diff --git a/.buildkite/hardware_tests/cpu.yaml b/.buildkite/hardware_tests/cpu.yaml
index e466e2a52444..92b0196ec2a2 100644
--- a/.buildkite/hardware_tests/cpu.yaml
+++ b/.buildkite/hardware_tests/cpu.yaml
@@ -12,15 +12,19 @@ steps:
   - vllm/_custom_ops.py
   - tests/kernels/attention/test_cpu_attn.py
   - tests/kernels/moe/test_cpu_fused_moe.py
+  - tests/kernels/moe/test_cpu_fp8_fused_moe.py
   - tests/kernels/test_onednn.py
   - tests/kernels/test_awq_int4_to_int8.py
+  - tests/kernels/quantization/test_cpu_fp8_scaled_mm.py
   commands:
     - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 20m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
       pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
       pytest -x -v -s tests/kernels/moe/test_cpu_fused_moe.py
+      pytest -x -v -s tests/kernels/moe/test_cpu_fp8_fused_moe.py
       pytest -x -v -s tests/kernels/test_onednn.py
-      pytest -x -v -s tests/kernels/test_awq_int4_to_int8.py"
+      pytest -x -v -s tests/kernels/test_awq_int4_to_int8.py
+      pytest -x -v -s tests/kernels/quantization/test_cpu_fp8_scaled_mm.py"
 
 - label: CPU-Compatibility Tests
   depends_on: []
@@ -46,7 +50,7 @@ steps:
   - tests/models/language/pooling/
   commands:
     - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 40m "
       pytest -x -v -s tests/models/language/generation -m cpu_model
       pytest -x -v -s tests/models/language/pooling -m cpu_model"
 
@@ -61,6 +65,7 @@ steps:
   - vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py
   - vllm/model_executor/layers/quantization/kernels/scaled_mm/cpu.py
   - vllm/model_executor/layers/quantization/kernels/mixed_precision/cpu.py
+  - vllm/model_executor/layers/fused_moe/experts/cpu_moe.py
   - tests/quantization/test_compressed_tensors.py
   - tests/quantization/test_cpu_wna16.py
   commands:
@@ -69,11 +74,11 @@ steps:
       pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs
       pytest -x -v -s tests/quantization/test_cpu_wna16.py"
       
-- label: CPU-Distributed Tests
+- label: CPU-Distributed Tests (PP+TP)
   depends_on: []
   device: intel_cpu
   no_plugin: true
-  source_file_dependencies:
+  source_file_dependencies: &cpu_distributed_deps
   - csrc/cpu/shm.cpp
   - vllm/v1/worker/cpu_worker.py
   - vllm/v1/worker/gpu_worker.py
@@ -82,10 +87,21 @@ steps:
   - vllm/platforms/cpu.py
   - vllm/distributed/parallel_state.py
   - vllm/distributed/device_communicators/cpu_communicator.py
+  - .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh
+  commands:
+    - |
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 10m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh tp_pp"
+
+- label: CPU-Distributed Tests (DP+TP)
+  depends_on: []
+  device: intel_cpu
+  no_plugin: true
+  source_file_dependencies: *cpu_distributed_deps
   commands:
     - |
       bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 10m "
-      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh"
+      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh dp_tp"
 
 - label: CPU-Multi-Modal Model Tests %N
   depends_on: []
@@ -99,7 +115,7 @@ steps:
     - |
       bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 45m "
       pytest -x -v -s tests/models/multimodal/generation --ignore=tests/models/multimodal/generation/test_pixtral.py -m cpu_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
-  parallelism: 2
+  parallelism: 3
 
 - label: "Arm CPU Test"
   depends_on: []
diff --git a/.buildkite/image_build/image_build.sh b/.buildkite/image_build/image_build.sh
index 9131dfc71a0a..10c03c3e1773 100755
--- a/.buildkite/image_build/image_build.sh
+++ b/.buildkite/image_build/image_build.sh
@@ -92,8 +92,8 @@ check_and_skip_if_image_exists() {
 }
 
 ecr_login() {
-    aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
-    aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
+    aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
+    aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com || true
 }
 
 prepare_cache_tags() {
@@ -192,6 +192,7 @@ export BUILDKITE_COMMIT
 export PARENT_COMMIT
 export IMAGE_TAG
 export IMAGE_TAG_LATEST
+export COMMIT="${COMMIT:-${BUILDKITE_COMMIT}}"
 export CACHE_FROM
 export CACHE_FROM_BASE_BRANCH
 export CACHE_FROM_MAIN
diff --git a/.buildkite/image_build/image_build_cpu.sh b/.buildkite/image_build/image_build_cpu.sh
index ccfe155fa2b7..035f070ab891 100755
--- a/.buildkite/image_build/image_build_cpu.sh
+++ b/.buildkite/image_build/image_build_cpu.sh
@@ -11,7 +11,7 @@ REPO=$2
 BUILDKITE_COMMIT=$3
 
 # authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
 
 # skip build if image already exists
 if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu) ]]; then
diff --git a/.buildkite/image_build/image_build_cpu_arm64.sh b/.buildkite/image_build/image_build_cpu_arm64.sh
index ff3d11c8d599..b561e2c2e463 100755
--- a/.buildkite/image_build/image_build_cpu_arm64.sh
+++ b/.buildkite/image_build/image_build_cpu_arm64.sh
@@ -11,7 +11,7 @@ REPO=$2
 BUILDKITE_COMMIT=$3
 
 # authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
 
 # skip build if image already exists
 if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu) ]]; then
diff --git a/.buildkite/image_build/image_build_torch_nightly.sh b/.buildkite/image_build/image_build_torch_nightly.sh
index a23c658d46b9..cbd08aa7bd0b 100755
--- a/.buildkite/image_build/image_build_torch_nightly.sh
+++ b/.buildkite/image_build/image_build_torch_nightly.sh
@@ -46,7 +46,7 @@ echo "Image not found, proceeding with build..."
 
 # --- CUDA 13.0 for nightly builds ---
 # Nightly CI uses CUDA 13.0 while regular CI stays on CUDA 12.9
-NIGHTLY_CUDA_VERSION="13.0.0"
+NIGHTLY_CUDA_VERSION="13.0.2"
 NIGHTLY_BUILD_BASE_IMAGE="nvidia/cuda:${NIGHTLY_CUDA_VERSION}-devel-ubuntu22.04"
 NIGHTLY_FINAL_BASE_IMAGE="nvidia/cuda:${NIGHTLY_CUDA_VERSION}-base-ubuntu22.04"
 
diff --git a/.buildkite/intel_jobs/engine_intel.yaml b/.buildkite/intel_jobs/engine_intel.yaml
new file mode 100644
index 000000000000..c66576d40991
--- /dev/null
+++ b/.buildkite/intel_jobs/engine_intel.yaml
@@ -0,0 +1,21 @@
+group: Engine Intel
+depends_on:
+  - image-build-xpu
+steps:
+- label: Engine (1 GPU)
+  timeout_in_minutes: 30
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/v1/engine/
+    - tests/v1/engine/
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py'
diff --git a/.buildkite/intel_jobs/kernels_intel.yaml b/.buildkite/intel_jobs/kernels_intel.yaml
new file mode 100644
index 000000000000..66a8db25f02e
--- /dev/null
+++ b/.buildkite/intel_jobs/kernels_intel.yaml
@@ -0,0 +1,21 @@
+group: Kernels Intel
+depends_on: 
+  - image-build-xpu
+steps:
+- label: vLLM IR Tests
+  timeout_in_minutes: 30
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/ir
+    - vllm/kernels
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      pytest -v -s kernels/ir'
diff --git a/.buildkite/intel_jobs/lora_intel.yaml b/.buildkite/intel_jobs/lora_intel.yaml
new file mode 100644
index 000000000000..6d5bddacf1bc
--- /dev/null
+++ b/.buildkite/intel_jobs/lora_intel.yaml
@@ -0,0 +1,135 @@
+group: LoRA Intel
+depends_on:
+  - image-build-xpu
+steps:
+- label: LoRA Runtime + Utils
+  timeout_in_minutes: 45
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/lora
+    - tests/lora
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      pytest -v -s lora/test_layers.py &&
+      pytest -v -s lora/test_lora_checkpoints.py &&
+      pytest -v -s lora/test_lora_functions.py &&
+      pytest -v -s lora/test_lora_huggingface.py &&
+      pytest -v -s lora/test_lora_manager.py &&
+      pytest -v -s lora/test_lora_utils.py &&
+      pytest -v -s lora/test_peft_helper.py &&
+      pytest -v -s lora/test_resolver.py &&
+      pytest -v -s lora/test_utils.py &&
+      pytest -v -s lora/test_add_lora.py  &&
+      pytest -v -s lora/test_worker.py'
+
+- label: LoRA Fused/MoE Kernels
+  timeout_in_minutes: 45
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/lora
+    - tests/lora
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      pytest -v -s lora/test_fused_moe_lora_kernel.py && 
+      pytest -v -s lora/test_moe_lora_align_sum.py'
+
+- label: LoRA Punica Kernels
+  timeout_in_minutes: 45
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/lora
+    - tests/lora
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      set -o pipefail &&
+      pytest -v -s lora/test_punica_ops.py --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[expand-0-xpu:0-dtype0-3-43264-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype1-1-2049-64-128-16]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-128-1-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-256-1-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-256-8-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[expand-0-xpu:0-dtype0-3-2049-128-8-16]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-128-8-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels[expand-0-xpu:0-dtype1-1-2049-256-128-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype0-3-64256-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype1-2-29696-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype1-3-49408-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype0-2-16384-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[expand-0-xpu:0-dtype0-2-51328-32-4-4]"'
+
+- label: LoRA Punica FP8/XPU Ops
+  timeout_in_minutes: 45
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/lora
+    - tests/lora
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      pytest -v -s lora/test_punica_ops_fp8.py &&
+      pytest -v -s lora/test_punica_xpu_ops.py'
+
+- label: LoRA Models
+  timeout_in_minutes: 45
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/lora
+    - tests/lora
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      (pytest -v -s lora/test_mixtral.py --deselect="tests/lora/test_mixtral.py::test_mixtral_lora[4]" || true) &&
+      pytest -v -s lora/test_quant_model.py --deselect="tests/lora/test_quant_model.py::test_quant_model_lora[model0]" --deselect="tests/lora/test_quant_model.py::test_quant_model_lora[model1]" --deselect="tests/lora/test_quant_model.py::test_quant_model_tp_equality[model0]" &&
+      pytest -v -s lora/test_transformers_model.py &&
+      pytest -v -s lora/test_chatglm3_tp.py &&
+      pytest -s -v lora/test_minicpmv_tp.py'
+
+- label: LoRA Multimodal
+  timeout_in_minutes: 45
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/lora
+    - tests/lora
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      pytest -v -s lora/test_default_mm_loras.py && 
+      pytest -v -s lora/test_whisper.py'
diff --git a/.buildkite/intel_jobs/misc_intel.yaml b/.buildkite/intel_jobs/misc_intel.yaml
new file mode 100644
index 000000000000..864128bb5338
--- /dev/null
+++ b/.buildkite/intel_jobs/misc_intel.yaml
@@ -0,0 +1,55 @@
+group: Miscellaneous Intel
+depends_on:
+  - image-build-xpu
+steps:
+- label: V1 Core + KV + Metrics
+  timeout_in_minutes: 30
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/
+    - tests/v1/core
+    - tests/v1/executor
+    - tests/v1/kv_offload
+    - tests/v1/worker
+    - tests/v1/kv_connector/unit
+    - tests/v1/metrics
+    - tests/entrypoints/openai/correctness/test_lmeval.py
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'pip install -r requirements/kv_connectors.txt &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      cd tests &&
+      pytest -v -s v1/executor'
+
+- label: V1 Sample + Logits
+  timeout_in_minutes: 30
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/
+    - tests/v1/sample
+    - tests/v1/logits_processors
+    - tests/v1/test_oracle.py
+    - tests/v1/test_request.py
+    - tests/v1/test_outputs.py
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      cd tests &&
+      pytest -v -s v1/logits_processors --ignore=v1/logits_processors/test_custom_online.py --ignore=v1/logits_processors/test_custom_offline.py &&
+      pytest -v -s v1/test_oracle.py &&
+      pytest -v -s v1/test_request.py &&
+      pytest -v -s v1/test_outputs.py'
diff --git a/.buildkite/intel_jobs/test-intel.yaml b/.buildkite/intel_jobs/test-intel.yaml
index c59be699502f..336f71fdbcda 100644
--- a/.buildkite/intel_jobs/test-intel.yaml
+++ b/.buildkite/intel_jobs/test-intel.yaml
@@ -61,5 +61,5 @@ steps:
         pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py --ignore=v1/worker/test_worker_memory_snapshot.py &&
         pytest -v -s v1/structured_output &&
         pytest -v -s v1/test_serial_utils.py &&
-        pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py &&
+        pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py &&
         pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py --ignore=v1/kv_connector/unit/test_hf3fs_client.py --ignore=v1/kv_connector/unit/test_hf3fs_connector.py --ignore=v1/kv_connector/unit/test_hf3fs_metadata_server.py'
diff --git a/.buildkite/release-pipeline.yaml b/.buildkite/release-pipeline.yaml
index 45b2996f7ead..8a900c0bf862 100644
--- a/.buildkite/release-pipeline.yaml
+++ b/.buildkite/release-pipeline.yaml
@@ -1,3 +1,13 @@
+# CUDA architecture lists — following PyTorch RELEASE.md
+# (https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
+# SM86 included for broader Ampere coverage; SM89 for marlin fp8 support
+env:
+  CUDA_ARCH_X86: "7.5 8.0 8.6 8.9 9.0 10.0 12.0+PTX"
+  # aarch64 only architectures: 8.7 for Orin, 11.0 for Thor (since CUDA 13)
+  CUDA_ARCH_AARCH64: "8.0 8.7 8.9 9.0 10.0 11.0 12.0+PTX"
+  CUDA_ARCH_X86_CU129: "7.5 8.0 8.6 8.9 9.0 10.0 12.0"
+  CUDA_ARCH_AARCH64_CU129: "8.0 8.7 8.9 9.0 10.0 12.0"
+
 steps:
   - input: "Provide Release version here"
     id: input-release-version
@@ -14,9 +24,7 @@ steps:
         agents:
           queue: arm64_cpu_queue_release
         commands:
-          # #NOTE: torch_cuda_arch_list is derived from upstream PyTorch build files here:
-          # https://github.com/pytorch/pytorch/blob/main/.ci/aarch64_linux/aarch64_ci_build.sh#L7
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64_CU129}\" --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
           - "bash .buildkite/scripts/upload-nightly-wheels.sh"
@@ -29,12 +37,10 @@ steps:
         agents:
           queue: arm64_cpu_queue_release
         commands:
-          # #NOTE: torch_cuda_arch_list is derived from upstream PyTorch build files here:
-          # https://github.com/pytorch/pytorch/blob/main/.ci/aarch64_linux/aarch64_ci_build.sh#L7
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04  --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64}\" --build-arg BUILD_OS=manylinux --build-arg BUILD_BASE_IMAGE=pytorch/manylinuxaarch64-builder:cuda13.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -47,7 +53,7 @@ steps:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_BUILD_ACL=ON --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -57,10 +63,10 @@ steps:
         agents:
           queue: cpu_queue_release
         commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86_CU129}\" --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_31"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -70,10 +76,10 @@ steps:
         agents:
           queue: cpu_queue_release
         commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86}\" --build-arg BUILD_OS=manylinux --build-arg BUILD_BASE_IMAGE=pytorch/manylinux2_28-builder:cuda13.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -86,7 +92,7 @@ steps:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_X86=true --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -98,105 +104,212 @@ steps:
     commands:
       - "bash .buildkite/scripts/generate-and-upload-nightly-index.sh"
 
+  - block: "Unblock to build release Docker images"
+    depends_on: ~
+    key: block-build-release-images
+    if: build.env("NIGHTLY") != "1"
+
   - group: "Build release Docker images"
     key: "build-release-images"
+    depends_on: block-build-release-images
+    allow_dependency_failure: true
     steps:
-      - label: "Build release image - x86_64 - CUDA 12.9"
+      - label: "Build release image - x86_64 - CUDA 13.0"
         depends_on: ~
         id: build-release-image-x86
         agents:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
+          - |
+            DOCKER_BUILDKIT=1 docker build \
+              $(bash .buildkite/scripts/docker-build-metadata-args.sh) \
+              --build-arg max_jobs=16 \
+              --build-arg USE_SCCACHE=1 \
+              --build-arg GIT_REPO_CHECK=1 \
+              --build-arg CUDA_VERSION=13.0.2 \
+              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86}" \
+              --build-arg INSTALL_KV_CONNECTORS=true \
+              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 \
+              --target vllm-openai \
+              --progress plain \
+              -f docker/Dockerfile .
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
           # re-tag to default image tag and push, just in case arm64 build fails
           - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
 
-      - label: "Build release image - aarch64 - CUDA 12.9"
+      - label: "Build release image - aarch64 - CUDA 13.0"
         depends_on: ~
         id: build-release-image-arm64
         agents:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
+          - |
+            DOCKER_BUILDKIT=1 docker build \
+              $(bash .buildkite/scripts/docker-build-metadata-args.sh) \
+              --build-arg max_jobs=16 \
+              --build-arg USE_SCCACHE=1 \
+              --build-arg GIT_REPO_CHECK=1 \
+              --build-arg CUDA_VERSION=13.0.2 \
+              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64}" \
+              --build-arg INSTALL_KV_CONNECTORS=true \
+              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 \
+              --target vllm-openai \
+              --progress plain \
+              -f docker/Dockerfile .
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
 
-      - label: "Build release image - x86_64 - CUDA 13.0"
+      - label: "Build release image - x86_64 - CUDA 12.9"
         depends_on: ~
-        id: build-release-image-x86-cuda-13-0
+        id: build-release-image-x86-cuda-12-9
         agents:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130"
+          - |
+            DOCKER_BUILDKIT=1 docker build \
+              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129) \
+              --build-arg max_jobs=16 \
+              --build-arg USE_SCCACHE=1 \
+              --build-arg GIT_REPO_CHECK=1 \
+              --build-arg CUDA_VERSION=12.9.1 \
+              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86_CU129}" \
+              --build-arg INSTALL_KV_CONNECTORS=true \
+              --target vllm-openai \
+              --progress plain \
+              -f docker/Dockerfile .
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129"
           # re-tag to default image tag and push, just in case arm64 build fails
-          - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
+          - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129"
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129"
 
-      - label: "Build release image - aarch64 - CUDA 13.0"
+      - label: "Build release image - aarch64 - CUDA 12.9"
         depends_on: ~
-        id: build-release-image-arm64-cuda-13-0
+        id: build-release-image-arm64-cuda-12-9
         agents:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          # compute capability 12.0 for RTX-50 series / RTX PRO 6000 Blackwell, 12.1 for DGX Spark
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0 12.1' --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130"
+          - |
+            DOCKER_BUILDKIT=1 docker build \
+              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129) \
+              --build-arg max_jobs=16 \
+              --build-arg USE_SCCACHE=1 \
+              --build-arg GIT_REPO_CHECK=1 \
+              --build-arg CUDA_VERSION=12.9.1 \
+              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64_CU129}" \
+              --build-arg INSTALL_KV_CONNECTORS=true \
+              --target vllm-openai \
+              --progress plain \
+              -f docker/Dockerfile .
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129"
 
-      - label: "Build release image - x86_64 - CUDA 12.9 - Ubuntu 24.04"
+      - label: "Build release image - x86_64 - CUDA 13.0 - Ubuntu 24.04"
         depends_on: ~
         id: build-release-image-x86-ubuntu2404
         agents:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
+          - |
+            DOCKER_BUILDKIT=1 docker build \
+              $(bash .buildkite/scripts/docker-build-metadata-args.sh ubuntu2404) \
+              --build-arg max_jobs=16 \
+              --build-arg USE_SCCACHE=1 \
+              --build-arg GIT_REPO_CHECK=1 \
+              --build-arg CUDA_VERSION=13.0.2 \
+              --build-arg UBUNTU_VERSION=24.04 \
+              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
+              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86}" \
+              --build-arg INSTALL_KV_CONNECTORS=true \
+              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 \
+              --target vllm-openai \
+              --progress plain \
+              -f docker/Dockerfile .
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404"
           - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"
 
-      - label: "Build release image - aarch64 - CUDA 12.9 - Ubuntu 24.04"
+      - label: "Build release image - aarch64 - CUDA 13.0 - Ubuntu 24.04"
         depends_on: ~
         id: build-release-image-arm64-ubuntu2404
         agents:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
+          - |
+            DOCKER_BUILDKIT=1 docker build \
+              $(bash .buildkite/scripts/docker-build-metadata-args.sh ubuntu2404) \
+              --build-arg max_jobs=16 \
+              --build-arg USE_SCCACHE=1 \
+              --build-arg GIT_REPO_CHECK=1 \
+              --build-arg CUDA_VERSION=13.0.2 \
+              --build-arg UBUNTU_VERSION=24.04 \
+              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
+              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64}" \
+              --build-arg INSTALL_KV_CONNECTORS=true \
+              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 \
+              --target vllm-openai \
+              --progress plain \
+              -f docker/Dockerfile .
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404"
 
-      - label: "Build release image - x86_64 - CUDA 13.0 - Ubuntu 24.04"
+      - label: "Build release image - x86_64 - CUDA 12.9 - Ubuntu 24.04"
         depends_on: ~
-        id: build-release-image-x86-cuda-13-0-ubuntu2404
+        id: build-release-image-x86-cuda-12-9-ubuntu2404
         agents:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0 12.1' --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu24.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404"
-          - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130-ubuntu2404"
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130-ubuntu2404"
+          - |
+            DOCKER_BUILDKIT=1 docker build \
+              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129-ubuntu2404) \
+              --build-arg max_jobs=16 \
+              --build-arg USE_SCCACHE=1 \
+              --build-arg GIT_REPO_CHECK=1 \
+              --build-arg CUDA_VERSION=12.9.1 \
+              --build-arg UBUNTU_VERSION=24.04 \
+              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
+              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86_CU129}" \
+              --build-arg INSTALL_KV_CONNECTORS=true \
+              --target vllm-openai \
+              --progress plain \
+              -f docker/Dockerfile .
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"
+          - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"
 
-      - label: "Build release image - aarch64 - CUDA 13.0 - Ubuntu 24.04"
+      - label: "Build release image - aarch64 - CUDA 12.9 - Ubuntu 24.04"
         depends_on: ~
-        id: build-release-image-arm64-cuda-13-0-ubuntu2404
+        id: build-release-image-arm64-cuda-12-9-ubuntu2404
         agents:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0 12.1' --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu24.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404"
+          - |
+            DOCKER_BUILDKIT=1 docker build \
+              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129-ubuntu2404) \
+              --build-arg max_jobs=16 \
+              --build-arg USE_SCCACHE=1 \
+              --build-arg GIT_REPO_CHECK=1 \
+              --build-arg CUDA_VERSION=12.9.1 \
+              --build-arg UBUNTU_VERSION=24.04 \
+              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
+              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64_CU129}" \
+              --build-arg INSTALL_KV_CONNECTORS=true \
+              --target vllm-openai \
+              --progress plain \
+              -f docker/Dockerfile .
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"
 
       - block: "Build release image for x86_64 CPU"
         key: block-cpu-release-image-build
         depends_on: ~
 
       - label: "Build release image - x86_64 - CPU"
+        key: build-cpu-release-image-x86
         depends_on:
           - block-cpu-release-image-build
           - input-release-version
@@ -215,7 +328,8 @@ steps:
         depends_on: ~
 
       - label: "Build release image - arm64 - CPU"
-        depends_on: 
+        key: build-cpu-release-image-arm64
+        depends_on:
           - block-arm64-cpu-release-image-build
           - input-release-version
         agents:
@@ -231,7 +345,7 @@ steps:
   - group: "Publish release images"
     key: "publish-release-images"
     steps:
-      - label: "Create multi-arch manifest - CUDA 12.9"
+      - label: "Create multi-arch manifest - CUDA 13.0"
         depends_on:
           - build-release-image-x86
           - build-release-image-arm64
@@ -243,7 +357,7 @@ steps:
           - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64 --amend"
           - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
 
-      - label: "Annotate release workflow - CUDA 12.9"
+      - label: "Annotate release workflow - CUDA 13.0"
         depends_on:
           - create-multi-arch-manifest
         id: annotate-release-workflow
@@ -252,19 +366,19 @@ steps:
         commands:
           - "bash .buildkite/scripts/annotate-release.sh"
 
-      - label: "Create multi-arch manifest - CUDA 13.0"
+      - label: "Create multi-arch manifest - CUDA 12.9"
         depends_on:
-          - build-release-image-x86-cuda-13-0
-          - build-release-image-arm64-cuda-13-0
-        id: create-multi-arch-manifest-cuda-13-0
+          - build-release-image-x86-cuda-12-9
+          - build-release-image-arm64-cuda-12-9
+        id: create-multi-arch-manifest-cuda-12-9
         agents:
           queue: small_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu130 --amend"
-          - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
+          - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu129 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu129 --amend"
+          - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129"
 
-      - label: "Create multi-arch manifest - CUDA 12.9 - Ubuntu 24.04"
+      - label: "Create multi-arch manifest - CUDA 13.0 - Ubuntu 24.04"
         depends_on:
           - build-release-image-x86-ubuntu2404
           - build-release-image-arm64-ubuntu2404
@@ -276,17 +390,17 @@ steps:
           - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-ubuntu2404 --amend"
           - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"
 
-      - label: "Create multi-arch manifest - CUDA 13.0 - Ubuntu 24.04"
+      - label: "Create multi-arch manifest - CUDA 12.9 - Ubuntu 24.04"
         depends_on:
-          - build-release-image-x86-cuda-13-0-ubuntu2404
-          - build-release-image-arm64-cuda-13-0-ubuntu2404
-        id: create-multi-arch-manifest-cuda-13-0-ubuntu2404
+          - build-release-image-x86-cuda-12-9-ubuntu2404
+          - build-release-image-arm64-cuda-12-9-ubuntu2404
+        id: create-multi-arch-manifest-cuda-12-9-ubuntu2404
         agents:
           queue: small_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu130-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu130-ubuntu2404 --amend"
-          - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130-ubuntu2404"
+          - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu129-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu129-ubuntu2404 --amend"
+          - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"
 
       - label: "Publish nightly multi-arch image to DockerHub"
         depends_on:
@@ -306,16 +420,51 @@ steps:
           DOCKER_BUILDKIT: "1"
           DOCKERHUB_USERNAME: "vllmbot"
 
-      - label: "Publish nightly multi-arch image to DockerHub - CUDA 13.0"
+      - label: "Publish nightly multi-arch image to DockerHub - CUDA 12.9"
         depends_on:
-          - create-multi-arch-manifest-cuda-13-0
+          - create-multi-arch-manifest-cuda-12-9
         if: build.env("NIGHTLY") == "1"
         agents:
           queue: small_cpu_queue_release
         commands:
-          - "bash .buildkite/scripts/push-nightly-builds.sh cu130"
+          - "bash .buildkite/scripts/push-nightly-builds.sh cu129"
           # Clean up old nightly builds (keep only last 14)
-          - "bash .buildkite/scripts/cleanup-nightly-builds.sh cu130-nightly-"
+          - "bash .buildkite/scripts/cleanup-nightly-builds.sh cu129-nightly-"
+        plugins:
+          - docker-login#v3.0.0:
+              username: vllmbot
+              password-env: DOCKERHUB_TOKEN
+        env:
+          DOCKER_BUILDKIT: "1"
+          DOCKERHUB_USERNAME: "vllmbot"
+
+      - block: "Publish release images to DockerHub"
+        key: block-publish-release-images
+        depends_on:
+          - create-multi-arch-manifest
+          - create-multi-arch-manifest-cuda-12-9
+          - create-multi-arch-manifest-ubuntu2404
+          - create-multi-arch-manifest-cuda-12-9-ubuntu2404
+          - build-rocm-release-image
+          - input-release-version
+          # Wait for CPU builds if their block steps were unblocked, so publish
+          # doesn't race the in-progress CPU build. allow_failure lets publish
+          # proceed when the operator legitimately leaves the CPU block steps
+          # unblocked or the CPU build fails.
+          - step: build-cpu-release-image-x86
+            allow_failure: true
+          - step: build-cpu-release-image-arm64
+            allow_failure: true
+        if: build.env("NIGHTLY") != "1"
+
+      - label: "Publish release images to DockerHub"
+        depends_on:
+          - block-publish-release-images
+        key: publish-release-images-dockerhub
+        agents:
+          queue: small_cpu_queue_release
+        commands:
+          - "bash .buildkite/scripts/publish-release-images.sh"
         plugins:
           - docker-login#v3.0.0:
               username: vllmbot
@@ -611,12 +760,14 @@ steps:
       - "bash tools/vllm-rocm/generate-rocm-wheels-root-index.sh"
     env:
       S3_BUCKET: "vllm-wheels"
-      VARIANT: "rocm721"
+      VARIANT: "rocm722"
 
   # ROCm Job 6: Build ROCm Release Docker Image
   - label: ":docker: Build release image - x86_64 - ROCm"
     id: build-rocm-release-image
     depends_on:
+      - step: block-build-release-images
+        allow_failure: true
       - step: build-rocm-base-wheels
         allow_failure: false
     agents:
diff --git a/.buildkite/scripts/annotate-release.sh b/.buildkite/scripts/annotate-release.sh
index 2da9db2f2e5d..afa884fba46b 100755
--- a/.buildkite/scripts/annotate-release.sh
+++ b/.buildkite/scripts/annotate-release.sh
@@ -8,112 +8,20 @@ if [ -z "${RELEASE_VERSION}" ]; then
   RELEASE_VERSION="1.0.0.dev"
 fi
 
-ROCM_BASE_CACHE_KEY=$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
-
 buildkite-agent annotate --style 'info' --context 'release-workflow' << EOF
 To download the wheel (by commit):
 \`\`\`
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_31_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_31_aarch64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_35_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_35_aarch64.whl .
 
-(Optional) For CUDA 13.0:
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux_2_35_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux_2_35_aarch64.whl .
+(Optional) For CUDA 12.9:
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux_2_31_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux_2_31_aarch64.whl .
 
 (Optional) For CPU:
 aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_x86_64.whl .
 aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_aarch64.whl .
 \`\`\`
 
-
-To download and upload the image:
-
-\`\`\`
-# Download images:
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu130
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu130
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm
-docker pull public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION}
-docker pull public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION}
-
-# Tag and push images:
-
-## CUDA
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64 vllm/vllm-openai:x86_64
-docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:latest-x86_64
-docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
-docker push vllm/vllm-openai:latest-x86_64
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu130 vllm/vllm-openai:x86_64-cu130
-docker tag vllm/vllm-openai:x86_64-cu130 vllm/vllm-openai:latest-x86_64-cu130
-docker tag vllm/vllm-openai:x86_64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130
-docker push vllm/vllm-openai:latest-x86_64-cu130
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64 vllm/vllm-openai:aarch64
-docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:latest-aarch64
-docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-docker push vllm/vllm-openai:latest-aarch64
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu130 vllm/vllm-openai:aarch64-cu130
-docker tag vllm/vllm-openai:aarch64-cu130 vllm/vllm-openai:latest-aarch64-cu130
-docker tag vllm/vllm-openai:aarch64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
-docker push vllm/vllm-openai:latest-aarch64-cu130
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
-
-## ROCm
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}
-docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:latest
-docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:v${RELEASE_VERSION}
-docker push vllm/vllm-openai-rocm:latest
-docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base
-docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:latest-base
-docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
-docker push vllm/vllm-openai-rocm:latest-base
-docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
-
-## CPU
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:x86_64
-docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:latest-x86_64
-docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
-docker push vllm/vllm-openai-cpu:latest-x86_64
-docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:arm64
-docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:latest-arm64
-docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-docker push vllm/vllm-openai-cpu:latest-arm64
-docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-
-# Create multi-arch manifest:
-
-docker manifest rm vllm/vllm-openai:latest
-docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-docker manifest push vllm/vllm-openai:latest
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
-
-docker manifest rm vllm/vllm-openai:latest-cu130
-docker manifest create vllm/vllm-openai:latest-cu130 vllm/vllm-openai:latest-x86_64-cu130 vllm/vllm-openai:latest-aarch64-cu130
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
-docker manifest push vllm/vllm-openai:latest-cu130
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu130
-
-docker manifest rm vllm/vllm-openai-cpu:latest || true
-docker manifest create vllm/vllm-openai-cpu:latest vllm/vllm-openai-cpu:latest-x86_64 vllm/vllm-openai-cpu:latest-arm64
-docker manifest create vllm/vllm-openai-cpu:v${RELEASE_VERSION} vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-docker manifest push vllm/vllm-openai-cpu:latest
-docker manifest push vllm/vllm-openai-cpu:v${RELEASE_VERSION}
-\`\`\`
+Docker images are published automatically by the "Publish release images to DockerHub" pipeline step.
 EOF
diff --git a/.buildkite/scripts/check-ray-compatibility.sh b/.buildkite/scripts/check-ray-compatibility.sh
index 1572fe94168d..b056d4403dbb 100644
--- a/.buildkite/scripts/check-ray-compatibility.sh
+++ b/.buildkite/scripts/check-ray-compatibility.sh
@@ -29,7 +29,7 @@ if python3 -c "import torch; assert torch.version.hip" 2>/dev/null; then
         TORCH_INDEX_URL=""
     fi
 else
-    TORCH_INDEX_URL="https://download.pytorch.org/whl/cu129"
+    TORCH_INDEX_URL="https://download.pytorch.org/whl/cu130"
 fi
 echo ">>> Using PyTorch index: ${TORCH_INDEX_URL:-PyPI default}"
 
diff --git a/.buildkite/scripts/ci-fetch-log.sh b/.buildkite/scripts/ci-fetch-log.sh
new file mode 100755
index 000000000000..02798b56f4a9
--- /dev/null
+++ b/.buildkite/scripts/ci-fetch-log.sh
@@ -0,0 +1,55 @@
+#!/bin/bash
+# Usage: ./ci-fetch-log.sh <buildkite_job_url> [output_file]
+#        ./ci-fetch-log.sh <build_number> <job_uuid> [output_file]
+#
+# Downloads the raw log for a Buildkite job from the public, unauthenticated
+# /organizations/<org>/pipelines/<pipeline>/builds/<n>/jobs/<uuid>/download
+# endpoint, then strips ANSI/timestamps via ci-clean-log.sh.
+#
+# Find <build_number> and <job_uuid> via:
+#   gh pr checks <PR> --repo vllm-project/vllm
+# Each failing row's URL is .../builds/<build_number>#<job_uuid>.
+
+set -euo pipefail
+
+ORG="vllm"
+PIPELINE="ci"
+
+usage() {
+    echo "Usage: $0 <buildkite_job_url> [output_file]"
+    echo "       $0 <build_number> <job_uuid> [output_file]"
+    exit 1
+}
+
+if [ $# -lt 1 ]; then usage; fi
+
+if [[ "$1" == https://* ]]; then
+    BUILD=$(echo "$1" | sed -nE 's#.*/builds/([0-9]+).*#\1#p')
+    JOB=$(echo "$1" | grep -oE '[0-9a-f]{8}-[0-9a-f-]+' | head -n 1)
+    OUT="${2:-ci-${BUILD}-${JOB:0:8}.log}"
+else
+    if [ $# -lt 2 ]; then usage; fi
+    BUILD="$1"
+    JOB="$2"
+    OUT="${3:-ci-${BUILD}-${JOB:0:8}.log}"
+fi
+
+if [ -z "$BUILD" ] || [ -z "$JOB" ]; then
+    echo "Could not parse build number or job UUID from: $1" >&2
+    usage
+fi
+
+COOKIES=$(mktemp)
+trap 'rm -f "$COOKIES"' EXIT
+
+# Buildkite issues a session cookie on first hit; subsequent /download needs it.
+curl -fsSL -c "$COOKIES" -A "vllm-ci-fetch-log" \
+    "https://buildkite.com/${ORG}/${PIPELINE}/builds/${BUILD}" -o /dev/null
+
+curl -fsSL -b "$COOKIES" -A "vllm-ci-fetch-log" \
+    "https://buildkite.com/organizations/${ORG}/pipelines/${PIPELINE}/builds/${BUILD}/jobs/${JOB}/download" \
+    -o "$OUT"
+
+bash "$(dirname "$0")/ci-clean-log.sh" "$OUT"
+
+echo "$OUT"
diff --git a/.buildkite/scripts/detect-manylinux-tag.py b/.buildkite/scripts/detect-manylinux-tag.py
new file mode 100644
index 000000000000..40fa6c6ffbb7
--- /dev/null
+++ b/.buildkite/scripts/detect-manylinux-tag.py
@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Detect the manylinux platform tag for a wheel and rename it in place.
+
+vLLM's build images produce wheels with the generic ``linux_<arch>`` platform
+tag, which installers like ``pip`` won't accept off PyPI/our index. We need to
+rewrite the platform tag to the appropriate ``manylinux_<major>_<minor>_<arch>``
+before uploading.
+
+Historically the tag was hard-coded per build (``manylinux_2_31`` for the
+Ubuntu 20.04-based image, ``manylinux_2_35`` for the Ubuntu 22.04-based
+images). That is brittle: bumping the base image silently produces wheels
+labelled with the wrong glibc requirement. This script asks ``auditwheel``
+to derive the tag from the symbol versions actually referenced by the
+binaries inside the wheel, so the label tracks reality.
+
+We can't simply call ``auditwheel repair`` -- it tries to graft external
+shared libraries into the wheel and fails on vLLM's CUDA/cuBLAS dependencies.
+Instead we use ``auditwheel.wheel_abi.analyze_wheel_abi`` directly, which is
+the same call that powers ``auditwheel show``, and read off
+``winfo.sym_policy.name``.
+
+Usage:
+    detect-manylinux-tag.py <wheel_path>
+
+The wheel is renamed in place; the new path is printed on stdout. All
+diagnostics go to stderr so callers can capture stdout safely.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+from auditwheel.error import (
+    AuditwheelError,
+    NonPlatformWheelError,
+    WheelToolsError,
+)
+from auditwheel.wheel_abi import analyze_wheel_abi
+from auditwheel.wheeltools import get_wheel_architecture, get_wheel_libc
+
+
+def detect_platform_tag(wheel_path: Path) -> str:
+    """Return the most precise platform tag the wheel is consistent with.
+
+    Mirrors ``auditwheel show`` but returns ``sym_policy`` rather than
+    ``overall_policy``: we only care about the glibc symbol versions used,
+    not about other policy axes (ISA extensions, blacklist, etc.) that
+    ``overall_policy`` folds in.
+    """
+    fn = wheel_path.name
+
+    try:
+        arch = get_wheel_architecture(fn)
+    except (WheelToolsError, NonPlatformWheelError):
+        # Architecture isn't deducible from the filename; let auditwheel
+        # infer it from the ELF binaries inside the wheel.
+        arch = None
+
+    try:
+        libc = get_wheel_libc(fn)
+    except WheelToolsError:
+        # An unrepaired wheel uses ``linux_<arch>``, which doesn't encode
+        # libc. Let auditwheel infer it from the ELF binaries.
+        libc = None
+
+    winfo = analyze_wheel_abi(
+        libc,
+        arch,
+        wheel_path,
+        frozenset(),
+        disable_isa_ext_check=False,
+        allow_graft=False,
+    )
+    return winfo.sym_policy.name
+
+
+def rename_wheel(wheel_path: Path, new_platform_tag: str) -> Path:
+    """Rename the wheel in place, replacing only its platform tag."""
+    # Wheel filename per PEP 427:
+    #   {distribution}-{version}(-{build})?-{python}-{abi}-{platform}.whl
+    # The platform tag is always the last ``-``-separated token before
+    # ``.whl``. Compound tags like ``manylinux_2_31_x86_64`` use ``_`` as the
+    # internal separator, so ``-``-splitting is unambiguous.
+    parts = wheel_path.stem.split("-")
+    if len(parts) < 5:
+        raise ValueError(f"Unrecognised wheel filename: {wheel_path.name}")
+    parts[-1] = new_platform_tag
+    new_path = wheel_path.with_name("-".join(parts) + ".whl")
+    if new_path != wheel_path:
+        wheel_path.rename(new_path)
+    return new_path
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description="Detect a wheel's manylinux platform tag with "
+        "auditwheel and rename the wheel in place."
+    )
+    parser.add_argument(
+        "wheel",
+        type=Path,
+        help="Path to the wheel to inspect and rename.",
+    )
+    args = parser.parse_args()
+
+    wheel_path: Path = args.wheel
+    if not wheel_path.is_file():
+        print(f"error: {wheel_path} is not a file", file=sys.stderr)
+        return 1
+
+    # Catch the things that ``analyze_wheel_abi`` and ``rename_wheel`` can
+    # raise: any subclass of ``AuditwheelError`` (pure-Python wheels,
+    # invalid libc, malformed wheels), filesystem errors, or our own
+    # ``ValueError`` for an unrecognised wheel filename. Print a single
+    # ``ERROR_TYPE: message`` line to stderr instead of a Python
+    # traceback, which is much friendlier in CI logs.
+    try:
+        new_tag = detect_platform_tag(wheel_path)
+        print(f"detected platform tag: {new_tag}", file=sys.stderr)
+        new_path = rename_wheel(wheel_path, new_tag)
+    except (AuditwheelError, ValueError, OSError) as e:
+        print(
+            f"error: failed to retag {wheel_path.name}: {type(e).__name__}: {e}",
+            file=sys.stderr,
+        )
+        return 2
+
+    if new_path != wheel_path:
+        print(f"renamed {wheel_path.name} -> {new_path.name}", file=sys.stderr)
+    else:
+        print(f"wheel already tagged {new_tag}", file=sys.stderr)
+
+    print(new_path)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/.buildkite/scripts/docker-build-metadata-args.sh b/.buildkite/scripts/docker-build-metadata-args.sh
new file mode 100644
index 000000000000..9aa6fa9314f7
--- /dev/null
+++ b/.buildkite/scripts/docker-build-metadata-args.sh
@@ -0,0 +1,54 @@
+#!/bin/bash
+# Emit docker build flags for release image provenance metadata.
+# Keep this helper best-effort: missing Buildkite metadata should fall back to
+# local/default values instead of blocking the Docker build.
+
+# Variant examples: "", "cu129", "ubuntu2404", "cu129-ubuntu2404".
+variant="${1:-}"
+variant_suffix="${variant:+-${variant}}"
+
+image_name="${VLLM_DOCKER_IMAGE_NAME:-vllm/vllm-openai}"
+staging_repo="${VLLM_STAGING_IMAGE_REPO:-public.ecr.aws/q9t5s3a7/vllm-release-repo}"
+build_commit="${VLLM_BUILD_COMMIT:-${BUILDKITE_COMMIT:-unknown}}"
+build_pipeline="${VLLM_BUILD_PIPELINE:-${BUILDKITE_PIPELINE_ID:-${BUILDKITE_PIPELINE_SLUG:-local}}}"
+build_url="${VLLM_BUILD_URL:-${BUILDKITE_BUILD_URL:-}}"
+tag_commit="${BUILDKITE_COMMIT:-${build_commit}}"
+
+if [[ -n "${BUILDKITE:-}" || -n "${BUILDKITE_COMMIT:-}" ]]; then
+  release_version="${RELEASE_VERSION:-}"
+  if command -v buildkite-agent >/dev/null 2>&1; then
+    release_version="${release_version:-$(buildkite-agent meta-data get release-version 2>/dev/null)}"
+  fi
+  release_version="${release_version#v}"
+  release_version="${release_version:-${tag_commit}}"
+
+  staging_image_ref="${staging_repo}:${tag_commit}-$(uname -m)${variant_suffix}"
+
+  if [[ "${NIGHTLY:-}" == "1" ]]; then
+    if [[ -z "${variant}" ]]; then
+      image_tag="${image_name}:nightly-${tag_commit}"
+    elif [[ "${variant}" == cu* ]]; then
+      cuda_variant="${variant%%-*}"
+      remaining_variant="${variant#${cuda_variant}}"
+      image_tag="${image_name}:${cuda_variant}-nightly-${tag_commit}${remaining_variant}"
+    else
+      image_tag="${image_name}:nightly-${tag_commit}${variant_suffix}"
+    fi
+  else
+    image_tag="${image_name}:v${release_version}${variant_suffix}"
+  fi
+else
+  image_tag="${VLLM_IMAGE_TAG:-local/vllm-openai:dev}"
+  staging_image_ref="${image_tag}"
+fi
+
+emit_arg() {
+  printf -- "--build-arg %s=%s " "$1" "$2"
+}
+
+emit_arg VLLM_BUILD_COMMIT "${build_commit}"
+emit_arg VLLM_BUILD_PIPELINE "${build_pipeline}"
+emit_arg VLLM_BUILD_URL "${build_url}"
+# This is the intended public tag. The final digest is only known after push.
+emit_arg VLLM_IMAGE_TAG "${image_tag}"
+printf -- "--tag %s " "${staging_image_ref}"
diff --git a/.buildkite/scripts/generate-and-upload-nightly-index.sh b/.buildkite/scripts/generate-and-upload-nightly-index.sh
index 7cef252c6078..502ed0609310 100755
--- a/.buildkite/scripts/generate-and-upload-nightly-index.sh
+++ b/.buildkite/scripts/generate-and-upload-nightly-index.sh
@@ -9,21 +9,14 @@ set -ex
 
 BUCKET="vllm-wheels"
 INDICES_OUTPUT_DIR="indices"
-DEFAULT_VARIANT_ALIAS="cu129" # align with vLLM_MAIN_CUDA_VERSION in vllm/envs.py
-PYTHON="${PYTHON_PROG:-python3}" # try to read from env var, otherwise use python3
+DEFAULT_VARIANT_ALIAS="cu130" # align with vLLM_MAIN_CUDA_VERSION in vllm/envs.py
 SUBPATH=$BUILDKITE_COMMIT
 S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
 
-# detect if python3.12+ is available
-has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)")
-if [[ "$has_new_python" -eq 0 ]]; then
-    # use new python from docker
-    docker pull python:3-slim
-    PYTHON="docker run --rm -u $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
-fi
-
-echo "Using python interpreter: $PYTHON"
-echo "Python version: $($PYTHON --version)"
+# Select python3 (>= 3.12) -- local if available, else a docker fallback.
+# shellcheck source=lib/select-python.sh
+source .buildkite/scripts/lib/select-python.sh
+select_python
 
 # ======== generate and upload indices ========
 
diff --git a/.buildkite/scripts/hardware_ci/run-amd-test.sh b/.buildkite/scripts/hardware_ci/run-amd-test.sh
index 703a7d753220..7e8ddb12ec98 100755
--- a/.buildkite/scripts/hardware_ci/run-amd-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-amd-test.sh
@@ -378,9 +378,11 @@ HF_MOUNT="/root/.cache/huggingface"
 # double-quotes will have been stripped by the calling shell.
 if [[ -n "${VLLM_TEST_COMMANDS:-}" ]]; then
   commands="${VLLM_TEST_COMMANDS}"
+  commands_source="env"
   echo "Commands sourced from VLLM_TEST_COMMANDS (quoting preserved)"
 else
   commands="$*"
+  commands_source="argv"
   if [[ -z "$commands" ]]; then
     echo "Error: No test commands provided." >&2
     echo "Usage:" >&2
@@ -397,9 +399,15 @@ fi
 
 echo "Raw commands: $commands"
 
-# Fix quoting before ROCm overrides (so overrides see correct structure)
-commands=$(re_quote_pytest_markers "$commands")
-echo "After re-quoting: $commands"
+# Only try to repair stripped pytest -m/-k quoting in legacy argv mode.
+# VLLM_TEST_COMMANDS preserves inner quoting already, and re-quoting that path
+# can corrupt embedded echo strings or otherwise well-formed shell fragments.
+if [[ "$commands_source" == "argv" ]]; then
+  commands=$(re_quote_pytest_markers "$commands")
+  echo "After re-quoting: $commands"
+else
+  echo "Skipping re-quoting for VLLM_TEST_COMMANDS input"
+fi
 
 commands=$(apply_rocm_test_overrides "$commands")
 echo "Final commands: $commands"
diff --git a/.buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh b/.buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh
index 4ff15067aa0f..8ac27ed6583a 100644
--- a/.buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh
@@ -3,42 +3,37 @@ set -euox pipefail
 export VLLM_CPU_CI_ENV=0
 export VLLM_CPU_KVCACHE_SPACE=1 # avoid OOM
 
-echo "--- PP+TP"
-vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 --max-model-len=4096 &
-server_pid=$!
-timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
-vllm bench serve \
-    --backend vllm \
-    --dataset-name random \
-    --model meta-llama/Llama-3.2-3B-Instruct \
-    --num-prompts 20 \
-    --result-dir ./test_results \
-    --result-filename tp_pp.json \
-    --save-result \
-    --endpoint /v1/completions
-kill -s SIGTERM $server_pid; wait $server_pid || true
-failed_req=$(jq '.failed' ./test_results/tp_pp.json)
-if [ "$failed_req" -ne 0 ]; then
-  echo "Some requests were failed!"
-  exit 1
-fi
+MODE=${1:-all}
 
-#echo "--- DP+TP"
-#vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -dp=2 --max-model-len=4096 &
-#server_pid=$!
-#timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
-#vllm bench serve \
-#    --backend vllm \
-#    --dataset-name random \
-#    --model meta-llama/Llama-3.2-3B-Instruct \
-#    --num-prompts 20 \
-#    --result-dir ./test_results \
-#    --result-filename dp_pp.json \
-#    --save-result \
-#    --endpoint /v1/completions
-#kill -s SIGTERM $server_pid; wait $server_pid || true
-#failed_req=$(jq '.failed' ./test_results/dp_pp.json)
-#if [ "$failed_req" -ne 0 ]; then
-#  echo "Some requests were failed!"
-#  exit 1
-#fi
+run_scenario() {
+    local label="$1" result_file="$2"
+    shift 2
+    echo "--- $label"
+    vllm serve meta-llama/Llama-3.2-3B-Instruct "$@" --max-model-len=4096 &
+    local server_pid=$!
+    timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
+    vllm bench serve \
+        --backend vllm \
+        --dataset-name random \
+        --model meta-llama/Llama-3.2-3B-Instruct \
+        --num-prompts 20 \
+        --result-dir ./test_results \
+        --result-filename "$result_file" \
+        --save-result \
+        --endpoint /v1/completions
+    kill -s SIGTERM "$server_pid"; wait "$server_pid" || true
+    if [ "$(jq '.failed' "./test_results/$result_file")" -ne 0 ]; then
+        echo "Some requests were failed in $label!"
+        exit 1
+    fi
+}
+
+case "$MODE" in
+    tp_pp) run_scenario "PP+TP" tp_pp.json -tp=2 -pp=2 ;;
+    dp_tp) run_scenario "DP+TP" dp_tp.json -tp=2 -dp=2 ;;
+    all)
+        run_scenario "PP+TP" tp_pp.json -tp=2 -pp=2
+        run_scenario "DP+TP" dp_tp.json -tp=2 -dp=2
+        ;;
+    *) echo "ERROR: unknown mode '$MODE' (expected: tp_pp | dp_tp | all)" >&2; exit 1 ;;
+esac
diff --git a/.buildkite/scripts/hardware_ci/run-cpu-test-arm.sh b/.buildkite/scripts/hardware_ci/run-cpu-test-arm.sh
index c2509a07b2c4..9c13fa79fcb2 100755
--- a/.buildkite/scripts/hardware_ci/run-cpu-test-arm.sh
+++ b/.buildkite/scripts/hardware_ci/run-cpu-test-arm.sh
@@ -31,6 +31,21 @@ function cpu_tests() {
     set -e
     pip list"
 
+  # Run kernel tests
+  docker exec cpu-test bash -c "
+    set -e
+    pytest -x -v -s tests/kernels/test_onednn.py
+    pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
+    pytest -x -v -s tests/kernels/core/test_cpu_activation.py
+    pytest -x -v -s tests/kernels/moe/test_moe.py -k test_cpu_fused_moe_basic"
+
+  # skip tests requiring model downloads if HF_TOKEN is not set
+  # due to rate-limits
+  if [ -z "$HF_TOKEN" ]; then
+    echo "Warning: HF_TOKEN is not set. Skipping tests that require model downloads."
+    return
+  fi
+
   # offline inference
   docker exec cpu-test bash -c "
     set -e
@@ -46,12 +61,6 @@ function cpu_tests() {
     set -e
     pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"
 
-  # Run kernel tests
-  docker exec cpu-test bash -c "
-    set -e
-    pytest -x -v -s tests/kernels/test_onednn.py
-    pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
-    pytest -x -v -s tests/kernels/moe/test_moe.py -k test_cpu_fused_moe_basic"
 
   # basic online serving
   docker exec cpu-test bash -c '
@@ -66,6 +75,21 @@ function cpu_tests() {
       --num-prompts 20 \
       --endpoint /v1/completions
     kill -s SIGTERM $server_pid &'
+
+  # smoke test for Gated DeltaNet
+  docker exec cpu-test bash -c '
+    set -e
+    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS vllm serve Qwen/Qwen3.5-0.8B --max-model-len 2048 &
+    server_pid=$!
+    timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
+    vllm bench serve \
+      --backend vllm \
+      --dataset-name random \
+      --model Qwen/Qwen3.5-0.8B \
+      --num-prompts 20 \
+      --endpoint /v1/completions
+    kill -s SIGTERM $server_pid &'
+
 }
 
 # All of CPU tests are expected to be finished less than 40 mins.
diff --git a/.buildkite/scripts/hardware_ci/run-cpu-test.sh b/.buildkite/scripts/hardware_ci/run-cpu-test.sh
index db75ad3083b2..27ec0068668f 100644
--- a/.buildkite/scripts/hardware_ci/run-cpu-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-cpu-test.sh
@@ -16,5 +16,5 @@ echo "--- :docker: Building Docker image"
 docker build --progress plain --tag "$IMAGE_NAME" --target vllm-test -f docker/Dockerfile.cpu .
 
 # Run the image, setting --shm-size=4g for tensor parallel.
-docker run --rm --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN -e VLLM_CPU_KVCACHE_SPACE=16 -e VLLM_CPU_CI_ENV=1 -e VLLM_CPU_SIM_MULTI_NUMA=1 --shm-size=4g "$IMAGE_NAME" \
+docker run --rm --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN -e VLLM_CPU_KVCACHE_SPACE=16 -e VLLM_CPU_CI_ENV=1 -e VLLM_CPU_SIM_MULTI_NUMA=1 -e VLLM_CPU_ATTN_SPLIT_KV=0 --shm-size=4g "$IMAGE_NAME" \
         timeout "$TIMEOUT_VAL" bash -c "set -euox pipefail; echo \"--- Print packages\"; pip list; echo \"--- Running tests\"; ${TEST_COMMAND}"
diff --git a/.buildkite/scripts/hardware_ci/run-intel-test.sh b/.buildkite/scripts/hardware_ci/run-intel-test.sh
index 836f0b849573..eae3b231b4be 100755
--- a/.buildkite/scripts/hardware_ci/run-intel-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-intel-test.sh
@@ -25,22 +25,100 @@ export PYTHONPATH=".."
 ###############################################################################
 
 cleanup_docker() {
+  # Share the same lock with image pull to avoid cleanup/pull races on one node.
+  local docker_lock="/tmp/docker-pull.lock"
+  exec 9>"$docker_lock"
+  flock 9
+
   docker_root=$(docker info -f '{{.DockerRootDir}}')
   if [ -z "$docker_root" ]; then
     echo "Failed to determine Docker root directory." >&2
-    exit 1
+    flock -u 9
+    return 1
   fi
   echo "Docker root directory: $docker_root"
 
   disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
   threshold=70
   if [ "$disk_usage" -gt "$threshold" ]; then
-    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
-    docker image prune -f
-    docker volume prune -f && docker system prune --force --filter "until=72h" --all
-    echo "Docker images and volumes cleanup completed."
+    echo "Disk usage is above $threshold%. Running aggressive CI image cleanup..."
+    cleanup_old_ci_images "${REGISTRY}/${REPO}" "${image_name}" "${DOCKER_IMAGE_CLEANUP_HOURS:-72}" 1
+  else
+    echo "Disk usage is below $threshold%. Checking old CI images anyway."
+    cleanup_old_ci_images "${REGISTRY}/${REPO}" "${image_name}" "${DOCKER_IMAGE_CLEANUP_HOURS:-72}" 0
+  fi
+  echo "Old CI image cleanup completed."
+
+  flock -u 9
+}
+
+cleanup_old_ci_images() {
+  local repo_prefix="$1"
+  local current_image_ref="$2"
+  local ttl_hours="$3"
+  local aggressive_cleanup="$4"
+
+  if [[ -z "$repo_prefix" || "$repo_prefix" == "/" ]]; then
+    echo "Skip old-image cleanup: invalid repo prefix '${repo_prefix}'"
+    return 0
+  fi
+
+  if ! [[ "$ttl_hours" =~ ^[0-9]+$ ]]; then
+    echo "Invalid DOCKER_IMAGE_CLEANUP_HOURS='${ttl_hours}', fallback to 72"
+    ttl_hours=72
+  fi
+
+  local now_epoch cutoff_epoch
+  now_epoch=$(date +%s)
+  cutoff_epoch=$((now_epoch - ttl_hours * 3600))
+
+  local -a used_image_ids
+  mapfile -t used_image_ids < <(docker ps -aq | xargs -r docker inspect --format '{{.Image}}' | sort -u)
+
+  local removed_count=0
+  local examined_count=0
+  declare -A seen_ids=()
+
+  while read -r image_ref image_id; do
+    [[ -z "$image_ref" || -z "$image_id" ]] && continue
+    ((examined_count++))
+
+    # Keep the image this job is going to use.
+    if [[ "$image_ref" == "$current_image_ref" ]]; then
+      continue
+    fi
+
+    # Avoid duplicate deletes when multiple tags point to same image id.
+    if [[ -n "${seen_ids[$image_id]:-}" ]]; then
+      continue
+    fi
+    seen_ids[$image_id]=1
+
+    # Never delete images that are used by any container on this node.
+    if printf '%s\n' "${used_image_ids[@]}" | grep -qx "$image_id"; then
+      continue
+    fi
+
+    local created created_epoch
+    created=$(docker image inspect -f '{{.Created}}' "$image_id" 2>/dev/null || true)
+    [[ -z "$created" ]] && continue
+    created_epoch=$(date -d "$created" +%s 2>/dev/null || true)
+    [[ -z "$created_epoch" ]] && continue
+
+    if (( created_epoch < cutoff_epoch )) || [[ "$aggressive_cleanup" == "1" ]]; then
+      if docker image rm -f "$image_id" >/dev/null 2>&1; then
+        ((removed_count++))
+      fi
+    fi
+  done < <(docker image ls --no-trunc "$repo_prefix" --format '{{.Repository}}:{{.Tag}} {{.ID}}')
+
+  # Also trim old dangling layers; this is safe and does not remove referenced images.
+  docker image prune -f --filter "until=${ttl_hours}h" >/dev/null 2>&1 || true
+
+  if [[ "$aggressive_cleanup" == "1" ]]; then
+    echo "Examined ${examined_count} images under ${repo_prefix}, removed ${removed_count} unused images under disk pressure."
   else
-    echo "Disk usage is below $threshold%. No cleanup needed."
+    echo "Examined ${examined_count} images under ${repo_prefix}, removed ${removed_count} old images (>${ttl_hours}h)."
   fi
 }
 
@@ -240,7 +318,6 @@ fi
 cleanup_docker
 
 aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
-aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
 
 # --- Build or pull test image ---
 IMAGE="${IMAGE_TAG_XPU:-${image_name}}"
@@ -266,8 +343,6 @@ fi
 
 remove_docker_container() {
   docker rm -f "${container_name}" || true
-  docker image rm -f "${image_name}" || true
-  docker system prune -f || true
 }
 trap remove_docker_container EXIT
 
@@ -283,6 +358,7 @@ docker run \
     --ipc=host \
     --privileged \
     -v /dev/dri/by-path:/dev/dri/by-path \
+    -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \
     --entrypoint="" \
     -e "HF_TOKEN=${HF_TOKEN:-}" \
     -e "ZE_AFFINITY_MASK=${ZE_AFFINITY_MASK:-}" \
diff --git a/.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh b/.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh
index feaf2b356267..61ebddf82e40 100755
--- a/.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh
@@ -136,8 +136,6 @@ run_and_track_test 3 "test_accuracy.py::test_lm_eval_accuracy_v1_engine" \
     "python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
 run_and_track_test 4 "test_quantization_accuracy.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py"
-run_and_track_test 5 "examples/offline_inference/tpu.py" \
-    "python3 /workspace/vllm/examples/offline_inference/tpu.py"
 run_and_track_test 6 "test_tpu_model_runner.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/worker/test_tpu_model_runner.py"
 run_and_track_test 7 "test_sampler.py" \
diff --git a/.buildkite/scripts/hardware_ci/run-xpu-test.sh b/.buildkite/scripts/hardware_ci/run-xpu-test.sh
index 6579810e9826..14bd08cfc1c4 100644
--- a/.buildkite/scripts/hardware_ci/run-xpu-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-xpu-test.sh
@@ -12,9 +12,7 @@ docker build -t "${image_name}" -f docker/Dockerfile.xpu .
 
 # Setup cleanup
 remove_docker_container() {
-  docker rm -f "${container_name}" || true;
-  docker image rm -f "${image_name}" || true;
-  docker system prune -f || true;
+  docker rm -f "${container_name}" || true
 }
 trap remove_docker_container EXIT
 
diff --git a/.buildkite/scripts/lib/manylinux.sh b/.buildkite/scripts/lib/manylinux.sh
new file mode 100644
index 000000000000..bde2dfe0a3dc
--- /dev/null
+++ b/.buildkite/scripts/lib/manylinux.sh
@@ -0,0 +1,127 @@
+#!/usr/bin/env bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Shared helper for rewriting a wheel's platform tag from the generic
+# ``linux_<arch>`` to the correct ``manylinux_<major>_<minor>_<arch>``.
+# After sourcing, call ``apply_manylinux_tag <wheel>`` on each wheel
+# that still carries the generic tag; the renamed path is printed on
+# stdout (logs go to stderr).
+#
+# Why a pinned Docker container instead of using whatever Python
+# happens to be on the agent:
+#   - vLLM's release agents are heterogeneous -- they don't agree on
+#     a Python minor version, and we can't rely on a particular
+#     ``auditwheel`` being installed.
+#   - ``detect-manylinux-tag.py`` reads ``auditwheel.wheel_abi`` and
+#     ``Policy.sym_policy``, which are *internal* APIs without a
+#     stability promise. Pinning both Python and auditwheel makes the
+#     detected tag a function of the inputs alone, and shifts version
+#     bumps from "implicit drift" to "deliberate, retested change".
+#   - Other release scripts (``generate-and-upload-nightly-index.sh``,
+#     ``upload-rocm-wheels.sh``) already use the python:3-slim image
+#     when the agent's interpreter is too old; this is the same idea
+#     made stricter.
+#
+# To keep the per-wheel cost down (the ROCm upload retags ~10 wheels
+# each run), we install auditwheel into a long-lived helper container
+# once on source, then ``docker exec`` into it for each call.
+#
+# Trap behaviour:
+# - Sourcing installs an EXIT trap that calls ``manylinux_cleanup`` to
+#   tear down the helper container. Any EXIT trap that was already in
+#   place when this file was sourced is captured and run AFTER our
+#   cleanup, so we don't silently clobber it.
+# - If a caller sets a new EXIT trap *after* sourcing, that trap will
+#   replace ours; in that case the caller should call
+#   ``manylinux_cleanup`` from their own handler.
+
+if [[ -n "${_MANYLINUX_LIB_SOURCED:-}" ]]; then
+    return 0
+fi
+_MANYLINUX_LIB_SOURCED=1
+
+# Pin both sides. Bump these deliberately and re-run a representative
+# wheel from each build target through the detection.
+_MANYLINUX_PYTHON_IMAGE="python:3.12-slim"
+_MANYLINUX_AUDITWHEEL_VERSION="6.6.0"
+
+# Resolve our own directory (and the sibling detect script) using the
+# canonical, symlink-resolved path. The container mounts cwd at the
+# same absolute path on both sides, so all paths we hand to it -- the
+# script, the wheel -- must canonicalise to a location under cwd.
+_MANYLINUX_LIB_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd -P)"
+_MANYLINUX_DETECT_SCRIPT="$(cd "${_MANYLINUX_LIB_DIR}/.." && pwd -P)/detect-manylinux-tag.py"
+_MANYLINUX_CWD="$(pwd -P)"
+
+docker pull --quiet "$_MANYLINUX_PYTHON_IMAGE" >/dev/null
+
+# Spin up a long-lived helper container so we install auditwheel once
+# and then ``docker exec`` into it for each wheel.
+#
+# The container runs as root so ``pip install`` can write into the
+# system site-packages; individual ``docker exec`` calls below pin
+# themselves to the host UID so any file rename happens with host
+# ownership, not root.
+_MANYLINUX_CONTAINER="$(docker run -d --rm \
+    -v "$_MANYLINUX_CWD:$_MANYLINUX_CWD" \
+    -w "$_MANYLINUX_CWD" \
+    "$_MANYLINUX_PYTHON_IMAGE" \
+    sleep infinity)"
+docker exec "$_MANYLINUX_CONTAINER" \
+    pip install --quiet --disable-pip-version-check \
+    --root-user-action=ignore \
+    "auditwheel==${_MANYLINUX_AUDITWHEEL_VERSION}"
+
+# Public cleanup -- safe to call multiple times.
+manylinux_cleanup() {
+    if [[ -n "${_MANYLINUX_CONTAINER:-}" ]]; then
+        docker rm -f "$_MANYLINUX_CONTAINER" >/dev/null 2>&1 || true
+        _MANYLINUX_CONTAINER=""
+    fi
+}
+
+# Capture any EXIT trap that was already in place so we can chain to
+# it rather than overwrite it. ``trap -p EXIT`` prints the handler in
+# eval-able form (``trap -- 'CMD' EXIT``) or nothing if unset; we
+# strip the wrapper to recover ``CMD``. Handles the common case --
+# CMDs without embedded single quotes -- and degrades gracefully (we
+# still run our own cleanup) for the pathological case.
+_manylinux_prev_exit_trap_cmd=""
+_manylinux_existing_exit_trap="$(trap -p EXIT)"
+if [[ -n "$_manylinux_existing_exit_trap" ]]; then
+    _tmp="${_manylinux_existing_exit_trap#trap -- \'}"
+    _manylinux_prev_exit_trap_cmd="${_tmp%\' EXIT}"
+    unset _tmp
+fi
+unset _manylinux_existing_exit_trap
+
+_manylinux_run_exit_chain() {
+    manylinux_cleanup
+    if [[ -n "$_manylinux_prev_exit_trap_cmd" ]]; then
+        eval "$_manylinux_prev_exit_trap_cmd"
+    fi
+}
+trap _manylinux_run_exit_chain EXIT
+
+# Detect the manylinux platform tag for a single wheel and rename it
+# in place, printing the renamed wheel path on stdout. Returns
+# non-zero on failure (which under ``set -e`` propagates to caller).
+#
+# The wheel must be reachable via a path under the host cwd so it's
+# visible inside the helper container; in CI the wheels always live
+# under ``artifacts/`` so this is fine.
+apply_manylinux_tag() {
+    local wheel="$1"
+    local abs_wheel
+    abs_wheel="$(realpath "$wheel")"
+    local new_wheel
+    new_wheel="$(docker exec -u "$(id -u):$(id -g)" \
+        "$_MANYLINUX_CONTAINER" \
+        python "$_MANYLINUX_DETECT_SCRIPT" "$abs_wheel")"
+    if [[ -z "$new_wheel" || ! -f "$new_wheel" ]]; then
+        echo "apply_manylinux_tag: detect-manylinux-tag.py did not produce a valid wheel path for $wheel" >&2
+        return 1
+    fi
+    printf '%s\n' "$new_wheel"
+}
diff --git a/.buildkite/scripts/lib/select-python.sh b/.buildkite/scripts/lib/select-python.sh
new file mode 100644
index 000000000000..bc53030a2b50
--- /dev/null
+++ b/.buildkite/scripts/lib/select-python.sh
@@ -0,0 +1,41 @@
+#!/usr/bin/env bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Pick a Python interpreter for buildkite scripts: prefer a local
+# ``python3`` if it is recent enough (>= 3.12), otherwise fall back to
+# a one-shot Docker container running ``python:3-slim``. After
+# ``select_python`` returns, ``$PYTHON`` is set in the caller's shell
+# and is safe to use as a command (e.g. ``$PYTHON some_script.py``).
+#
+# The 3.12 threshold matches what the existing nightly-index work
+# expects -- typing features used by ``generate-nightly-index.py``.
+# This helper does not pin the *minor* version; if you need stricter
+# reproducibility (e.g. relying on auditwheel internals), invoke
+# Docker yourself with a pinned tag rather than calling this.
+
+if [[ -n "${_SELECT_PYTHON_LIB_SOURCED:-}" ]]; then
+    return 0
+fi
+_SELECT_PYTHON_LIB_SOURCED=1
+
+# Sets ``PYTHON`` in the caller's shell and exports it. Idempotent --
+# calling twice is safe and the second call simply re-runs the probe.
+select_python() {
+    local py="${PYTHON_PROG:-python3}"
+    local has_new_python
+    has_new_python=$("$py" -c \
+        "print(1 if __import__('sys').version_info >= (3,12) else 0)" \
+        2>/dev/null || echo 0)
+    if [[ "$has_new_python" -eq 0 ]]; then
+        # ``-u $(id -u):$(id -g)`` so files created via the container
+        # end up owned by the host user, not root.
+        docker pull python:3-slim
+        PYTHON="docker run --rm -u $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
+    else
+        PYTHON="$py"
+    fi
+    export PYTHON
+    echo "Using python interpreter: $PYTHON"
+    echo "Python version: $($PYTHON --version)"
+}
diff --git a/.buildkite/scripts/publish-release-images.sh b/.buildkite/scripts/publish-release-images.sh
new file mode 100755
index 000000000000..ec319aa76006
--- /dev/null
+++ b/.buildkite/scripts/publish-release-images.sh
@@ -0,0 +1,180 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Publish release Docker images from ECR to DockerHub.
+# Pulls per-arch images, tags with latest and versioned tags, pushes them,
+# then creates and pushes multi-arch manifests.
+
+set -euo pipefail
+
+RELEASE_VERSION=$(buildkite-agent meta-data get release-version --default "" | sed 's/^v//')
+if [ -z "${RELEASE_VERSION}" ]; then
+  echo "ERROR: release-version metadata not set"
+  exit 1
+fi
+
+COMMIT="$BUILDKITE_COMMIT"
+ROCM_BASE_CACHE_KEY=$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
+
+echo "========================================"
+echo "Publishing release images v${RELEASE_VERSION}"
+echo "  Commit: ${COMMIT}"
+echo "  ROCm base cache key: ${ROCM_BASE_CACHE_KEY}"
+echo "========================================"
+
+# Login to ECR to pull staging images
+aws ecr-public get-login-password --region us-east-1 | \
+  docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
+
+# ---- CUDA (default: 13.0) ----
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64 vllm/vllm-openai:latest-x86_64
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
+docker push vllm/vllm-openai:latest-x86_64
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64 vllm/vllm-openai:latest-aarch64
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+docker push vllm/vllm-openai:latest-aarch64
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+
+docker manifest rm vllm/vllm-openai:latest || true
+docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION} || true
+docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+docker manifest push vllm/vllm-openai:latest
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
+
+# ---- CUDA 12.9 ----
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129 vllm/vllm-openai:latest-x86_64-cu129
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
+docker push vllm/vllm-openai:latest-x86_64-cu129
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129 vllm/vllm-openai:latest-aarch64-cu129
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
+docker push vllm/vllm-openai:latest-aarch64-cu129
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
+
+docker manifest rm vllm/vllm-openai:latest-cu129 || true
+docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-cu129 || true
+docker manifest create vllm/vllm-openai:latest-cu129 vllm/vllm-openai:latest-x86_64-cu129 vllm/vllm-openai:latest-aarch64-cu129
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
+docker manifest push vllm/vllm-openai:latest-cu129
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu129
+
+# ---- Ubuntu 24.04 (CUDA 13.0) ----
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404 vllm/vllm-openai:latest-x86_64-ubuntu2404
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404
+docker push vllm/vllm-openai:latest-x86_64-ubuntu2404
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404 vllm/vllm-openai:latest-aarch64-ubuntu2404
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
+docker push vllm/vllm-openai:latest-aarch64-ubuntu2404
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
+
+docker manifest rm vllm/vllm-openai:latest-ubuntu2404 || true
+docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404 || true
+docker manifest create vllm/vllm-openai:latest-ubuntu2404 vllm/vllm-openai:latest-x86_64-ubuntu2404 vllm/vllm-openai:latest-aarch64-ubuntu2404
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
+docker manifest push vllm/vllm-openai:latest-ubuntu2404
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404
+
+# ---- Ubuntu 24.04 (CUDA 12.9) ----
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404
+docker push vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404 vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
+docker push vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
+
+docker manifest rm vllm/vllm-openai:latest-cu129-ubuntu2404 || true
+docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404 || true
+docker manifest create vllm/vllm-openai:latest-cu129-ubuntu2404 vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404 vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
+docker manifest push vllm/vllm-openai:latest-cu129-ubuntu2404
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404
+
+# ---- ROCm ----
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm vllm/vllm-openai-rocm:latest
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+docker push vllm/vllm-openai-rocm:latest
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:latest-base
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+docker push vllm/vllm-openai-rocm:latest-base
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+
+# ---- CPU ----
+# CPU images are behind separate block steps and may not have been built.
+# All-or-nothing: inspect both arches first, then either publish everything
+# (per-arch + multi-arch manifest) or skip everything. Publishing only one
+# arch would leave `:latest-x86_64` pointing at the new release while the
+# `:latest` multi-arch manifest still resolves to the previous release.
+
+CPU_X86_TAG=public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION}
+CPU_ARM_TAG=public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION}
+
+CPU_X86_AVAILABLE=false
+CPU_ARM_AVAILABLE=false
+docker manifest inspect "${CPU_X86_TAG}" >/dev/null 2>&1 && CPU_X86_AVAILABLE=true
+docker manifest inspect "${CPU_ARM_TAG}" >/dev/null 2>&1 && CPU_ARM_AVAILABLE=true
+
+if [ "$CPU_X86_AVAILABLE" = "true" ] && [ "$CPU_ARM_AVAILABLE" = "true" ]; then
+  docker pull "${CPU_X86_TAG}"
+  docker tag "${CPU_X86_TAG}" vllm/vllm-openai-cpu:latest-x86_64
+  docker tag "${CPU_X86_TAG}" vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+  docker push vllm/vllm-openai-cpu:latest-x86_64
+  docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+
+  docker pull "${CPU_ARM_TAG}"
+  docker tag "${CPU_ARM_TAG}" vllm/vllm-openai-cpu:latest-arm64
+  docker tag "${CPU_ARM_TAG}" vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+  docker push vllm/vllm-openai-cpu:latest-arm64
+  docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+
+  docker manifest rm vllm/vllm-openai-cpu:latest || true
+  docker manifest rm vllm/vllm-openai-cpu:v${RELEASE_VERSION} || true
+  docker manifest create vllm/vllm-openai-cpu:latest vllm/vllm-openai-cpu:latest-x86_64 vllm/vllm-openai-cpu:latest-arm64
+  docker manifest create vllm/vllm-openai-cpu:v${RELEASE_VERSION} vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+  docker manifest push vllm/vllm-openai-cpu:latest
+  docker manifest push vllm/vllm-openai-cpu:v${RELEASE_VERSION}
+elif [ "$CPU_X86_AVAILABLE" = "false" ] && [ "$CPU_ARM_AVAILABLE" = "false" ]; then
+  echo "WARNING: Neither CPU image found in ECR, skipping CPU publish (ensure block-cpu-release-image-build and block-arm64-cpu-release-image-build were unblocked and the builds finished pushing)"
+else
+  # Partial state: one arch built, the other did not. Fail loudly rather than
+  # ship a Docker Hub state where `:latest-${arch}` and `:latest` (multi-arch)
+  # disagree on which release they point at.
+  echo "ERROR: Partial CPU build detected (x86_64=${CPU_X86_AVAILABLE}, arm64=${CPU_ARM_AVAILABLE})."
+  echo "       Refusing to publish to avoid split-tag drift between per-arch and multi-arch tags."
+  echo "       Re-run the missing CPU build and retry, or manually publish if a single-arch release is intended."
+  exit 1
+fi
+
+echo ""
+echo "Successfully published release images for v${RELEASE_VERSION}"
diff --git a/.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh b/.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh
index de48eb282a65..0eadfa1f80b4 100755
--- a/.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh
+++ b/.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh
@@ -51,6 +51,7 @@ vllm serve "$MODEL" \
   --offload-num-in-group 2 \
   --offload-prefetch-step 1 \
   --offload-params w13_weight w2_weight \
+  --generation-config vllm \
   --port "$PORT" \
   ${EXTRA_ARGS+"${EXTRA_ARGS[@]}"} &
 SERVER_PID=$!
diff --git a/.buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_dp4_async_eplb.sh b/.buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_dp4_async_eplb.sh
new file mode 100755
index 000000000000..06743f16b687
--- /dev/null
+++ b/.buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_dp4_async_eplb.sh
@@ -0,0 +1,55 @@
+#!/usr/bin/env bash
+set -euxo pipefail
+
+# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
+THRESHOLD=${1:-0.8}
+NUM_Q=${2:-1319}
+PORT=${3:-8050}
+OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
+mkdir -p "${OUT_DIR}"
+
+wait_for_server() {
+  local port=$1
+  timeout 600 bash -c '
+    until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
+      sleep 1
+    done'
+}
+
+MODEL="Qwen/Qwen3-30B-A3B-FP8"
+BACK="allgather_reducescatter"
+
+cleanup() {
+  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
+    kill "${SERVER_PID}" 2>/dev/null || true
+    for _ in {1..20}; do
+      kill -0 "${SERVER_PID}" 2>/dev/null || break
+      sleep 0.5
+    done
+    kill -9 "${SERVER_PID}" 2>/dev/null || true
+  fi
+}
+trap cleanup EXIT
+
+VLLM_DEEP_GEMM_WARMUP=skip \
+vllm serve "$MODEL" \
+--enforce-eager \
+--data-parallel-size 4 \
+--enable-expert-parallel \
+--enable-eplb \
+--all2all-backend "$BACK" \
+--eplb-config '{"window_size":20, "step_interval":100, "use_async":true}' \
+--trust-remote-code \
+--max-model-len 2048 \
+--port "$PORT" &
+SERVER_PID=$!
+wait_for_server "$PORT"
+
+TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
+OUT="${OUT_DIR}/${TAG}_${BACK}.json"
+python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
+python3 - <<PY
+import json; acc=json.load(open('${OUT}'))['accuracy']
+print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
+assert acc >= ${THRESHOLD}, f"${MODEL} ${BACK} accuracy {acc}"
+PY
diff --git a/.buildkite/scripts/tool_call/run-bfcl-eval.sh b/.buildkite/scripts/tool_call/run-bfcl-eval.sh
index f3e5009e6fe3..3748cab62c7c 100755
--- a/.buildkite/scripts/tool_call/run-bfcl-eval.sh
+++ b/.buildkite/scripts/tool_call/run-bfcl-eval.sh
@@ -28,6 +28,7 @@
 #   BFCL_MAX_MODEL_LEN  - Max model length (default: 4096)
 #   BFCL_PORT           - Server port (default: 8000)
 #   BFCL_REASONING_PARSER - Reasoning parser name (default: disabled)
+#   BFCL_TEMPERATURE    - Temperature (default: 0.0)
 #   BFCL_EXTRA_ARGS     - Additional vLLM server args
 
 set -euo pipefail
@@ -43,6 +44,7 @@ TP_SIZE="${BFCL_TP_SIZE:-1}"
 MAX_MODEL_LEN="${BFCL_MAX_MODEL_LEN:-4096}"
 PORT="${BFCL_PORT:-8000}"
 REASONING_PARSER="${BFCL_REASONING_PARSER:-}"
+TEMPERATURE="${BFCL_TEMPERATURE:-0.0}"
 EXTRA_ARGS="${BFCL_EXTRA_ARGS:-}"
 
 # Set up output directory
@@ -139,7 +141,7 @@ echo "vLLM server is ready. (started in ${SECONDS_WAITED}s)"
 # be patched in-process so BFCL knows to use the OpenAI-compatible handler
 # against our local vLLM server.
 bfcl_exit_code=0
-python3 - "$MODEL" "$TEST_CATEGORY" "$NUM_THREADS" "$PORT" "$API_TYPE" "$OUTPUT_DIR" << 'PYEOF' || bfcl_exit_code=$?
+python3 - "$MODEL" "$TEST_CATEGORY" "$NUM_THREADS" "$PORT" "$API_TYPE" "$TEMPERATURE" "$OUTPUT_DIR" << 'PYEOF' || bfcl_exit_code=$?
 import os
 import sys
 
@@ -148,7 +150,8 @@ test_category = sys.argv[2]
 num_threads = int(sys.argv[3])
 port = sys.argv[4]
 api_type = sys.argv[5]
-output_dir = sys.argv[6] if len(sys.argv) > 6 and sys.argv[6] else os.getcwd()
+temperature = float(sys.argv[6])
+output_dir = sys.argv[7] if len(sys.argv) > 7 and sys.argv[7] else os.getcwd()
 
 os.environ["OPENAI_BASE_URL"] = f"http://localhost:{port}/v1"
 os.environ["OPENAI_API_KEY"] = "dummy"
@@ -204,6 +207,7 @@ gen_kwargs["model"] = [model]
 gen_kwargs["test_category"] = [c.strip() for c in test_category.split(",")]
 gen_kwargs["skip_server_setup"] = True
 gen_kwargs["num_threads"] = num_threads
+gen_kwargs["temperature"] = temperature
 generate(**gen_kwargs)
 
 # ---- evaluate ----
diff --git a/.buildkite/scripts/upload-nightly-wheels.sh b/.buildkite/scripts/upload-nightly-wheels.sh
index cc72cda7d505..8cef31908809 100644
--- a/.buildkite/scripts/upload-nightly-wheels.sh
+++ b/.buildkite/scripts/upload-nightly-wheels.sh
@@ -2,14 +2,18 @@
 
 set -ex
 
-# Upload a single wheel to S3 (rename linux -> manylinux).
+# Upload a single wheel to S3, after detecting and applying the appropriate
+# manylinux platform tag with auditwheel.
 # Index generation is handled separately by generate-and-upload-nightly-index.sh.
 
+# shellcheck source=lib/manylinux.sh
+source .buildkite/scripts/lib/manylinux.sh
+
 BUCKET="vllm-wheels"
 SUBPATH=$BUILDKITE_COMMIT
 S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
 
-# ========= collect, rename & upload the wheel ==========
+# ========= locate the wheel ==========
 
 # Assume wheels are in artifacts/dist/*.whl
 wheel_files=(artifacts/dist/*.whl)
@@ -21,19 +25,9 @@ if [[ ${#wheel_files[@]} -ne 1 ]]; then
 fi
 wheel="${wheel_files[0]}"
 
-# default build image uses ubuntu 20.04, which corresponds to manylinux_2_31
-# we also accept params as manylinux tag
-# refer to https://github.com/mayeut/pep600_compliance?tab=readme-ov-file#acceptable-distros-to-build-wheels
-manylinux_version="${1:-manylinux_2_31}"
+# ========= detect manylinux tag and rename ==========
 
-# Rename 'linux' to the appropriate manylinux version in the wheel filename
-if [[ "$wheel" != *"linux"* ]]; then
-  echo "Error: Wheel filename does not contain 'linux': $wheel"
-  exit 1
-fi
-new_wheel="${wheel/linux/$manylinux_version}"
-mv -- "$wheel" "$new_wheel"
-wheel="$new_wheel"
+wheel="$(apply_manylinux_tag "$wheel")"
 echo "Renamed wheel to: $wheel"
 
 # Extract the version from the wheel
diff --git a/.buildkite/scripts/upload-release-wheels-pypi.sh b/.buildkite/scripts/upload-release-wheels-pypi.sh
index 058e5bbe4f4c..7e2077a2692c 100644
--- a/.buildkite/scripts/upload-release-wheels-pypi.sh
+++ b/.buildkite/scripts/upload-release-wheels-pypi.sh
@@ -39,10 +39,11 @@ fi
 
 set -x # avoid printing secrets above
 
-# install twine from pypi
+# install twine and sdist build prerequisites from pypi
 python3 -m venv /tmp/vllm-release-env
 source /tmp/vllm-release-env/bin/activate
 pip install twine
+pip install -r requirements/build/cuda.txt
 python3 -m twine --version
 
 # copy release wheels to local directory
diff --git a/.buildkite/scripts/upload-rocm-wheels.sh b/.buildkite/scripts/upload-rocm-wheels.sh
index a42848a16ffe..1f3655631204 100755
--- a/.buildkite/scripts/upload-rocm-wheels.sh
+++ b/.buildkite/scripts/upload-rocm-wheels.sh
@@ -20,10 +20,6 @@ BUCKET="${S3_BUCKET:-vllm-wheels}"
 ROCM_SUBPATH="rocm/${BUILDKITE_COMMIT}"
 S3_COMMIT_PREFIX="s3://$BUCKET/$ROCM_SUBPATH/"
 INDICES_OUTPUT_DIR="rocm-indices"
-PYTHON="${PYTHON_PROG:-python3}"
-
-# ROCm uses manylinux_2_35 (Ubuntu 22.04 based)
-MANYLINUX_VERSION="manylinux_2_35"
 
 echo "========================================"
 echo "ROCm Wheel Upload Configuration"
@@ -34,19 +30,21 @@ echo "Commit: $BUILDKITE_COMMIT"
 echo "Branch: $BUILDKITE_BRANCH"
 echo "========================================"
 
-# ======== Part 0: Setup Python ========
+# ======== Part 0: Setup Python and helpers ========
 
-# Detect if python3.12+ is available
-has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)" 2>/dev/null || echo 0)
-if [[ "$has_new_python" -eq 0 ]]; then
-    # Use new python from docker
-    # Use --user to ensure files are created with correct ownership (not root)
-    docker pull python:3-slim
-    PYTHON="docker run --rm --user $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
-fi
+# Pick a Python interpreter for index generation -- local if recent
+# enough, else a one-shot docker fallback.
+# shellcheck source=lib/select-python.sh
+source .buildkite/scripts/lib/select-python.sh
+select_python
 
-echo "Using python interpreter: $PYTHON"
-echo "Python version: $($PYTHON --version)"
+# Set up auditwheel-in-a-container for the manylinux retagging step.
+# Distinct from select_python: ``manylinux.sh`` deliberately pins both
+# the Python and auditwheel versions (the script reads auditwheel
+# internals) and so always runs in a known-good container regardless
+# of what's on the agent.
+# shellcheck source=lib/manylinux.sh
+source .buildkite/scripts/lib/manylinux.sh
 
 # ======== Part 1: Collect and prepare wheels ========
 
@@ -63,11 +61,18 @@ if [ "$WHEEL_COUNT" -eq 0 ]; then
     exit 1
 fi
 
-# Rename linux to manylinux in wheel filenames
+# Detect the appropriate manylinux platform tag for any wheel that still
+# carries the generic ``linux_<arch>`` tag, and rename it in place. We use
+# auditwheel via ``apply_manylinux_tag`` (see lib/manylinux.sh) rather than
+# a hard-coded ``manylinux_2_35`` string so that the label tracks the actual
+# glibc symbol versions used by the binaries (and stays correct if the
+# rocm_base image is rebased).
+#
+# The ``linux``/``manylinux`` filter below skips both pre-tagged wheels
+# (e.g. upstream torch) and pure-Python ``-any.whl`` wheels.
 for wheel in all-rocm-wheels/*.whl; do
     if [[ "$wheel" == *"linux"* ]] && [[ "$wheel" != *"manylinux"* ]]; then
-        new_wheel="${wheel/linux/$MANYLINUX_VERSION}"
-        mv -- "$wheel" "$new_wheel"
+        new_wheel="$(apply_manylinux_tag "$wheel")"
         echo "Renamed: $(basename "$wheel") -> $(basename "$new_wheel")"
     fi
 done
diff --git a/.buildkite/test-amd.yaml b/.buildkite/test-amd.yaml
index dda5d4064c3d..c5369950fb30 100644
--- a/.buildkite/test-amd.yaml
+++ b/.buildkite/test-amd.yaml
@@ -1,8 +1,8 @@
 # In this file, you can add more tests to run either by adding a new step or
 # adding a new command to an existing step. See different options here for examples.
 
-# This script will be feed into Jinja template in `test-template-aws.j2` at
-# https://github.com/vllm-project/buildkite-ci/blob/main/scripts/test-template-aws.j2
+# This script will be feed into Jinja template in `test-template-amd.j2` at
+# https://github.com/vllm-project/buildkite-ci/blob/main/scripts/test-template-amd.j2
 # to generate the final pipeline yaml file.
 
 # Documentation
@@ -39,8 +39,8 @@
 #####################################################################################################################################
 #                                                                                                                                   #
 # IMPORTANT:                                                                                                                        #
-#   * Currently AMD CI has MI250 agents, MI325 agents, and MI355 agents. All upcoming feature improvements are tracked in:          #
-#         https://github.com/vllm-project/vllm/issues/34994                                                                         #
+#   * Currently AMD CI has MI250 agents, MI300 agents, MI325 agents, and MI355 agents. All upcoming feature improvements are        #
+#         tracked in: https://github.com/vllm-project/vllm/issues/34994                                                             #
 #                                                                                                                                   #
 #-----------------------------------------------------------------------------------------------------------------------------------#
 #                                                                                                                                   #
@@ -104,193 +104,193 @@
 #####################################################################################################################################
 
 
-
-
 steps:
 
+#########################################################################################################################################
+#                                                                                                                                       #
+#                                                         MI250 (gfx90a) tests                                                          #
+#                                                                                                                                       #
+#########################################################################################################################################
 
-#####################################################################################################################################
-#                                                                                                                                   #
-#  MI250 test definitions ( currently the test set is completely mirrored // TBD which tests are to be routed there ultimately)     #
-#                                                                                                                                   #
-#####################################################################################################################################
+#-----------------------------------------------------  mi250 · basic_correctness  -----------------------------------------------------#
 
-- label: Pytorch Nightly Dependency Override Check # TBD
+- label: Distributed Model Tests (2 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  optional: true
-  soft_fail: true
+  agent_pool: mi250_2
+  num_gpus: 2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - requirements/test/nightly-torch.txt
+  - vllm/model_executor/model_loader/sharded_state_loader.py
+  - vllm/model_executor/models/
+  - vllm/model_executor/layers/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - tests/basic_correctness/
+  - tests/model_executor/model_loader/test_sharded_state_loader.py
+  - tests/models/
+  - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-  - bash standalone_tests/pytorch_nightly_dependency.sh
+  - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
+  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py -m '(not slow_test)'
+  - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
+  - pytest models/language -v -s -m 'distributed(num_gpus=2)'
+  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest models/multimodal/generation/test_whisper.py -v -s -m 'distributed(num_gpus=2)'
 
+#--------------------------------------------------------  mi250 · benchmarks  ---------------------------------------------------------#
 
-- label: Async Engine, Inputs, Utils, Worker # TBD
+- label: Benchmarks # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
-  working_dir: "/vllm-workspace/tests"
+  working_dir: "/vllm-workspace/.buildkite"
   source_file_dependencies:
-  - vllm/
-  - tests/detokenizer
-  - tests/multimodal
-  - tests/utils_
+  - benchmarks/
+  - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s detokenizer
-  - pytest -v -s -m 'not cpu_test' multimodal
-  - pytest -v -s utils_
+  - bash scripts/run-benchmarks.sh
 
+#----------------------------------------------------------  mi250 · compile  ----------------------------------------------------------#
 
-- label: Async Engine, Inputs, Utils, Worker, Config (CPU) # TBD
+- label: PyTorch Compilation Unit Tests # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
+  torch_nightly: true
   optional: true
-  no_gpu: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/test_inputs.py
-  - tests/test_outputs.py
-  - tests/test_pooling_params.py
-  - tests/test_ray_env.py
-  - tests/multimodal
-  - tests/renderers
-  - tests/standalone_tests/lazy_imports.py
-  - tests/tokenizers_
-  - tests/tool_parsers
-  - tests/transformers_utils
-  - tests/config
+  - vllm/compilation/
+  - vllm/model_executor/layers/
+  - vllm/v1/worker/
+  - vllm/v1/attention/
+  - vllm/v1/cudagraph_dispatcher.py
+  - vllm/config/compilation.py
+  - csrc/
+  - tests/compile
+  - vllm/platforms/rocm.py
   commands:
-  - python3 standalone_tests/lazy_imports.py
-  - pytest -v -s test_inputs.py
-  - pytest -v -s test_outputs.py
-  - pytest -v -s test_pooling_params.py
-  - pytest -v -s test_ray_env.py
-  - pytest -v -s -m 'cpu_test' multimodal
-  - pytest -v -s renderers
-  - pytest -v -s tokenizers_
-  - pytest -v -s tool_parsers
-  - pytest -v -s transformers_utils
-  - pytest -v -s config
-
+  - "find compile/ -maxdepth 1 -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
-- label: Python-only Installation # TBD
+- label: PyTorch Fullgraph # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   optional: true
+  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - tests/standalone_tests/python_only_compile.sh
-  - setup.py
+  - vllm/compilation/
+  - vllm/model_executor/
+  - vllm/v1/attention/
+  - vllm/config/compilation.py
+  - csrc/
+  - tests/compile
   - vllm/platforms/rocm.py
   commands:
-  - bash standalone_tests/python_only_compile.sh
-
+  - pytest -v -s compile/fullgraph/test_full_graph.py -k 'not test_fp8_kv_scale_compile'
 
-- label: Basic Correctness # TBD
+- label: PyTorch Fullgraph Smoke Test # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
-  fast_check: true
+  optional: true
   torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/basic_correctness/test_basic_correctness
-  - tests/basic_correctness/test_cpu_offload
-  - tests/basic_correctness/test_cumem.py
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s basic_correctness/test_cumem.py
-  - pytest -v -s basic_correctness/test_basic_correctness.py
-  - pytest -v -s basic_correctness/test_cpu_offload.py
-
-
-- label: Entrypoints Unit Tests # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  fast_check: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/entrypoints
-  - tests/entrypoints/
+  - vllm/compilation/
+  - vllm/model_executor/
+  - vllm/v1/attention/
+  - vllm/config/compilation.py
+  - csrc/
+  - tests/compile
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s entrypoints/openai/tool_parsers
-  - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/rpc --ignore=entrypoints/sleep --ignore=entrypoints/serve/instrumentator --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling
-
+  - "find compile/fullgraph/ -name 'test_*.py' -not -name 'test_full_graph.py' -exec pytest -s -v {} \\\\;"
 
-- label: Entrypoints Integration (LLM) # TBD
+- label: Distributed Compile + RPC Tests (2 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  fast_check: true
-  torch_nightly: true
+  agent_pool: mi250_2
+  num_gpus: 2
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/llm
-  - tests/entrypoints/offline_mode
+  - vllm/compilation/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/worker/worker_base.py
+  - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/compile/fullgraph/test_basic_correctness.py
+  - tests/compile/test_wrapper.py
+  - tests/entrypoints/llm/test_collective_rpc.py
+  - vllm/platforms/rocm.py
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
-  - pytest -v -s entrypoints/llm/test_generate.py
-  - pytest -v -s entrypoints/offline_mode
+  - pytest -v -s entrypoints/llm/test_collective_rpc.py
+  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
+  - pytest -v -s ./compile/test_wrapper.py
 
+#--------------------------------------------------------  mi250 · distributed  --------------------------------------------------------#
 
-- label: Entrypoints Integration (API Server 2) # TBD
+- label: Distributed Comm Ops # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  fast_check: true
-  torch_nightly: true
+  agent_pool: mi250_2
+  num_gpus: 2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/rpc
-  - tests/entrypoints/serve/instrumentator
-  - tests/tool_use
+  - vllm/distributed
+  - tests/distributed
+  - vllm/platforms/rocm.py
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/serve/instrumentator
-  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/rpc
-  - pytest -v -s tool_use
-
+  - pytest -v -s distributed/test_comm_ops.py
+  - pytest -v -s distributed/test_shm_broadcast.py
+  - pytest -v -s distributed/test_shm_buffer.py
+  - pytest -v -s distributed/test_shm_storage.py
 
-- label: Entrypoints Integration (Responses API) # TBD
+- label: Distributed Torchrun + Shutdown Tests (2 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  fast_check: true
-  torch_nightly: true
+  agent_pool: mi250_2
+  num_gpus: 2
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/openai/responses
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/worker/worker_base.py
+  - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/distributed/
+  - tests/v1/shutdown
+  - tests/v1/worker/test_worker_memory_snapshot.py
+  - vllm/platforms/rocm.py
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai/responses
-
+  - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
+  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
+  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
+  - pytest -v -s v1/worker/test_worker_memory_snapshot.py
 
-- label: EPLB Algorithm # TBD
+- label: Elastic EP Scaling Test # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
+  agent_pool: mi250_4
+  num_gpus: 4
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/distributed/eplb
-  - tests/distributed/test_eplb_algo.py
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/compilation/
+  - tests/distributed/
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s distributed/test_eplb_algo.py
-
+  - pytest -v -s distributed/test_elastic_ep.py
 
 - label: EPLB Execution # TBD
   timeout_in_minutes: 180
@@ -307,8 +307,7 @@ steps:
   - pytest -v -s distributed/test_eplb_execute.py
   - pytest -v -s distributed/test_eplb_spec_decode.py
 
-
-- label: Elastic EP Scaling Test # TBD
+- label: Pipeline + Context Parallelism (4 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_4
@@ -318,338 +317,303 @@ steps:
   - vllm/distributed/
   - vllm/engine/
   - vllm/executor/
-  - vllm/compilation/
+  - vllm/model_executor/models/
+  - vllm/model_executor/layers/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
   - tests/distributed/
+  - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s distributed/test_elastic_ep.py
+  - pytest -v -s distributed/test_pp_cudagraph.py
+  - pytest -v -s distributed/test_pipeline_parallel.py
 
+#----------------------------------------------------------  mi250 · engine  -----------------------------------------------------------#
 
-- label: Metrics, Tracing (2 GPUs) # TBD
+- label: Engine # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
-  num_gpus: 2
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/v1/tracing
+  - tests/engine
+  - tests/test_sequence
+  - tests/test_config
+  - tests/test_logger
+  - tests/test_vllm_port
   commands:
-  - "pip install \
-      'opentelemetry-sdk>=1.26.0' \
-      'opentelemetry-api>=1.26.0' \
-      'opentelemetry-exporter-otlp>=1.26.0' \
-      'opentelemetry-semantic-conventions-ai>=0.4.1'"
-  - pytest -v -s v1/tracing
+  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
 
+#-----------------------------------------------------------  mi250 · evals  -----------------------------------------------------------#
 
-- label: Regression # TBD
+- label: Multi-Modal Accuracy Eval (Small Models) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
-  working_dir: "/vllm-workspace/tests"
+  optional: true
+  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
   source_file_dependencies:
-  - vllm/
-  - tests/test_regression
+  - vllm/multimodal/
+  - vllm/inputs/
+  - vllm/v1/core/
+  - vllm/platforms/rocm.py
+  - vllm/model_executor/model_loader/
   commands:
-  - pip install modelscope
-  - pytest -v -s test_regression.py
+  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
 
+#---------------------------------------------------------  mi250 · examples  ----------------------------------------------------------#
 
-- label: Engine # TBD
+- label: Examples # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   optional: true
-  working_dir: "/vllm-workspace/tests"
+  working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
-  - vllm/
-  - tests/engine
-  - tests/test_sequence
-  - tests/test_config
-  - tests/test_logger
-  - tests/test_vllm_port
+  - vllm/entrypoints
+  - vllm/multimodal
+  - examples/
+  - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
+    - pip install tensorizer
+    # Basic
+    - python3 basic/offline_inference/chat.py --attention-backend TRITON_ATTN
+    - python3 basic/offline_inference/generate.py --model facebook/opt-125m
+    - python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10
+    - python3 basic/offline_inference/classify.py
+    - python3 basic/offline_inference/embed.py
+    - python3 basic/offline_inference/score.py
+    # Multi-modal models
+    - python3 generate/multimodal/audio_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
+    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+    # Pooling models
+    - python3 pooling/embed/vision_embedding_offline.py --seed 0
+    # Features demo
+    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
+    - python3 deployment/llm_engine_example.py
+    - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
+#----------------------------------------------------------  mi250 · kernels  ----------------------------------------------------------#
 
-- label: Engine (1 GPU) # TBD
+- label: Kernels Core Operation Test # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/v1/
-  - tests/v1/engine/
+  - csrc/
+  - tests/kernels/core
+  - tests/kernels/test_top_k_per_row.py
+  - tests/kernels/test_concat_mla_q.py
+  - vllm/model_executor/layers/rotary_embedding/
+  - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s v1/engine/test_preprocess_error_handling.py
-  - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
-
+  - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py  kernels/test_concat_mla_q.py kernels/test_top_k_per_row.py
 
-- label: e2e Scheduling (1 GPU) # TBD
+- label: Kernels Helion Test # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/v1/
-  - tests/v1/e2e/general/
+  - vllm/utils/import_utils.py
+  - tests/kernels/helion/
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s v1/e2e/general/test_async_scheduling.py
-
+  - pip install helion==1.0.0
+  - pytest -v -s kernels/helion/
 
-- label: e2e Core (1 GPU) # TBD
+- label: Kernels Mamba Test # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/v1/
-  - tests/v1/e2e/general/
+  - csrc/mamba/
+  - tests/kernels/mamba
+  - vllm/model_executor/layers/mamba/ops
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
+  - pytest -v -s kernels/mamba
 
+#-----------------------------------------------------------  mi250 · lora  ------------------------------------------------------------#
 
-- label: Spec Decode Speculators + MTP # TBD
+- label: LoRA %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
+  parallelism: 4
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/v1/spec_decode/
-  - vllm/v1/worker/gpu/spec_decode/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/sample/
-  - vllm/model_executor/layers/
-  - vllm/transformers_utils/configs/speculators/
-  - tests/v1/e2e/spec_decode/
+  - vllm/lora
+  - tests/lora
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
+  - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_llm_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py --ignore=lora/test_qwen35_densemodel_lora.py
 
+#------------------------------------------------------  mi250 · model_executor  -------------------------------------------------------#
 
-- label: Spec Decode Ngram + Suffix # TBD
+- label: Model Executor # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   optional: true
+  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/v1/spec_decode/
-  - vllm/v1/worker/gpu/spec_decode/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/sample/
-  - vllm/model_executor/layers/
-  - tests/v1/e2e/spec_decode/
+  - vllm/engine/arg_utils.py
+  - vllm/config/model.py
+  - vllm/model_executor
+  - tests/model_executor
+  - tests/entrypoints/openai/completion/test_tensorizer_entrypoint.py
+  - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s v1/e2e/spec_decode -k "ngram or suffix"
+  - apt-get update && apt-get install -y curl libsodium23
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s model_executor -m '(not slow_test)'
+  - pytest -v -s entrypoints/openai/completion/test_tensorizer_entrypoint.py
 
+#----------------------------------------------------------  mi250 · models  -----------------------------------------------------------#
 
-- label: Spec Decode Draft Model # TBD
+- label: Basic Models Test (Other CPU) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
+  no_gpu: true
   optional: true
+  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/v1/spec_decode/
-  - vllm/v1/worker/gpu/spec_decode/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/sample/
-  - vllm/model_executor/layers/
-  - tests/v1/e2e/spec_decode/
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/models/test_utils.py
+  - tests/models/test_vision.py
   commands:
-  - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
-
+  - pytest -v -s models/test_utils.py models/test_vision.py
 
-- label: V1 e2e (2 GPUs) # TBD
+- label: Basic Models Tests (Extra Initialization) %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
-  optional: true
+  torch_nightly: true
+  parallelism: 2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/v1/e2e
+  - vllm/model_executor/models/
+  - vllm/model_executor/layers/
+  - tests/models/test_initialization.py
+  - tests/models/registry.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
   commands:
-    - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "tensor_parallelism"
-
+  - pytest -v -s models/test_initialization.py -k 'not test_can_initialize_small_subset' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
 
-- label: V1 Sample + Logits # TBD
-  timeout_in_minutes: 60
+- label: Basic Models Tests (Initialization) # TBD
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
-  optional: true
+  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/v1/sample
-  - tests/v1/logits_processors
-  - tests/v1/test_oracle.py
-  - tests/v1/test_request.py
-  - tests/v1/test_outputs.py
+  - tests/models/test_initialization.py
+  - tests/models/registry.py
   commands:
-  - pytest -v -s v1/sample
-  - pytest -v -s v1/logits_processors
-  - pytest -v -s v1/test_oracle.py
-  - pytest -v -s v1/test_request.py
-  - pytest -v -s v1/test_outputs.py
-
+  - pytest -v -s models/test_initialization.py::test_can_initialize_small_subset
 
-- label: V1 Core + KV + Metrics # TBD
-  timeout_in_minutes: 60
+- label: Basic Models Tests (Other) # TBD
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
-  optional: true
+  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/v1/core
-  - tests/v1/executor
-  - tests/v1/kv_offload
-  - tests/v1/worker
-  - tests/v1/kv_connector/unit
-  - tests/v1/metrics
-  - tests/entrypoints/openai/correctness/test_lmeval.py
+  - tests/models/test_terratorch.py
+  - tests/models/test_transformers.py
+  - tests/models/test_registry.py
   commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - pytest -v -s -m 'not cpu_test' v1/core
-  - pytest -v -s v1/executor
-  - pytest -v -s v1/kv_offload
-  - pytest -v -s v1/worker
-  - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
-  - pytest -v -s -m 'not cpu_test' v1/metrics
-  - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
-  - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
-
+  - pytest -v -s models/test_terratorch.py models/test_transformers.py models/test_registry.py
 
-- label: V1 attention (H100-MI250) # TBD
+- label: Language Models Test (MTEB) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/config/attention.py
-  - vllm/model_executor/layers/attention
-  - vllm/v1/attention
-  - tests/v1/attention
-  - vllm/_aiter_ops.py
-  - vllm/envs.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/models/language/pooling_mteb_test
   commands:
-  - pytest -v -s v1/attention
-
+  - pytest -v -s models/language/pooling_mteb_test
 
-- label: V1 others (CPU) # TBD
+- label: Language Models Test (PPL) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
-  no_gpu: true
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/v1
+  - tests/models/language/generation_ppl_test
   commands:
-  - pytest -v -s -m 'cpu_test' v1/core
-  - pytest -v -s v1/structured_output
-  - pytest -v -s v1/test_serial_utils.py
-  - pytest -v -s -m 'cpu_test' v1/kv_connector/unit
-  - pytest -v -s -m 'cpu_test' v1/metrics
-
+  - pytest -v -s models/language/generation_ppl_test
 
-- label: Examples # TBD
+- label: Language Models Tests (Extra Standard) %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
-  optional: true
-  working_dir: "/vllm-workspace/examples"
+  torch_nightly: true
+  parallelism: 2
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/entrypoints
-  - vllm/multimodal
-  - examples/
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/model_executor/layers/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - tests/models/language/pooling/test_embedding.py
+  - tests/models/language/generation/test_common.py
+  - tests/models/language/pooling/test_classification.py
+  - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-    - pip install tensorizer
-    # Basic
-    - python3 basic/offline_inference/chat.py --attention-backend TRITON_ATTN
-    - python3 basic/offline_inference/generate.py --model facebook/opt-125m
-    - python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10
-    - python3 basic/offline_inference/classify.py
-    - python3 basic/offline_inference/embed.py
-    - python3 basic/offline_inference/score.py
-    # Multi-modal models
-    - python3 offline_inference/audio_language.py --seed 0
-    - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
-    # Pooling models
-    - python3 pooling/embed/vision_embedding_offline.py --seed 0
-    # Features demo
-    - python3 offline_inference/prefix_caching.py
-    - python3 offline_inference/llm_engine_example.py
-    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
-
+  - pip freeze | grep -E 'torch'
+  - pytest -v -s models/language -m 'core_model and slow_test' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
 
-- label: Platform Tests (CUDA) # TBD
+- label: Multi-Modal Models (Extended Generation 2) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/cuda
+  - tests/models/multimodal/generation
   commands:
-  - pytest -v -s cuda/test_cuda_context.py
-  - pytest -v -s cuda/test_platform_no_cuda_init.py
-
+  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+  - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
 
-- label: Samplers Test # TBD
+- label: Multi-Modal Models (Extended Pooling) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/layers
-  - vllm/sampling_metadata.py
-  - vllm/v1/sample/
-  - vllm/beam_search.py
-  - tests/samplers
-  - tests/conftest.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s samplers
-
-
-- label: LoRA %N # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  parallelism: 4
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/lora
-  - tests/lora
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/models/multimodal/pooling
   commands:
-  - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_llm_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py
-
+  - pytest -v -s models/multimodal/pooling -m 'not core_model'
 
-- label: PyTorch Compilation Unit Tests # TBD
+- label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl" # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
@@ -657,56 +621,75 @@ steps:
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/compilation/
-  - vllm/model_executor/layers/
-  - vllm/v1/worker/
-  - vllm/v1/attention/
-  - vllm/v1/cudagraph_dispatcher.py
-  - vllm/config/compilation.py
-  - csrc/
-  - tests/compile
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/models/multimodal
   commands:
-  - "find compile/ -maxdepth 1 -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
+  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "not qwen2 and not qwen3 and not gemma"
+  - pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model
 
+#----------------------------------------------------------  mi250 · plugins  ----------------------------------------------------------#
 
-- label: PyTorch Fullgraph Smoke Test # TBD
+- label: Plugin Tests (2 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
+  agent_pool: mi250_2
+  num_gpus: 2
   optional: true
-  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/compilation/
-  - vllm/model_executor/
-  - vllm/v1/attention/
-  - vllm/config/compilation.py
-  - csrc/
-  - tests/compile
+  - vllm/plugins/
+  - tests/plugins/
   - vllm/platforms/rocm.py
   commands:
-  - "find compile/fullgraph/ -name 'test_*.py' -not -name 'test_full_graph.py' -exec pytest -s -v {} \\\\;"
+  # BEGIN: platform plugin and general plugin tests, all the code in-between runs on dummy platform
+  - pip install -e ./plugins/vllm_add_dummy_platform
+  - pytest -v -s plugins_tests/test_platform_plugins.py
+  - pip uninstall vllm_add_dummy_platform -y
+  # END: platform plugin tests
+  # BEGIN: `io_processor` plugins test, all the code in between uses the `prithvi_io_processor` plugin
+  - pip install -e ./plugins/prithvi_io_processor_plugin
+  - pytest -v -s plugins_tests/test_io_processor_plugins.py
+  - pytest -v -s plugins_tests/test_terratorch_io_processor_plugins.py
+  - pip uninstall prithvi_io_processor_plugin -y
+  # END: `io_processor` plugins test
+  # BEGIN: `bge_m3_sparse io_processor` test
+  - pip install -e ./plugins/bge_m3_sparse_plugin
+  - pytest -v -s plugins_tests/test_bge_m3_sparse_io_processor_plugins.py
+  - pip uninstall bge_m3_sparse_plugin -y
+  # END: `bge_m3_sparse io_processor` test
+  # BEGIN: `stat_logger` plugins test
+  - pip install -e ./plugins/vllm_add_dummy_stat_logger
+  - pytest -v -s plugins_tests/test_stats_logger_plugins.py
+  - pip uninstall dummy_stat_logger -y
+  # END: `stat_logger` plugins test
+  # BEGIN: other tests
+  - pytest -v -s plugins_tests/test_scheduler_plugins.py
+  - pip install -e ./plugins/vllm_add_dummy_model
+  - pytest -v -s distributed/test_distributed_oot.py
+  - pytest -v -s entrypoints/openai/chat_completion/test_oot_registration.py
+  - pytest -v -s models/test_oot_registration.py
+  - pytest -v -s plugins/lora_resolvers
 
+#------------------------------------------------------------  mi250 · v1  -------------------------------------------------------------#
 
-- label: PyTorch Fullgraph # TBD
+- label: Batch Invariance (H100-MI250) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   optional: true
-  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/compilation/
-  - vllm/model_executor/
-  - vllm/v1/attention/
-  - vllm/config/compilation.py
-  - csrc/
-  - tests/compile
+  - vllm/v1/attention
+  - vllm/model_executor/layers
+  - tests/v1/determinism/
+  - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s compile/fullgraph/test_full_graph.py -k 'not test_fp8_kv_scale_compile'
-
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pip install pytest-timeout pytest-forked
+  - pytest -v -s v1/determinism/test_batch_invariance.py
+  - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
 
 - label: Cudagraph # TBD
   timeout_in_minutes: 180
@@ -724,1214 +707,923 @@ steps:
   - pytest -v -s v1/cudagraph/test_cudagraph_dispatch.py
   - pytest -v -s v1/cudagraph/test_cudagraph_mode.py
 
-
-- label: Kernels Core Operation Test # TBD
+- label: e2e Core (1 GPU) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - csrc/
-  - tests/kernels/core
-  - tests/kernels/test_top_k_per_row.py
-  - tests/kernels/test_concat_mla_q.py
-  - vllm/model_executor/layers/rotary_embedding/
-  - vllm/_aiter_ops.py
+  - vllm/v1/
+  - tests/v1/e2e/general/
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s kernels/core kernels/test_top_k_per_row.py
-
+  - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
 
-- label: Kernels Mamba Test # TBD
+- label: e2e Scheduling (1 GPU) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - csrc/mamba/
-  - tests/kernels/mamba
-  - vllm/model_executor/layers/mamba/ops
+  - vllm/v1/
+  - tests/v1/e2e/general/
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s kernels/mamba
-
+  - pytest -v -s v1/e2e/general/test_async_scheduling.py
 
-- label: Kernels Helion Test # TBD
+- label: Engine (1 GPU) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/utils/import_utils.py
-  - tests/kernels/helion/
+  - vllm/v1/
+  - tests/v1/engine/
   - vllm/platforms/rocm.py
   commands:
-  - pip install helion==0.3.3
-  - pytest -v -s kernels/helion/
-
+  - pytest -v -s v1/engine/test_preprocess_error_handling.py
+  - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
 
-- label: Model Executor # TBD
+- label: Spec Decode Draft Model # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   optional: true
-  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/engine/arg_utils.py
-  - vllm/config/model.py
-  - vllm/model_executor
-  - tests/model_executor
-  - tests/entrypoints/openai/completion/test_tensorizer_entrypoint.py
-  - vllm/_aiter_ops.py
+  - vllm/v1/spec_decode/
+  - vllm/v1/worker/gpu/spec_decode/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/sample/
+  - vllm/model_executor/layers/
+  - tests/v1/e2e/spec_decode/
   - vllm/platforms/rocm.py
   commands:
-  - apt-get update && apt-get install -y curl libsodium23
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s model_executor -m '(not slow_test)'
-  - pytest -v -s entrypoints/openai/completion/test_tensorizer_entrypoint.py
-
+  - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
 
-- label: Benchmarks # TBD
+- label: Spec Decode Speculators + MTP # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
-  working_dir: "/vllm-workspace/.buildkite"
+  optional: true
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - benchmarks/
+  - vllm/v1/spec_decode/
+  - vllm/v1/worker/gpu/spec_decode/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/sample/
+  - vllm/model_executor/layers/
+  - vllm/transformers_utils/configs/speculators/
+  - tests/v1/e2e/spec_decode/
   - vllm/platforms/rocm.py
   commands:
-  - bash scripts/run-benchmarks.sh
-
+  - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
 
-- label: Benchmarks CLI Test # TBD
+- label: V1 attention (H100-MI250) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/benchmarks/
+  - vllm/config/attention.py
+  - vllm/model_executor/layers/attention
+  - vllm/v1/attention
+  - tests/v1/attention
+  - vllm/_aiter_ops.py
+  - vllm/envs.py
+  - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s benchmarks/
-
+  - pytest -v -s v1/attention
 
-- label: OpenAI API correctness # TBD
-  timeout_in_minutes: 180
+- label: V1 Core + KV + Metrics # TBD
+  timeout_in_minutes: 60
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - csrc/
-  - vllm/entrypoints/openai/
-  - vllm/model_executor/models/whisper.py
-  - vllm/model_executor/layers/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  - vllm/model_executor/model_loader/
+  - vllm/
+  - tests/v1/core
+  - tests/v1/executor
+  - tests/v1/kv_offload
+  - tests/v1/worker
+  - tests/v1/kv_connector/unit
+  - tests/v1/metrics
+  - tests/entrypoints/openai/correctness/test_lmeval.py
   commands:
-  - bash ../tools/install_torchcodec_rocm.sh || exit 1
-  - pytest -s entrypoints/openai/correctness/
-
+  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
+  - pytest -v -s -m 'not cpu_test' v1/core
+  - pytest -v -s v1/executor
+  - pytest -v -s v1/kv_offload
+  - pytest -v -s v1/worker
+  - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
+  - pytest -v -s -m 'not cpu_test' v1/metrics
+  - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
+  - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
-- label: Basic Models Tests (Initialization) # TBD
-  timeout_in_minutes: 180
+- label: V1 Sample + Logits # TBD
+  timeout_in_minutes: 60
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
-  torch_nightly: true
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/models/test_initialization.py
-  - tests/models/registry.py
+  - tests/v1/sample
+  - tests/v1/logits_processors
+  - tests/v1/test_oracle.py
+  - tests/v1/test_request.py
+  - tests/v1/test_outputs.py
   commands:
-  - pytest -v -s models/test_initialization.py::test_can_initialize_small_subset
-
+  - pytest -v -s v1/sample
+  - pytest -v -s v1/logits_processors
+  - pytest -v -s v1/test_oracle.py
+  - pytest -v -s v1/test_request.py
+  - pytest -v -s v1/test_outputs.py
 
-- label: Basic Models Tests (Extra Initialization) %N # TBD
+- label: Distributed DP Tests (2 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  torch_nightly: true
-  parallelism: 2
+  agent_pool: mi250_2
+  num_gpus: 2
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/models/
-  - vllm/model_executor/layers/
-  - tests/models/test_initialization.py
-  - tests/models/registry.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/worker/worker_base.py
+  - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/v1/distributed
+  - tests/entrypoints/openai/test_multi_api_servers.py
+  - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s models/test_initialization.py -k 'not test_can_initialize_small_subset' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
-
+  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
+  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
+  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
+  - DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py
 
-- label: Basic Models Tests (Other) # TBD
+- label: NixlConnector PD + Spec Decode acceptance (2 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  torch_nightly: true
+  agent_pool: mi250_2
+  num_gpus: 2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/test_terratorch.py
-  - tests/models/test_transformers.py
-  - tests/models/test_registry.py
+  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+  - vllm/v1/worker/kv_connector_model_runner_mixin.py
+  - tests/v1/kv_connector/nixl_integration/
+  - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s models/test_terratorch.py models/test_transformers.py models/test_registry.py
-
+  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
+  - ATTENTION_BACKEND=ROCM_ATTN bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
 
-- label: Basic Models Test (Other CPU) # TBD
+- label: V1 e2e (2 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  no_gpu: true
+  agent_pool: mi250_2
   optional: true
-  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/models/test_utils.py
-  - tests/models/test_vision.py
+  - tests/v1/e2e
   commands:
-  - pytest -v -s models/test_utils.py models/test_vision.py
-
+    - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "tensor_parallelism"
 
-- label: Language Models Tests (Extra Standard) %N # TBD
+- label: Distributed NixlConnector PD accuracy (4 GPUs)  # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  torch_nightly: true
-  parallelism: 2
+  agent_pool: mi250_4
+  num_gpus: 4
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/model_executor/layers/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - tests/models/language/pooling/test_embedding.py
-  - tests/models/language/generation/test_common.py
-  - tests/models/language/pooling/test_classification.py
-  - vllm/_aiter_ops.py
+  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+  - tests/v1/kv_connector/nixl_integration/
   - vllm/platforms/rocm.py
   commands:
-  - pip freeze | grep -E 'torch'
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - pytest -v -s models/language -m 'core_model and slow_test' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
+  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
+  - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
+#-------------------------------------------------------------  mi250 · misc  ------------------------------------------------------------#
 
-- label: Language Models Test (PPL) # TBD
+- label: Async Engine, Inputs, Utils, Worker, Config (CPU) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
+  optional: true
+  no_gpu: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/models/language/generation_ppl_test
+  - tests/test_inputs.py
+  - tests/test_outputs.py
+  - tests/test_pooling_params.py
+  - tests/test_ray_env.py
+  - tests/multimodal
+  - tests/renderers
+  - tests/standalone_tests/lazy_imports.py
+  - tests/tokenizers_
+  - tests/reasoning
+  - tests/tool_parsers
+  - tests/transformers_utils
+  - tests/config
   commands:
-  - pytest -v -s models/language/generation_ppl_test
+  - python3 standalone_tests/lazy_imports.py
+  - pytest -v -s test_inputs.py
+  - pytest -v -s test_outputs.py
+  - pytest -v -s test_pooling_params.py
+  - pytest -v -s test_ray_env.py
+  - pytest -v -s -m 'cpu_test' multimodal
+  - pytest -v -s renderers
+  - pytest -v -s tokenizers_
+  - pytest -v -s reasoning --ignore=reasoning/test_seedoss_reasoning_parser.py --ignore=reasoning/test_glm4_moe_reasoning_parser.py
+  - pytest -v -s tool_parsers
+  - pytest -v -s transformers_utils
+  - pytest -v -s config
 
+#########################################################################################################################################
+#                                                                                                                                       #
+#                                                         MI300 (gfx942) tests                                                          #
+#                                                                                                                                       #
+#########################################################################################################################################
 
-- label: Language Models Test (Extended Pooling)  # TBD
+#-----------------------------------------------------  mi300 · basic_correctness  -----------------------------------------------------#
+
+- label: Basic Correctness # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  fast_check: true
+  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/models/language/pooling
+  - tests/basic_correctness/test_basic_correctness
+  - tests/basic_correctness/test_cpu_offload
+  - tests/basic_correctness/test_cumem.py
   commands:
-  - pytest -v -s models/language/pooling -m 'not core_model'
-
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s basic_correctness/test_cumem.py
+  - pytest -v -s basic_correctness/test_basic_correctness.py
+  - pytest -v -s basic_correctness/test_cpu_offload.py
 
-- label: Language Models Test (MTEB) # TBD
+- label: Distributed Model Tests (2 GPUs) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_2
+  num_gpus: 2
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/language/pooling_mteb_test
+  - vllm/model_executor/model_loader/sharded_state_loader.py
+  - vllm/model_executor/models/
+  - vllm/model_executor/layers/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
+  - tests/basic_correctness/
+  - tests/model_executor/model_loader/test_sharded_state_loader.py
+  - tests/models/
   commands:
-  - pytest -v -s models/language/pooling_mteb_test
+  - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
+  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py -m '(not slow_test)'
+  - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
+  - pytest models/language -v -s -m 'distributed(num_gpus=2)'
+  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/generation/test_phi4siglip.py
+  - pytest models/multimodal/generation/test_phi4siglip.py -v -s -m 'distributed(num_gpus=2)'
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest models/multimodal/generation/test_whisper.py -v -s -m 'distributed(num_gpus=2)'
 
+#--------------------------------------------------------  mi300 · benchmarks  ---------------------------------------------------------#
 
-- label: Multi-Modal Processor (CPU) # TBD
+- label: Benchmarks # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  no_gpu: true
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   optional: true
+  working_dir: "/vllm-workspace/.buildkite"
+  source_file_dependencies:
+  - benchmarks/
+  - vllm/platforms/rocm.py
+  commands:
+  - bash scripts/run-benchmarks.sh
+
+- label: Benchmarks CLI Test # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/models/multimodal
-  - tests/models/registry.py
+  - tests/benchmarks/
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py
+  - pytest -v -s benchmarks/
 
+#----------------------------------------------------------  mi300 · compile  ----------------------------------------------------------#
 
-- label: Multi-Modal Accuracy Eval (Small Models) # TBD
+- label: Fusion E2E Config Sweep (H100-MI300) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  optional: true
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  num_gpus: 1
+  working_dir: "/vllm-workspace/"
   source_file_dependencies:
-  - vllm/multimodal/
-  - vllm/inputs/
-  - vllm/v1/core/
+  - csrc/quantization/
+  - vllm/compilation/
+  - vllm/model_executor/layers/layernorm.py
+  - vllm/model_executor/layers/activation.py
+  - vllm/model_executor/layers/attention/attention.py
+  - vllm/model_executor/layers/quantization/input_quant_fp8.py
+  - tests/compile/fusions_e2e/
+  - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
-  - vllm/model_executor/model_loader/
   commands:
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
+  - rocm-smi
+  - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "llama-3"
 
+- label: Fusion E2E Quick (H100-MI300) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  num_gpus: 1
+  working_dir: "/vllm-workspace/"
+  source_file_dependencies:
+  - csrc/quantization/
+  - vllm/model_executor/
+  - vllm/v1/attention/
+  - vllm/compilation/
+  - tests/compile/fusions_e2e/
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
+  commands:
+  - rocm-smi
+  # Run all models and attn backends but only Inductor partition and native custom ops
+  - "pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k 'inductor_partition and not +rms_norm and not +quant_fp8'"
+  # Different from CUDA, Qwen requires +rms_norm and +quant_fp8 as rms+quant fusion is only supported on AITER
+  - "pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k 'inductor_partition and +rms_norm and +quant_fp8 and qwen3'"
 
-- label: "Multi-Modal Models (Standard) 1: qwen2" # TBD
+- label: PyTorch Compilation Passes Unit Tests # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  torch_nightly: true
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/models/multimodal
+  - tests/compile/passes
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen2"
-  - pytest -v -s models/multimodal/generation/test_ultravox.py -m core_model
-
+  - pytest -s -v compile/passes --ignore compile/passes/distributed
 
-- label: "Multi-Modal Models (Standard) 2: qwen3 + gemma" # TBD
+- label: Pytorch Nightly Dependency Override Check # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  torch_nightly: true
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
+  soft_fail: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
+  - requirements/test/nightly-torch.txt
+  - vllm/platforms/rocm.py
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen3 or gemma"
-  - pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model
-
+  - bash standalone_tests/pytorch_nightly_dependency.sh
 
-- label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl" # TBD
+- label: Distributed Compile Unit Tests (2xH100-2xMI300) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  torch_nightly: true
-  working_dir: "/vllm-workspace/tests"
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_2
+  num_gpus: 2
+  working_dir: "/vllm-workspace/"
   source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
+  - vllm/compilation/
+  - vllm/model_executor/layers
+  - tests/compile/passes/distributed/
+  - tests/compile/fusions_e2e/
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "not qwen2 and not qwen3 and not gemma"
-  - pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model
+  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+  - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/passes/distributed/test_async_tp.py
+  - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py::test_tp2_ar_rms_fusions
 
+#-----------------------------------------------------------  mi300 · cuda  ------------------------------------------------------------#
 
-- label: "Multi-Modal Models (Standard) 4: other + whisper" # TBD
+- label: Platform Tests (CUDA) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  torch_nightly: true
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/models/multimodal/generation
-  - tests/models/multimodal/test_mapping.py
+  - tests/cuda
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/generation/test_ultravox.py --ignore models/multimodal/generation/test_qwen2_5_vl.py --ignore models/multimodal/generation/test_qwen2_vl.py --ignore models/multimodal/generation/test_whisper.py  --ignore models/multimodal/generation/test_memory_leak.py --ignore models/multimodal/processing
-  - pytest -v -s models/multimodal/generation/test_memory_leak.py -m core_model
-  - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model
+  - pytest -v -s cuda/test_cuda_context.py
+  - pytest -v -s cuda/test_platform_no_cuda_init.py
 
+#--------------------------------------------------------  mi300 · detokenizer  --------------------------------------------------------#
 
-- label: Multi-Modal Models (Extended Generation 1) # TBD
+- label: Async Engine, Inputs, Utils, Worker # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/models/multimodal/generation
-  - tests/models/multimodal/test_mapping.py
+  - tests/detokenizer
+  - tests/multimodal
+  - tests/utils_
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation -m 'not core_model' --ignore models/multimodal/generation/test_common.py
-  - pytest -v -s models/multimodal/test_mapping.py
+  - pytest -v -s detokenizer
+  - pytest -v -s -m 'not cpu_test' multimodal
+  - pytest -v -s utils_
 
+#--------------------------------------------------------  mi300 · distributed  --------------------------------------------------------#
 
-- label: Multi-Modal Models (Extended Generation 2) # TBD
+- label: EPLB Algorithm # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal/generation
+  - vllm/distributed/eplb
+  - tests/distributed/test_eplb_algo.py
+  - vllm/platforms/rocm.py
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
-
+  - pytest -v -s distributed/test_eplb_algo.py
+  - pytest -v -s distributed/test_eplb_utils.py
 
-- label: Multi-Modal Models (Extended Generation 3) # TBD
+- label: Distributed Tests (2xH100-2xMI250) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  working_dir: "/vllm-workspace/tests"
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_2
+  num_gpus: 2
+  working_dir: "/vllm-workspace/"
   source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal/generation
+  - vllm/distributed/
+  - vllm/v1/distributed/
+  - vllm/model_executor/layers/fused_moe/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - tests/distributed/test_context_parallel.py
+  - examples/features/data_parallel/data_parallel_offline.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
-
+  - pytest -v -s tests/distributed/test_context_parallel.py
+  - VLLM_LOGGING_LEVEL=DEBUG python3 examples/features/data_parallel/data_parallel_offline.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=allgather_reducescatter --disable-nccl-for-dp-synchronization
 
-- label: Multi-Modal Models (Extended Pooling) # TBD
+- label: Distributed Tests (4xA100-4xMI300) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
+  num_gpus: 4
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/models/multimodal/pooling
   commands:
-  - pytest -v -s models/multimodal/pooling -m 'not core_model'
-
+  - pytest -v -s distributed/test_custom_all_reduce.py
+  - torchrun --nproc_per_node=2 distributed/test_ca_buffer_sharing.py
+  - TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
+  - pytest -v -s -x lora/test_mixtral.py
 
-- label: Distributed Comm Ops # TBD
+- label: Distributed Torchrun + Examples (4 GPUs) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  num_gpus: 2
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
+  num_gpus: 4
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/distributed
-  - tests/distributed
+  - vllm/distributed/
+  - tests/distributed/test_torchrun_example.py
+  - tests/distributed/test_torchrun_example_moe.py
+  - examples/rl/
+  - tests/examples/features/data_parallel/data_parallel_offline.py
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s distributed/test_comm_ops.py
-  - pytest -v -s distributed/test_shm_broadcast.py
-  - pytest -v -s distributed/test_shm_buffer.py
-  - pytest -v -s distributed/test_shm_storage.py
-
+  - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
+  - PP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
+  - TP_SIZE=4 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  - PP_SIZE=2 TP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  - DP_SIZE=4 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  - python3 ../examples/features/data_parallel/data_parallel_offline.py --enforce-eager
+  # rlhf examples
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 ../examples/rl/rlhf_nccl.py
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 ../examples/rl/rlhf_ipc.py
 
-- label: Distributed DP Tests (2 GPUs) # TBD
+- label: Elastic EP Scaling Test # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_2
-  num_gpus: 2
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
+  num_gpus: 4
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/distributed/
   - vllm/engine/
   - vllm/executor/
-  - vllm/worker/worker_base.py
-  - vllm/v1/engine/
-  - vllm/v1/worker/
-  - tests/v1/distributed
-  - tests/entrypoints/openai/test_multi_api_servers.py
+  - vllm/compilation/
+  - tests/distributed/
   - vllm/platforms/rocm.py
   commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
-  - DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py
-
+  - pytest -v -s distributed/test_elastic_ep.py
 
-- label: Distributed Compile + RPC Tests (2 GPUs) # TBD
+- label: RayExecutorV2 (4 GPUs) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_2
-  num_gpus: 2
-  optional: true
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
+  num_gpus: 4
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/compilation/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/worker/worker_base.py
-  - vllm/v1/engine/
-  - vllm/v1/worker/
-  - tests/compile/fullgraph/test_basic_correctness.py
-  - tests/compile/test_wrapper.py
-  - tests/entrypoints/llm/test_collective_rpc.py
+  - vllm/v1/executor/ray_executor_v2.py
+  - vllm/v1/executor/abstract.py
+  - vllm/v1/executor/multiproc_executor.py
+  - tests/distributed/test_ray_v2_executor.py
+  - tests/distributed/test_ray_v2_executor_e2e.py
+  - tests/distributed/test_pipeline_parallel.py
+  - tests/basic_correctness/test_basic_correctness.py
   - vllm/platforms/rocm.py
   commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - pytest -v -s entrypoints/llm/test_collective_rpc.py
-  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
-  - pytest -v -s ./compile/test_wrapper.py
+  - export VLLM_USE_RAY_V2_EXECUTOR_BACKEND=1
+  - pytest -v -s distributed/test_ray_v2_executor.py
+  - pytest -v -s distributed/test_ray_v2_executor_e2e.py
+  - pytest -v -s distributed/test_pipeline_parallel.py -k "ray"
+  - TARGET_TEST_SUITE=L4 pytest -v -s basic_correctness/test_basic_correctness.py -k "ray"
 
-
-- label: Distributed Torchrun + Shutdown Tests (2 GPUs) # TBD
+- label: Distributed Tests (8xH100-8xMI300) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_2
-  num_gpus: 2
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_8
+  num_gpus: 8
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
+  - examples/features/torchrun/torchrun_dp_example_offline.py
+  - vllm/config/parallel.py
   - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/worker/worker_base.py
-  - vllm/v1/engine/
-  - vllm/v1/worker/
-  - tests/distributed/
-  - tests/v1/shutdown
-  - tests/v1/worker/test_worker_memory_snapshot.py
+  - vllm/v1/engine/llm_engine.py
+  - vllm/v1/executor/uniproc_executor.py
+  - vllm/v1/worker/gpu_worker.py
   - vllm/platforms/rocm.py
   commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
-  - pytest -v -s v1/worker/test_worker_memory_snapshot.py
+  - torchrun --nproc-per-node=8 ../examples/features/torchrun/torchrun_dp_example_offline.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 
+#--------------------------------------------------------  mi300 · entrypoints  --------------------------------------------------------#
 
-- label: Distributed Model Tests (2 GPUs) # TBD
+- label: Entrypoints Integration (API Server 2) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_2
-  num_gpus: 2
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  fast_check: true
+  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/model_loader/sharded_state_loader.py
-  - vllm/model_executor/models/
-  - vllm/model_executor/layers/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - tests/basic_correctness/
-  - tests/model_executor/model_loader/test_sharded_state_loader.py
-  - tests/models/
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/entrypoints/rpc
+  - tests/entrypoints/serve/instrumentator
+  - tests/tool_use
   commands:
-  - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py -m '(not slow_test)'
-  - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
-  - pytest models/language -v -s -m 'distributed(num_gpus=2)'
-  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py
-  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest models/multimodal/generation/test_whisper.py -v -s -m 'distributed(num_gpus=2)'
-
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/serve/instrumentator
+  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/rpc
+  - pytest -v -s tool_use
 
-- label: Plugin Tests (2 GPUs) # TBD
+- label: Entrypoints Integration (API Server openai - Part 1) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_2
-  num_gpus: 2
-  optional: true
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  fast_check: true
+  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/plugins/
-  - tests/plugins/
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/entrypoints/openai
+  - tests/entrypoints/test_chat_utils
   commands:
-  # BEGIN: platform plugin and general plugin tests, all the code in-between runs on dummy platform
-  - pip install -e ./plugins/vllm_add_dummy_platform
-  - pytest -v -s plugins_tests/test_platform_plugins.py
-  - pip uninstall vllm_add_dummy_platform -y
-  # END: platform plugin tests
-  # BEGIN: `io_processor` plugins test, all the code in between uses the `prithvi_io_processor` plugin
-  - pip install -e ./plugins/prithvi_io_processor_plugin
-  - pytest -v -s plugins_tests/test_io_processor_plugins.py
-  - pip uninstall prithvi_io_processor_plugin -y
-  # END: `io_processor` plugins test
-  # BEGIN: `bge_m3_sparse io_processor` test
-  - pip install -e ./plugins/bge_m3_sparse_plugin
-  - pytest -v -s plugins_tests/test_bge_m3_sparse_io_processor_plugins.py
-  - pip uninstall bge_m3_sparse_plugin -y
-  # END: `bge_m3_sparse io_processor` test
-  # BEGIN: `stat_logger` plugins test
-  - pip install -e ./plugins/vllm_add_dummy_stat_logger
-  - pytest -v -s plugins_tests/test_stats_logger_plugins.py
-  - pip uninstall dummy_stat_logger -y
-  # END: `stat_logger` plugins test
-  # BEGIN: other tests
-  - pytest -v -s plugins_tests/test_scheduler_plugins.py
-  - pip install -e ./plugins/vllm_add_dummy_model
-  - pytest -v -s distributed/test_distributed_oot.py
-  - pytest -v -s entrypoints/openai/chat_completion/test_oot_registration.py
-  - pytest -v -s models/test_oot_registration.py
-  - pytest -v -s plugins/lora_resolvers
-  # END: other tests
-
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/openai/chat_completion --ignore=entrypoints/openai/chat_completion/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/chat_completion/test_oot_registration.py
 
-- label: Pipeline + Context Parallelism (4 GPUs) # TBD
+- label: Entrypoints Integration (API Server openai - Part 2) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_4
-  num_gpus: 4
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  fast_check: true
+  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/model_executor/models/
-  - vllm/model_executor/layers/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - tests/distributed/
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/entrypoints/openai
+  - tests/entrypoints/test_chat_utils
   commands:
-  - pytest -v -s distributed/test_pp_cudagraph.py
-  - pytest -v -s distributed/test_pipeline_parallel.py
-
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
+  - pytest -v -s entrypoints/openai/speech_to_text/
+  - pytest -v -s entrypoints/test_chat_utils.py
 
-- label: Ray Dependency Compatibility Check # TBD
+- label: Entrypoints Integration (API Server openai - Part 3) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  working_dir: "/"
-  source_file_dependencies:
-  - requirements/
-  - setup.py
-  - vllm/platforms/rocm.py
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  fast_check: true
+  torch_nightly: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/entrypoints/openai
+  - tests/entrypoints/test_chat_utils
   commands:
-  - bash /vllm-workspace/.buildkite/scripts/check-ray-compatibility.sh
-
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/speech_to_text/ --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/test_multi_api_servers.py
 
-- label: Distributed NixlConnector PD accuracy (4 GPUs)  # TBD
+- label: Entrypoints Integration (LLM) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_4
-  num_gpus: 4
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
+  fast_check: true
+  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-  - tests/v1/kv_connector/nixl_integration/
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/entrypoints/llm
+  - tests/entrypoints/offline_mode
   commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
+  - pytest -v -s entrypoints/llm/test_generate.py
+  - pytest -v -s entrypoints/offline_mode
 
-- label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs) # TBD
+- label: Entrypoints Integration (Pooling) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_4
-  num_gpus: 4
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  fast_check: true
+  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-  - tests/v1/kv_connector/nixl_integration/
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/entrypoints/pooling
   commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - DP_EP=1 ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/pooling
 
-- label: NixlConnector PD + Spec Decode acceptance (2 GPUs) # TBD
+- label: Entrypoints Integration (Responses API) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_2
-  num_gpus: 2
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  fast_check: true
+  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-  - vllm/v1/worker/kv_connector_model_runner_mixin.py
-  - tests/v1/kv_connector/nixl_integration/
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/entrypoints/openai/responses
   commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
-
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/openai/responses
 
-- label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs) # TBD
+- label: Entrypoints Unit Tests # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_4
-  num_gpus: 4
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  fast_check: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-  - tests/v1/kv_connector/nixl_integration/
+  - vllm/entrypoints
+  - tests/entrypoints/
   - vllm/platforms/rocm.py
   commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - CROSS_LAYERS_BLOCKS=True ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
+  - pytest -v -s entrypoints/openai/tool_parsers
+  - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/rpc --ignore=entrypoints/sleep --ignore=entrypoints/serve/instrumentator --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling
 
-- label: Hyrbid SSM NixlConnector PD accuracy tests (4 GPUs) # TBD
+- label: OpenAI API correctness # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_4
-  num_gpus: 4
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-  - tests/v1/kv_connector/nixl_integration/
+  - csrc/
+  - vllm/entrypoints/openai/
+  - vllm/model_executor/models/whisper.py
+  - vllm/model_executor/layers/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
+  - vllm/model_executor/model_loader/
   commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - HYBRID_SSM=1 ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+  - bash ../tools/install_torchcodec_rocm.sh || exit 1
+  - pytest -s entrypoints/openai/correctness/
 
+#-----------------------------------------------------------  mi300 · evals  -----------------------------------------------------------#
 
-- label: Distributed Tests (2 GPUs)(H100-MI250) # TBD
+- label: DeepSeek V2-Lite Prefetch Offload Accuracy (H100-MI300) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi325_2
-  num_gpus: 2
-  working_dir: "/vllm-workspace/"
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  num_gpus: 1
+  working_dir: "/vllm-workspace"
   source_file_dependencies:
-  - vllm/distributed/
-  - vllm/v1/distributed/
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
   - vllm/model_executor/layers/fused_moe/
+  - vllm/model_executor/layers/quantization/
   - vllm/v1/attention/backends/
+  - vllm/v1/attention/backends/mla/
   - vllm/v1/attention/selector.py
-  - tests/distributed/test_context_parallel.py
-  - examples/offline_inference/data_parallel.py
+  - .buildkite/scripts/scheduled_integration_test/
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - pytest -v -s tests/distributed/test_context_parallel.py
-  - VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=allgather_reducescatter --disable-nccl-for-dp-synchronization
-
-
-#####################################################################################################################################
-#                                                                                                                                   #
-#                                                             gfx942                                                                #
-#                                                                                                                                   #
-#####################################################################################################################################
-
+  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh 0.25 200 8030
 
-- label: Entrypoints Integration (LLM) # 13.1m
-  timeout_in_minutes: 22
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  fast_check: true
-  torch_nightly: true
+- label: LM Eval Small Models # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/llm
-  - tests/entrypoints/offline_mode
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
-  - pytest -v -s entrypoints/llm/test_generate.py
-  - pytest -v -s entrypoints/offline_mode
-
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt
 
-- label: Entrypoints Integration (API Server openai - Part 1) # TBD
+- label: LM Eval Small Models (MI300) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  fast_check: true
-  torch_nightly: true
-  working_dir: "/vllm-workspace/tests"
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
   source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/openai
-  - tests/entrypoints/test_chat_utils
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai/chat_completion --ignore=entrypoints/openai/chat_completion/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/chat_completion/test_oot_registration.py
-
+  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-small-rocm.txt
 
-- label: Entrypoints Integration (API Server openai - Part 2) # TBD
+- label: GPQA Eval (GPT-OSS) (2xH100-2xMI300) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  fast_check: true
-  torch_nightly: true
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_2
+  num_gpus: 2
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/openai
-  - tests/entrypoints/test_chat_utils
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/model_executor/layers/fused_moe/
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
+  - tests/evals/gpt_oss/
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
-  - pytest -v -s entrypoints/openai/speech_to_text/
-  - pytest -v -s entrypoints/test_chat_utils.py
-
+    - uv pip install --system 'gpt-oss[eval]==0.0.5'
+    - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-gfx942.txt
 
-- label: Entrypoints Integration (API Server openai - Part 3) # TBD
+- label: LM Eval Small Models (2xB200-2xMI300) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  fast_check: true
-  torch_nightly: true
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/openai
-  - tests/entrypoints/test_chat_utils
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/speech_to_text/ --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-mi3xx-fp8-and-mixed.txt
 
+- label: DeepSeek V2-Lite Accuracy (4xH100-4xMI300) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
+  num_gpus: 4
+  optional: true
+  working_dir: "/vllm-workspace"
+  source_file_dependencies:
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/distributed/eplb
+  - vllm/model_executor/layers/fused_moe/
+  - vllm/model_executor/layers/quantization/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/backends/mla/
+  - vllm/v1/attention/selector.py
+  - .buildkite/scripts/scheduled_integration_test/
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
+  commands:
+  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010
 
-- label: Entrypoints Integration (API Server 2) #26.9m
-  timeout_in_minutes: 45
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+- label: LM Eval Large Models (4xA100-4xMI300) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
+  num_gpus: 4
   optional: true
-  fast_check: true
-  torch_nightly: true
-  working_dir: "/vllm-workspace/tests"
+  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
   source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/rpc
-  - tests/entrypoints/serve/instrumentator
-  - tests/tool_use
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/serve/instrumentator
-  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/rpc
-  - pytest -v -s tool_use
+  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
-
-- label: Entrypoints Integration (Pooling) # 22.8m
-  timeout_in_minutes: 48
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  fast_check: true
-  torch_nightly: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/pooling
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/pooling
-
-
-- label: Distributed Torchrun + Examples (4 GPUs) # TBD
-  timeout_in_minutes: 80
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
-  num_gpus: 4
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/distributed/
-  - tests/distributed/test_torchrun_example.py
-  - tests/distributed/test_torchrun_example_moe.py
-  - examples/rl/
-  - tests/examples/offline_inference/data_parallel.py
-  - vllm/platforms/rocm.py
-  commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
-  - PP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
-  - TP_SIZE=4 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
-  - PP_SIZE=2 TP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
-  - DP_SIZE=4 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
-  - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
-  - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
-  # rlhf examples
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 ../examples/rl/rlhf_nccl.py
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 ../examples/rl/rlhf_ipc.py
-
-
-- label: Distributed DP Tests (4 GPUs) # TBD
-  timeout_in_minutes: 60
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
-  num_gpus: 4
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/distributed/
-  - tests/v1/distributed
-  - tests/v1/engine/test_engine_core_client.py
-  - tests/distributed/test_utils
-  - vllm/platforms/rocm.py
-  commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py
-  - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
-  - pytest -v -s distributed/test_utils.py
-
-
-- label: Distributed Compile + Comm (4 GPUs) # TBD
-  timeout_in_minutes: 40
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
-  num_gpus: 4
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/distributed/
-  - tests/distributed/test_pynccl
-  - tests/distributed/test_events
-  - tests/compile/fullgraph/test_basic_correctness.py
-  - tests/distributed/test_symm_mem_allreduce.py
-  - tests/distributed/test_multiproc_executor.py
-  - vllm/platforms/rocm.py
-  commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - pytest -v -s compile/fullgraph/test_basic_correctness.py
-  - pytest -v -s distributed/test_pynccl.py
-  - pytest -v -s distributed/test_events.py
-  - pytest -v -s distributed/test_symm_mem_allreduce.py
-  - pytest -v -s distributed/test_multiproc_executor.py::test_multiproc_executor_multi_node
-
-
-- label: Distributed Tests (8 GPUs)(H100-MI325) # 6.4m
-  timeout_in_minutes: 10
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_8
-  num_gpus: 8
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - examples/offline_inference/torchrun_dp_example.py
-  - vllm/config/parallel.py
-  - vllm/distributed/
-  - vllm/v1/engine/llm_engine.py
-  - vllm/v1/executor/uniproc_executor.py
-  - vllm/v1/worker/gpu_worker.py
-  - vllm/platforms/rocm.py
-  commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - torchrun --nproc-per-node=8 ../examples/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
-
-
-- label: Elastic EP Scaling Test # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
-  num_gpus: 4
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/compilation/
-  - tests/distributed/
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s distributed/test_elastic_ep.py
-
-
-- label: Engine # 11.3m
-  timeout_in_minutes: 35
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/engine
-  - tests/test_sequence
-  - tests/test_config
-  - tests/test_logger
-  - tests/test_vllm_port
-  commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
-
-
-- label: Engine (1 GPU) # TBD
+- label: Qwen3-30B-A3B-FP8-block Accuracy (4xH100-4xMI300) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
   optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/v1/engine/
-  - tests/v1/engine/
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s v1/engine/test_preprocess_error_handling.py
-  - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
-
-
-- label: e2e Scheduling (1 GPU) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/v1/
-  - tests/v1/e2e/general/
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s v1/e2e/general/test_async_scheduling.py
-
-
-- label: e2e Core (1 GPU) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/v1/
-  - tests/v1/e2e/
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
-
-
-- label: Spec Decode Eagle # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/v1/spec_decode/
-  - vllm/v1/worker/gpu/spec_decode/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/sample/
-  - vllm/model_executor/layers/
-  - tests/v1/e2e/spec_decode/
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
-
-
-- label: Spec Decode Speculators + MTP # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/v1/spec_decode/
-  - vllm/v1/worker/gpu/spec_decode/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/sample/
-  - vllm/model_executor/layers/
-  - vllm/transformers_utils/configs/speculators/
-  - tests/v1/e2e/spec_decode/
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
-
-
-- label: Spec Decode Ngram + Suffix # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/v1/spec_decode/
-  - vllm/v1/worker/gpu/spec_decode/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/sample/
-  - vllm/model_executor/layers/
-  - tests/v1/e2e/spec_decode/
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s v1/e2e/spec_decode -k "ngram or suffix"
-
-
-- label: Spec Decode Draft Model # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
+  working_dir: "/vllm-workspace"
   source_file_dependencies:
-  - vllm/v1/spec_decode/
-  - vllm/v1/worker/gpu/spec_decode/
+  - vllm/model_executor/models/
   - vllm/model_executor/model_loader/
-  - vllm/v1/sample/
-  - vllm/model_executor/layers/
-  - tests/v1/e2e/spec_decode/
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
-
-
-- label: V1 e2e (2 GPUs) # 7.1m
-  timeout_in_minutes: 12
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_2
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/v1/e2e
-  commands:
-    - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "tensor_parallelism"
-
-
-- label: V1 e2e (4 GPUs) # 52.6m
-  timeout_in_minutes: 106
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/v1/e2e
-  commands:
-    - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle_correctness_heavy"
-  
-
-- label: V1 e2e (4xH100-4xMI325) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
-  optional: true
-  source_file_dependencies:
-    - vllm/v1/attention/backends/utils.py
-    - vllm/v1/worker/gpu_model_runner.py
-    - tests/v1/e2e/test_hybrid_chunked_prefill.py
-  commands:
-    - pytest -v -s v1/e2e/test_hybrid_chunked_prefill.py
-
-
-- label: V1 Spec Decode # TBD
-  timeout_in_minutes: 40
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/v1/spec_decode
-  commands:
-  - pytest -v -s -m 'not slow_test' v1/spec_decode
-
-
-- label: V1 Sample + Logits # TBD
-  timeout_in_minutes: 40
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/v1/sample
-  - tests/v1/logits_processors
-  - tests/v1/test_oracle.py
-  - tests/v1/test_request.py
-  - tests/v1/test_outputs.py
-  commands:
-  - pytest -v -s v1/sample
-  - pytest -v -s v1/logits_processors
-  - pytest -v -s v1/test_oracle.py
-  - pytest -v -s v1/test_request.py
-  - pytest -v -s v1/test_outputs.py
-
-
-- label: V1 Core + KV + Metrics # TBD
-  timeout_in_minutes: 40
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/v1/core
-  - tests/v1/executor
-  - tests/v1/kv_offload
-  - tests/v1/worker
-  - tests/v1/kv_connector/unit
-  - tests/v1/metrics
-  - tests/entrypoints/openai/correctness/test_lmeval.py
-  commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - pytest -v -s -m 'not cpu_test' v1/core
-  - pytest -v -s v1/executor
-  - pytest -v -s v1/kv_offload
-  - pytest -v -s v1/worker
-  - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
-  - pytest -v -s -m 'not cpu_test' v1/metrics
-  - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
-  # - export HSA_NO_SCRATCH_RECLAIM=1
-  - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
-
-
-
-- label: Acceptance Length Test (Large Models) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/v1/spec_decode/
-  - vllm/model_executor/models/mlp_speculator.py
-  - tests/v1/spec_decode/test_acceptance_length.py
-  - vllm/platforms/rocm.py
-  commands:
-  - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
-  - pytest -v -s v1/spec_decode/test_acceptance_length.py
-
-
-- label: V1 attention (H100-MI325) # 14.5m
-  timeout_in_minutes: 40
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/config/attention.py
-  - vllm/model_executor/layers/attention
-  - vllm/v1/attention
-  - tests/v1/attention
+  - vllm/model_executor/layers/quantization/
+  - vllm/distributed/eplb
+  - vllm/model_executor/layers/fused_moe/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - .buildkite/scripts/scheduled_integration_test/
   - vllm/_aiter_ops.py
-  - vllm/envs.py
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s v1/attention
-
+  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020
 
-- label: Batch Invariance (H100-MI325) # 5.2m
-  timeout_in_minutes: 12
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+- label: Qwen3-Next-80B-A3B-Instruct MTP Async EPLB Accuracy # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
+  num_gpus: 4
   optional: true
-  working_dir: "/vllm-workspace/tests"
+  working_dir: "/vllm-workspace"
   source_file_dependencies:
-  - vllm/v1/attention
-  - vllm/model_executor/layers
-  - tests/v1/determinism/
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/spec_decode/
+  - vllm/distributed/eplb
+  - vllm/model_executor/layers/fused_moe/
+  - vllm/model_executor/layers/quantization/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - .buildkite/scripts/scheduled_integration_test/
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pip install pytest-timeout pytest-forked
-  - pytest -v -s v1/determinism/test_batch_invariance.py
-  - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
-
+  - bash .buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh 0.8 1319 8040
 
-- label: V1 others (CPU) # 10.4m
-  timeout_in_minutes: 28
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  no_gpu: true
+- label: LM Eval Large Models (8xH200-8xMI300) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_8
   optional: true
+  num_gpus: 8
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/v1
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/model_executor/layers/quantization/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/model_executor/layers/layernorm.py
+  - csrc/
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
+  - tests/evals/
   commands:
-  - pytest -v -s -m 'cpu_test' v1/core
-  - pytest -v -s v1/structured_output
-  - pytest -v -s v1/test_serial_utils.py
-  - pytest -v -s -m 'cpu_test' v1/kv_connector/unit
-  - pytest -v -s -m 'cpu_test' v1/metrics
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-mi3xx.txt
 
+#---------------------------------------------------------  mi300 · examples  ----------------------------------------------------------#
 
-- label: Examples # 24.5m
-  timeout_in_minutes: 55
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+- label: Examples # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   optional: true
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
@@ -1949,67 +1641,25 @@ steps:
     - python3 basic/offline_inference/embed.py
     - python3 basic/offline_inference/score.py
     # Multi-modal models
-    - python3 offline_inference/audio_language.py --seed 0
-    - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
+    - python3 generate/multimodal/audio_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
+    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
     # Pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # Features demo
-    - python3 offline_inference/prefix_caching.py
-    - python3 offline_inference/llm_engine_example.py
-    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
-
-
-- label: Platform Tests (CUDA) # 5.0m
-  timeout_in_minutes: 9
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/cuda
-  commands:
-  - pytest -v -s cuda/test_cuda_context.py
-  - pytest -v -s cuda/test_platform_no_cuda_init.py
+    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
+    - python3 deployment/llm_engine_example.py
+    - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
+#----------------------------------------------------------  mi300 · kernels  ----------------------------------------------------------#
 
-- label: PyTorch Compilation Passes Unit Tests # TBD
+- label: Kernels Attention Test %N # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/compile/passes
-  commands:
-  - pytest -s -v compile/passes --ignore compile/passes/distributed
-
-
-- label: Kernels Core Operation Test # 26.8m
-  timeout_in_minutes: 38
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - csrc/
-  - tests/kernels/core
-  - tests/kernels/test_top_k_per_row.py
-  - tests/kernels/test_concat_mla_q.py
-  - vllm/model_executor/layers/rotary_embedding/
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s kernels/core kernels/test_top_k_per_row.py
-
-
-- label: Kernels Attention Test %N # 17.7m
-  timeout_in_minutes: 28
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   parallelism: 2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -2023,29 +1673,26 @@ steps:
   commands:
   - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
 
-
-- label: Kernels Quantization Test %N # 15.2m
-  timeout_in_minutes: 24
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  parallelism: 2
+- label: Kernels Core Operation Test # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - csrc/quantization/
-  - vllm/model_executor/layers/quantization
-  - tests/kernels/quantization
-  - tests/kernels/quantization/test_rocm_skinny_gemms.py
+  - csrc/
+  - tests/kernels/core
+  - tests/kernels/test_top_k_per_row.py
+  - tests/kernels/test_concat_mla_q.py
+  - vllm/model_executor/layers/rotary_embedding/
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
-  - vllm/model_executor/kernels/
   commands:
-  - pytest -v -s kernels/quantization --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-
+  - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py  kernels/test_concat_mla_q.py kernels/test_top_k_per_row.py
 
 - label: Kernels MoE Test %N # TBD
-  timeout_in_minutes: 19
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   parallelism: 4
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -2062,11 +1709,27 @@ steps:
   - pytest -v -s kernels/moe --ignore=kernels/moe/test_modular_oai_triton_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
   - pytest -v -s kernels/moe/test_modular_oai_triton_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
 
+- label: Kernels Quantization Test %N # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  parallelism: 2
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - csrc/quantization/
+  - vllm/model_executor/layers/quantization
+  - tests/kernels/quantization
+  - tests/kernels/quantization/test_rocm_skinny_gemms.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
+  - vllm/model_executor/kernels/
+  commands:
+  - pytest -v -s kernels/quantization --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
 
-- label: Kernels FP8 MoE Test # TBD
+- label: Kernels FP8 MoE Test (2xH100-2xMI300) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_2
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_2
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -2080,40 +1743,222 @@ steps:
   commands:
     - pytest -v -s kernels/moe/test_deepep_moe.py
 
+#-----------------------------------------------------------  mi300 · lora  ------------------------------------------------------------#
+
+- label: LoRA TP (Distributed) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
+  num_gpus: 4
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/lora
+  - tests/lora
+  - vllm/platforms/rocm.py
+  commands:
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+  - pytest -v -s -x lora/test_chatglm3_tp.py
+  - pytest -v -s -x lora/test_llama_tp.py
+  - pytest -v -s -x lora/test_llm_with_multi_loras.py
+  - pytest -v -s -x lora/test_olmoe_tp.py
+  - pytest -v -s -x lora/test_gptoss_tp.py
+  - pytest -v -s -x lora/test_qwen35_densemodel_lora.py
+
+#----------------------------------------------------------  mi300 · models  -----------------------------------------------------------#
+
+- label: Language Models Test (Extended Pooling)  # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/language/pooling
+  commands:
+  - pytest -v -s models/language/pooling -m 'not core_model'
+
+- label: Language Models Tests (Standard) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
+  torch_nightly: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/language
+  commands:
+  - pip freeze | grep -E 'torch'
+  - pytest -v -s models/language -m 'core_model and (not slow_test)'
+
+- label: Multi-Modal Models (Extended Generation 1) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal/generation
+  - tests/models/multimodal/test_mapping.py
+  commands:
+  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@rocm-7.0-v2.3.0'
+  - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
+  - pytest -v -s models/language/generation -m hybrid_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
+
+
+- label: Multi-Modal Models (Extended Generation 2) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal/generation
+  commands:
+  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@rocm-7.0-v2.3.0'
+  - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
+  - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
+
+
+- label: Multi-Modal Models (Extended Generation 3) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal/generation
+  commands:
+  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+  - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
+
+- label: "Multi-Modal Models (Standard) 1: qwen2" # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  torch_nightly: true
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal
+  commands:
+  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen2"
+  - pytest -v -s models/multimodal/generation/test_ultravox.py -m core_model
+
+- label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl" # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  torch_nightly: true
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal/generation
+  - tests/models/multimodal/test_mapping.py
+  commands:
+  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "not qwen2 and not qwen3 and not gemma"
+  - pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model
+
+- label: "Multi-Modal Models (Standard) 4: other + whisper" # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  torch_nightly: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal/generation
+  - tests/models/multimodal/test_mapping.py
+  commands:
+  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+  - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/generation/test_ultravox.py --ignore models/multimodal/generation/test_qwen2_5_vl.py --ignore models/multimodal/generation/test_qwen2_vl.py --ignore models/multimodal/generation/test_whisper.py  --ignore models/multimodal/generation/test_memory_leak.py --ignore models/multimodal/processing
+  - pytest -v -s models/multimodal/generation/test_memory_leak.py -m core_model
+  - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model
+
+- label: Multi-Modal Processor # 1h 42m
+  timeout_in_minutes: 138
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal
+  - tests/models/registry.py
+  commands:
+  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+  - pytest -v -s models/multimodal/processing/test_tensor_schema.py
+
+- label: Multi-Modal Processor (CPU) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  no_gpu: true
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal
+  - tests/models/registry.py
+  commands:
+  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+  - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py
 
-- label: ROCm AITER Ops Test # TBD
+- label: Quantized Models Test # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
+  - vllm/model_executor/layers/quantization
   - vllm/_aiter_ops.py
-  - vllm/envs.py
   - vllm/platforms/rocm.py
-  - tests/rocm/aiter/
-  - vllm/v1/attention/backends/mla/rocm_aiter_mla.py
-  - vllm/v1/attention/selector.py
+  - tests/models/quantization
+  - vllm/model_executor/model_loader/
   commands:
-  - pytest -v -s rocm/aiter/
-
+  - pytest -v -s models/quantization
 
-- label: Benchmarks # 8.2m
-  timeout_in_minutes: 20
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+- label: Transformers Nightly Models # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   optional: true
-  working_dir: "/vllm-workspace/.buildkite"
+  working_dir: "/vllm-workspace/"
   source_file_dependencies:
-  - benchmarks/
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/multimodal/
+  - vllm/model_executor/layers/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
+  - tests/models/
+  - examples/
   commands:
-  - bash scripts/run-benchmarks.sh
+  - pip install --upgrade git+https://github.com/huggingface/transformers
+  - pytest -v -s tests/models/test_initialization.py
+  - pytest -v -s tests/models/test_transformers.py
+  - pytest -v -s tests/models/multimodal/processing/
+  - pytest -v -s tests/models/multimodal/test_mapping.py
+  - python3 examples/basic/offline_inference/chat.py
+  - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
 
+#-------------------------------------------------------  mi300 · quantization  --------------------------------------------------------#
 
-- label: Quantization # 36.1m
-  timeout_in_minutes: 60
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+- label: Quantization # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - csrc/
@@ -2134,262 +1979,292 @@ steps:
   - uv pip install --system conch-triton-kernels
   - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
 
+#-----------------------------------------------------------  mi300 · rocm  ------------------------------------------------------------#
 
-- label: Language Models Tests (Standard) # 22.8m
-  timeout_in_minutes: 38
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  torch_nightly: true
+- label: ROCm AITER Ops Test # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/language
+  - vllm/_aiter_ops.py
+  - vllm/envs.py
+  - vllm/platforms/rocm.py
+  - tests/rocm/aiter/
+  - vllm/v1/attention/backends/mla/rocm_aiter_mla.py
+  - vllm/v1/attention/selector.py
   commands:
-  - pip freeze | grep -E 'torch'
-  - pytest -v -s models/language -m 'core_model and (not slow_test)'
+  - pytest -v -s rocm/aiter/
 
+#---------------------------------------------------------  mi300 · samplers  ----------------------------------------------------------#
 
-- label: Language Models Tests (Hybrid) %N # 34.9m
-  timeout_in_minutes: 55
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  torch_nightly: true
-  parallelism: 2
+- label: Samplers Test # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/language/generation
+  - vllm/model_executor/layers
+  - vllm/sampling_metadata.py
+  - vllm/v1/sample/
+  - vllm/beam_search.py
+  - tests/samplers
+  - tests/conftest.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
   commands:
-  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
-  - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
-  - pytest -v -s models/language/generation -m hybrid_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
+  - pytest -v -s samplers
 
+#------------------------------------------------------------  mi300 · misc  ------------------------------------------------------------#
 
-- label: Language Models Test (Extended Generation) # 32.2m
-  timeout_in_minutes: 55
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+- label: Python-only Installation # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/language/generation
+  - tests/standalone_tests/python_only_compile.sh
+  - setup.py
+  - vllm/platforms/rocm.py
   commands:
-  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
-  - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
-  - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
-
+  - bash standalone_tests/python_only_compile.sh
 
-- label: Multi-Modal Processor # 1h 42m
-  timeout_in_minutes: 138
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
+- label: Regression # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/models/multimodal
-  - tests/models/registry.py
+  - tests/test_regression
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/processing/test_tensor_schema.py
+  - pip install modelscope
+  - pytest -v -s test_regression.py
 
+#---------------------------------------------------------  mi300 · ray_compat  ---------------------------------------------------------#
 
-- label: "Multi-Modal Models (Standard) 1: qwen2" # TBD
+- label: Ray Dependency Compatibility Check # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  torch_nightly: true
-  optional: true
-  working_dir: "/vllm-workspace/tests"
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  working_dir: "/"
   source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
+  - requirements/
+  - setup.py
+  - vllm/platforms/rocm.py
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen2"
-  - pytest -v -s models/multimodal/generation/test_ultravox.py -m core_model
+  - bash /vllm-workspace/.buildkite/scripts/check-ray-compatibility.sh
 
+#------------------------------------------------------------  mi300 · v1  -------------------------------------------------------------#
 
-- label: "Multi-Modal Models (Standard) 2: qwen3 + gemma" # TBD
+- label: Acceptance Length Test (Large Models) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  torch_nightly: true
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
+  - vllm/v1/spec_decode/
+  - vllm/model_executor/models/mlp_speculator.py
+  - tests/v1/spec_decode/test_acceptance_length.py
+  - vllm/platforms/rocm.py
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen3 or gemma"
-  - pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model
-
+  - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
+  - pytest -v -s v1/spec_decode/test_acceptance_length.py -m slow_test
 
-- label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl" # TBD
+- label: e2e Core (1 GPU) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  torch_nightly: true
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal/generation
-  - tests/models/multimodal/test_mapping.py
+  - vllm/v1/
+  - tests/v1/e2e/
+  - vllm/platforms/rocm.py
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "not qwen2 and not qwen3 and not gemma"
-  - pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model
-
+  - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
 
-- label: "Multi-Modal Models (Standard) 4: other + whisper" # TBD
+- label: e2e Scheduling (1 GPU) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  torch_nightly: true
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal/generation
+  - vllm/v1/
+  - tests/v1/e2e/general/
+  - vllm/platforms/rocm.py
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/generation/test_ultravox.py --ignore models/multimodal/generation/test_qwen2_5_vl.py --ignore models/multimodal/generation/test_qwen2_vl.py --ignore models/multimodal/generation/test_whisper.py  --ignore models/multimodal/generation/test_memory_leak.py --ignore models/multimodal/processing
-  - pytest -v -s models/multimodal/generation/test_memory_leak.py -m core_model
-  - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model
+  - pytest -v -s v1/e2e/general/test_async_scheduling.py
 
+- label: Engine (1 GPU) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/v1/engine/
+  - tests/v1/engine/
+  - vllm/platforms/rocm.py
+  commands:
+  - pytest -v -s v1/engine/test_preprocess_error_handling.py
+  - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
 
-- label: Multi-Modal Models (Extended Generation 1) # 1h 2m
-  timeout_in_minutes: 106
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+- label: Spec Decode Draft Model # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal/generation
-  - tests/models/multimodal/test_mapping.py
+  - vllm/v1/spec_decode/
+  - vllm/v1/worker/gpu/spec_decode/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/sample/
+  - vllm/model_executor/layers/
+  - tests/v1/e2e/spec_decode/
+  - vllm/platforms/rocm.py
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation -m 'not core_model' --ignore models/multimodal/generation/test_common.py
-  - pytest -v -s models/multimodal/test_mapping.py
+  - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
 
+- label: Spec Decode Eagle # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/v1/spec_decode/
+  - vllm/v1/worker/gpu/spec_decode/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/sample/
+  - vllm/model_executor/layers/
+  - tests/v1/e2e/spec_decode/
+  - vllm/platforms/rocm.py
+  commands:
+  - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
 
-- label: Multi-Modal Models (Extended Generation 2) # TBD
+- label: Spec Decode Ngram + Suffix # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal/generation
+  - vllm/v1/spec_decode/
+  - vllm/v1/worker/gpu/spec_decode/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/sample/
+  - vllm/model_executor/layers/
+  - tests/v1/e2e/spec_decode/
+  - vllm/platforms/rocm.py
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
+  - pytest -v -s v1/e2e/spec_decode -k "ngram or suffix"
 
+- label: Spec Decode Speculators + MTP # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/v1/spec_decode/
+  - vllm/v1/worker/gpu/spec_decode/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/sample/
+  - vllm/model_executor/layers/
+  - vllm/transformers_utils/configs/speculators/
+  - tests/v1/e2e/spec_decode/
+  - vllm/platforms/rocm.py
+  commands:
+  - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
 
-- label: Multi-Modal Models (Extended Generation 3) # TBD
+- label: V1 attention (H100-MI300) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal/generation
+  - vllm/config/attention.py
+  - vllm/model_executor/layers/attention
+  - vllm/v1/attention
+  - tests/v1/attention
+  - vllm/_aiter_ops.py
+  - vllm/envs.py
+  - vllm/platforms/rocm.py
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
-
+  - pytest -v -s v1/attention
 
-- label: Multi-Modal Models (Extended Pooling) # TBD
+- label: V1 Core + KV + Metrics # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/models/multimodal/pooling
+  - tests/v1/core
+  - tests/v1/executor
+  - tests/v1/kv_offload
+  - tests/v1/worker
+  - tests/v1/kv_connector/unit
+  - tests/v1/metrics
+  - tests/entrypoints/openai/correctness/test_lmeval.py
   commands:
-  - pytest -v -s models/multimodal/pooling -m 'not core_model'
-
+  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
+  - pytest -v -s -m 'not cpu_test' v1/core
+  - pytest -v -s v1/executor
+  - pytest -v -s v1/kv_offload
+  - pytest -v -s v1/worker
+  - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
+  - pytest -v -s -m 'not cpu_test' v1/metrics
+  - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
+  # - export HSA_NO_SCRATCH_RECLAIM=1
+  - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
-- label: Quantized Models Test # 21.4m
-  timeout_in_minutes: 38
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+- label: V1 others (CPU) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  no_gpu: true
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/layers/quantization
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  - tests/models/quantization
-  - vllm/model_executor/model_loader/
+  - vllm/
+  - tests/v1
   commands:
-  - pytest -v -s models/quantization
-
+  - pytest -v -s -m 'cpu_test' v1/core
+  - pytest -v -s v1/structured_output
+  - pytest -v -s v1/test_serial_utils.py
+  - pytest -v -s -m 'cpu_test' v1/kv_connector/unit
+  - pytest -v -s -m 'cpu_test' v1/metrics
 
-- label: Transformers Nightly Models # 50.9m
-  timeout_in_minutes: 102
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+- label: V1 Sample + Logits # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
   optional: true
-  working_dir: "/vllm-workspace/"
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/multimodal/
-  - vllm/model_executor/layers/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  - tests/models/
-  - examples/
+  - vllm/
+  - tests/v1/sample
+  - tests/v1/logits_processors
+  - tests/v1/test_oracle.py
+  - tests/v1/test_request.py
+  - tests/v1/test_outputs.py
   commands:
-  - pip install --upgrade git+https://github.com/huggingface/transformers
-  - pytest -v -s tests/models/test_initialization.py
-  - pytest -v -s tests/models/test_transformers.py
-  - pytest -v -s tests/models/multimodal/processing/
-  - pytest -v -s tests/models/multimodal/test_mapping.py
-  - python3 examples/basic/offline_inference/chat.py
-  - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
-  - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
-
+  - pytest -v -s v1/sample
+  - pytest -v -s v1/logits_processors
+  - pytest -v -s v1/test_oracle.py
+  - pytest -v -s v1/test_request.py
+  - pytest -v -s v1/test_outputs.py
 
-- label: Quantized MoE Test (B200-MI325) # TBD
+- label: Distributed DP Tests (2 GPUs) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  working_dir: "/vllm-workspace/"
-  source_file_dependencies:
-  - tests/quantization/test_gfx3xx_moe.py
-  - vllm/model_executor/models/deepseek_v2.py
-  - vllm/model_executor/models/gpt_oss.py
-  - vllm/model_executor/models/llama4.py
-  - vllm/model_executor/layers/fused_moe
-  - vllm/model_executor/layers/quantization/compressed_tensors
-  - vllm/model_executor/layers/quantization/modelopt.py
-  - vllm/model_executor/layers/quantization/mxfp4.py
-  - vllm/v1/attention/backends/triton_attn.py
-  - vllm/v1/attention/backends/rocm_attn.py
-  - vllm/v1/attention/backends/rocm_aiter_fa.py
-  - vllm/v1/attention/backends/mla/
-  - vllm/v1/attention/selector.py
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  - vllm/model_executor/model_loader/
-  commands:
-  - pytest -s -v tests/quantization/test_gfx3xx_moe.py
-
-
-- label: Distributed DP Tests (2 GPUs) # 56.1m
-  timeout_in_minutes: 102
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_2
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_2
   num_gpus: 2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -2403,158 +2278,115 @@ steps:
   - tests/entrypoints/openai/test_multi_api_servers.py
   - vllm/platforms/rocm.py
   commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
   - DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py
 
-
-- label: Distributed Compile + RPC Tests (2 GPUs) # 56.1m
-  timeout_in_minutes: 102
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_2
+- label: Distributed Tests (2xH100-2xMI300) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_2
   num_gpus: 2
-  working_dir: "/vllm-workspace/tests"
+  working_dir: "/vllm-workspace/"
   source_file_dependencies:
-  - vllm/compilation/
   - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/worker/worker_base.py
-  - vllm/v1/engine/
-  - vllm/v1/worker/
-  - tests/compile/fullgraph/test_basic_correctness.py
-  - tests/compile/test_wrapper.py
-  - tests/entrypoints/llm/test_collective_rpc.py
+  - vllm/v1/distributed/
+  - vllm/model_executor/layers/fused_moe/
+  - tests/v1/distributed/test_dbo.py
+  - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - pytest -v -s entrypoints/llm/test_collective_rpc.py
-  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
-  - pytest -v -s ./compile/test_wrapper.py
-
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_async_new_apis.py
+  - VLLM_LOGGING_LEVEL=DEBUG python3 examples/features/data_parallel/data_parallel_offline.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
+  - pytest -v -s tests/v1/distributed/test_dbo.py
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 pytest -v -s tests/distributed/test_weight_transfer.py
+  - pytest -v -s tests/distributed/test_packed_tensor.py
 
-- label: Distributed Torchrun + Shutdown Tests (2 GPUs) # 56.1m
-  timeout_in_minutes: 102
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_2
+- label: Metrics, Tracing (2 GPUs) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_2
   num_gpus: 2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/worker/worker_base.py
-  - vllm/v1/engine/
-  - vllm/v1/worker/
-  - tests/distributed/
-  - tests/v1/shutdown
-  - tests/v1/worker/test_worker_memory_snapshot.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/v1/tracing
   commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
-  - pytest -v -s v1/worker/test_worker_memory_snapshot.py
-
+  - "pip install \
+      'opentelemetry-sdk>=1.26.0' \
+      'opentelemetry-api>=1.26.0' \
+      'opentelemetry-exporter-otlp>=1.26.0' \
+      'opentelemetry-semantic-conventions-ai>=0.4.1'"
+  - pytest -v -s v1/tracing
 
-- label: Distributed Model Tests (2 GPUs) # 19.3m
-  timeout_in_minutes: 38
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_2
-  num_gpus: 2
+- label: V1 e2e (2 GPUs) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_2
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/model_loader/sharded_state_loader.py
-  - vllm/model_executor/models/
-  - vllm/model_executor/layers/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  - tests/basic_correctness/
-  - tests/model_executor/model_loader/test_sharded_state_loader.py
-  - tests/models/
+  - vllm/
+  - tests/v1/e2e
   commands:
-  - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py -m '(not slow_test)'
-  - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
-  - pytest models/language -v -s -m 'distributed(num_gpus=2)'
-  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py
-  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest models/multimodal/generation/test_whisper.py -v -s -m 'distributed(num_gpus=2)'
-
+    - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "tensor_parallelism"
 
-- label: LoRA TP (Distributed) # 9.8m
-  timeout_in_minutes: 18
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
+- label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
   num_gpus: 4
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/lora
-  - tests/lora
+  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+  - tests/v1/kv_connector/nixl_integration/
   - vllm/platforms/rocm.py
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
-  - pytest -v -s -x lora/test_chatglm3_tp.py
-  - pytest -v -s -x lora/test_llama_tp.py
-  - pytest -v -s -x lora/test_llm_with_multi_loras.py
-  - pytest -v -s -x lora/test_olmoe_tp.py
-  - pytest -v -s -x lora/test_gptoss_tp.py
-  - pytest -v -s -x lora/test_qwen35_densemodel_lora.py
-
-
-- label: Weight Loading Multiple GPU # 7.5m
-  timeout_in_minutes: 14
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_2
-  num_gpus: 2
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/weight_loading
-  commands:
-  - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-amd.txt
-
+  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
+  - CROSS_LAYERS_BLOCKS=True ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
-- label: Weight Loading Multiple GPU - Large Models # 12.6m
-  timeout_in_minutes: 26
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_2
-  num_gpus: 2
-  optional: true
+- label: Distributed DP Tests (4 GPUs) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
+  num_gpus: 4
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/weight_loading
+  - vllm/distributed/
+  - tests/v1/distributed
+  - tests/v1/engine/test_engine_core_client.py
+  - tests/distributed/test_utils
+  - vllm/platforms/rocm.py
   commands:
-  - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large-amd.txt
-
+  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
+  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
+  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
+  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py
+  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py
+  - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
+  - pytest -v -s distributed/test_utils.py
 
-- label: Ray Dependency Compatibility Check # TBD
+- label: Distributed NixlConnector PD accuracy (4 GPUs)  # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
+  num_gpus: 4
   optional: true
-  working_dir: "/"
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - requirements/
-  - setup.py
+  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+  - tests/v1/kv_connector/nixl_integration/
   - vllm/platforms/rocm.py
   commands:
-  - bash /vllm-workspace/.buildkite/scripts/check-ray-compatibility.sh
-
+  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
+  - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
-- label: Distributed NixlConnector PD accuracy (4 GPUs)  # 27.4m
-  timeout_in_minutes: 44
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
+- label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
   num_gpus: 4
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
@@ -2562,15 +2394,13 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
+  - DP_EP=1 ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
-- label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs) # TBD
+- label: Hyrbid SSM NixlConnector PD accuracy tests (4 GPUs) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
   num_gpus: 4
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
@@ -2578,166 +2408,157 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - CROSS_LAYERS_BLOCKS=True ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
+  - HYBRID_SSM=1 ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
-- label: Distributed Tests (4 GPUs)(A100-MI325) # 20.9m
-  timeout_in_minutes: 37
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
-  num_gpus: 4
+- label: V1 e2e (4 GPUs) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
+  - tests/v1/e2e
   commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - pytest -v -s distributed/test_custom_all_reduce.py
-  - torchrun --nproc_per_node=2 distributed/test_ca_buffer_sharing.py
-  - TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
-  - pytest -v -s -x lora/test_mixtral.py
-
+    - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle_correctness_heavy"
 
-- label: Distributed Tests (2 GPUs)(H100-MI325) # TBD
+- label: V1 e2e (4xH100-4xMI300) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_2
-  num_gpus: 2
-  working_dir: "/vllm-workspace/"
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_4
+  optional: true
   source_file_dependencies:
-  - vllm/distributed/
-  - vllm/v1/distributed/
-  - vllm/model_executor/layers/fused_moe/
-  - tests/v1/distributed/test_dbo.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+    - vllm/v1/attention/backends/utils.py
+    - vllm/v1/worker/gpu_model_runner.py
+    - tests/v1/e2e/test_hybrid_chunked_prefill.py
   commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - pytest -v -s tests/v1/distributed/test_dbo.py
+    - pytest -v -s v1/e2e/test_hybrid_chunked_prefill.py
 
+#------------------------------------------------------  mi300 · weight_loading  -------------------------------------------------------#
 
-- label: Distributed Compile Unit Tests (2xH100-2xMI325) # 14.3m
-  timeout_in_minutes: 32
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_2
+- label: Weight Loading Multiple GPU # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_2
   num_gpus: 2
-  working_dir: "/vllm-workspace/"
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/compilation/
-  - vllm/model_executor/layers
-  - tests/compile/passes/distributed/
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/weight_loading
   commands:
-  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-  - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/passes/distributed/test_async_tp.py
-  - pytest -v -s tests/compile/passes/distributed/test_sequence_parallelism.py
-  # TODO: this test is not supported on ROCm, there are aiter kernels for this.
-  # - pytest -v -s tests/compile/passes/distributed/test_fusion_all_reduce.py
-  # - pytest -v -s tests/compile/distributed/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm
-  # - "VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'"
-
+  - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-amd.txt
 
-- label: LM Eval Small Models # 13.3m
-  timeout_in_minutes: 23
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
+- label: Weight Loading Multiple GPU - Large Models # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_2
+  num_gpus: 2
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/weight_loading
   commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt
+  - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large-amd.txt
 
+#########################################################################################################################################
+#                                                                                                                                       #
+#                                                         MI325 (gfx942) tests                                                          #
+#                                                                                                                                       #
+#########################################################################################################################################
 
-- label: LM Eval Small Models (MI325) # TBD
+#----------------------------------------------------------  mi325 · compile  ----------------------------------------------------------#
+
+- label: Distributed Compile + RPC Tests (2 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
+  agent_pool: mi325_2
+  num_gpus: 2
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/_aiter_ops.py
+  - vllm/compilation/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/worker/worker_base.py
+  - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/compile/fullgraph/test_basic_correctness.py
+  - tests/compile/test_wrapper.py
+  - tests/entrypoints/llm/test_collective_rpc.py
   - vllm/platforms/rocm.py
   commands:
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-small-rocm.txt
+  - pytest -v -s entrypoints/llm/test_collective_rpc.py
+  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
+  - pytest -v -s ./compile/test_wrapper.py
 
+#--------------------------------------------------------  mi325 · distributed  --------------------------------------------------------#
 
-- label: LM Eval Small Models (B200-MI325) # TBD
+- label: Distributed Torchrun + Shutdown Tests (2 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
   agent_pool: mi325_2
+  num_gpus: 2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/_aiter_ops.py
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/worker/worker_base.py
+  - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/distributed/
+  - tests/v1/shutdown
+  - tests/v1/worker/test_worker_memory_snapshot.py
   - vllm/platforms/rocm.py
   commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-mi3xx-fp8-and-mixed.txt
-
+  - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
+  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
+  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
+  - pytest -v -s v1/worker/test_worker_memory_snapshot.py
 
-- label: LM Eval Large Models (H200-MI325) # TBD
+- label: Distributed Compile + Comm (4 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_8
-  optional: true
-  num_gpus: 8
+  agent_pool: mi325_4
+  num_gpus: 4
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/model_executor/layers/quantization/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/model_executor/layers/layernorm.py
-  - csrc/
-  - vllm/_aiter_ops.py
+  - vllm/distributed/
+  - tests/distributed/test_pynccl
+  - tests/distributed/test_events
+  - tests/compile/fullgraph/test_basic_correctness.py
+  - tests/distributed/test_symm_mem_allreduce.py
+  - tests/distributed/test_multiproc_executor.py
   - vllm/platforms/rocm.py
-  - tests/evals/
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-mi3xx.txt
+  - pytest -v -s compile/fullgraph/test_basic_correctness.py
+  - pytest -v -s distributed/test_pynccl.py
+  - pytest -v -s distributed/test_events.py
+  - pytest -v -s distributed/test_symm_mem_allreduce.py
+  - pytest -v -s distributed/test_multiproc_executor.py::test_multiproc_executor_multi_node
 
+#----------------------------------------------------------  mi325 · engine  -----------------------------------------------------------#
 
-- label: LM Eval Large Models (4 GPUs)(FP8) # 24.8m
-  timeout_in_minutes: 42
+- label: Engine # TBD
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
-  num_gpus: 4
-  optional: true
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
+  agent_pool: mi325_1
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/engine
+  - tests/test_sequence
+  - tests/test_config
+  - tests/test_logger
+  - tests/test_vllm_port
   commands:
-  - export VLLM_USE_DEEP_GEMM=0
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-rocm-fp8.txt --tp-size=4
+  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
 
+#-----------------------------------------------------------  mi325 · evals  -----------------------------------------------------------#
 
-- label: LM Eval Large Models (4 GPUs)(A100-MI325) # 17.3m
-  timeout_in_minutes: 27
+- label: LM Eval Large Models (4xH100-4xMI325) # TBD
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
   agent_pool: mi325_4
   num_gpus: 4
@@ -2753,11 +2574,10 @@ steps:
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
-
+  - export VLLM_USE_DEEP_GEMM=0
+  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-rocm-fp8.txt --tp-size=4
 
-- label: ROCm LM Eval Large Models (8 Card) # TBD
+- label: ROCm LM Eval Large Models (8 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
   agent_pool: mi325_8
@@ -2778,237 +2598,150 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-rocm.txt --tp-size=8
 
+#----------------------------------------------------------  mi325 · models  -----------------------------------------------------------#
 
-- label: GPQA Eval (GPT-OSS) (H100-MI325) # TBD
+- label: Language Models Test (Extended Generation) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_2
-  num_gpus: 2
-  optional: true
+  agent_pool: mi325_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/model_executor/layers/fused_moe/
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  - tests/evals/gpt_oss/
+  - vllm/
+  - tests/models/language/generation
   commands:
-    - uv pip install --system 'gpt-oss[eval]==0.0.5'
-    - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-gfx942.txt
-
+  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
+  - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
+  - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
 
-- label: DeepSeek V2-Lite Accuracy # 6.7m
-  timeout_in_minutes: 12
+- label: Language Models Tests (Hybrid) %N # TBD
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
-  num_gpus: 4
+  agent_pool: mi325_1
+  torch_nightly: true
+  parallelism: 2
   optional: true
-  working_dir: "/vllm-workspace"
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/distributed/eplb
-  - vllm/model_executor/layers/fused_moe/
-  - vllm/model_executor/layers/quantization/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/backends/mla/
-  - vllm/v1/attention/selector.py
-  - .buildkite/scripts/scheduled_integration_test/
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/models/language/generation
   commands:
-  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010
-
+  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
+  - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
+  - pytest -v -s models/language/generation -m hybrid_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
 
-- label: DeepSeek V2-Lite Prefetch Offload Accuracy (H100-MI325) # TBD
+- label: Multi-Modal Models (Extended Pooling) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
   agent_pool: mi325_1
-  num_gpus: 1
-  working_dir: "/vllm-workspace"
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/model_executor/layers/fused_moe/
-  - vllm/model_executor/layers/quantization/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/backends/mla/
-  - vllm/v1/attention/selector.py
-  - .buildkite/scripts/scheduled_integration_test/
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/models/multimodal/pooling
   commands:
-  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh 0.25 200 8030
-
+  - pytest -v -s models/multimodal/pooling -m 'not core_model'
 
-- label: Qwen3-30B-A3B-FP8-block Accuracy # 6.4m
-  timeout_in_minutes: 11
+- label: "Multi-Modal Models (Standard) 2: qwen3 + gemma" # TBD
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
+  agent_pool: mi325_1
+  torch_nightly: true
   optional: true
-  working_dir: "/vllm-workspace"
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/model_executor/layers/quantization/
-  - vllm/distributed/eplb
-  - vllm/model_executor/layers/fused_moe/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - .buildkite/scripts/scheduled_integration_test/
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/models/multimodal
   commands:
-  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020
+  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen3 or gemma"
+  - pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model
 
+#------------------------------------------------------------  mi325 · v1  -------------------------------------------------------------#
 
-- label: Qwen3-Next-80B-A3B-Instruct MTP Async EPLB Accuracy # 10.9m
-  timeout_in_minutes: 22
+- label: V1 Spec Decode # TBD
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
-  num_gpus: 4
-  optional: true
-  working_dir: "/vllm-workspace"
+  agent_pool: mi325_1
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/spec_decode/
-  - vllm/distributed/eplb
-  - vllm/model_executor/layers/fused_moe/
-  - vllm/model_executor/layers/quantization/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - .buildkite/scripts/scheduled_integration_test/
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/v1/spec_decode
   commands:
-  - bash .buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh 0.8 1319 8040
-
-##### .buildkite/test_areas/compile.yaml #####
-# Slowly setting up the tests so that it is also easier for the
-# CI team to review and upstream to the pipelinev2.
-# The following tests are important for vLLM IR Ops refactoring,
-# which affects fusion passes on ROCm. So we have to
-# enable them as as soon as possible.
+  - pytest -v -s -m 'not slow_test' v1/spec_decode
 
-## TODO: Enable the test in this group
-# # corresponds to .buildkite/test_areas/compile.yaml
-# - label: Fusion and Compile Unit Tests (2xB200-2xMI325) # TBD
-#   timeout_in_minutes: 180
-#   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325, tj]
-#   agent_pool: mi325_1 # changed to 1 GPU until the fusion all reduce is enabled then only revert back to 2 GPUs
-#   num_gpus: 1
-#   working_dir: "/vllm-workspace/"
-#   source_file_dependencies:
-#   - csrc/quantization/fp4/
-#   - vllm/model_executor/layers/quantization/
-#   - vllm/model_executor/layers/layernorm.py
-#   - vllm/model_executor/layers/activation.py
-#   - vllm/model_executor/layers/attention/attention.py
-#   - vllm/v1/attention/backends/flashinfer.py
-#   - vllm/compilation/ # TODO(luka) limit to vllm/compilation/passes
-#   - tests/compile/test_fusion_attn.py
-#   - tests/compile/test_silu_mul_quant_fusion.py
-#   - tests/compile/distributed/test_fusion_all_reduce.py
-#   - tests/compile/fullgraph/test_full_graph.py
-#   commands:
-#     - rocm-smi
-#     # we run all backend tests on ROCm
-#     # These two tests are covered in "PyTorch Compilation Passes Unit Tests"
-#     # - "pytest -v -s tests/compile/passes/test_fusion_attn.py"
-#     # - "pytest -v -s tests/compile/passes/test_silu_mul_quant_fusion.py"
-#     # TODO: this test is not supported on ROCm, there are aiter kernels for this.
-#     # - pytest -v -s tests/compile/passes/distributed/test_fusion_all_reduce.py
-#     # TODO: find out more details
-#     # - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile
+#########################################################################################################################################
+#                                                                                                                                       #
+#                                                         MI355 (gfx950) tests                                                          #
+#                                                                                                                                       #
+#########################################################################################################################################
 
+#--------------------------------------------------------  mi355 · benchmarks  ---------------------------------------------------------#
 
-- label: Fusion E2E Quick (H100-MI325) # TBD
+- label: Attention Benchmarks Smoke Test (B200-MI355) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  num_gpus: 1
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  agent_pool: mi355_2
+  num_gpus: 2
   working_dir: "/vllm-workspace/"
   source_file_dependencies:
-  - csrc/quantization/
-  - vllm/model_executor/
+  - benchmarks/attention_benchmarks/
   - vllm/v1/attention/
-  - vllm/compilation/
-  - tests/compile/fusions_e2e/
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-  - rocm-smi
-  # Run all models and attn backends but only Inductor partition and native custom ops
-  - "pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k 'inductor_partition and not +rms_norm and not +quant_fp8'"
-  # Different from CUDA, Qwen requires +rms_norm and +quant_fp8 as rms+quant fusion is only supported on AITER
-  - "pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k 'inductor_partition and +rms_norm and +quant_fp8 and qwen3'"
+  - python3 benchmarks/attention_benchmarks/benchmark.py --backends ROCM_ATTN ROCM_AITER_FA ROCM_AITER_UNIFIED_ATTN --batch-specs "8q1s1k" --repeats 1 --warmup-iters 1
 
+#--------------------------------------------------------  mi355 · distributed  --------------------------------------------------------#
 
-- label: Fusion E2E Config Sweep (H100-MI325) # TBD
+- label: Distributed Tests (2xH100-2xMI355) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  num_gpus: 1
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  agent_pool: mi355_2
+  num_gpus: 2
+  optional: true
   working_dir: "/vllm-workspace/"
   source_file_dependencies:
-  - csrc/quantization/
-  - vllm/compilation/
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/attention/attention.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - tests/compile/fusions_e2e/
+  - vllm/distributed/
+  - vllm/v1/distributed/
+  - vllm/model_executor/layers/fused_moe/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - tests/distributed/test_context_parallel.py
+  - tests/v1/distributed/test_dbo.py
+  - examples/features/data_parallel/data_parallel_offline.py
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-  - rocm-smi
-  - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "llama-3"
-
-## There are no ops on ROCm for these tests.
-## The test still passes but the logs are not useful.
-## fused ops just call torch.ops.symm_mem which
-## exists in ROCm even though they don't work
-# - label: AsyncTP Correctness Tests (2xH100-2xMI325)
-# - label: Fusion E2E TP2 Quick (H100-MI325)
-# - label: Fusion E2E TP2 AsyncTP Config Sweep (H100-MI325)
-# - label: Fusion E2E TP2 (B200-MI325)
-# - label: Sequence Parallel Correctness Tests (2xH100-2xMI325)
-
+  - pytest -v -s tests/distributed/test_context_parallel.py
+  - pytest -v -s tests/v1/distributed/test_dbo.py
 
-#####################################################################################################################################
-#                                                                                                                                   #
-#                                                             gfx950                                                                #
-#                                                                                                                                   #
-#####################################################################################################################################
+#--------------------------------------------------------  mi355 · entrypoints  --------------------------------------------------------#
 
-- label: Entrypoints Integration (API Server openai - Part 1) # TBD
+- label: Entrypoints Integration (API Server 2) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
+  optional: true
   fast_check: true
   torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/entrypoints/openai
-  - tests/entrypoints/test_chat_utils
+  - tests/entrypoints/rpc
+  - tests/entrypoints/serve/instrumentator
+  - tests/tool_use
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai/chat_completion --ignore=entrypoints/openai/chat_completion/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/chat_completion/test_oot_registration.py
-
+  - pytest -v -s entrypoints/serve/instrumentator
+  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/rpc
+  - pytest -v -s tool_use
 
-- label: Entrypoints Integration (API Server openai - Part 2) # TBD
+- label: Entrypoints Integration (API Server openai - Part 1) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
   fast_check: true
   torch_nightly: true
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -3016,17 +2749,15 @@ steps:
   - tests/entrypoints/test_chat_utils
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
-  - pytest -v -s entrypoints/openai/speech_to_text/
-  - pytest -v -s entrypoints/test_chat_utils.py
-
+  - pytest -v -s entrypoints/openai/chat_completion --ignore=entrypoints/openai/chat_completion/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/chat_completion/test_oot_registration.py
 
-- label: Entrypoints Integration (API Server openai - Part 3) # TBD
+- label: Entrypoints Integration (API Server openai - Part 2) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
   fast_check: true
   torch_nightly: true
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -3034,28 +2765,24 @@ steps:
   - tests/entrypoints/test_chat_utils
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/speech_to_text/ --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses
-
+  - pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
+  - pytest -v -s entrypoints/openai/speech_to_text/
+  - pytest -v -s entrypoints/test_chat_utils.py
 
-- label: Entrypoints Integration (API Server 2) # TBD
+- label: Entrypoints Integration (API Server openai - Part 3) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
-  optional: true
   fast_check: true
   torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/entrypoints/rpc
-  - tests/entrypoints/serve/instrumentator
-  - tests/tool_use
+  - tests/entrypoints/openai
+  - tests/entrypoints/test_chat_utils
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/serve/instrumentator
-  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/rpc
-  - pytest -v -s tool_use
-
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/speech_to_text/ --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/test_multi_api_servers.py
 
 - label: Entrypoints Integration (Pooling) # TBD
   timeout_in_minutes: 180
@@ -3071,95 +2798,112 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -v -s entrypoints/pooling
 
+#-----------------------------------------------------------  mi355 · evals  -----------------------------------------------------------#
 
-- label: Regression # TBD
+- label: GPQA Eval (GPT-OSS) (2xB200-2xMI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_1
+  agent_pool: mi355_2
+  num_gpus: 2
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/test_regression
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/model_executor/layers/fused_moe/
+  - tests/evals/gpt_oss/
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
   commands:
-  - pip install modelscope
-  - pytest -v -s test_regression.py
-
+    - uv pip install --system 'gpt-oss[eval]==0.0.5'
+    - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-gfx950.txt
 
-- label: V1 Spec Decode # TBD
-  timeout_in_minutes: 60
+- label: LM Eval Qwen3-5 Models (B200-MI355) # TBD
+  timeout_in_minutes: 120
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_1
+  agent_pool: mi355_2
+  num_gpus: 2
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/v1/spec_decode
+  - vllm/model_executor/models/qwen3_5.py
+  - vllm/model_executor/models/qwen3_5_mtp.py
+  - vllm/transformers_utils/configs/qwen3_5.py
+  - vllm/transformers_utils/configs/qwen3_5_moe.py
+  - vllm/model_executor/models/qwen.py
+  - vllm/model_executor/models/qwen2.py
+  - vllm/model_executor/models/qwen3.py
+  - vllm/model_executor/models/qwen3_next.py
+  - vllm/model_executor/models/qwen3_next_mtp.py
+  - vllm/model_executor/layers/fla/ops/
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s -m 'not slow_test' v1/spec_decode
-
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-qwen35-mi355.txt
 
-- label: V1 Sample + Logits # TBD
-  timeout_in_minutes: 60
+- label: LM Eval Small Models (2xB200-2xMI355) # TBD
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_1
+  agent_pool: mi355_2
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/v1/sample
-  - tests/v1/logits_processors
-  - tests/v1/test_oracle.py
-  - tests/v1/test_request.py
-  - tests/v1/test_outputs.py
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s v1/sample
-  - pytest -v -s v1/logits_processors
-  - pytest -v -s v1/test_oracle.py
-  - pytest -v -s v1/test_request.py
-  - pytest -v -s v1/test_outputs.py
-
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-mi3xx-fp8-and-mixed.txt
 
-- label: V1 Core + KV + Metrics # TBD
-  timeout_in_minutes: 60
+- label: Qwen3-30B-A3B-FP8-block Accuracy (B200-MI355) # TBD
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_1
-  working_dir: "/vllm-workspace/tests"
+  agent_pool: mi355_2
+  num_gpus: 2
+  working_dir: "/vllm-workspace"
   source_file_dependencies:
-  - vllm/
-  - tests/v1/core
-  - tests/v1/executor
-  - tests/v1/kv_offload
-  - tests/v1/worker
-  - tests/v1/kv_connector/unit
-  - tests/v1/metrics
-  - tests/entrypoints/openai/correctness/test_lmeval.py
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/model_executor/layers/quantization/
+  - vllm/model_executor/layers/fused_moe/
+  - vllm/distributed/eplb
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - .buildkite/scripts/scheduled_integration_test/
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
   commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - pytest -v -s -m 'not cpu_test' v1/core
-  - pytest -v -s v1/executor
-  - pytest -v -s v1/kv_offload
-  - pytest -v -s v1/worker
-  - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
-  - pytest -v -s -m 'not cpu_test' v1/metrics
-  - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
-  - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
-
+  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
 
-- label: V1 attention (B200-MI355) # TBD
+- label: LM Eval Large Models (4xH100-4xMI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_1
-  working_dir: "/vllm-workspace/tests"
+  agent_pool: mi355_4
+  num_gpus: 4
+  optional: true
+  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
   source_file_dependencies:
-  - vllm/config/attention.py
-  - vllm/model_executor/layers/attention
-  - vllm/v1/attention
-  - tests/v1/attention
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
   - vllm/_aiter_ops.py
-  - vllm/envs.py
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s v1/attention
+  - export VLLM_USE_DEEP_GEMM=0
+  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-rocm-fp8.txt --tp-size=4
 
+#---------------------------------------------------------  mi355 · examples  ----------------------------------------------------------#
 
 - label: Examples # TBD
   timeout_in_minutes: 180
@@ -3181,19 +2925,44 @@ steps:
   - python3 basic/offline_inference/embed.py
   - python3 basic/offline_inference/score.py
   # Multi-modal models
-  - python3 offline_inference/audio_language.py --seed 0
-  - python3 offline_inference/vision_language.py --seed 0
-  - python3 offline_inference/vision_language_multi_image.py --seed 0
-  - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
+  - python3 generate/multimodal/audio_language_offline.py --seed 0
+  - python3 generate/multimodal/vision_language_offline.py --seed 0
+  - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
+  - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
   # Pooling models
   - python3 pooling/embed/vision_embedding_offline.py --seed 0
   # Features demo
-  - python3 offline_inference/prefix_caching.py
-  - python3 offline_inference/llm_engine_example.py
-  - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-  - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-  - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+  - python3 features/automatic_prefix_caching/prefix_caching_offline.py
+  - python3 deployment/llm_engine_example.py
+  - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+  - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+  - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
+#----------------------------------------------------------  mi355 · kernels  ----------------------------------------------------------#
+
+- label: Kernels (B200-MI355) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  agent_pool: mi355_1
+  working_dir: "/vllm-workspace/"
+  source_file_dependencies:
+  - csrc/quantization/fp4/
+  - csrc/attention/mla/
+  - csrc/quantization/cutlass_w8a8/moe/
+  - vllm/model_executor/layers/fused_moe/cutlass_moe.py
+  - vllm/v1/attention/backends/triton_attn.py
+  - vllm/v1/attention/backends/rocm_attn.py
+  - vllm/v1/attention/backends/rocm_aiter_fa.py
+  - vllm/v1/attention/backends/rocm_aiter_unified_attn.py
+  - vllm/v1/attention/backends/mla/aiter_triton_mla.py
+  - vllm/v1/attention/backends/mla/rocm_aiter_mla.py
+  - vllm/v1/attention/selector.py
+  - vllm/platforms/rocm.py
+  - vllm/_aiter_ops.py
+  commands:
+  - rocm-smi
+  - python3 examples/basic/offline_inference/chat.py
+  - pytest -v -s tests/kernels/attention/test_attention_selector.py
 
 - label: Kernels Attention Test %N # TBD
   timeout_in_minutes: 180
@@ -3213,25 +2982,6 @@ steps:
   commands:
   - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
 
-
-- label: Kernels Quantization Test %N # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_1
-  parallelism: 2
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - csrc/quantization/
-  - vllm/model_executor/layers/quantization
-  - tests/kernels/quantization
-  - tests/kernels/quantization/test_rocm_skinny_gemms.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  - vllm/model_executor/kernels/
-  commands:
-  - pytest -v -s kernels/quantization --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-
-
 - label: Kernels MoE Test %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
@@ -3252,54 +3002,40 @@ steps:
   - pytest -v -s kernels/moe --ignore=kernels/moe/test_modular_oai_triton_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
   - pytest -v -s kernels/moe/test_modular_oai_triton_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
 
-
-- label: Kernels FP8 MoE Test # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_2
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - csrc/moe/
-  - csrc/quantization/w8a8/cutlass/moe/
-  - vllm/model_executor/layers/fused_moe/
-  - tests/kernels/moe/test_deepep_moe.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  - vllm/envs.py
-  commands:
-    - pytest -v -s kernels/moe/test_deepep_moe.py
-
-
-- label: Quantization # TBD
+- label: Kernels Quantization Test %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
+  parallelism: 2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - csrc/
+  - csrc/quantization/
   - vllm/model_executor/layers/quantization
-  - tests/quantization
+  - tests/kernels/quantization
+  - tests/kernels/quantization/test_rocm_skinny_gemms.py
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
+  - vllm/model_executor/kernels/
   commands:
-  - uv pip install --system torchao==0.17.0
-  - uv pip install --system conch-triton-kernels
-  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
-
+  - pytest -v -s kernels/quantization --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
 
-- label: Language Models Tests (Standard) # TBD
+- label: Kernels FP8 MoE Test (2xH100-2xMI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_1
-  torch_nightly: true
+  agent_pool: mi355_2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/language
+  - csrc/moe/
+  - csrc/quantization/w8a8/cutlass/moe/
+  - vllm/model_executor/layers/fused_moe/
+  - tests/kernels/moe/test_deepep_moe.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
+  - vllm/envs.py
   commands:
-  - pip freeze | grep -E 'torch'
-  - pytest -v -s models/language -m 'core_model and (not slow_test)'
+    - pytest -v -s kernels/moe/test_deepep_moe.py
 
+#----------------------------------------------------------  mi355 · models  -----------------------------------------------------------#
 
 - label: Language Models Test (Extended Generation) # TBD
   timeout_in_minutes: 180
@@ -3310,15 +3046,15 @@ steps:
   - vllm/
   - tests/models/language/generation
   commands:
-  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
-  - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
+  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@rocm-7.0-v2.3.0'
+  - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
   - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
 
-
 - label: Language Models Test (Extended Pooling) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -3326,72 +3062,46 @@ steps:
   commands:
   - pytest -v -s models/language/pooling -m 'not core_model'
 
-
-- label: "Multi-Modal Models (Standard) 1: qwen2" # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_1
-  torch_nightly: true
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
-  commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen2"
-  - pytest -v -s models/multimodal/generation/test_ultravox.py -m core_model
-
-
-- label: "Multi-Modal Models (Standard) 2: qwen3 + gemma" # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_1
-  torch_nightly: true
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
-  commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen3 or gemma"
-  - pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model
-
-
-- label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl" # TBD
+- label: Language Models Test (PPL) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
-  torch_nightly: true
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal/generation
-  - tests/models/multimodal/test_mapping.py
+  - vllm/model_executor/models/qwen3_5.py
+  - vllm/model_executor/models/qwen3_5_mtp.py
+  - vllm/transformers_utils/configs/qwen3_5.py
+  - vllm/transformers_utils/configs/qwen3_5_moe.py
+  - vllm/model_executor/models/qwen.py
+  - vllm/model_executor/models/qwen2.py
+  - vllm/model_executor/models/qwen3.py
+  - vllm/model_executor/models/qwen3_next.py
+  - vllm/model_executor/models/qwen3_next_mtp.py
+  - vllm/model_executor/layers/fla/ops/
+  - vllm/_aiter_ops.py
+  - vllm/v1/attention/backends/triton_attn.py
+  - vllm/v1/attention/backends/rocm_attn.py
+  - vllm/v1/attention/backends/rocm_aiter_unified_attn.py
+  - vllm/v1/attention/backends/rocm_aiter_fa.py
+  - vllm/v1/attention/backends/flex_attention.py
+  - vllm/v1/attention/ops/
+  - vllm/platforms/rocm.py
+  - tests/models/language/generation_ppl_test
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "not qwen2 and not qwen3 and not gemma"
-  - pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model
-
+  - pytest -v -s models/language/generation_ppl_test
 
-- label: "Multi-Modal Models (Standard) 4: other + whisper" # TBD
+- label: Language Models Tests (Standard) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
   torch_nightly: true
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/models/multimodal/generation
+  - tests/models/language
   commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/generation/test_ultravox.py --ignore models/multimodal/generation/test_qwen2_5_vl.py --ignore models/multimodal/generation/test_qwen2_vl.py --ignore models/multimodal/generation/test_whisper.py  --ignore models/multimodal/generation/test_memory_leak.py --ignore models/multimodal/processing
-  - pytest -v -s models/multimodal/generation/test_memory_leak.py -m core_model
-  - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model
-
+  - pip freeze | grep -E 'torch'
+  - pytest -v -s models/language -m 'core_model and (not slow_test)'
 
 - label: Multi-Modal Models (Extended Generation 1) # TBD
   timeout_in_minutes: 180
@@ -3408,21 +3118,6 @@ steps:
   - pytest -v -s models/multimodal/generation -m 'not core_model' --ignore models/multimodal/generation/test_common.py
   - pytest -v -s models/multimodal/test_mapping.py
 
-
-- label: Multi-Modal Models (Extended Generation 2) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal/generation
-  commands:
-  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
-
-
 - label: Multi-Modal Models (Extended Generation 3) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
@@ -3436,7 +3131,6 @@ steps:
   - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
   - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
 
-
 - label: Multi-Modal Models (Extended Pooling) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
@@ -3449,303 +3143,254 @@ steps:
   commands:
   - pytest -v -s models/multimodal/pooling -m 'not core_model'
 
-
-- label: Quantized Models Test # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_1
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/model_executor/layers/quantization
-  - tests/models/quantization
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  - vllm/model_executor/model_loader/
-  commands:
-  - pytest -v -s models/quantization
-
-
-- label: Kernels (B200-MI355) # TBD
+- label: "Multi-Modal Models (Standard) 1: qwen2" # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
-  working_dir: "/vllm-workspace/"
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - csrc/attention/mla/
-  - csrc/quantization/cutlass_w8a8/moe/
-  - vllm/model_executor/layers/fused_moe/cutlass_moe.py
-  - vllm/v1/attention/backends/triton_attn.py
-  - vllm/v1/attention/backends/rocm_attn.py
-  - vllm/v1/attention/backends/rocm_aiter_fa.py
-  - vllm/v1/attention/backends/rocm_aiter_unified_attn.py
-  - vllm/v1/attention/backends/mla/aiter_triton_mla.py
-  - vllm/v1/attention/backends/mla/rocm_aiter_mla.py
-  - vllm/v1/attention/selector.py
-  - vllm/platforms/rocm.py
-  - vllm/_aiter_ops.py
-  commands:
-  - rocm-smi
-  - python3 examples/basic/offline_inference/chat.py
-  - pytest -v -s tests/kernels/attention/test_attention_selector.py
-
-
-- label: Weight Loading Multiple GPU # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_2
-  num_gpus: 2
+  torch_nightly: true
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/weight_loading
+  - tests/models/multimodal
   commands:
-  - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-amd.txt
-
+  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen2"
+  - pytest -v -s models/multimodal/generation/test_ultravox.py -m core_model
 
-- label: Weight Loading Multiple GPU - Large Models # TBD
+- label: "Multi-Modal Models (Standard) 4: other + whisper" # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_2
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
+  agent_pool: mi355_1
+  torch_nightly: true
   optional: true
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/weight_loading
+  - tests/models/multimodal/generation
   commands:
-  - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large-amd.txt
-
+  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+  - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/generation/test_ultravox.py --ignore models/multimodal/generation/test_qwen2_5_vl.py --ignore models/multimodal/generation/test_qwen2_vl.py --ignore models/multimodal/generation/test_whisper.py  --ignore models/multimodal/generation/test_memory_leak.py --ignore models/multimodal/processing
+  - pytest -v -s models/multimodal/generation/test_memory_leak.py -m core_model
+  - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model
 
-- label: Ray Dependency Compatibility Check # TBD
+- label: Quantized Models Test # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
-  optional: true
-  working_dir: "/"
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - requirements/
-  - setup.py
+  - vllm/model_executor/layers/quantization
+  - tests/models/quantization
+  - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
+  - vllm/model_executor/model_loader/
   commands:
-  - bash /vllm-workspace/.buildkite/scripts/check-ray-compatibility.sh
+  - pytest -v -s models/quantization
 
+#-------------------------------------------------------  mi355 · quantization  --------------------------------------------------------#
 
-- label: Distributed NixlConnector PD accuracy (4 GPUs) # TBD
+- label: Quantization # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_4
-  num_gpus: 4
-  optional: true
+  agent_pool: mi355_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-  - tests/v1/kv_connector/nixl_integration/
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - tests/quantization
+  - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+  - uv pip install --system torchao==0.17.0
+  - uv pip install --system conch-triton-kernels
+  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
+
+# - label: Quantized MoE Test (B200-MI355) # TBD
+#   timeout_in_minutes: 180
+#   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+#   agent_pool: mi355_1
+#   working_dir: "/vllm-workspace/"
+#   source_file_dependencies:
+#   - tests/quantization/test_gfx950_moe.py
+#   - vllm/model_executor/models/deepseek_v2.py
+#   - vllm/model_executor/models/gpt_oss.py
+#   - vllm/model_executor/models/llama4.py
+#   - vllm/model_executor/layers/fused_moe
+#   - vllm/model_executor/layers/quantization/compressed_tensors
+#   - vllm/model_executor/layers/quantization/modelopt.py
+#   - vllm/model_executor/layers/quantization/mxfp4.py
+#   - vllm/v1/attention/backends/triton_attn.py
+#   - vllm/v1/attention/backends/rocm_attn.py
+#   - vllm/v1/attention/backends/rocm_aiter_fa.py
+#   - vllm/v1/attention/backends/mla/
+#   - vllm/v1/attention/selector.py
+#   - vllm/model_executor/layers/layernorm.py
+#   - vllm/_aiter_ops.py
+#   - vllm/platforms/rocm.py
+#   - vllm/model_executor/model_loader/
+#   commands:
+#   - pytest -s -v tests/quantization/test_gfx950_moe.py
 
+#------------------------------------------------------------  mi355 · v1  -------------------------------------------------------------#
 
-- label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs) # TBD
+- label: V1 attention (B200-MI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_4
-  num_gpus: 4
-  optional: true
+  agent_pool: mi355_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-  - tests/v1/kv_connector/nixl_integration/
+  - vllm/config/attention.py
+  - vllm/model_executor/layers/attention
+  - vllm/v1/attention
+  - tests/v1/attention
+  - vllm/_aiter_ops.py
+  - vllm/envs.py
   - vllm/platforms/rocm.py
   commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - DP_EP=1 ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
+  - pytest -v -s v1/attention
 
-- label: NixlConnector PD + Spec Decode acceptance (2 GPUs) # TBD
-  timeout_in_minutes: 180
+- label: V1 Core + KV + Metrics # TBD
+  timeout_in_minutes: 60
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_2
-  num_gpus: 2
+  agent_pool: mi355_1
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-  - vllm/v1/worker/kv_connector_model_runner_mixin.py
-  - tests/v1/kv_connector/nixl_integration/
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/v1/core
+  - tests/v1/executor
+  - tests/v1/kv_offload
+  - tests/v1/worker
+  - tests/v1/kv_connector/unit
+  - tests/v1/metrics
+  - tests/entrypoints/openai/correctness/test_lmeval.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
-
+  - pytest -v -s -m 'not cpu_test' v1/core
+  - pytest -v -s v1/executor
+  - pytest -v -s v1/kv_offload
+  - pytest -v -s v1/worker
+  - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
+  - pytest -v -s -m 'not cpu_test' v1/metrics
+  - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
+  - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
-- label: Distributed Tests (2 GPUs)(H100-MI355) # TBD
-  timeout_in_minutes: 180
+- label: V1 Sample + Logits # TBD
+  timeout_in_minutes: 60
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_2
-  num_gpus: 2
+  agent_pool: mi355_1
   optional: true
-  working_dir: "/vllm-workspace/"
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/distributed/
-  - vllm/v1/distributed/
-  - vllm/model_executor/layers/fused_moe/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - tests/distributed/test_context_parallel.py
-  - tests/v1/distributed/test_dbo.py
-  - examples/offline_inference/data_parallel.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/v1/sample
+  - tests/v1/logits_processors
+  - tests/v1/test_oracle.py
+  - tests/v1/test_request.py
+  - tests/v1/test_outputs.py
   commands:
-  - export TORCH_NCCL_BLOCKING_WAIT=1
-  - pytest -v -s tests/distributed/test_context_parallel.py
-  - VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
-  - pytest -v -s tests/v1/distributed/test_dbo.py
-
+  - pytest -v -s v1/sample
+  - pytest -v -s v1/logits_processors
+  - pytest -v -s v1/test_oracle.py
+  - pytest -v -s v1/test_request.py
+  - pytest -v -s v1/test_outputs.py
 
-- label: Distributed Compile Unit Tests (2xH100-2xMI355) # TBD
-  timeout_in_minutes: 180
+- label: V1 Spec Decode # TBD
+  timeout_in_minutes: 60
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_2
-  num_gpus: 2
-  optional: true
-  working_dir: "/vllm-workspace/"
+  agent_pool: mi355_1
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/compilation/
-  - vllm/model_executor/layers
-  - tests/compile/passes/distributed/
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/v1/spec_decode
   commands:
-  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-  - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/passes/distributed/test_async_tp.py
-  - pytest -v -s tests/compile/passes/distributed/test_sequence_parallelism.py
-  # TODO: this test is not supported on ROCm, there are aiter kernels for this.
-  # - pytest -v -s tests/compile/passes/distributed/test_fusion_all_reduce.py
-  # - pytest -v -s tests/compile/distributed/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm
-  # - "VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'"
-
+  - pytest -v -s -m 'not slow_test' v1/spec_decode
 
-- label: LM Eval Small Models (B200-MI355) # TBD
+- label: NixlConnector PD + Spec Decode acceptance (2 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_2
+  num_gpus: 2
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/_aiter_ops.py
+  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+  - vllm/v1/worker/kv_connector_model_runner_mixin.py
+  - tests/v1/kv_connector/nixl_integration/
   - vllm/platforms/rocm.py
   commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-mi3xx-fp8-and-mixed.txt
-
+  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
+  - ATTENTION_BACKEND=ROCM_ATTN bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
 
-- label: LM Eval Large Models (4 GPUs)(FP8) # TBD
+- label: Distributed NixlConnector PD accuracy (4 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_4
   num_gpus: 4
   optional: true
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/_aiter_ops.py
+  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+  - tests/v1/kv_connector/nixl_integration/
   - vllm/platforms/rocm.py
   commands:
-  - export VLLM_USE_DEEP_GEMM=0
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-rocm-fp8.txt --tp-size=4
-
+  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
+  - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
-- label: GPQA Eval (GPT-OSS) (B200-MI355) # TBD
+- label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs) # TBD
   timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx955nightly, amdmi355]
-  agent_pool: mi355_2
-  num_gpus: 2
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  agent_pool: mi355_4
+  num_gpus: 4
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/model_executor/layers/fused_moe/
-  - tests/evals/gpt_oss/
-  - vllm/_aiter_ops.py
+  - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+  - tests/v1/kv_connector/nixl_integration/
   - vllm/platforms/rocm.py
   commands:
-    - uv pip install --system 'gpt-oss[eval]==0.0.5'
-    - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-gfx950.txt
+  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
+  - DP_EP=1 ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
+#------------------------------------------------------  mi355 · weight_loading  -------------------------------------------------------#
 
-- label: Qwen3-30B-A3B-FP8-block Accuracy (B200-MI355) # TBD
+- label: Weight Loading Multiple GPU # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_2
   num_gpus: 2
-  working_dir: "/vllm-workspace"
+  working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/model_executor/layers/quantization/
-  - vllm/model_executor/layers/fused_moe/
-  - vllm/distributed/eplb
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - .buildkite/scripts/scheduled_integration_test/
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/weight_loading
   commands:
-  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
-
+  - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-amd.txt
 
-- label: Attention Benchmarks Smoke Test (B200-MI355) # TBD
+- label: Weight Loading Multiple GPU - Large Models # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_2
+  working_dir: "/vllm-workspace/tests"
   num_gpus: 2
-  working_dir: "/vllm-workspace/"
+  optional: true
   source_file_dependencies:
-  - benchmarks/attention_benchmarks/
-  - vllm/v1/attention/
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/weight_loading
   commands:
-  - python3 benchmarks/attention_benchmarks/benchmark.py --backends ROCM_ATTN ROCM_AITER_FA ROCM_AITER_UNIFIED_ATTN --batch-specs "8q1s1k" --repeats 1 --warmup-iters 1
+  - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large-amd.txt
 
+#-----------------------------------------------------------  mi355 · misc  ------------------------------------------------------------#
 
-- label: LM Eval Qwen3-5 Models (B200-MI355) # TBD
-  timeout_in_minutes: 120
+- label: Regression # TBD
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
-  agent_pool: mi355_2
-  num_gpus: 2
+  agent_pool: mi355_1
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/model_executor/models/qwen3_5.py
-  - vllm/model_executor/models/qwen3_5_mtp.py
-  - vllm/transformers_utils/configs/qwen3_5.py
-  - vllm/transformers_utils/configs/qwen3_5_moe.py
-  - vllm/model_executor/models/qwen.py
-  - vllm/model_executor/models/qwen2.py
-  - vllm/model_executor/models/qwen3.py
-  - vllm/model_executor/models/qwen3_next.py
-  - vllm/model_executor/models/qwen3_next_mtp.py
-  - vllm/model_executor/layers/fla/ops/
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/test_regression
   commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-qwen35-mi355.txt
+  - pip install modelscope
+  - pytest -v -s test_regression.py
diff --git a/.buildkite/test_areas/attention.yaml b/.buildkite/test_areas/attention.yaml
index 4bcf116f2756..d3947a03162b 100644
--- a/.buildkite/test_areas/attention.yaml
+++ b/.buildkite/test_areas/attention.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: V1 attention (H100)
+  key: v1-attention-h100
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
@@ -14,8 +15,9 @@ steps:
     - pytest -v -s v1/attention
 
 - label: V1 attention (B200)
+  key: v1-attention-b200
   timeout_in_minutes: 30
-  device: b200
+  device: b200-k8s
   source_file_dependencies:
     - vllm/config/attention.py
     - vllm/model_executor/layers/attention
diff --git a/.buildkite/test_areas/basic_correctness.yaml b/.buildkite/test_areas/basic_correctness.yaml
index 042734e8433b..5d547cd48637 100644
--- a/.buildkite/test_areas/basic_correctness.yaml
+++ b/.buildkite/test_areas/basic_correctness.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Basic Correctness
+  key: basic-correctness
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
diff --git a/.buildkite/test_areas/benchmarks.yaml b/.buildkite/test_areas/benchmarks.yaml
index 4cda6fff1443..85f804780179 100644
--- a/.buildkite/test_areas/benchmarks.yaml
+++ b/.buildkite/test_areas/benchmarks.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Benchmarks CLI Test
+  key: benchmarks-cli-test
   timeout_in_minutes: 20
   device: h200_18gb
   source_file_dependencies:
@@ -12,7 +13,8 @@ steps:
   - pytest -v -s benchmarks/
 
 - label: Attention Benchmarks Smoke Test (B200)
-  device: b200
+  key: attention-benchmarks-smoke-test-b200
+  device: b200-k8s
   num_gpus: 2
   optional: true
   working_dir: "/vllm-workspace/"
diff --git a/.buildkite/test_areas/compile.yaml b/.buildkite/test_areas/compile.yaml
index aa46447c24af..01248738d519 100644
--- a/.buildkite/test_areas/compile.yaml
+++ b/.buildkite/test_areas/compile.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Sequence Parallel Correctness Tests (2 GPUs)
+  key: sequence-parallel-correctness-tests-2-gpus
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
   num_devices: 2
@@ -17,6 +18,7 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
 
 - label: Sequence Parallel Correctness Tests (2xH100)
+  key: sequence-parallel-correctness-tests-2xh100
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
   device: h100
@@ -27,6 +29,7 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
 
 - label: AsyncTP Correctness Tests (2xH100)
+  key: asynctp-correctness-tests-2xh100
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
   device: h100
@@ -37,9 +40,10 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
 
 - label: AsyncTP Correctness Tests (B200)
+  key: asynctp-correctness-tests-b200
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
-  device: b200
+  device: b200-k8s
   optional: true
   num_devices: 2
   commands:
@@ -47,6 +51,7 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
 
 - label: Distributed Compile Unit Tests (2xH100)
+  key: distributed-compile-unit-tests-2xh100
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
   device: h100
@@ -60,9 +65,10 @@ steps:
   - pytest -s -v tests/compile/passes/distributed
 
 - label: Fusion and Compile Unit Tests (2xB200)
+  key: fusion-and-compile-unit-tests-2xb200
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
-  device: b200
+  device: b200-k8s
   source_file_dependencies:
   - csrc/quantization/fp4/
   - vllm/model_executor/layers/quantization/
@@ -89,6 +95,7 @@ steps:
     - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile
 
 - label: Fusion E2E Quick (H100)
+  key: fusion-e2e-quick-h100
   timeout_in_minutes: 15
   working_dir: "/vllm-workspace/"
   device: h100
@@ -107,6 +114,7 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and not +rms_norm and +quant_fp8 and (qwen3 or deepseek)"
 
 - label: Fusion E2E Config Sweep (H100)
+  key: fusion-e2e-config-sweep-h100
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
   device: h100
@@ -126,9 +134,10 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "llama-3"
 
 - label: Fusion E2E Config Sweep (B200)
+  key: fusion-e2e-config-sweep-b200
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
-  device: b200
+  device: b200-k8s
   num_devices: 1
   optional: true
   commands:
@@ -139,6 +148,7 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and (FLASHINFER and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek)) or llama-3)"
 
 - label: Fusion E2E TP2 Quick (H100)
+  key: fusion-e2e-tp2-quick-h100
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
   device: h100
@@ -156,6 +166,7 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))"
 
 - label: Fusion E2E TP2 AR-RMS Config Sweep (H100)
+  key: fusion-e2e-tp2-ar-rms-config-sweep-h100
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/"
   device: h100
@@ -175,6 +186,7 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "llama-3"
 
 - label: Fusion E2E TP2 AsyncTP Config Sweep (H100)
+  key: fusion-e2e-tp2-asynctp-config-sweep-h100
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/"
   device: h100
@@ -194,9 +206,10 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "llama-3"
 
 - label: Fusion E2E TP2 (B200)
+  key: fusion-e2e-tp2-b200
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
-  device: b200
+  device: b200-k8s
   num_devices: 2
   source_file_dependencies:
     - csrc/quantization/
diff --git a/.buildkite/test_areas/cuda.yaml b/.buildkite/test_areas/cuda.yaml
index 4d1efdb13c88..6254a6ba3dd5 100644
--- a/.buildkite/test_areas/cuda.yaml
+++ b/.buildkite/test_areas/cuda.yaml
@@ -3,16 +3,22 @@ depends_on:
   - image-build
 steps:
 - label: Platform Tests (CUDA)
+  key: platform-tests-cuda
   timeout_in_minutes: 15
   device: h200_18gb
   source_file_dependencies:
-  - vllm/
+  - vllm/envs.py
+  - vllm/logger.py
+  - vllm/platforms/
+  - vllm/plugins/
+  - vllm/utils/
   - tests/cuda
   commands:
     - pytest -v -s cuda/test_cuda_context.py
     - pytest -v -s cuda/test_platform_no_cuda_init.py
 
 - label: Cudagraph
+  key: cudagraph
   timeout_in_minutes: 20
   source_file_dependencies:
   - tests/v1/cudagraph
diff --git a/.buildkite/test_areas/disaggregated.yaml b/.buildkite/test_areas/disaggregated.yaml
new file mode 100644
index 000000000000..e68b9e1add8b
--- /dev/null
+++ b/.buildkite/test_areas/disaggregated.yaml
@@ -0,0 +1,106 @@
+group: Disaggregated
+depends_on: 
+  - image-build
+steps:
+- label: Distributed NixlConnector PD accuracy (4 GPUs)
+  key: distributed-nixlconnector-pd-accuracy-4-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+- label: Distributed FlashInfer NixlConnector PD accuracy (4 GPUs)
+  key: distributed-flashinfer-nixlconnector-pd-accuracy-4-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - FLASHINFER=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs)
+  key: dp-ep-distributed-nixlconnector-pd-accuracy-tests-4-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs)
+  key: crosslayer-kv-layout-distributed-nixlconnector-pd-accuracy-tests-4-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - CROSS_LAYERS_BLOCKS=True bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: Hybrid SSM NixlConnector PD accuracy tests (4 GPUs)
+  key: hybrid-ssm-nixlconnector-pd-accuracy-tests-4-gpus
+  timeout_in_minutes: 20
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - HYBRID_SSM=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: MultiConnector (Nixl+Offloading) PD accuracy (2 GPUs)
+  key: multiconnector-nixl-offloading-pd-accuracy-2-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/run_multi_connector_accuracy_test.sh
+
+- label: NixlConnector PD + Spec Decode acceptance (2 GPUs)
+  key: nixlconnector-pd-spec-decode-acceptance-2-gpus
+  timeout_in_minutes: 30
+  device: a100
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/v1/worker/kv_connector_model_runner_mixin.py
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
+
+- label: MultiConnector (Nixl+Offloading) PD edge cases (2 GPUs)
+  key: multiconnector-nixl-offloading-pd-edge-cases-2-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/run_multi_connector_edge_case_test.sh
\ No newline at end of file
diff --git a/.buildkite/test_areas/distributed.yaml b/.buildkite/test_areas/distributed.yaml
index 1c9ba95183c1..8aa41a9a26ab 100644
--- a/.buildkite/test_areas/distributed.yaml
+++ b/.buildkite/test_areas/distributed.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Distributed Comm Ops
+  key: distributed-comm-ops
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -16,6 +17,7 @@ steps:
   - pytest -v -s distributed/test_shm_storage.py
 
 - label: Distributed DP Tests (2 GPUs)
+  key: distributed-dp-tests-2-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -37,6 +39,7 @@ steps:
   - DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py
 
 - label: Distributed Compile + RPC Tests (2 GPUs)
+  key: distributed-compile-rpc-tests-2-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -59,6 +62,7 @@ steps:
   - pytest -v -s ./compile/test_wrapper.py
 
 - label: Distributed Torchrun + Shutdown Tests (2 GPUs)
+  key: distributed-torchrun-shutdown-tests-2-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -81,6 +85,7 @@ steps:
   - pytest -v -s v1/worker/test_worker_memory_snapshot.py
 
 - label: Distributed Torchrun + Examples (4 GPUs)
+  key: distributed-torchrun-examples-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace"
   num_devices: 4
@@ -88,9 +93,8 @@ steps:
   - vllm/distributed/
   - tests/distributed/test_torchrun_example.py
   - tests/distributed/test_torchrun_example_moe.py
-  - examples/offline_inference/rlhf_colocate.py
   - examples/rl/
-  - tests/examples/offline_inference/data_parallel.py
+  - tests/examples/features/data_parallel/data_parallel_offline.py
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
   - export NCCL_CUMEM_HOST_ENABLE=0
@@ -107,12 +111,13 @@ steps:
   # test with torchrun tp=2 and dp=2 with ep
   - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with internal dp
-  - python3 examples/offline_inference/data_parallel.py --enforce-eager
+  - python3 examples/features/data_parallel/data_parallel_offline.py --enforce-eager
   # rlhf examples
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_nccl.py
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_ipc.py
 
 - label: Distributed DP Tests (4 GPUs)
+  key: distributed-dp-tests-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -133,6 +138,7 @@ steps:
   - pytest -v -s distributed/test_utils.py
 
 - label: Distributed Compile + Comm (4 GPUs)
+  key: distributed-compile-comm-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -154,24 +160,28 @@ steps:
   - pytest -v -s distributed/test_multiproc_executor.py::test_multiproc_executor_multi_node
 
 - label: Distributed Tests (8 GPUs)(H100)
+  key: distributed-tests-8-gpus-h100
   timeout_in_minutes: 10
   device: h100
   num_devices: 8
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - examples/offline_inference/torchrun_dp_example.py
+  - examples/features/torchrun/torchrun_dp_example_offline.py
   - vllm/config/parallel.py
   - vllm/distributed/
   - vllm/v1/engine/llm_engine.py
   - vllm/v1/executor/uniproc_executor.py
   - vllm/v1/worker/gpu_worker.py
+  - tests/distributed/test_mnnvl_alltoall.py
+
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
   - export NCCL_CUMEM_HOST_ENABLE=0
   # test with torchrun tp=2 and dp=4 with ep
-  - torchrun --nproc-per-node=8 ../examples/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
+  - torchrun --nproc-per-node=8 ../examples/features/torchrun/torchrun_dp_example_offline.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 
 - label: Distributed Tests (4 GPUs)(A100)
+  key: distributed-tests-4-gpus-a100
   device: a100
   optional: true
   num_devices: 4
@@ -186,6 +196,7 @@ steps:
   - pytest -v -s -x lora/test_mixtral.py
 
 - label: Distributed Tests (2 GPUs)(H100)
+  key: distributed-tests-2-gpus-h100
   timeout_in_minutes: 15
   device: h100
   optional: true
@@ -194,12 +205,14 @@ steps:
   commands:
     - pytest -v -s tests/distributed/test_context_parallel.py
     - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_async_new_apis.py
-    - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
+    - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/features/data_parallel/data_parallel_offline.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
     - pytest -v -s tests/v1/distributed/test_dbo.py
-    - TP_SIZE=1 DP_SIZE=2 pytest -v -s tests/v1/distributed/test_eagle_dp.py
+    - VLLM_ALLOW_INSECURE_SERIALIZATION=1 pytest -v -s tests/distributed/test_weight_transfer.py
+    - pytest -v -s tests/distributed/test_packed_tensor.py
 
 - label: Distributed Tests (2 GPUs)(B200)
-  device: b200
+  key: distributed-tests-2-gpus-b200
+  device: b200-k8s
   optional: true
   working_dir: "/vllm-workspace/"
   num_devices: 2
@@ -207,8 +220,12 @@ steps:
     - pytest -v -s tests/distributed/test_context_parallel.py
     - pytest -v -s tests/distributed/test_nccl_symm_mem_allreduce.py
     - pytest -v -s tests/v1/distributed/test_dbo.py
+    - pytest -v -s tests/distributed/test_mnnvl_alltoall.py
+
+    
 
 - label: 2 Node Test (4 GPUs)
+  key: 2-node-test-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -221,96 +238,12 @@ steps:
   - vllm/executor/
   - vllm/model_executor/models/
   - tests/distributed/
-  - tests/examples/offline_inference/data_parallel.py
-  commands:
-    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
-
-- label: Distributed NixlConnector PD accuracy (4 GPUs)
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
-- label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs)
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
-- label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs)
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - CROSS_LAYERS_BLOCKS=True bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
-- label: Hyrbid SSM NixlConnector PD accuracy tests (4 GPUs)
-  timeout_in_minutes: 20
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - HYBRID_SSM=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
-- label: MultiConnector (Nixl+Offloading) PD accuracy (2 GPUs)
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 2
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
-    - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
-    - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/run_multi_connector_accuracy_test.sh
-
-- label: NixlConnector PD + Spec Decode acceptance (2 GPUs)
-  timeout_in_minutes: 30
-  device: a100
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 2
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - vllm/v1/worker/kv_connector_model_runner_mixin.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
-
-- label: MultiConnector (Nixl+Offloading) PD edge cases (2 GPUs)
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 2
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
-    - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
-    - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
-    - tests/v1/kv_connector/nixl_integration/
+  - tests/examples/features/data_parallel/data_parallel_offline.py
   commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/run_multi_connector_edge_case_test.sh
+    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
 
 - label: Pipeline + Context Parallelism (4 GPUs)
+  key: pipeline-context-parallelism-4-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -325,6 +258,7 @@ steps:
   - pytest -v -s distributed/test_pipeline_parallel.py
 
 - label: RayExecutorV2 (4 GPUs)
+  key: rayexecutorv2-4-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
diff --git a/.buildkite/test_areas/docker.yaml b/.buildkite/test_areas/docker.yaml
new file mode 100644
index 000000000000..9bf96221abe0
--- /dev/null
+++ b/.buildkite/test_areas/docker.yaml
@@ -0,0 +1,16 @@
+group: Docker
+depends_on:
+  - image-build-cpu
+steps:
+- label: Docker Build Metadata
+  timeout_in_minutes: 10
+  device: cpu-small
+  source_file_dependencies:
+    - .buildkite/release-pipeline.yaml
+    - .buildkite/scripts/docker-build-metadata-args.sh
+    - docker/Dockerfile
+    - docker/Dockerfile.cpu
+    - docker/docker-bake.hcl
+    - tests/tools/test_docker_build_metadata_args.py
+  commands:
+    - pytest -v -s tools/test_docker_build_metadata_args.py
diff --git a/.buildkite/test_areas/e2e_integration.yaml b/.buildkite/test_areas/e2e_integration.yaml
index 5b7f96bc7a26..bb8aa14eac18 100644
--- a/.buildkite/test_areas/e2e_integration.yaml
+++ b/.buildkite/test_areas/e2e_integration.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: DeepSeek V2-Lite Accuracy
+  key: deepseek-v2-lite-accuracy
   timeout_in_minutes: 60
   device: h100
   optional: true
@@ -12,6 +13,7 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010
 
 - label: Qwen3-30B-A3B-FP8-block Accuracy
+  key: qwen3-30b-a3b-fp8-block-accuracy
   timeout_in_minutes: 60
   device: h100
   optional: true
@@ -21,15 +23,27 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020
 
 - label: Qwen3-30B-A3B-FP8-block Accuracy (B200)
+  key: qwen3-30b-a3b-fp8-block-accuracy-b200
   timeout_in_minutes: 60
-  device: b200
+  device: b200-k8s
   optional: true
   num_devices: 2
   working_dir: "/vllm-workspace"
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
 
+- label: Qwen3-30B-A3B-FP8 DP4 Async EPLB Accuracy
+  key: qwen3-30b-a3b-fp8-dp4-async-eplb-accuracy
+  timeout_in_minutes: 60
+  device: h100
+  optional: true
+  num_devices: 4
+  working_dir: "/vllm-workspace"
+  commands:
+  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_dp4_async_eplb.sh 0.8 200 8050
+
 - label: DeepSeek V2-Lite Prefetch Offload Accuracy (H100)
+  key: deepseek-v2-lite-prefetch-offload-accuracy-h100
   timeout_in_minutes: 60
   device: h100
   optional: true
diff --git a/.buildkite/test_areas/engine.yaml b/.buildkite/test_areas/engine.yaml
index 5e4361ec9ad6..c8b2d66a413a 100644
--- a/.buildkite/test_areas/engine.yaml
+++ b/.buildkite/test_areas/engine.yaml
@@ -3,19 +3,32 @@ depends_on:
   - image-build
 steps:
 - label: Engine
+  key: engine
   timeout_in_minutes: 15
   device: h200_18gb
   source_file_dependencies:
-  - vllm/
+  - vllm/compilation/
+  - vllm/config/
+  - vllm/engine/
+  - vllm/entrypoints/logger.py
+  - vllm/envs.py
+  - vllm/logger.py
+  - vllm/logging_utils/
+  - vllm/platforms/
+  - vllm/sequence.py
+  - vllm/triton_utils/
+  - vllm/utils/
   - tests/engine
   - tests/test_sequence
   - tests/test_config
   - tests/test_logger
   - tests/test_vllm_port
+  - tests/test_jit_monitor.py
   commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
+  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py test_jit_monitor.py
 
 - label: Engine (1 GPU)
+  key: engine-1-gpu
   timeout_in_minutes: 30
   source_file_dependencies:
     - vllm/v1/engine/
@@ -25,6 +38,7 @@ steps:
     - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
 
 - label: e2e Scheduling (1 GPU)
+  key: e2e-scheduling-1-gpu
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -34,6 +48,7 @@ steps:
     - pytest -v -s v1/e2e/general/test_async_scheduling.py
 
 - label: e2e Core (1 GPU)
+  key: e2e-core-1-gpu
   timeout_in_minutes: 30
   source_file_dependencies:
     - vllm/v1/
@@ -42,38 +57,63 @@ steps:
     - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
 
 - label: V1 e2e (2 GPUs)
+  key: v1-e2e-2-gpus
   timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
   optional: true
   num_devices: 2
   source_file_dependencies:
-    - vllm/
-    - tests/v1/e2e
+    - vllm/compilation/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/envs.py
+    - vllm/forward_context.py
+    - vllm/inputs/
+    - vllm/logger.py
+    - vllm/logging_utils/
+    - vllm/model_executor/
+    - vllm/multimodal/
+    - vllm/platforms/
+    - vllm/sampling_params.py
+    - vllm/transformers_utils/
+    - vllm/triton_utils/
+    - vllm/utils/
+    - vllm/v1/
+    - tests/v1/e2e/spec_decode
   commands:
     # Only run tests that need exactly 2 GPUs
     - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "tensor_parallelism"
-  mirror:
-    amd:
-      device: mi325_2
-      depends_on:
-      - image-build-amd
 
 - label: V1 e2e (4 GPUs)
+  key: v1-e2e-4-gpus
   timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
   optional: true
   num_devices: 4
   source_file_dependencies:
-    - vllm/
-    - tests/v1/e2e
+    - vllm/compilation/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/envs.py
+    - vllm/forward_context.py
+    - vllm/inputs/
+    - vllm/logger.py
+    - vllm/logging_utils/
+    - vllm/model_executor/
+    - vllm/multimodal/
+    - vllm/platforms/
+    - vllm/sampling_params.py
+    - vllm/transformers_utils/
+    - vllm/triton_utils/
+    - vllm/utils/
+    - vllm/v1/
+    - tests/v1/e2e/spec_decode
   commands:
     # Only run tests that need 4 GPUs
     - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle_correctness_heavy"
-  mirror:
-    amd:
-      device: mi325_4
-      depends_on:
-      - image-build-amd
 
 - label: V1 e2e (4xH100)
+  key: v1-e2e-4xh100
   timeout_in_minutes: 60
   device: h100
   num_devices: 4
diff --git a/.buildkite/test_areas/entrypoints.yaml b/.buildkite/test_areas/entrypoints.yaml
index 8c2b529a8068..ba92d3a3aec0 100644
--- a/.buildkite/test_areas/entrypoints.yaml
+++ b/.buildkite/test_areas/entrypoints.yaml
@@ -2,7 +2,8 @@ group: Entrypoints
 depends_on: 
   - image-build
 steps:
-- label: Entrypoints Unit Tests  
+- label: Entrypoints Unit Tests
+  key: entrypoints-unit-tests
   timeout_in_minutes: 10
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -13,6 +14,7 @@ steps:
   - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/rpc --ignore=entrypoints/sleep --ignore=entrypoints/serve/instrumentator --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling
 
 - label: Entrypoints Integration (LLM)
+  key: entrypoints-integration-llm
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -24,8 +26,14 @@ steps:
   - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
   - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
   - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
+  mirror:
+    amd:
+      device: mi300_1
+      depends_on:
+      - image-build-amd
 
 - label: Entrypoints Integration (API Server openai - Part 1)
+  key: entrypoints-integration-api-server-openai-part-1
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -35,14 +43,10 @@ steps:
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -v -s entrypoints/openai/chat_completion --ignore=entrypoints/openai/chat_completion/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/chat_completion/test_oot_registration.py
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
 
 
 - label: Entrypoints Integration (API Server openai - Part 2)
+  key: entrypoints-integration-api-server-openai-part-2
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -53,13 +57,9 @@ steps:
   - pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
   - pytest -v -s entrypoints/openai/speech_to_text/
   - pytest -v -s entrypoints/test_chat_utils.py
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
 
 - label: Entrypoints Integration (API Server openai - Part 3)
+  key: entrypoints-integration-api-server-openai-part-3
   timeout_in_minutes: 50
   device: h200_18gb
   working_dir: "/vllm-workspace/tests"
@@ -72,6 +72,7 @@ steps:
   - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/speech_to_text/ --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/test_multi_api_servers.py
 
 - label: Entrypoints Integration (API Server 2)
+  key: entrypoints-integration-api-server-2
   timeout_in_minutes: 130
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -86,6 +87,7 @@ steps:
   - pytest -v -s tool_use
 
 - label: Entrypoints Integration (Pooling)
+  key: entrypoints-integration-pooling
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -96,6 +98,7 @@ steps:
   - pytest -v -s entrypoints/pooling
 
 - label: Entrypoints Integration (Responses API)
+  key: entrypoints-integration-responses-api
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -105,6 +108,7 @@ steps:
   - pytest -v -s entrypoints/openai/responses
 
 - label: OpenAI API Correctness
+  key: openai-api-correctness
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
diff --git a/.buildkite/test_areas/expert_parallelism.yaml b/.buildkite/test_areas/expert_parallelism.yaml
index c2adf52a2d57..0f7ab0d7157c 100644
--- a/.buildkite/test_areas/expert_parallelism.yaml
+++ b/.buildkite/test_areas/expert_parallelism.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: EPLB Algorithm
+  key: eplb-algorithm
   timeout_in_minutes: 15
   device: h200_18gb
   working_dir: "/vllm-workspace/tests"
@@ -15,6 +16,7 @@ steps:
   - pytest -v -s distributed/test_eplb_utils.py
 
 - label: EPLB Execution # 17min
+  key: eplb-execution
   timeout_in_minutes: 27
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -26,6 +28,7 @@ steps:
   - pytest -v -s distributed/test_eplb_spec_decode.py
 
 - label: Elastic EP Scaling Test
+  key: elastic-ep-scaling-test
   timeout_in_minutes: 20
   device: h100
   working_dir: "/vllm-workspace/tests"
diff --git a/.buildkite/test_areas/kernels.yaml b/.buildkite/test_areas/kernels.yaml
index 8b9765130aee..34e1e4832d9d 100644
--- a/.buildkite/test_areas/kernels.yaml
+++ b/.buildkite/test_areas/kernels.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: vLLM IR Tests
+  key: vllm-ir-tests
   timeout_in_minutes: 10
   device: h200_18gb
   working_dir: "/vllm-workspace/"
@@ -14,6 +15,7 @@ steps:
     - pytest -v -s tests/kernels/ir
 
 - label: Kernels Core Operation Test
+  key: kernels-core-operation-test
   timeout_in_minutes: 75
   source_file_dependencies:
   - csrc/
@@ -23,6 +25,7 @@ steps:
     - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py  kernels/test_concat_mla_q.py
 
 - label: Kernels MiniMax Reduce RMS Test (2 GPUs)
+  key: kernels-minimax-reduce-rms-test-2-gpus
   timeout_in_minutes: 15
   num_devices: 2
   device: h100
@@ -36,6 +39,7 @@ steps:
     - pytest -v -s kernels/core/test_minimax_reduce_rms.py
 
 - label: Kernels Attention Test %N
+  key: kernels-attention-test
   timeout_in_minutes: 35
   source_file_dependencies:
   - csrc/attention/
@@ -49,6 +53,7 @@ steps:
   parallelism: 2
 
 - label: Kernels Quantization Test %N
+  key: kernels-quantization-test
   timeout_in_minutes: 90
   source_file_dependencies:
   - csrc/quantization/
@@ -59,6 +64,7 @@ steps:
   parallelism: 2
 
 - label: Kernels MoE Test %N
+  key: kernels-moe-test
   timeout_in_minutes: 25
   source_file_dependencies:
   - csrc/quantization/cutlass_w8a8/moe/
@@ -74,6 +80,7 @@ steps:
   parallelism: 5
 
 - label: Kernels Mamba Test
+  key: kernels-mamba-test
   timeout_in_minutes: 45
   source_file_dependencies:
   - csrc/mamba/
@@ -82,7 +89,18 @@ steps:
   commands:
     - pytest -v -s kernels/mamba
 
+- label: Kernels KDA Test
+  timeout_in_minutes: 20
+  source_file_dependencies:
+  - vllm/model_executor/layers/fla/ops/kda.py
+  - vllm/model_executor/layers/fla/ops/chunk_delta_h.py
+  - vllm/model_executor/layers/fla/ops/l2norm.py
+  - tests/kernels/test_kda.py
+  commands:
+    - pytest -v -s kernels/test_kda.py
+
 - label: Kernels DeepGEMM Test (H100)
+  key: kernels-deepgemm-test-h100
   timeout_in_minutes: 45
   device: h100
   num_devices: 1
@@ -95,16 +113,19 @@ steps:
   - tests/kernels/moe/test_deepgemm.py
   - tests/kernels/moe/test_batched_deepgemm.py
   - tests/kernels/attention/test_deepgemm_attention.py
+  - tests/quantization/test_cutlass_w4a16.py
   commands:
     - pytest -v -s kernels/quantization/test_block_fp8.py
     - pytest -v -s kernels/moe/test_deepgemm.py
     - pytest -v -s kernels/moe/test_batched_deepgemm.py
     - pytest -v -s kernels/attention/test_deepgemm_attention.py
+    - pytest -v -s quantization/test_cutlass_w4a16.py
 
 - label: Kernels (B200)
+  key: kernels-b200
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
-  device: b200
+  device: b200-k8s
   # optional: true
   source_file_dependencies:
   - csrc/quantization/fp4/
@@ -141,6 +162,7 @@ steps:
     - pytest -v -s tests/kernels/quantization/test_nvfp4_qutlass.py
     - pytest -v -s tests/kernels/quantization/test_mxfp4_qutlass.py
     - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
+    - pytest -v -s tests/kernels/moe/test_mxfp4_moe.py
     - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
     - pytest -v -s tests/kernels/moe/test_flashinfer.py
     - pytest -v -s tests/kernels/moe/test_flashinfer_moe.py
@@ -149,17 +171,19 @@ steps:
     - pytest -v -s tests/models/quantization/test_nvfp4.py
 
 - label: Kernels Helion Test
+  key: kernels-helion-test
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
   - vllm/utils/import_utils.py
   - tests/kernels/helion/
   commands:
-    - pip install helion==0.3.3
+    - pip install helion==1.0.0
     - pytest -v -s kernels/helion/
 
  
 - label: Kernels FP8 MoE Test (1 H100)
+  key: kernels-fp8-moe-test-1-h100
   timeout_in_minutes: 90
   device: h100
   num_devices: 1
@@ -176,6 +200,7 @@ steps:
     - pytest -v -s kernels/moe/test_triton_moe_ptpc_fp8.py
 
 - label: Kernels FP8 MoE Test (2 H100s)
+  key: kernels-fp8-moe-test-2-h100s
   timeout_in_minutes: 90
   device: h100
   num_devices: 2
@@ -185,8 +210,9 @@ steps:
     - pytest -v -s kernels/moe/test_deepep_moe.py
 
 - label: Kernels Fp4 MoE Test (B200)
+  key: kernels-fp4-moe-test-b200
   timeout_in_minutes: 60
-  device: b200
+  device: b200-k8s
   num_devices: 1
   optional: true
   commands:
@@ -197,6 +223,7 @@ steps:
 
 
 - label: Kernels FusedMoE Layer Test (2 H100s)
+  key: kernels-fusedmoe-layer-test-2-h100s
   timeout_in_minutes: 90
   device: h100
   num_devices: 2
@@ -213,6 +240,7 @@ steps:
 
 
 - label: Kernels FusedMoE Layer Test (2 B200s)
+  key: kernels-fusedmoe-layer-test-2-b200s
   timeout_in_minutes: 90
   device: b200
   num_devices: 2
diff --git a/.buildkite/test_areas/lm_eval.yaml b/.buildkite/test_areas/lm_eval.yaml
index 39029efe9cd9..e5a163d17c7e 100644
--- a/.buildkite/test_areas/lm_eval.yaml
+++ b/.buildkite/test_areas/lm_eval.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: LM Eval Small Models
+  key: lm-eval-small-models
   timeout_in_minutes: 75
   source_file_dependencies:
   - csrc/
@@ -24,6 +25,7 @@ steps:
 #   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
 - label: LM Eval Large Models (4 GPUs)(H100)
+  key: lm-eval-large-models-4-gpus-h100
   device: h100
   optional: true
   num_devices: 4
@@ -36,6 +38,7 @@ steps:
     - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
 
 - label: LM Eval Small Models (B200)
+  key: lm-eval-small-models-b200
   timeout_in_minutes: 120
   device: b200
   optional: true
@@ -46,8 +49,9 @@ steps:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt
 
 - label: LM Eval Qwen3.5 Models (B200)
+  key: lm-eval-qwen3-5-models-b200
   timeout_in_minutes: 120
-  device: b200
+  device: b200-k8s
   optional: true
   num_devices: 2
   source_file_dependencies:
@@ -62,6 +66,7 @@ steps:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-qwen35-blackwell.txt
 
 - label: LM Eval Large Models (H200)
+  key: lm-eval-large-models-h200
   timeout_in_minutes: 60
   device: h200
   optional: true
@@ -70,6 +75,7 @@ steps:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-h200.txt
 
 - label: MoE Refactor Integration Test (H100 - TEMPORARY)
+  key: moe-refactor-integration-test-h100-temporary
   device: h100
   optional: true
   num_devices: 2
@@ -77,13 +83,15 @@ steps:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-h100.txt
   
 - label: MoE Refactor Integration Test (B200 - TEMPORARY)
-  device: b200
+  key: moe-refactor-integration-test-b200-temporary
+  device: b200-k8s
   optional: true
   num_devices: 2
   commands:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-b200.txt
 
 - label: MoE Refactor Integration Test (B200 DP - TEMPORARY)
+  key: moe-refactor-integration-test-b200-dp-temporary
   device: b200
   optional: true
   num_devices: 2
@@ -91,7 +99,19 @@ steps:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor-dp-ep/config-b200.txt
 
 
+- label: LM Eval TurboQuant KV Cache
+  key: lm-eval-turboquant-kv-cache
+  timeout_in_minutes: 75
+  source_file_dependencies:
+  - vllm/model_executor/layers/quantization/turboquant/
+  - vllm/v1/attention/backends/turboquant_attn.py
+  - vllm/v1/attention/ops/triton_turboquant_decode.py
+  - vllm/v1/attention/ops/triton_turboquant_store.py
+  commands:
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/models-turboquant.txt
+
 - label: GPQA Eval (GPT-OSS) (H100)
+  key: gpqa-eval-gpt-oss-h100
   timeout_in_minutes: 120
   device: h100
   optional: true
@@ -105,6 +125,7 @@ steps:
     - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-h100.txt
 
 - label: GPQA Eval (GPT-OSS) (B200)
+  key: gpqa-eval-gpt-oss-b200
   timeout_in_minutes: 120
   device: b200
   optional: true
@@ -116,3 +137,10 @@ steps:
   commands:
     - uv pip install --system 'gpt-oss[eval]==0.0.5'
     - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-b200.txt
+
+- label: MRCR Eval Small Models
+  timeout_in_minutes: 30
+  source_file_dependencies:
+  - tests/evals/mrcr/
+  commands:
+    - pytest -s -v evals/mrcr/test_mrcr_correctness.py --config-list-file=evals/mrcr/configs/models-small.txt
diff --git a/.buildkite/test_areas/lora.yaml b/.buildkite/test_areas/lora.yaml
index 21f392ff737b..ebffcc59fc3b 100644
--- a/.buildkite/test_areas/lora.yaml
+++ b/.buildkite/test_areas/lora.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: LoRA %N
+  key: lora
   timeout_in_minutes: 30
   source_file_dependencies:
   - vllm/lora
@@ -13,10 +14,12 @@ steps:
 
 
 - label: LoRA TP (Distributed)
+  key: lora-tp-distributed
   timeout_in_minutes: 30
   num_devices: 4
   source_file_dependencies:
   - vllm/lora
+  - vllm/model_executor/layers/fused_moe/
   - tests/lora
   commands:
     # FIXIT: find out which code initialize cuda before running the test
diff --git a/.buildkite/test_areas/misc.yaml b/.buildkite/test_areas/misc.yaml
index 0cf9ec433925..da04c18017db 100644
--- a/.buildkite/test_areas/misc.yaml
+++ b/.buildkite/test_areas/misc.yaml
@@ -3,25 +3,40 @@ depends_on:
   - image-build
 steps:
 - label: V1 Spec Decode
+  key: v1-spec-decode
   timeout_in_minutes: 30
   source_file_dependencies:
-    - vllm/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/inputs/
+    - vllm/model_executor/
+    - vllm/platforms/
+    - vllm/sampling_params.py
+    - vllm/transformers_utils/
+    - vllm/utils/
+    - vllm/v1/
     - tests/v1/spec_decode
   commands:
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     # TODO: create another `optional` test group for slow tests
     - pytest -v -s -m 'not slow_test' v1/spec_decode
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
 
 - label: V1 Sample + Logits
+  key: v1-sample-logits
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
-    - vllm/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/inputs/
+    - vllm/logger.py
+    - vllm/model_executor/
+    - vllm/platforms/
+    - vllm/sampling_params.py
+    - vllm/transformers_utils/
+    - vllm/utils/
+    - vllm/v1/
     - tests/v1/sample
     - tests/v1/logits_processors
     - tests/v1/test_oracle.py
@@ -36,14 +51,31 @@ steps:
     - pytest -v -s v1/test_outputs.py
   mirror:
     amd:
-      device: mi325_1
+      device: mi300_1
       depends_on:
       - image-build-amd
 
 - label: V1 Core + KV + Metrics
+  key: v1-core-kv-metrics
   timeout_in_minutes: 30
   source_file_dependencies:
-    - vllm/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/entrypoints/pooling/
+    - vllm/inputs/
+    - vllm/lora/
+    - vllm/model_executor/
+    - vllm/multimodal/
+    - vllm/outputs.py
+    - vllm/platforms/
+    - vllm/pooling_params.py
+    - vllm/profiler/
+    - vllm/sampling_params.py
+    - vllm/tokenizers/
+    - vllm/transformers_utils/
+    - vllm/utils/
+    - vllm/v1/
     - tests/v1/core
     - tests/v1/executor
     - tests/v1/kv_offload
@@ -64,17 +96,27 @@ steps:
     # Integration test for streaming correctness (requires special branch).
     - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
 
 - label: V1 Others (CPU)
+  key: v1-others-cpu
   depends_on:
     - image-build-cpu
   source_file_dependencies:
-    - vllm/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/inputs/
+    - vllm/lora/
+    - vllm/multimodal/
+    - vllm/outputs.py
+    - vllm/platforms/
+    - vllm/pooling_params.py
+    - vllm/profiler/
+    - vllm/sampling_params.py
+    - vllm/tokenizers/
+    - vllm/transformers_utils/
+    - vllm/utils/
+    - vllm/v1/
     - tests/v1
   device: cpu-small
   commands:
@@ -86,10 +128,21 @@ steps:
     - pytest -v -s -m 'cpu_test' v1/metrics
 
 - label: Regression
+  key: regression
   timeout_in_minutes: 20
   device: h200_18gb
   source_file_dependencies:
-  - vllm/
+  - vllm/config/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/inputs/
+  - vllm/model_executor/
+  - vllm/multimodal/
+  - vllm/platforms/
+  - vllm/sampling_params.py
+  - vllm/transformers_utils/
+  - vllm/utils/
+  - vllm/v1/
   - tests/test_regression
   commands:
   - pip install modelscope
@@ -97,6 +150,7 @@ steps:
   working_dir: "/vllm-workspace/tests" # optional
 
 - label: Examples
+  key: examples
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
@@ -113,25 +167,37 @@ steps:
     - python3 basic/offline_inference/embed.py
     - python3 basic/offline_inference/score.py
     # for multi-modal models
-    - python3 offline_inference/audio_language.py --seed 0
-    - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
+    - python3 generate/multimodal/audio_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
+    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
      # for pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # for features demo
-    - python3 offline_inference/prefix_caching.py
-    - python3 offline_inference/llm_engine_example.py
-    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
+    - python3 deployment/llm_engine_example.py
+    - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
     # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 - label: Metrics, Tracing (2 GPUs)
+  key: metrics-tracing-2-gpus
   timeout_in_minutes: 20
   num_devices: 2
   source_file_dependencies:
-  - vllm/
+  - vllm/config/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/inputs/
+  - vllm/model_executor/
+  - vllm/multimodal/
+  - vllm/platforms/
+  - vllm/sampling_params.py
+  - vllm/tracing/
+  - vllm/transformers_utils/
+  - vllm/utils/
+  - vllm/v1/
   - tests/v1/tracing
   commands:
   - "pip install \
@@ -142,6 +208,7 @@ steps:
   - pytest -v -s v1/tracing
 
 - label: Python-only Installation
+  key: python-only-installation
   depends_on: ~
   timeout_in_minutes: 20
   source_file_dependencies:
@@ -151,9 +218,22 @@ steps:
   - bash standalone_tests/python_only_compile.sh
 
 - label: Async Engine, Inputs, Utils, Worker
+  key: async-engine-inputs-utils-worker
   timeout_in_minutes: 50
   source_file_dependencies:
-  - vllm/
+  - vllm/assets/
+  - vllm/config/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/inputs/
+  - vllm/model_executor/
+  - vllm/multimodal/
+  - vllm/platforms/
+  - vllm/sampling_params.py
+  - vllm/tokenizers/
+  - vllm/transformers_utils/
+  - vllm/utils/
+  - vllm/v1/
   - tests/detokenizer
   - tests/multimodal
   - tests/utils_
@@ -163,11 +243,35 @@ steps:
   - pytest -v -s utils_
 
 - label: Async Engine, Inputs, Utils, Worker, Config (CPU)
-  depends_on: 
+  key: async-engine-inputs-utils-worker-config-cpu
+  depends_on:
   - image-build-cpu
   timeout_in_minutes: 30
   source_file_dependencies:
-  - vllm/
+  - vllm/assets/
+  - vllm/config/
+  - vllm/engine/arg_utils.py
+  - vllm/entrypoints/chat_utils.py
+  - vllm/entrypoints/mcp/
+  - vllm/entrypoints/openai/chat_completion/protocol.py
+  - vllm/entrypoints/openai/engine/protocol.py
+  - vllm/envs.py
+  - vllm/exceptions.py
+  - vllm/inputs/
+  - vllm/model_executor/layers/quantization/quark/
+  - vllm/multimodal/
+  - vllm/outputs.py
+  - vllm/platforms/
+  - vllm/pooling_params.py
+  - vllm/ray/
+  - vllm/reasoning/
+  - vllm/renderers/
+  - vllm/sampling_params.py
+  - vllm/tokenizers/
+  - vllm/tool_parsers/
+  - vllm/transformers_utils/
+  - vllm/utils/
+  - vllm/v1/
   - tests/test_inputs.py
   - tests/test_outputs.py
   - tests/test_pooling_params.py
@@ -190,12 +294,13 @@ steps:
   - pytest -v -s -m 'cpu_test' multimodal
   - pytest -v -s renderers
   - pytest -v -s tokenizers_
-  - pytest -v -s reasoning --ignore=reasoning/test_seedoss_reasoning_parser.py --ignore=reasoning/test_glm4_moe_reasoning_parser.py --ignore=reasoning/test_gemma4_reasoning_parser.py
+  - pytest -v -s reasoning --ignore=reasoning/test_seedoss_reasoning_parser.py --ignore=reasoning/test_glm4_moe_reasoning_parser.py
   - pytest -v -s tool_parsers
   - pytest -v -s transformers_utils
   - pytest -v -s config
 
 - label: Batch Invariance (H100)
+  key: batch-invariance-h100
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
@@ -211,8 +316,9 @@ steps:
     - VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
 
 - label: Batch Invariance (B200)
+  key: batch-invariance-b200
   timeout_in_minutes: 30
-  device: b200
+  device: b200-k8s
   source_file_dependencies:
     - vllm/v1/attention
     - vllm/model_executor/layers
@@ -227,6 +333,7 @@ steps:
     - pytest -v -s v1/determinism/test_nvfp4_batch_invariant.py
   
 - label: Acceptance Length Test (Large Models) # optional
+  key: acceptance-length-test-large-models
   timeout_in_minutes: 25
   gpu: h100
   optional: true
diff --git a/.buildkite/test_areas/model_executor.yaml b/.buildkite/test_areas/model_executor.yaml
index 212abfdbb906..c41ef8a7110d 100644
--- a/.buildkite/test_areas/model_executor.yaml
+++ b/.buildkite/test_areas/model_executor.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Model Executor
+  key: model-executor
   timeout_in_minutes: 35
   source_file_dependencies:
   - vllm/engine/arg_utils.py
diff --git a/.buildkite/test_areas/model_runner_v2.yaml b/.buildkite/test_areas/model_runner_v2.yaml
index 7aa1870f0db5..9dfd046289e8 100644
--- a/.buildkite/test_areas/model_runner_v2.yaml
+++ b/.buildkite/test_areas/model_runner_v2.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Model Runner V2 Core Tests
+  key: model-runner-v2-core-tests
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/v1/worker/gpu/
@@ -25,16 +26,18 @@ steps:
   - pytest -v -s entrypoints/llm/test_struct_output_generate.py -k "xgrammar and not speculative_config6 and not speculative_config7 and not speculative_config8 and not speculative_config0"
 
 - label: Model Runner V2 Examples
+  key: model-runner-v2-examples
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
     - vllm/v1/worker/gpu/
     - vllm/v1/core/sched/
     - vllm/v1/worker/gpu_worker.py
-    - examples/offline_inference/
     - examples/basic/offline_inference/
+    - examples/generate/multimodal/
+    - examples/features/
     - examples/pooling/embed/vision_embedding_offline.py
-    - examples/others/tensorize_vllm_model.py
+    - examples/features/tensorize_vllm_model.py
   commands:
     - set -x
     - export VLLM_USE_V2_MODEL_RUNNER=1
@@ -44,21 +47,22 @@ steps:
     #- python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10  # TODO
     #- python3 basic/offline_inference/embed.py   # TODO
     # for multi-modal models
-    - python3 offline_inference/audio_language.py --seed 0
-    - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
+    - python3 generate/multimodal/audio_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
+    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
     # for pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # for features demo
-    - python3 offline_inference/prefix_caching.py
-    - python3 offline_inference/llm_engine_example.py
-    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
+    - python3 deployment/llm_engine_example.py
+    - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
     # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 - label: Model Runner V2 Distributed (2 GPUs)
+  key: model-runner-v2-distributed-2-gpus
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -79,6 +83,7 @@ steps:
     - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
 
 - label: Model Runner V2 Pipeline Parallelism (4 GPUs)
+  key: model-runner-v2-pipeline-parallelism-4-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -94,6 +99,7 @@ steps:
     - pytest -v -s distributed/test_pp_cudagraph.py -k "not ray"
 
 - label: Model Runner V2 Spec Decode
+  key: model-runner-v2-spec-decode
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
diff --git a/.buildkite/test_areas/models_basic.yaml b/.buildkite/test_areas/models_basic.yaml
index 10b038d8b8a8..8fca203de44f 100644
--- a/.buildkite/test_areas/models_basic.yaml
+++ b/.buildkite/test_areas/models_basic.yaml
@@ -3,8 +3,8 @@ depends_on:
   - image-build
 steps:
 - label: Basic Models Tests (Initialization)
+  key: basic-models-tests-initialization
   timeout_in_minutes: 45
-  device: h200_18gb
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -17,6 +17,7 @@ steps:
     torch_nightly: {}
 
 - label: Basic Models Tests (Extra Initialization) %N
+  key: basic-models-tests-extra-initialization
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/model_executor/models/
@@ -32,6 +33,7 @@ steps:
     torch_nightly: {}
 
 - label: Basic Models Tests (Other)
+  key: basic-models-tests-other
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/
@@ -40,14 +42,9 @@ steps:
   - tests/models/test_registry.py
   commands:
     - pytest -v -s models/test_terratorch.py models/test_transformers.py models/test_registry.py
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
-
 
 - label: Basic Models Test (Other CPU) # 5min
+  key: basic-models-test-other-cpu
   depends_on:
   - image-build-cpu
   timeout_in_minutes: 10
@@ -60,6 +57,7 @@ steps:
     - pytest -v -s models/test_utils.py models/test_vision.py
 
 - label: Transformers Nightly Models
+  key: transformers-nightly-models
   working_dir: "/vllm-workspace/"
   optional: true
   soft_fail: true
@@ -70,6 +68,22 @@ steps:
     - pytest -v -s tests/models/multimodal/processing/
     - pytest -v -s tests/models/multimodal/test_mapping.py
     - python3 examples/basic/offline_inference/chat.py
-    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
+    - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
+    # Whisper needs spawn method to avoid deadlock
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
+
+- label: Transformers Backward Compatibility Models Test
+  key: transformers-backward-compatibility-models-test
+  working_dir: "/vllm-workspace/"
+  optional: true
+  soft_fail: true
+  commands:
+    - pip install transformers==4.57.5
+    - pytest -v -s tests/models/test_initialization.py
+    - pytest -v -s tests/models/test_transformers.py
+    - pytest -v -s tests/models/multimodal/processing/
+    - pytest -v -s tests/models/multimodal/test_mapping.py
+    - python3 examples/basic/offline_inference/chat.py
+    - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
-    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
diff --git a/.buildkite/test_areas/models_distributed.yaml b/.buildkite/test_areas/models_distributed.yaml
index 55e7410b8af4..b5758c55affa 100644
--- a/.buildkite/test_areas/models_distributed.yaml
+++ b/.buildkite/test_areas/models_distributed.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Distributed Model Tests (2 GPUs)
+  key: distributed-model-tests-2-gpus
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
diff --git a/.buildkite/test_areas/models_language.yaml b/.buildkite/test_areas/models_language.yaml
index c13371e25f1d..b560c5a4769a 100644
--- a/.buildkite/test_areas/models_language.yaml
+++ b/.buildkite/test_areas/models_language.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Language Models Tests (Standard)
+  key: language-models-tests-standard
   timeout_in_minutes: 25
   source_file_dependencies:
   - vllm/
@@ -15,6 +16,7 @@ steps:
     torch_nightly: {}
 
 - label: Language Models Tests (Extra Standard) %N
+  key: language-models-tests-extra-standard
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/model_executor/models/
@@ -31,6 +33,7 @@ steps:
     torch_nightly: {}
 
 - label: Language Models Tests (Hybrid) %N
+  key: language-models-tests-hybrid
   timeout_in_minutes: 75
   source_file_dependencies:
   - vllm/
@@ -45,8 +48,17 @@ steps:
   parallelism: 2
   mirror:
     torch_nightly: {}
+    amd:
+      device: mi300_1
+      depends_on:
+      - image-build-amd
+      commands:
+      - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
+      - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
+      - pytest -v -s models/language/generation -m hybrid_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
 
 - label: Language Models Test (Extended Generation) # 80min
+  key: language-models-test-extended-generation
   timeout_in_minutes: 110
   optional: true
   source_file_dependencies:
@@ -58,17 +70,9 @@ steps:
     - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.3.0'
     - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
     - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
-      commands:
-      - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
-      - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
-      - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
 
 - label: Language Models Test (PPL)
+  key: language-models-test-ppl
   timeout_in_minutes: 110
   device: h200_18gb
   optional: true
@@ -79,6 +83,7 @@ steps:
     - pytest -v -s models/language/generation_ppl_test
 
 - label: Language Models Test (Extended Pooling)  # 36min
+  key: language-models-test-extended-pooling
   timeout_in_minutes: 50
   optional: true
   source_file_dependencies:
@@ -86,13 +91,9 @@ steps:
   - tests/models/language/pooling
   commands:
     - pytest -v -s models/language/pooling -m 'not core_model'
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
 
 - label: Language Models Test (MTEB)
+  key: language-models-test-mteb
   timeout_in_minutes: 110
   device: h200_18gb
   optional: true
diff --git a/.buildkite/test_areas/models_multimodal.yaml b/.buildkite/test_areas/models_multimodal.yaml
index ff0fd2e7a626..1f66393df818 100644
--- a/.buildkite/test_areas/models_multimodal.yaml
+++ b/.buildkite/test_areas/models_multimodal.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: "Multi-Modal Models (Standard) 1: qwen2"
+  key: multi-modal-models-standard-1-qwen2
   timeout_in_minutes: 45
   device: h200_18gb
   source_file_dependencies:
@@ -14,11 +15,12 @@ steps:
     - pytest -v -s models/multimodal/generation/test_ultravox.py -m core_model
   mirror:
     amd:
-      device: mi325_1
+      device: mi300_1
       depends_on:
       - image-build-amd
 
 - label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
+  key: multi-modal-models-standard-2-qwen3-gemma
   timeout_in_minutes: 45
   device: h200_18gb
   source_file_dependencies:
@@ -28,13 +30,15 @@ steps:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen3 or gemma"
     - pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model
+    - pytest -v -s models/multimodal/generation/test_vit_cudagraph.py -m core_model
   mirror:
     amd:
-      device: mi325_1
+      device: mi300_1
       depends_on:
       - image-build-amd
 
 - label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl"
+  key: multi-modal-models-standard-3-llava-qwen2-vl
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/
@@ -45,11 +49,12 @@ steps:
     - pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model
   mirror:
     amd:
-      device: mi325_1
+      device: mi300_1
       depends_on:
       - image-build-amd
 
 - label: "Multi-Modal Models (Standard) 4: other + whisper"
+  key: multi-modal-models-standard-4-other-whisper
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/
@@ -59,14 +64,10 @@ steps:
     - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/generation/test_ultravox.py --ignore models/multimodal/generation/test_qwen2_5_vl.py --ignore models/multimodal/generation/test_qwen2_vl.py --ignore models/multimodal/generation/test_whisper.py  --ignore models/multimodal/generation/test_memory_leak.py --ignore models/multimodal/processing
     - pytest models/multimodal/generation/test_memory_leak.py -m core_model
     - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
 
 - label: Multi-Modal Processor (CPU)
-  depends_on: 
+  key: multi-modal-processor-cpu
+  depends_on:
   - image-build-cpu
   timeout_in_minutes: 60
   source_file_dependencies:
@@ -79,6 +80,7 @@ steps:
     - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py
 
 - label: Multi-Modal Processor # 44min
+  key: multi-modal-processor
   timeout_in_minutes: 60
   device: h200_18gb
   source_file_dependencies:
@@ -90,6 +92,7 @@ steps:
     - pytest -v -s models/multimodal/processing/test_tensor_schema.py
 
 - label: Multi-Modal Accuracy Eval (Small Models) # 50min
+  key: multi-modal-accuracy-eval-small-models
   timeout_in_minutes: 70
   working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
   source_file_dependencies:
@@ -100,6 +103,7 @@ steps:
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
 
 - label: Multi-Modal Models (Extended Generation 1)
+  key: multi-modal-models-extended-generation-1
   optional: true
   source_file_dependencies:
   - vllm/
@@ -111,11 +115,12 @@ steps:
     - pytest -v -s models/multimodal/test_mapping.py
   mirror:
     amd:
-      device: mi325_1
+      device: mi300_1
       depends_on:
       - image-build-amd
 
 - label: Multi-Modal Models (Extended Generation 2)
+  key: multi-modal-models-extended-generation-2
   optional: true
   source_file_dependencies:
   - vllm/
@@ -125,6 +130,7 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
 
 - label: Multi-Modal Models (Extended Generation 3)
+  key: multi-modal-models-extended-generation-3
   optional: true
   source_file_dependencies:
   - vllm/
@@ -134,6 +140,7 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
 
 - label: Multi-Modal Models (Extended Pooling)
+  key: multi-modal-models-extended-pooling
   optional: true
   device: h200_18gb
   source_file_dependencies:
diff --git a/.buildkite/test_areas/plugins.yaml b/.buildkite/test_areas/plugins.yaml
index 8e0eb0284019..0d23180f3ef7 100644
--- a/.buildkite/test_areas/plugins.yaml
+++ b/.buildkite/test_areas/plugins.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Plugin Tests (2 GPUs)
+  key: plugin-tests-2-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
diff --git a/.buildkite/test_areas/pytorch.yaml b/.buildkite/test_areas/pytorch.yaml
index a3648219d89d..c58a9986beba 100644
--- a/.buildkite/test_areas/pytorch.yaml
+++ b/.buildkite/test_areas/pytorch.yaml
@@ -3,9 +3,33 @@ depends_on:
   - image-build
 steps:
 - label: PyTorch Compilation Unit Tests
+  key: pytorch-compilation-unit-tests
   timeout_in_minutes: 10
   source_file_dependencies:
-    - vllm/
+    - vllm/__init__.py
+    - vllm/_aiter_ops.py
+    - vllm/_custom_ops.py
+    - vllm/compilation/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/env_override.py
+    - vllm/envs.py
+    - vllm/forward_context.py
+    - vllm/inputs/
+    - vllm/ir/
+    - vllm/kernels/
+    - vllm/logger.py
+    - vllm/model_executor/
+    - vllm/multimodal/
+    - vllm/platforms/
+    - vllm/plugins/
+    - vllm/sampling_params.py
+    - vllm/sequence.py
+    - vllm/transformers_utils/
+    - vllm/triton_utils/
+    - vllm/utils/
+    - vllm/v1/
     - tests/compile
   commands:
   # Run unit tests defined directly under compile/,
@@ -18,27 +42,99 @@ steps:
   - "find compile/ -maxdepth 1 -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Compilation Unit Tests (H100)
+  key: pytorch-compilation-unit-tests-h100
   timeout_in_minutes: 30
   device: h100
   num_devices: 1
   source_file_dependencies:
-    - vllm/
+    - vllm/__init__.py
+    - vllm/_aiter_ops.py
+    - vllm/_custom_ops.py
+    - vllm/compilation/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/env_override.py
+    - vllm/envs.py
+    - vllm/forward_context.py
+    - vllm/inputs/
+    - vllm/ir/
+    - vllm/kernels/
+    - vllm/logger.py
+    - vllm/model_executor/
+    - vllm/multimodal/
+    - vllm/platforms/
+    - vllm/plugins/
+    - vllm/sampling_params.py
+    - vllm/sequence.py
+    - vllm/transformers_utils/
+    - vllm/triton_utils/
+    - vllm/utils/
+    - vllm/v1/
     - tests/compile/h100/
   commands:
   - "find compile/h100/ -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Compilation Passes Unit Tests
+  key: pytorch-compilation-passes-unit-tests
   timeout_in_minutes: 20
   source_file_dependencies:
-    - vllm/
+    - vllm/__init__.py
+    - vllm/_aiter_ops.py
+    - vllm/_custom_ops.py
+    - vllm/compilation/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/env_override.py
+    - vllm/envs.py
+    - vllm/forward_context.py
+    - vllm/inputs/
+    - vllm/ir/
+    - vllm/kernels/
+    - vllm/logger.py
+    - vllm/model_executor/
+    - vllm/multimodal/
+    - vllm/platforms/
+    - vllm/plugins/
+    - vllm/sampling_params.py
+    - vllm/sequence.py
+    - vllm/transformers_utils/
+    - vllm/triton_utils/
+    - vllm/utils/
+    - vllm/v1/
     - tests/compile/passes
   commands:
   - pytest -s -v compile/passes --ignore compile/passes/distributed
 
 - label: PyTorch Fullgraph Smoke Test
+  key: pytorch-fullgraph-smoke-test
   timeout_in_minutes: 35
   source_file_dependencies:
-  - vllm/
+  - vllm/__init__.py
+  - vllm/_aiter_ops.py
+  - vllm/_custom_ops.py
+  - vllm/compilation/
+  - vllm/config/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/env_override.py
+  - vllm/envs.py
+  - vllm/forward_context.py
+  - vllm/inputs/
+  - vllm/ir/
+  - vllm/kernels/
+  - vllm/logger.py
+  - vllm/model_executor/
+  - vllm/multimodal/
+  - vllm/platforms/
+  - vllm/plugins/
+  - vllm/sampling_params.py
+  - vllm/sequence.py
+  - vllm/transformers_utils/
+  - vllm/triton_utils/
+  - vllm/utils/
+  - vllm/v1/
   - tests/compile
   commands:
   # Run smoke tests under fullgraph directory, except test_full_graph.py
@@ -48,16 +144,41 @@ steps:
   - "find compile/fullgraph/ -name 'test_*.py' -not -name 'test_full_graph.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Fullgraph
+  key: pytorch-fullgraph
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
-  - vllm/
+  - vllm/__init__.py
+  - vllm/_aiter_ops.py
+  - vllm/_custom_ops.py
+  - vllm/compilation/
+  - vllm/config/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/env_override.py
+  - vllm/envs.py
+  - vllm/forward_context.py
+  - vllm/inputs/
+  - vllm/ir/
+  - vllm/kernels/
+  - vllm/logger.py
+  - vllm/model_executor/
+  - vllm/multimodal/
+  - vllm/platforms/
+  - vllm/plugins/
+  - vllm/sampling_params.py
+  - vllm/sequence.py
+  - vllm/transformers_utils/
+  - vllm/triton_utils/
+  - vllm/utils/
+  - vllm/v1/
   - tests/compile
   commands:
     # fp8 kv scales not supported on sm89, tested on Blackwell instead
   - pytest -v -s compile/fullgraph/test_full_graph.py -k 'not test_fp8_kv_scale_compile'
 
 - label: Pytorch Nightly Dependency Override Check # 2min
+  key: pytorch-nightly-dependency-override-check
   # if this test fails, it means the nightly torch version is not compatible with some
   # of the dependencies. Please check the error message and add the package to whitelist
   # in /vllm/tools/pre_commit/generate_nightly_torch_test.py
diff --git a/.buildkite/test_areas/quantization.yaml b/.buildkite/test_areas/quantization.yaml
index a42d59b021c6..8a9a36da4481 100644
--- a/.buildkite/test_areas/quantization.yaml
+++ b/.buildkite/test_areas/quantization.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Quantization
+  key: quantization
   timeout_in_minutes: 90
   source_file_dependencies:
   - csrc/
@@ -21,9 +22,10 @@ steps:
   - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
 
 - label: Quantized MoE Test (B200)
+  key: quantized-moe-test-b200
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
-  device: b200
+  device: b200-k8s
   source_file_dependencies:
   - tests/quantization/test_blackwell_moe.py
   - vllm/model_executor/models/deepseek_v2.py
@@ -38,6 +40,7 @@ steps:
     - pytest -s -v tests/quantization/test_blackwell_moe.py
 
 - label: Quantized Models Test
+  key: quantized-models-test
   timeout_in_minutes: 60
   source_file_dependencies:
   - vllm/model_executor/layers/quantization
diff --git a/.buildkite/test_areas/ray_compat.yaml b/.buildkite/test_areas/ray_compat.yaml
index 3485e346532c..9207621a5830 100644
--- a/.buildkite/test_areas/ray_compat.yaml
+++ b/.buildkite/test_areas/ray_compat.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Ray Dependency Compatibility Check
+  key: ray-dependency-compatibility-check
   # Informational only — does not block the pipeline.
   # If this fails, it means the PR introduces a dependency that
   # conflicts with Ray's dependency constraints.
diff --git a/.buildkite/test_areas/samplers.yaml b/.buildkite/test_areas/samplers.yaml
index 2052a379827a..48e9f55571e4 100644
--- a/.buildkite/test_areas/samplers.yaml
+++ b/.buildkite/test_areas/samplers.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Samplers Test
+  key: samplers-test
   timeout_in_minutes: 75
   source_file_dependencies:
   - vllm/model_executor/layers
@@ -10,11 +11,13 @@ steps:
   - tests/samplers
   - tests/conftest.py
   commands:
-    - pytest -v -s samplers
+    # VLLM_USE_FLASHINFER_SAMPLER defaults to 1 now, so we need to pin both
+    # values explicitly to still cover the PyTorch-native (Triton) path.
+    - VLLM_USE_FLASHINFER_SAMPLER=0 pytest -v -s samplers
     - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
   mirror:
     amd:
-      device: mi325_1
+      device: mi250_1
       depends_on:
       - image-build-amd
       commands:
diff --git a/.buildkite/test_areas/spec_decode.yaml b/.buildkite/test_areas/spec_decode.yaml
index a0b730968675..5253f54735aa 100644
--- a/.buildkite/test_areas/spec_decode.yaml
+++ b/.buildkite/test_areas/spec_decode.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Spec Decode Eagle
+  key: spec-decode-eagle
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -12,7 +13,20 @@ steps:
   commands:
     - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
 
+- label: Spec Decode Eagle Nightly B200
+  key: spec-decode-eagle-nightly-b200
+  timeout_in_minutes: 30
+  device: b200
+  optional: true
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/v1/worker/gpu/spec_decode/
+    - tests/v1/e2e/spec_decode/
+  commands:
+    - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
+
 - label: Spec Decode Speculators + MTP
+  key: spec-decode-speculators-mtp
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -23,7 +37,21 @@ steps:
   commands:
     - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
 
+- label: Spec Decode Speculators + MTP Nightly B200
+  key: spec-decode-speculators-mtp-nightly-b200
+  timeout_in_minutes: 30
+  device: b200
+  optional: true
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/v1/worker/gpu/spec_decode/
+    - vllm/transformers_utils/configs/speculators/
+    - tests/v1/e2e/spec_decode/
+  commands:
+    - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
+  
 - label: Spec Decode Ngram + Suffix
+  key: spec-decode-ngram-suffix
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -34,6 +62,7 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "ngram or suffix"
 
 - label: Spec Decode Draft Model
+  key: spec-decode-draft-model
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -42,3 +71,42 @@ steps:
     - tests/v1/e2e/spec_decode/
   commands:
     - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
+
+- label: Spec Decode Draft Model Nightly B200
+  key: spec-decode-draft-model-nightly-b200
+  timeout_in_minutes: 30
+  device: b200-k8s
+  optional: true
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/v1/worker/gpu/spec_decode/
+    - tests/v1/e2e/spec_decode/
+  commands:
+    - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
+
+- label: DFlash Speculators Correctness
+  key: dflash-speculators-correctness
+  timeout_in_minutes: 30
+  device: h100
+  optional: true
+  num_devices: 1
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/model_executor/models/qwen3_dflash.py
+    - tests/v1/spec_decode/test_speculators_dflash.py
+  commands:
+    - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
+    - pytest -v -s v1/spec_decode/test_speculators_dflash.py -m slow_test
+
+- label: Spec Decode MTP hybrid (B200)
+  timeout_in_minutes: 30
+  device: b200
+  optional: true
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/v1/worker/gpu/spec_decode/
+    - vllm/model_executor/models/qwen3_5.py
+    - vllm/model_executor/models/qwen3_5_mtp.py
+    - tests/v1/e2e/spec_decode/
+  commands:
+    - pytest -v -s v1/e2e/spec_decode -k "qwen3_5-hybrid"
diff --git a/.buildkite/test_areas/weight_loading.yaml b/.buildkite/test_areas/weight_loading.yaml
index 8e86374a8ad0..01c6bb7809bc 100644
--- a/.buildkite/test_areas/weight_loading.yaml
+++ b/.buildkite/test_areas/weight_loading.yaml
@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Weight Loading Multiple GPU  # 33min
+  key: weight-loading-multiple-gpu
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index e1a2a5820839..44cf10076ee7 100644
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -6,8 +6,8 @@
 /vllm/distributed/kv_transfer @NickLucche @ApostaC @orozery @xuechendi
 /vllm/lora @jeejeelee
 /vllm/model_executor/layers/attention @LucasWilkinson @MatthewBonanni
-/vllm/model_executor/layers/fused_moe @mgoin @pavanimajety
-/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety
+/vllm/model_executor/layers/fused_moe @mgoin @pavanimajety @zyongye
+/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety @zyongye
 /vllm/model_executor/layers/mamba @tdoublep @tomeras91
 /vllm/model_executor/layers/mamba/gdn_linear_attn.py @tdoublep @ZJY0516 @vadiklyutiy
 /vllm/model_executor/layers/rotary_embedding.py @vadiklyutiy
@@ -18,7 +18,8 @@
 /vllm/kernels/helion @ProExpertProg @zou3519
 /vllm/multimodal @DarkLight1337 @ywang96 @NickLucche @tjtanaa
 /vllm/vllm_flash_attn @LucasWilkinson @MatthewBonanni
-CMakeLists.txt @tlrmchlsmth @LucasWilkinson
+/CMakeLists.txt @tlrmchlsmth @LucasWilkinson @Harry-Chen
+/cmake @tlrmchlsmth @LucasWilkinson @Harry-Chen
 
 # Any change to the VllmConfig changes can have a large user-facing impact,
 # so spam a lot of people
@@ -44,8 +45,9 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /vllm/pooling_params.py @noooop @DarkLight1337
 /vllm/tokenizers @DarkLight1337 @njhill
 /vllm/renderers @DarkLight1337 @njhill
-/vllm/reasoning @aarnphm @chaunceyjiang
-/vllm/tool_parsers @aarnphm @chaunceyjiang
+/vllm/reasoning @aarnphm @chaunceyjiang @sfeng33 @bbrowning
+/vllm/tool_parsers @aarnphm @chaunceyjiang @sfeng33 @bbrowning
+/vllm/parser @aarnphm @chaunceyjiang @sfeng33 @bbrowning
 
 # vLLM V1
 /vllm/v1/attention @LucasWilkinson @MatthewBonanni
@@ -69,6 +71,10 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /vllm/v1/worker/gpu @WoosukKwon @njhill
 /vllm/v1/worker/gpu/kv_connector.py @orozery
 
+# CI & building
+/.buildkite @Harry-Chen
+/docker/Dockerfile @Harry-Chen
+
 # Test ownership
 /.buildkite/lm-eval-harness @mgoin 
 /tests/distributed/test_multi_node_assignment.py @youkaichao
@@ -76,11 +82,11 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/distributed/test_same_node.py @youkaichao
 /tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @aarnphm @NickLucche
 /tests/evals @mgoin @vadiklyutiy
-/tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256
+/tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256 @zyongye
 /tests/kernels/ir @ProExpertProg @tjtanaa
 /tests/models @DarkLight1337 @ywang96
 /tests/multimodal @DarkLight1337 @ywang96 @NickLucche
-/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety
+/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety @zyongye
 /tests/test_inputs.py @DarkLight1337 @ywang96
 /tests/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
 /tests/v1/structured_output @mgoin @russellb @aarnphm
@@ -91,7 +97,10 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/v1/kv_connector/nixl_integration @NickLucche
 /tests/v1/kv_connector @ApostaC @orozery
 /tests/v1/kv_offload @ApostaC @orozery
-/tests/v1/determinism @yewentao256 
+/tests/v1/determinism @yewentao256
+/tests/reasoning @aarnphm @chaunceyjiang @sfeng33 @bbrowning
+/tests/tool_parsers @aarnphm @chaunceyjiang @sfeng33 @bbrowning
+/tests/tool_use @aarnphm @chaunceyjiang @sfeng33 @bbrowning
 
 # Transformers modeling backend
 /vllm/model_executor/models/transformers @hmellor
@@ -143,6 +152,12 @@ mkdocs.yaml @hmellor
 # MTP-specific files
 /vllm/model_executor/models/deepseek_mtp.py @luccafong
 
+# DeepseekV4-specific files
+/vllm/v1/attention/ops/deepseek_v4_ops @zyongye
+/vllm/model_executor/layers/deepseek_compressor.py @zyongye
+/vllm/model_executor/layers/deepseek_v4_attention.py @zyongye
+/vllm/model_executor/layers/sparse_attn_indexer.py @zyongye
+
 # Mistral-specific files
 /vllm/model_executor/models/mistral*.py @patrickvonplaten
 /vllm/model_executor/models/mixtral*.py @patrickvonplaten
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index 8043df65d558..8a3934670e44 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -15,7 +15,6 @@ PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTT
 - [ ] The test plan, such as providing test command.
 - [ ] The test results, such as pasting the results comparison before and after, or e2e results
 - [ ] (Optional) The necessary documentation update, such as updating `supported_models.md` and `examples` for a new model.
-- [ ] (Optional) Release notes update. If your change is user facing, please update the release notes draft in the [Google Doc](https://docs.google.com/document/d/1YyVqrgX4gHTtrstbq8oWUImOyPCKSGnJ7xtTpmXzlRs/edit?tab=t.0).
 </details>
 
 **BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing>** (anything written below this line will be removed by GitHub Actions)
diff --git a/.github/mergify.yml b/.github/mergify.yml
index baf65e14a882..2d36e3507028 100644
--- a/.github/mergify.yml
+++ b/.github/mergify.yml
@@ -262,7 +262,7 @@ pull_request_rules:
       - files~=^docker/Dockerfile.xpu
       - files~=^\\.buildkite/intel_jobs/
       - files=\.buildkite/ci_config_intel.yaml
-      - files=vllm/model_executor/layers/fused_moe/xpu_fused_moe.py
+      - files=vllm/model_executor/layers/fused_moe/experts/xpu_moe.py
       - files=vllm/model_executor/kernels/linear/mixed_precision/xpu.py
       - files=vllm/model_executor/kernels/linear/mxfp8/xpu.py
       - files=vllm/model_executor/kernels/linear/scaled_mm/xpu.py
@@ -308,8 +308,7 @@ pull_request_rules:
       - files=benchmarks/benchmark_serving_structured_output.py
       - files=benchmarks/run_structured_output_benchmark.sh
       - files=docs/features/structured_outputs.md
-      - files=examples/offline_inference/structured_outputs.py
-      - files=examples/online_serving/structured_outputs/structured_outputs.py
+      - files=^examples/features/structured_outputs/
       - files~=^tests/v1/structured_output/
       - files=tests/entrypoints/llm/test_struct_output_generate.py
       - files~=^vllm/v1/structured_output/
@@ -325,7 +324,7 @@ pull_request_rules:
     - or:
       - files~=^vllm/v1/spec_decode/
       - files~=^tests/v1/spec_decode/
-      - files~=^examples/.*(spec_decode|mlpspeculator|eagle|speculation).*\.py
+      - files=^examples/features/speculative_decoding/
       - files~=^vllm/model_executor/models/.*eagle.*\.py
       - files=vllm/model_executor/models/mlp_speculator.py
       - files~=^vllm/transformers_utils/configs/(eagle|medusa|mlp_speculator)\.py
@@ -389,11 +388,7 @@ pull_request_rules:
       - files~=^tests/entrypoints/anthropic/.*tool.*
       - files~=^vllm/tool_parsers/
       - files=docs/features/tool_calling.md
-      - files~=^examples/tool_chat_*
-      - files=examples/offline_inference/chat_with_tools.py
-      - files=examples/online_serving/openai_chat_completion_client_with_tools_required.py
-      - files=examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py
-      - files=examples/online_serving/openai_chat_completion_client_with_tools.py
+      - files~=^examples/tool_calling/
   actions:
     label:
       add:
@@ -482,9 +477,7 @@ pull_request_rules:
   conditions:
     - label != stale
     - or:
-      - files~=^examples/online_serving/disaggregated[^/]*/.*
-      - files~=^examples/offline_inference/disaggregated[^/]*/.*
-      - files~=^examples/others/lmcache/
+      - files~=^examples/disaggregated/
       - files~=^tests/v1/kv_connector/
       - files~=^vllm/distributed/kv_transfer/
       - title~=(?i)\bP/?D\b
diff --git a/.github/workflows/macos-smoke-test.yml b/.github/workflows/macos-smoke-test.yml
index b8e7cebf2dc1..ea1c8b0feac3 100644
--- a/.github/workflows/macos-smoke-test.yml
+++ b/.github/workflows/macos-smoke-test.yml
@@ -45,6 +45,7 @@ jobs:
       - name: Smoke test vllm serve
         run: |
           # Start server in background
+          VLLM_CPU_KVCACHE_SPACE=1 \
           vllm serve Qwen/Qwen3-0.6B \
             --max-model-len=2K \
             --load-format=dummy \
diff --git a/.github/workflows/new_pr_bot.yml b/.github/workflows/new_pr_bot.yml
index ef5e30952c62..27100f9f4da0 100644
--- a/.github/workflows/new_pr_bot.yml
+++ b/.github/workflows/new_pr_bot.yml
@@ -62,14 +62,14 @@ jobs:
             const prAuthor = context.payload.pull_request.user.login;
 
             const { data: searchResults } = await github.rest.search.issuesAndPullRequests({
-              q: `repo:${owner}/${repo} type:pr author:${prAuthor}`,
+              q: `repo:${owner}/${repo} type:pr is:merged author:${prAuthor}`,
               per_page: 1,
             });
 
-            const authorPRCount = searchResults.total_count;
-            console.log(`Found ${authorPRCount} PRs by ${prAuthor}`);
+            const mergedPRCount = searchResults.total_count;
+            console.log(`Found ${mergedPRCount} merged PRs by ${prAuthor}`);
 
-            if (authorPRCount === 1) {
+            if (mergedPRCount === 0) {
               console.log(`Posting welcome comment for first-time contributor: ${prAuthor}`);
               await github.rest.issues.createComment({
                 owner,
@@ -98,5 +98,5 @@ jobs:
                 ].join('\n'),
               });
             } else {
-              console.log(`Skipping comment for ${prAuthor} - not their first PR (${authorPRCount} PRs found)`);
+              console.log(`Skipping comment for ${prAuthor} - not a first-time contributor (${mergedPRCount} merged PRs)`);
             }
diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml
index 8ab8d3e7035f..1dd31b0e50f6 100644
--- a/.github/workflows/pre-commit.yml
+++ b/.github/workflows/pre-commit.yml
@@ -16,11 +16,7 @@ permissions:
 
 jobs:
   pre-run-check:
-    if: >-
-      github.event_name == 'pull_request' &&
-      (github.event.action != 'labeled' ||
-       github.event.label.name == 'ready' ||
-       github.event.label.name == 'verified')
+    if: github.event_name == 'pull_request'
     runs-on: ubuntu-latest
     steps:
     - name: Check PR label and author merge count
@@ -49,12 +45,7 @@ jobs:
 
   pre-commit:
     needs: pre-run-check
-    if: >-
-      always() &&
-      (github.event.action != 'labeled' ||
-       github.event.label.name == 'ready' ||
-       github.event.label.name == 'verified') &&
-      (needs.pre-run-check.result == 'success' || needs.pre-run-check.result == 'skipped')
+    if: always() && (needs.pre-run-check.result == 'success' || needs.pre-run-check.result == 'skipped')
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # v6.0.1
diff --git a/.github/workflows/scripts/build.sh b/.github/workflows/scripts/build.sh
index 30c63ee0da91..5bb9f7196806 100644
--- a/.github/workflows/scripts/build.sh
+++ b/.github/workflows/scripts/build.sh
@@ -14,7 +14,7 @@ $python_executable -m pip install -r requirements/build/cuda.txt -r requirements
 # Limit the number of parallel jobs to avoid OOM
 export MAX_JOBS=1
 # Make sure release wheels are built for the following architectures
-export TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.9 9.0+PTX"
+export TORCH_CUDA_ARCH_LIST="7.5 8.0 8.6 8.9 9.0 10.0 12.0+PTX"
 
 bash tools/check_repo.sh
 
diff --git a/.gitignore b/.gitignore
index 134bbc5cc893..e53d19b35340 100644
--- a/.gitignore
+++ b/.gitignore
@@ -237,6 +237,7 @@ ep_kernels_workspace/
 
 # Allow tracked library source folders under submodules (e.g., benchmarks/lib)
 !vllm/benchmarks/lib/
+!.buildkite/scripts/lib/
 
 # Generated gRPC protobuf files (compiled at build time from vllm_engine.proto)
 vllm/grpc/vllm_engine_pb2.py
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 33b1db69dec4..f1e0afebf213 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -131,6 +131,16 @@ repos:
         --python-version, "3.12",
       ]
       files: ^requirements/(common|xpu|test/xpu)\.(in|txt)$
+    - id: pip-compile
+      alias: pip-compile-docs
+      name: pip-compile-docs
+      args: [
+        requirements/docs.in,
+        -o, requirements/docs.txt,
+        --python-platform, x86_64-manylinux_2_28,
+        --python-version, "3.12",
+      ]
+      files: ^requirements/docs\.(in|txt)$
 - repo: local
   hooks:
   - id: format-torch-nightly-test
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 1e479fd03d91..c1029af69437 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -9,7 +9,44 @@ build:
     python: "3.12"
   jobs:
     post_checkout:
-      # - bash docs/maybe_skip_pr_build.sh
+      - |
+        if [ "$READTHEDOCS_VERSION_TYPE" = "external" ]; then
+          MAX_WAIT=300
+          INTERVAL=60
+          ELAPSED=0
+          while :; do
+            RAW=$(curl -sS -w "\n%{http_code}" "https://api.github.com/repos/vllm-project/vllm/commits/${READTHEDOCS_GIT_COMMIT_HASH}/check-runs?check_name=pre-run-check&filter=latest")
+            HTTP_CODE=$(printf %s "$RAW" | tail -n1)
+            BODY=$(printf %s "$RAW" | head -n -1)
+            if [ "$HTTP_CODE" != "200" ]; then
+              echo "GitHub API returned HTTP $HTTP_CODE (likely rate-limited); skipping pre-run-check gate."
+              break
+            fi
+            STATUS=$(printf %s "$BODY" | python3 -c "import sys, json; r=json.load(sys.stdin).get(\"check_runs\",[]); print((r[0].get(\"status\") or \"\") if r else \"none\")")
+            CONCLUSION=$(printf %s "$BODY" | python3 -c "import sys, json; r=json.load(sys.stdin).get(\"check_runs\",[]); print((r[0].get(\"conclusion\") or \"\") if r else \"\")")
+            if [ "$STATUS" = "none" ]; then
+              echo "no pre-run-check found for this commit; skipping gate."
+              break
+            fi
+            if [ -n "$CONCLUSION" ]; then
+              echo "pre-run-check conclusion: $CONCLUSION"
+              if [ "$CONCLUSION" = "failure" ] || [ "$CONCLUSION" = "cancelled" ] || [ "$CONCLUSION" = "timed_out" ]; then
+                echo "pre-run-check did not pass; failing docs build."
+                exit 1
+              fi
+              break
+            fi
+            if [ "$ELAPSED" -ge "$MAX_WAIT" ]; then
+              echo "pre-run-check status=$STATUS after ${MAX_WAIT}s; skipping gate."
+              break
+            fi
+            echo "pre-run-check status=$STATUS; waiting ${INTERVAL}s..."
+            sleep "$INTERVAL"
+            ELAPSED=$((ELAPSED + INTERVAL))
+          done
+        else
+          echo "Not a PR build (version type=$READTHEDOCS_VERSION_TYPE); skipping pre-run-check gate."
+        fi
       - git fetch origin main --unshallow --no-tags --filter=blob:none || true
     pre_create_environment:
       - pip install uv
diff --git a/CMakeLists.txt b/CMakeLists.txt
index f24c12eff83c..38af64851ed9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -13,8 +13,12 @@ cmake_minimum_required(VERSION 3.26)
 # cmake --install . --component _C
 project(vllm_extensions LANGUAGES CXX)
 
-set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD 20)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
+set(CMAKE_CUDA_STANDARD 20)
+set(CMAKE_CUDA_STANDARD_REQUIRED ON)
+set(CMAKE_HIP_STANDARD 20)
+set(CMAKE_HIP_STANDARD_REQUIRED ON)
 
 
 # CUDA by default, can be overridden by using -DVLLM_TARGET_DEVICE=... (used by setup.py)
@@ -34,10 +38,10 @@ install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
 # Supported python versions.  These versions will be searched in order, the
 # first match will be selected.  These should be kept in sync with setup.py.
 #
-set(PYTHON_SUPPORTED_VERSIONS "3.10" "3.11" "3.12" "3.13")
+set(PYTHON_SUPPORTED_VERSIONS "3.10" "3.11" "3.12" "3.13" "3.14")
 
 # Supported AMD GPU architectures.
-set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1150;gfx1151;gfx1152;gfx1153;gfx1200;gfx1201")
+set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1102;gfx1103;gfx1150;gfx1151;gfx1152;gfx1153;gfx1200;gfx1201")
 
 # ROCm installation prefix. Default to /opt/rocm but allow override via
 # -DROCM_PATH=/your/rocm/path when invoking cmake.
@@ -94,12 +98,15 @@ find_package(Torch REQUIRED)
 # This check must happen after find_package(Torch) because that's when CMAKE_CUDA_COMPILER_VERSION gets defined
 if(DEFINED CMAKE_CUDA_COMPILER_VERSION AND
    CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 13.0)
-  set(CUDA_SUPPORTED_ARCHS "7.5;8.0;8.6;8.7;8.9;9.0;10.0;11.0;12.0;12.1")
+   # starting from CUDA 12.9 and Blackwell (10.0), we use family-specific targets (10.0f, 12.0f, etc)
+   # to support the whole generation without specifying all sub-architectures
+   # see: https://developer.nvidia.com/blog/nvidia-blackwell-and-nvidia-cuda-12-9-introduce-family-specific-architecture-features/
+  set(CUDA_SUPPORTED_ARCHS "7.5;8.0;8.6;8.7;8.9;9.0;10.0;11.0;12.0")
 elseif(DEFINED CMAKE_CUDA_COMPILER_VERSION AND
    CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8)
-  set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0;12.1")
+  set(CUDA_SUPPORTED_ARCHS "7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;10.3;12.0;12.1")
 else()
-  set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0")
+  set(CUDA_SUPPORTED_ARCHS "7.0;7.5;8.0;8.6;8.7;8.9;9.0")
 endif()
 
 #
@@ -304,10 +311,12 @@ set(VLLM_EXT_SRC
   "csrc/quantization/activation_kernels.cu"
   "csrc/cuda_utils_kernels.cu"
   "csrc/custom_all_reduce.cu"
-  "csrc/torch_bindings.cpp")
+  "csrc/torch_bindings.cpp"
+  "csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
-  list(APPEND VLLM_EXT_SRC "csrc/minimax_reduce_rms_kernel.cu")
+  list(APPEND VLLM_EXT_SRC
+    "csrc/minimax_reduce_rms_kernel.cu")
 
   SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
 
@@ -923,6 +932,14 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
       SRCS "${SRCS}"
       CUDA_ARCHS "${FP4_ARCHS}")
     list(APPEND VLLM_STABLE_EXT_SRC "${SRCS}")
+    # nvfp4_kv_cache_kernels uses non-stable torch API and is called directly
+    # from cache_kernels.cu, so it belongs in _C rather than _C_stable.
+    set(NVFP4_KV_SRC "csrc/nvfp4_kv_cache_kernels.cu")
+    set_gencode_flags_for_srcs(
+      SRCS "${NVFP4_KV_SRC}"
+      CUDA_ARCHS "${FP4_ARCHS}")
+    target_sources(_C PRIVATE ${NVFP4_KV_SRC})
+    target_compile_definitions(_C PRIVATE ENABLE_NVFP4_SM120=1)
     list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM120=1")
     list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM120=1")
     message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
@@ -944,11 +961,19 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
       "csrc/libtorch_stable/quantization/fp4/activation_nvfp4_quant_fusion_kernels.cu"
       "csrc/libtorch_stable/quantization/fp4/nvfp4_experts_quant.cu"
       "csrc/libtorch_stable/quantization/fp4/nvfp4_scaled_mm_kernels.cu"
-      "csrc/libtorch_stable/quantization/fp4/nvfp4_blockwise_moe_kernel.cu")
+      "csrc/libtorch_stable/quantization/fp4/nvfp4_blockwise_moe_kernel.cu"
+      "csrc/libtorch_stable/quantization/fp4/mxfp4_experts_quant.cu"
+      "csrc/libtorch_stable/quantization/fp4/mxfp4_blockwise_moe_kernel.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${FP4_ARCHS}")
     list(APPEND VLLM_STABLE_EXT_SRC "${SRCS}")
+    set(NVFP4_KV_SRC "csrc/nvfp4_kv_cache_kernels.cu")
+    set_gencode_flags_for_srcs(
+      SRCS "${NVFP4_KV_SRC}"
+      CUDA_ARCHS "${FP4_ARCHS}")
+    target_sources(_C PRIVATE ${NVFP4_KV_SRC})
+    target_compile_definitions(_C PRIVATE ENABLE_NVFP4_SM100=1)
     list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM100=1")
     list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
     message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
@@ -1026,13 +1051,13 @@ endif()
 set(VLLM_MOE_EXT_SRC
   "csrc/moe/torch_bindings.cpp"
   "csrc/moe/moe_align_sum_kernels.cu"
-  "csrc/moe/topk_softmax_kernels.cu")
+  "csrc/moe/topk_softmax_kernels.cu"
+  "csrc/moe/topk_softplus_sqrt_kernels.cu")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_MOE_EXT_SRC
     "csrc/moe/moe_wna16.cu"
-    "csrc/moe/grouped_topk_kernels.cu"
-    "csrc/moe/router_gemm.cu")
+    "csrc/moe/grouped_topk_kernels.cu")
 endif()
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
diff --git a/README.md b/README.md
index d94e33ba8b0a..42777436c63d 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@ Easy, fast, and cheap LLM serving for everyone
 | <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://blog.vllm.ai/"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
 </p>
 
-🔥 We have built a vllm website to help you get started with vllm. Please visit [vllm.ai](https://vllm.ai) to learn more.
+🔥 We have built a vLLM website to help you get started with vLLM. Please visit [vllm.ai](https://vllm.ai) to learn more.
 For events, please visit [vllm.ai/events](https://vllm.ai/events) to join us.
 
 ---
@@ -50,7 +50,7 @@ vLLM is flexible and easy to use with:
 - Efficient multi-LoRA support for dense and MoE layers
 - Support for NVIDIA GPUs, AMD GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more.
 
-vLLM seamlessly supports 200+ model architectures on HuggingFace, including:
+vLLM seamlessly supports 200+ model architectures on Hugging Face, including:
 
 - Decoder-only LLMs (e.g., Llama, Qwen, Gemma)
 - Mixture-of-Expert LLMs (e.g., Mixtral, DeepSeek-V3, Qwen-MoE, GPT-OSS)
diff --git a/tests/entrypoints/pooling/pooling/__init__.py b/benchmarks/__init__.py
similarity index 100%
rename from tests/entrypoints/pooling/pooling/__init__.py
rename to benchmarks/__init__.py
diff --git a/benchmarks/attention_benchmarks/mla_runner.py b/benchmarks/attention_benchmarks/mla_runner.py
index f8bc7b4a10ed..b58ddffcd0b1 100644
--- a/benchmarks/attention_benchmarks/mla_runner.py
+++ b/benchmarks/attention_benchmarks/mla_runner.py
@@ -404,6 +404,7 @@ def _build_attention_metadata(
         query_start_loc=q_start_gpu,
         query_start_loc_cpu=q_start_cpu,
         seq_lens=seq_lens_gpu,
+        seq_lens_cpu_upper_bound=seq_lens_cpu,
         _seq_lens_cpu=seq_lens_cpu,
         _num_computed_tokens_cpu=num_computed_tokens_cpu,
         slot_mapping=slot_mapping,
diff --git a/benchmarks/kernels/__init__.py b/benchmarks/kernels/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/benchmarks/kernels/benchmark_cutlass_moe_fp8.py b/benchmarks/kernels/benchmark_cutlass_moe_fp8.py
index 3f80b024e108..03d7fb386f74 100644
--- a/benchmarks/kernels/benchmark_cutlass_moe_fp8.py
+++ b/benchmarks/kernels/benchmark_cutlass_moe_fp8.py
@@ -16,7 +16,7 @@
     maybe_make_prepare_finalize,
 )
 from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
-from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
+from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import CutlassExpertsFp8
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
 from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
diff --git a/benchmarks/kernels/benchmark_cutlass_moe_nvfp4.py b/benchmarks/kernels/benchmark_cutlass_moe_nvfp4.py
index 2d4afd38c097..7379bf858889 100644
--- a/benchmarks/kernels/benchmark_cutlass_moe_nvfp4.py
+++ b/benchmarks/kernels/benchmark_cutlass_moe_nvfp4.py
@@ -22,7 +22,7 @@
     fp8_w8a8_moe_quant_config,
     nvfp4_moe_quant_config,
 )
-from vllm.model_executor.layers.fused_moe.cutlass_moe import (
+from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import (
     CutlassExpertsFp4,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
diff --git a/benchmarks/kernels/benchmark_grouped_gemm_cutlass.py b/benchmarks/kernels/benchmark_grouped_gemm_cutlass.py
index dd4060bbdb94..04fc2960d1e4 100644
--- a/benchmarks/kernels/benchmark_grouped_gemm_cutlass.py
+++ b/benchmarks/kernels/benchmark_grouped_gemm_cutlass.py
@@ -13,7 +13,7 @@
     maybe_make_prepare_finalize,
 )
 from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
-from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
+from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import CutlassExpertsFp8
 from vllm.model_executor.layers.fused_moe.fused_moe import (
     fused_experts,
     fused_topk,
diff --git a/benchmarks/kernels/benchmark_vit_fp8_attn.py b/benchmarks/kernels/benchmark_vit_fp8_attn.py
new file mode 100644
index 000000000000..7d7a067dde9d
--- /dev/null
+++ b/benchmarks/kernels/benchmark_vit_fp8_attn.py
@@ -0,0 +1,324 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Benchmarks FP8 vs BF16 ViT attention via FlashInfer cuDNN backend.
+#
+# == Usage Examples ==
+#
+# Benchmark mode (default, FlashInfer CUDAGraph Bench)
+#   python3 benchmark_vit_fp8_attn.py
+#
+# Profile mode (PyTorch profiler, saves TensorBoard traces):
+#   python3 benchmark_vit_fp8_attn.py --profile
+#   python3 benchmark_vit_fp8_attn.py --profile --profile-output-dir ./profile_traces
+#
+# Custom seq_lens:
+#   python3 benchmark_vit_fp8_attn.py --seq-lens 4096 8192 16384
+
+from functools import partial
+
+import numpy as np
+import torch
+from torch.profiler import ProfilerActivity, profile, record_function
+
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+
+# Qwen3-VL defaults
+NUM_HEADS = 16
+HEAD_DIM = 72
+DEFAULT_SEQ_LENS = [2304, 4096, 8192, 16384]
+
+
+def _setup_fp8_attention(num_heads: int, head_dim: int) -> tuple:
+    """Create FP8 and BF16 attention modules + workspace."""
+    from types import SimpleNamespace
+    from unittest.mock import patch
+
+    from vllm.config import VllmConfig, set_current_vllm_config
+    from vllm.config.multimodal import MultiModalConfig
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+        _get_flashinfer_workspace_buffer,
+    )
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+    old_dtype = torch.get_default_dtype()
+    torch.set_default_dtype(torch.bfloat16)
+
+    backend_patch = patch(
+        "vllm.model_executor.layers.attention.mm_encoder_attention"
+        ".get_vit_attn_backend",
+        return_value=AttentionBackendEnum.FLASHINFER,
+    )
+
+    # FP8 attention
+    mm_config_fp8 = MultiModalConfig(mm_encoder_attn_dtype="fp8")
+    vllm_config_fp8 = VllmConfig()
+    vllm_config_fp8.model_config = SimpleNamespace(multimodal_config=mm_config_fp8)
+    with set_current_vllm_config(vllm_config_fp8), backend_patch:
+        attn_fp8 = MMEncoderAttention(
+            num_heads=num_heads,
+            head_size=head_dim,
+            prefix="visual.blocks.0.attn",
+        ).to("cuda")
+
+    # BF16 attention (no FP8)
+    with set_current_vllm_config(VllmConfig()), backend_patch:
+        attn_bf16 = MMEncoderAttention(
+            num_heads=num_heads,
+            head_size=head_dim,
+            prefix="visual.blocks.0.attn",
+        ).to("cuda")
+
+    torch.set_default_dtype(old_dtype)
+
+    workspace = _get_flashinfer_workspace_buffer()
+    return attn_fp8, attn_bf16, workspace
+
+
+def _build_meta(
+    seq_len: int,
+    num_heads: int,
+    head_dim: int,
+    fp8: bool,
+):
+    """Build cu_seqlens, max_seqlen, sequence_lengths."""
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+    )
+    from vllm.utils.math_utils import round_up
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+    cu_np = np.array([0, seq_len], dtype=np.int32)
+    fp8_padded = num_heads * round_up(head_dim, 16) if fp8 else None
+
+    seq_lengths = MMEncoderAttention.maybe_compute_seq_lens(
+        AttentionBackendEnum.FLASHINFER, cu_np, torch.device("cuda")
+    )
+    max_seqlen = torch.tensor(
+        MMEncoderAttention.compute_max_seqlen(AttentionBackendEnum.FLASHINFER, cu_np),
+        dtype=torch.int32,
+    )
+    cu_seqlens = MMEncoderAttention.maybe_recompute_cu_seqlens(
+        AttentionBackendEnum.FLASHINFER,
+        cu_np,
+        num_heads * head_dim,
+        1,
+        torch.device("cuda"),
+        fp8_padded_hidden_size=fp8_padded,
+    )
+    return cu_seqlens, max_seqlen, seq_lengths
+
+
+def run_benchmark(
+    seq_lens: list[int],
+    num_heads: int,
+    head_dim: int,
+    method: str,
+):
+    """Benchmark FP8 vs BF16 attention across seq_lens.
+
+    Uses FlashInfer GPU-level timing to measure pure kernel time,
+    excluding CPU launch overhead.
+    """
+    if method == "cupti":
+        from flashinfer.testing import bench_gpu_time_with_cupti as bench_fn
+
+        bench_fn = partial(bench_fn, use_cuda_graph=True, cold_l2_cache=False)
+    elif method == "cudagraph":
+        from flashinfer.testing import (
+            bench_gpu_time_with_cudagraph as bench_fn,
+        )
+
+        bench_fn = partial(bench_fn, cold_l2_cache=False)
+    else:
+        raise ValueError(f"Invalid method: {method}")
+
+    attn_fp8, attn_bf16, workspace = _setup_fp8_attention(num_heads, head_dim)
+
+    print(f"Timing method: {method}")
+    print(f"{'seq_len':>8} {'BF16 (us)':>12} {'FP8 (us)':>12} {'Speedup':>10}")
+    print("-" * 46)
+
+    for seq_len in seq_lens:
+        torch.manual_seed(42)
+
+        q = torch.randn(
+            seq_len,
+            num_heads,
+            head_dim,
+            device="cuda",
+            dtype=torch.bfloat16,
+        )
+        k = torch.randn_like(q)
+        v = torch.randn_like(q)
+
+        cu_fp8, max_s, seq_l = _build_meta(seq_len, num_heads, head_dim, fp8=True)
+        # we can reuse cu_fp8 for cu_bf16 since q, k, and v are contiguous
+        cu_bf16 = cu_fp8.clone()
+
+        def bf16_fn(q=q, k=k, v=v, cu=cu_bf16, ms=max_s, sl=seq_l):
+            attn_bf16._forward_flashinfer(q, k, v, cu, ms, sl)
+
+        def fp8_fn(q=q, k=k, v=v, cu=cu_fp8, ms=max_s, sl=seq_l):
+            attn_fp8._forward_flashinfer(q, k, v, cu, ms, sl)
+
+        # bench_fn returns List[float] of per-iteration times in ms
+        bf16_times = bench_fn(bf16_fn)
+        fp8_times = bench_fn(fp8_fn)
+
+        bf16_us = np.median(bf16_times) * 1e3  # ms -> us
+        fp8_us = np.median(fp8_times) * 1e3
+        speedup = bf16_us / fp8_us if fp8_us > 0 else float("inf")
+
+        print(f"{seq_len:>8} {bf16_us:>12.1f} {fp8_us:>12.1f} {speedup:>9.2f}x")
+
+
+def _make_trace_handler(output_dir: str, worker_name: str, label: str):
+    """Create a trace handler that saves to TensorBoard and prints summary."""
+
+    def handler(prof):
+        torch.profiler.tensorboard_trace_handler(output_dir, worker_name)(prof)
+        print(f"\n{'=' * 80}")
+        print(label)
+        print(f"{'=' * 80}")
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))
+
+    return handler
+
+
+def run_profile(
+    seq_len: int,
+    num_heads: int,
+    head_dim: int,
+    warmup: int,
+    output_dir: str,
+):
+    """Profile FP8 vs BF16 attention with PyTorch profiler."""
+    attn_fp8, attn_bf16, workspace = _setup_fp8_attention(num_heads, head_dim)
+
+    torch.manual_seed(42)
+    q = torch.randn(
+        seq_len,
+        num_heads,
+        head_dim,
+        device="cuda",
+        dtype=torch.bfloat16,
+    )
+    k = torch.randn_like(q)
+    v = torch.randn_like(q)
+
+    cu_fp8, max_s, seq_l = _build_meta(seq_len, num_heads, head_dim, fp8=True)
+    # we can reuse cu_fp8 for cu_bf16 since q, k, and v are contiguous
+    cu_bf16 = cu_fp8.clone()
+
+    sched = torch.profiler.schedule(wait=0, warmup=warmup, active=1)
+
+    # Profile BF16 (warmup handled by profiler schedule)
+    with profile(
+        activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+        schedule=sched,
+        on_trace_ready=_make_trace_handler(
+            output_dir,
+            f"bf16_h{head_dim}_s{seq_len}",
+            f"BF16 Attention (seq_len={seq_len}, heads={num_heads}, "
+            f"head_dim={head_dim})",
+        ),
+    ) as prof_bf16:
+        for _ in range(warmup + 1):
+            with record_function("bf16_attention"):
+                attn_bf16._forward_flashinfer(
+                    q.clone(), k.clone(), v.clone(), cu_bf16, max_s, seq_l
+                )
+                torch.accelerator.synchronize()
+            prof_bf16.step()
+
+    # Profile FP8 (warmup handled by profiler schedule)
+    with profile(
+        activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+        schedule=sched,
+        on_trace_ready=_make_trace_handler(
+            output_dir,
+            f"fp8_h{head_dim}_s{seq_len}",
+            f"FP8 Attention (seq_len={seq_len}, heads={num_heads}, "
+            f"head_dim={head_dim})",
+        ),
+    ) as prof_fp8:
+        for _ in range(warmup + 1):
+            with record_function("fp8_attention"):
+                attn_fp8._forward_flashinfer(
+                    q.clone(), k.clone(), v.clone(), cu_fp8, max_s, seq_l
+                )
+                torch.accelerator.synchronize()
+            prof_fp8.step()
+
+    print(f"\nTensorBoard traces saved to: {output_dir}")
+    print(f"View with: tensorboard --logdir={output_dir}")
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(description="Benchmark FP8 vs BF16 ViT attention.")
+    parser.add_argument(
+        "--seq-lens",
+        type=int,
+        nargs="+",
+        default=DEFAULT_SEQ_LENS,
+        help="Sequence lengths to benchmark",
+    )
+    parser.add_argument(
+        "--num-heads",
+        type=int,
+        default=NUM_HEADS,
+    )
+    parser.add_argument(
+        "--head-dim",
+        type=int,
+        default=HEAD_DIM,
+    )
+    parser.add_argument(
+        "--method",
+        choices=["cupti", "cudagraph"],
+        default="cudagraph",
+        help="GPU timing method: cupti (CUPTI kernel timing) or "
+        "cudagraph (CUDA graph capture/replay). Default: cudagraph",
+    )
+    parser.add_argument(
+        "--warmup",
+        type=int,
+        default=10,
+        help="Warmup iterations (profile mode only)",
+    )
+    parser.add_argument(
+        "--profile",
+        action="store_true",
+        help="Run PyTorch profiler instead of benchmark",
+    )
+    parser.add_argument(
+        "--profile-seq-len",
+        type=int,
+        default=8192,
+        help="Sequence length for profiling (default: 8192)",
+    )
+    parser.add_argument(
+        "--profile-output-dir",
+        type=str,
+        default="./profile_traces",
+        help="Output directory for TensorBoard traces (default: ./profile_traces)",
+    )
+    args = parser.parse_args()
+
+    if args.profile:
+        run_profile(
+            args.profile_seq_len,
+            args.num_heads,
+            args.head_dim,
+            args.warmup,
+            args.profile_output_dir,
+        )
+    else:
+        run_benchmark(
+            args.seq_lens,
+            args.num_heads,
+            args.head_dim,
+            args.method,
+        )
diff --git a/benchmarks/kernels/ir/__init__.py b/benchmarks/kernels/ir/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/benchmarks/kernels/ir/bench_ir_ops.py b/benchmarks/kernels/ir/bench_ir_ops.py
new file mode 100644
index 000000000000..b23c4e8ae327
--- /dev/null
+++ b/benchmarks/kernels/ir/bench_ir_ops.py
@@ -0,0 +1,378 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Generic benchmark harness for vLLM IR ops.
+
+Usage:
+    python benchmarks/kernels/ir/bench_ir_ops.py
+    python benchmarks/kernels/ir/bench_ir_ops.py --ops rms_norm
+    python benchmarks/kernels/ir/bench_ir_ops.py --ops rms_norm,silu_mul
+    python benchmarks/kernels/ir/bench_ir_ops.py --no-cuda-graph
+    python benchmarks/kernels/ir/bench_ir_ops.py --ops rms_norm --save-path ./results/
+"""
+
+import argparse
+import contextlib
+import csv
+import dataclasses
+import datetime
+import math
+import os
+import subprocess
+import sys
+import tempfile
+
+# Ensure repo root is on sys.path so `benchmarks` is importable as a package.
+_REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
+if _REPO_ROOT not in sys.path:
+    sys.path.insert(0, _REPO_ROOT)
+
+# Suppress noisy C++ warnings from vllm kernel registration (written to fd 2
+# directly by the dynamic linker, so Python-level sys.stderr redirect won't
+# catch them).
+_saved_fd = os.dup(2)
+try:
+    with open(os.devnull, "w") as _devnull:
+        os.dup2(_devnull.fileno(), 2)
+        import torch
+
+        import vllm.kernels  # noqa: E402, F401
+finally:
+    os.dup2(_saved_fd, 2)
+    os.close(_saved_fd)
+
+from tqdm import tqdm  # noqa: E402
+
+from benchmarks.kernels.ir.shapes import SHAPE_CONFIGS  # noqa: E402  # isort: skip
+from vllm.ir.op import IrOp  # noqa: E402
+from vllm.platforms import current_platform  # noqa: E402
+from vllm.triton_utils import triton  # noqa: E402
+
+
+@dataclasses.dataclass(frozen=True)
+class BenchConfig:
+    use_cuda_graph: bool = True
+    warmup: int = 25
+    rep: int = 100
+
+
+def _pkg_version(name: str) -> str:
+    from importlib.metadata import PackageNotFoundError, version
+
+    with contextlib.suppress(PackageNotFoundError):
+        return version(name)
+    return "not installed"
+
+
+_METADATA_LABELS = {
+    "timestamp": "Timestamp",
+    "git_commit": "Git commit",
+    "vllm": "vLLM",
+    "pytorch": "PyTorch",
+    "cuda_runtime": "CUDA runtime",
+    "triton": "Triton",
+    "cutlass": "CUTLASS",
+    "helion": "Helion",
+    "device": "Device",
+    "bench_mode": "Bench mode",
+    "warmup": "Warmup",
+    "rep": "Repetitions",
+}
+
+
+def collect_env_metadata(cfg: BenchConfig) -> dict[str, str]:
+    from vllm.collect_env import get_env_info
+
+    env = get_env_info()
+
+    git_sha = "unknown"
+    with contextlib.suppress(subprocess.CalledProcessError, FileNotFoundError):
+        git_sha = (
+            subprocess.check_output(
+                ["git", "rev-parse", "--short", "HEAD"], stderr=subprocess.DEVNULL
+            )
+            .decode()
+            .strip()
+        )
+
+    device_name = current_platform.get_device_name()
+
+    warmup_note = " ms" if not cfg.use_cuda_graph else " ms (ignored)"
+    rep_note = " replays" if cfg.use_cuda_graph else " ms"
+
+    return {
+        "timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "git_commit": git_sha,
+        "vllm": str(env.vllm_version),
+        "pytorch": str(env.torch_version),
+        "cuda_runtime": str(env.cuda_runtime_version),
+        "triton": triton.__version__,
+        "cutlass": _pkg_version("nvidia-cutlass-dsl"),
+        "helion": _pkg_version("helion"),
+        "device": device_name,
+        "bench_mode": "cuda_graph" if cfg.use_cuda_graph else "eager",
+        "warmup": f"{cfg.warmup}{warmup_note}",
+        "rep": f"{cfg.rep}{rep_note}",
+    }
+
+
+def print_metadata(metadata: dict[str, str]):
+    print("=" * 60)
+    for key, val in metadata.items():
+        print(f"{_METADATA_LABELS.get(key, key) + ':':<16}{val}")
+    print("=" * 60)
+
+
+def _clone_args(args: tuple) -> tuple:
+    return tuple(a.clone() if isinstance(a, torch.Tensor) else a for a in args)
+
+
+# TODO(gmagogsfm): When the `maybe_inplace` PR lands, ops marked as
+# inplace=True will mutate bench_args across iterations. Both CUDA graph
+# and eager modes will accumulate drift from repeated in-place mutation.
+# We need to re-clone inputs per iteration for inplace ops.
+def _bench_one(fn, args, cfg: BenchConfig) -> float:
+    bench_args = _clone_args(args)
+    bench_fn = lambda: fn(*bench_args)
+
+    if cfg.use_cuda_graph:
+        ms = triton.testing.do_bench_cudagraph(bench_fn, rep=cfg.rep, quantiles=[0.5])
+    else:
+        ms = triton.testing.do_bench(
+            bench_fn, warmup=cfg.warmup, rep=cfg.rep, quantiles=[0.5]
+        )
+    return ms * 1000
+
+
+# TODO(gmagogsfm): Once compiled native implementation lands (#38775),
+# the benchmark baseline should be the compiled native (what vLLM runs by
+# default) rather than the uncompiled native implementation.
+def collect_timings(
+    op: IrOp, shape_configs: list[dict], cfg: BenchConfig
+) -> tuple[list[str], list[str], dict[str, dict[str, float]]]:
+    def fmt(v) -> str:
+        return str(v).split(".")[-1] if isinstance(v, torch.dtype) else str(v)
+
+    case_names = [
+        "_".join(f"{k}={fmt(v)}" for k, v in kwargs.items()) for kwargs in shape_configs
+    ]
+    providers = [n for n, impl in op.impls.items() if impl.supported]
+
+    results: dict[str, dict[str, float]] = {c: {} for c in case_names}
+    for provider in providers:
+        impl = op.impls[provider]
+        desc = f"{op.name} / {provider}"
+        for case_name, kwargs in tqdm(
+            zip(case_names, shape_configs),
+            desc=desc,
+            total=len(case_names),
+            unit=" cases",
+        ):
+            args = op.generate_inputs(**kwargs)
+            if impl.supports_args(*args):
+                results[case_name][provider] = _bench_one(impl.impl_fn, args, cfg)
+            else:
+                results[case_name][provider] = float("nan")
+
+    return case_names, providers, results
+
+
+def analyze_results(
+    op_name: str,
+    case_names: list[str],
+    providers: list[str],
+    results: dict[str, dict[str, float]],
+) -> tuple[list[dict[str, str]], list[dict[str, str]], list[str]]:
+    native_col = "native"
+    non_native = [p for p in providers if p != native_col]
+
+    header_cols = ["case"]
+    for p in providers:
+        header_cols.append(f"{p} (us)")
+    for p in non_native:
+        header_cols.append(f"{p} speedup")
+
+    detail_rows: list[dict[str, str]] = []
+    speedup_data: dict[str, list[tuple[float, str]]] = {p: [] for p in non_native}
+
+    for case_name in case_names:
+        timings = results[case_name]
+        row: dict[str, str] = {"case": case_name}
+
+        for p in providers:
+            val = timings.get(p, float("nan"))
+            row[f"{p} (us)"] = f"{val:.2f}" if not math.isnan(val) else "n/a"
+
+        native_us = timings.get(native_col, float("nan"))
+        for p in non_native:
+            p_us = timings.get(p, float("nan"))
+            if not math.isnan(native_us) and not math.isnan(p_us) and p_us > 0:
+                speedup = native_us / p_us
+                row[f"{p} speedup"] = f"{speedup:.2f}x"
+                speedup_data[p].append((speedup, case_name))
+            else:
+                row[f"{p} speedup"] = "n/a"
+
+        detail_rows.append(row)
+
+    summary_rows: list[dict[str, str]] = []
+    for p in non_native:
+        entries = speedup_data[p]
+        if not entries:
+            continue
+        speedups = [s for s, _ in entries]
+        geomean = math.exp(sum(math.log(s) for s in speedups) / len(speedups))
+        best_val, best_case = max(entries)
+        worst_val, worst_case = min(entries)
+        wins = sum(1 for s in speedups if s > 1.0)
+        losses = sum(1 for s in speedups if s < 1.0)
+        total = len(speedups)
+
+        print(f"\n{p} vs native ({wins}/{total} faster, {losses}/{total} slower):")
+        print(f"  geomean speedup: {geomean:.2f}x")
+        print(f"  best:            {best_val:.2f}x  ({best_case})")
+        print(f"  worst:           {worst_val:.2f}x  ({worst_case})")
+
+        summary_rows.append(
+            {
+                "op": op_name,
+                "provider": p,
+                "geomean_speedup": f"{geomean:.2f}",
+                "best_speedup": f"{best_val:.2f}",
+                "best_case": best_case,
+                "worst_speedup": f"{worst_val:.2f}",
+                "worst_case": worst_case,
+                "wins": str(wins),
+                "losses": str(losses),
+                "total": str(total),
+            }
+        )
+
+    return detail_rows, summary_rows, header_cols
+
+
+def write_csv(path: str, rows: list[dict[str, str]], fieldnames: list[str]):
+    with open(path, "w", newline="") as f:
+        writer = csv.DictWriter(f, fieldnames=fieldnames)
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def save_results(
+    save_dir: str,
+    op_name: str,
+    detail_rows: list[dict[str, str]],
+    header_cols: list[str],
+    all_summary_rows: list[dict[str, str]],
+    metadata: dict[str, str],
+):
+    write_csv(
+        os.path.join(save_dir, f"{op_name}_detail.csv"),
+        detail_rows,
+        header_cols,
+    )
+    if all_summary_rows:
+        write_csv(
+            os.path.join(save_dir, "summary.csv"),
+            all_summary_rows,
+            list(all_summary_rows[0].keys()),
+        )
+    write_csv(
+        os.path.join(save_dir, "metadata.csv"),
+        [metadata],
+        list(metadata.keys()),
+    )
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Benchmark vLLM IR ops")
+    parser.add_argument(
+        "--ops",
+        type=str,
+        default=None,
+        help="Comma-separated list of op names to benchmark (substring match)",
+    )
+    parser.add_argument(
+        "--no-cuda-graph",
+        action="store_true",
+        help="Disable CUDA graph; use do_bench with L2 cache flushing instead",
+    )
+    parser.add_argument(
+        "--warmup",
+        type=int,
+        default=25,
+        help="Warmup time in ms (do_bench) or ignored with CUDA graph (default: 25)",
+    )
+    parser.add_argument(
+        "--rep",
+        type=int,
+        default=100,
+        help="Repetition time in ms (do_bench) or number of graph replays "
+        "(do_bench_cudagraph) (default: 100)",
+    )
+    parser.add_argument(
+        "--save-path",
+        type=str,
+        default=None,
+        help="Directory to save results (default: auto-created temp dir)",
+    )
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    cfg = BenchConfig(
+        use_cuda_graph=not args.no_cuda_graph,
+        warmup=args.warmup,
+        rep=args.rep,
+    )
+
+    torch.set_default_device(current_platform.device_type)
+
+    metadata = collect_env_metadata(cfg)
+    print_metadata(metadata)
+
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    save_dir = args.save_path or os.path.join(
+        tempfile.gettempdir(), f"vllm_ir_bench_{timestamp}"
+    )
+    os.makedirs(save_dir, exist_ok=True)
+
+    op_filters = [f.strip() for f in args.ops.split(",")] if args.ops else None
+    all_summary_rows: list[dict[str, str]] = []
+
+    for op in IrOp.registry.values():
+        if op_filters and not any(f in op.name for f in op_filters):
+            continue
+        if not op.has_input_generator:
+            print(f"Skipping op '{op.name}': no input generator registered")
+            continue
+        if op.name not in SHAPE_CONFIGS:
+            raise RuntimeError(
+                f"No benchmark shape config for op '{op.name}'. "
+                f"Add it to benchmarks/kernels/ir/shapes.py"
+            )
+
+        case_names, providers, results = collect_timings(
+            op, SHAPE_CONFIGS[op.name], cfg
+        )
+        detail_rows, summary_rows, header_cols = analyze_results(
+            op.name, case_names, providers, results
+        )
+        all_summary_rows.extend(summary_rows)
+
+        save_results(
+            save_dir,
+            op.name,
+            detail_rows,
+            header_cols,
+            all_summary_rows,
+            metadata,
+        )
+
+    print(f"\nResults saved to: {save_dir}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/kernels/ir/shapes.py b/benchmarks/kernels/ir/shapes.py
new file mode 100644
index 000000000000..6cc44cf6cec1
--- /dev/null
+++ b/benchmarks/kernels/ir/shapes.py
@@ -0,0 +1,29 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Shape configurations for IR op benchmarks.
+"""
+
+import torch
+
+NUM_TOKENS = [1, 2, 4, 16, 64, 256, 1024, 4096, 16384]
+COMMON_HIDDEN_SIZES = [
+    2048,  # Llama 3.2 1B, Qwen 3 MoE 30B-A3B, Gemma 3n
+    3072,  # Gemma 7B/9B
+    4096,  # Llama 3 8B, Qwen 3 8B, Mistral 7B
+    5120,  # Llama 4 Scout 17B-16E
+    7168,  # DeepSeek V3
+    8192,  # Llama 3 70B
+    16384,  # Llama 3 405B
+]
+
+# Each entry maps an op name to a list of kwarg dicts that will be passed
+# to that op's registered input generator via op.generate_inputs(**kwargs).
+SHAPE_CONFIGS: dict[str, list[dict]] = {
+    "rms_norm": [
+        {"num_tokens": n, "hidden_size": d, "dtype": dtype}
+        for dtype in [torch.float16, torch.bfloat16, torch.float32]
+        for d in COMMON_HIDDEN_SIZES
+        for n in NUM_TOKENS
+    ],
+}
diff --git a/benchmarks/multi_turn/benchmark_serving_multi_turn.py b/benchmarks/multi_turn/benchmark_serving_multi_turn.py
index 881039f43f07..2f56099c66fd 100644
--- a/benchmarks/multi_turn/benchmark_serving_multi_turn.py
+++ b/benchmarks/multi_turn/benchmark_serving_multi_turn.py
@@ -217,6 +217,7 @@ async def send_request(
     min_tokens: int | None = None,
     max_tokens: int | None = None,
     timeout_sec: int = 120,
+    conversation_id: str | None = None,
 ) -> ServerResponse:
     payload = {
         "model": model,
@@ -225,6 +226,9 @@ async def send_request(
         "temperature": 0.0,
     }
 
+    if conversation_id is not None:
+        payload["conversation_id"] = conversation_id
+
     if stream:
         payload["stream"] = True
         payload["stream_options"] = {"include_usage": False}
@@ -419,6 +423,7 @@ async def send_turn(
         min_tokens,
         max_tokens,
         req_args.timeout_sec,
+        conversation_id=conv_id,
     )
 
     if response.valid is False:
@@ -1468,6 +1473,12 @@ async def main() -> None:
         "(for example: --warmup-percentages=0%%,50%%)",
     )
 
+    parser.add_argument(
+        "--trust-remote-code",
+        action="store_true",
+        help="Trust remote code when loading the tokenizer.",
+    )
+
     args = parser.parse_args()
 
     logger.info(args)
@@ -1510,7 +1521,9 @@ async def main() -> None:
     np.random.seed(args.seed)
 
     logger.info("Loading tokenizer")
-    tokenizer = AutoTokenizer.from_pretrained(args.model)
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.model, trust_remote_code=args.trust_remote_code
+    )
 
     await get_server_info(args.url)
 
diff --git a/cmake/cpu_extension.cmake b/cmake/cpu_extension.cmake
index 0389ce1299a8..361f08b51054 100644
--- a/cmake/cpu_extension.cmake
+++ b/cmake/cpu_extension.cmake
@@ -1,7 +1,7 @@
 include(FetchContent)
 
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
-set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD 20)
 set(CMAKE_CXX_EXTENSIONS ON)
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
@@ -30,6 +30,26 @@ else()
     list(APPEND CXX_COMPILE_FLAGS
         "-fopenmp"
         "-DVLLM_CPU_EXTENSION")
+
+    # locate PyTorch's libgomp (e.g. site-packages/torch.libs/libgomp-947d5fa1.so.1.0.0)
+    # and create a local shim dir with it. When PyTorch is built from source or packaged
+    # by a distro (common on RISC-V, s390x, Fedora/RHEL aarch64), no vendored libgomp
+    # exists and the shim dir is empty; fall back to the system libgomp in that case.
+    vllm_prepare_torch_gomp_shim(VLLM_TORCH_GOMP_SHIM_DIR)
+
+    if(VLLM_TORCH_GOMP_SHIM_DIR)
+        find_library(OPEN_MP
+            NAMES gomp
+            PATHS "${VLLM_TORCH_GOMP_SHIM_DIR}"
+            NO_DEFAULT_PATH
+            REQUIRED
+        )
+        # Use the same libgomp as PyTorch at runtime
+        set(ENV{LD_LIBRARY_PATH} "${VLLM_TORCH_GOMP_SHIM_DIR}:$ENV{LD_LIBRARY_PATH}")
+    else()
+        # Fall back to system / toolchain libgomp
+        find_library(OPEN_MP NAMES gomp REQUIRED)
+    endif()
 endif()
 
 if (NOT MACOSX_FOUND)
@@ -146,16 +166,49 @@ elseif (S390_FOUND)
         "-mtune=native")
 elseif (CMAKE_SYSTEM_PROCESSOR MATCHES "riscv64")
     message(STATUS "RISC-V detected")
-    if(RVV_BF16_FOUND)
-        message(STATUS "BF16 extension detected")
-        set(MARCH_FLAGS -march=rv64gcv_zvfh_zfbfmin_zvfbfmin_zvl128b -mrvv-vector-bits=zvl -mabi=lp64d)
-        add_compile_definitions(RISCV_BF16_SUPPORT)
-    elseif (RVV_FP16_FOUND)
-        message(WARNING "BF16 functionality is not available")
-        set(MARCH_FLAGS -march=rv64gcv_zvfh_zvl128b -mrvv-vector-bits=zvl -mabi=lp64d)
+    # VLLM_RVV_VLEN selects the target VLEN. Auto-detected from /proc/cpuinfo
+    # by default; override with -DVLLM_RVV_VLEN=128 or -DVLLM_RVV_VLEN=256.
+    if(NOT DEFINED VLLM_RVV_VLEN)
+        # Auto-detect: find the largest zvl<N>b in /proc/cpuinfo isa line.
+        if(EXISTS /proc/cpuinfo)
+            file(READ /proc/cpuinfo _cpuinfo)
+            set(_best 0)
+            foreach(_n IN ITEMS 128 256 512 1024)
+                if(_cpuinfo MATCHES "zvl${_n}b")
+                    set(_best ${_n})
+                endif()
+            endforeach()
+            if(_best GREATER 0)
+                set(VLLM_RVV_VLEN ${_best})
+            endif()
+        endif()
+        # If auto-detect failed (no /proc/cpuinfo or no zvl<N>b reported)
+        # but the compiler supports RVV, require explicit specification.
+        if(NOT DEFINED VLLM_RVV_VLEN AND (RVV_FP16_FOUND OR RVV_BF16_FOUND))
+            message(FATAL_ERROR
+                "RISC-V RVV is available but VLEN could not be auto-detected. "
+                "Please specify VLEN explicitly:\n"
+                "  -DVLLM_RVV_VLEN=128   (for VLEN=128 hardware)\n"
+                "  -DVLLM_RVV_VLEN=256   (for VLEN=256 hardware, e.g. Spacemit X100)\n"
+                "  -DVLLM_RVV_VLEN=0     (force scalar, no RVV)")
+        endif()
+    endif()
+    if(VLLM_RVV_VLEN AND VLLM_RVV_VLEN GREATER 0)
+        message(STATUS "RISC-V RVV VLEN=${VLLM_RVV_VLEN}")
+        if(RVV_BF16_FOUND)
+            message(STATUS "BF16 extension detected")
+            set(MARCH_FLAGS -march=rv64gcv_zvfh_zfbfmin_zvfbfmin_zvl${VLLM_RVV_VLEN}b -mrvv-vector-bits=zvl -mabi=lp64d)
+            add_compile_definitions(RISCV_BF16_SUPPORT)
+        elseif(RVV_FP16_FOUND)
+            message(WARNING "BF16 functionality is not available")
+            set(MARCH_FLAGS -march=rv64gcv_zvfh_zvl${VLLM_RVV_VLEN}b -mrvv-vector-bits=zvl -mabi=lp64d)
+        else()
+            message(STATUS "compile riscv with scalar (no FP16/BF16)")
+            set(MARCH_FLAGS -march=rv64gc)
+        endif()
     else()
         message(STATUS "compile riscv with scalar")
-        list(APPEND CXX_COMPILE_FLAGS "-march=rv64gc")
+        set(MARCH_FLAGS -march=rv64gc)
     endif()
     list(APPEND CXX_COMPILE_FLAGS ${MARCH_FLAGS})
 else()
@@ -175,20 +228,6 @@ if (ENABLE_X86_ISA OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND
         if(NOT NPROC)
             set(NPROC 4)
         endif()
-        # locate PyTorch's libgomp (e.g. site-packages/torch.libs/libgomp-947d5fa1.so.1.0.0)
-        # and create a local shim dir with it
-        vllm_prepare_torch_gomp_shim(VLLM_TORCH_GOMP_SHIM_DIR)
-
-        find_library(OPEN_MP
-            NAMES gomp
-            PATHS ${VLLM_TORCH_GOMP_SHIM_DIR}
-            NO_DEFAULT_PATH
-            REQUIRED
-        )
-        # Set LD_LIBRARY_PATH to include the shim dir at build time to use the same libgomp as PyTorch
-        if (OPEN_MP)
-            set(ENV{LD_LIBRARY_PATH} "${VLLM_TORCH_GOMP_SHIM_DIR}:$ENV{LD_LIBRARY_PATH}")
-        endif()
 
         # Fetch and populate ACL
         if(DEFINED ENV{ACL_ROOT_DIR} AND IS_DIRECTORY "$ENV{ACL_ROOT_DIR}")
@@ -287,14 +326,6 @@ if (ENABLE_X86_ISA OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND
     set(ONEDNN_VERBOSE "ON")
     set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
 
-    # TODO: Refactor this
-    if (ENABLE_X86_ISA)
-        # Note: only enable oneDNN for AVX512
-        list(APPEND DNNL_COMPILE_FLAGS ${CXX_COMPILE_FLAGS_AVX512})
-    else()
-        list(APPEND DNNL_COMPILE_FLAGS ${CXX_COMPILE_FLAGS})
-    endif()
-
     set(VLLM_BUILD_TYPE ${CMAKE_BUILD_TYPE})
     set(CMAKE_BUILD_TYPE "Release") # remove oneDNN debug symbols to reduce size
     FetchContent_MakeAvailable(oneDNN)
@@ -307,8 +338,14 @@ if (ENABLE_X86_ISA OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND
         PRIVATE ${oneDNN_SOURCE_DIR}/src
     )
     target_link_libraries(dnnl_ext dnnl torch)
-    target_compile_options(dnnl_ext PRIVATE ${DNNL_COMPILE_FLAGS} -fPIC)
+    if (ENABLE_X86_ISA)
+        target_compile_options(dnnl_ext PRIVATE ${CXX_COMPILE_FLAGS_AVX2} -fPIC)
+    else()
+        target_compile_options(dnnl_ext PRIVATE ${CXX_COMPILE_FLAGS} -fPIC)
+    endif()
     list(APPEND LIBS dnnl_ext)
+
+
     set(USE_ONEDNN ON)
 else()
     set(USE_ONEDNN OFF)
@@ -360,6 +397,7 @@ set(VLLM_EXT_SRC
 if (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND)
     set(VLLM_EXT_SRC
         "csrc/cpu/shm.cpp"
+        "csrc/cpu/activation_lut_bf16.cpp"
         ${VLLM_EXT_SRC})
 endif()
 
@@ -371,12 +409,15 @@ endif()
 
 if (ENABLE_X86_ISA)
     set(VLLM_EXT_SRC_SGL
+        "csrc/cpu/sgl-kernels/fla.cpp"
+        "csrc/cpu/sgl-kernels/conv.cpp"
         "csrc/cpu/sgl-kernels/gemm.cpp"
         "csrc/cpu/sgl-kernels/gemm_int8.cpp"
         "csrc/cpu/sgl-kernels/gemm_fp8.cpp"
         "csrc/cpu/sgl-kernels/gemm_int4.cpp"
         "csrc/cpu/sgl-kernels/moe.cpp"
         "csrc/cpu/sgl-kernels/moe_int8.cpp"
+        "csrc/cpu/sgl-kernels/moe_int4.cpp"
         "csrc/cpu/sgl-kernels/moe_fp8.cpp")
 
     set(VLLM_EXT_SRC_AVX512
@@ -395,10 +436,11 @@ if (ENABLE_X86_ISA)
         "csrc/cpu/pos_encoding.cpp"
         "csrc/moe/dynamic_4bit_int_moe_cpu.cpp") 
 
-    set(VLLM_EXT_SRC_AVX2 
+    set(VLLM_EXT_SRC_AVX2
         "csrc/cpu/utils.cpp"
         "csrc/cpu/spec_decode_utils.cpp"
         "csrc/cpu/cpu_attn.cpp"
+        "csrc/cpu/dnnl_kernels.cpp"
         "csrc/cpu/torch_bindings.cpp"
         # TODO: Remove these files
         "csrc/cpu/activation.cpp"
@@ -413,7 +455,7 @@ if (ENABLE_X86_ISA)
 
     set(_C_LIBS numa dnnl_ext)
     set(_C_AVX512_LIBS numa dnnl_ext)
-    set(_C_AVX2_LIBS numa)
+    set(_C_AVX2_LIBS numa dnnl_ext)
 
     # AMX + AVX512F + AVX512BF16 + AVX512VNNI
     define_extension_target(
diff --git a/cmake/external_projects/deepgemm.cmake b/cmake/external_projects/deepgemm.cmake
index c3a48a64fc77..07328c271388 100644
--- a/cmake/external_projects/deepgemm.cmake
+++ b/cmake/external_projects/deepgemm.cmake
@@ -20,7 +20,7 @@ else()
   FetchContent_Declare(
     deepgemm
     GIT_REPOSITORY https://github.com/deepseek-ai/DeepGEMM.git
-    GIT_TAG 477618cd51baffca09c4b0b87e97c03fe827ef03
+    GIT_TAG 891d57b4db1071624b5c8fa0d1e51cb317fa709f
     GIT_SUBMODULES "third-party/cutlass" "third-party/fmt"
     GIT_PROGRESS TRUE
     CONFIGURE_COMMAND ""
@@ -76,7 +76,6 @@ if(DEEPGEMM_ARCHS)
     "${deepgemm_SOURCE_DIR}/third-party/fmt/include")
 
   target_compile_options(_deep_gemm_C PRIVATE
-    $<$<COMPILE_LANGUAGE:CXX>:-std=c++17>
     $<$<COMPILE_LANGUAGE:CXX>:-O3>
     $<$<COMPILE_LANGUAGE:CXX>:-Wno-psabi>
     $<$<COMPILE_LANGUAGE:CXX>:-Wno-deprecated-declarations>)
@@ -120,6 +119,11 @@ if(DEEPGEMM_ARCHS)
     COMPONENT _deep_gemm_C
     FILES_MATCHING PATTERN "*.py")
 
+  install(DIRECTORY "${deepgemm_SOURCE_DIR}/deep_gemm/mega/"
+    DESTINATION vllm/third_party/deep_gemm/mega
+    COMPONENT _deep_gemm_C
+    FILES_MATCHING PATTERN "*.py")
+
   # Generate envs.py (normally generated by DeepGEMM's setup.py build step)
   file(WRITE "${CMAKE_CURRENT_BINARY_DIR}/deep_gemm_envs.py"
     "# Pre-installed environment variables\npersistent_envs = dict()\n")
diff --git a/cmake/external_projects/flashmla.cmake b/cmake/external_projects/flashmla.cmake
index 0f16b9161fa3..65986df55012 100644
--- a/cmake/external_projects/flashmla.cmake
+++ b/cmake/external_projects/flashmla.cmake
@@ -19,7 +19,7 @@ else()
   FetchContent_Declare(
         flashmla
         GIT_REPOSITORY https://github.com/vllm-project/FlashMLA
-        GIT_TAG 692917b1cda61b93ac9ee2d846ec54e75afe87b1
+        GIT_TAG a6ec2ba7bd0a7dff98b3f4d3e6b52b159c48d78b
         GIT_PROGRESS TRUE
         CONFIGURE_COMMAND ""
         BUILD_COMMAND ""
diff --git a/csrc/activation_kernels.cu b/csrc/activation_kernels.cu
index 758a77795553..303433392c32 100644
--- a/csrc/activation_kernels.cu
+++ b/csrc/activation_kernels.cu
@@ -11,29 +11,74 @@
 namespace vllm {
 
 template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&),
-          bool act_first>
+          bool act_first, bool HAS_CLAMP>
 __device__ __forceinline__ scalar_t compute(const scalar_t& x,
-                                            const scalar_t& y) {
-  return act_first ? ACT_FN(x) * y : x * ACT_FN(y);
+                                            const scalar_t& y,
+                                            const float limit) {
+  if constexpr (act_first) {
+    scalar_t gate = x;
+    scalar_t up = y;
+    if constexpr (HAS_CLAMP) {
+      gate = (scalar_t)fminf((float)gate, limit);
+      up = (scalar_t)fmaxf(fminf((float)up, limit), -limit);
+    }
+    return ACT_FN(gate) * up;
+  } else {
+    scalar_t gate = x;
+    scalar_t up = y;
+    if constexpr (HAS_CLAMP) {
+      gate = (scalar_t)fmaxf(fminf((float)gate, limit), -limit);
+      up = (scalar_t)fminf((float)up, limit);
+    }
+    return gate * ACT_FN(up);
+  }
 }
 
 template <typename packed_t, packed_t (*PACKED_ACT_FN)(const packed_t&),
-          bool act_first>
+          bool act_first, bool HAS_CLAMP>
 __device__ __forceinline__ packed_t packed_compute(const packed_t& x,
-                                                   const packed_t& y) {
-  return act_first ? packed_mul(PACKED_ACT_FN(x), y)
-                   : packed_mul(x, PACKED_ACT_FN(y));
+                                                   const packed_t& y,
+                                                   const float limit) {
+  if constexpr (act_first) {
+    packed_t gate = x;
+    packed_t up = y;
+    if constexpr (HAS_CLAMP) {
+      float2 g = cast_to_float2(gate);
+      float2 u = cast_to_float2(up);
+      g.x = fminf(g.x, limit);
+      g.y = fminf(g.y, limit);
+      u.x = fmaxf(fminf(u.x, limit), -limit);
+      u.y = fmaxf(fminf(u.y, limit), -limit);
+      gate = cast_to_packed<packed_t>(g);
+      up = cast_to_packed<packed_t>(u);
+    }
+    return packed_mul(PACKED_ACT_FN(gate), up);
+  } else {
+    packed_t gate = x;
+    packed_t up = y;
+    if constexpr (HAS_CLAMP) {
+      float2 g = cast_to_float2(gate);
+      float2 u = cast_to_float2(up);
+      g.x = fmaxf(fminf(g.x, limit), -limit);
+      g.y = fmaxf(fminf(g.y, limit), -limit);
+      u.x = fminf(u.x, limit);
+      u.y = fminf(u.y, limit);
+      gate = cast_to_packed<packed_t>(g);
+      up = cast_to_packed<packed_t>(u);
+    }
+    return packed_mul(gate, PACKED_ACT_FN(up));
+  }
 }
 
 // Activation and gating kernel template.
 template <typename scalar_t, typename packed_t,
           scalar_t (*ACT_FN)(const scalar_t&),
           packed_t (*PACKED_ACT_FN)(const packed_t&), bool act_first,
-          bool use_vec, bool use_256b = false>
+          bool use_vec, bool HAS_CLAMP, bool use_256b = false>
 __global__ void act_and_mul_kernel(
     scalar_t* __restrict__ out,          // [..., d]
     const scalar_t* __restrict__ input,  // [..., 2, d]
-    const int d) {
+    const int d, const float limit) {
   const scalar_t* x_ptr = input + blockIdx.x * 2 * d;
   const scalar_t* y_ptr = x_ptr + d;
   scalar_t* out_ptr = out + blockIdx.x * d;
@@ -58,8 +103,9 @@ __global__ void act_and_mul_kernel(
       }
 #pragma unroll
       for (int j = 0; j < pvec_t::NUM_ELTS; j++) {
-        x.elts[j] = packed_compute<packed_t, PACKED_ACT_FN, act_first>(
-            x.elts[j], y.elts[j]);
+        x.elts[j] =
+            packed_compute<packed_t, PACKED_ACT_FN, act_first, HAS_CLAMP>(
+                x.elts[j], y.elts[j], limit);
       }
       if constexpr (use_256b) {
         st256(x, &out_vec[i]);
@@ -72,7 +118,8 @@ __global__ void act_and_mul_kernel(
     for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
       const scalar_t x = VLLM_LDG(&x_ptr[idx]);
       const scalar_t y = VLLM_LDG(&y_ptr[idx]);
-      out_ptr[idx] = compute<scalar_t, ACT_FN, act_first>(x, y);
+      out_ptr[idx] =
+          compute<scalar_t, ACT_FN, act_first, HAS_CLAMP>(x, y, limit);
     }
   }
 }
@@ -151,8 +198,11 @@ packed_gelu_tanh_kernel(const packed_t& val) {
 
 // Launch activation and gating kernel.
 // Use ACT_FIRST (bool) indicating whether to apply the activation function
-// first.
-#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL, PACKED_KERNEL, ACT_FIRST)        \
+// first. HAS_CLAMP (bool) enables pre-activation clamping: gate input is
+// clamped (max only) and up input is clamped (both sides) before the
+// activation function is applied.
+#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL, PACKED_KERNEL, ACT_FIRST,        \
+                                      HAS_CLAMP, LIMIT)                        \
   auto dtype = input.scalar_type();                                            \
   int d = input.size(-1) / 2;                                                  \
   int64_t num_tokens = input.numel() / input.size(-1);                         \
@@ -177,8 +227,8 @@ packed_gelu_tanh_kernel(const packed_t& val) {
             scalar_t, typename vllm::PackedTypeConverter<scalar_t>::Type,      \
             KERNEL<scalar_t>,                                                  \
             PACKED_KERNEL<typename vllm::PackedTypeConverter<scalar_t>::Type>, \
-            ACT_FIRST, true, true><<<grid, block, 0, stream>>>(                \
-            out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d);          \
+            ACT_FIRST, true, HAS_CLAMP, true><<<grid, block, 0, stream>>>(     \
+            out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d, LIMIT);   \
       });                                                                      \
     } else {                                                                   \
       VLLM_DISPATCH_FLOATING_TYPES(dtype, "act_and_mul_kernel", [&] {          \
@@ -186,8 +236,8 @@ packed_gelu_tanh_kernel(const packed_t& val) {
             scalar_t, typename vllm::PackedTypeConverter<scalar_t>::Type,      \
             KERNEL<scalar_t>,                                                  \
             PACKED_KERNEL<typename vllm::PackedTypeConverter<scalar_t>::Type>, \
-            ACT_FIRST, true, false><<<grid, block, 0, stream>>>(               \
-            out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d);          \
+            ACT_FIRST, true, HAS_CLAMP, false><<<grid, block, 0, stream>>>(    \
+            out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d, LIMIT);   \
       });                                                                      \
     }                                                                          \
   } else {                                                                     \
@@ -197,8 +247,8 @@ packed_gelu_tanh_kernel(const packed_t& val) {
           scalar_t, typename vllm::PackedTypeConverter<scalar_t>::Type,        \
           KERNEL<scalar_t>,                                                    \
           PACKED_KERNEL<typename vllm::PackedTypeConverter<scalar_t>::Type>,   \
-          ACT_FIRST, false><<<grid, block, 0, stream>>>(                       \
-          out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d);            \
+          ACT_FIRST, false, HAS_CLAMP><<<grid, block, 0, stream>>>(            \
+          out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d, LIMIT);     \
     });                                                                        \
   }
 
@@ -206,7 +256,14 @@ void silu_and_mul(torch::Tensor& out,    // [..., d]
                   torch::Tensor& input)  // [..., 2 * d]
 {
   LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel, vllm::packed_silu_kernel,
-                                true);
+                                true, false, 0.0f);
+}
+
+void silu_and_mul_clamp(torch::Tensor& out,    // [..., d]
+                        torch::Tensor& input,  // [..., 2 * d]
+                        double limit) {
+  LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel, vllm::packed_silu_kernel,
+                                true, true, (float)limit);
 }
 
 void mul_and_silu(torch::Tensor& out,    // [..., d]
@@ -215,21 +272,21 @@ void mul_and_silu(torch::Tensor& out,    // [..., d]
   // The difference between mul_and_silu and silu_and_mul is that mul_and_silu
   // applies the silu to the latter half of the input.
   LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel, vllm::packed_silu_kernel,
-                                false);
+                                false, false, 0.0f);
 }
 
 void gelu_and_mul(torch::Tensor& out,    // [..., d]
                   torch::Tensor& input)  // [..., 2 * d]
 {
   LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_kernel, vllm::packed_gelu_kernel,
-                                true);
+                                true, false, 0.0f);
 }
 
 void gelu_tanh_and_mul(torch::Tensor& out,    // [..., d]
                        torch::Tensor& input)  // [..., 2 * d]
 {
-  LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_tanh_kernel,
-                                vllm::packed_gelu_tanh_kernel, true);
+  LAUNCH_ACTIVATION_GATE_KERNEL(
+      vllm::gelu_tanh_kernel, vllm::packed_gelu_tanh_kernel, true, false, 0.0f);
 }
 
 namespace vllm {
diff --git a/csrc/cache.h b/csrc/cache.h
index 821d5e719a44..a9e74b0dc2df 100644
--- a/csrc/cache.h
+++ b/csrc/cache.h
@@ -12,7 +12,8 @@ void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
 
 void swap_blocks_batch(const torch::Tensor& src_ptrs,
                        const torch::Tensor& dst_ptrs,
-                       const torch::Tensor& sizes);
+                       const torch::Tensor& sizes,
+                       bool is_src_access_order_any);
 
 void reshape_and_cache(torch::Tensor& key, torch::Tensor& value,
                        torch::Tensor& key_cache, torch::Tensor& value_cache,
diff --git a/csrc/cache_kernels.cu b/csrc/cache_kernels.cu
index d1cfbaeb05d2..9130dd2ccae7 100644
--- a/csrc/cache_kernels.cu
+++ b/csrc/cache_kernels.cu
@@ -77,7 +77,8 @@ void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
 
 void swap_blocks_batch(const torch::Tensor& src_ptrs,
                        const torch::Tensor& dst_ptrs,
-                       const torch::Tensor& sizes) {
+                       const torch::Tensor& sizes,
+                       bool is_src_access_order_any) {
   TORCH_CHECK(src_ptrs.device().is_cpu(), "src_ptrs must be on CPU");
   TORCH_CHECK(dst_ptrs.device().is_cpu(), "dst_ptrs must be on CPU");
   TORCH_CHECK(sizes.device().is_cpu(), "sizes must be on CPU");
@@ -97,13 +98,13 @@ void swap_blocks_batch(const torch::Tensor& src_ptrs,
 
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  // Use cuMemcpyBatchAsync (CUDA 12.8+) to submit all copies in a single
-  // driver call, amortizing per-copy submission overhead.
-  // int64_t and CUdeviceptr/size_t are both 8 bytes on 64-bit platforms,
-  // so we reinterpret_cast the tensor data directly to avoid copies.
-  static_assert(sizeof(CUdeviceptr) == sizeof(int64_t));
+  // Use cuMemcpyBatchAsync / hipMemcpyBatchAsync to submit all copies in a
+  // single driver call, amortizing per-copy submission overhead. int64_t
+  // and CUdeviceptr/void*/size_t are all 8 bytes on 64-bit platforms, so we
+  // reinterpret_cast the tensor data directly to avoid copies.
   static_assert(sizeof(size_t) == sizeof(int64_t));
 #if !defined(USE_ROCM) && defined(CUDA_VERSION) && CUDA_VERSION >= 12080
+  static_assert(sizeof(CUdeviceptr) == sizeof(int64_t));
   // Resolve cuMemcpyBatchAsync at runtime via cuGetProcAddress so that
   // binaries compiled with CUDA 12.8+ still work on older drivers, and
   // we avoid the CUDA 13.0 header remapping (#define to _v2 signature).
@@ -124,7 +125,12 @@ void swap_blocks_batch(const torch::Tensor& src_ptrs,
 
   if (batch_fn != nullptr) {
     CUmemcpyAttributes attr = {};
-    attr.srcAccessOrder = CU_MEMCPY_SRC_ACCESS_ORDER_STREAM;
+    // ANY lets the DMA engine prefetch source bytes out of stream order,
+    // which is only safe when no GPU stream is concurrently writing the
+    // source.
+    attr.srcAccessOrder = is_src_access_order_any
+                              ? CU_MEMCPY_SRC_ACCESS_ORDER_ANY
+                              : CU_MEMCPY_SRC_ACCESS_ORDER_STREAM;
     size_t attrs_idx = 0;
     size_t fail_idx = 0;
     CUresult result = batch_fn(reinterpret_cast<CUdeviceptr*>(dst_data),
@@ -134,12 +140,30 @@ void swap_blocks_batch(const torch::Tensor& src_ptrs,
                                &fail_idx, static_cast<CUstream>(stream));
     TORCH_CHECK(result == CUDA_SUCCESS, "cuMemcpyBatchAsync failed at index ",
                 fail_idx, " with error ", result);
-  } else
+    return;
+  }
+#elif defined(USE_ROCM) && defined(HIP_VERSION) && HIP_VERSION >= 70100000
+  // ROCm 7.1+ exposes hipMemcpyBatchAsync. The 7.2.1 implementation early-
+  // returns hipErrorNotSupported whenever numAttrs > 0 (see ROCm/clr @
+  // rocm-7.2.1 hipamd/src/hip_memory.cpp:2819-2822), so call with
+  // numAttrs=0.
+  {
+    hipMemcpyAttributes attr = {};
+    size_t attrs_idx = 0;
+    size_t fail_idx = 0;
+    hipError_t result = hipMemcpyBatchAsync(
+        reinterpret_cast<void**>(dst_data), reinterpret_cast<void**>(src_data),
+        reinterpret_cast<size_t*>(size_data), static_cast<size_t>(n), &attr,
+        &attrs_idx, 0, &fail_idx, static_cast<hipStream_t>(stream));
+    TORCH_CHECK(result == hipSuccess, "hipMemcpyBatchAsync failed at index ",
+                fail_idx, " with error ", result);
+    return;
+  }
 #endif
   {
-    // Fallback for CUDA < 12.8, older drivers, and ROCm:
-    // individual async copies.
-    // cudaMemcpyDefault lets the driver infer direction from pointer types.
+    // Fallback for CUDA < 12.8, older CUDA drivers, and ROCm < 7.1:
+    // individual async copies. cudaMemcpyDefault lets the driver infer
+    // direction from pointer types.
     for (int64_t i = 0; i < n; i++) {
       cudaMemcpyAsync(reinterpret_cast<void*>(dst_data[i]),
                       reinterpret_cast<void*>(src_data[i]),
@@ -599,6 +623,11 @@ __global__ void cp_gather_indexer_k_quant_cache_kernel(
   const int head_idx = (blockIdx.y * blockDim.x + threadIdx.x) * VEC_SIZE;
   // Find batch index within a block
   __shared__ int batch_idx[BLOCK_Y_SIZE];
+  if (threadIdx.x == 0) {
+    batch_idx[threadIdx.y] = -1;
+  }
+  __syncthreads();
+
   for (int iter = 0; iter < cuda_utils::ceil_div(batch_size, int(blockDim.x));
        iter++) {
     int tid = iter * blockDim.x + threadIdx.x;
@@ -611,16 +640,18 @@ __global__ void cp_gather_indexer_k_quant_cache_kernel(
     }
   }
 
-#ifndef USE_ROCM
-  __syncwarp();
-#endif
+  __syncthreads();
 
-  if (head_idx >= head_dim || token_idx >= num_tokens) {
+  // num_tokens may be an allocation upper bound when Python avoids a D2H sync.
+  // Only tokens covered by the exact device-side cu_seq_lens are valid to
+  // gather.
+  const int batch = batch_idx[threadIdx.y];
+  if (head_idx >= head_dim || token_idx >= num_tokens || batch < 0) {
     return;
   }
-  const int inbatch_seq_idx = token_idx - cu_seq_lens[batch_idx[threadIdx.y]];
-  const int block_idx = block_table[batch_idx[threadIdx.y] * num_blocks +
-                                    inbatch_seq_idx / cache_block_size];
+  const int inbatch_seq_idx = token_idx - cu_seq_lens[batch];
+  const int block_idx =
+      block_table[batch * num_blocks + inbatch_seq_idx / cache_block_size];
   const int64_t src_block_offset = block_idx * block_stride;
   const int64_t cache_inblock_offset =
       (inbatch_seq_idx % cache_block_size) * head_dim + head_idx;
@@ -724,6 +755,28 @@ void reshape_and_cache_flash(
   int num_tokens = slot_mapping.size(0);
   int num_heads = key.size(1);
   int head_size = key.size(2);
+
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(key));
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  if (kv_cache_dtype == "nvfp4") {
+#if defined(ENABLE_NVFP4_SM100) || defined(ENABLE_NVFP4_SM120)
+    // NVFP4 dispatch is compiled separately for SM100+.
+    extern void reshape_and_cache_nvfp4_dispatch(
+        torch::Tensor & key, torch::Tensor & value, torch::Tensor & key_cache,
+        torch::Tensor & value_cache, torch::Tensor & slot_mapping,
+        torch::Tensor & k_scale, torch::Tensor & v_scale);
+    reshape_and_cache_nvfp4_dispatch(key, value, key_cache, value_cache,
+                                     slot_mapping, k_scale, v_scale);
+    return;
+#else
+    TORCH_CHECK(false,
+                "NVFP4 KV cache requires SM100+ (Blackwell). "
+                "Please rebuild vllm with a Blackwell-compatible CUDA target.");
+#endif
+  }
+
+  // Original FP8/auto path.
   int block_size = key_cache.size(1);
 
   int64_t key_stride = key.stride(0);
@@ -741,8 +794,6 @@ void reshape_and_cache_flash(
 
   dim3 grid(num_tokens);
   dim3 block(std::min(num_heads * head_size, 512));
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(key));
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
   DISPATCH_BY_KV_CACHE_DTYPE(key.dtype(), kv_cache_dtype,
                              CALL_RESHAPE_AND_CACHE_FLASH);
@@ -1470,6 +1521,9 @@ void concat_mla_q(torch::Tensor& ql_nope,  // [num_tokens, num_heads, nope_dim]
   TORCH_CHECK(ql_nope.stride(2) == 1, "ql_nope must have stride 1 in dim 2");
   TORCH_CHECK(q_pe.stride(2) == 1, "q_pe must have stride 1 in dim 2");
   TORCH_CHECK(q_out.stride(2) == 1, "q_out must have stride 1 in dim 2");
+  TORCH_CHECK(ql_nope.scalar_type() == at::ScalarType::Half ||
+                  ql_nope.scalar_type() == at::ScalarType::BFloat16,
+              "ql_nope must be float16 or bfloat16 dtype");
 
   if (num_tokens == 0) return;
 
@@ -1481,7 +1535,7 @@ void concat_mla_q(torch::Tensor& ql_nope,  // [num_tokens, num_heads, nope_dim]
   const at::cuda::OptionalCUDAGuard device_guard(device_of(ql_nope));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  VLLM_DISPATCH_FLOATING_TYPES(ql_nope.scalar_type(), "concat_mla_q", [&] {
+  VLLM_DISPATCH_HALF_TYPES(ql_nope.scalar_type(), "concat_mla_q", [&] {
     vllm::ConcatMLAQKernel<scalar_t, 512><<<grid_size, block_size, 0, stream>>>(
         q_out.data_ptr<scalar_t>(), ql_nope.data_ptr<scalar_t>(),
         q_pe.data_ptr<scalar_t>(), num_tokens, num_heads, q_out.stride(0),
diff --git a/csrc/cpu/activation_lut_bf16.cpp b/csrc/cpu/activation_lut_bf16.cpp
new file mode 100644
index 000000000000..0ff2567e1ee8
--- /dev/null
+++ b/csrc/cpu/activation_lut_bf16.cpp
@@ -0,0 +1,71 @@
+#include "cpu_types.hpp"
+
+#include <array>
+#include <cstdint>
+#include <mutex>
+#include <string>
+
+#include <ATen/ops/empty.h>
+#include <ATen/ops/gelu.h>
+#include <c10/util/BFloat16.h>
+
+constexpr uint32_t ActivationLutSize = 1u << 16;
+
+at::Tensor gelu_reference(const at::Tensor& x) { return at::gelu(x, "none"); }
+
+void maybe_init_activation_lut_bf16(
+    uint16_t* lut, std::once_flag& once,
+    at::Tensor (*activation)(const at::Tensor&)) {
+  std::call_once(once, [&]() {
+    auto lut_input =
+        at::empty({static_cast<int64_t>(ActivationLutSize)},
+                  at::TensorOptions().device(at::kCPU).dtype(at::kFloat));
+    auto* lut_input_ptr = lut_input.data_ptr<float>();
+#pragma omp parallel for
+    for (uint32_t i = 0; i < ActivationLutSize; ++i) {
+      lut_input_ptr[i] = c10::detail::f32_from_bits(static_cast<uint16_t>(i));
+    }
+
+    auto lut_output = activation(lut_input);
+    const auto* lut_output_ptr = lut_output.data_ptr<float>();
+#pragma omp parallel for
+    for (uint32_t i = 0; i < ActivationLutSize; ++i) {
+      lut[i] = c10::detail::round_to_nearest_even(lut_output_ptr[i]);
+    }
+  });
+}
+
+void activation_lut_bf16(torch::Tensor& out, torch::Tensor& input,
+                         const uint16_t* lut, const char* op_name) {
+  TORCH_CHECK(input.scalar_type() == at::kBFloat16, op_name,
+              ": input must be bfloat16");
+  TORCH_CHECK(out.scalar_type() == at::kBFloat16, op_name,
+              ": out must be bfloat16");
+  TORCH_CHECK(input.is_contiguous(), op_name, ": input must be contiguous");
+  TORCH_CHECK(out.is_contiguous(), op_name, ": out must be contiguous");
+
+  const auto* src =
+      reinterpret_cast<const uint16_t*>(input.data_ptr<at::BFloat16>());
+  auto* dst = reinterpret_cast<uint16_t*>(out.data_ptr<at::BFloat16>());
+  const int64_t n = input.numel();
+
+  CPU_KERNEL_GUARD_IN(activation_lut_bf16_impl)
+#pragma omp parallel for
+  for (int64_t i = 0; i < n; ++i) {
+    dst[i] = lut[src[i]];
+  }
+  CPU_KERNEL_GUARD_OUT(activation_lut_bf16_impl)
+}
+
+void activation_lut_bf16(torch::Tensor& out, torch::Tensor& input,
+                         const std::string& activation) {
+  if (activation == "gelu") {
+    static std::array<uint16_t, ActivationLutSize> lut{};
+    static std::once_flag once;
+    maybe_init_activation_lut_bf16(lut.data(), once, gelu_reference);
+    activation_lut_bf16(out, input, lut.data(), "gelu_lut");
+    return;
+  }
+
+  TORCH_CHECK(false, "Unsupported activation: ", activation);
+}
diff --git a/csrc/cpu/cpu_arch_macros.h b/csrc/cpu/cpu_arch_macros.h
index c73b62ecdec9..9be45a3efcef 100644
--- a/csrc/cpu/cpu_arch_macros.h
+++ b/csrc/cpu/cpu_arch_macros.h
@@ -61,8 +61,23 @@
 #endif
 
 #ifdef __aarch64__
-  // Implementation copied from Arm Optimized Routines (expf AdvSIMD)
+  // Implementation of neon_expf copied from Arm Optimized Routines (expf
+  // AdvSIMD)
   // https://github.com/ARM-software/optimized-routines/blob/master/math/aarch64/advsimd/expf.c
+  //
+  // Additional fast exponential intended for cases where outputs will be
+  // downcasted to FP16 / BF16 (e.g. attention softmax). Accurate within 1 ULP
+  // for FP16 Accurate within 1 ULP for BF16 for inputs in [-87.683, 88.376] &
+  // clamps inputs outside this range to 0 / inf. Implementation is similar to
+  // exp_u20, but:
+  // - uses a third degree polynomial approximation for exp(r) instead of a
+  // fifth degree one, with coefficients re-tuned.
+  // - does not split natural log (ln) into high / low parts
+  // - clamps exp(x) to 0 for x < -87.683113f and inf for x > 88.3762589f
+  // exp(x) = 2^n (exp(r))
+  // r = x - n*ln2, with n = round(x/ln2)
+  // exp(r) ~ poly(r) = 1 + r + r^2 * (c3 + c2 * r)
+  // n = round(x / ln2), r = x - n*ln2
   #include <limits>
   #define DEFINE_FAST_EXP                                                      \
     const float32x4_t inv_ln2 = vdupq_n_f32(0x1.715476p+0f);                   \
@@ -106,7 +121,38 @@
                           result.val[2] = neon_expf(vec.reg.val[2]);           \
                           result.val[3] = neon_expf(vec.reg.val[3]);           \
                           return vec_op::FP32Vec16(result);                    \
-                        };
+                        };                                                     \
+    const float32x4_t lower_bound = vdupq_n_f32(-0x1.5ebb82p+6f);              \
+    const float32x4_t upper_bound = vdupq_n_f32(0x1.61814ap+6f);               \
+    constexpr float ln2 = 0x1.62e43p-1f;                                       \
+    constexpr float f_c2 = 0x1.5592ecp-3f;                                     \
+    const float32x4_t f_c3 = vdupq_n_f32(0x1.017d34p-1f);                      \
+    auto neon_expf_f16 = [&](float32x4_t values) __attribute__((               \
+                             always_inline)) {                                 \
+      const uint32x4_t lt_lower = vcltq_f32(values, lower_bound);              \
+      const uint32x4_t gt_upper = vcgtq_f32(values, upper_bound);              \
+      float32x4_t n = vrndaq_f32(vmulq_f32(values, inv_ln2));                  \
+      float32x4_t r = vfmsq_n_f32(values, n, ln2);                             \
+      uint32x4_t e = vshlq_n_u32(vreinterpretq_u32_s32(vcvtq_s32_f32(n)), 23); \
+      float32x4_t r2 = vmulq_f32(r, r);                                        \
+      float32x4_t q = vfmaq_n_f32(f_c3, r, f_c2);                              \
+      float32x4_t s = vaddq_f32(vdupq_n_f32(1.0f), r);                         \
+      float32x4_t p = vfmaq_f32(s, q, r2);                                     \
+      float32x4_t y =                                                          \
+          vreinterpretq_f32_u32(vaddq_u32(vreinterpretq_u32_f32(p), e));       \
+      y = vbslq_f32(lt_lower, vdupq_n_f32(0.0f), y);                           \
+      y = vbslq_f32(gt_upper, vdupq_n_f32(INFINITY), y);                       \
+      return y;                                                                \
+    };                                                                         \
+    auto fast_exp_f16 = [&](const vec_op::FP32Vec16& vec)                      \
+                            __attribute__((always_inline)) {                   \
+                              float32x4x4_t result;                            \
+                              result.val[0] = neon_expf_f16(vec.reg.val[0]);   \
+                              result.val[1] = neon_expf_f16(vec.reg.val[1]);   \
+                              result.val[2] = neon_expf_f16(vec.reg.val[2]);   \
+                              result.val[3] = neon_expf_f16(vec.reg.val[3]);   \
+                              return vec_op::FP32Vec16(result);                \
+                            };
 
 #endif  // __aarch64__
 
diff --git a/csrc/cpu/cpu_attn.cpp b/csrc/cpu/cpu_attn.cpp
index a582b4b4d7cc..4750dd78838d 100644
--- a/csrc/cpu/cpu_attn.cpp
+++ b/csrc/cpu/cpu_attn.cpp
@@ -1,5 +1,16 @@
 #include "cpu_attn_dispatch_generated.h"
 
+// Maps kv_cache_dtype string to Fp8KVCacheDataType enum.
+// "auto" -> kAuto(0); "fp8"/"fp8_e4m3" -> kFp8E4M3; "fp8_e5m2" -> kFp8E5M2.
+static inline cpu_attention::Fp8KVCacheDataType parse_fp8_kv_dtype(
+    const std::string& kv_cache_dtype) {
+  if (kv_cache_dtype == "fp8_e5m2")
+    return cpu_attention::Fp8KVCacheDataType::kFp8E5M2;
+  if (kv_cache_dtype == "fp8_e4m3" || kv_cache_dtype == "fp8")
+    return cpu_attention::Fp8KVCacheDataType::kFp8E4M3;
+  return cpu_attention::Fp8KVCacheDataType::kAuto;
+}
+
 torch::Tensor get_scheduler_metadata(
     const int64_t num_req, const int64_t num_heads_q,
     const int64_t num_heads_kv, const int64_t head_dim,
@@ -18,6 +29,8 @@ torch::Tensor get_scheduler_metadata(
     isa = cpu_attention::ISA::NEON;
   } else if (isa_hint == "vxe") {
     isa = cpu_attention::ISA::VXE;
+  } else if (isa_hint == "vsx") {
+    isa = cpu_attention::ISA::VSX;
   } else {
     TORCH_CHECK(false, "Unsupported CPU attention ISA hint: " + isa_hint);
   }
@@ -49,7 +62,7 @@ torch::Tensor get_scheduler_metadata(
   input.enable_kv_split = enable_kv_split;
 
   VLLM_DISPATCH_FLOATING_TYPES(dtype, "get_scheduler_metadata", [&]() {
-    CPU_ATTN_DISPATCH(head_dim, isa, [&]() {
+    CPU_ATTN_DISPATCH(head_dim, isa, 0, [&]() {
       input.elem_size = sizeof(scalar_t);
       input.q_buffer_elem_size = sizeof(attn_impl::q_buffer_t);
       input.logits_buffer_elem_size = sizeof(attn_impl::logits_buffer_t);
@@ -72,7 +85,9 @@ void cpu_attn_reshape_and_cache(
         key_cache,  // [num_blocks, num_kv_heads, block_size, head_size]
     torch::Tensor&
         value_cache,  // [num_blocks, num_kv_heads, block_size, head_size]
-    const torch::Tensor& slot_mapping, const std::string& isa) {
+    const torch::Tensor& slot_mapping, const std::string& isa,
+    const double k_scale = 1.0, const double v_scale = 1.0,
+    const std::string& kv_cache_dtype = "auto") {
   TORCH_CHECK_EQ(key.dim(), 3);
   TORCH_CHECK_EQ(value.dim(), 3);
   TORCH_CHECK_EQ(key_cache.dim(), 4);
@@ -80,18 +95,30 @@ void cpu_attn_reshape_and_cache(
   TORCH_CHECK_EQ(key.stride(2), 1);
   TORCH_CHECK_EQ(value.stride(2), 1);
 
+  const int64_t kv_cache_idx =
+      static_cast<int64_t>(parse_fp8_kv_dtype(kv_cache_dtype));
+  const bool is_fp8 = (kv_cache_idx != 0);
+
+  if (is_fp8) {
+    TORCH_CHECK(key_cache.scalar_type() == at::ScalarType::Byte,
+                "key_cache must be uint8 for FP8 path");
+    TORCH_CHECK(value_cache.scalar_type() == at::ScalarType::Byte,
+                "value_cache must be uint8 for FP8 path");
+    TORCH_CHECK(k_scale > 0, "k_scale must be positive for FP8 path");
+    TORCH_CHECK(v_scale > 0, "v_scale must be positive for FP8 path");
+  }
+
+  const float k_inv = is_fp8 ? 1.0f / static_cast<float>(k_scale) : 0.0f;
+  const float v_inv = is_fp8 ? 1.0f / static_cast<float>(v_scale) : 0.0f;
+
   const int64_t token_num = key.size(0);
-  const int64_t key_token_num_stride = key.stride(0);
-  const int64_t value_token_num_stride = value.stride(0);
-  const int64_t head_num = value.size(1);
-  const int64_t key_head_num_stride = key.stride(1);
-  const int64_t value_head_num_stride = value.stride(1);
+  const int64_t head_num = key.size(1);
+  const int64_t head_dim = key.size(2);
   const int64_t num_blocks = key_cache.size(0);
   const int64_t num_blocks_stride = key_cache.stride(0);
   const int64_t cache_head_num_stride = key_cache.stride(1);
   const int64_t block_size = key_cache.size(2);
   const int64_t block_size_stride = key_cache.stride(2);
-  const int64_t head_dim = key.size(-1);
 
   cpu_attention::ISA isa_tag = [&]() {
     if (isa == "amx") {
@@ -104,21 +131,31 @@ void cpu_attn_reshape_and_cache(
       return cpu_attention::ISA::NEON;
     } else if (isa == "vxe") {
       return cpu_attention::ISA::VXE;
+    } else if (isa == "vsx") {
+      return cpu_attention::ISA::VSX;
     } else {
       TORCH_CHECK(false, "Invalid ISA type: " + isa);
     }
   }();
 
+  if (is_fp8) {
+    TORCH_CHECK(isa_tag == cpu_attention::ISA::AMX ||
+                    isa_tag == cpu_attention::ISA::VEC,
+                "FP8 KV cache is only supported on x86 (AMX/VEC) ISA");
+  }
+
   VLLM_DISPATCH_FLOATING_TYPES(
       key.scalar_type(), "cpu_attn_reshape_and_cache", [&]() {
-        CPU_ATTN_DISPATCH(head_dim, isa_tag, [&]() {
+        CPU_ATTN_DISPATCH(head_dim, isa_tag, kv_cache_idx, [&]() {
+          using kv_t = typename attn_impl::kv_cache_t;
           attn_impl::reshape_and_cache(
               key.data_ptr<scalar_t>(), value.data_ptr<scalar_t>(),
-              key_cache.data_ptr<scalar_t>(), value_cache.data_ptr<scalar_t>(),
-              slot_mapping.data_ptr<int64_t>(), token_num, key_token_num_stride,
-              value_token_num_stride, head_num, key_head_num_stride,
-              value_head_num_stride, num_blocks, num_blocks_stride,
-              cache_head_num_stride, block_size, block_size_stride);
+              reinterpret_cast<kv_t*>(key_cache.data_ptr()),
+              reinterpret_cast<kv_t*>(value_cache.data_ptr()),
+              slot_mapping.data_ptr<int64_t>(), token_num, key.stride(0),
+              value.stride(0), head_num, key.stride(1), value.stride(1),
+              num_blocks, num_blocks_stride, cache_head_num_stride, block_size,
+              block_size_stride, k_inv, v_inv);
         });
       });
 }
@@ -137,13 +174,26 @@ void cpu_attention_with_kv_cache(
     const int64_t sliding_window_left, const int64_t sliding_window_right,
     const torch::Tensor& block_table,  // [num_tokens, max_block_num]
     const double softcap, const torch::Tensor& scheduler_metadata,
-    const std::optional<torch::Tensor>& s_aux  // [num_heads]
-) {
+    const std::optional<torch::Tensor>& s_aux,  // [num_heads]
+    const double k_scale = 1.0, const double v_scale = 1.0,
+    const std::string& kv_cache_dtype = "auto") {
   TORCH_CHECK_EQ(query.dim(), 3);
   TORCH_CHECK_EQ(query.stride(2), 1);
   TORCH_CHECK_EQ(key_cache.dim(), 4);
   TORCH_CHECK_EQ(value_cache.dim(), 4);
 
+  const int64_t kv_cache_idx =
+      static_cast<int64_t>(parse_fp8_kv_dtype(kv_cache_dtype));
+  const bool is_fp8 = (kv_cache_idx != 0);
+  if (is_fp8) {
+    TORCH_CHECK(key_cache.scalar_type() == at::ScalarType::Byte,
+                "key_cache must be uint8 for FP8 path");
+    TORCH_CHECK(value_cache.scalar_type() == at::ScalarType::Byte,
+                "value_cache must be uint8 for FP8 path");
+    TORCH_CHECK(k_scale > 0, "k_scale must be positive for FP8 path");
+    TORCH_CHECK(v_scale > 0, "v_scale must be positive for FP8 path");
+  }
+
   cpu_attention::AttentionInput input;
   input.metadata = reinterpret_cast<cpu_attention::AttentionMetadata*>(
       scheduler_metadata.data_ptr());
@@ -165,25 +215,32 @@ void cpu_attention_with_kv_cache(
   input.block_table = block_table.data_ptr<int32_t>();
   input.alibi_slopes =
       alibi_slopes.has_value() ? alibi_slopes->data_ptr<float>() : nullptr;
-  // For now sink must be bf16
   input.s_aux = s_aux.has_value() ? s_aux->data_ptr<c10::BFloat16>() : nullptr;
   input.scale = scale;
   input.causal = causal;
   input.sliding_window_left = sliding_window_left;
   input.sliding_window_right = sliding_window_right;
   if (input.causal) {
-    // to make boundary calculation easier
     input.sliding_window_right = 0;
   }
-  float softcap_fp32 = softcap;
-  input.softcap = softcap_fp32;
+  input.softcap = static_cast<float>(softcap);
+
+  if (is_fp8) {
+    input.k_scale_fp8 = static_cast<float>(k_scale);
+    input.v_scale_fp8 = static_cast<float>(v_scale);
+    TORCH_CHECK(input.metadata->isa == cpu_attention::ISA::AMX ||
+                    input.metadata->isa == cpu_attention::ISA::VEC,
+                "FP8 KV cache is only supported on x86 (AMX/VEC) ISA");
+  }
 
   VLLM_DISPATCH_FLOATING_TYPES(
       query.scalar_type(), "cpu_attention_with_kv_cache", [&]() {
-        CPU_ATTN_DISPATCH(query.size(2), input.metadata->isa, [&]() {
-          TORCH_CHECK_EQ(input.block_size % attn_impl::BlockSizeAlignment, 0);
-          cpu_attention::AttentionMainLoop<attn_impl> mainloop;
-          mainloop(&input);
-        });
+        CPU_ATTN_DISPATCH(
+            query.size(2), input.metadata->isa, kv_cache_idx, [&]() {
+              TORCH_CHECK_EQ(input.block_size % attn_impl::BlockSizeAlignment,
+                             0);
+              cpu_attention::AttentionMainLoop<attn_impl> mainloop;
+              mainloop(&input);
+            });
       });
 }
diff --git a/csrc/cpu/cpu_attn_amx.hpp b/csrc/cpu/cpu_attn_amx.hpp
index 1c8644d52329..6a0341085dce 100644
--- a/csrc/cpu/cpu_attn_amx.hpp
+++ b/csrc/cpu/cpu_attn_amx.hpp
@@ -1,6 +1,7 @@
 #ifndef CPU_ATTN_AMX_HPP
 #define CPU_ATTN_AMX_HPP
 
+#include "cpu_attn_fp8.hpp"
 #include "cpu_attn_impl.hpp"
 
 namespace cpu_attention {
@@ -21,9 +22,10 @@ typedef struct __tile_config {
 // 2-2-4 pattern, for 16 < m <= 32
 // TILE 0, 1: load A matrix, row num should be 16, m - 16
 // TILE 2, 3: load B matrix, row num should be 16
-// TILE 4, 5, 6, 7: store results C matrix, row num should be 16, 16, m - 16, m
-// - 16
-template <typename kv_cache_t>
+// TILE 4, 5, 6, 7: store results C matrix, row num should be 16, 16,
+// m - 16, m - 16
+// q_buffer_t: A (Q/P) tile type; kv_cache_t: B (K/V cache) tile type.
+template <typename q_buffer_t, typename kv_cache_t>
 class TileGemm224 {
  public:
   template <AttentionGemmPhase phase, int32_t k_size>
@@ -42,13 +44,56 @@ class TileGemm224 {
   }
 };
 
-template <>
-class TileGemm224<c10::BFloat16> {
+// Dequantize one FP8 tile (AMX_TILE_ROW_NUM rows x 32 cols) to BF16.
+template <typename kv_cache_t>
+FORCE_INLINE void deq_tile_amx(const uint8_t* src, c10::BFloat16* dst) {
+  for (int r = 0; r < AMX_TILE_ROW_NUM; ++r) {
+    if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e4m3fn>) {
+      vec_op::BF16Vec32(src + r * 32, vec_op::fp8_bf16_e4m3_tag{})
+          .save(dst + r * 32);
+    } else {
+      vec_op::BF16Vec32(src + r * 32, vec_op::fp8_bf16_e5m2_tag{})
+          .save(dst + r * 32);
+    }
+  }
+}
+
+// For FP8: dequant src into scratch and return scratch.
+// For BF16: return src directly (scratch is unused; the compiler elides it).
+template <typename kv_cache_t>
+FORCE_INLINE const c10::BFloat16* prepare_b_tile(const kv_cache_t* src,
+                                                 c10::BFloat16* scratch) {
+  if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
+                std::is_same_v<kv_cache_t, c10::Float8_e5m2>) {
+    deq_tile_amx<kv_cache_t>(reinterpret_cast<const uint8_t*>(src), scratch);
+    return scratch;
+  } else {
+    return reinterpret_cast<const c10::BFloat16*>(src);
+  }
+}
+
+// Handles both BF16 and FP8 KV cache (2-2-4 pattern).
+template <typename kv_cache_t>
+class TileGemm224<c10::BFloat16, kv_cache_t> {
+  static_assert(std::is_same_v<kv_cache_t, c10::BFloat16> ||
+                    std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
+                    std::is_same_v<kv_cache_t, c10::Float8_e5m2>,
+                "kv_cache_t must be BFloat16, Float8_e4m3fn, or Float8_e5m2");
+
+  static constexpr bool fp8_kv =
+      std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
+      std::is_same_v<kv_cache_t, c10::Float8_e5m2>;
+
+  static constexpr int64_t tile_elems = AMX_TILE_BYTES / sizeof(c10::BFloat16);
+  // BF16 path: scratch_elems=1 so the scratch array is eliminated by the
+  // compiler.
+  static constexpr int64_t scratch_elems = fp8_kv ? tile_elems : 1;
+
  public:
   template <AttentionGemmPhase phase, int32_t k_size>
   FORCE_INLINE static void gemm(const int32_t m_size,
                                 c10::BFloat16* __restrict__ a_tile,
-                                c10::BFloat16* __restrict__ b_tile,
+                                kv_cache_t* __restrict__ b_tile,
                                 float* __restrict__ c_tile, const int64_t lda,
                                 const int64_t ldb, const int64_t ldc,
                                 const int32_t block_size,
@@ -56,6 +101,7 @@ class TileGemm224<c10::BFloat16> {
                                 const bool accum_c) {
     const int32_t k_times =
         dynamic_k_size / (AMX_TILE_ROW_NUM * 4 / sizeof(c10::BFloat16));
+
     c10::BFloat16* __restrict__ a_tile_0 = a_tile;
     c10::BFloat16* __restrict__ a_tile_1 = a_tile + lda * AMX_TILE_ROW_NUM;
     const int64_t a_tile_stride = [&]() {
@@ -70,8 +116,8 @@ class TileGemm224<c10::BFloat16> {
       }
     }();
 
-    c10::BFloat16* __restrict__ b_tile_2 = b_tile;
-    c10::BFloat16* __restrict__ b_tile_3 = [&]() {
+    kv_cache_t* __restrict__ b_tile_2 = b_tile;
+    kv_cache_t* __restrict__ b_tile_3 = [&]() {
       if constexpr (phase == AttentionGemmPhase::QK) {
         // k_cache is prepacked
         return b_tile + (k_size * AMX_TILE_ROW_BYTES / 4);
@@ -106,11 +152,16 @@ class TileGemm224<c10::BFloat16> {
       _tile_zero(7);
     }
 
+    alignas(64) c10::BFloat16 scratch_2[scratch_elems];
+    alignas(64) c10::BFloat16 scratch_3[scratch_elems];
     for (int32_t k = 0; k < k_times; ++k) {
+      const c10::BFloat16* load_2 = prepare_b_tile(b_tile_2, scratch_2);
+      const c10::BFloat16* load_3 = prepare_b_tile(b_tile_3, scratch_3);
+
       _tile_loadd(0, a_tile_0, a_tile_stride);
-      _tile_stream_loadd(2, b_tile_2, b_tile_stride);
+      _tile_stream_loadd(2, const_cast<c10::BFloat16*>(load_2), b_tile_stride);
       _tile_dpbf16ps(4, 0, 2);
-      _tile_stream_loadd(3, b_tile_3, b_tile_stride);
+      _tile_stream_loadd(3, const_cast<c10::BFloat16*>(load_3), b_tile_stride);
       _tile_dpbf16ps(5, 0, 3);
       _tile_loadd(1, a_tile_1, a_tile_stride);
       _tile_dpbf16ps(6, 1, 2);
@@ -154,13 +205,13 @@ class TileGemm224<c10::BFloat16> {
 };
 
 // 1-2-2 pattern, for 0 < m <= 16
-// TILE 0, (1): load A matrix, use extra 1 tile for prefetch, row num should be
-// m, m
-// TILE 2, 3, (4, 5): load B matrix, use extra 2 tiles for prefetch, row
-// num should be 16
-// TILE 6, 7, (6, 7): store results C matrix, row num should be
-// m
-template <typename kv_cache_t>
+// TILE 0, (1): load A matrix, use extra 1 tile for prefetch, row num should
+// be m, m
+// TILE 2, 3, (4, 5): load B matrix, use extra 2 tiles for prefetch, row num
+// should be 16
+// TILE 6, 7: store results C matrix, row num should be m
+// q_buffer_t: A (Q/P) tile type; kv_cache_t: B (K/V cache) tile type.
+template <typename q_buffer_t, typename kv_cache_t>
 class TileGemm122 {
  public:
   template <AttentionGemmPhase phase, int32_t k_size>
@@ -179,13 +230,26 @@ class TileGemm122 {
   }
 };
 
-template <>
-class TileGemm122<c10::BFloat16> {
+// Handles both BF16 and FP8 KV cache (1-2-2 pattern).
+template <typename kv_cache_t>
+class TileGemm122<c10::BFloat16, kv_cache_t> {
+  static_assert(std::is_same_v<kv_cache_t, c10::BFloat16> ||
+                    std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
+                    std::is_same_v<kv_cache_t, c10::Float8_e5m2>,
+                "kv_cache_t must be BFloat16, Float8_e4m3fn, or Float8_e5m2");
+
+  static constexpr bool fp8_kv =
+      std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
+      std::is_same_v<kv_cache_t, c10::Float8_e5m2>;
+
+  static constexpr int64_t tile_elems = AMX_TILE_BYTES / sizeof(c10::BFloat16);
+  static constexpr int64_t scratch_elems = fp8_kv ? tile_elems : 1;
+
  public:
   template <AttentionGemmPhase phase, int32_t k_size>
   FORCE_INLINE static void gemm(const int32_t m_size,
                                 c10::BFloat16* __restrict__ a_tile,
-                                c10::BFloat16* __restrict__ b_tile,
+                                kv_cache_t* __restrict__ b_tile,
                                 float* __restrict__ c_tile, const int64_t lda,
                                 const int64_t ldb, const int64_t ldc,
                                 const int32_t block_size,
@@ -215,21 +279,19 @@ class TileGemm122<c10::BFloat16> {
       }
     }();
 
-    c10::BFloat16* __restrict__ b_tile_2 = b_tile;
-    c10::BFloat16* __restrict__ b_tile_3 = [&]() {
+    kv_cache_t* __restrict__ b_tile_2 = b_tile;
+    kv_cache_t* __restrict__ b_tile_3 = [&]() {
       if constexpr (phase == AttentionGemmPhase::QK) {
-        // k_cache is prepacked
         return b_tile + (k_size * AMX_TILE_ROW_BYTES / 4);
       } else if constexpr (phase == AttentionGemmPhase::PV) {
-        // v_cache is prepacked
         return b_tile + (block_size * AMX_TILE_ROW_BYTES / 4);
       } else {
         TORCH_CHECK(false, "Unreachable");
       }
     }();
-    c10::BFloat16* __restrict__ b_tile_4 =
+    kv_cache_t* __restrict__ b_tile_4 =
         b_tile_2 + AMX_TILE_BYTES / sizeof(c10::BFloat16);
-    c10::BFloat16* __restrict__ b_tile_5 =
+    kv_cache_t* __restrict__ b_tile_5 =
         b_tile_3 + AMX_TILE_BYTES / sizeof(c10::BFloat16);
     int64_t b_stride = AMX_TILE_ROW_BYTES;
 
@@ -250,16 +312,25 @@ class TileGemm122<c10::BFloat16> {
       _tile_zero(7);
     }
 
+    alignas(64) c10::BFloat16 scratch_2[scratch_elems];
+    alignas(64) c10::BFloat16 scratch_3[scratch_elems];
+    alignas(64) c10::BFloat16 scratch_4[scratch_elems];
+    alignas(64) c10::BFloat16 scratch_5[scratch_elems];
     for (int32_t k = 0; k < k_group_times; ++k) {
+      const c10::BFloat16* load_2 = prepare_b_tile(b_tile_2, scratch_2);
+      const c10::BFloat16* load_3 = prepare_b_tile(b_tile_3, scratch_3);
+      const c10::BFloat16* load_4 = prepare_b_tile(b_tile_4, scratch_4);
+      const c10::BFloat16* load_5 = prepare_b_tile(b_tile_5, scratch_5);
+
       _tile_loadd(0, a_tile_0, a_tile_stride);
-      _tile_stream_loadd(2, b_tile_2, b_stride);
+      _tile_stream_loadd(2, const_cast<c10::BFloat16*>(load_2), b_stride);
       _tile_dpbf16ps(6, 0, 2);
-      _tile_stream_loadd(3, b_tile_3, b_stride);
+      _tile_stream_loadd(3, const_cast<c10::BFloat16*>(load_3), b_stride);
       _tile_dpbf16ps(7, 0, 3);
       _tile_loadd(1, a_tile_1, a_tile_stride);
-      _tile_stream_loadd(4, b_tile_4, b_stride);
+      _tile_stream_loadd(4, const_cast<c10::BFloat16*>(load_4), b_stride);
       _tile_dpbf16ps(6, 1, 4);
-      _tile_stream_loadd(5, b_tile_5, b_stride);
+      _tile_stream_loadd(5, const_cast<c10::BFloat16*>(load_5), b_stride);
       _tile_dpbf16ps(7, 1, 5);
 
       // update ptrs
@@ -279,10 +350,13 @@ class TileGemm122<c10::BFloat16> {
     }
 
     if (has_tail) {
+      const c10::BFloat16* load_2 = prepare_b_tile(b_tile_2, scratch_2);
+      const c10::BFloat16* load_3 = prepare_b_tile(b_tile_3, scratch_3);
+
       _tile_loadd(0, a_tile_0, a_tile_stride);
-      _tile_stream_loadd(2, b_tile_2, b_stride);
+      _tile_stream_loadd(2, const_cast<c10::BFloat16*>(load_2), b_stride);
       _tile_dpbf16ps(6, 0, 2);
-      _tile_stream_loadd(3, b_tile_3, b_stride);
+      _tile_stream_loadd(3, const_cast<c10::BFloat16*>(load_3), b_stride);
       _tile_dpbf16ps(7, 0, 3);
     }
 
@@ -302,21 +376,25 @@ class TileGemm122<c10::BFloat16> {
     _tile_loadconfig(&config);
   }
 };
+
 }  // namespace
 
-template <typename scalar_t, int64_t head_dim>
-class AttentionImpl<ISA::AMX, scalar_t, head_dim> {
+template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
+class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
+  static constexpr bool fp8_kv =
+      std::is_same_v<kv_cache_scalar_t, c10::Float8_e4m3fn> ||
+      std::is_same_v<kv_cache_scalar_t, c10::Float8_e5m2>;
+
  public:
   using query_t = scalar_t;
   using q_buffer_t = scalar_t;
-  using kv_cache_t = scalar_t;
+  using kv_cache_t = kv_cache_scalar_t;
   using logits_buffer_t = float;
   using partial_output_buffer_t = float;
   using prob_buffer_t = scalar_t;
 
   constexpr static int64_t BlockSizeAlignment =
-      AMX_TILE_ROW_BYTES /
-      sizeof(kv_cache_t);  // KV token num unit of QK and PV phases
+      32;  // AMX_TILE_ROW_NUM = 16 tokens/tile; 32 = 2 tiles
   constexpr static int64_t HeadDimAlignment =
       2 * (AMX_TILE_ROW_BYTES / 4);  // headdim num unit of PV phase
   constexpr static int64_t MaxQHeadNumPerIteration = 32;
@@ -324,6 +402,9 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim> {
   constexpr static ISA ISAType = ISA::AMX;
   constexpr static bool scale_on_logits = true;
 
+  float k_scale = 1.0f;
+  float v_scale = 1.0f;
+
  public:
   AttentionImpl() : current_q_head_num_(0) {
     // Use all columns in AMX tiles
@@ -332,21 +413,50 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim> {
 
   ~AttentionImpl() { _tile_release(); }
 
+  void init_from_input(const AttentionInput* input) {
+    if constexpr (fp8_kv) {
+      k_scale = input->k_scale_fp8;
+      v_scale = input->v_scale_fp8;
+    }
+  }
+
+  float get_output_v_scale() const noexcept {
+    if constexpr (fp8_kv) {
+      // AMX dequant places FP8 payload into a BF16 field (exponent bias 127).
+      // Correction = 2^(127 - FP8_bias): E4M3 bias=7 → 2^120, E5M2 bias=15 →
+      // 2^112.
+      constexpr float bias =
+          std::is_same_v<kv_cache_t, c10::Float8_e5m2> ? 0x1p112f : 0x1p120f;
+      return v_scale * bias;
+    }
+    return 1.0f;
+  }
+
   template <template <typename tile_gemm_t> typename attention>
   FORCE_INLINE void execute_attention(DEFINE_CPU_ATTENTION_PARAMS) {
+    if constexpr (fp8_kv) {
+      // Same bias correction as get_output_v_scale: AMX FP8→BF16 dequant
+      // shifts the exponent bias from FP8 to BF16 (127), so we multiply by
+      // 2^(127-FP8_bias) to recover the true value. E4M3: 2^120, E5M2: 2^112.
+      const float bias =
+          std::is_same_v<kv_cache_t, c10::Float8_e5m2> ? 0x1p112f : 0x1p120f;
+      scale *= k_scale * bias;
+    }
     if (q_head_num > AMX_TILE_ROW_NUM) {
       if (q_head_num != current_q_head_num_) {
         current_q_head_num_ = q_head_num;
-        TileGemm224<kv_cache_t>::init_tile_config(q_head_num, amx_tile_config_);
+        TileGemm224<q_buffer_t, kv_cache_t>::init_tile_config(q_head_num,
+                                                              amx_tile_config_);
       }
-      attention<TileGemm224<kv_cache_t>> attention_iteration;
+      attention<TileGemm224<q_buffer_t, kv_cache_t>> attention_iteration;
       attention_iteration(CPU_ATTENTION_PARAMS);
     } else {
       if (q_head_num != current_q_head_num_) {
         current_q_head_num_ = q_head_num;
-        TileGemm122<kv_cache_t>::init_tile_config(q_head_num, amx_tile_config_);
+        TileGemm122<q_buffer_t, kv_cache_t>::init_tile_config(q_head_num,
+                                                              amx_tile_config_);
       }
-      attention<TileGemm122<kv_cache_t>> attention_iteration;
+      attention<TileGemm122<q_buffer_t, kv_cache_t>> attention_iteration;
       attention_iteration(CPU_ATTENTION_PARAMS);
     }
   }
@@ -411,13 +521,26 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim> {
   // reshape KV to AMX friendly layout
   static void reshape_and_cache(
       const scalar_t* __restrict__ key, const scalar_t* __restrict__ value,
-      scalar_t* __restrict__ key_cache, scalar_t* __restrict__ value_cache,
+      kv_cache_t* __restrict__ key_cache, kv_cache_t* __restrict__ value_cache,
       const int64_t* __restrict__ slot_mapping, const int64_t token_num,
       const int64_t key_token_num_stride, const int64_t value_token_num_stride,
       const int64_t head_num, const int64_t key_head_num_stride,
       const int64_t value_head_num_stride, const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride) {
+      const int64_t block_size, const int64_t block_size_stride,
+      const float k_inv = 0.0f, const float v_inv = 0.0f) {
+    if constexpr (fp8_kv) {
+      constexpr auto qfn = select_fp8_quant_fn<kv_cache_t>();
+      reshape_and_cache_fp8_amx_impl<scalar_t, qfn>(
+          key, value, reinterpret_cast<uint8_t*>(key_cache),
+          reinterpret_cast<uint8_t*>(value_cache), slot_mapping, token_num,
+          head_num, head_dim, block_size, key_token_num_stride,
+          key_head_num_stride, value_token_num_stride, value_head_num_stride,
+          num_blocks_stride, cache_head_num_stride, num_blocks_stride,
+          cache_head_num_stride, k_inv, v_inv);
+      return;
+    }
+
     // For AMX 2D tiles, size of each line is 64 bytes
     constexpr int64_t amx_tile_row_size = AMX_TILE_ROW_BYTES;
     // For AMX B matrix, N always is 16
@@ -426,6 +549,9 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim> {
     // For now suppose block_size is divisible by amx_tile_column_num
     TORCH_CHECK_EQ(block_size % amx_b_tile_k_size, 0);
 
+    scalar_t* __restrict__ kc = reinterpret_cast<scalar_t*>(key_cache);
+    scalar_t* __restrict__ vc = reinterpret_cast<scalar_t*>(value_cache);
+
 #pragma omp parallel for collapse(2)
     for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
       for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
@@ -453,8 +579,7 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim> {
           constexpr int64_t quadword_num_per_group =
               token_num_per_group * quadword_num;
           int32_t* key_cache_start_ptr =
-              reinterpret_cast<int32_t*>(key_cache +
-                                         block_idx * num_blocks_stride +
+              reinterpret_cast<int32_t*>(kc + block_idx * num_blocks_stride +
                                          head_idx * cache_head_num_stride) +
               group_idx * quadword_num_per_group + group_offset;
 
@@ -483,7 +608,7 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim> {
                                             token_idx * value_token_num_stride +
                                             head_idx * value_head_num_stride;
           scalar_t* value_cache_start_ptr =
-              value_cache + block_idx * num_blocks_stride +
+              vc + block_idx * num_blocks_stride +
               head_idx * cache_head_num_stride +
               sub_group_idx * token_num_per_sub_group * amx_b_tile_n_size +
               sub_group_offset;
diff --git a/csrc/cpu/cpu_attn_fp8.hpp b/csrc/cpu/cpu_attn_fp8.hpp
new file mode 100644
index 000000000000..764b6ed7f84a
--- /dev/null
+++ b/csrc/cpu/cpu_attn_fp8.hpp
@@ -0,0 +1,214 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#pragma once
+#include <algorithm>
+#include <cmath>
+#include <cstdint>
+#include <limits>
+#include <type_traits>
+
+#include "cpu/utils.hpp"
+
+typedef uint32_t __attribute__((__may_alias__)) u32_alias_t;
+typedef uint16_t __attribute__((__may_alias__)) u16_alias_t;
+typedef float __attribute__((__may_alias__)) f32_alias_t;
+
+// Reference scalar dequant — used to verify vectorized AMX dequant.
+inline float fp8e4m3_to_float_scalar(uint8_t b, float scale) noexcept {
+  // NaN encoding in E4M3
+  if ((b & 0x7F) == 0x7F) return std::numeric_limits<float>::quiet_NaN();
+  uint32_t b_u32 = static_cast<uint32_t>(b);
+  uint32_t sign = (b_u32 & 0x80) << 24;
+  uint32_t payload = (b_u32 & 0x7F) << 20;
+  uint32_t bits = sign | payload;
+  float b_f32_unscaled = *reinterpret_cast<const f32_alias_t*>(&bits);
+  float b_f32_scaled = b_f32_unscaled * scale * 0x1p120f;
+  return b_f32_scaled;
+}
+
+inline uint8_t float_to_fp8e4m3_scalar(float v, float inv_scale) noexcept {
+  v *= inv_scale;
+  constexpr float fp8_max = 448.0f;
+  v = std::max(-fp8_max, std::min(fp8_max, v));
+  if (v == 0.0f) return 0;
+
+  // Inverse mapping of fp8e4m3_to_float_scalar: shift the effective exponent
+  // bias from fp32 (127) back to fp8 e4m3 (7), then pack sign|payload.
+  float v_f32_unscaled = v * 0x1p-120f;
+  uint32_t bits = *reinterpret_cast<const u32_alias_t*>(&v_f32_unscaled);
+  uint8_t sign = static_cast<uint8_t>((bits >> 24) & 0x80);
+  uint8_t payload = static_cast<uint8_t>((bits >> 20) & 0x7F);
+  if (payload == 0) return sign;
+  payload = std::min<uint8_t>(payload, 0x7E);  // keep 0x7F as NaN encoding
+  return static_cast<uint8_t>(sign | payload);
+}
+
+// ---------------------------------------------------------------------------
+// AMX reshape impl — parameterised on the quantisation function.
+// Writes key/value into uint8 FP8 KV cache using the AMX tile-friendly layout.
+// K: halfword-packed (2 FP8 per uint16, token_num_per_group=16).
+// V: sub-group packing (token_num_per_sub_group=2, head_elems_per_group=16).
+// block_size must be divisible by 32.
+// ---------------------------------------------------------------------------
+template <typename scalar_t, uint8_t (*quant_fn)(float, float)>
+inline void reshape_and_cache_fp8_amx_impl(
+    const scalar_t* key_ptr, const scalar_t* value_ptr, uint8_t* key_cache_ptr,
+    uint8_t* value_cache_ptr, const int64_t* slot_ptr, int64_t token_num,
+    int64_t head_num, int64_t head_dim, int64_t block_size, int64_t k_stride0,
+    int64_t k_stride1, int64_t v_stride0, int64_t v_stride1, int64_t kc_stride0,
+    int64_t kc_stride1, int64_t vc_stride0, int64_t vc_stride1, float k_inv,
+    float v_inv) {
+  constexpr int64_t token_num_per_group = 16;  // AMX_TILE_ROW_NUM
+  const int64_t halfword_num = head_dim / 2;   // 2 FP8 per uint16
+  const int64_t halfword_num_per_group = token_num_per_group * halfword_num;
+  constexpr int64_t head_elems_per_group = 16;
+  constexpr int64_t token_num_per_sub_group = 2;  // = 4 / sizeof(BF16)
+  const int64_t group_num = head_dim / head_elems_per_group;
+  const int64_t group_size = block_size * head_elems_per_group;
+
+#pragma omp parallel for collapse(2) schedule(static)
+  for (int64_t tok = 0; tok < token_num; ++tok) {
+    for (int64_t h = 0; h < head_num; ++h) {
+      const int64_t slot = slot_ptr[tok];
+      if (slot < 0) continue;
+      const int64_t block_idx = slot / block_size;
+      const int64_t block_offset = slot % block_size;
+
+      // Key: halfword-packed, 2 FP8 per uint16
+      {
+        const scalar_t* ksrc = key_ptr + tok * k_stride0 + h * k_stride1;
+        const int64_t group_idx = block_offset / token_num_per_group;
+        const int64_t group_offset = block_offset % token_num_per_group;
+        uint16_t* kdst =
+            reinterpret_cast<uint16_t*>(key_cache_ptr + block_idx * kc_stride0 +
+                                        h * kc_stride1) +
+            group_idx * halfword_num_per_group + group_offset;
+        for (int64_t j = 0; j < halfword_num; ++j) {
+          uint8_t fp8_0 = quant_fn(static_cast<float>(ksrc[j * 2]), k_inv);
+          uint8_t fp8_1 = quant_fn(static_cast<float>(ksrc[j * 2 + 1]), k_inv);
+          uint8_t bytes[2] = {fp8_0, fp8_1};
+          uint16_t hw = *reinterpret_cast<const u16_alias_t*>(bytes);
+          kdst[j * token_num_per_group] = hw;
+        }
+      }
+
+      // Value: sub-group packing (token_num_per_sub_group = 2)
+      {
+        const scalar_t* vsrc = value_ptr + tok * v_stride0 + h * v_stride1;
+        const int64_t sub_group_idx = block_offset / token_num_per_sub_group;
+        const int64_t sub_group_offset = block_offset % token_num_per_sub_group;
+        uint8_t* vdst =
+            value_cache_ptr + block_idx * vc_stride0 + h * vc_stride1 +
+            sub_group_idx * token_num_per_sub_group * head_elems_per_group +
+            sub_group_offset;
+        for (int64_t i = 0; i < group_num; ++i) {
+          for (int64_t j = 0; j < head_elems_per_group; ++j)
+            vdst[j * token_num_per_sub_group] =
+                quant_fn(static_cast<float>(vsrc[j]), v_inv);
+          vsrc += head_elems_per_group;
+          vdst += group_size;
+        }
+      }
+    }
+  }
+}
+
+// ---------------------------------------------------------------------------
+// FP8 E5M2 scalar helpers
+// ---------------------------------------------------------------------------
+
+// Reference scalar dequant — used to verify vectorized AMX dequant.
+// FP8 E5M2: s[7] e[6:2] m[1:0], exponent bias = 15 (same as FP16).
+// Byte b → FP16 bits = b << 8 (no bias correction needed).
+inline float fp8e5m2_to_float_scalar(uint8_t b, float scale) noexcept {
+  const uint8_t exp_bits = (b >> 2) & 0x1F;
+  const uint8_t mant_bits = b & 0x03;
+  // NaN: exp=11111, mant!=00
+  if (exp_bits == 0x1F && mant_bits != 0)
+    return std::numeric_limits<float>::quiet_NaN();
+  const uint32_t sign = static_cast<uint32_t>(b & 0x80) << 24;
+  if (exp_bits == 0x1F)
+    return sign ? -std::numeric_limits<float>::infinity()
+                : std::numeric_limits<float>::infinity();
+  if (exp_bits == 0) {  // subnormal: (-1)^s * 2^-14 * mant/4
+    if (mant_bits == 0) return 0.0f;
+    float v = mant_bits * 0x1p-16f;
+    return (sign ? -v : v) * scale;
+  }
+  // Normal: FP32 exp = exp5 - 15 + 127, mantissa top 2 bits
+  uint32_t fp32_bits = sign |
+                       ((static_cast<uint32_t>(exp_bits) - 15 + 127) << 23) |
+                       (static_cast<uint32_t>(mant_bits) << 21);
+  float val = *reinterpret_cast<const f32_alias_t*>(&fp32_bits);
+  return val * scale;
+}
+
+inline uint8_t float_to_fp8e5m2_scalar(float v, float inv_scale) noexcept {
+  v *= inv_scale;
+  constexpr float fp8_e5m2_max = 57344.0f;
+  v = std::max(-fp8_e5m2_max, std::min(fp8_e5m2_max, v));
+  if (v == 0.0f) return 0;
+  uint32_t bits = *reinterpret_cast<const u32_alias_t*>(&v);
+  const uint8_t sign = static_cast<uint8_t>((bits >> 24) & 0x80);
+  const int32_t exp_fp32 = static_cast<int32_t>((bits >> 23) & 0xFF) - 127;
+  const uint8_t mant2 = static_cast<uint8_t>((bits >> 21) & 0x03);
+  if (exp_fp32 < -14) {  // subnormal in E5M2
+    const int shift = -14 - exp_fp32;
+    if (shift + 21 >= 32)
+      return sign;  // underflow: too small for E5M2 subnormal
+    const uint32_t m = (0x800000u | (bits & 0x7FFFFFu)) >> (shift + 21);
+    return sign | static_cast<uint8_t>(std::min<uint32_t>(m, 3u));
+  }
+  const uint8_t exp5 = static_cast<uint8_t>(exp_fp32 + 15);
+  return sign | (exp5 << 2) | mant2;
+}
+
+// ---------------------------------------------------------------------------
+// Select the FP8 quant function at compile time based on kv_cache_t.
+// ---------------------------------------------------------------------------
+template <typename kv_cache_t>
+constexpr auto select_fp8_quant_fn() {
+  if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e5m2>)
+    return float_to_fp8e5m2_scalar;
+  else
+    return float_to_fp8e4m3_scalar;
+}
+
+// ---------------------------------------------------------------------------
+// VEC reshape impl — parameterised on the quantisation function.
+// Writes key (column-major) and value (row-major) into uint8 FP8 KV cache.
+// The pragma omp must live outside VLLM_DISPATCH_FLOATING_TYPES because
+// #pragma cannot appear inside variadic macro arguments.
+// ---------------------------------------------------------------------------
+template <typename scalar_t, uint8_t (*quant_fn)(float, float)>
+inline void reshape_and_cache_fp8_vec_impl(
+    const scalar_t* key_ptr, const scalar_t* value_ptr, uint8_t* key_cache_ptr,
+    uint8_t* value_cache_ptr, const int64_t* slot_ptr, int64_t token_num,
+    int64_t head_num, int64_t head_dim, int64_t block_size, int64_t k_stride0,
+    int64_t k_stride1, int64_t v_stride0, int64_t v_stride1, int64_t kc_stride0,
+    int64_t kc_stride1, int64_t vc_stride0, int64_t vc_stride1, float k_inv,
+    float v_inv) {
+#pragma omp parallel for collapse(2) schedule(static)
+  for (int64_t tok = 0; tok < token_num; ++tok) {
+    for (int64_t h = 0; h < head_num; ++h) {
+      const int64_t slot = slot_ptr[tok];
+      if (slot < 0) continue;
+      const int64_t block_idx = slot / block_size;
+      const int64_t block_offset = slot % block_size;
+
+      // Key layout: column-major within block
+      const scalar_t* ksrc = key_ptr + tok * k_stride0 + h * k_stride1;
+      uint8_t* kdst = key_cache_ptr + block_idx * kc_stride0 + h * kc_stride1 +
+                      block_offset;
+      for (int64_t i = 0; i < head_dim; ++i)
+        kdst[i * block_size] = quant_fn(static_cast<float>(ksrc[i]), k_inv);
+
+      // Value layout: row-major within block (contiguous head_dim bytes)
+      const scalar_t* vsrc = value_ptr + tok * v_stride0 + h * v_stride1;
+      uint8_t* vdst = value_cache_ptr + block_idx * vc_stride0 +
+                      h * vc_stride1 + block_offset * head_dim;
+      for (int64_t i = 0; i < head_dim; ++i)
+        vdst[i] = quant_fn(static_cast<float>(vsrc[i]), v_inv);
+    }
+  }
+}
diff --git a/csrc/cpu/cpu_attn_impl.hpp b/csrc/cpu/cpu_attn_impl.hpp
index c15799fa950d..b9987fb26c19 100644
--- a/csrc/cpu/cpu_attn_impl.hpp
+++ b/csrc/cpu/cpu_attn_impl.hpp
@@ -12,10 +12,24 @@
 #include "cpu/utils.hpp"
 
 namespace cpu_attention {
-enum class ISA { AMX, VEC, VEC16, NEON, VXE };
+enum class ISA { AMX, VEC, VEC16, NEON, VXE, VSX };
 
-template <ISA isa, typename scalar_t, int64_t head_dim>
-class AttentionImpl {};
+// Mirrors csrc/attention/dtype_fp8.cuh Fp8KVCacheDataType exactly.
+enum class Fp8KVCacheDataType {
+  kAuto = 0,
+  kFp8E4M3 = 1,
+  kFp8E5M2 = 2,
+};
+
+struct AttentionInput;
+
+template <ISA isa, typename scalar_t, int64_t head_dim,
+          typename kv_cache_scalar_t = scalar_t>
+class AttentionImpl {
+ public:
+  void init_from_input(const AttentionInput*) {}
+  float get_output_v_scale() const noexcept { return 1.0f; }
+};
 
 struct AttentionWorkItemGroup {
   int32_t req_id;
@@ -147,6 +161,12 @@ struct AttentionMetadata {
       case ISA::NEON:
         ss << "NEON, ";
         break;
+      case ISA::VXE:
+        ss << "VXE, ";
+        break;
+      case ISA::VSX:
+        ss << "VSX, ";
+        break;
     }
     ss << "workitem_group_num: " << workitem_group_num
        << ", reduction_item_num: " << reduction_item_num
@@ -777,6 +797,9 @@ struct AttentionInput {
   int32_t sliding_window_left;
   int32_t sliding_window_right;
   float softcap;
+  // FP8 KV cache scales (used by FP8 attention implementations)
+  float k_scale_fp8 = 1.0f;
+  float v_scale_fp8 = 1.0f;
 };
 
 #define DEFINE_CPU_ATTENTION_PARAMS                                         \
@@ -1149,7 +1172,11 @@ class AttentionMainLoop {
                        bool use_sink) {
 #ifdef DEFINE_FAST_EXP
       DEFINE_FAST_EXP
+      bool constexpr IsReducedPrecision =
+          std::is_same_v<query_t, c10::BFloat16> ||
+          std::is_same_v<query_t, c10::Half>;
 #endif
+
       using prob_buffer_vec_t = typename VecTypeTrait<prob_buffer_t>::vec_t;
       static_assert(sizeof(prob_buffer_t) <= sizeof(logits_buffer_t));
 
@@ -1198,8 +1225,17 @@ class AttentionMainLoop {
             vec = vec - max_vec;
 
             // compute exp
-#ifdef DEFINE_FAST_EXP
-            vec = fast_exp(vec);
+
+#if defined(DEFINE_FAST_EXP)
+  #ifdef __aarch64__
+            if constexpr (IsReducedPrecision) {
+              vec = fast_exp_f16(vec);
+            } else
+  #endif
+            {
+              vec = fast_exp(vec);
+            }
+
             prob_buffer_vec_t output_vec(vec);
             output_vec.save(curr_prob_buffer_iter);
 #else
@@ -1255,7 +1291,11 @@ class AttentionMainLoop {
                        int32_t kv_tile_token_num, float softcap_scale) {
 #ifdef DEFINE_FAST_EXP
       DEFINE_FAST_EXP
+      bool constexpr IsReducedPrecision =
+          std::is_same_v<query_t, c10::BFloat16> ||
+          std::is_same_v<query_t, c10::Half>;
 #endif
+
       float inv_softcap_scale = 1.0 / softcap_scale;
       vec_op::FP32Vec16 softcap_scale_vec(softcap_scale);
       vec_op::FP32Vec16 inv_softcap_scale_vec(inv_softcap_scale);
@@ -1269,8 +1309,15 @@ class AttentionMainLoop {
           vec_op::FP32Vec16 vec(curr_logits_buffer_iter);
           vec = vec * inv_softcap_scale_vec;
 
-#ifdef DEFINE_FAST_EXP
-          vec = fast_exp(vec);
+#if defined(DEFINE_FAST_EXP)
+  #ifdef __aarch64__
+          if constexpr (IsReducedPrecision) {
+            vec = fast_exp_f16(vec);
+          } else
+  #endif
+          {
+            vec = fast_exp(vec);
+          }
           vec_op::FP32Vec16 inv_vec = ones_vec / vec;
           vec = (vec - inv_vec) / (vec + inv_vec);
 #else
@@ -1347,6 +1394,13 @@ class AttentionMainLoop {
       }
 
       attention_impl_t attn_impl;
+      constexpr bool fp8_kv = std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
+                              std::is_same_v<kv_cache_t, c10::Float8_e5m2>;
+      float output_v_scale = 1.0f;
+      if constexpr (fp8_kv) {
+        attn_impl.init_from_input(input);
+        output_v_scale = attn_impl.get_output_v_scale();
+      }
 
       // general information
       const int32_t q_head_num = input->num_heads;
@@ -1726,7 +1780,7 @@ class AttentionMainLoop {
                                reinterpret_cast<query_t*>(input->output) +
                                    output_buffer_offset,
                                sum_buffer, actual_q_heads_per_kv,
-                               actual_q_token_num, q_head_num);
+                               actual_q_token_num, q_head_num, output_v_scale);
                 } else {
                   const int32_t stride =
                       actual_q_heads_per_kv * split_kv_q_token_num_threshold;
@@ -1796,7 +1850,7 @@ class AttentionMainLoop {
               split_output_buffer,
               reinterpret_cast<query_t*>(input->output) + output_buffer_offset,
               split_sum_buffer, actual_q_heads_per_kv, curr_output_token_num,
-              q_head_num);
+              q_head_num, output_v_scale);
         }
       }
     }
@@ -1920,8 +1974,8 @@ class AttentionMainLoop {
                     query_t* __restrict__ curr_output_buffer,
                     float* __restrict__ sum_buffer,
                     const int32_t q_heads_per_kv,
-                    const int32_t actual_q_token_num,
-                    const int32_t q_head_num) {
+                    const int32_t actual_q_token_num, const int32_t q_head_num,
+                    const float v_scale = 1.0f) {
     // final output
     using output_vec_t = typename VecTypeTrait<query_t>::vec_t;
 
@@ -1935,7 +1989,7 @@ class AttentionMainLoop {
           curr_partial_output_buffer;
       query_t* __restrict__ curr_output_buffer_iter = curr_output_buffer;
       for (int32_t head_idx = 0; head_idx < q_heads_per_kv; ++head_idx) {
-        vec_op::FP32Vec16 inv_sum_scale_vec(1.0 / *curr_sum_buffer);
+        vec_op::FP32Vec16 inv_sum_scale_vec(v_scale / *curr_sum_buffer);
 
         for (int32_t i = 0; i < group_num_per_head; ++i) {
           vec_op::FP32Vec16 vec(curr_partial_output_buffer_iter);
diff --git a/csrc/cpu/cpu_attn_neon.hpp b/csrc/cpu/cpu_attn_neon.hpp
index 3523893c38c5..db4c5df2e88d 100644
--- a/csrc/cpu/cpu_attn_neon.hpp
+++ b/csrc/cpu/cpu_attn_neon.hpp
@@ -248,8 +248,8 @@ class TileGemmNeonFMLA {
 }  // namespace
 
 // this is similar to "ISA::VEC" at the moment
-template <typename scalar_t, int64_t head_dim>
-class AttentionImpl<ISA::NEON, scalar_t, head_dim> {
+template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
+class AttentionImpl<ISA::NEON, scalar_t, head_dim, kv_cache_scalar_t> {
  public:
   using query_t = scalar_t;
   using q_buffer_t = float;
@@ -343,7 +343,8 @@ class AttentionImpl<ISA::NEON, scalar_t, head_dim> {
       const int64_t head_num, const int64_t key_head_num_stride,
       const int64_t value_head_num_stride, const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride) {
+      const int64_t block_size, const int64_t block_size_stride,
+      const float /*k_inv*/ = 0.0f, const float /*v_inv*/ = 0.0f) {
 #pragma omp parallel for collapse(2)
     for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
       for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
@@ -388,7 +389,7 @@ class AttentionImpl<ISA::NEON, scalar_t, head_dim> {
 #ifdef ARM_BF16_SUPPORT
 // For BF16 on Arm, reuse the BFMMLA kernels with 32-token alignment.
 template <int64_t head_dim>
-class AttentionImpl<ISA::NEON, c10::BFloat16, head_dim>
+class AttentionImpl<ISA::NEON, c10::BFloat16, head_dim, c10::BFloat16>
     : public AttentionImplNEONBFMMLA<BLOCK_SIZE_ALIGNMENT, ISA::NEON,
                                      head_dim> {};
 #endif
diff --git a/csrc/cpu/cpu_attn_neon_bfmmla.hpp b/csrc/cpu/cpu_attn_neon_bfmmla.hpp
index fb133aa13098..4e4578a74f5b 100644
--- a/csrc/cpu/cpu_attn_neon_bfmmla.hpp
+++ b/csrc/cpu/cpu_attn_neon_bfmmla.hpp
@@ -602,7 +602,8 @@ class AttentionImplNEONBFMMLA {
       [[maybe_unused]] const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
       const int64_t block_size,
-      [[maybe_unused]] const int64_t block_size_stride) {
+      [[maybe_unused]] const int64_t block_size_stride,
+      const float /*k_inv*/ = 0.0f, const float /*v_inv*/ = 0.0f) {
     const int64_t k_block_stride = (head_dim / TILE_K) * K_INNER_STRIDE;
     const int64_t v_pair_stride =
         (block_size / V_TOKENS_PER_ROW_BLOCK) * V_INNER_STRIDE;
diff --git a/csrc/cpu/cpu_attn_vec.hpp b/csrc/cpu/cpu_attn_vec.hpp
index f51a232ba955..c3983e0578a5 100644
--- a/csrc/cpu/cpu_attn_vec.hpp
+++ b/csrc/cpu/cpu_attn_vec.hpp
@@ -1,11 +1,37 @@
 #ifndef CPU_ATTN_VEC_HPP
 #define CPU_ATTN_VEC_HPP
 
+#include "cpu_attn_fp8.hpp"
 #include "cpu_attn_impl.hpp"
 
 namespace cpu_attention {
 
 namespace {
+
+// Load 32 kv_cache_t elements starting at ptr and return them as two FP32Vec16s
+// covering the lower 16 and upper 16 positions.
+// For FP8: both halves come from a single BF16Vec32 dequant of 32 bytes.
+// For BF16/FP16/FP32: two separate vector loads at ptr and ptr+16.
+template <typename kv_cache_t>
+FORCE_INLINE std::pair<vec_op::FP32Vec16, vec_op::FP32Vec16> load_b_pair_vec(
+    const kv_cache_t* ptr) {
+  if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e4m3fn>) {
+    // BF16 container, but values are in the FP16 exponent range (bias 15 not
+    // 127).
+    vec_op::BF16Vec32 bf16_b_reg(reinterpret_cast<const uint8_t*>(ptr),
+                                 vec_op::fp8_e4m3_tag{});
+    return {vec_op::FP32Vec16(bf16_b_reg, 0), vec_op::FP32Vec16(bf16_b_reg, 1)};
+  } else if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e5m2>) {
+    vec_op::BF16Vec32 bf16_b_reg(reinterpret_cast<const uint8_t*>(ptr),
+                                 vec_op::fp8_e5m2_tag{});
+    return {vec_op::FP32Vec16(bf16_b_reg, 0), vec_op::FP32Vec16(bf16_b_reg, 1)};
+  } else {
+    using load_vec_t = typename VecTypeTrait<kv_cache_t>::vec_t;
+    return std::make_pair(vec_op::FP32Vec16(load_vec_t(ptr)),
+                          vec_op::FP32Vec16(load_vec_t(ptr + 16)));
+  }
+}
+
 // 8-2-16 pattern, 8 regs for A, 2 regs for B, 16 regs for C, [8, K] @ [k, 32]
 template <typename kv_cache_t>
 class TileGemm82 {
@@ -54,10 +80,7 @@ class TileGemm82 {
                          const int32_t block_size, const int32_t dynamic_k_size,
                          const bool accum_c) {
     static_assert(0 < M && M <= 8);
-    using load_vec_t = typename VecTypeTrait<kv_cache_t>::vec_t;
 
-    kv_cache_t* __restrict__ curr_b_0 = b_tile;
-    kv_cache_t* __restrict__ curr_b_1 = b_tile + 16;
     float* __restrict__ curr_c_0 = c_tile;
     float* __restrict__ curr_c_1 = c_tile + 16;
 
@@ -76,16 +99,14 @@ class TileGemm82 {
     }
 
     float* __restrict__ curr_a = a_tile;
+    kv_cache_t* __restrict__ curr_b = b_tile;
+
     for (int32_t k = 0; k < dynamic_k_size; ++k) {
-      load_vec_t b_0_reg(curr_b_0);
-      vec_op::FP32Vec16 fp32_b_0_reg(b_0_reg);
-      load_vec_t b_1_reg(curr_b_1);
-      vec_op::FP32Vec16 fp32_b_1_reg(b_1_reg);
+      auto [fp32_b_0_reg, fp32_b_1_reg] = load_b_pair_vec(curr_b);
 
       float* __restrict__ curr_m_a = curr_a;
       vec_op::unroll_loop<int32_t, M>([&](int32_t i) {
-        float v = *curr_m_a;
-        vec_op::FP32Vec16 a_reg(v);
+        vec_op::FP32Vec16 a_reg(*curr_m_a);
         c_regs[i * 2] = c_regs[i * 2] + a_reg * fp32_b_0_reg;
         c_regs[i * 2 + 1] = c_regs[i * 2 + 1] + a_reg * fp32_b_1_reg;
 
@@ -95,8 +116,7 @@ class TileGemm82 {
 
       // update
       curr_a += 1;
-      curr_b_0 += ldb;
-      curr_b_1 += ldb;
+      curr_b += ldb;
     }
 
     vec_op::unroll_loop<int32_t, M>([&](int32_t i) {
@@ -109,15 +129,20 @@ class TileGemm82 {
     });
   }
 };
+
 }  // namespace
 
 // This is a general but naive implementation based on vector instructions
-template <typename scalar_t, int64_t head_dim>
-class AttentionImpl<ISA::VEC, scalar_t, head_dim> {
+template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
+class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
+  static constexpr bool fp8_kv =
+      std::is_same_v<kv_cache_scalar_t, c10::Float8_e4m3fn> ||
+      std::is_same_v<kv_cache_scalar_t, c10::Float8_e5m2>;
+
  public:
   using query_t = scalar_t;
   using q_buffer_t = float;
-  using kv_cache_t = scalar_t;
+  using kv_cache_t = kv_cache_scalar_t;
   using logits_buffer_t = float;
   using partial_output_buffer_t = float;
   using prob_buffer_t = float;
@@ -129,11 +154,45 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim> {
   constexpr static int64_t MaxQHeadNumPerIteration = 8;
   constexpr static int64_t HeadDim = head_dim;
   constexpr static ISA ISAType = ISA::VEC;
-  constexpr static bool scale_on_logits = false;  // apply scale on q_buffer
+  constexpr static bool scale_on_logits = fp8_kv;
+
+  float k_scale = 1.0f;
+  float v_scale = 1.0f;
 
  public:
+  void init_from_input(const AttentionInput* input) {
+    if constexpr (fp8_kv) {
+      k_scale = input->k_scale_fp8;
+      v_scale = input->v_scale_fp8;
+    }
+  }
+
+  float get_output_v_scale() const noexcept {
+    if constexpr (fp8_kv) {
+      // VEC dequant unpacks FP8 into a pseudo-FP16 layout (exponent bias 15).
+      // E4M3 (bias=7) needs correction 2^(15-7) = 2^8; E5M2 bias matches FP16
+      // so no correction.
+      if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e5m2>) {
+        return v_scale;
+      } else {
+        return v_scale * 0x1p8f;
+      }
+    }
+    return 1.0f;
+  }
+
   template <template <typename tile_gemm_t> typename attention>
   FORCE_INLINE void execute_attention(DEFINE_CPU_ATTENTION_PARAMS) {
+    if constexpr (fp8_kv) {
+      // Same bias correction as get_output_v_scale: VEC FP8→pseudo-FP16 dequant
+      // uses bias 15; E4M3 (bias=7) needs ×2^8, E5M2 (bias=15) needs no
+      // correction.
+      if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e5m2>) {
+        scale *= k_scale;
+      } else {
+        scale *= k_scale * 0x1p8f;
+      }
+    }
     attention<TileGemm82<kv_cache_t>> attention_iteration;
     attention_iteration(CPU_ATTENTION_PARAMS);
   }
@@ -161,17 +220,19 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim> {
                               // row-major
   }
 
-  // Copy q to q_buffer and cast it to fp32
-  static void copy_q_heads_tile(
-      scalar_t* __restrict__ src,  // [q_num, q_heads_per_kv, head_size]
-      float* __restrict__ q_buffer, const int32_t q_num,
-      const int32_t q_heads_per_kv, const int64_t q_num_stride,
-      const int64_t q_head_stride, float scale) {
+  // Copy q to q_buffer and cast it to fp32.
+  // FP8: QK scale is folded into execute_attention; copy Q unscaled here.
+  void copy_q_heads_tile(scalar_t* __restrict__ src,
+                         float* __restrict__ q_buffer, const int32_t q_num,
+                         const int32_t q_heads_per_kv,
+                         const int64_t q_num_stride,
+                         const int64_t q_head_stride, float scale) {
     static_assert(head_dim % 16 == 0);
     constexpr int32_t unroll_size = head_dim / 16;
     using load_vec_t = typename VecTypeTrait<scalar_t>::vec_t;
 
-    vec_op::FP32Vec16 scale_vec(scale);
+    const float effective_scale = fp8_kv ? 1.0f : scale;
+    vec_op::FP32Vec16 scale_vec(effective_scale);
     for (int32_t q_num_idx = 0; q_num_idx < q_num; ++q_num_idx) {
       for (int32_t q_head_idx = 0; q_head_idx < q_heads_per_kv; ++q_head_idx) {
         scalar_t* __restrict__ curr_q =
@@ -196,13 +257,26 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim> {
   // reshape K as column-major and V as row-major
   static void reshape_and_cache(
       const scalar_t* __restrict__ key, const scalar_t* __restrict__ value,
-      scalar_t* __restrict__ key_cache, scalar_t* __restrict__ value_cache,
+      kv_cache_t* __restrict__ key_cache, kv_cache_t* __restrict__ value_cache,
       const int64_t* __restrict__ slot_mapping, const int64_t token_num,
       const int64_t key_token_num_stride, const int64_t value_token_num_stride,
       const int64_t head_num, const int64_t key_head_num_stride,
       const int64_t value_head_num_stride, const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride) {
+      const int64_t block_size, const int64_t block_size_stride,
+      const float k_inv = 0.0f, const float v_inv = 0.0f) {
+    if constexpr (fp8_kv) {
+      constexpr auto qfn = select_fp8_quant_fn<kv_cache_t>();
+      reshape_and_cache_fp8_vec_impl<scalar_t, qfn>(
+          key, value, reinterpret_cast<uint8_t*>(key_cache),
+          reinterpret_cast<uint8_t*>(value_cache), slot_mapping, token_num,
+          head_num, head_dim, block_size, key_token_num_stride,
+          key_head_num_stride, value_token_num_stride, value_head_num_stride,
+          num_blocks_stride, cache_head_num_stride, num_blocks_stride,
+          cache_head_num_stride, k_inv, v_inv);
+      return;
+    }
+
 #pragma omp parallel for collapse(2)
     for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
       for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
@@ -220,8 +294,9 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim> {
                                           token_idx * key_token_num_stride +
                                           head_idx * key_head_num_stride;
           scalar_t* key_cache_start_ptr =
-              key_cache + block_idx * num_blocks_stride +
-              head_idx * cache_head_num_stride + block_offset;
+              reinterpret_cast<scalar_t*>(key_cache) +
+              block_idx * num_blocks_stride + head_idx * cache_head_num_stride +
+              block_offset;
 
 #pragma GCC unroll 8
           for (int64_t i = 0, j = 0; i < head_dim; ++i, j += block_size) {
@@ -234,8 +309,9 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim> {
                                             token_idx * value_token_num_stride +
                                             head_idx * value_head_num_stride;
           scalar_t* value_cache_start_ptr =
-              value_cache + block_idx * num_blocks_stride +
-              head_idx * cache_head_num_stride + block_offset * head_dim;
+              reinterpret_cast<scalar_t*>(value_cache) +
+              block_idx * num_blocks_stride + head_idx * cache_head_num_stride +
+              block_offset * head_dim;
           std::memcpy(value_cache_start_ptr, value_start_ptr,
                       sizeof(scalar_t) * head_dim);
         }
@@ -243,6 +319,7 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim> {
     }
   }
 };
+
 }  // namespace cpu_attention
 
 #endif
diff --git a/csrc/cpu/cpu_attn_vec16.hpp b/csrc/cpu/cpu_attn_vec16.hpp
index 06e4ad7624e9..bc15d614a7ed 100644
--- a/csrc/cpu/cpu_attn_vec16.hpp
+++ b/csrc/cpu/cpu_attn_vec16.hpp
@@ -116,9 +116,9 @@ class TileGemm161 {
 }  // namespace
 
 // This is a general but naive implementation based on vector instructions
-template <typename scalar_t, int64_t head_dim>
-class AttentionImpl<ISA::VEC16, scalar_t, head_dim>
-    : public AttentionImpl<ISA::VEC, scalar_t, head_dim> {
+template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
+class AttentionImpl<ISA::VEC16, scalar_t, head_dim, kv_cache_scalar_t>
+    : public AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
  public:
   using query_t = scalar_t;
   using q_buffer_t = float;
diff --git a/csrc/cpu/cpu_attn_vsx.hpp b/csrc/cpu/cpu_attn_vsx.hpp
new file mode 100644
index 000000000000..c7e1502bcb05
--- /dev/null
+++ b/csrc/cpu/cpu_attn_vsx.hpp
@@ -0,0 +1,359 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#ifndef CPU_ATTN_VSX_HPP
+#define CPU_ATTN_VSX_HPP
+
+#include "cpu_attn_impl.hpp"
+#include <altivec.h>
+#include <type_traits>
+
+namespace cpu_attention {
+
+namespace {
+
+// ppc64le Vector = 16 bytes (128 bits)
+#define BLOCK_SIZE_ALIGNMENT 32
+#define HEAD_SIZE_ALIGNMENT 32
+#define MAX_Q_HEAD_NUM_PER_ITER 16
+
+template <typename kv_cache_t>
+FORCE_INLINE void load_row8_B_as_f32(const kv_cache_t* p, __vector float& b0,
+                                     __vector float& b1);
+
+// [1] Float Specialization
+template <>
+FORCE_INLINE void load_row8_B_as_f32<float>(const float* p, __vector float& b0,
+                                            __vector float& b1) {
+  b0 = vec_xl(0, const_cast<float*>(p));
+  b1 = vec_xl(0, const_cast<float*>(p + 4));
+}
+
+// [2] BFloat16 Specialization (Little Endian ppc64le)
+// On ppc64le (LE): BF16 bits should land in the HIGH 16 bits of each float32.
+// Byte layout of float32 on LE: [byte0(LSB), byte1, byte2, byte3(MSB)]
+// We need BF16 in bytes2-3 (high half) with bytes0-1 zeroed.
+// vec_mergeh on LE interleaves elements 0..3: result_i = {a[i], b[i]}
+// So vec_mergeh(zeros_u16, raw_u16) gives for each uint16 pair:
+//   uint16[2i]   = zeros[i]  -> low 16 bits of uint32  -> zeroed mantissa LSBs
+//   uint16[2i+1] = raw[i]    -> high 16 bits of uint32 -> BF16 bits
+// Cast to float32 gives exactly (bf16_bits << 16) per element.
+template <>
+FORCE_INLINE void load_row8_B_as_f32<c10::BFloat16>(const c10::BFloat16* p,
+                                                    __vector float& b0,
+                                                    __vector float& b1) {
+  __vector unsigned short raw = vec_xl(
+      0, reinterpret_cast<unsigned short*>(const_cast<c10::BFloat16*>(p)));
+  __vector unsigned short zeros = vec_splat_u16(0);
+
+  // LE: zeros in low 16 bits, raw in high 16 bits → bf16 << 16 == float32
+  b0 = (__vector float)vec_mergeh(zeros, raw);
+  b1 = (__vector float)vec_mergel(zeros, raw);
+}
+
+// Note: c10::Half (FP16) is not supported on PowerPC architecture
+
+template <int32_t M, typename kv_cache_t>
+FORCE_INLINE void gemm_micro_ppc64le_Mx8_Ku4(
+    const float* __restrict A,       // [M x K]
+    const kv_cache_t* __restrict B,  // [K x 8]
+    float* __restrict C,             // [M x 8]
+    int64_t lda, int64_t ldb, int64_t ldc, int32_t K, bool accumulate) {
+  static_assert(1 <= M && M <= 8, "M must be in [1,8]");
+
+#define ROWS_APPLY(OP) OP(0) OP(1) OP(2) OP(3) OP(4) OP(5) OP(6) OP(7)
+#define IF_M(i) if constexpr (M > (i))
+
+  // 1. Define A pointers
+#define DECL_A(i) const float* a##i = A + (i) * lda;
+  ROWS_APPLY(DECL_A)
+#undef DECL_A
+
+  // 2. Define Accumulators (2 vectors covers 8 columns)
+#define DECL_ACC(i) __vector float acc##i##_0, acc##i##_1;
+  ROWS_APPLY(DECL_ACC)
+#undef DECL_ACC
+
+  // 3. Initialize Accumulators (Load C or Zero)
+#define INIT_ACC(i)                                                  \
+  IF_M(i) {                                                          \
+    if (accumulate) {                                                \
+      acc##i##_0 = vec_xl(0, const_cast<float*>(C + (i) * ldc + 0)); \
+      acc##i##_1 = vec_xl(0, const_cast<float*>(C + (i) * ldc + 4)); \
+    } else {                                                         \
+      acc##i##_0 = vec_splats(0.0f);                                 \
+      acc##i##_1 = vec_splats(0.0f);                                 \
+    }                                                                \
+  }
+  ROWS_APPLY(INIT_ACC)
+#undef INIT_ACC
+
+  int32_t k = 0;
+
+  for (; k + 3 < K; k += 4) {
+    // Load 4 values of A for each Row M: A[k...k+3]
+#define LOAD_A4(i)        \
+  __vector float a##i##v; \
+  IF_M(i) a##i##v = vec_xl(0, const_cast<float*>(a##i + k));
+    ROWS_APPLY(LOAD_A4)
+#undef LOAD_A4
+
+    // FMA for specific lane L of A
+    // ppc64le: vec_madd(b, vec_splat(a, lane), acc)
+#define FMAS_LANE(i, aiv, L)                        \
+  IF_M(i) {                                         \
+    __vector float a_broad = vec_splat(aiv, L);     \
+    acc##i##_0 = vec_madd(b0, a_broad, acc##i##_0); \
+    acc##i##_1 = vec_madd(b1, a_broad, acc##i##_1); \
+  }
+
+    // Unroll K=0..3
+    {
+      __vector float b0, b1;
+      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 0) * ldb, b0, b1);
+#define STEP_K0(i) FMAS_LANE(i, a##i##v, 0)
+      ROWS_APPLY(STEP_K0)
+#undef STEP_K0
+    }
+    {
+      __vector float b0, b1;
+      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 1) * ldb, b0, b1);
+#define STEP_K1(i) FMAS_LANE(i, a##i##v, 1)
+      ROWS_APPLY(STEP_K1)
+#undef STEP_K1
+    }
+    {
+      __vector float b0, b1;
+      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 2) * ldb, b0, b1);
+#define STEP_K2(i) FMAS_LANE(i, a##i##v, 2)
+      ROWS_APPLY(STEP_K2)
+#undef STEP_K2
+    }
+    {
+      __vector float b0, b1;
+      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 3) * ldb, b0, b1);
+#define STEP_K3(i) FMAS_LANE(i, a##i##v, 3)
+      ROWS_APPLY(STEP_K3)
+#undef STEP_K3
+    }
+#undef FMAS_LANE
+  }
+
+  for (; k < K; ++k) {
+    __vector float b0, b1;
+    load_row8_B_as_f32<kv_cache_t>(B + (int64_t)k * ldb, b0, b1);
+#define TAIL_ROW(i)                              \
+  IF_M(i) {                                      \
+    __vector float ai = vec_splats(*(a##i + k)); \
+    acc##i##_0 = vec_madd(b0, ai, acc##i##_0);   \
+    acc##i##_1 = vec_madd(b1, ai, acc##i##_1);   \
+  }
+    ROWS_APPLY(TAIL_ROW)
+#undef TAIL_ROW
+  }
+
+#define STORE_ROW(i)                           \
+  IF_M(i) {                                    \
+    vec_xst(acc##i##_0, 0, C + (i) * ldc + 0); \
+    vec_xst(acc##i##_1, 0, C + (i) * ldc + 4); \
+  }
+  ROWS_APPLY(STORE_ROW)
+#undef STORE_ROW
+
+#undef ROWS_APPLY
+#undef IF_M
+}
+
+template <int32_t N, typename kv_cache_t>
+FORCE_INLINE void gemm_macro_ppc64le_Mx8_Ku4(const float* __restrict A,
+                                             const kv_cache_t* __restrict B,
+                                             float* __restrict C, int32_t M,
+                                             int32_t K, int64_t lda,
+                                             int64_t ldb, int64_t ldc,
+                                             bool accumulate) {
+  static_assert(N % 8 == 0, "N must be a multiple of 8");
+  for (int32_t m = 0; m < M;) {
+    int32_t mb = (M - m >= 8) ? 8 : (M - m >= 4) ? 4 : (M - m >= 2) ? 2 : 1;
+    const float* Ab = A + m * lda;
+    float* Cb = C + m * ldc;
+
+    for (int32_t n = 0; n < N; n += 8) {
+      const kv_cache_t* Bn = B + n;
+      float* Cn = Cb + n;
+      switch (mb) {
+        case 8:
+          gemm_micro_ppc64le_Mx8_Ku4<8, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc,
+                                                    K, accumulate);
+          break;
+        case 4:
+          gemm_micro_ppc64le_Mx8_Ku4<4, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc,
+                                                    K, accumulate);
+          break;
+        case 2:
+          gemm_micro_ppc64le_Mx8_Ku4<2, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc,
+                                                    K, accumulate);
+          break;
+        default:
+          gemm_micro_ppc64le_Mx8_Ku4<1, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc,
+                                                    K, accumulate);
+          break;
+      }
+    }
+    m += mb;
+  }
+}
+
+template <typename kv_cache_t>
+class TileGemmPPC64 {
+ public:
+  template <AttentionGemmPhase phase, int32_t k_size>
+  FORCE_INLINE static void gemm(const int32_t m_size,
+                                float* __restrict__ a_tile,
+                                kv_cache_t* __restrict__ b_tile,
+                                float* __restrict__ c_tile, const int64_t lda,
+                                const int64_t ldb, const int64_t ldc,
+                                const int32_t block_size,
+                                const int32_t dynamic_k_size,
+                                const bool accum_c) {
+    if constexpr (phase == AttentionGemmPhase::QK) {
+      gemm_macro_ppc64le_Mx8_Ku4<BLOCK_SIZE_ALIGNMENT, kv_cache_t>(
+          a_tile, b_tile, c_tile, m_size, k_size, lda, ldb, ldc, accum_c);
+    } else {
+      gemm_macro_ppc64le_Mx8_Ku4<HEAD_SIZE_ALIGNMENT, kv_cache_t>(
+          a_tile, b_tile, c_tile, m_size, dynamic_k_size, lda, ldb, ldc,
+          accum_c);
+    }
+  }
+};
+
+}  // namespace
+
+template <typename scalar_t, int64_t head_dim>
+class AttentionImpl<ISA::VSX, scalar_t, head_dim> {
+ public:
+  using query_t = scalar_t;
+  using q_buffer_t = float;
+  using kv_cache_t = scalar_t;
+  using logits_buffer_t = float;
+  using partial_output_buffer_t = float;
+  using prob_buffer_t = float;
+
+  constexpr static int64_t BlockSizeAlignment = BLOCK_SIZE_ALIGNMENT;
+  constexpr static int64_t HeadDimAlignment = HEAD_SIZE_ALIGNMENT;
+  constexpr static int64_t MaxQHeadNumPerIteration = MAX_Q_HEAD_NUM_PER_ITER;
+  constexpr static int64_t HeadDim = head_dim;
+  constexpr static ISA ISAType = ISA::VSX;
+  constexpr static bool scale_on_logits =
+      false;  // Scale is applied to Q during copy
+
+ public:
+  AttentionImpl() {}
+
+  template <template <typename tile_gemm_t> typename attention>
+  FORCE_INLINE void execute_attention(DEFINE_CPU_ATTENTION_PARAMS) {
+    attention<TileGemmPPC64<kv_cache_t>> attention_iteration;
+    attention_iteration(CPU_ATTENTION_PARAMS);
+  }
+
+  // Strides for Memory Layout
+  constexpr static int64_t k_cache_token_group_stride(
+      const int32_t block_size) {
+    return BlockSizeAlignment;  // [head_dim, block_size] layout
+  }
+
+  constexpr static int64_t v_cache_token_group_stride(
+      const int32_t block_size) {
+    return head_dim * BlockSizeAlignment;
+  }
+
+  constexpr static int64_t v_cache_head_group_stride(const int32_t block_size) {
+    return HeadDimAlignment;
+  }
+
+  static void copy_q_heads_tile(scalar_t* __restrict__ src,
+                                float* __restrict__ q_buffer,
+                                const int32_t q_num,
+                                const int32_t q_heads_per_kv,
+                                const int64_t q_num_stride,
+                                const int64_t q_head_stride, float scale) {
+    __vector float scale_vec = vec_splats(scale);
+    constexpr bool is_bf16 = std::is_same<scalar_t, c10::BFloat16>::value;
+
+    for (int32_t i = 0; i < q_num; ++i) {
+      for (int32_t h = 0; h < q_heads_per_kv; ++h) {
+        scalar_t* curr_src = src + i * q_num_stride + h * q_head_stride;
+        float* curr_dst =
+            q_buffer + i * q_heads_per_kv * head_dim + h * head_dim;
+
+        int32_t d = 0;
+        for (; d <= head_dim - 8; d += 8) {
+          __vector float v0, v1;
+          load_row8_B_as_f32<scalar_t>(curr_src + d, v0, v1);
+
+          v0 = vec_mul(v0, scale_vec);
+          v1 = vec_mul(v1, scale_vec);
+
+          vec_xst(v0, 0, curr_dst + d);
+          vec_xst(v1, 0, curr_dst + d + 4);
+        }
+
+        for (; d < head_dim; ++d) {
+          float val = static_cast<float>(curr_src[d]);
+          curr_dst[d] = val * scale;
+        }
+      }
+    }
+  }
+
+  static void reshape_and_cache(
+      const scalar_t* __restrict__ key, const scalar_t* __restrict__ value,
+      scalar_t* __restrict__ key_cache, scalar_t* __restrict__ value_cache,
+      const int64_t* __restrict__ slot_mapping, const int64_t token_num,
+      const int64_t key_token_num_stride, const int64_t value_token_num_stride,
+      const int64_t head_num, const int64_t key_head_num_stride,
+      const int64_t value_head_num_stride, const int64_t num_blocks,
+      const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
+      const int64_t block_size, const int64_t block_size_stride,
+      const float k_inv = 0.0f, const float v_inv = 0.0f) {
+    // k_inv and v_inv are unused on VSX: FP8 KV cache is not supported on
+    // PowerPC. The parameters are present to match the common interface.
+#pragma omp parallel for collapse(2)
+    for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
+      for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
+        const int64_t pos = slot_mapping[token_idx];
+        if (pos < 0) continue;
+
+        const int64_t block_idx = pos / block_size;
+        const int64_t block_offset = pos % block_size;
+
+        {
+          const scalar_t* key_src = key + token_idx * key_token_num_stride +
+                                    head_idx * key_head_num_stride;
+          scalar_t* key_dst = key_cache + block_idx * num_blocks_stride +
+                              head_idx * cache_head_num_stride + block_offset;
+
+          for (int64_t i = 0, j = 0; i < head_dim; ++i, j += block_size) {
+            key_dst[j] = key_src[i];
+          }
+        }
+
+        {
+          const scalar_t* val_src = value + token_idx * value_token_num_stride +
+                                    head_idx * value_head_num_stride;
+          scalar_t* val_dst = value_cache + block_idx * num_blocks_stride +
+                              head_idx * cache_head_num_stride +
+                              block_offset * head_dim;
+
+          std::memcpy(val_dst, val_src, sizeof(scalar_t) * head_dim);
+        }
+      }
+    }
+  }
+};
+
+}  // namespace cpu_attention
+
+#undef BLOCK_SIZE_ALIGNMENT
+#undef HEAD_SIZE_ALIGNMENT
+#undef MAX_Q_HEAD_NUM_PER_ITER
+
+#endif  // CPU_ATTN_VSX_HPP
diff --git a/csrc/cpu/cpu_attn_vxe.hpp b/csrc/cpu/cpu_attn_vxe.hpp
index 45db4ebd7396..cbfda4cf7842 100644
--- a/csrc/cpu/cpu_attn_vxe.hpp
+++ b/csrc/cpu/cpu_attn_vxe.hpp
@@ -244,8 +244,8 @@ class TileGemmS390X {
 
 }  // namespace
 
-template <typename scalar_t, int64_t head_dim>
-class AttentionImpl<ISA::VXE, scalar_t, head_dim> {
+template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
+class AttentionImpl<ISA::VXE, scalar_t, head_dim, kv_cache_scalar_t> {
  public:
   using query_t = scalar_t;
   using q_buffer_t = float;
@@ -342,7 +342,8 @@ class AttentionImpl<ISA::VXE, scalar_t, head_dim> {
       const int64_t head_num, const int64_t key_head_num_stride,
       const int64_t value_head_num_stride, const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride) {
+      const int64_t block_size, const int64_t block_size_stride,
+      const float /*k_inv*/ = 0.0f, const float /*v_inv*/ = 0.0f) {
 #pragma omp parallel for collapse(2)
     for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
       for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
diff --git a/csrc/cpu/cpu_types_arm.hpp b/csrc/cpu/cpu_types_arm.hpp
index f9975b4e29cd..b408731f40d1 100644
--- a/csrc/cpu/cpu_types_arm.hpp
+++ b/csrc/cpu/cpu_types_arm.hpp
@@ -15,6 +15,9 @@ using namespace at::vec;
 
 namespace vec_op {
 
+struct fp8_e4m3_tag {};
+struct fp8_e5m2_tag {};
+
 #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)         \
   AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \
   AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)  \
@@ -322,6 +325,9 @@ struct BF16Vec32 : public VectorizedRegWrapper<BF16Vec32, 4, c10::BFloat16> {
     reg.val[2] = vec8_data.reg.val[0];
     reg.val[3] = vec8_data.reg.val[0];
   };
+
+  explicit BF16Vec32(const uint8_t*, fp8_e4m3_tag) : Base() {}
+  explicit BF16Vec32(const uint8_t*, fp8_e5m2_tag) : Base() {}
 };
 
 struct FP32Vec4 : public VectorizedRegWrapper<FP32Vec4, 1, float> {
@@ -480,6 +486,10 @@ struct FP32Vec16 : public VectorizedRegWrapper<FP32Vec16, 4, float> {
 
   explicit FP32Vec16(const BF16Vec8& v) : FP32Vec16(FP32Vec8(v)) {};
 
+  // FP8 stub: dead code on ARM (fp8 KV cache is x86-only), needed for
+  // load_b_pair_vec template to compile on all platforms.
+  explicit FP32Vec16(const BF16Vec32&, int) : Base() {}
+
   explicit FP32Vec16(const FP16Vec16& v) {
     reg.val[0] = Vectorized<float>(vcvt_f32_f16(vget_low_f16(v.reg.val[0])));
     reg.val[1] = Vectorized<float>(vcvt_f32_f16(vget_high_f16(v.reg.val[0])));
diff --git a/csrc/cpu/cpu_types_riscv.hpp b/csrc/cpu/cpu_types_riscv.hpp
index 910ee5c11331..e617d98dd002 100644
--- a/csrc/cpu/cpu_types_riscv.hpp
+++ b/csrc/cpu/cpu_types_riscv.hpp
@@ -1,832 +1,25 @@
 #ifndef CPU_TYPES_RISCV_HPP
 #define CPU_TYPES_RISCV_HPP
 
-#include <algorithm>
-#include <cmath>
-#include <cstring>
-#include <iostream>
-#include <limits>
-#include <riscv_vector.h>
-#include <torch/all.h>
+// RISC-V Vector (RVV) CPU type definitions for vLLM.
+//
+// Supports multiple VLENs via compile-time dispatch. The compiler defines
+// __riscv_v_min_vlen from the zvl<N>b extension in -march. The defs header
+// maps VLEN to the correct LMUL suffixes, and the impl header provides
+// VLEN-independent class implementations.
+//
+// To add support for a new VLEN, add the LMUL mapping in
+// cpu_types_riscv_defs.hpp (the impl header needs no changes).
 
-// ============================================================================
-// Vector Register Type Definitions (VLEN=128 bits)
-// ============================================================================
-
-typedef vfloat16m1_t fixed_vfloat16m1_t
-    __attribute__((riscv_rvv_vector_bits(128)));
-typedef vfloat16m2_t fixed_vfloat16m2_t
-    __attribute__((riscv_rvv_vector_bits(256)));
-
-typedef vfloat32m1_t fixed_vfloat32m1_t
-    __attribute__((riscv_rvv_vector_bits(128)));
-typedef vfloat32m2_t fixed_vfloat32m2_t
-    __attribute__((riscv_rvv_vector_bits(256)));
-typedef vfloat32m4_t fixed_vfloat32m4_t
-    __attribute__((riscv_rvv_vector_bits(512)));
-typedef vfloat32m8_t fixed_vfloat32m8_t
-    __attribute__((riscv_rvv_vector_bits(1024)));
-
-typedef vint32m2_t fixed_vint32m2_t __attribute__((riscv_rvv_vector_bits(256)));
-typedef vint32m4_t fixed_vint32m4_t __attribute__((riscv_rvv_vector_bits(512)));
-
-typedef vuint16m1_t fixed_vuint16m1_t
-    __attribute__((riscv_rvv_vector_bits(128)));
-typedef vuint16m2_t fixed_vuint16m2_t
-    __attribute__((riscv_rvv_vector_bits(256)));
-typedef vuint16m4_t fixed_vuint16m4_t
-    __attribute__((riscv_rvv_vector_bits(512)));
-
-#ifdef RISCV_BF16_SUPPORT
-typedef vbfloat16m1_t fixed_vbfloat16m1_t
-    __attribute__((riscv_rvv_vector_bits(128)));
-typedef vbfloat16m2_t fixed_vbfloat16m2_t
-    __attribute__((riscv_rvv_vector_bits(256)));
-typedef vbfloat16m4_t fixed_vbfloat16m4_t
-    __attribute__((riscv_rvv_vector_bits(512)));
-#endif
-
-namespace vec_op {
-
-#ifdef RISCV_BF16_SUPPORT
-  #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)         \
-    AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \
-    AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)  \
-    AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__)
-#else
-  #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)         \
-    AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \
-    AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)
+#ifndef __riscv_vector
+  #error "cpu_types_riscv.hpp included in a non-RVV translation unit"
 #endif
 
-#define VLLM_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...) \
-  AT_DISPATCH_SWITCH(TYPE, NAME, VLLM_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
-
-#define FORCE_INLINE __attribute__((always_inline)) inline
-
-namespace {
-template <typename T, T... indexes, typename F>
-constexpr void unroll_loop_item(std::integer_sequence<T, indexes...>, F&& f) {
-  (f(std::integral_constant<T, indexes>{}), ...);
-};
-}  // namespace
-
-template <typename T, T count, typename F,
-          typename = std::enable_if_t<std::is_invocable_v<F, T>>>
-constexpr void unroll_loop(F&& f) {
-  unroll_loop_item(std::make_integer_sequence<T, count>{}, std::forward<F>(f));
-}
-
-template <typename T>
-struct Vec {
-  constexpr static int get_elem_num() { return T::VEC_ELEM_NUM; };
-};
-
-struct FP32Vec8;
-struct FP32Vec16;
-
-// ============================================================================
-// FP16 Implementation
-// ============================================================================
-
-struct FP16Vec8 : public Vec<FP16Vec8> {
-  constexpr static int VEC_ELEM_NUM = 8;
-  fixed_vfloat16m1_t reg;
-
-  explicit FP16Vec8(const void* ptr)
-      : reg(__riscv_vle16_v_f16m1(static_cast<const _Float16*>(ptr),
-                                  VEC_ELEM_NUM)) {};
-
-  explicit FP16Vec8(const FP32Vec8&);
-
-  void save(void* ptr) const {
-    __riscv_vse16_v_f16m1(static_cast<_Float16*>(ptr), reg, VEC_ELEM_NUM);
-  }
-  void save(void* ptr, int elem_num) const {
-    __riscv_vse16_v_f16m1(static_cast<_Float16*>(ptr), reg, elem_num);
-  }
-  void save_strided(void* ptr, ptrdiff_t stride) const {
-    ptrdiff_t byte_stride = stride * sizeof(_Float16);
-    __riscv_vsse16_v_f16m1(static_cast<_Float16*>(ptr), byte_stride, reg,
-                           VEC_ELEM_NUM);
-  }
-};
-
-struct FP16Vec16 : public Vec<FP16Vec16> {
-  constexpr static int VEC_ELEM_NUM = 16;
-  fixed_vfloat16m2_t reg;
-
-  explicit FP16Vec16(const void* ptr)
-      : reg(__riscv_vle16_v_f16m2(static_cast<const _Float16*>(ptr),
-                                  VEC_ELEM_NUM)) {};
-
-  explicit FP16Vec16(const FP32Vec16& vec);
-
-  void save(void* ptr) const {
-    __riscv_vse16_v_f16m2(static_cast<_Float16*>(ptr), reg, VEC_ELEM_NUM);
-  }
-  void save(void* ptr, int elem_num) const {
-    __riscv_vse16_v_f16m2(static_cast<_Float16*>(ptr), reg, elem_num);
-  }
-  void save_strided(void* ptr, ptrdiff_t stride) const {
-    ptrdiff_t byte_stride = stride * sizeof(_Float16);
-    __riscv_vsse16_v_f16m2(static_cast<_Float16*>(ptr), byte_stride, reg,
-                           VEC_ELEM_NUM);
-  }
-};
-
-// ============================================================================
-// BF16 Implementation
-// ============================================================================
-
-#ifdef RISCV_BF16_SUPPORT
-
-FORCE_INLINE fixed_vuint16m1_t bf16_to_u16(fixed_vbfloat16m1_t v) {
-  return __riscv_vreinterpret_v_bf16m1_u16m1(v);
-}
-FORCE_INLINE fixed_vuint16m2_t bf16_to_u16(fixed_vbfloat16m2_t v) {
-  return __riscv_vreinterpret_v_bf16m2_u16m2(v);
-}
-FORCE_INLINE fixed_vuint16m4_t bf16_to_u16(fixed_vbfloat16m4_t v) {
-  return __riscv_vreinterpret_v_bf16m4_u16m4(v);
-}
-
-struct BF16Vec8 : public Vec<BF16Vec8> {
-  constexpr static int VEC_ELEM_NUM = 8;
-  fixed_vbfloat16m1_t reg;
-
-  explicit BF16Vec8(const void* ptr)
-      : reg(__riscv_vreinterpret_v_u16m1_bf16m1(__riscv_vle16_v_u16m1(
-            reinterpret_cast<const uint16_t*>(ptr), VEC_ELEM_NUM))) {};
-
-  explicit BF16Vec8(fixed_vbfloat16m1_t data) : reg(data) {};
-  explicit BF16Vec8(const FP32Vec8&);
-
-  void save(void* ptr) const {
-    __riscv_vse16_v_u16m1(reinterpret_cast<uint16_t*>(ptr), bf16_to_u16(reg),
-                          VEC_ELEM_NUM);
-  }
-  void save(void* ptr, int elem_num) const {
-    __riscv_vse16_v_u16m1(reinterpret_cast<uint16_t*>(ptr), bf16_to_u16(reg),
-                          elem_num);
-  }
-  void save_strided(void* ptr, ptrdiff_t stride) const {
-    ptrdiff_t byte_stride = stride * sizeof(uint16_t);
-    __riscv_vsse16_v_u16m1(reinterpret_cast<uint16_t*>(ptr), byte_stride,
-                           bf16_to_u16(reg), VEC_ELEM_NUM);
-  }
-};
-
-struct BF16Vec16 : public Vec<BF16Vec16> {
-  constexpr static int VEC_ELEM_NUM = 16;
-  fixed_vbfloat16m2_t reg;
-
-  explicit BF16Vec16(const void* ptr)
-      : reg(__riscv_vreinterpret_v_u16m2_bf16m2(__riscv_vle16_v_u16m2(
-            reinterpret_cast<const uint16_t*>(ptr), VEC_ELEM_NUM))) {};
-
-  explicit BF16Vec16(fixed_vbfloat16m2_t data) : reg(data) {};
-  explicit BF16Vec16(const FP32Vec16&);
-
-  void save(void* ptr) const {
-    __riscv_vse16_v_u16m2(reinterpret_cast<uint16_t*>(ptr), bf16_to_u16(reg),
-                          VEC_ELEM_NUM);
-  }
-  void save(void* ptr, int elem_num) const {
-    __riscv_vse16_v_u16m2(reinterpret_cast<uint16_t*>(ptr), bf16_to_u16(reg),
-                          elem_num);
-  }
-  void save_strided(void* ptr, ptrdiff_t stride) const {
-    ptrdiff_t byte_stride = stride * sizeof(uint16_t);
-    __riscv_vsse16_v_u16m2(reinterpret_cast<uint16_t*>(ptr), byte_stride,
-                           bf16_to_u16(reg), VEC_ELEM_NUM);
-  }
-};
-
-struct BF16Vec32 : public Vec<BF16Vec32> {
-  constexpr static int VEC_ELEM_NUM = 32;
-  fixed_vbfloat16m4_t reg;
-
-  explicit BF16Vec32(const void* ptr)
-      : reg(__riscv_vreinterpret_v_u16m4_bf16m4(__riscv_vle16_v_u16m4(
-            reinterpret_cast<const uint16_t*>(ptr), VEC_ELEM_NUM))) {};
-
-  explicit BF16Vec32(fixed_vbfloat16m4_t data) : reg(data) {};
-
-  explicit BF16Vec32(const BF16Vec8& v) {
-    fixed_vuint16m1_t u16_val = bf16_to_u16(v.reg);
-    fixed_vuint16m4_t u16_combined =
-        __riscv_vcreate_v_u16m1_u16m4(u16_val, u16_val, u16_val, u16_val);
-    reg = __riscv_vreinterpret_v_u16m4_bf16m4(u16_combined);
-  };
-
-  void save(void* ptr) const {
-    __riscv_vse16_v_u16m4(reinterpret_cast<uint16_t*>(ptr), bf16_to_u16(reg),
-                          VEC_ELEM_NUM);
-  }
-  void save(void* ptr, int elem_num) const {
-    __riscv_vse16_v_u16m4(reinterpret_cast<uint16_t*>(ptr), bf16_to_u16(reg),
-                          elem_num);
-  }
-  void save_strided(void* ptr, ptrdiff_t stride) const {
-    ptrdiff_t byte_stride = stride * sizeof(uint16_t);
-    __riscv_vsse16_v_u16m4(reinterpret_cast<uint16_t*>(ptr), byte_stride,
-                           bf16_to_u16(reg), VEC_ELEM_NUM);
-  }
-};
-
-#else
-// ============================================================================
-// BF16 Fallback Implementation (FP32 Simulation)
-// ============================================================================
-
-struct BF16Vec8 : public Vec<BF16Vec8> {
-  constexpr static int VEC_ELEM_NUM = 8;
-  fixed_vfloat32m2_t reg_fp32;
-  explicit BF16Vec8(const void* ptr) {
-    const uint16_t* u16 = static_cast<const uint16_t*>(ptr);
-    float tmp[8];
-    for (int i = 0; i < 8; ++i) {
-      uint32_t v = static_cast<uint32_t>(u16[i]) << 16;
-      std::memcpy(&tmp[i], &v, 4);
-    }
-    reg_fp32 = __riscv_vle32_v_f32m2(tmp, 8);
-  }
-  explicit BF16Vec8(const FP32Vec8&);
-  void save(void* ptr) const {
-    float tmp[8];
-    __riscv_vse32_v_f32m2(tmp, reg_fp32, 8);
-    uint16_t* u16 = static_cast<uint16_t*>(ptr);
-    for (int i = 0; i < 8; ++i) {
-      uint32_t v;
-      std::memcpy(&v, &tmp[i], 4);
-      u16[i] = static_cast<uint16_t>(v >> 16);
-    }
-  }
-  void save(void* ptr, int elem_num) const {
-    float tmp[8];
-    __riscv_vse32_v_f32m2(tmp, reg_fp32, 8);
-    uint16_t* u16 = static_cast<uint16_t*>(ptr);
-    for (int i = 0; i < elem_num; ++i) {
-      uint32_t v;
-      std::memcpy(&v, &tmp[i], 4);
-      u16[i] = static_cast<uint16_t>(v >> 16);
-    }
-  }
-  void save_strided(void* ptr, ptrdiff_t stride) const {
-    float tmp[8];
-    __riscv_vse32_v_f32m2(tmp, reg_fp32, 8);
-    uint8_t* u8 = static_cast<uint8_t*>(ptr);
-    ptrdiff_t byte_stride = stride * sizeof(uint16_t);
-    for (int i = 0; i < 8; ++i) {
-      uint32_t v;
-      std::memcpy(&v, &tmp[i], 4);
-      uint16_t val = static_cast<uint16_t>(v >> 16);
-      *reinterpret_cast<uint16_t*>(u8 + i * byte_stride) = val;
-    }
-  }
-};
-
-struct BF16Vec16 : public Vec<BF16Vec16> {
-  constexpr static int VEC_ELEM_NUM = 16;
-  fixed_vfloat32m4_t reg_fp32;
-  explicit BF16Vec16(const void* ptr) {
-    const uint16_t* u16 = static_cast<const uint16_t*>(ptr);
-    float tmp[16];
-    for (int i = 0; i < 16; ++i) {
-      uint32_t v = static_cast<uint32_t>(u16[i]) << 16;
-      std::memcpy(&tmp[i], &v, 4);
-    }
-    reg_fp32 = __riscv_vle32_v_f32m4(tmp, 16);
-  }
-  explicit BF16Vec16(const FP32Vec16&);
-  void save(void* ptr) const {
-    float tmp[16];
-    __riscv_vse32_v_f32m4(tmp, reg_fp32, 16);
-    uint16_t* u16 = static_cast<uint16_t*>(ptr);
-    for (int i = 0; i < 16; ++i) {
-      uint32_t v;
-      std::memcpy(&v, &tmp[i], 4);
-      u16[i] = static_cast<uint16_t>(v >> 16);
-    }
-  }
-  void save(void* ptr, int elem_num) const {
-    float tmp[16];
-    __riscv_vse32_v_f32m4(tmp, reg_fp32, 16);
-    uint16_t* u16 = static_cast<uint16_t*>(ptr);
-    for (int i = 0; i < elem_num; ++i) {
-      uint32_t v;
-      std::memcpy(&v, &tmp[i], 4);
-      u16[i] = static_cast<uint16_t>(v >> 16);
-    }
-  }
-  void save_strided(void* ptr, ptrdiff_t stride) const {
-    float tmp[16];
-    __riscv_vse32_v_f32m4(tmp, reg_fp32, 16);
-    uint8_t* u8 = static_cast<uint8_t*>(ptr);
-    ptrdiff_t byte_stride = stride * sizeof(uint16_t);
-    for (int i = 0; i < 16; ++i) {
-      uint32_t v;
-      std::memcpy(&v, &tmp[i], 4);
-      uint16_t val = static_cast<uint16_t>(v >> 16);
-      *reinterpret_cast<uint16_t*>(u8 + i * byte_stride) = val;
-    }
-  }
-};
-
-struct BF16Vec32 : public Vec<BF16Vec32> {
-  constexpr static int VEC_ELEM_NUM = 32;
-  fixed_vfloat32m8_t reg_fp32;
-
-  explicit BF16Vec32(const void* ptr) {
-    const uint16_t* u16 = static_cast<const uint16_t*>(ptr);
-    float tmp[32];
-    for (int i = 0; i < 32; ++i) {
-      uint32_t v = static_cast<uint32_t>(u16[i]) << 16;
-      std::memcpy(&tmp[i], &v, 4);
-    }
-    reg_fp32 = __riscv_vle32_v_f32m8(tmp, 32);
-  }
-
-  explicit BF16Vec32(const BF16Vec8& v) {
-    float tmp_small[8];
-    __riscv_vse32_v_f32m2(tmp_small, v.reg_fp32, 8);
-    float tmp_large[32];
-    for (int i = 0; i < 4; ++i) {
-      std::memcpy(tmp_large + (i * 8), tmp_small, 8 * sizeof(float));
-    }
-    reg_fp32 = __riscv_vle32_v_f32m8(tmp_large, 32);
-  }
-
-  void save(void* ptr) const {
-    float tmp[32];
-    __riscv_vse32_v_f32m8(tmp, reg_fp32, 32);
-    uint16_t* u16 = static_cast<uint16_t*>(ptr);
-    for (int i = 0; i < 32; ++i) {
-      uint32_t v;
-      std::memcpy(&v, &tmp[i], 4);
-      u16[i] = static_cast<uint16_t>(v >> 16);
-    }
-  }
-
-  void save(void* ptr, int elem_num) const {
-    float tmp[32];
-    __riscv_vse32_v_f32m8(tmp, reg_fp32, 32);
-    uint16_t* u16 = static_cast<uint16_t*>(ptr);
-    for (int i = 0; i < elem_num; ++i) {
-      uint32_t v;
-      std::memcpy(&v, &tmp[i], 4);
-      u16[i] = static_cast<uint16_t>(v >> 16);
-    }
-  }
-
-  void save_strided(void* ptr, ptrdiff_t stride) const {
-    float tmp[32];
-    __riscv_vse32_v_f32m8(tmp, reg_fp32, 32);
-    uint8_t* u8 = static_cast<uint8_t*>(ptr);
-    ptrdiff_t byte_stride = stride * sizeof(uint16_t);
-    for (int i = 0; i < 32; ++i) {
-      uint32_t v;
-      std::memcpy(&v, &tmp[i], 4);
-      uint16_t val = static_cast<uint16_t>(v >> 16);
-      *reinterpret_cast<uint16_t*>(u8 + i * byte_stride) = val;
-    }
-  }
-};
-#endif
-
-// ============================================================================
-// FP32 Implementation
-// ============================================================================
-
-struct FP32Vec4 : public Vec<FP32Vec4> {
-  constexpr static int VEC_ELEM_NUM = 4;
-  fixed_vfloat32m1_t reg;
-  explicit FP32Vec4(float v) : reg(__riscv_vfmv_v_f_f32m1(v, VEC_ELEM_NUM)) {};
-  explicit FP32Vec4() : reg(__riscv_vfmv_v_f_f32m1(0.0f, VEC_ELEM_NUM)) {};
-  explicit FP32Vec4(const float* ptr)
-      : reg(__riscv_vle32_v_f32m1(ptr, VEC_ELEM_NUM)) {};
-  explicit FP32Vec4(fixed_vfloat32m1_t data) : reg(data) {};
-  explicit FP32Vec4(const FP32Vec4& data) : reg(data.reg) {};
-  void save(float* ptr) const { __riscv_vse32_v_f32m1(ptr, reg, VEC_ELEM_NUM); }
-  void save(float* ptr, int elem_num) const {
-    __riscv_vse32_v_f32m1(ptr, reg, elem_num);
-  }
-};
-
-struct FP32Vec8 : public Vec<FP32Vec8> {
-  constexpr static int VEC_ELEM_NUM = 8;
-  fixed_vfloat32m2_t reg;
-
-  explicit FP32Vec8(float v) : reg(__riscv_vfmv_v_f_f32m2(v, VEC_ELEM_NUM)) {};
-  explicit FP32Vec8() : reg(__riscv_vfmv_v_f_f32m2(0.0f, VEC_ELEM_NUM)) {};
-  explicit FP32Vec8(const float* ptr)
-      : reg(__riscv_vle32_v_f32m2(ptr, VEC_ELEM_NUM)) {};
-  explicit FP32Vec8(fixed_vfloat32m2_t data) : reg(data) {};
-  explicit FP32Vec8(const FP32Vec8& data) : reg(data.reg) {};
-  explicit FP32Vec8(const FP16Vec8& v)
-      : reg(__riscv_vfwcvt_f_f_v_f32m2(v.reg, VEC_ELEM_NUM)) {};
-  explicit FP32Vec8(fixed_vfloat16m1_t v)
-      : reg(__riscv_vfwcvt_f_f_v_f32m2(v, VEC_ELEM_NUM)) {};
-
-#ifdef RISCV_BF16_SUPPORT
-  explicit FP32Vec8(fixed_vbfloat16m1_t v)
-      : reg(__riscv_vfwcvtbf16_f_f_v_f32m2(v, VEC_ELEM_NUM)) {};
-  explicit FP32Vec8(const BF16Vec8& v)
-      : reg(__riscv_vfwcvtbf16_f_f_v_f32m2(v.reg, VEC_ELEM_NUM)) {};
-#else
-  explicit FP32Vec8(const BF16Vec8& v) : reg(v.reg_fp32) {};
+#ifndef __riscv_v_min_vlen
+  #error "compiler did not define __riscv_v_min_vlen; pass -march=...zvl<N>b"
 #endif
 
-  float reduce_sum() const {
-    fixed_vfloat32m1_t scalar = __riscv_vfmv_s_f_f32m1(0.0f, 1);
-    scalar = __riscv_vfredusum_vs_f32m2_f32m1(reg, scalar, VEC_ELEM_NUM);
-    return __riscv_vfmv_f_s_f32m1_f32(scalar);
-  }
-
-  FP32Vec8 operator*(const FP32Vec8& b) const {
-    return FP32Vec8(__riscv_vfmul_vv_f32m2(reg, b.reg, VEC_ELEM_NUM));
-  }
-  FP32Vec8 operator+(const FP32Vec8& b) const {
-    return FP32Vec8(__riscv_vfadd_vv_f32m2(reg, b.reg, VEC_ELEM_NUM));
-  }
-  FP32Vec8 operator-(const FP32Vec8& b) const {
-    return FP32Vec8(__riscv_vfsub_vv_f32m2(reg, b.reg, VEC_ELEM_NUM));
-  }
-  FP32Vec8 operator/(const FP32Vec8& b) const {
-    return FP32Vec8(__riscv_vfdiv_vv_f32m2(reg, b.reg, VEC_ELEM_NUM));
-  }
-
-  FP32Vec8 min(const FP32Vec8& b) const {
-    return FP32Vec8(__riscv_vfmin_vv_f32m2(reg, b.reg, VEC_ELEM_NUM));
-  }
-  FP32Vec8 max(const FP32Vec8& b) const {
-    return FP32Vec8(__riscv_vfmax_vv_f32m2(reg, b.reg, VEC_ELEM_NUM));
-  }
-  FP32Vec8 abs() const {
-    return FP32Vec8(__riscv_vfabs_v_f32m2(reg, VEC_ELEM_NUM));
-  }
-
-  FP32Vec8 min(const FP32Vec8& b, int elem_num) const {
-    return FP32Vec8(__riscv_vfmin_vv_f32m2(reg, b.reg, elem_num));
-  }
-  FP32Vec8 max(const FP32Vec8& b, int elem_num) const {
-    return FP32Vec8(__riscv_vfmax_vv_f32m2(reg, b.reg, elem_num));
-  }
-
-  FP32Vec8 clamp(const FP32Vec8& min_v, const FP32Vec8& max_v) const {
-    fixed_vfloat32m2_t temp =
-        __riscv_vfmax_vv_f32m2(min_v.reg, reg, VEC_ELEM_NUM);
-    return FP32Vec8(__riscv_vfmin_vv_f32m2(max_v.reg, temp, VEC_ELEM_NUM));
-  }
-
-  void save(float* ptr) const { __riscv_vse32_v_f32m2(ptr, reg, VEC_ELEM_NUM); }
-  void save(float* ptr, int elem_num) const {
-    __riscv_vse32_v_f32m2(ptr, reg, elem_num);
-  }
-  void save_strided(float* ptr, ptrdiff_t stride) const {
-    ptrdiff_t byte_stride = stride * sizeof(float);
-    __riscv_vsse32_v_f32m2(ptr, byte_stride, reg, VEC_ELEM_NUM);
-  }
-
-  FP32Vec8 exp() const {
-    const float inv_ln2 = 1.44269504088896341f;
-    fixed_vfloat32m2_t x_scaled =
-        __riscv_vfmul_vf_f32m2(reg, inv_ln2, VEC_ELEM_NUM);
-    fixed_vint32m2_t n_int = __riscv_vfcvt_x_f_v_i32m2(x_scaled, VEC_ELEM_NUM);
-    fixed_vfloat32m2_t n_float = __riscv_vfcvt_f_x_v_f32m2(n_int, VEC_ELEM_NUM);
-
-    fixed_vfloat32m2_t r =
-        __riscv_vfsub_vv_f32m2(x_scaled, n_float, VEC_ELEM_NUM);
-
-    fixed_vfloat32m2_t poly =
-        __riscv_vfmv_v_f_f32m2(0.001333355810164f, VEC_ELEM_NUM);
-    poly = __riscv_vfmul_vv_f32m2(poly, r, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m2(poly, 0.009618129107628f, VEC_ELEM_NUM);
-    poly = __riscv_vfmul_vv_f32m2(poly, r, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m2(poly, 0.055504108664821f, VEC_ELEM_NUM);
-    poly = __riscv_vfmul_vv_f32m2(poly, r, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m2(poly, 0.240226506959101f, VEC_ELEM_NUM);
-    poly = __riscv_vfmul_vv_f32m2(poly, r, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m2(poly, 0.693147180559945f, VEC_ELEM_NUM);
-    poly = __riscv_vfmul_vv_f32m2(poly, r, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m2(poly, 1.0f, VEC_ELEM_NUM);
-
-    fixed_vint32m2_t biased_exp =
-        __riscv_vadd_vx_i32m2(n_int, 127, VEC_ELEM_NUM);
-    biased_exp = __riscv_vmax_vx_i32m2(biased_exp, 0, VEC_ELEM_NUM);
-    fixed_vint32m2_t exponent_bits =
-        __riscv_vsll_vx_i32m2(biased_exp, 23, VEC_ELEM_NUM);
-    fixed_vfloat32m2_t scale =
-        __riscv_vreinterpret_v_i32m2_f32m2(exponent_bits);
-
-    return FP32Vec8(__riscv_vfmul_vv_f32m2(poly, scale, VEC_ELEM_NUM));
-  }
-
-  FP32Vec8 tanh() const {
-    fixed_vfloat32m2_t x_clamped = __riscv_vfmin_vf_f32m2(
-        __riscv_vfmax_vf_f32m2(reg, -9.0f, VEC_ELEM_NUM), 9.0f, VEC_ELEM_NUM);
-    fixed_vfloat32m2_t x2 =
-        __riscv_vfmul_vf_f32m2(x_clamped, 2.0f, VEC_ELEM_NUM);
-    FP32Vec8 exp_val = FP32Vec8(x2).exp();
-    fixed_vfloat32m2_t num =
-        __riscv_vfsub_vf_f32m2(exp_val.reg, 1.0f, VEC_ELEM_NUM);
-    fixed_vfloat32m2_t den =
-        __riscv_vfadd_vf_f32m2(exp_val.reg, 1.0f, VEC_ELEM_NUM);
-    return FP32Vec8(__riscv_vfdiv_vv_f32m2(num, den, VEC_ELEM_NUM));
-  }
-
-  FP32Vec8 er() const {
-    const float p = 0.3275911f, a1 = 0.254829592f, a2 = -0.284496736f,
-                a3 = 1.421413741f, a4 = -1.453152027f, a5 = 1.061405429f;
-    fixed_vfloat32m2_t abs_x = __riscv_vfabs_v_f32m2(reg, VEC_ELEM_NUM);
-
-    fixed_vfloat32m2_t t = __riscv_vfadd_vf_f32m2(
-        __riscv_vfmul_vf_f32m2(abs_x, p, VEC_ELEM_NUM), 1.0f, VEC_ELEM_NUM);
-    t = __riscv_vfrdiv_vf_f32m2(t, 1.0f, VEC_ELEM_NUM);
-
-    fixed_vfloat32m2_t poly = __riscv_vfmv_v_f_f32m2(a5, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m2(__riscv_vfmul_vv_f32m2(poly, t, VEC_ELEM_NUM),
-                                  a4, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m2(__riscv_vfmul_vv_f32m2(poly, t, VEC_ELEM_NUM),
-                                  a3, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m2(__riscv_vfmul_vv_f32m2(poly, t, VEC_ELEM_NUM),
-                                  a2, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m2(__riscv_vfmul_vv_f32m2(poly, t, VEC_ELEM_NUM),
-                                  a1, VEC_ELEM_NUM);
-    poly = __riscv_vfmul_vv_f32m2(poly, t, VEC_ELEM_NUM);
-
-    fixed_vfloat32m2_t exp_val =
-        FP32Vec8(__riscv_vfneg_v_f32m2(
-                     __riscv_vfmul_vv_f32m2(abs_x, abs_x, VEC_ELEM_NUM),
-                     VEC_ELEM_NUM))
-            .exp()
-            .reg;
-    fixed_vfloat32m2_t res = __riscv_vfrsub_vf_f32m2(
-        __riscv_vfmul_vv_f32m2(poly, exp_val, VEC_ELEM_NUM), 1.0f,
-        VEC_ELEM_NUM);
-
-    vbool16_t mask = __riscv_vmflt_vf_f32m2_b16(reg, 0.0f, VEC_ELEM_NUM);
-    return FP32Vec8(__riscv_vfneg_v_f32m2_m(mask, res, VEC_ELEM_NUM));
-  }
-};
-
-struct FP32Vec16 : public Vec<FP32Vec16> {
-  constexpr static int VEC_ELEM_NUM = 16;
-  fixed_vfloat32m4_t reg;
-
-  explicit FP32Vec16(float v) : reg(__riscv_vfmv_v_f_f32m4(v, VEC_ELEM_NUM)) {};
-  explicit FP32Vec16() : reg(__riscv_vfmv_v_f_f32m4(0.0f, VEC_ELEM_NUM)) {};
-  explicit FP32Vec16(const float* ptr)
-      : reg(__riscv_vle32_v_f32m4(ptr, VEC_ELEM_NUM)) {};
-  explicit FP32Vec16(fixed_vfloat32m4_t data) : reg(data) {};
-  explicit FP32Vec16(const FP32Vec8& data)
-      : reg(__riscv_vcreate_v_f32m2_f32m4(data.reg, data.reg)) {};
-  explicit FP32Vec16(const FP32Vec16& data) : reg(data.reg) {};
-  explicit FP32Vec16(const FP16Vec16& v);
-
-#ifdef RISCV_BF16_SUPPORT
-  explicit FP32Vec16(fixed_vbfloat16m2_t v)
-      : reg(__riscv_vfwcvtbf16_f_f_v_f32m4(v, VEC_ELEM_NUM)) {};
-  explicit FP32Vec16(const BF16Vec16& v)
-      : reg(__riscv_vfwcvtbf16_f_f_v_f32m4(v.reg, VEC_ELEM_NUM)) {};
-#else
-  explicit FP32Vec16(const BF16Vec16& v) : reg(v.reg_fp32) {};
-#endif
-
-  FP32Vec16 operator+(const FP32Vec16& b) const {
-    return FP32Vec16(__riscv_vfadd_vv_f32m4(reg, b.reg, VEC_ELEM_NUM));
-  }
-  FP32Vec16 operator-(const FP32Vec16& b) const {
-    return FP32Vec16(__riscv_vfsub_vv_f32m4(reg, b.reg, VEC_ELEM_NUM));
-  }
-  FP32Vec16 operator*(const FP32Vec16& b) const {
-    return FP32Vec16(__riscv_vfmul_vv_f32m4(reg, b.reg, VEC_ELEM_NUM));
-  }
-  FP32Vec16 operator/(const FP32Vec16& b) const {
-    return FP32Vec16(__riscv_vfdiv_vv_f32m4(reg, b.reg, VEC_ELEM_NUM));
-  }
-
-  FP32Vec16 fma(const FP32Vec16& a, const FP32Vec16& b) const {
-    return FP32Vec16(__riscv_vfmacc_vv_f32m4(reg, a.reg, b.reg, VEC_ELEM_NUM));
-  }
-
-  float reduce_sum() const {
-    fixed_vfloat32m1_t scalar = __riscv_vfmv_s_f_f32m1(0.0f, 1);
-    scalar = __riscv_vfredusum_vs_f32m4_f32m1(reg, scalar, VEC_ELEM_NUM);
-    return __riscv_vfmv_f_s_f32m1_f32(scalar);
-  }
-
-  float reduce_max() const {
-    fixed_vfloat32m1_t scalar =
-        __riscv_vfmv_s_f_f32m1(std::numeric_limits<float>::lowest(), 1);
-    scalar = __riscv_vfredmax_vs_f32m4_f32m1(reg, scalar, VEC_ELEM_NUM);
-    return __riscv_vfmv_f_s_f32m1_f32(scalar);
-  }
-
-  float reduce_min() const {
-    fixed_vfloat32m1_t scalar =
-        __riscv_vfmv_s_f_f32m1(std::numeric_limits<float>::max(), 1);
-    scalar = __riscv_vfredmin_vs_f32m4_f32m1(reg, scalar, VEC_ELEM_NUM);
-    return __riscv_vfmv_f_s_f32m1_f32(scalar);
-  }
-
-  template <int group_size>
-  float reduce_sub_sum(int idx) {
-    static_assert(VEC_ELEM_NUM % group_size == 0);
-    const int start = idx * group_size;
-    vuint32m4_t indices = __riscv_vid_v_u32m4(VEC_ELEM_NUM);
-    vbool8_t mask = __riscv_vmand_mm_b8(
-        __riscv_vmsgeu_vx_u32m4_b8(indices, start, VEC_ELEM_NUM),
-        __riscv_vmsltu_vx_u32m4_b8(indices, start + group_size, VEC_ELEM_NUM),
-        VEC_ELEM_NUM);
-    fixed_vfloat32m1_t scalar = __riscv_vfmv_s_f_f32m1(0.0f, 1);
-    scalar =
-        __riscv_vfredusum_vs_f32m4_f32m1_m(mask, reg, scalar, VEC_ELEM_NUM);
-    return __riscv_vfmv_f_s_f32m1_f32(scalar);
-  };
-
-  FP32Vec16 max(const FP32Vec16& b) const {
-    return FP32Vec16(__riscv_vfmax_vv_f32m4(reg, b.reg, VEC_ELEM_NUM));
-  }
-  FP32Vec16 min(const FP32Vec16& b) const {
-    return FP32Vec16(__riscv_vfmin_vv_f32m4(reg, b.reg, VEC_ELEM_NUM));
-  }
-  FP32Vec16 abs() const {
-    return FP32Vec16(__riscv_vfabs_v_f32m4(reg, VEC_ELEM_NUM));
-  }
-
-  FP32Vec16 clamp(const FP32Vec16& min_v, const FP32Vec16& max_v) const {
-    return FP32Vec16(__riscv_vfmin_vv_f32m4(
-        max_v.reg, __riscv_vfmax_vv_f32m4(min_v.reg, reg, VEC_ELEM_NUM),
-        VEC_ELEM_NUM));
-  }
-
-  void save(float* ptr) const { __riscv_vse32_v_f32m4(ptr, reg, VEC_ELEM_NUM); }
-  void save(float* ptr, int elem_num) const {
-    __riscv_vse32_v_f32m4(ptr, reg, elem_num);
-  }
-  void save_strided(float* ptr, ptrdiff_t stride) const {
-    ptrdiff_t byte_stride = stride * sizeof(float);
-    __riscv_vsse32_v_f32m4(ptr, byte_stride, reg, VEC_ELEM_NUM);
-  }
-
-  FP32Vec16 exp() const {
-    const float inv_ln2 = 1.44269504088896341f;
-    fixed_vfloat32m4_t x_scaled =
-        __riscv_vfmul_vf_f32m4(reg, inv_ln2, VEC_ELEM_NUM);
-    fixed_vint32m4_t n_int = __riscv_vfcvt_x_f_v_i32m4(x_scaled, VEC_ELEM_NUM);
-    fixed_vfloat32m4_t n_float = __riscv_vfcvt_f_x_v_f32m4(n_int, VEC_ELEM_NUM);
-    fixed_vfloat32m4_t r =
-        __riscv_vfsub_vv_f32m4(x_scaled, n_float, VEC_ELEM_NUM);
-
-    fixed_vfloat32m4_t poly =
-        __riscv_vfmv_v_f_f32m4(0.001333355810164f, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m4(__riscv_vfmul_vv_f32m4(poly, r, VEC_ELEM_NUM),
-                                  0.009618129107628f, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m4(__riscv_vfmul_vv_f32m4(poly, r, VEC_ELEM_NUM),
-                                  0.055504108664821f, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m4(__riscv_vfmul_vv_f32m4(poly, r, VEC_ELEM_NUM),
-                                  0.240226506959101f, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m4(__riscv_vfmul_vv_f32m4(poly, r, VEC_ELEM_NUM),
-                                  0.693147180559945f, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m4(__riscv_vfmul_vv_f32m4(poly, r, VEC_ELEM_NUM),
-                                  1.0f, VEC_ELEM_NUM);
-
-    fixed_vint32m4_t biased_exp = __riscv_vmax_vx_i32m4(
-        __riscv_vadd_vx_i32m4(n_int, 127, VEC_ELEM_NUM), 0, VEC_ELEM_NUM);
-    fixed_vfloat32m4_t scale = __riscv_vreinterpret_v_i32m4_f32m4(
-        __riscv_vsll_vx_i32m4(biased_exp, 23, VEC_ELEM_NUM));
-
-    return FP32Vec16(__riscv_vfmul_vv_f32m4(poly, scale, VEC_ELEM_NUM));
-  }
-
-  FP32Vec16 tanh() const {
-    fixed_vfloat32m4_t x_clamped = __riscv_vfmin_vf_f32m4(
-        __riscv_vfmax_vf_f32m4(reg, -9.0f, VEC_ELEM_NUM), 9.0f, VEC_ELEM_NUM);
-    FP32Vec16 exp_val =
-        FP32Vec16(__riscv_vfmul_vf_f32m4(x_clamped, 2.0f, VEC_ELEM_NUM)).exp();
-    return FP32Vec16(__riscv_vfdiv_vv_f32m4(
-        __riscv_vfsub_vf_f32m4(exp_val.reg, 1.0f, VEC_ELEM_NUM),
-        __riscv_vfadd_vf_f32m4(exp_val.reg, 1.0f, VEC_ELEM_NUM), VEC_ELEM_NUM));
-  }
-
-  FP32Vec16 er() const {
-    const float p = 0.3275911f, a1 = 0.254829592f, a2 = -0.284496736f,
-                a3 = 1.421413741f, a4 = -1.453152027f, a5 = 1.061405429f;
-    fixed_vfloat32m4_t abs_x = __riscv_vfabs_v_f32m4(reg, VEC_ELEM_NUM);
-    fixed_vfloat32m4_t t = __riscv_vfrdiv_vf_f32m4(
-        __riscv_vfadd_vf_f32m4(__riscv_vfmul_vf_f32m4(abs_x, p, VEC_ELEM_NUM),
-                               1.0f, VEC_ELEM_NUM),
-        1.0f, VEC_ELEM_NUM);
-
-    fixed_vfloat32m4_t poly = __riscv_vfmv_v_f_f32m4(a5, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m4(__riscv_vfmul_vv_f32m4(poly, t, VEC_ELEM_NUM),
-                                  a4, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m4(__riscv_vfmul_vv_f32m4(poly, t, VEC_ELEM_NUM),
-                                  a3, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m4(__riscv_vfmul_vv_f32m4(poly, t, VEC_ELEM_NUM),
-                                  a2, VEC_ELEM_NUM);
-    poly = __riscv_vfadd_vf_f32m4(__riscv_vfmul_vv_f32m4(poly, t, VEC_ELEM_NUM),
-                                  a1, VEC_ELEM_NUM);
-    poly = __riscv_vfmul_vv_f32m4(poly, t, VEC_ELEM_NUM);
-
-    fixed_vfloat32m4_t exp_val =
-        FP32Vec16(__riscv_vfneg_v_f32m4(
-                      __riscv_vfmul_vv_f32m4(abs_x, abs_x, VEC_ELEM_NUM),
-                      VEC_ELEM_NUM))
-            .exp()
-            .reg;
-    fixed_vfloat32m4_t res = __riscv_vfrsub_vf_f32m4(
-        __riscv_vfmul_vv_f32m4(poly, exp_val, VEC_ELEM_NUM), 1.0f,
-        VEC_ELEM_NUM);
-
-    vbool8_t mask = __riscv_vmflt_vf_f32m4_b8(reg, 0.0f, VEC_ELEM_NUM);
-    return FP32Vec16(__riscv_vfneg_v_f32m4_m(mask, res, VEC_ELEM_NUM));
-  }
-};
-
-// ============================================================================
-// Type Traits & Global Helpers
-// ============================================================================
-
-template <typename T>
-struct VecType {
-  using vec_type = void;
-  using vec_t = void;
-};
-
-template <typename T>
-using vec_t = typename VecType<T>::vec_type;
-
-template <>
-struct VecType<float> {
-  using vec_type = FP32Vec8;
-  using vec_t = FP32Vec8;
-};
-template <>
-struct VecType<c10::Half> {
-  using vec_type = FP16Vec8;
-  using vec_t = FP16Vec8;
-};
-template <>
-struct VecType<c10::BFloat16> {
-  using vec_type = BF16Vec8;
-  using vec_t = BF16Vec8;
-};
-
-template <typename T>
-void storeFP32(float v, T* ptr) {
-  *ptr = v;
-}
-template <>
-inline void storeFP32<c10::Half>(float v, c10::Half* ptr) {
-  *reinterpret_cast<_Float16*>(ptr) = static_cast<_Float16>(v);
-}
-
-inline FP16Vec16::FP16Vec16(const FP32Vec16& v) {
-  reg = __riscv_vfncvt_f_f_w_f16m2(v.reg, VEC_ELEM_NUM);
-}
-inline FP16Vec8::FP16Vec8(const FP32Vec8& v) {
-  reg = __riscv_vfncvt_f_f_w_f16m1(v.reg, VEC_ELEM_NUM);
-}
-inline FP32Vec16::FP32Vec16(const FP16Vec16& v) {
-  reg = __riscv_vfwcvt_f_f_v_f32m4(v.reg, VEC_ELEM_NUM);
-}
-inline void fma(FP32Vec16& acc, const FP32Vec16& a, const FP32Vec16& b) {
-  acc = acc.fma(a, b);
-}
-
-#ifdef RISCV_BF16_SUPPORT
-template <>
-inline void storeFP32<c10::BFloat16>(float v, c10::BFloat16* ptr) {
-  *ptr = static_cast<__bf16>(v);
-};
-inline BF16Vec8::BF16Vec8(const FP32Vec8& v)
-    : reg(__riscv_vfncvtbf16_f_f_w_bf16m1(v.reg, VEC_ELEM_NUM)) {};
-inline BF16Vec16::BF16Vec16(const FP32Vec16& v)
-    : reg(__riscv_vfncvtbf16_f_f_w_bf16m2(v.reg, VEC_ELEM_NUM)) {};
-#else
-template <>
-inline void storeFP32<c10::BFloat16>(float v, c10::BFloat16* ptr) {
-  uint32_t val;
-  std::memcpy(&val, &v, 4);
-  *reinterpret_cast<uint16_t*>(ptr) = static_cast<uint16_t>(val >> 16);
-}
-inline BF16Vec8::BF16Vec8(const FP32Vec8& v) : reg_fp32(v.reg) {}
-inline BF16Vec16::BF16Vec16(const FP32Vec16& v) : reg_fp32(v.reg) {}
-#endif
-
-inline void prefetch(const void* addr) { __builtin_prefetch(addr, 0, 1); }
-
-}  // namespace vec_op
-
-#ifndef CPU_KERNEL_GUARD_IN
-  #define CPU_KERNEL_GUARD_IN(NAME)
-#endif
-
-#ifndef CPU_KERNEL_GUARD_OUT
-  #define CPU_KERNEL_GUARD_OUT(NAME)
-#endif
+#include "cpu_types_riscv_defs.hpp"
+#include "cpu_types_riscv_impl.hpp"
 
-#endif  // CPU_TYPES_RISCV_HPP
\ No newline at end of file
+#endif  // CPU_TYPES_RISCV_HPP
diff --git a/csrc/cpu/cpu_types_riscv_defs.hpp b/csrc/cpu/cpu_types_riscv_defs.hpp
new file mode 100644
index 000000000000..04ce2728f032
--- /dev/null
+++ b/csrc/cpu/cpu_types_riscv_defs.hpp
@@ -0,0 +1,98 @@
+#ifndef CPU_TYPES_RISCV_DEFS_HPP
+#define CPU_TYPES_RISCV_DEFS_HPP
+
+// VLEN-to-LMUL mapping for RISC-V Vector extension.
+//
+// LMUL_<N> expands to the LMUL suffix giving N total bits of vector data:
+//   VLEN=128: LMUL_128=m1,  LMUL_256=m2,  LMUL_512=m4,  LMUL_1024=m8
+//   VLEN=256: LMUL_128=mf2, LMUL_256=m1,  LMUL_512=m2,  LMUL_1024=m4
+
+#include <riscv_vector.h>
+
+#if __riscv_v_min_vlen == 128
+  #define LMUL_128 m1
+  #define LMUL_256 m2
+  #define LMUL_512 m4
+  #define LMUL_1024 m8
+  #define BOOL_256 b16
+  #define BOOL_512 b8
+#elif __riscv_v_min_vlen == 256
+  #define LMUL_128 mf2
+  #define LMUL_256 m1
+  #define LMUL_512 m2
+  #define LMUL_1024 m4
+  #define BOOL_256 b32
+  #define BOOL_512 b16
+#else
+  #error "cpu_types_riscv_defs.hpp: unsupported __riscv_v_min_vlen"
+#endif
+
+// Token-paste helpers.
+#define _RVV_P2(a, b) a##b
+#define _RVV_P3(a, b, c) a##b##c
+#define _RVV_P4(a, b, c, d) a##b##c##d
+#define RVVTYPE(base, lmul, suffix) _RVV_P3(base, lmul, suffix)
+#define RVVI(base, lmul) _RVV_P2(base, lmul)
+#define RVVI3(base, lmul, suffix) _RVV_P3(base, lmul, suffix)
+#define RVVI4(a, b, c, d) _RVV_P4(a, b, c, d)
+// For mask intrinsics: RVVIB(base, LMUL_256, BOOL_256) → base##m2##_##b16
+#define _RVV_PB(base, lmul, btype) base##lmul##_##btype
+#define RVVIB(base, lmul, btype) _RVV_PB(base, lmul, btype)
+
+// ---- Semantic fixed-vector typedefs (named by element count) ----
+
+// float16
+typedef RVVTYPE(vfloat16, LMUL_128, _t) fixed_fp16x8_t
+    __attribute__((riscv_rvv_vector_bits(128)));
+typedef RVVTYPE(vfloat16, LMUL_256, _t) fixed_fp16x16_t
+    __attribute__((riscv_rvv_vector_bits(256)));
+
+// float32
+typedef RVVTYPE(vfloat32, LMUL_128, _t) fixed_fp32x4_t
+    __attribute__((riscv_rvv_vector_bits(128)));
+typedef RVVTYPE(vfloat32, LMUL_256, _t) fixed_fp32x8_t
+    __attribute__((riscv_rvv_vector_bits(256)));
+typedef RVVTYPE(vfloat32, LMUL_512, _t) fixed_fp32x16_t
+    __attribute__((riscv_rvv_vector_bits(512)));
+typedef RVVTYPE(vfloat32, LMUL_1024, _t) fixed_fp32x32_t
+    __attribute__((riscv_rvv_vector_bits(1024)));
+
+// int32
+typedef RVVTYPE(vint32, LMUL_256, _t) fixed_i32x8_t
+    __attribute__((riscv_rvv_vector_bits(256)));
+typedef RVVTYPE(vint32, LMUL_512, _t) fixed_i32x16_t
+    __attribute__((riscv_rvv_vector_bits(512)));
+
+// uint16
+typedef RVVTYPE(vuint16, LMUL_128, _t) fixed_u16x8_t
+    __attribute__((riscv_rvv_vector_bits(128)));
+typedef RVVTYPE(vuint16, LMUL_256, _t) fixed_u16x16_t
+    __attribute__((riscv_rvv_vector_bits(256)));
+typedef RVVTYPE(vuint16, LMUL_512, _t) fixed_u16x32_t
+    __attribute__((riscv_rvv_vector_bits(512)));
+
+// bfloat16
+#ifdef RISCV_BF16_SUPPORT
+typedef RVVTYPE(vbfloat16, LMUL_128, _t) fixed_bf16x8_t
+    __attribute__((riscv_rvv_vector_bits(128)));
+typedef RVVTYPE(vbfloat16, LMUL_256, _t) fixed_bf16x16_t
+    __attribute__((riscv_rvv_vector_bits(256)));
+typedef RVVTYPE(vbfloat16, LMUL_512, _t) fixed_bf16x32_t
+    __attribute__((riscv_rvv_vector_bits(512)));
+#endif
+
+// ---- Reduction accumulator type (always m1 = one register of f32) ----
+// Used for scalar reductions; only element [0] is meaningful.
+typedef vfloat32m1_t rvv_f32_accum_t
+    __attribute__((riscv_rvv_vector_bits(__riscv_v_min_vlen)));
+
+// ---- Mask types for f32 elements ----
+#if __riscv_v_min_vlen == 128
+typedef vbool16_t rvv_mask_f32x8_t;
+typedef vbool8_t rvv_mask_f32x16_t;
+#elif __riscv_v_min_vlen == 256
+typedef vbool32_t rvv_mask_f32x8_t;
+typedef vbool16_t rvv_mask_f32x16_t;
+#endif
+
+#endif  // CPU_TYPES_RISCV_DEFS_HPP
diff --git a/csrc/cpu/cpu_types_riscv_impl.hpp b/csrc/cpu/cpu_types_riscv_impl.hpp
new file mode 100644
index 000000000000..3952a811c2c5
--- /dev/null
+++ b/csrc/cpu/cpu_types_riscv_impl.hpp
@@ -0,0 +1,905 @@
+#ifndef CPU_TYPES_RISCV_IMPL_HPP
+#define CPU_TYPES_RISCV_IMPL_HPP
+
+// Shared implementation of RVV vector-type wrapper classes.
+// This file is VLEN-independent: it uses the semantic type names and
+// RVVI() intrinsic macros from cpu_types_riscv_defs.hpp.
+//
+// DO NOT include this file directly; include cpu_types_riscv.hpp instead.
+
+#include <algorithm>
+#include <cmath>
+#include <cstring>
+#include <iostream>
+#include <limits>
+#include <torch/all.h>
+namespace vec_op {
+
+// BFloat16 is always supported on RISC-V: natively when RISCV_BF16_SUPPORT
+// is defined, otherwise via the FP32-simulation fallback path.
+#define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)         \
+  AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \
+  AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)  \
+  AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__)
+
+#define VLLM_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...) \
+  AT_DISPATCH_SWITCH(TYPE, NAME, VLLM_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
+
+#define FORCE_INLINE __attribute__((always_inline)) inline
+
+namespace {
+template <typename T, T... indexes, typename F>
+constexpr void unroll_loop_item(std::integer_sequence<T, indexes...>, F&& f) {
+  (f(std::integral_constant<T, indexes>{}), ...);
+};
+}  // namespace
+
+template <typename T, T count, typename F,
+          typename = std::enable_if_t<std::is_invocable_v<F, T>>>
+constexpr void unroll_loop(F&& f) {
+  unroll_loop_item(std::make_integer_sequence<T, count>{}, std::forward<F>(f));
+}
+
+template <typename T>
+struct Vec {
+  constexpr static int get_elem_num() { return T::VEC_ELEM_NUM; };
+};
+
+struct FP32Vec8;
+struct FP32Vec16;
+
+// ============================================================================
+// FP16 Implementation
+// ============================================================================
+
+struct FP16Vec8 : public Vec<FP16Vec8> {
+  constexpr static int VEC_ELEM_NUM = 8;
+  fixed_fp16x8_t reg;
+
+  explicit FP16Vec8(const void* ptr)
+      : reg(RVVI(__riscv_vle16_v_f16, LMUL_128)(
+            static_cast<const _Float16*>(ptr), VEC_ELEM_NUM)) {};
+
+  explicit FP16Vec8(const FP32Vec8&);
+
+  void save(void* ptr) const {
+    RVVI(__riscv_vse16_v_f16, LMUL_128)(static_cast<_Float16*>(ptr), reg,
+                                        VEC_ELEM_NUM);
+  }
+  void save(void* ptr, int elem_num) const {
+    RVVI(__riscv_vse16_v_f16, LMUL_128)(static_cast<_Float16*>(ptr), reg,
+                                        elem_num);
+  }
+  void save_strided(void* ptr, ptrdiff_t stride) const {
+    ptrdiff_t byte_stride = stride * sizeof(_Float16);
+    RVVI(__riscv_vsse16_v_f16, LMUL_128)(static_cast<_Float16*>(ptr),
+                                         byte_stride, reg, VEC_ELEM_NUM);
+  }
+};
+
+struct FP16Vec16 : public Vec<FP16Vec16> {
+  constexpr static int VEC_ELEM_NUM = 16;
+  fixed_fp16x16_t reg;
+
+  explicit FP16Vec16(const void* ptr)
+      : reg(RVVI(__riscv_vle16_v_f16, LMUL_256)(
+            static_cast<const _Float16*>(ptr), VEC_ELEM_NUM)) {};
+
+  explicit FP16Vec16(const FP32Vec16& vec);
+
+  void save(void* ptr) const {
+    RVVI(__riscv_vse16_v_f16, LMUL_256)(static_cast<_Float16*>(ptr), reg,
+                                        VEC_ELEM_NUM);
+  }
+  void save(void* ptr, int elem_num) const {
+    RVVI(__riscv_vse16_v_f16, LMUL_256)(static_cast<_Float16*>(ptr), reg,
+                                        elem_num);
+  }
+  void save_strided(void* ptr, ptrdiff_t stride) const {
+    ptrdiff_t byte_stride = stride * sizeof(_Float16);
+    RVVI(__riscv_vsse16_v_f16, LMUL_256)(static_cast<_Float16*>(ptr),
+                                         byte_stride, reg, VEC_ELEM_NUM);
+  }
+};
+
+// ============================================================================
+// BF16 Implementation
+// ============================================================================
+
+#ifdef RISCV_BF16_SUPPORT
+
+FORCE_INLINE fixed_u16x8_t bf16_to_u16(fixed_bf16x8_t v) {
+  return RVVI4(__riscv_vreinterpret_v_bf16, LMUL_128, _u16, LMUL_128)(v);
+}
+FORCE_INLINE fixed_u16x16_t bf16_to_u16(fixed_bf16x16_t v) {
+  return RVVI4(__riscv_vreinterpret_v_bf16, LMUL_256, _u16, LMUL_256)(v);
+}
+FORCE_INLINE fixed_u16x32_t bf16_to_u16(fixed_bf16x32_t v) {
+  return RVVI4(__riscv_vreinterpret_v_bf16, LMUL_512, _u16, LMUL_512)(v);
+}
+
+struct BF16Vec8 : public Vec<BF16Vec8> {
+  constexpr static int VEC_ELEM_NUM = 8;
+  fixed_bf16x8_t reg;
+
+  explicit BF16Vec8(const void* ptr)
+      : reg(RVVI4(__riscv_vreinterpret_v_u16, LMUL_128, _bf16,
+                  LMUL_128)(RVVI(__riscv_vle16_v_u16, LMUL_128)(
+            reinterpret_cast<const uint16_t*>(ptr), VEC_ELEM_NUM))) {};
+
+  explicit BF16Vec8(fixed_bf16x8_t data) : reg(data) {};
+  explicit BF16Vec8(const FP32Vec8&);
+
+  void save(void* ptr) const {
+    RVVI(__riscv_vse16_v_u16, LMUL_128)(reinterpret_cast<uint16_t*>(ptr),
+                                        bf16_to_u16(reg), VEC_ELEM_NUM);
+  }
+  void save(void* ptr, int elem_num) const {
+    RVVI(__riscv_vse16_v_u16, LMUL_128)(reinterpret_cast<uint16_t*>(ptr),
+                                        bf16_to_u16(reg), elem_num);
+  }
+  void save_strided(void* ptr, ptrdiff_t stride) const {
+    ptrdiff_t byte_stride = stride * sizeof(uint16_t);
+    RVVI(__riscv_vsse16_v_u16, LMUL_128)(reinterpret_cast<uint16_t*>(ptr),
+                                         byte_stride, bf16_to_u16(reg),
+                                         VEC_ELEM_NUM);
+  }
+};
+
+struct BF16Vec16 : public Vec<BF16Vec16> {
+  constexpr static int VEC_ELEM_NUM = 16;
+  fixed_bf16x16_t reg;
+
+  explicit BF16Vec16(const void* ptr)
+      : reg(RVVI4(__riscv_vreinterpret_v_u16, LMUL_256, _bf16,
+                  LMUL_256)(RVVI(__riscv_vle16_v_u16, LMUL_256)(
+            reinterpret_cast<const uint16_t*>(ptr), VEC_ELEM_NUM))) {};
+
+  explicit BF16Vec16(fixed_bf16x16_t data) : reg(data) {};
+  explicit BF16Vec16(const FP32Vec16&);
+
+  void save(void* ptr) const {
+    RVVI(__riscv_vse16_v_u16, LMUL_256)(reinterpret_cast<uint16_t*>(ptr),
+                                        bf16_to_u16(reg), VEC_ELEM_NUM);
+  }
+  void save(void* ptr, int elem_num) const {
+    RVVI(__riscv_vse16_v_u16, LMUL_256)(reinterpret_cast<uint16_t*>(ptr),
+                                        bf16_to_u16(reg), elem_num);
+  }
+  void save_strided(void* ptr, ptrdiff_t stride) const {
+    ptrdiff_t byte_stride = stride * sizeof(uint16_t);
+    RVVI(__riscv_vsse16_v_u16, LMUL_256)(reinterpret_cast<uint16_t*>(ptr),
+                                         byte_stride, bf16_to_u16(reg),
+                                         VEC_ELEM_NUM);
+  }
+};
+
+struct BF16Vec32 : public Vec<BF16Vec32> {
+  constexpr static int VEC_ELEM_NUM = 32;
+  fixed_bf16x32_t reg;
+
+  explicit BF16Vec32(const void* ptr)
+      : reg(RVVI4(__riscv_vreinterpret_v_u16, LMUL_512, _bf16,
+                  LMUL_512)(RVVI(__riscv_vle16_v_u16, LMUL_512)(
+            reinterpret_cast<const uint16_t*>(ptr), VEC_ELEM_NUM))) {};
+
+  explicit BF16Vec32(fixed_bf16x32_t data) : reg(data) {};
+
+  explicit BF16Vec32(const BF16Vec8& v) {
+    fixed_u16x8_t u16_val = bf16_to_u16(v.reg);
+    fixed_u16x32_t u16_combined =
+        RVVI4(__riscv_vcreate_v_u16, LMUL_128, _u16, LMUL_512)(
+            u16_val, u16_val, u16_val, u16_val);
+    reg = RVVI4(__riscv_vreinterpret_v_u16, LMUL_512, _bf16,
+                LMUL_512)(u16_combined);
+  };
+
+  void save(void* ptr) const {
+    RVVI(__riscv_vse16_v_u16, LMUL_512)(reinterpret_cast<uint16_t*>(ptr),
+                                        bf16_to_u16(reg), VEC_ELEM_NUM);
+  }
+  void save(void* ptr, int elem_num) const {
+    RVVI(__riscv_vse16_v_u16, LMUL_512)(reinterpret_cast<uint16_t*>(ptr),
+                                        bf16_to_u16(reg), elem_num);
+  }
+  void save_strided(void* ptr, ptrdiff_t stride) const {
+    ptrdiff_t byte_stride = stride * sizeof(uint16_t);
+    RVVI(__riscv_vsse16_v_u16, LMUL_512)(reinterpret_cast<uint16_t*>(ptr),
+                                         byte_stride, bf16_to_u16(reg),
+                                         VEC_ELEM_NUM);
+  }
+};
+
+#else
+// ============================================================================
+// BF16 Fallback Implementation (FP32 Simulation)
+// ============================================================================
+
+struct BF16Vec8 : public Vec<BF16Vec8> {
+  constexpr static int VEC_ELEM_NUM = 8;
+  fixed_fp32x8_t reg_fp32;
+  explicit BF16Vec8(const void* ptr) {
+    const uint16_t* u16 = static_cast<const uint16_t*>(ptr);
+    float tmp[8];
+    for (int i = 0; i < 8; ++i) {
+      uint32_t v = static_cast<uint32_t>(u16[i]) << 16;
+      std::memcpy(&tmp[i], &v, 4);
+    }
+    reg_fp32 = RVVI(__riscv_vle32_v_f32, LMUL_256)(tmp, 8);
+  }
+  explicit BF16Vec8(const FP32Vec8&);
+  void save(void* ptr) const {
+    float tmp[8];
+    RVVI(__riscv_vse32_v_f32, LMUL_256)(tmp, reg_fp32, 8);
+    uint16_t* u16 = static_cast<uint16_t*>(ptr);
+    for (int i = 0; i < 8; ++i) {
+      uint32_t v;
+      std::memcpy(&v, &tmp[i], 4);
+      u16[i] = static_cast<uint16_t>(v >> 16);
+    }
+  }
+  void save(void* ptr, int elem_num) const {
+    float tmp[8];
+    RVVI(__riscv_vse32_v_f32, LMUL_256)(tmp, reg_fp32, 8);
+    uint16_t* u16 = static_cast<uint16_t*>(ptr);
+    for (int i = 0; i < elem_num; ++i) {
+      uint32_t v;
+      std::memcpy(&v, &tmp[i], 4);
+      u16[i] = static_cast<uint16_t>(v >> 16);
+    }
+  }
+  void save_strided(void* ptr, ptrdiff_t stride) const {
+    float tmp[8];
+    RVVI(__riscv_vse32_v_f32, LMUL_256)(tmp, reg_fp32, 8);
+    uint8_t* u8 = static_cast<uint8_t*>(ptr);
+    ptrdiff_t byte_stride = stride * sizeof(uint16_t);
+    for (int i = 0; i < 8; ++i) {
+      uint32_t v;
+      std::memcpy(&v, &tmp[i], 4);
+      uint16_t val = static_cast<uint16_t>(v >> 16);
+      *reinterpret_cast<uint16_t*>(u8 + i * byte_stride) = val;
+    }
+  }
+};
+
+struct BF16Vec16 : public Vec<BF16Vec16> {
+  constexpr static int VEC_ELEM_NUM = 16;
+  fixed_fp32x16_t reg_fp32;
+  explicit BF16Vec16(const void* ptr) {
+    const uint16_t* u16 = static_cast<const uint16_t*>(ptr);
+    float tmp[16];
+    for (int i = 0; i < 16; ++i) {
+      uint32_t v = static_cast<uint32_t>(u16[i]) << 16;
+      std::memcpy(&tmp[i], &v, 4);
+    }
+    reg_fp32 = RVVI(__riscv_vle32_v_f32, LMUL_512)(tmp, 16);
+  }
+  explicit BF16Vec16(const FP32Vec16&);
+  void save(void* ptr) const {
+    float tmp[16];
+    RVVI(__riscv_vse32_v_f32, LMUL_512)(tmp, reg_fp32, 16);
+    uint16_t* u16 = static_cast<uint16_t*>(ptr);
+    for (int i = 0; i < 16; ++i) {
+      uint32_t v;
+      std::memcpy(&v, &tmp[i], 4);
+      u16[i] = static_cast<uint16_t>(v >> 16);
+    }
+  }
+  void save(void* ptr, int elem_num) const {
+    float tmp[16];
+    RVVI(__riscv_vse32_v_f32, LMUL_512)(tmp, reg_fp32, 16);
+    uint16_t* u16 = static_cast<uint16_t*>(ptr);
+    for (int i = 0; i < elem_num; ++i) {
+      uint32_t v;
+      std::memcpy(&v, &tmp[i], 4);
+      u16[i] = static_cast<uint16_t>(v >> 16);
+    }
+  }
+  void save_strided(void* ptr, ptrdiff_t stride) const {
+    float tmp[16];
+    RVVI(__riscv_vse32_v_f32, LMUL_512)(tmp, reg_fp32, 16);
+    uint8_t* u8 = static_cast<uint8_t*>(ptr);
+    ptrdiff_t byte_stride = stride * sizeof(uint16_t);
+    for (int i = 0; i < 16; ++i) {
+      uint32_t v;
+      std::memcpy(&v, &tmp[i], 4);
+      uint16_t val = static_cast<uint16_t>(v >> 16);
+      *reinterpret_cast<uint16_t*>(u8 + i * byte_stride) = val;
+    }
+  }
+};
+
+struct BF16Vec32 : public Vec<BF16Vec32> {
+  constexpr static int VEC_ELEM_NUM = 32;
+  fixed_fp32x32_t reg_fp32;
+
+  explicit BF16Vec32(const void* ptr) {
+    const uint16_t* u16 = static_cast<const uint16_t*>(ptr);
+    float tmp[32];
+    for (int i = 0; i < 32; ++i) {
+      uint32_t v = static_cast<uint32_t>(u16[i]) << 16;
+      std::memcpy(&tmp[i], &v, 4);
+    }
+    reg_fp32 = RVVI(__riscv_vle32_v_f32, LMUL_1024)(tmp, 32);
+  }
+
+  explicit BF16Vec32(const BF16Vec8& v) {
+    float tmp_small[8];
+    RVVI(__riscv_vse32_v_f32, LMUL_256)(tmp_small, v.reg_fp32, 8);
+    float tmp_large[32];
+    for (int i = 0; i < 4; ++i) {
+      std::memcpy(tmp_large + (i * 8), tmp_small, 8 * sizeof(float));
+    }
+    reg_fp32 = RVVI(__riscv_vle32_v_f32, LMUL_1024)(tmp_large, 32);
+  }
+
+  void save(void* ptr) const {
+    float tmp[32];
+    RVVI(__riscv_vse32_v_f32, LMUL_1024)(tmp, reg_fp32, 32);
+    uint16_t* u16 = static_cast<uint16_t*>(ptr);
+    for (int i = 0; i < 32; ++i) {
+      uint32_t v;
+      std::memcpy(&v, &tmp[i], 4);
+      u16[i] = static_cast<uint16_t>(v >> 16);
+    }
+  }
+
+  void save(void* ptr, int elem_num) const {
+    float tmp[32];
+    RVVI(__riscv_vse32_v_f32, LMUL_1024)(tmp, reg_fp32, 32);
+    uint16_t* u16 = static_cast<uint16_t*>(ptr);
+    for (int i = 0; i < elem_num; ++i) {
+      uint32_t v;
+      std::memcpy(&v, &tmp[i], 4);
+      u16[i] = static_cast<uint16_t>(v >> 16);
+    }
+  }
+
+  void save_strided(void* ptr, ptrdiff_t stride) const {
+    float tmp[32];
+    RVVI(__riscv_vse32_v_f32, LMUL_1024)(tmp, reg_fp32, 32);
+    uint8_t* u8 = static_cast<uint8_t*>(ptr);
+    ptrdiff_t byte_stride = stride * sizeof(uint16_t);
+    for (int i = 0; i < 32; ++i) {
+      uint32_t v;
+      std::memcpy(&v, &tmp[i], 4);
+      uint16_t val = static_cast<uint16_t>(v >> 16);
+      *reinterpret_cast<uint16_t*>(u8 + i * byte_stride) = val;
+    }
+  }
+};
+#endif
+
+// ============================================================================
+// FP32 Implementation
+// ============================================================================
+
+struct FP32Vec4 : public Vec<FP32Vec4> {
+  constexpr static int VEC_ELEM_NUM = 4;
+  fixed_fp32x4_t reg;
+  explicit FP32Vec4(float v)
+      : reg(RVVI(__riscv_vfmv_v_f_f32, LMUL_128)(v, VEC_ELEM_NUM)) {};
+  explicit FP32Vec4()
+      : reg(RVVI(__riscv_vfmv_v_f_f32, LMUL_128)(0.0f, VEC_ELEM_NUM)) {};
+  explicit FP32Vec4(const float* ptr)
+      : reg(RVVI(__riscv_vle32_v_f32, LMUL_128)(ptr, VEC_ELEM_NUM)) {};
+  explicit FP32Vec4(fixed_fp32x4_t data) : reg(data) {};
+  explicit FP32Vec4(const FP32Vec4& data) : reg(data.reg) {};
+  void save(float* ptr) const {
+    RVVI(__riscv_vse32_v_f32, LMUL_128)(ptr, reg, VEC_ELEM_NUM);
+  }
+  void save(float* ptr, int elem_num) const {
+    RVVI(__riscv_vse32_v_f32, LMUL_128)(ptr, reg, elem_num);
+  }
+};
+
+struct FP32Vec8 : public Vec<FP32Vec8> {
+  constexpr static int VEC_ELEM_NUM = 8;
+  fixed_fp32x8_t reg;
+
+  explicit FP32Vec8(float v)
+      : reg(RVVI(__riscv_vfmv_v_f_f32, LMUL_256)(v, VEC_ELEM_NUM)) {};
+  explicit FP32Vec8()
+      : reg(RVVI(__riscv_vfmv_v_f_f32, LMUL_256)(0.0f, VEC_ELEM_NUM)) {};
+  explicit FP32Vec8(const float* ptr)
+      : reg(RVVI(__riscv_vle32_v_f32, LMUL_256)(ptr, VEC_ELEM_NUM)) {};
+  explicit FP32Vec8(fixed_fp32x8_t data) : reg(data) {};
+  explicit FP32Vec8(const FP32Vec8& data) : reg(data.reg) {};
+  explicit FP32Vec8(const FP16Vec8& v)
+      : reg(RVVI(__riscv_vfwcvt_f_f_v_f32, LMUL_256)(v.reg, VEC_ELEM_NUM)) {};
+  explicit FP32Vec8(fixed_fp16x8_t v)
+      : reg(RVVI(__riscv_vfwcvt_f_f_v_f32, LMUL_256)(v, VEC_ELEM_NUM)) {};
+
+#ifdef RISCV_BF16_SUPPORT
+  explicit FP32Vec8(fixed_bf16x8_t v)
+      : reg(RVVI(__riscv_vfwcvtbf16_f_f_v_f32, LMUL_256)(v, VEC_ELEM_NUM)) {};
+  explicit FP32Vec8(const BF16Vec8& v)
+      : reg(RVVI(__riscv_vfwcvtbf16_f_f_v_f32, LMUL_256)(v.reg, VEC_ELEM_NUM)) {
+        };
+#else
+  explicit FP32Vec8(const BF16Vec8& v) : reg(v.reg_fp32) {};
+#endif
+
+  float reduce_sum() const {
+    rvv_f32_accum_t scalar = __riscv_vfmv_s_f_f32m1(0.0f, 1);
+    scalar = RVVI3(__riscv_vfredusum_vs_f32, LMUL_256, _f32m1)(reg, scalar,
+                                                               VEC_ELEM_NUM);
+    return __riscv_vfmv_f_s_f32m1_f32(scalar);
+  }
+
+  FP32Vec8 operator*(const FP32Vec8& b) const {
+    return FP32Vec8(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_256)(reg, b.reg, VEC_ELEM_NUM));
+  }
+  FP32Vec8 operator+(const FP32Vec8& b) const {
+    return FP32Vec8(
+        RVVI(__riscv_vfadd_vv_f32, LMUL_256)(reg, b.reg, VEC_ELEM_NUM));
+  }
+  FP32Vec8 operator-(const FP32Vec8& b) const {
+    return FP32Vec8(
+        RVVI(__riscv_vfsub_vv_f32, LMUL_256)(reg, b.reg, VEC_ELEM_NUM));
+  }
+  FP32Vec8 operator/(const FP32Vec8& b) const {
+    return FP32Vec8(
+        RVVI(__riscv_vfdiv_vv_f32, LMUL_256)(reg, b.reg, VEC_ELEM_NUM));
+  }
+
+  FP32Vec8 min(const FP32Vec8& b) const {
+    return FP32Vec8(
+        RVVI(__riscv_vfmin_vv_f32, LMUL_256)(reg, b.reg, VEC_ELEM_NUM));
+  }
+  FP32Vec8 max(const FP32Vec8& b) const {
+    return FP32Vec8(
+        RVVI(__riscv_vfmax_vv_f32, LMUL_256)(reg, b.reg, VEC_ELEM_NUM));
+  }
+  FP32Vec8 abs() const {
+    return FP32Vec8(RVVI(__riscv_vfabs_v_f32, LMUL_256)(reg, VEC_ELEM_NUM));
+  }
+
+  FP32Vec8 min(const FP32Vec8& b, int elem_num) const {
+    return FP32Vec8(RVVI(__riscv_vfmin_vv_f32, LMUL_256)(reg, b.reg, elem_num));
+  }
+  FP32Vec8 max(const FP32Vec8& b, int elem_num) const {
+    return FP32Vec8(RVVI(__riscv_vfmax_vv_f32, LMUL_256)(reg, b.reg, elem_num));
+  }
+
+  FP32Vec8 clamp(const FP32Vec8& min_v, const FP32Vec8& max_v) const {
+    fixed_fp32x8_t temp =
+        RVVI(__riscv_vfmax_vv_f32, LMUL_256)(min_v.reg, reg, VEC_ELEM_NUM);
+    return FP32Vec8(
+        RVVI(__riscv_vfmin_vv_f32, LMUL_256)(max_v.reg, temp, VEC_ELEM_NUM));
+  }
+
+  void save(float* ptr) const {
+    RVVI(__riscv_vse32_v_f32, LMUL_256)(ptr, reg, VEC_ELEM_NUM);
+  }
+  void save(float* ptr, int elem_num) const {
+    RVVI(__riscv_vse32_v_f32, LMUL_256)(ptr, reg, elem_num);
+  }
+  void save_strided(float* ptr, ptrdiff_t stride) const {
+    ptrdiff_t byte_stride = stride * sizeof(float);
+    RVVI(__riscv_vsse32_v_f32, LMUL_256)(ptr, byte_stride, reg, VEC_ELEM_NUM);
+  }
+
+  FP32Vec8 exp() const {
+    // Clamp input to prevent NaN: exp(-inf) must return 0, not NaN.
+    // Without clamping, -inf * 0.0 = NaN in the final poly * scale step.
+    // Matches the clamping strategy used by x86 AVX-512 and ARM NEON.
+    constexpr float exp_lo = -87.3365447505f;  // ln(FLT_MIN)
+    constexpr float exp_hi = 88.7228391117f;   // ln(FLT_MAX)
+    fixed_fp32x8_t x = RVVI(__riscv_vfmin_vf_f32, LMUL_256)(
+        RVVI(__riscv_vfmax_vf_f32, LMUL_256)(reg, exp_lo, VEC_ELEM_NUM), exp_hi,
+        VEC_ELEM_NUM);
+
+    const float inv_ln2 = 1.44269504088896341f;
+    fixed_fp32x8_t x_scaled =
+        RVVI(__riscv_vfmul_vf_f32, LMUL_256)(x, inv_ln2, VEC_ELEM_NUM);
+    fixed_i32x8_t n_int =
+        RVVI(__riscv_vfcvt_x_f_v_i32, LMUL_256)(x_scaled, VEC_ELEM_NUM);
+    fixed_fp32x8_t n_float =
+        RVVI(__riscv_vfcvt_f_x_v_f32, LMUL_256)(n_int, VEC_ELEM_NUM);
+
+    fixed_fp32x8_t r =
+        RVVI(__riscv_vfsub_vv_f32, LMUL_256)(x_scaled, n_float, VEC_ELEM_NUM);
+
+    fixed_fp32x8_t poly =
+        RVVI(__riscv_vfmv_v_f_f32, LMUL_256)(0.001333355810164f, VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfmul_vv_f32, LMUL_256)(poly, r, VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_256)(poly, 0.009618129107628f,
+                                                VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfmul_vv_f32, LMUL_256)(poly, r, VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_256)(poly, 0.055504108664821f,
+                                                VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfmul_vv_f32, LMUL_256)(poly, r, VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_256)(poly, 0.240226506959101f,
+                                                VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfmul_vv_f32, LMUL_256)(poly, r, VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_256)(poly, 0.693147180559945f,
+                                                VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfmul_vv_f32, LMUL_256)(poly, r, VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_256)(poly, 1.0f, VEC_ELEM_NUM);
+
+    fixed_i32x8_t biased_exp =
+        RVVI(__riscv_vadd_vx_i32, LMUL_256)(n_int, 127, VEC_ELEM_NUM);
+    biased_exp =
+        RVVI(__riscv_vmax_vx_i32, LMUL_256)(biased_exp, 0, VEC_ELEM_NUM);
+    fixed_i32x8_t exponent_bits =
+        RVVI(__riscv_vsll_vx_i32, LMUL_256)(biased_exp, 23, VEC_ELEM_NUM);
+    fixed_fp32x8_t scale = RVVI4(__riscv_vreinterpret_v_i32, LMUL_256, _f32,
+                                 LMUL_256)(exponent_bits);
+
+    return FP32Vec8(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_256)(poly, scale, VEC_ELEM_NUM));
+  }
+
+  FP32Vec8 tanh() const {
+    fixed_fp32x8_t x_clamped = RVVI(__riscv_vfmin_vf_f32, LMUL_256)(
+        RVVI(__riscv_vfmax_vf_f32, LMUL_256)(reg, -9.0f, VEC_ELEM_NUM), 9.0f,
+        VEC_ELEM_NUM);
+    fixed_fp32x8_t x2 =
+        RVVI(__riscv_vfmul_vf_f32, LMUL_256)(x_clamped, 2.0f, VEC_ELEM_NUM);
+    FP32Vec8 exp_val = FP32Vec8(x2).exp();
+    fixed_fp32x8_t num =
+        RVVI(__riscv_vfsub_vf_f32, LMUL_256)(exp_val.reg, 1.0f, VEC_ELEM_NUM);
+    fixed_fp32x8_t den =
+        RVVI(__riscv_vfadd_vf_f32, LMUL_256)(exp_val.reg, 1.0f, VEC_ELEM_NUM);
+    return FP32Vec8(
+        RVVI(__riscv_vfdiv_vv_f32, LMUL_256)(num, den, VEC_ELEM_NUM));
+  }
+
+  FP32Vec8 er() const {
+    const float p = 0.3275911f, a1 = 0.254829592f, a2 = -0.284496736f,
+                a3 = 1.421413741f, a4 = -1.453152027f, a5 = 1.061405429f;
+    fixed_fp32x8_t abs_x =
+        RVVI(__riscv_vfabs_v_f32, LMUL_256)(reg, VEC_ELEM_NUM);
+
+    fixed_fp32x8_t t = RVVI(__riscv_vfadd_vf_f32, LMUL_256)(
+        RVVI(__riscv_vfmul_vf_f32, LMUL_256)(abs_x, p, VEC_ELEM_NUM), 1.0f,
+        VEC_ELEM_NUM);
+    t = RVVI(__riscv_vfrdiv_vf_f32, LMUL_256)(t, 1.0f, VEC_ELEM_NUM);
+
+    fixed_fp32x8_t poly =
+        RVVI(__riscv_vfmv_v_f_f32, LMUL_256)(a5, VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_256)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_256)(poly, t, VEC_ELEM_NUM), a4,
+        VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_256)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_256)(poly, t, VEC_ELEM_NUM), a3,
+        VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_256)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_256)(poly, t, VEC_ELEM_NUM), a2,
+        VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_256)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_256)(poly, t, VEC_ELEM_NUM), a1,
+        VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfmul_vv_f32, LMUL_256)(poly, t, VEC_ELEM_NUM);
+
+    fixed_fp32x8_t exp_val = FP32Vec8(RVVI(__riscv_vfneg_v_f32, LMUL_256)(
+                                          RVVI(__riscv_vfmul_vv_f32, LMUL_256)(
+                                              abs_x, abs_x, VEC_ELEM_NUM),
+                                          VEC_ELEM_NUM))
+                                 .exp()
+                                 .reg;
+    fixed_fp32x8_t res = RVVI(__riscv_vfrsub_vf_f32, LMUL_256)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_256)(poly, exp_val, VEC_ELEM_NUM), 1.0f,
+        VEC_ELEM_NUM);
+
+    rvv_mask_f32x8_t mask = RVVIB(__riscv_vmflt_vf_f32, LMUL_256, BOOL_256)(
+        reg, 0.0f, VEC_ELEM_NUM);
+    return FP32Vec8(
+        RVVI3(__riscv_vfneg_v_f32, LMUL_256, _m)(mask, res, VEC_ELEM_NUM));
+  }
+};
+
+struct FP32Vec16 : public Vec<FP32Vec16> {
+  constexpr static int VEC_ELEM_NUM = 16;
+  fixed_fp32x16_t reg;
+
+  explicit FP32Vec16(float v)
+      : reg(RVVI(__riscv_vfmv_v_f_f32, LMUL_512)(v, VEC_ELEM_NUM)) {};
+  explicit FP32Vec16()
+      : reg(RVVI(__riscv_vfmv_v_f_f32, LMUL_512)(0.0f, VEC_ELEM_NUM)) {};
+  explicit FP32Vec16(const float* ptr)
+      : reg(RVVI(__riscv_vle32_v_f32, LMUL_512)(ptr, VEC_ELEM_NUM)) {};
+  explicit FP32Vec16(fixed_fp32x16_t data) : reg(data) {};
+  explicit FP32Vec16(const FP32Vec8& data)
+      : reg(RVVI4(__riscv_vcreate_v_f32, LMUL_256, _f32, LMUL_512)(
+            data.reg, data.reg)) {};
+  explicit FP32Vec16(const FP32Vec16& data) : reg(data.reg) {};
+  explicit FP32Vec16(const FP16Vec16& v);
+
+#ifdef RISCV_BF16_SUPPORT
+  explicit FP32Vec16(fixed_bf16x16_t v)
+      : reg(RVVI(__riscv_vfwcvtbf16_f_f_v_f32, LMUL_512)(v, VEC_ELEM_NUM)) {};
+  explicit FP32Vec16(const BF16Vec16& v)
+      : reg(RVVI(__riscv_vfwcvtbf16_f_f_v_f32, LMUL_512)(v.reg, VEC_ELEM_NUM)) {
+        };
+#else
+  explicit FP32Vec16(const BF16Vec16& v) : reg(v.reg_fp32) {};
+#endif
+
+  FP32Vec16 operator+(const FP32Vec16& b) const {
+    return FP32Vec16(
+        RVVI(__riscv_vfadd_vv_f32, LMUL_512)(reg, b.reg, VEC_ELEM_NUM));
+  }
+  FP32Vec16 operator-(const FP32Vec16& b) const {
+    return FP32Vec16(
+        RVVI(__riscv_vfsub_vv_f32, LMUL_512)(reg, b.reg, VEC_ELEM_NUM));
+  }
+  FP32Vec16 operator*(const FP32Vec16& b) const {
+    return FP32Vec16(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_512)(reg, b.reg, VEC_ELEM_NUM));
+  }
+  FP32Vec16 operator/(const FP32Vec16& b) const {
+    return FP32Vec16(
+        RVVI(__riscv_vfdiv_vv_f32, LMUL_512)(reg, b.reg, VEC_ELEM_NUM));
+  }
+
+  FP32Vec16 fma(const FP32Vec16& a, const FP32Vec16& b) const {
+    return FP32Vec16(
+        RVVI(__riscv_vfmacc_vv_f32, LMUL_512)(reg, a.reg, b.reg, VEC_ELEM_NUM));
+  }
+
+  float reduce_sum() const {
+    rvv_f32_accum_t scalar = __riscv_vfmv_s_f_f32m1(0.0f, 1);
+    scalar = RVVI3(__riscv_vfredusum_vs_f32, LMUL_512, _f32m1)(reg, scalar,
+                                                               VEC_ELEM_NUM);
+    return __riscv_vfmv_f_s_f32m1_f32(scalar);
+  }
+
+  float reduce_max() const {
+    rvv_f32_accum_t scalar =
+        __riscv_vfmv_s_f_f32m1(std::numeric_limits<float>::lowest(), 1);
+    scalar = RVVI3(__riscv_vfredmax_vs_f32, LMUL_512, _f32m1)(reg, scalar,
+                                                              VEC_ELEM_NUM);
+    return __riscv_vfmv_f_s_f32m1_f32(scalar);
+  }
+
+  float reduce_min() const {
+    rvv_f32_accum_t scalar =
+        __riscv_vfmv_s_f_f32m1(std::numeric_limits<float>::max(), 1);
+    scalar = RVVI3(__riscv_vfredmin_vs_f32, LMUL_512, _f32m1)(reg, scalar,
+                                                              VEC_ELEM_NUM);
+    return __riscv_vfmv_f_s_f32m1_f32(scalar);
+  }
+
+  template <int group_size>
+  float reduce_sub_sum(int idx) {
+    static_assert(VEC_ELEM_NUM % group_size == 0);
+    const int start = idx * group_size;
+    auto indices = RVVI(__riscv_vid_v_u32, LMUL_512)(VEC_ELEM_NUM);
+    rvv_mask_f32x16_t mask = RVVI(__riscv_vmand_mm_, BOOL_512)(
+        RVVIB(__riscv_vmsgeu_vx_u32, LMUL_512, BOOL_512)(indices, start,
+                                                         VEC_ELEM_NUM),
+        RVVIB(__riscv_vmsltu_vx_u32, LMUL_512, BOOL_512)(
+            indices, start + group_size, VEC_ELEM_NUM),
+        VEC_ELEM_NUM);
+    rvv_f32_accum_t scalar = __riscv_vfmv_s_f_f32m1(0.0f, 1);
+    scalar = RVVI3(__riscv_vfredusum_vs_f32, LMUL_512, _f32m1_m)(
+        mask, reg, scalar, VEC_ELEM_NUM);
+    return __riscv_vfmv_f_s_f32m1_f32(scalar);
+  };
+
+  FP32Vec16 max(const FP32Vec16& b) const {
+    return FP32Vec16(
+        RVVI(__riscv_vfmax_vv_f32, LMUL_512)(reg, b.reg, VEC_ELEM_NUM));
+  }
+  FP32Vec16 min(const FP32Vec16& b) const {
+    return FP32Vec16(
+        RVVI(__riscv_vfmin_vv_f32, LMUL_512)(reg, b.reg, VEC_ELEM_NUM));
+  }
+  FP32Vec16 abs() const {
+    return FP32Vec16(RVVI(__riscv_vfabs_v_f32, LMUL_512)(reg, VEC_ELEM_NUM));
+  }
+
+  FP32Vec16 clamp(const FP32Vec16& min_v, const FP32Vec16& max_v) const {
+    return FP32Vec16(RVVI(__riscv_vfmin_vv_f32, LMUL_512)(
+        max_v.reg,
+        RVVI(__riscv_vfmax_vv_f32, LMUL_512)(min_v.reg, reg, VEC_ELEM_NUM),
+        VEC_ELEM_NUM));
+  }
+
+  void save(float* ptr) const {
+    RVVI(__riscv_vse32_v_f32, LMUL_512)(ptr, reg, VEC_ELEM_NUM);
+  }
+  void save(float* ptr, int elem_num) const {
+    RVVI(__riscv_vse32_v_f32, LMUL_512)(ptr, reg, elem_num);
+  }
+  void save_strided(float* ptr, ptrdiff_t stride) const {
+    ptrdiff_t byte_stride = stride * sizeof(float);
+    RVVI(__riscv_vsse32_v_f32, LMUL_512)(ptr, byte_stride, reg, VEC_ELEM_NUM);
+  }
+
+  FP32Vec16 exp() const {
+    // Clamp input to prevent NaN: exp(-inf) must return 0, not NaN.
+    // Without clamping, -inf * 0.0 = NaN in the final poly * scale step.
+    // Matches the clamping strategy used by x86 AVX-512 and ARM NEON.
+    constexpr float exp_lo = -87.3365447505f;  // ln(FLT_MIN)
+    constexpr float exp_hi = 88.7228391117f;   // ln(FLT_MAX)
+    fixed_fp32x16_t x = RVVI(__riscv_vfmin_vf_f32, LMUL_512)(
+        RVVI(__riscv_vfmax_vf_f32, LMUL_512)(reg, exp_lo, VEC_ELEM_NUM), exp_hi,
+        VEC_ELEM_NUM);
+
+    const float inv_ln2 = 1.44269504088896341f;
+    fixed_fp32x16_t x_scaled =
+        RVVI(__riscv_vfmul_vf_f32, LMUL_512)(x, inv_ln2, VEC_ELEM_NUM);
+    fixed_i32x16_t n_int =
+        RVVI(__riscv_vfcvt_x_f_v_i32, LMUL_512)(x_scaled, VEC_ELEM_NUM);
+    fixed_fp32x16_t n_float =
+        RVVI(__riscv_vfcvt_f_x_v_f32, LMUL_512)(n_int, VEC_ELEM_NUM);
+    fixed_fp32x16_t r =
+        RVVI(__riscv_vfsub_vv_f32, LMUL_512)(x_scaled, n_float, VEC_ELEM_NUM);
+
+    fixed_fp32x16_t poly =
+        RVVI(__riscv_vfmv_v_f_f32, LMUL_512)(0.001333355810164f, VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_512)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_512)(poly, r, VEC_ELEM_NUM),
+        0.009618129107628f, VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_512)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_512)(poly, r, VEC_ELEM_NUM),
+        0.055504108664821f, VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_512)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_512)(poly, r, VEC_ELEM_NUM),
+        0.240226506959101f, VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_512)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_512)(poly, r, VEC_ELEM_NUM),
+        0.693147180559945f, VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_512)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_512)(poly, r, VEC_ELEM_NUM), 1.0f,
+        VEC_ELEM_NUM);
+
+    fixed_i32x16_t biased_exp = RVVI(__riscv_vmax_vx_i32, LMUL_512)(
+        RVVI(__riscv_vadd_vx_i32, LMUL_512)(n_int, 127, VEC_ELEM_NUM), 0,
+        VEC_ELEM_NUM);
+    fixed_fp32x16_t scale =
+        RVVI4(__riscv_vreinterpret_v_i32, LMUL_512, _f32, LMUL_512)(
+            RVVI(__riscv_vsll_vx_i32, LMUL_512)(biased_exp, 23, VEC_ELEM_NUM));
+
+    return FP32Vec16(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_512)(poly, scale, VEC_ELEM_NUM));
+  }
+
+  FP32Vec16 tanh() const {
+    fixed_fp32x16_t x_clamped = RVVI(__riscv_vfmin_vf_f32, LMUL_512)(
+        RVVI(__riscv_vfmax_vf_f32, LMUL_512)(reg, -9.0f, VEC_ELEM_NUM), 9.0f,
+        VEC_ELEM_NUM);
+    FP32Vec16 exp_val = FP32Vec16(RVVI(__riscv_vfmul_vf_f32, LMUL_512)(
+                                      x_clamped, 2.0f, VEC_ELEM_NUM))
+                            .exp();
+    return FP32Vec16(RVVI(__riscv_vfdiv_vv_f32, LMUL_512)(
+        RVVI(__riscv_vfsub_vf_f32, LMUL_512)(exp_val.reg, 1.0f, VEC_ELEM_NUM),
+        RVVI(__riscv_vfadd_vf_f32, LMUL_512)(exp_val.reg, 1.0f, VEC_ELEM_NUM),
+        VEC_ELEM_NUM));
+  }
+
+  FP32Vec16 er() const {
+    const float p = 0.3275911f, a1 = 0.254829592f, a2 = -0.284496736f,
+                a3 = 1.421413741f, a4 = -1.453152027f, a5 = 1.061405429f;
+    fixed_fp32x16_t abs_x =
+        RVVI(__riscv_vfabs_v_f32, LMUL_512)(reg, VEC_ELEM_NUM);
+    fixed_fp32x16_t t = RVVI(__riscv_vfrdiv_vf_f32, LMUL_512)(
+        RVVI(__riscv_vfadd_vf_f32, LMUL_512)(
+            RVVI(__riscv_vfmul_vf_f32, LMUL_512)(abs_x, p, VEC_ELEM_NUM), 1.0f,
+            VEC_ELEM_NUM),
+        1.0f, VEC_ELEM_NUM);
+
+    fixed_fp32x16_t poly =
+        RVVI(__riscv_vfmv_v_f_f32, LMUL_512)(a5, VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_512)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_512)(poly, t, VEC_ELEM_NUM), a4,
+        VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_512)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_512)(poly, t, VEC_ELEM_NUM), a3,
+        VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_512)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_512)(poly, t, VEC_ELEM_NUM), a2,
+        VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfadd_vf_f32, LMUL_512)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_512)(poly, t, VEC_ELEM_NUM), a1,
+        VEC_ELEM_NUM);
+    poly = RVVI(__riscv_vfmul_vv_f32, LMUL_512)(poly, t, VEC_ELEM_NUM);
+
+    fixed_fp32x16_t exp_val =
+        FP32Vec16(RVVI(__riscv_vfneg_v_f32, LMUL_512)(
+                      RVVI(__riscv_vfmul_vv_f32, LMUL_512)(abs_x, abs_x,
+                                                           VEC_ELEM_NUM),
+                      VEC_ELEM_NUM))
+            .exp()
+            .reg;
+    fixed_fp32x16_t res = RVVI(__riscv_vfrsub_vf_f32, LMUL_512)(
+        RVVI(__riscv_vfmul_vv_f32, LMUL_512)(poly, exp_val, VEC_ELEM_NUM), 1.0f,
+        VEC_ELEM_NUM);
+
+    rvv_mask_f32x16_t mask = RVVIB(__riscv_vmflt_vf_f32, LMUL_512, BOOL_512)(
+        reg, 0.0f, VEC_ELEM_NUM);
+    return FP32Vec16(
+        RVVI3(__riscv_vfneg_v_f32, LMUL_512, _m)(mask, res, VEC_ELEM_NUM));
+  }
+};
+
+// ============================================================================
+// Type Traits & Global Helpers
+// ============================================================================
+
+template <typename T>
+struct VecType {
+  using vec_type = void;
+  using vec_t = void;
+};
+
+template <typename T>
+using vec_t = typename VecType<T>::vec_type;
+
+template <>
+struct VecType<float> {
+  using vec_type = FP32Vec8;
+  using vec_t = FP32Vec8;
+};
+template <>
+struct VecType<c10::Half> {
+  using vec_type = FP16Vec8;
+  using vec_t = FP16Vec8;
+};
+template <>
+struct VecType<c10::BFloat16> {
+  using vec_type = BF16Vec8;
+  using vec_t = BF16Vec8;
+};
+
+template <typename T>
+void storeFP32(float v, T* ptr) {
+  *ptr = v;
+}
+template <>
+inline void storeFP32<c10::Half>(float v, c10::Half* ptr) {
+  *reinterpret_cast<_Float16*>(ptr) = static_cast<_Float16>(v);
+}
+
+inline FP16Vec16::FP16Vec16(const FP32Vec16& v) {
+  reg = RVVI(__riscv_vfncvt_f_f_w_f16, LMUL_256)(v.reg, VEC_ELEM_NUM);
+}
+inline FP16Vec8::FP16Vec8(const FP32Vec8& v) {
+  reg = RVVI(__riscv_vfncvt_f_f_w_f16, LMUL_128)(v.reg, VEC_ELEM_NUM);
+}
+inline FP32Vec16::FP32Vec16(const FP16Vec16& v) {
+  reg = RVVI(__riscv_vfwcvt_f_f_v_f32, LMUL_512)(v.reg, VEC_ELEM_NUM);
+}
+inline void fma(FP32Vec16& acc, const FP32Vec16& a, const FP32Vec16& b) {
+  acc = acc.fma(a, b);
+}
+
+#ifdef RISCV_BF16_SUPPORT
+template <>
+inline void storeFP32<c10::BFloat16>(float v, c10::BFloat16* ptr) {
+  *ptr = static_cast<__bf16>(v);
+};
+inline BF16Vec8::BF16Vec8(const FP32Vec8& v)
+    : reg(RVVI(__riscv_vfncvtbf16_f_f_w_bf16, LMUL_128)(v.reg, VEC_ELEM_NUM)) {
+      };
+inline BF16Vec16::BF16Vec16(const FP32Vec16& v)
+    : reg(RVVI(__riscv_vfncvtbf16_f_f_w_bf16, LMUL_256)(v.reg, VEC_ELEM_NUM)) {
+      };
+#else
+template <>
+inline void storeFP32<c10::BFloat16>(float v, c10::BFloat16* ptr) {
+  uint32_t val;
+  std::memcpy(&val, &v, 4);
+  *reinterpret_cast<uint16_t*>(ptr) = static_cast<uint16_t>(val >> 16);
+}
+inline BF16Vec8::BF16Vec8(const FP32Vec8& v) : reg_fp32(v.reg) {}
+inline BF16Vec16::BF16Vec16(const FP32Vec16& v) : reg_fp32(v.reg) {}
+#endif
+
+inline void prefetch(const void* addr) { __builtin_prefetch(addr, 0, 1); }
+
+}  // namespace vec_op
+
+#ifndef CPU_KERNEL_GUARD_IN
+  #define CPU_KERNEL_GUARD_IN(NAME)
+#endif
+
+#ifndef CPU_KERNEL_GUARD_OUT
+  #define CPU_KERNEL_GUARD_OUT(NAME)
+#endif
+
+#endif  // CPU_TYPES_RISCV_IMPL_HPP
diff --git a/csrc/cpu/cpu_types_scalar.hpp b/csrc/cpu/cpu_types_scalar.hpp
index f9da78283da5..d1c2fc85933a 100644
--- a/csrc/cpu/cpu_types_scalar.hpp
+++ b/csrc/cpu/cpu_types_scalar.hpp
@@ -6,6 +6,9 @@
 
 namespace vec_op {
 
+struct fp8_e4m3_tag {};
+struct fp8_e5m2_tag {};
+
 #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)            \
   AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)    \
   AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__) \
@@ -145,6 +148,9 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
   }
 
   void save(void* ptr) const { *reinterpret_cast<f16x32_t*>(ptr) = reg; }
+
+  explicit BF16Vec32(const uint8_t*, fp8_e4m3_tag) : reg{} {}
+  explicit BF16Vec32(const uint8_t*, fp8_e5m2_tag) : reg{} {}
 };
 
 struct FP32Vec4 : public Vec<FP32Vec4> {
@@ -302,6 +308,10 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
 
   FP32Vec16(const BF16Vec8& v) : FP32Vec16(FP32Vec8(v)) {};
 
+  // FP8 stub: dead code on scalar path (fp8 KV cache is x86-only), needed for
+  // load_b_pair_vec template to compile on all platforms.
+  explicit FP32Vec16(const BF16Vec32&, int) : reg{} {}
+
   FP32Vec16 operator*(const FP32Vec16& b) const {
     f32x16_t ret;
     unroll_loop<int, VEC_ELEM_NUM>(
diff --git a/csrc/cpu/cpu_types_vsx.hpp b/csrc/cpu/cpu_types_vsx.hpp
index 089b9840ea2e..87c7a9dd51f4 100644
--- a/csrc/cpu/cpu_types_vsx.hpp
+++ b/csrc/cpu/cpu_types_vsx.hpp
@@ -9,6 +9,10 @@
 
 namespace vec_op {
 
+// FP8 tag types for tag dispatch (see cpu_attn_vec.hpp)
+struct fp8_e4m3_tag {};
+struct fp8_e5m2_tag {};
+
 // FIXME: FP16 is not fully supported in Torch-CPU
 #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)         \
   AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \
@@ -142,6 +146,9 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
       : reg({vec8_data.reg, vec8_data.reg, vec8_data.reg, vec8_data.reg}) {}
 
   void save(void* ptr) const { *reinterpret_cast<ss16x8x4_t*>(ptr) = reg; }
+
+  explicit BF16Vec32(const uint8_t*, fp8_e4m3_tag) : reg{} {}
+  explicit BF16Vec32(const uint8_t*, fp8_e5m2_tag) : reg{} {}
 };
 
 struct FP32Vec4 : public Vec<FP32Vec4> {
@@ -404,6 +411,10 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
 
   explicit FP32Vec16(const BF16Vec8& v) : FP32Vec16(FP32Vec8(v)) {}
 
+  // FP8 stub: dead code on PowerPC (fp8 KV cache is x86-only), needed for
+  // load_b_pair_vec template to compile on all platforms.
+  explicit FP32Vec16(const BF16Vec32&, int) : reg{} {}
+
   explicit FP32Vec16(const INT32Vec16& v) {
     reg.val[0] = vec_ctf(v.reg.val[0], 0);
     reg.val[1] = vec_ctf(v.reg.val[1], 0);
diff --git a/csrc/cpu/cpu_types_vxe.hpp b/csrc/cpu/cpu_types_vxe.hpp
index 700ba0306239..2e0af466b649 100644
--- a/csrc/cpu/cpu_types_vxe.hpp
+++ b/csrc/cpu/cpu_types_vxe.hpp
@@ -8,6 +8,9 @@
 #include <torch/all.h>
 namespace vec_op {
 
+struct fp8_e4m3_tag {};
+struct fp8_e5m2_tag {};
+
 #define vec_neg(a) (-(a))
 #define vec_add(a, b) ((a) + (b))
 #define vec_sub(a, b) ((a) - (b))
@@ -241,6 +244,9 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
   explicit BF16Vec32(const BF16Vec8& vec8_data)
       : reg({vec8_data.reg, vec8_data.reg, vec8_data.reg, vec8_data.reg}) {}
 
+  explicit BF16Vec32(const uint8_t*, fp8_e4m3_tag) : reg{} {}
+  explicit BF16Vec32(const uint8_t*, fp8_e5m2_tag) : reg{} {}
+
   void save(void* ptr) const { *reinterpret_cast<ss16x8x4_t*>(ptr) = reg; }
 };
 
@@ -682,6 +688,10 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
 
   explicit FP32Vec16(const BF16Vec8& v) : FP32Vec16(FP32Vec8(v)) {}
 
+  // FP8 stub: dead code on s390x (fp8 KV cache is x86-only), needed for
+  // load_b_pair_vec template to compile on all platforms.
+  explicit FP32Vec16(const BF16Vec32&, int) : reg{} {}
+
   FP32Vec16 operator*(const FP32Vec16& b) const {
     return FP32Vec16(f32x4x4_t({vec_mul(reg.val[0], b.reg.val[0]),
                                 vec_mul(reg.val[1], b.reg.val[1]),
diff --git a/csrc/cpu/cpu_types_x86.hpp b/csrc/cpu/cpu_types_x86.hpp
index d94af338ac1c..396b9b7e041f 100644
--- a/csrc/cpu/cpu_types_x86.hpp
+++ b/csrc/cpu/cpu_types_x86.hpp
@@ -11,6 +11,17 @@ static_assert(false, "AVX2 must be supported for the current implementation.");
 
 namespace vec_op {
 
+// Tags for FP8 BF16Vec32 constructors (avoid overload collision with
+// BF16Vec32(void*)).
+// VEC path (FP8 → pseudo-FP16 layout, scale correction applied later):
+struct fp8_e4m3_tag {};  // E4M3 → pseudo-FP16; BF16 value = true_E4M3 * 2^-8
+struct fp8_e5m2_tag {};  // E5M2 → FP16 bits directly (same exponent bias=15)
+// AMX path (FP8 → unscaled BF16, no FP32 round-trip):
+// BF16 value = true_E4M3 * 2^-120 (E4M3) or true_E5M2 * 2^-112 (E5M2).
+// Exponent rebiasing is folded into k/v scales by the caller.
+struct fp8_bf16_e4m3_tag {};
+struct fp8_bf16_e5m2_tag {};
+
 #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)            \
   AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)    \
   AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__) \
@@ -111,9 +122,17 @@ struct FP16Vec16 : public Vec<FP16Vec16> {
   void save(void* ptr) const { _mm256_storeu_si256((__m256i*)ptr, reg); }
 
   void save(void* ptr, const int elem_num) const {
+#ifdef __AVX512BW__
     constexpr uint32_t M = 0xFFFFFFFF;
     __mmask16 mask = _cvtu32_mask16(M >> (32 - elem_num));
     _mm256_mask_storeu_epi16(ptr, mask, reg);
+#else
+    // Fallback for lack of 16-bit masked store
+    int16_t tmp[VEC_ELEM_NUM];
+    _mm256_storeu_si256((__m256i*)tmp, reg);
+    for (int i = 0; i < elem_num; ++i)
+      reinterpret_cast<int16_t*>(ptr)[i] = tmp[i];
+#endif
   }
 };
 
@@ -150,9 +169,17 @@ struct BF16Vec16 : public Vec<BF16Vec16> {
   void save(void* ptr) const { _mm256_storeu_si256((__m256i*)ptr, reg); }
 
   void save(void* ptr, const int elem_num) const {
+#ifdef __AVX512BW__
     constexpr uint32_t M = 0xFFFFFFFF;
     __mmask16 mask = _cvtu32_mask16(M >> (32 - elem_num));
     _mm256_mask_storeu_epi16(ptr, mask, reg);
+#else
+    // Fallback for lack of 16-bit masked store
+    int16_t tmp[VEC_ELEM_NUM];
+    _mm256_storeu_si256((__m256i*)tmp, reg);
+    for (int i = 0; i < elem_num; ++i)
+      reinterpret_cast<int16_t*>(ptr)[i] = tmp[i];
+#endif
   }
 };
 
@@ -176,6 +203,50 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
                                (__m128i)vec8_data.reg, 2),
             (__m128i)vec8_data.reg, 3)) {}
 
+  // Decode 32 FP8-E4M3 bytes to pseudo-FP16 layout (stored in the BF16
+  // register).  Result = true_E4M3 * 2^-8; caller applies scale * 2^8.
+  explicit BF16Vec32(const uint8_t* ptr, fp8_e4m3_tag) {
+    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
+    __m512i b16 = _mm512_cvtepu8_epi16(b8);
+    __m512i sign =
+        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x80)), 8);
+    __m512i payload =
+        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x7F)), 7);
+    reg = _mm512_or_si512(sign, payload);
+  }
+
+  // Decode 32 FP8-E5M2 bytes to FP16 layout.
+  // E5M2 and FP16 share the same 5-bit exponent bias (15), so FP8 byte b maps
+  // directly to FP16 bits by shifting left 8 — no sign/payload reconstruction.
+  explicit BF16Vec32(const uint8_t* ptr, fp8_e5m2_tag) {
+    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
+    reg = _mm512_slli_epi16(_mm512_cvtepu8_epi16(b8), 8);
+  }
+
+  // Direct FP8-E4M3 → unscaled BF16 for AMX (no FP32 round-trip).
+  // BF16 value = true_E4M3 * 2^-120; exponent rebiasing folded into k/v scales.
+  explicit BF16Vec32(const uint8_t* ptr, fp8_bf16_e4m3_tag) {
+    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
+    __m512i b16 = _mm512_cvtepu8_epi16(b8);
+    __m512i sign =
+        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x80)), 8);
+    __m512i payload =
+        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x7F)), 4);
+    reg = _mm512_or_si512(sign, payload);
+  }
+
+  // Direct FP8-E5M2 → unscaled BF16 for AMX (no FP32 round-trip).
+  // BF16 value = true_E5M2 * 2^-112; exponent rebiasing folded into k/v scales.
+  explicit BF16Vec32(const uint8_t* ptr, fp8_bf16_e5m2_tag) {
+    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
+    __m512i b16 = _mm512_cvtepu8_epi16(b8);
+    __m512i sign =
+        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x80)), 8);
+    __m512i payload =
+        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x7F)), 5);
+    reg = _mm512_or_si512(sign, payload);
+  }
+
   void save(void* ptr) const { *reinterpret_cast<__m512i*>(ptr) = reg; }
 };
 #else
@@ -192,13 +263,83 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
   explicit BF16Vec32(__m256i low, __m256i high)
       : reg_low(low), reg_high(high) {}
 
+  explicit BF16Vec32()
+      : reg_low(_mm256_setzero_si256()), reg_high(_mm256_setzero_si256()) {}
+
   explicit BF16Vec32(BF16Vec8& vec8_data)
-      : reg_low((__m256i)_mm256_inserti32x4(
-            _mm256_castsi128_si256((__m128i)vec8_data.reg),
-            (__m128i)vec8_data.reg, 1)),
-        reg_high((__m256i)_mm256_inserti32x4(
-            _mm256_castsi128_si256((__m128i)vec8_data.reg),
-            (__m128i)vec8_data.reg, 1)) {}
+      : reg_low(_mm256_broadcastsi128_si256((__m128i)vec8_data.reg)),
+        reg_high(_mm256_broadcastsi128_si256((__m128i)vec8_data.reg)) {}
+
+  // E4M3 decode (AVX2 path) — same bit-layout trick as the AVX512 variant
+  // above.  Result = true_E4M3 * 2^-8; caller applies scale * 2^8.
+  explicit BF16Vec32(const uint8_t* ptr, fp8_e4m3_tag) {
+    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
+    __m128i b8_low = _mm256_extracti128_si256(b8, 0);
+    __m128i b8_high = _mm256_extracti128_si256(b8, 1);
+    __m256i b16_low = _mm256_cvtepu8_epi16(b8_low);
+    __m256i b16_high = _mm256_cvtepu8_epi16(b8_high);
+
+    __m256i sign_low = _mm256_slli_epi16(
+        _mm256_and_si256(b16_low, _mm256_set1_epi16(0x80)), 8);
+    __m256i payload_low = _mm256_slli_epi16(
+        _mm256_and_si256(b16_low, _mm256_set1_epi16(0x7F)), 7);
+    __m256i sign_high = _mm256_slli_epi16(
+        _mm256_and_si256(b16_high, _mm256_set1_epi16(0x80)), 8);
+    __m256i payload_high = _mm256_slli_epi16(
+        _mm256_and_si256(b16_high, _mm256_set1_epi16(0x7F)), 7);
+    reg_low = _mm256_or_si256(sign_low, payload_low);
+    reg_high = _mm256_or_si256(sign_high, payload_high);
+  }
+
+  // E5M2 decode (AVX2 path) — b << 8 maps to FP16 bits; see AVX512 variant
+  // above.
+  explicit BF16Vec32(const uint8_t* ptr, fp8_e5m2_tag) {
+    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
+    __m128i b8_low = _mm256_extracti128_si256(b8, 0);
+    __m128i b8_high = _mm256_extracti128_si256(b8, 1);
+    reg_low = _mm256_slli_epi16(_mm256_cvtepu8_epi16(b8_low), 8);
+    reg_high = _mm256_slli_epi16(_mm256_cvtepu8_epi16(b8_high), 8);
+  }
+
+  // Direct FP8-E4M3 → unscaled BF16 for AMX (AVX2 path, no FP32 round-trip).
+  // BF16 value = true_E4M3 * 2^-120; exponent rebiasing folded into k/v scales.
+  explicit BF16Vec32(const uint8_t* ptr, fp8_bf16_e4m3_tag) {
+    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
+    __m128i b8_low = _mm256_extracti128_si256(b8, 0);
+    __m128i b8_high = _mm256_extracti128_si256(b8, 1);
+    __m256i b16_low = _mm256_cvtepu8_epi16(b8_low);
+    __m256i b16_high = _mm256_cvtepu8_epi16(b8_high);
+    reg_low = _mm256_or_si256(
+        _mm256_slli_epi16(_mm256_and_si256(b16_low, _mm256_set1_epi16(0x80)),
+                          8),
+        _mm256_slli_epi16(_mm256_and_si256(b16_low, _mm256_set1_epi16(0x7F)),
+                          4));
+    reg_high = _mm256_or_si256(
+        _mm256_slli_epi16(_mm256_and_si256(b16_high, _mm256_set1_epi16(0x80)),
+                          8),
+        _mm256_slli_epi16(_mm256_and_si256(b16_high, _mm256_set1_epi16(0x7F)),
+                          4));
+  }
+
+  // Direct FP8-E5M2 → unscaled BF16 for AMX (AVX2 path, no FP32 round-trip).
+  // BF16 value = true_E5M2 * 2^-112; exponent rebiasing folded into k/v scales.
+  explicit BF16Vec32(const uint8_t* ptr, fp8_bf16_e5m2_tag) {
+    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
+    __m128i b8_low = _mm256_extracti128_si256(b8, 0);
+    __m128i b8_high = _mm256_extracti128_si256(b8, 1);
+    __m256i b16_low = _mm256_cvtepu8_epi16(b8_low);
+    __m256i b16_high = _mm256_cvtepu8_epi16(b8_high);
+    reg_low = _mm256_or_si256(
+        _mm256_slli_epi16(_mm256_and_si256(b16_low, _mm256_set1_epi16(0x80)),
+                          8),
+        _mm256_slli_epi16(_mm256_and_si256(b16_low, _mm256_set1_epi16(0x7F)),
+                          5));
+    reg_high = _mm256_or_si256(
+        _mm256_slli_epi16(_mm256_and_si256(b16_high, _mm256_set1_epi16(0x80)),
+                          8),
+        _mm256_slli_epi16(_mm256_and_si256(b16_high, _mm256_set1_epi16(0x7F)),
+                          5));
+  }
 
   void save(void* ptr) const {
     _mm256_storeu_si256((__m256i*)ptr, reg_low);
@@ -390,6 +531,11 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
       : reg(_mm512_castsi512_ps(
             _mm512_bslli_epi128(_mm512_cvtepu16_epi32(v.reg), 2))) {}
 
+  explicit FP32Vec16(const BF16Vec32& v, int upper) {
+    __m256i v_half_i = _mm512_extracti32x8_epi32(v.reg, upper);
+    reg = _mm512_cvtph_ps(v_half_i);
+  }
+
   explicit FP32Vec16(const FP16Vec16& v) : reg(_mm512_cvtph_ps(v.reg)) {}
 
   explicit FP32Vec16(const FP16Vec8& v) : FP32Vec16(FP32Vec8(v)) {}
@@ -494,6 +640,14 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
   explicit FP32Vec16(const FP32Vec8& data)
       : reg_low(data.reg), reg_high(data.reg) {}
 
+  explicit FP32Vec16(const BF16Vec32& v, int upper) {
+    const __m256i& half = upper ? v.reg_high : v.reg_low;
+    __m128i lo = _mm256_extractf128_si256(half, 0);
+    __m128i hi = _mm256_extractf128_si256(half, 1);
+    reg_low = _mm256_cvtph_ps(lo);
+    reg_high = _mm256_cvtph_ps(hi);
+  }
+
   explicit FP32Vec16(const FP16Vec16& v) {
     __m128i low = _mm256_extractf128_si256(v.reg, 0);
     __m128i high = _mm256_extractf128_si256(v.reg, 1);
@@ -535,6 +689,11 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
                      _mm256_sub_ps(reg_high, b.reg_high));
   }
 
+  FP32Vec16 operator-() const {
+    const __m256 neg = _mm256_set1_ps(-0.0f);
+    return FP32Vec16(_mm256_xor_ps(reg_low, neg), _mm256_xor_ps(reg_high, neg));
+  }
+
   FP32Vec16 operator/(const FP32Vec16& b) const {
     return FP32Vec16(_mm256_div_ps(reg_low, b.reg_low),
                      _mm256_div_ps(reg_high, b.reg_high));
@@ -600,6 +759,85 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
     _mm256_storeu_ps(ptr, reg_low);
     _mm256_storeu_ps(ptr + 8, reg_high);
   }
+
+  void save(float* ptr, const int elem_num) const {
+    // Partial store: cmpgt produces a sign-bit mask (0xFFFFFFFF/0 per lane)
+    // for the first elem_num lanes, applied across the two 8-wide halves.
+    if (elem_num <= 8) {
+      __m256i mask =
+          _mm256_cmpgt_epi32(_mm256_set1_epi32(elem_num),
+                             _mm256_setr_epi32(0, 1, 2, 3, 4, 5, 6, 7));
+      _mm256_maskstore_ps(ptr, mask, reg_low);
+    } else {
+      _mm256_storeu_ps(ptr, reg_low);
+      __m256i mask =
+          _mm256_cmpgt_epi32(_mm256_set1_epi32(elem_num - 8),
+                             _mm256_setr_epi32(0, 1, 2, 3, 4, 5, 6, 7));
+      _mm256_maskstore_ps(ptr + 8, mask, reg_high);
+    }
+  }
+
+  FP32Vec16 clamp(const FP32Vec16& min, const FP32Vec16& max) const {
+    return FP32Vec16(
+        _mm256_min_ps(max.reg_low, _mm256_max_ps(min.reg_low, reg_low)),
+        _mm256_min_ps(max.reg_high, _mm256_max_ps(min.reg_high, reg_high)));
+  }
+
+  FP32Vec16 abs() const {
+    const __m256 sign_mask = _mm256_set1_ps(-0.0f);
+    return FP32Vec16(_mm256_andnot_ps(sign_mask, reg_low),
+                     _mm256_andnot_ps(sign_mask, reg_high));
+  }
+
+  FP32Vec16 min(const FP32Vec16& b) const {
+    return FP32Vec16(_mm256_min_ps(reg_low, b.reg_low),
+                     _mm256_min_ps(reg_high, b.reg_high));
+  }
+
+  // Partial element-wise min over the first elem_num lanes only (tail path).
+  // Scalar via AliasReg: AVX2 has no masked vminps, so we spill, loop, reload.
+  FP32Vec16 min(const FP32Vec16& b, const int elem_num) const {
+    AliasReg ar_this_low, ar_this_high, ar_b_low, ar_b_high;
+    ar_this_low.reg = reg_low;
+    ar_this_high.reg = reg_high;
+    ar_b_low.reg = b.reg_low;
+    ar_b_high.reg = b.reg_high;
+    for (int i = 0; i < elem_num && i < 8; ++i)
+      ar_this_low.values[i] =
+          std::min(ar_this_low.values[i], ar_b_low.values[i]);
+    for (int i = 0; i < elem_num - 8 && i < 8; ++i)
+      ar_this_high.values[i] =
+          std::min(ar_this_high.values[i], ar_b_high.values[i]);
+    return FP32Vec16(ar_this_low.reg, ar_this_high.reg);
+  }
+
+  // Partial element-wise max over the first elem_num lanes only (tail path).
+  // Scalar via AliasReg: AVX2 has no masked vmaxps, so we spill, loop, reload.
+  FP32Vec16 max(const FP32Vec16& b, const int elem_num) const {
+    AliasReg ar_this_low, ar_this_high, ar_b_low, ar_b_high;
+    ar_this_low.reg = reg_low;
+    ar_this_high.reg = reg_high;
+    ar_b_low.reg = b.reg_low;
+    ar_b_high.reg = b.reg_high;
+    for (int i = 0; i < elem_num && i < 8; ++i)
+      ar_this_low.values[i] =
+          std::max(ar_this_low.values[i], ar_b_low.values[i]);
+    for (int i = 0; i < elem_num - 8 && i < 8; ++i)
+      ar_this_high.values[i] =
+          std::max(ar_this_high.values[i], ar_b_high.values[i]);
+    return FP32Vec16(ar_this_low.reg, ar_this_high.reg);
+  }
+
+  float reduce_min() const {
+    __m256 v = _mm256_min_ps(reg_low, reg_high);
+    __m256 v_shuffled = _mm256_permute_ps(v, 0b00001011);
+    __m256 v_min = _mm256_min_ps(v, v_shuffled);
+    v_shuffled = _mm256_permute_ps(v_min, 0b00000001);
+    v_min = _mm256_min_ps(v_min, v_shuffled);
+    v_shuffled = _mm256_permute2f128_ps(v_min, v_min, 0b00000001);
+    v_min = _mm256_min_ps(v_min, v_shuffled);
+    return _mm256_cvtss_f32(v_min);
+  }
 };
 #endif
 
@@ -652,6 +890,34 @@ struct INT8Vec64 : public Vec<INT8Vec64> {
   // non-temporal save
   void nt_save(int8_t* ptr) { _mm512_stream_si512((__m512i*)ptr, reg); }
 };
+#else
+struct INT8Vec16 : public Vec<INT8Vec16> {
+  constexpr static int VEC_ELEM_NUM = 16;
+  union AliasReg {
+    __m128i reg;
+    int8_t values[VEC_ELEM_NUM];
+  };
+
+  __m128i reg;
+
+  explicit INT8Vec16(const FP32Vec16& vec) {
+    __m256i lo_i32 = _mm256_cvtps_epi32(vec.reg_low);
+    __m256i hi_i32 = _mm256_cvtps_epi32(vec.reg_high);
+    __m256i packed16 = _mm256_packs_epi32(lo_i32, hi_i32);
+    packed16 = _mm256_permute4x64_epi64(packed16, 0xD8);
+    __m256i packed8 = _mm256_packs_epi16(packed16, _mm256_setzero_si256());
+    packed8 = _mm256_permute4x64_epi64(packed8, 0xD8);
+    reg = _mm256_castsi256_si128(packed8);
+  }
+
+  void save(int8_t* ptr) const { _mm_storeu_si128((__m128i*)ptr, reg); }
+
+  void save(int8_t* ptr, const int elem_num) const {
+    AliasReg ar;
+    ar.reg = reg;
+    for (int i = 0; i < elem_num; ++i) ptr[i] = ar.values[i];
+  }
+};
 #endif
 
 template <typename T>
diff --git a/csrc/cpu/dnnl_kernels.cpp b/csrc/cpu/dnnl_kernels.cpp
index 80be42bb7639..058fe25b0e26 100644
--- a/csrc/cpu/dnnl_kernels.cpp
+++ b/csrc/cpu/dnnl_kernels.cpp
@@ -215,7 +215,7 @@ void dynamic_quant_epilogue(const float* input, scalar_t* output,
         float zp_scale_val = a_scale[i] * static_cast<float>(azp[i]);
         token_zp_scale_vec = cvt_vec_t(zp_scale_val);
       }
-      for (; j < hidden_size - vec_elem_num; ++j) {
+      for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
         cvt_vec_t elems_fp32(input_ptr + j);
         elems_fp32 = elems_fp32 * token_scale_vec;
         if constexpr (AZP) {
diff --git a/csrc/cpu/generate_cpu_attn_dispatch.py b/csrc/cpu/generate_cpu_attn_dispatch.py
index bbcd6d85b1d1..2cfd599f09cd 100644
--- a/csrc/cpu/generate_cpu_attn_dispatch.py
+++ b/csrc/cpu/generate_cpu_attn_dispatch.py
@@ -20,73 +20,98 @@
     "VEC16": 2,
     "NEON": 3,
     "VXE": 4,
+    "VSX": 5,
+}
+
+# KV cache index: 0 = auto (same as scalar_t), 1 = fp8_e4m3, 2 = fp8_e5m2
+KV_CACHE_IDX = {
+    "auto": 0,
+    "fp8_e4m3": 1,
+    "fp8_e5m2": 2,
+}
+
+# C++ type for each kv_cache index
+KV_CACHE_CPP_TYPES = {
+    "auto": "scalar_t",
+    "fp8_e4m3": "c10::Float8_e4m3fn",
+    "fp8_e5m2": "c10::Float8_e5m2",
 }
 
 # ISAs supported for head_dims divisible by 32
-ISA_FOR_32 = ["AMX", "NEON", "VEC", "VEC16", "VXE"]
+ISA_FOR_32 = ["AMX", "NEON", "VEC", "VEC16", "VXE", "VSX"]
 
 # ISAs supported for head_dims divisible by 16 only
 ISA_FOR_16 = ["VEC16"]
 
+# ISAs that support FP8 KV cache (x86 AVX2/AVX-512 required)
+ISA_FOR_FP8 = ["AMX", "VEC"]
+
 
-def encode_params(head_dim: int, isa_type: str) -> int:
-    """Encode head_dim and ISA type into a single int64_t."""
+def encode_params(head_dim: int, isa_type: str, kv_cache: str = "auto") -> int:
+    """Encode head_dim, ISA type, and KV cache type into a single int64_t."""
     isa_val = ISA_TYPES[isa_type]
-    # Encoding: (head_dim << 8) | isa_type
-    # This allows head_dim up to 2^56 - 1 and 256 ISA types
-    return (head_dim << 8) | isa_val
+    kv_val = KV_CACHE_IDX[kv_cache]
+    # Encoding: (head_dim << 16) | (kv_cache_idx << 8) | isa_type
+    # This allows head_dim up to 2^48 - 1, 256 KV cache types, and 256 ISA types
+    return (head_dim << 16) | (kv_val << 8) | isa_val
+
+
+def _make_case(
+    head_dim: int, isa: str, kv_cache: str = "auto", isa_override: str | None = None
+) -> str:
+    """Generate a single switch case line."""
+    encoded = encode_params(head_dim, isa, kv_cache)
+    actual_isa = isa_override if isa_override else isa
+    cpp_type = KV_CACHE_CPP_TYPES[kv_cache]
+    attn_impl = (
+        f"cpu_attention::AttentionImpl<"
+        f"cpu_attention::ISA::{actual_isa}, \\\n"
+        f"                                                       "
+        f"scalar_t, head_dim, {cpp_type}>"
+    )
+    comment = (
+        f"head_dim={head_dim}, isa={isa}"
+        if kv_cache == "auto"
+        else f"head_dim={head_dim}, isa={isa}, kv_cache={kv_cache}"
+    )
+    return (
+        f"""      case {encoded}LL: {{ """
+        f"""/* {comment} */ \\"""
+        f"""
+        constexpr size_t head_dim = {head_dim}; \\"""
+        f"""
+        using attn_impl = {attn_impl}; \\"""
+        f"""
+        return __VA_ARGS__(); \\"""
+        f"""
+      }} \\"""
+    )
 
 
-def generate_cases_for_isa_group(isa_list: list[str]) -> str:
+def generate_cases_for_isa_group(isa_list: list[str], include_fp8: bool = False) -> str:
     """Generate switch cases for a specific ISA group."""
     cases = []
 
-    # Generate cases for head_dims divisible by 32
+    # Non-FP8 cases for head_dims divisible by 32
     for head_dim in HEAD_DIMS_32:
         for isa in isa_list:
             if isa not in ISA_FOR_32:
                 continue
-            encoded = encode_params(head_dim, isa)
-            case_str = (
-                f"""      case {encoded}LL: {{ """
-                f"""/* head_dim={head_dim}, isa={isa} */ \\"""
-                f"""
-        constexpr size_t head_dim = {head_dim}; \\"""
-                f"""
-        using attn_impl = cpu_attention::AttentionImpl<"""
-                f"""cpu_attention::ISA::{isa}, \\"""
-                f"""
-                                                       """
-                f"""scalar_t, head_dim>; \\"""
-                f"""
-        return __VA_ARGS__(); \\"""
-                f"""
-      }} \\"""
-            )
-            cases.append(case_str)
+            cases.append(_make_case(head_dim, isa, "auto"))
 
-    # Generate cases for head_dims divisible by 16 only
+    # Non-FP8 cases for head_dims divisible by 16 only
     for head_dim in HEAD_DIMS_16:
         for isa in isa_list:
-            encoded = encode_params(head_dim, isa)
-            case_str = (
-                f"""      case {encoded}LL: {{ """
-                f"""/* head_dim={head_dim}, isa={isa} """
-                f"""(using VEC16) */ \\"""
-                f"""
-        constexpr size_t head_dim = {head_dim}; \\"""
-                f"""
-        using attn_impl = cpu_attention::AttentionImpl<"""
-                f"""cpu_attention::ISA::VEC16, \\"""
-                f"""
-                                                       """
-                f"""scalar_t, head_dim>; \\"""
-                f"""
-        return __VA_ARGS__(); \\"""
-                f"""
-      }} \\"""
-            )
-            cases.append(case_str)
+            cases.append(_make_case(head_dim, isa, "auto", isa_override="VEC16"))
+
+    # FP8 cases: only AMX and VEC, only head_dims divisible by 32
+    if include_fp8:
+        for fp8_type in ("fp8_e4m3", "fp8_e5m2"):
+            for head_dim in HEAD_DIMS_32:
+                for isa in isa_list:
+                    if isa not in ISA_FOR_FP8:
+                        continue
+                    cases.append(_make_case(head_dim, isa, fp8_type))
 
     return "\n".join(cases)
 
@@ -94,8 +119,9 @@ def generate_cases_for_isa_group(isa_list: list[str]) -> str:
 def generate_helper_function() -> str:
     """Generate helper function to encode parameters."""
     return """
-inline int64_t encode_cpu_attn_params(int64_t head_dim, cpu_attention::ISA isa) {
-  return (head_dim << 8) | static_cast<int64_t>(isa);
+inline int64_t encode_cpu_attn_params(int64_t head_dim, cpu_attention::ISA isa,
+                                      int64_t kv_cache_idx = 0) {
+  return (head_dim << 16) | (kv_cache_idx << 8) | static_cast<int64_t>(isa);
 }
 """
 
@@ -123,93 +149,94 @@ def generate_header_file() -> str:
   #include "cpu_attn_vxe.hpp"
 #endif
 
+#ifdef __powerpc__
+  #include "cpu_attn_vsx.hpp"
+#endif
+
 """
 
     header += generate_helper_function()
 
     # Generate dispatch macro with conditional compilation for different ISA sets
     header += """
-// Dispatch macro using encoded parameters
-"""
-
-    # x86_64 with AMX
-    header += """#if defined(CPU_CAPABILITY_AMXBF16)
-#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, ...) \\
-  [&] { \\
-    int64_t encoded_params = encode_cpu_attn_params(HEAD_DIM, ISA_TYPE); \\
-    switch (encoded_params) { \\
-"""
-    header += generate_cases_for_isa_group(["AMX", "VEC", "VEC16"])
-    header += """
-      default: { \\
-        TORCH_CHECK(false, "Unsupported CPU attention configuration: head_dim=" + \\
-                    std::to_string(HEAD_DIM) + " isa=" + \\
-                    std::to_string(static_cast<int>(ISA_TYPE))); \\
-      } \\
-    } \\
-  }()
-
-"""
-
-    # ARM64 with NEON
-    header += """#elif defined(__aarch64__)
-#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, ...) \\
-  [&] { \\
-    int64_t encoded_params = encode_cpu_attn_params(HEAD_DIM, ISA_TYPE); \\
-    switch (encoded_params) { \\
-"""
-    header += generate_cases_for_isa_group(["NEON", "VEC", "VEC16"])
-    header += """
-      default: { \\
-        TORCH_CHECK(false, "Unsupported CPU attention configuration: head_dim=" + \\
-                    std::to_string(HEAD_DIM) + " isa=" + \\
-                    std::to_string(static_cast<int>(ISA_TYPE))); \\
-      } \\
-    } \\
-  }()
-
-"""
-
-    # s390x with VXE
-    header += """#elif defined(__s390x__)
-#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, ...) \\
-  [&] { \\
-    int64_t encoded_params = encode_cpu_attn_params(HEAD_DIM, ISA_TYPE); \\
-    switch (encoded_params) { \\
-"""
-    header += generate_cases_for_isa_group(["VXE", "VEC", "VEC16"])
-    header += """
-      default: { \\
-        TORCH_CHECK(false, "Unsupported CPU attention configuration: head_dim=" + \\
-                    std::to_string(HEAD_DIM) + " isa=" + \\
-                    std::to_string(static_cast<int>(ISA_TYPE))); \\
-      } \\
-    } \\
-  }()
-
-"""
-
-    # Fallback: VEC and VEC16 only
-    header += """#else
-#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, ...) \\
-  [&] { \\
-    int64_t encoded_params = encode_cpu_attn_params(HEAD_DIM, ISA_TYPE); \\
-    switch (encoded_params) { \\
+// Dispatch macro using encoded parameters.
+// KV_CACHE_IDX: Fp8KVCacheDataType enum value (kAuto=0, kFp8E4M3=1, kFp8E5M2=2).
+// FP8 cases (kv_cache_idx != 0) are generated on x86 platforms with AVX2 or
+// AVX-512: BF16Vec32 FP8 constructors have both AVX-512 and AVX2 implementations
+// in cpu_types_x86.hpp. Non-x86 platforms (#else fallback) have fp8=False.
 """
-    header += generate_cases_for_isa_group(["VEC", "VEC16"])
-    header += """
-      default: { \\
-        TORCH_CHECK(false, "Unsupported CPU attention configuration: head_dim=" + \\
-                    std::to_string(HEAD_DIM) + " isa=" + \\
-                    std::to_string(static_cast<int>(ISA_TYPE))); \\
-      } \\
-    } \\
-  }()
-
-#endif  /* CPU_CAPABILITY_AMXBF16 / __aarch64__ / __s390x__ */
 
-#endif  // CPU_ATTN_DISPATCH_GENERATED_H
-"""
+    def _macro_block(guard: str, isa_list: list[str], fp8: bool) -> str:
+        """Return one CPU_ATTN_DISPATCH macro block for a given guard."""
+        enc = (
+            "    int64_t encoded_params = encode_cpu_attn_params("
+            "HEAD_DIM, ISA_TYPE, KV_CACHE_IDX); \\"
+        )
+        cases = generate_cases_for_isa_group(isa_list, include_fp8=fp8)
+        tail = (
+            "\n"
+            "      default: { \\\n"
+            "        TORCH_CHECK(false, "
+            '"Unsupported CPU attention configuration: head_dim=" + \\\n'
+            '                    std::to_string(HEAD_DIM) + " isa=" + \\\n'
+            "                    std::to_string(static_cast<int>(ISA_TYPE))"
+            " + \\\n"
+            '                    " kv_cache_idx=" + '
+            "std::to_string(KV_CACHE_IDX)); \\\n"
+            "      } \\\n"
+            "    } \\\n"
+            "  }()\n\n"
+        )
+        return (
+            f"{guard}\n"
+            "#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, KV_CACHE_IDX, ...) \\\n"
+            "  [&] { \\\n"
+            f"{enc}\n"
+            "    switch (encoded_params) { \\\n"
+            f"{cases}"
+            f"{tail}"
+        )
+
+    header += _macro_block(
+        "#if defined(CPU_CAPABILITY_AMXBF16)",
+        ["AMX", "VEC", "VEC16"],
+        fp8=True,
+    )
+    header += _macro_block(
+        "#elif defined(__aarch64__)",
+        ["NEON", "VEC", "VEC16"],
+        fp8=False,
+    )
+    header += _macro_block(
+        "#elif defined(__s390x__)",
+        ["VXE", "VEC", "VEC16"],
+        fp8=False,
+    )
+    header += _macro_block(
+        "#elif defined(__powerpc__)",
+        ["VSX", "VEC", "VEC16"],
+        fp8=False,
+    )
+    header += _macro_block(
+        "#elif defined(__AVX512F__)",
+        ["VEC", "VEC16"],
+        fp8=True,
+    )
+    header += _macro_block(
+        "#elif defined(__AVX2__)",
+        ["VEC", "VEC16"],
+        fp8=False,
+    )
+    header += _macro_block(
+        "#else",
+        ["VEC", "VEC16"],
+        fp8=False,
+    )
+    header += (
+        "#endif  /* CPU_CAPABILITY_AMXBF16 / __aarch64__ / "
+        "__s390x__ / __powerpc__ */\n\n"
+        "#endif  // CPU_ATTN_DISPATCH_GENERATED_H\n"
+    )
 
     return header
 
diff --git a/csrc/cpu/pos_encoding.cpp b/csrc/cpu/pos_encoding.cpp
index 74bb014cf39e..9f41e4e222bd 100644
--- a/csrc/cpu/pos_encoding.cpp
+++ b/csrc/cpu/pos_encoding.cpp
@@ -178,7 +178,12 @@ void rotary_embedding_gptj_impl(
 
 void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
                       std::optional<torch::Tensor> key, int64_t head_size,
-                      torch::Tensor& cos_sin_cache, bool is_neox) {
+                      torch::Tensor& cos_sin_cache, bool is_neox,
+                      int64_t rope_dim_offset, bool inverse) {
+  TORCH_CHECK(rope_dim_offset == 0,
+              "rope_dim_offset != 0 is not supported on CPU");
+  TORCH_CHECK(!inverse, "inverse rotary embedding is not supported on CPU");
+
   int num_tokens = positions.numel();
   int rot_dim = cos_sin_cache.size(1);
   int num_heads = query.size(-1) / head_size;
diff --git a/csrc/cpu/sgl-kernels/common.h b/csrc/cpu/sgl-kernels/common.h
index 31be725fab29..425d5d131f63 100644
--- a/csrc/cpu/sgl-kernels/common.h
+++ b/csrc/cpu/sgl-kernels/common.h
@@ -1,13 +1,12 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
+// clang-format off
+
 #pragma once
 
 #include <ATen/ATen.h>
 #include <ATen/Parallel.h>
-#include <ATen/record_function.h>
-
-// clang-format off
 
 #if defined(_OPENMP)
 #include <omp.h>
@@ -16,40 +15,157 @@
 namespace {
 
 // dispatch bool
-#define AT_DISPATCH_BOOL(BOOL_V, BOOL_NAME, ...)                                 \
-  [&] {                                                                          \
-    if (BOOL_V) {                                                                \
-      constexpr bool BOOL_NAME = true;                                           \
-      return __VA_ARGS__();                                                      \
-    } else {                                                                     \
-      constexpr bool BOOL_NAME = false;                                          \
-      return __VA_ARGS__();                                                      \
-    }                                                                            \
+#define AT_DISPATCH_BOOL(BOOL_V, BOOL_NAME, ...) \
+  [&] {                                          \
+    if (BOOL_V) {                                \
+      constexpr bool BOOL_NAME = true;           \
+      return __VA_ARGS__();                      \
+    } else {                                     \
+      constexpr bool BOOL_NAME = false;          \
+      return __VA_ARGS__();                      \
+    }                                            \
+  }()
+
+#define AT_DISPATCH_BOOL2(BOOL_V1, BOOL_NAME1, BOOL_V2, BOOL_NAME2, ...) \
+  [&] {                                                                  \
+    if (BOOL_V1) {                                                       \
+      constexpr bool BOOL_NAME1 = true;                                  \
+      if (BOOL_V2) {                                                     \
+        constexpr bool BOOL_NAME2 = true;                                \
+        return __VA_ARGS__();                                            \
+      } else {                                                           \
+        constexpr bool BOOL_NAME2 = false;                               \
+        return __VA_ARGS__();                                            \
+      }                                                                  \
+    } else {                                                             \
+      constexpr bool BOOL_NAME1 = false;                                 \
+      if (BOOL_V2) {                                                     \
+        constexpr bool BOOL_NAME2 = true;                                \
+        return __VA_ARGS__();                                            \
+      } else {                                                           \
+        constexpr bool BOOL_NAME2 = false;                               \
+        return __VA_ARGS__();                                            \
+      }                                                                  \
+    }                                                                    \
+  }()
+
+// dispatch: bfloat16, float16, int8_t, fp8_e4m3, uint8_t(mxfp4/int4)
+#define CPU_DISPATCH_PACKED_TYPES(TYPE, ...)                     \
+  [&] {                                                          \
+    switch (TYPE) {                                              \
+      case at::ScalarType::BFloat16: {                           \
+        using packed_t = at::BFloat16;                           \
+        return __VA_ARGS__();                                    \
+      }                                                          \
+      case at::ScalarType::Half: {                               \
+        using packed_t = at::Half;                               \
+        return __VA_ARGS__();                                    \
+      }                                                          \
+      case at::ScalarType::Char: {                               \
+        using packed_t = int8_t;                                 \
+        return __VA_ARGS__();                                    \
+      }                                                          \
+      case at::ScalarType::Float8_e4m3fn: {                      \
+        using packed_t = at::Float8_e4m3fn;                      \
+        return __VA_ARGS__();                                    \
+      }                                                          \
+      case at::ScalarType::Byte: {                               \
+        using packed_t = uint8_t;                                \
+        return __VA_ARGS__();                                    \
+      }                                                          \
+      default:                                                   \
+        TORCH_CHECK(false, "Unsupported floating data type.\n"); \
+    }                                                            \
+  }()
+
+// Helper MICRO for CPU_DISPATCH_FLOATING_TYPES_EXT:
+//   TYPE1: the primary dtype (input, output, weight);
+//   TYPE2: defined as PARAM_T input
+#define CPU_DISPATCH_TYPE1_WITH_PARAM(TYPE1, PARAM_T, ...)   \
+  switch (TYPE1) {                                           \
+    case at::ScalarType::BFloat16: {                         \
+      using scalar_t = at::BFloat16;                         \
+      using param_t = PARAM_T;                               \
+      return __VA_ARGS__();                                  \
+    }                                                        \
+    case at::ScalarType::Half: {                             \
+      using scalar_t = at::Half;                             \
+      using param_t = PARAM_T;                               \
+      return __VA_ARGS__();                                  \
+    }                                                        \
+    case at::ScalarType::Float: {                            \
+      using scalar_t = float;                                \
+      using param_t = PARAM_T;                               \
+      return __VA_ARGS__();                                  \
+    }                                                        \
+    default:                                                 \
+      TORCH_CHECK(false, "Unsupported floating data type."); \
+  }
+
+// Helper MICRO for CPU_DISPATCH_REDUCED_FLOATING_TYPES_EXT:
+//   TYPE1: the primary dtype (input, output, weight);
+//   TYPE2: defined as PARAM_T input
+#define CPU_DISPATCH_TYPE1_WITH_PARAM_REDUCED(TYPE1, PARAM_T, ...) \
+  switch (TYPE1) {                                                 \
+    case at::ScalarType::BFloat16: {                               \
+      using scalar_t = at::BFloat16;                               \
+      using param_t = PARAM_T;                                     \
+      return __VA_ARGS__();                                        \
+    }                                                              \
+    case at::ScalarType::Half: {                                   \
+      using scalar_t = at::Half;                                   \
+      using param_t = PARAM_T;                                     \
+      return __VA_ARGS__();                                        \
+    }                                                              \
+    default:                                                       \
+      TORCH_CHECK(false, "Unsupported floating data type.");       \
+  }
+
+// Helper MICRO for CPU_DISPATCH_REDUCED_FLOATING_TYPES_EXT:
+//   TYPE1: the dtype both for scalar_t and param_t
+#define CPU_DISPATCH_TYPE1_WITH_SAME_PARAM_REDUCED(TYPE1, ...)       \
+  switch (TYPE1) {                                                   \
+    case at::ScalarType::BFloat16: {                                 \
+      using scalar_t = at::BFloat16;                                 \
+      using param_t = at::BFloat16;                                  \
+      return __VA_ARGS__();                                          \
+    }                                                                \
+    case at::ScalarType::Half: {                                     \
+      using scalar_t = at::Half;                                     \
+      using param_t = at::Half;                                      \
+      return __VA_ARGS__();                                          \
+    }                                                                \
+    default:                                                         \
+      TORCH_CHECK(false, "Unsupported reduced floating data type."); \
+  }
+
+// dispatch with mixed dtypes (TYPE1, TYPE2):
+//   TYPE1: the primary dtype (input, output, weight);
+//   TYPE2: the secondary dtype (bias, etc.).
+#define CPU_DISPATCH_FLOATING_TYPES_EXT(TYPE1, TYPE2, ...)            \
+  [&] {                                                               \
+    if (TYPE2 == at::kFloat) {                                        \
+      CPU_DISPATCH_TYPE1_WITH_PARAM(TYPE1, float, __VA_ARGS__)        \
+    } else if (TYPE2 == at::ScalarType::BFloat16) {                   \
+      CPU_DISPATCH_TYPE1_WITH_PARAM(TYPE1, at::BFloat16, __VA_ARGS__) \
+    } else if (TYPE2 == at::ScalarType::Half) {                       \
+      CPU_DISPATCH_TYPE1_WITH_PARAM(TYPE1, at::Half, __VA_ARGS__)     \
+    } else {                                                          \
+      TORCH_CHECK(false, "Unsupported floating data type.");          \
+    }                                                                 \
   }()
 
-// dispatch: bfloat16, float16, int8_t, fp8_e4m3
-#define CPU_DISPATCH_PACKED_TYPES(TYPE, ...)                                    \
-  [&] {                                                                         \
-    switch (TYPE) {                                                             \
-      case at::ScalarType::BFloat16 : {                                         \
-        using packed_t = at::BFloat16;                                          \
-        return __VA_ARGS__();                                                   \
-      }                                                                         \
-      case at::ScalarType::Half: {                                              \
-        using packed_t = at::Half;                                              \
-        return __VA_ARGS__();                                                   \
-      }                                                                         \
-      case at::ScalarType::Char : {                                             \
-        using packed_t = int8_t;                                                \
-        return __VA_ARGS__();                                                   \
-      }                                                                         \
-      case at::ScalarType::Float8_e4m3fn : {                                    \
-        using packed_t = at::Float8_e4m3fn;                                     \
-        return __VA_ARGS__();                                                   \
-      }                                                                         \
-      default:                                                                  \
-        TORCH_CHECK(false, "Unsupported floating data type.\n");                \
-    }                                                                           \
+// dispatch with mixed dtypes (reduced one, no float for TYPE1) (TYPE1, TYPE2):
+//   TYPE1: the primary dtype (input, output, weight);
+//   TYPE2: the secondary dtype (bias, etc.).
+#define CPU_DISPATCH_REDUCED_FLOATING_TYPES_EXT(TYPE1, TYPE2, ...)     \
+  [&] {                                                                \
+    if (TYPE2 == at::kFloat) {                                         \
+      CPU_DISPATCH_TYPE1_WITH_PARAM_REDUCED(TYPE1, float, __VA_ARGS__) \
+    } else {                                                           \
+      TORCH_CHECK(TYPE1 == TYPE2);                                     \
+      CPU_DISPATCH_TYPE1_WITH_SAME_PARAM_REDUCED(TYPE1, __VA_ARGS__)   \
+    }                                                                  \
   }()
 
 #define UNUSED(x) (void)(x)
@@ -70,13 +186,51 @@ namespace {
 #define CHECK_DIM(d, x) TORCH_CHECK(x.dim() == d, #x " must be a " #d "D tensor")
 
 #define CHECK_EQ(a, b) TORCH_CHECK((a) == (b), "CHECK_EQ(" #a ", " #b ") failed. ", a, " vs ", b)
+#define CHECK_GT(a, b) TORCH_CHECK((a) > (b), "CHECK_GT(" #a ", " #b ") failed. ", a, " vs ", b)
+#define CHECK_GE(a, b) TORCH_CHECK((a) >= (b), "CHECK_GE(" #a ", " #b ") failed. ", a, " vs ", b)
+
+template <bool is_only_lastdim_contiguous>
+static inline void CHECK_INPUT_SHAPE_DTYPE(const at::Tensor& tensor, const at::IntArrayRef sizes, at::ScalarType st) {
+  TORCH_CHECK(tensor.sizes() == sizes, "Input tensor shape mismatch: expected ", sizes, ", got ", tensor.sizes());
+  TORCH_CHECK(tensor.scalar_type() == st, "Input tensor dtype mismatch");
+  if constexpr (is_only_lastdim_contiguous) {
+    CHECK_LAST_DIM_CONTIGUOUS_INPUT(tensor);
+  } else {
+    CHECK_INPUT(tensor);
+  }
+}
 
-// parallel routines
+// [NB] Parallel Routines
+//
+//  * at::parallel_for - applies for most of generic use cases, this will be compiled
+//                       against openmp in default torch release.
+//
+//  * parallel_for     - same function as above, can choose payload partition scheme in
+//                       balance211.
+//
+//  * parallel_2d      - parallel for 2 dimensions, used in GEMM, etc.
+//                       this one will do payload balance across 2 dimensions.
+//
+
+// grain size for each thread
 constexpr int GRAIN_SIZE = 1024;
 
 template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
-inline T div_up(T x, T y) { return (x + y - 1) / y; }
+inline T div_up(T x, T y) {
+  return (x + y - 1) / y;
+}
+
+// you can only use at::get_thread_num() with at::parallel_for()
+// as it is lazy initialized, otherwise it will always return 0.
+inline int get_thread_num() {
+#if defined(_OPENMP)
+  return omp_get_thread_num();
+#else
+  return 0;
+#endif
+}
 
+// balance payload across each thread
 template <typename T>
 inline void balance211(T n, T nth, T ith, T& n_start, T& n_end) {
 #if 0
@@ -94,10 +248,10 @@ inline void balance211(T n, T nth, T ith, T& n_start, T& n_end) {
     }
     n_end += n_start;
 #else
-    // pytorch aten partition pattern
-    T n_my = div_up(n, nth);
-    n_start = ith * n_my;
-    n_end = std::min(n_start + n_my, n);
+  // pytorch aten partition pattern
+  T n_my = div_up(n, nth);
+  n_start = ith * n_my;
+  n_end = std::min(n_start + n_my, n);
 #endif
 }
 
@@ -105,23 +259,15 @@ template <typename func_t>
 inline void parallel_for(int n, const func_t& f) {
 #if defined(_OPENMP)
 #pragma omp parallel
-{
+  {
     int nth = omp_get_num_threads();
     int ith = omp_get_thread_num();
     int tbegin, tend;
     balance211(n, nth, ith, tbegin, tend);
     f(tbegin, tend);
-}
-#else
-    f(0, n);
-#endif
-}
-
-inline int get_thread_num() {
-#if defined(_OPENMP)
-  return omp_get_thread_num();
+  }
 #else
-  return 0;
+  f(0, n);
 #endif
 }
 
@@ -137,7 +283,6 @@ int inline adjust_num_threads(int m) {
 
 template <typename func_t>
 inline void parallel_2d(int m, int n, const func_t& f) {
-
   // make sure we have even num_threads
   int nth = adjust_num_threads(m);
 
@@ -165,31 +310,59 @@ inline void parallel_2d(int m, int n, const func_t& f) {
 
 #if defined(_OPENMP)
 #pragma omp parallel num_threads(nth)
-{
-  int ith = omp_get_thread_num();
-  int ith_m = ith / nth_n;
-  int ith_n = ith % nth_n;
+  {
+    int ith = omp_get_thread_num();
+    int ith_m = ith / nth_n;
+    int ith_n = ith % nth_n;
 
-  int thread_block_m = div_up(m, nth_m);
-  int thread_block_n = div_up(n, nth_n);
+    int thread_block_m = div_up(m, nth_m);
+    int thread_block_n = div_up(n, nth_n);
 
-  int begin_m = ith_m * thread_block_m;
-  int end_m = std::min(m, begin_m + thread_block_m);
-  int begin_n = ith_n * thread_block_n;
-  int end_n = std::min(n, begin_n + thread_block_n);
+    int begin_m = ith_m * thread_block_m;
+    int end_m = std::min(m, begin_m + thread_block_m);
+    int begin_n = ith_n * thread_block_n;
+    int end_n = std::min(n, begin_n + thread_block_n);
 
-  f(begin_m, end_m, begin_n, end_n);
-}
+    f(begin_m, end_m, begin_n, end_n);
+  }
 #else
   f(0, m, 0, n);
 #endif
 }
 
+// limit max cache blocks
+// when we need to do pre-unpack for weights, e.g. fp8
+#define MAX_CACHE_BLOCK_SIZE 4
+
 template <typename T>
-int get_cache_blocks(int BLOCK_SIZE, int K) {
+inline int get_cache_blocks(int chunk_size) {
   // L2 2MB and ratio of 50%
   const int L2_size = 2048 * 1024 >> 1;
-  return std::max(1, int(L2_size / (BLOCK_SIZE * K * sizeof(T))));
+  return std::max(1, int(L2_size / (chunk_size * sizeof(T))));
+}
+
+template <>
+inline int get_cache_blocks<at::Float8_e4m3fn>(int chunk_size) {
+  // fp8 uses bf16 as accumulate type
+  int cache_block_size = get_cache_blocks<at::BFloat16>(chunk_size);
+  return std::min(MAX_CACHE_BLOCK_SIZE, cache_block_size);
+}
+
+// 2d sequential loop in range : [mb0, mb1), [nb0, nb1)
+template <typename T, typename func_t>
+inline void loop_2d(int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1, int64_t chunk_size, const func_t& f) {
+  // get number of blocks for L2 in most inner loop
+  int64_t cache_blocks_nb = get_cache_blocks<T>(chunk_size);
+
+  // loop order: [NB / cache_blocks_nb, MB, cache_blocks_nb]
+  // TODO: implement reverse order of [MB / cache_blocks_mb, NB, cache_blocks_mb]
+  for (int64_t nbb = nb0; nbb < nb1; nbb += cache_blocks_nb) {
+    for (int64_t mb = mb0; mb < mb1; ++mb) {
+      for (int64_t nb = nbb; nb < std::min(nbb + cache_blocks_nb, nb1); ++nb) {
+        f(mb, nb, nb - nbb);
+      }
+    }
+  }
 }
 
 // data indexing for dimension collapse
@@ -243,4 +416,10 @@ struct Unroll<1> {
   }
 };
 
-} // anonymous namespace
+// conditional data ptr for optional tensor
+template <typename T>
+inline T* conditional_data_ptr(const std::optional<at::Tensor>& opt) {
+  return opt.has_value() ? opt.value().data_ptr<T>() : nullptr;
+}
+
+}  // anonymous namespace
diff --git a/csrc/cpu/sgl-kernels/conv.cpp b/csrc/cpu/sgl-kernels/conv.cpp
new file mode 100644
index 000000000000..10ed1f85fb77
--- /dev/null
+++ b/csrc/cpu/sgl-kernels/conv.cpp
@@ -0,0 +1,709 @@
+// Adapted from
+// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
+
+// clang-format off
+
+#include "common.h"
+#include "gemm.h"
+#include "vec.h"
+
+namespace {
+
+template <typename scalar_t>
+inline void copy_stub(scalar_t* __restrict__ y, const scalar_t* __restrict__ x, int64_t size) {
+  using Vec = at::vec::Vectorized<scalar_t>;
+  const bool is_padding = (x == nullptr);
+  for (int64_t d = 0; d < size; d += Vec::size()) {
+    Vec data_vec = is_padding ? Vec(0.f) : Vec::loadu(x + d);
+    data_vec.store(y + d);
+  }
+}
+
+// no remainder
+template <typename scalar_t>
+void inline update_conv_state(
+    scalar_t* __restrict__ conv_states,
+    const scalar_t* __restrict__ input,
+    int64_t width,
+    int64_t dim,
+    int64_t seqlen,
+    bool has_initial_states) {
+  // width for `conv_states`
+  int64_t width1 = width - 1;
+  int64_t w = 0;
+  for (; w < width1 - seqlen; ++w) {
+    scalar_t* y = conv_states + w * dim;
+    const scalar_t* x = has_initial_states ? conv_states + (w + seqlen) * dim : nullptr;
+    copy_stub(y, x, dim);
+  }
+  for (; w < width1; ++w) {
+    scalar_t* y = conv_states + w * dim;
+    const scalar_t* x = input + (w + seqlen - width1) * dim;
+    copy_stub(y, x, dim);
+  }
+}
+
+// A : [M, BLOCK_N]
+// B : [BLOCK_N, K], prepacked as [K/2, BLOCK_N, 2]
+// C : [M, BLOCK_N]
+// bias : [BLOCK_N]
+//
+// lda : leading dimension of `input` and `out`
+//
+template <typename scalar_t, int K, int BLOCK_N, bool has_bias, bool has_silu>
+struct tinygemm_kernel {
+  static inline void apply(
+      const scalar_t* __restrict__ A,
+      const scalar_t* __restrict__ B,
+      scalar_t* __restrict__ C,
+      const scalar_t* __restrict__ bias,
+      const scalar_t* __restrict__ conv_states,
+      bool has_initial_state,
+      int64_t M,
+      int64_t lda,
+      bool is_first_token) {
+    TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
+  }
+};
+
+#if defined(CPU_CAPABILITY_AVX512)
+template <int K, int BLOCK_N, bool has_bias, bool has_silu>
+struct tinygemm_kernel<at::BFloat16, K, BLOCK_N, has_bias, has_silu> {
+  static inline void apply(
+      const at::BFloat16* __restrict__ A,
+      const at::BFloat16* __restrict__ B,
+      at::BFloat16* __restrict__ C,
+      const at::BFloat16* __restrict__ bias,
+      const at::BFloat16* __restrict__ conv_states,
+      bool has_initial_state,
+      int64_t M,
+      int64_t lda,
+      bool is_first_token) {
+    assert(K == 4);
+    constexpr int ROWS = K;
+    constexpr int COLS = BLOCK_N / block_size_n();
+
+    // leading dimension size for b for next block [K/2, 32, 2]
+    constexpr int ldb = block_size_n() * K;
+
+    __m512bh va[ROWS * COLS];
+    __m512bh vb[ROWS * COLS];
+    __m512 vc[COLS * 2];
+
+    // k: {-3, -2, -1} -> {0, 1, 2}
+    auto set_conv_states = [&](int k, int col) -> __m512i {
+      return has_initial_state ? _mm512_loadu_si512(conv_states + (k + K - 1) * lda + col * 32)
+                               : _mm512_setzero_si512();
+    };
+
+#define MM512_LOAD_A(idx)                                                 \
+  ((idx) < 0 && is_first_token) ? (__m512bh)(set_conv_states((idx), col)) \
+                                : (__m512bh)(_mm512_loadu_si512(A + (idx) * lda + col * 32))
+
+#define MM512_PACK_A(ap, bp, a, b)                       \
+  do {                                                   \
+    __m512i r0 = (__m512i)(a);                           \
+    __m512i r1 = (__m512i)(b);                           \
+    __m512i d0 = _mm512_unpacklo_epi16(r0, r1);          \
+    __m512i d1 = _mm512_unpackhi_epi16(r0, r1);          \
+    r0 = _mm512_shuffle_i32x4(d0, d1, 0x88);             \
+    r1 = _mm512_shuffle_i32x4(d0, d1, 0xdd);             \
+    (ap) = (__m512bh)_mm512_shuffle_i32x4(r0, r1, 0x88); \
+    (bp) = (__m512bh)_mm512_shuffle_i32x4(r0, r1, 0xdd); \
+  } while (0)
+
+    // step 0 : preload a at time step [-3][-2][-1]
+    auto preloada = [&](auto i) {
+      constexpr int col = i;
+      int64_t m = 0;
+      va[1 * COLS + col] = MM512_LOAD_A(m - 3);
+      va[2 * COLS + col] = MM512_LOAD_A(m - 2);
+      va[3 * COLS + col] = MM512_LOAD_A(m - 1);
+    };
+    Unroll<COLS>{}(preloada);
+
+    auto loada = [&](auto i, int64_t m) {
+      constexpr int col = i;
+      // update previous time step
+      va[0 * COLS + col] = va[1 * COLS + col];
+      va[1 * COLS + col] = va[2 * COLS + col];
+      va[2 * COLS + col] = va[3 * COLS + col];
+      // load current time step
+      va[3 * COLS + col] = MM512_LOAD_A(m);
+    };
+
+    // step 1 : load weight for just once
+    auto loadb = [&](auto i) {
+      constexpr int row = i / COLS;
+      constexpr int col = i % COLS;
+      vb[row * COLS + col] = (__m512bh)(_mm512_loadu_si512(B + col * ldb + row * 32));
+    };
+    Unroll<ROWS * COLS>{}(loadb);
+
+    // [NB] accumulates 4x32 bfloat16 blocks
+    //
+    //   +------------+------------+
+    //   |    col0    |    col1    |
+    //   +------------+------------+
+    //   |  va0  va1  |  va0  va1  |
+    //   |  va2  va3  |  va2  va3  |
+    //   +------------+------------+
+    //   |  vc0  vc1  |  vc0  vc1  |
+    //   +------------+------------+
+    //
+    //  * va and vb shares the same memory layout
+    //  * block_n 32 with 4 rows equals to 4 registers
+    //  * 37 uops with avx512bf16 v.s. 57 uops with avx512f
+    //
+    auto compute = [&](auto i) {
+      constexpr int col = i;
+
+      // init accumulators
+      if constexpr (has_bias) {
+        __m512i b16 = _mm512_loadu_si512(reinterpret_cast<const __m512i*>(bias + col * 32));
+        vc[col * 2 + 0] = CVT_BF16_TO_FP32(_mm512_extracti32x8_epi32(b16, 0));
+        vc[col * 2 + 1] = CVT_BF16_TO_FP32(_mm512_extracti32x8_epi32(b16, 1));
+      } else {
+        vc[col * 2 + 0] = _mm512_set1_ps(0.f);
+        vc[col * 2 + 1] = _mm512_set1_ps(0.f);
+      }
+
+      // convert to vnni2 format
+      __m512bh va0, va1, va2, va3;
+      MM512_PACK_A(va0, va1, va[0 * COLS + col], va[1 * COLS + col]);
+      MM512_PACK_A(va2, va3, va[2 * COLS + col], va[3 * COLS + col]);
+
+      // accumulate
+      vc[col * 2 + 0] = _mm512_dpbf16_ps(vc[col * 2 + 0], va0, vb[0 * COLS + col]);
+      vc[col * 2 + 0] = _mm512_dpbf16_ps(vc[col * 2 + 0], va2, vb[2 * COLS + col]);
+      vc[col * 2 + 1] = _mm512_dpbf16_ps(vc[col * 2 + 1], va1, vb[1 * COLS + col]);
+      vc[col * 2 + 1] = _mm512_dpbf16_ps(vc[col * 2 + 1], va3, vb[3 * COLS + col]);
+    };
+
+    using fVec = at::vec::Vectorized<float>;
+    using bVec = at::vec::Vectorized<at::BFloat16>;
+    const fVec one = fVec(1.f);
+    auto storec = [&](auto i, int64_t m) {
+      constexpr int col = i;
+      fVec x0 = fVec(vc[col * 2 + 0]);
+      fVec x1 = fVec(vc[col * 2 + 1]);
+      if constexpr (has_silu) {
+        x0 = x0 / (one + x0.neg().exp_u20());
+        x1 = x1 / (one + x1.neg().exp_u20());
+      }
+      bVec out_vec = convert_from_float_ext<at::BFloat16>(x0, x1);
+      out_vec.store(C + m * lda + col * 32);
+    };
+
+    for (int64_t m = 0; m < M; ++m) {
+      // step 3.a : load a at current time step
+      Unroll<COLS>{}(loada, m);
+      // step 3.b : accumulate for window size (4)
+      Unroll<COLS>{}(compute);
+      // step 3.c : store c at current time step
+      Unroll<COLS>{}(storec, m);
+    }
+  }
+};
+#endif
+
+#define LAUNCH_TINYGEMM_KERNEL(K, NB_SIZE)                                                   \
+  tinygemm_kernel<scalar_t, K, NB_SIZE, has_bias, has_silu>::apply(                          \
+      input + bs * seqlen * dim + mb_start * dim + nb_start,                                 \
+      weight + nb_start * width,                                                             \
+      out + bs * seqlen * dim + mb_start * dim + nb_start,                                   \
+      has_bias ? bias + nb_start : nullptr,                                                  \
+      has_conv_states ? conv_states + conv_state_index * (K - 1) * dim + nb_start : nullptr, \
+      has_initial_states_value,                                                              \
+      mb_size,                                                                               \
+      dim,                                                                                   \
+      mb_start == 0);
+
+template <typename scalar_t>
+void causal_conv1d_fwd_kernel_impl(
+    scalar_t* __restrict__ out,
+    const scalar_t* __restrict__ input,
+    const scalar_t* __restrict__ weight,
+    const scalar_t* __restrict__ bias,
+    scalar_t* __restrict__ conv_states,
+    const int32_t* __restrict__ conv_indices,
+    const bool* __restrict__ has_initial_state,
+    bool silu_activation,
+    int64_t batch,
+    int64_t dim,
+    int64_t seqlen,
+    int64_t width,
+    int64_t num_seq_blocks) {
+  // handle 32 x 64 per block
+  constexpr int64_t BLOCK_M = block_size_m();
+  constexpr int64_t BLOCK_N = block_size_n() * 2;
+  const int64_t NB = div_up(dim, BLOCK_N);
+
+  const int64_t num_blocks_per_seq = div_up(seqlen, BLOCK_M);
+  const bool has_conv_states = conv_states != nullptr;
+  const bool has_conv_indices = conv_indices != nullptr;
+
+  // parallel on [batch, seq, NB]
+  AT_DISPATCH_BOOL2(bias != nullptr, has_bias, silu_activation, has_silu, [&] {
+    at::parallel_for(0, num_seq_blocks * NB, 0, [&](int64_t begin, int64_t end) {
+      int64_t mb{0}, nb{0};
+      data_index_init(begin, mb, num_seq_blocks, nb, NB);
+
+      for (int64_t i = begin; i < end; ++i) {
+        int64_t bs = mb / num_blocks_per_seq;
+
+        int64_t mb_start = (mb % num_blocks_per_seq) * BLOCK_M;
+        int64_t mb_size = std::min(seqlen - mb_start, BLOCK_M);
+        int64_t nb_start = nb * BLOCK_N;
+        int64_t nb_size = std::min(dim - nb_start, BLOCK_N);
+
+        const bool has_initial_states_value = has_conv_states ? has_initial_state[bs] : false;
+        int32_t conv_state_index = has_conv_indices ? conv_indices[bs] : bs;
+
+        switch (width << 4 | nb_size >> 4) {
+          case 0x42:
+            LAUNCH_TINYGEMM_KERNEL(4, 32);
+            break;
+          case 0x44:
+            LAUNCH_TINYGEMM_KERNEL(4, 64);
+            break;
+          default:
+            TORCH_CHECK(false, "Unexpected block size, ", width, " x ", nb_size);
+        }
+
+        // move to the next index
+        data_index_step(mb, num_seq_blocks, nb, NB);
+      }
+    });
+  });
+
+  // update conv_states if necessary
+  if (has_conv_states) {
+    at::parallel_for(0, batch, 0, [&](int64_t begin, int64_t end) {
+      for (int64_t bs = begin; bs < end; ++bs) {
+        update_conv_state(
+            conv_states + bs * (width - 1) * dim, input + bs * seqlen * dim, width, dim, seqlen, has_initial_state[bs]);
+      }
+    });
+  }
+}
+
+#define LAUNCH_TINYGEMM_VARLEN_KERNEL(K, NB_SIZE)                   \
+  tinygemm_kernel<scalar_t, K, NB_SIZE, has_bias, has_silu>::apply( \
+      input + batch_offset * dim + mb_start * dim + nb_start,       \
+      weight + nb_start * width,                                    \
+      out + batch_offset * dim + mb_start * dim + nb_start,         \
+      has_bias ? bias + nb_start : nullptr,                         \
+      nullptr,                                                      \
+      false,                                                        \
+      mb_size,                                                      \
+      dim,                                                          \
+      mb_start == 0);
+
+// TODO: add `has_initial_state` support for varlen kernel
+template <typename scalar_t>
+void causal_conv1d_fwd_varlen_kernel_impl(
+    scalar_t* __restrict__ out,
+    const scalar_t* __restrict__ input,
+    const scalar_t* __restrict__ weight,
+    const scalar_t* __restrict__ bias,
+    scalar_t* __restrict__ conv_states,
+    const int32_t* __restrict__ query_start_loc,
+    const int32_t* __restrict__ conv_indices,
+    const bool* __restrict__ has_initial_state,
+    const int32_t* __restrict__ block_indices,
+    bool silu_activation,
+    int64_t batch,
+    int64_t dim,
+    int64_t width,
+    int64_t num_seq_blocks) {
+  // handle 32 x 64 per block
+  constexpr int64_t BLOCK_M = block_size_m();
+  constexpr int64_t BLOCK_N = block_size_n() * 2;
+  const int64_t NB = div_up(dim, BLOCK_N);
+
+  const bool has_conv_states = conv_states != nullptr;
+  const bool has_conv_indices = conv_indices != nullptr;
+
+  // parallel on [batch, seq, NB]
+  AT_DISPATCH_BOOL2(bias != nullptr, has_bias, silu_activation, has_silu, [&] {
+    at::parallel_for(0, num_seq_blocks * NB, 0, [&](int64_t begin, int64_t end) {
+      int64_t mb{0}, nb{0};
+      data_index_init(begin, mb, num_seq_blocks, nb, NB);
+
+      for (int64_t i = begin; i < end; ++i) {
+        int32_t bs = block_indices[mb * 2 + 0];
+        int32_t batch_offset = query_start_loc[bs];
+        int32_t seqlen = query_start_loc[bs + 1] - query_start_loc[bs];
+
+        int64_t mb_start = block_indices[mb * 2 + 1] * BLOCK_M;
+        int64_t mb_size = std::min(seqlen - mb_start, BLOCK_M);
+        int64_t nb_start = nb * BLOCK_N;
+        int64_t nb_size = std::min(dim - nb_start, BLOCK_N);
+
+        switch (width << 4 | nb_size >> 4) {
+          case 0x42:
+            LAUNCH_TINYGEMM_VARLEN_KERNEL(4, 32);
+            break;
+          case 0x44:
+            LAUNCH_TINYGEMM_VARLEN_KERNEL(4, 64);
+            break;
+          default:
+            TORCH_CHECK(false, "Unexpected block size, ", width, " x ", nb_size);
+        }
+
+        // move to the next index
+        data_index_step(mb, num_seq_blocks, nb, NB);
+      }
+    });
+  });
+
+  // update conv_states if necessary
+  if (has_conv_states) {
+    at::parallel_for(0, batch, 0, [&](int64_t begin, int64_t end) {
+      for (int64_t bs = begin; bs < end; ++bs) {
+        int32_t conv_state_index = has_conv_indices ? conv_indices[bs] : bs;
+        int32_t seqlen = query_start_loc[bs + 1] - query_start_loc[bs];
+        int32_t batch_offset = query_start_loc[bs];
+        update_conv_state(
+            conv_states + conv_state_index * (width - 1) * dim,
+            input + batch_offset * dim,
+            width,
+            dim,
+            seqlen,
+            /* has_initial_state */ false);
+      }
+    });
+  }
+}
+
+template <typename scalar_t>
+void causal_conv1d_update_kernel_impl(
+    scalar_t* __restrict__ out,
+    const scalar_t* __restrict__ input,
+    scalar_t* __restrict__ conv_states,
+    const scalar_t* __restrict__ weight,
+    const scalar_t* __restrict__ bias,
+    const int32_t* __restrict__ conv_indices,
+    bool silu_activation,
+    int64_t batch,
+    int64_t dim,
+    int64_t seqlen,
+    int64_t width) {
+  // handle 32 x 64 per block
+  constexpr int64_t BLOCK_M = block_size_m();
+  constexpr int64_t BLOCK_N = block_size_n() * 2;
+  const int64_t NB = div_up(dim, BLOCK_N);
+
+  const bool has_conv_states = conv_states != nullptr;
+  const bool has_conv_indices = conv_indices != nullptr;
+
+  // parallel on [batch, NB]
+  AT_DISPATCH_BOOL2(bias != nullptr, has_bias, silu_activation, has_silu, [&] {
+    at::parallel_for(0, batch * NB, 0, [&](int64_t begin, int64_t end) {
+      int64_t bs{0}, nb{0};
+      data_index_init(begin, bs, batch, nb, NB);
+
+      for (int64_t i = begin; i < end; ++i) {
+        int64_t mb_start = 0;
+        int64_t mb_size = 1;
+        int64_t nb_start = nb * BLOCK_N;
+        int64_t nb_size = std::min(dim - nb_start, BLOCK_N);
+
+        const bool has_initial_states_value = true;
+        int32_t conv_state_index = has_conv_indices ? conv_indices[bs] : bs;
+
+        switch (width << 4 | nb_size >> 4) {
+          case 0x42:
+            LAUNCH_TINYGEMM_KERNEL(4, 32);
+            break;
+          case 0x44:
+            LAUNCH_TINYGEMM_KERNEL(4, 64);
+            break;
+          default:
+            TORCH_CHECK(false, "Unexpected block size, ", width, " x ", nb_size);
+        }
+
+        // move to the next index
+        data_index_step(bs, batch, nb, NB);
+      }
+    });
+  });
+
+#define CONV_STATE_INDEXR(w) conv_states + conv_state_index*(width - 1) * dim + (w) * dim
+
+  // update conv_states
+  at::parallel_for(0, batch, 0, [&](int64_t begin, int64_t end) {
+    for (int64_t bs = begin; bs < end; ++bs) {
+      // update old states, range [1, width - 1)
+      int32_t conv_state_index = has_conv_indices ? conv_indices[bs] : bs;
+      for (int64_t w = 1; w < width - 1; ++w) {
+        std::memcpy(CONV_STATE_INDEXR(w - 1), CONV_STATE_INDEXR(w), dim * sizeof(scalar_t));
+      }
+      // copy new states
+      std::memcpy(CONV_STATE_INDEXR(width - 2), input + bs * dim, dim * sizeof(scalar_t));
+    }
+  });
+}
+
+}  // anonymous namespace
+
+// from [dim, width] or [N, K]
+// to [N/BLOCK_N, K/2, BLOCK_N, 2]
+at::Tensor causal_conv1d_weight_pack(const at::Tensor& weight) {
+  CHECK_INPUT(weight);
+
+  int64_t dim = weight.size(0);
+  int64_t width = weight.size(1);
+  constexpr int64_t BLOCK_N = block_size_n();
+  TORCH_CHECK(width == 4, "causal_conv1d_weight_pack: support only width of 4");
+  TORCH_CHECK(dim % BLOCK_N == 0, "causal_conv1d_weight_pack: invalid dim size ", dim);
+
+  const int64_t N = dim, K2 = width >> 1;
+  const int64_t NB = div_up(N, BLOCK_N);
+
+  auto packed_weight = at::empty_like(weight);
+  AT_DISPATCH_REDUCED_FLOATING_TYPES(weight.scalar_type(), "causal_conv1d_fwd_kernel_impl", [&] {
+    // cast to float32 as vnni size is 2
+    const float* w_data = reinterpret_cast<float*>(weight.data_ptr<scalar_t>());
+    float* packed_data = reinterpret_cast<float*>(packed_weight.data_ptr<scalar_t>());
+
+    at::parallel_for(0, NB * K2 * BLOCK_N, 0, [&](int64_t begin, int64_t end) {
+      int64_t nb{0}, k2{0}, n{0};
+      data_index_init(begin, nb, NB, k2, K2, n, BLOCK_N);
+
+      // TODO: optimize this if we need to online prepacking.
+      for (int64_t i = begin; i < end; ++i) {
+        packed_data[i] = w_data[nb * BLOCK_N * K2 + n * K2 + k2];
+
+        // move to the next index
+        data_index_step(nb, NB, k2, K2, n, BLOCK_N);
+      }
+    });
+  });
+  return packed_weight;
+}
+
+#define CHECK_OPTIONAL_SHAPE_DTYPE(OPT, SIZE, DTYPE) \
+  if (OPT.has_value()) {                             \
+    const auto tensor = OPT.value();                 \
+    CHECK_CONTIGUOUS(tensor);                        \
+    CHECK_EQ(tensor.size(0), SIZE);                  \
+    CHECK_EQ(tensor.scalar_type(), DTYPE);           \
+  }
+
+template <int BLOCK_M>
+int64_t get_block_count(const std::optional<at::Tensor>& offsets, int64_t batch, int64_t seqlen) {
+  if (offsets.has_value()) {
+    const int32_t* offsets_data = offsets.value().data_ptr<int32_t>();
+    int32_t num_seq_blocks = 0;
+    for (int64_t row = 0; row < batch; ++row) {
+      num_seq_blocks += div_up(offsets_data[row + 1] - offsets_data[row], BLOCK_M);
+    }
+    return num_seq_blocks;
+  }
+  return batch * div_up(seqlen, int64_t(BLOCK_M));
+}
+
+template <int BLOCK_M>
+at::Tensor get_block_indices(const std::optional<at::Tensor>& offsets, int64_t num_seq_blocks) {
+  if (!offsets.has_value()) {
+    return at::Tensor();
+  }
+
+  const at::Tensor& offsets_ = offsets.value();
+  at::Tensor indices = at::empty({num_seq_blocks, 2}, offsets_.options());
+
+  int64_t batch = offsets_.size(0) - 1;
+
+  const int32_t* offsets_data = offsets_.data_ptr<int32_t>();
+  int32_t* indices_data = indices.data_ptr<int32_t>();
+
+  int64_t idx = 0;
+  for (int32_t row = 0; row < batch; ++row) {
+    int32_t blocks = div_up(offsets_data[row + 1] - offsets_data[row], BLOCK_M);
+
+    for (int32_t col = 0; col < blocks; ++col) {
+      indices_data[idx * 2 + 0] = row;
+      indices_data[idx * 2 + 1] = col;
+      idx++;
+    }
+  }
+  return indices;
+}
+
+// API aligned with GPUs
+//
+//   x: (batch, dim, seqlen) or (dim, cu_seq_len) for varlen
+//   weight: (dim, width)
+//   bias: (dim,)
+//   query_start_loc: (batch + 1) int32
+//   cache_indices: (batch)  int32
+//   has_initial_state: (batch) bool
+//   conv_states: (..., dim, width - 1) itype
+//   activation: either None or "silu" or "swish"
+//   pad_slot_id: int
+//
+at::Tensor causal_conv1d_fwd_cpu(
+    const at::Tensor& x,
+    const at::Tensor& weight,
+    const std::optional<at::Tensor>& bias,
+    const std::optional<at::Tensor>& conv_states,
+    const std::optional<at::Tensor>& query_start_loc,
+    const std::optional<at::Tensor>& conv_state_indices,
+    const std::optional<at::Tensor>& has_initial_state,
+    bool silu_activation,
+    int64_t pad_slot_id,
+    bool is_vnni) {
+  CHECK_CONTIGUOUS(weight);
+  auto packed_w = is_vnni ? weight : causal_conv1d_weight_pack(weight);
+
+  const bool is_var_seqlen = query_start_loc.has_value();
+  const int64_t input_ndim = is_var_seqlen ? 2 : 3;
+  TORCH_CHECK(x.dim() == input_ndim, "causal_conv1d_fwd_cpu: expect x to be ", input_ndim, "D tensor.");
+  TORCH_CHECK(x.stride(-2) == 1 && x.stride(-1) == x.size(-2), "causal_conv1d_fwd_cpu: expect x to be transposed.");
+
+  const int64_t batch = is_var_seqlen ? query_start_loc.value().size(0) - 1 : x.size(0);
+  const int64_t dim = x.size(-2);
+  const int64_t seqlen = x.size(-1);
+  const int64_t width = weight.size(-1);
+
+  const auto scalar_type = x.scalar_type();
+  CHECK_EQ(weight.scalar_type(), scalar_type);
+  CHECK_OPTIONAL_SHAPE_DTYPE(bias, dim, scalar_type);
+  CHECK_OPTIONAL_SHAPE_DTYPE(query_start_loc, batch + 1, at::kInt);
+  CHECK_OPTIONAL_SHAPE_DTYPE(conv_state_indices, batch, at::kInt);
+  CHECK_OPTIONAL_SHAPE_DTYPE(has_initial_state, batch, at::kBool);
+
+  if (conv_states.has_value()) {
+    auto& conv_states_val = conv_states.value();
+    int64_t padded_batch = conv_states_val.size(0);
+    CHECK_EQ(conv_states_val.scalar_type(), scalar_type);
+    CHECK_GE(padded_batch, batch);
+    CHECK_EQ(conv_states_val.size(1), dim);
+    CHECK_EQ(conv_states_val.size(2), width - 1);
+
+    // adjust `conv_states` to be contiguous on `dim`
+    // should happen only once
+    if (conv_states_val.stride(-2) != 1) {
+      auto conv_states_copy = conv_states_val.clone();
+      conv_states_val.as_strided_({padded_batch, dim, width - 1}, {(width - 1) * dim, 1, dim});
+      conv_states_val.copy_(conv_states_copy);
+    }
+  }
+
+  // block size for sequence blocks, 32
+  constexpr int64_t BLOCK_M = block_size_m();
+
+  // total number of sequence blocks
+  int64_t num_seq_blocks = get_block_count<BLOCK_M>(query_start_loc, batch, seqlen);
+
+  at::Tensor out = at::empty_like(x);
+  AT_DISPATCH_REDUCED_FLOATING_TYPES(scalar_type, "causal_conv1d_fwd_kernel_impl", [&] {
+    if (is_var_seqlen) {
+      // record seq blocks in Coordinate format, aka [num_seq_blocks, 2]
+      at::Tensor block_indices = get_block_indices<BLOCK_M>(query_start_loc, num_seq_blocks);
+
+      causal_conv1d_fwd_varlen_kernel_impl(
+          out.data_ptr<scalar_t>(),
+          x.data_ptr<scalar_t>(),
+          packed_w.data_ptr<scalar_t>(),
+          conditional_data_ptr<scalar_t>(bias),
+          conditional_data_ptr<scalar_t>(conv_states),
+          conditional_data_ptr<int32_t>(query_start_loc),
+          conditional_data_ptr<int32_t>(conv_state_indices),
+          conditional_data_ptr<bool>(has_initial_state),
+          block_indices.data_ptr<int32_t>(),
+          silu_activation,
+          batch,
+          dim,
+          width,
+          num_seq_blocks);
+    } else {
+      causal_conv1d_fwd_kernel_impl<scalar_t>(
+          out.data_ptr<scalar_t>(),
+          x.data_ptr<scalar_t>(),
+          packed_w.data_ptr<scalar_t>(),
+          conditional_data_ptr<scalar_t>(bias),
+          conditional_data_ptr<scalar_t>(conv_states),
+          conditional_data_ptr<int32_t>(conv_state_indices),
+          conditional_data_ptr<bool>(has_initial_state),
+          silu_activation,
+          batch,
+          dim,
+          seqlen,
+          width,
+          num_seq_blocks);
+    }
+  });
+  return out;
+}
+
+// API aligned with GPUs
+//
+//   x: (batch, dim) or (batch, dim, seqlen)
+//   conv_state: (..., dim, state_len), where state_len >= width - 1
+//   weight: (dim, width)
+//   bias: (dim,)
+//   cache_seqlens: (batch,), dtype int32.
+//   conv_state_indices: (batch,), dtype int32
+//   pad_slot_id: int
+//   out: (batch, dim) or (batch, dim, seqlen)
+//
+at::Tensor causal_conv1d_update_cpu(
+    const at::Tensor& x,
+    const at::Tensor& conv_states,
+    const at::Tensor& weight,
+    const std::optional<at::Tensor>& bias,
+    bool silu_activation,
+    const std::optional<at::Tensor>& cache_seqlens,
+    const std::optional<at::Tensor>& conv_state_indices,
+    int64_t pad_slot_id,
+    bool is_vnni) {
+  CHECK_CONTIGUOUS(x);
+  CHECK_CONTIGUOUS(weight);
+  auto packed_w = is_vnni ? weight : causal_conv1d_weight_pack(weight);
+
+  // TODO: add multi-token prediction support
+  TORCH_CHECK(x.dim() == 2, "causal_conv1d_update_cpu: expect x to be 2D tensor.");
+  TORCH_CHECK(!cache_seqlens.has_value(), "causal_conv1d_update_cpu: don't support cache_seqlens.");
+
+  int64_t batch = x.size(0);
+  int64_t dim = x.size(1);
+  int64_t seqlen = 1;
+  int64_t width = weight.size(-1);
+
+  const auto scalar_type = x.scalar_type();
+  CHECK_EQ(weight.scalar_type(), scalar_type);
+  CHECK_OPTIONAL_SHAPE_DTYPE(bias, dim, scalar_type);
+  CHECK_OPTIONAL_SHAPE_DTYPE(conv_state_indices, batch, at::kInt);
+
+  CHECK_EQ(conv_states.scalar_type(), scalar_type);
+  CHECK_EQ(conv_states.size(1), dim);
+  CHECK_EQ(conv_states.size(2), width - 1);
+
+  // adjust `conv_states` to be contiguous on `dim`
+  if (conv_states.stride(-2) != 1) {
+    int64_t num_cache_lines = conv_states.size(0);
+    auto conv_states_copy = conv_states.clone();
+    conv_states.as_strided_({num_cache_lines, dim, width - 1}, {(width - 1) * dim, 1, dim});
+    conv_states.copy_(conv_states_copy);
+  }
+
+  at::Tensor out = at::empty_like(x);
+  AT_DISPATCH_REDUCED_FLOATING_TYPES(scalar_type, "causal_conv1d_update_kernel_impl", [&] {
+    causal_conv1d_update_kernel_impl<scalar_t>(
+        out.data_ptr<scalar_t>(),
+        x.data_ptr<scalar_t>(),
+        conv_states.data_ptr<scalar_t>(),
+        packed_w.data_ptr<scalar_t>(),
+        conditional_data_ptr<scalar_t>(bias),
+        conditional_data_ptr<int32_t>(conv_state_indices),
+        silu_activation,
+        batch,
+        dim,
+        seqlen,
+        width);
+  });
+  return out;
+}
diff --git a/csrc/cpu/sgl-kernels/fla.cpp b/csrc/cpu/sgl-kernels/fla.cpp
new file mode 100644
index 000000000000..b78e9863bf06
--- /dev/null
+++ b/csrc/cpu/sgl-kernels/fla.cpp
@@ -0,0 +1,1394 @@
+// Adapted from
+// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
+
+// clang-format off
+
+#include "common.h"
+#include "gemm.h"
+#include "vec.h"
+#include "vec_pack.h"
+
+namespace {
+// For this cpu kernel, we have some innovations aside from the existing gpu kernels:
+// 1) Use less parallel loops, i.e. 4 including l2_norm.
+// 2) Fuse part of l2_norm with the rest of the computation.
+
+#define THREAD_BUFFER_ALLOC(dst, base_ptr, offset, type, size) \
+  type* dst = reinterpret_cast<type*>((base_ptr) + (offset));  \
+  offset += (size);
+
+template <typename scalar_t>
+inline void fill_stub(scalar_t* __restrict__ out, float val, int size) {
+  using Vec = at::vec::Vectorized<scalar_t>;
+  constexpr int kVecSize = Vec::size();
+  const Vec data_vec = Vec(static_cast<scalar_t>(val));
+  int d = 0;
+#pragma GCC unroll 4
+  for (; d <= size - kVecSize; d += kVecSize) {
+    data_vec.store(out + d);
+  }
+  if (size - d > 0) {
+    data_vec.store(out + d, size - d);
+  }
+}
+
+template <typename scalar_t, int64_t chunk_size = 64>
+void chunk_gated_delta_rule_kernel_impl(
+    scalar_t* __restrict__ out,                  // [B, T, HV, EV]
+    float* __restrict__ final_state_data,        // [N, HV, EK, EV]
+    const scalar_t* __restrict__ q_orig,         // [B, T, HK, EK]
+    const scalar_t* __restrict__ k_orig,         // [B, T, HK, EK]
+    const scalar_t* __restrict__ v_orig,         // [B, T, HV, EV]
+    const float* __restrict__ g_orig,            // [B, T, HV] FP32
+    const scalar_t* __restrict__ b_orig,         // [B, T, HV]
+    const int32_t* __restrict__ cu_seqlens_ptr,  // [N + 1] INT32
+    float* __restrict__ buff,
+    scalar_t* __restrict__ reduced_buff,
+    scalar_t* __restrict__ thread_buff,
+    const int32_t* __restrict__ chunk_offsets_ptr,
+    const int32_t* __restrict__ chunk_indices_ptr,
+    bool use_qk_l2norm_in_kernel,
+    const int64_t& batch_size,
+    const int64_t& global_seq_len,
+    const int64_t& qk_num_head,
+    const int64_t& v_num_head,
+    const int64_t& qk_head_size,
+    const int64_t& v_head_size,
+    const int64_t& qStrideH,
+    const int64_t& qStrideT,
+    const int64_t& kStrideH,
+    const int64_t& kStrideT,
+    const int64_t& vStrideH,
+    const int64_t& vStrideT,
+    const int64_t& oStrideH,
+    const int64_t& oStrideT,
+    const int64_t& global_total_seq_length,
+    const int64_t& global_num_chunk,
+    const int64_t& buff_size_16bit_per_thread,
+    double eps = 1e-5) {
+  int64_t gStrideH = 1;
+  int64_t gStrideT = v_num_head;
+  int64_t bStrideH = 1;
+  int64_t bStrideT = v_num_head;
+  int64_t final_state_StrideN = v_num_head * qk_head_size * v_head_size;
+  int64_t final_state_StrideH = qk_head_size * v_head_size;
+  int64_t final_state_StrideE = v_head_size;
+  int64_t head_group = v_num_head / qk_num_head;
+  float scale = 1.0 / std::sqrt(qk_head_size);
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int64_t VecSize = bVec::size();
+  constexpr int64_t fVecSize = fVec::size();
+
+  // Data pointers
+  float* g_pad = buff;
+  float* core_attn_out = g_pad + v_num_head * global_total_seq_length;
+  float* decay_mask = core_attn_out + batch_size * v_num_head * global_total_seq_length * v_head_size;
+  float* v_beta_attn = decay_mask + v_num_head * global_total_seq_length * chunk_size;
+
+  scalar_t* q_pad = reduced_buff;
+  scalar_t* k_pad = q_pad + qk_num_head * global_total_seq_length * qk_head_size;
+  scalar_t* v_pad = k_pad + qk_num_head * global_total_seq_length * qk_head_size;
+  scalar_t* k_beta = v_pad + v_num_head * global_total_seq_length * v_head_size;
+  scalar_t* v_beta = k_beta + v_num_head * global_total_seq_length * qk_head_size;
+  scalar_t* k_cumdecay_reduced = v_beta + v_num_head * global_total_seq_length * v_head_size;
+  scalar_t* q_norm_sum = k_cumdecay_reduced + v_num_head * global_total_seq_length * qk_head_size;
+  scalar_t* k_norm_sum = q_norm_sum + qk_num_head * global_seq_len;
+
+  if (use_qk_l2norm_in_kernel) {
+    at::parallel_for(0, qk_num_head * global_seq_len, 0, [&](int64_t begin, int64_t end) {
+      int64_t h_qk = 0, l = 0;
+      data_index_init(begin, h_qk, qk_num_head, l, global_seq_len);
+      for (int64_t i = begin; i < end; ++i) {
+        auto q_norm_sum_ptr = q_norm_sum + h_qk * global_seq_len + l;
+        auto k_norm_sum_ptr = k_norm_sum + h_qk * global_seq_len + l;
+        float sum_q = float(0);
+        float sum_k = float(0);
+        fVec sum_q_fvec = fVec(float(0));
+        fVec sum_k_fvec = fVec(float(0));
+        int64_t q_offset = l * qStrideT + h_qk * qStrideH;
+        int64_t k_offset = l * qStrideT + h_qk * qStrideH;
+        int64_t d;
+        for (d = 0; d <= qk_head_size - VecSize; d += VecSize) {
+          bVec q_bvec = bVec::loadu(q_orig + q_offset + d);
+          fVec q_fvec0, q_fvec1;
+          std::tie(q_fvec0, q_fvec1) = at::vec::convert_to_float(q_bvec);
+          sum_q_fvec += q_fvec0 * q_fvec0;
+          sum_q_fvec += q_fvec1 * q_fvec1;
+          bVec k_bvec = bVec::loadu(k_orig + k_offset + d);
+          fVec k_fvec0, k_fvec1;
+          std::tie(k_fvec0, k_fvec1) = at::vec::convert_to_float(k_bvec);
+          sum_k_fvec += k_fvec0 * k_fvec0;
+          sum_k_fvec += k_fvec1 * k_fvec1;
+        }
+        sum_q += vec_reduce_sum(sum_q_fvec);
+        sum_k += vec_reduce_sum(sum_k_fvec);
+        q_norm_sum_ptr[0] = static_cast<scalar_t>(float(1) / std::sqrt(sum_q + eps));
+        k_norm_sum_ptr[0] = static_cast<scalar_t>(float(1) / std::sqrt(sum_k + eps));
+        data_index_step(h_qk, qk_num_head, l, global_seq_len);
+      }
+    });
+  }
+
+  // query = query * scale
+  // k_beta = key * beta.unsqueeze(-1)
+  // v_beta = value * beta.unsqueeze(-1)
+  // Padding for q/k/v/beta
+  at::parallel_for(0, qk_num_head * global_num_chunk, 1, [&](int64_t begin, int64_t end) {
+    int ompIdx = at::get_thread_num();
+    int64_t h_qk = 0, c = 0;
+    data_index_init(begin, h_qk, qk_num_head, c, global_num_chunk);
+    for ([[maybe_unused]] auto z : c10::irange(begin, end)) {
+      int64_t ib = chunk_indices_ptr[c * 2];      // idx_batch
+      int64_t ic = chunk_indices_ptr[c * 2 + 1];  // idx_chunk
+      int64_t l_orig = cu_seqlens_ptr[ib] + ic * chunk_size;
+      int64_t l = c * chunk_size;
+      bool is_tail = (c + 1 == chunk_offsets_ptr[ib + 1]);
+      int64_t seq_len = cu_seqlens_ptr[ib + 1] - cu_seqlens_ptr[ib];
+      int64_t real_chunk_size = is_tail ? seq_len - ic * chunk_size : chunk_size;
+      auto q_orig_ptr = q_orig + h_qk * qStrideH + l_orig * qStrideT;
+      auto k_orig_ptr = k_orig + h_qk * kStrideH + l_orig * kStrideT;
+      auto v_orig_ptr = v_orig + l_orig * vStrideT;
+      auto b_orig_ptr = b_orig + l_orig * bStrideT;
+      auto q_pad_ptr = q_pad + h_qk * global_total_seq_length * qk_head_size + l * qk_head_size;
+      auto k_pad_ptr = k_pad + h_qk * global_total_seq_length * qk_head_size + l * qk_head_size;
+      auto v_pad_ptr = v_pad + l * v_head_size;
+      auto k_beta_ptr = k_beta + l * qk_head_size;
+      auto v_beta_ptr = v_beta + l * v_head_size;
+
+      for (int64_t j = 0; j < real_chunk_size; j++) {
+        auto curr_q_orig = q_orig_ptr + j * qStrideT;
+        auto curr_k_orig = k_orig_ptr + j * kStrideT;
+        auto curr_q_pad = q_pad_ptr + j * qk_head_size;
+        auto curr_k_pad = k_pad_ptr + j * qk_head_size;
+        auto q_scale =
+            use_qk_l2norm_in_kernel ? *(q_norm_sum + h_qk * global_seq_len + l_orig + j) : static_cast<scalar_t>(1);
+        auto k_scale =
+            use_qk_l2norm_in_kernel ? *(k_norm_sum + h_qk * global_seq_len + l_orig + j) : static_cast<scalar_t>(1);
+        auto q_scale_vec = bVec(q_scale);
+        auto k_scale_vec = bVec(k_scale);
+        int64_t i = 0;
+        scalar_t scale_reduced = static_cast<scalar_t>(scale);
+        auto vec_scale_reduced = bVec(scale_reduced);
+        for (; i < fVecSize * (qk_head_size / fVecSize); i += fVecSize) {
+          auto tmp0 = bVec::loadu(curr_q_orig + i, fVecSize);
+          auto tmp1 = tmp0 * q_scale_vec * vec_scale_reduced;
+          tmp1.store(curr_q_pad + i, fVecSize);
+          auto tmp3 = bVec::loadu(curr_k_orig + i, fVecSize);
+          auto tmp4 = tmp3 * k_scale_vec;
+          tmp4.store(curr_k_pad + i, fVecSize);
+        }
+
+        for (auto hi = 0; hi < head_group; hi++) {
+          int64_t h = h_qk * head_group + hi;
+          auto curr_v_orig = v_orig_ptr + h * vStrideH + j * vStrideT;
+          auto curr_b_orig = b_orig_ptr + h * bStrideH + j * bStrideT;
+          scalar_t b_orig_val_reduced = *(curr_b_orig);
+          auto curr_v_pad = v_pad_ptr + h * global_total_seq_length * v_head_size + j * v_head_size;
+          auto curr_k_beta = k_beta_ptr + h * global_total_seq_length * qk_head_size + j * qk_head_size;
+          auto curr_v_beta = v_beta_ptr + h * global_total_seq_length * v_head_size + j * v_head_size;
+
+          // query = query * scale
+          // k_beta = key * beta.unsqueeze(-1)
+          int64_t i = 0;
+          auto vec_b_reduced = bVec(b_orig_val_reduced);
+          for (; i < fVecSize * (qk_head_size / fVecSize); i += fVecSize) {
+            auto tmp0 = bVec::loadu(curr_k_orig + i, fVecSize);
+            auto tmp2 = tmp0 * k_scale_vec * vec_b_reduced;
+            tmp2.store(curr_k_beta + i, fVecSize);
+          }
+          // v_beta = value * beta.unsqueeze(-1)
+          i = 0;
+          for (; i < VecSize * (v_head_size / VecSize); i += VecSize) {
+            auto tmp3 = bVec::loadu(curr_v_orig + i);
+            tmp3.store(curr_v_pad + i);
+            auto tmp5 = tmp3 * vec_b_reduced;
+            tmp5.store(curr_v_beta + i);
+          }
+        }
+      }
+
+      for (int64_t j = real_chunk_size; j < chunk_size; j++) {
+        auto curr_q_pad = q_pad_ptr + j * qk_head_size;
+        auto curr_k_pad = k_pad_ptr + j * qk_head_size;
+        int64_t i = 0;
+        auto vec_zero = bVec(0.0);
+        for (; i < VecSize * (qk_head_size / VecSize); i += VecSize) {
+          vec_zero.store(curr_q_pad + i);
+          vec_zero.store(curr_k_pad + i);
+        }
+        for (auto hi = 0; hi < head_group; hi++) {
+          int64_t h = h_qk * head_group + hi;
+          auto curr_v_pad = v_pad_ptr + h * global_total_seq_length * v_head_size + j * v_head_size;
+          auto curr_k_beta = k_beta_ptr + h * global_total_seq_length * qk_head_size + j * qk_head_size;
+          auto curr_v_beta = v_beta_ptr + h * global_total_seq_length * v_head_size + j * v_head_size;
+          int64_t i = 0;
+          for (; i < VecSize * (qk_head_size / VecSize); i += VecSize) {
+            vec_zero.store(curr_k_beta + i);
+          }
+          i = 0;
+          for (; i < VecSize * (v_head_size / VecSize); i += VecSize) {
+            vec_zero.store(curr_v_pad + i);
+            vec_zero.store(curr_v_beta + i);
+          }
+        }
+      }
+      // Move to the next query
+      data_index_step(h_qk, qk_num_head, c, global_num_chunk);
+    }
+  });
+
+  at::parallel_for(0, v_num_head * global_num_chunk, 1, [&](int64_t begin, int64_t end) {
+    int64_t h = 0, c = 0;
+    data_index_init(begin, h, v_num_head, c, global_num_chunk);
+    int ompIdx = at::get_thread_num();
+    int64_t offset = 0;
+    scalar_t* thread_buff_ptr = thread_buff + ompIdx * buff_size_16bit_per_thread;
+    THREAD_BUFFER_ALLOC(k_transpose, thread_buff_ptr, offset, scalar_t, qk_head_size * chunk_size);
+    THREAD_BUFFER_ALLOC(v_pack, thread_buff_ptr, offset, scalar_t, chunk_size * v_head_size);
+    THREAD_BUFFER_ALLOC(k_beta_g, thread_buff_ptr, offset, scalar_t, chunk_size * qk_head_size);
+    THREAD_BUFFER_ALLOC(k_beta_g_pack, thread_buff_ptr, offset, scalar_t, chunk_size * qk_head_size);
+    THREAD_BUFFER_ALLOC(curr_attn, thread_buff_ptr, offset, float, chunk_size* chunk_size * 2);
+    THREAD_BUFFER_ALLOC(curr_attn_reduced, thread_buff_ptr, offset, scalar_t, chunk_size * chunk_size);
+    THREAD_BUFFER_ALLOC(k_cumdecay, thread_buff_ptr, offset, float, chunk_size* qk_head_size * 2);
+    THREAD_BUFFER_ALLOC(row, thread_buff_ptr, offset, float, chunk_size * 2);
+    THREAD_BUFFER_ALLOC(updated, thread_buff_ptr, offset, float, chunk_size * 2);
+    for ([[maybe_unused]] auto z : c10::irange(begin, end)) {
+      int64_t ib = chunk_indices_ptr[c * 2];      // idx_batch
+      int64_t ic = chunk_indices_ptr[c * 2 + 1];  // idx_chunk
+      int64_t l_orig = cu_seqlens_ptr[ib] + ic * chunk_size;
+      int64_t seq_len = cu_seqlens_ptr[ib + 1] - cu_seqlens_ptr[ib];
+      int64_t h_qk = h / head_group;
+      auto curr_g_orig = g_orig + h * gStrideH + l_orig * gStrideT;
+      auto curr_g_pad = g_pad + h * global_total_seq_length + c * chunk_size;
+      auto curr_decay_mask = decay_mask + h * global_total_seq_length * chunk_size + c * chunk_size * chunk_size;
+      auto curr_k_pad = k_pad + h_qk * global_total_seq_length * qk_head_size + c * chunk_size * qk_head_size;
+      auto curr_k_beta = k_beta + h * global_total_seq_length * qk_head_size + c * chunk_size * qk_head_size;
+      auto curr_k_cumdecay_reduced =
+          k_cumdecay_reduced + h * global_total_seq_length * qk_head_size + c * chunk_size * qk_head_size;
+      auto curr_v_beta = v_beta + h * global_total_seq_length * v_head_size + c * chunk_size * v_head_size;
+      auto curr_value = v_beta_attn + h * global_total_seq_length * v_head_size + c * chunk_size * v_head_size;
+
+      float acc_val = 0;
+      for (int64_t i = 0; i < chunk_size; i++) {
+        // Padding for g
+        // g = g.cumsum(dim=-1)
+        // g: [B, HV, num_chunk, chunk_size]
+        if (ic * chunk_size + i < seq_len) {
+          acc_val += curr_g_orig[i * gStrideT];
+        }
+        curr_g_pad[i] = acc_val;
+        // decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril()
+        // decay_mask: [B, HV, num_chunk, chunk_size, chunk_size]
+        float curr_g_pad_i = static_cast<float>(curr_g_pad[i]);
+        auto vec_curr_g_pad_i = fVec(curr_g_pad_i);
+        int64_t j = 0;
+        int64_t len = i + 1;
+        for (; j < fVecSize * (len / fVecSize); j += fVecSize) {
+          auto tmp0 = fVec::loadu(curr_g_pad + j);
+          auto tmp1 = vec_curr_g_pad_i - tmp0;
+          auto tmp2 = tmp1.exp_u20();
+          tmp2.store(curr_decay_mask + i * chunk_size + j);
+        }
+        if (j < len) {
+          auto tmp0 = fVec::loadu(curr_g_pad + j, len - j);
+          auto tmp1 = vec_curr_g_pad_i - tmp0;
+          auto tmp2 = tmp1.exp_u20();
+          tmp2.store(curr_decay_mask + i * chunk_size + j, len - j);
+        }
+      }
+
+      // attn = k_beta @ key.transpose(-1, -2)
+      // attn: [B, HV, num_chunk, chunk_size, chunk_size]
+      // transpose and pack for key
+      pack_vnni<scalar_t>(
+          /*    dst */ k_transpose,
+          /*    src */ curr_k_pad,
+          /*     N  */ chunk_size,
+          /*     K  */ qk_head_size,
+          /* ld_src */ qk_head_size,
+          /* ld_dst */ chunk_size);
+      // k_beta @ key.transpose(-1, -2)
+      at::native::cpublas::brgemm(
+          /*     M */ chunk_size,
+          /*     N */ chunk_size,
+          /*     K */ qk_head_size,
+          /*   lda */ qk_head_size,
+          /*   ldb */ chunk_size,
+          /*   ldc */ chunk_size,
+          /* add_C */ false,
+          /*     A */ curr_k_beta,
+          /*     B */ k_transpose,
+          /*     C */ curr_attn);
+      // attn = attn * decay_mask
+      for (int64_t m = 0; m < chunk_size; m++) {
+        at::vec::map2<float>(
+            [](fVec x, fVec y) { return fVec(0) - x * y; },
+            curr_attn + m * chunk_size,
+            curr_attn + m * chunk_size,
+            curr_decay_mask + m * chunk_size,
+            chunk_size);
+      }
+
+      // chunk decay
+      // attn: [B, HV, num_chunk, chunk_size, chunk_size]
+      // mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=0)
+      // attn = -attn.masked_fill(mask, 0)
+      // attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2) [B, HV, num_chunk, i]
+      // attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
+      // attn = -attn.masked_fill(mask, 0)
+      for (int i = 0; i < chunk_size; i++) {
+        const auto vec_zero = fVec(0);
+        int64_t len = chunk_size - i;
+        int64_t front = len % fVecSize;
+        int64_t j = i;
+        // first masked vec for alignment
+        if (front > 0) {
+          vec_zero.store(curr_attn + i * chunk_size + j, front);
+          j += front;
+        }
+        for (; j < fVecSize * (chunk_size / fVecSize); j += fVecSize) {
+          vec_zero.store(curr_attn + i * chunk_size + j);
+        }
+      }
+      for (int i = 1; i < chunk_size; i++) {
+        // row = attn[..., i, :i] [B, HK, num_chunk, i]
+        int64_t j = 0;
+        int64_t len = i;
+        for (; j < fVecSize * (len / fVecSize); j += fVecSize) {
+          auto tmp0 = fVec::loadu(curr_attn + i * chunk_size + j);
+          tmp0.store(row + j);
+        }
+        if (j < len) {
+          auto tmp0 = fVec::loadu(curr_attn + i * chunk_size + j, len - j);
+          tmp0.store(row + j, len - j);
+        }
+        // (row.unsqueeze(-1) * sub).sum(-2)
+        fill_stub(updated, 0, i);
+        for (int k = 0; k < i; k++) {
+          float row_k = row[k];
+          auto vec_row_k = fVec(row_k);
+          int64_t j = 0;
+          int64_t len = i;
+          for (; j < fVecSize * (len / fVecSize); j += fVecSize) {
+            auto tmp0 = fVec::loadu(curr_attn + k * chunk_size + j);
+            auto tmp1 = vec_row_k * tmp0;
+            auto tmp2 = fVec::loadu(updated + j);
+            auto tmp3 = tmp1 + tmp2;
+            tmp3.store(updated + j);
+          }
+          if (j < len) {
+            auto tmp0 = fVec::loadu(curr_attn + k * chunk_size + j, len - j);
+            auto tmp1 = vec_row_k * tmp0;
+            auto tmp2 = fVec::loadu(updated + j);
+            auto tmp3 = tmp1 + tmp2;
+            tmp3.store(updated + j, len - j);
+          }
+        }
+        // attn[..., i, :i] = row + sum(...)
+        j = 0;
+        len = i;
+        for (; j < fVecSize * (len / fVecSize); j += fVecSize) {
+          auto tmp0 = fVec::loadu(row + j);
+          auto tmp1 = fVec::loadu(updated + j);
+          auto tmp2 = tmp0 + tmp1;
+          tmp2.store(curr_attn + i * chunk_size + j);
+        }
+        if (j < len) {
+          auto tmp0 = fVec::loadu(row + j, len - j);
+          auto tmp1 = fVec::loadu(updated + j, len - j);
+          auto tmp2 = tmp0 + tmp1;
+          tmp2.store(curr_attn + i * chunk_size + j, len - j);
+        }
+      }
+      for (int i = 0; i < chunk_size; i++) {
+        curr_attn[i * chunk_size + i] += 1.0f;
+        at::vec::map<scalar_t>(
+            [](fVec x) { return x; }, curr_attn_reduced + i * chunk_size, curr_attn + i * chunk_size, chunk_size);
+      }
+
+      // v_beta_attn = attn @ v_beta
+      // k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
+      // v_beta_attn: [B, HV, num_chunk, chunk_size, EV]
+      // k_beta_g = k_beta * g: [B, HV, num_chunk, chunk_size, EK]
+      // k_cumdecay: [B, HV, num_chunk, chunk_size, EK]
+      // pack for value
+      pack_vnni2<scalar_t>(
+          /*    dst */ v_pack,
+          /*    src */ curr_v_beta,
+          /*     N  */ chunk_size,
+          /*     K  */ v_head_size,
+          /* ld_src */ v_head_size,
+          /* ld_dst */ v_head_size);
+      // value = attn @ v_beta
+      at::native::cpublas::brgemm(
+          /*     M */ chunk_size,
+          /*     N */ v_head_size,
+          /*     K */ chunk_size,
+          /*   lda */ chunk_size,
+          /*   ldb */ v_head_size,
+          /*   ldc */ v_head_size,
+          /* add_C */ false,
+          /*     A */ curr_attn_reduced,
+          /*     B */ v_pack,
+          /*     C */ curr_value);
+      // k_beta_g = k_beta * g.exp().unsqueeze(-1)
+      for (int64_t j = 0; j < chunk_size; j++) {
+        int64_t i = 0;
+        float g_exp = std::exp(curr_g_pad[j]);
+        scalar_t g_exp_reduced = static_cast<scalar_t>(g_exp);
+        auto vec_g_exp_reduced = bVec(g_exp_reduced);
+        for (; i < VecSize * (qk_head_size / VecSize); i += VecSize) {
+          auto tmp0 = bVec::loadu(curr_k_beta + j * qk_head_size + i);
+          auto tmp1 = tmp0 * vec_g_exp_reduced;
+          tmp1.store(k_beta_g + j * qk_head_size + i);
+        }
+      }
+      // pack for k_beta_g
+      pack_vnni2<scalar_t>(
+          /*    dst */ k_beta_g_pack,
+          /*    src */ k_beta_g,
+          /*     N  */ chunk_size,
+          /*     K  */ qk_head_size,
+          /* ld_src */ qk_head_size,
+          /* ld_dst */ qk_head_size);
+      // k_cumdecay = attn @ k_beta_g
+      at::native::cpublas::brgemm(
+          /*     M */ chunk_size,
+          /*     N */ qk_head_size,
+          /*     K */ chunk_size,
+          /*   lda */ chunk_size,
+          /*   ldb */ qk_head_size,
+          /*   ldc */ qk_head_size,
+          /* add_C */ false,
+          /*     A */ curr_attn_reduced,
+          /*     B */ k_beta_g_pack,
+          /*     C */ k_cumdecay);
+      for (int i = 0; i < chunk_size; i++) {
+        at::vec::map<scalar_t>(
+            [](fVec x) { return x; },
+            curr_k_cumdecay_reduced + i * qk_head_size,
+            k_cumdecay + i * qk_head_size,
+            qk_head_size);
+      }
+
+      // Move to the next query
+      data_index_step(h, v_num_head, c, global_num_chunk);
+    }
+  });
+
+  // for each chunk
+  at::parallel_for(0, batch_size * v_num_head, 1, [&](int64_t begin, int64_t end) {
+    int64_t b = 0, h = 0;
+    data_index_init(begin, b, batch_size, h, v_num_head);
+    int ompIdx = at::get_thread_num();
+    int64_t offset =
+        /* k_transpose */ qk_head_size * chunk_size +
+        /* v_pack */ chunk_size * v_head_size +
+        /* k_beta_g  */ chunk_size * qk_head_size +
+        /* k_beta_g_pack  */ chunk_size * qk_head_size +
+        /* attn */ chunk_size * chunk_size * 2 +
+        /* attn_reduced */ chunk_size * chunk_size +
+        /* k_cumdecay */ chunk_size * qk_head_size * 2 +
+        /* row */ chunk_size * 2 +
+        /* updated */ chunk_size * 2;
+    scalar_t* thread_buff_ptr = thread_buff + ompIdx * buff_size_16bit_per_thread;
+    THREAD_BUFFER_ALLOC(
+        curr_last_recurrent_state_reduced, thread_buff_ptr, offset, scalar_t, qk_head_size * v_head_size);
+    THREAD_BUFFER_ALLOC(
+        curr_last_recurrent_state_pack_reduced, thread_buff_ptr, offset, scalar_t, qk_head_size * v_head_size);
+    THREAD_BUFFER_ALLOC(k_transpose_i, thread_buff_ptr, offset, scalar_t, qk_head_size * chunk_size);
+    THREAD_BUFFER_ALLOC(attn_i, thread_buff_ptr, offset, float, chunk_size* chunk_size * 2);
+    THREAD_BUFFER_ALLOC(attn_i_reduced, thread_buff_ptr, offset, scalar_t, chunk_size * chunk_size);
+    THREAD_BUFFER_ALLOC(v_prime, thread_buff_ptr, offset, float, chunk_size* v_head_size * 2);
+    THREAD_BUFFER_ALLOC(v_prime_reduced, thread_buff_ptr, offset, scalar_t, chunk_size * v_head_size);
+    THREAD_BUFFER_ALLOC(v_prime_pack_reduced, thread_buff_ptr, offset, scalar_t, chunk_size * v_head_size);
+    THREAD_BUFFER_ALLOC(qg, thread_buff_ptr, offset, scalar_t, chunk_size * qk_head_size);
+    THREAD_BUFFER_ALLOC(attn_inter, thread_buff_ptr, offset, float, chunk_size* v_head_size * 2);
+    THREAD_BUFFER_ALLOC(kg, thread_buff_ptr, offset, scalar_t, chunk_size * qk_head_size);
+    THREAD_BUFFER_ALLOC(kg_transpose, thread_buff_ptr, offset, scalar_t, qk_head_size * chunk_size);
+    THREAD_BUFFER_ALLOC(kgv, thread_buff_ptr, offset, float, qk_head_size* v_head_size * 2);
+
+    for ([[maybe_unused]] auto z : c10::irange(begin, end)) {
+      int64_t start_q = cu_seqlens_ptr[b];
+      int64_t seq_len = cu_seqlens_ptr[b + 1] - start_q;
+      int64_t num_chunk = chunk_offsets_ptr[b + 1] - chunk_offsets_ptr[b];
+      int64_t chunk_offset = chunk_offsets_ptr[b];
+      int64_t len_offset = chunk_offset * chunk_size;
+
+      int64_t h_qk = h / head_group;
+      auto out_ptr = out + start_q * oStrideT;
+      auto curr_q = q_pad + len_offset * qk_head_size +
+                    h_qk * global_total_seq_length * qk_head_size;  // [num_chunk, chunk_size, EK]
+      auto curr_k = k_pad + len_offset * qk_head_size +
+                    h_qk * global_total_seq_length * qk_head_size;            // [num_chunk, chunk_size, EK]
+      auto curr_v = v_beta_attn + h * global_total_seq_length * v_head_size;  // [num_chunk, chunk_size, EV]
+      auto curr_decay_mask =
+          decay_mask + h * global_total_seq_length * chunk_size;  // [num_chunk, chunk_size, chunk_size]
+      auto curr_k_cumdecay_reduced =
+          k_cumdecay_reduced + h * global_total_seq_length * qk_head_size;  // [num_chunk, chunk_size, EK]
+      auto curr_last_recurrent_state =
+          final_state_data + b * final_state_StrideN + h * final_state_StrideH;  // [EK, EV]
+      auto curr_g_pad = g_pad + len_offset + h * global_total_seq_length;        // [num_chunk, chunk_size]
+      auto curr_core_attn_out = core_attn_out + len_offset * v_head_size +
+                                h * global_total_seq_length * v_head_size;  // [num_chunk, chunk_size, EV]
+      for (int64_t c = 0; c < num_chunk; c++) {
+        for (int i = 0; i < qk_head_size; i++) {
+          at::vec::map<scalar_t>(
+              [](fVec x) { return x; },
+              curr_last_recurrent_state_reduced + i * v_head_size,
+              curr_last_recurrent_state + i * v_head_size,
+              v_head_size);
+        }
+        auto q_i = curr_q + c * chunk_size * qk_head_size;                                   // [chunk_size, EK]
+        auto k_i = curr_k + c * chunk_size * qk_head_size;                                   // [chunk_size, EK]
+        auto v_i = curr_v + (chunk_offset + c) * chunk_size * v_head_size;                   // [chunk_size, EV]
+        auto decay_mask_i = curr_decay_mask + (chunk_offset + c) * chunk_size * chunk_size;  // [chunk_size, chunk_size]
+        auto k_cumdecay_i_reduced =
+            curr_k_cumdecay_reduced + (chunk_offset + c) * chunk_size * qk_head_size;  // [chunk_size, EK]
+        auto g_pad_i = curr_g_pad + c * chunk_size;                                    // [chunk_size]
+        auto core_attn_out_i = curr_core_attn_out + c * chunk_size * v_head_size;      // [chunk_size, EV]
+
+        // attn_i = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
+        // k_transpose_i = k_i.transpose(-1, -2)
+        pack_vnni<scalar_t>(
+            /*    dst */ k_transpose_i,
+            /*    src */ k_i,
+            /*     N  */ chunk_size,
+            /*     K  */ qk_head_size,
+            /* ld_src */ qk_head_size,
+            /* ld_dst */ chunk_size);
+        // attn_i = q_i @ k_transpose_i
+        at::native::cpublas::brgemm(
+            /* M */ chunk_size,
+            /* N */ chunk_size,
+            /* K */ qk_head_size,
+            /* lda */ qk_head_size,
+            /* ldb */ chunk_size,
+            /* ldc */ chunk_size,
+            /* add_C */ false,
+            /* A */ q_i,
+            /* B */ k_transpose_i,
+            /* C */ attn_i);
+        // attn_i = attn_i * decay_mask_i
+        for (int64_t m = 0; m < chunk_size; m++) {
+          auto attn_i_m = attn_i + m * chunk_size;
+          auto attn_i_reduced_m = attn_i_reduced + m * chunk_size;
+          auto decay_mask_i_m = decay_mask_i + m * chunk_size;
+          int64_t n = 0;
+          for (; n < fVecSize * (chunk_size / fVecSize); n += fVecSize) {
+            auto tmp0 = fVec::loadu(attn_i_m + n);
+            auto tmp1 = fVec::loadu(decay_mask_i_m + n);
+            auto tmp2 = tmp0 * tmp1;
+            auto tmp3 = at::vec::convert<scalar_t>(tmp2);
+            tmp3.store(attn_i_reduced_m + n, fVecSize);
+          }
+          if (n < chunk_size) {
+            auto tmp0 = fVec::loadu(attn_i_m + n, chunk_size - n);
+            auto tmp1 = fVec::loadu(decay_mask_i_m + n, chunk_size - n);
+            auto tmp2 = tmp0 * tmp1;
+            auto tmp3 = at::vec::convert<scalar_t>(tmp2);
+            tmp3.store(attn_i_reduced_m + n, chunk_size - n);
+          }
+        }
+        // mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=1)
+        // attn_i = attn_i.masked_fill_(mask, 0)
+        for (int i = 0; i < chunk_size - 1; i++) {
+          const auto vec_zero = bVec(0);
+          int64_t len = chunk_size - i - 1;
+          int64_t front = len % VecSize;
+          int64_t j = i + 1;
+          // first masked vec for alignment
+          if (front > 0) {
+            vec_zero.store(attn_i_reduced + i * chunk_size + j, front);
+            j += front;
+          }
+          for (; j < VecSize * (chunk_size / VecSize); j += VecSize) {
+            vec_zero.store(attn_i_reduced + i * chunk_size + j);
+          }
+        }
+
+        // pack for curr_last_recurrent_state
+        pack_vnni2<scalar_t>(
+            /*    dst */ curr_last_recurrent_state_pack_reduced,
+            /*    src */ curr_last_recurrent_state_reduced,
+            /*     N  */ qk_head_size,
+            /*     K  */ v_head_size,
+            /* ld_src */ v_head_size,
+            /* ld_dst */ v_head_size);
+
+        // v_prime = k_cumdecay_i @ curr_last_recurrent_state: [chunk_size, EV]
+        // k_cumdecay_i: [chunk_size, EK]
+        // curr_last_recurrent_state: [EK, EV]
+        at::native::cpublas::brgemm(
+            /*     M */ chunk_size,
+            /*     N */ v_head_size,
+            /*     K */ qk_head_size,
+            /*   lda */ qk_head_size,
+            /*   ldb */ v_head_size,
+            /*   ldc */ v_head_size,
+            /* add_C */ false,
+            /*     A */ k_cumdecay_i_reduced,
+            /*     B */ curr_last_recurrent_state_pack_reduced,
+            /*     C */ v_prime);
+
+        // v_new = v_prime = v_i - v_prime
+        // v_i: [chunk_size, EV]
+        for (int64_t m = 0; m < chunk_size; m++) {
+          int64_t i = 0;
+          for (; i < fVecSize * (v_head_size / fVecSize); i += fVecSize) {
+            auto tmp0 = fVec::loadu(v_i + m * v_head_size + i);
+            auto tmp1 = fVec::loadu(v_prime + m * v_head_size + i);
+            auto tmp2 = tmp0 - tmp1;
+            auto tmp3 = at::vec::convert<scalar_t>(tmp2);
+            tmp3.store(v_prime_reduced + m * v_head_size + i, fVecSize);
+          }
+        }
+
+        // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+        // qg = q_i * g[:, :, i, :, None].exp(): [chunk_size, EK]
+        // q_i: [chunk_size, EK]
+        // g[:, :, i, :, None]: [chunk_size, 1]
+        for (int64_t m = 0; m < chunk_size; m++) {
+          auto g_pad_i_m = g_pad_i + m;
+          auto g_exp = std::exp(*g_pad_i_m);
+          int64_t i = 0;
+          scalar_t g_exp_reduced = static_cast<scalar_t>(g_exp);
+          auto vec_g_exp_reduced = bVec(g_exp_reduced);
+          for (; i < VecSize * (qk_head_size / VecSize); i += VecSize) {
+            auto tmp0 = bVec::loadu(q_i + m * qk_head_size + i);
+            auto tmp2 = tmp0 * vec_g_exp_reduced;
+            tmp2.store(qg + m * qk_head_size + i);
+          }
+        }
+        // attn_inter = qg @ curr_last_recurrent_state: [chunk_size, EV]
+        // curr_last_recurrent_state: [EK, EV]
+        at::native::cpublas::brgemm(
+            /* M */ chunk_size,
+            /* N */ v_head_size,
+            /* K */ qk_head_size,
+            /* lda */ qk_head_size,
+            /* ldb */ v_head_size,
+            /* ldc */ v_head_size,
+            /* add_C */ false,
+            /* A */ qg,
+            /* B */ curr_last_recurrent_state_pack_reduced,
+            /* C */ attn_inter);
+
+        // core_attn_out[:, :, i] = attn_inter + attn_i @ v_new
+        // pack for v_prime
+        pack_vnni2<scalar_t>(
+            /*    dst */ v_prime_pack_reduced,
+            /*    src */ v_prime_reduced,
+            /*     N  */ chunk_size,
+            /*     K  */ v_head_size,
+            /* ld_src */ v_head_size,
+            /* ld_dst */ v_head_size);
+        // attn_inter = attn_inter + attn_i @ v_new: [chunk_size, EV]
+        // attn_i: [chunk_size, chunk_size]
+        // v_new: [chunk_size, EV]
+        at::native::cpublas::brgemm(
+            /* M */ chunk_size,
+            /* N */ v_head_size,
+            /* K */ chunk_size,
+            /* lda */ chunk_size,
+            /* ldb */ v_head_size,
+            /* ldc */ v_head_size,
+            /* add_C */ true,
+            /* A */ attn_i_reduced,
+            /* B */ v_prime_pack_reduced,
+            /* C */ attn_inter);
+
+        // core_attn_out[:, :, i] = attn_inter
+        for (int64_t m = 0; m < chunk_size; m++) {
+          at::vec::map<float>(
+              [](fVec x) { return x; }, core_attn_out_i + m * v_head_size, attn_inter + m * v_head_size, v_head_size);
+        }
+
+        // last_recurrent_state = (
+        //     last_recurrent_state * g[:, :, i, -1, None, None].exp()
+        //     + (k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]).transpose(-1, -2) @ v_new
+        // )
+        // 1) last_recurrent_state * g[:, :, i, -1, None, None].exp()
+        // curr_last_recurrent_state: [EK, EV]
+        // g[:, :, i, -1, None, None]: [1, 1]
+        // last_recurrent_state * g[:, :, i, -1, None, None].exp(): [EK, EV]
+        auto g_pad_i_last = g_pad_i + chunk_size - 1;
+        auto g_exp_last = std::exp(g_pad_i_last[0]);
+        for (int64_t m = 0; m < qk_head_size; m++) {
+          int64_t i = 0;
+          auto vec_g_exp_last = fVec(g_exp_last);
+          for (; i < fVecSize * (v_head_size / fVecSize); i += fVecSize) {
+            auto tmp0 = bVec::loadu(curr_last_recurrent_state_reduced + m * v_head_size + i);
+            auto tmp1 = at::vec::convert<float>(tmp0);
+            auto tmp2 = tmp1 * vec_g_exp_last;
+            tmp2.store(curr_last_recurrent_state + m * v_head_size + i);
+          }
+          if (i < v_head_size) {
+            auto tmp0 = bVec::loadu(curr_last_recurrent_state_reduced + m * v_head_size + i, v_head_size - i);
+            auto tmp1 = at::vec::convert<float>(tmp0);
+            auto tmp2 = tmp1 * vec_g_exp_last;
+            tmp2.store(curr_last_recurrent_state + m * v_head_size + i, v_head_size - i);
+          }
+        }
+        // 2) (k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]).transpose(-1, -2) @ v_new
+        // k_i: [chunk_size, EK]
+        // g[:, :, i, -1, None]: [1]
+        // g[:, :, i]: [chunk_size]
+        // (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]: [chunk_size, 1]
+        // kg = k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]: [chunk_size, EK]
+        // (k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]).transpose(-1, -2): [EK, chunk_size]
+        // v_new: [chunk_size, EV]
+        // (k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]).transpose(-1, -2) @ v_new: [EK, EV]
+        // kg = k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]
+        for (int64_t m = 0; m < chunk_size; m++) {
+          auto g_exp = std::exp((g_pad_i_last[0] - g_pad_i[m]));
+          int64_t i = 0;
+          scalar_t g_exp_reduced = static_cast<scalar_t>(g_exp);
+          auto vec_g_exp_reduced = bVec(g_exp_reduced);
+          for (; i < VecSize * (qk_head_size / VecSize); i += VecSize) {
+            auto tmp0 = bVec::loadu(k_i + m * qk_head_size + i);
+            auto tmp2 = tmp0 * vec_g_exp_reduced;
+            tmp2.store(kg + m * qk_head_size + i);
+          }
+        }
+        // kg.transpose(-1, -2): [EK, chunk_size]
+        at::native::utils::transpose<scalar_t>(
+            /* M */ chunk_size,
+            /* N */ qk_head_size,
+            /* src */ kg,
+            /* ld_src */ qk_head_size,
+            /* dst */ kg_transpose,
+            /* ld_dst */ chunk_size);
+        // kgv = kg.transpose(-1, -2) @ v_new
+        // v_new: [chunk_size, EV]
+        at::native::cpublas::brgemm(
+            /* M */ qk_head_size,
+            /* N */ v_head_size,
+            /* K */ chunk_size,
+            /* lda */ chunk_size,
+            /* ldb */ v_head_size,
+            /* ldc */ v_head_size,
+            /* add_C */ false,
+            /* A */ kg_transpose,
+            /* B */ v_prime_pack_reduced,
+            /* C */ kgv);
+        // last_recurrent_state = 1) + 2)
+        for (int64_t m = 0; m < qk_head_size; m++) {
+          at::vec::map2<float>(
+              [](fVec x, fVec y) { return x + y; },
+              curr_last_recurrent_state + m * v_head_size,
+              curr_last_recurrent_state + m * v_head_size,
+              kgv + m * v_head_size,
+              v_head_size);
+        }
+      }
+
+      // core_attn_out -> output
+      // output: [B, T, HV, EV]
+      // core_attn_out: [B, HV, padded_T, EV]
+      auto curr_out = out_ptr + h * oStrideH;
+      for (int64_t m = 0; m < seq_len; m++) {
+        at::vec::map<scalar_t>(
+            [](fVec x) { return x; }, curr_out + m * oStrideT, curr_core_attn_out + m * v_head_size, v_head_size);
+      }
+
+      // Move to the next query
+      data_index_step(b, batch_size, h, v_num_head);
+    }
+  });
+}
+
+inline float softplus(float x, double threshold = 20.0) {
+  if (x > threshold)
+    return x;
+  else if (x < -threshold)
+    return std::exp(x);
+  else
+    return std::log1p(std::exp(x));
+}
+
+inline at::vec::Vectorized<float> softplus(const at::vec::Vectorized<float>& x, double threshold = 20.0) {
+  using Vec = at::vec::Vectorized<float>;
+  Vec mask_hi = x > Vec(threshold);
+  Vec mask_lo = x < Vec(-threshold);
+
+  Vec expx = x.exp_u20();
+  Vec log1pex = (expx + Vec(1.0f)).log();
+
+  return Vec::blendv(Vec::blendv(log1pex, expx, mask_lo), x, mask_hi);
+}
+
+template <typename scalar_t, typename param_t>
+void fused_sigmoid_gating_delta_rule_update_kernel_impl(
+    const scalar_t* __restrict__ q_ptr,
+    const scalar_t* __restrict__ k_ptr,
+    const scalar_t* __restrict__ v_ptr,
+    const param_t* __restrict__ A_log_ptr,
+    const scalar_t* __restrict__ a_ptr,
+    const scalar_t* __restrict__ dt_bias_ptr,
+    const scalar_t* __restrict__ b_ptr,
+    const int32_t* __restrict__ indices_ptr,
+    float* __restrict__ state_ptr,
+    scalar_t* __restrict__ o_ptr,
+    float* __restrict__ qk_scale_buf,
+    int64_t seq_len,
+    int64_t batch_size,
+    int64_t num_heads,
+    int64_t head_dim,
+    int64_t v_num_heads,
+    int64_t v_head_dim,
+    int64_t q_strideB,
+    int64_t q_strideS,
+    int64_t q_strideH,
+    int64_t k_strideB,
+    int64_t k_strideS,
+    int64_t k_strideH,
+    int64_t v_strideB,
+    int64_t v_strideS,
+    int64_t v_strideH,
+    bool use_qk_l2norm_in_kernel,
+    double softplus_threshold) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+
+  constexpr int64_t VecSize = bVec::size();
+  constexpr int64_t fVecSize = fVec::size();
+  int64_t group_size = v_num_heads / num_heads;
+  double scale = 1 / std::sqrt(head_dim);
+  fVec scale_vec = fVec(scale);
+  if (use_qk_l2norm_in_kernel) {
+    float eps = 1e-5;
+    at::parallel_for(0, batch_size * seq_len * num_heads, 0, [&](int64_t begin, int64_t end) {
+      int64_t bi{0}, si{0}, ni{0};
+      data_index_init(begin, bi, batch_size, si, seq_len, ni, num_heads);
+      for (int64_t i = begin; i < end; ++i) {
+        float sum_q = float(0);
+        float sum_k = float(0);
+        fVec sum_q_fvec = fVec(float(0));
+        fVec sum_k_fvec = fVec(float(0));
+        int64_t q_offset = bi * q_strideB + si * q_strideS + ni * q_strideH;
+        int64_t k_offset = bi * k_strideB + si * k_strideS + ni * k_strideH;
+        int64_t q_scale_offset = bi * seq_len * num_heads + si * num_heads + ni;
+        int64_t k_scale_offset = q_scale_offset + batch_size * seq_len * num_heads;
+        int64_t d;
+#pragma GCC unroll 4
+        for (d = 0; d <= head_dim - VecSize; d += VecSize) {
+          bVec q_bvec = bVec::loadu(q_ptr + q_offset + d);
+          fVec q_fvec0, q_fvec1;
+          std::tie(q_fvec0, q_fvec1) = at::vec::convert_to_float(q_bvec);
+          sum_q_fvec += q_fvec0 * q_fvec0;
+          sum_q_fvec += q_fvec1 * q_fvec1;
+          bVec k_bvec = bVec::loadu(k_ptr + k_offset + d);
+          fVec k_fvec0, k_fvec1;
+          std::tie(k_fvec0, k_fvec1) = at::vec::convert_to_float(k_bvec);
+          sum_k_fvec += k_fvec0 * k_fvec0;
+          sum_k_fvec += k_fvec1 * k_fvec1;
+        }
+#pragma GCC unroll 4
+        for (; d < head_dim; ++d) {
+          float q_val = static_cast<float>(q_ptr[q_offset + d]);
+          sum_q += q_val * q_val;
+          float k_val = static_cast<float>(k_ptr[k_offset + d]);
+          sum_k += k_val * k_val;
+        }
+
+        sum_q += vec_reduce_sum(sum_q_fvec);
+        sum_k += vec_reduce_sum(sum_k_fvec);
+        qk_scale_buf[q_scale_offset] = float(1) / std::sqrt(sum_q + eps);
+        qk_scale_buf[k_scale_offset] = float(1) / std::sqrt(sum_k + eps);
+
+        data_index_step(bi, batch_size, si, seq_len, ni, num_heads);
+      }
+    });
+  }
+  at::parallel_for(0, batch_size * seq_len * v_num_heads, 0, [&](int64_t begin, int64_t end) {
+    int64_t bi{0}, si{0}, ni{0};
+    data_index_init(begin, bi, batch_size, si, seq_len, ni, v_num_heads);
+    for (int64_t i = begin; i < end; ++i) {
+      int64_t cache_index = indices_ptr[bi];
+      int64_t state_offset = (cache_index * v_num_heads + ni) * head_dim * v_head_dim;
+      float g_val = -std::exp(float(A_log_ptr[ni])) *
+                    softplus(float(a_ptr[bi * v_num_heads + ni]) + float(dt_bias_ptr[ni]), softplus_threshold);
+      float g_val_exp = std::exp(g_val);
+      fVec g_val_exp_vec = fVec(g_val_exp);
+      int64_t q_offset = si * q_strideS + bi * q_strideB + (ni / group_size) * q_strideH;
+      int64_t k_offset = si * k_strideS + bi * k_strideB + (ni / group_size) * k_strideH;
+      int64_t q_scale_offset = bi * seq_len * num_heads + si * num_heads + (ni / group_size);
+      int64_t k_scale_offset = q_scale_offset + batch_size * seq_len * num_heads;
+      float q_scale = use_qk_l2norm_in_kernel ? qk_scale_buf[q_scale_offset] : 1.0f;
+      float k_scale = use_qk_l2norm_in_kernel ? qk_scale_buf[k_scale_offset] : 1.0f;
+      int64_t v_offset = si * v_strideS + bi * v_strideB + ni * v_strideH;
+      int64_t o_offset = ((bi * seq_len + si) * v_num_heads + ni) * v_head_dim;
+      float beta_val = 1 / (1 + std::exp(-b_ptr[ni]));
+      fVec beta_vec = fVec(beta_val);
+      int64_t dvi = 0;
+      for (; dvi <= v_head_dim - VecSize; dvi += VecSize) {
+        fVec kv_mem_vec0 = fVec(float(0));
+        fVec kv_mem_vec1 = fVec(float(0));
+        for (int di = 0; di < head_dim; ++di) {
+          fVec k_val_vec = fVec(k_ptr[k_offset + di] * k_scale);
+          fVec state_vec0 = fVec::loadu(state_ptr + state_offset + di * v_head_dim + dvi);
+          fVec state_vec1 = fVec::loadu(state_ptr + state_offset + di * v_head_dim + dvi + fVecSize);
+          kv_mem_vec0 = kv_mem_vec0 + state_vec0 * g_val_exp_vec * k_val_vec;
+          kv_mem_vec1 = kv_mem_vec1 + state_vec1 * g_val_exp_vec * k_val_vec;
+        }
+        bVec v_bvec = bVec::loadu(v_ptr + v_offset + dvi);
+        fVec v_vec0, v_vec1;
+        std::tie(v_vec0, v_vec1) = at::vec::convert_to_float(v_bvec);
+        fVec dt_vec0 = (v_vec0 - kv_mem_vec0) * beta_vec;
+        fVec dt_vec1 = (v_vec1 - kv_mem_vec1) * beta_vec;
+        fVec o_vec0 = fVec(float(0));
+        fVec o_vec1 = fVec(float(0));
+        for (int di = 0; di < head_dim; ++di) {
+          fVec q_vec = fVec(q_ptr[q_offset + di] * q_scale);
+          fVec k_vec = fVec(k_ptr[k_offset + di] * k_scale);
+          fVec state_vec0 = fVec::loadu(state_ptr + state_offset + di * v_head_dim + dvi);
+          fVec state_vec1 = fVec::loadu(state_ptr + state_offset + di * v_head_dim + dvi + fVecSize);
+          state_vec0 = state_vec0 * g_val_exp_vec + k_vec * dt_vec0;
+          state_vec1 = state_vec1 * g_val_exp_vec + k_vec * dt_vec1;
+          o_vec0 = o_vec0 + state_vec0 * q_vec * scale_vec;
+          o_vec1 = o_vec1 + state_vec1 * q_vec * scale_vec;
+          state_vec0.store(state_ptr + state_offset + di * v_head_dim + dvi);
+          state_vec1.store(state_ptr + state_offset + di * v_head_dim + dvi + fVecSize);
+        }
+        bVec o_vec = at::vec::convert_from_float<scalar_t>(o_vec0, o_vec1);
+        o_vec.store(o_ptr + o_offset + dvi);
+      }
+      for (; dvi < v_head_dim; ++dvi) {
+        float kv_mem_val = 0;
+        for (int di = 0; di < head_dim; ++di) {
+          float k_val = k_ptr[k_offset + di] * k_scale;
+          state_ptr[state_offset + di * v_head_dim + dvi] *= g_val_exp;
+          kv_mem_val += state_ptr[state_offset + di * v_head_dim + dvi] * k_val;
+        }
+        float v_val = v_ptr[v_offset + dvi];
+        float dt_val = (v_val - kv_mem_val) * beta_val;
+        float o_val = 0;
+        for (int di = 0; di < head_dim; ++di) {
+          float q_val = q_ptr[q_offset + di] * q_scale;
+          float k_val = k_ptr[k_offset + di] * k_scale;
+          state_ptr[state_offset + di * v_head_dim + dvi] += k_val * dt_val;
+          o_val += state_ptr[state_offset + di * v_head_dim + dvi] * q_val * scale;
+        }
+        o_ptr[o_offset + dvi] = o_val;
+      }
+      data_index_step(bi, batch_size, si, seq_len, ni, v_num_heads);
+    }
+  });
+}
+
+template <typename scalar_t>
+void fused_gdn_gating_kernel_impl(
+    float* __restrict__ A_log,
+    const scalar_t* __restrict__ a,
+    const scalar_t* __restrict__ b,
+    const scalar_t* __restrict__ dt_bias,
+    float* __restrict__ out,
+    scalar_t* __restrict__ beta,
+    int64_t batch,
+    int64_t num_heads) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int vec_size = bVec::size();
+  constexpr int fvec_size = fVec::size();
+  const fVec neg_one(-1.0f);
+  const fVec one(1.0f);
+  at::parallel_for(0, batch, 0, [&](int64_t begin, int64_t end) {
+    for (int64_t i = begin; i < end; ++i) {
+      int64_t j = 0;
+      for (; j < num_heads - (num_heads % vec_size); j += vec_size) {
+        fVec A_log_vec0 = fVec::loadu(A_log + j);
+        fVec A_log_vec1 = fVec::loadu(A_log + j + fvec_size);
+        bVec dt_bias_vec = bVec::loadu(dt_bias + j);
+        bVec a_bvec = bVec::loadu(a + i * num_heads + j);
+        bVec b_bvec = bVec::loadu(b + i * num_heads + j);
+        fVec a0, a1, dt_bias_vec0, dt_bias_vec1, b0, b1;
+        std::tie(a0, a1) = at::vec::convert_to_float(a_bvec);
+        std::tie(b0, b1) = at::vec::convert_to_float(b_bvec);
+        std::tie(dt_bias_vec0, dt_bias_vec1) = at::vec::convert_to_float(dt_bias_vec);
+
+        fVec g0 = neg_one * A_log_vec0.exp_u20() * softplus(a0 + dt_bias_vec0);
+        fVec g1 = neg_one * A_log_vec1.exp_u20() * softplus(a1 + dt_bias_vec1);
+        fVec beta0 = one / (one + (neg_one * b0).exp_u20());
+        fVec beta1 = one / (one + (neg_one * b1).exp_u20());
+
+        g0.store(out + i * num_heads + j);
+        g1.store(out + i * num_heads + j + fvec_size);
+        bVec beta_vec = at::vec::convert_from_float<scalar_t>(beta0, beta1);
+        beta_vec.store(beta + i * num_heads + j);
+      }
+      for (; j < num_heads; ++j) {
+        out[i * num_heads + j] = -std::exp(A_log[j]) * softplus(float(a[i * num_heads + j]) + float(dt_bias[j]));
+        beta[i * num_heads + j] = 1 / (1 + std::exp(-b[i * num_heads + j]));
+      }
+    }
+  });
+}
+
+template <typename scalar_t>
+void fused_gdn_gating_kernel_impl(
+    scalar_t* __restrict__ A_log,
+    const scalar_t* __restrict__ a,
+    const scalar_t* __restrict__ b,
+    const scalar_t* __restrict__ dt_bias,
+    float* __restrict__ out,
+    scalar_t* __restrict__ beta,
+    int64_t batch,
+    int64_t num_heads) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int vec_size = bVec::size();
+  constexpr int fvec_size = fVec::size();
+  const fVec neg_one(-1.0f);
+  const fVec one(1.0f);
+  at::parallel_for(0, batch, 0, [&](int64_t begin, int64_t end) {
+    for (int64_t i = begin; i < end; ++i) {
+      int64_t j = 0;
+      for (; j < num_heads - (num_heads % vec_size); j += vec_size) {
+        bVec A_log_bvec = bVec::loadu(A_log + j);
+        fVec A_log_vec0, A_log_vec1;
+        std::tie(A_log_vec0, A_log_vec1) = at::vec::convert_to_float(A_log_bvec);
+        bVec dt_bias_vec = bVec::loadu(dt_bias + j);
+        bVec a_bvec = bVec::loadu(a + i * num_heads + j);
+        bVec b_bvec = bVec::loadu(b + i * num_heads + j);
+        fVec a0, a1, dt_bias_vec0, dt_bias_vec1, b0, b1;
+        std::tie(a0, a1) = at::vec::convert_to_float(a_bvec);
+        std::tie(b0, b1) = at::vec::convert_to_float(b_bvec);
+        std::tie(dt_bias_vec0, dt_bias_vec1) = at::vec::convert_to_float(dt_bias_vec);
+
+        fVec g0 = neg_one * A_log_vec0.exp_u20() * softplus(a0 + dt_bias_vec0);
+        fVec g1 = neg_one * A_log_vec1.exp_u20() * softplus(a1 + dt_bias_vec1);
+        fVec beta0 = one / (one + (neg_one * b0).exp_u20());
+        fVec beta1 = one / (one + (neg_one * b1).exp_u20());
+
+        g0.store(out + i * num_heads + j);
+        g1.store(out + i * num_heads + j + fvec_size);
+        bVec beta_vec = at::vec::convert_from_float<scalar_t>(beta0, beta1);
+        beta_vec.store(beta + i * num_heads + j);
+      }
+      for (; j < num_heads; ++j) {
+        out[i * num_heads + j] = -std::exp(float(A_log[j])) * softplus(float(a[i * num_heads + j]) + float(dt_bias[j]));
+        beta[i * num_heads + j] = 1 / (1 + std::exp(-b[i * num_heads + j]));
+      }
+    }
+  });
+}
+
+}  // anonymous namespace
+
+template <bool is_last_dim_contiguous>
+inline void
+CHECK_INPUT_SHAPE_DTYPE(const at::Tensor& tensor, const int64_t& dim, const at::IntArrayRef& sizes, at::ScalarType st) {
+  TORCH_CHECK(tensor.sizes() == sizes, "Input tensor shape mismatch: expected ", sizes, ", got ", tensor.sizes());
+  TORCH_CHECK(tensor.dtype() == st, "Input tensor dtype mismatch");
+  CHECK_DIM(dim, tensor);
+  if (is_last_dim_contiguous) {
+    CHECK_LAST_DIM_CONTIGUOUS_INPUT(tensor);
+  } else {
+    CHECK_CONTIGUOUS(tensor);
+  }
+}
+
+// query: [B, T, HK, EK]
+// key: [B, T, HK, EK]
+// value: [B, T, HV, EV]
+// g: [B, T, HV] FP32
+// beta: [B, T, HV]
+// initial_state: [N, HV, EK, EV] FP32
+// output_final_state: bool
+// cu_seqlens: [N + 1] INT32
+// head_first: bool
+// use_qk_l2norm_in_kernel: bool
+std::tuple<at::Tensor, at::Tensor> chunk_gated_delta_rule_cpu(
+    const at::Tensor& query,
+    const at::Tensor& key,
+    const at::Tensor& value,
+    const at::Tensor& g,
+    const at::Tensor& beta,
+    const at::Tensor& initial_state,
+    bool output_final_state,
+    const at::Tensor& cu_seqlens,
+    bool head_first,
+    bool use_qk_l2norm_in_kernel,
+    double eps = 1e-5) {
+  TORCH_CHECK(head_first == false, "chunk_gated_delta_rule_cpu does not support head first");
+  int64_t B = query.size(0);
+  int64_t global_seq_len = query.size(1);
+  int64_t qk_num_head = query.size(2);
+  int64_t qk_head_size = query.size(3);
+  int64_t v_num_head = value.size(2);
+  int64_t v_head_size = value.size(3);
+  int64_t batch_size = initial_state.size(0);
+  CHECK_EQ(B, 1);
+  TORCH_CHECK(v_num_head % qk_num_head == 0, "expect v_num_head multiple of qk_num_head.");
+  TORCH_CHECK(qk_head_size % 32 == 0, "expect qk_head_size to be multiples of 32.");
+  TORCH_CHECK(v_head_size % 32 == 0, "expect v_head_size to be multiples of 32.");
+  CHECK_INPUT_SHAPE_DTYPE<true>(query, 4, {B, global_seq_len, qk_num_head, qk_head_size}, at::kBFloat16);
+  CHECK_INPUT_SHAPE_DTYPE<true>(key, 4, {B, global_seq_len, qk_num_head, qk_head_size}, at::kBFloat16);
+  CHECK_INPUT_SHAPE_DTYPE<true>(value, 4, {B, global_seq_len, v_num_head, v_head_size}, at::kBFloat16);
+  CHECK_INPUT_SHAPE_DTYPE<false>(g, 3, {B, global_seq_len, v_num_head}, at::kFloat);
+  CHECK_INPUT_SHAPE_DTYPE<false>(beta, 3, {B, global_seq_len, v_num_head}, at::kBFloat16);
+  CHECK_INPUT_SHAPE_DTYPE<false>(cu_seqlens, 1, {batch_size + 1}, at::kInt);
+  CHECK_INPUT_SHAPE_DTYPE<false>(initial_state, 4, {batch_size, v_num_head, qk_head_size, v_head_size}, at::kFloat);
+
+  at::Tensor output = at::empty_like(value, value.options());  // [B, T, HV, EV]
+  at::Tensor final_state = initial_state.to(at::kFloat);       // [N, HV, EK, EV]
+
+  // Strides
+  int64_t qStrideH = query.stride(2);
+  int64_t qStrideT = query.stride(1);
+  int64_t kStrideH = key.stride(2);
+  int64_t kStrideT = key.stride(1);
+  int64_t vStrideH = value.stride(2);
+  int64_t vStrideT = value.stride(1);
+  int64_t oStrideH = output.stride(2);
+  int64_t oStrideT = output.stride(1);
+
+  constexpr int64_t chunk_size = 64;
+  // Deduce the global chunks
+  // e.g. cu_seqlens: [0, 5, 13, 16], chunk_size = 4
+  // chunk_offsets: [0, 2, 4, 5]
+  // chunk_indices (batch_id, local_chunk_id): [[0, 0], [0, 1], [1, 0], [1, 1], [2, 0]]
+  at::Tensor chunk_offsets = at::empty(batch_size + 1, cu_seqlens.options());
+  auto chunk_offsets_ptr = chunk_offsets.data_ptr<int32_t>();
+  chunk_offsets_ptr[0] = 0;
+  int32_t* cu_seqlens_ptr = cu_seqlens.data_ptr<int32_t>();
+  int64_t s = 0;
+  int64_t e = 0;
+  int64_t s_pad = 0;
+  int64_t e_pad = 0;
+  for (int64_t b = 0; b < batch_size; b++) {
+    e = cu_seqlens_ptr[b + 1];
+    int64_t seq_len = e - s;
+    int64_t pad_size = (chunk_size - seq_len % chunk_size) % chunk_size;
+    int64_t total_seq_length = seq_len + pad_size;
+    e_pad = s_pad + total_seq_length;
+    chunk_offsets[b + 1] = e_pad / chunk_size;
+    s = e;
+    s_pad = e_pad;
+  }
+  int64_t global_total_seq_length = e_pad;
+  int64_t global_num_chunk = chunk_offsets_ptr[batch_size];
+  at::Tensor chunk_indices = at::empty(global_num_chunk * 2, cu_seqlens.options());
+  auto chunk_indices_ptr = chunk_indices.data_ptr<int32_t>();
+  int64_t curr_c = 0;
+  for (int64_t b = 0; b < batch_size; b++) {
+    int64_t batch_chunk_num = chunk_offsets_ptr[b + 1] - chunk_offsets_ptr[b];
+    for (int64_t c = 0; c < batch_chunk_num; c++) {
+      chunk_indices_ptr[curr_c * 2] = b;
+      chunk_indices_ptr[curr_c * 2 + 1] = c;
+      curr_c += 1;
+    }
+  }
+
+  // Allocate buffer
+  int64_t buff_size = v_num_head * global_total_seq_length                               // g_pad_data
+                      + batch_size * v_num_head * global_total_seq_length * v_head_size  // core_attn
+                      + v_num_head * global_total_seq_length * chunk_size                // decay_mask
+                      + v_num_head * global_total_seq_length * v_head_size;              // v_beta_attn
+  at::Tensor buff_data = at::empty({buff_size}, query.options().dtype(at::kFloat));
+  int64_t reduced_buff_size = qk_num_head * global_total_seq_length * qk_head_size    // q_pad_data
+                              + qk_num_head * global_total_seq_length * qk_head_size  // k_pad_data
+                              + v_num_head * global_total_seq_length * v_head_size    // v_pad_data
+                              + v_num_head * global_total_seq_length * qk_head_size   // k_beta_data
+                              + v_num_head * global_total_seq_length * v_head_size    // v_beta_data
+                              + v_num_head * global_total_seq_length * qk_head_size   // k_cumdecay_reduced
+                              + qk_num_head * global_seq_len                          // q_norm_sum
+                              + qk_num_head * global_seq_len;                         // k_norm_sum
+  at::Tensor reduced_buff_data = at::empty({reduced_buff_size}, query.options());
+  int64_t num_thread = at::get_num_threads();
+  int64_t buff_size_16bit_per_thread =
+      /* k_transpose */ qk_head_size * chunk_size +
+      /* v_pack */ chunk_size * v_head_size +
+      /* k_beta_g  */ chunk_size * qk_head_size +
+      /* k_beta_g_pack  */ chunk_size * qk_head_size +
+      /* attn */ chunk_size * chunk_size * 2 +
+      /* attn_reduced */ chunk_size * chunk_size +
+      /* k_cumdecay */ chunk_size * qk_head_size * 2 +
+      /* row */ chunk_size * 2 +
+      /* updated */ chunk_size * 2 +
+      /* curr_last_recurrent_state_reduced  */ qk_head_size * v_head_size +
+      /* curr_last_recurrent_state_pack_reduced   */ qk_head_size * v_head_size +
+      /* k_transpose_i  */ qk_head_size * chunk_size +
+      /* attn_i   */ chunk_size * chunk_size * 2 +
+      /* attn_i_reduced     */ chunk_size * chunk_size +
+      /* v_prime */ chunk_size * v_head_size * 2 +
+      /* v_prime_reduced */ chunk_size * v_head_size +
+      /* v_prime_pack_reduced */ chunk_size * v_head_size +
+      /* qg */ chunk_size * qk_head_size +
+      /* attn_inter */ chunk_size * v_head_size * 2 +
+      /* kg */ chunk_size * qk_head_size +
+      /* kg_transpose */ qk_head_size * chunk_size +
+      /* kgv */ qk_head_size * v_head_size * 2;
+  at::Tensor thread_buff_data = at::empty({num_thread, buff_size_16bit_per_thread}, query.options());
+
+  AT_DISPATCH_REDUCED_FLOATING_TYPES(query.scalar_type(), "chunk_gated_delta_rule_kernel", [&] {
+    chunk_gated_delta_rule_kernel_impl<scalar_t, chunk_size>(
+        output.data_ptr<scalar_t>(),
+        final_state.data_ptr<float>(),
+        query.data_ptr<scalar_t>(),
+        key.data_ptr<scalar_t>(),
+        value.data_ptr<scalar_t>(),
+        g.data_ptr<float>(),
+        beta.data_ptr<scalar_t>(),
+        cu_seqlens_ptr,
+        buff_data.data_ptr<float>(),
+        reduced_buff_data.data_ptr<scalar_t>(),
+        thread_buff_data.data_ptr<scalar_t>(),
+        chunk_offsets_ptr,
+        chunk_indices_ptr,
+        use_qk_l2norm_in_kernel,
+        batch_size,
+        global_seq_len,
+        qk_num_head,
+        v_num_head,
+        qk_head_size,
+        v_head_size,
+        qStrideH,
+        qStrideT,
+        kStrideH,
+        kStrideT,
+        vStrideH,
+        vStrideT,
+        oStrideH,
+        oStrideT,
+        global_total_seq_length,
+        global_num_chunk,
+        buff_size_16bit_per_thread,
+        eps);
+  });
+  return std::make_tuple(std::move(output), std::move(final_state));
+}
+
+// A_log: [v_num_heads]
+// dt_bias: [v_num_heads]
+// query: [seq_len, batch_size, num_heads, head_dim]
+// key: [seq_len, batch_size, num_heads, head_dim]
+// value: [seq_len, batch_size, v_num_heads, v_head_dim]
+// a: [batch_size, v_num_heads]
+// b: [batch_size, v_num_heads]
+// initial_state_source:[num_tokens, v_num_heads, head_dim, v_head_dim]
+// initial_state_indices: [batch_size]
+// cu_seqlens: [batch_size + 1]
+at::Tensor fused_sigmoid_gating_delta_rule_update_cpu(
+    const at::Tensor& A_log,
+    const at::Tensor& dt_bias,
+    const at::Tensor& q,
+    const at::Tensor& k,
+    const at::Tensor& v,
+    const at::Tensor& a,
+    const at::Tensor& b,
+    at::Tensor& initial_state_source,
+    const at::Tensor& initial_state_indices,
+    const at::Tensor& cu_seqlens,
+    bool use_qk_l2norm_in_kernel,
+    double softplus_beta = 1.0,
+    double softplus_threshold = 20.0) {
+  CHECK_DIM(4, q);
+  CHECK_DIM(4, v);
+  CHECK_LAST_DIM_CONTIGUOUS_INPUT(q);
+  int64_t seq_len = q.size(0);
+  int64_t batch_size = q.size(1);
+  int64_t num_heads = q.size(2);
+  int64_t head_dim = q.size(3);
+  int64_t v_num_heads = v.size(2);
+  int64_t v_head_dim = v.size(3);
+  CHECK_INPUT_SHAPE_DTYPE<true>(k, {seq_len, batch_size, num_heads, head_dim}, q.scalar_type());
+  CHECK_INPUT_SHAPE_DTYPE<true>(v, {seq_len, batch_size, v_num_heads, v_head_dim}, q.scalar_type());
+  CHECK_INPUT_SHAPE_DTYPE<true>(a, {batch_size, v_num_heads}, q.scalar_type());
+  CHECK_INPUT_SHAPE_DTYPE<true>(dt_bias, {v_num_heads}, q.scalar_type());
+  CHECK_INPUT_SHAPE_DTYPE<true>(b, {batch_size, v_num_heads}, q.scalar_type());
+  CHECK_INPUT_SHAPE_DTYPE<true>(initial_state_indices, {batch_size}, at::kInt);
+  CHECK_INPUT_SHAPE_DTYPE<true>(cu_seqlens, {batch_size + 1}, at::kInt);
+  CHECK_INPUT_SHAPE_DTYPE<true>(
+      initial_state_source, {initial_state_source.size(0), v_num_heads, head_dim, v_head_dim}, at::kFloat);
+  CHECK(initial_state_source.size(0) >= batch_size);
+  CHECK_EQ(v_num_heads % num_heads, 0);
+  TORCH_CHECK(
+      A_log.sizes() == at::IntArrayRef({v_num_heads}),
+      "Input tensor shape mismatch: expected ",
+      at::IntArrayRef({v_num_heads}),
+      ", got ",
+      A_log.sizes());
+
+  int64_t q_strideB = q.stride(1);
+  int64_t q_strideS = q.stride(0);
+  int64_t q_strideH = q.stride(2);
+  int64_t k_strideB = k.stride(1);
+  int64_t k_strideS = k.stride(0);
+  int64_t k_strideH = k.stride(2);
+  int64_t v_strideB = v.stride(1);
+  int64_t v_strideS = v.stride(0);
+  int64_t v_strideH = v.stride(2);
+  at::Tensor core_attn_out = at::empty({batch_size, seq_len, v_num_heads, v_head_dim}, q.options());
+  at::Tensor qk_scale_buf = at::empty({2 * batch_size, seq_len, num_heads}, at::kFloat);
+
+  CPU_DISPATCH_REDUCED_FLOATING_TYPES_EXT(
+      q.scalar_type(), A_log.scalar_type(), "fused_sigmoid_gating_delta_rule_update_kernel_impl", [&] {
+        fused_sigmoid_gating_delta_rule_update_kernel_impl<scalar_t, param_t>(
+            q.data_ptr<scalar_t>(),
+            k.data_ptr<scalar_t>(),
+            v.data_ptr<scalar_t>(),
+            A_log.data_ptr<param_t>(),
+            a.data_ptr<scalar_t>(),
+            dt_bias.data_ptr<scalar_t>(),
+            b.data_ptr<scalar_t>(),
+            initial_state_indices.data_ptr<int32_t>(),
+            initial_state_source.data_ptr<float>(),
+            core_attn_out.data_ptr<scalar_t>(),
+            qk_scale_buf.data_ptr<float>(),
+            seq_len,
+            batch_size,
+            num_heads,
+            head_dim,
+            v_num_heads,
+            v_head_dim,
+            q_strideB,
+            q_strideS,
+            q_strideH,
+            k_strideB,
+            k_strideS,
+            k_strideH,
+            v_strideB,
+            v_strideS,
+            v_strideH,
+            use_qk_l2norm_in_kernel,
+            softplus_threshold);
+      });
+  return core_attn_out;
+}
+
+// A_log: [num_v_heads]
+// a: [batch, num_v_heads]
+// b: [batch, num_v_heads]
+// dt_bias: [num_v_heads]
+// -A_log.float().exp() * F.softplus(a.float() + dt_bias)
+std::tuple<at::Tensor, at::Tensor>
+fused_gdn_gating_cpu(const at::Tensor& A_log, const at::Tensor& a, const at::Tensor& b, const at::Tensor& dt_bias) {
+  CHECK_DIM(1, A_log);
+  CHECK_DIM(2, a);
+  CHECK_DIM(2, b);
+  CHECK_DIM(1, dt_bias);
+  CHECK_CONTIGUOUS(a);
+  CHECK_EQ(A_log.size(0), a.size(1));
+  CHECK_EQ(A_log.size(0), dt_bias.size(0));
+  int batch = a.size(0);
+  int num_heads = a.size(1);
+  CHECK_EQ(b.size(0), batch);
+  CHECK_EQ(b.size(1), num_heads);
+  at::Tensor out = at::empty({1, batch, num_heads}, a.options().dtype(at::kFloat));
+  at::Tensor beta = at::empty({1, batch, num_heads}, b.options());
+  CPU_DISPATCH_REDUCED_FLOATING_TYPES_EXT(a.scalar_type(), A_log.scalar_type(), "fused_gdn_gating_kernel", [&] {
+    fused_gdn_gating_kernel_impl<scalar_t>(
+        A_log.data_ptr<param_t>(),
+        a.data_ptr<scalar_t>(),
+        b.data_ptr<scalar_t>(),
+        dt_bias.data_ptr<scalar_t>(),
+        out.data_ptr<float>(),
+        beta.data_ptr<scalar_t>(),
+        batch,
+        num_heads);
+  });
+  return std::make_tuple(out, beta);
+}
diff --git a/csrc/cpu/sgl-kernels/gemm.cpp b/csrc/cpu/sgl-kernels/gemm.cpp
index 65c56943c56f..38e6d9f4ce9d 100644
--- a/csrc/cpu/sgl-kernels/gemm.cpp
+++ b/csrc/cpu/sgl-kernels/gemm.cpp
@@ -1,11 +1,12 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
-#include "common.h"
-#include "vec.h"
+// clang-format off
+
 #include "gemm.h"
 
-// clang-format off
+#include "common.h"
+#include "vec.h"
 
 namespace {
 
@@ -26,13 +27,13 @@ inline void s8s8_compensation(int8_t* __restrict__ packed, int K) {
   const __m512i off = _mm512_set1_epi8(static_cast<char>(0x80));
   for (int k = 0; k < K / 4; ++k) {
     for (int col = 0; col < COLS; ++col) {
-      __m512i vb = _mm512_loadu_si512((const __m512i *)(packed + k * BLOCK_N * 4 + col * 64));
+      __m512i vb = _mm512_loadu_si512((const __m512i*)(packed + k * BLOCK_N * 4 + col * 64));
       vcomp[col] = _mm512_dpbusd_epi32(vcomp[col], off, vb);
     }
   }
 
   for (int col = 0; col < COLS; ++col) {
-    _mm512_storeu_si512((__m512i *)(packed + offset + col * 64), vcomp[col]);
+    _mm512_storeu_si512((__m512i*)(packed + offset + col * 64), vcomp[col]);
   }
 #else
   TORCH_CHECK(false, "s8s8_compensation not implemented!");
@@ -69,6 +70,43 @@ inline void pack_vnni<int8_t>(int8_t* __restrict__ packed, const int8_t* __restr
   s8s8_compensation<BLOCK_N>(packed, K);
 }
 
+// uint8_t: mxfp4 or int4
+// pack to vnni2 format as they are computed with bfloat16
+//
+// from [N, K'/2, 2] to [K'/2, N, 2], view 2x int4 as unit8:
+// from [N,    K   ] to [K,    N   ] where K = K'/2
+//
+template <>
+inline void pack_vnni<uint8_t>(uint8_t* __restrict__ packed, const uint8_t* __restrict__ weight, int N, int K) {
+  constexpr int BLOCK_N = block_size_n();
+
+  uint8_t unpacked[2 * BLOCK_N];
+
+  // 32-way pack (align with BLOCK_N), faster for avx512 unpacking
+  //
+  // for a range of (64):
+  //   {0, 1, 2, ..., 63}
+  //
+  // original format:
+  //   { 1|0,  3|2, ..., 63|62}
+  //
+  // packed format:
+  //   {32|0, 31|1, ..., 63|31}
+  //
+  for (int k = 0; k < K; ++k) {
+    // unpack first
+    for (int n = 0; n < N; ++n) {
+      uint8_t value = weight[n * K + k];
+      unpacked[n * 2 + 0] = value & 0xF;  // lower 4 bits
+      unpacked[n * 2 + 1] = value >> 4;   // higher 4 bits
+    }
+    // re-pack to 32-way
+    for (int n = 0; n < N; ++n) {
+      packed[k * N + n] = (unpacked[n + BLOCK_N] << 4) | unpacked[n];
+    }
+  }
+}
+
 template <typename scalar_t>
 inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ input, int64_t size) {
   using bVec = at::vec::Vectorized<scalar_t>;
@@ -76,7 +114,7 @@ inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ inpu
   constexpr int kVecSize = bVec::size();
 
   int64_t d;
-  #pragma GCC unroll 4
+#pragma GCC unroll 4
   for (d = 0; d <= size - kVecSize; d += kVecSize) {
     fVec data0 = fVec::loadu(input + d);
     fVec data1 = fVec::loadu(input + d + fVec::size());
@@ -89,13 +127,34 @@ inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ inpu
 }
 
 template <typename scalar_t>
-inline void copy_add_stub(scalar_t* __restrict__ out, const float* __restrict__ input, const float* __restrict__ bias, int64_t size) {
+inline void copy_stub(float* __restrict__ out, const scalar_t* __restrict__ input, int64_t size) {
   using bVec = at::vec::Vectorized<scalar_t>;
   using fVec = at::vec::Vectorized<float>;
   constexpr int kVecSize = bVec::size();
 
   int64_t d;
-  #pragma GCC unroll 4
+#pragma GCC unroll 4
+  for (d = 0; d <= size - kVecSize; d += kVecSize) {
+    fVec data0, data1;
+    bVec b_vec = bVec::loadu(input + d);
+    std::tie(data0, data1) = at::vec::convert_to_float(b_vec);
+    data0.store(out + d);
+    data1.store(out + d + fVec::size());
+  }
+  for (; d < size; ++d) {
+    out[d] = static_cast<float>(input[d]);
+  }
+}
+
+template <typename scalar_t>
+inline void copy_add_stub(
+    scalar_t* __restrict__ out, const float* __restrict__ input, const float* __restrict__ bias, int64_t size) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int kVecSize = bVec::size();
+
+  int64_t d;
+#pragma GCC unroll 4
   for (d = 0; d <= size - kVecSize; d += kVecSize) {
     fVec data0 = fVec::loadu(input + d) + fVec::loadu(bias + d);
     fVec data1 = fVec::loadu(input + d + fVec::size()) + fVec::loadu(bias + d + fVec::size());
@@ -107,11 +166,51 @@ inline void copy_add_stub(scalar_t* __restrict__ out, const float* __restrict__
   }
 }
 
+template <typename scalar_t, bool has_bias>
+inline void scalar_sigmoid_and_mul(
+    scalar_t* __restrict__ out,
+    const float* __restrict__ input,
+    const float* __restrict__ bias,
+    const scalar_t* __restrict__ mul,
+    int SIZE) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  // scalar sigmoid
+  const fVec one = fVec(1.f);
+  fVec X;
+  if constexpr (has_bias) {
+    assert(bias != nullptr);
+    X = fVec(input[0] + bias[0]);
+  } else {
+    X = fVec(input[0]);
+  }
+  X = one / (one + X.neg().exp_u20());
+
+  // vec mul
+  constexpr int kVecSize = bVec::size();
+  for (int d = 0; d < SIZE; d += kVecSize) {
+    bVec m_bvec = bVec::loadu(mul + d);
+    fVec m_fvec0, m_fvec1;
+    std::tie(m_fvec0, m_fvec1) = at::vec::convert_to_float(m_bvec);
+    m_fvec0 = m_fvec0 * X;
+    m_fvec1 = m_fvec1 * X;
+
+    bVec out_vec = convert_from_float_ext<scalar_t>(m_fvec0, m_fvec1);
+    out_vec.store(out + d);
+  }
+}
+
 template <typename scalar_t, bool has_bias, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn {
   static inline void apply(
-      const scalar_t* __restrict__ A, const scalar_t* __restrict__ B, scalar_t* __restrict__ C,
-      const float* __restrict__ bias, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
+      const scalar_t* __restrict__ A,
+      const scalar_t* __restrict__ B,
+      scalar_t* __restrict__ C,
+      const float* __restrict__ bias,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
     TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
   }
 };
@@ -120,9 +219,14 @@ struct tinygemm_kernel_nn {
 template <bool has_bias, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
   static inline void apply(
-      const at::BFloat16* __restrict__ A, const at::BFloat16* __restrict__ B, at::BFloat16* __restrict__ C,
-      const float* __restrict__ bias, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
-
+      const at::BFloat16* __restrict__ A,
+      const at::BFloat16* __restrict__ B,
+      at::BFloat16* __restrict__ C,
+      const float* __restrict__ bias,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
     constexpr int ROWS = BLOCK_M;
     constexpr int COLS = BLOCK_N / 16;
 
@@ -145,7 +249,7 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
 
     const int64_t K2 = K >> 1;
     const int64_t lda2 = lda >> 1;
-    const int64_t ldb2 = ldb; // ldb * 2 >> 1;
+    const int64_t ldb2 = ldb;  // ldb * 2 >> 1;
     const float* a_ptr = reinterpret_cast<const float*>(A);
     const float* b_ptr = reinterpret_cast<const float*>(B);
 
@@ -180,9 +284,7 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
               (__m512i)(_mm512_cvtne2ps_pbh(vc[row * COLS + col + 1], vc[row * COLS + col])));
         }
       } else {
-        _mm256_storeu_si256(
-            reinterpret_cast<__m256i*>(C + row * ldc + col * 16),
-            (__m256i)(_mm512_cvtneps_pbh(vc[i])));
+        _mm256_storeu_si256(reinterpret_cast<__m256i*>(C + row * ldc + col * 16), (__m256i)(_mm512_cvtneps_pbh(vc[i])));
       }
     };
     Unroll<ROWS * COLS>{}(storec);
@@ -190,22 +292,33 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
 };
 #endif
 
-#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)                          \
-    tinygemm_kernel_nn<scalar_t, has_bias, MB_SIZE, NB_SIZE>::apply(         \
-        A + mb_start * lda, B + nb_start * 2, C + mb_start * ldc + nb_start, \
-        has_bias ? bias + nb_start : nullptr, K, lda, ldb, ldc);
+#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)                \
+  tinygemm_kernel_nn<scalar_t, has_bias, MB_SIZE, NB_SIZE>::apply( \
+      A + mb_start * lda,                                          \
+      B + nb_start * 2,                                            \
+      C + mb_start * ldc + nb_start,                               \
+      has_bias ? bias + nb_start : nullptr,                        \
+      K,                                                           \
+      lda,                                                         \
+      ldb,                                                         \
+      ldc);
 
 template <typename scalar_t, bool has_bias>
 struct brgemm {
   static inline void apply(
-      const scalar_t* __restrict__ A, const scalar_t* __restrict__ B, scalar_t* __restrict__ C,
-      float* __restrict__ Ctmp, const float* __restrict__ bias,
-      int64_t M, int64_t N, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
-
+      const scalar_t* __restrict__ A,
+      const scalar_t* __restrict__ B,
+      scalar_t* __restrict__ C,
+      float* __restrict__ Ctmp,
+      const float* __restrict__ bias,
+      int64_t M,
+      int64_t N,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
     constexpr int BLOCK_N = block_size_n();
-    at::native::cpublas::brgemm(
-        M, N, K, lda, ldb, BLOCK_N, /* add_C */false,
-        A, B, Ctmp);
+    at::native::cpublas::brgemm(M, N, K, lda, ldb, BLOCK_N, /* add_C */ false, A, B, Ctmp);
 
     // copy from Ctmp to C
     for (int64_t m = 0; m < M; ++m) {
@@ -216,6 +329,21 @@ struct brgemm {
       }
     }
   }
+  static inline void apply(
+      const float* __restrict__ A,
+      const float* __restrict__ B,
+      scalar_t* __restrict__ C,
+      float* __restrict__ Ctmp,
+      const float* __restrict__ bias,
+      int64_t M,
+      int64_t N,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
+    constexpr int BLOCK_N = block_size_n();
+    at::native::cpublas::brgemm(M, N, K, lda, ldb, BLOCK_N, /* add_C */ false, A, B, Ctmp);
+  }
 };
 
 template <typename scalar_t, bool has_bias>
@@ -232,15 +360,12 @@ void tinygemm_kernel(
     int64_t ldb,
     int64_t ldc,
     bool brg) {
-
   if (brg) {
-    brgemm<scalar_t, has_bias>::apply(
-        A, B, C, Ctmp, bias,
-        M, N, K, lda, ldb, ldc);
+    brgemm<scalar_t, has_bias>::apply(A, B, C, Ctmp, bias, M, N, K, lda, ldb, ldc);
     return;
   }
 
-  // pattern: 1-4-16
+  // pattern: 1-4-16, N = 16, 32, 48, 64
   constexpr int64_t BLOCK_M = 4;
   constexpr int64_t BLOCK_N = 64;
   const int64_t MB = div_up(M, BLOCK_M);
@@ -252,25 +377,88 @@ void tinygemm_kernel(
       int64_t nb_start = nb * BLOCK_N;
       int64_t nb_size = std::min(BLOCK_N, N - nb_start);
 
-      switch(mb_size << 4 | nb_size >> 4) {
+      switch (mb_size << 4 | nb_size >> 4) {
         // mb_size = 1
-        case 0x12: LAUNCH_TINYGEMM_KERNEL_NN(1, 32); break;
-        case 0x14: LAUNCH_TINYGEMM_KERNEL_NN(1, 64); break;
+        case 0x11:
+          LAUNCH_TINYGEMM_KERNEL_NN(1, 16);
+          break;
+        case 0x12:
+          LAUNCH_TINYGEMM_KERNEL_NN(1, 32);
+          break;
+        case 0x13:
+          LAUNCH_TINYGEMM_KERNEL_NN(1, 48);
+          break;
+        case 0x14:
+          LAUNCH_TINYGEMM_KERNEL_NN(1, 64);
+          break;
         // mb_size = 2
-        case 0x22: LAUNCH_TINYGEMM_KERNEL_NN(2, 32); break;
-        case 0x24: LAUNCH_TINYGEMM_KERNEL_NN(2, 64); break;
+        case 0x21:
+          LAUNCH_TINYGEMM_KERNEL_NN(2, 16);
+          break;
+        case 0x22:
+          LAUNCH_TINYGEMM_KERNEL_NN(2, 32);
+          break;
+        case 0x23:
+          LAUNCH_TINYGEMM_KERNEL_NN(2, 48);
+          break;
+        case 0x24:
+          LAUNCH_TINYGEMM_KERNEL_NN(2, 64);
+          break;
         // mb_size = 3
-        case 0x32: LAUNCH_TINYGEMM_KERNEL_NN(3, 32); break;
-        case 0x34: LAUNCH_TINYGEMM_KERNEL_NN(3, 64); break;
+        case 0x31:
+          LAUNCH_TINYGEMM_KERNEL_NN(3, 16);
+          break;
+        case 0x32:
+          LAUNCH_TINYGEMM_KERNEL_NN(3, 32);
+          break;
+        case 0x33:
+          LAUNCH_TINYGEMM_KERNEL_NN(3, 48);
+          break;
+        case 0x34:
+          LAUNCH_TINYGEMM_KERNEL_NN(3, 64);
+          break;
         // mb_size = 4
-        case 0x42: LAUNCH_TINYGEMM_KERNEL_NN(4, 32); break;
-        case 0x44: LAUNCH_TINYGEMM_KERNEL_NN(4, 64); break;
-        default: TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", nb_size);
+        case 0x41:
+          LAUNCH_TINYGEMM_KERNEL_NN(4, 16);
+          break;
+        case 0x42:
+          LAUNCH_TINYGEMM_KERNEL_NN(4, 32);
+          break;
+        case 0x43:
+          LAUNCH_TINYGEMM_KERNEL_NN(4, 48);
+          break;
+        case 0x44:
+          LAUNCH_TINYGEMM_KERNEL_NN(4, 64);
+          break;
+        default:
+          TORCH_CHECK(false, "Unexpected block size, ", mb_size, " x ", nb_size);
       }
     }
   }
 }
 
+template <typename scalar_t, bool has_bias>
+void tinygemm_kernel(
+    const float* __restrict__ A,
+    const float* __restrict__ B,
+    scalar_t* __restrict__ C,
+    float* __restrict__ Ctmp,
+    const float* __restrict__ bias,
+    int64_t M,
+    int64_t N,
+    int64_t K,
+    int64_t lda,
+    int64_t ldb,
+    int64_t ldc,
+    bool brg) {
+  TORCH_CHECK(brg, "Expected to use fp32 brgemm for small N GEMM");
+  if (brg) {
+    brgemm<scalar_t, has_bias>::apply(A, B, C, Ctmp, bias, M, N, K, lda, ldb, ldc);
+    return;
+  }
+  // TODO : add intrinsic path
+}
+
 template <typename scalar_t>
 void weight_packed_linear_kernel_impl(
     scalar_t* __restrict__ out,
@@ -282,29 +470,20 @@ void weight_packed_linear_kernel_impl(
     int64_t K,
     int64_t mat1_strideM,
     int64_t out_strideM) {
-
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
   const int64_t MB = div_up(M, BLOCK_M);
   const int64_t NB = div_up(N, BLOCK_N);
 
-  // use avx512-bf16 when a) M is small; b) dtype is bfloat16, otherwise use amx
-  const bool use_brgemm = (M > 4) || (!std::is_same_v<scalar_t, at::BFloat16>);
-
-  // l2 cache block for n
-  int64_t cache_blocks_nb = get_cache_blocks<scalar_t>(BLOCK_N, K);
+  const bool use_brgemm = can_use_brgemm<scalar_t>(M);
 
   // parallel on [MB, NB]
   AT_DISPATCH_BOOL(bias != nullptr, has_bias, [&] {
-    parallel_2d(MB, NB, [&](int64_t begin_mb, int64_t end_mb, int64_t begin_nb, int64_t end_nb) {
-
+    parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
       // for brgemm, use float32 for accumulate
       alignas(64) float Ctmp[BLOCK_M * BLOCK_N];
 
-      for (int64_t nbb = begin_nb; nbb < end_nb; nbb += cache_blocks_nb) {
-      for (int64_t mb = begin_mb; mb < end_mb; ++mb) {
-      for (int64_t nb = nbb; nb < std::min(nbb + cache_blocks_nb, end_nb); ++nb) {
-
+      loop_2d<scalar_t>(mb0, mb1, nb0, nb1, BLOCK_N * K, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
         int64_t mb_start = mb * BLOCK_M;
         int64_t mb_size = std::min(M - mb_start, BLOCK_M);
         int64_t nb_start = nb * BLOCK_N;
@@ -323,7 +502,82 @@ void weight_packed_linear_kernel_impl(
             /* ldb */ nb_size,
             /* ldc */ out_strideM,
             /* brg */ use_brgemm);
-      }}}
+      });
+
+      if (use_brgemm) {
+        at::native::cpublas::brgemm_release();
+      }
+    });
+  });
+}
+
+template <typename scalar_t>
+void weight_packed_linear_kernel_impl(
+    scalar_t* __restrict__ out,
+    const scalar_t* __restrict__ mat1,
+    const float* __restrict__ mat2,
+    const float* __restrict__ bias,
+    const scalar_t* __restrict__ post_mul_mat,
+    int64_t M,
+    int64_t N,
+    int64_t K,
+    int64_t mat1_strideM,
+    int64_t out_strideM) {
+  constexpr int64_t BLOCK_M = block_size_m();
+  constexpr int64_t BLOCK_N = block_size_n();
+  const int64_t MB = div_up(M, BLOCK_M);
+  const int64_t NB = div_up(N, BLOCK_N);
+
+  const bool use_brgemm = true;  // TODO: add intrinsic path
+  // parallel on [MB, NB]
+  AT_DISPATCH_BOOL(bias != nullptr, has_bias, [&] {
+    parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
+      // for brgemm, use float32 for accumulate
+      alignas(64) float Atmp[BLOCK_M * K];
+      alignas(64) float Ctmp[BLOCK_M * BLOCK_N];
+
+      loop_2d<float>(mb0, mb1, nb0, nb1, BLOCK_N * K, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+        int64_t mb_start = mb * BLOCK_M;
+        int64_t mb_size = std::min(M - mb_start, BLOCK_M);
+        int64_t nb_start = nb * BLOCK_N;
+        int64_t nb_size = std::min(N - nb_start, BLOCK_N);
+        for (int64_t m = 0; m < mb_size; ++m) {
+          copy_stub<scalar_t>(Atmp + m * K, mat1 + mb_start * mat1_strideM + m * K, K);
+        }
+        tinygemm_kernel<scalar_t, has_bias>(
+            /*   A */ Atmp,
+            /*   B */ mat2 + nb_start * K /* nb * BLOCK_N * K */,
+            /*   C */ out + mb_start * out_strideM + nb_start,
+            /* Ctmp*/ Ctmp,
+            /* bias*/ bias + nb_start,
+            /*   M */ mb_size,
+            /*   N */ nb_size,
+            /*   K */ K,
+            /* lda */ mat1_strideM,
+            /* ldb */ nb_size,
+            /* ldc */ out_strideM,
+            /* brg */ use_brgemm);
+
+        if (post_mul_mat != nullptr) {
+          for (int64_t m = 0; m < mb_size; ++m) {
+            scalar_sigmoid_and_mul<scalar_t, has_bias>(
+                out + mb_start * out_strideM + nb_start + m * out_strideM,
+                Ctmp + m * BLOCK_N,
+                bias + nb_start,
+                post_mul_mat + mb_start * out_strideM + m * out_strideM,
+                out_strideM);
+          }
+        } else {
+          for (int64_t m = 0; m < mb_size; ++m) {
+            if constexpr (has_bias) {
+              copy_add_stub(
+                  out + mb_start * out_strideM + nb_start + m * out_strideM, Ctmp + m * BLOCK_N, bias + nb_start, N);
+            } else {
+              copy_stub(out + mb_start * out_strideM + nb_start + m * out_strideM, Ctmp + m * BLOCK_N, N);
+            }
+          }
+        }
+      });
 
       if (use_brgemm) {
         at::native::cpublas::brgemm_release();
@@ -332,20 +586,38 @@ void weight_packed_linear_kernel_impl(
   });
 }
 
-} // anonymous namespace
+}  // anonymous namespace
 
 // tinygemm interface
 template <typename scalar_t>
-void tinygemm_kernel(const scalar_t* __restrict__ A, const scalar_t* __restrict__ B, scalar_t* __restrict__ C,
-    float* __restrict__ Ctmp, int64_t M, int64_t N, int64_t K, int64_t lda, int64_t ldb, int64_t ldc, bool brg) {
+void tinygemm_kernel(
+    const scalar_t* __restrict__ A,
+    const scalar_t* __restrict__ B,
+    scalar_t* __restrict__ C,
+    float* __restrict__ Ctmp,
+    int64_t M,
+    int64_t N,
+    int64_t K,
+    int64_t lda,
+    int64_t ldb,
+    int64_t ldc,
+    bool brg) {
   tinygemm_kernel<scalar_t, false>(A, B, C, Ctmp, nullptr, M, N, K, lda, ldb, ldc, brg);
 }
 
-#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE)                                             \
-    template void tinygemm_kernel<TYPE>(                                                \
-        const TYPE* __restrict__ A, const TYPE* __restrict__ B, TYPE* __restrict__ C,   \
-        float* __restrict__ Ctmp, int64_t M, int64_t N, int64_t K, int64_t lda,         \
-        int64_t ldb, int64_t ldc, bool brg)
+#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE) \
+  template void tinygemm_kernel<TYPE>(      \
+      const TYPE* __restrict__ A,           \
+      const TYPE* __restrict__ B,           \
+      TYPE* __restrict__ C,                 \
+      float* __restrict__ Ctmp,             \
+      int64_t M,                            \
+      int64_t N,                            \
+      int64_t K,                            \
+      int64_t lda,                          \
+      int64_t ldb,                          \
+      int64_t ldc,                          \
+      bool brg)
 
 INSTANTIATE_TINYGEMM_TEMPLATE(at::BFloat16);
 INSTANTIATE_TINYGEMM_TEMPLATE(at::Half);
@@ -359,14 +631,23 @@ at::Tensor convert_weight_packed(at::Tensor& weight) {
 
   const int64_t ndim = weight.ndimension();
   TORCH_CHECK(ndim == 2 || ndim == 3, "expect weight to be 2d or 3d, got ", ndim, "d tensor.");
+
+  if (ndim == 2 && weight.size(0) < TILE_N) {
+    // for 2D weight and small OC shape, we use fma linear path, which needs transpose not pack
+    return weight.to(at::kFloat).t().contiguous();
+  }
+
   const auto st = weight.scalar_type();
   const int64_t E = ndim == 3 ? weight.size(0) : 1;
   const int64_t OC = ndim == 3 ? weight.size(1) : weight.size(0);
   const int64_t IC = ndim == 3 ? weight.size(2) : weight.size(1);
 
+  // mxfp4 or int4 are packed with uint8
+  const int64_t actual_IC = st == at::kByte ? IC * 2 : IC;
+
   // we handle 2 TILE_N at a time.
   TORCH_CHECK(OC % TILE_N == 0, "invalid weight out features ", OC);
-  TORCH_CHECK(IC % TILE_K == 0, "invalid weight input features ", IC);
+  TORCH_CHECK(actual_IC % TILE_K == 0, "invalid weight input features ", actual_IC);
 
   constexpr int64_t BLOCK_N = block_size_n();
   const int64_t NB = div_up(OC, BLOCK_N);
@@ -375,12 +656,14 @@ at::Tensor convert_weight_packed(at::Tensor& weight) {
   auto packed_weight = at::empty({}, weight.options());
   const int64_t stride = OC * IC;
 
-  TORCH_CHECK(st == at::kBFloat16 || st == at::kHalf || st == at::kChar || st == at::kFloat8_e4m3fn,
-      "expect weight to be bfloat16, float16, int8 or fp8_e4m3.");
+  // Note: for `kByte` (uint8), it represents either `mxfp4` or `int4`.
+  TORCH_CHECK(
+      st == at::kBFloat16 || st == at::kHalf || st == at::kChar || st == at::kFloat8_e4m3fn || st == at::kByte,
+      "expect weight to be bfloat16, float16, int8, fp8_e4m3 or uint8(mxfp4 or int4).");
 
   CPU_DISPATCH_PACKED_TYPES(st, [&] {
     // adjust most inner dimension size
-    const int packed_row_size = get_row_size<packed_t>(IC);
+    const int packed_row_size = get_row_size<packed_t>(actual_IC);
     auto sizes = weight.sizes().vec();
     sizes[ndim - 1] = packed_row_size;
     packed_weight.resize_(sizes);
@@ -399,10 +682,7 @@ at::Tensor convert_weight_packed(at::Tensor& weight) {
         int64_t n = nb * BLOCK_N;
         int64_t n_size = std::min(BLOCK_N, OC - n);
         pack_vnni<packed_t>(
-            packed_data + e * OC * packed_row_size + n * packed_row_size,
-            w_data + e * stride + n * IC,
-            n_size,
-            IC);
+            packed_data + e * OC * packed_row_size + n * packed_row_size, w_data + e * stride + n * IC, n_size, IC);
 
         // move to the next index
         data_index_step(e, E, nb, NB);
@@ -412,33 +692,141 @@ at::Tensor convert_weight_packed(at::Tensor& weight) {
   return packed_weight;
 }
 
+at::Tensor convert_scale_packed(at::Tensor& scale) {
+  CHECK_INPUT(scale);
+
+  const int64_t ndim = scale.ndimension();
+  TORCH_CHECK(ndim == 2 || ndim == 3, "expect scale to be 2d or 3d, got ", ndim, "d tensor.");
+  const auto st = scale.scalar_type();
+  const int64_t E = ndim == 3 ? scale.size(0) : 1;
+  const int64_t N = ndim == 3 ? scale.size(1) : scale.size(0);
+  // number of groups, e.g. K/32
+  const int64_t G = ndim == 3 ? scale.size(2) : scale.size(1);
+
+  constexpr int64_t BLOCK_N = block_size_n();
+  TORCH_CHECK(N % BLOCK_N == 0, "invalid weight out features ", N);
+  const int64_t NB = N / BLOCK_N;
+
+  auto packed_scale = at::empty_like(scale);
+  TORCH_CHECK(st == at::kByte, "expect scale to be uint8.");
+
+  const uint8_t* s_data = scale.data_ptr<uint8_t>();
+  uint8_t* packed_data = packed_scale.data_ptr<uint8_t>();
+
+  // parallel on src {E, NB, BLOCK_N, G}, dst {E, NB, G, BLOCK_N}
+  at::parallel_for(0, E * NB * BLOCK_N * G, 0, [&](int64_t begin, int64_t end) {
+    int64_t e{0}, nb{0}, n{0}, g{0};
+    data_index_init(begin, e, E, nb, NB, n, BLOCK_N, g, G);
+
+    for (int64_t i = begin; i < end; ++i) {
+      packed_data[e * N * G + nb * G * BLOCK_N + g * BLOCK_N + n] = s_data[i];
+      // move to the next index
+      data_index_step(e, E, nb, NB, n, BLOCK_N, g, G);
+    }
+  });
+  return packed_scale;
+}
+
 // mat1 : [M, K]
-// mat2 : [N, K]
+// mat2 : [N, K] ([K, N] if use_fma_gemm)
 // bias : [N]
 // out  : [M, N]
 //
-at::Tensor weight_packed_linear(at::Tensor& mat1, at::Tensor& mat2,
-    const std::optional<at::Tensor>& bias, bool is_vnni) {
-  RECORD_FUNCTION(
-    "sgl-kernel::weight_packed_linear", std::vector<c10::IValue>({mat1, mat2, bias}));
-
+at::Tensor
+weight_packed_linear(at::Tensor& mat1, at::Tensor& mat2, const std::optional<at::Tensor>& bias, bool is_vnni) {
   auto packed_w = is_vnni ? mat2 : convert_weight_packed(mat2);
+  bool use_fma_gemm = false;
+  if (packed_w.scalar_type() == at::kFloat) {
+    use_fma_gemm = true;
+  }
+
+  int64_t M = mat1.size(0);
+  int64_t K = mat1.size(1);
+  int64_t N = use_fma_gemm ? mat2.size(1) : mat2.size(0);
 
   CHECK_LAST_DIM_CONTIGUOUS_INPUT(mat1);
   CHECK_INPUT(mat2);
-
-  int64_t M = mat1.size(0);
-  int64_t N = mat2.size(0);
-  int64_t K = mat2.size(1);
-  CHECK_EQ(mat1.size(1), K);
   CHECK_DIM(2, mat1);
   CHECK_DIM(2, mat2);
+  if (!use_fma_gemm) {
+    CHECK_EQ(mat1.size(1), K);
+  }
 
+  auto dispatch_type = mat1.scalar_type();
   auto out = at::empty({M, N}, mat1.options());
-
   // strides
-  int64_t mat1_strideM = mat1.stride(0);
   int64_t out_strideM = out.stride(0);
+  int64_t mat1_strideM = mat1.stride(0);
+
+  const bool has_bias = bias.has_value();
+  const float* bias_data = nullptr;
+  if (has_bias) {
+    CHECK_EQ(bias.value().size(0), N);
+    bias_data = bias.value().data_ptr<float>();
+  }
+
+  AT_DISPATCH_REDUCED_FLOATING_TYPES(dispatch_type, "weight_packed_linear_kernel_impl", [&] {
+    if (use_fma_gemm) {
+      weight_packed_linear_kernel_impl<scalar_t>(
+          out.data_ptr<scalar_t>(),
+          mat1.data_ptr<scalar_t>(),
+          packed_w.data_ptr<float>(),
+          bias_data,
+          nullptr,
+          M,
+          N,
+          K,
+          mat1_strideM,
+          out_strideM);
+    } else {
+      weight_packed_linear_kernel_impl<scalar_t>(
+          out.data_ptr<scalar_t>(),
+          mat1.data_ptr<scalar_t>(),
+          packed_w.data_ptr<scalar_t>(),
+          bias_data,
+          M,
+          N,
+          K,
+          mat1_strideM,
+          out_strideM);
+    }
+  });
+
+  return out;
+}
+
+// mat1         : [M, K]
+// mat2         : [K, 1]
+// post_mul_mat : [M, K]
+// bias         : [N]
+// out          : [M, N]
+//
+at::Tensor fused_linear_sigmoid_mul(
+    at::Tensor& mat1,
+    at::Tensor& mat2,
+    const std::optional<at::Tensor>& bias,
+    bool is_vnni,
+    const at::Tensor& post_mul_mat) {
+  auto packed_w = is_vnni ? mat2 : convert_weight_packed(mat2);
+  TORCH_CHECK(packed_w.scalar_type() == at::kFloat, "fused_linear_sigmoid_mul requires packed float weight")
+
+  int64_t M = mat1.size(0);
+  int64_t K = mat1.size(1);
+  int64_t N = mat2.size(1);
+
+  CHECK_LAST_DIM_CONTIGUOUS_INPUT(mat1);
+  CHECK_INPUT(mat2);
+  CHECK_DIM(2, mat1);
+  CHECK_DIM(2, mat2);
+
+  int64_t out_strideM = post_mul_mat.size(1);
+  int64_t mat1_strideM = mat1.stride(0);
+  auto dispatch_type = mat1.scalar_type();
+  auto out = at::empty({M, out_strideM}, mat1.options());
+
+  TORCH_CHECK(
+      N == 1 && out_strideM % 32 == 0,
+      "post_mul_mat tensor size(1) should be 32 dividable, and the mat2 OC=1 (Mx1 as linear output shape)")
 
   const bool has_bias = bias.has_value();
   const float* bias_data = nullptr;
@@ -447,12 +835,13 @@ at::Tensor weight_packed_linear(at::Tensor& mat1, at::Tensor& mat2,
     bias_data = bias.value().data_ptr<float>();
   }
 
-  AT_DISPATCH_REDUCED_FLOATING_TYPES(mat1.scalar_type(), "weight_packed_linear_kernel_impl", [&] {
+  AT_DISPATCH_REDUCED_FLOATING_TYPES(dispatch_type, "fused_linear_sigmoid_mul", [&] {
     weight_packed_linear_kernel_impl<scalar_t>(
         out.data_ptr<scalar_t>(),
         mat1.data_ptr<scalar_t>(),
-        packed_w.data_ptr<scalar_t>(),
+        packed_w.data_ptr<float>(),
         bias_data,
+        post_mul_mat.data_ptr<scalar_t>(),
         M,
         N,
         K,
diff --git a/csrc/cpu/sgl-kernels/gemm.h b/csrc/cpu/sgl-kernels/gemm.h
index aa78c8807fb8..b43be94ffdbb 100644
--- a/csrc/cpu/sgl-kernels/gemm.h
+++ b/csrc/cpu/sgl-kernels/gemm.h
@@ -1,8 +1,12 @@
-#pragma once
+// Adapted from
+// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
+
+// clang-format off
 
+#pragma once
 #include <ATen/native/CPUBlas.h>
 
-// clang-format off
+#include "common.h"
 
 // amx-bf16
 #define TILE_M 16
@@ -10,20 +14,42 @@
 #define TILE_K 32
 
 // block size for AMX gemm
-constexpr int block_size_m() { return 2 * TILE_M; }
-constexpr int block_size_n() { return 2 * TILE_N; }
+constexpr int block_size_m() {
+  return 2 * TILE_M;
+}
+constexpr int block_size_n() {
+  return 2 * TILE_N;
+}
 
 // define threshold using brgemm (intel AMX)
-template <typename T> inline bool can_use_brgemm(int M);
-template <> inline bool can_use_brgemm<at::BFloat16>(int M) { return M > 4; }
-template <> inline bool can_use_brgemm<at::Half>(int M) { return true; }
-template <> inline bool can_use_brgemm<int8_t>(int M) { return M > 4; }
-template <> inline bool can_use_brgemm<uint8_t>(int M) { return M > 4; }
-template <> inline bool can_use_brgemm<at::Float8_e4m3fn>(int M) { return M > 4; }
-template <> inline bool can_use_brgemm<at::quint4x2>(int M) { return M > 4; }
+template <typename T>
+inline bool can_use_brgemm(int M);
+template <>
+inline bool can_use_brgemm<at::BFloat16>(int M) {
+  return M > 4;
+}
+template <>
+inline bool can_use_brgemm<at::Half>(int M) {
+  return true;
+}
+// this requires PyTorch 2.7 or above
+template <>
+inline bool can_use_brgemm<int8_t>(int M) {
+  return M > 4;
+}
+
+template <>
+inline bool can_use_brgemm<uint8_t>(int M) {
+  return M > 4;
+}
+
+template <>
+inline bool can_use_brgemm<at::Float8_e4m3fn>(int M) {
+  return M > 4;
+}
 
 // work around compiler internal error
-#define BLOCK_K 128 // 4 * TILE_K
+#define BLOCK_K 128  // 4 * TILE_K
 
 // adjust leading dimension size for K
 template <typename T>
@@ -36,18 +62,44 @@ inline int64_t get_row_size<int8_t>(int64_t K) {
   return K + sizeof(int32_t);
 }
 
+// uint8: mxfp4 or int4
+template <>
+inline int64_t get_row_size<uint8_t>(int64_t K) {
+  return K >> 1;
+}
+
 inline int64_t get_row_size(int64_t K, bool use_int8_w8a8) {
   return use_int8_w8a8 ? K + sizeof(int32_t) : K;
 }
 
+enum class CPUQuantMethod : int64_t { BF16 = 0, INT8_W8A8 = 1, FP8_W8A16 = 2, INT4_W4A8 = 3 };
+
+constexpr bool operator==(CPUQuantMethod a, int64_t b) {
+  return static_cast<int64_t>(a) == b;
+}
+
+constexpr bool operator==(int64_t a, CPUQuantMethod b) {
+  return a == static_cast<int64_t>(b);
+}
+
+enum class CPUQuantAlgo : int64_t { AWQ = 0, GPTQ = 1 };
+
+constexpr bool operator==(CPUQuantAlgo a, int64_t b) {
+  return static_cast<int64_t>(a) == b;
+}
+
+constexpr bool operator==(int64_t a, CPUQuantAlgo b) {
+  return a == static_cast<int64_t>(b);
+}
+
 inline int64_t get_4bit_block_k_size(int64_t group_size) {
   return group_size > 128 ? 128 : group_size;
 }
 
-// pack weight into vnni format
+// pack weight to vnni format
 at::Tensor convert_weight_packed(at::Tensor& weight);
 
-// pack weight to vnni format for int4 (adapted from sglang)
+// pack weight to vnni format for int4
 std::tuple<at::Tensor, at::Tensor, at::Tensor>
 convert_weight_packed_scale_zp(at::Tensor qweight, at::Tensor qzeros, at::Tensor scales);
 
@@ -105,35 +157,6 @@ void fused_experts_fp8_kernel_impl(
     int64_t topk,
     int64_t num_tokens_post_pad);
 
-// moe implementations for int4 w4a16
-template <typename scalar_t>
-void fused_experts_int4_w4a16_kernel_impl(
-    scalar_t* __restrict__ output,
-    scalar_t* __restrict__ ic0,
-    scalar_t* __restrict__ ic1,
-    scalar_t* __restrict__ ic2,
-    scalar_t* __restrict__ A_tmp,
-    scalar_t* __restrict__ B_tmp,
-    float* __restrict__ C_tmp,
-    const scalar_t* __restrict__ input,
-    const at::quint4x2* __restrict__ packed_w1,
-    const at::quint4x2* __restrict__ packed_w2,
-    const uint8_t* __restrict__ w1z,
-    const uint8_t* __restrict__ w2z,
-    const scalar_t* __restrict__ w1s,
-    const scalar_t* __restrict__ w2s,
-    int group_size,
-    const float* __restrict__ topk_weights,
-    const int32_t* __restrict__ sorted_ids,
-    const int32_t* __restrict__ expert_ids,
-    const int32_t* __restrict__ offsets,
-    int64_t M,
-    int64_t N,
-    int64_t K,
-    int64_t E,
-    int64_t topk,
-    int64_t num_tokens_post_pad);
-
 // shared expert implementation for int8 w8a8
 template <typename scalar_t>
 void shared_expert_int8_kernel_impl(
@@ -153,6 +176,37 @@ void shared_expert_int8_kernel_impl(
     int64_t N,
     int64_t K);
 
+template <typename scalar_t>
+void fused_experts_int4_w4a8_kernel_impl(
+    scalar_t* __restrict__ output,
+    scalar_t* __restrict__ ic0,
+    scalar_t* __restrict__ ic1,
+    scalar_t* __restrict__ ic2,
+    uint8_t* __restrict__ A_tmp,
+    uint8_t* __restrict__ Aq_tmp,
+    float* __restrict__ As_tmp,
+    int32_t* __restrict__ Azp_tmp,
+    float* __restrict__ C_tmp,
+    int8_t* __restrict__ dqB_tmp,
+    const scalar_t* __restrict__ input,
+    const uint8_t* __restrict__ packed_w1,
+    const uint8_t* __restrict__ packed_w2,
+    const int8_t* __restrict__ w1z,
+    const int8_t* __restrict__ w2z,
+    const float* __restrict__ w1s,
+    const float* __restrict__ w2s,
+    int group_size,
+    const float* __restrict__ topk_weights,
+    const int32_t* __restrict__ sorted_ids,
+    const int32_t* __restrict__ expert_ids,
+    const int32_t* __restrict__ offsets,
+    int64_t M,
+    int64_t N,
+    int64_t K,
+    int64_t E,
+    int64_t topk,
+    int64_t num_tokens_post_pad);
+
 template <typename scalar_t>
 void shared_expert_fp8_kernel_impl(
     scalar_t* __restrict__ output,
@@ -204,6 +258,7 @@ void tinygemm_kernel(
     int64_t ldc,
     bool brg);
 
+// block quantization
 template <typename scalar_t>
 void tinygemm_kernel(
     const scalar_t* __restrict__ A,
@@ -219,33 +274,26 @@ void tinygemm_kernel(
     int64_t ldb,
     int64_t ldc,
     bool brg,
-    int64_t block_size_K);
+    int64_t block_size_K,
+    bool do_unpack = true);
 
+// per tensor quantization
 template <typename scalar_t>
 void tinygemm_kernel(
     const scalar_t* __restrict__ A,
-    const at::quint4x2* __restrict__ B,
+    const at::Float8_e4m3fn* __restrict__ B,
     scalar_t* __restrict__ C,
-    const uint8_t* __restrict__ Bz,
-    const scalar_t* __restrict__ Bs,
     scalar_t* __restrict__ Btmp,
     float* __restrict__ Ctmp,
+    float scale,
     int64_t M,
     int64_t N,
     int64_t K,
-    int group_size,
     int64_t lda,
     int64_t ldb,
     int64_t ldc,
-    int64_t strideBz,
-    int64_t strideBs,
     bool brg);
 
-// int4 scaled GEMM (adapted from sglang)
-at::Tensor int4_scaled_mm_cpu(
-    at::Tensor& x, at::Tensor& w, at::Tensor& w_zeros, at::Tensor& w_scales, std::optional<at::Tensor> bias);
-
-// int4 tinygemm kernel interface(adapted from sglang)
 template <typename scalar_t>
 void tinygemm_kernel(
     scalar_t* C,
@@ -266,34 +314,21 @@ void tinygemm_kernel(
     bool store_out,
     bool use_brgemm);
 
-// TODO: debug print, remove me later
-inline void print_16x32i(const __m512i x) {
-  int32_t a[16];
-  _mm512_storeu_si512((__m512i *)a, x);
-
-  for (int i = 0; i < 16; i++){
-    std::cout << a[i] << " ";
-  }
-  std::cout << std::endl;
-}
-
-inline void print_16x32(const __m512 x) {
-  float a[16];
-  _mm512_storeu_ps((__m512 *)a, x);
-
-  for (int i = 0; i < 16; i++){
-    std::cout << a[i] << " ";
-  }
-  std::cout << std::endl;
-}
-
-
-inline void print_32x8u(const __m256i x) {
-  uint8_t a[32];
-  _mm256_storeu_si256((__m256i *)a, x);
-
-  for (int i = 0; i < 32; ++i) {
-    std::cout << int32_t(a[i]) << " ";
-  }
-  std::cout << std::endl;
-}
+// mxfp4
+template <typename scalar_t>
+void tinygemm_kernel(
+    const scalar_t* __restrict__ A,
+    const uint8_t* __restrict__ B,
+    scalar_t* __restrict__ C,
+    scalar_t* __restrict__ Btmp,
+    float* __restrict__ Ctmp,
+    const uint8_t* __restrict__ scale,
+    int64_t M,
+    int64_t N,
+    int64_t K,
+    int64_t lda,
+    int64_t ldb,
+    int64_t ldc,
+    bool brg,
+    int64_t block_size_K,
+    bool do_unpack = true);
diff --git a/csrc/cpu/sgl-kernels/gemm_fp8.cpp b/csrc/cpu/sgl-kernels/gemm_fp8.cpp
index ef29181cee56..dfc304468571 100644
--- a/csrc/cpu/sgl-kernels/gemm_fp8.cpp
+++ b/csrc/cpu/sgl-kernels/gemm_fp8.cpp
@@ -1,14 +1,11 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
-#include "common.h"
-#include "vec.h"
-#include "gemm.h"
-
 // clang-format off
 
-// we use 4x32 for BLOCK_M
-#define BLOCK_SIZE_M_SCALE 4
+#include "common.h"
+#include "gemm.h"
+#include "vec.h"
 
 namespace {
 
@@ -19,7 +16,7 @@ inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ inpu
   constexpr int kVecSize = bVec::size();
 
   int64_t d;
-  #pragma GCC unroll 4
+#pragma GCC unroll 4
   for (d = 0; d <= size - kVecSize; d += kVecSize) {
     fVec data0 = fVec::loadu(input + d);
     fVec data1 = fVec::loadu(input + d + fVec::size());
@@ -32,13 +29,14 @@ inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ inpu
 }
 
 template <typename scalar_t>
-inline void copy_add_stub(scalar_t* __restrict__ out, const float* __restrict__ input, const float* __restrict__ bias, int64_t size) {
+inline void copy_add_stub(
+    scalar_t* __restrict__ out, const float* __restrict__ input, const float* __restrict__ bias, int64_t size) {
   using bVec = at::vec::Vectorized<scalar_t>;
   using fVec = at::vec::Vectorized<float>;
   constexpr int kVecSize = bVec::size();
 
   int64_t d;
-  #pragma GCC unroll 4
+#pragma GCC unroll 4
   for (d = 0; d <= size - kVecSize; d += kVecSize) {
     fVec data0 = fVec::loadu(input + d) + fVec::loadu(bias + d);
     fVec data1 = fVec::loadu(input + d + fVec::size()) + fVec::loadu(bias + d + fVec::size());
@@ -49,21 +47,41 @@ inline void copy_add_stub(scalar_t* __restrict__ out, const float* __restrict__
     out[d] = static_cast<scalar_t>(input[d] + bias[d]);
   }
 }
+template <typename scalar_t>
+inline void copy_mul_stub(scalar_t* __restrict__ out, const float* __restrict__ input, int size, float scale) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int kVecSize = bVec::size();
+  const fVec vscale = fVec(scale);
+
+  int d;
+#pragma GCC unroll 4
+  for (d = 0; d <= size - kVecSize; d += kVecSize) {
+    fVec data0 = fVec::loadu(input + d) * vscale;
+    fVec data1 = fVec::loadu(input + d + fVec::size()) * vscale;
+    bVec out_vec = convert_from_float_ext<scalar_t>(data0, data1);
+    out_vec.store(out + d);
+  }
+  for (; d < size; ++d) {
+    out[d] = static_cast<scalar_t>(input[d] * scale);
+  }
+}
 
 inline void unpack_B(
     at::BFloat16* __restrict__ Btmp,
     const at::Float8_e4m3fn* __restrict__ packed_B,
-    int N,
-    int K,
-    int ldb,
-    int ldb_tmp,
+    int64_t N,
+    int64_t K,
+    int64_t ldb,
+    int64_t ldb_tmp,
     float scale) {
 #if defined(CPU_CAPABILITY_AVX512)
   // [K/2, N, 2]
-  const int K2 = K >> 1;
-  const int ldb2 = ldb; // ldb * 2 >> 1;
+  const int64_t K2 = K >> 1;
+  const int64_t ldb2 = ldb;  // ldb * 2 >> 1;
   const uint16_t* b_ptr = reinterpret_cast<const uint16_t*>(packed_B);
-  const __m512 vd = _mm512_set1_ps(scale);
+  const __m512 vexp = _mm512_castsi512_ps(_mm512_set1_epi32(kFP8_BIAS));
+  const __m512 vd = _mm512_mul_ps(_mm512_set1_ps(scale), vexp);
 
   constexpr int BLOCK_N = block_size_n();
   static_assert(BLOCK_N == 32);
@@ -72,7 +90,7 @@ inline void unpack_B(
   constexpr int PREFETCH_SIZE_K = 64;
 
 #pragma GCC unroll 4
-  for (int k = 0; k < K2; ++k) {
+  for (int64_t k = 0; k < K2; ++k) {
     __m512i b8 = _mm512_loadu_si512(b_ptr + k * ldb2);
     if constexpr (PREFETCH_SIZE_K > 0) {
       _mm_prefetch(b_ptr + (k + PREFETCH_SIZE_K) * ldb2, _MM_HINT_T0);
@@ -81,8 +99,8 @@ inline void unpack_B(
     __m256i b8_0 = _mm512_extracti32x8_epi32(b8, 0);
     __m256i b8_1 = _mm512_extracti32x8_epi32(b8, 1);
 
-    __m512bh bf16_0 = CVT_FP8_TO_BF16(b8_0);
-    __m512bh bf16_1 = CVT_FP8_TO_BF16(b8_1);
+    __m512bh bf16_0 = CVT_FP8_TO_BF16_EXT(b8_0);
+    __m512bh bf16_1 = CVT_FP8_TO_BF16_EXT(b8_1);
 
     // Apply scale
     __m512 f0_lo = CVT_BF16_TO_FP32(_mm512_extracti32x8_epi32((__m512i)bf16_0, 0));
@@ -106,26 +124,139 @@ inline void unpack_B(
 #endif
 }
 
-template <typename scalar_t, typename packed_t, bool has_bias, int BLOCK_M, int BLOCK_N>
+inline void unpack_B(
+    at::BFloat16* __restrict__ Btmp,
+    const at::Float8_e4m3fn* __restrict__ packed_B,
+    int N,
+    int K,
+    int ldb,
+    int ldb_tmp) {
+#if defined(CPU_CAPABILITY_AVX512)
+  // [K/2, N, 2]
+  const int K2 = K >> 1;
+  const int ldb2 = ldb;  // ldb * 2 >> 1;
+  const uint16_t* b_ptr = reinterpret_cast<const uint16_t*>(packed_B);
+
+  // prefetch distance
+  constexpr int PREFETCH_SIZE_K = 64;
+#pragma GCC unroll 4
+  for (int k = 0; k < K2; ++k) {
+    __m512i b8 = _mm512_loadu_si512(b_ptr + k * ldb2);
+    if constexpr (PREFETCH_SIZE_K > 0) {
+      _mm_prefetch(b_ptr + (k + PREFETCH_SIZE_K) * ldb2, _MM_HINT_T0);
+    }
+
+    __m256i b8_0 = _mm512_extracti32x8_epi32(b8, 0);
+    __m256i b8_1 = _mm512_extracti32x8_epi32(b8, 1);
+
+    __m512bh bf16_0 = CVT_FP8_TO_BF16(b8_0);
+    __m512bh bf16_1 = CVT_FP8_TO_BF16(b8_1);
+    _mm512_storeu_si512(Btmp + k * ldb_tmp * 2 + 0, (__m512i)bf16_0);
+    _mm512_storeu_si512(Btmp + k * ldb_tmp * 2 + 32, (__m512i)bf16_1);
+  }
+#else
+  TORCH_CHECK(false, "unpack_B: scalar path not implemented!");
+#endif
+}
+
+// mxfp4
+inline void unpack_B(
+    at::BFloat16* __restrict__ Btmp,
+    const uint8_t* __restrict__ packed_B,
+    int64_t N,
+    int64_t K,
+    int64_t ldb,
+    int64_t ldb_tmp,
+    const uint8_t* __restrict__ scale) {
+#if defined(CPU_CAPABILITY_AVX512)
+  // [K/2, N, 2]
+  const int64_t K2 = K >> 1;
+  const int64_t ldb2 = ldb;                                           // ldb * 2 >> 1;
+  const uint8_t* b_ptr = reinterpret_cast<const uint8_t*>(packed_B);  // 2 * 4 bit = 8 bit
+
+  constexpr int BLOCK_N = block_size_n();
+  static_assert(BLOCK_N == 32);
+
+  // prefetch distance
+  constexpr int PREFETCH_SIZE_K = 64;
+
+  // exponent bias 127
+  const __m512i off = _mm512_set1_epi16(0x7F);
+
+  // load 32 bytes only once for each block
+  __m256i s8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(scale));
+  __m512i s16 = _mm512_slli_epi16(_mm512_sub_epi16(_mm512_cvtepu8_epi16(s8), off), 0x7);
+
+  // holds Nx2(64) scales, interleaved as 2 belongs to K dimension
+  // e.g. vs0: { s0,  s0,  s1,  s1, ..., s15, s15}
+  //      vs1: {s16, s16, s17, s17, ..., s31, s31}
+  auto [vscale0, vscale1] = transpose_2x32_16bit(s16, s16);
+
+#pragma GCC unroll 4
+  for (int64_t k = 0; k < K2; ++k) {
+    __m256i b4 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(b_ptr + k * ldb2));
+    if constexpr (PREFETCH_SIZE_K > 0) {
+      _mm_prefetch(b_ptr + (k + PREFETCH_SIZE_K) * ldb2, _MM_HINT_T0);
+    }
+    auto [vb0, vb1] = CVT_MXFP4_TO_BF16(b4, vscale0, vscale1);
+
+    _mm512_storeu_si512(Btmp + k * ldb_tmp * 2 + 0, (__m512i)vb0);
+    _mm512_storeu_si512(Btmp + k * ldb_tmp * 2 + 32, (__m512i)vb1);
+  }
+#else
+  TORCH_CHECK(false, "unpack_B: scalar path not implemented!");
+#endif
+}
+
+template <typename scalar_t, typename packed_t, typename param_t, bool has_bias, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn {
   static inline void apply(
-      const scalar_t* __restrict__ A, const packed_t* __restrict__ B, scalar_t* __restrict__ C,
-      const float* __restrict__ bias, const float* __restrict__ scale, int K, int lda, int ldb, int ldc, int64_t block_size_K) {
+      const scalar_t* __restrict__ A,
+      const packed_t* __restrict__ B,
+      scalar_t* __restrict__ C,
+      const float* __restrict__ bias,
+      const param_t* __restrict__ scale,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc,
+      int64_t block_size_K) {
     TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
   }
 };
 
+template <typename scalar_t, int BLOCK_M, int BLOCK_N>
+struct tinygemm_kernel_nn2 {
+  static inline void apply(
+      const scalar_t* __restrict__ A,
+      const at::Float8_e4m3fn* __restrict__ B,
+      scalar_t* __restrict__ C,
+      float scale,
+      int K,
+      int lda,
+      int ldb,
+      int ldc) {
+    TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
+  }
+};
 #if defined(CPU_CAPABILITY_AVX512)
 template <bool has_bias, int BLOCK_M, int BLOCK_N>
-struct tinygemm_kernel_nn<at::BFloat16, at::Float8_e4m3fn, has_bias, BLOCK_M, BLOCK_N> {
+struct tinygemm_kernel_nn<at::BFloat16, at::Float8_e4m3fn, float, has_bias, BLOCK_M, BLOCK_N> {
   static inline void apply(
-      const at::BFloat16* __restrict__ A, const at::Float8_e4m3fn* __restrict__ B, at::BFloat16* __restrict__ C,
-      const float* __restrict__ bias, const float* __restrict__ scale, int K, int lda, int ldb, int ldc, int64_t block_size_K) {
-
+      const at::BFloat16* __restrict__ A,
+      const at::Float8_e4m3fn* __restrict__ B,
+      at::BFloat16* __restrict__ C,
+      const float* __restrict__ bias,
+      const float* __restrict__ scale,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc,
+      int64_t block_size_K) {
     constexpr int ROWS = BLOCK_M;
     constexpr int COLS = BLOCK_N / 16;
 
-    const int KB = div_up(K, BLOCK_K);
+    const int64_t KB = div_up(K, (int64_t)BLOCK_K);
 
     // prefetch distance
     constexpr int PREFETCH_SIZE_K = 64;
@@ -139,6 +270,8 @@ struct tinygemm_kernel_nn<at::BFloat16, at::Float8_e4m3fn, has_bias, BLOCK_M, BL
     // block quant scale
     __m512 vscale;
 
+    const __m512 vexp = _mm512_castsi512_ps(_mm512_set1_epi32(kFP8_BIAS));
+
     auto loadc = [&](auto i) {
       constexpr int col = i % COLS;
       if constexpr (has_bias) {
@@ -149,8 +282,8 @@ struct tinygemm_kernel_nn<at::BFloat16, at::Float8_e4m3fn, has_bias, BLOCK_M, BL
     };
     Unroll<ROWS * COLS>{}(loadc);
 
-    const int lda2 = lda >> 1;
-    const int ldb2 = ldb; // ldb * 2 >> 1;
+    const int64_t lda2 = lda >> 1;
+    const int64_t ldb2 = ldb;  // ldb * 2 >> 1;
     const float* a_ptr = reinterpret_cast<const float*>(A);
     const uint16_t* b_ptr = reinterpret_cast<const uint16_t*>(B);
 
@@ -170,34 +303,31 @@ struct tinygemm_kernel_nn<at::BFloat16, at::Float8_e4m3fn, has_bias, BLOCK_M, BL
           if constexpr (PREFETCH_SIZE_K > 0) {
             _mm_prefetch(b_ptr + (k + PREFETCH_SIZE_K) * ldb2 + col * 16, _MM_HINT_T0);
           }
-          vb[col + 0] = CVT_FP8_TO_BF16(_mm512_extracti32x8_epi32(b8, 0));
-          vb[col + 1] = CVT_FP8_TO_BF16(_mm512_extracti32x8_epi32(b8, 1));
+          vb[col + 0] = CVT_FP8_TO_BF16_EXT(_mm512_extracti32x8_epi32(b8, 0));
+          vb[col + 1] = CVT_FP8_TO_BF16_EXT(_mm512_extracti32x8_epi32(b8, 1));
         }
       }
       vsum[i] = _mm512_dpbf16_ps(vsum[i], va, vb[col]);
     };
 
-    constexpr int BLOCK_K2 = BLOCK_K >> 1;
-    for (int kb = 0; kb < KB; ++kb) {
-      int kb_start = kb * BLOCK_K2;
-      int kb_end = std::min(K, kb_start + BLOCK_K2);
+    constexpr int64_t BLOCK_K2 = BLOCK_K >> 1;
+    for (int64_t kb = 0; kb < KB; ++kb) {
+      int64_t kb_start = kb * BLOCK_K2;
+      int64_t kb_end = std::min(K >> 1, kb_start + BLOCK_K2);
       // 1. load scale vector
       vscale = _mm512_set1_ps(scale[kb]);
+      vscale = _mm512_mul_ps(vscale, vexp);
       if constexpr (PREFETCH_SIZE_KB > 0) {
         _mm_prefetch(scale + kb + PREFETCH_SIZE_KB, _MM_HINT_T0);
       }
       // 2. zero vsum for each block
-      Unroll<ROWS * COLS>{}([&](auto i) {
-        vsum[i] = _mm512_setzero_ps();
-      });
+      Unroll<ROWS * COLS>{}([&](auto i) { vsum[i] = _mm512_setzero_ps(); });
       // 3. accumulate across each block
       for (int k = kb_start; k < kb_end; ++k) {
         Unroll<ROWS * COLS>{}(compute, k);
       }
       // 4. apply scale
-      Unroll<ROWS * COLS>{}([&](auto i) {
-        vc[i] = _mm512_fmadd_ps(vsum[i], vscale, vc[i]);
-      });
+      Unroll<ROWS * COLS>{}([&](auto i) { vc[i] = _mm512_fmadd_ps(vsum[i], vscale, vc[i]); });
     }
 
     auto storec = [&](auto i) {
@@ -213,14 +343,196 @@ struct tinygemm_kernel_nn<at::BFloat16, at::Float8_e4m3fn, has_bias, BLOCK_M, BL
     Unroll<ROWS * COLS>{}(storec);
   }
 };
-#endif
 
-#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)                          \
-    tinygemm_kernel_nn<scalar_t, at::Float8_e4m3fn, has_bias, MB_SIZE, NB_SIZE>::apply(         \
-        A + mb_start * lda, B + nb_start * 2, C + mb_start * ldc + nb_start, \
-        has_bias ? bias + nb_start : nullptr, scale, K, lda, ldb, ldc, block_size_K);
+template <int BLOCK_M, int BLOCK_N>
+struct tinygemm_kernel_nn2<at::BFloat16, BLOCK_M, BLOCK_N> {
+  static inline void apply(
+      const at::BFloat16* __restrict__ A,
+      const at::Float8_e4m3fn* __restrict__ B,
+      at::BFloat16* __restrict__ C,
+      float scale,
+      int K,
+      int lda,
+      int ldb,
+      int ldc) {
+    constexpr int ROWS = BLOCK_M;
+    constexpr int COLS = BLOCK_N / 16;
+
+    // prefetch distance
+    constexpr int PREFETCH_SIZE_K = 64;
+
+    __m512bh va;
+    __m512bh vb[COLS];
+    __m512 vc[ROWS * COLS];
+
+    const __m512 vscale = _mm512_set1_ps(scale);
+
+    auto loadc = [&](auto i) { vc[i] = _mm512_setzero_ps(); };
+    Unroll<ROWS * COLS>{}(loadc);
+
+    const int K2 = K >> 1;
+    const int lda2 = lda >> 1;
+    const int ldb2 = ldb;  // ldb * 2 >> 1;
+    const float* a_ptr = reinterpret_cast<const float*>(A);
+    const uint16_t* b_ptr = reinterpret_cast<const uint16_t*>(B);
+
+    auto compute = [&](auto i, int k) {
+      constexpr int row = i / COLS;
+      constexpr int col = i % COLS;
+
+      if constexpr (col == 0) {
+        va = (__m512bh)(_mm512_set1_ps(a_ptr[row * lda2 + k]));
+      }
+      if constexpr (row == 0) {
+        if constexpr (col % 2 == 0) {
+          __m512i b8 = _mm512_loadu_si512(b_ptr + k * ldb2 + col * 16);
+          if constexpr (PREFETCH_SIZE_K > 0) {
+            _mm_prefetch(b_ptr + (k + PREFETCH_SIZE_K) * ldb2 + col * 16, _MM_HINT_T0);
+          }
+          vb[col + 0] = CVT_FP8_TO_BF16(_mm512_extracti32x8_epi32(b8, 0));
+          vb[col + 1] = CVT_FP8_TO_BF16(_mm512_extracti32x8_epi32(b8, 1));
+        }
+      }
+      vc[i] = _mm512_dpbf16_ps(vc[i], va, vb[col]);
+    };
+    for (int k = 0; k < K2; ++k) {
+      Unroll<ROWS * COLS>{}(compute, k);
+    }
+
+    auto storec = [&](auto i) {
+      constexpr int row = i / COLS;
+      constexpr int col = i % COLS;
+      // for COLS = 2, 4 use 512bit store
+      if constexpr (col % 2 == 0) {
+        __m512 vc0 = _mm512_mul_ps(vc[row * COLS + col + 0], vscale);
+        __m512 vc1 = _mm512_mul_ps(vc[row * COLS + col + 1], vscale);
+        _mm512_storeu_si512(
+            reinterpret_cast<__m512i*>((C + row * ldc + col * 16)), (__m512i)(_mm512_cvtne2ps_pbh(vc1, vc0)));
+      }
+    };
+    Unroll<ROWS * COLS>{}(storec);
+  }
+};
+
+template <bool has_bias, int BLOCK_M, int BLOCK_N>
+struct tinygemm_kernel_nn<at::BFloat16, uint8_t, uint8_t, has_bias, BLOCK_M, BLOCK_N> {
+  static inline void apply(
+      const at::BFloat16* __restrict__ A,
+      const uint8_t* __restrict__ B,
+      at::BFloat16* __restrict__ C,
+      const float* __restrict__ bias,
+      const uint8_t* __restrict__ scale,
+      int K,
+      int lda,
+      int ldb,
+      int ldc,
+      int64_t block_size_K) {
+    // mxfp4 supports only group size of 32
+    // expect weight packed in 32-way, vnni2 format Nx2(64)
+    assert(block_size_K == 32);
+    assert(BLOCK_N == 32);
+
+    constexpr int ROWS = BLOCK_M;
+    constexpr int COLS = BLOCK_N / 16;
+
+    // prefetch distance
+    constexpr int PREFETCH_SIZE_K = 64;
+    constexpr int PREFETCH_SIZE_KB = 1;
+
+    __m512bh va;
+    __m512bh vb[COLS];
+    __m512 vc[ROWS * COLS];
+
+    // holds Nx2(64) scales, interleaved as 2 belongs to K dimension
+    // e.g. vs0: { s0,  s0,  s1,  s1, ..., s15, s15}
+    //      vs1: {s16, s16, s17, s17, ..., s31, s31}
+    __m512i vscale[COLS];
+
+    // exponent bias 127
+    const __m512i off = _mm512_set1_epi16(0x7F);
+
+    auto loadc = [&](auto i) {
+      constexpr int col = i % COLS;
+      if constexpr (has_bias) {
+        vc[i] = _mm512_loadu_ps(bias + col * 16);
+      } else {
+        vc[i] = _mm512_setzero_ps();
+      }
+    };
+    Unroll<ROWS * COLS>{}(loadc);
+
+    const int64_t K2 = K >> 1;
+    const int64_t lda2 = lda >> 1;
+    const int64_t ldb2 = ldb;  // ldb * 2 >> 1;
+    const float* a_ptr = reinterpret_cast<const float*>(A);
+    const uint8_t* b_ptr = reinterpret_cast<const uint8_t*>(B);
+
+    auto compute = [&](auto i, int k) {
+      constexpr int row = i / COLS;
+      constexpr int col = i % COLS;
+
+      if constexpr (col == 0) {
+        va = (__m512bh)(_mm512_set1_ps(a_ptr[row * lda2 + k]));
+        if constexpr (PREFETCH_SIZE_K > 0) {
+          _mm_prefetch(a_ptr + row * lda2 + k + PREFETCH_SIZE_K, _MM_HINT_T0);
+        }
+      }
+      if constexpr (row == 0) {
+        // load 32 * 2 (64) int4 at a time
+        if constexpr (col % 2 == 0) {
+          __m256i b4 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(b_ptr + k * ldb2 + col * 16));
+          if constexpr (PREFETCH_SIZE_K > 0) {
+            _mm_prefetch(b_ptr + (k + PREFETCH_SIZE_K) * ldb2 + col * 16, _MM_HINT_T0);
+          }
+          std::tie(vb[col + 0], vb[col + 1]) = CVT_MXFP4_TO_BF16(b4, vscale[col + 0], vscale[col + 1]);
+        }
+      }
+      vc[i] = _mm512_dpbf16_ps(vc[i], va, vb[col]);
+    };
+
+    for (int64_t k = 0; k < K2; ++k) {
+      // update scales every 16x2 K
+      if ((k & 15) == 0) {
+        __m256i s8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(scale + (k >> 4) * 32));
+        __m512i s16 = _mm512_slli_epi16(_mm512_sub_epi16(_mm512_cvtepu8_epi16(s8), off), 0x7);
+        std::tie(vscale[0], vscale[1]) = transpose_2x32_16bit(s16, s16);
+      }
+      Unroll<ROWS * COLS>{}(compute, k);
+    }
+
+    auto storec = [&](auto i) {
+      constexpr int row = i / COLS;
+      constexpr int col = i % COLS;
+      // for COLS = 2,4 use 512bit store
+      if constexpr (col % 2 == 0) {
+        _mm512_storeu_si512(
+            reinterpret_cast<__m512i*>((C + row * ldc + col * 16)),
+            (__m512i)(_mm512_cvtne2ps_pbh(vc[row * COLS + col + 1], vc[row * COLS + col])));
+      }
+    };
+    Unroll<ROWS * COLS>{}(storec);
+  }
+};
+#endif
 
-template <typename scalar_t, typename packed_t, bool has_bias>
+#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)                                   \
+  tinygemm_kernel_nn<scalar_t, packed_t, param_t, has_bias, MB_SIZE, NB_SIZE>::apply( \
+      A + mb_start * lda,                                                             \
+      B + nb_start * 2,                                                               \
+      C + mb_start * ldc + nb_start,                                                  \
+      has_bias ? bias + nb_start : nullptr,                                           \
+      scale,                                                                          \
+      K,                                                                              \
+      lda,                                                                            \
+      ldb,                                                                            \
+      ldc,                                                                            \
+      block_size_K);
+
+#define LAUNCH_TINYGEMM_KERNEL_NN2(MB_SIZE, NB_SIZE)      \
+  tinygemm_kernel_nn2<scalar_t, MB_SIZE, NB_SIZE>::apply( \
+      A + mb_start * lda, B + nb_start * 2, C + mb_start * ldc + nb_start, scale, K, lda, ldb, ldc);
+
+template <typename scalar_t, typename packed_t, typename param_t, bool has_bias>
 struct brgemm {
   static inline void apply(
       const scalar_t* __restrict__ A,
@@ -229,19 +541,22 @@ struct brgemm {
       scalar_t* __restrict__ Btmp,
       float* __restrict__ Ctmp,
       const float* __restrict__ bias,
-      const float* __restrict__ scale,
+      const param_t* __restrict__ scale,
       int M,
       int N,
       int K,
       int lda,
       int ldb,
-      int ldc) {
+      int ldc,
+      bool do_unpack = true) {
     TORCH_CHECK(false, "struct brgemm: primary template not implemented!");
   }
 };
+template <typename scalar_t>
+struct brgemm2 {};
 
 template <bool has_bias>
-struct brgemm<at::BFloat16, at::Float8_e4m3fn, has_bias> {
+struct brgemm<at::BFloat16, at::Float8_e4m3fn, float, has_bias> {
   static inline void apply(
       const at::BFloat16* __restrict__ A,
       const at::Float8_e4m3fn* __restrict__ B,
@@ -255,22 +570,101 @@ struct brgemm<at::BFloat16, at::Float8_e4m3fn, has_bias> {
       int K,
       int lda,
       int ldb,
-      int ldc) {
-
+      int ldc,
+      bool do_unpack = true) {
     constexpr int BLOCK_N = block_size_n();
 
     // [K, BLOCK_N] -> [K / 2, BLOCK_N * 2]
     const int ldb_tmp = BLOCK_N;
 
+    if (do_unpack) {
+      for (int k = 0; k < K; k += BLOCK_K) {
+        int kb_size = std::min(BLOCK_K, K - k);
+
+        int idx = k >> 7;  // k / BLOCK_K where BLOCK_K = 128
+        unpack_B(Btmp + k * ldb_tmp, B + k * ldb, N, kb_size, ldb, ldb_tmp, scale[idx]);
+      }
+    }
+
+    at::native::cpublas::brgemm(M, N, K, lda, ldb_tmp, BLOCK_N, /* add_C */ false, A, Btmp, Ctmp);
+
+    // copy from Ctmp to C
+    for (int m = 0; m < M; ++m) {
+      if constexpr (has_bias) {
+        copy_add_stub(C + m * ldc, Ctmp + m * BLOCK_N, bias, N);
+      } else {
+        copy_stub(C + m * ldc, Ctmp + m * BLOCK_N, N);
+      }
+    }
+  }
+};
+
+template <>
+struct brgemm2<at::BFloat16> {
+  static inline void apply(
+      const at::BFloat16* __restrict__ A,
+      const at::Float8_e4m3fn* __restrict__ B,
+      at::BFloat16* __restrict__ C,
+      at::BFloat16* __restrict__ Btmp,
+      float* __restrict__ Ctmp,
+      float scale,
+      int M,
+      int N,
+      int K,
+      int lda,
+      int ldb,
+      int ldc) {
+    constexpr int BLOCK_N = block_size_n();
+
+    // [BLOCK_K, BLOCK_N] -> [BLOCK_K / 2, BLOCK_N * 2]
+    const int ldb_tmp = block_size_n();
+
+    // accumulate across K per BLOCK_K
     for (int k = 0; k < K; k += BLOCK_K) {
       int kb_size = std::min(BLOCK_K, K - k);
+      unpack_B(Btmp, B + k * ldb, N, kb_size, ldb, ldb_tmp);
+
+      const bool add_C = (k != 0);
+      at::native::cpublas::brgemm(M, N, kb_size, lda, ldb_tmp, BLOCK_N, add_C, A + k, Btmp, Ctmp);
+    }
+
+    // copy from Ctmp to C and mul scale
+    for (int m = 0; m < M; ++m) {
+      copy_mul_stub(C + m * ldc, Ctmp + m * BLOCK_N, N, scale);
+    }
+  }
+};
+
+template <bool has_bias>
+struct brgemm<at::BFloat16, uint8_t, uint8_t, has_bias> {
+  static inline void apply(
+      const at::BFloat16* __restrict__ A,
+      const uint8_t* __restrict__ B,
+      at::BFloat16* __restrict__ C,
+      at::BFloat16* __restrict__ Btmp,
+      float* __restrict__ Ctmp,
+      const float* __restrict__ bias,
+      const uint8_t* __restrict__ scale,
+      int M,
+      int N,
+      int K,
+      int lda,
+      int ldb,
+      int ldc,
+      bool do_unpack = true) {
+    constexpr int BLOCK_N = block_size_n();
+
+    // [K, BLOCK_N] -> [K / 2, BLOCK_N * 2]
+    const int ldb_tmp = BLOCK_N;
 
-      int idx = k >> 7; // k / BLOCK_K where BLOCK_K = 128
-      unpack_B(Btmp + k * ldb_tmp, B + k * ldb, N, kb_size, ldb, ldb_tmp, scale[idx]);
+    if (do_unpack) {
+      // group size 32 for mxfp4
+      for (int k = 0; k < K; k += 32) {
+        unpack_B(Btmp + k * ldb_tmp, B + k * (ldb >> 1), N, 32, ldb, ldb_tmp, scale + (k >> 5) * BLOCK_N);
+      }
     }
 
-    at::native::cpublas::brgemm(
-        M, N, K, lda, ldb_tmp, BLOCK_N, /* add_C */ false, A, Btmp, Ctmp);
+    at::native::cpublas::brgemm(M, N, K, lda, ldb_tmp, BLOCK_N, /* add_C */ false, A, Btmp, Ctmp);
 
     // copy from Ctmp to C
     for (int m = 0; m < M; ++m) {
@@ -283,14 +677,14 @@ struct brgemm<at::BFloat16, at::Float8_e4m3fn, has_bias> {
   }
 };
 
-template <typename scalar_t, bool has_bias>
+template <typename scalar_t, typename packed_t, typename param_t, bool has_bias>
 void tinygemm_kernel(
     const scalar_t* __restrict__ A,
-    const at::Float8_e4m3fn* __restrict__ B,
+    const packed_t* __restrict__ B,
     scalar_t* __restrict__ C,
     scalar_t* __restrict__ Btmp,
     float* __restrict__ Ctmp,
-    const float* __restrict__ scale,
+    const param_t* __restrict__ scale,
     const float* __restrict__ bias,
     int64_t M,
     int64_t N,
@@ -299,11 +693,11 @@ void tinygemm_kernel(
     int64_t ldb,
     int64_t ldc,
     bool brg,
-    int64_t block_size_K) {
-
+    int64_t block_size_K,
+    bool do_unpack = true) {
   if (brg) {
-    brgemm<scalar_t, at::Float8_e4m3fn, has_bias>::apply(
-        A, B, C, Btmp, Ctmp, bias, scale, M, N, K, lda, ldb, ldc);
+    brgemm<scalar_t, packed_t, param_t, has_bias>::apply(
+        A, B, C, Btmp, Ctmp, bias, scale, M, N, K, lda, ldb, ldc, do_unpack);
     return;
   }
 
@@ -319,23 +713,136 @@ void tinygemm_kernel(
       int64_t nb_start = nb * BLOCK_N;
       int64_t nb_size = std::min(BLOCK_N, N - nb_start);
 
-      switch(mb_size << 4 | nb_size >> 4) {
-        case 0x12: LAUNCH_TINYGEMM_KERNEL_NN(1, 32); break;
-        case 0x22: LAUNCH_TINYGEMM_KERNEL_NN(2, 32); break;
-        case 0x32: LAUNCH_TINYGEMM_KERNEL_NN(3, 32); break;
-        case 0x42: LAUNCH_TINYGEMM_KERNEL_NN(4, 32); break;
-        default: TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", nb_size);
+      switch (mb_size << 4 | nb_size >> 4) {
+        case 0x12:
+          LAUNCH_TINYGEMM_KERNEL_NN(1, 32);
+          break;
+        case 0x22:
+          LAUNCH_TINYGEMM_KERNEL_NN(2, 32);
+          break;
+        case 0x32:
+          LAUNCH_TINYGEMM_KERNEL_NN(3, 32);
+          break;
+        case 0x42:
+          LAUNCH_TINYGEMM_KERNEL_NN(4, 32);
+          break;
+        default:
+          TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", "nb_size");
       }
     }
   }
 }
 
 template <typename scalar_t>
-void fp8_scaled_mm_kernel_impl(
+void tinygemm_kernel2(
+    const scalar_t* __restrict__ A,
+    const at::Float8_e4m3fn* __restrict__ B,
+    scalar_t* __restrict__ C,
+    scalar_t* __restrict__ Btmp,
+    float* __restrict__ Ctmp,
+    float scale,
+    int64_t M,
+    int64_t N,
+    int64_t K,
+    int64_t lda,
+    int64_t ldb,
+    int64_t ldc,
+    bool brg) {
+  if (brg) {
+    brgemm2<scalar_t>::apply(A, B, C, Btmp, Ctmp, scale, M, N, K, lda, ldb, ldc);
+    return;
+  }
+
+  // pattern: 1-8-8
+  if (M == 1) {
+    constexpr int64_t BLOCK_N = 128;
+    const int64_t NB = div_up(N, BLOCK_N);
+    int64_t mb_start = 0;
+
+    for (int64_t nb = 0; nb < NB; ++nb) {
+      int64_t nb_start = nb * BLOCK_N;
+      int64_t nb_size = std::min(BLOCK_N, N - nb_start);
+
+      switch (nb_size >> 4) {
+        case 2:
+          LAUNCH_TINYGEMM_KERNEL_NN2(1, 32);
+          break;
+        case 4:
+          LAUNCH_TINYGEMM_KERNEL_NN2(1, 64);
+          break;
+        case 6:
+          LAUNCH_TINYGEMM_KERNEL_NN2(1, 96);
+          break;
+        case 8:
+          LAUNCH_TINYGEMM_KERNEL_NN2(1, 128);
+          break;
+        default:
+          TORCH_CHECK(false, "Unexpected block size, 1x", "nb_size");
+      }
+    }
+    return;
+  }
+
+  // pattern: 1-4-16
+  constexpr int64_t BLOCK_M = 4;
+  constexpr int64_t BLOCK_N = 64;
+  const int64_t MB = div_up(M, BLOCK_M);
+  const int64_t NB = div_up(N, BLOCK_N);
+  for (int64_t mb = 0; mb < MB; ++mb) {
+    int64_t mb_start = mb * BLOCK_M;
+    int64_t mb_size = std::min(BLOCK_M, M - mb_start);
+    for (int64_t nb = 0; nb < NB; ++nb) {
+      int64_t nb_start = nb * BLOCK_N;
+      int64_t nb_size = std::min(BLOCK_N, N - nb_start);
+
+      switch (mb_size << 4 | nb_size >> 4) {
+        // mb_size = 1
+        case 0x12:
+          LAUNCH_TINYGEMM_KERNEL_NN2(1, 32);
+          break;
+        case 0x14:
+          LAUNCH_TINYGEMM_KERNEL_NN2(1, 64);
+          break;
+        // mb_size = 2
+        case 0x22:
+          LAUNCH_TINYGEMM_KERNEL_NN2(2, 32);
+          break;
+        case 0x24:
+          LAUNCH_TINYGEMM_KERNEL_NN2(2, 64);
+          break;
+        // mb_size = 3
+        case 0x32:
+          LAUNCH_TINYGEMM_KERNEL_NN2(3, 32);
+          break;
+        case 0x34:
+          LAUNCH_TINYGEMM_KERNEL_NN2(3, 64);
+          break;
+        // mb_size = 4
+        case 0x42:
+          LAUNCH_TINYGEMM_KERNEL_NN2(4, 32);
+          break;
+        case 0x44:
+          LAUNCH_TINYGEMM_KERNEL_NN2(4, 64);
+          break;
+        default:
+          TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", "nb_size");
+      }
+    }
+  }
+}
+
+// NB: fp8/fp4 scaled mm kernel implementation
+//
+//        scalar_t     packed_t     param_t
+//   FP8    BF16         FP8         FP32
+//  MXFP4   BF16          U8           U8
+//
+template <typename scalar_t, typename packed_t, typename param_t, typename func_t>
+void fp_scaled_mm_kernel_impl(
     scalar_t* __restrict__ out,
     const scalar_t* __restrict__ mat1,
-    const at::Float8_e4m3fn* __restrict__ mat2,
-    const float* __restrict__ scales2,
+    const packed_t* __restrict__ mat2,
+    const param_t* __restrict__ scales2,
     const float* __restrict__ bias,
     scalar_t* __restrict__ buffer,
     int64_t M,
@@ -345,42 +852,41 @@ void fp8_scaled_mm_kernel_impl(
     int64_t out_strideM,
     int64_t block_size_N,
     int64_t block_size_K,
-    int64_t buffer_size_per_thread) {
-
-  constexpr int64_t BLOCK_M = block_size_m() * BLOCK_SIZE_M_SCALE;
+    int64_t buffer_size_per_thread,
+    const func_t& scale_offset_per_block) {
+  constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
   const int64_t MB = div_up(M, BLOCK_M);
   const int64_t NB = div_up(N, BLOCK_N);
 
-  const int64_t scale_size_K = div_up(K, block_size_K);
-  const int64_t blocks_n_per_group = block_size_N / BLOCK_N;
+  const bool use_brgemm = can_use_brgemm<packed_t>(M);
 
-  const bool use_brgemm = can_use_brgemm<at::Float8_e4m3fn>(M);
+  // use K/2 for mxfp4 and K for fp8
+  const int64_t packed_K = get_row_size<packed_t>(K);
 
   // parallel on [MB, NB]
   AT_DISPATCH_BOOL(bias != nullptr, has_bias, [&] {
-    at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
-      int64_t mb{0}, nb{0};
-      data_index_init(begin, mb, MB, nb, NB);
-
-      int tid = at::get_thread_num();
+    parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
+      int tid = get_thread_num();
       scalar_t* __restrict__ Btmp = buffer + tid * buffer_size_per_thread;
-      float* __restrict__ Ctmp = (float*)((void*)(Btmp + BLOCK_N * K));
+      float* __restrict__ Ctmp = (float*)((void*)(Btmp + MAX_CACHE_BLOCK_SIZE * BLOCK_N * K));
 
-      for (int64_t i = begin; i < end; ++i) {
-        UNUSED(i);
-        const float* scale_ptr = scales2 + (nb / blocks_n_per_group) * scale_size_K;
+      loop_2d<packed_t>(mb0, mb1, nb0, nb1, BLOCK_N * K, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+        const param_t* scale_ptr = scales2 + scale_offset_per_block(nb);
 
         int64_t mb_start = mb * BLOCK_M;
         int64_t mb_size = std::min(M - mb_start, BLOCK_M);
         int64_t nb_start = nb * BLOCK_N;
         int64_t nb_size = std::min(N - nb_start, BLOCK_N);
 
-        tinygemm_kernel<scalar_t, has_bias>(
+        // only do unpacking for the first row
+        bool do_unpack = (mb == mb0);
+
+        tinygemm_kernel<scalar_t, packed_t, param_t, has_bias>(
             /*   A            */ mat1 + mb_start * mat1_strideM,
-            /*   B            */ mat2 + nb_start * K, // nb * BLOCK_N * K
+            /*   B            */ mat2 + nb_start * packed_K,  // nb * BLOCK_N * K
             /*   C            */ out + mb_start * out_strideM + nb_start,
-            /*   Btmp         */ Btmp,
+            /*   Btmp         */ Btmp + nb_offset * BLOCK_N * K,
             /*   Ctmp         */ Ctmp,
             /*   scale        */ scale_ptr,
             /*   bias         */ bias + nb_start,
@@ -391,11 +897,9 @@ void fp8_scaled_mm_kernel_impl(
             /*   ldb          */ nb_size,
             /*   ldc          */ out_strideM,
             /*   brg          */ use_brgemm,
-            /*   block_size_K */ block_size_K);
-
-        // move to the next index
-        data_index_step(mb, MB, nb, NB);
-      }
+            /*   block_size_K */ block_size_K,
+            /*   do_unpack    */ do_unpack);
+      });
 
       if (use_brgemm) {
         at::native::cpublas::brgemm_release();
@@ -404,7 +908,7 @@ void fp8_scaled_mm_kernel_impl(
   });
 }
 
-} // anonymous namespace
+}  // anonymous namespace
 
 // tinygemm interface
 template <typename scalar_t>
@@ -422,42 +926,126 @@ void tinygemm_kernel(
     int64_t ldb,
     int64_t ldc,
     bool brg,
-    int64_t block_size_K) {
-  tinygemm_kernel<scalar_t, false>(A, B, C, Btmp, Ctmp, scale, nullptr, M, N, K, lda, ldb, ldc, brg, block_size_K);
+    int64_t block_size_K,
+    bool do_unpack) {
+  tinygemm_kernel<scalar_t, at::Float8_e4m3fn, float, false>(
+      A, B, C, Btmp, Ctmp, scale, nullptr, M, N, K, lda, ldb, ldc, brg, block_size_K, do_unpack);
 }
 
-#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE)    \
+template <typename scalar_t>
+void tinygemm_kernel(
+    const scalar_t* __restrict__ A,
+    const at::Float8_e4m3fn* __restrict__ B,
+    scalar_t* __restrict__ C,
+    scalar_t* __restrict__ Btmp,
+    float* __restrict__ Ctmp,
+    float scale,
+    int64_t M,
+    int64_t N,
+    int64_t K,
+    int64_t lda,
+    int64_t ldb,
+    int64_t ldc,
+    bool brg) {
+  tinygemm_kernel2<scalar_t>(A, B, C, Btmp, Ctmp, scale, M, N, K, lda, ldb, ldc, brg);
+}
+
+template <typename scalar_t>
+void tinygemm_kernel(
+    const scalar_t* __restrict__ A,
+    const uint8_t* __restrict__ B,
+    scalar_t* __restrict__ C,
+    scalar_t* __restrict__ Btmp,
+    float* __restrict__ Ctmp,
+    const uint8_t* __restrict__ scale,
+    int64_t M,
+    int64_t N,
+    int64_t K,
+    int64_t lda,
+    int64_t ldb,
+    int64_t ldc,
+    bool brg,
+    int64_t block_size_K,
+    bool do_unpack) {
+  tinygemm_kernel<scalar_t, uint8_t, uint8_t, false>(
+      A, B, C, Btmp, Ctmp, scale, nullptr, M, N, K, lda, ldb, ldc, brg, block_size_K, do_unpack);
+}
+
+#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE_A, TYPE_B, TYPE_S) \
+  template void tinygemm_kernel<TYPE_A>(                      \
+      const TYPE_A* __restrict__ A,                           \
+      const TYPE_B* __restrict__ B,                           \
+      TYPE_A* __restrict__ C,                                 \
+      TYPE_A* __restrict__ Btmp,                              \
+      float* __restrict__ Ctmp,                               \
+      const TYPE_S* __restrict__ scale,                       \
+      int64_t M,                                              \
+      int64_t N,                                              \
+      int64_t K,                                              \
+      int64_t lda,                                            \
+      int64_t ldb,                                            \
+      int64_t ldc,                                            \
+      bool brg,                                               \
+      int64_t block_size_K,                                   \
+      bool do_unpack)
+
+INSTANTIATE_TINYGEMM_TEMPLATE(at::BFloat16, at::Float8_e4m3fn, float);
+INSTANTIATE_TINYGEMM_TEMPLATE(at::Half, at::Float8_e4m3fn, float);
+INSTANTIATE_TINYGEMM_TEMPLATE(at::BFloat16, uint8_t, uint8_t);
+INSTANTIATE_TINYGEMM_TEMPLATE(at::Half, uint8_t, uint8_t);
+
+#define INSTANTIATE_TINYGEMM_TEMPLATE2(TYPE)   \
   template void tinygemm_kernel<TYPE>(         \
       const TYPE* __restrict__ A,              \
       const at::Float8_e4m3fn* __restrict__ B, \
       TYPE* __restrict__ C,                    \
       TYPE* __restrict__ Btmp,                 \
       float* __restrict__ Ctmp,                \
-      const float* __restrict__ scale,         \
+      float scale,                             \
       int64_t M,                               \
       int64_t N,                               \
       int64_t K,                               \
       int64_t lda,                             \
       int64_t ldb,                             \
       int64_t ldc,                             \
-      bool brg,                                \
-      int64_t block_size_K)
+      bool brg)
 
-INSTANTIATE_TINYGEMM_TEMPLATE(at::BFloat16);
-INSTANTIATE_TINYGEMM_TEMPLATE(at::Half);
+INSTANTIATE_TINYGEMM_TEMPLATE2(at::BFloat16);
 
-at::Tensor fp8_scaled_mm_cpu(at::Tensor& mat1, at::Tensor& mat2, at::Tensor& scales2,
-    std::vector<int64_t> block_size, std::optional<at::Tensor>& bias,
-    at::ScalarType out_dtype, bool is_vnni) {
-  RECORD_FUNCTION("sgl-kernel::fp8_scaled_mm_cpu", std::vector<c10::IValue>({mat1, mat2, scales2, block_size, bias}));
+inline const float* get_bias_data(const std::optional<at::Tensor>& bias, int64_t N) {
+  if (bias.has_value()) {
+    const auto& bias_ref = bias.value();
+    CHECK_EQ(bias_ref.size(0), N);
+    return bias_ref.data_ptr<float>();
+  }
+  return nullptr;
+}
 
+// FP8 and MXFP4 WoQ uses the same pattern:
+//   Btmp : [T, BLOCK_N * K]
+//   Ctmp : [T, BLOCK_M * BLOCK_N]
+inline at::Tensor alloc_thread_buffer(const at::TensorOptions& options, int64_t K) {
+  constexpr int64_t BLOCK_M = block_size_m();
+  constexpr int64_t BLOCK_N = block_size_n();
+  int num_threads = at::get_num_threads();
+  int64_t size_per_thread = MAX_CACHE_BLOCK_SIZE * BLOCK_N * K + BLOCK_M * BLOCK_N * 2;
+  return at::empty({num_threads, size_per_thread}, options);
+}
+
+at::Tensor fp8_scaled_mm_cpu(
+    at::Tensor& mat1,
+    at::Tensor& mat2,
+    at::Tensor& scales2,
+    std::vector<int64_t> block_size,
+    const std::optional<at::Tensor>& bias,
+    at::ScalarType out_dtype,
+    bool is_vnni) {
   auto packed_w = is_vnni ? mat2 : convert_weight_packed(mat2);
 
   CHECK_LAST_DIM_CONTIGUOUS_INPUT(mat1);
   CHECK_INPUT(mat2);
   CHECK_INPUT(scales2);
-  TORCH_CHECK(scales2.scalar_type() == at::kFloat,
-      "fp8_scaled_mm_cpu: expect scales2 to be float32.");
+  TORCH_CHECK(scales2.scalar_type() == at::kFloat, "fp8_scaled_mm_cpu: expect scales2 to be float32.");
 
   int64_t M = mat1.size(0);
   int64_t N = mat2.size(0);
@@ -467,13 +1055,10 @@ at::Tensor fp8_scaled_mm_cpu(at::Tensor& mat1, at::Tensor& mat2, at::Tensor& sca
   CHECK_DIM(2, mat1);
   CHECK_DIM(2, mat2);
 
-  TORCH_CHECK(block_size.size() == 2,
-      "fp8_scaled_mm_cpu: expect block_size.size() to be 2.");
-
+  TORCH_CHECK(block_size.size() == 2, "fp8_scaled_mm_cpu: expect block_size.size() to be 2.");
   int64_t block_size_N = block_size[0];
   int64_t block_size_K = block_size[1];
 
-  constexpr int64_t BLOCK_M = block_size_m() * BLOCK_SIZE_M_SCALE;
   constexpr int64_t BLOCK_N = block_size_n();
   TORCH_CHECK(block_size_N % BLOCK_N == 0, "fp8_scaled_mm_cpu: expect block_size_N to be multiples of BLOCK_N");
   TORCH_CHECK(block_size_K == BLOCK_K, "fp8_scaled_mm_cpu: expect block_size_K equals to BLOCK_K");
@@ -481,49 +1066,94 @@ at::Tensor fp8_scaled_mm_cpu(at::Tensor& mat1, at::Tensor& mat2, at::Tensor& sca
   CHECK_EQ(scales2.size(1), div_up(K, block_size_K));
 
   const auto st = mat1.scalar_type();
-  TORCH_CHECK(st == at::kBFloat16 || st == at::kHalf,
-      "fp8_scaled_mm_cpu: expect A to be bfloat16 or half.");
-  TORCH_CHECK(st == out_dtype,
-      "fp8_scaled_mm_cpu: expect A has same dtype with out_dtype.");
-  TORCH_CHECK(mat2.scalar_type() == at::kFloat8_e4m3fn,
-      "fp8_scaled_mm_cpu: expect mat2 to be fp8_e4m3.");
-  TORCH_CHECK(scales2.scalar_type() == at::kFloat,
-      "fp8_scaled_mm_cpu: expect scales to be float32.");
+  TORCH_CHECK(st == at::kBFloat16 || st == at::kHalf, "fp8_scaled_mm_cpu: expect A to be bfloat16 or half.");
+  TORCH_CHECK(st == out_dtype, "fp8_scaled_mm_cpu: expect A has same dtype with out_dtype.");
+  TORCH_CHECK(mat2.scalar_type() == at::kFloat8_e4m3fn, "fp8_scaled_mm_cpu: expect mat2 to be fp8_e4m3.");
+  TORCH_CHECK(scales2.scalar_type() == at::kFloat, "fp8_scaled_mm_cpu: expect scales to be float32.");
   auto out = at::empty({M, N}, mat1.options().dtype(out_dtype));
 
-  // strides
-  int64_t mat1_strideM = mat1.stride(0);
-  int64_t out_strideM = out.stride(0);
-
-  const bool has_bias = bias.has_value();
-  const float* bias_data = nullptr;
-  if (has_bias) {
-    CHECK_EQ(bias.value().size(0), N);
-    bias_data = bias.value().data_ptr<float>();
-  }
-
-  // Btmp : [T, BLOCK_N * K]
-  // Ctmp : [T, BLOCK_M * BLOCK_N]
-  int num_threads = at::get_num_threads();
-  int64_t size_per_thread = BLOCK_N * K + BLOCK_M * BLOCK_N * 2;
-  auto buffer = at::empty({num_threads, size_per_thread}, mat1.options());
+  auto buffer = alloc_thread_buffer(mat1.options(), K);
 
   AT_DISPATCH_REDUCED_FLOATING_TYPES(out_dtype, "fp8_scaled_mm_kernel_impl", [&] {
-    fp8_scaled_mm_kernel_impl<scalar_t>(
+    // used for lambda computing scale offset for each block
+    //   fp8 block gemm sale shape: [N/128, K/128]
+    //   for each block: [1, K/128]
+    const int64_t scale_size_K = div_up(K, block_size_K);
+    const int64_t blocks_n_per_group = block_size_N / BLOCK_N;
+
+    fp_scaled_mm_kernel_impl<scalar_t, at::Float8_e4m3fn, float>(
         out.data_ptr<scalar_t>(),
         mat1.data_ptr<scalar_t>(),
         packed_w.data_ptr<at::Float8_e4m3fn>(),
         scales2.data_ptr<float>(),
-        bias_data,
+        get_bias_data(bias, N),
         buffer.data_ptr<scalar_t>(),
         M,
         N,
         K,
-        mat1_strideM,
-        out_strideM,
+        mat1.stride(0),
+        out.stride(0),
         block_size_N,
         block_size_K,
-        size_per_thread);
+        buffer.size(-1),
+        [&](int64_t nb) { return (nb / blocks_n_per_group) * scale_size_K; });
+  });
+
+  return out;
+}
+
+// mat1 : [M, K] bfloat16
+// mat2 : [N, K / 2] uint8, actual layout: [N / BLOCK_N, K / 2, BLOCK_N, 2]
+// scales2: [N, K / G], actual layout: [N / BLOCK_N, K / G, BLOCK_N]
+at::Tensor mxfp4_scaled_mm_cpu(
+    at::Tensor& mat1, at::Tensor& mat2, at::Tensor& scales2, const std::optional<at::Tensor>& bias, bool is_vnni) {
+  auto packed_w = is_vnni ? mat2 : convert_weight_packed(mat2);
+
+  CHECK_INPUT(mat1);
+  CHECK_INPUT(mat2);
+  CHECK_INPUT(scales2);
+
+  int64_t M = mat1.size(0);
+  int64_t N = mat2.size(0);
+  int64_t K = mat2.size(1) * 2;
+
+  // mxfp4 supports only group size of 32 (2^5)
+  constexpr int64_t group_size = 32;
+  constexpr int64_t BLOCK_N = block_size_n();
+
+  CHECK_EQ(mat1.size(1), K);
+  CHECK_EQ(scales2.numel(), N * K >> 5);
+
+  const auto st = mat1.scalar_type();
+  TORCH_CHECK(st == at::kBFloat16 || st == at::kHalf, "mxfp4_scaled_mm_cpu: expect A to be bfloat16 or half.");
+  TORCH_CHECK(mat2.scalar_type() == at::kByte, "mxfp4_scaled_mm_cpu: expect mat2 to be uint8.");
+  TORCH_CHECK(scales2.scalar_type() == at::kByte, "mxfp4_scaled_mm_cpu: expect scales to be uint8.");
+  auto out = at::empty({M, N}, mat1.options());
+
+  auto buffer = alloc_thread_buffer(mat1.options(), K);
+
+  AT_DISPATCH_REDUCED_FLOATING_TYPES(st, "mxfp4_scaled_mm_kernel_impl", [&] {
+    // used for lambda computing scale offset for each block
+    //   mxfp4 block gemm sale shape: [N/BLOCK_N, K/32, BLOCK_N]
+    //   for each block: [K/32, BLOCK_N]
+    const int64_t s_strideN = (K >> 5) * BLOCK_N;
+
+    fp_scaled_mm_kernel_impl<scalar_t, uint8_t, uint8_t>(
+        out.data_ptr<scalar_t>(),
+        mat1.data_ptr<scalar_t>(),
+        packed_w.data_ptr<uint8_t>(),
+        scales2.data_ptr<uint8_t>(),
+        get_bias_data(bias, N),
+        buffer.data_ptr<scalar_t>(),
+        M,
+        N,
+        K,
+        mat1.stride(0),
+        out.stride(0),
+        /* block_size_N */ 1,
+        /* block_size_K */ group_size,
+        buffer.size(-1),
+        [&](int64_t nb) { return nb * s_strideN; });
   });
 
   return out;
diff --git a/csrc/cpu/sgl-kernels/gemm_int4.cpp b/csrc/cpu/sgl-kernels/gemm_int4.cpp
index 4a04c50660e2..5b66b2a5aee7 100644
--- a/csrc/cpu/sgl-kernels/gemm_int4.cpp
+++ b/csrc/cpu/sgl-kernels/gemm_int4.cpp
@@ -1,10 +1,10 @@
-// SPDX-License-Identifier: Apache-2.0
-// Adapted from sgl-project/sglang
-// https://github.com/sgl-project/sglang/pull/8226
+// Adapted from
+// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
-#include <ATen/ATen.h>
+// clang-format off
+
+#include <torch/all.h>
 
-#include "common.h"
 #include "gemm.h"
 #include "vec.h"
 
@@ -24,19 +24,22 @@ struct ActDtype<false> {
   using type = uint8_t;
 };
 
+#if defined(CPU_CAPABILITY_AVX512)
 struct alignas(32) m256i_wrapper {
   __m256i data;
 };
 
-#if defined(CPU_CAPABILITY_AVX512)
-inline std::array<m256i_wrapper, 2> load_zps_4vnni(
-    const int8_t* __restrict__ zps) {
-  __m256i vzps_low = _mm256_set1_epi64x(*reinterpret_cast<const int64_t*>(zps));
-  __m256i vzps_high =
-      _mm256_set1_epi64x(*reinterpret_cast<const int64_t*>(zps + 8));
+inline std::array<m256i_wrapper, 2> load_zps_4vnni(const int8_t* __restrict__ zps) {
+  // broadcast 01234567 to
+  // 01234567012345670123456701234567
+  __m256i vzps_low = _mm256_set1_epi64x(*reinterpret_cast<const long*>(zps));
+  __m256i vzps_high = _mm256_set1_epi64x(*reinterpret_cast<const long*>(zps + 8));
+  // shuffle from
+  // 01234567012345670123456701234567
+  // to
+  // 00001111222233334444555566667777
   __m256i shuffle_mask =
-      _mm256_set_epi8(7, 7, 7, 7, 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 3, 3, 3,
-                      3, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0);
+      _mm256_set_epi8(7, 7, 7, 7, 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0);
   vzps_low = _mm256_shuffle_epi8(vzps_low, shuffle_mask);
   vzps_high = _mm256_shuffle_epi8(vzps_high, shuffle_mask);
   m256i_wrapper vzps_low_wp, vzps_high_wp;
@@ -45,8 +48,7 @@ inline std::array<m256i_wrapper, 2> load_zps_4vnni(
   return {vzps_low_wp, vzps_high_wp};
 }
 
-inline std::array<m256i_wrapper, 2> load_uint4_as_int8(
-    const uint8_t* __restrict__ qB) {
+inline std::array<m256i_wrapper, 2> load_uint4_as_int8(const uint8_t* __restrict__ qB) {
   __m256i packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(qB));
   const __m256i low_mask = _mm256_set1_epi8(0x0f);
   __m256i high = _mm256_srli_epi16(packed, 4);
@@ -58,37 +60,42 @@ inline std::array<m256i_wrapper, 2> load_uint4_as_int8(
   return {low_wp, high_wp};
 }
 
-template <int N, int ldb>
-void _dequant_weight_zp_only(const uint8_t* __restrict__ B, int8_t* dqB,
-                             const int8_t* __restrict__ qzeros, int64_t K) {
-  #pragma GCC unroll 2
+template <int64_t N, int64_t ldb>
+void _dequant_weight_zp_only(const uint8_t* __restrict__ B, int8_t* dqB, const int8_t* __restrict__ qzeros, int64_t K) {
+  // unpack weight int8 -> two int4
+  // subtract zero point
+  // B shape = [K, ldb] = [K, N / 2], actual shape = [K / 4, N / 2, 4]
+  // dqB shape = [K, N], actual shape = [K / 4, N, 4]
+#pragma GCC unroll 2
   for (int n = 0; n < N; n += 16) {
     auto [zps_low_wp, zps_high_wp] = load_zps_4vnni(&qzeros[n]);
     auto zps_low = zps_low_wp.data;
     auto zps_high = zps_high_wp.data;
     for (int k = 0; k < K; k += 4) {
-      auto [vb_low_wp, vb_high_wp] =
-          load_uint4_as_int8(B + ldb * k + n / 2 * 4);
+      auto [vb_low_wp, vb_high_wp] = load_uint4_as_int8(B + ldb * k + n / 2 * 4);
       auto vb_low = vb_low_wp.data;
       auto vb_high = vb_high_wp.data;
       vb_high = _mm256_sub_epi8(vb_high, zps_high);
       vb_low = _mm256_sub_epi8(vb_low, zps_low);
-      _mm256_storeu_si256(reinterpret_cast<__m256i_u*>(dqB + N * k + n * 4),
-                          vb_low);
-      _mm256_storeu_si256(
-          reinterpret_cast<__m256i_u*>(dqB + N * k + (n + 8) * 4), vb_high);
+      // store vb to B
+      _mm256_storeu_si256(reinterpret_cast<__m256i_u*>(dqB + N * k + n * 4), vb_low);
+      _mm256_storeu_si256(reinterpret_cast<__m256i_u*>(dqB + N * k + (n + 8) * 4), vb_high);
     }
   }
 }
 
-template <bool sym_quant_act, int N, bool accum>
-void _dequant_and_store(float* __restrict__ output,
-                        const int32_t* __restrict__ input,
-                        const float* __restrict__ scale_a,
-                        const int32_t* __restrict__ zp_a,
-                        const float* __restrict__ scale_b,
-                        const int32_t* __restrict__ comp_b, int M, int ldi,
-                        int ldo, int ldsa = 1) {
+template <bool accum, int64_t N, bool sym_quant_act>
+void _dequant_and_store(
+    float* __restrict__ output,
+    const int32_t* __restrict__ input,
+    const float* __restrict__ scale_a,
+    const int32_t* __restrict__ zp_a,
+    const float* __restrict__ scale_b,
+    const int32_t* __restrict__ comp_b,
+    int M,
+    int ldi,
+    int ldo,
+    int ldsa = 1) {
   for (int m = 0; m < M; ++m) {
     float a_scale = *(scale_a + m * ldsa);
     __m512 va_scale = _mm512_set1_ps(a_scale);
@@ -99,7 +106,7 @@ void _dequant_and_store(float* __restrict__ output,
       va_zp = _mm512_set1_epi32(a_zp);
     }
     int n = 0;
-  #pragma GCC unroll 2
+#pragma GCC unroll 2
     for (; n < N; n += 16) {
       __m512i vc = _mm512_loadu_si512(input + m * ldi + n);
       if constexpr (!sym_quant_act) {
@@ -122,8 +129,7 @@ void _dequant_and_store(float* __restrict__ output,
       if constexpr (sym_quant_act) {
         dq_val = (float)input[m * ldi + n] * a_scale * scale_b[n];
       } else {
-        dq_val = (float)(input[m * ldi + n] - a_zp * comp_b[n]) * a_scale *
-                 scale_b[n];
+        dq_val = (float)(input[m * ldi + n] - a_zp * comp_b[n]) * a_scale * scale_b[n];
       }
       if constexpr (accum) {
         output[m * ldo + n] += dq_val;
@@ -135,9 +141,10 @@ void _dequant_and_store(float* __restrict__ output,
 }
 
 #else
-template <int N, int ldb>
-void _dequant_weight_zp_only(const uint8_t* B, int8_t* dqB,
-                             const int8_t* qzeros, int64_t K) {
+template <int64_t N, int64_t ldb>
+void _dequant_weight_zp_only(const uint8_t* B, int8_t* dqB, const int8_t* qzeros, int64_t K) {
+  // B shape = [K, N / 2]
+  // dqB shape = [K, N]
   for (int k = 0; k < K; ++k) {
     for (int n = 0; n < N / 2; ++n) {
       int32_t b = (int32_t)B[k * ldb + n];
@@ -158,20 +165,31 @@ inline __m512i combine_m256i(std::array<m256i_wrapper, 2> two_256) {
   return combine_m256i(two_256[0].data, two_256[1].data);
 }
 
+// negate elements in a according to b's sign
 static inline __m512i _mm512_sign_epi8(__m512i a, __m512i b) {
   __m512i zero = _mm512_setzero_si512();
   __mmask64 blt0 = _mm512_movepi8_mask(b);
   return _mm512_mask_sub_epi8(a, blt0, zero, a);
 }
 
-template <bool sym_quant_act, int M, int N, int ldb>
-void _dequant_gemm_accum_small_M(float* __restrict__ C, const uint8_t* A,
-                                 const float* scales_a, const int32_t* qzeros_a,
-                                 const uint8_t* B, const float* scales_b,
-                                 const int8_t* qzeros_b, int64_t K, int64_t lda,
-                                 int64_t ldc) {
+template <int64_t M, int64_t N, int64_t ldb, bool sym_quant_act>
+void _dequant_gemm_accum_small_M(
+    float* __restrict__ C,
+    const uint8_t* A,
+    const float* scales_a,
+    const int32_t* qzeros_a,
+    const uint8_t* B,
+    const float* scales_b,
+    const int8_t* qzeros_b,
+    int64_t K,
+    int64_t lda,
+    int64_t ldc) {
+  // if sym_quant_act is true, A pointer type is passed in as uint8_t* but actually int8_t*.
+
   constexpr int COLS = N / 16;
-  __m512i ones = _mm512_set1_epi8(1);
+  // Computing compensation is faster than loading it for small M
+  // because it's memory bound.
+  __m512i ones = _mm512_set1_epi8(1);  // used for computing compensation
   __m512i va;
   __m512i vb[COLS];
   __m512i vc[M * COLS];
@@ -179,6 +197,7 @@ void _dequant_gemm_accum_small_M(float* __restrict__ C, const uint8_t* A,
   __m512i vzps[COLS];
   __m512i vcompensate[COLS];
 
+  // Load scales and zps
   Unroll<COLS>{}([&](auto i) {
     vscales[i] = _mm512_loadu_ps(scales_b + i * 16);
     vzps[i] = combine_m256i(load_zps_4vnni(qzeros_b + i * 16));
@@ -214,25 +233,25 @@ void _dequant_gemm_accum_small_M(float* __restrict__ C, const uint8_t* A,
     }
   };
 
+  // Accumulate along k
   constexpr const int unroll = 4;
   int k = 0;
   for (; k < K / 4 / unroll; k++) {
-    Unroll<unroll>{}(
-        [&](auto i) { Unroll<M * COLS>{}(compute, 4 * (k * unroll + i)); });
+    Unroll<unroll>{}([&](auto i) { Unroll<M * COLS>{}(compute, 4 * (k * unroll + i)); });
   }
   k *= 4 * unroll;
   for (; k < K; k += 4) {
     Unroll<M * COLS>{}(compute, k);
   }
 
+  // Store to C
   auto store = [&](auto i) {
     constexpr const int row = i / COLS;
     constexpr const int col = i % COLS;
+    // compute (qC - compensate * zp_a) * scale_a * scale_b
     __m512 vc_float;
     if constexpr (!sym_quant_act) {
-      vc[i] = _mm512_sub_epi32(
-          vc[i], _mm512_mullo_epi32(vcompensate[col],
-                                    _mm512_set1_epi32(*(qzeros_a + row))));
+      vc[i] = _mm512_sub_epi32(vc[i], _mm512_mullo_epi32(vcompensate[col], _mm512_set1_epi32(*(qzeros_a + row))));
     }
     vc_float = _mm512_cvtepi32_ps(vc[i]);
     vc_float = _mm512_mul_ps(vc_float, _mm512_set1_ps(*(scales_a + row)));
@@ -245,17 +264,28 @@ void _dequant_gemm_accum_small_M(float* __restrict__ C, const uint8_t* A,
   Unroll<M * COLS>{}(store);
 }
 
-  #define CALL_DEQUANT_GEMM_ACCUM_SMALL_M(M)               \
-    _dequant_gemm_accum_small_M<sym_quant_act, M, N, ldb>( \
-        C, A, scales_a, qzeros_a, B, scales_b, qzeros_b, K, lda, ldc);
+#define CALL_DEQUANT_GEMM_ACCUM_SMALL_M(M) \
+  _dequant_gemm_accum_small_M<M, N, ldb, sym_quant_act>(C, A, scales_a, qzeros_a, B, scales_b, qzeros_b, K, lda, ldc);
 #endif
 
-template <bool sym_quant_act, int N, int ldb>
-void _dequant_gemm_accum(float* C, const uint8_t* A, const float* scales_a,
-                         const int32_t* qzeros_a, const uint8_t* B,
-                         const float* scales_b, const int8_t* qzeros_b,
-                         const int32_t* compensation, int8_t* dqB, int64_t M,
-                         int64_t K, int64_t lda, int64_t ldc, bool use_brgemm) {
+template <int64_t N, int64_t ldb, bool sym_quant_act>
+void _dequant_gemm_accum(
+    float* C,
+    const uint8_t* A,
+    const float* scales_a,
+    const int32_t* qzeros_a,
+    const uint8_t* B,
+    const float* scales_b,
+    const int8_t* qzeros_b,
+    const int32_t* compensation,
+    int8_t* dqB,
+    int64_t M,
+    int64_t K,
+    int64_t lda,
+    int64_t ldc,
+    bool use_brgemm) {
+  // Compute GEMM int8 * int8 -> int32
+  // dequant result to float by applying scales/qzeros
 #if defined(CPU_CAPABILITY_AVX512)
   if (!use_brgemm) {
     switch (M) {
@@ -282,14 +312,12 @@ void _dequant_gemm_accum(float* C, const uint8_t* A, const float* scales_a,
   Tin* A_ptr = (Tin*)A;
   if (use_brgemm) {
     int32_t C_i32[M * N];
-    at::native::cpublas::brgemm(M, N, K, lda, N /*ldb*/, N /*ldc*/,
-                                false /* add_C */, A_ptr, dqB, C_i32,
-                                true /* is_vnni */);
+    at::native::cpublas::brgemm(
+        M, N, K, lda, N /*ldb*/, N /*ldc*/, false /* add_C */, A_ptr, dqB, C_i32, true /* is_vnni */);
     _mm_prefetch(B + N * K / 2, _MM_HINT_T0);
     _mm_prefetch(A + K, _MM_HINT_T0);
-    _dequant_and_store<sym_quant_act, N, true>(C, C_i32, scales_a, qzeros_a,
-                                               scales_b, compensation, M,
-                                               N /*ldi*/, ldc, 1 /*ldsa*/);
+    _dequant_and_store<true, N, sym_quant_act>(
+        C, C_i32, scales_a, qzeros_a, scales_b, compensation, M, N /*ldi*/, ldc, 1 /*ldsa*/);
   } else
 #endif
   {
@@ -297,13 +325,13 @@ void _dequant_gemm_accum(float* C, const uint8_t* A, const float* scales_a,
   }
 }
 
-template <int N>
+template <int64_t N>
 inline void copy_bias(const float* bias_ptr, float* y_buf, int64_t m) {
   if (bias_ptr) {
     for (int i = 0; i < m; ++i) {
       int j = 0;
 #if defined(CPU_CAPABILITY_AVX512)
-  #pragma GCC unroll 2
+#pragma GCC unroll 2
       for (; j < N; j += 16) {
         __m512 bias_vec = _mm512_loadu_ps(bias_ptr + j);
         _mm512_storeu_ps(y_buf + i * N + j, bias_vec);
@@ -313,11 +341,11 @@ inline void copy_bias(const float* bias_ptr, float* y_buf, int64_t m) {
         y_buf[i * N + j] = bias_ptr[j];
       }
     }
-  } else {
+  } else {  // initialize to zero
     for (int i = 0; i < m; ++i) {
       int j = 0;
 #if defined(CPU_CAPABILITY_AVX512)
-  #pragma GCC unroll 2
+#pragma GCC unroll 2
       for (; j < N; j += 16) {
         __m512 zero_vec = _mm512_setzero_ps();
         _mm512_storeu_ps(y_buf + i * N + j, zero_vec);
@@ -330,14 +358,13 @@ inline void copy_bias(const float* bias_ptr, float* y_buf, int64_t m) {
   }
 }
 
-template <int N, typename out_dtype>
-inline void store_out(const float* y_buf, out_dtype* c_ptr, int64_t m,
-                      int64_t lda) {
+template <typename out_dtype, int64_t N>
+inline void store_out(const float* y_buf, out_dtype* c_ptr, int64_t m, /* int64_t n, */ int64_t lda) {
   for (int i = 0; i < m; ++i) {
     int j = 0;
     if constexpr (std::is_same<out_dtype, float>::value) {
 #if defined(CPU_CAPABILITY_AVX512)
-  #pragma GCC unroll 2
+#pragma GCC unroll 2
       for (; j < N; j += 16) {
         __m512 y_vec = _mm512_loadu_ps(y_buf + i * N + j);
         _mm512_storeu_ps(c_ptr + i * lda + j, y_vec);
@@ -348,12 +375,11 @@ inline void store_out(const float* y_buf, out_dtype* c_ptr, int64_t m,
       }
     } else if constexpr (std::is_same<out_dtype, at::BFloat16>::value) {
 #if defined(CPU_CAPABILITY_AVX512)
-  #pragma GCC unroll 2
+#pragma GCC unroll 2
       for (; j < N; j += 16) {
         __m512 y_vec = _mm512_loadu_ps(y_buf + i * N + j);
         __m256i y_bf16_vec = at::vec::cvtfp32_bf16(y_vec);
-        _mm256_storeu_si256(reinterpret_cast<__m256i*>(c_ptr + i * lda + j),
-                            y_bf16_vec);
+        _mm256_storeu_si256(reinterpret_cast<__m256i*>(c_ptr + i * lda + j), y_bf16_vec);
       }
 #endif
       for (; j < N; ++j) {
@@ -361,12 +387,11 @@ inline void store_out(const float* y_buf, out_dtype* c_ptr, int64_t m,
       }
     } else if constexpr (std::is_same<out_dtype, at::Half>::value) {
 #if defined(CPU_CAPABILITY_AVX512)
-  #pragma GCC unroll 2
+#pragma GCC unroll 2
       for (; j < N; j += 16) {
         __m512 y_vec = _mm512_loadu_ps(y_buf + i * N + j);
         __m256i y_fp16_vec = at::vec::cvtfp32_fp16(y_vec);
-        _mm256_storeu_si256(reinterpret_cast<__m256i*>(c_ptr + i * lda + j),
-                            y_fp16_vec);
+        _mm256_storeu_si256(reinterpret_cast<__m256i*>(c_ptr + i * lda + j), y_fp16_vec);
       }
 #endif
       for (; j < N; ++j) {
@@ -392,16 +417,25 @@ void fill_val_stub(int32_t* __restrict__ output, int32_t value, int64_t size) {
   }
 }
 
-template <bool sym_quant_act, typename act_dtype, typename out_dtype>
+template <typename act_dtype, typename out_dtype, bool sym_quant_act>
 void _da8w4_linear_impl(
-    act_dtype* __restrict__ input, const float* __restrict__ input_scales,
+    act_dtype* __restrict__ input,
+    const float* __restrict__ input_scales,
     const int32_t* __restrict__ input_qzeros,
-    const uint8_t* __restrict__ weight, const float* __restrict__ weight_scales,
-    const int8_t* __restrict__ weight_qzeros, const float* __restrict__ bias,
-    out_dtype* __restrict__ output, float* __restrict__ output_temp,
-    int8_t* __restrict__ dequant_weight_temp, int64_t M, int64_t N, int64_t K,
+    const uint8_t* __restrict__ weight,
+    const float* __restrict__ weight_scales,
+    const int8_t* __restrict__ weight_qzeros,
+    const float* __restrict__ bias,
+    out_dtype* __restrict__ output,
+    float* __restrict__ output_temp,
+    int8_t* __restrict__ dequant_weight_temp,
+    int64_t M,
+    int64_t N,
+    int64_t K,
     int64_t num_groups) {
-  const bool use_brgemm = can_use_brgemm<act_dtype>(M);
+  // weight + compensation shape = [Nc, Kc, BLOCK_N * _block_k / 2 + BLOCK_N*sizeof(int32_t)]
+  // scales/qzeros shape = [Nc, G, BLOCK_N]
+  const bool use_brgemm = can_use_brgemm<int8_t>(M);
   int64_t block_m = [&]() -> long {
     if (M <= 48) {
       return M;
@@ -432,30 +466,24 @@ void _da8w4_linear_impl(
       int64_t mc_end = parallel_on_M ? mc + 1 : Mc;
 
       for (int mci = mc; mci < mc_end; ++mci) {
-        int64_t m_size =
-            mci * block_m + block_m > M ? M - mci * block_m : block_m;
+        int64_t m_size = mci * block_m + block_m > M ? M - mci * block_m : block_m;
+        // copy bias to y_buf if bias is not None
         auto bias_data = bias ? bias + nc * BLOCK_N : nullptr;
         copy_bias<BLOCK_N>(bias_data, C_tmp, m_size);
         for (int kci = 0; kci < Kc; ++kci) {
           int32_t* compensation_ptr =
               sym_quant_act
                   ? nullptr
-                  : (int32_t*)(void*)(weight +
-                                      (nc * Kc + kci) *
-                                          (BLOCK_N *
-                                           (_block_k / 2 + sizeof(int32_t))) +
-                                      _block_k * BLOCK_N / 2);
-          _dequant_gemm_accum<sym_quant_act, BLOCK_N, BLOCK_N / 2>(
+                  : (int32_t*)(void*)(weight + (nc * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))) +
+                                      _block_k * BLOCK_N / 2) /*Bcomp*/;
+          _dequant_gemm_accum<BLOCK_N, BLOCK_N / 2, sym_quant_act>(
               /*C*/ C_tmp,
               /*A*/ (uint8_t*)input + mci * block_m * K + kci * _block_k,
               /*scales_a*/ input_scales + mci * block_m,
               /*qzeros_a*/ input_qzeros + mci * block_m,
-              /*B*/ weight + (nc * Kc + kci) *
-                                 (BLOCK_N * (_block_k / 2 + sizeof(int32_t))),
-              /*scales_b*/ weight_scales + nc * BLOCK_N * num_groups +
-                  kci / block_per_group * BLOCK_N,
-              /*qzeros_b*/ weight_qzeros + nc * BLOCK_N * num_groups +
-                  kci / block_per_group * BLOCK_N,
+              /*B*/ weight + (nc * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))),
+              /*scales_b*/ weight_scales + nc * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N,
+              /*qzeros_b*/ weight_qzeros + nc * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N,
               /*Bcomp*/ compensation_ptr,
               /*dqB_tmp*/ dqB_tmp,
               /*M*/ m_size,
@@ -464,8 +492,8 @@ void _da8w4_linear_impl(
               /*ldc*/ BLOCK_N,
               /*use_brgemm*/ use_brgemm);
         }
-        store_out<BLOCK_N>(C_tmp, output + mci * block_m * N + nc * BLOCK_N,
-                           m_size, N /*lda*/);
+        // store y_buf to output with dtype conversion
+        store_out<out_dtype, BLOCK_N>(C_tmp, output + mci * block_m * N + nc * BLOCK_N, m_size, N /*lda*/);
       }
     }
     if (use_brgemm) {
@@ -476,15 +504,16 @@ void _da8w4_linear_impl(
 
 }  // anonymous namespace
 
-std::tuple<at::Tensor, at::Tensor, at::Tensor>
-convert_int4_weight_packed_with_compensation(const at::Tensor& weight,
-                                             const at::Tensor& scales,
-                                             const at::Tensor& qzeros) {
-  TORCH_CHECK(weight.dim() == 2,
-              "DA8W4 CPU: Weight should be a 2D tensor for packing");
-  TORCH_CHECK(
-      weight.size(1) % 2 == 0,
-      "DA8W4 CPU: Weight should have even number of columns for packing");
+/*
+return: packed_weight, packed_scales, packed_qzeros
+*/
+std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_int4_weight_packed_with_compensation(
+    const at::Tensor& weight, const at::Tensor& scales, const at::Tensor& qzeros) {
+  // weight shape = [N, K]
+  // scales shape = [N, G]
+  // qzeros shape = [N, G]
+  TORCH_CHECK(weight.dim() == 2, "DA8W4 CPU: Weight should be a 2D tensor for packing");
+  TORCH_CHECK(weight.size(1) % 2 == 0, "DA8W4 CPU: Weight should have even number of columns for packing");
 
   auto new_scales = scales;
   auto new_qzeros = qzeros;
@@ -505,20 +534,21 @@ convert_int4_weight_packed_with_compensation(const at::Tensor& weight,
   int64_t Nc = N / block_n;
   int64_t Kc = K / _block_k;
 
+  // Reorder weight to [N/block_n, K/_block_k, _block_k, block_n]
+  // Reorder scales/qzeros to [N/block_n, G, block_n]
+  // weight + compensation shape = [Nc, Kc, block_n * _block_k / 2 + block_n*sizeof(int32_t)]
+  // scales/qzeros shape = [Nc, G, block_n]
   auto weight_view = weight.view({Nc, block_n, Kc, _block_k});
   at::Tensor weight_reordered = weight_view.permute({0, 2, 3, 1}).contiguous();
   at::Tensor blocked_weight;
-  at::Tensor blocked_scales =
-      new_scales.view({Nc, block_n, G}).permute({0, 2, 1}).contiguous();
-  at::Tensor blocked_qzeros =
-      new_qzeros.view({Nc, block_n, G}).permute({0, 2, 1}).contiguous();
-  auto weight_sub_qzero = weight.view({Nc, block_n, G, -1}).to(at::kInt) -
-                          new_qzeros.view({Nc, block_n, G, -1});
+  at::Tensor blocked_scales = new_scales.view({Nc, block_n, G}).permute({0, 2, 1}).contiguous();
+  at::Tensor blocked_qzeros = new_qzeros.view({Nc, block_n, G}).permute({0, 2, 1}).contiguous();
+  // Compensation = Σ(k)(W[k][n] - ZP[n]) for each block.
+  auto weight_sub_qzero = weight.view({Nc, block_n, G, -1}).to(at::kInt) - new_qzeros.view({Nc, block_n, G, -1});
   weight_sub_qzero = weight_sub_qzero.view({Nc, block_n, Kc, _block_k});
   at::Tensor compensation = weight_sub_qzero.sum(-1);
   compensation = compensation.permute({0, 2, 1}).contiguous().to(at::kInt);
-  int64_t buffer_size_nbytes =
-      _block_k * block_n / 2 + block_n * sizeof(int32_t);
+  int64_t buffer_size_nbytes = _block_k * block_n / 2 + block_n * sizeof(int32_t);
   blocked_weight = at::empty({Nc, Kc, buffer_size_nbytes}, weight.options());
 
   auto weight_ptr = weight_reordered.data_ptr<uint8_t>();
@@ -528,24 +558,25 @@ convert_int4_weight_packed_with_compensation(const at::Tensor& weight,
   at::parallel_for(0, num_blocks, 1, [&](int64_t begin, int64_t end) {
     for (const auto i : c10::irange(begin, end)) {
       auto in_ptr = weight_ptr + i * _block_k * block_n;
-      auto out_ptr =
-          blocked_weight_ptr + i * block_n * (_block_k / 2 + sizeof(int32_t));
+      auto out_ptr = blocked_weight_ptr + i * block_n * (_block_k / 2 + sizeof(int32_t));
       int32_t* comp_in_prt = compensation_ptr + i * block_n;
-      int32_t* comp_out_prt =
-          (int32_t*)(void*)(blocked_weight_ptr +
-                            i * block_n * (_block_k / 2 + sizeof(int32_t)) +
-                            _block_k * block_n / 2);
+      int32_t* comp_out_prt = (int32_t*)(void*)(blocked_weight_ptr + i * block_n * (_block_k / 2 + sizeof(int32_t)) +
+                                                _block_k * block_n / 2);
+      // Reorder weight block to VNNI4 and pack two lanes along N
+      // N=16 viewed as two lanes: a0, ...a7, b0, ...b7
+      // pack two lanes: [a0, b0], ..., [a7, b7]
+      // plain shape = [_block_k, block_n]
+      // packed shape = [_block_k / 4, block_n / 2, 4] viewed as [_block_k, block_n / 2]
       constexpr int n_group_size = 8;
       constexpr int vnni_size = 4;
-      constexpr int n_group = block_n / n_group_size;
+      constexpr int n_group = block_n / n_group_size;  // 4
       for (int nb = 0; nb < n_group; nb += 2) {
         for (int k = 0; k < _block_k; k += vnni_size) {
           for (int ni = 0; ni < n_group_size; ++ni) {
             for (int ki = 0; ki < vnni_size; ++ki) {
               int src_idx_1 = nb * n_group_size + ni + (k + ki) * block_n;
               int src_idx_2 = (nb + 1) * n_group_size + ni + (k + ki) * block_n;
-              int dst_idx = (nb / 2 * n_group_size + ni) * vnni_size +
-                            k * block_n / 2 + ki;
+              int dst_idx = (nb / 2 * n_group_size + ni) * vnni_size + k * block_n / 2 + ki;
               uint8_t src_1 = *(in_ptr + src_idx_1);
               uint8_t src_2 = *(in_ptr + src_idx_2);
               uint8_t dst = (src_1 & 0x0f) | ((src_2 & 0x0f) << 4);
@@ -554,39 +585,128 @@ convert_int4_weight_packed_with_compensation(const at::Tensor& weight,
           }
         }
       }
+      // compensation [block_n]
       for (int nb = 0; nb < block_n; nb++) {
         *(comp_out_prt + nb) = *(comp_in_prt + nb);
       }
     }
   });
 
-  return std::make_tuple(std::move(blocked_weight), std::move(blocked_scales),
-                         std::move(blocked_qzeros));
+  return std::make_tuple(std::move(blocked_weight), std::move(blocked_scales), std::move(blocked_qzeros));
 }
 
-std::tuple<at::Tensor, at::Tensor> autoawq_to_int4pack(at::Tensor qweight,
-                                                       at::Tensor qzeros) {
-  auto bitshifts = at::tensor({0, 4, 1, 5, 2, 6, 3, 7}, at::kInt) * 4;
-  auto qweight_unsq = qweight.unsqueeze(-1);
-  auto unpacked = at::bitwise_right_shift(qweight_unsq, bitshifts) & 0xF;
-  auto qweight_final = unpacked.flatten(-2).transpose(-1, -2).to(at::kByte);
+std::tuple<at::Tensor, at::Tensor> unpack_4bit_to_32bit_signed(const at::Tensor& qweight, const at::Tensor& qzeros) {
+  TORCH_CHECK(qweight.scalar_type() == at::kInt, "qweight must be int32");
+  TORCH_CHECK(qzeros.scalar_type() == at::kInt, "qzeros must be int32");
+  const auto W0 = qweight.size(0);
+  const auto W1 = qweight.size(1);
+  const auto Z0 = qzeros.size(0);
+  const auto Z1 = qzeros.size(1);
+
+  // unpacked_weights: (W0 * 8, W1), int8
+  auto unpacked_weights = at::zeros({W0 * 8, W1}, at::TensorOptions().dtype(at::kChar));
+  // unpacked_zeros: (Z0, Z1 * 8), int8
+  auto unpacked_zeros = at::zeros({Z0, Z1 * 8}, at::TensorOptions().dtype(at::kChar));
+
+  const int32_t* qw_ptr = qweight.data_ptr<int32_t>();
+  const int32_t* qz_ptr = qzeros.data_ptr<int32_t>();
+  int8_t* uw_ptr = unpacked_weights.data_ptr<int8_t>();
+  int8_t* uz_ptr = unpacked_zeros.data_ptr<int8_t>();
+
+  // ---- unpack qweight ----
+  for (int64_t row = 0; row < W0 * 8; ++row) {
+    const int i = row & 7;         // row % 8
+    const int src_row = row >> 3;  // row // 8
+    const int shift = 4 * i;
+    for (int64_t col = 0; col < W1; ++col) {
+      int32_t v = qw_ptr[src_row * W1 + col];
+      uw_ptr[row * W1 + col] = static_cast<int8_t>((v >> shift) & 0xF);
+    }
+  }
+  // ---- unpack qzeros ----
+  for (int64_t col = 0; col < Z1 * 8; ++col) {
+    const int i = col & 7;
+    const int src_col = col >> 3;
+    const int shift = 4 * i;
+
+    for (int64_t row = 0; row < Z0; ++row) {
+      int32_t v = qz_ptr[row * Z1 + src_col];
+      uz_ptr[row * (Z1 * 8) + col] = static_cast<int8_t>((v >> shift) & 0xF);
+    }
+  }
 
-  auto qzeros_unsq = qzeros.unsqueeze(-1);
-  auto qzeros_unpacked = at::bitwise_right_shift(qzeros_unsq, bitshifts) & 0xF;
-  auto qzeros_final = qzeros_unpacked.flatten(-2).to(at::kByte);
+  return std::make_tuple(unpacked_weights, unpacked_zeros + 1);
+}
 
-  return std::make_tuple(qweight_final, qzeros_final);
+std::tuple<at::Tensor, at::Tensor>
+autogptq_to_int4pack(const at::Tensor& qweight_tensor, const at::Tensor& qzeros_tensor) {
+  TORCH_CHECK(qweight_tensor.scalar_type() == at::kInt, "qweight_tensor must be int32");
+  TORCH_CHECK(qzeros_tensor.scalar_type() == at::kInt, "qzeros_tensor must be int32");
+  TORCH_CHECK(qweight_tensor.is_cpu(), "CPU only implementation");
+  if (qweight_tensor.dim() == 3) {
+    const int64_t B = qweight_tensor.size(0);
+    std::vector<at::Tensor> qweight_list;
+    std::vector<at::Tensor> qzeros_list;
+    qweight_list.reserve(B);
+    qzeros_list.reserve(B);
+    for (int64_t i = 0; i < B; ++i) {
+      auto outputs = unpack_4bit_to_32bit_signed(qweight_tensor[i], qzeros_tensor[i]);
+      at::Tensor unpacked_qweight = std::get<0>(outputs);
+      at::Tensor unpacked_qzeros = std::get<1>(outputs);
+      qweight_list.push_back(unpacked_qweight.transpose(0, 1).contiguous().to(at::kByte));
+      qzeros_list.push_back(unpacked_qzeros.contiguous().to(at::kByte));
+    }
+    return std::make_tuple(at::stack(qweight_list).detach(), at::stack(qzeros_list).detach());
+  }
+  auto outputs = unpack_4bit_to_32bit_signed(qweight_tensor, qzeros_tensor);
+  at::Tensor unpacked_qweight = std::get<0>(outputs);
+  at::Tensor unpacked_qzeros = std::get<1>(outputs);
+  at::Tensor return_qweight = unpacked_qweight.transpose(0, 1).contiguous().to(at::kByte);
+  at::Tensor return_qzeros = unpacked_qzeros.contiguous().to(at::kByte);
+  return std::make_tuple(return_qweight, return_qzeros);
+}
+
+std::tuple<at::Tensor, at::Tensor> int4pack(at::Tensor qweight, at::Tensor qzeros, int64_t quant_method_4bit) {
+  if (quant_method_4bit == CPUQuantAlgo::AWQ) {
+    // autoawq unpacking
+    qweight = qweight.contiguous();
+    qzeros = qzeros.contiguous();
+    // bitshifts: [0, 4, 1, 5, 2, 6, 3, 7] * 4
+    auto bitshifts = at::tensor({0, 4, 1, 5, 2, 6, 3, 7}, at::kInt) * 4;
+    auto qweight_unsq = qweight.unsqueeze(-1);  // [..., K, N/8, 1]
+    auto unpacked = (at::bitwise_right_shift(qweight_unsq, bitshifts) & 0xF).contiguous();
+    auto qweight_final = unpacked.flatten(-2).transpose(-1, -2).to(at::kByte).clone();
+    auto qzeros_unsq = qzeros.unsqueeze(-1);
+    auto qzeros_unpacked = (at::bitwise_right_shift(qzeros_unsq, bitshifts) & 0xF).contiguous();
+    auto qzeros_final = qzeros_unpacked.flatten(-2).to(at::kByte).clone();
+    return std::make_tuple(qweight_final, qzeros_final);
+  } else if (quant_method_4bit == CPUQuantAlgo::GPTQ) {
+    // autogptq unpacking
+    auto outputs = autogptq_to_int4pack(qweight, qzeros);
+    at::Tensor unpacked_qweight = std::get<0>(outputs);
+    at::Tensor unpacked_qzeros = std::get<1>(outputs);
+    return std::make_tuple(unpacked_qweight, unpacked_qzeros);
+  } else {
+    TORCH_CHECK(false, "CPU int4 pack only support AWQ or GPTQ...");
+  }
 }
 
 std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_weight_packed_scale_zp(
-    at::Tensor qweight, at::Tensor qzeros, at::Tensor scales) {
-  auto res = autoawq_to_int4pack(qweight, qzeros);
-  auto _qweight = std::get<0>(res);
-  auto _qzeros = std::get<1>(res);
+    at::Tensor qweight,  // awq: (*, K, N / 8)  ||  gptq: (*, K / 8, N) , int32
+    at::Tensor qzeros,   // awq: (*, K / group_size, N / 8) ||  gptq: (*, K / group_size, N / 8) , int32
+    at::Tensor scales,   // awq: (*, K / group_size, N) ||  gptq: (*, K / group_size, N) , bfloat16
+    int64_t quant_method_4bit) {
+  at::Tensor _qweight;
+  at::Tensor _qzeros;
+
+  auto res = int4pack(qweight, qzeros, quant_method_4bit);
+  _qweight = std::get<0>(res);
+  _qzeros = std::get<1>(res);
+
   auto _scales = scales;
-  _qzeros = _qzeros.transpose(-2, -1).contiguous();
+  _qzeros = _qzeros.transpose(-2, -1).contiguous();  // .T
   _scales = _scales.transpose(-2, -1).contiguous();
-  if (_qweight.dim() == 3) {
+  if (_qweight.dim() == 3) {  // Dim=3 for MOE packing, TODO: refine a unified loop
     int64_t E = _qweight.size(0);
     int64_t K = _qweight.size(2);
     int64_t G = _scales.size(2);
@@ -595,17 +715,12 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_weight_packed_scale_zp(
     int64_t block_n = block_size_n();
     int64_t Nc = _qweight.size(1) / block_n;
     int64_t Kc = K / _block_k;
-    int64_t buffer_size_nbytes =
-        _block_k * block_n / 2 + block_n * sizeof(int32_t);
-    auto blocked_weight =
-        at::empty({E, Nc, Kc, buffer_size_nbytes}, _qweight.options());
-    auto blocked_scales =
-        at::empty({E, Nc, G, block_n}, _scales.options()).to(at::kFloat);
-    auto blocked_qzeros =
-        at::empty({E, Nc, G, block_n}, _qzeros.options()).to(at::kChar);
+    int64_t buffer_size_nbytes = _block_k * block_n / 2 + block_n * sizeof(int32_t);
+    auto blocked_weight = at::empty({E, Nc, Kc, buffer_size_nbytes}, _qweight.options());
+    auto blocked_scales = at::empty({E, Nc, G, block_n}, _scales.options()).to(at::kFloat);
+    auto blocked_qzeros = at::empty({E, Nc, G, block_n}, _qzeros.options()).to(at::kChar);
     for (int i = 0; i < _qweight.size(0); i++) {
-      auto res_ = convert_int4_weight_packed_with_compensation(
-          _qweight[i], _scales[i], _qzeros[i]);
+      auto res_ = convert_int4_weight_packed_with_compensation(_qweight[i], _scales[i], _qzeros[i]);
       blocked_weight[i] = std::get<0>(res_);
       blocked_scales[i] = std::get<1>(res_);
       blocked_qzeros[i] = std::get<2>(res_);
@@ -614,8 +729,7 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_weight_packed_scale_zp(
     _scales = blocked_scales;
     _qzeros = blocked_qzeros;
   } else {
-    auto res_ = convert_int4_weight_packed_with_compensation(_qweight, _scales,
-                                                             _qzeros);
+    auto res_ = convert_int4_weight_packed_with_compensation(_qweight, _scales, _qzeros);
     _qweight = std::get<0>(res_);
     _scales = std::get<1>(res_);
     _qzeros = std::get<2>(res_);
@@ -624,89 +738,93 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_weight_packed_scale_zp(
   return std::make_tuple(_qweight, _qzeros, _scales);
 }
 
-at::Tensor int4_scaled_mm_cpu_with_quant(const at::Tensor& input,
-                                         const at::Tensor& weight,
-                                         const at::Tensor& weight_scales,
-                                         const at::Tensor& weight_qzeros,
-                                         const std::optional<at::Tensor>& bias,
-                                         at::ScalarType output_dtype) {
-  RECORD_FUNCTION("vllm::int4_scaled_mm_cpu_with_quant",
-                  std::vector<c10::IValue>({input, weight}));
-
+at::Tensor int4_scaled_mm_cpu_with_quant(
+    const at::Tensor& input,
+    const at::Tensor& weight,
+    const at::Tensor& weight_scales,
+    const at::Tensor& weight_qzeros,
+    const std::optional<at::Tensor>& bias,
+    at::ScalarType output_dtype) {
   int64_t M_a = input.size(0);
   int64_t K_a = input.size(1);
   int64_t lda = input.stride(0);
 
   const auto st = input.scalar_type();
   TORCH_CHECK(
-      st == at::kBFloat16 || st == at::kHalf,
-      "int4_scaled_mm_cpu_with_quant: expect A to be bfloat16 or half.");
+      st == at::kBFloat16 || st == at::kHalf, "int4_scaled_mm_cpu_with_quant: expect A to be bfloat16 or half.");
 
-  constexpr bool sym_quant_act = false;
+  constexpr bool sym_quant_act = false;  // TODO: add sym quant path
   using Tin = typename ActDtype<sym_quant_act>::type;
-  int64_t act_buffer_size =
-      M_a * K_a + M_a * sizeof(float) + M_a * sizeof(int32_t);
-  auto act_buffer =
-      at::empty({act_buffer_size}, input.options().dtype(at::kByte));
+  int64_t act_buffer_size = /* act quant */ M_a * K_a +
+                            /* act scale */ M_a * sizeof(float) +
+                            /* act zp */ M_a * sizeof(int32_t);
+  auto act_buffer = at::empty({act_buffer_size}, input.options().dtype(at::kByte));
+  // asym path, activation quants into uint8_t
   auto Aq_data = act_buffer.data_ptr<uint8_t>();
   auto As_data = reinterpret_cast<float*>(Aq_data + M_a * K_a);
   auto Azp_data = reinterpret_cast<int32_t*>(As_data + M_a);
-  fill_val_stub(Azp_data, 128, M_a);
+  fill_val_stub(Azp_data, 128, M_a);  // sym_a s8s8 is unified to u8s8 with compensation (128)
 
   auto out_sizes = input.sizes().vec();
   int64_t N = weight_scales.size(0) * weight_scales.size(-1);
   out_sizes.back() = N;
   auto output = at::empty(out_sizes, input.options());
+  // weight + compensation shape = [Nc, Kc, BLOCK_N * _block_k / 2 + BLOCK_N*sizeof(int32_t)]
+  // scales/qzeros shape = [Nc, G, BLOCK_N]
   int64_t Nc = weight.size(0);
   int64_t Kc = weight.size(1);
   int64_t _block_k = K_a / Kc;
   TORCH_CHECK(N == Nc * BLOCK_N, "DA8W4: weight and input shapes mismatch");
+  // scales/qzeros shape = [Nc, G, BLOCK_N]
   int64_t num_groups = weight_scales.size(1);
 
   const uint8_t* b_ptr = weight.data_ptr<uint8_t>();
   const float* b_scales_ptr = weight_scales.data_ptr<float>();
   const int8_t* b_qzeros_ptr = weight_qzeros.data_ptr<int8_t>();
-  const float* bias_ptr =
-      bias.has_value() ? bias.value().data_ptr<float>() : nullptr;
+  const float* bias_ptr = bias.has_value() ? bias.value().data_ptr<float>() : nullptr;
   int num_threads = at::get_num_threads();
-  int64_t temp_buffer_size = num_threads * BLOCK_M * BLOCK_N * sizeof(float) +
-                             num_threads * _block_k * BLOCK_N;
-  auto c_temp_buffer =
-      at::empty({temp_buffer_size}, input.options().dtype(at::kChar));
+  int64_t temp_buffer_size = /* output temp */ num_threads * BLOCK_M * BLOCK_N * sizeof(float) +
+                             /*  weight dequant temp */ num_threads * _block_k * BLOCK_N;
+  auto c_temp_buffer = at::empty({temp_buffer_size}, input.options().dtype(at::kChar));
   float* c_temp_ptr = (float*)((void*)(c_temp_buffer.data_ptr<int8_t>()));
-  int8_t* dqB_temp_ptr =
-      (int8_t*)((void*)(c_temp_ptr + num_threads * BLOCK_M * BLOCK_N));
-
-#define LAUNCH_DA8W4_LINEAR_WITH_QUANT_IMPL(sym_quant_act)                 \
-  AT_DISPATCH_FLOATING_TYPES_AND2(                                         \
-      at::ScalarType::BFloat16, at::ScalarType::Half, output_dtype,        \
-      "int4_scaled_mm_cpu", [&] {                                          \
-        const scalar_t* __restrict__ A_data = input.data_ptr<scalar_t>();  \
-        scalar_t* __restrict__ c_ptr = output.data_ptr<scalar_t>();        \
-        at::parallel_for(0, M_a, 0, [&](int64_t begin, int64_t end) {      \
-          for (int64_t m = begin; m < end; ++m) {                          \
-            quantize_row_int8<scalar_t>(Aq_data + m * K_a, As_data[m],     \
-                                        A_data + m * lda, K_a);            \
-          }                                                                \
-        });                                                                \
-        _da8w4_linear_impl<sym_quant_act, Tin, scalar_t>(                  \
-            Aq_data, As_data, Azp_data, b_ptr, b_scales_ptr, b_qzeros_ptr, \
-            bias_ptr, c_ptr, c_temp_ptr, dqB_temp_ptr, M_a, N, K_a,        \
-            num_groups);                                                   \
+  int8_t* dqB_temp_ptr = (int8_t*)((void*)(c_temp_ptr + num_threads * BLOCK_M * BLOCK_N));
+
+#define LAUNCH_DA8W4_LINEAR_WITH_QUANT_IMPL(sym_quant_act)                                                 \
+  AT_DISPATCH_FLOATING_TYPES_AND2(                                                                         \
+      at::ScalarType::BFloat16, at::ScalarType::Half, output_dtype, "int4_scaled_mm_cpu_with_quant", [&] { \
+        const scalar_t* __restrict__ A_data = input.data_ptr<scalar_t>();                                  \
+        scalar_t* __restrict__ c_ptr = output.data_ptr<scalar_t>();                                        \
+        at::parallel_for(0, M_a, 0, [&](int64_t begin, int64_t end) {                                      \
+          for (int64_t m = begin; m < end; ++m) {                                                          \
+            quantize_row_int8<scalar_t>(Aq_data + m * K_a, As_data[m], A_data + m * lda, K_a);             \
+          }                                                                                                \
+        });                                                                                                \
+        _da8w4_linear_impl<Tin, scalar_t, sym_quant_act>(                                                  \
+            Aq_data,                                                                                       \
+            As_data,                                                                                       \
+            Azp_data,                                                                                      \
+            b_ptr,                                                                                         \
+            b_scales_ptr,                                                                                  \
+            b_qzeros_ptr,                                                                                  \
+            bias_ptr,                                                                                      \
+            c_ptr,                                                                                         \
+            c_temp_ptr,                                                                                    \
+            dqB_temp_ptr,                                                                                  \
+            M_a,                                                                                           \
+            N,                                                                                             \
+            K_a,                                                                                           \
+            num_groups);                                                                                   \
       });
 
   LAUNCH_DA8W4_LINEAR_WITH_QUANT_IMPL(sym_quant_act);
 
   return output;
 }
-
-namespace {
-
 template <typename scalar_t>
-inline void copy_stub(scalar_t* __restrict__ out,
-                      const float* __restrict__ input, int64_t size) {
+inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ input, int64_t size) {
   using Vec = at::vec::Vectorized<scalar_t>;
   using fVec = at::vec::Vectorized<float>;
+// no remainder
 #pragma GCC unroll 4
   for (int64_t d = 0; d < size; d += Vec::size()) {
     fVec x0 = fVec::loadu(input + d);
@@ -716,40 +834,61 @@ inline void copy_stub(scalar_t* __restrict__ out,
   }
 }
 
-}  // anonymous namespace
-
 template <typename scalar_t>
-void tinygemm_kernel(scalar_t* C, float* C_temp, const uint8_t* A,
-                     const float* scales_a, const int32_t* qzeros_a,
-                     const uint8_t* B, const float* scales_b,
-                     const int8_t* qzeros_b, const int32_t* compensation,
-                     int8_t* dqB_tmp, int64_t M, int64_t K, int64_t lda,
-                     int64_t ldc_f, int64_t ldc_s, bool store_out,
-                     bool use_brgemm) {
-  _dequant_gemm_accum<false, BLOCK_N, BLOCK_N / 2>(
-      C_temp, A, scales_a, qzeros_a, B, scales_b, qzeros_b, compensation,
-      dqB_tmp, M, K, lda, ldc_f, use_brgemm);
+void tinygemm_kernel(
+    scalar_t* C,
+    float* C_temp,
+    const uint8_t* A,
+    const float* scales_a,
+    const int32_t* qzeros_a,
+    const uint8_t* B,
+    const float* scales_b,
+    const int8_t* qzeros_b,
+    const int32_t* compensation,
+    int8_t* dqB_tmp,
+    int64_t M,
+    int64_t K,
+    int64_t lda,
+    int64_t ldc_f,
+    int64_t ldc_s,
+    bool store_out,
+    bool use_brgemm) {
+  // TODO: add sym quant act, now only asym
+  _dequant_gemm_accum<BLOCK_N, BLOCK_N / 2, false>(
+      C_temp, A, scales_a, qzeros_a, B, scales_b, qzeros_b, compensation, dqB_tmp, M, K, lda, ldc_f, use_brgemm);
   if (store_out) {
+    // copy from Ctmp to C
     for (int64_t m = 0; m < M; ++m) {
       copy_stub<scalar_t>(C + m * ldc_s, C_temp + m * ldc_f, BLOCK_N);
     }
   }
 }
 
-#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE)                                 \
-  template void tinygemm_kernel<TYPE>(                                      \
-      TYPE * C, float* C_temp, const uint8_t* A, const float* scales_a,     \
-      const int32_t* qzeros_a, const uint8_t* B, const float* scales_b,     \
-      const int8_t* qzeros_b, const int32_t* compensation, int8_t* dqB_tmp, \
-      int64_t M, int64_t K, int64_t lda, int64_t ldc_f, int64_t ldc_s,      \
-      bool store_out, bool use_brgemm)
+#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE) \
+  template void tinygemm_kernel<TYPE>(      \
+      TYPE * C,                             \
+      float* C_temp,                        \
+      const uint8_t* A,                     \
+      const float* scales_a,                \
+      const int32_t* qzeros_a,              \
+      const uint8_t* B,                     \
+      const float* scales_b,                \
+      const int8_t* qzeros_b,               \
+      const int32_t* compensation,          \
+      int8_t* dqB_tmp,                      \
+      int64_t M,                            \
+      int64_t K,                            \
+      int64_t lda,                          \
+      int64_t ldc_f,                        \
+      int64_t ldc_s,                        \
+      bool store_out,                       \
+      bool use_brgemm)
 
 INSTANTIATE_TINYGEMM_TEMPLATE(at::BFloat16);
 INSTANTIATE_TINYGEMM_TEMPLATE(at::Half);
 
-at::Tensor int4_scaled_mm_cpu(at::Tensor& x, at::Tensor& w, at::Tensor& w_zeros,
-                              at::Tensor& w_scales,
-                              std::optional<at::Tensor> bias) {
-  return int4_scaled_mm_cpu_with_quant(x, w, w_scales, w_zeros, bias,
-                                       x.scalar_type());
+// int4 gemm dispatch api register
+at::Tensor int4_scaled_mm_cpu(
+    at::Tensor& x, at::Tensor& w, at::Tensor& w_zeros, at::Tensor& w_scales, std::optional<at::Tensor> bias) {
+  return int4_scaled_mm_cpu_with_quant(x, w, w_scales, w_zeros, bias, x.scalar_type());
 }
diff --git a/csrc/cpu/sgl-kernels/gemm_int8.cpp b/csrc/cpu/sgl-kernels/gemm_int8.cpp
index 4d6560cceb1a..c9b79de80105 100644
--- a/csrc/cpu/sgl-kernels/gemm_int8.cpp
+++ b/csrc/cpu/sgl-kernels/gemm_int8.cpp
@@ -1,20 +1,83 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
+// clang-format off
+
 #include "common.h"
-#include "vec.h"
 #include "gemm.h"
-
-// clang-format off
+#include "vec.h"
 
 namespace {
 
+template <typename scalar_t, bool has_bias, int BLOCK_N>
+struct scale_C {
+  static inline void apply(
+      scalar_t* __restrict__ C,
+      const int32_t* __restrict__ Ctmp,
+      const int32_t* __restrict__ Bcomp,
+      const float* __restrict__ bias,
+      float As,
+      const float* __restrict__ Bs) {
+    TORCH_CHECK(false, "scale_C: scalar path not implemented!");
+  }
+};
+
+#if defined(CPU_CAPABILITY_AVX512)
+template <bool has_bias, int BLOCK_N>
+struct scale_C<at::BFloat16, has_bias, BLOCK_N> {
+  static inline void apply(
+      at::BFloat16* __restrict__ C,
+      const int32_t* __restrict__ Ctmp,
+      const int32_t* __restrict__ Bcomp,
+      const float* __restrict__ bias,
+      float As,
+      const float* __restrict__ Bs) {
+    constexpr int COLS = BLOCK_N / 16;
+    static_assert(COLS % 2 == 0);
+
+    __m512 vc[COLS];
+    __m512 vd0 = _mm512_set1_ps(As);
+
+    auto compute = [&](auto col) {
+      __m512 vd1 = _mm512_loadu_ps(Bs + col * 16);
+      __m512i vcomp = _mm512_loadu_si512(Bcomp + col * 16);
+      __m512i vc32 = _mm512_loadu_si512(Ctmp + col * 16);
+      vc[col] = _mm512_cvtepi32_ps(_mm512_sub_epi32(vc32, vcomp));
+      if constexpr (has_bias) {
+        __m512 vbias = _mm512_loadu_ps(bias + col * 16);
+        vc[col] = _mm512_fmadd_ps(_mm512_mul_ps(vc[col], vd0), vd1, vbias);
+      } else {
+        vc[col] = _mm512_mul_ps(_mm512_mul_ps(vc[col], vd0), vd1);
+      }
+    };
+    Unroll<COLS>{}(compute);
+
+    auto storec = [&](auto col) {
+      // for COLS = 2, 4 use 512bit store
+      if constexpr (col % 2 == 0) {
+        _mm512_storeu_si512(
+            reinterpret_cast<__m512i*>((C + col * 16)), (__m512i)(_mm512_cvtne2ps_pbh(vc[col + 1], vc[col + 0])));
+      }
+    };
+    Unroll<COLS>{}(storec);
+  }
+};
+#endif
+
 template <typename scalar_t, bool has_bias, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn {
   static inline void apply(
-      const uint8_t* __restrict__ A, const int8_t* __restrict__ B, scalar_t* __restrict__ C,
-      const float* __restrict__ As, const float* __restrict__ Bs, const int32_t* __restrict__ Bcomp,
-      const float* __restrict__ bias, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
+      const uint8_t* __restrict__ A,
+      const int8_t* __restrict__ B,
+      scalar_t* __restrict__ C,
+      const float* __restrict__ As,
+      const float* __restrict__ Bs,
+      const int32_t* __restrict__ Bcomp,
+      const float* __restrict__ bias,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
     TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
   }
 };
@@ -23,10 +86,17 @@ struct tinygemm_kernel_nn {
 template <bool has_bias, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
   static inline void apply(
-      const uint8_t* __restrict__ A, const int8_t* __restrict__ B, at::BFloat16* __restrict__ C,
-      const float* __restrict__ As, const float* __restrict__ Bs, const int32_t* __restrict__ Bcomp,
-      const float* __restrict__ bias, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
-
+      const uint8_t* __restrict__ A,
+      const int8_t* __restrict__ B,
+      at::BFloat16* __restrict__ C,
+      const float* __restrict__ As,
+      const float* __restrict__ Bs,
+      const int32_t* __restrict__ Bcomp,
+      const float* __restrict__ bias,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
     constexpr int ROWS = BLOCK_M;
     constexpr int COLS = BLOCK_N / 16;
     static_assert(COLS % 2 == 0);
@@ -38,10 +108,10 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
     __m512i vb[COLS];
     __m512i vc[ROWS * COLS];
     __m512i vcomp[COLS];
-    __m512  vd0;
-    __m512  vd1[COLS];
+    __m512 vd0;
+    __m512 vd1[COLS];
 
-    // oops! 4x4 spills but luckily we use 4x2
+    // oops! 4x4 spills but we use 4x2
     __m512 vbias[COLS];
 
     // [NOTE]: s8s8 igemm compensation in avx512-vnni
@@ -54,14 +124,12 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
     // 1) 128 * b is pre-computed when packing B to vnni formats
     // 2) a + 128 is fused when dynamically quantize A
     //
-    auto loadc = [&](auto i) {
-      vc[i] = _mm512_set1_epi32(0);
-    };
+    auto loadc = [&](auto i) { vc[i] = _mm512_set1_epi32(0); };
     Unroll<ROWS * COLS>{}(loadc);
 
     const int64_t K4 = K >> 2;
     const int64_t lda4 = lda >> 2;
-    const int64_t ldb4 = ldb; // ldb * 4 >> 2;
+    const int64_t ldb4 = ldb;  // ldb * 4 >> 2;
     const int32_t* a_ptr = reinterpret_cast<const int32_t*>(A);
     const int32_t* b_ptr = reinterpret_cast<const int32_t*>(B);
 
@@ -89,7 +157,7 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
       constexpr int col = i % COLS;
 
       // load a scale
-      if constexpr(col == 0) {
+      if constexpr (col == 0) {
         vd0 = _mm512_set1_ps(As[row]);
       }
       // load b scale and vcomp per 2 vectors
@@ -120,8 +188,7 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
         }
 
         _mm512_storeu_si512(
-            reinterpret_cast<__m512i*>((C + row * ldc + col * 16)),
-            (__m512i)(_mm512_cvtne2ps_pbh(vc1, vc0)));
+            reinterpret_cast<__m512i*>((C + row * ldc + col * 16)), (__m512i)(_mm512_cvtne2ps_pbh(vc1, vc0)));
       }
     };
     Unroll<ROWS * COLS>{}(storec);
@@ -129,11 +196,19 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
 };
 #endif
 
-#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)                          \
-    tinygemm_kernel_nn<scalar_t, has_bias, MB_SIZE, NB_SIZE>::apply(         \
-        A + mb_start * lda, B + nb_start * 4, C + mb_start * ldc + nb_start, \
-        As + mb_start, Bs + nb_start, Bcomp + nb_start,                      \
-        has_bias ? bias + nb_start : nullptr, K, lda, ldb, ldc);
+#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)                \
+  tinygemm_kernel_nn<scalar_t, has_bias, MB_SIZE, NB_SIZE>::apply( \
+      A + mb_start * lda,                                          \
+      B + nb_start * 4,                                            \
+      C + mb_start * ldc + nb_start,                               \
+      As + mb_start,                                               \
+      Bs + nb_start,                                               \
+      Bcomp + nb_start,                                            \
+      has_bias ? bias + nb_start : nullptr,                        \
+      K,                                                           \
+      lda,                                                         \
+      ldb,                                                         \
+      ldc);
 
 template <typename scalar_t, bool has_bias>
 void tinygemm_kernel(
@@ -151,10 +226,20 @@ void tinygemm_kernel(
     int64_t ldb,
     int64_t ldc,
     bool brg) {
-
   // B compensation
   const int32_t* Bcomp = reinterpret_cast<const int32_t*>(B + block_size_n() * K);
 
+  if (brg) {
+    constexpr int BLOCK_N = block_size_n();
+    at::native::cpublas::brgemm(M, N, K, lda, ldb, BLOCK_N, /* add_C */ false, A, B, Ctmp);
+
+    // apply compensation and scale
+    for (int64_t m = 0; m < M; ++m) {
+      scale_C<scalar_t, has_bias, BLOCK_N>::apply(C + m * ldc, Ctmp + m * BLOCK_N, Bcomp, bias, As[m], Bs);
+    }
+    return;
+  }
+
   // pattern: 1-4-16
   constexpr int64_t BLOCK_M = 4;
   constexpr int64_t BLOCK_N = 64;
@@ -167,26 +252,43 @@ void tinygemm_kernel(
       int64_t nb_start = nb * BLOCK_N;
       int64_t nb_size = std::min(BLOCK_N, N - nb_start);
 
-      switch(mb_size << 4 | nb_size >> 4) {
+      switch (mb_size << 4 | nb_size >> 4) {
         // mb_size = 1
-        case 0x12: LAUNCH_TINYGEMM_KERNEL_NN(1, 32); break;
-        case 0x14: LAUNCH_TINYGEMM_KERNEL_NN(1, 64); break;
+        case 0x12:
+          LAUNCH_TINYGEMM_KERNEL_NN(1, 32);
+          break;
+        case 0x14:
+          LAUNCH_TINYGEMM_KERNEL_NN(1, 64);
+          break;
         // mb_size = 2
-        case 0x22: LAUNCH_TINYGEMM_KERNEL_NN(2, 32); break;
-        case 0x24: LAUNCH_TINYGEMM_KERNEL_NN(2, 64); break;
+        case 0x22:
+          LAUNCH_TINYGEMM_KERNEL_NN(2, 32);
+          break;
+        case 0x24:
+          LAUNCH_TINYGEMM_KERNEL_NN(2, 64);
+          break;
         // mb_size = 3
-        case 0x32: LAUNCH_TINYGEMM_KERNEL_NN(3, 32); break;
-        case 0x34: LAUNCH_TINYGEMM_KERNEL_NN(3, 64); break;
+        case 0x32:
+          LAUNCH_TINYGEMM_KERNEL_NN(3, 32);
+          break;
+        case 0x34:
+          LAUNCH_TINYGEMM_KERNEL_NN(3, 64);
+          break;
         // mb_size = 4
-        case 0x42: LAUNCH_TINYGEMM_KERNEL_NN(4, 32); break;
-        case 0x44: LAUNCH_TINYGEMM_KERNEL_NN(4, 64); break;
-        default: TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", nb_size);
+        case 0x42:
+          LAUNCH_TINYGEMM_KERNEL_NN(4, 32);
+          break;
+        case 0x44:
+          LAUNCH_TINYGEMM_KERNEL_NN(4, 64);
+          break;
+        default:
+          TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", "nb_size");
       }
     }
   }
 }
 
-template<typename scalar_t>
+template <typename scalar_t>
 void int8_scaled_mm_kernel_impl(
     scalar_t* __restrict__ out,
     const uint8_t* __restrict__ mat1,
@@ -197,28 +299,22 @@ void int8_scaled_mm_kernel_impl(
     int64_t M,
     int64_t N,
     int64_t K) {
-
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
   const int64_t MB = div_up(M, BLOCK_M);
   const int64_t NB = div_up(N, BLOCK_N);
 
-  // TODO: brgemm u8s8 depends on PyTorch 2.7 release.
-  const bool use_brgemm = false;
+  const bool use_brgemm = can_use_brgemm<int8_t>(M);
 
   // K + 4 after compensation
   const int64_t packed_row_size = get_row_size<int8_t>(K);
 
   AT_DISPATCH_BOOL(bias != nullptr, has_bias, [&] {
-    at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
-      int64_t mb{0}, nb{0};
-      data_index_init(begin, mb, MB, nb, NB);
-
+    parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
       // for brgemm, use int32_t for accumulate
       alignas(64) int32_t Ctmp[BLOCK_M * BLOCK_N];
 
-      for (int i = begin; i < end; ++i) {
-        UNUSED(i);
+      loop_2d<int8_t>(mb0, mb1, nb0, nb1, BLOCK_N * K, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
         int mb_start = mb * BLOCK_M;
         int mb_size = std::min(M - mb_start, BLOCK_M);
         int nb_start = nb * BLOCK_N;
@@ -239,10 +335,7 @@ void int8_scaled_mm_kernel_impl(
             /* ldb */ nb_size,
             /* ldc */ N,
             /* brg */ use_brgemm);
-
-        // move to the next index
-        data_index_step(mb, MB, nb, NB);
-      }
+      });
 
       if (use_brgemm) {
         at::native::cpublas::brgemm_release();
@@ -251,28 +344,47 @@ void int8_scaled_mm_kernel_impl(
   });
 }
 
-} // anonymous namespace
+}  // anonymous namespace
 
 // tinygemm interface
 template <typename scalar_t>
-void tinygemm_kernel(const uint8_t* __restrict__ A, const int8_t* __restrict__ B, scalar_t* __restrict__ C,
-    int32_t* __restrict__ Ctmp,  const float* __restrict__ As, const float* __restrict__ Bs,
-    int64_t M, int64_t N, int64_t K, int64_t lda, int64_t ldb, int64_t ldc, bool brg) {
+void tinygemm_kernel(
+    const uint8_t* __restrict__ A,
+    const int8_t* __restrict__ B,
+    scalar_t* __restrict__ C,
+    int32_t* __restrict__ Ctmp,
+    const float* __restrict__ As,
+    const float* __restrict__ Bs,
+    int64_t M,
+    int64_t N,
+    int64_t K,
+    int64_t lda,
+    int64_t ldb,
+    int64_t ldc,
+    bool brg) {
   tinygemm_kernel<scalar_t, false>(A, B, C, Ctmp, As, Bs, nullptr, M, N, K, lda, ldb, ldc, brg);
 }
 
-#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE)                                                     \
-    template void tinygemm_kernel<TYPE>(                                                        \
-        const uint8_t* __restrict__ A, const int8_t* __restrict__ B, TYPE* __restrict__ C,      \
-        int32_t* __restrict__ Ctmp, const float* __restrict__ As, const float* __restrict__ Bs, \
-        int64_t M, int64_t N, int64_t K, int64_t lda, int64_t ldb, int64_t ldc, bool brg)
+#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE) \
+  template void tinygemm_kernel<TYPE>(      \
+      const uint8_t* __restrict__ A,        \
+      const int8_t* __restrict__ B,         \
+      TYPE* __restrict__ C,                 \
+      int32_t* __restrict__ Ctmp,           \
+      const float* __restrict__ As,         \
+      const float* __restrict__ Bs,         \
+      int64_t M,                            \
+      int64_t N,                            \
+      int64_t K,                            \
+      int64_t lda,                          \
+      int64_t ldb,                          \
+      int64_t ldc,                          \
+      bool brg)
 
 INSTANTIATE_TINYGEMM_TEMPLATE(at::BFloat16);
 INSTANTIATE_TINYGEMM_TEMPLATE(at::Half);
 
 std::tuple<at::Tensor, at::Tensor> per_token_quant_int8_cpu(at::Tensor& A) {
-  RECORD_FUNCTION("sgl-kernel::per_token_quant_int8_cpu", std::vector<c10::IValue>({A}));
-
   CHECK_LAST_DIM_CONTIGUOUS_INPUT(A);
   CHECK_DIM(2, A);
 
@@ -281,8 +393,7 @@ std::tuple<at::Tensor, at::Tensor> per_token_quant_int8_cpu(at::Tensor& A) {
   int64_t lda = A.stride(0);
 
   const auto st = A.scalar_type();
-  TORCH_CHECK(st == at::kBFloat16 || st == at::kHalf,
-      "per_token_quant_int8: expect A to be bfloat16 or half.");
+  TORCH_CHECK(st == at::kBFloat16 || st == at::kHalf, "per_token_quant_int8: expect A to be bfloat16 or half.");
 
   auto Aq = at::empty({M, K}, A.options().dtype(at::kByte));
   auto As = at::empty({M}, A.options().dtype(at::kFloat));
@@ -292,13 +403,9 @@ std::tuple<at::Tensor, at::Tensor> per_token_quant_int8_cpu(at::Tensor& A) {
     float* __restrict__ As_data = As.data_ptr<float>();
     const scalar_t* __restrict__ A_data = A.data_ptr<scalar_t>();
 
-    at::parallel_for(0, M, 0, [&] (int64_t begin, int64_t end) {
+    at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
       for (int64_t m = begin; m < end; ++m) {
-        quantize_row_int8<scalar_t>(
-            Aq_data + m * K,
-            As_data[m],
-            A_data + m * lda,
-            K);
+        quantize_row_int8<scalar_t>(Aq_data + m * K, As_data[m], A_data + m * lda, K);
       }
     });
   });
@@ -315,11 +422,14 @@ std::tuple<at::Tensor, at::Tensor> per_token_quant_int8_cpu(at::Tensor& A) {
 // bias    : [N]
 // out     : [M, N]
 //
-at::Tensor int8_scaled_mm_cpu(at::Tensor& mat1, at::Tensor& mat2,
-    at::Tensor& scales1, at::Tensor& scales2,
-    std::optional<at::Tensor>& bias, at::ScalarType out_dtype, bool is_vnni) {
-  RECORD_FUNCTION("sgl-kernel::int8_scaled_mm_cpu", std::vector<c10::IValue>({mat1, mat2, scales1, scales2, bias}));
-
+at::Tensor int8_scaled_mm_cpu(
+    at::Tensor& mat1,
+    at::Tensor& mat2,
+    at::Tensor& scales1,
+    at::Tensor& scales2,
+    const std::optional<at::Tensor>& bias,
+    at::ScalarType out_dtype,
+    bool is_vnni) {
   auto packed_w = is_vnni ? mat2 : convert_weight_packed(mat2);
 
   CHECK_INPUT(mat1);
@@ -340,7 +450,8 @@ at::Tensor int8_scaled_mm_cpu(at::Tensor& mat1, at::Tensor& mat2,
 
   TORCH_CHECK(mat1.scalar_type() == at::kByte, "int8_scaled_mm: expect mat1 to be uint8.");
   TORCH_CHECK(mat2.scalar_type() == at::kChar, "int8_scaled_mm: expect mat2 to be int8.");
-  TORCH_CHECK(scales1.scalar_type() == at::kFloat && scales2.scalar_type() == at::kFloat,
+  TORCH_CHECK(
+      scales1.scalar_type() == at::kFloat && scales2.scalar_type() == at::kFloat,
       "int8_scaled_mm: expect scales to be float32.");
 
   auto out = at::empty({M, N}, mat1.options().dtype(out_dtype));
@@ -368,10 +479,13 @@ at::Tensor int8_scaled_mm_cpu(at::Tensor& mat1, at::Tensor& mat2,
 }
 
 // fused `per_token_quant_int8_cpu` and `int8_scaled_mm_cpu`
-at::Tensor int8_scaled_mm_with_quant(at::Tensor& mat1, at::Tensor& mat2, at::Tensor& scales2,
-    const std::optional<at::Tensor>& bias, at::ScalarType out_dtype, bool is_vnni) {
-  RECORD_FUNCTION("sgl-kernel::int8_scaled_mm_cpu", std::vector<c10::IValue>({mat1, mat2, scales2, bias}));
-
+at::Tensor int8_scaled_mm_with_quant(
+    at::Tensor& mat1,
+    at::Tensor& mat2,
+    at::Tensor& scales2,
+    const std::optional<at::Tensor>& bias,
+    at::ScalarType out_dtype,
+    bool is_vnni) {
   auto packed_w = is_vnni ? mat2 : convert_weight_packed(mat2);
 
   CHECK_LAST_DIM_CONTIGUOUS_INPUT(mat1);
@@ -390,14 +504,10 @@ at::Tensor int8_scaled_mm_with_quant(at::Tensor& mat1, at::Tensor& mat2, at::Ten
   CHECK_EQ(scales2.numel(), N);
 
   const auto st = mat1.scalar_type();
-  TORCH_CHECK(st == at::kBFloat16 || st == at::kHalf,
-      "int8_scaled_mm_with_quant: expect A to be bfloat16 or half.");
-  TORCH_CHECK(st == out_dtype,
-      "int8_scaled_mm_with_quant: expect A has same dtype with out_dtype.");
-  TORCH_CHECK(mat2.scalar_type() == at::kChar,
-      "int8_scaled_mm_with_quant: expect mat2 to be int8.");
-  TORCH_CHECK(scales2.scalar_type() == at::kFloat,
-      "int8_scaled_mm_with_quant: expect scales to be float32.");
+  TORCH_CHECK(st == at::kBFloat16 || st == at::kHalf, "int8_scaled_mm_with_quant: expect A to be bfloat16 or half.");
+  TORCH_CHECK(st == out_dtype, "int8_scaled_mm_with_quant: expect A has same dtype with out_dtype.");
+  TORCH_CHECK(mat2.scalar_type() == at::kChar, "int8_scaled_mm_with_quant: expect mat2 to be int8.");
+  TORCH_CHECK(scales2.scalar_type() == at::kFloat, "int8_scaled_mm_with_quant: expect scales to be float32.");
 
   const int64_t buffer_size = M * K + M * sizeof(float);
   auto buffer = at::empty({buffer_size}, mat1.options().dtype(at::kByte));
@@ -415,13 +525,9 @@ at::Tensor int8_scaled_mm_with_quant(at::Tensor& mat1, at::Tensor& mat2, at::Ten
     float* __restrict__ As_data = (float*)((void*)(Aq_data + M * K));
     const scalar_t* __restrict__ A_data = mat1.data_ptr<scalar_t>();
 
-    at::parallel_for(0, M, 0, [&] (int64_t begin, int64_t end) {
+    at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
       for (int64_t m = begin; m < end; ++m) {
-        quantize_row_int8<scalar_t>(
-            Aq_data + m * K,
-            As_data[m],
-            A_data + m * lda,
-            K);
+        quantize_row_int8<scalar_t>(Aq_data + m * K, As_data[m], A_data + m * lda, K);
       }
     });
 
diff --git a/csrc/cpu/sgl-kernels/moe.cpp b/csrc/cpu/sgl-kernels/moe.cpp
index c01bfd376d4f..c248f678beee 100644
--- a/csrc/cpu/sgl-kernels/moe.cpp
+++ b/csrc/cpu/sgl-kernels/moe.cpp
@@ -1,12 +1,13 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
+// clang-format off
+
+#include "moe.h"
+
 #include "common.h"
-#include "vec.h"
 #include "gemm.h"
 
-// clang-format off
-
 namespace {
 
 // [NOTE]: Fused MoE kernel with AMX
@@ -30,109 +31,6 @@ namespace {
 //     3. abstract at::native::cpublas::brgemm with WoQ gemm (M = 1 & M != 1)
 //
 
-template <typename scalar_t>
-inline void fill_stub(scalar_t* __restrict__ out, scalar_t val, int64_t size) {
-  using Vec = at::vec::Vectorized<scalar_t>;
-  const Vec data_vec(val);
-  at::vec::map<scalar_t>([data_vec](Vec out) { return out = data_vec; }, out, out, size);
-}
-
-template <typename scalar_t>
-inline void copy_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t size) {
-  using Vec = at::vec::Vectorized<scalar_t>;
-  // no remainder
-  #pragma GCC unroll 4
-  for (int64_t d = 0; d < size; d += Vec::size()) {
-    Vec data = Vec::loadu(input + d);
-    data.store(out + d);
-  }
-}
-
-template <typename scalar_t>
-inline void copy_mul_stub(scalar_t* __restrict__ out, const float* __restrict__ input, float weight, int64_t size) {
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-  const fVec weight_vec = fVec(weight);
-  int64_t d;
-  #pragma GCC unroll 4
-  for (d = 0; d <= size - kVecSize; d += kVecSize) {
-    fVec data0 = fVec::loadu(input + d) * weight_vec;
-    fVec data1 = fVec::loadu(input + d + fVec::size()) * weight_vec;
-    bVec out_vec = convert_from_float_ext<scalar_t>(data0, data1);
-    out_vec.store(out + d);
-  }
-  for (; d < size; ++d) {
-    out[d] = static_cast<scalar_t>(input[d] * weight);
-  }
-}
-
-// acc from [topk, K] to [K]
-template <typename scalar_t>
-inline void sum_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t topk, int64_t K) {
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-  if (topk == 1) {
-    // do copy for topk = 1
-    copy_stub(out, input, K);
-  } else {
-    // do sum for topk != 1
-    int64_t d;
-    #pragma GCC unroll 4
-    for (d = 0; d <= K - kVecSize; d += kVecSize) {
-      fVec sum_fvec0 = fVec(0.f);
-      fVec sum_fvec1 = fVec(0.f);
-      for (int t = 0; t < topk; ++t) {
-        bVec x_bvec = bVec::loadu(input + t * K + d);
-        fVec x_fvec0, x_fvec1;
-        std::tie(x_fvec0, x_fvec1) = at::vec::convert_to_float(x_bvec);
-
-        sum_fvec0 += x_fvec0;
-        sum_fvec1 += x_fvec1;
-      }
-      bVec out_bvec = convert_from_float_ext<scalar_t>(sum_fvec0, sum_fvec1);
-      out_bvec.store(out + d);
-    }
-    for (; d < K; ++d) {
-      float sum_val = 0.f;
-      for (int t = 0; t < topk; ++t) {
-        sum_val += static_cast<float>(input[t * K + d]);
-      }
-      out[d] = static_cast<scalar_t>(sum_val);
-    }
-  }
-}
-
-// out = input + input2 * scale
-template <typename scalar_t>
-inline void add_mul_stub(scalar_t* __restrict__ out, const float* __restrict__ input,
-    const scalar_t* __restrict__ input2, float scale, int64_t size) {
-
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-  const fVec s_vec = fVec(scale);
-  int64_t d;
-  #pragma GCC unroll 4
-  for (d = 0; d <= size - kVecSize; d += kVecSize) {
-    fVec x0 = fVec::loadu(input + d);
-    fVec x1 = fVec::loadu(input + d + fVec::size());
-
-    bVec y_bvec = bVec::loadu(input2 + d);
-    fVec y0, y1;
-    std::tie(y0, y1) = at::vec::convert_to_float(y_bvec);
-
-    x0 = x0 + y0 * s_vec;
-    x1 = x1 + y1 * s_vec;
-    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
-    out_vec.store(out + d);
-  }
-  for (; d < size; ++d) {
-    out[d] = static_cast<scalar_t>(input[d] + float(input2[d]) * scale);
-  }
-}
-
 template <int BLOCK_M>
 int moe_align_block_size(
     int32_t* __restrict__ sorted_ids,
@@ -144,8 +42,7 @@ int moe_align_block_size(
     int num_experts,
     int numel,
     int num_threads) {
-
-  #define T_INDEX(tt) total_cnts + (tt) * num_experts
+#define T_INDEX(tt) total_cnts + (tt) * num_experts
 
   // accumulate count of expert ids locally
   at::parallel_for(0, numel, 0, [&](int begin, int end) {
@@ -160,8 +57,7 @@ int moe_align_block_size(
   using iVec = at::vec::Vectorized<int32_t>;
   for (int t = 0; t < num_threads; ++t) {
     at::vec::map2<int32_t>(
-        [](iVec x, iVec y) { return x + y; },
-        T_INDEX(t + 1), T_INDEX(t + 1), T_INDEX(t), num_experts);
+        [](iVec x, iVec y) { return x + y; }, T_INDEX(t + 1), T_INDEX(t + 1), T_INDEX(t), num_experts);
   }
 
   // the last row holds sums of each experts
@@ -201,7 +97,9 @@ int moe_align_block_size(
   // padding value for sorted_ids: numel
   auto sorted_id_size = [=](const int32_t* sorted_ids_ptr) {
     for (int d = 0; d < BLOCK_M; ++d) {
-      if (sorted_ids_ptr[d] == numel) { return d; }
+      if (sorted_ids_ptr[d] == numel) {
+        return d;
+      }
     }
     return BLOCK_M;
   };
@@ -215,7 +113,7 @@ int moe_align_block_size(
       offsets[mb + 1] = sorted_id_size(sorted_ids + mb * BLOCK_M);
     }
   });
-  // TODO: do we need to vectorize this ?
+  // TODO: do we need to vecterize this ?
   for (int mb = 0; mb < num_token_blocks; ++mb) {
     offsets[mb + 1] += offsets[mb];
   }
@@ -236,7 +134,6 @@ inline void silu_and_mul(
     const float* __restrict__ input1,  // y: y0, y1
     int64_t m_size,
     int64_t N) {
-
   using bVec = at::vec::Vectorized<scalar_t>;
   using fVec = at::vec::Vectorized<float>;
 
@@ -269,8 +166,14 @@ inline void silu_and_mul(
 template <typename scalar_t, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn2 {
   static inline void apply(
-      const scalar_t* __restrict__ A, const scalar_t* __restrict__ B0, const scalar_t* __restrict__ B1,
-      scalar_t* __restrict__ C, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
+      const scalar_t* __restrict__ A,
+      const scalar_t* __restrict__ B0,
+      const scalar_t* __restrict__ B1,
+      scalar_t* __restrict__ C,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
     TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
   }
 };
@@ -279,9 +182,14 @@ struct tinygemm_kernel_nn2 {
 template <int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn2<at::BFloat16, BLOCK_M, BLOCK_N> {
   static inline void apply(
-      const at::BFloat16* __restrict__ A, const at::BFloat16* __restrict__ B0, const at::BFloat16* __restrict__ B1,
-      at::BFloat16* __restrict__ C, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
-
+      const at::BFloat16* __restrict__ A,
+      const at::BFloat16* __restrict__ B0,
+      const at::BFloat16* __restrict__ B1,
+      at::BFloat16* __restrict__ C,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
     constexpr int ROWS = BLOCK_M;
     constexpr int COLS = BLOCK_N / 16;
 
@@ -304,7 +212,7 @@ struct tinygemm_kernel_nn2<at::BFloat16, BLOCK_M, BLOCK_N> {
 
     const int64_t K2 = K >> 1;
     const int64_t lda2 = lda >> 1;
-    const int64_t ldb2 = ldb; // ldb * 2 >> 1;
+    const int64_t ldb2 = ldb;  // ldb * 2 >> 1;
     const float* a_ptr = reinterpret_cast<const float*>(A);
     const float* b0_ptr = reinterpret_cast<const float*>(B0);
     const float* b1_ptr = reinterpret_cast<const float*>(B1);
@@ -352,17 +260,16 @@ struct tinygemm_kernel_nn2<at::BFloat16, BLOCK_M, BLOCK_N> {
         _mm512_storeu_si512(
             reinterpret_cast<__m512i*>((C + row * ldc + col * 16)),
             (__m512i)(_mm512_cvtne2ps_pbh(__m512(x1), __m512(x0))));
-        }
+      }
     };
     Unroll<ROWS * COLS>{}(storec);
   }
 };
 #endif
 
-#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)                          \
-    tinygemm_kernel_nn2<scalar_t, MB_SIZE, NB_SIZE>::apply(                  \
-        A + mb_start * lda, B0 + nb_start * 2, B1 + nb_start * 2,            \
-        C + mb_start * ldc + nb_start, K, lda, ldb, ldc);
+#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)       \
+  tinygemm_kernel_nn2<scalar_t, MB_SIZE, NB_SIZE>::apply( \
+      A + mb_start * lda, B0 + nb_start * 2, B1 + nb_start * 2, C + mb_start * ldc + nb_start, K, lda, ldb, ldc);
 
 template <typename scalar_t>
 void tinygemm_kernel(
@@ -376,7 +283,6 @@ void tinygemm_kernel(
     int64_t lda,
     int64_t ldb,
     int64_t ldc) {
-
   // pattern: 1-(2+2)-(8+8)
   constexpr int64_t BLOCK_M = 4;
   constexpr int64_t BLOCK_N = 32;
@@ -389,16 +295,25 @@ void tinygemm_kernel(
       int64_t nb_start = nb * BLOCK_N;
       int64_t nb_size = std::min(BLOCK_N, N - nb_start);
 
-      switch(mb_size << 4 | nb_size >> 4) {
+      switch (mb_size << 4 | nb_size >> 4) {
         // mb_size = 1
-        case 0x12: LAUNCH_TINYGEMM_KERNEL_NN(1, 32); break;
+        case 0x12:
+          LAUNCH_TINYGEMM_KERNEL_NN(1, 32);
+          break;
         // mb_size = 2
-        case 0x22: LAUNCH_TINYGEMM_KERNEL_NN(2, 32); break;
+        case 0x22:
+          LAUNCH_TINYGEMM_KERNEL_NN(2, 32);
+          break;
         // mb_size = 3
-        case 0x32: LAUNCH_TINYGEMM_KERNEL_NN(3, 32); break;
+        case 0x32:
+          LAUNCH_TINYGEMM_KERNEL_NN(3, 32);
+          break;
         // mb_size = 4
-        case 0x42: LAUNCH_TINYGEMM_KERNEL_NN(4, 32); break;
-        default: TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", nb_size);
+        case 0x42:
+          LAUNCH_TINYGEMM_KERNEL_NN(4, 32);
+          break;
+        default:
+          TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", "nb_size");
       }
     }
   }
@@ -407,8 +322,13 @@ void tinygemm_kernel(
 template <typename scalar_t, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn {
   static inline void apply(
-      const scalar_t* __restrict__ A, const scalar_t* __restrict__ B, float* __restrict__ C,
-      int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
+      const scalar_t* __restrict__ A,
+      const scalar_t* __restrict__ B,
+      float* __restrict__ C,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
     TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
   }
 };
@@ -417,9 +337,13 @@ struct tinygemm_kernel_nn {
 template <int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn<at::BFloat16, BLOCK_M, BLOCK_N> {
   static inline void apply(
-      const at::BFloat16* __restrict__ A, const at::BFloat16* __restrict__ B, float* __restrict__ C,
-      int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
-
+      const at::BFloat16* __restrict__ A,
+      const at::BFloat16* __restrict__ B,
+      float* __restrict__ C,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
     constexpr int ROWS = BLOCK_M;
     constexpr int COLS = BLOCK_N / 16;
 
@@ -432,14 +356,12 @@ struct tinygemm_kernel_nn<at::BFloat16, BLOCK_M, BLOCK_N> {
     __m512bh vb[COLS];
     __m512 vc[ROWS * COLS];
 
-    auto loadc = [&](auto i) {
-      vc[i] = _mm512_set1_ps(0.f);
-    };
+    auto loadc = [&](auto i) { vc[i] = _mm512_set1_ps(0.f); };
     Unroll<ROWS * COLS>{}(loadc);
 
     const int64_t K2 = K >> 1;
     const int64_t lda2 = lda >> 1;
-    const int64_t ldb2 = ldb; // ldb * 2 >> 1;
+    const int64_t ldb2 = ldb;  // ldb * 2 >> 1;
     const float* a_ptr = reinterpret_cast<const float*>(A);
     const float* b_ptr = reinterpret_cast<const float*>(B);
 
@@ -466,17 +388,15 @@ struct tinygemm_kernel_nn<at::BFloat16, BLOCK_M, BLOCK_N> {
       constexpr int row = i / COLS;
       constexpr int col = i % COLS;
       _mm512_storeu_ps(reinterpret_cast<__m512*>(C + row * ldc + col * 16), vc[i]);
-
     };
     Unroll<ROWS * COLS>{}(storec);
   }
 };
 #endif
 
-#define LAUNCH_TINYGEMM_KERNEL_NN2(MB_SIZE, NB_SIZE)                         \
-    tinygemm_kernel_nn<scalar_t, MB_SIZE, NB_SIZE>::apply(                   \
-        A + mb_start * lda, B + nb_start * 2, C + mb_start * ldc + nb_start, \
-        K, lda, ldb, ldc);
+#define LAUNCH_TINYGEMM_KERNEL_NN2(MB_SIZE, NB_SIZE)     \
+  tinygemm_kernel_nn<scalar_t, MB_SIZE, NB_SIZE>::apply( \
+      A + mb_start * lda, B + nb_start * 2, C + mb_start * ldc + nb_start, K, lda, ldb, ldc);
 
 template <typename scalar_t>
 void tinygemm_kernel(
@@ -489,7 +409,6 @@ void tinygemm_kernel(
     int64_t lda,
     int64_t ldb,
     int64_t ldc) {
-
   // pattern: 1-2-8
   constexpr int64_t BLOCK_M = 4;
   constexpr int64_t BLOCK_N = 32;
@@ -502,16 +421,25 @@ void tinygemm_kernel(
       int64_t nb_start = nb * BLOCK_N;
       int64_t nb_size = std::min(BLOCK_N, N - nb_start);
 
-      switch(mb_size << 4 | nb_size >> 4) {
+      switch (mb_size << 4 | nb_size >> 4) {
         // mb_size = 1
-        case 0x12: LAUNCH_TINYGEMM_KERNEL_NN2(1, 32); break;
+        case 0x12:
+          LAUNCH_TINYGEMM_KERNEL_NN2(1, 32);
+          break;
         // mb_size = 2
-        case 0x22: LAUNCH_TINYGEMM_KERNEL_NN2(2, 32); break;
+        case 0x22:
+          LAUNCH_TINYGEMM_KERNEL_NN2(2, 32);
+          break;
         // mb_size = 3
-        case 0x32: LAUNCH_TINYGEMM_KERNEL_NN2(3, 32); break;
+        case 0x32:
+          LAUNCH_TINYGEMM_KERNEL_NN2(3, 32);
+          break;
         // mb_size = 4
-        case 0x42: LAUNCH_TINYGEMM_KERNEL_NN2(4, 32); break;
-        default: TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", nb_size);
+        case 0x42:
+          LAUNCH_TINYGEMM_KERNEL_NN2(4, 32);
+          break;
+        default:
+          TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", "nb_size");
       }
     }
   }
@@ -537,7 +465,6 @@ void fused_experts_kernel_impl(
     int64_t E,
     int64_t topk,
     int64_t num_tokens_post_pad) {
-
   // handle 2 tiles per block
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
@@ -552,39 +479,36 @@ void fused_experts_kernel_impl(
   const int64_t stride_e = 2 * N * K;
   const int64_t stride_n = K;
 
+  int64_t avg_M = std::max(int64_t(1), M * topk / E);
+  const bool use_brgemm = can_use_brgemm<scalar_t>(avg_M);
+
   // here we only parallel on half of 2N to fuse silu_and_mul with gemm
-  at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
+  parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
     // get local pointers
-    int tid = at::get_thread_num();
+    int tid = get_thread_num();
     scalar_t* __restrict__ A = A_tmp + tid * BLOCK_M * K;
     float* __restrict__ C0 = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
     float* __restrict__ C1 = C0 + BLOCK_M * BLOCK_N;
 
-    bool is_brgemm_used = false;
-
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB;
-      int64_t nb = i % NB;
-
-      // nb0 from top half and nb1 from bottom half
-      int64_t nb0 = nb, nb1 = nb + NB;
-      int64_t n_size = std::min(N - nb0 * BLOCK_N, BLOCK_N);
+    loop_2d<scalar_t>(mb0, mb1, nb0, nb1, BLOCK_N * K * 2, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+      // nb_upper from top half and nb_lower from bottom half
+      int64_t nb_upper = nb, nb_lower = nb + NB;
+      int64_t n_size = std::min(N - nb * BLOCK_N, BLOCK_N);
 
       // B shape [K, n_size] in vnni format
       int32_t expert_id = expert_ids[mb];
-      const scalar_t* __restrict__ B0 = packed_w1 + expert_id * stride_e + nb0 * BLOCK_N * stride_n;
-      const scalar_t* __restrict__ B1 = packed_w1 + expert_id * stride_e + nb1 * BLOCK_N * stride_n;
+      const scalar_t* __restrict__ B0 = packed_w1 + expert_id * stride_e + nb_upper * BLOCK_N * stride_n;
+      const scalar_t* __restrict__ B1 = packed_w1 + expert_id * stride_e + nb_lower * BLOCK_N * stride_n;
 
-      // 1.a load A
-      const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
       int64_t m_size = offsets[mb + 1] - offsets[mb];
 
-      const bool use_brgemm = can_use_brgemm<scalar_t>(m_size);
-      is_brgemm_used = is_brgemm_used || use_brgemm;
-
-      for (int64_t m = 0; m < m_size; ++m) {
-        int32_t index = A_ids[m] / topk;
-        copy_stub(A + m * K, input + index * K, K);
+      if (nb_offset == 0) {
+        // 1.a load A
+        const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
+        for (int64_t m = 0; m < m_size; ++m) {
+          int32_t index = A_ids[m] / topk;
+          copy_stub(A + m * K, input + index * K, K);
+        }
       }
 
       if (use_brgemm) {
@@ -616,12 +540,7 @@ void fused_experts_kernel_impl(
 
         // 1.d silu and mul
         const int64_t offset = offsets[mb];
-        silu_and_mul<scalar_t, BLOCK_N>(
-            ic1 + offset * N + nb * BLOCK_N,
-            C0,
-            C1,
-            m_size,
-            N);
+        silu_and_mul<scalar_t, BLOCK_N>(ic1 + offset * N + nb * BLOCK_N, C0, C1, m_size, N);
       } else {
         // fused 1.bcd: silu_and_mul(A @ B0, A @ B1)
         const int64_t offset = offsets[mb];
@@ -637,9 +556,9 @@ void fused_experts_kernel_impl(
             /* ldb   */ n_size,
             /* ldc   */ N);
       }
-    }
+    });
 
-    if (is_brgemm_used) {
+    if (use_brgemm) {
       at::native::cpublas::brgemm_release();
     }
   });
@@ -654,24 +573,16 @@ void fused_experts_kernel_impl(
   const int64_t stride_oc = IC;
 
   // parallel on [MB2, NB2]
-  at::parallel_for(0, MB2 * NB2, 0, [&](int64_t begin, int64_t end) {
+  parallel_2d(MB2, NB2, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
     // get local pointers
-    int tid = at::get_thread_num();
+    int tid = get_thread_num();
     // we won't be using C1 for gemm2
     float* __restrict__ C = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
 
-    bool is_brgemm_used = false;
-
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB2;
-      int64_t nb = i % NB2;
-
+    loop_2d<scalar_t>(mb0, mb1, nb0, nb1, BLOCK_N * IC, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
       int64_t m_size = offsets[mb + 1] - offsets[mb];
       int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
 
-      const bool use_brgemm = can_use_brgemm<scalar_t>(m_size);
-      is_brgemm_used = is_brgemm_used || use_brgemm;
-
       // A ptr from ic1 of [M * topk, N] in sorted order
       // so as to avoid copy A to tmp buffer again
       const scalar_t* __restrict__ A = ic1 + offsets[mb] * N;
@@ -714,9 +625,9 @@ void fused_experts_kernel_impl(
         float weight = topk_weights[index];
         copy_mul_stub(ic2 + index * K + nb * BLOCK_N, C + m * BLOCK_N, weight, n_size);
       }
-    }
+    });
 
-    if (is_brgemm_used) {
+    if (use_brgemm) {
       at::native::cpublas::brgemm_release();
     }
   });
@@ -743,7 +654,6 @@ void shared_expert_kernel_impl(
     int64_t M,
     int64_t N,
     int64_t K) {
-
   // handle 2 tiles per block
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
@@ -755,36 +665,29 @@ void shared_expert_kernel_impl(
   TORCH_CHECK(N % BLOCK_N == 0, "Fixme when N is not multiples of ", BLOCK_N);
   const int64_t stride_n = K;
 
+  const bool use_brgemm = can_use_brgemm<scalar_t>(M);
+
+  const bool apply_scaling_factor = fused_experts_out != nullptr;
+
   // here we only parallel on half of 2N to fuse silu_and_mul with gemm
-  at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
+  parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
     // get local pointers
-    int tid = at::get_thread_num();
+    int tid = get_thread_num();
     float* __restrict__ C0 = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
     float* __restrict__ C1 = C0 + BLOCK_M * BLOCK_N;
 
-    bool is_brgemm_used = false;
-
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB;
-      int64_t nb = i % NB;
-
-      // nb0 from top half and nb1 from bottom half
-      int64_t nb0 = nb, nb1 = nb + NB;
-      int64_t n_size = std::min(N - nb0 * BLOCK_N, BLOCK_N);
+    loop_2d<scalar_t>(mb0, mb1, nb0, nb1, BLOCK_N * K * 2, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+      // nb_upper from top half and nb_lower from bottom half
+      int64_t nb_upper = nb, nb_lower = nb + NB;
+      int64_t n_size = std::min(N - nb * BLOCK_N, BLOCK_N);
       int64_t m_size = std::min(M - mb * BLOCK_M, BLOCK_M);
 
-      //int64_t mb_start = mb * BLOCK_M;
-      //int64_t mb_size = std::min(M - mb_start, BLOCK_M);
-
       // A shape [m_size, K]
       const scalar_t* A = input + mb * BLOCK_M * K;
 
       // B shape [K, n_size] in vnni format
-      const scalar_t* __restrict__ B0 = packed_w1 + nb0 * BLOCK_N * stride_n;
-      const scalar_t* __restrict__ B1 = packed_w1 + nb1 * BLOCK_N * stride_n;
-
-      const bool use_brgemm = can_use_brgemm<scalar_t>(m_size);
-      is_brgemm_used = is_brgemm_used || use_brgemm;
+      const scalar_t* __restrict__ B0 = packed_w1 + nb_upper * BLOCK_N * stride_n;
+      const scalar_t* __restrict__ B1 = packed_w1 + nb_lower * BLOCK_N * stride_n;
 
       if (use_brgemm) {
         // 1.b gemm: C0 = A @ B0
@@ -814,12 +717,7 @@ void shared_expert_kernel_impl(
             /* C     */ C1);
 
         // 1.d silu and mul
-        silu_and_mul<scalar_t, BLOCK_N>(
-            ic1 + mb * BLOCK_M * N + nb * BLOCK_N,
-            C0,
-            C1,
-            m_size,
-            N);
+        silu_and_mul<scalar_t, BLOCK_N>(ic1 + mb * BLOCK_M * N + nb * BLOCK_N, C0, C1, m_size, N);
       } else {
         // fused 1.bcd: silu_and_mul(A @ B0, A @ B1)
         tinygemm_kernel(
@@ -834,9 +732,9 @@ void shared_expert_kernel_impl(
             /* ldb   */ n_size,
             /* ldc   */ N);
       }
-    }
+    });
 
-    if (is_brgemm_used) {
+    if (use_brgemm) {
       at::native::cpublas::brgemm_release();
     }
   });
@@ -850,24 +748,16 @@ void shared_expert_kernel_impl(
   const int64_t stride_oc = IC;
 
   // parallel on [MB2, NB2]
-  at::parallel_for(0, MB2 * NB2, 0, [&](int64_t begin, int64_t end) {
+  parallel_2d(MB2, NB2, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
     // get local pointers
-    int tid = at::get_thread_num();
+    int tid = get_thread_num();
     // we won't be using C1 for gemm2
     float* __restrict__ C = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
 
-    bool is_brgemm_used = false;
-
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB2;
-      int64_t nb = i % NB2;
-
+    loop_2d<scalar_t>(mb0, mb1, nb0, nb1, BLOCK_N * IC, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
       int64_t m_size = std::min(M - mb * BLOCK_M, BLOCK_M);
       int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
 
-      const bool use_brgemm = can_use_brgemm<scalar_t>(m_size);
-      is_brgemm_used = is_brgemm_used || use_brgemm;
-
       // A shape [m_size, IC]
       const scalar_t* __restrict__ A = ic1 + mb * BLOCK_M * N;
 
@@ -902,19 +792,21 @@ void shared_expert_kernel_impl(
 
       // 2.b copy from C to output and add fused_experts_out
       scalar_t* __restrict__ out = output + mb * BLOCK_M * K + nb * BLOCK_N;
-      const scalar_t* __restrict__ fused_out = fused_experts_out + mb * BLOCK_M * K + nb * BLOCK_N;
+      const scalar_t* __restrict__ fused_out =
+          apply_scaling_factor ? fused_experts_out + mb * BLOCK_M * K + nb * BLOCK_N : nullptr;
       for (int64_t m = 0; m < m_size; ++m) {
-        add_mul_stub(out + m * K, C + m * BLOCK_N, fused_out + m * K, routed_scaling_factor, n_size);
+        const scalar_t* __restrict__ fused_out_row = apply_scaling_factor ? (fused_out + m * K) : nullptr;
+        add_mul_stub(out + m * K, C + m * BLOCK_N, fused_out_row, routed_scaling_factor, n_size);
       }
-    }
+    });
 
-    if (is_brgemm_used) {
+    if (use_brgemm) {
       at::native::cpublas::brgemm_release();
     }
   });
 }
 
-} // anonymous namespace
+}  // anonymous namespace
 
 // common checks
 static inline void check_moe_scales(
@@ -922,14 +814,10 @@ static inline void check_moe_scales(
     bool use_fp8_w8a16,
     const std::optional<at::Tensor>& w1_scale,
     const std::optional<at::Tensor>& w2_scale,
-    const std::optional<std::vector<int64_t>> block_size,
-    const std::optional<at::Tensor>& a1_scale,
-    const std::optional<at::Tensor>& a2_scale) {
+    const std::optional<std::vector<int64_t>> block_size) {
   if (use_int8_w8a8) {
     TORCH_CHECK(w1_scale.has_value(), "missing w1_scale for int8 w8a8.");
     TORCH_CHECK(w2_scale.has_value(), "missing w2_scale for int8 w8a8.");
-    TORCH_CHECK(!a1_scale.has_value(), "static quantization for activation not supported.");
-    TORCH_CHECK(!a2_scale.has_value(), "static quantization for activation not supported.");
   }
   if (use_fp8_w8a16) {
     TORCH_CHECK(w1_scale.has_value(), "missing w1_scale for fp8 w8a16.");
@@ -939,16 +827,16 @@ static inline void check_moe_scales(
   }
 }
 
-#define CHECK_MOE_SCALES_FP8(DIM0, DIM1)                 \
-    auto w1s = w1_scale.value();                         \
-    auto w2s = w2_scale.value();                         \
-    auto block_size_val = block_size.value();            \
-    int64_t block_size_N = block_size_val[0];            \
-    int64_t block_size_K = block_size_val[1];            \
-    TORCH_CHECK(w1s.size(DIM0) == 2 * N / block_size_N); \
-    TORCH_CHECK(w1s.size(DIM1) == K / block_size_K);     \
-    TORCH_CHECK(w2s.size(DIM0) == K / block_size_N);     \
-    TORCH_CHECK(w2s.size(DIM1) == N / block_size_K)
+#define CHECK_MOE_SCALES_FP8(DIM0, DIM1)                      \
+  auto w1s = w1_scale.value();                                \
+  auto w2s = w2_scale.value();                                \
+  auto block_size_val = block_size.value();                   \
+  int64_t block_size_N = block_size_val[0];                   \
+  int64_t block_size_K = block_size_val[1];                   \
+  TORCH_CHECK(w1s.size(DIM0) == div_up(2 * N, block_size_N)); \
+  TORCH_CHECK(w1s.size(DIM1) == div_up(K, block_size_K));     \
+  TORCH_CHECK(w2s.size(DIM0) == div_up(K, block_size_N));     \
+  TORCH_CHECK(w2s.size(DIM1) == div_up(N, block_size_K))
 
 // hidden_states: [M, K]
 // w1: [E, 2N, K]
@@ -956,6 +844,7 @@ static inline void check_moe_scales(
 // topk_weights: [M, topk]
 // topk_ids: [M, topk] (int32_t)
 //
+
 at::Tensor fused_experts_cpu(
     at::Tensor& hidden_states,
     at::Tensor& w1,
@@ -963,16 +852,13 @@ at::Tensor fused_experts_cpu(
     at::Tensor& topk_weights,
     at::Tensor& topk_ids,
     bool inplace,
-    bool use_int8_w8a8,
-    bool use_fp8_w8a16,
+    int64_t moe_comp_method,
     const std::optional<at::Tensor>& w1_scale,
     const std::optional<at::Tensor>& w2_scale,
+    const std::optional<at::Tensor>& w1_zero,
+    const std::optional<at::Tensor>& w2_zero,
     const std::optional<std::vector<int64_t>> block_size,
-    const std::optional<at::Tensor>& a1_scale,
-    const std::optional<at::Tensor>& a2_scale,
     bool is_vnni) {
-  RECORD_FUNCTION("sgl-kernel::fused_experts_cpu", std::vector<c10::IValue>({hidden_states, w1, w2, topk_weights, topk_ids}));
-
   auto packed_w1 = is_vnni ? w1 : convert_weight_packed(w1);
   auto packed_w2 = is_vnni ? w2 : convert_weight_packed(w2);
 
@@ -985,32 +871,49 @@ at::Tensor fused_experts_cpu(
   CHECK_INPUT(w2);
   CHECK_EQ(topk_weights.sizes(), topk_ids.sizes());
   CHECK_DIM(2, hidden_states);
-  CHECK_DIM(3, w1);
-  CHECK_DIM(3, w2);
+  if (moe_comp_method == CPUQuantMethod::INT4_W4A8 && is_vnni) {
+    CHECK_DIM(4, w1);
+    CHECK_DIM(4, w2);
+  } else {
+    CHECK_DIM(3, w1);
+    CHECK_DIM(3, w2);
+  }
   CHECK_DIM(2, topk_weights);
   CHECK_DIM(2, topk_ids);
 
   CHECK_EQ(topk_ids.scalar_type(), at::kInt);
-  CHECK_EQ(topk_weights.scalar_type(), at::kFloat);
+
+  // TODO: support topk_weights to be bf16 or fp16 in the kernel.
+  // The topk_weights of llama4 is computed via Llama4MoE:custom_routing_function and is bf16/fp16
+  // while the kernel currently only supports it to be float32
+  auto topk_weights_ = topk_weights.to(at::kFloat);
+  CHECK_EQ(topk_weights_.scalar_type(), at::kFloat);
 
   int64_t M = hidden_states.size(0);
   int64_t K = hidden_states.size(1);
-  int64_t N = w1.size(1) / 2;
+  int64_t N = moe_comp_method == CPUQuantMethod::INT4_W4A8 ? w1_scale.value().size(1) * w1_scale.value().size(3) / 2
+                                                           : w1.size(1) / 2;
   int64_t E = w1.size(0);
-  int64_t topk = topk_weights.size(1);
+  int64_t topk = topk_weights_.size(1);
 
   // we use int32_t compensation for int8 w8a8
-  int64_t packed_K = get_row_size(K, use_int8_w8a8);
-  int64_t packed_N = get_row_size(N, use_int8_w8a8);
+  int64_t packed_K = get_row_size(K, moe_comp_method == CPUQuantMethod::INT8_W8A8);
+  int64_t packed_N = get_row_size(N, moe_comp_method == CPUQuantMethod::INT8_W8A8);
 
   // check weight shapes
   CHECK_EQ(w2.size(0), E);
-  CHECK_EQ(w2.size(1), K);
-  CHECK_EQ(packed_w1.size(2), packed_K);
-  CHECK_EQ(packed_w2.size(2), packed_N);
-
+  if (!(moe_comp_method == CPUQuantMethod::INT4_W4A8)) {
+    CHECK_EQ(w2.size(1), K);
+    CHECK_EQ(packed_w1.size(2), packed_K / (moe_comp_method == CPUQuantMethod::INT4_W4A8 ? 2 : 1));
+    CHECK_EQ(packed_w2.size(2), packed_N / (moe_comp_method == CPUQuantMethod::INT4_W4A8 ? 2 : 1));
+  }
   // check scales
-  check_moe_scales(use_int8_w8a8, use_fp8_w8a16, w1_scale, w2_scale, block_size, a1_scale, a2_scale);
+  check_moe_scales(
+      moe_comp_method == CPUQuantMethod::INT8_W8A8,
+      moe_comp_method == CPUQuantMethod::FP8_W8A16,
+      w1_scale,
+      w2_scale,
+      block_size);
 
   at::Tensor out_hidden_states = inplace ? hidden_states : at::empty_like(hidden_states);
 
@@ -1031,8 +934,8 @@ at::Tensor fused_experts_cpu(
   int32_t* __restrict__ sorted_ids = buffer.data_ptr<int32_t>();
   int32_t* __restrict__ expert_ids = sorted_ids + max_num_tokens_padded;
   int32_t* __restrict__ total_cnts = expert_ids + max_num_blocks;
-  int32_t* __restrict__ cumsums    = total_cnts + (num_threads + 1) * E;
-  int32_t* __restrict__ offsets    = cumsums    + (E + 1);
+  int32_t* __restrict__ cumsums = total_cnts + (num_threads + 1) * E;
+  int32_t* __restrict__ offsets = cumsums + (E + 1);
 
   // init sorted_ids with `numel` as the padding number
   // init expert_ids with `num_experts`
@@ -1064,26 +967,31 @@ at::Tensor fused_experts_cpu(
   //
   // for fp8 w8a16:
   //   7. intermediate_cache0 : [M * topk, 2N]
-  //   8. B_tmp : [T, BLOCK_N, std::max(K, N)]
+  //   8. B_tmp : [T, MAX_CACHE_BLOCK_SIZE, BLOCK_N, std::max(K, N)]
   //
-  int64_t buffer_size_nbytes = M * topk * N * 2 + M * topk * K * 2 +
-      num_threads * BLOCK_M * K * (use_int8_w8a8 ? 1 : 2) +
+  int64_t buffer_size_nbytes =
+      M * topk * N * 2 + M * topk * K * 2 +
+      num_threads * BLOCK_M * K *
+          (moe_comp_method == CPUQuantMethod::INT8_W8A8 | moe_comp_method == CPUQuantMethod::INT4_W4A8 ? 1 : 2) +
       num_threads * 2 * BLOCK_M * BLOCK_N * sizeof(float);
 
-  if (use_int8_w8a8) {
+  if (moe_comp_method == CPUQuantMethod::INT8_W8A8) {
     buffer_size_nbytes += std::max(M * K, M * topk * N) + M * topk * sizeof(float);
   }
-  if (use_fp8_w8a16) {
-    buffer_size_nbytes += M * topk * 2 * N * 2 + num_threads * BLOCK_N * std::max(K, N) * 2;
+  if (moe_comp_method == CPUQuantMethod::FP8_W8A16) {
+    buffer_size_nbytes += M * topk * 2 * N * 2 + num_threads * MAX_CACHE_BLOCK_SIZE * BLOCK_N * std::max(K, N) * 2;
+  }
+  if (moe_comp_method == CPUQuantMethod::INT4_W4A8) {
+    buffer_size_nbytes += M * topk * 2 * N * 2 + std::max(M * K, M * topk * N) + M * topk * sizeof(float) +
+                          num_threads * 2 * get_4bit_block_k_size(K / w1_scale.value().size(2)) * BLOCK_N;
   }
-
   auto buffer2 = at::empty({buffer_size_nbytes}, hidden_states.options().dtype(at::kChar));
 
   AT_DISPATCH_REDUCED_FLOATING_TYPES(st, "fused_experts_kernel_impl", [&] {
     scalar_t* __restrict__ intermediate_cache1 = (scalar_t*)((void*)(buffer2.data_ptr<int8_t>()));
     scalar_t* __restrict__ intermediate_cache2 = intermediate_cache1 + M * topk * N;
 
-    if (use_int8_w8a8) {
+    if (moe_comp_method == CPUQuantMethod::INT8_W8A8) {
       uint8_t* __restrict__ A_tmp = (uint8_t*)((void*)(intermediate_cache2 + M * topk * K));
       float* __restrict__ C_tmp = (float*)((void*)(A_tmp + num_threads * BLOCK_M * K));
       uint8_t* __restrict__ Aq_tmp = (uint8_t*)((void*)(C_tmp + num_threads * 2 * BLOCK_M * BLOCK_N));
@@ -1107,7 +1015,7 @@ at::Tensor fused_experts_cpu(
           packed_w2.data_ptr<int8_t>(),
           w1s.data_ptr<float>(),
           w2s.data_ptr<float>(),
-          topk_weights.data_ptr<float>(),
+          topk_weights_.data_ptr<float>(),
           sorted_ids,
           expert_ids,
           offsets,
@@ -1117,7 +1025,7 @@ at::Tensor fused_experts_cpu(
           E,
           topk,
           num_tokens_post_pad);
-    } else if (use_fp8_w8a16) {
+    } else if (moe_comp_method == CPUQuantMethod::FP8_W8A16) {
       // here we just ignore C_tmp as it is not used
       scalar_t* __restrict__ A_tmp = (scalar_t*)((void*)(intermediate_cache2 + M * topk * K));
       float* __restrict__ C_tmp = (float*)((void*)(A_tmp + num_threads * BLOCK_M * K));
@@ -1140,6 +1048,48 @@ at::Tensor fused_experts_cpu(
           w2s.data_ptr<float>(),
           block_size_N,
           block_size_K,
+          topk_weights_.data_ptr<float>(),
+          sorted_ids,
+          expert_ids,
+          offsets,
+          M,
+          N,
+          K,
+          E,
+          topk,
+          num_tokens_post_pad);
+    } else if (moe_comp_method == CPUQuantMethod::INT4_W4A8) {
+      uint8_t* __restrict__ A_tmp = (uint8_t*)((void*)(intermediate_cache2 + M * topk * K));
+      float* __restrict__ C_tmp = (float*)((void*)(A_tmp + num_threads * BLOCK_M * K));
+      scalar_t* __restrict__ intermediate_cache0 = (scalar_t*)((void*)(C_tmp + num_threads * 2 * BLOCK_M * BLOCK_N));
+      uint8_t* __restrict__ Aq_tmp = (uint8_t*)((void*)(intermediate_cache0 + M * topk * 2 * N));
+      float* __restrict__ As_tmp = (float*)((void*)(Aq_tmp + std::max(M * K, M * topk * N)));
+      int8_t* __restrict__ dqB_tmp = (int8_t*)((void*)(As_tmp + M * topk));
+
+      // weight + compensation shape = [Nc, Kc, block_n * block_k / 2 + block_n*sizeof(int32_t)]
+      // scales/qzeros shape = [E, Nc, G, block_n]
+      int64_t num_groups = w1_scale.value().size(2);
+      const int group_size = K / num_groups;
+      // TODO: check scales and zeros
+      fused_experts_int4_w4a8_kernel_impl<scalar_t>(
+          out_hidden_states.data_ptr<scalar_t>(),
+          intermediate_cache0,
+          intermediate_cache1,
+          intermediate_cache2,
+          A_tmp,
+          Aq_tmp,
+          As_tmp,
+          nullptr,
+          C_tmp,
+          dqB_tmp,
+          hidden_states.data_ptr<scalar_t>(),
+          packed_w1.data_ptr<uint8_t>(),
+          packed_w2.data_ptr<uint8_t>(),
+          w1_zero.value().data_ptr<int8_t>(),
+          w2_zero.value().data_ptr<int8_t>(),
+          w1_scale.value().data_ptr<float>(),
+          w2_scale.value().data_ptr<float>(),
+          group_size,
           topk_weights.data_ptr<float>(),
           sorted_ids,
           expert_ids,
@@ -1163,7 +1113,7 @@ at::Tensor fused_experts_cpu(
           hidden_states.data_ptr<scalar_t>(),
           packed_w1.data_ptr<scalar_t>(),
           packed_w2.data_ptr<scalar_t>(),
-          topk_weights.data_ptr<float>(),
+          topk_weights_.data_ptr<float>(),
           sorted_ids,
           expert_ids,
           offsets,
@@ -1188,34 +1138,37 @@ at::Tensor shared_expert_cpu(
     at::Tensor& hidden_states,
     at::Tensor& w1,
     at::Tensor& w2,
-    at::Tensor& fused_experts_out,
-    double routed_scaling_factor,
+    const std::optional<at::Tensor>& fused_experts_out,
+    const std::optional<double> routed_scaling_factor,
     bool inplace,
     bool use_int8_w8a8,
     bool use_fp8_w8a16,
-    std::optional<at::Tensor>& w1_scale,
-    std::optional<at::Tensor>& w2_scale,
-    std::optional<std::vector<int64_t>> block_size,
-    std::optional<at::Tensor>& a1_scale,
-    std::optional<at::Tensor>& a2_scale,
+    const std::optional<at::Tensor>& w1_scale,
+    const std::optional<at::Tensor>& w2_scale,
+    const std::optional<std::vector<int64_t>> block_size,
     bool is_vnni) {
-  RECORD_FUNCTION("sgl-kernel::shared_expert_cpu", std::vector<c10::IValue>({hidden_states, w1, w2}));
-
   auto packed_w1 = is_vnni ? w1 : convert_weight_packed(w1);
   auto packed_w2 = is_vnni ? w2 : convert_weight_packed(w2);
 
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
 
+  double routed_scaling_factor_value = 0;
+  if (routed_scaling_factor.has_value()) {
+    TORCH_CHECK(fused_experts_out.has_value(), "shared_expert_cpu: expect fused_experts_out.");
+    const auto fused_experts_out_tensor = fused_experts_out.value();
+    routed_scaling_factor_value = routed_scaling_factor.value();
+    CHECK_INPUT(fused_experts_out_tensor);
+    CHECK_EQ(hidden_states.sizes(), fused_experts_out_tensor.sizes());
+  }
+
   const auto st = hidden_states.scalar_type();
   CHECK_INPUT(hidden_states);
-  CHECK_INPUT(fused_experts_out);
   CHECK_INPUT(w1);
   CHECK_INPUT(w2);
   CHECK_DIM(2, hidden_states);
   CHECK_DIM(2, w1);
   CHECK_DIM(2, w2);
-  CHECK_EQ(hidden_states.sizes(), fused_experts_out.sizes());
   CHECK_EQ(hidden_states.scalar_type(), st);
 
   int64_t M = hidden_states.size(0);
@@ -1232,7 +1185,7 @@ at::Tensor shared_expert_cpu(
   CHECK_EQ(packed_w2.size(1), packed_N);
 
   // check scales
-  check_moe_scales(use_int8_w8a8, use_fp8_w8a16, w1_scale, w2_scale, block_size, a1_scale, a2_scale);
+  check_moe_scales(use_int8_w8a8, use_fp8_w8a16, w1_scale, w2_scale, block_size);
 
   at::Tensor out_hidden_states = inplace ? hidden_states : at::empty_like(hidden_states);
 
@@ -1246,7 +1199,7 @@ at::Tensor shared_expert_cpu(
   //
   // for fp8 w8a16:
   //   5. intermediate_cache0 : [M, 2N]
-  //   6. B_tmp: [T, BLOCK_M, max(K, N)]
+  //   6. B_tmp: [T, MAX_CACHE_BLOCK_SIZE, BLOCK_M, max(K, N)]
   //
   int num_threads = at::get_num_threads();
   int64_t buffer_size_nbytes = M * N * 2 + num_threads * 2 * BLOCK_M * BLOCK_N * sizeof(float);
@@ -1255,7 +1208,7 @@ at::Tensor shared_expert_cpu(
     buffer_size_nbytes += std::max(M * K, M * N) + M * sizeof(float);
   }
   if (use_fp8_w8a16) {
-    buffer_size_nbytes += M * 2 * N * 2 + num_threads * BLOCK_M * std::max(K, N) * 2;
+    buffer_size_nbytes += M * 2 * N * 2 + num_threads * MAX_CACHE_BLOCK_SIZE * BLOCK_M * std::max(K, N) * 2;
   }
 
   auto buffer = at::empty({buffer_size_nbytes}, hidden_states.options().dtype(at::kChar));
@@ -1283,8 +1236,8 @@ at::Tensor shared_expert_cpu(
           packed_w2.data_ptr<int8_t>(),
           w1s.data_ptr<float>(),
           w2s.data_ptr<float>(),
-          fused_experts_out.data_ptr<scalar_t>(),
-          routed_scaling_factor,
+          conditional_data_ptr<scalar_t>(fused_experts_out),
+          routed_scaling_factor_value,
           M,
           N,
           K);
@@ -1306,8 +1259,8 @@ at::Tensor shared_expert_cpu(
           w2s.data_ptr<float>(),
           block_size_N,
           block_size_K,
-          fused_experts_out.data_ptr<scalar_t>(),
-          routed_scaling_factor,
+          conditional_data_ptr<scalar_t>(fused_experts_out),
+          routed_scaling_factor_value,
           M,
           N,
           K);
@@ -1319,8 +1272,8 @@ at::Tensor shared_expert_cpu(
           hidden_states.data_ptr<scalar_t>(),
           packed_w1.data_ptr<scalar_t>(),
           packed_w2.data_ptr<scalar_t>(),
-          fused_experts_out.data_ptr<scalar_t>(),
-          routed_scaling_factor,
+          conditional_data_ptr<scalar_t>(fused_experts_out),
+          routed_scaling_factor_value,
           M,
           N,
           K);
diff --git a/csrc/cpu/sgl-kernels/moe.h b/csrc/cpu/sgl-kernels/moe.h
new file mode 100644
index 000000000000..d80ed50cf9dd
--- /dev/null
+++ b/csrc/cpu/sgl-kernels/moe.h
@@ -0,0 +1,178 @@
+// Adapted from
+// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
+
+// clang-format off
+
+#pragma once
+#include "vec.h"
+
+template <typename scalar_t>
+inline void fill_stub(scalar_t* __restrict__ out, scalar_t val, int64_t size) {
+  using Vec = at::vec::Vectorized<scalar_t>;
+  const Vec data_vec(val);
+  at::vec::map<scalar_t>([data_vec](Vec out) { return out = data_vec; }, out, out, size);
+}
+
+template <typename scalar_t>
+inline void copy_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t size) {
+  using Vec = at::vec::Vectorized<scalar_t>;
+  constexpr int kVecSize = Vec::size();
+  int64_t d;
+#pragma GCC unroll 4
+  for (d = 0; d <= size - kVecSize; d += kVecSize) {
+    Vec data = Vec::loadu(input + d);
+    data.store(out + d);
+  }
+  for (; d < size; ++d) {
+    out[d] = input[d];
+  }
+}
+
+template <typename scalar_t>
+inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ input, int64_t size) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int kVecSize = bVec::size();
+  int64_t d;
+#pragma GCC unroll 4
+  for (d = 0; d <= size - kVecSize; d += kVecSize) {
+    auto [x0, x1] = load_float_vec2(input + d);
+    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
+    out_vec.store(out + d);
+  }
+  for (; d < size; ++d) {
+    out[d] = static_cast<scalar_t>(input[d]);
+  }
+}
+
+template <>
+inline void copy_stub<uint8_t>(uint8_t* __restrict__ out, const uint8_t* __restrict__ input, int64_t size) {
+  // size might be 64x + 32
+  std::memcpy(out, input, size * sizeof(uint8_t));
+}
+
+template <typename scalar_t, typename input_t>
+inline void copy_mul_stub(scalar_t* __restrict__ out, const input_t* __restrict__ input, float weight, int64_t size) {
+  static_assert(
+      std::is_same_v<input_t, float> || std::is_same_v<input_t, scalar_t>,
+      "copy_mul_stub only supports input_t == float or input_t == scalar_t");
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int kVecSize = bVec::size();
+  const fVec weight_vec = fVec(weight);
+  int64_t d;
+#pragma GCC unroll 4
+  for (d = 0; d <= size - kVecSize; d += kVecSize) {
+    auto [x0, x1] = load_float_vec2(input + d);
+    x0 = x0 * weight_vec;
+    x1 = x1 * weight_vec;
+    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
+    out_vec.store(out + d);
+  }
+  for (; d < size; ++d) {
+    out[d] = static_cast<scalar_t>(input[d] * weight);
+  }
+}
+
+// acc from [topk, K] to [K]
+template <typename scalar_t>
+inline void sum_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t topk, int64_t K) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int kVecSize = bVec::size();
+  if (topk == 1) {
+    // do copy for topk = 1
+    copy_stub(out, input, K);
+  } else {
+    // do sum for topk != 1
+    int64_t d;
+#pragma GCC unroll 4
+    for (d = 0; d <= K - kVecSize; d += kVecSize) {
+      fVec sum_fvec0 = fVec(0.f);
+      fVec sum_fvec1 = fVec(0.f);
+      for (int t = 0; t < topk; ++t) {
+        bVec x_bvec = bVec::loadu(input + t * K + d);
+        fVec x_fvec0, x_fvec1;
+        std::tie(x_fvec0, x_fvec1) = at::vec::convert_to_float(x_bvec);
+
+        sum_fvec0 += x_fvec0;
+        sum_fvec1 += x_fvec1;
+      }
+      bVec out_bvec = convert_from_float_ext<scalar_t>(sum_fvec0, sum_fvec1);
+      out_bvec.store(out + d);
+    }
+    for (; d < K; ++d) {
+      float sum_val = 0.f;
+      for (int t = 0; t < topk; ++t) {
+        sum_val += static_cast<float>(input[t * K + d]);
+      }
+      out[d] = static_cast<scalar_t>(sum_val);
+    }
+  }
+}
+
+// out = input + input2 * scale
+template <typename scalar_t, typename input_t>
+inline void add_mul_stub(
+    scalar_t* __restrict__ out,
+    const input_t* __restrict__ input,
+    const scalar_t* __restrict__ input2,
+    float scale,
+    int64_t size) {
+  static_assert(
+      std::is_same_v<input_t, float> || std::is_same_v<input_t, scalar_t>,
+      "add_mul_stub only supports input_t == float or input_t == scalar_t");
+
+  // out = input (without scale factor)
+  if (input2 == nullptr) {
+    copy_stub(out, input, size);
+    return;
+  }
+
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int kVecSize = bVec::size();
+  const fVec s_vec = fVec(scale);
+  int64_t d;
+#pragma GCC unroll 4
+  for (d = 0; d <= size - kVecSize; d += kVecSize) {
+    auto [x0, x1] = load_float_vec2(input + d);
+
+    bVec y_bvec = bVec::loadu(input2 + d);
+    fVec y0, y1;
+    std::tie(y0, y1) = at::vec::convert_to_float(y_bvec);
+
+    x0 = x0 + y0 * s_vec;
+    x1 = x1 + y1 * s_vec;
+    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
+    out_vec.store(out + d);
+  }
+  for (; d < size; ++d) {
+    out[d] = static_cast<scalar_t>(input[d] + float(input2[d]) * scale);
+  }
+}
+
+template <typename scalar_t>
+inline void silu_and_mul_stub(
+    scalar_t* __restrict__ out, const scalar_t* __restrict__ input, const scalar_t* __restrict__ input2, int64_t size) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  const fVec one = fVec(1.f);
+
+  // no remainder
+#pragma GCC unroll 4
+  for (int64_t d = 0; d < size; d += bVec::size()) {
+    bVec x = bVec::loadu(input + d);
+    fVec x0, x1;
+    std::tie(x0, x1) = at::vec::convert_to_float(x);
+    bVec y = bVec::loadu(input2 + d);
+    fVec y0, y1;
+    std::tie(y0, y1) = at::vec::convert_to_float(y);
+    x0 = x0 / (one + x0.neg().exp_u20());
+    x1 = x1 / (one + x1.neg().exp_u20());
+    x0 = x0 * y0;
+    x1 = x1 * y1;
+    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
+    out_vec.store(out + d);
+  }
+}
diff --git a/csrc/cpu/sgl-kernels/moe_fp8.cpp b/csrc/cpu/sgl-kernels/moe_fp8.cpp
index 84a6af267740..f93630ae2646 100644
--- a/csrc/cpu/sgl-kernels/moe_fp8.cpp
+++ b/csrc/cpu/sgl-kernels/moe_fp8.cpp
@@ -1,147 +1,11 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
-#include "common.h"
-#include "gemm.h"
-#include "vec.h"
-
 // clang-format off
 
-namespace {
-
-template <typename scalar_t>
-inline void copy_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t size) {
-  using Vec = at::vec::Vectorized<scalar_t>;
-  // no remainder
-  #pragma GCC unroll 4
-  for (int64_t d = 0; d < size; d += Vec::size()) {
-    Vec data = Vec::loadu(input + d);
-    data.store(out + d);
-  }
-}
-
-template <typename scalar_t>
-inline void copy_mul_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, float weight, int64_t size) {
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-  const fVec weight_vec = fVec(weight);
-  int64_t d;
-  #pragma GCC unroll 4
-  for (d = 0; d <= size - kVecSize; d += kVecSize) {
-    bVec x = bVec::loadu(input + d);
-    fVec x0, x1;
-    std::tie(x0, x1) = at::vec::convert_to_float(x);
-    x0 = x0 * weight_vec;
-    x1 = x1 * weight_vec;
-    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
-    out_vec.store(out + d);
-  }
-  for (; d < size; ++d) {
-    out[d] = static_cast<scalar_t>(input[d] * weight);
-  }
-}
-
-// acc from [topk, K] to [K]
-template <typename scalar_t>
-inline void sum_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t topk, int64_t K) {
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-  if (topk == 1) {
-    // do copy for topk = 1
-    copy_stub(out, input, K);
-  } else {
-    // do sum for topk != 1
-    int64_t d;
-    #pragma GCC unroll 4
-    for (d = 0; d <= K - kVecSize; d += kVecSize) {
-      fVec sum_fvec0 = fVec(0.f);
-      fVec sum_fvec1 = fVec(0.f);
-      for (int t = 0; t < topk; ++t) {
-        bVec x_bvec = bVec::loadu(input + t * K + d);
-        fVec x_fvec0, x_fvec1;
-        std::tie(x_fvec0, x_fvec1) = at::vec::convert_to_float(x_bvec);
-
-        sum_fvec0 += x_fvec0;
-        sum_fvec1 += x_fvec1;
-      }
-      bVec out_bvec = convert_from_float_ext<scalar_t>(sum_fvec0, sum_fvec1);
-      out_bvec.store(out + d);
-    }
-    for (; d < K; ++d) {
-      float sum_val = 0.f;
-      for (int t = 0; t < topk; ++t) {
-        sum_val += static_cast<float>(input[t * K + d]);
-      }
-      out[d] = static_cast<scalar_t>(sum_val);
-    }
-  }
-}
-
-// out = input + input2 * scale
-template <typename scalar_t>
-inline void add_mul_stub(
-    scalar_t* __restrict__ out,
-    const scalar_t* __restrict__ input,
-    const scalar_t* __restrict__ input2,
-    float scale,
-    int64_t size) {
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-  const fVec s_vec = fVec(scale);
-
-  int64_t d;
-#pragma GCC unroll 4
-  for (d = 0; d <= size - kVecSize; d += kVecSize) {
-    bVec x_bvec = bVec::loadu(input + d);
-    fVec x0, x1;
-    std::tie(x0, x1) = at::vec::convert_to_float(x_bvec);
-
-    bVec y_bvec = bVec::loadu(input2 + d);
-    fVec y0, y1;
-    std::tie(y0, y1) = at::vec::convert_to_float(y_bvec);
-
-    x0 = x0 + y0 * s_vec;
-    x1 = x1 + y1 * s_vec;
-    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
-    out_vec.store(out + d);
-  }
-  for (; d < size; ++d) {
-    out[d] = static_cast<scalar_t>(input[d] + float(input2[d]) * scale);
-  }
-}
-
-template <typename scalar_t>
-inline void silu_and_mul_stub(
-    scalar_t* __restrict__ out,
-    const scalar_t* __restrict__ input,
-    const scalar_t* __restrict__ input2,
-    int64_t size) {
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  const fVec one = fVec(1.f);
-
-  // no remainder
-#pragma GCC unroll 4
-  for (int64_t d = 0; d < size; d += bVec::size()) {
-    bVec x = bVec::loadu(input + d);
-    fVec x0, x1;
-    std::tie(x0, x1) = at::vec::convert_to_float(x);
-    bVec y = bVec::loadu(input2 + d);
-    fVec y0, y1;
-    std::tie(y0, y1) = at::vec::convert_to_float(y);
-    x0 = x0 / (one + x0.neg().exp_u20());
-    x1 = x1 / (one + x1.neg().exp_u20());
-    x0 = x0 * y0;
-    x1 = x1 * y1;
-    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
-    out_vec.store(out + d);
-  }
-}
-
-} // anonymous namespace
+#include "common.h"
+#include "gemm.h"
+#include "moe.h"
 
 template <typename scalar_t>
 void fused_experts_fp8_kernel_impl(
@@ -169,7 +33,6 @@ void fused_experts_fp8_kernel_impl(
     int64_t E,
     int64_t topk,
     int64_t num_tokens_post_pad) {
-
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
 
@@ -183,35 +46,39 @@ void fused_experts_fp8_kernel_impl(
   const int64_t stride_e = 2 * N * K;
   const int64_t stride_n = K;
 
+  int64_t avg_M = std::max(int64_t(1), M * topk / E);
+  const bool use_brgemm = can_use_brgemm<at::Float8_e4m3fn>(avg_M);
+
+  int64_t B_tmp_size_per_thread = MAX_CACHE_BLOCK_SIZE * BLOCK_N * std::max(K, N);
+
   // here we only parallel on half of 2N to fuse silu_and_mul with gemm
-  at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
+  parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
     // get local pointers
-    int tid = at::get_thread_num();
+    int tid = get_thread_num();
     scalar_t* __restrict__ A = A_tmp + tid * BLOCK_M * K;
 
-    bool is_brgemm_used = false;
-
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB;
-      int64_t nb = i % NB;
-
+    loop_2d<at::Float8_e4m3fn>(mb0, mb1, nb0, nb1, BLOCK_N * K, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
       int64_t n_size = std::min(2 * N - nb * BLOCK_N, BLOCK_N);
 
       // B shape [K, n_size] in vnni format
       int32_t expert_id = expert_ids[mb];
       const at::Float8_e4m3fn* __restrict__ B = packed_w1 + expert_id * stride_e + nb * BLOCK_N * stride_n;
-      const float* __restrict__ Bs = w1s + expert_id * scale_size_N * scale_size_K + (nb / blocks_n_per_group) * scale_size_K;
+      const float* __restrict__ Bs =
+          w1s + expert_id * scale_size_N * scale_size_K + (nb / blocks_n_per_group) * scale_size_K;
 
-      // 1.a load A
-      const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
-      int64_t m_size = offsets[mb + 1] - offsets[mb];
+      // do unpacking for the first row or a new expert
+      int32_t pre_expert_id = mb == 0 ? -1 : expert_ids[mb - 1];
+      bool do_unpack = (mb == mb0) || (expert_id != pre_expert_id);
 
-      const bool use_brgemm = can_use_brgemm<at::Float8_e4m3fn>(m_size);
-      is_brgemm_used = is_brgemm_used || use_brgemm;
+      int64_t m_size = offsets[mb + 1] - offsets[mb];
 
-      for (int64_t m = 0; m < m_size; ++m) {
-        int32_t index = A_ids[m] / topk;
-        copy_stub(A + m * K, input + index * K, K);
+      if (nb_offset == 0) {
+        // 1.a load A
+        const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
+        for (int64_t m = 0; m < m_size; ++m) {
+          int32_t index = A_ids[m] / topk;
+          copy_stub(A + m * K, input + index * K, K);
+        }
       }
 
       const int64_t offset = offsets[mb];
@@ -219,7 +86,7 @@ void fused_experts_fp8_kernel_impl(
           /*   A            */ A,
           /*   B            */ B,
           /*   C            */ ic0 + offset * 2 * N + nb * BLOCK_N,
-          /*   Btmp         */ B_tmp + tid * BLOCK_N * std::max(K, N),
+          /*   Btmp         */ B_tmp + tid * B_tmp_size_per_thread + nb_offset * BLOCK_N * K,
           /*   Ctmp         */ C_tmp + tid * 2 * BLOCK_M * BLOCK_N,
           /*   scale        */ Bs,
           /*   M            */ m_size,
@@ -229,10 +96,11 @@ void fused_experts_fp8_kernel_impl(
           /*   ldb          */ n_size,
           /*   ldc          */ 2 * N,
           /*   brg          */ use_brgemm,
-          /*   block_size_K */ block_size_K);
-    }
+          /*   block_size_K */ block_size_K,
+          /*   do_unpack    */ do_unpack);
+    });
 
-    if (is_brgemm_used) {
+    if (use_brgemm) {
       at::native::cpublas::brgemm_release();
     }
   });
@@ -240,11 +108,7 @@ void fused_experts_fp8_kernel_impl(
   // stage 1.5: intermediate_cache1 = silu(intermediate_cache0)
   at::parallel_for(0, M * topk, 0, [&](int64_t begin, int64_t end) {
     for (int64_t m = begin; m < end; ++m) {
-      silu_and_mul_stub(
-          ic1 + m * N,
-          ic0 + m * 2 * N,
-          ic0 + m * 2 * N + N,
-          N);
+      silu_and_mul_stub(ic1 + m * N, ic0 + m * 2 * N, ic0 + m * 2 * N + N, N);
     }
   });
 
@@ -260,22 +124,14 @@ void fused_experts_fp8_kernel_impl(
   const int64_t stride_oc = IC;
 
   // parallel on [MB2, NB2]
-  at::parallel_for(0, MB2 * NB2, 0, [&](int64_t begin, int64_t end) {
-    int tid = at::get_thread_num();
+  parallel_2d(MB2, NB2, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
+    int tid = get_thread_num();
     alignas(64) scalar_t C[BLOCK_M * BLOCK_K];
 
-    bool is_brgemm_used = false;
-
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB2;
-      int64_t nb = i % NB2;
-
+    loop_2d<at::Float8_e4m3fn>(mb0, mb1, nb0, nb1, BLOCK_N * IC, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
       int64_t m_size = offsets[mb + 1] - offsets[mb];
       int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
 
-      const bool use_brgemm = can_use_brgemm<at::Float8_e4m3fn>(m_size);
-      is_brgemm_used = is_brgemm_used || use_brgemm;
-
       // A ptr from ic1 of [M * topk, N] in sorted order
       // so as to avoid copy A to tmp buffer again
       const scalar_t* __restrict__ A = ic1 + offsets[mb] * N;
@@ -284,13 +140,18 @@ void fused_experts_fp8_kernel_impl(
       // B shape [IC, n_size] in vnni format
       int32_t expert_id = expert_ids[mb];
       const at::Float8_e4m3fn* __restrict__ B = packed_w2 + expert_id * stride_e2 + nb * BLOCK_N * stride_oc;
-      const float* __restrict__ Bs = w2s + expert_id * scale_size_N * scale_size_K + (nb / blocks_n_per_group) * scale_size_K;
+      const float* __restrict__ Bs =
+          w2s + expert_id * scale_size_N * scale_size_K + (nb / blocks_n_per_group) * scale_size_K;
+
+      // do unpacking for the first row or a new expert
+      int32_t pre_expert_id = mb == 0 ? -1 : expert_ids[mb - 1];
+      bool do_unpack = (mb == mb0) || (expert_id != pre_expert_id);
 
       tinygemm_kernel<scalar_t>(
           /*   A            */ A,
           /*   B            */ B,
           /*   C            */ C,
-          /*   Btmp         */ B_tmp + tid * BLOCK_N * std::max(K, N),
+          /*   Btmp         */ B_tmp + tid * B_tmp_size_per_thread + nb_offset * BLOCK_N * IC,
           /*   Ctmp         */ C_tmp + tid * 2 * BLOCK_M * BLOCK_N,
           /*   scale        */ Bs,
           /*   M            */ m_size,
@@ -300,7 +161,8 @@ void fused_experts_fp8_kernel_impl(
           /*   ldb          */ n_size,
           /*   ldc          */ BLOCK_N,
           /*   brg          */ use_brgemm,
-          /*   block_size_K */ block_size_K);
+          /*   block_size_K */ block_size_K,
+          /*   do_unpack    */ do_unpack);
 
       // 2.b copy from C to ic2 in original order
       //   and also mul topk_weights in float32
@@ -309,9 +171,9 @@ void fused_experts_fp8_kernel_impl(
         float weight = topk_weights[index];
         copy_mul_stub(ic2 + index * K + nb * BLOCK_N, C + m * BLOCK_N, weight, n_size);
       }
-    }
+    });
 
-    if (is_brgemm_used) {
+    if (use_brgemm) {
       at::native::cpublas::brgemm_release();
     }
   });
@@ -374,7 +236,6 @@ void shared_expert_fp8_kernel_impl(
     int64_t M,
     int64_t N,
     int64_t K) {
-
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
 
@@ -385,21 +246,25 @@ void shared_expert_fp8_kernel_impl(
   int64_t blocks_n_per_group = block_size_N / BLOCK_N;
 
   const bool use_brgemm = can_use_brgemm<at::Float8_e4m3fn>(M);
+  const bool apply_scaling_factor = fused_experts_out != nullptr;
 
-  at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
-    int tid = at::get_thread_num();
+  int64_t B_tmp_size_per_thread = MAX_CACHE_BLOCK_SIZE * BLOCK_N * std::max(K, N);
 
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB;
-      int64_t nb = i % NB;
+  parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
+    int tid = get_thread_num();
+
+    loop_2d<at::Float8_e4m3fn>(mb0, mb1, nb0, nb1, BLOCK_N * K, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
       int64_t m_size = std::min(M - mb * BLOCK_M, BLOCK_M);
       int64_t n_size = std::min(2 * N - nb * BLOCK_N, BLOCK_N);
 
+      // do unpacking for the first row
+      bool do_unpack = (mb == mb0);
+
       tinygemm_kernel<scalar_t>(
           /*   A            */ input + mb * BLOCK_M * K,
           /*   B            */ packed_w1 + nb * BLOCK_N * K,
           /*   C            */ ic0 + mb * BLOCK_M * 2 * N + nb * BLOCK_N,
-          /*   Btmp         */ B_tmp + tid * BLOCK_N * std::max(K, N),
+          /*   Btmp         */ B_tmp + tid * B_tmp_size_per_thread + nb_offset * BLOCK_N * K,
           /*   Ctmp         */ C_tmp + tid * 2 * BLOCK_M * BLOCK_N,
           /*   scale        */ w1s + (nb / blocks_n_per_group) * scale_size_K,
           /*   M            */ m_size,
@@ -409,8 +274,9 @@ void shared_expert_fp8_kernel_impl(
           /*   ldb          */ n_size,
           /*   ldc          */ 2 * N,
           /*   brg          */ use_brgemm,
-          /*   block_size_K */ block_size_K);
-    }
+          /*   block_size_K */ block_size_K,
+          /*   do_unpack    */ do_unpack);
+    });
 
     if (use_brgemm) {
       at::native::cpublas::brgemm_release();
@@ -420,11 +286,7 @@ void shared_expert_fp8_kernel_impl(
   // stage 1.5: intermediate_cache1 = silu(intermediate_cache0)
   at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
     for (int64_t m = begin; m < end; ++m) {
-      silu_and_mul_stub(
-          ic1 + m * N,
-          ic0 + m * 2 * N,
-          ic0 + m * 2 * N + N,
-          N);
+      silu_and_mul_stub(ic1 + m * N, ic0 + m * 2 * N, ic0 + m * 2 * N + N, N);
     }
   });
 
@@ -437,22 +299,23 @@ void shared_expert_fp8_kernel_impl(
   scale_size_K = div_up(N, block_size_K);
 
   // parallel on [MB2, NB2]
-  at::parallel_for(0, MB2 * NB2, 0, [&](int64_t begin, int64_t end) {
-    int tid = at::get_thread_num();
+  parallel_2d(MB2, NB2, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
+    int tid = get_thread_num();
     alignas(64) scalar_t C[BLOCK_M * BLOCK_K];
 
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB2;
-      int64_t nb = i % NB2;
+    loop_2d<at::Float8_e4m3fn>(mb0, mb1, nb0, nb1, BLOCK_N * IC, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
       int64_t m_size = std::min(M - mb * BLOCK_M, BLOCK_M);
       int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
 
+      // do unpacking for the first row
+      bool do_unpack = (mb == mb0);
+
       // 2.a gemm: C = A @ B
       tinygemm_kernel<scalar_t>(
           /*   A            */ ic1 + mb * BLOCK_M * N,
           /*   B            */ packed_w2 + nb * BLOCK_N * N,
           /*   C            */ C,
-          /*   Btmp         */ B_tmp + tid * BLOCK_N * std::max(K, N),
+          /*   Btmp         */ B_tmp + tid * B_tmp_size_per_thread + nb_offset * BLOCK_N * IC,
           /*   Ctmp         */ C_tmp + tid * 2 * BLOCK_M * BLOCK_N,
           /*   scale        */ w2s + (nb / blocks_n_per_group) * scale_size_K,
           /*   M            */ m_size,
@@ -462,15 +325,18 @@ void shared_expert_fp8_kernel_impl(
           /*   ldb          */ n_size,
           /*   ldc          */ BLOCK_N,
           /*   brg          */ use_brgemm,
-          /*   block_size_K */ block_size_K);
+          /*   block_size_K */ block_size_K,
+          /*   do_unpack    */ do_unpack);
 
       // 2.b copy from C to output and add fused_experts_out
       scalar_t* __restrict__ out = output + mb * BLOCK_M * K + nb * BLOCK_N;
-      const scalar_t* __restrict__ fused_out = fused_experts_out + mb * BLOCK_M * K + nb * BLOCK_N;
+      const scalar_t* __restrict__ fused_out =
+          apply_scaling_factor ? fused_experts_out + mb * BLOCK_M * K + nb * BLOCK_N : nullptr;
       for (int64_t m = 0; m < m_size; ++m) {
-        add_mul_stub(out + m * K, C + m * BLOCK_N, fused_out + m * K, routed_scaling_factor, n_size);
+        const scalar_t* __restrict__ fused_out_row = apply_scaling_factor ? (fused_out + m * K) : nullptr;
+        add_mul_stub(out + m * K, C + m * BLOCK_N, fused_out_row, routed_scaling_factor, n_size);
       }
-    }
+    });
   });
 
   if (use_brgemm) {
diff --git a/csrc/cpu/sgl-kernels/moe_int4.cpp b/csrc/cpu/sgl-kernels/moe_int4.cpp
new file mode 100644
index 000000000000..c97784847a9e
--- /dev/null
+++ b/csrc/cpu/sgl-kernels/moe_int4.cpp
@@ -0,0 +1,323 @@
+// Adapted from
+// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
+
+// clang-format off
+
+#include "common.h"
+#include "gemm.h"
+#include "moe.h"
+
+template <int64_t N>
+inline void copy_bias(const float* bias_ptr, float* y_buf, int64_t m, int64_t ldn) {
+  using Vec = at::vec::Vectorized<float>;
+  constexpr int kVecSize = Vec::size();
+  static_assert(N % kVecSize == 0, "copy_bias requires N to be a multiple of Vectorized<float>::size()");
+  const bool has_bias = bias_ptr != nullptr;
+  const Vec zero_vec(0.f);
+  for (int i = 0; i < m; ++i) {
+#pragma GCC unroll 2
+    for (int j = 0; j < N; j += kVecSize) {
+      Vec vec = has_bias ? Vec::loadu(bias_ptr + j) : zero_vec;
+      vec.store(y_buf + i * ldn + j);
+    }
+  }
+}
+
+template <typename scalar_t>
+void fused_experts_int4_w4a8_kernel_impl(
+    scalar_t* __restrict__ output,
+    scalar_t* __restrict__ ic0,
+    scalar_t* __restrict__ ic1,
+    scalar_t* __restrict__ ic2,
+    uint8_t* __restrict__ A_tmp,
+    uint8_t* __restrict__ Aq_tmp,
+    float* __restrict__ As_tmp,
+    int32_t* __restrict__ Azp_tmp,
+    float* __restrict__ C_tmp,
+    int8_t* __restrict__ dqB_tmp,
+    const scalar_t* __restrict__ input,
+    const uint8_t* __restrict__ packed_w1,
+    const uint8_t* __restrict__ packed_w2,
+    const int8_t* __restrict__ w1z,
+    const int8_t* __restrict__ w2z,
+    const float* __restrict__ w1s,
+    const float* __restrict__ w2s,
+    int group_size,
+    const float* __restrict__ topk_weights,
+    const int32_t* __restrict__ sorted_ids,
+    const int32_t* __restrict__ expert_ids,
+    const int32_t* __restrict__ offsets,
+    int64_t M,
+    int64_t N,
+    int64_t K,
+    int64_t E,
+    int64_t topk,
+    int64_t num_tokens_post_pad) {
+  constexpr int64_t BLOCK_M = block_size_m();
+  constexpr int64_t BLOCK_N = block_size_n();
+  int num_threads = at::get_num_threads();
+  // int64_t buffer_size_nbytes = M * topk * N * 2
+  //                              M * topk * K * 2 +
+  //                              num_threads * BLOCK_M * K +
+  //                              num_threads * 2 * BLOCK_M * BLOCK_N * sizeof(float)  +
+  //                              M * topk * 2 * N * 2 +
+  //                              max(M * K, M * topk * N)  +
+  //                              M * topk * sizeof(float);
+
+  // intermediate_cache1 (scalar_t):     START + M * topk * N
+  // intermediate_cache2 (scalar_t):     + M * topk * K
+  // A_tmp (uint8_t):                    + num_threads * BLOCK_M * K
+  // C_tmp (float):                      + num_threads * 2 * BLOCK_M * BLOCK_N
+  // intermediate_cache0 (scalar_t):     + M * topk * 2 * N
+  // Aq_tmp (uint8_t):                   + max(M * K, M * topk * N)
+  // As_tmp (float):                     + M * topk
+  // dqB_tmp (int8_t)                    + num_threads * _block_k * BlOCK_N
+
+  // stage 0: quantize input to uint8, [M, K]
+  at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
+    for (int64_t m = begin; m < end; ++m) {
+      quantize_row_int8<scalar_t>(Aq_tmp + m * K, As_tmp[m], input + m * K, K);
+    }
+  });
+  int64_t _block_k = get_4bit_block_k_size(group_size);
+  auto Azp = at::ones({M * topk}).to(at::kInt).mul(128);
+  auto Azp_ptr = Azp.data_ptr<int32_t>();
+  // stage 1: intermediate_cache0 = hidden_states @ w1
+  const int64_t MB = div_up(num_tokens_post_pad, BLOCK_M);
+  const int64_t NB = div_up(N, BLOCK_N);
+
+  int64_t block_per_group = group_size / _block_k;
+  int64_t Kc = K / _block_k;
+  int64_t num_groups = K / group_size;
+
+  const int64_t stride_e = 2 * NB * Kc * (BLOCK_N * (_block_k / 2 + sizeof(int32_t)));
+  const bool sym_quant_act = false;
+  // weight + compensation shape = [E, Nc, Kc, block_n * _block_k / 2 + block_n*sizeof(int32_t)]
+  // scales/qzeros shape = [E, Nc, G, block_n]
+
+  // here we only parallel on half of 2N to fuse silu_and_mul with gemm
+  at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
+    // get local pointers
+    int tid = at::get_thread_num();
+    int8_t* dqB_tmp1 = dqB_tmp + tid * 2 * _block_k * BLOCK_N;
+    int8_t* dqB_tmp2 = dqB_tmp1 + _block_k * BLOCK_N;
+    alignas(64) float As[BLOCK_M];
+    uint8_t* __restrict__ A = A_tmp + tid * BLOCK_M * K;
+    float* __restrict__ C0 = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
+    float* __restrict__ C1 = C0 + BLOCK_M * BLOCK_N;
+    bool is_brgemm_used = false;
+    for (int64_t i = begin; i < end; ++i) {
+      int64_t mb = i / NB;
+      int64_t nb = i % NB;
+      int64_t nb1 = nb + NB;
+      int64_t n_size = std::min(N - nb * BLOCK_N, BLOCK_N);
+      // B shape [K, n_size] in vnni format
+      int32_t expert_id = expert_ids[mb];
+      const uint8_t* __restrict__ B = packed_w1 + expert_id * stride_e;
+      // Bz and Bs: [E, K/gs, 2N]
+      const int8_t* __restrict__ Bz = w1z + expert_id * (num_groups) * (2 * N);
+      const float* __restrict__ Bs = w1s + expert_id * (num_groups) * (2 * N);
+
+      // 1.a load A
+      const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
+      int64_t m_size = offsets[mb + 1] - offsets[mb];
+      const bool use_brgemm = can_use_brgemm<int8_t>(m_size);
+      is_brgemm_used = is_brgemm_used || use_brgemm;
+      // copy to A [BLOCK_M, K]
+      for (int64_t m = 0; m < m_size; ++m) {
+        int32_t index = A_ids[m] / topk;
+        copy_stub(A + m * K, Aq_tmp + index * K, K);
+        As[m] = As_tmp[index];
+      }
+      const int64_t offset = offsets[mb];
+      copy_bias<BLOCK_N>(nullptr, C0, m_size, BLOCK_N);
+      copy_bias<BLOCK_N>(nullptr, C1, m_size, BLOCK_N);
+      for (int kci = 0; kci < Kc; ++kci) {
+        int32_t* compensation_ptr =
+            sym_quant_act ? nullptr
+                          : (int32_t*)(void*)(B + (nb * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))) +
+                                              _block_k * BLOCK_N / 2) /*Bcomp*/;
+        tinygemm_kernel<scalar_t>(
+            ic0 + offset * 2 * N + nb * BLOCK_N,
+            C0,
+            A + kci * _block_k,
+            As,
+            Azp_ptr,
+            B + (nb * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))) /*B*/,
+            Bs + nb * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N /*scales_b*/,
+            Bz + nb * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N /*qzeros_b*/,
+            compensation_ptr,
+            dqB_tmp1,
+            m_size,
+            _block_k,
+            K,
+            BLOCK_N,
+            2 * N,
+            kci == Kc - 1,
+            use_brgemm);
+      }
+
+      for (int kci = 0; kci < Kc; ++kci) {
+        int32_t* compensation_ptr =
+            sym_quant_act ? nullptr
+                          : (int32_t*)(void*)(B + (nb1 * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))) +
+                                              _block_k * BLOCK_N / 2) /*Bcomp*/;
+        tinygemm_kernel<scalar_t>(
+            ic0 + offset * 2 * N + nb1 * BLOCK_N,
+            C1,
+            A + kci * _block_k,
+            As,
+            Azp_ptr,
+            B + (nb1 * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))) /*B*/,
+            Bs + nb1 * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N /*scales_b*/,
+            Bz + nb1 * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N /*qzeros_b*/,
+            compensation_ptr,
+            dqB_tmp2,
+            m_size,
+            _block_k,
+            K,
+            BLOCK_N,
+            2 * N,
+            kci == Kc - 1,
+            use_brgemm);
+      }
+    }
+
+    if (is_brgemm_used) {
+      at::native::cpublas::brgemm_release();
+    }
+  });
+
+  // stage 1.5: intermediate_cache1 = silu(intermediate_cache0)
+  at::parallel_for(0, M * topk, 0, [&](int64_t begin, int64_t end) {
+    for (int64_t m = begin; m < end; ++m) {
+      silu_and_mul_stub(ic1 + m * N, ic0 + m * 2 * N, ic0 + m * 2 * N + N, N);
+    }
+  });
+
+  // stage 1.5: quantize ic1 to uint8, [M * topk, N]
+  at::parallel_for(0, M * topk, 0, [&](int64_t begin, int64_t end) {
+    for (int64_t m = begin; m < end; ++m) {
+      quantize_row_int8<scalar_t>(Aq_tmp + m * N, As_tmp[m], ic1 + m * N, N);
+    }
+  });
+  // stage 2: intermediate_cache2 = intermediate_cache1 @ w2
+  //   w2 : [E, K, N] as [E, OC, IC]
+  const int64_t OC = K;  // rename K as OC
+  const int64_t IC = N;  // rename N as IC
+  const int64_t MB2 = MB;
+  const int64_t NB2 = div_up(OC, BLOCK_N);
+  const int64_t stride_oc = IC;
+  num_groups = IC / group_size;
+  Kc = IC / _block_k;
+  const int64_t stride_e2 = NB2 * Kc * (BLOCK_N * (_block_k / 2 + sizeof(int32_t)));
+  // parallel on [MB2, NB2]
+  at::parallel_for(0, MB2 * NB2, 0, [&](int64_t begin, int64_t end) {
+    int tid = at::get_thread_num();
+    int8_t* dqB_tmp1 = dqB_tmp + tid * 2 * _block_k * BLOCK_N;
+    float* __restrict__ C2 = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
+    bool is_brgemm_used = false;
+    for (int64_t i = begin; i < end; ++i) {
+      int64_t mb = i / NB2;
+      int64_t nb = i % NB2;
+
+      int64_t m_size = offsets[mb + 1] - offsets[mb];
+      int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
+      const bool use_brgemm = can_use_brgemm<int8_t>(m_size);
+      is_brgemm_used = is_brgemm_used || use_brgemm;
+      const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
+
+      // B shape [IC, n_size] in vnni format
+      int32_t expert_id = expert_ids[mb];
+      const uint8_t* __restrict__ B = packed_w2 + expert_id * stride_e2;
+
+      // Bz and Bs: [E, IC/gs, OC]
+      const int8_t* __restrict__ Bz = w2z + expert_id * (num_groups)*OC;
+      const float* __restrict__ Bs = w2s + expert_id * (num_groups)*OC;
+
+      // A ptr from ic1 of [M * topk, N] in sorted order
+      // so as to avoid copy A to tmp buffer again
+      const uint8_t* __restrict__ A = Aq_tmp + offsets[mb] * IC;
+      const float* __restrict__ As = As_tmp + offsets[mb];
+      copy_bias<BLOCK_N>(nullptr, C2, m_size, BLOCK_N);
+      for (int kci = 0; kci < Kc; ++kci) {
+        int32_t* compensation_ptr =
+            sym_quant_act ? nullptr
+                          : (int32_t*)(void*)(B + (nb * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))) +
+                                              _block_k * BLOCK_N / 2) /*Bcomp*/;
+        tinygemm_kernel<scalar_t>(
+            nullptr, /*store_out is false*/
+            C2,
+            A + kci * _block_k,
+            As,
+            Azp_ptr,
+            B + (nb * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))),
+            Bs + nb * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N /*scales_b*/,
+            Bz + nb * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N /*zeros_b*/,
+            compensation_ptr,
+            dqB_tmp1,
+            m_size,
+            _block_k,
+            IC,
+            BLOCK_N,
+            BLOCK_N,
+            false,
+            use_brgemm);
+      }
+
+      // 2.b copy from C to ic2 in original order
+      //   and also mul topk_weights in float32
+      for (int64_t m = 0; m < m_size; ++m) {
+        int32_t index = A_ids[m];
+        float weight = topk_weights[index];
+        copy_mul_stub(ic2 + index * K + nb * BLOCK_N, C2 + m * BLOCK_N, weight, n_size);
+      }
+    }
+
+    if (is_brgemm_used) {
+      at::native::cpublas::brgemm_release();
+    }
+  });
+
+  // stage 3: out = intermediate_cache2.sum(dim=1)
+  //   from [M, topk, K] to [M, K]
+  at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
+    for (int64_t m = begin; m < end; ++m) {
+      sum_stub(output + m * K, ic2 + m * topk * K, topk, K);
+    }
+  });
+}
+
+#define INSTANTIATE_MOE_INT4_W4A8_TEMPLATE(TYPE)           \
+  template void fused_experts_int4_w4a8_kernel_impl<TYPE>( \
+      TYPE* __restrict__ output,                           \
+      TYPE* __restrict__ ic0,                              \
+      TYPE* __restrict__ ic1,                              \
+      TYPE* __restrict__ ic2,                              \
+      uint8_t* __restrict__ A_tmp,                         \
+      uint8_t* __restrict__ Aq_tmp,                        \
+      float* __restrict__ As_tmp,                          \
+      int32_t* __restrict__ Azp_tmp,                       \
+      float* __restrict__ C_tmp,                           \
+      int8_t* __restrict__ dqB_tmp,                        \
+      const TYPE* __restrict__ input,                      \
+      const uint8_t* __restrict__ packed_w1,               \
+      const uint8_t* __restrict__ packed_w2,               \
+      const int8_t* __restrict__ w1z,                      \
+      const int8_t* __restrict__ w2z,                      \
+      const float* __restrict__ w1s,                       \
+      const float* __restrict__ w2s,                       \
+      int group_size,                                      \
+      const float* __restrict__ topk_weights,              \
+      const int32_t* __restrict__ sorted_ids,              \
+      const int32_t* __restrict__ expert_ids,              \
+      const int32_t* __restrict__ offsets,                 \
+      int64_t M,                                           \
+      int64_t N,                                           \
+      int64_t K,                                           \
+      int64_t E,                                           \
+      int64_t topk,                                        \
+      int64_t num_tokens_post_pad)
+
+INSTANTIATE_MOE_INT4_W4A8_TEMPLATE(at::BFloat16);
+INSTANTIATE_MOE_INT4_W4A8_TEMPLATE(at::Half);
diff --git a/csrc/cpu/sgl-kernels/moe_int8.cpp b/csrc/cpu/sgl-kernels/moe_int8.cpp
index e28b4fc4ee59..3bdd5892d0b4 100644
--- a/csrc/cpu/sgl-kernels/moe_int8.cpp
+++ b/csrc/cpu/sgl-kernels/moe_int8.cpp
@@ -1,124 +1,145 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
+// clang-format off
+
 #include "common.h"
-#include "vec.h"
 #include "gemm.h"
-
-// clang-format off
+#include "moe.h"
 
 namespace {
 
-template <typename scalar_t>
-inline void copy_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t size) {
-  using Vec = at::vec::Vectorized<scalar_t>;
-  // no remainder
-  #pragma GCC unroll 4
-  for (int64_t d = 0; d < size; d += Vec::size()) {
-    Vec data = Vec::loadu(input + d);
-    data.store(out + d);
-  }
-}
-
-template <>
-inline void copy_stub<uint8_t>(uint8_t* __restrict__ out, const uint8_t* __restrict__ input, int64_t size) {
-  // size might be 64x + 32
-  std::memcpy(out, input, size * sizeof(uint8_t));
-}
-
-template <typename scalar_t>
-inline void copy_mul_stub(scalar_t* __restrict__ out, const float* __restrict__ input, float weight, int64_t size) {
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-  const fVec weight_vec = fVec(weight);
-  int64_t d;
-  #pragma GCC unroll 4
-  for (d = 0; d <= size - kVecSize; d += kVecSize) {
-    fVec data0 = fVec::loadu(input + d) * weight_vec;
-    fVec data1 = fVec::loadu(input + d + fVec::size()) * weight_vec;
-    bVec out_vec = convert_from_float_ext<scalar_t>(data0, data1);
-    out_vec.store(out + d);
-  }
-  for (; d < size; ++d) {
-    out[d] = static_cast<scalar_t>(input[d] * weight);
-  }
-}
+template <typename scalar_t, int BLOCK_N>
+inline void silu_and_mul(
+    scalar_t* __restrict__ C,
+    const int32_t* __restrict__ C0,  // x: x0, x1
+    const int32_t* __restrict__ C1,  // y: y0, y1
+    const float* __restrict__ As,
+    const float* __restrict__ Bs0,
+    const float* __restrict__ Bs1,
+    const int32_t* __restrict__ Bcomp0,
+    const int32_t* __restrict__ Bcomp1,
+    int64_t m_size,
+    int64_t N) {
+#if defined(CPU_CAPABILITY_AVX512)
+  constexpr int COLS = BLOCK_N / 16;
+  static_assert(COLS % 2 == 0);
+
+  __m512 vc0[COLS];
+  __m512 vc1[COLS];
+  __m512i vcomp0[COLS];
+  __m512i vcomp1[COLS];
+  __m512 vas;
+  __m512 vbs0[COLS];
+  __m512 vbs1[COLS];
+
+  auto load_scale_and_comp = [&](auto col) {
+    vcomp0[col] = _mm512_loadu_si512(Bcomp0 + col * 16);
+    vcomp1[col] = _mm512_loadu_si512(Bcomp1 + col * 16);
+    vbs0[col] = _mm512_loadu_ps(Bs0 + col * 16);
+    vbs1[col] = _mm512_loadu_ps(Bs1 + col * 16);
+  };
+  Unroll<COLS>{}(load_scale_and_comp);
+
+  auto scalec = [&](auto col, int64_t m) {
+    // update As
+    vas = _mm512_set1_ps(As[m]);
+    // C = As * (C - Bcomp) * Bs
+    __m512i vc32_0 = _mm512_loadu_si512(C0 + m * BLOCK_N + col * 16);
+    __m512i vc32_1 = _mm512_loadu_si512(C1 + m * BLOCK_N + col * 16);
+    vc0[col] = _mm512_cvtepi32_ps(_mm512_sub_epi32(vc32_0, vcomp0[col]));
+    vc1[col] = _mm512_cvtepi32_ps(_mm512_sub_epi32(vc32_1, vcomp1[col]));
+    vc0[col] = _mm512_mul_ps(_mm512_mul_ps(vc0[col], vas), vbs0[col]);
+    vc1[col] = _mm512_mul_ps(_mm512_mul_ps(vc1[col], vas), vbs1[col]);
+  };
 
-// acc from [topk, K] to [K]
-template <typename scalar_t>
-inline void sum_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t topk, int64_t K) {
   using bVec = at::vec::Vectorized<scalar_t>;
   using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-  if (topk == 1) {
-    // do copy for topk = 1
-    copy_stub(out, input, K);
-  } else {
-    // do sum for topk != 1
-    int64_t d;
-    #pragma GCC unroll 4
-    for (d = 0; d <= K - kVecSize; d += kVecSize) {
-      fVec sum_fvec0 = fVec(0.f);
-      fVec sum_fvec1 = fVec(0.f);
-      for (int t = 0; t < topk; ++t) {
-        bVec x_bvec = bVec::loadu(input + t * K + d);
-        fVec x_fvec0, x_fvec1;
-        std::tie(x_fvec0, x_fvec1) = at::vec::convert_to_float(x_bvec);
-
-        sum_fvec0 += x_fvec0;
-        sum_fvec1 += x_fvec1;
-      }
-      bVec out_bvec = convert_from_float_ext<scalar_t>(sum_fvec0, sum_fvec1);
-      out_bvec.store(out + d);
-    }
-    for (; d < K; ++d) {
-      float sum_val = 0.f;
-      for (int t = 0; t < topk; ++t) {
-        sum_val += static_cast<float>(input[t * K + d]);
-      }
-      out[d] = static_cast<scalar_t>(sum_val);
+  const fVec one = fVec(1.f);
+  auto silu_and_mul = [&](auto col) {
+    fVec x = fVec(vc0[col]);
+    fVec y = fVec(vc1[col]);
+    x = x / (one + x.neg().exp_u20());
+    vc0[col] = x * y;
+  };
+
+  auto storec = [&](auto col, int64_t m) {
+    if constexpr (col % 2 == 0) {
+      fVec x0 = fVec(vc0[col + 0]);
+      fVec x1 = fVec(vc0[col + 1]);
+      bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
+      out_vec.store(C + m * N + col * 16);
     }
+  };
+
+  for (int64_t m = 0; m < m_size; ++m) {
+    Unroll<COLS>{}(scalec, m);
+    Unroll<COLS>{}(silu_and_mul);
+    Unroll<COLS>{}(storec, m);
   }
+#else
+  TORCH_CHECK(false, "silu_and_mul: scalar path not implemented!");
+#endif
 }
 
-// out = input + input2 * scale
-template <typename scalar_t>
-inline void add_mul_stub(scalar_t* __restrict__ out, const float* __restrict__ input,
-    const scalar_t* __restrict__ input2, float scale, int64_t size) {
-
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-  const fVec s_vec = fVec(scale);
-  int64_t d;
-  #pragma GCC unroll 4
-  for (d = 0; d <= size - kVecSize; d += kVecSize) {
-    fVec x0 = fVec::loadu(input + d);
-    fVec x1 = fVec::loadu(input + d + fVec::size());
-
-    bVec y_bvec = bVec::loadu(input2 + d);
-    fVec y0, y1;
-    std::tie(y0, y1) = at::vec::convert_to_float(y_bvec);
-
-    x0 = x0 + y0 * s_vec;
-    x1 = x1 + y1 * s_vec;
-    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
-    out_vec.store(out + d);
-  }
-  for (; d < size; ++d) {
-    out[d] = static_cast<scalar_t>(input[d] + float(input2[d]) * scale);
+template <int BLOCK_N>
+inline void scale_C(
+    float* __restrict__ C,
+    const int32_t* __restrict__ Ctmp,
+    const float* __restrict__ As,
+    const float* __restrict__ Bs,
+    const int32_t* __restrict__ Bcomp,
+    int64_t m_size) {
+#if defined(CPU_CAPABILITY_AVX512)
+  constexpr int COLS = BLOCK_N / 16;
+  static_assert(COLS % 2 == 0);
+
+  __m512 vc[COLS];
+  __m512i vcomp[COLS];
+  __m512 vas;
+  __m512 vbs[COLS];
+
+  auto load_scale_and_comp = [&](auto col) {
+    vcomp[col] = _mm512_loadu_si512(Bcomp + col * 16);
+    vbs[col] = _mm512_loadu_ps(Bs + col * 16);
+  };
+  Unroll<COLS>{}(load_scale_and_comp);
+
+  auto scalec = [&](auto col, int64_t m) {
+    // update As
+    vas = _mm512_set1_ps(As[m]);
+    // C = As * (C - Bcomp) * Bs
+    __m512i vc32 = _mm512_loadu_si512(Ctmp + m * BLOCK_N + col * 16);
+    vc[col] = _mm512_cvtepi32_ps(_mm512_sub_epi32(vc32, vcomp[col]));
+    vc[col] = _mm512_mul_ps(_mm512_mul_ps(vc[col], vas), vbs[col]);
+    _mm512_storeu_ps(C + m * BLOCK_N + col * 16, vc[col]);
+  };
+
+  for (int64_t m = 0; m < m_size; ++m) {
+    Unroll<COLS>{}(scalec, m);
   }
+#else
+  TORCH_CHECK(false, "scale_C: scalar path not implemented!");
+#endif
 }
 
 /// gemm for w13
 template <typename scalar_t, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_vnni {
   static inline void apply(
-      const uint8_t* __restrict__ A, const int8_t* __restrict__ B0, const int8_t* __restrict__ B1, scalar_t* __restrict__ C,
-      const float* __restrict__ As, const float* __restrict__ Bs0, const float* __restrict__ Bs1,
-      const int32_t* __restrict__ Bcomp0, const int32_t* __restrict__ Bcomp1,
-      int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
+      const uint8_t* __restrict__ A,
+      const int8_t* __restrict__ B0,
+      const int8_t* __restrict__ B1,
+      scalar_t* __restrict__ C,
+      const float* __restrict__ As,
+      const float* __restrict__ Bs0,
+      const float* __restrict__ Bs1,
+      const int32_t* __restrict__ Bcomp0,
+      const int32_t* __restrict__ Bcomp1,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
     TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
   }
 };
@@ -127,11 +148,19 @@ struct tinygemm_kernel_vnni {
 template <int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_vnni<at::BFloat16, BLOCK_M, BLOCK_N> {
   static inline void apply(
-      const uint8_t* __restrict__ A, const int8_t* __restrict__ B0, const int8_t* __restrict__ B1, at::BFloat16* __restrict__ C,
-      const float* __restrict__ As, const float* __restrict__ Bs0, const float* __restrict__ Bs1,
-      const int32_t* __restrict__ Bcomp0, const int32_t* __restrict__ Bcomp1,
-      int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
-
+      const uint8_t* __restrict__ A,
+      const int8_t* __restrict__ B0,
+      const int8_t* __restrict__ B1,
+      at::BFloat16* __restrict__ C,
+      const float* __restrict__ As,
+      const float* __restrict__ Bs0,
+      const float* __restrict__ Bs1,
+      const int32_t* __restrict__ Bcomp0,
+      const int32_t* __restrict__ Bcomp1,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
     constexpr int ROWS = BLOCK_M;
     constexpr int COLS = BLOCK_N / 16;
     static_assert(COLS % 2 == 0);
@@ -143,9 +172,9 @@ struct tinygemm_kernel_vnni<at::BFloat16, BLOCK_M, BLOCK_N> {
     __m512i vc1[ROWS * COLS];
     __m512i vcomp0[COLS];
     __m512i vcomp1[COLS];
-    __m512  was;
-    __m512  vbs0[COLS];
-    __m512  vbs1[COLS];
+    __m512 vas;
+    __m512 vbs0[COLS];
+    __m512 vbs1[COLS];
 
     auto loadc = [&](auto i) {
       vc0[i] = _mm512_set1_epi32(0);
@@ -155,7 +184,7 @@ struct tinygemm_kernel_vnni<at::BFloat16, BLOCK_M, BLOCK_N> {
 
     const int64_t K4 = K >> 2;
     const int64_t lda4 = lda >> 2;
-    const int64_t ldb4 = ldb; // ldb * 4 >> 2;
+    const int64_t ldb4 = ldb;  // ldb * 4 >> 2;
     const int32_t* a_ptr = reinterpret_cast<const int32_t*>(A);
     const int32_t* b0_ptr = reinterpret_cast<const int32_t*>(B0);
     const int32_t* b1_ptr = reinterpret_cast<const int32_t*>(B1);
@@ -183,8 +212,8 @@ struct tinygemm_kernel_vnni<at::BFloat16, BLOCK_M, BLOCK_N> {
       constexpr int col = i % COLS;
 
       // load a scale
-      if constexpr(col == 0) {
-        was = _mm512_set1_ps(As[row]);
+      if constexpr (col == 0) {
+        vas = _mm512_set1_ps(As[row]);
       }
       // load b scale and vcomp
       if constexpr (row == 0) {
@@ -195,8 +224,8 @@ struct tinygemm_kernel_vnni<at::BFloat16, BLOCK_M, BLOCK_N> {
       }
       __m512 c0 = _mm512_cvtepi32_ps(_mm512_sub_epi32(vc0[i], vcomp0[col]));
       __m512 c1 = _mm512_cvtepi32_ps(_mm512_sub_epi32(vc1[i], vcomp1[col]));
-      vc0[i] = _mm512_castps_si512(_mm512_mul_ps(_mm512_mul_ps(c0, was), vbs0[col]));
-      vc1[i] = _mm512_castps_si512(_mm512_mul_ps(_mm512_mul_ps(c1, was), vbs1[col]));
+      vc0[i] = _mm512_castps_si512(_mm512_mul_ps(_mm512_mul_ps(c0, vas), vbs0[col]));
+      vc1[i] = _mm512_castps_si512(_mm512_mul_ps(_mm512_mul_ps(c1, vas), vbs1[col]));
     };
     Unroll<ROWS * COLS>{}(scalec);
 
@@ -221,19 +250,28 @@ struct tinygemm_kernel_vnni<at::BFloat16, BLOCK_M, BLOCK_N> {
         _mm512_storeu_si512(
             reinterpret_cast<__m512i*>((C + row * ldc + col * 16)),
             (__m512i)(_mm512_cvtne2ps_pbh(__m512(x1), __m512(x0))));
-        }
+      }
     };
     Unroll<ROWS * COLS>{}(storec);
   }
 };
 #endif
 
-#define LAUNCH_TINYGEMM_KERNEL_VNNI(MB_SIZE, NB_SIZE)                        \
-    tinygemm_kernel_vnni<scalar_t, MB_SIZE, NB_SIZE>::apply(                 \
-        A + mb_start * lda, B0 + nb_start * 4, B1 + nb_start * 4,            \
-        C + mb_start * ldc + nb_start, As + mb_start,                        \
-        Bs0 + nb_start, Bs1 + nb_start, Bcomp0 + nb_start, Bcomp1 + nb_start,\
-        K, lda, ldb, ldc);
+#define LAUNCH_TINYGEMM_KERNEL_VNNI(MB_SIZE, NB_SIZE)      \
+  tinygemm_kernel_vnni<scalar_t, MB_SIZE, NB_SIZE>::apply( \
+      A + mb_start * lda,                                  \
+      B0 + nb_start * 4,                                   \
+      B1 + nb_start * 4,                                   \
+      C + mb_start * ldc + nb_start,                       \
+      As + mb_start,                                       \
+      Bs0 + nb_start,                                      \
+      Bs1 + nb_start,                                      \
+      Bcomp0 + nb_start,                                   \
+      Bcomp1 + nb_start,                                   \
+      K,                                                   \
+      lda,                                                 \
+      ldb,                                                 \
+      ldc);
 
 template <typename scalar_t>
 void tinygemm_kernel(
@@ -250,7 +288,6 @@ void tinygemm_kernel(
     int64_t lda,
     int64_t ldb,
     int64_t ldc) {
-
   const int32_t* Bcomp0 = reinterpret_cast<const int32_t*>(B0 + block_size_n() * K);
   const int32_t* Bcomp1 = reinterpret_cast<const int32_t*>(B1 + block_size_n() * K);
 
@@ -266,12 +303,21 @@ void tinygemm_kernel(
       int64_t nb_start = nb * BLOCK_N;
       int64_t nb_size = std::min(BLOCK_N, N - nb_start);
 
-      switch(mb_size << 4 | nb_size >> 4) {
-        case 0x12: LAUNCH_TINYGEMM_KERNEL_VNNI(1, 32); break;
-        case 0x22: LAUNCH_TINYGEMM_KERNEL_VNNI(2, 32); break;
-        case 0x32: LAUNCH_TINYGEMM_KERNEL_VNNI(3, 32); break;
-        case 0x42: LAUNCH_TINYGEMM_KERNEL_VNNI(4, 32); break;
-        default: TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", nb_size);
+      switch (mb_size << 4 | nb_size >> 4) {
+        case 0x12:
+          LAUNCH_TINYGEMM_KERNEL_VNNI(1, 32);
+          break;
+        case 0x22:
+          LAUNCH_TINYGEMM_KERNEL_VNNI(2, 32);
+          break;
+        case 0x32:
+          LAUNCH_TINYGEMM_KERNEL_VNNI(3, 32);
+          break;
+        case 0x42:
+          LAUNCH_TINYGEMM_KERNEL_VNNI(4, 32);
+          break;
+        default:
+          TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", "nb_size");
       }
     }
   }
@@ -281,9 +327,16 @@ void tinygemm_kernel(
 template <typename scalar_t, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_vnni2 {
   static inline void apply(
-      const uint8_t* __restrict__ A, const int8_t* __restrict__ B, float* __restrict__ C,
-      const float* __restrict__ As, const float* __restrict__ Bs, const int32_t* __restrict__ Bcomp,
-      int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
+      const uint8_t* __restrict__ A,
+      const int8_t* __restrict__ B,
+      float* __restrict__ C,
+      const float* __restrict__ As,
+      const float* __restrict__ Bs,
+      const int32_t* __restrict__ Bcomp,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
     TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
   }
 };
@@ -292,10 +345,16 @@ struct tinygemm_kernel_vnni2 {
 template <int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_vnni2<at::BFloat16, BLOCK_M, BLOCK_N> {
   static inline void apply(
-      const uint8_t* __restrict__ A, const int8_t* __restrict__ B, float* __restrict__ C,
-      const float* __restrict__ As, const float* __restrict__ Bs, const int32_t* __restrict__ Bcomp,
-      int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
-
+      const uint8_t* __restrict__ A,
+      const int8_t* __restrict__ B,
+      float* __restrict__ C,
+      const float* __restrict__ As,
+      const float* __restrict__ Bs,
+      const int32_t* __restrict__ Bcomp,
+      int64_t K,
+      int64_t lda,
+      int64_t ldb,
+      int64_t ldc) {
     constexpr int ROWS = BLOCK_M;
     constexpr int COLS = BLOCK_N / 16;
     static_assert(COLS % 2 == 0);
@@ -304,17 +363,15 @@ struct tinygemm_kernel_vnni2<at::BFloat16, BLOCK_M, BLOCK_N> {
     __m512i vb[COLS];
     __m512i vc[ROWS * COLS];
     __m512i vcomp[COLS];
-    __m512  was;
-    __m512  vbs[COLS];
+    __m512 vas;
+    __m512 vbs[COLS];
 
-    auto loadc = [&](auto i) {
-      vc[i] = _mm512_set1_epi32(0);
-    };
+    auto loadc = [&](auto i) { vc[i] = _mm512_set1_epi32(0); };
     Unroll<ROWS * COLS>{}(loadc);
 
     const int64_t K4 = K >> 2;
     const int64_t lda4 = lda >> 2;
-    const int64_t ldb4 = ldb; // ldb * 4 >> 2;
+    const int64_t ldb4 = ldb;  // ldb * 4 >> 2;
     const int32_t* a_ptr = reinterpret_cast<const int32_t*>(A);
     const int32_t* b_ptr = reinterpret_cast<const int32_t*>(B);
 
@@ -339,8 +396,8 @@ struct tinygemm_kernel_vnni2<at::BFloat16, BLOCK_M, BLOCK_N> {
       constexpr int col = i % COLS;
 
       // load a scale
-      if constexpr(col == 0) {
-        was = _mm512_set1_ps(As[row]);
+      if constexpr (col == 0) {
+        vas = _mm512_set1_ps(As[row]);
       }
       // load b scale and vcomp per 2 vectors
       // also load bias if any
@@ -353,7 +410,7 @@ struct tinygemm_kernel_vnni2<at::BFloat16, BLOCK_M, BLOCK_N> {
         }
       }
       __m512 x = _mm512_cvtepi32_ps(_mm512_sub_epi32(vc[i], vcomp[col]));
-      x = _mm512_mul_ps(_mm512_mul_ps(x, was), vbs[col]);
+      x = _mm512_mul_ps(_mm512_mul_ps(x, vas), vbs[col]);
       _mm512_storeu_ps(reinterpret_cast<__m512*>(C + row * ldc + col * 16), x);
     };
     Unroll<ROWS * COLS>{}(storec);
@@ -361,11 +418,18 @@ struct tinygemm_kernel_vnni2<at::BFloat16, BLOCK_M, BLOCK_N> {
 };
 #endif
 
-#define LAUNCH_TINYGEMM_KERNEL_VNNI2(MB_SIZE, NB_SIZE)                       \
-    tinygemm_kernel_vnni2<scalar_t, MB_SIZE, NB_SIZE>::apply(                \
-        A + mb_start * lda, B + nb_start * 4, C + mb_start * ldc + nb_start, \
-        As + mb_start, Bs + nb_start, Bcomp + nb_start,                      \
-        K, lda, ldb, ldc);
+#define LAUNCH_TINYGEMM_KERNEL_VNNI2(MB_SIZE, NB_SIZE)      \
+  tinygemm_kernel_vnni2<scalar_t, MB_SIZE, NB_SIZE>::apply( \
+      A + mb_start * lda,                                   \
+      B + nb_start * 4,                                     \
+      C + mb_start * ldc + nb_start,                        \
+      As + mb_start,                                        \
+      Bs + nb_start,                                        \
+      Bcomp + nb_start,                                     \
+      K,                                                    \
+      lda,                                                  \
+      ldb,                                                  \
+      ldc);
 
 template <typename scalar_t>
 void tinygemm_kernel(
@@ -380,7 +444,6 @@ void tinygemm_kernel(
     int64_t lda,
     int64_t ldb,
     int64_t ldc) {
-
   // B compensation
   const int32_t* Bcomp = reinterpret_cast<const int32_t*>(B + block_size_n() * K);
 
@@ -396,18 +459,27 @@ void tinygemm_kernel(
       int64_t nb_start = nb * BLOCK_N;
       int64_t nb_size = std::min(BLOCK_N, N - nb_start);
 
-      switch(mb_size << 4 | nb_size >> 4) {
-        case 0x12: LAUNCH_TINYGEMM_KERNEL_VNNI2(1, 32); break;
-        case 0x22: LAUNCH_TINYGEMM_KERNEL_VNNI2(2, 32); break;
-        case 0x32: LAUNCH_TINYGEMM_KERNEL_VNNI2(3, 32); break;
-        case 0x42: LAUNCH_TINYGEMM_KERNEL_VNNI2(4, 32); break;
-        default: TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", nb_size);
+      switch (mb_size << 4 | nb_size >> 4) {
+        case 0x12:
+          LAUNCH_TINYGEMM_KERNEL_VNNI2(1, 32);
+          break;
+        case 0x22:
+          LAUNCH_TINYGEMM_KERNEL_VNNI2(2, 32);
+          break;
+        case 0x32:
+          LAUNCH_TINYGEMM_KERNEL_VNNI2(3, 32);
+          break;
+        case 0x42:
+          LAUNCH_TINYGEMM_KERNEL_VNNI2(4, 32);
+          break;
+        default:
+          TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", "nb_size");
       }
     }
   }
 }
 
-} // anonymous namespace
+}  // anonymous namespace
 
 template <typename scalar_t>
 void fused_experts_int8_kernel_impl(
@@ -433,7 +505,6 @@ void fused_experts_int8_kernel_impl(
     int64_t E,
     int64_t topk,
     int64_t num_tokens_post_pad) {
-
   // handle 2 tiles per block
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
@@ -441,11 +512,7 @@ void fused_experts_int8_kernel_impl(
   // stage 0: quantize input to uint8, [M, K]
   at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
     for (int64_t m = begin; m < end; ++m) {
-      quantize_row_int8<scalar_t>(
-          Aq_tmp + m * K,
-          As_tmp[m],
-          input + m * K,
-          K);
+      quantize_row_int8<scalar_t>(Aq_tmp + m * K, As_tmp[m], input + m * K, K);
     }
   });
 
@@ -462,66 +529,107 @@ void fused_experts_int8_kernel_impl(
 
   const int64_t stride_e = 2 * N * packed_K;
   const int64_t stride_n = packed_K;
+
+  int64_t avg_M = std::max(int64_t(1), M * topk / E);
+  const bool use_brgemm = can_use_brgemm<int8_t>(avg_M);
+
   // here we only parallel on half of 2N to fuse silu_and_mul with gemm
-  at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
+  parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
     // get local pointers
-    int tid = at::get_thread_num();
+    int tid = get_thread_num();
     uint8_t* __restrict__ A = A_tmp + tid * BLOCK_M * K;
+    int32_t* __restrict__ C0 = reinterpret_cast<int32_t*>(C_tmp) + tid * 2 * BLOCK_M * BLOCK_N;
+    int32_t* __restrict__ C1 = C0 + BLOCK_M * BLOCK_N;
 
     alignas(64) float As[BLOCK_M];
 
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB;
-      int64_t nb = i % NB;
-
-      // nb0 from top half and nb1 from bottom half
-      int64_t nb0 = nb, nb1 = nb + NB;
-      int64_t n_size = std::min(N - nb0 * BLOCK_N, BLOCK_N);
+    loop_2d<int8_t>(mb0, mb1, nb0, nb1, BLOCK_N * K * 2, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+      // nb_upper from top half and nb_lower from bottom half
+      int64_t nb_upper = nb, nb_lower = nb + NB;
+      int64_t n_size = std::min(N - nb * BLOCK_N, BLOCK_N);
 
       // B shape [K, n_size] in vnni format
       int32_t expert_id = expert_ids[mb];
-      const int8_t* __restrict__ B0 = packed_w1 + expert_id * stride_e + nb0 * BLOCK_N * stride_n;
-      const int8_t* __restrict__ B1 = packed_w1 + expert_id * stride_e + nb1 * BLOCK_N * stride_n;
-      const float* __restrict__ Bs0 = w1s + expert_id * 2 * N + nb0 * BLOCK_N;
-      const float* __restrict__ Bs1 = w1s + expert_id * 2 * N + nb1 * BLOCK_N;
+      const int8_t* __restrict__ B0 = packed_w1 + expert_id * stride_e + nb_upper * BLOCK_N * stride_n;
+      const int8_t* __restrict__ B1 = packed_w1 + expert_id * stride_e + nb_lower * BLOCK_N * stride_n;
+      const float* __restrict__ Bs0 = w1s + expert_id * 2 * N + nb_upper * BLOCK_N;
+      const float* __restrict__ Bs1 = w1s + expert_id * 2 * N + nb_lower * BLOCK_N;
 
-      // 1.a load A
-      const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
       int64_t m_size = offsets[mb + 1] - offsets[mb];
 
-      for (int64_t m = 0; m < m_size; ++m) {
-        int32_t index = A_ids[m] / topk;
-        copy_stub(A + m * K, Aq_tmp + index * K, K);
-        As[m] = As_tmp[index];
+      if (nb_offset == 0) {
+        // 1.a load A
+        const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
+        for (int64_t m = 0; m < m_size; ++m) {
+          int32_t index = A_ids[m] / topk;
+          copy_stub(A + m * K, Aq_tmp + index * K, K);
+          As[m] = As_tmp[index];
+        }
       }
 
-      // fused 1.b: silu_and_mul(A @ B0, A @ B1)
-      const int64_t offset = offsets[mb];
-      tinygemm_kernel(
-          /* A     */ A,
-          /* B0    */ B0,
-          /* B1    */ B1,
-          /* C     */ ic1 + offset * N + nb * BLOCK_N,
-          /* As    */ As,
-          /* Bs0   */ Bs0,
-          /* Bs1   */ Bs1,
-          /* M     */ m_size,
-          /* N     */ n_size,
-          /* K     */ K,
-          /* lda   */ K,
-          /* ldb   */ n_size,
-          /* ldc   */ N);
+      if (use_brgemm) {
+        // 1.b gemm: C0 = A @ B0
+        at::native::cpublas::brgemm(
+            /* M     */ m_size,
+            /* N     */ n_size,
+            /* K     */ K,
+            /* lda   */ K,
+            /* ldb   */ n_size,
+            /* ldc   */ BLOCK_N,
+            /* add_C */ false,
+            /* A     */ A,
+            /* B     */ B0,
+            /* C     */ C0);
+
+        // 1.c gemm: C1 = A @ B1
+        at::native::cpublas::brgemm(
+            /* M     */ m_size,
+            /* N     */ n_size,
+            /* K     */ K,
+            /* lda   */ K,
+            /* ldb   */ n_size,
+            /* ldc   */ BLOCK_N,
+            /* add_C */ false,
+            /* A     */ A,
+            /* B     */ B1,
+            /* C     */ C1);
+
+        const int32_t* Bcomp0 = reinterpret_cast<const int32_t*>(B0 + block_size_n() * K);
+        const int32_t* Bcomp1 = reinterpret_cast<const int32_t*>(B1 + block_size_n() * K);
+
+        // 1.d silu and mul
+        const int64_t offset = offsets[mb];
+        silu_and_mul<scalar_t, BLOCK_N>(
+            ic1 + offset * N + nb * BLOCK_N, C0, C1, As, Bs0, Bs1, Bcomp0, Bcomp1, m_size, N);
+      } else {
+        // fused 1.bcd: silu_and_mul(A @ B0, A @ B1)
+        const int64_t offset = offsets[mb];
+        tinygemm_kernel(
+            /* A     */ A,
+            /* B0    */ B0,
+            /* B1    */ B1,
+            /* C     */ ic1 + offset * N + nb * BLOCK_N,
+            /* As    */ As,
+            /* Bs0   */ Bs0,
+            /* Bs1   */ Bs1,
+            /* M     */ m_size,
+            /* N     */ n_size,
+            /* K     */ K,
+            /* lda   */ K,
+            /* ldb   */ n_size,
+            /* ldc   */ N);
+      }
+    });
+
+    if (use_brgemm) {
+      at::native::cpublas::brgemm_release();
     }
   });
 
   // stage 1.5: quantize ic1 to uint8, [M * topk, N]
   at::parallel_for(0, M * topk, 0, [&](int64_t begin, int64_t end) {
     for (int64_t m = begin; m < end; ++m) {
-      quantize_row_int8<scalar_t>(
-          Aq_tmp + m * N,
-          As_tmp[m],
-          ic1 + m * N,
-          N);
+      quantize_row_int8<scalar_t>(Aq_tmp + m * N, As_tmp[m], ic1 + m * N, N);
     }
   });
 
@@ -535,16 +643,13 @@ void fused_experts_int8_kernel_impl(
   const int64_t stride_oc = packed_N;
 
   // parallel on [MB2, NB2]
-  at::parallel_for(0, MB2 * NB2, 0, [&](int64_t begin, int64_t end) {
+  parallel_2d(MB2, NB2, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
     // get local pointers
-    int tid = at::get_thread_num();
-    // we won't be using C1 for gemm2
+    int tid = get_thread_num();
     float* __restrict__ C = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
+    int32_t* __restrict__ C32 = reinterpret_cast<int32_t*>(C + BLOCK_M * BLOCK_N);
 
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB2;
-      int64_t nb = i % NB2;
-
+    loop_2d<int8_t>(mb0, mb1, nb0, nb1, BLOCK_N * IC, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
       int64_t m_size = offsets[mb + 1] - offsets[mb];
       int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
 
@@ -560,18 +665,36 @@ void fused_experts_int8_kernel_impl(
       const float* __restrict__ Bs = w2s + expert_id * K + nb * BLOCK_N;
 
       // 2.a gemm: C = A @ B
-      tinygemm_kernel<scalar_t>(
-          /* A     */ A,
-          /* B     */ B,
-          /* C     */ C,
-          /* As    */ As,
-          /* Bs    */ Bs,
-          /* M     */ m_size,
-          /* N     */ n_size,
-          /* K     */ IC,
-          /* lda   */ IC,
-          /* ldb   */ n_size,
-          /* ldc   */ BLOCK_N);
+      if (use_brgemm) {
+        at::native::cpublas::brgemm(
+            /* M     */ m_size,
+            /* N     */ n_size,
+            /* K     */ IC,
+            /* lda   */ IC,
+            /* ldb   */ n_size,
+            /* ldc   */ BLOCK_N,
+            /* add_C */ false,
+            /* A     */ A,
+            /* B     */ B,
+            /* C     */ C32);
+
+        // apply scales
+        const int32_t* Bcomp = reinterpret_cast<const int32_t*>(B + block_size_n() * IC);
+        scale_C<BLOCK_N>(C, C32, As, Bs, Bcomp, m_size);
+      } else {
+        tinygemm_kernel<scalar_t>(
+            /* A     */ A,
+            /* B     */ B,
+            /* C     */ C,
+            /* As    */ As,
+            /* Bs    */ Bs,
+            /* M     */ m_size,
+            /* N     */ n_size,
+            /* K     */ IC,
+            /* lda   */ IC,
+            /* ldb   */ n_size,
+            /* ldc   */ BLOCK_N);
+      }
 
       // 2.b copy from C to ic2 in original order
       //   and also mul topk_weights in float32
@@ -580,6 +703,10 @@ void fused_experts_int8_kernel_impl(
         float weight = topk_weights[index];
         copy_mul_stub(ic2 + index * K + nb * BLOCK_N, C + m * BLOCK_N, weight, n_size);
       }
+    });
+
+    if (use_brgemm) {
+      at::native::cpublas::brgemm_release();
     }
   });
 
@@ -592,17 +719,30 @@ void fused_experts_int8_kernel_impl(
   });
 }
 
-#define INSTANTIATE_MOE_INT8_TEMPLATE(TYPE)                                                  \
-  template void fused_experts_int8_kernel_impl<TYPE> (                                       \
-      TYPE* __restrict__ output, TYPE* __restrict__ ic1,                                     \
-      TYPE* __restrict__ ic2, uint8_t* __restrict__ A_tmp,                                   \
-      float* __restrict__ C_tmp, uint8_t* __restrict__ Aq_tmp,                               \
-      float* __restrict__ As_tmp, const TYPE* __restrict__ input,                            \
-      const int8_t* __restrict__ packed_w1, const int8_t* __restrict__ packed_w2,            \
-      const float* __restrict__ w1s, const float* __restrict__ w2s,                          \
-      const float* __restrict__ topk_weights, const int32_t* __restrict__ sorted_ids,        \
-      const int32_t* __restrict__ expert_ids, const int32_t* __restrict__ offsets,           \
-      int64_t M, int64_t N, int64_t K, int64_t E, int64_t topk, int64_t num_tokens_post_pad)
+#define INSTANTIATE_MOE_INT8_TEMPLATE(TYPE)           \
+  template void fused_experts_int8_kernel_impl<TYPE>( \
+      TYPE* __restrict__ output,                      \
+      TYPE* __restrict__ ic1,                         \
+      TYPE* __restrict__ ic2,                         \
+      uint8_t* __restrict__ A_tmp,                    \
+      float* __restrict__ C_tmp,                      \
+      uint8_t* __restrict__ Aq_tmp,                   \
+      float* __restrict__ As_tmp,                     \
+      const TYPE* __restrict__ input,                 \
+      const int8_t* __restrict__ packed_w1,           \
+      const int8_t* __restrict__ packed_w2,           \
+      const float* __restrict__ w1s,                  \
+      const float* __restrict__ w2s,                  \
+      const float* __restrict__ topk_weights,         \
+      const int32_t* __restrict__ sorted_ids,         \
+      const int32_t* __restrict__ expert_ids,         \
+      const int32_t* __restrict__ offsets,            \
+      int64_t M,                                      \
+      int64_t N,                                      \
+      int64_t K,                                      \
+      int64_t E,                                      \
+      int64_t topk,                                   \
+      int64_t num_tokens_post_pad)
 
 INSTANTIATE_MOE_INT8_TEMPLATE(at::BFloat16);
 INSTANTIATE_MOE_INT8_TEMPLATE(at::Half);
@@ -624,7 +764,6 @@ void shared_expert_int8_kernel_impl(
     int64_t M,
     int64_t N,
     int64_t K) {
-
   // handle 2 tiles per block
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
@@ -632,15 +771,11 @@ void shared_expert_int8_kernel_impl(
   // stage 0: quantize input to uint8, [M, K]
   at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
     for (int64_t m = begin; m < end; ++m) {
-      quantize_row_int8<scalar_t>(
-          Aq_tmp + m * K,
-          As_tmp[m],
-          input + m * K,
-          K);
+      quantize_row_int8<scalar_t>(Aq_tmp + m * K, As_tmp[m], input + m * K, K);
     }
   });
 
-   // stage 1: intermediate_cache1 = silu(hidden_states @ w1)
+  // stage 1: intermediate_cache1 = silu(hidden_states @ w1)
   const int64_t MB = div_up(M, BLOCK_M);
   const int64_t NB = div_up(N, BLOCK_N);
 
@@ -651,15 +786,20 @@ void shared_expert_int8_kernel_impl(
   const int64_t packed_N = get_row_size<int8_t>(N);
   const int64_t stride_n = packed_K;
 
+  const bool use_brgemm = can_use_brgemm<int8_t>(M);
+  const bool apply_scaling_factor = fused_experts_out != nullptr;
+
   // here we only parallel on half of 2N to fuse silu_and_mul with gemm
-  at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB;
-      int64_t nb = i % NB;
-
-      // nb0 from top half and nb1 from bottom half
-      int64_t nb0 = nb, nb1 = nb + NB;
-      int64_t n_size = std::min(N - nb0 * BLOCK_N, BLOCK_N);
+  parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
+    // get local pointers
+    int tid = get_thread_num();
+    int32_t* __restrict__ C0 = reinterpret_cast<int32_t*>(C_tmp) + tid * 2 * BLOCK_M * BLOCK_N;
+    int32_t* __restrict__ C1 = C0 + BLOCK_M * BLOCK_N;
+
+    loop_2d<int8_t>(mb0, mb1, nb0, nb1, BLOCK_N * K * 2, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+      // nb_upper from top half and nb_lower from bottom half
+      int64_t nb_upper = nb, nb_lower = nb + NB;
+      int64_t n_size = std::min(N - nb * BLOCK_N, BLOCK_N);
       int64_t m_size = std::min(M - mb * BLOCK_M, BLOCK_M);
 
       // A shape [m_size, K]
@@ -667,37 +807,72 @@ void shared_expert_int8_kernel_impl(
       const float* As = As_tmp + mb * BLOCK_M;
 
       // B shape [K, n_size] in vnni format
-      const int8_t* __restrict__ B0 = packed_w1 + nb0 * BLOCK_N * stride_n;
-      const int8_t* __restrict__ B1 = packed_w1 + nb1 * BLOCK_N * stride_n;
-      const float* __restrict__ Bs0 = w1s + nb0 * BLOCK_N;
-      const float* __restrict__ Bs1 = w1s + nb1 * BLOCK_N;
-
-      // fused 1.b: silu_and_mul(A @ B0, A @ B1)
-      tinygemm_kernel(
-          /* A     */ A,
-          /* B0    */ B0,
-          /* B1    */ B1,
-          /* C     */ ic1 + mb * BLOCK_M * N + nb * BLOCK_N,
-          /* As    */ As,
-          /* Bs0   */ Bs0,
-          /* Bs1   */ Bs1,
-          /* M     */ m_size,
-          /* N     */ n_size,
-          /* K     */ K,
-          /* lda   */ K,
-          /* ldb   */ n_size,
-          /* ldc   */ N);
+      const int8_t* __restrict__ B0 = packed_w1 + nb_upper * BLOCK_N * stride_n;
+      const int8_t* __restrict__ B1 = packed_w1 + nb_lower * BLOCK_N * stride_n;
+      const float* __restrict__ Bs0 = w1s + nb_upper * BLOCK_N;
+      const float* __restrict__ Bs1 = w1s + nb_lower * BLOCK_N;
+
+      if (use_brgemm) {
+        // 1.b gemm: C0 = A @ B0
+        at::native::cpublas::brgemm(
+            /* M     */ m_size,
+            /* N     */ n_size,
+            /* K     */ K,
+            /* lda   */ K,
+            /* ldb   */ n_size,
+            /* ldc   */ BLOCK_N,
+            /* add_C */ false,
+            /* A     */ A,
+            /* B     */ B0,
+            /* C     */ C0);
+
+        // 1.c gemm: C1 = A @ B1
+        at::native::cpublas::brgemm(
+            /* M     */ m_size,
+            /* N     */ n_size,
+            /* K     */ K,
+            /* lda   */ K,
+            /* ldb   */ n_size,
+            /* ldc   */ BLOCK_N,
+            /* add_C */ false,
+            /* A     */ A,
+            /* B     */ B1,
+            /* C     */ C1);
+
+        const int32_t* Bcomp0 = reinterpret_cast<const int32_t*>(B0 + block_size_n() * K);
+        const int32_t* Bcomp1 = reinterpret_cast<const int32_t*>(B1 + block_size_n() * K);
+
+        // 1.d silu and mul
+        silu_and_mul<scalar_t, BLOCK_N>(
+            ic1 + mb * BLOCK_M * N + nb * BLOCK_N, C0, C1, As, Bs0, Bs1, Bcomp0, Bcomp1, m_size, N);
+      } else {
+        // fused 1.bcd: silu_and_mul(A @ B0, A @ B1)
+        tinygemm_kernel(
+            /* A     */ A,
+            /* B0    */ B0,
+            /* B1    */ B1,
+            /* C     */ ic1 + mb * BLOCK_M * N + nb * BLOCK_N,
+            /* As    */ As,
+            /* Bs0   */ Bs0,
+            /* Bs1   */ Bs1,
+            /* M     */ m_size,
+            /* N     */ n_size,
+            /* K     */ K,
+            /* lda   */ K,
+            /* ldb   */ n_size,
+            /* ldc   */ N);
+      }
+    });
+
+    if (use_brgemm) {
+      at::native::cpublas::brgemm_release();
     }
   });
 
   // stage 1.5: quantize ic1 to uint8, [M * topk, N]
   at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
     for (int64_t m = begin; m < end; ++m) {
-      quantize_row_int8<scalar_t>(
-          Aq_tmp + m * N,
-          As_tmp[m],
-          ic1 + m * N,
-          N);
+      quantize_row_int8<scalar_t>(Aq_tmp + m * N, As_tmp[m], ic1 + m * N, N);
     }
   });
 
@@ -710,16 +885,13 @@ void shared_expert_int8_kernel_impl(
   const int64_t stride_oc = packed_N;
 
   // parallel on [MB2, NB2]
-  at::parallel_for(0, MB2 * NB2, 0, [&](int64_t begin, int64_t end) {
+  parallel_2d(MB2, NB2, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
     // get local pointers
-    int tid = at::get_thread_num();
-    // we won't be using C1 for gemm2
+    int tid = get_thread_num();
     float* __restrict__ C = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
+    int32_t* __restrict__ C32 = reinterpret_cast<int32_t*>(C + BLOCK_M * BLOCK_N);
 
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB2;
-      int64_t nb = i % NB2;
-
+    loop_2d<int8_t>(mb0, mb1, nb0, nb1, BLOCK_N * IC, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
       int64_t m_size = std::min(M - mb * BLOCK_M, BLOCK_M);
       int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
 
@@ -731,39 +903,71 @@ void shared_expert_int8_kernel_impl(
       const int8_t* __restrict__ B = packed_w2 + nb * BLOCK_N * stride_oc;
       const float* __restrict__ Bs = w2s + nb * BLOCK_N;
 
-      // 2.a gemm: C = A @ B
-      tinygemm_kernel<scalar_t>(
-          /* A     */ A,
-          /* B     */ B,
-          /* C     */ C,
-          /* As    */ As,
-          /* Bs    */ Bs,
-          /* M     */ m_size,
-          /* N     */ n_size,
-          /* K     */ IC,
-          /* lda   */ IC,
-          /* ldb   */ n_size,
-          /* ldc   */ BLOCK_N);
+      if (use_brgemm) {
+        at::native::cpublas::brgemm(
+            /* M     */ m_size,
+            /* N     */ n_size,
+            /* K     */ IC,
+            /* lda   */ IC,
+            /* ldb   */ n_size,
+            /* ldc   */ BLOCK_N,
+            /* add_C */ false,
+            /* A     */ A,
+            /* B     */ B,
+            /* C     */ C32);
+
+        // apply scales
+        const int32_t* Bcomp = reinterpret_cast<const int32_t*>(B + block_size_n() * IC);
+        scale_C<BLOCK_N>(C, C32, As, Bs, Bcomp, m_size);
+      } else {
+        // 2.a gemm: C = A @ B
+        tinygemm_kernel<scalar_t>(
+            /* A     */ A,
+            /* B     */ B,
+            /* C     */ C,
+            /* As    */ As,
+            /* Bs    */ Bs,
+            /* M     */ m_size,
+            /* N     */ n_size,
+            /* K     */ IC,
+            /* lda   */ IC,
+            /* ldb   */ n_size,
+            /* ldc   */ BLOCK_N);
+      }
 
       // 2.b copy from C to output and add fused_experts_out
       scalar_t* __restrict__ out = output + mb * BLOCK_M * K + nb * BLOCK_N;
-      const scalar_t* __restrict__ fused_out = fused_experts_out + mb * BLOCK_M * K + nb * BLOCK_N;
+      const scalar_t* __restrict__ fused_out =
+          apply_scaling_factor ? fused_experts_out + mb * BLOCK_M * K + nb * BLOCK_N : nullptr;
       for (int64_t m = 0; m < m_size; ++m) {
-        add_mul_stub(out + m * K, C + m * BLOCK_N, fused_out + m * K, routed_scaling_factor, n_size);
+        const scalar_t* __restrict__ fused_out_row = apply_scaling_factor ? (fused_out + m * K) : nullptr;
+        add_mul_stub(out + m * K, C + m * BLOCK_N, fused_out_row, routed_scaling_factor, n_size);
       }
+    });
+
+    if (use_brgemm) {
+      at::native::cpublas::brgemm_release();
     }
   });
 }
 
-#define INSTANTIATE_SHARED_EXPERT_INT8_TEMPLATE(TYPE)                                        \
-  template void shared_expert_int8_kernel_impl<TYPE> (                                       \
-      TYPE* __restrict__ output, TYPE* __restrict__ ic1,                                     \
-      float* __restrict__ C_tmp, uint8_t* __restrict__ Aq_tmp,                               \
-      float* __restrict__ As_tmp, const TYPE* __restrict__ input,                            \
-      const int8_t* __restrict__ packed_w1, const int8_t* __restrict__ packed_w2,            \
-      const float* __restrict__ w1s, const float* __restrict__ w2s,                          \
-      const TYPE* __restrict__ fused_experts_out, float routed_scaling_factor,               \
-      int64_t M, int64_t N, int64_t K)
+#define INSTANTIATE_SHARED_EXPERT_INT8_TEMPLATE(TYPE) \
+  template void shared_expert_int8_kernel_impl<TYPE>( \
+      TYPE* __restrict__ output,                      \
+      TYPE* __restrict__ ic1,                         \
+      float* __restrict__ C_tmp,                      \
+      uint8_t* __restrict__ Aq_tmp,                   \
+      float* __restrict__ As_tmp,                     \
+      const TYPE* __restrict__ input,                 \
+      const int8_t* __restrict__ packed_w1,           \
+      const int8_t* __restrict__ packed_w2,           \
+      const float* __restrict__ w1s,                  \
+      const float* __restrict__ w2s,                  \
+      const TYPE* __restrict__ fused_experts_out,     \
+      float routed_scaling_factor,                    \
+      int64_t M,                                      \
+      int64_t N,                                      \
+      int64_t K)
 
 INSTANTIATE_SHARED_EXPERT_INT8_TEMPLATE(at::BFloat16);
 INSTANTIATE_SHARED_EXPERT_INT8_TEMPLATE(at::Half);
diff --git a/csrc/cpu/sgl-kernels/vec.h b/csrc/cpu/sgl-kernels/vec.h
index 160845c9b1cb..c045a8cc7dca 100644
--- a/csrc/cpu/sgl-kernels/vec.h
+++ b/csrc/cpu/sgl-kernels/vec.h
@@ -1,10 +1,10 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
-#pragma once
-
 // clang-format off
 
+#pragma once
+
 #if defined(__AVX512F__) && defined(__AVX512BF16__) && defined(__AMX_BF16__)
 #define CPU_CAPABILITY_AVX512
 #endif
@@ -16,38 +16,51 @@ namespace {
 
 using namespace at::vec;
 
-template <typename scalar_t,
-          typename std::enable_if_t<is_reduced_floating_point_v<scalar_t>, int> = 0>
+template <typename scalar_t, typename std::enable_if_t<is_reduced_floating_point_v<scalar_t>, int> = 0>
 inline Vectorized<scalar_t> convert_from_float_ext(const Vectorized<float>& a, const Vectorized<float>& b) {
   return at::vec::convert_from_float<scalar_t>(a, b);
 }
 
+// allow f16, bf16
+template <typename scalar_t, typename std::enable_if_t<is_reduced_floating_point_v<scalar_t>, int> = 1>
+inline std::tuple<Vectorized<float>, Vectorized<float>> load_float_vec2(const scalar_t* __restrict__ data) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  bVec x_vec = bVec::loadu(data);
+  fVec x0, x1;
+  std::tie(x0, x1) = at::vec::convert_to_float(x_vec);
+  return std::make_tuple(x0, x1);
+}
+
+// allow  f32
+inline std::tuple<Vectorized<float>, Vectorized<float>> load_float_vec2(const float* __restrict__ data) {
+  using fVec = at::vec::Vectorized<float>;
+  fVec x0 = fVec::loadu(data);
+  fVec x1 = fVec::loadu(data + fVec::size());
+  return std::make_tuple(x0, x1);
+}
+
 #if defined(CPU_CAPABILITY_AVX512)
 
 // `at::vec::convert_from_float<>` from PyTorch doesn't have avx512-bf16 intrinsics
 // use native instruction for bfloat16->float32 conversion
 template <>
-inline Vectorized<at::BFloat16> convert_from_float_ext<at::BFloat16>(const Vectorized<float>& a, const Vectorized<float>& b) {
+inline Vectorized<at::BFloat16>
+convert_from_float_ext<at::BFloat16>(const Vectorized<float>& a, const Vectorized<float>& b) {
   return (__m512i)(_mm512_cvtne2ps_pbh(__m512(b), __m512(a)));
 }
 
-#define CVT_BF16_TO_FP32(a) \
-    _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(a), 16))
+#define CVT_BF16_TO_FP32(a) _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(a), 16))
 
-#define CVT_FP16_TO_FP32(a) \
-    _mm512_cvtps_ph(a, (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC))
+#define CVT_FP16_TO_FP32(a) _mm512_cvtph_ps(a)
 
 // this doesn't handle NaN.
 inline __m512bh cvt_e4m3_bf16_intrinsic_no_nan(__m256i fp8_vec) {
   const __m512i x = _mm512_cvtepu8_epi16(fp8_vec);
-
-  const __m512i mant = _mm512_slli_epi16(_mm512_and_si512(x, _mm512_set1_epi16(0x07)), 4);
-  const __m512i raw_exp = _mm512_srli_epi16(_mm512_and_si512(x, _mm512_set1_epi16(0x78)), 3);
-  const __m512i exp = _mm512_slli_epi16(_mm512_add_epi16(raw_exp, _mm512_set1_epi16(120)), 7);
-  const __m512i nonsign = _mm512_or_si512(exp, mant);
-
-  const __m512i sign = _mm512_slli_epi16(_mm512_and_si512(x, _mm512_set1_epi16(0x80)), 8);
-  const __m512i combined = _mm512_or_si512(nonsign, sign);
+  __m512i combined = _mm512_add_epi16(x, _mm512_set1_epi16(0x0780));
+  combined = _mm512_slli_epi16(combined, 4);
+  combined = _mm512_and_si512(combined, _mm512_set1_epi16(0x87f0));
+  combined = _mm512_add_epi16(combined, _mm512_set1_epi16(0x3c00));
 
   const __mmask32 is_nonzero = _mm512_cmpneq_epi16_mask(x, _mm512_setzero_si512());
   return (__m512bh)_mm512_maskz_mov_epi16(is_nonzero, combined);
@@ -113,10 +126,77 @@ inline __m512bh CVT_FP8_TO_BF16(__m256i a) {
 #endif
 }
 
+// faster version of float8_e4m3fn conversion to bfloat16
+//
+// we mapped cuda implementation from below link and vectorized with avx512:
+// https://github.com/thu-pacman/chitu/blob/1ed2078ec26581ebdca05b7306d4385f86edaa7c/csrc/cuda/marlin/marlin_gemm/dequant.h#L387
+//
+inline __attribute__((always_inline)) __m512bh CVT_FP8_TO_BF16_EXT(__m256i a) {
+  const __m512i mask0 = _mm512_set1_epi16(0x80);  // sign bit
+  const __m512i mask1 = _mm512_set1_epi16(0x7F);  // exponent and mantissa
+  const __m512i mask2 = _mm512_set1_epi16(0x4000);
+
+  __m512i x = _mm512_cvtepu8_epi16(a);
+  __m512i vsign = _mm512_and_si512(x, mask0);
+  vsign = _mm512_slli_epi16(vsign, 8);
+
+  __m512i vexp_and_mant = _mm512_and_si512(x, mask1);
+  vexp_and_mant = _mm512_slli_epi16(vexp_and_mant, 4);
+
+  // _MM_TERNLOG_A | _MM_TERNLOG_B | _MM_TERNLOG_C: 0b11111110
+  return (__m512bh)(_mm512_ternarylogic_epi32(vsign, mask2, vexp_and_mant, 0b11111110));
+}
+
+// bias for conversion of fp8 to bf16 1/256 in float32
+#define kFP8_BIAS 0x3b800000
+
+// remove warning: ignoring attributes on template argument ‘__m512bh’ [-Wignored-attributes]
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wignored-attributes"
+
+#define MXFP4_VALUES \
+  -6.0f, -4.0f, -3.0f, -2.0f, -1.5f, -1.0f, -0.5f, -0.0f, 6.0f, 4.0f, 3.0f, 2.0f, 1.5f, 1.0f, 0.5f, 0.0f
+
+// convert 64 mxfp4 to 2x bf16 vectors, expect input 32-way packing
+inline std::tuple<__m512bh, __m512bh> cvt_mxfp4_e2m1_bf16_intrinsic_lut(__m256i a, __m512i s0, __m512i s1) {
+  // LUT
+  const __m512 values = _mm512_set_ps(MXFP4_VALUES);
+  const __m512i lut = (__m512i)(_mm512_cvtne2ps_pbh(values, values));
+
+  const __m512i abs_mask = _mm512_set1_epi16(0x7FFF);
+  const __m512i zero = _mm512_setzero_si512();
+
+  // expand values to 16-bit integers
+  __m512i x0 = _mm512_cvtepu8_epi16(a);
+  __m512i x1 = _mm512_srli_epi32(x0, 4);
+
+  // LUT to convert mxfp4 values to bf16
+  x0 = _mm512_permutexvar_epi16(x0, lut);
+  x1 = _mm512_permutexvar_epi16(x1, lut);
+
+  // check for zeros
+  __mmask32 mask0 = _mm512_cmp_epi16_mask(_mm512_and_si512(x0, abs_mask), zero, _MM_CMPINT_EQ);
+  __mmask32 mask1 = _mm512_cmp_epi16_mask(_mm512_and_si512(x1, abs_mask), zero, _MM_CMPINT_EQ);
+
+  // emulate bf16 mul with scale factor
+  x0 = _mm512_add_epi16(x0, s0);
+  x1 = _mm512_add_epi16(x1, s1);
+
+  // blend with zero
+  x0 = _mm512_mask_blend_epi16(mask0, x0, zero);
+  x1 = _mm512_mask_blend_epi16(mask1, x1, zero);
+
+  return std::make_tuple(__m512bh(x0), __m512bh(x1));
+}
+
+#define CVT_MXFP4_TO_BF16(a, s0, s1) cvt_mxfp4_e2m1_bf16_intrinsic_lut(a, s0, s1)
+
+#pragma GCC diagnostic pop
+
 #endif
 
 // vector to scalar reduction
-#if defined(CPU_CAPABILITY_AVX512) && 0
+#if defined(CPU_CAPABILITY_AVX512)
 inline float vec_reduce_sum(const Vectorized<float>& a) {
   return _mm512_reduce_add_ps(__m512(a));
 }
@@ -136,10 +216,9 @@ inline float vec_reduce_max(const Vectorized<float>& a) {
 
 // https://github.com/InternLM/lmdeploy/blob/086481ed84b59bee3b8e4274e5fc69620040c048/lmdeploy/pytorch/kernels/cuda/w8a8_triton_kernels.py#L282
 template <typename scalar_t>
-inline void quantize_row_int8(uint8_t* __restrict__ Aq, float& As,
-    const scalar_t* __restrict__ A, int64_t K, float eps = 1e-7) {
-
-  float amax = 0.f; // absolute max
+inline void
+quantize_row_int8(uint8_t* __restrict__ Aq, float& As, const scalar_t* __restrict__ A, int64_t K, float eps = 1e-7) {
+  float amax = 0.f;  // absolute max
   for (int64_t k = 0; k < K; ++k) {
     const float val = static_cast<float>(A[k]);
     amax = std::max(amax, std::abs(val));
@@ -158,9 +237,8 @@ inline void quantize_row_int8(uint8_t* __restrict__ Aq, float& As,
 
 #if defined(CPU_CAPABILITY_AVX512)
 template <>
-inline void quantize_row_int8<at::BFloat16>(uint8_t* __restrict__ Aq, float& As,
-    const at::BFloat16* __restrict__ A, int64_t K, float eps) {
-
+inline void quantize_row_int8<at::BFloat16>(
+    uint8_t* __restrict__ Aq, float& As, const at::BFloat16* __restrict__ A, int64_t K, float eps) {
   const __m512 signBit = _mm512_set1_ps(-0.0f);
   const __m512i off = _mm512_set1_epi32(128);
 
@@ -200,7 +278,7 @@ inline void quantize_row_int8<at::BFloat16>(uint8_t* __restrict__ Aq, float& As,
 // transpose utils
 // taken from my PR in ggml: https://github.com/ggml-org/llama.cpp/pull/8998
 #if defined(CPU_CAPABILITY_AVX512)
-inline void transpose_16x16_32bit(__m512i * v) {
+inline void transpose_16x16_32bit(__m512i* v) {
   __m512i v1[16];
   v1[0] = _mm512_unpacklo_epi32(v[0], v[1]);
   v1[1] = _mm512_unpackhi_epi32(v[0], v[1]);
@@ -293,16 +371,56 @@ inline std::tuple<__m512i, __m512i> transpose_2x32_16bit(__m512i r0, __m512i r1)
 }
 #pragma GCC diagnostic pop
 
-#endif
-
-// TODO: debug print, remove me later
-template<typename scalar_t>
-void print_array(scalar_t* ptr, int size) {
-  for (int d = 0; d < size; ++d) {
-    if (d % 16 == 0) { std::cout << std::endl; }
-    std::cout << ptr[d] << " ";
-  }
-  std::cout << std::endl;
+inline __attribute__((always_inline)) __m512 _mm512_fexp_u20_ps(const __m512 values) {
+  const __m512 vec_c0 = _mm512_set1_ps(0.00010703434948458272f);
+  const __m512 vec_c1 = _mm512_set1_ps(0.30354260500649682f);
+  const __m512 vec_c2 = _mm512_set1_ps(-0.22433836478672356);
+  const __m512 vec_c3 = _mm512_set1_ps(-0.079204240219773236);
+
+  const __m512 vec_exp_log2ef = _mm512_castsi512_ps(_mm512_set1_epi32(0x3fb8aa3b));  // log2(e)
+
+  const __m512 vec_a = _mm512_set1_ps(std::pow(2, 23) / std::log2(2));
+  const __m512 vec_b = _mm512_set1_ps(std::pow(2, 23) * 127.f);
+
+  const __m512 vec_ln_flt_min = _mm512_castsi512_ps(_mm512_set1_epi32(0xc2aeac50));
+  const __m512 vec_ln_flt_max = _mm512_castsi512_ps(_mm512_set1_epi32(0x42b17218));
+  __m512i vec_infinity = _mm512_set1_epi32(0x7F800000);
+  __m512i vec_zero = _mm512_setzero_epi32();
+
+  // Fast Exponential Computation on SIMD Architectures
+  // A. Cristiano I. Malossi, Yves Ineichen, Costas Bekas, and Alessandro
+  // Curioni exp(x) = 2**(x * log2(e))
+  //        = 2**xi * 2**xf   - TIPS we are using  the EEEE floating point
+  //        representation with identification to the exponent and the
+  //        mentissa
+  //  2**xf will be approximated to a polynomial of degree 3 computed with
+  //  Horner method
+  // mask for the boundary condition
+  auto min_mask = _mm512_cmp_ps_mask(values, vec_ln_flt_min, _CMP_LT_OS);
+  auto max_mask = _mm512_cmp_ps_mask(values, vec_ln_flt_max, _CMP_GT_OS);
+
+  // transformation with log2(e)
+  auto vec_src = _mm512_mul_ps(values, vec_exp_log2ef);
+  auto vec_fractional = _mm512_sub_ps(vec_src, _mm512_floor_ps(vec_src));
+
+  // compute polynomial using Horner Scheme, for superscalar processor
+  auto vec_res = _mm512_fmadd_ps(vec_fractional, vec_c3, vec_c2);
+  vec_res = _mm512_fmadd_ps(vec_fractional, vec_res, vec_c1);
+  vec_res = _mm512_fmadd_ps(vec_fractional, vec_res, vec_c0);
+
+  vec_src = _mm512_sub_ps(vec_src, vec_res);
+  // the tips is here, headache in perspective
+  auto tmp = _mm512_fmadd_ps(vec_a, vec_src, vec_b);
+  // headache bis - we loose precision with the cast but it "fits", but ok
+  // after f32 -> f16 later
+  __m512i casted_integer = _mm512_cvttps_epi32(tmp);
+  // boundary condition, lower than the min -> 0
+  casted_integer = _mm512_mask_mov_epi32(casted_integer, min_mask, vec_zero);
+  // boundary condition, larger than the max -> +oo
+  casted_integer = _mm512_mask_mov_epi32(casted_integer, max_mask, vec_infinity);
+  // final interpretation to float
+  return _mm512_castsi512_ps(casted_integer);
 }
+#endif
 
-} // anonymous namespace
+}  // anonymous namespace
diff --git a/csrc/cpu/sgl-kernels/vec_pack.h b/csrc/cpu/sgl-kernels/vec_pack.h
new file mode 100644
index 000000000000..d245d2a40743
--- /dev/null
+++ b/csrc/cpu/sgl-kernels/vec_pack.h
@@ -0,0 +1,299 @@
+// Adapted from
+// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
+
+// clang-format off
+
+// To use the transpose functions
+#include <ATen/native/cpu/utils.h>
+
+#include "vec.h"
+
+namespace {
+
+using namespace at::vec;
+
+template <typename index_t>
+inline index_t get_index(index_t* ind, int i) {
+  return (ind == nullptr) ? (index_t)i : ind[i];
+}
+
+#if defined(CPU_CAPABILITY_AVX512)
+// key: from [N, 32] to [32/2, N, 2]
+template <typename scalar_t, typename index_t>
+inline void pack_vnni_Nx32(
+    scalar_t* __restrict__ dst,
+    const scalar_t* __restrict__ src,
+    const index_t* __restrict__ ind,
+    int N,
+    int ld_src,
+    int ld_dst) {
+  __m512i vinputs[16];
+
+  int n = 0;
+  for (; n < N; ++n) {
+    index_t index = get_index(ind, n);
+    vinputs[n] = _mm512_loadu_si512(src + index * ld_src);
+  }
+  // padding with zero to avoid uninitialized vectors
+  for (; n < 16; ++n) {
+    vinputs[n] = _mm512_set1_epi32(0);
+  }
+
+  // pack key
+  transpose_16x16_32bit(vinputs);
+
+  const __mmask16 vmask = (1 << N) - 1;
+  for (int k = 0; k < 16; ++k) {
+    _mm512_mask_storeu_epi32(dst + k * ld_dst * 2, vmask, vinputs[k]);
+  }
+}
+
+template <typename scalar_t, typename index_t>
+inline void pack_vnni_N_remainder(
+    scalar_t* __restrict__ dst,
+    const scalar_t* __restrict__ src,
+    const index_t* __restrict__ ind,
+    int N,
+    int K,
+    int ld_src,
+    int ld_dst) {
+  __m512i vinputs[16];
+
+  int K2 = K >> 1;
+  const __mmask16 vmask = (1 << K2) - 1;
+
+  int n = 0;
+  for (; n < N; ++n) {
+    index_t index = get_index(ind, n);
+    vinputs[n] = _mm512_maskz_loadu_epi32(vmask, src + index * ld_src);
+  }
+  // padding with zero to avoid uninitialized vectors
+  for (; n < 16; ++n) {
+    vinputs[n] = _mm512_set1_epi32(0);
+  }
+
+  // pack key
+  transpose_16x16_32bit(vinputs);
+
+  const __mmask16 vmask2 = (1 << N) - 1;
+  for (int k = 0; k < K2; ++k) {
+    _mm512_mask_storeu_epi32(dst + k * ld_dst * 2, vmask2, vinputs[k]);
+  }
+}
+
+// value: from [K, 32] to [K/2, 32, 2]
+template <typename scalar_t, typename index_t>
+inline void pack_vnni_Kx32(
+    scalar_t* __restrict__ dst,
+    const scalar_t* __restrict__ src,
+    const index_t* __restrict__ ind,
+    int K,
+    int ld_src,
+    int ld_dst) {
+  __m512i vinputs[2];
+
+  int k = 0;
+  for (; k < K; ++k) {
+    index_t index = get_index(ind, k);
+    vinputs[k] = _mm512_loadu_si512(src + index * ld_src);
+  }
+  // padding with zero to avoid uninitialized vectors
+  for (; k < 2; ++k) {
+    vinputs[k] = _mm512_set1_epi32(0);
+  }
+
+  // pack value
+  __m512i d0, d1;
+  std::tie(d0, d1) = transpose_2x32_16bit(vinputs[0], vinputs[1]);
+  _mm512_storeu_si512(dst + 0 * ld_dst * 2, d0);
+  _mm512_storeu_si512(dst + 0 * ld_dst * 2 + 32, d1);
+}
+
+template <typename scalar_t, typename index_t>
+inline void pack_vnni_K_remainder(
+    scalar_t* __restrict__ dst,
+    const scalar_t* __restrict__ src,
+    const index_t* __restrict__ ind,
+    int K,
+    int N,
+    int ld_src,
+    int ld_dst) {
+  __m512i vinputs[2];
+
+  const __mmask32 vmask = (1 << N) - 1;
+
+  int k = 0;
+  for (; k < K; ++k) {
+    index_t index = get_index(ind, k);
+    vinputs[k] = _mm512_maskz_loadu_epi16(vmask, src + index * ld_src);
+  }
+  // padding with zero to avoid uninitialized vectors
+  for (; k < 2; ++k) {
+    vinputs[k] = _mm512_set1_epi32(0);
+  }
+
+  // pack value
+  __m512i d0, d1;
+  std::tie(d0, d1) = transpose_2x32_16bit(vinputs[0], vinputs[1]);
+
+  if (N <= 16) {
+    // 2N * 16bits: N * 32bits
+    const __mmask16 vmask2 = (1 << N) - 1;
+    _mm512_mask_storeu_epi32(dst + 0 * ld_dst * 2, vmask2, d0);
+  } else {
+    // 2(N-16) * 16bits: (N-16) * 32bits
+    const __mmask16 vmask2 = (1 << (N - 16)) - 1;
+    _mm512_storeu_epi32(dst + 0 * ld_dst * 2, d0);
+    _mm512_mask_storeu_epi32(dst + 0 * ld_dst * 2 + 32, vmask2, d1);
+  }
+}
+#endif
+
+// convert to vnni format
+// from [N, K/2, 2] to [K/2, N, 2] for bfloat16 and float16
+template <typename scalar_t, typename index_t, bool is_indexed>
+void pack_vnni(
+    scalar_t* __restrict__ dst,
+    const scalar_t* __restrict__ src,
+    const index_t* __restrict__ ind,
+    int N,
+    int K,
+    int ld_src,
+    int ld_dst) {
+#if defined(CPU_CAPABILITY_AVX512)
+  const int NB = div_up(N, 16);
+  const int KB = K / 32;
+  const int K_remainder = K - KB * 32;
+
+  for (int nb = 0; nb < NB; ++nb) {
+    int nb_size = std::min(N - nb * 16, 16);
+    for (int kb = 0; kb < KB; ++kb) {
+      // handle 16x512bits each block
+      pack_vnni_Nx32<scalar_t, index_t>(
+          /*    dst */ dst + ((kb * 32) >> 1) * ld_dst * 2 + nb * 16 * 2,
+          /*    src */ src + kb * 32 + (is_indexed ? 0 : nb * 16 * ld_src),
+          /*    ind */ is_indexed ? ind + nb * 16 : nullptr,
+          /*      N */ nb_size,
+          /* ld_src */ ld_src,
+          /* ld_dst */ ld_dst);
+    }
+    if (K_remainder > 0) {
+      pack_vnni_N_remainder<scalar_t, index_t>(
+          /*    dst */ dst + ((KB * 32) >> 1) * ld_dst * 2 + nb * 16 * 2,
+          /*    src */ src + KB * 32 + (is_indexed ? 0 : nb * 16 * ld_src),
+          /*    ind */ is_indexed ? ind + nb * 16 : nullptr,
+          /*      N */ nb_size,
+          /*      K */ K_remainder,
+          /* ld_src */ ld_src,
+          /* ld_dst */ ld_dst);
+    }
+  }
+#else
+  for (int n = 0; n < N; ++n) {
+    index_t index = get_index(ind, n);
+    for (int k = 0; k < K / 2; ++k) {
+      for (int d = 0; d < 2; ++d) {
+        dst[k * ld_dst * 2 + n * 2 + d] = src[index * ld_src + k * 2 + d];
+      }
+    }
+  }
+#endif
+}
+
+template <typename scalar_t>
+void pack_vnni(scalar_t* __restrict__ dst, const scalar_t* __restrict__ src, int N, int K, int ld_src, int ld_dst) {
+  pack_vnni<scalar_t, int32_t, false>(dst, src, nullptr, N, K, ld_src, ld_dst);
+}
+
+template <typename scalar_t, typename index_t>
+void pack_vnni(
+    scalar_t* __restrict__ dst,
+    const scalar_t* __restrict__ src,
+    const index_t* __restrict__ ind,
+    int N,
+    int K,
+    int ld_src,
+    int ld_dst) {
+  assert(ind != nullptr);
+  pack_vnni<scalar_t, index_t, true>(dst, src, ind, N, K, ld_src, ld_dst);
+}
+
+// convert to vnni format
+// from [K/2, 2, N] to [K/2, N, 2] for bfloat16 and float16
+template <typename scalar_t, typename index_t, bool is_indexed>
+void pack_vnni2(
+    scalar_t* __restrict__ dst,
+    const scalar_t* __restrict__ src,
+    const index_t* __restrict__ ind,
+    int K,
+    int N,
+    int ld_src,
+    int ld_dst) {
+#if defined(CPU_CAPABILITY_AVX512)
+  const int KB = div_up(K, 2);
+  const int NB = N / 32;
+  const int N_remainder = N - NB * 32;
+
+  for (int kb = 0; kb < KB; ++kb) {
+    int kb_size = std::min(K - kb * 2, 2);
+    for (int nb = 0; nb < NB; ++nb) {
+      // handle 2x512bits each block
+      pack_vnni_Kx32<scalar_t, index_t>(
+          /*    dst */ dst + ((kb * 2) >> 1) * ld_dst * 2 + nb * 32 * 2,
+          /*    src */ src + (is_indexed ? 0 : kb * 2 * ld_src) + nb * 32,
+          /*    ind */ is_indexed ? ind + kb * 2 : nullptr,
+          /*      K */ kb_size,
+          /* ld_src */ ld_src,
+          /* ld_dst */ ld_dst);
+    }
+    if (N_remainder > 0) {
+      pack_vnni_K_remainder(
+          /*    dst */ dst + ((kb * 2) >> 1) * ld_dst * 2 + NB * 32 * 2,
+          /*    src */ src + (is_indexed ? 0 : kb * 2 * ld_src) + NB * 32,
+          /*    ind */ is_indexed ? ind + kb * 2 : nullptr,
+          /*      K */ kb_size,
+          /*      N */ N_remainder,
+          /* ld_src */ ld_src,
+          /* ld_dst */ ld_dst);
+    }
+  }
+#else
+  int k = 0;
+  for (; k < (K >> 1) * 2; k += 2) {
+    index_t index0 = get_index(ind, k + 0);
+    index_t index1 = get_index(ind, k + 1);
+    for (int n = 0; n < N; ++n) {
+      dst[(k >> 1) * ld_dst * 2 + n * 2 + 0] = src[index0 * ld_src + n];
+      dst[(k >> 1) * ld_dst * 2 + n * 2 + 1] = src[index1 * ld_src + n];
+    }
+  }
+  if (K % 2 != 0) {
+    index_t index = get_index(ind, K - 1);
+    for (int n = 0; n < N; ++n) {
+      dst[(K >> 1) * ld_dst * 2 + n * 2 + 0] = src[index * ld_src + n];
+      dst[(K >> 1) * ld_dst * 2 + n * 2 + 1] = 0;
+    }
+    k += 2;
+  }
+#endif
+}
+
+template <typename scalar_t>
+void pack_vnni2(scalar_t* __restrict__ dst, const scalar_t* __restrict__ src, int K, int N, int ld_src, int ld_dst) {
+  pack_vnni2<scalar_t, int32_t, false>(dst, src, nullptr, K, N, ld_src, ld_dst);
+}
+
+template <typename scalar_t, typename index_t>
+void pack_vnni2(
+    scalar_t* __restrict__ dst,
+    const scalar_t* __restrict__ src,
+    const index_t* __restrict__ ind,
+    int K,
+    int N,
+    int ld_src,
+    int ld_dst) {
+  assert(ind != nullptr);
+  pack_vnni2<scalar_t, index_t, true>(dst, src, ind, K, N, ld_src, ld_dst);
+}
+
+}  // anonymous namespace
diff --git a/csrc/cpu/torch_bindings.cpp b/csrc/cpu/torch_bindings.cpp
index fcf7064f606a..9d3c354558e9 100644
--- a/csrc/cpu/torch_bindings.cpp
+++ b/csrc/cpu/torch_bindings.cpp
@@ -56,6 +56,8 @@ void shm_send_tensor_list(int64_t handle,
 
 std::vector<torch::Tensor> shm_recv_tensor_list(int64_t handle, int64_t src);
 
+// SGL CPU kernels
+
 at::Tensor weight_packed_linear(at::Tensor& mat1, at::Tensor& mat2,
                                 const std::optional<at::Tensor>& bias,
                                 bool is_vnni);
@@ -65,26 +67,40 @@ at::Tensor convert_weight_packed(at::Tensor& weight);
 at::Tensor fused_experts_cpu(
     at::Tensor& hidden_states, at::Tensor& w1, at::Tensor& w2,
     at::Tensor& topk_weights, at::Tensor& topk_ids, bool inplace,
-    bool use_int8_w8a8, bool use_fp8_w8a16,
-    const std::optional<at::Tensor>& w1_scale,
+    int64_t moe_comp_method, const std::optional<at::Tensor>& w1_scale,
     const std::optional<at::Tensor>& w2_scale,
-    const std::optional<std::vector<int64_t>> block_size,
-    const std::optional<at::Tensor>& a1_scale,
-    const std::optional<at::Tensor>& a2_scale, bool is_vnni);
+    const std::optional<at::Tensor>& w1_zero,
+    const std::optional<at::Tensor>& w2_zero,
+    const std::optional<std::vector<int64_t>> block_size, bool is_vnni);
 
 at::Tensor int8_scaled_mm_with_quant(at::Tensor& mat1, at::Tensor& mat2,
                                      at::Tensor& scales2,
                                      const std::optional<at::Tensor>& bias,
                                      at::ScalarType out_dtype, bool is_vnni);
 
+// Adapted from sglang: FP8 W8A16 kernel
+at::Tensor fp8_scaled_mm_cpu(at::Tensor& mat1, at::Tensor& mat2,
+                             at::Tensor& scales2,
+                             std::vector<int64_t> block_size,
+                             const std::optional<at::Tensor>& bias,
+                             at::ScalarType out_dtype, bool is_vnni);
+
 // Adapted from sglang: INT4 W4A8 kernels
 std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_weight_packed_scale_zp(
-    at::Tensor qweight, at::Tensor qzeros, at::Tensor scales);
+    at::Tensor qweight,  // awq: (*, K, N / 8)  ||  gptq: (*, K / 8, N) , int32
+    at::Tensor qzeros,   // awq: (*, K / group_size, N / 8) ||  gptq: (*, K /
+                         // group_size, N / 8) , int32
+    at::Tensor scales,   // awq: (*, K / group_size, N) ||  gptq: (*, K /
+                         // group_size, N) , bfloat16
+    int64_t quant_method_4bit);
 
 at::Tensor int4_scaled_mm_cpu(at::Tensor& x, at::Tensor& w, at::Tensor& w_zeros,
                               at::Tensor& w_scales,
                               std::optional<at::Tensor> bias);
 
+void activation_lut_bf16(torch::Tensor& out, torch::Tensor& input,
+                         const std::string& activation);
+
 torch::Tensor get_scheduler_metadata(
     const int64_t num_req, const int64_t num_heads_q,
     const int64_t num_heads_kv, const int64_t head_dim,
@@ -98,7 +114,9 @@ void cpu_attn_reshape_and_cache(const torch::Tensor& key,
                                 torch::Tensor& key_cache,
                                 torch::Tensor& value_cache,
                                 const torch::Tensor& slot_mapping,
-                                const std::string& isa);
+                                const std::string& isa, const double k_scale,
+                                const double v_scale,
+                                const std::string& kv_cache_dtype);
 
 void cpu_attention_with_kv_cache(
     const torch::Tensor& query, const torch::Tensor& key_cache,
@@ -109,7 +127,8 @@ void cpu_attention_with_kv_cache(
     const int64_t sliding_window_left, const int64_t sliding_window_right,
     const torch::Tensor& block_table, const double softcap,
     const torch::Tensor& scheduler_metadata,
-    const std::optional<torch::Tensor>& s_aux);
+    const std::optional<torch::Tensor>& s_aux, const double k_scale,
+    const double v_scale, const std::string& kv_cache_dtype);
 
 // Note: just for avoiding importing errors
 void placeholder_op() { TORCH_CHECK(false, "Unimplemented"); }
@@ -138,6 +157,8 @@ void compute_slot_mapping_kernel_impl(const torch::Tensor query_start_loc,
                                       torch::Tensor slot_mapping,
                                       const int64_t block_size);
 
+void init_cpu_memory_env(std::vector<int64_t> node_ids);
+
 namespace cpu_utils {
 void eagle_prepare_inputs_padded_kernel_impl(
     const torch::Tensor& cu_num_draft_tokens,
@@ -231,6 +252,15 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("gelu_quick(Tensor! out, Tensor input) -> ()");
   ops.impl("gelu_quick", torch::kCPU, &gelu_quick);
 
+#if (defined(__aarch64__) && !defined(__APPLE__))
+
+  ops.def(
+      "activation_lut_bf16(Tensor! out, Tensor input, str activation)"
+      " -> ()");
+  ops.impl("activation_lut_bf16", torch::kCPU, &activation_lut_bf16);
+
+#endif  // (defined(__aarch64__) && !defined(__APPLE__))
+
   // Layernorm
   // Apply Root Mean Square (RMS) Normalization to the input tensor.
   ops.def(
@@ -249,12 +279,13 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def(
       "rotary_embedding(Tensor positions, Tensor! query,"
       "                 Tensor!? key, int head_size,"
-      "                 Tensor cos_sin_cache, bool is_neox) -> ()");
+      "                 Tensor cos_sin_cache, bool is_neox, int "
+      "rope_dim_offset=0, bool inverse=False) -> ()");
   ops.impl("rotary_embedding", torch::kCPU, &rotary_embedding);
 
   // Quantization
-#if defined(__AVX512F__) || (defined(__aarch64__) && !defined(__APPLE__)) || \
-    defined(__powerpc64__)
+#if defined(__AVX512F__) || defined(__AVX2__) || \
+    (defined(__aarch64__) && !defined(__APPLE__)) || defined(__powerpc64__)
   // Helper function to release oneDNN handlers
   ops.def("release_dnnl_matmul_handler(int handler) -> ()",
           &release_dnnl_matmul_handler);
@@ -335,10 +366,10 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("convert_weight_packed(Tensor! weight) -> Tensor");
   ops.impl("convert_weight_packed", torch::kCPU, &convert_weight_packed);
   ops.def(
-      "fused_experts_cpu(Tensor! hidden_states, Tensor w1, Tensor w2, Tensor "
-      "topk_weights, Tensor topk_ids, bool inplace, bool use_int8_w8a8, bool "
-      "use_fp8_w8a16, Tensor? w1_scale, Tensor? w2_scale, SymInt[]? "
-      "block_size, Tensor? a1_scale, Tensor? a2_scale, bool is_vnni) -> "
+      "fused_experts_cpu(Tensor hidden_states, Tensor w1, Tensor w2, Tensor "
+      "topk_weights, Tensor topk_ids, bool "
+      "inplace, int moe_comp_method, Tensor? w1_scale, Tensor? w2_scale, "
+      "Tensor? w1_zero, Tensor? w2_zero, int[]? block_size, bool is_vnni) -> "
       "Tensor");
   ops.impl("fused_experts_cpu", torch::kCPU, &fused_experts_cpu);
   ops.def(
@@ -349,8 +380,9 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
 
   // Adapted from sglang: INT4 W4A8 kernels
   ops.def(
-      "convert_weight_packed_scale_zp(Tensor qweight, Tensor qzeros, "
-      "Tensor scales) -> (Tensor, Tensor, Tensor)");
+      "convert_weight_packed_scale_zp(Tensor weight, Tensor qzeros, Tensor "
+      "scales, int quant_method_4bit) -> (Tensor, "
+      "Tensor, Tensor)");
   ops.impl("convert_weight_packed_scale_zp", torch::kCPU,
            &convert_weight_packed_scale_zp);
 
@@ -358,6 +390,13 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "int4_scaled_mm_cpu(Tensor(a0!) x, Tensor(a1!) w, Tensor(a2!) w_zeros, "
       "Tensor(a3!) w_scales, Tensor? bias) -> Tensor");
   ops.impl("int4_scaled_mm_cpu", torch::kCPU, &int4_scaled_mm_cpu);
+
+  // Adapted from sglang: FP8 W8A16 kernel
+  ops.def(
+      "fp8_scaled_mm_cpu(Tensor(a0!) mat1, Tensor(a1!) mat2, Tensor(a2!) "
+      "scales2, SymInt[] block_size, Tensor? bias, ScalarType out_dtype, "
+      "bool is_vnni) -> Tensor");
+  ops.impl("fp8_scaled_mm_cpu", torch::kCPU, &fp8_scaled_mm_cpu);
 #endif
 
   // CPU attention kernels
@@ -369,15 +408,18 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       &get_scheduler_metadata);
   ops.def(
       "cpu_attn_reshape_and_cache(Tensor key, Tensor value, Tensor(a2!) "
-      "key_cache, Tensor(a3!) value_cache, Tensor slot_mapping, str "
-      "isa) -> ()",
+      "key_cache, Tensor(a3!) value_cache, Tensor slot_mapping, str isa, "
+      "float k_scale=1.0, float v_scale=1.0, str kv_cache_dtype=\"auto\") -> "
+      "()",
       &cpu_attn_reshape_and_cache);
   ops.def(
       "cpu_attention_with_kv_cache(Tensor query, Tensor key_cache, Tensor "
       "value_cache, Tensor(a3!) output, Tensor query_start_loc, Tensor "
       "seq_lens, float scale, bool causal, Tensor? alibi_slopes, SymInt "
       "sliding_window_left, SymInt sliding_window_right, Tensor block_table, "
-      "float softcap, Tensor scheduler_metadata, Tensor? s_aux) -> ()",
+      "float softcap, Tensor scheduler_metadata, Tensor? s_aux, "
+      "float k_scale=1.0, float v_scale=1.0, str kv_cache_dtype=\"auto\") -> "
+      "()",
       &cpu_attention_with_kv_cache);
 
   // placeholders
@@ -419,6 +461,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "block_size) -> ()",
       &compute_slot_mapping_kernel_impl);
 
+  ops.def("init_cpu_memory_env(SymInt[] node_ids) -> ()", &init_cpu_memory_env);
+
   // Speculative decoding kernels
   ops.def(
       "eagle_prepare_inputs_padded_kernel_impl(Tensor cu_num_draft_tokens, "
diff --git a/csrc/cpu/utils.cpp b/csrc/cpu/utils.cpp
index 55d0a3d17572..fd06e01e4d60 100644
--- a/csrc/cpu/utils.cpp
+++ b/csrc/cpu/utils.cpp
@@ -13,13 +13,80 @@
 #include "cpu/utils.hpp"
 
 #ifdef VLLM_NUMA_DISABLED
-std::string init_cpu_threads_env(const std::string& cpu_ids) {
-  return std::string(
-      "Warning: NUMA is not enabled in this build. `init_cpu_threads_env` has "
-      "no effect to setup thread affinity.");
-}
+void init_cpu_memory_env(std::vector<int64_t> node_ids) {}
+#else
+void init_cpu_memory_env(std::vector<int64_t> node_ids) {
+  // Memory node binding
+  if (numa_available() != -1) {
+    // Concatenate all node_ids into a single comma-separated string
+    if (!node_ids.empty()) {
+      std::string node_ids_str;
+      for (const int node_id : node_ids) {
+        if (!node_ids_str.empty()) {
+          node_ids_str += ",";
+        }
+        node_ids_str += std::to_string(node_id);
+      }
 
-#endif
+      bitmask* mask = numa_parse_nodestring(node_ids_str.c_str());
+      bitmask* src_mask = numa_get_mems_allowed();
+
+      int pid = getpid();
+
+      if (mask && src_mask) {
+        // move all existing pages to the specified numa node.
+        *(src_mask->maskp) = *(src_mask->maskp) ^ *(mask->maskp);
+        int page_num = numa_migrate_pages(pid, src_mask, mask);
+        if (page_num == -1) {
+          TORCH_WARN("numa_migrate_pages failed. errno: " +
+                     std::to_string(errno));
+        }
+
+        // Restrict memory allocation to the selected NUMA node(s).
+        // Enhances memory locality for the threads bound to those NUMA CPUs.
+        if (node_ids.size() > 1) {
+          errno = 0;
+          numa_set_interleave_mask(mask);
+          if (errno != 0) {
+            TORCH_WARN("numa_set_interleave_mask failed. errno: " +
+                       std::to_string(errno));
+          } else {
+            TORCH_WARN(
+                "NUMA binding: Using INTERLEAVE policy for memory "
+                "allocation across multiple NUMA nodes (nodes: " +
+                node_ids_str +
+                "). Memory allocations will be "
+                "interleaved across the specified NUMA nodes.");
+          }
+        } else {
+          errno = 0;
+          numa_set_membind(mask);
+          if (errno != 0) {
+            TORCH_WARN("numa_set_membind failed. errno: " +
+                       std::to_string(errno));
+          } else {
+            TORCH_WARN(
+                "NUMA binding: Using MEMBIND policy for memory "
+                "allocation on the NUMA nodes (" +
+                node_ids_str +
+                "). Memory allocations will be "
+                "strictly bound to these NUMA nodes.");
+          }
+        }
+
+        numa_set_strict(1);
+
+        numa_free_nodemask(mask);
+        numa_free_nodemask(src_mask);
+      } else {
+        TORCH_WARN(
+            "numa_parse_nodestring or numa_get_run_node_mask failed. errno: " +
+            std::to_string(errno));
+      }
+    }
+  }
+}
+#endif  // VLLM_NUMA_DISABLED
 
 namespace cpu_utils {
 ScratchPadManager::ScratchPadManager() : size_(0), ptr_(nullptr) {
diff --git a/csrc/cpu/utils.hpp b/csrc/cpu/utils.hpp
index f237bba088b1..394e67e3a034 100644
--- a/csrc/cpu/utils.hpp
+++ b/csrc/cpu/utils.hpp
@@ -54,12 +54,34 @@ struct Counter {
 };
 
 inline int64_t get_available_l2_size() {
+#if defined(__s390x__) || defined(__powerpc__)
+  static int64_t size = []() {
+    uint32_t l2_cache_size = 0;
+    auto caps = at::cpu::get_cpu_capabilities();
+    auto it = caps.find("l2_cache_size");
+    if (it != caps.end()) {
+      l2_cache_size = static_cast<uint32_t>(it->second.toInt());
+    }
+    if (l2_cache_size == 0) {
+      long sys_l2 = sysconf(_SC_LEVEL2_CACHE_SIZE);
+      if (sys_l2 > 0) {
+        l2_cache_size = static_cast<uint32_t>(sys_l2);
+      }
+    }
+    if (l2_cache_size == 0) {
+      l2_cache_size = 256 * 1024;
+    }
+    return static_cast<int64_t>(l2_cache_size) >> 1;  // use 50% of L2 cache
+  }();
+  return size;
+#else
   static int64_t size = []() {
     auto caps = at::cpu::get_cpu_capabilities();
     const uint32_t l2_cache_size = caps.at("l2_cache_size").toInt();
     return l2_cache_size >> 1;  // use 50% of L2 cache
   }();
   return size;
+#endif
 }
 
 template <int32_t alignment_v, typename T>
diff --git a/csrc/cumem_allocator.cpp b/csrc/cumem_allocator.cpp
index 9ef623bf7f1f..0b720d356e78 100644
--- a/csrc/cumem_allocator.cpp
+++ b/csrc/cumem_allocator.cpp
@@ -232,28 +232,6 @@ void unmap_and_release(unsigned long long device, ssize_t size,
     }
   }
 
-  // ROCm workaround: hipMemRelease does not return physical VRAM to the
-  // free pool while the virtual-address reservation is still held.
-  // Cycling cuMemAddressFree → cuMemAddressReserve (at the same address)
-  // forces the driver to actually release the physical pages while keeping
-  // the same VA available for a later create_and_map.
-  if (first_error == no_error) {
-    first_error = cuMemAddressFree(d_mem, size);
-    if (first_error == no_error) {
-      CUdeviceptr d_mem_new = 0;
-      first_error = cuMemAddressReserve(&d_mem_new, size, 0, d_mem, 0);
-      if (first_error == no_error && d_mem_new != d_mem) {
-        cuMemAddressFree(d_mem_new, size);
-        snprintf(error_msg, sizeof(error_msg),
-                 "ROCm: VA re-reserve got %p instead of %p", (void*)d_mem_new,
-                 (void*)d_mem);
-        error_code = CUresult(1);
-        std::cerr << error_msg << std::endl;
-        return;
-      }
-    }
-  }
-
   if (first_error != no_error) {
     CUDA_CHECK(first_error);
   }
diff --git a/csrc/cutlass_extensions/common.hpp b/csrc/cutlass_extensions/common.hpp
index c32d3a0606af..ca96b0ef3fef 100644
--- a/csrc/cutlass_extensions/common.hpp
+++ b/csrc/cutlass_extensions/common.hpp
@@ -96,44 +96,14 @@ struct enable_sm90_or_later : Kernel {
 };
 
 template <typename Kernel>
-struct enable_sm90_only : Kernel {
+struct enable_sm100_to_sm120 : Kernel {
   template <typename... Args>
   CUTLASS_DEVICE void operator()(Args&&... args) {
 #if defined __CUDA_ARCH__
-  #if __CUDA_ARCH__ == 900
+  #if (__CUDA_ARCH__ >= 1000 && __CUDA_ARCH__ < 1200)
     Kernel::operator()(std::forward<Args>(args)...);
   #else
-    printf("This kernel only supports sm90.\n");
-    asm("trap;");
-  #endif
-#endif
-  }
-};
-
-template <typename Kernel>
-struct enable_sm100f_only : Kernel {
-  template <typename... Args>
-  CUTLASS_DEVICE void operator()(Args&&... args) {
-#if defined __CUDA_ARCH__
-  #if __CUDA_ARCH__ == 1000 || __CUDA_ARCH__ == 1030
-    Kernel::operator()(std::forward<Args>(args)...);
-  #else
-    printf("This kernel only supports sm100f.\n");
-    asm("trap;");
-  #endif
-#endif
-  }
-};
-
-template <typename Kernel>
-struct enable_sm100a_only : Kernel {
-  template <typename... Args>
-  CUTLASS_DEVICE void operator()(Args&&... args) {
-#if defined __CUDA_ARCH__
-  #if __CUDA_ARCH__ == 1000
-    Kernel::operator()(std::forward<Args>(args)...);
-  #else
-    printf("This kernel only supports sm100a.\n");
+    printf("This kernel only supports sm[100, 120).\n");
     asm("trap;");
   #endif
 #endif
@@ -148,7 +118,7 @@ struct enable_sm120_only : Kernel {
   #if __CUDA_ARCH__ == 1200
     Kernel::operator()(std::forward<Args>(args)...);
   #else
-    printf("This kernel only supports sm120.\n");
+    printf("This kernel only supports sm120a.\n");
     asm("trap;");
   #endif
 #endif
@@ -160,8 +130,13 @@ template <typename Kernel>
 struct enable_sm120_family : Kernel {
   template <typename... Args>
   CUTLASS_DEVICE void operator()(Args&&... args) {
-#if defined __CUDA_ARCH__ && (__CUDA_ARCH__ >= 1200 && __CUDA_ARCH__ < 1300)
+#if defined __CUDA_ARCH__
+  #if (__CUDA_ARCH__ >= 1200 && __CUDA_ARCH__ < 1300)
     Kernel::operator()(std::forward<Args>(args)...);
+  #else
+    printf("This kernel only supports sm120f.\n");
+    asm("trap;");
+  #endif
 #endif
   }
 };
diff --git a/csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu b/csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu
new file mode 100644
index 000000000000..2f2e7ecc1829
--- /dev/null
+++ b/csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu
@@ -0,0 +1,511 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ * SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+ *
+ * Horizontally-fused DeepseekV4-MLA kernel:
+ *   - Q side:  per-head RMSNorm (no weight) + GPT-J RoPE on last ROPE_DIM
+ *   - KV side: GPT-J RoPE on last ROPE_DIM + UE8M0 FP8 quant on NoPE + paged
+ *              cache insert
+ *
+ * Structured after `applyMLARopeAndAssignQKVKernelGeneration` in
+ * TensorRT-LLM's mlaKernels.cu: one kernel, one grid, with head-slot
+ * dispatch choosing Q vs KV work per warp.  The per-warp RMSNorm/RoPE
+ * skeleton is adapted from vllm-deepseek_v4's existing
+ * `fusedQKNormRopeKernel` (csrc/fused_qknorm_rope_kernel.cu).
+ *
+ * Assumptions (hard-coded for DeepseekV4 attention):
+ *   HEAD_DIM  = 512
+ *   ROPE_DIM  = 64   (RoPE applied to dims [NOPE_DIM, HEAD_DIM))
+ *   NOPE_DIM  = 448
+ *   QUANT_BLOCK = 64 (UE8M0 FP8 quant block)
+ *   FP8_MAX   = 448.0f
+ *   is_neox=false (GPT-J interleaved pairs)
+ *   cos_sin_cache layout [max_pos, rope_dim] = cos || sin (cos first, sin
+ *     second along last dim; each half is rope_dim/2 = 32 values)
+ *
+ * Cache layout per paged-cache block (block_size tokens):
+ *   [0,            bs*576):          token data, 448 fp8 + 128 bf16 each
+ *   [bs*576,       bs*576 + bs*8):   UE8M0 scales, 7 real + 1 pad per token
+ */
+
+#include <cmath>
+#ifndef USE_ROCM
+  #include <cuda_fp8.h>
+#else
+  #include <hip/hip_fp8.h>
+#endif
+#include <cuda_runtime.h>
+#include <type_traits>
+
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include <torch/cuda.h>
+
+#include "cuda_compat.h"
+#include "dispatch_utils.h"
+#include "type_convert.cuh"
+
+#ifndef FINAL_MASK
+  #ifdef USE_ROCM
+    #define FINAL_MASK 0xffffffffffffffffULL
+  #else
+    #define FINAL_MASK 0xffffffffu
+  #endif
+#endif
+
+#ifdef USE_ROCM
+// ROCm-compatible FP8 conversion helpers
+__device__ __forceinline__ uint8_t rocm_cvt_float_to_fp8_e4m3(float val) {
+  #if defined(HIP_FP8_TYPE_OCP)
+  __hip_fp8_e4m3 fp8_val(val);
+  #else
+  __hip_fp8_e4m3_fnuz fp8_val(val);
+  #endif
+  return reinterpret_cast<uint8_t&>(fp8_val);
+}
+#endif
+
+namespace vllm {
+namespace deepseek_v4_fused_ops {
+
+namespace {
+inline int getSMVersion() {
+  auto* props = at::cuda::getCurrentDeviceProperties();
+  return props->major * 10 + props->minor;
+}
+}  // namespace
+
+// ────────────────────────────────────────────────────────────────────────────
+// Constants
+// ────────────────────────────────────────────────────────────────────────────
+constexpr int kHeadDim = 512;
+constexpr int kRopeDim = 64;
+constexpr int kNopeDim = kHeadDim - kRopeDim;  // 448
+constexpr int kQuantBlock = 64;
+constexpr int kNumQuantBlocks = kNopeDim / kQuantBlock;   // 7
+constexpr int kScaleBytesPerToken = kNumQuantBlocks + 1;  // 8 (7 real + 1 pad)
+constexpr int kTokenDataBytes = kNopeDim + kRopeDim * 2;  // 448 + 128 = 576
+constexpr float kFp8Max = 448.0f;
+
+// Per-warp layout:  32 lanes × 16 elems/lane = 512 elems = HEAD_DIM.
+constexpr int kNumLanes = 32;
+constexpr int kElemsPerLane = kHeadDim / kNumLanes;  // 16
+
+// ────────────────────────────────────────────────────────────────────────────
+// Small inline helpers
+// ────────────────────────────────────────────────────────────────────────────
+__device__ __forceinline__ float warp4MaxAbs(float val) {
+  // Reduce absolute max across 4 consecutive lanes (lane id & 3 group).
+  float peer = __shfl_xor_sync(FINAL_MASK, val, 1);
+  val = fmaxf(val, peer);
+  peer = __shfl_xor_sync(FINAL_MASK, val, 2);
+  val = fmaxf(val, peer);
+  return val;
+}
+
+template <typename T>
+__device__ __forceinline__ float warpSum(float val) {
+#pragma unroll
+  for (int mask = 16; mask > 0; mask >>= 1) {
+    val += __shfl_xor_sync(FINAL_MASK, val, mask, 32);
+  }
+  return val;
+}
+
+// ────────────────────────────────────────────────────────────────────────────
+// Kernel
+// ────────────────────────────────────────────────────────────────────────────
+//
+// Grid: 1D, gridDim.x = ceil(num_tokens_full * (num_heads_q + 1) /
+// warps_per_block) Block: blockDim.x = 256 threads (8 warps per block) Each
+// warp handles one (token, head_slot) pair. head_slot < num_heads_q          →
+// Q branch (RMSNorm + RoPE, in place) head_slot == num_heads_q         → KV
+// branch (RoPE + UE8M0 quant + insert)
+//
+// With DP padding, q/kv/position_ids can have more rows than slot_mapping.
+// The Q branch covers all `num_tokens_full` rows (downstream attention uses
+// them).  The KV branch only inserts the first `num_tokens_insert` tokens
+// (= slot_mapping length) into the paged cache.
+//
+template <typename scalar_t_in>
+__global__ void fusedDeepseekV4QNormRopeKVRopeQuantInsertKernel(
+    scalar_t_in* __restrict__ q_inout,         // [N, H, 512] bf16, in place
+    scalar_t_in const* __restrict__ kv_in,     // [N, 512] bf16
+    uint8_t* __restrict__ k_cache,             // [num_blocks, block_stride]
+    int64_t const* __restrict__ slot_mapping,  // [num_tokens_insert] i64
+    int64_t const* __restrict__ position_ids,  // [N] i64
+    float const* __restrict__ cos_sin_cache,   // [max_pos, 64] fp32
+    float const eps,
+    int const num_tokens_full,    // = q.size(0) = kv.size(0)
+    int const num_tokens_insert,  // = slot_mapping.size(0), ≤ num_tokens_full
+    int const num_heads_q,        // H
+    int const cache_block_size,   // tokens per paged-cache block
+    int const kv_block_stride) {  // bytes per paged-cache block
+#if (!defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 800) && !defined(USE_ROCM)
+  // BF16 _typeConvert specialization is unavailable on pre-Ampere.  The
+  // DeepseekV4 kernel only runs with bf16 inputs in practice, so compile a
+  // no-op stub for sm_70/sm_75 to keep multi-arch builds happy.
+  if constexpr (std::is_same_v<scalar_t_in, c10::BFloat16>) {
+    return;
+  } else {
+#endif
+    using Converter = vllm::_typeConvert<scalar_t_in>;
+
+    int const warpsPerBlock = blockDim.x / 32;
+    int const warpId = threadIdx.x / 32;
+    int const laneId = threadIdx.x % 32;
+    int const globalWarpIdx = blockIdx.x * warpsPerBlock + warpId;
+
+    int const total_slots_per_token = num_heads_q + 1;
+    int const tokenIdx = globalWarpIdx / total_slots_per_token;
+    int const slotIdx = globalWarpIdx % total_slots_per_token;
+    if (tokenIdx >= num_tokens_full) return;
+
+    bool const isKV = (slotIdx == num_heads_q);
+    // KV branch: skip DP-padded tokens (no slot reserved for them).
+    if (isKV && tokenIdx >= num_tokens_insert) return;
+
+    // PDL: wait for predecessor kernel (upstream q/kv producer) to signal
+    // before touching any global memory.  No-op when PDL is not enabled on
+    // the launch.  The CUDA runtime wrapper emits the griddepcontrol.wait
+    // PTX with the required memory clobber internally.
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
+    cudaGridDependencySynchronize();
+#endif
+
+    // Dim range this lane owns within the 512-wide head.
+    int const dim_base = laneId * kElemsPerLane;  // in [0, 512) step 16
+
+    // ── Load 16 bf16 → 16 fp32 registers (one 16-byte + one 16-byte LDG) ────
+    float elements[kElemsPerLane];
+    float sumOfSquares = 0.0f;
+
+    scalar_t_in const* src_ptr;
+    if (isKV) {
+      src_ptr = kv_in + static_cast<int64_t>(tokenIdx) * kHeadDim + dim_base;
+    } else {
+      int64_t const q_row_offset =
+          (static_cast<int64_t>(tokenIdx) * num_heads_q + slotIdx) * kHeadDim +
+          dim_base;
+      src_ptr = q_inout + q_row_offset;
+    }
+
+    // Two 16-byte loads per thread (8 bf16 each).  Use uint4 as the vector
+    // type and bitcast to scalar_t_in packed pairs for conversion.
+    uint4 v0 = *reinterpret_cast<uint4 const*>(src_ptr);
+    uint4 v1 = *reinterpret_cast<uint4 const*>(src_ptr + 8);
+
+    {
+      typename Converter::packed_hip_type const* p0 =
+          reinterpret_cast<typename Converter::packed_hip_type const*>(&v0);
+      typename Converter::packed_hip_type const* p1 =
+          reinterpret_cast<typename Converter::packed_hip_type const*>(&v1);
+// Each packed_hip_type holds 2 bf16 → 4 packed = 8 elems per uint4.
+#pragma unroll
+      for (int i = 0; i < 4; i++) {
+        float2 f2 = Converter::convert(p0[i]);
+        elements[2 * i] = f2.x;
+        elements[2 * i + 1] = f2.y;
+      }
+#pragma unroll
+      for (int i = 0; i < 4; i++) {
+        float2 f2 = Converter::convert(p1[i]);
+        elements[8 + 2 * i] = f2.x;
+        elements[8 + 2 * i + 1] = f2.y;
+      }
+    }
+
+    // ── Q branch: RMSNorm with no weight (has_weight=False) ─────────────────
+    // Variance + rsqrt + multiply all in fp32, no intermediate bf16 round.
+    // The downstream bf16 round only happens at the final store.
+    if (!isKV) {
+#pragma unroll
+      for (int i = 0; i < kElemsPerLane; i++) {
+        sumOfSquares += elements[i] * elements[i];
+      }
+      sumOfSquares = warpSum<float>(sumOfSquares);
+      float const rms_rcp =
+          rsqrtf(sumOfSquares / static_cast<float>(kHeadDim) + eps);
+#pragma unroll
+      for (int i = 0; i < kElemsPerLane; i++) {
+        elements[i] = elements[i] * rms_rcp;
+      }
+    }
+
+    // ── GPT-J RoPE on dims [NOPE_DIM, HEAD_DIM) ─────────────────────────────
+    // All math in fp32.  cos_sin_cache is loaded as fp32 (its native storage).
+    bool const is_rope_lane = dim_base >= kNopeDim;
+    if (is_rope_lane) {
+      int64_t const pos = position_ids[tokenIdx];
+      constexpr int kHalfRope = kRopeDim / 2;  // 32
+      float const* cos_ptr = cos_sin_cache + pos * kRopeDim;
+      float const* sin_ptr = cos_ptr + kHalfRope;
+
+      int const rope_local_base = dim_base - kNopeDim;  // in [0, 64) step 16
+#pragma unroll
+      for (int p = 0; p < kElemsPerLane / 2; p++) {
+        int const pair_dim = rope_local_base + 2 * p;
+        int const half_idx = pair_dim / 2;
+        float const cos_v = VLLM_LDG(cos_ptr + half_idx);
+        float const sin_v = VLLM_LDG(sin_ptr + half_idx);
+        float const x_even = elements[2 * p];
+        float const x_odd = elements[2 * p + 1];
+        elements[2 * p] = x_even * cos_v - x_odd * sin_v;
+        elements[2 * p + 1] = x_even * sin_v + x_odd * cos_v;
+      }
+    }
+
+    // ═══════════════════════════════════════════════════════════════════════
+    // Q branch: cast to bf16 and store back in place.
+    // ═══════════════════════════════════════════════════════════════════════
+    if (!isKV) {
+      uint4 out0, out1;
+      typename Converter::packed_hip_type* po0 =
+          reinterpret_cast<typename Converter::packed_hip_type*>(&out0);
+      typename Converter::packed_hip_type* po1 =
+          reinterpret_cast<typename Converter::packed_hip_type*>(&out1);
+#pragma unroll
+      for (int i = 0; i < 4; i++) {
+        po0[i] = Converter::convert(
+            make_float2(elements[2 * i], elements[2 * i + 1]));
+      }
+#pragma unroll
+      for (int i = 0; i < 4; i++) {
+        po1[i] = Converter::convert(
+            make_float2(elements[8 + 2 * i], elements[8 + 2 * i + 1]));
+      }
+      scalar_t_in* dst =
+          q_inout +
+          (static_cast<int64_t>(tokenIdx) * num_heads_q + slotIdx) * kHeadDim +
+          dim_base;
+      *reinterpret_cast<uint4*>(dst) = out0;
+      *reinterpret_cast<uint4*>(dst + 8) = out1;
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
+      cudaTriggerProgrammaticLaunchCompletion();
+#endif
+      return;
+    }
+
+    // ═══════════════════════════════════════════════════════════════════════
+    // KV branch.
+    // ═══════════════════════════════════════════════════════════════════════
+    int64_t const slot_id = slot_mapping[tokenIdx];
+    if (slot_id < 0) {
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
+      cudaTriggerProgrammaticLaunchCompletion();
+#endif
+      return;
+    }
+
+    int64_t const block_idx = slot_id / cache_block_size;
+    int64_t const pos_in_block = slot_id % cache_block_size;
+    uint8_t* block_base =
+        k_cache + block_idx * static_cast<int64_t>(kv_block_stride);
+    uint8_t* token_fp8_ptr = block_base + pos_in_block * kTokenDataBytes;
+    uint8_t* token_bf16_ptr = token_fp8_ptr + kNopeDim;
+    uint8_t* token_scale_ptr =
+        block_base + static_cast<int64_t>(cache_block_size) * kTokenDataBytes +
+        pos_in_block * kScaleBytesPerToken;
+
+    // Round K to bf16 first, matching the unfused reference path where K is
+    // materialized as bf16 before K quantization.  absmax, clamp, and FP8
+    // quant below all run on these bf16-rounded values.
+#pragma unroll
+    for (int i = 0; i < kElemsPerLane; i++) {
+      elements[i] = Converter::convert(Converter::convert(elements[i]));
+    }
+
+    // Per-quant-block absmax must be computed by ALL 32 lanes (warp-collective
+    // shuffle requires full participation).  RoPE lanes contribute garbage,
+    // but their values are gated out below via `!is_rope_lane`.
+    float local_absmax = 0.0f;
+#pragma unroll
+    for (int i = 0; i < kElemsPerLane; i++) {
+      local_absmax = fmaxf(local_absmax, fabsf(elements[i]));
+    }
+    float const absmax = fmaxf(warp4MaxAbs(local_absmax), 1e-4f);
+    float const exponent = ceilf(log2f(absmax / kFp8Max));
+    float const inv_scale = exp2f(-exponent);
+
+    if (!is_rope_lane) {
+      // ── NoPE lane: UE8M0 FP8 quant ───────────────────────────────────────
+      uint8_t out_bytes[kElemsPerLane];
+#pragma unroll
+      for (int i = 0; i < kElemsPerLane; i++) {
+        float scaled = elements[i] * inv_scale;
+        scaled = fminf(fmaxf(scaled, -kFp8Max), kFp8Max);
+#ifndef USE_ROCM
+        __nv_fp8_storage_t s =
+            __nv_cvt_float_to_fp8(scaled, __NV_SATFINITE, __NV_E4M3);
+        out_bytes[i] = static_cast<uint8_t>(s);
+#else
+      out_bytes[i] = rocm_cvt_float_to_fp8_e4m3(scaled);
+#endif
+      }
+      // One 16-byte STG per lane.
+      *reinterpret_cast<uint4*>(token_fp8_ptr + dim_base) =
+          *reinterpret_cast<uint4 const*>(out_bytes);
+
+      // Lane (4k) of each 4-lane group writes the scale byte for block k<7.
+      if ((laneId & 3) == 0) {
+        int const q_block_idx = laneId >> 2;  // 0..6 for NoPE lanes
+        float encoded = fmaxf(fminf(exponent + 127.0f, 255.0f), 0.0f);
+        token_scale_ptr[q_block_idx] = static_cast<uint8_t>(encoded);
+      }
+      // Lane 0 also writes the padding byte at index 7.
+      if (laneId == 0) {
+        token_scale_ptr[kNumQuantBlocks] = 0;  // pad
+      }
+    } else {
+      // ── RoPE lane: cast back to bf16 and store to cache bf16 tail ────────
+      uint4 out0, out1;
+      typename Converter::packed_hip_type* po0 =
+          reinterpret_cast<typename Converter::packed_hip_type*>(&out0);
+      typename Converter::packed_hip_type* po1 =
+          reinterpret_cast<typename Converter::packed_hip_type*>(&out1);
+#pragma unroll
+      for (int i = 0; i < 4; i++) {
+        po0[i] = Converter::convert(
+            make_float2(elements[2 * i], elements[2 * i + 1]));
+      }
+#pragma unroll
+      for (int i = 0; i < 4; i++) {
+        po1[i] = Converter::convert(
+            make_float2(elements[8 + 2 * i], elements[8 + 2 * i + 1]));
+      }
+      int const rope_local_base = dim_base - kNopeDim;  // in [0, 64)
+      scalar_t_in* bf16_dst =
+          reinterpret_cast<scalar_t_in*>(token_bf16_ptr) + rope_local_base;
+      *reinterpret_cast<uint4*>(bf16_dst) = out0;
+      *reinterpret_cast<uint4*>(bf16_dst + 8) = out1;
+    }
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
+    cudaTriggerProgrammaticLaunchCompletion();
+#endif
+#if (!defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 800) && !defined(USE_ROCM)
+  }
+#endif
+}
+
+// ────────────────────────────────────────────────────────────────────────────
+// Launch wrapper
+// ────────────────────────────────────────────────────────────────────────────
+template <typename scalar_t_in>
+void launchFusedDeepseekV4QNormRopeKVRopeQuantInsert(
+    scalar_t_in* q_inout, scalar_t_in const* kv_in, uint8_t* k_cache,
+    int64_t const* slot_mapping, int64_t const* position_ids,
+    float const* cos_sin_cache, float const eps, int const num_tokens_full,
+    int const num_tokens_insert, int const num_heads_q,
+    int const cache_block_size, int const kv_block_stride,
+    cudaStream_t stream) {
+  constexpr int kBlockSize = 256;
+  constexpr int kWarpsPerBlock = kBlockSize / 32;
+  int64_t const total_warps =
+      static_cast<int64_t>(num_tokens_full) * (num_heads_q + 1);
+  int const grid =
+      static_cast<int>((total_warps + kWarpsPerBlock - 1) / kWarpsPerBlock);
+
+  // PDL: enable programmatic stream serialization whenever the hardware
+  // supports it (SM90+).  On pre-Hopper GPUs the attribute is unavailable,
+  // so leave numAttrs = 0 and launch as a regular kernel.
+#ifndef USE_ROCM
+  static int const sm_version = getSMVersion();
+  // Host-side guard: the device kernel body is compiled as a no-op for
+  // bf16 on pre-Ampere (sm_70/sm_75) because _typeConvert<BFloat16> is
+  // unavailable there.  Refuse the launch loudly instead of silently
+  // skipping the work.
+  TORCH_CHECK(
+      sm_version >= 80,
+      "fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert requires sm_80+ "
+      "(Ampere or newer); got sm_",
+      sm_version);
+  cudaLaunchConfig_t config;
+  config.gridDim = dim3(grid);
+  config.blockDim = dim3(kBlockSize);
+  config.dynamicSmemBytes = 0;
+  config.stream = stream;
+  cudaLaunchAttribute attrs[1];
+  attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
+  attrs[0].val.programmaticStreamSerializationAllowed = 1;
+  config.attrs = attrs;
+  config.numAttrs = (sm_version >= 90) ? 1 : 0;
+
+  cudaLaunchKernelEx(
+      &config, fusedDeepseekV4QNormRopeKVRopeQuantInsertKernel<scalar_t_in>,
+      q_inout, kv_in, k_cache, slot_mapping, position_ids, cos_sin_cache, eps,
+      num_tokens_full, num_tokens_insert, num_heads_q, cache_block_size,
+      kv_block_stride);
+#else
+  // ROCm: use standard kernel launch syntax (no PDL/stream serialization)
+  // clang-format off
+  fusedDeepseekV4QNormRopeKVRopeQuantInsertKernel<scalar_t_in>
+      <<<grid, kBlockSize, 0, stream>>>(
+          q_inout, kv_in, k_cache, slot_mapping, position_ids, cos_sin_cache,
+          eps, num_tokens_full, num_tokens_insert, num_heads_q,
+          cache_block_size, kv_block_stride);
+#endif
+}
+
+}  // namespace deepseek_v4_fused_ops
+}  // namespace vllm
+
+// ────────────────────────────────────────────────────────────────────────────
+// Torch op wrapper
+// ────────────────────────────────────────────────────────────────────────────
+void fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert(
+    torch::Tensor& q,                    // [N, H, 512] bf16, in place
+    torch::Tensor const& kv,             // [N, 512] bf16 (read-only)
+    torch::Tensor& k_cache,              // [num_blocks, block_bytes] uint8
+    torch::Tensor const& slot_mapping,   // [N] int64
+    torch::Tensor const& position_ids,   // [N] int64
+    torch::Tensor const& cos_sin_cache,  // [max_pos, rope_dim] bf16
+    double eps, int64_t cache_block_size) {
+  TORCH_CHECK(q.is_cuda() && q.is_contiguous(), "q must be contiguous CUDA");
+  TORCH_CHECK(kv.is_cuda() && kv.is_contiguous(), "kv must be contiguous CUDA");
+  TORCH_CHECK(k_cache.is_cuda(), "k_cache must be CUDA");
+  TORCH_CHECK(slot_mapping.is_cuda() && slot_mapping.dtype() == torch::kInt64,
+              "slot_mapping must be int64 CUDA");
+  TORCH_CHECK(position_ids.is_cuda() && position_ids.dtype() == torch::kInt64,
+              "position_ids must be int64 CUDA");
+  TORCH_CHECK(cos_sin_cache.is_cuda(), "cos_sin_cache must be CUDA");
+  TORCH_CHECK(q.dim() == 3 && q.size(2) == 512, "q shape [N, H, 512]");
+  TORCH_CHECK(kv.dim() == 2 && kv.size(1) == 512, "kv shape [N, 512]");
+  TORCH_CHECK(q.dtype() == kv.dtype(), "q and kv dtype must match");
+  TORCH_CHECK(k_cache.dtype() == torch::kUInt8, "k_cache must be uint8");
+  TORCH_CHECK(cos_sin_cache.dim() == 2 && cos_sin_cache.size(1) == 64,
+              "cos_sin_cache shape [max_pos, 64]");
+  TORCH_CHECK(cos_sin_cache.dtype() == torch::kFloat32,
+              "cos_sin_cache must be float32");
+
+  // With DP padding, slot_mapping can be shorter than q/kv/positions.
+  // Q-norm+RoPE runs on all q.size(0) rows (downstream attention uses them);
+  // KV quant+insert runs only on the first slot_mapping.size(0) rows.
+  int const num_tokens_full = static_cast<int>(q.size(0));
+  int const num_tokens_insert = static_cast<int>(slot_mapping.size(0));
+  TORCH_CHECK(static_cast<int>(kv.size(0)) == num_tokens_full &&
+                  static_cast<int>(position_ids.size(0)) == num_tokens_full,
+              "q/kv/position_ids row counts must match");
+  TORCH_CHECK(num_tokens_insert <= num_tokens_full,
+              "slot_mapping must not exceed q row count");
+  int const num_heads_q = static_cast<int>(q.size(1));
+  int const cache_block_size_i = static_cast<int>(cache_block_size);
+  int const kv_block_stride = static_cast<int>(k_cache.stride(0));
+
+  at::cuda::OptionalCUDAGuard device_guard(device_of(q));
+  auto stream = at::cuda::getCurrentCUDAStream();
+
+  VLLM_DISPATCH_HALF_TYPES(
+      q.scalar_type(), "fused_deepseek_v4_qnorm_rope_kv_insert", [&] {
+        using qkv_scalar_t = scalar_t;
+        vllm::deepseek_v4_fused_ops::
+            launchFusedDeepseekV4QNormRopeKVRopeQuantInsert<qkv_scalar_t>(
+                reinterpret_cast<qkv_scalar_t*>(q.data_ptr()),
+                reinterpret_cast<qkv_scalar_t const*>(kv.data_ptr()),
+                reinterpret_cast<uint8_t*>(k_cache.data_ptr()),
+                reinterpret_cast<int64_t const*>(slot_mapping.data_ptr()),
+                reinterpret_cast<int64_t const*>(position_ids.data_ptr()),
+                cos_sin_cache.data_ptr<float>(), static_cast<float>(eps),
+                num_tokens_full, num_tokens_insert, num_heads_q,
+                cache_block_size_i, kv_block_stride, stream);
+      });
+}
diff --git a/csrc/layernorm_kernels.cu b/csrc/layernorm_kernels.cu
index 9766103f7646..e617e45dc58b 100644
--- a/csrc/layernorm_kernels.cu
+++ b/csrc/layernorm_kernels.cu
@@ -77,7 +77,8 @@ __global__ void rms_norm_kernel(
 #pragma unroll
     for (int j = 0; j < VEC_SIZE; j++) {
       float x = static_cast<float>(src1.val[j]);
-      dst.val[j] = ((scalar_t)(x * s_variance)) * src2.val[j];
+      float w = static_cast<float>(src2.val[j]);
+      dst.val[j] = static_cast<scalar_t>(x * s_variance * w);
     }
     v_out[i] = dst;
   }
@@ -134,10 +135,17 @@ fused_add_rms_norm_kernel(
   for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
     int id = blockIdx.x * vec_hidden_size + idx;
     int64_t strided_id = blockIdx.x * vec_input_stride + idx;
-    _f16Vec<scalar_t, width> temp = residual_v[id];
-    temp *= s_variance;
-    temp *= weight_v[idx];
-    input_v[strided_id] = temp;
+    _f16Vec<scalar_t, width> res = residual_v[id];
+    _f16Vec<scalar_t, width> w = weight_v[idx];
+    _f16Vec<scalar_t, width> out;
+    using Converter = _typeConvert<scalar_t>;
+#pragma unroll
+    for (int j = 0; j < width; ++j) {
+      float x = Converter::convert(res.data[j]);
+      float wf = Converter::convert(w.data[j]);
+      out.data[j] = Converter::convert(x * s_variance * wf);
+    }
+    input_v[strided_id] = out;
   }
 }
 
@@ -174,8 +182,8 @@ fused_add_rms_norm_kernel(
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
     float x = (float)residual[blockIdx.x * hidden_size + idx];
-    input[blockIdx.x * input_stride + idx] =
-        ((scalar_t)(x * s_variance)) * weight[idx];
+    float w = (float)weight[idx];
+    input[blockIdx.x * input_stride + idx] = (scalar_t)(x * s_variance * w);
   }
 }
 
diff --git a/csrc/layernorm_quant_kernels.cu b/csrc/layernorm_quant_kernels.cu
index f96386252c31..b2f546b3db1d 100644
--- a/csrc/layernorm_quant_kernels.cu
+++ b/csrc/layernorm_quant_kernels.cu
@@ -65,9 +65,16 @@ __global__ void rms_norm_static_fp8_quant_kernel(
 #pragma unroll
     for (int j = 0; j < VEC_SIZE; j++) {
       float x = static_cast<float>(src1.val[j]);
-      float const out_norm = ((scalar_t)(x * s_variance)) * src2.val[j];
+      float w = static_cast<float>(src2.val[j]);
+      // Round normalized result through scalar_t to match the precision of the
+      // unfused composite (rms_norm writes scalar_t, then
+      // static_scaled_fp8_quant re-loads it as float before FP8 conversion).
+      // Without this round, the fused path is strictly more accurate and
+      // disagrees with the composite at exact E4M3 quantization tie boundaries.
+      scalar_t out_norm = static_cast<scalar_t>(x * s_variance * w);
       out[blockIdx.x * hidden_size + idx * VEC_SIZE + j] =
-          scaled_fp8_conversion<true, fp8_type>(out_norm, scale_inv);
+          scaled_fp8_conversion<true, fp8_type>(static_cast<float>(out_norm),
+                                                scale_inv);
     }
   }
 }
@@ -127,13 +134,21 @@ fused_add_rms_norm_static_fp8_quant_kernel(
 
   for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
     int id = blockIdx.x * vec_hidden_size + idx;
-    _f16Vec<scalar_t, width> temp = residual_v[id];
-    temp *= s_variance;
-    temp *= weight_v[idx];
+    _f16Vec<scalar_t, width> res = residual_v[id];
+    _f16Vec<scalar_t, width> w = weight_v[idx];
+    using Converter = _typeConvert<scalar_t>;
+    using HipT = typename Converter::hip_type;
 #pragma unroll
     for (int i = 0; i < width; ++i) {
-      out[id * width + i] =
-          scaled_fp8_conversion<true, fp8_type>(float(temp.data[i]), scale_inv);
+      float x = Converter::convert(res.data[i]);
+      float wf = Converter::convert(w.data[i]);
+      // See note in rms_norm_static_fp8_quant_kernel: round through scalar_t
+      // to match the unfused composite path at FP8 boundaries. We use the
+      // backend's hip_type for the intermediate since c10::Half/BFloat16 has
+      // ambiguous conversions on CUDA and no implicit conversion on ROCm.
+      HipT out_norm_h = Converter::convert(x * s_variance * wf);
+      out[id * width + i] = scaled_fp8_conversion<true, fp8_type>(
+          Converter::convert(out_norm_h), scale_inv);
     }
   }
 }
@@ -176,9 +191,12 @@ fused_add_rms_norm_static_fp8_quant_kernel(
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
     float x = (float)residual[blockIdx.x * hidden_size + idx];
-    float const out_norm = ((scalar_t)(x * s_variance)) * weight[idx];
-    out[blockIdx.x * hidden_size + idx] =
-        scaled_fp8_conversion<true, fp8_type>(out_norm, scale_inv);
+    float w = (float)weight[idx];
+    // See note in rms_norm_static_fp8_quant_kernel: round through scalar_t
+    // to match the unfused composite path at FP8 boundaries.
+    scalar_t out_norm = static_cast<scalar_t>(x * s_variance * w);
+    out[blockIdx.x * hidden_size + idx] = scaled_fp8_conversion<true, fp8_type>(
+        static_cast<float>(out_norm), scale_inv);
   }
 }
 
diff --git a/csrc/libtorch_stable/ops.h b/csrc/libtorch_stable/ops.h
index 8153102c598a..176cd5006335 100644
--- a/csrc/libtorch_stable/ops.h
+++ b/csrc/libtorch_stable/ops.h
@@ -134,4 +134,13 @@ void silu_and_mul_nvfp4_quant(torch::stable::Tensor& out,
                               torch::stable::Tensor& input,
                               torch::stable::Tensor& input_global_scale);
 
+void cutlass_mxfp4_group_mm(torch::stable::Tensor& output,
+                            const torch::stable::Tensor& a,
+                            const torch::stable::Tensor& b,
+                            const torch::stable::Tensor& a_blockscale,
+                            const torch::stable::Tensor& b_blockscales,
+                            const torch::stable::Tensor& problem_sizes,
+                            const torch::stable::Tensor& expert_offsets,
+                            const torch::stable::Tensor& sf_offsets);
+
 #endif
diff --git a/csrc/libtorch_stable/quantization/fp4/mxfp4_blockwise_moe_kernel.cu b/csrc/libtorch_stable/quantization/fp4/mxfp4_blockwise_moe_kernel.cu
new file mode 100644
index 000000000000..8a493fdf22c3
--- /dev/null
+++ b/csrc/libtorch_stable/quantization/fp4/mxfp4_blockwise_moe_kernel.cu
@@ -0,0 +1,468 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ * SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+ *
+ * MXFP4 x MXFP4 block-scaled grouped GEMM kernel for MoE on SM100.
+ * Uses Cutlass mx_float4_t operands, E8M0 block scales, and 32-element groups.
+ */
+
+#include <torch/csrc/stable/library.h>
+#include <torch/csrc/stable/tensor.h>
+#include "libtorch_stable/torch_utils.h"
+
+#include <cutlass/arch/arch.h>
+
+#include "cutlass_extensions/common.hpp"
+
+#include "cute/tensor.hpp"
+#include "cutlass/tensor_ref.h"
+#include "cutlass/epilogue/collective/default_epilogue.hpp"
+#include "cutlass/epilogue/thread/linear_combination.h"
+#include "cutlass/gemm/dispatch_policy.hpp"
+#include "cutlass/gemm/group_array_problem_shape.hpp"
+#include "cutlass/gemm/collective/collective_builder.hpp"
+#include "cutlass/epilogue/collective/collective_builder.hpp"
+#include "cutlass/gemm/device/gemm_universal_adapter.h"
+#include "cutlass/gemm/kernel/gemm_universal.hpp"
+
+#include "cutlass/util/packed_stride.hpp"
+#include <cassert>
+
+using namespace cute;
+
+// Offset-computation kernel for MXFP4 grouped GEMM (group size 32).
+template <typename ElementAB, typename ElementC, typename ElementSF,
+          typename LayoutSFA, typename LayoutSFB, typename ScaleConfig>
+__global__ void __mxfp4_get_group_gemm_starts(
+    ElementAB** a_offsets, ElementAB** b_offsets, ElementC** out_offsets,
+    ElementSF** a_scales_offsets, ElementSF** b_scales_offsets,
+    LayoutSFA* layout_sfa_base_as_int, LayoutSFB* layout_sfb_base_as_int,
+    ElementAB* a_base_as_int, ElementAB* b_base_as_int,
+    ElementC* out_base_as_int, ElementSF* a_scales_base_as_int,
+    ElementSF* b_scales_base_as_int, const int32_t* expert_offsets,
+    const int32_t* sf_offsets, const int32_t* problem_sizes_as_shapes,
+    int64_t* a_strides, int64_t* b_strides, int64_t* c_strides,
+    const int64_t a_stride_val, const int64_t b_stride_val,
+    const int64_t c_stride_val, const int K, const int N) {
+  int64_t expert_id = threadIdx.x;
+  if (expert_id >= gridDim.x * blockDim.x) {
+    return;
+  }
+  int64_t expert_offset = static_cast<int64_t>(expert_offsets[expert_id]);
+  int64_t sf_offset = static_cast<int64_t>(sf_offsets[expert_id]);
+  int64_t group_size = 32;
+  int64_t m = static_cast<int64_t>(problem_sizes_as_shapes[expert_id * 3]);
+  int64_t n = static_cast<int64_t>(problem_sizes_as_shapes[expert_id * 3 + 1]);
+  int64_t k = static_cast<int64_t>(problem_sizes_as_shapes[expert_id * 3 + 2]);
+  assert((m >= 0 && n == N && k == K && k % 2 == 0) &&
+         "unexpected problem sizes");
+
+  int64_t half_k = static_cast<int64_t>(k / 2);
+  int64_t group_k = static_cast<int64_t>(k / group_size);
+  // Shape of A as uint8/byte = [M, K // 2]
+  a_offsets[expert_id] = a_base_as_int + expert_offset * half_k;
+  // Shape of B as uint8/byte = [E, N, K // 2]
+  b_offsets[expert_id] = b_base_as_int + expert_id * n * half_k;
+  // Shape of C = [M, N]
+  out_offsets[expert_id] = out_base_as_int + expert_offset * n;
+  // Shape of a_scale = [sum(sf_sizes), K // group_size]
+  a_scales_offsets[expert_id] = a_scales_base_as_int + sf_offset * group_k;
+
+  assert((reinterpret_cast<uintptr_t>(a_scales_offsets[expert_id]) % 128) ==
+             0 &&
+         "TMA requires 128-byte alignment");
+
+  // Shape of B scale = [E, N, K // group_size]
+  b_scales_offsets[expert_id] = b_scales_base_as_int + expert_id * n * group_k;
+  assert((reinterpret_cast<uintptr_t>(b_scales_offsets[expert_id]) % 128) ==
+             0 &&
+         "TMA requires 128-byte alignment");
+
+  // Initialize strides
+  a_strides[expert_id] = a_stride_val;
+  b_strides[expert_id] = b_stride_val;
+  c_strides[expert_id] = c_stride_val;
+
+  LayoutSFA* layout_sfa_ptr = layout_sfa_base_as_int + expert_id;
+  LayoutSFB* layout_sfb_ptr = layout_sfb_base_as_int + expert_id;
+
+  *layout_sfa_ptr = ScaleConfig::tile_atom_to_shape_SFA(cute::make_shape(
+      static_cast<int>(m), static_cast<int>(n), static_cast<int>(k), 1));
+  *layout_sfb_ptr = ScaleConfig::tile_atom_to_shape_SFB(cute::make_shape(
+      static_cast<int>(m), static_cast<int>(n), static_cast<int>(k), 1));
+}
+
+#define __CALL_MXFP4_GET_STARTS_KERNEL(ELEMENT_AB_TYPE, SF_TYPE,               \
+                                       TENSOR_C_TYPE, C_TYPE, LayoutSFA,       \
+                                       LayoutSFB, ScaleConfig)                 \
+  else if (out_tensors.scalar_type() == TENSOR_C_TYPE) {                       \
+    __mxfp4_get_group_gemm_starts<ELEMENT_AB_TYPE, C_TYPE, SF_TYPE, LayoutSFA, \
+                                  LayoutSFB, ScaleConfig>                      \
+        <<<1, num_experts, 0, stream>>>(                                       \
+            static_cast<ELEMENT_AB_TYPE**>(a_starts.data_ptr()),               \
+            static_cast<ELEMENT_AB_TYPE**>(b_starts.data_ptr()),               \
+            static_cast<C_TYPE**>(out_starts.data_ptr()),                      \
+            static_cast<SF_TYPE**>(a_scales_starts.data_ptr()),                \
+            static_cast<SF_TYPE**>(b_scales_starts.data_ptr()),                \
+            reinterpret_cast<LayoutSFA*>(layout_sfa.data_ptr()),               \
+            reinterpret_cast<LayoutSFB*>(layout_sfb.data_ptr()),               \
+            static_cast<ELEMENT_AB_TYPE*>(a_tensors.data_ptr()),               \
+            static_cast<ELEMENT_AB_TYPE*>(b_tensors.data_ptr()),               \
+            static_cast<C_TYPE*>(out_tensors.data_ptr()),                      \
+            static_cast<SF_TYPE*>(a_scales.data_ptr()),                        \
+            static_cast<SF_TYPE*>(b_scales.data_ptr()),                        \
+            static_cast<int32_t*>(expert_offsets.data_ptr()),                  \
+            static_cast<int32_t*>(sf_offsets.data_ptr()),                      \
+            static_cast<int32_t*>(problem_sizes.data_ptr()),                   \
+            static_cast<int64_t*>(a_strides.data_ptr()),                       \
+            static_cast<int64_t*>(b_strides.data_ptr()),                       \
+            static_cast<int64_t*>(c_strides.data_ptr()), a_stride_val,         \
+            b_stride_val, c_stride_val, K, N);                                 \
+  }
+
+template <typename LayoutSFA, typename LayoutSFB, typename ScaleConfig>
+void mxfp4_run_get_group_gemm_starts(
+    const torch::stable::Tensor& a_starts,
+    const torch::stable::Tensor& b_starts,
+    const torch::stable::Tensor& out_starts,
+    const torch::stable::Tensor& a_scales_starts,
+    const torch::stable::Tensor& b_scales_starts,
+    const torch::stable::Tensor& layout_sfa,
+    const torch::stable::Tensor& layout_sfb,
+    const torch::stable::Tensor& a_strides,
+    const torch::stable::Tensor& b_strides,
+    const torch::stable::Tensor& c_strides, int64_t a_stride_val,
+    int64_t b_stride_val, int64_t c_stride_val,
+    torch::stable::Tensor const& a_tensors,
+    torch::stable::Tensor const& b_tensors,
+    torch::stable::Tensor const& out_tensors,
+    torch::stable::Tensor const& a_scales,
+    torch::stable::Tensor const& b_scales,
+    torch::stable::Tensor const& expert_offsets,
+    torch::stable::Tensor const& sf_offsets,
+    torch::stable::Tensor const& problem_sizes, int M, int N, int K) {
+  int num_experts = (int)expert_offsets.size(0);
+  auto stream = get_current_cuda_stream(a_tensors.get_device_index());
+
+  STD_TORCH_CHECK(out_tensors.size(1) == N,
+                  "Output tensor shape doesn't match expected shape");
+  STD_TORCH_CHECK(K / 2 == b_tensors.size(2),
+                  "b_tensors(dim = 2) and a_tensors(dim = 1) trailing"
+                  " dimension must match");
+  if (false) {
+  }
+  // MXFP4 uses E8M0 (float_ue8m0_t) scale factors
+  __CALL_MXFP4_GET_STARTS_KERNEL(cutlass::float_e2m1_t, cutlass::float_ue8m0_t,
+                                 torch::headeronly::ScalarType::BFloat16,
+                                 cutlass::bfloat16_t, LayoutSFA, LayoutSFB,
+                                 ScaleConfig)
+  __CALL_MXFP4_GET_STARTS_KERNEL(cutlass::float_e2m1_t, cutlass::float_ue8m0_t,
+                                 torch::headeronly::ScalarType::Half, half,
+                                 LayoutSFA, LayoutSFB, ScaleConfig)
+  else {
+    STD_TORCH_CHECK(false, "Invalid output type (must be float16 or bfloat16)");
+  }
+}
+
+template <typename OutType>
+void run_mxfp4_blockwise_scaled_group_mm_sm100(
+    torch::stable::Tensor& output, const torch::stable::Tensor& a,
+    const torch::stable::Tensor& b, const torch::stable::Tensor& a_blockscale,
+    const torch::stable::Tensor& b_blockscales,
+    const torch::stable::Tensor& problem_sizes,
+    const torch::stable::Tensor& expert_offsets,
+    const torch::stable::Tensor& sf_offsets, int M, int N, int K) {
+  using ProblemShape =
+      cutlass::gemm::GroupProblemShape<Shape<int32_t, int32_t, int32_t>>;
+  using ElementType = cutlass::float_e2m1_t;
+  using ElementSFType = cutlass::float_ue8m0_t;
+  using ElementA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
+  using ElementB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
+
+  using ElementC = OutType;
+  using ElementD = ElementC;
+  using ElementAccumulator = float;
+  // Layout definitions
+  using LayoutA = cutlass::layout::RowMajor;
+  using LayoutB = cutlass::layout::ColumnMajor;
+  using LayoutC = cutlass::layout::RowMajor;
+  using LayoutD = LayoutC;
+
+  static constexpr int AlignmentA = 32;
+  static constexpr int AlignmentB = 32;
+  static constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value;
+  static constexpr int AlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
+
+  // Architecture definitions
+  using ArchTag = cutlass::arch::Sm100;
+  using EpilogueOperatorClass = cutlass::arch::OpClassTensorOp;
+  using MainloopOperatorClass = cutlass::arch::OpClassBlockScaledTensorOp;
+  using StageCountType = cutlass::gemm::collective::StageCountAuto;
+
+  using ClusterShape = Shape<_1, _1, _1>;
+  struct MMA1SMConfig {
+    using MmaTileShape = Shape<_128, _128, _128>;
+    using KernelSchedule =
+        cutlass::gemm::KernelPtrArrayTmaWarpSpecialized1SmMxf4Sm100;
+    using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecialized1Sm;
+  };
+
+  using CollectiveEpilogue =
+      typename cutlass::epilogue::collective::CollectiveBuilder<
+          ArchTag, EpilogueOperatorClass, typename MMA1SMConfig::MmaTileShape,
+          ClusterShape, Shape<_128, _64>, ElementAccumulator,
+          ElementAccumulator, ElementC, LayoutC*, AlignmentC, ElementD,
+          LayoutC*, AlignmentD,
+          typename MMA1SMConfig::EpilogueSchedule>::CollectiveOp;
+
+  using CollectiveMainloop =
+      typename cutlass::gemm::collective::CollectiveBuilder<
+          ArchTag, MainloopOperatorClass, ElementA, LayoutA*, AlignmentA,
+          ElementB, LayoutB*, AlignmentB, ElementAccumulator,
+          typename MMA1SMConfig::MmaTileShape, ClusterShape,
+          cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
+              sizeof(typename CollectiveEpilogue::SharedStorage))>,
+          typename MMA1SMConfig::KernelSchedule>::CollectiveOp;
+
+  using GemmKernel =
+      cutlass::gemm::kernel::GemmUniversal<ProblemShape, CollectiveMainloop,
+                                           CollectiveEpilogue>;
+
+  using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
+  using StrideA = typename Gemm::GemmKernel::InternalStrideA;
+  using StrideB = typename Gemm::GemmKernel::InternalStrideB;
+  using StrideC = typename Gemm::GemmKernel::InternalStrideC;
+  using StrideD = typename Gemm::GemmKernel::InternalStrideD;
+
+  using LayoutSFA =
+      typename Gemm::GemmKernel::CollectiveMainloop::InternalLayoutSFA;
+  using LayoutSFB =
+      typename Gemm::GemmKernel::CollectiveMainloop::InternalLayoutSFB;
+  using ScaleConfig =
+      typename Gemm::GemmKernel::CollectiveMainloop::Sm1xxBlkScaledConfig;
+
+  using UnderlyingProblemShape = ProblemShape::UnderlyingProblemShape;
+  int num_experts = static_cast<int>(expert_offsets.size(0));
+
+  torch::stable::Tensor a_ptrs =
+      torch::stable::empty(num_experts, torch::headeronly::ScalarType::Long,
+                           std::nullopt, a.device());
+  torch::stable::Tensor b_ptrs =
+      torch::stable::empty(num_experts, torch::headeronly::ScalarType::Long,
+                           std::nullopt, a.device());
+  torch::stable::Tensor out_ptrs =
+      torch::stable::empty(num_experts, torch::headeronly::ScalarType::Long,
+                           std::nullopt, a.device());
+  torch::stable::Tensor a_scales_ptrs =
+      torch::stable::empty(num_experts, torch::headeronly::ScalarType::Long,
+                           std::nullopt, a.device());
+  torch::stable::Tensor b_scales_ptrs =
+      torch::stable::empty(num_experts, torch::headeronly::ScalarType::Long,
+                           std::nullopt, a.device());
+  torch::stable::Tensor layout_sfa = torch::stable::empty(
+      {num_experts, 5}, torch::headeronly::ScalarType::Long, std::nullopt,
+      a.device());
+  torch::stable::Tensor layout_sfb = torch::stable::empty(
+      {num_experts, 5}, torch::headeronly::ScalarType::Long, std::nullopt,
+      a.device());
+  torch::stable::Tensor a_strides1 =
+      torch::stable::empty(num_experts, torch::headeronly::ScalarType::Long,
+                           std::nullopt, a.device());
+  torch::stable::Tensor b_strides1 =
+      torch::stable::empty(num_experts, torch::headeronly::ScalarType::Long,
+                           std::nullopt, a.device());
+  torch::stable::Tensor c_strides1 =
+      torch::stable::empty(num_experts, torch::headeronly::ScalarType::Long,
+                           std::nullopt, a.device());
+
+  mxfp4_run_get_group_gemm_starts<LayoutSFA, LayoutSFB, ScaleConfig>(
+      a_ptrs, b_ptrs, out_ptrs, a_scales_ptrs, b_scales_ptrs, layout_sfa,
+      layout_sfb, a_strides1, b_strides1, c_strides1, a.stride(0) * 2,
+      b.stride(1) * 2, output.stride(0), a, b, output, a_blockscale,
+      b_blockscales, expert_offsets, sf_offsets, problem_sizes, M, N, K);
+
+  // Create an instance of the GEMM
+  Gemm gemm_op;
+
+  UnderlyingProblemShape* problem_sizes_as_shapes =
+      static_cast<UnderlyingProblemShape*>(problem_sizes.data_ptr());
+
+  // Set the Scheduler info
+  cutlass::KernelHardwareInfo hw_info;
+  using RasterOrderOptions = typename cutlass::gemm::kernel::detail::
+      PersistentTileSchedulerSm100GroupParams<
+          typename ProblemShape::UnderlyingProblemShape>::RasterOrderOptions;
+  typename Gemm::GemmKernel::TileSchedulerArguments scheduler;
+  scheduler.raster_order = RasterOrderOptions::AlongM;
+  hw_info.device_id = a.get_device_index();
+  static std::unordered_map<int, int> cached_sm_counts;
+  if (cached_sm_counts.find(hw_info.device_id) == cached_sm_counts.end()) {
+    cached_sm_counts[hw_info.device_id] =
+        cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
+            hw_info.device_id);
+  }
+  hw_info.sm_count = min(cached_sm_counts[hw_info.device_id], INT_MAX);
+
+  // Mainloop Arguments
+  typename GemmKernel::MainloopArguments mainloop_args{
+      static_cast<const ElementType**>(a_ptrs.data_ptr()),
+      static_cast<StrideA*>(a_strides1.data_ptr()),
+      static_cast<const ElementType**>(b_ptrs.data_ptr()),
+      static_cast<StrideB*>(b_strides1.data_ptr()),
+      static_cast<const ElementSFType**>(a_scales_ptrs.data_ptr()),
+      reinterpret_cast<LayoutSFA*>(layout_sfa.data_ptr()),
+      static_cast<const ElementSFType**>(b_scales_ptrs.data_ptr()),
+      reinterpret_cast<LayoutSFB*>(layout_sfb.data_ptr())};
+
+  // Epilogue Arguments
+  typename GemmKernel::EpilogueArguments epilogue_args{
+      {},  // epilogue.thread
+      nullptr,
+      static_cast<StrideC*>(c_strides1.data_ptr()),
+      static_cast<ElementD**>(out_ptrs.data_ptr()),
+      static_cast<StrideC*>(c_strides1.data_ptr())};
+  auto& fusion_args = epilogue_args.thread;
+  // Scalar epilogue (CUTLASS grouped GEMM): D = 1 * accum + 0 * C
+  fusion_args.alpha_ptr = nullptr;
+  fusion_args.beta_ptr = nullptr;
+  fusion_args.alpha = 1.0f;
+  fusion_args.alpha_ptr_array = nullptr;
+  fusion_args.dAlpha = {_0{}, _0{}, 0};
+  fusion_args.beta = 0.0f;
+  fusion_args.beta_ptr_array = nullptr;
+  fusion_args.dBeta = {_0{}, _0{}, 0};
+
+  // Gemm Arguments
+  typename GemmKernel::Arguments args{
+      cutlass::gemm::GemmUniversalMode::kGrouped,
+      {num_experts, problem_sizes_as_shapes, nullptr},
+      mainloop_args,
+      epilogue_args,
+      hw_info,
+      scheduler};
+
+  size_t workspace_size = Gemm::get_workspace_size(args);
+  auto workspace =
+      torch::stable::empty(workspace_size, torch::headeronly::ScalarType::Byte,
+                           std::nullopt, a.device());
+  const cudaStream_t stream = get_current_cuda_stream(a.get_device_index());
+
+  auto can_implement_status = gemm_op.can_implement(args);
+  STD_TORCH_CHECK(
+      can_implement_status == cutlass::Status::kSuccess,
+      "Failed to implement MXFP4 GEMM: status=", (int)can_implement_status);
+
+  // Run the GEMM
+  auto status = gemm_op.initialize(args, workspace.data_ptr());
+  STD_TORCH_CHECK(status == cutlass::Status::kSuccess,
+                  "Failed to initialize MXFP4 GEMM: status=", (int)status,
+                  " workspace_size=", workspace_size,
+                  " num_experts=", num_experts, " M=", M, " N=", N, " K=", K);
+
+  status = gemm_op.run(args, workspace.data_ptr(), stream);
+  STD_TORCH_CHECK(status == cutlass::Status::kSuccess,
+                  "Failed to run MXFP4 GEMM");
+}
+
+template <typename OutType>
+void run_mxfp4_blockwise_scaled_group_mm(
+    torch::stable::Tensor& output, const torch::stable::Tensor& a,
+    const torch::stable::Tensor& b, const torch::stable::Tensor& a_blockscale,
+    const torch::stable::Tensor& b_blockscales,
+    const torch::stable::Tensor& problem_sizes,
+    const torch::stable::Tensor& expert_offsets,
+    const torch::stable::Tensor& sf_offsets, int M, int N, int K) {
+  int32_t version_num = get_sm_version_num();
+#if defined ENABLE_NVFP4_SM100 && ENABLE_NVFP4_SM100
+  if (version_num >= 100 && version_num < 120) {
+    run_mxfp4_blockwise_scaled_group_mm_sm100<OutType>(
+        output, a, b, a_blockscale, b_blockscales, problem_sizes,
+        expert_offsets, sf_offsets, M, N, K);
+    return;
+  }
+#endif
+  STD_TORCH_CHECK_NOT_IMPLEMENTED(
+      false,
+      "No compiled cutlass_mxfp4_group_mm kernel for CUDA device capability: ",
+      version_num, ". Required capability: 100");
+}
+
+#if defined ENABLE_NVFP4_SM100 && ENABLE_NVFP4_SM100
+constexpr auto MXFP4_FLOAT4_E2M1X2 = torch::headeronly::ScalarType::Byte;
+// E8M0 scale factors stored as uint8
+constexpr auto MXFP4_SF_DTYPE = torch::headeronly::ScalarType::Byte;
+#endif
+
+#define CHECK_TYPE(x, st, m)             \
+  STD_TORCH_CHECK(x.scalar_type() == st, \
+                  ": Inconsistency of torch::stable::Tensor type:", m)
+#define CHECK_TH_CUDA(x, m) \
+  STD_TORCH_CHECK(x.is_cuda(), m, ": must be a CUDA tensor.")
+#define CHECK_CONTIGUOUS(x, m) \
+  STD_TORCH_CHECK(x.is_contiguous(), m, ": must be contiguous.")
+#define CHECK_INPUT(x, st, m) \
+  CHECK_TH_CUDA(x, m);        \
+  CHECK_CONTIGUOUS(x, m);     \
+  CHECK_TYPE(x, st, m)
+
+void cutlass_mxfp4_group_mm(torch::stable::Tensor& output,
+                            const torch::stable::Tensor& a,
+                            const torch::stable::Tensor& b,
+                            const torch::stable::Tensor& a_blockscale,
+                            const torch::stable::Tensor& b_blockscales,
+                            const torch::stable::Tensor& problem_sizes,
+                            const torch::stable::Tensor& expert_offsets,
+                            const torch::stable::Tensor& sf_offsets) {
+#if defined ENABLE_NVFP4_SM100 && ENABLE_NVFP4_SM100
+  // Input validation
+  CHECK_INPUT(a, MXFP4_FLOAT4_E2M1X2, "a");
+  CHECK_INPUT(b, MXFP4_FLOAT4_E2M1X2, "b");
+  // MXFP4 uses E8M0 scale factors (stored as uint8)
+  CHECK_INPUT(a_blockscale, MXFP4_SF_DTYPE, "a_blockscale");
+  CHECK_INPUT(b_blockscales, MXFP4_SF_DTYPE, "b_blockscales");
+
+  STD_TORCH_CHECK(
+      a_blockscale.dim() == 2,
+      "expected a_blockscale to be of shape [num_experts, rounded_m,"
+      " k // group_size], observed rank: ",
+      a_blockscale.dim())
+  STD_TORCH_CHECK(b_blockscales.dim() == 3,
+                  "expected b_blockscale to be of shape: "
+                  " [num_experts, n, k // group_size], observed rank: ",
+                  b_blockscales.dim())
+  STD_TORCH_CHECK(problem_sizes.dim() == 2,
+                  "problem_sizes must be a 2D tensor");
+  STD_TORCH_CHECK(problem_sizes.size(1) == 3,
+                  "problem_sizes must have the shape (num_experts, 3)");
+  STD_TORCH_CHECK(
+      problem_sizes.size(0) == expert_offsets.size(0),
+      "Number of experts in problem_sizes must match expert_offsets");
+  STD_TORCH_CHECK(
+      problem_sizes.scalar_type() == torch::headeronly::ScalarType::Int,
+      "problem_sizes must be int32.");
+
+  int M = static_cast<int>(a.size(0));
+  int N = static_cast<int>(b.size(1));
+  int E = static_cast<int>(b.size(0));
+  int K = static_cast<int>(2 * b.size(2));
+
+  if (output.scalar_type() == torch::headeronly::ScalarType::BFloat16) {
+    run_mxfp4_blockwise_scaled_group_mm<cutlass::bfloat16_t>(
+        output, a, b, a_blockscale, b_blockscales, problem_sizes,
+        expert_offsets, sf_offsets, M, N, K);
+  } else {
+    run_mxfp4_blockwise_scaled_group_mm<cutlass::half_t>(
+        output, a, b, a_blockscale, b_blockscales, problem_sizes,
+        expert_offsets, sf_offsets, M, N, K);
+  }
+#else
+  STD_TORCH_CHECK_NOT_IMPLEMENTED(
+      false,
+      "No compiled cutlass_mxfp4_group_mm kernel; build vLLM with "
+      "SM100 block-scaled FP4 MoE (ENABLE_NVFP4_SM100) and CUDA 12.8+.");
+#endif
+}
+
+STABLE_TORCH_LIBRARY_IMPL(_C, CUDA, m) {
+  m.impl("cutlass_mxfp4_group_mm", TORCH_BOX(&cutlass_mxfp4_group_mm));
+}
diff --git a/csrc/libtorch_stable/quantization/fp4/mxfp4_experts_quant.cu b/csrc/libtorch_stable/quantization/fp4/mxfp4_experts_quant.cu
new file mode 100644
index 000000000000..78e4eda0c012
--- /dev/null
+++ b/csrc/libtorch_stable/quantization/fp4/mxfp4_experts_quant.cu
@@ -0,0 +1,432 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ * SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+ *
+ * MXFP4 activation quantization kernel for MoE experts.
+ * Quantizes BF16/FP16 activations to MXFP4: E2M1 values with E8M0 block scales
+ * over 32-element groups.
+ *
+ * Uses PACK16 E2M1 conversion helpers (nvfp4_utils.cuh) configured for:
+ *   - Block size 32 (2 threads per SF in PACK16 mode)
+ *   - E8M0 (power-of-two) scale factors
+ *   - SF layout: [numMTiles, numKTiles, 32, 4, 4] where numKTiles=ceil(K/128)
+ */
+
+// MXFP4 requires PACK16 mode (16 elements per thread) so that
+// 2 threads cover 32-element blocks. This requires CUDA >= 12.9.
+// Must be defined before any header that (transitively) includes
+// nvfp4_utils.cuh.
+#define NVFP4_ENABLE_ELTS16 1
+
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include <cuda_runtime.h>
+#include <cuda_fp8.h>
+
+#include <torch/csrc/stable/library.h>
+#include <torch/csrc/stable/tensor.h>
+#include "libtorch_stable/torch_utils.h"
+#include "libtorch_stable/dispatch_utils.h"
+#include "cuda_vec_utils.cuh"
+#include "cuda_utils.h"
+
+#include "nvfp4_utils.cuh"
+static_assert(CVT_FP4_ELTS_PER_THREAD == 16,
+              "MXFP4 experts quant requires PACK16 mode (CUDA >= 12.9)");
+
+#include "launch_bounds_utils.h"
+
+namespace vllm {
+
+// MXFP4 block size constants
+static constexpr int MXFP4_SF_VEC_SIZE = 32;
+
+// For PACK16 mode (CVT_FP4_ELTS_PER_THREAD=16): 2 threads per SF
+// For PACK8 mode (CVT_FP4_ELTS_PER_THREAD=8): 4 threads per SF
+static constexpr int MXFP4_NUM_THREADS_PER_SF =
+    MXFP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD;
+
+// MXFP4 quantization kernel for experts.
+// Uses 32-element blocks with E8M0 (UE8M0) scale factors.
+// When FUSE_SILU_MUL=true, expects input with gate||up layout and fuses
+// SiLU(gate)*up before quantization.
+template <class Type, bool FUSE_SILU_MUL = false,
+          bool SMALL_NUM_EXPERTS = false>
+__global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
+    mxfp4_cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
+                          fp4_packed_t* out, uint32_t* SFout,
+                          uint32_t* input_offset_by_experts,
+                          uint32_t* output_scale_offset_by_experts,
+                          int n_experts, bool low_latency) {
+  using PackedVec = PackedVec<Type, CVT_FP4_PACK16>;
+  static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
+                "Vec size is not matched.");
+
+  // MXFP4: numKTiles = ceil(numCols / 128) since block_size=32, 4 SFs/tile
+  int32_t const numKTiles = (numCols + 127) / 128;
+
+  int tid = blockIdx.x * blockDim.x + threadIdx.x;
+  int colsPerRow = numCols / CVT_FP4_ELTS_PER_THREAD;
+  int inColsPerRow = FUSE_SILU_MUL ? colsPerRow * 2 : colsPerRow;
+
+  for (int globalIdx = tid; globalIdx < numRows * colsPerRow;
+       globalIdx += gridDim.x * blockDim.x) {
+    int rowIdx = globalIdx / colsPerRow;
+    int colIdx = globalIdx % colsPerRow;
+
+    int rowIdx_in_expert = 0;
+    int expert_idx = 0;
+
+    if constexpr (SMALL_NUM_EXPERTS) {
+      for (int i = 0; i < n_experts; i++) {
+        uint32_t current_offset = __ldca(&input_offset_by_experts[i]);
+        uint32_t next_offset = __ldca(&input_offset_by_experts[i + 1]);
+        if (rowIdx >= current_offset && rowIdx < next_offset) {
+          rowIdx_in_expert = rowIdx - current_offset;
+          expert_idx = i;
+          break;
+        }
+      }
+    } else {
+      uint32_t local_offsets[17];
+      for (int chunk_start = 0; chunk_start < n_experts; chunk_start += 16) {
+        *reinterpret_cast<int4*>(local_offsets) =
+            __ldca(reinterpret_cast<const int4*>(
+                &input_offset_by_experts[chunk_start]));
+        *reinterpret_cast<int4*>(local_offsets + 4) =
+            __ldca(reinterpret_cast<const int4*>(
+                &input_offset_by_experts[chunk_start + 4]));
+        *reinterpret_cast<int4*>(local_offsets + 8) =
+            __ldca(reinterpret_cast<const int4*>(
+                &input_offset_by_experts[chunk_start + 8]));
+        *reinterpret_cast<int4*>(local_offsets + 12) =
+            __ldca(reinterpret_cast<const int4*>(
+                &input_offset_by_experts[chunk_start + 12]));
+        local_offsets[16] = __ldca(&input_offset_by_experts[chunk_start + 16]);
+
+#pragma unroll
+        for (int i = 0; i < 16; i++) {
+          if (rowIdx >= local_offsets[i] && rowIdx < local_offsets[i + 1]) {
+            rowIdx_in_expert = rowIdx - local_offsets[i];
+            expert_idx = chunk_start + i;
+            break;
+          }
+        }
+      }
+    }
+
+    // Load input and optionally apply fused SiLU+Mul
+    int64_t inOffset = rowIdx * inColsPerRow + colIdx;
+    PackedVec in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
+    PackedVec quant_input;
+    if constexpr (FUSE_SILU_MUL) {
+      PackedVec in_vec_up =
+          reinterpret_cast<PackedVec const*>(in)[inOffset + colsPerRow];
+      quant_input = compute_silu_mul(in_vec, in_vec_up);
+    } else {
+      quant_input = in_vec;
+    }
+
+    // In PACK16 mode, each thread outputs 16 E2M1 values = u32x2
+    int64_t outOffset = rowIdx * colsPerRow + colIdx;
+    auto& out_pos = out[outOffset];
+
+    uint32_t* SFout_in_expert =
+        SFout + output_scale_offset_by_experts[expert_idx] * numKTiles;
+
+    // Use MXFP4_NUM_THREADS_PER_SF (2 for PACK16) for 32-element blocks
+    auto sf_out =
+        cvt_quant_to_fp4_get_sf_out_offset<uint32_t, MXFP4_NUM_THREADS_PER_SF>(
+            rowIdx_in_expert, colIdx, numKTiles, SFout_in_expert);
+
+    // Block E8M0 scales only; no extra tensor-level scale in this path
+    constexpr float SFScaleVal = 1.0f;
+    // UE8M0_SF=true for MXFP4 E8M0 scale factors
+    out_pos =
+        cvt_warp_fp16_to_fp4<Type, MXFP4_NUM_THREADS_PER_SF, /*UE8M0_SF=*/true>(
+            quant_input, SFScaleVal, sf_out);
+  }
+}
+
+// Large M_topk variant using shared memory for expert offsets
+template <class Type, bool FUSE_SILU_MUL = false,
+          bool SMALL_NUM_EXPERTS = false>
+__global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
+    mxfp4_cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
+                          fp4_packed_t* out, uint32_t* SFout,
+                          uint32_t* input_offset_by_experts,
+                          uint32_t* output_scale_offset_by_experts,
+                          int n_experts) {
+  using PackedVec = PackedVec<Type, CVT_FP4_PACK16>;
+  static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
+                "Vec size is not matched.");
+
+  // MXFP4: numKTiles = ceil(numCols / 128)
+  int32_t const numKTiles = (numCols + 127) / 128;
+
+  extern __shared__ uint32_t shared_input_offsets[];
+
+  if constexpr (SMALL_NUM_EXPERTS) {
+    for (int i = threadIdx.x; i < n_experts + 1; i += blockDim.x) {
+      shared_input_offsets[i] = input_offset_by_experts[i];
+    }
+  } else {
+    for (int i = threadIdx.x * 4; i < n_experts; i += blockDim.x * 4) {
+      *reinterpret_cast<int4*>(&shared_input_offsets[i]) =
+          *reinterpret_cast<const int4*>(&input_offset_by_experts[i]);
+    }
+    if (threadIdx.x == 0) {
+      shared_input_offsets[n_experts] = input_offset_by_experts[n_experts];
+    }
+  }
+
+  __syncthreads();
+
+  int tid = blockIdx.x * blockDim.x + threadIdx.x;
+  int colsPerRow = numCols / CVT_FP4_ELTS_PER_THREAD;
+  int inColsPerRow = FUSE_SILU_MUL ? colsPerRow * 2 : colsPerRow;
+
+  for (int globalIdx = tid; globalIdx < numRows * colsPerRow;
+       globalIdx += gridDim.x * blockDim.x) {
+    int rowIdx = globalIdx / colsPerRow;
+    int colIdx = globalIdx % colsPerRow;
+
+    int rowIdx_in_expert = 0;
+    int expert_idx = 0;
+
+    // Binary search through experts using shared memory
+    int left = 0, right = n_experts - 1;
+    while (left <= right) {
+      int mid = (left + right) / 2;
+      uint32_t mid_offset = shared_input_offsets[mid];
+      uint32_t next_offset = shared_input_offsets[mid + 1];
+
+      if (rowIdx >= mid_offset && rowIdx < next_offset) {
+        rowIdx_in_expert = rowIdx - mid_offset;
+        expert_idx = mid;
+        break;
+      } else if (rowIdx < mid_offset) {
+        right = mid - 1;
+      } else {
+        left = mid + 1;
+      }
+    }
+
+    int64_t inOffset = rowIdx * inColsPerRow + colIdx;
+    PackedVec in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
+    PackedVec quant_input;
+    if constexpr (FUSE_SILU_MUL) {
+      PackedVec in_vec_up =
+          reinterpret_cast<PackedVec const*>(in)[inOffset + colsPerRow];
+      quant_input = compute_silu_mul(in_vec, in_vec_up);
+    } else {
+      quant_input = in_vec;
+    }
+
+    int64_t outOffset = rowIdx * colsPerRow + colIdx;
+    auto& out_pos = out[outOffset];
+
+    // MXFP4 has no global scale - only block-level E8M0 scale factors
+    constexpr float SFScaleVal = 1.0f;
+
+    uint32_t* SFout_in_expert =
+        SFout + output_scale_offset_by_experts[expert_idx] * numKTiles;
+
+    auto sf_out =
+        cvt_quant_to_fp4_get_sf_out_offset<uint32_t, MXFP4_NUM_THREADS_PER_SF>(
+            rowIdx_in_expert, colIdx, numKTiles, SFout_in_expert);
+
+    out_pos =
+        cvt_warp_fp16_to_fp4<Type, MXFP4_NUM_THREADS_PER_SF, /*UE8M0_SF=*/true>(
+            quant_input, SFScaleVal, sf_out);
+  }
+}
+
+template <typename T, bool FUSE_SILU_MUL = false>
+void mxfp4_quant_impl(void* output, void* output_scale, void* input,
+                      void* input_offset_by_experts,
+                      void* output_scale_offset_by_experts, int m_topk, int k,
+                      int n_experts, cudaStream_t stream) {
+  int multiProcessorCount =
+      get_device_attribute(cudaDevAttrMultiProcessorCount, -1);
+
+  int const workSizePerRow = k / ELTS_PER_THREAD;
+  int const totalWorkSize = m_topk * workSizePerRow;
+  dim3 block(std::min(workSizePerRow, 512));
+  int const numBlocksPerSM =
+      vllm_runtime_blocks_per_sm(static_cast<int>(block.x));
+  dim3 grid(std::min(static_cast<int>((totalWorkSize + block.x - 1) / block.x),
+                     multiProcessorCount * numBlocksPerSM));
+  while (grid.x <= multiProcessorCount && block.x > 64) {
+    grid.x *= 2;
+    block.x = (block.x + 1) / 2;
+  }
+
+  int const blockRepeat =
+      (totalWorkSize + block.x * grid.x - 1) / (block.x * grid.x);
+  if (blockRepeat > 1) {
+    size_t shared_mem_size = (n_experts + 1) * sizeof(uint32_t);
+    if (n_experts >= 4) {
+      mxfp4_cvt_fp16_to_fp4<T, FUSE_SILU_MUL, false>
+          <<<grid, block, shared_mem_size, stream>>>(
+              m_topk, k, reinterpret_cast<T*>(input),
+              reinterpret_cast<fp4_packed_t*>(output),
+              reinterpret_cast<uint32_t*>(output_scale),
+              reinterpret_cast<uint32_t*>(input_offset_by_experts),
+              reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
+              n_experts);
+    } else {
+      mxfp4_cvt_fp16_to_fp4<T, FUSE_SILU_MUL, true>
+          <<<grid, block, shared_mem_size, stream>>>(
+              m_topk, k, reinterpret_cast<T*>(input),
+              reinterpret_cast<fp4_packed_t*>(output),
+              reinterpret_cast<uint32_t*>(output_scale),
+              reinterpret_cast<uint32_t*>(input_offset_by_experts),
+              reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
+              n_experts);
+    }
+  } else {
+    if (n_experts >= 16) {
+      mxfp4_cvt_fp16_to_fp4<T, FUSE_SILU_MUL, false>
+          <<<grid, block, 0, stream>>>(
+              m_topk, k, reinterpret_cast<T*>(input),
+              reinterpret_cast<fp4_packed_t*>(output),
+              reinterpret_cast<uint32_t*>(output_scale),
+              reinterpret_cast<uint32_t*>(input_offset_by_experts),
+              reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
+              n_experts, /* bool low_latency */ true);
+    } else {
+      mxfp4_cvt_fp16_to_fp4<T, FUSE_SILU_MUL, true><<<grid, block, 0, stream>>>(
+          m_topk, k, reinterpret_cast<T*>(input),
+          reinterpret_cast<fp4_packed_t*>(output),
+          reinterpret_cast<uint32_t*>(output_scale),
+          reinterpret_cast<uint32_t*>(input_offset_by_experts),
+          reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
+          n_experts, /* bool low_latency */ true);
+    }
+  }
+}
+
+}  // namespace vllm
+
+/*Quantization entry for mxfp4 experts quantization*/
+#define CHECK_TH_CUDA(x, m) \
+  STD_TORCH_CHECK(x.is_cuda(), m, "must be a CUDA tensor")
+#define CHECK_CONTIGUOUS(x, m) \
+  STD_TORCH_CHECK(x.is_contiguous(), m, "must be contiguous")
+#define CHECK_INPUT(x, m) \
+  CHECK_TH_CUDA(x, m);    \
+  CHECK_CONTIGUOUS(x, m);
+
+constexpr auto HALF = torch::headeronly::ScalarType::Half;
+constexpr auto BF16 = torch::headeronly::ScalarType::BFloat16;
+constexpr auto INT = torch::headeronly::ScalarType::Int;
+constexpr auto UINT8 = torch::headeronly::ScalarType::Byte;
+
+static constexpr int MXFP4_BLOCK_SIZE = 32;
+
+static void validate_mxfp4_experts_quant_inputs(
+    torch::stable::Tensor const& output,
+    torch::stable::Tensor const& output_scale,
+    torch::stable::Tensor const& input,
+    torch::stable::Tensor const& input_offset_by_experts,
+    torch::stable::Tensor const& output_scale_offset_by_experts,
+    int64_t n_experts, int64_t m_topk, int64_t k) {
+  CHECK_INPUT(output, "output");
+  CHECK_INPUT(output_scale, "output_scale");
+  CHECK_INPUT(input, "input");
+  CHECK_INPUT(input_offset_by_experts, "input_offset_by_experts");
+  CHECK_INPUT(output_scale_offset_by_experts, "output_scale_offset_by_experts");
+
+  STD_TORCH_CHECK(output.dim() == 2);
+  STD_TORCH_CHECK(output_scale.dim() == 2);
+  STD_TORCH_CHECK(input.dim() == 2);
+  STD_TORCH_CHECK(input_offset_by_experts.dim() == 1);
+  STD_TORCH_CHECK(output_scale_offset_by_experts.dim() == 1);
+
+  STD_TORCH_CHECK(input.scalar_type() == HALF || input.scalar_type() == BF16);
+  STD_TORCH_CHECK(input_offset_by_experts.scalar_type() == INT);
+  STD_TORCH_CHECK(output_scale_offset_by_experts.scalar_type() == INT);
+  // output is uint8 (two mxfp4 values packed into one uint8)
+  // output_scale is int32 (four E8M0 values packed into one int32)
+  STD_TORCH_CHECK(output.scalar_type() == UINT8);
+  STD_TORCH_CHECK(output_scale.scalar_type() == INT);
+
+  STD_TORCH_CHECK(k % MXFP4_BLOCK_SIZE == 0, "k must be a multiple of 32");
+  STD_TORCH_CHECK(input_offset_by_experts.size(0) == n_experts + 1);
+  STD_TORCH_CHECK(output_scale_offset_by_experts.size(0) == n_experts + 1);
+  STD_TORCH_CHECK(output.size(0) == m_topk);
+  STD_TORCH_CHECK(output.size(1) == k / 2);
+  int scales_k = k / MXFP4_BLOCK_SIZE;
+  // K-dimension scale columns padded to a multiple of 4 for swizzle layout
+  int padded_k = (scales_k + (4 - 1)) / 4 * 4;
+  // 4 = 4 E8M0 values packed into one int32
+  STD_TORCH_CHECK(output_scale.size(1) * 4 == padded_k);
+}
+
+void mxfp4_experts_quant(
+    torch::stable::Tensor& output, torch::stable::Tensor& output_scale,
+    torch::stable::Tensor const& input,
+    torch::stable::Tensor const& input_offset_by_experts,
+    torch::stable::Tensor const& output_scale_offset_by_experts,
+    int64_t n_experts) {
+  auto m_topk = input.size(0);
+  auto k = input.size(1);
+
+  validate_mxfp4_experts_quant_inputs(
+      output, output_scale, input, input_offset_by_experts,
+      output_scale_offset_by_experts, n_experts, m_topk, k);
+
+  const torch::stable::accelerator::DeviceGuard device_guard(
+      input.get_device_index());
+  const cudaStream_t stream = get_current_cuda_stream(input.get_device_index());
+
+  VLLM_STABLE_DISPATCH_HALF_TYPES(
+      input.scalar_type(), "mxfp4_experts_quant_kernel", [&] {
+        using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
+        vllm::mxfp4_quant_impl<cuda_type, /*FUSE_SILU_MUL=*/false>(
+            output.data_ptr(), output_scale.data_ptr(), input.data_ptr(),
+            input_offset_by_experts.data_ptr(),
+            output_scale_offset_by_experts.data_ptr(), m_topk, k, n_experts,
+            stream);
+      });
+}
+
+void silu_and_mul_mxfp4_experts_quant(
+    torch::stable::Tensor& output, torch::stable::Tensor& output_scale,
+    torch::stable::Tensor const& input,
+    torch::stable::Tensor const& input_offset_by_experts,
+    torch::stable::Tensor const& output_scale_offset_by_experts,
+    int64_t n_experts) {
+  auto m_topk = input.size(0);
+  auto k_times_2 = input.size(1);
+  STD_TORCH_CHECK(k_times_2 % 2 == 0, "input width must be even (gate || up)");
+  auto k = k_times_2 / 2;
+
+  validate_mxfp4_experts_quant_inputs(
+      output, output_scale, input, input_offset_by_experts,
+      output_scale_offset_by_experts, n_experts, m_topk, k);
+
+  const torch::stable::accelerator::DeviceGuard device_guard(
+      input.get_device_index());
+  const cudaStream_t stream = get_current_cuda_stream(input.get_device_index());
+
+  VLLM_STABLE_DISPATCH_HALF_TYPES(
+      input.scalar_type(), "silu_mul_mxfp4_experts_quant_kernel", [&] {
+        using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
+        vllm::mxfp4_quant_impl<cuda_type, /*FUSE_SILU_MUL=*/true>(
+            output.data_ptr(), output_scale.data_ptr(), input.data_ptr(),
+            input_offset_by_experts.data_ptr(),
+            output_scale_offset_by_experts.data_ptr(), m_topk, k, n_experts,
+            stream);
+      });
+}
+
+// Registered here (not torch_bindings.cpp) because VLLM_GPU_FLAGS is applied
+// only under COMPILE_LANGUAGE:CUDA, so ENABLE_NVFP4_SM100 is invisible to
+// .cpp files and cannot gate the registration from there.
+STABLE_TORCH_LIBRARY_IMPL(_C, CUDA, m) {
+  m.impl("mxfp4_experts_quant", TORCH_BOX(&mxfp4_experts_quant));
+  m.impl("silu_and_mul_mxfp4_experts_quant",
+         TORCH_BOX(&silu_and_mul_mxfp4_experts_quant));
+}
diff --git a/csrc/libtorch_stable/quantization/fp4/nvfp4_experts_quant.cu b/csrc/libtorch_stable/quantization/fp4/nvfp4_experts_quant.cu
index f90bd543ab9f..744ae4f73112 100644
--- a/csrc/libtorch_stable/quantization/fp4/nvfp4_experts_quant.cu
+++ b/csrc/libtorch_stable/quantization/fp4/nvfp4_experts_quant.cu
@@ -277,7 +277,9 @@ void quant_impl(void* output, void* output_scale, void* input,
       (totalWorkSize + block.x * grid.x - 1) / (block.x * grid.x);
   if (blockRepeat > 1) {
     size_t shared_mem_size = (n_experts + 1) * sizeof(uint32_t);
-    if (n_experts >= 4) {
+    // The shared-memory vectorized offset load only handles full 4-expert
+    // chunks. Use the scalar specialization for the remainder cases.
+    if (n_experts >= 4 && n_experts % 4 == 0) {
       cvt_fp16_to_fp4<T, FUSE_SILU_MUL, false, false>
           <<<grid, block, shared_mem_size, stream>>>(
               m_topk, k, reinterpret_cast<T*>(input),
@@ -299,7 +301,9 @@ void quant_impl(void* output, void* output_scale, void* input,
               n_experts);
     }
   } else {
-    if (n_experts >= 16) {
+    // The low-latency vectorized expert lookup only handles full 16-expert
+    // chunks. Fall back to the scalar lookup path for the remainder cases.
+    if (n_experts >= 16 && n_experts % 16 == 0) {
       cvt_fp16_to_fp4<T, FUSE_SILU_MUL, false, false>
           <<<grid, block, 0, stream>>>(
               m_topk, k, reinterpret_cast<T*>(input),
diff --git a/csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm.cuh b/csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm.cuh
index 546e1eec64bb..e98433bed25e 100644
--- a/csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm.cuh
+++ b/csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm.cuh
@@ -141,7 +141,7 @@ struct cutlass_3x_gemm_sm100 {
               sizeof(typename CollectiveEpilogue::SharedStorage))>,
           KernelSchedule>::CollectiveOp;
 
-  using GemmKernel = enable_sm100f_only<cutlass::gemm::kernel::GemmUniversal<
+  using GemmKernel = enable_sm100_to_sm120<cutlass::gemm::kernel::GemmUniversal<
       Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue, void>>;
 };
 
diff --git a/csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm_blockwise_sm100_fp8_dispatch.cuh b/csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm_blockwise_sm100_fp8_dispatch.cuh
index cf84fc3a6de1..8f74f2991469 100644
--- a/csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm_blockwise_sm100_fp8_dispatch.cuh
+++ b/csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm_blockwise_sm100_fp8_dispatch.cuh
@@ -125,7 +125,7 @@ struct cutlass_3x_gemm_fp8_blockwise {
           MainloopScheduler
       >::CollectiveOp>;
 
-  using KernelType = enable_sm100f_only<cutlass::gemm::kernel::GemmUniversal<
+  using KernelType = enable_sm100_to_sm120<cutlass::gemm::kernel::GemmUniversal<
       Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue>>;
 
   struct GemmKernel : public KernelType {};
diff --git a/csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm_sm100_fp8_dispatch.cuh b/csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm_sm100_fp8_dispatch.cuh
index 46c36d13ece4..5cd55f0198c2 100644
--- a/csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm_sm100_fp8_dispatch.cuh
+++ b/csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm_sm100_fp8_dispatch.cuh
@@ -92,7 +92,7 @@ struct cutlass_3x_gemm_sm100_fp8 {
   // -----------------------------------------------------------
   // Kernel definition
   // -----------------------------------------------------------
-  using GemmKernel = enable_sm100f_only<cutlass::gemm::kernel::GemmUniversal<
+  using GemmKernel = enable_sm100_to_sm120<cutlass::gemm::kernel::GemmUniversal<
       Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue, void>>;
 };
 
diff --git a/csrc/libtorch_stable/quantization/w8a8/fp8/per_token_group_quant.cu b/csrc/libtorch_stable/quantization/w8a8/fp8/per_token_group_quant.cu
index 8b0356815bb7..5029e3903752 100644
--- a/csrc/libtorch_stable/quantization/w8a8/fp8/per_token_group_quant.cu
+++ b/csrc/libtorch_stable/quantization/w8a8/fp8/per_token_group_quant.cu
@@ -236,17 +236,41 @@ void per_token_group_quant_8bit(const torch::stable::Tensor& input,
 #undef LAUNCH_KERNEL
 }
 
-template <typename T, typename DST_DTYPE>
-__global__ void per_token_group_quant_8bit_packed_kernel(
+// Register-resident fast path for group_size==128.
+//
+// Each thread holds 16 source elements (32 B = uint4 x 2) in registers across
+// the absmax reduce -> scale compute -> quantize pipeline. No shared memory.
+// UE8M0 scale extracted via bit math (bit-exact with exp2f(ceilf(log2f))).
+//
+// Loads two contiguous uint4s (16 B + 16 B = 32 B) per thread; on Blackwell
+// nvcc fuses these into a single 256-bit LDG.E.256.
+//
+// Constraints: GROUP_SIZE % (THREADS_PER_GROUP * VEC_SIZE) == 0; for
+// THREADS_PER_GROUP=8 and bf16/fp16 (VEC_SIZE=16), this means GROUP_SIZE=128.
+template <typename T, typename DST_DTYPE, int GROUP_SIZE>
+__global__ void per_token_group_quant_8bit_packed_register_kernel(
     const T* __restrict__ input, void* __restrict__ output_q,
-    unsigned int* __restrict__ output_s_packed, const int group_size,
-    const int num_groups_padded, const int groups_per_block,
-    const int padded_groups_per_row, const int groups_per_row, const int mn,
-    const int tma_aligned_mn, const int num_scale_elems, const float eps,
+    unsigned int* __restrict__ output_s_packed, const int64_t num_groups_padded,
+    const int groups_per_block, const int padded_groups_per_row,
+    const int groups_per_row, const int mn, const int output_q_mn_extent,
+    const int tma_aligned_mn, const int64_t num_scale_elems, const float eps,
     const float min_8bit, const float max_8bit) {
-  const int threads_per_group = 16;
-  const int64_t local_group_id = threadIdx.x / threads_per_group;
-  const int lane_id = threadIdx.x % threads_per_group;
+  static_assert(GROUP_SIZE == 128, "fast path supports GROUP_SIZE==128");
+  constexpr int THREADS_PER_GROUP = 8;
+  constexpr int VEC_SIZE = 32 / sizeof(T);  // 16 for bf16/fp16
+  static_assert(GROUP_SIZE == THREADS_PER_GROUP * VEC_SIZE,
+                "GROUP_SIZE must equal THREADS_PER_GROUP * VEC_SIZE");
+  // Each group's 8 threads must live in a single warp octet so the
+  // 0xffu << (threadIdx.x & 24u) shuffle mask selects exactly the lanes
+  // that share a group. Requires 32 % THREADS_PER_GROUP == 0 and the host
+  // to launch num_threads as a multiple of THREADS_PER_GROUP (which it does
+  // via num_threads = groups_per_block * THREADS_PER_GROUP).
+  static_assert(32 % THREADS_PER_GROUP == 0,
+                "THREADS_PER_GROUP must divide warp size for the shuffle "
+                "mask to be valid");
+
+  const int local_group_id = threadIdx.x / THREADS_PER_GROUP;
+  const int lane_id = threadIdx.x % THREADS_PER_GROUP;
 
   const int64_t block_group_id = blockIdx.x * groups_per_block;
   const int64_t global_group_id = block_group_id + local_group_id;
@@ -254,141 +278,207 @@ __global__ void per_token_group_quant_8bit_packed_kernel(
     return;
   }
 
-  // map flat group id to 2D indices (mn_idx, sf_k_idx)
   const int sf_k_idx =
       static_cast<int>(global_group_id % padded_groups_per_row);
   const int mn_idx = static_cast<int>(global_group_id / padded_groups_per_row);
-
-  // whether it is a valid group (not padding)
   const bool is_valid_group = (mn_idx < mn) && (sf_k_idx < groups_per_row);
 
-  // shared memory to cache each group's data to avoid double DRAM reads.
-  extern __shared__ __align__(16) char smem_raw[];
-  T* smem = reinterpret_cast<T*>(smem_raw);
-  T* smem_group = smem + local_group_id * group_size;
-
-  // compute scale for valid groups
-  float y_s = 0.f;
+  // Load 16 input elements (32 B) into registers as two adjacent uint4
+  // loads. nvcc keeps these as 2x LDG.E.128 on sm_100; the per-thread cost
+  // is dominated by HBM bandwidth at large MN, so a fused 256-bit load via
+  // inline PTX gave no measurable speedup.
+  // alignas(16) is required so the uint4* reinterpret_cast below is
+  // well-defined for T == bf16/fp16 (default alignof is 2).
+  alignas(16) T regs[VEC_SIZE];
+  float local_absmax = eps;
   if (is_valid_group) {
     const T* group_input =
-        input + static_cast<int64_t>(mn_idx) * groups_per_row * group_size +
-        sf_k_idx * group_size;
-    y_s = ComputeGroupScale<T, true>(group_input, smem_group, group_size,
-                                     lane_id, threads_per_group, eps, max_8bit);
+        input + static_cast<int64_t>(mn_idx) * groups_per_row * GROUP_SIZE +
+        sf_k_idx * GROUP_SIZE + lane_id * VEC_SIZE;
+    uint4* dst = reinterpret_cast<uint4*>(&regs[0]);
+    const uint4* src = reinterpret_cast<const uint4*>(group_input);
+    dst[0] = src[0];
+    dst[1] = src[1];
+#pragma unroll
+    for (int i = 0; i < VEC_SIZE; ++i) {
+      float v = fabsf(static_cast<float>(regs[i]));
+      local_absmax = fmaxf(local_absmax, v);
+    }
   }
 
-  // pack 4 scales into a uint32 exponent
+  // 8-lane subgroup shuffle reduce (octet of the warp). The mask selects the
+  // 8 lanes within the warp that share a group.
+  unsigned mask = 0xffu << (threadIdx.x & 24u);
+  local_absmax = fmaxf(local_absmax, __shfl_xor_sync(mask, local_absmax, 4));
+  local_absmax = fmaxf(local_absmax, __shfl_xor_sync(mask, local_absmax, 2));
+  local_absmax = fmaxf(local_absmax, __shfl_xor_sync(mask, local_absmax, 1));
+
+  float y_s = local_absmax / max_8bit;
+  y_s = fmaxf(y_s, 1e-10f);
+  uint32_t bits = __float_as_uint(y_s);
+  uint32_t exp_bits = (bits >> 23) & 0xffu;
+  uint32_t mant_bits = bits & 0x7fffffu;
+  uint8_t exp_byte =
+      static_cast<uint8_t>(exp_bits + (mant_bits != 0u ? 1u : 0u));
+
+  // Lane 0 writes the packed scale byte.
   if (lane_id == 0) {
-    // each uint32 in output_s_packed stores 4 packed scales
     const int sf_k_pack_idx = sf_k_idx / 4;
     const int pos = sf_k_idx % 4;
     const int out_idx = sf_k_pack_idx * tma_aligned_mn + mn_idx;
-
     if (is_valid_group) {
-      // reinterpret the UE8M0 scale y_s as IEEE bits, extract the 8-bit
-      // exponent, and place it into the correct byte of the 32-bit word.
-      const unsigned int bits = __float_as_uint(y_s);
-      const uint8_t exponent = static_cast<uint8_t>((bits >> 23u) & 0xffu);
-      reinterpret_cast<uint8_t*>(output_s_packed)[out_idx * 4 + pos] = exponent;
+      reinterpret_cast<uint8_t*>(output_s_packed)[out_idx * 4 + pos] = exp_byte;
     } else if (out_idx < num_scale_elems) {
-      // write zero for padding groups if within bounds of output_s_packed
       reinterpret_cast<uint8_t*>(output_s_packed)[out_idx * 4 + pos] = 0;
     }
   }
 
-  __syncthreads();
+  // For padded mn rows that fall within output_q's allocated extent, write
+  // a uint4 of zeros to keep the buffer clean for downstream TMA loads.
+  // Skip writes for sf_k padding (those positions don't exist in output_q).
+  if (!is_valid_group) {
+    if (sf_k_idx < groups_per_row && mn_idx >= mn &&
+        mn_idx < output_q_mn_extent) {
+      DST_DTYPE* group_output =
+          static_cast<DST_DTYPE*>(output_q) +
+          static_cast<int64_t>(mn_idx) * groups_per_row * GROUP_SIZE +
+          sf_k_idx * GROUP_SIZE + lane_id * VEC_SIZE;
+      *reinterpret_cast<uint4*>(group_output) = make_uint4(0, 0, 0, 0);
+    }
+    return;
+  }
 
-  if (is_valid_group) {
-    DST_DTYPE* group_output =
-        static_cast<DST_DTYPE*>(output_q) +
-        static_cast<int64_t>(mn_idx) * groups_per_row * group_size +
-        sf_k_idx * group_size;
-    QuantizeGroup<T, DST_DTYPE>(smem_group, group_output, group_size, lane_id,
-                                threads_per_group, y_s, min_8bit, max_8bit);
+  // Reconstruct y_s as a power-of-2 float and use its reciprocal.
+  float y_s_q = __uint_as_float(static_cast<uint32_t>(exp_byte) << 23);
+  float inv_y = 1.0f / y_s_q;
+
+  // Quantize and pack into 16 fp8/int8 bytes (= uint4). VEC_SIZE==16 so we
+  // fill four 32-bit words, four bytes each.
+  uint32_t packed_lo = 0;
+  uint32_t packed_lo_hi = 0;
+  uint32_t packed_hi_lo = 0;
+  uint32_t packed_hi = 0;
+#pragma unroll
+  for (int i = 0; i < VEC_SIZE; ++i) {
+    float q =
+        fminf(fmaxf(static_cast<float>(regs[i]) * inv_y, min_8bit), max_8bit);
+    DST_DTYPE qb = DST_DTYPE(q);
+    uint8_t byte = *reinterpret_cast<uint8_t*>(&qb);
+    const int shift = (i & 3) * 8;
+    if (i < 4) {
+      packed_lo |= static_cast<uint32_t>(byte) << shift;
+    } else if (i < 8) {
+      packed_lo_hi |= static_cast<uint32_t>(byte) << shift;
+    } else if (i < 12) {
+      packed_hi_lo |= static_cast<uint32_t>(byte) << shift;
+    } else {
+      packed_hi |= static_cast<uint32_t>(byte) << shift;
+    }
   }
+
+  uint4 packed_out =
+      make_uint4(packed_lo, packed_lo_hi, packed_hi_lo, packed_hi);
+  DST_DTYPE* group_output =
+      static_cast<DST_DTYPE*>(output_q) +
+      static_cast<int64_t>(mn_idx) * groups_per_row * GROUP_SIZE +
+      sf_k_idx * GROUP_SIZE + lane_id * VEC_SIZE;
+  *reinterpret_cast<uint4*>(group_output) = packed_out;
 }
 
+// Public entry point: register-resident packed quant kernel.
+// Constraints: group_size == 128 and bf16/fp16 input.
 void per_token_group_quant_8bit_packed(const torch::stable::Tensor& input,
                                        torch::stable::Tensor& output_q,
                                        torch::stable::Tensor& output_s_packed,
                                        int64_t group_size, double eps,
                                        double min_8bit, double max_8bit) {
+  STD_TORCH_CHECK(group_size == 128,
+                  "per_token_group_quant_8bit_packed only supports "
+                  "group_size==128, got ",
+                  group_size, ".");
+  const auto in_dtype = input.scalar_type();
+  STD_TORCH_CHECK(
+      in_dtype == torch::headeronly::ScalarType::Half ||
+          in_dtype == torch::headeronly::ScalarType::BFloat16,
+      "per_token_group_quant_8bit_packed only supports bf16/fp16 input.");
+
   STD_TORCH_CHECK(input.is_contiguous());
   STD_TORCH_CHECK(output_q.is_contiguous());
 
   const int64_t k = input.size(-1);
-  STD_TORCH_CHECK(k % group_size == 0, "Last dimension (", k,
-                  ") must be divisible by group_size (", group_size, ").");
+  STD_TORCH_CHECK(k % group_size == 0, "input last dim k=", k,
+                  " is not divisible by group_size=", group_size, ".");
 
   const int64_t mn = input.numel() / k;
   const int64_t groups_per_row = k / group_size;
-
-  STD_TORCH_CHECK(output_s_packed.dim() == 2,
-                  "output_s_packed must be 2D, got dim=", output_s_packed.dim(),
-                  ".");
-
   const int64_t k_num_packed_sfk = (groups_per_row + 3) / 4;
   const int64_t tma_aligned_mn = ((mn + 3) / 4) * 4;
 
+  // output_q may be allocated with extra padded mn rows (e.g.,
+  // (tma_aligned_mn, k)) so the kernel can zero-fill them in-line and the
+  // caller can use torch.empty instead of torch.zeros. The grid only covers
+  // up to tma_aligned_mn, so we cap the extent there.
+  const int64_t output_q_mn_actual = output_q.numel() / k;
+  STD_TORCH_CHECK(output_q_mn_actual >= mn,
+                  "output_q must have at least mn rows; got ",
+                  output_q_mn_actual, " rows for mn=", mn, ".");
+  const int64_t output_q_mn_extent =
+      output_q_mn_actual < tma_aligned_mn ? output_q_mn_actual : tma_aligned_mn;
+
   STD_TORCH_CHECK(
       output_s_packed.scalar_type() == torch::headeronly::ScalarType::Int,
-      "output_s_packed must have dtype int32 for UE8M0-packed scales.");
-  // DeepGEMM expects SFA scales in MN-major form with shape
-  // [mn, ceil_div(K, 128 * 4)] and TMA-aligned stride on the last
-  // dimension.
+      "output_s_packed must be int32 for UE8M0-packed scales.");
   STD_TORCH_CHECK(output_s_packed.size(0) == mn &&
                       output_s_packed.size(1) == k_num_packed_sfk,
                   "output_s_packed shape must be [", mn, ", ", k_num_packed_sfk,
-                  "], but got [", output_s_packed.size(0), ", ",
+                  "]; got [", output_s_packed.size(0), ", ",
                   output_s_packed.size(1), "].");
-  // Verify column-major TMA-aligned layout
   STD_TORCH_CHECK(output_s_packed.stride(0) == 1 &&
                       output_s_packed.stride(1) == tma_aligned_mn,
-                  "output_s_packed must have strides [1, ", tma_aligned_mn,
-                  "], but got [", output_s_packed.stride(0), ", ",
+                  "output_s_packed strides must be [1, ", tma_aligned_mn,
+                  "]; got [", output_s_packed.stride(0), ", ",
                   output_s_packed.stride(1), "].");
 
   cudaStream_t stream = get_current_cuda_stream();
 
-  constexpr int THREADS_PER_GROUP = 16;
-
-  // Expand the grid to cover MN and K padding so every byte in
-  // output_s_packed is written (padding bytes get zeroed by the kernel).
+  constexpr int THREADS_PER_GROUP = 8;
   const int64_t padded_groups_per_row = k_num_packed_sfk * 4;
   const int64_t num_groups_padded = tma_aligned_mn * padded_groups_per_row;
-  // Number of elements in output_s_packed.
   const int64_t num_scale_elems = mn + (k_num_packed_sfk - 1) * tma_aligned_mn;
-
   const int groups_per_block = GetGroupsPerBlock(num_groups_padded);
 
   auto dst_type = output_q.scalar_type();
-  const int num_blocks = num_groups_padded / groups_per_block;
+  const int64_t num_blocks = num_groups_padded / groups_per_block;
   const int num_threads = groups_per_block * THREADS_PER_GROUP;
-
-#define LAUNCH_PACKED_KERNEL(T, DST_DTYPE)                                     \
-  do {                                                                         \
-    dim3 grid(num_blocks);                                                     \
-    dim3 block(num_threads);                                                   \
-    size_t smem_bytes =                                                        \
-        static_cast<size_t>(groups_per_block) * group_size * sizeof(T);        \
-    per_token_group_quant_8bit_packed_kernel<T, DST_DTYPE>                     \
-        <<<grid, block, smem_bytes, stream>>>(                                 \
-            static_cast<const T*>(input.data_ptr()), output_q.data_ptr(),      \
-            reinterpret_cast<unsigned int*>(output_s_packed.data_ptr()),       \
-            static_cast<int>(group_size), static_cast<int>(num_groups_padded), \
-            groups_per_block, static_cast<int>(padded_groups_per_row),         \
-            static_cast<int>(groups_per_row), static_cast<int>(mn),            \
-            static_cast<int>(tma_aligned_mn),                                  \
-            static_cast<int>(num_scale_elems), static_cast<float>(eps),        \
-            static_cast<float>(min_8bit), static_cast<float>(max_8bit));       \
+  // CUDA caps grid.x at 2^31 - 1; this fits any realistic shape but guard
+  // against pathological inputs.
+  STD_TORCH_CHECK(num_blocks <= static_cast<int64_t>(INT32_MAX),
+                  "per_token_group_quant_8bit_packed grid too large: ",
+                  num_blocks, " blocks (max ", INT32_MAX, ").");
+
+#define LAUNCH_REG_KERNEL(T, DST_DTYPE)                                   \
+  do {                                                                    \
+    dim3 grid(static_cast<unsigned int>(num_blocks));                     \
+    dim3 block(num_threads);                                              \
+    per_token_group_quant_8bit_packed_register_kernel<T, DST_DTYPE, 128>  \
+        <<<grid, block, 0, stream>>>(                                     \
+            static_cast<const T*>(input.data_ptr()), output_q.data_ptr(), \
+            reinterpret_cast<unsigned int*>(output_s_packed.data_ptr()),  \
+            num_groups_padded, groups_per_block,                          \
+            static_cast<int>(padded_groups_per_row),                      \
+            static_cast<int>(groups_per_row), static_cast<int>(mn),       \
+            static_cast<int>(output_q_mn_extent),                         \
+            static_cast<int>(tma_aligned_mn), num_scale_elems,            \
+            static_cast<float>(eps), static_cast<float>(min_8bit),        \
+            static_cast<float>(max_8bit));                                \
   } while (0)
 
-  VLLM_STABLE_DISPATCH_FLOATING_TYPES(
-      input.scalar_type(), "per_token_group_quant_8bit_packed", ([&] {
+  VLLM_STABLE_DISPATCH_HALF_TYPES(
+      input.scalar_type(), "per_token_group_quant_8bit_packed_register", ([&] {
         if (dst_type == torch::headeronly::ScalarType::Float8_e4m3fn) {
-          LAUNCH_PACKED_KERNEL(scalar_t, __nv_fp8_e4m3);
+          LAUNCH_REG_KERNEL(scalar_t, __nv_fp8_e4m3);
         } else if (dst_type == torch::headeronly::ScalarType::Char) {
-          LAUNCH_PACKED_KERNEL(scalar_t, int8_t);
+          LAUNCH_REG_KERNEL(scalar_t, int8_t);
         } else {
           STD_TORCH_CHECK(
               false,
@@ -397,7 +487,7 @@ void per_token_group_quant_8bit_packed(const torch::stable::Tensor& input,
         }
       }));
 
-#undef LAUNCH_PACKED_KERNEL
+#undef LAUNCH_REG_KERNEL
 }
 
 void per_token_group_quant_fp8(const torch::stable::Tensor& input,
diff --git a/csrc/libtorch_stable/quantization/w8a8/per_token_group_quant_8bit.h b/csrc/libtorch_stable/quantization/w8a8/per_token_group_quant_8bit.h
index d67fd2b336ec..6630c0decee6 100644
--- a/csrc/libtorch_stable/quantization/w8a8/per_token_group_quant_8bit.h
+++ b/csrc/libtorch_stable/quantization/w8a8/per_token_group_quant_8bit.h
@@ -8,3 +8,13 @@ void per_token_group_quant_8bit(const torch::stable::Tensor& input,
                                 torch::stable::Tensor& output_s,
                                 int64_t group_size, double eps, double min_8bit,
                                 double max_8bit, bool scale_ue8m0 = false);
+
+// Public op: register-resident packed quant for the DeepGEMM Blackwell path.
+// Restricted to group_size == 128 and bf16/fp16 input; other configurations
+// raise STD_TORCH_CHECK. The legacy shared-memory fallback was removed because
+// no production caller (deep_gemm_moe / input_quant_fp8) uses other shapes.
+void per_token_group_quant_8bit_packed(const torch::stable::Tensor& input,
+                                       torch::stable::Tensor& output_q,
+                                       torch::stable::Tensor& output_s_packed,
+                                       int64_t group_size, double eps,
+                                       double min_8bit, double max_8bit);
diff --git a/csrc/libtorch_stable/torch_bindings.cpp b/csrc/libtorch_stable/torch_bindings.cpp
index c31844948e5f..124512e81628 100644
--- a/csrc/libtorch_stable/torch_bindings.cpp
+++ b/csrc/libtorch_stable/torch_bindings.cpp
@@ -116,6 +116,12 @@ STABLE_TORCH_LIBRARY_FRAGMENT(_C, ops) {
       " Tensor a_blockscale, Tensor b_blockscales, Tensor alphas,"
       " Tensor problem_sizes, Tensor expert_offsets, Tensor sf_offsets) -> ()");
 
+  // cutlass mxfp4 block scaled group GEMM (MXFP4 x MXFP4 MoE)
+  ops.def(
+      "cutlass_mxfp4_group_mm(Tensor! out, Tensor a, Tensor b,"
+      " Tensor a_blockscale, Tensor b_blockscales,"
+      " Tensor problem_sizes, Tensor expert_offsets, Tensor sf_offsets) -> ()");
+
   // Compute NVFP4 block quantized tensor.
   ops.def(
       "scaled_fp4_quant(Tensor input,"
@@ -149,6 +155,19 @@ STABLE_TORCH_LIBRARY_FRAGMENT(_C, ops) {
       "Tensor input, Tensor input_global_scale, Tensor input_offset_by_experts,"
       "Tensor output_scale_offset_by_experts) -> ()");
 
+  // Compute MXFP4 experts quantization (32-element blocks, E8M0 SFs).
+  ops.def(
+      "mxfp4_experts_quant(Tensor! output, Tensor! output_scale,"
+      "Tensor input, Tensor input_offset_by_experts,"
+      "Tensor output_scale_offset_by_experts, int n_experts) -> ()");
+
+  // Fused SiLU+Mul+MXFP4 experts quantization.
+  ops.def(
+      "silu_and_mul_mxfp4_experts_quant(Tensor! output, Tensor! "
+      "output_scale,"
+      "Tensor input, Tensor input_offset_by_experts,"
+      "Tensor output_scale_offset_by_experts, int n_experts) -> ()");
+
   // Fused SiLU+Mul+NVFP4 quantization.
   ops.def(
       "silu_and_mul_nvfp4_quant(Tensor! result, Tensor! result_block_scale, "
@@ -233,9 +252,8 @@ STABLE_TORCH_LIBRARY_IMPL(_C, CUDA, ops) {
   ops.impl("silu_and_mul_scaled_fp4_experts_quant",
            TORCH_BOX(&silu_and_mul_scaled_fp4_experts_quant));
   ops.impl("silu_and_mul_nvfp4_quant", TORCH_BOX(&silu_and_mul_nvfp4_quant));
-
-  // W4A8 ops: impl registrations are in the source files
-  // (w4a8_mm_entry.cu and w4a8_grouped_mm_entry.cu)
+  // mxfp4_experts_quant: registered in mxfp4_experts_quant.cu (SM100 only).
+  // W4A8 ops: registered in w4a8_mm_entry.cu / w4a8_grouped_mm_entry.cu.
 #endif
 }
 
diff --git a/csrc/moe/moe_ops.h b/csrc/moe/moe_ops.h
index d8d962887dab..ac0e8d59f604 100644
--- a/csrc/moe/moe_ops.h
+++ b/csrc/moe/moe_ops.h
@@ -12,6 +12,15 @@ void topk_sigmoid(torch::Tensor& topk_weights, torch::Tensor& topk_indices,
                   torch::Tensor& gating_output, bool renormalize,
                   std::optional<torch::Tensor> bias);
 
+void topk_softplus_sqrt(torch::Tensor& topk_weights,
+                        torch::Tensor& topk_indices,
+                        torch::Tensor& token_expert_indices,
+                        torch::Tensor& gating_output, bool renormalize,
+                        double routed_scaling_factor,
+                        const c10::optional<torch::Tensor>& correction_bias,
+                        const c10::optional<torch::Tensor>& input_ids,
+                        const c10::optional<torch::Tensor>& tid2eid);
+
 void moe_sum(torch::Tensor& input, torch::Tensor& output);
 
 void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
@@ -58,10 +67,6 @@ void shuffle_rows(const torch::Tensor& input_tensor,
                   torch::Tensor& output_tensor);
 
 #ifndef USE_ROCM
-// cuBLAS bf16 x bf16 -> fp32 router GEMM (fallback for non-SM90 / batch > 16)
-torch::Tensor router_gemm_bf16_fp32(torch::Tensor const& input,
-                                    torch::Tensor const& weight);
-
 // DeepSeek V3 optimized router GEMM kernel for SM90+
 // Computes output = mat_a @ mat_b.T where:
 //   mat_a: [num_tokens, hidden_dim] in bf16
diff --git a/csrc/moe/router_gemm.cu b/csrc/moe/router_gemm.cu
deleted file mode 100644
index a939f8846ff1..000000000000
--- a/csrc/moe/router_gemm.cu
+++ /dev/null
@@ -1,52 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-// bf16 x bf16 -> fp32 router GEMM via cuBLAS.
-// Uses CUBLAS_COMPUTE_32F so bf16 operands accumulate into fp32,
-// matching TRT-LLM's cuBLAS fallback behaviour in dsv3RouterGemmOp.
-
-#include <torch/all.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <cublas_v2.h>
-
-// cuBLAS column-major math for row-major PyTorch tensors:
-//   weight[N,K]_row  lda=K  -> cuBLAS sees (K,N) col-major; CUBLAS_OP_T ->
-//   (N,K) input[M,K]_row   ldb=K  -> cuBLAS sees (K,M) col-major; CUBLAS_OP_N
-//   -> (K,M) out[M,N]_row     ldc=N  -> cuBLAS sees (N,M) col-major (written as
-//   output^T)
-// cuBLAS: C(N,M) = weight(N,K) @ input(K,M)  =>  C^T = output[M,N]
-// params: m=N, n=M, k=K, lda=K (weight), ldb=K (input), ldc=N (output)
-
-torch::Tensor router_gemm_bf16_fp32(torch::Tensor const& input,
-                                    torch::Tensor const& weight) {
-  TORCH_CHECK(input.dtype() == torch::kBFloat16,
-              "router_gemm_bf16_fp32: input must be bfloat16");
-  TORCH_CHECK(weight.dtype() == torch::kBFloat16,
-              "router_gemm_bf16_fp32: weight must be bfloat16");
-  TORCH_CHECK(input.dim() == 2 && weight.dim() == 2,
-              "router_gemm_bf16_fp32: input and weight must be 2-D");
-  TORCH_CHECK(input.size(1) == weight.size(1),
-              "router_gemm_bf16_fp32: inner dimensions must match");
-
-  int64_t const M = input.size(0);
-  int64_t const N = weight.size(0);
-  int64_t const K = input.size(1);
-
-  auto out = torch::empty({M, N}, input.options().dtype(torch::kFloat32));
-
-  cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();
-  TORCH_CUDABLAS_CHECK(
-      cublasSetStream(handle, at::cuda::getCurrentCUDAStream()));
-
-  float const alpha = 1.0f;
-  float const beta = 0.0f;
-
-  TORCH_CUDABLAS_CHECK(cublasGemmEx(
-      handle, CUBLAS_OP_T, CUBLAS_OP_N, static_cast<int>(N),
-      static_cast<int>(M), static_cast<int>(K), &alpha, weight.data_ptr(),
-      CUDA_R_16BF, static_cast<int>(K), input.data_ptr(), CUDA_R_16BF,
-      static_cast<int>(K), &beta, out.data_ptr(), CUDA_R_32F,
-      static_cast<int>(N), CUBLAS_COMPUTE_32F, CUBLAS_GEMM_DEFAULT));
-
-  return out;
-}
diff --git a/csrc/moe/topk_softmax_kernels.cu b/csrc/moe/topk_softmax_kernels.cu
index 833036da528e..57461a044f9d 100644
--- a/csrc/moe/topk_softmax_kernels.cu
+++ b/csrc/moe/topk_softmax_kernels.cu
@@ -126,7 +126,9 @@ __launch_bounds__(TPB) __global__
     {
         const int idx = thread_row_offset + ii;
         const float val = toFloat(input[idx]);
-        const float softmax_val = expf(val - float_max) * normalizing_factor;
+        float softmax_val = expf(val - float_max) * normalizing_factor;
+        // Clamp NaN/Inf to 0 to prevent duplicate expert IDs downstream.
+        if (isnan(softmax_val) || isinf(softmax_val)) softmax_val = 0.f;
         output[idx] = softmax_val;
     }
 }
@@ -147,7 +149,9 @@ __launch_bounds__(TPB) __global__
     {
         const int idx = thread_row_offset + ii;
         const float val = toFloat(input[idx]);
-        const float sigmoid_val = 1.0f / (1.0f + __expf(-val));
+        float sigmoid_val = 1.0f / (1.0f + __expf(-val));
+        // Clamp NaN/Inf to 0 to prevent duplicate expert IDs downstream.
+        if (isnan(sigmoid_val) || isinf(sigmoid_val)) sigmoid_val = 0.f;
         output[idx] = sigmoid_val;
     }
 }
@@ -442,6 +446,19 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
       }
     }
 
+    // Fix: clamp NaN/Inf values to 0 to prevent duplicate expert IDs.
+    // NaN gating (from degenerate hidden states in CUDA graph padding) causes
+    // softmax to produce all-NaN, which makes the argmax loop always pick
+    // expert 0 for every top-k slot, producing duplicate expert IDs that
+    // crash FlashInfer's three-step MoE sort.
+    // With 0s, the argmax uses index tie-breaking to pick [0,1,2,...,k-1].
+#pragma unroll
+    for (int ii = 0; ii < VPT; ++ii) {
+      if (isnan(row_chunk[ii]) || isinf(row_chunk[ii])) {
+        row_chunk[ii] = 0.f;
+      }
+    }
+
     static constexpr int COLS_PER_GROUP_LDG = ELTS_PER_LDG * THREADS_PER_ROW;
 
     // If bias is not null, use biased value for selection
diff --git a/csrc/moe/topk_softplus_sqrt_kernels.cu b/csrc/moe/topk_softplus_sqrt_kernels.cu
new file mode 100644
index 000000000000..43d461a0179a
--- /dev/null
+++ b/csrc/moe/topk_softplus_sqrt_kernels.cu
@@ -0,0 +1,726 @@
+/*
+ * Adapted from
+ * https://github.com/NVIDIA/TensorRT-LLM/blob/v0.7.1/cpp/tensorrt_llm/kernels/mixtureOfExperts/moe_kernels.cu
+ * Copyright (c) 2024, The vLLM team.
+ * SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION &
+ * AFFILIATES. All rights reserved. SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <type_traits>
+#include <torch/all.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include "../cuda_compat.h"
+#include "../cub_helpers.h"
+#ifndef USE_ROCM
+  #include <cuda_bf16.h>
+  #include <cuda_fp16.h>
+#else
+  #include <hip/hip_bf16.h>
+  #include <hip/hip_fp16.h>
+typedef __hip_bfloat16 __nv_bfloat16;
+typedef __hip_bfloat162 __nv_bfloat162;
+#endif
+
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+
+namespace vllm {
+namespace moe {
+
+/// Aligned array type
+template <typename T,
+          /// Number of elements in the array
+          int N,
+          /// Alignment requirement in bytes
+          int Alignment = sizeof(T) * N>
+struct alignas(Alignment) AlignedArray {
+  T data[N];
+};
+
+template <typename T>
+__device__ __forceinline__ float toFloat(T value) {
+  if constexpr (std::is_same_v<T, float>) {
+    return value;
+  } else if constexpr (std::is_same_v<T, __nv_bfloat16>) {
+    return __bfloat162float(value);
+  } else if constexpr (std::is_same_v<T, __half>) {
+    return __half2float(value);
+  }
+}
+
+// ====================== TopK softplus_sqrt things
+// ===============================
+
+/*
+  A Top-K gating softplus_sqrt written to exploit when the number of experts in
+  the MoE layers are a small power of 2. This allows us to cleanly share the
+  rows among the threads in a single warp and eliminate communication between
+  warps (so no need to use shared mem).
+
+  It fuses the sigmoid, max and argmax into a single kernel.
+
+  Limitations:
+  1) This implementation is optimized for when the number of experts is a small
+  power of 2. Additionally it also supports when number of experts is multiple
+  of 64 which is still faster than the computing sigmoid and topK separately
+  (only tested on CUDA yet). 2) This implementation assumes k is small, but will
+  work for any k.
+*/
+
+template <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG,
+          int WARP_SIZE_PARAM, bool USE_HASH, typename IndType,
+          typename InputType = float>
+__launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
+    void topkGatingSoftplusSqrt(
+        const InputType* input, const bool* finished, float* output,
+        const int num_rows, IndType* indices, int* source_rows, const int k,
+        const int start_expert, const int end_expert, const bool renormalize,
+        double routed_scaling_factor, const float* correction_bias,
+        const IndType* input_ids, const IndType* tid2eid) {
+  static_assert(std::is_same_v<InputType, float> ||
+                    std::is_same_v<InputType, __nv_bfloat16> ||
+                    std::is_same_v<InputType, __half>,
+                "InputType must be float, __nv_bfloat16, or __half");
+
+  // We begin by enforcing compile time assertions and setting up compile time
+  // constants.
+  static_assert(BYTES_PER_LDG == (BYTES_PER_LDG & -BYTES_PER_LDG),
+                "BYTES_PER_LDG must be power of 2");
+  static_assert(BYTES_PER_LDG <= 16, "BYTES_PER_LDG must be leq 16");
+
+  // Number of bytes each thread pulls in per load
+  static constexpr int ELTS_PER_LDG = BYTES_PER_LDG / sizeof(InputType);
+  static constexpr int ELTS_PER_ROW = NUM_EXPERTS;
+  static constexpr int THREADS_PER_ROW = ELTS_PER_ROW / VPT;
+  static constexpr int LDG_PER_THREAD = VPT / ELTS_PER_LDG;
+
+  if constexpr (std::is_same_v<InputType, __nv_bfloat16> ||
+                std::is_same_v<InputType, __half>) {
+    static_assert(ELTS_PER_LDG == 1 || ELTS_PER_LDG % 2 == 0,
+                  "ELTS_PER_LDG must be 1 or even for 16-bit conversion");
+  }
+
+  // Restrictions based on previous section.
+  static_assert(
+      VPT % ELTS_PER_LDG == 0,
+      "The elements per thread must be a multiple of the elements per ldg");
+  static_assert(WARP_SIZE_PARAM % THREADS_PER_ROW == 0,
+                "The threads per row must cleanly divide the threads per warp");
+  static_assert(THREADS_PER_ROW == (THREADS_PER_ROW & -THREADS_PER_ROW),
+                "THREADS_PER_ROW must be power of 2");
+  static_assert(THREADS_PER_ROW <= WARP_SIZE_PARAM,
+                "THREADS_PER_ROW can be at most warp size");
+
+  // We have NUM_EXPERTS elements per row. We specialize for small #experts
+  static constexpr int ELTS_PER_WARP = WARP_SIZE_PARAM * VPT;
+  static constexpr int ROWS_PER_WARP = ELTS_PER_WARP / ELTS_PER_ROW;
+  static constexpr int ROWS_PER_CTA = WARPS_PER_CTA * ROWS_PER_WARP;
+
+  // Restrictions for previous section.
+  static_assert(ELTS_PER_WARP % ELTS_PER_ROW == 0,
+                "The elts per row must cleanly divide the total elt per warp");
+
+  // ===================== From this point, we finally start computing run-time
+  // variables. ========================
+
+  // Compute CTA and warp rows. We pack multiple rows into a single warp, and a
+  // block contains WARPS_PER_CTA warps. This, each block processes a chunk of
+  // rows. We start by computing the start row for each block.
+  const int cta_base_row = blockIdx.x * ROWS_PER_CTA;
+
+  // Now, using the base row per thread block, we compute the base row per warp.
+  const int warp_base_row = cta_base_row + threadIdx.y * ROWS_PER_WARP;
+
+  // The threads in a warp are split into sub-groups that will work on a row.
+  // We compute row offset for each thread sub-group
+  const int thread_row_in_warp = threadIdx.x / THREADS_PER_ROW;
+  const int thread_row = warp_base_row + thread_row_in_warp;
+
+  // Threads with indices out of bounds should early exit here.
+  if (thread_row >= num_rows) {
+    return;
+  }
+  const bool row_is_active = finished ? !finished[thread_row] : true;
+
+  // We finally start setting up the read pointers for each thread. First, each
+  // thread jumps to the start of the row it will read.
+  const InputType* thread_row_ptr = input + thread_row * ELTS_PER_ROW;
+
+  // Now, we compute the group each thread belong to in order to determine the
+  // first column to start loads.
+  const int thread_group_idx = threadIdx.x % THREADS_PER_ROW;
+  const int first_elt_read_by_thread = thread_group_idx * ELTS_PER_LDG;
+  const InputType* thread_read_ptr = thread_row_ptr + first_elt_read_by_thread;
+
+  // Finally, we pull in the data from global mem
+  float row_chunk[VPT];
+
+#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
+  asm volatile("griddepcontrol.wait;");
+#endif
+
+  // NOTE(zhuhaoran): dispatch different input types loading, BF16/FP16 convert
+  // to float
+  if constexpr (std::is_same_v<InputType, float>) {
+    using VecType = AlignedArray<float, ELTS_PER_LDG>;
+    VecType* row_chunk_vec_ptr = reinterpret_cast<VecType*>(&row_chunk);
+    const VecType* vec_thread_read_ptr =
+        reinterpret_cast<const VecType*>(thread_read_ptr);
+#pragma unroll
+    for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
+      row_chunk_vec_ptr[ii] = vec_thread_read_ptr[ii * THREADS_PER_ROW];
+    }
+  } else if constexpr (std::is_same_v<InputType, __nv_bfloat16>) {
+    if constexpr (ELTS_PER_LDG >= 2) {
+      using VecType = AlignedArray<__nv_bfloat16, ELTS_PER_LDG>;
+      float2* row_chunk_f2 = reinterpret_cast<float2*>(row_chunk);
+      const VecType* vec_thread_read_ptr =
+          reinterpret_cast<const VecType*>(thread_read_ptr);
+#pragma unroll
+      for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
+        VecType vec = vec_thread_read_ptr[ii * THREADS_PER_ROW];
+        int base_idx_f2 = ii * ELTS_PER_LDG / 2;
+#pragma unroll
+        for (int jj = 0; jj < ELTS_PER_LDG / 2; ++jj) {
+          row_chunk_f2[base_idx_f2 + jj] = __bfloat1622float2(
+              *reinterpret_cast<const __nv_bfloat162*>(vec.data + jj * 2));
+        }
+      }
+    } else {  // ELTS_PER_LDG == 1
+#pragma unroll
+      for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
+        const __nv_bfloat16* scalar_ptr =
+            thread_read_ptr + ii * THREADS_PER_ROW;
+        row_chunk[ii] = __bfloat162float(*scalar_ptr);
+      }
+    }
+  } else if constexpr (std::is_same_v<InputType, __half>) {
+    if constexpr (ELTS_PER_LDG >= 2) {
+      using VecType = AlignedArray<__half, ELTS_PER_LDG>;
+      float2* row_chunk_f2 = reinterpret_cast<float2*>(row_chunk);
+      const VecType* vec_thread_read_ptr =
+          reinterpret_cast<const VecType*>(thread_read_ptr);
+#pragma unroll
+      for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
+        VecType vec = vec_thread_read_ptr[ii * THREADS_PER_ROW];
+        int base_idx_f2 = ii * ELTS_PER_LDG / 2;
+#pragma unroll
+        for (int jj = 0; jj < ELTS_PER_LDG / 2; ++jj) {
+          row_chunk_f2[base_idx_f2 + jj] = __half22float2(
+              *reinterpret_cast<const __half2*>(vec.data + jj * 2));
+        }
+      }
+    } else {  // ELTS_PER_LDG == 1
+#pragma unroll
+      for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
+        const __half* scalar_ptr = thread_read_ptr + ii * THREADS_PER_ROW;
+        row_chunk[ii] = __half2float(*scalar_ptr);
+      }
+    }
+  }
+  constexpr float threshold = 20.0f;
+  constexpr float beta = 1.0f;
+
+  // Hash MoE path: indices are predetermined from lookup table
+  if constexpr (USE_HASH) {
+    const IndType token_id = input_ids[thread_row];
+    const IndType* expert_indices_for_token = tid2eid + token_id * k;
+#pragma unroll
+    for (int ii = 0; ii < VPT; ++ii) {
+      float val = row_chunk[ii];
+      float val_b = val * beta;
+      val = (val_b > threshold) ? val : (__logf(1.0f + __expf(val_b))) / beta;
+      row_chunk[ii] = sqrtf(val);
+    }
+    float selected_sum = 0.f;
+#pragma unroll
+    for (int k_idx = 0; k_idx < k; ++k_idx) {
+      const int expert = expert_indices_for_token[k_idx];
+      const int idx = k * thread_row + k_idx;
+      for (int ii = 0; ii < VPT; ++ii) {
+        const int group_id = ii / ELTS_PER_LDG;
+        const int local_id = ii % ELTS_PER_LDG;
+        const int expert_idx = first_elt_read_by_thread +
+                               group_id * THREADS_PER_ROW * ELTS_PER_LDG +
+                               local_id;
+        if (expert == expert_idx) {
+          indices[idx] = expert;
+          selected_sum += row_chunk[ii];
+          break;
+        }
+      }
+    }
+    // Compute per-thread scale (using warp reduction when renormalizing).
+    // THREADS_PER_ROW-parameterized butterfly works for both warp sizes (32
+    // on CUDA, 64 on ROCm CDNA) and any THREADS_PER_ROW the dispatch picks.
+    if (renormalize) {
+#pragma unroll
+      for (int mask = THREADS_PER_ROW / 2; mask > 0; mask /= 2) {
+        selected_sum +=
+            VLLM_SHFL_XOR_SYNC_WIDTH(selected_sum, mask, THREADS_PER_ROW);
+      }
+    }
+    float scale = static_cast<float>(routed_scaling_factor);
+    if (renormalize) {
+      const float denom = selected_sum > 0.f ? selected_sum : 1.f;
+      scale /= denom;
+    }
+
+#pragma unroll
+    for (int k_idx = 0; k_idx < k; ++k_idx) {
+      const int expert = expert_indices_for_token[k_idx];
+      const int idx = k * thread_row + k_idx;
+      for (int ii = 0; ii < VPT; ++ii) {
+        const int group_id = ii / ELTS_PER_LDG;
+        const int local_id = ii % ELTS_PER_LDG;
+        const int expert_idx = first_elt_read_by_thread +
+                               group_id * THREADS_PER_ROW * ELTS_PER_LDG +
+                               local_id;
+        if (expert == expert_idx) {
+          output[idx] = row_chunk[ii] * scale;
+          break;
+        }
+      }
+    }
+#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
+    asm volatile("griddepcontrol.launch_dependents;");
+#endif
+    return;
+  }
+
+#pragma unroll
+  for (int ii = 0; ii < VPT; ++ii) {
+    float val = row_chunk[ii];
+    float val_b = val * beta;
+    // Compute softplus: log(1 + exp(val)) with numerical stability
+    // When val > threshold, softplus(x) ≈ x to avoid exp overflow
+    val = (val_b > threshold) ? val : (__logf(1.0f + __expf(val_b))) / beta;
+    val = sqrtf(val);
+    if (correction_bias) {
+      const int group_id = ii / ELTS_PER_LDG;
+      const int local_id = ii % ELTS_PER_LDG;
+      const int expert_idx = first_elt_read_by_thread +
+                             group_id * THREADS_PER_ROW * ELTS_PER_LDG +
+                             local_id;
+      val = val + correction_bias[expert_idx];
+    }
+    row_chunk[ii] = val;
+  }
+
+  // Original TopK path: find top-k experts by score
+  // Now, sigmoid_res contains the sigmoid of the row chunk. Now, I want to find
+  // the topk elements in each row, along with the max index.
+  int start_col = first_elt_read_by_thread;
+  static constexpr int COLS_PER_GROUP_LDG = ELTS_PER_LDG * THREADS_PER_ROW;
+
+  float selected_sum = 0.f;
+  for (int k_idx = 0; k_idx < k; ++k_idx) {
+    // First, each thread does the local argmax
+    float max_val = row_chunk[0];
+    int expert = start_col;
+#pragma unroll
+    for (int ldg = 0, col = start_col; ldg < LDG_PER_THREAD;
+         ++ldg, col += COLS_PER_GROUP_LDG) {
+#pragma unroll
+      for (int ii = 0; ii < ELTS_PER_LDG; ++ii) {
+        float val = row_chunk[ldg * ELTS_PER_LDG + ii];
+
+        // No check on the experts here since columns with the smallest index
+        // are processed first and only updated if > (not >=)
+        if (val > max_val) {
+          max_val = val;
+          expert = col + ii;
+        }
+      }
+    }
+
+// Now, we perform the argmax reduce. We use the butterfly pattern so threads
+// reach consensus about the max. This will be useful for K > 1 so that the
+// threads can agree on "who" had the max value. That thread can then blank out
+// their max with -inf and the warp can run more iterations...
+#pragma unroll
+    for (int mask = THREADS_PER_ROW / 2; mask > 0; mask /= 2) {
+      float other_max =
+          VLLM_SHFL_XOR_SYNC_WIDTH(max_val, mask, THREADS_PER_ROW);
+      int other_expert =
+          VLLM_SHFL_XOR_SYNC_WIDTH(expert, mask, THREADS_PER_ROW);
+
+      // We want lower indices to "win" in every thread so we break ties this
+      // way
+      if (other_max > max_val ||
+          (other_max == max_val && other_expert < expert)) {
+        max_val = other_max;
+        expert = other_expert;
+      }
+    }
+
+    // Write the max for this k iteration to global memory.
+    if (thread_group_idx == 0) {
+      // Add a guard to ignore experts not included by this node
+      const bool node_uses_expert =
+          expert >= start_expert && expert < end_expert;
+      const bool should_process_row = row_is_active && node_uses_expert;
+
+      // The lead thread from each sub-group will write out the final results to
+      // global memory. (This will be a single) thread per row of the
+      // input/output matrices.
+      const int idx = k * thread_row + k_idx;
+      if (correction_bias != nullptr) {
+        max_val -= correction_bias[expert];
+      }
+      output[idx] = max_val;
+      indices[idx] = should_process_row ? (expert - start_expert) : NUM_EXPERTS;
+      source_rows[idx] = k_idx * num_rows + thread_row;
+      if (renormalize) {
+        selected_sum += max_val;
+      }
+    }
+
+    // Finally, we clear the value in the thread with the current max if there
+    // is another iteration to run.
+    if (k_idx + 1 < k) {
+      const int ldg_group_for_expert = expert / COLS_PER_GROUP_LDG;
+      const int thread_to_clear_in_group =
+          (expert / ELTS_PER_LDG) % THREADS_PER_ROW;
+
+      // Only the thread in the group which produced the max will reset the
+      // "winning" value to -inf.
+      if (thread_group_idx == thread_to_clear_in_group) {
+        const int offset_for_expert = expert % ELTS_PER_LDG;
+        // Safe to set to any negative value since row_chunk values must be
+        // between 0 and 1.
+        row_chunk[ldg_group_for_expert * ELTS_PER_LDG + offset_for_expert] =
+            -10000.f;
+      }
+    }
+  }
+
+  // Apply renormalization and routed scaling factor to final weights.
+  if (thread_group_idx == 0) {
+    float scale = static_cast<float>(routed_scaling_factor);
+    if (renormalize) {
+      const float denom = selected_sum > 0.f ? selected_sum : 1.f;
+      scale /= denom;
+    }
+    for (int k_idx = 0; k_idx < k; ++k_idx) {
+      const int idx = k * thread_row + k_idx;
+      output[idx] = output[idx] * scale;
+    }
+  }
+#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
+  asm volatile("griddepcontrol.launch_dependents;");
+#endif
+}
+
+namespace detail {
+// Constructs some constants needed to partition the work across threads at
+// compile time.
+template <int EXPERTS, int BYTES_PER_LDG, int WARP_SIZE_PARAM,
+          typename InputType>
+struct TopkConstants {
+  static constexpr int ELTS_PER_LDG = BYTES_PER_LDG / sizeof(InputType);
+  static_assert(EXPERTS / (ELTS_PER_LDG * WARP_SIZE_PARAM) == 0 ||
+                    EXPERTS % (ELTS_PER_LDG * WARP_SIZE_PARAM) == 0,
+                "");
+  static constexpr int VECs_PER_THREAD =
+      MAX(1, EXPERTS / (ELTS_PER_LDG * WARP_SIZE_PARAM));
+  static constexpr int VPT = VECs_PER_THREAD * ELTS_PER_LDG;
+  static constexpr int THREADS_PER_ROW = EXPERTS / VPT;
+  static const int ROWS_PER_WARP = WARP_SIZE_PARAM / THREADS_PER_ROW;
+};
+}  // namespace detail
+
+#define DISPATCH_HASH(use_hash, USE_HASH, ...)                                 \
+  if (use_hash) {                                                              \
+    const bool USE_HASH = true;                                                \
+    static_assert(USE_HASH == true, "USE_HASH must be compile-time constant"); \
+    __VA_ARGS__                                                                \
+  } else {                                                                     \
+    const bool USE_HASH = false;                                               \
+    static_assert(USE_HASH == false,                                           \
+                  "USE_HASH must be compile-time constant");                   \
+    __VA_ARGS__                                                                \
+  }
+
+template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM,
+          int MAX_BYTES_PER_LDG, typename IndType, typename InputType>
+void topkGatingSoftplusSqrtLauncherHelper(
+    const InputType* input, const bool* finished, float* output,
+    IndType* indices, int* source_row, const int num_rows, const int k,
+    const int start_expert, const int end_expert, const bool renormalize,
+    double routed_scaling_factor, const float* correction_bias,
+    const bool use_hash, const IndType* input_ids, const IndType* tid2eid,
+    cudaStream_t stream) {
+  static constexpr int BYTES_PER_LDG =
+      MIN(MAX_BYTES_PER_LDG, sizeof(InputType) * EXPERTS);
+  using Constants =
+      detail::TopkConstants<EXPERTS, BYTES_PER_LDG, WARP_SIZE_PARAM, InputType>;
+  static constexpr int VPT = Constants::VPT;
+  static constexpr int ROWS_PER_WARP = Constants::ROWS_PER_WARP;
+  const int num_warps = (num_rows + ROWS_PER_WARP - 1) / ROWS_PER_WARP;
+  const int num_blocks = (num_warps + WARPS_PER_TB - 1) / WARPS_PER_TB;
+  dim3 block_dim(WARP_SIZE_PARAM, WARPS_PER_TB);
+  DISPATCH_HASH(use_hash, USE_HASH, {
+    auto* kernel =
+        &topkGatingSoftplusSqrt<VPT, EXPERTS, WARPS_PER_TB, BYTES_PER_LDG,
+                                WARP_SIZE_PARAM, USE_HASH, IndType, InputType>;
+#ifndef USE_ROCM
+    cudaLaunchConfig_t config = {};
+    config.gridDim = num_blocks;
+    config.blockDim = block_dim;
+    config.dynamicSmemBytes = 0;
+    config.stream = stream;
+    cudaLaunchAttribute attrs[1];
+    attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
+    attrs[0].val.programmaticStreamSerializationAllowed = 1;
+    config.numAttrs = 1;
+    config.attrs = attrs;
+    cudaLaunchKernelEx(&config, kernel, input, finished, output, num_rows,
+                       indices, source_row, k, start_expert, end_expert,
+                       renormalize, routed_scaling_factor, correction_bias,
+                       input_ids, tid2eid);
+#else
+    kernel<<<num_blocks, block_dim, 0, stream>>>(
+        input, finished, output, num_rows, indices, source_row, k, start_expert,
+        end_expert, renormalize, routed_scaling_factor, correction_bias,
+        input_ids, tid2eid);
+#endif
+  })
+}
+
+#ifndef USE_ROCM
+  #define LAUNCH_SOFTPLUS_SQRT(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)           \
+    static_assert(WARP_SIZE == 32,                                             \
+                  "Unsupported warp size. Only 32 is supported for CUDA");     \
+    topkGatingSoftplusSqrtLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, WARP_SIZE, \
+                                         MAX_BYTES>(                           \
+        gating_output, nullptr, topk_weights, topk_indices,                    \
+        token_expert_indices, num_tokens, topk, 0, num_experts, renormalize,   \
+        routed_scaling_factor, correction_bias, use_hash, input_ids, tid2eid,  \
+        stream);
+#else
+  #define LAUNCH_SOFTPLUS_SQRT(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)           \
+    if (WARP_SIZE == 64) {                                                     \
+      topkGatingSoftplusSqrtLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 64,      \
+                                           MAX_BYTES>(                         \
+          gating_output, nullptr, topk_weights, topk_indices,                  \
+          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
+          routed_scaling_factor, correction_bias, use_hash, input_ids,         \
+          tid2eid, stream);                                                    \
+    } else if (WARP_SIZE == 32) {                                              \
+      topkGatingSoftplusSqrtLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32,      \
+                                           MAX_BYTES>(                         \
+          gating_output, nullptr, topk_weights, topk_indices,                  \
+          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
+          routed_scaling_factor, correction_bias, use_hash, input_ids,         \
+          tid2eid, stream);                                                    \
+    } else {                                                                   \
+      assert(false &&                                                          \
+             "Unsupported warp size. Only 32 and 64 are supported for ROCm");  \
+    }
+#endif
+
+template <typename IndType, typename InputType>
+void topkGatingSoftplusSqrtKernelLauncher(
+    const InputType* gating_output, float* topk_weights, IndType* topk_indices,
+    int* token_expert_indices, const int num_tokens, const int num_experts,
+    const int topk, const bool renormalize, double routed_scaling_factor,
+    const float* correction_bias, const bool use_hash, const IndType* input_ids,
+    const IndType* tid2eid, cudaStream_t stream) {
+  static constexpr int WARPS_PER_TB = 4;
+  static constexpr int BYTES_PER_LDG_POWER_OF_2 = 16;
+  // for bfloat16 dtype, we need 4 bytes loading to make sure num_experts
+  // elements can be loaded by a warp
+  static constexpr int BYTES_PER_LDG_MULTIPLE_64 =
+      (std::is_same_v<InputType, __nv_bfloat16> ||
+       std::is_same_v<InputType, __half>)
+          ? 4
+          : 8;
+  // Narrower LDG (ELTS_PER_LDG=1) used by 192/320/448/576 on ROCm WARP_SIZE=64
+  // where ELTS_PER_LDG=2 fails the EXPERTS%(ELTS_PER_LDG*WARP_SIZE)==0 check.
+  // On CUDA WARP_SIZE=32 the wider LDG already aligns, so the alias collapses
+  // back to BYTES_PER_LDG_MULTIPLE_64 — no behavioral change for CUDA.
+#ifdef USE_ROCM
+  static constexpr int BYTES_PER_LDG_MULTIPLE_64_NARROW =
+      (std::is_same_v<InputType, __nv_bfloat16> ||
+       std::is_same_v<InputType, __half>)
+          ? 2
+          : 4;
+#else
+  static constexpr int BYTES_PER_LDG_MULTIPLE_64_NARROW =
+      BYTES_PER_LDG_MULTIPLE_64;
+#endif
+  switch (num_experts) {
+    case 1:
+      LAUNCH_SOFTPLUS_SQRT(1, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+      break;
+    case 2:
+      LAUNCH_SOFTPLUS_SQRT(2, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+      break;
+    case 4:
+      LAUNCH_SOFTPLUS_SQRT(4, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+      break;
+    case 8:
+      LAUNCH_SOFTPLUS_SQRT(8, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+      break;
+    case 16:
+      LAUNCH_SOFTPLUS_SQRT(16, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+      break;
+    case 32:
+      LAUNCH_SOFTPLUS_SQRT(32, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+      break;
+    case 64:
+      LAUNCH_SOFTPLUS_SQRT(64, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+      break;
+    case 128:
+      LAUNCH_SOFTPLUS_SQRT(128, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+      break;
+    case 256:
+      LAUNCH_SOFTPLUS_SQRT(256, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+      break;
+    case 512:
+      LAUNCH_SOFTPLUS_SQRT(512, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+      break;
+      // Multiples of 64 that are not powers of 2. The kernel requires
+      // EXPERTS % (ELTS_PER_LDG * WARP_SIZE) == 0. With ELTS_PER_LDG=2
+      // (BYTES_PER_LDG_MULTIPLE_64), this holds for all five values on CUDA
+      // WARP_SIZE=32 but only for 384 on ROCm WARP_SIZE=64. The other four
+      // use BYTES_PER_LDG_MULTIPLE_64_NARROW (ELTS_PER_LDG=1), which
+      // satisfies the assertion for any multiple of 64 on either backend;
+      // on CUDA the narrow alias collapses back to the wider load, so CUDA
+      // behavior is unchanged.
+    case 192:
+      LAUNCH_SOFTPLUS_SQRT(192, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64_NARROW);
+      break;
+    case 320:
+      LAUNCH_SOFTPLUS_SQRT(320, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64_NARROW);
+      break;
+    case 384:
+      LAUNCH_SOFTPLUS_SQRT(384, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+      break;
+    case 448:
+      LAUNCH_SOFTPLUS_SQRT(448, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64_NARROW);
+      break;
+    case 576:
+      LAUNCH_SOFTPLUS_SQRT(576, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64_NARROW);
+      break;
+    default: {
+      TORCH_CHECK(false, "Unsupported expert number: ", num_experts);
+    }
+  }
+}
+
+}  // namespace moe
+}  // namespace vllm
+
+template <typename ComputeType>
+void dispatch_topk_softplus_sqrt_launch(
+    const ComputeType* gating_output, torch::Tensor& topk_weights,
+    torch::Tensor& topk_indices, torch::Tensor& token_expert_indices,
+    int num_tokens, int num_experts, int topk, bool renormalize,
+    double routed_scaling_factor,
+    const c10::optional<torch::Tensor>& correction_bias,
+    const c10::optional<torch::Tensor>& input_ids,
+    const c10::optional<torch::Tensor>& tid2eid, cudaStream_t stream) {
+  const float* bias_ptr = nullptr;
+  if (correction_bias.has_value()) {
+    bias_ptr = correction_bias.value().data_ptr<float>();
+  }
+  bool use_hash = false;
+  if (tid2eid.has_value()) {
+    TORCH_CHECK(input_ids.has_value(), "input_ids is required for hash MoE");
+    use_hash = true;
+  }
+  if (topk_indices.scalar_type() == at::ScalarType::Int) {
+    const int* input_ids_ptr = nullptr;
+    const int* tid2eid_ptr = nullptr;
+    if (tid2eid.has_value()) {
+      input_ids_ptr = input_ids.value().data_ptr<int>();
+      tid2eid_ptr = tid2eid.value().data_ptr<int>();
+    }
+
+    vllm::moe::topkGatingSoftplusSqrtKernelLauncher<int, ComputeType>(
+        gating_output, topk_weights.data_ptr<float>(),
+        topk_indices.data_ptr<int>(), token_expert_indices.data_ptr<int>(),
+        num_tokens, num_experts, topk, renormalize, routed_scaling_factor,
+        bias_ptr, use_hash, input_ids_ptr, tid2eid_ptr, stream);
+  } else if (topk_indices.scalar_type() == at::ScalarType::UInt32) {
+    const uint32_t* input_ids_ptr = nullptr;
+    const uint32_t* tid2eid_ptr = nullptr;
+    if (tid2eid.has_value()) {
+      input_ids_ptr = input_ids.value().data_ptr<uint32_t>();
+      tid2eid_ptr = tid2eid.value().data_ptr<uint32_t>();
+    }
+    vllm::moe::topkGatingSoftplusSqrtKernelLauncher<uint32_t, ComputeType>(
+        gating_output, topk_weights.data_ptr<float>(),
+        topk_indices.data_ptr<uint32_t>(), token_expert_indices.data_ptr<int>(),
+        num_tokens, num_experts, topk, renormalize, routed_scaling_factor,
+        bias_ptr, use_hash, input_ids_ptr, tid2eid_ptr, stream);
+  } else {
+    TORCH_CHECK(topk_indices.scalar_type() == at::ScalarType::Long);
+
+    const int64_t* input_ids_ptr = nullptr;
+    const int64_t* tid2eid_ptr = nullptr;
+    if (tid2eid.has_value()) {
+      input_ids_ptr = input_ids.value().data_ptr<int64_t>();
+      tid2eid_ptr = tid2eid.value().data_ptr<int64_t>();
+    }
+
+    vllm::moe::topkGatingSoftplusSqrtKernelLauncher<int64_t, ComputeType>(
+        gating_output, topk_weights.data_ptr<float>(),
+        topk_indices.data_ptr<int64_t>(), token_expert_indices.data_ptr<int>(),
+        num_tokens, num_experts, topk, renormalize, routed_scaling_factor,
+        bias_ptr, use_hash, input_ids_ptr, tid2eid_ptr, stream);
+  }
+}
+
+void topk_softplus_sqrt(
+    torch::Tensor& topk_weights,          // [num_tokens, topk]
+    torch::Tensor& topk_indices,          // [num_tokens, topk]
+    torch::Tensor& token_expert_indices,  // [num_tokens, topk]
+    torch::Tensor& gating_output,         // [num_tokens, num_experts]
+    bool renormalize, double routed_scaling_factor,
+    const c10::optional<torch::Tensor>& correction_bias,
+    const c10::optional<torch::Tensor>& input_ids,
+    const c10::optional<torch::Tensor>& tid2eid) {
+  const int num_experts = gating_output.size(-1);
+  const auto num_tokens = gating_output.numel() / num_experts;
+  const int topk = topk_weights.size(-1);
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(gating_output));
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  if (gating_output.scalar_type() == at::ScalarType::Float) {
+    dispatch_topk_softplus_sqrt_launch<float>(
+        gating_output.data_ptr<float>(), topk_weights, topk_indices,
+        token_expert_indices, num_tokens, num_experts, topk, renormalize,
+        routed_scaling_factor, correction_bias, input_ids, tid2eid, stream);
+  } else if (gating_output.scalar_type() == at::ScalarType::Half) {
+    dispatch_topk_softplus_sqrt_launch<__half>(
+        reinterpret_cast<const __half*>(gating_output.data_ptr<at::Half>()),
+        topk_weights, topk_indices, token_expert_indices, num_tokens,
+        num_experts, topk, renormalize, routed_scaling_factor, correction_bias,
+        input_ids, tid2eid, stream);
+  } else if (gating_output.scalar_type() == at::ScalarType::BFloat16) {
+    dispatch_topk_softplus_sqrt_launch<__nv_bfloat16>(
+        reinterpret_cast<const __nv_bfloat16*>(
+            gating_output.data_ptr<at::BFloat16>()),
+        topk_weights, topk_indices, token_expert_indices, num_tokens,
+        num_experts, topk, renormalize, routed_scaling_factor, correction_bias,
+        input_ids, tid2eid, stream);
+  } else {
+    TORCH_CHECK(false, "Unsupported gating_output data type: ",
+                gating_output.scalar_type());
+  }
+}
\ No newline at end of file
diff --git a/csrc/moe/torch_bindings.cpp b/csrc/moe/torch_bindings.cpp
index 7b627a6f8760..8940e341cd01 100644
--- a/csrc/moe/torch_bindings.cpp
+++ b/csrc/moe/torch_bindings.cpp
@@ -16,6 +16,13 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "bias) -> ()");
   m.impl("topk_sigmoid", torch::kCUDA, &topk_sigmoid);
 
+  m.def(
+      "topk_softplus_sqrt(Tensor! topk_weights, Tensor! topk_indices, Tensor! "
+      "token_expert_indices, Tensor gating_output, bool renormalize, float "
+      "routed_scaling_factor, Tensor? "
+      "bias, Tensor? input_ids, Tensor? tid2eid) -> ()");
+  m.impl("topk_softplus_sqrt", torch::kCUDA, &topk_softplus_sqrt);
+
   // Calculate the result of moe by summing up the partial results
   // from all selected experts.
   m.def("moe_sum(Tensor input, Tensor! output) -> ()");
@@ -125,10 +132,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "Tensor)");
   m.impl("grouped_topk", torch::kCUDA, &grouped_topk);
 
-  // cuBLAS bf16 x bf16 -> fp32 router GEMM (fallback for non-SM90 / batch > 16)
-  m.def("router_gemm_bf16_fp32(Tensor input, Tensor weight) -> Tensor");
-  m.impl("router_gemm_bf16_fp32", torch::kCUDA, &router_gemm_bf16_fp32);
-
   // DeepSeek V3 optimized router GEMM for SM90+
   m.def("dsv3_router_gemm(Tensor! output, Tensor mat_a, Tensor mat_b) -> ()");
   // conditionally compiled so impl registration is in source file
diff --git a/csrc/nvfp4_kv_cache_kernels.cu b/csrc/nvfp4_kv_cache_kernels.cu
new file mode 100644
index 000000000000..d6aa715c203d
--- /dev/null
+++ b/csrc/nvfp4_kv_cache_kernels.cu
@@ -0,0 +1,275 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+// NVFP4 KV cache store kernel.
+// Quantizes bf16 key/value to packed FP4 + FP8 block scales and writes them
+// into the paged KV cache.
+//
+// Per page layout: [K_data | K_scale | V_data | V_scale]
+// Both data and scale regions are contiguous per head, enabling direct
+// TMA descriptor use.
+//
+// Reuses device functions from nvfp4_utils.cuh:
+//   - cvt_warp_fp16_to_fp4()  for bf16 → fp4 quantization + block scale
+//   - pack_fp4()              for packing float pairs to fp4
+//   - reciprocal_approximate_ftz() for fast reciprocal
+
+#define NVFP4_ENABLE_ELTS16 1
+#include "libtorch_stable/quantization/fp4/nvfp4_utils.cuh"
+
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include <torch/all.h>
+
+#include "dispatch_utils.h"
+
+namespace vllm {
+
+// Compute swizzled scale offset for SM100 trtllm-gen MHA kernel.
+// The swizzle pattern for HND layout is:
+//   [T//4, 4, 4, S//4] → permute(0, 2, 3, 1) → reshape to [T, S]
+// where T = block_size (page_size), S = scale_dim = head_size // 16.
+//
+// For a linear (t, s) position, the swizzled position is:
+//   swizzled_t = (t / 4) * 4 + (s / (S / 4))
+//   swizzled_s = (s % (S / 4)) * 4 + (t % 4)
+__device__ __forceinline__ int swizzle_scale_offset(int t, int s,
+                                                    int scale_dim) {
+  int s_group = scale_dim / 4;
+  int swizzled_t = (t / 4) * 4 + (s / s_group);
+  int swizzled_s = (s % s_group) * 4 + (t % 4);
+  return swizzled_t * scale_dim + swizzled_s;
+}
+
+// Kernel: quantize bf16 key/value to NVFP4 and store in paged KV cache.
+//
+// Takes separate data and scale cache pointers for K and V.
+// Within each KV side, data and scale are separate contiguous regions.
+//
+// Threading: one CUDA block per token, threads process heads and
+// groups of 16 elements within each head.
+template <typename scalar_t>
+__global__ void reshape_and_cache_nvfp4_kernel(
+    const scalar_t* __restrict__ key,      // [num_tokens, num_heads, head_size]
+    const scalar_t* __restrict__ value,    // [num_tokens, num_heads, head_size]
+    uint8_t* __restrict__ key_data_cache,  // data region for K
+    uint8_t* __restrict__ value_data_cache,    // data region for V
+    uint8_t* __restrict__ key_scale_cache,     // scale region for K
+    uint8_t* __restrict__ value_scale_cache,   // scale region for V
+    const int64_t* __restrict__ slot_mapping,  // [num_actual_tokens]
+    const float* __restrict__ k_scale_ptr,     // pointer to checkpoint k_scale
+    const float* __restrict__ v_scale_ptr,     // pointer to checkpoint v_scale
+    const int64_t key_stride,                  // key.stride(0) in elements
+    const int64_t value_stride,                // value.stride(0) in elements
+    const int num_heads, const int head_size, const int block_size,
+    const int64_t data_block_stride,         // data cache stride for dim 0
+    const int64_t data_head_stride,          // data cache stride for heads
+    const int64_t data_block_offset_stride,  // data cache stride for tokens
+    const int64_t scale_block_stride,        // scale cache stride for dim 0
+    const int64_t scale_head_stride,         // scale cache stride for heads
+    const int64_t scale_block_offset_stride  // scale cache stride for tokens
+) {
+  using CudaType = typename CUDATypeConverter<scalar_t>::Type;
+  using PVec = PackedVec<CudaType, CVT_FP4_PACK16>;
+
+  static constexpr int ELTS = CVT_FP4_ELTS_PER_THREAD;  // 16 or 8
+  static constexpr int THREADS_PER_SF = CVT_FP4_SF_VEC_SIZE / ELTS;
+
+  const int64_t token_idx = blockIdx.x;
+  const int64_t slot_idx = slot_mapping[token_idx];
+  if (slot_idx < 0) return;
+
+  const int64_t block_idx = slot_idx / block_size;
+  const int block_offset = static_cast<int>(slot_idx % block_size);
+
+  const int scale_dim = head_size / 16;
+  const int groups_per_head = head_size / CVT_FP4_SF_VEC_SIZE;
+
+  const int total_groups = num_heads * groups_per_head;
+  const int tid = threadIdx.x;
+  const int num_thread_groups = blockDim.x / THREADS_PER_SF;
+  const int tg_id = tid / THREADS_PER_SF;
+  const int tg_lane = tid % THREADS_PER_SF;
+
+  // Process both K (kv=0) and V (kv=1)
+#pragma unroll
+  for (int kv = 0; kv < 2; kv++) {
+    const scalar_t* __restrict__ src = (kv == 0) ? key : value;
+    const float global_scale = 1.0f / ((kv == 0) ? *k_scale_ptr : *v_scale_ptr);
+    const int64_t src_stride = (kv == 0) ? key_stride : value_stride;
+    uint8_t* __restrict__ data_cache =
+        (kv == 0) ? key_data_cache : value_data_cache;
+    uint8_t* __restrict__ sc_cache =
+        (kv == 0) ? key_scale_cache : value_scale_cache;
+
+    // Source pointer for this token (use actual stride, not assumed contiguous)
+    const CudaType* __restrict__ token_src =
+        reinterpret_cast<const CudaType*>(src) + token_idx * src_stride;
+
+    // Destination bases in data and scale caches for this token's block
+    uint8_t* __restrict__ data_block =
+        data_cache + block_idx * data_block_stride;
+    uint8_t* __restrict__ scale_block =
+        sc_cache + block_idx * scale_block_stride;
+
+    for (int g = tg_id; g < total_groups; g += num_thread_groups) {
+      const int head = g / groups_per_head;
+      const int group_in_head = g % groups_per_head;
+
+      // Load 16 (or 8) bf16 elements from source
+      PVec in_vec;
+      const CudaType* __restrict__ src_ptr =
+          token_src + head * head_size + group_in_head * CVT_FP4_SF_VEC_SIZE +
+          tg_lane * ELTS;
+
+#pragma unroll
+      for (int i = 0; i < ELTS / 2; i++) {
+        in_vec.elts[i] = reinterpret_cast<
+            const typename PackedTypeConverter<CudaType>::Type*>(src_ptr)[i];
+      }
+
+      // Quantize: produces packed fp4 and writes scale factor.
+      uint8_t sf_val;
+      uint8_t* sf_out_ptr = (tg_lane == 0) ? &sf_val : nullptr;
+
+      fp4_packed_t packed = cvt_warp_fp16_to_fp4<CudaType, THREADS_PER_SF>(
+          in_vec, global_scale, sf_out_ptr);
+
+      // Write packed FP4 data to data cache
+      uint8_t* __restrict__ data_dst = data_block + head * data_head_stride +
+                                       block_offset * data_block_offset_stride;
+
+#if CVT_FP4_PACK16
+      {
+        // 16 elements → 8 bytes (u32x2)
+        int data_byte_offset = group_in_head * 8;
+        reinterpret_cast<uint64_t*>(data_dst + data_byte_offset)[0] =
+            (uint64_t(packed.hi) << 32) | uint64_t(packed.lo);
+      }
+#else
+      {
+        // 8 elements → 4 bytes (uint32_t)
+        int data_byte_offset =
+            group_in_head * CVT_FP4_SF_VEC_SIZE / 2 + tg_lane * ELTS / 2;
+        reinterpret_cast<uint32_t*>(data_dst + data_byte_offset)[0] = packed;
+      }
+#endif
+
+      // Write block scale to scale cache.
+      // K (kv==0): linear layout (no swizzle).
+      // V (kv==1): swizzled layout for SM100 trtllm-gen MHA kernel.
+      if (sf_out_ptr != nullptr) {
+        int scale_idx = group_in_head;
+        uint8_t* __restrict__ scale_dst;
+        if (kv == 0) {
+          scale_dst = scale_block + head * scale_head_stride +
+                      block_offset * scale_block_offset_stride + scale_idx;
+        } else {
+          int swizzled_offset =
+              swizzle_scale_offset(block_offset, scale_idx, scale_dim);
+          int swizzled_t = swizzled_offset / scale_dim;
+          int swizzled_s = swizzled_offset % scale_dim;
+          scale_dst = scale_block + head * scale_head_stride +
+                      swizzled_t * scale_block_offset_stride + swizzled_s;
+        }
+        *scale_dst = sf_val;
+      }
+    }
+  }
+}
+
+}  // namespace vllm
+
+// Non-template entry point callable from cache_kernels.cu.
+// Receives key_cache/value_cache as kv_cache[:, 0] and kv_cache[:, 1].
+// Each KV side contains both data and scale:
+//   page = [K_data | K_scale | V_data | V_scale]
+void reshape_and_cache_nvfp4_dispatch(torch::Tensor& key, torch::Tensor& value,
+                                      torch::Tensor& key_cache,
+                                      torch::Tensor& value_cache,
+                                      torch::Tensor& slot_mapping,
+                                      torch::Tensor& k_scale,
+                                      torch::Tensor& v_scale) {
+  int num_tokens = slot_mapping.size(0);
+  int num_heads = key.size(1);
+  int head_size = key.size(2);
+  int data_dim = head_size / 2;
+  int scale_dim = head_size / 16;
+  int full_dim = data_dim + scale_dim;
+
+  // key_cache is kv_cache[:, 0] with shape
+  // [num_blocks, block_size, num_heads, full_dim] in logical order.
+  // Strides encode the physical layout (HND or NHD).
+  TORCH_CHECK(key_cache.dim() == 4, "key_cache must be 4D");
+  TORCH_CHECK(key_cache.size(3) == full_dim,
+              "key_cache last dim must be data_dim + scale_dim, got ",
+              key_cache.size(3), " expected ", full_dim);
+
+  int block_size = key_cache.size(1);
+
+  TORCH_CHECK(head_size % 16 == 0,
+              "head_size must be divisible by 16 for NVFP4 KV cache");
+  TORCH_CHECK(block_size % 4 == 0,
+              "block_size must be divisible by 4 for NVFP4 KV cache swizzle");
+
+  // Detect physical layout from strides (based on full_dim).
+  // HND: head stride > block_offset stride.
+  bool is_hnd = key_cache.stride(2) > key_cache.stride(1);
+
+  int64_t data_block_stride = key_cache.stride(0);  // page_bytes
+  int64_t data_head_stride, data_block_offset_stride;
+  if (is_hnd) {
+    data_head_stride = (int64_t)block_size * data_dim;
+    data_block_offset_stride = data_dim;
+  } else {
+    data_head_stride = data_dim;
+    data_block_offset_stride = (int64_t)num_heads * data_dim;
+  }
+
+  // Page layout: [K_data | K_scale | V_data | V_scale]
+  // Scale follows data within each KV side.
+  int64_t data_per_kv = (int64_t)num_heads * block_size * data_dim;
+
+  uint8_t* key_scale_ptr = key_cache.data_ptr<uint8_t>() + data_per_kv;
+  uint8_t* value_scale_ptr = value_cache.data_ptr<uint8_t>() + data_per_kv;
+
+  // Scale strides: same page stride, inner strides from layout.
+  int64_t scale_block_stride = data_block_stride;
+  int64_t scale_head_stride, scale_block_offset_stride;
+  if (is_hnd) {
+    scale_head_stride = (int64_t)block_size * scale_dim;
+    scale_block_offset_stride = scale_dim;
+  } else {
+    scale_head_stride = scale_dim;
+    scale_block_offset_stride = (int64_t)num_heads * scale_dim;
+  }
+
+  const float* k_scale_ptr = k_scale.data_ptr<float>();
+  const float* v_scale_ptr = v_scale.data_ptr<float>();
+
+  int groups_per_head = head_size / CVT_FP4_SF_VEC_SIZE;
+  int total_groups = num_heads * groups_per_head;
+  constexpr int THREADS_PER_SF = CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD;
+  int num_threads = std::min(total_groups * THREADS_PER_SF, 512);
+  num_threads = ((num_threads + 31) / 32) * 32;
+
+  dim3 grid(num_tokens);
+  dim3 block(num_threads);
+
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(key));
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  AT_DISPATCH_REDUCED_FLOATING_TYPES(
+      key.scalar_type(), "reshape_and_cache_nvfp4", [&] {
+        vllm::reshape_and_cache_nvfp4_kernel<scalar_t>
+            <<<grid, block, 0, stream>>>(
+                key.data_ptr<scalar_t>(), value.data_ptr<scalar_t>(),
+                key_cache.data_ptr<uint8_t>(), value_cache.data_ptr<uint8_t>(),
+                key_scale_ptr, value_scale_ptr,
+                slot_mapping.data_ptr<int64_t>(), k_scale_ptr, v_scale_ptr,
+                key.stride(0), value.stride(0), num_heads, head_size,
+                block_size, data_block_stride, data_head_stride,
+                data_block_offset_stride, scale_block_stride, scale_head_stride,
+                scale_block_offset_stride);
+      });
+}
diff --git a/csrc/ops.h b/csrc/ops.h
index 0a0b6c2d7d07..16a78f570cf6 100644
--- a/csrc/ops.h
+++ b/csrc/ops.h
@@ -1,6 +1,7 @@
 #pragma once
 
 #include <optional>
+#include <string>
 #include <torch/library.h>
 #include <tuple>
 
@@ -99,6 +100,11 @@ void fused_qk_norm_rope(torch::Tensor& qkv, int64_t num_heads_q,
                         bool is_neox, torch::Tensor& position_ids,
                         int64_t forced_token_heads_per_warp);
 
+void fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert(
+    torch::Tensor& q, torch::Tensor const& kv, torch::Tensor& k_cache,
+    torch::Tensor const& slot_mapping, torch::Tensor const& position_ids,
+    torch::Tensor const& cos_sin_cache, double eps, int64_t cache_block_size);
+
 void apply_repetition_penalties_(torch::Tensor& logits,
                                  const torch::Tensor& prompt_mask,
                                  const torch::Tensor& output_mask,
@@ -152,10 +158,13 @@ void silu_and_mul_per_block_quant(torch::Tensor& out,
 
 void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
                       std::optional<torch::Tensor> key, int64_t head_size,
-                      torch::Tensor& cos_sin_cache, bool is_neox);
+                      torch::Tensor& cos_sin_cache, bool is_neox,
+                      int64_t rope_dim_offset, bool inverse);
 
 void silu_and_mul(torch::Tensor& out, torch::Tensor& input);
 
+void silu_and_mul_clamp(torch::Tensor& out, torch::Tensor& input, double limit);
+
 void silu_and_mul_quant(torch::Tensor& out, torch::Tensor& input,
                         torch::Tensor& scale);
 
diff --git a/csrc/persistent_topk.cuh b/csrc/persistent_topk.cuh
index 694fedad39f1..8b9d10ff83dd 100644
--- a/csrc/persistent_topk.cuh
+++ b/csrc/persistent_topk.cuh
@@ -18,7 +18,6 @@ namespace persistent {
 // Constants
 // ============================================================================
 
-constexpr int TopK = 2048;
 constexpr int kThreadsPerBlock = 1024;
 constexpr int RADIX = 256;
 
@@ -128,11 +127,12 @@ struct RadixRowState {
 
 struct PersistentTopKParams {
   const float* __restrict__ input;  // [num_rows, stride]
-  int32_t* __restrict__ output;     // [num_rows, TopK]
+  int32_t* __restrict__ output;     // [num_rows, top_k]
   int32_t* __restrict__ lengths;    // [num_rows]
   RadixRowState* row_states;        // large path: per-group state
   uint32_t num_rows;
   uint32_t stride;
+  uint32_t top_k;           // actual k value for output stride
   uint32_t chunk_size;      // large path: elements per CTA
   uint32_t ctas_per_group;  // 1=medium, >1=large
   uint32_t max_seq_len;     // max seq_len across all rows (for early CTA exit)
@@ -154,6 +154,7 @@ __device__ __forceinline__ uint32_t decode_bin(float x) {
   return key >> 5;
 }
 
+template <int TopK>
 __device__ __noinline__ void histogram_2048_topk(
     const float* __restrict__ logits, int32_t* __restrict__ output_indices,
     int32_t seq_len) {
@@ -418,6 +419,7 @@ __device__ __noinline__ void histogram_2048_topk(
 // by: DarkSharpness
 // which at the same time is an optimized topk kernel copied from tilelang
 // kernel
+template <int TopK>
 __device__ __noinline__ void histogram_256_topk(
     const float* __restrict__ logits, int* __restrict__ output_indices,
     int logits_offset, int seq_len) {
@@ -649,7 +651,7 @@ __device__ __forceinline__ void wait_ge(int* ptr, int target_val,
 // Adapted from https://github.com/flashinfer-ai/flashinfer/pull/2215
 // ============================================================================
 
-template <uint32_t VEC_SIZE>
+template <int TopK, uint32_t VEC_SIZE>
 __device__ void radix_topk(const float* __restrict__ row_input,
                            int32_t* __restrict__ row_output, uint32_t seq_len,
                            uint32_t my_chunk_start, uint32_t chunk_size,
@@ -857,7 +859,7 @@ __device__ void radix_topk(const float* __restrict__ row_input,
 // see filtered_topk.cuh)
 // ============================================================================
 
-template <uint32_t VEC_SIZE = 1>
+template <int TopK = 2048, uint32_t VEC_SIZE = 1>
 __global__ void __launch_bounds__(kThreadsPerBlock, 2)
     persistent_topk_kernel(PersistentTopKParams params) {
   const uint32_t tx = threadIdx.x;
@@ -885,27 +887,14 @@ __global__ void __launch_bounds__(kThreadsPerBlock, 2)
   uint32_t* shared_ordered =
       reinterpret_cast<uint32_t*>(smem_raw + kFixedSmemLarge);
 
-  // RadixRowState for multi-CTA cooperative radix
+  // RadixRowState for multi-CTA cooperative radix.
+  // Zero-initialization is done host-side via cudaMemsetAsync in topk.cu
+  // before launch — that gives a stream-ordered happens-before edge for all
+  // CTAs, which the previous in-kernel init (CTA-0 only + intra-CTA
+  // __syncthreads) did not provide and which manifested as a race against
+  // CTA-1+'s first red_release on arrival_counter.
   RadixRowState* state = &params.row_states[group_id];
 
-  // -- Initialize RadixRowState (only needed if large rows exist) --
-  if (params.max_seq_len > RADIX_THRESHOLD) {
-    if (cta_in_group == 0) {
-      for (uint32_t buf = 0; buf < 3; buf++) {
-        for (uint32_t i = tx; i < RADIX; i += kThreadsPerBlock) {
-          state->histogram[buf][i] = 0;
-        }
-      }
-      if (tx == 0) {
-        state->remaining_k = 0;
-        state->prefix = 0;
-        state->arrival_counter = 0;
-        state->output_counter = 0;
-      }
-    }
-    __syncthreads();
-  }
-
   int barrier_phase = 0;
   const uint32_t total_iters = (params.num_rows + num_groups - 1) / num_groups;
 
@@ -915,7 +904,7 @@ __global__ void __launch_bounds__(kThreadsPerBlock, 2)
     if (row_idx >= params.num_rows) break;
 
     const uint32_t seq_len = params.lengths[row_idx];
-    int32_t* row_output = params.output + row_idx * TopK;
+    int32_t* row_output = params.output + row_idx * params.top_k;
     const float* row_input = params.input + row_idx * params.stride;
 
     if (seq_len <= RADIX_THRESHOLD) {
@@ -927,19 +916,19 @@ __global__ void __launch_bounds__(kThreadsPerBlock, 2)
             row_output[i] = (i < seq_len) ? static_cast<int32_t>(i) : -1;
           }
         } else if (seq_len <= static_cast<uint32_t>(HIST2048_THRESHOLD)) {
-          histogram_2048_topk(row_input, row_output, seq_len);
+          histogram_2048_topk<TopK>(row_input, row_output, seq_len);
         } else {
-          histogram_256_topk(row_input, row_output, 0, seq_len);
+          histogram_256_topk<TopK>(row_input, row_output, 0, seq_len);
         }
       }
       continue;
     }
 
     const uint32_t my_chunk_start = cta_in_group * chunk_size;
-    radix_topk<VEC_SIZE>(row_input, row_output, seq_len, my_chunk_start,
-                         chunk_size, local_histogram, suffix_sum,
-                         shared_scalars, shared_ordered, state, cta_in_group,
-                         ctas_per_group, barrier_phase, iter, tx);
+    radix_topk<TopK, VEC_SIZE>(
+        row_input, row_output, seq_len, my_chunk_start, chunk_size,
+        local_histogram, suffix_sum, shared_scalars, shared_ordered, state,
+        cta_in_group, ctas_per_group, barrier_phase, iter, tx);
   }
 }
 
@@ -1011,7 +1000,6 @@ struct FilteredTopKTraits<float> {
   }
 };
 
-constexpr uint32_t FILTERED_TOPK_MAX_K = 2048;
 constexpr uint32_t FILTERED_TOPK_BLOCK_THREADS = 1024;
 constexpr uint32_t FILTERED_TOPK_SMEM_INPUT_SIZE =
     16 * 1024;  // 16K indices per buffer
@@ -1025,7 +1013,7 @@ constexpr size_t FILTERED_TOPK_SMEM_DYNAMIC =
  * \tparam IdType Index type (int32_t)
  * \tparam VEC_SIZE Vector size for input loads (1, 2, 4, or 8)
  */
-template <typename DType, typename IdType, int VEC_SIZE>
+template <typename DType, typename IdType, int VEC_SIZE, uint32_t MAX_K = 2048>
 __global__ void __launch_bounds__(FILTERED_TOPK_BLOCK_THREADS)
     FilteredTopKUnifiedKernel(const DType* __restrict__ input,
                               IdType* __restrict__ output,
@@ -1059,7 +1047,7 @@ __global__ void __launch_bounds__(FILTERED_TOPK_BLOCK_THREADS)
   alignas(128) __shared__ int s_counter;
   alignas(128) __shared__ int s_threshold_bin_id;
   alignas(128) __shared__ int s_num_input[2];
-  alignas(128) __shared__ int s_indices[FILTERED_TOPK_MAX_K];
+  alignas(128) __shared__ int s_indices[MAX_K];
 
   auto& s_histogram = s_histogram_buf[0];
 
@@ -1280,7 +1268,7 @@ constexpr int ComputeFilteredTopKVecSize(uint32_t max_len) {
   return static_cast<int>(g);
 }
 
-template <typename DType, typename IdType>
+template <typename DType, typename IdType, uint32_t MAX_K = 2048>
 cudaError_t FilteredTopKRaggedTransform(DType* input, IdType* output_indices,
                                         IdType* lengths, uint32_t num_rows,
                                         uint32_t top_k_val, uint32_t max_len,
@@ -1297,7 +1285,7 @@ cudaError_t FilteredTopKRaggedTransform(DType* input, IdType* output_indices,
 
 #define DISPATCH_VEC_SIZE(VS)                                               \
   if (vec_size == VS) {                                                     \
-    auto kernel = FilteredTopKUnifiedKernel<DType, IdType, VS>;             \
+    auto kernel = FilteredTopKUnifiedKernel<DType, IdType, VS, MAX_K>;      \
     FLASHINFER_CUDA_CALL(cudaFuncSetAttribute(                              \
         kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));   \
     FLASHINFER_CUDA_CALL(cudaLaunchKernel((void*)kernel, grid, block, args, \
diff --git a/csrc/pos_encoding_kernels.cu b/csrc/pos_encoding_kernels.cu
index b5645b33b907..d03c6a5cf0dd 100644
--- a/csrc/pos_encoding_kernels.cu
+++ b/csrc/pos_encoding_kernels.cu
@@ -7,33 +7,34 @@
 
 namespace vllm {
 
-template <typename scalar_t, bool IS_NEOX>
+template <typename scalar_t, typename cache_t, bool IS_NEOX>
 inline __device__ void apply_token_rotary_embedding(
-    scalar_t* __restrict__ arr, const scalar_t* __restrict__ cos_ptr,
-    const scalar_t* __restrict__ sin_ptr, int rot_offset, int embed_dim) {
+    scalar_t* __restrict__ arr, const cache_t* __restrict__ cos_ptr,
+    const cache_t* __restrict__ sin_ptr, int rot_offset, int embed_dim,
+    const bool inverse) {
   int x_index, y_index;
-  scalar_t cos, sin;
+  float cos_f, sin_f;
   if (IS_NEOX) {
-    // GPT-NeoX style rotary embedding.
     x_index = rot_offset;
     y_index = embed_dim + rot_offset;
-    cos = VLLM_LDG(cos_ptr + x_index);
-    sin = VLLM_LDG(sin_ptr + x_index);
+    cos_f = static_cast<float>(VLLM_LDG(cos_ptr + x_index));
+    sin_f = static_cast<float>(VLLM_LDG(sin_ptr + x_index));
   } else {
-    // GPT-J style rotary embedding.
     x_index = 2 * rot_offset;
     y_index = 2 * rot_offset + 1;
-    cos = VLLM_LDG(cos_ptr + x_index / 2);
-    sin = VLLM_LDG(sin_ptr + x_index / 2);
+    cos_f = static_cast<float>(VLLM_LDG(cos_ptr + x_index / 2));
+    sin_f = static_cast<float>(VLLM_LDG(sin_ptr + x_index / 2));
   }
-
-  const scalar_t x = arr[x_index];
-  const scalar_t y = arr[y_index];
-  arr[x_index] = x * cos - y * sin;
-  arr[y_index] = y * cos + x * sin;
+  if (inverse) {
+    sin_f = -sin_f;
+  }
+  const float x_f = static_cast<float>(arr[x_index]);
+  const float y_f = static_cast<float>(arr[y_index]);
+  arr[x_index] = static_cast<scalar_t>(x_f * cos_f - y_f * sin_f);
+  arr[y_index] = static_cast<scalar_t>(y_f * cos_f + x_f * sin_f);
 }
 
-template <typename scalar_t, bool IS_NEOX>
+template <typename scalar_t, typename cache_t, bool IS_NEOX>
 inline __device__ void apply_rotary_embedding(
     scalar_t* __restrict__ query,  // [batch_size, seq_len, num_heads,
                                    // head_size] or [num_tokens, num_heads,
@@ -42,22 +43,23 @@ inline __device__ void apply_rotary_embedding(
                                    // [batch_size, seq_len, num_kv_heads,
                                    // head_size] or [num_tokens, num_kv_heads,
                                    // head_size]
-    const scalar_t* cache_ptr, const int head_size, const int num_heads,
+    const cache_t* cache_ptr, const int head_size, const int num_heads,
     const int num_kv_heads, const int rot_dim, const int token_idx,
     const int64_t query_stride, const int64_t key_stride,
-    const int64_t head_stride) {
+    const int64_t head_stride, const int64_t rope_dim_offset,
+    const bool inverse) {
   const int embed_dim = rot_dim / 2;
-  const scalar_t* cos_ptr = cache_ptr;
-  const scalar_t* sin_ptr = cache_ptr + embed_dim;
+  const cache_t* cos_ptr = cache_ptr;
+  const cache_t* sin_ptr = cache_ptr + embed_dim;
 
   const int nq = num_heads * embed_dim;
   for (int i = threadIdx.x; i < nq; i += blockDim.x) {
     const int head_idx = i / embed_dim;
     const int64_t token_head =
-        token_idx * query_stride + head_idx * head_stride;
+        token_idx * query_stride + head_idx * head_stride + rope_dim_offset;
     const int rot_offset = i % embed_dim;
-    apply_token_rotary_embedding<scalar_t, IS_NEOX>(
-        query + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim);
+    apply_token_rotary_embedding<scalar_t, cache_t, IS_NEOX>(
+        query + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim, inverse);
   }
 
   if (key != nullptr) {
@@ -65,15 +67,15 @@ inline __device__ void apply_rotary_embedding(
     for (int i = threadIdx.x; i < nk; i += blockDim.x) {
       const int head_idx = i / embed_dim;
       const int64_t token_head =
-          token_idx * key_stride + head_idx * head_stride;
+          token_idx * key_stride + head_idx * head_stride + rope_dim_offset;
       const int rot_offset = i % embed_dim;
-      apply_token_rotary_embedding<scalar_t, IS_NEOX>(
-          key + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim);
+      apply_token_rotary_embedding<scalar_t, cache_t, IS_NEOX>(
+          key + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim, inverse);
     }
   }
 }
 
-template <typename scalar_t, bool IS_NEOX>
+template <typename scalar_t, typename cache_t, bool IS_NEOX>
 __global__ void rotary_embedding_kernel(
     const int64_t* __restrict__ positions,  // [batch_size, seq_len] or
                                             // [num_tokens]
@@ -84,19 +86,18 @@ __global__ void rotary_embedding_kernel(
                                  // [batch_size, seq_len, num_kv_heads,
                                  // head_size] or [num_tokens, num_kv_heads,
                                  // head_size]
-    const scalar_t* __restrict__ cos_sin_cache,  // [max_position, 2, rot_dim //
-                                                 // 2]
+    const cache_t* __restrict__ cos_sin_cache,  // [max_position, rot_dim]
     const int rot_dim, const int64_t query_stride, const int64_t key_stride,
     const int64_t head_stride, const int num_heads, const int num_kv_heads,
-    const int head_size) {
-  // Each thread block is responsible for one token.
+    const int head_size, const int64_t rope_dim_offset, const bool inverse) {
   const int token_idx = blockIdx.x;
   int64_t pos = positions[token_idx];
-  const scalar_t* cache_ptr = cos_sin_cache + pos * rot_dim;
+  const cache_t* cache_ptr = cos_sin_cache + pos * rot_dim;
 
-  apply_rotary_embedding<scalar_t, IS_NEOX>(
+  apply_rotary_embedding<scalar_t, cache_t, IS_NEOX>(
       query, key, cache_ptr, head_size, num_heads, num_kv_heads, rot_dim,
-      token_idx, query_stride, key_stride, head_stride);
+      token_idx, query_stride, key_stride, head_stride, rope_dim_offset,
+      inverse);
 }
 
 }  // namespace vllm
@@ -115,7 +116,7 @@ void rotary_embedding(
     // [num_tokens, num_heads, head_size]
     int64_t head_size,
     torch::Tensor& cos_sin_cache,  // [max_position, rot_dim]
-    bool is_neox) {
+    bool is_neox, int64_t rope_dim_offset, bool inverse) {
   // num_tokens = batch_size * seq_len
   int64_t num_tokens = positions.numel();
   int positions_ndim = positions.dim();
@@ -154,6 +155,8 @@ void rotary_embedding(
   int seq_dim_idx = positions_ndim - 1;
   int64_t query_stride = query.stride(seq_dim_idx);
   int64_t key_stride = key.has_value() ? key->stride(seq_dim_idx) : 0;
+
+  TORCH_CHECK((rot_dim + rope_dim_offset) <= head_size);
   // Determine head stride: for [*, heads, head_size] use stride of last dim;
   // for flat [*, heads*head_size], heads blocks are contiguous of size
   // head_size
@@ -166,19 +169,27 @@ void rotary_embedding(
   const at::cuda::OptionalCUDAGuard device_guard(device_of(query));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   VLLM_DISPATCH_FLOATING_TYPES(query.scalar_type(), "rotary_embedding", [&] {
-    if (is_neox) {
-      vllm::rotary_embedding_kernel<scalar_t, true><<<grid, block, 0, stream>>>(
-          positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
-          key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
-          cos_sin_cache.data_ptr<scalar_t>(), rot_dim, query_stride, key_stride,
-          head_stride, num_heads, num_kv_heads, head_size);
-    } else {
-      vllm::rotary_embedding_kernel<scalar_t, false>
-          <<<grid, block, 0, stream>>>(
-              positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
-              key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
-              cos_sin_cache.data_ptr<scalar_t>(), rot_dim, query_stride,
-              key_stride, head_stride, num_heads, num_kv_heads, head_size);
-    }
+    using query_t = scalar_t;
+    VLLM_DISPATCH_FLOATING_TYPES(
+        cos_sin_cache.scalar_type(), "rotary_embedding_cache", [&] {
+          using cache_t = scalar_t;
+          if (is_neox) {
+            vllm::rotary_embedding_kernel<query_t, cache_t, true>
+                <<<grid, block, 0, stream>>>(
+                    positions.data_ptr<int64_t>(), query.data_ptr<query_t>(),
+                    key.has_value() ? key->data_ptr<query_t>() : nullptr,
+                    cos_sin_cache.data_ptr<cache_t>(), rot_dim, query_stride,
+                    key_stride, head_stride, num_heads, num_kv_heads, head_size,
+                    rope_dim_offset, inverse);
+          } else {
+            vllm::rotary_embedding_kernel<query_t, cache_t, false>
+                <<<grid, block, 0, stream>>>(
+                    positions.data_ptr<int64_t>(), query.data_ptr<query_t>(),
+                    key.has_value() ? key->data_ptr<query_t>() : nullptr,
+                    cos_sin_cache.data_ptr<cache_t>(), rot_dim, query_stride,
+                    key_stride, head_stride, num_heads, num_kv_heads, head_size,
+                    rope_dim_offset, inverse);
+          }
+        });
   });
 }
diff --git a/csrc/quantization/gguf/moe.cuh b/csrc/quantization/gguf/moe.cuh
index df9b84abcc13..a2f9f46c8f89 100644
--- a/csrc/quantization/gguf/moe.cuh
+++ b/csrc/quantization/gguf/moe.cuh
@@ -1,7 +1,7 @@
 #include <cstdint>
 
 /* Adapted from ./csrc/quantization/gguf/mmq.cuh
-   based on ./vllm/model_executor/layers/fused_moe/fused_moe.py */
+   based on ./vllm/model_executor/layers/fused_moe/experts/triton_moe.py */
 template <typename scalar_t, int qk, int qr, int qi, bool need_sum,
           typename block_q_t, int mmq_x, int mmq_y, int nwarps,
           allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles,
diff --git a/csrc/quantization/gptq/compat.cuh b/csrc/quantization/gptq/compat.cuh
index 1b3fb3d39103..f95f3fd95860 100644
--- a/csrc/quantization/gptq/compat.cuh
+++ b/csrc/quantization/gptq/compat.cuh
@@ -42,8 +42,8 @@ __device__ __forceinline__ void atomicAdd_half2(half2* address, half2 val) {
 }
 
 //
-
-#if defined(__CUDA_ARCH__) || defined(USE_ROCM)
+#if defined(__CUDA_ARCH__) || \
+    (defined(USE_ROCM) && (HIP_VERSION_MAJOR * 100 + HIP_VERSION_MINOR) < 713)
   #if __CUDA_ARCH__ < 700 || defined(USE_ROCM)
 
 __device__ __forceinline__ void atomicAdd(half* address, half val) {
diff --git a/csrc/rocm/attention.cu b/csrc/rocm/attention.cu
index a339c5641bb4..9e6c0726d19e 100644
--- a/csrc/rocm/attention.cu
+++ b/csrc/rocm/attention.cu
@@ -40,15 +40,6 @@ using __hip_fp8_e5m2 = __hip_fp8_e5m2_fnuz;
   #define __HIP__FP8MFMA__
 #endif
 
-#if defined(__HIPCC__) && (defined(__gfx1100__) || defined(__gfx1101__) || \
-                           defined(__gfx1150__) || defined(__gfx1151__))
-  #define __HIP__GFX11__
-#endif
-
-#if defined(__HIPCC__) && (defined(__gfx1200__) || defined(__gfx1201__))
-  #define __HIP__GFX12__
-#endif
-
 #if defined(NDEBUG)
   #undef NDEBUG
   #include <assert.h>
@@ -1629,7 +1620,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
   }
 }
 
-#elif defined(__HIP__GFX11__)
+#elif defined(__GFX11__)
 
 using floatx8 = __attribute__((__vector_size__(8 * sizeof(float)))) float;
 
@@ -2388,7 +2379,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
   out_ptr[threadIdx.x] = from_float<scalar_t>(acc);
 }
 
-#elif defined(__HIP__GFX12__)
+#elif defined(__GFX12__)
 
 using floatx8 = __attribute__((__vector_size__(8 * sizeof(float)))) float;
 
diff --git a/csrc/rocm/skinny_gemms.cu b/csrc/rocm/skinny_gemms.cu
index 60e10e53391a..3342db37be90 100644
--- a/csrc/rocm/skinny_gemms.cu
+++ b/csrc/rocm/skinny_gemms.cu
@@ -26,16 +26,11 @@
   #define __HIP__GFX9__
 #endif
 
-#if defined(__HIPCC__) &&                                                    \
-    (defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1150__) || \
-     defined(__gfx1151__) || defined(__gfx1200__) || defined(__gfx1201__))
+// Combined RDNA macro (gfx11 + gfx12) - both use 32-wide wavefronts
+#if defined(__GFX11__) || defined(__GFX12__)
   #define __HIP__GFX1X__
 #endif
 
-#if defined(__HIPCC__) && (defined(__gfx1200__) || defined(__gfx1201__))
-  #define __HIP__GFX12__
-#endif
-
 #if defined(__HIPCC__) && (defined(__gfx942__) || defined(__gfx950__))
   #define __HIP__MI3XX__
 #endif
@@ -1845,7 +1840,7 @@ torch::Tensor wvSplitKrc(const at::Tensor& in_a, const at::Tensor& in_b,
   return out_c;
 }
 
-#if defined(__HIP__MI3XX__) || defined(__HIP__GFX12__)
+#if defined(__HIP__MI3XX__) || defined(__GFX12__)
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
 __global__ void __launch_bounds__(WvPrGrp* THRDS)
@@ -1893,7 +1888,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   float sB = *s_B;
 
   while (m < M) {
-  #ifdef __HIP__GFX12__
+  #ifdef __GFX12__
     // gfx12: per-lane scalar accumulation via v_dot4_f32_fp8_fp8
     float sum[N][YTILE] = {};
   #else
@@ -1931,7 +1926,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   #pragma unroll
       for (uint32_t k2 = 0; k2 < UNRL; k2++) {
         for (uint32_t n = 0; n < N; n++) {
-  #ifdef __HIP__GFX12__
+  #ifdef __GFX12__
           // gfx12: 4 x dot4 per A_CHUNK=16 bytes (4 FP8 per dot4)
           for (int y = 0; y < YTILE; ++y) {
     #pragma unroll
@@ -1955,7 +1950,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     }
 
     // Final reduction
-  #ifdef __HIP__GFX12__
+  #ifdef __GFX12__
     // gfx12 wave32: DPP row_shr within 16-lane rows + cross-row shuffle
     for (int n = 0; n < N; n++) {
       for (int y = 0; y < YTILE; y++) {
@@ -1993,7 +1988,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   #endif
 
     const bool writeback_lane =
-  #ifdef __HIP__GFX12__
+  #ifdef __GFX12__
         threadIdx.x == (THRDS - 1);
   #else
         threadIdx.x == 0;
@@ -2009,7 +2004,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
       for (int n = 0; n < N; n++) {
         for (int y = 0; y < YTILE; y++) {
           if (y + m >= M) break;  // To avoid mem access fault.
-  #ifdef __HIP__GFX12__
+  #ifdef __GFX12__
           float result = sum[n][y] * sA * sB;
   #else
           float result = sum[n][y][0] * sA * sB;
@@ -2027,7 +2022,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     m += CuCount * _WvPrGrp * YTILE;
   }
 }
-#else   // !defined(__HIP__MI3XX__) && !defined(__HIP__GFX12__)
+#else   // !defined(__HIP__MI3XX__) && !defined(__GFX12__)
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
 __global__ void wvSplitKQ_hf_sml_(const int K, const int Kap, const int Kbp,
@@ -2039,9 +2034,9 @@ __global__ void wvSplitKQ_hf_sml_(const int K, const int Kap, const int Kbp,
                                   const int _WvPrGrp, const int CuCount) {
   UNREACHABLE_CODE
 }
-#endif  // defined(__HIP__MI3XX__) || defined(__HIP__GFX12__)
+#endif  // defined(__HIP__MI3XX__) || defined(__GFX12__)
 
-#if defined(__HIP__MI3XX__) || defined(__HIP__GFX12__)
+#if defined(__HIP__MI3XX__) || defined(__GFX12__)
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
 __global__ void __launch_bounds__(WvPrGrp* THRDS)
@@ -2088,7 +2083,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   float sB = *s_B;
 
   while (m < M) {
-  #ifdef __HIP__GFX12__
+  #ifdef __GFX12__
     // gfx12: per-lane scalar accumulation via v_dot4_f32_fp8_fp8
     float sum[N][YTILE] = {};
   #else
@@ -2128,7 +2123,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   #pragma unroll
       for (uint32_t k2 = 0; k2 < UNRL; k2++) {
         for (uint32_t n = 0; n < N; n++) {
-  #ifdef __HIP__GFX12__
+  #ifdef __GFX12__
           // gfx12: 4 x dot4 per A_CHUNK=16 bytes (4 FP8 per dot4)
           for (int y = 0; y < YTILE; ++y) {
     #pragma unroll
@@ -2152,7 +2147,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     }
 
     // Final reduction
-  #ifdef __HIP__GFX12__
+  #ifdef __GFX12__
     // gfx12 wave32: DPP row_shr within 16-lane rows + cross-row shuffle
     for (int n = 0; n < N; n++) {
       for (int y = 0; y < YTILE; y++) {
@@ -2190,7 +2185,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   #endif
 
     const bool writeback_lane =
-  #ifdef __HIP__GFX12__
+  #ifdef __GFX12__
         threadIdx.x == (THRDS - 1);
   #else
         threadIdx.x == 0;
@@ -2206,7 +2201,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
       for (int n = 0; n < N; n++) {
         for (int y = 0; y < YTILE; y++) {
           if (y + m >= M) break;  // To avoid mem access fault.
-  #ifdef __HIP__GFX12__
+  #ifdef __GFX12__
           float result = sum[n][y] * sA * sB;
   #else
           float result = sum[n][y][0] * sA * sB;
@@ -2224,7 +2219,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     m += CuCount * _WvPrGrp * YTILE;
   }
 }
-#else   // !defined(__HIP__MI3XX__) && !defined(__HIP__GFX12__)
+#else   // !defined(__HIP__MI3XX__) && !defined(__GFX12__)
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
 __global__ void wvSplitKQ_hf_(const int K, const int Kap, const int Kbp,
@@ -2236,7 +2231,7 @@ __global__ void wvSplitKQ_hf_(const int K, const int Kap, const int Kbp,
                               const int CuCount) {
   UNREACHABLE_CODE
 }
-#endif  // defined(__HIP__MI3XX__) || defined(__HIP__GFX12__)
+#endif  // defined(__HIP__MI3XX__) || defined(__GFX12__)
 
 void wvSplitKQ(const at::Tensor& in_b, const at::Tensor& in_a,
                const std::optional<at::Tensor>& in_bias, at::Tensor& out_c,
diff --git a/csrc/sampler.cu b/csrc/sampler.cu
index c0cc03a08ad7..14d84013c08d 100644
--- a/csrc/sampler.cu
+++ b/csrc/sampler.cu
@@ -258,7 +258,13 @@ __device__ bool processHistogramStep(
   auto processBins = [&](float logit, int idx) {
     if (isPartialMatch<patternShift>(logit, logitPattern)) {
       uint32_t binIdx = extractBinIdx<step>(logit);
-      if (binIdx < thresholdBinIdx) {
+      // Only write elements with binIdx < thresholdBinIdx when:
+      // 1. This is step 0 and the threshold bin is small enough (no step 1)
+      // 2. This is step >= 1 (where pattern matching filters correctly)
+      // This prevents duplicates when step 0 and step 1 both run.
+      bool shouldWriteDirectly =
+          (step == 0 && smemFinalBinSize[0] <= kNumFinalItems) || (step >= 1);
+      if (binIdx < thresholdBinIdx && shouldWriteDirectly) {
         // The element is part of the top-k selection
         int dstIdx = atomicAdd(&smemFoundTopKValues[0], 1);
 
diff --git a/csrc/topk.cu b/csrc/topk.cu
index f48e7cbc4fc8..9ca9aa8824d8 100644
--- a/csrc/topk.cu
+++ b/csrc/topk.cu
@@ -10,33 +10,17 @@
   #include "persistent_topk.cuh"
 #endif
 
-void persistent_topk(const torch::Tensor& logits, const torch::Tensor& lengths,
-                     torch::Tensor& output, torch::Tensor& workspace, int64_t k,
-                     int64_t max_seq_len) {
-#ifndef USE_ROCM
-  TORCH_CHECK(logits.is_cuda(), "logits must be CUDA tensor");
-  TORCH_CHECK(lengths.is_cuda(), "lengths must be CUDA tensor");
-  TORCH_CHECK(output.is_cuda(), "output must be CUDA tensor");
-  TORCH_CHECK(logits.dtype() == torch::kFloat32, "Only float32 supported");
-  TORCH_CHECK(lengths.dtype() == torch::kInt32, "lengths must be int32");
-  TORCH_CHECK(output.dtype() == torch::kInt32, "output must be int32");
-  TORCH_CHECK(logits.dim() == 2, "logits must be 2D");
-  TORCH_CHECK(lengths.dim() == 1 || lengths.dim() == 2,
-              "lengths must be 1D or 2D");
-  TORCH_CHECK(lengths.is_contiguous(), "lengths must be contiguous");
-  TORCH_CHECK(output.dim() == 2, "output must be 2D");
+namespace {
 
-  const int64_t num_rows = logits.size(0);
-  const int64_t stride = logits.size(1);
-
-  TORCH_CHECK(lengths.numel() == num_rows, "lengths size mismatch");
-  TORCH_CHECK(output.size(0) == num_rows && output.size(1) == k,
-              "output size mismatch");
+#ifndef USE_ROCM
+template <int TopK>
+void launch_persistent_topk(const torch::Tensor& logits,
+                            const torch::Tensor& lengths, torch::Tensor& output,
+                            torch::Tensor& workspace, int64_t max_seq_len) {
   namespace P = vllm::persistent;
 
-  TORCH_CHECK(k == P::TopK, "k must be 2048");
-  TORCH_CHECK(k <= stride, "k out of range");
-
+  const int64_t num_rows = logits.size(0);
+  const int64_t stride = logits.stride(0);
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
   static int num_sms = 0;
@@ -50,18 +34,17 @@ void persistent_topk(const torch::Tensor& logits, const torch::Tensor& lengths,
   }
 
   if (num_rows > 32 && max_smem_per_block >= 128 * 1024) {
-    cudaError_t status = vllm::FilteredTopKRaggedTransform<float, int32_t>(
-        logits.data_ptr<float>(), output.data_ptr<int32_t>(),
-        lengths.data_ptr<int32_t>(), static_cast<uint32_t>(num_rows),
-        static_cast<uint32_t>(k), static_cast<uint32_t>(stride), stream);
+    cudaError_t status =
+        vllm::FilteredTopKRaggedTransform<float, int32_t, TopK>(
+            logits.data_ptr<float>(), output.data_ptr<int32_t>(),
+            lengths.data_ptr<int32_t>(), static_cast<uint32_t>(num_rows),
+            static_cast<uint32_t>(TopK), static_cast<uint32_t>(stride), stream);
     TORCH_CHECK(status == cudaSuccess,
                 "FilteredTopK failed: ", cudaGetErrorString(status));
   } else {
     TORCH_CHECK(workspace.is_cuda(), "workspace must be CUDA tensor");
     TORCH_CHECK(workspace.dtype() == torch::kUInt8, "workspace must be uint8");
 
-    // Smem cap: smaller smem → more CTAs/group → more per-row parallelism for
-    // large path. Empirically tuned.
     int effective_max_smem;
     if (num_rows <= 4) {
       effective_max_smem =
@@ -99,37 +82,125 @@ void persistent_topk(const torch::Tensor& logits, const torch::Tensor& lengths,
     size_t smem_size = P::kFixedSmemLarge + chunk_size * sizeof(uint32_t);
     if (smem_size < P::kSmemMedium) smem_size = P::kSmemMedium;
 
+    // Query occupancy for the instantiation that will actually launch;
+    // overestimating it deadlocks the cooperative barrier.
     int occupancy = 1;
-    cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-        &occupancy, P::persistent_topk_kernel<4>, P::kThreadsPerBlock,
-        smem_size);
+    cudaError_t occ_err = cudaSuccess;
+    if (vec_size == 4) {
+      occ_err = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+          &occupancy, P::persistent_topk_kernel<TopK, 4>, P::kThreadsPerBlock,
+          smem_size);
+    } else if (vec_size == 2) {
+      occ_err = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+          &occupancy, P::persistent_topk_kernel<TopK, 2>, P::kThreadsPerBlock,
+          smem_size);
+    } else {
+      occ_err = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+          &occupancy, P::persistent_topk_kernel<TopK, 1>, P::kThreadsPerBlock,
+          smem_size);
+    }
+    TORCH_CHECK(occ_err == cudaSuccess,
+                "persistent_topk occupancy query failed: ",
+                cudaGetErrorString(occ_err));
     if (occupancy < 1) occupancy = 1;
 
-    uint32_t max_resident_ctas = static_cast<uint32_t>(num_sms) * occupancy;
+    // The cooperative spin-wait barrier only runs when at least one row hits
+    // the radix path (seq_len > RADIX_THRESHOLD). Below that, non-CTA-0 CTAs
+    // early-exit, so oversubscription can't deadlock and headroom is wasted.
+    const bool needs_cooperative =
+        static_cast<uint32_t>(max_seq_len) > P::RADIX_THRESHOLD;
+
+    const uint32_t hw_resident_cap =
+        static_cast<uint32_t>(num_sms) * static_cast<uint32_t>(occupancy);
+    uint32_t max_resident_ctas = hw_resident_cap;
+    if (needs_cooperative) {
+      // Reserve one CTA per SM when occupancy allows; fall back to a single
+      // CTA when occupancy == 1 (the most deadlock-prone case — any straggler
+      // kernel that takes the only slot on one SM hangs the barrier). Never
+      // drop below one full group's worth.
+      uint32_t headroom = (occupancy > 1) ? static_cast<uint32_t>(num_sms) : 1u;
+      if (max_resident_ctas >= headroom + ctas_per_group) {
+        max_resident_ctas -= headroom;
+      }
+    }
     uint32_t num_groups = std::min(max_resident_ctas / ctas_per_group,
                                    static_cast<uint32_t>(num_rows));
     if (num_groups == 0) num_groups = 1;
     uint32_t total_ctas = num_groups * ctas_per_group;
 
+    // If the cooperative launch wouldn't fit, fall back to FilteredTopK
+    // instead of deadlocking. Only relevant when needs_cooperative.
+    if (needs_cooperative && total_ctas > hw_resident_cap) {
+      TORCH_CHECK(max_smem_per_block >= 128 * 1024,
+                  "persistent_topk would oversubscribe and the FilteredTopK "
+                  "fallback requires >=128KB smem per block (have ",
+                  max_smem_per_block, "). total_ctas=", total_ctas,
+                  " > num_sms*occupancy=", hw_resident_cap, " (TopK=", TopK,
+                  ", vec_size=", vec_size, ", ctas_per_group=", ctas_per_group,
+                  ", smem=", smem_size, ").");
+      cudaError_t status =
+          vllm::FilteredTopKRaggedTransform<float, int32_t, TopK>(
+              logits.data_ptr<float>(), output.data_ptr<int32_t>(),
+              lengths.data_ptr<int32_t>(), static_cast<uint32_t>(num_rows),
+              static_cast<uint32_t>(TopK), static_cast<uint32_t>(stride),
+              stream);
+      TORCH_CHECK(status == cudaSuccess,
+                  "FilteredTopK fallback failed: ", cudaGetErrorString(status));
+      return;
+    }
+
     size_t state_bytes = num_groups * sizeof(P::RadixRowState);
     TORCH_CHECK(workspace.size(0) >= static_cast<int64_t>(state_bytes),
                 "workspace too small, need ", state_bytes, " bytes");
 
+    // Zero the per-group RadixRowState region before launch.
+    //
+    // Issued UNCONDITIONALLY so the memset is captured as its own node in
+    // the cudagraph (a separate cudaMemsetAsync node, sequenced before the
+    // persistent_topk_kernel launch on the same stream). The previous
+    // host-side guard `if (needs_cooperative)` was evaluated at capture time;
+    // when capture-time max_seq_len <= RADIX_THRESHOLD (always true under
+    // FULL_DECODE_ONLY with max_model_len < 32 K) the memset would NOT be
+    // captured, leaving the workspace state to accumulate across replays.
+    // That's a latent correctness bug if the runtime data ever takes the
+    // radix path, and removes one variable while debugging hangs in the
+    // decode/medium paths.
+    //
+    // Cost is sub-microsecond: state_bytes = num_groups * sizeof(RadixRowState)
+    // is ~3 KB per group, ~100 KB for the largest grids on this hardware.
+    //
+    // Why the memset is required (regardless of which path the kernel takes):
+    //   1. arrival_counter accumulates within a launch and is never reset,
+    //      so a prior call leaves it at a large positive value. Without this
+    //      reset, the very first wait_ge in the next call sees counter >>
+    //      target and returns instantly, breaking the barrier.
+    //   2. The previous in-kernel init only ran in CTA-0 with intra-CTA
+    //      __syncthreads(), so it had no happens-before edge to CTA-1+'s
+    //      first red_release. cudaMemsetAsync is stream-ordered: the zero
+    //      is globally visible before any CTA runs.
+    {
+      cudaError_t mz_err = cudaMemsetAsync(workspace.data_ptr<uint8_t>(), 0,
+                                           state_bytes, stream);
+      TORCH_CHECK(mz_err == cudaSuccess,
+                  "row_states memset failed: ", cudaGetErrorString(mz_err));
+    }
+
     P::PersistentTopKParams params;
     params.input = logits.data_ptr<float>();
     params.output = output.data_ptr<int32_t>();
     params.lengths = lengths.data_ptr<int32_t>();
     params.num_rows = static_cast<uint32_t>(num_rows);
     params.stride = static_cast<uint32_t>(stride);
+    params.top_k = static_cast<uint32_t>(TopK);
     params.chunk_size = chunk_size;
     params.row_states =
         reinterpret_cast<P::RadixRowState*>(workspace.data_ptr<uint8_t>());
     params.ctas_per_group = ctas_per_group;
     params.max_seq_len = static_cast<uint32_t>(max_seq_len);
 
-  #define LAUNCH_PERSISTENT(VS)                                               \
+  #define LAUNCH_PERSISTENT(TOPK_VAL, VS)                                     \
     do {                                                                      \
-      auto kernel = &P::persistent_topk_kernel<VS>;                           \
+      auto kernel = &P::persistent_topk_kernel<TOPK_VAL, VS>;                 \
       cudaError_t err = cudaFuncSetAttribute(                                 \
           kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size);    \
       TORCH_CHECK(err == cudaSuccess,                                         \
@@ -138,11 +209,11 @@ void persistent_topk(const torch::Tensor& logits, const torch::Tensor& lengths,
     } while (0)
 
     if (vec_size == 4) {
-      LAUNCH_PERSISTENT(4);
+      LAUNCH_PERSISTENT(TopK, 4);
     } else if (vec_size == 2) {
-      LAUNCH_PERSISTENT(2);
+      LAUNCH_PERSISTENT(TopK, 2);
     } else {
-      LAUNCH_PERSISTENT(1);
+      LAUNCH_PERSISTENT(TopK, 1);
     }
   #undef LAUNCH_PERSISTENT
   }
@@ -150,6 +221,46 @@ void persistent_topk(const torch::Tensor& logits, const torch::Tensor& lengths,
   cudaError_t err = cudaGetLastError();
   TORCH_CHECK(err == cudaSuccess,
               "persistent_topk failed: ", cudaGetErrorString(err));
+}
+#endif
+
+}  // anonymous namespace
+
+void persistent_topk(const torch::Tensor& logits, const torch::Tensor& lengths,
+                     torch::Tensor& output, torch::Tensor& workspace, int64_t k,
+                     int64_t max_seq_len) {
+#ifndef USE_ROCM
+  TORCH_CHECK(logits.is_cuda(), "logits must be CUDA tensor");
+  TORCH_CHECK(lengths.is_cuda(), "lengths must be CUDA tensor");
+  TORCH_CHECK(output.is_cuda(), "output must be CUDA tensor");
+  TORCH_CHECK(logits.dtype() == torch::kFloat32, "Only float32 supported");
+  TORCH_CHECK(lengths.dtype() == torch::kInt32, "lengths must be int32");
+  TORCH_CHECK(output.dtype() == torch::kInt32, "output must be int32");
+  TORCH_CHECK(logits.dim() == 2, "logits must be 2D");
+  TORCH_CHECK(lengths.dim() == 1 || lengths.dim() == 2,
+              "lengths must be 1D or 2D");
+  TORCH_CHECK(lengths.is_contiguous(), "lengths must be contiguous");
+  TORCH_CHECK(output.dim() == 2, "output must be 2D");
+
+  const int64_t num_rows = logits.size(0);
+  const int64_t stride = logits.stride(0);
+
+  TORCH_CHECK(lengths.numel() == num_rows, "lengths size mismatch");
+  TORCH_CHECK(output.size(0) == num_rows && output.size(1) == k,
+              "output size mismatch");
+  TORCH_CHECK(k == 512 || k == 1024 || k == 2048,
+              "persistent_topk supports k=512, k=1024, or k=2048, got k=", k);
+
+  if (k == 512) {
+    launch_persistent_topk<512>(logits, lengths, output, workspace,
+                                max_seq_len);
+  } else if (k == 1024) {
+    launch_persistent_topk<1024>(logits, lengths, output, workspace,
+                                 max_seq_len);
+  } else {
+    launch_persistent_topk<2048>(logits, lengths, output, workspace,
+                                 max_seq_len);
+  }
 #else
   TORCH_CHECK(false, "persistent_topk is not supported on ROCm");
 #endif
diff --git a/csrc/torch_bindings.cpp b/csrc/torch_bindings.cpp
index 48062c3f47b2..7562d90c0b99 100644
--- a/csrc/torch_bindings.cpp
+++ b/csrc/torch_bindings.cpp
@@ -106,6 +106,12 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("silu_and_mul(Tensor! result, Tensor input) -> ()");
   ops.impl("silu_and_mul", torch::kCUDA, &silu_and_mul);
 
+  // SwiGLU activation with input clamping.
+  ops.def(
+      "silu_and_mul_with_clamp(Tensor! result, Tensor input, float limit) "
+      "-> ()");
+  ops.impl("silu_and_mul_with_clamp", torch::kCUDA, &silu_and_mul_clamp);
+
   ops.def(
       "silu_and_mul_quant(Tensor! result, Tensor input, Tensor scale) -> ()");
   ops.impl("silu_and_mul_quant", torch::kCUDA, &silu_and_mul_quant);
@@ -177,6 +183,17 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "int forced_token_heads_per_warp=-1) -> ()");
   ops.impl("fused_qk_norm_rope", torch::kCUDA, &fused_qk_norm_rope);
 
+  // Horizontally-fused DeepseekV4-MLA: per-head RMSNorm + GPT-J RoPE for Q, and
+  // GPT-J RoPE + UE8M0 FP8 quant + paged cache insert for KV, all in one
+  // kernel launch.
+  ops.def(
+      "fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert("
+      "Tensor! q, Tensor kv, Tensor! k_cache, "
+      "Tensor slot_mapping, Tensor position_ids, Tensor cos_sin_cache, "
+      "float eps, int cache_block_size) -> ()");
+  ops.impl("fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert", torch::kCUDA,
+           &fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert);
+
   // Apply repetition penalties to logits in-place
   ops.def(
       "apply_repetition_penalties_(Tensor! logits, Tensor prompt_mask, "
@@ -240,7 +257,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def(
       "rotary_embedding(Tensor positions, Tensor! query,"
       "                 Tensor!? key, int head_size,"
-      "                 Tensor cos_sin_cache, bool is_neox) -> ()");
+      "                 Tensor cos_sin_cache, bool is_neox, int "
+      "rope_dim_offset=0, bool inverse=False) -> ()");
   ops.impl("rotary_embedding", torch::kCUDA, &rotary_embedding);
 
   // Quantization ops
@@ -535,7 +553,8 @@ TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cache_ops), cache_ops) {
   // Batch swap: submit all block copies in a single driver call.
   cache_ops.def(
       "swap_blocks_batch(Tensor src_ptrs, Tensor dst_ptrs,"
-      "                  Tensor sizes) -> ()");
+      "                  Tensor sizes,"
+      "                  bool is_src_access_order_any=False) -> ()");
   cache_ops.impl("swap_blocks_batch", torch::kCPU, &swap_blocks_batch);
 
   // Reshape the key and value tensors and cache them.
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 12942b5c807b..1051ca97d3d3 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -22,7 +22,7 @@
 #   docker buildx bake -f docker/docker-bake.hcl -f docker/versions.json
 # =============================================================================
 
-ARG CUDA_VERSION=13.0.0
+ARG CUDA_VERSION=13.0.2
 ARG PYTHON_VERSION=3.12
 ARG UBUNTU_VERSION=22.04
 
@@ -41,6 +41,13 @@ ARG BUILD_BASE_IMAGE=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04
 # Using cuda base image with minimal dependencies necessary for JIT compilation (FlashInfer, DeepGEMM, EP kernels)
 ARG FINAL_BASE_IMAGE=nvidia/cuda:${CUDA_VERSION}-base-ubuntu${UBUNTU_VERSION}
 
+# OS family of BUILD_BASE_IMAGE. Controls package manager (apt vs dnf) and
+# Python bootstrap. Set to "manylinux" alongside a manylinux build base such
+# as pytorch/manylinux2_28-builder:cuda13.0 to produce wheels with a glibc
+# 2.28 floor (matches PyTorch's own published wheels). Default stays on
+# Ubuntu for backwards compatibility.
+ARG BUILD_OS=ubuntu
+
 # By parameterizing the Deadsnakes repository URL, we allow third-party to use
 # their own mirror. When doing so, we don't benefit from the transparent
 # installation of the GPG key of the PPA, as done by add-apt-repository, so we
@@ -94,35 +101,64 @@ FROM ${BUILD_BASE_IMAGE} AS base
 
 ARG CUDA_VERSION
 ARG PYTHON_VERSION
+ARG BUILD_OS
 
 ENV DEBIAN_FRONTEND=noninteractive
 
-# Install system dependencies including build tools
-RUN apt-get update -y \
-    && apt-get install -y --no-install-recommends \
-        ccache \
-        software-properties-common \
-        git \
-        curl \
-        sudo \
-        python3-pip \
-        libibverbs-dev \
-        # Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
-        # as it was causing spam when compiling the CUTLASS kernels
-        gcc-10 \
-        g++-10 \
-    && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10 \
-    # Install python dev headers if available (needed for cmake FindPython on Ubuntu 24.04
-    # which ships cmake 3.28 and requires Development.SABIModule; silently skipped on
-    # Ubuntu 20.04/22.04 where python3.x-dev is not available without a PPA)
-    && (apt-get install -y --no-install-recommends python${PYTHON_VERSION}-dev 2>/dev/null || true) \
-    && rm -rf /var/lib/apt/lists/* \
-    && curl -LsSf https://astral.sh/uv/install.sh | sh \
-    && $HOME/.local/bin/uv venv /opt/venv --python ${PYTHON_VERSION} \
+# Install system dependencies including build tools.
+# The Ubuntu path uses apt + deadsnakes-via-uv for Python; the manylinux path
+# (AlmaLinux 8, e.g. pytorch/manylinux2_28-builder) uses dnf and the Python
+# interpreters pre-installed at /opt/python/cpXY-cpXY/.
+RUN if [ "${BUILD_OS}" = "manylinux" ]; then \
+        # rdma-core-devel provides libibverbs headers; ccache lives in EPEL,
+        # which the pytorch manylinux image already enables. git/curl/sudo
+        # are typically pre-installed but listed defensively.
+        dnf install -y --setopt=install_weak_deps=False \
+            ccache \
+            git \
+            curl \
+            sudo \
+            rdma-core-devel \
+        && dnf clean all \
+        && rm -rf /var/cache/dnf; \
+    else \
+        apt-get update -y \
+        && apt-get install -y --no-install-recommends \
+            ccache \
+            software-properties-common \
+            git \
+            curl \
+            sudo \
+            python3-pip \
+            libibverbs-dev \
+            # Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
+            # as it was causing spam when compiling the CUTLASS kernels
+            gcc-10 \
+            g++-10 \
+        && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10 \
+        # Install python dev headers if available (needed for cmake FindPython on Ubuntu 24.04
+        # which ships cmake 3.28 and requires Development.SABIModule; silently skipped on
+        # Ubuntu 20.04/22.04 where python3.x-dev is not available without a PPA)
+        && (apt-get install -y --no-install-recommends python${PYTHON_VERSION}-dev 2>/dev/null || true) \
+        && rm -rf /var/lib/apt/lists/*; \
+    fi
+
+# Install uv and bootstrap /opt/venv. Both paths converge on /opt/venv so all
+# downstream stages stay distro-agnostic.
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh \
+    && if [ "${BUILD_OS}" = "manylinux" ]; then \
+           # manylinux images ship Python at /opt/python/cpXY-cpXY/; point uv
+           # at the matching interpreter rather than letting it fetch one.
+           PYV_NODOT=$(echo ${PYTHON_VERSION} | tr -d '.') \
+           && MANYLINUX_PY=/opt/python/cp${PYV_NODOT}-cp${PYV_NODOT}/bin/python${PYTHON_VERSION} \
+           && $HOME/.local/bin/uv venv /opt/venv --python "$MANYLINUX_PY"; \
+       else \
+           $HOME/.local/bin/uv venv /opt/venv --python ${PYTHON_VERSION}; \
+       fi \
     && rm -f /usr/bin/python3 /usr/bin/python3-config /usr/bin/pip \
-    && ln -s /opt/venv/bin/python3 /usr/bin/python3 \
-    && ln -s /opt/venv/bin/python3-config /usr/bin/python3-config \
-    && ln -s /opt/venv/bin/pip /usr/bin/pip \
+    && ln -sf /opt/venv/bin/python3 /usr/bin/python3 \
+    && ln -sf /opt/venv/bin/python3-config /usr/bin/python3-config \
+    && ln -sf /opt/venv/bin/pip /usr/bin/pip \
     && python3 --version && python3 -m pip --version
 
 # Activate virtual environment and add uv to PATH
@@ -188,7 +224,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 # Explicitly set the list to avoid issues with torch 2.2
 # See https://github.com/pytorch/pytorch/pull/123243
 # From versions.json: .torch.cuda_arch_list
-ARG torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0 10.0 12.0'
+ARG torch_cuda_arch_list='7.5 8.0 8.6 8.9 9.0 10.0 11.0 12.0+PTX'
 ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
 #################### BUILD BASE IMAGE ####################
 
@@ -433,6 +469,7 @@ FROM base AS dev
 ARG PIP_INDEX_URL UV_INDEX_URL
 ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
 ARG PYTORCH_CUDA_INDEX_BASE_URL
+ARG BUILD_OS
 
 # This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
 # Reference: https://github.com/astral-sh/uv/pull/1694
@@ -442,7 +479,11 @@ ENV UV_INDEX_STRATEGY="unsafe-best-match"
 ENV UV_LINK_MODE=copy
 
 # Install libnuma-dev, required by fastsafetensors (fixes #20384)
-RUN apt-get update && apt-get install -y --no-install-recommends libnuma-dev && rm -rf /var/lib/apt/lists/*
+RUN if [ "${BUILD_OS}" = "manylinux" ]; then \
+        dnf install -y numactl-devel && dnf clean all && rm -rf /var/cache/dnf; \
+    else \
+        apt-get update && apt-get install -y --no-install-recommends libnuma-dev && rm -rf /var/lib/apt/lists/*; \
+    fi
 
 
 # We can specify the standard or nightly build of PyTorch
@@ -538,9 +579,11 @@ RUN CUDA_VERSION_DASH=$(echo $CUDA_VERSION | cut -d. -f1,2 | tr '.' '-') && \
         cuda-nvrtc-${CUDA_VERSION_DASH} \
         cuda-cuobjdump-${CUDA_VERSION_DASH} \
         libcurand-dev-${CUDA_VERSION_DASH} \
-        libcublas-${CUDA_VERSION_DASH} \
+        libcublas-dev-${CUDA_VERSION_DASH} \
         # Required by fastsafetensors (fixes #20384)
-        libnuma-dev && \
+        libnuma-dev \
+        # numactl CLI for NUMA binding at runtime
+        numactl && \
     # Fixes nccl_allocator requiring nccl.h at runtime
     # https://github.com/vllm-project/vllm/blob/1336a1ea244fa8bfd7e72751cabbdb5b68a0c11a/vllm/distributed/device_communicators/pynccl_allocator.py#L22
     # NCCL packages don't use the cuda-MAJOR-MINOR naming convention,
@@ -580,33 +623,10 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 # Install FlashInfer JIT cache (requires CUDA-version-specific index URL)
 # https://docs.flashinfer.ai/installation.html
 # From versions.json: .flashinfer.version
-# 0.6.7: CUTLASS 4.4.2 bump, fixes TMA grouped GEMM on SM12x (flashinfer#2798)
-# TODO: bump to 0.6.8 when released for NVFP4/MXFP4 group GEMMs on
-#   SM120/SM121 (RTX 50 / DGX Spark) via flashinfer#2738
-ARG FLASHINFER_VERSION=0.6.7
+ARG FLASHINFER_VERSION=0.6.8.post1
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system flashinfer-jit-cache==${FLASHINFER_VERSION} \
-        --extra-index-url https://flashinfer.ai/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
-    && flashinfer show-config
-
-# Pre-download FlashInfer TRTLLM BMM headers for air-gapped environments.
-# At runtime, MoE JIT compilation downloads these from edge.urm.nvidia.com
-# which fails without internet. This step caches them at build time.
-RUN python3 <<'PYEOF'
-from flashinfer.jit import env as jit_env
-from flashinfer.jit.cubin_loader import download_trtllm_headers, get_cubin
-from flashinfer.artifacts import ArtifactPath, CheckSumHash
-
-download_trtllm_headers(
-    'bmm',
-    jit_env.FLASHINFER_CUBIN_DIR / 'flashinfer' / 'trtllm' / 'batched_gemm' / 'trtllmGen_bmm_export',
-    f'{ArtifactPath.TRTLLM_GEN_BMM}/include/trtllmGen_bmm_export',
-    ArtifactPath.TRTLLM_GEN_BMM,
-    get_cubin(f'{ArtifactPath.TRTLLM_GEN_BMM}/checksums.txt', CheckSumHash.TRTLLM_GEN_BMM),
-)
-
-print('FlashInfer TRTLLM BMM headers downloaded successfully')
-PYEOF
+        --extra-index-url https://flashinfer.ai/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
 
 # ============================================================
 # OPENAI API SERVER DEPENDENCIES
@@ -642,7 +662,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     else \
         BITSANDBYTES_VERSION="${BITSANDBYTES_VERSION_X86}"; \
     fi; \
-    uv pip install --system accelerate hf_transfer modelscope \
+    uv pip install --system accelerate modelscope \
         "bitsandbytes>=${BITSANDBYTES_VERSION}" "timm${TIMM_VERSION}" "runai-model-streamer[s3,gcs,azure]${RUNAI_MODEL_STREAMER_VERSION}"
 
 # ============================================================
@@ -688,6 +708,13 @@ RUN --mount=type=bind,from=build,src=/tmp/ep_kernels_workspace/dist,target=/vllm
     uv pip install --system ep_kernels/dist/*.whl --verbose \
         --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
 
+# Download FlashInfer precompiled cubins AFTER all pip installs are done.
+# This must run after the vLLM wheel and EP kernels installs above, because
+# those can reinstall/touch flashinfer packages. Downloading cubins earlier
+# (in the flashinfer-jit-cache layer) causes ~2.5 GB of layer duplication
+# when a later pip install overwrites flashinfer package files.
+RUN flashinfer show-config && flashinfer download-cubin
+
 # CUDA image changed from /usr/local/nvidia to /usr/local/cuda in 12.8 but will
 # return to /usr/local/nvidia in 13.0 to allow container providers to mount drivers
 # consistently from the host (see https://github.com/vllm-project/vllm/issues/18859).
@@ -756,9 +783,10 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system -e tests/vllm_test_utils
 
 # enable fast downloads from hf (for testing)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system hf_transfer
-ENV HF_HUB_ENABLE_HF_TRANSFER 1
+ENV HF_XET_HIGH_PERFORMANCE 1
+
+# increase timeout for hf downloads (for testing)
+ENV HF_HUB_DOWNLOAD_TIMEOUT 60
 
 # Copy in the v1 package for testing (it isn't distributed yet)
 COPY vllm/v1 /usr/local/lib/python${PYTHON_VERSION}/dist-packages/vllm/v1
@@ -776,6 +804,10 @@ FROM vllm-base AS vllm-openai-base
 ARG TARGETPLATFORM
 ARG INSTALL_KV_CONNECTORS=false
 ARG CUDA_VERSION
+ARG VLLM_BUILD_COMMIT
+ARG VLLM_BUILD_PIPELINE
+ARG VLLM_BUILD_URL
+ARG VLLM_IMAGE_TAG
 
 ARG PIP_INDEX_URL UV_INDEX_URL
 ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
@@ -785,7 +817,7 @@ ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
 ENV UV_HTTP_TIMEOUT=500
 
 # install kv_connectors if requested
-ARG torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0 10.0 12.0'
+ARG torch_cuda_arch_list='7.5 8.0 8.6 8.9 9.0 10.0 11.0 12.0+PTX'
 ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
 RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,source=requirements/kv_connectors.txt,target=/tmp/kv_connectors.txt,ro \
@@ -812,11 +844,23 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     fi
 
 ENV VLLM_USAGE_SOURCE production-docker-image
+ENV VLLM_BUILD_COMMIT=${VLLM_BUILD_COMMIT:-unknown} \
+    VLLM_BUILD_PIPELINE=${VLLM_BUILD_PIPELINE:-local} \
+    VLLM_BUILD_URL=${VLLM_BUILD_URL:-} \
+    VLLM_IMAGE_TAG=${VLLM_IMAGE_TAG:-local/vllm-openai:dev}
+LABEL org.opencontainers.image.source="https://github.com/vllm-project/vllm" \
+      org.opencontainers.image.revision="${VLLM_BUILD_COMMIT}" \
+      org.opencontainers.image.version="${VLLM_IMAGE_TAG}" \
+      org.opencontainers.image.url="${VLLM_BUILD_URL}" \
+      ai.vllm.build.commit="${VLLM_BUILD_COMMIT}" \
+      ai.vllm.build.pipeline="${VLLM_BUILD_PIPELINE}" \
+      ai.vllm.build.url="${VLLM_BUILD_URL}" \
+      ai.vllm.image.tag="${VLLM_IMAGE_TAG}"
 
 # define sagemaker first, so it is not default from `docker build`
 FROM vllm-openai-base AS vllm-sagemaker
 
-COPY examples/online_serving/sagemaker-entrypoint.sh .
+COPY examples/deployment/sagemaker-entrypoint.sh .
 RUN chmod +x sagemaker-entrypoint.sh
 ENTRYPOINT ["./sagemaker-entrypoint.sh"]
 
diff --git a/docker/Dockerfile.cpu b/docker/Dockerfile.cpu
index 0600f7da82f9..d15ced8e0111 100644
--- a/docker/Dockerfile.cpu
+++ b/docker/Dockerfile.cpu
@@ -173,7 +173,8 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 COPY --from=vllm-test-deps /vllm-workspace/requirements/test/cpu.txt requirements/test/cpu.txt
 
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install -r requirements/dev.txt && \
+    uv pip install -r requirements/lint.txt && \
+    uv pip install -r requirements/test/cpu.txt && \
     pre-commit install --hook-type pre-commit --hook-type commit-msg
 
 ENTRYPOINT ["bash"]
@@ -191,12 +192,19 @@ ADD ./tests/ ./tests/
 ADD ./examples/ ./examples/
 ADD ./benchmarks/ ./benchmarks/
 ADD ./vllm/collect_env.py .
+ADD ./docker/ ./docker/
 ADD ./.buildkite/ ./.buildkite/
 
 # install development dependencies (for testing)
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install -e tests/vllm_test_utils
 
+# enable fast downloads from hf (for testing)
+ENV HF_XET_HIGH_PERFORMANCE 1
+
+# increase timeout for hf downloads (for testing)
+ENV HF_HUB_DOWNLOAD_TIMEOUT 60
+
 ######################### RELEASE IMAGE #########################
 FROM base AS vllm-openai
 
diff --git a/docker/Dockerfile.nightly_torch b/docker/Dockerfile.nightly_torch
index 39e1cc187592..6b9d85c9d17f 100644
--- a/docker/Dockerfile.nightly_torch
+++ b/docker/Dockerfile.nightly_torch
@@ -77,7 +77,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system $pkgs --index-url https://download.pytorch.org/whl/nightly/cu128
 
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system numba==0.61.2
+    uv pip install --system numba==0.65.0
 
 RUN --mount=type=cache,target=/root/.cache/uv \
 uv pip install --system -r requirements/common.txt
@@ -217,16 +217,13 @@ RUN pip install setuptools==75.6.0 packaging==23.2 ninja==1.11.1.3 build==1.2.2.
 
 
 # build flashinfer for torch nightly from source around 10 mins
-# release version: v0.6.7
-# 0.6.7: CUTLASS 4.4.2 bump, fixes TMA grouped GEMM on SM12x (flashinfer#2798)
-# TODO: bump to 0.6.8 when released for NVFP4/MXFP4 group GEMMs on
-#   SM120/SM121 (RTX 50 / DGX Spark) via flashinfer#2738
+# release version: v0.6.8.post1
 # todo(elainewy): cache flashinfer build result for faster build
 ENV CCACHE_DIR=/root/.cache/ccache
 RUN --mount=type=cache,target=/root/.cache/ccache \
     --mount=type=cache,target=/root/.cache/uv \
     echo "git clone flashinfer..." \
-    && git clone --depth 1 --branch v0.6.7 --recursive https://github.com/flashinfer-ai/flashinfer.git \
+    && git clone --depth 1 --branch v0.6.8.post1 --recursive https://github.com/flashinfer-ai/flashinfer.git \
     && cd flashinfer \
     && git submodule update --init --recursive \
     && echo "finish git clone flashinfer..." \
@@ -272,9 +269,10 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system -e tests/vllm_test_utils
 
 # enable fast downloads from hf (for testing)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system hf_transfer
-ENV HF_HUB_ENABLE_HF_TRANSFER 1
+ENV HF_XET_HIGH_PERFORMANCE 1
+
+# increase timeout for hf downloads (for testing)
+ENV HF_HUB_DOWNLOAD_TIMEOUT 60
 
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system -r requirements/test/nightly-torch.txt
diff --git a/docker/Dockerfile.rocm b/docker/Dockerfile.rocm
index 801847d4999d..92ac17bcd7e1 100644
--- a/docker/Dockerfile.rocm
+++ b/docker/Dockerfile.rocm
@@ -2,6 +2,11 @@
 ARG REMOTE_VLLM="0"
 ARG COMMON_WORKDIR=/app
 ARG BASE_IMAGE=rocm/vllm-dev:base
+# AMD NIC backend
+ARG NIC_BACKEND=none
+# AMD AINIC apt repo settings
+ARG AINIC_VERSION=1.117.5
+ARG UBUNTU_CODENAME=jammy
 
 # Sccache configuration (only used in release pipeline)
 ARG USE_SCCACHE
@@ -119,10 +124,10 @@ COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/vllm/v1 /vllm_v1
 
 # RIXL/UCX build stages
 FROM base AS build_rixl
-ARG RIXL_BRANCH="f33a5599"
+ARG RIXL_BRANCH="39be1de8"
 ARG RIXL_REPO="https://github.com/ROCm/RIXL.git"
-ARG UCX_BRANCH="da3fac2a"
-ARG UCX_REPO="https://github.com/ROCm/ucx.git"
+ARG UCX_BRANCH="bfb51733"
+ARG UCX_REPO="https://github.com/openucx/ucx.git"
 ENV ROCM_PATH=/opt/rocm
 ENV UCX_HOME=/usr/local/ucx
 ENV RIXL_HOME=/usr/local/rixl
@@ -160,7 +165,7 @@ RUN cd /usr/local/src && \
         --disable-doxygen-doc \
         --enable-optimizations \
         --enable-devel-headers \
-        --with-rocm=/opt/rocm \
+        --with-rocm=${ROCM_PATH} \
         --with-verbs \
         --with-dm \
         --enable-mt && \
@@ -181,7 +186,13 @@ RUN git clone ${RIXL_REPO} /opt/rixl && \
     ninja install
 
 # Generate RIXL wheel
-RUN cd /opt/rixl && mkdir -p /app/install && \
+# Exclude libcore and libpull from auditwheel: transitive dependencies
+# that are not shipped in the wheel and vary across base images.
+RUN cd /opt/rixl && \
+    sed -i "s/--exclude 'libamdhip64\*'/--exclude 'libamdhip64*' --exclude 'libcore*' --exclude 'libpull*'/" \
+        contrib/build-wheel.sh && \
+    mkdir -p /app/install && \
+    _ucx_install_dir=${UCX_HOME} \
     ./contrib/build-wheel.sh \
         --output-dir /app/install \
         --rocm-dir ${ROCM_PATH} \
@@ -190,11 +201,12 @@ RUN cd /opt/rixl && mkdir -p /app/install && \
 
 # DeepEP build stage
 FROM base AS build_deep
-ARG ROCSHMEM_BRANCH="ba0bf0f3"
+ARG ROCSHMEM_BRANCH="f0acb0c6"
 ARG ROCSHMEM_REPO="https://github.com/ROCm/rocm-systems.git"
-ARG DEEPEP_BRANCH="e84464ec"
+ARG DEEPEP_BRANCH="a9ea9774"
 ARG DEEPEP_REPO="https://github.com/ROCm/DeepEP.git"
 ARG DEEPEP_NIC="cx7"
+ARG DEEPEP_ROCM_ARCH="gfx942;gfx950"
 ENV ROCSHMEM_DIR=/opt/rocshmem
 
 RUN git clone ${ROCSHMEM_REPO} \
@@ -202,20 +214,56 @@ RUN git clone ${ROCSHMEM_REPO} \
  && git checkout ${ROCSHMEM_BRANCH} \
  && mkdir -p projects/rocshmem/build \
  && cd projects/rocshmem/build \
- && cmake .. \
-    -DCMAKE_INSTALL_PREFIX="${ROCSHMEM_DIR}" \
-    -DROCM_PATH=/opt/rocm \
-    -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
-    -DUSE_EXTERNAL_MPI=OFF \
- && make -j \
- && make install
+ && INSTALL_PREFIX=${ROCSHMEM_DIR} \
+    ../scripts/build_configs/all_backends -DUSE_EXTERNAL_MPI=OFF
 
 # Build DeepEP wheel.
 # DeepEP looks for rocshmem at ROCSHMEM_DIR.
 RUN git clone ${DEEPEP_REPO} \
  && cd DeepEP \
  && git checkout ${DEEPEP_BRANCH} \
- && python3 setup.py --variant rocm --nic ${DEEPEP_NIC} bdist_wheel --dist-dir=/app/deep_install
+ && python3 setup.py --variant rocm --rocm-explicit-ctx --nic ${DEEPEP_NIC} bdist_wheel --dist-dir=/app/deep_install
+
+# MoRI runtime dependencies live in Dockerfile.rocm so NIC backend changes do
+# not force users to rebuild the long-lived Dockerfile.rocm_base image.
+FROM base AS mori_base
+ARG NIC_BACKEND
+ARG AINIC_VERSION
+ARG UBUNTU_CODENAME
+RUN /bin/bash -lc 'set -euo pipefail; \
+ echo "[MORI] Install MoRI proxy deps"; \
+ pip install --quiet --ignore-installed blinker && \
+ pip install --quiet quart msgpack aiohttp pyzmq; \
+ echo "[MORI] NIC_BACKEND=${NIC_BACKEND}"; \
+  \
+  # NIC backend deps — mori auto-detects NIC at runtime (MORI_DEVICE_NIC env var override).
+  # Only vendor packages are installed here for dlopen (e.g. libionic.so); no compile-time flags needed.
+  case "${NIC_BACKEND}" in \
+    # default: mlx5
+    none) \
+      ;; \
+    # AMD NIC
+    ainic) \
+      apt-get update && apt-get install -y --no-install-recommends ca-certificates curl gnupg apt-transport-https && \
+      rm -rf /var/lib/apt/lists/* && mkdir -p /etc/apt/keyrings; \
+      curl -fsSL https://repo.radeon.com/rocm/rocm.gpg.key | gpg --dearmor > /etc/apt/keyrings/amdainic.gpg; \
+      echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/amdainic.gpg] https://repo.radeon.com/amdainic/pensando/ubuntu/${AINIC_VERSION} ${UBUNTU_CODENAME} main" \
+        > /etc/apt/sources.list.d/amdainic.list; \
+      apt-get update && apt-get install -y --no-install-recommends \
+          libionic-dev \
+          ionic-common \
+      ; \
+      rm -rf /var/lib/apt/lists/*; \
+      ;; \
+    # TODO: Add Broadcom bnxt packages/repos here later.
+    # bnxt) \
+    #   echo "[MORI] Add Broadcom bnxt packages/repos here later."; \
+    #   ;; \
+    *) \
+      echo "ERROR: unknown NIC_BACKEND=${NIC_BACKEND}. Use one of: none, ainic"; \
+      exit 2; \
+      ;; \
+  esac;'
 
 # -----------------------
 # vLLM wheel release build stage (for building distributable wheels)
@@ -319,7 +367,7 @@ COPY --from=build_vllm_wheel_release ${COMMON_WORKDIR}/vllm/vllm/v1 /vllm_v1
 
 # -----------------------
 # Test vLLM image
-FROM base AS test
+FROM mori_base AS test
 
 RUN python3 -m pip install --upgrade pip && rm -rf /var/lib/apt/lists/*
 
@@ -338,6 +386,16 @@ RUN --mount=type=bind,from=export_vllm,src=/,target=/install \
 # above are not available once that RUN step completes.
 COPY --from=export_vllm /*.whl /opt/vllm-wheels/
 
+# Update rdma-core to support latest rocshmem
+ARG DEEPEP_NIC
+RUN if [ "${DEEPEP_NIC}" = "cx7" ] || [ "${DEEPEP_NIC}" = "io" ]; then \
+    git clone --branch v62.0 --depth 1 https://github.com/linux-rdma/rdma-core.git /tmp/rdma-core && \
+    cd /tmp/rdma-core && \
+    mkdir -p build && cd build && \
+    cmake -GNinja -DCMAKE_INSTALL_PREFIX=/usr -DNO_MAN_PAGES=1 .. && \
+    ninja &&     ninja install &&     ldconfig &&     rm -rf /tmp/rdma-core; \
+fi
+
 # Install RIXL wheel
 RUN --mount=type=bind,from=build_rixl,src=/app/install,target=/rixl_install \
     uv pip install --system /rixl_install/*.whl
@@ -365,9 +423,10 @@ RUN cd /vllm-workspace \
     && python3 -m pip install pytest-shard
 
 # enable fast downloads from hf (for testing)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system hf_transfer
-ENV HF_HUB_ENABLE_HF_TRANSFER=1
+ENV HF_XET_HIGH_PERFORMANCE=1
+
+# increase timeout for hf downloads (for testing)
+ENV HF_HUB_DOWNLOAD_TIMEOUT 60
 
 # install audio decode package `torchcodec` from source (required due to 
 # ROCm and torch version mismatch) for tests with datasets package
@@ -385,6 +444,10 @@ COPY --from=export_vllm /vllm_v1 /usr/local/lib/python${PYTHON_VERSION}/dist-pac
 ENV MIOPEN_DEBUG_CONV_DIRECT=0
 ENV MIOPEN_DEBUG_CONV_GEMM=0
 
+# Use legacy IPC mode for HSA to avoid GPU memory pinning issues with UCX rocm_ipc
+# See: https://github.com/ROCm/rocm-libraries/issues/6266
+ENV HSA_ENABLE_IPC_MODE_LEGACY=1
+
 # Source code is used in the `python_only_compile.sh` test
 # We hide it inside `src/` so that this source code
 # will not be imported by other tests
@@ -409,7 +472,7 @@ RUN printf '%s\n' \
 
 # -----------------------
 # Final vLLM image
-FROM base AS final
+FROM mori_base AS final
 
 RUN python3 -m pip install --upgrade pip && rm -rf /var/lib/apt/lists/*
 
@@ -444,14 +507,24 @@ RUN --mount=type=bind,from=export_vllm,src=/,target=/install \
     && pip uninstall -y vllm \
     && uv pip install --system *.whl
 
+# Install RIXL wheel
+RUN --mount=type=bind,from=build_rixl,src=/app/install,target=/rixl_install \
+    uv pip install --system /rixl_install/*.whl
+
 ARG COMMON_WORKDIR
 ARG BASE_IMAGE
+ARG NIC_BACKEND
+ARG AINIC_VERSION
 
 # Copy over the benchmark scripts as well
 COPY --from=export_vllm /benchmarks ${COMMON_WORKDIR}/vllm/benchmarks
 COPY --from=export_vllm /examples ${COMMON_WORKDIR}/vllm/examples
 COPY --from=export_vllm /docker ${COMMON_WORKDIR}/vllm/docker
 
+# Use legacy IPC mode for HSA to avoid GPU memory pinning issues with UCX rocm_ipc
+# See: https://github.com/ROCm/rocm-libraries/issues/6266
+ENV HSA_ENABLE_IPC_MODE_LEGACY=1
+
 ENV TOKENIZERS_PARALLELISM=false
 
 # ENV that can improve safe tensor loading, and end-to-end time
@@ -463,7 +536,9 @@ ENV HIP_FORCE_DEV_KERNARG=1
 # Workaround for ROCm profiler limits
 RUN echo "ROCTRACER_MAX_EVENTS=10000000" > ${COMMON_WORKDIR}/libkineto.conf
 ENV KINETO_CONFIG="${COMMON_WORKDIR}/libkineto.conf"
-RUN echo "VLLM_BASE_IMAGE=${BASE_IMAGE}" >> ${COMMON_WORKDIR}/versions.txt
+RUN echo "VLLM_BASE_IMAGE=${BASE_IMAGE}" >> ${COMMON_WORKDIR}/versions.txt \
+    && echo "MORI_NIC_BACKEND=${NIC_BACKEND}" >> ${COMMON_WORKDIR}/versions.txt \
+    && echo "AINIC_VERSION=${AINIC_VERSION}" >> ${COMMON_WORKDIR}/versions.txt
 
 CMD ["/bin/bash"]
 
diff --git a/docker/Dockerfile.rocm_base b/docker/Dockerfile.rocm_base
index 1ab2d7229bc9..cb6d09f5bb6c 100644
--- a/docker/Dockerfile.rocm_base
+++ b/docker/Dockerfile.rocm_base
@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=rocm/dev-ubuntu-22.04:7.2.1-complete
+ARG BASE_IMAGE=rocm/dev-ubuntu-22.04:7.2.2-complete
 ARG TRITON_BRANCH="ba5c1517"
 ARG TRITON_REPO="https://github.com/ROCm/triton.git"
 ARG PYTORCH_BRANCH="8514f051" # release/2.10 as of 3/17
@@ -9,9 +9,9 @@ ARG PYTORCH_AUDIO_BRANCH="v2.9.0"
 ARG PYTORCH_AUDIO_REPO="https://github.com/pytorch/audio.git"
 ARG FA_BRANCH="0e60e394"
 ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
-ARG AITER_BRANCH="v0.1.10.post3"
+ARG AITER_BRANCH="v0.1.13"
 ARG AITER_REPO="https://github.com/ROCm/aiter.git"
-ARG MORI_BRANCH="2d02c6a9"
+ARG MORI_BRANCH="v1.1.0"
 ARG MORI_REPO="https://github.com/ROCm/mori.git"
 
 # Sccache configuration (only used in release pipeline)
@@ -104,6 +104,28 @@ ENV SCCACHE_REGION=${USE_SCCACHE:+${SCCACHE_REGION_NAME}}
 ENV SCCACHE_S3_NO_CREDENTIALS=${USE_SCCACHE:+${SCCACHE_S3_NO_CREDENTIALS}}
 ENV SCCACHE_IDLE_TIMEOUT=${USE_SCCACHE:+0}
 
+# torch profiler hotfix for 7.2.2: rebuild CLR with https://github.com/ROCm/rocm-systems/pull/5062
+# will be removed once we move to ROCm 7.2.3
+RUN apt-get update && apt-get install -y rocm-llvm-dev
+RUN pip install CppHeaderParser
+RUN git clone --no-checkout --filter=blob:none https://github.com/ROCm/rocm-systems /tmp/rocm-systems \
+    && cd /tmp/rocm-systems \
+    && git sparse-checkout init --cone \
+    && git sparse-checkout set projects/hip projects/clr \
+    && git checkout 35e8c7bf8911862e5389509800e65fdf125412b3 \
+    && export CLR_DIR=/tmp/rocm-systems/projects/clr \
+    && export HIP_DIR=/tmp/rocm-systems/projects/hip \
+    && mkdir -p $CLR_DIR/build && cd $CLR_DIR/build \
+    && cmake \
+        -DHIP_COMMON_DIR=$HIP_DIR \
+        -DCMAKE_PREFIX_PATH="/opt/rocm/" \
+        -DCLR_BUILD_HIP=ON \
+        -DCLR_BUILD_OCL=OFF \
+        -DHIP_PLATFORM=amd \
+        .. \
+    && make -j$(nproc) \
+    && make install \
+    && rm -rf /tmp/rocm-systems
 
 ###
 ### Triton Build
@@ -153,8 +175,6 @@ RUN git clone ${PYTORCH_REPO} pytorch
 RUN cd pytorch && git checkout ${PYTORCH_BRANCH}
 RUN cd pytorch \
     && pip install -r requirements.txt && git submodule update --init --recursive
-RUN cd pytorch/third_party/kineto \
-    && git remote add rocm https://github.com/ROCm/kineto && git fetch rocm && git checkout 2d73be3 
 RUN cd pytorch && python3 tools/amd_build/build_amd.py \
     && if [ "$USE_SCCACHE" = "1" ]; then \
            export HIP_CLANG_PATH=/opt/sccache-wrappers \
diff --git a/docker/Dockerfile.s390x b/docker/Dockerfile.s390x
index 6b5d965be765..554a7257c236 100644
--- a/docker/Dockerfile.s390x
+++ b/docker/Dockerfile.s390x
@@ -42,7 +42,7 @@ FROM python-install AS pyarrow
 # Build Apache Arrow
 WORKDIR /tmp
 RUN --mount=type=cache,target=/root/.cache/uv \
-    git clone https://github.com/apache/arrow.git && \
+    git clone https://github.com/apache/arrow.git  -b maint-19.0.1 && \
     cd arrow/cpp && \
     mkdir release && cd release && \
     cmake -DCMAKE_BUILD_TYPE=Release \
@@ -68,19 +68,6 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install -r requirements-build.txt && \
     python setup.py build_ext --build-type=$ARROW_BUILD_TYPE --bundle-arrow-cpp bdist_wheel
 
-FROM python-install AS numa-build
-# Install numactl (needed for numa.h dependency)
-WORKDIR /tmp
-RUN curl -LO https://github.com/numactl/numactl/archive/refs/tags/v2.0.16.tar.gz && \
-    tar -xvzf v2.0.16.tar.gz && \
-    cd numactl-2.0.16 && \
-    ./autogen.sh && \
-    ./configure && \
-    make
-
-# Set include path
-ENV C_INCLUDE_PATH="/usr/local/include:$C_INCLUDE_PATH"
-
 FROM python-install AS rust
 ENV CARGO_HOME=/root/.cargo
 ENV RUSTUP_HOME=/root/.rustup
@@ -91,6 +78,18 @@ RUN curl https://sh.rustup.rs -sSf | sh -s -- -y && \
     rustup default stable && \
     rustup show
 
+FROM python-install AS numa-build
+WORKDIR /tmp
+RUN curl -LO https://github.com/numactl/numactl/archive/refs/tags/v2.0.19.tar.gz && \
+    tar -xvzf v2.0.19.tar.gz && \
+    cd numactl-2.0.19 && \
+    ./autogen.sh && \
+    ./configure && \
+    make
+
+# Set include path
+ENV C_INCLUDE_PATH="/usr/local/include:$C_INCLUDE_PATH"
+
 FROM python-install AS torch-vision
 # Install torchvision
 ARG TORCH_VISION_VERSION=v0.26.0
@@ -133,7 +132,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     git clone --recursive https://github.com/numba/llvmlite.git -b v0.44.0 && \
     git clone --recursive https://github.com/numba/numba.git -b ${NUMBA_VERSION} && \
     cd llvm-project && mkdir build && cd  build && \
-    uv pip install 'cmake<4' setuptools numpy && \
+    uv pip install 'cmake<4' 'setuptools<70' numpy && \
     export PREFIX=/usr/local && CMAKE_ARGS="${CMAKE_ARGS} -DLLVM_ENABLE_PROJECTS=lld;libunwind;compiler-rt" \
     CFLAGS="$(echo $CFLAGS | sed 's/-fno-plt //g')" \
     CXXFLAGS="$(echo $CXXFLAGS | sed 's/-fno-plt //g')" \
@@ -193,27 +192,22 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     cd opencv-python && \
     python -m build --wheel --installer=uv --outdir /tmp/opencv-python/dist
 
-# Build Outlines Core
-FROM python-install AS outlines-core-builder
+## Todo(r3hankhan123): Remove guidance-builder stage once vLLM upgrades to new version of llguidance that fixes s390x issues. See https://github.com/guidance-ai/llguidance/issues/330
+FROM python-install AS guidance-builder
 WORKDIR /tmp
 ENV CARGO_HOME=/root/.cargo
 ENV RUSTUP_HOME=/root/.rustup
 ENV PATH="$CARGO_HOME/bin:$RUSTUP_HOME/bin:$PATH"
-COPY requirements/common.txt /tmp/requirements/common.txt
-ARG OUTLINES_CORE_VERSION
 RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,from=rust,source=/root/.cargo,target=/root/.cargo,rw \
     --mount=type=bind,from=rust,source=/root/.rustup,target=/root/.rustup,rw \
-    OUTLINES_CORE_VERSION=${OUTLINES_CORE_VERSION:-$(grep -E '^outlines_core\s*==\s*[0-9.]+' /tmp/requirements/common.txt | grep -Eo '[0-9.]+')} && \
-    if [ -z "${OUTLINES_CORE_VERSION}" ]; then echo "ERROR: Could not determine outlines_core version"; exit 1; fi && \
-    git clone https://github.com/dottxt-ai/outlines-core.git && \
-    cd outlines-core && \
-    git checkout tags/${OUTLINES_CORE_VERSION} && \
-    sed -i "s/version = \"0.0.0\"/version = \"${OUTLINES_CORE_VERSION}\"/" Cargo.toml && \
+    git clone https://github.com/guidance-ai/llguidance.git && \
+    cd llguidance && \
+    git checkout s390x-fix-v2  && \
     uv pip install maturin && \
-    python -m maturin build --release --out dist
+    python -m maturin build --release --out dist --compatibility linux
 
-# Final build stage
+# # Final build stage
 FROM python-install AS vllm-cpu
 ARG PYTHON_VERSION
 ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
@@ -229,10 +223,12 @@ ENV PKG_CONFIG_PATH="/opt/rh/gcc-toolset-14/root/usr/lib64/pkgconfig:/usr/local/
 ENV PATH="${VIRTUAL_ENV:+${VIRTUAL_ENV}/bin}:/opt/rh/gcc-toolset-14/root/usr/bin:/usr/local/bin:$CARGO_HOME/bin:$RUSTUP_HOME/bin:$PATH"
 ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
+# Force pure Python protobuf to avoid s390x C++ extension crashes
+ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
 COPY . /workspace/vllm
 WORKDIR /workspace/vllm
 
-RUN --mount=type=bind,from=numa-build,src=/tmp/numactl-2.0.16,target=/numactl \
+RUN --mount=type=bind,from=numa-build,src=/tmp/numactl-2.0.19,target=/numactl \
     make -C /numactl install
 
 # Install dependencies, including PyTorch and Apache Arrow
@@ -245,22 +241,22 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,from=numba-builder,source=/tmp/llvmlite/dist,target=/tmp/llvmlite-wheels/ \
     --mount=type=bind,from=numba-builder,source=/tmp/numba/dist,target=/tmp/numba-wheels/ \
     --mount=type=bind,from=opencv-builder,source=/tmp/opencv-python/dist,target=/tmp/opencv-wheels/ \
-    --mount=type=bind,from=outlines-core-builder,source=/tmp/outlines-core/dist,target=/tmp/outlines-core/dist/ \
-     ARROW_WHL_FILE=$(ls /tmp/arrow-wheels/pyarrow-*.whl) && \
+    --mount=type=bind,from=guidance-builder,source=/tmp/llguidance/dist,target=/tmp/guidance-wheels/ \
+     ARROW_WHL_FILE=$(ls /tmp/arrow-wheels/*.whl) && \
      VISION_WHL_FILE=$(ls /tmp/vision-wheels/*.whl) && \
      HF_XET_WHL_FILE=$(ls /tmp/hf-xet-wheels/*.whl) && \
      LLVM_WHL_FILE=$(ls /tmp/llvmlite-wheels/*.whl) && \
      NUMBA_WHL_FILE=$(ls /tmp/numba-wheels/*.whl) && \
      OPENCV_WHL_FILE=$(ls /tmp/opencv-wheels/*.whl) && \
-     OUTLINES_CORE_WHL_FILE=$(ls /tmp/outlines-core/dist/*.whl) && \
-     uv pip install -v \
-        $ARROW_WHL_FILE  \
+     GUIDANCE_WHL_FILE=$(ls /tmp/guidance-wheels/*.whl) && \
+     uv pip install -v \    
+        $ARROW_WHL_FILE \
         $VISION_WHL_FILE \
         $HF_XET_WHL_FILE \
         $LLVM_WHL_FILE \
         $NUMBA_WHL_FILE \
         $OPENCV_WHL_FILE \
-        $OUTLINES_CORE_WHL_FILE \
+        $GUIDANCE_WHL_FILE \
         --index-strategy unsafe-best-match \
         -r requirements/build/cpu.txt \
         -r requirements/cpu.txt
@@ -271,6 +267,10 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     VLLM_TARGET_DEVICE=cpu VLLM_CPU_MOE_PREPACK=0 python setup.py bdist_wheel && \
     uv pip install "$(echo dist/*.whl)[tensorizer]"
 
+# Remove protobuf C++ extension that crashes on s390x
+RUN rm -rf /opt/vllm/lib64/python${PYTHON_VERSION}/site-packages/google/_upb/*.so \
+           /opt/vllm/lib64/python${PYTHON_VERSION}/site-packages/google/protobuf/pyext/*.so 2>/dev/null || true
+
 # setup non-root user for vllm
 RUN umask 002 && \
     /usr/sbin/useradd --uid 2000 --gid 0 vllm && \
diff --git a/docker/Dockerfile.xpu b/docker/Dockerfile.xpu
index 555c1f144205..e348562a47f7 100644
--- a/docker/Dockerfile.xpu
+++ b/docker/Dockerfile.xpu
@@ -5,9 +5,6 @@ WORKDIR /workspace/
 ARG PYTHON_VERSION=3.12
 ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/xpu"
 
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && \
-    echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list
-
 RUN apt clean && apt-get update -y && \
     apt-get install -y --no-install-recommends --fix-missing \
     curl \
@@ -26,8 +23,20 @@ RUN apt clean && apt-get update -y && \
     python3.12-dev \
     python3-pip
 
-RUN apt update && apt upgrade -y && \
-    apt install -y intel-oneapi-compiler-dpcpp-cpp-2025.3
+# Add oneAPI repo, pin oneAPI to 2025.3, then install pinned packages in one layer.
+RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && \
+    echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list && \
+    printf '%s\n' \
+    'Package: intel-oneapi-* intel-deep-learning-essentials* intel-pti*' \
+    'Pin: version 2025.3*' \
+    'Pin-Priority: 1001' \
+    > /etc/apt/preferences.d/oneapi-2025.3.pref && \
+    apt-get update -y && \
+    apt-get install -y --no-install-recommends \
+    intel-oneapi-compiler-dpcpp-cpp-2025.3 \
+    intel-oneapi-mkl-devel-2025.3 \
+    intel-oneapi-dnnl-devel-2025.3 && \
+    rm -rf /var/lib/apt/lists/*
 
 # Install UMD
 RUN mkdir neo && \
@@ -50,9 +59,9 @@ RUN curl -LsSf https://astral.sh/uv/install.sh | sh
 RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
 
-# This oneccl contains the BMG support which is not the case for default version of oneapi 2025.2.
-ARG ONECCL_INSTALLER="intel-oneccl-2021.15.7.8_offline.sh"
-RUN wget "https://github.com/uxlfoundation/oneCCL/releases/download/2021.15.7/${ONECCL_INSTALLER}" && \
+# This oneccl contains the BMG support which is not the case for default version of oneapi 2025.3.
+ARG ONECCL_INSTALLER="intel-oneccl-2021.15.9.14_offline.sh"
+RUN wget "https://github.com/uxlfoundation/oneCCL/releases/download/2021.15.9/${ONECCL_INSTALLER}" && \
     bash "${ONECCL_INSTALLER}" -a --silent --eula accept && \
     rm "${ONECCL_INSTALLER}" && \
     echo "source /opt/intel/oneapi/setvars.sh --force" >> /root/.bashrc && \
@@ -164,7 +173,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 # FIX triton
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip uninstall triton triton-xpu && \
-    uv pip install triton-xpu==3.6.0
+    uv pip install triton-xpu==3.7.0
 
 # remove torch bundled oneccl to avoid conflicts
 RUN --mount=type=cache,target=/root/.cache/uv \
diff --git a/docker/docker-bake.hcl b/docker/docker-bake.hcl
index e1c2fbba63a6..94ca8397561a 100644
--- a/docker/docker-bake.hcl
+++ b/docker/docker-bake.hcl
@@ -20,13 +20,29 @@ variable "NVCC_THREADS" {
 }
 
 variable "TORCH_CUDA_ARCH_LIST" {
-  default = "8.0 8.9 9.0 10.0"
+  default = "8.0 8.9 9.0 10.0 11.0 12.0"
 }
 
 variable "COMMIT" {
   default = ""
 }
 
+variable "VLLM_BUILD_COMMIT" {
+  default = "unknown"
+}
+
+variable "VLLM_BUILD_PIPELINE" {
+  default = "local"
+}
+
+variable "VLLM_BUILD_URL" {
+  default = ""
+}
+
+variable "VLLM_IMAGE_TAG" {
+  default = "local/vllm-openai:dev"
+}
+
 # Groups
 
 group "default" {
@@ -46,6 +62,10 @@ target "_common" {
     max_jobs             = MAX_JOBS
     nvcc_threads         = NVCC_THREADS
     torch_cuda_arch_list = TORCH_CUDA_ARCH_LIST
+    VLLM_BUILD_COMMIT    = VLLM_BUILD_COMMIT != "unknown" ? VLLM_BUILD_COMMIT : (COMMIT != "" ? COMMIT : "unknown")
+    VLLM_BUILD_PIPELINE  = VLLM_BUILD_PIPELINE
+    VLLM_BUILD_URL       = VLLM_BUILD_URL
+    VLLM_IMAGE_TAG       = VLLM_IMAGE_TAG
   }
 }
 
@@ -56,10 +76,16 @@ target "_labels" {
     "org.opencontainers.image.title"       = "vLLM"
     "org.opencontainers.image.description" = "vLLM: A high-throughput and memory-efficient inference and serving engine for LLMs"
     "org.opencontainers.image.licenses"    = "Apache-2.0"
-    "org.opencontainers.image.revision"    = COMMIT
+    "org.opencontainers.image.revision"    = VLLM_BUILD_COMMIT != "unknown" ? VLLM_BUILD_COMMIT : (COMMIT != "" ? COMMIT : "unknown")
+    "org.opencontainers.image.version"     = VLLM_IMAGE_TAG
+    "org.opencontainers.image.url"         = VLLM_BUILD_URL
+    "ai.vllm.build.commit"                 = VLLM_BUILD_COMMIT != "unknown" ? VLLM_BUILD_COMMIT : (COMMIT != "" ? COMMIT : "unknown")
+    "ai.vllm.build.pipeline"               = VLLM_BUILD_PIPELINE
+    "ai.vllm.build.url"                    = VLLM_BUILD_URL
+    "ai.vllm.image.tag"                    = VLLM_IMAGE_TAG
   }
   annotations = [
-      "index,manifest:org.opencontainers.image.revision=${COMMIT}",
+    "index,manifest:org.opencontainers.image.revision=${VLLM_BUILD_COMMIT != "unknown" ? VLLM_BUILD_COMMIT : (COMMIT != "" ? COMMIT : "unknown")}",
   ]
 }
 
@@ -88,7 +114,6 @@ target "test-ubuntu2404" {
   args = {
     UBUNTU_VERSION          = "24.04"
     GDRCOPY_OS_VERSION      = "Ubuntu24_04"
-    FLASHINFER_AOT_COMPILE  = "true"
   }
   output = ["type=docker"]
 }
@@ -100,7 +125,6 @@ target "openai-ubuntu2404" {
   args = {
     UBUNTU_VERSION          = "24.04"
     GDRCOPY_OS_VERSION      = "Ubuntu24_04"
-    FLASHINFER_AOT_COMPILE  = "true"
   }
   output = ["type=docker"]
 }
diff --git a/docker/versions.json b/docker/versions.json
index 625e363f64ce..75652823db0b 100644
--- a/docker/versions.json
+++ b/docker/versions.json
@@ -2,7 +2,7 @@
   "_comment": "Auto-generated from Dockerfile ARGs. Do not edit manually. Run: python tools/generate_versions_json.py",
   "variable": {
     "CUDA_VERSION": {
-      "default": "13.0.0"
+      "default": "13.0.2"
     },
     "PYTHON_VERSION": {
       "default": "3.12"
@@ -11,10 +11,13 @@
       "default": "22.04"
     },
     "BUILD_BASE_IMAGE": {
-      "default": "nvidia/cuda:13.0.0-devel-ubuntu22.04"
+      "default": "nvidia/cuda:13.0.2-devel-ubuntu22.04"
     },
     "FINAL_BASE_IMAGE": {
-      "default": "nvidia/cuda:13.0.0-base-ubuntu22.04"
+      "default": "nvidia/cuda:13.0.2-base-ubuntu22.04"
+    },
+    "BUILD_OS": {
+      "default": "ubuntu"
     },
     "GET_PIP_URL": {
       "default": "https://bootstrap.pypa.io/get-pip.py"
@@ -32,7 +35,7 @@
       "default": "false"
     },
     "TORCH_CUDA_ARCH_LIST": {
-      "default": "7.0 7.5 8.0 8.9 9.0 10.0 12.0"
+      "default": "7.5 8.0 8.6 8.9 9.0 10.0 11.0 12.0+PTX"
     },
     "MAX_JOBS": {
       "default": "2"
@@ -65,7 +68,7 @@
       "default": "true"
     },
     "FLASHINFER_VERSION": {
-      "default": "0.6.7"
+      "default": "0.6.8.post1"
     },
     "GDRCOPY_CUDA_VERSION": {
       "default": "12.8"
diff --git a/docs/assets/contributing/dockerfile-stages-dependency.png b/docs/assets/contributing/dockerfile-stages-dependency.png
index a6b0bf7b8fd4..d9d501319595 100644
Binary files a/docs/assets/contributing/dockerfile-stages-dependency.png and b/docs/assets/contributing/dockerfile-stages-dependency.png differ
diff --git a/docs/assets/contributing/vllm_bench_serve_dataset_stats.png b/docs/assets/contributing/vllm_bench_serve_dataset_stats.png
new file mode 100644
index 000000000000..72c19d3d7c07
Binary files /dev/null and b/docs/assets/contributing/vllm_bench_serve_dataset_stats.png differ
diff --git a/docs/assets/contributing/vllm_bench_serve_timeline.html b/docs/assets/contributing/vllm_bench_serve_timeline.html
new file mode 100644
index 000000000000..d463e202b6d1
--- /dev/null
+++ b/docs/assets/contributing/vllm_bench_serve_timeline.html
@@ -0,0 +1,3888 @@
+<html>
+<head><meta charset="utf-8" /></head>
+<body>
+    <div>                        <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
+        <script type="text/javascript">/**
+* plotly.js v3.3.1
+* Copyright 2012-2025, Plotly, Inc.
+* All rights reserved.
+* Licensed under the MIT license
+*/
+(
+ function(root, factory) {
+  if (typeof module === "object" && module.exports) {
+   module.exports = factory();
+  } else {
+   root.moduleName = factory();
+  }
+} (typeof self !== "undefined" ? self : this, () => {
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t=["ERROR:"];for(e=0;e<arguments.length;e++)t.push(arguments[e]);console.error.apply(console,t)}if(l3.notifyOnLogging>0){var r=[];for(e=0;e<arguments.length;e++)r.push(arguments[e]);DO(r.join("<br>"),"stick")}}});var w6=ye((Rrr,hte)=>{"use strict";hte.exports=function(){}});var zO=ye((Drr,dte)=>{"use strict";dte.exports=function(t,r){if(r instanceof RegExp){for(var n=r.toString(),i=0;i<t.length;i++)if(t[i]instanceof RegExp&&t[i].toString()===n)return t;t.push(r)}else(r||r===0)&&t.indexOf(r)===-1&&t.push(r);return t}});var pte=ye((Frr,vte)=>{vte.exports=Jtt;function Jtt(){var e=new Float32Array(16);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var mte=ye((zrr,gte)=>{gte.exports=$tt;function $tt(e){var t=new Float32Array(16);return 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r=t[0],n=t[1],i=t[2],a=t[3],o=t[4],s=t[5],l=t[6],u=t[7],c=t[8],f=t[9],h=t[10],d=t[11],v=t[12],_=t[13],b=t[14],p=t[15];return e[0]=s*(h*p-d*b)-f*(l*p-u*b)+_*(l*d-u*h),e[1]=-(n*(h*p-d*b)-f*(i*p-a*b)+_*(i*d-a*h)),e[2]=n*(l*p-u*b)-s*(i*p-a*b)+_*(i*u-a*l),e[3]=-(n*(l*d-u*h)-s*(i*d-a*h)+f*(i*u-a*l)),e[4]=-(o*(h*p-d*b)-c*(l*p-u*b)+v*(l*d-u*h)),e[5]=r*(h*p-d*b)-c*(i*p-a*b)+v*(i*d-a*h),e[6]=-(r*(l*p-u*b)-o*(i*p-a*b)+v*(i*u-a*l)),e[7]=r*(l*d-u*h)-o*(i*d-a*h)+c*(i*u-a*l),e[8]=o*(f*p-d*_)-c*(s*p-u*_)+v*(s*d-u*f),e[9]=-(r*(f*p-d*_)-c*(n*p-a*_)+v*(n*d-a*f)),e[10]=r*(s*p-u*_)-o*(n*p-a*_)+v*(n*u-a*s),e[11]=-(r*(s*d-u*f)-o*(n*d-a*f)+c*(n*u-a*s)),e[12]=-(o*(f*b-h*_)-c*(s*b-l*_)+v*(s*h-l*f)),e[13]=r*(f*b-h*_)-c*(n*b-i*_)+v*(n*h-i*f),e[14]=-(r*(s*b-l*_)-o*(n*b-i*_)+v*(n*l-i*s)),e[15]=r*(s*h-l*f)-o*(n*h-i*f)+c*(n*l-i*s),e}});var kte=ye((Vrr,Ete)=>{Ete.exports=nrt;function nrt(e){var 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ore=ye((nir,are)=>{are.exports=xrt;function xrt(e,t,r,n,i,a,o){var s=1/(r-t),l=1/(i-n),u=1/(a-o);return e[0]=a*2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=a*2*l,e[6]=0,e[7]=0,e[8]=(r+t)*s,e[9]=(i+n)*l,e[10]=(o+a)*u,e[11]=-1,e[12]=0,e[13]=0,e[14]=o*a*2*u,e[15]=0,e}});var lre=ye((air,sre)=>{sre.exports=brt;function brt(e,t,r,n,i){var a=1/Math.tan(t/2),o=1/(n-i);return e[0]=a/r,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=a,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=(i+n)*o,e[11]=-1,e[12]=0,e[13]=0,e[14]=2*i*n*o,e[15]=0,e}});var cre=ye((oir,ure)=>{ure.exports=wrt;function wrt(e,t,r,n){var i=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),o=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(o+s),u=2/(i+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=u,e[6]=0,e[7]=0,e[8]=-((o-s)*l*.5),e[9]=(i-a)*u*.5,e[10]=n/(r-n),e[11]=-1,e[12]=0,e[13]=0,e[14]=n*r/(r-n),e[15]=0,e}});var hre=ye((sir,fre)=>{fre.exports=Trt;function Trt(e,t,r,n,i,a,o){var 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t(r.slice(0,2)).concat(t(r.slice(2,4)))}};fh.convertCssMatrix=function(e){if(e){var t=e.length;if(t===16)return e;if(t===6)return[e[0],e[1],0,0,e[2],e[3],0,0,0,0,1,0,e[4],e[5],0,1]}return[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]};fh.inverseTransformMatrix=function(e){var t=[];return Ert.invert(t,e),[[t[0],t[1],t[2],t[3]],[t[4],t[5],t[6],t[7]],[t[8],t[9],t[10],t[11]],[t[12],t[13],t[14],t[15]]]}});var NS=ye((hir,Tre)=>{"use strict";var krt=Oa(),yre=G1(),Crt=T6(),Lrt=qO();function Prt(e){var t;if(typeof e=="string"){if(t=document.getElementById(e),t===null)throw new Error("No DOM element with id '"+e+"' exists on the page.");return t}else if(e==null)throw new Error("DOM element provided is null or undefined");return e}function Irt(e){var t=krt.select(e);return t.node()instanceof HTMLElement&&t.size()&&t.classed("js-plotly-plot")}function _re(e){var t=e&&e.parentNode;t&&t.removeChild(e)}function Rrt(e,t){xre("global",e,t)}function xre(e,t,r){var n="plotly.js-style-"+e,i=document.getElementById(n);if(!(i&&i.matches(".no-inline-styles"))){i||(i=document.createElement("style"),i.setAttribute("id",n),i.appendChild(document.createTextNode("")),document.head.appendChild(i));var a=i.sheet;a?a.insertRule?a.insertRule(t+"{"+r+"}",0):a.addRule?a.addRule(t,r,0):yre.warn("addStyleRule failed"):yre.warn("Cannot addRelatedStyleRule, probably due to strict CSP...")}}function Drt(e){var t="plotly.js-style-"+e,r=document.getElementById(t);r&&_re(r)}function Frt(e,t,r,n,i,a){var o=n.split(":"),s=i.split(":"),l="data-btn-style-event-added";a||(a=document),a.querySelectorAll(e).forEach(function(u){u.getAttribute(l)||(u.addEventListener("mouseenter",function(){var c=this.querySelector(r);c&&(c.style[o[0]]=o[1])}),u.addEventListener("mouseleave",function(){var c=this.querySelector(r);c&&(t&&this.matches(t)?c.style[o[0]]=o[1]:c.style[s[0]]=s[1])}),u.setAttribute(l,!0))})}function zrt(e){var t=wre(e),r=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];return 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button.maplibregl-ctrl-geolocate.maplibregl-ctrl-geolocate-background-error .maplibregl-ctrl-icon{background-image:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' width='29' height='29' fill='%23e54e33' viewBox='0 0 20 20'%3E%3Cpath d='M10 4C9 4 9 5 9 5v.1A5 5 0 0 0 5.1 9H5s-1 0-1 1 1 1 1 1h.1A5 5 0 0 0 9 14.9v.1s0 1 1 1 1-1 1-1v-.1a5 5 0 0 0 3.9-3.9h.1s1 0 1-1-1-1-1-1h-.1A5 5 0 0 0 11 5.1V5s0-1-1-1m0 2.5a3.5 3.5 0 1 1 0 7 3.5 3.5 0 1 1 0-7'/%3E%3C/svg%3E")}.maplibregl-ctrl button.maplibregl-ctrl-geolocate.maplibregl-ctrl-geolocate-waiting .maplibregl-ctrl-icon{animation:maplibregl-spin 2s linear infinite}@media (forced-colors:active){.maplibregl-ctrl button.maplibregl-ctrl-geolocate .maplibregl-ctrl-icon{background-image:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' width='29' height='29' fill='%23fff' viewBox='0 0 20 20'%3E%3Cpath d='M10 4C9 4 9 5 9 5v.1A5 5 0 0 0 5.1 9H5s-1 0-1 1 1 1 1 1h.1A5 5 0 0 0 9 14.9v.1s0 1 1 1 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u=n("uniformtext.mode");u&&n("uniformtext.minsize"),n("autosize",!(e.width&&e.height)),n("width"),n("height"),n("minreducedwidth"),n("minreducedheight"),n("margin.l"),n("margin.r"),n("margin.t"),n("margin.b"),n("margin.pad"),n("margin.autoexpand"),e.width&&e.height&&xa.sanitizeMargins(t),Xl.getComponentMethod("grid","sizeDefaults")(e,t),n("paper_bgcolor"),n("separators",r.decimal+r.thousands),n("hidesources"),n("colorway"),n("datarevision");var 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h=t._transformModules;for(s=0;s<h.length;s++)l=h[s],l.supplyLayoutDefaults&&l.supplyLayoutDefaults(e,t,r,n);for(o in i)l=i[o],l.supplyLayoutDefaults&&l.supplyLayoutDefaults(e,t,r)};xa.purge=function(e){var t=e._fullLayout||{};t._glcontainer!==void 0&&(t._glcontainer.selectAll(".gl-canvas").remove(),t._glcontainer.remove(),t._glcanvas=null),t._modeBar&&t._modeBar.destroy(),e._transitionData&&(e._transitionData._interruptCallbacks&&(e._transitionData._interruptCallbacks.length=0),e._transitionData._animationRaf&&window.cancelAnimationFrame(e._transitionData._animationRaf)),Ga.clearThrottle(),Ga.clearResponsive(e),delete e.data,delete e.layout,delete e._fullData,delete e._fullLayout,delete e.calcdata,delete e.empty,delete e.fid,delete e.undoqueue,delete e.undonum,delete e.autoplay,delete e.changed,delete e._promises,delete e._redrawTimer,delete e._hmlumcount,delete e._hmpixcount,delete e._transitionData,delete e._transitioning,delete e._initialAutoSize,delete 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xa.doAutoMargin(e)}};function gat(e){if("_redrawFromAutoMarginCount"in e._fullLayout)return!1;var t=Sp.list(e,"",!0);for(var r in t)if(t[r].autoshift||t[r].shift)return!0;return!1}xa.doAutoMargin=function(e){var t=e._fullLayout,r=t.width,n=t.height;t._size||(t._size={}),Gne(t);var i=t._size,a=t.margin,o={t:0,b:0,l:0,r:0},s=Ga.extendFlat({},i),l=a.l,u=a.r,c=a.t,f=a.b,h=t._pushmargin,d=t._pushmarginIds,v=t.minreducedwidth,_=t.minreducedheight;if(a.autoexpand!==!1){for(var b in h)d[b]||delete h[b];var p=e._fullLayout._reservedMargin;for(var k in p)for(var E in p[k]){var S=p[k][E];o[E]=Math.max(o[E],S)}h.base={l:{val:0,size:l},r:{val:1,size:u},t:{val:1,size:c},b:{val:0,size:f}};for(var L in o){var x=0;for(var C in h)C!=="base"&&JS(h[C][L].size)&&(x=h[C][L].size>x?h[C][L].size:x);var M=Math.max(0,a[L]-x);o[L]=Math.max(0,o[L]-M)}for(var g in h){var P=h[g].l||{},T=h[g].b||{},z=P.val,O=P.size,V=T.val,G=T.size,Z=r-o.r-o.l,j=n-o.t-o.b;for(var N in h){if(JS(O)&&h[N].r){var 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hs;var n=ca(e,2);return vs(t,r,"M"+n+","+n+"H-"+n+"L"+n+",-"+n+"H-"+n+"Z")},noDot:!0},bowtie:{n:26,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,"M"+n+","+n+"V-"+n+"L-"+n+","+n+"V-"+n+"Z")},noDot:!0},"circle-cross":{n:27,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,"M0,"+n+"V-"+n+"M"+n+",0H-"+n+"M"+n+",0A"+n+","+n+" 0 1,1 0,-"+n+"A"+n+","+n+" 0 0,1 "+n+",0Z")},needLine:!0,noDot:!0},"circle-x":{n:28,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2),i=ca(e/goe,2);return vs(t,r,"M"+i+","+i+"L-"+i+",-"+i+"M"+i+",-"+i+"L-"+i+","+i+"M"+n+",0A"+n+","+n+" 0 1,1 0,-"+n+"A"+n+","+n+" 0 0,1 "+n+",0Z")},needLine:!0,noDot:!0},"square-cross":{n:29,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,"M0,"+n+"V-"+n+"M"+n+",0H-"+n+"M"+n+","+n+"H-"+n+"V-"+n+"H"+n+"Z")},needLine:!0,noDot:!0},"square-x":{n:30,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return 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T=[i||"noSh",u||"noBg",c||"noFg",a,o].join(";"),z=d._defs.select(".patterns").selectAll("#"+v).data([T],Du.identity);z.exit().remove(),z.enter().append("pattern").each(function(){var O=dd.select(this);if(O.attr({id:v,width:_+"px",height:b+"px",patternUnits:"userSpaceOnUse",patternTransform:h?"scale(0.8)":""}),u){var V=Hq(u),G=Kd.tinyRGB(V),Z=V.getAlpha(),j=O.selectAll("rect").data([0]);j.exit().remove(),j.enter().append("rect").attr({width:_+"px",height:b+"px",fill:G,"fill-opacity":Z})}var N=O.selectAll(L).data([0]);N.exit().remove(),N.enter().append(L).attr(x)}),e.style("fill",jq(v,r)).style("fill-opacity",null),e.classed("pattern_filled",!0)};Aa.initGradients=function(e){var t=e._fullLayout,r=Du.ensureSingle(t._defs,"g","gradients");r.selectAll("linearGradient,radialGradient").remove(),dd.select(e).selectAll(".gradient_filled").classed("gradient_filled",!1)};Aa.initPatterns=function(e){var t=e._fullLayout,r=Du.ensureSingle(t._defs,"g","patterns");r.selectAll("pattern").remove(),dd.select(e).selectAll(".pattern_filled").classed("pattern_filled",!1)};Aa.getPatternAttr=function(e,t,r){return e&&Du.isArrayOrTypedArray(e)?t<e.length?e[t]:r:e};Aa.pointStyle=function(e,t,r,n){if(e.size()){var i=Aa.makePointStyleFns(t);e.each(function(a){Aa.singlePointStyle(a,dd.select(this),t,i,r,n)})}};Aa.singlePointStyle=function(e,t,r,n,i,a){var o=r.marker,s=o.line;if(a&&a.i>=0&&e.i===void 0&&(e.i=a.i),t.style("opacity",n.selectedOpacityFn?n.selectedOpacityFn(e):e.mo===void 0?o.opacity:e.mo),n.ms2mrc){var l;e.ms==="various"||o.size==="various"?l=3:l=n.ms2mrc(e.ms),e.mrc=l,n.selectedSizeFn&&(l=e.mrc=n.selectedSizeFn(e));var u=Aa.symbolNumber(e.mx||o.symbol)||0;e.om=u%200>=100;var c=Xq(e,r),f=Wq(e,r);t.attr("d",Doe(u,l,c,f))}var h=!1,d,v,_;if(e.so)_=s.outlierwidth,v=s.outliercolor,d=o.outliercolor;else{var 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C=yc.nestedProperty(t,S).get(),M=yc.simpleMap(C,t.r2l),g=M[1]<M[0];g&&M.reverse();var P=yc.simpleMap([L,x],t.r2l);if(L!==void 0&&M[0]<P[0]&&(C[g?1:0]=L),x!==void 0&&M[1]>P[1]&&(C[g?0:1]=x),C[0]===C[1]){var T=t.l2r(L),z=t.l2r(x);if(L!==void 0){var O=T+1;x!==void 0&&(O=Math.min(O,z)),C[g?1:0]=O}if(x!==void 0){var V=z+1;L!==void 0&&(V=Math.max(V,T)),C[g?0:1]=V}}}},t.cleanRange=function(S,L){t._cleanRange(S,L),t.limitRange(S)},t._cleanRange=function(S,L){L||(L={}),S||(S="range");var x=yc.nestedProperty(t,S).get(),C,M;if(t.type==="date"?M=yc.dfltRange(t.calendar):i==="y"?M=mL.DFLTRANGEY:t._name==="realaxis"?M=[0,1]:M=L.dfltRange||mL.DFLTRANGEX,M=M.slice(),(t.rangemode==="tozero"||t.rangemode==="nonnegative")&&(M[0]=0),!x||x.length!==2){yc.nestedProperty(t,S).set(M);return}var g=x[0]===null,P=x[1]===null;for(t.type==="date"&&!t.autorange&&(x[0]=yc.cleanDate(x[0],Tg,t.calendar),x[1]=yc.cleanDate(x[1],Tg,t.calendar)),C=0;C<2;C++)if(t.type==="date"){if(!yc.isDateTime(x[C],t.calendar)){t[S]=M;break}if(t.r2l(x[0])===t.r2l(x[1])){var T=yc.constrain(t.r2l(x[0]),yc.MIN_MS+1e3,yc.MAX_MS-1e3);x[0]=t.l2r(T-1e3),x[1]=t.l2r(T+1e3);break}}else{if(!gm(x[C]))if(!(g||P)&&gm(x[1-C]))x[C]=x[1-C]*(C?10:.1);else{t[S]=M;break}if(x[C]<-fL?x[C]=-fL:x[C]>fL&&(x[C]=fL),x[0]===x[1]){var z=Math.max(1,Math.abs(x[0]*1e-6));x[0]-=z,x[1]+=z}}},t.setScale=function(S){var L=r._size;if(t.overlaying){var x=vL.getFromId({_fullLayout:r},t.overlaying);t.domain=x.domain}var C=S&&t._r?"_r":"range",M=t.calendar;t.cleanRange(C);var g=t.r2l(t[C][0],M),P=t.r2l(t[C][1],M),T=i==="y";if(T?(t._offset=L.t+(1-t.domain[1])*L.h,t._length=L.h*(t.domain[1]-t.domain[0]),t._m=t._length/(g-P),t._b=-t._m*P):(t._offset=L.l+t.domain[0]*L.w,t._length=L.w*(t.domain[1]-t.domain[0]),t._m=t._length/(P-g),t._b=-t._m*g),t._rangebreaks=[],t._lBreaks=0,t._m2=0,t._B=[],t.rangebreaks){var z,O;if(t._rangebreaks=t.locateBreaks(Math.min(g,P),Math.max(g,P)),t._rangebreaks.length){for(z=0;z<t._rangebreaks.length;z++)O=t._rangebreaks[z],t._lBreaks+=Math.abs(O.max-O.min);var V=T;g>P&&(V=!V),V&&t._rangebreaks.reverse();var G=V?-1:1;for(t._m2=G*t._length/(Math.abs(P-g)-t._lBreaks),t._B.push(-t._m2*(T?P:g)),z=0;z<t._rangebreaks.length;z++)O=t._rangebreaks[z],t._B.push(t._B[t._B.length-1]-G*t._m2*(O.max-O.min));for(z=0;z<t._rangebreaks.length;z++)O=t._rangebreaks[z],O.pmin=b(O.min),O.pmax=b(O.max)}}if(!isFinite(t._m)||!isFinite(t._b)||t._length<0)throw r._replotting=!1,new Error("Something went wrong with axis scaling")},t.maskBreaks=function(S){var L=t.rangebreaks||[],x,C,M,g,P;L._cachedPatterns||(L._cachedPatterns=L.map(function(te){return te.enabled&&te.bounds?yc.simpleMap(te.bounds,te.pattern?n_:t.d2c):null})),L._cachedValues||(L._cachedValues=L.map(function(te){return te.enabled&&te.values?yc.simpleMap(te.values,t.d2c).sort(yc.sorterAsc):null}));for(var T=0;T<L.length;T++){var z=L[T];if(z.enabled){if(z.bounds){var O=z.pattern;switch(x=L._cachedPatterns[T],C=x[0],M=x[1],O){case gL:P=new Date(S),g=P.getUTCDay(),C>M&&(M+=7,g<C&&(g+=7));break;case pL:P=new Date(S);var V=P.getUTCHours(),G=P.getUTCMinutes(),Z=P.getUTCSeconds(),j=P.getUTCMilliseconds();g=V+(G/60+Z/3600+j/36e5),C>M&&(M+=24,g<C&&(g+=24));break;case"":g=S;break}if(g>=C&&g<M)return Tg}else for(var N=L._cachedValues[T],H=0;H<N.length;H++)if(C=N[H],M=C+z.dvalue,S>=C&&S<M)return Tg}}return S},t.locateBreaks=function(S,L){var x,C,M,g,P=[];if(!t.rangebreaks)return P;var T=t.rangebreaks.slice().sort(function(_e,Ee){return 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P.sort(function(_e,Ee){return _e.min-Ee.min}),P},t.makeCalcdata=function(S,L,x){var C,M,g,P,T=t.type,z=T==="date"&&S[L+"calendar"];if(L in S){if(C=S[L],P=S._length||yc.minRowLength(C),yc.isTypedArray(C)&&(T==="linear"||T==="log")){if(P===C.length)return C;if(C.subarray)return C.subarray(0,P)}if(T==="multicategory")return c(C,P);for(M=new Array(P),g=0;g<P;g++)M[g]=t.d2c(C[g],0,z,x)}else{var O=L+"0"in S?t.d2c(S[L+"0"],0,z):0,V=S["d"+L]?Number(S["d"+L]):1;for(C=S[{x:"y",y:"x"}[L]],P=S._length||C.length,M=new Array(P),g=0;g<P;g++)M[g]=O+g*V}if(t.rangebreaks)for(g=0;g<P;g++)M[g]=t.maskBreaks(M[g]);return M},t.isValidRange=function(S,L){return Array.isArray(S)&&S.length===2&&(L&&S[0]===null||gm(t.r2l(S[0])))&&(L&&S[1]===null||gm(t.r2l(S[1])))},t.getAutorangeDflt=function(S,L){var x=!t.isValidRange(S,"nullOk");return x&&L&&L.reverseDflt?x="reversed":S&&(S[0]===null&&S[1]===null?x=!0:S[0]===null&&S[1]!==null?x="min":S[0]!==null&&S[1]===null&&(x="max")),x},t.isReversed=function(){var S=t.autorange;return S==="reversed"||S==="min reversed"||S==="max reversed"},t.isPtWithinRange=function(S,L){var x=t.c2l(S[i],null,L),C=t.r2l(t.range[0]),M=t.r2l(t.range[1]);return C<M?C<=x&&x<=M:M<=x&&x<=C},t._emptyCategories=function(){t._categories=[],t._categoriesMap={}},t.clearCalc=function(){var S=t._matchGroup;if(S){var L=null,x=null;for(var C in S){var M=r[vL.id2name(C)];if(M._categories){L=M._categories,x=M._categoriesMap;break}}L&&x?(t._categories=L,t._categoriesMap=x):t._emptyCategories()}else t._emptyCategories();if(t._initialCategories)for(var g=0;g<t._initialCategories.length;g++)u(t._initialCategories[g])},t.sortByInitialCategories=function(){var S=[];if(t._emptyCategories(),t._initialCategories)for(var L=0;L<t._initialCategories.length;L++)u(t._initialCategories[L]);S=S.concat(t._traceIndices);var x=t._matchGroup;for(var C in x)if(n!==C){var M=r[vL.id2name(C)];M._categories=t._categories,M._categoriesMap=t._categoriesMap,S=S.concat(M._traceIndices)}return S};var 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C=vB(u),M=C[0],g=C[1],P=Ih(x.dtick),T=n==="log"&&!(P||x.dtick.charAt(0)==="L"),z=Qn.tickFirst(x,r);if(S){if(t._tmin=z,z<M!==c)break;(n==="category"||n==="multicategory")&&(g=c?Math.max(-.5,g):Math.min(t._categories.length-.5,g))}var O=null,V=z,G;if(S){var Z;P?Z=t.dtick:n==="date"?typeof t.dtick=="string"&&t.dtick.charAt(0)==="M"&&(Z=z3*t.dtick.substring(1)):Z=t._roughDTick,G=Math.round((t.r2l(V)-t.r2l(t.tick0))/Z)-1}var j=x.dtick;for(x.rangebreaks&&x._tick0Init!==x.tick0&&(V=oB(V,t),c||(V=Qn.tickIncrement(V,j,!c,i))),S&&o&&(V=Qn.tickIncrement(V,j,!c,i),G--);c?V>=g:V<=g;V=Qn.tickIncrement(V,j,c,i)){if(S&&G++,x.rangebreaks&&!c){if(V<M)continue;if(x.maskBreaks(V)===CL&&oB(V,x)>=h)break}if(b.length>d||V===O)break;O=V;var N={value:V};S?(T&&V!==(V|0)&&(N.simpleLabel=!0),a>1&&G%a&&(N.skipLabel=!0),b.push(N)):(N.minor=!0,p.push(N))}}if(!p||p.length<2)l=!1;else{var H=(p[1].value-p[0].value)*(s?-1:1);ylt(H,t.tickformat)||(l=!1)}if(!l)k=b;else{var te=b.concat(p);o&&b.length&&(te=te.slice(1)),te=te.sort(function(Gt,Nt){return Gt.value-Nt.value}).filter(function(Gt,Nt,Jt){return Nt===0||Gt.value!==Jt[Nt-1].value});var oe=te.map(function(Gt,Nt){return Gt.minor===void 0&&!Gt.skipLabel?Nt:null}).filter(function(Gt){return Gt!==null});oe.forEach(function(Gt){l.map(function(Nt){var Jt=Gt+Nt;Jt>=0&&Jt<te.length&&Jo.pushUnique(k,te[Jt])})})}if(E){var _e=t.minor.ticks==="inside"&&t.ticks==="outside"||t.minor.ticks==="outside"&&t.ticks==="inside";if(!_e){for(var Ee=b.map(function(Gt){return Gt.value}),Ce=[],me=0;me<p.length;me++){var ie=p[me],Se=ie.value;if(Ee.indexOf(Se)===-1){for(var Le=!1,Ae=0;!Le&&Ae<b.length;Ae++)1e7+b[Ae].value===1e7+Se&&(Le=!0);Le||Ce.push(ie)}}p=Ce}}o&&Wst(k,t,t._definedDelta);var Fe;if(t.rangebreaks){var Pe=t._id.charAt(0)==="y",ge=1;t.tickmode==="auto"&&(ge=t.tickfont?t.tickfont.size:12);var Re=NaN;for(Fe=b.length-1;Fe>-1;Fe--){if(b[Fe].drop){b.splice(Fe,1);continue}b[Fe].value=oB(b[Fe].value,t);var ce=t.c2p(b[Fe].value);(Pe?Re>ce-ge:Re<ce+ge)?b.splice(c?Fe+1:Fe,1):Re=ce}}yB(t)&&Math.abs(u[1]-u[0])===360&&b.pop(),t._tmax=(b[b.length-1]||{}).value,t._prevDateHead="",t._inCalcTicks=!0;var Ze,ut=function(Gt){Gt.text="",t._prevDateHead=Ze};b=b.concat(p);function pt(Gt,Nt){var Jt=Qn.tickText(Gt,Nt.value,!1,Nt.simpleLabel),sr=Nt.periodX;return sr!==void 0&&(Jt.periodX=sr,(sr>h||sr<f)&&(sr>h&&(Jt.periodX=h),sr<f&&(Jt.periodX=f),ut(Jt))),Jt}var Zt;for(Fe=0;Fe<b.length;Fe++){var st=b[Fe].minor,lt=b[Fe].value;st?(l&&k.indexOf(b[Fe])!==-1?Zt=pt(t,b[Fe]):Zt={x:lt},Zt.minor=!0,_.push(Zt)):(Ze=t._prevDateHead,Zt=pt(t,b[Fe]),(b[Fe].skipLabel||l&&k.indexOf(b[Fe])===-1)&&ut(Zt),v.push(Zt))}return v=v.concat(_),t._inCalcTicks=!1,o&&v.length&&(v[0].noTick=!0),v};function Tse(e,t){return e.rangebreaks&&(t=t.filter(function(r){return e.maskBreaks(r.x)!==CL})),t}function Xst(e){var t=e._mainAxis,r=[];if(t._vals){for(var n=0;n<t._vals.length;n++)if(!t._vals[n].noTick){var i=t.l2p(t._vals[n].x),a=e.p2l(i),o=Qn.tickText(e,a);t._vals[n].minor&&(o.minor=!0,o.text=""),r.push(o)}}return r=Tse(e,r),r}function gse(e,t){var r=Jo.simpleMap(e.range,e.r2l),n=vB(r),i=Math.min(n[0],n[1]),a=Math.max(n[0],n[1]),o=e.type==="category"?e.d2l_noadd:e.d2l;e.type==="log"&&String(e.dtick).charAt(0)!=="L"&&(e.dtick="L"+Math.pow(10,Math.floor(Math.min(e.range[0],e.range[1]))-1));for(var s=[],l=0;l<=1;l++)if(!(t!==void 0&&(t&&l||t===!1&&!l))&&!(l&&!e.minor)){var u=l?e.minor.tickvals:e.tickvals,c=l?[]:e.ticktext;if(u){Jo.isArrayOrTypedArray(c)||(c=[]);for(var f=0;f<u.length;f++){var h=o(u[f]);if(h>i&&h<a){var d=Qn.tickText(e,h,!1,String(c[f]));l&&(d.minor=!0,d.text=""),s.push(d)}}}}return s=Tse(e,s),s}var bL=[2,5,10],mse=[1,2,3,6,12],yse=[1,2,5,10,15,30],Zst=[1,2,3,7,14],Ase=[-.046,0,.301,.477,.602,.699,.778,.845,.903,.954,1],Sse=[-.301,0,.301,.699,1],Yst=[15,30,45,90,180];function _m(e,t,r){return t*Jo.roundUp(e/t,r)}Qn.autoTicks=function(e,t,r){var n;function i(f){return Math.pow(f,Math.floor(Math.log(t)/Math.LN10))}if(e.type==="date"){e.tick0=Jo.dateTick0(e.calendar,0);var a=2*t;if(a>TL)t/=TL,n=i(10),e.dtick="M"+12*_m(t,n,bL);else if(a>z3)t/=z3,e.dtick="M"+_m(t,1,mse);else if(a>Ov){if(e.dtick=_m(t,Ov,e._hasDayOfWeekBreaks?[1,2,7,14]:Zst),!r){var o=Qn.getTickFormat(e),s=e.ticklabelmode==="period";s&&(e._rawTick0=e.tick0),/%[uVW]/.test(o)?e.tick0=Jo.dateTick0(e.calendar,2):e.tick0=Jo.dateTick0(e.calendar,1),s&&(e._dowTick0=e.tick0)}}else a>xm?e.dtick=_m(t,xm,mse):a>sM?e.dtick=_m(t,sM,yse):a>EL?e.dtick=_m(t,EL,yse):(n=i(10),e.dtick=_m(t,n,bL))}else if(e.type==="log"){e.tick0=0;var l=Jo.simpleMap(e.range,e.r2l);if(e._isMinor&&(t*=1.5),t>.7)e.dtick=Math.ceil(t);else if(Math.abs(l[1]-l[0])<1){var u=1.5*Math.abs((l[1]-l[0])/t);t=Math.abs(Math.pow(10,l[1])-Math.pow(10,l[0]))/u,n=i(10),e.dtick="L"+_m(t,n,bL)}else e.dtick=t>.3?"D2":"D1"}else e.type==="category"||e.type==="multicategory"?(e.tick0=0,e.dtick=Math.ceil(Math.max(t,1))):yB(e)?(e.tick0=0,n=1,e.dtick=_m(t,n,Yst)):(e.tick0=0,n=i(10),e.dtick=_m(t,n,bL));if(e.dtick===0&&(e.dtick=1),!Ih(e.dtick)&&typeof e.dtick!="string"){var c=e.dtick;throw e.dtick=1,"ax.dtick error: "+String(c)}};function Mse(e){var t=e.dtick;if(e._tickexponent=0,!Ih(t)&&typeof t!="string"&&(t=1),(e.type==="category"||e.type==="multicategory")&&(e._tickround=null),e.type==="date"){var r=e.r2l(e.tick0),n=e.l2r(r).replace(/(^-|i)/g,""),i=n.length;if(String(t).charAt(0)==="M")i>10||n.slice(5)!=="01-01"?e._tickround="d":e._tickround=+t.slice(1)%12===0?"y":"m";else if(t>=Ov&&i<=10||t>=Ov*15)e._tickround="d";else if(t>=sM&&i<=16||t>=xm)e._tickround="M";else if(t>=EL&&i<=19||t>=sM)e._tickround="S";else{var a=e.l2r(r+t).replace(/^-/,"").length;e._tickround=Math.max(i,a)-20,e._tickround<0&&(e._tickround=4)}}else if(Ih(t)||t.charAt(0)==="L"){var o=e.range.map(e.r2d||Number);Ih(t)||(t=Number(t.slice(1))),e._tickround=2-Math.floor(Math.log(t)/Math.LN10+.01);var s=Math.max(Math.abs(o[0]),Math.abs(o[1])),l=Math.floor(Math.log(s)/Math.LN10+.01),u=e.minexponent===void 0?3:e.minexponent;Math.abs(l)>u&&(O3(e.exponentformat)&&e.exponentformat!=="SI extended"&&!pB(l)||O3(e.exponentformat)&&e.exponentformat==="SI extended"&&!gB(l)?e._tickexponent=3*Math.round((l-1)/3):e._tickexponent=l)}else e._tickround=null}Qn.tickIncrement=function(e,t,r,n){var i=r?-1:1;if(Ih(t))return Jo.increment(e,i*t);var a=t.charAt(0),o=i*Number(t.slice(1));if(a==="M")return Jo.incrementMonth(e,o,n);if(a==="L")return Math.log(Math.pow(10,e)+o)/Math.LN10;if(a==="D"){var s=t==="D2"?Sse:Ase,l=e+i*.01,u=Jo.roundUp(Jo.mod(l,1),s,r);return Math.floor(l)+Math.log(b0.round(Math.pow(10,u),1))/Math.LN10}throw"unrecognized dtick "+String(t)};Qn.tickFirst=function(e,t){var r=e.r2l||Number,n=Jo.simpleMap(e.range,r,void 0,void 0,t),i=n[1]<n[0],a=i?Math.floor:Math.ceil,o=vB(n)[0],s=e.dtick,l=r(e.tick0);if(Ih(s)){var u=a((o-l)/s)*s+l;return(e.type==="category"||e.type==="multicategory")&&(u=Jo.constrain(u,0,e._categories.length-1)),u}var c=s.charAt(0),f=Number(s.slice(1));if(c==="M"){for(var h=0,d=l,v,_,b;h<10;){if(v=Qn.tickIncrement(d,s,i,e.calendar),(v-o)*(d-o)<=0)return i?Math.min(d,v):Math.max(d,v);_=(o-(d+v)/2)/(v-d),b=c+(Math.abs(Math.round(_))||1)*f,d=Qn.tickIncrement(d,b,_<0?!i:i,e.calendar),h++}return Jo.error("tickFirst did not converge",e),d}else{if(c==="L")return Math.log(a((Math.pow(10,o)-l)/f)*f+l)/Math.LN10;if(c==="D"){var p=s==="D2"?Sse:Ase,k=Jo.roundUp(Jo.mod(o,1),p,i);return Math.floor(o)+Math.log(b0.round(Math.pow(10,k),1))/Math.LN10}else throw"unrecognized dtick "+String(s)}};Qn.tickText=function(e,t,r,n){var i=Ese(e,t),a=e.tickmode==="array",o=r||a,s=e.type,l=s==="category"?e.d2l_noadd:e.d2l,u,c=function(b){var p=e.l2p(b);return p>=0&&p<=e._length?b:null};if(a&&Jo.isArrayOrTypedArray(e.ticktext)){var f=Jo.simpleMap(e.range,e.r2l),h=(Math.abs(f[1]-f[0])-(e._lBreaks||0))/1e4;for(u=0;u<e.ticktext.length&&!(Math.abs(t-l(e.tickvals[u]))<h);u++);if(u<e.ticktext.length)return i.text=String(e.ticktext[u]),i.xbnd=[c(i.x-.5),c(i.x+e.dtick-.5)],i}function d(b){if(b===void 0)return!0;if(r)return b==="none";var p={first:e._tmin,last:e._tmax}[b];return b!=="all"&&t!==p}var v=r?"never":e.exponentformat!=="none"&&d(e.showexponent)?"hide":"";if(s==="date"?Kst(e,i,r,o):s==="log"?Jst(e,i,r,o,v):s==="category"?$st(e,i):s==="multicategory"?Qst(e,i,r):yB(e)?tlt(e,i,r,o,v):elt(e,i,r,o,v),n||(e.tickprefix&&!d(e.showtickprefix)&&(i.text=e.tickprefix+i.text),e.ticksuffix&&!d(e.showticksuffix)&&(i.text+=e.ticksuffix)),e.labelalias&&e.labelalias.hasOwnProperty(i.text)){var _=e.labelalias[i.text];typeof 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n=t._id+"divider",i=r.vals,a=r.layer.selectAll("path."+n).data(i,PL);a.exit().remove(),a.enter().insert("path",":first-child").classed(n,1).classed("crisp",1).call(oM.stroke,t.dividercolor).style("stroke-width",Xp.crispRound(e,t.dividerwidth,1)+"px"),a.attr("transform",r.transFn).attr("d",r.path)}Qn.getPxPosition=function(e,t){var r=e._fullLayout._size,n=t._id.charAt(0),i=t.side,a;if(t.anchor!=="free"?a=t._anchorAxis:n==="x"?a={_offset:r.t+(1-(t.position||0))*r.h,_length:0}:n==="y"&&(a={_offset:r.l+(t.position||0)*r.w+t._shift,_length:0}),i==="top"||i==="left")return a._offset;if(i==="bottom"||i==="right")return a._offset+a._length};function xse(e){var t=e.title.font.size,r=(e.title.text.match(kb.BR_TAG_ALL)||[]).length;return e.title.hasOwnProperty("standoff")?t*(Lb+r*lM):r?t*(r+1)*lM:t}function flt(e,t){var 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st=E.constructUpdate(),lt=Object.keys(st);for(k=0;k<lt.length;k++)S=lt[k],x=C[S]=C[S]||[],x[P[p]]=st[S]}for(L=Object.keys(C),p=0;p<L.length;p++)for(S=L[p],k=0;k<M.length;k++)C[S].hasOwnProperty(k)||(C[S][k]=void 0);O?q3.call("_guiUpdate",r,C,{shapes:z},M):q3.call("_guiRestyle",r,C,M)}}});var LB=ye((sar,ile)=>{"use strict";ile.exports={scrollBarWidth:6,scrollBarMinHeight:20,scrollBarColor:"#808BA4",scrollBarMargin:4,scrollBarEnterAttrs:{rx:20,ry:3,width:0,height:0},titlePad:2,itemGap:5}});var ole=ye((lar,ale)=>{"use strict";var nle=qa(),PB=FL();ale.exports=function(t,r,n){var i=r._inHover,a=PB.isGrouped(r),o=PB.isReversed(r),s={},l=[],u=!1,c={},f=0,h=0,d,v;function _(H,te,oe){if(r.visible!==!1&&!(n&&H!==r._id))if(te===""||!PB.isGrouped(r)){var _e="~~i"+f;l.push(_e),s[_e]=[oe],f++}else l.indexOf(te)===-1?(l.push(te),u=!0,s[te]=[oe]):s[te].push(oe)}for(d=0;d<t.length;d++){var 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P=C[0].trace,T=P.marker||{},z=T.line||{},O=T.cornerradius?"M6,3a3,3,0,0,1-3,3H-3a3,3,0,0,1-3-3V-3a3,3,0,0,1,3-3H3a3,3,0,0,1,3,3Z":"M6,6H-6V-6H6Z",V=g?P.visible&&P.type===g:IB.traceIs(P,"bar"),G=qv.select(M).select("g.legendpoints").selectAll("path.legend"+g).data(V?[C]:[]);G.enter().append("path").classed("legend"+g,!0).attr("d",O).attr("transform",l),G.exit().remove(),G.each(function(Z){var j=qv.select(this),N=Z[0],H=u(N.mlw,T.line,N3,Rb);j.style("stroke-width",H+"px");var te=N.mcc;if(!n._inHover&&"mc"in N){var oe=RB(T),_e=oe.mid;_e===void 0&&(_e=(oe.max+oe.min)/2),te=Kp.tryColorscale(T,"")(_e)}var Ee=te||N.mc||T.color,Ce=T.pattern,me=Kp.getPatternAttr,ie=Ce&&(me(Ce.shape,0,"")||me(Ce.path,0,""));if(ie){var Se=me(Ce.bgcolor,0,null),Le=me(Ce.fgcolor,0,null),Ae=Ce.fgopacity,Fe=mle(Ce.size,8,10),Pe=mle(Ce.solidity,.5,1),ge="legend-"+P.uid;j.call(Kp.pattern,"legend",r,ge,ie,Fe,Pe,te,Ce.fillmode,Se,Le,Ae)}else j.call(w0.fill,Ee);H&&w0.stroke(j,N.mlc||z.color)})}function b(C){var M=C[0].trace,g=qv.select(this).select("g.legendpoints").selectAll("path.legendbox").data(M.visible&&IB.traceIs(M,"box-violin")?[C]:[]);g.enter().append("path").classed("legendbox",!0).attr("d","M6,6H-6V-6H6Z").attr("transform",l),g.exit().remove(),g.each(function(){var P=qv.select(this);if((M.boxpoints==="all"||M.points==="all")&&w0.opacity(M.fillcolor)===0&&w0.opacity((M.line||{}).color)===0){var T=_v.minExtend(M,{marker:{size:a?vle:_v.constrain(M.marker.size,2,16),sizeref:1,sizemin:1,sizemode:"diameter"}});g.call(Kp.pointStyle,T,r)}else{var z=u(void 0,M.line,N3,Rb);P.style("stroke-width",z+"px").call(w0.fill,M.fillcolor),z&&w0.stroke(P,M.line.color)}})}function p(C){var M=C[0].trace,g=qv.select(this).select("g.legendpoints").selectAll("path.legendcandle").data(M.visible&&M.type==="candlestick"?[C,C]:[]);g.enter().append("path").classed("legendcandle",!0).attr("d",function(P,T){return 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Fb=Oa(),My=Eo(),Cle=cd(),zf=Dr(),iut=zf.pushUnique,NB=zf.strTranslate,nut=zf.strRotate,aut=_3(),T0=ru(),out=$se(),bm=So(),vd=ka(),jL=yv(),wm=ho(),sut=hd().zindexSeparator,j3=qa(),Mg=ip(),zb=zS(),lut=EB(),uut=BB(),Ole=zb.YANGLE,UB=Math.PI*Ole/180,cut=1/Math.sin(UB),fut=Math.cos(UB),hut=Math.sin(UB),Qc=zb.HOVERARROWSIZE,sl=zb.HOVERTEXTPAD,Lle={box:!0,ohlc:!0,violin:!0,candlestick:!0},dut={scatter:!0,scattergl:!0,splom:!0};function Ple(e,t){return e.distance-t.distance}VB.hover=function(t,r,n,i){t=zf.getGraphDiv(t);var a=r.target;zf.throttle(t._fullLayout._uid+zb.HOVERID,zb.HOVERMINTIME,function(){vut(t,r,n,i,a)})};VB.loneHover=function(t,r){var n=!0;Array.isArray(t)||(n=!1,t=[t]);var i=r.gd,a=Vle(i),o=Gle(i),s=t.map(function(b){var p=b._x0||b.x0||b.x||0,k=b._x1||b.x1||b.x||0,E=b._y0||b.y0||b.y||0,S=b._y1||b.y1||b.y||0,L=b.eventData;if(L){var x=Math.min(p,k),C=Math.max(p,k),M=Math.min(E,S),g=Math.max(E,S),P=b.trace;if(j3.traceIs(P,"gl3d")){var T=i._fullLayout[P.scene]._scene.container,z=T.offsetLeft,O=T.offsetTop;x+=z,C+=z,M+=O,g+=O}L.bbox={x0:x+o,x1:C+o,y0:M+a,y1:g+a},r.inOut_bbox&&r.inOut_bbox.push(L.bbox)}else L=!1;return{color:b.color||vd.defaultLine,x0:b.x0||b.x||0,x1:b.x1||b.x||0,y0:b.y0||b.y||0,y1:b.y1||b.y||0,xLabel:b.xLabel,yLabel:b.yLabel,zLabel:b.zLabel,text:b.text,name:b.name,idealAlign:b.idealAlign,borderColor:b.borderColor,fontFamily:b.fontFamily,fontSize:b.fontSize,fontColor:b.fontColor,fontWeight:b.fontWeight,fontStyle:b.fontStyle,fontVariant:b.fontVariant,nameLength:b.nameLength,textAlign:b.textAlign,trace:b.trace||{index:0,hoverinfo:""},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:b.hovertemplate||!1,hovertemplateLabels:b.hovertemplateLabels||!1,eventData:L}}),l=!1,u=Ble(s,{gd:i,hovermode:"closest",rotateLabels:l,bgColor:r.bgColor||vd.background,container:Fb.select(r.container),outerContainer:r.outerContainer||r.container}),c=u.hoverLabels,f=5,h=0,d=0;c.sort(function(b,p){return b.y0-p.y0}).each(function(b,p){var k=b.y0-b.by/2;k-f<h?b.offset=h-k+f:b.offset=0,h=k+b.by+b.offset,p===r.anchorIndex&&(d=b.offset)}).each(function(b){b.offset-=d});var v=i._fullLayout._invScaleX,_=i._fullLayout._invScaleY;return Ule(c,l,v,_),n?c:c.node()};function vut(e,t,r,n,i){r||(r="xy"),typeof r=="string"&&(r=r.split(sut)[0]);var a=Array.isArray(r)?r:[r],o,s=e._fullLayout,l=s.hoversubplots,u=s._plots||[],c=u[r],f=s._has("cartesian"),h=t.hovermode||s.hovermode,d=(h||"").charAt(0)==="x",v=(h||"").charAt(0)==="y",_,b;if(f&&(d||v)&&l==="axis"){for(var p=a.length,k=0;k<p;k++)if(o=a[k],u[o]){_=wm.getFromId(e,o,"x"),b=wm.getFromId(e,o,"y");var E=(d?_:b)._subplotsWith;if(E&&E.length)for(var S=0;S<E.length;S++)iut(a,E[S])}}if(c&&l!=="single"){var L=c.overlays.map(function(wi){return wi.id});a=a.concat(L)}for(var x=a.length,C=new Array(x),M=new Array(x),g=!1,P=0;P<x;P++)if(o=a[P],u[o])g=!0,C[P]=u[o].xaxis,M[P]=u[o].yaxis;else if(s[o]&&s[o]._subplot){var T=s[o]._subplot;C[P]=T.xaxis,M[P]=T.yaxis}else{zf.warn("Unrecognized subplot: "+o);return}if(h&&!g&&(h="closest"),["x","y","closest","x unified","y unified"].indexOf(h)===-1||!e.calcdata||e.querySelector(".zoombox")||e._dragging)return jL.unhoverRaw(e,t);var z=s.hoverdistance;z===-1&&(z=1/0);var O=s.spikedistance;O===-1&&(O=1/0);var V=[],G=[],Z,j,N,H,te,oe,_e,Ee,Ce,me,ie,Se,Le,Ae={hLinePoint:null,vLinePoint:null},Fe=!1;if(Array.isArray(t))for(h="array",N=0;N<t.length;N++)te=e.calcdata[t[N].curveNumber||0],te&&(oe=te[0].trace,te[0].trace.hoverinfo!=="skip"&&(G.push(te),oe.orientation==="h"&&(Fe=!0)));else{var Pe=e.calcdata.slice();for(Pe.sort(function(wi,On){var qn=wi[0].trace.zorder||0,Fn=On[0].trace.zorder||0;return qn-Fn}),H=0;H<Pe.length;H++)te=Pe[H],oe=te[0].trace,oe.hoverinfo!=="skip"&&Mg.isTraceInSubplots(oe,a)&&(G.push(te),oe.orientation==="h"&&(Fe=!0));var ge=!i,Re,ce;if(ge)"xpx"in t?Re=t.xpx:Re=C[0]._length/2,"ypx"in t?ce=t.ypx:ce=M[0]._length/2;else{if(aut.triggerHandler(e,"plotly_beforehover",t)===!1)return;var Ze=i.getBoundingClientRect();Re=t.clientX-Ze.left,ce=t.clientY-Ze.top,s._calcInverseTransform(e);var ut=zf.apply3DTransform(s._invTransform)(Re,ce);if(Re=ut[0],ce=ut[1],Re<0||Re>C[0]._length||ce<0||ce>M[0]._length)return jL.unhoverRaw(e,t)}if(t.pointerX=Re+C[0]._offset,t.pointerY=ce+M[0]._offset,"xval"in t?Z=Mg.flat(a,t.xval):Z=Mg.p2c(C,Re),"yval"in t?j=Mg.flat(a,t.yval):j=Mg.p2c(M,ce),!My(Z[0])||!My(j[0]))return zf.warn("Fx.hover failed",t,e),jL.unhoverRaw(e,t)}var pt=1/0;function Zt(wi,On){for(H=0;H<G.length;H++)if(te=G[H],!(!te||!te[0]||!te[0].trace)&&(oe=te[0].trace,!(oe.visible!==!0||oe._length===0)&&["carpet","contourcarpet"].indexOf(oe._module.name)===-1)){if(Ce=h,Mg.isUnifiedHover(Ce)&&(Ce=Ce.charAt(0)),oe.type==="splom"?(Ee=0,_e=a[Ee]):(_e=Mg.getSubplot(oe),Ee=a.indexOf(_e)),Se={cd:te,trace:oe,xa:C[Ee],ya:M[Ee],maxHoverDistance:z,maxSpikeDistance:O,index:!1,distance:Math.min(pt,z),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:vd.defaultLine,name:oe.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},s[_e]&&(Se.subplot=s[_e]._subplot),s._splomScenes&&s._splomScenes[oe.uid]&&(Se.scene=s._splomScenes[oe.uid]),Ce==="array"){var qn=t[H];"pointNumber"in qn?(Se.index=qn.pointNumber,Ce="closest"):(Ce="","xval"in qn&&(me=qn.xval,Ce="x"),"yval"in qn&&(ie=qn.yval,Ce=Ce?"closest":"y"))}else wi!==void 0&&On!==void 0?(me=wi,ie=On):(me=Z[Ee],ie=j[Ee]);if(Le=V.length,z!==0)if(oe._module&&oe._module.hoverPoints){var Fn=oe._module.hoverPoints(Se,me,ie,Ce,{finiteRange:!0,hoverLayer:s._hoverlayer,hoversubplots:l,gd:e});if(Fn)for(var ra,la=0;la<Fn.length;la++)ra=Fn[la],My(ra.x0)&&My(ra.y0)&&V.push(yut(ra,h))}else zf.log("Unrecognized trace type in hover:",oe);if(h==="closest"&&V.length>Le&&(V.splice(0,Le),pt=V[0].distance),f&&O!==0&&V.length===0){Se.distance=O,Se.index=!1;var Ut=oe._module.hoverPoints(Se,me,ie,"closest",{hoverLayer:s._hoverlayer});if(Ut&&(Ut=Ut.filter(function(ri){return ri.spikeDistance<=O})),Ut&&Ut.length){var wt,rr=Ut.filter(function(ri){return ri.xa.showspikes&&ri.xa.spikesnap!=="hovered data"});if(rr.length){var nr=rr[0];My(nr.x0)&&My(nr.y0)&&(wt=lt(nr),(!Ae.vLinePoint||Ae.vLinePoint.spikeDistance>wt.spikeDistance)&&(Ae.vLinePoint=wt))}var Er=Ut.filter(function(ri){return ri.ya.showspikes&&ri.ya.spikesnap!=="hovered data"});if(Er.length){var Xr=Er[0];My(Xr.x0)&&My(Xr.y0)&&(wt=lt(Xr),(!Ae.hLinePoint||Ae.hLinePoint.spikeDistance>wt.spikeDistance)&&(Ae.hLinePoint=wt))}}}}}Zt();function st(wi,On,qn){for(var Fn=null,ra=1/0,la,Ut=0;Ut<wi.length;Ut++)_&&_._id!==wi[Ut].xa._id||b&&b._id!==wi[Ut].ya._id||(la=wi[Ut].spikeDistance,qn&&Ut===0&&(la=-1/0),la<=ra&&la<=On&&(Fn=wi[Ut],ra=la));return Fn}function lt(wi){return wi?{xa:wi.xa,ya:wi.ya,x:wi.xSpike!==void 0?wi.xSpike:(wi.x0+wi.x1)/2,y:wi.ySpike!==void 0?wi.ySpike:(wi.y0+wi.y1)/2,distance:wi.distance,spikeDistance:wi.spikeDistance,curveNumber:wi.trace.index,color:wi.color,pointNumber:wi.index}:null}var Gt={fullLayout:s,container:s._hoverlayer,event:t},Nt=e._spikepoints,Jt={vLinePoint:Ae.vLinePoint,hLinePoint:Ae.hLinePoint};e._spikepoints=Jt;var sr=function(){var wi=V.filter(function(qn){return _&&_._id===qn.xa._id&&b&&b._id===qn.ya._id}),On=V.filter(function(qn){return!(_&&_._id===qn.xa._id&&b&&b._id===qn.ya._id)});wi.sort(Ple),On.sort(Ple),V=wi.concat(On),V=xut(V,h)};sr();var wr=h.charAt(0),cr=(wr==="x"||wr==="y")&&V[0]&&dut[V[0].trace.type];if(f&&O!==0&&V.length!==0){var $e=V.filter(function(wi){return wi.ya.showspikes}),St=st($e,O,cr);Ae.hLinePoint=lt(St);var Qt=V.filter(function(wi){return wi.xa.showspikes}),Vt=st(Qt,O,cr);Ae.vLinePoint=lt(Vt)}if(V.length===0){var _t=jL.unhoverRaw(e,t);return f&&(Ae.hLinePoint!==null||Ae.vLinePoint!==null)&&Dle(Nt)&&Rle(e,Ae,Gt),_t}if(f&&Dle(Nt)&&Rle(e,Ae,Gt),Mg.isXYhover(Ce)&&V[0].length!==0&&V[0].trace.type!=="splom"){var It=V[0];Lle[It.trace.type]?V=V.filter(wi=>wi.trace.index===It.trace.index):V=[It];var mt=V.length,er=zle("x",It,s),lr=zle("y",It,s);Zt(er,lr);var Tr=[],Lr={},ti=0,Br=function(wi){var On=Lle[wi.trace.type]?qle(wi):wi.trace.index;if(!Lr[On])ti++,Lr[On]=ti,Tr.push(wi);else{var qn=Lr[On]-1,Fn=Tr[qn];qn>0&&Math.abs(wi.distance)<Math.abs(Fn.distance)&&(Tr[qn]=wi)}},Vr;for(Vr=0;Vr<mt;Vr++)Br(V[Vr]);for(Vr=V.length-1;Vr>mt-1;Vr--)Br(V[Vr]);V=Tr,sr()}var dt=e._hoverdata,Ge=[],Je=Vle(e),je=Gle(e);for(let wi of V){var tt=Mg.makeEventData(wi,wi.trace,wi.cd);if(wi.hovertemplate!==!1){var xt=!1;wi.cd[wi.index]&&wi.cd[wi.index].ht&&(xt=wi.cd[wi.index].ht),wi.hovertemplate=xt||wi.trace.hovertemplate||!1}if(wi.xa&&wi.ya){var Ie=wi.x0+wi.xa._offset,xe=wi.x1+wi.xa._offset,ke=wi.y0+wi.ya._offset,vt=wi.y1+wi.ya._offset,ir=Math.min(Ie,xe),ar=Math.max(Ie,xe),vr=Math.min(ke,vt),ii=Math.max(ke,vt);tt.bbox={x0:ir+je,x1:ar+je,y0:vr+Je,y1:ii+Je}}wi.eventData=[tt],Ge.push(tt)}e._hoverdata=Ge;var pi=h==="y"&&(G.length>1||V.length>1)||h==="closest"&&Fe&&V.length>1,$r=vd.combine(s.plot_bgcolor||vd.background,s.paper_bgcolor),di=Ble(V,{gd:e,hovermode:h,rotateLabels:pi,bgColor:$r,container:s._hoverlayer,outerContainer:s._paper.node(),commonLabelOpts:s.hoverlabel,hoverdistance:s.hoverdistance}),ji=di.hoverLabels;if(Mg.isUnifiedHover(h)||(gut(ji,pi,s,di.commonLabelBoundingBox),Ule(ji,pi,s._invScaleX,s._invScaleY)),i&&i.tagName){var In=j3.getComponentMethod("annotations","hasClickToShow")(e,Ge);out(Fb.select(i),In?"pointer":"")}!i||n||!_ut(e,t,dt)||(dt&&e.emit("plotly_unhover",{event:t,points:dt}),e.emit("plotly_hover",{event:t,points:e._hoverdata,xaxes:C,yaxes:M,xvals:Z,yvals:j}))}function qle(e){return[e.trace.index,e.index,e.x0,e.y0,e.name,e.attr,e.xa?e.xa._id:"",e.ya?e.ya._id:""].join(",")}var put=/<extra>([\s\S]*)<\/extra>/;function Ble(e,t){var r=t.gd,n=r._fullLayout,i=t.hovermode,a=t.rotateLabels,o=t.bgColor,s=t.container,l=t.outerContainer,u=t.commonLabelOpts||{};if(e.length===0)return[[]];var c=t.fontFamily||zb.HOVERFONT,f=t.fontSize||zb.HOVERFONTSIZE,h=t.fontWeight||n.font.weight,d=t.fontStyle||n.font.style,v=t.fontVariant||n.font.variant,_=t.fontTextcase||n.font.textcase,b=t.fontLineposition||n.font.lineposition,p=t.fontShadow||n.font.shadow,k=e[0],E=k.xa,S=k.ya,L=i.charAt(0),x=L+"Label",C=k[x];if(C===void 0&&E.type==="multicategory")for(var M=0;M<e.length&&(C=e[M][x],C===void 0);M++);var g=H3(r,l),P=g.top,T=g.width,z=g.height,O=C!==void 0&&k.distance<=t.hoverdistance&&(i==="x"||i==="y");if(O){var V=!0,G,Z;for(G=0;G<e.length;G++)if(V&&e[G].zLabel===void 0&&(V=!1),Z=e[G].hoverinfo||e[G].trace.hoverinfo,Z){var j=Array.isArray(Z)?Z:Z.split("+");if(j.indexOf("all")===-1&&j.indexOf(i)===-1){O=!1;break}}V&&(O=!1)}var N=s.selectAll("g.axistext").data(O?[0]:[]);N.enter().append("g").classed("axistext",!0),N.exit().remove();var H={minX:0,maxX:0,minY:0,maxY:0};if(N.each(function(){var _t=Fb.select(this),It=zf.ensureSingle(_t,"path","",function(ii){ii.style({"stroke-width":"1px"})}),mt=zf.ensureSingle(_t,"text","",function(ii){ii.attr("data-notex",1)}),er=u.bgcolor||vd.defaultLine,lr=u.bordercolor||vd.contrast(er),Tr=vd.contrast(er),Lr=u.font,ti={weight:Lr.weight||h,style:Lr.style||d,variant:Lr.variant||v,textcase:Lr.textcase||_,lineposition:Lr.lineposition||b,shadow:Lr.shadow||p,family:Lr.family||c,size:Lr.size||f,color:Lr.color||Tr};It.style({fill:er,stroke:lr}),mt.text(C).call(bm.font,ti).call(T0.positionText,0,0).call(T0.convertToTspans,r),_t.attr("transform","");var Br=H3(r,mt.node()),Vr,dt;if(i==="x"){var Ge=E.side==="top"?"-":"";mt.attr("text-anchor","middle").call(T0.positionText,0,E.side==="top"?P-Br.bottom-Qc-sl:P-Br.top+Qc+sl),Vr=E._offset+(k.x0+k.x1)/2,dt=S._offset+(E.side==="top"?0:S._length);var Je=Br.width/2+sl,je=Vr;Vr<Je?je=Je:Vr>n.width-Je&&(je=n.width-Je),It.attr("d","M"+(Vr-je)+",0L"+(Vr-je+Qc)+","+Ge+Qc+"H"+Je+"v"+Ge+(sl*2+Br.height)+"H"+-Je+"V"+Ge+Qc+"H"+(Vr-je-Qc)+"Z"),Vr=je,H.minX=Vr-Je,H.maxX=Vr+Je,E.side==="top"?(H.minY=dt-(sl*2+Br.height),H.maxY=dt-sl):(H.minY=dt+sl,H.maxY=dt+(sl*2+Br.height))}else{var tt,xt,Ie;S.side==="right"?(tt="start",xt=1,Ie="",Vr=E._offset+E._length):(tt="end",xt=-1,Ie="-",Vr=E._offset),dt=S._offset+(k.y0+k.y1)/2,mt.attr("text-anchor",tt),It.attr("d","M0,0L"+Ie+Qc+","+Qc+"V"+(sl+Br.height/2)+"h"+Ie+(sl*2+Br.width)+"V-"+(sl+Br.height/2)+"H"+Ie+Qc+"V-"+Qc+"Z"),H.minY=dt-(sl+Br.height/2),H.maxY=dt+(sl+Br.height/2),S.side==="right"?(H.minX=Vr+Qc,H.maxX=Vr+Qc+(sl*2+Br.width)):(H.minX=Vr-Qc-(sl*2+Br.width),H.maxX=Vr-Qc);var xe=Br.height/2,ke=P-Br.top-xe,vt="clip"+n._uid+"commonlabel"+S._id,ir;if(Vr<Br.width+2*sl+Qc){ir="M-"+(Qc+sl)+"-"+xe+"h-"+(Br.width-sl)+"V"+xe+"h"+(Br.width-sl)+"Z";var ar=Br.width-Vr+sl;T0.positionText(mt,ar,ke),tt==="end"&&mt.selectAll("tspan").each(function(){var ii=Fb.select(this),pi=bm.tester.append("text").text(ii.text()).call(bm.font,ti),$r=H3(r,pi.node());Math.round($r.width)<Math.round(Br.width)&&ii.attr("x",ar-$r.width),pi.remove()})}else T0.positionText(mt,xt*(sl+Qc),ke),ir=null;var vr=n._topclips.selectAll("#"+vt).data(ir?[0]:[]);vr.enter().append("clipPath").attr("id",vt).append("path"),vr.exit().remove(),vr.select("path").attr("d",ir),bm.setClipUrl(mt,ir?vt:null,r)}_t.attr("transform",NB(Vr,dt))}),Mg.isUnifiedHover(i)){s.selectAll("g.hovertext").remove();let _t=e.filter(It=>It.hoverinfo!=="none");if(_t.length===0)return[];var te=n.hoverlabel,oe=te.font,_e=_t[0],Ee=((i==="x unified"?_e.xa:_e.ya).unifiedhovertitle||{}).text,Ce=Ee?zf.hovertemplateString({data:i==="x unified"?[{xa:_e.xa,x:_e.xVal}]:[{ya:_e.ya,y:_e.yVal}],fallback:_e.trace.hovertemplatefallback,locale:n._d3locale,template:Ee}):C,me={showlegend:!0,legend:{title:{text:Ce,font:oe},font:oe,bgcolor:te.bgcolor,bordercolor:te.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:n.legend?n.legend.traceorder:void 0,orientation:"v"}},ie={font:oe};lut(me,ie,r._fullData);var Se=ie.legend;Se.entries=[];for(var Le=0;Le<_t.length;Le++){var Ae=_t[Le];if(Ae.hoverinfo!=="none"){var Fe=Ile(Ae,!0,i,n,C),Pe=Fe[0],ge=Fe[1];Ae.name=ge,ge!==""?Ae.text=ge+" : "+Pe:Ae.text=Pe;var Re=Ae.cd[Ae.index];Re&&(Re.mc&&(Ae.mc=Re.mc),Re.mcc&&(Ae.mc=Re.mcc),Re.mlc&&(Ae.mlc=Re.mlc),Re.mlcc&&(Ae.mlc=Re.mlcc),Re.mlw&&(Ae.mlw=Re.mlw),Re.mrc&&(Ae.mrc=Re.mrc),Re.dir&&(Ae.dir=Re.dir)),Ae._distinct=!0,Se.entries.push([Ae])}}Se.entries.sort(function(It,mt){return It[0].trace.index-mt[0].trace.index}),Se.layer=s,Se._inHover=!0,Se._groupTitleFont=te.grouptitlefont,uut(r,Se);var ce=s.select("g.legend"),Ze=H3(r,ce.node()),ut=Ze.width+2*sl,pt=Ze.height+2*sl,Zt=_t[0],st=(Zt.x0+Zt.x1)/2,lt=(Zt.y0+Zt.y1)/2,Gt=!(j3.traceIs(Zt.trace,"bar-like")||j3.traceIs(Zt.trace,"box-violin")),Nt,Jt;L==="y"?Gt?(Jt=lt-sl,Nt=lt+sl):(Jt=Math.min.apply(null,_t.map(function(It){return Math.min(It.y0,It.y1)})),Nt=Math.max.apply(null,_t.map(function(It){return Math.max(It.y0,It.y1)}))):Jt=Nt=zf.mean(_t.map(function(It){return(It.y0+It.y1)/2}))-pt/2;var sr,wr;L==="x"?Gt?(sr=st+sl,wr=st-sl):(sr=Math.max.apply(null,_t.map(function(It){return Math.max(It.x0,It.x1)})),wr=Math.min.apply(null,_t.map(function(It){return Math.min(It.x0,It.x1)}))):sr=wr=zf.mean(_t.map(function(It){return(It.x0+It.x1)/2}))-ut/2;var cr=E._offset,$e=S._offset;Nt+=$e,sr+=cr,wr+=cr-ut,Jt+=$e-pt;var St,Qt;return sr+ut<T&&sr>=0?St=sr:wr+ut<T&&wr>=0?St=wr:cr+ut<T?St=cr:sr-st<st-wr+ut?St=T-ut:St=0,St+=sl,Nt+pt<z&&Nt>=0?Qt=Nt:Jt+pt<z&&Jt>=0?Qt=Jt:$e+pt<z?Qt=$e:Nt-lt<lt-Jt+pt?Qt=z-pt:Qt=0,Qt+=sl,ce.attr("transform",NB(St-1,Qt-1)),ce}var Vt=s.selectAll("g.hovertext").data(e,function(_t){return qle(_t)});return Vt.enter().append("g").classed("hovertext",!0).each(function(){var _t=Fb.select(this);_t.append("rect").call(vd.fill,vd.addOpacity(o,.8)),_t.append("text").classed("name",!0),_t.append("path").style("stroke-width","1px"),_t.append("text").classed("nums",!0).call(bm.font,{weight:h,style:d,variant:v,textcase:_,lineposition:b,shadow:p,family:c,size:f})}),Vt.exit().remove(),Vt.each(function(_t){var It=Fb.select(this).attr("transform",""),mt=_t.color;Array.isArray(mt)&&(mt=mt[_t.eventData[0].pointNumber]);var er=_t.bgcolor||mt,lr=vd.combine(vd.opacity(er)?er:vd.defaultLine,o),Tr=vd.combine(vd.opacity(mt)?mt:vd.defaultLine,o),Lr=_t.borderColor||vd.contrast(lr),ti=Ile(_t,O,i,n,C,It),Br=ti[0],Vr=ti[1],dt=It.select("text.nums").call(bm.font,{family:_t.fontFamily||c,size:_t.fontSize||f,color:_t.fontColor||Lr,weight:_t.fontWeight||h,style:_t.fontStyle||d,variant:_t.fontVariant||v,textcase:_t.fontTextcase||_,lineposition:_t.fontLineposition||b,shadow:_t.fontShadow||p}).text(Br).attr("data-notex",1).call(T0.positionText,0,0).call(T0.convertToTspans,r),Ge=It.select("text.name"),Je=0,je=0;if(Vr&&Vr!==Br){Ge.call(bm.font,{family:_t.fontFamily||c,size:_t.fontSize||f,color:Tr,weight:_t.fontWeight||h,style:_t.fontStyle||d,variant:_t.fontVariant||v,textcase:_t.fontTextcase||_,lineposition:_t.fontLineposition||b,shadow:_t.fontShadow||p}).text(Vr).attr("data-notex",1).call(T0.positionText,0,0).call(T0.convertToTspans,r);var tt=H3(r,Ge.node());Je=tt.width+2*sl,je=tt.height+2*sl}else 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Ee=Nle(Z,t),Ce=Z.anchor,me=Ce==="end"?-1:1,ie,Se;if(Ce==="middle")ie=Z.crossPos+(H?b(Ee.y-Z.by/2):_(Z.bx/2+Z.tx2width/2)),Se=ie+(H?b(Z.by):_(Z.bx));else if(H)ie=Z.crossPos+b(Qc+Ee.y)-b(Z.by/2-Qc),Se=ie+b(Z.by);else{var Le=_(me*Qc+Ee.x),Ae=Le+_(me*Z.bx);ie=Z.crossPos+Math.min(Le,Ae),Se=Z.crossPos+Math.max(Le,Ae)}H?d!==void 0&&v!==void 0&&Math.min(Se,v)-Math.max(ie,d)>1&&(N.side==="left"?(oe=N._mainLinePosition,_e=r.width):_e=N._mainLinePosition):f!==void 0&&h!==void 0&&Math.min(Se,h)-Math.max(ie,f)>1&&(N.side==="top"?(oe=N._mainLinePosition,_e=r.height):_e=N._mainLinePosition)}u[c++]=[{datum:Z,traceIndex:Z.trace.index,dp:0,pos:Z.pos,posref:Z.posref,size:Z.by*(H?cut:1)/2,pmin:oe,pmax:_e}]}),u.sort(function(Z,j){return Z[0].posref-j[0].posref||s*(j[0].traceIndex-Z[0].traceIndex)});var p,k,E,S,L,x,C;function M(Z){var j=Z[0],N=Z[Z.length-1];if(k=j.pmin-j.pos-j.dp+j.size,E=N.pos+N.dp+N.size-j.pmax,k>.01){for(L=Z.length-1;L>=0;L--)Z[L].dp+=k;p=!1}if(!(E<.01)){if(k<-.01){for(L=Z.length-1;L>=0;L--)Z[L].dp-=E;p=!1}if(p){var H=0;for(S=0;S<Z.length;S++)x=Z[S],x.pos+x.dp+x.size>j.pmax&&H++;for(S=Z.length-1;S>=0&&!(H<=0);S--)x=Z[S],x.pos>j.pmax-1&&(x.del=!0,H--);for(S=0;S<Z.length&&!(H<=0);S++)if(x=Z[S],x.pos<j.pmin+1)for(x.del=!0,H--,E=x.size*2,L=Z.length-1;L>=0;L--)Z[L].dp-=E;for(S=Z.length-1;S>=0&&!(H<=0);S--)x=Z[S],x.pos+x.dp+x.size>j.pmax&&(x.del=!0,H--)}}}for(;!p&&o<=l;){for(o++,p=!0,S=0;S<u.length-1;){var g=u[S],P=u[S+1],T=g[g.length-1],z=P[0];if(k=T.pos+T.dp+T.size-z.pos-z.dp+z.size,k>.01){for(L=P.length-1;L>=0;L--)P[L].dp+=k;for(g.push.apply(g,P),u.splice(S+1,1),C=0,L=g.length-1;L>=0;L--)C+=g[L].dp;for(E=C/g.length,L=g.length-1;L>=0;L--)g[L].dp-=E;p=!1}else S++}u.forEach(M)}for(S=u.length-1;S>=0;S--){var O=u[S];for(L=O.length-1;L>=0;L--){var V=O[L],G=V.datum;G.offset=V.dp,G.del=V.del}}}function Nle(e,t){var r=0,n=e.offset;return t&&(n*=-hut,r=e.offset*fut),{x:r,y:n}}function mut(e){var t={start:1,end:-1,middle:0}[e.anchor],r=t*(Qc+sl),n=r+t*(e.txwidth+sl),i=e.anchor==="middle";return i&&(r-=e.tx2width/2,n+=e.txwidth/2+sl),{alignShift:t,textShiftX:r,text2ShiftX:n}}function Ule(e,t,r,n){var i=function(o){return o*r},a=function(o){return o*n};e.each(function(o){var s=Fb.select(this);if(o.del)return s.remove();var l=s.select("text.nums"),u=o.anchor,c=u==="end"?-1:1,f=mut(o),h=Nle(o,t),d=h.x,v=h.y,_=u==="middle",b="hoverlabel"in o.trace?o.trace.hoverlabel.showarrow:!0,p;_?p="M-"+i(o.bx/2+o.tx2width/2)+","+a(v-o.by/2)+"h"+i(o.bx)+"v"+a(o.by)+"h-"+i(o.bx)+"Z":b?p="M0,0L"+i(c*Qc+d)+","+a(Qc+v)+"v"+a(o.by/2-Qc)+"h"+i(c*o.bx)+"v-"+a(o.by)+"H"+i(c*Qc+d)+"V"+a(v-Qc)+"Z":p="M"+i(c*Qc+d)+","+a(v-o.by/2)+"h"+i(c*o.bx)+"v"+a(o.by)+"h"+i(-c*o.bx)+"Z",s.select("path").attr("d",p);var k=d+f.textShiftX,E=v+o.ty0-o.by/2+sl,S=o.textAlign||"auto";S!=="auto"&&(S==="left"&&u!=="start"?(l.attr("text-anchor","start"),k=_?-o.bx/2-o.tx2width/2+sl:-o.bx-sl):S==="right"&&u!=="end"&&(l.attr("text-anchor","end"),k=_?o.bx/2-o.tx2width/2-sl:o.bx+sl)),l.call(T0.positionText,i(k),a(E)),o.tx2width&&(s.select("text.name").call(T0.positionText,i(f.text2ShiftX+f.alignShift*sl+d),a(v+o.ty0-o.by/2+sl)),s.select("rect").call(bm.setRect,i(f.text2ShiftX+(f.alignShift-1)*o.tx2width/2+d),a(v-o.by/2-1),i(o.tx2width),a(o.by+2)))})}function yut(e,t){var r=e.index,n=e.trace||{},i=e.cd[0],a=e.cd[r]||{};function o(h){return h||My(h)&&h===0}var s=Array.isArray(r)?function(h,d){var v=zf.castOption(i,r,h);return o(v)?v:zf.extractOption({},n,"",d)}:function(h,d){return zf.extractOption(a,n,h,d)};function l(h,d,v){var _=s(d,v);o(_)&&(e[h]=_)}if(l("hoverinfo","hi","hoverinfo"),l("bgcolor","hbg","hoverlabel.bgcolor"),l("borderColor","hbc","hoverlabel.bordercolor"),l("fontFamily","htf","hoverlabel.font.family"),l("fontSize","hts","hoverlabel.font.size"),l("fontColor","htc","hoverlabel.font.color"),l("fontWeight","htw","hoverlabel.font.weight"),l("fontStyle","hty","hoverlabel.font.style"),l("fontVariant","htv","hoverlabel.font.variant"),l("nameLength","hnl","hoverlabel.namelength"),l("textAlign","hta","hoverlabel.align"),e.posref=t==="y"||t==="closest"&&n.orientation==="h"?e.xa._offset+(e.x0+e.x1)/2:e.ya._offset+(e.y0+e.y1)/2,e.x0=zf.constrain(e.x0,0,e.xa._length),e.x1=zf.constrain(e.x1,0,e.xa._length),e.y0=zf.constrain(e.y0,0,e.ya._length),e.y1=zf.constrain(e.y1,0,e.ya._length),e.xLabelVal!==void 0&&(e.xLabel="xLabel"in e?e.xLabel:wm.hoverLabelText(e.xa,e.xLabelVal,n.xhoverformat),e.xVal=e.xa.c2d(e.xLabelVal)),e.yLabelVal!==void 0&&(e.yLabel="yLabel"in e?e.yLabel:wm.hoverLabelText(e.ya,e.yLabelVal,n.yhoverformat),e.yVal=e.ya.c2d(e.yLabelVal)),e.zLabelVal!==void 0&&e.zLabel===void 0&&(e.zLabel=String(e.zLabelVal)),!isNaN(e.xerr)&&!(e.xa.type==="log"&&e.xerr<=0)){var u=wm.tickText(e.xa,e.xa.c2l(e.xerr),"hover").text;e.xerrneg!==void 0?e.xLabel+=" +"+u+" / -"+wm.tickText(e.xa,e.xa.c2l(e.xerrneg),"hover").text:e.xLabel+=" \xB1 "+u,t==="x"&&(e.distance+=1)}if(!isNaN(e.yerr)&&!(e.ya.type==="log"&&e.yerr<=0)){var c=wm.tickText(e.ya,e.ya.c2l(e.yerr),"hover").text;e.yerrneg!==void 0?e.yLabel+=" +"+c+" / -"+wm.tickText(e.ya,e.ya.c2l(e.yerrneg),"hover").text:e.yLabel+=" \xB1 "+c,t==="y"&&(e.distance+=1)}var f=e.hoverinfo||e.trace.hoverinfo;return f&&f!=="all"&&(f=Array.isArray(f)?f:f.split("+"),f.indexOf("x")===-1&&(e.xLabel=void 0),f.indexOf("y")===-1&&(e.yLabel=void 0),f.indexOf("z")===-1&&(e.zLabel=void 0),f.indexOf("text")===-1&&(e.text=void 0),f.indexOf("name")===-1&&(e.name=void 0)),e}function Rle(e,t,r){var n=r.container,i=r.fullLayout,a=i._size,o=r.event,s=!!t.hLinePoint,l=!!t.vLinePoint,u,c;if(n.selectAll(".spikeline").remove(),!!(l||s)){var f=vd.combine(i.plot_bgcolor,i.paper_bgcolor);if(s){var h=t.hLinePoint,d,v;u=h&&h.xa,c=h&&h.ya;var _=c.spikesnap;_==="cursor"?(d=o.pointerX,v=o.pointerY):(d=u._offset+h.x,v=c._offset+h.y);var b=Cle.readability(h.color,f)<1.5?vd.contrast(f):h.color,p=c.spikemode,k=c.spikethickness,E=c.spikecolor||b,S=wm.getPxPosition(e,c),L,x;if(p.indexOf("toaxis")!==-1||p.indexOf("across")!==-1){if(p.indexOf("toaxis")!==-1&&(L=S,x=d),p.indexOf("across")!==-1){var C=c._counterDomainMin,M=c._counterDomainMax;c.anchor==="free"&&(C=Math.min(C,c.position),M=Math.max(M,c.position)),L=a.l+C*a.w,x=a.l+M*a.w}n.insert("line",":first-child").attr({x1:L,x2:x,y1:v,y2:v,"stroke-width":k,stroke:E,"stroke-dasharray":bm.dashStyle(c.spikedash,k)}).classed("spikeline",!0).classed("crisp",!0),n.insert("line",":first-child").attr({x1:L,x2:x,y1:v,y2:v,"stroke-width":k+2,stroke:f}).classed("spikeline",!0).classed("crisp",!0)}p.indexOf("marker")!==-1&&n.insert("circle",":first-child").attr({cx:S+(c.side!=="right"?k:-k),cy:v,r:k,fill:E}).classed("spikeline",!0)}if(l){var g=t.vLinePoint,P,T;u=g&&g.xa,c=g&&g.ya;var z=u.spikesnap;z==="cursor"?(P=o.pointerX,T=o.pointerY):(P=u._offset+g.x,T=c._offset+g.y);var O=Cle.readability(g.color,f)<1.5?vd.contrast(f):g.color,V=u.spikemode,G=u.spikethickness,Z=u.spikecolor||O,j=wm.getPxPosition(e,u),N,H;if(V.indexOf("toaxis")!==-1||V.indexOf("across")!==-1){if(V.indexOf("toaxis")!==-1&&(N=j,H=T),V.indexOf("across")!==-1){var 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ie=H[Ee][1],Se=H[Ee][2],Le=j.append(Ce?"rect":"circle").attr("data-i",N).attr("data-j",Ee).style({fill:Due.background,stroke:Due.defaultLine,"stroke-width":1,"shape-rendering":"crispEdges"});if(Ce){var Ae=me[1]-ie,Fe=me[2]-Se,Pe=Fe?5:Math.max(Math.min(25,Math.abs(Ae)-5),5),ge=Ae?5:Math.max(Math.min(25,Math.abs(Fe)-5),5);Le.classed(Fe?"cursor-ew-resize":"cursor-ns-resize",!0).attr("width",Pe).attr("height",ge).attr("x",ie-Pe/2).attr("y",Se-ge/2).attr("transform",bct(Ae/2,Fe/2))}else Le.classed("cursor-grab",!0).attr("r",5).attr("cx",ie).attr("cy",Se);d[N][Ee]={element:Le.node(),gd:a,prepFn:L,doneFn:C,clickFn:g},Rue.init(d[N][Ee])}}}function T(j,N){if(t.length)for(var H=0;H<t.length;H++)for(var te=0;te<t[H].length;te++)for(var oe=0;oe+2<t[H][te].length;oe+=2)t[H][te][oe+1]=p[H][te][oe+1]+j,t[H][te][oe+2]=p[H][te][oe+2]+N}function z(j,N){T(j,N),o()}function O(j){_=+j.srcElement.getAttribute("data-i"),_||(_=0),v[_].moveFn=z}function V(){s()}function G(j){j===2&&Dct(a)}function 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vce(e){this.container=e.container,this.element=document.createElement("div"),this.update(e.graphInfo,e.buttons),this.container.appendChild(this.element)}var Tm=vce.prototype;Tm.update=function(e,t){this.graphInfo=e;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i="modebar-"+n._uid;this.element.setAttribute("id",i),this.element.setAttribute("role","toolbar"),this._uid=i,this.element.className="modebar modebar--custom",r.displayModeBar==="hover"&&(this.element.className+=" modebar--hover ease-bg"),n.modebar.orientation==="v"&&(this.element.className+=" vertical",t=t.reverse());var a=n.modebar,o="#"+i+" .modebar-group";document.querySelectorAll(o).forEach(function(f){f.style.backgroundColor=a.bgcolor});var s=!this.hasButtons(t),l=this.hasLogo!==r.displaylogo,u=this.locale!==r.locale;if(this.locale=r.locale,(s||l||u)&&(this.removeAllButtons(),this.updateButtons(t),r.watermark||r.displaylogo)){var c=this.getLogo();r.watermark&&(c.className=c.className+" watermark"),n.modebar.orientation==="v"?this.element.insertBefore(c,this.element.childNodes[0]):this.element.appendChild(c),this.hasLogo=!0}this.updateActiveButton(),fP.setStyleOnHover("#"+i+" .modebar-btn",".active",".icon path","fill: "+a.activecolor,"fill: "+a.color,this.element)};Tm.updateButtons=function(e){var t=this;this.buttons=e,this.buttonElements=[],this.buttonsNames=[],this.buttons.forEach(function(r){var n=t.createGroup();r.forEach(function(i){var a=i.name;if(!a)throw new Error("must provide button 'name' in button config");if(t.buttonsNames.indexOf(a)!==-1)throw new Error("button name '"+a+"' is taken");t.buttonsNames.push(a);var o=t.createButton(i);t.buttonElements.push(o),n.appendChild(o)}),t.element.appendChild(n)})};Tm.createGroup=function(){var e=document.createElement("div");e.className="modebar-group";var t=this.graphInfo._fullLayout.modebar;return e.style.backgroundColor=t.bgcolor,e};Tm.createButton=function(e){var t=this,r=document.createElement("button");r.setAttribute("type","button"),r.setAttribute("rel","tooltip"),r.className="modebar-btn";var n=e.title;n===void 0?n=e.name:typeof n=="function"&&(n=n(this.graphInfo)),(n||n===0)&&(r.setAttribute("data-title",n),r.setAttribute("aria-label",n)),e.attr!==void 0&&r.setAttribute("data-attr",e.attr);var i=e.val;i!==void 0&&(typeof i=="function"&&(i=i(this.graphInfo)),r.setAttribute("data-val",i));var a=e.click;if(typeof a!="function")throw new Error("must provide button 'click' function in button config");r.addEventListener("click",function(s){e.click(t.graphInfo,s),t.updateActiveButton(s.currentTarget)}),r.setAttribute("data-toggle",e.toggle||!1),e.toggle&&lN.select(r).classed("active",!0);var o=e.icon;return typeof o=="function"?r.appendChild(o()):r.appendChild(this.createIcon(o||dce.question)),r.setAttribute("data-gravity",e.gravity||"n"),r};Tm.createIcon=function(e){var t=Kct(e.height)?Number(e.height):e.ascent-e.descent,r="http://www.w3.org/2000/svg",n;if(e.path){n=document.createElementNS(r,"svg"),n.setAttribute("viewBox",[0,0,e.width,t].join(" ")),n.setAttribute("class","icon");var i=document.createElementNS(r,"path");i.setAttribute("d",e.path),e.transform?i.setAttribute("transform",e.transform):e.ascent!==void 0&&i.setAttribute("transform","matrix(1 0 0 -1 0 "+e.ascent+")"),n.appendChild(i)}if(e.svg){var a=$ct.parseFromString(e.svg,"application/xml");n=a.childNodes[0]}return n.setAttribute("height","1em"),n.setAttribute("width","1em"),n};Tm.updateActiveButton=function(e){var t=this.graphInfo._fullLayout,r=e!==void 0?e.getAttribute("data-attr"):null;this.buttonElements.forEach(function(n){var i=n.getAttribute("data-val")||!0,a=n.getAttribute("data-attr"),o=n.getAttribute("data-toggle")==="true",s=lN.select(n),l=function(f,h){var d=t.modebar,v=f.querySelector(".icon path");v&&(h||f.matches(":hover")?v.style.fill=d.activecolor:v.style.fill=d.color)};if(o){if(a===r){var u=!s.classed("active");s.classed("active",u),l(n,u)}}else{var c=a===null?a:fP.nestedProperty(t,a).get();s.classed("active",c===i),l(n,c===i)}})};Tm.hasButtons=function(e){var t=this.buttons;if(!t||e.length!==t.length)return!1;for(var r=0;r<e.length;++r){if(e[r].length!==t[r].length)return!1;for(var n=0;n<e[r].length;n++)if(e[r][n].name!==t[r][n].name)return!1}return!0};function Qct(e){return e+" (v"+Jct+")"}Tm.getLogo=function(){var e=this.createGroup(),t=document.createElement("a");return t.href="https://plotly.com/",t.target="_blank",t.setAttribute("data-title",Qct(fP._(this.graphInfo,"Produced with Plotly.js"))),t.className="modebar-btn plotlyjsicon modebar-btn--logo",t.appendChild(this.createIcon(dce.newplotlylogo)),e.appendChild(t),e};Tm.removeAllButtons=function(){for(;this.element.firstChild;)this.element.removeChild(this.element.firstChild);this.hasLogo=!1};Tm.destroy=function(){fP.removeElement(this.container.querySelector(".modebar"))};function eft(e,t){var r=e._fullLayout,n=new vce({graphInfo:e,container:r._modebardiv.node(),buttons:t});return r._privateplot&&lN.select(n.element).append("span").classed("badge-private float--left",!0).text("PRIVATE"),n}pce.exports=eft});var _ce=ye((Gar,yce)=>{"use strict";var tft=hf(),mce=Ru(),uN=qa(),rft=ip().isUnifiedHover,ift=gce(),hP=aN(),nft=oN().DRAW_MODES,aft=Dr().extendDeep;yce.exports=function(t){var r=t._fullLayout,n=t._context,i=r._modeBar;if(!n.displayModeBar&&!n.watermark){i&&(i.destroy(),delete r._modeBar);return}if(!Array.isArray(n.modeBarButtonsToRemove))throw new Error(["*modeBarButtonsToRemove* configuration options","must be an array."].join(" "));if(!Array.isArray(n.modeBarButtonsToAdd))throw new Error(["*modeBarButtonsToAdd* configuration options","must be an array."].join(" "));var a=n.modeBarButtons,o;Array.isArray(a)&&a.length?o=fft(a):!n.displayModeBar&&n.watermark?o=[]:o=oft(t),i?i.update(t,o):r._modeBar=ift(t,o)};function oft(e){var t=e._fullLayout,r=e._fullData,n=e._context;function i(N,H){if(typeof H=="string"){if(H.toLowerCase()===N.toLowerCase())return!0}else{var te=H.name,oe=H._cat||H.name;if(te===N||oe===N.toLowerCase())return!0}return!1}var a=t.modebar.add;typeof a=="string"&&(a=[a]);var o=t.modebar.remove;typeof o=="string"&&(o=[o]);var s=n.modeBarButtonsToAdd.concat(a.filter(function(N){for(var H=0;H<n.modeBarButtonsToRemove.length;H++)if(i(N,n.modeBarButtonsToRemove[H]))return!1;return!0})),l=n.modeBarButtonsToRemove.concat(o.filter(function(N){for(var H=0;H<n.modeBarButtonsToAdd.length;H++)if(i(N,n.modeBarButtonsToAdd[H]))return!1;return!0})),u=t._has("cartesian"),c=t._has("gl3d"),f=t._has("geo"),h=t._has("pie"),d=t._has("funnelarea"),v=t._has("ternary"),_=t._has("mapbox"),b=t._has("map"),p=t._has("polar"),k=t._has("smith"),E=t._has("sankey"),S=sft(t),L=rft(t.hovermode),x=[];function C(N){if(N.length){for(var H=[],te=0;te<N.length;te++){for(var oe=N[te],_e=hP[oe],Ee=_e.name.toLowerCase(),Ce=(_e._cat||_e.name).toLowerCase(),me=!1,ie=0;ie<l.length;ie++){var Se=l[ie].toLowerCase();if(Se===Ee||Se===Ce){me=!0;break}}me||H.push(hP[oe])}x.push(H)}}var M=["toImage"];n.showEditInChartStudio?M.push("editInChartStudio"):n.showSendToCloud&&M.push("sendDataToCloud"),C(M);var g=[],P=[],T=[],z=[];(u||h||d||v)+f+c+_+b+p+k>1?(P=["toggleHover"],T=["resetViews"]):f?(g=["zoomInGeo","zoomOutGeo"],P=["hoverClosestGeo"],T=["resetGeo"]):c?(P=["hoverClosest3d"],T=["resetCameraDefault3d","resetCameraLastSave3d"]):_?(g=["zoomInMapbox","zoomOutMapbox"],P=["toggleHover"],T=["resetViewMapbox"]):b?(g=["zoomInMap","zoomOutMap"],P=["toggleHover"],T=["resetViewMap"]):h?P=["hoverClosestPie"]:E?(P=["hoverClosestCartesian","hoverCompareCartesian"],T=["resetViewSankey"]):P=["toggleHover"],u&&P.push("toggleSpikelines","hoverClosestCartesian","hoverCompareCartesian"),(uft(r)||L)&&(P=[]),u&&!S&&(g=["zoomIn2d","zoomOut2d","autoScale2d"],T[0]!=="resetViews"&&(T=["resetScale2d"])),c?z=["zoom3d","pan3d","orbitRotation","tableRotation"]:u&&!S||v?z=["zoom2d","pan2d"]:_||b||f?z=["pan2d"]:p&&(z=["zoom2d"]),lft(r)&&z.push("select2d","lasso2d");var O=[],V=function(N){O.indexOf(N)===-1&&P.indexOf(N)!==-1&&O.push(N)};if(Array.isArray(s)){for(var G=[],Z=0;Z<s.length;Z++){var j=s[Z];typeof j=="string"?(j=j.toLowerCase(),nft.indexOf(j)!==-1?(t._has("mapbox")||t._has("map")||t._has("cartesian"))&&z.push(j):j==="togglespikelines"?V("toggleSpikelines"):j==="togglehover"?V("toggleHover"):j==="hovercompare"?V("hoverCompareCartesian"):j==="hoverclosest"?(V("hoverClosestCartesian"),V("hoverClosestGeo"),V("hoverClosest3d"),V("hoverClosestPie")):j==="v1hovermode"&&(V("hoverClosestCartesian"),V("hoverCompareCartesian"),V("hoverClosestGeo"),V("hoverClosest3d"),V("hoverClosestPie"))):G.push(j)}s=G}return C(z),C(g.concat(T)),C(O),cft(x,s)}function sft(e){for(var t=tft.list({_fullLayout:e},null,!0),r=0;r<t.length;r++){var n=t[r].modebardisable;if(!t[r].fixedrange&&n!=="autoscale+zoominout"&&n!=="zoominout+autoscale")return!1}return!0}function lft(e){for(var t=!1,r=0;r<e.length&&!t;r++){var n=e[r];!n._module||!n._module.selectPoints||(uN.traceIs(n,"scatter-like")?(mce.hasMarkers(n)||mce.hasText(n))&&(t=!0):uN.traceIs(n,"box-violin")?(n.boxpoints==="all"||n.points==="all")&&(t=!0):t=!0)}return t}function uft(e){for(var t=0;t<e.length;t++)if(!uN.traceIs(e[t],"noHover"))return!1;return!0}function cft(e,t){if(t.length)if(Array.isArray(t[0]))for(var r=0;r<t.length;r++)e.push(t[r]);else e.push(t);return e}function fft(e){for(var t=aft([],e),r=0;r<t.length;r++)for(var n=t[r],i=0;i<n.length;i++){var a=n[i];if(typeof a=="string")if(hP[a]!==void 0)t[r][i]=hP[a];else throw new Error(["*modeBarButtons* configuration options","invalid button name"].join(" "))}return t}});var cN=ye((Har,xce)=>{"use strict";xce.exports={moduleType:"component",name:"modebar",layoutAttributes:sN(),supplyLayoutDefaults:hce(),manage:_ce()}});var fN=ye((jar,bce)=>{"use strict";var hft=$h().FROM_BL;bce.exports=function(t,r,n){n===void 0&&(n=hft[t.constraintoward||"center"]);var i=[t.r2l(t.range[0]),t.r2l(t.range[1])],a=i[0]+(i[1]-i[0])*n;t.range=t._input.range=[t.l2r(a+(i[0]-a)*r),t.l2r(a+(i[1]-a)*r)],t.setScale()}});var Nb=ye(_M=>{"use strict";var Bb=Dr(),hN=Ag(),kg=hf().id2name,dft=Rd(),wce=fN(),vft=ym(),pft=fs().ALMOST_EQUAL,gft=$h().FROM_BL;_M.handleDefaults=function(e,t,r){var n=r.axIds,i=r.axHasImage,a=t._axisConstraintGroups=[],o=t._axisMatchGroups=[],s,l,u,c,f,h,d,v;for(s=0;s<n.length;s++)c=kg(n[s]),f=e[c],h=t[c],mft(f,h,{axIds:n,layoutOut:t,hasImage:i[c]});function _(M,g){for(s=0;s<M.length;s++){l=M[s];for(u in l)t[kg(u)][g]=l}}for(_(o,"_matchGroup"),s=0;s<a.length;s++){l=a[s];for(u in l)if(h=t[kg(u)],h.fixedrange){for(var b in l){var p=kg(b);(e[p]||{}).fixedrange===!1&&Bb.warn("fixedrange was specified as false for axis "+p+" but was overridden because another axis in its constraint group has fixedrange true"),t[p].fixedrange=!0}break}}for(s=0;s<a.length;){l=a[s];for(u in l){h=t[kg(u)],h._matchGroup&&Object.keys(h._matchGroup).length===Object.keys(l).length&&(a.splice(s,1),s--);break}s++}_(a,"_constraintGroup");var k=["constrain","range","autorange","rangemode","rangebreaks","categoryorder","categoryarray"],E=!1,S=!1;function L(){v=h[d],d==="rangebreaks"&&(S=h._hasDayOfWeekBreaks)}for(s=0;s<o.length;s++){l=o[s];for(var x=0;x<k.length;x++){d=k[x],v=null;var C;for(u in l)if(c=kg(u),f=e[c],h=t[c],d in h){if(!h.matches&&(C=h,d in f)){L();break}v===null&&d in f&&L()}if(d==="range"&&v&&f.range&&f.range.length===2&&f.range[0]!==null&&f.range[1]!==null&&(E=!0),d==="autorange"&&v===null&&E&&(v=!1),v===null&&d in C&&(v=C[d]),v!==null)for(u in l)h=t[kg(u)],h[d]=d==="range"?v.slice():v,d==="rangebreaks"&&(h._hasDayOfWeekBreaks=S,vft(h,t))}}};function mft(e,t,r){var n=r.axIds,i=r.layoutOut,a=r.hasImage,o=i._axisConstraintGroups,s=i._axisMatchGroups,l=t._id,u=l.charAt(0),c=((i._splomAxes||{})[u]||{})[l]||{},f=t._id,h=f.charAt(0)==="x";t._matchGroup=null,t._constraintGroup=null;function d(z,O){return Bb.coerce(e,t,dft,z,O)}d("constrain",a?"domain":"range"),Bb.coerce(e,t,{constraintoward:{valType:"enumerated",values:h?["left","center","right"]:["bottom","middle","top"],dflt:h?"center":"middle"}},"constraintoward");var v=t.type,_,b,p=[];for(_=0;_<n.length;_++)if(b=n[_],b!==f){var k=i[kg(b)];k.type===v&&p.push(b)}var E=Sce(o,f);if(E){var S=[];for(_=0;_<p.length;_++)b=p[_],E[b]||S.push(b);p=S}var L=p.length,x,C;L&&(e.matches||c.matches)&&(x=Bb.coerce(e,t,{matches:{valType:"enumerated",values:p,dflt:p.indexOf(c.matches)!==-1?c.matches:void 0}},"matches"));var M=a&&!h?t.anchor:void 0;if(L&&!x&&(e.scaleanchor||M)&&(C=Bb.coerce(e,t,{scaleanchor:{valType:"enumerated",values:p.concat([!1])}},"scaleanchor",M)),x){t._matchGroup=dN(s,f,x,1);var g=i[kg(x)],P=Tce(i,t)/Tce(i,g);h!==(x.charAt(0)==="x")&&(P=(h?"x":"y")+P),dN(o,f,x,P)}else e.matches&&n.indexOf(e.matches)!==-1&&Bb.warn("ignored "+t._name+'.matches: "'+e.matches+'" to avoid an infinite loop');if(C){var T=d("scaleratio");T||(T=t.scaleratio=1),dN(o,f,C,T)}else e.scaleanchor&&n.indexOf(e.scaleanchor)!==-1&&Bb.warn("ignored "+t._name+'.scaleanchor: "'+e.scaleanchor+'" to avoid either an infinite loop and possibly inconsistent scaleratios, or because this axis declares a *matches* constraint.')}function Tce(e,t){var r=t.domain;return r||(r=e[kg(t.overlaying)].domain),r[1]-r[0]}function Sce(e,t){for(var r=0;r<e.length;r++)if(e[r][t])return e[r];return null}function dN(e,t,r,n){var i,a,o,s,l,u=Sce(e,t);u===null?(u={},u[t]=1,l=e.length,e.push(u)):l=e.indexOf(u);var c=Object.keys(u);for(i=0;i<e.length;i++)if(o=e[i],i!==l&&o[r]){var f=o[r];for(a=0;a<c.length;a++)s=c[a],o[s]=vN(f,vN(n,u[s]));e.splice(l,1);return}if(n!==1)for(a=0;a<c.length;a++){var 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h=Object.keys(o),d=1/0,v=0,_=1/0,b={},p={},k=!1;for(a=0;a<h.length;a++)s=h[a],p[s]=l=r[kg(s)],l._inputDomain?l.domain=l._inputDomain.slice():l._inputDomain=l.domain.slice(),l._inputRange||(l._inputRange=l.range.slice()),l.setScale(),b[s]=u=Math.abs(l._m)/o[s],d=Math.min(d,u),(l.constrain==="domain"||!l._constraintShrinkable)&&(_=Math.min(_,u)),delete l._constraintShrinkable,v=Math.max(v,u),l.constrain==="domain"&&(k=!0);if(!(d>pft*v&&!k)){for(a=0;a<h.length;a++)if(s=h[a],u=b[s],l=p[s],c=l.constrain,u!==_||c==="domain")if(f=u/_,c==="range")wce(l,f);else{var E=l._inputDomain,S=(l.domain[1]-l.domain[0])/(E[1]-E[0]),L=(l.r2l(l.range[1])-l.r2l(l.range[0]))/(l.r2l(l._inputRange[1])-l.r2l(l._inputRange[0]));if(f/=S,f*L<1){l.domain=l._input.domain=E.slice(),wce(l,f);continue}if(L<1&&(l.range=l._input.range=l._inputRange.slice(),f*=L),l.autorange){var 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ce=Pe._lw/2;if(Pe._id.charAt(0)==="x"){if(ge){if(Re==="top")return ge._offset-n-ce}else return r.t+r.h*(1-(Pe.position||0))+ce%1;return ge._offset+ge._length+n+ce}if(ge){if(Re==="right")return ge._offset+ge._length+n+ce}else return r.l+r.w*(Pe.position||0)+ce%1;return ge._offset-n-ce}for(a=0;a<i.length;a++){l=i[a];var h=l._anchorAxis;l._linepositions={},l._lw=$3.crispRound(e,l.linewidth,1),l._mainLinePosition=f(l,h,l.side),l._mainMirrorPosition=l.mirror&&h?f(l,h,Cy.OPPOSITE_SIDE[l.side]):null}var d=[],v=[],_=[],b=xM.opacity(t.paper_bgcolor)===1&&xM.opacity(t.plot_bgcolor)===1&&t.paper_bgcolor===t.plot_bgcolor;for(o in t._plots)if(s=t._plots[o],s.mainplot)s.bg&&s.bg.remove(),s.bg=void 0;else{var p=s.xaxis.domain,k=s.yaxis.domain,E=s.plotgroup;if(Tft(p,k,_)&&o.indexOf(wft)===-1){var S=E.node(),L=s.bg=S0.ensureSingle(E,"rect","bg");S.insertBefore(L.node(),S.childNodes[0]),v.push(o)}else E.select("rect.bg").remove(),_.push([p,k]),b||(d.push(o),v.push(o))}var x=t._bgLayer.selectAll(".bg").data(d);for(x.enter().append("rect").classed("bg",!0),x.exit().remove(),x.each(function(Pe){t._plots[Pe].bg=vP.select(this)}),a=0;a<v.length;a++)s=t._plots[v[a]],u=s.xaxis,c=s.yaxis,s.bg&&u._offset!==void 0&&c._offset!==void 0&&s.bg.call($3.setRect,u._offset-n,c._offset-n,u._length+2*n,c._length+2*n).call(xM.fill,t.plot_bgcolor).style("stroke-width",0);if(!t._hasOnlyLargeSploms)for(o in t._plots){s=t._plots[o],u=s.xaxis,c=s.yaxis;var C=s.clipId="clip"+t._uid+o+"plot",M=S0.ensureSingleById(t._clips,"clipPath",C,function(Pe){Pe.classed("plotclip",!0).append("rect")});s.clipRect=M.select("rect").attr({width:u._length,height:c._length}),$3.setTranslate(s.plot,u._offset,c._offset);var g,P;s._hasClipOnAxisFalse?(g=null,P=C):(g=C,P=null),$3.setClipUrl(s.plot,g,e),s.layerClipId=P}var T,z,O,V,G,Z,j,N,H,te,oe,_e,Ee;function Ce(Pe){return"M"+T+","+Pe+"H"+z}function me(Pe){return"M"+u._offset+","+Pe+"h"+u._length}function ie(Pe){return"M"+Pe+","+N+"V"+j}function 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Fe="M0,0";kce(c,o)&&(oe=dP(c,"bottom",u,i),j=c._offset+c._length+(oe?n:0),_e=dP(c,"top",u,i),N=c._offset-(_e?n:0),H=f(c,u,"left"),te=f(c,u,"right"),Ee=!c._anchorAxis||o!==c._mainSubplot,Ee&&(c.mirror==="allticks"||c.mirror==="all")&&(c._linepositions[o]=[H,te]),Fe=Le(c,ie,Se),Ee&&c.showline&&(c.mirror==="all"||c.mirror==="allticks")&&(Fe+=ie(H)+ie(te)),s.ylines.style("stroke-width",c._lw+"px").call(xM.stroke,c.showline?c.linecolor:"rgba(0,0,0,0)")),s.ylines.attr("d",Fe)}return bM.makeClipPaths(e),Jp.previousPromises(e)}function kce(e,t){return(e.ticks||e.showline)&&(t===e._mainSubplot||e.mirror==="all"||e.mirror==="allticks")}function Cce(e,t,r){if(!r.showline||!r._lw)return!1;if(r.mirror==="all"||r.mirror==="allticks")return!0;var n=r._anchorAxis;if(!n)return!1;var i=Cy.FROM_BL[t];return r.side===t?n.domain[i]===e.domain[i]:r.mirror&&n.domain[1-i]===e.domain[1-i]}function dP(e,t,r,n){if(Cce(e,t,r))return r._lw;for(var i=0;i<n.length;i++){var a=n[i];if(a._mainAxis===r._mainAxis&&Cce(e,t,a))return a._lw}return 0}pd.drawMainTitle=function(e){var t=e._fullLayout.title,r=e._fullLayout,n=Pft(r),i=Ift(r),a=Lft(r,i),o=Cft(r,n);if(Mce.draw(e,"gtitle",{propContainer:r,propName:"title.text",subtitlePropName:"title.subtitle.text",placeholder:r._dfltTitle.plot,subtitlePlaceholder:r._dfltTitle.subtitle,attributes:{x:o,y:a,"text-anchor":n,dy:i}}),t.text&&t.automargin){var s=vP.select(e).selectAll(".gtitle"),l=$3.bBox(vP.select(e).selectAll(".g-gtitle").node()).height,u=Eft(e,t,l);if(u>0){kft(e,a,u,l),s.attr({x:o,y:a,"text-anchor":n,dy:Lce(t.yanchor)}).call(pN.positionText,o,a);var c=(t.text.match(pN.BR_TAG_ALL)||[]).length;if(c){var f=Cy.LINE_SPACING*c+Cy.MID_SHIFT;t.y===0&&(f=-f),s.selectAll(".line").each(function(){var b=+this.getAttribute("dy").slice(0,-2)-f+"em";this.setAttribute("dy",b)})}var h=vP.select(e).selectAll(".gtitle-subtitle");if(h.node()){var d=s.node().getBBox(),v=d.y+d.height,_=v+Mce.SUBTITLE_PADDING_EM*t.subtitle.font.size;h.attr({x:o,y:_,"text-anchor":n,dy:Lce(t.yanchor)}).call(pN.positionText,o,_)}}}};function Sft(e,t,r,n,i){var a=t.yref==="paper"?e._fullLayout._size.h:e._fullLayout.height,o=S0.isTopAnchor(t)?n:n-i,s=r==="b"?a-o:o;return S0.isTopAnchor(t)&&r==="t"||S0.isBottomAnchor(t)&&r==="b"?!1:s<i}function Mft(e,t,r,n,i){var a=0;return r==="middle"&&(a+=i/2),e==="t"?(r==="top"&&(a+=i),a+=n-t*n):(r==="bottom"&&(a+=i),a+=t*n),a}function Eft(e,t,r){var n=t.y,i=t.yanchor,a=n>.5?"t":"b",o=e._fullLayout.margin[a],s=0;return t.yref==="paper"?s=r+t.pad.t+t.pad.b:t.yref==="container"&&(s=Mft(a,n,i,e._fullLayout.height,r)+t.pad.t+t.pad.b),s>o?s:0}function kft(e,t,r,n){var i="title.automargin",a=e._fullLayout.title,o=a.y>.5?"t":"b",s={x:a.x,y:a.y,t:0,b:0},l={};a.yref==="paper"&&Sft(e,a,o,t,n)?s[o]=r:a.yref==="container"&&(l[o]=r,e._fullLayout._reservedMargin[i]=l),Jp.allowAutoMargin(e,i),Jp.autoMargin(e,i,s)}function Cft(e,t){var r=e.title,n=e._size,i=0;switch(t===Rce?i=r.pad.l:t===Dce&&(i=-r.pad.r),r.xref){case"paper":return n.l+n.w*r.x+i;case"container":default:return e.width*r.x+i}}function Lft(e,t){var r=e.title,n=e._size,i=0;if(t==="0em"||!t?i=-r.pad.b:t===Cy.CAP_SHIFT+"em"&&(i=r.pad.t),r.y==="auto")return n.t/2;switch(r.yref){case"paper":return n.t+n.h-n.h*r.y+i;case"container":default:return e.height-e.height*r.y+i}}function Lce(e){return e==="top"?Cy.CAP_SHIFT+.3+"em":e==="bottom"?"-0.3em":Cy.MID_SHIFT+"em"}function Pft(e){var t=e.title,r=bft;return S0.isRightAnchor(t)?r=Dce:S0.isLeftAnchor(t)&&(r=Rce),r}function Ift(e){var t=e.title,r="0em";return S0.isTopAnchor(t)?r=Cy.CAP_SHIFT+"em":S0.isMiddleAnchor(t)&&(r=Cy.MID_SHIFT+"em"),r}pd.doTraceStyle=function(e){var t=e.calcdata,r=[],n;for(n=0;n<t.length;n++){var i=t[n],a=i[0]||{},o=a.trace||{},s=o._module||{},l=s.arraysToCalcdata;l&&l(i,o);var u=s.editStyle;u&&r.push({fn:u,cd0:a})}if(r.length){for(n=0;n<r.length;n++){var c=r[n];c.fn(e,c.cd0)}gN(e),pd.redrawReglTraces(e)}return Jp.style(e),Nv.getComponentMethod("legend","draw")(e),Jp.previousPromises(e)};pd.doColorBars=function(e){return Nv.getComponentMethod("colorbar","draw")(e),Jp.previousPromises(e)};pd.layoutReplot=function(e){var t=e.layout;return e.layout=void 0,Nv.call("_doPlot",e,"",t)};pd.doLegend=function(e){return Nv.getComponentMethod("legend","draw")(e),Jp.previousPromises(e)};pd.doTicksRelayout=function(e){return bM.draw(e,"redraw"),e._fullLayout._hasOnlyLargeSploms&&(Nv.subplotsRegistry.splom.updateGrid(e),gN(e),pd.redrawReglTraces(e)),pd.drawMainTitle(e),Jp.previousPromises(e)};pd.doModeBar=function(e){var t=e._fullLayout;Pce.manage(e);for(var r=0;r<t._basePlotModules.length;r++){var n=t._basePlotModules[r].updateFx;n&&n(e)}return Jp.previousPromises(e)};pd.doCamera=function(e){for(var t=e._fullLayout,r=t._subplots.gl3d,n=0;n<r.length;n++){var i=t[r[n]],a=i._scene;a.setViewport(i)}};pd.drawData=function(e){var t=e._fullLayout;gN(e);for(var r=t._basePlotModules,n=0;n<r.length;n++)r[n].plot(e);return pd.redrawReglTraces(e),Jp.style(e),Nv.getComponentMethod("selections","draw")(e),Nv.getComponentMethod("shapes","draw")(e),Nv.getComponentMethod("annotations","draw")(e),Nv.getComponentMethod("images","draw")(e),t._replotting=!1,Jp.previousPromises(e)};pd.redrawReglTraces=function(e){var t=e._fullLayout;if(t._has("regl")){var r=e._fullData,n=[],i=[],a,o;for(t._hasOnlyLargeSploms&&t._splomGrid.draw(),a=0;a<r.length;a++){var s=r[a];s.visible===!0&&s._length!==0&&(s.type==="splom"?t._splomScenes[s.uid].draw():s.type==="scattergl"?S0.pushUnique(n,s.xaxis+s.yaxis):s.type==="scatterpolargl"&&S0.pushUnique(i,s.subplot))}for(a=0;a<n.length;a++)o=t._plots[n[a]],o._scene&&o._scene.draw();for(a=0;a<i.length;a++)o=t[i[a]]._subplot,o._scene&&o._scene.draw()}};pd.doAutoRangeAndConstraints=function(e){for(var t=bM.list(e,"",!0),r,n={},i=0;i<t.length;i++)if(r=t[i],!n[r._id]){n[r._id]=1,xft(e,r),Ece(e,r);var a=r._matchGroup;if(a)for(var o in a){var s=bM.getFromId(e,o);Ece(e,s,r.range),n[o]=1}}_ft(e)};pd.finalDraw=function(e){Nv.getComponentMethod("rangeslider","draw")(e),Nv.getComponentMethod("rangeselector","draw")(e)};pd.drawMarginPushers=function(e){Nv.getComponentMethod("legend","draw")(e),Nv.getComponentMethod("rangeselector","draw")(e),Nv.getComponentMethod("sliders","draw")(e),Nv.getComponentMethod("updatemenus","draw")(e),Nv.getComponentMethod("colorbar","draw")(e)}});var _N=ye((Zar,Bce)=>{"use strict";var Rft=u_().readPaths,Dft=nP(),Fce=Q1().clearOutlineControllers,mN=ka(),zce=So(),Fft=vl().arrayEditor,Oce=f_(),zft=Oce.getPathString;Bce.exports={draw:pP,drawOne:qce,activateLastSelection:Bft};function pP(e){var t=e._fullLayout;Fce(e),t._selectionLayer.selectAll("path").remove();for(var r in t._plots){var n=t._plots[r].selectionLayer;n&&n.selectAll("path").remove()}for(var i=0;i<t.selections.length;i++)qce(e,i)}function gP(e){return e._context.editSelection}function qce(e,t){e._fullLayout._paperdiv.selectAll('.selectionlayer [data-index="'+t+'"]').remove();var r=Oce.makeSelectionsOptionsAndPlotinfo(e,t),n=r.options,i=r.plotinfo;if(!n._input)return;a(e._fullLayout._selectionLayer);function a(o){var s=zft(e,n),l={"data-index":t,"fill-rule":"evenodd",d:s},u=n.opacity,c="rgba(0,0,0,0)",f=n.line.color||mN.contrast(e._fullLayout.plot_bgcolor),h=n.line.width,d=n.line.dash;h||(h=5,d="solid");var v=gP(e)&&e._fullLayout._activeSelectionIndex===t;v&&(c=e._fullLayout.activeselection.fillcolor,u=e._fullLayout.activeselection.opacity);for(var _=[],b=1;b>=0;b--){var p=o.append("path").attr(l).style("opacity",b?.1:u).call(mN.stroke,f).call(mN.fill,c).call(zce.dashLine,b?"solid":d,b?4+h:h);if(Oft(p,e,n),v){var k=Fft(e.layout,"selections",n);p.style({cursor:"move"});var E={element:p.node(),plotinfo:i,gd:e,editHelpers:k,isActiveSelection:!0},S=Rft(s,e);Dft(S,p,E)}else p.style("pointer-events",b?"all":"none");_[b]=p}var L=_[0],x=_[1];x.node().addEventListener("click",function(){return qft(e,L)})}}function Oft(e,t,r){var n=r.xref+r.yref;zce.setClipUrl(e,"clip"+t._fullLayout._uid+n,t)}function qft(e,t){if(gP(e)){var r=t.node(),n=+r.getAttribute("data-index");if(n>=0){if(n===e._fullLayout._activeSelectionIndex){yN(e);return}e._fullLayout._activeSelectionIndex=n,e._fullLayout._deactivateSelection=yN,pP(e)}}}function Bft(e){if(gP(e)){var t=e._fullLayout.selections.length-1;e._fullLayout._activeSelectionIndex=t,e._fullLayout._deactivateSelection=yN,pP(e)}}function yN(e){if(gP(e)){var t=e._fullLayout._activeSelectionIndex;t>=0&&(Fce(e),delete e._fullLayout._activeSelectionIndex,pP(e))}}});var Uce=ye((Yar,Nce)=>{function Nft(){var e,t=0,r=!1;function n(i,a){return e.list.push({type:i,data:a?JSON.parse(JSON.stringify(a)):void 0}),e}return e={list:[],segmentId:function(){return t++},checkIntersection:function(i,a){return n("check",{seg1:i,seg2:a})},segmentChop:function(i,a){return n("div_seg",{seg:i,pt:a}),n("chop",{seg:i,pt:a})},statusRemove:function(i){return n("pop_seg",{seg:i})},segmentUpdate:function(i){return n("seg_update",{seg:i})},segmentNew:function(i,a){return n("new_seg",{seg:i,primary:a})},segmentRemove:function(i){return n("rem_seg",{seg:i})},tempStatus:function(i,a,o){return n("temp_status",{seg:i,above:a,below:o})},rewind:function(i){return n("rewind",{seg:i})},status:function(i,a,o){return n("status",{seg:i,above:a,below:o})},vert:function(i){return i===r?e:(r=i,n("vert",{x:i}))},log:function(i){return typeof i!="string"&&(i=JSON.stringify(i,!1,"  ")),n("log",{txt:i})},reset:function(){return n("reset")},selected:function(i){return n("selected",{segs:i})},chainStart:function(i){return n("chain_start",{seg:i})},chainRemoveHead:function(i,a){return n("chain_rem_head",{index:i,pt:a})},chainRemoveTail:function(i,a){return n("chain_rem_tail",{index:i,pt:a})},chainNew:function(i,a){return n("chain_new",{pt1:i,pt2:a})},chainMatch:function(i){return 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M=_._invScaleX,g=_._invScaleY,P=L,T=x,z="M"+L+","+x,O=n.xaxes[0],V=n.yaxes[0],G=O._length,Z=V._length,j=e.altKey&&!(IM(i)&&l),N,H,te,oe,_e,Ee,Ce;bfe(e,v,n),o&&(N=uht([[L,x]],bP.BENDPX));var me=p.selectAll("path.select-outline-"+E.id).data([1]),ie=u?_.newshape:_.newselection;u&&(n.hasText=ie.label.text||ie.label.texttemplate);var Se=u&&!l?ie.fillcolor:"rgba(0,0,0,0)",Le=ie.line.color||(a?CM.contrast(v._fullLayout.plot_bgcolor):"#7f7f7f");me.enter().append("path").attr("class","select-outline select-outline-"+E.id).style({opacity:u?ie.opacity/2:1,"stroke-dasharray":Jft(ie.line.dash,ie.line.width),"stroke-width":ie.line.width+"px","shape-rendering":"crispEdges"}).call(CM.stroke,Le).call(CM.fill,Se).attr("fill-rule","evenodd").classed("cursor-move",!!u).attr("transform",S).attr("d",z+"Z");var Ae=p.append("path").attr("class","zoombox-corners").style({fill:CM.background,stroke:CM.defaultLine,"stroke-width":1}).attr("transform",S).attr("d","M0,0Z");if(u&&n.hasText){var 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o&&(N.addPt([P,T]),oe=N.filtered);if(n.selectionDefs&&n.selectionDefs.length?(te=wfe(n.mergedPolygons,oe,j),oe.subtract=j,H=CN(n.selectionDefs.concat([oe]))):(te=[oe],H=MN(oe)),gfe(Afe(te,l),me,n),c){var Jt=TN(v,!1),sr=Jt.eventData?Jt.eventData.points.slice():[];Jt=TN(v,!1,H,Re,n),H=Jt.selectionTesters,Ce=Jt.eventData;var wr;N?wr=N.filtered:wr=Efe(te),LM.throttle(Pe,bP.SELECTDELAY,function(){ge=Sfe(H,Re);for(var cr=ge.slice(),$e=0;$e<sr.length;$e++){for(var St=sr[$e],Qt=!1,Vt=0;Vt<cr.length;Vt++)if(cr[Vt].curveNumber===St.curveNumber&&cr[Vt].pointNumber===St.pointNumber){Qt=!0;break}Qt||cr.push(St)}cr.length&&(Ce||(Ce={}),Ce.points=cr),ce(Ce,wr),Eht(v,Ce)})}},n.clickFn=function(Ze,ut){if(Ae.remove(),v._fullLayout._activeShapeIndex>=0){v._fullLayout._deactivateShape(v);return}if(!u){var pt=_.clickmode;LM.done(Pe).then(function(){if(LM.clear(Pe),Ze===2){for(me.remove(),_e=0;_e<Re.length;_e++)Ee=Re[_e],Ee._module.selectPoints(Ee,!1);if(DM(v,Re),RM(n),PN(v),Re.length){var Zt=Re[0].xaxis,st=Re[0].yaxis;if(Zt&&st){for(var lt=[],Gt=v._fullLayout.selections,Nt=0;Nt<Gt.length;Nt++){var Jt=Gt[Nt];Jt&&(Jt.xref!==Zt._id||Jt.yref!==st._id)&&lt.push(Jt)}lt.length<Gt.length&&(v._fullLayout._noEmitSelectedAtStart=!0,PM.call("_guiRelayout",v,{selections:lt}))}}}else pt.indexOf("select")>-1&&xfe(ut,v,n.xaxes,n.yaxes,n.subplot,n,me),pt==="event"&&FM(v,void 0);$ft.click(v,ut,E.id)}).catch(xP.error)}},n.doneFn=function(){Ae.remove(),LM.done(Pe).then(function(){LM.clear(Pe),!b&&oe&&n.selectionDefs&&(oe.subtract=j,n.selectionDefs.push(oe),n.mergedPolygons.length=0,[].push.apply(n.mergedPolygons,te)),(b||u)&&RM(n,b),n.doneFnCompleted&&n.doneFnCompleted(ge),c&&FM(v,Ce)}).catch(xP.error)}}function xfe(e,t,r,n,i,a,o){var s=t._hoverdata,l=t._fullLayout,u=l.clickmode,c=u.indexOf("event")>-1,f=[],h,d,v,_,b,p,k,E,S,L;if(mht(s)){bfe(e,t,a),h=wP(t,r,n,i);var x=yht(s,h),C=x.pointNumbers.length>0;if(C?_ht(h,x):xht(h)&&(k=vfe(x))){for(o&&o.remove(),L=0;L<h.length;L++)d=h[L],d._module.selectPoints(d,!1);DM(t,h),RM(a),c&&PN(t)}else{E=e.shiftKey&&(k!==void 0?k:vfe(x)),v=dht(x.pointNumber,x.searchInfo,E);var M=a.selectionDefs.concat([v]);for(_=CN(M,_),L=0;L<h.length;L++)if(b=h[L]._module.selectPoints(h[L],_),p=Tfe(b,h[L]),f.length)for(var g=0;g<p.length;g++)f.push(p[g]);else f=p;if(S={points:f},DM(t,h,S),v&&a&&a.selectionDefs.push(v),o){var P=a.mergedPolygons,T=AN(a.dragmode);gfe(Afe(P,T),o,a)}c&&FM(t,S)}}}function dht(e,t,r){return{pointNumber:e,searchInfo:t,subtract:!!r}}function wN(e){return"pointNumber"in e&&"searchInfo"in e}function vht(e){return{xmin:0,xmax:0,ymin:0,ymax:0,pts:[],contains:function(t,r,n,i){var a=e.searchInfo.cd[0].trace.index,o=i.cd[0].trace.index;return o===a&&n===e.pointNumber},isRect:!1,degenerate:!1,subtract:!!e.subtract}}function CN(e){if(!e.length)return;for(var t=[],r=wN(e[0])?0:e[0][0][0],n=r,i=wN(e[0])?0:e[0][0][1],a=i,o=0;o<e.length;o++)if(wN(e[o]))t.push(vht(e[o]));else{var s=MN(e[o]);s.subtract=!!e[o].subtract,t.push(s),r=Math.min(r,s.xmin),n=Math.max(n,s.xmax),i=Math.min(i,s.ymin),a=Math.max(a,s.ymax)}function l(u,c,f,h){for(var d=!1,v=0;v<t.length;v++)t[v].contains(u,c,f,h)&&(d=!t[v].subtract);return d}return{xmin:r,xmax:n,ymin:i,ymax:a,pts:[],contains:l,isRect:!1,degenerate:!1}}function bfe(e,t,r){var n=t._fullLayout,i=r.plotinfo,a=r.dragmode,o=n._lastSelectedSubplot&&n._lastSelectedSubplot===i.id,s=(e.shiftKey||e.altKey)&&!(IM(a)&&AN(a));o&&s&&i.selection&&i.selection.selectionDefs&&!r.selectionDefs?(r.selectionDefs=i.selection.selectionDefs,r.mergedPolygons=i.selection.mergedPolygons):(!s||!i.selection)&&RM(r),o||(mfe(t),n._lastSelectedSubplot=i.id)}function pht(e){return e._fullLayout._activeShapeIndex>=0}function ght(e){return e._fullLayout._activeSelectionIndex>=0}function RM(e,t){var r=e.dragmode,n=e.plotinfo,i=e.gd;pht(i)&&i._fullLayout._deactivateShape(i),ght(i)&&i._fullLayout._deactivateSelection(i);var a=i._fullLayout,o=a._zoomlayer,s=IM(r),l=SN(r);if(s||l){var u=o.selectAll(".select-outline-"+n.id);if(u&&i._fullLayout._outlining){var c;s&&(c=iht(u,e)),c&&PM.call("_guiRelayout",i,{shapes:c});var f;l&&!kN(e)&&(f=nht(u,e)),f&&(i._fullLayout._noEmitSelectedAtStart=!0,PM.call("_guiRelayout",i,{selections:f}).then(function(){t&&aht(i)})),i._fullLayout._outlining=!1}}n.selection={},n.selection.selectionDefs=e.selectionDefs=[],n.selection.mergedPolygons=e.mergedPolygons=[]}function dfe(e){return e._id}function wP(e,t,r,n){if(!e.calcdata)return[];var i=[],a=t.map(dfe),o=r.map(dfe),s,l,u;for(u=0;u<e.calcdata.length;u++)if(s=e.calcdata[u],l=s[0].trace,!(l.visible!==!0||!l._module||!l._module.selectPoints))if(kN({subplot:n})&&(l.subplot===n||l.geo===n))i.push(_P(l._module,s,t[0],r[0]));else if(l.type==="splom"){if(l._xaxes[a[0]]&&l._yaxes[o[0]]){var c=_P(l._module,s,t[0],r[0]);c.scene=e._fullLayout._splomScenes[l.uid],i.push(c)}}else if(l.type==="sankey"){var f=_P(l._module,s,t[0],r[0]);i.push(f)}else{if(a.indexOf(l.xaxis)===-1&&(!l._xA||!l._xA.overlaying)||o.indexOf(l.yaxis)===-1&&(!l._yA||!l._yA.overlaying))continue;i.push(_P(l._module,s,E0(e,l.xaxis),E0(e,l.yaxis)))}return i}function _P(e,t,r,n){return{_module:e,cd:t,xaxis:r,yaxis:n}}function mht(e){return e&&Array.isArray(e)&&e[0].hoverOnBox!==!0}function yht(e,t){var r=e[0],n=-1,i=[],a,o;for(o=0;o<t.length;o++)if(a=t[o],r.fullData.index===a.cd[0].trace.index){if(r.hoverOnBox===!0)break;r.pointNumber!==void 0?n=r.pointNumber:r.binNumber!==void 0&&(n=r.binNumber,i=r.pointNumbers);break}return{pointNumber:n,pointNumbers:i,searchInfo:a}}function vfe(e){var t=e.searchInfo.cd[0].trace,r=e.pointNumber,n=e.pointNumbers,i=n.length>0,a=i?n[0]:r;return t.selectedpoints?t.selectedpoints.indexOf(a)>-1:!1}function _ht(e,t){var r=[],n,i,a,o;for(o=0;o<e.length;o++)n=e[o],n.cd[0].trace.selectedpoints&&n.cd[0].trace.selectedpoints.length>0&&r.push(n);if(r.length===1&&(a=r[0]===t.searchInfo,a&&(i=t.searchInfo.cd[0].trace,i.selectedpoints.length===t.pointNumbers.length))){for(o=0;o<t.pointNumbers.length;o++)if(i.selectedpoints.indexOf(t.pointNumbers[o])<0)return!1;return!0}return!1}function xht(e){var t=0,r,n,i;for(i=0;i<e.length;i++)if(r=e[i],n=r.cd[0].trace,n.selectedpoints&&(n.selectedpoints.length>1||(t+=n.selectedpoints.length,t>1)))return!1;return t===1}function DM(e,t,r){var n;for(n=0;n<t.length;n++){var i=t[n].cd[0].trace._fullInput,a=e._fullLayout._tracePreGUI[i.uid]||{};a.selectedpoints===void 0&&(a.selectedpoints=i._input.selectedpoints||null)}var o;if(r){var s=r.points||[];for(n=0;n<t.length;n++)o=t[n].cd[0].trace,o._input.selectedpoints=o._fullInput.selectedpoints=[],o._fullInput!==o&&(o.selectedpoints=[]);for(var l=0;l<s.length;l++){var u=s[l],c=u.data,f=u.fullData,h=u.pointIndex,d=u.pointIndices;d?([].push.apply(c.selectedpoints,d),o._fullInput!==o&&[].push.apply(f.selectedpoints,d)):(c.selectedpoints.push(h),o._fullInput!==o&&f.selectedpoints.push(h))}}else for(n=0;n<t.length;n++)o=t[n].cd[0].trace,delete o.selectedpoints,delete o._input.selectedpoints,o._fullInput!==o&&delete o._fullInput.selectedpoints;bht(e,t)}function bht(e,t){for(var r=!1,n=0;n<t.length;n++){var i=t[n],a=i.cd;PM.traceIs(a[0].trace,"regl")&&(r=!0);var o=i._module,s=o.styleOnSelect||o.style;s&&(s(e,a,a[0].node3),a[0].nodeRangePlot3&&s(e,a,a[0].nodeRangePlot3))}r&&(sht(e),lht(e))}function wfe(e,t,r){for(var n=r?ufe.difference:ufe.union,i=n({regions:e},{regions:[t]}),a=i.regions.reverse(),o=0;o<a.length;o++){var s=a[o];s.subtract=LN(s,a.slice(0,o))}return a}function Tfe(e,t){if(Array.isArray(e))for(var r=t.cd,n=t.cd[0].trace,i=0;i<e.length;i++)e[i]=Qft(e[i],n,r);return e}function Afe(e,t){for(var r=[],n=0;n<e.length;n++){r[n]=[];for(var 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Zfe=Dr(),MP=ho(),Nht=Yd(),Uht=RN(),Yfe=f_();Kfe.exports=function(t,r){Nht(t,r,{name:"selections",handleItemDefaults:Vht});for(var n=r.selections,i=0;i<n.length;i++){var a=n[i];a&&a.path===void 0&&(a.x0===void 0||a.x1===void 0||a.y0===void 0||a.y1===void 0)&&(r.selections[i]=null)}};function Vht(e,t,r){function n(x,C){return Zfe.coerce(e,t,Uht,x,C)}var i=n("path"),a=i?"path":"rect",o=n("type",a),s=o!=="path";s&&delete t.path,n("opacity"),n("line.color"),n("line.width"),n("line.dash");for(var l=["x","y"],u=0;u<2;u++){var c=l[u],f={_fullLayout:r},h,d,v,_=MP.coerceRef(e,t,f,c);if(h=MP.getFromId(f,_),h._selectionIndices.push(t._index),v=Yfe.rangeToShapePosition(h),d=Yfe.shapePositionToRange(h),s){var b=c+"0",p=c+"1",k=e[b],E=e[p];e[b]=d(e[b],!0),e[p]=d(e[p],!0),MP.coercePosition(t,f,n,_,b),MP.coercePosition(t,f,n,_,p);var S=t[b],L=t[p];S!==void 0&&L!==void 0&&(t[b]=v(S),t[p]=v(L),e[b]=k,e[p]=E)}}s&&Zfe.noneOrAll(e,t,["x0","x1","y0","y1"])}});var Qfe=ye((yor,$fe)=>{"use 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ihe=_N(),BM=Lfe();nhe.exports={moduleType:"component",name:"selections",layoutAttributes:RN(),supplyLayoutDefaults:Jfe(),supplyDrawNewSelectionDefaults:Qfe(),includeBasePlot:qM()("selections"),draw:ihe.draw,drawOne:ihe.drawOne,reselect:BM.reselect,prepSelect:BM.prepSelect,clearOutline:BM.clearOutline,clearSelectionsCache:BM.clearSelectionsCache,selectOnClick:BM.selectOnClick}});var NN=ye((bor,Ahe)=>{"use strict";var qN=Oa(),k0=Dr(),ahe=k0.numberFormat,Hht=cd(),jht=RL(),EP=qa(),vhe=k0.strTranslate,Wht=ru(),ohe=ka(),d_=So(),Xht=vf(),she=ho(),Zht=Sg(),Yht=yv(),phe=Eg(),kP=phe.selectingOrDrawing,Kht=phe.freeMode,Jht=$h().FROM_TL,$ht=vM(),Qht=wM().redrawReglTraces,edt=Mc(),FN=hf().getFromId,tdt=Of().prepSelect,rdt=Of().clearOutline,idt=Of().selectOnClick,DN=fN(),BN=hd(),lhe=BN.MINDRAG,np=BN.MINZOOM,uhe=!0;function ndt(e,t,r,n,i,a,o,s){var l=e._fullLayout._zoomlayer,u=o+s==="nsew",c=(o+s).length===1,f,h,d,v,_,b,p,k,E,S,L,x,C,M,g,P,T,z,O,V,G,Z,j;r+=t.yaxis._shift;function N(){if(f=t.xaxis,h=t.yaxis,E=f._length,S=h._length,p=f._offset,k=h._offset,d={},d[f._id]=f,v={},v[h._id]=h,o&&s)for(var It=t.overlays,mt=0;mt<It.length;mt++){var er=It[mt].xaxis;d[er._id]=er;var lr=It[mt].yaxis;v[lr._id]=lr}_=dhe(d),b=dhe(v),C=che(_,s),M=che(b,o),g=!M&&!C,x=hhe(e,e._fullLayout._axisMatchGroups,d,v),L=hhe(e,e._fullLayout._axisConstraintGroups,d,v,x);var Tr=L.isSubplotConstrained||x.isSubplotConstrained;P=s||Tr,T=o||Tr;var Lr=e._fullLayout;z=Lr._has("scattergl"),O=Lr._has("splom"),V=Lr._has("svg")}N();var H=sdt(M+C,e._fullLayout.dragmode,u),te=mhe(t,o+s+"drag",H,r,n,i,a);if(g&&!u)return te.onmousedown=null,te.style.pointerEvents="none",te;var oe={element:te,gd:e,plotinfo:t};oe.prepFn=function(It,mt,er){var lr=oe.dragmode,Tr=e._fullLayout.dragmode;Tr!==lr&&(oe.dragmode=Tr),N(),Z=e._fullLayout._invScaleX,j=e._fullLayout._invScaleY,g||(u?It.shiftKey?Tr==="pan"?Tr="zoom":kP(Tr)||(Tr="pan"):It.ctrlKey&&(Tr="pan"):Tr="pan"),Kht(Tr)?oe.minDrag=1:oe.minDrag=void 0,kP(Tr)?(oe.xaxes=_,oe.yaxes=b,tdt(It,mt,er,oe,Tr)):(oe.clickFn=Ee,kP(lr)&&_e(),g||(Tr==="zoom"?(oe.moveFn=Ze,oe.doneFn=pt,oe.minDrag=1,ce(It,mt,er)):Tr==="pan"&&(oe.moveFn=Jt,oe.doneFn=$e))),e._fullLayout._redrag=function(){var Lr=e._dragdata;if(Lr&&Lr.element===te){var ti=e._fullLayout.dragmode;kP(ti)||(N(),St([0,0,E,S]),oe.moveFn(Lr.dx,Lr.dy))}}};function _e(){oe.plotinfo.selection=!1,rdt(e)}function Ee(It,mt){var er=oe.gd;if(er._fullLayout._activeShapeIndex>=0){er._fullLayout._deactivateShape(er);return}var lr=er._fullLayout.clickmode;if(ON(er),It===2&&!c&&cr(),u)lr.indexOf("select")>-1&&idt(mt,er,_,b,t.id,oe),lr.indexOf("event")>-1&&Xht.click(er,mt,t.id);else if(It===1&&c){var Tr=o?h:f,Lr=o==="s"||s==="w"?0:1,ti=Tr._name+".range["+Lr+"]",Br=adt(Tr,Lr),Vr="left",dt="middle";if(Tr.fixedrange)return;o?(dt=o==="n"?"top":"bottom",Tr.side==="right"&&(Vr="right")):s==="e"&&(Vr="right"),er._context.showAxisRangeEntryBoxes&&qN.select(te).call(Wht.makeEditable,{gd:er,immediate:!0,background:er._fullLayout.paper_bgcolor,text:String(Br),fill:Tr.tickfont?Tr.tickfont.color:"#444",horizontalAlign:Vr,verticalAlign:dt}).on("edit",function(Ge){var Je=Tr.d2r(Ge);Je!==void 0&&EP.call("_guiRelayout",er,ti,Je)})}}Yht.init(oe);var Ce,me,ie,Se,Le,Ae,Fe,Pe,ge,Re;function ce(It,mt,er){var lr=te.getBoundingClientRect();Ce=mt-lr.left,me=er-lr.top,e._fullLayout._calcInverseTransform(e);var Tr=k0.apply3DTransform(e._fullLayout._invTransform)(Ce,me);Ce=Tr[0],me=Tr[1],ie={l:Ce,r:Ce,w:0,t:me,b:me,h:0},Se=e._hmpixcount?e._hmlumcount/e._hmpixcount:Hht(e._fullLayout.plot_bgcolor).getLuminance(),Le="M0,0H"+E+"V"+S+"H0V0",Ae=!1,Fe="xy",Re=!1,Pe=yhe(l,Se,p,k,Le),ge=_he(l,p,k)}function Ze(It,mt){if(e._transitioningWithDuration)return!1;var er=Math.max(0,Math.min(E,Z*It+Ce)),lr=Math.max(0,Math.min(S,j*mt+me)),Tr=Math.abs(er-Ce),Lr=Math.abs(lr-me);ie.l=Math.min(Ce,er),ie.r=Math.max(Ce,er),ie.t=Math.min(me,lr),ie.b=Math.max(me,lr);function ti(){Fe="",ie.r=ie.l,ie.t=ie.b,ge.attr("d","M0,0Z")}if(L.isSubplotConstrained)Tr>np||Lr>np?(Fe="xy",Tr/E>Lr/S?(Lr=Tr*S/E,me>lr?ie.t=me-Lr:ie.b=me+Lr):(Tr=Lr*E/S,Ce>er?ie.l=Ce-Tr:ie.r=Ce+Tr),ge.attr("d",CP(ie))):ti();else if(x.isSubplotConstrained)if(Tr>np||Lr>np){Fe="xy";var Br=Math.min(ie.l/E,(S-ie.b)/S),Vr=Math.max(ie.r/E,(S-ie.t)/S);ie.l=Br*E,ie.r=Vr*E,ie.b=(1-Br)*S,ie.t=(1-Vr)*S,ge.attr("d",CP(ie))}else ti();else!M||Lr<Math.min(Math.max(Tr*.6,lhe),np)?Tr<lhe||!C?ti():(ie.t=0,ie.b=S,Fe="x",ge.attr("d",ldt(ie,me))):!C||Tr<Math.min(Lr*.6,np)?(ie.l=0,ie.r=E,Fe="y",ge.attr("d",udt(ie,Ce))):(Fe="xy",ge.attr("d",CP(ie)));ie.w=ie.r-ie.l,ie.h=ie.b-ie.t,Fe&&(Re=!0),e._dragged=Re,xhe(Pe,ge,ie,Le,Ae,Se),ut(),e.emit("plotly_relayouting",G),Ae=!0}function ut(){G={},(Fe==="xy"||Fe==="x")&&(zN(_,ie.l/E,ie.r/E,G,L.xaxes),sr("x",G)),(Fe==="xy"||Fe==="y")&&(zN(b,(S-ie.b)/S,(S-ie.t)/S,G,L.yaxes),sr("y",G))}function pt(){ut(),ON(e),$e(),whe(e)}var Zt=[0,0,E,S],st=null,lt=BN.REDRAWDELAY,Gt=t.mainplot?e._fullLayout._plots[t.mainplot]:t;function Nt(It){if(!e._context._scrollZoom.cartesian&&!e._fullLayout._enablescrollzoom)return;if(_e(),e._transitioningWithDuration){It.preventDefault(),It.stopPropagation();return}N(),clearTimeout(st);var mt=-It.deltaY;if(isFinite(mt)||(mt=It.wheelDelta/10),!isFinite(mt)){k0.log("Did not find wheel motion attributes: ",It);return}var er=Math.exp(-Math.min(Math.max(mt,-20),20)/200),lr=Gt.draglayer.select(".nsewdrag").node().getBoundingClientRect(),Tr=(It.clientX-lr.left)/lr.width,Lr=(lr.bottom-It.clientY)/lr.height,ti;function Br(Vr,dt,Ge){if(Vr.fixedrange)return;var Je=k0.simpleMap(Vr.range,Vr.r2l),je=Je[0]+(Je[1]-Je[0])*dt;function tt(xt){return Vr.l2r(je+(xt-je)*Ge)}Vr.range=Je.map(tt)}if(P){for(s||(Tr=.5),ti=0;ti<_.length;ti++)Br(_[ti],Tr,er);sr("x"),Zt[2]*=er,Zt[0]+=Zt[2]*Tr*(1/er-1)}if(T){for(o||(Lr=.5),ti=0;ti<b.length;ti++)Br(b[ti],Lr,er);sr("y"),Zt[3]*=er,Zt[1]+=Zt[3]*(1-Lr)*(1/er-1)}St(Zt),wr(),e.emit("plotly_relayouting",G),st=setTimeout(function(){e._fullLayout&&(Zt=[0,0,E,S],$e())},lt),It.preventDefault()}o.length*s.length!==1&&The(te,Nt);function Jt(It,mt){if(It=It*Z,mt=mt*j,e._transitioningWithDuration)return;if(e._fullLayout._replotting=!0,C==="ew"||M==="ns"){var er=C?-It:0,lr=M?-mt:0;if(x.isSubplotConstrained){if(C&&M){var 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Ge;if(x.isSubplotConstrained||!C&&M.length===1){for(Ge=0;Ge<_.length;Ge++)_[Ge].range=_[Ge]._r.slice(),DN(_[Ge],1-mt/S);It=mt*E/S,Vr=It/2}if(x.isSubplotConstrained||!M&&C.length===1){for(Ge=0;Ge<b.length;Ge++)b[Ge].range=b[Ge]._r.slice(),DN(b[Ge],1-It/E);mt=It*S/E,dt=mt/2}}(!x.isSubplotConstrained||!M)&&sr("x"),(!x.isSubplotConstrained||!C)&&sr("y");var Je=E-It,je=S-mt;x.isSubplotConstrained&&!(C&&M)&&(C?(dt=Vr?0:It*S/E,je=Je*S/E):(Vr=dt?0:mt*E/S,Je=je*E/S)),St([Vr,dt,Je,je]),wr(),e.emit("plotly_relayouting",G)}function sr(It,mt){for(var er=x.isSubplotConstrained?{x:b,y:_}[It]:x[It+"axes"],lr=x.isSubplotConstrained?{x:_,y:b}[It]:[],Tr=0;Tr<er.length;Tr++){var Lr=er[Tr],ti=Lr._id,Br=x.xLinks[ti]||x.yLinks[ti],Vr=lr[0]||d[Br]||v[Br];Vr&&(mt?(mt[Lr._name+".range[0]"]=mt[Vr._name+".range[0]"],mt[Lr._name+".range[1]"]=mt[Vr._name+".range[1]"]):Lr.range=Vr.range.slice())}}function wr(){var It=[],mt;function er(ti){for(mt=0;mt<ti.length;mt++)ti[mt].fixedrange||It.push(ti[mt]._id)}function lr(ti,Br){for(mt=0;mt<ti.length;mt++){var Vr=ti[mt],dt=Vr[Br];!Vr.fixedrange&&dt.tickmode==="sync"&&It.push(dt._id)}}for(P&&(er(_),er(L.xaxes),er(x.xaxes),lr(t.overlays,"xaxis")),T&&(er(b),er(L.yaxes),er(x.yaxes),lr(t.overlays,"yaxis")),G={},mt=0;mt<It.length;mt++){var Tr=It[mt],Lr=FN(e,Tr);she.drawOne(e,Lr,{skipTitle:!0}),G[Lr._name+".range[0]"]=Lr.range[0],G[Lr._name+".range[1]"]=Lr.range[1]}she.redrawComponents(e,It)}function cr(){if(!e._transitioningWithDuration){var It=e._context.doubleClick,mt=[];C&&(mt=mt.concat(_)),M&&(mt=mt.concat(b)),x.xaxes&&(mt=mt.concat(x.xaxes)),x.yaxes&&(mt=mt.concat(x.yaxes));var er={},lr,Tr;if(It==="reset+autosize")for(It="autosize",Tr=0;Tr<mt.length;Tr++){lr=mt[Tr];var Lr=lr._rangeInitial0,ti=lr._rangeInitial1,Br=Lr!==void 0||ti!==void 0;if(Br&&(Lr!==void 0&&Lr!==lr.range[0]||ti!==void 0&&ti!==lr.range[1])||!Br&&lr.autorange!==!0){It="reset";break}}if(It==="autosize")for(Tr=0;Tr<mt.length;Tr++)lr=mt[Tr],lr.fixedrange||(er[lr._name+".autorange"]=!0);else if(It==="reset"){for((C||L.isSubplotConstrained)&&(mt=mt.concat(L.xaxes)),M&&!L.isSubplotConstrained&&(mt=mt.concat(L.yaxes)),L.isSubplotConstrained&&(C?M||(mt=mt.concat(b)):mt=mt.concat(_)),Tr=0;Tr<mt.length;Tr++)if(lr=mt[Tr],!lr.fixedrange){var Vr=lr._name,dt=lr._autorangeInitial;lr._rangeInitial0===void 0&&lr._rangeInitial1===void 0?er[Vr+".autorange"]=!0:lr._rangeInitial0===void 0?(er[Vr+".autorange"]=dt,er[Vr+".range"]=[null,lr._rangeInitial1]):lr._rangeInitial1===void 0?(er[Vr+".range"]=[lr._rangeInitial0,null],er[Vr+".autorange"]=dt):er[Vr+".range"]=[lr._rangeInitial0,lr._rangeInitial1]}}e.emit("plotly_doubleclick",null),EP.call("_guiRelayout",e,er)}}function $e(){St([0,0,E,S]),k0.syncOrAsync([edt.previousPromises,function(){e._fullLayout._replotting=!1,EP.call("_guiRelayout",e,G)}],e)}function St(It){var mt=e._fullLayout,er=mt._plots,lr=mt._subplots.cartesian,Tr,Lr,ti,Br;if(O&&EP.subplotsRegistry.splom.drag(e),z){for(Tr=0;Tr<lr.length;Tr++)if(Lr=er[lr[Tr]],ti=Lr.xaxis,Br=Lr.yaxis,Lr._scene){ti.limitRange&&ti.limitRange(),Br.limitRange&&Br.limitRange();var Vr=k0.simpleMap(ti.range,ti.r2l),dt=k0.simpleMap(Br.range,Br.r2l);Lr._scene.update({range:[Vr[0],dt[0],Vr[1],dt[1]]})}}if((O||z)&&($ht(e),Qht(e)),V){var Ge=It[2]/f._length,Je=It[3]/h._length;for(Tr=0;Tr<lr.length;Tr++){Lr=er[lr[Tr]],ti=Lr.xaxis,Br=Lr.yaxis;var je=(P||x.isSubplotConstrained)&&!ti.fixedrange&&d[ti._id],tt=(T||x.isSubplotConstrained)&&!Br.fixedrange&&v[Br._id],xt,Ie,xe,ke;if(je?(xt=Ge,xe=s||x.isSubplotConstrained?It[0]:_t(ti,xt)):x.xaHash[ti._id]?(xt=Ge,xe=It[0]*ti._length/f._length):x.yaHash[ti._id]?(xt=Je,xe=M==="ns"?-It[1]*ti._length/h._length:_t(ti,xt,{n:"top",s:"bottom"}[M])):(xt=Qt(ti,Ge,Je),xe=Vt(ti,xt)),xt>1&&(ti.maxallowed!==void 0&&P===(ti.range[0]<ti.range[1]?"e":"w")||ti.minallowed!==void 0&&P===(ti.range[0]<ti.range[1]?"w":"e"))&&(xt=1,xe=0),tt?(Ie=Je,ke=o||x.isSubplotConstrained?It[1]:_t(Br,Ie)):x.yaHash[Br._id]?(Ie=Je,ke=It[1]*Br._length/h._length):x.xaHash[Br._id]?(Ie=Ge,ke=C==="ew"?-It[0]*Br._length/f._length:_t(Br,Ie,{e:"right",w:"left"}[C])):(Ie=Qt(Br,Ge,Je),ke=Vt(Br,Ie)),Ie>1&&(Br.maxallowed!==void 0&&T===(Br.range[0]<Br.range[1]?"n":"s")||Br.minallowed!==void 0&&T===(Br.range[0]<Br.range[1]?"s":"n"))&&(Ie=1,ke=0),!(!xt&&!Ie)){xt||(xt=1),Ie||(Ie=1);var vt=ti._offset-xe/xt,ir=Br._offset-ke/Ie;Lr.clipRect.call(d_.setTranslate,xe,ke).call(d_.setScale,xt,Ie),Lr.plot.call(d_.setTranslate,vt,ir).call(d_.setScale,1/xt,1/Ie),(xt!==Lr.xScaleFactor||Ie!==Lr.yScaleFactor)&&(d_.setPointGroupScale(Lr.zoomScalePts,xt,Ie),d_.setTextPointsScale(Lr.zoomScaleTxt,xt,Ie)),d_.hideOutsideRangePoints(Lr.clipOnAxisFalseTraces,Lr),Lr.xScaleFactor=xt,Lr.yScaleFactor=Ie}}}}function Qt(It,mt,er){return It.fixedrange?0:P&&L.xaHash[It._id]?mt:T&&(L.isSubplotConstrained?L.xaHash:L.yaHash)[It._id]?er:0}function Vt(It,mt){return mt?(It.range=It._r.slice(),DN(It,mt),_t(It,mt)):0}function _t(It,mt,er){return It._length*(1-mt)*Jht[er||It.constraintoward||"middle"]}return te}function ghe(e,t,r,n){var i=k0.ensureSingle(e.draglayer,t,r,function(a){a.classed("drag",!0).style({fill:"transparent","stroke-width":0}).attr("data-subplot",e.id)});return i.call(Zht,n),i.node()}function mhe(e,t,r,n,i,a,o){var s=ghe(e,"rect",t,r);return qN.select(s).call(d_.setRect,n,i,a,o),s}function che(e,t){for(var r=0;r<e.length;r++)if(!e[r].fixedrange)return t;return""}function adt(e,t){var r=e.range[t],n=Math.abs(r-e.range[1-t]),i;return e.type==="date"?r:e.type==="log"?(i=Math.ceil(Math.max(0,-Math.log(n)/Math.LN10))+3,ahe("."+i+"g")(Math.pow(10,r))):(i=Math.floor(Math.log(Math.abs(r))/Math.LN10)-Math.floor(Math.log(n)/Math.LN10)+4,ahe("."+String(i)+"g")(r))}function zN(e,t,r,n,i){for(var a=0;a<e.length;a++){var 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o._matchGroup)if(f!==u){var h=n[Vv.id2name(f)];h.autorange=o.autorange,h.range=o.range.slice(),h._input.range=o.range.slice()}}}return!0}function GP(e,t){var r=t?function(n){var i=[],a=!0;for(var o in t){var s=Vv.getFromId(n,o);if(i.push(o),(s.ticklabelposition||"").indexOf("inside")!==-1&&s._anchorAxis&&i.push(s._anchorAxis._id),s._matchGroup)for(var l in s._matchGroup)t[l]||i.push(l)}return Vv.draw(n,i,{skipTitle:a})}:function(n){return Vv.draw(n,"redraw")};e.push(Edt,Au.doAutoRangeAndConstraints,r,Au.drawData,Au.finalDraw)}var ede=/^[xyz]axis[0-9]*\.range(\[[0|1]\])?$/,tde=/^[xyz]axis[0-9]*\.autorange$/,qdt=/^[xyz]axis[0-9]*\.domain(\[[0|1]\])?$/;function rde(e,t){var r=e.layout,n=e._fullLayout,i=n._guiEditing,a=DP(n._preGUI,i),o=Object.keys(t),s=Vv.list(e),l=Sa.extendDeepAll({},t),u={},c,f,h;for(o=Object.keys(t),f=0;f<o.length;f++)if(o[f].indexOf("allaxes")===0){for(h=0;h<s.length;h++){var 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z==="reverse"?(G.range?G.range.reverse():(E(V+".autorange",!0),G.range=[1,0]),Z.autorange?b.calc=!0:b.plot=!0):(C==="dragmode"&&(g===!1&&j!==!1||g!==!1&&j===!1)||n._has("scatter-like")&&n._has("regl")&&C==="dragmode"&&(g==="lasso"||g==="select")&&!(j==="lasso"||j==="select")?b.plot=!0:N?v_.update(b,N):b.calc=!0,M.set(g))}}for(c in u){var ge=RP.applyContainerArrayChanges(e,a(r,c),u[c],b,a);ge||(b.plot=!0)}for(var Re in S){L=Vv.getFromId(e,Re);var ce=L&&L._constraintGroup;if(ce){b.calc=!0;for(var Ze in ce)S[Ze]||(Vv.getFromId(e,Ze)._constraintShrinkable=!0)}}(ide(e)||t.height||t.width)&&(b.plot=!0);var ut=n.shapes;for(f=0;f<ut.length;f++)if(ut[f].showlegend){b.calc=!0;break}return(b.plot||b.calc)&&(b.layoutReplot=!0),{flags:b,rangesAltered:S,undoit:k,redoit:p,eventData:l}}function ide(e){var t=e._fullLayout,r=t.width,n=t.height;return e.layout.autosize&&Qo.plotAutoSize(e,e.layout,t),t.width!==r||t.height!==n}function NP(e,t,r,n){e=Sa.getGraphDiv(e),vh.clearPromiseQueue(e),Sa.isPlainObject(t)||(t={}),Sa.isPlainObject(r)||(r={}),Object.keys(t).length&&(e.changed=!0),Object.keys(r).length&&(e.changed=!0);var i=vh.coerceTraceIndices(e,n),a=$he(e,Sa.extendFlat({},t),i),o=a.flags,s=rde(e,Sa.extendFlat({},r)),l=s.flags;(o.calc||l.calc)&&(e.calcdata=void 0),o.clearAxisTypes&&vh.clearAxisTypes(e,i,r);var u=[];l.layoutReplot?u.push(Au.layoutReplot):o.fullReplot?u.push(_l._doPlot):(u.push(Qo.previousPromises),Qhe(e,l,s)||Qo.supplyDefaults(e),o.style&&u.push(Au.doTraceStyle),(o.colorbars||l.colorbars)&&u.push(Au.doColorBars),l.legend&&u.push(Au.doLegend),l.layoutstyle&&u.push(Au.layoutStyles),l.axrange&&GP(u,s.rangesAltered),l.ticks&&u.push(Au.doTicksRelayout),l.modebar&&u.push(Au.doModeBar),l.camera&&u.push(Au.doCamera),u.push(jM)),u.push(Qo.rehover,Qo.redrag,Qo.reselect),ap.add(e,NP,[e,a.undoit,s.undoit,a.traces],NP,[e,a.redoit,s.redoit,a.traces]);var c=Sa.syncOrAsync(u,e);return(!c||!c.then)&&(c=Promise.resolve(e)),c.then(function(){return e.emit("plotly_update",{data:a.eventData,layout:s.eventData}),e})}function KN(e){return function(r){r._fullLayout._guiEditing=!0;var n=e.apply(null,arguments);return r._fullLayout._guiEditing=!1,n}}var Bdt=[{pattern:/^hiddenlabels/,attr:"legend.uirevision"},{pattern:/^((x|y)axis\d*)\.((auto)?range|title\.text)/},{pattern:/axis\d*\.showspikes$/,attr:"modebar.uirevision"},{pattern:/(hover|drag)mode$/,attr:"modebar.uirevision"},{pattern:/^(scene\d*)\.camera/},{pattern:/^(geo\d*)\.(projection|center|fitbounds)/},{pattern:/^(ternary\d*\.[abc]axis)\.(min|title\.text)$/},{pattern:/^(polar\d*\.radialaxis)\.((auto)?range|angle|title\.text)/},{pattern:/^(polar\d*\.angularaxis)\.rotation/},{pattern:/^(mapbox\d*)\.(center|zoom|bearing|pitch)/},{pattern:/^(map\d*)\.(center|zoom|bearing|pitch)/},{pattern:/^legend\.(x|y)$/,attr:"editrevision"},{pattern:/^(shapes|annotations)/,attr:"editrevision"},{pattern:/^title\.text$/,attr:"editrevision"}],Ndt=[{pattern:/^selectedpoints$/,attr:"selectionrevision"},{pattern:/(^|value\.)visible$/,attr:"legend.uirevision"},{pattern:/^dimensions\[\d+\]\.constraintrange/},{pattern:/^node\.(x|y|groups)/},{pattern:/^level$/},{pattern:/(^|value\.)name$/},{pattern:/colorbar\.title\.text$/},{pattern:/colorbar\.(x|y)$/,attr:"editrevision"}];function Bhe(e,t){for(var r=0;r<t.length;r++){var n=t[r],i=e.match(n.pattern);if(i){var a=i[1]||"";return{head:a,tail:e.slice(a.length+1),attr:n.attr}}}}function Nhe(e,t){var r=Ec(t,e).get();if(r!==void 0)return r;var n=e.split(".");for(n.pop();n.length>1;)if(n.pop(),r=Ec(t,n.join(".")+".uirevision").get(),r!==void 0)return r;return t.uirevision}function Udt(e,t){for(var r=0;r<t.length;r++)if(t[r]._fullInput.uid===e)return r;return-1}function Vdt(e,t,r){for(var n=0;n<t.length;n++)if(t[n].uid===e)return n;return!t[r]||t[r].uid?-1:r}function Uhe(e,t){var r=Sa.isPlainObject(e),n=Array.isArray(e);return r||n?(r&&Sa.isPlainObject(t)||n&&Array.isArray(t))&&JSON.stringify(e)===JSON.stringify(t):e===t}function Gdt(e,t,r,n){var i=n._preGUI,a,o,s,l,u,c,f,h,d,v,_=[],b={},p={};for(a in i){if(u=Bhe(a,Bdt),u){if(d=u.head,v=u.tail,o=u.attr||d+".uirevision",s=Ec(n,o).get(),l=s&&Nhe(o,t),l&&l===s){if(c=i[a],c===null&&(c=void 0),f=Ec(t,a),h=f.get(),Uhe(h,c)){h===void 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Sa.warn("unrecognized GUI edit: "+a+" in trace uid "+P);delete T[a]}}}function Hdt(e,t,r,n){var i,a;function o(){return _l.addFrames(e,i)}e=Sa.getGraphDiv(e),vh.clearPromiseQueue(e);var s=e._fullData,l=e._fullLayout;if(!Sa.isPlotDiv(e)||!s||!l)a=_l.newPlot(e,t,r,n);else{if(Sa.isPlainObject(t)){var u=t;t=u.data,r=u.layout,n=u.config,i=u.frames}var c=!1;if(n){let O=Sa.extendDeepAll({},e._context);e._context=void 0,Hhe(e,n),c=!vh.collectionsAreEqual(O,e._context)}if(c){let O=e._ev.eventNames().map(V=>[V,e._ev.listeners(V)]);a=_l.newPlot(e,t,r,n).then(()=>{for(let[V,G]of O)G.forEach(Z=>e.on(V,Z));return _l.react(e,t,r,n)})}else{e.data=t||[],vh.cleanData(e.data),e.layout=r||{},vh.cleanLayout(e.layout),Gdt(e.data,e.layout,s,l),Qo.supplyDefaults(e,{skipUpdateCalc:!0});var f=e._fullData,h=e._fullLayout,d=h.datarevision===void 0,v=h.transition,_=Wdt(e,l,h,d,v),b=_.newDataRevision,p=jdt(e,s,f,d,v,b);if(ide(e)&&(_.layoutReplot=!0),p.calc||_.calc){e.calcdata=void 0;for(var k=Object.getOwnPropertyNames(h),E=0;E<k.length;E++){var S=k[E],L=S.substring(0,5);if(L==="xaxis"||L==="yaxis"){var x=h[S]._emptyCategories;x&&x()}}}else Qo.supplyDefaultsUpdateCalc(e.calcdata,f);var C=[];if(i&&(e._transitionData={},Qo.createTransitionData(e),C.push(o)),h.transition&&(p.anim||_.anim))_.ticks&&C.push(Au.doTicksRelayout),Qo.doCalcdata(e),Au.doAutoRangeAndConstraints(e),C.push(function(){return Qo.transitionFromReact(e,p,_,l)});else if(p.fullReplot||_.layoutReplot)e._fullLayout._skipDefaults=!0,C.push(_l._doPlot);else{for(var M in _.arrays){var g=_.arrays[M];if(g.length){var P=L0.getComponentMethod(M,"drawOne");if(P!==Sa.noop)for(var T=0;T<g.length;T++)P(e,g[T]);else{var z=L0.getComponentMethod(M,"draw");if(z===Sa.noop)throw new Error("cannot draw components: 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f={getValObject:c,flags:s,immutable:n,transition:i,newDataRevision:a,gd:e},h={};for(l=0;l<t.length;l++)if(r[l]){if(u=r[l]._fullInput,h[u.uid])continue;h[u.uid]=1,UP(t[l]._fullInput,u,[],f)}return(s.calc||s.plot)&&(s.fullReplot=!0),i&&s.nChanges&&s.nChangesAnim&&(s.anim=s.nChanges===s.nChangesAnim&&o?"all":"some"),s}function Wdt(e,t,r,n,i){var a=v_.layoutFlags();a.arrays={},a.rangesAltered={},a.nChanges=0,a.nChangesAnim=0;function o(h){return VP.getLayoutValObject(r,h)}for(var s in r)if(!(!s.startsWith("xaxis")&&!s.startsWith("yaxis"))&&t[s]){var l=r[s].domain,u=t[s].domain,c=t[s]._inputDomain;t[s]._inputDomain&&(l[0]===c[0]&&l[1]===c[1]?r[s].domain=t[s].domain:(l[0]!==u[0]||l[1]!==u[1])&&(r[s]._inputDomain=null))}var f={getValObject:o,flags:a,immutable:n,transition:i,gd:e};return UP(t,r,[],f),(a.plot||a.calc)&&(a.layoutReplot=!0),i&&a.nChanges&&a.nChangesAnim&&(a.anim=a.nChanges===a.nChangesAnim?"all":"some"),a}function UP(e,t,r,n){var i,a,o,s=n.getValObject,l=n.flags,u=n.immutable,c=n.inArray,f=n.arrayIndex;function h(){var V=i.editType;if(c&&V.indexOf("arraydraw")!==-1){Sa.pushUnique(l.arrays[c],f);return}v_.update(l,i),V!=="none"&&l.nChanges++,n.transition&&i.anim&&l.nChangesAnim++,(ede.test(o)||tde.test(o))&&(l.rangesAltered[r[0]]=1),a==="datarevision"&&(l.newDataRevision=1)}function d(V){return V.valType==="data_array"||V.arrayOk}for(a in e){if(l.calc&&!n.transition)return;var v=e[a],_=t[a],b=r.concat(a);if(o=b.join("."),!(a.charAt(0)==="_"||typeof v=="function"||v===_)){if((a==="tick0"||a==="dtick")&&r[0]!=="geo"){var p=t.tickmode;if(p==="auto"||p==="array"||!p)continue}if(!(a==="range"&&t.autorange)&&!((a==="zmin"||a==="zmax")&&t.type==="contourcarpet")&&(i=s(b),!!i&&!(i._compareAsJSON&&JSON.stringify(v)===JSON.stringify(_)))){var k=i.valType,E,S=d(i),L=Array.isArray(v),x=Array.isArray(_);if(L&&x){var C="_input_"+a,M=e[C],g=t[C];if(Array.isArray(M)&&M===g)continue}if(_===void 0)S&&L?l.calc=!0:h();else 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tpt=Nb().getAxisGroup;Hde.exports=function(t,r,n,i,a){var o=r.orientation,s=r[{v:"x",h:"y"}[o]+"axis"],l=tpt(n,s)+o,u=n._alignmentOpts||{},c=i("alignmentgroup"),f=u[l];f||(f=u[l]={});var h=f[c];h?h.traces.push(r):h=f[c]={traces:[r],alignmentIndex:Object.keys(f).length,offsetGroups:{}};var d=i("offsetgroup")||"",v=h.offsetGroups,_=v[d];r._offsetIndex=0,(a!=="group"||d)&&(_||(_=v[d]={offsetIndex:Object.keys(v).length}),r._offsetIndex=_.offsetIndex)}});var aU=ye((Yor,jde)=>{"use strict";var rpt=Dr(),ipt=Hb(),npt=pf();jde.exports=function(t,r){var n,i,a,o=r.scattermode;function s(h){return rpt.coerce(i._input,i,npt,h)}if(r.scattermode==="group")for(a=0;a<t.length;a++)i=t[a],i.type==="scatter"&&(n=i._input,ipt(n,i,r,s,o));for(a=0;a<t.length;a++){var l=t[a];if(l.type==="scatter"){var u=l.fill;if(!(u==="none"||u==="toself")&&(l.opacity=void 0,u==="tonexty"||u==="tonextx"))for(var c=a-1;c>=0;c--){var f=t[c];if(f.type==="scatter"&&f.xaxis===l.xaxis&&f.yaxis===l.yaxis){f.opacity=void 0;break}}}}}});var Xde=ye((Kor,Wde)=>{"use strict";var apt=Dr(),opt=X6();Wde.exports=function(e,t){function r(i,a){return apt.coerce(e,t,opt,i,a)}var n=t.barmode==="group";t.scattermode==="group"&&r("scattergap",n?t.bargap:.2)}});var Dg=ye((Jor,Yde)=>{"use strict";var spt=Eo(),Zde=Dr(),lpt=Zde.dateTime2ms,$P=Zde.incrementMonth,upt=fs(),cpt=upt.ONEAVGMONTH;Yde.exports=function(t,r,n,i){if(r.type!=="date")return{vals:i};var a=t[n+"periodalignment"];if(!a)return{vals:i};var o=t[n+"period"],s;if(spt(o)){if(o=+o,o<=0)return{vals:i}}else if(typeof o=="string"&&o.charAt(0)==="M"){var l=+o.substring(1);if(l>0&&Math.round(l)===l)s=l;else return{vals:i}}for(var u=r.calendar,c=a==="start",f=a==="end",h=t[n+"period0"],d=lpt(h,u)||0,v=[],_=[],b=[],p=i.length,k=0;k<p;k++){var E=i[k],S,L,x;if(s){for(S=Math.round((E-d)/(s*cpt)),x=$P(d,s*S,u);x>E;)x=$P(x,-s,u);for(;x<=E;)x=$P(x,s,u);L=$P(x,-s,u)}else{for(S=Math.round((E-d)/o),x=d+S*o;x>E;)x-=o;for(;x<=E;)x+=o;L=x-o}v[k]=c?L:f?x:(L+x)/2,_[k]=L,b[k]=x}return{vals:v,starts:_,ends:b}}});var F0=ye(($or,Jde)=>{"use strict";var oU=pv().hasColorscale,sU=gv(),Kde=Ru();Jde.exports=function(t,r){Kde.hasLines(r)&&oU(r,"line")&&sU(t,r,{vals:r.line.color,containerStr:"line",cLetter:"c"}),Kde.hasMarkers(r)&&(oU(r,"marker")&&sU(t,r,{vals:r.marker.color,containerStr:"marker",cLetter:"c"}),oU(r,"marker.line")&&sU(t,r,{vals:r.marker.line.color,containerStr:"marker.line",cLetter:"c"}))}});var km=ye((Qor,$de)=>{"use strict";var Yf=Dr();$de.exports=function(t,r){for(var n=0;n<t.length;n++)t[n].i=n;Yf.mergeArray(r.text,t,"tx"),Yf.mergeArray(r.texttemplate,t,"txt"),Yf.mergeArray(r.hovertext,t,"htx"),Yf.mergeArray(r.customdata,t,"data"),Yf.mergeArray(r.textposition,t,"tp"),r.textfont&&(Yf.mergeArrayCastPositive(r.textfont.size,t,"ts"),Yf.mergeArray(r.textfont.color,t,"tc"),Yf.mergeArray(r.textfont.family,t,"tf"),Yf.mergeArray(r.textfont.weight,t,"tw"),Yf.mergeArray(r.textfont.style,t,"ty"),Yf.mergeArray(r.textfont.variant,t,"tv"),Yf.mergeArray(r.textfont.textcase,t,"tC"),Yf.mergeArray(r.textfont.lineposition,t,"tE"),Yf.mergeArray(r.textfont.shadow,t,"tS"));var i=r.marker;if(i){Yf.mergeArrayCastPositive(i.size,t,"ms"),Yf.mergeArrayCastPositive(i.opacity,t,"mo"),Yf.mergeArray(i.symbol,t,"mx"),Yf.mergeArray(i.angle,t,"ma"),Yf.mergeArray(i.standoff,t,"mf"),Yf.mergeArray(i.color,t,"mc");var a=i.line;i.line&&(Yf.mergeArray(a.color,t,"mlc"),Yf.mergeArrayCastPositive(a.width,t,"mlw"));var o=i.gradient;o&&o.type!=="none"&&(Yf.mergeArray(o.type,t,"mgt"),Yf.mergeArray(o.color,t,"mgc"))}}});var z0=ye((esr,eve)=>{"use strict";var Qde=Dr();eve.exports=function(t,r){Qde.isArrayOrTypedArray(r.selectedpoints)&&Qde.tagSelected(t,r)}});var O0=ye((tsr,sve)=>{"use strict";var tve=Eo(),uU=Dr(),KM=ho(),rve=Dg(),lU=fs().BADNUM,cU=Ru(),fpt=F0(),hpt=km(),dpt=z0();function vpt(e,t){var r=e._fullLayout,n=t._xA=KM.getFromId(e,t.xaxis||"x","x"),i=t._yA=KM.getFromId(e,t.yaxis||"y","y"),a=n.makeCalcdata(t,"x"),o=i.makeCalcdata(t,"y"),s=rve(t,n,"x",a),l=rve(t,i,"y",o),u=s.vals,c=l.vals,f=t._length,h=new Array(f),d=t.ids,v=fU(t,r,n,i),_=!1,b,p,k,E,S,L;ave(r,t);var x="x",C="y",M;if(v)uU.pushUnique(v.traceIndices,t.index),b=v.orientation==="v",b?(C="s",M="x"):(x="s",M="y"),S=v.stackgaps==="interpolate";else{var g=nve(t,f);ive(e,t,n,i,u,c,g)}var P=!!t.xperiodalignment,T=!!t.yperiodalignment;for(p=0;p<f;p++){var z=h[p]={},O=tve(u[p]),V=tve(c[p]);O&&V?(z[x]=u[p],z[C]=c[p],P&&(z.orig_x=a[p],z.xEnd=s.ends[p],z.xStart=s.starts[p]),T&&(z.orig_y=o[p],z.yEnd=l.ends[p],z.yStart=l.starts[p])):v&&(b?O:V)?(z[M]=b?u[p]:c[p],z.gap=!0,S?(z.s=lU,_=!0):z.s=0):z[x]=z[C]=lU,d&&(z.id=String(d[p]))}if(hpt(h,t),fpt(e,t),dpt(h,t),v){for(p=0;p<h.length;)h[p][M]===lU?h.splice(p,1):p++;if(uU.sort(h,function(N,H){return N[M]-H[M]||N.i-H.i}),_){for(p=0;p<h.length-1&&h[p].gap;)p++;for(L=h[p].s,L||(L=h[p].s=0),k=0;k<p;k++)h[k].s=L;for(E=h.length-1;E>p&&h[E].gap;)E--;for(L=h[E].s,k=h.length-1;k>E;k--)h[k].s=L;for(;p<E;)if(p++,h[p].gap){for(k=p+1;h[k].gap;)k++;for(var G=h[p-1][M],Z=h[p-1].s,j=(h[k].s-Z)/(h[k][M]-G);p<k;)h[p].s=Z+(h[p][M]-G)*j,p++}}}return h}function ive(e,t,r,n,i,a,o){var s=t._length,l=e._fullLayout,u=r._id,c=n._id,f=l._firstScatter[ove(t)]===t.uid,h=(fU(t,l,r,n)||{}).orientation,d=t.fill;r._minDtick=0,n._minDtick=0;var v={padded:!0},_={padded:!0};o&&(v.ppad=_.ppad=o);var b=s<2||i[0]!==i[s-1]||a[0]!==a[s-1];b&&(d==="tozerox"||d==="tonextx"&&(f||h==="h"))?v.tozero=!0:!(t.error_y||{}).visible&&(d==="tonexty"||d==="tozeroy"||!cU.hasMarkers(t)&&!cU.hasText(t))&&(v.padded=!1,v.ppad=0),b&&(d==="tozeroy"||d==="tonexty"&&(f||h==="v"))?_.tozero=!0:(d==="tonextx"||d==="tozerox")&&(_.padded=!1),u&&(t._extremes[u]=KM.findExtremes(r,i,v)),c&&(t._extremes[c]=KM.findExtremes(n,a,_))}function nve(e,t){if(cU.hasMarkers(e)){var r=e.marker,n=1.6*(e.marker.sizeref||1),i;if(e.marker.sizemode==="area"?i=function(u){return Math.max(Math.sqrt((u||0)/n),3)}:i=function(u){return Math.max((u||0)/n,3)},uU.isArrayOrTypedArray(r.size)){var a={type:"linear"};KM.setConvert(a);for(var o=a.makeCalcdata(e.marker,"size"),s=new Array(t),l=0;l<t;l++)s[l]=i(o[l]);return s}else return i(r.size)}}function ave(e,t){var r=ove(t),n=e._firstScatter;n[r]||(n[r]=t.uid)}function ove(e){var t=e.stackgroup;return e.xaxis+e.yaxis+e.type+(t?"-"+t:"")}function fU(e,t,r,n){var i=e.stackgroup;if(i){var a=t._scatterStackOpts[r._id+n._id][i],o=a.orientation==="v"?n:r;if(o.type==="linear"||o.type==="log")return a}}sve.exports={calc:vpt,calcMarkerSize:nve,calcAxisExpansion:ive,setFirstScatter:ave,getStackOpts:fU}});var uve=ye((rsr,lve)=>{"use strict";lve.exports=QP;var ppt=Dr().distinctVals;function QP(e,t){this.traces=e,this.sepNegVal=t.sepNegVal,this.overlapNoMerge=t.overlapNoMerge;for(var r=1/0,n=t.posAxis._id.charAt(0),i=[],a=0;a<e.length;a++){for(var o=e[a],s=0;s<o.length;s++){var l=o[s],u=l.p;u===void 0&&(u=l[n]),u!==void 0&&i.push(u)}o[0]&&o[0].width1&&(r=Math.min(o[0].width1,r))}this.positions=i;var c=ppt(i);this.distinctPositions=c.vals,c.vals.length===1&&r!==1/0?this.minDiff=r:this.minDiff=Math.min(c.minDiff,r);var f=(t.posAxis||{}).type;(f==="category"||f==="multicategory")&&(this.minDiff=1),this.binWidth=this.minDiff,this.bins={}}QP.prototype.put=function(t,r,n){var i=this.getLabel(t,r,n),a=this.bins[i]||0;return this.bins[i]=a+n,a};QP.prototype.get=function(t,r,n){var i=this.getLabel(t,r,n);return this.bins[i]||0};QP.prototype.getLabel=function(t,r,n){var i=n<0&&this.sepNegVal?"v":"^",a=this.overlapNoMerge?t:Math.round(t/this.binWidth);return i+a+"g"+r}});var jb=ye((isr,dve)=>{"use strict";var q0=Eo(),p_=Dr().isArrayOrTypedArray,iT=fs().BADNUM,gpt=qa(),JM=ho(),mpt=Nb().getAxisGroup,eI=uve();function ypt(e,t){for(var r=t.xaxis,n=t.yaxis,i=e._fullLayout,a=e._fullData,o=e.calcdata,s=[],l=[],u=0;u<a.length;u++){var c=a[u];if(c.visible===!0&&gpt.traceIs(c,"bar")&&c.xaxis===r._id&&c.yaxis===n._id&&(c.orientation==="h"?s.push(o[u]):l.push(o[u]),c._computePh))for(var f=e.calcdata[u],h=0;h<f.length;h++)typeof f[h].ph0=="function"&&(f[h].ph0=f[h].ph0()),typeof f[h].ph1=="function"&&(f[h].ph1=f[h].ph1())}var d={xCat:r.type==="category"||r.type==="multicategory",yCat:n.type==="category"||n.type==="multicategory",mode:i.barmode,norm:i.barnorm,gap:i.bargap,groupgap:i.bargroupgap};dU(e,r,n,l,d),dU(e,n,r,s,d)}function dU(e,t,r,n,i){if(n.length){var a,o,s,l,u;switch(bpt(r,n),i.mode){case"overlay":hU(e,t,r,n,i);break;case"group":for(a=[],o=[],s=0;s<n.length;s++)l=n[s],u=l[0].trace,u.offset===void 0?o.push(l):a.push(l);o.length&&wpt(e,t,r,o,i),a.length&&hU(e,t,r,a,i);break;case"stack":case"relative":for(a=[],o=[],s=0;s<n.length;s++)l=n[s],u=l[0].trace,u.base===void 0?o.push(l):a.push(l);xpt(o),o.length&&Tpt(e,t,r,o,i),a.length&&hU(e,t,r,a,i);break}_pt(n),Cpt(n,t)}}function _pt(e){var t,r,n,i,a,o,s;for(t=0;t<e.length;t++)r=e[t],n=r[0].trace,i=r[0].t,i.cornerradiusvalue===void 0&&(a=n.marker?n.marker.cornerradius:void 0,a!==void 0&&(o=q0(a)?+a:+a.slice(0,-1),s=q0(a)?"px":"%",i.cornerradiusvalue=o,i.cornerradiusform=s))}function xpt(e){if(!(e.length<2)){var t,r,n,i,a,o,s;for(t=0;t<e.length&&(r=e[t],n=r[0].trace,a=n.marker?n.marker.cornerradius:void 0,a===void 0);t++);if(a!==void 0)for(o=q0(a)?+a:+a.slice(0,-1),s=q0(a)?"px":"%",t=0;t<e.length;t++)r=e[t],i=r[0].t,i.cornerradiusvalue=o,i.cornerradiusform=s}}function bpt(e,t){var r,n;for(r=0;r<t.length;r++){var i=t[r],a=i[0].trace,o=a.type==="funnel"?a._base:a.base,s,l=a.orientation==="h"?a.xcalendar:a.ycalendar,u=e.type==="category"||e.type==="multicategory"?function(){return null}:e.d2c;if(p_(o)){for(n=0;n<Math.min(o.length,i.length);n++)s=u(o[n],0,l),q0(s)?(i[n].b=+s,i[n].hasB=1):i[n].b=0;for(;n<i.length;n++)i[n].b=0}else{s=u(o,0,l);var c=q0(s);for(s=c?s:0,n=0;n<i.length;n++)i[n].b=s,c&&(i[n].hasB=1)}}}function hU(e,t,r,n,i){for(var a=0;a<n.length;a++){var o=n[a],s=new eI([o],{posAxis:t,sepNegVal:!1,overlapNoMerge:!i.norm});vU(e,t,s,i),i.norm?(hve(s),pU(r,s,i)):fve(r,s)}}function wpt(e,t,r,n,i){var a=new eI(n,{posAxis:t,sepNegVal:!1,overlapNoMerge:!i.norm});vU(e,t,a,i),Ept(a,t),i.norm?(hve(a),pU(r,a,i)):fve(r,a)}function Tpt(e,t,r,n,i){var a=new eI(n,{posAxis:t,sepNegVal:i.mode==="relative",overlapNoMerge:!(i.norm||i.mode==="stack"||i.mode==="relative")});vU(e,t,a,i),Mpt(r,a,i);for(var o=0;o<n.length;o++)for(var s=n[o],l=s[0].t.offsetindex,u=0;u<s.length;u++){var c=s[u];if(c.s!==iT){var f=c.b+c.s===a.get(c.p,l,c.s);f&&(c._outmost=!0)}}i.norm&&pU(r,a,i)}function vU(e,t,r,n){var i=e._fullLayout,a=r.positions,o=r.distinctPositions,s=r.minDiff,l=r.traces,u=l.length,c=a.length!==o.length,f=s*(1-n.gap),h,d,v,_;if(t._id==="angularaxis")h=f,d=h*(1-(n.groupgap||0)),v=-d/2;else{var b=mpt(i,t._id)+l[0][0].trace.orientation;_=i._alignmentOpts[b]||{}}for(var p=0;p<u;p++){var k=l[p],E=k[0].trace;if(t._id!=="angularaxis"){var S=_[E.alignmentgroup]||{},L=Object.keys(S.offsetGroups||{}).length;L?h=f/L:h=c?f/u:f,d=h*(1-(n.groupgap||0)),L?v=((2*E._offsetIndex+1-L)*h-d)/2:v=c?((2*p+1-u)*h-d)/2:-d/2}var x=k[0].t;x.barwidth=d,x.offsetindex=E._offsetIndex||0,x.poffset=v,x.bargroupwidth=f,x.bardelta=s}r.binWidth=l[0][0].t.barwidth/100,Apt(r),Spt(t,r),t._id==="angularaxis"?cve(t,r):cve(t,r,c)}function Apt(e){var t=e.traces,r,n;for(r=0;r<t.length;r++){var i=t[r],a=i[0],o=a.trace,s=a.t,l=o._offset||o.offset,u=s.poffset,c;if(p_(l)){for(c=Array.prototype.slice.call(l,0,i.length),n=0;n<c.length;n++)q0(c[n])||(c[n]=u);for(n=c.length;n<i.length;n++)c.push(u);s.poffset=c}else l!==void 0&&(s.poffset=l);var f=o._width||o.width,h=s.barwidth;if(p_(f)){var d=Array.prototype.slice.call(f,0,i.length);for(n=0;n<d.length;n++)q0(d[n])||(d[n]=h);for(n=d.length;n<i.length;n++)d.push(h);if(s.barwidth=d,l===void 0){for(c=[],n=0;n<i.length;n++)c.push(u+(h-d[n])/2);s.poffset=c}}else f!==void 0&&(s.barwidth=f,l===void 0&&(s.poffset=u+(h-f)/2))}}function Spt(e,t){for(var r=t.traces,n=nT(e),i=0;i<r.length;i++)for(var a=r[i],o=a[0].t,s=o.poffset,l=p_(s),u=o.barwidth,c=p_(u),f=0;f<a.length;f++){var h=a[f],d=h.w=c?u[f]:u;h.p===void 0&&(h.p=h[n],h["orig_"+n]=h[n]);var v=(l?s[f]:s)+d/2;h[n]=h.p+v}}function cve(e,t,r){var n=t.traces,i=t.minDiff,a=i/2;JM.minDtick(e,t.minDiff,t.distinctPositions[0],r);for(var o=0;o<n.length;o++){var s=n[o],l=s[0],u=l.trace,c=[],f,h,d,v;for(v=0;v<s.length;v++)f=s[v],h=f.p-a,d=f.p+a,c.push(h,d);if(u.width||u.offset){var _=l.t,b=_.poffset,p=_.barwidth,k=p_(b),E=p_(p);for(v=0;v<s.length;v++){f=s[v];var S=k?b[v]:b,L=E?p[v]:p;h=f.p+S,d=h+L,c.push(h,d)}}u._extremes[e._id]=JM.findExtremes(e,c,{padded:!1})}}function fve(e,t){for(var r=t.traces,n=nT(e),i=0;i<r.length;i++){for(var a=r[i],o=a[0].trace,s=o.type==="scatter",l=o.orientation==="v",u=[],c=!1,f=0;f<a.length;f++){var h=a[f],d=s?0:h.b,v=s?l?h.y:h.x:d+h.s;h[n]=v,u.push(v),h.hasB&&u.push(d),(!h.hasB||!h.b)&&(c=!0)}o._extremes[e._id]=JM.findExtremes(e,u,{tozero:c,padded:!0})}}function Mpt(e,t,r){var n=nT(e),i=t.traces,a,o,s,l,u,c,f;for(l=0;l<i.length;l++)if(a=i[l],o=a[0].trace,o.type==="funnel")for(f=a[0].t.offsetindex,u=0;u<a.length;u++)c=a[u],c.s!==iT&&t.put(c.p,f,-.5*c.s);for(l=0;l<i.length;l++){a=i[l],o=a[0].trace,s=o.type==="funnel",f=o.type==="barpolar"?0:a[0].t.offsetindex;var h=[];for(u=0;u<a.length;u++)if(c=a[u],c.s!==iT){var 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S=n("domain",f);S[0]>S[1]-1/4096&&(r.domain=f),lI.noneOrAll(t.domain,r.domain,f),r.tickmode==="sync"&&(r.tickmode="auto")}return n("layer"),r}});var ope=ye((xsr,ape)=>{"use strict";var Wb=Dr(),$ve=ka(),c0t=ip().isUnifiedHover,f0t=HB(),Qve=vl(),h0t=c3(),epe=Rd(),d0t=SU(),tpe=i4(),v0t=Nb(),rpe=uI(),kU=hf(),Cm=kU.id2name,ipe=kU.name2id,p0t=hd().AX_ID_PATTERN,npe=qa(),cI=npe.traceIs,EU=npe.getComponentMethod;function fI(e,t,r){Array.isArray(e[t])?e[t].push(r):e[t]=[r]}ape.exports=function(t,r,n){var i=r.autotypenumbers,a={},o={},s={},l={},u={},c={},f={},h={},d={},v={},_,b;for(_=0;_<n.length;_++){var p=n[_];if(cI(p,"cartesian")){var k;if(p.xaxis)k=Cm(p.xaxis),fI(a,k,p);else if(p.xaxes)for(b=0;b<p.xaxes.length;b++)fI(a,Cm(p.xaxes[b]),p);var E;if(p.yaxis)E=Cm(p.yaxis),fI(a,E,p);else if(p.yaxes)for(b=0;b<p.yaxes.length;b++)fI(a,Cm(p.yaxes[b]),p);if(p.type==="funnel"?p.orientation==="h"?(k&&(o[k]=!0),E&&(f[E]=!0)):E&&(s[E]=!0):p.type==="image"?(E&&(h[E]=!0),k&&(h[k]=!0)):(E&&(u[E]=!0,c[E]=!0),(!cI(p,"carpet")||p.type==="carpet"&&!p._cheater)&&k&&(l[k]=!0)),p.type==="carpet"&&p._cheater&&k&&(o[k]=!0),cI(p,"2dMap")&&(d[k]=!0,d[E]=!0),cI(p,"oriented")){var S=p.orientation==="h"?E:k;v[S]=!0}}}var L=r._subplots,x=L.xaxis,C=L.yaxis,M=Wb.simpleMap(x,Cm),g=Wb.simpleMap(C,Cm),P=M.concat(g),T=$ve.background;x.length&&C.length&&(T=Wb.coerce(t,r,h0t,"plot_bgcolor"));var z=$ve.combine(T,r.paper_bgcolor),O,V,G,Z,j;function N(){var St=a[O]||[];j._traceIndices=St.map(function(Qt){return Qt.index}),j._annIndices=[],j._shapeIndices=[],j._selectionIndices=[],j._imgIndices=[],j._subplotsWith=[],j._counterAxes=[],j._name=j._attr=O,j._id=V}function H(St,Qt){return Wb.coerce(Z,j,epe,St,Qt)}function te(St,Qt){return Wb.coerce2(Z,j,epe,St,Qt)}function oe(St){return St==="x"?C:x}function 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0),pt=te("spikemode",Ae?"across":void 0),Zt=te("spikesnap"),st=H("showspikes",!!Re||!!ce||!!Ze||!!ut||!!pt||!!Zt);st||(delete j.spikecolor,delete j.spikethickness,delete j.spikedash,delete j.spikemode,delete j.spikesnap);var lt=Cm(Z.overlaying),Gt=[0,1];if(r[lt]!==void 0){var Nt=Cm(r[lt].anchor);r[Nt]!==void 0&&(Gt=r[Nt].domain)}rpe(Z,j,H,{letter:G,counterAxes:Ee[G],overlayableAxes:_e(G,O),grid:r.grid,overlayingDomain:Gt}),H("title.standoff"),Se(),j._input=Z}for(_=0;_<ie.length;){V=ie[_++],O=Cm(V),G=O.charAt(0),Wb.isPlainObject(t[O])||(t[O]={}),Z=t[O],j=Qve.newContainer(r,O,G+"axis"),N();var Jt={letter:G,font:r.font,outerTicks:d[O],showGrid:!v[O],data:[],bgColor:z,calendar:r.calendar,automargin:!0,visibleDflt:!1,reverseDflt:!1,autotypenumbersDflt:i,splomStash:((r._splomAxes||{})[G]||{})[V]};H("uirevision",r.uirevision),j.type=me[V]||"linear",tpe(Z,j,H,Jt,r),rpe(Z,j,H,{letter:G,counterAxes:Ee[G],overlayableAxes:_e(G,O),grid:r.grid}),H("fixedrange"),H("modebardisable"),Se(),j._input=Z}var sr=EU("rangeslider","handleDefaults"),wr=EU("rangeselector","handleDefaults");for(_=0;_<M.length;_++)O=M[_],Z=t[O],j=r[O],sr(t,r,O),j.type==="date"&&wr(Z,j,r,g,j.calendar),H("fixedrange"),H("modebardisable");for(_=0;_<g.length;_++){O=g[_],Z=t[O],j=r[O];var cr=r[Cm(j.anchor)],$e=EU("rangeslider","isVisible")(cr);H("fixedrange",$e),H("modebardisable")}v0t.handleDefaults(t,r,{axIds:Ce.concat(ie).sort(kU.idSort),axHasImage:h})}});var upe=ye((bsr,lpe)=>{"use strict";var g0t=Oa(),spe=qa(),hI=Dr(),Qp=So(),dI=ho();lpe.exports=function(t,r,n,i){var a=t._fullLayout;if(r.length===0){dI.redrawComponents(t);return}function o(b){var p=b.xaxis,k=b.yaxis;a._defs.select("#"+b.clipId+"> rect").call(Qp.setTranslate,0,0).call(Qp.setScale,1,1),b.plot.call(Qp.setTranslate,p._offset,k._offset).call(Qp.setScale,1,1);var E=b.plot.selectAll(".scatterlayer .trace");E.selectAll(".point").call(Qp.setPointGroupScale,1,1),E.selectAll(".textpoint").call(Qp.setTextPointsScale,1,1),E.call(Qp.hideOutsideRangePoints,b)}function s(b,p){var k=b.plotinfo,E=k.xaxis,S=k.yaxis,L=E._length,x=S._length,C=!!b.xr1,M=!!b.yr1,g=[];if(C){var P=hI.simpleMap(b.xr0,E.r2l),T=hI.simpleMap(b.xr1,E.r2l),z=P[1]-P[0],O=T[1]-T[0];g[0]=(P[0]*(1-p)+p*T[0]-P[0])/(P[1]-P[0])*L,g[2]=L*(1-p+p*O/z),E.range[0]=E.l2r(P[0]*(1-p)+p*T[0]),E.range[1]=E.l2r(P[1]*(1-p)+p*T[1])}else g[0]=0,g[2]=L;if(M){var V=hI.simpleMap(b.yr0,S.r2l),G=hI.simpleMap(b.yr1,S.r2l),Z=V[1]-V[0],j=G[1]-G[0];g[1]=(V[1]*(1-p)+p*G[1]-V[1])/(V[0]-V[1])*x,g[3]=x*(1-p+p*j/Z),S.range[0]=E.l2r(V[0]*(1-p)+p*G[0]),S.range[1]=S.l2r(V[1]*(1-p)+p*G[1])}else g[1]=0,g[3]=x;dI.drawOne(t,E,{skipTitle:!0}),dI.drawOne(t,S,{skipTitle:!0}),dI.redrawComponents(t,[E._id,S._id]);var N=C?L/g[2]:1,H=M?x/g[3]:1,te=C?g[0]:0,oe=M?g[1]:0,_e=C?g[0]/g[2]*L:0,Ee=M?g[1]/g[3]*x:0,Ce=E._offset-_e,me=S._offset-Ee;k.clipRect.call(Qp.setTranslate,te,oe).call(Qp.setScale,1/N,1/H),k.plot.call(Qp.setTranslate,Ce,me).call(Qp.setScale,N,H),Qp.setPointGroupScale(k.zoomScalePts,1/N,1/H),Qp.setTextPointsScale(k.zoomScaleTxt,1/N,1/H)}var l;i&&(l=i());function u(){for(var b={},p=0;p<r.length;p++){var k=r[p],E=k.plotinfo.xaxis,S=k.plotinfo.yaxis;k.xr1&&(b[E._name+".range"]=k.xr1.slice()),k.yr1&&(b[S._name+".range"]=k.yr1.slice())}return l&&l(),spe.call("relayout",t,b).then(function(){for(var L=0;L<r.length;L++)o(r[L].plotinfo)})}function c(){for(var b={},p=0;p<r.length;p++){var k=r[p],E=k.plotinfo.xaxis,S=k.plotinfo.yaxis;k.xr0&&(b[E._name+".range"]=k.xr0.slice()),k.yr0&&(b[S._name+".range"]=k.yr0.slice())}return spe.call("relayout",t,b).then(function(){for(var L=0;L<r.length;L++)o(r[L].plotinfo)})}var f,h,d,v=g0t.ease(n.easing);t._transitionData._interruptCallbacks.push(function(){return window.cancelAnimationFrame(d),d=null,c()});function _(){h=Date.now();for(var b=Math.min(1,(h-f)/n.duration),p=v(b),k=0;k<r.length;k++)s(r[k],p);h-f>n.duration?(u(),d=window.cancelAnimationFrame(_)):d=window.requestAnimationFrame(_)}return f=Date.now(),d=window.requestAnimationFrame(_),Promise.resolve()}});var ph=ye(xv=>{"use strict";var pI=Oa(),cpe=qa(),Xb=Dr(),m0t=Mc(),y0t=So(),fpe=Id().getModuleCalcData,g_=hf(),zg=hd(),_0t=Wp(),iu=Xb.ensureSingle;function vI(e,t,r){return Xb.ensureSingle(e,t,r,function(n){n.datum(r)})}var 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i=e._fullLayout,a=i._subplots.cartesian,o=e.calcdata,s;if(!Array.isArray(t))for(t=[],s=0;s<o.length;s++)t.push(s);for(var l=i._zindices,u=0;u<l.length;u++){var c=l[u];for(s=0;s<a.length;s++){var f=a[s],h=i._plots[f];if(u>0){var d=h.id;if(d.indexOf(Zb)!==-1)continue;d+=Zb+(u+1),h=Xb.extendFlat({},h,{id:d,plot:i._cartesianlayer.selectAll(".subplot").select("."+d)})}for(var v=[],_,b=0;b<o.length;b++){var p=o[b],k=p[0].trace;c===(k.zorder||0)&&k.xaxis+k.yaxis===f&&((t.indexOf(k.index)!==-1||k.carpet)&&(_&&_[0].trace.xaxis+_[0].trace.yaxis===f&&["tonextx","tonexty","tonext"].indexOf(k.fill)!==-1&&v.indexOf(_)===-1&&v.push(_),v.push(p)),_=p)}hpe(e,h,v,r,n)}}};function hpe(e,t,r,n,i){for(var a=zg.traceLayerClasses,o=e._fullLayout,s=o._zindices,l=o._modules,u,c,f,h=[],d=[],v=0;v<s.length;v++)for(var _=s[v],b=0;b<l.length;b++){u=l[b];var p=u.name,k=cpe.modules[p].categories;if(k.svg){var 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o,s,l=gT.getRefType(a.xref),u=gT.getRefType(a.yref);a.xref!=="paper"&&l!=="domain"&&(o=gT.getFromId(t,a.xref),s=h0e(o,a,mT.paramIsX),s&&(a._extremes[o._id]=gT.findExtremes(o,s,hgt(a)))),a.yref!=="paper"&&u!=="domain"&&(o=gT.getFromId(t,a.yref),s=h0e(o,a,mT.paramIsY),s&&(a._extremes[o._id]=gT.findExtremes(o,s,dgt(a))))}};function hgt(e){return v0e(e.line.width,e.xsizemode,e.x0,e.x1,e.path,!1)}function dgt(e){return v0e(e.line.width,e.ysizemode,e.y0,e.y1,e.path,!0)}function v0e(e,t,r,n,i,a){var o=e/2,s=a;if(t==="pixel"){var l=i?d0e.extractPathCoords(i,a?mT.paramIsY:mT.paramIsX):[r,n],u=GU.aggNums(Math.max,null,l),c=GU.aggNums(Math.min,null,l),f=c<0?Math.abs(c)+o:o,h=u>0?u+o:o;return{ppad:o,ppadplus:s?f:h,ppadminus:s?h:f}}else return{ppad:o}}function h0e(e,t,r){var n=e._id.charAt(0)==="x"?"x":"y",i=e.type==="category"||e.type==="multicategory",a,o,s=0,l=0,u=i?e.r2c:e.d2c,c=t[n+"sizemode"]==="scaled";if(c?(a=t[n+"0"],o=t[n+"1"],i&&(s=t[n+"0shift"],l=t[n+"1shift"])):(a=t[n+"anchor"],o=t[n+"anchor"]),a!==void 0)return[u(a)+s,u(o)+l];if(t.path){var f=1/0,h=-1/0,d=t.path.match(mT.segmentRE),v,_,b,p,k;for(e.type==="date"&&(u=d0e.decodeDate(u)),v=0;v<d.length;v++)_=d[v],b=r[_.charAt(0)].drawn,b!==void 0&&(p=d[v].slice(1).match(mT.paramRE),!(!p||p.length<b)&&(k=u(p[b]),k<f&&(f=k),k>h&&(h=k)));if(h>=f)return[f,h]}}});var _0e=ye((Gsr,y0e)=>{"use strict";var m0e=lP();y0e.exports={moduleType:"component",name:"shapes",layoutAttributes:VU(),supplyLayoutDefaults:l0e(),supplyDrawNewShapeDefaults:f0e(),includeBasePlot:qM()("shapes"),calcAutorange:g0e(),draw:m0e.draw,drawOne:m0e.drawOne}});var HU=ye((jsr,b0e)=>{"use strict";var x0e=hd(),vgt=vl().templatedArray,Hsr=OM();b0e.exports=vgt("image",{visible:{valType:"boolean",dflt:!0,editType:"arraydraw"},source:{valType:"string",editType:"arraydraw"},layer:{valType:"enumerated",values:["below","above"],dflt:"above",editType:"arraydraw"},sizex:{valType:"number",dflt:0,editType:"arraydraw"},sizey:{valType:"number",dflt:0,editType:"arraydraw"},sizing:{valType:"enumerated",values:["fill","contain","stretch"],dflt:"contain",editType:"arraydraw"},opacity:{valType:"number",min:0,max:1,dflt:1,editType:"arraydraw"},x:{valType:"any",dflt:0,editType:"arraydraw"},y:{valType:"any",dflt:0,editType:"arraydraw"},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left",editType:"arraydraw"},yanchor:{valType:"enumerated",values:["top","middle","bottom"],dflt:"top",editType:"arraydraw"},xref:{valType:"enumerated",values:["paper",x0e.idRegex.x.toString()],dflt:"paper",editType:"arraydraw"},yref:{valType:"enumerated",values:["paper",x0e.idRegex.y.toString()],dflt:"paper",editType:"arraydraw"},editType:"arraydraw"})});var T0e=ye((Wsr,w0e)=>{"use strict";var pgt=Dr(),jU=ho(),ggt=Yd(),mgt=HU(),ygt="images";w0e.exports=function(t,r){var n={name:ygt,handleItemDefaults:_gt};ggt(t,r,n)};function _gt(e,t,r){function n(h,d){return pgt.coerce(e,t,mgt,h,d)}var i=n("source"),a=n("visible",!!i);if(!a)return t;n("layer"),n("xanchor"),n("yanchor"),n("sizex"),n("sizey"),n("sizing"),n("opacity");for(var o={_fullLayout:r},s=["x","y"],l=0;l<2;l++){var u=s[l],c=jU.coerceRef(e,t,o,u,"paper",void 0);if(c!=="paper"){var f=jU.getFromId(o,c);f._imgIndices.push(t._index)}jU.coercePosition(t,o,n,c,u,0)}return t}});var E0e=ye((Xsr,M0e)=>{"use strict";var A0e=Oa(),xgt=So(),yT=ho(),S0e=hf(),bgt=Wp();M0e.exports=function(t){var r=t._fullLayout,n=[],i={},a=[],o,s;for(s=0;s<r.images.length;s++){var l=r.images[s];if(l.visible)if(l.layer==="below"&&l.xref!=="paper"&&l.yref!=="paper"){o=S0e.ref2id(l.xref)+S0e.ref2id(l.yref);var u=r._plots[o];if(!u){a.push(l);continue}u.mainplot&&(o=u.mainplot.id),i[o]||(i[o]=[]),i[o].push(l)}else l.layer==="above"?n.push(l):a.push(l)}var c={x:{left:{sizing:"xMin",offset:0},center:{sizing:"xMid",offset:-1/2},right:{sizing:"xMax",offset:-1}},y:{top:{sizing:"YMin",offset:0},middle:{sizing:"YMid",offset:-1/2},bottom:{sizing:"YMax",offset:-1}}};function f(S){var L=A0e.select(this);if(this._imgSrc!==S.source)if(L.attr("xmlns",bgt.svg),!t._context.staticPlot||S.source&&S.source.slice(0,5)==="data:")L.attr("xlink:href",S.source),this._imgSrc=S.source;else{var x=new Promise(function(C){var M=new Image;this.img=M,M.setAttribute("crossOrigin","anonymous"),M.onerror=g,M.onload=function(){var P=document.createElement("canvas");P.width=this.width,P.height=this.height;var T=P.getContext("2d",{willReadFrequently:!0});T.drawImage(this,0,0);var z=P.toDataURL("image/png");L.attr("xlink:href",z),C()},L.on("error",g),M.src=S.source,this._imgSrc=S.source;function g(){L.remove(),C()}}.bind(this));t._promises.push(x)}}function h(S){var L=A0e.select(this),x=yT.getFromId(t,S.xref),C=yT.getFromId(t,S.yref),M=yT.getRefType(S.xref)==="domain",g=yT.getRefType(S.yref)==="domain",P=r._size,T,z;x!==void 0?T=typeof S.xref=="string"&&M?x._length*S.sizex:Math.abs(x.l2p(S.sizex)-x.l2p(0)):T=S.sizex*P.w,C!==void 0?z=typeof S.yref=="string"&&g?C._length*S.sizey:Math.abs(C.l2p(S.sizey)-C.l2p(0)):z=S.sizey*P.h;var O=T*c.x[S.xanchor].offset,V=z*c.y[S.yanchor].offset,G=c.x[S.xanchor].sizing+c.y[S.yanchor].sizing,Z,j;switch(x!==void 0?Z=typeof S.xref=="string"&&M?x._length*S.x+x._offset:x.r2p(S.x)+x._offset:Z=S.x*P.w+P.l,Z+=O,C!==void 0?j=typeof S.yref=="string"&&g?C._length*(1-S.y)+C._offset:C.r2p(S.y)+C._offset:j=P.h-S.y*P.h+P.t,j+=V,S.sizing){case"fill":G+=" slice";break;case"stretch":G="none";break}L.attr({x:Z,y:j,width:T,height:z,preserveAspectRatio:G,opacity:S.opacity});var N=x&&yT.getRefType(S.xref)!=="domain"?x._id:"",H=C&&yT.getRefType(S.yref)!=="domain"?C._id:"",te=N+H;xgt.setClipUrl(L,te?"clip"+r._uid+te:null,t)}function d(S){return[S.xref,S.x,S.sizex,S.yref,S.y,S.sizey].join("_")}function v(S,L){return S._index-L._index}var _=r._imageLowerLayer.selectAll("image").data(a,d),b=r._imageUpperLayer.selectAll("image").data(n,d);_.enter().append("image"),b.enter().append("image"),_.exit().remove(),b.exit().remove(),_.each(function(S){f.bind(this)(S),h.bind(this)(S)}),b.each(function(S){f.bind(this)(S),h.bind(this)(S)}),_.sort(v),b.sort(v);var p=Object.keys(r._plots);for(s=0;s<p.length;s++){o=p[s];var k=r._plots[o];if(k.imagelayer){var E=k.imagelayer.selectAll("image").data(i[o]||[],d);E.enter().append("image"),E.exit().remove(),E.each(function(S){f.bind(this)(S),h.bind(this)(S)}),E.sort(v)}}}});var L0e=ye((Zsr,C0e)=>{"use strict";var k0e=Eo(),wgt=m6();C0e.exports=function(t,r,n,i){r=r||{};var a=n==="log"&&r.type==="linear",o=n==="linear"&&r.type==="log";if(a||o){for(var s=t._fullLayout.images,l=r._id.charAt(0),u,c,f=0;f<s.length;f++)if(u=s[f],c="images["+f+"].",u[l+"ref"]===r._id){var h=u[l],d=u["size"+l],v=null,_=null;if(a){v=wgt(h,r.range);var b=d/Math.pow(10,v)/2;_=2*Math.log(b+Math.sqrt(1+b*b))/Math.LN10}else v=Math.pow(10,h),_=v*(Math.pow(10,d/2)-Math.pow(10,-d/2));k0e(v)?k0e(_)||(_=null):(v=null,_=null),i(c+l,v),i(c+"size"+l,_)}}}});var I0e=ye((Ysr,P0e)=>{"use strict";P0e.exports={moduleType:"component",name:"images",layoutAttributes:HU(),supplyLayoutDefaults:T0e(),includeBasePlot:qM()("images"),draw:E0e(),convertCoords:L0e()}});var _I=ye((Ksr,R0e)=>{"use strict";R0e.exports={name:"updatemenus",containerClassName:"updatemenu-container",headerGroupClassName:"updatemenu-header-group",headerClassName:"updatemenu-header",headerArrowClassName:"updatemenu-header-arrow",dropdownButtonGroupClassName:"updatemenu-dropdown-button-group",dropdownButtonClassName:"updatemenu-dropdown-button",buttonClassName:"updatemenu-button",itemRectClassName:"updatemenu-item-rect",itemTextClassName:"updatemenu-item-text",menuIndexAttrName:"updatemenu-active-index",autoMarginIdRoot:"updatemenu-",blankHeaderOpts:{label:"  "},minWidth:30,minHeight:30,textPadX:24,arrowPadX:16,rx:2,ry:2,textOffsetX:12,textOffsetY:3,arrowOffsetX:4,gapButtonHeader:5,gapButton:2,activeColor:"#F4FAFF",hoverColor:"#F4FAFF",arrowSymbol:{left:"\u25C4",right:"\u25BA",up:"\u25B2",down:"\u25BC"}}});var WU=ye((Jsr,F0e)=>{"use strict";var Tgt=ec(),Agt=Lh(),Sgt=Ao().extendFlat,Mgt=mc().overrideAll,Egt=E6(),D0e=vl().templatedArray,kgt=D0e("button",{visible:{valType:"boolean"},method:{valType:"enumerated",values:["restyle","relayout","animate","update","skip"],dflt:"restyle"},args:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},args2:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},label:{valType:"string",dflt:""},execute:{valType:"boolean",dflt:!0}});F0e.exports=Mgt(D0e("updatemenu",{_arrayAttrRegexps:[/^updatemenus\[(0|[1-9][0-9]+)\]\.buttons/],visible:{valType:"boolean"},type:{valType:"enumerated",values:["dropdown","buttons"],dflt:"dropdown"},direction:{valType:"enumerated",values:["left","right","up","down"],dflt:"down"},active:{valType:"integer",min:-1,dflt:0},showactive:{valType:"boolean",dflt:!0},buttons:kgt,x:{valType:"number",min:-2,max:3,dflt:-.05},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"right"},y:{valType:"number",min:-2,max:3,dflt:1},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"top"},pad:Sgt(Egt({editType:"arraydraw"}),{}),font:Tgt({}),bgcolor:{valType:"color"},bordercolor:{valType:"color",dflt:Agt.borderLine},borderwidth:{valType:"number",min:0,dflt:1,editType:"arraydraw"}}),"arraydraw","from-root")});var B0e=ye(($sr,q0e)=>{"use strict";var xI=Dr(),z0e=Yd(),O0e=WU(),Cgt=_I(),Lgt=Cgt.name,Pgt=O0e.buttons;q0e.exports=function(t,r){var n={name:Lgt,handleItemDefaults:Igt};z0e(t,r,n)};function Igt(e,t,r){function n(o,s){return xI.coerce(e,t,O0e,o,s)}var i=z0e(e,t,{name:"buttons",handleItemDefaults:Rgt}),a=n("visible",i.length>0);a&&(n("active"),n("direction"),n("type"),n("showactive"),n("x"),n("y"),xI.noneOrAll(e,t,["x","y"]),n("xanchor"),n("yanchor"),n("pad.t"),n("pad.r"),n("pad.b"),n("pad.l"),xI.coerceFont(n,"font",r.font),n("bgcolor",r.paper_bgcolor),n("bordercolor"),n("borderwidth"))}function Rgt(e,t){function r(i,a){return xI.coerce(e,t,Pgt,i,a)}var n=r("visible",e.method==="skip"||Array.isArray(e.args));n&&(r("method"),r("args"),r("args2"),r("label"),r("execute"))}});var V0e=ye((Qsr,U0e)=>{"use strict";U0e.exports=Mf;var qg=Oa(),N0e=ka(),_T=So(),bI=Dr();function Mf(e,t,r){this.gd=e,this.container=t,this.id=r,this.position=null,this.translateX=null,this.translateY=null,this.hbar=null,this.vbar=null,this.bg=this.container.selectAll("rect.scrollbox-bg").data([0]),this.bg.exit().on(".drag",null).on("wheel",null).remove(),this.bg.enter().append("rect").classed("scrollbox-bg",!0).style("pointer-events","all").attr({opacity:0,x:0,y:0,width:0,height:0})}Mf.barWidth=2;Mf.barLength=20;Mf.barRadius=2;Mf.barPad=1;Mf.barColor="#808BA4";Mf.prototype.enable=function(t,r,n){var i=this.gd._fullLayout,a=i.width,o=i.height;this.position=t;var s=this.position.l,l=this.position.w,u=this.position.t,c=this.position.h,f=this.position.direction,h=f==="down",d=f==="left",v=f==="right",_=f==="up",b=l,p=c,k,E,S,L;!h&&!d&&!v&&!_&&(this.position.direction="down",h=!0);var x=h||_;x?(k=s,E=k+b,h?(S=u,L=Math.min(S+p,o),p=L-S):(L=u+p,S=Math.max(L-p,0),p=L-S)):(S=u,L=S+p,d?(E=s+b,k=Math.max(E-b,0),b=E-k):(k=s,E=Math.min(k+b,a),b=E-k)),this._box={l:k,t:S,w:b,h:p};var C=l>b,M=Mf.barLength+2*Mf.barPad,g=Mf.barWidth+2*Mf.barPad,P=s,T=u+c;T+g>o&&(T=o-g);var z=this.container.selectAll("rect.scrollbar-horizontal").data(C?[0]:[]);z.exit().on(".drag",null).remove(),z.enter().append("rect").classed("scrollbar-horizontal",!0).call(N0e.fill,Mf.barColor),C?(this.hbar=z.attr({rx:Mf.barRadius,ry:Mf.barRadius,x:P,y:T,width:M,height:g}),this._hbarXMin=P+M/2,this._hbarTranslateMax=b-M):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var O=c>p,V=Mf.barWidth+2*Mf.barPad,G=Mf.barLength+2*Mf.barPad,Z=s+l,j=u;Z+V>a&&(Z=a-V);var N=this.container.selectAll("rect.scrollbar-vertical").data(O?[0]:[]);N.exit().on(".drag",null).remove(),N.enter().append("rect").classed("scrollbar-vertical",!0).call(N0e.fill,Mf.barColor),O?(this.vbar=N.attr({rx:Mf.barRadius,ry:Mf.barRadius,x:Z,y:j,width:V,height:G}),this._vbarYMin=j+G/2,this._vbarTranslateMax=p-G):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var H=this.id,te=k-.5,oe=O?E+V+.5:E+.5,_e=S-.5,Ee=C?L+g+.5:L+.5,Ce=i._topdefs.selectAll("#"+H).data(C||O?[0]:[]);if(Ce.exit().remove(),Ce.enter().append("clipPath").attr("id",H).append("rect"),C||O?(this._clipRect=Ce.select("rect").attr({x:Math.floor(te),y:Math.floor(_e),width:Math.ceil(oe)-Math.floor(te),height:Math.ceil(Ee)-Math.floor(_e)}),this.container.call(_T.setClipUrl,H,this.gd),this.bg.attr({x:s,y:u,width:l,height:c})):(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(_T.setClipUrl,null),delete this._clipRect),C||O){var me=qg.behavior.drag().on("dragstart",function(){qg.event.sourceEvent.preventDefault()}).on("drag",this._onBoxDrag.bind(this));this.container.on("wheel",null).on("wheel",this._onBoxWheel.bind(this)).on(".drag",null).call(me);var ie=qg.behavior.drag().on("dragstart",function(){qg.event.sourceEvent.preventDefault(),qg.event.sourceEvent.stopPropagation()}).on("drag",this._onBarDrag.bind(this));C&&this.hbar.on(".drag",null).call(ie),O&&this.vbar.on(".drag",null).call(ie)}this.setTranslate(r,n)};Mf.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(_T.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(".drag",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(".drag",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)};Mf.prototype._onBoxDrag=function(){var t=this.translateX,r=this.translateY;this.hbar&&(t-=qg.event.dx),this.vbar&&(r-=qg.event.dy),this.setTranslate(t,r)};Mf.prototype._onBoxWheel=function(){var t=this.translateX,r=this.translateY;this.hbar&&(t+=qg.event.deltaY),this.vbar&&(r+=qg.event.deltaY),this.setTranslate(t,r)};Mf.prototype._onBarDrag=function(){var t=this.translateX,r=this.translateY;if(this.hbar){var n=t+this._hbarXMin,i=n+this._hbarTranslateMax,a=bI.constrain(qg.event.x,n,i),o=(a-n)/(i-n),s=this.position.w-this._box.w;t=o*s}if(this.vbar){var l=r+this._vbarYMin,u=l+this._vbarTranslateMax,c=bI.constrain(qg.event.y,l,u),f=(c-l)/(u-l),h=this.position.h-this._box.h;r=f*h}this.setTranslate(t,r)};Mf.prototype.setTranslate=function(t,r){var n=this.position.w-this._box.w,i=this.position.h-this._box.h;if(t=bI.constrain(t||0,0,n),r=bI.constrain(r||0,0,i),this.translateX=t,this.translateY=r,this.container.call(_T.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-r),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+r-.5)}),this.hbar){var a=t/n;this.hbar.call(_T.setTranslate,t+a*this._hbarTranslateMax,r)}if(this.vbar){var o=r/i;this.vbar.call(_T.setTranslate,t,r+o*this._vbarTranslateMax)}}});var $0e=ye((elr,J0e)=>{"use strict";var xT=Oa(),s4=Mc(),l4=ka(),bT=So(),e0=Dr(),wI=ru(),Dgt=vl().arrayEditor,H0e=$h().LINE_SPACING,es=_I(),Fgt=V0e();J0e.exports=function(t){var r=t._fullLayout,n=e0.filterVisible(r[es.name]);function i(h){s4.autoMargin(t,Y0e(h))}var a=r._menulayer.selectAll("g."+es.containerClassName).data(n.length>0?[0]:[]);if(a.enter().append("g").classed(es.containerClassName,!0).style("cursor","pointer"),a.exit().each(function(){xT.select(this).selectAll("g."+es.headerGroupClassName).each(i)}).remove(),n.length!==0){var o=a.selectAll("g."+es.headerGroupClassName).data(n,zgt);o.enter().append("g").classed(es.headerGroupClassName,!0);for(var s=e0.ensureSingle(a,"g",es.dropdownButtonGroupClassName,function(h){h.style("pointer-events","all")}),l=0;l<n.length;l++){var u=n[l];Vgt(t,u)}var c="updatemenus"+r._uid,f=new Fgt(t,s,c);o.enter().size()&&(s.node().parentNode.appendChild(s.node()),s.call(ZU)),o.exit().each(function(h){s.call(ZU),i(h)}).remove(),o.each(function(h){var d=xT.select(this),v=h.type==="dropdown"?s:null;s4.manageCommandObserver(t,h,h.buttons,function(_){XU(t,h,h.buttons[_.index],d,v,f,_.index,!0)}),h.type==="dropdown"?(W0e(t,d,s,f,h),j0e(s,h)&&u4(t,d,s,f,h)):u4(t,d,null,null,h)})}};function zgt(e){return e._index}function Ogt(e){return+e.attr(es.menuIndexAttrName)==-1}function j0e(e,t){return+e.attr(es.menuIndexAttrName)===t._index}function XU(e,t,r,n,i,a,o,s){t.active=o,Dgt(e.layout,es.name,t).applyUpdate("active",o),t.type==="buttons"?u4(e,n,null,null,t):t.type==="dropdown"&&(i.attr(es.menuIndexAttrName,"-1"),W0e(e,n,i,a,t),s||u4(e,n,i,a,t))}function W0e(e,t,r,n,i){var a=e0.ensureSingle(t,"g",es.headerClassName,function(h){h.style("pointer-events","all")}),o=i._dims,s=i.active,l=i.buttons[s]||es.blankHeaderOpts,u={y:i.pad.t,yPad:0,x:i.pad.l,xPad:0,index:0},c={width:o.headerWidth,height:o.headerHeight};a.call(YU,i,l,e).call(K0e,i,u,c);var f=e0.ensureSingle(t,"text",es.headerArrowClassName,function(h){h.attr("text-anchor","end").call(bT.font,i.font).text(es.arrowSymbol[i.direction])});f.attr({x:o.headerWidth-es.arrowOffsetX+i.pad.l,y:o.headerHeight/2+es.textOffsetY+i.pad.t}),a.on("click",function(){r.call(ZU,String(j0e(r,i)?-1:i._index)),u4(e,t,r,n,i)}),a.on("mouseover",function(){a.call(X0e)}),a.on("mouseout",function(){a.call(Z0e,i)}),bT.setTranslate(t,o.lx,o.ly)}function u4(e,t,r,n,i){r||(r=t,r.attr("pointer-events","all"));var a=!Ogt(r)||i.type==="buttons"?i.buttons:[],o=i.type==="dropdown"?es.dropdownButtonClassName:es.buttonClassName,s=r.selectAll("g."+o).data(e0.filterVisible(a)),l=s.enter().append("g").classed(o,!0),u=s.exit();i.type==="dropdown"?(l.attr("opacity","0").transition().attr("opacity","1"),u.transition().attr("opacity","0").remove()):u.remove();var c=0,f=0,h=i._dims,d=["up","down"].indexOf(i.direction)!==-1;i.type==="dropdown"&&(d?f=h.headerHeight+es.gapButtonHeader:c=h.headerWidth+es.gapButtonHeader),i.type==="dropdown"&&i.direction==="up"&&(f=-es.gapButtonHeader+es.gapButton-h.openHeight),i.type==="dropdown"&&i.direction==="left"&&(c=-es.gapButtonHeader+es.gapButton-h.openWidth);var v={x:h.lx+c+i.pad.l,y:h.ly+f+i.pad.t,yPad:es.gapButton,xPad:es.gapButton,index:0},_={l:v.x+i.borderwidth,t:v.y+i.borderwidth};s.each(function(b,p){var k=xT.select(this);k.call(YU,i,b,e).call(K0e,i,v),k.on("click",function(){xT.event.defaultPrevented||(b.execute&&(b.args2&&i.active===p?(XU(e,i,b,t,r,n,-1),s4.executeAPICommand(e,b.method,b.args2)):(XU(e,i,b,t,r,n,p),s4.executeAPICommand(e,b.method,b.args))),e.emit("plotly_buttonclicked",{menu:i,button:b,active:i.active}))}),k.on("mouseover",function(){k.call(X0e)}),k.on("mouseout",function(){k.call(Z0e,i),s.call(G0e,i)})}),s.call(G0e,i),d?(_.w=Math.max(h.openWidth,h.headerWidth),_.h=v.y-_.t):(_.w=v.x-_.l,_.h=Math.max(h.openHeight,h.headerHeight)),_.direction=i.direction,n&&(s.size()?qgt(e,t,r,n,i,_):Bgt(n))}function qgt(e,t,r,n,i,a){var o=i.direction,s=o==="up"||o==="down",l=i._dims,u=i.active,c,f,h;if(s)for(f=0,h=0;h<u;h++)f+=l.heights[h]+es.gapButton;else for(c=0,h=0;h<u;h++)c+=l.widths[h]+es.gapButton;n.enable(a,c,f),n.hbar&&n.hbar.attr("opacity","0").transition().attr("opacity","1"),n.vbar&&n.vbar.attr("opacity","0").transition().attr("opacity","1")}function Bgt(e){var t=!!e.hbar,r=!!e.vbar;t&&e.hbar.transition().attr("opacity","0").each("end",function(){t=!1,r||e.disable()}),r&&e.vbar.transition().attr("opacity","0").each("end",function(){r=!1,t||e.disable()})}function YU(e,t,r,n){e.call(Ngt,t).call(Ugt,t,r,n)}function Ngt(e,t){var r=e0.ensureSingle(e,"rect",es.itemRectClassName,function(n){n.attr({rx:es.rx,ry:es.ry,"shape-rendering":"crispEdges"})});r.call(l4.stroke,t.bordercolor).call(l4.fill,t.bgcolor).style("stroke-width",t.borderwidth+"px")}function Ugt(e,t,r,n){var i=e0.ensureSingle(e,"text",es.itemTextClassName,function(s){s.attr({"text-anchor":"start","data-notex":1})}),a=r.label,o=n._fullLayout._meta;o&&(a=e0.templateString(a,o)),i.call(bT.font,t.font).text(a).call(wI.convertToTspans,n)}function G0e(e,t){var r=t.active;e.each(function(n,i){var a=xT.select(this);i===r&&t.showactive&&a.select("rect."+es.itemRectClassName).call(l4.fill,es.activeColor)})}function X0e(e){e.select("rect."+es.itemRectClassName).call(l4.fill,es.hoverColor)}function Z0e(e,t){e.select("rect."+es.itemRectClassName).call(l4.fill,t.bgcolor)}function Vgt(e,t){var r=t._dims={width1:0,height1:0,heights:[],widths:[],totalWidth:0,totalHeight:0,openWidth:0,openHeight:0,lx:0,ly:0},n=bT.tester.selectAll("g."+es.dropdownButtonClassName).data(e0.filterVisible(t.buttons));n.enter().append("g").classed(es.dropdownButtonClassName,!0);var i=["up","down"].indexOf(t.direction)!==-1;n.each(function(c,f){var h=xT.select(this);h.call(YU,t,c,e);var d=h.select("."+es.itemTextClassName),v=d.node()&&bT.bBox(d.node()).width,_=Math.max(v+es.textPadX,es.minWidth),b=t.font.size*H0e,p=wI.lineCount(d),k=Math.max(b*p,es.minHeight)+es.textOffsetY;k=Math.ceil(k),_=Math.ceil(_),r.widths[f]=_,r.heights[f]=k,r.height1=Math.max(r.height1,k),r.width1=Math.max(r.width1,_),i?(r.totalWidth=Math.max(r.totalWidth,_),r.openWidth=r.totalWidth,r.totalHeight+=k+es.gapButton,r.openHeight+=k+es.gapButton):(r.totalWidth+=_+es.gapButton,r.openWidth+=_+es.gapButton,r.totalHeight=Math.max(r.totalHeight,k),r.openHeight=r.totalHeight)}),i?r.totalHeight-=es.gapButton:r.totalWidth-=es.gapButton,r.headerWidth=r.width1+es.arrowPadX,r.headerHeight=r.height1,t.type==="dropdown"&&(i?(r.width1+=es.arrowPadX,r.totalHeight=r.height1):r.totalWidth=r.width1,r.totalWidth+=es.arrowPadX),n.remove();var a=r.totalWidth+t.pad.l+t.pad.r,o=r.totalHeight+t.pad.t+t.pad.b,s=e._fullLayout._size;r.lx=s.l+s.w*t.x,r.ly=s.t+s.h*(1-t.y);var l="left";e0.isRightAnchor(t)&&(r.lx-=a,l="right"),e0.isCenterAnchor(t)&&(r.lx-=a/2,l="center");var u="top";e0.isBottomAnchor(t)&&(r.ly-=o,u="bottom"),e0.isMiddleAnchor(t)&&(r.ly-=o/2,u="middle"),r.totalWidth=Math.ceil(r.totalWidth),r.totalHeight=Math.ceil(r.totalHeight),r.lx=Math.round(r.lx),r.ly=Math.round(r.ly),s4.autoMargin(e,Y0e(t),{x:t.x,y:t.y,l:a*({right:1,center:.5}[l]||0),r:a*({left:1,center:.5}[l]||0),b:o*({top:1,middle:.5}[u]||0),t:o*({bottom:1,middle:.5}[u]||0)})}function Y0e(e){return es.autoMarginIdRoot+e._index}function K0e(e,t,r,n){n=n||{};var i=e.select("."+es.itemRectClassName),a=e.select("."+es.itemTextClassName),o=t.borderwidth,s=r.index,l=t._dims;bT.setTranslate(e,o+r.x,o+r.y);var u=["up","down"].indexOf(t.direction)!==-1,c=n.height||(u?l.heights[s]:l.height1);i.attr({x:0,y:0,width:n.width||(u?l.width1:l.widths[s]),height:c});var f=t.font.size*H0e,h=wI.lineCount(a),d=(h-1)*f/2;wI.positionText(a,es.textOffsetX,c/2-d+es.textOffsetY),u?r.y+=l.heights[s]+r.yPad:r.x+=l.widths[s]+r.xPad,r.index++}function ZU(e,t){e.attr(es.menuIndexAttrName,t||"-1").selectAll("g."+es.dropdownButtonClassName).remove()}});var ege=ye((tlr,Q0e)=>{"use strict";var Ggt=_I();Q0e.exports={moduleType:"component",name:Ggt.name,layoutAttributes:WU(),supplyLayoutDefaults:B0e(),draw:$0e()}});var c4=ye((rlr,tge)=>{"use strict";tge.exports={name:"sliders",containerClassName:"slider-container",groupClassName:"slider-group",inputAreaClass:"slider-input-area",railRectClass:"slider-rail-rect",railTouchRectClass:"slider-rail-touch-rect",gripRectClass:"slider-grip-rect",tickRectClass:"slider-tick-rect",inputProxyClass:"slider-input-proxy",labelsClass:"slider-labels",labelGroupClass:"slider-label-group",labelClass:"slider-label",currentValueClass:"slider-current-value",railHeight:5,menuIndexAttrName:"slider-active-index",autoMarginIdRoot:"slider-",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:"#bec8d9",railBgColor:"#f8fafc",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:"#bec8d9",gripBgColor:"#f6f8fa",gripBgActiveColor:"#dbdde0",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:"#333",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:"#333",minorTickLength:4,currentValuePadding:8,currentValueInset:0}});var KU=ye((ilr,nge)=>{"use strict";var rge=ec(),Hgt=E6(),jgt=Ao().extendDeepAll,Wgt=mc().overrideAll,Xgt=US(),ige=vl().templatedArray,$b=c4(),Zgt=ige("step",{visible:{valType:"boolean",dflt:!0},method:{valType:"enumerated",values:["restyle","relayout","animate","update","skip"],dflt:"restyle"},args:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},label:{valType:"string"},value:{valType:"string"},execute:{valType:"boolean",dflt:!0}});nge.exports=Wgt(ige("slider",{visible:{valType:"boolean",dflt:!0},active:{valType:"number",min:0,dflt:0},steps:Zgt,lenmode:{valType:"enumerated",values:["fraction","pixels"],dflt:"fraction"},len:{valType:"number",min:0,dflt:1},x:{valType:"number",min:-2,max:3,dflt:0},pad:jgt(Hgt({editType:"arraydraw"}),{},{t:{dflt:20}}),xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left"},y:{valType:"number",min:-2,max:3,dflt:0},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"top"},transition:{duration:{valType:"number",min:0,dflt:150},easing:{valType:"enumerated",values:Xgt.transition.easing.values,dflt:"cubic-in-out"}},currentvalue:{visible:{valType:"boolean",dflt:!0},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left"},offset:{valType:"number",dflt:10},prefix:{valType:"string"},suffix:{valType:"string"},font:rge({})},font:rge({}),activebgcolor:{valType:"color",dflt:$b.gripBgActiveColor},bgcolor:{valType:"color",dflt:$b.railBgColor},bordercolor:{valType:"color",dflt:$b.railBorderColor},borderwidth:{valType:"number",min:0,dflt:$b.railBorderWidth},ticklen:{valType:"number",min:0,dflt:$b.tickLength},tickcolor:{valType:"color",dflt:$b.tickColor},tickwidth:{valType:"number",min:0,dflt:1},minorticklen:{valType:"number",min:0,dflt:$b.minorTickLength}}),"arraydraw","from-root")});var lge=ye((nlr,sge)=>{"use strict";var wT=Dr(),age=Yd(),oge=KU(),Ygt=c4(),Kgt=Ygt.name,Jgt=oge.steps;sge.exports=function(t,r){age(t,r,{name:Kgt,handleItemDefaults:$gt})};function $gt(e,t,r){function n(f,h){return wT.coerce(e,t,oge,f,h)}for(var i=age(e,t,{name:"steps",handleItemDefaults:Qgt}),a=0,o=0;o<i.length;o++)i[o].visible&&a++;var s;if(a<2?s=t.visible=!1:s=n("visible"),!!s){t._stepCount=a;var l=t._visibleSteps=wT.filterVisible(i),u=n("active");(i[u]||{}).visible||(t.active=l[0]._index),n("x"),n("y"),wT.noneOrAll(e,t,["x","y"]),n("xanchor"),n("yanchor"),n("len"),n("lenmode"),n("pad.t"),n("pad.r"),n("pad.b"),n("pad.l"),wT.coerceFont(n,"font",r.font);var c=n("currentvalue.visible");c&&(n("currentvalue.xanchor"),n("currentvalue.prefix"),n("currentvalue.suffix"),n("currentvalue.offset"),wT.coerceFont(n,"currentvalue.font",t.font)),n("transition.duration"),n("transition.easing"),n("bgcolor"),n("activebgcolor"),n("bordercolor"),n("borderwidth"),n("ticklen"),n("tickwidth"),n("tickcolor"),n("minorticklen")}}function Qgt(e,t){function r(a,o){return wT.coerce(e,t,Jgt,a,o)}var n;if(e.method!=="skip"&&!Array.isArray(e.args)?n=t.visible=!1:n=r("visible"),n){r("method"),r("args");var i=r("label","step-"+t._index);r("value",i),r("execute")}}});var yge=ye((alr,mge)=>{"use strict";var Bg=Oa(),TI=Mc(),__=ka(),Ng=So(),t0=Dr(),emt=t0.strTranslate,f4=ru(),tmt=vl().arrayEditor,Rs=c4(),QU=$h(),fge=QU.LINE_SPACING,JU=QU.FROM_TL,$U=QU.FROM_BR;mge.exports=function(t){var r=t._context.staticPlot,n=t._fullLayout,i=rmt(n,t),a=n._infolayer.selectAll("g."+Rs.containerClassName).data(i.length>0?[0]:[]);a.enter().append("g").classed(Rs.containerClassName,!0).style("cursor",r?null:"ew-resize");function o(c){c._commandObserver&&(c._commandObserver.remove(),delete c._commandObserver),TI.autoMargin(t,hge(c))}if(a.exit().each(function(){Bg.select(this).selectAll("g."+Rs.groupClassName).each(o)}).remove(),i.length!==0){var s=a.selectAll("g."+Rs.groupClassName).data(i,imt);s.enter().append("g").classed(Rs.groupClassName,!0),s.exit().each(o).remove();for(var l=0;l<i.length;l++){var u=i[l];nmt(t,u)}s.each(function(c){var f=Bg.select(this);umt(c),TI.manageCommandObserver(t,c,c._visibleSteps,function(h){var d=f.data()[0];d.active!==h.index&&(d._dragging||vge(t,f,d,h.index,!1,!0))}),amt(t,Bg.select(this),c)})}};function hge(e){return Rs.autoMarginIdRoot+e._index}function rmt(e,t){for(var r=e[Rs.name],n=[],i=0;i<r.length;i++){var a=r[i];a.visible&&(a._gd=t,n.push(a))}return n}function imt(e){return e._index}function nmt(e,t){var r=Ng.tester.selectAll("g."+Rs.labelGroupClass).data(t._visibleSteps);r.enter().append("g").classed(Rs.labelGroupClass,!0);var n=0,i=0;r.each(function(v){var _=Bg.select(this),b=dge(_,{step:v},t),p=b.node();if(p){var k=Ng.bBox(p);i=Math.max(i,k.height),n=Math.max(n,k.width)}}),r.remove();var a=t._dims={};a.inputAreaWidth=Math.max(Rs.railWidth,Rs.gripHeight);var o=e._fullLayout._size;a.lx=o.l+o.w*t.x,a.ly=o.t+o.h*(1-t.y),t.lenmode==="fraction"?a.outerLength=Math.round(o.w*t.len):a.outerLength=t.len,a.inputAreaStart=0,a.inputAreaLength=Math.round(a.outerLength-t.pad.l-t.pad.r);var s=a.inputAreaLength-2*Rs.stepInset,l=s/(t._stepCount-1),u=n+Rs.labelPadding;if(a.labelStride=Math.max(1,Math.ceil(u/l)),a.labelHeight=i,a.currentValueMaxWidth=0,a.currentValueHeight=0,a.currentValueTotalHeight=0,a.currentValueMaxLines=1,t.currentvalue.visible){var c=Ng.tester.append("g");r.each(function(v){var _=AI(c,t,v.label),b=_.node()&&Ng.bBox(_.node())||{width:0,height:0},p=f4.lineCount(_);a.currentValueMaxWidth=Math.max(a.currentValueMaxWidth,Math.ceil(b.width)),a.currentValueHeight=Math.max(a.currentValueHeight,Math.ceil(b.height)),a.currentValueMaxLines=Math.max(a.currentValueMaxLines,p)}),a.currentValueTotalHeight=a.currentValueHeight+t.currentvalue.offset,c.remove()}a.height=a.currentValueTotalHeight+Rs.tickOffset+t.ticklen+Rs.labelOffset+a.labelHeight+t.pad.t+t.pad.b;var f="left";t0.isRightAnchor(t)&&(a.lx-=a.outerLength,f="right"),t0.isCenterAnchor(t)&&(a.lx-=a.outerLength/2,f="center");var h="top";t0.isBottomAnchor(t)&&(a.ly-=a.height,h="bottom"),t0.isMiddleAnchor(t)&&(a.ly-=a.height/2,h="middle"),a.outerLength=Math.ceil(a.outerLength),a.height=Math.ceil(a.height),a.lx=Math.round(a.lx),a.ly=Math.round(a.ly);var d={y:t.y,b:a.height*$U[h],t:a.height*JU[h]};t.lenmode==="fraction"?(d.l=0,d.xl=t.x-t.len*JU[f],d.r=0,d.xr=t.x+t.len*$U[f]):(d.x=t.x,d.l=a.outerLength*JU[f],d.r=a.outerLength*$U[f]),TI.autoMargin(e,hge(t),d)}function amt(e,t,r){(r.steps[r.active]||{}).visible||(r.active=r._visibleSteps[0]._index),t.call(AI,r).call(fmt,r).call(smt,r).call(lmt,r).call(cmt,e,r).call(omt,e,r);var n=r._dims;Ng.setTranslate(t,n.lx+r.pad.l,n.ly+r.pad.t),t.call(gge,r,!1),t.call(AI,r)}function AI(e,t,r){if(t.currentvalue.visible){var n=t._dims,i,a;switch(t.currentvalue.xanchor){case"right":i=n.inputAreaLength-Rs.currentValueInset-n.currentValueMaxWidth,a="left";break;case"center":i=n.inputAreaLength*.5,a="middle";break;default:i=Rs.currentValueInset,a="left"}var o=t0.ensureSingle(e,"text",Rs.labelClass,function(h){h.attr({"text-anchor":a,"data-notex":1})}),s=t.currentvalue.prefix?t.currentvalue.prefix:"";if(typeof r=="string")s+=r;else{var l=t.steps[t.active].label,u=t._gd._fullLayout._meta;u&&(l=t0.templateString(l,u)),s+=l}t.currentvalue.suffix&&(s+=t.currentvalue.suffix),o.call(Ng.font,t.currentvalue.font).text(s).call(f4.convertToTspans,t._gd);var c=f4.lineCount(o),f=(n.currentValueMaxLines+1-c)*t.currentvalue.font.size*fge;return f4.positionText(o,i,f),o}}function omt(e,t,r){var n=t0.ensureSingle(e,"rect",Rs.gripRectClass,function(i){i.call(pge,t,e,r).style("pointer-events","all")});n.attr({width:Rs.gripWidth,height:Rs.gripHeight,rx:Rs.gripRadius,ry:Rs.gripRadius}).call(__.stroke,r.bordercolor).call(__.fill,r.bgcolor).style("stroke-width",r.borderwidth+"px")}function dge(e,t,r){var n=t0.ensureSingle(e,"text",Rs.labelClass,function(o){o.attr({"text-anchor":"middle","data-notex":1})}),i=t.step.label,a=r._gd._fullLayout._meta;return a&&(i=t0.templateString(i,a)),n.call(Ng.font,r.font).text(i).call(f4.convertToTspans,r._gd),n}function smt(e,t){var r=t0.ensureSingle(e,"g",Rs.labelsClass),n=t._dims,i=r.selectAll("g."+Rs.labelGroupClass).data(n.labelSteps);i.enter().append("g").classed(Rs.labelGroupClass,!0),i.exit().remove(),i.each(function(a){var o=Bg.select(this);o.call(dge,a,t),Ng.setTranslate(o,eV(t,a.fraction),Rs.tickOffset+t.ticklen+t.font.size*fge+Rs.labelOffset+n.currentValueTotalHeight)})}function uge(e,t,r,n,i){var a=Math.round(n*(r._stepCount-1)),o=r._visibleSteps[a]._index;o!==r.active&&vge(e,t,r,o,!0,i)}function vge(e,t,r,n,i,a){var o=r.active;r.active=n,tmt(e.layout,Rs.name,r).applyUpdate("active",n);var s=r.steps[r.active];t.call(gge,r,a),t.call(AI,r),e.emit("plotly_sliderchange",{slider:r,step:r.steps[r.active],interaction:i,previousActive:o}),s&&s.method&&i&&(t._nextMethod?(t._nextMethod.step=s,t._nextMethod.doCallback=i,t._nextMethod.doTransition=a):(t._nextMethod={step:s,doCallback:i,doTransition:a},t._nextMethodRaf=window.requestAnimationFrame(function(){var l=t._nextMethod.step;l.method&&(l.execute&&TI.executeAPICommand(e,l.method,l.args),t._nextMethod=null,t._nextMethodRaf=null)})))}function pge(e,t,r){if(t._context.staticPlot)return;var n=r.node(),i=Bg.select(t);function a(){return r.data()[0]}function o(){var s=a();t.emit("plotly_sliderstart",{slider:s});var l=r.select("."+Rs.gripRectClass);Bg.event.stopPropagation(),Bg.event.preventDefault(),l.call(__.fill,s.activebgcolor);var u=cge(s,Bg.mouse(n)[0]);uge(t,r,s,u,!0),s._dragging=!0;function c(){var h=a(),d=cge(h,Bg.mouse(n)[0]);uge(t,r,h,d,!1)}i.on("mousemove",c),i.on("touchmove",c);function f(){var h=a();h._dragging=!1,l.call(__.fill,h.bgcolor),i.on("mouseup",null),i.on("mousemove",null),i.on("touchend",null),i.on("touchmove",null),t.emit("plotly_sliderend",{slider:h,step:h.steps[h.active]})}i.on("mouseup",f),i.on("touchend",f)}e.on("mousedown",o),e.on("touchstart",o)}function lmt(e,t){var r=e.selectAll("rect."+Rs.tickRectClass).data(t._visibleSteps),n=t._dims;r.enter().append("rect").classed(Rs.tickRectClass,!0),r.exit().remove(),r.attr({width:t.tickwidth+"px","shape-rendering":"crispEdges"}),r.each(function(i,a){var o=a%n.labelStride===0,s=Bg.select(this);s.attr({height:o?t.ticklen:t.minorticklen}).call(__.fill,t.tickcolor),Ng.setTranslate(s,eV(t,a/(t._stepCount-1))-.5*t.tickwidth,(o?Rs.tickOffset:Rs.minorTickOffset)+n.currentValueTotalHeight)})}function umt(e){var t=e._dims;t.labelSteps=[];for(var r=e._stepCount,n=0;n<r;n+=t.labelStride)t.labelSteps.push({fraction:n/(r-1),step:e._visibleSteps[n]})}function gge(e,t,r){for(var n=e.select("rect."+Rs.gripRectClass),i=0,a=0;a<t._stepCount;a++)if(t._visibleSteps[a]._index===t.active){i=a;break}var o=eV(t,i/(t._stepCount-1));if(!t._invokingCommand){var s=n;r&&t.transition.duration>0&&(s=s.transition().duration(t.transition.duration).ease(t.transition.easing)),s.attr("transform",emt(o-Rs.gripWidth*.5,t._dims.currentValueTotalHeight))}}function eV(e,t){var r=e._dims;return r.inputAreaStart+Rs.stepInset+(r.inputAreaLength-2*Rs.stepInset)*Math.min(1,Math.max(0,t))}function cge(e,t){var r=e._dims;return Math.min(1,Math.max(0,(t-Rs.stepInset-r.inputAreaStart)/(r.inputAreaLength-2*Rs.stepInset-2*r.inputAreaStart)))}function cmt(e,t,r){var n=r._dims,i=t0.ensureSingle(e,"rect",Rs.railTouchRectClass,function(a){a.call(pge,t,e,r).style("pointer-events","all")});i.attr({width:n.inputAreaLength,height:Math.max(n.inputAreaWidth,Rs.tickOffset+r.ticklen+n.labelHeight)}).call(__.fill,r.bgcolor).attr("opacity",0),Ng.setTranslate(i,0,n.currentValueTotalHeight)}function fmt(e,t){var r=t._dims,n=r.inputAreaLength-Rs.railInset*2,i=t0.ensureSingle(e,"rect",Rs.railRectClass);i.attr({width:n,height:Rs.railWidth,rx:Rs.railRadius,ry:Rs.railRadius,"shape-rendering":"crispEdges"}).call(__.stroke,t.bordercolor).call(__.fill,t.bgcolor).style("stroke-width",t.borderwidth+"px"),Ng.setTranslate(i,Rs.railInset,(r.inputAreaWidth-Rs.railWidth)*.5+r.currentValueTotalHeight)}});var xge=ye((olr,_ge)=>{"use strict";var hmt=c4();_ge.exports={moduleType:"component",name:hmt.name,layoutAttributes:KU(),supplyLayoutDefaults:lge(),draw:yge()}});var SI=ye((slr,wge)=>{"use strict";var bge=Lh();wge.exports={bgcolor:{valType:"color",dflt:bge.background,editType:"plot"},bordercolor:{valType:"color",dflt:bge.defaultLine,editType:"plot"},borderwidth:{valType:"integer",dflt:0,min:0,editType:"plot"},autorange:{valType:"boolean",dflt:!0,editType:"calc",impliedEdits:{"range[0]":void 0,"range[1]":void 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dmt=hf(),vmt=ru(),Sge=MI(),pmt=$h().LINE_SPACING,EI=Sge.name;function Mge(e){var t=e&&e[EI];return t&&t.visible}kI.isVisible=Mge;kI.makeData=function(e){for(var t=dmt.list({_fullLayout:e},"x",!0),r=e.margin,n=[],i=0;i<t.length;i++){var a=t[i];if(Mge(a)){n.push(a);var o=a[EI];o._id=EI+a._id,o._height=(e.height-r.b-r.t)*o.thickness,o._offsetShift=Math.floor(o.borderwidth/2)}}e._rangeSliderData=n};kI.autoMarginOpts=function(e,t){var r=e._fullLayout,n=t[EI],i=t._id.charAt(0),a=0,o=0;if(t.side==="bottom"&&(a=t._depth,t.title.text!==r._dfltTitle[i])){o=1.5*t.title.font.size+10+n._offsetShift;var s=(t.title.text.match(vmt.BR_TAG_ALL)||[]).length;o+=s*t.title.font.size*pmt}return{x:0,y:t._counterDomainMin,l:0,r:0,t:0,b:n._height+a+Math.max(r.margin.b,o),pad:Sge.extraPad+n._offsetShift*2}}});var Pge=ye((flr,Lge)=>{"use strict";var CI=Dr(),kge=vl(),Cge=hf(),gmt=SI(),mmt=tV();Lge.exports=function(t,r,n){var i=t[n],a=r[n];if(!(i.rangeslider||r._requestRangeslider[a._id]))return;CI.isPlainObject(i.rangeslider)||(i.rangeslider={});var o=i.rangeslider,s=kge.newContainer(a,"rangeslider");function l(L,x){return CI.coerce(o,s,gmt,L,x)}var u,c;function f(L,x){return CI.coerce(u,c,mmt,L,x)}var h=l("visible");if(h){l("bgcolor",r.plot_bgcolor),l("bordercolor"),l("borderwidth"),l("thickness"),l("autorange",!a.isValidRange(o.range)),l("range");var d=r._subplots;if(d)for(var v=d.cartesian.filter(function(L){return L.slice(0,Math.max(0,L.indexOf("y")))===Cge.name2id(n)}).map(function(L){return L.slice(L.indexOf("y"),L.length)}),_=CI.simpleMap(v,Cge.id2name),b=0;b<_.length;b++){var p=_[b];u=o[p]||{},c=kge.newContainer(s,p,"yaxis");var k=r[p],E;u.range&&k.isValidRange(u.range)&&(E="fixed");var S=f("rangemode",E);S!=="match"&&f("range",k.range.slice())}s._input=o}}});var Rge=ye((hlr,Ige)=>{"use strict";var ymt=hf().list,_mt=Ag().getAutoRange,xmt=MI();Ige.exports=function(t){for(var r=ymt(t,"x",!0),n=0;n<r.length;n++){var i=r[n],a=i[xmt.name];a&&a.visible&&a.autorange&&(a._input.autorange=!0,a._input.range=a.range=_mt(t,i))}}});var Oge=ye((dlr,zge)=>{"use strict";var LI=Oa(),bmt=qa(),wmt=Mc(),Jf=Dr(),PI=Jf.strTranslate,Fge=So(),x_=ka(),Tmt=Eb(),Amt=ph(),rV=hf(),Smt=yv(),Mmt=Sg(),rl=MI();zge.exports=function(e){for(var t=e._fullLayout,r=t._rangeSliderData,n=0;n<r.length;n++){var i=r[n][rl.name];i._clipId=i._id+"-"+t._uid}function a(s){return s._name}var o=t._infolayer.selectAll("g."+rl.containerClassName).data(r,a);o.exit().each(function(s){var l=s[rl.name];t._topdefs.select("#"+l._clipId).remove()}).remove(),r.length!==0&&(o.enter().append("g").classed(rl.containerClassName,!0).attr("pointer-events","all"),o.each(function(s){var l=LI.select(this),u=s[rl.name],c=t[rV.id2name(s.anchor)],f=u[rV.id2name(s.anchor)];if(u.range){var h=Jf.simpleMap(u.range,s.r2l),d=Jf.simpleMap(s.range,s.r2l),v;d[0]<d[1]?v=[Math.min(h[0],d[0]),Math.max(h[1],d[1])]:v=[Math.max(h[0],d[0]),Math.min(h[1],d[1])],u.range=u._input.range=Jf.simpleMap(v,s.l2r)}s.cleanRange("rangeslider.range");var _=t._size,b=s.domain;u._width=_.w*(b[1]-b[0]);var p=Math.round(_.l+_.w*b[0]),k=Math.round(_.t+_.h*(1-s._counterDomainMin)+(s.side==="bottom"?s._depth:0)+u._offsetShift+rl.extraPad);l.attr("transform",PI(p,k)),u._rl=Jf.simpleMap(u.range,s.r2l);var E=u._rl[0],S=u._rl[1],L=S-E;if(u.p2d=function(G){return G/u._width*L+E},u.d2p=function(G){return(G-E)/L*u._width},s.rangebreaks){var x=s.locateBreaks(E,S);if(x.length){var C,M,g=0;for(C=0;C<x.length;C++)M=x[C],g+=M.max-M.min;var P=u._width/(S-E-g),T=[-P*E];for(C=0;C<x.length;C++)M=x[C],T.push(T[T.length-1]-P*(M.max-M.min));for(u.d2p=function(G){for(var Z=T[0],j=0;j<x.length;j++){var N=x[j];if(G>=N.max)Z=T[j+1];else if(G<N.min)break}return 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i=e.select("rect."+rl.slideBoxClassName).node(),a=e.select("rect."+rl.grabAreaMinClassName).node(),o=e.select("rect."+rl.grabAreaMaxClassName).node();function s(){var l=LI.event,u=l.target,c=Dge(l),f=c-e.node().getBoundingClientRect().left,h=n.d2p(r._rl[0]),d=n.d2p(r._rl[1]),v=Smt.coverSlip();this.addEventListener("touchmove",_),this.addEventListener("touchend",b),v.addEventListener("mousemove",_),v.addEventListener("mouseup",b);function _(p){var k=Dge(p),E=+k-c,S,L,x;switch(u){case i:if(x="ew-resize",h+E>r._length||d+E<0)return;S=h+E,L=d+E;break;case a:if(x="col-resize",h+E>r._length)return;S=h+E,L=d;break;case o:if(x="col-resize",d+E<0)return;S=h,L=d+E;break;default:x="ew-resize",S=f,L=f+E;break}if(L<S){var C=L;L=S,S=C}n._pixelMin=S,n._pixelMax=L,Mmt(LI.select(v),x),kmt(e,t,r,n)}function b(){v.removeEventListener("mousemove",_),v.removeEventListener("mouseup",b),this.removeEventListener("touchmove",_),this.removeEventListener("touchend",b),Jf.removeElement(v)}}e.on("mousedown",s),e.on("touchstart",s)}function kmt(e,t,r,n){function i(s){return r.l2r(Jf.constrain(s,n._rl[0],n._rl[1]))}var a=i(n.p2d(n._pixelMin)),o=i(n.p2d(n._pixelMax));window.requestAnimationFrame(function(){bmt.call("_guiRelayout",t,r._name+".range",[a,o])})}function Cmt(e,t,r,n,i,a){var o=rl.handleWidth/2;function s(p){return Jf.constrain(p,0,n._width)}function l(p){return Jf.constrain(p,0,n._height)}function u(p){return Jf.constrain(p,-o,n._width+o)}var c=s(n.d2p(r._rl[0])),f=s(n.d2p(r._rl[1]));if(e.select("rect."+rl.slideBoxClassName).attr("x",c).attr("width",f-c),e.select("rect."+rl.maskMinClassName).attr("width",c),e.select("rect."+rl.maskMaxClassName).attr("x",f).attr("width",n._width-f),a.rangemode!=="match"){var h=n._height-l(n.d2pOppAxis(i._rl[1])),d=n._height-l(n.d2pOppAxis(i._rl[0]));e.select("rect."+rl.maskMinOppAxisClassName).attr("x",c).attr("height",h).attr("width",f-c),e.select("rect."+rl.maskMaxOppAxisClassName).attr("x",c).attr("y",d).attr("height",n._height-d).attr("width",f-c),e.select("rect."+rl.slideBoxClassName).attr("y",h).attr("height",d-h)}var v=.5,_=Math.round(u(c-o))-v,b=Math.round(u(f-o))+v;e.select("g."+rl.grabberMinClassName).attr("transform",PI(_,v)),e.select("g."+rl.grabberMaxClassName).attr("transform",PI(b,v))}function Lmt(e,t,r,n){var i=Jf.ensureSingle(e,"rect",rl.bgClassName,function(l){l.attr({x:0,y:0,"shape-rendering":"crispEdges"})}),a=n.borderwidth%2===0?n.borderwidth:n.borderwidth-1,o=-n._offsetShift,s=Fge.crispRound(t,n.borderwidth);i.attr({width:n._width+a,height:n._height+a,transform:PI(o,o),"stroke-width":s}).call(x_.stroke,n.bordercolor).call(x_.fill,n.bgcolor)}function Pmt(e,t,r,n){var i=t._fullLayout,a=Jf.ensureSingleById(i._topdefs,"clipPath",n._clipId,function(o){o.append("rect").attr({x:0,y:0})});a.select("rect").attr({width:n._width,height:n._height})}function Imt(e,t,r,n){var i=t.calcdata,a=e.selectAll("g."+rl.rangePlotClassName).data(r._subplotsWith,Jf.identity);a.enter().append("g").attr("class",function(s){return rl.rangePlotClassName+" "+s}).call(Fge.setClipUrl,n._clipId,t),a.order(),a.exit().remove();var o;a.each(function(s,l){var u=LI.select(this),c=l===0,f=rV.getFromId(t,s,"y"),h=f._name,d=n[h],v={data:[],layout:{xaxis:{type:r.type,domain:[0,1],range:n.range.slice(),calendar:r.calendar},width:n._width,height:n._height,margin:{t:0,b:0,l:0,r:0}},_context:t._context};r.rangebreaks&&(v.layout.xaxis.rangebreaks=r.rangebreaks),v.layout[h]={type:f.type,domain:[0,1],range:d.rangemode!=="match"?d.range.slice():f.range.slice(),calendar:f.calendar},f.rangebreaks&&(v.layout[h].rangebreaks=f.rangebreaks),wmt.supplyDefaults(v);var 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L,x=E.select("path.yerror");if(d.visible&&b_(S.x)&&b_(S.yh)&&b_(S.ys)){var C=d.width;L="M"+(S.x-C)+","+S.yh+"h"+2*C+"m-"+C+",0V"+S.ys,S.noYS||(L+="m-"+C+",0h"+2*C),a=!x.size(),a?x=E.append("path").style("vector-effect",u?"none":"non-scaling-stroke").classed("yerror",!0):l&&(x=x.transition().duration(i.duration).ease(i.easing)),x.attr("d",L)}else x.remove();var M=E.select("path.xerror");if(h.visible&&b_(S.y)&&b_(S.xh)&&b_(S.xs)){var g=(h.copy_ystyle?d:h).width;L="M"+S.xh+","+(S.y-g)+"v"+2*g+"m0,-"+g+"H"+S.xs,S.noXS||(L+="m0,-"+g+"v"+2*g),a=!M.size(),a?M=E.append("path").style("vector-effect",u?"none":"non-scaling-stroke").classed("xerror",!0):l&&(M=M.transition().duration(i.duration).ease(i.easing)),M.attr("d",L)}else M.remove()}})}})};function Tyt(e,t,r){var n={x:t.c2p(e.x),y:r.c2p(e.y)};return e.yh!==void 0&&(n.yh=r.c2p(e.yh),n.ys=r.c2p(e.ys),b_(n.ys)||(n.noYS=!0,n.ys=r.c2p(e.ys,!0))),e.xh!==void 0&&(n.xh=t.c2p(e.xh),n.xs=t.c2p(e.xs),b_(n.xs)||(n.noXS=!0,n.xs=t.c2p(e.xs,!0))),n}});var Tme=ye((klr,wme)=>{"use strict";var Ayt=Oa(),bme=ka();wme.exports=function(t){t.each(function(r){var n=r[0].trace,i=n.error_y||{},a=n.error_x||{},o=Ayt.select(this);o.selectAll("path.yerror").style("stroke-width",i.thickness+"px").call(bme.stroke,i.color),a.copy_ystyle&&(a=i),o.selectAll("path.xerror").style("stroke-width",a.thickness+"px").call(bme.stroke,a.color)})}});var Mme=ye((Clr,Sme)=>{"use strict";var h4=Dr(),Ame=mc().overrideAll,d4=hV(),Qb={error_x:h4.extendFlat({},d4),error_y:h4.extendFlat({},d4)};delete Qb.error_x.copy_zstyle;delete Qb.error_y.copy_zstyle;delete Qb.error_y.copy_ystyle;var v4={error_x:h4.extendFlat({},d4),error_y:h4.extendFlat({},d4),error_z:h4.extendFlat({},d4)};delete v4.error_x.copy_ystyle;delete v4.error_y.copy_ystyle;delete v4.error_z.copy_ystyle;delete v4.error_z.copy_zstyle;Sme.exports={moduleType:"component",name:"errorbars",schema:{traces:{scatter:Qb,bar:Qb,histogram:Qb,scatter3d:Ame(v4,"calc","nested"),scattergl:Ame(Qb,"calc","nested")}},supplyDefaults:hme(),calc:mme(),makeComputeError:dV(),plot:xme(),style:Tme(),hoverInfo:Syt};function Syt(e,t,r){(t.error_y||{}).visible&&(r.yerr=e.yh-e.y,t.error_y.symmetric||(r.yerrneg=e.y-e.ys)),(t.error_x||{}).visible&&(r.xerr=e.xh-e.x,t.error_x.symmetric||(r.xerrneg=e.x-e.xs))}});var kme=ye((Llr,Eme)=>{"use strict";Eme.exports={cn:{colorbar:"colorbar",cbbg:"cbbg",cbfill:"cbfill",cbfills:"cbfills",cbline:"cbline",cblines:"cblines",cbaxis:"cbaxis",cbtitleunshift:"cbtitleunshift",cbtitle:"cbtitle",cboutline:"cboutline",crisp:"crisp",jsPlaceholder:"js-placeholder"}}});var Fme=ye((Plr,Dme)=>{"use strict";var w_=Oa(),gV=cd(),qI=Mc(),Cme=qa(),Dy=ho(),OI=yv(),B0=Dr(),Vg=B0.strTranslate,Rme=Ao().extendFlat,mV=Sg(),Ug=So(),yV=ka(),Myt=Eb(),Eyt=ru(),kyt=pv().flipScale,Cyt=i4(),Lyt=uI(),Pyt=Rd(),_V=$h(),Lme=_V.LINE_SPACING,Pme=_V.FROM_TL,Ime=_V.FROM_BR,gf=kme().cn;function Iyt(e){var t=e._fullLayout,r=t._infolayer.selectAll("g."+gf.colorbar).data(Ryt(e),function(n){return n._id});r.enter().append("g").attr("class",function(n){return n._id}).classed(gf.colorbar,!0),r.each(function(n){var i=w_.select(this);B0.ensureSingle(i,"rect",gf.cbbg),B0.ensureSingle(i,"g",gf.cbfills),B0.ensureSingle(i,"g",gf.cblines),B0.ensureSingle(i,"g",gf.cbaxis,function(o){o.classed(gf.crisp,!0)}),B0.ensureSingle(i,"g",gf.cbtitleunshift,function(o){o.append("g").classed(gf.cbtitle,!0)}),B0.ensureSingle(i,"rect",gf.cboutline);var a=Dyt(i,n,e);a&&a.then&&(e._promises||[]).push(a),e._context.edits.colorbarPosition&&Fyt(i,n,e)}),r.exit().each(function(n){qI.autoMargin(e,n._id)}).remove(),r.order()}function Ryt(e){var t=e._fullLayout,r=e.calcdata,n=[],i,a,o,s;function l(E){return Rme(E,{_fillcolor:null,_line:{color:null,width:null,dash:null},_levels:{start:null,end:null,size:null},_filllevels:null,_fillgradient:null,_zrange:null})}function u(){typeof s.calc=="function"?s.calc(e,o,i):(i._fillgradient=a.reversescale?kyt(a.colorscale):a.colorscale,i._zrange=[a[s.min],a[s.max]])}for(var c=0;c<r.length;c++){var f=r[c];if(o=f[0].trace,!!o._module){var h=o._module.colorbar;if(o.visible===!0&&h)for(var d=Array.isArray(h),v=d?h:[h],_=0;_<v.length;_++){s=v[_];var b=s.container;a=b?o[b]:o,a&&a.showscale&&(i=l(a.colorbar),i._id="cb"+o.uid+(d&&b?"-"+b:""),i._traceIndex=o.index,i._propPrefix=(b?b+".":"")+"colorbar.",i._meta=o._meta,u(),n.push(i))}}}for(var p in t._colorAxes)if(a=t[p],a.showscale){var k=t._colorAxes[p];i=l(a.colorbar),i._id="cb"+p,i._propPrefix=p+".colorbar.",i._meta=t._meta,s={min:"cmin",max:"cmax"},k[0]!=="heatmap"&&(o=k[1],s.calc=o._module.colorbar.calc),u(),n.push(i)}return n}function Dyt(e,t,r){var n=t.orientation==="v",i=t.len,a=t.lenmode,o=t.thickness,s=t.thicknessmode,l=t.outlinewidth,u=t.borderwidth,c=t.bgcolor,f=t.xanchor,h=t.yanchor,d=t.xpad,v=t.ypad,_=t.x,b=n?t.y:1-t.y,p=t.yref==="paper",k=t.xref==="paper",E=r._fullLayout,S=E._size,L=t._fillcolor,x=t._line,C=t.title,M=C.side,g=t._zrange||w_.extent((typeof L=="function"?L:x.color).domain()),P=typeof x.color=="function"?x.color:function(){return x.color},T=typeof L=="function"?L:function(){return L},z=t._levels,O=zyt(r,t,g),V=O.fill,G=O.line,Z=Math.round(o*(s==="fraction"?n?S.w:S.h:1)),j=Z/(n?S.w:S.h),N=Math.round(i*(a==="fraction"?n?S.h:S.w:1)),H=N/(n?S.h:S.w),te=k?S.w:r._fullLayout.width,oe=p?S.h:r._fullLayout.height,_e=Math.round(n?_*te+d:b*oe+v),Ee={center:.5,right:1}[f]||0,Ce={top:1,middle:.5}[h]||0,me=n?_-Ee*j:b-Ce*j,ie=n?b-Ce*H:_-Ee*H,Se=Math.round(n?oe*(1-ie):te*ie);t._lenFrac=H,t._thickFrac=j,t._uFrac=me,t._vFrac=ie;var Le=t._axis=Oyt(r,t,g);Le.position=j+(n?_+d/S.w:b+v/S.h);var Ae=["top","bottom"].indexOf(M)!==-1;if(n&&Ae&&(Le.title.side=M,Le.titlex=_+d/S.w,Le.titley=ie+(C.side==="top"?H-v/S.h:v/S.h)),!n&&!Ae&&(Le.title.side=M,Le.titley=b+v/S.h,Le.titlex=ie+d/S.w),x.color&&t.tickmode==="auto"){Le.tickmode="linear",Le.tick0=z.start;var Fe=z.size,Pe=B0.constrain(N/50,4,15)+1,ge=(g[1]-g[0])/((t.nticks||Pe)*Fe);if(ge>1){var Re=Math.pow(10,Math.floor(Math.log(ge)/Math.LN10));Fe*=Re*B0.roundUp(ge/Re,[2,5,10]),(Math.abs(z.start)/z.size+1e-6)%1<2e-6&&(Le.tick0=0)}Le.dtick=Fe}Le.domain=n?[ie+v/S.h,ie+H-v/S.h]:[ie+d/S.w,ie+H-d/S.w],Le.setScale(),e.attr("transform",Vg(Math.round(S.l),Math.round(S.t)));var ce=e.select("."+gf.cbtitleunshift).attr("transform",Vg(-Math.round(S.l),-Math.round(S.t))),Ze=Le.ticklabelposition,ut=Le.title.font.size,pt=e.select("."+gf.cbaxis),Zt,st=0,lt=0;function Gt(cr,$e){var 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lt&&(M==="right"&&(Le.domain[0]+=(lt+ut/2)/S.w),cr.attr("transform",Vg(St[0],St[1])),Le.setScale())}e.selectAll("."+gf.cbfills+",."+gf.cblines).attr("transform",n?Vg(0,Math.round(S.h*(1-Le.domain[1]))):Vg(Math.round(S.w*Le.domain[0]),0)),pt.attr("transform",n?Vg(0,Math.round(-S.t)):Vg(Math.round(-S.l),0));var mt=e.select("."+gf.cbfills).selectAll("rect."+gf.cbfill).attr("style","").data(V);mt.enter().append("rect").classed(gf.cbfill,!0).attr("style",""),mt.exit().remove();var er=g.map(Le.c2p).map(Math.round).sort(function(Br,Vr){return Br-Vr});mt.each(function(Br,Vr){var dt=[Vr===0?g[0]:(V[Vr]+V[Vr-1])/2,Vr===V.length-1?g[1]:(V[Vr]+V[Vr+1])/2].map(Le.c2p).map(Math.round);n&&(dt[1]=B0.constrain(dt[1]+(dt[1]>dt[0])?1:-1,er[0],er[1]));var Ge=w_.select(this).attr(n?"x":"y",_e).attr(n?"y":"x",w_.min(dt)).attr(n?"width":"height",Math.max(Z,2)).attr(n?"height":"width",Math.max(w_.max(dt)-w_.min(dt),2));if(t._fillgradient)Ug.gradient(Ge,r,t._id,n?"vertical":"horizontalreversed",t._fillgradient,"fill");else{var Je=T(Br).replace("e-","");Ge.attr("fill",gV(Je).toHexString())}});var lr=e.select("."+gf.cblines).selectAll("path."+gf.cbline).data(x.color&&x.width?G:[]);lr.enter().append("path").classed(gf.cbline,!0),lr.exit().remove(),lr.each(function(Br){var Vr=_e,dt=Math.round(Le.c2p(Br))+x.width/2%1;w_.select(this).attr("d","M"+(n?Vr+","+dt:dt+","+Vr)+(n?"h":"v")+Z).call(Ug.lineGroupStyle,x.width,P(Br),x.dash)}),pt.selectAll("g."+Le._id+"tick,path").remove();var Tr=_e+Z+(l||0)/2-(t.ticks==="outside"?1:0),Lr=Dy.calcTicks(Le),ti=Dy.getTickSigns(Le)[2];return Dy.drawTicks(r,Le,{vals:Le.ticks==="inside"?Dy.clipEnds(Le,Lr):Lr,layer:pt,path:Dy.makeTickPath(Le,Tr,ti),transFn:Dy.makeTransTickFn(Le)}),Dy.drawLabels(r,Le,{vals:Lr,layer:pt,transFn:Dy.makeTransTickLabelFn(Le),labelFns:Dy.makeLabelFns(Le,Tr)})}function wr(){var cr,$e=Z+l/2;Ze.indexOf("inside")===-1&&(cr=Ug.bBox(pt.node()),$e+=n?cr.width:cr.height),Zt=ce.select("text");var St=0,Qt=n&&M==="top",Vt=!n&&M==="right",_t=0;if(Zt.node()&&!Zt.classed(gf.jsPlaceholder)){var It,mt=ce.select(".h"+Le._id+"title-math-group").node();mt&&(n&&Ae||!n&&!Ae)?(cr=Ug.bBox(mt),St=cr.width,It=cr.height):(cr=Ug.bBox(ce.node()),St=cr.right-S.l-(n?_e:Se),It=cr.bottom-S.t-(n?Se:_e),!n&&M==="top"&&($e+=cr.height,_t=cr.height)),Vt&&(Zt.attr("transform",Vg(St/2+ut/2,0)),St*=2),$e=Math.max($e,n?St:It)}var er=(n?d:v)*2+$e+u+l/2,lr=0;!n&&C.text&&h==="bottom"&&b<=0&&(lr=er/2,er+=lr,_t+=lr),E._hColorbarMoveTitle=lr,E._hColorbarMoveCBTitle=_t;var Tr=u+l,Lr=(n?_e:Se)-Tr/2-(n?d:0),ti=(n?Se:_e)-(n?N:v+_t-lr);e.select("."+gf.cbbg).attr("x",Lr).attr("y",ti).attr(n?"width":"height",Math.max(er-lr,2)).attr(n?"height":"width",Math.max(N+Tr,2)).call(yV.fill,c).call(yV.stroke,t.bordercolor).style("stroke-width",u);var Br=Vt?Math.max(St-10,0):0;e.selectAll("."+gf.cboutline).attr("x",(n?_e:Se+d)+Br).attr("y",(n?Se+v-N:_e)+(Qt?st:0)).attr(n?"width":"height",Math.max(Z,2)).attr(n?"height":"width",Math.max(N-(n?2*v+st:2*d+Br),2)).call(yV.stroke,t.outlinecolor).style({fill:"none","stroke-width":l});var Vr=n?Ee*er:0,dt=n?0:(1-Ce)*er-_t;if(Vr=k?S.l-Vr:-Vr,dt=p?S.t-dt:-dt,e.attr("transform",Vg(Vr,dt)),!n&&(u||gV(c).getAlpha()&&!gV.equals(E.paper_bgcolor,c))){var Ge=pt.selectAll("text"),Je=Ge[0].length,je=e.select("."+gf.cbbg).node(),tt=Ug.bBox(je),xt=Ug.getTranslate(e),Ie=2;Ge.each(function(di,ji){var In=0,wi=Je-1;if(ji===In||ji===wi){var On=Ug.bBox(this),qn=Ug.getTranslate(this),Fn;if(ji===wi){var ra=On.right+qn.x,la=tt.right+xt.x+Se-u-Ie+_;Fn=la-ra,Fn>0&&(Fn=0)}else if(ji===In){var Ut=On.left+qn.x,wt=tt.left+xt.x+Se+u+Ie;Fn=wt-Ut,Fn<0&&(Fn=0)}Fn&&(Je<3?this.setAttribute("transform","translate("+Fn+",0) "+this.getAttribute("transform")):this.setAttribute("visibility","hidden"))}})}var xe={},ke=Pme[f],vt=Ime[f],ir=Pme[h],ar=Ime[h],vr=er-Z;n?(a==="pixels"?(xe.y=b,xe.t=N*ir,xe.b=N*ar):(xe.t=xe.b=0,xe.yt=b+i*ir,xe.yb=b-i*ar),s==="pixels"?(xe.x=_,xe.l=er*ke,xe.r=er*vt):(xe.l=vr*ke,xe.r=vr*vt,xe.xl=_-o*ke,xe.xr=_+o*vt)):(a==="pixels"?(xe.x=_,xe.l=N*ke,xe.r=N*vt):(xe.l=xe.r=0,xe.xl=_+i*ke,xe.xr=_-i*vt),s==="pixels"?(xe.y=1-b,xe.t=er*ir,xe.b=er*ar):(xe.t=vr*ir,xe.b=vr*ar,xe.yt=b-o*ir,xe.yb=b+o*ar));var ii=t.y<.5?"b":"t",pi=t.x<.5?"l":"r";r._fullLayout._reservedMargin[t._id]={};var 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n=t._levels,i=[],a=[],o,s,l=n.end+n.size/100,u=n.size,c=1.001*r[0]-.001*r[1],f=1.001*r[1]-.001*r[0];for(s=0;s<1e5&&(o=n.start+s*u,!(u>0?o>=l:o<=l));s++)o>c&&o<f&&i.push(o);if(t._fillgradient)a=[0];else if(typeof t._fillcolor=="function"){var h=t._filllevels;if(h)for(l=h.end+h.size/100,u=h.size,s=0;s<1e5&&(o=h.start+s*u,!(u>0?o>=l:o<=l));s++)o>r[0]&&o<r[1]&&a.push(o);else a=i.map(function(d){return d-n.size/2}),a.push(a[a.length-1]+n.size)}else t._fillcolor&&typeof t._fillcolor=="string"&&(a=[0]);return n.size<0&&(i.reverse(),a.reverse()),{line:i,fill:a}}function Oyt(e,t,r){var 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L.q1=gh.interp(C,.25),L.q3=gh.interp(C,.75);L.lf=b1e(L,C,M),L.uf=w1e(L,C,M),L.lo=T1e(L),L.uo=A1e(L);var pt=S1e(L,M);L.ln=L.med-pt,L.un=L.med+pt,Fe=Math.min(Fe,L.ln),Pe=Math.max(Pe,L.un),L.pts2=x.filter(T),o.push(L)}r.notched&&gh.isTypedArray(me)&&(me=Array.from(me)),r._extremes[c._id]=YI.findExtremes(c,r.notched?me.concat([Fe,Pe]):me,{padded:!0})}return S_t(o,r),o.length>0?(o[0].t={num:n[s],dPos:S,posLetter:d,valLetter:f,labels:{med:Oy(t,"median:"),min:Oy(t,"min:"),q1:Oy(t,"q1:"),q3:Oy(t,"q3:"),max:Oy(t,"max:"),mean:r.boxmean==="sd"||r.sizemode==="sd"?Oy(t,"mean \xB1 \u03C3:").replace("\u03C3",r.sdmultiple===1?"\u03C3":r.sdmultiple+"\u03C3"):Oy(t,"mean:"),lf:Oy(t,"lower fence:"),uf:Oy(t,"upper fence:")}},n[s]++,o):[{t:{empty:!0}}]};function w_t(e,t,r,n){var i=t in e,a=t+"0"in e,o="d"+t in e;if(i||a&&o){var s=r.makeCalcdata(e,t),l=b_t(e,r,t,s).vals;return[l,s]}var u;a?u=e[t+"0"]:"name"in 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k=e.selectAll("path.box").data(r.type!=="violin"||r.box.visible?a2.identity:[]);k.enter().append("path").style("vector-effect",i?"none":"non-scaling-stroke").attr("class","box"),k.exit().remove(),k.each(function(E){if(E.empty)return LT.select(this).attr("d","M0,0Z");var S=s.c2l(E.pos+u,!0),L=s.l2p(S-b)+f,x=s.l2p(S+p)+f,C=l?(L+x)/2:s.l2p(S)+f,M=r.whiskerwidth,g=l?L*M+(1-M)*C:s.l2p(S-c)+f,P=l?x*M+(1-M)*C:s.l2p(S+c)+f,T=s.l2p(S-b*_)+f,z=s.l2p(S+p*_)+f,O=r.sizemode==="sd",V=o.c2p(O?E.mean-E.sd:E.q1,!0),G=O?o.c2p(E.mean+E.sd,!0):o.c2p(E.q3,!0),Z=a2.constrain(O?o.c2p(E.mean,!0):o.c2p(E.med,!0),Math.min(V,G)+1,Math.max(V,G)-1),j=E.lf===void 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d,v=h.pts2,_=Math.max((h.max-h.min)/10,h.q3-h.q1),b=_*1e-9,p=_*L_t,k=[],E=0,S;if(r.jitter){if(_===0)for(E=1,k=new Array(v.length),d=0;d<v.length;d++)k[d]=1;else for(d=0;d<v.length;d++){var L=Math.max(0,d-P1e),x=v[L].v,C=Math.min(v.length-1,d+P1e),M=v[C].v;l!=="all"&&(v[d].v<h.lf?M=Math.min(M,h.lf):x=Math.max(x,h.uf));var g=Math.sqrt(p*(C-L)/(M-x+b))||0;g=a2.constrain(Math.abs(g),0,1),k.push(g),E=Math.max(g,E)}S=r.jitter*2/(E||1)}for(d=0;d<v.length;d++){var P=v[d],T=P.v,z=r.jitter?S*k[d]*(a2.pseudoRandom()-.5):0,O=h.pos+s+o*(r.pointpos+z);r.orientation==="h"?(P.y=O,P.x=T):(P.x=O,P.y=T),l==="suspectedoutliers"&&T<h.uo&&T>h.lo&&(P.so=!0)}return v});f.enter().append("path").classed("point",!0),f.exit().remove(),f.call(C_t.translatePoints,i,a)}function D1e(e,t,r,n){var i=t.val,a=t.pos,o=!!a.rangebreaks,s=n.bPos,l=n.bPosPxOffset||0,u=r.boxmean||(r.meanline||{}).visible,c,f;Array.isArray(n.bdPos)?(c=n.bdPos[0],f=n.bdPos[1]):(c=n.bdPos,f=n.bdPos);var h=e.selectAll("path.mean").data(r.type==="box"&&r.boxmean||r.type==="violin"&&r.box.visible&&r.meanline.visible?a2.identity:[]);h.enter().append("path").attr("class","mean").style({fill:"none","vector-effect":"non-scaling-stroke"}),h.exit().remove(),h.each(function(d){var v=a.c2l(d.pos+s,!0),_=a.l2p(v-c)+l,b=a.l2p(v+f)+l,p=o?(_+b)/2:a.l2p(v)+l,k=i.c2p(d.mean,!0),E=i.c2p(d.mean-d.sd,!0),S=i.c2p(d.mean+d.sd,!0);r.orientation==="h"?LT.select(this).attr("d","M"+k+","+_+"V"+b+(u==="sd"?"m0,0L"+E+","+p+"L"+k+","+_+"L"+S+","+p+"Z":"")):LT.select(this).attr("d","M"+_+","+k+"H"+b+(u==="sd"?"m0,0L"+p+","+E+"L"+_+","+k+"L"+p+","+S+"Z":""))})}F1e.exports={plot:P_t,plotBoxAndWhiskers:I1e,plotPoints:R1e,plotBoxMean:D1e}});var $I=ye((fur,z1e)=>{"use strict";var DV=Oa(),FV=ka(),zV=So();function I_t(e,t,r){var n=r||DV.select(e).selectAll("g.trace.boxes");n.style("opacity",function(i){return i[0].trace.opacity}),n.each(function(i){var a=DV.select(this),o=i[0].trace,s=o.line.width;function 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i=e.cd,a=e.xa,o=e.ya,s=i[0].trace,l=i[0].t,u=s.type==="violin",c,f,h,d,v,_,b,p,k,E,S,L=l.bdPos,x,C,M=l.wHover,g=function(Pe){return h.c2l(Pe.pos)+l.bPos-h.c2l(_)};u&&s.side!=="both"?(s.side==="positive"&&(k=function(Pe){var ge=g(Pe);return A_.inbox(ge,ge+M,E)},x=L,C=0),s.side==="negative"&&(k=function(Pe){var ge=g(Pe);return A_.inbox(ge-M,ge,E)},x=0,C=L)):(k=function(Pe){var ge=g(Pe);return A_.inbox(ge-M,ge+M,E)},x=C=L);var P;u?P=function(Pe){return A_.inbox(Pe.span[0]-v,Pe.span[1]-v,E)}:P=function(Pe){return A_.inbox(Pe.min-v,Pe.max-v,E)},s.orientation==="h"?(v=t,_=r,b=P,p=k,c="y",h=o,f="x",d=a):(v=r,_=t,b=k,p=P,c="x",h=a,f="y",d=o);var T=Math.min(1,L/Math.abs(h.r2c(h.range[1])-h.r2c(h.range[0])));E=e.maxHoverDistance-T,S=e.maxSpikeDistance-T;function z(Pe){return(b(Pe)+p(Pe))/2}var O=A_.getDistanceFunction(n,b,p,z);if(A_.getClosest(i,O,e),e.index===!1)return[];var V=i[e.index],G=s.line.color,Z=(s.marker||{}).color;O1e.opacity(G)&&s.line.width?e.color=G:O1e.opacity(Z)&&s.boxpoints?e.color=Z:e.color=s.fillcolor,e[c+"0"]=h.c2p(V.pos+l.bPos-C,!0),e[c+"1"]=h.c2p(V.pos+l.bPos+x,!0),e[c+"LabelVal"]=V.orig_p!==void 0?V.orig_p:V.pos;var j=c+"Spike";e.spikeDistance=z(V)*S/E,e[j]=h.c2p(V.pos,!0);var N=s.boxmean||s.sizemode==="sd"||(s.meanline||{}).visible,H=s.boxpoints||s.points,te=H&&N?["max","uf","q3","med","mean","q1","lf","min"]:H&&!N?["max","uf","q3","med","q1","lf","min"]:!H&&N?["max","q3","med","mean","q1","min"]:["max","q3","med","q1","min"],oe=d.range[1]<d.range[0];s.orientation===(oe?"v":"h")&&te.reverse();for(var _e=e.spikeDistance,Ee=e[j],Ce=[],me=0;me<te.length;me++){var ie=te[me];if(ie in V){var Se=V[ie],Le=d.c2p(Se,!0),Ae=OV.extendFlat({},e);Ae.attr=ie,Ae[f+"0"]=Ae[f+"1"]=Le,Ae[f+"LabelVal"]=Se,Ae[f+"Label"]=(l.labels?l.labels[ie]+" ":"")+D_t.hoverLabelText(d,Se,s[f+"hoverformat"]),Ae.hoverOnBox=!0,ie==="mean"&&"sd"in 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s=r.x.length,l=r.y.length,u=-1/0,c=1/0;for(i=0;i<l;i++)c=Math.min(c,n[i][0]),c=Math.min(c,n[i][s-1]),u=Math.max(u,n[i][0]),u=Math.max(u,n[i][s-1]);for(i=1;i<s-1;i++)c=Math.min(c,n[0][i]),c=Math.min(c,n[l-1][i]),u=Math.max(u,n[0][i]),u=Math.max(u,n[l-1][i]);var f=t.value,h,d;switch(t._operation){case">":f>u&&(r.prefixBoundary=!0);break;case"<":(f<c||r.starts.length&&f===c)&&(r.prefixBoundary=!0);break;case"[]":h=Math.min(f[0],f[1]),d=Math.max(f[0],f[1]),(d<c||h>u||r.starts.length&&d===c)&&(r.prefixBoundary=!0);break;case"][":h=Math.min(f[0],f[1]),d=Math.max(f[0],f[1]),h<c&&d>u&&(r.prefixBoundary=!0);break}break}}});var k8=ye(jv=>{"use strict";var D4=Oa(),zd=Dr(),qy=So(),Ibt=tc(),Abe=ru(),wbe=ho(),Tbe=ym(),Rbt=d8(),Sbe=gG(),Mbe=mG(),Dbt=xG(),Fbt=bG(),Ebe=wG(),R4=P4(),Dm=R4.LABELOPTIMIZER;jv.plot=function(t,r,n,i){var a=r.xaxis,o=r.yaxis;zd.makeTraceGroups(i,n,"contour").each(function(s){var l=D4.select(this),u=s[0],c=u.trace,f=u.x,h=u.y,d=c.contours,v=Dbt(d,r,u),_=zd.ensureSingle(l,"g","heatmapcoloring"),b=[];d.coloring==="heatmap"&&(b=[s]),Rbt(t,r,b,_),Sbe(v),Mbe(v);var p=a.c2p(f[0],!0),k=a.c2p(f[f.length-1],!0),E=o.c2p(h[0],!0),S=o.c2p(h[h.length-1],!0),L=[[p,S],[k,S],[k,E],[p,E]],x=v;d.type==="constraint"&&(x=Fbt(v,d._operation)),zbt(l,L,d),Obt(l,x,L,d),qbt(l,v,t,u,d),Nbt(l,r,t,u,L)})};function zbt(e,t,r){var n=zd.ensureSingle(e,"g","contourbg"),i=n.selectAll("path").data(r.coloring==="fill"?[0]:[]);i.enter().append("path"),i.exit().remove(),i.attr("d","M"+t.join("L")+"Z").style("stroke","none")}function Obt(e,t,r,n){var i=n.coloring==="fill"||n.type==="constraint"&&n._operation!=="=",a="M"+r.join("L")+"Z";i&&Ebe(t,n);var o=zd.ensureSingle(e,"g","contourfill"),s=o.selectAll("path").data(i?t:[]);s.enter().append("path"),s.exit().remove(),s.each(function(l){var u=(l.prefixBoundary?a:"")+kbe(l,r);u?D4.select(this).attr("d",u).style("stroke","none"):D4.select(this).remove()})}function kbe(e,t){var r="",n=0,i=e.edgepaths.map(function(p,k){return k}),a=!0,o,s,l,u,c,f;function h(p){return Math.abs(p[1]-t[0][1])<.01}function d(p){return Math.abs(p[1]-t[2][1])<.01}function v(p){return Math.abs(p[0]-t[0][0])<.01}function _(p){return Math.abs(p[0]-t[2][0])<.01}for(;i.length;){for(f=qy.smoothopen(e.edgepaths[n],e.smoothing),r+=a?f:f.replace(/^M/,"L"),i.splice(i.indexOf(n),1),o=e.edgepaths[n][e.edgepaths[n].length-1],u=-1,l=0;l<4;l++){if(!o){zd.log("Missing end?",n,e);break}for(h(o)&&!_(o)?s=t[1]:v(o)?s=t[0]:d(o)?s=t[3]:_(o)&&(s=t[2]),c=0;c<e.edgepaths.length;c++){var b=e.edgepaths[c][0];Math.abs(o[0]-s[0])<.01?Math.abs(o[0]-b[0])<.01&&(b[1]-o[1])*(s[1]-b[1])>=0&&(s=b,u=c):Math.abs(o[1]-s[1])<.01?Math.abs(o[1]-b[1])<.01&&(b[0]-o[0])*(s[0]-b[0])>=0&&(s=b,u=c):zd.log("endpt to newendpt is not vert. or 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a};jv.createLineClip=function(e,t,r,n){var i=r._fullLayout._clips,a=t?"clipline"+n:null,o=i.selectAll("#"+a).data(t?[0]:[]);return o.exit().remove(),o.enter().append("clipPath").classed("contourlineclip",!0).attr("id",a),qy.setClipUrl(e,a,r),o};jv.labelFormatter=function(e,t){var r=e._fullLayout,n=t.trace,i=n.contours,a={type:"linear",_id:"ycontour",showexponent:"all",exponentformat:"B"};if(i.labelformat)a.tickformat=i.labelformat,Tbe(a,r);else{var o=Ibt.extractOpts(n);if(o&&o.colorbar&&o.colorbar._axis)a=o.colorbar._axis;else{if(i.type==="constraint"){var s=i.value;zd.isArrayOrTypedArray(s)?a.range=[s[0],s[s.length-1]]:a.range=[s,s]}else a.range=[i.start,i.end],a.nticks=(i.end-i.start)/i.size;a.range[0]===a.range[1]&&(a.range[1]+=a.range[0]||1),a.nticks||(a.nticks=1e3),Tbe(a,r),wbe.prepTicks(a),a._tmin=null,a._tmax=null}}return function(l){return wbe.tickText(a,l).text}};jv.calcTextOpts=function(e,t,r,n){var i=t(e);r.text(i).call(Abe.convertToTspans,n);var a=r.node(),o=qy.bBox(a,!0);return{text:i,width:o.width,height:o.height,fontSize:+a.style["font-size"].replace("px",""),level:e,dy:(o.top+o.bottom)/2}};jv.findBestTextLocation=function(e,t,r,n,i){var a=r.width,o,s,l,u,c;t.isClosed?(s=t.len/Dm.INITIALSEARCHPOINTS,o=t.min+s/2,l=t.max):(s=(t.len-a)/(Dm.INITIALSEARCHPOINTS+1),o=t.min+s+a/2,l=t.max-(s+a)/2);for(var f=1/0,h=0;h<Dm.ITERATIONS;h++){for(var d=o;d<l;d+=s){var v=zd.getTextLocation(e,t.total,d,a),_=Bbt(v,r,n,i);_<f&&(f=_,c=v,u=d)}if(f>Dm.MAXCOST*2)break;h&&(s/=2),o=u-s/2,l=o+s*1.5}if(f<=Dm.MAXCOST)return c};function Bbt(e,t,r,n){var i=t.width/2,a=t.height/2,o=e.x,s=e.y,l=e.theta,u=Math.cos(l)*i,c=Math.sin(l)*i,f=(o>n.center?n.right-o:o-n.left)/(u+Math.abs(Math.sin(l)*a)),h=(s>n.middle?n.bottom-s:s-n.top)/(Math.abs(c)+Math.cos(l)*a);if(f<1||h<1)return 1/0;var d=Dm.EDGECOST*(1/(f-1)+1/(h-1));d+=Dm.ANGLECOST*l*l;for(var v=o-u,_=s-c,b=o+u,p=s+c,k=0;k<r.length;k++){var E=r[k],S=Math.cos(E.theta)*E.width/2,L=Math.sin(E.theta)*E.width/2,x=zd.segmentDistance(v,_,b,p,E.x-S,E.y-L,E.x+S,E.y+L)*2/(t.height+E.height),C=E.level===t.level,M=C?Dm.SAMELEVELDISTANCE:1;if(x<=M)return 1/0;var g=Dm.NEIGHBORCOST*(C?Dm.SAMELEVELFACTOR:1);d+=g/(x-M)}return d}jv.addLabelData=function(e,t,r,n){var i=t.fontSize,a=t.width+i/3,o=Math.max(0,t.height-i/3),s=e.x,l=e.y,u=e.theta,c=Math.sin(u),f=Math.cos(u),h=function(v,_){return[s+v*f-_*c,l+v*c+_*f]},d=[h(-a/2,-o/2),h(-a/2,o/2),h(a/2,o/2),h(a/2,-o/2)];r.push({text:t.text,x:s,y:l,dy:t.dy,theta:u,level:t.level,width:a,height:o}),n.push(d)};jv.drawLabels=function(e,t,r,n,i){var a=e.selectAll("text").data(t,function(u){return u.text+","+u.x+","+u.y+","+u.theta});if(a.exit().remove(),a.enter().append("text").attr({"data-notex":1,"text-anchor":"middle"}).each(function(u){var c=u.x+Math.sin(u.theta)*u.dy,f=u.y-Math.cos(u.theta)*u.dy;D4.select(this).text(u.text).attr({x:c,y:f,transform:"rotate("+180*u.theta/Math.PI+" "+c+" 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r=t.contours,n=r.start,i=Gbt(r),a=r.size||1,o=Math.floor((i-n)/a)+1,s=r.coloring==="lines"?0:1,l=TG.extractOpts(t);isFinite(a)||(a=1,o=1);var u=l.reversescale?TG.flipScale(l.colorscale):l.colorscale,c=u.length,f=new Array(c),h=new Array(c),d,v,_=l.min,b=l.max;if(r.coloring==="heatmap"){for(v=0;v<c;v++)d=u[v],f[v]=d[0]*(b-_)+_,h[v]=d[1];var p=Vbt.extent([_,b,r.start,r.start+a*(o-1)]),k=p[_<b?0:1],E=p[_<b?1:0];k!==_&&(f.splice(0,0,k),h.splice(0,0,h[0])),E!==b&&(f.push(E),h.push(h[h.length-1]))}else{var S=t._input&&typeof t._input.zmin=="number"&&typeof t._input.zmax=="number";for(S&&(n<=_||i>=b)&&(n<=_&&(n=_),i>=b&&(i=b),o=Math.floor((i-n)/a)+1,s=0),v=0;v<c;v++)d=u[v],f[v]=(d[0]*(o+s-1)-s/2)*a+n,h[v]=d[1];(S||t.autocontour)&&(f[0]>_&&(f.unshift(_),h.unshift(h[0])),f[f.length-1]<b&&(f.push(b),h.push(h[h.length-1])))}return TG.makeColorScaleFunc({domain:f,range:h},{noNumericCheck:!0})}});var L8=ye((wcr,Pbe)=>{"use strict";var C8=Oa(),Lbe=So(),Hbt=v8(),jbt=AG();Pbe.exports=function(t){var r=C8.select(t).selectAll("g.contour");r.style("opacity",function(n){return n[0].trace.opacity}),r.each(function(n){var i=C8.select(this),a=n[0].trace,o=a.contours,s=a.line,l=o.size||1,u=o.start,c=o.type==="constraint",f=!c&&o.coloring==="lines",h=!c&&o.coloring==="fill",d=f||h?jbt(a):null;i.selectAll("g.contourlevel").each(function(b){C8.select(this).selectAll("path").call(Lbe.lineGroupStyle,s.width,f?d(b.level):s.color,s.dash)});var v=o.labelfont;if(i.selectAll("g.contourlabels text").each(function(b){Lbe.font(C8.select(this),{weight:v.weight,style:v.style,variant:v.variant,textcase:v.textcase,lineposition:v.lineposition,shadow:v.shadow,family:v.family,size:v.size,color:v.color||(f?d(b.level):s.color)})}),c)i.selectAll("g.contourfill path").style("fill",a.fillcolor);else if(h){var _;i.selectAll("g.contourfill path").style("fill",function(b){return _===void 0&&(_=b.level),d(b.level+.5*l)}),_===void 0&&(_=u),i.selectAll("g.contourbg path").style("fill",d(_-.5*l))}}),Hbt(t)}});var P8=ye((Tcr,Rbe)=>{"use strict";var Ibe=tc(),Wbt=AG(),Xbt=L4();function Zbt(e,t,r){var n=t.contours,i=t.line,a=n.size||1,o=n.coloring,s=Wbt(t,{isColorbar:!0});if(o==="heatmap"){var l=Ibe.extractOpts(t);r._fillgradient=l.reversescale?Ibe.flipScale(l.colorscale):l.colorscale,r._zrange=[l.min,l.max]}else o==="fill"&&(r._fillcolor=s);r._line={color:o==="lines"?s:i.color,width:n.showlines!==!1?i.width:0,dash:i.dash},r._levels={start:n.start,end:Xbt(n),size:a}}Rbe.exports={min:"zmin",max:"zmax",calc:Zbt}});var SG=ye((Acr,Dbe)=>{"use strict";var I8=ka(),Ybt=g8();Dbe.exports=function(t,r,n,i,a){a||(a={}),a.isContour=!0;var o=Ybt(t,r,n,i,a);return o&&o.forEach(function(s){var l=s.trace;l.contours.type==="constraint"&&(l.fillcolor&&I8.opacity(l.fillcolor)?s.color=I8.addOpacity(l.fillcolor,1):l.contours.showlines&&I8.opacity(l.line.color)&&(s.color=I8.addOpacity(l.line.color,1)))}),o}});var zbe=ye((Scr,Fbe)=>{"use 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n2e=ye((Icr,i2e)=>{"use strict";var t2e=Dr(),d2t=Sm(),BT=Ru(),v2t=$p(),p2t=R0(),r2e=tT(),g2t=D0(),m2t=Rg(),y2t=kG();i2e.exports=function(t,r,n,i){function a(h,d){return t2e.coerce(t,r,y2t,h,d)}var o=a("a"),s=a("b"),l=a("c"),u;if(o?(u=o.length,s?(u=Math.min(u,s.length),l&&(u=Math.min(u,l.length))):l?u=Math.min(u,l.length):u=0):s&&l&&(u=Math.min(s.length,l.length)),!u){r.visible=!1;return}r._length=u,a("sum"),a("text"),a("hovertext"),r.hoveron!=="fills"&&(a("hovertemplate"),a("hovertemplatefallback"));var c=u<d2t.PTS_LINESONLY?"lines+markers":"lines";a("mode",c),BT.hasMarkers(r)&&v2t(t,r,n,i,a,{gradient:!0}),BT.hasLines(r)&&(p2t(t,r,n,i,a,{backoff:!0}),r2e(t,r,a),a("connectgaps")),BT.hasText(r)&&(a("texttemplate"),a("texttemplatefallback"),g2t(t,r,i,a));var f=[];(BT.hasMarkers(r)||BT.hasText(r))&&(a("cliponaxis"),a("marker.maxdisplayed"),f.push("points")),a("fill"),r.fill!=="none"&&(m2t(t,r,n,a),BT.hasLines(r)||r2e(t,r,a)),(r.fill==="tonext"||r.fill==="toself")&&f.push("fills"),a("hoveron",f.join("+")||"points"),t2e.coerceSelectionMarkerOpacity(r,a)}});var o2e=ye((Rcr,a2e)=>{"use strict";var CG=ho();a2e.exports=function(t,r,n){var i={},a=n[r.subplot]._subplot;return i.aLabel=CG.tickText(a.aaxis,t.a,!0).text,i.bLabel=CG.tickText(a.baxis,t.b,!0).text,i.cLabel=CG.tickText(a.caxis,t.c,!0).text,i}});var c2e=ye((Dcr,u2e)=>{"use strict";var LG=Eo(),_2t=F0(),x2t=km(),b2t=z0(),w2t=O0().calcMarkerSize,s2e=["a","b","c"],l2e={a:["b","c"],b:["a","c"],c:["a","b"]};u2e.exports=function(t,r){var n=t._fullLayout[r.subplot],i=n.sum,a=r.sum||i,o={a:r.a,b:r.b,c:r.c},s=r.ids,l,u,c,f,h,d;for(l=0;l<s2e.length;l++)if(c=s2e[l],!o[c]){for(h=o[l2e[c][0]],d=o[l2e[c][1]],f=new Array(h.length),u=0;u<h.length;u++)f[u]=a-h[u]-d[u];o[c]=f}var v=r._length,_=new 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f=o.cd[o.index],h=o.trace,d=o.subplot;o.a=f.a,o.b=f.b,o.c=f.c,o.xLabelVal=void 0,o.yLabelVal=void 0;var v={};v[h.subplot]={_subplot:d};var _=h._module.formatLabels(f,h,v);o.aLabel=_.aLabel,o.bLabel=_.bLabel,o.cLabel=_.cLabel;var b=f.hi||h.hoverinfo,p=[];function k(S,L){p.push(S._hovertitle+": "+L)}if(!h.hovertemplate){var E=b.split("+");E.indexOf("all")!==-1&&(E=["a","b","c"]),E.indexOf("a")!==-1&&k(d.aaxis,o.aLabel),E.indexOf("b")!==-1&&k(d.baxis,o.bLabel),E.indexOf("c")!==-1&&k(d.caxis,o.cLabel)}return o.extraText=p.join("<br>"),o.hovertemplate=h.hovertemplate,a}});var g2e=ye((Ocr,p2e)=>{"use strict";p2e.exports=function(t,r,n,i,a){if(r.xa&&(t.xaxis=r.xa),r.ya&&(t.yaxis=r.ya),i[a]){var o=i[a];t.a=o.a,t.b=o.b,t.c=o.c}else t.a=r.a,t.b=r.b,t.c=r.c;return t}});var E2e=ye((qcr,M2e)=>{"use strict";var w2e=Oa(),S2t=cd(),PG=qa(),By=Dr(),Fm=By.strTranslate,R8=By._,UT=ka(),D8=So(),z4=ym(),IG=Ao().extendFlat,M2t=Mc(),E_=ho(),m2e=yv(),y2e=vf(),T2e=Eg(),_2e=T2e.freeMode,E2t=T2e.rectMode,RG=Eb(),k2t=Of().prepSelect,C2t=Of().selectOnClick,L2t=Of().clearOutline,P2t=Of().clearSelectionsCache,A2e=hd();function S2e(e,t){this.id=e.id,this.graphDiv=e.graphDiv,this.init(t),this.makeFramework(t),this.updateFx(t),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}M2e.exports=S2e;var zm=S2e.prototype;zm.init=function(e){this.container=e._ternarylayer,this.defs=e._defs,this.layoutId=e._uid,this.traceHash={},this.layers={}};zm.plot=function(e,t){var r=this,n=t[r.id],i=t._size;r._hasClipOnAxisFalse=!1;for(var a=0;a<e.length;a++){var o=e[a][0].trace;if(o.cliponaxis===!1){r._hasClipOnAxisFalse=!0;break}}r.updateLayers(n),r.adjustLayout(n,i),M2t.generalUpdatePerTraceModule(r.graphDiv,r,e,n),r.layers.plotbg.select("path").call(UT.fill,n.bgcolor)};zm.makeFramework=function(e){var t=this,r=t.graphDiv,n=e[t.id],i=t.clipId="clip"+t.layoutId+t.id,a=t.clipIdRelative="clip-relative"+t.layoutId+t.id;t.clipDef=By.ensureSingleById(e._clips,"clipPath",i,function(o){o.append("path").attr("d","M0,0Z")}),t.clipDefRelative=By.ensureSingleById(e._clips,"clipPath",a,function(o){o.append("path").attr("d","M0,0Z")}),t.plotContainer=By.ensureSingle(t.container,"g",t.id),t.updateLayers(n),D8.setClipUrl(t.layers.backplot,i,r),D8.setClipUrl(t.layers.grids,i,r)};zm.updateFx=function(e){e._ternarylayer.selectAll("g.toplevel").style("cursor",e.dragmode==="pan"?"move":"crosshair")};zm.updateLayers=function(e){var t=this,r=t.layers,n=["draglayer","plotbg","backplot","grids"];e.aaxis.layer==="below traces"&&n.push("aaxis","aline"),e.baxis.layer==="below traces"&&n.push("baxis","bline"),e.caxis.layer==="below traces"&&n.push("caxis","cline"),n.push("frontplot"),e.aaxis.layer==="above traces"&&n.push("aaxis","aline"),e.baxis.layer==="above traces"&&n.push("baxis","bline"),e.caxis.layer==="above traces"&&n.push("caxis","cline");var i=t.plotContainer.selectAll("g.toplevel").data(n,String),a=["agrid","bgrid","cgrid"];i.enter().append("g").attr("class",function(o){return"toplevel "+o}).each(function(o){var s=w2e.select(this);r[o]=s,o==="frontplot"?s.append("g").classed("scatterlayer",!0):o==="backplot"?s.append("g").classed("maplayer",!0):o==="plotbg"?s.append("path").attr("d","M0,0Z"):o==="aline"||o==="bline"||o==="cline"?s.append("path"):o==="grids"&&a.forEach(function(l){r[l]=s.append("g").classed("grid "+l,!0)})}),i.order()};var NT=Math.sqrt(4/3);zm.adjustLayout=function(e,t){var r=this,n=e.domain,i=(n.x[0]+n.x[1])/2,a=(n.y[0]+n.y[1])/2,o=n.x[1]-n.x[0],s=n.y[1]-n.y[0],l=o*t.w,u=s*t.h,c=e.sum,f=e.aaxis.min,h=e.baxis.min,d=e.caxis.min,v,_,b,p,k,E;l>NT*u?(p=u,b=p*NT):(b=l,p=b/NT),k=o*b/l,E=s*p/u,v=t.l+t.w*i-b/2,_=t.t+t.h*(1-a)-p/2,r.x0=v,r.y0=_,r.w=b,r.h=p,r.sum=c,r.xaxis={type:"linear",range:[f+2*d-c,c-f-2*h],domain:[i-k/2,i+k/2],_id:"x"},z4(r.xaxis,r.graphDiv._fullLayout),r.xaxis.setScale(),r.xaxis.isPtWithinRange=function(V){return V.a>=r.aaxis.range[0]&&V.a<=r.aaxis.range[1]&&V.b>=r.baxis.range[1]&&V.b<=r.baxis.range[0]&&V.c>=r.caxis.range[1]&&V.c<=r.caxis.range[0]},r.yaxis={type:"linear",range:[f,c-h-d],domain:[a-E/2,a+E/2],_id:"y"},z4(r.yaxis,r.graphDiv._fullLayout),r.yaxis.setScale(),r.yaxis.isPtWithinRange=function(){return!0};var S=r.yaxis.domain[0],L=r.aaxis=IG({},e.aaxis,{range:[f,c-h-d],side:"left",tickangle:(+e.aaxis.tickangle||0)-30,domain:[S,S+E*NT],anchor:"free",position:0,_id:"y",_length:b});z4(L,r.graphDiv._fullLayout),L.setScale();var x=r.baxis=IG({},e.baxis,{range:[c-f-d,h],side:"bottom",domain:r.xaxis.domain,anchor:"free",position:0,_id:"x",_length:b});z4(x,r.graphDiv._fullLayout),x.setScale();var C=r.caxis=IG({},e.caxis,{range:[c-f-h,d],side:"right",tickangle:(+e.caxis.tickangle||0)+30,domain:[S,S+E*NT],anchor:"free",position:0,_id:"y",_length:b});z4(C,r.graphDiv._fullLayout),C.setScale();var M="M"+v+","+(_+p)+"h"+b+"l-"+b/2+",-"+p+"Z";r.clipDef.select("path").attr("d",M),r.layers.plotbg.select("path").attr("d",M);var g="M0,"+p+"h"+b+"l-"+b/2+",-"+p+"Z";r.clipDefRelative.select("path").attr("d",g);var P=Fm(v,_);r.plotContainer.selectAll(".scatterlayer,.maplayer").attr("transform",P),r.clipDefRelative.select("path").attr("transform",null);var T=Fm(v-x._offset,_+p);r.layers.baxis.attr("transform",T),r.layers.bgrid.attr("transform",T);var z=Fm(v+b/2,_)+"rotate(30)"+Fm(0,-L._offset);r.layers.aaxis.attr("transform",z),r.layers.agrid.attr("transform",z);var O=Fm(v+b/2,_)+"rotate(-30)"+Fm(0,-C._offset);r.layers.caxis.attr("transform",O),r.layers.cgrid.attr("transform",O),r.drawAxes(!0),r.layers.aline.select("path").attr("d",L.showline?"M"+v+","+(_+p)+"l"+b/2+",-"+p:"M0,0").call(UT.stroke,L.linecolor||"#000").style("stroke-width",(L.linewidth||0)+"px"),r.layers.bline.select("path").attr("d",x.showline?"M"+v+","+(_+p)+"h"+b:"M0,0").call(UT.stroke,x.linecolor||"#000").style("stroke-width",(x.linewidth||0)+"px"),r.layers.cline.select("path").attr("d",C.showline?"M"+(v+b/2)+","+_+"l"+b/2+","+p:"M0,0").call(UT.stroke,C.linecolor||"#000").style("stroke-width",(C.linewidth||0)+"px"),r.graphDiv._context.staticPlot||r.initInteractions(),D8.setClipUrl(r.layers.frontplot,r._hasClipOnAxisFalse?null:r.clipId,r.graphDiv)};zm.drawAxes=function(e){var t=this,r=t.graphDiv,n=t.id.slice(7)+"title",i=t.layers,a=t.aaxis,o=t.baxis,s=t.caxis;if(t.drawAx(a),t.drawAx(o),t.drawAx(s),e){var 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t=this,r=t.graphDiv,n=e._name,i=n.charAt(0),a=e._id,o=t.layers[n],s=30,l=i+"tickLayout",u=I2t(e);t[l]!==u&&(o.selectAll("."+a+"tick").remove(),t[l]=u),e.setScale();var c=E_.calcTicks(e),f=E_.clipEnds(e,c),h=E_.makeTransTickFn(e),d=E_.getTickSigns(e)[2],v=By.deg2rad(s),_=d*(e.linewidth||1)/2,b=d*e.ticklen,p=t.w,k=t.h,E=i==="b"?"M0,"+_+"l"+Math.sin(v)*b+","+Math.cos(v)*b:"M"+_+",0l"+Math.cos(v)*b+","+-Math.sin(v)*b,S={a:"M0,0l"+k+",-"+p/2,b:"M0,0l-"+p/2+",-"+k,c:"M0,0l-"+k+","+p/2}[i];E_.drawTicks(r,e,{vals:e.ticks==="inside"?f:c,layer:o,path:E,transFn:h,crisp:!1}),E_.drawGrid(r,e,{vals:f,layer:t.layers[i+"grid"],path:S,transFn:h,crisp:!1}),E_.drawLabels(r,e,{vals:c,layer:o,transFn:h,labelFns:E_.makeLabelFns(e,0,s)})};function I2t(e){return e.ticks+String(e.ticklen)+String(e.showticklabels)}var 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NG=ye((Wcr,U2e)=>{"use strict";var qG=b4(),BG=Dr().extendFlat;U2e.exports={violinmode:BG({},qG.boxmode,{}),violingap:BG({},qG.boxgap,{}),violingroupgap:BG({},qG.boxgroupgap,{})}});var W2e=ye((Xcr,j2e)=>{"use strict";var V2e=Dr(),nwt=ka(),G2e=T4(),H2e=OG();j2e.exports=function(t,r,n,i){function a(L,x){return V2e.coerce(t,r,H2e,L,x)}function o(L,x){return V2e.coerce2(t,r,H2e,L,x)}if(G2e.handleSampleDefaults(t,r,a,i),r.visible!==!1){a("bandwidth"),a("side");var s=a("width");s||(a("scalegroup",r.name),a("scalemode"));var l=a("span"),u;Array.isArray(l)&&(u="manual"),a("spanmode",u);var c=a("line.color",(t.marker||{}).color||n),f=a("line.width"),h=a("fillcolor",nwt.addOpacity(r.line.color,.5));G2e.handlePointsDefaults(t,r,a,{prefix:""});var d=o("box.width"),v=o("box.fillcolor",h),_=o("box.line.color",c),b=o("box.line.width",f),p=a("box.visible",!!(d||v||_||b));p||(r.box={visible:!1});var k=o("meanline.color",c),E=o("meanline.width",f),S=a("meanline.visible",!!(k||E));S||(r.meanline={visible:!1}),a("quartilemethod"),a("zorder")}}});var Z2e=ye((Zcr,X2e)=>{"use strict";var awt=Dr(),owt=NG(),swt=ZI();X2e.exports=function(t,r,n){function i(a,o){return awt.coerce(t,r,owt,a,o)}swt._supply(t,r,n,i,"violin")}});var O8=ye(s2=>{"use strict";var lwt=Dr(),uwt={gaussian:function(e){return 1/Math.sqrt(2*Math.PI)*Math.exp(-.5*e*e)}};s2.makeKDE=function(e,t,r){var n=r.length,i=uwt.gaussian,a=e.bandwidth,o=1/(n*a);return function(s){for(var l=0,u=0;u<n;u++)l+=i((s-r[u])/a);return o*l}};s2.getPositionOnKdePath=function(e,t,r){var n,i;t.orientation==="h"?(n="y",i="x"):(n="x",i="y");var a=lwt.findPointOnPath(e.path,r,i,{pathLength:e.pathLength}),o=e.posCenterPx,s=a[n],l=t.side==="both"?2*o-s:o;return[s,l]};s2.getKdeValue=function(e,t,r){var n=e.pts.map(s2.extractVal),i=s2.makeKDE(e,t,n);return i(r)/e.posDensityScale};s2.extractVal=function(e){return e.v}});var J2e=ye((Kcr,K2e)=>{"use 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C=i._violinScaleGroupStats,M=r.scalegroup,g=C[M];g?(g.maxKDE=Math.max(g.maxKDE,l),g.maxCount=Math.max(g.maxCount,u)):C[M]={maxKDE:l,maxCount:u}}return n[0].t.labels.kde=UG._(t,"kde:"),n};function hwt(e,t,r){var n=Math.min(t,r/1.349);return 1.059*n*Math.pow(e,-.2)}function dwt(e,t,r){var n=t.max-t.min;if(!n)return e.bandwidth?e.bandwidth:0;if(e.bandwidth)return Math.max(e.bandwidth,n/1e4);var i=r.length,a=UG.stdev(r,i-1,t.mean);return Math.max(hwt(i,a,t.q3-t.q1),n/100)}function vwt(e,t,r,n){var i=e.spanmode,a=e.span||[],o=[t.min,t.max],s=[t.min-2*n,t.max+2*n],l;function u(f){var h=a[f],d=r.type==="multicategory"?r.r2c(h):r.d2c(h,0,e[t.valLetter+"calendar"]);return d===fwt?s[f]:d}i==="soft"?l=s:i==="hard"?l=o:l=[u(0),u(1)];var c={type:"linear",range:l};return VG.setConvert(c),c.cleanRange(),l}});var ewe=ye((Jcr,Q2e)=>{"use strict";var pwt=KI().setPositionOffset,$2e=["v","h"];Q2e.exports=function(t,r){for(var n=t.calcdata,i=r.xaxis,a=r.yaxis,o=0;o<$2e.length;o++){for(var s=$2e[o],l=s==="h"?a:i,u=[],c=0;c<n.length;c++){var f=n[c],h=f[0].t,d=f[0].trace;d.visible===!0&&d.type==="violin"&&!h.empty&&d.orientation===s&&d.xaxis===i._id&&d.yaxis===a._id&&u.push(c)}pwt("violin",t,u,l)}}});var rwe=ye(($cr,twe)=>{"use strict";var GG=Oa(),HG=Dr(),gwt=So(),jG=JI(),mwt=yU(),ywt=O8();twe.exports=function(t,r,n,i){var a=t._context.staticPlot,o=t._fullLayout,s=r.xaxis,l=r.yaxis;function u(c,f){var h=mwt(c,{xaxis:s,yaxis:l,trace:f,connectGaps:!0,baseTolerance:.75,shape:"spline",simplify:!0,linearized:!0});return gwt.smoothopen(h[0],1)}HG.makeTraceGroups(i,n,"trace violins").each(function(c){var f=GG.select(this),h=c[0],d=h.t,v=h.trace;if(v.visible!==!0||d.empty){f.remove();return}var _=d.bPos,b=d.bdPos,p=r[d.valLetter+"axis"],k=r[d.posLetter+"axis"],E=v.side==="both",S=E||v.side==="positive",L=E||v.side==="negative",x=f.selectAll("path.violin").data(HG.identity);x.enter().append("path").style("vector-effect",a?"none":"non-scaling-stroke").attr("class","violin"),x.exit().remove(),x.each(function(V){var G=GG.select(this),Z=V.density,j=Z.length,N=k.c2l(V.pos+_,!0),H=k.l2p(N),te;if(v.width)te=d.maxKDE/b;else{var oe=o._violinScaleGroupStats[v.scalegroup];te=v.scalemode==="count"?oe.maxKDE/b*(oe.maxCount/V.pts.length):oe.maxKDE/b}var _e,Ee,Ce,me,ie,Se,Le;if(S){for(Se=new Array(j),me=0;me<j;me++)Le=Se[me]={},Le[d.posLetter]=N+Z[me].v/te,Le[d.valLetter]=p.c2l(Z[me].t,!0);_e=u(Se,v)}if(L){for(Se=new Array(j),ie=0,me=j-1;ie<j;ie++,me--)Le=Se[ie]={},Le[d.posLetter]=N-Z[me].v/te,Le[d.valLetter]=p.c2l(Z[me].t,!0);Ee=u(Se,v)}if(E)Ce=_e+"L"+Ee.slice(1)+"Z";else{var Ae=[H,p.c2p(Z[0].t)],Fe=[H,p.c2p(Z[j-1].t)];v.orientation==="h"&&(Ae.reverse(),Fe.reverse()),S?Ce="M"+Ae+"L"+_e.slice(1)+"L"+Fe:Ce="M"+Fe+"L"+Ee.slice(1)+"L"+Ae}G.attr("d",Ce),V.posCenterPx=H,V.posDensityScale=te*b,V.path=G.node(),V.pathLength=V.path.getTotalLength()/(E?2:1)});var C=v.box,M=C.width,g=(C.line||{}).width,P,T;E?(P=b*M,T=0):S?(P=[0,b*M/2],T=g*{x:1,y:-1}[d.posLetter]):(P=[b*M/2,0],T=g*{x:-1,y:1}[d.posLetter]),jG.plotBoxAndWhiskers(f,{pos:k,val:p},v,{bPos:_,bdPos:P,bPosPxOffset:T}),jG.plotBoxMean(f,{pos:k,val:p},v,{bPos:_,bdPos:P,bPosPxOffset:T});var z;!v.box.visible&&v.meanline.visible&&(z=HG.identity);var O=f.selectAll("path.meanline").data(z||[]);O.enter().append("path").attr("class","meanline").style("fill","none").style("vector-effect",a?"none":"non-scaling-stroke"),O.exit().remove(),O.each(function(V){var G=p.c2p(V.mean,!0),Z=ywt.getPositionOnKdePath(V,v,G);GG.select(this).attr("d",v.orientation==="h"?"M"+G+","+Z[0]+"V"+Z[1]:"M"+Z[0]+","+G+"H"+Z[1])}),jG.plotPoints(f,{x:s,y:l},v,d)})}});var awe=ye((Qcr,nwe)=>{"use strict";var iwe=Oa(),HT=ka(),_wt=op().stylePoints;nwe.exports=function(t){var r=iwe.select(t).selectAll("g.trace.violins");r.style("opacity",function(n){return n[0].trace.opacity}),r.each(function(n){var i=n[0].trace,a=iwe.select(this),o=i.box||{},s=o.line||{},l=i.meanline||{},u=l.width;a.selectAll("path.violin").style("stroke-width",i.line.width+"px").call(HT.stroke,i.line.color).call(HT.fill,i.fillcolor),a.selectAll("path.box").style("stroke-width",s.width+"px").call(HT.stroke,s.color).call(HT.fill,o.fillcolor);var c={"stroke-width":u+"px","stroke-dasharray":2*u+"px,"+u+"px"};a.selectAll("path.mean").style(c).call(HT.stroke,l.color),a.selectAll("path.meanline").style(c).call(HT.stroke,l.color),_wt(a,i,t)})}});var uwe=ye((efr,lwe)=>{"use strict";var xwt=ka(),WG=Dr(),bwt=ho(),owe=qV(),swe=O8();lwe.exports=function(t,r,n,i,a){a||(a={});var o=a.hoverLayer,s=t.cd,l=s[0].trace,u=l.hoveron,c=u.indexOf("violins")!==-1,f=u.indexOf("kde")!==-1,h=[],d,v;if(c||f){var _=owe.hoverOnBoxes(t,r,n,i);if(f&&_.length>0){var b=t.xa,p=t.ya,k,E,S,L,x;l.orientation==="h"?(x=r,k="y",S=p,E="x",L=b):(x=n,k="x",S=b,E="y",L=p);var C=s[t.index];if(x>=C.span[0]&&x<=C.span[1]){var M=WG.extendFlat({},t),g=L.c2p(x,!0),P=swe.getKdeValue(C,l,x),T=swe.getPositionOnKdePath(C,l,g),z=S._offset,O=S._length;M[k+"0"]=T[0],M[k+"1"]=T[1],M[E+"0"]=M[E+"1"]=g,M[E+"Label"]=E+": "+bwt.hoverLabelText(L,x,l[E+"hoverformat"])+", "+s[0].t.labels.kde+" "+P.toFixed(3);for(var V=0,G=0;G<_.length;G++)if(_[G].attr==="med"){V=G;break}M.spikeDistance=_[V].spikeDistance;var Z=k+"Spike";M[Z]=_[V][Z],_[V].spikeDistance=void 0,_[V][Z]=void 0,M.hovertemplate=!1,h.push(M),v={},v[k+"1"]=WG.constrain(z+T[0],z,z+O),v[k+"2"]=WG.constrain(z+T[1],z,z+O),v[E+"1"]=v[E+"2"]=L._offset+g}}c&&(h=h.concat(_))}u.indexOf("points")!==-1&&(d=owe.hoverOnPoints(t,r,n));var j=o.selectAll(".violinline-"+l.uid).data(v?[0]:[]);return j.enter().append("line").classed("violinline-"+l.uid,!0).attr("stroke-width",1.5),j.exit().remove(),j.attr(v).call(xwt.stroke,t.color),i==="closest"?d?[d]:h:(d&&h.push(d),h)}});var fwe=ye((tfr,cwe)=>{"use strict";cwe.exports={attributes:OG(),layoutAttributes:NG(),supplyDefaults:W2e(),crossTraceDefaults:T4().crossTraceDefaults,supplyLayoutDefaults:Z2e(),calc:J2e(),crossTraceCalc:ewe(),plot:rwe(),style:awe(),styleOnSelect:op().styleOnSelect,hoverPoints:uwe(),selectPoints:BV(),moduleType:"trace",name:"violin",basePlotModule:ph(),categories:["cartesian","svg","symbols","oriented","box-violin","showLegend","violinLayout","zoomScale"],meta:{}}});var dwe=ye((rfr,hwe)=>{"use strict";hwe.exports=fwe()});var pwe=ye((ifr,vwe)=>{"use strict";vwe.exports={eventDataKeys:["percentInitial","percentPrevious","percentTotal"]}});var ZG=ye((nfr,_we)=>{"use strict";var jc=Lm(),XG=pf().line,wwt=Gl(),gwe=df().axisHoverFormat,{hovertemplateAttrs:Twt,texttemplateAttrs:Awt,templatefallbackAttrs:mwe}=Ll(),ywe=pwe(),Ny=Ao().extendFlat,Swt=ka();_we.exports={x:jc.x,x0:jc.x0,dx:jc.dx,y:jc.y,y0:jc.y0,dy:jc.dy,xperiod:jc.xperiod,yperiod:jc.yperiod,xperiod0:jc.xperiod0,yperiod0:jc.yperiod0,xperiodalignment:jc.xperiodalignment,yperiodalignment:jc.yperiodalignment,xhoverformat:gwe("x"),yhoverformat:gwe("y"),hovertext:jc.hovertext,hovertemplate:Twt({},{keys:ywe.eventDataKeys}),hovertemplatefallback:mwe(),hoverinfo:Ny({},wwt.hoverinfo,{flags:["name","x","y","text","percent initial","percent previous","percent total"]}),textinfo:{valType:"flaglist",flags:["label","text","percent initial","percent previous","percent total","value"],extras:["none"],editType:"plot",arrayOk:!1},texttemplate:Awt({editType:"plot"},{keys:ywe.eventDataKeys.concat(["label","value"])}),texttemplatefallback:mwe({editType:"plot"}),text:jc.text,textposition:jc.textposition,insidetextanchor:Ny({},jc.insidetextanchor,{dflt:"middle"}),textangle:Ny({},jc.textangle,{dflt:0}),textfont:jc.textfont,insidetextfont:jc.insidetextfont,outsidetextfont:jc.outsidetextfont,constraintext:jc.constraintext,cliponaxis:jc.cliponaxis,orientation:Ny({},jc.orientation,{}),offset:Ny({},jc.offset,{arrayOk:!1}),width:Ny({},jc.width,{arrayOk:!1}),marker:Mwt(),connector:{fillcolor:{valType:"color",editType:"style"},line:{color:Ny({},XG.color,{dflt:Swt.defaultLine}),width:Ny({},XG.width,{dflt:0,editType:"plot"}),dash:XG.dash,editType:"style"},visible:{valType:"boolean",dflt:!0,editType:"plot"},editType:"plot"},offsetgroup:jc.offsetgroup,alignmentgroup:jc.alignmentgroup,zorder:jc.zorder};function Mwt(){var e=Ny({},jc.marker);return delete e.pattern,delete e.cornerradius,e}});var YG=ye((afr,xwe)=>{"use strict";xwe.exports={funnelmode:{valType:"enumerated",values:["stack","group","overlay"],dflt:"stack",editType:"calc"},funnelgap:{valType:"number",min:0,max:1,editType:"calc"},funnelgroupgap:{valType:"number",min:0,max:1,dflt:0,editType:"calc"}}});var JG=ye((ofr,wwe)=>{"use strict";var q8=Dr(),Ewt=Hb(),kwt=r0().handleText,Cwt=eT(),Lwt=Ig(),bwe=ZG(),KG=ka();function Pwt(e,t,r,n){function i(f,h){return q8.coerce(e,t,bwe,f,h)}var a=Cwt(e,t,n,i);if(!a){t.visible=!1;return}Lwt(e,t,n,i),i("xhoverformat"),i("yhoverformat"),i("orientation",t.y&&!t.x?"v":"h"),i("offset"),i("width");var o=i("text");i("hovertext"),i("hovertemplate"),i("hovertemplatefallback");var s=i("textposition");kwt(e,t,n,i,s,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!0,moduleHasCliponaxis:!0,moduleHasTextangle:!0,moduleHasInsideanchor:!0}),t.textposition!=="none"&&!t.texttemplate&&i("textinfo",q8.isArrayOrTypedArray(o)?"text+value":"value");var l=i("marker.color",r);i("marker.line.color",KG.defaultLine),i("marker.line.width");var u=i("connector.visible");if(u){i("connector.fillcolor",Iwt(l));var c=i("connector.line.width");c&&(i("connector.line.color"),i("connector.line.dash"))}i("zorder")}function Iwt(e){var t=q8.isArrayOrTypedArray(e)?"#000":e;return KG.addOpacity(t,.5*KG.opacity(t))}function Rwt(e,t){var r,n;function i(o){return q8.coerce(n._input,n,bwe,o)}for(var a=0;a<e.length;a++)n=e[a],n.type==="funnel"&&(r=n._input,Ewt(r,n,t,i,t.funnelmode))}wwe.exports={supplyDefaults:Pwt,crossTraceDefaults:Rwt}});var Awe=ye((sfr,Twe)=>{"use strict";var Dwt=Dr(),Fwt=YG();Twe.exports=function(e,t,r){var n=!1;function i(s,l){return Dwt.coerce(e,t,Fwt,s,l)}for(var a=0;a<r.length;a++){var o=r[a];if(o.visible&&o.type==="funnel"){n=!0;break}}n&&(i("funnelmode"),i("funnelgap",.2),i("funnelgroupgap"))}});var Mwe=ye((lfr,Swe)=>{"use strict";var jT=Dr();Swe.exports=function(t,r){for(var n=0;n<t.length;n++)t[n].i=n;jT.mergeArray(r.text,t,"tx"),jT.mergeArray(r.hovertext,t,"htx");var i=r.marker;if(i){jT.mergeArray(i.opacity,t,"mo"),jT.mergeArray(i.color,t,"mc");var a=i.line;a&&(jT.mergeArray(a.color,t,"mlc"),jT.mergeArrayCastPositive(a.width,t,"mlw"))}}});var Lwe=ye((ufr,Cwe)=>{"use strict";var Ewe=ho(),kwe=Dg(),zwt=Mwe(),Owt=z0(),O4=fs().BADNUM;Cwe.exports=function(t,r){var n=Ewe.getFromId(t,r.xaxis||"x"),i=Ewe.getFromId(t,r.yaxis||"y"),a,o,s,l,u,c,f,h;r.orientation==="h"?(a=n.makeCalcdata(r,"x"),s=i.makeCalcdata(r,"y"),l=kwe(r,i,"y",s),u=!!r.yperiodalignment,c="y"):(a=i.makeCalcdata(r,"y"),s=n.makeCalcdata(r,"x"),l=kwe(r,n,"x",s),u=!!r.xperiodalignment,c="x"),o=l.vals;var d=Math.min(o.length,a.length),v=new Array(d);for(r._base=[],f=0;f<d;f++){a[f]<0&&(a[f]=O4);var _=!1;a[f]!==O4&&f+1<d&&a[f+1]!==O4&&(_=!0),h=v[f]={p:o[f],s:a[f],cNext:_},r._base[f]=-.5*h.s,u&&(v[f].orig_p=s[f],v[f][c+"End"]=l.ends[f],v[f][c+"Start"]=l.starts[f]),r.ids&&(h.id=String(r.ids[f])),f===0&&(v[0].vTotal=0),v[0].vTotal+=$G(h.s),h.begR=$G(h.s)/$G(v[0].s)}var b;for(f=0;f<d;f++)h=v[f],h.s!==O4&&(h.sumR=h.s/v[0].vTotal,h.difR=b!==void 0?h.s/b:1,b=h.s);return zwt(v,r),Owt(v,r),v};function $G(e){return e===O4?0:e}});var Rwe=ye((cfr,Iwe)=>{"use strict";var Pwe=jb().setGroupPositions;Iwe.exports=function(t,r){var n=t._fullLayout,i=t._fullData,a=t.calcdata,o=r.xaxis,s=r.yaxis,l=[],u=[],c=[],f,h;for(h=0;h<i.length;h++){var d=i[h],v=d.orientation==="h";d.visible===!0&&d.xaxis===o._id&&d.yaxis===s._id&&d.type==="funnel"&&(f=a[h],v?c.push(f):u.push(f),l.push(f))}var _={mode:n.funnelmode,norm:n.funnelnorm,gap:n.funnelgap,groupgap:n.funnelgroupgap};for(Pwe(t,o,s,u,_),Pwe(t,s,o,c,_),h=0;h<l.length;h++){f=l[h];for(var b=0;b<f.length;b++)b+1<f.length&&(f[b].nextP0=f[b+1].p0,f[b].nextS0=f[b+1].s0,f[b].nextP1=f[b+1].p1,f[b].nextS1=f[b+1].s1)}}});var Owe=ye((ffr,zwe)=>{"use strict";var B8=Oa(),L_=Dr(),Dwe=So(),C_=fs().BADNUM,qwt=n2(),Bwt=bv().clearMinTextSize;zwe.exports=function(t,r,n,i){var a=t._fullLayout;Bwt("funnel",a),Nwt(t,r,n,i),Uwt(t,r,n,i),qwt.plot(t,r,n,i,{mode:a.funnelmode,norm:a.funnelmode,gap:a.funnelgap,groupgap:a.funnelgroupgap})};function Nwt(e,t,r,n){var i=t.xaxis,a=t.yaxis;L_.makeTraceGroups(n,r,"trace bars").each(function(o){var s=B8.select(this),l=o[0].trace,u=L_.ensureSingle(s,"g","regions");if(!l.connector||!l.connector.visible){u.remove();return}var c=l.orientation==="h",f=u.selectAll("g.region").data(L_.identity);f.enter().append("g").classed("region",!0),f.exit().remove();var h=f.size();f.each(function(d,v){if(!(v!==h-1&&!d.cNext)){var _=Fwe(d,i,a,c),b=_[0],p=_[1],k="";b[0]!==C_&&p[0]!==C_&&b[1]!==C_&&p[1]!==C_&&b[2]!==C_&&p[2]!==C_&&b[3]!==C_&&p[3]!==C_&&(c?k+="M"+b[0]+","+p[1]+"L"+b[2]+","+p[2]+"H"+b[3]+"L"+b[1]+","+p[1]+"Z":k+="M"+b[1]+","+p[1]+"L"+b[2]+","+p[3]+"V"+p[2]+"L"+b[1]+","+p[0]+"Z"),k===""&&(k="M0,0Z"),L_.ensureSingle(B8.select(this),"path").attr("d",k).call(Dwe.setClipUrl,t.layerClipId,e)}})})}function Uwt(e,t,r,n){var i=t.xaxis,a=t.yaxis;L_.makeTraceGroups(n,r,"trace bars").each(function(o){var s=B8.select(this),l=o[0].trace,u=L_.ensureSingle(s,"g","lines");if(!l.connector||!l.connector.visible||!l.connector.line.width){u.remove();return}var c=l.orientation==="h",f=u.selectAll("g.line").data(L_.identity);f.enter().append("g").classed("line",!0),f.exit().remove();var h=f.size();f.each(function(d,v){if(!(v!==h-1&&!d.cNext)){var _=Fwe(d,i,a,c),b=_[0],p=_[1],k="";b[3]!==void 0&&p[3]!==void 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l=o.marker;q4.select(this).call(QG.fill,s.mc||l.color).call(QG.stroke,s.mlc||l.line.color).call(qwe.dashLine,l.line.dash,s.mlw||l.line.width).style("opacity",o.selectedpoints&&!s.selected?Vwt:1)}}),jwt(a,o,e),a.selectAll(".regions").each(function(){q4.select(this).selectAll("path").style("stroke-width",0).call(QG.fill,o.connector.fillcolor)}),a.selectAll(".lines").each(function(){var s=o.connector.line;qwe.lineGroupStyle(q4.select(this).selectAll("path"),s.width,s.color,s.dash)})})}Bwe.exports={style:Wwt}});var Gwe=ye((dfr,Vwe)=>{"use strict";var Uwe=ka().opacity,Xwt=ET().hoverOnBars,eH=Dr().formatPercent;Vwe.exports=function(t,r,n,i,a){var o=Xwt(t,r,n,i,a);if(o){var s=o.cd,l=s[0].trace,u=l.orientation==="h",c=o.index,f=s[c],h=u?"x":"y";o[h+"LabelVal"]=f.s,o.percentInitial=f.begR,o.percentInitialLabel=eH(f.begR,1),o.percentPrevious=f.difR,o.percentPreviousLabel=eH(f.difR,1),o.percentTotal=f.sumR,o.percentTotalLabel=eH(f.sumR,1);var d=f.hi||l.hoverinfo,v=[];if(d&&d!=="none"&&d!=="skip"){var _=d==="all",b=d.split("+"),p=function(k){return _||b.indexOf(k)!==-1};p("percent initial")&&v.push(o.percentInitialLabel+" of initial"),p("percent previous")&&v.push(o.percentPreviousLabel+" of previous"),p("percent total")&&v.push(o.percentTotalLabel+" of total")}return o.extraText=v.join("<br>"),o.color=Zwt(l,f),[o]}};function Zwt(e,t){var r=e.marker,n=t.mc||r.color,i=t.mlc||r.line.color,a=t.mlw||r.line.width;if(Uwe(n))return n;if(Uwe(i)&&a)return i}});var jwe=ye((vfr,Hwe)=>{"use strict";Hwe.exports=function(t,r){return t.x="xVal"in r?r.xVal:r.x,t.y="yVal"in r?r.yVal:r.y,"percentInitial"in r&&(t.percentInitial=r.percentInitial),"percentPrevious"in r&&(t.percentPrevious=r.percentPrevious),"percentTotal"in r&&(t.percentTotal=r.percentTotal),r.xa&&(t.xaxis=r.xa),r.ya&&(t.yaxis=r.ya),t}});var Xwe=ye((pfr,Wwe)=>{"use 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rH(e){return{marker:{color:WT({},_c.marker.color,{arrayOk:!1,editType:"style"}),line:{color:WT({},_c.marker.line.color,{arrayOk:!1,editType:"style"}),width:WT({},_c.marker.line.width,{arrayOk:!1,editType:"style"}),editType:"style"},editType:"style"},editType:"style"}}t3e.exports={measure:{valType:"data_array",dflt:[],editType:"calc"},base:{valType:"number",dflt:null,arrayOk:!1,editType:"calc"},x:_c.x,x0:_c.x0,dx:_c.dx,y:_c.y,y0:_c.y0,dy:_c.dy,xperiod:_c.xperiod,yperiod:_c.yperiod,xperiod0:_c.xperiod0,yperiod0:_c.yperiod0,xperiodalignment:_c.xperiodalignment,yperiodalignment:_c.yperiodalignment,xhoverformat:$we("x"),yhoverformat:$we("y"),hovertext:_c.hovertext,hovertemplate:Kwt({},{keys:e3e.eventDataKeys}),hovertemplatefallback:Qwe(),hoverinfo:WT({},Ywt.hoverinfo,{flags:["name","x","y","text","initial","delta","final"]}),textinfo:{valType:"flaglist",flags:["label","text","initial","delta","final"],extras:["none"],editType:"plot",arrayOk:!1},texttemplate:Jwt({editType:"plot"},{keys:e3e.eventDataKeys.concat(["label"])}),texttemplatefallback:Qwe({editType:"plot"}),text:_c.text,textposition:_c.textposition,insidetextanchor:_c.insidetextanchor,textangle:_c.textangle,textfont:_c.textfont,insidetextfont:_c.insidetextfont,outsidetextfont:_c.outsidetextfont,constraintext:_c.constraintext,cliponaxis:_c.cliponaxis,orientation:_c.orientation,offset:_c.offset,width:_c.width,increasing:rH("increasing"),decreasing:rH("decreasing"),totals:rH("intermediate 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aH(e,t,r){e(t+".marker.color",r),e(t+".marker.line.color",i3t.defaultLine),e(t+".marker.line.width")}function s3t(e,t,r,n){function i(u,c){return n3e.coerce(e,t,a3e,u,c)}var a=t3t(e,t,n,i);if(!a){t.visible=!1;return}r3t(e,t,n,i),i("xhoverformat"),i("yhoverformat"),i("measure"),i("orientation",t.x&&!t.y?"h":"v"),i("base"),i("offset"),i("width"),i("text"),i("hovertext"),i("hovertemplate"),i("hovertemplatefallback");var o=i("textposition");e3t(e,t,n,i,o,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!0,moduleHasCliponaxis:!0,moduleHasTextangle:!0,moduleHasInsideanchor:!0}),t.textposition!=="none"&&(i("texttemplate"),i("texttemplatefallback"),t.texttemplate||i("textinfo")),aH(i,"increasing",n3t),aH(i,"decreasing",a3t),aH(i,"totals",o3t);var s=i("connector.visible");if(s){i("connector.mode");var l=i("connector.line.width");l&&(i("connector.line.color"),i("connector.line.dash"))}i("zorder")}function l3t(e,t){var r,n;function i(o){return 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y3e=ye((Afr,m3e)=>{"use strict";var g3e=jb().setGroupPositions;m3e.exports=function(t,r){var n=t._fullLayout,i=t._fullData,a=t.calcdata,o=r.xaxis,s=r.yaxis,l=[],u=[],c=[],f,h;for(h=0;h<i.length;h++){var d=i[h];d.visible===!0&&d.xaxis===o._id&&d.yaxis===s._id&&d.type==="waterfall"&&(f=a[h],d.orientation==="h"?c.push(f):u.push(f),l.push(f))}var v={mode:n.waterfallmode,norm:n.waterfallnorm,gap:n.waterfallgap,groupgap:n.waterfallgroupgap};for(g3e(t,o,s,u,v),g3e(t,s,o,c,v),h=0;h<l.length;h++){f=l[h];for(var _=0;_<f.length;_++){var b=f[_];b.isSum===!1&&(b.s0+=_===0?0:f[_-1].s),_+1<f.length&&(f[_].nextP0=f[_+1].p0,f[_].nextS0=f[_+1].s0)}}}});var b3e=ye((Sfr,x3e)=>{"use strict";var _3e=Oa(),N8=Dr(),h3t=So(),ZT=fs().BADNUM,d3t=n2(),v3t=bv().clearMinTextSize;x3e.exports=function(t,r,n,i){var a=t._fullLayout;v3t("waterfall",a),d3t.plot(t,r,n,i,{mode:a.waterfallmode,norm:a.waterfallmode,gap:a.waterfallgap,groupgap:a.waterfallgroupgap}),p3t(t,r,n,i)};function p3t(e,t,r,n){var i=t.xaxis,a=t.yaxis;N8.makeTraceGroups(n,r,"trace bars").each(function(o){var s=_3e.select(this),l=o[0].trace,u=N8.ensureSingle(s,"g","lines");if(!l.connector||!l.connector.visible){u.remove();return}var c=l.orientation==="h",f=l.connector.mode,h=u.selectAll("g.line").data(N8.identity);h.enter().append("g").classed("line",!0),h.exit().remove();var d=h.size();h.each(function(v,_){if(!(_!==d-1&&!v.cNext)){var b=g3t(v,i,a,c),p=b[0],k=b[1],E="";p[0]!==ZT&&k[0]!==ZT&&p[1]!==ZT&&k[1]!==ZT&&(f==="spanning"&&!v.isSum&&_>0&&(c?E+="M"+p[0]+","+k[1]+"V"+k[0]:E+="M"+p[1]+","+k[0]+"H"+p[0]),f!=="between"&&(v.isSum||_<d-1)&&(c?E+="M"+p[1]+","+k[0]+"V"+k[1]:E+="M"+p[0]+","+k[1]+"H"+p[1]),p[2]!==ZT&&k[2]!==ZT&&(c?E+="M"+p[1]+","+k[1]+"V"+k[2]:E+="M"+p[1]+","+k[1]+"H"+p[2])),E===""&&(E="M0,0Z"),N8.ensureSingle(_3e.select(this),"path").attr("d",E).call(h3t.setClipUrl,t.layerClipId,e)}})})}function g3t(e,t,r,n){var i=[],a=[],o=n?t:r,s=n?r:t;return i[0]=o.c2p(e.s0,!0),a[0]=s.c2p(e.p0,!0),i[1]=o.c2p(e.s1,!0),a[1]=s.c2p(e.p1,!0),i[2]=o.c2p(e.nextS0,!0),a[2]=s.c2p(e.nextP0,!0),n?[i,a]:[a,i]}});var S3e=ye((Mfr,A3e)=>{"use strict";var U8=Oa(),w3e=So(),T3e=ka(),m3t=N1().DESELECTDIM,y3t=N0(),_3t=bv().resizeText,x3t=y3t.styleTextPoints;function b3t(e,t,r){var n=r||U8.select(e).selectAll('g[class^="waterfalllayer"]').selectAll("g.trace");_3t(e,n,"waterfall"),n.style("opacity",function(i){return i[0].trace.opacity}),n.each(function(i){var a=U8.select(this),o=i[0].trace;a.selectAll(".point > path").each(function(s){if(!s.isBlank){var l=o[s.dir].marker;U8.select(this).call(T3e.fill,l.color).call(T3e.stroke,l.line.color).call(w3e.dashLine,l.line.dash,l.line.width).style("opacity",o.selectedpoints&&!s.selected?m3t:1)}}),x3t(a,o,e),a.selectAll(".lines").each(function(){var s=o.connector.line;w3e.lineGroupStyle(U8.select(this).selectAll("path"),s.width,s.color,s.dash)})})}A3e.exports={style:b3t}});var L3e=ye((Efr,C3e)=>{"use strict";var w3t=ho().hoverLabelText,M3e=ka().opacity,T3t=ET().hoverOnBars,E3e=XT(),k3e={increasing:E3e.INCREASING.SYMBOL,decreasing:E3e.DECREASING.SYMBOL};C3e.exports=function(t,r,n,i,a){var o=T3t(t,r,n,i,a);if(!o)return;var s=o.cd,l=s[0].trace,u=l.orientation==="h",c=u?"x":"y",f=u?t.xa:t.ya;function h(x){return w3t(f,x,l[c+"hoverformat"])}var d=o.index,v=s[d],_=v.isSum?v.b+v.s:v.rawS;o.initial=v.b+v.s-_,o.delta=_,o.final=o.initial+o.delta;var b=h(Math.abs(o.delta));o.deltaLabel=_<0?"("+b+")":b,o.finalLabel=h(o.final),o.initialLabel=h(o.initial);var p=v.hi||l.hoverinfo,k=[];if(p&&p!=="none"&&p!=="skip"){var E=p==="all",S=p.split("+"),L=function(x){return E||S.indexOf(x)!==-1};v.isSum||(L("final")&&(u?!L("x"):!L("y"))&&k.push(o.finalLabel),L("delta")&&(_<0?k.push(o.deltaLabel+" "+k3e.decreasing):k.push(o.deltaLabel+" "+k3e.increasing)),L("initial")&&k.push("Initial: "+o.initialLabel))}return k.length&&(o.extraText=k.join("<br>")),o.color=A3t(l,v),[o]};function A3t(e,t){var r=e[t.dir].marker,n=r.color,i=r.line.color,a=r.line.width;if(M3e(n))return n;if(M3e(i)&&a)return i}});var I3e=ye((kfr,P3e)=>{"use strict";P3e.exports=function(t,r){return t.x="xVal"in r?r.xVal:r.x,t.y="yVal"in r?r.yVal:r.y,"initial"in r&&(t.initial=r.initial),"delta"in r&&(t.delta=r.delta),"final"in r&&(t.final=r.final),r.xa&&(t.xaxis=r.xa),r.ya&&(t.yaxis=r.ya),t}});var D3e=ye((Cfr,R3e)=>{"use strict";R3e.exports={attributes:iH(),layoutAttributes:nH(),supplyDefaults:oH().supplyDefaults,crossTraceDefaults:oH().crossTraceDefaults,supplyLayoutDefaults:u3e(),calc:p3e(),crossTraceCalc:y3e(),plot:b3e(),style:S3e().style,hoverPoints:L3e(),eventData:I3e(),selectPoints:kT(),moduleType:"trace",name:"waterfall",basePlotModule:ph(),categories:["bar-like","cartesian","svg","oriented","showLegend","zoomScale"],meta:{}}});var z3e=ye((Lfr,F3e)=>{"use strict";F3e.exports=D3e()});var YT=ye((Pfr,O3e)=>{"use strict";O3e.exports={colormodel:{rgb:{min:[0,0,0],max:[255,255,255],fmt:function(e){return e.slice(0,3)},suffix:["","",""]},rgba:{min:[0,0,0,0],max:[255,255,255,1],fmt:function(e){return e.slice(0,4)},suffix:["","","",""]},rgba256:{colormodel:"rgba",zminDflt:[0,0,0,0],zmaxDflt:[255,255,255,255],min:[0,0,0,0],max:[255,255,255,1],fmt:function(e){return e.slice(0,4)},suffix:["","","",""]},hsl:{min:[0,0,0],max:[360,100,100],fmt:function(e){var t=e.slice(0,3);return t[1]=t[1]+"%",t[2]=t[2]+"%",t},suffix:["\xB0","%","%"]},hsla:{min:[0,0,0,0],max:[360,100,100,1],fmt:function(e){var t=e.slice(0,4);return t[1]=t[1]+"%",t[2]=t[2]+"%",t},suffix:["\xB0","%","%",""]}}}});var uH=ye((Ifr,B3e)=>{"use strict";var S3t=Gl(),M3t=pf().zorder,{hovertemplateAttrs:E3t,templatefallbackAttrs:k3t}=Ll(),q3e=Ao().extendFlat,C3t=YT().colormodel,N4=["rgb","rgba","rgba256","hsl","hsla"],L3t=[],P3t=[];for(KT=0;KT<N4.length;KT++)B4=C3t[N4[KT]],L3t.push("For the `"+N4[KT]+"` colormodel, it is ["+(B4.zminDflt||B4.min).join(", ")+"]."),P3t.push("For the `"+N4[KT]+"` colormodel, it is 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r};Hy.readUInt16LE=function(e,t){return e[t]|e[t+1]<<8};Hy.readUInt16BE=function(e,t){return e[t+1]|e[t]<<8};Hy.readUInt32LE=function(e,t){return e[t]|e[t+1]<<8|e[t+2]<<16|e[t+3]*16777216};Hy.readUInt32BE=function(e,t){return e[t+3]|e[t+2]<<8|e[t+1]<<16|e[t]*16777216};function oD(e,t,r){Error.call(this),Error.captureStackTrace?Error.captureStackTrace(this,this.constructor):this.stack=new Error().stack||"",this.name=this.constructor.name,this.message=e,t&&(this.code=t),r&&(this.statusCode=r)}oD.prototype=Object.create(Error.prototype);oD.prototype.constructor=oD;Hy.ProbeError=oD});var G4e=ye((bdr,sD)=>{"use strict";var bA=rc().readUInt16BE,oW=rc().readUInt32BE;function EE(e,t){if(e.length<4+t)return null;var r=oW(e,t);return e.length<r+t||r<8?null:{boxtype:String.fromCharCode.apply(null,e.slice(t+4,t+8)),data:e.slice(t+8,t+r),end:t+r}}sD.exports.unbox=EE;function nkt(e,t){for(var r=0;;){var 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n=EE(e,r);if(!n)break;n.boxtype==="iprp"&&skt(n.data,t),n.boxtype==="iloc"&&akt(n.data,t),n.boxtype==="iinf"&&okt(n.data,t),r=n.end}}function ukt(e){var t=e.reduce(function(i,a){return i.width>a.width||i.width===a.width&&i.height>a.height?i:a}),r=e.reduce(function(i,a){return i.height>a.height||i.height===a.height&&i.width>a.width?i:a}),n;return t.width>r.height||t.width===r.height&&t.height>r.width?n=t:n=r,n}sD.exports.readSizeFromMeta=function(e){var t={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(lkt(e,t),!!t.sizes.length){var r=ukt(t.sizes),n=1;t.transforms.forEach(function(a){var o={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},s={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(a.type==="imir"&&(a.value===0?n=s[n]:(n=s[n],n=o[n],n=o[n])),a.type==="irot")for(var l=0;l<a.value;l++)n=o[n]});var i=null;return t.item_inf.Exif&&(i=t.item_loc[t.item_inf.Exif]),{width:r.width,height:r.height,orientation:t.transforms.length?n:null,variants:t.sizes,exif_location:i}}};sD.exports.getMimeType=function(e){var t=String.fromCharCode.apply(null,e.slice(0,4)),r={};r[t]=!0;for(var n=8;n<e.length;n+=4)r[String.fromCharCode.apply(null,e.slice(n,n+4))]=!0;if(!(!r.mif1&&!r.msf1&&!r.miaf))return t==="avif"||t==="avis"||t==="avio"?{type:"avif",mime:"image/avif"}:t==="heic"||t==="heix"?{type:"heic",mime:"image/heic"}:t==="hevc"||t==="hevx"?{type:"heic",mime:"image/heic-sequence"}:r.avif||r.avis?{type:"avif",mime:"image/avif"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:"heif",mime:"image/heif-sequence"}:{type:"heif",mime:"image/heif"}:{type:"avif",mime:"image/avif"}}});var uD=ye((wdr,sW)=>{"use strict";function lD(e,t){var r=new Error(e);return r.code=t,r}function ckt(e){try{return decodeURIComponent(escape(e))}catch(t){return e}}function jy(e,t,r){this.input=e.subarray(t,r),this.start=t;var n=String.fromCharCode.apply(null,this.input.subarray(0,4));if(n!=="II*\0"&&n!=="MM\0*")throw lD("invalid TIFF signature","EBADDATA");this.big_endian=n[0]==="M"}jy.prototype.each=function(e){this.aborted=!1;var t=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:t}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,e)}};jy.prototype.read_uint16=function(e){var t=this.input;if(e+2>t.length)throw lD("unexpected EOF","EBADDATA");return this.big_endian?t[e]*256+t[e+1]:t[e]+t[e+1]*256};jy.prototype.read_uint32=function(e){var t=this.input;if(e+4>t.length)throw lD("unexpected EOF","EBADDATA");return this.big_endian?t[e]*16777216+t[e+1]*65536+t[e+2]*256+t[e+3]:t[e]+t[e+1]*256+t[e+2]*65536+t[e+3]*16777216};jy.prototype.is_subifd_link=function(e,t){return e===0&&t===34665||e===0&&t===34853||e===34665&&t===40965};jy.prototype.exif_format_length=function(e){switch(e){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}};jy.prototype.exif_format_read=function(e,t){var r;switch(e){case 1:case 2:return r=this.input[t],r;case 6:return r=this.input[t],r|(r&128)*33554430;case 3:return r=this.read_uint16(t),r;case 8:return r=this.read_uint16(t),r|(r&32768)*131070;case 4:return r=this.read_uint32(t),r;case 9:return r=this.read_uint32(t),r|0;case 5:case 10:case 11:case 12:return null;case 7:return null;default:return null}};jy.prototype.scan_ifd=function(e,t,r){var n=this.read_uint16(t);t+=2;for(var i=0;i<n;i++){var a=this.read_uint16(t),o=this.read_uint16(t+2),s=this.read_uint32(t+4),l=this.exif_format_length(o),u=s*l,c=u<=4?t+8:this.read_uint32(t+8),f=!1;if(c+u>this.input.length)throw lD("unexpected EOF","EBADDATA");for(var h=[],d=c,v=0;v<s;v++,d+=l){var _=this.exif_format_read(o,d);if(_===null){h=null;break}h.push(_)}Array.isArray(h)&&o===2&&(h=ckt(String.fromCharCode.apply(null,h)),h&&h[h.length-1]==="\0"&&(h=h.slice(0,-1))),this.is_subifd_link(e,a)&&Array.isArray(h)&&Number.isInteger(h[0])&&h[0]>0&&(this.ifds_to_read.push({id:a,offset:h[0]}),f=!0);var b={is_big_endian:this.big_endian,ifd:e,tag:a,format:o,count:s,entry_offset:t+this.start,data_length:u,data_offset:c+this.start,value:h,is_subifd_link:f};if(r(b)===!1){this.aborted=!0;return}t+=12}e===0&&this.ifds_to_read.push({id:1,offset:this.read_uint32(t)})};sW.exports.ExifParser=jy;sW.exports.get_orientation=function(e){var t=0;try{return new jy(e,0,e.length).each(function(r){if(r.ifd===0&&r.tag===274&&Array.isArray(r.value))return t=r.value[0],!1}),t}catch(r){return-1}}});var j4e=ye((Tdr,H4e)=>{"use strict";var fkt=rc().str2arr,hkt=rc().sliceEq,dkt=rc().readUInt32BE,cD=G4e(),vkt=uD(),pkt=fkt("ftyp");H4e.exports=function(e){if(hkt(e,4,pkt)){var t=cD.unbox(e,0);if(t){var r=cD.getMimeType(t.data);if(r){for(var n,i=t.end;;){var a=cD.unbox(e,i);if(!a)break;if(i=a.end,a.boxtype==="mdat")return;if(a.boxtype==="meta"){n=a.data;break}}if(n){var o=cD.readSizeFromMeta(n);if(o){var s={width:o.width,height:o.height,type:r.type,mime:r.mime,wUnits:"px",hUnits:"px"};if(o.variants.length>1&&(s.variants=o.variants),o.orientation&&(s.orientation=o.orientation),o.exif_location&&o.exif_location.offset+o.exif_location.length<=e.length){var l=dkt(e,o.exif_location.offset),u=e.slice(o.exif_location.offset+l+4,o.exif_location.offset+o.exif_location.length),c=vkt.get_orientation(u);c>0&&(s.orientation=c)}return s}}}}}}});var Z4e=ye((Adr,X4e)=>{"use strict";var gkt=rc().str2arr,mkt=rc().sliceEq,W4e=rc().readUInt16LE,ykt=gkt("BM");X4e.exports=function(e){if(!(e.length<26)&&mkt(e,0,ykt))return{width:W4e(e,18),height:W4e(e,22),type:"bmp",mime:"image/bmp",wUnits:"px",hUnits:"px"}}});var Q4e=ye((Sdr,$4e)=>{"use strict";var J4e=rc().str2arr,Y4e=rc().sliceEq,K4e=rc().readUInt16LE,_kt=J4e("GIF87a"),xkt=J4e("GIF89a");$4e.exports=function(e){if(!(e.length<10)&&!(!Y4e(e,0,_kt)&&!Y4e(e,0,xkt)))return{width:K4e(e,6),height:K4e(e,8),type:"gif",mime:"image/gif",wUnits:"px",hUnits:"px"}}});var rEe=ye((Mdr,tEe)=>{"use strict";var lW=rc().readUInt16LE,bkt=0,wkt=1,eEe=16;tEe.exports=function(e){var t=lW(e,0),r=lW(e,2),n=lW(e,4);if(!(t!==bkt||r!==wkt||!n)){for(var i=[],a={width:0,height:0},o=0;o<n;o++){var s=e[6+eEe*o]||256,l=e[6+eEe*o+1]||256,u={width:s,height:l};i.push(u),(s>a.width||l>a.height)&&(a=u)}return{width:a.width,height:a.height,variants:i,type:"ico",mime:"image/x-icon",wUnits:"px",hUnits:"px"}}}});var nEe=ye((Edr,iEe)=>{"use strict";var uW=rc().readUInt16BE,Tkt=rc().str2arr,Akt=rc().sliceEq,Skt=uD(),Mkt=Tkt("Exif\0\0");iEe.exports=function(e){if(!(e.length<2)&&!(e[0]!==255||e[1]!==216||e[2]!==255))for(var t=2;;){for(;;){if(e.length-t<2)return;if(e[t++]===255)break}for(var r=e[t++],n;r===255;)r=e[t++];if(208<=r&&r<=217||r===1)n=0;else if(192<=r&&r<=254){if(e.length-t<2)return;n=uW(e,t)-2,t+=2}else return;if(r===217||r===218)return;var i;if(r===225&&n>=10&&Akt(e,t,Mkt)&&(i=Skt.get_orientation(e.slice(t+6,t+n))),n>=5&&192<=r&&r<=207&&r!==196&&r!==200&&r!==204){if(e.length-t<n)return;var a={width:uW(e,t+3),height:uW(e,t+1),type:"jpg",mime:"image/jpeg",wUnits:"px",hUnits:"px"};return i>0&&(a.orientation=i),a}t+=n}}});var uEe=ye((kdr,lEe)=>{"use strict";var sEe=rc().str2arr,aEe=rc().sliceEq,oEe=rc().readUInt32BE,Ekt=sEe(`\x89PNG\r
+
+`),kkt=sEe("IHDR");lEe.exports=function(e){if(!(e.length<24)&&aEe(e,0,Ekt)&&aEe(e,12,kkt))return{width:oEe(e,16),height:oEe(e,20),type:"png",mime:"image/png",wUnits:"px",hUnits:"px"}}});var hEe=ye((Cdr,fEe)=>{"use strict";var Ckt=rc().str2arr,Lkt=rc().sliceEq,cEe=rc().readUInt32BE,Pkt=Ckt("8BPS\0
");fEe.exports=function(e){if(!(e.length<22)&&Lkt(e,0,Pkt))return{width:cEe(e,18),height:cEe(e,14),type:"psd",mime:"image/vnd.adobe.photoshop",wUnits:"px",hUnits:"px"}}});var pEe=ye((Ldr,vEe)=>{"use strict";function Ikt(e){return e===32||e===9||e===13||e===10}function wA(e){return typeof e=="number"&&isFinite(e)&&e>0}function Rkt(e){var t=0,r=e.length;for(e[0]===239&&e[1]===187&&e[2]===191&&(t=3);t<r&&Ikt(e[t]);)t++;return t===r?!1:e[t]===60}var Dkt=/<[-_.:a-zA-Z0-9][^>]*>/,Fkt=/^<([-_.:a-zA-Z0-9]+:)?svg\s/,zkt=/[^-]\bwidth="([^%]+?)"|[^-]\bwidth='([^%]+?)'/,Okt=/\bheight="([^%]+?)"|\bheight='([^%]+?)'/,qkt=/\bview[bB]ox="(.+?)"|\bview[bB]ox='(.+?)'/,dEe=/in$|mm$|cm$|pt$|pc$|px$|em$|ex$/;function Bkt(e){var t=e.match(zkt),r=e.match(Okt),n=e.match(qkt);return{width:t&&(t[1]||t[2]),height:r&&(r[1]||r[2]),viewbox:n&&(n[1]||n[2])}}function Um(e){return dEe.test(e)?e.match(dEe)[0]:"px"}vEe.exports=function(e){if(Rkt(e)){for(var t="",r=0;r<e.length;r++)t+=String.fromCharCode(e[r]);var n=(t.match(Dkt)||[""])[0];if(Fkt.test(n)){var i=Bkt(n),a=parseFloat(i.width),o=parseFloat(i.height);if(i.width&&i.height)return!wA(a)||!wA(o)?void 0:{width:a,height:o,type:"svg",mime:"image/svg+xml",wUnits:Um(i.width),hUnits:Um(i.height)};var s=(i.viewbox||"").split(" "),l={width:s[2],height:s[3]},u=parseFloat(l.width),c=parseFloat(l.height);if(!(!wA(u)||!wA(c))&&Um(l.width)===Um(l.height)){var f=u/c;return i.width?wA(a)?{width:a,height:a/f,type:"svg",mime:"image/svg+xml",wUnits:Um(i.width),hUnits:Um(i.width)}:void 0:i.height?wA(o)?{width:o*f,height:o,type:"svg",mime:"image/svg+xml",wUnits:Um(i.height),hUnits:Um(i.height)}:void 0:{width:u,height:c,type:"svg",mime:"image/svg+xml",wUnits:Um(l.width),hUnits:Um(l.height)}}}}}});var xEe=ye((Pdr,_Ee)=>{"use strict";var yEe=rc().str2arr,gEe=rc().sliceEq,Nkt=rc().readUInt16LE,Ukt=rc().readUInt16BE,Vkt=rc().readUInt32LE,Gkt=rc().readUInt32BE,Hkt=yEe("II*\0"),jkt=yEe("MM\0*");function fD(e,t,r){return r?Ukt(e,t):Nkt(e,t)}function cW(e,t,r){return r?Gkt(e,t):Vkt(e,t)}function mEe(e,t,r){var n=fD(e,t+2,r),i=cW(e,t+4,r);return i!==1||n!==3&&n!==4?null:n===3?fD(e,t+8,r):cW(e,t+8,r)}_Ee.exports=function(e){if(!(e.length<8)&&!(!gEe(e,0,Hkt)&&!gEe(e,0,jkt))){var t=e[0]===77,r=cW(e,4,t)-8;if(!(r<0)){var n=r+8;if(!(e.length-n<2)){var i=fD(e,n+0,t)*12;if(!(i<=0)&&(n+=2,!(e.length-n<i))){var a,o,s,l;for(a=0;a<i;a+=12)l=fD(e,n+a,t),l===256?o=mEe(e,n+a,t):l===257&&(s=mEe(e,n+a,t));if(o&&s)return{width:o,height:s,type:"tiff",mime:"image/tiff",wUnits:"px",hUnits:"px"}}}}}}});var SEe=ye((Idr,AEe)=>{"use strict";var TEe=rc().str2arr,bEe=rc().sliceEq,wEe=rc().readUInt16LE,fW=rc().readUInt32LE,Wkt=uD(),Xkt=TEe("RIFF"),Zkt=TEe("WEBP");function Ykt(e,t){if(!(e[t+3]!==157||e[t+4]!==1||e[t+5]!==42))return{width:wEe(e,t+6)&16383,height:wEe(e,t+8)&16383,type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}function Kkt(e,t){if(e[t]===47){var r=fW(e,t+1);return{width:(r&16383)+1,height:(r>>14&16383)+1,type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}}function Jkt(e,t){return{width:(e[t+6]<<16|e[t+5]<<8|e[t+4])+1,height:(e[t+9]<<t|e[t+8]<<8|e[t+7])+1,type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}AEe.exports=function(e){if(!(e.length<16)&&!(!bEe(e,0,Xkt)&&!bEe(e,8,Zkt))){var t=12,r=null,n=0,i=fW(e,4)+8;if(!(i>e.length)){for(;t+8<i;){if(e[t]===0){t++;continue}var a=String.fromCharCode.apply(null,e.slice(t,t+4)),o=fW(e,t+4);a==="VP8 "&&o>=10?r=r||Ykt(e,t+8):a==="VP8L"&&o>=9?r=r||Kkt(e,t+8):a==="VP8X"&&o>=10?r=r||Jkt(e,t+8):a==="EXIF"&&(n=Wkt.get_orientation(e.slice(t+8,t+8+o)),t=1/0),t+=8+o}if(r)return n>0&&(r.orientation=n),r}}}});var EEe=ye((Rdr,MEe)=>{"use strict";MEe.exports={avif:j4e(),bmp:Z4e(),gif:Q4e(),ico:rEe(),jpeg:nEe(),png:uEe(),psd:hEe(),svg:pEe(),tiff:xEe(),webp:SEe()}});var kEe=ye((Ddr,dW)=>{"use strict";var hW=EEe();function $kt(e){for(var t=Object.keys(hW),r=0;r<t.length;r++){var 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PEe.constrain((i-e)*t,r,n)}}function sCt(e,t){return function(r){return PEe.constrain(r,e,t)}}function lCt(e){var t=rCt.colormodel[e.colormodel],r=t.colormodel||e.colormodel,n=r.length;e._sArray=[];for(var i=0;i<n;i++)t.min[i]!==e.zmin[i]||t.max[i]!==e.zmax[i]?e._sArray.push(oCt(e.zmin[i],(t.max[i]-t.min[i])/(e.zmax[i]-e.zmin[i]),t.min[i],t.max[i])):e._sArray.push(sCt(t.min[i],t.max[i]));return function(a){for(var o=a.slice(0,n),s=0;s<n;s++){var l=o[s];if(!iCt(l))return!1;o[s]=e._sArray[s](l)}return o}}});var zEe=ye((Odr,FEe)=>{"use strict";var uCt=Oa(),A2=Dr(),DEe=A2.strTranslate,cCt=Wp(),fCt=YT(),hCt=QV(),dCt=h8().STYLE;FEe.exports=function(t,r,n,i){var a=r.xaxis,o=r.yaxis,s=!t._context._exportedPlot&&hCt();A2.makeTraceGroups(i,n,"im").each(function(l){var u=uCt.select(this),c=l[0],f=c.trace,h=(f.zsmooth==="fast"||f.zsmooth===!1&&s)&&!f._hasZ&&f._hasSource&&a.type==="linear"&&o.type==="linear";f._realImage=h;var 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Le=f._image;Le.onload=function(){Se.drawImage(Le,0,0),f._canvas={el:ie,source:f.source},me()},Le.setAttribute("src",f.source)}}).then(function(){var me,ie;if(f._hasZ)ie=G(function(Ae,Fe){var Pe=d[Fe][Ae];return A2.isTypedArray(Pe)&&(Pe=Array.from(Pe)),Pe}),me=ie.toDataURL("image/png");else if(f._hasSource)if(h)me=f.source;else{var Se=f._canvas.el.getContext("2d",{willReadFrequently:!0}),Le=Se.getImageData(0,0,b,p).data;ie=G(function(Ae,Fe){var Pe=4*(Fe*b+Ae);return[Le[Pe],Le[Pe+1],Le[Pe+2],Le[Pe+3]]}),me=ie.toDataURL("image/png")}Z.attr({"xlink:href":me,height:z,width:T,x:S,y:C})});t._promises.push(Ce)})}});var qEe=ye((qdr,OEe)=>{"use strict";var vCt=Oa();OEe.exports=function(t){vCt.select(t).selectAll(".im image").style("opacity",function(r){return r[0].trace.opacity})}});var VEe=ye((Bdr,UEe)=>{"use strict";var BEe=vf(),NEe=Dr(),dD=NEe.isArrayOrTypedArray,pCt=YT();UEe.exports=function(t,r,n){var i=t.cd[0],a=i.trace,o=t.xa,s=t.ya;if(!(BEe.inbox(r-i.x0,r-(i.x0+i.w*a.dx),0)>0||BEe.inbox(n-i.y0,n-(i.y0+i.h*a.dy),0)>0)){var l=Math.floor((r-i.x0)/a.dx),u=Math.floor(Math.abs(n-i.y0)/a.dy),c;if(a._hasZ?c=i.z[u][l]:a._hasSource&&(c=a._canvas.el.getContext("2d",{willReadFrequently:!0}).getImageData(l,u,1,1).data),!!c){var f=i.hi||a.hoverinfo,h;if(f){var d=f.split("+");d.indexOf("all")!==-1&&(d=["color"]),d.indexOf("color")!==-1&&(h=!0)}var v=pCt.colormodel[a.colormodel],_=v.colormodel||a.colormodel,b=_.length,p=a._scaler(c),k=v.suffix,E=[];(a.hovertemplate||h)&&(E.push("["+[p[0]+k[0],p[1]+k[1],p[2]+k[2]].join(", ")),b===4&&E.push(", "+p[3]+k[3]),E.push("]"),E=E.join(""),t.extraText=_.toUpperCase()+": "+E);var S;dD(a.hovertext)&&dD(a.hovertext[u])?S=a.hovertext[u][l]:dD(a.text)&&dD(a.text[u])&&(S=a.text[u][l]);var L=s.c2p(i.y0+(u+.5)*a.dy),x=i.x0+(l+.5)*a.dx,C=i.y0+(u+.5)*a.dy,M="["+c.slice(0,a.colormodel.length).join(", 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vW=Gl(),gCt=Cc().attributes,mCt=ec(),yCt=Lh(),{hovertemplateAttrs:_Ct,texttemplateAttrs:xCt,templatefallbackAttrs:YEe}=Ll(),S2=Ao().extendFlat,bCt=Pd().pattern,vD=mCt({editType:"plot",arrayOk:!0,colorEditType:"plot"});KEe.exports={labels:{valType:"data_array",editType:"calc"},label0:{valType:"number",dflt:0,editType:"calc"},dlabel:{valType:"number",dflt:1,editType:"calc"},values:{valType:"data_array",editType:"calc"},marker:{colors:{valType:"data_array",editType:"calc"},line:{color:{valType:"color",dflt:yCt.defaultLine,arrayOk:!0,editType:"style"},width:{valType:"number",min:0,dflt:0,arrayOk:!0,editType:"style"},editType:"calc"},pattern:bCt,editType:"calc"},text:{valType:"data_array",editType:"plot"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"style"},scalegroup:{valType:"string",dflt:"",editType:"calc"},textinfo:{valType:"flaglist",flags:["label","text","value","percent"],extras:["none"],editType:"calc"},hoverinfo:S2({},vW.hoverinfo,{flags:["label","text","value","percent","name"]}),hovertemplate:_Ct({},{keys:["label","color","value","percent","text"]}),hovertemplatefallback:YEe(),texttemplate:xCt({editType:"plot"},{keys:["label","color","value","percent","text"]}),texttemplatefallback:YEe({editType:"plot"}),textposition:{valType:"enumerated",values:["inside","outside","auto","none"],dflt:"auto",arrayOk:!0,editType:"plot"},textfont:S2({},vD,{}),insidetextorientation:{valType:"enumerated",values:["horizontal","radial","tangential","auto"],dflt:"auto",editType:"plot"},insidetextfont:S2({},vD,{}),outsidetextfont:S2({},vD,{}),automargin:{valType:"boolean",dflt:!1,editType:"plot"},showlegend:S2({},vW.showlegend,{arrayOk:!0}),legend:S2({},vW.legend,{arrayOk:!0}),title:{text:{valType:"string",dflt:"",editType:"plot"},font:S2({},vD,{}),position:{valType:"enumerated",values:["top left","top center","top right","middle center","bottom left","bottom center","bottom right"],editType:"plot"},editType:"plot"},domain:gCt({name:"pie",trace:!0,editType:"calc"}),hole:{valType:"number",min:0,max:1,dflt:0,editType:"calc"},sort:{valType:"boolean",dflt:!0,editType:"calc"},direction:{valType:"enumerated",values:["clockwise","counterclockwise"],dflt:"counterclockwise",editType:"calc"},rotation:{valType:"angle",dflt:0,editType:"calc"},pull:{valType:"number",min:0,max:1,dflt:0,arrayOk:!0,editType:"calc"}}});var E2=ye((Hdr,QEe)=>{"use strict";var wCt=Eo(),kE=Dr(),TCt=M2(),ACt=Cc().defaults,SCt=r0().handleText,MCt=Dr().coercePattern;function JEe(e,t){var r=kE.isArrayOrTypedArray(e),n=kE.isArrayOrTypedArray(t),i=Math.min(r?e.length:1/0,n?t.length:1/0);if(isFinite(i)||(i=0),i&&n){for(var a,o=0;o<i;o++){var s=t[o];if(wCt(s)&&s>0){a=!0;break}}a||(i=0)}return{hasLabels:r,hasValues:n,len:i}}function $Ee(e,t,r,n,i){var a=n("marker.line.width");a&&n("marker.line.color",i?void 0:r.paper_bgcolor);var o=n("marker.colors");MCt(n,"marker.pattern",o),e.marker&&!t.marker.pattern.fgcolor&&(t.marker.pattern.fgcolor=e.marker.colors),t.marker.pattern.bgcolor||(t.marker.pattern.bgcolor=r.paper_bgcolor)}function ECt(e,t,r,n){function i(k,E){return kE.coerce(e,t,TCt,k,E)}var a=i("labels"),o=i("values"),s=JEe(a,o),l=s.len;if(t._hasLabels=s.hasLabels,t._hasValues=s.hasValues,!t._hasLabels&&t._hasValues&&(i("label0"),i("dlabel")),!l){t.visible=!1;return}t._length=l,$Ee(e,t,n,i,!0),i("scalegroup");var u=i("text"),c=i("texttemplate");i("texttemplatefallback");var f;if(c||(f=i("textinfo",kE.isArrayOrTypedArray(u)?"text+percent":"percent")),i("hovertext"),i("hovertemplate"),i("hovertemplatefallback"),c||f&&f!=="none"){var h=i("textposition");SCt(e,t,n,i,h,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1});var d=Array.isArray(h)||h==="auto",v=d||h==="outside";v&&i("automargin"),(h==="inside"||h==="auto"||Array.isArray(h))&&i("insidetextorientation")}else f==="none"&&i("textposition","none");ACt(t,n,i);var _=i("hole"),b=i("title.text");if(b){var p=i("title.position",_?"middle center":"top center");!_&&p==="middle center"&&(t.title.position="top center"),kE.coerceFont(i,"title.font",n.font)}i("sort"),i("direction"),i("rotation"),i("pull")}QEe.exports={handleLabelsAndValues:JEe,handleMarkerDefaults:$Ee,supplyDefaults:ECt}});var pD=ye((jdr,eke)=>{"use strict";eke.exports={hiddenlabels:{valType:"data_array",editType:"calc"},piecolorway:{valType:"colorlist",editType:"calc"},extendpiecolors:{valType:"boolean",dflt:!0,editType:"calc"}}});var rke=ye((Wdr,tke)=>{"use strict";var kCt=Dr(),CCt=pD();tke.exports=function(t,r){function n(i,a){return kCt.coerce(t,r,CCt,i,a)}n("hiddenlabels"),n("piecolorway",r.colorway),n("extendpiecolors")}});var TA=ye((Xdr,ake)=>{"use strict";var LCt=Eo(),pW=cd(),PCt=ka(),ICt={};function RCt(e,t){var r=[],n=e._fullLayout,i=n.hiddenlabels||[],a=t.labels,o=t.marker.colors||[],s=t.values,l=t._length,u=t._hasValues&&l,c,f;if(t.dlabel)for(a=new Array(l),c=0;c<l;c++)a[c]=String(t.label0+c*t.dlabel);var h={},d=ike(n["_"+t.type+"colormap"]),v=0,_=!1;for(c=0;c<l;c++){var b,p,k;if(u){if(b=s[c],!LCt(b))continue;b=+b}else b=1;p=a[c],(p===void 0||p==="")&&(p=c),p=String(p);var E=h[p];E===void 0?(h[p]=r.length,k=i.indexOf(p)!==-1,k||(v+=b),r.push({v:b,label:p,color:d(o[c],p),i:c,pts:[c],hidden:k})):(_=!0,f=r[E],f.v+=b,f.pts.push(c),f.hidden||(v+=b),f.color===!1&&o[c]&&(f.color=d(o[c],p)))}r=r.filter(function(L){return L.v>=0});var S=t.type==="funnelarea"?_:t.sort;return S&&r.sort(function(L,x){return x.v-L.v}),r[0]&&(r[0].vTotal=v),r}function ike(e){return function(r,n){return!r||(r=pW(r),!r.isValid())?!1:(r=PCt.addOpacity(r,r.getAlpha()),e[n]||(e[n]=r),r)}}function DCt(e,t){var r=(t||{}).type;r||(r="pie");var n=e._fullLayout,i=e.calcdata,a=n[r+"colorway"],o=n["_"+r+"colormap"];n["extend"+r+"colors"]&&(a=nke(a,ICt));for(var s=0,l=0;l<i.length;l++){var u=i[l],c=u[0].trace.type;if(c===r)for(var f=0;f<u.length;f++){var h=u[f];h.color===!1&&(o[h.label]?h.color=o[h.label]:(o[h.label]=h.color=a[s%a.length],s++))}}}function nke(e,t){var r,n=JSON.stringify(e),i=t[n];if(!i){for(i=e.slice(),r=0;r<e.length;r++)i.push(pW(e[r]).lighten(20).toHexString());for(r=0;r<e.length;r++)i.push(pW(e[r]).darken(20).toHexString());t[n]=i}return i}ake.exports={calc:RCt,crossTraceCalc:DCt,makePullColorFn:ike,generateExtendedColors:nke}});var ske=ye((Zdr,oke)=>{"use strict";var FCt=ip().appendArrayMultiPointValues;oke.exports=function(t,r){var n={curveNumber:r.index,pointNumbers:t.pts,data:r._input,fullData:r,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return t.pts.length===1&&(n.pointNumber=n.i=t.pts[0]),FCt(n,r,t.pts),r.type==="funnelarea"&&(delete n.v,delete n.i),n}});var _D=ye((Ydr,kke)=>{"use strict";var Fp=Oa(),zCt=Mc(),gD=vf(),dke=ka(),Wy=So(),rv=Dr(),OCt=rv.strScale,lke=rv.strTranslate,gW=ru(),vke=bv(),qCt=vke.recordMinTextSize,BCt=vke.clearMinTextSize,pke=e2().TEXTPAD,ns=l_(),mD=ske(),uke=Dr().isValidTextValue;function NCt(e,t){var r=e._context.staticPlot,n=e._fullLayout,i=n._size;BCt("pie",n),yke(t,e),Ske(t,i);var a=rv.makeTraceGroups(n._pielayer,t,"trace").each(function(o){var s=Fp.select(this),l=o[0],u=l.trace;YCt(o),s.attr("stroke-linejoin","round"),s.each(function(){var c=Fp.select(this).selectAll("g.slice").data(o);c.enter().append("g").classed("slice",!0),c.exit().remove();var f=[[[],[]],[[],[]]],h=!1;c.each(function(S,L){if(S.hidden){Fp.select(this).selectAll("path,g").remove();return}S.pointNumber=S.i,S.curveNumber=u.index,f[S.pxmid[1]<0?0:1][S.pxmid[0]<0?0:1].push(S);var x=l.cx,C=l.cy,M=Fp.select(this),g=M.selectAll("path.surface").data([S]);if(g.enter().append("path").classed("surface",!0).style({"pointer-events":r?"none":"all"}),M.call(gke,e,o),u.pull){var P=+ns.castOption(u.pull,S.pts)||0;P>0&&(x+=P*S.pxmid[0],C+=P*S.pxmid[1])}S.cxFinal=x,S.cyFinal=C;function T(N,H,te,oe){var _e=oe*(H[0]-N[0]),Ee=oe*(H[1]-N[1]);return"a"+oe*l.r+","+oe*l.r+" 0 "+S.largeArc+(te?" 1 ":" 0 ")+_e+","+Ee}var z=u.hole;if(S.v===l.vTotal){var O="M"+(x+S.px0[0])+","+(C+S.px0[1])+T(S.px0,S.pxmid,!0,1)+T(S.pxmid,S.px0,!0,1)+"Z";z?g.attr("d","M"+(x+z*S.px0[0])+","+(C+z*S.px0[1])+T(S.px0,S.pxmid,!1,z)+T(S.pxmid,S.px0,!1,z)+"Z"+O):g.attr("d",O)}else{var V=T(S.px0,S.px1,!0,1);if(z){var G=1-z;g.attr("d","M"+(x+z*S.px1[0])+","+(C+z*S.px1[1])+T(S.px1,S.px0,!1,z)+"l"+G*S.px0[0]+","+G*S.px0[1]+V+"Z")}else g.attr("d","M"+x+","+C+"l"+S.px0[0]+","+S.px0[1]+V+"Z")}Mke(e,S,l);var Z=ns.castOption(u.textposition,S.pts),j=M.selectAll("g.slicetext").data(S.text&&Z!=="none"?[0]:[]);j.enter().append("g").classed("slicetext",!0),j.exit().remove(),j.each(function(){var N=rv.ensureSingle(Fp.select(this),"text","",function(ie){ie.attr("data-notex",1)}),H=rv.ensureUniformFontSize(e,Z==="outside"?VCt(u,S,n.font):mke(u,S,n.font));N.text(S.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(Wy.font,H).call(gW.convertToTspans,e);var te=Wy.bBox(N.node()),oe;if(Z==="outside")oe=hke(te,S);else if(oe=_ke(te,S,l),Z==="auto"&&oe.scale<1){var _e=rv.ensureUniformFontSize(e,u.outsidetextfont);N.call(Wy.font,_e),te=Wy.bBox(N.node()),oe=hke(te,S)}var Ee=oe.textPosAngle,Ce=Ee===void 0?S.pxmid:yD(l.r,Ee);if(oe.targetX=x+Ce[0]*oe.rCenter+(oe.x||0),oe.targetY=C+Ce[1]*oe.rCenter+(oe.y||0),Eke(oe,te),oe.outside){var me=oe.targetY;S.yLabelMin=me-te.height/2,S.yLabelMid=me,S.yLabelMax=me+te.height/2,S.labelExtraX=0,S.labelExtraY=0,h=!0}oe.fontSize=H.size,qCt(u.type,oe,n),o[L].transform=oe,rv.setTransormAndDisplay(N,oe)})});var d=Fp.select(this).selectAll("g.titletext").data(u.title.text?[0]:[]);if(d.enter().append("g").classed("titletext",!0),d.exit().remove(),d.each(function(){var S=rv.ensureSingle(Fp.select(this),"text","",function(C){C.attr("data-notex",1)}),L=u.title.text;u._meta&&(L=rv.templateString(L,u._meta)),S.text(L).attr({class:"titletext",transform:"","text-anchor":"middle"}).call(Wy.font,u.title.font).call(gW.convertToTspans,e);var x;u.title.position==="middle center"?x=jCt(l):x=Tke(l,i),S.attr("transform",lke(x.x,x.y)+OCt(Math.min(1,x.scale))+lke(x.tx,x.ty))}),h&&XCt(f,u),UCt(c,u),h&&u.automargin){var v=Wy.bBox(s.node()),_=u.domain,b=i.w*(_.x[1]-_.x[0]),p=i.h*(_.y[1]-_.y[0]),k=(.5*b-l.r)/i.w,E=(.5*p-l.r)/i.h;zCt.autoMargin(e,"pie."+u.uid+".automargin",{xl:_.x[0]-k,xr:_.x[1]+k,yb:_.y[0]-E,yt:_.y[1]+E,l:Math.max(l.cx-l.r-v.left,0),r:Math.max(v.right-(l.cx+l.r),0),b:Math.max(v.bottom-(l.cy+l.r),0),t:Math.max(l.cy-l.r-v.top,0),pad:5})}})});setTimeout(function(){a.selectAll("tspan").each(function(){var o=Fp.select(this);o.attr("dy")&&o.attr("dy",o.attr("dy"))})},0)}function UCt(e,t){e.each(function(r){var n=Fp.select(this);if(!r.labelExtraX&&!r.labelExtraY){n.select("path.textline").remove();return}var i=n.select("g.slicetext text");r.transform.targetX+=r.labelExtraX,r.transform.targetY+=r.labelExtraY,rv.setTransormAndDisplay(i,r.transform);var a=r.cxFinal+r.pxmid[0],o=r.cyFinal+r.pxmid[1],s="M"+a+","+o,l=(r.yLabelMax-r.yLabelMin)*(r.pxmid[0]<0?-1:1)/4;if(r.labelExtraX){var u=r.labelExtraX*r.pxmid[1]/r.pxmid[0],c=r.yLabelMid+r.labelExtraY-(r.cyFinal+r.pxmid[1]);Math.abs(u)>Math.abs(c)?s+="l"+c*r.pxmid[0]/r.pxmid[1]+","+c+"H"+(a+r.labelExtraX+l):s+="l"+r.labelExtraX+","+u+"v"+(c-u)+"h"+l}else s+="V"+(r.yLabelMid+r.labelExtraY)+"h"+l;rv.ensureSingle(n,"path","textline").call(dke.stroke,t.outsidetextfont.color).attr({"stroke-width":Math.min(2,t.outsidetextfont.size/8),d:s,fill:"none"})})}function gke(e,t,r){var n=r[0],i=n.cx,a=n.cy,o=n.trace,s=o.type==="funnelarea";"_hasHoverLabel"in o||(o._hasHoverLabel=!1),"_hasHoverEvent"in o||(o._hasHoverEvent=!1),e.on("mouseover",function(l){var u=t._fullLayout,c=t._fullData[o.index];if(!(t._dragging||u.hovermode===!1)){var f=c.hoverinfo;if(Array.isArray(f)&&(f=gD.castHoverinfo({hoverinfo:[ns.castOption(f,l.pts)],_module:o._module},u,0)),f==="all"&&(f="label+text+value+percent+name"),c.hovertemplate||f!=="none"&&f!=="skip"&&f){var h=l.rInscribed||0,d=i+l.pxmid[0]*(1-h),v=a+l.pxmid[1]*(1-h),_=u.separators,b=[];if(f&&f.indexOf("label")!==-1&&b.push(l.label),l.text=ns.castOption(c.hovertext||c.text,l.pts),f&&f.indexOf("text")!==-1){var p=l.text;rv.isValidTextValue(p)&&b.push(p)}l.value=l.v,l.valueLabel=ns.formatPieValue(l.v,_),f&&f.indexOf("value")!==-1&&b.push(l.valueLabel),l.percent=l.v/n.vTotal,l.percentLabel=ns.formatPiePercent(l.percent,_),f&&f.indexOf("percent")!==-1&&b.push(l.percentLabel);var k=c.hoverlabel,E=k.font,S=[];gD.loneHover({trace:o,x0:d-h*n.r,x1:d+h*n.r,y:v,_x0:s?i+l.TL[0]:d-h*n.r,_x1:s?i+l.TR[0]:d+h*n.r,_y0:s?a+l.TL[1]:v-h*n.r,_y1:s?a+l.BL[1]:v+h*n.r,text:b.join("<br>"),name:c.hovertemplate||f.indexOf("name")!==-1?c.name:void 0,idealAlign:l.pxmid[0]<0?"left":"right",color:ns.castOption(k.bgcolor,l.pts)||l.color,borderColor:ns.castOption(k.bordercolor,l.pts),fontFamily:ns.castOption(E.family,l.pts),fontSize:ns.castOption(E.size,l.pts),fontColor:ns.castOption(E.color,l.pts),nameLength:ns.castOption(k.namelength,l.pts),textAlign:ns.castOption(k.align,l.pts),hovertemplate:ns.castOption(c.hovertemplate,l.pts),hovertemplateLabels:l,eventData:[mD(l,c)]},{container:u._hoverlayer.node(),outerContainer:u._paper.node(),gd:t,inOut_bbox:S}),l.bbox=S[0],o._hasHoverLabel=!0}o._hasHoverEvent=!0,t.emit("plotly_hover",{points:[mD(l,c)],event:Fp.event})}}),e.on("mouseout",function(l){var u=t._fullLayout,c=t._fullData[o.index],f=Fp.select(this).datum();o._hasHoverEvent&&(l.originalEvent=Fp.event,t.emit("plotly_unhover",{points:[mD(f,c)],event:Fp.event}),o._hasHoverEvent=!1),o._hasHoverLabel&&(gD.loneUnhover(u._hoverlayer.node()),o._hasHoverLabel=!1)}),e.on("click",function(l){var u=t._fullLayout,c=t._fullData[o.index];t._dragging||u.hovermode===!1||(t._hoverdata=[mD(l,c)],gD.click(t,Fp.event))})}function VCt(e,t,r){var n=ns.castOption(e.outsidetextfont.color,t.pts)||ns.castOption(e.textfont.color,t.pts)||r.color,i=ns.castOption(e.outsidetextfont.family,t.pts)||ns.castOption(e.textfont.family,t.pts)||r.family,a=ns.castOption(e.outsidetextfont.size,t.pts)||ns.castOption(e.textfont.size,t.pts)||r.size,o=ns.castOption(e.outsidetextfont.weight,t.pts)||ns.castOption(e.textfont.weight,t.pts)||r.weight,s=ns.castOption(e.outsidetextfont.style,t.pts)||ns.castOption(e.textfont.style,t.pts)||r.style,l=ns.castOption(e.outsidetextfont.variant,t.pts)||ns.castOption(e.textfont.variant,t.pts)||r.variant,u=ns.castOption(e.outsidetextfont.textcase,t.pts)||ns.castOption(e.textfont.textcase,t.pts)||r.textcase,c=ns.castOption(e.outsidetextfont.lineposition,t.pts)||ns.castOption(e.textfont.lineposition,t.pts)||r.lineposition,f=ns.castOption(e.outsidetextfont.shadow,t.pts)||ns.castOption(e.textfont.shadow,t.pts)||r.shadow;return{color:n,family:i,size:a,weight:o,style:s,variant:l,textcase:u,lineposition:c,shadow:f}}function mke(e,t,r){var n=ns.castOption(e.insidetextfont.color,t.pts);!n&&e._input.textfont&&(n=ns.castOption(e._input.textfont.color,t.pts));var i=ns.castOption(e.insidetextfont.family,t.pts)||ns.castOption(e.textfont.family,t.pts)||r.family,a=ns.castOption(e.insidetextfont.size,t.pts)||ns.castOption(e.textfont.size,t.pts)||r.size,o=ns.castOption(e.insidetextfont.weight,t.pts)||ns.castOption(e.textfont.weight,t.pts)||r.weight,s=ns.castOption(e.insidetextfont.style,t.pts)||ns.castOption(e.textfont.style,t.pts)||r.style,l=ns.castOption(e.insidetextfont.variant,t.pts)||ns.castOption(e.textfont.variant,t.pts)||r.variant,u=ns.castOption(e.insidetextfont.textcase,t.pts)||ns.castOption(e.textfont.textcase,t.pts)||r.textcase,c=ns.castOption(e.insidetextfont.lineposition,t.pts)||ns.castOption(e.textfont.lineposition,t.pts)||r.lineposition,f=ns.castOption(e.insidetextfont.shadow,t.pts)||ns.castOption(e.textfont.shadow,t.pts)||r.shadow;return{color:n||dke.contrast(t.color),family:i,size:a,weight:o,style:s,variant:l,textcase:u,lineposition:c,shadow:f}}function yke(e,t){for(var r,n,i=0;i<e.length;i++)if(r=e[i][0],n=r.trace,n.title.text){var a=n.title.text;n._meta&&(a=rv.templateString(a,n._meta));var o=Wy.tester.append("text").attr("data-notex",1).text(a).call(Wy.font,n.title.font).call(gW.convertToTspans,t),s=Wy.bBox(o.node(),!0);r.titleBox={width:s.width,height:s.height},o.remove()}}function _ke(e,t,r){var n=r.r||t.rpx1,i=t.rInscribed,a=t.startangle===t.stopangle;if(a)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var o=t.ring,s=o===1&&Math.abs(t.startangle-t.stopangle)===Math.PI*2,l=t.halfangle,u=t.midangle,c=r.trace.insidetextorientation,f=c==="horizontal",h=c==="tangential",d=c==="radial",v=c==="auto",_=[],b;if(!v){var p=function(M,g){if(GCt(t,M)){var P=Math.abs(M-t.startangle),T=Math.abs(M-t.stopangle),z=P<T?P:T;g==="tan"?b=fke(e,n,o,z,0):b=cke(e,n,o,z,Math.PI/2),b.textPosAngle=M,_.push(b)}},k;if(f||h){for(k=4;k>=-4;k-=2)p(Math.PI*k,"tan");for(k=4;k>=-4;k-=2)p(Math.PI*(k+1),"tan")}if(f||d){for(k=4;k>=-4;k-=2)p(Math.PI*(k+1.5),"rad");for(k=4;k>=-4;k-=2)p(Math.PI*(k+.5),"rad")}}if(s||v||f){var E=Math.sqrt(e.width*e.width+e.height*e.height);if(b={scale:i*n*2/E,rCenter:1-i,rotate:0},b.textPosAngle=(t.startangle+t.stopangle)/2,b.scale>=1)return b;_.push(b)}(v||d)&&(b=cke(e,n,o,l,u),b.textPosAngle=(t.startangle+t.stopangle)/2,_.push(b)),(v||h)&&(b=fke(e,n,o,l,u),b.textPosAngle=(t.startangle+t.stopangle)/2,_.push(b));for(var S=0,L=0,x=0;x<_.length;x++){var C=_[x].scale;if(L<C&&(L=C,S=x),!v&&L>=1)break}return _[S]}function GCt(e,t){var r=e.startangle,n=e.stopangle;return r>t&&t>n||r<t&&t<n}function cke(e,t,r,n,i){t=Math.max(0,t-2*pke);var a=e.width/e.height,o=wke(a,n,t,r);return{scale:o*2/e.height,rCenter:xke(a,o/t),rotate:bke(i)}}function fke(e,t,r,n,i){t=Math.max(0,t-2*pke);var a=e.height/e.width,o=wke(a,n,t,r);return{scale:o*2/e.width,rCenter:xke(a,o/t),rotate:bke(i+Math.PI/2)}}function xke(e,t){return Math.cos(t)-e*t}function bke(e){return(180/Math.PI*e+720)%180-90}function wke(e,t,r,n){var i=e+1/(2*Math.tan(t));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(e*e+n/2)+e))}function HCt(e,t){return e.v===t.vTotal&&!t.trace.hole?1:Math.min(1/(1+1/Math.sin(e.halfangle)),e.ring/2)}function hke(e,t){var r=t.pxmid[0],n=t.pxmid[1],i=e.width/2,a=e.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function jCt(e){var t=Math.sqrt(e.titleBox.width*e.titleBox.width+e.titleBox.height*e.titleBox.height);return{x:e.cx,y:e.cy,scale:e.trace.hole*e.r*2/t,tx:0,ty:-e.titleBox.height/2+e.trace.title.font.size}}function Tke(e,t){var r=1,n=1,i,a=e.trace,o={x:e.cx,y:e.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=Ake(a),a.title.position.indexOf("top")!==-1?(o.y-=(1+i)*e.r,s.ty-=e.titleBox.height):a.title.position.indexOf("bottom")!==-1&&(o.y+=(1+i)*e.r);var l=WCt(e.r,e.trace.aspectratio),u=t.w*(a.domain.x[1]-a.domain.x[0])/2;return a.title.position.indexOf("left")!==-1?(u=u+l,o.x-=(1+i)*l,s.tx+=e.titleBox.width/2):a.title.position.indexOf("center")!==-1?u*=2:a.title.position.indexOf("right")!==-1&&(u=u+l,o.x+=(1+i)*l,s.tx-=e.titleBox.width/2),r=u/e.titleBox.width,n=mW(e,t)/e.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function WCt(e,t){return e/(t===void 0?1:t)}function mW(e,t){var r=e.trace,n=t.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(e.titleBox.height,n/2)}function Ake(e){var t=e.pull;if(!t)return 0;var r;if(rv.isArrayOrTypedArray(t))for(t=0,r=0;r<e.pull.length;r++)e.pull[r]>t&&(t=e.pull[r]);return t}function XCt(e,t){var r,n,i,a,o,s,l,u,c,f,h,d,v;function _(E,S){return E.pxmid[1]-S.pxmid[1]}function b(E,S){return S.pxmid[1]-E.pxmid[1]}function p(E,S){S||(S={});var L=S.labelExtraY+(n?S.yLabelMax:S.yLabelMin),x=n?E.yLabelMin:E.yLabelMax,C=n?E.yLabelMax:E.yLabelMin,M=E.cyFinal+o(E.px0[1],E.px1[1]),g=L-x,P,T,z,O,V,G;if(g*l>0&&(E.labelExtraY=g),!!rv.isArrayOrTypedArray(t.pull))for(T=0;T<f.length;T++)z=f[T],!(z===E||(ns.castOption(t.pull,E.pts)||0)>=(ns.castOption(t.pull,z.pts)||0))&&((E.pxmid[1]-z.pxmid[1])*l>0?(O=z.cyFinal+o(z.px0[1],z.px1[1]),g=O-x-E.labelExtraY,g*l>0&&(E.labelExtraY+=g)):(C+E.labelExtraY-M)*l>0&&(P=3*s*Math.abs(T-f.indexOf(E)),V=z.cxFinal+a(z.px0[0],z.px1[0]),G=V+P-(E.cxFinal+E.pxmid[0])-E.labelExtraX,G*s>0&&(E.labelExtraX+=G)))}for(n=0;n<2;n++)for(i=n?_:b,o=n?Math.max:Math.min,l=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,u=e[n][r],u.sort(i),c=e[1-n][r],f=c.concat(u),d=[],h=0;h<u.length;h++)u[h].yLabelMid!==void 0&&d.push(u[h]);for(v=!1,h=0;n&&h<c.length;h++)if(c[h].yLabelMid!==void 0){v=c[h];break}for(h=0;h<d.length;h++){var k=h&&d[h-1];v&&!h&&(k=v),p(d[h],k)}}}function Ske(e,t){for(var r=[],n=0;n<e.length;n++){var i=e[n][0],a=i.trace,o=a.domain,s=t.w*(o.x[1]-o.x[0]),l=t.h*(o.y[1]-o.y[0]);a.title.text&&a.title.position!=="middle center"&&(l-=mW(i,t));var u=s/2,c=l/2;a.type==="funnelarea"&&!a.scalegroup&&(c/=a.aspectratio),i.r=Math.min(u,c)/(1+Ake(a)),i.cx=t.l+t.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=t.t+t.h*(1-a.domain.y[0])-l/2,a.title.text&&a.title.position.indexOf("bottom")!==-1&&(i.cy-=mW(i,t)),a.scalegroup&&r.indexOf(a.scalegroup)===-1&&r.push(a.scalegroup)}ZCt(e,r)}function ZCt(e,t){for(var r,n,i,a=0;a<t.length;a++){var o=1/0,s=t[a];for(n=0;n<e.length;n++)if(r=e[n][0],i=r.trace,i.scalegroup===s){var l;if(i.type==="pie")l=r.r*r.r;else if(i.type==="funnelarea"){var u,c;i.aspectratio>1?(u=r.r,c=u/i.aspectratio):(c=r.r,u=c*i.aspectratio),u*=(1+i.baseratio)/2,l=u*c}o=Math.min(o,l/r.vTotal)}for(n=0;n<e.length;n++)if(r=e[n][0],i=r.trace,i.scalegroup===s){var f=o*r.vTotal;i.type==="funnelarea"&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}function YCt(e){var t=e[0],r=t.r,n=t.trace,i=ns.getRotationAngle(n.rotation),a=2*Math.PI/t.vTotal,o="px0",s="px1",l,u,c;if(n.direction==="counterclockwise"){for(l=0;l<e.length&&e[l].hidden;l++);if(l===e.length)return;i+=a*e[l].v,a*=-1,o="px1",s="px0"}for(c=yD(r,i),l=0;l<e.length;l++)u=e[l],!u.hidden&&(u[o]=c,u.startangle=i,i+=a*u.v/2,u.pxmid=yD(r,i),u.midangle=i,i+=a*u.v/2,c=yD(r,i),u.stopangle=i,u[s]=c,u.largeArc=u.v>t.vTotal/2?1:0,u.halfangle=Math.PI*Math.min(u.v/t.vTotal,.5),u.ring=1-n.hole,u.rInscribed=HCt(u,t))}function yD(e,t){return[e*Math.sin(t),-e*Math.cos(t)]}function Mke(e,t,r){var n=e._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&o!=="none"){var s=o.split("+"),l=function(S){return s.indexOf(S)!==-1},u=l("label"),c=l("text"),f=l("value"),h=l("percent"),d=n.separators,v;if(v=u?[t.label]:[],c){var _=ns.getFirstFilled(i.text,t.pts);uke(_)&&v.push(_)}f&&v.push(ns.formatPieValue(t.v,d)),h&&v.push(ns.formatPiePercent(t.v/r.vTotal,d)),t.text=v.join("<br>")}function b(S){return{label:S.label,value:S.v,valueLabel:ns.formatPieValue(S.v,n.separators),percent:S.v/r.vTotal,percentLabel:ns.formatPiePercent(S.v/r.vTotal,n.separators),color:S.color,text:S.text,customdata:rv.castOption(i,S.i,"customdata")}}if(a){var p=rv.castOption(i,t.i,"texttemplate");if(!p)t.text="";else{var k=b(t),E=ns.getFirstFilled(i.text,t.pts);(uke(E)||E==="")&&(k.text=E),t.text=rv.texttemplateString({data:[k,i._meta],fallback:i.texttemplatefallback,labels:k,locale:e._fullLayout._d3locale,template:p})}}}function Eke(e,t){var r=e.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(t.left+t.right)/2,o=(t.top+t.bottom)/2;e.textX=a*n-o*i,e.textY=a*i+o*n,e.noCenter=!0}kke.exports={plot:NCt,formatSliceLabel:Mke,transformInsideText:_ke,determineInsideTextFont:mke,positionTitleOutside:Tke,prerenderTitles:yke,layoutAreas:Ske,attachFxHandlers:gke,computeTransform:Eke}});var Pke=ye((Kdr,Lke)=>{"use strict";var Cke=Oa(),KCt=B3(),JCt=bv().resizeText;Lke.exports=function(t){var 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s=i.separators,l=n[0],u=e.data.data,c=l.hierarchy,f=Rl.isHierarchyRoot(e),h=Rl.getParent(c,e),d=Rl.getValue(e);if(!a){var v=o.split("+"),_=function(g){return v.indexOf(g)!==-1},b=[],p;if(_("label")&&u.label&&b.push(u.label),u.hasOwnProperty("v")&&_("value")&&b.push(Rl.formatValue(u.v,s)),!f){_("current path")&&b.push(Rl.getPath(e.data));var k=0;_("percent parent")&&k++,_("percent entry")&&k++,_("percent root")&&k++;var E=k>1;if(k){var S,L=function(g){p=Rl.formatPercent(S,s),E&&(p+=" of "+g),b.push(p)};_("percent parent")&&!f&&(S=d/Rl.getValue(h),L("parent")),_("percent entry")&&(S=d/Rl.getValue(t),L("entry")),_("percent root")&&(S=d/Rl.getValue(c),L("root"))}}return _("text")&&(p=Av.castOption(r,u.i,"text"),Av.isValidTextValue(p)&&b.push(p)),b.join("<br>")}var x=Av.castOption(r,u.i,"texttemplate");if(!x)return"";var C={};u.label&&(C.label=u.label),u.hasOwnProperty("v")&&(C.value=u.v,C.valueLabel=Rl.formatValue(u.v,s)),C.currentPath=Rl.getPath(e.data),f||(C.percentParent=d/Rl.getValue(h),C.percentParentLabel=Rl.formatPercent(C.percentParent,s),C.parent=Rl.getPtLabel(h)),C.percentEntry=d/Rl.getValue(t),C.percentEntryLabel=Rl.formatPercent(C.percentEntry,s),C.entry=Rl.getPtLabel(t),C.percentRoot=d/Rl.getValue(c),C.percentRootLabel=Rl.formatPercent(C.percentRoot,s),C.root=Rl.getPtLabel(c),u.hasOwnProperty("color")&&(C.color=u.color);var M=Av.castOption(r,u.i,"text");return(Av.isValidTextValue(M)||M==="")&&(C.text=M),C.customdata=Av.castOption(r,u.i,"customdata"),Av.texttemplateString({data:[C,r._meta],fallback:r.texttemplatefallback,labels:C,locale:i._d3locale,template:x})};function uLt(e){return e.rpx0===0&&Av.isFullCircle([e.x0,e.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(e.halfangle)),e.ring/2))}function w6e(e){return cLt(e.rpx1,e.transform.textPosAngle)}function 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c=a("text");a("texttemplate"),a("texttemplatefallback"),r.texttemplate||a("textinfo",z6e.isArrayOrTypedArray(c)?"text+label":"label"),a("hovertext"),a("hovertemplate"),a("hovertemplatefallback");var f=a("pathbar.visible"),h="auto";_Lt(t,r,i,a,h,{hasPathbar:f,moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),a("textposition");var d=r.textposition.indexOf("bottom")!==-1;bLt(t,r,i,a);var v=r._hasColorscale=wLt(t,"marker","colors")||(t.marker||{}).coloraxis;v?TLt(t,r,i,a,{prefix:"marker.",cLetter:"c"}):a("marker.depthfade",!(r.marker.colors||[]).length);var _=r.textfont.size*2;a("marker.pad.t",d?_/4:_),a("marker.pad.l",_/4),a("marker.pad.r",_/4),a("marker.pad.b",d?_:_/4),a("marker.cornerradius"),r._hovered={marker:{line:{width:2,color:mLt.contrast(i.paper_bgcolor)}}},f&&(a("pathbar.thickness",r.pathbar.textfont.size+2*xLt),a("pathbar.side"),a("pathbar.edgeshape")),a("sort"),a("root.color"),yLt(r,i,a),r._length=null}});var U6e=ye((s0r,N6e)=>{"use strict";var ALt=Dr(),SLt=jW();N6e.exports=function(t,r){function n(i,a){return ALt.coerce(t,r,SLt,i,a)}n("treemapcolorway",r.colorway),n("extendtreemapcolors")}});var XW=ye(WW=>{"use strict";var V6e=RE();WW.calc=function(e,t){return V6e.calc(e,t)};WW.crossTraceCalc=function(e){return V6e._runCrossTraceCalc("treemap",e)}});var ZW=ye((u0r,G6e)=>{"use strict";G6e.exports=function e(t,r,n){var i;n.swapXY&&(i=t.x0,t.x0=t.y0,t.y0=i,i=t.x1,t.x1=t.y1,t.y1=i),n.flipX&&(i=t.x0,t.x0=r[0]-t.x1,t.x1=r[0]-i),n.flipY&&(i=t.y0,t.y0=r[1]-t.y1,t.y1=r[1]-i);var a=t.children;if(a)for(var o=0;o<a.length;o++)e(a[o],r,n)}});var YW=ye((c0r,H6e)=>{"use strict";var zA=PE(),MLt=ZW();H6e.exports=function(t,r,n){var i=n.flipX,a=n.flipY,o=n.packing==="dice-slice",s=n.pad[a?"bottom":"top"],l=n.pad[i?"right":"left"],u=n.pad[i?"left":"right"],c=n.pad[a?"top":"bottom"],f;o&&(f=l,l=s,s=f,f=u,u=c,c=f);var h=zA.treemap().tile(ELt(n.packing,n.squarifyratio)).paddingInner(n.pad.inner).paddingLeft(l).paddingRight(u).paddingTop(s).paddingBottom(c).size(o?[r[1],r[0]]:r)(t);return(o||i||a)&&MLt(h,r,{swapXY:o,flipX:i,flipY:a}),h};function ELt(e,t){switch(e){case"squarify":return zA.treemapSquarify.ratio(t);case"binary":return zA.treemapBinary;case"dice":return zA.treemapDice;case"slice":return zA.treemapSlice;default:return zA.treemapSliceDice}}});var HD=ye((f0r,Z6e)=>{"use strict";var j6e=Oa(),OA=ka(),W6e=Dr(),KW=Ky(),kLt=bv().resizeText,CLt=ND();function LLt(e){var t=e._fullLayout._treemaplayer.selectAll(".trace");kLt(e,t,"treemap"),t.each(function(r){var n=j6e.select(this),i=r[0],a=i.trace;n.style("opacity",a.opacity),n.selectAll("path.surface").each(function(o){j6e.select(this).call(X6e,o,a,e,{hovered:!1})})})}function X6e(e,t,r,n,i){var a=(i||{}).hovered,o=t.data.data,s=o.i,l,u,c=o.color,f=KW.isHierarchyRoot(t),h=1;if(a)l=r._hovered.marker.line.color,u=r._hovered.marker.line.width;else if(f&&c===r.root.color)h=100,l="rgba(0,0,0,0)",u=0;else if(l=W6e.castOption(r,s,"marker.line.color")||OA.defaultLine,u=W6e.castOption(r,s,"marker.line.width")||0,!r._hasColorscale&&!t.onPathbar){var d=r.marker.depthfade;if(d){var v=OA.combine(OA.addOpacity(r._backgroundColor,.75),c),_;if(d===!0){var b=KW.getMaxDepth(r);isFinite(b)?KW.isLeaf(t)?_=0:_=r._maxVisibleLayers-(t.data.depth-r._entryDepth):_=t.data.height+1}else _=t.data.depth-r._entryDepth,r._atRootLevel||_++;if(_>0)for(var p=0;p<_;p++){var k=.5*p/_;c=OA.combine(OA.addOpacity(v,k),c)}}}e.call(CLt,t,r,n,c).style("stroke-width",u).call(OA.stroke,l).style("opacity",h)}Z6e.exports={style:LLt,styleOne:X6e}});var Q6e=ye((h0r,$6e)=>{"use strict";var Y6e=Oa(),jD=Dr(),K6e=So(),PLt=ru(),ILt=YW(),J6e=HD().styleOne,JW=O2(),qA=Ky(),RLt=HE(),$W=!0;$6e.exports=function(t,r,n,i,a){var o=a.barDifY,s=a.width,l=a.height,u=a.viewX,c=a.viewY,f=a.pathSlice,h=a.toMoveInsideSlice,d=a.strTransform,v=a.hasTransition,_=a.handleSlicesExit,b=a.makeUpdateSliceInterpolator,p=a.makeUpdateTextInterpolator,k={},E=t._context.staticPlot,S=t._fullLayout,L=r[0],x=L.trace,C=L.hierarchy,M=s/x._entryDepth,g=qA.listPath(n.data,"id"),P=ILt(C.copy(),[s,l],{packing:"dice",pad:{inner:0,top:0,left:0,right:0,bottom:0}}).descendants();P=P.filter(function(z){var O=g.indexOf(z.data.id);return O===-1?!1:(z.x0=M*O,z.x1=M*(O+1),z.y0=o,z.y1=o+l,z.onPathbar=!0,!0)}),P.reverse(),i=i.data(P,qA.getPtId),i.enter().append("g").classed("pathbar",!0),_(i,$W,k,[s,l],f),i.order();var T=i;v&&(T=T.transition().each("end",function(){var z=Y6e.select(this);qA.setSliceCursor(z,t,{hideOnRoot:!1,hideOnLeaves:!1,isTransitioning:!1})})),T.each(function(z){z._x0=u(z.x0),z._x1=u(z.x1),z._y0=c(z.y0),z._y1=c(z.y1),z._hoverX=u(z.x1-Math.min(s,l)/2),z._hoverY=c(z.y1-l/2);var O=Y6e.select(this),V=jD.ensureSingle(O,"path","surface",function(N){N.style("pointer-events",E?"none":"all")});v?V.transition().attrTween("d",function(N){var H=b(N,$W,k,[s,l]);return function(te){return f(H(te))}}):V.attr("d",f),O.call(RLt,n,t,r,{styleOne:J6e,eventDataKeys:JW.eventDataKeys,transitionTime:JW.CLICK_TRANSITION_TIME,transitionEasing:JW.CLICK_TRANSITION_EASING}).call(qA.setSliceCursor,t,{hideOnRoot:!1,hideOnLeaves:!1,isTransitioning:t._transitioning}),V.call(J6e,z,x,t,{hovered:!1}),z._text=(qA.getPtLabel(z)||"").split("<br>").join(" ")||"";var G=jD.ensureSingle(O,"g","slicetext"),Z=jD.ensureSingle(G,"text","",function(N){N.attr("data-notex",1)}),j=jD.ensureUniformFontSize(t,qA.determineTextFont(x,z,S.font,{onPathbar:!0}));Z.text(z._text||" ").classed("slicetext",!0).attr("text-anchor","start").call(K6e.font,j).call(PLt.convertToTspans,t),z.textBB=K6e.bBox(Z.node()),z.transform=h(z,{fontSize:j.size,onPathbar:!0}),z.transform.fontSize=j.size,v?Z.transition().attrTween("transform",function(N){var H=p(N,$W,k,[s,l]);return function(te){return d(H(te))}}):Z.attr("transform",d(z))})}});var iLe=ye((d0r,rLe)=>{"use strict";var eLe=Oa(),QW=(F2(),ob(D2)).interpolate,Z_=Ky(),WE=Dr(),tLe=e2().TEXTPAD,DLt=n2(),FLt=DLt.toMoveInsideBar,zLt=bv(),eX=zLt.recordMinTextSize,OLt=O2(),qLt=Q6e();function B2(e){return Z_.isHierarchyRoot(e)?"":Z_.getPtId(e)}rLe.exports=function(t,r,n,i,a){var 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u(c).value}}}},614:function(i,a,o){var s=o(3236),l=s([`precision highp float;
+
+precision highp float;
+#define GLSLIFY 1
+
+vec3 getOrthogonalVector(vec3 v) {
+  // Return up-vector for only-z vector.
+  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).
+  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.
+  // Assign z = 0, x = -b, y = a:
+  // a*-b + b*a + c*0 = -ba + ba + 0 = 0
+  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {
+    return normalize(vec3(-v.y, v.x, 0.0));
+  } else {
+    return normalize(vec3(0.0, v.z, -v.y));
+  }
+}
+
+// Calculate the cone vertex and normal at the given index.
+//
+// The returned vertex is for a cone with its top at origin and height of 1.0,
+// pointing in the direction of the vector attribute.
+//
+// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.
+// These vertices are used to make up the triangles of the cone by the following:
+//   segment + 0 top vertex
+//   segment + 1 perimeter vertex a+1
+//   segment + 2 perimeter vertex a
+//   segment + 3 center base vertex
+//   segment + 4 perimeter vertex a
+//   segment + 5 perimeter vertex a+1
+// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.
+// To go from index to segment, floor(index / 6)
+// To go from segment to angle, 2*pi * (segment/segmentCount)
+// To go from index to segment index, index - (segment*6)
+//
+vec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {
+
+  const float segmentCount = 8.0;
+
+  float index = rawIndex - floor(rawIndex /
+    (segmentCount * 6.0)) *
+    (segmentCount * 6.0);
+
+  float segment = floor(0.001 + index/6.0);
+  float segmentIndex = index - (segment*6.0);
+
+  normal = -normalize(d);
+
+  if (segmentIndex > 2.99 && segmentIndex < 3.01) {
+    return mix(vec3(0.0), -d, coneOffset);
+  }
+
+  float nextAngle = (
+    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||
+    (segmentIndex > 4.99 &&  segmentIndex < 5.01)
+  ) ? 1.0 : 0.0;
+  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);
+
+  vec3 v1 = mix(d, vec3(0.0), coneOffset);
+  vec3 v2 = v1 - d;
+
+  vec3 u = getOrthogonalVector(d);
+  vec3 v = normalize(cross(u, d));
+
+  vec3 x = u * cos(angle) * length(d)*0.25;
+  vec3 y = v * sin(angle) * length(d)*0.25;
+  vec3 v3 = v2 + x + y;
+  if (segmentIndex < 3.0) {
+    vec3 tx = u * sin(angle);
+    vec3 ty = v * -cos(angle);
+    vec3 tangent = tx + ty;
+    normal = normalize(cross(v3 - v1, tangent));
+  }
+
+  if (segmentIndex == 0.0) {
+    return mix(d, vec3(0.0), coneOffset);
+  }
+  return v3;
+}
+
+attribute vec3 vector;
+attribute vec4 color, position;
+attribute vec2 uv;
+
+uniform float vectorScale, coneScale, coneOffset;
+uniform mat4 model, view, projection, inverseModel;
+uniform vec3 eyePosition, lightPosition;
+
+varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;
+varying vec4 f_color;
+varying vec2 f_uv;
+
+void main() {
+  // Scale the vector magnitude to stay constant with
+  // model & view changes.
+  vec3 normal;
+  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);
+  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);
+
+  //Lighting geometry parameters
+  vec4 cameraCoordinate = view * conePosition;
+  cameraCoordinate.xyz /= cameraCoordinate.w;
+  f_lightDirection = lightPosition - cameraCoordinate.xyz;
+  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;
+  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);
+
+  // vec4 m_position  = model * vec4(conePosition, 1.0);
+  vec4 t_position  = view * conePosition;
+  gl_Position      = projection * t_position;
+
+  f_color          = color;
+  f_data           = conePosition.xyz;
+  f_position       = position.xyz;
+  f_uv             = uv;
+}
+`]),u=s([`#extension GL_OES_standard_derivatives : enable
+
+precision highp float;
+#define GLSLIFY 1
+
+float beckmannDistribution(float x, float roughness) {
+  float NdotH = max(x, 0.0001);
+  float cos2Alpha = NdotH * NdotH;
+  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;
+  float roughness2 = roughness * roughness;
+  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;
+  return exp(tan2Alpha / roughness2) / denom;
+}
+
+float cookTorranceSpecular(
+  vec3 lightDirection,
+  vec3 viewDirection,
+  vec3 surfaceNormal,
+  float roughness,
+  float fresnel) {
+
+  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);
+  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);
+
+  //Half angle vector
+  vec3 H = normalize(lightDirection + viewDirection);
+
+  //Geometric term
+  float NdotH = max(dot(surfaceNormal, H), 0.0);
+  float VdotH = max(dot(viewDirection, H), 0.000001);
+  float LdotH = max(dot(lightDirection, H), 0.000001);
+  float G1 = (2.0 * NdotH * VdotN) / VdotH;
+  float G2 = (2.0 * NdotH * LdotN) / LdotH;
+  float G = min(1.0, min(G1, G2));
+  
+  //Distribution term
+  float D = beckmannDistribution(NdotH, roughness);
+
+  //Fresnel term
+  float F = pow(1.0 - VdotN, fresnel);
+
+  //Multiply terms and done
+  return  G * F * D / max(3.14159265 * VdotN, 0.000001);
+}
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform vec3 clipBounds[2];
+uniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;
+uniform sampler2D texture;
+
+varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;
+varying vec4 f_color;
+varying vec2 f_uv;
+
+void main() {
+  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;
+  vec3 N = normalize(f_normal);
+  vec3 L = normalize(f_lightDirection);
+  vec3 V = normalize(f_eyeDirection);
+
+  if(gl_FrontFacing) {
+    N = -N;
+  }
+
+  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));
+  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);
+
+  vec4 surfaceColor = f_color * texture2D(texture, f_uv);
+  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);
+
+  gl_FragColor = litColor * opacity;
+}
+`]),c=s([`precision highp float;
+
+precision highp float;
+#define GLSLIFY 1
+
+vec3 getOrthogonalVector(vec3 v) {
+  // Return up-vector for only-z vector.
+  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).
+  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.
+  // Assign z = 0, x = -b, y = a:
+  // a*-b + b*a + c*0 = -ba + ba + 0 = 0
+  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {
+    return normalize(vec3(-v.y, v.x, 0.0));
+  } else {
+    return normalize(vec3(0.0, v.z, -v.y));
+  }
+}
+
+// Calculate the cone vertex and normal at the given index.
+//
+// The returned vertex is for a cone with its top at origin and height of 1.0,
+// pointing in the direction of the vector attribute.
+//
+// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.
+// These vertices are used to make up the triangles of the cone by the following:
+//   segment + 0 top vertex
+//   segment + 1 perimeter vertex a+1
+//   segment + 2 perimeter vertex a
+//   segment + 3 center base vertex
+//   segment + 4 perimeter vertex a
+//   segment + 5 perimeter vertex a+1
+// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.
+// To go from index to segment, floor(index / 6)
+// To go from segment to angle, 2*pi * (segment/segmentCount)
+// To go from index to segment index, index - (segment*6)
+//
+vec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {
+
+  const float segmentCount = 8.0;
+
+  float index = rawIndex - floor(rawIndex /
+    (segmentCount * 6.0)) *
+    (segmentCount * 6.0);
+
+  float segment = floor(0.001 + index/6.0);
+  float segmentIndex = index - (segment*6.0);
+
+  normal = -normalize(d);
+
+  if (segmentIndex > 2.99 && segmentIndex < 3.01) {
+    return mix(vec3(0.0), -d, coneOffset);
+  }
+
+  float nextAngle = (
+    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||
+    (segmentIndex > 4.99 &&  segmentIndex < 5.01)
+  ) ? 1.0 : 0.0;
+  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);
+
+  vec3 v1 = mix(d, vec3(0.0), coneOffset);
+  vec3 v2 = v1 - d;
+
+  vec3 u = getOrthogonalVector(d);
+  vec3 v = normalize(cross(u, d));
+
+  vec3 x = u * cos(angle) * length(d)*0.25;
+  vec3 y = v * sin(angle) * length(d)*0.25;
+  vec3 v3 = v2 + x + y;
+  if (segmentIndex < 3.0) {
+    vec3 tx = u * sin(angle);
+    vec3 ty = v * -cos(angle);
+    vec3 tangent = tx + ty;
+    normal = normalize(cross(v3 - v1, tangent));
+  }
+
+  if (segmentIndex == 0.0) {
+    return mix(d, vec3(0.0), coneOffset);
+  }
+  return v3;
+}
+
+attribute vec4 vector;
+attribute vec4 position;
+attribute vec4 id;
+
+uniform mat4 model, view, projection;
+uniform float vectorScale, coneScale, coneOffset;
+
+varying vec3 f_position;
+varying vec4 f_id;
+
+void main() {
+  vec3 normal;
+  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);
+  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);
+  gl_Position = projection * (view * conePosition);
+  f_id        = id;
+  f_position  = position.xyz;
+}
+`]),f=s([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform vec3  clipBounds[2];
+uniform float pickId;
+
+varying vec3 f_position;
+varying vec4 f_id;
+
+void main() {
+  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;
+
+  gl_FragColor = vec4(pickId, f_id.xyz);
+}`]);a.meshShader={vertex:l,fragment:u,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},a.pickShader={vertex:c,fragment:f,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},620:function(i){i.exports=["precision","highp","mediump","lowp","attribute","const","uniform","varying","break","continue","do","for","while","if","else","in","out","inout","float","int","uint","void","bool","true","false","discard","return","mat2","mat3","mat4","vec2","vec3","vec4","ivec2","ivec3","ivec4","bvec2","bvec3","bvec4","sampler1D","sampler2D","sampler3D","samplerCube","sampler1DShadow","sampler2DShadow","struct","asm","class","union","enum","typedef","template","this","packed","goto","switch","default","inline","noinline","volatile","public","static","extern","external","interface","long","short","double","half","fixed","unsigned","input","output","hvec2","hvec3","hvec4","dvec2","dvec3","dvec4","fvec2","fvec3","fvec4","sampler2DRect","sampler3DRect","sampler2DRectShadow","sizeof","cast","namespace","using"]},665:function(i,a,o){"use 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+#define GLSLIFY 1
+attribute vec2 position;
+varying vec2 uv;
+void main() {
+  uv = position;
+  gl_Position = vec4(position, 0, 1);
+}`]),c=s([`precision mediump float;
+#define GLSLIFY 1
+
+uniform sampler2D accumBuffer;
+varying vec2 uv;
+
+void main() {
+  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));
+  gl_FragColor = min(vec4(1,1,1,1), accum);
+}`]);i.exports=function(f){return l(f,u,c,null,[{name:"position",type:"vec2"}])}},811:function(i){i.exports=a;function a(o,s){return o[0]=1/s[0],o[1]=1/s[1],o[2]=1/s[2],o}},840:function(i,a,o){var s=o(3236),l=s([`precision highp float;
+#define GLSLIFY 1
+
+attribute vec3 position, normal;
+attribute vec4 color;
+attribute vec2 uv;
+
+uniform mat4 model
+           , view
+           , projection
+           , inverseModel;
+uniform vec3 eyePosition
+           , lightPosition;
+
+varying vec3 f_normal
+           , f_lightDirection
+           , f_eyeDirection
+           , f_data;
+varying vec4 f_color;
+varying vec2 f_uv;
+
+vec4 project(vec3 p) {
+  return projection * (view * (model * vec4(p, 1.0)));
+}
+
+void main() {
+  gl_Position      = project(position);
+
+  //Lighting geometry parameters
+  vec4 cameraCoordinate = view * vec4(position , 1.0);
+  cameraCoordinate.xyz /= cameraCoordinate.w;
+  f_lightDirection = lightPosition - cameraCoordinate.xyz;
+  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;
+  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);
+
+  f_color          = color;
+  f_data           = position;
+  f_uv             = uv;
+}
+`]),u=s([`#extension GL_OES_standard_derivatives : enable
+
+precision highp float;
+#define GLSLIFY 1
+
+float beckmannDistribution(float x, float roughness) {
+  float NdotH = max(x, 0.0001);
+  float cos2Alpha = NdotH * NdotH;
+  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;
+  float roughness2 = roughness * roughness;
+  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;
+  return exp(tan2Alpha / roughness2) / denom;
+}
+
+float cookTorranceSpecular(
+  vec3 lightDirection,
+  vec3 viewDirection,
+  vec3 surfaceNormal,
+  float roughness,
+  float fresnel) {
+
+  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);
+  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);
+
+  //Half angle vector
+  vec3 H = normalize(lightDirection + viewDirection);
+
+  //Geometric term
+  float NdotH = max(dot(surfaceNormal, H), 0.0);
+  float VdotH = max(dot(viewDirection, H), 0.000001);
+  float LdotH = max(dot(lightDirection, H), 0.000001);
+  float G1 = (2.0 * NdotH * VdotN) / VdotH;
+  float G2 = (2.0 * NdotH * LdotN) / LdotH;
+  float G = min(1.0, min(G1, G2));
+  
+  //Distribution term
+  float D = beckmannDistribution(NdotH, roughness);
+
+  //Fresnel term
+  float F = pow(1.0 - VdotN, fresnel);
+
+  //Multiply terms and done
+  return  G * F * D / max(3.14159265 * VdotN, 0.000001);
+}
+
+//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform vec3 clipBounds[2];
+uniform float roughness
+            , fresnel
+            , kambient
+            , kdiffuse
+            , kspecular;
+uniform sampler2D texture;
+
+varying vec3 f_normal
+           , f_lightDirection
+           , f_eyeDirection
+           , f_data;
+varying vec4 f_color;
+varying vec2 f_uv;
+
+void main() {
+  if (f_color.a == 0.0 ||
+    outOfRange(clipBounds[0], clipBounds[1], f_data)
+  ) discard;
+
+  vec3 N = normalize(f_normal);
+  vec3 L = normalize(f_lightDirection);
+  vec3 V = normalize(f_eyeDirection);
+
+  if(gl_FrontFacing) {
+    N = -N;
+  }
+
+  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));
+  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d
+
+  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);
+
+  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);
+  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);
+
+  gl_FragColor = litColor * f_color.a;
+}
+`]),c=s([`precision highp float;
+#define GLSLIFY 1
+
+attribute vec3 position;
+attribute vec4 color;
+attribute vec2 uv;
+
+uniform mat4 model, view, projection;
+
+varying vec4 f_color;
+varying vec3 f_data;
+varying vec2 f_uv;
+
+void main() {
+  gl_Position = projection * (view * (model * vec4(position, 1.0)));
+  f_color = color;
+  f_data  = position;
+  f_uv    = uv;
+}`]),f=s([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform vec3 clipBounds[2];
+uniform sampler2D texture;
+uniform float opacity;
+
+varying vec4 f_color;
+varying vec3 f_data;
+varying vec2 f_uv;
+
+void main() {
+  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;
+
+  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;
+}`]),h=s([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+attribute vec3 position;
+attribute vec4 color;
+attribute vec2 uv;
+attribute float pointSize;
+
+uniform mat4 model, view, projection;
+uniform vec3 clipBounds[2];
+
+varying vec4 f_color;
+varying vec2 f_uv;
+
+void main() {
+  if (outOfRange(clipBounds[0], clipBounds[1], position)) {
+
+    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);
+  } else {
+    gl_Position = projection * (view * (model * vec4(position, 1.0)));
+  }
+  gl_PointSize = pointSize;
+  f_color = color;
+  f_uv = uv;
+}`]),d=s([`precision highp float;
+#define GLSLIFY 1
+
+uniform sampler2D texture;
+uniform float opacity;
+
+varying vec4 f_color;
+varying vec2 f_uv;
+
+void main() {
+  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);
+  if(dot(pointR, pointR) > 0.25) {
+    discard;
+  }
+  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;
+}`]),v=s([`precision highp float;
+#define GLSLIFY 1
+
+attribute vec3 position;
+attribute vec4 id;
+
+uniform mat4 model, view, projection;
+
+varying vec3 f_position;
+varying vec4 f_id;
+
+void main() {
+  gl_Position = projection * (view * (model * vec4(position, 1.0)));
+  f_id        = id;
+  f_position  = position;
+}`]),_=s([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform vec3  clipBounds[2];
+uniform float pickId;
+
+varying vec3 f_position;
+varying vec4 f_id;
+
+void main() {
+  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;
+
+  gl_FragColor = vec4(pickId, f_id.xyz);
+}`]),b=s([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+attribute vec3  position;
+attribute float pointSize;
+attribute vec4  id;
+
+uniform mat4 model, view, projection;
+uniform vec3 clipBounds[2];
+
+varying vec3 f_position;
+varying vec4 f_id;
+
+void main() {
+  if (outOfRange(clipBounds[0], clipBounds[1], position)) {
+
+    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);
+  } else {
+    gl_Position  = projection * (view * (model * vec4(position, 1.0)));
+    gl_PointSize = pointSize;
+  }
+  f_id         = id;
+  f_position   = position;
+}`]),p=s([`precision highp float;
+#define GLSLIFY 1
+
+attribute vec3 position;
+
+uniform mat4 model, view, projection;
+
+void main() {
+  gl_Position = projection * (view * (model * vec4(position, 1.0)));
+}`]),k=s([`precision highp float;
+#define GLSLIFY 1
+
+uniform vec3 contourColor;
+
+void main() {
+  gl_FragColor = vec4(contourColor, 1.0);
+}
+`]);a.meshShader={vertex:l,fragment:u,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},a.wireShader={vertex:c,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},a.pointShader={vertex:h,fragment:d,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},a.pickShader={vertex:v,fragment:_,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},a.pointPickShader={vertex:b,fragment:_,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},a.contourShader={vertex:p,fragment:k,attributes:[{name:"position",type:"vec3"}]}},855:function(i,a,o){"use strict";i.exports={init:E,sweepBipartite:x,sweepComplete:C,scanBipartite:M,scanComplete:g};var s=o(1888),l=o(8828),u=o(4192),c=1<<28,f=1024,h=s.mallocInt32(f),d=s.mallocInt32(f),v=s.mallocInt32(f),_=s.mallocInt32(f),b=s.mallocInt32(f),p=s.mallocInt32(f),k=s.mallocDouble(f*8);function E(P){var T=l.nextPow2(P);h.length<T&&(s.free(h),h=s.mallocInt32(T)),d.length<T&&(s.free(d),d=s.mallocInt32(T)),v.length<T&&(s.free(v),v=s.mallocInt32(T)),_.length<T&&(s.free(_),_=s.mallocInt32(T)),b.length<T&&(s.free(b),b=s.mallocInt32(T)),p.length<T&&(s.free(p),p=s.mallocInt32(T));var z=8*T;k.length<z&&(s.free(k),k=s.mallocDouble(z))}function S(P,T,z,O){var V=T[O],G=P[z-1];P[V]=G,T[G]=V}function L(P,T,z,O){P[z]=O,T[O]=z}function x(P,T,z,O,V,G,Z,j,N,H){for(var te=0,oe=2*P,_e=P-1,Ee=oe-1,Ce=z;Ce<O;++Ce){var me=G[Ce],ie=oe*Ce;k[te++]=V[ie+_e],k[te++]=-(me+1),k[te++]=V[ie+Ee],k[te++]=me}for(var Ce=Z;Ce<j;++Ce){var me=H[Ce]+c,Se=oe*Ce;k[te++]=N[Se+_e],k[te++]=-me,k[te++]=N[Se+Ee],k[te++]=me}var Le=te>>>1;u(k,Le);for(var Ae=0,Fe=0,Ce=0;Ce<Le;++Ce){var Pe=k[2*Ce+1]|0;if(Pe>=c)Pe=Pe-c|0,S(v,_,Fe--,Pe);else 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ce=(ge>>1)-1;Re===0?S(h,d,Ae--,ce):Re===1?S(v,_,Fe--,ce):Re===2&&S(b,p,Pe--,ce)}}}function M(P,T,z,O,V,G,Z,j,N,H,te,oe){var _e=0,Ee=2*P,Ce=T,me=T+P,ie=1,Se=1;O?Se=c:ie=c;for(var Le=V;Le<G;++Le){var Ae=Le+ie,Fe=Ee*Le;k[_e++]=Z[Fe+Ce],k[_e++]=-Ae,k[_e++]=Z[Fe+me],k[_e++]=Ae}for(var Le=N;Le<H;++Le){var Ae=Le+Se,Pe=Ee*Le;k[_e++]=te[Pe+Ce],k[_e++]=-Ae}var ge=_e>>>1;u(k,ge);for(var Re=0,Le=0;Le<ge;++Le){var ce=k[2*Le+1]|0;if(ce<0){var Ae=-ce,Ze=!1;if(Ae>=c?(Ze=!O,Ae-=c):(Ze=!!O,Ae-=1),Ze)L(h,d,Re++,Ae);else{var ut=oe[Ae],pt=Ee*Ae,Zt=te[pt+T+1],st=te[pt+T+1+P];e:for(var lt=0;lt<Re;++lt){var Gt=h[lt],Nt=Ee*Gt;if(!(st<Z[Nt+T+1]||Z[Nt+T+1+P]<Zt)){for(var Jt=T+2;Jt<P;++Jt)if(te[pt+Jt+P]<Z[Nt+Jt]||Z[Nt+Jt+P]<te[pt+Jt])continue e;var sr=j[Gt],wr;if(O?wr=z(ut,sr):wr=z(sr,ut),wr!==void 0)return wr}}}}else S(h,d,Re--,ce-ie)}}function g(P,T,z,O,V,G,Z,j,N,H,te){for(var oe=0,_e=2*P,Ee=T,Ce=T+P,me=O;me<V;++me){var ie=me+c,Se=_e*me;k[oe++]=G[Se+Ee],k[oe++]=-ie,k[oe++]=G[Se+Ce],k[oe++]=ie}for(var me=j;me<N;++me){var ie=me+1,Le=_e*me;k[oe++]=H[Le+Ee],k[oe++]=-ie}var Ae=oe>>>1;u(k,Ae);for(var Fe=0,me=0;me<Ae;++me){var Pe=k[2*me+1]|0;if(Pe<0){var ie=-Pe;if(ie>=c)h[Fe++]=ie-c;else{ie-=1;var ge=te[ie],Re=_e*ie,ce=H[Re+T+1],Ze=H[Re+T+1+P];e:for(var ut=0;ut<Fe;++ut){var pt=h[ut],Zt=Z[pt];if(Zt===ge)break;var st=_e*pt;if(!(Ze<G[st+T+1]||G[st+T+1+P]<ce)){for(var lt=T+2;lt<P;++lt)if(H[Re+lt+P]<G[st+lt]||G[st+lt+P]<H[Re+lt])continue e;var Gt=z(Zt,ge);if(Gt!==void 0)return Gt}}}}else{for(var ie=Pe-c,ut=Fe-1;ut>=0;--ut)if(h[ut]===ie){for(var lt=ut+1;lt<Fe;++lt)h[lt-1]=h[lt];break}--Fe}}}},868:function(i,a,o){i.exports=o(1387)},869:function(i,a,o){"use strict";var s=o(2651),l=o(5716);i.exports=u;function u(c,f){var h=l(c),d=l(f);if(h===0)return[s(0),s(1)];if(d===0)return[s(0),s(0)];d<0&&(c=c.neg(),f=f.neg());var v=c.gcd(f);return v.cmpn(1)?[c.div(v),f.div(v)]:[c,f]}},870:function(i,a,o){"use strict";var s=o(1433);function l(c){this.gl=c,this._elements=null,this._attributes=null,this._elementsType=c.UNSIGNED_SHORT}l.prototype.bind=function(){s(this.gl,this._elements,this._attributes)},l.prototype.update=function(c,f,h){this._elements=f,this._attributes=c,this._elementsType=h||this.gl.UNSIGNED_SHORT},l.prototype.dispose=function(){},l.prototype.unbind=function(){},l.prototype.draw=function(c,f,h){h=h||0;var d=this.gl;this._elements?d.drawElements(c,f,this._elementsType,h):d.drawArrays(c,h,f)};function u(c){return new l(c)}i.exports=u},946:function(i,a,o){"use strict";var s=o(1369),l=o(4025);i.exports=u;function u(c){var f=c[0],h=c[1];if(f.cmpn(0)===0)return 0;var d=f.abs().divmod(h.abs()),v=d.div,_=s(v),b=d.mod,p=f.negative!==h.negative?-1:1;if(b.cmpn(0)===0)return p*_;if(_){var k=l(_)+4,E=s(b.ushln(k).divRound(h));return p*(_+E*Math.pow(2,-k))}else{var S=h.bitLength()-b.bitLength()+53,E=s(b.ushln(S).divRound(h));return S<1023?p*E*Math.pow(2,-S):(E*=Math.pow(2,-1023),p*E*Math.pow(2,1023-S))}}},990:function(i,a,o){var s=o(9405),l=o(3236),u=l([`precision highp float;
+#define GLSLIFY 1
+
+attribute vec4 uv;
+attribute vec3 f;
+attribute vec3 normal;
+
+uniform vec3 objectOffset;
+uniform mat4 model, view, projection, inverseModel;
+uniform vec3 lightPosition, eyePosition;
+uniform sampler2D colormap;
+
+varying float value, kill;
+varying vec3 worldCoordinate;
+varying vec2 planeCoordinate;
+varying vec3 lightDirection, eyeDirection, surfaceNormal;
+varying vec4 vColor;
+
+void main() {
+  vec3 localCoordinate = vec3(uv.zw, f.x);
+  worldCoordinate = objectOffset + localCoordinate;
+  mat4 objectOffsetTranslation = mat4(1.0) + mat4(vec4(0), vec4(0), vec4(0), vec4(objectOffset, 0));
+  vec4 worldPosition = (model * objectOffsetTranslation) * vec4(localCoordinate, 1.0);
+  vec4 clipPosition = projection * (view * worldPosition);
+  gl_Position = clipPosition;
+  kill = f.y;
+  value = f.z;
+  planeCoordinate = uv.xy;
+
+  vColor = texture2D(colormap, vec2(value, value));
+
+  //Lighting geometry parameters
+  vec4 cameraCoordinate = view * worldPosition;
+  cameraCoordinate.xyz /= cameraCoordinate.w;
+  lightDirection = lightPosition - cameraCoordinate.xyz;
+  eyeDirection   = eyePosition - cameraCoordinate.xyz;
+  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);
+}
+`]),c=l([`precision highp float;
+#define GLSLIFY 1
+
+float beckmannDistribution(float x, float roughness) {
+  float NdotH = max(x, 0.0001);
+  float cos2Alpha = NdotH * NdotH;
+  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;
+  float roughness2 = roughness * roughness;
+  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;
+  return exp(tan2Alpha / roughness2) / denom;
+}
+
+float beckmannSpecular(
+  vec3 lightDirection,
+  vec3 viewDirection,
+  vec3 surfaceNormal,
+  float roughness) {
+  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);
+}
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform vec3 lowerBound, upperBound;
+uniform float contourTint;
+uniform vec4 contourColor;
+uniform sampler2D colormap;
+uniform vec3 clipBounds[2];
+uniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;
+uniform float vertexColor;
+
+varying float value, kill;
+varying vec3 worldCoordinate;
+varying vec3 lightDirection, eyeDirection, surfaceNormal;
+varying vec4 vColor;
+
+void main() {
+  if (
+    kill > 0.0 ||
+    vColor.a == 0.0 ||
+    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)
+  ) discard;
+
+  vec3 N = normalize(surfaceNormal);
+  vec3 V = normalize(eyeDirection);
+  vec3 L = normalize(lightDirection);
+
+  if(gl_FrontFacing) {
+    N = -N;
+  }
+
+  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);
+  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);
+
+  //decide how to interpolate color \u2014 in vertex or in fragment
+  vec4 surfaceColor =
+    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +
+    step(.5, vertexColor) * vColor;
+
+  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);
+
+  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;
+}
+`]),f=l([`precision highp float;
+#define GLSLIFY 1
+
+attribute vec4 uv;
+attribute float f;
+
+uniform vec3 objectOffset;
+uniform mat3 permutation;
+uniform mat4 model, view, projection;
+uniform float height, zOffset;
+uniform sampler2D colormap;
+
+varying float value, kill;
+varying vec3 worldCoordinate;
+varying vec2 planeCoordinate;
+varying vec3 lightDirection, eyeDirection, surfaceNormal;
+varying vec4 vColor;
+
+void main() {
+  vec3 dataCoordinate = permutation * vec3(uv.xy, height);
+  worldCoordinate = objectOffset + dataCoordinate;
+  mat4 objectOffsetTranslation = mat4(1.0) + mat4(vec4(0), vec4(0), vec4(0), vec4(objectOffset, 0));
+  vec4 worldPosition = (model * objectOffsetTranslation) * vec4(dataCoordinate, 1.0);
+
+  vec4 clipPosition = projection * (view * worldPosition);
+  clipPosition.z += zOffset;
+
+  gl_Position = clipPosition;
+  value = f + objectOffset.z;
+  kill = -1.0;
+  planeCoordinate = uv.zw;
+
+  vColor = texture2D(colormap, vec2(value, value));
+
+  //Don't do lighting for contours
+  surfaceNormal   = vec3(1,0,0);
+  eyeDirection    = vec3(0,1,0);
+  lightDirection  = vec3(0,0,1);
+}
+`]),h=l([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform vec2 shape;
+uniform vec3 clipBounds[2];
+uniform float pickId;
+
+varying float value, kill;
+varying vec3 worldCoordinate;
+varying vec2 planeCoordinate;
+varying vec3 surfaceNormal;
+
+vec2 splitFloat(float v) {
+  float vh = 255.0 * v;
+  float upper = floor(vh);
+  float lower = fract(vh);
+  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);
+}
+
+void main() {
+  if ((kill > 0.0) ||
+      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;
+
+  vec2 ux = splitFloat(planeCoordinate.x / shape.x);
+  vec2 uy = splitFloat(planeCoordinate.y / shape.y);
+  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));
+}
+`]);a.createShader=function(d){var v=s(d,u,c,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return v.attributes.uv.location=0,v.attributes.f.location=1,v.attributes.normal.location=2,v},a.createPickShader=function(d){var v=s(d,u,h,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return v.attributes.uv.location=0,v.attributes.f.location=1,v.attributes.normal.location=2,v},a.createContourShader=function(d){var v=s(d,f,c,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return v.attributes.uv.location=0,v.attributes.f.location=1,v},a.createPickContourShader=function(d){var v=s(d,f,h,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return v.attributes.uv.location=0,v.attributes.f.location=1,v}},1085:function(i,a,o){var s=o(1371);i.exports=l;function l(u,c,f){c=typeof c=="number"?c:1,f=f||": ";var h=u.split(/\r?\n/),d=String(h.length+c-1).length;return h.map(function(v,_){var b=_+c,p=String(b).length,k=s(b,d-p);return k+f+v}).join(`
+`)}},1091:function(i){i.exports=a;function a(){var o=new Float32Array(3);return o[0]=0,o[1]=0,o[2]=0,o}},1125:function(i,a,o){"use strict";i.exports=u;var s=o(3250)[3];function l(c,f,h,d){for(var v=0;v<2;++v){var _=c[v],b=f[v],p=Math.min(_,b),k=Math.max(_,b),E=h[v],S=d[v],L=Math.min(E,S),x=Math.max(E,S);if(x<p||k<L)return!1}return!0}function u(c,f,h,d){var v=s(c,h,d),_=s(f,h,d);if(v>0&&_>0||v<0&&_<0)return!1;var b=s(h,c,f),p=s(d,c,f);return b>0&&p>0||b<0&&p<0?!1:v===0&&_===0&&b===0&&p===0?l(c,f,h,d):!0}},1278:function(i,a,o){"use strict";var s=o(2361),l=Math.pow(2,-1074),u=-1>>>0;i.exports=c;function c(f,h){if(isNaN(f)||isNaN(h))return NaN;if(f===h)return f;if(f===0)return h<0?-l:l;var d=s.hi(f),v=s.lo(f);return h>f==f>0?v===u?(d+=1,v=0):v+=1:v===0?(v=u,d-=1):v-=1,s.pack(v,d)}},1283:function(i,a,o){var s=o(9405),l=o(3236),u=l([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+attribute vec3 position;
+attribute vec4 color;
+attribute vec2 glyph;
+attribute vec4 id;
+
+uniform vec4 highlightId;
+uniform float highlightScale;
+uniform mat4 model, view, projection;
+uniform vec3 clipBounds[2];
+
+varying vec4 interpColor;
+varying vec4 pickId;
+varying vec3 dataCoordinate;
+
+void main() {
+  if (outOfRange(clipBounds[0], clipBounds[1], position)) {
+
+    gl_Position = vec4(0,0,0,0);
+  } else {
+    float scale = 1.0;
+    if(distance(highlightId, id) < 0.0001) {
+      scale = highlightScale;
+    }
+
+    vec4 worldPosition = model * vec4(position, 1);
+    vec4 viewPosition = view * worldPosition;
+    viewPosition = viewPosition / viewPosition.w;
+    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));
+
+    gl_Position = clipPosition;
+    interpColor = color;
+    pickId = id;
+    dataCoordinate = position;
+  }
+}`]),c=l([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+attribute vec3 position;
+attribute vec4 color;
+attribute vec2 glyph;
+attribute vec4 id;
+
+uniform mat4 model, view, projection;
+uniform vec2 screenSize;
+uniform vec3 clipBounds[2];
+uniform float highlightScale, pixelRatio;
+uniform vec4 highlightId;
+
+varying vec4 interpColor;
+varying vec4 pickId;
+varying vec3 dataCoordinate;
+
+void main() {
+  if (outOfRange(clipBounds[0], clipBounds[1], position)) {
+
+    gl_Position = vec4(0,0,0,0);
+  } else {
+    float scale = pixelRatio;
+    if(distance(highlightId.bgr, id.bgr) < 0.001) {
+      scale *= highlightScale;
+    }
+
+    vec4 worldPosition = model * vec4(position, 1.0);
+    vec4 viewPosition = view * worldPosition;
+    vec4 clipPosition = projection * viewPosition;
+    clipPosition /= clipPosition.w;
+
+    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);
+    interpColor = color;
+    pickId = id;
+    dataCoordinate = position;
+  }
+}`]),f=l([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+attribute vec3 position;
+attribute vec4 color;
+attribute vec2 glyph;
+attribute vec4 id;
+
+uniform float highlightScale;
+uniform vec4 highlightId;
+uniform vec3 axes[2];
+uniform mat4 model, view, projection;
+uniform vec2 screenSize;
+uniform vec3 clipBounds[2];
+uniform float scale, pixelRatio;
+
+varying vec4 interpColor;
+varying vec4 pickId;
+varying vec3 dataCoordinate;
+
+void main() {
+  if (outOfRange(clipBounds[0], clipBounds[1], position)) {
+
+    gl_Position = vec4(0,0,0,0);
+  } else {
+    float lscale = pixelRatio * scale;
+    if(distance(highlightId, id) < 0.0001) {
+      lscale *= highlightScale;
+    }
+
+    vec4 clipCenter   = projection * (view * (model * vec4(position, 1)));
+    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;
+    vec4 clipPosition = projection * (view * (model * vec4(dataPosition, 1)));
+
+    gl_Position = clipPosition;
+    interpColor = color;
+    pickId = id;
+    dataCoordinate = dataPosition;
+  }
+}
+`]),h=l([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform vec3 fragClipBounds[2];
+uniform float opacity;
+
+varying vec4 interpColor;
+varying vec3 dataCoordinate;
+
+void main() {
+  if (
+    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||
+    interpColor.a * opacity == 0.
+  ) discard;
+  gl_FragColor = interpColor * opacity;
+}
+`]),d=l([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform vec3 fragClipBounds[2];
+uniform float pickGroup;
+
+varying vec4 pickId;
+varying vec3 dataCoordinate;
+
+void main() {
+  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;
+
+  gl_FragColor = vec4(pickGroup, pickId.bgr);
+}`]),v=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],_={vertex:u,fragment:h,attributes:v},b={vertex:c,fragment:h,attributes:v},p={vertex:f,fragment:h,attributes:v},k={vertex:u,fragment:d,attributes:v},E={vertex:c,fragment:d,attributes:v},S={vertex:f,fragment:d,attributes:v};function L(x,C){var M=s(x,C),g=M.attributes;return g.position.location=0,g.color.location=1,g.glyph.location=2,g.id.location=3,M}a.createPerspective=function(x){return L(x,_)},a.createOrtho=function(x){return L(x,b)},a.createProject=function(x){return L(x,p)},a.createPickPerspective=function(x){return L(x,k)},a.createPickOrtho=function(x){return L(x,E)},a.createPickProject=function(x){return L(x,S)}},1303:function(i,a,o){"use strict";i.exports=u;var s=o(3250);function l(c,f){var h,d;if(f[0][0]<f[1][0])h=f[0],d=f[1];else if(f[0][0]>f[1][0])h=f[1],d=f[0];else{var v=Math.min(c[0][1],c[1][1]),_=Math.max(c[0][1],c[1][1]),b=Math.min(f[0][1],f[1][1]),p=Math.max(f[0][1],f[1][1]);return _<b?_-b:v>p?v-p:_-p}var k,E;c[0][1]<c[1][1]?(k=c[0],E=c[1]):(k=c[1],E=c[0]);var S=s(d,h,k);return S||(S=s(d,h,E),S)?S:E-d}function u(c,f){var h,d;if(f[0][0]<f[1][0])h=f[0],d=f[1];else if(f[0][0]>f[1][0])h=f[1],d=f[0];else return l(f,c);var v,_;if(c[0][0]<c[1][0])v=c[0],_=c[1];else if(c[0][0]>c[1][0])v=c[1],_=c[0];else return-l(c,f);var b=s(h,d,_),p=s(h,d,v);if(b<0){if(p<=0)return b}else if(b>0){if(p>=0)return b}else if(p)return p;if(b=s(_,v,d),p=s(_,v,h),b<0){if(p<=0)return b}else if(b>0){if(p>=0)return b}else if(p)return p;return d[0]-_[0]}},1318:function(i){"use strict";i.exports=a;function a(o,s){return o[0].mul(s[1]).cmp(s[0].mul(o[1]))}},1338:function(i){"use strict";function a(l,u,c){var f=l[c]|0;if(f<=0)return[];var h=new Array(f),d;if(c===l.length-1)for(d=0;d<f;++d)h[d]=u;else for(d=0;d<f;++d)h[d]=a(l,u,c+1);return h}function o(l,u){var c,f;for(c=new Array(l),f=0;f<l;++f)c[f]=u;return c}function s(l,u){switch(typeof u=="undefined"&&(u=0),typeof l){case"number":if(l>0)return o(l|0,u);break;case"object":if(typeof l.length=="number")return a(l,u,0);break}return[]}i.exports=s},1369:function(i,a,o){"use strict";var s=o(5716);i.exports=l;function l(u){var c=u.length,f=u.words,h=0;if(c===1)h=f[0];else if(c===2)h=f[0]+f[1]*67108864;else for(var d=0;d<c;d++){var v=f[d];h+=v*Math.pow(67108864,d)}return s(u)*h}},1371:function(i,a,o){"use strict";var s=o(3233);i.exports=function(u,c,f){return f=typeof f!="undefined"?f+"":" ",s(f,c)+u}},1373:function(i){i.exports=a;function a(o,s,l){return o[0]=s[0]/l[0],o[1]=s[1]/l[1],o[2]=s[2]/l[2],o[3]=s[3]/l[3],o}},1387:function(i){i.exports=a;function a(o){var s=o[0],l=o[1],u=o[2];return Math.sqrt(s*s+l*l+u*u)}},1433:function(i){"use strict";function a(o,s,l){s?s.bind():o.bindBuffer(o.ELEMENT_ARRAY_BUFFER,null);var u=o.getParameter(o.MAX_VERTEX_ATTRIBS)|0;if(l){if(l.length>u)throw new Error("gl-vao: Too many vertex attributes");for(var c=0;c<l.length;++c){var f=l[c];if(f.buffer){var h=f.buffer,d=f.size||4,v=f.type||o.FLOAT,_=!!f.normalized,b=f.stride||0,p=f.offset||0;h.bind(),o.enableVertexAttribArray(c),o.vertexAttribPointer(c,d,v,_,b,p)}else{if(typeof f=="number")o.vertexAttrib1f(c,f);else if(f.length===1)o.vertexAttrib1f(c,f[0]);else if(f.length===2)o.vertexAttrib2f(c,f[0],f[1]);else if(f.length===3)o.vertexAttrib3f(c,f[0],f[1],f[2]);else if(f.length===4)o.vertexAttrib4f(c,f[0],f[1],f[2],f[3]);else throw new Error("gl-vao: Invalid vertex attribute");o.disableVertexAttribArray(c)}}for(;c<u;++c)o.disableVertexAttribArray(c)}else{o.bindBuffer(o.ARRAY_BUFFER,null);for(var c=0;c<u;++c)o.disableVertexAttribArray(c)}}i.exports=a},1463:function(i){i.exports=a;function a(o,s,l,u){return o[0]=s,o[1]=l,o[2]=u,o}},1493:function(i,a,o){"use strict";var s=o(3236),l=o(9405),u=s([`precision mediump float;
+#define GLSLIFY 1
+
+attribute vec3 position, color;
+attribute float weight;
+
+uniform mat4 model, view, projection;
+uniform vec3 coordinates[3];
+uniform vec4 colors[3];
+uniform vec2 screenShape;
+uniform float lineWidth;
+
+varying vec4 fragColor;
+
+void main() {
+  vec3 vertexPosition = mix(coordinates[0],
+    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));
+
+  vec4 clipPos = projection * (view * (model * vec4(vertexPosition, 1.0)));
+  vec2 clipOffset = (projection * (view * (model * vec4(color, 0.0)))).xy;
+  vec2 delta = weight * clipOffset * screenShape;
+  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;
+
+  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);
+  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];
+}
+`]),c=s([`precision mediump float;
+#define GLSLIFY 1
+
+varying vec4 fragColor;
+
+void main() {
+  gl_FragColor = fragColor;
+}`]);i.exports=function(f){return l(f,u,c,null,[{name:"position",type:"vec3"},{name:"color",type:"vec3"},{name:"weight",type:"float"}])}},1498:function(i){i.exports=a;function a(o,s){return o[0]=-s[0],o[1]=-s[1],o[2]=-s[2],o[3]=-s[3],o}},1533:function(i,a,o){"use strict";var s=o(6859);i.exports=l;function l(u){return u&&typeof u=="object"&&!!u.words}},1538:function(i){(function(){"use strict";if(typeof ses!="undefined"&&ses.ok&&!ses.ok())return;function o(T){T.permitHostObjects___&&T.permitHostObjects___(o)}typeof ses!="undefined"&&(ses.weakMapPermitHostObjects=o);var s=!1;if(typeof WeakMap=="function"){var l=WeakMap;if(!(typeof navigator!="undefined"&&/Firefox/.test(navigator.userAgent))){var u=new l,c=Object.freeze({});if(u.set(c,1),u.get(c)!==1)s=!0;else{i.exports=WeakMap;return}}}var f=Object.prototype.hasOwnProperty,h=Object.getOwnPropertyNames,d=Object.defineProperty,v=Object.isExtensible,_="weakmap:",b=_+"ident:"+Math.random()+"___";if(typeof crypto!="undefined"&&typeof crypto.getRandomValues=="function"&&typeof ArrayBuffer=="function"&&typeof Uint8Array=="function"){var p=new ArrayBuffer(25),k=new Uint8Array(p);crypto.getRandomValues(k),b=_+"rand:"+Array.prototype.map.call(k,function(T){return(T%36).toString(36)}).join("")+"___"}function E(T){return!(T.substr(0,_.length)==_&&T.substr(T.length-3)==="___")}if(d(Object,"getOwnPropertyNames",{value:function(z){return h(z).filter(E)}}),"getPropertyNames"in Object){var S=Object.getPropertyNames;d(Object,"getPropertyNames",{value:function(z){return S(z).filter(E)}})}function L(T){if(T!==Object(T))throw new TypeError("Not an object: "+T);var z=T[b];if(z&&z.key===T)return z;if(v(T)){z={key:T};try{return d(T,b,{value:z,writable:!1,enumerable:!1,configurable:!1}),z}catch(O){return}}}(function(){var T=Object.freeze;d(Object,"freeze",{value:function(G){return L(G),T(G)}});var z=Object.seal;d(Object,"seal",{value:function(G){return L(G),z(G)}});var O=Object.preventExtensions;d(Object,"preventExtensions",{value:function(G){return L(G),O(G)}})})();function x(T){return T.prototype=null,Object.freeze(T)}var C=!1;function M(){!C&&typeof console!="undefined"&&(C=!0,console.warn("WeakMap should be invoked as new WeakMap(), not WeakMap(). 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this.delete___(z)},writable:!0,configurable:!0}}),typeof l=="function"?function(){s&&typeof Proxy!="undefined"&&(Proxy=void 0);function T(){this instanceof P||M();var z=new l,O=void 0,V=!1;function G(H,te){return O?z.has(H)?z.get(H):O.get___(H,te):z.get(H,te)}function Z(H){return z.has(H)||(O?O.has___(H):!1)}var j;s?j=function(H,te){return z.set(H,te),z.has(H)||(O||(O=new P),O.set(H,te)),this}:j=function(H,te){if(V)try{z.set(H,te)}catch(oe){O||(O=new P),O.set___(H,te)}else z.set(H,te);return this};function N(H){var te=!!z.delete(H);return O&&O.delete___(H)||te}return Object.create(P.prototype,{get___:{value:x(G)},has___:{value:x(Z)},set___:{value:x(j)},delete___:{value:x(N)},permitHostObjects___:{value:x(function(H){if(H===o)V=!0;else throw new Error("bogus call to permitHostObjects___")})}})}T.prototype=P.prototype,i.exports=T,Object.defineProperty(WeakMap.prototype,"constructor",{value:WeakMap,enumerable:!1,configurable:!0,writable:!0})}():(typeof Proxy!="undefined"&&(Proxy=void 0),i.exports=P)})()},1570:function(i){"use strict";i.exports=o;var a=[function(){function l(c,f,h,d){for(var v=Math.min(h,d)|0,_=Math.max(h,d)|0,b=c[2*v],p=c[2*v+1];b<p;){var k=b+p>>1,E=f[2*k+1];if(E===_)return k;_<E?p=k:b=k+1}return b}function u(c,f,h,d){for(var v=c.length,_=[],b=0;b<v;++b)var p=c[b],k=p.length;return _}return u},function(){function l(c,f,h,d){for(var v=Math.min(h,d)|0,_=Math.max(h,d)|0,b=c[2*v],p=c[2*v+1];b<p;){var k=b+p>>1,E=f[2*k+1];if(E===_)return k;_<E?p=k:b=k+1}return b}function u(c,f,h,d){for(var v=c.length,_=[],b=0;b<v;++b){var p=c[b],k=p.length;if(k===2){var E=(d[p[0]]<<0)+(d[p[1]]<<1);if(E===0||E===3)continue;switch(E){case 0:break;case 1:_.push([l(h,f,p[0],p[1])]);break;case 2:_.push([l(h,f,p[1],p[0])]);break;case 3:break}}}return _}return u},function(){function l(c,f,h,d){for(var v=Math.min(h,d)|0,_=Math.max(h,d)|0,b=c[2*v],p=c[2*v+1];b<p;){var k=b+p>>1,E=f[2*k+1];if(E===_)return k;_<E?p=k:b=k+1}return b}function u(c,f,h,d){for(var v=c.length,_=[],b=0;b<v;++b){var p=c[b],k=p.length;if(k===3){var E=(d[p[0]]<<0)+(d[p[1]]<<1)+(d[p[2]]<<2);if(E===0||E===7)continue;switch(E){case 0:break;case 1:_.push([l(h,f,p[0],p[2]),l(h,f,p[0],p[1])]);break;case 2:_.push([l(h,f,p[1],p[0]),l(h,f,p[1],p[2])]);break;case 3:_.push([l(h,f,p[0],p[2]),l(h,f,p[1],p[2])]);break;case 4:_.push([l(h,f,p[2],p[1]),l(h,f,p[2],p[0])]);break;case 5:_.push([l(h,f,p[2],p[1]),l(h,f,p[0],p[1])]);break;case 6:_.push([l(h,f,p[1],p[0]),l(h,f,p[2],p[0])]);break;case 7:break}}else if(k===2){var E=(d[p[0]]<<0)+(d[p[1]]<<1);if(E===0||E===3)continue;switch(E){case 0:break;case 1:_.push([l(h,f,p[0],p[1])]);break;case 2:_.push([l(h,f,p[1],p[0])]);break;case 3:break}}}return _}return u},function(){function l(c,f,h,d){for(var v=Math.min(h,d)|0,_=Math.max(h,d)|0,b=c[2*v],p=c[2*v+1];b<p;){var k=b+p>>1,E=f[2*k+1];if(E===_)return k;_<E?p=k:b=k+1}return b}function u(c,f,h,d){for(var v=c.length,_=[],b=0;b<v;++b){var p=c[b],k=p.length;if(k===4){var E=(d[p[0]]<<0)+(d[p[1]]<<1)+(d[p[2]]<<2)+(d[p[3]]<<3);if(E===0||E===15)continue;switch(E){case 0:break;case 1:_.push([l(h,f,p[0],p[1]),l(h,f,p[0],p[2]),l(h,f,p[0],p[3])]);break;case 2:_.push([l(h,f,p[1],p[2]),l(h,f,p[1],p[0]),l(h,f,p[1],p[3])]);break;case 3:_.push([l(h,f,p[1],p[2]),l(h,f,p[0],p[2]),l(h,f,p[0],p[3])],[l(h,f,p[1],p[3]),l(h,f,p[1],p[2]),l(h,f,p[0],p[3])]);break;case 4:_.push([l(h,f,p[2],p[0]),l(h,f,p[2],p[1]),l(h,f,p[2],p[3])]);break;case 5:_.push([l(h,f,p[0],p[1]),l(h,f,p[2],p[1]),l(h,f,p[0],p[3])],[l(h,f,p[2],p[1]),l(h,f,p[2],p[3]),l(h,f,p[0],p[3])]);break;case 6:_.push([l(h,f,p[2],p[0]),l(h,f,p[1],p[0]),l(h,f,p[1],p[3])],[l(h,f,p[2],p[3]),l(h,f,p[2],p[0]),l(h,f,p[1],p[3])]);break;case 7:_.push([l(h,f,p[0],p[3]),l(h,f,p[1],p[3]),l(h,f,p[2],p[3])]);break;case 8:_.push([l(h,f,p[3],p[1]),l(h,f,p[3],p[0]),l(h,f,p[3],p[2])]);break;case 9:_.push([l(h,f,p[3],p[1]),l(h,f,p[0],p[1]),l(h,f,p[0],p[2])],[l(h,f,p[3],p[2]),l(h,f,p[3],p[1]),l(h,f,p[0],p[2])]);break;case 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E=(d[p[0]]<<0)+(d[p[1]]<<1);if(E===0||E===3)continue;switch(E){case 0:break;case 1:_.push([l(h,f,p[0],p[1])]);break;case 2:_.push([l(h,f,p[1],p[0])]);break;case 3:break}}}return _}return u}];function o(s){return a[s]()}},1682:function(i){"use strict";function a(l,u){for(var c=1,f=l.length,h=l[0],d=l[0],v=1;v<f;++v)if(d=h,h=l[v],u(h,d)){if(v===c){c++;continue}l[c++]=h}return l.length=c,l}function o(l){for(var u=1,c=l.length,f=l[0],h=l[0],d=1;d<c;++d,h=f)if(h=f,f=l[d],f!==h){if(d===u){u++;continue}l[u++]=f}return l.length=u,l}function s(l,u,c){return l.length===0?l:u?(c||l.sort(u),a(l,u)):(c||l.sort(),o(l))}i.exports=s},1755:function(i){"use strict";"use restrict";i.exports=a;function a(s){this.roots=new Array(s),this.ranks=new Array(s);for(var l=0;l<s;++l)this.roots[l]=l,this.ranks[l]=0}var o=a.prototype;Object.defineProperty(o,"length",{get:function(){return this.roots.length}}),o.makeSet=function(){var s=this.roots.length;return this.roots.push(s),this.ranks.push(0),s},o.find=function(s){for(var l=s,u=this.roots;u[s]!==s;)s=u[s];for(;u[l]!==s;){var c=u[l];u[l]=s,l=c}return s},o.link=function(s,l){var u=this.find(s),c=this.find(l);if(u!==c){var f=this.ranks,h=this.roots,d=f[u],v=f[c];d<v?h[u]=c:v<d?h[c]=u:(h[c]=u,++f[u])}}},1811:function(i,a,o){"use strict";var s=o(2478),l=o(7442),u=o(7608),c=o(5567),f=o(2408),h=o(7089),d=o(6582),v=o(7656),_=o(2504),b=o(3536),p=[0,0,0];i.exports=L;function k(x){this._components=x.slice(),this._time=[0],this.prevMatrix=x.slice(),this.nextMatrix=x.slice(),this.computedMatrix=x.slice(),this.computedInverse=x.slice(),this.computedEye=[0,0,0],this.computedUp=[0,0,0],this.computedCenter=[0,0,0],this.computedRadius=[0],this._limits=[-1/0,1/0]}var E=k.prototype;E.recalcMatrix=function(x){var C=this._time,M=s.le(C,x),g=this.computedMatrix;if(!(M<0)){var P=this._components;if(M===C.length-1)for(var T=16*M,z=0;z<16;++z)g[z]=P[T++];else{for(var O=C[M+1]-C[M],T=16*M,V=this.prevMatrix,G=!0,z=0;z<16;++z)V[z]=P[T++];for(var Z=this.nextMatrix,z=0;z<16;++z)Z[z]=P[T++],G=G&&V[z]===Z[z];if(O<1e-6||G)for(var z=0;z<16;++z)g[z]=V[z];else l(g,V,Z,(x-C[M])/O)}var j=this.computedUp;j[0]=g[1],j[1]=g[5],j[2]=g[9],b(j,j);var N=this.computedInverse;u(N,g);var H=this.computedEye,te=N[15];H[0]=N[12]/te,H[1]=N[13]/te,H[2]=N[14]/te;for(var oe=this.computedCenter,_e=Math.exp(this.computedRadius[0]),z=0;z<3;++z)oe[z]=H[z]-g[2+4*z]*_e}},E.idle=function(x){if(!(x<this.lastT())){for(var C=this._components,M=C.length-16,g=0;g<16;++g)C.push(C[M++]);this._time.push(x)}},E.flush=function(x){var C=s.gt(this._time,x)-2;C<0||(this._time.splice(0,C),this._components.splice(0,16*C))},E.lastT=function(){return this._time[this._time.length-1]},E.lookAt=function(x,C,M,g){this.recalcMatrix(x),C=C||this.computedEye,M=M||p,g=g||this.computedUp,this.setMatrix(x,d(this.computedMatrix,C,M,g));for(var P=0,T=0;T<3;++T)P+=Math.pow(M[T]-C[T],2);P=Math.log(Math.sqrt(P)),this.computedRadius[0]=P},E.rotate=function(x,C,M,g){this.recalcMatrix(x);var P=this.computedInverse;C&&f(P,P,C),M&&c(P,P,M),g&&h(P,P,g),this.setMatrix(x,u(this.computedMatrix,P))};var S=[0,0,0];E.pan=function(x,C,M,g){S[0]=-(C||0),S[1]=-(M||0),S[2]=-(g||0),this.recalcMatrix(x);var P=this.computedInverse;v(P,P,S),this.setMatrix(x,u(P,P))},E.translate=function(x,C,M,g){S[0]=C||0,S[1]=M||0,S[2]=g||0,this.recalcMatrix(x);var P=this.computedMatrix;v(P,P,S),this.setMatrix(x,P)},E.setMatrix=function(x,C){if(!(x<this.lastT())){this._time.push(x);for(var M=0;M<16;++M)this._components.push(C[M])}},E.setDistance=function(x,C){this.computedRadius[0]=C},E.setDistanceLimits=function(x,C){var M=this._limits;M[0]=x,M[1]=C},E.getDistanceLimits=function(x){var C=this._limits;return x?(x[0]=C[0],x[1]=C[1],x):C};function L(x){x=x||{};var C=x.matrix||[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];return new k(C)}},1848:function(i,a,o){var s=o(4905),l=o(6468);i.exports=u;function u(c){for(var f=Array.isArray(c)?c:s(c),h=0;h<f.length;h++){var d=f[h];if(d.type==="preprocessor"){var v=d.data.match(/\#define\s+SHADER_NAME(_B64)?\s+(.+)$/);if(v&&v[2]){var _=v[1],b=v[2];return(_?l(b):b).trim()}}}}},1879:function(i,a,o){"use strict";var s=o(3236),l=o(9405),u=s([`precision highp float;
+#define GLSLIFY 1
+
+attribute vec3 position;
+
+uniform mat4 model, view, projection;
+uniform vec3 offset, majorAxis, minorAxis, screenAxis;
+uniform float lineWidth;
+uniform vec2 screenShape;
+
+vec3 project(vec3 p) {
+  vec4 pp = projection * (view * (model * vec4(p, 1.0)));
+  return pp.xyz / max(pp.w, 0.0001);
+}
+
+void main() {
+  vec3 major = position.x * majorAxis;
+  vec3 minor = position.y * minorAxis;
+
+  vec3 vPosition = major + minor + offset;
+  vec3 pPosition = project(vPosition);
+  vec3 offset = project(vPosition + screenAxis * position.z);
+
+  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;
+
+  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);
+}
+`]),c=s([`precision highp float;
+#define GLSLIFY 1
+
+uniform vec4 color;
+void main() {
+  gl_FragColor = color;
+}`]);a.n=function(_){return l(_,u,c,null,[{name:"position",type:"vec3"}])};var f=s([`precision highp float;
+#define GLSLIFY 1
+
+attribute vec3 position;
+
+uniform mat4 model, view, projection;
+uniform vec3 offset, axis, alignDir, alignOpt;
+uniform float scale, angle, pixelScale;
+uniform vec2 resolution;
+
+vec3 project(vec3 p) {
+  vec4 pp = projection * (view * (model * vec4(p, 1.0)));
+  return pp.xyz / max(pp.w, 0.0001);
+}
+
+float computeViewAngle(vec3 a, vec3 b) {
+  vec3 A = project(a);
+  vec3 B = project(b);
+
+  return atan(
+    (B.y - A.y) * resolution.y,
+    (B.x - A.x) * resolution.x
+  );
+}
+
+const float PI = 3.141592;
+const float TWO_PI = 2.0 * PI;
+const float HALF_PI = 0.5 * PI;
+const float ONE_AND_HALF_PI = 1.5 * PI;
+
+int option = int(floor(alignOpt.x + 0.001));
+float hv_ratio =       alignOpt.y;
+bool enableAlign =    (alignOpt.z != 0.0);
+
+float mod_angle(float a) {
+  return mod(a, PI);
+}
+
+float positive_angle(float a) {
+  return mod_angle((a < 0.0) ?
+    a + TWO_PI :
+    a
+  );
+}
+
+float look_upwards(float a) {
+  float b = positive_angle(a);
+  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?
+    b - PI :
+    b;
+}
+
+float look_horizontal_or_vertical(float a, float ratio) {
+  // ratio controls the ratio between being horizontal to (vertical + horizontal)
+  // if ratio is set to 0.5 then it is 50%, 50%.
+  // when using a higher ratio e.g. 0.75 the result would
+  // likely be more horizontal than vertical.
+
+  float b = positive_angle(a);
+
+  return
+    (b < (      ratio) * HALF_PI) ? 0.0 :
+    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :
+    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :
+    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :
+                                    0.0;
+}
+
+float roundTo(float a, float b) {
+  return float(b * floor((a + 0.5 * b) / b));
+}
+
+float look_round_n_directions(float a, int n) {
+  float b = positive_angle(a);
+  float div = TWO_PI / float(n);
+  float c = roundTo(b, div);
+  return look_upwards(c);
+}
+
+float applyAlignOption(float rawAngle, float delta) {
+  return
+    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions
+    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical
+    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis
+    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards
+    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal
+                    rawAngle;                // otherwise return back raw input angle
+}
+
+bool isAxisTitle = (axis.x == 0.0) &&
+                   (axis.y == 0.0) &&
+                   (axis.z == 0.0);
+
+void main() {
+  //Compute world offset
+  float axisDistance = position.z;
+  vec3 dataPosition = axisDistance * axis + offset;
+
+  float beta = angle; // i.e. user defined attributes for each tick
+
+  float axisAngle;
+  float clipAngle;
+  float flip;
+
+  if (enableAlign) {
+    axisAngle = (isAxisTitle) ? HALF_PI :
+                      computeViewAngle(dataPosition, dataPosition + axis);
+    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);
+
+    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;
+    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;
+
+    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),
+                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;
+
+    beta += applyAlignOption(clipAngle, flip * PI);
+  }
+
+  //Compute plane offset
+  vec2 planeCoord = position.xy * pixelScale;
+
+  mat2 planeXform = scale * mat2(
+     cos(beta), sin(beta),
+    -sin(beta), cos(beta)
+  );
+
+  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;
+
+  //Compute clip position
+  vec3 clipPosition = project(dataPosition);
+
+  //Apply text offset in clip coordinates
+  clipPosition += vec3(viewOffset, 0.0);
+
+  //Done
+  gl_Position = vec4(clipPosition, 1.0);
+}
+`]),h=s([`precision highp float;
+#define GLSLIFY 1
+
+uniform vec4 color;
+void main() {
+  gl_FragColor = color;
+}`]);a.Q=function(_){return l(_,f,h,null,[{name:"position",type:"vec3"}])};var d=s([`precision highp float;
+#define GLSLIFY 1
+
+attribute vec3 position;
+attribute vec3 normal;
+
+uniform mat4 model, view, projection;
+uniform vec3 enable;
+uniform vec3 bounds[2];
+
+varying vec3 colorChannel;
+
+void main() {
+
+  vec3 signAxis = sign(bounds[1] - bounds[0]);
+
+  vec3 realNormal = signAxis * normal;
+
+  if(dot(realNormal, enable) > 0.0) {
+    vec3 minRange = min(bounds[0], bounds[1]);
+    vec3 maxRange = max(bounds[0], bounds[1]);
+    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));
+    gl_Position = projection * (view * (model * vec4(nPosition, 1.0)));
+  } else {
+    gl_Position = vec4(0,0,0,0);
+  }
+
+  colorChannel = abs(realNormal);
+}
+`]),v=s([`precision highp float;
+#define GLSLIFY 1
+
+uniform vec4 colors[3];
+
+varying vec3 colorChannel;
+
+void main() {
+  gl_FragColor = colorChannel.x * colors[0] +
+                 colorChannel.y * colors[1] +
+                 colorChannel.z * colors[2];
+}`]);a.bg=function(_){return l(_,d,v,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},1888:function(i,a,o){"use strict";var s=o(8828),l=o(1338),u=o(4793).hp;o.g.__TYPEDARRAY_POOL||(o.g.__TYPEDARRAY_POOL={UINT8:l([32,0]),UINT16:l([32,0]),UINT32:l([32,0]),BIGUINT64:l([32,0]),INT8:l([32,0]),INT16:l([32,0]),INT32:l([32,0]),BIGINT64:l([32,0]),FLOAT:l([32,0]),DOUBLE:l([32,0]),DATA:l([32,0]),UINT8C:l([32,0]),BUFFER:l([32,0])});var c=typeof Uint8ClampedArray!="undefined",f=typeof BigUint64Array!="undefined",h=typeof BigInt64Array!="undefined",d=o.g.__TYPEDARRAY_POOL;d.UINT8C||(d.UINT8C=l([32,0])),d.BIGUINT64||(d.BIGUINT64=l([32,0])),d.BIGINT64||(d.BIGINT64=l([32,0])),d.BUFFER||(d.BUFFER=l([32,0]));var v=d.DATA,_=d.BUFFER;a.free=function(j){if(u.isBuffer(j))_[s.log2(j.length)].push(j);else{if(Object.prototype.toString.call(j)!=="[object ArrayBuffer]"&&(j=j.buffer),!j)return;var N=j.length||j.byteLength,H=s.log2(N)|0;v[H].push(j)}};function b(Z){if(Z){var 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+#define GLSLIFY 1
+
+attribute vec3 position, offset;
+attribute vec4 color;
+uniform mat4 model, view, projection;
+uniform float capSize;
+varying vec4 fragColor;
+varying vec3 fragPosition;
+
+void main() {
+  vec4 worldPosition  = model * vec4(position, 1.0);
+  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);
+  gl_Position         = projection * (view * worldPosition);
+  fragColor           = color;
+  fragPosition        = position;
+}`]),c=s([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform vec3 clipBounds[2];
+uniform float opacity;
+varying vec3 fragPosition;
+varying vec4 fragColor;
+
+void main() {
+  if (
+    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||
+    fragColor.a * opacity == 0.
+  ) discard;
+
+  gl_FragColor = opacity * fragColor;
+}`]);i.exports=function(f){return l(f,u,c,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},3502:function(i,a,o){i.exports=u;var s=o(5995),l=o(9127);function u(c,f){return l(s(c,f))}},3508:function(i,a,o){var 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Fe=L,Ae=0;Ae<3;++Ae)x(L[Ae],Ae,this.bounds,oe,_e);for(var Pe=this.gl,ge=C,Ae=0;Ae<3;++Ae)this.backgroundEnable[Ae]?ge[Ae]=_e[Ae]:ge[Ae]=0;this._background.draw(G,Z,j,N,ge,this.backgroundColor),this._lines.bind(G,Z,j,this);for(var Ae=0;Ae<3;++Ae){var Re=[0,0,0];_e[Ae]>0?Re[Ae]=N[1][Ae]:Re[Ae]=N[0][Ae];for(var ce=0;ce<2;++ce){var Ze=(Ae+1+ce)%3,ut=(Ae+1+(ce^1))%3;this.gridEnable[Ze]&&this._lines.drawGrid(Ze,ut,this.bounds,Re,this.gridColor[Ze],this.gridWidth[Ze]*this.pixelRatio)}for(var ce=0;ce<2;++ce){var Ze=(Ae+1+ce)%3,ut=(Ae+1+(ce^1))%3;this.zeroEnable[ut]&&Math.min(N[0][ut],N[1][ut])<=0&&Math.max(N[0][ut],N[1][ut])>=0&&this._lines.drawZero(Ze,ut,this.bounds,Re,this.zeroLineColor[ut],this.zeroLineWidth[ut]*this.pixelRatio)}}for(var Ae=0;Ae<3;++Ae){this.lineEnable[Ae]&&this._lines.drawAxisLine(Ae,this.bounds,Fe[Ae].primalOffset,this.lineColor[Ae],this.lineWidth[Ae]*this.pixelRatio),this.lineMirror[Ae]&&this._lines.drawAxisLine(Ae,this.bounds,Fe[Ae].mirrorOffset,this.lineColor[Ae],this.lineWidth[Ae]*this.pixelRatio);for(var pt=p(P,Fe[Ae].primalMinor),Zt=p(T,Fe[Ae].mirrorMinor),st=this.lineTickLength,ce=0;ce<3;++ce){var lt=Le/G[5*ce];pt[ce]*=st[ce]*lt,Zt[ce]*=st[ce]*lt}this.lineTickEnable[Ae]&&this._lines.drawAxisTicks(Ae,Fe[Ae].primalOffset,pt,this.lineTickColor[Ae],this.lineTickWidth[Ae]*this.pixelRatio),this.lineTickMirror[Ae]&&this._lines.drawAxisTicks(Ae,Fe[Ae].mirrorOffset,Zt,this.lineTickColor[Ae],this.lineTickWidth[Ae]*this.pixelRatio)}this._lines.unbind(),this._text.bind(G,Z,j,this.pixelRatio);var Gt,Nt=.5,Jt,sr;function wr(_t){sr=[0,0,0],sr[_t]=1}function cr(_t,It,mt){var er=(_t+1)%3,lr=(_t+2)%3,Tr=It[er],Lr=It[lr],ti=mt[er],Br=mt[lr];if(Tr>0&&Br>0){wr(er);return}else if(Tr>0&&Br<0){wr(er);return}else 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ce=0;ce<3;++ce)Qt[ce]+=Le*$e[ce]*this.labelPad[ce]/G[5*ce];Qt[Ae]+=.5*(N[0][Ae]+N[1][Ae]),this._text.drawLabel(Ae,this.labelSize[Ae],this.labelAngle[Ae],Qt,this.labelColor[Ae],[0,0,0],sr,Gt)}}this._text.unbind()},E.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null};function O(V,G){var Z=new k(V);return Z.update(G),Z}},3840:function(i){"use strict";i.exports=L;var a=0,o=1;function s(x,C,M,g,P,T){this._color=x,this.key=C,this.value=M,this.left=g,this.right=P,this._count=T}function l(x){return new s(x._color,x.key,x.value,x.left,x.right,x._count)}function u(x,C){return new s(x,C.key,C.value,C.left,C.right,C._count)}function c(x){x._count=1+(x.left?x.left._count:0)+(x.right?x.right._count:0)}function f(x,C){this._compare=x,this.root=C}var h=f.prototype;Object.defineProperty(h,"keys",{get:function(){var x=[];return 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vt=Ie.length;(!xe||xe<0)&&(xe=0),(!ke||ke<0||ke>vt)&&(ke=vt);for(var ir="",ar=xe;ar<ke;++ar)ir+=je[Ie[ar]];return ir}function lt(Ie,xe,ke){for(var vt=Ie.slice(xe,ke),ir="",ar=0;ar<vt.length-1;ar+=2)ir+=String.fromCharCode(vt[ar]+vt[ar+1]*256);return ir}T.prototype.slice=function(xe,ke){var vt=this.length;xe=~~xe,ke=ke===void 0?vt:~~ke,xe<0?(xe+=vt,xe<0&&(xe=0)):xe>vt&&(xe=vt),ke<0?(ke+=vt,ke<0&&(ke=0)):ke>vt&&(ke=vt),ke<xe&&(ke=xe);var ir=this.subarray(xe,ke);return Object.setPrototypeOf(ir,T.prototype),ir};function Gt(Ie,xe,ke){if(Ie%1!==0||Ie<0)throw new RangeError("offset is not uint");if(Ie+xe>ke)throw new RangeError("Trying to access beyond buffer length")}T.prototype.readUintLE=T.prototype.readUIntLE=function(xe,ke,vt){xe=xe>>>0,ke=ke>>>0,vt||Gt(xe,ke,this.length);for(var ir=this[xe],ar=1,vr=0;++vr<ke&&(ar*=256);)ir+=this[xe+vr]*ar;return ir},T.prototype.readUintBE=T.prototype.readUIntBE=function(xe,ke,vt){xe=xe>>>0,ke=ke>>>0,vt||Gt(xe,ke,this.length);for(var 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ir=ke+this[++xe]*Math.pow(2,8)+this[++xe]*Math.pow(2,16)+this[++xe]*Math.pow(2,24),ar=this[++xe]+this[++xe]*Math.pow(2,8)+this[++xe]*Math.pow(2,16)+vt*Math.pow(2,24);return BigInt(ir)+(BigInt(ar)<<BigInt(32))}),T.prototype.readBigUInt64BE=tt(function(xe){xe=xe>>>0,mt(xe,"offset");var ke=this[xe],vt=this[xe+7];(ke===void 0||vt===void 0)&&er(xe,this.length-8);var ir=ke*Math.pow(2,24)+this[++xe]*Math.pow(2,16)+this[++xe]*Math.pow(2,8)+this[++xe],ar=this[++xe]*Math.pow(2,24)+this[++xe]*Math.pow(2,16)+this[++xe]*Math.pow(2,8)+vt;return(BigInt(ir)<<BigInt(32))+BigInt(ar)}),T.prototype.readIntLE=function(xe,ke,vt){xe=xe>>>0,ke=ke>>>0,vt||Gt(xe,ke,this.length);for(var ir=this[xe],ar=1,vr=0;++vr<ke&&(ar*=256);)ir+=this[xe+vr]*ar;return ar*=128,ir>=ar&&(ir-=Math.pow(2,8*ke)),ir},T.prototype.readIntBE=function(xe,ke,vt){xe=xe>>>0,ke=ke>>>0,vt||Gt(xe,ke,this.length);for(var ir=ke,ar=1,vr=this[xe+--ir];ir>0&&(ar*=256);)vr+=this[xe+--ir]*ar;return ar*=128,vr>=ar&&(vr-=Math.pow(2,8*ke)),vr},T.prototype.readInt8=function(xe,ke){return xe=xe>>>0,ke||Gt(xe,1,this.length),this[xe]&128?(255-this[xe]+1)*-1:this[xe]},T.prototype.readInt16LE=function(xe,ke){xe=xe>>>0,ke||Gt(xe,2,this.length);var vt=this[xe]|this[xe+1]<<8;return vt&32768?vt|4294901760:vt},T.prototype.readInt16BE=function(xe,ke){xe=xe>>>0,ke||Gt(xe,2,this.length);var vt=this[xe+1]|this[xe]<<8;return vt&32768?vt|4294901760:vt},T.prototype.readInt32LE=function(xe,ke){return xe=xe>>>0,ke||Gt(xe,4,this.length),this[xe]|this[xe+1]<<8|this[xe+2]<<16|this[xe+3]<<24},T.prototype.readInt32BE=function(xe,ke){return xe=xe>>>0,ke||Gt(xe,4,this.length),this[xe]<<24|this[xe+1]<<16|this[xe+2]<<8|this[xe+3]},T.prototype.readBigInt64LE=tt(function(xe){xe=xe>>>0,mt(xe,"offset");var ke=this[xe],vt=this[xe+7];(ke===void 0||vt===void 0)&&er(xe,this.length-8);var ir=this[xe+4]+this[xe+5]*Math.pow(2,8)+this[xe+6]*Math.pow(2,16)+(vt<<24);return(BigInt(ir)<<BigInt(32))+BigInt(ke+this[++xe]*Math.pow(2,8)+this[++xe]*Math.pow(2,16)+this[++xe]*Math.pow(2,24))}),T.prototype.readBigInt64BE=tt(function(xe){xe=xe>>>0,mt(xe,"offset");var ke=this[xe],vt=this[xe+7];(ke===void 0||vt===void 0)&&er(xe,this.length-8);var ir=(ke<<24)+this[++xe]*Math.pow(2,16)+this[++xe]*Math.pow(2,8)+this[++xe];return(BigInt(ir)<<BigInt(32))+BigInt(this[++xe]*Math.pow(2,24)+this[++xe]*Math.pow(2,16)+this[++xe]*Math.pow(2,8)+vt)}),T.prototype.readFloatLE=function(xe,ke){return xe=xe>>>0,ke||Gt(xe,4,this.length),x.read(this,xe,!0,23,4)},T.prototype.readFloatBE=function(xe,ke){return xe=xe>>>0,ke||Gt(xe,4,this.length),x.read(this,xe,!1,23,4)},T.prototype.readDoubleLE=function(xe,ke){return xe=xe>>>0,ke||Gt(xe,8,this.length),x.read(this,xe,!0,52,8)},T.prototype.readDoubleBE=function(xe,ke){return xe=xe>>>0,ke||Gt(xe,8,this.length),x.read(this,xe,!1,52,8)};function Nt(Ie,xe,ke,vt,ir,ar){if(!T.isBuffer(Ie))throw new TypeError('"buffer" argument must be a Buffer instance');if(xe>ir||xe<ar)throw new RangeError('"value" argument is out of bounds');if(ke+vt>Ie.length)throw new RangeError("Index out of range")}T.prototype.writeUintLE=T.prototype.writeUIntLE=function(xe,ke,vt,ir){if(xe=+xe,ke=ke>>>0,vt=vt>>>0,!ir){var ar=Math.pow(2,8*vt)-1;Nt(this,xe,ke,vt,ar,0)}var vr=1,ii=0;for(this[ke]=xe&255;++ii<vt&&(vr*=256);)this[ke+ii]=xe/vr&255;return ke+vt},T.prototype.writeUintBE=T.prototype.writeUIntBE=function(xe,ke,vt,ir){if(xe=+xe,ke=ke>>>0,vt=vt>>>0,!ir){var ar=Math.pow(2,8*vt)-1;Nt(this,xe,ke,vt,ar,0)}var vr=vt-1,ii=1;for(this[ke+vr]=xe&255;--vr>=0&&(ii*=256);)this[ke+vr]=xe/ii&255;return ke+vt},T.prototype.writeUint8=T.prototype.writeUInt8=function(xe,ke,vt){return xe=+xe,ke=ke>>>0,vt||Nt(this,xe,ke,1,255,0),this[ke]=xe&255,ke+1},T.prototype.writeUint16LE=T.prototype.writeUInt16LE=function(xe,ke,vt){return xe=+xe,ke=ke>>>0,vt||Nt(this,xe,ke,2,65535,0),this[ke]=xe&255,this[ke+1]=xe>>>8,ke+2},T.prototype.writeUint16BE=T.prototype.writeUInt16BE=function(xe,ke,vt){return xe=+xe,ke=ke>>>0,vt||Nt(this,xe,ke,2,65535,0),this[ke]=xe>>>8,this[ke+1]=xe&255,ke+2},T.prototype.writeUint32LE=T.prototype.writeUInt32LE=function(xe,ke,vt){return xe=+xe,ke=ke>>>0,vt||Nt(this,xe,ke,4,4294967295,0),this[ke+3]=xe>>>24,this[ke+2]=xe>>>16,this[ke+1]=xe>>>8,this[ke]=xe&255,ke+4},T.prototype.writeUint32BE=T.prototype.writeUInt32BE=function(xe,ke,vt){return xe=+xe,ke=ke>>>0,vt||Nt(this,xe,ke,4,4294967295,0),this[ke]=xe>>>24,this[ke+1]=xe>>>16,this[ke+2]=xe>>>8,this[ke+3]=xe&255,ke+4};function Jt(Ie,xe,ke,vt,ir){It(xe,vt,ir,Ie,ke,7);var ar=Number(xe&BigInt(4294967295));Ie[ke++]=ar,ar=ar>>8,Ie[ke++]=ar,ar=ar>>8,Ie[ke++]=ar,ar=ar>>8,Ie[ke++]=ar;var vr=Number(xe>>BigInt(32)&BigInt(4294967295));return Ie[ke++]=vr,vr=vr>>8,Ie[ke++]=vr,vr=vr>>8,Ie[ke++]=vr,vr=vr>>8,Ie[ke++]=vr,ke}function sr(Ie,xe,ke,vt,ir){It(xe,vt,ir,Ie,ke,7);var ar=Number(xe&BigInt(4294967295));Ie[ke+7]=ar,ar=ar>>8,Ie[ke+6]=ar,ar=ar>>8,Ie[ke+5]=ar,ar=ar>>8,Ie[ke+4]=ar;var vr=Number(xe>>BigInt(32)&BigInt(4294967295));return Ie[ke+3]=vr,vr=vr>>8,Ie[ke+2]=vr,vr=vr>>8,Ie[ke+1]=vr,vr=vr>>8,Ie[ke]=vr,ke+8}T.prototype.writeBigUInt64LE=tt(function(xe){var ke=arguments.length>1&&arguments[1]!==void 0?arguments[1]:0;return Jt(this,xe,ke,BigInt(0),BigInt("0xffffffffffffffff"))}),T.prototype.writeBigUInt64BE=tt(function(xe){var ke=arguments.length>1&&arguments[1]!==void 0?arguments[1]:0;return sr(this,xe,ke,BigInt(0),BigInt("0xffffffffffffffff"))}),T.prototype.writeIntLE=function(xe,ke,vt,ir){if(xe=+xe,ke=ke>>>0,!ir){var ar=Math.pow(2,8*vt-1);Nt(this,xe,ke,vt,ar-1,-ar)}var vr=0,ii=1,pi=0;for(this[ke]=xe&255;++vr<vt&&(ii*=256);)xe<0&&pi===0&&this[ke+vr-1]!==0&&(pi=1),this[ke+vr]=(xe/ii>>0)-pi&255;return ke+vt},T.prototype.writeIntBE=function(xe,ke,vt,ir){if(xe=+xe,ke=ke>>>0,!ir){var ar=Math.pow(2,8*vt-1);Nt(this,xe,ke,vt,ar-1,-ar)}var vr=vt-1,ii=1,pi=0;for(this[ke+vr]=xe&255;--vr>=0&&(ii*=256);)xe<0&&pi===0&&this[ke+vr+1]!==0&&(pi=1),this[ke+vr]=(xe/ii>>0)-pi&255;return ke+vt},T.prototype.writeInt8=function(xe,ke,vt){return xe=+xe,ke=ke>>>0,vt||Nt(this,xe,ke,1,127,-128),xe<0&&(xe=255+xe+1),this[ke]=xe&255,ke+1},T.prototype.writeInt16LE=function(xe,ke,vt){return xe=+xe,ke=ke>>>0,vt||Nt(this,xe,ke,2,32767,-32768),this[ke]=xe&255,this[ke+1]=xe>>>8,ke+2},T.prototype.writeInt16BE=function(xe,ke,vt){return xe=+xe,ke=ke>>>0,vt||Nt(this,xe,ke,2,32767,-32768),this[ke]=xe>>>8,this[ke+1]=xe&255,ke+2},T.prototype.writeInt32LE=function(xe,ke,vt){return xe=+xe,ke=ke>>>0,vt||Nt(this,xe,ke,4,2147483647,-2147483648),this[ke]=xe&255,this[ke+1]=xe>>>8,this[ke+2]=xe>>>16,this[ke+3]=xe>>>24,ke+4},T.prototype.writeInt32BE=function(xe,ke,vt){return xe=+xe,ke=ke>>>0,vt||Nt(this,xe,ke,4,2147483647,-2147483648),xe<0&&(xe=4294967295+xe+1),this[ke]=xe>>>24,this[ke+1]=xe>>>16,this[ke+2]=xe>>>8,this[ke+3]=xe&255,ke+4},T.prototype.writeBigInt64LE=tt(function(xe){var ke=arguments.length>1&&arguments[1]!==void 0?arguments[1]:0;return Jt(this,xe,ke,-BigInt("0x8000000000000000"),BigInt("0x7fffffffffffffff"))}),T.prototype.writeBigInt64BE=tt(function(xe){var ke=arguments.length>1&&arguments[1]!==void 0?arguments[1]:0;return sr(this,xe,ke,-BigInt("0x8000000000000000"),BigInt("0x7fffffffffffffff"))});function wr(Ie,xe,ke,vt,ir,ar){if(ke+vt>Ie.length)throw new RangeError("Index out of range");if(ke<0)throw new RangeError("Index out of range")}function cr(Ie,xe,ke,vt,ir){return xe=+xe,ke=ke>>>0,ir||wr(Ie,xe,ke,4,34028234663852886e22,-34028234663852886e22),x.write(Ie,xe,ke,vt,23,4),ke+4}T.prototype.writeFloatLE=function(xe,ke,vt){return cr(this,xe,ke,!0,vt)},T.prototype.writeFloatBE=function(xe,ke,vt){return cr(this,xe,ke,!1,vt)};function $e(Ie,xe,ke,vt,ir){return xe=+xe,ke=ke>>>0,ir||wr(Ie,xe,ke,8,17976931348623157e292,-17976931348623157e292),x.write(Ie,xe,ke,vt,52,8),ke+8}T.prototype.writeDoubleLE=function(xe,ke,vt){return $e(this,xe,ke,!0,vt)},T.prototype.writeDoubleBE=function(xe,ke,vt){return $e(this,xe,ke,!1,vt)},T.prototype.copy=function(xe,ke,vt,ir){if(!T.isBuffer(xe))throw new TypeError("argument should be a Buffer");if(vt||(vt=0),!ir&&ir!==0&&(ir=this.length),ke>=xe.length&&(ke=xe.length),ke||(ke=0),ir>0&&ir<vt&&(ir=vt),ir===vt||xe.length===0||this.length===0)return 0;if(ke<0)throw new RangeError("targetStart out of bounds");if(vt<0||vt>=this.length)throw new RangeError("Index out of range");if(ir<0)throw new RangeError("sourceEnd out of bounds");ir>this.length&&(ir=this.length),xe.length-ke<ir-vt&&(ir=xe.length-ke+vt);var ar=ir-vt;return this===xe&&typeof Uint8Array.prototype.copyWithin=="function"?this.copyWithin(ke,vt,ir):Uint8Array.prototype.set.call(xe,this.subarray(vt,ir),ke),ar},T.prototype.fill=function(xe,ke,vt,ir){if(typeof xe=="string"){if(typeof ke=="string"?(ir=ke,ke=0,vt=this.length):typeof vt=="string"&&(ir=vt,vt=this.length),ir!==void 0&&typeof ir!="string")throw new TypeError("encoding must be a string");if(typeof ir=="string"&&!T.isEncoding(ir))throw new TypeError("Unknown encoding: "+ir);if(xe.length===1){var ar=xe.charCodeAt(0);(ir==="utf8"&&ar<128||ir==="latin1")&&(xe=ar)}}else typeof xe=="number"?xe=xe&255:typeof xe=="boolean"&&(xe=Number(xe));if(ke<0||this.length<ke||this.length<vt)throw new RangeError("Out of range index");if(vt<=ke)return this;ke=ke>>>0,vt=vt===void 0?this.length:vt>>>0,xe||(xe=0);var vr;if(typeof xe=="number")for(vr=ke;vr<vt;++vr)this[vr]=xe;else{var ii=T.isBuffer(xe)?xe:T.from(xe,ir),pi=ii.length;if(pi===0)throw new TypeError('The value "'+xe+'" is invalid for argument 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T[0]>2&&T[1]>2&&M(P.pick(-1,-1).lo(1,1).hi(T[0]-2,T[1]-2),g.pick(-1,-1,0).lo(1,1).hi(T[0]-2,T[1]-2),g.pick(-1,-1,1).lo(1,1).hi(T[0]-2,T[1]-2)),T[1]>2&&(C(P.pick(0,-1).lo(1).hi(T[1]-2),g.pick(0,-1,1).lo(1).hi(T[1]-2)),x(g.pick(0,-1,0).lo(1).hi(T[1]-2))),T[1]>2&&(C(P.pick(T[0]-1,-1).lo(1).hi(T[1]-2),g.pick(T[0]-1,-1,1).lo(1).hi(T[1]-2)),x(g.pick(T[0]-1,-1,0).lo(1).hi(T[1]-2))),T[0]>2&&(C(P.pick(-1,0).lo(1).hi(T[0]-2),g.pick(-1,0,0).lo(1).hi(T[0]-2)),x(g.pick(-1,0,1).lo(1).hi(T[0]-2))),T[0]>2&&(C(P.pick(-1,T[1]-1).lo(1).hi(T[0]-2),g.pick(-1,T[1]-1,0).lo(1).hi(T[0]-2)),x(g.pick(-1,T[1]-1,1).lo(1).hi(T[0]-2))),g.set(0,0,0,0),g.set(0,0,1,0),g.set(T[0]-1,0,0,0),g.set(T[0]-1,0,1,0),g.set(0,T[1]-1,0,0),g.set(0,T[1]-1,1,0),g.set(T[0]-1,T[1]-1,0,0),g.set(T[0]-1,T[1]-1,1,0),g}}function S(L){var x=L.join(),T=v[x];if(T)return T;for(var C=L.length,M=[b,p],g=1;g<=C;++g)M.push(k(g));var P=E,T=P.apply(void 0,M);return v[x]=T,T}i.exports=function(x,C,M){if(Array.isArray(M)||(typeof M=="string"?M=s(C.dimension,M):M=s(C.dimension,"clamp")),C.size===0)return x;if(C.dimension===0)return x.set(0),x;var g=S(M);return g(x,C)}},6204:function(i){"use strict";i.exports=a;function a(o){var s,l,u,c=o.length,f=0;for(s=0;s<c;++s)f+=o[s].length;var h=new Array(f),d=0;for(s=0;s<c;++s){var v=o[s],_=v.length;for(l=0;l<_;++l){var b=h[d++]=new Array(_-1),p=0;for(u=0;u<_;++u)u!==l&&(b[p++]=v[u]);if(l&1){var k=b[1];b[1]=b[0],b[0]=k}}}return h}},6296:function(i,a,o){"use strict";i.exports=h;var s=o(7261),l=o(9977),u=o(1811);function c(d,v){this._controllerNames=Object.keys(d),this._controllerList=this._controllerNames.map(function(_){return d[_]}),this._mode=v,this._active=d[v],this._active||(this._mode="turntable",this._active=d.turntable),this.modes=this._controllerNames,this.computedMatrix=this._active.computedMatrix,this.computedEye=this._active.computedEye,this.computedUp=this._active.computedUp,this.computedCenter=this._active.computedCenter,this.computedRadius=this._active.computedRadius}var f=c.prototype;f.flush=function(d){for(var v=this._controllerList,_=0;_<v.length;++_)v[_].flush(d)},f.idle=function(d){for(var v=this._controllerList,_=0;_<v.length;++_)v[_].idle(d)},f.lookAt=function(d,v,_,b){for(var p=this._controllerList,k=0;k<p.length;++k)p[k].lookAt(d,v,_,b)},f.rotate=function(d,v,_,b){for(var p=this._controllerList,k=0;k<p.length;++k)p[k].rotate(d,v,_,b)},f.pan=function(d,v,_,b){for(var p=this._controllerList,k=0;k<p.length;++k)p[k].pan(d,v,_,b)},f.translate=function(d,v,_,b){for(var p=this._controllerList,k=0;k<p.length;++k)p[k].translate(d,v,_,b)},f.setMatrix=function(d,v){for(var _=this._controllerList,b=0;b<_.length;++b)_[b].setMatrix(d,v)},f.setDistanceLimits=function(d,v){for(var _=this._controllerList,b=0;b<_.length;++b)_[b].setDistanceLimits(d,v)},f.setDistance=function(d,v){for(var _=this._controllerList,b=0;b<_.length;++b)_[b].setDistance(d,v)},f.recalcMatrix=function(d){this._active.recalcMatrix(d)},f.getDistance=function(d){return this._active.getDistance(d)},f.getDistanceLimits=function(d){return this._active.getDistanceLimits(d)},f.lastT=function(){return this._active.lastT()},f.setMode=function(d){if(d!==this._mode){var v=this._controllerNames.indexOf(d);if(!(v<0)){var _=this._active,b=this._controllerList[v],p=Math.max(_.lastT(),b.lastT());_.recalcMatrix(p),b.setMatrix(p,_.computedMatrix),this._active=b,this._mode=d,this.computedMatrix=this._active.computedMatrix,this.computedEye=this._active.computedEye,this.computedUp=this._active.computedUp,this.computedCenter=this._active.computedCenter,this.computedRadius=this._active.computedRadius}}},f.getMode=function(){return this._mode};function h(d){d=d||{};var v=d.eye||[0,0,1],_=d.center||[0,0,0],b=d.up||[0,1,0],p=d.distanceLimits||[0,1/0],k=d.mode||"turntable",E=s(),S=l(),L=u();return E.setDistanceLimits(p[0],p[1]),E.lookAt(0,v,_,b),S.setDistanceLimits(p[0],p[1]),S.lookAt(0,v,_,b),L.setDistanceLimits(p[0],p[1]),L.lookAt(0,v,_,b),new c({turntable:E,orbit:S,matrix:L},k)}},6330:function(i,a,o){"use strict";var s=o(1533);i.exports=l;function l(u){return Array.isArray(u)&&u.length===2&&s(u[0])&&s(u[1])}},6405:function(i,a,o){"use strict";var s=o(2931);i.exports=function(u,c){var f=u.positions,h=u.vectors,d={positions:[],vertexIntensity:[],vertexIntensityBounds:u.vertexIntensityBounds,vectors:[],cells:[],coneOffset:u.coneOffset,colormap:u.colormap};if(u.positions.length===0)return c&&(c[0]=[0,0,0],c[1]=[0,0,0]),d;for(var v=0,_=1/0,b=-1/0,p=1/0,k=-1/0,E=1/0,S=-1/0,L=null,x=null,C=[],M=1/0,g=!1,P=u.coneSizemode==="raw",T=0;T<f.length;T++){var z=f[T];_=Math.min(z[0],_),b=Math.max(z[0],b),p=Math.min(z[1],p),k=Math.max(z[1],k),E=Math.min(z[2],E),S=Math.max(z[2],S);var O=h[T];if(s.length(O)>v&&(v=s.length(O)),T&&!P){var V=2*s.distance(L,z)/(s.length(x)+s.length(O));V?(M=Math.min(M,V),g=!1):g=!0}g||(L=z,x=O),C.push(O)}var G=[_,p,E],Z=[b,k,S];c&&(c[0]=G,c[1]=Z),v===0&&(v=1);var j=1/v;isFinite(M)||(M=1),d.vectorScale=M;var N=u.coneSize||(P?1:.5);u.absoluteConeSize&&(N=u.absoluteConeSize*j),d.coneScale=N;for(var T=0,H=0;T<f.length;T++)for(var z=f[T],te=z[0],oe=z[1],_e=z[2],Ee=C[T],Ce=s.length(Ee)*j,me=0,ie=8;me<ie;me++){d.positions.push([te,oe,_e,H++]),d.positions.push([te,oe,_e,H++]),d.positions.push([te,oe,_e,H++]),d.positions.push([te,oe,_e,H++]),d.positions.push([te,oe,_e,H++]),d.positions.push([te,oe,_e,H++]),d.vectors.push(Ee),d.vectors.push(Ee),d.vectors.push(Ee),d.vectors.push(Ee),d.vectors.push(Ee),d.vectors.push(Ee),d.vertexIntensity.push(Ce,Ce,Ce),d.vertexIntensity.push(Ce,Ce,Ce);var Se=d.positions.length;d.cells.push([Se-6,Se-5,Se-4],[Se-3,Se-2,Se-1])}return d};var l=o(614);i.exports.createMesh=o(9060),i.exports.createConeMesh=function(u,c){return i.exports.createMesh(u,c,{shaders:l,traceType:"cone"})}},6429:function(i,a,o){"use strict";i.exports=x;var s=o(8828),l=o(6760),u=o(5202),c=o(3250),f=new Array(16),h=new Array(8),d=new Array(8),v=new Array(3),_=[0,0,0];(function(){for(var C=0;C<8;++C)h[C]=[1,1,1,1],d[C]=[1,1,1]})();function b(C,M,g){for(var P=0;P<4;++P){C[P]=g[12+P];for(var T=0;T<3;++T)C[P]+=M[T]*g[4*T+P]}}var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function k(C){for(var M=0;M<p.length;++M)if(C=u.positive(C,p[M]),C.length<3)return 0;for(var g=C[0],P=g[0]/g[3],T=g[1]/g[3],z=0,M=1;M+1<C.length;++M){var O=C[M],V=C[M+1],G=O[0]/O[3],Z=O[1]/O[3],j=V[0]/V[3],N=V[1]/V[3],H=G-P,te=Z-T,oe=j-P,_e=N-T;z+=Math.abs(H*_e-te*oe)}return z}var E=[1,1,1],S=[0,0,0],L={cubeEdges:E,axis:S};function x(C,M,g,P,T){l(f,M,C),l(f,g,f);for(var z=0,O=0;O<2;++O){v[2]=P[O][2];for(var V=0;V<2;++V){v[1]=P[V][1];for(var G=0;G<2;++G)v[0]=P[G][0],b(h[z],v,f),z+=1}}for(var Z=-1,O=0;O<8;++O){for(var j=h[O][3],N=0;N<3;++N)d[O][N]=h[O][N]/j;T&&(d[O][2]*=-1),j<0&&(Z<0||d[O][2]<d[Z][2])&&(Z=O)}if(Z<0){Z=0;for(var H=0;H<3;++H){for(var te=(H+2)%3,oe=(H+1)%3,_e=-1,Ee=-1,Ce=0;Ce<2;++Ce){var me=Ce<<H,ie=me+(Ce<<te)+(1-Ce<<oe),Se=me+(1-Ce<<te)+(Ce<<oe);c(d[me],d[ie],d[Se],_)<0||(Ce?_e=1:Ee=1)}if(_e<0||Ee<0){Ee>_e&&(Z|=1<<H);continue}for(var Ce=0;Ce<2;++Ce){var me=Ce<<H,ie=me+(Ce<<te)+(1-Ce<<oe),Se=me+(1-Ce<<te)+(Ce<<oe),Le=k([h[me],h[ie],h[Se],h[me+(1<<te)+(1<<oe)]]);Ce?_e=Le:Ee=Le}if(Ee>_e){Z|=1<<H;continue}}}for(var Ae=7^Z,Fe=-1,O=0;O<8;++O)O===Z||O===Ae||(Fe<0||d[Fe][1]>d[O][1])&&(Fe=O);for(var Pe=-1,O=0;O<3;++O){var ge=Fe^1<<O;if(!(ge===Z||ge===Ae)){Pe<0&&(Pe=ge);var oe=d[ge];oe[0]<d[Pe][0]&&(Pe=ge)}}for(var Re=-1,O=0;O<3;++O){var ge=Fe^1<<O;if(!(ge===Z||ge===Ae||ge===Pe)){Re<0&&(Re=ge);var oe=d[ge];oe[0]>d[Re][0]&&(Re=ge)}}var ce=E;ce[0]=ce[1]=ce[2]=0,ce[s.log2(Pe^Fe)]=Fe&Pe,ce[s.log2(Fe^Re)]=Fe&Re;var Ze=Re^7;Ze===Z||Ze===Ae?(Ze=Pe^7,ce[s.log2(Re^Ze)]=Ze&Re):ce[s.log2(Pe^Ze)]=Ze&Pe;for(var ut=S,pt=Z,H=0;H<3;++H)pt&1<<H?ut[H]=-1:ut[H]=1;return L}},6444:function(i,a){"use strict";a.create=s,a.equal=l;function o(u,c){var f=u+"",h=f.indexOf("."),d=0;h>=0&&(d=f.length-h-1);var v=Math.pow(10,d),_=Math.round(u*c*v),b=_+"";if(b.indexOf("e")>=0)return b;var p=_/v,k=_%v;_<0?(p=-Math.ceil(p)|0,k=-k|0):(p=Math.floor(p)|0,k=k|0);var E=""+p;if(_<0&&(E="-"+E),d){for(var S=""+k;S.length<d;)S="0"+S;return E+"."+S}else return E}function s(u,c){for(var f=[],h=0;h<3;++h){for(var d=[],v=.5*(u[0][h]+u[1][h]),_=0;_*c[h]<=u[1][h];++_)d.push({x:_*c[h],text:o(c[h],_)});for(var _=-1;_*c[h]>=u[0][h];--_)d.push({x:_*c[h],text:o(c[h],_)});f.push(d)}return f}function l(u,c){for(var f=0;f<3;++f){if(u[f].length!==c[f].length)return!1;for(var h=0;h<u[f].length;++h){var d=u[f][h],v=c[f][h];if(d.x!==v.x||d.text!==v.text||d.font!==v.font||d.fontColor!==v.fontColor||d.fontSize!==v.fontSize||d.dx!==v.dx||d.dy!==v.dy)return!1}}return!0}},6468:function(i){i.exports=function(o){return atob(o)}},6504:function(i,a,o){"use strict";var s=o(869);i.exports=l;function l(u,c){return s(u[0].mul(c[0]),u[1].mul(c[1]))}},6582:function(i,a,o){var s=o(7894);i.exports=l;function l(u,c,f,h){var d,v,_,b,p,k,E,S,L,x,C=c[0],M=c[1],g=c[2],P=h[0],T=h[1],z=h[2],O=f[0],V=f[1],G=f[2];return Math.abs(C-O)<1e-6&&Math.abs(M-V)<1e-6&&Math.abs(g-G)<1e-6?s(u):(E=C-O,S=M-V,L=g-G,x=1/Math.sqrt(E*E+S*S+L*L),E*=x,S*=x,L*=x,d=T*L-z*S,v=z*E-P*L,_=P*S-T*E,x=Math.sqrt(d*d+v*v+_*_),x?(x=1/x,d*=x,v*=x,_*=x):(d=0,v=0,_=0),b=S*_-L*v,p=L*d-E*_,k=E*v-S*d,x=Math.sqrt(b*b+p*p+k*k),x?(x=1/x,b*=x,p*=x,k*=x):(b=0,p=0,k=0),u[0]=d,u[1]=b,u[2]=E,u[3]=0,u[4]=v,u[5]=p,u[6]=S,u[7]=0,u[8]=_,u[9]=k,u[10]=L,u[11]=0,u[12]=-(d*C+v*M+_*g),u[13]=-(b*C+p*M+k*g),u[14]=-(E*C+S*M+L*g),u[15]=1,u)}},6621:function(i){i.exports=a;function a(o,s,l){return o[0]=s[0]*l,o[1]=s[1]*l,o[2]=s[2]*l,o}},6658:function(i){i.exports=a;function a(o,s,l,u){var c=s[0],f=s[1],h=s[2];return o[0]=c+u*(l[0]-c),o[1]=f+u*(l[1]-f),o[2]=h+u*(l[2]-h),o}},6690:function(i){i.exports=a;function a(o,s,l){return o[0]=s[0]/l[0],o[1]=s[1]/l[1],o[2]=s[2]/l[2],o}},6729:function(i,a,o){"use strict";var s=o(3642),l=o(395);i.exports=u;function u(d){var v,_,b,p,k,E,S,L,g,x,C;if(d||(d={}),L=(d.nshades||72)-1,S=d.format||"hex",E=d.colormap,E||(E="jet"),typeof E=="string"){if(E=E.toLowerCase(),!s[E])throw Error(E+" not a supported colorscale");k=s[E]}else if(Array.isArray(E))k=E.slice();else throw Error("unsupported colormap option",E);if(k.length>L+1)throw new Error(E+" map requires nshades to be at least size "+k.length);Array.isArray(d.alpha)?d.alpha.length!==2?x=[1,1]:x=d.alpha.slice():typeof d.alpha=="number"?x=[d.alpha,d.alpha]:x=[1,1],v=k.map(function(z){return Math.round(z.index*L)}),x[0]=Math.min(Math.max(x[0],0),1),x[1]=Math.min(Math.max(x[1],0),1);var M=k.map(function(z,O){var V=k[O].index,G=k[O].rgb.slice();return G.length===4&&G[3]>=0&&G[3]<=1||(G[3]=x[0]+(x[1]-x[0])*V),G}),g=[];for(C=0;C<v.length-1;++C){p=v[C+1]-v[C],_=M[C],b=M[C+1];for(var P=0;P<p;P++){var T=P/p;g.push([Math.round(l(_[0],b[0],T)),Math.round(l(_[1],b[1],T)),Math.round(l(_[2],b[2],T)),l(_[3],b[3],T)])}}return g.push(k[k.length-1].rgb.concat(x[1])),S==="hex"?g=g.map(f):S==="rgbaString"?g=g.map(h):S==="float"&&(g=g.map(c)),g}function c(d){return[d[0]/255,d[1]/255,d[2]/255,d[3]]}function f(d){for(var v,_="#",b=0;b<3;++b)v=d[b],v=v.toString(16),_+=("00"+v).substr(v.length);return _}function h(d){return"rgba("+d.join(",")+")"}},6740:function(i,a,o){var s=o(3236),l=s([`precision highp float;
+
+precision highp float;
+#define GLSLIFY 1
+
+vec3 getOrthogonalVector(vec3 v) {
+  // Return up-vector for only-z vector.
+  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).
+  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.
+  // Assign z = 0, x = -b, y = a:
+  // a*-b + b*a + c*0 = -ba + ba + 0 = 0
+  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {
+    return normalize(vec3(-v.y, v.x, 0.0));
+  } else {
+    return normalize(vec3(0.0, v.z, -v.y));
+  }
+}
+
+// Calculate the tube vertex and normal at the given index.
+//
+// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.
+//
+// Each tube segment is made up of a ring of vertices.
+// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.
+// The indexes of tube segments run from 0 to 8.
+//
+vec3 getTubePosition(vec3 d, float index, out vec3 normal) {
+  float segmentCount = 8.0;
+
+  float angle = 2.0 * 3.14159 * (index / segmentCount);
+
+  vec3 u = getOrthogonalVector(d);
+  vec3 v = normalize(cross(u, d));
+
+  vec3 x = u * cos(angle) * length(d);
+  vec3 y = v * sin(angle) * length(d);
+  vec3 v3 = x + y;
+
+  normal = normalize(v3);
+
+  return v3;
+}
+
+attribute vec4 vector;
+attribute vec4 color, position;
+attribute vec2 uv;
+
+uniform float vectorScale, tubeScale;
+uniform mat4 model, view, projection, inverseModel;
+uniform vec3 eyePosition, lightPosition;
+
+varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;
+varying vec4 f_color;
+varying vec2 f_uv;
+
+void main() {
+  // Scale the vector magnitude to stay constant with
+  // model & view changes.
+  vec3 normal;
+  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);
+  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);
+
+  //Lighting geometry parameters
+  vec4 cameraCoordinate = view * tubePosition;
+  cameraCoordinate.xyz /= cameraCoordinate.w;
+  f_lightDirection = lightPosition - cameraCoordinate.xyz;
+  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;
+  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);
+
+  // vec4 m_position  = model * vec4(tubePosition, 1.0);
+  vec4 t_position  = view * tubePosition;
+  gl_Position      = projection * t_position;
+
+  f_color          = color;
+  f_data           = tubePosition.xyz;
+  f_position       = position.xyz;
+  f_uv             = uv;
+}
+`]),u=s([`#extension GL_OES_standard_derivatives : enable
+
+precision highp float;
+#define GLSLIFY 1
+
+float beckmannDistribution(float x, float roughness) {
+  float NdotH = max(x, 0.0001);
+  float cos2Alpha = NdotH * NdotH;
+  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;
+  float roughness2 = roughness * roughness;
+  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;
+  return exp(tan2Alpha / roughness2) / denom;
+}
+
+float cookTorranceSpecular(
+  vec3 lightDirection,
+  vec3 viewDirection,
+  vec3 surfaceNormal,
+  float roughness,
+  float fresnel) {
+
+  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);
+  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);
+
+  //Half angle vector
+  vec3 H = normalize(lightDirection + viewDirection);
+
+  //Geometric term
+  float NdotH = max(dot(surfaceNormal, H), 0.0);
+  float VdotH = max(dot(viewDirection, H), 0.000001);
+  float LdotH = max(dot(lightDirection, H), 0.000001);
+  float G1 = (2.0 * NdotH * VdotN) / VdotH;
+  float G2 = (2.0 * NdotH * LdotN) / LdotH;
+  float G = min(1.0, min(G1, G2));
+  
+  //Distribution term
+  float D = beckmannDistribution(NdotH, roughness);
+
+  //Fresnel term
+  float F = pow(1.0 - VdotN, fresnel);
+
+  //Multiply terms and done
+  return  G * F * D / max(3.14159265 * VdotN, 0.000001);
+}
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform vec3 clipBounds[2];
+uniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;
+uniform sampler2D texture;
+
+varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;
+varying vec4 f_color;
+varying vec2 f_uv;
+
+void main() {
+  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;
+  vec3 N = normalize(f_normal);
+  vec3 L = normalize(f_lightDirection);
+  vec3 V = normalize(f_eyeDirection);
+
+  if(gl_FrontFacing) {
+    N = -N;
+  }
+
+  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));
+  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);
+
+  vec4 surfaceColor = f_color * texture2D(texture, f_uv);
+  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);
+
+  gl_FragColor = litColor * opacity;
+}
+`]),c=s([`precision highp float;
+
+precision highp float;
+#define GLSLIFY 1
+
+vec3 getOrthogonalVector(vec3 v) {
+  // Return up-vector for only-z vector.
+  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).
+  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.
+  // Assign z = 0, x = -b, y = a:
+  // a*-b + b*a + c*0 = -ba + ba + 0 = 0
+  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {
+    return normalize(vec3(-v.y, v.x, 0.0));
+  } else {
+    return normalize(vec3(0.0, v.z, -v.y));
+  }
+}
+
+// Calculate the tube vertex and normal at the given index.
+//
+// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.
+//
+// Each tube segment is made up of a ring of vertices.
+// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.
+// The indexes of tube segments run from 0 to 8.
+//
+vec3 getTubePosition(vec3 d, float index, out vec3 normal) {
+  float segmentCount = 8.0;
+
+  float angle = 2.0 * 3.14159 * (index / segmentCount);
+
+  vec3 u = getOrthogonalVector(d);
+  vec3 v = normalize(cross(u, d));
+
+  vec3 x = u * cos(angle) * length(d);
+  vec3 y = v * sin(angle) * length(d);
+  vec3 v3 = x + y;
+
+  normal = normalize(v3);
+
+  return v3;
+}
+
+attribute vec4 vector;
+attribute vec4 position;
+attribute vec4 id;
+
+uniform mat4 model, view, projection;
+uniform float tubeScale;
+
+varying vec3 f_position;
+varying vec4 f_id;
+
+void main() {
+  vec3 normal;
+  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);
+  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);
+
+  gl_Position = projection * (view * tubePosition);
+  f_id        = id;
+  f_position  = position.xyz;
+}
+`]),f=s([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform vec3  clipBounds[2];
+uniform float pickId;
+
+varying vec3 f_position;
+varying vec4 f_id;
+
+void main() {
+  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;
+
+  gl_FragColor = vec4(pickId, f_id.xyz);
+}`]);a.meshShader={vertex:l,fragment:u,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},a.pickShader={vertex:c,fragment:f,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},6743:function(i){i.exports=a;function a(o,s,l){var u=s[0],c=s[1],f=s[2],h=s[3],d=u+u,v=c+c,_=f+f,b=u*d,p=u*v,k=u*_,E=c*v,S=c*_,L=f*_,x=h*d,C=h*v,M=h*_;return o[0]=1-(E+L),o[1]=p+M,o[2]=k-C,o[3]=0,o[4]=p-M,o[5]=1-(b+L),o[6]=S+x,o[7]=0,o[8]=k+C,o[9]=S-x,o[10]=1-(b+E),o[11]=0,o[12]=l[0],o[13]=l[1],o[14]=l[2],o[15]=1,o}},6760:function(i){i.exports=a;function a(o,s,l){var u=s[0],c=s[1],f=s[2],h=s[3],d=s[4],v=s[5],_=s[6],b=s[7],p=s[8],k=s[9],E=s[10],S=s[11],L=s[12],x=s[13],C=s[14],M=s[15],g=l[0],P=l[1],T=l[2],z=l[3];return 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b=["","0","00","000","0000","00000","000000","0000000","00000000","000000000","0000000000","00000000000","000000000000","0000000000000","00000000000000","000000000000000","0000000000000000","00000000000000000","000000000000000000","0000000000000000000","00000000000000000000","000000000000000000000","0000000000000000000000","00000000000000000000000","000000000000000000000000","0000000000000000000000000"],p=[0,0,25,16,12,11,10,9,8,8,7,7,7,7,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5],k=[0,0,33554432,43046721,16777216,48828125,60466176,40353607,16777216,43046721,1e7,19487171,35831808,62748517,7529536,11390625,16777216,24137569,34012224,47045881,64e6,4084101,5153632,6436343,7962624,9765625,11881376,14348907,17210368,20511149,243e5,28629151,33554432,39135393,45435424,52521875,60466176];f.prototype.toString=function(N,H){N=N||10,H=H|0||1;var te;if(N===16||N==="hex"){te="";for(var oe=0,_e=0,Ee=0;Ee<this.length;Ee++){var Ce=this.words[Ee],me=((Ce<<oe|_e)&16777215).toString(16);_e=Ce>>>24-oe&16777215,_e!==0||Ee!==this.length-1?te=b[6-me.length]+me+te:te=me+te,oe+=2,oe>=26&&(oe-=26,Ee--)}for(_e!==0&&(te=_e.toString(16)+te);te.length%H!==0;)te="0"+te;return this.negative!==0&&(te="-"+te),te}if(N===(N|0)&&N>=2&&N<=36){var ie=p[N],Se=k[N];te="";var Le=this.clone();for(Le.negative=0;!Le.isZero();){var Ae=Le.modn(Se).toString(N);Le=Le.idivn(Se),Le.isZero()?te=Ae+te:te=b[ie-Ae.length]+Ae+te}for(this.isZero()&&(te="0"+te);te.length%H!==0;)te="0"+te;return this.negative!==0&&(te="-"+te),te}u(!1,"Base should be between 2 and 36")},f.prototype.toNumber=function(){var N=this.words[0];return this.length===2?N+=this.words[1]*67108864:this.length===3&&this.words[2]===1?N+=4503599627370496+this.words[1]*67108864:this.length>2&&u(!1,"Number can only safely store up to 53 bits"),this.negative!==0?-N:N},f.prototype.toJSON=function(){return this.toString(16)},f.prototype.toBuffer=function(N,H){return u(typeof h!="undefined"),this.toArrayLike(h,N,H)},f.prototype.toArray=function(N,H){return this.toArrayLike(Array,N,H)},f.prototype.toArrayLike=function(N,H,te){var oe=this.byteLength(),_e=te||Math.max(1,oe);u(oe<=_e,"byte array longer than desired length"),u(_e>0,"Requested array length <= 0"),this.strip();var Ee=H==="le",Ce=new N(_e),me,ie,Se=this.clone();if(Ee){for(ie=0;!Se.isZero();ie++)me=Se.andln(255),Se.iushrn(8),Ce[ie]=me;for(;ie<_e;ie++)Ce[ie]=0}else{for(ie=0;ie<_e-oe;ie++)Ce[ie]=0;for(ie=0;!Se.isZero();ie++)me=Se.andln(255),Se.iushrn(8),Ce[_e-ie-1]=me}return Ce},Math.clz32?f.prototype._countBits=function(N){return 32-Math.clz32(N)}:f.prototype._countBits=function(N){var H=N,te=0;return H>=4096&&(te+=13,H>>>=13),H>=64&&(te+=7,H>>>=7),H>=8&&(te+=4,H>>>=4),H>=2&&(te+=2,H>>>=2),te+H},f.prototype._zeroBits=function(N){if(N===0)return 26;var H=N,te=0;return(H&8191)===0&&(te+=13,H>>>=13),(H&127)===0&&(te+=7,H>>>=7),(H&15)===0&&(te+=4,H>>>=4),(H&3)===0&&(te+=2,H>>>=2),(H&1)===0&&te++,te},f.prototype.bitLength=function(){var N=this.words[this.length-1],H=this._countBits(N);return(this.length-1)*26+H};function E(j){for(var N=new Array(j.bitLength()),H=0;H<N.length;H++){var te=H/26|0,oe=H%26;N[H]=(j.words[te]&1<<oe)>>>oe}return N}f.prototype.zeroBits=function(){if(this.isZero())return 0;for(var N=0,H=0;H<this.length;H++){var te=this._zeroBits(this.words[H]);if(N+=te,te!==26)break}return N},f.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},f.prototype.toTwos=function(N){return this.negative!==0?this.abs().inotn(N).iaddn(1):this.clone()},f.prototype.fromTwos=function(N){return this.testn(N-1)?this.notn(N).iaddn(1).ineg():this.clone()},f.prototype.isNeg=function(){return this.negative!==0},f.prototype.neg=function(){return this.clone().ineg()},f.prototype.ineg=function(){return this.isZero()||(this.negative^=1),this},f.prototype.iuor=function(N){for(;this.length<N.length;)this.words[this.length++]=0;for(var H=0;H<N.length;H++)this.words[H]=this.words[H]|N.words[H];return this.strip()},f.prototype.ior=function(N){return u((this.negative|N.negative)===0),this.iuor(N)},f.prototype.or=function(N){return this.length>N.length?this.clone().ior(N):N.clone().ior(this)},f.prototype.uor=function(N){return this.length>N.length?this.clone().iuor(N):N.clone().iuor(this)},f.prototype.iuand=function(N){var H;this.length>N.length?H=N:H=this;for(var te=0;te<H.length;te++)this.words[te]=this.words[te]&N.words[te];return this.length=H.length,this.strip()},f.prototype.iand=function(N){return u((this.negative|N.negative)===0),this.iuand(N)},f.prototype.and=function(N){return this.length>N.length?this.clone().iand(N):N.clone().iand(this)},f.prototype.uand=function(N){return this.length>N.length?this.clone().iuand(N):N.clone().iuand(this)},f.prototype.iuxor=function(N){var H,te;this.length>N.length?(H=this,te=N):(H=N,te=this);for(var oe=0;oe<te.length;oe++)this.words[oe]=H.words[oe]^te.words[oe];if(this!==H)for(;oe<H.length;oe++)this.words[oe]=H.words[oe];return this.length=H.length,this.strip()},f.prototype.ixor=function(N){return u((this.negative|N.negative)===0),this.iuxor(N)},f.prototype.xor=function(N){return this.length>N.length?this.clone().ixor(N):N.clone().ixor(this)},f.prototype.uxor=function(N){return this.length>N.length?this.clone().iuxor(N):N.clone().iuxor(this)},f.prototype.inotn=function(N){u(typeof N=="number"&&N>=0);var H=Math.ceil(N/26)|0,te=N%26;this._expand(H),te>0&&H--;for(var oe=0;oe<H;oe++)this.words[oe]=~this.words[oe]&67108863;return te>0&&(this.words[oe]=~this.words[oe]&67108863>>26-te),this.strip()},f.prototype.notn=function(N){return this.clone().inotn(N)},f.prototype.setn=function(N,H){u(typeof N=="number"&&N>=0);var te=N/26|0,oe=N%26;return this._expand(te+1),H?this.words[te]=this.words[te]|1<<oe:this.words[te]=this.words[te]&~(1<<oe),this.strip()},f.prototype.iadd=function(N){var H;if(this.negative!==0&&N.negative===0)return this.negative=0,H=this.isub(N),this.negative^=1,this._normSign();if(this.negative===0&&N.negative!==0)return N.negative=0,H=this.isub(N),N.negative=1,H._normSign();var te,oe;this.length>N.length?(te=this,oe=N):(te=N,oe=this);for(var _e=0,Ee=0;Ee<oe.length;Ee++)H=(te.words[Ee]|0)+(oe.words[Ee]|0)+_e,this.words[Ee]=H&67108863,_e=H>>>26;for(;_e!==0&&Ee<te.length;Ee++)H=(te.words[Ee]|0)+_e,this.words[Ee]=H&67108863,_e=H>>>26;if(this.length=te.length,_e!==0)this.words[this.length]=_e,this.length++;else if(te!==this)for(;Ee<te.length;Ee++)this.words[Ee]=te.words[Ee];return this},f.prototype.add=function(N){var H;return N.negative!==0&&this.negative===0?(N.negative=0,H=this.sub(N),N.negative^=1,H):N.negative===0&&this.negative!==0?(this.negative=0,H=N.sub(this),this.negative=1,H):this.length>N.length?this.clone().iadd(N):N.clone().iadd(this)},f.prototype.isub=function(N){if(N.negative!==0){N.negative=0;var H=this.iadd(N);return N.negative=1,H._normSign()}else if(this.negative!==0)return this.negative=0,this.iadd(N),this.negative=1,this._normSign();var te=this.cmp(N);if(te===0)return this.negative=0,this.length=1,this.words[0]=0,this;var oe,_e;te>0?(oe=this,_e=N):(oe=N,_e=this);for(var Ee=0,Ce=0;Ce<_e.length;Ce++)H=(oe.words[Ce]|0)-(_e.words[Ce]|0)+Ee,Ee=H>>26,this.words[Ce]=H&67108863;for(;Ee!==0&&Ce<oe.length;Ce++)H=(oe.words[Ce]|0)+Ee,Ee=H>>26,this.words[Ce]=H&67108863;if(Ee===0&&Ce<oe.length&&oe!==this)for(;Ce<oe.length;Ce++)this.words[Ce]=oe.words[Ce];return this.length=Math.max(this.length,Ce),oe!==this&&(this.negative=1),this.strip()},f.prototype.sub=function(N){return this.clone().isub(N)};function S(j,N,H){H.negative=N.negative^j.negative;var te=j.length+N.length|0;H.length=te,te=te-1|0;var oe=j.words[0]|0,_e=N.words[0]|0,Ee=oe*_e,Ce=Ee&67108863,me=Ee/67108864|0;H.words[0]=Ce;for(var ie=1;ie<te;ie++){for(var Se=me>>>26,Le=me&67108863,Ae=Math.min(ie,N.length-1),Fe=Math.max(0,ie-j.length+1);Fe<=Ae;Fe++){var Pe=ie-Fe|0;oe=j.words[Pe]|0,_e=N.words[Fe]|0,Ee=oe*_e+Le,Se+=Ee/67108864|0,Le=Ee&67108863}H.words[ie]=Le|0,me=Se|0}return me!==0?H.words[ie]=me|0:H.length--,H.strip()}var L=function(N,H,te){var oe=N.words,_e=H.words,Ee=te.words,Ce=0,me,ie,Se,Le=oe[0]|0,Ae=Le&8191,Fe=Le>>>13,Pe=oe[1]|0,ge=Pe&8191,Re=Pe>>>13,ce=oe[2]|0,Ze=ce&8191,ut=ce>>>13,pt=oe[3]|0,Zt=pt&8191,st=pt>>>13,lt=oe[4]|0,Gt=lt&8191,Nt=lt>>>13,Jt=oe[5]|0,sr=Jt&8191,wr=Jt>>>13,cr=oe[6]|0,$e=cr&8191,St=cr>>>13,Qt=oe[7]|0,Vt=Qt&8191,_t=Qt>>>13,It=oe[8]|0,mt=It&8191,er=It>>>13,lr=oe[9]|0,Tr=lr&8191,Lr=lr>>>13,ti=_e[0]|0,Br=ti&8191,Vr=ti>>>13,dt=_e[1]|0,Ge=dt&8191,Je=dt>>>13,je=_e[2]|0,tt=je&8191,xt=je>>>13,Ie=_e[3]|0,xe=Ie&8191,ke=Ie>>>13,vt=_e[4]|0,ir=vt&8191,ar=vt>>>13,vr=_e[5]|0,ii=vr&8191,pi=vr>>>13,$r=_e[6]|0,di=$r&8191,ji=$r>>>13,In=_e[7]|0,wi=In&8191,On=In>>>13,qn=_e[8]|0,Fn=qn&8191,ra=qn>>>13,la=_e[9]|0,Ut=la&8191,wt=la>>>13;te.negative=N.negative^H.negative,te.length=19,me=Math.imul(Ae,Br),ie=Math.imul(Ae,Vr),ie=ie+Math.imul(Fe,Br)|0,Se=Math.imul(Fe,Vr);var rr=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(rr>>>26)|0,rr&=67108863,me=Math.imul(ge,Br),ie=Math.imul(ge,Vr),ie=ie+Math.imul(Re,Br)|0,Se=Math.imul(Re,Vr),me=me+Math.imul(Ae,Ge)|0,ie=ie+Math.imul(Ae,Je)|0,ie=ie+Math.imul(Fe,Ge)|0,Se=Se+Math.imul(Fe,Je)|0;var nr=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(nr>>>26)|0,nr&=67108863,me=Math.imul(Ze,Br),ie=Math.imul(Ze,Vr),ie=ie+Math.imul(ut,Br)|0,Se=Math.imul(ut,Vr),me=me+Math.imul(ge,Ge)|0,ie=ie+Math.imul(ge,Je)|0,ie=ie+Math.imul(Re,Ge)|0,Se=Se+Math.imul(Re,Je)|0,me=me+Math.imul(Ae,tt)|0,ie=ie+Math.imul(Ae,xt)|0,ie=ie+Math.imul(Fe,tt)|0,Se=Se+Math.imul(Fe,xt)|0;var Er=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Er>>>26)|0,Er&=67108863,me=Math.imul(Zt,Br),ie=Math.imul(Zt,Vr),ie=ie+Math.imul(st,Br)|0,Se=Math.imul(st,Vr),me=me+Math.imul(Ze,Ge)|0,ie=ie+Math.imul(Ze,Je)|0,ie=ie+Math.imul(ut,Ge)|0,Se=Se+Math.imul(ut,Je)|0,me=me+Math.imul(ge,tt)|0,ie=ie+Math.imul(ge,xt)|0,ie=ie+Math.imul(Re,tt)|0,Se=Se+Math.imul(Re,xt)|0,me=me+Math.imul(Ae,xe)|0,ie=ie+Math.imul(Ae,ke)|0,ie=ie+Math.imul(Fe,xe)|0,Se=Se+Math.imul(Fe,ke)|0;var Xr=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Xr>>>26)|0,Xr&=67108863,me=Math.imul(Gt,Br),ie=Math.imul(Gt,Vr),ie=ie+Math.imul(Nt,Br)|0,Se=Math.imul(Nt,Vr),me=me+Math.imul(Zt,Ge)|0,ie=ie+Math.imul(Zt,Je)|0,ie=ie+Math.imul(st,Ge)|0,Se=Se+Math.imul(st,Je)|0,me=me+Math.imul(Ze,tt)|0,ie=ie+Math.imul(Ze,xt)|0,ie=ie+Math.imul(ut,tt)|0,Se=Se+Math.imul(ut,xt)|0,me=me+Math.imul(ge,xe)|0,ie=ie+Math.imul(ge,ke)|0,ie=ie+Math.imul(Re,xe)|0,Se=Se+Math.imul(Re,ke)|0,me=me+Math.imul(Ae,ir)|0,ie=ie+Math.imul(Ae,ar)|0,ie=ie+Math.imul(Fe,ir)|0,Se=Se+Math.imul(Fe,ar)|0;var ri=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(ri>>>26)|0,ri&=67108863,me=Math.imul(sr,Br),ie=Math.imul(sr,Vr),ie=ie+Math.imul(wr,Br)|0,Se=Math.imul(wr,Vr),me=me+Math.imul(Gt,Ge)|0,ie=ie+Math.imul(Gt,Je)|0,ie=ie+Math.imul(Nt,Ge)|0,Se=Se+Math.imul(Nt,Je)|0,me=me+Math.imul(Zt,tt)|0,ie=ie+Math.imul(Zt,xt)|0,ie=ie+Math.imul(st,tt)|0,Se=Se+Math.imul(st,xt)|0,me=me+Math.imul(Ze,xe)|0,ie=ie+Math.imul(Ze,ke)|0,ie=ie+Math.imul(ut,xe)|0,Se=Se+Math.imul(ut,ke)|0,me=me+Math.imul(ge,ir)|0,ie=ie+Math.imul(ge,ar)|0,ie=ie+Math.imul(Re,ir)|0,Se=Se+Math.imul(Re,ar)|0,me=me+Math.imul(Ae,ii)|0,ie=ie+Math.imul(Ae,pi)|0,ie=ie+Math.imul(Fe,ii)|0,Se=Se+Math.imul(Fe,pi)|0;var Qr=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Qr>>>26)|0,Qr&=67108863,me=Math.imul($e,Br),ie=Math.imul($e,Vr),ie=ie+Math.imul(St,Br)|0,Se=Math.imul(St,Vr),me=me+Math.imul(sr,Ge)|0,ie=ie+Math.imul(sr,Je)|0,ie=ie+Math.imul(wr,Ge)|0,Se=Se+Math.imul(wr,Je)|0,me=me+Math.imul(Gt,tt)|0,ie=ie+Math.imul(Gt,xt)|0,ie=ie+Math.imul(Nt,tt)|0,Se=Se+Math.imul(Nt,xt)|0,me=me+Math.imul(Zt,xe)|0,ie=ie+Math.imul(Zt,ke)|0,ie=ie+Math.imul(st,xe)|0,Se=Se+Math.imul(st,ke)|0,me=me+Math.imul(Ze,ir)|0,ie=ie+Math.imul(Ze,ar)|0,ie=ie+Math.imul(ut,ir)|0,Se=Se+Math.imul(ut,ar)|0,me=me+Math.imul(ge,ii)|0,ie=ie+Math.imul(ge,pi)|0,ie=ie+Math.imul(Re,ii)|0,Se=Se+Math.imul(Re,pi)|0,me=me+Math.imul(Ae,di)|0,ie=ie+Math.imul(Ae,ji)|0,ie=ie+Math.imul(Fe,di)|0,Se=Se+Math.imul(Fe,ji)|0;var Oi=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Oi>>>26)|0,Oi&=67108863,me=Math.imul(Vt,Br),ie=Math.imul(Vt,Vr),ie=ie+Math.imul(_t,Br)|0,Se=Math.imul(_t,Vr),me=me+Math.imul($e,Ge)|0,ie=ie+Math.imul($e,Je)|0,ie=ie+Math.imul(St,Ge)|0,Se=Se+Math.imul(St,Je)|0,me=me+Math.imul(sr,tt)|0,ie=ie+Math.imul(sr,xt)|0,ie=ie+Math.imul(wr,tt)|0,Se=Se+Math.imul(wr,xt)|0,me=me+Math.imul(Gt,xe)|0,ie=ie+Math.imul(Gt,ke)|0,ie=ie+Math.imul(Nt,xe)|0,Se=Se+Math.imul(Nt,ke)|0,me=me+Math.imul(Zt,ir)|0,ie=ie+Math.imul(Zt,ar)|0,ie=ie+Math.imul(st,ir)|0,Se=Se+Math.imul(st,ar)|0,me=me+Math.imul(Ze,ii)|0,ie=ie+Math.imul(Ze,pi)|0,ie=ie+Math.imul(ut,ii)|0,Se=Se+Math.imul(ut,pi)|0,me=me+Math.imul(ge,di)|0,ie=ie+Math.imul(ge,ji)|0,ie=ie+Math.imul(Re,di)|0,Se=Se+Math.imul(Re,ji)|0,me=me+Math.imul(Ae,wi)|0,ie=ie+Math.imul(Ae,On)|0,ie=ie+Math.imul(Fe,wi)|0,Se=Se+Math.imul(Fe,On)|0;var $i=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+($i>>>26)|0,$i&=67108863,me=Math.imul(mt,Br),ie=Math.imul(mt,Vr),ie=ie+Math.imul(er,Br)|0,Se=Math.imul(er,Vr),me=me+Math.imul(Vt,Ge)|0,ie=ie+Math.imul(Vt,Je)|0,ie=ie+Math.imul(_t,Ge)|0,Se=Se+Math.imul(_t,Je)|0,me=me+Math.imul($e,tt)|0,ie=ie+Math.imul($e,xt)|0,ie=ie+Math.imul(St,tt)|0,Se=Se+Math.imul(St,xt)|0,me=me+Math.imul(sr,xe)|0,ie=ie+Math.imul(sr,ke)|0,ie=ie+Math.imul(wr,xe)|0,Se=Se+Math.imul(wr,ke)|0,me=me+Math.imul(Gt,ir)|0,ie=ie+Math.imul(Gt,ar)|0,ie=ie+Math.imul(Nt,ir)|0,Se=Se+Math.imul(Nt,ar)|0,me=me+Math.imul(Zt,ii)|0,ie=ie+Math.imul(Zt,pi)|0,ie=ie+Math.imul(st,ii)|0,Se=Se+Math.imul(st,pi)|0,me=me+Math.imul(Ze,di)|0,ie=ie+Math.imul(Ze,ji)|0,ie=ie+Math.imul(ut,di)|0,Se=Se+Math.imul(ut,ji)|0,me=me+Math.imul(ge,wi)|0,ie=ie+Math.imul(ge,On)|0,ie=ie+Math.imul(Re,wi)|0,Se=Se+Math.imul(Re,On)|0,me=me+Math.imul(Ae,Fn)|0,ie=ie+Math.imul(Ae,ra)|0,ie=ie+Math.imul(Fe,Fn)|0,Se=Se+Math.imul(Fe,ra)|0;var tn=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(tn>>>26)|0,tn&=67108863,me=Math.imul(Tr,Br),ie=Math.imul(Tr,Vr),ie=ie+Math.imul(Lr,Br)|0,Se=Math.imul(Lr,Vr),me=me+Math.imul(mt,Ge)|0,ie=ie+Math.imul(mt,Je)|0,ie=ie+Math.imul(er,Ge)|0,Se=Se+Math.imul(er,Je)|0,me=me+Math.imul(Vt,tt)|0,ie=ie+Math.imul(Vt,xt)|0,ie=ie+Math.imul(_t,tt)|0,Se=Se+Math.imul(_t,xt)|0,me=me+Math.imul($e,xe)|0,ie=ie+Math.imul($e,ke)|0,ie=ie+Math.imul(St,xe)|0,Se=Se+Math.imul(St,ke)|0,me=me+Math.imul(sr,ir)|0,ie=ie+Math.imul(sr,ar)|0,ie=ie+Math.imul(wr,ir)|0,Se=Se+Math.imul(wr,ar)|0,me=me+Math.imul(Gt,ii)|0,ie=ie+Math.imul(Gt,pi)|0,ie=ie+Math.imul(Nt,ii)|0,Se=Se+Math.imul(Nt,pi)|0,me=me+Math.imul(Zt,di)|0,ie=ie+Math.imul(Zt,ji)|0,ie=ie+Math.imul(st,di)|0,Se=Se+Math.imul(st,ji)|0,me=me+Math.imul(Ze,wi)|0,ie=ie+Math.imul(Ze,On)|0,ie=ie+Math.imul(ut,wi)|0,Se=Se+Math.imul(ut,On)|0,me=me+Math.imul(ge,Fn)|0,ie=ie+Math.imul(ge,ra)|0,ie=ie+Math.imul(Re,Fn)|0,Se=Se+Math.imul(Re,ra)|0,me=me+Math.imul(Ae,Ut)|0,ie=ie+Math.imul(Ae,wt)|0,ie=ie+Math.imul(Fe,Ut)|0,Se=Se+Math.imul(Fe,wt)|0;var fn=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(fn>>>26)|0,fn&=67108863,me=Math.imul(Tr,Ge),ie=Math.imul(Tr,Je),ie=ie+Math.imul(Lr,Ge)|0,Se=Math.imul(Lr,Je),me=me+Math.imul(mt,tt)|0,ie=ie+Math.imul(mt,xt)|0,ie=ie+Math.imul(er,tt)|0,Se=Se+Math.imul(er,xt)|0,me=me+Math.imul(Vt,xe)|0,ie=ie+Math.imul(Vt,ke)|0,ie=ie+Math.imul(_t,xe)|0,Se=Se+Math.imul(_t,ke)|0,me=me+Math.imul($e,ir)|0,ie=ie+Math.imul($e,ar)|0,ie=ie+Math.imul(St,ir)|0,Se=Se+Math.imul(St,ar)|0,me=me+Math.imul(sr,ii)|0,ie=ie+Math.imul(sr,pi)|0,ie=ie+Math.imul(wr,ii)|0,Se=Se+Math.imul(wr,pi)|0,me=me+Math.imul(Gt,di)|0,ie=ie+Math.imul(Gt,ji)|0,ie=ie+Math.imul(Nt,di)|0,Se=Se+Math.imul(Nt,ji)|0,me=me+Math.imul(Zt,wi)|0,ie=ie+Math.imul(Zt,On)|0,ie=ie+Math.imul(st,wi)|0,Se=Se+Math.imul(st,On)|0,me=me+Math.imul(Ze,Fn)|0,ie=ie+Math.imul(Ze,ra)|0,ie=ie+Math.imul(ut,Fn)|0,Se=Se+Math.imul(ut,ra)|0,me=me+Math.imul(ge,Ut)|0,ie=ie+Math.imul(ge,wt)|0,ie=ie+Math.imul(Re,Ut)|0,Se=Se+Math.imul(Re,wt)|0;var yn=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(yn>>>26)|0,yn&=67108863,me=Math.imul(Tr,tt),ie=Math.imul(Tr,xt),ie=ie+Math.imul(Lr,tt)|0,Se=Math.imul(Lr,xt),me=me+Math.imul(mt,xe)|0,ie=ie+Math.imul(mt,ke)|0,ie=ie+Math.imul(er,xe)|0,Se=Se+Math.imul(er,ke)|0,me=me+Math.imul(Vt,ir)|0,ie=ie+Math.imul(Vt,ar)|0,ie=ie+Math.imul(_t,ir)|0,Se=Se+Math.imul(_t,ar)|0,me=me+Math.imul($e,ii)|0,ie=ie+Math.imul($e,pi)|0,ie=ie+Math.imul(St,ii)|0,Se=Se+Math.imul(St,pi)|0,me=me+Math.imul(sr,di)|0,ie=ie+Math.imul(sr,ji)|0,ie=ie+Math.imul(wr,di)|0,Se=Se+Math.imul(wr,ji)|0,me=me+Math.imul(Gt,wi)|0,ie=ie+Math.imul(Gt,On)|0,ie=ie+Math.imul(Nt,wi)|0,Se=Se+Math.imul(Nt,On)|0,me=me+Math.imul(Zt,Fn)|0,ie=ie+Math.imul(Zt,ra)|0,ie=ie+Math.imul(st,Fn)|0,Se=Se+Math.imul(st,ra)|0,me=me+Math.imul(Ze,Ut)|0,ie=ie+Math.imul(Ze,wt)|0,ie=ie+Math.imul(ut,Ut)|0,Se=Se+Math.imul(ut,wt)|0;var Sn=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Sn>>>26)|0,Sn&=67108863,me=Math.imul(Tr,xe),ie=Math.imul(Tr,ke),ie=ie+Math.imul(Lr,xe)|0,Se=Math.imul(Lr,ke),me=me+Math.imul(mt,ir)|0,ie=ie+Math.imul(mt,ar)|0,ie=ie+Math.imul(er,ir)|0,Se=Se+Math.imul(er,ar)|0,me=me+Math.imul(Vt,ii)|0,ie=ie+Math.imul(Vt,pi)|0,ie=ie+Math.imul(_t,ii)|0,Se=Se+Math.imul(_t,pi)|0,me=me+Math.imul($e,di)|0,ie=ie+Math.imul($e,ji)|0,ie=ie+Math.imul(St,di)|0,Se=Se+Math.imul(St,ji)|0,me=me+Math.imul(sr,wi)|0,ie=ie+Math.imul(sr,On)|0,ie=ie+Math.imul(wr,wi)|0,Se=Se+Math.imul(wr,On)|0,me=me+Math.imul(Gt,Fn)|0,ie=ie+Math.imul(Gt,ra)|0,ie=ie+Math.imul(Nt,Fn)|0,Se=Se+Math.imul(Nt,ra)|0,me=me+Math.imul(Zt,Ut)|0,ie=ie+Math.imul(Zt,wt)|0,ie=ie+Math.imul(st,Ut)|0,Se=Se+Math.imul(st,wt)|0;var Ba=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Ba>>>26)|0,Ba&=67108863,me=Math.imul(Tr,ir),ie=Math.imul(Tr,ar),ie=ie+Math.imul(Lr,ir)|0,Se=Math.imul(Lr,ar),me=me+Math.imul(mt,ii)|0,ie=ie+Math.imul(mt,pi)|0,ie=ie+Math.imul(er,ii)|0,Se=Se+Math.imul(er,pi)|0,me=me+Math.imul(Vt,di)|0,ie=ie+Math.imul(Vt,ji)|0,ie=ie+Math.imul(_t,di)|0,Se=Se+Math.imul(_t,ji)|0,me=me+Math.imul($e,wi)|0,ie=ie+Math.imul($e,On)|0,ie=ie+Math.imul(St,wi)|0,Se=Se+Math.imul(St,On)|0,me=me+Math.imul(sr,Fn)|0,ie=ie+Math.imul(sr,ra)|0,ie=ie+Math.imul(wr,Fn)|0,Se=Se+Math.imul(wr,ra)|0,me=me+Math.imul(Gt,Ut)|0,ie=ie+Math.imul(Gt,wt)|0,ie=ie+Math.imul(Nt,Ut)|0,Se=Se+Math.imul(Nt,wt)|0;var ua=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(ua>>>26)|0,ua&=67108863,me=Math.imul(Tr,ii),ie=Math.imul(Tr,pi),ie=ie+Math.imul(Lr,ii)|0,Se=Math.imul(Lr,pi),me=me+Math.imul(mt,di)|0,ie=ie+Math.imul(mt,ji)|0,ie=ie+Math.imul(er,di)|0,Se=Se+Math.imul(er,ji)|0,me=me+Math.imul(Vt,wi)|0,ie=ie+Math.imul(Vt,On)|0,ie=ie+Math.imul(_t,wi)|0,Se=Se+Math.imul(_t,On)|0,me=me+Math.imul($e,Fn)|0,ie=ie+Math.imul($e,ra)|0,ie=ie+Math.imul(St,Fn)|0,Se=Se+Math.imul(St,ra)|0,me=me+Math.imul(sr,Ut)|0,ie=ie+Math.imul(sr,wt)|0,ie=ie+Math.imul(wr,Ut)|0,Se=Se+Math.imul(wr,wt)|0;var ma=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(ma>>>26)|0,ma&=67108863,me=Math.imul(Tr,di),ie=Math.imul(Tr,ji),ie=ie+Math.imul(Lr,di)|0,Se=Math.imul(Lr,ji),me=me+Math.imul(mt,wi)|0,ie=ie+Math.imul(mt,On)|0,ie=ie+Math.imul(er,wi)|0,Se=Se+Math.imul(er,On)|0,me=me+Math.imul(Vt,Fn)|0,ie=ie+Math.imul(Vt,ra)|0,ie=ie+Math.imul(_t,Fn)|0,Se=Se+Math.imul(_t,ra)|0,me=me+Math.imul($e,Ut)|0,ie=ie+Math.imul($e,wt)|0,ie=ie+Math.imul(St,Ut)|0,Se=Se+Math.imul(St,wt)|0;var Wa=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Wa>>>26)|0,Wa&=67108863,me=Math.imul(Tr,wi),ie=Math.imul(Tr,On),ie=ie+Math.imul(Lr,wi)|0,Se=Math.imul(Lr,On),me=me+Math.imul(mt,Fn)|0,ie=ie+Math.imul(mt,ra)|0,ie=ie+Math.imul(er,Fn)|0,Se=Se+Math.imul(er,ra)|0,me=me+Math.imul(Vt,Ut)|0,ie=ie+Math.imul(Vt,wt)|0,ie=ie+Math.imul(_t,Ut)|0,Se=Se+Math.imul(_t,wt)|0;var Fa=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Fa>>>26)|0,Fa&=67108863,me=Math.imul(Tr,Fn),ie=Math.imul(Tr,ra),ie=ie+Math.imul(Lr,Fn)|0,Se=Math.imul(Lr,ra),me=me+Math.imul(mt,Ut)|0,ie=ie+Math.imul(mt,wt)|0,ie=ie+Math.imul(er,Ut)|0,Se=Se+Math.imul(er,wt)|0;var Wo=(Ce+me|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Wo>>>26)|0,Wo&=67108863,me=Math.imul(Tr,Ut),ie=Math.imul(Tr,wt),ie=ie+Math.imul(Lr,Ut)|0,Se=Math.imul(Lr,wt);var da=(Ce+me|0)+((ie&8191)<<13)|0;return Ce=(Se+(ie>>>13)|0)+(da>>>26)|0,da&=67108863,Ee[0]=rr,Ee[1]=nr,Ee[2]=Er,Ee[3]=Xr,Ee[4]=ri,Ee[5]=Qr,Ee[6]=Oi,Ee[7]=$i,Ee[8]=tn,Ee[9]=fn,Ee[10]=yn,Ee[11]=Sn,Ee[12]=Ba,Ee[13]=ua,Ee[14]=ma,Ee[15]=Wa,Ee[16]=Fa,Ee[17]=Wo,Ee[18]=da,Ce!==0&&(Ee[19]=Ce,te.length++),te};Math.imul||(L=S);function x(j,N,H){H.negative=N.negative^j.negative,H.length=j.length+N.length;for(var te=0,oe=0,_e=0;_e<H.length-1;_e++){var Ee=oe;oe=0;for(var Ce=te&67108863,me=Math.min(_e,N.length-1),ie=Math.max(0,_e-j.length+1);ie<=me;ie++){var Se=_e-ie,Le=j.words[Se]|0,Ae=N.words[ie]|0,Fe=Le*Ae,Pe=Fe&67108863;Ee=Ee+(Fe/67108864|0)|0,Pe=Pe+Ce|0,Ce=Pe&67108863,Ee=Ee+(Pe>>>26)|0,oe+=Ee>>>26,Ee&=67108863}H.words[_e]=Ce,te=Ee,Ee=oe}return te!==0?H.words[_e]=te:H.length--,H.strip()}function C(j,N,H){var te=new M;return te.mulp(j,N,H)}f.prototype.mulTo=function(N,H){var te,oe=this.length+N.length;return this.length===10&&N.length===10?te=L(this,N,H):oe<63?te=S(this,N,H):oe<1024?te=x(this,N,H):te=C(this,N,H),te};function M(j,N){this.x=j,this.y=N}M.prototype.makeRBT=function(N){for(var H=new Array(N),te=f.prototype._countBits(N)-1,oe=0;oe<N;oe++)H[oe]=this.revBin(oe,te,N);return H},M.prototype.revBin=function(N,H,te){if(N===0||N===te-1)return N;for(var oe=0,_e=0;_e<H;_e++)oe|=(N&1)<<H-_e-1,N>>=1;return oe},M.prototype.permute=function(N,H,te,oe,_e,Ee){for(var Ce=0;Ce<Ee;Ce++)oe[Ce]=H[N[Ce]],_e[Ce]=te[N[Ce]]},M.prototype.transform=function(N,H,te,oe,_e,Ee){this.permute(Ee,N,H,te,oe,_e);for(var Ce=1;Ce<_e;Ce<<=1)for(var me=Ce<<1,ie=Math.cos(2*Math.PI/me),Se=Math.sin(2*Math.PI/me),Le=0;Le<_e;Le+=me)for(var Ae=ie,Fe=Se,Pe=0;Pe<Ce;Pe++){var ge=te[Le+Pe],Re=oe[Le+Pe],ce=te[Le+Pe+Ce],Ze=oe[Le+Pe+Ce],ut=Ae*ce-Fe*Ze;Ze=Ae*Ze+Fe*ce,ce=ut,te[Le+Pe]=ge+ce,oe[Le+Pe]=Re+Ze,te[Le+Pe+Ce]=ge-ce,oe[Le+Pe+Ce]=Re-Ze,Pe!==me&&(ut=ie*Ae-Se*Fe,Fe=ie*Fe+Se*Ae,Ae=ut)}},M.prototype.guessLen13b=function(N,H){var te=Math.max(H,N)|1,oe=te&1,_e=0;for(te=te/2|0;te;te=te>>>1)_e++;return 1<<_e+1+oe},M.prototype.conjugate=function(N,H,te){if(!(te<=1))for(var oe=0;oe<te/2;oe++){var _e=N[oe];N[oe]=N[te-oe-1],N[te-oe-1]=_e,_e=H[oe],H[oe]=-H[te-oe-1],H[te-oe-1]=-_e}},M.prototype.normalize13b=function(N,H){for(var te=0,oe=0;oe<H/2;oe++){var _e=Math.round(N[2*oe+1]/H)*8192+Math.round(N[2*oe]/H)+te;N[oe]=_e&67108863,_e<67108864?te=0:te=_e/67108864|0}return N},M.prototype.convert13b=function(N,H,te,oe){for(var _e=0,Ee=0;Ee<H;Ee++)_e=_e+(N[Ee]|0),te[2*Ee]=_e&8191,_e=_e>>>13,te[2*Ee+1]=_e&8191,_e=_e>>>13;for(Ee=2*H;Ee<oe;++Ee)te[Ee]=0;u(_e===0),u((_e&-8192)===0)},M.prototype.stub=function(N){for(var H=new Array(N),te=0;te<N;te++)H[te]=0;return H},M.prototype.mulp=function(N,H,te){var oe=2*this.guessLen13b(N.length,H.length),_e=this.makeRBT(oe),Ee=this.stub(oe),Ce=new Array(oe),me=new Array(oe),ie=new Array(oe),Se=new Array(oe),Le=new Array(oe),Ae=new Array(oe),Fe=te.words;Fe.length=oe,this.convert13b(N.words,N.length,Ce,oe),this.convert13b(H.words,H.length,Se,oe),this.transform(Ce,Ee,me,ie,oe,_e),this.transform(Se,Ee,Le,Ae,oe,_e);for(var Pe=0;Pe<oe;Pe++){var ge=me[Pe]*Le[Pe]-ie[Pe]*Ae[Pe];ie[Pe]=me[Pe]*Ae[Pe]+ie[Pe]*Le[Pe],me[Pe]=ge}return this.conjugate(me,ie,oe),this.transform(me,ie,Fe,Ee,oe,_e),this.conjugate(Fe,Ee,oe),this.normalize13b(Fe,oe),te.negative=N.negative^H.negative,te.length=N.length+H.length,te.strip()},f.prototype.mul=function(N){var H=new f(null);return H.words=new Array(this.length+N.length),this.mulTo(N,H)},f.prototype.mulf=function(N){var H=new f(null);return H.words=new Array(this.length+N.length),C(this,N,H)},f.prototype.imul=function(N){return this.clone().mulTo(N,this)},f.prototype.imuln=function(N){u(typeof N=="number"),u(N<67108864);for(var H=0,te=0;te<this.length;te++){var oe=(this.words[te]|0)*N,_e=(oe&67108863)+(H&67108863);H>>=26,H+=oe/67108864|0,H+=_e>>>26,this.words[te]=_e&67108863}return H!==0&&(this.words[te]=H,this.length++),this},f.prototype.muln=function(N){return this.clone().imuln(N)},f.prototype.sqr=function(){return this.mul(this)},f.prototype.isqr=function(){return this.imul(this.clone())},f.prototype.pow=function(N){var H=E(N);if(H.length===0)return new f(1);for(var te=this,oe=0;oe<H.length&&H[oe]===0;oe++,te=te.sqr());if(++oe<H.length)for(var _e=te.sqr();oe<H.length;oe++,_e=_e.sqr())H[oe]!==0&&(te=te.mul(_e));return te},f.prototype.iushln=function(N){u(typeof N=="number"&&N>=0);var H=N%26,te=(N-H)/26,oe=67108863>>>26-H<<26-H,_e;if(H!==0){var Ee=0;for(_e=0;_e<this.length;_e++){var Ce=this.words[_e]&oe,me=(this.words[_e]|0)-Ce<<H;this.words[_e]=me|Ee,Ee=Ce>>>26-H}Ee&&(this.words[_e]=Ee,this.length++)}if(te!==0){for(_e=this.length-1;_e>=0;_e--)this.words[_e+te]=this.words[_e];for(_e=0;_e<te;_e++)this.words[_e]=0;this.length+=te}return this.strip()},f.prototype.ishln=function(N){return u(this.negative===0),this.iushln(N)},f.prototype.iushrn=function(N,H,te){u(typeof N=="number"&&N>=0);var oe;H?oe=(H-H%26)/26:oe=0;var _e=N%26,Ee=Math.min((N-_e)/26,this.length),Ce=67108863^67108863>>>_e<<_e,me=te;if(oe-=Ee,oe=Math.max(0,oe),me){for(var ie=0;ie<Ee;ie++)me.words[ie]=this.words[ie];me.length=Ee}if(Ee!==0)if(this.length>Ee)for(this.length-=Ee,ie=0;ie<this.length;ie++)this.words[ie]=this.words[ie+Ee];else this.words[0]=0,this.length=1;var Se=0;for(ie=this.length-1;ie>=0&&(Se!==0||ie>=oe);ie--){var Le=this.words[ie]|0;this.words[ie]=Se<<26-_e|Le>>>_e,Se=Le&Ce}return me&&Se!==0&&(me.words[me.length++]=Se),this.length===0&&(this.words[0]=0,this.length=1),this.strip()},f.prototype.ishrn=function(N,H,te){return u(this.negative===0),this.iushrn(N,H,te)},f.prototype.shln=function(N){return this.clone().ishln(N)},f.prototype.ushln=function(N){return this.clone().iushln(N)},f.prototype.shrn=function(N){return this.clone().ishrn(N)},f.prototype.ushrn=function(N){return this.clone().iushrn(N)},f.prototype.testn=function(N){u(typeof N=="number"&&N>=0);var H=N%26,te=(N-H)/26,oe=1<<H;if(this.length<=te)return!1;var _e=this.words[te];return!!(_e&oe)},f.prototype.imaskn=function(N){u(typeof N=="number"&&N>=0);var H=N%26,te=(N-H)/26;if(u(this.negative===0,"imaskn works only with positive numbers"),this.length<=te)return this;if(H!==0&&te++,this.length=Math.min(te,this.length),H!==0){var oe=67108863^67108863>>>H<<H;this.words[this.length-1]&=oe}return this.strip()},f.prototype.maskn=function(N){return this.clone().imaskn(N)},f.prototype.iaddn=function(N){return u(typeof N=="number"),u(N<67108864),N<0?this.isubn(-N):this.negative!==0?this.length===1&&(this.words[0]|0)<N?(this.words[0]=N-(this.words[0]|0),this.negative=0,this):(this.negative=0,this.isubn(N),this.negative=1,this):this._iaddn(N)},f.prototype._iaddn=function(N){this.words[0]+=N;for(var H=0;H<this.length&&this.words[H]>=67108864;H++)this.words[H]-=67108864,H===this.length-1?this.words[H+1]=1:this.words[H+1]++;return this.length=Math.max(this.length,H+1),this},f.prototype.isubn=function(N){if(u(typeof N=="number"),u(N<67108864),N<0)return this.iaddn(-N);if(this.negative!==0)return this.negative=0,this.iaddn(N),this.negative=1,this;if(this.words[0]-=N,this.length===1&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var H=0;H<this.length&&this.words[H]<0;H++)this.words[H]+=67108864,this.words[H+1]-=1;return this.strip()},f.prototype.addn=function(N){return this.clone().iaddn(N)},f.prototype.subn=function(N){return this.clone().isubn(N)},f.prototype.iabs=function(){return this.negative=0,this},f.prototype.abs=function(){return this.clone().iabs()},f.prototype._ishlnsubmul=function(N,H,te){var oe=N.length+te,_e;this._expand(oe);var Ee,Ce=0;for(_e=0;_e<N.length;_e++){Ee=(this.words[_e+te]|0)+Ce;var me=(N.words[_e]|0)*H;Ee-=me&67108863,Ce=(Ee>>26)-(me/67108864|0),this.words[_e+te]=Ee&67108863}for(;_e<this.length-te;_e++)Ee=(this.words[_e+te]|0)+Ce,Ce=Ee>>26,this.words[_e+te]=Ee&67108863;if(Ce===0)return this.strip();for(u(Ce===-1),Ce=0,_e=0;_e<this.length;_e++)Ee=-(this.words[_e]|0)+Ce,Ce=Ee>>26,this.words[_e]=Ee&67108863;return this.negative=1,this.strip()},f.prototype._wordDiv=function(N,H){var te=this.length-N.length,oe=this.clone(),_e=N,Ee=_e.words[_e.length-1]|0,Ce=this._countBits(Ee);te=26-Ce,te!==0&&(_e=_e.ushln(te),oe.iushln(te),Ee=_e.words[_e.length-1]|0);var me=oe.length-_e.length,ie;if(H!=="mod"){ie=new f(null),ie.length=me+1,ie.words=new Array(ie.length);for(var Se=0;Se<ie.length;Se++)ie.words[Se]=0}var Le=oe.clone()._ishlnsubmul(_e,1,me);Le.negative===0&&(oe=Le,ie&&(ie.words[me]=1));for(var Ae=me-1;Ae>=0;Ae--){var Fe=(oe.words[_e.length+Ae]|0)*67108864+(oe.words[_e.length+Ae-1]|0);for(Fe=Math.min(Fe/Ee|0,67108863),oe._ishlnsubmul(_e,Fe,Ae);oe.negative!==0;)Fe--,oe.negative=0,oe._ishlnsubmul(_e,1,Ae),oe.isZero()||(oe.negative^=1);ie&&(ie.words[Ae]=Fe)}return ie&&ie.strip(),oe.strip(),H!=="div"&&te!==0&&oe.iushrn(te),{div:ie||null,mod:oe}},f.prototype.divmod=function(N,H,te){if(u(!N.isZero()),this.isZero())return{div:new f(0),mod:new f(0)};var oe,_e,Ee;return this.negative!==0&&N.negative===0?(Ee=this.neg().divmod(N,H),H!=="mod"&&(oe=Ee.div.neg()),H!=="div"&&(_e=Ee.mod.neg(),te&&_e.negative!==0&&_e.iadd(N)),{div:oe,mod:_e}):this.negative===0&&N.negative!==0?(Ee=this.divmod(N.neg(),H),H!=="mod"&&(oe=Ee.div.neg()),{div:oe,mod:Ee.mod}):(this.negative&N.negative)!==0?(Ee=this.neg().divmod(N.neg(),H),H!=="div"&&(_e=Ee.mod.neg(),te&&_e.negative!==0&&_e.isub(N)),{div:Ee.div,mod:_e}):N.length>this.length||this.cmp(N)<0?{div:new f(0),mod:this}:N.length===1?H==="div"?{div:this.divn(N.words[0]),mod:null}:H==="mod"?{div:null,mod:new f(this.modn(N.words[0]))}:{div:this.divn(N.words[0]),mod:new f(this.modn(N.words[0]))}:this._wordDiv(N,H)},f.prototype.div=function(N){return this.divmod(N,"div",!1).div},f.prototype.mod=function(N){return this.divmod(N,"mod",!1).mod},f.prototype.umod=function(N){return this.divmod(N,"mod",!0).mod},f.prototype.divRound=function(N){var H=this.divmod(N);if(H.mod.isZero())return H.div;var te=H.div.negative!==0?H.mod.isub(N):H.mod,oe=N.ushrn(1),_e=N.andln(1),Ee=te.cmp(oe);return Ee<0||_e===1&&Ee===0?H.div:H.div.negative!==0?H.div.isubn(1):H.div.iaddn(1)},f.prototype.modn=function(N){u(N<=67108863);for(var H=(1<<26)%N,te=0,oe=this.length-1;oe>=0;oe--)te=(H*te+(this.words[oe]|0))%N;return te},f.prototype.idivn=function(N){u(N<=67108863);for(var H=0,te=this.length-1;te>=0;te--){var oe=(this.words[te]|0)+H*67108864;this.words[te]=oe/N|0,H=oe%N}return this.strip()},f.prototype.divn=function(N){return this.clone().idivn(N)},f.prototype.egcd=function(N){u(N.negative===0),u(!N.isZero());var H=this,te=N.clone();H.negative!==0?H=H.umod(N):H=H.clone();for(var oe=new f(1),_e=new f(0),Ee=new f(0),Ce=new f(1),me=0;H.isEven()&&te.isEven();)H.iushrn(1),te.iushrn(1),++me;for(var ie=te.clone(),Se=H.clone();!H.isZero();){for(var Le=0,Ae=1;(H.words[0]&Ae)===0&&Le<26;++Le,Ae<<=1);if(Le>0)for(H.iushrn(Le);Le-- >0;)(oe.isOdd()||_e.isOdd())&&(oe.iadd(ie),_e.isub(Se)),oe.iushrn(1),_e.iushrn(1);for(var Fe=0,Pe=1;(te.words[0]&Pe)===0&&Fe<26;++Fe,Pe<<=1);if(Fe>0)for(te.iushrn(Fe);Fe-- >0;)(Ee.isOdd()||Ce.isOdd())&&(Ee.iadd(ie),Ce.isub(Se)),Ee.iushrn(1),Ce.iushrn(1);H.cmp(te)>=0?(H.isub(te),oe.isub(Ee),_e.isub(Ce)):(te.isub(H),Ee.isub(oe),Ce.isub(_e))}return{a:Ee,b:Ce,gcd:te.iushln(me)}},f.prototype._invmp=function(N){u(N.negative===0),u(!N.isZero());var H=this,te=N.clone();H.negative!==0?H=H.umod(N):H=H.clone();for(var oe=new f(1),_e=new f(0),Ee=te.clone();H.cmpn(1)>0&&te.cmpn(1)>0;){for(var Ce=0,me=1;(H.words[0]&me)===0&&Ce<26;++Ce,me<<=1);if(Ce>0)for(H.iushrn(Ce);Ce-- >0;)oe.isOdd()&&oe.iadd(Ee),oe.iushrn(1);for(var ie=0,Se=1;(te.words[0]&Se)===0&&ie<26;++ie,Se<<=1);if(ie>0)for(te.iushrn(ie);ie-- >0;)_e.isOdd()&&_e.iadd(Ee),_e.iushrn(1);H.cmp(te)>=0?(H.isub(te),oe.isub(_e)):(te.isub(H),_e.isub(oe))}var Le;return H.cmpn(1)===0?Le=oe:Le=_e,Le.cmpn(0)<0&&Le.iadd(N),Le},f.prototype.gcd=function(N){if(this.isZero())return N.abs();if(N.isZero())return this.abs();var H=this.clone(),te=N.clone();H.negative=0,te.negative=0;for(var oe=0;H.isEven()&&te.isEven();oe++)H.iushrn(1),te.iushrn(1);do{for(;H.isEven();)H.iushrn(1);for(;te.isEven();)te.iushrn(1);var _e=H.cmp(te);if(_e<0){var Ee=H;H=te,te=Ee}else if(_e===0||te.cmpn(1)===0)break;H.isub(te)}while(!0);return te.iushln(oe)},f.prototype.invm=function(N){return this.egcd(N).a.umod(N)},f.prototype.isEven=function(){return(this.words[0]&1)===0},f.prototype.isOdd=function(){return(this.words[0]&1)===1},f.prototype.andln=function(N){return this.words[0]&N},f.prototype.bincn=function(N){u(typeof N=="number");var H=N%26,te=(N-H)/26,oe=1<<H;if(this.length<=te)return this._expand(te+1),this.words[te]|=oe,this;for(var _e=oe,Ee=te;_e!==0&&Ee<this.length;Ee++){var Ce=this.words[Ee]|0;Ce+=_e,_e=Ce>>>26,Ce&=67108863,this.words[Ee]=Ce}return _e!==0&&(this.words[Ee]=_e,this.length++),this},f.prototype.isZero=function(){return this.length===1&&this.words[0]===0},f.prototype.cmpn=function(N){var H=N<0;if(this.negative!==0&&!H)return-1;if(this.negative===0&&H)return 1;this.strip();var te;if(this.length>1)te=1;else{H&&(N=-N),u(N<=67108863,"Number is too big");var oe=this.words[0]|0;te=oe===N?0:oe<N?-1:1}return this.negative!==0?-te|0:te},f.prototype.cmp=function(N){if(this.negative!==0&&N.negative===0)return-1;if(this.negative===0&&N.negative!==0)return 1;var H=this.ucmp(N);return this.negative!==0?-H|0:H},f.prototype.ucmp=function(N){if(this.length>N.length)return 1;if(this.length<N.length)return-1;for(var H=0,te=this.length-1;te>=0;te--){var oe=this.words[te]|0,_e=N.words[te]|0;if(oe!==_e){oe<_e?H=-1:oe>_e&&(H=1);break}}return H},f.prototype.gtn=function(N){return this.cmpn(N)===1},f.prototype.gt=function(N){return this.cmp(N)===1},f.prototype.gten=function(N){return this.cmpn(N)>=0},f.prototype.gte=function(N){return this.cmp(N)>=0},f.prototype.ltn=function(N){return this.cmpn(N)===-1},f.prototype.lt=function(N){return this.cmp(N)===-1},f.prototype.lten=function(N){return this.cmpn(N)<=0},f.prototype.lte=function(N){return this.cmp(N)<=0},f.prototype.eqn=function(N){return this.cmpn(N)===0},f.prototype.eq=function(N){return this.cmp(N)===0},f.red=function(N){return new G(N)},f.prototype.toRed=function(N){return u(!this.red,"Already a number in reduction context"),u(this.negative===0,"red works only with positives"),N.convertTo(this)._forceRed(N)},f.prototype.fromRed=function(){return u(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},f.prototype._forceRed=function(N){return this.red=N,this},f.prototype.forceRed=function(N){return u(!this.red,"Already a number in reduction context"),this._forceRed(N)},f.prototype.redAdd=function(N){return u(this.red,"redAdd works only with red numbers"),this.red.add(this,N)},f.prototype.redIAdd=function(N){return u(this.red,"redIAdd works only with red numbers"),this.red.iadd(this,N)},f.prototype.redSub=function(N){return u(this.red,"redSub works only with red numbers"),this.red.sub(this,N)},f.prototype.redISub=function(N){return u(this.red,"redISub works only with red numbers"),this.red.isub(this,N)},f.prototype.redShl=function(N){return u(this.red,"redShl works only with red numbers"),this.red.shl(this,N)},f.prototype.redMul=function(N){return u(this.red,"redMul works only with red numbers"),this.red._verify2(this,N),this.red.mul(this,N)},f.prototype.redIMul=function(N){return u(this.red,"redMul works only with red numbers"),this.red._verify2(this,N),this.red.imul(this,N)},f.prototype.redSqr=function(){return u(this.red,"redSqr works only with red numbers"),this.red._verify1(this),this.red.sqr(this)},f.prototype.redISqr=function(){return u(this.red,"redISqr works only with red numbers"),this.red._verify1(this),this.red.isqr(this)},f.prototype.redSqrt=function(){return u(this.red,"redSqrt works only with red numbers"),this.red._verify1(this),this.red.sqrt(this)},f.prototype.redInvm=function(){return u(this.red,"redInvm works only with red numbers"),this.red._verify1(this),this.red.invm(this)},f.prototype.redNeg=function(){return u(this.red,"redNeg works only with red numbers"),this.red._verify1(this),this.red.neg(this)},f.prototype.redPow=function(N){return u(this.red&&!N.red,"redPow(normalNum)"),this.red._verify1(this),this.red.pow(this,N)};var g={k256:null,p224:null,p192:null,p25519:null};function P(j,N){this.name=j,this.p=new f(N,16),this.n=this.p.bitLength(),this.k=new f(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}P.prototype._tmp=function(){var N=new f(null);return N.words=new Array(Math.ceil(this.n/13)),N},P.prototype.ireduce=function(N){var H=N,te;do this.split(H,this.tmp),H=this.imulK(H),H=H.iadd(this.tmp),te=H.bitLength();while(te>this.n);var oe=te<this.n?-1:H.ucmp(this.p);return oe===0?(H.words[0]=0,H.length=1):oe>0?H.isub(this.p):H.strip!==void 0?H.strip():H._strip(),H},P.prototype.split=function(N,H){N.iushrn(this.n,0,H)},P.prototype.imulK=function(N){return N.imul(this.k)};function T(){P.call(this,"k256","ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f")}c(T,P),T.prototype.split=function(N,H){for(var te=4194303,oe=Math.min(N.length,9),_e=0;_e<oe;_e++)H.words[_e]=N.words[_e];if(H.length=oe,N.length<=9){N.words[0]=0,N.length=1;return}var Ee=N.words[9];for(H.words[H.length++]=Ee&te,_e=10;_e<N.length;_e++){var Ce=N.words[_e]|0;N.words[_e-10]=(Ce&te)<<4|Ee>>>22,Ee=Ce}Ee>>>=22,N.words[_e-10]=Ee,Ee===0&&N.length>10?N.length-=10:N.length-=9},T.prototype.imulK=function(N){N.words[N.length]=0,N.words[N.length+1]=0,N.length+=2;for(var H=0,te=0;te<N.length;te++){var oe=N.words[te]|0;H+=oe*977,N.words[te]=H&67108863,H=oe*64+(H/67108864|0)}return N.words[N.length-1]===0&&(N.length--,N.words[N.length-1]===0&&N.length--),N};function z(){P.call(this,"p224","ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001")}c(z,P);function O(){P.call(this,"p192","ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff")}c(O,P);function V(){P.call(this,"25519","7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed")}c(V,P),V.prototype.imulK=function(N){for(var H=0,te=0;te<N.length;te++){var oe=(N.words[te]|0)*19+H,_e=oe&67108863;oe>>>=26,N.words[te]=_e,H=oe}return H!==0&&(N.words[N.length++]=H),N},f._prime=function(N){if(g[N])return g[N];var H;if(N==="k256")H=new T;else if(N==="p224")H=new z;else if(N==="p192")H=new O;else if(N==="p25519")H=new V;else throw new Error("Unknown prime "+N);return g[N]=H,H};function G(j){if(typeof j=="string"){var N=f._prime(j);this.m=N.p,this.prime=N}else u(j.gtn(1),"modulus must be greater than 1"),this.m=j,this.prime=null}G.prototype._verify1=function(N){u(N.negative===0,"red works only with positives"),u(N.red,"red works only with red numbers")},G.prototype._verify2=function(N,H){u((N.negative|H.negative)===0,"red works only with positives"),u(N.red&&N.red===H.red,"red works only with red numbers")},G.prototype.imod=function(N){return this.prime?this.prime.ireduce(N)._forceRed(this):N.umod(this.m)._forceRed(this)},G.prototype.neg=function(N){return N.isZero()?N.clone():this.m.sub(N)._forceRed(this)},G.prototype.add=function(N,H){this._verify2(N,H);var te=N.add(H);return te.cmp(this.m)>=0&&te.isub(this.m),te._forceRed(this)},G.prototype.iadd=function(N,H){this._verify2(N,H);var te=N.iadd(H);return te.cmp(this.m)>=0&&te.isub(this.m),te},G.prototype.sub=function(N,H){this._verify2(N,H);var te=N.sub(H);return te.cmpn(0)<0&&te.iadd(this.m),te._forceRed(this)},G.prototype.isub=function(N,H){this._verify2(N,H);var te=N.isub(H);return te.cmpn(0)<0&&te.iadd(this.m),te},G.prototype.shl=function(N,H){return this._verify1(N),this.imod(N.ushln(H))},G.prototype.imul=function(N,H){return this._verify2(N,H),this.imod(N.imul(H))},G.prototype.mul=function(N,H){return this._verify2(N,H),this.imod(N.mul(H))},G.prototype.isqr=function(N){return this.imul(N,N.clone())},G.prototype.sqr=function(N){return this.mul(N,N)},G.prototype.sqrt=function(N){if(N.isZero())return N.clone();var H=this.m.andln(3);if(u(H%2===1),H===3){var te=this.m.add(new f(1)).iushrn(2);return this.pow(N,te)}for(var oe=this.m.subn(1),_e=0;!oe.isZero()&&oe.andln(1)===0;)_e++,oe.iushrn(1);u(!oe.isZero());var Ee=new f(1).toRed(this),Ce=Ee.redNeg(),me=this.m.subn(1).iushrn(1),ie=this.m.bitLength();for(ie=new f(2*ie*ie).toRed(this);this.pow(ie,me).cmp(Ce)!==0;)ie.redIAdd(Ce);for(var Se=this.pow(ie,oe),Le=this.pow(N,oe.addn(1).iushrn(1)),Ae=this.pow(N,oe),Fe=_e;Ae.cmp(Ee)!==0;){for(var Pe=Ae,ge=0;Pe.cmp(Ee)!==0;ge++)Pe=Pe.redSqr();u(ge<Fe);var Re=this.pow(Se,new f(1).iushln(Fe-ge-1));Le=Le.redMul(Re),Se=Re.redSqr(),Ae=Ae.redMul(Se),Fe=ge}return Le},G.prototype.invm=function(N){var H=N._invmp(this.m);return H.negative!==0?(H.negative=0,this.imod(H).redNeg()):this.imod(H)},G.prototype.pow=function(N,H){if(H.isZero())return new f(1).toRed(this);if(H.cmpn(1)===0)return N.clone();var te=4,oe=new Array(1<<te);oe[0]=new f(1).toRed(this),oe[1]=N;for(var _e=2;_e<oe.length;_e++)oe[_e]=this.mul(oe[_e-1],N);var Ee=oe[0],Ce=0,me=0,ie=H.bitLength()%26;for(ie===0&&(ie=26),_e=H.length-1;_e>=0;_e--){for(var Se=H.words[_e],Le=ie-1;Le>=0;Le--){var Ae=Se>>Le&1;if(Ee!==oe[0]&&(Ee=this.sqr(Ee)),Ae===0&&Ce===0){me=0;continue}Ce<<=1,Ce|=Ae,me++,!(me!==te&&(_e!==0||Le!==0))&&(Ee=this.mul(Ee,oe[Ce]),me=0,Ce=0)}ie=26}return Ee},G.prototype.convertTo=function(N){var H=N.umod(this.m);return H===N?H.clone():H},G.prototype.convertFrom=function(N){var H=N.clone();return H.red=null,H},f.mont=function(N){return new Z(N)};function Z(j){G.call(this,j),this.shift=this.m.bitLength(),this.shift%26!==0&&(this.shift+=26-this.shift%26),this.r=new f(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}c(Z,G),Z.prototype.convertTo=function(N){return this.imod(N.ushln(this.shift))},Z.prototype.convertFrom=function(N){var H=this.imod(N.mul(this.rinv));return H.red=null,H},Z.prototype.imul=function(N,H){if(N.isZero()||H.isZero())return N.words[0]=0,N.length=1,N;var te=N.imul(H),oe=te.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),_e=te.isub(oe).iushrn(this.shift),Ee=_e;return _e.cmp(this.m)>=0?Ee=_e.isub(this.m):_e.cmpn(0)<0&&(Ee=_e.iadd(this.m)),Ee._forceRed(this)},Z.prototype.mul=function(N,H){if(N.isZero()||H.isZero())return new f(0)._forceRed(this);var te=N.mul(H),oe=te.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),_e=te.isub(oe).iushrn(this.shift),Ee=_e;return 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g,P=s.mallocDouble(2*M*x),T=s.mallocInt32(x);if(x=f(k,M,P,T),x>0){if(M===1&&L)l.init(x),g=l.sweepComplete(M,S,0,x,P,T,0,x,P,T);else{var z=s.mallocDouble(2*M*C),O=s.mallocInt32(C);C=f(E,M,z,O),C>0&&(l.init(x+C),M===1?g=l.sweepBipartite(M,S,0,x,P,T,0,C,z,O):g=u(M,S,L,x,P,T,C,z,O),s.free(z),s.free(O))}s.free(P),s.free(T)}return g}}}var d;function v(k,E){d.push([k,E])}function _(k){return d=[],h(k,k,v,!0),d}function b(k,E){return d=[],h(k,E,v,!1),d}function p(k,E,S){switch(arguments.length){case 1:return _(k);case 2:return typeof E=="function"?h(k,k,E,!0):b(k,E);case 3:return h(k,E,S,!1);default:throw new Error("box-intersect: Invalid arguments")}}},6894:function(i){i.exports=a;function a(o,s,l,u){var c=l[1],f=l[2],h=s[1]-c,d=s[2]-f,v=Math.sin(u),_=Math.cos(u);return o[0]=s[0],o[1]=c+h*_-d*v,o[2]=f+h*v+d*_,o}},7004:function(i){"use strict";i.exports=a;function a(o){for(var s=o.length,l=o[o.length-1],u=s,c=s-2;c>=0;--c){var f=l,h=o[c];l=f+h;var d=l-f,v=h-d;v&&(o[--u]=l,l=v)}for(var 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_e=l.nextPow2(g*te);z.length<_e&&(s.free(z),z=s.mallocDouble(_e))}function V(N,H,te,oe,_e,Ee,Ce,me,ie){var Se=M*N;T[Se]=H,T[Se+1]=te,T[Se+2]=oe,T[Se+3]=_e,T[Se+4]=Ee,T[Se+5]=Ce;var Le=g*N;z[Le]=me,z[Le+1]=ie}function G(N,H,te,oe,_e,Ee,Ce,me,ie,Se,Le){var Ae=2*N,Fe=ie*Ae,Pe=Se[Fe+H];e:for(var ge=_e,Re=_e*Ae;ge<Ee;++ge,Re+=Ae){var ce=Ce[Re+H],Ze=Ce[Re+H+N];if(!(Pe<ce||Ze<Pe)&&!(oe&&Pe===ce)){for(var ut=me[ge],pt=H+1;pt<N;++pt){var ce=Ce[Re+pt],Ze=Ce[Re+pt+N],Zt=Se[Fe+pt],st=Se[Fe+pt+N];if(Ze<Zt||st<ce)continue e}var lt;if(oe?lt=te(Le,ut):lt=te(ut,Le),lt!==void 0)return lt}}}function Z(N,H,te,oe,_e,Ee,Ce,me,ie,Se){var Le=2*N,Ae=me*Le,Fe=ie[Ae+H];e:for(var Pe=oe,ge=oe*Le;Pe<_e;++Pe,ge+=Le){var Re=Ce[Pe];if(Re!==Se){var ce=Ee[ge+H],Ze=Ee[ge+H+N];if(!(Fe<ce||Ze<Fe)){for(var ut=H+1;ut<N;++ut){var ce=Ee[ge+ut],Ze=Ee[ge+ut+N],pt=ie[Ae+ut],Zt=ie[Ae+ut+N];if(Ze<pt||Zt<ce)continue e}var st=te(Re,Se);if(st!==void 0)return st}}}}function j(N,H,te,oe,_e,Ee,Ce,me,ie){O(N,oe+Ce);var Se=0,Le=2*N,Ae;for(V(Se++,0,0,oe,0,Ce,te?16:0,-1/0,1/0),te||V(Se++,0,0,Ce,0,oe,1,-1/0,1/0);Se>0;){Se-=1;var Fe=Se*M,Pe=T[Fe],ge=T[Fe+1],Re=T[Fe+2],ce=T[Fe+3],Ze=T[Fe+4],ut=T[Fe+5],pt=Se*g,Zt=z[pt],st=z[pt+1],lt=ut&1,Gt=!!(ut&16),Nt=_e,Jt=Ee,sr=me,wr=ie;if(lt&&(Nt=me,Jt=ie,sr=_e,wr=Ee),!(ut&2&&(Re=S(N,Pe,ge,Re,Nt,Jt,st),ge>=Re))&&!(ut&4&&(ge=L(N,Pe,ge,Re,Nt,Jt,Zt),ge>=Re))){var cr=Re-ge,$e=Ze-ce;if(Gt){if(N*cr*(cr+$e)<p){if(Ae=h.scanComplete(N,Pe,H,ge,Re,Nt,Jt,ce,Ze,sr,wr),Ae!==void 0)return Ae;continue}}else if(N*Math.min(cr,$e)<_){if(Ae=c(N,Pe,H,lt,ge,Re,Nt,Jt,ce,Ze,sr,wr),Ae!==void 0)return Ae;continue}else if(N*cr*$e<b){if(Ae=h.scanBipartite(N,Pe,H,lt,ge,Re,Nt,Jt,ce,Ze,sr,wr),Ae!==void 0)return Ae;continue}var St=k(N,Pe,ge,Re,Nt,Jt,Zt,st);if(ge<St)if(N*(St-ge)<_){if(Ae=f(N,Pe+1,H,ge,St,Nt,Jt,ce,Ze,sr,wr),Ae!==void 0)return Ae}else if(Pe===N-2){if(lt?Ae=h.sweepBipartite(N,H,ce,Ze,sr,wr,ge,St,Nt,Jt):Ae=h.sweepBipartite(N,H,ge,St,Nt,Jt,ce,Ze,sr,wr),Ae!==void 0)return Ae}else V(Se++,Pe+1,ge,St,ce,Ze,lt,-1/0,1/0),V(Se++,Pe+1,ce,Ze,ge,St,lt^1,-1/0,1/0);if(St<Re){var Qt=d(N,Pe,ce,Ze,sr,wr),Vt=sr[Le*Qt+Pe],_t=E(N,Pe,Qt,Ze,sr,wr,Vt);if(_t<Ze&&V(Se++,Pe,St,Re,_t,Ze,(lt|4)+(Gt?16:0),Vt,st),ce<Qt&&V(Se++,Pe,St,Re,ce,Qt,(lt|2)+(Gt?16:0),Zt,Vt),Qt+1===_t){if(Gt?Ae=Z(N,Pe,H,St,Re,Nt,Jt,Qt,sr,wr[Qt]):Ae=G(N,Pe,H,lt,St,Re,Nt,Jt,Qt,sr,wr[Qt]),Ae!==void 0)return Ae}else if(Qt<_t){var It;if(Gt){if(It=x(N,Pe,St,Re,Nt,Jt,Vt),St<It){var mt=E(N,Pe,St,It,Nt,Jt,Vt);if(Pe===N-2){if(St<mt&&(Ae=h.sweepComplete(N,H,St,mt,Nt,Jt,Qt,_t,sr,wr),Ae!==void 0)||mt<It&&(Ae=h.sweepBipartite(N,H,mt,It,Nt,Jt,Qt,_t,sr,wr),Ae!==void 0))return Ae}else St<mt&&V(Se++,Pe+1,St,mt,Qt,_t,16,-1/0,1/0),mt<It&&(V(Se++,Pe+1,mt,It,Qt,_t,0,-1/0,1/0),V(Se++,Pe+1,Qt,_t,mt,It,1,-1/0,1/0))}}else lt?It=C(N,Pe,St,Re,Nt,Jt,Vt):It=x(N,Pe,St,Re,Nt,Jt,Vt),St<It&&(Pe===N-2?lt?Ae=h.sweepBipartite(N,H,Qt,_t,sr,wr,St,It,Nt,Jt):Ae=h.sweepBipartite(N,H,St,It,Nt,Jt,Qt,_t,sr,wr):(V(Se++,Pe+1,St,It,Qt,_t,lt,-1/0,1/0),V(Se++,Pe+1,Qt,_t,St,It,lt^1,-1/0,1/0)))}}}}}},7163:function(i){i.exports=function(s){return s!=null&&(a(s)||o(s)||!!s._isBuffer)};function a(s){return!!s.constructor&&typeof s.constructor.isBuffer=="function"&&s.constructor.isBuffer(s)}function o(s){return typeof s.readFloatLE=="function"&&typeof s.slice=="function"&&a(s.slice(0,0))}},7169:function(i,a,o){"use strict";var s=typeof WeakMap=="undefined"?o(1538):WeakMap,l=o(2762),u=o(8116),c=new s;function f(h){var d=c.get(h),v=d&&(d._triangleBuffer.handle||d._triangleBuffer.buffer);if(!v||!h.isBuffer(v)){var _=l(h,new Float32Array([-1,-1,-1,4,4,-1]));d=u(h,[{buffer:_,type:h.FLOAT,size:2}]),d._triangleBuffer=_,c.set(h,d)}d.bind(),h.drawArrays(h.TRIANGLES,0,3),d.unbind()}i.exports=f},7182:function(i,a,o){var s={identity:o(7894),translate:o(7656),multiply:o(6760),create:o(6864),scale:o(2504),fromRotationTranslation:o(6743)},l=s.create(),u=s.create();i.exports=function(f,h,d,v,_,b){return s.identity(f),s.fromRotationTranslation(f,b,h),f[3]=_[0],f[7]=_[1],f[11]=_[2],f[15]=_[3],s.identity(u),v[2]!==0&&(u[9]=v[2],s.multiply(f,f,u)),v[1]!==0&&(u[9]=0,u[8]=v[1],s.multiply(f,f,u)),v[0]!==0&&(u[8]=0,u[4]=v[0],s.multiply(f,f,u)),s.scale(f,f,d),f}},7201:function(i,a,o){"use strict";var s=1e-6,l=1e-6,u=o(9405),c=o(2762),f=o(8116),h=o(7766),d=o(8406),v=o(6760),_=o(7608),b=o(9618),p=o(6729),k=o(7765),E=o(1888),S=o(840),L=o(7626),x=S.meshShader,C=S.wireShader,M=S.pointShader,g=S.pickShader,P=S.pointPickShader,T=S.contourShader,z=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function O(me,ie,Se,Le,Ae,Fe,Pe,ge,Re,ce,Ze,ut,pt,Zt,st,lt,Gt,Nt,Jt,sr,wr,cr,$e,St,Qt,Vt,_t){this.gl=me,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=ie,this.dirty=!0,this.triShader=Se,this.lineShader=Le,this.pointShader=Ae,this.pickShader=Fe,this.pointPickShader=Pe,this.contourShader=ge,this.trianglePositions=Re,this.triangleColors=Ze,this.triangleNormals=pt,this.triangleUVs=ut,this.triangleIds=ce,this.triangleVAO=Zt,this.triangleCount=0,this.lineWidth=1,this.edgePositions=st,this.edgeColors=Gt,this.edgeUVs=Nt,this.edgeIds=lt,this.edgeVAO=Jt,this.edgeCount=0,this.pointPositions=sr,this.pointColors=cr,this.pointUVs=$e,this.pointSizes=St,this.pointIds=wr,this.pointVAO=Qt,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=Vt,this.contourVAO=_t,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=z,this._view=z,this._projection=z,this._resolution=[1,1]}var 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ie=k(this.cells,this.intensity,me.intensity),Se=ie.cells,Le=ie.vertexIds,Ae=ie.vertexWeights,Fe=Se.length,Pe=E.mallocFloat32(2*3*Fe),ge=0,Re=0;Re<Fe;++Re)for(var ce=Se[Re],Ze=0;Ze<2;++Ze){var ut=ce[0];ce.length===2&&(ut=ce[Ze]);for(var pt=Le[ut][0],Zt=Le[ut][1],st=Ae[ut],lt=1-st,Gt=this.positions[pt],Nt=this.positions[Zt],Jt=0;Jt<3;++Jt)Pe[ge++]=st*Gt[Jt]+lt*Nt[Jt]}this.contourCount=ge/3|0,this.contourPositions.update(Pe.subarray(0,ge)),E.free(Pe)},V.update=function(me){me=me||{};var ie=this.gl;this.dirty=!0,"contourEnable"in me&&(this.contourEnable=me.contourEnable),"contourColor"in me&&(this.contourColor=me.contourColor),"lineWidth"in me&&(this.lineWidth=me.lineWidth),"lightPosition"in me&&(this.lightPosition=me.lightPosition),this.hasAlpha=!1,"opacity"in me&&(this.opacity=me.opacity,this.opacity<1&&(this.hasAlpha=!0)),"opacityscale"in me&&(this.opacityscale=me.opacityscale,this.hasAlpha=!0),"ambient"in me&&(this.ambientLight=me.ambient),"diffuse"in me&&(this.diffuseLight=me.diffuse),"specular"in me&&(this.specularLight=me.specular),"roughness"in me&&(this.roughness=me.roughness),"fresnel"in me&&(this.fresnel=me.fresnel),me.texture?(this.texture.dispose(),this.texture=h(ie,me.texture)):me.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=ie.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=ie.LINEAR,this.texture.setPixels(Z(me.colormap,this.opacityscale)),this.texture.generateMipmap());var Se=me.cells,Le=me.positions;if(!(!Le||!Se)){var Ae=[],Fe=[],Pe=[],ge=[],Re=[],ce=[],Ze=[],ut=[],pt=[],Zt=[],st=[],lt=[],Gt=[],Nt=[];this.cells=Se,this.positions=Le;var Jt=me.vertexNormals,sr=me.cellNormals,wr=me.vertexNormalsEpsilon===void 0?s:me.vertexNormalsEpsilon,cr=me.faceNormalsEpsilon===void 0?l:me.faceNormalsEpsilon;me.useFacetNormals&&!sr&&(sr=d.faceNormals(Se,Le,cr)),!sr&&!Jt&&(Jt=d.vertexNormals(Se,Le,wr));var $e=me.vertexColors,St=me.cellColors,Qt=me.meshColor||[1,1,1,1],Vt=me.vertexUVs,_t=me.vertexIntensity,It=me.cellUVs,mt=me.cellIntensity,er=1/0,lr=-1/0;if(!Vt&&!It)if(_t)if(me.vertexIntensityBounds)er=+me.vertexIntensityBounds[0],lr=+me.vertexIntensityBounds[1];else for(var Tr=0;Tr<_t.length;++Tr){var Lr=_t[Tr];er=Math.min(er,Lr),lr=Math.max(lr,Lr)}else if(mt)if(me.cellIntensityBounds)er=+me.cellIntensityBounds[0],lr=+me.cellIntensityBounds[1];else for(var Tr=0;Tr<mt.length;++Tr){var Lr=mt[Tr];er=Math.min(er,Lr),lr=Math.max(lr,Lr)}else for(var Tr=0;Tr<Le.length;++Tr){var Lr=Le[Tr][2];er=Math.min(er,Lr),lr=Math.max(lr,Lr)}_t?this.intensity=_t:mt?this.intensity=mt:this.intensity=j(Le),this.pickVertex=!(mt||St);var ti=me.pointSizes,Br=me.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(var Tr=0;Tr<Le.length;++Tr)for(var Vr=Le[Tr],dt=0;dt<3;++dt)isNaN(Vr[dt])||!isFinite(Vr[dt])||(this.bounds[0][dt]=Math.min(this.bounds[0][dt],Vr[dt]),this.bounds[1][dt]=Math.max(this.bounds[1][dt],Vr[dt]));var Ge=0,Je=0,je=0;e:for(var Tr=0;Tr<Se.length;++Tr){var tt=Se[Tr];switch(tt.length){case 1:for(var xt=tt[0],Vr=Le[xt],dt=0;dt<3;++dt)if(isNaN(Vr[dt])||!isFinite(Vr[dt]))continue e;Zt.push(Vr[0],Vr[1],Vr[2]);var Ie;$e?Ie=$e[xt]:St?Ie=St[Tr]:Ie=Qt,this.opacityscale&&_t?Fe.push(Ie[0],Ie[1],Ie[2],this.opacity*G((_t[xt]-er)/(lr-er),this.opacityscale)):Ie.length===3?st.push(Ie[0],Ie[1],Ie[2],this.opacity):(st.push(Ie[0],Ie[1],Ie[2],Ie[3]*this.opacity),Ie[3]<1&&(this.hasAlpha=!0));var xe;Vt?xe=Vt[xt]:_t?xe=[(_t[xt]-er)/(lr-er),0]:It?xe=It[Tr]:mt?xe=[(mt[Tr]-er)/(lr-er),0]:xe=[(Vr[2]-er)/(lr-er),0],lt.push(xe[0],xe[1]),ti?Gt.push(ti[xt]):Gt.push(Br),Nt.push(Tr),je+=1;break;case 2:for(var dt=0;dt<2;++dt)for(var xt=tt[dt],Vr=Le[xt],ke=0;ke<3;++ke)if(isNaN(Vr[ke])||!isFinite(Vr[ke]))continue e;for(var dt=0;dt<2;++dt){var xt=tt[dt],Vr=Le[xt];ce.push(Vr[0],Vr[1],Vr[2]);var Ie;$e?Ie=$e[xt]:St?Ie=St[Tr]:Ie=Qt,this.opacityscale&&_t?Fe.push(Ie[0],Ie[1],Ie[2],this.opacity*G((_t[xt]-er)/(lr-er),this.opacityscale)):Ie.length===3?Ze.push(Ie[0],Ie[1],Ie[2],this.opacity):(Ze.push(Ie[0],Ie[1],Ie[2],Ie[3]*this.opacity),Ie[3]<1&&(this.hasAlpha=!0));var xe;Vt?xe=Vt[xt]:_t?xe=[(_t[xt]-er)/(lr-er),0]:It?xe=It[Tr]:mt?xe=[(mt[Tr]-er)/(lr-er),0]:xe=[(Vr[2]-er)/(lr-er),0],ut.push(xe[0],xe[1]),pt.push(Tr)}Je+=1;break;case 3:for(var dt=0;dt<3;++dt)for(var xt=tt[dt],Vr=Le[xt],ke=0;ke<3;++ke)if(isNaN(Vr[ke])||!isFinite(Vr[ke]))continue e;for(var dt=0;dt<3;++dt){var xt=tt[2-dt],Vr=Le[xt];Ae.push(Vr[0],Vr[1],Vr[2]);var Ie;$e?Ie=$e[xt]:St?Ie=St[Tr]:Ie=Qt,Ie?this.opacityscale&&_t?Fe.push(Ie[0],Ie[1],Ie[2],this.opacity*G((_t[xt]-er)/(lr-er),this.opacityscale)):Ie.length===3?Fe.push(Ie[0],Ie[1],Ie[2],this.opacity):(Fe.push(Ie[0],Ie[1],Ie[2],Ie[3]*this.opacity),Ie[3]<1&&(this.hasAlpha=!0)):Fe.push(.5,.5,.5,1);var xe;Vt?xe=Vt[xt]:_t?xe=[(_t[xt]-er)/(lr-er),0]:It?xe=It[Tr]:mt?xe=[(mt[Tr]-er)/(lr-er),0]:xe=[(Vr[2]-er)/(lr-er),0],ge.push(xe[0],xe[1]);var vt;Jt?vt=Jt[xt]:vt=sr[Tr],Pe.push(vt[0],vt[1],vt[2]),Re.push(Tr)}Ge+=1;break;default:break}}this.pointCount=je,this.edgeCount=Je,this.triangleCount=Ge,this.pointPositions.update(Zt),this.pointColors.update(st),this.pointUVs.update(lt),this.pointSizes.update(Gt),this.pointIds.update(new Uint32Array(Nt)),this.edgePositions.update(ce),this.edgeColors.update(Ze),this.edgeUVs.update(ut),this.edgeIds.update(new Uint32Array(pt)),this.trianglePositions.update(Ae),this.triangleColors.update(Fe),this.triangleUVs.update(ge),this.triangleNormals.update(Pe),this.triangleIds.update(new Uint32Array(Re))}},V.drawTransparent=V.draw=function(me){me=me||{};for(var ie=this.gl,Se=me.model||z,Le=me.view||z,Ae=me.projection||z,Fe=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],Pe=0;Pe<3;++Pe)Fe[0][Pe]=Math.max(Fe[0][Pe],this.clipBounds[0][Pe]),Fe[1][Pe]=Math.min(Fe[1][Pe],this.clipBounds[1][Pe]);var ge={model:Se,view:Le,projection:Ae,inverseModel:z.slice(),clipBounds:Fe,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};ge.inverseModel=_(ge.inverseModel,ge.model),ie.disable(ie.CULL_FACE),this.texture.bind(0);var Re=new Array(16);v(Re,ge.view,ge.model),v(Re,ge.projection,Re),_(Re,Re);for(var Pe=0;Pe<3;++Pe)ge.eyePosition[Pe]=Re[12+Pe]/Re[15];for(var ce=Re[15],Pe=0;Pe<3;++Pe)ce+=this.lightPosition[Pe]*Re[4*Pe+3];for(var Pe=0;Pe<3;++Pe){for(var Ze=Re[12+Pe],ut=0;ut<3;++ut)Ze+=Re[4*ut+Pe]*this.lightPosition[ut];ge.lightPosition[Pe]=Ze/ce}if(this.triangleCount>0){var pt=this.triShader;pt.bind(),pt.uniforms=ge,this.triangleVAO.bind(),ie.drawArrays(ie.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind()}if(this.edgeCount>0&&this.lineWidth>0){var pt=this.lineShader;pt.bind(),pt.uniforms=ge,this.edgeVAO.bind(),ie.lineWidth(this.lineWidth*this.pixelRatio),ie.drawArrays(ie.LINES,0,this.edgeCount*2),this.edgeVAO.unbind()}if(this.pointCount>0){var pt=this.pointShader;pt.bind(),pt.uniforms=ge,this.pointVAO.bind(),ie.drawArrays(ie.POINTS,0,this.pointCount),this.pointVAO.unbind()}if(this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0){var pt=this.contourShader;pt.bind(),pt.uniforms=ge,this.contourVAO.bind(),ie.drawArrays(ie.LINES,0,this.contourCount),this.contourVAO.unbind()}},V.drawPick=function(me){me=me||{};for(var ie=this.gl,Se=me.model||z,Le=me.view||z,Ae=me.projection||z,Fe=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],Pe=0;Pe<3;++Pe)Fe[0][Pe]=Math.max(Fe[0][Pe],this.clipBounds[0][Pe]),Fe[1][Pe]=Math.min(Fe[1][Pe],this.clipBounds[1][Pe]);this._model=[].slice.call(Se),this._view=[].slice.call(Le),this._projection=[].slice.call(Ae),this._resolution=[ie.drawingBufferWidth,ie.drawingBufferHeight];var ge={model:Se,view:Le,projection:Ae,clipBounds:Fe,pickId:this.pickId/255},Re=this.pickShader;if(Re.bind(),Re.uniforms=ge,this.triangleCount>0&&(this.triangleVAO.bind(),ie.drawArrays(ie.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),ie.lineWidth(this.lineWidth*this.pixelRatio),ie.drawArrays(ie.LINES,0,this.edgeCount*2),this.edgeVAO.unbind()),this.pointCount>0){var Re=this.pointPickShader;Re.bind(),Re.uniforms=ge,this.pointVAO.bind(),ie.drawArrays(ie.POINTS,0,this.pointCount),this.pointVAO.unbind()}},V.pick=function(me){if(!me||me.id!==this.pickId)return null;for(var ie=me.value[0]+256*me.value[1]+65536*me.value[2],Se=this.cells[ie],Le=this.positions,Ae=new Array(Se.length),Fe=0;Fe<Se.length;++Fe)Ae[Fe]=Le[Se[Fe]];var Pe=me.coord[0],ge=me.coord[1];if(!this.pickVertex){var Re=this.positions[Se[0]],ce=this.positions[Se[1]],Ze=this.positions[Se[2]],ut=[(Re[0]+ce[0]+Ze[0])/3,(Re[1]+ce[1]+Ze[1])/3,(Re[2]+ce[2]+Ze[2])/3];return{_cellCenter:!0,position:[Pe,ge],index:ie,cell:Se,cellId:ie,intensity:this.intensity[ie],dataCoordinate:ut}}var pt=L(Ae,[Pe*this.pixelRatio,this._resolution[1]-ge*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!pt)return null;for(var Zt=pt[2],st=0,Fe=0;Fe<Se.length;++Fe)st+=Zt[Fe]*this.intensity[Se[Fe]];return{position:pt[1],index:Se[pt[0]],cell:Se,cellId:ie,intensity:st,dataCoordinate:this.positions[Se[pt[0]]]}},V.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()};function N(me){var ie=u(me,x.vertex,x.fragment);return ie.attributes.position.location=0,ie.attributes.color.location=2,ie.attributes.uv.location=3,ie.attributes.normal.location=4,ie}function H(me){var ie=u(me,C.vertex,C.fragment);return ie.attributes.position.location=0,ie.attributes.color.location=2,ie.attributes.uv.location=3,ie}function te(me){var ie=u(me,M.vertex,M.fragment);return ie.attributes.position.location=0,ie.attributes.color.location=2,ie.attributes.uv.location=3,ie.attributes.pointSize.location=4,ie}function oe(me){var ie=u(me,g.vertex,g.fragment);return ie.attributes.position.location=0,ie.attributes.id.location=1,ie}function _e(me){var ie=u(me,P.vertex,P.fragment);return ie.attributes.position.location=0,ie.attributes.id.location=1,ie.attributes.pointSize.location=4,ie}function Ee(me){var ie=u(me,T.vertex,T.fragment);return ie.attributes.position.location=0,ie}function Ce(me,ie){arguments.length===1&&(ie=me,me=ie.gl);var Se=me.getExtension("OES_standard_derivatives")||me.getExtension("MOZ_OES_standard_derivatives")||me.getExtension("WEBKIT_OES_standard_derivatives");if(!Se)throw new Error("derivatives not supported");var Le=N(me),Ae=H(me),Fe=te(me),Pe=oe(me),ge=_e(me),Re=Ee(me),ce=h(me,b(new Uint8Array([255,255,255,255]),[1,1,4]));ce.generateMipmap(),ce.minFilter=me.LINEAR_MIPMAP_LINEAR,ce.magFilter=me.LINEAR;var Ze=c(me),ut=c(me),pt=c(me),Zt=c(me),st=c(me),lt=f(me,[{buffer:Ze,type:me.FLOAT,size:3},{buffer:st,type:me.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:ut,type:me.FLOAT,size:4},{buffer:pt,type:me.FLOAT,size:2},{buffer:Zt,type:me.FLOAT,size:3}]),Gt=c(me),Nt=c(me),Jt=c(me),sr=c(me),wr=f(me,[{buffer:Gt,type:me.FLOAT,size:3},{buffer:sr,type:me.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:Nt,type:me.FLOAT,size:4},{buffer:Jt,type:me.FLOAT,size:2}]),cr=c(me),$e=c(me),St=c(me),Qt=c(me),Vt=c(me),_t=f(me,[{buffer:cr,type:me.FLOAT,size:3},{buffer:Vt,type:me.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:$e,type:me.FLOAT,size:4},{buffer:St,type:me.FLOAT,size:2},{buffer:Qt,type:me.FLOAT,size:1}]),It=c(me),mt=f(me,[{buffer:It,type:me.FLOAT,size:3}]),er=new O(me,ce,Le,Ae,Fe,Pe,ge,Re,Ze,st,ut,pt,Zt,lt,Gt,sr,Nt,Jt,wr,cr,Vt,$e,St,Qt,_t,It,mt);return er.update(ie),er}i.exports=Ce},7261:function(i,a,o){"use strict";i.exports=E;var s=o(9215),l=o(7608),u=o(6079),c=o(5911),f=o(3536),h=o(244);function d(S,L,x){return Math.sqrt(Math.pow(S,2)+Math.pow(L,2)+Math.pow(x,2))}function v(S){return Math.min(1,Math.max(-1,S))}function _(S){var L=Math.abs(S[0]),x=Math.abs(S[1]),C=Math.abs(S[2]),M=[0,0,0];L>Math.max(x,C)?M[2]=1:x>Math.max(L,C)?M[0]=1:M[1]=1;for(var g=0,P=0,T=0;T<3;++T)g+=S[T]*S[T],P+=M[T]*S[T];for(var T=0;T<3;++T)M[T]-=P/g*S[T];return f(M,M),M}function b(S,L,x,C,M,g,P,T){this.center=s(x),this.up=s(C),this.right=s(M),this.radius=s([g]),this.angle=s([P,T]),this.angle.bounds=[[-1/0,-Math.PI/2],[1/0,Math.PI/2]],this.setDistanceLimits(S,L),this.computedCenter=this.center.curve(0),this.computedUp=this.up.curve(0),this.computedRight=this.right.curve(0),this.computedRadius=this.radius.curve(0),this.computedAngle=this.angle.curve(0),this.computedToward=[0,0,0],this.computedEye=[0,0,0],this.computedMatrix=new Array(16);for(var z=0;z<16;++z)this.computedMatrix[z]=.5;this.recalcMatrix(0)}var p=b.prototype;p.setDistanceLimits=function(S,L){S>0?S=Math.log(S):S=-1/0,L>0?L=Math.log(L):L=1/0,L=Math.max(L,S),this.radius.bounds[0][0]=S,this.radius.bounds[1][0]=L},p.getDistanceLimits=function(S){var L=this.radius.bounds[0];return S?(S[0]=Math.exp(L[0][0]),S[1]=Math.exp(L[1][0]),S):[Math.exp(L[0][0]),Math.exp(L[1][0])]},p.recalcMatrix=function(S){this.center.curve(S),this.up.curve(S),this.right.curve(S),this.radius.curve(S),this.angle.curve(S);for(var L=this.computedUp,x=this.computedRight,C=0,M=0,g=0;g<3;++g)M+=L[g]*x[g],C+=L[g]*L[g];for(var P=Math.sqrt(C),T=0,g=0;g<3;++g)x[g]-=L[g]*M/C,T+=x[g]*x[g],L[g]/=P;for(var z=Math.sqrt(T),g=0;g<3;++g)x[g]/=z;var O=this.computedToward;c(O,L,x),f(O,O);for(var V=Math.exp(this.computedRadius[0]),G=this.computedAngle[0],Z=this.computedAngle[1],j=Math.cos(G),N=Math.sin(G),H=Math.cos(Z),te=Math.sin(Z),oe=this.computedCenter,_e=j*H,Ee=N*H,Ce=te,me=-j*te,ie=-N*te,Se=H,Le=this.computedEye,Ae=this.computedMatrix,g=0;g<3;++g){var Fe=_e*x[g]+Ee*O[g]+Ce*L[g];Ae[4*g+1]=me*x[g]+ie*O[g]+Se*L[g],Ae[4*g+2]=Fe,Ae[4*g+3]=0}var Pe=Ae[1],ge=Ae[5],Re=Ae[9],ce=Ae[2],Ze=Ae[6],ut=Ae[10],pt=ge*ut-Re*Ze,Zt=Re*ce-Pe*ut,st=Pe*Ze-ge*ce,lt=d(pt,Zt,st);pt/=lt,Zt/=lt,st/=lt,Ae[0]=pt,Ae[4]=Zt,Ae[8]=st;for(var g=0;g<3;++g)Le[g]=oe[g]+Ae[2+4*g]*V;for(var g=0;g<3;++g){for(var T=0,Gt=0;Gt<3;++Gt)T+=Ae[g+4*Gt]*Le[Gt];Ae[12+g]=-T}Ae[15]=1},p.getMatrix=function(S,L){this.recalcMatrix(S);var x=this.computedMatrix;if(L){for(var C=0;C<16;++C)L[C]=x[C];return L}return x};var k=[0,0,0];p.rotate=function(S,L,x,C){if(this.angle.move(S,L,x),C){this.recalcMatrix(S);var M=this.computedMatrix;k[0]=M[2],k[1]=M[6],k[2]=M[10];for(var g=this.computedUp,P=this.computedRight,T=this.computedToward,z=0;z<3;++z)M[4*z]=g[z],M[4*z+1]=P[z],M[4*z+2]=T[z];u(M,M,C,k);for(var z=0;z<3;++z)g[z]=M[4*z],P[z]=M[4*z+1];this.up.set(S,g[0],g[1],g[2]),this.right.set(S,P[0],P[1],P[2])}},p.pan=function(S,L,x,C){L=L||0,x=x||0,C=C||0,this.recalcMatrix(S);var M=this.computedMatrix,g=Math.exp(this.computedRadius[0]),P=M[1],T=M[5],z=M[9],O=d(P,T,z);P/=O,T/=O,z/=O;var V=M[0],G=M[4],Z=M[8],j=V*P+G*T+Z*z;V-=P*j,G-=T*j,Z-=z*j;var N=d(V,G,Z);V/=N,G/=N,Z/=N;var H=V*L+P*x,te=G*L+T*x,oe=Z*L+z*x;this.center.move(S,H,te,oe);var _e=Math.exp(this.computedRadius[0]);_e=Math.max(1e-4,_e+C),this.radius.set(S,Math.log(_e))},p.translate=function(S,L,x,C){this.center.move(S,L||0,x||0,C||0)},p.setMatrix=function(S,L,x,C){var M=1;typeof x=="number"&&(M=x|0),(M<0||M>3)&&(M=1);var g=(M+2)%3,P=(M+1)%3;L||(this.recalcMatrix(S),L=this.computedMatrix);var T=L[M],z=L[M+4],O=L[M+8];if(C){var G=Math.abs(T),Z=Math.abs(z),j=Math.abs(O),N=Math.max(G,Z,j);G===N?(T=T<0?-1:1,z=O=0):j===N?(O=O<0?-1:1,T=z=0):(z=z<0?-1:1,T=O=0)}else{var V=d(T,z,O);T/=V,z/=V,O/=V}var H=L[g],te=L[g+4],oe=L[g+8],_e=H*T+te*z+oe*O;H-=T*_e,te-=z*_e,oe-=O*_e;var Ee=d(H,te,oe);H/=Ee,te/=Ee,oe/=Ee;var Ce=z*oe-O*te,me=O*H-T*oe,ie=T*te-z*H,Se=d(Ce,me,ie);Ce/=Se,me/=Se,ie/=Se,this.center.jump(S,cr,$e,St),this.radius.idle(S),this.up.jump(S,T,z,O),this.right.jump(S,H,te,oe);var Le,Ae;if(M===2){var Fe=L[1],Pe=L[5],ge=L[9],Re=Fe*H+Pe*te+ge*oe,ce=Fe*Ce+Pe*me+ge*ie;Zt<0?Le=-Math.PI/2:Le=Math.PI/2,Ae=Math.atan2(ce,Re)}else{var Ze=L[2],ut=L[6],pt=L[10],Zt=Ze*T+ut*z+pt*O,st=Ze*H+ut*te+pt*oe,lt=Ze*Ce+ut*me+pt*ie;Le=Math.asin(v(Zt)),Ae=Math.atan2(lt,st)}this.angle.jump(S,Ae,Le),this.recalcMatrix(S);var Gt=L[2],Nt=L[6],Jt=L[10],sr=this.computedMatrix;l(sr,L);var wr=sr[15],cr=sr[12]/wr,$e=sr[13]/wr,St=sr[14]/wr,Qt=Math.exp(this.computedRadius[0]);this.center.jump(S,cr-Gt*Qt,$e-Nt*Qt,St-Jt*Qt)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(S){this.center.idle(S),this.up.idle(S),this.right.idle(S),this.radius.idle(S),this.angle.idle(S)},p.flush=function(S){this.center.flush(S),this.up.flush(S),this.right.flush(S),this.radius.flush(S),this.angle.flush(S)},p.setDistance=function(S,L){L>0&&this.radius.set(S,Math.log(L))},p.lookAt=function(S,L,x,C){this.recalcMatrix(S),L=L||this.computedEye,x=x||this.computedCenter,C=C||this.computedUp;var M=C[0],g=C[1],P=C[2],T=d(M,g,P);if(!(T<1e-6)){M/=T,g/=T,P/=T;var z=L[0]-x[0],O=L[1]-x[1],V=L[2]-x[2],G=d(z,O,V);if(!(G<1e-6)){z/=G,O/=G,V/=G;var Z=this.computedRight,j=Z[0],N=Z[1],H=Z[2],te=M*j+g*N+P*H;j-=te*M,N-=te*g,H-=te*P;var oe=d(j,N,H);if(!(oe<.01&&(j=g*V-P*O,N=P*z-M*V,H=M*O-g*z,oe=d(j,N,H),oe<1e-6))){j/=oe,N/=oe,H/=oe,this.up.set(S,M,g,P),this.right.set(S,j,N,H),this.center.set(S,x[0],x[1],x[2]),this.radius.set(S,Math.log(G));var _e=g*H-P*N,Ee=P*j-M*H,Ce=M*N-g*j,me=d(_e,Ee,Ce);_e/=me,Ee/=me,Ce/=me;var ie=M*z+g*O+P*V,Se=j*z+N*O+H*V,Le=_e*z+Ee*O+Ce*V,Ae=Math.asin(v(ie)),Fe=Math.atan2(Le,Se),Pe=this.angle._state,ge=Pe[Pe.length-1],Re=Pe[Pe.length-2];ge=ge%(2*Math.PI);var ce=Math.abs(ge+2*Math.PI-Fe),Ze=Math.abs(ge-Fe),ut=Math.abs(ge-2*Math.PI-Fe);ce<Ze&&(ge+=2*Math.PI),ut<Ze&&(ge-=2*Math.PI),this.angle.jump(this.angle.lastT(),ge,Re),this.angle.set(S,Fe,Ae)}}}};function E(S){S=S||{};var L=S.center||[0,0,0],x=S.up||[0,1,0],C=S.right||_(x),M=S.radius||1,g=S.theta||0,P=S.phi||0;if(L=[].slice.call(L,0,3),x=[].slice.call(x,0,3),f(x,x),C=[].slice.call(C,0,3),f(C,C),"eye"in S){var T=S.eye,z=[T[0]-L[0],T[1]-L[1],T[2]-L[2]];c(C,z,x),d(C[0],C[1],C[2])<1e-6?C=_(x):f(C,C),M=d(z[0],z[1],z[2]);var O=h(x,z)/M,V=h(C,z)/M;P=Math.acos(O),g=Math.acos(V)}return M=Math.log(M),new b(S.zoomMin,S.zoomMax,L,x,C,M,g,P)}},7319:function(i,a,o){var s=o(3236),l=o(9405),u=s([`precision highp float;
+#define GLSLIFY 1
+
+attribute vec3 position, nextPosition;
+attribute float arcLength, lineWidth;
+attribute vec4 color;
+
+uniform vec2 screenShape;
+uniform float pixelRatio;
+uniform mat4 model, view, projection;
+
+varying vec4 fragColor;
+varying vec3 worldPosition;
+varying float pixelArcLength;
+
+vec4 project(vec3 p) {
+  return projection * (view * (model * vec4(p, 1.0)));
+}
+
+void main() {
+  vec4 startPoint = project(position);
+  vec4 endPoint   = project(nextPosition);
+
+  vec2 A = startPoint.xy / startPoint.w;
+  vec2 B =   endPoint.xy /   endPoint.w;
+
+  float clipAngle = atan(
+    (B.y - A.y) * screenShape.y,
+    (B.x - A.x) * screenShape.x
+  );
+
+  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(
+    sin(clipAngle),
+    -cos(clipAngle)
+  ) / screenShape;
+
+  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);
+
+  worldPosition = position;
+  pixelArcLength = arcLength;
+  fragColor = color;
+}
+`]),c=s([`precision highp float;
+#define GLSLIFY 1
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform vec3      clipBounds[2];
+uniform sampler2D dashTexture;
+uniform float     dashScale;
+uniform float     opacity;
+
+varying vec3    worldPosition;
+varying float   pixelArcLength;
+varying vec4    fragColor;
+
+void main() {
+  if (
+    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||
+    fragColor.a * opacity == 0.
+  ) discard;
+
+  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;
+  if(dashWeight < 0.5) {
+    discard;
+  }
+  gl_FragColor = fragColor * opacity;
+}
+`]),f=s([`precision highp float;
+#define GLSLIFY 1
+
+#define FLOAT_MAX  1.70141184e38
+#define FLOAT_MIN  1.17549435e-38
+
+// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl
+vec4 packFloat(float v) {
+  float av = abs(v);
+
+  //Handle special cases
+  if(av < FLOAT_MIN) {
+    return vec4(0.0, 0.0, 0.0, 0.0);
+  } else if(v > FLOAT_MAX) {
+    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;
+  } else if(v < -FLOAT_MAX) {
+    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;
+  }
+
+  vec4 c = vec4(0,0,0,0);
+
+  //Compute exponent and mantissa
+  float e = floor(log2(av));
+  float m = av * pow(2.0, -e) - 1.0;
+
+  //Unpack mantissa
+  c[1] = floor(128.0 * m);
+  m -= c[1] / 128.0;
+  c[2] = floor(32768.0 * m);
+  m -= c[2] / 32768.0;
+  c[3] = floor(8388608.0 * m);
+
+  //Unpack exponent
+  float ebias = e + 127.0;
+  c[0] = floor(ebias / 2.0);
+  ebias -= c[0] * 2.0;
+  c[1] += floor(ebias) * 128.0;
+
+  //Unpack sign bit
+  c[0] += 128.0 * step(0.0, -v);
+
+  //Scale back to range
+  return c / 255.0;
+}
+
+bool outOfRange(float a, float b, float p) {
+  return ((p > max(a, b)) || 
+          (p < min(a, b)));
+}
+
+bool outOfRange(vec2 a, vec2 b, vec2 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y));
+}
+
+bool outOfRange(vec3 a, vec3 b, vec3 p) {
+  return (outOfRange(a.x, b.x, p.x) ||
+          outOfRange(a.y, b.y, p.y) ||
+          outOfRange(a.z, b.z, p.z));
+}
+
+bool outOfRange(vec4 a, vec4 b, vec4 p) {
+  return outOfRange(a.xyz, b.xyz, p.xyz);
+}
+
+uniform float pickId;
+uniform vec3 clipBounds[2];
+
+varying vec3 worldPosition;
+varying float pixelArcLength;
+varying vec4 fragColor;
+
+void main() {
+  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;
+
+  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);
+}`]),h=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];a.createShader=function(d){return l(d,u,c,null,h)},a.createPickShader=function(d){return l(d,u,f,null,h)}},7352:function(i,a,o){"use strict";var s=o(5721),l=o(4750),u=o(2690);i.exports=c;function c(f){var h=f.length;if(h===0)return[];if(h===1)return[[0]];var d=f[0].length;return d===0?[]:d===1?s(f):d===2?l(f):u(f,d)}},7399:function(i){i.exports=a;function a(o,s){var l=s[0],u=s[1],c=s[2],f=s[3],h=l+l,d=u+u,v=c+c,_=l*h,b=u*h,p=u*d,k=c*h,E=c*d,S=c*v,L=f*h,x=f*d,C=f*v;return o[0]=1-p-S,o[1]=b+C,o[2]=k-x,o[3]=0,o[4]=b-C,o[5]=1-_-S,o[6]=E+L,o[7]=0,o[8]=k+x,o[9]=E-L,o[10]=1-_-p,o[11]=0,o[12]=0,o[13]=0,o[14]=0,o[15]=1,o}},7417:function(i){i.exports=a;function a(o,s,l){return o[0]=Math.max(s[0],l[0]),o[1]=Math.max(s[1],l[1]),o[2]=Math.max(s[2],l[2]),o}},7442:function(i,a,o){var s=o(6658),l=o(7182),u=o(2652),c=o(9921),f=o(8648),h=b(),d=b(),v=b();i.exports=_;function _(E,S,L,x){if(c(S)===0||c(L)===0)return!1;var C=u(S,h.translate,h.scale,h.skew,h.perspective,h.quaternion),M=u(L,d.translate,d.scale,d.skew,d.perspective,d.quaternion);return!C||!M?!1:(s(v.translate,h.translate,d.translate,x),s(v.skew,h.skew,d.skew,x),s(v.scale,h.scale,d.scale,x),s(v.perspective,h.perspective,d.perspective,x),f(v.quaternion,h.quaternion,d.quaternion,x),l(E,v.translate,v.scale,v.skew,v.perspective,v.quaternion),!0)}function b(){return{translate:p(),scale:p(1),skew:p(),perspective:k(),quaternion:k()}}function p(E){return[E||0,E||0,E||0]}function k(){return[0,0,0,1]}},7507:function(i,a){"use strict";a.byteLength=d,a.toByteArray=_,a.fromByteArray=k;for(var o=[],s=[],l=typeof Uint8Array!="undefined"?Uint8Array:Array,u="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/",c=0,f=u.length;c<f;++c)o[c]=u[c],s[u.charCodeAt(c)]=c;s[45]=62,s[95]=63;function h(E){var S=E.length;if(S%4>0)throw new Error("Invalid string. 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O=E[T],V=E[z];x[2*g]=T,x[2*g+1]=z,M[g++]=(V-L)/(V-O)}}return k.shape[0]=g,s(M,[g])}function _(k,E){var S=l.mallocInt32(E*2),L=k.shape[0],x=k.data;S[0]=0;for(var C=0,M=0;M<L;++M){var g=x[2*M];if(g!==C){for(S[2*C+1]=M;++C<g;)S[2*C]=M,S[2*C+1]=M;S[2*C]=M}}for(S[2*C+1]=L;++C<E;)S[2*C]=S[2*C+1]=L;return S}function b(k){for(var E=k.shape[0]|0,S=k.data,L=new Array(E),x=0;x<E;++x)L[x]=[S[2*x],S[2*x+1]];return L}function p(k,E,S,L){S=S||0,typeof L=="undefined"&&(L=f(k));var x=k.length;if(x===0||L<1)return{cells:[],vertexIds:[],vertexWeights:[]};var C=h(E,+S),M=d(k,L),g=v(M,E,C,+S),P=_(M,E.length|0),T=c(L)(k,M.data,P,C),z=b(M),O=[].slice.call(g.data,0,g.shape[0]);return l.free(C),l.free(M.data),l.free(g.data),l.free(P),{cells:T,vertexIds:z,vertexWeights:O}}},7766:function(i,a,o){"use strict";var s=o(9618),l=o(5298),u=o(1888);i.exports=g;var c=null,f=null,h=null;function 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p(P,T,z,O,V,G){this.gl=P,this.handle=T,this.format=V,this.type=G,this._shape=[z,O],this._mipLevels=[0],this._magFilter=P.NEAREST,this._minFilter=P.NEAREST,this._wrapS=P.CLAMP_TO_EDGE,this._wrapT=P.CLAMP_TO_EDGE,this._anisoSamples=1;var Z=this,j=[this._wrapS,this._wrapT];Object.defineProperties(j,[{get:function(){return Z._wrapS},set:function(H){return Z.wrapS=H}},{get:function(){return Z._wrapT},set:function(H){return Z.wrapT=H}}]),this._wrapVector=j;var N=[this._shape[0],this._shape[1]];Object.defineProperties(N,[{get:function(){return Z._shape[0]},set:function(H){return Z.width=H}},{get:function(){return Z._shape[1]},set:function(H){return Z.height=H}}]),this._shapeVector=N}var k=p.prototype;Object.defineProperties(k,{minFilter:{get:function(){return this._minFilter},set:function(P){this.bind();var T=this.gl;if(this.type===T.FLOAT&&c.indexOf(P)>=0&&(T.getExtension("OES_texture_float_linear")||(P=T.NEAREST)),f.indexOf(P)<0)throw new Error("gl-texture2d: Unknown filter mode "+P);return T.texParameteri(T.TEXTURE_2D,T.TEXTURE_MIN_FILTER,P),this._minFilter=P}},magFilter:{get:function(){return this._magFilter},set:function(P){this.bind();var T=this.gl;if(this.type===T.FLOAT&&c.indexOf(P)>=0&&(T.getExtension("OES_texture_float_linear")||(P=T.NEAREST)),f.indexOf(P)<0)throw new Error("gl-texture2d: Unknown filter mode "+P);return T.texParameteri(T.TEXTURE_2D,T.TEXTURE_MAG_FILTER,P),this._magFilter=P}},mipSamples:{get:function(){return this._anisoSamples},set:function(P){var T=this._anisoSamples;if(this._anisoSamples=Math.max(P,1)|0,T!==this._anisoSamples){var z=this.gl.getExtension("EXT_texture_filter_anisotropic");z&&this.gl.texParameterf(this.gl.TEXTURE_2D,z.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(P){if(this.bind(),h.indexOf(P)<0)throw new Error("gl-texture2d: Unknown wrap mode "+P);return 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Z=this._mipLevels.indexOf(O)<0;Z?(V.texImage2D(V.TEXTURE_2D,0,this.format,this.format,this.type,G),this._mipLevels.push(O)):V.texSubImage2D(V.TEXTURE_2D,O,T,z,this.format,this.type,G)}else if(P.shape&&P.stride&&P.data){if(P.shape.length<2||T+P.shape[1]>this._shape[1]>>>O||z+P.shape[0]>this._shape[0]>>>O||T<0||z<0)throw new Error("gl-texture2d: Texture dimensions are out of bounds");S(V,T,z,O,this.format,this.type,this._mipLevels,P)}else throw new Error("gl-texture2d: Unsupported data type")};function E(P,T){return P.length===3?T[2]===1&&T[1]===P[0]*P[2]&&T[0]===P[2]:T[0]===1&&T[1]===P[0]}function S(P,T,z,O,V,G,Z,j){var N=j.dtype,H=j.shape.slice();if(H.length<2||H.length>3)throw new Error("gl-texture2d: Invalid ndarray, must be 2d or 3d");var te=0,oe=0,_e=E(H,j.stride.slice());N==="float32"?te=P.FLOAT:N==="float64"?(te=P.FLOAT,_e=!1,N="float32"):N==="uint8"?te=P.UNSIGNED_BYTE:(te=P.UNSIGNED_BYTE,_e=!1,N="uint8");var Ee=1;if(H.length===2)oe=P.LUMINANCE,H=[H[0],H[1],1],j=s(j.data,H,[j.stride[0],j.stride[1],1],j.offset);else if(H.length===3){if(H[2]===1)oe=P.ALPHA;else if(H[2]===2)oe=P.LUMINANCE_ALPHA;else if(H[2]===3)oe=P.RGB;else if(H[2]===4)oe=P.RGBA;else throw new Error("gl-texture2d: Invalid shape for pixel coords");Ee=H[2]}else throw new Error("gl-texture2d: Invalid shape for texture");if((oe===P.LUMINANCE||oe===P.ALPHA)&&(V===P.LUMINANCE||V===P.ALPHA)&&(oe=V),oe!==V)throw new Error("gl-texture2d: Incompatible texture format for setPixels");var Ce=j.size,me=Z.indexOf(O)<0;if(me&&Z.push(O),te===G&&_e)j.offset===0&&j.data.length===Ce?me?P.texImage2D(P.TEXTURE_2D,O,V,H[0],H[1],0,V,G,j.data):P.texSubImage2D(P.TEXTURE_2D,O,T,z,H[0],H[1],V,G,j.data):me?P.texImage2D(P.TEXTURE_2D,O,V,H[0],H[1],0,V,G,j.data.subarray(j.offset,j.offset+Ce)):P.texSubImage2D(P.TEXTURE_2D,O,T,z,H[0],H[1],V,G,j.data.subarray(j.offset,j.offset+Ce));else{var ie;G===P.FLOAT?ie=u.mallocFloat32(Ce):ie=u.mallocUint8(Ce);var Se=s(ie,H,[H[2],H[2]*H[0],1]);te===P.FLOAT&&G===P.UNSIGNED_BYTE?_(Se,j):l.assign(Se,j),me?P.texImage2D(P.TEXTURE_2D,O,V,H[0],H[1],0,V,G,ie.subarray(0,Ce)):P.texSubImage2D(P.TEXTURE_2D,O,T,z,H[0],H[1],V,G,ie.subarray(0,Ce)),G===P.FLOAT?u.freeFloat32(ie):u.freeUint8(ie)}}function L(P){var T=P.createTexture();return P.bindTexture(P.TEXTURE_2D,T),P.texParameteri(P.TEXTURE_2D,P.TEXTURE_MIN_FILTER,P.NEAREST),P.texParameteri(P.TEXTURE_2D,P.TEXTURE_MAG_FILTER,P.NEAREST),P.texParameteri(P.TEXTURE_2D,P.TEXTURE_WRAP_S,P.CLAMP_TO_EDGE),P.texParameteri(P.TEXTURE_2D,P.TEXTURE_WRAP_T,P.CLAMP_TO_EDGE),T}function x(P,T,z,O,V){var G=P.getParameter(P.MAX_TEXTURE_SIZE);if(T<0||T>G||z<0||z>G)throw new Error("gl-texture2d: Invalid texture shape");if(V===P.FLOAT&&!P.getExtension("OES_texture_float"))throw new Error("gl-texture2d: Floating point textures not supported on this platform");var Z=L(P);return P.texImage2D(P.TEXTURE_2D,0,O,T,z,0,O,V,null),new p(P,Z,T,z,O,V)}function C(P,T,z,O,V,G){var Z=L(P);return 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Re=Fe.axesProject,ce=Fe.gl,Ze=Ae.uniforms,ut=Pe.model||_,pt=Pe.view||_,Zt=Pe.projection||_,st=Fe.axesBounds,lt=oe(Fe.clipBounds),Gt;Fe.axes&&Fe.axes.lastCubeProps?Gt=Fe.axes.lastCubeProps.axis:Gt=[1,1,1],P[0]=2/ce.drawingBufferWidth,P[1]=2/ce.drawingBufferHeight,Ae.bind(),Ze.view=pt,Ze.projection=Zt,Ze.screenSize=P,Ze.highlightId=Fe.highlightId,Ze.highlightScale=Fe.highlightScale,Ze.clipBounds=lt,Ze.pickGroup=Fe.pickId/255,Ze.pixelRatio=ge;for(var Nt=0;Nt<3;++Nt)if(Re[Nt]){Ze.scale=Fe.projectScale[Nt],Ze.opacity=Fe.projectOpacity[Nt];for(var Jt=G,sr=0;sr<16;++sr)Jt[sr]=0;for(var sr=0;sr<4;++sr)Jt[5*sr]=1;Jt[5*Nt]=0,Gt[Nt]<0?Jt[12+Nt]=st[0][Nt]:Jt[12+Nt]=st[1][Nt],f(Jt,ut,Jt),Ze.model=Jt;var wr=(Nt+1)%3,cr=(Nt+2)%3,$e=N(T),St=N(z);$e[wr]=1,St[cr]=1;var Qt=L(Zt,pt,ut,H(O,$e)),Vt=L(Zt,pt,ut,H(V,St));if(Math.abs(Qt[1])>Math.abs(Vt[1])){var _t=Qt;Qt=Vt,Vt=_t,_t=$e,$e=St,St=_t;var It=wr;wr=cr,cr=It}Qt[0]<0&&($e[wr]=-1),Vt[1]>0&&(St[cr]=-1);for(var mt=0,er=0,sr=0;sr<4;++sr)mt+=Math.pow(ut[4*wr+sr],2),er+=Math.pow(ut[4*cr+sr],2);$e[wr]/=Math.sqrt(mt),St[cr]/=Math.sqrt(er),Ze.axes[0]=$e,Ze.axes[1]=St,Ze.fragClipBounds[0]=te(Z,lt[0],Nt,-1e8),Ze.fragClipBounds[1]=te(Z,lt[1],Nt,1e8),Fe.vao.bind(),Fe.vao.draw(ce.TRIANGLES,Fe.vertexCount),Fe.lineWidth>0&&(ce.lineWidth(Fe.lineWidth*ge),Fe.vao.draw(ce.LINES,Fe.lineVertexCount,Fe.vertexCount)),Fe.vao.unbind()}}var Ee=[-1e8,-1e8,-1e8],Ce=[1e8,1e8,1e8],me=[Ee,Ce];function ie(Ae,Fe,Pe,ge,Re,ce,Ze){var ut=Pe.gl;if((ce===Pe.projectHasAlpha||Ze)&&_e(Fe,Pe,ge,Re),ce===Pe.hasAlpha||Ze){Ae.bind();var pt=Ae.uniforms;pt.model=ge.model||_,pt.view=ge.view||_,pt.projection=ge.projection||_,P[0]=2/ut.drawingBufferWidth,P[1]=2/ut.drawingBufferHeight,pt.screenSize=P,pt.highlightId=Pe.highlightId,pt.highlightScale=Pe.highlightScale,pt.fragClipBounds=me,pt.clipBounds=Pe.axes.bounds,pt.opacity=Pe.opacity,pt.pickGroup=Pe.pickId/255,pt.pixelRatio=Re,Pe.vao.bind(),Pe.vao.draw(ut.TRIANGLES,Pe.vertexCount),Pe.lineWidth>0&&(ut.lineWidth(Pe.lineWidth*Re),Pe.vao.draw(ut.LINES,Pe.lineVertexCount,Pe.vertexCount)),Pe.vao.unbind()}}g.draw=function(Ae){var Fe=this.useOrtho?this.orthoShader:this.shader;ie(Fe,this.projectShader,this,Ae,this.pixelRatio,!1,!1)},g.drawTransparent=function(Ae){var Fe=this.useOrtho?this.orthoShader:this.shader;ie(Fe,this.projectShader,this,Ae,this.pixelRatio,!0,!1)},g.drawPick=function(Ae){var Fe=this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader;ie(Fe,this.pickProjectShader,this,Ae,1,!0,!0)},g.pick=function(Ae){if(!Ae||Ae.id!==this.pickId)return null;var Fe=Ae.value[2]+(Ae.value[1]<<8)+(Ae.value[0]<<16);if(Fe>=this.pointCount||Fe<0)return null;var Pe=this.points[Fe],ge=this._selectResult;ge.index=Fe;for(var Re=0;Re<3;++Re)ge.position[Re]=ge.dataCoordinate[Re]=Pe[Re];return ge},g.highlight=function(Ae){if(!Ae)this.highlightId=[1,1,1,1];else{var Fe=Ae.index,Pe=Fe&255,ge=Fe>>8&255,Re=Fe>>16&255;this.highlightId=[Pe/255,ge/255,Re/255,0]}};function Se(Ae,Fe,Pe,ge){var Re;E(Ae)?Fe<Ae.length?Re=Ae[Fe]:Re=void 0:Re=Ae,Re=v(Re);var ce=!0;s(Re)&&(Re="\u25BC",ce=!1),Pe||(Pe={});var Ze=Pe.family;E(Ze)&&(Ze=Ze[Fe]),Ze||(Ze="normal");var ut=Pe.weight;E(ut)&&(ut=ut[Fe]),ut||(ut="normal");var pt=Pe.style;E(pt)&&(pt=pt[Fe]),pt||(pt="normal");var Zt=Pe.variant;E(Zt)&&(Zt=Zt[Fe]),Zt||(Zt="normal");var st=d(Re,{family:Ze,weight:ut,style:pt,variant:Zt},ge),st=d(Re,Pe,ge);return{mesh:st[0],lines:st[1],bounds:st[2],visible:ce}}g.update=function(Ae){if(Ae=Ae||{},"perspective"in Ae&&(this.useOrtho=!Ae.perspective),"orthographic"in Ae&&(this.useOrtho=!!Ae.orthographic),"lineWidth"in Ae&&(this.lineWidth=Ae.lineWidth),"project"in Ae)if(E(Ae.project))this.axesProject=Ae.project;else{var Fe=!!Ae.project;this.axesProject=[Fe,Fe,Fe]}if("projectScale"in Ae)if(E(Ae.projectScale))this.projectScale=Ae.projectScale.slice();else{var Pe=+Ae.projectScale;this.projectScale=[Pe,Pe,Pe]}if(this.projectHasAlpha=!1,"projectOpacity"in Ae){if(E(Ae.projectOpacity))this.projectOpacity=Ae.projectOpacity.slice();else{var Pe=+Ae.projectOpacity;this.projectOpacity=[Pe,Pe,Pe]}for(var ge=0;ge<3;++ge)this.projectOpacity[ge]=C(this.projectOpacity[ge]),this.projectOpacity[ge]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in Ae&&(this.opacity=C(Ae.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var Re=Ae.position,ce={family:Ae.font||"normal",style:Ae.fontStyle||"normal",weight:Ae.fontWeight||"normal",variant:Ae.fontVariant||"normal"},Ze=Ae.alignment||[0,0],ut,pt;if(Ze.length===2)ut=Ze[0],pt=Ze[1];else{ut=[],pt=[];for(var ge=0;ge<Ze.length;++ge)ut[ge]=Ze[ge][0],pt[ge]=Ze[ge][1]}var Zt=[1/0,1/0,1/0],st=[-1/0,-1/0,-1/0],lt=Ae.glyph,Gt=Ae.color,Nt=Ae.size,Jt=Ae.angle,sr=Ae.lineColor,wr=-1,cr=0,$e=0,St=0;if(Re.length){St=Re.length;e:for(var ge=0;ge<St;++ge){for(var Qt=Re[ge],Vt=0;Vt<3;++Vt)if(isNaN(Qt[Vt])||!isFinite(Qt[Vt]))continue e;var _t=Se(lt,ge,ce,this.pixelRatio),It=_t.mesh,mt=_t.lines,er=_t.bounds;cr+=It.cells.length*3,$e+=mt.edges.length*2}}var lr=cr+$e,Tr=c.mallocFloat(3*lr),Lr=c.mallocFloat(4*lr),ti=c.mallocFloat(2*lr),Br=c.mallocUint32(lr);if(lr>0){var Vr=0,dt=cr,Ge=[0,0,0,1],Je=[0,0,0,1],je=E(Gt)&&E(Gt[0]),tt=E(sr)&&E(sr[0]);e:for(var ge=0;ge<St;++ge){wr+=1;for(var Qt=Re[ge],Vt=0;Vt<3;++Vt){if(isNaN(Qt[Vt])||!isFinite(Qt[Vt]))continue e;st[Vt]=Math.max(st[Vt],Qt[Vt]),Zt[Vt]=Math.min(Zt[Vt],Qt[Vt])}var _t=Se(lt,ge,ce,this.pixelRatio),It=_t.mesh,mt=_t.lines,er=_t.bounds,xt=_t.visible;if(!xt)Ge=[1,1,1,0];else if(E(Gt)){var Ie;if(je?ge<Gt.length?Ie=Gt[ge]:Ie=[0,0,0,0]:Ie=Gt,Ie.length===3){for(var Vt=0;Vt<3;++Vt)Ge[Vt]=Ie[Vt];Ge[3]=1}else if(Ie.length===4){for(var Vt=0;Vt<4;++Vt)Ge[Vt]=Ie[Vt];!this.hasAlpha&&Ie[3]<1&&(this.hasAlpha=!0)}}else Ge[0]=Ge[1]=Ge[2]=0,Ge[3]=1;if(!xt)Je=[1,1,1,0];else if(E(sr)){var Ie;if(tt?ge<sr.length?Ie=sr[ge]:Ie=[0,0,0,0]:Ie=sr,Ie.length===3){for(var Vt=0;Vt<3;++Vt)Je[Vt]=Ie[Vt];Je[Vt]=1}else if(Ie.length===4){for(var Vt=0;Vt<4;++Vt)Je[Vt]=Ie[Vt];!this.hasAlpha&&Ie[3]<1&&(this.hasAlpha=!0)}}else Je[0]=Je[1]=Je[2]=0,Je[3]=1;var xe=.5;xt?E(Nt)?ge<Nt.length?xe=+Nt[ge]:xe=12:Nt?xe=+Nt:this.useOrtho&&(xe=12):xe=0;var ke=0;E(Jt)?ge<Jt.length?ke=+Jt[ge]:ke=0:Jt&&(ke=+Jt);for(var vt=Math.cos(ke),ir=Math.sin(ke),Qt=Re[ge],Vt=0;Vt<3;++Vt)st[Vt]=Math.max(st[Vt],Qt[Vt]),Zt[Vt]=Math.min(Zt[Vt],Qt[Vt]);var ar=ut,vr=pt,ar=0;E(ut)?ge<ut.length?ar=ut[ge]:ar=0:ut&&(ar=ut);var vr=0;E(pt)?ge<pt.length?vr=pt[ge]:vr=0:pt&&(vr=pt),ar*=ar>0?1-er[0][0]:ar<0?1+er[1][0]:1,vr*=vr>0?1-er[0][1]:vr<0?1+er[1][1]:1;for(var ii=[ar,vr],In=It.cells||[],wi=It.positions||[],Vt=0;Vt<In.length;++Vt)for(var pi=In[Vt],$r=0;$r<3;++$r){for(var di=0;di<3;++di)Tr[3*Vr+di]=Qt[di];for(var di=0;di<4;++di)Lr[4*Vr+di]=Ge[di];Br[Vr]=wr;var ji=wi[pi[$r]];ti[2*Vr]=xe*(vt*ji[0]-ir*ji[1]+ii[0]),ti[2*Vr+1]=xe*(ir*ji[0]+vt*ji[1]+ii[1]),Vr+=1}for(var In=mt.edges,wi=mt.positions,Vt=0;Vt<In.length;++Vt)for(var pi=In[Vt],$r=0;$r<2;++$r){for(var di=0;di<3;++di)Tr[3*dt+di]=Qt[di];for(var di=0;di<4;++di)Lr[4*dt+di]=Je[di];Br[dt]=wr;var ji=wi[pi[$r]];ti[2*dt]=xe*(vt*ji[0]-ir*ji[1]+ii[0]),ti[2*dt+1]=xe*(ir*ji[0]+vt*ji[1]+ii[1]),dt+=1}}}this.bounds=[Zt,st],this.points=Re,this.pointCount=Re.length,this.vertexCount=cr,this.lineVertexCount=$e,this.pointBuffer.update(Tr),this.colorBuffer.update(Lr),this.glyphBuffer.update(ti),this.idBuffer.update(Br),c.free(Tr),c.free(Lr),c.free(ti),c.free(Br)},g.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()};function Le(Ae){var 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strict";var s=o(7842),l=o(6504);i.exports=u;function u(c,f){for(var h=s(f),d=c.length,v=new Array(d),_=0;_<d;++_)v[_]=l(c[_],h);return v}},9060:function(i,a,o){"use strict";var s=o(9405),l=o(2762),u=o(8116),c=o(7766),f=o(6760),h=o(7608),d=o(9618),v=o(6729),_=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function b(C,M,g,P,T,z,O,V,G,Z,j){this.gl=C,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=M,this.dirty=!0,this.triShader=g,this.pickShader=P,this.trianglePositions=T,this.triangleVectors=z,this.triangleColors=V,this.triangleUVs=G,this.triangleIds=O,this.triangleVAO=Z,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=j,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=_,this._view=_,this._projection=_,this._resolution=[1,1]}var p=b.prototype;p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(C){this.pickId=C};function k(C){for(var M=v({colormap:C,nshades:256,format:"rgba"}),g=new Uint8Array(256*4),P=0;P<256;++P){for(var T=M[P],z=0;z<3;++z)g[4*P+z]=T[z];g[4*P+3]=T[3]*255}return d(g,[256,256,4],[4,0,1])}function E(C){for(var M=C.length,g=new Array(M),P=0;P<M;++P)g[P]=C[P][2];return g}p.update=function(C){C=C||{};var M=this.gl;this.dirty=!0,"lightPosition"in C&&(this.lightPosition=C.lightPosition),"opacity"in C&&(this.opacity=C.opacity),"ambient"in C&&(this.ambientLight=C.ambient),"diffuse"in C&&(this.diffuseLight=C.diffuse),"specular"in C&&(this.specularLight=C.specular),"roughness"in C&&(this.roughness=C.roughness),"fresnel"in C&&(this.fresnel=C.fresnel),C.tubeScale!==void 0&&(this.tubeScale=C.tubeScale),C.vectorScale!==void 0&&(this.vectorScale=C.vectorScale),C.coneScale!==void 0&&(this.coneScale=C.coneScale),C.coneOffset!==void 0&&(this.coneOffset=C.coneOffset),C.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=M.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=M.LINEAR,this.texture.setPixels(k(C.colormap)),this.texture.generateMipmap());var g=C.cells,P=C.positions,T=C.vectors;if(!(!P||!g||!T)){var z=[],O=[],V=[],G=[],Z=[];this.cells=g,this.positions=P,this.vectors=T;var j=C.meshColor||[1,1,1,1],N=C.vertexIntensity,H=1/0,te=-1/0;if(N)if(C.vertexIntensityBounds)H=+C.vertexIntensityBounds[0],te=+C.vertexIntensityBounds[1];else for(var oe=0;oe<N.length;++oe){var _e=N[oe];H=Math.min(H,_e),te=Math.max(te,_e)}else for(var oe=0;oe<P.length;++oe){var _e=P[oe][2];H=Math.min(H,_e),te=Math.max(te,_e)}N?this.intensity=N:this.intensity=E(P),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(var oe=0;oe<P.length;++oe)for(var Ee=P[oe],Ce=0;Ce<3;++Ce)isNaN(Ee[Ce])||!isFinite(Ee[Ce])||(this.bounds[0][Ce]=Math.min(this.bounds[0][Ce],Ee[Ce]),this.bounds[1][Ce]=Math.max(this.bounds[1][Ce],Ee[Ce]));var me=0;e:for(var oe=0;oe<g.length;++oe){var ie=g[oe];switch(ie.length){case 3:for(var Ce=0;Ce<3;++Ce)for(var Se=ie[Ce],Ee=P[Se],Le=0;Le<3;++Le)if(isNaN(Ee[Le])||!isFinite(Ee[Le]))continue e;for(var Ce=0;Ce<3;++Ce){var Se=ie[2-Ce],Ee=P[Se];z.push(Ee[0],Ee[1],Ee[2],Ee[3]);var Ae=T[Se];O.push(Ae[0],Ae[1],Ae[2],Ae[3]||0);var Fe=j;Fe.length===3?V.push(Fe[0],Fe[1],Fe[2],1):V.push(Fe[0],Fe[1],Fe[2],Fe[3]);var Pe;N?Pe=[(N[Se]-H)/(te-H),0]:Pe=[(Ee[2]-H)/(te-H),0],G.push(Pe[0],Pe[1]),Z.push(oe)}me+=1;break;default:break}}this.triangleCount=me,this.trianglePositions.update(z),this.triangleVectors.update(O),this.triangleColors.update(V),this.triangleUVs.update(G),this.triangleIds.update(new Uint32Array(Z))}},p.drawTransparent=p.draw=function(C){C=C||{};for(var M=this.gl,g=C.model||_,P=C.view||_,T=C.projection||_,z=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],O=0;O<3;++O)z[0][O]=Math.max(z[0][O],this.clipBounds[0][O]),z[1][O]=Math.min(z[1][O],this.clipBounds[1][O]);var V={model:g,view:P,projection:T,inverseModel:_.slice(),clipBounds:z,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};V.inverseModel=h(V.inverseModel,V.model),M.disable(M.CULL_FACE),this.texture.bind(0);var G=new Array(16);f(G,V.view,V.model),f(G,V.projection,G),h(G,G);for(var O=0;O<3;++O)V.eyePosition[O]=G[12+O]/G[15];for(var Z=G[15],O=0;O<3;++O)Z+=this.lightPosition[O]*G[4*O+3];for(var O=0;O<3;++O){for(var j=G[12+O],N=0;N<3;++N)j+=G[4*N+O]*this.lightPosition[N];V.lightPosition[O]=j/Z}if(this.triangleCount>0){var H=this.triShader;H.bind(),H.uniforms=V,this.triangleVAO.bind(),M.drawArrays(M.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind()}},p.drawPick=function(C){C=C||{};for(var M=this.gl,g=C.model||_,P=C.view||_,T=C.projection||_,z=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],O=0;O<3;++O)z[0][O]=Math.max(z[0][O],this.clipBounds[0][O]),z[1][O]=Math.min(z[1][O],this.clipBounds[1][O]);this._model=[].slice.call(g),this._view=[].slice.call(P),this._projection=[].slice.call(T),this._resolution=[M.drawingBufferWidth,M.drawingBufferHeight];var V={model:g,view:P,projection:T,clipBounds:z,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},G=this.pickShader;G.bind(),G.uniforms=V,this.triangleCount>0&&(this.triangleVAO.bind(),M.drawArrays(M.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind())},p.pick=function(C){if(!C||C.id!==this.pickId)return null;var M=C.value[0]+256*C.value[1]+65536*C.value[2],g=this.cells[M],P=this.positions[g[1]].slice(0,3),T={position:P,dataCoordinate:P,index:Math.floor(g[1]/48)};return this.traceType==="cone"?T.index=Math.floor(g[1]/48):this.traceType==="streamtube"&&(T.intensity=this.intensity[g[1]],T.velocity=this.vectors[g[1]].slice(0,3),T.divergence=this.vectors[g[1]][3],T.index=M),T},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()};function S(C,M){var g=s(C,M.meshShader.vertex,M.meshShader.fragment,null,M.meshShader.attributes);return g.attributes.position.location=0,g.attributes.color.location=2,g.attributes.uv.location=3,g.attributes.vector.location=4,g}function L(C,M){var g=s(C,M.pickShader.vertex,M.pickShader.fragment,null,M.pickShader.attributes);return g.attributes.position.location=0,g.attributes.id.location=1,g.attributes.vector.location=4,g}function x(C,M,g){var P=g.shaders;arguments.length===1&&(M=C,C=M.gl);var T=S(C,P),z=L(C,P),O=c(C,d(new Uint8Array([255,255,255,255]),[1,1,4]));O.generateMipmap(),O.minFilter=C.LINEAR_MIPMAP_LINEAR,O.magFilter=C.LINEAR;var V=l(C),G=l(C),Z=l(C),j=l(C),N=l(C),H=u(C,[{buffer:V,type:C.FLOAT,size:4},{buffer:N,type:C.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:Z,type:C.FLOAT,size:4},{buffer:j,type:C.FLOAT,size:2},{buffer:G,type:C.FLOAT,size:4}]),te=new b(C,O,T,z,V,G,N,Z,j,H,g.traceType||"cone");return te.update(M),te}i.exports=x},9127:function(i,a,o){"use strict";i.exports=u;var s=o(6204),l=o(5771);function u(c){return l(s(c))}},9131:function(i,a,o){var s=o(5177),l=o(9288);i.exports=u;function u(c,f){return f=f||1,c[0]=Math.random(),c[1]=Math.random(),c[2]=Math.random(),c[3]=Math.random(),s(c,c),l(c,c,f),c}},9165:function(i,a,o){"use strict";i.exports=b;var s=o(2762),l=o(8116),u=o(3436),c=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function f(p,k,E,S){this.gl=p,this.shader=S,this.buffer=k,this.vao=E,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var h=f.prototype;h.isOpaque=function(){return!this.hasAlpha},h.isTransparent=function(){return this.hasAlpha},h.drawTransparent=h.draw=function(p){var k=this.gl,E=this.shader.uniforms;this.shader.bind();var S=E.view=p.view||c,L=E.projection=p.projection||c;E.model=p.model||c,E.clipBounds=this.clipBounds,E.opacity=this.opacity;var x=S[12],C=S[13],M=S[14],g=S[15],P=p._ortho||!1,T=P?2:1,z=T*this.pixelRatio*(L[3]*x+L[7]*C+L[11]*M+L[15]*g)/k.drawingBufferHeight;this.vao.bind();for(var O=0;O<3;++O)k.lineWidth(this.lineWidth[O]*this.pixelRatio),E.capSize=this.capSize[O]*z,this.lineCount[O]&&k.drawArrays(k.LINES,this.lineOffset[O],this.lineCount[O]);this.vao.unbind()};function d(p,k){for(var E=0;E<3;++E)p[0][E]=Math.min(p[0][E],k[E]),p[1][E]=Math.max(p[1][E],k[E])}var v=function(){for(var p=new Array(3),k=0;k<3;++k){for(var E=[],S=1;S<=2;++S)for(var L=-1;L<=1;L+=2){var x=(S+k)%3,C=[0,0,0];C[x]=L,E.push(C)}p[k]=E}return p}();function _(p,k,E,S){for(var L=v[S],x=0;x<L.length;++x){var C=L[x];p.push(k[0],k[1],k[2],E[0],E[1],E[2],E[3],C[0],C[1],C[2])}return L.length}h.update=function(p){p=p||{},"lineWidth"in p&&(this.lineWidth=p.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in p&&(this.capSize=p.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in p&&(this.opacity=+p.opacity,this.opacity<1&&(this.hasAlpha=!0));var k=p.color||[[0,0,0],[0,0,0],[0,0,0]],E=p.position,S=p.error;if(Array.isArray(k[0])||(k=[k,k,k]),E&&S){var L=[],x=E.length,C=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var M=0;M<3;++M){this.lineOffset[M]=C;e:for(var g=0;g<x;++g){for(var P=E[g],T=0;T<3;++T)if(isNaN(P[T])||!isFinite(P[T]))continue e;var z=S[g],O=k[M];if(Array.isArray(O[0])&&(O=k[g]),O.length===3?O=[O[0],O[1],O[2],1]:O.length===4&&(O=[O[0],O[1],O[2],O[3]],!this.hasAlpha&&O[3]<1&&(this.hasAlpha=!0)),!(isNaN(z[0][M])||isNaN(z[1][M]))){if(z[0][M]<0){var V=P.slice();V[M]+=z[0][M],L.push(P[0],P[1],P[2],O[0],O[1],O[2],O[3],0,0,0,V[0],V[1],V[2],O[0],O[1],O[2],O[3],0,0,0),d(this.bounds,V),C+=2+_(L,V,O,M)}if(z[1][M]>0){var V=P.slice();V[M]+=z[1][M],L.push(P[0],P[1],P[2],O[0],O[1],O[2],O[3],0,0,0,V[0],V[1],V[2],O[0],O[1],O[2],O[3],0,0,0),d(this.bounds,V),C+=2+_(L,V,O,M)}}}this.lineCount[M]=C-this.lineOffset[M]}this.buffer.update(L)}},h.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()};function b(p){var k=p.gl,E=s(k),S=l(k,[{buffer:E,type:k.FLOAT,size:3,offset:0,stride:40},{buffer:E,type:k.FLOAT,size:4,offset:12,stride:40},{buffer:E,type:k.FLOAT,size:3,offset:28,stride:40}]),L=u(k);L.attributes.position.location=0,L.attributes.color.location=1,L.attributes.offset.location=2;var x=new f(k,E,S,L);return x.update(p),x}},9215:function(i,a,o){"use strict";i.exports=d;var s=o(4769),l=o(2478);function u(v,_,b){return Math.min(_,Math.max(v,b))}function c(v,_,b){this.dimension=v.length,this.bounds=[new Array(this.dimension),new Array(this.dimension)];for(var p=0;p<this.dimension;++p)this.bounds[0][p]=-1/0,this.bounds[1][p]=1/0;this._state=v.slice().reverse(),this._velocity=_.slice().reverse(),this._time=[b],this._scratch=[v.slice(),v.slice(),v.slice(),v.slice(),v.slice()]}var f=c.prototype;f.flush=function(v){var _=l.gt(this._time,v)-1;_<=0||(this._time.splice(0,_),this._state.splice(0,_*this.dimension),this._velocity.splice(0,_*this.dimension))},f.curve=function(v){var _=this._time,b=_.length,p=l.le(_,v),k=this._scratch[0],E=this._state,S=this._velocity,L=this.dimension,x=this.bounds;if(p<0)for(var C=L-1,M=0;M<L;++M,--C)k[M]=E[C];else if(p>=b-1)for(var C=E.length-1,g=v-_[b-1],M=0;M<L;++M,--C)k[M]=E[C]+g*S[C];else{for(var C=L*(p+1)-1,P=_[p],T=_[p+1],z=T-P||1,O=this._scratch[1],V=this._scratch[2],G=this._scratch[3],Z=this._scratch[4],j=!0,M=0;M<L;++M,--C)O[M]=E[C],G[M]=S[C]*z,V[M]=E[C+L],Z[M]=S[C+L]*z,j=j&&O[M]===V[M]&&G[M]===Z[M]&&G[M]===0;if(j)for(var M=0;M<L;++M)k[M]=O[M];else s(O,G,V,Z,(v-P)/z,k)}for(var N=x[0],H=x[1],M=0;M<L;++M)k[M]=u(N[M],H[M],k[M]);return k},f.dcurve=function(v){var _=this._time,b=_.length,p=l.le(_,v),k=this._scratch[0],E=this._state,S=this._velocity,L=this.dimension;if(p>=b-1)for(var x=E.length-1,C=v-_[b-1],M=0;M<L;++M,--x)k[M]=S[x];else{for(var x=L*(p+1)-1,g=_[p],P=_[p+1],T=P-g||1,z=this._scratch[1],O=this._scratch[2],V=this._scratch[3],G=this._scratch[4],Z=!0,M=0;M<L;++M,--x)z[M]=E[x],V[M]=S[x]*T,O[M]=E[x+L],G[M]=S[x+L]*T,Z=Z&&z[M]===O[M]&&V[M]===G[M]&&V[M]===0;if(Z)for(var M=0;M<L;++M)k[M]=0;else{s.derivative(z,V,O,G,(v-g)/T,k);for(var M=0;M<L;++M)k[M]/=T}}return k},f.lastT=function(){var v=this._time;return v[v.length-1]},f.stable=function(){for(var v=this._velocity,_=v.length,b=this.dimension-1;b>=0;--b)if(v[--_])return!1;return!0},f.jump=function(v){var _=this.lastT(),b=this.dimension;if(!(v<_||arguments.length!==b+1)){var p=this._state,k=this._velocity,E=p.length-this.dimension,S=this.bounds,L=S[0],x=S[1];this._time.push(_,v);for(var C=0;C<2;++C)for(var M=0;M<b;++M)p.push(p[E++]),k.push(0);this._time.push(v);for(var M=b;M>0;--M)p.push(u(L[M-1],x[M-1],arguments[M])),k.push(0)}},f.push=function(v){var _=this.lastT(),b=this.dimension;if(!(v<_||arguments.length!==b+1)){var p=this._state,k=this._velocity,E=p.length-this.dimension,S=v-_,L=this.bounds,x=L[0],C=L[1],M=S>1e-6?1/S:0;this._time.push(v);for(var g=b;g>0;--g){var P=u(x[g-1],C[g-1],arguments[g]);p.push(P),k.push((P-p[E++])*M)}}},f.set=function(v){var _=this.dimension;if(!(v<this.lastT()||arguments.length!==_+1)){var b=this._state,p=this._velocity,k=this.bounds,E=k[0],S=k[1];this._time.push(v);for(var L=_;L>0;--L)b.push(u(E[L-1],S[L-1],arguments[L])),p.push(0)}},f.move=function(v){var _=this.lastT(),b=this.dimension;if(!(v<=_||arguments.length!==b+1)){var p=this._state,k=this._velocity,E=p.length-this.dimension,S=this.bounds,L=S[0],x=S[1],C=v-_,M=C>1e-6?1/C:0;this._time.push(v);for(var g=b;g>0;--g){var P=arguments[g];p.push(u(L[g-1],x[g-1],p[E++]+P)),k.push(P*M)}}},f.idle=function(v){var _=this.lastT();if(!(v<_)){var b=this.dimension,p=this._state,k=this._velocity,E=p.length-b,S=this.bounds,L=S[0],x=S[1],C=v-_;this._time.push(v);for(var M=b-1;M>=0;--M)p.push(u(L[M],x[M],p[E]+C*k[E])),k.push(0),E+=1}};function h(v){for(var _=new Array(v),b=0;b<v;++b)_[b]=0;return _}function d(v,_,b){switch(arguments.length){case 0:return new c([0],[0],0);case 1:if(typeof v=="number"){var p=h(v);return new c(p,p,0)}else return new c(v,h(v.length),0);case 2:if(typeof _=="number"){var p=h(v.length);return new c(v,p,+_)}else b=0;case 3:if(v.length!==_.length)throw new Error("state and velocity lengths must match");return new c(v,_,b)}}},9216:function(i){"use strict";i.exports=l,i.exports.isMobile=l,i.exports.default=l;var a=/(android|bb\d+|meego).+mobile|armv7l|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series[46]0|samsungbrowser.*mobile|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i,o=/CrOS/,s=/android|ipad|playbook|silk/i;function l(u){u||(u={});var c=u.ua;if(!c&&typeof navigator!="undefined"&&(c=navigator.userAgent),c&&c.headers&&typeof c.headers["user-agent"]=="string"&&(c=c.headers["user-agent"]),typeof c!="string")return!1;var f=a.test(c)&&!o.test(c)||!!u.tablet&&s.test(c);return!f&&u.tablet&&u.featureDetect&&navigator&&navigator.maxTouchPoints>1&&c.indexOf("Macintosh")!==-1&&c.indexOf("Safari")!==-1&&(f=!0),f}},9226:function(i){i.exports=a;function a(o,s){return o[0]=Math.ceil(s[0]),o[1]=Math.ceil(s[1]),o[2]=Math.ceil(s[2]),o}},9265:function(i){i.exports=a;function a(o,s){return o[0]===s[0]&&o[1]===s[1]&&o[2]===s[2]}},9288:function(i){i.exports=a;function a(o,s,l){return o[0]=s[0]*l,o[1]=s[1]*l,o[2]=s[2]*l,o[3]=s[3]*l,o}},9346:function(i){"use strict";var a=new Float64Array(4),o=new Float64Array(4),s=new Float64Array(4);function l(u,c,f,h,d){a.length<h.length&&(a=new Float64Array(h.length),o=new Float64Array(h.length),s=new Float64Array(h.length));for(var v=0;v<h.length;++v)a[v]=u[v]-h[v],o[v]=c[v]-u[v],s[v]=f[v]-u[v];for(var _=0,b=0,p=0,k=0,E=0,S=0,v=0;v<h.length;++v){var L=o[v],x=s[v],C=a[v];_+=L*L,b+=L*x,p+=x*x,k+=C*L,E+=C*x,S+=C*C}var M=Math.abs(_*p-b*b),g=b*E-p*k,P=b*k-_*E,T;if(g+P<=M)if(g<0)P<0&&k<0?(P=0,-k>=_?(g=1,T=_+2*k+S):(g=-k/_,T=k*g+S)):(g=0,E>=0?(P=0,T=S):-E>=p?(P=1,T=p+2*E+S):(P=-E/p,T=E*P+S));else if(P<0)P=0,k>=0?(g=0,T=S):-k>=_?(g=1,T=_+2*k+S):(g=-k/_,T=k*g+S);else{var z=1/M;g*=z,P*=z,T=g*(_*g+b*P+2*k)+P*(b*g+p*P+2*E)+S}else{var O,V,G,Z;g<0?(O=b+k,V=p+E,V>O?(G=V-O,Z=_-2*b+p,G>=Z?(g=1,P=0,T=_+2*k+S):(g=G/Z,P=1-g,T=g*(_*g+b*P+2*k)+P*(b*g+p*P+2*E)+S)):(g=0,V<=0?(P=1,T=p+2*E+S):E>=0?(P=0,T=S):(P=-E/p,T=E*P+S))):P<0?(O=b+E,V=_+k,V>O?(G=V-O,Z=_-2*b+p,G>=Z?(P=1,g=0,T=p+2*E+S):(P=G/Z,g=1-P,T=g*(_*g+b*P+2*k)+P*(b*g+p*P+2*E)+S)):(P=0,V<=0?(g=1,T=_+2*k+S):k>=0?(g=0,T=S):(g=-k/_,T=k*g+S))):(G=p+E-b-k,G<=0?(g=0,P=1,T=p+2*E+S):(Z=_-2*b+p,G>=Z?(g=1,P=0,T=_+2*k+S):(g=G/Z,P=1-g,T=g*(_*g+b*P+2*k)+P*(b*g+p*P+2*E)+S)))}for(var j=1-g-P,v=0;v<h.length;++v)d[v]=j*u[v]+g*c[v]+P*f[v];return T<0?0:T}i.exports=l},9362:function(i){"use strict";i.exports=a;function a(o,s,l){var u=o+s,c=u-o,f=u-c,h=s-c,d=o-f;return l?(l[0]=d+h,l[1]=u,l):[d+h,u]}},9366:function(i,a,o){"use strict";var s=o(4359);i.exports=u;var l={};function u(c,f,h){var d=[f.style,f.weight,f.variant,f.family].join("_"),v=l[d];if(v||(v=l[d]={}),c in v)return v[c];var _={textAlign:"center",textBaseline:"middle",lineHeight:1,font:f.family,fontStyle:f.style,fontWeight:f.weight,fontVariant:f.variant,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0}};_.triangles=!0;var b=s(c,_);_.triangles=!1;var p=s(c,_),k,E;if(h&&h!==1){for(k=0;k<b.positions.length;++k)for(E=0;E<b.positions[k].length;++E)b.positions[k][E]/=h;for(k=0;k<p.positions.length;++k)for(E=0;E<p.positions[k].length;++E)p.positions[k][E]/=h}var S=[[1/0,1/0],[-1/0,-1/0]],L=p.positions.length;for(k=0;k<L;++k){var x=p.positions[k];for(E=0;E<2;++E)S[0][E]=Math.min(S[0][E],x[E]),S[1][E]=Math.max(S[1][E],x[E])}return v[c]=[b,p,S]}},9405:function(i,a,o){"use strict";var s=o(3327),l=o(8731),u=o(216),c=o(5091),f=o(2145),h=o(8866);function d(p){this.gl=p,this.gl.lastAttribCount=0,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}var v=d.prototype;v.bind=function(){this.program||this._relink();var p,k=this.gl.getProgramParameter(this.program,this.gl.ACTIVE_ATTRIBUTES),E=this.gl.lastAttribCount;if(k>E)for(p=E;p<k;p++)this.gl.enableVertexAttribArray(p);else if(E>k)for(p=k;p<E;p++)this.gl.disableVertexAttribArray(p);this.gl.lastAttribCount=k,this.gl.useProgram(this.program)},v.dispose=function(){for(var p=this.gl.lastAttribCount,k=0;k<p;k++)this.gl.disableVertexAttribArray(k);this.gl.lastAttribCount=0,this._fref&&this._fref.dispose(),this._vref&&this._vref.dispose(),this.attributes=this.types=this.vertShader=this.fragShader=this.program=this._relink=this._fref=this._vref=null};function _(p,k){return p.name<k.name?-1:1}v.update=function(p,k,E,S){if(!k||arguments.length===1){var L=p;p=L.vertex,k=L.fragment,E=L.uniforms,S=L.attributes}var x=this,C=x.gl,M=x._vref;x._vref=c.shader(C,C.VERTEX_SHADER,p),M&&M.dispose(),x.vertShader=x._vref.shader;var g=this._fref;if(x._fref=c.shader(C,C.FRAGMENT_SHADER,k),g&&g.dispose(),x.fragShader=x._fref.shader,!E||!S){var P=C.createProgram();if(C.attachShader(P,x.fragShader),C.attachShader(P,x.vertShader),C.linkProgram(P),!C.getProgramParameter(P,C.LINK_STATUS)){var T=C.getProgramInfoLog(P);throw new h(T,"Error linking program:"+T)}E=E||f.uniforms(C,P),S=S||f.attributes(C,P),C.deleteProgram(P)}S=S.slice(),S.sort(_);var z=[],O=[],V=[],G;for(G=0;G<S.length;++G){var Z=S[G];if(Z.type.indexOf("mat")>=0){for(var j=Z.type.charAt(Z.type.length-1)|0,N=new Array(j),H=0;H<j;++H)N[H]=V.length,O.push(Z.name+"["+H+"]"),typeof Z.location=="number"?V.push(Z.location+H):Array.isArray(Z.location)&&Z.location.length===j&&typeof Z.location[H]=="number"?V.push(Z.location[H]|0):V.push(-1);z.push({name:Z.name,type:Z.type,locations:N})}else z.push({name:Z.name,type:Z.type,locations:[V.length]}),O.push(Z.name),typeof Z.location=="number"?V.push(Z.location|0):V.push(-1)}var te=0;for(G=0;G<V.length;++G)if(V[G]<0){for(;V.indexOf(te)>=0;)te+=1;V[G]=te}var oe=new Array(E.length);function _e(){x.program=c.program(C,x._vref,x._fref,O,V);for(var Ee=0;Ee<E.length;++Ee)oe[Ee]=C.getUniformLocation(x.program,E[Ee].name)}_e(),x._relink=_e,x.types={uniforms:u(E),attributes:u(S)},x.attributes=l(C,x,z,V),Object.defineProperty(x,"uniforms",s(C,x,E,oe))};function b(p,k,E,S,L){var x=new d(p);return x.update(k,E,S,L),x}i.exports=b},9499:function(i,a,o){"use strict";i.exports=Fe;var s=o(8828),l=o(2762),u=o(8116),c=o(7766),f=o(1888),h=o(6729),d=o(5298),v=o(9994),_=o(9618),b=o(3711),p=o(6760),k=o(7608),E=o(2478),S=o(6199),L=o(990),x=L.createShader,C=L.createContourShader,M=L.createPickShader,g=L.createPickContourShader,P=4*10,T=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],z=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],O=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];(function(){for(var Pe=0;Pe<3;++Pe){var ge=O[Pe],Re=(Pe+1)%3,ce=(Pe+2)%3;ge[Re+0]=1,ge[ce+3]=1,ge[Pe+6]=1}})();function V(Pe,ge,Re,ce,Ze){this.position=Pe,this.index=ge,this.uv=Re,this.level=ce,this.dataCoordinate=Ze}var G=256;function Z(Pe,ge,Re,ce,Ze,ut,pt,Zt,st,lt,Gt,Nt,Jt,sr,wr){this.gl=Pe,this.shape=ge,this.bounds=Re,this.objectOffset=wr,this.intensityBounds=[],this._shader=ce,this._pickShader=Ze,this._coordinateBuffer=ut,this._vao=pt,this._colorMap=Zt,this._contourShader=st,this._contourPickShader=lt,this._contourBuffer=Gt,this._contourVAO=Nt,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new V([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=Jt,this._dynamicVAO=sr,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[_(f.mallocFloat(1024),[0,0]),_(f.mallocFloat(1024),[0,0]),_(f.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var j=Z.prototype;j.genColormap=function(Pe,ge){var Re=!1,ce=v([h({colormap:Pe,nshades:G,format:"rgba"}).map(function(Ze,ut){var pt=ge?N(ut/255,ge):Ze[3];return pt<1&&(Re=!0),[Ze[0],Ze[1],Ze[2],255*pt]})]);return d.divseq(ce,255),this.hasAlphaScale=Re,ce},j.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},j.isOpaque=function(){return!this.isTransparent()},j.pickSlots=1,j.setPickBase=function(Pe){this.pickId=Pe};function N(Pe,ge){if(!ge||!ge.length)return 1;for(var Re=0;Re<ge.length;++Re){if(ge.length<2)return 1;if(ge[Re][0]===Pe)return ge[Re][1];if(ge[Re][0]>Pe&&Re>0){var ce=(ge[Re][0]-Pe)/(ge[Re][0]-ge[Re-1][0]);return ge[Re][1]*(1-ce)+ce*ge[Re-1][1]}}return 1}var H=[0,0,0],te={showSurface:!1,showContour:!1,projections:[T.slice(),T.slice(),T.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function oe(Pe,ge){var Re,ce,Ze,ut=ge.axes&&ge.axes.lastCubeProps.axis||H,pt=ge.showSurface,Zt=ge.showContour;for(Re=0;Re<3;++Re)for(pt=pt||ge.surfaceProject[Re],ce=0;ce<3;++ce)Zt=Zt||ge.contourProject[Re][ce];for(Re=0;Re<3;++Re){var st=te.projections[Re];for(ce=0;ce<16;++ce)st[ce]=0;for(ce=0;ce<4;++ce)st[5*ce]=1;st[5*Re]=0,st[12+Re]=ge.axesBounds[+(ut[Re]>0)][Re],p(st,Pe.model,st);var lt=te.clipBounds[Re];for(Ze=0;Ze<2;++Ze)for(ce=0;ce<3;++ce)lt[Ze][ce]=Pe.clipBounds[Ze][ce];lt[0][Re]=-1e8,lt[1][Re]=1e8}return te.showSurface=pt,te.showContour=Zt,te}var _e={model:T,view:T,projection:T,inverseModel:T.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},Ee=T.slice(),Ce=[1,0,0,0,1,0,0,0,1];function me(Pe,ge){Pe=Pe||{};var Re=this.gl;Re.disable(Re.CULL_FACE),this._colorMap.bind(0);var ce=_e;ce.model=Pe.model||T,ce.view=Pe.view||T,ce.projection=Pe.projection||T,ce.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],ce.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],ce.objectOffset=this.objectOffset,ce.contourColor=this.contourColor[0],ce.inverseModel=k(ce.inverseModel,ce.model);for(var Ze=0;Ze<2;++Ze)for(var ut=ce.clipBounds[Ze],pt=0;pt<3;++pt)ut[pt]=Math.min(Math.max(this.clipBounds[Ze][pt],-1e8),1e8);ce.kambient=this.ambientLight,ce.kdiffuse=this.diffuseLight,ce.kspecular=this.specularLight,ce.roughness=this.roughness,ce.fresnel=this.fresnel,ce.opacity=this.opacity,ce.height=0,ce.permutation=Ce,ce.vertexColor=this.vertexColor;var Zt=Ee;for(p(Zt,ce.view,ce.model),p(Zt,ce.projection,Zt),k(Zt,Zt),Ze=0;Ze<3;++Ze)ce.eyePosition[Ze]=Zt[12+Ze]/Zt[15];var st=Zt[15];for(Ze=0;Ze<3;++Ze)st+=this.lightPosition[Ze]*Zt[4*Ze+3];for(Ze=0;Ze<3;++Ze){var lt=Zt[12+Ze];for(pt=0;pt<3;++pt)lt+=Zt[4*pt+Ze]*this.lightPosition[pt];ce.lightPosition[Ze]=lt/st}var Gt=oe(ce,this);if(Gt.showSurface){for(this._shader.bind(),this._shader.uniforms=ce,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(Re.TRIANGLES,this._vertexCount),Ze=0;Ze<3;++Ze)!this.surfaceProject[Ze]||!this.vertexCount||(this._shader.uniforms.model=Gt.projections[Ze],this._shader.uniforms.clipBounds=Gt.clipBounds[Ze],this._vao.draw(Re.TRIANGLES,this._vertexCount));this._vao.unbind()}if(Gt.showContour){var Nt=this._contourShader;ce.kambient=1,ce.kdiffuse=0,ce.kspecular=0,ce.opacity=1,Nt.bind(),Nt.uniforms=ce;var Jt=this._contourVAO;for(Jt.bind(),Ze=0;Ze<3;++Ze)for(Nt.uniforms.permutation=O[Ze],Re.lineWidth(this.contourWidth[Ze]*this.pixelRatio),pt=0;pt<this.contourLevels[Ze].length;++pt)pt===this.highlightLevel[Ze]?(Nt.uniforms.contourColor=this.highlightColor[Ze],Nt.uniforms.contourTint=this.highlightTint[Ze]):(pt===0||pt-1===this.highlightLevel[Ze])&&(Nt.uniforms.contourColor=this.contourColor[Ze],Nt.uniforms.contourTint=this.contourTint[Ze]),this._contourCounts[Ze][pt]&&(Nt.uniforms.height=this.contourLevels[Ze][pt],Jt.draw(Re.LINES,this._contourCounts[Ze][pt],this._contourOffsets[Ze][pt]));for(Ze=0;Ze<3;++Ze)for(Nt.uniforms.model=Gt.projections[Ze],Nt.uniforms.clipBounds=Gt.clipBounds[Ze],pt=0;pt<3;++pt)if(this.contourProject[Ze][pt]){Nt.uniforms.permutation=O[pt],Re.lineWidth(this.contourWidth[pt]*this.pixelRatio);for(var sr=0;sr<this.contourLevels[pt].length;++sr)sr===this.highlightLevel[pt]?(Nt.uniforms.contourColor=this.highlightColor[pt],Nt.uniforms.contourTint=this.highlightTint[pt]):(sr===0||sr-1===this.highlightLevel[pt])&&(Nt.uniforms.contourColor=this.contourColor[pt],Nt.uniforms.contourTint=this.contourTint[pt]),this._contourCounts[pt][sr]&&(Nt.uniforms.height=this.contourLevels[pt][sr],Jt.draw(Re.LINES,this._contourCounts[pt][sr],this._contourOffsets[pt][sr]))}for(Jt.unbind(),Jt=this._dynamicVAO,Jt.bind(),Ze=0;Ze<3;++Ze)if(this._dynamicCounts[Ze]!==0)for(Nt.uniforms.model=ce.model,Nt.uniforms.clipBounds=ce.clipBounds,Nt.uniforms.permutation=O[Ze],Re.lineWidth(this.dynamicWidth[Ze]*this.pixelRatio),Nt.uniforms.contourColor=this.dynamicColor[Ze],Nt.uniforms.contourTint=this.dynamicTint[Ze],Nt.uniforms.height=this.dynamicLevel[Ze],Jt.draw(Re.LINES,this._dynamicCounts[Ze],this._dynamicOffsets[Ze]),pt=0;pt<3;++pt)this.contourProject[pt][Ze]&&(Nt.uniforms.model=Gt.projections[pt],Nt.uniforms.clipBounds=Gt.clipBounds[pt],Jt.draw(Re.LINES,this._dynamicCounts[Ze],this._dynamicOffsets[Ze]));Jt.unbind()}}j.draw=function(Pe){return me.call(this,Pe,!1)},j.drawTransparent=function(Pe){return me.call(this,Pe,!0)};var ie={model:T,view:T,projection:T,inverseModel:T,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};j.drawPick=function(Pe){Pe=Pe||{};var ge=this.gl;ge.disable(ge.CULL_FACE);var Re=ie;Re.model=Pe.model||T,Re.view=Pe.view||T,Re.projection=Pe.projection||T,Re.shape=this._field[2].shape,Re.pickId=this.pickId/255,Re.lowerBound=this.bounds[0],Re.upperBound=this.bounds[1],Re.objectOffset=this.objectOffset,Re.permutation=Ce;for(var ce=0;ce<2;++ce)for(var Ze=Re.clipBounds[ce],ut=0;ut<3;++ut)Ze[ut]=Math.min(Math.max(this.clipBounds[ce][ut],-1e8),1e8);var pt=oe(Re,this);if(pt.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=Re,this._vao.bind(),this._vao.draw(ge.TRIANGLES,this._vertexCount),ce=0;ce<3;++ce)this.surfaceProject[ce]&&(this._pickShader.uniforms.model=pt.projections[ce],this._pickShader.uniforms.clipBounds=pt.clipBounds[ce],this._vao.draw(ge.TRIANGLES,this._vertexCount));this._vao.unbind()}if(pt.showContour){var Zt=this._contourPickShader;Zt.bind(),Zt.uniforms=Re;var st=this._contourVAO;for(st.bind(),ut=0;ut<3;++ut)for(ge.lineWidth(this.contourWidth[ut]*this.pixelRatio),Zt.uniforms.permutation=O[ut],ce=0;ce<this.contourLevels[ut].length;++ce)this._contourCounts[ut][ce]&&(Zt.uniforms.height=this.contourLevels[ut][ce],st.draw(ge.LINES,this._contourCounts[ut][ce],this._contourOffsets[ut][ce]));for(ce=0;ce<3;++ce)for(Zt.uniforms.model=pt.projections[ce],Zt.uniforms.clipBounds=pt.clipBounds[ce],ut=0;ut<3;++ut)if(this.contourProject[ce][ut]){Zt.uniforms.permutation=O[ut],ge.lineWidth(this.contourWidth[ut]*this.pixelRatio);for(var lt=0;lt<this.contourLevels[ut].length;++lt)this._contourCounts[ut][lt]&&(Zt.uniforms.height=this.contourLevels[ut][lt],st.draw(ge.LINES,this._contourCounts[ut][lt],this._contourOffsets[ut][lt]))}st.unbind()}},j.pick=function(Pe){if(!Pe||Pe.id!==this.pickId)return null;var ge=this._field[2].shape,Re=this._pickResult,ce=ge[0]*(Pe.value[0]+(Pe.value[2]>>4)/16)/255,Ze=Math.floor(ce),ut=ce-Ze,pt=ge[1]*(Pe.value[1]+(Pe.value[2]&15)/16)/255,Zt=Math.floor(pt),st=pt-Zt;Ze+=1,Zt+=1;var lt=Re.position;lt[0]=lt[1]=lt[2]=0;for(var Gt=0;Gt<2;++Gt)for(var Nt=Gt?ut:1-ut,Jt=0;Jt<2;++Jt)for(var sr=Jt?st:1-st,wr=Ze+Gt,cr=Zt+Jt,$e=Nt*sr,St=0;St<3;++St)lt[St]+=this._field[St].get(wr,cr)*$e;for(var Qt=this._pickResult.level,Vt=0;Vt<3;++Vt)if(Qt[Vt]=E.le(this.contourLevels[Vt],lt[Vt]),Qt[Vt]<0)this.contourLevels[Vt].length>0&&(Qt[Vt]=0);else if(Qt[Vt]<this.contourLevels[Vt].length-1){var _t=this.contourLevels[Vt][Qt[Vt]],It=this.contourLevels[Vt][Qt[Vt]+1];Math.abs(_t-lt[Vt])>Math.abs(It-lt[Vt])&&(Qt[Vt]+=1)}for(Re.index[0]=ut<.5?Ze:Ze+1,Re.index[1]=st<.5?Zt:Zt+1,Re.uv[0]=ce/ge[0],Re.uv[1]=pt/ge[1],St=0;St<3;++St)Re.dataCoordinate[St]=this._field[St].get(Re.index[0],Re.index[1]);return Re},j.padField=function(Pe,ge){var Re=ge.shape.slice(),ce=Pe.shape.slice();d.assign(Pe.lo(1,1).hi(Re[0],Re[1]),ge),d.assign(Pe.lo(1).hi(Re[0],1),ge.hi(Re[0],1)),d.assign(Pe.lo(1,ce[1]-1).hi(Re[0],1),ge.lo(0,Re[1]-1).hi(Re[0],1)),d.assign(Pe.lo(0,1).hi(1,Re[1]),ge.hi(1)),d.assign(Pe.lo(ce[0]-1,1).hi(1,Re[1]),ge.lo(Re[0]-1)),Pe.set(0,0,ge.get(0,0)),Pe.set(0,ce[1]-1,ge.get(0,Re[1]-1)),Pe.set(ce[0]-1,0,ge.get(Re[0]-1,0)),Pe.set(ce[0]-1,ce[1]-1,ge.get(Re[0]-1,Re[1]-1))};function Se(Pe,ge){return Array.isArray(Pe)?[ge(Pe[0]),ge(Pe[1]),ge(Pe[2])]:[ge(Pe),ge(Pe),ge(Pe)]}function Le(Pe){return Array.isArray(Pe)?Pe.length===3?[Pe[0],Pe[1],Pe[2],1]:[Pe[0],Pe[1],Pe[2],Pe[3]]:[0,0,0,1]}function Ae(Pe){if(Array.isArray(Pe)){if(Array.isArray(Pe))return[Le(Pe[0]),Le(Pe[1]),Le(Pe[2])];var ge=Le(Pe);return[ge.slice(),ge.slice(),ge.slice()]}}j.update=function(Pe){Pe=Pe||{},this.objectOffset=Pe.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in Pe&&(this.contourWidth=Se(Pe.contourWidth,Number)),"showContour"in Pe&&(this.showContour=Se(Pe.showContour,Boolean)),"showSurface"in Pe&&(this.showSurface=!!Pe.showSurface),"contourTint"in Pe&&(this.contourTint=Se(Pe.contourTint,Boolean)),"contourColor"in Pe&&(this.contourColor=Ae(Pe.contourColor)),"contourProject"in Pe&&(this.contourProject=Se(Pe.contourProject,function(yn){return Se(yn,Boolean)})),"surfaceProject"in Pe&&(this.surfaceProject=Pe.surfaceProject),"dynamicColor"in Pe&&(this.dynamicColor=Ae(Pe.dynamicColor)),"dynamicTint"in Pe&&(this.dynamicTint=Se(Pe.dynamicTint,Number)),"dynamicWidth"in Pe&&(this.dynamicWidth=Se(Pe.dynamicWidth,Number)),"opacity"in Pe&&(this.opacity=Pe.opacity),"opacityscale"in Pe&&(this.opacityscale=Pe.opacityscale),"colorBounds"in Pe&&(this.colorBounds=Pe.colorBounds),"vertexColor"in Pe&&(this.vertexColor=Pe.vertexColor?1:0),"colormap"in Pe&&this._colorMap.setPixels(this.genColormap(Pe.colormap,this.opacityscale));var ge=Pe.field||Pe.coords&&Pe.coords[2]||null,Re=!1;if(ge||(this._field[2].shape[0]||this._field[2].shape[2]?ge=this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):ge=this._field[2].hi(0,0)),"field"in Pe||"coords"in Pe){var ce=(ge.shape[0]+2)*(ge.shape[1]+2);ce>this._field[2].data.length&&(f.freeFloat(this._field[2].data),this._field[2].data=f.mallocFloat(s.nextPow2(ce))),this._field[2]=_(this._field[2].data,[ge.shape[0]+2,ge.shape[1]+2]),this.padField(this._field[2],ge),this.shape=ge.shape.slice();for(var Ze=this.shape,ut=0;ut<2;++ut)this._field[2].size>this._field[ut].data.length&&(f.freeFloat(this._field[ut].data),this._field[ut].data=f.mallocFloat(this._field[2].size)),this._field[ut]=_(this._field[ut].data,[Ze[0]+2,Ze[1]+2]);if(Pe.coords){var pt=Pe.coords;if(!Array.isArray(pt)||pt.length!==3)throw new Error("gl-surface: invalid coordinates for x/y");for(ut=0;ut<2;++ut){var Zt=pt[ut];for(Jt=0;Jt<2;++Jt)if(Zt.shape[Jt]!==Ze[Jt])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[ut],Zt)}}else if(Pe.ticks){var st=Pe.ticks;if(!Array.isArray(st)||st.length!==2)throw new Error("gl-surface: invalid ticks");for(ut=0;ut<2;++ut){var lt=st[ut];if((Array.isArray(lt)||lt.length)&&(lt=_(lt)),lt.shape[0]!==Ze[ut])throw new Error("gl-surface: invalid tick length");var Gt=_(lt.data,Ze);Gt.stride[ut]=lt.stride[0],Gt.stride[ut^1]=0,this.padField(this._field[ut],Gt)}}else{for(ut=0;ut<2;++ut){var Nt=[0,0];Nt[ut]=1,this._field[ut]=_(this._field[ut].data,[Ze[0]+2,Ze[1]+2],Nt,0)}this._field[0].set(0,0,0);for(var Jt=0;Jt<Ze[0];++Jt)this._field[0].set(Jt+1,0,Jt);for(this._field[0].set(Ze[0]+1,0,Ze[0]-1),this._field[1].set(0,0,0),Jt=0;Jt<Ze[1];++Jt)this._field[1].set(0,Jt+1,Jt);this._field[1].set(0,Ze[1]+1,Ze[1]-1)}var sr=this._field,wr=_(f.mallocFloat(sr[2].size*3*2),[3,Ze[0]+2,Ze[1]+2,2]);for(ut=0;ut<3;++ut)S(wr.pick(ut),sr[ut],"mirror");var cr=_(f.mallocFloat(sr[2].size*3),[Ze[0]+2,Ze[1]+2,3]);for(ut=0;ut<Ze[0]+2;++ut)for(Jt=0;Jt<Ze[1]+2;++Jt){var $e=wr.get(0,ut,Jt,0),St=wr.get(0,ut,Jt,1),Qt=wr.get(1,ut,Jt,0),Vt=wr.get(1,ut,Jt,1),_t=wr.get(2,ut,Jt,0),It=wr.get(2,ut,Jt,1),mt=Qt*It-Vt*_t,er=_t*St-It*$e,lr=$e*Vt-St*Qt,Tr=Math.sqrt(mt*mt+er*er+lr*lr);Tr<1e-8?(Tr=Math.max(Math.abs(mt),Math.abs(er),Math.abs(lr)),Tr<1e-8?(lr=1,er=mt=0,Tr=1):Tr=1/Tr):Tr=1/Math.sqrt(Tr),cr.set(ut,Jt,0,mt*Tr),cr.set(ut,Jt,1,er*Tr),cr.set(ut,Jt,2,lr*Tr)}f.free(wr.data);var Lr=[1/0,1/0,1/0],ti=[-1/0,-1/0,-1/0],Br=1/0,Vr=-1/0,dt=(Ze[0]-1)*(Ze[1]-1)*6,Ge=f.mallocFloat(s.nextPow2(10*dt)),Je=0,je=0;for(ut=0;ut<Ze[0]-1;++ut)e:for(Jt=0;Jt<Ze[1]-1;++Jt){for(var tt=0;tt<2;++tt)for(var xt=0;xt<2;++xt)for(var Ie=0;Ie<3;++Ie){var xe=this._field[Ie].get(1+ut+tt,1+Jt+xt);if(isNaN(xe)||!isFinite(xe))continue e}for(Ie=0;Ie<6;++Ie){var ke=ut+z[Ie][0],vt=Jt+z[Ie][1],ir=this._field[0].get(ke+1,vt+1),ar=this._field[1].get(ke+1,vt+1);xe=this._field[2].get(ke+1,vt+1),mt=cr.get(ke+1,vt+1,0),er=cr.get(ke+1,vt+1,1),lr=cr.get(ke+1,vt+1,2),Pe.intensity&&(vr=Pe.intensity.get(ke,vt));var vr=Pe.intensity?Pe.intensity.get(ke,vt):xe+this.objectOffset[2];Ge[Je++]=ke,Ge[Je++]=vt,Ge[Je++]=ir,Ge[Je++]=ar,Ge[Je++]=xe,Ge[Je++]=0,Ge[Je++]=vr,Ge[Je++]=mt,Ge[Je++]=er,Ge[Je++]=lr,Lr[0]=Math.min(Lr[0],ir+this.objectOffset[0]),Lr[1]=Math.min(Lr[1],ar+this.objectOffset[1]),Lr[2]=Math.min(Lr[2],xe+this.objectOffset[2]),Br=Math.min(Br,vr),ti[0]=Math.max(ti[0],ir+this.objectOffset[0]),ti[1]=Math.max(ti[1],ar+this.objectOffset[1]),ti[2]=Math.max(ti[2],xe+this.objectOffset[2]),Vr=Math.max(Vr,vr),je+=1}}for(Pe.intensityBounds&&(Br=+Pe.intensityBounds[0],Vr=+Pe.intensityBounds[1]),ut=6;ut<Je;ut+=10)Ge[ut]=(Ge[ut]-Br)/(Vr-Br);this._vertexCount=je,this._coordinateBuffer.update(Ge.subarray(0,Je)),f.freeFloat(Ge),f.free(cr.data),this.bounds=[Lr,ti],this.intensity=Pe.intensity||this._field[2],(this.intensityBounds[0]!==Br||this.intensityBounds[1]!==Vr)&&(Re=!0),this.intensityBounds=[Br,Vr]}if("levels"in Pe){var ii=Pe.levels;for(Array.isArray(ii[0])?ii=ii.slice():ii=[[],[],ii],ut=0;ut<3;++ut)ii[ut]=ii[ut].slice(),ii[ut].sort(function(yn,Sn){return yn-Sn});for(ut=0;ut<3;++ut)for(Jt=0;Jt<ii[ut].length;++Jt)ii[ut][Jt]-=this.objectOffset[ut];e:for(ut=0;ut<3;++ut){if(ii[ut].length!==this.contourLevels[ut].length){Re=!0;break}for(Jt=0;Jt<ii[ut].length;++Jt)if(ii[ut][Jt]!==this.contourLevels[ut][Jt]){Re=!0;break e}}this.contourLevels=ii}if(Re){sr=this._field,Ze=this.shape;for(var pi=[],$r=0;$r<3;++$r){var di=this.contourLevels[$r],ji=[],In=[],wi=[0,0,0];for(ut=0;ut<di.length;++ut){var On=b(this._field[$r],di[ut]);ji.push(pi.length/5|0),je=0;e:for(Jt=0;Jt<On.cells.length;++Jt){var qn=On.cells[Jt];for(Ie=0;Ie<2;++Ie){var Fn=On.positions[qn[Ie]],ra=Fn[0],la=Math.floor(ra)|0,Ut=ra-la,wt=Fn[1],rr=Math.floor(wt)|0,nr=wt-rr,Er=!1;t:for(var Xr=0;Xr<3;++Xr){wi[Xr]=0;var ri=($r+Xr+1)%3;for(tt=0;tt<2;++tt){var Qr=tt?Ut:1-Ut;for(ke=Math.min(Math.max(la+tt,0),Ze[0])|0,xt=0;xt<2;++xt){var Oi=xt?nr:1-nr;if(vt=Math.min(Math.max(rr+xt,0),Ze[1])|0,Xr<2?xe=this._field[ri].get(ke,vt):xe=(this.intensity.get(ke,vt)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(xe)||isNaN(xe)){Er=!0;break t}var $i=Qr*Oi;wi[Xr]+=$i*xe}}}if(!Er)pi.push(wi[0],wi[1],Fn[0],Fn[1],wi[2]),je+=1;else{if(Ie>0){for(var tn=0;tn<5;++tn)pi.pop();je-=1}continue e}}}In.push(je)}this._contourOffsets[$r]=ji,this._contourCounts[$r]=In}var fn=f.mallocFloat(pi.length);for(ut=0;ut<pi.length;++ut)fn[ut]=pi[ut];this._contourBuffer.update(fn),f.freeFloat(fn)}},j.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var Pe=0;Pe<3;++Pe)f.freeFloat(this._field[Pe].data)},j.highlight=function(Pe){var ge;if(!Pe){this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],this.highlightLevel=[-1,-1,-1];return}for(ge=0;ge<3;++ge)this.enableHighlight[ge]?this.highlightLevel[ge]=Pe.level[ge]:this.highlightLevel[ge]=-1;var Re;for(this.snapToData?Re=Pe.dataCoordinate:Re=Pe.position,ge=0;ge<3;++ge)Re[ge]-=this.objectOffset[ge];if(!((!this.enableDynamic[0]||Re[0]===this.dynamicLevel[0])&&(!this.enableDynamic[1]||Re[1]===this.dynamicLevel[1])&&(!this.enableDynamic[2]||Re[2]===this.dynamicLevel[2]))){for(var ce=0,Ze=this.shape,ut=f.mallocFloat(12*Ze[0]*Ze[1]),pt=0;pt<3;++pt){if(!this.enableDynamic[pt]){this.dynamicLevel[pt]=NaN,this._dynamicCounts[pt]=0;continue}this.dynamicLevel[pt]=Re[pt];var Zt=(pt+1)%3,st=(pt+2)%3,lt=this._field[pt],Gt=this._field[Zt],Nt=this._field[st],Jt=b(lt,Re[pt]),sr=Jt.cells,wr=Jt.positions;for(this._dynamicOffsets[pt]=ce,ge=0;ge<sr.length;++ge)for(var cr=sr[ge],$e=0;$e<2;++$e){var St=wr[cr[$e]],Qt=+St[0],Vt=Qt|0,_t=Math.min(Vt+1,Ze[0])|0,It=Qt-Vt,mt=1-It,er=+St[1],lr=er|0,Tr=Math.min(lr+1,Ze[1])|0,Lr=er-lr,ti=1-Lr,Br=mt*ti,Vr=mt*Lr,dt=It*ti,Ge=It*Lr,Je=Br*Gt.get(Vt,lr)+Vr*Gt.get(Vt,Tr)+dt*Gt.get(_t,lr)+Ge*Gt.get(_t,Tr),je=Br*Nt.get(Vt,lr)+Vr*Nt.get(Vt,Tr)+dt*Nt.get(_t,lr)+Ge*Nt.get(_t,Tr);if(isNaN(Je)||isNaN(je)){$e&&(ce-=1);break}ut[2*ce+0]=Je,ut[2*ce+1]=je,ce+=1}this._dynamicCounts[pt]=ce-this._dynamicOffsets[pt]}this._dynamicBuffer.update(ut.subarray(0,2*ce)),f.freeFloat(ut)}};function Fe(Pe){var ge=Pe.gl,Re=x(ge),ce=M(ge),Ze=C(ge),ut=g(ge),pt=l(ge),Zt=u(ge,[{buffer:pt,size:4,stride:P,offset:0},{buffer:pt,size:3,stride:P,offset:16},{buffer:pt,size:3,stride:P,offset:28}]),st=l(ge),lt=u(ge,[{buffer:st,size:4,stride:20,offset:0},{buffer:st,size:1,stride:20,offset:16}]),Gt=l(ge),Nt=u(ge,[{buffer:Gt,size:2,type:ge.FLOAT}]),Jt=c(ge,1,G,ge.RGBA,ge.UNSIGNED_BYTE);Jt.minFilter=ge.LINEAR,Jt.magFilter=ge.LINEAR;var sr=new Z(ge,[0,0],[[0,0,0],[0,0,0]],Re,ce,pt,Zt,Jt,Ze,ut,st,lt,Gt,Nt,[0,0,0]),wr={levels:[[],[],[]]};for(var cr in Pe)wr[cr]=Pe[cr];return wr.colormap=wr.colormap||"jet",sr.update(wr),sr}},9507:function(i){i.exports=!0},9618:function(i,a,o){var s=o(7163),l=typeof Float64Array!="undefined";function u(b,p){return b[0]-p[0]}function c(){var b=this.stride,p=new Array(b.length),k;for(k=0;k<p.length;++k)p[k]=[Math.abs(b[k]),k];p.sort(u);var E=new Array(p.length);for(k=0;k<E.length;++k)E[k]=p[k][1];return E}var f={T:function(b){function p(E){this.data=E}var k=p.prototype;return k.dtype=b,k.index=function(){return-1},k.size=0,k.dimension=-1,k.shape=k.stride=k.order=[],k.lo=k.hi=k.transpose=k.step=function(){return new p(this.data)},k.get=k.set=function(){},k.pick=function(){return null},function(S){return new p(S)}},0:function(b,p){function k(S,L){this.data=S,this.offset=L}var E=k.prototype;return E.dtype=b,E.index=function(){return this.offset},E.dimension=0,E.size=1,E.shape=E.stride=E.order=[],E.lo=E.hi=E.transpose=E.step=function(){return new k(this.data,this.offset)},E.pick=function(){return p(this.data)},E.valueOf=E.get=function(){return b==="generic"?this.data.get(this.offset):this.data[this.offset]},E.set=function(L){return b==="generic"?this.data.set(this.offset,L):this.data[this.offset]=L},function(L,x,C,M){return new k(L,M)}},1:function(b,p,k){function E(L,x,C,M){this.data=L,this.shape=[x],this.stride=[C],this.offset=M|0}var S=E.prototype;return S.dtype=b,S.dimension=1,Object.defineProperty(S,"size",{get:function(){return this.shape[0]}}),S.order=[0],S.set=function(x,C){return b==="generic"?this.data.set(this.offset+this.stride[0]*x,C):this.data[this.offset+this.stride[0]*x]=C},S.get=function(x){return b==="generic"?this.data.get(this.offset+this.stride[0]*x):this.data[this.offset+this.stride[0]*x]},S.index=function(x){return this.offset+this.stride[0]*x},S.hi=function(x){return new E(this.data,typeof x!="number"||x<0?this.shape[0]:x|0,this.stride[0],this.offset)},S.lo=function(x){var C=this.offset,M=0,g=this.shape[0],P=this.stride[0];return typeof x=="number"&&x>=0&&(M=x|0,C+=P*M,g-=M),new E(this.data,g,P,C)},S.step=function(x){var C=this.shape[0],M=this.stride[0],g=this.offset,P=0,T=Math.ceil;return typeof x=="number"&&(P=x|0,P<0?(g+=M*(C-1),C=T(-C/P)):C=T(C/P),M*=P),new E(this.data,C,M,g)},S.transpose=function(x){x=x===void 0?0:x|0;var C=this.shape,M=this.stride;return new E(this.data,C[x],M[x],this.offset)},S.pick=function(x){var C=[],M=[],g=this.offset;typeof x=="number"&&x>=0?g=g+this.stride[0]*x|0:(C.push(this.shape[0]),M.push(this.stride[0]));var P=p[C.length+1];return P(this.data,C,M,g)},function(x,C,M,g){return new E(x,C[0],M[0],g)}},2:function(b,p,k){function E(L,x,C,M,g,P){this.data=L,this.shape=[x,C],this.stride=[M,g],this.offset=P|0}var S=E.prototype;return S.dtype=b,S.dimension=2,Object.defineProperty(S,"size",{get:function(){return 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yX=ye((K0r,OPe)=>{"use strict";OPe.exports={solid:[[],0],dot:[[.5,1],200],dash:[[.5,1],50],longdash:[[.5,1],10],dashdot:[[.5,.625,.875,1],50],longdashdot:[[.5,.7,.8,1],10]}});var QD=ye((J0r,qPe)=>{"use strict";qPe.exports={circle:"\u25CF","circle-open":"\u25CB",square:"\u25A0","square-open":"\u25A1",diamond:"\u25C6","diamond-open":"\u25C7",cross:"+",x:"\u274C"}});var NPe=ye(($0r,BPe)=>{"use strict";var hIt=qa();function _X(e,t,r,n){if(!t||!t.visible)return null;for(var i=hIt.getComponentMethod("errorbars","makeComputeError")(t),a=new Array(e.length),o=0;o<e.length;o++){var s=i(+e[o],o);if(n.type==="log"){var l=n.c2l(e[o]),u=e[o]-s[0],c=e[o]+s[1];if(a[o]=[(n.c2l(u,!0)-l)*r,(n.c2l(c,!0)-l)*r],u>0){var f=n.c2l(u);n._lowerLogErrorBound||(n._lowerLogErrorBound=f),n._lowerErrorBound=Math.min(n._lowerLogErrorBound,f)}}else a[o]=[-s[0]*r,s[1]*r]}return a}function dIt(e){for(var t=0;t<e.length;t++)if(e[t])return e[t].length;return 0}function vIt(e,t,r){var 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wX=XPe.prototype;wX.handlePick=function(e){if(e.object&&(e.object===this.linePlot||e.object===this.delaunayMesh||e.object===this.textMarkers||e.object===this.scatterPlot)){var t=e.index=e.data.index;return e.object.highlight&&e.object.highlight(null),this.scatterPlot&&(e.object=this.scatterPlot,this.scatterPlot.highlight(e.data)),e.textLabel="",this.textLabels&&(Qy.isArrayOrTypedArray(this.textLabels)?(this.textLabels[t]||this.textLabels[t]===0)&&(e.textLabel=this.textLabels[t]):e.textLabel=this.textLabels),e.traceCoordinate=[this.data.x[t],this.data.y[t],this.data.z[t]],!0}};function AIt(e,t,r){var n=(r+1)%3,i=(r+2)%3,a=[],o=[],s;for(s=0;s<e.length;++s){var l=e[s];isNaN(l[n])||!isFinite(l[n])||isNaN(l[i])||!isFinite(l[i])||(a.push([l[n],l[i]]),o.push(s))}var u=yIt(a);for(s=0;s<u.length;++s)for(var c=u[s],f=0;f<c.length;++f)c[f]=o[c[f]];return{positions:e,cells:u,meshColor:t}}function SIt(e){for(var t=[0,0,0],r=[[0,0,0],[0,0,0],[0,0,0]],n=[1,1,1],i=0;i<3;i++){var a=e[i];a&&a.copy_zstyle!==!1&&e[2].visible!==!1&&(a=e[2]),!(!a||!a.visible)&&(t[i]=a.width/2,r[i]=WPe(a.color),n[i]=a.thickness)}return{capSize:t,color:r,lineWidth:n}}function VPe(e){return e==null?0:e.indexOf("left")>-1?-1:e.indexOf("right")>-1?1:0}function GPe(e){return e==null?0:e.indexOf("top")>-1?-1:e.indexOf("bottom")>-1?1:0}function MIt(e){var t=0,r=0,n=[t,r];if(Array.isArray(e))for(var i=0;i<e.length;i++)n[i]=[t,r],e[i]&&(n[i][0]=VPe(e[i]),n[i][1]=GPe(e[i]));else n[0]=VPe(e),n[1]=GPe(e);return n}function EIt(e,t){return t(e*4)}function kIt(e){return xIt[e]}function bX(e,t,r,n,i){var a=null;if(Qy.isArrayOrTypedArray(e)){a=[];for(var o=0;o<t;o++)e[o]===void 0?a[o]=n:a[o]=r(e[o],i)}else a=r(e,Qy.identity);return a}function CIt(e,t){var r=[],n=e.fullSceneLayout,i=e.dataScale,a=n.xaxis,o=n.yaxis,s=n.zaxis,l=t.marker,u=t.line,c=t.x||[],f=t.y||[],h=t.z||[],d=c.length,v=t.xcalendar,_=t.ycalendar,b=t.zcalendar,p,k,E,S,L,x;for(L=0;L<d;L++)p=a.d2l(c[L],0,v)*i[0],k=o.d2l(f[L],0,_)*i[1],E=s.d2l(h[L],0,b)*i[2],r[L]=[p,k,E];if(Array.isArray(t.text))x=t.text;else if(Qy.isTypedArray(t.text))x=Array.from(t.text);else if(t.text!==void 0)for(x=new Array(d),L=0;L<d;L++)x[L]=t.text;function C(oe,_e){var Ee=n[oe];return bIt.tickText(Ee,Ee.d2l(_e),!0).text}var M=t.texttemplate;if(M){var g=e.fullLayout,P=g._d3locale,T=Array.isArray(M),z=T?Math.min(M.length,d):d,O=T?function(oe){return M[oe]}:function(){return M};for(x=new Array(z),L=0;L<z;L++){var V={x:c[L],y:f[L],z:h[L]},G={xLabel:C("xaxis",c[L]),yLabel:C("yaxis",f[L]),zLabel:C("zaxis",h[L])},Z={};wIt(Z,t,L),x[L]=Qy.texttemplateString({data:[Z,V,t._meta],fallback:t.texttemplatefallback,labels:G,locale:P,template:O(L)})}}if(S={position:r,mode:t.mode,text:x},"line"in t&&(S.lineColor=eF(u,1,d),S.lineWidth=u.width,S.lineDashes=u.dash),"marker"in t){var j=_It(t);S.scatterColor=eF(l,1,d),S.scatterSize=bX(l.size,d,EIt,20,j),S.scatterMarker=bX(l.symbol,d,kIt,"\u25CF"),S.scatterLineWidth=l.line.width,S.scatterLineColor=eF(l.line,1,d),S.scatterAngle=0}"textposition"in t&&(S.textOffset=MIt(t.textposition),S.textColor=eF(t.textfont,1,d),S.textSize=bX(t.textfont.size,d,Qy.identity,12),S.textFontFamily=t.textfont.family,S.textFontWeight=t.textfont.weight,S.textFontStyle=t.textfont.style,S.textFontVariant=t.textfont.variant,S.textAngle=0);var N=["x","y","z"];for(S.project=[!1,!1,!1],S.projectScale=[1,1,1],S.projectOpacity=[1,1,1],L=0;L<3;++L){var H=t.projection[N[L]];(S.project[L]=H.show)&&(S.projectOpacity[L]=H.opacity,S.projectScale[L]=H.scale)}S.errorBounds=TIt(t,i,n);var te=SIt([t.error_x,t.error_y,t.error_z]);return S.errorColor=te.color,S.errorLineWidth=te.lineWidth,S.errorCapSize=te.capSize,S.delaunayAxis=t.surfaceaxis,S.delaunayColor=WPe(t.surfacecolor),S}function HPe(e){if(Qy.isArrayOrTypedArray(e)){var t=e[0];return Qy.isArrayOrTypedArray(t)&&(e=t),"rgb("+e.slice(0,3).map(function(r){return Math.round(r*255)})+")"}return null}function jPe(e){return Qy.isArrayOrTypedArray(e)?e.length===4&&typeof e[0]=="number"?HPe(e):e.map(HPe):null}wX.update=function(e){var t=this.scene.glplot.gl,r,n,i,a,o=xX.solid;this.data=e;var s=CIt(this.scene,e);"mode"in s&&(this.mode=s.mode),"lineDashes"in s&&s.lineDashes in xX&&(o=xX[s.lineDashes]),this.color=jPe(s.scatterColor)||jPe(s.lineColor),this.dataPoints=s.position,r={gl:this.scene.glplot.gl,position:s.position,color:s.lineColor,lineWidth:s.lineWidth||1,dashes:o[0],dashScale:o[1],opacity:e.opacity,connectGaps:e.connectgaps},this.mode.indexOf("lines")!==-1?this.linePlot?this.linePlot.update(r):(this.linePlot=pIt(r),this.linePlot._trace=this,this.scene.glplot.add(this.linePlot)):this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose(),this.linePlot=null);var l=e.opacity;if(e.marker&&e.marker.opacity!==void 0&&(l*=e.marker.opacity),n={gl:this.scene.glplot.gl,position:s.position,color:s.scatterColor,size:s.scatterSize,glyph:s.scatterMarker,opacity:l,orthographic:!0,lineWidth:s.scatterLineWidth,lineColor:s.scatterLineColor,project:s.project,projectScale:s.projectScale,projectOpacity:s.projectOpacity},this.mode.indexOf("markers")!==-1?this.scatterPlot?this.scatterPlot.update(n):(this.scatterPlot=UPe(n),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),a={gl:this.scene.glplot.gl,position:s.position,glyph:s.text,color:s.textColor,size:s.textSize,angle:s.textAngle,alignment:s.textOffset,font:s.textFontFamily,fontWeight:s.textFontWeight,fontStyle:s.textFontStyle,fontVariant:s.textFontVariant,orthographic:!0,lineWidth:0,project:!1,opacity:e.opacity},this.textLabels=e.hovertext||e.text,this.mode.indexOf("text")!==-1?this.textMarkers?this.textMarkers.update(a):(this.textMarkers=UPe(a),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),i={gl:this.scene.glplot.gl,position:s.position,color:s.errorColor,error:s.errorBounds,lineWidth:s.errorLineWidth,capSize:s.errorCapSize,opacity:e.opacity},this.errorBars?s.errorBounds?this.errorBars.update(i):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):s.errorBounds&&(this.errorBars=gIt(i),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),s.delaunayAxis>=0){var u=AIt(s.position,s.delaunayColor,s.delaunayAxis);u.opacity=e.opacity,this.delaunayMesh?this.delaunayMesh.update(u):(u.gl=t,this.delaunayMesh=mIt(u),this.delaunayMesh._trace=this,this.scene.glplot.add(this.delaunayMesh))}else this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose(),this.delaunayMesh=null)};wX.dispose=function(){this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose()),this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose()),this.errorBars&&(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose()),this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose()),this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose())};function LIt(e,t){var r=new XPe(e,t.uid);return r.update(t),r}ZPe.exports=LIt});var EX=ye((egr,QPe)=>{"use strict";var e1=pf(),PIt=ec(),MX=Tu(),TX=df().axisHoverFormat,{hovertemplateAttrs:IIt,texttemplateAttrs:RIt,templatefallbackAttrs:KPe}=Ll(),JPe=Gl(),DIt=yX(),FIt=QD(),Kg=Ao().extendFlat,zIt=mc().overrideAll,$Pe=Z1(),OIt=e1.line,V2=e1.marker,qIt=V2.line,BIt=Kg({width:OIt.width,dash:{valType:"enumerated",values:$Pe(DIt),dflt:"solid"}},MX("line"));function AX(e){return{show:{valType:"boolean",dflt:!1},opacity:{valType:"number",min:0,max:1,dflt:1},scale:{valType:"number",min:0,max:10,dflt:2/3}}}var SX=QPe.exports=zIt({x:e1.x,y:e1.y,z:{valType:"data_array"},text:Kg({},e1.text,{}),texttemplate:RIt(),texttemplatefallback:KPe({editType:"calc"}),hovertext:Kg({},e1.hovertext,{}),hovertemplate:IIt(),hovertemplatefallback:KPe(),xhoverformat:TX("x"),yhoverformat:TX("y"),zhoverformat:TX("z"),mode:Kg({},e1.mode,{dflt:"lines+markers"}),surfaceaxis:{valType:"enumerated",values:[-1,0,1,2],dflt:-1},surfacecolor:{valType:"color"},projection:{x:AX("x"),y:AX("y"),z:AX("z")},connectgaps:e1.connectgaps,line:BIt,marker:Kg({symbol:{valType:"enumerated",values:$Pe(FIt),dflt:"circle",arrayOk:!0},size:Kg({},V2.size,{dflt:8}),sizeref:V2.sizeref,sizemin:V2.sizemin,sizemode:V2.sizemode,opacity:Kg({},V2.opacity,{arrayOk:!1}),colorbar:V2.colorbar,line:Kg({width:Kg({},qIt.width,{arrayOk:!1})},MX("marker.line"))},MX("marker")),textposition:Kg({},e1.textposition,{dflt:"top center"}),textfont:PIt({noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0,editType:"calc",colorEditType:"style",arrayOk:!0,variantValues:["normal","small-caps"]}),opacity:JPe.opacity,hoverinfo:Kg({},JPe.hoverinfo)},"calc","nested");SX.x.editType=SX.y.editType=SX.z.editType="calc+clearAxisTypes"});var rIe=ye((tgr,tIe)=>{"use strict";var eIe=qa(),NIt=Dr(),kX=Ru(),UIt=$p(),VIt=R0(),GIt=D0(),HIt=EX();tIe.exports=function(t,r,n,i){function a(d,v){return NIt.coerce(t,r,HIt,d,v)}var o=jIt(t,r,a,i);if(!o){r.visible=!1;return}a("text"),a("hovertext"),a("hovertemplate"),a("hovertemplatefallback"),a("xhoverformat"),a("yhoverformat"),a("zhoverformat"),a("mode"),kX.hasMarkers(r)&&UIt(t,r,n,i,a,{noSelect:!0,noAngle:!0}),kX.hasLines(r)&&(a("connectgaps"),VIt(t,r,n,i,a)),kX.hasText(r)&&(a("texttemplate"),a("texttemplatefallback"),GIt(t,r,i,a,{noSelect:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0}));var s=(r.line||{}).color,l=(r.marker||{}).color;a("surfaceaxis")>=0&&a("surfacecolor",s||l);for(var u=["x","y","z"],c=0;c<3;++c){var f="projection."+u[c];a(f+".show")&&(a(f+".opacity"),a(f+".scale"))}var h=eIe.getComponentMethod("errorbars","supplyDefaults");h(t,r,s||l||n,{axis:"z"}),h(t,r,s||l||n,{axis:"y",inherit:"z"}),h(t,r,s||l||n,{axis:"x",inherit:"z"})};function jIt(e,t,r,n){var i=0,a=r("x"),o=r("y"),s=r("z"),l=eIe.getComponentMethod("calendars","handleTraceDefaults");return l(e,t,["x","y","z"],n),a&&o&&s&&(i=Math.min(a.length,o.length,s.length),t._length=t._xlength=t._ylength=t._zlength=i),i}});var nIe=ye((rgr,iIe)=>{"use strict";var WIt=km(),XIt=F0();iIe.exports=function(t,r){var n=[{x:!1,y:!1,trace:r,t:{}}];return WIt(n,r),XIt(t,r),n}});var oIe=ye((igr,aIe)=>{aIe.exports=ZIt;function ZIt(e,t){if(typeof e!="string")throw new TypeError("must specify type string");if(t=t||{},typeof document=="undefined"&&!t.canvas)return null;var r=t.canvas||document.createElement("canvas");typeof t.width=="number"&&(r.width=t.width),typeof t.height=="number"&&(r.height=t.height);var n=t,i;try{var a=[e];e.indexOf("webgl")===0&&a.push("experimental-"+e);for(var o=0;o<a.length;o++)if(i=r.getContext(a[o],n),i)return i}catch(s){i=null}return i||null}});var lIe=ye((ngr,sIe)=>{var YIt=oIe();sIe.exports=function(t){return YIt("webgl",t)}});var CX=ye((agr,cIe)=>{"use strict";var uIe=ka(),KIt=function(){};cIe.exports=function(t){for(var r in t)typeof t[r]=="function"&&(t[r]=KIt);t.destroy=function(){t.container.parentNode.removeChild(t.container)};var n=document.createElement("div");n.className="no-webgl",n.style.cursor="pointer",n.style.fontSize="24px",n.style.color=uIe.defaults[0],n.style.position="absolute",n.style.left=n.style.top="0px",n.style.width=n.style.height="100%",n.style["background-color"]=uIe.lightLine,n.style["z-index"]=30;var i=document.createElement("p");return i.textContent="WebGL is not supported by your browser - visit https://get.webgl.org for more info",i.style.position="relative",i.style.top="50%",i.style.left="50%",i.style.height="30%",i.style.width="50%",i.style.margin="-15% 0 0 -25%",n.appendChild(i),t.container.appendChild(n),t.container.style.background="#FFFFFF",t.container.onclick=function(){window.open("https://get.webgl.org")},!1}});var dIe=ye((ogr,hIe)=>{"use strict";var G2=Jy(),JIt=Dr(),$It=["xaxis","yaxis","zaxis"];function fIe(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickFontWeight=["normal","normal","normal","normal"],this.tickFontStyle=["normal","normal","normal","normal"],this.tickFontVariant=["normal","normal","normal","normal"],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open Sans"],this.labelSize=[20,20,20],this.labelFontWeight=["normal","normal","normal","normal"],this.labelFontStyle=["normal","normal","normal","normal"],this.labelFontVariant=["normal","normal","normal","normal"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}var QIt=fIe.prototype;QIt.merge=function(e,t){for(var r=this,n=0;n<3;++n){var i=t[$It[n]];if(!i.visible){r.tickEnable[n]=!1,r.labelEnable[n]=!1,r.lineEnable[n]=!1,r.lineTickEnable[n]=!1,r.gridEnable[n]=!1,r.zeroEnable[n]=!1,r.backgroundEnable[n]=!1;continue}r.labels[n]=e._meta?JIt.templateString(i.title.text,e._meta):i.title.text,"font"in i.title&&(i.title.font.color&&(r.labelColor[n]=G2(i.title.font.color)),i.title.font.family&&(r.labelFont[n]=i.title.font.family),i.title.font.size&&(r.labelSize[n]=i.title.font.size),i.title.font.weight&&(r.labelFontWeight[n]=i.title.font.weight),i.title.font.style&&(r.labelFontStyle[n]=i.title.font.style),i.title.font.variant&&(r.labelFontVariant[n]=i.title.font.variant)),"showline"in i&&(r.lineEnable[n]=i.showline),"linecolor"in i&&(r.lineColor[n]=G2(i.linecolor)),"linewidth"in i&&(r.lineWidth[n]=i.linewidth),"showgrid"in i&&(r.gridEnable[n]=i.showgrid),"gridcolor"in i&&(r.gridColor[n]=G2(i.gridcolor)),"gridwidth"in i&&(r.gridWidth[n]=i.gridwidth),i.type==="log"?r.zeroEnable[n]=!1:"zeroline"in i&&(r.zeroEnable[n]=i.zeroline),"zerolinecolor"in i&&(r.zeroLineColor[n]=G2(i.zerolinecolor)),"zerolinewidth"in i&&(r.zeroLineWidth[n]=i.zerolinewidth),"ticks"in i&&i.ticks?r.lineTickEnable[n]=!0:r.lineTickEnable[n]=!1,"ticklen"in i&&(r.lineTickLength[n]=r._defaultLineTickLength[n]=i.ticklen),"tickcolor"in i&&(r.lineTickColor[n]=G2(i.tickcolor)),"tickwidth"in i&&(r.lineTickWidth[n]=i.tickwidth),"tickangle"in i&&(r.tickAngle[n]=i.tickangle==="auto"?-3600:Math.PI*-i.tickangle/180),"showticklabels"in i&&(r.tickEnable[n]=i.showticklabels),"tickfont"in i&&(i.tickfont.color&&(r.tickColor[n]=G2(i.tickfont.color)),i.tickfont.family&&(r.tickFont[n]=i.tickfont.family),i.tickfont.size&&(r.tickSize[n]=i.tickfont.size),i.tickfont.weight&&(r.tickFontWeight[n]=i.tickfont.weight),i.tickfont.style&&(r.tickFontStyle[n]=i.tickfont.style),i.tickfont.variant&&(r.tickFontVariant[n]=i.tickfont.variant)),"mirror"in i?["ticks","all","allticks"].indexOf(i.mirror)!==-1?(r.lineTickMirror[n]=!0,r.lineMirror[n]=!0):i.mirror===!0?(r.lineTickMirror[n]=!1,r.lineMirror[n]=!0):(r.lineTickMirror[n]=!1,r.lineMirror[n]=!1):r.lineMirror[n]=!1,"showbackground"in i&&i.showbackground!==!1?(r.backgroundEnable[n]=!0,r.backgroundColor[n]=G2(i.backgroundcolor)):r.backgroundEnable[n]=!1}};function e8t(e,t){var r=new fIe;return r.merge(e,t),r}hIe.exports=e8t});var gIe=ye((sgr,pIe)=>{"use strict";var t8t=Jy(),r8t=["xaxis","yaxis","zaxis"];function vIe(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}var i8t=vIe.prototype;i8t.merge=function(e){for(var t=0;t<3;++t){var r=e[r8t[t]];if(!r.visible){this.enabled[t]=!1,this.drawSides[t]=!1;continue}this.enabled[t]=r.showspikes,this.colors[t]=t8t(r.spikecolor),this.drawSides[t]=r.spikesides,this.lineWidth[t]=r.spikethickness}};function n8t(e){var t=new vIe;return t.merge(e),t}pIe.exports=n8t});var _Ie=ye((lgr,yIe)=>{"use strict";yIe.exports=u8t;var mIe=ho(),a8t=Dr(),o8t=["xaxis","yaxis","zaxis"],s8t=[0,0,0];function l8t(e){for(var t=new Array(3),r=0;r<3;++r){for(var n=e[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;t[r]=i}return t}function u8t(e){for(var t=e.axesOptions,r=e.glplot.axesPixels,n=e.fullSceneLayout,i=[[],[],[]],a=0;a<3;++a){var o=n[o8t[a]];if(o._length=(r[a].hi-r[a].lo)*r[a].pixelsPerDataUnit/e.dataScale[a],Math.abs(o._length)===1/0||isNaN(o._length))i[a]=[];else{o._input_range=o.range.slice(),o.range[0]=r[a].lo/e.dataScale[a],o.range[1]=r[a].hi/e.dataScale[a],o._m=1/(e.dataScale[a]*r[a].pixelsPerDataUnit),o.range[0]===o.range[1]&&(o.range[0]-=1,o.range[1]+=1);var s=o.tickmode;if(o.tickmode==="auto"){o.tickmode="linear";var l=o.nticks||a8t.constrain(o._length/40,4,9);mIe.autoTicks(o,Math.abs(o.range[1]-o.range[0])/l)}for(var u=mIe.calcTicks(o,{msUTC:!0}),c=0;c<u.length;++c)u[c].x=u[c].x*e.dataScale[a],o.type==="date"&&(u[c].text=u[c].text.replace(/\<br\>/g," "));i[a]=u,o.tickmode=s}}t.ticks=i;for(var a=0;a<3;++a){s8t[a]=.5*(e.glplot.bounds[0][a]+e.glplot.bounds[1][a]);for(var c=0;c<2;++c)t.bounds[c][a]=e.glplot.bounds[c][a]}e.contourLevels=l8t(i)}});var MIe=ye((ugr,SIe)=>{"use strict";var wIe=Od().gl_plot3d,c8t=wIe.createCamera,xIe=wIe.createScene,f8t=lIe(),h8t=RL(),iF=qa(),up=Dr(),rF=up.preserveDrawingBuffer(),nF=ho(),Jg=vf(),d8t=Jy(),v8t=CX(),p8t=UU(),g8t=dIe(),m8t=gIe(),y8t=_Ie(),_8t=Ag().applyAutorangeOptions,YE,tF,TIe=!1;function AIe(e,t){var r=document.createElement("div"),n=e.container;this.graphDiv=e.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=e.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=t,this.id=e.id||"scene",this.fullSceneLayout=t[this.id],this.plotArgs=[[],{},{}],this.axesOptions=g8t(t,t[this.id]),this.spikeOptions=m8t(t[this.id]),this.container=r,this.staticMode=!!e.staticPlot,this.pixelRatio=this.pixelRatio||e.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=iF.getComponentMethod("annotations3d","convert"),this.drawAnnotations=iF.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var Sv=AIe.prototype;Sv.prepareOptions=function(){var e=this,t={canvas:e.canvas,gl:e.gl,glOptions:{preserveDrawingBuffer:rF,premultipliedAlpha:!0,antialias:!0},container:e.container,axes:e.axesOptions,spikes:e.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:e.camera,pixelRatio:e.pixelRatio};if(e.staticMode){if(!tF&&(YE=document.createElement("canvas"),tF=f8t({canvas:YE,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}),!tF))throw new Error("error creating static canvas/context for image server");t.gl=tF,t.canvas=YE}return t};var bIe=!0;Sv.tryCreatePlot=function(){var e=this,t=e.prepareOptions(),r=!0;try{e.glplot=xIe(t)}catch(n){if(e.staticMode||!bIe||rF)r=!1;else{up.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{rF=t.glOptions.preserveDrawingBuffer=!0,e.glplot=xIe(t)}catch(i){rF=t.glOptions.preserveDrawingBuffer=!1,r=!1}}}return bIe=!1,r};Sv.initializeGLCamera=function(){var e=this,t=e.fullSceneLayout.camera,r=t.projection.type==="orthographic";e.camera=c8t(e.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:r,zoomMin:.01,zoomMax:100,mode:"orbit"})};Sv.initializeGLPlot=function(){var e=this;e.initializeGLCamera();var t=e.tryCreatePlot();if(!t)return v8t(e);e.traces={},e.make4thDimension();var r=e.graphDiv,n=r.layout,i=function(){var o={};return e.isCameraChanged(n)&&(o[e.id+".camera"]=e.getCamera()),e.isAspectChanged(n)&&(o[e.id+".aspectratio"]=e.glplot.getAspectratio(),n[e.id].aspectmode!=="manual"&&(e.fullSceneLayout.aspectmode=n[e.id].aspectmode=o[e.id+".aspectmode"]="manual")),o},a=function(o){if(o.fullSceneLayout.dragmode!==!1){var s=i();o.saveLayout(n),o.graphDiv.emit("plotly_relayout",s)}};return e.glplot.canvas&&(e.glplot.canvas.addEventListener("mouseup",function(){a(e)}),e.glplot.canvas.addEventListener("touchstart",function(){TIe=!0}),e.glplot.canvas.addEventListener("wheel",function(o){if(r._context._scrollZoom.gl3d){if(e.camera._ortho){var s=o.deltaX>o.deltaY?1.1:.9090909090909091,l=e.glplot.getAspectratio();e.glplot.setAspectratio({x:s*l.x,y:s*l.y,z:s*l.z})}a(e)}},h8t?{passive:!1}:!1),e.glplot.canvas.addEventListener("mousemove",function(){if(e.fullSceneLayout.dragmode!==!1&&e.camera.mouseListener.buttons!==0){var o=i();e.graphDiv.emit("plotly_relayouting",o)}}),e.staticMode||e.glplot.canvas.addEventListener("webglcontextlost",function(o){r&&r.emit&&r.emit("plotly_webglcontextlost",{event:o,layer:e.id})},!1)),e.glplot.oncontextloss=function(){e.recoverContext()},e.glplot.onrender=function(){e.render()},!0};Sv.render=function(){var e=this,t=e.graphDiv,r,n=e.svgContainer,i=e.container.getBoundingClientRect();t._fullLayout._calcInverseTransform(t);var a=t._fullLayout._invScaleX,o=t._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,"viewBox","0 0 "+s+" "+l),n.setAttributeNS(null,"width",s),n.setAttributeNS(null,"height",l),y8t(e),e.glplot.axes.update(e.axesOptions);for(var u=Object.keys(e.traces),c=null,f=e.glplot.selection,h=0;h<u.length;++h)r=e.traces[u[h]],r.data.hoverinfo!=="skip"&&r.handlePick(f)&&(c=r),r.setContourLevels&&r.setContourLevels();function d(P,T,z){var O=e.fullSceneLayout[P+"axis"];return O.type!=="log"&&(T=O.d2l(T)),nF.hoverLabelText(O,T,z)}if(c!==null){var v=p8t(e.glplot.cameraParams,f.dataCoordinate);r=c.data;var _=t._fullData[r.index],b=f.index,p={xLabel:d("x",f.traceCoordinate[0],r.xhoverformat),yLabel:d("y",f.traceCoordinate[1],r.yhoverformat),zLabel:d("z",f.traceCoordinate[2],r.zhoverformat)},k=Jg.castHoverinfo(_,e.fullLayout,b),E=(k||"").split("+"),S=k&&k==="all";!_.hovertemplate&&!S&&(E.indexOf("x")===-1&&(p.xLabel=void 0),E.indexOf("y")===-1&&(p.yLabel=void 0),E.indexOf("z")===-1&&(p.zLabel=void 0),E.indexOf("text")===-1&&(f.textLabel=void 0),E.indexOf("name")===-1&&(c.name=void 0));var L,x=[];r.type==="cone"||r.type==="streamtube"?(p.uLabel=d("x",f.traceCoordinate[3],r.uhoverformat),(S||E.indexOf("u")!==-1)&&x.push("u: "+p.uLabel),p.vLabel=d("y",f.traceCoordinate[4],r.vhoverformat),(S||E.indexOf("v")!==-1)&&x.push("v: "+p.vLabel),p.wLabel=d("z",f.traceCoordinate[5],r.whoverformat),(S||E.indexOf("w")!==-1)&&x.push("w: "+p.wLabel),p.normLabel=f.traceCoordinate[6].toPrecision(3),(S||E.indexOf("norm")!==-1)&&x.push("norm: "+p.normLabel),r.type==="streamtube"&&(p.divergenceLabel=f.traceCoordinate[7].toPrecision(3),(S||E.indexOf("divergence")!==-1)&&x.push("divergence: "+p.divergenceLabel)),f.textLabel&&x.push(f.textLabel),L=x.join("<br>")):r.type==="isosurface"||r.type==="volume"?(p.valueLabel=nF.hoverLabelText(e._mockAxis,e._mockAxis.d2l(f.traceCoordinate[3]),r.valuehoverformat),x.push("value: "+p.valueLabel),f.textLabel&&x.push(f.textLabel),L=x.join("<br>")):L=f.textLabel;var C={x:f.traceCoordinate[0],y:f.traceCoordinate[1],z:f.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:b};Jg.appendArrayPointValue(C,_,b),r._module.eventData&&(C=_._module.eventData(C,f,_,{},b));var M={points:[C]};if(e.fullSceneLayout.hovermode){var g=[];Jg.loneHover({trace:_,x:(.5+.5*v[0]/v[3])*s,y:(.5-.5*v[1]/v[3])*l,xLabel:p.xLabel,yLabel:p.yLabel,zLabel:p.zLabel,text:L,name:c.name,color:Jg.castHoverOption(_,b,"bgcolor")||c.color,borderColor:Jg.castHoverOption(_,b,"bordercolor"),fontFamily:Jg.castHoverOption(_,b,"font.family"),fontSize:Jg.castHoverOption(_,b,"font.size"),fontColor:Jg.castHoverOption(_,b,"font.color"),nameLength:Jg.castHoverOption(_,b,"namelength"),textAlign:Jg.castHoverOption(_,b,"align"),hovertemplate:up.castOption(_,b,"hovertemplate"),hovertemplateLabels:up.extendFlat({},C,p),eventData:[C]},{container:n,gd:t,inOut_bbox:g}),C.bbox=g[0]}f.distance<5&&(f.buttons||TIe)?t.emit("plotly_click",M):t.emit("plotly_hover",M),this.oldEventData=M}else Jg.loneUnhover(n),this.oldEventData&&t.emit("plotly_unhover",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)};Sv.recoverContext=function(){var e=this;e.glplot.dispose();var t=function(){if(e.glplot.gl.isContextLost()){requestAnimationFrame(t);return}if(!e.initializeGLPlot()){up.error("Catastrophic and unrecoverable WebGL error. Context lost.");return}e.plot.apply(e,e.plotArgs)};requestAnimationFrame(t)};var KE=["xaxis","yaxis","zaxis"];function x8t(e,t,r){for(var n=e.fullSceneLayout,i=0;i<3;i++){var a=KE[i],o=a.charAt(0),s=n[a],l=t[o],u=t[o+"calendar"],c=t["_"+o+"length"];if(!up.isArrayOrTypedArray(l))r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],c-1);else for(var f,h=0;h<(c||l.length);h++)if(up.isArrayOrTypedArray(l[h]))for(var d=0;d<l[h].length;++d)f=s.d2l(l[h][d],0,u),!isNaN(f)&&isFinite(f)&&(r[0][i]=Math.min(r[0][i],f),r[1][i]=Math.max(r[1][i],f));else f=s.d2l(l[h],0,u),!isNaN(f)&&isFinite(f)&&(r[0][i]=Math.min(r[0][i],f),r[1][i]=Math.max(r[1][i],f))}}function b8t(e,t){for(var r=e.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=KE[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var u=n[l];if(u.visible){var c=s.r2l(u[o]);!isNaN(c)&&isFinite(c)&&(t[0][i]=Math.min(t[0][i],c),t[1][i]=Math.max(t[1][i],c))}}}Sv.plot=function(e,t,r){var n=this;if(n.plotArgs=[e,t,r],!n.glplot.contextLost){var i,a,o,s,l,u,c=t[n.id],f=r[n.id];n.fullLayout=t,n.fullSceneLayout=c,n.axesOptions.merge(t,c),n.spikeOptions.merge(c),n.setViewport(c),n.updateFx(c.dragmode,c.hovermode),n.camera.enableWheel=n.graphDiv._context._scrollZoom.gl3d,n.glplot.setClearColor(d8t(c.bgcolor)),n.setConvert(l),e?Array.isArray(e)||(e=[e]):e=[];var h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(o=0;o<e.length;++o)i=e[o],!(i.visible!==!0||i._length===0)&&x8t(this,i,h);b8t(this,h);var d=[1,1,1];for(s=0;s<3;++s)h[1][s]===h[0][s]?d[s]=1:d[s]=1/(h[1][s]-h[0][s]);for(n.dataScale=d,n.convertAnnotations(this),o=0;o<e.length;++o)i=e[o],!(i.visible!==!0||i._length===0)&&(a=n.traces[i.uid],a?a.data.type===i.type?a.update(i):(a.dispose(),a=i._module.plot(this,i),n.traces[i.uid]=a):(a=i._module.plot(this,i),n.traces[i.uid]=a),a.name=i.name);var v=Object.keys(n.traces);e:for(o=0;o<v.length;++o){for(s=0;s<e.length;++s)if(e[s].uid===v[o]&&e[s].visible===!0&&e[s]._length!==0)continue e;a=n.traces[v[o]],a.dispose(),delete n.traces[v[o]]}n.glplot.objects.sort(function(oe,_e){return oe._trace.data.index-_e._trace.data.index});var _=[[0,0,0],[0,0,0]],b=[],p={};for(o=0;o<3;++o){l=c[KE[o]],u=l.type,u in p?(p[u].acc*=d[o],p[u].count+=1):p[u]={acc:d[o],count:1};var k;if(l.autorange){_[0][o]=1/0,_[1][o]=-1/0;var E=n.glplot.objects,S=n.fullSceneLayout.annotations||[],L=l._name.charAt(0);for(s=0;s<E.length;s++){var x=E[s],C=x.bounds,M=x._trace.data._pad||0;x.constructor.name==="ErrorBars"&&l._lowerLogErrorBound?_[0][o]=Math.min(_[0][o],l._lowerLogErrorBound):_[0][o]=Math.min(_[0][o],C[0][o]/d[o]-M),_[1][o]=Math.max(_[1][o],C[1][o]/d[o]+M)}for(s=0;s<S.length;s++){var g=S[s];if(g.visible){var P=l.r2l(g[L]);_[0][o]=Math.min(_[0][o],P),_[1][o]=Math.max(_[1][o],P)}}if("rangemode"in l&&l.rangemode==="tozero"&&(_[0][o]=Math.min(_[0][o],0),_[1][o]=Math.max(_[1][o],0)),_[0][o]>_[1][o])_[0][o]=-1,_[1][o]=1;else{var T=_[1][o]-_[0][o];_[0][o]-=T/32,_[1][o]+=T/32}if(k=[_[0][o],_[1][o]],k=_8t(k,l),_[0][o]=k[0],_[1][o]=k[1],l.isReversed()){var z=_[0][o];_[0][o]=_[1][o],_[1][o]=z}}else k=l.range,_[0][o]=l.r2l(k[0]),_[1][o]=l.r2l(k[1]);_[0][o]===_[1][o]&&(_[0][o]-=1,_[1][o]+=1),b[o]=_[1][o]-_[0][o],l.range=[_[0][o],_[1][o]],l.limitRange(),n.glplot.setBounds(o,{min:l.range[0]*d[o],max:l.range[1]*d[o]})}var O,V=c.aspectmode;if(V==="cube")O=[1,1,1];else if(V==="manual"){var G=c.aspectratio;O=[G.x,G.y,G.z]}else if(V==="auto"||V==="data"){var Z=[1,1,1];for(o=0;o<3;++o){l=c[KE[o]],u=l.type;var j=p[u];Z[o]=Math.pow(j.acc,1/j.count)/d[o]}V==="data"||Math.max.apply(null,Z)/Math.min.apply(null,Z)<=4?O=Z:O=[1,1,1]}else throw new Error("scene.js aspectRatio was not one of the enumerated types");c.aspectratio.x=f.aspectratio.x=O[0],c.aspectratio.y=f.aspectratio.y=O[1],c.aspectratio.z=f.aspectratio.z=O[2],n.glplot.setAspectratio(c.aspectratio),n.viewInitial.aspectratio||(n.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),n.viewInitial.aspectmode||(n.viewInitial.aspectmode=c.aspectmode);var N=c.domain||null,H=t._size||null;if(N&&H){var te=n.container.style;te.position="absolute",te.left=H.l+N.x[0]*H.w+"px",te.top=H.t+(1-N.y[1])*H.h+"px",te.width=H.w*(N.x[1]-N.x[0])+"px",te.height=H.h*(N.y[1]-N.y[0])+"px"}n.glplot.redraw()}};Sv.destroy=function(){var e=this;e.glplot&&(e.camera.mouseListener.enabled=!1,e.container.removeEventListener("wheel",e.camera.wheelListener),e.camera=null,e.glplot.dispose(),e.container.parentNode.removeChild(e.container),e.glplot=null)};function w8t(e){return[[e.eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]}function T8t(e){return{up:{x:e.up[0],y:e.up[1],z:e.up[2]},center:{x:e.center[0],y:e.center[1],z:e.center[2]},eye:{x:e.eye[0],y:e.eye[1],z:e.eye[2]},projection:{type:e._ortho===!0?"orthographic":"perspective"}}}Sv.getCamera=function(){var e=this;return e.camera.view.recalcMatrix(e.camera.view.lastT()),T8t(e.camera)};Sv.setViewport=function(e){var t=this,r=e.camera;t.camera.lookAt.apply(this,w8t(r)),t.glplot.setAspectratio(e.aspectratio);var n=r.projection.type==="orthographic",i=t.camera._ortho;n!==i&&(t.glplot.redraw(),t.glplot.clearRGBA(),t.glplot.dispose(),t.initializeGLPlot())};Sv.isCameraChanged=function(e){var t=this,r=t.getCamera(),n=up.nestedProperty(e,t.id+".camera"),i=n.get();function a(u,c,f,h){var d=["up","center","eye"],v=["x","y","z"];return c[d[f]]&&u[d[f]][v[h]]===c[d[f]][v[h]]}var o=!1;if(i===void 0)o=!0;else{for(var s=0;s<3;s++)for(var l=0;l<3;l++)if(!a(r,i,s,l)){o=!0;break}(!i.projection||r.projection&&r.projection.type!==i.projection.type)&&(o=!0)}return o};Sv.isAspectChanged=function(e){var t=this,r=t.glplot.getAspectratio(),n=up.nestedProperty(e,t.id+".aspectratio"),i=n.get();return i===void 0||i.x!==r.x||i.y!==r.y||i.z!==r.z};Sv.saveLayout=function(e){var t=this,r=t.fullLayout,n,i,a,o,s,l,u=t.isCameraChanged(e),c=t.isAspectChanged(e),f=u||c;if(f){var h={};if(u&&(n=t.getCamera(),i=up.nestedProperty(e,t.id+".camera"),a=i.get(),h[t.id+".camera"]=a),c&&(o=t.glplot.getAspectratio(),s=up.nestedProperty(e,t.id+".aspectratio"),l=s.get(),h[t.id+".aspectratio"]=l),iF.call("_storeDirectGUIEdit",e,r._preGUI,h),u){i.set(n);var d=up.nestedProperty(r,t.id+".camera");d.set(n)}if(c){s.set(o);var v=up.nestedProperty(r,t.id+".aspectratio");v.set(o),t.glplot.redraw()}}return f};Sv.updateFx=function(e,t){var r=this,n=r.camera;if(n)if(e==="orbit")n.mode="orbit",n.keyBindingMode="rotate";else if(e==="turntable"){n.up=[0,0,1],n.mode="turntable",n.keyBindingMode="rotate";var i=r.graphDiv,a=i._fullLayout,o=r.fullSceneLayout.camera,s=o.up.x,l=o.up.y,u=o.up.z;if(u/Math.sqrt(s*s+l*l+u*u)<.999){var c=r.id+".camera.up",f={x:0,y:0,z:1},h={};h[c]=f;var d=i.layout;iF.call("_storeDirectGUIEdit",d,a._preGUI,h),o.up=f,up.nestedProperty(d,c).set(f)}}else n.keyBindingMode=e;r.fullSceneLayout.hovermode=t};function A8t(e,t,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<t;++a)for(var o=0;o<4;++o){var s=4*(t*n+a)+o,l=4*(t*i+a)+o,u=e[s];e[s]=e[l],e[l]=u}}function S8t(e,t,r){for(var n=0;n<r;++n)for(var i=0;i<t;++i){var a=4*(t*n+i),o=e[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)e[a+l]=Math.min(s*e[a+l],255)}}Sv.toImage=function(e){var t=this;e||(e="png"),t.staticMode&&t.container.appendChild(YE),t.glplot.redraw();var r=t.glplot.gl,n=r.drawingBufferWidth,i=r.drawingBufferHeight;r.bindFramebuffer(r.FRAMEBUFFER,null);var a=new Uint8Array(n*i*4);r.readPixels(0,0,n,i,r.RGBA,r.UNSIGNED_BYTE,a),A8t(a,n,i),S8t(a,n,i);var o=document.createElement("canvas");o.width=n,o.height=i;var s=o.getContext("2d",{willReadFrequently:!0}),l=s.createImageData(n,i);l.data.set(a),s.putImageData(l,0,0);var u;switch(e){case"jpeg":u=o.toDataURL("image/jpeg");break;case"webp":u=o.toDataURL("image/webp");break;default:u=o.toDataURL("image/png")}return t.staticMode&&t.container.removeChild(YE),u};Sv.setConvert=function(){for(var e=this,t=0;t<3;t++){var r=e.fullSceneLayout[KE[t]];nF.setConvert(r,e.fullLayout),r.setScale=up.noop}};Sv.make4thDimension=function(){var e=this,t=e.graphDiv,r=t._fullLayout;e._mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},nF.setConvert(e._mockAxis,r)};SIe.exports=AIe});var kIe=ye((cgr,EIe)=>{"use strict";EIe.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}});var PX=ye((fgr,CIe)=>{"use strict";var M8t=ka(),xs=Rd(),LX=Ao().extendFlat,E8t=mc().overrideAll;CIe.exports=E8t({visible:xs.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:M8t.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 0.5)"},showaxeslabels:{valType:"boolean",dflt:!0},color:xs.color,categoryorder:xs.categoryorder,categoryarray:xs.categoryarray,title:{text:xs.title.text,font:xs.title.font},type:LX({},xs.type,{values:["-","linear","log","date","category"]}),autotypenumbers:xs.autotypenumbers,autorange:xs.autorange,autorangeoptions:{minallowed:xs.autorangeoptions.minallowed,maxallowed:xs.autorangeoptions.maxallowed,clipmin:xs.autorangeoptions.clipmin,clipmax:xs.autorangeoptions.clipmax,include:xs.autorangeoptions.include,editType:"plot"},rangemode:xs.rangemode,minallowed:xs.minallowed,maxallowed:xs.maxallowed,range:LX({},xs.range,{items:[{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}},{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}}],anim:!1}),tickmode:xs.minor.tickmode,nticks:xs.nticks,tick0:xs.tick0,dtick:xs.dtick,tickvals:xs.tickvals,ticktext:xs.ticktext,ticks:xs.ticks,mirror:xs.mirror,ticklen:xs.ticklen,tickwidth:xs.tickwidth,tickcolor:xs.tickcolor,showticklabels:xs.showticklabels,labelalias:xs.labelalias,tickfont:xs.tickfont,tickangle:xs.tickangle,tickprefix:xs.tickprefix,showtickprefix:xs.showtickprefix,ticksuffix:xs.ticksuffix,showticksuffix:xs.showticksuffix,showexponent:xs.showexponent,exponentformat:xs.exponentformat,minexponent:xs.minexponent,separatethousands:xs.separatethousands,tickformat:xs.tickformat,tickformatstops:xs.tickformatstops,hoverformat:xs.hoverformat,showline:xs.showline,linecolor:xs.linecolor,linewidth:xs.linewidth,showgrid:xs.showgrid,gridcolor:LX({},xs.gridcolor,{dflt:"rgb(204, 204, 204)"}),gridwidth:xs.gridwidth,zeroline:xs.zeroline,zerolinecolor:xs.zerolinecolor,zerolinewidth:xs.zerolinewidth},"plot","from-root")});var FX=ye((hgr,LIe)=>{"use strict";var IX=PX(),k8t=Cc().attributes,RX=Ao().extendFlat,C8t=Dr().counterRegex;function DX(e,t,r){return{x:{valType:"number",dflt:e,editType:"camera"},y:{valType:"number",dflt:t,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}LIe.exports={_arrayAttrRegexps:[C8t("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:RX(DX(0,0,1),{}),center:RX(DX(0,0,0),{}),eye:RX(DX(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:k8t({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:IX,yaxis:IX,zaxis:IX,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot"}});var DIe=ye((dgr,RIe)=>{"use strict";var L8t=cd().mix,PIe=Dr(),P8t=vl(),I8t=PX(),R8t=SU(),D8t=i4(),IIe=["xaxis","yaxis","zaxis"],F8t=100*136/187;RIe.exports=function(t,r,n){var i,a;function o(u,c){return PIe.coerce(i,a,I8t,u,c)}for(var s=0;s<IIe.length;s++){var l=IIe[s];i=t[l]||{},a=P8t.newContainer(r,l),a._id=l[0]+n.scene,a._name=l,R8t(i,a,o,n),D8t(i,a,o,{font:n.font,letter:l[0],data:n.data,showGrid:!0,noAutotickangles:!0,noMinorloglabels:!0,noTicklabelindex:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelshift:!0,noTicklabelstandoff:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,noInsiderange:!0,noUnifiedhovertitle:!0,bgColor:n.bgColor,calendar:n.calendar},n.fullLayout),o("gridcolor",L8t(a.color,n.bgColor,F8t).toRgbString()),o("title.text",l[0]),a.setScale=PIe.noop,o("showspikes")&&(o("spikesides"),o("spikethickness"),o("spikecolor",a.color)),o("showaxeslabels"),o("showbackground")&&o("backgroundcolor")}}});var qIe=ye((vgr,OIe)=>{"use strict";var z8t=Dr(),O8t=ka(),q8t=qa(),B8t=k_(),N8t=DIe(),FIe=FX(),U8t=Id().getSubplotData,zIe="gl3d";OIe.exports=function(t,r,n){var i=r._basePlotModules.length>1;function a(o){if(!i){var s=z8t.validate(t[o],FIe[o]);if(s)return t[o]}}B8t(t,r,n,{type:zIe,attributes:FIe,handleDefaults:V8t,fullLayout:r,font:r.font,fullData:n,getDfltFromLayout:a,autotypenumbersDflt:r.autotypenumbers,paper_bgcolor:r.paper_bgcolor,calendar:r.calendar})};function V8t(e,t,r,n){for(var i=r("bgcolor"),a=O8t.combine(i,n.paper_bgcolor),o=["up","center","eye"],s=0;s<o.length;s++)r("camera."+o[s]+".x"),r("camera."+o[s]+".y"),r("camera."+o[s]+".z");r("camera.projection.type");var l=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),u=l?"manual":"auto",c=r("aspectmode",u);l||(e.aspectratio=t.aspectratio={x:1,y:1,z:1},c==="manual"&&(t.aspectmode="auto"),e.aspectmode=t.aspectmode);var f=U8t(n.fullData,zIe,n.id);N8t(e,t,{font:n.font,scene:n.id,data:f,bgColor:a,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),q8t.getComponentMethod("annotations3d","handleDefaults")(e,t,n);var h=n.getDfltFromLayout("dragmode");if(h!==!1&&!h)if(h="orbit",e.camera&&e.camera.up){var d=e.camera.up.x,v=e.camera.up.y,_=e.camera.up.z;_!==0&&(!d||!v||!_||_/Math.sqrt(d*d+v*v+_*_)>.999)&&(h="turntable")}else h="turntable";r("dragmode",h),r("hovermode",n.getDfltFromLayout("hovermode"))}});var $_=ye(cp=>{"use strict";var G8t=mc().overrideAll,H8t=B1(),j8t=MIe(),W8t=Id().getSubplotData,X8t=Dr(),Z8t=Wp(),GA="gl3d",zX="scene";cp.name=GA;cp.attr=zX;cp.idRoot=zX;cp.idRegex=cp.attrRegex=X8t.counterRegex("scene");cp.attributes=kIe();cp.layoutAttributes=FX();cp.baseLayoutAttrOverrides=G8t({hoverlabel:H8t.hoverlabel},"plot","nested");cp.supplyLayoutDefaults=qIe();cp.plot=function(t){for(var r=t._fullLayout,n=t._fullData,i=r._subplots[GA],a=0;a<i.length;a++){var o=i[a],s=W8t(n,GA,o),l=r[o],u=l.camera,c=l._scene;c||(c=new j8t({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},r),l._scene=c),c.viewInitial||(c.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),c.plot(s,r,t.layout)}};cp.clean=function(e,t,r,n){for(var i=n._subplots[GA]||[],a=0;a<i.length;a++){var o=i[a];!t[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}};cp.toSVG=function(e){for(var t=e._fullLayout,r=t._subplots[GA],n=t._size,i=0;i<r.length;i++){var a=t[r[i]],o=a.domain,s=a._scene,l=s.toImage("png"),u=t._glimages.append("svg:image");u.attr({xmlns:Z8t.svg,"xlink:href":l,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}};cp.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var r=t.slice(5);return r==="1"&&(r=""),zX+r}};cp.updateFx=function(e){for(var t=e._fullLayout,r=t._subplots[GA],n=0;n<r.length;n++){var i=t[r[n]]._scene;i.updateFx(t.dragmode,t.hovermode)}}});var NIe=ye((ggr,BIe)=>{"use strict";BIe.exports={plot:YPe(),attributes:EX(),markerSymbols:QD(),supplyDefaults:rIe(),colorbar:[{container:"marker",min:"cmin",max:"cmax"},{container:"line",min:"cmin",max:"cmax"}],calc:nIe(),moduleType:"trace",name:"scatter3d",basePlotModule:$_(),categories:["gl3d","symbols","showLegend","scatter-like"],meta:{}}});var VIe=ye((mgr,UIe)=>{"use strict";UIe.exports=NIe()});var JE=ye((ygr,jIe)=>{"use strict";var GIe=ka(),Y8t=Tu(),OX=df().axisHoverFormat,{hovertemplateAttrs:K8t,templatefallbackAttrs:J8t}=Ll(),HIe=Gl(),qX=Ao().extendFlat,$8t=mc().overrideAll;function BX(e){return{valType:"boolean",dflt:!1}}function NX(e){return{show:{valType:"boolean",dflt:!1},start:{valType:"number",dflt:null,editType:"plot"},end:{valType:"number",dflt:null,editType:"plot"},size:{valType:"number",dflt:null,min:0,editType:"plot"},project:{x:BX("x"),y:BX("y"),z:BX("z")},color:{valType:"color",dflt:GIe.defaultLine},usecolormap:{valType:"boolean",dflt:!1},width:{valType:"number",min:1,max:16,dflt:2},highlight:{valType:"boolean",dflt:!0},highlightcolor:{valType:"color",dflt:GIe.defaultLine},highlightwidth:{valType:"number",min:1,max:16,dflt:2}}}var UX=jIe.exports=$8t(qX({z:{valType:"data_array"},x:{valType:"data_array"},y:{valType:"data_array"},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:K8t(),hovertemplatefallback:J8t(),xhoverformat:OX("x"),yhoverformat:OX("y"),zhoverformat:OX("z"),connectgaps:{valType:"boolean",dflt:!1,editType:"calc"},surfacecolor:{valType:"data_array"}},Y8t("",{colorAttr:"z or surfacecolor",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:"calc"}),{contours:{x:NX("x"),y:NX("y"),z:NX("z")},hidesurface:{valType:"boolean",dflt:!1},lightposition:{x:{valType:"number",min:-1e5,max:1e5,dflt:10},y:{valType:"number",min:-1e5,max:1e5,dflt:1e4},z:{valType:"number",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:"number",min:0,max:1,dflt:.8},diffuse:{valType:"number",min:0,max:1,dflt:.8},specular:{valType:"number",min:0,max:2,dflt:.05,description:"Represents the level that incident rays are reflected in a single direction, causing shine."},roughness:{valType:"number",min:0,max:1,dflt:.5,description:"Alters specular reflection; the rougher the surface, the wider and less contrasty the shine."},fresnel:{valType:"number",min:0,max:5,dflt:.2}},opacity:{valType:"number",min:0,max:1,dflt:1},opacityscale:{valType:"any",editType:"calc"},hoverinfo:qX({},HIe.hoverinfo),showlegend:qX({},HIe.showlegend,{dflt:!1})}),"calc","nested");UX.x.editType=UX.y.editType=UX.z.editType="calc+clearAxisTypes"});var GX=ye((_gr,ZIe)=>{"use strict";var Q8t=qa(),WIe=Dr(),eRt=Qh(),tRt=JE(),VX=.1;function rRt(e,t){for(var r=[],n=32,i=0;i<n;i++){var a=i/(n-1),o=t+(1-t)*(1-Math.pow(Math.sin(e*a*Math.PI),2));r.push([a,Math.max(0,Math.min(1,o))])}return r}function iRt(e){var t=0;if(!Array.isArray(e)||e.length<2||!e[0]||!e[e.length-1]||+e[0][0]!=0||+e[e.length-1][0]!=1)return!1;for(var r=0;r<e.length;r++){var n=e[r];if(n.length!==2||+n[0]<t)return!1;t=+n[0]}return!0}function nRt(e,t,r,n){var i,a;function o(b,p){return WIe.coerce(e,t,tRt,b,p)}var s=o("x"),l=o("y"),u=o("z");if(!u||!u.length||s&&s.length<1||l&&l.length<1){t.visible=!1;return}t._xlength=Array.isArray(s)&&WIe.isArrayOrTypedArray(s[0])?u.length:u[0].length,t._ylength=u.length;var c=Q8t.getComponentMethod("calendars","handleTraceDefaults");c(e,t,["x","y","z"],n),o("text"),o("hovertext"),o("hovertemplate"),o("hovertemplatefallback"),o("xhoverformat"),o("yhoverformat"),o("zhoverformat"),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lightposition.x","lightposition.y","lightposition.z","hidesurface","connectgaps","opacity"].forEach(function(b){o(b)});var f=o("surfacecolor"),h=["x","y","z"];for(i=0;i<3;++i){var d="contours."+h[i],v=o(d+".show"),_=o(d+".highlight");if(v||_)for(a=0;a<3;++a)o(d+".project."+h[a]);v&&(o(d+".color"),o(d+".width"),o(d+".usecolormap")),_&&(o(d+".highlightcolor"),o(d+".highlightwidth")),o(d+".start"),o(d+".end"),o(d+".size")}eRt(e,t,n,o,{prefix:"",cLetter:"c"}),XIe(e,t,n,o),t._length=null}function XIe(e,t,r,n){var i=n("opacityscale");i==="max"?t.opacityscale=[[0,VX],[1,1]]:i==="min"?t.opacityscale=[[0,1],[1,VX]]:i==="extremes"?t.opacityscale=rRt(1,VX):iRt(i)||(t.opacityscale=void 0)}ZIe.exports={supplyDefaults:nRt,opacityscaleDefaults:XIe}});var JIe=ye((xgr,KIe)=>{"use strict";var YIe=gv();KIe.exports=function(t,r){r.surfacecolor?YIe(t,r,{vals:r.surfacecolor,containerStr:"",cLetter:"c"}):YIe(t,r,{vals:r.z,containerStr:"",cLetter:"c"})}});var i8e=ye((bgr,r8e)=>{"use strict";var aRt=Od().gl_surface3d,HA=Od().ndarray,oRt=Od().ndarray_linear_interpolate.d2,sRt=o8(),lRt=s8(),$E=Dr().isArrayOrTypedArray,uRt=$y().parseColorScale,$Ie=Jy(),cRt=tc().extractOpts;function e8e(e,t,r){this.scene=e,this.uid=r,this.surface=t,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var $g=e8e.prototype;$g.getXat=function(e,t,r,n){var i=$E(this.data.x)?$E(this.data.x[0])?this.data.x[t][e]:this.data.x[e]:e;return r===void 0?i:n.d2l(i,0,r)};$g.getYat=function(e,t,r,n){var i=$E(this.data.y)?$E(this.data.y[0])?this.data.y[t][e]:this.data.y[t]:t;return r===void 0?i:n.d2l(i,0,r)};$g.getZat=function(e,t,r,n){var i=this.data.z[t][e];return i===null&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[t][e]),r===void 0?i:n.d2l(i,0,r)};$g.handlePick=function(e){if(e.object===this.surface){var t=(e.data.index[0]-1)/this.dataScaleX-1,r=(e.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(t),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);e.index=[n,i],e.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],e.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=e.dataCoordinate[a];o!=null&&(e.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return $E(s)&&s[i]&&s[i][n]!==void 0?e.textLabel=s[i][n]:s?e.textLabel=s:e.textLabel="",e.data.dataCoordinate=e.dataCoordinate.slice(),this.surface.highlight(e.data),this.scene.glplot.spikes.position=e.dataCoordinate,!0}};function fRt(e){var t=e[0].rgb,r=e[e.length-1].rgb;return t[0]===r[0]&&t[1]===r[1]&&t[2]===r[2]&&t[3]===r[3]}var jA=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function hRt(e,t){if(e<t)return 0;for(var r=0;Math.floor(e%t)===0;)e/=t,r++;return r}function HX(e){for(var t=[],r=0;r<jA.length;r++){var n=jA[r];t.push(hRt(e,n))}return t}function dRt(e){for(var t=HX(e),r=e,n=0;n<jA.length;n++)if(t[n]>0){r=jA[n];break}return r}function vRt(e,t){if(!(e<1||t<1)){for(var r=HX(e),n=HX(t),i=1,a=0;a<jA.length;a++)i*=Math.pow(jA[a],Math.max(r[a],n[a]));return i}}function pRt(e){if(e.length!==0){for(var t=1,r=0;r<e.length;r++)t=vRt(t,e[r]);return t}}$g.calcXnums=function(e){var t,r=[];for(t=1;t<e;t++){var n=this.getXat(t-1,0),i=this.getXat(t,0);i!==n&&n!==void 0&&n!==null&&i!==void 0&&i!==null?r[t-1]=Math.abs(i-n):r[t-1]=0}var a=0;for(t=1;t<e;t++)a+=r[t-1];for(t=1;t<e;t++)r[t-1]===0?r[t-1]=1:r[t-1]=Math.round(a/r[t-1]);return r};$g.calcYnums=function(e){var t,r=[];for(t=1;t<e;t++){var n=this.getYat(0,t-1),i=this.getYat(0,t);i!==n&&n!==void 0&&n!==null&&i!==void 0&&i!==null?r[t-1]=Math.abs(i-n):r[t-1]=0}var a=0;for(t=1;t<e;t++)a+=r[t-1];for(t=1;t<e;t++)r[t-1]===0?r[t-1]=1:r[t-1]=Math.round(a/r[t-1]);return r};var t8e=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],QIe=t8e[9],aF=t8e[13];$g.estimateScale=function(e,t){for(var r=t===0?this.calcXnums(e):this.calcYnums(e),n=1+pRt(r);n<QIe;)n*=2;for(;n>aF;)n--,n/=dRt(n),n++,n<QIe&&(n=aF);var i=Math.round(n/e);return i>1?i:1};function gRt(e,t,r){var n=r[8]+r[2]*t[0]+r[5]*t[1];return e[0]=(r[6]+r[0]*t[0]+r[3]*t[1])/n,e[1]=(r[7]+r[1]*t[0]+r[4]*t[1])/n,e}function mRt(e,t,r){return yRt(e,t,gRt,r),e}function yRt(e,t,r,n){for(var i=[0,0],a=e.shape[0],o=e.shape[1],s=0;s<a;s++)for(var l=0;l<o;l++)r(i,[s,l],n),e.set(s,l,oRt(t,i[0],i[1]));return e}$g.refineCoords=function(e){for(var t=this.dataScaleX,r=this.dataScaleY,n=e[0].shape[0],i=e[0].shape[1],a=Math.floor(e[0].shape[0]*t+1)|0,o=Math.floor(e[0].shape[1]*r+1)|0,s=1+n+1,l=1+i+1,u=HA(new Float32Array(s*l),[s,l]),c=[1/t,0,0,0,1/r,0,0,0,1],f=0;f<e.length;++f){this.surface.padField(u,e[f]);var h=HA(new Float32Array(a*o),[a,o]);mRt(h,u,c),e[f]=h}};function _Rt(e,t){for(var r=!1,n=0;n<e.length;n++)if(t===e[n]){r=!0;break}r===!1&&e.push(t)}$g.setContourLevels=function(){var e=[[],[],[]],t=[!1,!1,!1],r=!1,n,i,a;for(n=0;n<3;++n)if(this.showContour[n]&&(r=!0,this.contourSize[n]>0&&this.contourStart[n]!==null&&this.contourEnd[n]!==null&&this.contourEnd[n]>this.contourStart[n]))for(t[n]=!0,i=this.contourStart[n];i<this.contourEnd[n];i+=this.contourSize[n])a=i*this.scene.dataScale[n],_Rt(e[n],a);if(r){var o=[[],[],[]];for(n=0;n<3;++n)this.showContour[n]&&(o[n]=t[n]?e[n]:this.scene.contourLevels[n]);this.surface.update({levels:o})}};$g.update=function(e){var t=this.scene,r=t.fullSceneLayout,n=this.surface,i=uRt(e),a=t.dataScale,o=e.z[0].length,s=e._ylength,l=t.contourLevels;this.data=e;var u,c,f,h,d=[];for(u=0;u<3;u++)for(d[u]=[],c=0;c<o;c++)d[u][c]=[];for(c=0;c<o;c++)for(f=0;f<s;f++)d[0][c][f]=this.getXat(c,f,e.xcalendar,r.xaxis),d[1][c][f]=this.getYat(c,f,e.ycalendar,r.yaxis),d[2][c][f]=this.getZat(c,f,e.zcalendar,r.zaxis);if(e.connectgaps)for(e._emptypoints=lRt(d[2]),sRt(d[2],e._emptypoints),e._interpolatedZ=[],c=0;c<o;c++)for(e._interpolatedZ[c]=[],f=0;f<s;f++)e._interpolatedZ[c][f]=d[2][c][f];for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)h=d[u][c][f],h==null?d[u][c][f]=NaN:h=d[u][c][f]*=a[u];for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)h=d[u][c][f],h!=null&&(this.minValues[u]>h&&(this.minValues[u]=h),this.maxValues[u]<h&&(this.maxValues[u]=h));for(u=0;u<3;u++)this.objectOffset[u]=.5*(this.minValues[u]+this.maxValues[u]);for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)h=d[u][c][f],h!=null&&(d[u][c][f]-=this.objectOffset[u]);var v=[HA(new Float32Array(o*s),[o,s]),HA(new Float32Array(o*s),[o,s]),HA(new Float32Array(o*s),[o,s])];for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)v[u].set(c,f,d[u][c][f]);d=[];var _={colormap:i,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!e.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:e.opacityscale,opacity:e.opacity},b=cRt(e);if(_.intensityBounds=[b.min,b.max],e.surfacecolor){var p=HA(new Float32Array(o*s),[o,s]);for(c=0;c<o;c++)for(f=0;f<s;f++)p.set(c,f,e.surfacecolor[f][c]);v.push(p)}else _.intensityBounds[0]*=a[2],_.intensityBounds[1]*=a[2];(aF<v[0].shape[0]||aF<v[0].shape[1])&&(this.refineData=!1),this.refineData===!0&&(this.dataScaleX=this.estimateScale(v[0].shape[0],0),this.dataScaleY=this.estimateScale(v[0].shape[1],1),(this.dataScaleX!==1||this.dataScaleY!==1)&&this.refineCoords(v)),e.surfacecolor&&(_.intensity=v.pop());var k=[!0,!0,!0],E=["x","y","z"];for(u=0;u<3;++u){var S=e.contours[E[u]];k[u]=S.highlight,_.showContour[u]=S.show||S.highlight,_.showContour[u]&&(_.contourProject[u]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[u]=!0,_.levels[u]=l[u],n.highlightColor[u]=_.contourColor[u]=$Ie(S.color),S.usecolormap?n.highlightTint[u]=_.contourTint[u]=0:n.highlightTint[u]=_.contourTint[u]=1,_.contourWidth[u]=S.width,this.contourStart[u]=S.start,this.contourEnd[u]=S.end,this.contourSize[u]=S.size):(this.showContour[u]=!1,this.contourStart[u]=null,this.contourEnd[u]=null,this.contourSize[u]=0),S.highlight&&(_.dynamicColor[u]=$Ie(S.highlightcolor),_.dynamicWidth[u]=S.highlightwidth))}fRt(i)&&(_.vertexColor=!0),_.objectOffset=this.objectOffset,_.coords=v,n.update(_),n.visible=e.visible,n.enableDynamic=k,n.enableHighlight=k,n.snapToData=!0,"lighting"in e&&(n.ambientLight=e.lighting.ambient,n.diffuseLight=e.lighting.diffuse,n.specularLight=e.lighting.specular,n.roughness=e.lighting.roughness,n.fresnel=e.lighting.fresnel),"lightposition"in e&&(n.lightPosition=[e.lightposition.x,e.lightposition.y,e.lightposition.z])};$g.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()};function xRt(e,t){var r=e.glplot.gl,n=aRt({gl:r}),i=new e8e(e,n,t.uid);return n._trace=i,i.update(t),e.glplot.add(n),i}r8e.exports=xRt});var a8e=ye((wgr,n8e)=>{"use strict";n8e.exports={attributes:JE(),supplyDefaults:GX().supplyDefaults,colorbar:{min:"cmin",max:"cmax"},calc:JIe(),plot:i8e(),moduleType:"trace",name:"surface",basePlotModule:$_(),categories:["gl3d","2dMap","showLegend"],meta:{}}});var s8e=ye((Tgr,o8e)=>{"use strict";o8e.exports=a8e()});var WA=ye((Agr,u8e)=>{"use strict";var bRt=Tu(),jX=df().axisHoverFormat,{hovertemplateAttrs:wRt,templatefallbackAttrs:TRt}=Ll(),Q_=JE(),l8e=Gl(),ex=Ao().extendFlat;u8e.exports=ex({x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},i:{valType:"data_array",editType:"calc"},j:{valType:"data_array",editType:"calc"},k:{valType:"data_array",editType:"calc"},text:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertemplate:wRt({editType:"calc"}),hovertemplatefallback:TRt({editType:"calc"}),xhoverformat:jX("x"),yhoverformat:jX("y"),zhoverformat:jX("z"),delaunayaxis:{valType:"enumerated",values:["x","y","z"],dflt:"z",editType:"calc"},alphahull:{valType:"number",dflt:-1,editType:"calc"},intensity:{valType:"data_array",editType:"calc"},intensitymode:{valType:"enumerated",values:["vertex","cell"],dflt:"vertex",editType:"calc"},color:{valType:"color",editType:"calc"},vertexcolor:{valType:"data_array",editType:"calc"},facecolor:{valType:"data_array",editType:"calc"}},bRt("",{colorAttr:"`intensity`",showScaleDflt:!0,editTypeOverride:"calc"}),{opacity:Q_.opacity,flatshading:{valType:"boolean",dflt:!1,editType:"calc"},contour:{show:ex({},Q_.contours.x.show,{}),color:Q_.contours.x.color,width:Q_.contours.x.width,editType:"calc"},lightposition:{x:ex({},Q_.lightposition.x,{dflt:1e5}),y:ex({},Q_.lightposition.y,{dflt:1e5}),z:ex({},Q_.lightposition.z,{dflt:0}),editType:"calc"},lighting:ex({vertexnormalsepsilon:{valType:"number",min:0,max:1,dflt:1e-12,editType:"calc",description:"Epsilon for vertex normals calculation avoids math issues arising from degenerate geometry."},facenormalsepsilon:{valType:"number",min:0,max:1,dflt:1e-6,editType:"calc",description:"Epsilon for face normals calculation avoids math issues arising from degenerate geometry."},editType:"calc"},Q_.lighting),hoverinfo:ex({},l8e.hoverinfo,{editType:"calc"}),showlegend:ex({},l8e.showlegend,{dflt:!1})})});var sF=ye((Sgr,f8e)=>{"use strict";var ARt=Tu(),oF=df().axisHoverFormat,{hovertemplateAttrs:SRt,templatefallbackAttrs:MRt}=Ll(),QE=WA(),c8e=Gl(),WX=Ao().extendFlat,ERt=mc().overrideAll;function XX(e){return{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}}}function ZX(e){return{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}}}var XA=f8e.exports=ERt(WX({x:{valType:"data_array"},y:{valType:"data_array"},z:{valType:"data_array"},value:{valType:"data_array"},isomin:{valType:"number"},isomax:{valType:"number"},surface:{show:{valType:"boolean",dflt:!0},count:{valType:"integer",dflt:2,min:1},fill:{valType:"number",min:0,max:1,dflt:1},pattern:{valType:"flaglist",flags:["A","B","C","D","E"],extras:["all","odd","even"],dflt:"all"}},spaceframe:{show:{valType:"boolean",dflt:!1},fill:{valType:"number",min:0,max:1,dflt:.15}},slices:{x:XX("x"),y:XX("y"),z:XX("z")},caps:{x:ZX("x"),y:ZX("y"),z:ZX("z")},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:SRt(),hovertemplatefallback:MRt(),xhoverformat:oF("x"),yhoverformat:oF("y"),zhoverformat:oF("z"),valuehoverformat:oF("value",1),showlegend:WX({},c8e.showlegend,{dflt:!1})},ARt("",{colorAttr:"`value`",showScaleDflt:!0,editTypeOverride:"calc"}),{opacity:QE.opacity,lightposition:QE.lightposition,lighting:QE.lighting,flatshading:QE.flatshading,contour:QE.contour,hoverinfo:WX({},c8e.hoverinfo)}),"calc","nested");XA.flatshading.dflt=!0;XA.lighting.facenormalsepsilon.dflt=0;XA.x.editType=XA.y.editType=XA.z.editType=XA.value.editType="calc+clearAxisTypes"});var YX=ye((Mgr,d8e)=>{"use strict";var kRt=Dr(),CRt=qa(),LRt=sF(),PRt=Qh();function IRt(e,t,r,n){function i(a,o){return kRt.coerce(e,t,LRt,a,o)}h8e(e,t,r,n,i)}function h8e(e,t,r,n,i){var a=i("isomin"),o=i("isomax");o!=null&&a!==void 0&&a!==null&&a>o&&(t.isomin=null,t.isomax=null);var s=i("x"),l=i("y"),u=i("z"),c=i("value");if(!s||!s.length||!l||!l.length||!u||!u.length||!c||!c.length){t.visible=!1;return}var f=CRt.getComponentMethod("calendars","handleTraceDefaults");f(e,t,["x","y","z"],n),i("valuehoverformat"),["x","y","z"].forEach(function(_){i(_+"hoverformat");var b="caps."+_,p=i(b+".show");p&&i(b+".fill");var k="slices."+_,E=i(k+".show");E&&(i(k+".fill"),i(k+".locations"))});var h=i("spaceframe.show");h&&i("spaceframe.fill");var d=i("surface.show");d&&(i("surface.count"),i("surface.fill"),i("surface.pattern"));var v=i("contour.show");v&&(i("contour.color"),i("contour.width")),["text","hovertext","hovertemplate","lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lighting.vertexnormalsepsilon","lighting.facenormalsepsilon","lightposition.x","lightposition.y","lightposition.z","flatshading","opacity"].forEach(function(_){i(_)}),PRt(e,t,n,i,{prefix:"",cLetter:"c"}),t._length=null}d8e.exports={supplyDefaults:IRt,supplyIsoDefaults:h8e}});var lF=ye((Egr,p8e)=>{"use strict";var JX=Dr(),RRt=gv();function DRt(e,t){t._len=Math.min(t.u.length,t.v.length,t.w.length,t.x.length,t.y.length,t.z.length),t._u=jm(t.u,t._len),t._v=jm(t.v,t._len),t._w=jm(t.w,t._len),t._x=jm(t.x,t._len),t._y=jm(t.y,t._len),t._z=jm(t.z,t._len);var r=v8e(t);t._gridFill=r.fill,t._Xs=r.Xs,t._Ys=r.Ys,t._Zs=r.Zs,t._len=r.len;var n=0,i,a,o;t.starts&&(i=jm(t.starts.x||[]),a=jm(t.starts.y||[]),o=jm(t.starts.z||[]),n=Math.min(i.length,a.length,o.length)),t._startsX=i||[],t._startsY=a||[],t._startsZ=o||[];var s=0,l=1/0,u;for(u=0;u<t._len;u++){var c=t._u[u],f=t._v[u],h=t._w[u],d=Math.sqrt(c*c+f*f+h*h);s=Math.max(s,d),l=Math.min(l,d)}for(RRt(e,t,{vals:[l,s],containerStr:"",cLetter:"c"}),u=0;u<n;u++){var v=i[u];r.xMax=Math.max(r.xMax,v),r.xMin=Math.min(r.xMin,v);var _=a[u];r.yMax=Math.max(r.yMax,_),r.yMin=Math.min(r.yMin,_);var b=o[u];r.zMax=Math.max(r.zMax,b),r.zMin=Math.min(r.zMin,b)}t._slen=n,t._normMax=s,t._xbnds=[r.xMin,r.xMax],t._ybnds=[r.yMin,r.yMax],t._zbnds=[r.zMin,r.zMax]}function v8e(e){var t=e._x,r=e._y,n=e._z,i=e._len,a,o,s,l=-1/0,u=1/0,c=-1/0,f=1/0,h=-1/0,d=1/0,v="",_,b,p,k,E,S,L,x,C;for(i&&(k=t[0],S=r[0],x=n[0]),i>1&&(E=t[i-1],L=r[i-1],C=n[i-1]),a=0;a<i;a++)l=Math.max(l,t[a]),u=Math.min(u,t[a]),c=Math.max(c,r[a]),f=Math.min(f,r[a]),h=Math.max(h,n[a]),d=Math.min(d,n[a]),!_&&t[a]!==k&&(_=!0,v+="x"),!b&&r[a]!==S&&(b=!0,v+="y"),!p&&n[a]!==x&&(p=!0,v+="z");_||(v+="x"),b||(v+="y"),p||(v+="z");var M=KX(e._x),g=KX(e._y),P=KX(e._z);v=v.replace("x",(k>E?"-":"+")+"x"),v=v.replace("y",(S>L?"-":"+")+"y"),v=v.replace("z",(x>C?"-":"+")+"z");var T=function(){i=0,M=[],g=[],P=[]};(!i||i<M.length*g.length*P.length)&&T();var z=function(Fe){return Fe==="x"?t:Fe==="y"?r:n},O=function(Fe){return Fe==="x"?M:Fe==="y"?g:P},V=function(Fe){return Fe[i-1]<Fe[0]?-1:1},G=z(v[1]),Z=z(v[3]),j=z(v[5]),N=O(v[1]).length,H=O(v[3]).length,te=O(v[5]).length,oe=!1,_e=function(Fe,Pe,ge){return 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a&&(te(Qt,"A")&&(Vr=Pe(null,[Vt,_t,It,er],ti,Br)||Vr),te(Qt,"B")&&(Vr=Pe(null,[_t,It,mt,Lr],ti,Br)||Vr),te(Qt,"C")&&(Vr=Pe(null,[_t,er,lr,Lr],ti,Br)||Vr),te(Qt,"D")&&(Vr=Pe(null,[It,er,Tr,Lr],ti,Br)||Vr),te(Qt,"E")&&(Vr=Pe(null,[_t,It,er,Lr],ti,Br)||Vr)),o&&(Vr=Pe(Qt,[_t,It,er,Lr],ti,Br)||Vr),Vr}function Re(Qt,Vt,_t,It,mt,er,lr,Tr){return[Tr[0]===!0?!0:Fe(Qt,Le([Vt,_t,It]),[Vt,_t,It],er,lr),Tr[1]===!0?!0:Fe(Qt,Le([It,mt,Vt]),[It,mt,Vt],er,lr)]}function ce(Qt,Vt,_t,It,mt,er,lr,Tr,Lr){return Tr?Re(Qt,Vt,_t,mt,It,er,lr,Lr):Re(Qt,_t,mt,It,Vt,er,lr,Lr)}function Ze(Qt,Vt,_t,It,mt,er,lr){var Tr=!1,Lr,ti,Br,Vr,dt=function(){Tr=Fe(Qt,[Lr,ti,Br],[-1,-1,-1],mt,er)||Tr,Tr=Fe(Qt,[Br,Vr,Lr],[-1,-1,-1],mt,er)||Tr},Ge=lr[0],Je=lr[1],je=lr[2];return Ge&&(Lr=Z(Le([p(Vt,_t-0,It-0)])[0],Le([p(Vt-1,_t-0,It-0)])[0],Ge),ti=Z(Le([p(Vt,_t-0,It-1)])[0],Le([p(Vt-1,_t-0,It-1)])[0],Ge),Br=Z(Le([p(Vt,_t-1,It-1)])[0],Le([p(Vt-1,_t-1,It-1)])[0],Ge),Vr=Z(Le([p(Vt,_t-1,It-0)])[0],Le([p(Vt-1,_t-1,It-0)])[0],Ge),dt()),Je&&(Lr=Z(Le([p(Vt-0,_t,It-0)])[0],Le([p(Vt-0,_t-1,It-0)])[0],Je),ti=Z(Le([p(Vt-0,_t,It-1)])[0],Le([p(Vt-0,_t-1,It-1)])[0],Je),Br=Z(Le([p(Vt-1,_t,It-1)])[0],Le([p(Vt-1,_t-1,It-1)])[0],Je),Vr=Z(Le([p(Vt-1,_t,It-0)])[0],Le([p(Vt-1,_t-1,It-0)])[0],Je),dt()),je&&(Lr=Z(Le([p(Vt-0,_t-0,It)])[0],Le([p(Vt-0,_t-0,It-1)])[0],je),ti=Z(Le([p(Vt-0,_t-1,It)])[0],Le([p(Vt-0,_t-1,It-1)])[0],je),Br=Z(Le([p(Vt-1,_t-1,It)])[0],Le([p(Vt-1,_t-1,It-1)])[0],je),Vr=Z(Le([p(Vt-1,_t-0,It)])[0],Le([p(Vt-1,_t-0,It-1)])[0],je),dt()),Tr}function ut(Qt,Vt,_t,It,mt,er,lr,Tr,Lr,ti,Br,Vr){var dt=Qt;return Vr?(a&&Qt==="even"&&(dt=null),ge(dt,Vt,_t,It,mt,er,lr,Tr,Lr,ti,Br)):(a&&Qt==="odd"&&(dt=null),ge(dt,Lr,Tr,lr,er,mt,It,_t,Vt,ti,Br))}function pt(Qt,Vt,_t,It,mt){for(var er=[],lr=0,Tr=0;Tr<Vt.length;Tr++)for(var Lr=Vt[Tr],ti=1;ti<_;ti++)for(var Br=1;Br<v;Br++)er.push(ce(Qt,p(Lr,Br-1,ti-1),p(Lr,Br-1,ti),p(Lr,Br,ti-1),p(Lr,Br,ti),_t,It,(Lr+Br+ti)%2,mt&&mt[lr]?mt[lr]:[])),lr++;return er}function Zt(Qt,Vt,_t,It,mt){for(var er=[],lr=0,Tr=0;Tr<Vt.length;Tr++)for(var Lr=Vt[Tr],ti=1;ti<d;ti++)for(var Br=1;Br<_;Br++)er.push(ce(Qt,p(ti-1,Lr,Br-1),p(ti,Lr,Br-1),p(ti-1,Lr,Br),p(ti,Lr,Br),_t,It,(ti+Lr+Br)%2,mt&&mt[lr]?mt[lr]:[])),lr++;return er}function st(Qt,Vt,_t,It,mt){for(var er=[],lr=0,Tr=0;Tr<Vt.length;Tr++)for(var Lr=Vt[Tr],ti=1;ti<v;ti++)for(var Br=1;Br<d;Br++)er.push(ce(Qt,p(Br-1,ti-1,Lr),p(Br-1,ti,Lr),p(Br,ti-1,Lr),p(Br,ti,Lr),_t,It,(Br+ti+Lr)%2,mt&&mt[lr]?mt[lr]:[])),lr++;return er}function lt(Qt,Vt,_t){for(var It=1;It<_;It++)for(var mt=1;mt<v;mt++)for(var er=1;er<d;er++)ut(Qt,p(er-1,mt-1,It-1),p(er-1,mt-1,It),p(er-1,mt,It-1),p(er-1,mt,It),p(er,mt-1,It-1),p(er,mt-1,It),p(er,mt,It-1),p(er,mt,It),Vt,_t,(er+mt+It)%2)}function Gt(Qt,Vt,_t){o=!0,lt(Qt,Vt,_t),o=!1}function Nt(Qt,Vt,_t){a=!0,lt(Qt,Vt,_t),a=!1}function Jt(Qt,Vt,_t,It,mt,er){for(var lr=[],Tr=0,Lr=0;Lr<Vt.length;Lr++)for(var ti=Vt[Lr],Br=1;Br<_;Br++)for(var Vr=1;Vr<v;Vr++)lr.push(Ze(Qt,ti,Vr,Br,_t,It,mt[Lr],er&&er[Tr]?er[Tr]:[])),Tr++;return lr}function sr(Qt,Vt,_t,It,mt,er){for(var lr=[],Tr=0,Lr=0;Lr<Vt.length;Lr++)for(var ti=Vt[Lr],Br=1;Br<d;Br++)for(var Vr=1;Vr<_;Vr++)lr.push(Ze(Qt,Br,ti,Vr,_t,It,mt[Lr],er&&er[Tr]?er[Tr]:[])),Tr++;return lr}function wr(Qt,Vt,_t,It,mt,er){for(var lr=[],Tr=0,Lr=0;Lr<Vt.length;Lr++)for(var ti=Vt[Lr],Br=1;Br<v;Br++)for(var Vr=1;Vr<d;Vr++)lr.push(Ze(Qt,Vr,Br,ti,_t,It,mt[Lr],er&&er[Tr]?er[Tr]:[])),Tr++;return lr}function cr(Qt,Vt){for(var _t=[],It=Qt;It<Vt;It++)_t.push(It);return _t}function $e(){for(var Qt=0;Qt<d;Qt++)for(var Vt=0;Vt<v;Vt++)for(var _t=0;_t<_;_t++){var It=p(Qt,Vt,_t);O(e._x[It],e._y[It],e._z[It],e._value[It])}}function St(){z(),$e();var Qt=null;if(r&&i&&(N(i),Gt(Qt,S,L)),t&&n){N(n);for(var Vt=e.surface.pattern,_t=e.surface.count,It=0;It<_t;It++){var mt=_t===1?.5:It/(_t-1),er=(1-mt)*S+mt*L,lr=Math.abs(er-k),Tr=Math.abs(er-E),Lr=lr>Tr?[k,er]:[er,E];Nt(Vt,Lr[0],Lr[1])}}var ti=[[Math.min(S,E),Math.max(S,E)],[Math.min(k,L),Math.max(k,L)]];["x","y","z"].forEach(function(Br){for(var Vr=[],dt=0;dt<ti.length;dt++){var Ge=0,Je=ti[dt][0],je=ti[dt][1],tt=e.slices[Br];if(tt.show&&tt.fill){N(tt.fill);var xt=[],Ie=[],xe=[];if(tt.locations.length)for(var ke=0;ke<tt.locations.length;ke++){var vt=ek(tt.locations[ke],Br==="x"?c:Br==="y"?f:h);vt.distRatio===0?xt.push(vt.id):vt.id>0&&(Ie.push(vt.id),Br==="x"?xe.push([vt.distRatio,0,0]):Br==="y"?xe.push([0,vt.distRatio,0]):xe.push([0,0,vt.distRatio]))}else Br==="x"?xt=cr(1,d-1):Br==="y"?xt=cr(1,v-1):xt=cr(1,_-1);Ie.length>0&&(Br==="x"?Vr[Ge]=Jt(Qt,Ie,Je,je,xe,Vr[Ge]):Br==="y"?Vr[Ge]=sr(Qt,Ie,Je,je,xe,Vr[Ge]):Vr[Ge]=wr(Qt,Ie,Je,je,xe,Vr[Ge]),Ge++),xt.length>0&&(Br==="x"?Vr[Ge]=pt(Qt,xt,Je,je,Vr[Ge]):Br==="y"?Vr[Ge]=Zt(Qt,xt,Je,je,Vr[Ge]):Vr[Ge]=st(Qt,xt,Je,je,Vr[Ge]),Ge++)}var ir=e.caps[Br];ir.show&&ir.fill&&(N(ir.fill),Br==="x"?Vr[Ge]=pt(Qt,[0,d-1],Je,je,Vr[Ge]):Br==="y"?Vr[Ge]=Zt(Qt,[0,v-1],Je,je,Vr[Ge]):Vr[Ge]=st(Qt,[0,_-1],Je,je,Vr[Ge]),Ge++)}}),s===0&&z(),e._meshX=x,e._meshY=C,e._meshZ=M,e._meshIntensity=g,e._Xs=c,e._Ys=f,e._Zs=h}return St(),e}function GRt(e,t){var r=e.glplot.gl,n=ORt({gl:r}),i=new _8e(e,n,t.uid);return n._trace=i,i.update(t),e.glplot.add(n),i}b8e.exports={findNearestOnAxis:ek,generateIsoMeshes:x8e,createIsosurfaceTrace:GRt}});var T8e=ye((Pgr,w8e)=>{"use 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HRt=Tu(),yh=sF(),jRt=JE(),M8e=Gl(),eZ=Ao().extendFlat,WRt=mc().overrideAll,fF=E8e.exports=WRt(eZ({x:yh.x,y:yh.y,z:yh.z,value:yh.value,isomin:yh.isomin,isomax:yh.isomax,surface:yh.surface,spaceframe:{show:{valType:"boolean",dflt:!1},fill:{valType:"number",min:0,max:1,dflt:1}},slices:yh.slices,caps:yh.caps,text:yh.text,hovertext:yh.hovertext,xhoverformat:yh.xhoverformat,yhoverformat:yh.yhoverformat,zhoverformat:yh.zhoverformat,valuehoverformat:yh.valuehoverformat,hovertemplate:yh.hovertemplate,hovertemplatefallback:yh.hovertemplatefallback},HRt("",{colorAttr:"`value`",showScaleDflt:!0,editTypeOverride:"calc"}),{colorbar:yh.colorbar,opacity:yh.opacity,opacityscale:jRt.opacityscale,lightposition:yh.lightposition,lighting:yh.lighting,flatshading:yh.flatshading,contour:yh.contour,hoverinfo:eZ({},M8e.hoverinfo),showlegend:eZ({},M8e.showlegend,{dflt:!1})}),"calc","nested");fF.x.editType=fF.y.editType=fF.z.editType=fF.value.editType="calc+clearAxisTypes"});var C8e=ye((Dgr,k8e)=>{"use 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i=L8e(n(r.xaxis,e._meshX,t.dataScale[0],e.xcalendar),n(r.yaxis,e._meshY,t.dataScale[1],e.ycalendar),n(r.zaxis,e._meshZ,t.dataScale[2],e.zcalendar)),a=L8e(e._meshI,e._meshJ,e._meshK),o={positions:i,cells:a,lightPosition:[e.lightposition.x,e.lightposition.y,e.lightposition.z],ambient:e.lighting.ambient,diffuse:e.lighting.diffuse,specular:e.lighting.specular,roughness:e.lighting.roughness,fresnel:e.lighting.fresnel,vertexNormalsEpsilon:e.lighting.vertexnormalsepsilon,faceNormalsEpsilon:e.lighting.facenormalsepsilon,opacity:e.opacity,opacityscale:e.opacityscale,contourEnable:e.contour.show,contourColor:eDt(e.contour.color).slice(0,3),contourWidth:e.contour.width,useFacetNormals:e.flatshading},s=tDt(e);o.vertexIntensity=e._meshIntensity,o.vertexIntensityBounds=[s.min,s.max],o.colormap=$Rt(e),this.mesh.update(o)};iZ.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};function iDt(e,t){var r=e.glplot.gl,n=JRt({gl:r}),i=new P8e(e,n,t.uid);return n._trace=i,i.update(t),e.glplot.add(n),i}I8e.exports=iDt});var F8e=ye((zgr,D8e)=>{"use strict";D8e.exports={attributes:tZ(),supplyDefaults:C8e(),calc:$X(),colorbar:{min:"cmin",max:"cmax"},plot:R8e(),moduleType:"trace",name:"volume",basePlotModule:$_(),categories:["gl3d","showLegend"],meta:{}}});var O8e=ye((Ogr,z8e)=>{"use strict";z8e.exports=F8e()});var N8e=ye((qgr,B8e)=>{"use strict";var nDt=qa(),q8e=Dr(),aDt=Qh(),oDt=WA();B8e.exports=function(t,r,n,i){function a(c,f){return q8e.coerce(t,r,oDt,c,f)}function o(c){var f=c.map(function(h){var d=a(h);return d&&q8e.isArrayOrTypedArray(d)?d:null});return f.every(function(h){return h&&h.length===f[0].length})&&f}var s=o(["x","y","z"]);if(!s){r.visible=!1;return}if(o(["i","j","k"]),r.i&&(!r.j||!r.k)||r.j&&(!r.k||!r.i)||r.k&&(!r.i||!r.j)){r.visible=!1;return}var l=nDt.getComponentMethod("calendars","handleTraceDefaults");l(t,r,["x","y","z"],i),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lighting.vertexnormalsepsilon","lighting.facenormalsepsilon","lightposition.x","lightposition.y","lightposition.z","flatshading","alphahull","delaunayaxis","opacity"].forEach(function(c){a(c)});var u=a("contour.show");u&&(a("contour.color"),a("contour.width")),"intensity"in t?(a("intensity"),a("intensitymode"),aDt(t,r,i,a,{prefix:"",cLetter:"c"})):(r.showscale=!1,"facecolor"in t?a("facecolor"):"vertexcolor"in t?a("vertexcolor"):a("color",n)),a("text"),a("hovertext"),a("hovertemplate"),a("hovertemplatefallback"),a("xhoverformat"),a("yhoverformat"),a("zhoverformat"),r._length=null}});var V8e=ye((Bgr,U8e)=>{"use strict";var sDt=gv();U8e.exports=function(t,r){r.intensity&&sDt(t,r,{vals:r.intensity,containerStr:"",cLetter:"c"})}});var X8e=ye((Ngr,W8e)=>{"use strict";var lDt=Od().gl_mesh3d,uDt=Od().delaunay_triangulate,cDt=Od().alpha_shape,fDt=Od().convex_hull,hDt=$y().parseColorScale,dDt=Dr().isArrayOrTypedArray,sZ=Jy(),vDt=tc().extractOpts,G8e=ZA();function j8e(e,t,r){this.scene=e,this.uid=r,this.mesh=t,this.name="",this.color="#fff",this.data=null,this.showContour=!1}var lZ=j8e.prototype;lZ.handlePick=function(e){if(e.object===this.mesh){var t=e.index=e.data.index;e.data._cellCenter?e.traceCoordinate=e.data.dataCoordinate:e.traceCoordinate=[this.data.x[t],this.data.y[t],this.data.z[t]];var r=this.data.hovertext||this.data.text;return dDt(r)&&r[t]!==void 0?e.textLabel=r[t]:r&&(e.textLabel=r),!0}};function H8e(e){for(var t=[],r=e.length,n=0;n<r;n++)t[n]=sZ(e[n]);return t}function nZ(e,t,r,n){for(var i=[],a=t.length,o=0;o<a;o++)i[o]=e.d2l(t[o],0,n)*r;return i}function aZ(e){for(var t=[],r=e.length,n=0;n<r;n++)t[n]=Math.round(e[n]);return t}function pDt(e,t){for(var r=["x","y","z"].indexOf(e),n=[],i=t.length,a=0;a<i;a++)n[a]=[t[a][(r+1)%3],t[a][(r+2)%3]];return uDt(n)}function oZ(e,t){for(var r=e.length,n=0;n<r;n++)if(e[n]<=-.5||e[n]>=t-.5)return!1;return!0}lZ.update=function(e){var t=this.scene,r=t.fullSceneLayout;this.data=e;var n=e.x.length,i=G8e(nZ(r.xaxis,e.x,t.dataScale[0],e.xcalendar),nZ(r.yaxis,e.y,t.dataScale[1],e.ycalendar),nZ(r.zaxis,e.z,t.dataScale[2],e.zcalendar)),a;if(e.i&&e.j&&e.k){if(e.i.length!==e.j.length||e.j.length!==e.k.length||!oZ(e.i,n)||!oZ(e.j,n)||!oZ(e.k,n))return;a=G8e(aZ(e.i),aZ(e.j),aZ(e.k))}else e.alphahull===0?a=fDt(i):e.alphahull>0?a=cDt(e.alphahull,i):a=pDt(e.delaunayaxis,i);var o={positions:i,cells:a,lightPosition:[e.lightposition.x,e.lightposition.y,e.lightposition.z],ambient:e.lighting.ambient,diffuse:e.lighting.diffuse,specular:e.lighting.specular,roughness:e.lighting.roughness,fresnel:e.lighting.fresnel,vertexNormalsEpsilon:e.lighting.vertexnormalsepsilon,faceNormalsEpsilon:e.lighting.facenormalsepsilon,opacity:e.opacity,contourEnable:e.contour.show,contourColor:sZ(e.contour.color).slice(0,3),contourWidth:e.contour.width,useFacetNormals:e.flatshading};if(e.intensity){var s=vDt(e);this.color="#fff";var l=e.intensitymode;o[l+"Intensity"]=e.intensity,o[l+"IntensityBounds"]=[s.min,s.max],o.colormap=hDt(e)}else e.vertexcolor?(this.color=e.vertexcolor[0],o.vertexColors=H8e(e.vertexcolor)):e.facecolor?(this.color=e.facecolor[0],o.cellColors=H8e(e.facecolor)):(this.color=e.color,o.meshColor=sZ(e.color));this.mesh.update(o)};lZ.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};function gDt(e,t){var r=e.glplot.gl,n=lDt({gl:r}),i=new j8e(e,n,t.uid);return n._trace=i,i.update(t),e.glplot.add(n),i}W8e.exports=gDt});var Y8e=ye((Ugr,Z8e)=>{"use strict";Z8e.exports={attributes:WA(),supplyDefaults:N8e(),calc:V8e(),colorbar:{min:"cmin",max:"cmax"},plot:X8e(),moduleType:"trace",name:"mesh3d",basePlotModule:$_(),categories:["gl3d","showLegend"],meta:{}}});var J8e=ye((Vgr,K8e)=>{"use strict";K8e.exports=Y8e()});var cZ=ye((Ggr,Q8e)=>{"use strict";var mDt=Tu(),YA=df().axisHoverFormat,{hovertemplateAttrs:yDt,templatefallbackAttrs:_Dt}=Ll(),xDt=WA(),$8e=Gl(),uZ=Ao().extendFlat,hF={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},sizemode:{valType:"enumerated",values:["scaled","absolute","raw"],editType:"calc",dflt:"scaled"},sizeref:{valType:"number",editType:"calc",min:0},anchor:{valType:"enumerated",editType:"calc",values:["tip","tail","cm","center"],dflt:"cm"},text:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertemplate:yDt({editType:"calc"},{keys:["norm"]}),hovertemplatefallback:_Dt({editType:"calc"}),uhoverformat:YA("u",1),vhoverformat:YA("v",1),whoverformat:YA("w",1),xhoverformat:YA("x"),yhoverformat:YA("y"),zhoverformat:YA("z"),showlegend:uZ({},$8e.showlegend,{dflt:!1})};uZ(hF,mDt("",{colorAttr:"u/v/w 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De=(1+S)*_(be),Be=0,et=1/0;Be<10&&n(et)>p;Be++)be-=et=(be+_(be)-De)/(1+o(be));return De=G(2+E),[ve*(1+o(be))/De,2*be/De]}wt.invert=function(ve,be){var De=1+S,Be=G(De/2);return[ve*2*Be/(1+o(be*=Be)),O((be+_(be))/De)]};function rr(){return t.geoProjection(wt).scale(173.044)}var nr=3+2*C;function Er(ve,be){var De=_(ve/=2),Be=o(ve),et=G(o(be)),We=o(be/=2),it=_(be)/(We+C*Be*et),Ft=G(2/(1+it*it)),Ht=G((C*We+(Be+De)*et)/(C*We+(Be-De)*et));return[nr*(Ft*(Ht-1/Ht)-2*u(Ht)),nr*(Ft*it*(Ht+1/Ht)-2*i(it))]}Er.invert=function(ve,be){if(!(We=Se.invert(ve/1.2,be*1.065)))return null;var De=We[0],Be=We[1],et=20,We;ve/=nr,be/=nr;do{var it=De/2,Ft=Be/2,Ht=_(it),tr=o(it),dr=_(Ft),Sr=o(Ft),Or=o(Be),Wr=G(Or),ni=dr/(Sr+C*tr*Wr),Pi=ni*ni,cn=G(2/(1+Pi)),ln=C*Sr+(tr+Ht)*Wr,Cn=C*Sr+(tr-Ht)*Wr,Kn=ln/Cn,Ta=G(Kn),fa=Ta-1/Ta,$a=Ta+1/Ta,Co=cn*fa-2*u(Ta)-ve,Qa=cn*ni*$a-2*i(ni)-be,mo=dr&&x*Wr*Ht*Pi/dr,Bo=(C*tr*Sr+Wr)/(2*(Sr+C*tr*Wr)*(Sr+C*tr*Wr)*Wr),Ps=-.5*ni*cn*cn*cn,Ts=Ps*mo,wo=Ps*Bo,To=(To=2*Sr+C*Wr*(tr-Ht))*To*Ta,hl=(C*tr*Sr*Wr+Or)/To,Ul=-(C*Ht*dr)/(Wr*To),Lu=fa*Ts-2*hl/Ta+cn*(hl+hl/Kn),au=fa*wo-2*Ul/Ta+cn*(Ul+Ul/Kn),Js=ni*$a*Ts-2*mo/(1+Pi)+cn*$a*mo+cn*ni*(hl-hl/Kn),Ql=ni*$a*wo-2*Bo/(1+Pi)+cn*$a*Bo+cn*ni*(Ul-Ul/Kn),dc=au*Js-Ql*Lu;if(!dc)break;var Tl=(Qa*au-Co*Ql)/dc,Al=(Co*Js-Qa*Lu)/dc;De-=Tl,Be=c(-S,f(S,Be-Al))}while((n(Tl)>p||n(Al)>p)&&--et>0);return n(n(Be)-S)<p?[0,Be]:et&&[De,Be]};function Xr(){return t.geoProjection(Er).scale(62.5271)}var ri=o(35*T);function Qr(ve,be){var De=b(be/2);return[ve*ri*G(1-De*De),(1+ri)*De]}Qr.invert=function(ve,be){var De=be/(1+ri);return[ve&&ve/(ri*G(1-De*De)),2*i(De)]};function Oi(){return t.geoProjection(Qr).scale(137.152)}function $i(ve,be){var De=be/2,Be=o(De);return[2*ve/M*o(be)*Be*Be,M*b(De)]}$i.invert=function(ve,be){var De=i(be/M),Be=o(De),et=2*De;return[ve*M/2/(o(et)*Be*Be),et]};function tn(){return t.geoProjection($i).scale(135.264)}function fn(ve){var be=1-ve,De=We(E,0)[0]-We(-E,0)[0],Be=We(0,S)[1]-We(0,-S)[1],et=G(2*Be/De);function We(Ht,tr){var dr=o(tr),Sr=_(tr);return[dr/(be+ve*dr)*Ht,be*tr+ve*Sr]}function it(Ht,tr){var dr=We(Ht,tr);return[dr[0]*et,dr[1]/et]}function Ft(Ht){return it(0,Ht)[1]}return it.invert=function(Ht,tr){var dr=Zt(Ft,tr),Sr=Ht/et*(ve+be/o(dr));return[Sr,dr]},it}function yn(){var ve=.5,be=t.geoProjectionMutator(fn),De=be(ve);return De.alpha=function(Be){return arguments.length?be(ve=+Be):ve},De.scale(168.725)}function Sn(ve){return[ve[0]/2,O(b(ve[1]/2*T))*P]}function Ba(ve){return[ve[0]*2,2*i(_(ve[1]*T))*P]}function ua(ve){ve==null&&(ve=t.geoOrthographic);var be=ve(),De=t.geoEquirectangular().scale(P).precision(0).clipAngle(null).translate([0,0]);function Be(We){return be(Sn(We))}be.invert&&(Be.invert=function(We){return Ba(be.invert(We))}),Be.stream=function(We){var it=be.stream(We),Ft=De.stream({point:function(Ht,tr){it.point(Ht/2,O(b(-tr/2*T))*P)},lineStart:function(){it.lineStart()},lineEnd:function(){it.lineEnd()},polygonStart:function(){it.polygonStart()},polygonEnd:function(){it.polygonEnd()}});return Ft.sphere=it.sphere,Ft};function et(We){Be[We]=function(){return arguments.length?(be[We].apply(be,arguments),Be):be[We]()}}return Be.rotate=function(We){return arguments.length?(De.rotate(We),Be):De.rotate()},Be.center=function(We){return arguments.length?(be.center(Sn(We)),Be):Ba(be.center())},et("angle"),et("clipAngle"),et("clipExtent"),et("fitExtent"),et("fitHeight"),et("fitSize"),et("fitWidth"),et("scale"),et("translate"),et("precision"),Be.scale(249.5)}function ma(ve,be){var De=2*E/be,Be=ve*ve;function et(We,it){var Ft=t.geoAzimuthalEquidistantRaw(We,it),Ht=Ft[0],tr=Ft[1],dr=Ht*Ht+tr*tr;if(dr>Be){var Sr=G(dr),Or=a(tr,Ht),Wr=De*d(Or/De),ni=Or-Wr,Pi=ve*o(ni),cn=(ve*_(ni)-ni*_(Pi))/(S-Pi),ln=Wa(ni,cn),Cn=(E-ve)/Fa(ln,Pi,E);Ht=Sr;var Kn=50,Ta;do Ht-=Ta=(ve+Fa(ln,Pi,Ht)*Cn-Sr)/(ln(Ht)*Cn);while(n(Ta)>p&&--Kn>0);tr=ni*_(Ht),Ht<S&&(tr-=cn*(Ht-S));var fa=_(Wr),$a=o(Wr);Ft[0]=Ht*$a-tr*fa,Ft[1]=Ht*fa+tr*$a}return Ft}return et.invert=function(We,it){var Ft=We*We+it*it;if(Ft>Be){var Ht=G(Ft),tr=a(it,We),dr=De*d(tr/De),Sr=tr-dr;We=Ht*o(Sr),it=Ht*_(Sr);for(var Or=We-S,Wr=_(We),ni=it/Wr,Pi=We<S?1/0:0,cn=10;;){var ln=ve*_(ni),Cn=ve*o(ni),Kn=_(Cn),Ta=S-Cn,fa=(ln-ni*Kn)/Ta,$a=Wa(ni,fa);if(n(Pi)<k||!--cn)break;ni-=Pi=(ni*Wr-fa*Or-it)/(Wr-Or*2*(Ta*(Cn+ni*ln*o(Cn)-Kn)-ln*(ln-ni*Kn))/(Ta*Ta))}Ht=ve+Fa($a,Cn,We)*(E-ve)/Fa($a,Cn,E),tr=dr+ni,We=Ht*o(tr),it=Ht*_(tr)}return t.geoAzimuthalEquidistantRaw.invert(We,it)},et}function Wa(ve,be){return function(De){var Be=ve*o(De);return De<S&&(Be-=be),G(1+Be*Be)}}function Fa(ve,be,De){for(var Be=50,et=(De-be)/Be,We=ve(be)+ve(De),it=1,Ft=be;it<Be;++it)We+=2*ve(Ft+=et);return We*.5*et}function Wo(){var ve=6,be=30*T,De=o(be),Be=_(be),et=t.geoProjectionMutator(ma),We=et(be,ve),it=We.stream,Ft=.01,Ht=-o(Ft*T),tr=_(Ft*T);return We.radius=function(dr){return arguments.length?(De=o(be=dr*T),Be=_(be),et(be,ve)):be*P},We.lobes=function(dr){return arguments.length?et(be,ve=+dr):ve},We.stream=function(dr){var Sr=We.rotate(),Or=it(dr),Wr=(We.rotate([0,0]),it(dr));return We.rotate(Sr),Or.sphere=function(){Wr.polygonStart(),Wr.lineStart();for(var ni=0,Pi=2*E/ve,cn=0;ni<ve;++ni,cn-=Pi)Wr.point(a(tr*o(cn),Ht)*P,O(tr*_(cn))*P),Wr.point(a(Be*o(cn-Pi/2),De)*P,O(Be*_(cn-Pi/2))*P);Wr.lineEnd(),Wr.polygonEnd()},Or},We.rotate([90,-40]).scale(91.7095).clipAngle(180-.001)}function da(ve,be,De,Be,et,We,it,Ft){arguments.length<8&&(Ft=0);function Ht(tr,dr){if(!dr)return[ve*tr/E,0];var Sr=dr*dr,Or=ve+Sr*(be+Sr*(De+Sr*Be)),Wr=dr*(et-1+Sr*(We-Ft+Sr*it)),ni=(Or*Or+Wr*Wr)/(2*Wr),Pi=tr*O(Or/ni)/E;return[ni*_(Pi),dr*(1+Sr*Ft)+ni*(1-o(Pi))]}return Ht.invert=function(tr,dr){var Sr=E*tr/ve,Or=dr,Wr,ni,Pi=50;do{var cn=Or*Or,ln=ve+cn*(be+cn*(De+cn*Be)),Cn=Or*(et-1+cn*(We-Ft+cn*it)),Kn=ln*ln+Cn*Cn,Ta=2*Cn,fa=Kn/Ta,$a=fa*fa,Co=O(ln/fa)/E,Qa=Sr*Co,mo=ln*ln,Bo=(2*be+cn*(4*De+cn*6*Be))*Or,Ps=et+cn*(3*We+cn*5*it),Ts=2*(ln*Bo+Cn*(Ps-1)),wo=2*(Ps-1),To=(Ts*Ta-Kn*wo)/(Ta*Ta),hl=o(Qa),Ul=_(Qa),Lu=fa*hl,au=fa*Ul,Js=Sr/E*(1/G(1-mo/$a))*(Bo*fa-ln*To)/$a,Ql=au-tr,dc=Or*(1+cn*Ft)+fa-Lu-dr,Tl=To*Ul+Lu*Js,Al=Lu*Co,X=1+To-(To*hl-au*Js),se=au*Co,Te=Tl*se-X*Al;if(!Te)break;Sr-=Wr=(dc*Tl-Ql*X)/Te,Or-=ni=(Ql*se-dc*Al)/Te}while((n(Wr)>p||n(ni)>p)&&--Pi>0);return[Sr,Or]},Ht}var Wn=da(2.8284,-1.6988,.75432,-.18071,1.76003,-.38914,.042555);function Ha(){return t.geoProjection(Wn).scale(149.995)}var vo=da(2.583819,-.835827,.170354,-.038094,1.543313,-.411435,.082742);function jn(){return t.geoProjection(vo).scale(153.93)}var Mt=da(5/6*E,-.62636,-.0344,0,1.3493,-.05524,0,.045);function kr(){return t.geoProjection(Mt).scale(130.945)}function Jr(ve,be){var De=ve*ve,Be=be*be;return[ve*(1-.162388*Be)*(.87-952426e-9*De*De),be*(1+Be/12)]}Jr.invert=function(ve,be){var De=ve,Be=be,et=50,We;do{var it=Be*Be;Be-=We=(Be*(1+it/12)-be)/(1+it/4)}while(n(We)>p&&--et>0);et=50,ve/=1-.162388*it;do{var Ft=(Ft=De*De)*Ft;De-=We=(De*(.87-952426e-9*Ft)-ve)/(.87-.00476213*Ft)}while(n(We)>p&&--et>0);return[De,Be]};function vi(){return t.geoProjection(Jr).scale(131.747)}var hn=da(2.6516,-.76534,.19123,-.047094,1.36289,-.13965,.031762);function An(){return t.geoProjection(hn).scale(131.087)}function Mn(ve){var be=ve(S,0)[0]-ve(-S,0)[0];function De(Be,et){var We=Be>0?-.5:.5,it=ve(Be+We*E,et);return it[0]-=We*be,it}return ve.invert&&(De.invert=function(Be,et){var We=Be>0?-.5:.5,it=ve.invert(Be+We*be,et),Ft=it[0]-We*E;return Ft<-E?Ft+=2*E:Ft>E&&(Ft-=2*E),it[0]=Ft,it}),De}function Li(ve,be){var De=v(ve),Be=v(be),et=o(be),We=o(ve)*et,it=_(ve)*et,Ft=_(Be*be);ve=n(a(it,Ft)),be=O(We),n(ve-S)>p&&(ve%=S);var Ht=_n(ve>E/4?S-ve:ve,be);return ve>E/4&&(Ft=Ht[0],Ht[0]=-Ht[1],Ht[1]=-Ft),Ht[0]*=De,Ht[1]*=-Be,Ht}Li.invert=function(ve,be){n(ve)>1&&(ve=v(ve)*2-ve),n(be)>1&&(be=v(be)*2-be);var De=v(ve),Be=v(be),et=-De*ve,We=-Be*be,it=We/et<1,Ft=ya(it?We:et,it?et:We),Ht=Ft[0],tr=Ft[1],dr=o(tr);return it&&(Ht=-S-Ht),[De*(a(_(Ht)*dr,-_(tr))+E),Be*O(o(Ht)*dr)]};function _n(ve,be){if(be===S)return[0,0];var De=_(be),Be=De*De,et=Be*Be,We=1+et,it=1+3*et,Ft=1-et,Ht=O(1/G(We)),tr=Ft+Be*We*Ht,dr=(1-De)/tr,Sr=G(dr),Or=dr*We,Wr=G(Or),ni=Sr*Ft,Pi,cn;if(ve===0)return[0,-(ni+Be*Wr)];var ln=o(be),Cn=1/ln,Kn=2*De*ln,Ta=(-3*Be+Ht*it)*Kn,fa=(-tr*ln-(1-De)*Ta)/(tr*tr),$a=.5*fa/Sr,Co=Ft*$a-2*Be*Sr*Kn,Qa=Be*We*fa+dr*it*Kn,mo=-Cn*Kn,Bo=-Cn*Qa,Ps=-2*Cn*Co,Ts=4*ve/E,wo;if(ve>.222*E||be<E/4&&ve>.175*E){if(Pi=(ni+Be*G(Or*(1+et)-ni*ni))/(1+et),ve>E/4)return[Pi,Pi];var To=Pi,hl=.5*Pi;Pi=.5*(hl+To),cn=50;do{var Ul=G(Or-Pi*Pi),Lu=Pi*(Ps+mo*Ul)+Bo*O(Pi/Wr)-Ts;if(!Lu)break;Lu<0?hl=Pi:To=Pi,Pi=.5*(hl+To)}while(n(To-hl)>p&&--cn>0)}else{Pi=p,cn=25;do{var au=Pi*Pi,Js=G(Or-au),Ql=Ps+mo*Js,dc=Pi*Ql+Bo*O(Pi/Wr)-Ts,Tl=Ql+(Bo-mo*au)/Js;Pi-=wo=Js?dc/Tl:0}while(n(wo)>p&&--cn>0)}return[Pi,-ni-Be*G(Or-Pi*Pi)]}function ya(ve,be){for(var De=0,Be=1,et=.5,We=50;;){var it=et*et,Ft=G(et),Ht=O(1/G(1+it)),tr=1-it+et*(1+it)*Ht,dr=(1-Ft)/tr,Sr=G(dr),Or=dr*(1+it),Wr=Sr*(1-it),ni=Or-ve*ve,Pi=G(ni),cn=be+Wr+et*Pi;if(n(Be-De)<k||--We===0||cn===0)break;cn>0?De=et:Be=et,et=.5*(De+Be)}if(!We)return null;var ln=O(Ft),Cn=o(ln),Kn=1/Cn,Ta=2*Ft*Cn,fa=(-3*et+Ht*(1+3*it))*Ta,$a=(-tr*Cn-(1-Ft)*fa)/(tr*tr),Co=.5*$a/Sr,Qa=(1-it)*Co-2*et*Sr*Ta,mo=-2*Kn*Qa,Bo=-Kn*Ta,Ps=-Kn*(et*(1+it)*$a+dr*(1+3*it)*Ta);return[E/4*(ve*(mo+Bo*Pi)+Ps*O(ve/G(Or))),ln]}function Jn(){return t.geoProjection(Mn(Li)).scale(239.75)}function Ma(ve,be,De){var Be,et,We;return ve?(Be=_o(ve,De),be?(et=_o(be,1-De),We=et[1]*et[1]+De*Be[0]*Be[0]*et[0]*et[0],[[Be[0]*et[2]/We,Be[1]*Be[2]*et[0]*et[1]/We],[Be[1]*et[1]/We,-Be[0]*Be[2]*et[0]*et[2]/We],[Be[2]*et[1]*et[2]/We,-De*Be[0]*Be[1]*et[0]/We]]):[[Be[0],0],[Be[1],0],[Be[2],0]]):(et=_o(be,1-De),[[0,et[0]/et[1]],[1/et[1],0],[et[2]/et[1],0]])}function _o(ve,be){var De,Be,et,We,it;if(be<p)return We=_(ve),Be=o(ve),De=be*(ve-We*Be)/4,[We-De*Be,Be+De*We,1-be*We*We/2,ve-De];if(be>=1-p)return De=(1-be)/4,Be=N(ve),We=Z(ve),et=1/Be,it=Be*j(ve),[We+De*(it-ve)/(Be*Be),et-De*We*et*(it-ve),et+De*We*et*(it+ve),2*i(s(ve))-S+De*(it-ve)/Be];var Ft=[1,0,0,0,0,0,0,0,0],Ht=[G(be),0,0,0,0,0,0,0,0],tr=0;for(Be=G(1-be),it=1;n(Ht[tr]/Ft[tr])>p&&tr<8;)De=Ft[tr++],Ht[tr]=(De-Be)/2,Ft[tr]=(De+Be)/2,Be=G(De*Be),it*=2;et=it*Ft[tr]*ve;do We=Ht[tr]*_(Be=et)/Ft[tr],et=(O(We)+et)/2;while(--tr);return[_(et),We=o(et),We/o(et-Be),et]}function No(ve,be,De){var Be=n(ve),et=n(be),We=j(et);if(Be){var it=1/_(Be),Ft=1/(b(Be)*b(Be)),Ht=-(Ft+De*(We*We*it*it)-1+De),tr=(De-1)*Ft,dr=(-Ht+G(Ht*Ht-4*tr))/2;return[po(i(1/G(dr)),De)*v(ve),po(i(G((dr/Ft-1)/De)),1-De)*v(be)]}return[0,po(i(We),1-De)*v(be)]}function po(ve,be){if(!be)return ve;if(be===1)return u(b(ve/2+L));for(var De=1,Be=G(1-be),et=G(be),We=0;n(et)>p;We++){if(ve%E){var it=i(Be*b(ve)/De);it<0&&(it+=E),ve+=it+~~(ve/E)*E}else ve+=ve;et=(De+Be)/2,Be=G(De*Be),et=((De=et)-Be)/2}return ve/(h(2,We)*De)}function Lo(ve,be){var De=(C-1)/(C+1),Be=G(1-De*De),et=po(S,Be*Be),We=-1,it=u(b(E/4+n(be)/2)),Ft=s(We*it)/G(De),Ht=ko(Ft*o(We*ve),Ft*_(We*ve)),tr=No(Ht[0],Ht[1],Be*Be);return[-tr[1],(be>=0?1:-1)*(.5*et-tr[0])]}function ko(ve,be){var De=ve*ve,Be=be+1,et=1-De-be*be;return[.5*((ve>=0?S:-S)-a(et,2*ve)),-.25*u(et*et+4*De)+.5*u(Be*Be+De)]}function Ds(ve,be){var De=be[0]*be[0]+be[1]*be[1];return[(ve[0]*be[0]+ve[1]*be[1])/De,(ve[1]*be[0]-ve[0]*be[1])/De]}Lo.invert=function(ve,be){var De=(C-1)/(C+1),Be=G(1-De*De),et=po(S,Be*Be),We=-1,it=Ma(.5*et-be,-ve,Be*Be),Ft=Ds(it[0],it[1]),Ht=a(Ft[1],Ft[0])/We;return[Ht,2*i(s(.5/We*u(De*Ft[0]*Ft[0]+De*Ft[1]*Ft[1])))-S]};function Fs(){return t.geoProjection(Mn(Lo)).scale(151.496)}function ll(ve){var be=_(ve),De=o(ve),Be=ul(ve);Be.invert=ul(-ve);function et(We,it){var Ft=Be(We,it);We=Ft[0],it=Ft[1];var Ht=_(it),tr=o(it),dr=o(We),Sr=V(be*Ht+De*tr*dr),Or=_(Sr),Wr=n(Or)>p?Sr/Or:1;return[Wr*De*_(We),(n(We)>S?Wr:-Wr)*(be*tr-De*Ht*dr)]}return et.invert=function(We,it){var Ft=G(We*We+it*it),Ht=-_(Ft),tr=o(Ft),dr=Ft*tr,Sr=-it*Ht,Or=Ft*be,Wr=G(dr*dr+Sr*Sr-Or*Or),ni=a(dr*Or+Sr*Wr,Sr*Or-dr*Wr),Pi=(Ft>S?-1:1)*a(We*Ht,Ft*o(ni)*tr+it*_(ni)*Ht);return Be.invert(Pi,ni)},et}function ul(ve){var be=_(ve),De=o(ve);return function(Be,et){var We=o(et),it=o(Be)*We,Ft=_(Be)*We,Ht=_(et);return[a(Ft,it*De-Ht*be),O(Ht*De+it*be)]}}function zl(){var ve=0,be=t.geoProjectionMutator(ll),De=be(ve),Be=De.rotate,et=De.stream,We=t.geoCircle();return De.parallel=function(it){if(!arguments.length)return ve*P;var Ft=De.rotate();return be(ve=it*T).rotate(Ft)},De.rotate=function(it){return arguments.length?(Be.call(De,[it[0],it[1]-ve*P]),We.center([-it[0],-it[1]]),De):(it=Be.call(De),it[1]+=ve*P,it)},De.stream=function(it){return it=et(it),it.sphere=function(){it.polygonStart();var Ft=.01,Ht=We.radius(90-Ft)().coordinates[0],tr=Ht.length-1,dr=-1,Sr;for(it.lineStart();++dr<tr;)it.point((Sr=Ht[dr])[0],Sr[1]);for(it.lineEnd(),Ht=We.radius(90+Ft)().coordinates[0],tr=Ht.length-1,it.lineStart();--dr>=0;)it.point((Sr=Ht[dr])[0],Sr[1]);it.lineEnd(),it.polygonEnd()},it},De.scale(79.4187).parallel(45).clipAngle(180-.001)}var us=3,il=O(1-1/us)*P,As=vr(0);function cl(ve){var be=il*T,De=xt(E,be)[0]-xt(-E,be)[0],Be=As(0,be)[1],et=xt(0,be)[1],We=M-et,it=g/ve,Ft=4/g,Ht=Be+We*We*4/g;function tr(dr,Sr){var Or,Wr=n(Sr);if(Wr>be){var ni=f(ve-1,c(0,l((dr+E)/it)));dr+=E*(ve-1)/ve-ni*it,Or=xt(dr,Wr),Or[0]=Or[0]*g/De-g*(ve-1)/(2*ve)+ni*g/ve,Or[1]=Be+(Or[1]-et)*4*We/g,Sr<0&&(Or[1]=-Or[1])}else Or=As(dr,Sr);return Or[0]*=Ft,Or[1]/=Ht,Or}return tr.invert=function(dr,Sr){dr/=Ft,Sr*=Ht;var Or=n(Sr);if(Or>Be){var Wr=f(ve-1,c(0,l((dr+E)/it)));dr=(dr+E*(ve-1)/ve-Wr*it)*De/g;var ni=xt.invert(dr,.25*(Or-Be)*g/We+et);return ni[0]-=E*(ve-1)/ve-Wr*it,Sr<0&&(ni[1]=-ni[1]),ni}return As.invert(dr,Sr)},tr}function Ks(ve,be){return[ve,be&1?90-p:il]}function zs(ve,be){return[ve,be&1?-90+p:-il]}function Io(ve){return[ve[0]*(1-p),ve[1]]}function ls(ve){var be=[].concat(r.range(-180,180+ve/2,ve).map(Ks),r.range(180,-180-ve/2,-ve).map(zs));return{type:"Polygon",coordinates:[ve===180?be.map(Io):be]}}function Zl(){var ve=4,be=t.geoProjectionMutator(cl),De=be(ve),Be=De.stream;return De.lobes=function(et){return arguments.length?be(ve=+et):ve},De.stream=function(et){var We=De.rotate(),it=Be(et),Ft=(De.rotate([0,0]),Be(et));return De.rotate(We),it.sphere=function(){t.geoStream(ls(180/ve),Ft)},it},De.scale(239.75)}function Su(ve){var be=1+ve,De=_(1/be),Be=O(De),et=2*G(E/(We=E+4*Be*be)),We,it=.5*et*(be+G(ve*(2+ve))),Ft=ve*ve,Ht=be*be;function tr(dr,Sr){var Or=1-_(Sr),Wr,ni;if(Or&&Or<2){var Pi=S-Sr,cn=25,ln;do{var Cn=_(Pi),Kn=o(Pi),Ta=Be+a(Cn,be-Kn),fa=1+Ht-2*be*Kn;Pi-=ln=(Pi-Ft*Be-be*Cn+fa*Ta-.5*Or*We)/(2*be*Cn*Ta)}while(n(ln)>k&&--cn>0);Wr=et*G(fa),ni=dr*Ta/E}else Wr=et*(ve+Or),ni=dr*Be/E;return[Wr*_(ni),it-Wr*o(ni)]}return tr.invert=function(dr,Sr){var Or=dr*dr+(Sr-=it)*Sr,Wr=(1+Ht-Or/(et*et))/(2*be),ni=V(Wr),Pi=_(ni),cn=Be+a(Pi,be-Wr);return[O(dr/G(Or))*E/cn,O(1-2*(ni-Ft*Be-be*Pi+(1+Ht-2*be*Wr)*cn)/We)]},tr}function nc(){var ve=1,be=t.geoProjectionMutator(Su),De=be(ve);return De.ratio=function(Be){return arguments.length?be(ve=+Be):ve},De.scale(167.774).center([0,18.67])}var bs=.7109889596207567,Rn=.0528035274542;function _a(ve,be){return be>-bs?(ve=sr(ve,be),ve[1]+=Rn,ve):_t(ve,be)}_a.invert=function(ve,be){return be>-bs?sr.invert(ve,be-Rn):_t.invert(ve,be)};function Vu(){return t.geoProjection(_a).rotate([-20,-55]).scale(164.263).center([0,-5.4036])}function Ol(ve,be){return n(be)>bs?(ve=sr(ve,be),ve[1]-=be>0?Rn:-Rn,ve):_t(ve,be)}Ol.invert=function(ve,be){return n(be)>bs?sr.invert(ve,be+(be>0?Rn:-Rn)):_t.invert(ve,be)};function xo(){return t.geoProjection(Ol).scale(152.63)}function Yl(ve,be,De,Be){var et=G(4*E/(2*De+(1+ve-be/2)*_(2*De)+(ve+be)/2*_(4*De)+be/2*_(6*De))),We=G(Be*_(De)*G((1+ve*o(2*De)+be*o(4*De))/(1+ve+be))),it=De*Ht(1);function Ft(Sr){return G(1+ve*o(2*Sr)+be*o(4*Sr))}function Ht(Sr){var Or=Sr*De;return(2*Or+(1+ve-be/2)*_(2*Or)+(ve+be)/2*_(4*Or)+be/2*_(6*Or))/De}function tr(Sr){return Ft(Sr)*_(Sr)}var dr=function(Sr,Or){var Wr=De*Zt(Ht,it*_(Or)/De,Or/E);isNaN(Wr)&&(Wr=De*v(Or));var ni=et*Ft(Wr);return[ni*We*Sr/E*o(Wr),ni/We*_(Wr)]};return dr.invert=function(Sr,Or){var Wr=Zt(tr,Or*We/et);return[Sr*E/(o(Wr)*et*We*Ft(Wr)),O(De*Ht(Wr/De)/it)]},De===0&&(et=G(Be/E),dr=function(Sr,Or){return[Sr*et,_(Or)/et]},dr.invert=function(Sr,Or){return[Sr/et,O(Or*et)]}),dr}function Ns(){var ve=1,be=0,De=45*T,Be=2,et=t.geoProjectionMutator(Yl),We=et(ve,be,De,Be);return We.a=function(it){return arguments.length?et(ve=+it,be,De,Be):ve},We.b=function(it){return arguments.length?et(ve,be=+it,De,Be):be},We.psiMax=function(it){return arguments.length?et(ve,be,De=+it*T,Be):De*P},We.ratio=function(it){return arguments.length?et(ve,be,De,Be=+it):Be},We.scale(180.739)}function Hl(ve,be,De,Be,et,We,it,Ft,Ht,tr,dr){if(dr.nanEncountered)return NaN;var Sr,Or,Wr,ni,Pi,cn,ln,Cn,Kn,Ta;if(Sr=De-be,Or=ve(be+Sr*.25),Wr=ve(De-Sr*.25),isNaN(Or)){dr.nanEncountered=!0;return}if(isNaN(Wr)){dr.nanEncountered=!0;return}return ni=Sr*(Be+4*Or+et)/12,Pi=Sr*(et+4*Wr+We)/12,cn=ni+Pi,Ta=(cn-it)/15,tr>Ht?(dr.maxDepthCount++,cn+Ta):Math.abs(Ta)<Ft?cn+Ta:(ln=be+Sr*.5,Cn=Hl(ve,be,ln,Be,Or,et,ni,Ft*.5,Ht,tr+1,dr),isNaN(Cn)?(dr.nanEncountered=!0,NaN):(Kn=Hl(ve,ln,De,et,Wr,We,Pi,Ft*.5,Ht,tr+1,dr),isNaN(Kn)?(dr.nanEncountered=!0,NaN):Cn+Kn))}function ac(ve,be,De,Be,et){var We={maxDepthCount:0,nanEncountered:!1};Be===void 0&&(Be=1e-8),et===void 0&&(et=20);var it=ve(be),Ft=ve(.5*(be+De)),Ht=ve(De),tr=(it+4*Ft+Ht)*(De-be)/6,dr=Hl(ve,be,De,it,Ft,Ht,tr,Be,et,1,We);return dr}function aa(ve,be,De){function Be(Wr){return ve+(1-ve)*h(1-h(Wr,be),1/be)}function et(Wr){return ac(Be,0,Wr,1e-4)}for(var We=1/et(1),it=1e3,Ft=(1+1e-8)*We,Ht=[],tr=0;tr<=it;tr++)Ht.push(et(tr/it)*Ft);function dr(Wr){var ni=0,Pi=it,cn=it>>1;do Ht[cn]>Wr?Pi=cn:ni=cn,cn=ni+Pi>>1;while(cn>ni);var ln=Ht[cn+1]-Ht[cn];return ln&&(ln=(Wr-Ht[cn+1])/ln),(cn+1+ln)/it}var Sr=2*dr(1)/E*We/De,Or=function(Wr,ni){var Pi=dr(n(_(ni))),cn=Be(Pi)*Wr;return Pi/=Sr,[cn,ni>=0?Pi:-Pi]};return Or.invert=function(Wr,ni){var Pi;return ni*=Sr,n(ni)<1&&(Pi=v(ni)*O(et(n(ni))*We)),[Wr/Be(n(ni)),Pi]},Or}function Oo(){var ve=0,be=2.5,De=1.183136,Be=t.geoProjectionMutator(aa),et=Be(ve,be,De);return et.alpha=function(We){return arguments.length?Be(ve=+We,be,De):ve},et.k=function(We){return arguments.length?Be(ve,be=+We,De):be},et.gamma=function(We){return arguments.length?Be(ve,be,De=+We):De},et.scale(152.63)}function qo(ve,be){return n(ve[0]-be[0])<p&&n(ve[1]-be[1])<p}function ql(ve,be){for(var De=-1,Be=ve.length,et=ve[0],We,it,Ft,Ht=[];++De<Be;){We=ve[De],it=(We[0]-et[0])/be,Ft=(We[1]-et[1])/be;for(var tr=0;tr<be;++tr)Ht.push([et[0]+tr*it,et[1]+tr*Ft]);et=We}return Ht.push(We),Ht}function Pc(ve){var be=[],De,Be,et,We,it,Ft,Ht,tr=ve[0].length;for(Ht=0;Ht<tr;++Ht)De=ve[0][Ht],Be=De[0][0],et=De[0][1],We=De[1][1],it=De[2][0],Ft=De[2][1],be.push(ql([[Be+p,et+p],[Be+p,We-p],[it-p,We-p],[it-p,Ft+p]],30));for(Ht=ve[1].length-1;Ht>=0;--Ht)De=ve[1][Ht],Be=De[0][0],et=De[0][1],We=De[1][1],it=De[2][0],Ft=De[2][1],be.push(ql([[it-p,Ft-p],[it-p,We+p],[Be+p,We+p],[Be+p,et-p]],30));return{type:"Polygon",coordinates:[r.merge(be)]}}function Do(ve,be,De){var Be,et;function We(Ht,tr){for(var dr=tr<0?-1:1,Sr=be[+(tr<0)],Or=0,Wr=Sr.length-1;Or<Wr&&Ht>Sr[Or][2][0];++Or);var ni=ve(Ht-Sr[Or][1][0],tr);return ni[0]+=ve(Sr[Or][1][0],dr*tr>dr*Sr[Or][0][1]?Sr[Or][0][1]:tr)[0],ni}De?We.invert=De(We):ve.invert&&(We.invert=function(Ht,tr){for(var dr=et[+(tr<0)],Sr=be[+(tr<0)],Or=0,Wr=dr.length;Or<Wr;++Or){var ni=dr[Or];if(ni[0][0]<=Ht&&Ht<ni[1][0]&&ni[0][1]<=tr&&tr<ni[1][1]){var Pi=ve.invert(Ht-ve(Sr[Or][1][0],0)[0],tr);return Pi[0]+=Sr[Or][1][0],qo(We(Pi[0],Pi[1]),[Ht,tr])?Pi:null}}});var it=t.geoProjection(We),Ft=it.stream;return it.stream=function(Ht){var tr=it.rotate(),dr=Ft(Ht),Sr=(it.rotate([0,0]),Ft(Ht));return it.rotate(tr),dr.sphere=function(){t.geoStream(Be,Sr)},dr},it.lobes=function(Ht){return arguments.length?(Be=Pc(Ht),be=Ht.map(function(tr){return tr.map(function(dr){return[[dr[0][0]*T,dr[0][1]*T],[dr[1][0]*T,dr[1][1]*T],[dr[2][0]*T,dr[2][1]*T]]})}),et=be.map(function(tr){return tr.map(function(dr){var Sr=ve(dr[0][0],dr[0][1])[0],Or=ve(dr[2][0],dr[2][1])[0],Wr=ve(dr[1][0],dr[0][1])[1],ni=ve(dr[1][0],dr[1][1])[1],Pi;return Wr>ni&&(Pi=Wr,Wr=ni,ni=Pi),[[Sr,Wr],[Or,ni]]})}),it):be.map(function(tr){return tr.map(function(dr){return[[dr[0][0]*P,dr[0][1]*P],[dr[1][0]*P,dr[1][1]*P],[dr[2][0]*P,dr[2][1]*P]]})})},be!=null&&it.lobes(be),it}var rf=[[[[-180,0],[-100,90],[-40,0]],[[-40,0],[30,90],[180,0]]],[[[-180,0],[-160,-90],[-100,0]],[[-100,0],[-60,-90],[-20,0]],[[-20,0],[20,-90],[80,0]],[[80,0],[140,-90],[180,0]]]];function Uf(){return Do(St,rf).scale(160.857)}var pl=[[[[-180,0],[-100,90],[-40,0]],[[-40,0],[30,90],[180,0]]],[[[-180,0],[-160,-90],[-100,0]],[[-100,0],[-60,-90],[-20,0]],[[-20,0],[20,-90],[80,0]],[[80,0],[140,-90],[180,0]]]];function Zc(){return Do(Ol,pl).scale(152.63)}var Kl=[[[[-180,0],[-100,90],[-40,0]],[[-40,0],[30,90],[180,0]]],[[[-180,0],[-160,-90],[-100,0]],[[-100,0],[-60,-90],[-20,0]],[[-20,0],[20,-90],[80,0]],[[80,0],[140,-90],[180,0]]]];function Os(){return Do(sr,Kl).scale(169.529)}var yu=[[[[-180,0],[-90,90],[0,0]],[[0,0],[90,90],[180,0]]],[[[-180,0],[-90,-90],[0,0]],[[0,0],[90,-90],[180,0]]]];function oc(){return Do(sr,yu).scale(169.529).rotate([20,0])}var Cf=[[[[-180,35],[-30,90],[0,35]],[[0,35],[30,90],[180,35]]],[[[-180,-10],[-102,-90],[-65,-10]],[[-65,-10],[5,-90],[77,-10]],[[77,-10],[103,-90],[180,-10]]]];function sc(){return Do(_a,Cf,st).rotate([-20,-55]).scale(164.263).center([0,-5.4036])}var Vh=[[[[-180,0],[-110,90],[-40,0]],[[-40,0],[0,90],[40,0]],[[40,0],[110,90],[180,0]]],[[[-180,0],[-110,-90],[-40,0]],[[-40,0],[0,-90],[40,0]],[[40,0],[110,-90],[180,0]]]];function Lf(){return Do(_t,Vh).scale(152.63).rotate([-20,0])}function cs(ve,be){return[3/g*ve*G(E*E/3-be*be),be]}cs.invert=function(ve,be){return[g/3*ve/G(E*E/3-be*be),be]};function nf(){return t.geoProjection(cs).scale(158.837)}function Vf(ve){function be(De,Be){if(n(n(Be)-S)<p)return[0,Be<0?-2:2];var et=_(Be),We=h((1+et)/(1-et),ve/2),it=.5*(We+1/We)+o(De*=ve);return[2*_(De)/it,(We-1/We)/it]}return be.invert=function(De,Be){var et=n(Be);if(n(et-2)<p)return De?null:[0,v(Be)*S];if(et>2)return null;De/=2,Be/=2;var We=De*De,it=Be*Be,Ft=2*Be/(1+We+it);return Ft=h((1+Ft)/(1-Ft),1/ve),[a(2*De,1-We-it)/ve,O((Ft-1)/(Ft+1))]},be}function Jl(){var ve=.5,be=t.geoProjectionMutator(Vf),De=be(ve);return De.spacing=function(Be){return arguments.length?be(ve=+Be):ve},De.scale(124.75)}var fl=E/C;function lc(ve,be){return[ve*(1+G(o(be)))/2,be/(o(be/2)*o(ve/6))]}lc.invert=function(ve,be){var De=n(ve),Be=n(be),et=p,We=S;Be<fl?We*=Be/fl:et+=6*V(fl/Be);for(var it=0;it<25;it++){var Ft=_(We),Ht=G(o(We)),tr=_(We/2),dr=o(We/2),Sr=_(et/6),Or=o(et/6),Wr=.5*et*(1+Ht)-De,ni=We/(dr*Or)-Be,Pi=Ht?-.25*et*Ft/Ht:0,cn=.5*(1+Ht),ln=(1+.5*We*tr/dr)/(dr*Or),Cn=We/dr*(Sr/6)/(Or*Or),Kn=Pi*Cn-ln*cn,Ta=(Wr*Cn-ni*cn)/Kn,fa=(ni*Pi-Wr*ln)/Kn;if(We-=Ta,et-=fa,n(Ta)<p&&n(fa)<p)break}return[ve<0?-et:et,be<0?-We:We]};function Fu(){return t.geoProjection(lc).scale(97.2672)}function Es(ve,be){var De=ve*ve,Be=be*be;return[ve*(.975534+Be*(-.119161+De*-.0143059+Be*-.0547009)),be*(1.00384+De*(.0802894+Be*-.02855+De*199025e-9)+Be*(.0998909+Be*-.0491032))]}Es.invert=function(ve,be){var De=v(ve)*E,Be=be/2,et=50;do{var We=De*De,it=Be*Be,Ft=De*Be,Ht=De*(.975534+it*(-.119161+We*-.0143059+it*-.0547009))-ve,tr=Be*(1.00384+We*(.0802894+it*-.02855+We*199025e-9)+it*(.0998909+it*-.0491032))-be,dr=.975534-it*(.119161+3*We*.0143059+it*.0547009),Sr=-Ft*(2*.119161+4*.0547009*it+2*.0143059*We),Or=Ft*(2*.0802894+4*199025e-9*We+2*-.02855*it),Wr=1.00384+We*(.0802894+199025e-9*We)+it*(3*(.0998909-.02855*We)-5*.0491032*it),ni=Sr*Or-Wr*dr,Pi=(tr*Sr-Ht*Wr)/ni,cn=(Ht*Or-tr*dr)/ni;De-=Pi,Be-=cn}while((n(Pi)>p||n(cn)>p)&&--et>0);return et&&[De,Be]};function Hs(){return t.geoProjection(Es).scale(139.98)}function Go(ve,be){return[_(ve)/o(be),b(be)*o(ve)]}Go.invert=function(ve,be){var De=ve*ve,Be=be*be,et=Be+1,We=De+et,it=ve?x*G((We-G(We*We-4*De))/De):1/G(et);return[O(ve*it),v(be)*V(it)]};function ps(){return t.geoProjection(Go).scale(144.049).clipAngle(90-.001)}function uc(ve){var be=o(ve),De=b(L+ve/2);function Be(et,We){var it=We-ve,Ft=n(it)<p?et*be:n(Ft=L+We/2)<p||n(n(Ft)-S)<p?0:et*it/u(b(Ft)/De);return[Ft,it]}return Be.invert=function(et,We){var it,Ft=We+ve;return[n(We)<p?et/be:n(it=L+Ft/2)<p||n(n(it)-S)<p?0:et*u(b(it)/De)/We,Ft]},Be}function xl(){return Vt(uc).parallel(40).scale(158.837)}function Gu(ve,be){return[ve,1.25*u(b(L+.4*be))]}Gu.invert=function(ve,be){return[ve,2.5*i(s(.8*be))-.625*E]};function qs(){return t.geoProjection(Gu).scale(108.318)}function ad(ve){var be=ve.length-1;function De(Be,et){for(var We=o(et),it=2/(1+We*o(Be)),Ft=it*We*_(Be),Ht=it*_(et),tr=be,dr=ve[tr],Sr=dr[0],Or=dr[1],Wr;--tr>=0;)dr=ve[tr],Sr=dr[0]+Ft*(Wr=Sr)-Ht*Or,Or=dr[1]+Ft*Or+Ht*Wr;return Sr=Ft*(Wr=Sr)-Ht*Or,Or=Ft*Or+Ht*Wr,[Sr,Or]}return De.invert=function(Be,et){var We=20,it=Be,Ft=et;do{for(var Ht=be,tr=ve[Ht],dr=tr[0],Sr=tr[1],Or=0,Wr=0,ni;--Ht>=0;)tr=ve[Ht],Or=dr+it*(ni=Or)-Ft*Wr,Wr=Sr+it*Wr+Ft*ni,dr=tr[0]+it*(ni=dr)-Ft*Sr,Sr=tr[1]+it*Sr+Ft*ni;Or=dr+it*(ni=Or)-Ft*Wr,Wr=Sr+it*Wr+Ft*ni,dr=it*(ni=dr)-Ft*Sr-Be,Sr=it*Sr+Ft*ni-et;var Pi=Or*Or+Wr*Wr,cn,ln;it-=cn=(dr*Or+Sr*Wr)/Pi,Ft-=ln=(Sr*Or-dr*Wr)/Pi}while(n(cn)+n(ln)>p*p&&--We>0);if(We){var Cn=G(it*it+Ft*Ft),Kn=2*i(Cn*.5),Ta=_(Kn);return[a(it*Ta,Cn*o(Kn)),Cn?O(Ft*Ta/Cn):0]}},De}var Po=[[.9972523,0],[.0052513,-.0041175],[.0074606,.0048125],[-.0153783,-.1968253],[.0636871,-.1408027],[.3660976,-.2937382]],od=[[.98879,0],[0,0],[-.050909,0],[0,0],[.075528,0]],Yo=[[.984299,0],[.0211642,.0037608],[-.1036018,-.0575102],[-.0329095,-.0320119],[.0499471,.1223335],[.026046,.0899805],[7388e-7,-.1435792],[.0075848,-.1334108],[-.0216473,.0776645],[-.0225161,.0853673]],Pa=[[.9245,0],[0,0],[.01943,0]],af=[[.721316,0],[0,0],[-.00881625,-.00617325]];function Hu(){return Bl(Po,[152,-64]).scale(1400).center([-160.908,62.4864]).clipAngle(30).angle(7.8)}function bl(){return Bl(od,[95,-38]).scale(1e3).clipAngle(55).center([-96.5563,38.8675])}function Gf(){return Bl(Yo,[120,-45]).scale(359.513).clipAngle(55).center([-117.474,53.0628])}function Ic(){return Bl(Pa,[-20,-18]).scale(209.091).center([20,16.7214]).clipAngle(82)}function yf(){return Bl(af,[165,10]).scale(250).clipAngle(130).center([-165,-10])}function Bl(ve,be){var De=t.geoProjection(ad(ve)).rotate(be).clipAngle(90),Be=t.geoRotation(be),et=De.center;return delete De.rotate,De.center=function(We){return arguments.length?et(Be(We)):Be.invert(et())},De}var wh=G(6),Qf=G(7);function _f(ve,be){var De=O(7*_(be)/(3*wh));return[wh*ve*(2*o(2*De/3)-1)/Qf,9*_(De/3)/Qf]}_f.invert=function(ve,be){var De=3*O(be*Qf/9);return[ve*Qf/(wh*(2*o(2*De/3)-1)),O(_(De)*3*wh/7)]};function Yc(){return t.geoProjection(_f).scale(164.859)}function eh(ve,be){for(var De=(1+x)*_(be),Be=be,et=0,We;et<25&&(Be-=We=(_(Be/2)+_(Be)-De)/(.5*o(Be/2)+o(Be)),!(n(We)<p));et++);return[ve*(1+2*o(Be)/o(Be/2))/(3*C),2*G(3)*_(Be/2)/G(2+C)]}eh.invert=function(ve,be){var De=be*G(2+C)/(2*G(3)),Be=2*O(De);return[3*C*ve/(1+2*o(Be)/o(Be/2)),O((De+_(Be))/(1+x))]};function th(){return t.geoProjection(eh).scale(188.209)}function ju(ve,be){for(var De=G(6/(4+E)),Be=(1+E/4)*_(be),et=be/2,We=0,it;We<25&&(et-=it=(et/2+_(et)-Be)/(.5+o(et)),!(n(it)<p));We++);return[De*(.5+o(et))*ve/1.5,De*et]}ju.invert=function(ve,be){var De=G(6/(4+E)),Be=be/De;return n(n(Be)-S)<p&&(Be=Be<0?-S:S),[1.5*ve/(De*(.5+o(Be))),O((Be/2+_(Be))/(1+E/4))]};function Hf(){return t.geoProjection(ju).scale(166.518)}function cc(ve,be){var De=be*be,Be=De*De,et=De*Be;return[ve*(.84719-.13063*De+et*et*(-.04515+.05494*De-.02326*Be+.00331*et)),be*(1.01183+Be*Be*(-.02625+.01926*De-.00396*Be))]}cc.invert=function(ve,be){var De=be,Be=25,et,We,it,Ft;do We=De*De,it=We*We,De-=et=(De*(1.01183+it*it*(-.02625+.01926*We-.00396*it))-be)/(1.01183+it*it*(9*-.02625+11*.01926*We+13*-.00396*it));while(n(et)>k&&--Be>0);return We=De*De,it=We*We,Ft=We*it,[ve/(.84719-.13063*We+Ft*Ft*(-.04515+.05494*We-.02326*it+.00331*Ft)),De]};function of(){return t.geoProjection(cc).scale(175.295)}function Nl(ve,be){return[ve*(1+o(be))/2,2*(be-b(be/2))]}Nl.invert=function(ve,be){for(var De=be/2,Be=0,et=1/0;Be<10&&n(et)>p;++Be){var We=o(be/2);be-=et=(be-b(be/2)-De)/(1-.5/(We*We))}return[2*ve/(1+o(be)),be]};function Kc(){return t.geoProjection(Nl).scale(152.63)}var Rc=[[[[-180,0],[-90,90],[0,0]],[[0,0],[90,90],[180,0]]],[[[-180,0],[-90,-90],[0,0]],[[0,0],[90,-90],[180,0]]]];function gs(){return Do(Ze(1/0),Rc).rotate([20,0]).scale(152.63)}function jf(ve,be){var De=_(be),Be=o(be),et=v(ve);if(ve===0||n(be)===S)return[0,be];if(be===0)return[ve,0];if(n(ve)===S)return[ve*Be,S*De];var We=E/(2*ve)-2*ve/E,it=2*be/E,Ft=(1-it*it)/(De-it),Ht=We*We,tr=Ft*Ft,dr=1+Ht/tr,Sr=1+tr/Ht,Or=(We*De/Ft-We/2)/dr,Wr=(tr*De/Ht+Ft/2)/Sr,ni=Or*Or+Be*Be/dr,Pi=Wr*Wr-(tr*De*De/Ht+Ft*De-1)/Sr;return[S*(Or+G(ni)*et),S*(Wr+G(Pi<0?0:Pi)*v(-be*We)*et)]}jf.invert=function(ve,be){ve/=S,be/=S;var De=ve*ve,Be=be*be,et=De+Be,We=E*E;return[ve?(et-1+G((1-et)*(1-et)+4*De))/(2*ve)*S:0,Zt(function(it){return et*(E*_(it)-2*it)*E+4*it*it*(be-_(it))+2*E*it-We*be},0)]};function Gh(){return t.geoProjection(jf).scale(127.267)}var rh=1.0148,sf=.23185,Th=-.14499,Mu=.02406,ih=rh,js=5*sf,Eu=7*Th,Dc=9*Mu,ks=1.790857183;function bc(ve,be){var De=be*be;return[ve,be*(rh+De*De*(sf+De*(Th+Mu*De)))]}bc.invert=function(ve,be){be>ks?be=ks:be<-ks&&(be=-ks);var De=be,Be;do{var et=De*De;De-=Be=(De*(rh+et*et*(sf+et*(Th+Mu*et)))-be)/(ih+et*et*(js+et*(Eu+Dc*et)))}while(n(Be)>p);return[ve,De]};function hu(){return t.geoProjection(bc).scale(139.319)}function _u(ve,be){if(n(be)<p)return[ve,0];var De=b(be),Be=ve*_(be);return[_(Be)/De,be+(1-o(Be))/De]}_u.invert=function(ve,be){if(n(be)<p)return[ve,0];var De=ve*ve+be*be,Be=be*.5,et=10,We;do{var it=b(Be),Ft=1/o(Be),Ht=De-2*be*Be+Be*Be;Be-=We=(it*Ht+2*(Be-be))/(2+Ht*Ft*Ft+2*(Be-be)*it)}while(n(We)>p&&--et>0);return it=b(Be),[(n(be)<n(Be+1/it)?O(ve*it):v(be)*v(ve)*(V(n(ve*it))+S))/_(Be),Be]};function nl(){return t.geoProjection(_u).scale(103.74)}function nh(ve,be){var De=Fc(ve[1],ve[0]),Be=Fc(be[1],be[0]),et=bd(De,Be),We=wc(De)/wc(Be);return zu([1,0,ve[0][0],0,1,ve[0][1]],zu([We,0,0,0,We,0],zu([o(et),_(et),0,-_(et),o(et),0],[1,0,-be[0][0],0,1,-be[0][1]])))}function Ah(ve){var be=1/(ve[0]*ve[4]-ve[1]*ve[3]);return[be*ve[4],-be*ve[1],be*(ve[1]*ve[5]-ve[2]*ve[4]),-be*ve[3],be*ve[0],be*(ve[2]*ve[3]-ve[0]*ve[5])]}function zu(ve,be){return[ve[0]*be[0]+ve[1]*be[3],ve[0]*be[1]+ve[1]*be[4],ve[0]*be[2]+ve[1]*be[5]+ve[2],ve[3]*be[0]+ve[4]*be[3],ve[3]*be[1]+ve[4]*be[4],ve[3]*be[2]+ve[4]*be[5]+ve[5]]}function Fc(ve,be){return[ve[0]-be[0],ve[1]-be[1]]}function wc(ve){return G(ve[0]*ve[0]+ve[1]*ve[1])}function bd(ve,be){return a(ve[0]*be[1]-ve[1]*be[0],ve[0]*be[0]+ve[1]*be[1])}function xf(ve,be,De){Be(ve,{transform:null});function Be(tr,dr){if(tr.edges=jl(tr.face),dr.face){var Sr=tr.shared=bf(tr.face,dr.face),Or=nh(Sr.map(dr.project),Sr.map(tr.project));tr.transform=dr.transform?zu(dr.transform,Or):Or;for(var Wr=dr.edges,ni=0,Pi=Wr.length;ni<Pi;++ni)Ou(Sr[0],Wr[ni][1])&&Ou(Sr[1],Wr[ni][0])&&(Wr[ni]=tr),Ou(Sr[0],Wr[ni][0])&&Ou(Sr[1],Wr[ni][1])&&(Wr[ni]=tr);for(Wr=tr.edges,ni=0,Pi=Wr.length;ni<Pi;++ni)Ou(Sr[0],Wr[ni][0])&&Ou(Sr[1],Wr[ni][1])&&(Wr[ni]=dr),Ou(Sr[0],Wr[ni][1])&&Ou(Sr[1],Wr[ni][0])&&(Wr[ni]=dr)}else tr.transform=dr.transform;return tr.children&&tr.children.forEach(function(cn){Be(cn,tr)}),tr}function et(tr,dr){var Sr=be(tr,dr),Or=Sr.project([tr*P,dr*P]),Wr;return(Wr=Sr.transform)?[Wr[0]*Or[0]+Wr[1]*Or[1]+Wr[2],-(Wr[3]*Or[0]+Wr[4]*Or[1]+Wr[5])]:(Or[1]=-Or[1],Or)}lf(ve)&&(et.invert=function(tr,dr){var Sr=We(ve,[tr,-dr]);return Sr&&(Sr[0]*=T,Sr[1]*=T,Sr)});function We(tr,dr){var Sr=tr.project.invert,Or=tr.transform,Wr=dr;if(Or&&(Or=Ah(Or),Wr=[Or[0]*Wr[0]+Or[1]*Wr[1]+Or[2],Or[3]*Wr[0]+Or[4]*Wr[1]+Or[5]]),Sr&&tr===it(ni=Sr(Wr)))return ni;for(var ni,Pi=tr.children,cn=0,ln=Pi&&Pi.length;cn<ln;++cn)if(ni=We(Pi[cn],dr))return ni}function it(tr){return be(tr[0]*T,tr[1]*T)}var Ft=t.geoProjection(et),Ht=Ft.stream;return Ft.stream=function(tr){var dr=Ft.rotate(),Sr=Ht(tr),Or=(Ft.rotate([0,0]),Ht(tr));return Ft.rotate(dr),Sr.sphere=function(){Or.polygonStart(),Or.lineStart(),Pf(Or,ve),Or.lineEnd(),Or.polygonEnd()},Sr},Ft.angle(De==null?-30:De*P)}function Pf(ve,be,De){var Be,et=be.edges,We=et.length,it,Ft={type:"MultiPoint",coordinates:be.face},Ht=be.face.filter(function(Pi){return n(Pi[1])!==90}),tr=t.geoBounds({type:"MultiPoint",coordinates:Ht}),dr=!1,Sr=-1,Or=tr[1][0]-tr[0][0],Wr=Or===180||Or===360?[(tr[0][0]+tr[1][0])/2,(tr[0][1]+tr[1][1])/2]:t.geoCentroid(Ft);if(De)for(;++Sr<We&&et[Sr]!==De;);++Sr;for(var ni=0;ni<We;++ni)it=et[(ni+Sr)%We],Array.isArray(it)?(dr||(ve.point((Be=t.geoInterpolate(it[0],Wr)(p))[0],Be[1]),dr=!0),ve.point((Be=t.geoInterpolate(it[1],Wr)(p))[0],Be[1])):(dr=!1,it!==De&&Pf(ve,it,be))}function Ou(ve,be){return ve&&be&&ve[0]===be[0]&&ve[1]===be[1]}function bf(ve,be){for(var De,Be,et=ve.length,We=null,it=0;it<et;++it){De=ve[it];for(var Ft=be.length;--Ft>=0;)if(Be=be[Ft],De[0]===Be[0]&&De[1]===Be[1]){if(We)return[We,De];We=De}}}function jl(ve){for(var be=ve.length,De=[],Be=ve[be-1],et=0;et<be;++et)De.push([Be,Be=ve[et]]);return De}function lf(ve){return ve.project.invert||ve.children&&ve.children.some(lf)}var Hh=[[0,90],[-90,0],[0,0],[90,0],[180,0],[0,-90]],If=[[0,2,1],[0,3,2],[5,1,2],[5,2,3],[0,1,4],[0,4,3],[5,4,1],[5,3,4]].map(function(ve){return ve.map(function(be){return Hh[be]})});function Cs(ve){ve=ve||function(De){var Be=t.geoCentroid({type:"MultiPoint",coordinates:De});return t.geoGnomonic().scale(1).translate([0,0]).rotate([-Be[0],-Be[1]])};var be=If.map(function(De){return{face:De,project:ve(De)}});return[-1,0,0,1,0,1,4,5].forEach(function(De,Be){var et=be[De];et&&(et.children||(et.children=[])).push(be[Be])}),xf(be[0],function(De,Be){return be[De<-E/2?Be<0?6:4:De<0?Be<0?2:0:De<E/2?Be<0?3:1:Be<0?7:5]}).angle(-30).scale(101.858).center([0,45])}var du=2/G(3);function ku(ve,be){var De=xt(ve,be);return[De[0]*du,De[1]]}ku.invert=function(ve,be){return xt.invert(ve/du,be)};function Wf(ve){ve=ve||function(De){var Be=t.geoCentroid({type:"MultiPoint",coordinates:De});return t.geoProjection(ku).translate([0,0]).scale(1).rotate(Be[1]>0?[-Be[0],0]:[180-Be[0],180])};var be=If.map(function(De){return{face:De,project:ve(De)}});return[-1,0,0,1,0,1,4,5].forEach(function(De,Be){var et=be[De];et&&(et.children||(et.children=[])).push(be[Be])}),xf(be[0],function(De,Be){return be[De<-E/2?Be<0?6:4:De<0?Be<0?2:0:De<E/2?Be<0?3:1:Be<0?7:5]}).angle(-30).scale(121.906).center([0,48.5904])}function Us(ve){ve=ve||function(it){var Ft=it.length===6?t.geoCentroid({type:"MultiPoint",coordinates:it}):it[0];return t.geoGnomonic().scale(1).translate([0,0]).rotate([-Ft[0],-Ft[1]])};var be=If.map(function(it){for(var Ft=it.map(Rf),Ht=Ft.length,tr=Ft[Ht-1],dr,Sr=[],Or=0;Or<Ht;++Or)dr=Ft[Or],Sr.push(Wu([tr[0]*.9486832980505138+dr[0]*.31622776601683794,tr[1]*.9486832980505138+dr[1]*.31622776601683794,tr[2]*.9486832980505138+dr[2]*.31622776601683794]),Wu([dr[0]*.9486832980505138+tr[0]*.31622776601683794,dr[1]*.9486832980505138+tr[1]*.31622776601683794,dr[2]*.9486832980505138+tr[2]*.31622776601683794])),tr=dr;return Sr}),De=[],Be=[-1,0,0,1,0,1,4,5];be.forEach(function(it,Ft){for(var Ht=If[Ft],tr=Ht.length,dr=De[Ft]=[],Sr=0;Sr<tr;++Sr)be.push([Ht[Sr],it[(Sr*2+2)%(2*tr)],it[(Sr*2+1)%(2*tr)]]),Be.push(Ft),dr.push(zc(Rf(it[(Sr*2+2)%(2*tr)]),Rf(it[(Sr*2+1)%(2*tr)])))});var et=be.map(function(it){return{project:ve(it),face:it}});Be.forEach(function(it,Ft){var Ht=et[it];Ht&&(Ht.children||(Ht.children=[])).push(et[Ft])});function We(it,Ft){var Ht=o(Ft),tr=[Ht*o(it),Ht*_(it),_(Ft)],dr=it<-E/2?Ft<0?6:4:it<0?Ft<0?2:0:it<E/2?Ft<0?3:1:Ft<0?7:5,Sr=De[dr];return et[wf(Sr[0],tr)<0?8+3*dr:wf(Sr[1],tr)<0?8+3*dr+1:wf(Sr[2],tr)<0?8+3*dr+2:dr]}return xf(et[0],We).angle(-30).scale(110.625).center([0,45])}function wf(ve,be){for(var De=0,Be=ve.length,et=0;De<Be;++De)et+=ve[De]*be[De];return et}function zc(ve,be){return[ve[1]*be[2]-ve[2]*be[1],ve[2]*be[0]-ve[0]*be[2],ve[0]*be[1]-ve[1]*be[0]]}function Wu(ve){return[a(ve[1],ve[0])*P,O(c(-1,f(1,ve[2])))*P]}function Rf(ve){var be=ve[0]*T,De=ve[1]*T,Be=o(De);return[Be*o(be),Be*_(be),_(De)]}function Xu(){}function uf(ve){if((De=ve.length)<4)return!1;for(var be=0,De,Be=ve[De-1][1]*ve[0][0]-ve[De-1][0]*ve[0][1];++be<De;)Be+=ve[be-1][1]*ve[be][0]-ve[be-1][0]*ve[be][1];return Be<=0}function Xf(ve,be){for(var De=be[0],Be=be[1],et=!1,We=0,it=ve.length,Ft=it-1;We<it;Ft=We++){var Ht=ve[We],tr=Ht[0],dr=Ht[1],Sr=ve[Ft],Or=Sr[0],Wr=Sr[1];dr>Be^Wr>Be&&De<(Or-tr)*(Be-dr)/(Wr-dr)+tr&&(et=!et)}return et}function Wl(ve,be){var De=be.stream,Be;if(!De)throw new Error("invalid projection");switch(ve&&ve.type){case"Feature":Be=Zu;break;case"FeatureCollection":Be=ah;break;default:Be=Tc;break}return Be(ve,De)}function ah(ve,be){return{type:"FeatureCollection",features:ve.features.map(function(De){return Zu(De,be)})}}function Zu(ve,be){return{type:"Feature",id:ve.id,properties:ve.properties,geometry:Tc(ve.geometry,be)}}function Oc(ve,be){return{type:"GeometryCollection",geometries:ve.geometries.map(function(De){return Tc(De,be)})}}function Tc(ve,be){if(!ve)return null;if(ve.type==="GeometryCollection")return Oc(ve,be);var De;switch(ve.type){case"Point":De=qc;break;case"MultiPoint":De=qc;break;case"LineString":De=cf;break;case"MultiLineString":De=cf;break;case"Polygon":De=fc;break;case"MultiPolygon":De=fc;break;case"Sphere":De=fc;break;default:return null}return t.geoStream(ve,be(De)),De.result()}var wl=[],vu=[],qc={point:function(ve,be){wl.push([ve,be])},result:function(){var ve=wl.length?wl.length<2?{type:"Point",coordinates:wl[0]}:{type:"MultiPoint",coordinates:wl}:null;return wl=[],ve}},cf={lineStart:Xu,point:function(ve,be){wl.push([ve,be])},lineEnd:function(){wl.length&&(vu.push(wl),wl=[])},result:function(){var ve=vu.length?vu.length<2?{type:"LineString",coordinates:vu[0]}:{type:"MultiLineString",coordinates:vu}:null;return vu=[],ve}},fc={polygonStart:Xu,lineStart:Xu,point:function(ve,be){wl.push([ve,be])},lineEnd:function(){var ve=wl.length;if(ve){do wl.push(wl[0].slice());while(++ve<4);vu.push(wl),wl=[]}},polygonEnd:Xu,result:function(){if(!vu.length)return null;var ve=[],be=[];return vu.forEach(function(De){uf(De)?ve.push([De]):be.push(De)}),be.forEach(function(De){var Be=De[0];ve.some(function(et){if(Xf(et[0],Be))return et.push(De),!0})||ve.push([De])}),vu=[],ve.length?ve.length>1?{type:"MultiPolygon",coordinates:ve}:{type:"Polygon",coordinates:ve[0]}:null}};function Bc(ve){var be=ve(S,0)[0]-ve(-S,0)[0];function De(Be,et){var We=n(Be)<S,it=ve(We?Be:Be>0?Be-E:Be+E,et),Ft=(it[0]-it[1])*x,Ht=(it[0]+it[1])*x;if(We)return[Ft,Ht];var tr=be*x,dr=Ft>0^Ht>0?-1:1;return[dr*Ft-v(Ht)*tr,dr*Ht-v(Ft)*tr]}return ve.invert&&(De.invert=function(Be,et){var We=(Be+et)*x,it=(et-Be)*x,Ft=n(We)<.5*be&&n(it)<.5*be;if(!Ft){var Ht=be*x,tr=We>0^it>0?-1:1,dr=-tr*Be+(it>0?1:-1)*Ht,Sr=-tr*et+(We>0?1:-1)*Ht;We=(-dr-Sr)*x,it=(dr-Sr)*x}var Or=ve.invert(We,it);return Ft||(Or[0]+=We>0?E:-E),Or}),t.geoProjection(De).rotate([-90,-90,45]).clipAngle(180-.001)}function At(){return Bc(Li).scale(176.423)}function Wt(){return Bc(Lo).scale(111.48)}function Cr(ve,be){if(!(0<=(be=+be)&&be<=20))throw new Error("invalid digits");function De(tr){var dr=tr.length,Sr=2,Or=new Array(dr);for(Or[0]=+tr[0].toFixed(be),Or[1]=+tr[1].toFixed(be);Sr<dr;)Or[Sr]=tr[Sr],++Sr;return Or}function Be(tr){return tr.map(De)}function et(tr){for(var dr=De(tr[0]),Sr=[dr],Or=1;Or<tr.length;Or++){var 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it=new Array(et);++Be<et;)ws(ve.coordinates[Be],it[Be]=[],De);Ho(De),be={type:"MultiPolygon",coordinates:it.filter(Ka)};break}default:return ve}return ve.bbox!=null&&(be.bbox=ve.bbox),be}function Ls(ve){if(ve==null)return ve;switch(ve.type){case"Feature":return ml(ve);case"FeatureCollection":{var be={type:"FeatureCollection",features:ve.features.map(ml)};return ve.bbox!=null&&(be.bbox=ve.bbox),be}default:return Ws(ve)}}function va(ve,be){var De=b(be/2),Be=_(L*De);return[ve*(.74482-.34588*Be*Be),1.70711*De]}va.invert=function(ve,be){var De=be/1.70711,Be=_(L*De);return[ve/(.74482-.34588*Be*Be),2*i(De)]};function no(){return t.geoProjection(va).scale(146.153)}function ys(ve,be,De){var Be=t.geoInterpolate(be,De),et=Be(.5),We=t.geoRotation([-et[0],-et[1]])(be),it=Be.distance/2,Ft=-O(_(We[1]*T)/_(it)),Ht=[-et[0],-et[1],-(We[0]>0?E-Ft:Ft)*P],tr=t.geoProjection(ve(it)).rotate(Ht),dr=t.geoRotation(Ht),Sr=tr.center;return delete tr.rotate,tr.center=function(Or){return arguments.length?Sr(dr(Or)):dr.invert(Sr())},tr.clipAngle(90)}function rs(ve){var be=o(ve);function De(Be,et){var We=t.geoGnomonicRaw(Be,et);return We[0]*=be,We}return De.invert=function(Be,et){return t.geoGnomonicRaw.invert(Be/be,et)},De}function $l(){return Cu([-158,21.5],[-77,39]).clipAngle(60).scale(400)}function Cu(ve,be){return ys(rs,ve,be)}function Yu(ve){if(!(ve*=2))return t.geoAzimuthalEquidistantRaw;var be=-ve/2,De=-be,Be=ve*ve,et=b(De),We=.5/_(De);function it(Ft,Ht){var tr=V(o(Ht)*o(Ft-be)),dr=V(o(Ht)*o(Ft-De)),Sr=Ht<0?-1:1;return tr*=tr,dr*=dr,[(tr-dr)/(2*ve),Sr*G(4*Be*dr-(Be-tr+dr)*(Be-tr+dr))/(2*ve)]}return it.invert=function(Ft,Ht){var tr=Ht*Ht,dr=o(G(tr+(Or=Ft+be)*Or)),Sr=o(G(tr+(Or=Ft+De)*Or)),Or,Wr;return[a(Wr=dr-Sr,Or=(dr+Sr)*et),(Ht<0?-1:1)*V(G(Or*Or+Wr*Wr)*We)]},it}function Nc(){return pu([-158,21.5],[-77,39]).clipAngle(130).scale(122.571)}function pu(ve,be){return ys(Yu,ve,be)}function Uc(ve,be){if(n(be)<p)return[ve,0];var De=n(be/S),Be=O(De);if(n(ve)<p||n(n(be)-S)<p)return[0,v(be)*E*b(Be/2)];var et=o(Be),We=n(E/ve-ve/E)/2,it=We*We,Ft=et/(De+et-1),Ht=Ft*(2/De-1),tr=Ht*Ht,dr=tr+it,Sr=Ft-tr,Or=it+Ft;return[v(ve)*E*(We*Sr+G(it*Sr*Sr-dr*(Ft*Ft-tr)))/dr,v(be)*E*(Ht*Or-We*G((it+1)*dr-Or*Or))/dr]}Uc.invert=function(ve,be){if(n(be)<p)return[ve,0];if(n(ve)<p)return[0,S*_(2*i(be/E))];var De=(ve/=E)*ve,Be=(be/=E)*be,et=De+Be,We=et*et,it=-n(be)*(1+et),Ft=it-2*Be+De,Ht=-2*it+1+2*Be+We,tr=Be/Ht+(2*Ft*Ft*Ft/(Ht*Ht*Ht)-9*it*Ft/(Ht*Ht))/27,dr=(it-Ft*Ft/(3*Ht))/Ht,Sr=2*G(-dr/3),Or=V(3*tr/(dr*Sr))/3;return[E*(et-1+G(1+2*(De-Be)+We))/(2*ve),v(be)*E*(-Sr*o(Or+E/3)-Ft/(3*Ht))]};function xu(){return t.geoProjection(Uc).scale(79.4183)}function Ac(ve,be){if(n(be)<p)return[ve,0];var De=n(be/S),Be=O(De);if(n(ve)<p||n(n(be)-S)<p)return[0,v(be)*E*b(Be/2)];var et=o(Be),We=n(E/ve-ve/E)/2,it=We*We,Ft=et*(G(1+it)-We*et)/(1+it*De*De);return[v(ve)*E*Ft,v(be)*E*G(1-Ft*(2*We+Ft))]}Ac.invert=function(ve,be){if(!ve)return[0,S*_(2*i(be/E))];var De=n(ve/E),Be=(1-De*De-(be/=E)*be)/(2*De),et=Be*Be,We=G(et+1);return[v(ve)*E*(We-Be),v(be)*S*_(2*a(G((1-2*Be*De)*(Be+We)-De),G(We+Be+De)))]};function Ua(){return t.geoProjection(Ac).scale(79.4183)}function oo(ve,be){if(n(be)<p)return[ve,0];var De=be/S,Be=O(De);if(n(ve)<p||n(n(be)-S)<p)return[0,E*b(Be/2)];var et=(E/ve-ve/E)/2,We=De/(1+o(Be));return[E*(v(ve)*G(et*et+1-We*We)-et),E*We]}oo.invert=function(ve,be){if(!be)return[ve,0];var De=be/E,Be=(E*E*(1-De*De)-ve*ve)/(2*E*ve);return[ve?E*(v(ve)*G(Be*Be+1)-Be):0,S*_(2*i(De))]};function Vc(){return t.geoProjection(oo).scale(79.4183)}function hc(ve,be){if(!be)return[ve,0];var De=n(be);if(!ve||De===S)return[0,be];var Be=De/S,et=Be*Be,We=(8*Be-et*(et+2)-5)/(2*et*(Be-1)),it=We*We,Ft=Be*We,Ht=et+it+2*Ft,tr=Be+3*We,dr=ve/S,Sr=dr+1/dr,Or=v(n(ve)-S)*G(Sr*Sr-4),Wr=Or*Or,ni=Ht*(et+it*Wr-1)+(1-et)*(et*(tr*tr+4*it)+12*Ft*it+4*it*it),Pi=(Or*(Ht+it-1)+2*G(ni))/(4*Ht+Wr);return[v(ve)*S*Pi,v(be)*S*G(1+Or*n(Pi)-Pi*Pi)]}hc.invert=function(ve,be){var De;if(!ve||!be)return[ve,be];be/=E;var Be=v(ve)*ve/S,et=(Be*Be-1+4*be*be)/n(Be),We=et*et,it=2*be,Ft=50;do{var Ht=it*it,tr=(8*it-Ht*(Ht+2)-5)/(2*Ht*(it-1)),dr=(3*it-Ht*it-10)/(2*Ht*it),Sr=tr*tr,Or=it*tr,Wr=it+tr,ni=Wr*Wr,Pi=it+3*tr,cn=ni*(Ht+Sr*We-1)+(1-Ht)*(Ht*(Pi*Pi+4*Sr)+Sr*(12*Or+4*Sr)),ln=-2*Wr*(4*Or*Sr+(1-4*Ht+3*Ht*Ht)*(1+dr)+Sr*(-6+14*Ht-We+(-8+8*Ht-2*We)*dr)+Or*(-8+12*Ht+(-10+10*Ht-We)*dr)),Cn=G(cn),Kn=et*(ni+Sr-1)+2*Cn-Be*(4*ni+We),Ta=et*(2*tr*dr+2*Wr*(1+dr))+ln/Cn-8*Wr*(et*(-1+Sr+ni)+2*Cn)*(1+dr)/(We+4*ni);it-=De=Kn/Ta}while(De>p&&--Ft>0);return[v(ve)*(G(et*et+4)+et)*E/4,S*it]};function Ku(){return t.geoProjection(hc).scale(127.16)}function 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r=this.graphDiv,n=t[this.id],i=t._size,a=n.domain,o=n.projection,s=n.lonaxis,l=n.lataxis,u=s._ax,c=l._ax,f=this.projection=Jzt(n),h=[[i.l+i.w*a.x[0],i.t+i.h*(1-a.y[1])],[i.l+i.w*a.x[1],i.t+i.h*(1-a.y[0])]],d=n.center||{},v=o.rotation||{},_=s.range||[],b=l.range||[];if(n.fitbounds){u._length=h[1][0]-h[0][0],c._length=h[1][1]-h[0][1],u.range=qDe(r,u),c.range=qDe(r,c);var p=(u.range[0]+u.range[1])/2,k=(c.range[0]+c.range[1])/2;if(n._isScoped)d={lon:p,lat:k};else if(n._isClipped){d={lon:p,lat:k},v={lon:p,lat:k,roll:v.roll};var E=o.type,S=fp.lonaxisSpan[E]/2||180,L=fp.lataxisSpan[E]/2||90;_=[p-S,p+S],b=[k-L,k+L]}else d={lon:p,lat:k},v={lon:p,lat:v.lat,roll:v.roll}}f.center([d.lon-v.lon,d.lat-v.lat]).rotate([-v.lon,-v.lat,v.roll]).parallels(o.parallels);var x=BDe(_,b);f.fitExtent(h,x);var C=this.bounds=f.getBounds(x),M=this.fitScale=f.scale(),g=f.translate();if(n.fitbounds){var 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e=this,t=e.graphDiv,r=t._fullLayout,n="clip"+r._uid+e.id;e.clipDef=r._clips.append("clipPath").attr("id",n),e.clipRect=e.clipDef.append("rect"),e.framework=t1.select(e.container).append("g").attr("class","geo "+e.id).call(ak.setClipUrl,n,t),e.project=function(i){var a=e.projection(i);return a?[a[0]-e.xaxis._offset,a[1]-e.yaxis._offset]:[null,null]},e.xaxis={_id:"x",c2p:function(i){return e.project(i)[0]}},e.yaxis={_id:"y",c2p:function(i){return e.project(i)[1]}},e.mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},UZ.setConvert(e.mockAxis,r)};Qg.saveViewInitial=function(e){var t=e.center||{},r=e.projection,n=r.rotation||{};this.viewInitial={fitbounds:e.fitbounds,"projection.scale":r.scale};var i;e._isScoped?i={"center.lon":t.lon,"center.lat":t.lat}:e._isClipped?i={"projection.rotation.lon":n.lon,"projection.rotation.lat":n.lat}:i={"center.lon":t.lon,"center.lat":t.lat,"projection.rotation.lon":n.lon},ok.extendFlat(this.viewInitial,i)};Qg.render=function(e){this._hasMarkerAngles&&e?this.plot(this._geoCalcData,this._fullLayout,[],!0):this._render()};Qg._render=function(){var e=this.projection,t=e.getPath(),r;function n(a){var o=e(a.lonlat);return o?Gzt(o[0],o[1]):null}function i(a){return e.isLonLatOverEdges(a.lonlat)?"none":null}for(r in this.basePaths)this.basePaths[r].attr("d",t);for(r in this.dataPaths)this.dataPaths[r].attr("d",function(a){return t(a.geojson)});for(r in this.dataPoints)this.dataPoints[r].attr("display",i).attr("transform",n)};function Jzt(e){var t=e.projection,r=t.type,n=fp.projNames[r];n="geo"+ok.titleCase(n);for(var i=VZ[n]||Uzt[n],a=i(),o=e._isSatellite?Math.acos(1/t.distance)*180/Math.PI:e._isClipped?fp.lonaxisSpan[r]/2:null,s=["center","rotate","parallels","clipExtent"],l=function(f){return f?a:[]},u=0;u<s.length;u++){var c=s[u];typeof a[c]!="function"&&(a[c]=l)}return a.isLonLatOverEdges=function(f){if(a(f)===null)return!0;if(o){var h=a.rotate(),d=Nzt(f,[-h[0],-h[1]]),v=o*Math.PI/180;return d>v}else return!1},a.getPath=function(){return Bzt().projection(a)},a.getBounds=function(f){return a.getPath().bounds(f)},a.precision(fp.precision),e._isSatellite&&a.tilt(t.tilt).distance(t.distance),o&&a.clipAngle(o-fp.clipPad),a}function $zt(e,t,r){var n=1e-6,i=2.5,a=t[e],o=fp.scopeDefaults[t.scope],s,l,u;e==="lonaxis"?(s=o.lonaxisRange,l=o.lataxisRange,u=function(k,E){return[k,E]}):e==="lataxis"&&(s=o.lataxisRange,l=o.lonaxisRange,u=function(k,E){return[E,k]});var c={type:"linear",range:[s[0],s[1]-n],tick0:a.tick0,dtick:a.dtick};UZ.setConvert(c,r);var f=UZ.calcTicks(c);!t.isScoped&&e==="lonaxis"&&f.pop();for(var h=f.length,d=new Array(h),v=0;v<h;v++)for(var _=f[v].x,b=d[v]=[],p=l[0];p<l[1]+i;p+=i)b.push(u(_,p));return{type:"MultiLineString",coordinates:d}}function BDe(e,t){var r=fp.clipPad,n=e[0]+r,i=e[1]-r,a=t[0]+r,o=t[1]-r;n>0&&i<0&&(i+=360);var s=(i-n)/4;return{type:"Polygon",coordinates:[[[n,a],[n,o],[n+s,o],[n+2*s,o],[n+3*s,o],[i,o],[i,a],[i-s,a],[i-2*s,a],[i-3*s,a],[n,a]]]}}});var GZ=ye((wmr,WDe)=>{"use strict";var e5=Lh(),Qzt=Cc().attributes,e7t=Pd().dash,QA=tk(),t7t=mc().overrideAll,HDe=Z1(),jDe={range:{valType:"info_array",items:[{valType:"number"},{valType:"number"}]},showgrid:{valType:"boolean",dflt:!1},tick0:{valType:"number",dflt:0},dtick:{valType:"number"},gridcolor:{valType:"color",dflt:e5.lightLine},gridwidth:{valType:"number",min:0,dflt:1},griddash:e7t},r7t=WDe.exports=t7t({domain:Qzt({name:"geo"},{}),fitbounds:{valType:"enumerated",values:[!1,"locations","geojson"],dflt:!1,editType:"plot"},resolution:{valType:"enumerated",values:[110,50],dflt:110,coerceNumber:!0},scope:{valType:"enumerated",values:HDe(QA.scopeDefaults),dflt:"world"},projection:{type:{valType:"enumerated",values:HDe(QA.projNames)},rotation:{lon:{valType:"number"},lat:{valType:"number"},roll:{valType:"number"}},tilt:{valType:"number",dflt:0},distance:{valType:"number",min:1.001,dflt:2},parallels:{valType:"info_array",items:[{valType:"number"},{valType:"number"}]},scale:{valType:"number",min:0,dflt:1}},center:{lon:{valType:"number"},lat:{valType:"number"}},visible:{valType:"boolean",dflt:!0},showcoastlines:{valType:"boolean"},coastlinecolor:{valType:"color",dflt:e5.defaultLine},coastlinewidth:{valType:"number",min:0,dflt:1},showland:{valType:"boolean",dflt:!1},landcolor:{valType:"color",dflt:QA.landColor},showocean:{valType:"boolean",dflt:!1},oceancolor:{valType:"color",dflt:QA.waterColor},showlakes:{valType:"boolean",dflt:!1},lakecolor:{valType:"color",dflt:QA.waterColor},showrivers:{valType:"boolean",dflt:!1},rivercolor:{valType:"color",dflt:QA.waterColor},riverwidth:{valType:"number",min:0,dflt:1},showcountries:{valType:"boolean"},countrycolor:{valType:"color",dflt:e5.defaultLine},countrywidth:{valType:"number",min:0,dflt:1},showsubunits:{valType:"boolean"},subunitcolor:{valType:"color",dflt:e5.defaultLine},subunitwidth:{valType:"number",min:0,dflt:1},showframe:{valType:"boolean"},framecolor:{valType:"color",dflt:e5.defaultLine},framewidth:{valType:"number",min:0,dflt:1},bgcolor:{valType:"color",dflt:e5.background},lonaxis:jDe,lataxis:jDe},"plot","from-root");r7t.uirevision={valType:"any",editType:"none"}});var YDe=ye((Tmr,ZDe)=>{"use strict";var DF=Dr(),i7t=k_(),n7t=Id().getSubplotData,FF=tk(),a7t=GZ(),XDe=FF.axesNames;ZDe.exports=function(t,r,n){i7t(t,r,n,{type:"geo",attributes:a7t,handleDefaults:o7t,fullData:n,partition:"y"})};function o7t(e,t,r,n){var i=n7t(n.fullData,"geo",n.id),a=i.map(function(oe){return oe.index}),o=r("resolution"),s=r("scope"),l=FF.scopeDefaults[s],u=r("projection.type",l.projType),c=t._isAlbersUsa=u==="albers usa";c&&(s=t.scope="usa");var f=t._isScoped=s!=="world",h=t._isSatellite=u==="satellite",d=t._isConic=u.indexOf("conic")!==-1||u==="albers",v=t._isClipped=!!FF.lonaxisSpan[u];if(e.visible===!1){var _=DF.extendDeep({},t._template);_.showcoastlines=!1,_.showcountries=!1,_.showframe=!1,_.showlakes=!1,_.showland=!1,_.showocean=!1,_.showrivers=!1,_.showsubunits=!1,_.lonaxis&&(_.lonaxis.showgrid=!1),_.lataxis&&(_.lataxis.showgrid=!1),t._template=_}for(var b=r("visible"),p,k=0;k<XDe.length;k++){var E=XDe[k],S=[30,10][k],L;if(f)L=l[E+"Range"];else{var x=FF[E+"Span"],C=(x[u]||x["*"])/2,M=r("projection.rotation."+E.slice(0,3),l.projRotate[k]);L=[M-C,M+C]}var g=r(E+".range",L);r(E+".tick0"),r(E+".dtick",S),p=r(E+".showgrid",b?void 0:!1),p&&(r(E+".gridcolor"),r(E+".gridwidth"),r(E+".griddash")),t[E]._ax={type:"linear",_id:E.slice(0,3),_traceIndices:a,setScale:DF.identity,c2l:DF.identity,r2l:DF.identity,autorange:!0,range:g.slice(),_m:1,_input:{}}}var P=t.lonaxis.range,T=t.lataxis.range,z=P[0],O=P[1];z>0&&O<0&&(O+=360);var V=(z+O)/2,G;if(!c){var Z=f?l.projRotate:[V,0,0];G=r("projection.rotation.lon",Z[0]),r("projection.rotation.lat",Z[1]),r("projection.rotation.roll",Z[2]),p=r("showcoastlines",!f&&b),p&&(r("coastlinecolor"),r("coastlinewidth")),p=r("showocean",b?void 0:!1),p&&r("oceancolor")}var j,N;if(c?(j=-96.6,N=38.7):(j=f?V:G,N=(T[0]+T[1])/2),r("center.lon",j),r("center.lat",N),h&&(r("projection.tilt"),r("projection.distance")),d){var H=l.projParallels||[0,60];r("projection.parallels",H)}r("projection.scale"),p=r("showland",b?void 0:!1),p&&r("landcolor"),p=r("showlakes",b?void 0:!1),p&&r("lakecolor"),p=r("showrivers",b?void 0:!1),p&&(r("rivercolor"),r("riverwidth")),p=r("showcountries",f&&s!=="usa"&&b),p&&(r("countrycolor"),r("countrywidth")),(s==="usa"||s==="north america"&&o===50)&&(r("showsubunits",b),r("subunitcolor"),r("subunitwidth")),f||(p=r("showframe",b),p&&(r("framecolor"),r("framewidth"))),r("bgcolor");var te=r("fitbounds");te&&(delete t.projection.scale,f?(delete t.center.lon,delete t.center.lat):v?(delete t.center.lon,delete t.center.lat,delete t.projection.rotation.lon,delete t.projection.rotation.lat,delete t.lonaxis.range,delete t.lataxis.range):(delete t.center.lon,delete t.center.lat,delete t.projection.rotation.lon))}});var HZ=ye((Amr,$De)=>{"use strict";var s7t=Id().getSubplotCalcData,l7t=Dr().counterRegex,u7t=GDe(),Xm="geo",KDe=l7t(Xm),JDe={};JDe[Xm]={valType:"subplotid",dflt:Xm,editType:"calc"};function c7t(e){for(var t=e._fullLayout,r=e.calcdata,n=t._subplots[Xm],i=0;i<n.length;i++){var a=n[i],o=s7t(r,Xm,a),s=t[a],l=s._subplot;l||(l=u7t({id:a,graphDiv:e,container:t._geolayer.node(),topojsonURL:e._context.topojsonURL,staticPlot:e._context.staticPlot}),t[a]._subplot=l),l.plot(o,t,e._promises)}}function f7t(e,t,r,n){for(var i=n._subplots[Xm]||[],a=0;a<i.length;a++){var o=i[a],s=n[o]._subplot;!t[o]&&s&&(s.framework.remove(),s.clipDef.remove())}}function h7t(e){for(var t=e._fullLayout,r=t._subplots[Xm],n=0;n<r.length;n++){var i=t[r[n]],a=i._subplot;a.updateFx(t,i)}}$De.exports={attr:Xm,name:Xm,idRoot:Xm,idRegex:KDe,attrRegex:KDe,attributes:JDe,layoutAttributes:GZ(),supplyLayoutDefaults:YDe(),plot:c7t,updateFx:h7t,clean:f7t}});var eFe=ye((Smr,QDe)=>{"use 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sFe=ye((kmr,oFe)=>{"use strict";var sk=Dr(),m7t=Qh(),y7t=t5(),_7t=["The library used by the *country names* `locationmode` option is changing in the next major version.","Some country names in existing plots may not work in the new version.","To ensure consistent behavior, consider setting `locationmode` to *ISO-3*."].join(" ");oFe.exports=function(t,r,n,i){function a(h,d){return sk.coerce(t,r,y7t,h,d)}var o=a("locations"),s=a("z");if(!(o&&o.length&&sk.isArrayOrTypedArray(s)&&s.length)){r.visible=!1;return}r._length=Math.min(o.length,s.length);var l=a("geojson"),u;(typeof l=="string"&&l!==""||sk.isPlainObject(l))&&(u="geojson-id");var c=a("locationmode",u);c==="country names"&&sk.warn(_7t),c==="geojson-id"&&a("featureidkey"),a("text"),a("hovertext"),a("hovertemplate"),a("hovertemplatefallback");var f=a("marker.line.width");f&&a("marker.line.color"),a("marker.opacity"),m7t(t,r,i,a,{prefix:"",cLetter:"z"}),sk.coerceSelectionMarkerOpacity(r,a)}});var zF=ye((Cmr,cFe)=>{"use strict";var 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n=r._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return r._scene||(n=r._scene={},n.init=function(){JF.extendFlat(n,a,i)},n.init(),n.update=function(s){var l=JF.repeat(s,n.count);if(n.fill2d&&n.fill2d.update(l),n.scatter2d&&n.scatter2d.update(l),n.line2d&&n.line2d.update(l),n.error2d&&n.error2d.update(l.concat(l)),n.select2d&&n.select2d.update(l),n.glText)for(var u=0;u<n.count;u++)n.glText[u].update(s)},n.draw=function(){for(var s=n.count,l=n.fill2d,u=n.error2d,c=n.line2d,f=n.scatter2d,h=n.glText,d=n.select2d,v=n.selectBatch,_=n.unselectBatch,b=0;b<s;b++){if(l&&n.fillOrder[b]&&l.draw(n.fillOrder[b]),c&&n.lineOptions[b]&&c.draw(b),u&&(n.errorXOptions[b]&&u.draw(b),n.errorYOptions[b]&&u.draw(b+s)),f&&n.markerOptions[b])if(_[b].length){var p=JF.repeat([],n.count);p[b]=_[b],f.draw(p)}else 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tOt=HF(),s5=Dr(),Vze=hf(),rOt=Ag().findExtremes,Gze=Dg(),sY=O0(),iOt=sY.calcMarkerSize,nOt=sY.calcAxisExpansion,aOt=sY.setFirstScatter,oOt=F0(),l5=J2(),sOt=oY(),Hze=fs().BADNUM,lOt=ox().TOO_MANY_POINTS;Wze.exports=function(t,r){var n=t._fullLayout,i=r._xA=Vze.getFromId(t,r.xaxis,"x"),a=r._yA=Vze.getFromId(t,r.yaxis,"y"),o=n._plots[r.xaxis+r.yaxis],s=r._length,l=s>=lOt,u=s*2,c={},f,h=i.makeCalcdata(r,"x"),d=a.makeCalcdata(r,"y"),v=Gze(r,i,"x",h),_=Gze(r,a,"y",d),b=v.vals,p=_.vals;r._x=b,r._y=p,r.xperiodalignment&&(r._origX=h,r._xStarts=v.starts,r._xEnds=v.ends),r.yperiodalignment&&(r._origY=d,r._yStarts=_.starts,r._yEnds=_.ends);var k=new Array(u),E=new Array(s);for(f=0;f<s;f++)k[f*2]=b[f]===Hze?NaN:b[f],k[f*2+1]=p[f]===Hze?NaN:p[f],E[f]=f;if(i.type==="log")for(f=0;f<u;f+=2)k[f]=i.c2l(k[f]);if(a.type==="log")for(f=1;f<u;f+=2)k[f]=a.c2l(k[f]);l&&i.type!=="log"&&a.type!=="log"?c.tree=tOt(k):c.ids=E,oOt(t,r);var S=uOt(t,o,r,k,b,p),L=sOt(t,o);aOt(n,r);var x;return l?S.marker&&(x=S.marker.sizeAvg||Math.max(S.marker.size,3)):x=iOt(r,s),nOt(t,r,i,a,b,p,x),S.errorX&&jze(r,i,S.errorX),S.errorY&&jze(r,a,S.errorY),S.fill&&!L.fill2d&&(L.fill2d=!0),S.marker&&!L.scatter2d&&(L.scatter2d=!0),S.line&&!L.line2d&&(L.line2d=!0),(S.errorX||S.errorY)&&!L.error2d&&(L.error2d=!0),S.text&&!L.glText&&(L.glText=!0),S.marker&&(S.marker.snap=s),L.lineOptions.push(S.line),L.errorXOptions.push(S.errorX),L.errorYOptions.push(S.errorY),L.fillOptions.push(S.fill),L.markerOptions.push(S.marker),L.markerSelectedOptions.push(S.markerSel),L.markerUnselectedOptions.push(S.markerUnsel),L.textOptions.push(S.text),L.textSelectedOptions.push(S.textSel),L.textUnselectedOptions.push(S.textUnsel),L.selectBatch.push([]),L.unselectBatch.push([]),c._scene=L,c.index=L.count,c.x=b,c.y=p,c.positions=k,L.count++,[{x:!1,y:!1,t:c,trace:r}]};function jze(e,t,r){var n=e._extremes[t._id],i=rOt(t,r._bnds,{padded:!0});n.min=n.min.concat(i.min),n.max=n.max.concat(i.max)}function uOt(e,t,r,n,i,a){var o=l5.style(e,r);if(o.marker&&(o.marker.positions=n),o.line&&n.length>1&&s5.extendFlat(o.line,l5.linePositions(e,r,n)),o.errorX||o.errorY){var s=l5.errorBarPositions(e,r,n,i,a);o.errorX&&s5.extendFlat(o.errorX,s.x),o.errorY&&s5.extendFlat(o.errorY,s.y)}return o.text&&(s5.extendFlat(o.text,{positions:n},l5.textPosition(e,r,o.text,o.marker)),s5.extendFlat(o.textSel,{positions:n},l5.textPosition(e,r,o.text,o.markerSel)),s5.extendFlat(o.textUnsel,{positions:n},l5.textPosition(e,r,o.text,o.markerUnsel))),o}});var lY=ye((cyr,Yze)=>{"use strict";var Zze=Dr(),cOt=ka(),fOt=N1().DESELECTDIM;function hOt(e){var t=e[0],r=t.trace,n=t.t,i=n._scene,a=n.index,o=i.selectBatch[a],s=i.unselectBatch[a],l=i.textOptions[a],u=i.textSelectedOptions[a]||{},c=i.textUnselectedOptions[a]||{},f=Zze.extendFlat({},l),h,d;if(o.length||s.length){var v=u.color,_=c.color,b=l.color,p=Zze.isArrayOrTypedArray(b);for(f.color=new Array(r._length),h=0;h<o.length;h++)d=o[h],f.color[d]=v||(p?b[d]:b);for(h=0;h<s.length;h++){d=s[h];var k=p?b[d]:b;f.color[d]=_||(v?k:cOt.addOpacity(k,fOt))}}i.glText[a].update(f)}Yze.exports={styleTextSelection:hOt}});var uY=ye((fyr,Jze)=>{"use strict";var Kze=Ru(),dOt=lY().styleTextSelection;Jze.exports=function(t,r){var n=t.cd,i=t.xaxis,a=t.yaxis,o=[],s=n[0].trace,l=n[0].t,u=s._length,c=l.x,f=l.y,h=l._scene,d=l.index;if(!h)return o;var v=Kze.hasText(s),_=Kze.hasMarkers(s),b=!_&&!v;if(s.visible!==!0||b)return o;var p=[],k=[];if(r!==!1&&!r.degenerate)for(var E=0;E<u;E++)r.contains([l.xpx[E],l.ypx[E]],!1,E,t)?(p.push(E),o.push({pointNumber:E,x:i.c2d(c[E]),y:a.c2d(f[E])})):k.push(E);if(_){var S=h.scatter2d;if(!p.length&&!k.length){var L=new Array(h.count);L[d]=h.markerOptions[d],S.update.apply(S,L)}else if(!h.selectBatch[d].length&&!h.unselectBatch[d].length){var x=new Array(h.count);x[d]=h.markerUnselectedOptions[d],S.update.apply(S,x)}}return h.selectBatch[d]=p,h.unselectBatch[d]=k,v&&dOt(n),o}});var Qze=ye((hyr,$ze)=>{"use strict";var vOt=UF();$ze.exports={moduleType:"trace",name:"scattergl",basePlotModule:ph(),categories:["gl","regl","cartesian","symbols","errorBarsOK","showLegend","scatter-like"],attributes:lk(),supplyDefaults:DFe(),crossTraceDefaults:aU(),colorbar:$d(),formatLabels:zFe(),calc:Xze(),hoverPoints:vOt.hoverPoints,selectPoints:uY(),meta:{}}});var t7e=ye((dyr,QF)=>{"use strict";var $F=ZE();QF.exports=e7e;QF.exports.to=e7e;QF.exports.from=pOt;function e7e(e,t){t==null&&(t=!0);var r=e[0],n=e[1],i=e[2],a=e[3];a==null&&(a=t?1:255),t&&(r*=255,n*=255,i*=255,a*=255),r=$F(r,0,255)&255,n=$F(n,0,255)&255,i=$F(i,0,255)&255,a=$F(a,0,255)&255;var o=r*16777216+(n<<16)+(i<<8)+a;return o}function pOt(e,t){e=+e;var r=e>>>24,n=(e&16711680)>>>16,i=(e&65280)>>>8,a=e&255;return t===!1?[r,n,i,a]:[r/255,n/255,i/255,a/255]}});var Oh=ye((vyr,i7e)=>{"use strict";var r7e=Object.getOwnPropertySymbols,gOt=Object.prototype.hasOwnProperty,mOt=Object.prototype.propertyIsEnumerable;function yOt(e){if(e==null)throw new TypeError("Object.assign cannot be called with null or undefined");return Object(e)}function _Ot(){try{if(!Object.assign)return!1;var e=new String("abc");if(e[5]="de",Object.getOwnPropertyNames(e)[0]==="5")return!1;for(var t={},r=0;r<10;r++)t["_"+String.fromCharCode(r)]=r;var n=Object.getOwnPropertyNames(t).map(function(a){return t[a]});if(n.join("")!=="0123456789")return!1;var i={};return"abcdefghijklmnopqrst".split("").forEach(function(a){i[a]=a}),Object.keys(Object.assign({},i)).join("")==="abcdefghijklmnopqrst"}catch(a){return!1}}i7e.exports=_Ot()?Object.assign:function(e,t){for(var r,n=yOt(e),i,a=1;a<arguments.length;a++){r=Object(arguments[a]);for(var o in r)gOt.call(r,o)&&(n[o]=r[o]);if(r7e){i=r7e(r);for(var s=0;s<i.length;s++)mOt.call(r,i[s])&&(n[i[s]]=r[i[s]])}}return n}});var a7e=ye((pyr,n7e)=>{n7e.exports=function(e){typeof e=="string"&&(e=[e]);for(var t=[].slice.call(arguments,1),r=[],n=0;n<e.length-1;n++)r.push(e[n],t[n]||"");return r.push(e[n]),r.join("")}});var cY=ye((gyr,o7e)=>{"use strict";o7e.exports=function(t,r,n){Array.isArray(n)||(n=[].slice.call(arguments,2));for(var i=0,a=n.length;i<a;i++){var o=n[i];for(var s in o)if(!(r[s]!==void 0&&!Array.isArray(r[s])&&t[s]===r[s])&&s in r){var l;if(o[s]===!0)l=r[s];else{if(o[s]===!1)continue;if(typeof o[s]=="function"&&(l=o[s](r[s],t,r),l===void 0))continue}t[s]=l}}return t}});var l7e=ye((myr,s7e)=>{"use strict";s7e.exports=typeof navigator!="undefined"&&(/MSIE/.test(navigator.userAgent)||/Trident\//.test(navigator.appVersion))});var ez=ye((yyr,u5)=>{"use strict";u5.exports=dk;u5.exports.float32=u5.exports.float=dk;u5.exports.fract32=u5.exports.fract=xOt;var u7e=new Float32Array(1);function xOt(e,t){if(e.length){if(e instanceof Float32Array)return new Float32Array(e.length);t instanceof Float32Array||(t=dk(e));for(var r=0,n=t.length;r<n;r++)t[r]=e[r]-t[r];return t}return dk(e-dk(e))}function dk(e){return e.length?e instanceof Float32Array?e:new Float32Array(e):(u7e[0]=e,u7e[0])}});var hY=ye((_yr,h7e)=>{"use strict";function bOt(e,t){var r=e==null?null:typeof Symbol!="undefined"&&e[Symbol.iterator]||e["@@iterator"];if(r!=null){var n,i,a,o,s=[],l=!0,u=!1;try{if(a=(r=r.call(e)).next,t===0){if(Object(r)!==r)return;l=!1}else for(;!(l=(n=a.call(r)).done)&&(s.push(n.value),s.length!==t);l=!0);}catch(c){u=!0,i=c}finally{try{if(!l&&r.return!=null&&(o=r.return(),Object(o)!==o))return}finally{if(u)throw i}}return s}}function wOt(e,t){return SOt(e)||bOt(e,t)||f7e(e,t)||kOt()}function TOt(e){return AOt(e)||MOt(e)||f7e(e)||EOt()}function AOt(e){if(Array.isArray(e))return fY(e)}function SOt(e){if(Array.isArray(e))return e}function MOt(e){if(typeof Symbol!="undefined"&&e[Symbol.iterator]!=null||e["@@iterator"]!=null)return Array.from(e)}function f7e(e,t){if(e){if(typeof e=="string")return fY(e,t);var 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+In order to be iterable, non-array objects must have a [Symbol.iterator]() method.`)}function kOt(){throw new TypeError(`Invalid attempt to destructure non-iterable instance.
+In order to be iterable, non-array objects must have a [Symbol.iterator]() method.`)}var COt=J_(),LOt=X2(),POt=t7e(),IOt=HF(),$2=Oh(),tz=a7e(),ROt=Zm(),DOt=cY(),FOt=Z2(),c7e=l7e(),rz=ez(),zOt=i5(),OOt=nv;function nv(e,t){var r=this;if(!(this instanceof nv))return new nv(e,t);typeof e=="function"?(t||(t={}),t.regl=e):(t=e,e=null),t&&t.length&&(t.positions=t),e=t.regl;var n=e._gl,i,a=[],o={},s=[],l=[null],u=[null],c=255,f=100;this.tooManyColors=c7e,i=e.texture({data:new Uint8Array(c*4),width:c,height:1,type:"uint8",format:"rgba",wrapS:"clamp",wrapT:"clamp",mag:"nearest",min:"nearest"}),$2(this,{regl:e,gl:n,groups:s,markerCache:u,markerTextures:l,palette:a,paletteIds:o,paletteTexture:i,maxColors:c,maxSize:f,canvas:n.canvas}),this.update(t);var h={uniforms:{constPointSize:!!t.constPointSize,opacity:e.prop("opacity"),paletteSize:function(b,p){return[r.tooManyColors?0:c,i.height]},pixelRatio:e.context("pixelRatio"),scale:e.prop("scale"),scaleFract:e.prop("scaleFract"),translate:e.prop("translate"),translateFract:e.prop("translateFract"),markerTexture:e.prop("markerTexture"),paletteTexture:i},attributes:{x:function(b,p){return p.xAttr||{buffer:p.positionBuffer,stride:8,offset:0}},y:function(b,p){return p.yAttr||{buffer:p.positionBuffer,stride:8,offset:4}},xFract:function(b,p){return p.xAttr?{constant:[0,0]}:{buffer:p.positionFractBuffer,stride:8,offset:0}},yFract:function(b,p){return p.yAttr?{constant:[0,0]}:{buffer:p.positionFractBuffer,stride:8,offset:4}},size:function(b,p){return p.size.length?{buffer:p.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(p.size*255/r.maxSize)]}},borderSize:function(b,p){return p.borderSize.length?{buffer:p.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(p.borderSize*255/r.maxSize)]}},colorId:function(b,p){return p.color.length?{buffer:p.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(p.color*4,p.color*4+4):[p.color]}},borderColorId:function(b,p){return p.borderColor.length?{buffer:p.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(p.borderColor*4,p.borderColor*4+4):[p.borderColor]}},isActive:function(b,p){return p.activation===!0?{constant:[1]}:p.activation?p.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},scissor:{enable:!0,box:e.prop("viewport")},viewport:e.prop("viewport"),stencil:{enable:!1},depth:{enable:!1},elements:e.prop("elements"),count:e.prop("count"),offset:e.prop("offset"),primitive:"points"},d=$2({},h);d.frag=tz([`precision highp float;
+#define GLSLIFY 1
+
+uniform float opacity;
+uniform sampler2D markerTexture;
+
+varying vec4 fragColor, fragBorderColor;
+varying float fragWidth, fragBorderColorLevel, fragColorLevel;
+
+float smoothStep(float x, float y) {
+  return 1.0 / (1.0 + exp(50.0*(x - y)));
+}
+
+void main() {
+  float dist = texture2D(markerTexture, gl_PointCoord).r, delta = fragWidth;
+
+  // max-distance alpha
+  if (dist < 0.003) discard;
+
+  // null-border case
+  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {
+    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);
+    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);
+  }
+  else {
+    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);
+    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);
+
+    vec4 color = fragBorderColor;
+    color.a *= borderColorAmt;
+    color = mix(color, fragColor, colorAmt);
+    color.a *= opacity;
+
+    gl_FragColor = color;
+  }
+
+}
+`]),d.vert=tz([`precision highp float;
+#define GLSLIFY 1
+
+attribute float x, y, xFract, yFract;
+attribute float size, borderSize;
+attribute vec4 colorId, borderColorId;
+attribute float isActive;
+
+// \`invariant\` effectively turns off optimizations for the position.
+// We need this because -fast-math on M1 Macs is re-ordering
+// floating point operations in a way that causes floating point
+// precision limits to put points in the wrong locations.
+invariant gl_Position;
+
+uniform bool constPointSize;
+uniform float pixelRatio;
+uniform vec2 scale, scaleFract, translate, translateFract, paletteSize;
+uniform sampler2D paletteTexture;
+
+const float maxSize = 100.;
+const float borderLevel = .5;
+
+varying vec4 fragColor, fragBorderColor;
+varying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;
+
+float pointSizeScale = (constPointSize) ? 2. : pixelRatio;
+
+bool isDirect = (paletteSize.x < 1.);
+
+vec4 getColor(vec4 id) {
+  return isDirect ? id / 255. : texture2D(paletteTexture,
+    vec2(
+      (id.x + .5) / paletteSize.x,
+      (id.y + .5) / paletteSize.y
+    )
+  );
+}
+
+void main() {
+  // ignore inactive points
+  if (isActive == 0.) return;
+
+  vec2 position = vec2(x, y);
+  vec2 positionFract = vec2(xFract, yFract);
+
+  vec4 color = getColor(colorId);
+  vec4 borderColor = getColor(borderColorId);
+
+  float size = size * maxSize / 255.;
+  float borderSize = borderSize * maxSize / 255.;
+
+  gl_PointSize = 2. * size * pointSizeScale;
+  fragPointSize = size * pixelRatio;
+
+  vec2 pos = (position + translate) * scale
+      + (positionFract + translateFract) * scale
+      + (position + translate) * scaleFract
+      + (positionFract + translateFract) * scaleFract;
+
+  gl_Position = vec4(pos * 2. - 1., 0., 1.);
+
+  fragColor = color;
+  fragBorderColor = borderColor;
+  fragWidth = 1. / gl_PointSize;
+
+  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);
+  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);
+}
+`]),this.drawMarker=e(d);var v=$2({},h);v.frag=tz([`precision highp float;
+#define GLSLIFY 1
+
+varying vec4 fragColor, fragBorderColor;
+varying float fragBorderRadius, fragWidth;
+
+uniform float opacity;
+
+float smoothStep(float edge0, float edge1, float x) {
+	float t;
+	t = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);
+	return t * t * (3.0 - 2.0 * t);
+}
+
+void main() {
+	float radius, alpha = 1.0, delta = fragWidth;
+
+	radius = length(2.0 * gl_PointCoord.xy - 1.0);
+
+	if (radius > 1.0 + delta) {
+		discard;
+	}
+
+	alpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);
+
+	float borderRadius = fragBorderRadius;
+	float ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);
+	vec4 color = mix(fragColor, fragBorderColor, ratio);
+	color.a *= alpha * opacity;
+	gl_FragColor = color;
+}
+`]),v.vert=tz([`precision highp float;
+#define GLSLIFY 1
+
+attribute float x, y, xFract, yFract;
+attribute float size, borderSize;
+attribute vec4 colorId, borderColorId;
+attribute float isActive;
+
+// \`invariant\` effectively turns off optimizations for the position.
+// We need this because -fast-math on M1 Macs is re-ordering
+// floating point operations in a way that causes floating point
+// precision limits to put points in the wrong locations.
+invariant gl_Position;
+
+uniform bool constPointSize;
+uniform float pixelRatio;
+uniform vec2 paletteSize, scale, scaleFract, translate, translateFract;
+uniform sampler2D paletteTexture;
+
+const float maxSize = 100.;
+
+varying vec4 fragColor, fragBorderColor;
+varying float fragBorderRadius, fragWidth;
+
+float pointSizeScale = (constPointSize) ? 2. : pixelRatio;
+
+bool isDirect = (paletteSize.x < 1.);
+
+vec4 getColor(vec4 id) {
+  return isDirect ? id / 255. : texture2D(paletteTexture,
+    vec2(
+      (id.x + .5) / paletteSize.x,
+      (id.y + .5) / paletteSize.y
+    )
+  );
+}
+
+void main() {
+  // ignore inactive points
+  if (isActive == 0.) return;
+
+  vec2 position = vec2(x, y);
+  vec2 positionFract = vec2(xFract, yFract);
+
+  vec4 color = getColor(colorId);
+  vec4 borderColor = getColor(borderColorId);
+
+  float size = size * maxSize / 255.;
+  float borderSize = borderSize * maxSize / 255.;
+
+  gl_PointSize = (size + borderSize) * pointSizeScale;
+
+  vec2 pos = (position + translate) * scale
+      + (positionFract + translateFract) * scale
+      + (position + translate) * scaleFract
+      + (positionFract + translateFract) * scaleFract;
+
+  gl_Position = vec4(pos * 2. - 1., 0., 1.);
+
+  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);
+  fragColor = color;
+  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;
+  fragWidth = 1. / gl_PointSize;
+}
+`]),c7e&&(v.frag=v.frag.replace("smoothstep","smoothStep"),d.frag=d.frag.replace("smoothstep","smoothStep")),this.drawCircle=e(v)}nv.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4};nv.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this};nv.prototype.draw=function(){for(var e=this,t=arguments.length,r=new Array(t),n=0;n<t;n++)r[n]=arguments[n];var i=this.groups;if(r.length===1&&Array.isArray(r[0])&&(r[0][0]===null||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach(function(o,s){e.drawItem(s)});return this};nv.prototype.drawItem=function(e,t){var r=this.groups,n=r[e];if(typeof t=="number"&&(e=t,n=r[t],t=null),!!(n&&n.count&&n.opacity)){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,t));for(var i=[],a=1;a<n.activation.length;a++)!n.activation[a]||n.activation[a]!==!0&&!n.activation[a].data.length||i.push.apply(i,TOt(this.getMarkerDrawOptions(a,n,t)));i.length&&this.drawMarker(i)}};nv.prototype.getMarkerDrawOptions=function(e,t,r){var n=t.range,i=t.tree,a=t.viewport,o=t.activation,s=t.selectionBuffer,l=t.count,u=this.regl;if(!i)return r?[$2({},t,{markerTexture:this.markerTextures[e],activation:o[e],count:r.length,elements:r,offset:0})]:[$2({},t,{markerTexture:this.markerTextures[e],activation:o[e],offset:0})];var c=[],f=i.range(n,{lod:!0,px:[(n[2]-n[0])/a.width,(n[3]-n[1])/a.height]});if(r){for(var h=o[e],d=h.data,v=new Uint8Array(l),_=0;_<r.length;_++){var b=r[_];v[b]=d?d[b]:1}s.subdata(v)}for(var p=f.length;p--;){var k=wOt(f[p],2),E=k[0],S=k[1];c.push($2({},t,{markerTexture:this.markerTextures[e],activation:r?s:o[e],offset:E,count:S-E}))}return c};nv.prototype.update=function(){for(var e=this,t=arguments.length,r=new Array(t),n=0;n<t;n++)r[n]=arguments[n];if(r.length){r.length===1&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,l=this.maxColors,u=this.palette;this.groups=i=r.map(function(c,f){var h=i[f];if(c===void 0)return h;c===null?c={positions:null}:typeof c=="function"?c={ondraw:c}:typeof c[0]=="number"&&(c={positions:c}),c=ROt(c,{positions:"positions data points",snap:"snap cluster lod tree",size:"sizes size radius",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",color:"colors color fill fill-color fillColor",borderColor:"borderColors borderColor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range dataBox databox",viewport:"viewport viewPort viewBox viewbox",opacity:"opacity alpha transparency",bounds:"bound bounds boundaries limits",tooManyColors:"tooManyColors palette paletteMode optimizePalette enablePalette"}),c.positions===null&&(c.positions=[]),c.tooManyColors!=null&&(e.tooManyColors=c.tooManyColors),h||(i[f]=h={id:f,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:"stream",type:"uint8"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"})},c=$2({},nv.defaults,c)),c.positions&&!("marker"in c)&&(c.marker=h.marker,delete h.marker),c.marker&&!("positions"in c)&&(c.positions=h.positions,delete h.positions);var d=0,v=0;if(DOt(h,c,[{snap:!0,size:function(j,N){return j==null&&(j=nv.defaults.size),d+=j&&j.length?1:0,j},borderSize:function(j,N){return j==null&&(j=nv.defaults.borderSize),d+=j&&j.length?1:0,j},opacity:parseFloat,color:function(j,N){return j==null&&(j=nv.defaults.color),j=e.updateColor(j),v++,j},borderColor:function(j,N){return j==null&&(j=nv.defaults.borderColor),j=e.updateColor(j),v++,j},bounds:function(j,N,H){return"range"in H||(H.range=null),j},positions:function(j,N,H){var te=N.snap,oe=N.positionBuffer,_e=N.positionFractBuffer,Ee=N.selectionBuffer;if(j.x||j.y)return j.x.length?N.xAttr={buffer:o.buffer(j.x),offset:0,stride:4,count:j.x.length}:N.xAttr={buffer:j.x.buffer,offset:j.x.offset*4||0,stride:(j.x.stride||1)*4,count:j.x.count},j.y.length?N.yAttr={buffer:o.buffer(j.y),offset:0,stride:4,count:j.y.length}:N.yAttr={buffer:j.y.buffer,offset:j.y.offset*4||0,stride:(j.y.stride||1)*4,count:j.y.count},N.count=Math.max(N.xAttr.count,N.yAttr.count),j;j=FOt(j,"float64");var Ce=N.count=Math.floor(j.length/2),me=N.bounds=Ce?LOt(j,2):null;if(!H.range&&!N.range&&(delete N.range,H.range=me),!H.marker&&!N.marker&&(delete N.marker,H.marker=null),te&&(te===!0||Ce>te)?N.tree=IOt(j,{bounds:me}):te&&te.length&&(N.tree=te),N.tree){var ie={primitive:"points",usage:"static",data:N.tree,type:"uint32"};N.elements?N.elements(ie):N.elements=o.elements(ie)}var Se=rz.float32(j);oe({data:Se,usage:"dynamic"});var Le=rz.fract32(j,Se);return _e({data:Le,usage:"dynamic"}),Ee({data:new Uint8Array(Ce),type:"uint8",usage:"stream"}),j}},{marker:function(j,N,H){var te=N.activation;if(te.forEach(function(Le){return Le&&Le.destroy&&Le.destroy()}),te.length=0,!j||typeof j[0]=="number"){var oe=e.addMarker(j);te[oe]=!0}else{for(var _e=[],Ee=0,Ce=Math.min(j.length,N.count);Ee<Ce;Ee++){var me=e.addMarker(j[Ee]);_e[me]||(_e[me]=new Uint8Array(N.count)),_e[me][Ee]=1}for(var ie=0;ie<_e.length;ie++)if(_e[ie]){var Se={data:_e[ie],type:"uint8",usage:"static"};te[ie]?te[ie](Se):te[ie]=o.buffer(Se),te[ie].data=_e[ie]}}return j},range:function(j,N,H){var te=N.bounds;if(te)return j||(j=te),N.scale=[1/(j[2]-j[0]),1/(j[3]-j[1])],N.translate=[-j[0],-j[1]],N.scaleFract=rz.fract(N.scale),N.translateFract=rz.fract(N.translate),j},viewport:function(j){var N=zOt(j||[a.drawingBufferWidth,a.drawingBufferHeight]);return N}}]),d){var _=h,b=_.count,p=_.size,k=_.borderSize,E=_.sizeBuffer,S=new Uint8Array(b*2);if(p.length||k.length)for(var L=0;L<b;L++)S[L*2]=Math.round((p[L]==null?p:p[L])*255/s),S[L*2+1]=Math.round((k[L]==null?k:k[L])*255/s);E({data:S,usage:"dynamic"})}if(v){var x=h,C=x.count,M=x.color,g=x.borderColor,P=x.colorBuffer,T;if(e.tooManyColors){if(M.length||g.length){T=new Uint8Array(C*8);for(var z=0;z<C;z++){var O=M[z];T[z*8]=u[O*4],T[z*8+1]=u[O*4+1],T[z*8+2]=u[O*4+2],T[z*8+3]=u[O*4+3];var V=g[z];T[z*8+4]=u[V*4],T[z*8+5]=u[V*4+1],T[z*8+6]=u[V*4+2],T[z*8+7]=u[V*4+3]}}}else if(M.length||g.length){T=new Uint8Array(C*4+2);for(var G=0;G<C;G++)M[G]!=null&&(T[G*4]=M[G]%l,T[G*4+1]=Math.floor(M[G]/l)),g[G]!=null&&(T[G*4+2]=g[G]%l,T[G*4+3]=Math.floor(g[G]/l))}P({data:T||new Uint8Array(0),type:"uint8",usage:"dynamic"})}return h})}};nv.prototype.addMarker=function(e){var t=this.markerTextures,r=this.regl,n=this.markerCache,i=e==null?0:n.indexOf(e);if(i>=0)return i;var a;if(e instanceof Uint8Array||e instanceof Uint8ClampedArray)a=e;else{a=new Uint8Array(e.length);for(var o=0,s=e.length;o<s;o++)a[o]=e[o]*255}var l=Math.floor(Math.sqrt(a.length));return i=t.length,n.push(e),t.push(r.texture({channels:1,data:a,radius:l,mag:"linear",min:"linear"})),i};nv.prototype.updateColor=function(e){var t=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(e)||(e=[e]);var i=[];if(typeof e[0]=="number"){var a=[];if(Array.isArray(e))for(var o=0;o<e.length;o+=4)a.push(e.slice(o,o+4));else for(var s=0;s<e.length;s+=4)a.push(e.subarray(s,s+4));e=a}for(var l=0;l<e.length;l++){var u=e[l];u=COt(u,"uint8");var c=POt(u,!1);if(t[c]==null){var f=r.length;t[c]=Math.floor(f/4),r[f]=u[0],r[f+1]=u[1],r[f+2]=u[2],r[f+3]=u[3]}i[l]=t[c]}return!this.tooManyColors&&r.length>n*4&&(this.tooManyColors=!0),this.updatePalette(r),i.length===1?i[0]:i};nv.prototype.updatePalette=function(e){if(!this.tooManyColors){var t=this.maxColors,r=this.paletteTexture,n=Math.ceil(e.length*.25/t);if(n>1){e=e.slice();for(var i=e.length*.25%t;i<n*t;i++)e.push(0,0,0,0)}r.height<n&&r.resize(t,n),r.subimage({width:Math.min(e.length*.25,t),height:n,data:e},0,0)}};nv.prototype.destroy=function(){return this.groups.forEach(function(e){e.sizeBuffer.destroy(),e.positionBuffer.destroy(),e.positionFractBuffer.destroy(),e.colorBuffer.destroy(),e.activation.forEach(function(t){return t&&t.destroy&&t.destroy()}),e.selectionBuffer.destroy(),e.elements&&e.elements.destroy()}),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach(function(e){return e&&e.destroy&&e.destroy()}),this};var qOt=Oh(),BOt=function(t,r){var n=new OOt(t,r),i=n.render.bind(n);return qOt(i,{render:i,update:n.update.bind(n),draw:n.draw.bind(n),destroy:n.destroy.bind(n),regl:n.regl,gl:n.gl,canvas:n.gl.canvas,groups:n.groups,markers:n.markerCache,palette:n.palette}),i};h7e.exports=BOt});var m7e=ye((xyr,gY)=>{"use strict";gY.exports=az;gY.exports.default=az;function az(e,t,r){r=r||2;var n=t&&t.length,i=n?t[0]*r:e.length,a=v7e(e,0,i,r,!0),o=[];if(!a||a.next===a.prev)return o;var s,l,u,c,f,h,d;if(n&&(a=HOt(e,t,a,r)),e.length>80*r){s=u=e[0],l=c=e[1];for(var v=r;v<i;v+=r)f=e[v],h=e[v+1],f<s&&(s=f),h<l&&(l=h),f>u&&(u=f),h>c&&(c=h);d=Math.max(u-s,c-l),d=d!==0?32767/d:0}return vk(a,o,r,s,l,d,0),o}function v7e(e,t,r,n,i){var a,o;if(i===pY(e,t,r,n)>0)for(a=t;a<r;a+=n)o=d7e(a,e[a],e[a+1],o);else for(a=r-n;a>=t;a-=n)o=d7e(a,e[a],e[a+1],o);return o&&oz(o,o.next)&&(gk(o),o=o.next),o}function Q2(e,t){if(!e)return e;t||(t=e);var r=e,n;do if(n=!1,!r.steiner&&(oz(r,r.next)||xh(r.prev,r,r.next)===0)){if(gk(r),r=t=r.prev,r===r.next)break;n=!0}else 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n=Object(e),i=n.length;if(i===0)return-1;for(var a=0;a<i;a++)if(t.call(r,n[a],a,n))return a;return-1}});var GY=ye((U1r,dqe)=>{"use strict";var yz=J_(),gNt=X2(),VY=Oh(),mNt=Zm(),yNt=Z2(),fqe=m7e(),_Nt=_7e(),{float32:xNt,fract32:UY}=ez(),bNt=NY(),hqe=i5(),wNt=cqe(),TNt=`
+precision highp float;
+
+attribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;
+attribute vec4 color;
+attribute float lineEnd, lineTop;
+
+uniform vec2 scale, scaleFract, translate, translateFract;
+uniform float thickness, pixelRatio, id, depth;
+uniform vec4 viewport;
+
+varying vec4 fragColor;
+varying vec2 tangent;
+
+vec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {
+	// the order is important
+	return position * scale + translate
+       + positionFract * scale + translateFract
+       + position * scaleFract
+       + positionFract * scaleFract;
+}
+
+void main() {
+	float lineStart = 1. - lineEnd;
+	float lineOffset = lineTop * 2. - 1.;
+
+	vec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);
+	tangent = normalize(diff * scale * viewport.zw);
+	vec2 normal = vec2(-tangent.y, tangent.x);
+
+	vec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart
+		+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd
+
+		+ thickness * normal * .5 * lineOffset / viewport.zw;
+
+	gl_Position = vec4(position * 2.0 - 1.0, depth, 1);
+
+	fragColor = color / 255.;
+}
+`,ANt=`
+precision highp float;
+
+uniform float dashLength, pixelRatio, thickness, opacity, id;
+uniform sampler2D dashTexture;
+
+varying vec4 fragColor;
+varying vec2 tangent;
+
+void main() {
+	float alpha = 1.;
+
+	float t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;
+	float dash = texture2D(dashTexture, vec2(t, .5)).r;
+
+	gl_FragColor = fragColor;
+	gl_FragColor.a *= alpha * opacity * dash;
+}
+`,SNt=`
+precision highp float;
+
+attribute vec2 position, positionFract;
+
+uniform vec4 color;
+uniform vec2 scale, scaleFract, translate, translateFract;
+uniform float pixelRatio, id;
+uniform vec4 viewport;
+uniform float opacity;
+
+varying vec4 fragColor;
+
+const float MAX_LINES = 256.;
+
+void main() {
+	float depth = (MAX_LINES - 4. - id) / (MAX_LINES);
+
+	vec2 position = position * scale + translate
+       + positionFract * scale + translateFract
+       + position * scaleFract
+       + positionFract * scaleFract;
+
+	gl_Position = vec4(position * 2.0 - 1.0, depth, 1);
+
+	fragColor = color / 255.;
+	fragColor.a *= opacity;
+}
+`,MNt=`
+precision highp float;
+varying vec4 fragColor;
+
+void main() {
+	gl_FragColor = fragColor;
+}
+`,ENt=`
+precision highp float;
+
+attribute vec2 aCoord, bCoord, nextCoord, prevCoord;
+attribute vec4 aColor, bColor;
+attribute float lineEnd, lineTop;
+
+uniform vec2 scale, translate;
+uniform float thickness, pixelRatio, id, depth;
+uniform vec4 viewport;
+uniform float miterLimit, miterMode;
+
+varying vec4 fragColor;
+varying vec4 startCutoff, endCutoff;
+varying vec2 tangent;
+varying vec2 startCoord, endCoord;
+varying float enableStartMiter, enableEndMiter;
+
+const float REVERSE_THRESHOLD = -.875;
+const float MIN_DIFF = 1e-6;
+
+// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead
+// TODO: precalculate dot products, normalize things beforehead etc.
+// TODO: refactor to rectangular algorithm
+
+float distToLine(vec2 p, vec2 a, vec2 b) {
+	vec2 diff = b - a;
+	vec2 perp = normalize(vec2(-diff.y, diff.x));
+	return dot(p - a, perp);
+}
+
+bool isNaN( float val ){
+  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;
+}
+
+void main() {
+	vec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;
+
+  vec2 adjustedScale;
+  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;
+  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;
+
+  vec2 scaleRatio = adjustedScale * viewport.zw;
+	vec2 normalWidth = thickness / scaleRatio;
+
+	float lineStart = 1. - lineEnd;
+	float lineBot = 1. - lineTop;
+
+	fragColor = (lineStart * aColor + lineEnd * bColor) / 255.;
+
+	if (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;
+
+	if (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);
+	if (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);
+
+
+	vec2 prevDiff = aCoord - prevCoord;
+	vec2 currDiff = bCoord - aCoord;
+	vec2 nextDiff = nextCoord - bCoord;
+
+	vec2 prevTangent = normalize(prevDiff * scaleRatio);
+	vec2 currTangent = normalize(currDiff * scaleRatio);
+	vec2 nextTangent = normalize(nextDiff * scaleRatio);
+
+	vec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);
+	vec2 currNormal = vec2(-currTangent.y, currTangent.x);
+	vec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);
+
+	vec2 startJoinDirection = normalize(prevTangent - currTangent);
+	vec2 endJoinDirection = normalize(currTangent - nextTangent);
+
+	// collapsed/unidirectional segment cases
+	// FIXME: there should be more elegant solution
+	vec2 prevTanDiff = abs(prevTangent - currTangent);
+	vec2 nextTanDiff = abs(nextTangent - currTangent);
+	if (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {
+		startJoinDirection = currNormal;
+	}
+	if (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {
+		endJoinDirection = currNormal;
+	}
+	if (aCoord == bCoord) {
+		endJoinDirection = startJoinDirection;
+		currNormal = prevNormal;
+		currTangent = prevTangent;
+	}
+
+	tangent = currTangent;
+
+	//calculate join shifts relative to normals
+	float startJoinShift = dot(currNormal, startJoinDirection);
+	float endJoinShift = dot(currNormal, endJoinDirection);
+
+	float startMiterRatio = abs(1. / startJoinShift);
+	float endMiterRatio = abs(1. / endJoinShift);
+
+	vec2 startJoin = startJoinDirection * startMiterRatio;
+	vec2 endJoin = endJoinDirection * endMiterRatio;
+
+	vec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;
+	startTopJoin = sign(startJoinShift) * startJoin * .5;
+	startBotJoin = -startTopJoin;
+
+	endTopJoin = sign(endJoinShift) * endJoin * .5;
+	endBotJoin = -endTopJoin;
+
+	vec2 aTopCoord = aCoord + normalWidth * startTopJoin;
+	vec2 bTopCoord = bCoord + normalWidth * endTopJoin;
+	vec2 aBotCoord = aCoord + normalWidth * startBotJoin;
+	vec2 bBotCoord = bCoord + normalWidth * endBotJoin;
+
+	//miter anti-clipping
+	float baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));
+	float abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));
+
+	//prevent close to reverse direction switch
+	bool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);
+	bool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);
+
+	if (prevReverse) {
+		//make join rectangular
+		vec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;
+		float normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);
+		aBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;
+		aTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;
+	}
+	else if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {
+		//handle miter clipping
+		bTopCoord -= normalWidth * endTopJoin;
+		bTopCoord += normalize(endTopJoin * normalWidth) * baClipping;
+	}
+
+	if (nextReverse) {
+		//make join rectangular
+		vec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;
+		float normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);
+		bBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;
+		bTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;
+	}
+	else if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {
+		//handle miter clipping
+		aBotCoord -= normalWidth * startBotJoin;
+		aBotCoord += normalize(startBotJoin * normalWidth) * abClipping;
+	}
+
+	vec2 aTopPosition = (aTopCoord) * adjustedScale + translate;
+	vec2 aBotPosition = (aBotCoord) * adjustedScale + translate;
+
+	vec2 bTopPosition = (bTopCoord) * adjustedScale + translate;
+	vec2 bBotPosition = (bBotCoord) * adjustedScale + translate;
+
+	//position is normalized 0..1 coord on the screen
+	vec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;
+
+	startCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;
+	endCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;
+
+	gl_Position = vec4(position  * 2.0 - 1.0, depth, 1);
+
+	enableStartMiter = step(dot(currTangent, prevTangent), .5);
+	enableEndMiter = step(dot(currTangent, nextTangent), .5);
+
+	//bevel miter cutoffs
+	if (miterMode == 1.) {
+		if (enableStartMiter == 1.) {
+			vec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;
+			startCutoff = vec4(aCoord, aCoord);
+			startCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;
+			startCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;
+			startCutoff += viewport.xyxy;
+			startCutoff += startMiterWidth.xyxy;
+		}
+
+		if (enableEndMiter == 1.) {
+			vec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;
+			endCutoff = vec4(bCoord, bCoord);
+			endCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;
+			endCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;
+			endCutoff += viewport.xyxy;
+			endCutoff += endMiterWidth.xyxy;
+		}
+	}
+
+	//round miter cutoffs
+	else if (miterMode == 2.) {
+		if (enableStartMiter == 1.) {
+			vec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;
+			startCutoff = vec4(aCoord, aCoord);
+			startCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;
+			startCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;
+			startCutoff += viewport.xyxy;
+			startCutoff += startMiterWidth.xyxy;
+		}
+
+		if (enableEndMiter == 1.) {
+			vec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;
+			endCutoff = vec4(bCoord, bCoord);
+			endCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;
+			endCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;
+			endCutoff += viewport.xyxy;
+			endCutoff += endMiterWidth.xyxy;
+		}
+	}
+}
+`,kNt=`
+precision highp float;
+
+uniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;
+uniform sampler2D dashTexture;
+
+varying vec4 fragColor;
+varying vec2 tangent;
+varying vec4 startCutoff, endCutoff;
+varying vec2 startCoord, endCoord;
+varying float enableStartMiter, enableEndMiter;
+
+float distToLine(vec2 p, vec2 a, vec2 b) {
+	vec2 diff = b - a;
+	vec2 perp = normalize(vec2(-diff.y, diff.x));
+	return dot(p - a, perp);
+}
+
+void main() {
+	float alpha = 1., distToStart, distToEnd;
+	float cutoff = thickness * .5;
+
+	//bevel miter
+	if (miterMode == 1.) {
+		if (enableStartMiter == 1.) {
+			distToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);
+			if (distToStart < -1.) {
+				discard;
+				return;
+			}
+			alpha *= min(max(distToStart + 1., 0.), 1.);
+		}
+
+		if (enableEndMiter == 1.) {
+			distToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);
+			if (distToEnd < -1.) {
+				discard;
+				return;
+			}
+			alpha *= min(max(distToEnd + 1., 0.), 1.);
+		}
+	}
+
+	// round miter
+	else if (miterMode == 2.) {
+		if (enableStartMiter == 1.) {
+			distToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);
+			if (distToStart < 0.) {
+				float radius = length(gl_FragCoord.xy - startCoord);
+
+				if(radius > cutoff + .5) {
+					discard;
+					return;
+				}
+
+				alpha -= smoothstep(cutoff - .5, cutoff + .5, radius);
+			}
+		}
+
+		if (enableEndMiter == 1.) {
+			distToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);
+			if (distToEnd < 0.) {
+				float radius = length(gl_FragCoord.xy - endCoord);
+
+				if(radius > cutoff + .5) {
+					discard;
+					return;
+				}
+
+				alpha -= smoothstep(cutoff - .5, cutoff + .5, radius);
+			}
+		}
+	}
+
+	float t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;
+	float dash = texture2D(dashTexture, vec2(t, .5)).r;
+
+	gl_FragColor = fragColor;
+	gl_FragColor.a *= alpha * opacity * dash;
+}
+`;dqe.exports=Wc;function Wc(e,t){if(!(this instanceof Wc))return new Wc(e,t);if(typeof e=="function"?(t||(t={}),t.regl=e):t=e,t.length&&(t.positions=t),e=t.regl,!e.hasExtension("ANGLE_instanced_arrays"))throw Error("regl-error2d: `ANGLE_instanced_arrays` extension should be enabled");this.gl=e._gl,this.regl=e,this.passes=[],this.shaders=Wc.shaders.has(e)?Wc.shaders.get(e):Wc.shaders.set(e,Wc.createShaders(e)).get(e),this.update(t)}Wc.dashMult=2;Wc.maxPatternLength=256;Wc.precisionThreshold=3e6;Wc.maxPoints=1e4;Wc.maxLines=2048;Wc.shaders=new bNt;Wc.createShaders=function(e){let t=e.buffer({usage:"static",type:"float",data:[0,1,0,0,1,1,1,0]}),r={primitive:"triangle strip",instances:e.prop("count"),count:4,offset:0,uniforms:{miterMode:(o,s)=>s.join==="round"?2:1,miterLimit:e.prop("miterLimit"),scale:e.prop("scale"),scaleFract:e.prop("scaleFract"),translateFract:e.prop("translateFract"),translate:e.prop("translate"),thickness:e.prop("thickness"),dashTexture:e.prop("dashTexture"),opacity:e.prop("opacity"),pixelRatio:e.context("pixelRatio"),id:e.prop("id"),dashLength:e.prop("dashLength"),viewport:(o,s)=>[s.viewport.x,s.viewport.y,o.viewportWidth,o.viewportHeight],depth:e.prop("depth")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:"add",alpha:"add"},func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},depth:{enable:(o,s)=>!s.overlay},stencil:{enable:!1},scissor:{enable:!0,box:e.prop("viewport")},viewport:e.prop("viewport")},n=e(VY({vert:TNt,frag:ANt,attributes:{lineEnd:{buffer:t,divisor:0,stride:8,offset:0},lineTop:{buffer:t,divisor:0,stride:8,offset:4},aCoord:{buffer:e.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:e.prop("positionBuffer"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:e.prop("positionFractBuffer"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:e.prop("positionFractBuffer"),stride:8,offset:16,divisor:1},color:{buffer:e.prop("colorBuffer"),stride:4,offset:0,divisor:1}}},r)),i;try{i=e(VY({cull:{enable:!0,face:"back"},vert:ENt,frag:kNt,attributes:{lineEnd:{buffer:t,divisor:0,stride:8,offset:0},lineTop:{buffer:t,divisor:0,stride:8,offset:4},aColor:{buffer:e.prop("colorBuffer"),stride:4,offset:0,divisor:1},bColor:{buffer:e.prop("colorBuffer"),stride:4,offset:4,divisor:1},prevCoord:{buffer:e.prop("positionBuffer"),stride:8,offset:0,divisor:1},aCoord:{buffer:e.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:e.prop("positionBuffer"),stride:8,offset:16,divisor:1},nextCoord:{buffer:e.prop("positionBuffer"),stride:8,offset:24,divisor:1}}},r))}catch(o){i=n}return{fill:e({primitive:"triangle",elements:(o,s)=>s.triangles,offset:0,vert:SNt,frag:MNt,uniforms:{scale:e.prop("scale"),color:e.prop("fill"),scaleFract:e.prop("scaleFract"),translateFract:e.prop("translateFract"),translate:e.prop("translate"),opacity:e.prop("opacity"),pixelRatio:e.context("pixelRatio"),id:e.prop("id"),viewport:(o,s)=>[s.viewport.x,s.viewport.y,o.viewportWidth,o.viewportHeight]},attributes:{position:{buffer:e.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:e.prop("positionFractBuffer"),stride:8,offset:8}},blend:r.blend,depth:{enable:!1},scissor:r.scissor,stencil:r.stencil,viewport:r.viewport}),rect:n,miter:i}};Wc.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null};Wc.prototype.render=function(...e){e.length&&this.update(...e),this.draw()};Wc.prototype.draw=function(...e){return(e.length?e:this.passes).forEach((t,r)=>{if(t&&Array.isArray(t))return this.draw(...t);typeof t=="number"&&(t=this.passes[t]),t&&t.count>1&&t.opacity&&(this.regl._refresh(),t.fill&&t.triangles&&t.triangles.length>2&&this.shaders.fill(t),t.thickness&&(t.scale[0]*t.viewport.width>Wc.precisionThreshold||t.scale[1]*t.viewport.height>Wc.precisionThreshold?this.shaders.rect(t):t.join==="rect"||!t.join&&(t.thickness<=2||t.count>=Wc.maxPoints)?this.shaders.rect(t):this.shaders.miter(t)))}),this};Wc.prototype.update=function(e){if(!e)return;e.length!=null?typeof e[0]=="number"&&(e=[{positions:e}]):Array.isArray(e)||(e=[e]);let{regl:t,gl:r}=this;if(e.forEach((i,a)=>{let o=this.passes[a];if(i!==void 0){if(i===null){this.passes[a]=null;return}if(typeof i[0]=="number"&&(i={positions:i}),i=mNt(i,{positions:"positions points data coords",thickness:"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth",join:"lineJoin linejoin join type mode",miterLimit:"miterlimit miterLimit",dashes:"dash dashes dasharray dash-array dashArray",color:"color colour stroke colors colours stroke-color strokeColor",fill:"fill fill-color fillColor",opacity:"alpha opacity",overlay:"overlay crease overlap intersect",close:"closed close closed-path closePath",range:"range dataBox",viewport:"viewport viewBox",hole:"holes hole hollow",splitNull:"splitNull"}),o||(this.passes[a]=o={id:a,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:t.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:"linear",min:"linear"}),colorBuffer:t.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array}),positionBuffer:t.buffer({usage:"dynamic",type:"float",data:new Uint8Array}),positionFractBuffer:t.buffer({usage:"dynamic",type:"float",data:new Uint8Array})},i=VY({},Wc.defaults,i)),i.thickness!=null&&(o.thickness=parseFloat(i.thickness)),i.opacity!=null&&(o.opacity=parseFloat(i.opacity)),i.miterLimit!=null&&(o.miterLimit=parseFloat(i.miterLimit)),i.overlay!=null&&(o.overlay=!!i.overlay,a<Wc.maxLines&&(o.depth=2*(Wc.maxLines-1-a%Wc.maxLines)/Wc.maxLines-1)),i.join!=null&&(o.join=i.join),i.hole!=null&&(o.hole=i.hole),i.fill!=null&&(o.fill=i.fill?yz(i.fill,"uint8"):null),i.viewport!=null&&(o.viewport=hqe(i.viewport)),o.viewport||(o.viewport=hqe([r.drawingBufferWidth,r.drawingBufferHeight])),i.close!=null&&(o.close=i.close),i.positions===null&&(i.positions=[]),i.positions){let u,c;if(i.positions.x&&i.positions.y){let v=i.positions.x,_=i.positions.y;c=o.count=Math.max(v.length,_.length),u=new Float64Array(c*2);for(let b=0;b<c;b++)u[b*2]=v[b],u[b*2+1]=_[b]}else u=yNt(i.positions,"float64"),c=o.count=Math.floor(u.length/2);let f=o.bounds=gNt(u,2);if(o.fill){let v=[],_={},b=0;for(let p=0,k=0,E=o.count;p<E;p++){let S=u[p*2],L=u[p*2+1];isNaN(S)||isNaN(L)||S==null||L==null?(S=u[b*2],L=u[b*2+1],_[p]=b):b=p,v[k++]=S,v[k++]=L}if(i.splitNull){o.count-1 in _||(_[o.count]=o.count-1);let p=Object.keys(_).map(Number).sort((L,x)=>L-x),k=[],E=0,S=o.hole!=null?o.hole[0]:null;if(S!=null){let L=wNt(p,x=>x>=S);p=p.slice(0,L),p.push(S)}for(let L=0;L<p.length;L++){let x=v.slice(E*2,p[L]*2).concat(S?v.slice(S*2):[]),C=(o.hole||[]).map(g=>g-S+(p[L]-E)),M=fqe(x,C);M=M.map(g=>g+E+(g+E<p[L]?0:S-p[L])),k.push(...M),E=p[L]+1}for(let L=0,x=k.length;L<x;L++)_[k[L]]!=null&&(k[L]=_[k[L]]);o.triangles=k}else{let p=fqe(v,o.hole||[]);for(let k=0,E=p.length;k<E;k++)_[p[k]]!=null&&(p[k]=_[p[k]]);o.triangles=p}}let h=new Float64Array(u);_Nt(h,2,f);let d=new Float64Array(c*2+6);o.close?u[0]===u[c*2-2]&&u[1]===u[c*2-1]?(d[0]=h[c*2-4],d[1]=h[c*2-3]):(d[0]=h[c*2-2],d[1]=h[c*2-1]):(d[0]=h[0],d[1]=h[1]),d.set(h,2),o.close?u[0]===u[c*2-2]&&u[1]===u[c*2-1]?(d[c*2+2]=h[2],d[c*2+3]=h[3],o.count-=1):(d[c*2+2]=h[0],d[c*2+3]=h[1],d[c*2+4]=h[2],d[c*2+5]=h[3]):(d[c*2+2]=h[c*2-2],d[c*2+3]=h[c*2-1],d[c*2+4]=h[c*2-2],d[c*2+5]=h[c*2-1]);var s=xNt(d);o.positionBuffer(s);var l=UY(d,s);o.positionFractBuffer(l)}if(i.range?o.range=i.range:o.range||(o.range=o.bounds),(i.range||i.positions)&&o.count){let u=o.bounds,c=u[2]-u[0],f=u[3]-u[1],h=o.range[2]-o.range[0],d=o.range[3]-o.range[1];o.scale=[c/h,f/d],o.translate=[-o.range[0]/h+u[0]/h||0,-o.range[1]/d+u[1]/d||0],o.scaleFract=UY(o.scale),o.translateFract=UY(o.translate)}if(i.dashes){let u=0,c;if(!i.dashes||i.dashes.length<2)u=1,c=new Uint8Array([255,255,255,255,255,255,255,255]);else{u=0;for(let d=0;d<i.dashes.length;++d)u+=i.dashes[d];c=new Uint8Array(u*Wc.dashMult);let f=0,h=255;for(let d=0;d<2;d++)for(let v=0;v<i.dashes.length;++v){for(let _=0,b=i.dashes[v]*Wc.dashMult*.5;_<b;++_)c[f++]=h;h^=255}}o.dashLength=u,o.dashTexture({channels:1,data:c,width:c.length,height:1,mag:"linear",min:"linear"},0,0)}if(i.color){let u=o.count,c=i.color;c||(c="transparent");let f=new Uint8Array(u*4+4);if(!Array.isArray(c)||typeof c[0]=="number"){let h=yz(c,"uint8");for(let d=0;d<u+1;d++)f.set(h,d*4)}else{for(let h=0;h<u;h++){let d=yz(c[h],"uint8");f.set(d,h*4)}f.set(yz(c[0],"uint8"),u*4)}o.colorBuffer({usage:"dynamic",type:"uint8",data:f})}}}),e.length<this.passes.length){for(let i=e.length;i<this.passes.length;i++){let a=this.passes[i];a&&(a.colorBuffer.destroy(),a.positionBuffer.destroy(),a.dashTexture.destroy())}this.passes.length=e.length}let n=[];for(let i=0;i<this.passes.length;i++)this.passes[i]!==null&&n.push(this.passes[i]);return this.passes=n,this};Wc.prototype.destroy=function(){return this.passes.forEach(e=>{e.colorBuffer.destroy(),e.positionBuffer.destroy(),e.dashTexture.destroy()}),this.passes.length=0,this}});var yqe=ye((V1r,mqe)=>{"use strict";var CNt=X2(),LNt=J_(),PNt=cY(),INt=Zm(),vqe=Oh(),pqe=Z2(),{float32:RNt,fract32:HY}=ez();mqe.exports=DNt;var gqe=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]];function DNt(e,t){if(typeof e=="function"?(t||(t={}),t.regl=e):t=e,t.length&&(t.positions=t),e=t.regl,!e.hasExtension("ANGLE_instanced_arrays"))throw Error("regl-error2d: `ANGLE_instanced_arrays` extension should be enabled");let r=e._gl,n,i,a,o,s,l,u={color:"black",capSize:5,lineWidth:1,opacity:1,viewport:null,range:null,offset:0,count:0,bounds:null,positions:[],errors:[]},c=[];return o=e.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array(0)}),i=e.buffer({usage:"dynamic",type:"float",data:new Uint8Array(0)}),a=e.buffer({usage:"dynamic",type:"float",data:new Uint8Array(0)}),s=e.buffer({usage:"dynamic",type:"float",data:new Uint8Array(0)}),l=e.buffer({usage:"static",type:"float",data:gqe}),v(t),n=e({vert:`
+		precision highp float;
+
+		attribute vec2 position, positionFract;
+		attribute vec4 error;
+		attribute vec4 color;
+
+		attribute vec2 direction, lineOffset, capOffset;
+
+		uniform vec4 viewport;
+		uniform float lineWidth, capSize;
+		uniform vec2 scale, scaleFract, translate, translateFract;
+
+		varying vec4 fragColor;
+
+		void main() {
+			fragColor = color / 255.;
+
+			vec2 pixelOffset = lineWidth * lineOffset + (capSize + lineWidth) * capOffset;
+
+			vec2 dxy = -step(.5, direction.xy) * error.xz + step(direction.xy, vec2(-.5)) * error.yw;
+
+			vec2 position = position + dxy;
+
+			vec2 pos = (position + translate) * scale
+				+ (positionFract + translateFract) * scale
+				+ (position + translate) * scaleFract
+				+ (positionFract + translateFract) * scaleFract;
+
+			pos += pixelOffset / viewport.zw;
+
+			gl_Position = vec4(pos * 2. - 1., 0, 1);
+		}
+		`,frag:`
+		precision highp float;
+
+		varying vec4 fragColor;
+
+		uniform float opacity;
+
+		void main() {
+			gl_FragColor = fragColor;
+			gl_FragColor.a *= opacity;
+		}
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Qe=we.format,at=ne.internalformat=Wi[Qe];ne.format=Bn[at],Qe in en&&("type"in we||(ne.type=en[Qe])),Qe in si&&(ne.compressed=!0),Ve=!0}!qt&&Ve?ne.channels=Go[ne.format]:qt&&!Ve&&ne.channels!==Hs[ne.format]&&(ne.format=ne.internalformat=Hs[ne.channels])}}function Ji(ne){Me.pixelStorei(Vf,ne.flipY),Me.pixelStorei(Jl,ne.premultiplyAlpha),Me.pixelStorei(fl,ne.colorSpace),Me.pixelStorei(nf,ne.unpackAlignment)}function Vi(){hi.call(this),this.xOffset=0,this.yOffset=0,this.data=null,this.needsFree=!1,this.element=null,this.needsCopy=!1}function Ni(ne,we){var Ue=null;if(_f(we)?Ue=we:we&&(mn(ne,we),"x"in we&&(ne.xOffset=we.x|0),"y"in we&&(ne.yOffset=we.y|0),_f(we.data)&&(Ue=we.data)),we.copy){var ft=jr.viewportWidth,Xt=jr.viewportHeight;ne.width=ne.width||ft-ne.xOffset,ne.height=ne.height||Xt-ne.yOffset,ne.needsCopy=!0}else if(!Ue)ne.width=ne.width||1,ne.height=ne.height||1,ne.channels=ne.channels||4;else if(Br(Ue))ne.channels=ne.channels||4,ne.data=Ue,!("type"in 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hr=ne.element,qt=ne.data,Ve=ne.internalformat,Qe=ne.format,at=ne.type,Ct=ne.width,Ot=ne.height;Ji(ne),hr?Me.texSubImage2D(we,Xt,Ue,ft,Qe,at,hr):ne.compressed?Me.compressedTexSubImage2D(we,Xt,Ue,ft,Ve,Ct,Ot,qt):ne.needsCopy?(Rr(),Me.copyTexSubImage2D(we,Xt,Ue,ft,ne.xOffset,ne.yOffset,Ct,Ot)):Me.texSubImage2D(we,Xt,Ue,ft,Ct,Ot,Qe,at,qt)}var na=[];function Ki(){return na.pop()||new Vi}function kn(ne){ne.needsFree&&H.freeType(ne.data),Vi.call(ne),na.push(ne)}function ta(){hi.call(this),this.genMipmaps=!1,this.mipmapHint=sc,this.mipmask=0,this.images=Array(16)}function oa(ne,we,Ue){var ft=ne.images[0]=Ki();ne.mipmask=1,ft.width=ne.width=we,ft.height=ne.height=Ue,ft.channels=ne.channels=4}function ba(ne,we){var Ue=null;if(_f(we))Ue=ne.images[0]=Ki(),li(Ue,ne),Ni(Ue,we),ne.mipmask=1;else if(mn(ne,we),Array.isArray(we.mipmap))for(var ft=we.mipmap,Xt=0;Xt<ft.length;++Xt)Ue=ne.images[Xt]=Ki(),li(Ue,ne),Ue.width>>=Xt,Ue.height>>=Xt,Ni(Ue,ft[Xt]),ne.mipmask|=1<<Xt;else 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at=Va();return typeof qt=="number"?typeof Ve=="number"?oa(at,qt|0,Ve|0):oa(at,qt|0,qt|0):qt?(Qs(Qe,qt),ba(at,qt)):oa(at,1,1),Qe.genMipmaps&&(at.mipmask=(at.width<<1)-1),Ue.mipmask=at.mipmask,li(Ue,at),Ue.internalformat=at.internalformat,ft.width=at.width,ft.height=at.height,ol(Ue),is(at,_n),al(Qe,_n),io(),Ml(at),Gr.profile&&(Ue.stats.size=cc(Ue.internalformat,Ue.type,at.width,at.height,Qe.genMipmaps,!1)),ft.format=xi[Ue.internalformat],ft.type=Ri[Ue.type],ft.mag=ci[Qe.magFilter],ft.min=an[Qe.minFilter],ft.wrapS=Zi[Qe.wrapS],ft.wrapT=Zi[Qe.wrapT],ft}function Xt(qt,Ve,Qe,at){var Ct=Ve|0,Ot=Qe|0,Rt=at|0,Bt=Ki();return li(Bt,Ue),Bt.width=0,Bt.height=0,Ni(Bt,qt),Bt.width=Bt.width||(Ue.width>>Rt)-Ct,Bt.height=Bt.height||(Ue.height>>Rt)-Ot,ol(Ue),Vn(Bt,_n,Ct,Ot,Rt),io(),kn(Bt),ft}function hr(qt,Ve){var Qe=qt|0,at=Ve|0||Qe;if(Qe===Ue.width&&at===Ue.height)return ft;ft.width=Ue.width=Qe,ft.height=Ue.height=at,ol(Ue);for(var Ct=0;Ue.mipmask>>Ct;++Ct){var Ot=Qe>>Ct,Rt=at>>Ct;if(!Ot||!Rt)break;Me.texImage2D(_n,Ct,Ue.format,Ot,Rt,0,Ue.format,Ue.type,null)}return io(),Gr.profile&&(Ue.stats.size=cc(Ue.internalformat,Ue.type,Qe,at,!1,!1)),ft}return ft(ne,we),ft.subimage=Xt,ft.resize=hr,ft._reglType="texture2d",ft._texture=Ue,Gr.profile&&(ft.stats=Ue.stats),ft.destroy=function(){Ue.decRef()},ft}function J(ne,we,Ue,ft,Xt,hr){var qt=new wa(ya);ss[qt.id]=qt,Nr.cubeCount++;var Ve=new Array(6);function Qe(Ot,Rt,Bt,Dt,yt,Pt){var ht,ur=qt.texInfo;for(zo.call(ur),ht=0;ht<6;++ht)Ve[ht]=Va();if(typeof Ot=="number"||!Ot){var br=Ot|0||1;for(ht=0;ht<6;++ht)oa(Ve[ht],br,br)}else if(typeof Ot=="object")if(Rt)ba(Ve[0],Ot),ba(Ve[1],Rt),ba(Ve[2],Bt),ba(Ve[3],Dt),ba(Ve[4],yt),ba(Ve[5],Pt);else if(Qs(ur,Ot),mn(qt,Ot),"faces"in Ot){var Ur=Ot.faces;for(ht=0;ht<6;++ht)li(Ve[ht],qt),ba(Ve[ht],Ur[ht])}else 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ft=0;ft<32;++ft)if((Ue.mipmask&1<<ft)!==0)if(Ue.target===_n)Me.texImage2D(_n,ft,Ue.internalformat,Ue.width>>ft,Ue.height>>ft,0,Ue.internalformat,Ue.type,null);else for(var Xt=0;Xt<6;++Xt)Me.texImage2D(Jn+Xt,ft,Ue.internalformat,Ue.width>>ft,Ue.height>>ft,0,Ue.internalformat,Ue.type,null);al(Ue.texInfo,Ue.target)})}function de(){for(var ne=0;ne<Vs;++ne){var we=Ys[ne];we&&(we.bindCount=0,we.unit=-1,Ys[ne]=null),Me.activeTexture(Fu+ne),Me.bindTexture(_n,null),Me.bindTexture(ya,null)}}return{create2D:D,createCube:J,clear:q,getTexture:function(ne){return null},restore:K,refresh:de}}var Nl=36161,Kc=32854,Rc=32855,gs=36194,jf=33189,Gh=36168,rh=34041,sf=35907,Th=34836,Mu=34842,ih=34843,js=[];js[Kc]=2,js[Rc]=2,js[gs]=2,js[jf]=2,js[Gh]=1,js[rh]=4,js[sf]=4,js[Th]=16,js[Mu]=8,js[ih]=6;function Eu(Me,bt,zt){return js[Me]*bt*zt}var Dc=function(Me,bt,zt,Rr,jr){var Nr={rgba4:Kc,rgb565:gs,"rgb5 a1":Rc,depth:jf,stencil:Gh,"depth 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en(){dt(Ui).forEach(function(Wi){Wi.renderbuffer=Me.createRenderbuffer(),Me.bindRenderbuffer(Nl,Wi.renderbuffer),Me.renderbufferStorage(Nl,Wi.format,Wi.width,Wi.height)}),Me.bindRenderbuffer(Nl,null)}return{create:Hn,clear:function(){dt(Ui).forEach(Ei)},restore:en}},ks=36160,bc=36161,hu=3553,_u=34069,nl=36064,nh=36096,Ah=36128,zu=33306,Fc=36053,wc=36193,bd=5121,xf=5126,Pf=6407,Ou=6408,bf=[];bf[Ou]=4,bf[Pf]=3;var jl=[];jl[bd]=1,jl[xf]=4,jl[wc]=2;function lf(Me,bt,zt,Rr,jr,Nr){var Gr={cur:null,next:null,dirty:!1,setFBO:null},mi=["rgba"],Ui=["rgba4","rgb565","rgb5 a1"];bt.ext_srgb&&Ui.push("srgba"),bt.ext_color_buffer_half_float&&Ui.push("rgba16f","rgb16f"),bt.webgl_color_buffer_float&&Ui.push("rgba32f");var qi=["uint8"];bt.oes_texture_half_float&&qi.push("half float","float16"),bt.oes_texture_float&&qi.push("float","float32");function Ei(Vi,Ni,pn){this.target=Vi,this.texture=Ni,this.renderbuffer=pn;var 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Ot=Qe.static,Rt=Object.keys(Ot);if(Rt.length>0&&typeof Ot[Rt[0]]=="number"){for(var Bt=[],Dt=0;Dt<Rt.length;++Dt)Bt.push([Ot[Rt[Dt]]|0,Rt[Dt]]);return Bt}}return null}function Zs(Ve,Qe,at){var Ct=Ve.static,Ot=Ve.dynamic;function Rt(ht){if(ht in Ct){var ur=bt.id(Ct[ht]),br=En(function(){return ur});return br.id=ur,br}else if(ht in Ot){var Ur=Ot[ht];return Un(Ur,function(Di,fi){var Ti=Di.invoke(fi,Ur),gn=fi.def(Di.shared.strings,".id(",Ti,")");return gn})}return null}var Bt=Rt(ot),Dt=Rt(Xe),yt=null,Pt;return Ln(Bt)&&Ln(Dt)?(yt=Ei.program(Dt.id,Bt.id,null,at),Pt=En(function(ht,ur){return ht.link(yt)})):Pt=new ei(Bt&&Bt.thisDep||Dt&&Dt.thisDep,Bt&&Bt.contextDep||Dt&&Dt.contextDep,Bt&&Bt.propDep||Dt&&Dt.propDep,function(ht,ur){var br=ht.shared.shader,Ur;Bt?Ur=Bt.append(ht,ur):Ur=ur.def(br,".",ot);var Di;Dt?Di=Dt.append(ht,ur):Di=ur.def(br,".",Xe);var fi=br+".program("+Di+","+Ur;return ur.def(fi+")")}),{frag:Bt,vert:Dt,progVar:Pt,program:yt}}function Va(Ve,Qe){var at=Ve.static,Ct=Ve.dynamic,Ot={},Rt=!1;function Bt(){if(ve in at){var rn=at[ve];return rn!==null&&qi.getVAO(rn)===null&&(rn=qi.createVAO(rn)),Rt=!0,Ot.vao=rn,En(function(Bi){var Gi=qi.getVAO(rn);return Gi?Bi.link(Gi):"null"})}else if(ve in Ct){Rt=!0;var Ci=Ct[ve];return Un(Ci,function(Bi,Gi){var sn=Bi.invoke(Gi,Ci);return Gi.def(Bi.shared.vao+".getVAO("+sn+")")})}return null}var Dt=Bt(),yt=!1;function Pt(){if(Tt in at){var rn=at[Tt];if(Ot.elements=rn,Ii(rn)){var Ci=Ot.elements=Nr.create(rn,!0);rn=Nr.getElements(Ci),yt=!0}else rn&&(rn=Nr.getElements(rn),yt=!0);var Bi=En(function(sn,zn){if(rn){var Ja=sn.link(rn);return sn.ELEMENTS=Ja,Ja}return sn.ELEMENTS=null,null});return Bi.value=rn,Bi}else if(Tt in Ct){yt=!0;var Gi=Ct[Tt];return Un(Gi,function(sn,zn){var Ja=sn.shared,co=Ja.isBufferArgs,ts=Ja.elements,so=sn.invoke(zn,Gi),Zo=zn.def("null"),ms=zn.def(co,"(",so,")"),ou=sn.cond(ms).then(Zo,"=",ts,".createStream(",so,");").else(Zo,"=",ts,".getElements(",so,");");return zn.entry(ou),zn.exit(sn.cond(ms).then(ts,".destroyStream(",Zo,");")),sn.ELEMENTS=Zo,Zo})}else if(Rt)return new ei(Dt.thisDep,Dt.contextDep,Dt.propDep,function(sn,zn){return zn.def(sn.shared.vao+".currentVAO?"+sn.shared.elements+".getElements("+sn.shared.vao+".currentVAO.elements):null")});return null}var ht=Pt();function ur(){if(Yt in at){var rn=at[Yt];return Ot.primitive=rn,En(function(Bi,Gi){return Sn[rn]})}else if(Yt in Ct){var Ci=Ct[Yt];return Un(Ci,function(Bi,Gi){var sn=Bi.constants.primTypes,zn=Bi.invoke(Gi,Ci);return Gi.def(sn,"[",zn,"]")})}else{if(yt)return Ln(ht)?ht.value?En(function(Bi,Gi){return Gi.def(Bi.ELEMENTS,".primType")}):En(function(){return Al}):new ei(ht.thisDep,ht.contextDep,ht.propDep,function(Bi,Gi){var sn=Bi.ELEMENTS;return Gi.def(sn,"?",sn,".primType:",Al)});if(Rt)return new ei(Dt.thisDep,Dt.contextDep,Dt.propDep,function(Bi,Gi){return Gi.def(Bi.shared.vao+".currentVAO?"+Bi.shared.vao+".currentVAO.primitive:"+Al)})}return null}function br(rn,Ci){if(rn in at){var Bi=at[rn]|0;return Ci?Ot.offset=Bi:Ot.instances=Bi,En(function(sn,zn){return Ci&&(sn.OFFSET=Bi),Bi})}else if(rn in Ct){var Gi=Ct[rn];return Un(Gi,function(sn,zn){var Ja=sn.invoke(zn,Gi);return Ci&&(sn.OFFSET=Ja),Ja})}else if(Ci){if(yt)return En(function(sn,zn){return sn.OFFSET=0,0});if(Rt)return new ei(Dt.thisDep,Dt.contextDep,Dt.propDep,function(sn,zn){return zn.def(sn.shared.vao+".currentVAO?"+sn.shared.vao+".currentVAO.offset:0")})}else if(Rt)return new ei(Dt.thisDep,Dt.contextDep,Dt.propDep,function(sn,zn){return zn.def(sn.shared.vao+".currentVAO?"+sn.shared.vao+".currentVAO.instances:-1")});return null}var Ur=br(xr,!0);function Di(){if(Kt in at){var rn=at[Kt]|0;return Ot.count=rn,En(function(){return rn})}else if(Kt in Ct){var Ci=Ct[Kt];return Un(Ci,function(zn,Ja){var co=zn.invoke(Ja,Ci);return co})}else if(yt)if(Ln(ht)){if(ht)return Ur?new ei(Ur.thisDep,Ur.contextDep,Ur.propDep,function(zn,Ja){var co=Ja.def(zn.ELEMENTS,".vertCount-",zn.OFFSET);return co}):En(function(zn,Ja){return Ja.def(zn.ELEMENTS,".vertCount")});var Bi=En(function(){return-1});return Bi}else{var Gi=new ei(ht.thisDep||Ur.thisDep,ht.contextDep||Ur.contextDep,ht.propDep||Ur.propDep,function(zn,Ja){var co=zn.ELEMENTS;return zn.OFFSET?Ja.def(co,"?",co,".vertCount-",zn.OFFSET,":-1"):Ja.def(co,"?",co,".vertCount:-1")});return Gi}else if(Rt){var sn=new ei(Dt.thisDep,Dt.contextDep,Dt.propDep,function(zn,Ja){return Ja.def(zn.shared.vao,".currentVAO?",zn.shared.vao,".currentVAO.count:-1")});return sn}return null}var fi=ur(),Ti=Di(),gn=br(Ir,!1);return{elements:ht,primitive:fi,count:Ti,instances:gn,offset:Ur,vao:Dt,vaoActive:Rt,elementsActive:yt,static:Ot}}function Ml(Ve,Qe){var at=Ve.static,Ct=Ve.dynamic,Ot={};return hi.forEach(function(Rt){var Bt=Ji(Rt);function Dt(yt,Pt){if(Rt in at){var ht=yt(at[Rt]);Ot[Bt]=En(function(){return ht})}else if(Rt in Ct){var ur=Ct[Rt];Ot[Bt]=Un(ur,function(br,Ur){return Pt(br,Ur,br.invoke(Ur,ur))})}}switch(Rt){case Cu:case Ho:case ws:case Vc:case va:case B:case Uc:case Ac:case Ua:case rs:return Dt(function(yt){return yt},function(yt,Pt,ht){return ht});case no:return Dt(function(yt){return Zr[yt]},function(yt,Pt,ht){var ur=yt.constants.compareFuncs;return Pt.def(ur,"[",ht,"]")});case ys:return Dt(function(yt){return yt},function(yt,Pt,ht){var ur=Pt.def("+",ht,"[0]"),br=Pt.def("+",ht,"[1]");return[ur,br]});case Ls:return Dt(function(yt){var Pt="srcRGB"in yt?yt.srcRGB:yt.src,ht="srcAlpha"in yt?yt.srcAlpha:yt.src,ur="dstRGB"in yt?yt.dstRGB:yt.dst,br="dstAlpha"in yt?yt.dstAlpha:yt.dst;return[bi[Pt],bi[ur],bi[ht],bi[br]]},function(yt,Pt,ht){var ur=yt.constants.blendFuncs;function br(Ci,Bi){var Gi=Pt.def('"',Ci,Bi,'" in ',ht,"?",ht,".",Ci,Bi,":",ht,".",Ci);return Gi}var Ur=br("src","RGB"),Di=br("dst","RGB"),fi=Pt.def(ur,"[",Ur,"]"),Ti=Pt.def(ur,"[",br("src","Alpha"),"]"),gn=Pt.def(ur,"[",Di,"]"),rn=Pt.def(ur,"[",br("dst","Alpha"),"]");return[fi,gn,Ti,rn]});case Ws:return Dt(function(yt){if(typeof yt=="string")return[xi[yt],xi[yt]];if(typeof yt=="object")return[xi[yt.rgb],xi[yt.alpha]]},function(yt,Pt,ht){var ur=yt.constants.blendEquations,br=Pt.def(),Ur=Pt.def(),Di=yt.cond("typeof ",ht,'==="string"');return Di.then(br,"=",Ur,"=",ur,"[",ht,"];"),Di.else(br,"=",ur,"[",ht,".rgb];",Ur,"=",ur,"[",ht,".alpha];"),Pt(Di),[br,Ur]});case ml:return Dt(function(yt){return M(4,function(Pt){return+yt[Pt]})},function(yt,Pt,ht){return M(4,function(ur){return Pt.def("+",ht,"[",ur,"]")})});case hc:return Dt(function(yt){return yt|0},function(yt,Pt,ht){return Pt.def(ht,"|0")});case Ku:return Dt(function(yt){var Pt=yt.cmp||"keep",ht=yt.ref||0,ur="mask"in yt?yt.mask:-1;return[Zr[Pt],ht,ur]},function(yt,Pt,ht){var ur=yt.constants.compareFuncs,br=Pt.def('"cmp" in ',ht,"?",ur,"[",ht,".cmp]",":",nt),Ur=Pt.def(ht,".ref|0"),Di=Pt.def('"mask" in ',ht,"?",ht,".mask|0:-1");return[br,Ur,Di]});case ue:case w:return Dt(function(yt){var Pt=yt.fail||"keep",ht=yt.zfail||"keep",ur=yt.zpass||"keep";return[Rt===w?se:X,ai[Pt],ai[ht],ai[ur]]},function(yt,Pt,ht){var ur=yt.constants.stencilOps;function br(Ur){return Pt.def('"',Ur,'" in ',ht,"?",ur,"[",ht,".",Ur,"]:",nt)}return[Rt===w?se:X,br("fail"),br("zfail"),br("zpass")]});case xu:return Dt(function(yt){var Pt=yt.factor|0,ht=yt.units|0;return[Pt,ht]},function(yt,Pt,ht){var ur=Pt.def(ht,".factor|0"),br=Pt.def(ht,".units|0");return[ur,br]});case Yu:return Dt(function(yt){var Pt=0;return yt==="front"?Pt=X:yt==="back"&&(Pt=se),Pt},function(yt,Pt,ht){return Pt.def(ht,'==="front"?',X,":",se)});case pu:return Dt(function(yt){return yt},function(yt,Pt,ht){return ht});case Nc:return Dt(function(yt){return gi[yt]},function(yt,Pt,ht){return Pt.def(ht+'==="cw"?'+Te+":"+Ne)});case $l:return Dt(function(yt){return yt.map(function(Pt){return!!Pt})},function(yt,Pt,ht){return M(4,function(ur){return"!!"+ht+"["+ur+"]"})});case oo:return Dt(function(yt){var Pt="value"in yt?yt.value:1,ht=!!yt.invert;return[Pt,ht]},function(yt,Pt,ht){var ur=Pt.def('"value" in ',ht,"?+",ht,".value:1"),br=Pt.def("!!",ht,".invert");return[ur,br]})}}),Ot}function zo(Ve,Qe){var at=Ve.static,Ct=Ve.dynamic,Ot={};return Object.keys(at).forEach(function(Rt){var Bt=at[Rt],Dt;if(typeof Bt=="number"||typeof Bt=="boolean")Dt=En(function(){return Bt});else if(typeof Bt=="function"){var yt=Bt._reglType;yt==="texture2d"||yt==="textureCube"?Dt=En(function(Pt){return Pt.link(Bt)}):(yt==="framebuffer"||yt==="framebufferCube")&&(Dt=En(function(Pt){return Pt.link(Bt.color[0])}))}else Mn(Bt)&&(Dt=En(function(Pt){var ht=Pt.global.def("[",M(Bt.length,function(ur){return Bt[ur]}),"]");return ht}));Dt.value=Bt,Ot[Rt]=Dt}),Object.keys(Ct).forEach(function(Rt){var Bt=Ct[Rt];Ot[Rt]=Un(Bt,function(Dt,yt){return Dt.invoke(yt,Bt)})}),Ot}function Qs(Ve,Qe){var at=Ve.static,Ct=Ve.dynamic,Ot={};return Object.keys(at).forEach(function(Rt){var Bt=at[Rt],Dt=bt.id(Rt),yt=new Yr;if(Ii(Bt))yt.state=La,yt.buffer=jr.getBuffer(jr.create(Bt,Sr,!1,!0)),yt.type=0;else{var Pt=jr.getBuffer(Bt);if(Pt)yt.state=La,yt.buffer=Pt,yt.type=0;else if("constant"in Bt){var ht=Bt.constant;yt.buffer="null",yt.state=Na,typeof ht=="number"?yt.x=ht:Ca.forEach(function(gn,rn){rn<ht.length&&(yt[gn]=ht[rn])})}else{Ii(Bt.buffer)?Pt=jr.getBuffer(jr.create(Bt.buffer,Sr,!1,!0)):Pt=jr.getBuffer(Bt.buffer);var ur=Bt.offset|0,br=Bt.stride|0,Ur=Bt.size|0,Di=!!Bt.normalized,fi=0;"type"in Bt&&(fi=ji[Bt.type]);var Ti=Bt.divisor|0;yt.buffer=Pt,yt.state=La,yt.size=Ur,yt.normalized=Di,yt.type=fi||Pt.dtype,yt.offset=ur,yt.stride=br,yt.divisor=Ti}}Ot[Rt]=En(function(gn,rn){var Ci=gn.attribCache;if(Dt in Ci)return Ci[Dt];var Bi={isStream:!1};return Object.keys(yt).forEach(function(Gi){Bi[Gi]=yt[Gi]}),yt.buffer&&(Bi.buffer=gn.link(yt.buffer),Bi.type=Bi.type||Bi.buffer+".dtype"),Ci[Dt]=Bi,Bi})}),Object.keys(Ct).forEach(function(Rt){var Bt=Ct[Rt];function Dt(yt,Pt){var ht=yt.invoke(Pt,Bt),ur=yt.shared,br=yt.constants,Ur=ur.isBufferArgs,Di=ur.buffer,fi={isStream:Pt.def(!1)},Ti=new Yr;Ti.state=La,Object.keys(Ti).forEach(function(Bi){fi[Bi]=Pt.def(""+Ti[Bi])});var gn=fi.buffer,rn=fi.type;Pt("if(",Ur,"(",ht,")){",fi.isStream,"=true;",gn,"=",Di,".createStream(",Sr,",",ht,");",rn,"=",gn,".dtype;","}else{",gn,"=",Di,".getBuffer(",ht,");","if(",gn,"){",rn,"=",gn,".dtype;",'}else if("constant" in ',ht,"){",fi.state,"=",Na,";","if(typeof "+ht+'.constant === "number"){',fi[Ca[0]],"=",ht,".constant;",Ca.slice(1).map(function(Bi){return fi[Bi]}).join("="),"=0;","}else{",Ca.map(function(Bi,Gi){return fi[Bi]+"="+ht+".constant.length>"+Gi+"?"+ht+".constant["+Gi+"]:0;"}).join(""),"}}else{","if(",Ur,"(",ht,".buffer)){",gn,"=",Di,".createStream(",Sr,",",ht,".buffer);","}else{",gn,"=",Di,".getBuffer(",ht,".buffer);","}",rn,'="type" in ',ht,"?",br.glTypes,"[",ht,".type]:",gn,".dtype;",fi.normalized,"=!!",ht,".normalized;");function Ci(Bi){Pt(fi[Bi],"=",ht,".",Bi,"|0;")}return Ci("size"),Ci("offset"),Ci("stride"),Ci("divisor"),Pt("}}"),Pt.exit("if(",fi.isStream,"){",Di,".destroyStream(",gn,");","}"),fi}Ot[Rt]=Un(Bt,Dt)}),Ot}function al(Ve){var Qe=Ve.static,at=Ve.dynamic,Ct={};return Object.keys(Qe).forEach(function(Ot){var Rt=Qe[Ot];Ct[Ot]=En(function(Bt,Dt){return typeof Rt=="number"||typeof Rt=="boolean"?""+Rt:Bt.link(Rt)})}),Object.keys(at).forEach(function(Ot){var Rt=at[Ot];Ct[Ot]=Un(Rt,function(Bt,Dt){return Bt.invoke(Dt,Rt)})}),Ct}function Vl(Ve,Qe,at,Ct,Ot){var Rt=Ve.static,Bt=Ve.dynamic,Dt=is(Ve,Qe),yt=oa(Ve,Ot),Pt=ba(Ve,yt,Ot),ht=Va(Ve,Ot),ur=Ml(Ve,Ot),br=Zs(Ve,Ot,Dt);function Ur(Ci){var Bi=Pt[Ci];Bi&&(ur[Ci]=Bi)}Ur(ee),Ur(Ji(Q));var Di=Object.keys(ur).length>0,fi={framebuffer:yt,draw:ht,shader:br,state:ur,dirty:Di,scopeVAO:null,drawVAO:null,useVAO:!1,attributes:{}};if(fi.profile=ta(Ve,Ot),fi.uniforms=zo(at,Ot),fi.drawVAO=fi.scopeVAO=ht.vao,!fi.drawVAO&&br.program&&!Dt&&zt.angle_instanced_arrays&&ht.static.elements){var Ti=!0,gn=br.program.attributes.map(function(Ci){var Bi=Qe.static[Ci];return Ti=Ti&&!!Bi,Bi});if(Ti&&gn.length>0){var rn=qi.getVAO(qi.createVAO({attributes:gn,elements:ht.static.elements}));fi.drawVAO=new ei(null,null,null,function(Ci,Bi){return Ci.link(rn)}),fi.useVAO=!0}}return Dt?fi.useVAO=!0:fi.attributes=Qs(Qe,Ot),fi.context=al(Ct,Ot),fi}function ss(Ve,Qe,at){var Ct=Ve.shared,Ot=Ct.context,Rt=Ve.scope();Object.keys(at).forEach(function(Bt){Qe.save(Ot,"."+Bt);var Dt=at[Bt],yt=Dt.append(Ve,Qe);Array.isArray(yt)?Rt(Ot,".",Bt,"=[",yt.join(),"];"):Rt(Ot,".",Bt,"=",yt,";")}),Qe(Rt)}function Vs(Ve,Qe,at,Ct){var Ot=Ve.shared,Rt=Ot.gl,Bt=Ot.framebuffer,Dt;ci&&(Dt=Qe.def(Ot.extensions,".webgl_draw_buffers"));var yt=Ve.constants,Pt=yt.drawBuffer,ht=yt.backBuffer,ur;at?ur=at.append(Ve,Qe):ur=Qe.def(Bt,".next"),Ct||Qe("if(",ur,"!==",Bt,".cur){"),Qe("if(",ur,"){",Rt,".bindFramebuffer(",qr,",",ur,".framebuffer);"),ci&&Qe(Dt,".drawBuffersWEBGL(",Pt,"[",ur,".colorAttachments.length]);"),Qe("}else{",Rt,".bindFramebuffer(",qr,",null);"),ci&&Qe(Dt,".drawBuffersWEBGL(",ht,");"),Qe("}",Bt,".cur=",ur,";"),Ct||Qe("}")}function Ys(Ve,Qe,at){var Ct=Ve.shared,Ot=Ct.gl,Rt=Ve.current,Bt=Ve.next,Dt=Ct.current,yt=Ct.next,Pt=Ve.cond(Dt,".dirty");hi.forEach(function(ht){var ur=Ji(ht);if(!(ur in at.state)){var br,Ur;if(ur in Bt){br=Bt[ur],Ur=Rt[ur];var Di=M(Zi[ur].length,function(Ti){return Pt.def(br,"[",Ti,"]")});Pt(Ve.cond(Di.map(function(Ti,gn){return Ti+"!=="+Ur+"["+gn+"]"}).join("||")).then(Ot,".",mn[ur],"(",Di,");",Di.map(function(Ti,gn){return Ur+"["+gn+"]="+Ti}).join(";"),";"))}else{br=Pt.def(yt,".",ur);var fi=Ve.cond(br,"!==",Dt,".",ur);Pt(fi),ur in li?fi(Ve.cond(br).then(Ot,".enable(",li[ur],");").else(Ot,".disable(",li[ur],");"),Dt,".",ur,"=",br,";"):fi(Ot,".",mn[ur],"(",br,");",Dt,".",ur,"=",br,";")}}}),Object.keys(at.state).length===0&&Pt(Dt,".dirty=false;"),Qe(Pt)}function wa(Ve,Qe,at,Ct){var Ot=Ve.shared,Rt=Ve.current,Bt=Ot.current,Dt=Ot.gl,yt;Si(Object.keys(at)).forEach(function(Pt){var ht=at[Pt];if(!(Ct&&!Ct(ht))){var ur=ht.append(Ve,Qe);if(li[Pt]){var br=li[Pt];Ln(ht)?(yt=Ve.link(ur,{stable:!0}),Qe(Ve.cond(yt).then(Dt,".enable(",br,");").else(Dt,".disable(",br,");")),Qe(Bt,".",Pt,"=",yt,";")):(Qe(Ve.cond(ur).then(Dt,".enable(",br,");").else(Dt,".disable(",br,");")),Qe(Bt,".",Pt,"=",ur,";"))}else if(Mn(ur)){var Ur=Rt[Pt];Qe(Dt,".",mn[Pt],"(",ur,");",ur.map(function(Di,fi){return Ur+"["+fi+"]="+Di}).join(";"),";")}else Ln(ht)?(yt=Ve.link(ur,{stable:!0}),Qe(Dt,".",mn[Pt],"(",yt,");",Bt,".",Pt,"=",yt,";")):Qe(Dt,".",mn[Pt],"(",ur,");",Bt,".",Pt,"=",ur,";")}})}function ol(Ve,Qe){Ri&&(Ve.instancing=Qe.def(Ve.shared.extensions,".angle_instanced_arrays"))}function io(Ve,Qe,at,Ct,Ot){var Rt=Ve.shared,Bt=Ve.stats,Dt=Rt.current,yt=Rt.timer,Pt=at.profile;function ht(){return typeof performance=="undefined"?"Date.now()":"performance.now()"}var ur,br;function Ur(Ci){ur=Qe.def(),Ci(ur,"=",ht(),";"),typeof Ot=="string"?Ci(Bt,".count+=",Ot,";"):Ci(Bt,".count++;"),Wi&&(Ct?(br=Qe.def(),Ci(br,"=",yt,".getNumPendingQueries();")):Ci(yt,".beginQuery(",Bt,");"))}function Di(Ci){Ci(Bt,".cpuTime+=",ht(),"-",ur,";"),Wi&&(Ct?Ci(yt,".pushScopeStats(",br,",",yt,".getNumPendingQueries(),",Bt,");"):Ci(yt,".endQuery();"))}function fi(Ci){var Bi=Qe.def(Dt,".profile");Qe(Dt,".profile=",Ci,";"),Qe.exit(Dt,".profile=",Bi,";")}var Ti;if(Pt){if(Ln(Pt)){Pt.enable?(Ur(Qe),Di(Qe.exit),fi("true")):fi("false");return}Ti=Pt.append(Ve,Qe),fi(Ti)}else Ti=Qe.def(Dt,".profile");var gn=Ve.block();Ur(gn),Qe("if(",Ti,"){",gn,"}");var rn=Ve.block();Di(rn),Qe.exit("if(",Ti,"){",rn,"}")}function 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t[0]=="number"?n=wz(t):Array.isArray(t)?n=t:(t.o?n=wz(t.o):t.pairs&&(n=t.pairs),t.fontSize&&(i=t.fontSize),t.threshold!=null&&(a=t.threshold))),n||(n=mBe),bz.font=i+"px "+e;for(var o=0;o<n.length;o++){var s=n[o],l=bz.measureText(s[0]).width+bz.measureText(s[1]).width,u=bz.measureText(s).width;if(Math.abs(l-u)>i*a){var c=(u-l)/i;r[s]=c*1e3}}return r}function wz(e){for(var t=[],r=e[0];r<=e[1];r++)for(var n=String.fromCharCode(r),i=e[0];i<e[1];i++){var a=String.fromCharCode(i),o=n+a;t.push(o)}return t}});var TBe=ye((f_r,wBe)=>{"use strict";wBe.exports=hx;hx.canvas=document.createElement("canvas");hx.cache={};function hx(o,t){t||(t={}),(typeof o=="string"||Array.isArray(o))&&(t.family=o);var r=Array.isArray(t.family)?t.family.join(", "):t.family;if(!r)throw Error("`family` must be defined");var n=t.size||t.fontSize||t.em||48,i=t.weight||t.fontWeight||"",a=t.style||t.fontStyle||"",o=[a,i,n].join(" ")+"px "+r,s=t.origin||"top";if(hx.cache[r]&&n<=hx.cache[r].em)return xBe(hx.cache[r],s);var l=t.canvas||hx.canvas,u=l.getContext("2d"),c={upper:t.upper!==void 0?t.upper:"H",lower:t.lower!==void 0?t.lower:"x",descent:t.descent!==void 0?t.descent:"p",ascent:t.ascent!==void 0?t.ascent:"h",tittle:t.tittle!==void 0?t.tittle:"i",overshoot:t.overshoot!==void 0?t.overshoot:"O"},f=Math.ceil(n*1.5);l.height=f,l.width=f*.5,u.font=o;var h="H",d={top:0};u.clearRect(0,0,f,f),u.textBaseline="top",u.fillStyle="black",u.fillText(h,0,0);var v=Km(u.getImageData(0,0,f,f));u.clearRect(0,0,f,f),u.textBaseline="bottom",u.fillText(h,0,f);var _=Km(u.getImageData(0,0,f,f));d.lineHeight=d.bottom=f-_+v,u.clearRect(0,0,f,f),u.textBaseline="alphabetic",u.fillText(h,0,f);var b=Km(u.getImageData(0,0,f,f)),p=f-b-1+v;d.baseline=d.alphabetic=p,u.clearRect(0,0,f,f),u.textBaseline="middle",u.fillText(h,0,f*.5);var k=Km(u.getImageData(0,0,f,f));d.median=d.middle=f-k-1+v-f*.5,u.clearRect(0,0,f,f),u.textBaseline="hanging",u.fillText(h,0,f*.5);var E=Km(u.getImageData(0,0,f,f));d.hanging=f-E-1+v-f*.5,u.clearRect(0,0,f,f),u.textBaseline="ideographic",u.fillText(h,0,f);var S=Km(u.getImageData(0,0,f,f));if(d.ideographic=f-S-1+v,c.upper&&(u.clearRect(0,0,f,f),u.textBaseline="top",u.fillText(c.upper,0,0),d.upper=Km(u.getImageData(0,0,f,f)),d.capHeight=d.baseline-d.upper),c.lower&&(u.clearRect(0,0,f,f),u.textBaseline="top",u.fillText(c.lower,0,0),d.lower=Km(u.getImageData(0,0,f,f)),d.xHeight=d.baseline-d.lower),c.tittle&&(u.clearRect(0,0,f,f),u.textBaseline="top",u.fillText(c.tittle,0,0),d.tittle=Km(u.getImageData(0,0,f,f))),c.ascent&&(u.clearRect(0,0,f,f),u.textBaseline="top",u.fillText(c.ascent,0,0),d.ascent=Km(u.getImageData(0,0,f,f))),c.descent&&(u.clearRect(0,0,f,f),u.textBaseline="top",u.fillText(c.descent,0,0),d.descent=bBe(u.getImageData(0,0,f,f))),c.overshoot){u.clearRect(0,0,f,f),u.textBaseline="top",u.fillText(c.overshoot,0,0);var L=bBe(u.getImageData(0,0,f,f));d.overshoot=L-p}for(var x in d)d[x]/=n;return d.em=n,hx.cache[r]=d,xBe(d,s)}function xBe(e,t){var r={};typeof t=="string"&&(t=e[t]);for(var n in e)n!=="em"&&(r[n]=e[n]-t);return r}function Km(e){for(var t=e.height,r=e.data,n=3;n<r.length;n+=4)if(r[n]!==0)return Math.floor((n-3)*.25/t)}function bBe(e){for(var t=e.height,r=e.data,n=r.length-1;n>0;n-=4)if(r[n]!==0)return Math.floor((n-3)*.25/t)}});var EBe=ye((h_r,MBe)=>{"use strict";var p5=Oqe(),xUt=Zm(),bUt=qqe(),wUt=Gqe(),TUt=NY(),lK=J_(),AUt=jqe(),dx=cBe(),SUt=i5(),MUt=hBe(),EUt=oK(),kUt=gBe(),CUt=_Be(),LUt=Oh(),PUt=TBe(),IUt=Z2(),RUt=nK(),ABe=RUt.nextPow2,SBe=new TUt,Sz=!1;document.body&&(Az=document.body.appendChild(document.createElement("div")),Az.style.font="italic small-caps bold condensed 16px/2 cursive",getComputedStyle(Az).fontStretch&&(Sz=!0),document.body.removeChild(Az));var Az,xc=function(t){DUt(t)?(t={regl:t},this.gl=t.regl._gl):this.gl=wUt(t),this.shader=SBe.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||bUt({gl:this.gl}),this.charBuffer=this.regl.buffer({type:"uint8",usage:"stream"}),this.sizeBuffer=this.regl.buffer({type:"float",usage:"stream"}),this.shader||(this.shader=this.createShader(),SBe.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(MUt(t)?t:{})};xc.prototype.createShader=function(){var t=this.regl,r=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop("count"),offset:t.prop("offset"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this("sizeBuffer")},width:{offset:0,stride:8,buffer:t.this("sizeBuffer")},char:t.this("charBuffer"),position:t.this("position")},uniforms:{atlasSize:function(i,a){return[a.atlas.width,a.atlas.height]},atlasDim:function(i,a){return[a.atlas.cols,a.atlas.rows]},atlas:function(i,a){return a.atlas.texture},charStep:function(i,a){return a.atlas.step},em:function(i,a){return a.atlas.em},color:t.prop("color"),opacity:t.prop("opacity"),viewport:t.this("viewportArray"),scale:t.this("scale"),align:t.prop("align"),baseline:t.prop("baseline"),translate:t.this("translate"),positionOffset:t.prop("positionOffset")},primitive:"points",viewport:t.this("viewport"),vert:`
+			precision highp float;
+			attribute float width, charOffset, char;
+			attribute vec2 position;
+			uniform float fontSize, charStep, em, align, baseline;
+			uniform vec4 viewport;
+			uniform vec4 color;
+			uniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;
+			varying vec2 charCoord, charId;
+			varying float charWidth;
+			varying vec4 fontColor;
+			void main () {
+				vec2 offset = floor(em * (vec2(align + charOffset, baseline)
+					+ vec2(positionOffset.x, -positionOffset.y)))
+					/ (viewport.zw * scale.xy);
+
+				vec2 position = (position + translate) * scale;
+				position += offset * scale;
+
+				charCoord = position * viewport.zw + viewport.xy;
+
+				gl_Position = vec4(position * 2. - 1., 0, 1);
+
+				gl_PointSize = charStep;
+
+				charId.x = mod(char, atlasDim.x);
+				charId.y = floor(char / atlasDim.x);
+
+				charWidth = width * em;
+
+				fontColor = color / 255.;
+			}`,frag:`
+			precision highp float;
+			uniform float fontSize, charStep, opacity;
+			uniform vec2 atlasSize;
+			uniform vec4 viewport;
+			uniform sampler2D atlas;
+			varying vec4 fontColor;
+			varying vec2 charCoord, charId;
+			varying float charWidth;
+
+			float lightness(vec4 color) {
+				return color.r * 0.299 + color.g * 0.587 + color.b * 0.114;
+			}
+
+			void main () {
+				vec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;
+				float halfCharStep = floor(charStep * .5 + .5);
+
+				// invert y and shift by 1px (FF expecially needs that)
+				uv.y = charStep - uv.y;
+
+				// ignore points outside of character bounding box
+				float halfCharWidth = ceil(charWidth * .5);
+				if (floor(uv.x) > halfCharStep + halfCharWidth ||
+					floor(uv.x) < halfCharStep - halfCharWidth) return;
+
+				uv += charId * charStep;
+				uv = uv / atlasSize;
+
+				vec4 color = fontColor;
+				vec4 mask = texture2D(atlas, uv);
+
+				float maskY = lightness(mask);
+				// float colorY = lightness(color);
+				color.a *= maskY;
+				color.a *= opacity;
+
+				// color.a += .1;
+
+				// antialiasing, see yiq color space y-channel formula
+				// color.rgb += (1. - color.rgb) * (1. - mask.rgb);
+
+				gl_FragColor = color;
+			}`}),n={};return{regl:t,draw:r,atlas:n}};xc.prototype.update=function(t){var r=this;if(typeof t=="string")t={text:t};else if(!t)return;t=xUt(t,{position:"position positions coord coords coordinates",font:"font fontFace fontface typeface cssFont css-font family fontFamily",fontSize:"fontSize fontsize size font-size",text:"text texts chars characters value values symbols",align:"align alignment textAlign textbaseline",baseline:"baseline textBaseline textbaseline",direction:"dir direction textDirection",color:"color colour fill fill-color fillColor textColor textcolor",kerning:"kerning kern",range:"range dataBox",viewport:"vp viewport viewBox viewbox viewPort",opacity:"opacity alpha transparency visible visibility opaque",offset:"offset positionOffset padding shift indent indentation"},!0),t.opacity!=null&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map(function(Ce){return parseFloat(Ce)}):this.opacity=parseFloat(t.opacity)),t.viewport!=null&&(this.viewport=SUt(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),this.viewport==null&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),t.kerning!=null&&(this.kerning=t.kerning),t.offset!=null&&(typeof t.offset=="number"&&(t.offset=[t.offset,0]),this.positionOffset=IUt(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),!this.font.length&&!t.font&&(t.font=xc.baseFontSize+"px sans-serif");var n=!1,i=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach(function(Ce,me){if(typeof Ce=="string")try{Ce=p5.parse(Ce)}catch(Ze){Ce=p5.parse(xc.baseFontSize+"px "+Ce)}else{var ie=Ce.style,Se=Ce.weight,Le=Ce.stretch,Ae=Ce.variant;Ce=p5.parse(p5.stringify(Ce)),ie&&(Ce.style=ie),Se&&(Ce.weight=Se),Le&&(Ce.stretch=Le),Ae&&(Ce.variant=Ae)}var Fe=p5.stringify({size:xc.baseFontSize,family:Ce.family,stretch:Sz?Ce.stretch:void 0,variant:Ce.variant,weight:Ce.weight,style:Ce.style}),Pe=EUt(Ce.size),ge=Math.round(Pe[0]*kUt(Pe[1]));if(ge!==r.fontSize[me]&&(i=!0,r.fontSize[me]=ge),(!r.font[me]||Fe!=r.font[me].baseString)&&(n=!0,r.font[me]=xc.fonts[Fe],!r.font[me])){var Re=Ce.family.join(", "),ce=[Ce.style];Ce.style!=Ce.variant&&ce.push(Ce.variant),Ce.variant!=Ce.weight&&ce.push(Ce.weight),Sz&&Ce.weight!=Ce.stretch&&ce.push(Ce.stretch),r.font[me]={baseString:Fe,family:Re,weight:Ce.weight,stretch:Ce.stretch,style:Ce.style,variant:Ce.variant,width:{},kerning:{},metrics:PUt(Re,{origin:"top",fontSize:xc.baseFontSize,fontStyle:ce.join(" ")})},xc.fonts[Fe]=r.font[me]}}),(n||i)&&this.font.forEach(function(Ce,me){var ie=p5.stringify({size:r.fontSize[me],family:Ce.family,stretch:Sz?Ce.stretch:void 0,variant:Ce.variant,weight:Ce.weight,style:Ce.style});if(r.fontAtlas[me]=r.shader.atlas[ie],!r.fontAtlas[me]){var Se=Ce.metrics;r.shader.atlas[ie]=r.fontAtlas[me]={fontString:ie,step:Math.ceil(r.fontSize[me]*Se.bottom*.5)*2,em:r.fontSize[me],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:r.regl.texture()}}t.text==null&&(t.text=r.text)}),typeof t.text=="string"&&t.position&&t.position.length>2){for(var a=Array(t.position.length*.5),o=0;o<a.length;o++)a[o]=t.text;t.text=a}var s;if(t.text!=null||n){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var l=1;l<t.text.length;l++)this.textOffsets[l]=this.textOffsets[l-1]+t.text[l-1].length,this.count+=t.text[l].length,this.counts.push(t.text[l].length);this.text=t.text.join("")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];s=[],this.font.forEach(function(Ce,me){xc.atlasContext.font=Ce.baseString;for(var ie=r.fontAtlas[me],Se=0;Se<r.text.length;Se++){var Le=r.text.charAt(Se);if(ie.ids[Le]==null&&(ie.ids[Le]=ie.chars.length,ie.chars.push(Le),s.push(Le)),Ce.width[Le]==null&&(Ce.width[Le]=xc.atlasContext.measureText(Le).width/xc.baseFontSize,r.kerning)){var Ae=[];for(var Fe in Ce.width)Ae.push(Fe+Le,Le+Fe);LUt(Ce.kerning,CUt(Ce.family,{pairs:Ae}))}}})}if(t.position)if(t.position.length>2){for(var u=!t.position[0].length,c=dx.mallocFloat(this.count*2),f=0,h=0;f<this.counts.length;f++){var d=this.counts[f];if(u)for(var v=0;v<d;v++)c[h++]=t.position[f*2],c[h++]=t.position[f*2+1];else for(var _=0;_<d;_++)c[h++]=t.position[f][0],c[h++]=t.position[f][1]}this.position.call?this.position({type:"float",data:c}):this.position=this.regl.buffer({type:"float",data:c}),dx.freeFloat(c)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||n){var b=dx.mallocUint8(this.count),p=dx.mallocFloat(this.count*2);this.textWidth=[];for(var k=0,E=0;k<this.counts.length;k++){for(var S=this.counts[k],L=this.font[k]||this.font[0],x=this.fontAtlas[k]||this.fontAtlas[0],C=0;C<S;C++){var M=this.text.charAt(E),g=this.text.charAt(E-1);if(b[E]=x.ids[M],p[E*2]=L.width[M],C){var P=p[E*2-2],T=p[E*2],z=p[E*2-1],O=z+P*.5+T*.5;if(this.kerning){var V=L.kerning[g+M];V&&(O+=V*.001)}p[E*2+1]=O}else p[E*2+1]=p[E*2]*.5;E++}this.textWidth.push(p.length?p[E*2-2]*.5+p[E*2-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:b,type:"uint8",usage:"stream"}),this.sizeBuffer({data:p,type:"float",usage:"stream"}),dx.freeUint8(b),dx.freeFloat(p),s.length&&this.font.forEach(function(Ce,me){var ie=r.fontAtlas[me],Se=ie.step,Le=Math.floor(xc.maxAtlasSize/Se),Ae=Math.min(Le,ie.chars.length),Fe=Math.ceil(ie.chars.length/Ae),Pe=ABe(Ae*Se),ge=ABe(Fe*Se);ie.width=Pe,ie.height=ge,ie.rows=Fe,ie.cols=Ae,ie.em&&ie.texture({data:AUt({canvas:xc.atlasCanvas,font:ie.fontString,chars:ie.chars,shape:[Pe,ge],step:[Se,Se]})})})}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map(function(Ce,me){var ie=Array.isArray(r.align)?r.align.length>1?r.align[me]:r.align[0]:r.align;if(typeof ie=="number")return ie;switch(ie){case"right":case"end":return-Ce;case"center":case"centre":case"middle":return-Ce*.5}return 0})),this.baseline==null&&t.baseline==null&&(t.baseline=0),t.baseline!=null&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map(function(Ce,me){var ie=(r.font[me]||r.font[0]).metrics,Se=0;return Se+=ie.bottom*.5,typeof Ce=="number"?Se+=Ce-ie.baseline:Se+=-ie[Ce],Se*=-1,Se})),t.color!=null)if(t.color||(t.color="transparent"),typeof t.color=="string"||!isNaN(t.color))this.color=lK(t.color,"uint8");else{var G;if(typeof t.color[0]=="number"&&t.color.length>this.counts.length){var Z=t.color.length;G=dx.mallocUint8(Z);for(var j=(t.color.subarray||t.color.slice).bind(t.color),N=0;N<Z;N+=4)G.set(lK(j(N,N+4),"uint8"),N)}else{var H=t.color.length;G=dx.mallocUint8(H*4);for(var te=0;te<H;te++)G.set(lK(t.color[te]||0,"uint8"),te*4)}this.color=G}if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity){var oe=this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2;if(oe){var _e=Math.max(this.position.length*.5||0,this.color.length*.25||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,this.positionOffset.length*.5||0);this.batch=Array(_e);for(var 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Dt=0;qt.mipmask>>Dt;++Dt)Me.texImage2D(Jn+Bt,Dt,qt.format,Rt>>Dt,Rt>>Dt,0,qt.format,qt.type,null);return io(),Gr.profile&&(qt.stats.size=cc(qt.internalformat,qt.type,Qe.width,Qe.height,!1,!0)),Qe}}return Qe(ne,we,Ue,ft,Xt,hr),Qe.subimage=at,Qe.resize=Ct,Qe._reglType="textureCube",Qe._texture=qt,Gr.profile&&(Qe.stats=qt.stats),Qe.destroy=function(){qt.decRef()},Qe}function q(){for(var ne=0;ne<Vs;++ne)Me.activeTexture(Fu+ne),Me.bindTexture(_n,null),Ys[ne]=null;dt(ss).forEach(Y),Nr.cubeCount=0,Nr.textureCount=0}Gr.profile&&(Nr.getTotalTextureSize=function(){var ne=0;return Object.keys(ss).forEach(function(we){ne+=ss[we].stats.size}),ne});function K(){for(var ne=0;ne<Vs;++ne){var we=Ys[ne];we&&(we.bindCount=0,we.unit=-1,Ys[ne]=null)}dt(ss).forEach(function(Ue){Ue.texture=Me.createTexture(),Me.bindTexture(Ue.target,Ue.texture);for(var ft=0;ft<32;++ft)if((Ue.mipmask&1<<ft)!==0)if(Ue.target===_n)Me.texImage2D(_n,ft,Ue.internalformat,Ue.width>>ft,Ue.height>>ft,0,Ue.internalformat,Ue.type,null);else for(var Xt=0;Xt<6;++Xt)Me.texImage2D(Jn+Xt,ft,Ue.internalformat,Ue.width>>ft,Ue.height>>ft,0,Ue.internalformat,Ue.type,null);al(Ue.texInfo,Ue.target)})}function de(){for(var ne=0;ne<Vs;++ne){var we=Ys[ne];we&&(we.bindCount=0,we.unit=-1,Ys[ne]=null),Me.activeTexture(Fu+ne),Me.bindTexture(_n,null),Me.bindTexture(ya,null)}}return{create2D:D,createCube:J,clear:q,getTexture:function(ne){return null},restore:K,refresh:de}}var Nl=36161,Kc=32854,Rc=32855,gs=36194,jf=33189,Gh=36168,rh=34041,sf=35907,Th=34836,Mu=34842,ih=34843,js=[];js[Kc]=2,js[Rc]=2,js[gs]=2,js[jf]=2,js[Gh]=1,js[rh]=4,js[sf]=4,js[Th]=16,js[Mu]=8,js[ih]=6;function Eu(Me,bt,zt){return js[Me]*bt*zt}var Dc=function(Me,bt,zt,Rr,jr){var Nr={rgba4:Kc,rgb565:gs,"rgb5 a1":Rc,depth:jf,stencil:Gh,"depth 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xi(pn.depthAttachment,ta,oa),xi(pn.stencilAttachment,ta,oa),xi(pn.depthStencilAttachment,ta,oa),pn.width=Vn.width=ta,pn.height=Vn.height=oa,hi(pn),Vn}return Vn(Vi,Ni),e(Vn,{resize:na,_reglType:"framebuffer",_framebuffer:pn,destroy:function(){Bn(pn),Zi(pn)},use:function(Ki){Gr.setFBO({framebuffer:Vn},Ki)}})}function mn(Vi){var Ni=Array(6);function pn(na){var Ki,kn={color:null},ta=0,oa=null,ba="rgba",is="uint8",Zs=1;if(typeof na=="number")ta=na|0;else if(!na)ta=1;else{var Va=na;if("shape"in Va){var Ml=Va.shape;ta=Ml[0]}else"radius"in Va&&(ta=Va.radius|0),"width"in Va?(ta=Va.width|0,"height"in Va):"height"in Va&&(ta=Va.height|0);("color"in Va||"colors"in Va)&&(oa=Va.color||Va.colors,Array.isArray(oa)),oa||("colorCount"in Va&&(Zs=Va.colorCount|0),"colorType"in Va&&(is=Va.colorType),"colorFormat"in Va&&(ba=Va.colorFormat)),"depth"in Va&&(kn.depth=Va.depth),"stencil"in Va&&(kn.stencil=Va.stencil),"depthStencil"in Va&&(kn.depthStencil=Va.depthStencil)}var 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hi=="function"&&hi._vao?hi._vao:null}function xi(hi){if(hi!==en.currentVAO){var li=si();hi?li.bindVertexArrayOES(hi.vao):li.bindVertexArrayOES(null),en.currentVAO=hi}}function Ri(hi){if(hi!==en.currentVAO){if(hi)hi.bindAttrs();else{for(var li=Mr(),mn=0;mn<Ui.length;++mn){var Ji=Ui[mn];Ji.buffer?(Me.enableVertexAttribArray(mn),Ji.buffer.bind(),Me.vertexAttribPointer(mn,Ji.size,Ji.type,Ji.normalized,Ji.stride,Ji.offfset),li&&Ji.divisor&&li.vertexAttribDivisorANGLE(mn,Ji.divisor)):(Me.disableVertexAttribArray(mn),Me.vertexAttrib4f(mn,Ji.x,Ji.y,Ji.z,Ji.w))}Gr.elements?Me.bindBuffer(Cs,Gr.elements.buffer.buffer):Me.bindBuffer(Cs,null)}en.currentVAO=hi}}function ci(){dt(Hn).forEach(function(hi){hi.destroy()})}function an(){this.id=++Ei,this.attributes=[],this.elements=null,this.ownsElements=!1,this.count=0,this.offset=0,this.instances=-1,this.primitive=4;var hi=si();hi?this.vao=hi.createVertexArrayOES():this.vao=null,Hn[this.id]=this,this.buffers=[]}an.prototype.bindAttrs=function(){for(var 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Ve.replace(".","_")}function Vi(Ve,Qe,at){var Ct=Ji(Ve);hi.push(Ve),Bn[Ct]=Zi[Ct]=!!at,li[Ct]=Qe}function Ni(Ve,Qe,at){var Ct=Ji(Ve);hi.push(Ve),Array.isArray(at)?(Zi[Ct]=at.slice(),Bn[Ct]=at.slice()):Zi[Ct]=Bn[Ct]=at,mn[Ct]=Qe}function pn(Ve){return!!isNaN(Ve)}Vi(ws,Pi),Vi(Ho,ni),Ni(ml,"blendColor",[0,0,0,0]),Ni(Ws,"blendEquationSeparate",[yr,yr]),Ni(Ls,"blendFuncSeparate",[gr,jt,gr,jt]),Vi(va,ln,!0),Ni(no,"depthFunc",Hr),Ni(ys,"depthRange",[0,1]),Ni(rs,"depthMask",!0),Ni($l,$l,[!0,!0,!0,!0]),Vi(Cu,Wr),Ni(Yu,"cullFace",se),Ni(Nc,Nc,Ne),Ni(pu,pu,1),Vi(Uc,Kn),Ni(xu,"polygonOffset",[0,0]),Vi(Ac,Ta),Vi(Ua,fa),Ni(oo,"sampleCoverage",[1,!1]),Vi(Vc,cn),Ni(hc,"stencilMask",-1),Ni(Ku,"stencilFunc",[kt,0,-1]),Ni(ue,"stencilOpSeparate",[X,nt,nt,nt]),Ni(w,"stencilOpSeparate",[se,nt,nt,nt]),Vi(B,Cn),Ni(Q,"scissor",[0,0,Me.drawingBufferWidth,Me.drawingBufferHeight]),Ni(ee,ee,[0,0,Me.drawingBufferWidth,Me.drawingBufferHeight]);var Vn={gl:Me,context:en,strings:bt,next:Bn,current:Zi,draw:Hn,elements:Nr,buffer:jr,shader:Ei,attributes:qi.state,vao:qi,uniforms:Ui,framebuffer:mi,extensions:zt,timer:Wi,isBufferArgs:Ii},na={primTypes:Sn,compareFuncs:Zr,blendFuncs:bi,blendEquations:xi,stencilOps:ai,glTypes:ji,orientationType:gi};ci&&(na.backBuffer=[se],na.drawBuffer=M(Rr.maxDrawbuffers,function(Ve){return Ve===0?[0]:M(Ve,function(Qe){return _i+Qe})}));var Ki=0;function kn(){var Ve=$n({cache:si}),Qe=Ve.link,at=Ve.global;Ve.id=Ki++,Ve.batchId="0";var Ct=Qe(Vn),Ot=Ve.shared={props:"a0"};Object.keys(Vn).forEach(function(Pt){Ot[Pt]=at.def(Ct,".",Pt)});var Rt=Ve.next={},Bt=Ve.current={};Object.keys(mn).forEach(function(Pt){Array.isArray(Zi[Pt])&&(Rt[Pt]=at.def(Ot.next,".",Pt),Bt[Pt]=at.def(Ot.current,".",Pt))});var Dt=Ve.constants={};Object.keys(na).forEach(function(Pt){Dt[Pt]=at.def(JSON.stringify(na[Pt]))}),Ve.invoke=function(Pt,ht){switch(ht.type){case Yn:var ur=["this",Ot.context,Ot.props,Ve.batchId];return Pt.def(Qe(ht.data),".call(",ur.slice(0,Math.max(ht.data.length+1,4)),")");case Dn:return Pt.def(Ot.props,ht.data);case Ka:return Pt.def(Ot.context,ht.data);case bo:return Pt.def("this",ht.data);case Xo:return ht.data.append(Ve,Pt),ht.data.ref;case Ss:return ht.data.toString();case as:return ht.data.map(function(br){return Ve.invoke(Pt,br)})}},Ve.attribCache={};var yt={};return Ve.scopeAttrib=function(Pt){var ht=bt.id(Pt);if(ht in yt)return yt[ht];var ur=qi.scope[ht];ur||(ur=qi.scope[ht]=new Yr);var br=yt[ht]=Qe(ur);return br},Ve}function ta(Ve){var Qe=Ve.static,at=Ve.dynamic,Ct;if(le in Qe){var Ot=!!Qe[le];Ct=En(function(Bt,Dt){return Ot}),Ct.enable=Ot}else if(le in at){var Rt=at[le];Ct=Un(Rt,function(Bt,Dt){return Bt.invoke(Dt,Rt)})}return Ct}function oa(Ve,Qe){var at=Ve.static,Ct=Ve.dynamic;if(qe in at){var Ot=at[qe];return Ot?(Ot=mi.getFramebuffer(Ot),En(function(Bt,Dt){var yt=Bt.link(Ot),Pt=Bt.shared;Dt.set(Pt.framebuffer,".next",yt);var ht=Pt.context;return Dt.set(ht,"."+Be,yt+".width"),Dt.set(ht,"."+et,yt+".height"),yt})):En(function(Bt,Dt){var yt=Bt.shared;Dt.set(yt.framebuffer,".next","null");var Pt=yt.context;return Dt.set(Pt,"."+Be,Pt+"."+Ht),Dt.set(Pt,"."+et,Pt+"."+tr),"null"})}else if(qe in Ct){var Rt=Ct[qe];return Un(Rt,function(Bt,Dt){var yt=Bt.invoke(Dt,Rt),Pt=Bt.shared,ht=Pt.framebuffer,ur=Dt.def(ht,".getFramebuffer(",yt,")");Dt.set(ht,".next",ur);var br=Pt.context;return Dt.set(br,"."+Be,ur+"?"+ur+".width:"+br+"."+Ht),Dt.set(br,"."+et,ur+"?"+ur+".height:"+br+"."+tr),ur})}else return null}function ba(Ve,Qe,at){var Ct=Ve.static,Ot=Ve.dynamic;function Rt(yt){if(yt in Ct){var Pt=Ct[yt],ht=!0,ur=Pt.x|0,br=Pt.y|0,Ur,Di;return"width"in Pt?Ur=Pt.width|0:ht=!1,"height"in Pt?Di=Pt.height|0:ht=!1,new ei(!ht&&Qe&&Qe.thisDep,!ht&&Qe&&Qe.contextDep,!ht&&Qe&&Qe.propDep,function(gn,rn){var Ci=gn.shared.context,Bi=Ur;"width"in Pt||(Bi=rn.def(Ci,".",Be,"-",ur));var Gi=Di;return"height"in Pt||(Gi=rn.def(Ci,".",et,"-",br)),[ur,br,Bi,Gi]})}else if(yt in Ot){var fi=Ot[yt],Ti=Un(fi,function(gn,rn){var Ci=gn.invoke(rn,fi),Bi=gn.shared.context,Gi=rn.def(Ci,".x|0"),sn=rn.def(Ci,".y|0"),zn=rn.def('"width" in ',Ci,"?",Ci,".width|0:","(",Bi,".",Be,"-",Gi,")"),Ja=rn.def('"height" in ',Ci,"?",Ci,".height|0:","(",Bi,".",et,"-",sn,")");return[Gi,sn,zn,Ja]});return Qe&&(Ti.thisDep=Ti.thisDep||Qe.thisDep,Ti.contextDep=Ti.contextDep||Qe.contextDep,Ti.propDep=Ti.propDep||Qe.propDep),Ti}else return Qe?new ei(Qe.thisDep,Qe.contextDep,Qe.propDep,function(gn,rn){var Ci=gn.shared.context;return[0,0,rn.def(Ci,".",Be),rn.def(Ci,".",et)]}):null}var Bt=Rt(ee);if(Bt){var Dt=Bt;Bt=new ei(Bt.thisDep,Bt.contextDep,Bt.propDep,function(yt,Pt){var ht=Dt.append(yt,Pt),ur=yt.shared.context;return Pt.set(ur,"."+We,ht[2]),Pt.set(ur,"."+it,ht[3]),ht})}return{viewport:Bt,scissor_box:Rt(Q)}}function is(Ve,Qe){var at=Ve.static,Ct=typeof at[ot]=="string"&&typeof at[Xe]=="string";if(Ct){if(Object.keys(Qe.dynamic).length>0)return null;var Ot=Qe.static,Rt=Object.keys(Ot);if(Rt.length>0&&typeof Ot[Rt[0]]=="number"){for(var Bt=[],Dt=0;Dt<Rt.length;++Dt)Bt.push([Ot[Rt[Dt]]|0,Rt[Dt]]);return Bt}}return null}function Zs(Ve,Qe,at){var Ct=Ve.static,Ot=Ve.dynamic;function Rt(ht){if(ht in Ct){var ur=bt.id(Ct[ht]),br=En(function(){return ur});return br.id=ur,br}else if(ht in Ot){var Ur=Ot[ht];return Un(Ur,function(Di,fi){var Ti=Di.invoke(fi,Ur),gn=fi.def(Di.shared.strings,".id(",Ti,")");return gn})}return null}var Bt=Rt(ot),Dt=Rt(Xe),yt=null,Pt;return Ln(Bt)&&Ln(Dt)?(yt=Ei.program(Dt.id,Bt.id,null,at),Pt=En(function(ht,ur){return ht.link(yt)})):Pt=new ei(Bt&&Bt.thisDep||Dt&&Dt.thisDep,Bt&&Bt.contextDep||Dt&&Dt.contextDep,Bt&&Bt.propDep||Dt&&Dt.propDep,function(ht,ur){var br=ht.shared.shader,Ur;Bt?Ur=Bt.append(ht,ur):Ur=ur.def(br,".",ot);var Di;Dt?Di=Dt.append(ht,ur):Di=ur.def(br,".",Xe);var fi=br+".program("+Di+","+Ur;return ur.def(fi+")")}),{frag:Bt,vert:Dt,progVar:Pt,program:yt}}function Va(Ve,Qe){var at=Ve.static,Ct=Ve.dynamic,Ot={},Rt=!1;function Bt(){if(ve in at){var rn=at[ve];return rn!==null&&qi.getVAO(rn)===null&&(rn=qi.createVAO(rn)),Rt=!0,Ot.vao=rn,En(function(Bi){var Gi=qi.getVAO(rn);return Gi?Bi.link(Gi):"null"})}else if(ve in Ct){Rt=!0;var Ci=Ct[ve];return Un(Ci,function(Bi,Gi){var sn=Bi.invoke(Gi,Ci);return Gi.def(Bi.shared.vao+".getVAO("+sn+")")})}return null}var Dt=Bt(),yt=!1;function Pt(){if(Tt in at){var rn=at[Tt];if(Ot.elements=rn,Ii(rn)){var Ci=Ot.elements=Nr.create(rn,!0);rn=Nr.getElements(Ci),yt=!0}else rn&&(rn=Nr.getElements(rn),yt=!0);var Bi=En(function(sn,zn){if(rn){var Ja=sn.link(rn);return sn.ELEMENTS=Ja,Ja}return sn.ELEMENTS=null,null});return Bi.value=rn,Bi}else if(Tt in Ct){yt=!0;var Gi=Ct[Tt];return Un(Gi,function(sn,zn){var Ja=sn.shared,co=Ja.isBufferArgs,ts=Ja.elements,so=sn.invoke(zn,Gi),Zo=zn.def("null"),ms=zn.def(co,"(",so,")"),ou=sn.cond(ms).then(Zo,"=",ts,".createStream(",so,");").else(Zo,"=",ts,".getElements(",so,");");return zn.entry(ou),zn.exit(sn.cond(ms).then(ts,".destroyStream(",Zo,");")),sn.ELEMENTS=Zo,Zo})}else if(Rt)return new ei(Dt.thisDep,Dt.contextDep,Dt.propDep,function(sn,zn){return zn.def(sn.shared.vao+".currentVAO?"+sn.shared.elements+".getElements("+sn.shared.vao+".currentVAO.elements):null")});return null}var ht=Pt();function ur(){if(Yt in at){var rn=at[Yt];return Ot.primitive=rn,En(function(Bi,Gi){return Sn[rn]})}else if(Yt in Ct){var Ci=Ct[Yt];return Un(Ci,function(Bi,Gi){var sn=Bi.constants.primTypes,zn=Bi.invoke(Gi,Ci);return Gi.def(sn,"[",zn,"]")})}else{if(yt)return Ln(ht)?ht.value?En(function(Bi,Gi){return Gi.def(Bi.ELEMENTS,".primType")}):En(function(){return Al}):new ei(ht.thisDep,ht.contextDep,ht.propDep,function(Bi,Gi){var sn=Bi.ELEMENTS;return Gi.def(sn,"?",sn,".primType:",Al)});if(Rt)return new ei(Dt.thisDep,Dt.contextDep,Dt.propDep,function(Bi,Gi){return Gi.def(Bi.shared.vao+".currentVAO?"+Bi.shared.vao+".currentVAO.primitive:"+Al)})}return null}function br(rn,Ci){if(rn in at){var Bi=at[rn]|0;return Ci?Ot.offset=Bi:Ot.instances=Bi,En(function(sn,zn){return Ci&&(sn.OFFSET=Bi),Bi})}else if(rn in Ct){var Gi=Ct[rn];return Un(Gi,function(sn,zn){var Ja=sn.invoke(zn,Gi);return Ci&&(sn.OFFSET=Ja),Ja})}else if(Ci){if(yt)return En(function(sn,zn){return sn.OFFSET=0,0});if(Rt)return new ei(Dt.thisDep,Dt.contextDep,Dt.propDep,function(sn,zn){return zn.def(sn.shared.vao+".currentVAO?"+sn.shared.vao+".currentVAO.offset:0")})}else if(Rt)return new ei(Dt.thisDep,Dt.contextDep,Dt.propDep,function(sn,zn){return zn.def(sn.shared.vao+".currentVAO?"+sn.shared.vao+".currentVAO.instances:-1")});return null}var Ur=br(xr,!0);function Di(){if(Kt in at){var rn=at[Kt]|0;return Ot.count=rn,En(function(){return rn})}else if(Kt in Ct){var Ci=Ct[Kt];return Un(Ci,function(zn,Ja){var co=zn.invoke(Ja,Ci);return co})}else if(yt)if(Ln(ht)){if(ht)return Ur?new ei(Ur.thisDep,Ur.contextDep,Ur.propDep,function(zn,Ja){var co=Ja.def(zn.ELEMENTS,".vertCount-",zn.OFFSET);return co}):En(function(zn,Ja){return Ja.def(zn.ELEMENTS,".vertCount")});var Bi=En(function(){return-1});return Bi}else{var Gi=new ei(ht.thisDep||Ur.thisDep,ht.contextDep||Ur.contextDep,ht.propDep||Ur.propDep,function(zn,Ja){var co=zn.ELEMENTS;return zn.OFFSET?Ja.def(co,"?",co,".vertCount-",zn.OFFSET,":-1"):Ja.def(co,"?",co,".vertCount:-1")});return Gi}else if(Rt){var sn=new ei(Dt.thisDep,Dt.contextDep,Dt.propDep,function(zn,Ja){return Ja.def(zn.shared.vao,".currentVAO?",zn.shared.vao,".currentVAO.count:-1")});return sn}return null}var fi=ur(),Ti=Di(),gn=br(Ir,!1);return{elements:ht,primitive:fi,count:Ti,instances:gn,offset:Ur,vao:Dt,vaoActive:Rt,elementsActive:yt,static:Ot}}function Ml(Ve,Qe){var at=Ve.static,Ct=Ve.dynamic,Ot={};return hi.forEach(function(Rt){var Bt=Ji(Rt);function Dt(yt,Pt){if(Rt in at){var ht=yt(at[Rt]);Ot[Bt]=En(function(){return ht})}else if(Rt in Ct){var ur=Ct[Rt];Ot[Bt]=Un(ur,function(br,Ur){return Pt(br,Ur,br.invoke(Ur,ur))})}}switch(Rt){case Cu:case Ho:case ws:case Vc:case va:case B:case Uc:case Ac:case Ua:case rs:return Dt(function(yt){return yt},function(yt,Pt,ht){return ht});case no:return Dt(function(yt){return Zr[yt]},function(yt,Pt,ht){var ur=yt.constants.compareFuncs;return Pt.def(ur,"[",ht,"]")});case ys:return Dt(function(yt){return yt},function(yt,Pt,ht){var ur=Pt.def("+",ht,"[0]"),br=Pt.def("+",ht,"[1]");return[ur,br]});case Ls:return Dt(function(yt){var Pt="srcRGB"in yt?yt.srcRGB:yt.src,ht="srcAlpha"in yt?yt.srcAlpha:yt.src,ur="dstRGB"in yt?yt.dstRGB:yt.dst,br="dstAlpha"in yt?yt.dstAlpha:yt.dst;return[bi[Pt],bi[ur],bi[ht],bi[br]]},function(yt,Pt,ht){var ur=yt.constants.blendFuncs;function br(Ci,Bi){var Gi=Pt.def('"',Ci,Bi,'" in ',ht,"?",ht,".",Ci,Bi,":",ht,".",Ci);return Gi}var Ur=br("src","RGB"),Di=br("dst","RGB"),fi=Pt.def(ur,"[",Ur,"]"),Ti=Pt.def(ur,"[",br("src","Alpha"),"]"),gn=Pt.def(ur,"[",Di,"]"),rn=Pt.def(ur,"[",br("dst","Alpha"),"]");return[fi,gn,Ti,rn]});case Ws:return Dt(function(yt){if(typeof yt=="string")return[xi[yt],xi[yt]];if(typeof yt=="object")return[xi[yt.rgb],xi[yt.alpha]]},function(yt,Pt,ht){var ur=yt.constants.blendEquations,br=Pt.def(),Ur=Pt.def(),Di=yt.cond("typeof ",ht,'==="string"');return Di.then(br,"=",Ur,"=",ur,"[",ht,"];"),Di.else(br,"=",ur,"[",ht,".rgb];",Ur,"=",ur,"[",ht,".alpha];"),Pt(Di),[br,Ur]});case ml:return Dt(function(yt){return M(4,function(Pt){return+yt[Pt]})},function(yt,Pt,ht){return M(4,function(ur){return Pt.def("+",ht,"[",ur,"]")})});case hc:return Dt(function(yt){return yt|0},function(yt,Pt,ht){return Pt.def(ht,"|0")});case Ku:return Dt(function(yt){var Pt=yt.cmp||"keep",ht=yt.ref||0,ur="mask"in yt?yt.mask:-1;return[Zr[Pt],ht,ur]},function(yt,Pt,ht){var ur=yt.constants.compareFuncs,br=Pt.def('"cmp" in ',ht,"?",ur,"[",ht,".cmp]",":",nt),Ur=Pt.def(ht,".ref|0"),Di=Pt.def('"mask" in ',ht,"?",ht,".mask|0:-1");return[br,Ur,Di]});case ue:case w:return Dt(function(yt){var Pt=yt.fail||"keep",ht=yt.zfail||"keep",ur=yt.zpass||"keep";return[Rt===w?se:X,ai[Pt],ai[ht],ai[ur]]},function(yt,Pt,ht){var ur=yt.constants.stencilOps;function br(Ur){return Pt.def('"',Ur,'" in ',ht,"?",ur,"[",ht,".",Ur,"]:",nt)}return[Rt===w?se:X,br("fail"),br("zfail"),br("zpass")]});case xu:return Dt(function(yt){var Pt=yt.factor|0,ht=yt.units|0;return[Pt,ht]},function(yt,Pt,ht){var ur=Pt.def(ht,".factor|0"),br=Pt.def(ht,".units|0");return[ur,br]});case Yu:return Dt(function(yt){var Pt=0;return yt==="front"?Pt=X:yt==="back"&&(Pt=se),Pt},function(yt,Pt,ht){return Pt.def(ht,'==="front"?',X,":",se)});case pu:return Dt(function(yt){return yt},function(yt,Pt,ht){return ht});case Nc:return Dt(function(yt){return gi[yt]},function(yt,Pt,ht){return Pt.def(ht+'==="cw"?'+Te+":"+Ne)});case $l:return Dt(function(yt){return yt.map(function(Pt){return!!Pt})},function(yt,Pt,ht){return M(4,function(ur){return"!!"+ht+"["+ur+"]"})});case oo:return Dt(function(yt){var Pt="value"in yt?yt.value:1,ht=!!yt.invert;return[Pt,ht]},function(yt,Pt,ht){var ur=Pt.def('"value" in ',ht,"?+",ht,".value:1"),br=Pt.def("!!",ht,".invert");return[ur,br]})}}),Ot}function zo(Ve,Qe){var at=Ve.static,Ct=Ve.dynamic,Ot={};return Object.keys(at).forEach(function(Rt){var Bt=at[Rt],Dt;if(typeof Bt=="number"||typeof Bt=="boolean")Dt=En(function(){return Bt});else if(typeof Bt=="function"){var yt=Bt._reglType;yt==="texture2d"||yt==="textureCube"?Dt=En(function(Pt){return Pt.link(Bt)}):(yt==="framebuffer"||yt==="framebufferCube")&&(Dt=En(function(Pt){return Pt.link(Bt.color[0])}))}else Mn(Bt)&&(Dt=En(function(Pt){var ht=Pt.global.def("[",M(Bt.length,function(ur){return Bt[ur]}),"]");return ht}));Dt.value=Bt,Ot[Rt]=Dt}),Object.keys(Ct).forEach(function(Rt){var Bt=Ct[Rt];Ot[Rt]=Un(Bt,function(Dt,yt){return Dt.invoke(yt,Bt)})}),Ot}function Qs(Ve,Qe){var at=Ve.static,Ct=Ve.dynamic,Ot={};return Object.keys(at).forEach(function(Rt){var Bt=at[Rt],Dt=bt.id(Rt),yt=new 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s=i[o],l=a[o]=new Array(4),u=CNe(e,t._diag[s][0]);u&&(l[0]=u.r2l(u.range[0]),l[2]=u.r2l(u.range[1]));var c=CNe(e,t._diag[s][1]);c&&(l[1]=c.r2l(c.range[0]),l[3]=c.r2l(c.range[1]))}r.selectBatch.length||r.unselectBatch.length?r.matrix.update({ranges:a},{ranges:a}):r.matrix.update({ranges:a})}function bK(e){var t=e._fullLayout,r=t._glcanvas.data()[0].regl,n=t._splomGrid;n||(n=t._splomGrid=kVt(r)),n.update(FVt(e))}function FVt(e){var t=e._context.plotGlPixelRatio,r=e._fullLayout,n=r._size,i=[0,0,r.width*t,r.height*t],a={},o;function s(x,C,M,g,P,T){M*=t,g*=t,P*=t,T*=t;var z=C[x+"color"],O=C[x+"width"],V=String(z+O);V in a?a[V].data.push(NaN,NaN,M,g,P,T):a[V]={data:[M,g,P,T],join:"rect",thickness:O*t,color:z,viewport:i,range:i,overlay:!1}}for(o in r._splomSubplots){var 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n._splomGrid),gx.clean(e,t,r,n)}RNe.exports={name:PNe,attr:gx.attr,attrRegex:gx.attrRegex,layoutAttributes:gx.layoutAttributes,supplyLayoutDefaults:gx.supplyLayoutDefaults,drawFramework:gx.drawFramework,plot:IVt,drag:RVt,updateGrid:bK,clean:zVt,updateFx:gx.updateFx,toSVG:gx.toSVG,reglPrecompiled:INe}});var ONe=ye((P_r,zNe)=>{"use strict";var FNe=kNe();FNe.basePlotModule=DNe(),zNe.exports=FNe});var BNe=ye((I_r,qNe)=>{"use strict";qNe.exports=ONe()});var AK=ye((R_r,NNe)=>{"use strict";var OVt=Tu(),wK=Rd(),TK=ec(),qVt=Cc().attributes,Fz=Ao().extendFlat,BVt=vl().templatedArray;NNe.exports={domain:qVt({name:"parcoords",trace:!0,editType:"plot"}),labelangle:{valType:"angle",dflt:0,editType:"plot"},labelside:{valType:"enumerated",values:["top","bottom"],dflt:"top",editType:"plot"},labelfont:TK({editType:"plot"}),tickfont:TK({autoShadowDflt:!0,editType:"plot"}),rangefont:TK({editType:"plot"}),dimensions:BVt("dimension",{label:{valType:"string",editType:"plot"},tickvals:Fz({},wK.tickvals,{editType:"plot"}),ticktext:Fz({},wK.ticktext,{editType:"plot"}),tickformat:Fz({},wK.tickformat,{editType:"plot"}),visible:{valType:"boolean",dflt:!0,editType:"plot"},range:{valType:"info_array",items:[{valType:"number",editType:"plot"},{valType:"number",editType:"plot"}],editType:"plot"},constraintrange:{valType:"info_array",freeLength:!0,dimensions:"1-2",items:[{valType:"any",editType:"plot"},{valType:"any",editType:"plot"}],editType:"plot"},multiselect:{valType:"boolean",dflt:!0,editType:"plot"},values:{valType:"data_array",editType:"calc"},editType:"calc"}),line:Fz({editType:"calc"},OVt("line",{colorscaleDflt:"Viridis",autoColorDflt:!1,editTypeOverride:"calc"})),unselected:{line:{color:{valType:"color",dflt:"#7f7f7f",editType:"plot"},opacity:{valType:"number",min:0,max:1,dflt:"auto",editType:"plot"},editType:"plot"},editType:"plot"}}});var Pk=ye((D_r,UNe)=>{"use strict";UNe.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:["contextLineLayer","focusLineLayer","pickLineLayer"],axisTitleOffset:28,axisExtentOffset:10,bar:{width:4,captureWidth:10,fillColor:"magenta",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:"axis-extent-text",parcoordsLineLayers:"parcoords-line-layers",parcoordsLineLayer:"parcoords-lines",parcoords:"parcoords",parcoordsControlView:"parcoords-control-view",yAxis:"y-axis",axisOverlays:"axis-overlays",axis:"axis",axisHeading:"axis-heading",axisTitle:"axis-title",axisExtent:"axis-extent",axisExtentTop:"axis-extent-top",axisExtentTopText:"axis-extent-top-text",axisExtentBottom:"axis-extent-bottom",axisExtentBottomText:"axis-extent-bottom-text",axisBrush:"axis-brush"},id:{filterBarPattern:"filter-bar-pattern"}}});var Jm=ye((F_r,GNe)=>{"use strict";var NVt=HS();function VNe(e){return[e]}GNe.exports={keyFun:function(e){return e.key},repeat:VNe,descend:NVt,wrap:VNe,unwrap:function(e){return e[0]}}});var EK=ye((z_r,eUe)=>{"use strict";var bh=Pk(),tm=Oa(),UVt=Jm().keyFun,zz=Jm().repeat,x5=Dr().sorterAsc,VVt=Dr().strTranslate,HNe=bh.bar.snapRatio;function jNe(e,t){return e*(1-HNe)+t*HNe}var WNe=bh.bar.snapClose;function GVt(e,t){return e*(1-WNe)+t*WNe}function qz(e,t,r,n){if(HVt(r,n))return r;var i=e?-1:1,a=0,o=t.length-1;if(i<0){var s=a;a=o,o=s}for(var l=t[a],u=l,c=a;i*c<i*o;c+=i){var f=c+i,h=t[f];if(i*r<i*GVt(l,h))return jNe(l,u);if(i*r<i*h||f===o)return jNe(h,l);u=l,l=h}}function HVt(e,t){for(var r=0;r<t.length;r++)if(e>=t[r][0]&&e<=t[r][1])return!0;return!1}function jVt(e){e.attr("x",-bh.bar.captureWidth/2).attr("width",bh.bar.captureWidth)}function WVt(e){e.attr("visibility","visible").style("visibility","visible").attr("fill","yellow").attr("opacity",0)}function XVt(e){if(!e.brush.filterSpecified)return"0,"+e.height;for(var t=XNe(e.brush.filter.getConsolidated(),e.height),r=[0],n,i,a,o=t.length?t[0][0]:null,s=0;s<t.length;s++)n=t[s],i=n[1]-n[0],r.push(o),r.push(i),a=s+1,a<t.length&&(o=t[a][0]-n[1]);return r.push(e.height),r}function XNe(e,t){return e.map(function(r){return r.map(function(n){return Math.max(0,n*t)}).sort(x5)})}function ZVt(e,t){var r=bh.bar.handleHeight;if(!(t>e[1]+r||t<e[0]-r))return t>=.9*e[1]+.1*e[0]?"n":t<=.9*e[0]+.1*e[1]?"s":"ns"}function ZNe(){tm.select(document.body).style("cursor",null)}function MK(e){e.attr("stroke-dasharray",XVt)}function Oz(e,t){var r=tm.select(e).selectAll(".highlight, .highlight-shadow"),n=t?r.transition().duration(bh.bar.snapDuration).each("end",t):r;MK(n)}function YNe(e,t){var r=e.brush,n=r.filterSpecified,i=NaN,a={},o;if(n){var s=e.height,l=r.filter.getConsolidated(),u=XNe(l,s),c=NaN,f=NaN,h=NaN;for(o=0;o<=u.length;o++){var d=u[o];if(d&&d[0]<=t&&t<=d[1]){c=o;break}else 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u[0]!==o[0]&&u[1]!==o[1]})),s.startExtent=a.region?o[a.region==="s"?1:0]:n,t.parent.inBrushDrag=!0,s.brushStartCallback()}function KNe(e,t){tm.event.sourceEvent.stopPropagation();var r=t.height-tm.mouse(e)[1]-2*bh.verticalPadding,n=t.brush.svgBrush;n.wasDragged=!0,n._dragging=!0,n.grabbingBar?n.newExtent=[r-n.grabPoint,r+n.barLength-n.grabPoint].map(t.unitToPaddedPx.invert):n.newExtent=[n.startExtent,t.unitToPaddedPx.invert(r)].sort(x5),t.brush.filterSpecified=!0,n.extent=n.stayingIntervals.concat([n.newExtent]),n.brushCallback(t),Oz(e.parentNode)}function KVt(e,t){var r=t.brush,n=r.filter,i=r.svgBrush;i._dragging||(JNe(e,t),KNe(e,t),t.brush.svgBrush.wasDragged=!1),i._dragging=!1;var a=tm.event;a.sourceEvent.stopPropagation();var o=i.grabbingBar;if(i.grabbingBar=!1,i.grabLocation=void 0,t.parent.inBrushDrag=!1,ZNe(),!i.wasDragged){i.wasDragged=void 0,i.clickableOrdinalRange?r.filterSpecified&&t.multiselect?i.extent.push(i.clickableOrdinalRange):(i.extent=[i.clickableOrdinalRange],r.filterSpecified=!0):o?(i.extent=i.stayingIntervals,i.extent.length===0&&SK(r)):SK(r),i.brushCallback(t),Oz(e.parentNode),i.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);return}var s=function(){n.set(n.getConsolidated())};if(t.ordinal){var l=t.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),i.newExtent=[qz(0,l,i.newExtent[0],i.stayingIntervals),qz(1,l,i.newExtent[1],i.stayingIntervals)];var u=i.newExtent[1]>i.newExtent[0];i.extent=i.stayingIntervals.concat(u?[i.newExtent]:[]),i.extent.length||SK(r),i.brushCallback(t),u?Oz(e.parentNode,s):(s(),Oz(e.parentNode))}else s();i.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}function JNe(e,t){var r=t.height-tm.mouse(e)[1]-2*bh.verticalPadding,n=YNe(t,r),i="crosshair";n.clickableOrdinalRange?i="pointer":n.region&&(i=n.region+"-resize"),tm.select(document.body).style("cursor",i)}function JVt(e){e.on("mousemove",function(t){tm.event.preventDefault(),t.parent.inBrushDrag||JNe(this,t)}).on("mouseleave",function(t){t.parent.inBrushDrag||ZNe()}).call(tm.behavior.drag().on("dragstart",function(t){YVt(this,t)}).on("drag",function(t){KNe(this,t)}).on("dragend",function(t){KVt(this,t)}))}function $Ne(e,t){return e[0]-t[0]}function $Vt(e,t,r){var n=r._context.staticPlot,i=e.selectAll(".background").data(zz);i.enter().append("rect").classed("background",!0).call(jVt).call(WVt).style("pointer-events",n?"none":"auto").attr("transform",VVt(0,bh.verticalPadding)),i.call(JVt).attr("height",function(s){return s.height-bh.verticalPadding});var a=e.selectAll(".highlight-shadow").data(zz);a.enter().append("line").classed("highlight-shadow",!0).attr("x",-bh.bar.width/2).attr("stroke-width",bh.bar.width+bh.bar.strokeWidth).attr("stroke",t).attr("opacity",bh.bar.strokeOpacity).attr("stroke-linecap","butt"),a.attr("y1",function(s){return s.height}).call(MK);var o=e.selectAll(".highlight").data(zz);o.enter().append("line").classed("highlight",!0).attr("x",-bh.bar.width/2).attr("stroke-width",bh.bar.width-bh.bar.strokeWidth).attr("stroke",bh.bar.fillColor).attr("opacity",bh.bar.fillOpacity).attr("stroke-linecap","butt"),o.attr("y1",function(s){return s.height}).call(MK)}function QVt(e,t,r){var n=e.selectAll("."+bh.cn.axisBrush).data(zz,UVt);n.enter().append("g").classed(bh.cn.axisBrush,!0),$Vt(n,t,r)}function eGt(e){return e.svgBrush.extent.map(function(t){return t.slice()})}function SK(e){e.filterSpecified=!1,e.svgBrush.extent=[[-1/0,1/0]]}function tGt(e){return function(r){var n=r.brush,i=eGt(n),a=i.slice();n.filter.set(a),e()}}function QNe(e){for(var t=e.slice(),r=[],n,i=t.shift();i;){for(n=i.slice();(i=t.shift())&&i[0]<=n[1];)n[1]=Math.max(n[1],i[1]);r.push(n)}return r.length===1&&r[0][0]>r[0][1]&&(r=[]),r}function rGt(){var e=[],t,r;return{set:function(n){e=n.map(function(i){return i.slice().sort(x5)}).sort($Ne),e.length===1&&e[0][0]===-1/0&&e[0][1]===1/0&&(e=[[0,-1]]),t=QNe(e),r=e.reduce(function(i,a){return[Math.min(i[0],a[0]),Math.max(i[1],a[1])]},[1/0,-1/0])},get:function(){return e.slice()},getConsolidated:function(){return t},getBounds:function(){return r}}}function iGt(e,t,r,n,i,a){var o=rGt();return o.set(r),{filter:o,filterSpecified:t,svgBrush:{extent:[],brushStartCallback:n,brushCallback:tGt(i),brushEndCallback:a}}}function nGt(e,t){if(Array.isArray(e[0])?(e=e.map(function(n){return n.sort(x5)}),t.multiselect?e=QNe(e.sort($Ne)):e=[e[0]]):e=[e.sort(x5)],t.tickvals){var r=t.tickvals.slice().sort(x5);if(e=e.map(function(n){var i=[qz(0,r,n[0],[]),qz(1,r,n[1],[])];if(i[1]>i[0])return i}).filter(function(n){return n}),!e.length)return}return e.length>1?e:e[0]}eUe.exports={makeBrush:iGt,ensureAxisBrush:QVt,cleanRanges:nGt}});var iUe=ye((O_r,rUe)=>{"use strict";var mx=Dr(),aGt=pv().hasColorscale,oGt=Qh(),sGt=Cc().defaults,lGt=Yd(),uGt=ho(),tUe=AK(),cGt=EK(),kK=Pk().maxDimensionCount,fGt=kz();function hGt(e,t,r,n,i){var a=i("line.color",r);if(aGt(e,"line")&&mx.isArrayOrTypedArray(a)){if(a.length)return i("line.colorscale"),oGt(e,t,n,i,{prefix:"line.",cLetter:"c"}),a.length;t.line.color=r}return 1/0}function dGt(e,t,r,n){function i(u,c){return mx.coerce(e,t,tUe.dimensions,u,c)}var a=i("values"),o=i("visible");if(a&&a.length||(o=t.visible=!1),o){i("label"),i("tickvals"),i("ticktext"),i("tickformat");var s=i("range");t._ax={_id:"y",type:"linear",showexponent:"all",exponentformat:"B",range:s},uGt.setConvert(t._ax,n.layout),i("multiselect");var l=i("constraintrange");l&&(t.constraintrange=cGt.cleanRanges(l,t))}}rUe.exports=function(t,r,n,i){function a(c,f){return mx.coerce(t,r,tUe,c,f)}var o=t.dimensions;Array.isArray(o)&&o.length>kK&&(mx.log("parcoords traces support up to "+kK+" dimensions at the moment"),o.splice(kK));var s=lGt(t,r,{name:"dimensions",layout:i,handleItemDefaults:dGt}),l=hGt(t,r,n,i,a);sGt(r,i,a),(!Array.isArray(s)||!s.length)&&(r.visible=!1),fGt(r,s,"values",l);var u=mx.extendFlat({},i.font,{size:Math.round(i.font.size/1.2)});mx.coerceFont(a,"labelfont",u),mx.coerceFont(a,"tickfont",u,{autoShadowDflt:!0}),mx.coerceFont(a,"rangefont",u),a("labelangle"),a("labelside"),a("unselected.line.color"),a("unselected.line.opacity")}});var aUe=ye((q_r,nUe)=>{"use strict";var vGt=Dr().isArrayOrTypedArray,CK=tc(),pGt=Jm().wrap;nUe.exports=function(t,r){var n,i;return CK.hasColorscale(r,"line")&&vGt(r.line.color)?(n=r.line.color,i=CK.extractOpts(r.line).colorscale,CK.calc(t,r,{vals:n,containerStr:"line",cLetter:"c"})):(n=gGt(r._length),i=[[0,r.line.color],[1,r.line.color]]),pGt({lineColor:n,cscale:i})};function gGt(e){for(var t=new Array(e),r=0;r<e;r++)t[r]=.5;return t}});function mGt(e){var c,f;var t,r=[],n=1,i;if(typeof e=="number")return{space:"rgb",values:[e>>>16,(e&65280)>>>8,e&255],alpha:1};if(typeof e=="number")return{space:"rgb",values:[e>>>16,(e&65280)>>>8,e&255],alpha:1};if(e=String(e).toLowerCase(),LK.default[e])r=LK.default[e].slice(),i="rgb";else if(e==="transparent")n=0,i="rgb",r=[0,0,0];else if(e[0]==="#"){var a=e.slice(1),o=a.length,s=o<=4;n=1,s?(r=[parseInt(a[0]+a[0],16),parseInt(a[1]+a[1],16),parseInt(a[2]+a[2],16)],o===4&&(n=parseInt(a[3]+a[3],16)/255)):(r=[parseInt(a[0]+a[1],16),parseInt(a[2]+a[3],16),parseInt(a[4]+a[5],16)],o===8&&(n=parseInt(a[6]+a[7],16)/255)),r[0]||(r[0]=0),r[1]||(r[1]=0),r[2]||(r[2]=0),i="rgb"}else if(t=/^((?:rgba?|hs[lvb]a?|hwba?|cmyk?|xy[zy]|gray|lab|lchu?v?|[ly]uv|lms|oklch|oklab|color))\s*\(([^\)]*)\)/.exec(e)){var l=t[1];i=l.replace(/a$/,"");var u=i==="cmyk"?4:i==="gray"?1:3;r=t[2].trim().split(/\s*[,\/]\s*|\s+/),i==="color"&&(i=r.shift()),r=r.map(function(h,d){if(h[h.length-1]==="%")return h=parseFloat(h)/100,d===3?h:i==="rgb"?h*255:i[0]==="h"||i[0]==="l"&&!d?h*100:i==="lab"?h*125:i==="lch"?d<2?h*150:h*360:i[0]==="o"&&!d?h:i==="oklab"?h*.4:i==="oklch"?d<2?h*.4:h*360:h;if(i[d]==="h"||d===2&&i[i.length-1]==="h"){if(oUe[h]!==void 0)return oUe[h];if(h.endsWith("deg"))return parseFloat(h);if(h.endsWith("turn"))return parseFloat(h)*360;if(h.endsWith("grad"))return parseFloat(h)*360/400;if(h.endsWith("rad"))return parseFloat(h)*180/Math.PI}return h==="none"?0:parseFloat(h)}),n=r.length>u?r.pop():1}else/[0-9](?:\s|\/|,)/.test(e)&&(r=e.match(/([0-9]+)/g).map(function(h){return parseFloat(h)}),i=((f=(c=e.match(/([a-z])/ig))==null?void 0:c.join(""))==null?void 0:f.toLowerCase())||"rgb");return{space:i,values:r,alpha:n}}var LK,sUe,oUe,lUe=gu(()=>{LK=gtt(gX(),1),sUe=mGt,oUe={red:0,orange:60,yellow:120,green:180,blue:240,purple:300}});var Ik,PK=gu(()=>{Ik={name:"rgb",min:[0,0,0],max:[255,255,255],channel:["red","green","blue"],alias:["RGB"]}});var Bz,uUe=gu(()=>{PK();Bz={name:"hsl",min:[0,0,0],max:[360,100,100],channel:["hue","saturation","lightness"],alias:["HSL"],rgb:function(e){var t=e[0]/360,r=e[1]/100,n=e[2]/100,i,a,o,s,l,u=0;if(r===0)return l=n*255,[l,l,l];for(a=n<.5?n*(1+r):n+r-n*r,i=2*n-a,s=[0,0,0];u<3;)o=t+1/3*-(u-1),o<0?o++:o>1&&o--,l=6*o<1?i+(a-i)*6*o:2*o<1?a:3*o<2?i+(a-i)*(2/3-o)*6:i,s[u++]=l*255;return s}};Ik.hsl=function(e){var t=e[0]/255,r=e[1]/255,n=e[2]/255,i=Math.min(t,r,n),a=Math.max(t,r,n),o=a-i,s,l,u;return a===i?s=0:t===a?s=(r-n)/o:r===a?s=2+(n-t)/o:n===a&&(s=4+(t-r)/o),s=Math.min(s*60,360),s<0&&(s+=360),u=(i+a)/2,a===i?l=0:u<=.5?l=o/(a+i):l=o/(2-a-i),[s,l*100,u*100]}});var fUe={};cee(fUe,{default:()=>cUe});function cUe(e){Array.isArray(e)&&e.raw&&(e=String.raw(...arguments)),e instanceof Number&&(e=+e);var t,r,n,i=sUe(e);if(!i.space)return[];let a=i.space[0]==="h"?Bz.min:Ik.min,o=i.space[0]==="h"?Bz.max:Ik.max;return t=Array(3),t[0]=Math.min(Math.max(i.values[0],a[0]),o[0]),t[1]=Math.min(Math.max(i.values[1],a[1]),o[1]),t[2]=Math.min(Math.max(i.values[2],a[2]),o[2]),i.space[0]==="h"&&(t=Bz.rgb(t)),t.push(Math.min(Math.max(i.alpha,0),1)),t}var hUe=gu(()=>{lUe();PK();uUe()});var IK=ye(Nz=>{"use strict";var yGt=Dr().isTypedArray;Nz.convertTypedArray=function(e){return yGt(e)?Array.prototype.slice.call(e):e};Nz.isOrdinal=function(e){return!!e.tickvals};Nz.isVisible=function(e){return e.visible||!("visible"in e)}});var wUe=ye((X_r,bUe)=>{"use strict";var _Gt=["precision highp float;","","varying vec4 fragColor;","","attribute vec4 p01_04, p05_08, p09_12, p13_16,","               p17_20, p21_24, p25_28, p29_32,","               p33_36, p37_40, p41_44, p45_48,","               p49_52, p53_56, p57_60, colors;","","uniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,","             loA, hiA, loB, hiB, loC, hiC, loD, hiD;","","uniform vec2 resolution, viewBoxPos, viewBoxSize;","uniform float maskHeight;","uniform float drwLayer; // 0: context, 1: focus, 2: pick","uniform vec4 contextColor;","uniform sampler2D maskTexture, palette;","","bool isPick    = (drwLayer > 1.5);","bool isContext = (drwLayer < 0.5);","","const vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);","const vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);","","float val(mat4 p, mat4 v) {","    return dot(matrixCompMult(p, v) * UNITS, UNITS);","}","","float axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {","    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);","    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);","    return y1 * (1.0 - ratio) + y2 * ratio;","}","","int iMod(int a, int b) {","    return a - b * (a / b);","}","","bool fOutside(float p, float lo, float hi) {","    return (lo < hi) && (lo > p || p > hi);","}","","bool vOutside(vec4 p, vec4 lo, vec4 hi) {","    return (","        fOutside(p[0], lo[0], hi[0]) ||","        fOutside(p[1], lo[1], hi[1]) ||","        fOutside(p[2], lo[2], hi[2]) ||","        fOutside(p[3], lo[3], hi[3])","    );","}","","bool mOutside(mat4 p, mat4 lo, mat4 hi) {","    return (","        vOutside(p[0], lo[0], hi[0]) ||","        vOutside(p[1], lo[1], hi[1]) ||","        vOutside(p[2], lo[2], hi[2]) ||","        vOutside(p[3], lo[3], hi[3])","    );","}","","bool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {","    return mOutside(A, loA, hiA) ||","           mOutside(B, loB, hiB) ||","           mOutside(C, loC, hiC) ||","           mOutside(D, loD, hiD);","}","","bool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {","    mat4 pnts[4];","    pnts[0] = A;","    pnts[1] = B;","    pnts[2] = C;","    pnts[3] = D;","","    for(int i = 0; i < 4; ++i) {","        for(int j = 0; j < 4; ++j) {","            for(int k = 0; k < 4; ++k) {","                if(0 == iMod(","                    int(255.0 * texture2D(maskTexture,","                        vec2(","                            (float(i * 2 + j / 2) + 0.5) / 8.0,","                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight","                        ))[3]","                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),","                    2","                )) return true;","            }","        }","    }","    return false;","}","","vec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {","    float x = 0.5 * sign(v) + 0.5;","    float y = axisY(x, A, B, C, D);","    float z = 1.0 - abs(v);","","    z += isContext ? 0.0 : 2.0 * float(","        outsideBoundingBox(A, B, C, D) ||","        outsideRasterMask(A, B, C, D)","    );","","    return vec4(","        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,","        z,","        1.0","    );","}","","void main() {","    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);","    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);","    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);","    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);","","    float v = colors[3];","","    gl_Position = position(isContext, v, A, B, C, D);","","    fragColor =","        isContext ? vec4(contextColor) :","        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));","}"].join(`
+`),xGt=["precision highp float;","","varying vec4 fragColor;","","void main() {","    gl_FragColor = fragColor;","}"].join(`
+`),Rk=Pk().maxDimensionCount,yUe=Dr(),dUe=1e-6,Uz=2048,bGt=new Uint8Array(4),vUe=new Uint8Array(4),pUe={shape:[256,1],format:"rgba",type:"uint8",mag:"nearest",min:"nearest"};function wGt(e){e.read({x:0,y:0,width:1,height:1,data:bGt})}function _Ue(e,t,r,n,i){var a=e._gl;a.enable(a.SCISSOR_TEST),a.scissor(t,r,n,i),e.clear({color:[0,0,0,0],depth:1})}function TGt(e,t,r,n,i,a){var o=a.key;function s(l){var u=Math.min(n,i-l*n);l===0&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],_Ue(e,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),!r.clearOnly&&(a.count=2*u,a.offset=2*l*n,t(a),l*n+u<i&&(r.currentRafs[o]=window.requestAnimationFrame(function(){s(l+1)})),r.drawCompleted=!1)}r.drawCompleted||(wGt(e),r.drawCompleted=!0),s(0)}function AGt(e){return Math.max(dUe,Math.min(1-dUe,e))}function SGt(e,t){for(var r=new Array(256),n=0;n<256;n++)r[n]=e(n/255).concat(t);return r}function RK(e,t){return(e>>>8*t)%256/255}function MGt(e,t,r){for(var n=new Array(e*(Rk+4)),i=0,a=0;a<e;a++){for(var o=0;o<Rk;o++)n[i++]=o<t.length?t[o].paddedUnitValues[a]:.5;n[i++]=RK(a,2),n[i++]=RK(a,1),n[i++]=RK(a,0),n[i++]=AGt(r[a])}return n}function EGt(e,t,r){for(var n=new Array(t*8),i=0,a=0;a<t;a++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=e*4+s,u=r[a*64+l];l===63&&o===0&&(u*=-1),n[i++]=u}return n}function gUe(e){var t="0"+e;return t.slice(-2)}function xUe(e){return e<Rk?"p"+gUe(e+1)+"_"+gUe(e+4):"colors"}function kGt(e,t,r){for(var n=0;n<=Rk;n+=4)e[xUe(n)](EGt(n/4,t,r))}function CGt(e){for(var t={},r=0;r<=Rk;r+=4)t[xUe(r)]=e.buffer({usage:"dynamic",type:"float",data:new Uint8Array(0)});return t}function LGt(e,t,r,n,i,a,o,s,l,u,c,f,h,d){for(var v=[[],[]],_=0;_<64;_++)v[0][_]=_===i?1:0,v[1][_]=_===a?1:0;o*=d,s*=d,l*=d,u*=d;var b=e.lines.canvasOverdrag*d,p=e.domain,k=e.canvasWidth*d,E=e.canvasHeight*d,S=e.pad.l*d,L=e.pad.b*d,x=e.layoutHeight*d,C=e.layoutWidth*d,M=e.deselectedLines.color,g=e.deselectedLines.opacity,P=yUe.extendFlat({key:c,resolution:[k,E],viewBoxPos:[o+b,s],viewBoxSize:[l,u],i0:i,i1:a,dim0A:v[0].slice(0,16),dim0B:v[0].slice(16,32),dim0C:v[0].slice(32,48),dim0D:v[0].slice(48,64),dim1A:v[1].slice(0,16),dim1B:v[1].slice(16,32),dim1C:v[1].slice(32,48),dim1D:v[1].slice(48,64),drwLayer:f,contextColor:[M[0]/255,M[1]/255,M[2]/255,g!=="auto"?M[3]*g:Math.max(1/255,Math.pow(1/e.lines.color.length,1/3))],scissorX:(n===t?0:o+b)+(S-b)+C*p.x[0],scissorWidth:(n===r?k-o+b:l+.5)+(n===t?o+b:0),scissorY:s+L+x*p.y[0],scissorHeight:u,viewportX:S-b+C*p.x[0],viewportY:L+x*p.y[0],viewportWidth:k,viewportHeight:E},h);return P}function mUe(e){var t=Uz-1,r=Math.max(0,Math.floor(e[0]*t),0),n=Math.min(t,Math.ceil(e[1]*t),t);return[Math.min(r,n),Math.max(r,n)]}bUe.exports=function(e,t){var r=t.context,n=t.pick,i=t.regl,a=i._gl,o=a.getParameter(a.ALIASED_LINE_WIDTH_RANGE),s=Math.max(o[0],Math.min(o[1],t.viewModel.plotGlPixelRatio)),l={currentRafs:{},drawCompleted:!0,clearOnly:!1},u,c,f,h,d=CGt(i),v,_=i.texture(pUe),b=[];k(t);var p=i({profile:!1,blend:{enable:r,func:{srcRGB:"src alpha",dstRGB:"one minus src 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$m(V.xScale(V.xIndex),0)}),Bd.select(this).attr("transform",$m(z.x,0)),k.each(function(V,G,Z){Z===z.parent.key&&(O.dimensions[G]=V)}),O.contextLayer&&O.contextLayer.render(O.panels,!1,!zK(O)),O.focusLayer.render&&O.focusLayer.render(O.panels)}).on("dragend",function(z){var O=z.parent;z.x=z.xScale(z.xIndex),z.canvasX=z.x*z.model.canvasPixelRatio,FK(k,O,u),Bd.select(this).attr("transform",function(V){return $m(V.x,0)}),O.contextLayer&&O.contextLayer.render(O.panels,!1,!zK(O)),O.focusLayer&&O.focusLayer.render(O.panels),O.pickLayer&&O.pickLayer.render(O.panels,!0),f.linePickActive(!0),i&&i.axesMoved&&i.axesMoved(O.key,O.dimensions.map(function(V){return V.crossfilterDimensionIndex}))})),k.exit().remove();var E=k.selectAll("."+Dl.cn.axisOverlays).data(Qm,tg);E.enter().append("g").classed(Dl.cn.axisOverlays,!0),E.selectAll("."+Dl.cn.axis).remove();var S=E.selectAll("."+Dl.cn.axis).data(Qm,tg);S.enter().append("g").classed(Dl.cn.axis,!0),S.each(function(z){var 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kHt(e){if(!e.parcatsViewModel.dragDimension&&e.parcatsViewModel.hoverinfoItems.indexOf("skip")===-1){yx.raiseToTop(this),WK(Fl.select(this));var t=Bk(e),r=HK(e);if(e.parcatsViewModel.graphDiv.emit("plotly_hover",{points:t,event:Fl.event,constraints:r}),e.parcatsViewModel.hoverinfoItems.indexOf("none")===-1){var n=Fl.mouse(this)[0],i=e.parcatsViewModel.graphDiv,a=e.parcatsViewModel.trace,o=i._fullLayout,s=o._paperdiv.node().getBoundingClientRect(),l=e.parcatsViewModel.graphDiv.getBoundingClientRect(),u,c,f;for(f=0;f<e.leftXs.length-1;f++)if(e.leftXs[f]+e.dimWidths[f]-2<=n&&n<=e.leftXs[f+1]+2){var h=e.parcatsViewModel.dimensions[f],d=e.parcatsViewModel.dimensions[f+1];u=(h.x+h.width+d.x)/2,c=(e.topYs[f]+e.topYs[f+1]+e.height)/2;break}var 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CHt(e){if(!e.parcatsViewModel.dragDimension&&(jK(Fl.select(this)),qk.loneUnhover(e.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),e.parcatsViewModel.pathSelection.sort(GK),e.parcatsViewModel.hoverinfoItems.indexOf("skip")===-1)){var t=Bk(e),r=HK(e);e.parcatsViewModel.graphDiv.emit("plotly_unhover",{points:t,event:Fl.event,constraints:r})}}function Bk(e){for(var t=[],r=cVe(e.parcatsViewModel),n=0;n<e.model.valueInds.length;n++){var i=e.model.valueInds[n];t.push({curveNumber:r,pointNumber:i})}return t}function HK(e){for(var t={},r=e.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[e.model.categoryInds[n]];t[i.containerInd]=a.categoryValue}return e.model.rawColor!==void 0&&(t.color=e.model.rawColor),t}function LHt(e){if(e.parcatsViewModel.hoverinfoItems.indexOf("skip")===-1){var t=Bk(e),r=HK(e);e.parcatsViewModel.graphDiv.emit("plotly_click",{points:t,event:Fl.event,constraints:r})}}function jK(e){e.attr("fill",function(t){return t.model.color}).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function WK(e){e.attr("fill-opacity",.8).attr("stroke",function(t){return VK.mostReadable(t.model.color,["black","white"])}).attr("stroke-width",.3)}function PHt(e){e.select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}function lVe(e){e.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function IHt(e){e.attr("stroke","black").attr("stroke-width",1.5)}function XK(e){e.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function Hz(e){var t=e.parcatsViewModel.pathSelection,r=e.categoryViewModel.model.dimensionInd,n=e.categoryViewModel.model.categoryInd;return t.filter(function(i){return i.model.categoryInds[r]===n&&i.model.color===e.color})}function RHt(e){var t=Fl.select(e.parentNode).selectAll("rect.bandrect");t.each(function(r){var n=Hz(r);WK(n),n.each(function(){yx.raiseToTop(this)})}),PHt(Fl.select(e.parentNode))}function DHt(e){var t=Fl.select(e).datum(),r=Hz(t);WK(r),r.each(function(){yx.raiseToTop(this)}),Fl.select(e.parentNode).selectAll("rect.bandrect").filter(function(n){return n.color===t.color}).each(function(){yx.raiseToTop(this),IHt(Fl.select(this))})}function ZK(e,t,r){var n=Fl.select(e).datum(),i=n.categoryViewModel.model,a=n.parcatsViewModel.graphDiv,o=Fl.select(e.parentNode).selectAll("rect.bandrect"),s=[];o.each(function(u){var c=Hz(u);c.each(function(f){Array.prototype.push.apply(s,Bk(f))})});var l={};l[i.dimensionInd]=i.categoryValue,a.emit(t,{points:s,event:r,constraints:l})}function YK(e,t,r){var n=Fl.select(e).datum(),i=n.categoryViewModel.model,a=n.parcatsViewModel.graphDiv,o=Hz(n),s=[];o.each(function(u){Array.prototype.push.apply(s,Bk(u))});var l={};l[i.dimensionInd]=i.categoryValue,n.rawColor!==void 0&&(l.color=n.rawColor),a.emit(t,{points:s,event:r,constraints:l})}function uVe(e,t,r){e._fullLayout._calcInverseTransform(e);var n=e._fullLayout._invScaleX,i=e._fullLayout._invScaleY,a=Fl.select(r.parentNode).select("rect.catrect"),o=a.node().getBoundingClientRect(),s=a.datum(),l=s.parcatsViewModel,u=l.model.dimensions[s.model.dimensionInd],c=l.trace,f=o.top+o.height/2,h,d;l.dimensions.length>1&&u.displayInd===l.dimensions.length-1?(h=o.left,d="left"):(h=o.left+o.width,d="right");var v=s.model.count,_=s.model.categoryLabel,b=v/s.parcatsViewModel.model.count,p={countLabel:v,categoryLabel:_,probabilityLabel:b.toFixed(3)},k=[];s.parcatsViewModel.hoverinfoItems.indexOf("count")!==-1&&k.push(["Count:",p.countLabel].join(" ")),s.parcatsViewModel.hoverinfoItems.indexOf("probability")!==-1&&k.push(["P("+p.categoryLabel+"):",p.probabilityLabel].join(" "));var E=k.join("<br>");return{trace:c,x:n*(h-t.left),y:i*(f-t.top),text:E,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", 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E=b/_,S=b/k,L=b/p,x={countLabel:b,categoryLabel:v,probabilityLabel:E.toFixed(3)},C=[];s.parcatsViewModel.hoverinfoItems.indexOf("count")!==-1&&C.push(["Count:",x.countLabel].join(" ")),s.parcatsViewModel.hoverinfoItems.indexOf("probability")!==-1&&(C.push("P(color \u2229 "+v+"): "+x.probabilityLabel),C.push("P("+v+" | color): "+S.toFixed(3)),C.push("P(color | "+v+"): "+L.toFixed(3)));var M=C.join("<br>"),g=VK.mostReadable(o.color,["black","white"]);return{trace:c,x:n*(h-t.left),y:i*(f-t.top),text:M,color:o.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:g,fontSize:10,idealAlign:d,hovertemplate:c.hovertemplate,hovertemplateLabels:x,eventData:[{data:c._input,fullData:c,category:v,count:_,probability:E,categorycount:p,colorcount:k,bandcolorcount:b}]}}function OHt(e){if(!e.parcatsViewModel.dragDimension&&e.parcatsViewModel.hoverinfoItems.indexOf("skip")===-1){var t=Fl.mouse(this)[1];if(t<-1)return;var r=e.parcatsViewModel.graphDiv,n=r._fullLayout,i=n._paperdiv.node().getBoundingClientRect(),a=e.parcatsViewModel.hoveron,o=this;if(a==="color"?(DHt(o),YK(o,"plotly_hover",Fl.event)):(RHt(o),ZK(o,"plotly_hover",Fl.event)),e.parcatsViewModel.hoverinfoItems.indexOf("none")===-1){var s;a==="category"?s=uVe(r,i,o):a==="color"?s=zHt(r,i,o):a==="dimension"&&(s=FHt(r,i,o)),s&&qk.loneHover(s,{container:n._hoverlayer.node(),outerContainer:n._paper.node(),gd:r})}}}function qHt(e){var t=e.parcatsViewModel;if(!t.dragDimension&&(jK(t.pathSelection),lVe(t.dimensionSelection.selectAll("g.category")),XK(t.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),qk.loneUnhover(t.graphDiv._fullLayout._hoverlayer.node()),t.pathSelection.sort(GK),t.hoverinfoItems.indexOf("skip")===-1)){var r=e.parcatsViewModel.hoveron,n=this;r==="color"?YK(n,"plotly_unhover",Fl.event):ZK(n,"plotly_unhover",Fl.event)}}function BHt(e){e.parcatsViewModel.arrangement!=="fixed"&&(e.dragDimensionDisplayInd=e.model.displayInd,e.initialDragDimensionDisplayInds=e.parcatsViewModel.model.dimensions.map(function(t){return t.displayInd}),e.dragHasMoved=!1,e.dragCategoryDisplayInd=null,Fl.select(this).selectAll("g.category").select("rect.catrect").each(function(t){var r=Fl.mouse(this)[0],n=Fl.mouse(this)[1];-2<=r&&r<=t.width+2&&-2<=n&&n<=t.height+2&&(e.dragCategoryDisplayInd=t.model.displayInd,e.initialDragCategoryDisplayInds=e.model.categories.map(function(i){return i.displayInd}),t.model.dragY=t.y,yx.raiseToTop(this.parentNode),Fl.select(this.parentNode).selectAll("rect.bandrect").each(function(i){i.y<n&&n<=i.y+i.height&&(e.potentialClickBand=this)}))}),e.parcatsViewModel.dragDimension=e,qk.loneUnhover(e.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function NHt(e){if(e.parcatsViewModel.arrangement!=="fixed"&&(e.dragHasMoved=!0,e.dragDimensionDisplayInd!==null)){var t=e.dragDimensionDisplayInd,r=t-1,n=t+1,i=e.parcatsViewModel.dimensions[t];if(e.dragCategoryDisplayInd!==null){var a=i.categories[e.dragCategoryDisplayInd];a.model.dragY+=Fl.event.dy;var o=a.model.dragY,s=a.model.displayInd,l=i.categories,u=l[s-1],c=l[s+1];u!==void 0&&o<u.y+u.height/2&&(a.model.displayInd=u.model.displayInd,u.model.displayInd=s),c!==void 0&&o+a.height>c.y+c.height/2&&(a.model.displayInd=c.model.displayInd,c.model.displayInd=s),e.dragCategoryDisplayInd=a.model.displayInd}if(e.dragCategoryDisplayInd===null||e.parcatsViewModel.arrangement==="freeform"){i.model.dragX=Fl.event.x;var f=e.parcatsViewModel.dimensions[r],h=e.parcatsViewModel.dimensions[n];f!==void 0&&i.model.dragX<f.x+f.width&&(i.model.displayInd=f.model.displayInd,f.model.displayInd=t),h!==void 0&&i.model.dragX+i.width>h.x&&(i.model.displayInd=h.model.displayInd,h.model.displayInd=e.dragDimensionDisplayInd),e.dragDimensionDisplayInd=i.model.displayInd}JK(e.parcatsViewModel),KK(e.parcatsViewModel),hVe(e.parcatsViewModel),fVe(e.parcatsViewModel)}}function UHt(e){if(e.parcatsViewModel.arrangement!=="fixed"&&e.dragDimensionDisplayInd!==null){Fl.select(this).selectAll("text").attr("font-weight","normal");var t={},r=cVe(e.parcatsViewModel),n=e.parcatsViewModel.model.dimensions.map(function(h){return h.displayInd}),i=e.initialDragDimensionDisplayInds.some(function(h,d){return h!==n[d]});i&&n.forEach(function(h,d){var v=e.parcatsViewModel.model.dimensions[d].containerInd;t["dimensions["+v+"].displayindex"]=h});var a=!1;if(e.dragCategoryDisplayInd!==null){var o=e.model.categories.map(function(h){return h.displayInd});if(a=e.initialDragCategoryDisplayInds.some(function(h,d){return h!==o[d]}),a){var s=e.model.categories.slice().sort(function(h,d){return h.displayInd-d.displayInd}),l=s.map(function(h){return h.categoryValue}),u=s.map(function(h){return h.categoryLabel});t["dimensions["+e.model.containerInd+"].categoryarray"]=[l],t["dimensions["+e.model.containerInd+"].ticktext"]=[u],t["dimensions["+e.model.containerInd+"].categoryorder"]="array"}}if(e.parcatsViewModel.hoverinfoItems.indexOf("skip")===-1&&!e.dragHasMoved&&e.potentialClickBand&&(e.parcatsViewModel.hoveron==="color"?YK(e.potentialClickBand,"plotly_click",Fl.event.sourceEvent):ZK(e.potentialClickBand,"plotly_click",Fl.event.sourceEvent)),e.model.dragX=null,e.dragCategoryDisplayInd!==null){var c=e.parcatsViewModel.dimensions[e.dragDimensionDisplayInd].categories[e.dragCategoryDisplayInd];c.model.dragY=null,e.dragCategoryDisplayInd=null}e.dragDimensionDisplayInd=null,e.parcatsViewModel.dragDimension=null,e.dragHasMoved=null,e.potentialClickBand=null,JK(e.parcatsViewModel),KK(e.parcatsViewModel);var f=Fl.transition().duration(300).ease("cubic-in-out");f.each(function(){hVe(e.parcatsViewModel,!0),fVe(e.parcatsViewModel,!0)}).each("end",function(){(i||a)&&SHt.restyle(e.parcatsViewModel.graphDiv,t,[r])})}}function cVe(e){for(var t,r=e.graphDiv._fullData,n=0;n<r.length;n++)if(e.key===r[n].uid){t=n;break}return t}function fVe(e,t){t===void 0&&(t=!1);function r(n){return t?n.transition():n}e.pathSelection.data(function(n){return n.paths},u1),r(e.pathSelection).attr("d",function(n){return n.svgD})}function hVe(e,t){t===void 0&&(t=!1);function r(l){return t?l.transition():l}e.dimensionSelection.data(function(l){return l.dimensions},u1);var n=e.dimensionSelection.selectAll("g.category").data(function(l){return l.categories},u1);r(e.dimensionSelection).attr("transform",function(l){return zk(l.x,0)}),r(n).attr("transform",function(l){return zk(0,l.y)});var i=n.select(".dimlabel");i.text(function(l,u){return u===0?l.parcatsViewModel.model.dimensions[l.model.dimensionInd].dimensionLabel:null});var a=n.select(".catlabel");a.attr("text-anchor",function(l){return Ok(l)?"start":"end"}).attr("x",function(l){return Ok(l)?l.width+5:-5}).each(function(l){var u,c;Ok(l)?(u=l.width+5,c="start"):(u=-5,c="end"),Fl.select(this).selectAll("tspan").attr("x",u).attr("text-anchor",c)});var o=n.selectAll("rect.bandrect").data(function(l){return l.bands},u1),s=o.enter().append("rect").attr("class","bandrect").attr("cursor","move").attr("stroke-opacity",0).attr("fill",function(l){return l.color}).attr("fill-opacity",0);o.attr("fill",function(l){return l.color}).attr("width",function(l){return l.width}).attr("height",function(l){return l.height}).attr("y",function(l){return l.y}),XK(s),o.each(function(){yx.raiseToTop(this)}),o.exit().remove()}function VHt(e,t,r){var n=r[0],i=t.margin||{l:80,r:80,t:100,b:80},a=n.trace,o=a.domain,s=t.width,l=t.height,u=Math.floor(s*(o.x[1]-o.x[0])),c=Math.floor(l*(o.y[1]-o.y[0])),f=o.x[0]*s+i.l,h=t.height-o.y[1]*t.height+i.t,d=a.line.shape,v;a.hoverinfo==="all"?v=["count","probability"]:v=(a.hoverinfo||"").split("+");var _={trace:a,key:a.uid,model:n,x:f,y:h,width:u,height:c,hoveron:a.hoveron,hoverinfoItems:v,arrangement:a.arrangement,bundlecolors:a.bundlecolors,sortpaths:a.sortpaths,labelfont:a.labelfont,categorylabelfont:a.tickfont,pathShape:d,dragDimension:null,margin:i,paths:[],dimensions:[],graphDiv:e,traceSelection:null,pathSelection:null,dimensionSelection:null};return n.dimensions&&(JK(_),KK(_)),_}function sVe(e,t,r,n,i){var a=[],o=[],s,l;for(l=0;l<r.length-1;l++)s=AHt(r[l]+e[l],e[l+1]),a.push(s(i)),o.push(s(1-i));var u="M "+e[0]+","+t[0];for(u+="l"+r[0]+",0 ",l=1;l<r.length;l++)u+="C"+a[l-1]+","+t[l-1]+" "+o[l-1]+","+t[l]+" "+e[l]+","+t[l],u+="l"+r[l]+",0 ";for(u+="l0,"+n+" ",u+="l -"+r[r.length-1]+",0 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Array(u.length),d=t[0].model.count,v=t[0].categories.map(function(O){return O.height}).reduce(function(O,V){return O+V}),_=0;_<u.length;_++){var b=u[_],p;d>0?p=v*(b.count/d):p=0;for(var k=new Array(n.length),E=0;E<b.categoryInds.length;E++){var S=b.categoryInds[E],L=i[E][S],x=a[E];k[x]=n[x][L],n[x][L]+=p;var C=e.dimensions[x].categories[L],M=C.bands.length,g=C.bands[M-1];if(g===void 0||b.rawColor!==g.rawColor){var P=g===void 0?0:g.y+g.height;C.bands.push({key:P,color:b.color,rawColor:b.rawColor,height:p,width:C.width,count:b.count,y:P,categoryViewModel:C,parcatsViewModel:e})}else{var T=C.bands[M-1];T.height+=p,T.count+=b.count}}var z;e.pathShape==="hspline"?z=sVe(s,k,l,p,.5):z=sVe(s,k,l,p,0),h[_]={key:b.valueInds[0],model:b,height:p,leftXs:s,topYs:k,dimWidths:l,svgD:z,parcatsViewModel:e}}e.paths=h}function JK(e){var t=e.model.dimensions.map(function(o){return{displayInd:o.displayInd,dimensionInd:o.dimensionInd}});t.sort(function(o,s){return o.displayInd-s.displayInd});var r=[];for(var n in t){var i=t[n].dimensionInd,a=e.model.dimensions[i];r.push(GHt(e,a))}e.dimensions=r}function GHt(e,t){var r=40,n=16,i=e.model.dimensions.length,a=t.displayInd,o,s,l;i>1?o=(e.width-2*r-n)/(i-1):o=0,s=r,l=s+o*a;var u=[],c=e.model.maxCats,f=t.categories.length,h=8,d=t.count,v=e.height-h*(c-1),_,b,p,k,E,S=(c-f)*h/2,L=t.categories.map(function(x){return{displayInd:x.displayInd,categoryInd:x.categoryInd}});for(L.sort(function(x,C){return x.displayInd-C.displayInd}),E=0;E<f;E++)k=L[E].categoryInd,b=t.categories[k],d>0?_=b.count/d*v:_=0,p={key:b.valueInds[0],model:b,width:n,height:_,y:b.dragY!==null?b.dragY:S,bands:[],parcatsViewModel:e},S=S+_+h,u.push(p);return{key:t.dimensionInd,x:t.dragX!==null?t.dragX:l,y:0,width:n,model:t,categories:u,parcatsViewModel:e,dragCategoryDisplayInd:null,dragDimensionDisplayInd:null,initialDragDimensionDisplayInds:null,initialDragCategoryDisplayInds:null,dragHasMoved:null,potentialClickBand:null}}});var $K=ye((ixr,pVe)=>{"use strict";var HHt=vVe();pVe.exports=function(t,r,n,i){var a=t._fullLayout,o=a._paper,s=a._size;HHt(t,o,r,{width:s.w,height:s.h,margin:{t:s.t,r:s.r,b:s.b,l:s.l}},n,i)}});var mVe=ye(jz=>{"use strict";var jHt=Id().getModuleCalcData,WHt=$K(),gVe="parcats";jz.name=gVe;jz.plot=function(e,t,r,n){var i=jHt(e.calcdata,gVe);if(i.length){var a=i[0];WHt(e,a,r,n)}};jz.clean=function(e,t,r,n){var i=n._has&&n._has("parcats"),a=t._has&&t._has("parcats");i&&!a&&n._paperdiv.selectAll(".parcats").remove()}});var _Ve=ye((axr,yVe)=>{"use strict";yVe.exports={attributes:UK(),supplyDefaults:iVe(),calc:aVe(),plot:$K(),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcats",basePlotModule:mVe(),categories:["noOpacity"],meta:{}}});var bVe=ye((oxr,xVe)=>{"use strict";xVe.exports=_Ve()});var c1=ye((sxr,kVe)=>{"use strict";var XHt=Z1(),wVe="1.13.4",MVe='\xA9 <a target="_blank" href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors',TVe=['\xA9 <a target="_blank" href="https://carto.com/">Carto</a>',MVe].join(" "),AVe=['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://www.openstreetmap.org/copyright">ODbL</a>'].join(" "),ZHt=['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://creativecommons.org/licenses/by-sa/3.0">CC BY SA</a>'].join(" "),EVe={"open-street-map":{id:"osm",version:8,sources:{"plotly-osm-tiles":{type:"raster",attribution:MVe,tiles:["https://a.tile.openstreetmap.org/{z}/{x}/{y}.png","https://b.tile.openstreetmap.org/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-osm-tiles",type:"raster",source:"plotly-osm-tiles",minzoom:0,maxzoom:22}],glyphs:"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf"},"white-bg":{id:"white-bg",version:8,sources:{},layers:[{id:"white-bg",type:"background",paint:{"background-color":"#FFFFFF"},minzoom:0,maxzoom:22}],glyphs:"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf"},"carto-positron":{id:"carto-positron",version:8,sources:{"plotly-carto-positron":{type:"raster",attribution:TVe,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-positron",type:"raster",source:"plotly-carto-positron",minzoom:0,maxzoom:22}],glyphs:"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf"},"carto-darkmatter":{id:"carto-darkmatter",version:8,sources:{"plotly-carto-darkmatter":{type:"raster",attribution:TVe,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-darkmatter",type:"raster",source:"plotly-carto-darkmatter",minzoom:0,maxzoom:22}],glyphs:"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf"},"stamen-terrain":{id:"stamen-terrain",version:8,sources:{"plotly-stamen-terrain":{type:"raster",attribution:AVe,tiles:["https://tiles.stadiamaps.com/tiles/stamen_terrain/{z}/{x}/{y}.png?api_key="],tileSize:256}},layers:[{id:"plotly-stamen-terrain",type:"raster",source:"plotly-stamen-terrain",minzoom:0,maxzoom:22}],glyphs:"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf"},"stamen-toner":{id:"stamen-toner",version:8,sources:{"plotly-stamen-toner":{type:"raster",attribution:AVe,tiles:["https://tiles.stadiamaps.com/tiles/stamen_toner/{z}/{x}/{y}.png?api_key="],tileSize:256}},layers:[{id:"plotly-stamen-toner",type:"raster",source:"plotly-stamen-toner",minzoom:0,maxzoom:22}],glyphs:"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf"},"stamen-watercolor":{id:"stamen-watercolor",version:8,sources:{"plotly-stamen-watercolor":{type:"raster",attribution:ZHt,tiles:["https://tiles.stadiamaps.com/tiles/stamen_watercolor/{z}/{x}/{y}.jpg?api_key="],tileSize:256}},layers:[{id:"plotly-stamen-watercolor",type:"raster",source:"plotly-stamen-watercolor",minzoom:0,maxzoom:22}],glyphs:"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf"}},SVe=XHt(EVe);kVe.exports={requiredVersion:wVe,styleUrlPrefix:"mapbox://styles/mapbox/",styleUrlSuffix:"v9",styleValuesMapbox:["basic","streets","outdoors","light","dark","satellite","satellite-streets"],styleValueDflt:"basic",stylesNonMapbox:EVe,styleValuesNonMapbox:SVe,traceLayerPrefix:"plotly-trace-layer-",layoutLayerPrefix:"plotly-layout-layer-",wrongVersionErrorMsg:["Your custom plotly.js bundle is not using the correct mapbox-gl version","Please install @plotly/mapbox-gl@"+wVe+"."].join(`
+`),noAccessTokenErrorMsg:["Missing Mapbox access token.","Mapbox trace type require a Mapbox access token to be registered.","For example:","  Plotly.newPlot(gd, data, layout, { mapboxAccessToken: 'my-access-token' });","More info here: https://www.mapbox.com/help/define-access-token/"].join(`
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ejt=IVe.exports=$Ht({_arrayAttrRegexps:[CVe.counterRegex("mapbox",".layers",!0)],domain:YHt({name:"mapbox"}),accesstoken:{valType:"string",noBlank:!0,strict:!0},style:{valType:"any",values:QK.styleValuesMapbox.concat(QK.styleValuesNonMapbox),dflt:QK.styleValueDflt},center:{lon:{valType:"number",dflt:0},lat:{valType:"number",dflt:0}},zoom:{valType:"number",dflt:1},bearing:{valType:"number",dflt:0},pitch:{valType:"number",dflt:0},bounds:{west:{valType:"number"},east:{valType:"number"},south:{valType:"number"},north:{valType:"number"}},layers:QHt("layer",{visible:{valType:"boolean",dflt:!0},sourcetype:{valType:"enumerated",values:["geojson","vector","raster","image"],dflt:"geojson"},source:{valType:"any"},sourcelayer:{valType:"string",dflt:""},sourceattribution:{valType:"string"},type:{valType:"enumerated",values:["circle","line","fill","symbol","raster"],dflt:"circle"},coordinates:{valType:"any"},below:{valType:"string"},color:{valType:"color",dflt:LVe},opacity:{valType:"number",min:0,max:1,dflt:1},minzoom:{valType:"number",min:0,max:24,dflt:0},maxzoom:{valType:"number",min:0,max:24,dflt:24},circle:{radius:{valType:"number",dflt:15}},line:{width:{valType:"number",dflt:2},dash:{valType:"data_array"}},fill:{outlinecolor:{valType:"color",dflt:LVe}},symbol:{icon:{valType:"string",dflt:"marker"},iconsize:{valType:"number",dflt:10},text:{valType:"string",dflt:""},placement:{valType:"enumerated",values:["point","line","line-center"],dflt:"point"},textfont:PVe,textposition:CVe.extendFlat({},JHt,{arrayOk:!1})}})},"plot","from-root");ejt.uirevision={valType:"any",editType:"none"}});var Wz=ye((uxr,zVe)=>{"use 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eJ=ye((cxr,OVe)=>{"use strict";var ljt=["Metropolis Black Italic","Metropolis Black","Metropolis Bold Italic","Metropolis Bold","Metropolis Extra Bold Italic","Metropolis Extra Bold","Metropolis Extra Light Italic","Metropolis Extra Light","Metropolis Light Italic","Metropolis Light","Metropolis Medium Italic","Metropolis Medium","Metropolis Regular Italic","Metropolis Regular","Metropolis Semi Bold Italic","Metropolis Semi Bold","Metropolis Thin Italic","Metropolis Thin","Open Sans Bold Italic","Open Sans Bold","Open Sans Extrabold Italic","Open Sans Extrabold","Open Sans Italic","Open Sans Light Italic","Open Sans Light","Open Sans Regular","Open Sans Semibold Italic","Open Sans Semibold","Klokantech Noto Sans Bold","Klokantech Noto Sans CJK Bold","Klokantech Noto Sans CJK Regular","Klokantech Noto Sans Italic","Klokantech Noto Sans Regular"];OVe.exports={isSupportedFont:function(e){return ljt.indexOf(e)!==-1}}});var NVe=ye((fxr,BVe)=>{"use strict";var 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p=this.isHidden,k=r.visible!==!0;k?p||v():p?k||_():l!==s?(v(),_()):(this.below!==o&&(v(!0),_(!0)),b()),this.clusterEnabled=s,this.isHidden=k,this.below=o,t[0].trace._glTrace=this};Gk.dispose=function(){for(var t=this.subplot.map,r=this.clusterEnabled?rg.cluster:rg.nonCluster,n=r.length-1;n>=0;n--){var i=r[n];t.removeLayer(this.layerIds[i]),t.removeSource(this.sourceIds[i])}};QVe.exports=function(t,r){var n=r[0].trace,i=n.cluster&&n.cluster.enabled,a=n.visible!==!0,o=new $Ve(t,n.uid,i,a),s=JVe(t.gd,r),l=o.below=t.belowLookup["trace-"+n.uid],u,c,f;if(i)for(o.addSource("circle",s.circle,n.cluster),u=0;u<rg.cluster.length;u++)c=rg.cluster[u],f=s[c],o.addLayer(c,f,l);else for(u=0;u<rg.nonCluster.length;u++)c=rg.nonCluster[u],f=s[c],o.addSource(c,f,n.cluster),o.addLayer(c,f,l);return r[0].trace._glTrace=o,o}});var Jz=ye((gxr,rGe)=>{"use strict";var Mjt=vf(),oJ=Dr(),Ejt=cT(),kjt=oJ.fillText,Cjt=fs().BADNUM,Ljt=c1().traceLayerPrefix;function Pjt(e,t,r){var n=e.cd,i=n[0].trace,a=e.xa,o=e.ya,s=e.subplot,l=[],u=Ljt+i.uid+"-circle",c=i.cluster&&i.cluster.enabled;if(c){var f=s.map.queryRenderedFeatures(null,{layers:[u]});l=f.map(function(M){return M.id})}var h=t>=0?Math.floor((t+180)/360):Math.ceil((t-180)/360),d=h*360,v=t-d;function _(M){var g=M.lonlat;if(g[0]===Cjt||c&&l.indexOf(M.i+1)===-1)return 1/0;var P=oJ.modHalf(g[0],360),T=g[1],z=s.project([P,T]),O=z.x-a.c2p([v,T]),V=z.y-o.c2p([P,r]),G=Math.max(3,M.mrc||0);return Math.max(Math.sqrt(O*O+V*V)-G,1-3/G)}if(Mjt.getClosest(n,_,e),e.index!==!1){var b=n[e.index],p=b.lonlat,k=[oJ.modHalf(p[0],360)+d,p[1]],E=a.c2p(k),S=o.c2p(k),L=b.mrc||1;e.x0=E-L,e.x1=E+L,e.y0=S-L,e.y1=S+L;var x={};x[i.subplot]={_subplot:s};var C=i._module.formatLabels(b,i,x);return e.lonLabel=C.lonLabel,e.latLabel=C.latLabel,e.color=Ejt(i,b),e.extraText=tGe(i,b,n[0].t.labels),e.hovertemplate=i.hovertemplate,[e]}}function tGe(e,t,r){if(e.hovertemplate)return;var n=t.hi||e.hoverinfo,i=n.split("+"),a=i.indexOf("all")!==-1,o=i.indexOf("lon")!==-1,s=i.indexOf("lat")!==-1,l=t.lonlat,u=[];function c(f){return f+"\xB0"}return a||o&&s?u.push("("+c(l[1])+", "+c(l[0])+")"):o?u.push(r.lon+c(l[0])):s&&u.push(r.lat+c(l[1])),(a||i.indexOf("text")!==-1)&&kjt(t,e,u),u.join("<br>")}rGe.exports={hoverPoints:Pjt,getExtraText:tGe}});var nGe=ye((mxr,iGe)=>{"use strict";iGe.exports=function(t,r){return t.lon=r.lon,t.lat=r.lat,t}});var oGe=ye((yxr,aGe)=>{"use strict";var Ijt=Dr(),Rjt=Ru(),Djt=fs().BADNUM;aGe.exports=function(t,r){var n=t.cd,i=t.xaxis,a=t.yaxis,o=[],s=n[0].trace,l;if(!Rjt.hasMarkers(s))return[];if(r===!1)for(l=0;l<n.length;l++)n[l].selected=0;else for(l=0;l<n.length;l++){var u=n[l],c=u.lonlat;if(c[0]!==Djt){var f=[Ijt.modHalf(c[0],360),c[1]],h=[i.c2p(f),a.c2p(f)];r.contains(h,null,l,t)?(o.push({pointNumber:l,lon:c[0],lat:c[1]}),u.selected=1):u.selected=0}}return o}});var uJ=ye((sJ,lJ)=>{(function(e,t){typeof sJ=="object"&&typeof 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this.int16[Oe+0]=$,this.int16[Oe+1]=ae,this.int16[Oe+2]=he,U},y}(en);kn.prototype.bytesPerElement=6,X("StructArrayLayout3i6",kn);var ta=function(m){function y(){m.apply(this,arguments)}return m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y,y.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},y.prototype.emplaceBack=function(U,$,ae){var he=this.length;return this.resize(he+1),this.emplace(he,U,$,ae)},y.prototype.emplace=function(U,$,ae,he){var Oe=U*2,rt=U*4;return this.uint32[Oe+0]=$,this.uint16[rt+2]=ae,this.uint16[rt+3]=he,U},y}(en);ta.prototype.bytesPerElement=8,X("StructArrayLayout1ul2ui8",ta);var oa=function(m){function y(){m.apply(this,arguments)}return m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y,y.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},y.prototype.emplaceBack=function(U,$){var ae=this.length;return this.resize(ae+1),this.emplace(ae,U,$)},y.prototype.emplace=function(U,$,ae){var he=U*2;return this.uint16[he+0]=$,this.uint16[he+1]=ae,U},y}(en);oa.prototype.bytesPerElement=4,X("StructArrayLayout2ui4",oa);var ba=function(m){function y(){m.apply(this,arguments)}return m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y,y.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},y.prototype.emplaceBack=function(U){var $=this.length;return this.resize($+1),this.emplace($,U)},y.prototype.emplace=function(U,$){var ae=U*1;return this.uint16[ae+0]=$,U},y}(en);ba.prototype.bytesPerElement=2,X("StructArrayLayout1ui2",ba);var is=function(m){function y(){m.apply(this,arguments)}return m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y,y.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},y.prototype.emplaceBack=function(U,$,ae,he){var Oe=this.length;return this.resize(Oe+1),this.emplace(Oe,U,$,ae,he)},y.prototype.emplace=function(U,$,ae,he,Oe){var rt=U*4;return this.float32[rt+0]=$,this.float32[rt+1]=ae,this.float32[rt+2]=he,this.float32[rt+3]=Oe,U},y}(en);is.prototype.bytesPerElement=16,X("StructArrayLayout4f16",is);var Zs=function(m){function y(){m.apply(this,arguments)}m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y;var I={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return I.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},I.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},I.x1.get=function(){return this._structArray.int16[this._pos2+2]},I.y1.get=function(){return this._structArray.int16[this._pos2+3]},I.x2.get=function(){return this._structArray.int16[this._pos2+4]},I.y2.get=function(){return this._structArray.int16[this._pos2+5]},I.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},I.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},I.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},I.anchorPoint.get=function(){return new u(this.anchorPointX,this.anchorPointY)},Object.defineProperties(y.prototype,I),y}(qi);Zs.prototype.size=20;var Va=function(m){function y(){m.apply(this,arguments)}return m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y,y.prototype.get=function(U){return new 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this._structArray.uint16[this._pos2+3]},I.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},I.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},I.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},I.segment.get=function(){return this._structArray.uint16[this._pos2+10]},I.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},I.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},I.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},I.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},I.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},I.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},I.placedOrientation.set=function(U){this._structArray.uint8[this._pos1+37]=U},I.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},I.hidden.set=function(U){this._structArray.uint8[this._pos1+38]=U},I.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},I.crossTileID.set=function(U){this._structArray.uint32[this._pos4+10]=U},I.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(y.prototype,I),y}(qi);Ml.prototype.size=48;var zo=function(m){function y(){m.apply(this,arguments)}return m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y,y.prototype.get=function(U){return new Ml(this,U)},y}(Vn);X("PlacedSymbolArray",zo);var Qs=function(m){function y(){m.apply(this,arguments)}m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y;var I={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return I.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},I.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},I.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},I.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},I.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},I.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},I.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},I.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},I.key.get=function(){return this._structArray.uint16[this._pos2+8]},I.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},I.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},I.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},I.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},I.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},I.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},I.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},I.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},I.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},I.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},I.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},I.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},I.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},I.useRuntimeCollisionCircles.get=function(){return this._structArray.uint16[this._pos2+22]},I.crossTileID.get=function(){return this._structArray.uint32[this._pos4+12]},I.crossTileID.set=function(U){this._structArray.uint32[this._pos4+12]=U},I.textBoxScale.get=function(){return this._structArray.float32[this._pos4+13]},I.textOffset0.get=function(){return this._structArray.float32[this._pos4+14]},I.textOffset1.get=function(){return this._structArray.float32[this._pos4+15]},I.collisionCircleDiameter.get=function(){return this._structArray.float32[this._pos4+16]},Object.defineProperties(y.prototype,I),y}(qi);Qs.prototype.size=68;var al=function(m){function y(){m.apply(this,arguments)}return m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y,y.prototype.get=function(U){return new Qs(this,U)},y}(na);X("SymbolInstanceArray",al);var Vl=function(m){function y(){m.apply(this,arguments)}return m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y,y.prototype.getoffsetX=function(U){return this.float32[U*1+0]},y}(Ki);X("GlyphOffsetArray",Vl);var ss=function(m){function y(){m.apply(this,arguments)}return m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y,y.prototype.getx=function(U){return this.int16[U*3+0]},y.prototype.gety=function(U){return this.int16[U*3+1]},y.prototype.gettileUnitDistanceFromAnchor=function(U){return this.int16[U*3+2]},y}(kn);X("SymbolLineVertexArray",ss);var Vs=function(m){function y(){m.apply(this,arguments)}m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y;var I={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return I.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},I.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+2]},I.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+3]},Object.defineProperties(y.prototype,I),y}(qi);Vs.prototype.size=8;var Ys=function(m){function y(){m.apply(this,arguments)}return m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y,y.prototype.get=function(U){return new Vs(this,U)},y}(ta);X("FeatureIndexArray",Ys);var wa=Wi([{name:"a_pos",components:2,type:"Int16"}],4),ol=wa.members,io=function(y){y===void 0&&(y=[]),this.segments=y};io.prototype.prepareSegment=function(y,I,U,$){var ae=this.segments[this.segments.length-1];return y>io.MAX_VERTEX_ARRAY_LENGTH&&te("Max vertices per segment is "+io.MAX_VERTEX_ARRAY_LENGTH+": bucket requested "+y),(!ae||ae.vertexLength+y>io.MAX_VERTEX_ARRAY_LENGTH||ae.sortKey!==$)&&(ae={vertexOffset:I.length,primitiveOffset:U.length,vertexLength:0,primitiveLength:0},$!==void 0&&(ae.sortKey=$),this.segments.push(ae)),ae},io.prototype.get=function(){return this.segments},io.prototype.destroy=function(){for(var y=0,I=this.segments;y<I.length;y+=1){var U=I[y];for(var $ in U.vaos)U.vaos[$].destroy()}},io.simpleSegment=function(y,I,U,$){return new io([{vertexOffset:y,primitiveOffset:I,vertexLength:U,primitiveLength:$,vaos:{},sortKey:0}])},io.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,X("SegmentVector",io);function Y(m,y){return m=p(Math.floor(m),0,255),y=p(Math.floor(y),0,255),256*m+y}var D=Wi([{name:"a_pattern_from",components:4,type:"Uint16"},{name:"a_pattern_to",components:4,type:"Uint16"},{name:"a_pixel_ratio_from",components:1,type:"Uint16"},{name:"a_pixel_ratio_to",components:1,type:"Uint16"}]),J=a(function(m){function y(I,U){var 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$=I.length,ae=U^$,he=0,Oe;$>=4;)Oe=I.charCodeAt(he)&255|(I.charCodeAt(++he)&255)<<8|(I.charCodeAt(++he)&255)<<16|(I.charCodeAt(++he)&255)<<24,Oe=(Oe&65535)*1540483477+(((Oe>>>16)*1540483477&65535)<<16),Oe^=Oe>>>24,Oe=(Oe&65535)*1540483477+(((Oe>>>16)*1540483477&65535)<<16),ae=(ae&65535)*1540483477+(((ae>>>16)*1540483477&65535)<<16)^Oe,$-=4,++he;switch($){case 3:ae^=(I.charCodeAt(he+2)&255)<<16;case 2:ae^=(I.charCodeAt(he+1)&255)<<8;case 1:ae^=I.charCodeAt(he)&255,ae=(ae&65535)*1540483477+(((ae>>>16)*1540483477&65535)<<16)}return ae^=ae>>>13,ae=(ae&65535)*1540483477+(((ae>>>16)*1540483477&65535)<<16),ae^=ae>>>15,ae>>>0}m.exports=y}),K=J,de=J,ne=q;K.murmur3=de,K.murmur2=ne;var we=function(){this.ids=[],this.positions=[],this.indexed=!1};we.prototype.add=function(y,I,U,$){this.ids.push(ft(y)),this.positions.push(I,U,$)},we.prototype.getPositions=function(y){for(var I=ft(y),U=0,$=this.ids.length-1;U<$;){var ae=U+$>>1;this.ids[ae]>=I?$=ae:U=ae+1}for(var he=[];this.ids[U]===I;){var 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U.id})};sh.prototype.populate=function(y,I,U){this.hasPattern=qw("line",this.layers,I);for(var $=this.layers[0].layout.get("line-sort-key"),ae=[],he=0,Oe=y;he<Oe.length;he+=1){var rt=Oe[he],gt=rt.feature,Et=rt.id,or=rt.index,_r=rt.sourceLayerIndex,pr=this.layers[0]._featureFilter.needGeometry,Fr=Ja(gt,pr);if(this.layers[0]._featureFilter.filter(new Gn(this.zoom),Fr,U)){var oi=$?$.evaluate(Fr,{},U):void 0,Hi={id:Et,properties:gt.properties,type:gt.type,sourceLayerIndex:_r,index:or,geometry:pr?Fr.geometry:zn(gt),patterns:{},sortKey:oi};ae.push(Hi)}}$&&ae.sort(function(za,Za){return za.sortKey-Za.sortKey});for(var Ai=0,bn=ae;Ai<bn.length;Ai+=1){var nn=bn[Ai],xn=nn,Pn=xn.geometry,Zn=xn.index,ga=xn.sourceLayerIndex;if(this.hasPattern){var ha=Jx("line",this.layers,nn,this.zoom,I);this.patternFeatures.push(ha)}else this.addFeature(nn,Pn,Zn,U,{});var eo=y[Zn].feature;I.featureIndex.insert(eo,Pn,Zn,ga,this.index)}},sh.prototype.update=function(y,I,U){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(y,I,this.stateDependentLayers,U)},sh.prototype.addFeatures=function(y,I,U){for(var $=0,ae=this.patternFeatures;$<ae.length;$+=1){var he=ae[$];this.addFeature(he,he.geometry,he.index,I,U)}},sh.prototype.isEmpty=function(){return this.layoutVertexArray.length===0},sh.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},sh.prototype.upload=function(y){this.uploaded||(this.layoutVertexArray2.length!==0&&(this.layoutVertexBuffer2=y.createVertexBuffer(this.layoutVertexArray2,jC)),this.layoutVertexBuffer=y.createVertexBuffer(this.layoutVertexArray,HC),this.indexBuffer=y.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(y),this.uploaded=!0},sh.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},sh.prototype.lineFeatureClips=function(y){if(y.properties&&y.properties.hasOwnProperty("mapbox_clip_start")&&y.properties.hasOwnProperty("mapbox_clip_end")){var I=+y.properties.mapbox_clip_start,U=+y.properties.mapbox_clip_end;return{start:I,end:U}}},sh.prototype.addFeature=function(y,I,U,$,ae){var he=this.layers[0].layout,Oe=he.get("line-join").evaluate(y,{}),rt=he.get("line-cap"),gt=he.get("line-miter-limit"),Et=he.get("line-round-limit");this.lineClips=this.lineFeatureClips(y);for(var or=0,_r=I;or<_r.length;or+=1){var pr=_r[or];this.addLine(pr,y,Oe,rt,gt,Et)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,y,U,ae,$)},sh.prototype.addLine=function(y,I,U,$,ae,he){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(var Oe=0;Oe<y.length-1;Oe++)this.totalDistance+=y[Oe].dist(y[Oe+1]);this.updateScaledDistance(),this.maxLineLength=Math.max(this.maxLineLength,this.totalDistance)}for(var rt=xS[I.type]==="Polygon",gt=y.length;gt>=2&&y[gt-1].equals(y[gt-2]);)gt--;for(var Et=0;Et<gt-1&&y[Et].equals(y[Et+1]);)Et++;if(!(gt<(rt?3:2))){U==="bevel"&&(ae=1.05);var or=this.overscaling<=16?eb*Ci/(512*this.overscaling):0,_r=this.segments.prepareSegment(gt*10,this.layoutVertexArray,this.indexArray),pr,Fr=void 0,oi=void 0,Hi=void 0,Ai=void 0;this.e1=this.e2=-1,rt&&(pr=y[gt-2],Ai=y[Et].sub(pr)._unit()._perp());for(var bn=Et;bn<gt;bn++)if(oi=bn===gt-1?rt?y[Et+1]:void 0:y[bn+1],!(oi&&y[bn].equals(oi))){Ai&&(Hi=Ai),pr&&(Fr=pr),pr=y[bn],Ai=oi?oi.sub(pr)._unit()._perp():Hi,Hi=Hi||Ai;var nn=Hi.add(Ai);(nn.x!==0||nn.y!==0)&&nn._unit();var xn=Hi.x*Ai.x+Hi.y*Ai.y,Pn=nn.x*Ai.x+nn.y*Ai.y,Zn=Pn!==0?1/Pn:1/0,ga=2*Math.sqrt(2-2*Pn),ha=Pn<WC&&Fr&&oi,eo=Hi.x*Ai.y-Hi.y*Ai.x>0;if(ha&&bn>Et){var za=pr.dist(Fr);if(za>2*or){var Za=pr.sub(pr.sub(Fr)._mult(or/za)._round());this.updateDistance(Fr,Za),this.addCurrentVertex(Za,Hi,0,0,_r),Fr=Za}}var Ko=Fr&&oi,to=Ko?U:rt?"butt":$;if(Ko&&to==="round"&&(Zn<he?to="miter":Zn<=2&&(to="fakeround")),to==="miter"&&Zn>ae&&(to="bevel"),to==="bevel"&&(Zn>2&&(to="flipbevel"),Zn<ae&&(to="miter")),Fr&&this.updateDistance(Fr,pr),to==="miter")nn._mult(Zn),this.addCurrentVertex(pr,nn,0,0,_r);else if(to==="flipbevel"){if(Zn>100)nn=Ai.mult(-1);else{var ao=Zn*Hi.add(Ai).mag()/Hi.sub(Ai).mag();nn._perp()._mult(ao*(eo?-1:1))}this.addCurrentVertex(pr,nn,0,0,_r),this.addCurrentVertex(pr,nn.mult(-1),0,0,_r)}else if(to==="bevel"||to==="fakeround"){var _s=-Math.sqrt(Zn*Zn-1),jo=eo?_s:0,El=eo?0:_s;if(Fr&&this.addCurrentVertex(pr,Hi,jo,El,_r),to==="fakeround")for(var Iu=Math.round(ga*180/Math.PI/bS),kl=1;kl<Iu;kl++){var Cl=kl/Iu;if(Cl!==.5){var yl=Cl-.5,Qu=1.0904+xn*(-3.2452+xn*(3.55645-xn*1.43519)),gc=.848013+xn*(-1.06021+xn*.215638);Cl=Cl+Cl*yl*(Cl-1)*(Qu*yl*yl+gc)}var 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rt=y.x,gt=y.y,Et=this.lineClips?this.scaledDistance*(tb-1):this.scaledDistance,or=Et*Uw;if(this.layoutVertexArray.emplaceBack((rt<<1)+($?1:0),(gt<<1)+(ae?1:0),Math.round(Nw*I)+128,Math.round(Nw*U)+128,(he===0?0:he<0?-1:1)+1|(or&63)<<2,or>>6),this.lineClips){var _r=this.scaledDistance-this.lineClips.start,pr=this.lineClips.end-this.lineClips.start,Fr=_r/pr;this.layoutVertexArray2.emplaceBack(Fr,this.lineClipsArray.length)}var oi=Oe.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,oi),Oe.primitiveLength++),ae?this.e2=oi:this.e1=oi},sh.prototype.updateScaledDistance=function(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance},sh.prototype.updateDistance=function(y,I){this.distance+=y.dist(I),this.updateScaledDistance()},X("LineBucket",sh,{omit:["layers","patternFeatures"]});var wS=new Nr({"line-cap":new Me(Rn.layout_line["line-cap"]),"line-join":new bt(Rn.layout_line["line-join"]),"line-miter-limit":new Me(Rn.layout_line["line-miter-limit"]),"line-round-limit":new Me(Rn.layout_line["line-round-limit"]),"line-sort-key":new bt(Rn.layout_line["line-sort-key"])}),TS=new Nr({"line-opacity":new bt(Rn.paint_line["line-opacity"]),"line-color":new bt(Rn.paint_line["line-color"]),"line-translate":new Me(Rn.paint_line["line-translate"]),"line-translate-anchor":new Me(Rn.paint_line["line-translate-anchor"]),"line-width":new bt(Rn.paint_line["line-width"]),"line-gap-width":new bt(Rn.paint_line["line-gap-width"]),"line-offset":new bt(Rn.paint_line["line-offset"]),"line-blur":new bt(Rn.paint_line["line-blur"]),"line-dasharray":new Rr(Rn.paint_line["line-dasharray"]),"line-pattern":new zt(Rn.paint_line["line-pattern"]),"line-gradient":new jr(Rn.paint_line["line-gradient"])}),Vw={paint:TS,layout:wS},aO=function(m){function y(){m.apply(this,arguments)}return m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y,y.prototype.possiblyEvaluate=function(U,$){return $=new Gn(Math.floor($.zoom),{now:$.now,fadeDuration:$.fadeDuration,zoomHistory:$.zoomHistory,transition:$.transition}),m.prototype.possiblyEvaluate.call(this,U,$)},y.prototype.evaluate=function(U,$,ae,he){return $=x({},$,{zoom:Math.floor($.zoom)}),m.prototype.evaluate.call(this,U,$,ae,he)},y}(bt),R=new aO(Vw.paint.properties["line-width"].specification);R.useIntegerZoom=!0;var A=function(m){function y(I){m.call(this,I,Vw),this.gradientVersion=0}return m&&(y.__proto__=m),y.prototype=Object.create(m&&m.prototype),y.prototype.constructor=y,y.prototype._handleSpecialPaintPropertyUpdate=function(U){if(U==="line-gradient"){var $=this._transitionablePaint._values["line-gradient"].value.expression;this.stepInterpolant=$._styleExpression.expression instanceof _u,this.gradientVersion=(this.gradientVersion+1)%d}},y.prototype.gradientExpression=function(){return 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Fr=I[pr],oi=$[pr].paddedRect,Hi=oi.x+Gc,Ai=oi.y+Gc,bn=Fr.data.width,nn=Fr.data.height;Sh.copy(Fr.data,gt,{x:0,y:0},{x:Hi,y:Ai},Fr.data),Sh.copy(Fr.data,gt,{x:0,y:nn-1},{x:Hi,y:Ai-1},{width:bn,height:1}),Sh.copy(Fr.data,gt,{x:0,y:0},{x:Hi,y:Ai+nn},{width:bn,height:1}),Sh.copy(Fr.data,gt,{x:bn-1,y:0},{x:Hi-1,y:Ai},{width:1,height:nn}),Sh.copy(Fr.data,gt,{x:0,y:0},{x:Hi+bn,y:Ai},{width:1,height:nn})}this.image=gt,this.iconPositions=U,this.patternPositions=$};gg.prototype.addImages=function(y,I,U){for(var $ in y){var ae=y[$],he={x:0,y:0,w:ae.data.width+2*Gc,h:ae.data.height+2*Gc};U.push(he),I[$]=new Zf(he,ae),ae.hasRenderCallback&&this.haveRenderCallbacks.push($)}},gg.prototype.patchUpdatedImages=function(y,I){y.dispatchRenderCallbacks(this.haveRenderCallbacks);for(var U in 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or=0,_r=m;or<_r.length;or+=1)for(var pr=_r[or],Fr=0,oi=pr.positionedGlyphs;Fr<oi.length;Fr+=1){var Hi=oi[Fr];Hi.x+=gt,Hi.y+=Et}}function rp(m,y,I){var U=SS(I),$=U.horizontalAlign,ae=U.verticalAlign,he=y[0],Oe=y[1],rt=he-m.displaySize[0]*$,gt=rt+m.displaySize[0],Et=Oe-m.displaySize[1]*ae,or=Et+m.displaySize[1];return{image:m,top:Et,bottom:or,left:rt,right:gt}}function dm(m,y,I,U,$,ae){var he=m.image,Oe;if(he.content){var rt=he.content,gt=he.pixelRatio||1;Oe=[rt[0]/gt,rt[1]/gt,he.displaySize[0]-rt[2]/gt,he.displaySize[1]-rt[3]/gt]}var Et=y.left*ae,or=y.right*ae,_r,pr,Fr,oi;I==="width"||I==="both"?(oi=$[0]+Et-U[3],pr=$[0]+or+U[1]):(oi=$[0]+(Et+or-he.displaySize[0])/2,pr=oi+he.displaySize[0]);var Hi=y.top*ae,Ai=y.bottom*ae;return I==="height"||I==="both"?(_r=$[1]+Hi-U[0],Fr=$[1]+Ai+U[2]):(_r=$[1]+(Hi+Ai-he.displaySize[1])/2,Fr=_r+he.displaySize[1]),{image:he,top:_r,right:pr,bottom:Fr,left:oi,collisionPadding:Oe}}var Wd=function(m){function 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Reduce your "text-size".')),m.addSymbols(m.text,oi,Ai,Oe,ae,he,gt,y,rt.lineStartIndex,rt.lineLength,_r,Fr);for(var bn=0,nn=Et;bn<nn.length;bn+=1){var xn=nn[bn];or[xn]=m.text.placedSymbolArray.length-1}return oi.length*4}function HQ(m){for(var y in m)return m[y];return null}function zet(m,y,I,U,$,ae,he,Oe,rt,gt,Et,or,_r,pr,Fr,oi,Hi,Ai,bn,nn,xn,Pn,Zn,ga,ha){var eo,za=m.addToLineVertexArray(y,I),Za,Ko,to,ao,_s=0,jo=0,El=0,Iu=0,kl=-1,Cl=-1,yl={},Qu=K(""),gc=0,Sf=0;if(Oe._unevaluatedLayout.getValue("text-radial-offset")===void 0?(eo=Oe.layout.get("text-offset").evaluate(xn,{},ga).map(function(CS){return CS*Tn}),gc=eo[0],Sf=eo[1]):(gc=Oe.layout.get("text-radial-offset").evaluate(xn,{},ga)*Tn,Sf=fO),m.allowVerticalPlacement&&U.vertical){var Ff=Oe.layout.get("text-rotate").evaluate(xn,{},ga),Yh=Ff+90,ch=U.vertical;to=new QC(rt,y,gt,Et,or,ch,_r,pr,Fr,Yh),he&&(ao=new QC(rt,y,gt,Et,or,he,Hi,Ai,Fr,Yh))}if($){var Xd=Oe.layout.get("icon-rotate").evaluate(xn,{}),Zd=Oe.layout.get("icon-text-fit")!=="none",Kh=NQ($,Xd,Zn,Zd),fv=he?NQ(he,Xd,Zn,Zd):void 0;Ko=new QC(rt,y,gt,Et,or,$,Hi,Ai,!1,Xd),_s=Kh.length*4;var hv=m.iconSizeData,Ch=null;hv.kind==="source"?(Ch=[Cd*Oe.layout.get("icon-size").evaluate(xn,{})],Ch[0]>R1&&te(m.layerIds[0]+': Value for "icon-size" is >= '+MS+'. 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Reduce your "icon-size".')),m.addSymbols(m.icon,Kh,Ch,nn,bn,xn,!1,y,za.lineStartIndex,za.lineLength,-1,ga),kl=m.icon.placedSymbolArray.length-1,fv&&(jo=fv.length*4,m.addSymbols(m.icon,fv,Ch,nn,bn,xn,uv.vertical,y,za.lineStartIndex,za.lineLength,-1,ga),Cl=m.icon.placedSymbolArray.length-1)}for(var dv in U.horizontal){var xp=U.horizontal[dv];if(!Za){Qu=K(xp.text);var gy=Oe.layout.get("text-rotate").evaluate(xn,{},ga);Za=new QC(rt,y,gt,Et,or,xp,_r,pr,Fr,gy)}var F1=xp.positionedLines.length===1;if(El+=GQ(m,y,xp,ae,Oe,Fr,xn,oi,za,U.vertical?uv.horizontal:uv.horizontalOnly,F1?Object.keys(U.horizontal):[dv],yl,kl,Pn,ga),F1)break}U.vertical&&(Iu+=GQ(m,y,U.vertical,ae,Oe,Fr,xn,oi,za,uv.vertical,["vertical"],yl,Cl,Pn,ga));var z1=Za?Za.boxStartIndex:m.collisionBoxArray.length,O1=Za?Za.boxEndIndex:m.collisionBoxArray.length,e3=to?to.boxStartIndex:m.collisionBoxArray.length,m0=to?to.boxEndIndex:m.collisionBoxArray.length,n6=Ko?Ko.boxStartIndex:m.collisionBoxArray.length,yO=Ko?Ko.boxEndIndex:m.collisionBoxArray.length,a6=ao?ao.boxStartIndex:m.collisionBoxArray.length,_O=ao?ao.boxEndIndex:m.collisionBoxArray.length,Hp=-1,t3=function(CS,oee){return CS&&CS.circleDiameter?Math.max(CS.circleDiameter,oee):oee};Hp=t3(Za,Hp),Hp=t3(to,Hp),Hp=t3(Ko,Hp),Hp=t3(ao,Hp);var o6=Hp>-1?1:0;o6&&(Hp*=ha/Tn),m.glyphOffsetArray.length>=Pu.MAX_GLYPHS&&te("Too many glyphs being rendered in a tile. See https://github.com/mapbox/mapbox-gl-js/issues/2907"),xn.sortKey!==void 0&&m.addToSortKeyRanges(m.symbolInstances.length,xn.sortKey),m.symbolInstances.emplaceBack(y.x,y.y,yl.right>=0?yl.right:-1,yl.center>=0?yl.center:-1,yl.left>=0?yl.left:-1,yl.vertical||-1,kl,Cl,Qu,z1,O1,e3,m0,n6,yO,a6,_O,gt,El,Iu,_s,jo,o6,0,_r,gc,Sf,Hp)}function Oet(m,y,I,U){var $=m.compareText;if(!(y in $))$[y]=[];else for(var ae=$[y],he=ae.length-1;he>=0;he--)if(U.dist(ae[he])<I)return!0;return $[y].push(U),!1}var qet=pg.VectorTileFeature.types,Bet=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function e6(m,y,I,U,$,ae,he,Oe,rt,gt,Et,or,_r){var pr=Oe?Math.min(R1,Math.round(Oe[0])):0,Fr=Oe?Math.min(R1,Math.round(Oe[1])):0;m.emplaceBack(y,I,Math.round(U*32),Math.round($*32),ae,he,(pr<<1)+(rt?1:0),Fr,gt*16,Et*16,or*256,_r*256)}function dO(m,y,I){m.emplaceBack(y.x,y.y,I),m.emplaceBack(y.x,y.y,I),m.emplaceBack(y.x,y.y,I),m.emplaceBack(y.x,y.y,I)}function Net(m){for(var y=0,I=m.sections;y<I.length;y+=1){var U=I[y];if(Ii(U.text))return!0}return!1}var Kw=function(y){this.layoutVertexArray=new Bn,this.indexArray=new pn,this.programConfigurations=y,this.segments=new io,this.dynamicLayoutVertexArray=new hi,this.opacityVertexArray=new li,this.placedSymbolArray=new zo};Kw.prototype.isEmpty=function(){return this.layoutVertexArray.length===0&&this.indexArray.length===0&&this.dynamicLayoutVertexArray.length===0&&this.opacityVertexArray.length===0},Kw.prototype.upload=function(y,I,U,$){this.isEmpty()||(U&&(this.layoutVertexBuffer=y.createVertexBuffer(this.layoutVertexArray,re.members),this.indexBuffer=y.createIndexBuffer(this.indexArray,I),this.dynamicLayoutVertexBuffer=y.createVertexBuffer(this.dynamicLayoutVertexArray,fe.members,!0),this.opacityVertexBuffer=y.createVertexBuffer(this.opacityVertexArray,Bet,!0),this.opacityVertexBuffer.itemSize=1),(U||$)&&this.programConfigurations.upload(y))},Kw.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},X("SymbolBuffers",Kw);var ES=function(y,I,U){this.layoutVertexArray=new y,this.layoutAttributes=I,this.indexArray=new U,this.segments=new io,this.collisionVertexArray=new Ni};ES.prototype.upload=function(y){this.layoutVertexBuffer=y.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=y.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=y.createVertexBuffer(this.collisionVertexArray,ze.members,!0)},ES.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},X("CollisionBuffers",ES);var Pu=function(y){this.collisionBoxArray=y.collisionBoxArray,this.zoom=y.zoom,this.overscaling=y.overscaling,this.layers=y.layers,this.layerIds=this.layers.map(function(rt){return rt.id}),this.index=y.index,this.pixelRatio=y.pixelRatio,this.sourceLayerIndex=y.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=sy([]),this.placementViewportMatrix=sy([]);var I=this.layers[0],U=I._unevaluatedLayout._values;this.textSizeData=_p(this.zoom,U["text-size"]),this.iconSizeData=_p(this.zoom,U["icon-size"]);var $=this.layers[0].layout,ae=$.get("symbol-sort-key"),he=$.get("symbol-z-order");this.canOverlap=$.get("text-allow-overlap")||$.get("icon-allow-overlap")||$.get("text-ignore-placement")||$.get("icon-ignore-placement"),this.sortFeaturesByKey=he!=="viewport-y"&&ae.constantOr(1)!==void 0;var Oe=he==="viewport-y"||he==="auto"&&!this.sortFeaturesByKey;this.sortFeaturesByY=Oe&&this.canOverlap,$.get("symbol-placement")==="point"&&(this.writingModes=$.get("text-writing-mode").map(function(rt){return uv[rt]})),this.stateDependentLayerIds=this.layers.filter(function(rt){return rt.isStateDependent()}).map(function(rt){return rt.id}),this.sourceID=y.sourceID};Pu.prototype.createArrays=function(){this.text=new Kw(new fi(this.layers,this.zoom,function(y){return/^text/.test(y)})),this.icon=new Kw(new fi(this.layers,this.zoom,function(y){return/^icon/.test(y)})),this.glyphOffsetArray=new Vl,this.lineVertexArray=new ss,this.symbolInstances=new al},Pu.prototype.calculateGlyphDependencies=function(y,I,U,$,ae){for(var he=0;he<y.length;he++)if(I[y.charCodeAt(he)]=!0,(U||$)&&ae){var Oe=zi[y.charAt(he)];Oe&&(I[Oe.charCodeAt(0)]=!0)}},Pu.prototype.populate=function(y,I,U){var $=this.layers[0],ae=$.layout,he=ae.get("text-font"),Oe=ae.get("text-field"),rt=ae.get("icon-image"),gt=(Oe.value.kind!=="constant"||Oe.value.value instanceof Jl&&!Oe.value.value.isEmpty()||Oe.value.value.toString().length>0)&&(he.value.kind!=="constant"||he.value.value.length>0),Et=rt.value.kind!=="constant"||!!rt.value.value||Object.keys(rt.parameters).length>0,or=ae.get("symbol-sort-key");if(this.features=[],!(!gt&&!Et)){for(var _r=I.iconDependencies,pr=I.glyphDependencies,Fr=I.availableImages,oi=new Gn(this.zoom),Hi=0,Ai=y;Hi<Ai.length;Hi+=1){var bn=Ai[Hi],nn=bn.feature,xn=bn.id,Pn=bn.index,Zn=bn.sourceLayerIndex,ga=$._featureFilter.needGeometry,ha=Ja(nn,ga);if($._featureFilter.filter(oi,ha,U)){ga||(ha.geometry=zn(nn));var eo=void 0;if(gt){var za=$.getValueAndResolveTokens("text-field",ha,U,Fr),Za=Jl.factory(za);Net(Za)&&(this.hasRTLText=!0),(!this.hasRTLText||yo()==="unavailable"||this.hasRTLText&&Ms.isParsed())&&(eo=yi(Za,$,ha))}var Ko=void 0;if(Et){var to=$.getValueAndResolveTokens("icon-image",ha,U,Fr);to instanceof fl?Ko=to:Ko=fl.fromString(to)}if(!(!eo&&!Ko)){var ao=this.sortFeaturesByKey?or.evaluate(ha,{},U):void 0,_s={id:xn,text:eo,icon:Ko,index:Pn,sourceLayerIndex:Zn,geometry:ha.geometry,properties:nn.properties,type:qet[nn.type],sortKey:ao};if(this.features.push(_s),Ko&&(_r[Ko.name]=!0),eo){var jo=he.evaluate(ha,{},U).join(","),El=ae.get("text-rotation-alignment")==="map"&&ae.get("symbol-placement")!=="point";this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(uv.vertical)>=0;for(var Iu=0,kl=eo.sections;Iu<kl.length;Iu+=1){var Cl=kl[Iu];if(Cl.image)_r[Cl.image.name]=!0;else{var yl=jt(eo.toString()),Qu=Cl.fontStack||jo,gc=pr[Qu]=pr[Qu]||{};this.calculateGlyphDependencies(Cl.text,gc,El,this.allowVerticalPlacement,yl)}}}}}}ae.get("symbol-placement")==="line"&&(this.features=vn(this.features)),this.sortFeaturesByKey&&this.features.sort(function(Sf,Ff){return Sf.sortKey-Ff.sortKey})}},Pu.prototype.update=function(y,I,U){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(y,I,this.layers,U),this.icon.programConfigurations.updatePaintArrays(y,I,this.layers,U))},Pu.prototype.isEmpty=function(){return this.symbolInstances.length===0&&!this.hasRTLText},Pu.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Pu.prototype.upload=function(y){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(y),this.iconCollisionBox.upload(y)),this.text.upload(y,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(y,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Pu.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Pu.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Pu.prototype.addToLineVertexArray=function(y,I){var U=this.lineVertexArray.length;if(y.segment!==void 0){for(var $=y.dist(I[y.segment+1]),ae=y.dist(I[y.segment]),he={},Oe=y.segment+1;Oe<I.length;Oe++)he[Oe]={x:I[Oe].x,y:I[Oe].y,tileUnitDistanceFromAnchor:$},Oe<I.length-1&&($+=I[Oe+1].dist(I[Oe]));for(var rt=y.segment||0;rt>=0;rt--)he[rt]={x:I[rt].x,y:I[rt].y,tileUnitDistanceFromAnchor:ae},rt>0&&(ae+=I[rt-1].dist(I[rt]));for(var gt=0;gt<I.length;gt++){var Et=he[gt];this.lineVertexArray.emplaceBack(Et.x,Et.y,Et.tileUnitDistanceFromAnchor)}}return{lineStartIndex:U,lineLength:this.lineVertexArray.length-U}},Pu.prototype.addSymbols=function(y,I,U,$,ae,he,Oe,rt,gt,Et,or,_r){for(var pr=y.indexArray,Fr=y.layoutVertexArray,oi=y.segments.prepareSegment(4*I.length,Fr,pr,this.canOverlap?he.sortKey:void 0),Hi=this.glyphOffsetArray.length,Ai=oi.vertexLength,bn=this.allowVerticalPlacement&&Oe===uv.vertical?Math.PI/2:0,nn=he.text&&he.text.sections,xn=0;xn<I.length;xn++){var Pn=I[xn],Zn=Pn.tl,ga=Pn.tr,ha=Pn.bl,eo=Pn.br,za=Pn.tex,Za=Pn.pixelOffsetTL,Ko=Pn.pixelOffsetBR,to=Pn.minFontScaleX,ao=Pn.minFontScaleY,_s=Pn.glyphOffset,jo=Pn.isSDF,El=Pn.sectionIndex,Iu=oi.vertexLength,kl=_s[1];e6(Fr,rt.x,rt.y,Zn.x,kl+Zn.y,za.x,za.y,U,jo,Za.x,Za.y,to,ao),e6(Fr,rt.x,rt.y,ga.x,kl+ga.y,za.x+za.w,za.y,U,jo,Ko.x,Za.y,to,ao),e6(Fr,rt.x,rt.y,ha.x,kl+ha.y,za.x,za.y+za.h,U,jo,Za.x,Ko.y,to,ao),e6(Fr,rt.x,rt.y,eo.x,kl+eo.y,za.x+za.w,za.y+za.h,U,jo,Ko.x,Ko.y,to,ao),dO(y.dynamicLayoutVertexArray,rt,bn),pr.emplaceBack(Iu,Iu+1,Iu+2),pr.emplaceBack(Iu+1,Iu+2,Iu+3),oi.vertexLength+=4,oi.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(_s[0]),(xn===I.length-1||El!==I[xn+1].sectionIndex)&&y.programConfigurations.populatePaintArrays(Fr.length,he,he.index,{},_r,nn&&nn[El])}y.placedSymbolArray.emplaceBack(rt.x,rt.y,Hi,this.glyphOffsetArray.length-Hi,Ai,gt,Et,rt.segment,U?U[0]:0,U?U[1]:0,$[0],$[1],Oe,0,!1,0,or)},Pu.prototype._addCollisionDebugVertex=function(y,I,U,$,ae,he){return I.emplaceBack(0,0),y.emplaceBack(U.x,U.y,$,ae,Math.round(he.x),Math.round(he.y))},Pu.prototype.addCollisionDebugVertices=function(y,I,U,$,ae,he,Oe){var rt=ae.segments.prepareSegment(4,ae.layoutVertexArray,ae.indexArray),gt=rt.vertexLength,Et=ae.layoutVertexArray,or=ae.collisionVertexArray,_r=Oe.anchorX,pr=Oe.anchorY;this._addCollisionDebugVertex(Et,or,he,_r,pr,new u(y,I)),this._addCollisionDebugVertex(Et,or,he,_r,pr,new u(U,I)),this._addCollisionDebugVertex(Et,or,he,_r,pr,new u(U,$)),this._addCollisionDebugVertex(Et,or,he,_r,pr,new u(y,$)),rt.vertexLength+=4;var Fr=ae.indexArray;Fr.emplaceBack(gt,gt+1),Fr.emplaceBack(gt+1,gt+2),Fr.emplaceBack(gt+2,gt+3),Fr.emplaceBack(gt+3,gt),rt.primitiveLength+=4},Pu.prototype.addDebugCollisionBoxes=function(y,I,U,$){for(var ae=y;ae<I;ae++){var he=this.collisionBoxArray.get(ae),Oe=he.x1,rt=he.y1,gt=he.x2,Et=he.y2;this.addCollisionDebugVertices(Oe,rt,gt,Et,$?this.textCollisionBox:this.iconCollisionBox,he.anchorPoint,U)}},Pu.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new ES(Ji,ct.members,oa),this.iconCollisionBox=new ES(Ji,ct.members,oa);for(var y=0;y<this.symbolInstances.length;y++){var I=this.symbolInstances.get(y);this.addDebugCollisionBoxes(I.textBoxStartIndex,I.textBoxEndIndex,I,!0),this.addDebugCollisionBoxes(I.verticalTextBoxStartIndex,I.verticalTextBoxEndIndex,I,!0),this.addDebugCollisionBoxes(I.iconBoxStartIndex,I.iconBoxEndIndex,I,!1),this.addDebugCollisionBoxes(I.verticalIconBoxStartIndex,I.verticalIconBoxEndIndex,I,!1)}},Pu.prototype._deserializeCollisionBoxesForSymbol=function(y,I,U,$,ae,he,Oe,rt,gt){for(var Et={},or=I;or<U;or++){var _r=y.get(or);Et.textBox={x1:_r.x1,y1:_r.y1,x2:_r.x2,y2:_r.y2,anchorPointX:_r.anchorPointX,anchorPointY:_r.anchorPointY},Et.textFeatureIndex=_r.featureIndex;break}for(var pr=$;pr<ae;pr++){var Fr=y.get(pr);Et.verticalTextBox={x1:Fr.x1,y1:Fr.y1,x2:Fr.x2,y2:Fr.y2,anchorPointX:Fr.anchorPointX,anchorPointY:Fr.anchorPointY},Et.verticalTextFeatureIndex=Fr.featureIndex;break}for(var oi=he;oi<Oe;oi++){var Hi=y.get(oi);Et.iconBox={x1:Hi.x1,y1:Hi.y1,x2:Hi.x2,y2:Hi.y2,anchorPointX:Hi.anchorPointX,anchorPointY:Hi.anchorPointY},Et.iconFeatureIndex=Hi.featureIndex;break}for(var Ai=rt;Ai<gt;Ai++){var bn=y.get(Ai);Et.verticalIconBox={x1:bn.x1,y1:bn.y1,x2:bn.x2,y2:bn.y2,anchorPointX:bn.anchorPointX,anchorPointY:bn.anchorPointY},Et.verticalIconFeatureIndex=bn.featureIndex;break}return Et},Pu.prototype.deserializeCollisionBoxes=function(y){this.collisionArrays=[];for(var I=0;I<this.symbolInstances.length;I++){var U=this.symbolInstances.get(I);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(y,U.textBoxStartIndex,U.textBoxEndIndex,U.verticalTextBoxStartIndex,U.verticalTextBoxEndIndex,U.iconBoxStartIndex,U.iconBoxEndIndex,U.verticalIconBoxStartIndex,U.verticalIconBoxEndIndex))}},Pu.prototype.hasTextData=function(){return this.text.segments.get().length>0},Pu.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Pu.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Pu.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Pu.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Pu.prototype.addIndicesForPlacedSymbol=function(y,I){for(var U=y.placedSymbolArray.get(I),$=U.vertexStartIndex+U.numGlyphs*4,ae=U.vertexStartIndex;ae<$;ae+=4)y.indexArray.emplaceBack(ae,ae+1,ae+2),y.indexArray.emplaceBack(ae+1,ae+2,ae+3)},Pu.prototype.getSortedSymbolIndexes=function(y){if(this.sortedAngle===y&&this.symbolInstanceIndexes!==void 0)return this.symbolInstanceIndexes;for(var I=Math.sin(y),U=Math.cos(y),$=[],ae=[],he=[],Oe=0;Oe<this.symbolInstances.length;++Oe){he.push(Oe);var rt=this.symbolInstances.get(Oe);$.push(Math.round(I*rt.anchorX+U*rt.anchorY)|0),ae.push(rt.featureIndex)}return he.sort(function(gt,Et){return $[gt]-$[Et]||ae[Et]-ae[gt]}),he},Pu.prototype.addToSortKeyRanges=function(y,I){var U=this.sortKeyRanges[this.sortKeyRanges.length-1];U&&U.sortKey===I?U.symbolInstanceEnd=y+1:this.sortKeyRanges.push({sortKey:I,symbolInstanceStart:y,symbolInstanceEnd:y+1})},Pu.prototype.sortFeatures=function(y){var I=this;if(this.sortFeaturesByY&&this.sortedAngle!==y&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(y),this.sortedAngle=y,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var U=0,$=this.symbolInstanceIndexes;U<$.length;U+=1){var ae=$[U],he=this.symbolInstances.get(ae);this.featureSortOrder.push(he.featureIndex),[he.rightJustifiedTextSymbolIndex,he.centerJustifiedTextSymbolIndex,he.leftJustifiedTextSymbolIndex].forEach(function(Oe,rt,gt){Oe>=0&&gt.indexOf(Oe)===rt&&I.addIndicesForPlacedSymbol(I.text,Oe)}),he.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,he.verticalPlacedTextSymbolIndex),he.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,he.placedIconSymbolIndex),he.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,he.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},X("SymbolBucket",Pu,{omit:["layers","collisionBoxArray","features","compareText"]}),Pu.MAX_GLYPHS=65535,Pu.addDynamicAttributes=dO;function Uet(m,y){return y.replace(/{([^{}]+)}/g,function(I,U){return U in m?String(m[U]):""})}var Vet=new Nr({"symbol-placement":new Me(Rn.layout_symbol["symbol-placement"]),"symbol-spacing":new Me(Rn.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new Me(Rn.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new bt(Rn.layout_symbol["symbol-sort-key"]),"symbol-z-order":new Me(Rn.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new Me(Rn.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new Me(Rn.layout_symbol["icon-ignore-placement"]),"icon-optional":new Me(Rn.layout_symbol["icon-optional"]),"icon-rotation-alignment":new Me(Rn.layout_symbol["icon-rotation-alignment"]),"icon-size":new bt(Rn.layout_symbol["icon-size"]),"icon-text-fit":new Me(Rn.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new Me(Rn.layout_symbol["icon-text-fit-padding"]),"icon-image":new bt(Rn.layout_symbol["icon-image"]),"icon-rotate":new bt(Rn.layout_symbol["icon-rotate"]),"icon-padding":new Me(Rn.layout_symbol["icon-padding"]),"icon-keep-upright":new 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y.overscaledZ===0||y.overscaledZ<this.overscaledZ&&y.canonical.x===this.canonical.x>>I&&y.canonical.y===this.canonical.y>>I},uh.prototype.children=function(y){if(this.overscaledZ>=y)return[new uh(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var I=this.canonical.z+1,U=this.canonical.x*2,$=this.canonical.y*2;return[new uh(I,this.wrap,I,U,$),new uh(I,this.wrap,I,U+1,$),new uh(I,this.wrap,I,U,$+1),new uh(I,this.wrap,I,U+1,$+1)]},uh.prototype.isLessThan=function(y){return this.wrap<y.wrap?!0:this.wrap>y.wrap?!1:this.overscaledZ<y.overscaledZ?!0:this.overscaledZ>y.overscaledZ?!1:this.canonical.x<y.canonical.x?!0:this.canonical.x>y.canonical.x?!1:this.canonical.y<y.canonical.y},uh.prototype.wrapped=function(){return new uh(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},uh.prototype.unwrapTo=function(y){return new uh(this.overscaledZ,y,this.canonical.z,this.canonical.x,this.canonical.y)},uh.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},uh.prototype.toUnwrapped=function(){return new ree(this.wrap,this.canonical)},uh.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},uh.prototype.getTilePoint=function(y){return this.canonical.getTilePoint(new nb(y.x-this.wrap,y.y))};function kS(m,y,I,U,$){m*=2,m<0&&(m=m*-1-1);var ae=1<<I;return(ae*ae*m+ae*$+U).toString(36)+I.toString(36)+y.toString(36)}function att(m,y,I){for(var U="",$,ae=m;ae>0;ae--)$=1<<ae-1,U+=(y&$?1:0)+(I&$?2:0);return U}X("CanonicalTileID",ab),X("OverscaledTileID",uh,{omit:["posMatrix"]});var vy=function(y,I,U){if(this.uid=y,I.height!==I.width)throw new RangeError("DEM tiles must be square");if(U&&U!=="mapbox"&&U!=="terrarium")return te('"'+U+'" is not a valid encoding type. Valid types include "mapbox" and "terrarium".');this.stride=I.height;var $=this.dim=I.height-2;this.data=new Uint32Array(I.data.buffer),this.encoding=U||"mapbox";for(var ae=0;ae<$;ae++)this.data[this._idx(-1,ae)]=this.data[this._idx(0,ae)],this.data[this._idx($,ae)]=this.data[this._idx($-1,ae)],this.data[this._idx(ae,-1)]=this.data[this._idx(ae,0)],this.data[this._idx(ae,$)]=this.data[this._idx(ae,$-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx($,-1)]=this.data[this._idx($-1,0)],this.data[this._idx(-1,$)]=this.data[this._idx(0,$-1)],this.data[this._idx($,$)]=this.data[this._idx($-1,$-1)]};vy.prototype.get=function(y,I){var U=new Uint8Array(this.data.buffer),$=this._idx(y,I)*4,ae=this.encoding==="terrarium"?this._unpackTerrarium:this._unpackMapbox;return ae(U[$],U[$+1],U[$+2])},vy.prototype.getUnpackVector=function(){return 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gt=this.deletedStates[y]&&this.deletedStates[y][$]&&this.deletedStates[y][$][rt]===null;gt&&delete this.deletedStates[y][$][rt]}}},Qw.prototype.removeFeatureState=function(y,I,U){var $=this.deletedStates[y]===null;if(!$){var ae=String(I);if(this.deletedStates[y]=this.deletedStates[y]||{},U&&I!==void 0)this.deletedStates[y][ae]!==null&&(this.deletedStates[y][ae]=this.deletedStates[y][ae]||{},this.deletedStates[y][ae][U]=null);else if(I!==void 0){var he=this.stateChanges[y]&&this.stateChanges[y][ae];if(he){this.deletedStates[y][ae]={};for(U in this.stateChanges[y][ae])this.deletedStates[y][ae][U]=null}else this.deletedStates[y][ae]=null}else this.deletedStates[y]=null}},Qw.prototype.getState=function(y,I){var U=String(I),$=this.state[y]||{},ae=this.stateChanges[y]||{},he=x({},$[U],ae[U]);if(this.deletedStates[y]===null)return{};if(this.deletedStates[y]){var Oe=this.deletedStates[y][I];if(Oe===null)return{};for(var rt in Oe)delete he[rt]}return 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py=function(y,I){this.tileID=y,this.x=y.canonical.x,this.y=y.canonical.y,this.z=y.canonical.z,this.grid=new au(Ci,16,0),this.grid3D=new au(Ci,16,0),this.featureIndexArray=new Ys,this.promoteId=I};py.prototype.insert=function(y,I,U,$,ae,he){var Oe=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(U,$,ae);for(var rt=he?this.grid3D:this.grid,gt=0;gt<I.length;gt++){for(var Et=I[gt],or=[1/0,1/0,-1/0,-1/0],_r=0;_r<Et.length;_r++){var pr=Et[_r];or[0]=Math.min(or[0],pr.x),or[1]=Math.min(or[1],pr.y),or[2]=Math.max(or[2],pr.x),or[3]=Math.max(or[3],pr.y)}or[0]<Ci&&or[1]<Ci&&or[2]>=0&&or[3]>=0&&rt.insert(Oe,or[0],or[1],or[2],or[3])}},py.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new pg.VectorTile(new Xa(this.rawTileData)).layers,this.sourceLayerCoder=new r6(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},py.prototype.query=function(y,I,U,$){var ae=this;this.loadVTLayers();for(var he=y.params||{},Oe=Ci/y.tileSize/y.scale,rt=be(he.filter),gt=y.queryGeometry,Et=y.queryPadding*Oe,or=nee(gt),_r=this.grid.query(or.minX-Et,or.minY-Et,or.maxX+Et,or.maxY+Et),pr=nee(y.cameraQueryGeometry),Fr=this.grid3D.query(pr.minX-Et,pr.minY-Et,pr.maxX+Et,pr.maxY+Et,function(Zn,ga,ha,eo){return gp(y.cameraQueryGeometry,Zn-Et,ga-Et,ha+Et,eo+Et)}),oi=0,Hi=Fr;oi<Hi.length;oi+=1){var Ai=Hi[oi];_r.push(Ai)}_r.sort(stt);for(var bn={},nn,xn=function(Zn){var ga=_r[Zn];if(ga!==nn){nn=ga;var ha=ae.featureIndexArray.get(ga),eo=null;ae.loadMatchingFeature(bn,ha.bucketIndex,ha.sourceLayerIndex,ha.featureIndex,rt,he.layers,he.availableImages,I,U,$,function(za,Za,Ko){return eo||(eo=zn(za)),Za.queryIntersectsFeature(gt,za,Ko,eo,ae.z,y.transform,Oe,y.pixelPosMatrix)})}},Pn=0;Pn<_r.length;Pn++)xn(Pn);return bn},py.prototype.loadMatchingFeature=function(y,I,U,$,ae,he,Oe,rt,gt,Et,or){var _r=this.bucketLayerIDs[I];if(!(he&&!N(he,_r))){var pr=this.sourceLayerCoder.decode(U),Fr=this.vtLayers[pr],oi=Fr.feature($);if(ae.needGeometry){var Hi=Ja(oi,!0);if(!ae.filter(new Gn(this.tileID.overscaledZ),Hi,this.tileID.canonical))return}else if(!ae.filter(new Gn(this.tileID.overscaledZ),oi))return;for(var Ai=this.getId(oi,pr),bn=0;bn<_r.length;bn++){var nn=_r[bn];if(!(he&&he.indexOf(nn)<0)){var xn=rt[nn];if(xn){var Pn={};Ai!==void 0&&Et&&(Pn=Et.getState(xn.sourceLayer||"_geojsonTileLayer",Ai));var Zn=x({},gt[nn]);Zn.paint=iee(Zn.paint,xn.paint,oi,Pn,Oe),Zn.layout=iee(Zn.layout,xn.layout,oi,Pn,Oe);var ga=!or||or(oi,xn,Pn);if(ga){var ha=new i6(oi,this.z,this.x,this.y,Ai);ha.layer=Zn;var eo=y[nn];eo===void 0&&(eo=y[nn]=[]),eo.push({featureIndex:$,feature:ha,intersectionZ:ga})}}}}}},py.prototype.lookupSymbolFeatures=function(y,I,U,$,ae,he,Oe,rt){var gt={};this.loadVTLayers();for(var Et=be(ae),or=0,_r=y;or<_r.length;or+=1){var pr=_r[or];this.loadMatchingFeature(gt,U,$,pr,Et,he,Oe,rt,I)}return gt},py.prototype.hasLayer=function(y){for(var I=0,U=this.bucketLayerIDs;I<U.length;I+=1)for(var $=U[I],ae=0,he=$;ae<he.length;ae+=1){var Oe=he[ae];if(y===Oe)return!0}return!1},py.prototype.getId=function(y,I){var U=y.id;if(this.promoteId){var $=typeof this.promoteId=="string"?this.promoteId:this.promoteId[I];U=y.properties[$],typeof U=="boolean"&&(U=Number(U))}return U},X("FeatureIndex",py,{omit:["rawTileData","sourceLayerCoder"]});function iee(m,y,I,U,$){return G(m,function(ae,he){var Oe=y instanceof Sc?y.get(he):null;return Oe&&Oe.evaluate?Oe.evaluate(I,U,$):Oe})}function nee(m){for(var y=1/0,I=1/0,U=-1/0,$=-1/0,ae=0,he=m;ae<he.length;ae+=1){var Oe=he[ae];y=Math.min(y,Oe.x),I=Math.min(I,Oe.y),U=Math.max(U,Oe.x),$=Math.max($,Oe.y)}return{minX:y,minY:I,maxX:U,maxY:$}}function stt(m,y){return y-m}var ltt=3e4,Zh=function(y,I){this.tileID=y,this.uid=g(),this.uses=0,this.tileSize=I,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state="loading"};Zh.prototype.registerFadeDuration=function(y){var I=y+this.timeAdded;I<ut.now()||this.fadeEndTime&&I<this.fadeEndTime||(this.fadeEndTime=I)},Zh.prototype.wasRequested=function(){return this.state==="errored"||this.state==="loaded"||this.state==="reloading"},Zh.prototype.loadVectorData=function(y,I,U){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",!y){this.collisionBoxArray=new Va;return}y.featureIndex&&(this.latestFeatureIndex=y.featureIndex,y.rawTileData?(this.latestRawTileData=y.rawTileData,this.latestFeatureIndex.rawTileData=y.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=y.collisionBoxArray,this.buckets=ott(y.buckets,I.style),this.hasSymbolBuckets=!1;for(var $ in this.buckets){var ae=this.buckets[$];if(ae instanceof Pu)if(this.hasSymbolBuckets=!0,U)ae.justReloaded=!0;else break}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var he in this.buckets){var Oe=this.buckets[he];if(Oe instanceof Pu&&Oe.hasRTLText){this.hasRTLText=!0,Xs();break}}this.queryPadding=0;for(var rt in this.buckets){var gt=this.buckets[rt];this.queryPadding=Math.max(this.queryPadding,I.style.getLayer(rt).queryRadius(gt))}y.imageAtlas&&(this.imageAtlas=y.imageAtlas),y.glyphAtlasImage&&(this.glyphAtlasImage=y.glyphAtlasImage)},Zh.prototype.unloadVectorData=function(){for(var y in this.buckets)this.buckets[y].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},Zh.prototype.getBucket=function(y){return this.buckets[y.id]},Zh.prototype.upload=function(y){for(var I in this.buckets){var U=this.buckets[I];U.uploadPending()&&U.upload(y)}var $=y.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new ib(y,this.imageAtlas.image,$.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new ib(y,this.glyphAtlasImage,$.ALPHA),this.glyphAtlasImage=null)},Zh.prototype.prepare=function(y){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(y,this.imageAtlasTexture)},Zh.prototype.queryRenderedFeatures=function(y,I,U,$,ae,he,Oe,rt,gt,Et){return!this.latestFeatureIndex||!this.latestFeatureIndex.rawTileData?{}:this.latestFeatureIndex.query({queryGeometry:$,cameraQueryGeometry:ae,scale:he,tileSize:this.tileSize,pixelPosMatrix:Et,transform:rt,params:Oe,queryPadding:this.queryPadding*gt},y,I,U)},Zh.prototype.querySourceFeatures=function(y,I){var U=this.latestFeatureIndex;if(!(!U||!U.rawTileData)){var $=U.loadVTLayers(),ae=I?I.sourceLayer:"",he=$._geojsonTileLayer||$[ae];if(he)for(var Oe=be(I&&I.filter),rt=this.tileID.canonical,gt=rt.z,Et=rt.x,or=rt.y,_r={z:gt,x:Et,y:or},pr=0;pr<he.length;pr++){var Fr=he.feature(pr);if(Oe.needGeometry){var oi=Ja(Fr,!0);if(!Oe.filter(new Gn(this.tileID.overscaledZ),oi,this.tileID.canonical))continue}else if(!Oe.filter(new Gn(this.tileID.overscaledZ),Fr))continue;var Hi=U.getId(Fr,ae),Ai=new i6(Fr,gt,Et,or,Hi);Ai.tile=_r,y.push(Ai)}}},Zh.prototype.hasData=function(){return this.state==="loaded"||this.state==="reloading"||this.state==="expired"},Zh.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},Zh.prototype.setExpiryData=function(y){var I=this.expirationTime;if(y.cacheControl){var U=me(y.cacheControl);U["max-age"]&&(this.expirationTime=Date.now()+U["max-age"]*1e3)}else y.expires&&(this.expirationTime=new Date(y.expires).getTime());if(this.expirationTime){var $=Date.now(),ae=!1;if(this.expirationTime>$)ae=!1;else if(!I)ae=!0;else if(this.expirationTime<I)ae=!0;else{var he=this.expirationTime-I;he?this.expirationTime=$+Math.max(he,ltt):ae=!0}ae?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},Zh.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-new Date().getTime(),Math.pow(2,31)-1)},Zh.prototype.setFeatureState=function(y,I){if(!(!this.latestFeatureIndex||!this.latestFeatureIndex.rawTileData||Object.keys(y).length===0)){var U=this.latestFeatureIndex.loadVTLayers();for(var $ in this.buckets)if(I.style.hasLayer($)){var ae=this.buckets[$],he=ae.layers[0].sourceLayer||"_geojsonTileLayer",Oe=U[he],rt=y[he];if(!(!Oe||!rt||Object.keys(rt).length===0)){ae.update(rt,Oe,this.imageAtlas&&this.imageAtlas.patternPositions||{});var gt=I&&I.style&&I.style.getLayer($);gt&&(this.queryPadding=Math.max(this.queryPadding,gt.queryRadius(ae)))}}}},Zh.prototype.holdingForFade=function(){return this.symbolFadeHoldUntil!==void 0},Zh.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<ut.now()},Zh.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 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i.AlphaImage({width:fn||1,height:yn||1});for(var Ba in wt){var ua=wt[Ba];for(var ma in ua){var Wa=ua[+ma];if(!(!Wa||Wa.bitmap.width===0||Wa.bitmap.height===0)){var Fa=rr[Ba][ma].rect;i.AlphaImage.copy(Wa.bitmap,Sn,{x:0,y:0},{x:Fa.x+u,y:Fa.y+u},Wa.bitmap)}}}this.image=Sn,this.positions=rr};i.register("GlyphAtlas",c);var f=function(wt){this.tileID=new i.OverscaledTileID(wt.tileID.overscaledZ,wt.tileID.wrap,wt.tileID.canonical.z,wt.tileID.canonical.x,wt.tileID.canonical.y),this.uid=wt.uid,this.zoom=wt.zoom,this.pixelRatio=wt.pixelRatio,this.tileSize=wt.tileSize,this.source=wt.source,this.overscaling=this.tileID.overscaleFactor(),this.showCollisionBoxes=wt.showCollisionBoxes,this.collectResourceTiming=!!wt.collectResourceTiming,this.returnDependencies=!!wt.returnDependencies,this.promoteId=wt.promoteId};f.prototype.parse=function(wt,rr,nr,Er,Xr){var ri=this;this.status="parsing",this.data=wt,this.collisionBoxArray=new i.CollisionBoxArray;var Qr=new i.DictionaryCoder(Object.keys(wt.layers).sort()),Oi=new i.FeatureIndex(this.tileID,this.promoteId);Oi.bucketLayerIDs=[];var $i={},tn={featureIndex:Oi,iconDependencies:{},patternDependencies:{},glyphDependencies:{},availableImages:nr},fn=rr.familiesBySource[this.source];for(var yn in fn){var Sn=wt.layers[yn];if(Sn){Sn.version===1&&i.warnOnce('Vector tile source "'+this.source+'" layer "'+yn+'" does not use vector tile spec v2 and therefore may have some rendering errors.');for(var Ba=Qr.encode(yn),ua=[],ma=0;ma<Sn.length;ma++){var Wa=Sn.feature(ma),Fa=Oi.getId(Wa,yn);ua.push({feature:Wa,id:Fa,index:ma,sourceLayerIndex:Ba})}for(var Wo=0,da=fn[yn];Wo<da.length;Wo+=1){var Wn=da[Wo],Ha=Wn[0];if(!(Ha.minzoom&&this.zoom<Math.floor(Ha.minzoom))&&!(Ha.maxzoom&&this.zoom>=Ha.maxzoom)&&Ha.visibility!=="none"){h(Wn,this.zoom,nr);var vo=$i[Ha.id]=Ha.createBucket({index:Oi.bucketLayerIDs.length,layers:Wn,zoom:this.zoom,pixelRatio:this.pixelRatio,overscaling:this.overscaling,collisionBoxArray:this.collisionBoxArray,sourceLayerIndex:Ba,sourceID:this.source});vo.populate(ua,tn,this.tileID.canonical),Oi.bucketLayerIDs.push(Wn.map(function(Li){return Li.id}))}}}}var jn,Mt,kr,Jr,vi=i.mapObject(tn.glyphDependencies,function(Li){return Object.keys(Li).map(Number)});Object.keys(vi).length?Er.send("getGlyphs",{uid:this.uid,stacks:vi},function(Li,_n){jn||(jn=Li,Mt=_n,Mn.call(ri))}):Mt={};var hn=Object.keys(tn.iconDependencies);hn.length?Er.send("getImages",{icons:hn,source:this.source,tileID:this.tileID,type:"icons"},function(Li,_n){jn||(jn=Li,kr=_n,Mn.call(ri))}):kr={};var An=Object.keys(tn.patternDependencies);An.length?Er.send("getImages",{icons:An,source:this.source,tileID:this.tileID,type:"patterns"},function(Li,_n){jn||(jn=Li,Jr=_n,Mn.call(ri))}):Jr={},Mn.call(this);function Mn(){if(jn)return Xr(jn);if(Mt&&kr&&Jr){var Li=new c(Mt),_n=new i.ImageAtlas(kr,Jr);for(var ya in $i){var Jn=$i[ya];Jn instanceof i.SymbolBucket?(h(Jn.layers,this.zoom,nr),i.performSymbolLayout(Jn,Mt,Li.positions,kr,_n.iconPositions,this.showCollisionBoxes,this.tileID.canonical)):Jn.hasPattern&&(Jn instanceof i.LineBucket||Jn instanceof i.FillBucket||Jn instanceof i.FillExtrusionBucket)&&(h(Jn.layers,this.zoom,nr),Jn.addFeatures(tn,this.tileID.canonical,_n.patternPositions))}this.status="done",Xr(null,{buckets:i.values($i).filter(function(Ma){return!Ma.isEmpty()}),featureIndex:Oi,collisionBoxArray:this.collisionBoxArray,glyphAtlasImage:Li.image,imageAtlas:_n,glyphMap:this.returnDependencies?Mt:null,iconMap:this.returnDependencies?kr:null,glyphPositions:this.returnDependencies?Li.positions:null})}}};function h(Ut,wt,rr){for(var nr=new i.EvaluationParameters(wt),Er=0,Xr=Ut;Er<Xr.length;Er+=1){var ri=Xr[Er];ri.recalculate(nr,rr)}}function d(Ut,wt){var rr=i.getArrayBuffer(Ut.request,function(nr,Er,Xr,ri){nr?wt(nr):Er&&wt(null,{vectorTile:new i.vectorTile.VectorTile(new i.pbf(Er)),rawData:Er,cacheControl:Xr,expires:ri})});return function(){rr.cancel(),wt()}}var v=function(wt,rr,nr,Er){this.actor=wt,this.layerIndex=rr,this.availableImages=nr,this.loadVectorData=Er||d,this.loading={},this.loaded={}};v.prototype.loadTile=function(wt,rr){var nr=this,Er=wt.uid;this.loading||(this.loading={});var Xr=wt&&wt.request&&wt.request.collectResourceTiming?new i.RequestPerformance(wt.request):!1,ri=this.loading[Er]=new f(wt);ri.abort=this.loadVectorData(wt,function(Qr,Oi){if(delete nr.loading[Er],Qr||!Oi)return ri.status="done",nr.loaded[Er]=ri,rr(Qr);var $i=Oi.rawData,tn={};Oi.expires&&(tn.expires=Oi.expires),Oi.cacheControl&&(tn.cacheControl=Oi.cacheControl);var fn={};if(Xr){var yn=Xr.finish();yn&&(fn.resourceTiming=JSON.parse(JSON.stringify(yn)))}ri.vectorTile=Oi.vectorTile,ri.parse(Oi.vectorTile,nr.layerIndex,nr.availableImages,nr.actor,function(Sn,Ba){if(Sn||!Ba)return rr(Sn);rr(null,i.extend({rawTileData:$i.slice(0)},Ba,tn,fn))}),nr.loaded=nr.loaded||{},nr.loaded[Er]=ri})},v.prototype.reloadTile=function(wt,rr){var nr=this,Er=this.loaded,Xr=wt.uid,ri=this;if(Er&&Er[Xr]){var Qr=Er[Xr];Qr.showCollisionBoxes=wt.showCollisionBoxes;var Oi=function($i,tn){var fn=Qr.reloadCallback;fn&&(delete Qr.reloadCallback,Qr.parse(Qr.vectorTile,ri.layerIndex,nr.availableImages,ri.actor,fn)),rr($i,tn)};Qr.status==="parsing"?Qr.reloadCallback=Oi:Qr.status==="done"&&(Qr.vectorTile?Qr.parse(Qr.vectorTile,this.layerIndex,this.availableImages,this.actor,Oi):Oi())}},v.prototype.abortTile=function(wt,rr){var nr=this.loading,Er=wt.uid;nr&&nr[Er]&&nr[Er].abort&&(nr[Er].abort(),delete nr[Er]),rr()},v.prototype.removeTile=function(wt,rr){var nr=this.loaded,Er=wt.uid;nr&&nr[Er]&&delete nr[Er],rr()};var _=i.window.ImageBitmap,b=function(){this.loaded={}};b.prototype.loadTile=function(wt,rr){var nr=wt.uid,Er=wt.encoding,Xr=wt.rawImageData,ri=_&&Xr instanceof _?this.getImageData(Xr):Xr,Qr=new i.DEMData(nr,ri,Er);this.loaded=this.loaded||{},this.loaded[nr]=Qr,rr(null,Qr)},b.prototype.getImageData=function(wt){(!this.offscreenCanvas||!this.offscreenCanvasContext)&&(this.offscreenCanvas=new OffscreenCanvas(wt.width,wt.height),this.offscreenCanvasContext=this.offscreenCanvas.getContext("2d")),this.offscreenCanvas.width=wt.width,this.offscreenCanvas.height=wt.height,this.offscreenCanvasContext.drawImage(wt,0,0,wt.width,wt.height);var rr=this.offscreenCanvasContext.getImageData(-1,-1,wt.width+2,wt.height+2);return this.offscreenCanvasContext.clearRect(0,0,this.offscreenCanvas.width,this.offscreenCanvas.height),new i.RGBAImage({width:rr.width,height:rr.height},rr.data)},b.prototype.removeTile=function(wt){var rr=this.loaded,nr=wt.uid;rr&&rr[nr]&&delete rr[nr]};var p=k;function k(Ut,wt){var rr=Ut&&Ut.type,nr;if(rr==="FeatureCollection")for(nr=0;nr<Ut.features.length;nr++)k(Ut.features[nr],wt);else if(rr==="GeometryCollection")for(nr=0;nr<Ut.geometries.length;nr++)k(Ut.geometries[nr],wt);else if(rr==="Feature")k(Ut.geometry,wt);else if(rr==="Polygon")E(Ut.coordinates,wt);else if(rr==="MultiPolygon")for(nr=0;nr<Ut.coordinates.length;nr++)E(Ut.coordinates[nr],wt);return Ut}function E(Ut,wt){if(Ut.length!==0){S(Ut[0],wt);for(var rr=1;rr<Ut.length;rr++)S(Ut[rr],!wt)}}function S(Ut,wt){for(var rr=0,nr=0,Er=Ut.length,Xr=Er-1;nr<Er;Xr=nr++)rr+=(Ut[nr][0]-Ut[Xr][0])*(Ut[Xr][1]+Ut[nr][1]);rr>=0!=!!wt&&Ut.reverse()}var L=i.vectorTile.VectorTileFeature.prototype.toGeoJSON,x=function(wt){this._feature=wt,this.extent=i.EXTENT,this.type=wt.type,this.properties=wt.tags,"id"in wt&&!isNaN(wt.id)&&(this.id=parseInt(wt.id,10))};x.prototype.loadGeometry=function(){if(this._feature.type===1){for(var wt=[],rr=0,nr=this._feature.geometry;rr<nr.length;rr+=1){var 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Ut=this.rawGeometry;this.geometry=[];for(var wt=0;wt<Ut.length;wt++){for(var rr=Ut[wt],nr=[],Er=0;Er<rr.length;Er++)nr.push(new i.Point$1(rr[Er][0],rr[Er][1]));this.geometry.push(nr)}return this.geometry},T.prototype.bbox=function(){this.geometry||this.loadGeometry();for(var Ut=this.geometry,wt=1/0,rr=-1/0,nr=1/0,Er=-1/0,Xr=0;Xr<Ut.length;Xr++)for(var ri=Ut[Xr],Qr=0;Qr<ri.length;Qr++){var Oi=ri[Qr];wt=Math.min(wt,Oi.x),rr=Math.max(rr,Oi.x),nr=Math.min(nr,Oi.y),Er=Math.max(Er,Oi.y)}return[wt,nr,rr,Er]},T.prototype.toGeoJSON=M.prototype.toGeoJSON;var z=Z,O=Z,V=j,G=g;function Z(Ut){var wt=new i.pbf;return N(Ut,wt),wt.finish()}function j(Ut,wt){wt=wt||{};var rr={};for(var nr in Ut)rr[nr]=new g(Ut[nr].features,wt),rr[nr].name=nr,rr[nr].version=wt.version,rr[nr].extent=wt.extent;return Z({layers:rr})}function N(Ut,wt){for(var rr in Ut.layers)wt.writeMessage(3,H,Ut.layers[rr])}function H(Ut,wt){wt.writeVarintField(15,Ut.version||1),wt.writeStringField(1,Ut.name||""),wt.writeVarintField(5,Ut.extent||4096);var rr,nr={keys:[],values:[],keycache:{},valuecache:{}};for(rr=0;rr<Ut.length;rr++)nr.feature=Ut.feature(rr),wt.writeMessage(2,te,nr);var Er=nr.keys;for(rr=0;rr<Er.length;rr++)wt.writeStringField(3,Er[rr]);var Xr=nr.values;for(rr=0;rr<Xr.length;rr++)wt.writeMessage(4,me,Xr[rr])}function te(Ut,wt){var rr=Ut.feature;rr.id!==void 0&&wt.writeVarintField(1,rr.id),wt.writeMessage(2,oe,Ut),wt.writeVarintField(3,rr.type),wt.writeMessage(4,Ce,rr)}function oe(Ut,wt){var rr=Ut.feature,nr=Ut.keys,Er=Ut.values,Xr=Ut.keycache,ri=Ut.valuecache;for(var Qr in rr.properties){var Oi=Xr[Qr];typeof Oi=="undefined"&&(nr.push(Qr),Oi=nr.length-1,Xr[Qr]=Oi),wt.writeVarint(Oi);var $i=rr.properties[Qr],tn=typeof $i;tn!=="string"&&tn!=="boolean"&&tn!=="number"&&($i=JSON.stringify($i));var fn=tn+":"+$i,yn=ri[fn];typeof yn=="undefined"&&(Er.push($i),yn=Er.length-1,ri[fn]=yn),wt.writeVarint(yn)}}function _e(Ut,wt){return(wt<<3)+(Ut&7)}function Ee(Ut){return Ut<<1^Ut>>31}function Ce(Ut,wt){for(var rr=Ut.loadGeometry(),nr=Ut.type,Er=0,Xr=0,ri=rr.length,Qr=0;Qr<ri;Qr++){var Oi=rr[Qr],$i=1;nr===1&&($i=Oi.length),wt.writeVarint(_e(1,$i));for(var tn=nr===3?Oi.length-1:Oi.length,fn=0;fn<tn;fn++){fn===1&&nr!==1&&wt.writeVarint(_e(2,tn-1));var yn=Oi[fn].x-Er,Sn=Oi[fn].y-Xr;wt.writeVarint(Ee(yn)),wt.writeVarint(Ee(Sn)),Er+=yn,Xr+=Sn}nr===3&&wt.writeVarint(_e(7,1))}}function me(Ut,wt){var rr=typeof Ut;rr==="string"?wt.writeStringField(1,Ut):rr==="boolean"?wt.writeBooleanField(7,Ut):rr==="number"&&(Ut%1!==0?wt.writeDoubleField(3,Ut):Ut<0?wt.writeSVarintField(6,Ut):wt.writeVarintField(5,Ut))}z.fromVectorTileJs=O,z.fromGeojsonVt=V,z.GeoJSONWrapper=G;function ie(Ut,wt,rr,nr,Er,Xr){if(!(Er-nr<=rr)){var ri=nr+Er>>1;Se(Ut,wt,ri,nr,Er,Xr%2),ie(Ut,wt,rr,nr,ri-1,Xr+1),ie(Ut,wt,rr,ri+1,Er,Xr+1)}}function Se(Ut,wt,rr,nr,Er,Xr){for(;Er>nr;){if(Er-nr>600){var ri=Er-nr+1,Qr=rr-nr+1,Oi=Math.log(ri),$i=.5*Math.exp(2*Oi/3),tn=.5*Math.sqrt(Oi*$i*(ri-$i)/ri)*(Qr-ri/2<0?-1:1),fn=Math.max(nr,Math.floor(rr-Qr*$i/ri+tn)),yn=Math.min(Er,Math.floor(rr+(ri-Qr)*$i/ri+tn));Se(Ut,wt,rr,fn,yn,Xr)}var Sn=wt[2*rr+Xr],Ba=nr,ua=Er;for(Le(Ut,wt,nr,rr),wt[2*Er+Xr]>Sn&&Le(Ut,wt,nr,Er);Ba<ua;){for(Le(Ut,wt,Ba,ua),Ba++,ua--;wt[2*Ba+Xr]<Sn;)Ba++;for(;wt[2*ua+Xr]>Sn;)ua--}wt[2*nr+Xr]===Sn?Le(Ut,wt,nr,ua):(ua++,Le(Ut,wt,ua,Er)),ua<=rr&&(nr=ua+1),rr<=ua&&(Er=ua-1)}}function Le(Ut,wt,rr,nr){Ae(Ut,rr,nr),Ae(wt,2*rr,2*nr),Ae(wt,2*rr+1,2*nr+1)}function Ae(Ut,wt,rr){var nr=Ut[wt];Ut[wt]=Ut[rr],Ut[rr]=nr}function Fe(Ut,wt,rr,nr,Er,Xr,ri){for(var Qr=[0,Ut.length-1,0],Oi=[],$i,tn;Qr.length;){var fn=Qr.pop(),yn=Qr.pop(),Sn=Qr.pop();if(yn-Sn<=ri){for(var Ba=Sn;Ba<=yn;Ba++)$i=wt[2*Ba],tn=wt[2*Ba+1],$i>=rr&&$i<=Er&&tn>=nr&&tn<=Xr&&Oi.push(Ut[Ba]);continue}var ua=Math.floor((Sn+yn)/2);$i=wt[2*ua],tn=wt[2*ua+1],$i>=rr&&$i<=Er&&tn>=nr&&tn<=Xr&&Oi.push(Ut[ua]);var ma=(fn+1)%2;(fn===0?rr<=$i:nr<=tn)&&(Qr.push(Sn),Qr.push(ua-1),Qr.push(ma)),(fn===0?Er>=$i:Xr>=tn)&&(Qr.push(ua+1),Qr.push(yn),Qr.push(ma))}return Oi}function Pe(Ut,wt,rr,nr,Er,Xr){for(var ri=[0,Ut.length-1,0],Qr=[],Oi=Er*Er;ri.length;){var $i=ri.pop(),tn=ri.pop(),fn=ri.pop();if(tn-fn<=Xr){for(var yn=fn;yn<=tn;yn++)ge(wt[2*yn],wt[2*yn+1],rr,nr)<=Oi&&Qr.push(Ut[yn]);continue}var Sn=Math.floor((fn+tn)/2),Ba=wt[2*Sn],ua=wt[2*Sn+1];ge(Ba,ua,rr,nr)<=Oi&&Qr.push(Ut[Sn]);var ma=($i+1)%2;($i===0?rr-Er<=Ba:nr-Er<=ua)&&(ri.push(fn),ri.push(Sn-1),ri.push(ma)),($i===0?rr+Er>=Ba:nr+Er>=ua)&&(ri.push(Sn+1),ri.push(tn),ri.push(ma))}return Qr}function ge(Ut,wt,rr,nr){var Er=Ut-rr,Xr=wt-nr;return Er*Er+Xr*Xr}var Re=function(Ut){return Ut[0]},ce=function(Ut){return Ut[1]},Ze=function(wt,rr,nr,Er,Xr){rr===void 0&&(rr=Re),nr===void 0&&(nr=ce),Er===void 0&&(Er=64),Xr===void 0&&(Xr=Float64Array),this.nodeSize=Er,this.points=wt;for(var ri=wt.length<65536?Uint16Array:Uint32Array,Qr=this.ids=new ri(wt.length),Oi=this.coords=new Xr(wt.length*2),$i=0;$i<wt.length;$i++)Qr[$i]=$i,Oi[2*$i]=rr(wt[$i]),Oi[2*$i+1]=nr(wt[$i]);ie(Qr,Oi,Er,0,Qr.length-1,0)};Ze.prototype.range=function(wt,rr,nr,Er){return Fe(this.ids,this.coords,wt,rr,nr,Er,this.nodeSize)},Ze.prototype.within=function(wt,rr,nr){return Pe(this.ids,this.coords,wt,rr,nr,this.nodeSize)};var ut={minZoom:0,maxZoom:16,minPoints:2,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(Ut){return Ut}},pt=function(wt){this.options=cr(Object.create(ut),wt),this.trees=new Array(this.options.maxZoom+1)};pt.prototype.load=function(wt){var rr=this.options,nr=rr.log,Er=rr.minZoom,Xr=rr.maxZoom,ri=rr.nodeSize;nr&&console.time("total time");var Qr="prepare "+wt.length+" points";nr&&console.time(Qr),this.points=wt;for(var Oi=[],$i=0;$i<wt.length;$i++)wt[$i].geometry&&Oi.push(st(wt[$i],$i));this.trees[Xr+1]=new Ze(Oi,$e,St,ri,Float32Array),nr&&console.timeEnd(Qr);for(var tn=Xr;tn>=Er;tn--){var fn=+Date.now();Oi=this._cluster(Oi,tn),this.trees[tn]=new Ze(Oi,$e,St,ri,Float32Array),nr&&console.log("z%d: %d clusters in %dms",tn,Oi.length,+Date.now()-fn)}return nr&&console.timeEnd("total time"),this},pt.prototype.getClusters=function(wt,rr){var nr=((wt[0]+180)%360+360)%360-180,Er=Math.max(-90,Math.min(90,wt[1])),Xr=wt[2]===180?180:((wt[2]+180)%360+360)%360-180,ri=Math.max(-90,Math.min(90,wt[3]));if(wt[2]-wt[0]>=360)nr=-180,Xr=180;else if(nr>Xr){var Qr=this.getClusters([nr,Er,180,ri],rr),Oi=this.getClusters([-180,Er,Xr,ri],rr);return Qr.concat(Oi)}for(var $i=this.trees[this._limitZoom(rr)],tn=$i.range(Nt(nr),Jt(ri),Nt(Xr),Jt(Er)),fn=[],yn=0,Sn=tn;yn<Sn.length;yn+=1){var Ba=Sn[yn],ua=$i.points[Ba];fn.push(ua.numPoints?lt(ua):this.points[ua.index])}return fn},pt.prototype.getChildren=function(wt){var rr=this._getOriginId(wt),nr=this._getOriginZoom(wt),Er="No cluster with the specified id.",Xr=this.trees[nr];if(!Xr)throw new Error(Er);var ri=Xr.points[rr];if(!ri)throw new Error(Er);for(var Qr=this.options.radius/(this.options.extent*Math.pow(2,nr-1)),Oi=Xr.within(ri.x,ri.y,Qr),$i=[],tn=0,fn=Oi;tn<fn.length;tn+=1){var yn=fn[tn],Sn=Xr.points[yn];Sn.parentId===wt&&$i.push(Sn.numPoints?lt(Sn):this.points[Sn.index])}if($i.length===0)throw new Error(Er);return $i},pt.prototype.getLeaves=function(wt,rr,nr){rr=rr||10,nr=nr||0;var Er=[];return this._appendLeaves(Er,wt,rr,nr,0),Er},pt.prototype.getTile=function(wt,rr,nr){var Er=this.trees[this._limitZoom(wt)],Xr=Math.pow(2,wt),ri=this.options,Qr=ri.extent,Oi=ri.radius,$i=Oi/Qr,tn=(nr-$i)/Xr,fn=(nr+1+$i)/Xr,yn={features:[]};return this._addTileFeatures(Er.range((rr-$i)/Xr,tn,(rr+1+$i)/Xr,fn),Er.points,rr,nr,Xr,yn),rr===0&&this._addTileFeatures(Er.range(1-$i/Xr,tn,1,fn),Er.points,Xr,nr,Xr,yn),rr===Xr-1&&this._addTileFeatures(Er.range(0,tn,$i/Xr,fn),Er.points,-1,nr,Xr,yn),yn.features.length?yn:null},pt.prototype.getClusterExpansionZoom=function(wt){for(var rr=this._getOriginZoom(wt)-1;rr<=this.options.maxZoom;){var nr=this.getChildren(wt);if(rr++,nr.length!==1)break;wt=nr[0].properties.cluster_id}return rr},pt.prototype._appendLeaves=function(wt,rr,nr,Er,Xr){for(var ri=this.getChildren(rr),Qr=0,Oi=ri;Qr<Oi.length;Qr+=1){var $i=Oi[Qr],tn=$i.properties;if(tn&&tn.cluster?Xr+tn.point_count<=Er?Xr+=tn.point_count:Xr=this._appendLeaves(wt,tn.cluster_id,nr,Er,Xr):Xr<Er?Xr++:wt.push($i),wt.length===nr)break}return Xr},pt.prototype._addTileFeatures=function(wt,rr,nr,Er,Xr,ri){for(var Qr=0,Oi=wt;Qr<Oi.length;Qr+=1){var $i=Oi[Qr],tn=rr[$i],fn=tn.numPoints,yn={type:1,geometry:[[Math.round(this.options.extent*(tn.x*Xr-nr)),Math.round(this.options.extent*(tn.y*Xr-Er))]],tags:fn?Gt(tn):this.points[tn.index].properties},Sn=void 0;fn?Sn=tn.id:this.options.generateId?Sn=tn.index:this.points[tn.index].id&&(Sn=this.points[tn.index].id),Sn!==void 0&&(yn.id=Sn),ri.features.push(yn)}},pt.prototype._limitZoom=function(wt){return Math.max(this.options.minZoom,Math.min(+wt,this.options.maxZoom+1))},pt.prototype._cluster=function(wt,rr){for(var nr=[],Er=this.options,Xr=Er.radius,ri=Er.extent,Qr=Er.reduce,Oi=Er.minPoints,$i=Xr/(ri*Math.pow(2,rr)),tn=0;tn<wt.length;tn++){var fn=wt[tn];if(!(fn.zoom<=rr)){fn.zoom=rr;for(var yn=this.trees[rr+1],Sn=yn.within(fn.x,fn.y,$i),Ba=fn.numPoints||1,ua=Ba,ma=0,Wa=Sn;ma<Wa.length;ma+=1){var Fa=Wa[ma],Wo=yn.points[Fa];Wo.zoom>rr&&(ua+=Wo.numPoints||1)}if(ua>=Oi){for(var da=fn.x*Ba,Wn=fn.y*Ba,Ha=Qr&&Ba>1?this._map(fn,!0):null,vo=(tn<<5)+(rr+1)+this.points.length,jn=0,Mt=Sn;jn<Mt.length;jn+=1){var kr=Mt[jn],Jr=yn.points[kr];if(!(Jr.zoom<=rr)){Jr.zoom=rr;var vi=Jr.numPoints||1;da+=Jr.x*vi,Wn+=Jr.y*vi,Jr.parentId=vo,Qr&&(Ha||(Ha=this._map(fn,!0)),Qr(Ha,this._map(Jr)))}}fn.parentId=vo,nr.push(Zt(da/ua,Wn/ua,vo,ua,Ha))}else if(nr.push(fn),ua>1)for(var hn=0,An=Sn;hn<An.length;hn+=1){var Mn=An[hn],Li=yn.points[Mn];Li.zoom<=rr||(Li.zoom=rr,nr.push(Li))}}}return nr},pt.prototype._getOriginId=function(wt){return wt-this.points.length>>5},pt.prototype._getOriginZoom=function(wt){return(wt-this.points.length)%32},pt.prototype._map=function(wt,rr){if(wt.numPoints)return rr?cr({},wt.properties):wt.properties;var nr=this.points[wt.index].properties,Er=this.options.map(nr);return rr&&Er===nr?cr({},Er):Er};function Zt(Ut,wt,rr,nr,Er){return{x:Ut,y:wt,zoom:1/0,id:rr,parentId:-1,numPoints:nr,properties:Er}}function st(Ut,wt){var rr=Ut.geometry.coordinates,nr=rr[0],Er=rr[1];return{x:Nt(nr),y:Jt(Er),zoom:1/0,index:wt,parentId:-1}}function lt(Ut){return{type:"Feature",id:Ut.id,properties:Gt(Ut),geometry:{type:"Point",coordinates:[sr(Ut.x),wr(Ut.y)]}}}function Gt(Ut){var wt=Ut.numPoints,rr=wt>=1e4?Math.round(wt/1e3)+"k":wt>=1e3?Math.round(wt/100)/10+"k":wt;return cr(cr({},Ut.properties),{cluster:!0,cluster_id:Ut.id,point_count:wt,point_count_abbreviated:rr})}function Nt(Ut){return Ut/360+.5}function Jt(Ut){var wt=Math.sin(Ut*Math.PI/180),rr=.5-.25*Math.log((1+wt)/(1-wt))/Math.PI;return rr<0?0:rr>1?1:rr}function sr(Ut){return(Ut-.5)*360}function wr(Ut){var wt=(180-Ut*360)*Math.PI/180;return 360*Math.atan(Math.exp(wt))/Math.PI-90}function cr(Ut,wt){for(var rr in wt)Ut[rr]=wt[rr];return Ut}function $e(Ut){return Ut.x}function St(Ut){return Ut.y}function Qt(Ut,wt,rr,nr){for(var Er=nr,Xr=rr-wt>>1,ri=rr-wt,Qr,Oi=Ut[wt],$i=Ut[wt+1],tn=Ut[rr],fn=Ut[rr+1],yn=wt+3;yn<rr;yn+=3){var Sn=Vt(Ut[yn],Ut[yn+1],Oi,$i,tn,fn);if(Sn>Er)Qr=yn,Er=Sn;else if(Sn===Er){var Ba=Math.abs(yn-Xr);Ba<ri&&(Qr=yn,ri=Ba)}}Er>nr&&(Qr-wt>3&&Qt(Ut,wt,Qr,nr),Ut[Qr+2]=Er,rr-Qr>3&&Qt(Ut,Qr,rr,nr))}function Vt(Ut,wt,rr,nr,Er,Xr){var ri=Er-rr,Qr=Xr-nr;if(ri!==0||Qr!==0){var Oi=((Ut-rr)*ri+(wt-nr)*Qr)/(ri*ri+Qr*Qr);Oi>1?(rr=Er,nr=Xr):Oi>0&&(rr+=ri*Oi,nr+=Qr*Oi)}return ri=Ut-rr,Qr=wt-nr,ri*ri+Qr*Qr}function _t(Ut,wt,rr,nr){var Er={id:typeof Ut=="undefined"?null:Ut,type:wt,geometry:rr,tags:nr,minX:1/0,minY:1/0,maxX:-1/0,maxY:-1/0};return It(Er),Er}function It(Ut){var wt=Ut.geometry,rr=Ut.type;if(rr==="Point"||rr==="MultiPoint"||rr==="LineString")mt(Ut,wt);else if(rr==="Polygon"||rr==="MultiLineString")for(var nr=0;nr<wt.length;nr++)mt(Ut,wt[nr]);else if(rr==="MultiPolygon")for(nr=0;nr<wt.length;nr++)for(var Er=0;Er<wt[nr].length;Er++)mt(Ut,wt[nr][Er])}function mt(Ut,wt){for(var rr=0;rr<wt.length;rr+=3)Ut.minX=Math.min(Ut.minX,wt[rr]),Ut.minY=Math.min(Ut.minY,wt[rr+1]),Ut.maxX=Math.max(Ut.maxX,wt[rr]),Ut.maxY=Math.max(Ut.maxY,wt[rr+1])}function er(Ut,wt){var rr=[];if(Ut.type==="FeatureCollection")for(var nr=0;nr<Ut.features.length;nr++)lr(rr,Ut.features[nr],wt,nr);else Ut.type==="Feature"?lr(rr,Ut,wt):lr(rr,{geometry:Ut},wt);return rr}function lr(Ut,wt,rr,nr){if(wt.geometry){var Er=wt.geometry.coordinates,Xr=wt.geometry.type,ri=Math.pow(rr.tolerance/((1<<rr.maxZoom)*rr.extent),2),Qr=[],Oi=wt.id;if(rr.promoteId?Oi=wt.properties[rr.promoteId]:rr.generateId&&(Oi=nr||0),Xr==="Point")Tr(Er,Qr);else if(Xr==="MultiPoint")for(var $i=0;$i<Er.length;$i++)Tr(Er[$i],Qr);else if(Xr==="LineString")Lr(Er,Qr,ri,!1);else if(Xr==="MultiLineString")if(rr.lineMetrics){for($i=0;$i<Er.length;$i++)Qr=[],Lr(Er[$i],Qr,ri,!1),Ut.push(_t(Oi,"LineString",Qr,wt.properties));return}else ti(Er,Qr,ri,!1);else if(Xr==="Polygon")ti(Er,Qr,ri,!0);else if(Xr==="MultiPolygon")for($i=0;$i<Er.length;$i++){var tn=[];ti(Er[$i],tn,ri,!0),Qr.push(tn)}else if(Xr==="GeometryCollection"){for($i=0;$i<wt.geometry.geometries.length;$i++)lr(Ut,{id:Oi,geometry:wt.geometry.geometries[$i],properties:wt.properties},rr,nr);return}else throw new Error("Input data is not a valid GeoJSON object.");Ut.push(_t(Oi,Xr,Qr,wt.properties))}}function Tr(Ut,wt){wt.push(Br(Ut[0])),wt.push(Vr(Ut[1])),wt.push(0)}function Lr(Ut,wt,rr,nr){for(var Er,Xr,ri=0,Qr=0;Qr<Ut.length;Qr++){var Oi=Br(Ut[Qr][0]),$i=Vr(Ut[Qr][1]);wt.push(Oi),wt.push($i),wt.push(0),Qr>0&&(nr?ri+=(Er*$i-Oi*Xr)/2:ri+=Math.sqrt(Math.pow(Oi-Er,2)+Math.pow($i-Xr,2))),Er=Oi,Xr=$i}var tn=wt.length-3;wt[2]=1,Qt(wt,0,tn,rr),wt[tn+2]=1,wt.size=Math.abs(ri),wt.start=0,wt.end=wt.size}function ti(Ut,wt,rr,nr){for(var Er=0;Er<Ut.length;Er++){var Xr=[];Lr(Ut[Er],Xr,rr,nr),wt.push(Xr)}}function Br(Ut){return Ut/360+.5}function Vr(Ut){var wt=Math.sin(Ut*Math.PI/180),rr=.5-.25*Math.log((1+wt)/(1-wt))/Math.PI;return rr<0?0:rr>1?1:rr}function dt(Ut,wt,rr,nr,Er,Xr,ri,Qr){if(rr/=wt,nr/=wt,Xr>=rr&&ri<nr)return Ut;if(ri<rr||Xr>=nr)return null;for(var Oi=[],$i=0;$i<Ut.length;$i++){var tn=Ut[$i],fn=tn.geometry,yn=tn.type,Sn=Er===0?tn.minX:tn.minY,Ba=Er===0?tn.maxX:tn.maxY;if(Sn>=rr&&Ba<nr){Oi.push(tn);continue}else if(Ba<rr||Sn>=nr)continue;var ua=[];if(yn==="Point"||yn==="MultiPoint")Ge(fn,ua,rr,nr,Er);else if(yn==="LineString")Je(fn,ua,rr,nr,Er,!1,Qr.lineMetrics);else if(yn==="MultiLineString")tt(fn,ua,rr,nr,Er,!1);else if(yn==="Polygon")tt(fn,ua,rr,nr,Er,!0);else if(yn==="MultiPolygon")for(var ma=0;ma<fn.length;ma++){var Wa=[];tt(fn[ma],Wa,rr,nr,Er,!0),Wa.length&&ua.push(Wa)}if(ua.length){if(Qr.lineMetrics&&yn==="LineString"){for(ma=0;ma<ua.length;ma++)Oi.push(_t(tn.id,yn,ua[ma],tn.tags));continue}(yn==="LineString"||yn==="MultiLineString")&&(ua.length===1?(yn="LineString",ua=ua[0]):yn="MultiLineString"),(yn==="Point"||yn==="MultiPoint")&&(yn=ua.length===3?"Point":"MultiPoint"),Oi.push(_t(tn.id,yn,ua,tn.tags))}}return Oi.length?Oi:null}function Ge(Ut,wt,rr,nr,Er){for(var Xr=0;Xr<Ut.length;Xr+=3){var ri=Ut[Xr+Er];ri>=rr&&ri<=nr&&(wt.push(Ut[Xr]),wt.push(Ut[Xr+1]),wt.push(Ut[Xr+2]))}}function Je(Ut,wt,rr,nr,Er,Xr,ri){for(var Qr=je(Ut),Oi=Er===0?Ie:xe,$i=Ut.start,tn,fn,yn=0;yn<Ut.length-3;yn+=3){var Sn=Ut[yn],Ba=Ut[yn+1],ua=Ut[yn+2],ma=Ut[yn+3],Wa=Ut[yn+4],Fa=Er===0?Sn:Ba,Wo=Er===0?ma:Wa,da=!1;ri&&(tn=Math.sqrt(Math.pow(Sn-ma,2)+Math.pow(Ba-Wa,2))),Fa<rr?Wo>rr&&(fn=Oi(Qr,Sn,Ba,ma,Wa,rr),ri&&(Qr.start=$i+tn*fn)):Fa>nr?Wo<nr&&(fn=Oi(Qr,Sn,Ba,ma,Wa,nr),ri&&(Qr.start=$i+tn*fn)):xt(Qr,Sn,Ba,ua),Wo<rr&&Fa>=rr&&(fn=Oi(Qr,Sn,Ba,ma,Wa,rr),da=!0),Wo>nr&&Fa<=nr&&(fn=Oi(Qr,Sn,Ba,ma,Wa,nr),da=!0),!Xr&&da&&(ri&&(Qr.end=$i+tn*fn),wt.push(Qr),Qr=je(Ut)),ri&&($i+=tn)}var Wn=Ut.length-3;Sn=Ut[Wn],Ba=Ut[Wn+1],ua=Ut[Wn+2],Fa=Er===0?Sn:Ba,Fa>=rr&&Fa<=nr&&xt(Qr,Sn,Ba,ua),Wn=Qr.length-3,Xr&&Wn>=3&&(Qr[Wn]!==Qr[0]||Qr[Wn+1]!==Qr[1])&&xt(Qr,Qr[0],Qr[1],Qr[2]),Qr.length&&wt.push(Qr)}function je(Ut){var wt=[];return wt.size=Ut.size,wt.start=Ut.start,wt.end=Ut.end,wt}function tt(Ut,wt,rr,nr,Er,Xr){for(var ri=0;ri<Ut.length;ri++)Je(Ut[ri],wt,rr,nr,Er,Xr,!1)}function xt(Ut,wt,rr,nr){Ut.push(wt),Ut.push(rr),Ut.push(nr)}function Ie(Ut,wt,rr,nr,Er,Xr){var ri=(Xr-wt)/(nr-wt);return Ut.push(Xr),Ut.push(rr+(Er-rr)*ri),Ut.push(1),ri}function xe(Ut,wt,rr,nr,Er,Xr){var ri=(Xr-rr)/(Er-rr);return Ut.push(wt+(nr-wt)*ri),Ut.push(Xr),Ut.push(1),ri}function ke(Ut,wt){var rr=wt.buffer/wt.extent,nr=Ut,Er=dt(Ut,1,-1-rr,rr,0,-1,2,wt),Xr=dt(Ut,1,1-rr,2+rr,0,-1,2,wt);return(Er||Xr)&&(nr=dt(Ut,1,-rr,1+rr,0,-1,2,wt)||[],Er&&(nr=vt(Er,1).concat(nr)),Xr&&(nr=nr.concat(vt(Xr,-1)))),nr}function vt(Ut,wt){for(var rr=[],nr=0;nr<Ut.length;nr++){var Er=Ut[nr],Xr=Er.type,ri;if(Xr==="Point"||Xr==="MultiPoint"||Xr==="LineString")ri=ir(Er.geometry,wt);else if(Xr==="MultiLineString"||Xr==="Polygon"){ri=[];for(var Qr=0;Qr<Er.geometry.length;Qr++)ri.push(ir(Er.geometry[Qr],wt))}else if(Xr==="MultiPolygon")for(ri=[],Qr=0;Qr<Er.geometry.length;Qr++){for(var Oi=[],$i=0;$i<Er.geometry[Qr].length;$i++)Oi.push(ir(Er.geometry[Qr][$i],wt));ri.push(Oi)}rr.push(_t(Er.id,Xr,ri,Er.tags))}return rr}function ir(Ut,wt){var rr=[];rr.size=Ut.size,Ut.start!==void 0&&(rr.start=Ut.start,rr.end=Ut.end);for(var nr=0;nr<Ut.length;nr+=3)rr.push(Ut[nr]+wt,Ut[nr+1],Ut[nr+2]);return rr}function ar(Ut,wt){if(Ut.transformed)return Ut;var rr=1<<Ut.z,nr=Ut.x,Er=Ut.y,Xr,ri,Qr;for(Xr=0;Xr<Ut.features.length;Xr++){var Oi=Ut.features[Xr],$i=Oi.geometry,tn=Oi.type;if(Oi.geometry=[],tn===1)for(ri=0;ri<$i.length;ri+=2)Oi.geometry.push(vr($i[ri],$i[ri+1],wt,rr,nr,Er));else for(ri=0;ri<$i.length;ri++){var fn=[];for(Qr=0;Qr<$i[ri].length;Qr+=2)fn.push(vr($i[ri][Qr],$i[ri][Qr+1],wt,rr,nr,Er));Oi.geometry.push(fn)}}return Ut.transformed=!0,Ut}function vr(Ut,wt,rr,nr,Er,Xr){return[Math.round(rr*(Ut*nr-Er)),Math.round(rr*(wt*nr-Xr))]}function ii(Ut,wt,rr,nr,Er){for(var Xr=wt===Er.maxZoom?0:Er.tolerance/((1<<wt)*Er.extent),ri={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:rr,y:nr,z:wt,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},Qr=0;Qr<Ut.length;Qr++){ri.numFeatures++,pi(ri,Ut[Qr],Xr,Er);var Oi=Ut[Qr].minX,$i=Ut[Qr].minY,tn=Ut[Qr].maxX,fn=Ut[Qr].maxY;Oi<ri.minX&&(ri.minX=Oi),$i<ri.minY&&(ri.minY=$i),tn>ri.maxX&&(ri.maxX=tn),fn>ri.maxY&&(ri.maxY=fn)}return ri}function pi(Ut,wt,rr,nr){var Er=wt.geometry,Xr=wt.type,ri=[];if(Xr==="Point"||Xr==="MultiPoint")for(var Qr=0;Qr<Er.length;Qr+=3)ri.push(Er[Qr]),ri.push(Er[Qr+1]),Ut.numPoints++,Ut.numSimplified++;else if(Xr==="LineString")$r(ri,Er,Ut,rr,!1,!1);else if(Xr==="MultiLineString"||Xr==="Polygon")for(Qr=0;Qr<Er.length;Qr++)$r(ri,Er[Qr],Ut,rr,Xr==="Polygon",Qr===0);else if(Xr==="MultiPolygon")for(var Oi=0;Oi<Er.length;Oi++){var $i=Er[Oi];for(Qr=0;Qr<$i.length;Qr++)$r(ri,$i[Qr],Ut,rr,!0,Qr===0)}if(ri.length){var tn=wt.tags||null;if(Xr==="LineString"&&nr.lineMetrics){tn={};for(var fn in wt.tags)tn[fn]=wt.tags[fn];tn.mapbox_clip_start=Er.start/Er.size,tn.mapbox_clip_end=Er.end/Er.size}var yn={geometry:ri,type:Xr==="Polygon"||Xr==="MultiPolygon"?3:Xr==="LineString"||Xr==="MultiLineString"?2:1,tags:tn};wt.id!==null&&(yn.id=wt.id),Ut.features.push(yn)}}function $r(Ut,wt,rr,nr,Er,Xr){var ri=nr*nr;if(nr>0&&wt.size<(Er?ri:nr)){rr.numPoints+=wt.length/3;return}for(var Qr=[],Oi=0;Oi<wt.length;Oi+=3)(nr===0||wt[Oi+2]>ri)&&(rr.numSimplified++,Qr.push(wt[Oi]),Qr.push(wt[Oi+1])),rr.numPoints++;Er&&di(Qr,Xr),Ut.push(Qr)}function di(Ut,wt){for(var rr=0,nr=0,Er=Ut.length,Xr=Er-2;nr<Er;Xr=nr,nr+=2)rr+=(Ut[nr]-Ut[Xr])*(Ut[nr+1]+Ut[Xr+1]);if(rr>0===wt)for(nr=0,Er=Ut.length;nr<Er/2;nr+=2){var ri=Ut[nr],Qr=Ut[nr+1];Ut[nr]=Ut[Er-2-nr],Ut[nr+1]=Ut[Er-1-nr],Ut[Er-2-nr]=ri,Ut[Er-1-nr]=Qr}}function ji(Ut,wt){return new In(Ut,wt)}function In(Ut,wt){wt=this.options=On(Object.create(this.options),wt);var rr=wt.debug;if(rr&&console.time("preprocess data"),wt.maxZoom<0||wt.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(wt.promoteId&&wt.generateId)throw new Error("promoteId and generateId cannot be used together.");var nr=er(Ut,wt);this.tiles={},this.tileCoords=[],rr&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",wt.indexMaxZoom,wt.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),nr=ke(nr,wt),nr.length&&this.splitTile(nr,0,0,0),rr&&(nr.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}In.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},In.prototype.splitTile=function(Ut,wt,rr,nr,Er,Xr,ri){for(var Qr=[Ut,wt,rr,nr],Oi=this.options,$i=Oi.debug;Qr.length;){nr=Qr.pop(),rr=Qr.pop(),wt=Qr.pop(),Ut=Qr.pop();var tn=1<<wt,fn=wi(wt,rr,nr),yn=this.tiles[fn];if(!yn&&($i>1&&console.time("creation"),yn=this.tiles[fn]=ii(Ut,wt,rr,nr,Oi),this.tileCoords.push({z:wt,x:rr,y:nr}),$i)){$i>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",wt,rr,nr,yn.numFeatures,yn.numPoints,yn.numSimplified),console.timeEnd("creation"));var Sn="z"+wt;this.stats[Sn]=(this.stats[Sn]||0)+1,this.total++}if(yn.source=Ut,Er){if(wt===Oi.maxZoom||wt===Er)continue;var Ba=1<<Er-wt;if(rr!==Math.floor(Xr/Ba)||nr!==Math.floor(ri/Ba))continue}else if(wt===Oi.indexMaxZoom||yn.numPoints<=Oi.indexMaxPoints)continue;if(yn.source=null,Ut.length!==0){$i>1&&console.time("clipping");var ua=.5*Oi.buffer/Oi.extent,ma=.5-ua,Wa=.5+ua,Fa=1+ua,Wo,da,Wn,Ha,vo,jn;Wo=da=Wn=Ha=null,vo=dt(Ut,tn,rr-ua,rr+Wa,0,yn.minX,yn.maxX,Oi),jn=dt(Ut,tn,rr+ma,rr+Fa,0,yn.minX,yn.maxX,Oi),Ut=null,vo&&(Wo=dt(vo,tn,nr-ua,nr+Wa,1,yn.minY,yn.maxY,Oi),da=dt(vo,tn,nr+ma,nr+Fa,1,yn.minY,yn.maxY,Oi),vo=null),jn&&(Wn=dt(jn,tn,nr-ua,nr+Wa,1,yn.minY,yn.maxY,Oi),Ha=dt(jn,tn,nr+ma,nr+Fa,1,yn.minY,yn.maxY,Oi),jn=null),$i>1&&console.timeEnd("clipping"),Qr.push(Wo||[],wt+1,rr*2,nr*2),Qr.push(da||[],wt+1,rr*2,nr*2+1),Qr.push(Wn||[],wt+1,rr*2+1,nr*2),Qr.push(Ha||[],wt+1,rr*2+1,nr*2+1)}}},In.prototype.getTile=function(Ut,wt,rr){var nr=this.options,Er=nr.extent,Xr=nr.debug;if(Ut<0||Ut>24)return null;var ri=1<<Ut;wt=(wt%ri+ri)%ri;var Qr=wi(Ut,wt,rr);if(this.tiles[Qr])return ar(this.tiles[Qr],Er);Xr>1&&console.log("drilling down to z%d-%d-%d",Ut,wt,rr);for(var Oi=Ut,$i=wt,tn=rr,fn;!fn&&Oi>0;)Oi--,$i=Math.floor($i/2),tn=Math.floor(tn/2),fn=this.tiles[wi(Oi,$i,tn)];return!fn||!fn.source?null:(Xr>1&&console.log("found parent tile z%d-%d-%d",Oi,$i,tn),Xr>1&&console.time("drilling down"),this.splitTile(fn.source,Oi,$i,tn,Ut,wt,rr),Xr>1&&console.timeEnd("drilling down"),this.tiles[Qr]?ar(this.tiles[Qr],Er):null)};function wi(Ut,wt,rr){return((1<<Ut)*rr+wt)*32+Ut}function On(Ut,wt){for(var rr in wt)Ut[rr]=wt[rr];return Ut}function qn(Ut,wt){var rr=Ut.tileID.canonical;if(!this._geoJSONIndex)return wt(null,null);var nr=this._geoJSONIndex.getTile(rr.z,rr.x,rr.y);if(!nr)return wt(null,null);var Er=new C(nr.features),Xr=z(Er);(Xr.byteOffset!==0||Xr.byteLength!==Xr.buffer.byteLength)&&(Xr=new Uint8Array(Xr)),wt(null,{vectorTile:Er,rawData:Xr.buffer})}var Fn=function(Ut){function wt(rr,nr,Er,Xr){Ut.call(this,rr,nr,Er,qn),Xr&&(this.loadGeoJSON=Xr)}return Ut&&(wt.__proto__=Ut),wt.prototype=Object.create(Ut&&Ut.prototype),wt.prototype.constructor=wt,wt.prototype.loadData=function(nr,Er){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=Er,this._pendingLoadDataParams=nr,this._state&&this._state!=="Idle"?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},wt.prototype._loadData=function(){var nr=this;if(!(!this._pendingCallback||!this._pendingLoadDataParams)){var Er=this._pendingCallback,Xr=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var ri=Xr&&Xr.request&&Xr.request.collectResourceTiming?new i.RequestPerformance(Xr.request):!1;this.loadGeoJSON(Xr,function(Qr,Oi){if(Qr||!Oi)return Er(Qr);if(typeof Oi!="object")return Er(new Error("Input data given to '"+Xr.source+"' is not a valid GeoJSON object."));p(Oi,!0);try{if(Xr.filter){var $i=i.createExpression(Xr.filter,{type:"boolean","property-type":"data-driven",overridable:!1,transition:!1});if($i.result==="error")throw new Error($i.value.map(function(Sn){return Sn.key+": "+Sn.message}).join(", "));var tn=Oi.features.filter(function(Sn){return $i.value.evaluate({zoom:0},Sn)});Oi={type:"FeatureCollection",features:tn}}nr._geoJSONIndex=Xr.cluster?new pt(ra(Xr)).load(Oi.features):ji(Oi,Xr.geojsonVtOptions)}catch(Sn){return Er(Sn)}nr.loaded={};var fn={};if(ri){var yn=ri.finish();yn&&(fn.resourceTiming={},fn.resourceTiming[Xr.source]=JSON.parse(JSON.stringify(yn)))}Er(null,fn)})}},wt.prototype.coalesce=function(){this._state==="Coalescing"?this._state="Idle":this._state==="NeedsLoadData"&&(this._state="Coalescing",this._loadData())},wt.prototype.reloadTile=function(nr,Er){var Xr=this.loaded,ri=nr.uid;return Xr&&Xr[ri]?Ut.prototype.reloadTile.call(this,nr,Er):this.loadTile(nr,Er)},wt.prototype.loadGeoJSON=function(nr,Er){if(nr.request)i.getJSON(nr.request,Er);else if(typeof nr.data=="string")try{return Er(null,JSON.parse(nr.data))}catch(Xr){return Er(new Error("Input data given to '"+nr.source+"' is not a valid GeoJSON object."))}else return Er(new Error("Input data given to '"+nr.source+"' is not a valid GeoJSON object."))},wt.prototype.removeSource=function(nr,Er){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),Er()},wt.prototype.getClusterExpansionZoom=function(nr,Er){try{Er(null,this._geoJSONIndex.getClusterExpansionZoom(nr.clusterId))}catch(Xr){Er(Xr)}},wt.prototype.getClusterChildren=function(nr,Er){try{Er(null,this._geoJSONIndex.getChildren(nr.clusterId))}catch(Xr){Er(Xr)}},wt.prototype.getClusterLeaves=function(nr,Er){try{Er(null,this._geoJSONIndex.getLeaves(nr.clusterId,nr.limit,nr.offset))}catch(Xr){Er(Xr)}},wt}(v);function ra(Ut){var wt=Ut.superclusterOptions,rr=Ut.clusterProperties;if(!rr||!wt)return wt;for(var nr={},Er={},Xr={accumulated:null,zoom:0},ri={properties:null},Qr=Object.keys(rr),Oi=0,$i=Qr;Oi<$i.length;Oi+=1){var tn=$i[Oi],fn=rr[tn],yn=fn[0],Sn=fn[1],Ba=i.createExpression(Sn),ua=i.createExpression(typeof yn=="string"?[yn,["accumulated"],["get",tn]]:yn);nr[tn]=Ba.value,Er[tn]=ua.value}return wt.map=function(ma){ri.properties=ma;for(var Wa={},Fa=0,Wo=Qr;Fa<Wo.length;Fa+=1){var da=Wo[Fa];Wa[da]=nr[da].evaluate(Xr,ri)}return Wa},wt.reduce=function(ma,Wa){ri.properties=Wa;for(var Fa=0,Wo=Qr;Fa<Wo.length;Fa+=1){var da=Wo[Fa];Xr.accumulated=ma[da],ma[da]=Er[da].evaluate(Xr,ri)}},wt}var la=function(wt){var rr=this;this.self=wt,this.actor=new i.Actor(wt,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:v,geojson:Fn},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(nr,Er){if(rr.workerSourceTypes[nr])throw new Error('Worker source with name "'+nr+'" already registered.');rr.workerSourceTypes[nr]=Er},this.self.registerRTLTextPlugin=function(nr){if(i.plugin.isParsed())throw new Error("RTL text plugin already registered.");i.plugin.applyArabicShaping=nr.applyArabicShaping,i.plugin.processBidirectionalText=nr.processBidirectionalText,i.plugin.processStyledBidirectionalText=nr.processStyledBidirectionalText}};return la.prototype.setReferrer=function(wt,rr){this.referrer=rr},la.prototype.setImages=function(wt,rr,nr){this.availableImages[wt]=rr;for(var Er in this.workerSources[wt]){var Xr=this.workerSources[wt][Er];for(var ri in Xr)Xr[ri].availableImages=rr}nr()},la.prototype.setLayers=function(wt,rr,nr){this.getLayerIndex(wt).replace(rr),nr()},la.prototype.updateLayers=function(wt,rr,nr){this.getLayerIndex(wt).update(rr.layers,rr.removedIds),nr()},la.prototype.loadTile=function(wt,rr,nr){this.getWorkerSource(wt,rr.type,rr.source).loadTile(rr,nr)},la.prototype.loadDEMTile=function(wt,rr,nr){this.getDEMWorkerSource(wt,rr.source).loadTile(rr,nr)},la.prototype.reloadTile=function(wt,rr,nr){this.getWorkerSource(wt,rr.type,rr.source).reloadTile(rr,nr)},la.prototype.abortTile=function(wt,rr,nr){this.getWorkerSource(wt,rr.type,rr.source).abortTile(rr,nr)},la.prototype.removeTile=function(wt,rr,nr){this.getWorkerSource(wt,rr.type,rr.source).removeTile(rr,nr)},la.prototype.removeDEMTile=function(wt,rr){this.getDEMWorkerSource(wt,rr.source).removeTile(rr)},la.prototype.removeSource=function(wt,rr,nr){if(!(!this.workerSources[wt]||!this.workerSources[wt][rr.type]||!this.workerSources[wt][rr.type][rr.source])){var 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Er=this;if(this.workerSources[wt]||(this.workerSources[wt]={}),this.workerSources[wt][rr]||(this.workerSources[wt][rr]={}),!this.workerSources[wt][rr][nr]){var Xr={send:function(ri,Qr,Oi){Er.actor.send(ri,Qr,Oi,wt)}};this.workerSources[wt][rr][nr]=new this.workerSourceTypes[rr](Xr,this.getLayerIndex(wt),this.getAvailableImages(wt))}return this.workerSources[wt][rr][nr]},la.prototype.getDEMWorkerSource=function(wt,rr){return this.demWorkerSources[wt]||(this.demWorkerSources[wt]={}),this.demWorkerSources[wt][rr]||(this.demWorkerSources[wt][rr]=new b),this.demWorkerSources[wt][rr]},la.prototype.enforceCacheSizeLimit=function(wt,rr){i.enforceCacheSizeLimit(rr)},typeof WorkerGlobalScope!="undefined"&&typeof self!="undefined"&&self instanceof WorkerGlobalScope&&(self.worker=new la(self)),la}),n(["./shared"],function(i){"use strict";var a=i.createCommonjsModule(function(Y){Y.exports?Y.exports=D:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=D,window.mapboxgl.notSupportedReason=J);function D(Ct){return!J(Ct)}function J(Ct){if(!q())return"not a browser";if(!K())return"insufficent Array support";if(!de())return"insufficient Function support";if(!ne())return"insufficient Object support";if(!we())return"insufficient JSON support";if(!Ue())return"insufficient worker support";if(!ft())return"insufficient Uint8ClampedArray support";if(!Xt())return"insufficient ArrayBuffer support";if(!hr())return"insufficient Canvas/getImageData support";if(!Ve(Ct&&Ct.failIfMajorPerformanceCaveat))return"insufficient WebGL support"}function q(){return typeof window!="undefined"&&typeof document!="undefined"}function K(){return 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ft=this.images[Ue];ft?de[Ue]={data:ft.data.clone(),pixelRatio:ft.pixelRatio,sdf:ft.sdf,version:ft.version,stretchX:ft.stretchX,stretchY:ft.stretchY,content:ft.content,hasRenderCallback:!!(ft.userImage&&ft.userImage.render)}:i.warnOnce('Image "'+Ue+'" could not be loaded. 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Qe=Ve[qt],at=Qe.stack,Ct=Qe.id,Ot=Qe.glyph;(hr[at]||(hr[at]={}))[Ct]=Ot&&{id:Ot.id,bitmap:Ot.bitmap.clone(),metrics:Ot.metrics}}J(null,hr)}})},P.prototype._doesCharSupportLocalGlyph=function(D){return!!this.localIdeographFontFamily&&(i.isChar["CJK Unified Ideographs"](D)||i.isChar["Hangul Syllables"](D)||i.isChar.Hiragana(D)||i.isChar.Katakana(D))},P.prototype._tinySDF=function(D,J,q){var K=this.localIdeographFontFamily;if(K&&this._doesCharSupportLocalGlyph(q)){var de=D.tinySDF;if(!de){var ne="400";/bold/i.test(J)?ne="900":/medium/i.test(J)?ne="500":/light/i.test(J)&&(ne="200"),de=D.tinySDF=new P.TinySDF(24,3,8,.25,K,ne)}return{id:q,bitmap:new i.AlphaImage({width:30,height:30},de.draw(String.fromCharCode(q))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},P.loadGlyphRange=E,P.TinySDF=S;var T=function(){this.specification=i.styleSpec.light.position};T.prototype.possiblyEvaluate=function(D,J){return i.sphericalToCartesian(D.expression.evaluate(J))},T.prototype.interpolate=function(D,J,q){return{x:i.number(D.x,J.x,q),y:i.number(D.y,J.y,q),z:i.number(D.z,J.z,q)}};var z=new i.Properties({anchor:new i.DataConstantProperty(i.styleSpec.light.anchor),position:new T,color:new i.DataConstantProperty(i.styleSpec.light.color),intensity:new i.DataConstantProperty(i.styleSpec.light.intensity)}),O="-transition",V=function(Y){function D(J){Y.call(this),this._transitionable=new i.Transitionable(z),this.setLight(J),this._transitioning=this._transitionable.untransitioned()}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getLight=function(){return this._transitionable.serialize()},D.prototype.setLight=function(q,K){if(K===void 0&&(K={}),!this._validate(i.validateLight,q,K))for(var de in q){var ne=q[de];i.endsWith(de,O)?this._transitionable.setTransition(de.slice(0,-O.length),ne):this._transitionable.setValue(de,ne)}},D.prototype.updateTransitions=function(q){this._transitioning=this._transitionable.transitioned(q,this._transitioning)},D.prototype.hasTransition=function(){return this._transitioning.hasTransition()},D.prototype.recalculate=function(q){this.properties=this._transitioning.possiblyEvaluate(q)},D.prototype._validate=function(q,K,de){return de&&de.validate===!1?!1:i.emitValidationErrors(this,q.call(i.validateStyle,i.extend({value:K,style:{glyphs:!0,sprite:!0},styleSpec:i.styleSpec})))},D}(i.Evented),G=function(D,J){this.width=D,this.height=J,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};G.prototype.getDash=function(D,J){var q=D.join(",")+String(J);return this.dashEntry[q]||(this.dashEntry[q]=this.addDash(D,J)),this.dashEntry[q]},G.prototype.getDashRanges=function(D,J,q){var K=D.length%2===1,de=[],ne=K?-D[D.length-1]*q:0,we=D[0]*q,Ue=!0;de.push({left:ne,right:we,isDash:Ue,zeroLength:D[0]===0});for(var ft=D[0],Xt=1;Xt<D.length;Xt++){Ue=!Ue;var hr=D[Xt];ne=ft*q,ft+=hr,we=ft*q,de.push({left:ne,right:we,isDash:Ue,zeroLength:hr===0})}return de},G.prototype.addRoundDash=function(D,J,q){for(var K=J/2,de=-q;de<=q;de++)for(var ne=this.nextRow+q+de,we=this.width*ne,Ue=0,ft=D[Ue],Xt=0;Xt<this.width;Xt++){Xt/ft.right>1&&(ft=D[++Ue]);var hr=Math.abs(Xt-ft.left),qt=Math.abs(Xt-ft.right),Ve=Math.min(hr,qt),Qe=void 0,at=de/q*(K+1);if(ft.isDash){var Ct=K-Math.abs(at);Qe=Math.sqrt(Ve*Ve+Ct*Ct)}else Qe=K-Math.sqrt(Ve*Ve+at*at);this.data[we+Xt]=Math.max(0,Math.min(255,Qe+128))}},G.prototype.addRegularDash=function(D){for(var J=D.length-1;J>=0;--J){var q=D[J],K=D[J+1];q.zeroLength?D.splice(J,1):K&&K.isDash===q.isDash&&(K.left=q.left,D.splice(J,1))}var de=D[0],ne=D[D.length-1];de.isDash===ne.isDash&&(de.left=ne.left-this.width,ne.right=de.right+this.width);for(var we=this.width*this.nextRow,Ue=0,ft=D[Ue],Xt=0;Xt<this.width;Xt++){Xt/ft.right>1&&(ft=D[++Ue]);var hr=Math.abs(Xt-ft.left),qt=Math.abs(Xt-ft.right),Ve=Math.min(hr,qt),Qe=ft.isDash?Ve:-Ve;this.data[we+Xt]=Math.max(0,Math.min(255,Qe+128))}},G.prototype.addDash=function(D,J){var q=J?7:0,K=2*q+1;if(this.nextRow+K>this.height)return i.warnOnce("LineAtlas out of space"),null;for(var de=0,ne=0;ne<D.length;ne++)de+=D[ne];if(de!==0){var we=this.width/de,Ue=this.getDashRanges(D,this.width,we);J?this.addRoundDash(Ue,we,q):this.addRegularDash(Ue)}var ft={y:(this.nextRow+q+.5)/this.height,height:2*q/this.height,width:de};return this.nextRow+=K,this.dirty=!0,ft},G.prototype.bind=function(D){var J=D.gl;this.texture?(J.bindTexture(J.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,J.texSubImage2D(J.TEXTURE_2D,0,0,0,this.width,this.height,J.ALPHA,J.UNSIGNED_BYTE,this.data))):(this.texture=J.createTexture(),J.bindTexture(J.TEXTURE_2D,this.texture),J.texParameteri(J.TEXTURE_2D,J.TEXTURE_WRAP_S,J.REPEAT),J.texParameteri(J.TEXTURE_2D,J.TEXTURE_WRAP_T,J.REPEAT),J.texParameteri(J.TEXTURE_2D,J.TEXTURE_MIN_FILTER,J.LINEAR),J.texParameteri(J.TEXTURE_2D,J.TEXTURE_MAG_FILTER,J.LINEAR),J.texImage2D(J.TEXTURE_2D,0,J.ALPHA,this.width,this.height,0,J.ALPHA,J.UNSIGNED_BYTE,this.data))};var Z=function Y(D,J){this.workerPool=D,this.actors=[],this.currentActor=0,this.id=i.uniqueId();for(var q=this.workerPool.acquire(this.id),K=0;K<q.length;K++){var de=q[K],ne=new Y.Actor(de,J,this.id);ne.name="Worker "+K,this.actors.push(ne)}};Z.prototype.broadcast=function(D,J,q){q=q||function(){},i.asyncAll(this.actors,function(K,de){K.send(D,J,de)},q)},Z.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},Z.prototype.remove=function(){this.actors.forEach(function(D){D.remove()}),this.actors=[],this.workerPool.release(this.id)},Z.Actor=i.Actor;function j(Y,D,J){var q=function(K,de){if(K)return J(K);if(de){var ne=i.pick(i.extend(de,Y),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);de.vector_layers&&(ne.vectorLayers=de.vector_layers,ne.vectorLayerIds=ne.vectorLayers.map(function(we){return we.id})),ne.tiles=D.canonicalizeTileset(ne,Y.url),J(null,ne)}};return Y.url?i.getJSON(D.transformRequest(D.normalizeSourceURL(Y.url),i.ResourceType.Source),q):i.browser.frame(function(){return q(null,Y)})}var N=function(D,J,q){this.bounds=i.LngLatBounds.convert(this.validateBounds(D)),this.minzoom=J||0,this.maxzoom=q||24};N.prototype.validateBounds=function(D){return!Array.isArray(D)||D.length!==4?[-180,-90,180,90]:[Math.max(-180,D[0]),Math.max(-90,D[1]),Math.min(180,D[2]),Math.min(90,D[3])]},N.prototype.contains=function(D){var J=Math.pow(2,D.z),q={minX:Math.floor(i.mercatorXfromLng(this.bounds.getWest())*J),minY:Math.floor(i.mercatorYfromLat(this.bounds.getNorth())*J),maxX:Math.ceil(i.mercatorXfromLng(this.bounds.getEast())*J),maxY:Math.ceil(i.mercatorYfromLat(this.bounds.getSouth())*J)},K=D.x>=q.minX&&D.x<q.maxX&&D.y>=q.minY&&D.y<q.maxY;return K};var H=function(Y){function D(J,q,K,de){if(Y.call(this),this.id=J,this.dispatcher=K,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,i.extend(this,i.pick(q,["url","scheme","tileSize","promoteId"])),this._options=i.extend({type:"vector"},q),this._collectResourceTiming=q.collectResourceTiming,this.tileSize!==512)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(de)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(){var q=this;this._loaded=!1,this.fire(new i.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=j(this._options,this.map._requestManager,function(K,de){q._tileJSONRequest=null,q._loaded=!0,K?q.fire(new i.ErrorEvent(K)):de&&(i.extend(q,de),de.bounds&&(q.tileBounds=new N(de.bounds,q.minzoom,q.maxzoom)),i.postTurnstileEvent(de.tiles,q.map._requestManager._customAccessToken),i.postMapLoadEvent(de.tiles,q.map._getMapId(),q.map._requestManager._skuToken,q.map._requestManager._customAccessToken),q.fire(new i.Event("data",{dataType:"source",sourceDataType:"metadata"})),q.fire(new i.Event("data",{dataType:"source",sourceDataType:"content"})))})},D.prototype.loaded=function(){return this._loaded},D.prototype.hasTile=function(q){return!this.tileBounds||this.tileBounds.contains(q.canonical)},D.prototype.onAdd=function(q){this.map=q,this.load()},D.prototype.setSourceProperty=function(q){this._tileJSONRequest&&this._tileJSONRequest.cancel(),q();var K=this.map.style.sourceCaches[this.id];K.clearTiles(),this.load()},D.prototype.setTiles=function(q){var K=this;return this.setSourceProperty(function(){K._options.tiles=q}),this},D.prototype.setUrl=function(q){var K=this;return this.setSourceProperty(function(){K.url=q,K._options.url=q}),this},D.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},D.prototype.serialize=function(){return i.extend({},this._options)},D.prototype.loadTile=function(q,K){var de=this.map._requestManager.normalizeTileURL(q.tileID.canonical.url(this.tiles,this.scheme)),ne={request:this.map._requestManager.transformRequest(de,i.ResourceType.Tile),uid:q.uid,tileID:q.tileID,zoom:q.tileID.overscaledZ,tileSize:this.tileSize*q.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:i.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};ne.request.collectResourceTiming=this._collectResourceTiming,!q.actor||q.state==="expired"?(q.actor=this.dispatcher.getActor(),q.request=q.actor.send("loadTile",ne,we.bind(this))):q.state==="loading"?q.reloadCallback=K:q.request=q.actor.send("reloadTile",ne,we.bind(this));function we(Ue,ft){if(delete q.request,q.aborted)return K(null);if(Ue&&Ue.status!==404)return K(Ue);ft&&ft.resourceTiming&&(q.resourceTiming=ft.resourceTiming),this.map._refreshExpiredTiles&&ft&&q.setExpiryData(ft),q.loadVectorData(ft,this.map.painter),i.cacheEntryPossiblyAdded(this.dispatcher),K(null),q.reloadCallback&&(this.loadTile(q,q.reloadCallback),q.reloadCallback=null)}},D.prototype.abortTile=function(q){q.request&&(q.request.cancel(),delete q.request),q.actor&&q.actor.send("abortTile",{uid:q.uid,type:this.type,source:this.id},void 0)},D.prototype.unloadTile=function(q){q.unloadVectorData(),q.actor&&q.actor.send("removeTile",{uid:q.uid,type:this.type,source:this.id},void 0)},D.prototype.hasTransition=function(){return!1},D}(i.Evented),te=function(Y){function D(J,q,K,de){Y.call(this),this.id=J,this.dispatcher=K,this.setEventedParent(de),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=i.extend({type:"raster"},q),i.extend(this,i.pick(q,["url","scheme","tileSize"]))}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(){var q=this;this._loaded=!1,this.fire(new i.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=j(this._options,this.map._requestManager,function(K,de){q._tileJSONRequest=null,q._loaded=!0,K?q.fire(new i.ErrorEvent(K)):de&&(i.extend(q,de),de.bounds&&(q.tileBounds=new N(de.bounds,q.minzoom,q.maxzoom)),i.postTurnstileEvent(de.tiles),i.postMapLoadEvent(de.tiles,q.map._getMapId(),q.map._requestManager._skuToken),q.fire(new i.Event("data",{dataType:"source",sourceDataType:"metadata"})),q.fire(new i.Event("data",{dataType:"source",sourceDataType:"content"})))})},D.prototype.loaded=function(){return this._loaded},D.prototype.onAdd=function(q){this.map=q,this.load()},D.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},D.prototype.serialize=function(){return i.extend({},this._options)},D.prototype.hasTile=function(q){return!this.tileBounds||this.tileBounds.contains(q.canonical)},D.prototype.loadTile=function(q,K){var de=this,ne=this.map._requestManager.normalizeTileURL(q.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);q.request=i.getImage(this.map._requestManager.transformRequest(ne,i.ResourceType.Tile),function(we,Ue){if(delete q.request,q.aborted)q.state="unloaded",K(null);else if(we)q.state="errored",K(we);else if(Ue){de.map._refreshExpiredTiles&&q.setExpiryData(Ue),delete Ue.cacheControl,delete Ue.expires;var ft=de.map.painter.context,Xt=ft.gl;q.texture=de.map.painter.getTileTexture(Ue.width),q.texture?q.texture.update(Ue,{useMipmap:!0}):(q.texture=new i.Texture(ft,Ue,Xt.RGBA,{useMipmap:!0}),q.texture.bind(Xt.LINEAR,Xt.CLAMP_TO_EDGE,Xt.LINEAR_MIPMAP_NEAREST),ft.extTextureFilterAnisotropic&&Xt.texParameterf(Xt.TEXTURE_2D,ft.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,ft.extTextureFilterAnisotropicMax)),q.state="loaded",i.cacheEntryPossiblyAdded(de.dispatcher),K(null)}})},D.prototype.abortTile=function(q,K){q.request&&(q.request.cancel(),delete q.request),K()},D.prototype.unloadTile=function(q,K){q.texture&&this.map.painter.saveTileTexture(q.texture),K()},D.prototype.hasTransition=function(){return!1},D}(i.Evented),oe=function(Y){function D(J,q,K,de){Y.call(this,J,q,K,de),this.type="raster-dem",this.maxzoom=22,this._options=i.extend({type:"raster-dem"},q),this.encoding=q.encoding||"mapbox"}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},D.prototype.loadTile=function(q,K){var de=this.map._requestManager.normalizeTileURL(q.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);q.request=i.getImage(this.map._requestManager.transformRequest(de,i.ResourceType.Tile),ne.bind(this)),q.neighboringTiles=this._getNeighboringTiles(q.tileID);function ne(Ue,ft){if(delete q.request,q.aborted)q.state="unloaded",K(null);else if(Ue)q.state="errored",K(Ue);else if(ft){this.map._refreshExpiredTiles&&q.setExpiryData(ft),delete ft.cacheControl,delete ft.expires;var Xt=i.window.ImageBitmap&&ft instanceof i.window.ImageBitmap&&i.offscreenCanvasSupported(),hr=Xt?ft:i.browser.getImageData(ft,1),qt={uid:q.uid,coord:q.tileID,source:this.id,rawImageData:hr,encoding:this.encoding};(!q.actor||q.state==="expired")&&(q.actor=this.dispatcher.getActor(),q.actor.send("loadDEMTile",qt,we.bind(this)))}}function we(Ue,ft){Ue&&(q.state="errored",K(Ue)),ft&&(q.dem=ft,q.needsHillshadePrepare=!0,q.state="loaded",K(null))}},D.prototype._getNeighboringTiles=function(q){var K=q.canonical,de=Math.pow(2,K.z),ne=(K.x-1+de)%de,we=K.x===0?q.wrap-1:q.wrap,Ue=(K.x+1+de)%de,ft=K.x+1===de?q.wrap+1:q.wrap,Xt={};return Xt[new i.OverscaledTileID(q.overscaledZ,we,K.z,ne,K.y).key]={backfilled:!1},Xt[new i.OverscaledTileID(q.overscaledZ,ft,K.z,Ue,K.y).key]={backfilled:!1},K.y>0&&(Xt[new i.OverscaledTileID(q.overscaledZ,we,K.z,ne,K.y-1).key]={backfilled:!1},Xt[new i.OverscaledTileID(q.overscaledZ,q.wrap,K.z,K.x,K.y-1).key]={backfilled:!1},Xt[new i.OverscaledTileID(q.overscaledZ,ft,K.z,Ue,K.y-1).key]={backfilled:!1}),K.y+1<de&&(Xt[new i.OverscaledTileID(q.overscaledZ,we,K.z,ne,K.y+1).key]={backfilled:!1},Xt[new i.OverscaledTileID(q.overscaledZ,q.wrap,K.z,K.x,K.y+1).key]={backfilled:!1},Xt[new i.OverscaledTileID(q.overscaledZ,ft,K.z,Ue,K.y+1).key]={backfilled:!1}),Xt},D.prototype.unloadTile=function(q){q.demTexture&&this.map.painter.saveTileTexture(q.demTexture),q.fbo&&(q.fbo.destroy(),delete q.fbo),q.dem&&delete q.dem,delete q.neighboringTiles,q.state="unloaded",q.actor&&q.actor.send("removeDEMTile",{uid:q.uid,source:this.id})},D}(te),_e=function(Y){function D(J,q,K,de){Y.call(this),this.id=J,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=K.getActor(),this.setEventedParent(de),this._data=q.data,this._options=i.extend({},q),this._collectResourceTiming=q.collectResourceTiming,this._resourceTiming=[],q.maxzoom!==void 0&&(this.maxzoom=q.maxzoom),q.type&&(this.type=q.type),q.attribution&&(this.attribution=q.attribution),this.promoteId=q.promoteId;var ne=i.EXTENT/this.tileSize;this.workerOptions=i.extend({source:this.id,cluster:q.cluster||!1,geojsonVtOptions:{buffer:(q.buffer!==void 0?q.buffer:128)*ne,tolerance:(q.tolerance!==void 0?q.tolerance:.375)*ne,extent:i.EXTENT,maxZoom:this.maxzoom,lineMetrics:q.lineMetrics||!1,generateId:q.generateId||!1},superclusterOptions:{maxZoom:q.clusterMaxZoom!==void 0?Math.min(q.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,minPoints:Math.max(2,q.clusterMinPoints||2),extent:i.EXTENT,radius:(q.clusterRadius||50)*ne,log:!1,generateId:q.generateId||!1},clusterProperties:q.clusterProperties,filter:q.filter},q.workerOptions)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(){var q=this;this.fire(new i.Event("dataloading",{dataType:"source"})),this._updateWorkerData(function(K){if(K){q.fire(new i.ErrorEvent(K));return}var de={dataType:"source",sourceDataType:"metadata"};q._collectResourceTiming&&q._resourceTiming&&q._resourceTiming.length>0&&(de.resourceTiming=q._resourceTiming,q._resourceTiming=[]),q.fire(new i.Event("data",de))})},D.prototype.onAdd=function(q){this.map=q,this.load()},D.prototype.setData=function(q){var K=this;return this._data=q,this.fire(new i.Event("dataloading",{dataType:"source"})),this._updateWorkerData(function(de){if(de){K.fire(new i.ErrorEvent(de));return}var ne={dataType:"source",sourceDataType:"content"};K._collectResourceTiming&&K._resourceTiming&&K._resourceTiming.length>0&&(ne.resourceTiming=K._resourceTiming,K._resourceTiming=[]),K.fire(new i.Event("data",ne))}),this},D.prototype.getClusterExpansionZoom=function(q,K){return this.actor.send("geojson.getClusterExpansionZoom",{clusterId:q,source:this.id},K),this},D.prototype.getClusterChildren=function(q,K){return this.actor.send("geojson.getClusterChildren",{clusterId:q,source:this.id},K),this},D.prototype.getClusterLeaves=function(q,K,de,ne){return this.actor.send("geojson.getClusterLeaves",{source:this.id,clusterId:q,limit:K,offset:de},ne),this},D.prototype._updateWorkerData=function(q){var K=this;this._loaded=!1;var de=i.extend({},this.workerOptions),ne=this._data;typeof ne=="string"?(de.request=this.map._requestManager.transformRequest(i.browser.resolveURL(ne),i.ResourceType.Source),de.request.collectResourceTiming=this._collectResourceTiming):de.data=JSON.stringify(ne),this.actor.send(this.type+".loadData",de,function(we,Ue){K._removed||Ue&&Ue.abandoned||(K._loaded=!0,Ue&&Ue.resourceTiming&&Ue.resourceTiming[K.id]&&(K._resourceTiming=Ue.resourceTiming[K.id].slice(0)),K.actor.send(K.type+".coalesce",{source:de.source},null),q(we))})},D.prototype.loaded=function(){return this._loaded},D.prototype.loadTile=function(q,K){var de=this,ne=q.actor?"reloadTile":"loadTile";q.actor=this.actor;var we={type:this.type,uid:q.uid,tileID:q.tileID,zoom:q.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:i.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};q.request=this.actor.send(ne,we,function(Ue,ft){return delete q.request,q.unloadVectorData(),q.aborted?K(null):Ue?K(Ue):(q.loadVectorData(ft,de.map.painter,ne==="reloadTile"),K(null))})},D.prototype.abortTile=function(q){q.request&&(q.request.cancel(),delete q.request),q.aborted=!0},D.prototype.unloadTile=function(q){q.unloadVectorData(),this.actor.send("removeTile",{uid:q.uid,type:this.type,source:this.id})},D.prototype.onRemove=function(){this._removed=!0,this.actor.send("removeSource",{type:this.type,source:this.id})},D.prototype.serialize=function(){return i.extend({},this._options,{type:this.type,data:this._data})},D.prototype.hasTransition=function(){return!1},D}(i.Evented),Ee=i.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),Ce=function(Y){function D(J,q,K,de){Y.call(this),this.id=J,this.dispatcher=K,this.coordinates=q.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(de),this.options=q}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(q,K){var de=this;this._loaded=!1,this.fire(new i.Event("dataloading",{dataType:"source"})),this.url=this.options.url,i.getImage(this.map._requestManager.transformRequest(this.url,i.ResourceType.Image),function(ne,we){de._loaded=!0,ne?de.fire(new i.ErrorEvent(ne)):we&&(de.image=we,q&&(de.coordinates=q),K&&K(),de._finishLoading())})},D.prototype.loaded=function(){return this._loaded},D.prototype.updateImage=function(q){var K=this;return!this.image||!q.url?this:(this.options.url=q.url,this.load(q.coordinates,function(){K.texture=null}),this)},D.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new i.Event("data",{dataType:"source",sourceDataType:"metadata"})))},D.prototype.onAdd=function(q){this.map=q,this.load()},D.prototype.setCoordinates=function(q){var K=this;this.coordinates=q;var de=q.map(i.MercatorCoordinate.fromLngLat);this.tileID=me(de),this.minzoom=this.maxzoom=this.tileID.z;var ne=de.map(function(we){return K.tileID.getTilePoint(we)._round()});return this._boundsArray=new i.StructArrayLayout4i8,this._boundsArray.emplaceBack(ne[0].x,ne[0].y,0,0),this._boundsArray.emplaceBack(ne[1].x,ne[1].y,i.EXTENT,0),this._boundsArray.emplaceBack(ne[3].x,ne[3].y,0,i.EXTENT),this._boundsArray.emplaceBack(ne[2].x,ne[2].y,i.EXTENT,i.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new i.Event("data",{dataType:"source",sourceDataType:"content"})),this},D.prototype.prepare=function(){if(!(Object.keys(this.tiles).length===0||!this.image)){var q=this.map.painter.context,K=q.gl;this.boundsBuffer||(this.boundsBuffer=q.createVertexBuffer(this._boundsArray,Ee.members)),this.boundsSegments||(this.boundsSegments=i.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new i.Texture(q,this.image,K.RGBA),this.texture.bind(K.LINEAR,K.CLAMP_TO_EDGE));for(var de in this.tiles){var ne=this.tiles[de];ne.state!=="loaded"&&(ne.state="loaded",ne.texture=this.texture)}}},D.prototype.loadTile=function(q,K){this.tileID&&this.tileID.equals(q.tileID.canonical)?(this.tiles[String(q.tileID.wrap)]=q,q.buckets={},K(null)):(q.state="errored",K(null))},D.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},D.prototype.hasTransition=function(){return!1},D}(i.Evented);function me(Y){for(var D=1/0,J=1/0,q=-1/0,K=-1/0,de=0,ne=Y;de<ne.length;de+=1){var we=ne[de];D=Math.min(D,we.x),J=Math.min(J,we.y),q=Math.max(q,we.x),K=Math.max(K,we.y)}var Ue=q-D,ft=K-J,Xt=Math.max(Ue,ft),hr=Math.max(0,Math.floor(-Math.log(Xt)/Math.LN2)),qt=Math.pow(2,hr);return new i.CanonicalTileID(hr,Math.floor((D+q)/2*qt),Math.floor((J+K)/2*qt))}var ie=function(Y){function D(J,q,K,de){Y.call(this,J,q,K,de),this.roundZoom=!0,this.type="video",this.options=q}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(){var q=this;this._loaded=!1;var K=this.options;this.urls=[];for(var de=0,ne=K.urls;de<ne.length;de+=1){var we=ne[de];this.urls.push(this.map._requestManager.transformRequest(we,i.ResourceType.Source).url)}i.getVideo(this.urls,function(Ue,ft){q._loaded=!0,Ue?q.fire(new i.ErrorEvent(Ue)):ft&&(q.video=ft,q.video.loop=!0,q.video.addEventListener("playing",function(){q.map.triggerRepaint()}),q.map&&q.video.play(),q._finishLoading())})},D.prototype.pause=function(){this.video&&this.video.pause()},D.prototype.play=function(){this.video&&this.video.play()},D.prototype.seek=function(q){if(this.video){var K=this.video.seekable;q<K.start(0)||q>K.end(0)?this.fire(new i.ErrorEvent(new i.ValidationError("sources."+this.id,null,"Playback for this video can be set only between the "+K.start(0)+" and "+K.end(0)+"-second mark."))):this.video.currentTime=q}},D.prototype.getVideo=function(){return this.video},D.prototype.onAdd=function(q){this.map||(this.map=q,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},D.prototype.prepare=function(){if(!(Object.keys(this.tiles).length===0||this.video.readyState<2)){var 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K=this.gl;K.bindFramebuffer(K.FRAMEBUFFER,q),this.current=q,this.dirty=!1}},D}(Jt),Ie=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return null},D.prototype.set=function(q){if(!(q===this.current&&!this.dirty)){var K=this.gl;K.bindRenderbuffer(K.RENDERBUFFER,q),this.current=q,this.dirty=!1}},D}(Jt),xe=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return null},D.prototype.set=function(q){if(!(q===this.current&&!this.dirty)){var K=this.gl;K.bindTexture(K.TEXTURE_2D,q),this.current=q,this.dirty=!1}},D}(Jt),ke=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return null},D.prototype.set=function(q){if(!(q===this.current&&!this.dirty)){var K=this.gl;K.bindBuffer(K.ARRAY_BUFFER,q),this.current=q,this.dirty=!1}},D}(Jt),vt=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return null},D.prototype.set=function(q){var K=this.gl;K.bindBuffer(K.ELEMENT_ARRAY_BUFFER,q),this.current=q,this.dirty=!1},D}(Jt),ir=function(Y){function D(J){Y.call(this,J),this.vao=J.extVertexArrayObject}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return null},D.prototype.set=function(q){!this.vao||q===this.current&&!this.dirty||(this.vao.bindVertexArrayOES(q),this.current=q,this.dirty=!1)},D}(Jt),ar=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return 4},D.prototype.set=function(q){if(!(q===this.current&&!this.dirty)){var K=this.gl;K.pixelStorei(K.UNPACK_ALIGNMENT,q),this.current=q,this.dirty=!1}},D}(Jt),vr=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return!1},D.prototype.set=function(q){if(!(q===this.current&&!this.dirty)){var K=this.gl;K.pixelStorei(K.UNPACK_PREMULTIPLY_ALPHA_WEBGL,q),this.current=q,this.dirty=!1}},D}(Jt),ii=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return!1},D.prototype.set=function(q){if(!(q===this.current&&!this.dirty)){var K=this.gl;K.pixelStorei(K.UNPACK_FLIP_Y_WEBGL,q),this.current=q,this.dirty=!1}},D}(Jt),pi=function(Y){function D(J,q){Y.call(this,J),this.context=J,this.parent=q}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return null},D}(Jt),$r=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.setDirty=function(){this.dirty=!0},D.prototype.set=function(q){if(!(q===this.current&&!this.dirty)){this.context.bindFramebuffer.set(this.parent);var K=this.gl;K.framebufferTexture2D(K.FRAMEBUFFER,K.COLOR_ATTACHMENT0,K.TEXTURE_2D,q,0),this.current=q,this.dirty=!1}},D}(pi),di=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.set=function(q){if(!(q===this.current&&!this.dirty)){this.context.bindFramebuffer.set(this.parent);var K=this.gl;K.framebufferRenderbuffer(K.FRAMEBUFFER,K.DEPTH_ATTACHMENT,K.RENDERBUFFER,q),this.current=q,this.dirty=!1}},D}(pi),ji=function(D,J,q,K){this.context=D,this.width=J,this.height=q;var de=D.gl,ne=this.framebuffer=de.createFramebuffer();this.colorAttachment=new $r(D,ne),K&&(this.depthAttachment=new di(D,ne))};ji.prototype.destroy=function(){var D=this.context.gl,J=this.colorAttachment.get();if(J&&D.deleteTexture(J),this.depthAttachment){var q=this.depthAttachment.get();q&&D.deleteRenderbuffer(q)}D.deleteFramebuffer(this.framebuffer)};var In=519,wi=function(D,J,q){this.func=D,this.mask=J,this.range=q};wi.ReadOnly=!1,wi.ReadWrite=!0,wi.disabled=new wi(In,wi.ReadOnly,[0,1]);var On=519,qn=7680,Fn=function(D,J,q,K,de,ne){this.test=D,this.ref=J,this.mask=q,this.fail=K,this.depthFail=de,this.pass=ne};Fn.disabled=new Fn({func:On,mask:0},0,0,qn,qn,qn);var ra=0,la=1,Ut=771,wt=function(D,J,q){this.blendFunction=D,this.blendColor=J,this.mask=q};wt.Replace=[la,ra],wt.disabled=new wt(wt.Replace,i.Color.transparent,[!1,!1,!1,!1]),wt.unblended=new wt(wt.Replace,i.Color.transparent,[!0,!0,!0,!0]),wt.alphaBlended=new wt([la,Ut],i.Color.transparent,[!0,!0,!0,!0]);var rr=1029,nr=2305,Er=function(D,J,q){this.enable=D,this.mode=J,this.frontFace=q};Er.disabled=new Er(!1,rr,nr),Er.backCCW=new Er(!0,rr,nr);var Xr=function(D){this.gl=D,this.extVertexArrayObject=this.gl.getExtension("OES_vertex_array_object"),this.clearColor=new sr(this),this.clearDepth=new wr(this),this.clearStencil=new cr(this),this.colorMask=new $e(this),this.depthMask=new St(this),this.stencilMask=new Qt(this),this.stencilFunc=new Vt(this),this.stencilOp=new _t(this),this.stencilTest=new It(this),this.depthRange=new mt(this),this.depthTest=new er(this),this.depthFunc=new lr(this),this.blend=new Tr(this),this.blendFunc=new Lr(this),this.blendColor=new ti(this),this.blendEquation=new Br(this),this.cullFace=new Vr(this),this.cullFaceSide=new dt(this),this.frontFace=new Ge(this),this.program=new Je(this),this.activeTexture=new je(this),this.viewport=new tt(this),this.bindFramebuffer=new xt(this),this.bindRenderbuffer=new Ie(this),this.bindTexture=new xe(this),this.bindVertexBuffer=new ke(this),this.bindElementBuffer=new vt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new ir(this),this.pixelStoreUnpack=new ar(this),this.pixelStoreUnpackPremultiplyAlpha=new vr(this),this.pixelStoreUnpackFlipY=new ii(this),this.extTextureFilterAnisotropic=D.getExtension("EXT_texture_filter_anisotropic")||D.getExtension("MOZ_EXT_texture_filter_anisotropic")||D.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=D.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=D.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&(D.getExtension("OES_texture_half_float_linear"),this.extRenderToTextureHalfFloat=D.getExtension("EXT_color_buffer_half_float")),this.extTimerQuery=D.getExtension("EXT_disjoint_timer_query"),this.maxTextureSize=D.getParameter(D.MAX_TEXTURE_SIZE)};Xr.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Xr.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Xr.prototype.createIndexBuffer=function(D,J){return new lt(this,D,J)},Xr.prototype.createVertexBuffer=function(D,J,q){return new Nt(this,D,J,q)},Xr.prototype.createRenderbuffer=function(D,J,q){var K=this.gl,de=K.createRenderbuffer();return this.bindRenderbuffer.set(de),K.renderbufferStorage(K.RENDERBUFFER,D,J,q),this.bindRenderbuffer.set(null),de},Xr.prototype.createFramebuffer=function(D,J,q){return new ji(this,D,J,q)},Xr.prototype.clear=function(D){var J=D.color,q=D.depth,K=this.gl,de=0;J&&(de|=K.COLOR_BUFFER_BIT,this.clearColor.set(J),this.colorMask.set([!0,!0,!0,!0])),typeof q!="undefined"&&(de|=K.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(q),this.depthMask.set(!0)),K.clear(de)},Xr.prototype.setCullFace=function(D){D.enable===!1?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(D.mode),this.frontFace.set(D.frontFace))},Xr.prototype.setDepthMode=function(D){D.func===this.gl.ALWAYS&&!D.mask?this.depthTest.set(!1):(this.depthTest.set(!0),this.depthFunc.set(D.func),this.depthMask.set(D.mask),this.depthRange.set(D.range))},Xr.prototype.setStencilMode=function(D){D.test.func===this.gl.ALWAYS&&!D.mask?this.stencilTest.set(!1):(this.stencilTest.set(!0),this.stencilMask.set(D.mask),this.stencilOp.set([D.fail,D.depthFail,D.pass]),this.stencilFunc.set({func:D.test.func,ref:D.ref,mask:D.test.mask}))},Xr.prototype.setColorMode=function(D){i.deepEqual(D.blendFunction,wt.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(D.blendFunction),this.blendColor.set(D.blendColor)),this.colorMask.set(D.mask)},Xr.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var ri=function(Y){function D(J,q,K){var de=this;Y.call(this),this.id=J,this.dispatcher=K,this.on("data",function(ne){ne.dataType==="source"&&ne.sourceDataType==="metadata"&&(de._sourceLoaded=!0),de._sourceLoaded&&!de._paused&&ne.dataType==="source"&&ne.sourceDataType==="content"&&(de.reload(),de.transform&&de.update(de.transform))}),this.on("error",function(){de._sourceErrored=!0}),this._source=Ae(J,q,K,this),this._tiles={},this._cache=new st(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new i.SourceFeatureState}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.onAdd=function(q){this.map=q,this._maxTileCacheSize=q?q._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(q)},D.prototype.onRemove=function(q){this._source&&this._source.onRemove&&this._source.onRemove(q)},D.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded||!this._source.loaded())return!1;for(var q in this._tiles){var K=this._tiles[q];if(K.state!=="loaded"&&K.state!=="errored")return!1}return!0},D.prototype.getSource=function(){return this._source},D.prototype.pause=function(){this._paused=!0},D.prototype.resume=function(){if(this._paused){var q=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,q&&this.reload(),this.transform&&this.update(this.transform)}},D.prototype._loadTile=function(q,K){return this._source.loadTile(q,K)},D.prototype._unloadTile=function(q){if(this._source.unloadTile)return this._source.unloadTile(q,function(){})},D.prototype._abortTile=function(q){if(this._source.abortTile)return this._source.abortTile(q,function(){})},D.prototype.serialize=function(){return this._source.serialize()},D.prototype.prepare=function(q){this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null);for(var K in this._tiles){var de=this._tiles[K];de.upload(q),de.prepare(this.map.style.imageManager)}},D.prototype.getIds=function(){return i.values(this._tiles).map(function(q){return q.tileID}).sort(Qr).map(function(q){return q.key})},D.prototype.getRenderableIds=function(q){var K=this,de=[];for(var ne in this._tiles)this._isIdRenderable(ne,q)&&de.push(this._tiles[ne]);return q?de.sort(function(we,Ue){var ft=we.tileID,Xt=Ue.tileID,hr=new i.Point(ft.canonical.x,ft.canonical.y)._rotate(K.transform.angle),qt=new i.Point(Xt.canonical.x,Xt.canonical.y)._rotate(K.transform.angle);return ft.overscaledZ-Xt.overscaledZ||qt.y-hr.y||qt.x-hr.x}).map(function(we){return we.tileID.key}):de.map(function(we){return we.tileID}).sort(Qr).map(function(we){return we.key})},D.prototype.hasRenderableParent=function(q){var K=this.findLoadedParent(q,0);return K?this._isIdRenderable(K.tileID.key):!1},D.prototype._isIdRenderable=function(q,K){return this._tiles[q]&&this._tiles[q].hasData()&&!this._coveredTiles[q]&&(K||!this._tiles[q].holdingForFade())},D.prototype.reload=function(){if(this._paused){this._shouldReloadOnResume=!0;return}this._cache.reset();for(var q in this._tiles)this._tiles[q].state!=="errored"&&this._reloadTile(q,"reloading")},D.prototype._reloadTile=function(q,K){var de=this._tiles[q];de&&(de.state!=="loading"&&(de.state=K),this._loadTile(de,this._tileLoaded.bind(this,de,q,K)))},D.prototype._tileLoaded=function(q,K,de,ne){if(ne){q.state="errored",ne.status!==404?this._source.fire(new i.ErrorEvent(ne,{tile:q})):this.update(this.transform);return}q.timeAdded=i.browser.now(),de==="expired"&&(q.refreshedUponExpiration=!0),this._setTileReloadTimer(K,q),this.getSource().type==="raster-dem"&&q.dem&&this._backfillDEM(q),this._state.initializeTileState(q,this.map?this.map.painter:null),this._source.fire(new i.Event("data",{dataType:"source",tile:q,coord:q.tileID}))},D.prototype._backfillDEM=function(q){for(var K=this.getRenderableIds(),de=0;de<K.length;de++){var ne=K[de];if(q.neighboringTiles&&q.neighboringTiles[ne]){var we=this.getTileByID(ne);Ue(q,we),Ue(we,q)}}function Ue(ft,Xt){ft.needsHillshadePrepare=!0;var hr=Xt.tileID.canonical.x-ft.tileID.canonical.x,qt=Xt.tileID.canonical.y-ft.tileID.canonical.y,Ve=Math.pow(2,ft.tileID.canonical.z),Qe=Xt.tileID.key;hr===0&&qt===0||Math.abs(qt)>1||(Math.abs(hr)>1&&(Math.abs(hr+Ve)===1?hr+=Ve:Math.abs(hr-Ve)===1&&(hr-=Ve)),!(!Xt.dem||!ft.dem)&&(ft.dem.backfillBorder(Xt.dem,hr,qt),ft.neighboringTiles&&ft.neighboringTiles[Qe]&&(ft.neighboringTiles[Qe].backfilled=!0)))}},D.prototype.getTile=function(q){return this.getTileByID(q.key)},D.prototype.getTileByID=function(q){return this._tiles[q]},D.prototype._retainLoadedChildren=function(q,K,de,ne){for(var we in this._tiles){var Ue=this._tiles[we];if(!(ne[we]||!Ue.hasData()||Ue.tileID.overscaledZ<=K||Ue.tileID.overscaledZ>de)){for(var ft=Ue.tileID;Ue&&Ue.tileID.overscaledZ>K+1;){var Xt=Ue.tileID.scaledTo(Ue.tileID.overscaledZ-1);Ue=this._tiles[Xt.key],Ue&&Ue.hasData()&&(ft=Xt)}for(var hr=ft;hr.overscaledZ>K;)if(hr=hr.scaledTo(hr.overscaledZ-1),q[hr.key]){ne[ft.key]=ft;break}}}},D.prototype.findLoadedParent=function(q,K){if(q.key in this._loadedParentTiles){var de=this._loadedParentTiles[q.key];return de&&de.tileID.overscaledZ>=K?de:null}for(var ne=q.overscaledZ-1;ne>=K;ne--){var we=q.scaledTo(ne),Ue=this._getLoadedTile(we);if(Ue)return Ue}},D.prototype._getLoadedTile=function(q){var K=this._tiles[q.key];if(K&&K.hasData())return K;var de=this._cache.getByKey(q.wrapped().key);return de},D.prototype.updateCacheSize=function(q){var K=Math.ceil(q.width/this._source.tileSize)+1,de=Math.ceil(q.height/this._source.tileSize)+1,ne=K*de,we=5,Ue=Math.floor(ne*we),ft=typeof this._maxTileCacheSize=="number"?Math.min(this._maxTileCacheSize,Ue):Ue;this._cache.setMaxSize(ft)},D.prototype.handleWrapJump=function(q){var K=this._prevLng===void 0?q:this._prevLng,de=q-K,ne=de/360,we=Math.round(ne);if(this._prevLng=q,we){var Ue={};for(var ft in this._tiles){var Xt=this._tiles[ft];Xt.tileID=Xt.tileID.unwrapTo(Xt.tileID.wrap+we),Ue[Xt.tileID.key]=Xt}this._tiles=Ue;for(var hr in this._timers)clearTimeout(this._timers[hr]),delete this._timers[hr];for(var qt in this._tiles){var Ve=this._tiles[qt];this._setTileReloadTimer(qt,Ve)}}},D.prototype.update=function(q){var K=this;if(this.transform=q,!(!this._sourceLoaded||this._paused)){this.updateCacheSize(q),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={};var de;this.used?this._source.tileID?de=q.getVisibleUnwrappedCoordinates(this._source.tileID).map(function(Ur){return new i.OverscaledTileID(Ur.canonical.z,Ur.wrap,Ur.canonical.z,Ur.canonical.x,Ur.canonical.y)}):(de=q.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(de=de.filter(function(Ur){return K._source.hasTile(Ur)}))):de=[];var ne=q.coveringZoomLevel(this._source),we=Math.max(ne-D.maxOverzooming,this._source.minzoom),Ue=Math.max(ne+D.maxUnderzooming,this._source.minzoom),ft=this._updateRetainedTiles(de,ne);if(Oi(this._source.type)){for(var Xt={},hr={},qt=Object.keys(ft),Ve=0,Qe=qt;Ve<Qe.length;Ve+=1){var at=Qe[Ve],Ct=ft[at],Ot=this._tiles[at];if(!(!Ot||Ot.fadeEndTime&&Ot.fadeEndTime<=i.browser.now())){var Rt=this.findLoadedParent(Ct,we);Rt&&(this._addTile(Rt.tileID),Xt[Rt.tileID.key]=Rt.tileID),hr[at]=Ct}}this._retainLoadedChildren(hr,ne,Ue,ft);for(var Bt in Xt)ft[Bt]||(this._coveredTiles[Bt]=!0,ft[Bt]=Xt[Bt])}for(var Dt in ft)this._tiles[Dt].clearFadeHold();for(var yt=i.keysDifference(this._tiles,ft),Pt=0,ht=yt;Pt<ht.length;Pt+=1){var ur=ht[Pt],br=this._tiles[ur];br.hasSymbolBuckets&&!br.holdingForFade()?br.setHoldDuration(this.map._fadeDuration):(!br.hasSymbolBuckets||br.symbolFadeFinished())&&this._removeTile(ur)}this._updateLoadedParentTileCache()}},D.prototype.releaseSymbolFadeTiles=function(){for(var q in this._tiles)this._tiles[q].holdingForFade()&&this._removeTile(q)},D.prototype._updateRetainedTiles=function(q,K){for(var de={},ne={},we=Math.max(K-D.maxOverzooming,this._source.minzoom),Ue=Math.max(K+D.maxUnderzooming,this._source.minzoom),ft={},Xt=0,hr=q;Xt<hr.length;Xt+=1){var qt=hr[Xt],Ve=this._addTile(qt);de[qt.key]=qt,!Ve.hasData()&&K<this._source.maxzoom&&(ft[qt.key]=qt)}this._retainLoadedChildren(ft,K,Ue,de);for(var Qe=0,at=q;Qe<at.length;Qe+=1){var Ct=at[Qe],Ot=this._tiles[Ct.key];if(!Ot.hasData()){if(K+1>this._source.maxzoom){var Rt=Ct.children(this._source.maxzoom)[0],Bt=this.getTile(Rt);if(Bt&&Bt.hasData()){de[Rt.key]=Rt;continue}}else{var Dt=Ct.children(this._source.maxzoom);if(de[Dt[0].key]&&de[Dt[1].key]&&de[Dt[2].key]&&de[Dt[3].key])continue}for(var yt=Ot.wasRequested(),Pt=Ct.overscaledZ-1;Pt>=we;--Pt){var ht=Ct.scaledTo(Pt);if(ne[ht.key]||(ne[ht.key]=!0,Ot=this.getTile(ht),!Ot&&yt&&(Ot=this._addTile(ht)),Ot&&(de[ht.key]=ht,yt=Ot.wasRequested(),Ot.hasData())))break}}}return de},D.prototype._updateLoadedParentTileCache=function(){this._loadedParentTiles={};for(var q in this._tiles){for(var K=[],de=void 0,ne=this._tiles[q].tileID;ne.overscaledZ>0;){if(ne.key in this._loadedParentTiles){de=this._loadedParentTiles[ne.key];break}K.push(ne.key);var we=ne.scaledTo(ne.overscaledZ-1);if(de=this._getLoadedTile(we),de)break;ne=we}for(var Ue=0,ft=K;Ue<ft.length;Ue+=1){var Xt=ft[Ue];this._loadedParentTiles[Xt]=de}}},D.prototype._addTile=function(q){var K=this._tiles[q.key];if(K)return K;K=this._cache.getAndRemove(q),K&&(this._setTileReloadTimer(q.key,K),K.tileID=q,this._state.initializeTileState(K,this.map?this.map.painter:null),this._cacheTimers[q.key]&&(clearTimeout(this._cacheTimers[q.key]),delete this._cacheTimers[q.key],this._setTileReloadTimer(q.key,K)));var de=!!K;return de||(K=new i.Tile(q,this._source.tileSize*q.overscaleFactor()),this._loadTile(K,this._tileLoaded.bind(this,K,q.key,K.state))),K?(K.uses++,this._tiles[q.key]=K,de||this._source.fire(new i.Event("dataloading",{tile:K,coord:K.tileID,dataType:"source"})),K):null},D.prototype._setTileReloadTimer=function(q,K){var de=this;q in this._timers&&(clearTimeout(this._timers[q]),delete this._timers[q]);var ne=K.getExpiryTimeout();ne&&(this._timers[q]=setTimeout(function(){de._reloadTile(q,"expired"),delete de._timers[q]},ne))},D.prototype._removeTile=function(q){var K=this._tiles[q];K&&(K.uses--,delete this._tiles[q],this._timers[q]&&(clearTimeout(this._timers[q]),delete this._timers[q]),!(K.uses>0)&&(K.hasData()&&K.state!=="reloading"?this._cache.add(K.tileID,K,K.getExpiryTimeout()):(K.aborted=!0,this._abortTile(K),this._unloadTile(K))))},D.prototype.clearTiles=function(){this._shouldReloadOnResume=!1,this._paused=!1;for(var q in this._tiles)this._removeTile(q);this._cache.reset()},D.prototype.tilesIn=function(q,K,de){var ne=this,we=[],Ue=this.transform;if(!Ue)return we;for(var ft=de?Ue.getCameraQueryGeometry(q):q,Xt=q.map(function(Pt){return Ue.pointCoordinate(Pt)}),hr=ft.map(function(Pt){return Ue.pointCoordinate(Pt)}),qt=this.getIds(),Ve=1/0,Qe=1/0,at=-1/0,Ct=-1/0,Ot=0,Rt=hr;Ot<Rt.length;Ot+=1){var Bt=Rt[Ot];Ve=Math.min(Ve,Bt.x),Qe=Math.min(Qe,Bt.y),at=Math.max(at,Bt.x),Ct=Math.max(Ct,Bt.y)}for(var Dt=function(Pt){var ht=ne._tiles[qt[Pt]];if(!ht.holdingForFade()){var ur=ht.tileID,br=Math.pow(2,Ue.zoom-ht.tileID.overscaledZ),Ur=K*ht.queryPadding*i.EXTENT/ht.tileSize/br,Di=[ur.getTilePoint(new i.MercatorCoordinate(Ve,Qe)),ur.getTilePoint(new i.MercatorCoordinate(at,Ct))];if(Di[0].x-Ur<i.EXTENT&&Di[0].y-Ur<i.EXTENT&&Di[1].x+Ur>=0&&Di[1].y+Ur>=0){var fi=Xt.map(function(gn){return ur.getTilePoint(gn)}),Ti=hr.map(function(gn){return ur.getTilePoint(gn)});we.push({tile:ht,tileID:ur,queryGeometry:fi,cameraQueryGeometry:Ti,scale:br})}}},yt=0;yt<qt.length;yt++)Dt(yt);return we},D.prototype.getVisibleCoordinates=function(q){for(var K=this,de=this.getRenderableIds(q).map(function(ft){return K._tiles[ft].tileID}),ne=0,we=de;ne<we.length;ne+=1){var Ue=we[ne];Ue.posMatrix=this.transform.calculatePosMatrix(Ue.toUnwrapped())}return de},D.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Oi(this._source.type))for(var q in this._tiles){var K=this._tiles[q];if(K.fadeEndTime!==void 0&&K.fadeEndTime>=i.browser.now())return!0}return!1},D.prototype.setFeatureState=function(q,K,de){q=q||"_geojsonTileLayer",this._state.updateState(q,K,de)},D.prototype.removeFeatureState=function(q,K,de){q=q||"_geojsonTileLayer",this._state.removeFeatureState(q,K,de)},D.prototype.getFeatureState=function(q,K){return q=q||"_geojsonTileLayer",this._state.getState(q,K)},D.prototype.setDependencies=function(q,K,de){var ne=this._tiles[q];ne&&ne.setDependencies(K,de)},D.prototype.reloadTilesForDependencies=function(q,K){for(var de in this._tiles){var ne=this._tiles[de];ne.hasDependency(q,K)&&this._reloadTile(de,"reloading")}this._cache.filter(function(we){return!we.hasDependency(q,K)})},D}(i.Evented);ri.maxOverzooming=10,ri.maxUnderzooming=3;function Qr(Y,D){var J=Math.abs(Y.wrap*2)-+(Y.wrap<0),q=Math.abs(D.wrap*2)-+(D.wrap<0);return Y.overscaledZ-D.overscaledZ||q-J||D.canonical.y-Y.canonical.y||D.canonical.x-Y.canonical.x}function Oi(Y){return Y==="raster"||Y==="image"||Y==="video"}function $i(){return new i.window.Worker(io.workerUrl)}var tn="mapboxgl_preloaded_worker_pool",fn=function(){this.active={}};fn.prototype.acquire=function(D){if(!this.workers)for(this.workers=[];this.workers.length<fn.workerCount;)this.workers.push(new $i);return this.active[D]=!0,this.workers.slice()},fn.prototype.release=function(D){delete this.active[D],this.numActive()===0&&(this.workers.forEach(function(J){J.terminate()}),this.workers=null)},fn.prototype.isPreloaded=function(){return!!this.active[tn]},fn.prototype.numActive=function(){return Object.keys(this.active).length};var yn=Math.floor(i.browser.hardwareConcurrency/2);fn.workerCount=Math.max(Math.min(yn,6),1);var Sn;function Ba(){return Sn||(Sn=new fn),Sn}function ua(){var Y=Ba();Y.acquire(tn)}function ma(){var Y=Sn;Y&&(Y.isPreloaded()&&Y.numActive()===1?(Y.release(tn),Sn=null):console.warn("Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()"))}function Wa(Y,D){var J={};for(var q in Y)q!=="ref"&&(J[q]=Y[q]);return i.refProperties.forEach(function(K){K in D&&(J[K]=D[K])}),J}function Fa(Y){Y=Y.slice();for(var D=Object.create(null),J=0;J<Y.length;J++)D[Y[J].id]=Y[J];for(var q=0;q<Y.length;q++)"ref"in Y[q]&&(Y[q]=Wa(Y[q],D[Y[q].ref]));return Y}function Wo(){var Y={},D=i.styleSpec.$version;for(var J in i.styleSpec.$root){var q=i.styleSpec.$root[J];if(q.required){var K=null;J==="version"?K=D:q.type==="array"?K=[]:K={},K!=null&&(Y[J]=K)}}return Y}var da={setStyle:"setStyle",addLayer:"addLayer",removeLayer:"removeLayer",setPaintProperty:"setPaintProperty",setLayoutProperty:"setLayoutProperty",setFilter:"setFilter",addSource:"addSource",removeSource:"removeSource",setGeoJSONSourceData:"setGeoJSONSourceData",setLayerZoomRange:"setLayerZoomRange",setLayerProperty:"setLayerProperty",setCenter:"setCenter",setZoom:"setZoom",setBearing:"setBearing",setPitch:"setPitch",setSprite:"setSprite",setGlyphs:"setGlyphs",setTransition:"setTransition",setLight:"setLight"};function Wn(Y,D,J){J.push({command:da.addSource,args:[Y,D[Y]]})}function Ha(Y,D,J){D.push({command:da.removeSource,args:[Y]}),J[Y]=!0}function vo(Y,D,J,q){Ha(Y,J,q),Wn(Y,D,J)}function jn(Y,D,J){var q;for(q in Y[J])if(Y[J].hasOwnProperty(q)&&q!=="data"&&!i.deepEqual(Y[J][q],D[J][q]))return!1;for(q in D[J])if(D[J].hasOwnProperty(q)&&q!=="data"&&!i.deepEqual(Y[J][q],D[J][q]))return!1;return!0}function Mt(Y,D,J,q){Y=Y||{},D=D||{};var K;for(K in Y)Y.hasOwnProperty(K)&&(D.hasOwnProperty(K)||Ha(K,J,q));for(K in D)D.hasOwnProperty(K)&&(Y.hasOwnProperty(K)?i.deepEqual(Y[K],D[K])||(Y[K].type==="geojson"&&D[K].type==="geojson"&&jn(Y,D,K)?J.push({command:da.setGeoJSONSourceData,args:[K,D[K].data]}):vo(K,D,J,q)):Wn(K,D,J))}function kr(Y,D,J,q,K,de){Y=Y||{},D=D||{};var ne;for(ne in Y)Y.hasOwnProperty(ne)&&(i.deepEqual(Y[ne],D[ne])||J.push({command:de,args:[q,ne,D[ne],K]}));for(ne in D)!D.hasOwnProperty(ne)||Y.hasOwnProperty(ne)||i.deepEqual(Y[ne],D[ne])||J.push({command:de,args:[q,ne,D[ne],K]})}function Jr(Y){return Y.id}function vi(Y,D){return Y[D.id]=D,Y}function hn(Y,D,J){Y=Y||[],D=D||[];var q=Y.map(Jr),K=D.map(Jr),de=Y.reduce(vi,{}),ne=D.reduce(vi,{}),we=q.slice(),Ue=Object.create(null),ft,Xt,hr,qt,Ve,Qe,at;for(ft=0,Xt=0;ft<q.length;ft++)hr=q[ft],ne.hasOwnProperty(hr)?Xt++:(J.push({command:da.removeLayer,args:[hr]}),we.splice(we.indexOf(hr,Xt),1));for(ft=0,Xt=0;ft<K.length;ft++)hr=K[K.length-1-ft],we[we.length-1-ft]!==hr&&(de.hasOwnProperty(hr)?(J.push({command:da.removeLayer,args:[hr]}),we.splice(we.lastIndexOf(hr,we.length-Xt),1)):Xt++,Qe=we[we.length-ft],J.push({command:da.addLayer,args:[ne[hr],Qe]}),we.splice(we.length-ft,0,hr),Ue[hr]=!0);for(ft=0;ft<K.length;ft++)if(hr=K[ft],qt=de[hr],Ve=ne[hr],!(Ue[hr]||i.deepEqual(qt,Ve))){if(!i.deepEqual(qt.source,Ve.source)||!i.deepEqual(qt["source-layer"],Ve["source-layer"])||!i.deepEqual(qt.type,Ve.type)){J.push({command:da.removeLayer,args:[hr]}),Qe=we[we.lastIndexOf(hr)+1],J.push({command:da.addLayer,args:[Ve,Qe]});continue}kr(qt.layout,Ve.layout,J,hr,null,da.setLayoutProperty),kr(qt.paint,Ve.paint,J,hr,null,da.setPaintProperty),i.deepEqual(qt.filter,Ve.filter)||J.push({command:da.setFilter,args:[hr,Ve.filter]}),(!i.deepEqual(qt.minzoom,Ve.minzoom)||!i.deepEqual(qt.maxzoom,Ve.maxzoom))&&J.push({command:da.setLayerZoomRange,args:[hr,Ve.minzoom,Ve.maxzoom]});for(at in qt)qt.hasOwnProperty(at)&&(at==="layout"||at==="paint"||at==="filter"||at==="metadata"||at==="minzoom"||at==="maxzoom"||(at.indexOf("paint.")===0?kr(qt[at],Ve[at],J,hr,at.slice(6),da.setPaintProperty):i.deepEqual(qt[at],Ve[at])||J.push({command:da.setLayerProperty,args:[hr,at,Ve[at]]})));for(at in Ve)!Ve.hasOwnProperty(at)||qt.hasOwnProperty(at)||at==="layout"||at==="paint"||at==="filter"||at==="metadata"||at==="minzoom"||at==="maxzoom"||(at.indexOf("paint.")===0?kr(qt[at],Ve[at],J,hr,at.slice(6),da.setPaintProperty):i.deepEqual(qt[at],Ve[at])||J.push({command:da.setLayerProperty,args:[hr,at,Ve[at]]}))}}function An(Y,D){if(!Y)return[{command:da.setStyle,args:[D]}];var J=[];try{if(!i.deepEqual(Y.version,D.version))return[{command:da.setStyle,args:[D]}];i.deepEqual(Y.center,D.center)||J.push({command:da.setCenter,args:[D.center]}),i.deepEqual(Y.zoom,D.zoom)||J.push({command:da.setZoom,args:[D.zoom]}),i.deepEqual(Y.bearing,D.bearing)||J.push({command:da.setBearing,args:[D.bearing]}),i.deepEqual(Y.pitch,D.pitch)||J.push({command:da.setPitch,args:[D.pitch]}),i.deepEqual(Y.sprite,D.sprite)||J.push({command:da.setSprite,args:[D.sprite]}),i.deepEqual(Y.glyphs,D.glyphs)||J.push({command:da.setGlyphs,args:[D.glyphs]}),i.deepEqual(Y.transition,D.transition)||J.push({command:da.setTransition,args:[D.transition]}),i.deepEqual(Y.light,D.light)||J.push({command:da.setLight,args:[D.light]});var q={},K=[];Mt(Y.sources,D.sources,K,q);var de=[];Y.layers&&Y.layers.forEach(function(ne){q[ne.source]?J.push({command:da.removeLayer,args:[ne.id]}):de.push(ne)}),J=J.concat(K),hn(de,D.layers,J)}catch(ne){console.warn("Unable to compute style diff:",ne),J=[{command:da.setStyle,args:[D]}]}return J}var Mn=function(D,J){this.reset(D,J)};Mn.prototype.reset=function(D,J){this.points=D||[],this._distances=[0];for(var q=1;q<this.points.length;q++)this._distances[q]=this._distances[q-1]+this.points[q].dist(this.points[q-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(J||0,this.length*.5),this.paddedLength=this.length-this.padding*2},Mn.prototype.lerp=function(D){if(this.points.length===1)return this.points[0];D=i.clamp(D,0,1);for(var J=1,q=this._distances[J],K=D*this.paddedLength+this.padding;q<K&&J<this._distances.length;)q=this._distances[++J];var de=J-1,ne=this._distances[de],we=q-ne,Ue=we>0?(K-ne)/we:0;return this.points[de].mult(1-Ue).add(this.points[J].mult(Ue))};var Li=function(D,J,q){var K=this.boxCells=[],de=this.circleCells=[];this.xCellCount=Math.ceil(D/q),this.yCellCount=Math.ceil(J/q);for(var ne=0;ne<this.xCellCount*this.yCellCount;ne++)K.push([]),de.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=D,this.height=J,this.xScale=this.xCellCount/D,this.yScale=this.yCellCount/J,this.boxUid=0,this.circleUid=0};Li.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Li.prototype.insert=function(D,J,q,K,de){this._forEachCell(J,q,K,de,this._insertBoxCell,this.boxUid++),this.boxKeys.push(D),this.bboxes.push(J),this.bboxes.push(q),this.bboxes.push(K),this.bboxes.push(de)},Li.prototype.insertCircle=function(D,J,q,K){this._forEachCell(J-K,q-K,J+K,q+K,this._insertCircleCell,this.circleUid++),this.circleKeys.push(D),this.circles.push(J),this.circles.push(q),this.circles.push(K)},Li.prototype._insertBoxCell=function(D,J,q,K,de,ne){this.boxCells[de].push(ne)},Li.prototype._insertCircleCell=function(D,J,q,K,de,ne){this.circleCells[de].push(ne)},Li.prototype._query=function(D,J,q,K,de,ne){if(q<0||D>this.width||K<0||J>this.height)return de?!1:[];var we=[];if(D<=0&&J<=0&&this.width<=q&&this.height<=K){if(de)return!0;for(var Ue=0;Ue<this.boxKeys.length;Ue++)we.push({key:this.boxKeys[Ue],x1:this.bboxes[Ue*4],y1:this.bboxes[Ue*4+1],x2:this.bboxes[Ue*4+2],y2:this.bboxes[Ue*4+3]});for(var ft=0;ft<this.circleKeys.length;ft++){var Xt=this.circles[ft*3],hr=this.circles[ft*3+1],qt=this.circles[ft*3+2];we.push({key:this.circleKeys[ft],x1:Xt-qt,y1:hr-qt,x2:Xt+qt,y2:hr+qt})}return ne?we.filter(ne):we}else{var Ve={hitTest:de,seenUids:{box:{},circle:{}}};return this._forEachCell(D,J,q,K,this._queryCell,we,Ve,ne),de?we.length>0:we}},Li.prototype._queryCircle=function(D,J,q,K,de){var ne=D-q,we=D+q,Ue=J-q,ft=J+q;if(we<0||ne>this.width||ft<0||Ue>this.height)return K?!1:[];var Xt=[],hr={hitTest:K,circle:{x:D,y:J,radius:q},seenUids:{box:{},circle:{}}};return this._forEachCell(ne,Ue,we,ft,this._queryCellCircle,Xt,hr,de),K?Xt.length>0:Xt},Li.prototype.query=function(D,J,q,K,de){return this._query(D,J,q,K,!1,de)},Li.prototype.hitTest=function(D,J,q,K,de){return this._query(D,J,q,K,!0,de)},Li.prototype.hitTestCircle=function(D,J,q,K){return this._queryCircle(D,J,q,!0,K)},Li.prototype._queryCell=function(D,J,q,K,de,ne,we,Ue){var ft=we.seenUids,Xt=this.boxCells[de];if(Xt!==null)for(var hr=this.bboxes,qt=0,Ve=Xt;qt<Ve.length;qt+=1){var Qe=Ve[qt];if(!ft.box[Qe]){ft.box[Qe]=!0;var at=Qe*4;if(D<=hr[at+2]&&J<=hr[at+3]&&q>=hr[at+0]&&K>=hr[at+1]&&(!Ue||Ue(this.boxKeys[Qe]))){if(we.hitTest)return ne.push(!0),!0;ne.push({key:this.boxKeys[Qe],x1:hr[at],y1:hr[at+1],x2:hr[at+2],y2:hr[at+3]})}}}var Ct=this.circleCells[de];if(Ct!==null)for(var Ot=this.circles,Rt=0,Bt=Ct;Rt<Bt.length;Rt+=1){var Dt=Bt[Rt];if(!ft.circle[Dt]){ft.circle[Dt]=!0;var yt=Dt*3;if(this._circleAndRectCollide(Ot[yt],Ot[yt+1],Ot[yt+2],D,J,q,K)&&(!Ue||Ue(this.circleKeys[Dt]))){if(we.hitTest)return ne.push(!0),!0;var Pt=Ot[yt],ht=Ot[yt+1],ur=Ot[yt+2];ne.push({key:this.circleKeys[Dt],x1:Pt-ur,y1:ht-ur,x2:Pt+ur,y2:ht+ur})}}}},Li.prototype._queryCellCircle=function(D,J,q,K,de,ne,we,Ue){var ft=we.circle,Xt=we.seenUids,hr=this.boxCells[de];if(hr!==null)for(var qt=this.bboxes,Ve=0,Qe=hr;Ve<Qe.length;Ve+=1){var at=Qe[Ve];if(!Xt.box[at]){Xt.box[at]=!0;var Ct=at*4;if(this._circleAndRectCollide(ft.x,ft.y,ft.radius,qt[Ct+0],qt[Ct+1],qt[Ct+2],qt[Ct+3])&&(!Ue||Ue(this.boxKeys[at])))return ne.push(!0),!0}}var Ot=this.circleCells[de];if(Ot!==null)for(var Rt=this.circles,Bt=0,Dt=Ot;Bt<Dt.length;Bt+=1){var yt=Dt[Bt];if(!Xt.circle[yt]){Xt.circle[yt]=!0;var Pt=yt*3;if(this._circlesCollide(Rt[Pt],Rt[Pt+1],Rt[Pt+2],ft.x,ft.y,ft.radius)&&(!Ue||Ue(this.circleKeys[yt])))return ne.push(!0),!0}}},Li.prototype._forEachCell=function(D,J,q,K,de,ne,we,Ue){for(var ft=this._convertToXCellCoord(D),Xt=this._convertToYCellCoord(J),hr=this._convertToXCellCoord(q),qt=this._convertToYCellCoord(K),Ve=ft;Ve<=hr;Ve++)for(var Qe=Xt;Qe<=qt;Qe++){var at=this.xCellCount*Qe+Ve;if(de.call(this,D,J,q,K,at,ne,we,Ue))return}},Li.prototype._convertToXCellCoord=function(D){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(D*this.xScale)))},Li.prototype._convertToYCellCoord=function(D){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(D*this.yScale)))},Li.prototype._circlesCollide=function(D,J,q,K,de,ne){var we=K-D,Ue=de-J,ft=q+ne;return ft*ft>we*we+Ue*Ue},Li.prototype._circleAndRectCollide=function(D,J,q,K,de,ne,we){var Ue=(ne-K)/2,ft=Math.abs(D-(K+Ue));if(ft>Ue+q)return!1;var Xt=(we-de)/2,hr=Math.abs(J-(de+Xt));if(hr>Xt+q)return!1;if(ft<=Ue||hr<=Xt)return!0;var qt=ft-Ue,Ve=hr-Xt;return qt*qt+Ve*Ve<=q*q};function _n(Y,D,J,q,K){var de=i.create();return D?(i.scale(de,de,[1/K,1/K,1]),J||i.rotateZ(de,de,q.angle)):i.multiply(de,q.labelPlaneMatrix,Y),de}function ya(Y,D,J,q,K){if(D){var de=i.clone(Y);return i.scale(de,de,[K,K,1]),J||i.rotateZ(de,de,-q.angle),de}else return q.glCoordMatrix}function Jn(Y,D){var J=[Y.x,Y.y,0,1];zl(J,J,D);var q=J[3];return{point:new i.Point(J[0]/q,J[1]/q),signedDistanceFromCamera:q}}function Ma(Y,D){return .5+.5*(Y/D)}function _o(Y,D){var J=Y[0]/Y[3],q=Y[1]/Y[3],K=J>=-D[0]&&J<=D[0]&&q>=-D[1]&&q<=D[1];return K}function No(Y,D,J,q,K,de,ne,we){var Ue=q?Y.textSizeData:Y.iconSizeData,ft=i.evaluateSizeForZoom(Ue,J.transform.zoom),Xt=[256/J.width*2+1,256/J.height*2+1],hr=q?Y.text.dynamicLayoutVertexArray:Y.icon.dynamicLayoutVertexArray;hr.clear();for(var qt=Y.lineVertexArray,Ve=q?Y.text.placedSymbolArray:Y.icon.placedSymbolArray,Qe=J.transform.width/J.transform.height,at=!1,Ct=0;Ct<Ve.length;Ct++){var Ot=Ve.get(Ct);if(Ot.hidden||Ot.writingMode===i.WritingMode.vertical&&!at){ul(Ot.numGlyphs,hr);continue}at=!1;var Rt=[Ot.anchorX,Ot.anchorY,0,1];if(i.transformMat4(Rt,Rt,D),!_o(Rt,Xt)){ul(Ot.numGlyphs,hr);continue}var Bt=Rt[3],Dt=Ma(J.transform.cameraToCenterDistance,Bt),yt=i.evaluateSizeForFeature(Ue,ft,Ot),Pt=ne?yt/Dt:yt*Dt,ht=new i.Point(Ot.anchorX,Ot.anchorY),ur=Jn(ht,K).point,br={},Ur=ko(Ot,Pt,!1,we,D,K,de,Y.glyphOffsetArray,qt,hr,ur,ht,br,Qe);at=Ur.useVertical,(Ur.notEnoughRoom||at||Ur.needsFlipping&&ko(Ot,Pt,!0,we,D,K,de,Y.glyphOffsetArray,qt,hr,ur,ht,br,Qe).notEnoughRoom)&&ul(Ot.numGlyphs,hr)}q?Y.text.dynamicLayoutVertexBuffer.updateData(hr):Y.icon.dynamicLayoutVertexBuffer.updateData(hr)}function po(Y,D,J,q,K,de,ne,we,Ue,ft,Xt){var hr=we.glyphStartIndex+we.numGlyphs,qt=we.lineStartIndex,Ve=we.lineStartIndex+we.lineLength,Qe=D.getoffsetX(we.glyphStartIndex),at=D.getoffsetX(hr-1),Ct=Fs(Y*Qe,J,q,K,de,ne,we.segment,qt,Ve,Ue,ft,Xt);if(!Ct)return null;var Ot=Fs(Y*at,J,q,K,de,ne,we.segment,qt,Ve,Ue,ft,Xt);return Ot?{first:Ct,last:Ot}:null}function Lo(Y,D,J,q){if(Y===i.WritingMode.horizontal){var K=Math.abs(J.y-D.y),de=Math.abs(J.x-D.x)*q;if(K>de)return{useVertical:!0}}return(Y===i.WritingMode.vertical?D.y<J.y:D.x>J.x)?{needsFlipping:!0}:null}function ko(Y,D,J,q,K,de,ne,we,Ue,ft,Xt,hr,qt,Ve){var Qe=D/24,at=Y.lineOffsetX*Qe,Ct=Y.lineOffsetY*Qe,Ot;if(Y.numGlyphs>1){var Rt=Y.glyphStartIndex+Y.numGlyphs,Bt=Y.lineStartIndex,Dt=Y.lineStartIndex+Y.lineLength,yt=po(Qe,we,at,Ct,J,Xt,hr,Y,Ue,de,qt);if(!yt)return{notEnoughRoom:!0};var Pt=Jn(yt.first.point,ne).point,ht=Jn(yt.last.point,ne).point;if(q&&!J){var ur=Lo(Y.writingMode,Pt,ht,Ve);if(ur)return ur}Ot=[yt.first];for(var br=Y.glyphStartIndex+1;br<Rt-1;br++)Ot.push(Fs(Qe*we.getoffsetX(br),at,Ct,J,Xt,hr,Y.segment,Bt,Dt,Ue,de,qt));Ot.push(yt.last)}else{if(q&&!J){var Ur=Jn(hr,K).point,Di=Y.lineStartIndex+Y.segment+1,fi=new i.Point(Ue.getx(Di),Ue.gety(Di)),Ti=Jn(fi,K),gn=Ti.signedDistanceFromCamera>0?Ti.point:Ds(hr,fi,Ur,1,K),rn=Lo(Y.writingMode,Ur,gn,Ve);if(rn)return rn}var Ci=Fs(Qe*we.getoffsetX(Y.glyphStartIndex),at,Ct,J,Xt,hr,Y.segment,Y.lineStartIndex,Y.lineStartIndex+Y.lineLength,Ue,de,qt);if(!Ci)return{notEnoughRoom:!0};Ot=[Ci]}for(var Bi=0,Gi=Ot;Bi<Gi.length;Bi+=1){var sn=Gi[Bi];i.addDynamicAttributes(ft,sn.point,sn.angle)}return{}}function Ds(Y,D,J,q,K){var de=Jn(Y.add(Y.sub(D)._unit()),K).point,ne=J.sub(de);return J.add(ne._mult(q/ne.mag()))}function Fs(Y,D,J,q,K,de,ne,we,Ue,ft,Xt,hr){var qt=q?Y-D:Y+D,Ve=qt>0?1:-1,Qe=0;q&&(Ve*=-1,Qe=Math.PI),Ve<0&&(Qe+=Math.PI);for(var at=Ve>0?we+ne:we+ne+1,Ct=K,Ot=K,Rt=0,Bt=0,Dt=Math.abs(qt),yt=[];Rt+Bt<=Dt;){if(at+=Ve,at<we||at>=Ue)return null;if(Ot=Ct,yt.push(Ct),Ct=hr[at],Ct===void 0){var Pt=new i.Point(ft.getx(at),ft.gety(at)),ht=Jn(Pt,Xt);if(ht.signedDistanceFromCamera>0)Ct=hr[at]=ht.point;else{var ur=at-Ve,br=Rt===0?de:new i.Point(ft.getx(ur),ft.gety(ur));Ct=Ds(br,Pt,Ot,Dt-Rt+1,Xt)}}Rt+=Bt,Bt=Ot.dist(Ct)}var Ur=(Dt-Rt)/Bt,Di=Ct.sub(Ot),fi=Di.mult(Ur)._add(Ot);fi._add(Di._unit()._perp()._mult(J*Ve));var Ti=Qe+Math.atan2(Ct.y-Ot.y,Ct.x-Ot.x);return yt.push(fi),{point:fi,angle:Ti,path:yt}}var ll=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ul(Y,D){for(var J=0;J<Y;J++){var q=D.length;D.resize(q+4),D.float32.set(ll,q*3)}}function zl(Y,D,J){var q=D[0],K=D[1];return Y[0]=J[0]*q+J[4]*K+J[12],Y[1]=J[1]*q+J[5]*K+J[13],Y[3]=J[3]*q+J[7]*K+J[15],Y}var us=100,il=function(D,J,q){J===void 0&&(J=new Li(D.width+2*us,D.height+2*us,25)),q===void 0&&(q=new Li(D.width+2*us,D.height+2*us,25)),this.transform=D,this.grid=J,this.ignoredGrid=q,this.pitchfactor=Math.cos(D._pitch)*D.cameraToCenterDistance,this.screenRightBoundary=D.width+us,this.screenBottomBoundary=D.height+us,this.gridRightBoundary=D.width+2*us,this.gridBottomBoundary=D.height+2*us};il.prototype.placeCollisionBox=function(D,J,q,K,de){var ne=this.projectAndGetPerspectiveRatio(K,D.anchorPointX,D.anchorPointY),we=q*ne.perspectiveRatio,Ue=D.x1*we+ne.point.x,ft=D.y1*we+ne.point.y,Xt=D.x2*we+ne.point.x,hr=D.y2*we+ne.point.y;return!this.isInsideGrid(Ue,ft,Xt,hr)||!J&&this.grid.hitTest(Ue,ft,Xt,hr,de)?{box:[],offscreen:!1}:{box:[Ue,ft,Xt,hr],offscreen:this.isOffscreen(Ue,ft,Xt,hr)}},il.prototype.placeCollisionCircles=function(D,J,q,K,de,ne,we,Ue,ft,Xt,hr,qt,Ve){var Qe=[],at=new i.Point(J.anchorX,J.anchorY),Ct=Jn(at,ne),Ot=Ma(this.transform.cameraToCenterDistance,Ct.signedDistanceFromCamera),Rt=Xt?de/Ot:de*Ot,Bt=Rt/i.ONE_EM,Dt=Jn(at,we).point,yt={},Pt=J.lineOffsetX*Bt,ht=J.lineOffsetY*Bt,ur=po(Bt,K,Pt,ht,!1,Dt,at,J,q,we,yt),br=!1,Ur=!1,Di=!0;if(ur){for(var fi=qt*.5*Ot+Ve,Ti=new i.Point(-us,-us),gn=new i.Point(this.screenRightBoundary,this.screenBottomBoundary),rn=new Mn,Ci=ur.first,Bi=ur.last,Gi=[],sn=Ci.path.length-1;sn>=1;sn--)Gi.push(Ci.path[sn]);for(var zn=1;zn<Bi.path.length;zn++)Gi.push(Bi.path[zn]);var Ja=fi*2.5;if(Ue){var co=Gi.map(function(Iv){return Jn(Iv,Ue)});co.some(function(Iv){return Iv.signedDistanceFromCamera<=0})?Gi=[]:Gi=co.map(function(Iv){return Iv.point})}var ts=[];if(Gi.length>0){for(var so=Gi[0].clone(),Zo=Gi[0].clone(),ms=1;ms<Gi.length;ms++)so.x=Math.min(so.x,Gi[ms].x),so.y=Math.min(so.y,Gi[ms].y),Zo.x=Math.max(Zo.x,Gi[ms].x),Zo.y=Math.max(Zo.y,Gi[ms].y);so.x>=Ti.x&&Zo.x<=gn.x&&so.y>=Ti.y&&Zo.y<=gn.y?ts=[Gi]:Zo.x<Ti.x||so.x>gn.x||Zo.y<Ti.y||so.y>gn.y?ts=[]:ts=i.clipLine([Gi],Ti.x,Ti.y,gn.x,gn.y)}for(var ou=0,Cv=ts;ou<Cv.length;ou+=1){var Lv=Cv[ou];rn.reset(Lv,fi*.25);var wd=0;rn.length<=.5*fi?wd=1:wd=Math.ceil(rn.paddedLength/Ja)+1;for(var Kv=0;Kv<wd;Kv++){var cg=Kv/Math.max(wd-1,1),pp=rn.lerp(cg),Td=pp.x+us,gp=pp.y+us;Qe.push(Td,gp,fi,0);var Vd=Td-fi,Ad=gp-fi,Pv=Td+fi,Jv=gp+fi;if(Di=Di&&this.isOffscreen(Vd,Ad,Pv,Jv),Ur=Ur||this.isInsideGrid(Vd,Ad,Pv,Jv),!D&&this.grid.hitTestCircle(Td,gp,fi,hr)&&(br=!0,!ft))return{circles:[],offscreen:!1,collisionDetected:br}}}}return{circles:!ft&&br||!Ur?[]:Qe,offscreen:Di,collisionDetected:br}},il.prototype.queryRenderedSymbols=function(D){if(D.length===0||this.grid.keysLength()===0&&this.ignoredGrid.keysLength()===0)return{};for(var J=[],q=1/0,K=1/0,de=-1/0,ne=-1/0,we=0,Ue=D;we<Ue.length;we+=1){var ft=Ue[we],Xt=new i.Point(ft.x+us,ft.y+us);q=Math.min(q,Xt.x),K=Math.min(K,Xt.y),de=Math.max(de,Xt.x),ne=Math.max(ne,Xt.y),J.push(Xt)}for(var hr=this.grid.query(q,K,de,ne).concat(this.ignoredGrid.query(q,K,de,ne)),qt={},Ve={},Qe=0,at=hr;Qe<at.length;Qe+=1){var Ct=at[Qe],Ot=Ct.key;if(qt[Ot.bucketInstanceId]===void 0&&(qt[Ot.bucketInstanceId]={}),!qt[Ot.bucketInstanceId][Ot.featureIndex]){var Rt=[new i.Point(Ct.x1,Ct.y1),new i.Point(Ct.x2,Ct.y1),new i.Point(Ct.x2,Ct.y2),new i.Point(Ct.x1,Ct.y2)];i.polygonIntersectsPolygon(J,Rt)&&(qt[Ot.bucketInstanceId][Ot.featureIndex]=!0,Ve[Ot.bucketInstanceId]===void 0&&(Ve[Ot.bucketInstanceId]=[]),Ve[Ot.bucketInstanceId].push(Ot.featureIndex))}}return Ve},il.prototype.insertCollisionBox=function(D,J,q,K,de){var ne=J?this.ignoredGrid:this.grid,we={bucketInstanceId:q,featureIndex:K,collisionGroupID:de};ne.insert(we,D[0],D[1],D[2],D[3])},il.prototype.insertCollisionCircles=function(D,J,q,K,de){for(var ne=J?this.ignoredGrid:this.grid,we={bucketInstanceId:q,featureIndex:K,collisionGroupID:de},Ue=0;Ue<D.length;Ue+=4)ne.insertCircle(we,D[Ue],D[Ue+1],D[Ue+2])},il.prototype.projectAndGetPerspectiveRatio=function(D,J,q){var K=[J,q,0,1];zl(K,K,D);var de=new i.Point((K[0]/K[3]+1)/2*this.transform.width+us,(-K[1]/K[3]+1)/2*this.transform.height+us);return{point:de,perspectiveRatio:.5+.5*(this.transform.cameraToCenterDistance/K[3])}},il.prototype.isOffscreen=function(D,J,q,K){return q<us||D>=this.screenRightBoundary||K<us||J>this.screenBottomBoundary},il.prototype.isInsideGrid=function(D,J,q,K){return q>=0&&D<this.gridRightBoundary&&K>=0&&J<this.gridBottomBoundary},il.prototype.getViewportMatrix=function(){var D=i.identity([]);return i.translate(D,D,[-us,-us,0]),D};function As(Y,D,J){return D*(i.EXTENT/(Y.tileSize*Math.pow(2,J-Y.tileID.overscaledZ)))}var cl=function(D,J,q,K){D?this.opacity=Math.max(0,Math.min(1,D.opacity+(D.placed?J:-J))):this.opacity=K&&q?1:0,this.placed=q};cl.prototype.isHidden=function(){return this.opacity===0&&!this.placed};var Ks=function(D,J,q,K,de){this.text=new cl(D?D.text:null,J,q,de),this.icon=new cl(D?D.icon:null,J,K,de)};Ks.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var zs=function(D,J,q){this.text=D,this.icon=J,this.skipFade=q},Io=function(){this.invProjMatrix=i.create(),this.viewportMatrix=i.create(),this.circles=[]},ls=function(D,J,q,K,de){this.bucketInstanceId=D,this.featureIndex=J,this.sourceLayerIndex=q,this.bucketIndex=K,this.tileID=de},Zl=function(D){this.crossSourceCollisions=D,this.maxGroupID=0,this.collisionGroups={}};Zl.prototype.get=function(D){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[D]){var J=++this.maxGroupID;this.collisionGroups[D]={ID:J,predicate:function(q){return q.collisionGroupID===J}}}return this.collisionGroups[D]};function Su(Y,D,J,q,K){var de=i.getAnchorAlignment(Y),ne=de.horizontalAlign,we=de.verticalAlign,Ue=-(ne-.5)*D,ft=-(we-.5)*J,Xt=i.evaluateVariableOffset(Y,q);return new i.Point(Ue+Xt[0]*K,ft+Xt[1]*K)}function nc(Y,D,J,q,K,de){var ne=Y.x1,we=Y.x2,Ue=Y.y1,ft=Y.y2,Xt=Y.anchorPointX,hr=Y.anchorPointY,qt=new i.Point(D,J);return q&&qt._rotate(K?de:-de),{x1:ne+qt.x,y1:Ue+qt.y,x2:we+qt.x,y2:ft+qt.y,anchorPointX:Xt,anchorPointY:hr}}var bs=function(D,J,q,K){this.transform=D.clone(),this.collisionIndex=new il(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=J,this.retainedQueryData={},this.collisionGroups=new Zl(q),this.collisionCircleArrays={},this.prevPlacement=K,K&&(K.prevPlacement=void 0),this.placedOrientations={}};bs.prototype.getBucketParts=function(D,J,q,K){var de=q.getBucket(J),ne=q.latestFeatureIndex;if(!(!de||!ne||J.id!==de.layerIds[0])){var we=q.collisionBoxArray,Ue=de.layers[0].layout,ft=Math.pow(2,this.transform.zoom-q.tileID.overscaledZ),Xt=q.tileSize/i.EXTENT,hr=this.transform.calculatePosMatrix(q.tileID.toUnwrapped()),qt=Ue.get("text-pitch-alignment")==="map",Ve=Ue.get("text-rotation-alignment")==="map",Qe=As(q,1,this.transform.zoom),at=_n(hr,qt,Ve,this.transform,Qe),Ct=null;if(qt){var Ot=ya(hr,qt,Ve,this.transform,Qe);Ct=i.multiply([],this.transform.labelPlaneMatrix,Ot)}this.retainedQueryData[de.bucketInstanceId]=new ls(de.bucketInstanceId,ne,de.sourceLayerIndex,de.index,q.tileID);var Rt={bucket:de,layout:Ue,posMatrix:hr,textLabelPlaneMatrix:at,labelToScreenMatrix:Ct,scale:ft,textPixelRatio:Xt,holdingForFade:q.holdingForFade(),collisionBoxArray:we,partiallyEvaluatedTextSize:i.evaluateSizeForZoom(de.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(de.sourceID)};if(K)for(var Bt=0,Dt=de.sortKeyRanges;Bt<Dt.length;Bt+=1){var yt=Dt[Bt],Pt=yt.sortKey,ht=yt.symbolInstanceStart,ur=yt.symbolInstanceEnd;D.push({sortKey:Pt,symbolInstanceStart:ht,symbolInstanceEnd:ur,parameters:Rt})}else D.push({symbolInstanceStart:0,symbolInstanceEnd:de.symbolInstances.length,parameters:Rt})}},bs.prototype.attemptAnchorPlacement=function(D,J,q,K,de,ne,we,Ue,ft,Xt,hr,qt,Ve,Qe,at){var Ct=[qt.textOffset0,qt.textOffset1],Ot=Su(D,q,K,Ct,de),Rt=this.collisionIndex.placeCollisionBox(nc(J,Ot.x,Ot.y,ne,we,this.transform.angle),hr,Ue,ft,Xt.predicate);if(at){var Bt=this.collisionIndex.placeCollisionBox(nc(at,Ot.x,Ot.y,ne,we,this.transform.angle),hr,Ue,ft,Xt.predicate);if(Bt.box.length===0)return}if(Rt.box.length>0){var Dt;return this.prevPlacement&&this.prevPlacement.variableOffsets[qt.crossTileID]&&this.prevPlacement.placements[qt.crossTileID]&&this.prevPlacement.placements[qt.crossTileID].text&&(Dt=this.prevPlacement.variableOffsets[qt.crossTileID].anchor),this.variableOffsets[qt.crossTileID]={textOffset:Ct,width:q,height:K,anchor:D,textBoxScale:de,prevAnchor:Dt},this.markUsedJustification(Ve,D,qt,Qe),Ve.allowVerticalPlacement&&(this.markUsedOrientation(Ve,Qe,qt),this.placedOrientations[qt.crossTileID]=Qe),{shift:Ot,placedGlyphBoxes:Rt}}},bs.prototype.placeLayerBucketPart=function(D,J,q){var K=this,de=D.parameters,ne=de.bucket,we=de.layout,Ue=de.posMatrix,ft=de.textLabelPlaneMatrix,Xt=de.labelToScreenMatrix,hr=de.textPixelRatio,qt=de.holdingForFade,Ve=de.collisionBoxArray,Qe=de.partiallyEvaluatedTextSize,at=de.collisionGroup,Ct=we.get("text-optional"),Ot=we.get("icon-optional"),Rt=we.get("text-allow-overlap"),Bt=we.get("icon-allow-overlap"),Dt=we.get("text-rotation-alignment")==="map",yt=we.get("text-pitch-alignment")==="map",Pt=we.get("icon-text-fit")!=="none",ht=we.get("symbol-z-order")==="viewport-y",ur=Rt&&(Bt||!ne.hasIconData()||Ot),br=Bt&&(Rt||!ne.hasTextData()||Ct);!ne.collisionArrays&&Ve&&ne.deserializeCollisionBoxes(Ve);var Ur=function(Ci,Bi){if(!J[Ci.crossTileID]){if(qt){K.placements[Ci.crossTileID]=new zs(!1,!1,!1);return}var Gi=!1,sn=!1,zn=!0,Ja=null,co={box:null,offscreen:null},ts={box:null,offscreen:null},so=null,Zo=null,ms=null,ou=0,Cv=0,Lv=0;Bi.textFeatureIndex?ou=Bi.textFeatureIndex:Ci.useRuntimeCollisionCircles&&(ou=Ci.featureIndex),Bi.verticalTextFeatureIndex&&(Cv=Bi.verticalTextFeatureIndex);var wd=Bi.textBox;if(wd){var Kv=function(vc){var eu=i.WritingMode.horizontal;if(ne.allowVerticalPlacement&&!vc&&K.prevPlacement){var Sd=K.prevPlacement.placedOrientations[Ci.crossTileID];Sd&&(K.placedOrientations[Ci.crossTileID]=Sd,eu=Sd,K.markUsedOrientation(ne,eu,Ci))}return eu},cg=function(vc,eu){if(ne.allowVerticalPlacement&&Ci.numVerticalGlyphVertices>0&&Bi.verticalTextBox)for(var Sd=0,ly=ne.writingModes;Sd<ly.length;Sd+=1){var T1=ly[Sd];if(T1===i.WritingMode.vertical?(co=eu(),ts=co):co=vc(),co&&co.box&&co.box.length)break}else co=vc()};if(we.get("text-variable-anchor")){var Vd=we.get("text-variable-anchor");if(K.prevPlacement&&K.prevPlacement.variableOffsets[Ci.crossTileID]){var Ad=K.prevPlacement.variableOffsets[Ci.crossTileID];Vd.indexOf(Ad.anchor)>0&&(Vd=Vd.filter(function(vc){return vc!==Ad.anchor}),Vd.unshift(Ad.anchor))}var Pv=function(vc,eu,Sd){for(var ly=vc.x2-vc.x1,T1=vc.y2-vc.y1,wu=Ci.textBoxScale,Nx=Pt&&!Bt?eu:null,om={box:[],offscreen:!1},kw=Rt?Vd.length*2:Vd.length,Rv=0;Rv<kw;++Rv){var sm=Vd[Rv%Vd.length],Cw=Rv>=Vd.length,Ux=K.attemptAnchorPlacement(sm,vc,ly,T1,wu,Dt,yt,hr,Ue,at,Cw,Ci,ne,Sd,Nx);if(Ux&&(om=Ux.placedGlyphBoxes,om&&om.box&&om.box.length)){Gi=!0,Ja=Ux.shift;break}}return om},Jv=function(){return Pv(wd,Bi.iconBox,i.WritingMode.horizontal)},Iv=function(){var vc=Bi.verticalTextBox,eu=co&&co.box&&co.box.length;return ne.allowVerticalPlacement&&!eu&&Ci.numVerticalGlyphVertices>0&&vc?Pv(vc,Bi.verticalIconBox,i.WritingMode.vertical):{box:null,offscreen:null}};cg(Jv,Iv),co&&(Gi=co.box,zn=co.offscreen);var ay=Kv(co&&co.box);if(!Gi&&K.prevPlacement){var fg=K.prevPlacement.variableOffsets[Ci.crossTileID];fg&&(K.variableOffsets[Ci.crossTileID]=fg,K.markUsedJustification(ne,fg.anchor,Ci,ay))}}else{var pp=function(vc,eu){var Sd=K.collisionIndex.placeCollisionBox(vc,Rt,hr,Ue,at.predicate);return Sd&&Sd.box&&Sd.box.length&&(K.markUsedOrientation(ne,eu,Ci),K.placedOrientations[Ci.crossTileID]=eu),Sd},Td=function(){return pp(wd,i.WritingMode.horizontal)},gp=function(){var vc=Bi.verticalTextBox;return ne.allowVerticalPlacement&&Ci.numVerticalGlyphVertices>0&&vc?pp(vc,i.WritingMode.vertical):{box:null,offscreen:null}};cg(Td,gp),Kv(co&&co.box&&co.box.length)}}if(so=co,Gi=so&&so.box&&so.box.length>0,zn=so&&so.offscreen,Ci.useRuntimeCollisionCircles){var oh=ne.text.placedSymbolArray.get(Ci.centerJustifiedTextSymbolIndex),hg=i.evaluateSizeForFeature(ne.textSizeData,Qe,oh),oy=we.get("text-padding"),jh=Ci.collisionCircleDiameter;Zo=K.collisionIndex.placeCollisionCircles(Rt,oh,ne.lineVertexArray,ne.glyphOffsetArray,hg,Ue,ft,Xt,q,yt,at.predicate,jh,oy),Gi=Rt||Zo.circles.length>0&&!Zo.collisionDetected,zn=zn&&Zo.offscreen}if(Bi.iconFeatureIndex&&(Lv=Bi.iconFeatureIndex),Bi.iconBox){var im=function(vc){var eu=Pt&&Ja?nc(vc,Ja.x,Ja.y,Dt,yt,K.transform.angle):vc;return K.collisionIndex.placeCollisionBox(eu,Bt,hr,Ue,at.predicate)};ts&&ts.box&&ts.box.length&&Bi.verticalIconBox?(ms=im(Bi.verticalIconBox),sn=ms.box.length>0):(ms=im(Bi.iconBox),sn=ms.box.length>0),zn=zn&&ms.offscreen}var b1=Ct||Ci.numHorizontalGlyphVertices===0&&Ci.numVerticalGlyphVertices===0,w1=Ot||Ci.numIconVertices===0;if(!b1&&!w1?sn=Gi=sn&&Gi:w1?b1||(sn=sn&&Gi):Gi=sn&&Gi,Gi&&so&&so.box&&(ts&&ts.box&&Cv?K.collisionIndex.insertCollisionBox(so.box,we.get("text-ignore-placement"),ne.bucketInstanceId,Cv,at.ID):K.collisionIndex.insertCollisionBox(so.box,we.get("text-ignore-placement"),ne.bucketInstanceId,ou,at.ID)),sn&&ms&&K.collisionIndex.insertCollisionBox(ms.box,we.get("icon-ignore-placement"),ne.bucketInstanceId,Lv,at.ID),Zo&&(Gi&&K.collisionIndex.insertCollisionCircles(Zo.circles,we.get("text-ignore-placement"),ne.bucketInstanceId,ou,at.ID),q)){var sy=ne.bucketInstanceId,nm=K.collisionCircleArrays[sy];nm===void 0&&(nm=K.collisionCircleArrays[sy]=new Io);for(var am=0;am<Zo.circles.length;am+=4)nm.circles.push(Zo.circles[am+0]),nm.circles.push(Zo.circles[am+1]),nm.circles.push(Zo.circles[am+2]),nm.circles.push(Zo.collisionDetected?1:0)}K.placements[Ci.crossTileID]=new zs(Gi||ur,sn||br,zn||ne.justReloaded),J[Ci.crossTileID]=!0}};if(ht)for(var Di=ne.getSortedSymbolIndexes(this.transform.angle),fi=Di.length-1;fi>=0;--fi){var Ti=Di[fi];Ur(ne.symbolInstances.get(Ti),ne.collisionArrays[Ti])}else for(var gn=D.symbolInstanceStart;gn<D.symbolInstanceEnd;gn++)Ur(ne.symbolInstances.get(gn),ne.collisionArrays[gn]);if(q&&ne.bucketInstanceId in this.collisionCircleArrays){var rn=this.collisionCircleArrays[ne.bucketInstanceId];i.invert(rn.invProjMatrix,Ue),rn.viewportMatrix=this.collisionIndex.getViewportMatrix()}ne.justReloaded=!1},bs.prototype.markUsedJustification=function(D,J,q,K){var de={left:q.leftJustifiedTextSymbolIndex,center:q.centerJustifiedTextSymbolIndex,right:q.rightJustifiedTextSymbolIndex},ne;K===i.WritingMode.vertical?ne=q.verticalPlacedTextSymbolIndex:ne=de[i.getAnchorJustification(J)];for(var we=[q.leftJustifiedTextSymbolIndex,q.centerJustifiedTextSymbolIndex,q.rightJustifiedTextSymbolIndex,q.verticalPlacedTextSymbolIndex],Ue=0,ft=we;Ue<ft.length;Ue+=1){var Xt=ft[Ue];Xt>=0&&(ne>=0&&Xt!==ne?D.text.placedSymbolArray.get(Xt).crossTileID=0:D.text.placedSymbolArray.get(Xt).crossTileID=q.crossTileID)}},bs.prototype.markUsedOrientation=function(D,J,q){for(var K=J===i.WritingMode.horizontal||J===i.WritingMode.horizontalOnly?J:0,de=J===i.WritingMode.vertical?J:0,ne=[q.leftJustifiedTextSymbolIndex,q.centerJustifiedTextSymbolIndex,q.rightJustifiedTextSymbolIndex],we=0,Ue=ne;we<Ue.length;we+=1){var ft=Ue[we];D.text.placedSymbolArray.get(ft).placedOrientation=K}q.verticalPlacedTextSymbolIndex&&(D.text.placedSymbolArray.get(q.verticalPlacedTextSymbolIndex).placedOrientation=de)},bs.prototype.commit=function(D){this.commitTime=D,this.zoomAtLastRecencyCheck=this.transform.zoom;var J=this.prevPlacement,q=!1;this.prevZoomAdjustment=J?J.zoomAdjustment(this.transform.zoom):0;var K=J?J.symbolFadeChange(D):1,de=J?J.opacities:{},ne=J?J.variableOffsets:{},we=J?J.placedOrientations:{};for(var Ue in this.placements){var ft=this.placements[Ue],Xt=de[Ue];Xt?(this.opacities[Ue]=new Ks(Xt,K,ft.text,ft.icon),q=q||ft.text!==Xt.text.placed||ft.icon!==Xt.icon.placed):(this.opacities[Ue]=new Ks(null,K,ft.text,ft.icon,ft.skipFade),q=q||ft.text||ft.icon)}for(var hr in de){var qt=de[hr];if(!this.opacities[hr]){var Ve=new Ks(qt,K,!1,!1);Ve.isHidden()||(this.opacities[hr]=Ve,q=q||qt.text.placed||qt.icon.placed)}}for(var Qe in ne)!this.variableOffsets[Qe]&&this.opacities[Qe]&&!this.opacities[Qe].isHidden()&&(this.variableOffsets[Qe]=ne[Qe]);for(var at in we)!this.placedOrientations[at]&&this.opacities[at]&&!this.opacities[at].isHidden()&&(this.placedOrientations[at]=we[at]);q?this.lastPlacementChangeTime=D:typeof this.lastPlacementChangeTime!="number"&&(this.lastPlacementChangeTime=J?J.lastPlacementChangeTime:D)},bs.prototype.updateLayerOpacities=function(D,J){for(var q={},K=0,de=J;K<de.length;K+=1){var ne=de[K],we=ne.getBucket(D);we&&ne.latestFeatureIndex&&D.id===we.layerIds[0]&&this.updateBucketOpacities(we,q,ne.collisionBoxArray)}},bs.prototype.updateBucketOpacities=function(D,J,q){var K=this;D.hasTextData()&&D.text.opacityVertexArray.clear(),D.hasIconData()&&D.icon.opacityVertexArray.clear(),D.hasIconCollisionBoxData()&&D.iconCollisionBox.collisionVertexArray.clear(),D.hasTextCollisionBoxData()&&D.textCollisionBox.collisionVertexArray.clear();var de=D.layers[0].layout,ne=new Ks(null,0,!1,!1,!0),we=de.get("text-allow-overlap"),Ue=de.get("icon-allow-overlap"),ft=de.get("text-variable-anchor"),Xt=de.get("text-rotation-alignment")==="map",hr=de.get("text-pitch-alignment")==="map",qt=de.get("icon-text-fit")!=="none",Ve=new Ks(null,0,we&&(Ue||!D.hasIconData()||de.get("icon-optional")),Ue&&(we||!D.hasTextData()||de.get("text-optional")),!0);!D.collisionArrays&&q&&(D.hasIconCollisionBoxData()||D.hasTextCollisionBoxData())&&D.deserializeCollisionBoxes(q);for(var Qe=function(Rt,Bt,Dt){for(var yt=0;yt<Bt/4;yt++)Rt.opacityVertexArray.emplaceBack(Dt)},at=function(Rt){var Bt=D.symbolInstances.get(Rt),Dt=Bt.numHorizontalGlyphVertices,yt=Bt.numVerticalGlyphVertices,Pt=Bt.crossTileID,ht=J[Pt],ur=K.opacities[Pt];ht?ur=ne:ur||(ur=Ve,K.opacities[Pt]=ur),J[Pt]=!0;var br=Dt>0||yt>0,Ur=Bt.numIconVertices>0,Di=K.placedOrientations[Bt.crossTileID],fi=Di===i.WritingMode.vertical,Ti=Di===i.WritingMode.horizontal||Di===i.WritingMode.horizontalOnly;if(br){var gn=ac(ur.text),rn=fi?aa:gn;Qe(D.text,Dt,rn);var Ci=Ti?aa:gn;Qe(D.text,yt,Ci);var Bi=ur.text.isHidden();[Bt.rightJustifiedTextSymbolIndex,Bt.centerJustifiedTextSymbolIndex,Bt.leftJustifiedTextSymbolIndex].forEach(function(Lv){Lv>=0&&(D.text.placedSymbolArray.get(Lv).hidden=Bi||fi?1:0)}),Bt.verticalPlacedTextSymbolIndex>=0&&(D.text.placedSymbolArray.get(Bt.verticalPlacedTextSymbolIndex).hidden=Bi||Ti?1:0);var Gi=K.variableOffsets[Bt.crossTileID];Gi&&K.markUsedJustification(D,Gi.anchor,Bt,Di);var sn=K.placedOrientations[Bt.crossTileID];sn&&(K.markUsedJustification(D,"left",Bt,sn),K.markUsedOrientation(D,sn,Bt))}if(Ur){var zn=ac(ur.icon),Ja=!(qt&&Bt.verticalPlacedIconSymbolIndex&&fi);if(Bt.placedIconSymbolIndex>=0){var co=Ja?zn:aa;Qe(D.icon,Bt.numIconVertices,co),D.icon.placedSymbolArray.get(Bt.placedIconSymbolIndex).hidden=ur.icon.isHidden()}if(Bt.verticalPlacedIconSymbolIndex>=0){var ts=Ja?aa:zn;Qe(D.icon,Bt.numVerticalIconVertices,ts),D.icon.placedSymbolArray.get(Bt.verticalPlacedIconSymbolIndex).hidden=ur.icon.isHidden()}}if(D.hasIconCollisionBoxData()||D.hasTextCollisionBoxData()){var so=D.collisionArrays[Rt];if(so){var Zo=new i.Point(0,0);if(so.textBox||so.verticalTextBox){var ms=!0;if(ft){var ou=K.variableOffsets[Pt];ou?(Zo=Su(ou.anchor,ou.width,ou.height,ou.textOffset,ou.textBoxScale),Xt&&Zo._rotate(hr?K.transform.angle:-K.transform.angle)):ms=!1}so.textBox&&Rn(D.textCollisionBox.collisionVertexArray,ur.text.placed,!ms||fi,Zo.x,Zo.y),so.verticalTextBox&&Rn(D.textCollisionBox.collisionVertexArray,ur.text.placed,!ms||Ti,Zo.x,Zo.y)}var Cv=!!(!Ti&&so.verticalIconBox);so.iconBox&&Rn(D.iconCollisionBox.collisionVertexArray,ur.icon.placed,Cv,qt?Zo.x:0,qt?Zo.y:0),so.verticalIconBox&&Rn(D.iconCollisionBox.collisionVertexArray,ur.icon.placed,!Cv,qt?Zo.x:0,qt?Zo.y:0)}}},Ct=0;Ct<D.symbolInstances.length;Ct++)at(Ct);if(D.sortFeatures(this.transform.angle),this.retainedQueryData[D.bucketInstanceId]&&(this.retainedQueryData[D.bucketInstanceId].featureSortOrder=D.featureSortOrder),D.hasTextData()&&D.text.opacityVertexBuffer&&D.text.opacityVertexBuffer.updateData(D.text.opacityVertexArray),D.hasIconData()&&D.icon.opacityVertexBuffer&&D.icon.opacityVertexBuffer.updateData(D.icon.opacityVertexArray),D.hasIconCollisionBoxData()&&D.iconCollisionBox.collisionVertexBuffer&&D.iconCollisionBox.collisionVertexBuffer.updateData(D.iconCollisionBox.collisionVertexArray),D.hasTextCollisionBoxData()&&D.textCollisionBox.collisionVertexBuffer&&D.textCollisionBox.collisionVertexBuffer.updateData(D.textCollisionBox.collisionVertexArray),D.bucketInstanceId in this.collisionCircleArrays){var Ot=this.collisionCircleArrays[D.bucketInstanceId];D.placementInvProjMatrix=Ot.invProjMatrix,D.placementViewportMatrix=Ot.viewportMatrix,D.collisionCircleArray=Ot.circles,delete this.collisionCircleArrays[D.bucketInstanceId]}},bs.prototype.symbolFadeChange=function(D){return this.fadeDuration===0?1:(D-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},bs.prototype.zoomAdjustment=function(D){return Math.max(0,(this.transform.zoom-D)/1.5)},bs.prototype.hasTransitions=function(D){return this.stale||D-this.lastPlacementChangeTime<this.fadeDuration},bs.prototype.stillRecent=function(D,J){var q=this.zoomAtLastRecencyCheck===J?1-this.zoomAdjustment(J):1;return this.zoomAtLastRecencyCheck=J,this.commitTime+this.fadeDuration*q>D},bs.prototype.setStale=function(){this.stale=!0};function Rn(Y,D,J,q,K){Y.emplaceBack(D?1:0,J?1:0,q||0,K||0),Y.emplaceBack(D?1:0,J?1:0,q||0,K||0),Y.emplaceBack(D?1:0,J?1:0,q||0,K||0),Y.emplaceBack(D?1:0,J?1:0,q||0,K||0)}var _a=Math.pow(2,25),Vu=Math.pow(2,24),Ol=Math.pow(2,17),xo=Math.pow(2,16),Yl=Math.pow(2,9),Ns=Math.pow(2,8),Hl=Math.pow(2,1);function ac(Y){if(Y.opacity===0&&!Y.placed)return 0;if(Y.opacity===1&&Y.placed)return 4294967295;var D=Y.placed?1:0,J=Math.floor(Y.opacity*127);return J*_a+D*Vu+J*Ol+D*xo+J*Yl+D*Ns+J*Hl+D}var aa=0,Oo=function(D){this._sortAcrossTiles=D.layout.get("symbol-z-order")!=="viewport-y"&&D.layout.get("symbol-sort-key").constantOr(1)!==void 0,this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oo.prototype.continuePlacement=function(D,J,q,K,de){for(var ne=this._bucketParts;this._currentTileIndex<D.length;){var we=D[this._currentTileIndex];if(J.getBucketParts(ne,K,we,this._sortAcrossTiles),this._currentTileIndex++,de())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,ne.sort(function(ft,Xt){return ft.sortKey-Xt.sortKey}));this._currentPartIndex<ne.length;){var Ue=ne[this._currentPartIndex];if(J.placeLayerBucketPart(Ue,this._seenCrossTileIDs,q),this._currentPartIndex++,de())return!0}return!1};var qo=function(D,J,q,K,de,ne,we){this.placement=new bs(D,de,ne,we),this._currentPlacementIndex=J.length-1,this._forceFullPlacement=q,this._showCollisionBoxes=K,this._done=!1};qo.prototype.isDone=function(){return this._done},qo.prototype.continuePlacement=function(D,J,q){for(var K=this,de=i.browser.now(),ne=function(){var hr=i.browser.now()-de;return K._forceFullPlacement?!1:hr>2};this._currentPlacementIndex>=0;){var we=D[this._currentPlacementIndex],Ue=J[we],ft=this.placement.collisionIndex.transform.zoom;if(Ue.type==="symbol"&&(!Ue.minzoom||Ue.minzoom<=ft)&&(!Ue.maxzoom||Ue.maxzoom>ft)){this._inProgressLayer||(this._inProgressLayer=new Oo(Ue));var Xt=this._inProgressLayer.continuePlacement(q[Ue.source],this.placement,this._showCollisionBoxes,Ue,ne);if(Xt)return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},qo.prototype.commit=function(D){return this.placement.commit(D),this.placement};var ql=512/i.EXTENT/2,Pc=function(D,J,q){this.tileID=D,this.indexedSymbolInstances={},this.bucketInstanceId=q;for(var K=0;K<J.length;K++){var de=J.get(K),ne=de.key;this.indexedSymbolInstances[ne]||(this.indexedSymbolInstances[ne]=[]),this.indexedSymbolInstances[ne].push({crossTileID:de.crossTileID,coord:this.getScaledCoordinates(de,D)})}};Pc.prototype.getScaledCoordinates=function(D,J){var q=J.canonical.z-this.tileID.canonical.z,K=ql/Math.pow(2,q);return{x:Math.floor((J.canonical.x*i.EXTENT+D.anchorX)*K),y:Math.floor((J.canonical.y*i.EXTENT+D.anchorY)*K)}},Pc.prototype.findMatches=function(D,J,q){for(var K=this.tileID.canonical.z<J.canonical.z?1:Math.pow(2,this.tileID.canonical.z-J.canonical.z),de=0;de<D.length;de++){var ne=D.get(de);if(!ne.crossTileID){var we=this.indexedSymbolInstances[ne.key];if(we)for(var Ue=this.getScaledCoordinates(ne,J),ft=0,Xt=we;ft<Xt.length;ft+=1){var hr=Xt[ft];if(Math.abs(hr.coord.x-Ue.x)<=K&&Math.abs(hr.coord.y-Ue.y)<=K&&!q[hr.crossTileID]){q[hr.crossTileID]=!0,ne.crossTileID=hr.crossTileID;break}}}}};var Do=function(){this.maxCrossTileID=0};Do.prototype.generate=function(){return++this.maxCrossTileID};var rf=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};rf.prototype.handleWrapJump=function(D){var J=Math.round((D-this.lng)/360);if(J!==0)for(var q in this.indexes){var K=this.indexes[q],de={};for(var ne in K){var we=K[ne];we.tileID=we.tileID.unwrapTo(we.tileID.wrap+J),de[we.tileID.key]=we}this.indexes[q]=de}this.lng=D},rf.prototype.addBucket=function(D,J,q){if(this.indexes[D.overscaledZ]&&this.indexes[D.overscaledZ][D.key]){if(this.indexes[D.overscaledZ][D.key].bucketInstanceId===J.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(D.overscaledZ,this.indexes[D.overscaledZ][D.key])}for(var K=0;K<J.symbolInstances.length;K++){var de=J.symbolInstances.get(K);de.crossTileID=0}this.usedCrossTileIDs[D.overscaledZ]||(this.usedCrossTileIDs[D.overscaledZ]={});var ne=this.usedCrossTileIDs[D.overscaledZ];for(var we in this.indexes){var Ue=this.indexes[we];if(Number(we)>D.overscaledZ)for(var ft in Ue){var Xt=Ue[ft];Xt.tileID.isChildOf(D)&&Xt.findMatches(J.symbolInstances,D,ne)}else{var hr=D.scaledTo(Number(we)),qt=Ue[hr.key];qt&&qt.findMatches(J.symbolInstances,D,ne)}}for(var Ve=0;Ve<J.symbolInstances.length;Ve++){var Qe=J.symbolInstances.get(Ve);Qe.crossTileID||(Qe.crossTileID=q.generate(),ne[Qe.crossTileID]=!0)}return this.indexes[D.overscaledZ]===void 0&&(this.indexes[D.overscaledZ]={}),this.indexes[D.overscaledZ][D.key]=new Pc(D,J.symbolInstances,J.bucketInstanceId),!0},rf.prototype.removeBucketCrossTileIDs=function(D,J){for(var q in J.indexedSymbolInstances)for(var K=0,de=J.indexedSymbolInstances[q];K<de.length;K+=1){var ne=de[K];delete this.usedCrossTileIDs[D][ne.crossTileID]}},rf.prototype.removeStaleBuckets=function(D){var J=!1;for(var q in this.indexes){var K=this.indexes[q];for(var de in K)D[K[de].bucketInstanceId]||(this.removeBucketCrossTileIDs(q,K[de]),delete K[de],J=!0)}return J};var Uf=function(){this.layerIndexes={},this.crossTileIDs=new Do,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Uf.prototype.addLayer=function(D,J,q){var K=this.layerIndexes[D.id];K===void 0&&(K=this.layerIndexes[D.id]=new rf);var de=!1,ne={};K.handleWrapJump(q);for(var we=0,Ue=J;we<Ue.length;we+=1){var ft=Ue[we],Xt=ft.getBucket(D);!Xt||D.id!==Xt.layerIds[0]||(Xt.bucketInstanceId||(Xt.bucketInstanceId=++this.maxBucketInstanceId),K.addBucket(ft.tileID,Xt,this.crossTileIDs)&&(de=!0),ne[Xt.bucketInstanceId]=!0)}return K.removeStaleBuckets(ne)&&(de=!0),de},Uf.prototype.pruneUnusedLayers=function(D){var J={};D.forEach(function(K){J[K]=!0});for(var q in this.layerIndexes)J[q]||delete this.layerIndexes[q]};var pl=function(Y,D){return i.emitValidationErrors(Y,D&&D.filter(function(J){return J.identifier!=="source.canvas"}))},Zc=i.pick(da,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),Kl=i.pick(da,["setCenter","setZoom","setBearing","setPitch"]),Os=Wo(),yu=function(Y){function D(J,q){var K=this;q===void 0&&(q={}),Y.call(this),this.map=J,this.dispatcher=new Z(Ba(),this),this.imageManager=new k,this.imageManager.setEventedParent(this),this.glyphManager=new P(J._requestManager,q.localIdeographFontFamily),this.lineAtlas=new G(256,512),this.crossTileSymbolIndex=new Uf,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new i.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("setReferrer",i.getReferrer());var de=this;this._rtlTextPluginCallback=D.registerForPluginStateChange(function(ne){var we={pluginStatus:ne.pluginStatus,pluginURL:ne.pluginURL};de.dispatcher.broadcast("syncRTLPluginState",we,function(Ue,ft){if(i.triggerPluginCompletionEvent(Ue),ft){var Xt=ft.every(function(qt){return qt});if(Xt)for(var hr in de.sourceCaches)de.sourceCaches[hr].reload()}})}),this.on("data",function(ne){if(!(ne.dataType!=="source"||ne.sourceDataType!=="metadata")){var we=K.sourceCaches[ne.sourceId];if(we){var Ue=we.getSource();if(!(!Ue||!Ue.vectorLayerIds))for(var ft in K._layers){var Xt=K._layers[ft];Xt.source===Ue.id&&K._validateLayer(Xt)}}}})}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.loadURL=function(q,K){var de=this;K===void 0&&(K={}),this.fire(new i.Event("dataloading",{dataType:"style"}));var ne=typeof K.validate=="boolean"?K.validate:!i.isMapboxURL(q);q=this.map._requestManager.normalizeStyleURL(q,K.accessToken);var we=this.map._requestManager.transformRequest(q,i.ResourceType.Style);this._request=i.getJSON(we,function(Ue,ft){de._request=null,Ue?de.fire(new i.ErrorEvent(Ue)):ft&&de._load(ft,ne)})},D.prototype.loadJSON=function(q,K){var de=this;K===void 0&&(K={}),this.fire(new i.Event("dataloading",{dataType:"style"})),this._request=i.browser.frame(function(){de._request=null,de._load(q,K.validate!==!1)})},D.prototype.loadEmpty=function(){this.fire(new i.Event("dataloading",{dataType:"style"})),this._load(Os,!1)},D.prototype._load=function(q,K){if(!(K&&pl(this,i.validateStyle(q)))){this._loaded=!0,this.stylesheet=q;for(var de in q.sources)this.addSource(de,q.sources[de],{validate:!1});q.sprite?this._loadSprite(q.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(q.glyphs);var ne=Fa(this.stylesheet.layers);this._order=ne.map(function(Xt){return Xt.id}),this._layers={},this._serializedLayers={};for(var we=0,Ue=ne;we<Ue.length;we+=1){var ft=Ue[we];ft=i.createStyleLayer(ft),ft.setEventedParent(this,{layer:{id:ft.id}}),this._layers[ft.id]=ft,this._serializedLayers[ft.id]=ft.serialize()}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new V(this.stylesheet.light),this.fire(new i.Event("data",{dataType:"style"})),this.fire(new i.Event("style.load"))}},D.prototype._loadSprite=function(q){var K=this;this._spriteRequest=_(q,this.map._requestManager,function(de,ne){if(K._spriteRequest=null,de)K.fire(new i.ErrorEvent(de));else if(ne)for(var we in ne)K.imageManager.addImage(we,ne[we]);K.imageManager.setLoaded(!0),K._availableImages=K.imageManager.listImages(),K.dispatcher.broadcast("setImages",K._availableImages),K.fire(new i.Event("data",{dataType:"style"}))})},D.prototype._validateLayer=function(q){var K=this.sourceCaches[q.source];if(K){var de=q.sourceLayer;if(de){var ne=K.getSource();(ne.type==="geojson"||ne.vectorLayerIds&&ne.vectorLayerIds.indexOf(de)===-1)&&this.fire(new i.ErrorEvent(new Error('Source layer "'+de+'" does not exist on source "'+ne.id+'" as specified by style layer "'+q.id+'"')))}}},D.prototype.loaded=function(){if(!this._loaded||Object.keys(this._updatedSources).length)return!1;for(var q in this.sourceCaches)if(!this.sourceCaches[q].loaded())return!1;return!!this.imageManager.isLoaded()},D.prototype._serializeLayers=function(q){for(var K=[],de=0,ne=q;de<ne.length;de+=1){var we=ne[de],Ue=this._layers[we];Ue.type!=="custom"&&K.push(Ue.serialize())}return K},D.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var q in this.sourceCaches)if(this.sourceCaches[q].hasTransition())return!0;for(var K in this._layers)if(this._layers[K].hasTransition())return!0;return!1},D.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},D.prototype.update=function(q){if(this._loaded){var K=this._changed;if(this._changed){var de=Object.keys(this._updatedLayers),ne=Object.keys(this._removedLayers);(de.length||ne.length)&&this._updateWorkerLayers(de,ne);for(var we in this._updatedSources){var Ue=this._updatedSources[we];Ue==="reload"?this._reloadSource(we):Ue==="clear"&&this._clearSource(we)}this._updateTilesForChangedImages();for(var ft in this._updatedPaintProps)this._layers[ft].updateTransitions(q);this.light.updateTransitions(q),this._resetUpdates()}var Xt={};for(var hr in this.sourceCaches){var qt=this.sourceCaches[hr];Xt[hr]=qt.used,qt.used=!1}for(var Ve=0,Qe=this._order;Ve<Qe.length;Ve+=1){var at=Qe[Ve],Ct=this._layers[at];Ct.recalculate(q,this._availableImages),!Ct.isHidden(q.zoom)&&Ct.source&&(this.sourceCaches[Ct.source].used=!0)}for(var Ot in Xt){var Rt=this.sourceCaches[Ot];Xt[Ot]!==Rt.used&&Rt.fire(new i.Event("data",{sourceDataType:"visibility",dataType:"source",sourceId:Ot}))}this.light.recalculate(q),this.z=q.zoom,K&&this.fire(new i.Event("data",{dataType:"style"}))}},D.prototype._updateTilesForChangedImages=function(){var q=Object.keys(this._changedImages);if(q.length){for(var K in this.sourceCaches)this.sourceCaches[K].reloadTilesForDependencies(["icons","patterns"],q);this._changedImages={}}},D.prototype._updateWorkerLayers=function(q,K){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers(q),removedIds:K})},D.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},D.prototype.setState=function(q){var K=this;if(this._checkLoaded(),pl(this,i.validateStyle(q)))return!1;q=i.clone$1(q),q.layers=Fa(q.layers);var de=An(this.serialize(),q).filter(function(we){return!(we.command in Kl)});if(de.length===0)return!1;var ne=de.filter(function(we){return!(we.command in Zc)});if(ne.length>0)throw new Error("Unimplemented: "+ne.map(function(we){return we.command}).join(", ")+".");return de.forEach(function(we){we.command!=="setTransition"&&K[we.command].apply(K,we.args)}),this.stylesheet=q,!0},D.prototype.addImage=function(q,K){if(this.getImage(q))return this.fire(new i.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage(q,K),this._afterImageUpdated(q)},D.prototype.updateImage=function(q,K){this.imageManager.updateImage(q,K)},D.prototype.getImage=function(q){return this.imageManager.getImage(q)},D.prototype.removeImage=function(q){if(!this.getImage(q))return this.fire(new i.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage(q),this._afterImageUpdated(q)},D.prototype._afterImageUpdated=function(q){this._availableImages=this.imageManager.listImages(),this._changedImages[q]=!0,this._changed=!0,this.dispatcher.broadcast("setImages",this._availableImages),this.fire(new i.Event("data",{dataType:"style"}))},D.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},D.prototype.addSource=function(q,K,de){var ne=this;if(de===void 0&&(de={}),this._checkLoaded(),this.sourceCaches[q]!==void 0)throw new Error("There is already a source with this ID");if(!K.type)throw new Error("The type property must be defined, but only the following properties were given: "+Object.keys(K).join(", ")+".");var we=["vector","raster","geojson","video","image"],Ue=we.indexOf(K.type)>=0;if(!(Ue&&this._validate(i.validateStyle.source,"sources."+q,K,null,de))){this.map&&this.map._collectResourceTiming&&(K.collectResourceTiming=!0);var ft=this.sourceCaches[q]=new ri(q,K,this.dispatcher);ft.style=this,ft.setEventedParent(this,function(){return{isSourceLoaded:ne.loaded(),source:ft.serialize(),sourceId:q}}),ft.onAdd(this.map),this._changed=!0}},D.prototype.removeSource=function(q){if(this._checkLoaded(),this.sourceCaches[q]===void 0)throw new Error("There is no source with this ID");for(var K in this._layers)if(this._layers[K].source===q)return this.fire(new i.ErrorEvent(new Error('Source "'+q+'" cannot be removed while layer "'+K+'" is using it.')));var de=this.sourceCaches[q];delete this.sourceCaches[q],delete this._updatedSources[q],de.fire(new i.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:q})),de.setEventedParent(null),de.clearTiles(),de.onRemove&&de.onRemove(this.map),this._changed=!0},D.prototype.setGeoJSONSourceData=function(q,K){this._checkLoaded();var de=this.sourceCaches[q].getSource();de.setData(K),this._changed=!0},D.prototype.getSource=function(q){return this.sourceCaches[q]&&this.sourceCaches[q].getSource()},D.prototype.addLayer=function(q,K,de){de===void 0&&(de={}),this._checkLoaded();var ne=q.id;if(this.getLayer(ne)){this.fire(new i.ErrorEvent(new Error('Layer with id "'+ne+'" already exists on this map')));return}var we;if(q.type==="custom"){if(pl(this,i.validateCustomStyleLayer(q)))return;we=i.createStyleLayer(q)}else{if(typeof q.source=="object"&&(this.addSource(ne,q.source),q=i.clone$1(q),q=i.extend(q,{source:ne})),this._validate(i.validateStyle.layer,"layers."+ne,q,{arrayIndex:-1},de))return;we=i.createStyleLayer(q),this._validateLayer(we),we.setEventedParent(this,{layer:{id:ne}}),this._serializedLayers[we.id]=we.serialize()}var Ue=K?this._order.indexOf(K):this._order.length;if(K&&Ue===-1){this.fire(new i.ErrorEvent(new Error('Layer with id "'+K+'" does not exist on this map.')));return}if(this._order.splice(Ue,0,ne),this._layerOrderChanged=!0,this._layers[ne]=we,this._removedLayers[ne]&&we.source&&we.type!=="custom"){var ft=this._removedLayers[ne];delete this._removedLayers[ne],ft.type!==we.type?this._updatedSources[we.source]="clear":(this._updatedSources[we.source]="reload",this.sourceCaches[we.source].pause())}this._updateLayer(we),we.onAdd&&we.onAdd(this.map)},D.prototype.moveLayer=function(q,K){this._checkLoaded(),this._changed=!0;var de=this._layers[q];if(!de){this.fire(new i.ErrorEvent(new Error("The layer '"+q+"' does not exist in the map's style and cannot be moved.")));return}if(q!==K){var ne=this._order.indexOf(q);this._order.splice(ne,1);var we=K?this._order.indexOf(K):this._order.length;if(K&&we===-1){this.fire(new i.ErrorEvent(new Error('Layer with id "'+K+'" does not exist on this map.')));return}this._order.splice(we,0,q),this._layerOrderChanged=!0}},D.prototype.removeLayer=function(q){this._checkLoaded();var K=this._layers[q];if(!K){this.fire(new i.ErrorEvent(new Error("The layer '"+q+"' does not exist in the map's style and cannot be removed.")));return}K.setEventedParent(null);var de=this._order.indexOf(q);this._order.splice(de,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[q]=K,delete this._layers[q],delete this._serializedLayers[q],delete this._updatedLayers[q],delete this._updatedPaintProps[q],K.onRemove&&K.onRemove(this.map)},D.prototype.getLayer=function(q){return this._layers[q]},D.prototype.hasLayer=function(q){return q in this._layers},D.prototype.setLayerZoomRange=function(q,K,de){this._checkLoaded();var ne=this.getLayer(q);if(!ne){this.fire(new i.ErrorEvent(new Error("The layer '"+q+"' does not exist in the map's style and cannot have zoom extent.")));return}ne.minzoom===K&&ne.maxzoom===de||(K!=null&&(ne.minzoom=K),de!=null&&(ne.maxzoom=de),this._updateLayer(ne))},D.prototype.setFilter=function(q,K,de){de===void 0&&(de={}),this._checkLoaded();var ne=this.getLayer(q);if(!ne){this.fire(new i.ErrorEvent(new Error("The layer '"+q+"' does not exist in the map's style and cannot be filtered.")));return}if(!i.deepEqual(ne.filter,K)){if(K==null){ne.filter=void 0,this._updateLayer(ne);return}this._validate(i.validateStyle.filter,"layers."+ne.id+".filter",K,null,de)||(ne.filter=i.clone$1(K),this._updateLayer(ne))}},D.prototype.getFilter=function(q){return i.clone$1(this.getLayer(q).filter)},D.prototype.setLayoutProperty=function(q,K,de,ne){ne===void 0&&(ne={}),this._checkLoaded();var we=this.getLayer(q);if(!we){this.fire(new i.ErrorEvent(new Error("The layer '"+q+"' does not exist in the map's style and cannot be styled.")));return}i.deepEqual(we.getLayoutProperty(K),de)||(we.setLayoutProperty(K,de,ne),this._updateLayer(we))},D.prototype.getLayoutProperty=function(q,K){var de=this.getLayer(q);if(!de){this.fire(new i.ErrorEvent(new Error("The layer '"+q+"' does not exist in the map's style.")));return}return de.getLayoutProperty(K)},D.prototype.setPaintProperty=function(q,K,de,ne){ne===void 0&&(ne={}),this._checkLoaded();var we=this.getLayer(q);if(!we){this.fire(new i.ErrorEvent(new Error("The layer '"+q+"' does not exist in the map's style and cannot be styled.")));return}if(!i.deepEqual(we.getPaintProperty(K),de)){var Ue=we.setPaintProperty(K,de,ne);Ue&&this._updateLayer(we),this._changed=!0,this._updatedPaintProps[q]=!0}},D.prototype.getPaintProperty=function(q,K){return this.getLayer(q).getPaintProperty(K)},D.prototype.setFeatureState=function(q,K){this._checkLoaded();var de=q.source,ne=q.sourceLayer,we=this.sourceCaches[de];if(we===void 0){this.fire(new i.ErrorEvent(new Error("The source '"+de+"' does not exist in the map's style.")));return}var Ue=we.getSource().type;if(Ue==="geojson"&&ne){this.fire(new i.ErrorEvent(new Error("GeoJSON sources cannot have a sourceLayer parameter.")));return}if(Ue==="vector"&&!ne){this.fire(new i.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")));return}q.id===void 0&&this.fire(new i.ErrorEvent(new Error("The feature id parameter must be provided."))),we.setFeatureState(ne,q.id,K)},D.prototype.removeFeatureState=function(q,K){this._checkLoaded();var de=q.source,ne=this.sourceCaches[de];if(ne===void 0){this.fire(new i.ErrorEvent(new Error("The source '"+de+"' does not exist in the map's style.")));return}var we=ne.getSource().type,Ue=we==="vector"?q.sourceLayer:void 0;if(we==="vector"&&!Ue){this.fire(new i.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")));return}if(K&&typeof q.id!="string"&&typeof q.id!="number"){this.fire(new i.ErrorEvent(new Error("A feature id is required to remove its specific state property.")));return}ne.removeFeatureState(Ue,q.id,K)},D.prototype.getFeatureState=function(q){this._checkLoaded();var K=q.source,de=q.sourceLayer,ne=this.sourceCaches[K];if(ne===void 0){this.fire(new i.ErrorEvent(new Error("The source '"+K+"' does not exist in the map's style.")));return}var we=ne.getSource().type;if(we==="vector"&&!de){this.fire(new i.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")));return}return q.id===void 0&&this.fire(new i.ErrorEvent(new Error("The feature id parameter must be provided."))),ne.getFeatureState(de,q.id)},D.prototype.getTransition=function(){return i.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},D.prototype.serialize=function(){return i.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:i.mapObject(this.sourceCaches,function(q){return q.serialize()}),layers:this._serializeLayers(this._order)},function(q){return q!==void 0})},D.prototype._updateLayer=function(q){this._updatedLayers[q.id]=!0,q.source&&!this._updatedSources[q.source]&&this.sourceCaches[q.source].getSource().type!=="raster"&&(this._updatedSources[q.source]="reload",this.sourceCaches[q.source].pause()),this._changed=!0},D.prototype._flattenAndSortRenderedFeatures=function(q){for(var K=this,de=function(Ti){return K._layers[Ti].type==="fill-extrusion"},ne={},we=[],Ue=this._order.length-1;Ue>=0;Ue--){var ft=this._order[Ue];if(de(ft)){ne[ft]=Ue;for(var Xt=0,hr=q;Xt<hr.length;Xt+=1){var qt=hr[Xt],Ve=qt[ft];if(Ve)for(var Qe=0,at=Ve;Qe<at.length;Qe+=1){var Ct=at[Qe];we.push(Ct)}}}}we.sort(function(Ti,gn){return gn.intersectionZ-Ti.intersectionZ});for(var Ot=[],Rt=this._order.length-1;Rt>=0;Rt--){var Bt=this._order[Rt];if(de(Bt))for(var Dt=we.length-1;Dt>=0;Dt--){var yt=we[Dt].feature;if(ne[yt.layer.id]<Rt)break;Ot.push(yt),we.pop()}else for(var Pt=0,ht=q;Pt<ht.length;Pt+=1){var ur=ht[Pt],br=ur[Bt];if(br)for(var Ur=0,Di=br;Ur<Di.length;Ur+=1){var fi=Di[Ur];Ot.push(fi.feature)}}}return Ot},D.prototype.queryRenderedFeatures=function(q,K,de){K&&K.filter&&this._validate(i.validateStyle.filter,"queryRenderedFeatures.filter",K.filter,null,K);var ne={};if(K&&K.layers){if(!Array.isArray(K.layers))return this.fire(new i.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var we=0,Ue=K.layers;we<Ue.length;we+=1){var ft=Ue[we],Xt=this._layers[ft];if(!Xt)return this.fire(new i.ErrorEvent(new Error("The layer '"+ft+"' does not exist in the map's style and cannot be queried for features."))),[];ne[Xt.source]=!0}}var hr=[];K.availableImages=this._availableImages;for(var qt in this.sourceCaches)K.layers&&!ne[qt]||hr.push(ce(this.sourceCaches[qt],this._layers,this._serializedLayers,q,K,de));return this.placement&&hr.push(Ze(this._layers,this._serializedLayers,this.sourceCaches,q,K,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(hr)},D.prototype.querySourceFeatures=function(q,K){K&&K.filter&&this._validate(i.validateStyle.filter,"querySourceFeatures.filter",K.filter,null,K);var de=this.sourceCaches[q];return de?ut(de,K):[]},D.prototype.addSourceType=function(q,K,de){if(D.getSourceType(q))return de(new Error('A source type called "'+q+'" already exists.'));if(D.setSourceType(q,K),!K.workerSourceURL)return de(null,null);this.dispatcher.broadcast("loadWorkerSource",{name:q,url:K.workerSourceURL},de)},D.prototype.getLight=function(){return this.light.getLight()},D.prototype.setLight=function(q,K){K===void 0&&(K={}),this._checkLoaded();var de=this.light.getLight(),ne=!1;for(var we in q)if(!i.deepEqual(q[we],de[we])){ne=!0;break}if(ne){var Ue={now:i.browser.now(),transition:i.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(q,K),this.light.updateTransitions(Ue)}},D.prototype._validate=function(q,K,de,ne,we){return we===void 0&&(we={}),we&&we.validate===!1?!1:pl(this,q.call(i.validateStyle,i.extend({key:K,style:this.serialize(),value:de,styleSpec:i.styleSpec},ne)))},D.prototype._remove=function(){this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),i.evented.off("pluginStateChange",this._rtlTextPluginCallback);for(var q in this._layers){var K=this._layers[q];K.setEventedParent(null)}for(var de in this.sourceCaches)this.sourceCaches[de].clearTiles(),this.sourceCaches[de].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},D.prototype._clearSource=function(q){this.sourceCaches[q].clearTiles()},D.prototype._reloadSource=function(q){this.sourceCaches[q].resume(),this.sourceCaches[q].reload()},D.prototype._updateSources=function(q){for(var K in this.sourceCaches)this.sourceCaches[K].update(q)},D.prototype._generateCollisionBoxes=function(){for(var q in this.sourceCaches)this._reloadSource(q)},D.prototype._updatePlacement=function(q,K,de,ne,we){we===void 0&&(we=!1);for(var Ue=!1,ft=!1,Xt={},hr=0,qt=this._order;hr<qt.length;hr+=1){var Ve=qt[hr],Qe=this._layers[Ve];if(Qe.type==="symbol"){if(!Xt[Qe.source]){var at=this.sourceCaches[Qe.source];Xt[Qe.source]=at.getRenderableIds(!0).map(function(Pt){return at.getTileByID(Pt)}).sort(function(Pt,ht){return ht.tileID.overscaledZ-Pt.tileID.overscaledZ||(Pt.tileID.isLessThan(ht.tileID)?-1:1)})}var Ct=this.crossTileSymbolIndex.addLayer(Qe,Xt[Qe.source],q.center.lng);Ue=Ue||Ct}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),we=we||this._layerOrderChanged||de===0,(we||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(i.browser.now(),q.zoom))&&(this.pauseablePlacement=new qo(q,this._order,we,K,de,ne,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,Xt),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(i.browser.now()),ft=!0),Ue&&this.pauseablePlacement.placement.setStale()),ft||Ue)for(var Ot=0,Rt=this._order;Ot<Rt.length;Ot+=1){var Bt=Rt[Ot],Dt=this._layers[Bt];Dt.type==="symbol"&&this.placement.updateLayerOpacities(Dt,Xt[Dt.source])}var yt=!this.pauseablePlacement.isDone()||this.placement.hasTransitions(i.browser.now());return yt},D.prototype._releaseSymbolFadeTiles=function(){for(var q in this.sourceCaches)this.sourceCaches[q].releaseSymbolFadeTiles()},D.prototype.getImages=function(q,K,de){this.imageManager.getImages(K.icons,de),this._updateTilesForChangedImages();var ne=this.sourceCaches[K.source];ne&&ne.setDependencies(K.tileID.key,K.type,K.icons)},D.prototype.getGlyphs=function(q,K,de){this.glyphManager.getGlyphs(K.stacks,de)},D.prototype.getResource=function(q,K,de){return i.makeRequest(K,de)},D}(i.Evented);yu.getSourceType=Fe,yu.setSourceType=Pe,yu.registerForPluginStateChange=i.registerForPluginStateChange;var oc=i.createLayout([{name:"a_pos",type:"Int16",components:2}]),Cf=`#ifdef GL_ES
+precision mediump float;
+#else
+#if !defined(lowp)
+#define lowp
+#endif
+#if !defined(mediump)
+#define mediump
+#endif
+#if !defined(highp)
+#define highp
+#endif
+#endif`,sc=`#ifdef GL_ES
+precision highp float;
+#else
+#if !defined(lowp)
+#define lowp
+#endif
+#if !defined(mediump)
+#define mediump
+#endif
+#if !defined(highp)
+#define highp
+#endif
+#endif
+vec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0
+);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}`,Vh=`uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,Lf="attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}",cs=`uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,nf="uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}",Vf=`varying vec3 v_data;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define mediump float radius
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define highp vec4 stroke_color
+#pragma mapbox: define mediump float stroke_width
+#pragma mapbox: define lowp float stroke_opacity
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize mediump float radius
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize highp vec4 stroke_color
+#pragma mapbox: initialize mediump float stroke_width
+#pragma mapbox: initialize lowp float stroke_opacity
+vec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,Jl=`uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define mediump float radius
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define highp vec4 stroke_color
+#pragma mapbox: define mediump float stroke_width
+#pragma mapbox: define lowp float stroke_opacity
+void main(void) {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize mediump float radius
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize highp vec4 stroke_color
+#pragma mapbox: initialize mediump float stroke_width
+#pragma mapbox: initialize lowp float stroke_opacity
+vec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}`,fl="void main() {gl_FragColor=vec4(1.0);}",lc="attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}",Fu=`uniform highp float u_intensity;varying vec2 v_extrude;
+#pragma mapbox: define highp float weight
+#define GAUSS_COEF 0.3989422804014327
+void main() {
+#pragma mapbox: initialize highp float weight
+float d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,Es=`uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;
+#pragma mapbox: define highp float weight
+#pragma mapbox: define mediump float radius
+const highp float ZERO=1.0/255.0/16.0;
+#define GAUSS_COEF 0.3989422804014327
+void main(void) {
+#pragma mapbox: initialize highp float weight
+#pragma mapbox: initialize mediump float radius
+vec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}`,Hs=`uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(0.0);
+#endif
+}`,Go="uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}",ps="varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}",uc="attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}",xl="varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}",Gu="attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}",qs="uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}",ad="attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}",Po=`#pragma mapbox: define highp vec4 color
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize lowp float opacity
+gl_FragColor=color*opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,od=`attribute vec2 a_pos;uniform mat4 u_matrix;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize lowp float opacity
+gl_Position=u_matrix*vec4(a_pos,0,1);}`,Yo=`varying vec2 v_pos;
+#pragma mapbox: define highp vec4 outline_color
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize highp vec4 outline_color
+#pragma mapbox: initialize lowp float opacity
+float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,Pa=`attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;
+#pragma mapbox: define highp vec4 outline_color
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize highp vec4 outline_color
+#pragma mapbox: initialize lowp float opacity
+gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`,af=`uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+void main() {
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,Hu=`uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+#pragma mapbox: define lowp float pixel_ratio_from
+#pragma mapbox: define lowp float pixel_ratio_to
+void main() {
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+#pragma mapbox: initialize lowp float pixel_ratio_from
+#pragma mapbox: initialize lowp float pixel_ratio_to
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`,bl=`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+void main() {
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,Gf=`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+#pragma mapbox: define lowp float pixel_ratio_from
+#pragma mapbox: define lowp float pixel_ratio_to
+void main() {
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+#pragma mapbox: initialize lowp float pixel_ratio_from
+#pragma mapbox: initialize lowp float pixel_ratio_to
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}`,Ic=`varying vec4 v_color;void main() {gl_FragColor=v_color;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,yf=`uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;
+#pragma mapbox: define highp float base
+#pragma mapbox: define highp float height
+#pragma mapbox: define highp vec4 color
+void main() {
+#pragma mapbox: initialize highp float base
+#pragma mapbox: initialize highp float height
+#pragma mapbox: initialize highp vec4 color
+vec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}`,Bl=`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;
+#pragma mapbox: define lowp float base
+#pragma mapbox: define lowp float height
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+#pragma mapbox: define lowp float pixel_ratio_from
+#pragma mapbox: define lowp float pixel_ratio_to
+void main() {
+#pragma mapbox: initialize lowp float base
+#pragma mapbox: initialize lowp float height
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+#pragma mapbox: initialize lowp float pixel_ratio_from
+#pragma mapbox: initialize lowp float pixel_ratio_to
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,wh=`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;
+#pragma mapbox: define lowp float base
+#pragma mapbox: define lowp float height
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+#pragma mapbox: define lowp float pixel_ratio_from
+#pragma mapbox: define lowp float pixel_ratio_to
+void main() {
+#pragma mapbox: initialize lowp float base
+#pragma mapbox: initialize lowp float height
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+#pragma mapbox: initialize lowp float pixel_ratio_from
+#pragma mapbox: initialize lowp float pixel_ratio_to
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0
+? a_pos
+: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}`,Qf=`#ifdef GL_ES
+precision highp float;
+#endif
+uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,_f="uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}",Yc=`uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;
+#define PI 3.141592653589793
+void main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,eh="uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}",th=`uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,ju=`
+#define scale 0.015873016
+attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define mediump float gapwidth
+#pragma mapbox: define lowp float offset
+#pragma mapbox: define mediump float width
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump float gapwidth
+#pragma mapbox: initialize lowp float offset
+#pragma mapbox: initialize mediump float width
+float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}`,Hf=`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,cc=`
+#define scale 0.015873016
+attribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define mediump float gapwidth
+#pragma mapbox: define lowp float offset
+#pragma mapbox: define mediump float width
+void main() {
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump float gapwidth
+#pragma mapbox: initialize lowp float offset
+#pragma mapbox: initialize mediump float width
+float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}`,of=`uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+#pragma mapbox: define lowp float pixel_ratio_from
+#pragma mapbox: define lowp float pixel_ratio_to
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+#pragma mapbox: initialize lowp float pixel_ratio_from
+#pragma mapbox: initialize lowp float pixel_ratio_to
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,Nl=`
+#define scale 0.015873016
+#define LINE_DISTANCE_SCALE 2.0
+attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp float offset
+#pragma mapbox: define mediump float gapwidth
+#pragma mapbox: define mediump float width
+#pragma mapbox: define lowp float floorwidth
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+#pragma mapbox: define lowp float pixel_ratio_from
+#pragma mapbox: define lowp float pixel_ratio_to
+void main() {
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize lowp float offset
+#pragma mapbox: initialize mediump float gapwidth
+#pragma mapbox: initialize mediump float width
+#pragma mapbox: initialize lowp float floorwidth
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+#pragma mapbox: initialize lowp float pixel_ratio_from
+#pragma mapbox: initialize lowp float pixel_ratio_to
+float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}`,Kc=`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define mediump float width
+#pragma mapbox: define lowp float floorwidth
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump float width
+#pragma mapbox: initialize lowp float floorwidth
+float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,Rc=`
+#define scale 0.015873016
+#define LINE_DISTANCE_SCALE 2.0
+attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define mediump float gapwidth
+#pragma mapbox: define lowp float offset
+#pragma mapbox: define mediump float width
+#pragma mapbox: define lowp float floorwidth
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump float gapwidth
+#pragma mapbox: initialize lowp float offset
+#pragma mapbox: initialize mediump float width
+#pragma mapbox: initialize lowp float floorwidth
+float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}`,gs=`uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,jf="uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}",Gh=`uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize lowp float opacity
+lowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,rh=`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize lowp float opacity
+vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?
+camera_to_anchor_distance/u_camera_to_center_distance :
+u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}`,sf=`#define SDF_PX 8.0
+uniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;
+#pragma mapbox: define highp vec4 fill_color
+#pragma mapbox: define highp vec4 halo_color
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp float halo_width
+#pragma mapbox: define lowp float halo_blur
+void main() {
+#pragma mapbox: initialize highp vec4 fill_color
+#pragma mapbox: initialize highp vec4 halo_color
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize lowp float halo_width
+#pragma mapbox: initialize lowp float halo_blur
+float EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,Th=`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;
+#pragma mapbox: define highp vec4 fill_color
+#pragma mapbox: define highp vec4 halo_color
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp float halo_width
+#pragma mapbox: define lowp float halo_blur
+void main() {
+#pragma mapbox: initialize highp vec4 fill_color
+#pragma mapbox: initialize highp vec4 halo_color
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize lowp float halo_width
+#pragma mapbox: initialize lowp float halo_blur
+vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?
+camera_to_anchor_distance/u_camera_to_center_distance :
+u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}`,Mu=`#define SDF_PX 8.0
+#define SDF 1.0
+#define ICON 0.0
+uniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;
+#pragma mapbox: define highp vec4 fill_color
+#pragma mapbox: define highp vec4 halo_color
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp float halo_width
+#pragma mapbox: define lowp float halo_blur
+void main() {
+#pragma mapbox: initialize highp vec4 fill_color
+#pragma mapbox: initialize highp vec4 halo_color
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize lowp float halo_width
+#pragma mapbox: initialize lowp float halo_blur
+float fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+return;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,ih=`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;
+#pragma mapbox: define highp vec4 fill_color
+#pragma mapbox: define highp vec4 halo_color
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp float halo_width
+#pragma mapbox: define lowp float halo_blur
+void main() {
+#pragma mapbox: initialize highp vec4 fill_color
+#pragma mapbox: initialize highp vec4 halo_color
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize lowp float halo_width
+#pragma mapbox: initialize lowp float halo_blur
+vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?
+camera_to_anchor_distance/u_camera_to_center_distance :
+u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}`,js=Us(Cf,sc),Eu=Us(Vh,Lf),Dc=Us(cs,nf),ks=Us(Vf,Jl),bc=Us(fl,lc),hu=Us(Fu,Es),_u=Us(Hs,Go),nl=Us(ps,uc),nh=Us(xl,Gu),Ah=Us(qs,ad),zu=Us(Po,od),Fc=Us(Yo,Pa),wc=Us(af,Hu),bd=Us(bl,Gf),xf=Us(Ic,yf),Pf=Us(Bl,wh),Ou=Us(Qf,_f),bf=Us(Yc,eh),jl=Us(th,ju),lf=Us(Hf,cc),Hh=Us(of,Nl),If=Us(Kc,Rc),Cs=Us(gs,jf),du=Us(Gh,rh),ku=Us(sf,Th),Wf=Us(Mu,ih);function Us(Y,D){var J=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,q=D.match(/attribute ([\w]+) ([\w]+)/g),K=Y.match(/uniform ([\w]+) ([\w]+)([\s]*)([\w]*)/g),de=D.match(/uniform ([\w]+) ([\w]+)([\s]*)([\w]*)/g),ne=de?de.concat(K):K,we={};return Y=Y.replace(J,function(Ue,ft,Xt,hr,qt){return we[qt]=!0,ft==="define"?`
+#ifndef HAS_UNIFORM_u_`+qt+`
+varying `+Xt+" "+hr+" "+qt+`;
+#else
+uniform `+Xt+" "+hr+" u_"+qt+`;
+#endif
+`:`
+#ifdef HAS_UNIFORM_u_`+qt+`
+    `+Xt+" "+hr+" "+qt+" = u_"+qt+`;
+#endif
+`}),D=D.replace(J,function(Ue,ft,Xt,hr,qt){var Ve=hr==="float"?"vec2":"vec4",Qe=qt.match(/color/)?"color":Ve;return we[qt]?ft==="define"?`
+#ifndef HAS_UNIFORM_u_`+qt+`
+uniform lowp float u_`+qt+`_t;
+attribute `+Xt+" "+Ve+" a_"+qt+`;
+varying `+Xt+" "+hr+" "+qt+`;
+#else
+uniform `+Xt+" "+hr+" u_"+qt+`;
+#endif
+`:Qe==="vec4"?`
+#ifndef HAS_UNIFORM_u_`+qt+`
+    `+qt+" = a_"+qt+`;
+#else
+    `+Xt+" "+hr+" "+qt+" = u_"+qt+`;
+#endif
+`:`
+#ifndef HAS_UNIFORM_u_`+qt+`
+    `+qt+" = unpack_mix_"+Qe+"(a_"+qt+", u_"+qt+`_t);
+#else
+    `+Xt+" "+hr+" "+qt+" = u_"+qt+`;
+#endif
+`:ft==="define"?`
+#ifndef HAS_UNIFORM_u_`+qt+`
+uniform lowp float u_`+qt+`_t;
+attribute `+Xt+" "+Ve+" a_"+qt+`;
+#else
+uniform `+Xt+" "+hr+" u_"+qt+`;
+#endif
+`:Qe==="vec4"?`
+#ifndef HAS_UNIFORM_u_`+qt+`
+    `+Xt+" "+hr+" "+qt+" = a_"+qt+`;
+#else
+    `+Xt+" "+hr+" "+qt+" = u_"+qt+`;
+#endif
+`:`
+#ifndef HAS_UNIFORM_u_`+qt+`
+    `+Xt+" "+hr+" "+qt+" = unpack_mix_"+Qe+"(a_"+qt+", u_"+qt+`_t);
+#else
+    `+Xt+" "+hr+" "+qt+" = u_"+qt+`;
+#endif
+`}),{fragmentSource:Y,vertexSource:D,staticAttributes:q,staticUniforms:ne}}var wf=Object.freeze({__proto__:null,prelude:js,background:Eu,backgroundPattern:Dc,circle:ks,clippingMask:bc,heatmap:hu,heatmapTexture:_u,collisionBox:nl,collisionCircle:nh,debug:Ah,fill:zu,fillOutline:Fc,fillOutlinePattern:wc,fillPattern:bd,fillExtrusion:xf,fillExtrusionPattern:Pf,hillshadePrepare:Ou,hillshade:bf,line:jl,lineGradient:lf,linePattern:Hh,lineSDF:If,raster:Cs,symbolIcon:du,symbolSDF:ku,symbolTextAndIcon:Wf}),zc=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};zc.prototype.bind=function(D,J,q,K,de,ne,we,Ue){this.context=D;for(var ft=this.boundPaintVertexBuffers.length!==K.length,Xt=0;!ft&&Xt<K.length;Xt++)this.boundPaintVertexBuffers[Xt]!==K[Xt]&&(ft=!0);var hr=!this.vao||this.boundProgram!==J||this.boundLayoutVertexBuffer!==q||ft||this.boundIndexBuffer!==de||this.boundVertexOffset!==ne||this.boundDynamicVertexBuffer!==we||this.boundDynamicVertexBuffer2!==Ue;!D.extVertexArrayObject||hr?this.freshBind(J,q,K,de,ne,we,Ue):(D.bindVertexArrayOES.set(this.vao),we&&we.bind(),de&&de.dynamicDraw&&de.bind(),Ue&&Ue.bind())},zc.prototype.freshBind=function(D,J,q,K,de,ne,we){var Ue,ft=D.numAttributes,Xt=this.context,hr=Xt.gl;if(Xt.extVertexArrayObject)this.vao&&this.destroy(),this.vao=Xt.extVertexArrayObject.createVertexArrayOES(),Xt.bindVertexArrayOES.set(this.vao),Ue=0,this.boundProgram=D,this.boundLayoutVertexBuffer=J,this.boundPaintVertexBuffers=q,this.boundIndexBuffer=K,this.boundVertexOffset=de,this.boundDynamicVertexBuffer=ne,this.boundDynamicVertexBuffer2=we;else{Ue=Xt.currentNumAttributes||0;for(var qt=ft;qt<Ue;qt++)hr.disableVertexAttribArray(qt)}J.enableAttributes(hr,D);for(var Ve=0,Qe=q;Ve<Qe.length;Ve+=1){var at=Qe[Ve];at.enableAttributes(hr,D)}ne&&ne.enableAttributes(hr,D),we&&we.enableAttributes(hr,D),J.bind(),J.setVertexAttribPointers(hr,D,de);for(var Ct=0,Ot=q;Ct<Ot.length;Ct+=1){var Rt=Ot[Ct];Rt.bind(),Rt.setVertexAttribPointers(hr,D,de)}ne&&(ne.bind(),ne.setVertexAttribPointers(hr,D,de)),K&&K.bind(),we&&(we.bind(),we.setVertexAttribPointers(hr,D,de)),Xt.currentNumAttributes=ft},zc.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};function Wu(Y){for(var D=[],J=0;J<Y.length;J++)if(Y[J]!==null){var q=Y[J].split(" ");D.push(q.pop())}return D}var Rf=function(D,J,q,K,de,ne){var we=D.gl;this.program=we.createProgram();for(var Ue=Wu(q.staticAttributes),ft=K?K.getBinderAttributes():[],Xt=Ue.concat(ft),hr=q.staticUniforms?Wu(q.staticUniforms):[],qt=K?K.getBinderUniforms():[],Ve=hr.concat(qt),Qe=[],at=0,Ct=Ve;at<Ct.length;at+=1){var Ot=Ct[at];Qe.indexOf(Ot)<0&&Qe.push(Ot)}var Rt=K?K.defines():[];ne&&Rt.push("#define OVERDRAW_INSPECTOR;");var Bt=Rt.concat(js.fragmentSource,q.fragmentSource).join(`
+`),Dt=Rt.concat(js.vertexSource,q.vertexSource).join(`
+`),yt=we.createShader(we.FRAGMENT_SHADER);if(we.isContextLost()){this.failedToCreate=!0;return}we.shaderSource(yt,Bt),we.compileShader(yt),we.attachShader(this.program,yt);var Pt=we.createShader(we.VERTEX_SHADER);if(we.isContextLost()){this.failedToCreate=!0;return}we.shaderSource(Pt,Dt),we.compileShader(Pt),we.attachShader(this.program,Pt),this.attributes={};var ht={};this.numAttributes=Xt.length;for(var ur=0;ur<this.numAttributes;ur++)Xt[ur]&&(we.bindAttribLocation(this.program,ur,Xt[ur]),this.attributes[Xt[ur]]=ur);we.linkProgram(this.program),we.deleteShader(Pt),we.deleteShader(yt);for(var br=0;br<Qe.length;br++){var Ur=Qe[br];if(Ur&&!ht[Ur]){var Di=we.getUniformLocation(this.program,Ur);Di&&(ht[Ur]=Di)}}this.fixedUniforms=de(D,ht),this.binderUniforms=K?K.getUniforms(D,ht):[]};Rf.prototype.draw=function(D,J,q,K,de,ne,we,Ue,ft,Xt,hr,qt,Ve,Qe,at,Ct){var Ot,Rt=D.gl;if(!this.failedToCreate){D.program.set(this.program),D.setDepthMode(q),D.setStencilMode(K),D.setColorMode(de),D.setCullFace(ne);for(var Bt in this.fixedUniforms)this.fixedUniforms[Bt].set(we[Bt]);Qe&&Qe.setUniforms(D,this.binderUniforms,qt,{zoom:Ve});for(var Dt=(Ot={},Ot[Rt.LINES]=2,Ot[Rt.TRIANGLES]=3,Ot[Rt.LINE_STRIP]=1,Ot)[J],yt=0,Pt=hr.get();yt<Pt.length;yt+=1){var ht=Pt[yt],ur=ht.vaos||(ht.vaos={}),br=ur[Ue]||(ur[Ue]=new zc);br.bind(D,this,ft,Qe?Qe.getPaintVertexBuffers():[],Xt,ht.vertexOffset,at,Ct),Rt.drawElements(J,ht.primitiveLength*Dt,Rt.UNSIGNED_SHORT,ht.primitiveOffset*Dt*2)}}};function Xu(Y,D,J){var q=1/As(J,1,D.transform.tileZoom),K=Math.pow(2,J.tileID.overscaledZ),de=J.tileSize*Math.pow(2,D.transform.tileZoom)/K,ne=de*(J.tileID.canonical.x+J.tileID.wrap*K),we=de*J.tileID.canonical.y;return{u_image:0,u_texsize:J.imageAtlasTexture.size,u_scale:[q,Y.fromScale,Y.toScale],u_fade:Y.t,u_pixel_coord_upper:[ne>>16,we>>16],u_pixel_coord_lower:[ne&65535,we&65535]}}function uf(Y,D,J,q){var K=J.imageManager.getPattern(Y.from.toString()),de=J.imageManager.getPattern(Y.to.toString()),ne=J.imageManager.getPixelSize(),we=ne.width,Ue=ne.height,ft=Math.pow(2,q.tileID.overscaledZ),Xt=q.tileSize*Math.pow(2,J.transform.tileZoom)/ft,hr=Xt*(q.tileID.canonical.x+q.tileID.wrap*ft),qt=Xt*q.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:K.tl,u_pattern_br_a:K.br,u_pattern_tl_b:de.tl,u_pattern_br_b:de.br,u_texsize:[we,Ue],u_mix:D.t,u_pattern_size_a:K.displaySize,u_pattern_size_b:de.displaySize,u_scale_a:D.fromScale,u_scale_b:D.toScale,u_tile_units_to_pixels:1/As(q,1,J.transform.tileZoom),u_pixel_coord_upper:[hr>>16,qt>>16],u_pixel_coord_lower:[hr&65535,qt&65535]}}var Xf=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_lightpos:new i.Uniform3f(Y,D.u_lightpos),u_lightintensity:new i.Uniform1f(Y,D.u_lightintensity),u_lightcolor:new i.Uniform3f(Y,D.u_lightcolor),u_vertical_gradient:new i.Uniform1f(Y,D.u_vertical_gradient),u_opacity:new i.Uniform1f(Y,D.u_opacity)}},Wl=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_lightpos:new i.Uniform3f(Y,D.u_lightpos),u_lightintensity:new i.Uniform1f(Y,D.u_lightintensity),u_lightcolor:new i.Uniform3f(Y,D.u_lightcolor),u_vertical_gradient:new i.Uniform1f(Y,D.u_vertical_gradient),u_height_factor:new i.Uniform1f(Y,D.u_height_factor),u_image:new i.Uniform1i(Y,D.u_image),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_pixel_coord_upper:new i.Uniform2f(Y,D.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(Y,D.u_pixel_coord_lower),u_scale:new i.Uniform3f(Y,D.u_scale),u_fade:new i.Uniform1f(Y,D.u_fade),u_opacity:new i.Uniform1f(Y,D.u_opacity)}},ah=function(Y,D,J,q){var K=D.style.light,de=K.properties.get("position"),ne=[de.x,de.y,de.z],we=i.create$1();K.properties.get("anchor")==="viewport"&&i.fromRotation(we,-D.transform.angle),i.transformMat3(ne,ne,we);var Ue=K.properties.get("color");return{u_matrix:Y,u_lightpos:ne,u_lightintensity:K.properties.get("intensity"),u_lightcolor:[Ue.r,Ue.g,Ue.b],u_vertical_gradient:+J,u_opacity:q}},Zu=function(Y,D,J,q,K,de,ne){return i.extend(ah(Y,D,J,q),Xu(de,D,ne),{u_height_factor:-Math.pow(2,K.overscaledZ)/ne.tileSize/8})},Oc=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix)}},Tc=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_image:new i.Uniform1i(Y,D.u_image),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_pixel_coord_upper:new i.Uniform2f(Y,D.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(Y,D.u_pixel_coord_lower),u_scale:new i.Uniform3f(Y,D.u_scale),u_fade:new i.Uniform1f(Y,D.u_fade)}},wl=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_world:new i.Uniform2f(Y,D.u_world)}},vu=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_world:new i.Uniform2f(Y,D.u_world),u_image:new i.Uniform1i(Y,D.u_image),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_pixel_coord_upper:new i.Uniform2f(Y,D.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(Y,D.u_pixel_coord_lower),u_scale:new i.Uniform3f(Y,D.u_scale),u_fade:new i.Uniform1f(Y,D.u_fade)}},qc=function(Y){return{u_matrix:Y}},cf=function(Y,D,J,q){return i.extend(qc(Y),Xu(J,D,q))},fc=function(Y,D){return{u_matrix:Y,u_world:D}},Bc=function(Y,D,J,q,K){return i.extend(cf(Y,D,J,q),{u_world:K})},At=function(Y,D){return{u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_scale_with_map:new i.Uniform1i(Y,D.u_scale_with_map),u_pitch_with_map:new i.Uniform1i(Y,D.u_pitch_with_map),u_extrude_scale:new i.Uniform2f(Y,D.u_extrude_scale),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix)}},Wt=function(Y,D,J,q){var K=Y.transform,de,ne;if(q.paint.get("circle-pitch-alignment")==="map"){var we=As(J,1,K.zoom);de=!0,ne=[we,we]}else de=!1,ne=K.pixelsToGLUnits;return{u_camera_to_center_distance:K.cameraToCenterDistance,u_scale_with_map:+(q.paint.get("circle-pitch-scale")==="map"),u_matrix:Y.translatePosMatrix(D.posMatrix,J,q.paint.get("circle-translate"),q.paint.get("circle-translate-anchor")),u_pitch_with_map:+de,u_device_pixel_ratio:i.browser.devicePixelRatio,u_extrude_scale:ne}},Cr=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_pixels_to_tile_units:new i.Uniform1f(Y,D.u_pixels_to_tile_units),u_extrude_scale:new i.Uniform2f(Y,D.u_extrude_scale),u_overscale_factor:new i.Uniform1f(Y,D.u_overscale_factor)}},Ar=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_inv_matrix:new i.UniformMatrix4f(Y,D.u_inv_matrix),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_viewport_size:new i.Uniform2f(Y,D.u_viewport_size)}},Kr=function(Y,D,J){var q=As(J,1,D.zoom),K=Math.pow(2,D.zoom-J.tileID.overscaledZ),de=J.tileID.overscaleFactor();return{u_matrix:Y,u_camera_to_center_distance:D.cameraToCenterDistance,u_pixels_to_tile_units:q,u_extrude_scale:[D.pixelsToGLUnits[0]/(q*K),D.pixelsToGLUnits[1]/(q*K)],u_overscale_factor:de}},ki=function(Y,D,J){return{u_matrix:Y,u_inv_matrix:D,u_camera_to_center_distance:J.cameraToCenterDistance,u_viewport_size:[J.width,J.height]}},Xi=function(Y,D){return{u_color:new i.UniformColor(Y,D.u_color),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_overlay:new i.Uniform1i(Y,D.u_overlay),u_overlay_scale:new i.Uniform1f(Y,D.u_overlay_scale)}},dn=function(Y,D,J){return J===void 0&&(J=1),{u_matrix:Y,u_color:D,u_overlay:0,u_overlay_scale:J}},wn=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix)}},Nn=function(Y){return{u_matrix:Y}},Yi=function(Y,D){return{u_extrude_scale:new i.Uniform1f(Y,D.u_extrude_scale),u_intensity:new i.Uniform1f(Y,D.u_intensity),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix)}},Qi=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_world:new i.Uniform2f(Y,D.u_world),u_image:new i.Uniform1i(Y,D.u_image),u_color_ramp:new i.Uniform1i(Y,D.u_color_ramp),u_opacity:new i.Uniform1f(Y,D.u_opacity)}},on=function(Y,D,J,q){return{u_matrix:Y,u_extrude_scale:As(D,1,J),u_intensity:q}},Fi=function(Y,D,J,q){var K=i.create();i.ortho(K,0,Y.width,Y.height,0,0,1);var de=Y.context.gl;return{u_matrix:K,u_world:[de.drawingBufferWidth,de.drawingBufferHeight],u_image:J,u_color_ramp:q,u_opacity:D.paint.get("heatmap-opacity")}},$n=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_image:new i.Uniform1i(Y,D.u_image),u_latrange:new i.Uniform2f(Y,D.u_latrange),u_light:new i.Uniform2f(Y,D.u_light),u_shadow:new i.UniformColor(Y,D.u_shadow),u_highlight:new i.UniformColor(Y,D.u_highlight),u_accent:new i.UniformColor(Y,D.u_accent)}},Ca=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_image:new i.Uniform1i(Y,D.u_image),u_dimension:new i.Uniform2f(Y,D.u_dimension),u_zoom:new i.Uniform1f(Y,D.u_zoom),u_unpack:new i.Uniform4f(Y,D.u_unpack)}},Ra=function(Y,D,J){var q=J.paint.get("hillshade-shadow-color"),K=J.paint.get("hillshade-highlight-color"),de=J.paint.get("hillshade-accent-color"),ne=J.paint.get("hillshade-illumination-direction")*(Math.PI/180);J.paint.get("hillshade-illumination-anchor")==="viewport"&&(ne-=Y.transform.angle);var we=!Y.options.moving;return{u_matrix:Y.transform.calculatePosMatrix(D.tileID.toUnwrapped(),we),u_image:0,u_latrange:Na(Y,D.tileID),u_light:[J.paint.get("hillshade-exaggeration"),ne],u_shadow:q,u_highlight:K,u_accent:de}},La=function(Y,D){var J=D.stride,q=i.create();return i.ortho(q,0,i.EXTENT,-i.EXTENT,0,0,1),i.translate(q,q,[0,-i.EXTENT,0]),{u_matrix:q,u_image:1,u_dimension:[J,J],u_zoom:Y.overscaledZ,u_unpack:D.getUnpackVector()}};function Na(Y,D){var J=Math.pow(2,D.canonical.z),q=D.canonical.y;return[new i.MercatorCoordinate(0,q/J).toLngLat().lat,new i.MercatorCoordinate(0,(q+1)/J).toLngLat().lat]}var Yn=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_ratio:new i.Uniform1f(Y,D.u_ratio),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_units_to_pixels:new i.Uniform2f(Y,D.u_units_to_pixels)}},Dn=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_ratio:new i.Uniform1f(Y,D.u_ratio),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_units_to_pixels:new i.Uniform2f(Y,D.u_units_to_pixels),u_image:new i.Uniform1i(Y,D.u_image),u_image_height:new i.Uniform1f(Y,D.u_image_height)}},Ka=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_ratio:new i.Uniform1f(Y,D.u_ratio),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_image:new i.Uniform1i(Y,D.u_image),u_units_to_pixels:new i.Uniform2f(Y,D.u_units_to_pixels),u_scale:new i.Uniform3f(Y,D.u_scale),u_fade:new i.Uniform1f(Y,D.u_fade)}},bo=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_ratio:new i.Uniform1f(Y,D.u_ratio),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_units_to_pixels:new i.Uniform2f(Y,D.u_units_to_pixels),u_patternscale_a:new i.Uniform2f(Y,D.u_patternscale_a),u_patternscale_b:new i.Uniform2f(Y,D.u_patternscale_b),u_sdfgamma:new i.Uniform1f(Y,D.u_sdfgamma),u_image:new i.Uniform1i(Y,D.u_image),u_tex_y_a:new i.Uniform1f(Y,D.u_tex_y_a),u_tex_y_b:new i.Uniform1f(Y,D.u_tex_y_b),u_mix:new i.Uniform1f(Y,D.u_mix)}},Xo=function(Y,D,J){var q=Y.transform;return{u_matrix:ml(Y,D,J),u_ratio:1/As(D,1,q.zoom),u_device_pixel_ratio:i.browser.devicePixelRatio,u_units_to_pixels:[1/q.pixelsToGLUnits[0],1/q.pixelsToGLUnits[1]]}},Ss=function(Y,D,J,q){return i.extend(Xo(Y,D,J),{u_image:0,u_image_height:q})},as=function(Y,D,J,q){var K=Y.transform,de=Ho(D,K);return{u_matrix:ml(Y,D,J),u_texsize:D.imageAtlasTexture.size,u_ratio:1/As(D,1,K.zoom),u_device_pixel_ratio:i.browser.devicePixelRatio,u_image:0,u_scale:[de,q.fromScale,q.toScale],u_fade:q.t,u_units_to_pixels:[1/K.pixelsToGLUnits[0],1/K.pixelsToGLUnits[1]]}},ws=function(Y,D,J,q,K){var de=Y.transform,ne=Y.lineAtlas,we=Ho(D,de),Ue=J.layout.get("line-cap")==="round",ft=ne.getDash(q.from,Ue),Xt=ne.getDash(q.to,Ue),hr=ft.width*K.fromScale,qt=Xt.width*K.toScale;return i.extend(Xo(Y,D,J),{u_patternscale_a:[we/hr,-ft.height/2],u_patternscale_b:[we/qt,-Xt.height/2],u_sdfgamma:ne.width/(Math.min(hr,qt)*256*i.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:ft.y,u_tex_y_b:Xt.y,u_mix:K.t})};function Ho(Y,D){return 1/As(Y,1,D.tileZoom)}function ml(Y,D,J){return Y.translatePosMatrix(D.tileID.posMatrix,D,J.paint.get("line-translate"),J.paint.get("line-translate-anchor"))}var Ws=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_tl_parent:new i.Uniform2f(Y,D.u_tl_parent),u_scale_parent:new i.Uniform1f(Y,D.u_scale_parent),u_buffer_scale:new i.Uniform1f(Y,D.u_buffer_scale),u_fade_t:new i.Uniform1f(Y,D.u_fade_t),u_opacity:new i.Uniform1f(Y,D.u_opacity),u_image0:new i.Uniform1i(Y,D.u_image0),u_image1:new i.Uniform1i(Y,D.u_image1),u_brightness_low:new i.Uniform1f(Y,D.u_brightness_low),u_brightness_high:new i.Uniform1f(Y,D.u_brightness_high),u_saturation_factor:new i.Uniform1f(Y,D.u_saturation_factor),u_contrast_factor:new i.Uniform1f(Y,D.u_contrast_factor),u_spin_weights:new i.Uniform3f(Y,D.u_spin_weights)}},Ls=function(Y,D,J,q,K){return{u_matrix:Y,u_tl_parent:D,u_scale_parent:J,u_buffer_scale:1,u_fade_t:q.mix,u_opacity:q.opacity*K.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:K.paint.get("raster-brightness-min"),u_brightness_high:K.paint.get("raster-brightness-max"),u_saturation_factor:ys(K.paint.get("raster-saturation")),u_contrast_factor:no(K.paint.get("raster-contrast")),u_spin_weights:va(K.paint.get("raster-hue-rotate"))}};function va(Y){Y*=Math.PI/180;var D=Math.sin(Y),J=Math.cos(Y);return[(2*J+1)/3,(-Math.sqrt(3)*D-J+1)/3,(Math.sqrt(3)*D-J+1)/3]}function no(Y){return Y>0?1/(1-Y):1+Y}function ys(Y){return Y>0?1-1/(1.001-Y):-Y}var rs=function(Y,D){return{u_is_size_zoom_constant:new i.Uniform1i(Y,D.u_is_size_zoom_constant),u_is_size_feature_constant:new i.Uniform1i(Y,D.u_is_size_feature_constant),u_size_t:new i.Uniform1f(Y,D.u_size_t),u_size:new i.Uniform1f(Y,D.u_size),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_pitch:new i.Uniform1f(Y,D.u_pitch),u_rotate_symbol:new i.Uniform1i(Y,D.u_rotate_symbol),u_aspect_ratio:new i.Uniform1f(Y,D.u_aspect_ratio),u_fade_change:new i.Uniform1f(Y,D.u_fade_change),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_label_plane_matrix:new i.UniformMatrix4f(Y,D.u_label_plane_matrix),u_coord_matrix:new i.UniformMatrix4f(Y,D.u_coord_matrix),u_is_text:new i.Uniform1i(Y,D.u_is_text),u_pitch_with_map:new i.Uniform1i(Y,D.u_pitch_with_map),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_texture:new i.Uniform1i(Y,D.u_texture)}},$l=function(Y,D){return{u_is_size_zoom_constant:new i.Uniform1i(Y,D.u_is_size_zoom_constant),u_is_size_feature_constant:new i.Uniform1i(Y,D.u_is_size_feature_constant),u_size_t:new i.Uniform1f(Y,D.u_size_t),u_size:new i.Uniform1f(Y,D.u_size),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_pitch:new i.Uniform1f(Y,D.u_pitch),u_rotate_symbol:new i.Uniform1i(Y,D.u_rotate_symbol),u_aspect_ratio:new i.Uniform1f(Y,D.u_aspect_ratio),u_fade_change:new i.Uniform1f(Y,D.u_fade_change),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_label_plane_matrix:new i.UniformMatrix4f(Y,D.u_label_plane_matrix),u_coord_matrix:new i.UniformMatrix4f(Y,D.u_coord_matrix),u_is_text:new i.Uniform1i(Y,D.u_is_text),u_pitch_with_map:new i.Uniform1i(Y,D.u_pitch_with_map),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_texture:new i.Uniform1i(Y,D.u_texture),u_gamma_scale:new i.Uniform1f(Y,D.u_gamma_scale),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_is_halo:new i.Uniform1i(Y,D.u_is_halo)}},Cu=function(Y,D){return{u_is_size_zoom_constant:new i.Uniform1i(Y,D.u_is_size_zoom_constant),u_is_size_feature_constant:new i.Uniform1i(Y,D.u_is_size_feature_constant),u_size_t:new i.Uniform1f(Y,D.u_size_t),u_size:new i.Uniform1f(Y,D.u_size),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_pitch:new i.Uniform1f(Y,D.u_pitch),u_rotate_symbol:new i.Uniform1i(Y,D.u_rotate_symbol),u_aspect_ratio:new i.Uniform1f(Y,D.u_aspect_ratio),u_fade_change:new i.Uniform1f(Y,D.u_fade_change),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_label_plane_matrix:new i.UniformMatrix4f(Y,D.u_label_plane_matrix),u_coord_matrix:new i.UniformMatrix4f(Y,D.u_coord_matrix),u_is_text:new i.Uniform1i(Y,D.u_is_text),u_pitch_with_map:new i.Uniform1i(Y,D.u_pitch_with_map),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_texsize_icon:new i.Uniform2f(Y,D.u_texsize_icon),u_texture:new i.Uniform1i(Y,D.u_texture),u_texture_icon:new i.Uniform1i(Y,D.u_texture_icon),u_gamma_scale:new i.Uniform1f(Y,D.u_gamma_scale),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_is_halo:new i.Uniform1i(Y,D.u_is_halo)}},Yu=function(Y,D,J,q,K,de,ne,we,Ue,ft){var Xt=K.transform;return{u_is_size_zoom_constant:+(Y==="constant"||Y==="source"),u_is_size_feature_constant:+(Y==="constant"||Y==="camera"),u_size_t:D?D.uSizeT:0,u_size:D?D.uSize:0,u_camera_to_center_distance:Xt.cameraToCenterDistance,u_pitch:Xt.pitch/360*2*Math.PI,u_rotate_symbol:+J,u_aspect_ratio:Xt.width/Xt.height,u_fade_change:K.options.fadeDuration?K.symbolFadeChange:1,u_matrix:de,u_label_plane_matrix:ne,u_coord_matrix:we,u_is_text:+Ue,u_pitch_with_map:+q,u_texsize:ft,u_texture:0}},Nc=function(Y,D,J,q,K,de,ne,we,Ue,ft,Xt){var hr=K.transform;return i.extend(Yu(Y,D,J,q,K,de,ne,we,Ue,ft),{u_gamma_scale:q?Math.cos(hr._pitch)*hr.cameraToCenterDistance:1,u_device_pixel_ratio:i.browser.devicePixelRatio,u_is_halo:+Xt})},pu=function(Y,D,J,q,K,de,ne,we,Ue,ft){return i.extend(Nc(Y,D,J,q,K,de,ne,we,!0,Ue,!0),{u_texsize_icon:ft,u_texture_icon:1})},Uc=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_opacity:new i.Uniform1f(Y,D.u_opacity),u_color:new i.UniformColor(Y,D.u_color)}},xu=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_opacity:new i.Uniform1f(Y,D.u_opacity),u_image:new i.Uniform1i(Y,D.u_image),u_pattern_tl_a:new i.Uniform2f(Y,D.u_pattern_tl_a),u_pattern_br_a:new i.Uniform2f(Y,D.u_pattern_br_a),u_pattern_tl_b:new i.Uniform2f(Y,D.u_pattern_tl_b),u_pattern_br_b:new i.Uniform2f(Y,D.u_pattern_br_b),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_mix:new i.Uniform1f(Y,D.u_mix),u_pattern_size_a:new i.Uniform2f(Y,D.u_pattern_size_a),u_pattern_size_b:new i.Uniform2f(Y,D.u_pattern_size_b),u_scale_a:new i.Uniform1f(Y,D.u_scale_a),u_scale_b:new i.Uniform1f(Y,D.u_scale_b),u_pixel_coord_upper:new i.Uniform2f(Y,D.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(Y,D.u_pixel_coord_lower),u_tile_units_to_pixels:new i.Uniform1f(Y,D.u_tile_units_to_pixels)}},Ac=function(Y,D,J){return{u_matrix:Y,u_opacity:D,u_color:J}},Ua=function(Y,D,J,q,K,de){return i.extend(uf(q,de,J,K),{u_matrix:Y,u_opacity:D})},oo={fillExtrusion:Xf,fillExtrusionPattern:Wl,fill:Oc,fillPattern:Tc,fillOutline:wl,fillOutlinePattern:vu,circle:At,collisionBox:Cr,collisionCircle:Ar,debug:Xi,clippingMask:wn,heatmap:Yi,heatmapTexture:Qi,hillshade:$n,hillshadePrepare:Ca,line:Yn,lineGradient:Dn,linePattern:Ka,lineSDF:bo,raster:Ws,symbolIcon:rs,symbolSDF:$l,symbolTextAndIcon:Cu,background:Uc,backgroundPattern:xu},Vc;function hc(Y,D,J,q,K,de,ne){for(var we=Y.context,Ue=we.gl,ft=Y.useProgram("collisionBox"),Xt=[],hr=0,qt=0,Ve=0;Ve<q.length;Ve++){var Qe=q[Ve],at=D.getTile(Qe),Ct=at.getBucket(J);if(Ct){var Ot=Qe.posMatrix;(K[0]!==0||K[1]!==0)&&(Ot=Y.translatePosMatrix(Qe.posMatrix,at,K,de));var Rt=ne?Ct.textCollisionBox:Ct.iconCollisionBox,Bt=Ct.collisionCircleArray;if(Bt.length>0){var Dt=i.create(),yt=Ot;i.mul(Dt,Ct.placementInvProjMatrix,Y.transform.glCoordMatrix),i.mul(Dt,Dt,Ct.placementViewportMatrix),Xt.push({circleArray:Bt,circleOffset:qt,transform:yt,invTransform:Dt}),hr+=Bt.length/4,qt=hr}Rt&&ft.draw(we,Ue.LINES,wi.disabled,Fn.disabled,Y.colorModeForRenderPass(),Er.disabled,Kr(Ot,Y.transform,at),J.id,Rt.layoutVertexBuffer,Rt.indexBuffer,Rt.segments,null,Y.transform.zoom,null,null,Rt.collisionVertexBuffer)}}if(!(!ne||!Xt.length)){var Pt=Y.useProgram("collisionCircle"),ht=new i.StructArrayLayout2f1f2i16;ht.resize(hr*4),ht._trim();for(var ur=0,br=0,Ur=Xt;br<Ur.length;br+=1)for(var Di=Ur[br],fi=0;fi<Di.circleArray.length/4;fi++){var Ti=fi*4,gn=Di.circleArray[Ti+0],rn=Di.circleArray[Ti+1],Ci=Di.circleArray[Ti+2],Bi=Di.circleArray[Ti+3];ht.emplace(ur++,gn,rn,Ci,Bi,0),ht.emplace(ur++,gn,rn,Ci,Bi,1),ht.emplace(ur++,gn,rn,Ci,Bi,2),ht.emplace(ur++,gn,rn,Ci,Bi,3)}(!Vc||Vc.length<hr*2)&&(Vc=Ku(hr));for(var Gi=we.createIndexBuffer(Vc,!0),sn=we.createVertexBuffer(ht,i.collisionCircleLayout.members,!0),zn=0,Ja=Xt;zn<Ja.length;zn+=1){var co=Ja[zn],ts=ki(co.transform,co.invTransform,Y.transform);Pt.draw(we,Ue.TRIANGLES,wi.disabled,Fn.disabled,Y.colorModeForRenderPass(),Er.disabled,ts,J.id,sn,Gi,i.SegmentVector.simpleSegment(0,co.circleOffset*2,co.circleArray.length,co.circleArray.length/2),null,Y.transform.zoom,null,null,null)}sn.destroy(),Gi.destroy()}}function Ku(Y){var D=Y*2,J=new i.StructArrayLayout3ui6;J.resize(D),J._trim();for(var q=0;q<D;q++){var K=q*6;J.uint16[K+0]=q*4+0,J.uint16[K+1]=q*4+1,J.uint16[K+2]=q*4+2,J.uint16[K+3]=q*4+2,J.uint16[K+4]=q*4+3,J.uint16[K+5]=q*4+0}return J}var ue=i.identity(new Float32Array(16));function w(Y,D,J,q,K){if(Y.renderPass==="translucent"){var de=Fn.disabled,ne=Y.colorModeForRenderPass(),we=J.layout.get("text-variable-anchor");we&&Q(q,Y,J,D,J.layout.get("text-rotation-alignment"),J.layout.get("text-pitch-alignment"),K),J.paint.get("icon-opacity").constantOr(1)!==0&&qe(Y,D,J,q,!1,J.paint.get("icon-translate"),J.paint.get("icon-translate-anchor"),J.layout.get("icon-rotation-alignment"),J.layout.get("icon-pitch-alignment"),J.layout.get("icon-keep-upright"),de,ne),J.paint.get("text-opacity").constantOr(1)!==0&&qe(Y,D,J,q,!0,J.paint.get("text-translate"),J.paint.get("text-translate-anchor"),J.layout.get("text-rotation-alignment"),J.layout.get("text-pitch-alignment"),J.layout.get("text-keep-upright"),de,ne),D.map.showCollisionBoxes&&(hc(Y,D,J,q,J.paint.get("text-translate"),J.paint.get("text-translate-anchor"),!0),hc(Y,D,J,q,J.paint.get("icon-translate"),J.paint.get("icon-translate-anchor"),!1))}}function B(Y,D,J,q,K,de){var ne=i.getAnchorAlignment(Y),we=ne.horizontalAlign,Ue=ne.verticalAlign,ft=-(we-.5)*D,Xt=-(Ue-.5)*J,hr=i.evaluateVariableOffset(Y,q);return new i.Point((ft/K+hr[0])*de,(Xt/K+hr[1])*de)}function Q(Y,D,J,q,K,de,ne){for(var we=D.transform,Ue=K==="map",ft=de==="map",Xt=0,hr=Y;Xt<hr.length;Xt+=1){var qt=hr[Xt],Ve=q.getTile(qt),Qe=Ve.getBucket(J);if(!(!Qe||!Qe.text||!Qe.text.segments.get().length)){var at=Qe.textSizeData,Ct=i.evaluateSizeForZoom(at,we.zoom),Ot=As(Ve,1,D.transform.zoom),Rt=_n(qt.posMatrix,ft,Ue,D.transform,Ot),Bt=J.layout.get("icon-text-fit")!=="none"&&Qe.hasIconData();if(Ct){var Dt=Math.pow(2,we.zoom-Ve.tileID.overscaledZ);ee(Qe,Ue,ft,ne,i.symbolSize,we,Rt,qt.posMatrix,Dt,Ct,Bt)}}}}function ee(Y,D,J,q,K,de,ne,we,Ue,ft,Xt){var hr=Y.text.placedSymbolArray,qt=Y.text.dynamicLayoutVertexArray,Ve=Y.icon.dynamicLayoutVertexArray,Qe={};qt.clear();for(var at=0;at<hr.length;at++){var Ct=hr.get(at),Ot=Y.allowVerticalPlacement&&!Ct.placedOrientation,Rt=!Ct.hidden&&Ct.crossTileID&&!Ot?q[Ct.crossTileID]:null;if(!Rt)ul(Ct.numGlyphs,qt);else{var Bt=new i.Point(Ct.anchorX,Ct.anchorY),Dt=Jn(Bt,J?we:ne),yt=Ma(de.cameraToCenterDistance,Dt.signedDistanceFromCamera),Pt=K.evaluateSizeForFeature(Y.textSizeData,ft,Ct)*yt/i.ONE_EM;J&&(Pt*=Y.tilePixelRatio/Ue);for(var ht=Rt.width,ur=Rt.height,br=Rt.anchor,Ur=Rt.textOffset,Di=Rt.textBoxScale,fi=B(br,ht,ur,Ur,Di,Pt),Ti=J?Jn(Bt.add(fi),ne).point:Dt.point.add(D?fi.rotate(-de.angle):fi),gn=Y.allowVerticalPlacement&&Ct.placedOrientation===i.WritingMode.vertical?Math.PI/2:0,rn=0;rn<Ct.numGlyphs;rn++)i.addDynamicAttributes(qt,Ti,gn);Xt&&Ct.associatedIconIndex>=0&&(Qe[Ct.associatedIconIndex]={shiftedAnchor:Ti,angle:gn})}}if(Xt){Ve.clear();for(var Ci=Y.icon.placedSymbolArray,Bi=0;Bi<Ci.length;Bi++){var Gi=Ci.get(Bi);if(Gi.hidden)ul(Gi.numGlyphs,Ve);else{var sn=Qe[Bi];if(!sn)ul(Gi.numGlyphs,Ve);else for(var zn=0;zn<Gi.numGlyphs;zn++)i.addDynamicAttributes(Ve,sn.shiftedAnchor,sn.angle)}}Y.icon.dynamicLayoutVertexBuffer.updateData(Ve)}Y.text.dynamicLayoutVertexBuffer.updateData(qt)}function le(Y,D,J){return J.iconsInText&&D?"symbolTextAndIcon":Y?"symbolSDF":"symbolIcon"}function qe(Y,D,J,q,K,de,ne,we,Ue,ft,Xt,hr){for(var qt=Y.context,Ve=qt.gl,Qe=Y.transform,at=we==="map",Ct=Ue==="map",Ot=at&&J.layout.get("symbol-placement")!=="point",Rt=at&&!Ct&&!Ot,Bt=J.layout.get("symbol-sort-key").constantOr(1)!==void 0,Dt=!1,yt=Y.depthModeForSublayer(0,wi.ReadOnly),Pt=J.layout.get("text-variable-anchor"),ht=[],ur=0,br=q;ur<br.length;ur+=1){var Ur=br[ur],Di=D.getTile(Ur),fi=Di.getBucket(J);if(fi){var Ti=K?fi.text:fi.icon;if(!(!Ti||!Ti.segments.get().length)){var gn=Ti.programConfigurations.get(J.id),rn=K||fi.sdfIcons,Ci=K?fi.textSizeData:fi.iconSizeData,Bi=Ct||Qe.pitch!==0,Gi=Y.useProgram(le(rn,K,fi),gn),sn=i.evaluateSizeForZoom(Ci,Qe.zoom),zn=void 0,Ja=[0,0],co=void 0,ts=void 0,so=null,Zo=void 0;if(K){if(co=Di.glyphAtlasTexture,ts=Ve.LINEAR,zn=Di.glyphAtlasTexture.size,fi.iconsInText){Ja=Di.imageAtlasTexture.size,so=Di.imageAtlasTexture;var ms=Ci.kind==="composite"||Ci.kind==="camera";Zo=Bi||Y.options.rotating||Y.options.zooming||ms?Ve.LINEAR:Ve.NEAREST}}else{var ou=J.layout.get("icon-size").constantOr(0)!==1||fi.iconsNeedLinear;co=Di.imageAtlasTexture,ts=rn||Y.options.rotating||Y.options.zooming||ou||Bi?Ve.LINEAR:Ve.NEAREST,zn=Di.imageAtlasTexture.size}var Cv=As(Di,1,Y.transform.zoom),Lv=_n(Ur.posMatrix,Ct,at,Y.transform,Cv),wd=ya(Ur.posMatrix,Ct,at,Y.transform,Cv),Kv=Pt&&fi.hasTextData(),cg=J.layout.get("icon-text-fit")!=="none"&&Kv&&fi.hasIconData();Ot&&No(fi,Ur.posMatrix,Y,K,Lv,wd,Ct,ft);var pp=Y.translatePosMatrix(Ur.posMatrix,Di,de,ne),Td=Ot||K&&Pt||cg?ue:Lv,gp=Y.translatePosMatrix(wd,Di,de,ne,!0),Vd=rn&&J.paint.get(K?"text-halo-width":"icon-halo-width").constantOr(1)!==0,Ad=void 0;rn?fi.iconsInText?Ad=pu(Ci.kind,sn,Rt,Ct,Y,pp,Td,gp,zn,Ja):Ad=Nc(Ci.kind,sn,Rt,Ct,Y,pp,Td,gp,K,zn,!0):Ad=Yu(Ci.kind,sn,Rt,Ct,Y,pp,Td,gp,K,zn);var Pv={program:Gi,buffers:Ti,uniformValues:Ad,atlasTexture:co,atlasTextureIcon:so,atlasInterpolation:ts,atlasInterpolationIcon:Zo,isSDF:rn,hasHalo:Vd};if(Bt&&fi.canOverlap){Dt=!0;for(var Jv=Ti.segments.get(),Iv=0,ay=Jv;Iv<ay.length;Iv+=1){var fg=ay[Iv];ht.push({segments:new i.SegmentVector([fg]),sortKey:fg.sortKey,state:Pv})}}else ht.push({segments:Ti.segments,sortKey:0,state:Pv})}}}Dt&&ht.sort(function(b1,w1){return b1.sortKey-w1.sortKey});for(var oh=0,hg=ht;oh<hg.length;oh+=1){var oy=hg[oh],jh=oy.state;if(qt.activeTexture.set(Ve.TEXTURE0),jh.atlasTexture.bind(jh.atlasInterpolation,Ve.CLAMP_TO_EDGE),jh.atlasTextureIcon&&(qt.activeTexture.set(Ve.TEXTURE1),jh.atlasTextureIcon&&jh.atlasTextureIcon.bind(jh.atlasInterpolationIcon,Ve.CLAMP_TO_EDGE)),jh.isSDF){var im=jh.uniformValues;jh.hasHalo&&(im.u_is_halo=1,Xe(jh.buffers,oy.segments,J,Y,jh.program,yt,Xt,hr,im)),im.u_is_halo=0}Xe(jh.buffers,oy.segments,J,Y,jh.program,yt,Xt,hr,jh.uniformValues)}}function Xe(Y,D,J,q,K,de,ne,we,Ue){var ft=q.context,Xt=ft.gl;K.draw(ft,Xt.TRIANGLES,de,ne,we,Er.disabled,Ue,J.id,Y.layoutVertexBuffer,Y.indexBuffer,D,J.paint,q.transform.zoom,Y.programConfigurations.get(J.id),Y.dynamicLayoutVertexBuffer,Y.opacityVertexBuffer)}function ot(Y,D,J,q){if(Y.renderPass==="translucent"){var K=J.paint.get("circle-opacity"),de=J.paint.get("circle-stroke-width"),ne=J.paint.get("circle-stroke-opacity"),we=J.layout.get("circle-sort-key").constantOr(1)!==void 0;if(!(K.constantOr(1)===0&&(de.constantOr(1)===0||ne.constantOr(1)===0))){for(var Ue=Y.context,ft=Ue.gl,Xt=Y.depthModeForSublayer(0,wi.ReadOnly),hr=Fn.disabled,qt=Y.colorModeForRenderPass(),Ve=[],Qe=0;Qe<q.length;Qe++){var at=q[Qe],Ct=D.getTile(at),Ot=Ct.getBucket(J);if(Ot){var Rt=Ot.programConfigurations.get(J.id),Bt=Y.useProgram("circle",Rt),Dt=Ot.layoutVertexBuffer,yt=Ot.indexBuffer,Pt=Wt(Y,at,Ct,J),ht={programConfiguration:Rt,program:Bt,layoutVertexBuffer:Dt,indexBuffer:yt,uniformValues:Pt};if(we)for(var ur=Ot.segments.get(),br=0,Ur=ur;br<Ur.length;br+=1){var Di=Ur[br];Ve.push({segments:new i.SegmentVector([Di]),sortKey:Di.sortKey,state:ht})}else Ve.push({segments:Ot.segments,sortKey:0,state:ht})}}we&&Ve.sort(function(co,ts){return co.sortKey-ts.sortKey});for(var fi=0,Ti=Ve;fi<Ti.length;fi+=1){var gn=Ti[fi],rn=gn.state,Ci=rn.programConfiguration,Bi=rn.program,Gi=rn.layoutVertexBuffer,sn=rn.indexBuffer,zn=rn.uniformValues,Ja=gn.segments;Bi.draw(Ue,ft.TRIANGLES,Xt,hr,qt,Er.disabled,zn,J.id,Gi,sn,Ja,J.paint,Y.transform.zoom,Ci)}}}}function Tt(Y,D,J,q){if(J.paint.get("heatmap-opacity")!==0)if(Y.renderPass==="offscreen"){var K=Y.context,de=K.gl,ne=Fn.disabled,we=new wt([de.ONE,de.ONE],i.Color.transparent,[!0,!0,!0,!0]);Yt(K,Y,J),K.clear({color:i.Color.transparent});for(var Ue=0;Ue<q.length;Ue++){var ft=q[Ue];if(!D.hasRenderableParent(ft)){var Xt=D.getTile(ft),hr=Xt.getBucket(J);if(hr){var qt=hr.programConfigurations.get(J.id),Ve=Y.useProgram("heatmap",qt),Qe=Y.transform,at=Qe.zoom;Ve.draw(K,de.TRIANGLES,wi.disabled,ne,we,Er.disabled,on(ft.posMatrix,Xt,at,J.paint.get("heatmap-intensity")),J.id,hr.layoutVertexBuffer,hr.indexBuffer,hr.segments,J.paint,Y.transform.zoom,qt)}}}K.viewport.set([0,0,Y.width,Y.height])}else Y.renderPass==="translucent"&&(Y.context.setColorMode(Y.colorModeForRenderPass()),xr(Y,J))}function Yt(Y,D,J){var q=Y.gl;Y.activeTexture.set(q.TEXTURE1),Y.viewport.set([0,0,D.width/4,D.height/4]);var K=J.heatmapFbo;if(K)q.bindTexture(q.TEXTURE_2D,K.colorAttachment.get()),Y.bindFramebuffer.set(K.framebuffer);else{var de=q.createTexture();q.bindTexture(q.TEXTURE_2D,de),q.texParameteri(q.TEXTURE_2D,q.TEXTURE_WRAP_S,q.CLAMP_TO_EDGE),q.texParameteri(q.TEXTURE_2D,q.TEXTURE_WRAP_T,q.CLAMP_TO_EDGE),q.texParameteri(q.TEXTURE_2D,q.TEXTURE_MIN_FILTER,q.LINEAR),q.texParameteri(q.TEXTURE_2D,q.TEXTURE_MAG_FILTER,q.LINEAR),K=J.heatmapFbo=Y.createFramebuffer(D.width/4,D.height/4,!1),Kt(Y,D,de,K)}}function Kt(Y,D,J,q){var K=Y.gl,de=Y.extRenderToTextureHalfFloat?Y.extTextureHalfFloat.HALF_FLOAT_OES:K.UNSIGNED_BYTE;K.texImage2D(K.TEXTURE_2D,0,K.RGBA,D.width/4,D.height/4,0,K.RGBA,de,null),q.colorAttachment.set(J)}function xr(Y,D){var J=Y.context,q=J.gl,K=D.heatmapFbo;if(K){J.activeTexture.set(q.TEXTURE0),q.bindTexture(q.TEXTURE_2D,K.colorAttachment.get()),J.activeTexture.set(q.TEXTURE1);var de=D.colorRampTexture;de||(de=D.colorRampTexture=new i.Texture(J,D.colorRamp,q.RGBA)),de.bind(q.LINEAR,q.CLAMP_TO_EDGE),Y.useProgram("heatmapTexture").draw(J,q.TRIANGLES,wi.disabled,Fn.disabled,Y.colorModeForRenderPass(),Er.disabled,Fi(Y,D,0,1),D.id,Y.viewportBuffer,Y.quadTriangleIndexBuffer,Y.viewportSegments,D.paint,Y.transform.zoom)}}function Ir(Y,D,J,q){if(Y.renderPass==="translucent"){var K=J.paint.get("line-opacity"),de=J.paint.get("line-width");if(!(K.constantOr(1)===0||de.constantOr(1)===0))for(var ne=Y.depthModeForSublayer(0,wi.ReadOnly),we=Y.colorModeForRenderPass(),Ue=J.paint.get("line-dasharray"),ft=J.paint.get("line-pattern"),Xt=ft.constantOr(1),hr=J.paint.get("line-gradient"),qt=J.getCrossfadeParameters(),Ve=Xt?"linePattern":Ue?"lineSDF":hr?"lineGradient":"line",Qe=Y.context,at=Qe.gl,Ct=!0,Ot=0,Rt=q;Ot<Rt.length;Ot+=1){var Bt=Rt[Ot],Dt=D.getTile(Bt);if(!(Xt&&!Dt.patternsLoaded())){var yt=Dt.getBucket(J);if(yt){var Pt=yt.programConfigurations.get(J.id),ht=Y.context.program.get(),ur=Y.useProgram(Ve,Pt),br=Ct||ur.program!==ht,Ur=ft.constantOr(null);if(Ur&&Dt.imageAtlas){var Di=Dt.imageAtlas,fi=Di.patternPositions[Ur.to.toString()],Ti=Di.patternPositions[Ur.from.toString()];fi&&Ti&&Pt.setConstantPatternPositions(fi,Ti)}var gn=Xt?as(Y,Dt,J,qt):Ue?ws(Y,Dt,J,Ue,qt):hr?Ss(Y,Dt,J,yt.lineClipsArray.length):Xo(Y,Dt,J);if(Xt)Qe.activeTexture.set(at.TEXTURE0),Dt.imageAtlasTexture.bind(at.LINEAR,at.CLAMP_TO_EDGE),Pt.updatePaintBuffers(qt);else if(Ue&&(br||Y.lineAtlas.dirty))Qe.activeTexture.set(at.TEXTURE0),Y.lineAtlas.bind(Qe);else if(hr){var rn=yt.gradients[J.id],Ci=rn.texture;if(J.gradientVersion!==rn.version){var Bi=256;if(J.stepInterpolant){var Gi=D.getSource().maxzoom,sn=Bt.canonical.z===Gi?Math.ceil(1<<Y.transform.maxZoom-Bt.canonical.z):1,zn=yt.maxLineLength/i.EXTENT,Ja=1024,co=zn*Ja*sn;Bi=i.clamp(i.nextPowerOfTwo(co),256,Qe.maxTextureSize)}rn.gradient=i.renderColorRamp({expression:J.gradientExpression(),evaluationKey:"lineProgress",resolution:Bi,image:rn.gradient||void 0,clips:yt.lineClipsArray}),rn.texture?rn.texture.update(rn.gradient):rn.texture=new i.Texture(Qe,rn.gradient,at.RGBA),rn.version=J.gradientVersion,Ci=rn.texture}Qe.activeTexture.set(at.TEXTURE0),Ci.bind(J.stepInterpolant?at.NEAREST:at.LINEAR,at.CLAMP_TO_EDGE)}ur.draw(Qe,at.TRIANGLES,ne,Y.stencilModeForClipping(Bt),we,Er.disabled,gn,J.id,yt.layoutVertexBuffer,yt.indexBuffer,yt.segments,J.paint,Y.transform.zoom,Pt,yt.layoutVertexBuffer2),Ct=!1}}}}}function ve(Y,D,J,q){var K=J.paint.get("fill-color"),de=J.paint.get("fill-opacity");if(de.constantOr(1)!==0){var ne=Y.colorModeForRenderPass(),we=J.paint.get("fill-pattern"),Ue=Y.opaquePassEnabledForLayer()&&!we.constantOr(1)&&K.constantOr(i.Color.transparent).a===1&&de.constantOr(0)===1?"opaque":"translucent";if(Y.renderPass===Ue){var ft=Y.depthModeForSublayer(1,Y.renderPass==="opaque"?wi.ReadWrite:wi.ReadOnly);be(Y,D,J,q,ft,ne,!1)}if(Y.renderPass==="translucent"&&J.paint.get("fill-antialias")){var Xt=Y.depthModeForSublayer(J.getPaintProperty("fill-outline-color")?2:0,wi.ReadOnly);be(Y,D,J,q,Xt,ne,!0)}}}function be(Y,D,J,q,K,de,ne){var we=Y.context.gl,Ue=J.paint.get("fill-pattern"),ft=Ue&&Ue.constantOr(1),Xt=J.getCrossfadeParameters(),hr,qt,Ve,Qe,at;ne?(qt=ft&&!J.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",hr=we.LINES):(qt=ft?"fillPattern":"fill",hr=we.TRIANGLES);for(var Ct=0,Ot=q;Ct<Ot.length;Ct+=1){var Rt=Ot[Ct],Bt=D.getTile(Rt);if(!(ft&&!Bt.patternsLoaded())){var Dt=Bt.getBucket(J);if(Dt){var yt=Dt.programConfigurations.get(J.id),Pt=Y.useProgram(qt,yt);ft&&(Y.context.activeTexture.set(we.TEXTURE0),Bt.imageAtlasTexture.bind(we.LINEAR,we.CLAMP_TO_EDGE),yt.updatePaintBuffers(Xt));var ht=Ue.constantOr(null);if(ht&&Bt.imageAtlas){var ur=Bt.imageAtlas,br=ur.patternPositions[ht.to.toString()],Ur=ur.patternPositions[ht.from.toString()];br&&Ur&&yt.setConstantPatternPositions(br,Ur)}var Di=Y.translatePosMatrix(Rt.posMatrix,Bt,J.paint.get("fill-translate"),J.paint.get("fill-translate-anchor"));if(!ne)Qe=Dt.indexBuffer,at=Dt.segments,Ve=ft?cf(Di,Y,Xt,Bt):qc(Di);else{Qe=Dt.indexBuffer2,at=Dt.segments2;var fi=[we.drawingBufferWidth,we.drawingBufferHeight];Ve=qt==="fillOutlinePattern"&&ft?Bc(Di,Y,Xt,Bt,fi):fc(Di,fi)}Pt.draw(Y.context,hr,K,Y.stencilModeForClipping(Rt),de,Er.disabled,Ve,J.id,Dt.layoutVertexBuffer,Qe,at,J.paint,Y.transform.zoom,yt)}}}}function De(Y,D,J,q){var K=J.paint.get("fill-extrusion-opacity");if(K!==0&&Y.renderPass==="translucent"){var de=new wi(Y.context.gl.LEQUAL,wi.ReadWrite,Y.depthRangeFor3D);if(K===1&&!J.paint.get("fill-extrusion-pattern").constantOr(1)){var ne=Y.colorModeForRenderPass();Be(Y,D,J,q,de,Fn.disabled,ne)}else Be(Y,D,J,q,de,Fn.disabled,wt.disabled),Be(Y,D,J,q,de,Y.stencilModeFor3D(),Y.colorModeForRenderPass())}}function Be(Y,D,J,q,K,de,ne){for(var we=Y.context,Ue=we.gl,ft=J.paint.get("fill-extrusion-pattern"),Xt=ft.constantOr(1),hr=J.getCrossfadeParameters(),qt=J.paint.get("fill-extrusion-opacity"),Ve=0,Qe=q;Ve<Qe.length;Ve+=1){var at=Qe[Ve],Ct=D.getTile(at),Ot=Ct.getBucket(J);if(Ot){var Rt=Ot.programConfigurations.get(J.id),Bt=Y.useProgram(Xt?"fillExtrusionPattern":"fillExtrusion",Rt);Xt&&(Y.context.activeTexture.set(Ue.TEXTURE0),Ct.imageAtlasTexture.bind(Ue.LINEAR,Ue.CLAMP_TO_EDGE),Rt.updatePaintBuffers(hr));var Dt=ft.constantOr(null);if(Dt&&Ct.imageAtlas){var yt=Ct.imageAtlas,Pt=yt.patternPositions[Dt.to.toString()],ht=yt.patternPositions[Dt.from.toString()];Pt&&ht&&Rt.setConstantPatternPositions(Pt,ht)}var ur=Y.translatePosMatrix(at.posMatrix,Ct,J.paint.get("fill-extrusion-translate"),J.paint.get("fill-extrusion-translate-anchor")),br=J.paint.get("fill-extrusion-vertical-gradient"),Ur=Xt?Zu(ur,Y,br,qt,at,hr,Ct):ah(ur,Y,br,qt);Bt.draw(we,we.gl.TRIANGLES,K,de,ne,Er.backCCW,Ur,J.id,Ot.layoutVertexBuffer,Ot.indexBuffer,Ot.segments,J.paint,Y.transform.zoom,Rt)}}}function et(Y,D,J,q){if(!(Y.renderPass!=="offscreen"&&Y.renderPass!=="translucent")){for(var K=Y.context,de=Y.depthModeForSublayer(0,wi.ReadOnly),ne=Y.colorModeForRenderPass(),we=Y.renderPass==="translucent"?Y.stencilConfigForOverlap(q):[{},q],Ue=we[0],ft=we[1],Xt=0,hr=ft;Xt<hr.length;Xt+=1){var qt=hr[Xt],Ve=D.getTile(qt);Ve.needsHillshadePrepare&&Y.renderPass==="offscreen"?it(Y,Ve,J,de,Fn.disabled,ne):Y.renderPass==="translucent"&&We(Y,Ve,J,de,Ue[qt.overscaledZ],ne)}K.viewport.set([0,0,Y.width,Y.height])}}function We(Y,D,J,q,K,de){var ne=Y.context,we=ne.gl,Ue=D.fbo;if(Ue){var ft=Y.useProgram("hillshade");ne.activeTexture.set(we.TEXTURE0),we.bindTexture(we.TEXTURE_2D,Ue.colorAttachment.get());var Xt=Ra(Y,D,J);ft.draw(ne,we.TRIANGLES,q,K,de,Er.disabled,Xt,J.id,Y.rasterBoundsBuffer,Y.quadTriangleIndexBuffer,Y.rasterBoundsSegments)}}function it(Y,D,J,q,K,de){var ne=Y.context,we=ne.gl,Ue=D.dem;if(Ue&&Ue.data){var ft=Ue.dim,Xt=Ue.stride,hr=Ue.getPixels();if(ne.activeTexture.set(we.TEXTURE1),ne.pixelStoreUnpackPremultiplyAlpha.set(!1),D.demTexture=D.demTexture||Y.getTileTexture(Xt),D.demTexture){var qt=D.demTexture;qt.update(hr,{premultiply:!1}),qt.bind(we.NEAREST,we.CLAMP_TO_EDGE)}else D.demTexture=new i.Texture(ne,hr,we.RGBA,{premultiply:!1}),D.demTexture.bind(we.NEAREST,we.CLAMP_TO_EDGE);ne.activeTexture.set(we.TEXTURE0);var Ve=D.fbo;if(!Ve){var Qe=new i.Texture(ne,{width:ft,height:ft,data:null},we.RGBA);Qe.bind(we.LINEAR,we.CLAMP_TO_EDGE),Ve=D.fbo=ne.createFramebuffer(ft,ft,!0),Ve.colorAttachment.set(Qe.texture)}ne.bindFramebuffer.set(Ve.framebuffer),ne.viewport.set([0,0,ft,ft]),Y.useProgram("hillshadePrepare").draw(ne,we.TRIANGLES,q,K,de,Er.disabled,La(D.tileID,Ue),J.id,Y.rasterBoundsBuffer,Y.quadTriangleIndexBuffer,Y.rasterBoundsSegments),D.needsHillshadePrepare=!1}}function Ft(Y,D,J,q){if(Y.renderPass==="translucent"&&J.paint.get("raster-opacity")!==0&&q.length)for(var K=Y.context,de=K.gl,ne=D.getSource(),we=Y.useProgram("raster"),Ue=Y.colorModeForRenderPass(),ft=ne instanceof Ce?[{},q]:Y.stencilConfigForOverlap(q),Xt=ft[0],hr=ft[1],qt=hr[hr.length-1].overscaledZ,Ve=!Y.options.moving,Qe=0,at=hr;Qe<at.length;Qe+=1){var Ct=at[Qe],Ot=Y.depthModeForSublayer(Ct.overscaledZ-qt,J.paint.get("raster-opacity")===1?wi.ReadWrite:wi.ReadOnly,de.LESS),Rt=D.getTile(Ct),Bt=Y.transform.calculatePosMatrix(Ct.toUnwrapped(),Ve);Rt.registerFadeDuration(J.paint.get("raster-fade-duration"));var Dt=D.findLoadedParent(Ct,0),yt=Ht(Rt,Dt,D,J,Y.transform),Pt=void 0,ht=void 0,ur=J.paint.get("raster-resampling")==="nearest"?de.NEAREST:de.LINEAR;K.activeTexture.set(de.TEXTURE0),Rt.texture.bind(ur,de.CLAMP_TO_EDGE,de.LINEAR_MIPMAP_NEAREST),K.activeTexture.set(de.TEXTURE1),Dt?(Dt.texture.bind(ur,de.CLAMP_TO_EDGE,de.LINEAR_MIPMAP_NEAREST),Pt=Math.pow(2,Dt.tileID.overscaledZ-Rt.tileID.overscaledZ),ht=[Rt.tileID.canonical.x*Pt%1,Rt.tileID.canonical.y*Pt%1]):Rt.texture.bind(ur,de.CLAMP_TO_EDGE,de.LINEAR_MIPMAP_NEAREST);var br=Ls(Bt,ht||[0,0],Pt||1,yt,J);ne instanceof Ce?we.draw(K,de.TRIANGLES,Ot,Fn.disabled,Ue,Er.disabled,br,J.id,ne.boundsBuffer,Y.quadTriangleIndexBuffer,ne.boundsSegments):we.draw(K,de.TRIANGLES,Ot,Xt[Ct.overscaledZ],Ue,Er.disabled,br,J.id,Y.rasterBoundsBuffer,Y.quadTriangleIndexBuffer,Y.rasterBoundsSegments)}}function Ht(Y,D,J,q,K){var de=q.paint.get("raster-fade-duration");if(de>0){var ne=i.browser.now(),we=(ne-Y.timeAdded)/de,Ue=D?(ne-D.timeAdded)/de:-1,ft=J.getSource(),Xt=K.coveringZoomLevel({tileSize:ft.tileSize,roundZoom:ft.roundZoom}),hr=!D||Math.abs(D.tileID.overscaledZ-Xt)>Math.abs(Y.tileID.overscaledZ-Xt),qt=hr&&Y.refreshedUponExpiration?1:i.clamp(hr?we:1-Ue,0,1);return Y.refreshedUponExpiration&&we>=1&&(Y.refreshedUponExpiration=!1),D?{opacity:1,mix:1-qt}:{opacity:qt,mix:0}}else return{opacity:1,mix:0}}function tr(Y,D,J){var q=J.paint.get("background-color"),K=J.paint.get("background-opacity");if(K!==0){var de=Y.context,ne=de.gl,we=Y.transform,Ue=we.tileSize,ft=J.paint.get("background-pattern");if(!Y.isPatternMissing(ft)){var Xt=!ft&&q.a===1&&K===1&&Y.opaquePassEnabledForLayer()?"opaque":"translucent";if(Y.renderPass===Xt){var hr=Fn.disabled,qt=Y.depthModeForSublayer(0,Xt==="opaque"?wi.ReadWrite:wi.ReadOnly),Ve=Y.colorModeForRenderPass(),Qe=Y.useProgram(ft?"backgroundPattern":"background"),at=we.coveringTiles({tileSize:Ue});ft&&(de.activeTexture.set(ne.TEXTURE0),Y.imageManager.bind(Y.context));for(var Ct=J.getCrossfadeParameters(),Ot=0,Rt=at;Ot<Rt.length;Ot+=1){var Bt=Rt[Ot],Dt=Y.transform.calculatePosMatrix(Bt.toUnwrapped()),yt=ft?Ua(Dt,K,Y,ft,{tileID:Bt,tileSize:Ue},Ct):Ac(Dt,K,q);Qe.draw(de,ne.TRIANGLES,qt,hr,Ve,Er.disabled,yt,J.id,Y.tileExtentBuffer,Y.quadTriangleIndexBuffer,Y.tileExtentSegments)}}}}}var dr=new i.Color(1,0,0,1),Sr=new i.Color(0,1,0,1),Or=new i.Color(0,0,1,1),Wr=new i.Color(1,0,1,1),ni=new i.Color(0,1,1,1);function Pi(Y){var D=Y.transform.padding,J=3;ln(Y,Y.transform.height-(D.top||0),J,dr),ln(Y,D.bottom||0,J,Sr),Cn(Y,D.left||0,J,Or),Cn(Y,Y.transform.width-(D.right||0),J,Wr);var q=Y.transform.centerPoint;cn(Y,q.x,Y.transform.height-q.y,ni)}function cn(Y,D,J,q){var K=20,de=2;Kn(Y,D-de/2,J-K/2,de,K,q),Kn(Y,D-K/2,J-de/2,K,de,q)}function ln(Y,D,J,q){Kn(Y,0,D+J/2,Y.transform.width,J,q)}function Cn(Y,D,J,q){Kn(Y,D-J/2,0,J,Y.transform.height,q)}function Kn(Y,D,J,q,K,de){var ne=Y.context,we=ne.gl;we.enable(we.SCISSOR_TEST),we.scissor(D*i.browser.devicePixelRatio,J*i.browser.devicePixelRatio,q*i.browser.devicePixelRatio,K*i.browser.devicePixelRatio),ne.clear({color:de}),we.disable(we.SCISSOR_TEST)}function Ta(Y,D,J){for(var q=0;q<J.length;q++)fa(Y,D,J[q])}function fa(Y,D,J){var q=Y.context,K=q.gl,de=J.posMatrix,ne=Y.useProgram("debug"),we=wi.disabled,Ue=Fn.disabled,ft=Y.colorModeForRenderPass(),Xt="$debug";q.activeTexture.set(K.TEXTURE0),Y.emptyTexture.bind(K.LINEAR,K.CLAMP_TO_EDGE),ne.draw(q,K.LINE_STRIP,we,Ue,ft,Er.disabled,dn(de,i.Color.red),Xt,Y.debugBuffer,Y.tileBorderIndexBuffer,Y.debugSegments);var hr=D.getTileByID(J.key).latestRawTileData,qt=hr&&hr.byteLength||0,Ve=Math.floor(qt/1024),Qe=D.getTile(J).tileSize,at=512/Math.min(Qe,512)*(J.overscaledZ/Y.transform.zoom)*.5,Ct=J.canonical.toString();J.overscaledZ!==J.canonical.z&&(Ct+=" => "+J.overscaledZ);var Ot=Ct+" "+Ve+"kb";$a(Y,Ot),ne.draw(q,K.TRIANGLES,we,Ue,wt.alphaBlended,Er.disabled,dn(de,i.Color.transparent,at),Xt,Y.debugBuffer,Y.quadTriangleIndexBuffer,Y.debugSegments)}function $a(Y,D){Y.initDebugOverlayCanvas();var J=Y.debugOverlayCanvas,q=Y.context.gl,K=Y.debugOverlayCanvas.getContext("2d");K.clearRect(0,0,J.width,J.height),K.shadowColor="white",K.shadowBlur=2,K.lineWidth=1.5,K.strokeStyle="white",K.textBaseline="top",K.font="bold 36px Open Sans, sans-serif",K.fillText(D,5,5),K.strokeText(D,5,5),Y.debugOverlayTexture.update(J),Y.debugOverlayTexture.bind(q.LINEAR,q.CLAMP_TO_EDGE)}function Co(Y,D,J){var q=Y.context,K=J.implementation;if(Y.renderPass==="offscreen"){var de=K.prerender;de&&(Y.setCustomLayerDefaults(),q.setColorMode(Y.colorModeForRenderPass()),de.call(K,q.gl,Y.transform.customLayerMatrix()),q.setDirty(),Y.setBaseState())}else if(Y.renderPass==="translucent"){Y.setCustomLayerDefaults(),q.setColorMode(Y.colorModeForRenderPass()),q.setStencilMode(Fn.disabled);var ne=K.renderingMode==="3d"?new wi(Y.context.gl.LEQUAL,wi.ReadWrite,Y.depthRangeFor3D):Y.depthModeForSublayer(0,wi.ReadOnly);q.setDepthMode(ne),K.render(q.gl,Y.transform.customLayerMatrix()),q.setDirty(),Y.setBaseState(),q.bindFramebuffer.set(null)}}var Qa={symbol:w,circle:ot,heatmap:Tt,line:Ir,fill:ve,"fill-extrusion":De,hillshade:et,raster:Ft,background:tr,debug:Ta,custom:Co},mo=function(D,J){this.context=new Xr(D),this.transform=J,this._tileTextures={},this.setup(),this.numSublayers=ri.maxUnderzooming+ri.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Uf,this.gpuTimers={}};mo.prototype.resize=function(D,J){if(this.width=D*i.browser.devicePixelRatio,this.height=J*i.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var q=0,K=this.style._order;q<K.length;q+=1){var de=K[q];this.style._layers[de].resize()}},mo.prototype.setup=function(){var D=this.context,J=new i.StructArrayLayout2i4;J.emplaceBack(0,0),J.emplaceBack(i.EXTENT,0),J.emplaceBack(0,i.EXTENT),J.emplaceBack(i.EXTENT,i.EXTENT),this.tileExtentBuffer=D.createVertexBuffer(J,oc.members),this.tileExtentSegments=i.SegmentVector.simpleSegment(0,0,4,2);var q=new i.StructArrayLayout2i4;q.emplaceBack(0,0),q.emplaceBack(i.EXTENT,0),q.emplaceBack(0,i.EXTENT),q.emplaceBack(i.EXTENT,i.EXTENT),this.debugBuffer=D.createVertexBuffer(q,oc.members),this.debugSegments=i.SegmentVector.simpleSegment(0,0,4,5);var K=new i.StructArrayLayout4i8;K.emplaceBack(0,0,0,0),K.emplaceBack(i.EXTENT,0,i.EXTENT,0),K.emplaceBack(0,i.EXTENT,0,i.EXTENT),K.emplaceBack(i.EXTENT,i.EXTENT,i.EXTENT,i.EXTENT),this.rasterBoundsBuffer=D.createVertexBuffer(K,Ee.members),this.rasterBoundsSegments=i.SegmentVector.simpleSegment(0,0,4,2);var de=new i.StructArrayLayout2i4;de.emplaceBack(0,0),de.emplaceBack(1,0),de.emplaceBack(0,1),de.emplaceBack(1,1),this.viewportBuffer=D.createVertexBuffer(de,oc.members),this.viewportSegments=i.SegmentVector.simpleSegment(0,0,4,2);var ne=new i.StructArrayLayout1ui2;ne.emplaceBack(0),ne.emplaceBack(1),ne.emplaceBack(3),ne.emplaceBack(2),ne.emplaceBack(0),this.tileBorderIndexBuffer=D.createIndexBuffer(ne);var we=new i.StructArrayLayout3ui6;we.emplaceBack(0,1,2),we.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=D.createIndexBuffer(we),this.emptyTexture=new i.Texture(D,{width:1,height:1,data:new Uint8Array([0,0,0,0])},D.gl.RGBA);var Ue=this.context.gl;this.stencilClearMode=new Fn({func:Ue.ALWAYS,mask:0},0,255,Ue.ZERO,Ue.ZERO,Ue.ZERO)},mo.prototype.clearStencil=function(){var D=this.context,J=D.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var q=i.create();i.ortho(q,0,this.width,this.height,0,0,1),i.scale(q,q,[J.drawingBufferWidth,J.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(D,J.TRIANGLES,wi.disabled,this.stencilClearMode,wt.disabled,Er.disabled,Nn(q),"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},mo.prototype._renderTileClippingMasks=function(D,J){if(!(this.currentStencilSource===D.source||!D.isTileClipped()||!J||!J.length)){this.currentStencilSource=D.source;var q=this.context,K=q.gl;this.nextStencilID+J.length>256&&this.clearStencil(),q.setColorMode(wt.disabled),q.setDepthMode(wi.disabled);var de=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(var ne=0,we=J;ne<we.length;ne+=1){var Ue=we[ne],ft=this._tileClippingMaskIDs[Ue.key]=this.nextStencilID++;de.draw(q,K.TRIANGLES,wi.disabled,new Fn({func:K.ALWAYS,mask:0},ft,255,K.KEEP,K.KEEP,K.REPLACE),wt.disabled,Er.disabled,Nn(Ue.posMatrix),"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},mo.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var D=this.nextStencilID++,J=this.context.gl;return new Fn({func:J.NOTEQUAL,mask:255},D,255,J.KEEP,J.KEEP,J.REPLACE)},mo.prototype.stencilModeForClipping=function(D){var J=this.context.gl;return new Fn({func:J.EQUAL,mask:255},this._tileClippingMaskIDs[D.key],0,J.KEEP,J.KEEP,J.REPLACE)},mo.prototype.stencilConfigForOverlap=function(D){var J,q=this.context.gl,K=D.sort(function(ft,Xt){return Xt.overscaledZ-ft.overscaledZ}),de=K[K.length-1].overscaledZ,ne=K[0].overscaledZ-de+1;if(ne>1){this.currentStencilSource=void 0,this.nextStencilID+ne>256&&this.clearStencil();for(var we={},Ue=0;Ue<ne;Ue++)we[Ue+de]=new Fn({func:q.GEQUAL,mask:255},Ue+this.nextStencilID,255,q.KEEP,q.KEEP,q.REPLACE);return this.nextStencilID+=ne,[we,K]}return[(J={},J[de]=Fn.disabled,J),K]},mo.prototype.colorModeForRenderPass=function(){var D=this.context.gl;if(this._showOverdrawInspector){var J=8,q=1/J;return new wt([D.CONSTANT_COLOR,D.ONE],new i.Color(q,q,q,0),[!0,!0,!0,!0])}else return this.renderPass==="opaque"?wt.unblended:wt.alphaBlended},mo.prototype.depthModeForSublayer=function(D,J,q){if(!this.opaquePassEnabledForLayer())return wi.disabled;var K=1-((1+this.currentLayer)*this.numSublayers+D)*this.depthEpsilon;return new wi(q||this.context.gl.LEQUAL,J,[K,K])},mo.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},mo.prototype.render=function(D,J){var q=this;this.style=D,this.options=J,this.lineAtlas=D.lineAtlas,this.imageManager=D.imageManager,this.glyphManager=D.glyphManager,this.symbolFadeChange=D.placement.symbolFadeChange(i.browser.now()),this.imageManager.beginFrame();var K=this.style._order,de=this.style.sourceCaches;for(var ne in de){var we=de[ne];we.used&&we.prepare(this.context)}var Ue={},ft={},Xt={};for(var hr in de){var qt=de[hr];Ue[hr]=qt.getVisibleCoordinates(),ft[hr]=Ue[hr].slice().reverse(),Xt[hr]=qt.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var Ve=0;Ve<K.length;Ve++){var Qe=K[Ve];if(this.style._layers[Qe].is3D()){this.opaquePassCutoff=Ve;break}}this.renderPass="offscreen";for(var at=0,Ct=K;at<Ct.length;at+=1){var Ot=Ct[at],Rt=this.style._layers[Ot];if(!(!Rt.hasOffscreenPass()||Rt.isHidden(this.transform.zoom))){var Bt=ft[Rt.source];Rt.type!=="custom"&&!Bt.length||this.renderLayer(this,de[Rt.source],Rt,Bt)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:J.showOverdrawInspector?i.Color.black:i.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=J.showOverdrawInspector,this.depthRangeFor3D=[0,1-(D._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass="opaque",this.currentLayer=K.length-1;this.currentLayer>=0;this.currentLayer--){var Dt=this.style._layers[K[this.currentLayer]],yt=de[Dt.source],Pt=Ue[Dt.source];this._renderTileClippingMasks(Dt,Pt),this.renderLayer(this,yt,Dt,Pt)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<K.length;this.currentLayer++){var ht=this.style._layers[K[this.currentLayer]],ur=de[ht.source],br=(ht.type==="symbol"?Xt:ft)[ht.source];this._renderTileClippingMasks(ht,Ue[ht.source]),this.renderLayer(this,ur,ht,br)}if(this.options.showTileBoundaries){var Ur,Di,fi=i.values(this.style._layers);fi.forEach(function(Ti){Ti.source&&!Ti.isHidden(q.transform.zoom)&&(Ti.source!==(Di&&Di.id)&&(Di=q.style.sourceCaches[Ti.source]),(!Ur||Ur.getSource().maxzoom<Di.getSource().maxzoom)&&(Ur=Di))}),Ur&&Qa.debug(this,Ur,Ur.getVisibleCoordinates())}this.options.showPadding&&Pi(this),this.context.setDefault()},mo.prototype.renderLayer=function(D,J,q,K){q.isHidden(this.transform.zoom)||q.type!=="background"&&q.type!=="custom"&&!K.length||(this.id=q.id,this.gpuTimingStart(q),Qa[q.type](D,J,q,K,this.style.placement.variableOffsets),this.gpuTimingEnd())},mo.prototype.gpuTimingStart=function(D){if(this.options.gpuTiming){var J=this.context.extTimerQuery,q=this.gpuTimers[D.id];q||(q=this.gpuTimers[D.id]={calls:0,cpuTime:0,query:J.createQueryEXT()}),q.calls++,J.beginQueryEXT(J.TIME_ELAPSED_EXT,q.query)}},mo.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var D=this.context.extTimerQuery;D.endQueryEXT(D.TIME_ELAPSED_EXT)}},mo.prototype.collectGpuTimers=function(){var D=this.gpuTimers;return this.gpuTimers={},D},mo.prototype.queryGpuTimers=function(D){var J={};for(var q in D){var K=D[q],de=this.context.extTimerQuery,ne=de.getQueryObjectEXT(K.query,de.QUERY_RESULT_EXT)/(1e3*1e3);de.deleteQueryEXT(K.query),J[q]=ne}return J},mo.prototype.translatePosMatrix=function(D,J,q,K,de){if(!q[0]&&!q[1])return D;var ne=de?K==="map"?this.transform.angle:0:K==="viewport"?-this.transform.angle:0;if(ne){var we=Math.sin(ne),Ue=Math.cos(ne);q=[q[0]*Ue-q[1]*we,q[0]*we+q[1]*Ue]}var ft=[de?q[0]:As(J,q[0],this.transform.zoom),de?q[1]:As(J,q[1],this.transform.zoom),0],Xt=new Float32Array(16);return i.translate(Xt,D,ft),Xt},mo.prototype.saveTileTexture=function(D){var J=this._tileTextures[D.size[0]];J?J.push(D):this._tileTextures[D.size[0]]=[D]},mo.prototype.getTileTexture=function(D){var J=this._tileTextures[D];return J&&J.length>0?J.pop():null},mo.prototype.isPatternMissing=function(D){if(!D)return!1;if(!D.from||!D.to)return!0;var J=this.imageManager.getPattern(D.from.toString()),q=this.imageManager.getPattern(D.to.toString());return!J||!q},mo.prototype.useProgram=function(D,J){this.cache=this.cache||{};var q=""+D+(J?J.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[q]||(this.cache[q]=new Rf(this.context,D,wf[D],J,oo[D],this._showOverdrawInspector)),this.cache[q]},mo.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},mo.prototype.setBaseState=function(){var D=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(D.FUNC_ADD)},mo.prototype.initDebugOverlayCanvas=function(){if(this.debugOverlayCanvas==null){this.debugOverlayCanvas=i.window.document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var D=this.context.gl;this.debugOverlayTexture=new i.Texture(this.context,this.debugOverlayCanvas,D.RGBA)}},mo.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var Bo=function(D,J){this.points=D,this.planes=J};Bo.fromInvProjectionMatrix=function(D,J,q){var K=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]],de=Math.pow(2,q),ne=K.map(function(ft){return i.transformMat4([],ft,D)}).map(function(ft){return i.scale$1([],ft,1/ft[3]/J*de)}),we=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]],Ue=we.map(function(ft){var Xt=i.sub([],ne[ft[0]],ne[ft[1]]),hr=i.sub([],ne[ft[2]],ne[ft[1]]),qt=i.normalize([],i.cross([],Xt,hr)),Ve=-i.dot(qt,ne[ft[1]]);return qt.concat(Ve)});return new Bo(ne,Ue)};var Ps=function(D,J){this.min=D,this.max=J,this.center=i.scale$2([],i.add([],this.min,this.max),.5)};Ps.prototype.quadrant=function(D){for(var J=[D%2===0,D<2],q=i.clone$2(this.min),K=i.clone$2(this.max),de=0;de<J.length;de++)q[de]=J[de]?this.min[de]:this.center[de],K[de]=J[de]?this.center[de]:this.max[de];return K[2]=this.max[2],new Ps(q,K)},Ps.prototype.distanceX=function(D){var J=Math.max(Math.min(this.max[0],D[0]),this.min[0]);return J-D[0]},Ps.prototype.distanceY=function(D){var J=Math.max(Math.min(this.max[1],D[1]),this.min[1]);return J-D[1]},Ps.prototype.intersects=function(D){for(var J=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],q=!0,K=0;K<D.planes.length;K++){for(var de=D.planes[K],ne=0,we=0;we<J.length;we++)ne+=i.dot$1(de,J[we])>=0;if(ne===0)return 0;ne!==J.length&&(q=!1)}if(q)return 2;for(var Ue=0;Ue<3;Ue++){for(var ft=Number.MAX_VALUE,Xt=-Number.MAX_VALUE,hr=0;hr<D.points.length;hr++){var qt=D.points[hr][Ue]-this.min[Ue];ft=Math.min(ft,qt),Xt=Math.max(Xt,qt)}if(Xt<0||ft>this.max[Ue]-this.min[Ue])return 0}return 1};var Ts=function(D,J,q,K){if(D===void 0&&(D=0),J===void 0&&(J=0),q===void 0&&(q=0),K===void 0&&(K=0),isNaN(D)||D<0||isNaN(J)||J<0||isNaN(q)||q<0||isNaN(K)||K<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=D,this.bottom=J,this.left=q,this.right=K};Ts.prototype.interpolate=function(D,J,q){return J.top!=null&&D.top!=null&&(this.top=i.number(D.top,J.top,q)),J.bottom!=null&&D.bottom!=null&&(this.bottom=i.number(D.bottom,J.bottom,q)),J.left!=null&&D.left!=null&&(this.left=i.number(D.left,J.left,q)),J.right!=null&&D.right!=null&&(this.right=i.number(D.right,J.right,q)),this},Ts.prototype.getCenter=function(D,J){var q=i.clamp((this.left+D-this.right)/2,0,D),K=i.clamp((this.top+J-this.bottom)/2,0,J);return new i.Point(q,K)},Ts.prototype.equals=function(D){return this.top===D.top&&this.bottom===D.bottom&&this.left===D.left&&this.right===D.right},Ts.prototype.clone=function(){return new Ts(this.top,this.bottom,this.left,this.right)},Ts.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var wo=function(D,J,q,K,de){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=de===void 0?!0:de,this._minZoom=D||0,this._maxZoom=J||22,this._minPitch=q==null?0:q,this._maxPitch=K==null?60:K,this.setMaxBounds(),this.width=0,this.height=0,this._center=new i.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Ts,this._posMatrixCache={},this._alignedPosMatrixCache={}},To={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};wo.prototype.clone=function(){var D=new wo(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return D.tileSize=this.tileSize,D.latRange=this.latRange,D.width=this.width,D.height=this.height,D._center=this._center,D.zoom=this.zoom,D.angle=this.angle,D._fov=this._fov,D._pitch=this._pitch,D._unmodified=this._unmodified,D._edgeInsets=this._edgeInsets.clone(),D._calcMatrices(),D},To.minZoom.get=function(){return this._minZoom},To.minZoom.set=function(Y){this._minZoom!==Y&&(this._minZoom=Y,this.zoom=Math.max(this.zoom,Y))},To.maxZoom.get=function(){return this._maxZoom},To.maxZoom.set=function(Y){this._maxZoom!==Y&&(this._maxZoom=Y,this.zoom=Math.min(this.zoom,Y))},To.minPitch.get=function(){return this._minPitch},To.minPitch.set=function(Y){this._minPitch!==Y&&(this._minPitch=Y,this.pitch=Math.max(this.pitch,Y))},To.maxPitch.get=function(){return this._maxPitch},To.maxPitch.set=function(Y){this._maxPitch!==Y&&(this._maxPitch=Y,this.pitch=Math.min(this.pitch,Y))},To.renderWorldCopies.get=function(){return this._renderWorldCopies},To.renderWorldCopies.set=function(Y){Y===void 0?Y=!0:Y===null&&(Y=!1),this._renderWorldCopies=Y},To.worldSize.get=function(){return this.tileSize*this.scale},To.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},To.size.get=function(){return new i.Point(this.width,this.height)},To.bearing.get=function(){return-this.angle/Math.PI*180},To.bearing.set=function(Y){var D=-i.wrap(Y,-180,180)*Math.PI/180;this.angle!==D&&(this._unmodified=!1,this.angle=D,this._calcMatrices(),this.rotationMatrix=i.create$2(),i.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},To.pitch.get=function(){return this._pitch/Math.PI*180},To.pitch.set=function(Y){var D=i.clamp(Y,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==D&&(this._unmodified=!1,this._pitch=D,this._calcMatrices())},To.fov.get=function(){return this._fov/Math.PI*180},To.fov.set=function(Y){Y=Math.max(.01,Math.min(60,Y)),this._fov!==Y&&(this._unmodified=!1,this._fov=Y/180*Math.PI,this._calcMatrices())},To.zoom.get=function(){return this._zoom},To.zoom.set=function(Y){var D=Math.min(Math.max(Y,this.minZoom),this.maxZoom);this._zoom!==D&&(this._unmodified=!1,this._zoom=D,this.scale=this.zoomScale(D),this.tileZoom=Math.floor(D),this.zoomFraction=D-this.tileZoom,this._constrain(),this._calcMatrices())},To.center.get=function(){return this._center},To.center.set=function(Y){Y.lat===this._center.lat&&Y.lng===this._center.lng||(this._unmodified=!1,this._center=Y,this._constrain(),this._calcMatrices())},To.padding.get=function(){return this._edgeInsets.toJSON()},To.padding.set=function(Y){this._edgeInsets.equals(Y)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,Y,1),this._calcMatrices())},To.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},wo.prototype.isPaddingEqual=function(D){return this._edgeInsets.equals(D)},wo.prototype.interpolatePadding=function(D,J,q){this._unmodified=!1,this._edgeInsets.interpolate(D,J,q),this._constrain(),this._calcMatrices()},wo.prototype.coveringZoomLevel=function(D){var J=(D.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/D.tileSize));return Math.max(0,J)},wo.prototype.getVisibleUnwrappedCoordinates=function(D){var J=[new i.UnwrappedTileID(0,D)];if(this._renderWorldCopies)for(var q=this.pointCoordinate(new i.Point(0,0)),K=this.pointCoordinate(new i.Point(this.width,0)),de=this.pointCoordinate(new i.Point(this.width,this.height)),ne=this.pointCoordinate(new i.Point(0,this.height)),we=Math.floor(Math.min(q.x,K.x,de.x,ne.x)),Ue=Math.floor(Math.max(q.x,K.x,de.x,ne.x)),ft=1,Xt=we-ft;Xt<=Ue+ft;Xt++)Xt!==0&&J.push(new i.UnwrappedTileID(Xt,D));return J},wo.prototype.coveringTiles=function(D){var J=this.coveringZoomLevel(D),q=J;if(D.minzoom!==void 0&&J<D.minzoom)return[];D.maxzoom!==void 0&&J>D.maxzoom&&(J=D.maxzoom);var K=i.MercatorCoordinate.fromLngLat(this.center),de=Math.pow(2,J),ne=[de*K.x,de*K.y,0],we=Bo.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,J),Ue=D.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(Ue=J);var ft=3,Xt=function(fi){return{aabb:new Ps([fi*de,0,0],[(fi+1)*de,de,0]),zoom:0,x:0,y:0,wrap:fi,fullyVisible:!1}},hr=[],qt=[],Ve=J,Qe=D.reparseOverscaled?q:J;if(this._renderWorldCopies)for(var at=1;at<=3;at++)hr.push(Xt(-at)),hr.push(Xt(at));for(hr.push(Xt(0));hr.length>0;){var Ct=hr.pop(),Ot=Ct.x,Rt=Ct.y,Bt=Ct.fullyVisible;if(!Bt){var Dt=Ct.aabb.intersects(we);if(Dt===0)continue;Bt=Dt===2}var yt=Ct.aabb.distanceX(ne),Pt=Ct.aabb.distanceY(ne),ht=Math.max(Math.abs(yt),Math.abs(Pt)),ur=ft+(1<<Ve-Ct.zoom)-2;if(Ct.zoom===Ve||ht>ur&&Ct.zoom>=Ue){qt.push({tileID:new i.OverscaledTileID(Ct.zoom===Ve?Qe:Ct.zoom,Ct.wrap,Ct.zoom,Ot,Rt),distanceSq:i.sqrLen([ne[0]-.5-Ot,ne[1]-.5-Rt])});continue}for(var br=0;br<4;br++){var Ur=(Ot<<1)+br%2,Di=(Rt<<1)+(br>>1);hr.push({aabb:Ct.aabb.quadrant(br),zoom:Ct.zoom+1,x:Ur,y:Di,wrap:Ct.wrap,fullyVisible:Bt})}}return qt.sort(function(fi,Ti){return fi.distanceSq-Ti.distanceSq}).map(function(fi){return fi.tileID})},wo.prototype.resize=function(D,J){this.width=D,this.height=J,this.pixelsToGLUnits=[2/D,-2/J],this._constrain(),this._calcMatrices()},To.unmodified.get=function(){return this._unmodified},wo.prototype.zoomScale=function(D){return Math.pow(2,D)},wo.prototype.scaleZoom=function(D){return Math.log(D)/Math.LN2},wo.prototype.project=function(D){var J=i.clamp(D.lat,-this.maxValidLatitude,this.maxValidLatitude);return new i.Point(i.mercatorXfromLng(D.lng)*this.worldSize,i.mercatorYfromLat(J)*this.worldSize)},wo.prototype.unproject=function(D){return new i.MercatorCoordinate(D.x/this.worldSize,D.y/this.worldSize).toLngLat()},To.point.get=function(){return this.project(this.center)},wo.prototype.setLocationAtPoint=function(D,J){var q=this.pointCoordinate(J),K=this.pointCoordinate(this.centerPoint),de=this.locationCoordinate(D),ne=new i.MercatorCoordinate(de.x-(q.x-K.x),de.y-(q.y-K.y));this.center=this.coordinateLocation(ne),this._renderWorldCopies&&(this.center=this.center.wrap())},wo.prototype.locationPoint=function(D){return this.coordinatePoint(this.locationCoordinate(D))},wo.prototype.pointLocation=function(D){return this.coordinateLocation(this.pointCoordinate(D))},wo.prototype.locationCoordinate=function(D){return i.MercatorCoordinate.fromLngLat(D)},wo.prototype.coordinateLocation=function(D){return D.toLngLat()},wo.prototype.pointCoordinate=function(D){var J=0,q=[D.x,D.y,0,1],K=[D.x,D.y,1,1];i.transformMat4(q,q,this.pixelMatrixInverse),i.transformMat4(K,K,this.pixelMatrixInverse);var de=q[3],ne=K[3],we=q[0]/de,Ue=K[0]/ne,ft=q[1]/de,Xt=K[1]/ne,hr=q[2]/de,qt=K[2]/ne,Ve=hr===qt?0:(J-hr)/(qt-hr);return new i.MercatorCoordinate(i.number(we,Ue,Ve)/this.worldSize,i.number(ft,Xt,Ve)/this.worldSize)},wo.prototype.coordinatePoint=function(D){var J=[D.x*this.worldSize,D.y*this.worldSize,0,1];return i.transformMat4(J,J,this.pixelMatrix),new i.Point(J[0]/J[3],J[1]/J[3])},wo.prototype.getBounds=function(){return new i.LngLatBounds().extend(this.pointLocation(new i.Point(0,0))).extend(this.pointLocation(new i.Point(this.width,0))).extend(this.pointLocation(new i.Point(this.width,this.height))).extend(this.pointLocation(new i.Point(0,this.height)))},wo.prototype.getMaxBounds=function(){return!this.latRange||this.latRange.length!==2||!this.lngRange||this.lngRange.length!==2?null:new i.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]])},wo.prototype.setMaxBounds=function(D){D?(this.lngRange=[D.getWest(),D.getEast()],this.latRange=[D.getSouth(),D.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},wo.prototype.calculatePosMatrix=function(D,J){J===void 0&&(J=!1);var q=D.key,K=J?this._alignedPosMatrixCache:this._posMatrixCache;if(K[q])return K[q];var de=D.canonical,ne=this.worldSize/this.zoomScale(de.z),we=de.x+Math.pow(2,de.z)*D.wrap,Ue=i.identity(new Float64Array(16));return i.translate(Ue,Ue,[we*ne,de.y*ne,0]),i.scale(Ue,Ue,[ne/i.EXTENT,ne/i.EXTENT,1]),i.multiply(Ue,J?this.alignedProjMatrix:this.projMatrix,Ue),K[q]=new Float32Array(Ue),K[q]},wo.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},wo.prototype._constrain=function(){if(!(!this.center||!this.width||!this.height||this._constraining)){this._constraining=!0;var D=-90,J=90,q=-180,K=180,de,ne,we,Ue,ft=this.size,Xt=this._unmodified;if(this.latRange){var hr=this.latRange;D=i.mercatorYfromLat(hr[1])*this.worldSize,J=i.mercatorYfromLat(hr[0])*this.worldSize,de=J-D<ft.y?ft.y/(J-D):0}if(this.lngRange){var qt=this.lngRange;q=i.mercatorXfromLng(qt[0])*this.worldSize,K=i.mercatorXfromLng(qt[1])*this.worldSize,ne=K-q<ft.x?ft.x/(K-q):0}var Ve=this.point,Qe=Math.max(ne||0,de||0);if(Qe){this.center=this.unproject(new i.Point(ne?(K+q)/2:Ve.x,de?(J+D)/2:Ve.y)),this.zoom+=this.scaleZoom(Qe),this._unmodified=Xt,this._constraining=!1;return}if(this.latRange){var at=Ve.y,Ct=ft.y/2;at-Ct<D&&(Ue=D+Ct),at+Ct>J&&(Ue=J-Ct)}if(this.lngRange){var Ot=Ve.x,Rt=ft.x/2;Ot-Rt<q&&(we=q+Rt),Ot+Rt>K&&(we=K-Rt)}(we!==void 0||Ue!==void 0)&&(this.center=this.unproject(new i.Point(we!==void 0?we:Ve.x,Ue!==void 0?Ue:Ve.y))),this._unmodified=Xt,this._constraining=!1}},wo.prototype._calcMatrices=function(){if(this.height){var D=this._fov/2,J=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(D)*this.height;var q=Math.PI/2+this._pitch,K=this._fov*(.5+J.y/this.height),de=Math.sin(K)*this.cameraToCenterDistance/Math.sin(i.clamp(Math.PI-q-K,.01,Math.PI-.01)),ne=this.point,we=ne.x,Ue=ne.y,ft=Math.cos(Math.PI/2-this._pitch)*de+this.cameraToCenterDistance,Xt=ft*1.01,hr=this.height/50,qt=new Float64Array(16);i.perspective(qt,this._fov,this.width/this.height,hr,Xt),qt[8]=-J.x*2/this.width,qt[9]=J.y*2/this.height,i.scale(qt,qt,[1,-1,1]),i.translate(qt,qt,[0,0,-this.cameraToCenterDistance]),i.rotateX(qt,qt,this._pitch),i.rotateZ(qt,qt,this.angle),i.translate(qt,qt,[-we,-Ue,0]),this.mercatorMatrix=i.scale([],qt,[this.worldSize,this.worldSize,this.worldSize]),i.scale(qt,qt,[1,1,i.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=qt,this.invProjMatrix=i.invert([],this.projMatrix);var Ve=this.width%2/2,Qe=this.height%2/2,at=Math.cos(this.angle),Ct=Math.sin(this.angle),Ot=we-Math.round(we)+at*Ve+Ct*Qe,Rt=Ue-Math.round(Ue)+at*Qe+Ct*Ve,Bt=new Float64Array(qt);if(i.translate(Bt,Bt,[Ot>.5?Ot-1:Ot,Rt>.5?Rt-1:Rt,0]),this.alignedProjMatrix=Bt,qt=i.create(),i.scale(qt,qt,[this.width/2,-this.height/2,1]),i.translate(qt,qt,[1,-1,0]),this.labelPlaneMatrix=qt,qt=i.create(),i.scale(qt,qt,[1,-1,1]),i.translate(qt,qt,[-1,-1,0]),i.scale(qt,qt,[2/this.width,2/this.height,1]),this.glCoordMatrix=qt,this.pixelMatrix=i.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),qt=i.invert(new Float64Array(16),this.pixelMatrix),!qt)throw new Error("failed to invert matrix");this.pixelMatrixInverse=qt,this._posMatrixCache={},this._alignedPosMatrixCache={}}},wo.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var D=this.pointCoordinate(new i.Point(0,0)),J=[D.x*this.worldSize,D.y*this.worldSize,0,1],q=i.transformMat4(J,J,this.pixelMatrix);return q[3]/this.cameraToCenterDistance},wo.prototype.getCameraPoint=function(){var D=this._pitch,J=Math.tan(D)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new i.Point(0,J))},wo.prototype.getCameraQueryGeometry=function(D){var J=this.getCameraPoint();if(D.length===1)return[D[0],J];for(var q=J.x,K=J.y,de=J.x,ne=J.y,we=0,Ue=D;we<Ue.length;we+=1){var ft=Ue[we];q=Math.min(q,ft.x),K=Math.min(K,ft.y),de=Math.max(de,ft.x),ne=Math.max(ne,ft.y)}return[new i.Point(q,K),new i.Point(de,K),new i.Point(de,ne),new i.Point(q,ne),new i.Point(q,K)]},Object.defineProperties(wo.prototype,To);function hl(Y,D){var J=!1,q=null,K=function(){q=null,J&&(Y(),q=setTimeout(K,D),J=!1)};return function(){return J=!0,q||K(),q}}var Ul=function(D){this._hashName=D&&encodeURIComponent(D),i.bindAll(["_getCurrentHash","_onHashChange","_updateHash"],this),this._updateHash=hl(this._updateHashUnthrottled.bind(this),30*1e3/100)};Ul.prototype.addTo=function(D){return this._map=D,i.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},Ul.prototype.remove=function(){return i.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Ul.prototype.getHashString=function(D){var J=this._map.getCenter(),q=Math.round(this._map.getZoom()*100)/100,K=Math.ceil((q*Math.LN2+Math.log(512/360/.5))/Math.LN10),de=Math.pow(10,K),ne=Math.round(J.lng*de)/de,we=Math.round(J.lat*de)/de,Ue=this._map.getBearing(),ft=this._map.getPitch(),Xt="";if(D?Xt+="/"+ne+"/"+we+"/"+q:Xt+=q+"/"+we+"/"+ne,(Ue||ft)&&(Xt+="/"+Math.round(Ue*10)/10),ft&&(Xt+="/"+Math.round(ft)),this._hashName){var hr=this._hashName,qt=!1,Ve=i.window.location.hash.slice(1).split("&").map(function(Qe){var at=Qe.split("=")[0];return at===hr?(qt=!0,at+"="+Xt):Qe}).filter(function(Qe){return Qe});return qt||Ve.push(hr+"="+Xt),"#"+Ve.join("&")}return"#"+Xt},Ul.prototype._getCurrentHash=function(){var D=this,J=i.window.location.hash.replace("#","");if(this._hashName){var q;return J.split("&").map(function(K){return K.split("=")}).forEach(function(K){K[0]===D._hashName&&(q=K)}),(q&&q[1]||"").split("/")}return J.split("/")},Ul.prototype._onHashChange=function(){var D=this._getCurrentHash();if(D.length>=3&&!D.some(function(q){return isNaN(q)})){var J=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(D[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+D[2],+D[1]],zoom:+D[0],bearing:J,pitch:+(D[4]||0)}),!0}return!1},Ul.prototype._updateHashUnthrottled=function(){var D=i.window.location.href.replace(/(#.+)?$/,this.getHashString());try{i.window.history.replaceState(i.window.history.state,null,D)}catch(J){}};var Lu={linearity:.3,easing:i.bezier(0,0,.3,1)},au=i.extend({deceleration:2500,maxSpeed:1400},Lu),Js=i.extend({deceleration:20,maxSpeed:1400},Lu),Ql=i.extend({deceleration:1e3,maxSpeed:360},Lu),dc=i.extend({deceleration:1e3,maxSpeed:90},Lu),Tl=function(D){this._map=D,this.clear()};Tl.prototype.clear=function(){this._inertiaBuffer=[]},Tl.prototype.record=function(D){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:i.browser.now(),settings:D})},Tl.prototype._drainInertiaBuffer=function(){for(var D=this._inertiaBuffer,J=i.browser.now(),q=160;D.length>0&&J-D[0].time>q;)D.shift()},Tl.prototype._onMoveEnd=function(D){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var J={zoom:0,bearing:0,pitch:0,pan:new i.Point(0,0),pinchAround:void 0,around:void 0},q=0,K=this._inertiaBuffer;q<K.length;q+=1){var de=K[q],ne=de.settings;J.zoom+=ne.zoomDelta||0,J.bearing+=ne.bearingDelta||0,J.pitch+=ne.pitchDelta||0,ne.panDelta&&J.pan._add(ne.panDelta),ne.around&&(J.around=ne.around),ne.pinchAround&&(J.pinchAround=ne.pinchAround)}var we=this._inertiaBuffer[this._inertiaBuffer.length-1],Ue=we.time-this._inertiaBuffer[0].time,ft={};if(J.pan.mag()){var Xt=X(J.pan.mag(),Ue,i.extend({},au,D||{}));ft.offset=J.pan.mult(Xt.amount/J.pan.mag()),ft.center=this._map.transform.center,Al(ft,Xt)}if(J.zoom){var hr=X(J.zoom,Ue,Js);ft.zoom=this._map.transform.zoom+hr.amount,Al(ft,hr)}if(J.bearing){var qt=X(J.bearing,Ue,Ql);ft.bearing=this._map.transform.bearing+i.clamp(qt.amount,-179,179),Al(ft,qt)}if(J.pitch){var Ve=X(J.pitch,Ue,dc);ft.pitch=this._map.transform.pitch+Ve.amount,Al(ft,Ve)}if(ft.zoom||ft.bearing){var Qe=J.pinchAround===void 0?J.around:J.pinchAround;ft.around=Qe?this._map.unproject(Qe):this._map.getCenter()}return this.clear(),i.extend(ft,{noMoveStart:!0})}};function Al(Y,D){(!Y.duration||Y.duration<D.duration)&&(Y.duration=D.duration,Y.easing=D.easing)}function X(Y,D,J){var q=J.maxSpeed,K=J.linearity,de=J.deceleration,ne=i.clamp(Y*K/(D/1e3),-q,q),we=Math.abs(ne)/(de*K);return{easing:J.easing,duration:we*1e3,amount:ne*(we/2)}}var se=function(Y){function D(q,K,de,ne){ne===void 0&&(ne={});var we=o.mousePos(K.getCanvasContainer(),de),Ue=K.unproject(we);Y.call(this,q,i.extend({point:we,lngLat:Ue,originalEvent:de},ne)),this._defaultPrevented=!1,this.target=K}Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D;var J={defaultPrevented:{configurable:!0}};return D.prototype.preventDefault=function(){this._defaultPrevented=!0},J.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(D.prototype,J),D}(i.Event),Te=function(Y){function D(q,K,de){var ne=q==="touchend"?de.changedTouches:de.touches,we=o.touchPos(K.getCanvasContainer(),ne),Ue=we.map(function(hr){return K.unproject(hr)}),ft=we.reduce(function(hr,qt,Ve,Qe){return hr.add(qt.div(Qe.length))},new i.Point(0,0)),Xt=K.unproject(ft);Y.call(this,q,{points:we,point:ft,lngLats:Ue,lngLat:Xt,originalEvent:de}),this._defaultPrevented=!1}Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D;var J={defaultPrevented:{configurable:!0}};return D.prototype.preventDefault=function(){this._defaultPrevented=!0},J.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(D.prototype,J),D}(i.Event),Ne=function(Y){function D(q,K,de){Y.call(this,q,{originalEvent:de}),this._defaultPrevented=!1}Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D;var J={defaultPrevented:{configurable:!0}};return D.prototype.preventDefault=function(){this._defaultPrevented=!0},J.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(D.prototype,J),D}(i.Event),He=function(D,J){this._map=D,this._clickTolerance=J.clickTolerance};He.prototype.reset=function(){delete this._mousedownPos},He.prototype.wheel=function(D){return this._firePreventable(new Ne(D.type,this._map,D))},He.prototype.mousedown=function(D,J){return this._mousedownPos=J,this._firePreventable(new se(D.type,this._map,D))},He.prototype.mouseup=function(D){this._map.fire(new se(D.type,this._map,D))},He.prototype.click=function(D,J){this._mousedownPos&&this._mousedownPos.dist(J)>=this._clickTolerance||this._map.fire(new se(D.type,this._map,D))},He.prototype.dblclick=function(D){return this._firePreventable(new se(D.type,this._map,D))},He.prototype.mouseover=function(D){this._map.fire(new se(D.type,this._map,D))},He.prototype.mouseout=function(D){this._map.fire(new se(D.type,this._map,D))},He.prototype.touchstart=function(D){return this._firePreventable(new Te(D.type,this._map,D))},He.prototype.touchmove=function(D){this._map.fire(new Te(D.type,this._map,D))},He.prototype.touchend=function(D){this._map.fire(new Te(D.type,this._map,D))},He.prototype.touchcancel=function(D){this._map.fire(new Te(D.type,this._map,D))},He.prototype._firePreventable=function(D){if(this._map.fire(D),D.defaultPrevented)return{}},He.prototype.isEnabled=function(){return!0},He.prototype.isActive=function(){return!1},He.prototype.enable=function(){},He.prototype.disable=function(){};var Ye=function(D){this._map=D};Ye.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Ye.prototype.mousemove=function(D){this._map.fire(new se(D.type,this._map,D))},Ye.prototype.mousedown=function(){this._delayContextMenu=!0},Ye.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new se("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Ye.prototype.contextmenu=function(D){this._delayContextMenu?this._contextMenuEvent=D:this._map.fire(new se(D.type,this._map,D)),this._map.listens("contextmenu")&&D.preventDefault()},Ye.prototype.isEnabled=function(){return!0},Ye.prototype.isActive=function(){return!1},Ye.prototype.enable=function(){},Ye.prototype.disable=function(){};var kt=function(D,J){this._map=D,this._el=D.getCanvasContainer(),this._container=D.getContainer(),this._clickTolerance=J.clickTolerance||1};kt.prototype.isEnabled=function(){return!!this._enabled},kt.prototype.isActive=function(){return!!this._active},kt.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},kt.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},kt.prototype.mousedown=function(D,J){this.isEnabled()&&D.shiftKey&&D.button===0&&(o.disableDrag(),this._startPos=this._lastPos=J,this._active=!0)},kt.prototype.mousemoveWindow=function(D,J){if(this._active){var q=J;if(!(this._lastPos.equals(q)||!this._box&&q.dist(this._startPos)<this._clickTolerance)){var K=this._startPos;this._lastPos=q,this._box||(this._box=o.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",D));var de=Math.min(K.x,q.x),ne=Math.max(K.x,q.x),we=Math.min(K.y,q.y),Ue=Math.max(K.y,q.y);o.setTransform(this._box,"translate("+de+"px,"+we+"px)"),this._box.style.width=ne-de+"px",this._box.style.height=Ue-we+"px"}}},kt.prototype.mouseupWindow=function(D,J){var q=this;if(this._active&&D.button===0){var K=this._startPos,de=J;if(this.reset(),o.suppressClick(),K.x===de.x&&K.y===de.y)this._fireEvent("boxzoomcancel",D);else return this._map.fire(new i.Event("boxzoomend",{originalEvent:D})),{cameraAnimation:function(ne){return ne.fitScreenCoordinates(K,de,q._map.getBearing(),{linear:!0})}}}},kt.prototype.keydown=function(D){this._active&&D.keyCode===27&&(this.reset(),this._fireEvent("boxzoomcancel",D))},kt.prototype.reset=function(){this._active=!1,this._container.classList.remove("mapboxgl-crosshair"),this._box&&(o.remove(this._box),this._box=null),o.enableDrag(),delete this._startPos,delete this._lastPos},kt.prototype._fireEvent=function(D,J){return this._map.fire(new i.Event(D,{originalEvent:J}))};function nt(Y,D){for(var J={},q=0;q<Y.length;q++)J[Y[q].identifier]=D[q];return J}function jt(Y){for(var D=new i.Point(0,0),J=0,q=Y;J<q.length;J+=1){var K=q[J];D._add(K)}return D.div(Y.length)}var gr=500,yr=500,Hr=30,qr=function(D){this.reset(),this.numTouches=D.numTouches};qr.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},qr.prototype.touchstart=function(D,J,q){(this.centroid||q.length>this.numTouches)&&(this.aborted=!0),!this.aborted&&(this.startTime===void 0&&(this.startTime=D.timeStamp),q.length===this.numTouches&&(this.centroid=jt(J),this.touches=nt(q,J)))},qr.prototype.touchmove=function(D,J,q){if(!(this.aborted||!this.centroid)){var K=nt(q,J);for(var de in this.touches){var ne=this.touches[de],we=K[de];(!we||we.dist(ne)>Hr)&&(this.aborted=!0)}}},qr.prototype.touchend=function(D,J,q){if((!this.centroid||D.timeStamp-this.startTime>yr)&&(this.aborted=!0),q.length===0){var K=!this.aborted&&this.centroid;if(this.reset(),K)return K}};var _i=function(D){this.singleTap=new qr(D),this.numTaps=D.numTaps,this.reset()};_i.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},_i.prototype.touchstart=function(D,J,q){this.singleTap.touchstart(D,J,q)},_i.prototype.touchmove=function(D,J,q){this.singleTap.touchmove(D,J,q)},_i.prototype.touchend=function(D,J,q){var K=this.singleTap.touchend(D,J,q);if(K){var de=D.timeStamp-this.lastTime<gr,ne=!this.lastTap||this.lastTap.dist(K)<Hr;if((!de||!ne)&&this.reset(),this.count++,this.lastTime=D.timeStamp,this.lastTap=K,this.count===this.numTaps)return this.reset(),K}};var bi=function(){this._zoomIn=new _i({numTouches:1,numTaps:2}),this._zoomOut=new _i({numTouches:2,numTaps:1}),this.reset()};bi.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},bi.prototype.touchstart=function(D,J,q){this._zoomIn.touchstart(D,J,q),this._zoomOut.touchstart(D,J,q)},bi.prototype.touchmove=function(D,J,q){this._zoomIn.touchmove(D,J,q),this._zoomOut.touchmove(D,J,q)},bi.prototype.touchend=function(D,J,q){var K=this,de=this._zoomIn.touchend(D,J,q),ne=this._zoomOut.touchend(D,J,q);if(de)return this._active=!0,D.preventDefault(),setTimeout(function(){return K.reset()},0),{cameraAnimation:function(we){return we.easeTo({duration:300,zoom:we.getZoom()+1,around:we.unproject(de)},{originalEvent:D})}};if(ne)return this._active=!0,D.preventDefault(),setTimeout(function(){return K.reset()},0),{cameraAnimation:function(we){return we.easeTo({duration:300,zoom:we.getZoom()-1,around:we.unproject(ne)},{originalEvent:D})}}},bi.prototype.touchcancel=function(){this.reset()},bi.prototype.enable=function(){this._enabled=!0},bi.prototype.disable=function(){this._enabled=!1,this.reset()},bi.prototype.isEnabled=function(){return this._enabled},bi.prototype.isActive=function(){return this._active};var Zr=0,ai=2,gi={};gi[Zr]=1,gi[ai]=2;function Ii(Y,D){var J=gi[D];return Y.buttons===void 0||(Y.buttons&J)!==J}var Si=function(D){this.reset(),this._clickTolerance=D.clickTolerance||1};Si.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Si.prototype._correctButton=function(D,J){return!1},Si.prototype._move=function(D,J){return{}},Si.prototype.mousedown=function(D,J){if(!this._lastPoint){var q=o.mouseButton(D);this._correctButton(D,q)&&(this._lastPoint=J,this._eventButton=q)}},Si.prototype.mousemoveWindow=function(D,J){var q=this._lastPoint;if(q){if(D.preventDefault(),Ii(D,this._eventButton)){this.reset();return}if(!(!this._moved&&J.dist(q)<this._clickTolerance))return this._moved=!0,this._lastPoint=J,this._move(q,J)}},Si.prototype.mouseupWindow=function(D){if(this._lastPoint){var J=o.mouseButton(D);J===this._eventButton&&(this._moved&&o.suppressClick(),this.reset())}},Si.prototype.enable=function(){this._enabled=!0},Si.prototype.disable=function(){this._enabled=!1,this.reset()},Si.prototype.isEnabled=function(){return this._enabled},Si.prototype.isActive=function(){return this._active};var ei=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.mousedown=function(q,K){Y.prototype.mousedown.call(this,q,K),this._lastPoint&&(this._active=!0)},D.prototype._correctButton=function(q,K){return K===Zr&&!q.ctrlKey},D.prototype._move=function(q,K){return{around:K,panDelta:K.sub(q)}},D}(Si),Ln=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype._correctButton=function(q,K){return K===Zr&&q.ctrlKey||K===ai},D.prototype._move=function(q,K){var de=.8,ne=(K.x-q.x)*de;if(ne)return this._active=!0,{bearingDelta:ne}},D.prototype.contextmenu=function(q){q.preventDefault()},D}(Si),En=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype._correctButton=function(q,K){return K===Zr&&q.ctrlKey||K===ai},D.prototype._move=function(q,K){var de=-.5,ne=(K.y-q.y)*de;if(ne)return this._active=!0,{pitchDelta:ne}},D.prototype.contextmenu=function(q){q.preventDefault()},D}(Si),Un=function(D){this._minTouches=1,this._clickTolerance=D.clickTolerance||1,this.reset()};Un.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new i.Point(0,0)},Un.prototype.touchstart=function(D,J,q){return this._calculateTransform(D,J,q)},Un.prototype.touchmove=function(D,J,q){if(!(!this._active||q.length<this._minTouches))return D.preventDefault(),this._calculateTransform(D,J,q)},Un.prototype.touchend=function(D,J,q){this._calculateTransform(D,J,q),this._active&&q.length<this._minTouches&&this.reset()},Un.prototype.touchcancel=function(){this.reset()},Un.prototype._calculateTransform=function(D,J,q){q.length>0&&(this._active=!0);var K=nt(q,J),de=new i.Point(0,0),ne=new i.Point(0,0),we=0;for(var Ue in K){var ft=K[Ue],Xt=this._touches[Ue];Xt&&(de._add(ft),ne._add(ft.sub(Xt)),we++,K[Ue]=ft)}if(this._touches=K,!(we<this._minTouches||!ne.mag())){var hr=ne.div(we);if(this._sum._add(hr),!(this._sum.mag()<this._clickTolerance)){var qt=de.div(we);return{around:qt,panDelta:hr}}}},Un.prototype.enable=function(){this._enabled=!0},Un.prototype.disable=function(){this._enabled=!1,this.reset()},Un.prototype.isEnabled=function(){return this._enabled},Un.prototype.isActive=function(){return this._active};var ia=function(){this.reset()};ia.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},ia.prototype._start=function(D){},ia.prototype._move=function(D,J,q){return{}},ia.prototype.touchstart=function(D,J,q){this._firstTwoTouches||q.length<2||(this._firstTwoTouches=[q[0].identifier,q[1].identifier],this._start([J[0],J[1]]))},ia.prototype.touchmove=function(D,J,q){if(this._firstTwoTouches){D.preventDefault();var K=this._firstTwoTouches,de=K[0],ne=K[1],we=Ea(q,J,de),Ue=Ea(q,J,ne);if(!(!we||!Ue)){var ft=this._aroundCenter?null:we.add(Ue).div(2);return this._move([we,Ue],ft,D)}}},ia.prototype.touchend=function(D,J,q){if(this._firstTwoTouches){var K=this._firstTwoTouches,de=K[0],ne=K[1],we=Ea(q,J,de),Ue=Ea(q,J,ne);we&&Ue||(this._active&&o.suppressClick(),this.reset())}},ia.prototype.touchcancel=function(){this.reset()},ia.prototype.enable=function(D){this._enabled=!0,this._aroundCenter=!!D&&D.around==="center"},ia.prototype.disable=function(){this._enabled=!1,this.reset()},ia.prototype.isEnabled=function(){return this._enabled},ia.prototype.isActive=function(){return this._active};function Ea(Y,D,J){for(var q=0;q<Y.length;q++)if(Y[q].identifier===J)return D[q]}var Ia=.1;function yo(Y,D){return Math.log(Y/D)/Math.LN2}var Da=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.reset=function(){Y.prototype.reset.call(this),delete this._distance,delete this._startDistance},D.prototype._start=function(q){this._startDistance=this._distance=q[0].dist(q[1])},D.prototype._move=function(q,K){var de=this._distance;if(this._distance=q[0].dist(q[1]),!(!this._active&&Math.abs(yo(this._distance,this._startDistance))<Ia))return this._active=!0,{zoomDelta:yo(this._distance,de),pinchAround:K}},D}(ia),go=25;function Is(Y,D){return Y.angleWith(D)*180/Math.PI}var Ms=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.reset=function(){Y.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},D.prototype._start=function(q){this._startVector=this._vector=q[0].sub(q[1]),this._minDiameter=q[0].dist(q[1])},D.prototype._move=function(q,K){var de=this._vector;if(this._vector=q[0].sub(q[1]),!(!this._active&&this._isBelowThreshold(this._vector)))return this._active=!0,{bearingDelta:Is(this._vector,de),pinchAround:K}},D.prototype._isBelowThreshold=function(q){this._minDiameter=Math.min(this._minDiameter,q.mag());var K=Math.PI*this._minDiameter,de=go/K*360,ne=Is(q,this._startVector);return Math.abs(ne)<de},D}(ia);function Xs(Y){return Math.abs(Y.y)>Math.abs(Y.x)}var Gn=100,ja=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.reset=function(){Y.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},D.prototype._start=function(q){this._lastPoints=q,Xs(q[0].sub(q[1]))&&(this._valid=!1)},D.prototype._move=function(q,K,de){var ne=q[0].sub(this._lastPoints[0]),we=q[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(ne,we,de.timeStamp),!!this._valid){this._lastPoints=q,this._active=!0;var Ue=(ne.y+we.y)/2,ft=-.5;return{pitchDelta:Ue*ft}}},D.prototype.gestureBeginsVertically=function(q,K,de){if(this._valid!==void 0)return this._valid;var ne=2,we=q.mag()>=ne,Ue=K.mag()>=ne;if(!(!we&&!Ue)){if(!we||!Ue)return this._firstMove===void 0&&(this._firstMove=de),de-this._firstMove<Gn?void 0:!1;var ft=q.y>0==K.y>0;return Xs(q)&&Xs(K)&&ft}},D}(ia),Fo={panStep:100,bearingStep:15,pitchStep:10},Uo=function(){var D=Fo;this._panStep=D.panStep,this._bearingStep=D.bearingStep,this._pitchStep=D.pitchStep,this._rotationDisabled=!1};Uo.prototype.reset=function(){this._active=!1},Uo.prototype.keydown=function(D){var J=this;if(!(D.altKey||D.ctrlKey||D.metaKey)){var q=0,K=0,de=0,ne=0,we=0;switch(D.keyCode){case 61:case 107:case 171:case 187:q=1;break;case 189:case 109:case 173:q=-1;break;case 37:D.shiftKey?K=-1:(D.preventDefault(),ne=-1);break;case 39:D.shiftKey?K=1:(D.preventDefault(),ne=1);break;case 38:D.shiftKey?de=1:(D.preventDefault(),we=-1);break;case 40:D.shiftKey?de=-1:(D.preventDefault(),we=1);break;default:return}return this._rotationDisabled&&(K=0,de=0),{cameraAnimation:function(Ue){var ft=Ue.getZoom();Ue.easeTo({duration:300,easeId:"keyboardHandler",easing:$s,zoom:q?Math.round(ft)+q*(D.shiftKey?2:1):ft,bearing:Ue.getBearing()+K*J._bearingStep,pitch:Ue.getPitch()+de*J._pitchStep,offset:[-ne*J._panStep,-we*J._panStep],center:Ue.getCenter()},{originalEvent:D})}}}},Uo.prototype.enable=function(){this._enabled=!0},Uo.prototype.disable=function(){this._enabled=!1,this.reset()},Uo.prototype.isEnabled=function(){return this._enabled},Uo.prototype.isActive=function(){return this._active},Uo.prototype.disableRotation=function(){this._rotationDisabled=!0},Uo.prototype.enableRotation=function(){this._rotationDisabled=!1};function $s(Y){return Y*(2-Y)}var Sl=4.000244140625,bu=1/100,dl=1/450,Sc=2,Me=function(D,J){this._map=D,this._el=D.getCanvasContainer(),this._handler=J,this._delta=0,this._defaultZoomRate=bu,this._wheelZoomRate=dl,i.bindAll(["_onTimeout"],this)};Me.prototype.setZoomRate=function(D){this._defaultZoomRate=D},Me.prototype.setWheelZoomRate=function(D){this._wheelZoomRate=D},Me.prototype.isEnabled=function(){return!!this._enabled},Me.prototype.isActive=function(){return!!this._active||this._finishTimeout!==void 0},Me.prototype.isZooming=function(){return!!this._zooming},Me.prototype.enable=function(D){this.isEnabled()||(this._enabled=!0,this._aroundCenter=D&&D.around==="center")},Me.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Me.prototype.wheel=function(D){if(this.isEnabled()){var J=D.deltaMode===i.window.WheelEvent.DOM_DELTA_LINE?D.deltaY*40:D.deltaY,q=i.browser.now(),K=q-(this._lastWheelEventTime||0);this._lastWheelEventTime=q,J!==0&&J%Sl===0?this._type="wheel":J!==0&&Math.abs(J)<4?this._type="trackpad":K>400?(this._type=null,this._lastValue=J,this._timeout=setTimeout(this._onTimeout,40,D)):this._type||(this._type=Math.abs(K*J)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,J+=this._lastValue)),D.shiftKey&&J&&(J=J/4),this._type&&(this._lastWheelEvent=D,this._delta-=J,this._active||this._start(D)),D.preventDefault()}},Me.prototype._onTimeout=function(D){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(D)},Me.prototype._start=function(D){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var J=o.mousePos(this._el,D);this._around=i.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(J)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},Me.prototype.renderFrame=function(){var D=this;if(this._frameId&&(this._frameId=null,!!this.isActive())){var J=this._map.transform;if(this._delta!==0){var q=this._type==="wheel"&&Math.abs(this._delta)>Sl?this._wheelZoomRate:this._defaultZoomRate,K=Sc/(1+Math.exp(-Math.abs(this._delta*q)));this._delta<0&&K!==0&&(K=1/K);var de=typeof this._targetZoom=="number"?J.zoomScale(this._targetZoom):J.scale;this._targetZoom=Math.min(J.maxZoom,Math.max(J.minZoom,J.scaleZoom(de*K))),this._type==="wheel"&&(this._startZoom=J.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var ne=typeof this._targetZoom=="number"?this._targetZoom:J.zoom,we=this._startZoom,Ue=this._easing,ft=!1,Xt;if(this._type==="wheel"&&we&&Ue){var hr=Math.min((i.browser.now()-this._lastWheelEventTime)/200,1),qt=Ue(hr);Xt=i.number(we,ne,qt),hr<1?this._frameId||(this._frameId=!0):ft=!0}else Xt=ne,ft=!0;return this._active=!0,ft&&(this._active=!1,this._finishTimeout=setTimeout(function(){D._zooming=!1,D._handler._triggerRenderFrame(),delete D._targetZoom,delete D._finishTimeout},200)),{noInertia:!0,needsRenderFrame:!ft,zoomDelta:Xt-J.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},Me.prototype._smoothOutEasing=function(D){var J=i.ease;if(this._prevEase){var q=this._prevEase,K=(i.browser.now()-q.start)/q.duration,de=q.easing(K+.01)-q.easing(K),ne=.27/Math.sqrt(de*de+1e-4)*.01,we=Math.sqrt(.27*.27-ne*ne);J=i.bezier(ne,we,.25,1)}return this._prevEase={start:i.browser.now(),duration:D,easing:J},J},Me.prototype.reset=function(){this._active=!1};var bt=function(D,J){this._clickZoom=D,this._tapZoom=J};bt.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},bt.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},bt.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},bt.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var zt=function(){this.reset()};zt.prototype.reset=function(){this._active=!1},zt.prototype.dblclick=function(D,J){return D.preventDefault(),{cameraAnimation:function(q){q.easeTo({duration:300,zoom:q.getZoom()+(D.shiftKey?-1:1),around:q.unproject(J)},{originalEvent:D})}}},zt.prototype.enable=function(){this._enabled=!0},zt.prototype.disable=function(){this._enabled=!1,this.reset()},zt.prototype.isEnabled=function(){return this._enabled},zt.prototype.isActive=function(){return this._active};var Rr=function(){this._tap=new _i({numTouches:1,numTaps:1}),this.reset()};Rr.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},Rr.prototype.touchstart=function(D,J,q){this._swipePoint||(this._tapTime&&D.timeStamp-this._tapTime>gr&&this.reset(),this._tapTime?q.length>0&&(this._swipePoint=J[0],this._swipeTouch=q[0].identifier):this._tap.touchstart(D,J,q))},Rr.prototype.touchmove=function(D,J,q){if(!this._tapTime)this._tap.touchmove(D,J,q);else if(this._swipePoint){if(q[0].identifier!==this._swipeTouch)return;var K=J[0],de=K.y-this._swipePoint.y;return this._swipePoint=K,D.preventDefault(),this._active=!0,{zoomDelta:de/128}}},Rr.prototype.touchend=function(D,J,q){if(this._tapTime)this._swipePoint&&q.length===0&&this.reset();else{var K=this._tap.touchend(D,J,q);K&&(this._tapTime=D.timeStamp)}},Rr.prototype.touchcancel=function(){this.reset()},Rr.prototype.enable=function(){this._enabled=!0},Rr.prototype.disable=function(){this._enabled=!1,this.reset()},Rr.prototype.isEnabled=function(){return this._enabled},Rr.prototype.isActive=function(){return this._active};var jr=function(D,J,q){this._el=D,this._mousePan=J,this._touchPan=q};jr.prototype.enable=function(D){this._inertiaOptions=D||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},jr.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},jr.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},jr.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var Nr=function(D,J,q){this._pitchWithRotate=D.pitchWithRotate,this._mouseRotate=J,this._mousePitch=q};Nr.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},Nr.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},Nr.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},Nr.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var Gr=function(D,J,q,K){this._el=D,this._touchZoom=J,this._touchRotate=q,this._tapDragZoom=K,this._rotationDisabled=!1,this._enabled=!0};Gr.prototype.enable=function(D){this._touchZoom.enable(D),this._rotationDisabled||this._touchRotate.enable(D),this._tapDragZoom.enable(),this._el.classList.add("mapboxgl-touch-zoom-rotate")},Gr.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},Gr.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},Gr.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},Gr.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},Gr.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(Y){return Y.zoom||Y.drag||Y.pitch||Y.rotate},Ui=function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D}(i.Event);function qi(Y){return Y.panDelta&&Y.panDelta.mag()||Y.zoomDelta||Y.bearingDelta||Y.pitchDelta}var Ei=function(D,J){this._map=D,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Tl(D),this._bearingSnap=J.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(J),i.bindAll(["handleEvent","handleWindowEvent"],this);var q=this._el;this._listeners=[[q,"touchstart",{passive:!0}],[q,"touchmove",{passive:!1}],[q,"touchend",void 0],[q,"touchcancel",void 0],[q,"mousedown",void 0],[q,"mousemove",void 0],[q,"mouseup",void 0],[i.window.document,"mousemove",{capture:!0}],[i.window.document,"mouseup",void 0],[q,"mouseover",void 0],[q,"mouseout",void 0],[q,"dblclick",void 0],[q,"click",void 0],[q,"keydown",{capture:!1}],[q,"keyup",void 0],[q,"wheel",{passive:!1}],[q,"contextmenu",void 0],[i.window,"blur",void 0]];for(var K=0,de=this._listeners;K<de.length;K+=1){var ne=de[K],we=ne[0],Ue=ne[1],ft=ne[2];o.addEventListener(we,Ue,we===i.window.document?this.handleWindowEvent:this.handleEvent,ft)}};Ei.prototype.destroy=function(){for(var D=0,J=this._listeners;D<J.length;D+=1){var q=J[D],K=q[0],de=q[1],ne=q[2];o.removeEventListener(K,de,K===i.window.document?this.handleWindowEvent:this.handleEvent,ne)}},Ei.prototype._addDefaultHandlers=function(D){var J=this._map,q=J.getCanvasContainer();this._add("mapEvent",new He(J,D));var K=J.boxZoom=new kt(J,D);this._add("boxZoom",K);var de=new bi,ne=new zt;J.doubleClickZoom=new bt(ne,de),this._add("tapZoom",de),this._add("clickZoom",ne);var we=new Rr;this._add("tapDragZoom",we);var Ue=J.touchPitch=new ja;this._add("touchPitch",Ue);var ft=new Ln(D),Xt=new En(D);J.dragRotate=new Nr(D,ft,Xt),this._add("mouseRotate",ft,["mousePitch"]),this._add("mousePitch",Xt,["mouseRotate"]);var hr=new ei(D),qt=new Un(D);J.dragPan=new jr(q,hr,qt),this._add("mousePan",hr),this._add("touchPan",qt,["touchZoom","touchRotate"]);var Ve=new Ms,Qe=new Da;J.touchZoomRotate=new Gr(q,Qe,Ve,we),this._add("touchRotate",Ve,["touchPan","touchZoom"]),this._add("touchZoom",Qe,["touchPan","touchRotate"]);var at=J.scrollZoom=new Me(J,this);this._add("scrollZoom",at,["mousePan"]);var Ct=J.keyboard=new Uo;this._add("keyboard",Ct),this._add("blockableMapEvent",new Ye(J));for(var Ot=0,Rt=["boxZoom","doubleClickZoom","tapDragZoom","touchPitch","dragRotate","dragPan","touchZoomRotate","scrollZoom","keyboard"];Ot<Rt.length;Ot+=1){var Bt=Rt[Ot];D.interactive&&D[Bt]&&J[Bt].enable(D[Bt])}},Ei.prototype._add=function(D,J,q){this._handlers.push({handlerName:D,handler:J,allowed:q}),this._handlersById[D]=J},Ei.prototype.stop=function(D){if(!this._updatingCamera){for(var J=0,q=this._handlers;J<q.length;J+=1){var K=q[J],de=K.handler;de.reset()}this._inertia.clear(),this._fireEvents({},{},D),this._changes=[]}},Ei.prototype.isActive=function(){for(var D=0,J=this._handlers;D<J.length;D+=1){var q=J[D],K=q.handler;if(K.isActive())return!0}return!1},Ei.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},Ei.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},Ei.prototype.isMoving=function(){return!!mi(this._eventsInProgress)||this.isZooming()},Ei.prototype._blockedByActive=function(D,J,q){for(var K in D)if(K!==q&&(!J||J.indexOf(K)<0))return!0;return!1},Ei.prototype.handleWindowEvent=function(D){this.handleEvent(D,D.type+"Window")},Ei.prototype._getMapTouches=function(D){for(var J=[],q=0,K=D;q<K.length;q+=1){var de=K[q],ne=de.target;this._el.contains(ne)&&J.push(de)}return J},Ei.prototype.handleEvent=function(D,J){if(D.type==="blur"){this.stop(!0);return}this._updatingCamera=!0;for(var q=D.type==="renderFrame"?void 0:D,K={needsRenderFrame:!1},de={},ne={},we=D.touches?this._getMapTouches(D.touches):void 0,Ue=we?o.touchPos(this._el,we):o.mousePos(this._el,D),ft=0,Xt=this._handlers;ft<Xt.length;ft+=1){var hr=Xt[ft],qt=hr.handlerName,Ve=hr.handler,Qe=hr.allowed;if(Ve.isEnabled()){var at=void 0;this._blockedByActive(ne,Qe,qt)?Ve.reset():Ve[J||D.type]&&(at=Ve[J||D.type](D,Ue,we),this.mergeHandlerResult(K,de,at,qt,q),at&&at.needsRenderFrame&&this._triggerRenderFrame()),(at||Ve.isActive())&&(ne[qt]=Ve)}}var Ct={};for(var Ot in this._previousActiveHandlers)ne[Ot]||(Ct[Ot]=q);this._previousActiveHandlers=ne,(Object.keys(Ct).length||qi(K))&&(this._changes.push([K,de,Ct]),this._triggerRenderFrame()),(Object.keys(ne).length||qi(K))&&this._map._stop(!0),this._updatingCamera=!1;var Rt=K.cameraAnimation;Rt&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],Rt(this._map))},Ei.prototype.mergeHandlerResult=function(D,J,q,K,de){if(q){i.extend(D,q);var ne={handlerName:K,originalEvent:q.originalEvent||de};q.zoomDelta!==void 0&&(J.zoom=ne),q.panDelta!==void 0&&(J.drag=ne),q.pitchDelta!==void 0&&(J.pitch=ne),q.bearingDelta!==void 0&&(J.rotate=ne)}},Ei.prototype._applyChanges=function(){for(var D={},J={},q={},K=0,de=this._changes;K<de.length;K+=1){var ne=de[K],we=ne[0],Ue=ne[1],ft=ne[2];we.panDelta&&(D.panDelta=(D.panDelta||new i.Point(0,0))._add(we.panDelta)),we.zoomDelta&&(D.zoomDelta=(D.zoomDelta||0)+we.zoomDelta),we.bearingDelta&&(D.bearingDelta=(D.bearingDelta||0)+we.bearingDelta),we.pitchDelta&&(D.pitchDelta=(D.pitchDelta||0)+we.pitchDelta),we.around!==void 0&&(D.around=we.around),we.pinchAround!==void 0&&(D.pinchAround=we.pinchAround),we.noInertia&&(D.noInertia=we.noInertia),i.extend(J,Ue),i.extend(q,ft)}this._updateMapTransform(D,J,q),this._changes=[]},Ei.prototype._updateMapTransform=function(D,J,q){var K=this._map,de=K.transform;if(!qi(D))return this._fireEvents(J,q,!0);var ne=D.panDelta,we=D.zoomDelta,Ue=D.bearingDelta,ft=D.pitchDelta,Xt=D.around,hr=D.pinchAround;hr!==void 0&&(Xt=hr),K._stop(!0),Xt=Xt||K.transform.centerPoint;var qt=de.pointLocation(ne?Xt.sub(ne):Xt);Ue&&(de.bearing+=Ue),ft&&(de.pitch+=ft),we&&(de.zoom+=we),de.setLocationAtPoint(qt,Xt),this._map._update(),D.noInertia||this._inertia.record(D),this._fireEvents(J,q,!0)},Ei.prototype._fireEvents=function(D,J,q){var K=this,de=mi(this._eventsInProgress),ne=mi(D),we={};for(var Ue in D){var ft=D[Ue],Xt=ft.originalEvent;this._eventsInProgress[Ue]||(we[Ue+"start"]=Xt),this._eventsInProgress[Ue]=D[Ue]}!de&&ne&&this._fireEvent("movestart",ne.originalEvent);for(var hr in we)this._fireEvent(hr,we[hr]);ne&&this._fireEvent("move",ne.originalEvent);for(var qt in D){var Ve=D[qt],Qe=Ve.originalEvent;this._fireEvent(qt,Qe)}var at={},Ct;for(var Ot in this._eventsInProgress){var Rt=this._eventsInProgress[Ot],Bt=Rt.handlerName,Dt=Rt.originalEvent;this._handlersById[Bt].isActive()||(delete this._eventsInProgress[Ot],Ct=J[Bt]||Dt,at[Ot+"end"]=Ct)}for(var yt in at)this._fireEvent(yt,at[yt]);var Pt=mi(this._eventsInProgress);if(q&&(de||ne)&&!Pt){this._updatingCamera=!0;var ht=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),ur=function(br){return br!==0&&-K._bearingSnap<br&&br<K._bearingSnap};ht?(ur(ht.bearing||this._map.getBearing())&&(ht.bearing=0),this._map.easeTo(ht,{originalEvent:Ct})):(this._map.fire(new i.Event("moveend",{originalEvent:Ct})),ur(this._map.getBearing())&&this._map.resetNorth()),this._updatingCamera=!1}},Ei.prototype._fireEvent=function(D,J){this._map.fire(new i.Event(D,J?{originalEvent:J}:{}))},Ei.prototype._requestFrame=function(){var D=this;return this._map.triggerRepaint(),this._map._renderTaskQueue.add(function(J){delete D._frameId,D.handleEvent(new Ui("renderFrame",{timeStamp:J})),D._applyChanges()})},Ei.prototype._triggerRenderFrame=function(){this._frameId===void 0&&(this._frameId=this._requestFrame())};var Hn=function(Y){function D(J,q){Y.call(this),this._moving=!1,this._zooming=!1,this.transform=J,this._bearingSnap=q.bearingSnap,i.bindAll(["_renderFrameCallback"],this)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getCenter=function(){return new i.LngLat(this.transform.center.lng,this.transform.center.lat)},D.prototype.setCenter=function(q,K){return this.jumpTo({center:q},K)},D.prototype.panBy=function(q,K,de){return q=i.Point.convert(q).mult(-1),this.panTo(this.transform.center,i.extend({offset:q},K),de)},D.prototype.panTo=function(q,K,de){return this.easeTo(i.extend({center:q},K),de)},D.prototype.getZoom=function(){return this.transform.zoom},D.prototype.setZoom=function(q,K){return this.jumpTo({zoom:q},K),this},D.prototype.zoomTo=function(q,K,de){return this.easeTo(i.extend({zoom:q},K),de)},D.prototype.zoomIn=function(q,K){return this.zoomTo(this.getZoom()+1,q,K),this},D.prototype.zoomOut=function(q,K){return this.zoomTo(this.getZoom()-1,q,K),this},D.prototype.getBearing=function(){return this.transform.bearing},D.prototype.setBearing=function(q,K){return this.jumpTo({bearing:q},K),this},D.prototype.getPadding=function(){return this.transform.padding},D.prototype.setPadding=function(q,K){return this.jumpTo({padding:q},K),this},D.prototype.rotateTo=function(q,K,de){return this.easeTo(i.extend({bearing:q},K),de)},D.prototype.resetNorth=function(q,K){return this.rotateTo(0,i.extend({duration:1e3},q),K),this},D.prototype.resetNorthPitch=function(q,K){return this.easeTo(i.extend({bearing:0,pitch:0,duration:1e3},q),K),this},D.prototype.snapToNorth=function(q,K){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(q,K):this},D.prototype.getPitch=function(){return this.transform.pitch},D.prototype.setPitch=function(q,K){return this.jumpTo({pitch:q},K),this},D.prototype.cameraForBounds=function(q,K){q=i.LngLatBounds.convert(q);var de=K&&K.bearing||0;return this._cameraForBoxAndBearing(q.getNorthWest(),q.getSouthEast(),de,K)},D.prototype._cameraForBoxAndBearing=function(q,K,de,ne){var we={top:0,bottom:0,right:0,left:0};if(ne=i.extend({padding:we,offset:[0,0],maxZoom:this.transform.maxZoom},ne),typeof ne.padding=="number"){var Ue=ne.padding;ne.padding={top:Ue,bottom:Ue,right:Ue,left:Ue}}ne.padding=i.extend(we,ne.padding);var ft=this.transform,Xt=ft.padding,hr=ft.project(i.LngLat.convert(q)),qt=ft.project(i.LngLat.convert(K)),Ve=hr.rotate(-de*Math.PI/180),Qe=qt.rotate(-de*Math.PI/180),at=new i.Point(Math.max(Ve.x,Qe.x),Math.max(Ve.y,Qe.y)),Ct=new i.Point(Math.min(Ve.x,Qe.x),Math.min(Ve.y,Qe.y)),Ot=at.sub(Ct),Rt=(ft.width-(Xt.left+Xt.right+ne.padding.left+ne.padding.right))/Ot.x,Bt=(ft.height-(Xt.top+Xt.bottom+ne.padding.top+ne.padding.bottom))/Ot.y;if(Bt<0||Rt<0){i.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset.");return}var Dt=Math.min(ft.scaleZoom(ft.scale*Math.min(Rt,Bt)),ne.maxZoom),yt=typeof ne.offset.x=="number"?new i.Point(ne.offset.x,ne.offset.y):i.Point.convert(ne.offset),Pt=(ne.padding.left-ne.padding.right)/2,ht=(ne.padding.top-ne.padding.bottom)/2,ur=new i.Point(Pt,ht),br=ur.rotate(de*Math.PI/180),Ur=yt.add(br),Di=Ur.mult(ft.scale/ft.zoomScale(Dt)),fi=ft.unproject(hr.add(qt).div(2).sub(Di));return{center:fi,zoom:Dt,bearing:de}},D.prototype.fitBounds=function(q,K,de){return this._fitInternal(this.cameraForBounds(q,K),K,de)},D.prototype.fitScreenCoordinates=function(q,K,de,ne,we){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(i.Point.convert(q)),this.transform.pointLocation(i.Point.convert(K)),de,ne),ne,we)},D.prototype._fitInternal=function(q,K,de){return q?(K=i.extend(q,K),delete K.padding,K.linear?this.easeTo(K,de):this.flyTo(K,de)):this},D.prototype.jumpTo=function(q,K){this.stop();var de=this.transform,ne=!1,we=!1,Ue=!1;return"zoom"in q&&de.zoom!==+q.zoom&&(ne=!0,de.zoom=+q.zoom),q.center!==void 0&&(de.center=i.LngLat.convert(q.center)),"bearing"in q&&de.bearing!==+q.bearing&&(we=!0,de.bearing=+q.bearing),"pitch"in q&&de.pitch!==+q.pitch&&(Ue=!0,de.pitch=+q.pitch),q.padding!=null&&!de.isPaddingEqual(q.padding)&&(de.padding=q.padding),this.fire(new i.Event("movestart",K)).fire(new i.Event("move",K)),ne&&this.fire(new i.Event("zoomstart",K)).fire(new i.Event("zoom",K)).fire(new i.Event("zoomend",K)),we&&this.fire(new i.Event("rotatestart",K)).fire(new i.Event("rotate",K)).fire(new i.Event("rotateend",K)),Ue&&this.fire(new i.Event("pitchstart",K)).fire(new i.Event("pitch",K)).fire(new i.Event("pitchend",K)),this.fire(new i.Event("moveend",K))},D.prototype.easeTo=function(q,K){var de=this;this._stop(!1,q.easeId),q=i.extend({offset:[0,0],duration:500,easing:i.ease},q),(q.animate===!1||!q.essential&&i.browser.prefersReducedMotion)&&(q.duration=0);var ne=this.transform,we=this.getZoom(),Ue=this.getBearing(),ft=this.getPitch(),Xt=this.getPadding(),hr="zoom"in q?+q.zoom:we,qt="bearing"in q?this._normalizeBearing(q.bearing,Ue):Ue,Ve="pitch"in q?+q.pitch:ft,Qe="padding"in q?q.padding:ne.padding,at=i.Point.convert(q.offset),Ct=ne.centerPoint.add(at),Ot=ne.pointLocation(Ct),Rt=i.LngLat.convert(q.center||Ot);this._normalizeCenter(Rt);var Bt=ne.project(Ot),Dt=ne.project(Rt).sub(Bt),yt=ne.zoomScale(hr-we),Pt,ht;q.around&&(Pt=i.LngLat.convert(q.around),ht=ne.locationPoint(Pt));var ur={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||hr!==we,this._rotating=this._rotating||Ue!==qt,this._pitching=this._pitching||Ve!==ft,this._padding=!ne.isPaddingEqual(Qe),this._easeId=q.easeId,this._prepareEase(K,q.noMoveStart,ur),this._ease(function(br){if(de._zooming&&(ne.zoom=i.number(we,hr,br)),de._rotating&&(ne.bearing=i.number(Ue,qt,br)),de._pitching&&(ne.pitch=i.number(ft,Ve,br)),de._padding&&(ne.interpolatePadding(Xt,Qe,br),Ct=ne.centerPoint.add(at)),Pt)ne.setLocationAtPoint(Pt,ht);else{var Ur=ne.zoomScale(ne.zoom-we),Di=hr>we?Math.min(2,yt):Math.max(.5,yt),fi=Math.pow(Di,1-br),Ti=ne.unproject(Bt.add(Dt.mult(br*fi)).mult(Ur));ne.setLocationAtPoint(ne.renderWorldCopies?Ti.wrap():Ti,Ct)}de._fireMoveEvents(K)},function(br){de._afterEase(K,br)},q),this},D.prototype._prepareEase=function(q,K,de){de===void 0&&(de={}),this._moving=!0,!K&&!de.moving&&this.fire(new i.Event("movestart",q)),this._zooming&&!de.zooming&&this.fire(new i.Event("zoomstart",q)),this._rotating&&!de.rotating&&this.fire(new i.Event("rotatestart",q)),this._pitching&&!de.pitching&&this.fire(new i.Event("pitchstart",q))},D.prototype._fireMoveEvents=function(q){this.fire(new i.Event("move",q)),this._zooming&&this.fire(new i.Event("zoom",q)),this._rotating&&this.fire(new i.Event("rotate",q)),this._pitching&&this.fire(new i.Event("pitch",q))},D.prototype._afterEase=function(q,K){if(!(this._easeId&&K&&this._easeId===K)){delete this._easeId;var de=this._zooming,ne=this._rotating,we=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,de&&this.fire(new i.Event("zoomend",q)),ne&&this.fire(new i.Event("rotateend",q)),we&&this.fire(new i.Event("pitchend",q)),this.fire(new i.Event("moveend",q))}},D.prototype.flyTo=function(q,K){var de=this;if(!q.essential&&i.browser.prefersReducedMotion){var ne=i.pick(q,["center","zoom","bearing","pitch","around"]);return this.jumpTo(ne,K)}this.stop(),q=i.extend({offset:[0,0],speed:1.2,curve:1.42,easing:i.ease},q);var we=this.transform,Ue=this.getZoom(),ft=this.getBearing(),Xt=this.getPitch(),hr=this.getPadding(),qt="zoom"in q?i.clamp(+q.zoom,we.minZoom,we.maxZoom):Ue,Ve="bearing"in q?this._normalizeBearing(q.bearing,ft):ft,Qe="pitch"in q?+q.pitch:Xt,at="padding"in q?q.padding:we.padding,Ct=we.zoomScale(qt-Ue),Ot=i.Point.convert(q.offset),Rt=we.centerPoint.add(Ot),Bt=we.pointLocation(Rt),Dt=i.LngLat.convert(q.center||Bt);this._normalizeCenter(Dt);var yt=we.project(Bt),Pt=we.project(Dt).sub(yt),ht=q.curve,ur=Math.max(we.width,we.height),br=ur/Ct,Ur=Pt.mag();if("minZoom"in q){var Di=i.clamp(Math.min(q.minZoom,Ue,qt),we.minZoom,we.maxZoom),fi=ur/we.zoomScale(Di-Ue);ht=Math.sqrt(fi/Ur*2)}var Ti=ht*ht;function gn(so){var Zo=(br*br-ur*ur+(so?-1:1)*Ti*Ti*Ur*Ur)/(2*(so?br:ur)*Ti*Ur);return Math.log(Math.sqrt(Zo*Zo+1)-Zo)}function rn(so){return(Math.exp(so)-Math.exp(-so))/2}function Ci(so){return(Math.exp(so)+Math.exp(-so))/2}function Bi(so){return rn(so)/Ci(so)}var Gi=gn(0),sn=function(so){return Ci(Gi)/Ci(Gi+ht*so)},zn=function(so){return ur*((Ci(Gi)*Bi(Gi+ht*so)-rn(Gi))/Ti)/Ur},Ja=(gn(1)-Gi)/ht;if(Math.abs(Ur)<1e-6||!isFinite(Ja)){if(Math.abs(ur-br)<1e-6)return this.easeTo(q,K);var co=br<ur?-1:1;Ja=Math.abs(Math.log(br/ur))/ht,zn=function(){return 0},sn=function(so){return Math.exp(co*ht*so)}}if("duration"in q)q.duration=+q.duration;else{var ts="screenSpeed"in q?+q.screenSpeed/ht:+q.speed;q.duration=1e3*Ja/ts}return q.maxDuration&&q.duration>q.maxDuration&&(q.duration=0),this._zooming=!0,this._rotating=ft!==Ve,this._pitching=Qe!==Xt,this._padding=!we.isPaddingEqual(at),this._prepareEase(K,!1),this._ease(function(so){var Zo=so*Ja,ms=1/sn(Zo);we.zoom=so===1?qt:Ue+we.scaleZoom(ms),de._rotating&&(we.bearing=i.number(ft,Ve,so)),de._pitching&&(we.pitch=i.number(Xt,Qe,so)),de._padding&&(we.interpolatePadding(hr,at,so),Rt=we.centerPoint.add(Ot));var ou=so===1?Dt:we.unproject(yt.add(Pt.mult(zn(Zo))).mult(ms));we.setLocationAtPoint(we.renderWorldCopies?ou.wrap():ou,Rt),de._fireMoveEvents(K)},function(){return de._afterEase(K)},q),this},D.prototype.isEasing=function(){return!!this._easeFrameId},D.prototype.stop=function(){return this._stop()},D.prototype._stop=function(q,K){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var de=this._onEaseEnd;delete this._onEaseEnd,de.call(this,K)}if(!q){var ne=this.handlers;ne&&ne.stop(!1)}return this},D.prototype._ease=function(q,K,de){de.animate===!1||de.duration===0?(q(1),K()):(this._easeStart=i.browser.now(),this._easeOptions=de,this._onEaseFrame=q,this._onEaseEnd=K,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},D.prototype._renderFrameCallback=function(){var q=Math.min((i.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(q)),q<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},D.prototype._normalizeBearing=function(q,K){q=i.wrap(q,-180,180);var de=Math.abs(q-K);return Math.abs(q-360-K)<de&&(q-=360),Math.abs(q+360-K)<de&&(q+=360),q},D.prototype._normalizeCenter=function(q){var K=this.transform;if(!(!K.renderWorldCopies||K.lngRange)){var de=q.lng-K.center.lng;q.lng+=de>180?-360:de<-180?360:0}},D}(i.Evented),en=function(D){D===void 0&&(D={}),this.options=D,i.bindAll(["_toggleAttribution","_updateEditLink","_updateData","_updateCompact"],this)};en.prototype.getDefaultPosition=function(){return"bottom-right"},en.prototype.onAdd=function(D){var J=this.options&&this.options.compact;return this._map=D,this._container=o.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),this._compactButton=o.create("button","mapboxgl-ctrl-attrib-button",this._container),this._compactButton.addEventListener("click",this._toggleAttribution),this._setElementTitle(this._compactButton,"ToggleAttribution"),this._innerContainer=o.create("div","mapboxgl-ctrl-attrib-inner",this._container),this._innerContainer.setAttribute("role","list"),J&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),J===void 0&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},en.prototype.onRemove=function(){o.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0,this._attribHTML=void 0},en.prototype._setElementTitle=function(D,J){var q=this._map._getUIString("AttributionControl."+J);D.title=q,D.setAttribute("aria-label",q)},en.prototype._toggleAttribution=function(){this._container.classList.contains("mapboxgl-compact-show")?(this._container.classList.remove("mapboxgl-compact-show"),this._compactButton.setAttribute("aria-pressed","false")):(this._container.classList.add("mapboxgl-compact-show"),this._compactButton.setAttribute("aria-pressed","true"))},en.prototype._updateEditLink=function(){var D=this._editLink;D||(D=this._editLink=this._container.querySelector(".mapbox-improve-map"));var J=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:this._map._requestManager._customAccessToken||i.config.ACCESS_TOKEN}];if(D){var q=J.reduce(function(K,de,ne){return de.value&&(K+=de.key+"="+de.value+(ne<J.length-1?"&":"")),K},"?");D.href=i.config.FEEDBACK_URL+"/"+q+(this._map._hash?this._map._hash.getHashString(!0):""),D.rel="noopener nofollow",this._setElementTitle(D,"MapFeedback")}},en.prototype._updateData=function(D){D&&(D.sourceDataType==="metadata"||D.sourceDataType==="visibility"||D.dataType==="style")&&(this._updateAttributions(),this._updateEditLink())},en.prototype._updateAttributions=function(){if(this._map.style){var D=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?D=D.concat(this.options.customAttribution.map(function(Ue){return typeof Ue!="string"?"":Ue})):typeof this.options.customAttribution=="string"&&D.push(this.options.customAttribution)),this._map.style.stylesheet){var J=this._map.style.stylesheet;this.styleOwner=J.owner,this.styleId=J.id}var q=this._map.style.sourceCaches;for(var K in q){var de=q[K];if(de.used){var ne=de.getSource();ne.attribution&&D.indexOf(ne.attribution)<0&&D.push(ne.attribution)}}D.sort(function(Ue,ft){return Ue.length-ft.length}),D=D.filter(function(Ue,ft){for(var Xt=ft+1;Xt<D.length;Xt++)if(D[Xt].indexOf(Ue)>=0)return!1;return!0});var we=D.join(" | ");we!==this._attribHTML&&(this._attribHTML=we,D.length?(this._innerContainer.innerHTML=we,this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null)}},en.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact","mapboxgl-compact-show")};var Wi=function(){i.bindAll(["_updateLogo"],this),i.bindAll(["_updateCompact"],this)};Wi.prototype.onAdd=function(D){this._map=D,this._container=o.create("div","mapboxgl-ctrl");var J=o.create("a","mapboxgl-ctrl-logo");return J.target="_blank",J.rel="noopener nofollow",J.href="https://www.mapbox.com/",J.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),J.setAttribute("rel","noopener nofollow"),this._container.appendChild(J),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._map.on("resize",this._updateCompact),this._updateCompact(),this._container},Wi.prototype.onRemove=function(){o.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},Wi.prototype.getDefaultPosition=function(){return"bottom-left"},Wi.prototype._updateLogo=function(D){(!D||D.sourceDataType==="metadata")&&(this._container.style.display=this._logoRequired()?"block":"none")},Wi.prototype._logoRequired=function(){if(this._map.style){var D=this._map.style.sourceCaches;for(var J in D){var q=D[J].getSource();if(q.mapbox_logo)return!0}return!1}},Wi.prototype._updateCompact=function(){var D=this._container.children;if(D.length){var J=D[0];this._map.getCanvasContainer().offsetWidth<250?J.classList.add("mapboxgl-compact"):J.classList.remove("mapboxgl-compact")}};var si=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};si.prototype.add=function(D){var J=++this._id,q=this._queue;return q.push({callback:D,id:J,cancelled:!1}),J},si.prototype.remove=function(D){for(var J=this._currentlyRunning,q=J?this._queue.concat(J):this._queue,K=0,de=q;K<de.length;K+=1){var ne=de[K];if(ne.id===D){ne.cancelled=!0;return}}},si.prototype.run=function(D){D===void 0&&(D=0);var J=this._currentlyRunning=this._queue;this._queue=[];for(var q=0,K=J;q<K.length;q+=1){var de=K[q];if(!de.cancelled&&(de.callback(D),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},si.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Mr={"AttributionControl.ToggleAttribution":"Toggle attribution","AttributionControl.MapFeedback":"Map feedback","FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"Mapbox logo","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm"},Yr=i.window.HTMLImageElement,xi=i.window.HTMLElement,Ri=i.window.ImageBitmap,ci=-2,an=22,Zi=0,Bn=60,hi={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:ci,maxZoom:an,minPitch:Zi,maxPitch:Bn,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:"sans-serif",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},li=function(Y){function D(q){var K=this;if(q=i.extend({},hi,q),q.minZoom!=null&&q.maxZoom!=null&&q.minZoom>q.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(q.minPitch!=null&&q.maxPitch!=null&&q.minPitch>q.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(q.minPitch!=null&&q.minPitch<Zi)throw new Error("minPitch must be greater than or equal to "+Zi);if(q.maxPitch!=null&&q.maxPitch>Bn)throw new Error("maxPitch must be less than or equal to "+Bn);var de=new wo(q.minZoom,q.maxZoom,q.minPitch,q.maxPitch,q.renderWorldCopies);if(Y.call(this,de,q),this._interactive=q.interactive,this._maxTileCacheSize=q.maxTileCacheSize,this._failIfMajorPerformanceCaveat=q.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=q.preserveDrawingBuffer,this._antialias=q.antialias,this._trackResize=q.trackResize,this._bearingSnap=q.bearingSnap,this._refreshExpiredTiles=q.refreshExpiredTiles,this._fadeDuration=q.fadeDuration,this._crossSourceCollisions=q.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=q.collectResourceTiming,this._renderTaskQueue=new si,this._controls=[],this._mapId=i.uniqueId(),this._locale=i.extend({},Mr,q.locale),this._clickTolerance=q.clickTolerance,this._requestManager=new i.RequestManager(q.transformRequest,q.accessToken),typeof q.container=="string"){if(this._container=i.window.document.getElementById(q.container),!this._container)throw new Error("Container '"+q.container+"' not found.")}else if(q.container instanceof xi)this._container=q.container;else throw new Error("Invalid type: 'container' must be a String or HTMLElement.");if(q.maxBounds&&this.setMaxBounds(q.maxBounds),i.bindAll(["_onWindowOnline","_onWindowResize","_onMapScroll","_contextLost","_contextRestored"],this),this._setupContainer(),this._setupPainter(),this.painter===void 0)throw new Error("Failed to initialize WebGL.");this.on("move",function(){return K._update(!1)}),this.on("moveend",function(){return K._update(!1)}),this.on("zoom",function(){return K._update(!0)}),typeof i.window!="undefined"&&(i.window.addEventListener("online",this._onWindowOnline,!1),i.window.addEventListener("resize",this._onWindowResize,!1),i.window.addEventListener("orientationchange",this._onWindowResize,!1)),this.handlers=new Ei(this,q);var ne=typeof q.hash=="string"&&q.hash||void 0;this._hash=q.hash&&new Ul(ne).addTo(this),(!this._hash||!this._hash._onHashChange())&&(this.jumpTo({center:q.center,zoom:q.zoom,bearing:q.bearing,pitch:q.pitch}),q.bounds&&(this.resize(),this.fitBounds(q.bounds,i.extend({},q.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=q.localIdeographFontFamily,q.style&&this.setStyle(q.style,{localIdeographFontFamily:q.localIdeographFontFamily}),q.attributionControl&&this.addControl(new en({customAttribution:q.customAttribution})),this.addControl(new Wi,q.logoPosition),this.on("style.load",function(){K.transform.unmodified&&K.jumpTo(K.style.stylesheet)}),this.on("data",function(we){K._update(we.dataType==="style"),K.fire(new i.Event(we.dataType+"data",we))}),this.on("dataloading",function(we){K.fire(new i.Event(we.dataType+"dataloading",we))})}Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D;var J={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return D.prototype._getMapId=function(){return this._mapId},D.prototype.addControl=function(K,de){if(de===void 0&&(K.getDefaultPosition?de=K.getDefaultPosition():de="top-right"),!K||!K.onAdd)return this.fire(new i.ErrorEvent(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));var ne=K.onAdd(this);this._controls.push(K);var we=this._controlPositions[de];return de.indexOf("bottom")!==-1?we.insertBefore(ne,we.firstChild):we.appendChild(ne),this},D.prototype.removeControl=function(K){if(!K||!K.onRemove)return this.fire(new i.ErrorEvent(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));var de=this._controls.indexOf(K);return de>-1&&this._controls.splice(de,1),K.onRemove(this),this},D.prototype.hasControl=function(K){return this._controls.indexOf(K)>-1},D.prototype.resize=function(K){var de=this._containerDimensions(),ne=de[0],we=de[1];this._resizeCanvas(ne,we),this.transform.resize(ne,we),this.painter.resize(ne,we);var Ue=!this._moving;return Ue&&(this.stop(),this.fire(new i.Event("movestart",K)).fire(new i.Event("move",K))),this.fire(new i.Event("resize",K)),Ue&&this.fire(new i.Event("moveend",K)),this},D.prototype.getBounds=function(){return this.transform.getBounds()},D.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},D.prototype.setMaxBounds=function(K){return this.transform.setMaxBounds(i.LngLatBounds.convert(K)),this._update()},D.prototype.setMinZoom=function(K){if(K=K==null?ci:K,K>=ci&&K<=this.transform.maxZoom)return this.transform.minZoom=K,this._update(),this.getZoom()<K&&this.setZoom(K),this;throw new Error("minZoom must be between "+ci+" and the current maxZoom, inclusive")},D.prototype.getMinZoom=function(){return this.transform.minZoom},D.prototype.setMaxZoom=function(K){if(K=K==null?an:K,K>=this.transform.minZoom)return this.transform.maxZoom=K,this._update(),this.getZoom()>K&&this.setZoom(K),this;throw new Error("maxZoom must be greater than the current minZoom")},D.prototype.getMaxZoom=function(){return this.transform.maxZoom},D.prototype.setMinPitch=function(K){if(K=K==null?Zi:K,K<Zi)throw new Error("minPitch must be greater than or equal to "+Zi);if(K>=Zi&&K<=this.transform.maxPitch)return this.transform.minPitch=K,this._update(),this.getPitch()<K&&this.setPitch(K),this;throw new Error("minPitch must be between "+Zi+" and the current maxPitch, inclusive")},D.prototype.getMinPitch=function(){return this.transform.minPitch},D.prototype.setMaxPitch=function(K){if(K=K==null?Bn:K,K>Bn)throw new Error("maxPitch must be less than or equal to "+Bn);if(K>=this.transform.minPitch)return this.transform.maxPitch=K,this._update(),this.getPitch()>K&&this.setPitch(K),this;throw new Error("maxPitch must be greater than the current minPitch")},D.prototype.getMaxPitch=function(){return this.transform.maxPitch},D.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},D.prototype.setRenderWorldCopies=function(K){return this.transform.renderWorldCopies=K,this._update()},D.prototype.project=function(K){return this.transform.locationPoint(i.LngLat.convert(K))},D.prototype.unproject=function(K){return this.transform.pointLocation(i.Point.convert(K))},D.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},D.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},D.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},D.prototype._createDelegatedListener=function(K,de,ne){var we=this,Ue;if(K==="mouseenter"||K==="mouseover"){var ft=!1,Xt=function(Ct){var Ot=we.getLayer(de)?we.queryRenderedFeatures(Ct.point,{layers:[de]}):[];Ot.length?ft||(ft=!0,ne.call(we,new se(K,we,Ct.originalEvent,{features:Ot}))):ft=!1},hr=function(){ft=!1};return{layer:de,listener:ne,delegates:{mousemove:Xt,mouseout:hr}}}else if(K==="mouseleave"||K==="mouseout"){var qt=!1,Ve=function(Ct){var Ot=we.getLayer(de)?we.queryRenderedFeatures(Ct.point,{layers:[de]}):[];Ot.length?qt=!0:qt&&(qt=!1,ne.call(we,new se(K,we,Ct.originalEvent)))},Qe=function(Ct){qt&&(qt=!1,ne.call(we,new se(K,we,Ct.originalEvent)))};return{layer:de,listener:ne,delegates:{mousemove:Ve,mouseout:Qe}}}else{var at=function(Ct){var Ot=we.getLayer(de)?we.queryRenderedFeatures(Ct.point,{layers:[de]}):[];Ot.length&&(Ct.features=Ot,ne.call(we,Ct),delete Ct.features)};return{layer:de,listener:ne,delegates:(Ue={},Ue[K]=at,Ue)}}},D.prototype.on=function(K,de,ne){if(ne===void 0)return Y.prototype.on.call(this,K,de);var we=this._createDelegatedListener(K,de,ne);this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[K]=this._delegatedListeners[K]||[],this._delegatedListeners[K].push(we);for(var Ue in we.delegates)this.on(Ue,we.delegates[Ue]);return this},D.prototype.once=function(K,de,ne){if(ne===void 0)return Y.prototype.once.call(this,K,de);var we=this._createDelegatedListener(K,de,ne);for(var Ue in we.delegates)this.once(Ue,we.delegates[Ue]);return this},D.prototype.off=function(K,de,ne){var we=this;if(ne===void 0)return Y.prototype.off.call(this,K,de);var Ue=function(ft){for(var Xt=ft[K],hr=0;hr<Xt.length;hr++){var qt=Xt[hr];if(qt.layer===de&&qt.listener===ne){for(var Ve in qt.delegates)we.off(Ve,qt.delegates[Ve]);return Xt.splice(hr,1),we}}};return this._delegatedListeners&&this._delegatedListeners[K]&&Ue(this._delegatedListeners),this},D.prototype.queryRenderedFeatures=function(K,de){if(!this.style)return[];de===void 0&&K!==void 0&&!(K instanceof i.Point)&&!Array.isArray(K)&&(de=K,K=void 0),de=de||{},K=K||[[0,0],[this.transform.width,this.transform.height]];var ne;if(K instanceof i.Point||typeof K[0]=="number")ne=[i.Point.convert(K)];else{var we=i.Point.convert(K[0]),Ue=i.Point.convert(K[1]);ne=[we,new i.Point(Ue.x,we.y),Ue,new i.Point(we.x,Ue.y),we]}return this.style.queryRenderedFeatures(ne,de,this.transform)},D.prototype.querySourceFeatures=function(K,de){return this.style.querySourceFeatures(K,de)},D.prototype.setStyle=function(K,de){return de=i.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},de),de.diff!==!1&&de.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&K?(this._diffStyle(K,de),this):(this._localIdeographFontFamily=de.localIdeographFontFamily,this._updateStyle(K,de))},D.prototype._getUIString=function(K){var de=this._locale[K];if(de==null)throw new Error("Missing UI string '"+K+"'");return de},D.prototype._updateStyle=function(K,de){if(this.style&&(this.style.setEventedParent(null),this.style._remove()),K)this.style=new yu(this,de||{});else return delete this.style,this;return this.style.setEventedParent(this,{style:this.style}),typeof K=="string"?this.style.loadURL(K):this.style.loadJSON(K),this},D.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new yu(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},D.prototype._diffStyle=function(K,de){var ne=this;if(typeof K=="string"){var we=this._requestManager.normalizeStyleURL(K),Ue=this._requestManager.transformRequest(we,i.ResourceType.Style);i.getJSON(Ue,function(ft,Xt){ft?ne.fire(new i.ErrorEvent(ft)):Xt&&ne._updateDiff(Xt,de)})}else typeof K=="object"&&this._updateDiff(K,de)},D.prototype._updateDiff=function(K,de){try{this.style.setState(K)&&this._update(!0)}catch(ne){i.warnOnce("Unable to perform style diff: "+(ne.message||ne.error||ne)+".  Rebuilding the style from scratch."),this._updateStyle(K,de)}},D.prototype.getStyle=function(){if(this.style)return this.style.serialize()},D.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():i.warnOnce("There is no style added to the map.")},D.prototype.addSource=function(K,de){return this._lazyInitEmptyStyle(),this.style.addSource(K,de),this._update(!0)},D.prototype.isSourceLoaded=function(K){var de=this.style&&this.style.sourceCaches[K];if(de===void 0){this.fire(new i.ErrorEvent(new Error("There is no source with ID '"+K+"'")));return}return de.loaded()},D.prototype.areTilesLoaded=function(){var K=this.style&&this.style.sourceCaches;for(var de in K){var ne=K[de],we=ne._tiles;for(var Ue in we){var ft=we[Ue];if(!(ft.state==="loaded"||ft.state==="errored"))return!1}}return!0},D.prototype.addSourceType=function(K,de,ne){return this._lazyInitEmptyStyle(),this.style.addSourceType(K,de,ne)},D.prototype.removeSource=function(K){return this.style.removeSource(K),this._update(!0)},D.prototype.getSource=function(K){return this.style.getSource(K)},D.prototype.addImage=function(K,de,ne){ne===void 0&&(ne={});var we=ne.pixelRatio;we===void 0&&(we=1);var Ue=ne.sdf;Ue===void 0&&(Ue=!1);var ft=ne.stretchX,Xt=ne.stretchY,hr=ne.content;this._lazyInitEmptyStyle();var qt=0;if(de instanceof Yr||Ri&&de instanceof Ri){var Ve=i.browser.getImageData(de),Qe=Ve.width,at=Ve.height,Ct=Ve.data;this.style.addImage(K,{data:new i.RGBAImage({width:Qe,height:at},Ct),pixelRatio:we,stretchX:ft,stretchY:Xt,content:hr,sdf:Ue,version:qt})}else{if(de.width===void 0||de.height===void 0)return this.fire(new i.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var Ot=de.width,Rt=de.height,Bt=de.data,Dt=de;this.style.addImage(K,{data:new i.RGBAImage({width:Ot,height:Rt},new Uint8Array(Bt)),pixelRatio:we,stretchX:ft,stretchY:Xt,content:hr,sdf:Ue,version:qt,userImage:Dt}),Dt.onAdd&&Dt.onAdd(this,K)}},D.prototype.updateImage=function(K,de){var ne=this.style.getImage(K);if(!ne)return this.fire(new i.ErrorEvent(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));var we=de instanceof Yr||Ri&&de instanceof Ri?i.browser.getImageData(de):de,Ue=we.width,ft=we.height,Xt=we.data;if(Ue===void 0||ft===void 0)return this.fire(new i.ErrorEvent(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(Ue!==ne.data.width||ft!==ne.data.height)return this.fire(new i.ErrorEvent(new Error("The width and height of the updated image must be that same as the previous version of the image")));var hr=!(de instanceof Yr||Ri&&de instanceof Ri);ne.data.replace(Xt,hr),this.style.updateImage(K,ne)},D.prototype.hasImage=function(K){return K?!!this.style.getImage(K):(this.fire(new i.ErrorEvent(new Error("Missing required image id"))),!1)},D.prototype.removeImage=function(K){this.style.removeImage(K)},D.prototype.loadImage=function(K,de){i.getImage(this._requestManager.transformRequest(K,i.ResourceType.Image),de)},D.prototype.listImages=function(){return this.style.listImages()},D.prototype.addLayer=function(K,de){return this._lazyInitEmptyStyle(),this.style.addLayer(K,de),this._update(!0)},D.prototype.moveLayer=function(K,de){return this.style.moveLayer(K,de),this._update(!0)},D.prototype.removeLayer=function(K){return this.style.removeLayer(K),this._update(!0)},D.prototype.getLayer=function(K){return this.style.getLayer(K)},D.prototype.setLayerZoomRange=function(K,de,ne){return this.style.setLayerZoomRange(K,de,ne),this._update(!0)},D.prototype.setFilter=function(K,de,ne){return ne===void 0&&(ne={}),this.style.setFilter(K,de,ne),this._update(!0)},D.prototype.getFilter=function(K){return this.style.getFilter(K)},D.prototype.setPaintProperty=function(K,de,ne,we){return we===void 0&&(we={}),this.style.setPaintProperty(K,de,ne,we),this._update(!0)},D.prototype.getPaintProperty=function(K,de){return this.style.getPaintProperty(K,de)},D.prototype.setLayoutProperty=function(K,de,ne,we){return we===void 0&&(we={}),this.style.setLayoutProperty(K,de,ne,we),this._update(!0)},D.prototype.getLayoutProperty=function(K,de){return this.style.getLayoutProperty(K,de)},D.prototype.setLight=function(K,de){return de===void 0&&(de={}),this._lazyInitEmptyStyle(),this.style.setLight(K,de),this._update(!0)},D.prototype.getLight=function(){return this.style.getLight()},D.prototype.setFeatureState=function(K,de){return this.style.setFeatureState(K,de),this._update()},D.prototype.removeFeatureState=function(K,de){return this.style.removeFeatureState(K,de),this._update()},D.prototype.getFeatureState=function(K){return this.style.getFeatureState(K)},D.prototype.getContainer=function(){return this._container},D.prototype.getCanvasContainer=function(){return this._canvasContainer},D.prototype.getCanvas=function(){return this._canvas},D.prototype._containerDimensions=function(){var K=0,de=0;return this._container&&(K=this._container.clientWidth||400,de=this._container.clientHeight||300),[K,de]},D.prototype._detectMissingCSS=function(){var K=i.window.getComputedStyle(this._missingCSSCanary).getPropertyValue("background-color");K!=="rgb(250, 128, 114)"&&i.warnOnce("This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.")},D.prototype._setupContainer=function(){var K=this._container;K.classList.add("mapboxgl-map");var de=this._missingCSSCanary=o.create("div","mapboxgl-canary",K);de.style.visibility="hidden",this._detectMissingCSS();var ne=this._canvasContainer=o.create("div","mapboxgl-canvas-container",K);this._interactive&&ne.classList.add("mapboxgl-interactive"),this._canvas=o.create("canvas","mapboxgl-canvas",ne),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map"),this._canvas.setAttribute("role","region");var we=this._containerDimensions();this._resizeCanvas(we[0],we[1]);var Ue=this._controlContainer=o.create("div","mapboxgl-control-container",K),ft=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach(function(Xt){ft[Xt]=o.create("div","mapboxgl-ctrl-"+Xt,Ue)}),this._container.addEventListener("scroll",this._onMapScroll,!1)},D.prototype._resizeCanvas=function(K,de){var ne=i.browser.devicePixelRatio||1;this._canvas.width=ne*K,this._canvas.height=ne*de,this._canvas.style.width=K+"px",this._canvas.style.height=de+"px"},D.prototype._setupPainter=function(){var K=i.extend({},a.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),de=this._canvas.getContext("webgl",K)||this._canvas.getContext("experimental-webgl",K);if(!de){this.fire(new i.ErrorEvent(new Error("Failed to initialize WebGL")));return}this.painter=new mo(de,this.transform),i.webpSupported.testSupport(de)},D.prototype._contextLost=function(K){K.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new i.Event("webglcontextlost",{originalEvent:K}))},D.prototype._contextRestored=function(K){this._setupPainter(),this.resize(),this._update(),this.fire(new i.Event("webglcontextrestored",{originalEvent:K}))},D.prototype._onMapScroll=function(K){if(K.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},D.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},D.prototype._update=function(K){return this.style?(this._styleDirty=this._styleDirty||K,this._sourcesDirty=!0,this.triggerRepaint(),this):this},D.prototype._requestRenderFrame=function(K){return this._update(),this._renderTaskQueue.add(K)},D.prototype._cancelRenderFrame=function(K){this._renderTaskQueue.remove(K)},D.prototype._render=function(K){var de=this,ne,we=0,Ue=this.painter.context.extTimerQuery;if(this.listens("gpu-timing-frame")&&(ne=Ue.createQueryEXT(),Ue.beginQueryEXT(Ue.TIME_ELAPSED_EXT,ne),we=i.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(K),!this._removed){var ft=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var Xt=this.transform.zoom,hr=i.browser.now();this.style.zoomHistory.update(Xt,hr);var qt=new i.EvaluationParameters(Xt,{now:hr,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),Ve=qt.crossFadingFactor();(Ve!==1||Ve!==this._crossFadingFactor)&&(ft=!0,this._crossFadingFactor=Ve),this.style.update(qt)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens("gpu-timing-layer")}),this.fire(new i.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new i.Event("load"))),this.style&&(this.style.hasTransitions()||ft)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens("gpu-timing-frame")){var Qe=i.browser.now()-we;Ue.endQueryEXT(Ue.TIME_ELAPSED_EXT,ne),setTimeout(function(){var Ot=Ue.getQueryObjectEXT(ne,Ue.QUERY_RESULT_EXT)/1e6;Ue.deleteQueryEXT(ne),de.fire(new i.Event("gpu-timing-frame",{cpuTime:Qe,gpuTime:Ot}))},50)}if(this.listens("gpu-timing-layer")){var at=this.painter.collectGpuTimers();setTimeout(function(){var Ot=de.painter.queryGpuTimers(at);de.fire(new i.Event("gpu-timing-layer",{layerTimes:Ot}))},50)}var Ct=this._sourcesDirty||this._styleDirty||this._placementDirty;return Ct||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new i.Event("idle")),this._loaded&&!this._fullyLoaded&&!Ct&&(this._fullyLoaded=!0),this}},D.prototype.remove=function(){this._hash&&this._hash.remove();for(var K=0,de=this._controls;K<de.length;K+=1){var ne=de[K];ne.onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),typeof i.window!="undefined"&&(i.window.removeEventListener("resize",this._onWindowResize,!1),i.window.removeEventListener("orientationchange",this._onWindowResize,!1),i.window.removeEventListener("online",this._onWindowOnline,!1));var we=this.painter.context.gl.getExtension("WEBGL_lose_context");we&&we.loseContext&&we.loseContext(),mn(this._canvasContainer),mn(this._controlContainer),mn(this._missingCSSCanary),this._container.classList.remove("mapboxgl-map"),this._removed=!0,this.fire(new i.Event("remove"))},D.prototype.triggerRepaint=function(){var K=this;this.style&&!this._frame&&(this._frame=i.browser.frame(function(de){K._frame=null,K._render(de)}))},D.prototype._onWindowOnline=function(){this._update()},D.prototype._onWindowResize=function(K){this._trackResize&&this.resize({originalEvent:K})._update()},J.showTileBoundaries.get=function(){return!!this._showTileBoundaries},J.showTileBoundaries.set=function(q){this._showTileBoundaries!==q&&(this._showTileBoundaries=q,this._update())},J.showPadding.get=function(){return!!this._showPadding},J.showPadding.set=function(q){this._showPadding!==q&&(this._showPadding=q,this._update())},J.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},J.showCollisionBoxes.set=function(q){this._showCollisionBoxes!==q&&(this._showCollisionBoxes=q,q?this.style._generateCollisionBoxes():this._update())},J.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},J.showOverdrawInspector.set=function(q){this._showOverdrawInspector!==q&&(this._showOverdrawInspector=q,this._update())},J.repaint.get=function(){return!!this._repaint},J.repaint.set=function(q){this._repaint!==q&&(this._repaint=q,this.triggerRepaint())},J.vertices.get=function(){return!!this._vertices},J.vertices.set=function(q){this._vertices=q,this._update()},D.prototype._setCacheLimits=function(K,de){i.setCacheLimits(K,de)},J.version.get=function(){return i.version},Object.defineProperties(D.prototype,J),D}(Hn);function mn(Y){Y.parentNode&&Y.parentNode.removeChild(Y)}var Ji={showCompass:!0,showZoom:!0,visualizePitch:!1},Vi=function(D){var J=this;this.options=i.extend({},Ji,D),this._container=o.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",function(q){return q.preventDefault()}),this.options.showZoom&&(i.bindAll(["_setButtonTitle","_updateZoomButtons"],this),this._zoomInButton=this._createButton("mapboxgl-ctrl-zoom-in",function(q){return J._map.zoomIn({},{originalEvent:q})}),o.create("span","mapboxgl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden",!0),this._zoomOutButton=this._createButton("mapboxgl-ctrl-zoom-out",function(q){return J._map.zoomOut({},{originalEvent:q})}),o.create("span","mapboxgl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden",!0)),this.options.showCompass&&(i.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-compass",function(q){J.options.visualizePitch?J._map.resetNorthPitch({},{originalEvent:q}):J._map.resetNorth({},{originalEvent:q})}),this._compassIcon=o.create("span","mapboxgl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden",!0))};Vi.prototype._updateZoomButtons=function(){var D=this._map.getZoom(),J=D===this._map.getMaxZoom(),q=D===this._map.getMinZoom();this._zoomInButton.disabled=J,this._zoomOutButton.disabled=q,this._zoomInButton.setAttribute("aria-disabled",J.toString()),this._zoomOutButton.setAttribute("aria-disabled",q.toString())},Vi.prototype._rotateCompassArrow=function(){var D=this.options.visualizePitch?"scale("+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+") rotateX("+this._map.transform.pitch+"deg) rotateZ("+this._map.transform.angle*(180/Math.PI)+"deg)":"rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassIcon.style.transform=D},Vi.prototype.onAdd=function(D){return this._map=D,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ni(this._map,this._compass,this.options.visualizePitch)),this._container},Vi.prototype.onRemove=function(){o.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Vi.prototype._createButton=function(D,J){var q=o.create("button",D,this._container);return q.type="button",q.addEventListener("click",J),q},Vi.prototype._setButtonTitle=function(D,J){var q=this._map._getUIString("NavigationControl."+J);D.title=q,D.setAttribute("aria-label",q)};var Ni=function(D,J,q){q===void 0&&(q=!1),this._clickTolerance=10,this.element=J,this.mouseRotate=new Ln({clickTolerance:D.dragRotate._mouseRotate._clickTolerance}),this.map=D,q&&(this.mousePitch=new En({clickTolerance:D.dragRotate._mousePitch._clickTolerance})),i.bindAll(["mousedown","mousemove","mouseup","touchstart","touchmove","touchend","reset"],this),o.addEventListener(J,"mousedown",this.mousedown),o.addEventListener(J,"touchstart",this.touchstart,{passive:!1}),o.addEventListener(J,"touchmove",this.touchmove),o.addEventListener(J,"touchend",this.touchend),o.addEventListener(J,"touchcancel",this.reset)};Ni.prototype.down=function(D,J){this.mouseRotate.mousedown(D,J),this.mousePitch&&this.mousePitch.mousedown(D,J),o.disableDrag()},Ni.prototype.move=function(D,J){var q=this.map,K=this.mouseRotate.mousemoveWindow(D,J);if(K&&K.bearingDelta&&q.setBearing(q.getBearing()+K.bearingDelta),this.mousePitch){var de=this.mousePitch.mousemoveWindow(D,J);de&&de.pitchDelta&&q.setPitch(q.getPitch()+de.pitchDelta)}},Ni.prototype.off=function(){var D=this.element;o.removeEventListener(D,"mousedown",this.mousedown),o.removeEventListener(D,"touchstart",this.touchstart,{passive:!1}),o.removeEventListener(D,"touchmove",this.touchmove),o.removeEventListener(D,"touchend",this.touchend),o.removeEventListener(D,"touchcancel",this.reset),this.offTemp()},Ni.prototype.offTemp=function(){o.enableDrag(),o.removeEventListener(i.window,"mousemove",this.mousemove),o.removeEventListener(i.window,"mouseup",this.mouseup)},Ni.prototype.mousedown=function(D){this.down(i.extend({},D,{ctrlKey:!0,preventDefault:function(){return D.preventDefault()}}),o.mousePos(this.element,D)),o.addEventListener(i.window,"mousemove",this.mousemove),o.addEventListener(i.window,"mouseup",this.mouseup)},Ni.prototype.mousemove=function(D){this.move(D,o.mousePos(this.element,D))},Ni.prototype.mouseup=function(D){this.mouseRotate.mouseupWindow(D),this.mousePitch&&this.mousePitch.mouseupWindow(D),this.offTemp()},Ni.prototype.touchstart=function(D){D.targetTouches.length!==1?this.reset():(this._startPos=this._lastPos=o.touchPos(this.element,D.targetTouches)[0],this.down({type:"mousedown",button:0,ctrlKey:!0,preventDefault:function(){return D.preventDefault()}},this._startPos))},Ni.prototype.touchmove=function(D){D.targetTouches.length!==1?this.reset():(this._lastPos=o.touchPos(this.element,D.targetTouches)[0],this.move({preventDefault:function(){return D.preventDefault()}},this._lastPos))},Ni.prototype.touchend=function(D){D.targetTouches.length===0&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ni.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};function pn(Y,D,J){if(Y=new i.LngLat(Y.lng,Y.lat),D){var q=new i.LngLat(Y.lng-360,Y.lat),K=new i.LngLat(Y.lng+360,Y.lat),de=J.locationPoint(Y).distSqr(D);J.locationPoint(q).distSqr(D)<de?Y=q:J.locationPoint(K).distSqr(D)<de&&(Y=K)}for(;Math.abs(Y.lng-J.center.lng)>180;){var ne=J.locationPoint(Y);if(ne.x>=0&&ne.y>=0&&ne.x<=J.width&&ne.y<=J.height)break;Y.lng>J.center.lng?Y.lng-=360:Y.lng+=360}return Y}var Vn={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function na(Y,D,J){var q=Y.classList;for(var K in Vn)q.remove("mapboxgl-"+J+"-anchor-"+K);q.add("mapboxgl-"+J+"-anchor-"+D)}var Ki=function(Y){function D(J,q){if(Y.call(this),(J instanceof i.window.HTMLElement||q)&&(J=i.extend({element:J},q)),i.bindAll(["_update","_onMove","_onUp","_addDragHandler","_onMapClick","_onKeyPress"],this),this._anchor=J&&J.anchor||"center",this._color=J&&J.color||"#3FB1CE",this._scale=J&&J.scale||1,this._draggable=J&&J.draggable||!1,this._clickTolerance=J&&J.clickTolerance||0,this._isDragging=!1,this._state="inactive",this._rotation=J&&J.rotation||0,this._rotationAlignment=J&&J.rotationAlignment||"auto",this._pitchAlignment=J&&J.pitchAlignment&&J.pitchAlignment!=="auto"?J.pitchAlignment:this._rotationAlignment,!J||!J.element){this._defaultMarker=!0,this._element=o.create("div"),this._element.setAttribute("aria-label","Map marker");var K=o.createNS("http://www.w3.org/2000/svg","svg"),de=41,ne=27;K.setAttributeNS(null,"display","block"),K.setAttributeNS(null,"height",de+"px"),K.setAttributeNS(null,"width",ne+"px"),K.setAttributeNS(null,"viewBox","0 0 "+ne+" "+de);var we=o.createNS("http://www.w3.org/2000/svg","g");we.setAttributeNS(null,"stroke","none"),we.setAttributeNS(null,"stroke-width","1"),we.setAttributeNS(null,"fill","none"),we.setAttributeNS(null,"fill-rule","evenodd");var Ue=o.createNS("http://www.w3.org/2000/svg","g");Ue.setAttributeNS(null,"fill-rule","nonzero");var ft=o.createNS("http://www.w3.org/2000/svg","g");ft.setAttributeNS(null,"transform","translate(3.0, 29.0)"),ft.setAttributeNS(null,"fill","#000000");for(var Xt=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}],hr=0,qt=Xt;hr<qt.length;hr+=1){var Ve=qt[hr],Qe=o.createNS("http://www.w3.org/2000/svg","ellipse");Qe.setAttributeNS(null,"opacity","0.04"),Qe.setAttributeNS(null,"cx","10.5"),Qe.setAttributeNS(null,"cy","5.80029008"),Qe.setAttributeNS(null,"rx",Ve.rx),Qe.setAttributeNS(null,"ry",Ve.ry),ft.appendChild(Qe)}var at=o.createNS("http://www.w3.org/2000/svg","g");at.setAttributeNS(null,"fill",this._color);var Ct=o.createNS("http://www.w3.org/2000/svg","path");Ct.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),at.appendChild(Ct);var Ot=o.createNS("http://www.w3.org/2000/svg","g");Ot.setAttributeNS(null,"opacity","0.25"),Ot.setAttributeNS(null,"fill","#000000");var Rt=o.createNS("http://www.w3.org/2000/svg","path");Rt.setAttributeNS(null,"d","M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z"),Ot.appendChild(Rt);var Bt=o.createNS("http://www.w3.org/2000/svg","g");Bt.setAttributeNS(null,"transform","translate(6.0, 7.0)"),Bt.setAttributeNS(null,"fill","#FFFFFF");var Dt=o.createNS("http://www.w3.org/2000/svg","g");Dt.setAttributeNS(null,"transform","translate(8.0, 8.0)");var yt=o.createNS("http://www.w3.org/2000/svg","circle");yt.setAttributeNS(null,"fill","#000000"),yt.setAttributeNS(null,"opacity","0.25"),yt.setAttributeNS(null,"cx","5.5"),yt.setAttributeNS(null,"cy","5.5"),yt.setAttributeNS(null,"r","5.4999962");var Pt=o.createNS("http://www.w3.org/2000/svg","circle");Pt.setAttributeNS(null,"fill","#FFFFFF"),Pt.setAttributeNS(null,"cx","5.5"),Pt.setAttributeNS(null,"cy","5.5"),Pt.setAttributeNS(null,"r","5.4999962"),Dt.appendChild(yt),Dt.appendChild(Pt),Ue.appendChild(ft),Ue.appendChild(at),Ue.appendChild(Ot),Ue.appendChild(Bt),Ue.appendChild(Dt),K.appendChild(Ue),K.setAttributeNS(null,"height",de*this._scale+"px"),K.setAttributeNS(null,"width",ne*this._scale+"px"),this._element.appendChild(K),this._offset=i.Point.convert(J&&J.offset||[0,-14])}else this._element=J.element,this._offset=i.Point.convert(J&&J.offset||[0,0]);this._element.classList.add("mapboxgl-marker"),this._element.addEventListener("dragstart",function(ht){ht.preventDefault()}),this._element.addEventListener("mousedown",function(ht){ht.preventDefault()}),na(this._element,this._anchor,"marker"),this._popup=null}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.addTo=function(q){return this.remove(),this._map=q,q.getCanvasContainer().appendChild(this._element),q.on("move",this._update),q.on("moveend",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this},D.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),o.remove(this._element),this._popup&&this._popup.remove(),this},D.prototype.getLngLat=function(){return this._lngLat},D.prototype.setLngLat=function(q){return this._lngLat=i.LngLat.convert(q),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},D.prototype.getElement=function(){return this._element},D.prototype.setPopup=function(q){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),q){if(!("offset"in q.options)){var K=38.1,de=13.5,ne=Math.sqrt(Math.pow(de,2)/2);q.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-K],"bottom-left":[ne,(K-de+ne)*-1],"bottom-right":[-ne,(K-de+ne)*-1],left:[de,(K-de)*-1],right:[-de,(K-de)*-1]}:this._offset}this._popup=q,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this},D.prototype._onKeyPress=function(q){var K=q.code,de=q.charCode||q.keyCode;(K==="Space"||K==="Enter"||de===32||de===13)&&this.togglePopup()},D.prototype._onMapClick=function(q){var K=q.originalEvent.target,de=this._element;this._popup&&(K===de||de.contains(K))&&this.togglePopup()},D.prototype.getPopup=function(){return this._popup},D.prototype.togglePopup=function(){var q=this._popup;if(q)q.isOpen()?q.remove():q.addTo(this._map);else return this;return this},D.prototype._update=function(q){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=pn(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var K="";this._rotationAlignment==="viewport"||this._rotationAlignment==="auto"?K="rotateZ("+this._rotation+"deg)":this._rotationAlignment==="map"&&(K="rotateZ("+(this._rotation-this._map.getBearing())+"deg)");var de="";this._pitchAlignment==="viewport"||this._pitchAlignment==="auto"?de="rotateX(0deg)":this._pitchAlignment==="map"&&(de="rotateX("+this._map.getPitch()+"deg)"),(!q||q.type==="moveend")&&(this._pos=this._pos.round()),o.setTransform(this._element,Vn[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px) "+de+" "+K)}},D.prototype.getOffset=function(){return this._offset},D.prototype.setOffset=function(q){return this._offset=i.Point.convert(q),this._update(),this},D.prototype._onMove=function(q){if(!this._isDragging){var K=this._clickTolerance||this._map._clickTolerance;this._isDragging=q.point.dist(this._pointerdownPos)>=K}this._isDragging&&(this._pos=q.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none",this._state==="pending"&&(this._state="active",this.fire(new i.Event("dragstart"))),this.fire(new i.Event("drag")))},D.prototype._onUp=function(){this._element.style.pointerEvents="auto",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),this._state==="active"&&this.fire(new i.Event("dragend")),this._state="inactive"},D.prototype._addDragHandler=function(q){this._element.contains(q.originalEvent.target)&&(q.preventDefault(),this._positionDelta=q.point.sub(this._pos).add(this._offset),this._pointerdownPos=q.point,this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},D.prototype.setDraggable=function(q){return this._draggable=!!q,this._map&&(q?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this},D.prototype.isDraggable=function(){return this._draggable},D.prototype.setRotation=function(q){return this._rotation=q||0,this._update(),this},D.prototype.getRotation=function(){return this._rotation},D.prototype.setRotationAlignment=function(q){return this._rotationAlignment=q||"auto",this._update(),this},D.prototype.getRotationAlignment=function(){return this._rotationAlignment},D.prototype.setPitchAlignment=function(q){return this._pitchAlignment=q&&q!=="auto"?q:this._rotationAlignment,this._update(),this},D.prototype.getPitchAlignment=function(){return this._pitchAlignment},D}(i.Evented),kn={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},ta;function oa(Y){ta!==void 0?Y(ta):i.window.navigator.permissions!==void 0?i.window.navigator.permissions.query({name:"geolocation"}).then(function(D){ta=D.state!=="denied",Y(ta)}):(ta=!!i.window.navigator.geolocation,Y(ta))}var ba=0,is=!1,Zs=function(Y){function D(J){Y.call(this),this.options=i.extend({},kn,J),i.bindAll(["_onSuccess","_onError","_onZoom","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.onAdd=function(q){return this._map=q,this._container=o.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),oa(this._setupUI),this._container},D.prototype.onRemove=function(){this._geolocationWatchID!==void 0&&(i.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),o.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,ba=0,is=!1},D.prototype._isOutOfMapMaxBounds=function(q){var K=this._map.getMaxBounds(),de=q.coords;return K&&(de.longitude<K.getWest()||de.longitude>K.getEast()||de.latitude<K.getSouth()||de.latitude>K.getNorth())},D.prototype._setErrorState=function(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break}},D.prototype._onSuccess=function(q){if(this._map){if(this._isOutOfMapMaxBounds(q)){this._setErrorState(),this.fire(new i.Event("outofmaxbounds",q)),this._updateMarker(),this._finish();return}if(this.options.trackUserLocation)switch(this._lastKnownPosition=q,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break}this.options.showUserLocation&&this._watchState!=="OFF"&&this._updateMarker(q),(!this.options.trackUserLocation||this._watchState==="ACTIVE_LOCK")&&this._updateCamera(q),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new i.Event("geolocate",q)),this._finish()}},D.prototype._updateCamera=function(q){var K=new i.LngLat(q.coords.longitude,q.coords.latitude),de=q.coords.accuracy,ne=this._map.getBearing(),we=i.extend({bearing:ne},this.options.fitBoundsOptions);this._map.fitBounds(K.toBounds(de),we,{geolocateSource:!0})},D.prototype._updateMarker=function(q){if(q){var K=new i.LngLat(q.coords.longitude,q.coords.latitude);this._accuracyCircleMarker.setLngLat(K).addTo(this._map),this._userLocationDotMarker.setLngLat(K).addTo(this._map),this._accuracy=q.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},D.prototype._updateCircleRadius=function(){var q=this._map._container.clientHeight/2,K=this._map.unproject([0,q]),de=this._map.unproject([1,q]),ne=K.distanceTo(de),we=Math.ceil(2*this._accuracy/ne);this._circleElement.style.width=we+"px",this._circleElement.style.height=we+"px"},D.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},D.prototype._onError=function(q){if(this._map){if(this.options.trackUserLocation)if(q.code===1){this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;var K=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=K,this._geolocateButton.setAttribute("aria-label",K),this._geolocationWatchID!==void 0&&this._clearWatch()}else{if(q.code===3&&is)return;this._setErrorState()}this._watchState!=="OFF"&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new i.Event("error",q)),this._finish()}},D.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},D.prototype._setupUI=function(q){var K=this;if(this._container.addEventListener("contextmenu",function(we){return we.preventDefault()}),this._geolocateButton=o.create("button","mapboxgl-ctrl-geolocate",this._container),o.create("span","mapboxgl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden",!0),this._geolocateButton.type="button",q===!1){i.warnOnce("Geolocation support is not available so the GeolocateControl will be disabled.");var de=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=de,this._geolocateButton.setAttribute("aria-label",de)}else{var ne=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.title=ne,this._geolocateButton.setAttribute("aria-label",ne)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=o.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new Ki(this._dotElement),this._circleElement=o.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Ki({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",function(we){var Ue=we.originalEvent&&we.originalEvent.type==="resize";!we.geolocateSource&&K._watchState==="ACTIVE_LOCK"&&!Ue&&(K._watchState="BACKGROUND",K._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),K._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),K.fire(new i.Event("trackuserlocationend")))})},D.prototype.trigger=function(){if(!this._setup)return i.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new i.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":ba--,is=!1,this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new i.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new i.Event("trackuserlocationstart"));break}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error");break}if(this._watchState==="OFF"&&this._geolocationWatchID!==void 0)this._clearWatch();else if(this._geolocationWatchID===void 0){this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),ba++;var q;ba>1?(q={maximumAge:6e5,timeout:0},is=!0):(q=this.options.positionOptions,is=!1),this._geolocationWatchID=i.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,q)}}else i.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},D.prototype._clearWatch=function(){i.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},D}(i.Evented),Va={maxWidth:100,unit:"metric"},Ml=function(D){this.options=i.extend({},Va,D),i.bindAll(["_onMove","setUnit"],this)};Ml.prototype.getDefaultPosition=function(){return"bottom-left"},Ml.prototype._onMove=function(){zo(this._map,this._container,this.options)},Ml.prototype.onAdd=function(D){return this._map=D,this._container=o.create("div","mapboxgl-ctrl mapboxgl-ctrl-scale",D.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container},Ml.prototype.onRemove=function(){o.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},Ml.prototype.setUnit=function(D){this.options.unit=D,zo(this._map,this._container,this.options)};function zo(Y,D,J){var q=J&&J.maxWidth||100,K=Y._container.clientHeight/2,de=Y.unproject([0,K]),ne=Y.unproject([q,K]),we=de.distanceTo(ne);if(J&&J.unit==="imperial"){var Ue=3.2808*we;if(Ue>5280){var ft=Ue/5280;Qs(D,q,ft,Y._getUIString("ScaleControl.Miles"))}else Qs(D,q,Ue,Y._getUIString("ScaleControl.Feet"))}else if(J&&J.unit==="nautical"){var Xt=we/1852;Qs(D,q,Xt,Y._getUIString("ScaleControl.NauticalMiles"))}else we>=1e3?Qs(D,q,we/1e3,Y._getUIString("ScaleControl.Kilometers")):Qs(D,q,we,Y._getUIString("ScaleControl.Meters"))}function Qs(Y,D,J,q){var K=Vl(J),de=K/J;Y.style.width=D*de+"px",Y.innerHTML=K+"&nbsp;"+q}function al(Y){var D=Math.pow(10,Math.ceil(-Math.log(Y)/Math.LN10));return Math.round(Y*D)/D}function Vl(Y){var D=Math.pow(10,(""+Math.floor(Y)).length-1),J=Y/D;return J=J>=10?10:J>=5?5:J>=3?3:J>=2?2:J>=1?1:al(J),D*J}var ss=function(D){this._fullscreen=!1,D&&D.container&&(D.container instanceof i.window.HTMLElement?this._container=D.container:i.warnOnce("Full screen control 'container' must be a DOM element.")),i.bindAll(["_onClickFullscreen","_changeIcon"],this),"onfullscreenchange"in i.window.document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in i.window.document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in i.window.document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in i.window.document&&(this._fullscreenchange="MSFullscreenChange")};ss.prototype.onAdd=function(D){return this._map=D,this._container||(this._container=this._map.getContainer()),this._controlContainer=o.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display="none",i.warnOnce("This device does not support fullscreen mode.")),this._controlContainer},ss.prototype.onRemove=function(){o.remove(this._controlContainer),this._map=null,i.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},ss.prototype._checkFullscreenSupport=function(){return!!(i.window.document.fullscreenEnabled||i.window.document.mozFullScreenEnabled||i.window.document.msFullscreenEnabled||i.window.document.webkitFullscreenEnabled)},ss.prototype._setupUI=function(){var D=this._fullscreenButton=o.create("button","mapboxgl-ctrl-fullscreen",this._controlContainer);o.create("span","mapboxgl-ctrl-icon",D).setAttribute("aria-hidden",!0),D.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),i.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},ss.prototype._updateTitle=function(){var D=this._getTitle();this._fullscreenButton.setAttribute("aria-label",D),this._fullscreenButton.title=D},ss.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},ss.prototype._isFullscreen=function(){return this._fullscreen},ss.prototype._changeIcon=function(){var D=i.window.document.fullscreenElement||i.window.document.mozFullScreenElement||i.window.document.webkitFullscreenElement||i.window.document.msFullscreenElement;D===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle("mapboxgl-ctrl-shrink"),this._fullscreenButton.classList.toggle("mapboxgl-ctrl-fullscreen"),this._updateTitle())},ss.prototype._onClickFullscreen=function(){this._isFullscreen()?i.window.document.exitFullscreen?i.window.document.exitFullscreen():i.window.document.mozCancelFullScreen?i.window.document.mozCancelFullScreen():i.window.document.msExitFullscreen?i.window.document.msExitFullscreen():i.window.document.webkitCancelFullScreen&&i.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Vs={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:"",maxWidth:"240px"},Ys=["a[href]","[tabindex]:not([tabindex='-1'])","[contenteditable]:not([contenteditable='false'])","button:not([disabled])","input:not([disabled])","select:not([disabled])","textarea:not([disabled])"].join(", "),wa=function(Y){function D(J){Y.call(this),this.options=i.extend(Object.create(Vs),J),i.bindAll(["_update","_onClose","remove","_onMouseMove","_onMouseUp","_onDrag"],this)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.addTo=function(q){return this._map&&this.remove(),this._map=q,this.options.closeOnClick&&this._map.on("click",this._onClose),this.options.closeOnMove&&this._map.on("move",this._onClose),this._map.on("remove",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on("mousemove",this._onMouseMove),this._map.on("mouseup",this._onMouseUp),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")):this._map.on("move",this._update),this.fire(new i.Event("open")),this},D.prototype.isOpen=function(){return!!this._map},D.prototype.remove=function(){return this._content&&o.remove(this._content),this._container&&(o.remove(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("move",this._onClose),this._map.off("click",this._onClose),this._map.off("remove",this.remove),this._map.off("mousemove",this._onMouseMove),this._map.off("mouseup",this._onMouseUp),this._map.off("drag",this._onDrag),delete this._map),this.fire(new i.Event("close")),this},D.prototype.getLngLat=function(){return this._lngLat},D.prototype.setLngLat=function(q){return this._lngLat=i.LngLat.convert(q),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on("move",this._update),this._map.off("mousemove",this._onMouseMove),this._container&&this._container.classList.remove("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.remove("mapboxgl-track-pointer")),this},D.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off("move",this._update),this._map.on("mousemove",this._onMouseMove),this._map.on("drag",this._onDrag),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")),this},D.prototype.getElement=function(){return this._container},D.prototype.setText=function(q){return this.setDOMContent(i.window.document.createTextNode(q))},D.prototype.setHTML=function(q){var K=i.window.document.createDocumentFragment(),de=i.window.document.createElement("body"),ne;for(de.innerHTML=q;ne=de.firstChild,!!ne;)K.appendChild(ne);return this.setDOMContent(K)},D.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},D.prototype.setMaxWidth=function(q){return this.options.maxWidth=q,this._update(),this},D.prototype.setDOMContent=function(q){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=o.create("div","mapboxgl-popup-content",this._container);return this._content.appendChild(q),this._createCloseButton(),this._update(),this._focusFirstElement(),this},D.prototype.addClassName=function(q){this._container&&this._container.classList.add(q)},D.prototype.removeClassName=function(q){this._container&&this._container.classList.remove(q)},D.prototype.setOffset=function(q){return this.options.offset=q,this._update(),this},D.prototype.toggleClassName=function(q){if(this._container)return this._container.classList.toggle(q)},D.prototype._createCloseButton=function(){this.options.closeButton&&(this._closeButton=o.create("button","mapboxgl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.setAttribute("aria-label","Close popup"),this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClose))},D.prototype._onMouseUp=function(q){this._update(q.point)},D.prototype._onMouseMove=function(q){this._update(q.point)},D.prototype._onDrag=function(q){this._update(q.point)},D.prototype._update=function(q){var K=this,de=this._lngLat||this._trackPointer;if(!(!this._map||!de||!this._content)&&(this._container||(this._container=o.create("div","mapboxgl-popup",this._map.getContainer()),this._tip=o.create("div","mapboxgl-popup-tip",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(" ").forEach(function(Ve){return K._container.classList.add(Ve)}),this._trackPointer&&this._container.classList.add("mapboxgl-popup-track-pointer")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=pn(this._lngLat,this._pos,this._map.transform)),!(this._trackPointer&&!q))){var ne=this._pos=this._trackPointer&&q?q:this._map.project(this._lngLat),we=this.options.anchor,Ue=ol(this.options.offset);if(!we){var ft=this._container.offsetWidth,Xt=this._container.offsetHeight,hr;ne.y+Ue.bottom.y<Xt?hr=["top"]:ne.y>this._map.transform.height-Xt?hr=["bottom"]:hr=[],ne.x<ft/2?hr.push("left"):ne.x>this._map.transform.width-ft/2&&hr.push("right"),hr.length===0?we="bottom":we=hr.join("-")}var qt=ne.add(Ue[we]).round();o.setTransform(this._container,Vn[we]+" translate("+qt.x+"px,"+qt.y+"px)"),na(this._container,we,"popup")}},D.prototype._focusFirstElement=function(){if(!(!this.options.focusAfterOpen||!this._container)){var q=this._container.querySelector(Ys);q&&q.focus()}},D.prototype._onClose=function(){this.remove()},D}(i.Evented);function ol(Y){if(Y)if(typeof Y=="number"){var D=Math.round(Math.sqrt(.5*Math.pow(Y,2)));return{center:new i.Point(0,0),top:new i.Point(0,Y),"top-left":new i.Point(D,D),"top-right":new i.Point(-D,D),bottom:new i.Point(0,-Y),"bottom-left":new i.Point(D,-D),"bottom-right":new i.Point(-D,-D),left:new i.Point(Y,0),right:new i.Point(-Y,0)}}else if(Y instanceof i.Point||Array.isArray(Y)){var J=i.Point.convert(Y);return{center:J,top:J,"top-left":J,"top-right":J,bottom:J,"bottom-left":J,"bottom-right":J,left:J,right:J}}else return{center:i.Point.convert(Y.center||[0,0]),top:i.Point.convert(Y.top||[0,0]),"top-left":i.Point.convert(Y["top-left"]||[0,0]),"top-right":i.Point.convert(Y["top-right"]||[0,0]),bottom:i.Point.convert(Y.bottom||[0,0]),"bottom-left":i.Point.convert(Y["bottom-left"]||[0,0]),"bottom-right":i.Point.convert(Y["bottom-right"]||[0,0]),left:i.Point.convert(Y.left||[0,0]),right:i.Point.convert(Y.right||[0,0])};else return ol(new i.Point(0,0))}var io={version:i.version,supported:a,setRTLTextPlugin:i.setRTLTextPlugin,getRTLTextPluginStatus:i.getRTLTextPluginStatus,Map:li,NavigationControl:Vi,GeolocateControl:Zs,AttributionControl:en,ScaleControl:Ml,FullscreenControl:ss,Popup:wa,Marker:Ki,Style:yu,LngLat:i.LngLat,LngLatBounds:i.LngLatBounds,Point:i.Point,MercatorCoordinate:i.MercatorCoordinate,Evented:i.Evented,config:i.config,prewarm:ua,clearPrewarmedResources:ma,get accessToken(){return i.config.ACCESS_TOKEN},set accessToken(Y){i.config.ACCESS_TOKEN=Y},get baseApiUrl(){return i.config.API_URL},set baseApiUrl(Y){i.config.API_URL=Y},get workerCount(){return fn.workerCount},set workerCount(Y){fn.workerCount=Y},get maxParallelImageRequests(){return i.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(Y){i.config.MAX_PARALLEL_IMAGE_REQUESTS=Y},clearStorage:function(D){i.clearTileCache(D)},workerUrl:""};return io}),r})});var fGe=ye((_xr,cGe)=>{"use strict";var iw=Dr(),Fjt=ru().sanitizeHTML,zjt=iJ(),sGe=c1();function lGe(e,t){this.subplot=e,this.uid=e.uid+"-"+t,this.index=t,this.idSource="source-"+this.uid,this.idLayer=sGe.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var ig=lGe.prototype;ig.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=$z(t)};ig.needsNewImage=function(e){var t=this.subplot.map;return t.getSource(this.idSource)&&this.sourceType==="image"&&e.sourcetype==="image"&&(this.source!==e.source||JSON.stringify(this.coordinates)!==JSON.stringify(e.coordinates))};ig.needsNewSource=function(e){return this.sourceType!==e.sourcetype||JSON.stringify(this.source)!==JSON.stringify(e.source)||this.layerType!==e.type};ig.needsNewLayer=function(e){return this.layerType!==e.type||this.below!==this.subplot.belowLookup["layout-"+this.index]};ig.lookupBelow=function(){return this.subplot.belowLookup["layout-"+this.index]};ig.updateImage=function(e){var t=this.subplot.map;t.getSource(this.idSource).updateImage({url:e.source,coordinates:e.coordinates});var r=this.findFollowingMapboxLayerId(this.lookupBelow());r!==null&&this.subplot.map.moveLayer(this.idLayer,r)};ig.updateSource=function(e){var t=this.subplot.map;if(t.getSource(this.idSource)&&t.removeSource(this.idSource),this.sourceType=e.sourcetype,this.source=e.source,!!$z(e)){var r=Ojt(e);t.addSource(this.idSource,r)}};ig.findFollowingMapboxLayerId=function(e){if(e==="traces")for(var t=this.subplot.getMapLayers(),r=0;r<t.length;r++){var n=t[r].id;if(typeof n=="string"&&n.indexOf(sGe.traceLayerPrefix)===0){e=n;break}}return e};ig.updateLayer=function(e){var t=this.subplot,r=uGe(e),n=this.lookupBelow(),i=this.findFollowingMapboxLayerId(n);this.removeLayer(),$z(e)&&t.addLayer({id:this.idLayer,source:this.idSource,"source-layer":e.sourcelayer||"",type:e.type,minzoom:e.minzoom,maxzoom:e.maxzoom,layout:r.layout,paint:r.paint},i),this.layerType=e.type,this.below=n};ig.updateStyle=function(e){if($z(e)){var t=uGe(e);this.subplot.setOptions(this.idLayer,"setLayoutProperty",t.layout),this.subplot.setOptions(this.idLayer,"setPaintProperty",t.paint)}};ig.removeLayer=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer)};ig.dispose=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer),e.getSource(this.idSource)&&e.removeSource(this.idSource)};function $z(e){if(!e.visible)return!1;var t=e.source;if(Array.isArray(t)&&t.length>0){for(var r=0;r<t.length;r++)if(typeof t[r]!="string"||t[r].length===0)return!1;return!0}return iw.isPlainObject(t)||typeof t=="string"&&t.length>0}function uGe(e){var t={},r={};switch(e.type){case"circle":iw.extendFlat(r,{"circle-radius":e.circle.radius,"circle-color":e.color,"circle-opacity":e.opacity});break;case"line":iw.extendFlat(r,{"line-width":e.line.width,"line-color":e.color,"line-opacity":e.opacity,"line-dasharray":e.line.dash});break;case"fill":iw.extendFlat(r,{"fill-color":e.color,"fill-outline-color":e.fill.outlinecolor,"fill-opacity":e.opacity});break;case"symbol":var n=e.symbol,i=zjt(n.textposition,n.iconsize);iw.extendFlat(t,{"icon-image":n.icon+"-15","icon-size":n.iconsize/10,"text-field":n.text,"text-size":n.textfont.size,"text-anchor":i.anchor,"text-offset":i.offset,"symbol-placement":n.placement}),iw.extendFlat(r,{"icon-color":e.color,"text-color":n.textfont.color,"text-opacity":e.opacity});break;case"raster":iw.extendFlat(r,{"raster-fade-duration":0,"raster-opacity":e.opacity});break}return{layout:t,paint:r}}function Ojt(e){var t=e.sourcetype,r=e.source,n={type:t},i;return t==="geojson"?i="data":t==="vector"?i=typeof r=="string"?"url":"tiles":t==="raster"?(i="tiles",n.tileSize=256):t==="image"&&(i="url",n.coordinates=e.coordinates),n[i]=r,e.sourceattribution&&(n.attribution=Fjt(e.sourceattribution)),n}cGe.exports=function(t,r,n){var i=new lGe(t,r);return i.update(n),i}});var xGe=ye((xxr,_Ge)=>{"use strict";var cJ=uJ(),fJ=Dr(),pGe=ix(),hGe=qa(),qjt=ho(),Bjt=yv(),Qz=vf(),gGe=Eg(),Njt=gGe.drawMode,Ujt=gGe.selectMode,Vjt=Of().prepSelect,Gjt=Of().clearOutline,Hjt=Of().clearSelectionsCache,jjt=Of().selectOnClick,_x=c1(),Wjt=fGe();function mGe(e,t){this.id=t,this.gd=e;var r=e._fullLayout,n=e._context;this.container=r._glcontainer.node(),this.isStatic=n.staticPlot,this.uid=r._uid+"-"+this.id,this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(r),this.map=null,this.accessToken=null,this.styleObj=null,this.traceHash={},this.layerList=[],this.belowLookup={},this.dragging=!1,this.wheeling=!1}var Nh=mGe.prototype;Nh.plot=function(e,t,r){var n=this,i=t[n.id];n.map&&i.accesstoken!==n.accessToken&&(n.map.remove(),n.map=null,n.styleObj=null,n.traceHash={},n.layerList=[]);var a;n.map?a=new Promise(function(o,s){n.updateMap(e,t,o,s)}):a=new Promise(function(o,s){n.createMap(e,t,o,s)}),r.push(a)};Nh.createMap=function(e,t,r,n){var i=this,a=t[i.id],o=i.styleObj=yGe(a.style,t);i.accessToken=a.accesstoken;var s=a.bounds,l=s?[[s.west,s.south],[s.east,s.north]]:null,u=i.map=new cJ.Map({container:i.div,style:o.style,center:hJ(a.center),zoom:a.zoom,bearing:a.bearing,pitch:a.pitch,maxBounds:l,interactive:!i.isStatic,preserveDrawingBuffer:i.isStatic,doubleClickZoom:!1,boxZoom:!1,attributionControl:!1}).addControl(new cJ.AttributionControl({compact:!0}));u._canvas.style.left="0px",u._canvas.style.top="0px",i.rejectOnError(n),i.isStatic||i.initFx(e,t);var c=[];c.push(new Promise(function(f){u.once("load",f)})),c=c.concat(pGe.fetchTraceGeoData(e)),Promise.all(c).then(function(){i.fillBelowLookup(e,t),i.updateData(e),i.updateLayout(t),i.resolveOnRender(r)}).catch(n)};Nh.updateMap=function(e,t,r,n){var i=this,a=i.map,o=t[this.id];i.rejectOnError(n);var s=[],l=yGe(o.style,t);JSON.stringify(i.styleObj)!==JSON.stringify(l)&&(i.styleObj=l,a.setStyle(l.style),i.traceHash={},s.push(new Promise(function(u){a.once("styledata",u)}))),s=s.concat(pGe.fetchTraceGeoData(e)),Promise.all(s).then(function(){i.fillBelowLookup(e,t),i.updateData(e),i.updateLayout(t),i.resolveOnRender(r)}).catch(n)};Nh.fillBelowLookup=function(e,t){var r=t[this.id],n=r.layers,i,a,o=this.belowLookup={},s=!1;for(i=0;i<e.length;i++){var l=e[i][0].trace,u=l._module;typeof l.below=="string"?a=l.below:u.getBelow&&(a=u.getBelow(l,this)),a===""&&(s=!0),o["trace-"+l.uid]=a||""}for(i=0;i<n.length;i++){var c=n[i];typeof c.below=="string"?a=c.below:s?a="traces":a="",o["layout-"+i]=a}var f={},h,d;for(h in o)a=o[h],f[a]?f[a].push(h):f[a]=[h];for(a in f){var v=f[a];if(v.length>1)for(i=0;i<v.length;i++)h=v[i],h.indexOf("trace-")===0?(d=h.split("trace-")[1],this.traceHash[d]&&(this.traceHash[d].below=null)):h.indexOf("layout-")===0&&(d=h.split("layout-")[1],this.layerList[d]&&(this.layerList[d].below=null))}};var dGe={choroplethmapbox:0,densitymapbox:1,scattermapbox:2};Nh.updateData=function(e){var t=this.traceHash,r,n,i,a,o=e.slice().sort(function(f,h){return dGe[f[0].trace.type]-dGe[h[0].trace.type]});for(i=0;i<o.length;i++){var s=o[i];n=s[0].trace,r=t[n.uid];var l=!1;r&&(r.type===n.type?(r.update(s),l=!0):r.dispose()),!l&&n._module&&(t[n.uid]=n._module.plot(this,s))}var u=Object.keys(t);e:for(i=0;i<u.length;i++){var c=u[i];for(a=0;a<e.length;a++)if(n=e[a][0].trace,c===n.uid)continue e;r=t[c],r.dispose(),delete t[c]}};Nh.updateLayout=function(e){var t=this.map,r=e[this.id];!this.dragging&&!this.wheeling&&(t.setCenter(hJ(r.center)),t.setZoom(r.zoom),t.setBearing(r.bearing),t.setPitch(r.pitch)),this.updateLayers(e),this.updateFramework(e),this.updateFx(e),this.map.resize(),this.gd._context._scrollZoom.mapbox?t.scrollZoom.enable():t.scrollZoom.disable()};Nh.resolveOnRender=function(e){var t=this.map;t.on("render",function r(){t.loaded()&&(t.off("render",r),setTimeout(e,10))})};Nh.rejectOnError=function(e){var t=this.map;function r(){e(new Error(_x.mapOnErrorMsg))}t.once("error",r),t.once("style.error",r),t.once("source.error",r),t.once("tile.error",r),t.once("layer.error",r)};Nh.createFramework=function(e){var t=this,r=t.div=document.createElement("div");r.id=t.uid,r.style.position="absolute",t.container.appendChild(r),t.xaxis={_id:"x",c2p:function(n){return t.project(n).x}},t.yaxis={_id:"y",c2p:function(n){return t.project(n).y}},t.updateFramework(e),t.mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},qjt.setConvert(t.mockAxis,e)};Nh.initFx=function(e,t){var r=this,n=r.gd,i=r.map;i.on("moveend",function(s){if(r.map){var l=n._fullLayout;if(s.originalEvent||r.wheeling){var u=l[r.id];hGe.call("_storeDirectGUIEdit",n.layout,l._preGUI,r.getViewEdits(u));var c=r.getView();u._input.center=u.center=c.center,u._input.zoom=u.zoom=c.zoom,u._input.bearing=u.bearing=c.bearing,u._input.pitch=u.pitch=c.pitch,n.emit("plotly_relayout",r.getViewEditsWithDerived(c))}s.originalEvent&&s.originalEvent.type==="mouseup"?r.dragging=!1:r.wheeling&&(r.wheeling=!1),l._rehover&&l._rehover()}}),i.on("wheel",function(){r.wheeling=!0}),i.on("mousemove",function(s){var l=r.div.getBoundingClientRect(),u=[s.originalEvent.offsetX,s.originalEvent.offsetY];s.target.getBoundingClientRect=function(){return l},r.xaxis.p2c=function(){return i.unproject(u).lng},r.yaxis.p2c=function(){return i.unproject(u).lat},n._fullLayout._rehover=function(){n._fullLayout._hoversubplot===r.id&&n._fullLayout[r.id]&&Qz.hover(n,s,r.id)},Qz.hover(n,s,r.id),n._fullLayout._hoversubplot=r.id});function a(){Qz.loneUnhover(t._hoverlayer)}i.on("dragstart",function(){r.dragging=!0,a()}),i.on("zoomstart",a),i.on("mouseout",function(){n._fullLayout._hoversubplot=null});function o(){var s=r.getView();n.emit("plotly_relayouting",r.getViewEditsWithDerived(s))}i.on("drag",o),i.on("zoom",o),i.on("dblclick",function(){var s=n._fullLayout[r.id];hGe.call("_storeDirectGUIEdit",n.layout,n._fullLayout._preGUI,r.getViewEdits(s));var l=r.viewInitial;i.setCenter(hJ(l.center)),i.setZoom(l.zoom),i.setBearing(l.bearing),i.setPitch(l.pitch);var u=r.getView();s._input.center=s.center=u.center,s._input.zoom=s.zoom=u.zoom,s._input.bearing=s.bearing=u.bearing,s._input.pitch=s.pitch=u.pitch,n.emit("plotly_doubleclick",null),n.emit("plotly_relayout",r.getViewEditsWithDerived(u))}),r.clearOutline=function(){Hjt(r.dragOptions),Gjt(r.dragOptions.gd)},r.onClickInPanFn=function(s){return function(l){var u=n._fullLayout.clickmode;u.indexOf("select")>-1&&jjt(l.originalEvent,n,[r.xaxis],[r.yaxis],r.id,s),u.indexOf("event")>-1&&Qz.click(n,l.originalEvent)}}};Nh.updateFx=function(e){var t=this,r=t.map,n=t.gd;if(t.isStatic)return;function i(l){var u=t.map.unproject(l);return[u.lng,u.lat]}var a=e.dragmode,o;o=function(l,u){if(u.isRect){var c=l.range={};c[t.id]=[i([u.xmin,u.ymin]),i([u.xmax,u.ymax])]}else{var f=l.lassoPoints={};f[t.id]=u.map(i)}};var s=t.dragOptions;t.dragOptions=fJ.extendDeep(s||{},{dragmode:e.dragmode,element:t.div,gd:n,plotinfo:{id:t.id,domain:e[t.id].domain,xaxis:t.xaxis,yaxis:t.yaxis,fillRangeItems:o},xaxes:[t.xaxis],yaxes:[t.yaxis],subplot:t.id}),r.off("click",t.onClickInPanHandler),Ujt(a)||Njt(a)?(r.dragPan.disable(),r.on("zoomstart",t.clearOutline),t.dragOptions.prepFn=function(l,u,c){Vjt(l,u,c,t.dragOptions,a)},Bjt.init(t.dragOptions)):(r.dragPan.enable(),r.off("zoomstart",t.clearOutline),t.div.onmousedown=null,t.div.ontouchstart=null,t.div.removeEventListener("touchstart",t.div._ontouchstart),t.onClickInPanHandler=t.onClickInPanFn(t.dragOptions),r.on("click",t.onClickInPanHandler))};Nh.updateFramework=function(e){var t=e[this.id].domain,r=e._size,n=this.div.style;n.width=r.w*(t.x[1]-t.x[0])+"px",n.height=r.h*(t.y[1]-t.y[0])+"px",n.left=r.l+t.x[0]*r.w+"px",n.top=r.t+(1-t.y[1])*r.h+"px",this.xaxis._offset=r.l+t.x[0]*r.w,this.xaxis._length=r.w*(t.x[1]-t.x[0]),this.yaxis._offset=r.t+(1-t.y[1])*r.h,this.yaxis._length=r.h*(t.y[1]-t.y[0])};Nh.updateLayers=function(e){var t=e[this.id],r=t.layers,n=this.layerList,i;if(r.length!==n.length){for(i=0;i<n.length;i++)n[i].dispose();for(n=this.layerList=[],i=0;i<r.length;i++)n.push(Wjt(this,i,r[i]))}else for(i=0;i<r.length;i++)n[i].update(r[i])};Nh.destroy=function(){this.map&&(this.map.remove(),this.map=null,this.container.removeChild(this.div))};Nh.toImage=function(){return this.map.stop(),this.map.getCanvas().toDataURL()};Nh.setOptions=function(e,t,r){for(var n in r)this.map[t](e,n,r[n])};Nh.getMapLayers=function(){return this.map.getStyle().layers};Nh.addLayer=function(e,t){var r=this.map;if(typeof t=="string"){if(t===""){r.addLayer(e,t);return}for(var n=this.getMapLayers(),i=0;i<n.length;i++)if(t===n[i].id){r.addLayer(e,t);return}fJ.warn(["Trying to add layer with *below* value",t,"referencing a layer that does not exist","or that does not yet exist."].join(" "))}r.addLayer(e)};Nh.project=function(e){return this.map.project(new cJ.LngLat(e[0],e[1]))};Nh.getView=function(){var e=this.map,t=e.getCenter(),r=t.lng,n=t.lat,i={lon:r,lat:n},a=e.getCanvas(),o=parseInt(a.style.width),s=parseInt(a.style.height);return{center:i,zoom:e.getZoom(),bearing:e.getBearing(),pitch:e.getPitch(),_derived:{coordinates:[e.unproject([0,0]).toArray(),e.unproject([o,0]).toArray(),e.unproject([o,s]).toArray(),e.unproject([0,s]).toArray()]}}};Nh.getViewEdits=function(e){for(var t=this.id,r=["center","zoom","bearing","pitch"],n={},i=0;i<r.length;i++){var a=r[i];n[t+"."+a]=e[a]}return n};Nh.getViewEditsWithDerived=function(e){var t=this.id,r=this.getViewEdits(e);return r[t+"._derived"]=e._derived,r};function yGe(e,t){var r={};if(fJ.isPlainObject(e))r.id=e.id,r.style=e;else if(typeof e=="string")if(r.id=e,_x.styleValuesMapbox.indexOf(e)!==-1)r.style=vGe(e);else if(_x.stylesNonMapbox[e]){r.style=_x.stylesNonMapbox[e];var n=r.style.sources["plotly-"+e],i=n?n.tiles:void 0;i&&i[0]&&i[0].slice(-9)==="?api_key="&&(i[0]+=t._mapboxAccessToken)}else r.style=e;else r.id=_x.styleValueDflt,r.style=vGe(_x.styleValueDflt);return r.transition={duration:0,delay:0},r}function vGe(e){return _x.styleUrlPrefix+e+"-"+_x.styleUrlSuffix}function hJ(e){return[e.lon,e.lat]}_Ge.exports=mGe});var TGe=ye((bxr,wGe)=>{"use strict";var dJ=Dr(),Xjt=k_(),Zjt=Yd(),bGe=Nk();wGe.exports=function(t,r,n){Xjt(t,r,n,{type:"mapbox",attributes:bGe,handleDefaults:Yjt,partition:"y",accessToken:r._mapboxAccessToken})};function Yjt(e,t,r,n){r("accesstoken",n.accessToken),r("style"),r("center.lon"),r("center.lat"),r("zoom"),r("bearing"),r("pitch");var i=r("bounds.west"),a=r("bounds.east"),o=r("bounds.south"),s=r("bounds.north");(i===void 0||a===void 0||o===void 0||s===void 0)&&delete t.bounds,Zjt(e,t,{name:"layers",handleItemDefaults:Kjt}),t._input=e}function Kjt(e,t){function r(l,u){return dJ.coerce(e,t,bGe.layers,l,u)}var n=r("visible");if(n){var i=r("sourcetype"),a=i==="raster"||i==="image";r("source"),r("sourceattribution"),i==="vector"&&r("sourcelayer"),i==="image"&&r("coordinates");var o;a&&(o="raster");var s=r("type",o);a&&s!=="raster"&&(s=t.type="raster",dJ.log("Source types *raster* and *image* must drawn *raster* layer type.")),r("below"),r("color"),r("opacity"),r("minzoom"),r("maxzoom"),s==="circle"&&r("circle.radius"),s==="line"&&(r("line.width"),r("line.dash")),s==="fill"&&r("fill.outlinecolor"),s==="symbol"&&(r("symbol.icon"),r("symbol.iconsize"),r("symbol.text"),dJ.coerceFont(r,"symbol.textfont",void 0,{noFontVariant:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0}),r("symbol.textposition"),r("symbol.placement"))}}});var e7=ye(Np=>{"use strict";var AGe=uJ(),rm=Dr(),vJ=rm.strTranslate,Jjt=rm.strScale,$jt=Id().getSubplotCalcData,Qjt=Wp(),eWt=Oa(),SGe=So(),tWt=ru(),rWt=xGe(),xx="mapbox",ey=Np.constants=c1();Np.name=xx;Np.attr="subplot";Np.idRoot=xx;Np.idRegex=Np.attrRegex=rm.counterRegex(xx);var iWt=["mapbox subplots and traces are deprecated!","Please consider switching to `map` subplots and traces.","Learn more at: https://plotly.com/python/maplibre-migration/","as well as https://plotly.com/javascript/maplibre-migration/"].join(" ");Np.attributes={subplot:{valType:"subplotid",dflt:"mapbox",editType:"calc"}};Np.layoutAttributes=Nk();Np.supplyLayoutDefaults=TGe();var MGe=!0;Np.plot=function(t){MGe&&(MGe=!1,rm.warn(iWt));var r=t._fullLayout,n=t.calcdata,i=r._subplots[xx];if(AGe.version!==ey.requiredVersion)throw new Error(ey.wrongVersionErrorMsg);var a=nWt(t,i);AGe.accessToken=a;for(var o=0;o<i.length;o++){var s=i[o],l=$jt(n,xx,s),u=r[s],c=u._subplot;c||(c=new rWt(t,s),r[s]._subplot=c),c.viewInitial||(c.viewInitial={center:rm.extendFlat({},u.center),zoom:u.zoom,bearing:u.bearing,pitch:u.pitch}),c.plot(l,r,t._promises)}};Np.clean=function(e,t,r,n){for(var i=n._subplots[xx]||[],a=0;a<i.length;a++){var o=i[a];!t[o]&&n[o]._subplot&&n[o]._subplot.destroy()}};Np.toSVG=function(e){for(var t=e._fullLayout,r=t._subplots[xx],n=t._size,i=0;i<r.length;i++){var a=t[r[i]],o=a.domain,s=a._subplot,l=s.toImage("png"),u=t._glimages.append("svg:image");u.attr({xmlns:Qjt.svg,"xlink:href":l,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"});var c=eWt.select(a._subplot.div),f=c.select(".mapboxgl-ctrl-logo").node().offsetParent===null;if(!f){var h=t._glimages.append("g");h.attr("transform",vJ(n.l+n.w*o.x[0]+10,n.t+n.h*(1-o.y[0])-31)),h.append("path").attr("d",ey.mapboxLogo.path0).style({opacity:.9,fill:"#ffffff","enable-background":"new"}),h.append("path").attr("d",ey.mapboxLogo.path1).style("opacity",.35).style("enable-background","new"),h.append("path").attr("d",ey.mapboxLogo.path2).style("opacity",.35).style("enable-background","new"),h.append("polygon").attr("points",ey.mapboxLogo.polygon).style({opacity:.9,fill:"#ffffff","enable-background":"new"})}var d=c.select(".mapboxgl-ctrl-attrib").text().replace("Improve this map",""),v=t._glimages.append("g"),_=v.append("text");_.text(d).classed("static-attribution",!0).attr({"font-size":12,"font-family":"Arial",color:"rgba(0, 0, 0, 0.75)","text-anchor":"end","data-unformatted":d});var b=SGe.bBox(_.node()),p=n.w*(o.x[1]-o.x[0]);if(b.width>p/2){var k=d.split("|").join("<br>");_.text(k).attr("data-unformatted",k).call(tWt.convertToTspans,e),b=SGe.bBox(_.node())}_.attr("transform",vJ(-3,-b.height+8)),v.insert("rect",".static-attribution").attr({x:-b.width-6,y:-b.height-3,width:b.width+6,height:b.height+3,fill:"rgba(255, 255, 255, 0.75)"});var E=1;b.width+6>p&&(E=p/(b.width+6));var S=[n.l+n.w*o.x[1],n.t+n.h*(1-o.y[0])];v.attr("transform",vJ(S[0],S[1])+Jjt(E))}};function nWt(e,t){var r=e._fullLayout,n=e._context;if(n.mapboxAccessToken==="")return"";for(var i=[],a=[],o=!1,s=!1,l=0;l<t.length;l++){var u=r[t[l]],c=u.accesstoken;EGe(u.style)&&(c?rm.pushUnique(i,c):(EGe(u._input.style)&&(rm.error("Uses Mapbox map style, but did not set an access token."),o=!0),s=!0)),c&&rm.pushUnique(a,c)}if(s){var f=o?ey.noAccessTokenErrorMsg:ey.missingStyleErrorMsg;throw rm.error(f),new Error(f)}return i.length?(i.length>1&&rm.warn(ey.multipleTokensErrorMsg),i[0]):(a.length&&rm.log(["Listed mapbox access token(s)",a.join(","),"but did not use a Mapbox map style, ignoring token(s)."].join(" ")),"")}function EGe(e){return typeof e=="string"&&(ey.styleValuesMapbox.indexOf(e)!==-1||e.indexOf("mapbox://")===0||e.indexOf("stamen")===0)}Np.updateFx=function(e){for(var t=e._fullLayout,r=t._subplots[xx],n=0;n<r.length;n++){var i=t[r[n]]._subplot;i.updateFx(t)}}});var CGe=ye((Axr,kGe)=>{"use strict";var Txr=["*scattermapbox* trace is deprecated!","Please consider switching to the *scattermap* trace type and `map` subplots.","Learn more at: https://plotly.com/python/maplibre-migration/","as well as https://plotly.com/javascript/maplibre-migration/"].join(" ");kGe.exports={attributes:Wz(),supplyDefaults:NVe(),colorbar:$d(),formatLabels:rJ(),calc:pF(),plot:eGe(),hoverPoints:Jz().hoverPoints,eventData:nGe(),selectPoints:oGe(),styleOnSelect:function(e,t){if(t){var r=t[0].trace;r._glTrace.update(t)}},moduleType:"trace",name:"scattermapbox",basePlotModule:e7(),categories:["mapbox","gl","symbols","showLegend","scatter-like"],meta:{}}});var PGe=ye((Sxr,LGe)=>{"use strict";LGe.exports=CGe()});var pJ=ye((Mxr,IGe)=>{"use strict";var f1=t5(),aWt=Tu(),{hovertemplateAttrs:oWt,templatefallbackAttrs:sWt}=Ll(),lWt=Gl(),bx=Ao().extendFlat;IGe.exports=bx({locations:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},geojson:{valType:"any",editType:"calc"},featureidkey:bx({},f1.featureidkey,{}),below:{valType:"string",editType:"plot"},text:f1.text,hovertext:f1.hovertext,marker:{line:{color:bx({},f1.marker.line.color,{editType:"plot"}),width:bx({},f1.marker.line.width,{editType:"plot"}),editType:"calc"},opacity:bx({},f1.marker.opacity,{editType:"plot"}),editType:"calc"},selected:{marker:{opacity:bx({},f1.selected.marker.opacity,{editType:"plot"}),editType:"plot"},editType:"plot"},unselected:{marker:{opacity:bx({},f1.unselected.marker.opacity,{editType:"plot"}),editType:"plot"},editType:"plot"},hoverinfo:f1.hoverinfo,hovertemplate:oWt({},{keys:["properties"]}),hovertemplatefallback:sWt(),showlegend:bx({},lWt.showlegend,{dflt:!1})},aWt("",{cLetter:"z",editTypeOverride:"calc"}))});var DGe=ye((Exr,RGe)=>{"use strict";var Hk=Dr(),uWt=Qh(),cWt=pJ();RGe.exports=function(t,r,n,i){function a(c,f){return Hk.coerce(t,r,cWt,c,f)}var o=a("locations"),s=a("z"),l=a("geojson");if(!Hk.isArrayOrTypedArray(o)||!o.length||!Hk.isArrayOrTypedArray(s)||!s.length||!(typeof l=="string"&&l!==""||Hk.isPlainObject(l))){r.visible=!1;return}a("featureidkey"),r._length=Math.min(o.length,s.length),a("below"),a("text"),a("hovertext"),a("hovertemplate"),a("hovertemplatefallback");var u=a("marker.line.width");u&&a("marker.line.color"),a("marker.opacity"),uWt(t,r,i,a,{prefix:"",cLetter:"z"}),Hk.coerceSelectionMarkerOpacity(r,a)}});var gJ=ye((kxr,OGe)=>{"use strict";var fWt=Eo(),h1=Dr(),hWt=tc(),dWt=So(),vWt=tx().makeBlank,FGe=ix();function pWt(e){var t=e[0].trace,r=t.visible===!0&&t._length!==0,n={layout:{visibility:"none"},paint:{}},i={layout:{visibility:"none"},paint:{}},a=t._opts={fill:n,line:i,geojson:vWt()};if(!r)return a;var o=FGe.extractTraceFeature(e);if(!o)return a;var s=hWt.makeColorScaleFuncFromTrace(t),l=t.marker,u=l.line||{},c;h1.isArrayOrTypedArray(l.opacity)&&(c=function(k){var E=k.mo;return fWt(E)?+h1.constrain(E,0,1):0});var f;h1.isArrayOrTypedArray(u.color)&&(f=function(k){return k.mlc});var h;h1.isArrayOrTypedArray(u.width)&&(h=function(k){return k.mlw});for(var d=0;d<e.length;d++){var v=e[d],_=v.fOut;if(_){var b=_.properties;b.fc=s(v.z),c&&(b.mo=c(v)),f&&(b.mlc=f(v)),h&&(b.mlw=h(v)),v.ct=b.ct,v._polygons=FGe.feature2polygons(_)}}var p=c?{type:"identity",property:"mo"}:l.opacity;return h1.extendFlat(n.paint,{"fill-color":{type:"identity",property:"fc"},"fill-opacity":p}),h1.extendFlat(i.paint,{"line-color":f?{type:"identity",property:"mlc"}:u.color,"line-width":h?{type:"identity",property:"mlw"}:u.width,"line-opacity":p}),n.layout.visibility="visible",i.layout.visibility="visible",a.geojson={type:"FeatureCollection",features:o},zGe(e),a}function zGe(e){var t=e[0].trace,r=t._opts,n;if(t.selectedpoints){for(var i=dWt.makeSelectedPointStyleFns(t),a=0;a<e.length;a++){var o=e[a];o.fOut&&(o.fOut.properties.mo2=i.selectedOpacityFn(o))}n={type:"identity",property:"mo2"}}else n=h1.isArrayOrTypedArray(t.marker.opacity)?{type:"identity",property:"mo"}:t.marker.opacity;return h1.extendFlat(r.fill.paint,{"fill-opacity":n}),h1.extendFlat(r.line.paint,{"line-opacity":n}),r}OGe.exports={convert:pWt,convertOnSelect:zGe}});var VGe=ye((Cxr,UGe)=>{"use strict";var BGe=gJ().convert,gWt=gJ().convertOnSelect,qGe=c1().traceLayerPrefix;function NGe(e,t){this.type="choroplethmapbox",this.subplot=e,this.uid=t,this.sourceId="source-"+t,this.layerList=[["fill",qGe+t+"-fill"],["line",qGe+t+"-line"]],this.below=null}var M5=NGe.prototype;M5.update=function(e){this._update(BGe(e)),e[0].trace._glTrace=this};M5.updateOnSelect=function(e){this._update(gWt(e))};M5._update=function(e){var t=this.subplot,r=this.layerList,n=t.belowLookup["trace-"+this.uid];t.map.getSource(this.sourceId).setData(e.geojson),n!==this.below&&(this._removeLayers(),this._addLayers(e,n),this.below=n);for(var i=0;i<r.length;i++){var a=r[i],o=a[0],s=a[1],l=e[o];t.setOptions(s,"setLayoutProperty",l.layout),l.layout.visibility==="visible"&&t.setOptions(s,"setPaintProperty",l.paint)}};M5._addLayers=function(e,t){for(var r=this.subplot,n=this.layerList,i=this.sourceId,a=0;a<n.length;a++){var o=n[a],s=o[0],l=e[s];r.addLayer({type:s,id:o[1],source:i,layout:l.layout,paint:l.paint},t)}};M5._removeLayers=function(){for(var e=this.subplot.map,t=this.layerList,r=t.length-1;r>=0;r--)e.removeLayer(t[r][1])};M5.dispose=function(){var e=this.subplot.map;this._removeLayers(),e.removeSource(this.sourceId)};UGe.exports=function(t,r){var n=r[0].trace,i=new NGe(t,n.uid),a=i.sourceId,o=BGe(r),s=i.below=t.belowLookup["trace-"+n.uid];return t.map.addSource(a,{type:"geojson",data:o.geojson}),i._addLayers(o,s),r[0].trace._glTrace=i,i}});var HGe=ye((Pxr,GGe)=>{"use strict";var Lxr=["*choroplethmapbox* trace is deprecated!","Please consider switching to the *choroplethmap* trace type and `map` subplots.","Learn more at: https://plotly.com/python/maplibre-migration/","as well as https://plotly.com/javascript/maplibre-migration/"].join(" ");GGe.exports={attributes:pJ(),supplyDefaults:DGe(),colorbar:S_(),calc:zF(),plot:VGe(),hoverPoints:qF(),eventData:BF(),selectPoints:NF(),styleOnSelect:function(e,t){if(t){var r=t[0].trace;r._glTrace.updateOnSelect(t)}},getBelow:function(e,t){for(var r=t.getMapLayers(),n=r.length-2;n>=0;n--){var i=r[n].id;if(typeof i=="string"&&i.indexOf("water")===0){for(var a=n+1;a<r.length;a++)if(i=r[a].id,typeof i=="string"&&i.indexOf("plotly-")===-1)return i}}},moduleType:"trace",name:"choroplethmapbox",basePlotModule:e7(),categories:["mapbox","gl","noOpacity","showLegend"],meta:{hr_name:"choropleth_mapbox"}}});var WGe=ye((Ixr,jGe)=>{"use strict";jGe.exports=HGe()});var yJ=ye((Rxr,ZGe)=>{"use strict";var mWt=Tu(),{hovertemplateAttrs:yWt,templatefallbackAttrs:_Wt}=Ll(),XGe=Gl(),t7=Wz(),mJ=Ao().extendFlat;ZGe.exports=mJ({lon:t7.lon,lat:t7.lat,z:{valType:"data_array",editType:"calc"},radius:{valType:"number",editType:"plot",arrayOk:!0,min:1,dflt:30},below:{valType:"string",editType:"plot"},text:t7.text,hovertext:t7.hovertext,hoverinfo:mJ({},XGe.hoverinfo,{flags:["lon","lat","z","text","name"]}),hovertemplate:yWt(),hovertemplatefallback:_Wt(),showlegend:mJ({},XGe.showlegend,{dflt:!1})},mWt("",{cLetter:"z",editTypeOverride:"calc"}))});var KGe=ye((Dxr,YGe)=>{"use strict";var xWt=Dr(),bWt=Qh(),wWt=yJ();YGe.exports=function(t,r,n,i){function a(u,c){return xWt.coerce(t,r,wWt,u,c)}var o=a("lon")||[],s=a("lat")||[],l=Math.min(o.length,s.length);if(!l){r.visible=!1;return}r._length=l,a("z"),a("radius"),a("below"),a("text"),a("hovertext"),a("hovertemplate"),a("hovertemplatefallback"),bWt(t,r,i,a,{prefix:"",cLetter:"z"})}});var QGe=ye((Fxr,$Ge)=>{"use strict";var _J=Eo(),TWt=Dr().isArrayOrTypedArray,xJ=fs().BADNUM,AWt=gv(),JGe=Dr()._;$Ge.exports=function(t,r){for(var n=r._length,i=new Array(n),a=r.z,o=TWt(a)&&a.length,s=0;s<n;s++){var l=i[s]={},u=r.lon[s],c=r.lat[s];if(l.lonlat=_J(u)&&_J(c)?[+u,+c]:[xJ,xJ],o){var f=a[s];l.z=_J(f)?f:xJ}}return AWt(t,r,{vals:o?a:[0,1],containerStr:"",cLetter:"z"}),n&&(i[0].t={labels:{lat:JGe(t,"lat:")+" ",lon:JGe(t,"lon:")+" "}}),i}});var nHe=ye((zxr,iHe)=>{"use strict";var SWt=Eo(),bJ=Dr(),eHe=ka(),tHe=tc(),rHe=fs().BADNUM,MWt=tx().makeBlank;iHe.exports=function(t){var r=t[0].trace,n=r.visible===!0&&r._length!==0,i={layout:{visibility:"none"},paint:{}},a=r._opts={heatmap:i,geojson:MWt()};if(!n)return a;var o=[],s,l=r.z,u=r.radius,c=bJ.isArrayOrTypedArray(l)&&l.length,f=bJ.isArrayOrTypedArray(u);for(s=0;s<t.length;s++){var h=t[s],d=h.lonlat;if(d[0]!==rHe){var v={};if(c){var _=h.z;v.z=_!==rHe?_:0}f&&(v.r=SWt(u[s])&&u[s]>0?+u[s]:0),o.push({type:"Feature",geometry:{type:"Point",coordinates:d},properties:v})}}var b=tHe.extractOpts(r),p=b.reversescale?tHe.flipScale(b.colorscale):b.colorscale,k=p[0][1],E=eHe.opacity(k)<1?k:eHe.addOpacity(k,0),S=["interpolate",["linear"],["heatmap-density"],0,E];for(s=1;s<p.length;s++)S.push(p[s][0],p[s][1]);var L=["interpolate",["linear"],["get","z"],b.min,0,b.max,1];return bJ.extendFlat(a.heatmap.paint,{"heatmap-weight":c?L:1/(b.max-b.min),"heatmap-color":S,"heatmap-radius":f?{type:"identity",property:"r"}:r.radius,"heatmap-opacity":r.opacity}),a.geojson={type:"FeatureCollection",features:o},a.heatmap.layout.visibility="visible",a}});var lHe=ye((Oxr,sHe)=>{"use strict";var aHe=nHe(),EWt=c1().traceLayerPrefix;function oHe(e,t){this.type="densitymapbox",this.subplot=e,this.uid=t,this.sourceId="source-"+t,this.layerList=[["heatmap",EWt+t+"-heatmap"]],this.below=null}var r7=oHe.prototype;r7.update=function(e){var t=this.subplot,r=this.layerList,n=aHe(e),i=t.belowLookup["trace-"+this.uid];t.map.getSource(this.sourceId).setData(n.geojson),i!==this.below&&(this._removeLayers(),this._addLayers(n,i),this.below=i);for(var a=0;a<r.length;a++){var o=r[a],s=o[0],l=o[1],u=n[s];t.setOptions(l,"setLayoutProperty",u.layout),u.layout.visibility==="visible"&&t.setOptions(l,"setPaintProperty",u.paint)}};r7._addLayers=function(e,t){for(var r=this.subplot,n=this.layerList,i=this.sourceId,a=0;a<n.length;a++){var o=n[a],s=o[0],l=e[s];r.addLayer({type:s,id:o[1],source:i,layout:l.layout,paint:l.paint},t)}};r7._removeLayers=function(){for(var e=this.subplot.map,t=this.layerList,r=t.length-1;r>=0;r--)e.removeLayer(t[r][1])};r7.dispose=function(){var e=this.subplot.map;this._removeLayers(),e.removeSource(this.sourceId)};sHe.exports=function(t,r){var n=r[0].trace,i=new oHe(t,n.uid),a=i.sourceId,o=aHe(r),s=i.below=t.belowLookup["trace-"+n.uid];return t.map.addSource(a,{type:"geojson",data:o.geojson}),i._addLayers(o,s),i}});var cHe=ye((qxr,uHe)=>{"use strict";var kWt=ho(),CWt=Jz().hoverPoints,LWt=Jz().getExtraText;uHe.exports=function(t,r,n){var i=CWt(t,r,n);if(i){var a=i[0],o=a.cd,s=o[0].trace,l=o[a.index];if(delete a.color,"z"in l){var u=a.subplot.mockAxis;a.z=l.z,a.zLabel=kWt.tickText(u,u.c2l(l.z),"hover").text}return a.extraText=LWt(s,l,o[0].t.labels),[a]}}});var hHe=ye((Bxr,fHe)=>{"use strict";fHe.exports=function(t,r){return t.lon=r.lon,t.lat=r.lat,t.z=r.z,t}});var vHe=ye((Uxr,dHe)=>{"use strict";var Nxr=["*densitymapbox* trace is deprecated!","Please consider switching to the *densitymap* trace type and `map` subplots.","Learn more at: https://plotly.com/python/maplibre-migration/","as well as https://plotly.com/javascript/maplibre-migration/"].join(" ");dHe.exports={attributes:yJ(),supplyDefaults:KGe(),colorbar:S_(),formatLabels:rJ(),calc:QGe(),plot:lHe(),hoverPoints:cHe(),eventData:hHe(),getBelow:function(e,t){for(var r=t.getMapLayers(),n=0;n<r.length;n++){var i=r[n],a=i.id;if(i.type==="symbol"&&typeof a=="string"&&a.indexOf("plotly-")===-1)return a}},moduleType:"trace",name:"densitymapbox",basePlotModule:e7(),categories:["mapbox","gl","showLegend"],meta:{hr_name:"density_mapbox"}}});var gHe=ye((Vxr,pHe)=>{"use strict";pHe.exports=vHe()});var yHe=ye((Gxr,mHe)=>{mHe.exports={version:8,name:"orto",metadata:{"maputnik:renderer":"mlgljs"},center:[1.537786,41.837539],zoom:12,bearing:0,pitch:0,light:{anchor:"viewport",color:"white",intensity:.4,position:[1.15,45,30]},sources:{ortoEsri:{type:"raster",tiles:["https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}"],tileSize:256,maxzoom:18,attribution:"ESRI &copy; <a 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wJ=ye((Zxr,FHe)=>{"use strict";var JWt=["Metropolis Black Italic","Metropolis Black","Metropolis Bold Italic","Metropolis Bold","Metropolis Extra Bold Italic","Metropolis Extra Bold","Metropolis Extra Light Italic","Metropolis Extra Light","Metropolis Light Italic","Metropolis Light","Metropolis Medium Italic","Metropolis Medium","Metropolis Regular Italic","Metropolis Regular","Metropolis Semi Bold Italic","Metropolis Semi Bold","Metropolis Thin Italic","Metropolis Thin","Open Sans Bold Italic","Open Sans Bold","Open Sans Extrabold Italic","Open Sans Extrabold","Open Sans Italic","Open Sans Light Italic","Open Sans Light","Open Sans Regular","Open Sans Semibold Italic","Open Sans Semibold","Klokantech Noto Sans Bold","Klokantech Noto Sans CJK Bold","Klokantech Noto Sans CJK Regular","Klokantech Noto Sans Italic","Klokantech Noto Sans Regular"];FHe.exports={isSupportedFont:function(e){return JWt.indexOf(e)!==-1}}});var qHe=ye((Yxr,OHe)=>{"use strict";var 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this.overwriteGetter("lab",ji(this.rgb))}overwriteGetter(A,F){return Object.defineProperty(this,A,{value:F}),F}toString(){let[A,F,W,re]=this.rgb;return`rgba(${[A,F,W].map(fe=>Math.round(255*fe)).join(",")},${re})`}}nr.black=new nr(0,0,0,1),nr.white=new nr(1,1,1,1),nr.transparent=new nr(0,0,0,0),nr.red=new nr(1,0,0,1);class Er{constructor(A,F,W){this.sensitivity=A?F?"variant":"case":F?"accent":"base",this.locale=W,this.collator=new Intl.Collator(this.locale?this.locale:[],{sensitivity:this.sensitivity,usage:"search"})}compare(A,F){return this.collator.compare(A,F)}resolvedLocale(){return new Intl.Collator(this.locale?this.locale:[]).resolvedOptions().locale}}class Xr{constructor(A,F,W,re,fe){this.text=A,this.image=F,this.scale=W,this.fontStack=re,this.textColor=fe}}class ri{constructor(A){this.sections=A}static fromString(A){return new ri([new Xr(A,null,null,null,null)])}isEmpty(){return 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A}possiblyEvaluate(A,F,W){let re=new Ac(this._properties);for(let fe of Object.keys(this._values))re._values[fe]=this._values[fe].possiblyEvaluate(A,F,W);return re}}class xu{constructor(A,F,W){this.property=A,this.value=F,this.parameters=W}isConstant(){return this.value.kind==="constant"}constantOr(A){return this.value.kind==="constant"?this.value.value:A}evaluate(A,F,W,re){return this.property.evaluate(this.value,this.parameters,A,F,W,re)}}class Ac{constructor(A){this._properties=A,this._values=Object.create(A.defaultPossiblyEvaluatedValues)}get(A){return this._values[A]}}class Ua{constructor(A){this.specification=A}possiblyEvaluate(A,F){if(A.isDataDriven())throw new Error("Value should not be data driven");return A.expression.evaluate(F)}interpolate(A,F,W){let re=Lo[this.specification.type];return re?re(A,F,W):A}}class oo{constructor(A,F){this.specification=A,this.overrides=F}possiblyEvaluate(A,F,W,re){return new xu(this,A.expression.kind==="constant"||A.expression.kind==="camera"?{kind:"constant",value:A.expression.evaluate(F,null,{},W,re)}:A.expression,F)}interpolate(A,F,W){if(A.value.kind!=="constant"||F.value.kind!=="constant")return A;if(A.value.value===void 0||F.value.value===void 0)return new xu(this,{kind:"constant",value:void 0},A.parameters);let re=Lo[this.specification.type];if(re){let fe=re(A.value.value,F.value.value,W);return new xu(this,{kind:"constant",value:fe},A.parameters)}return A}evaluate(A,F,W,re,fe,pe){return A.kind==="constant"?A.value:A.evaluate(F,W,re,fe,pe)}}class Vc extends oo{possiblyEvaluate(A,F,W,re){if(A.value===void 0)return new xu(this,{kind:"constant",value:void 0},F);if(A.expression.kind==="constant"){let fe=A.expression.evaluate(F,null,{},W,re),pe=A.property.specification.type==="resolvedImage"&&typeof fe!="string"?fe.name:fe,ze=this._calculate(pe,pe,pe,F);return new xu(this,{kind:"constant",value:ze},F)}if(A.expression.kind==="camera"){let fe=this._calculate(A.expression.evaluate({zoom:F.zoom-1}),A.expression.evaluate({zoom:F.zoom}),A.expression.evaluate({zoom:F.zoom+1}),F);return new xu(this,{kind:"constant",value:fe},F)}return new xu(this,A.expression,F)}evaluate(A,F,W,re,fe,pe){if(A.kind==="source"){let ze=A.evaluate(F,W,re,fe,pe);return this._calculate(ze,ze,ze,F)}return A.kind==="composite"?this._calculate(A.evaluate({zoom:Math.floor(F.zoom)-1},W,re),A.evaluate({zoom:Math.floor(F.zoom)},W,re),A.evaluate({zoom:Math.floor(F.zoom)+1},W,re),F):A.value}_calculate(A,F,W,re){return re.zoom>re.zoomHistory.lastIntegerZoom?{from:A,to:F}:{from:W,to:F}}interpolate(A){return A}}class hc{constructor(A){this.specification=A}possiblyEvaluate(A,F,W,re){if(A.value!==void 0){if(A.expression.kind==="constant"){let fe=A.expression.evaluate(F,null,{},W,re);return this._calculate(fe,fe,fe,F)}return this._calculate(A.expression.evaluate(new rs(Math.floor(F.zoom-1),F)),A.expression.evaluate(new rs(Math.floor(F.zoom),F)),A.expression.evaluate(new rs(Math.floor(F.zoom+1),F)),F)}}_calculate(A,F,W,re){return re.zoom>re.zoomHistory.lastIntegerZoom?{from:A,to:F}:{from:W,to:F}}interpolate(A){return A}}class Ku{constructor(A){this.specification=A}possiblyEvaluate(A,F,W,re){return!!A.expression.evaluate(F,null,{},W,re)}interpolate(){return!1}}class ue{constructor(A){this.properties=A,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[];for(let F in A){let W=A[F];W.specification.overridable&&this.overridableProperties.push(F);let re=this.defaultPropertyValues[F]=new $l(W,void 0),fe=this.defaultTransitionablePropertyValues[F]=new Cu(W);this.defaultTransitioningPropertyValues[F]=fe.untransitioned(),this.defaultPossiblyEvaluatedValues[F]=re.possiblyEvaluate({})}}}Fi("DataDrivenProperty",oo),Fi("DataConstantProperty",Ua),Fi("CrossFadedDataDrivenProperty",Vc),Fi("CrossFadedProperty",hc),Fi("ColorRampProperty",Ku);let w="-transition";class B extends Re{constructor(A,F){if(super(),this.id=A.id,this.type=A.type,this._featureFilter={filter:()=>!0,needGeometry:!1},A.type!=="custom"&&(this.metadata=A.metadata,this.minzoom=A.minzoom,this.maxzoom=A.maxzoom,A.type!=="background"&&(this.source=A.source,this.sourceLayer=A["source-layer"],this.filter=A.filter),F.layout&&(this._unevaluatedLayout=new Uc(F.layout)),F.paint)){this._transitionablePaint=new Yu(F.paint);for(let W in A.paint)this.setPaintProperty(W,A.paint[W],{validate:!1});for(let W in A.layout)this.setLayoutProperty(W,A.layout[W],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new Ac(F.paint)}}getCrossfadeParameters(){return this._crossfadeParameters}getLayoutProperty(A){return A==="visibility"?this.visibility:this._unevaluatedLayout.getValue(A)}setLayoutProperty(A,F,W={}){F!=null&&this._validate(Nn,`layers.${this.id}.layout.${A}`,A,F,W)||(A!=="visibility"?this._unevaluatedLayout.setValue(A,F):this.visibility=F)}getPaintProperty(A){return A.endsWith(w)?this._transitionablePaint.getTransition(A.slice(0,-11)):this._transitionablePaint.getValue(A)}setPaintProperty(A,F,W={}){if(F!=null&&this._validate(wn,`layers.${this.id}.paint.${A}`,A,F,W))return!1;if(A.endsWith(w))return this._transitionablePaint.setTransition(A.slice(0,-11),F||void 0),!1;{let re=this._transitionablePaint._values[A],fe=re.property.specification["property-type"]==="cross-faded-data-driven",pe=re.value.isDataDriven(),ze=re.value;this._transitionablePaint.setValue(A,F),this._handleSpecialPaintPropertyUpdate(A);let Ke=this._transitionablePaint._values[A].value;return Ke.isDataDriven()||pe||fe||this._handleOverridablePaintPropertyUpdate(A,ze,Ke)}}_handleSpecialPaintPropertyUpdate(A){}_handleOverridablePaintPropertyUpdate(A,F,W){return!1}isHidden(A){return!!(this.minzoom&&A<this.minzoom)||!!(this.maxzoom&&A>=this.maxzoom)||this.visibility==="none"}updateTransitions(A){this._transitioningPaint=this._transitionablePaint.transitioned(A,this._transitioningPaint)}hasTransition(){return this._transitioningPaint.hasTransition()}recalculate(A,F){A.getCrossfadeParameters&&(this._crossfadeParameters=A.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(A,void 0,F)),this.paint=this._transitioningPaint.possiblyEvaluate(A,void 0,F)}serialize(){let A={id:this.id,type:this.type,source:this.source,"source-layer":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(A.layout=A.layout||{},A.layout.visibility=this.visibility),M(A,(F,W)=>!(F===void 0||W==="layout"&&!Object.keys(F).length||W==="paint"&&!Object.keys(F).length))}_validate(A,F,W,re,fe={}){return(!fe||fe.validate!==!1)&&Yi(this,A.call(ki,{key:F,layerType:this.type,objectKey:W,value:re,styleSpec:ce,style:{glyphs:!0,sprite:!0}}))}is3D(){return!1}isTileClipped(){return!1}hasOffscreenPass(){return!1}resize(){}isStateDependent(){for(let A in this.paint._values){let F=this.paint.get(A);if(F instanceof xu&&Nl(F.property.specification)&&(F.value.kind==="source"||F.value.kind==="composite")&&F.value.isStateDependent)return!0}return!1}}let Q={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array};class ee{constructor(A,F){this._structArray=A,this._pos1=F*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8}}class le{constructor(){this.isTransferred=!1,this.capacity=-1,this.resize(0)}static serialize(A,F){return A._trim(),F&&(A.isTransferred=!0,F.push(A.arrayBuffer)),{length:A.length,arrayBuffer:A.arrayBuffer}}static deserialize(A){let F=Object.create(this.prototype);return F.arrayBuffer=A.arrayBuffer,F.length=A.length,F.capacity=A.arrayBuffer.byteLength/F.bytesPerElement,F._refreshViews(),F}_trim(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())}clear(){this.length=0}resize(A){this.reserve(A),this.length=A}reserve(A){if(A>this.capacity){this.capacity=Math.max(A,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);let F=this.uint8;this._refreshViews(),F&&this.uint8.set(F)}}_refreshViews(){throw new Error("_refreshViews() must be implemented by each concrete StructArray layout")}}function qe(R,A=1){let F=0,W=0;return{members:R.map(re=>{let fe=Q[re.type].BYTES_PER_ELEMENT,pe=F=Xe(F,Math.max(A,fe)),ze=re.components||1;return W=Math.max(W,fe),F+=fe*ze,{name:re.name,type:re.type,components:ze,offset:pe}}),size:Xe(F,Math.max(W,A)),alignment:A}}function Xe(R,A){return Math.ceil(R/A)*A}class ot extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F){let W=this.length;return this.resize(W+1),this.emplace(W,A,F)}emplace(A,F,W){let re=2*A;return this.int16[re+0]=F,this.int16[re+1]=W,A}}ot.prototype.bytesPerElement=4,Fi("StructArrayLayout2i4",ot);class Tt extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W){let re=this.length;return this.resize(re+1),this.emplace(re,A,F,W)}emplace(A,F,W,re){let fe=3*A;return this.int16[fe+0]=F,this.int16[fe+1]=W,this.int16[fe+2]=re,A}}Tt.prototype.bytesPerElement=6,Fi("StructArrayLayout3i6",Tt);class Yt extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,re){let fe=this.length;return this.resize(fe+1),this.emplace(fe,A,F,W,re)}emplace(A,F,W,re,fe){let pe=4*A;return this.int16[pe+0]=F,this.int16[pe+1]=W,this.int16[pe+2]=re,this.int16[pe+3]=fe,A}}Yt.prototype.bytesPerElement=8,Fi("StructArrayLayout4i8",Yt);class Kt extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,re,fe,pe){let ze=this.length;return this.resize(ze+1),this.emplace(ze,A,F,W,re,fe,pe)}emplace(A,F,W,re,fe,pe,ze){let Ke=6*A;return this.int16[Ke+0]=F,this.int16[Ke+1]=W,this.int16[Ke+2]=re,this.int16[Ke+3]=fe,this.int16[Ke+4]=pe,this.int16[Ke+5]=ze,A}}Kt.prototype.bytesPerElement=12,Fi("StructArrayLayout2i4i12",Kt);class xr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,re,fe,pe){let ze=this.length;return this.resize(ze+1),this.emplace(ze,A,F,W,re,fe,pe)}emplace(A,F,W,re,fe,pe,ze){let Ke=4*A,ct=8*A;return this.int16[Ke+0]=F,this.int16[Ke+1]=W,this.uint8[ct+4]=re,this.uint8[ct+5]=fe,this.uint8[ct+6]=pe,this.uint8[ct+7]=ze,A}}xr.prototype.bytesPerElement=8,Fi("StructArrayLayout2i4ub8",xr);class Ir extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F){let W=this.length;return this.resize(W+1),this.emplace(W,A,F)}emplace(A,F,W){let re=2*A;return this.float32[re+0]=F,this.float32[re+1]=W,A}}Ir.prototype.bytesPerElement=8,Fi("StructArrayLayout2f8",Ir);class ve extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W,re,fe,pe,ze,Ke,ct,Lt){let $t=this.length;return this.resize($t+1),this.emplace($t,A,F,W,re,fe,pe,ze,Ke,ct,Lt)}emplace(A,F,W,re,fe,pe,ze,Ke,ct,Lt,$t){let fr=10*A;return this.uint16[fr+0]=F,this.uint16[fr+1]=W,this.uint16[fr+2]=re,this.uint16[fr+3]=fe,this.uint16[fr+4]=pe,this.uint16[fr+5]=ze,this.uint16[fr+6]=Ke,this.uint16[fr+7]=ct,this.uint16[fr+8]=Lt,this.uint16[fr+9]=$t,A}}ve.prototype.bytesPerElement=20,Fi("StructArrayLayout10ui20",ve);class be extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W,re,fe,pe,ze,Ke,ct,Lt,$t,fr){let mr=this.length;return this.resize(mr+1),this.emplace(mr,A,F,W,re,fe,pe,ze,Ke,ct,Lt,$t,fr)}emplace(A,F,W,re,fe,pe,ze,Ke,ct,Lt,$t,fr,mr){let Pr=12*A;return this.int16[Pr+0]=F,this.int16[Pr+1]=W,this.int16[Pr+2]=re,this.int16[Pr+3]=fe,this.uint16[Pr+4]=pe,this.uint16[Pr+5]=ze,this.uint16[Pr+6]=Ke,this.uint16[Pr+7]=ct,this.int16[Pr+8]=Lt,this.int16[Pr+9]=$t,this.int16[Pr+10]=fr,this.int16[Pr+11]=mr,A}}be.prototype.bytesPerElement=24,Fi("StructArrayLayout4i4ui4i24",be);class De extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W){let re=this.length;return this.resize(re+1),this.emplace(re,A,F,W)}emplace(A,F,W,re){let fe=3*A;return this.float32[fe+0]=F,this.float32[fe+1]=W,this.float32[fe+2]=re,A}}De.prototype.bytesPerElement=12,Fi("StructArrayLayout3f12",De);class Be extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)}emplaceBack(A){let F=this.length;return this.resize(F+1),this.emplace(F,A)}emplace(A,F){return this.uint32[1*A+0]=F,A}}Be.prototype.bytesPerElement=4,Fi("StructArrayLayout1ul4",Be);class et extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W,re,fe,pe,ze,Ke,ct){let Lt=this.length;return this.resize(Lt+1),this.emplace(Lt,A,F,W,re,fe,pe,ze,Ke,ct)}emplace(A,F,W,re,fe,pe,ze,Ke,ct,Lt){let $t=10*A,fr=5*A;return this.int16[$t+0]=F,this.int16[$t+1]=W,this.int16[$t+2]=re,this.int16[$t+3]=fe,this.int16[$t+4]=pe,this.int16[$t+5]=ze,this.uint32[fr+3]=Ke,this.uint16[$t+8]=ct,this.uint16[$t+9]=Lt,A}}et.prototype.bytesPerElement=20,Fi("StructArrayLayout6i1ul2ui20",et);class We extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,re,fe,pe){let ze=this.length;return this.resize(ze+1),this.emplace(ze,A,F,W,re,fe,pe)}emplace(A,F,W,re,fe,pe,ze){let Ke=6*A;return this.int16[Ke+0]=F,this.int16[Ke+1]=W,this.int16[Ke+2]=re,this.int16[Ke+3]=fe,this.int16[Ke+4]=pe,this.int16[Ke+5]=ze,A}}We.prototype.bytesPerElement=12,Fi("StructArrayLayout2i2i2i12",We);class it extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,re,fe){let pe=this.length;return this.resize(pe+1),this.emplace(pe,A,F,W,re,fe)}emplace(A,F,W,re,fe,pe){let ze=4*A,Ke=8*A;return this.float32[ze+0]=F,this.float32[ze+1]=W,this.float32[ze+2]=re,this.int16[Ke+6]=fe,this.int16[Ke+7]=pe,A}}it.prototype.bytesPerElement=16,Fi("StructArrayLayout2f1f2i16",it);class Ft extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,re,fe,pe){let ze=this.length;return this.resize(ze+1),this.emplace(ze,A,F,W,re,fe,pe)}emplace(A,F,W,re,fe,pe,ze){let Ke=16*A,ct=4*A,Lt=8*A;return this.uint8[Ke+0]=F,this.uint8[Ke+1]=W,this.float32[ct+1]=re,this.float32[ct+2]=fe,this.int16[Lt+6]=pe,this.int16[Lt+7]=ze,A}}Ft.prototype.bytesPerElement=16,Fi("StructArrayLayout2ub2f2i16",Ft);class Ht extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W){let re=this.length;return this.resize(re+1),this.emplace(re,A,F,W)}emplace(A,F,W,re){let fe=3*A;return this.uint16[fe+0]=F,this.uint16[fe+1]=W,this.uint16[fe+2]=re,A}}Ht.prototype.bytesPerElement=6,Fi("StructArrayLayout3ui6",Ht);class tr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W,re,fe,pe,ze,Ke,ct,Lt,$t,fr,mr,Pr,zr,ui,yi){let vn=this.length;return this.resize(vn+1),this.emplace(vn,A,F,W,re,fe,pe,ze,Ke,ct,Lt,$t,fr,mr,Pr,zr,ui,yi)}emplace(A,F,W,re,fe,pe,ze,Ke,ct,Lt,$t,fr,mr,Pr,zr,ui,yi,vn){let zi=24*A,un=12*A,Tn=48*A;return this.int16[zi+0]=F,this.int16[zi+1]=W,this.uint16[zi+2]=re,this.uint16[zi+3]=fe,this.uint32[un+2]=pe,this.uint32[un+3]=ze,this.uint32[un+4]=Ke,this.uint16[zi+10]=ct,this.uint16[zi+11]=Lt,this.uint16[zi+12]=$t,this.float32[un+7]=fr,this.float32[un+8]=mr,this.uint8[Tn+36]=Pr,this.uint8[Tn+37]=zr,this.uint8[Tn+38]=ui,this.uint32[un+10]=yi,this.int16[zi+22]=vn,A}}tr.prototype.bytesPerElement=48,Fi("StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48",tr);class dr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W,re,fe,pe,ze,Ke,ct,Lt,$t,fr,mr,Pr,zr,ui,yi,vn,zi,un,Tn,pa,ro,Vo,Xa,sa,Mo,fo){let lo=this.length;return this.resize(lo+1),this.emplace(lo,A,F,W,re,fe,pe,ze,Ke,ct,Lt,$t,fr,mr,Pr,zr,ui,yi,vn,zi,un,Tn,pa,ro,Vo,Xa,sa,Mo,fo)}emplace(A,F,W,re,fe,pe,ze,Ke,ct,Lt,$t,fr,mr,Pr,zr,ui,yi,vn,zi,un,Tn,pa,ro,Vo,Xa,sa,Mo,fo,lo){let Xn=32*A,Ro=16*A;return this.int16[Xn+0]=F,this.int16[Xn+1]=W,this.int16[Xn+2]=re,this.int16[Xn+3]=fe,this.int16[Xn+4]=pe,this.int16[Xn+5]=ze,this.int16[Xn+6]=Ke,this.int16[Xn+7]=ct,this.uint16[Xn+8]=Lt,this.uint16[Xn+9]=$t,this.uint16[Xn+10]=fr,this.uint16[Xn+11]=mr,this.uint16[Xn+12]=Pr,this.uint16[Xn+13]=zr,this.uint16[Xn+14]=ui,this.uint16[Xn+15]=yi,this.uint16[Xn+16]=vn,this.uint16[Xn+17]=zi,this.uint16[Xn+18]=un,this.uint16[Xn+19]=Tn,this.uint16[Xn+20]=pa,this.uint16[Xn+21]=ro,this.uint16[Xn+22]=Vo,this.uint32[Ro+12]=Xa,this.float32[Ro+13]=sa,this.float32[Ro+14]=Mo,this.uint16[Xn+30]=fo,this.uint16[Xn+31]=lo,A}}dr.prototype.bytesPerElement=64,Fi("StructArrayLayout8i15ui1ul2f2ui64",dr);class Sr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A){let F=this.length;return this.resize(F+1),this.emplace(F,A)}emplace(A,F){return this.float32[1*A+0]=F,A}}Sr.prototype.bytesPerElement=4,Fi("StructArrayLayout1f4",Sr);class Or extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W){let re=this.length;return this.resize(re+1),this.emplace(re,A,F,W)}emplace(A,F,W,re){let fe=3*A;return this.uint16[6*A+0]=F,this.float32[fe+1]=W,this.float32[fe+2]=re,A}}Or.prototype.bytesPerElement=12,Fi("StructArrayLayout1ui2f12",Or);class Wr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W){let re=this.length;return this.resize(re+1),this.emplace(re,A,F,W)}emplace(A,F,W,re){let fe=4*A;return this.uint32[2*A+0]=F,this.uint16[fe+2]=W,this.uint16[fe+3]=re,A}}Wr.prototype.bytesPerElement=8,Fi("StructArrayLayout1ul2ui8",Wr);class ni extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F){let W=this.length;return this.resize(W+1),this.emplace(W,A,F)}emplace(A,F,W){let re=2*A;return this.uint16[re+0]=F,this.uint16[re+1]=W,A}}ni.prototype.bytesPerElement=4,Fi("StructArrayLayout2ui4",ni);class Pi extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A){let F=this.length;return this.resize(F+1),this.emplace(F,A)}emplace(A,F){return this.uint16[1*A+0]=F,A}}Pi.prototype.bytesPerElement=2,Fi("StructArrayLayout1ui2",Pi);class cn extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W,re){let fe=this.length;return this.resize(fe+1),this.emplace(fe,A,F,W,re)}emplace(A,F,W,re,fe){let pe=4*A;return this.float32[pe+0]=F,this.float32[pe+1]=W,this.float32[pe+2]=re,this.float32[pe+3]=fe,A}}cn.prototype.bytesPerElement=16,Fi("StructArrayLayout4f16",cn);class ln extends ee{get anchorPointX(){return this._structArray.int16[this._pos2+0]}get anchorPointY(){return this._structArray.int16[this._pos2+1]}get x1(){return this._structArray.int16[this._pos2+2]}get y1(){return this._structArray.int16[this._pos2+3]}get x2(){return this._structArray.int16[this._pos2+4]}get y2(){return this._structArray.int16[this._pos2+5]}get featureIndex(){return this._structArray.uint32[this._pos4+3]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+8]}get bucketIndex(){return this._structArray.uint16[this._pos2+9]}get anchorPoint(){return new u(this.anchorPointX,this.anchorPointY)}}ln.prototype.size=20;class Cn extends et{get(A){return new ln(this,A)}}Fi("CollisionBoxArray",Cn);class Kn extends ee{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get glyphStartIndex(){return this._structArray.uint16[this._pos2+2]}get numGlyphs(){return this._structArray.uint16[this._pos2+3]}get vertexStartIndex(){return this._structArray.uint32[this._pos4+2]}get lineStartIndex(){return this._structArray.uint32[this._pos4+3]}get lineLength(){return this._structArray.uint32[this._pos4+4]}get segment(){return this._structArray.uint16[this._pos2+10]}get lowerSize(){return this._structArray.uint16[this._pos2+11]}get upperSize(){return this._structArray.uint16[this._pos2+12]}get lineOffsetX(){return this._structArray.float32[this._pos4+7]}get lineOffsetY(){return this._structArray.float32[this._pos4+8]}get writingMode(){return this._structArray.uint8[this._pos1+36]}get placedOrientation(){return this._structArray.uint8[this._pos1+37]}set placedOrientation(A){this._structArray.uint8[this._pos1+37]=A}get hidden(){return this._structArray.uint8[this._pos1+38]}set hidden(A){this._structArray.uint8[this._pos1+38]=A}get crossTileID(){return this._structArray.uint32[this._pos4+10]}set crossTileID(A){this._structArray.uint32[this._pos4+10]=A}get associatedIconIndex(){return this._structArray.int16[this._pos2+22]}}Kn.prototype.size=48;class Ta extends tr{get(A){return new Kn(this,A)}}Fi("PlacedSymbolArray",Ta);class fa extends ee{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get rightJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+2]}get centerJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+3]}get leftJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+4]}get verticalPlacedTextSymbolIndex(){return this._structArray.int16[this._pos2+5]}get placedIconSymbolIndex(){return this._structArray.int16[this._pos2+6]}get verticalPlacedIconSymbolIndex(){return this._structArray.int16[this._pos2+7]}get key(){return this._structArray.uint16[this._pos2+8]}get textBoxStartIndex(){return this._structArray.uint16[this._pos2+9]}get textBoxEndIndex(){return this._structArray.uint16[this._pos2+10]}get verticalTextBoxStartIndex(){return this._structArray.uint16[this._pos2+11]}get verticalTextBoxEndIndex(){return this._structArray.uint16[this._pos2+12]}get iconBoxStartIndex(){return this._structArray.uint16[this._pos2+13]}get iconBoxEndIndex(){return this._structArray.uint16[this._pos2+14]}get verticalIconBoxStartIndex(){return this._structArray.uint16[this._pos2+15]}get verticalIconBoxEndIndex(){return this._structArray.uint16[this._pos2+16]}get featureIndex(){return this._structArray.uint16[this._pos2+17]}get numHorizontalGlyphVertices(){return this._structArray.uint16[this._pos2+18]}get numVerticalGlyphVertices(){return this._structArray.uint16[this._pos2+19]}get numIconVertices(){return this._structArray.uint16[this._pos2+20]}get numVerticalIconVertices(){return this._structArray.uint16[this._pos2+21]}get useRuntimeCollisionCircles(){return this._structArray.uint16[this._pos2+22]}get crossTileID(){return this._structArray.uint32[this._pos4+12]}set crossTileID(A){this._structArray.uint32[this._pos4+12]=A}get textBoxScale(){return this._structArray.float32[this._pos4+13]}get collisionCircleDiameter(){return this._structArray.float32[this._pos4+14]}get textAnchorOffsetStartIndex(){return this._structArray.uint16[this._pos2+30]}get textAnchorOffsetEndIndex(){return this._structArray.uint16[this._pos2+31]}}fa.prototype.size=64;class $a extends dr{get(A){return new fa(this,A)}}Fi("SymbolInstanceArray",$a);class Co extends Sr{getoffsetX(A){return this.float32[1*A+0]}}Fi("GlyphOffsetArray",Co);class Qa extends Tt{getx(A){return this.int16[3*A+0]}gety(A){return this.int16[3*A+1]}gettileUnitDistanceFromAnchor(A){return this.int16[3*A+2]}}Fi("SymbolLineVertexArray",Qa);class mo extends ee{get textAnchor(){return this._structArray.uint16[this._pos2+0]}get textOffset0(){return this._structArray.float32[this._pos4+1]}get textOffset1(){return this._structArray.float32[this._pos4+2]}}mo.prototype.size=12;class Bo extends Or{get(A){return new mo(this,A)}}Fi("TextAnchorOffsetArray",Bo);class Ps extends ee{get featureIndex(){return this._structArray.uint32[this._pos4+0]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+2]}get bucketIndex(){return this._structArray.uint16[this._pos2+3]}}Ps.prototype.size=8;class Ts extends Wr{get(A){return new Ps(this,A)}}Fi("FeatureIndexArray",Ts);class wo extends ot{}class To extends ot{}class hl extends ot{}class Ul extends Kt{}class Lu extends xr{}class au extends Ir{}class Js extends ve{}class Ql extends be{}class dc extends De{}class Tl extends Be{}class Al extends We{}class X extends Ft{}class se extends Ht{}class Te extends ni{}let Ne=qe([{name:"a_pos",components:2,type:"Int16"}],4),{members:He}=Ne;class Ye{constructor(A=[]){this.segments=A}prepareSegment(A,F,W,re){let fe=this.segments[this.segments.length-1];return A>Ye.MAX_VERTEX_ARRAY_LENGTH&&T(`Max vertices per segment is ${Ye.MAX_VERTEX_ARRAY_LENGTH}: bucket requested ${A}`),(!fe||fe.vertexLength+A>Ye.MAX_VERTEX_ARRAY_LENGTH||fe.sortKey!==re)&&(fe={vertexOffset:F.length,primitiveOffset:W.length,vertexLength:0,primitiveLength:0},re!==void 0&&(fe.sortKey=re),this.segments.push(fe)),fe}get(){return this.segments}destroy(){for(let A of this.segments)for(let F in A.vaos)A.vaos[F].destroy()}static simpleSegment(A,F,W,re){return new Ye([{vertexOffset:A,primitiveOffset:F,vertexLength:W,primitiveLength:re,vaos:{},sortKey:0}])}}function kt(R,A){return 256*(R=E(Math.floor(R),0,255))+E(Math.floor(A),0,255)}Ye.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,Fi("SegmentVector",Ye);let nt=qe([{name:"a_pattern_from",components:4,type:"Uint16"},{name:"a_pattern_to",components:4,type:"Uint16"},{name:"a_pixel_ratio_from",components:1,type:"Uint16"},{name:"a_pixel_ratio_to",components:1,type:"Uint16"}]);var jt={exports:{}},gr={exports:{}};gr.exports=function(R,A){var F,W,re,fe,pe,ze,Ke,ct;for(W=R.length-(F=3&R.length),re=A,pe=3432918353,ze=461845907,ct=0;ct<W;)Ke=255&R.charCodeAt(ct)|(255&R.charCodeAt(++ct))<<8|(255&R.charCodeAt(++ct))<<16|(255&R.charCodeAt(++ct))<<24,++ct,re=27492+(65535&(fe=5*(65535&(re=(re^=Ke=(65535&(Ke=(Ke=(65535&Ke)*pe+(((Ke>>>16)*pe&65535)<<16)&4294967295)<<15|Ke>>>17))*ze+(((Ke>>>16)*ze&65535)<<16)&4294967295)<<13|re>>>19))+((5*(re>>>16)&65535)<<16)&4294967295))+((58964+(fe>>>16)&65535)<<16);switch(Ke=0,F){case 3:Ke^=(255&R.charCodeAt(ct+2))<<16;case 2:Ke^=(255&R.charCodeAt(ct+1))<<8;case 1:re^=Ke=(65535&(Ke=(Ke=(65535&(Ke^=255&R.charCodeAt(ct)))*pe+(((Ke>>>16)*pe&65535)<<16)&4294967295)<<15|Ke>>>17))*ze+(((Ke>>>16)*ze&65535)<<16)&4294967295}return re^=R.length,re=2246822507*(65535&(re^=re>>>16))+((2246822507*(re>>>16)&65535)<<16)&4294967295,re=3266489909*(65535&(re^=re>>>13))+((3266489909*(re>>>16)&65535)<<16)&4294967295,(re^=re>>>16)>>>0};var yr=gr.exports,Hr={exports:{}};Hr.exports=function(R,A){for(var F,W=R.length,re=A^W,fe=0;W>=4;)F=1540483477*(65535&(F=255&R.charCodeAt(fe)|(255&R.charCodeAt(++fe))<<8|(255&R.charCodeAt(++fe))<<16|(255&R.charCodeAt(++fe))<<24))+((1540483477*(F>>>16)&65535)<<16),re=1540483477*(65535&re)+((1540483477*(re>>>16)&65535)<<16)^(F=1540483477*(65535&(F^=F>>>24))+((1540483477*(F>>>16)&65535)<<16)),W-=4,++fe;switch(W){case 3:re^=(255&R.charCodeAt(fe+2))<<16;case 2:re^=(255&R.charCodeAt(fe+1))<<8;case 1:re=1540483477*(65535&(re^=255&R.charCodeAt(fe)))+((1540483477*(re>>>16)&65535)<<16)}return re=1540483477*(65535&(re^=re>>>13))+((1540483477*(re>>>16)&65535)<<16),(re^=re>>>15)>>>0};var qr=yr,_i=Hr.exports;jt.exports=qr,jt.exports.murmur3=qr,jt.exports.murmur2=_i;var bi=o(jt.exports);class Zr{constructor(){this.ids=[],this.positions=[],this.indexed=!1}add(A,F,W,re){this.ids.push(ai(A)),this.positions.push(F,W,re)}getPositions(A){if(!this.indexed)throw new Error("Trying to get index, but feature positions are not indexed");let F=ai(A),W=0,re=this.ids.length-1;for(;W<re;){let pe=W+re>>1;this.ids[pe]>=F?re=pe:W=pe+1}let fe=[];for(;this.ids[W]===F;)fe.push({index:this.positions[3*W],start:this.positions[3*W+1],end:this.positions[3*W+2]}),W++;return fe}static serialize(A,F){let W=new Float64Array(A.ids),re=new Uint32Array(A.positions);return gi(W,re,0,W.length-1),F&&F.push(W.buffer,re.buffer),{ids:W,positions:re}}static deserialize(A){let F=new Zr;return F.ids=A.ids,F.positions=A.positions,F.indexed=!0,F}}function ai(R){let A=+R;return!isNaN(A)&&A<=Number.MAX_SAFE_INTEGER?A:bi(String(R))}function gi(R,A,F,W){for(;F<W;){let re=R[F+W>>1],fe=F-1,pe=W+1;for(;;){do fe++;while(R[fe]<re);do pe--;while(R[pe]>re);if(fe>=pe)break;Ii(R,fe,pe),Ii(A,3*fe,3*pe),Ii(A,3*fe+1,3*pe+1),Ii(A,3*fe+2,3*pe+2)}pe-F<W-pe?(gi(R,A,F,pe),F=pe+1):(gi(R,A,pe+1,W),W=pe)}}function Ii(R,A,F){let W=R[A];R[A]=R[F],R[F]=W}Fi("FeaturePositionMap",Zr);class Si{constructor(A,F){this.gl=A.gl,this.location=F}}class ei extends Si{constructor(A,F){super(A,F),this.current=0}set(A){this.current!==A&&(this.current=A,this.gl.uniform1f(this.location,A))}}class Ln extends Si{constructor(A,F){super(A,F),this.current=[0,0,0,0]}set(A){A[0]===this.current[0]&&A[1]===this.current[1]&&A[2]===this.current[2]&&A[3]===this.current[3]||(this.current=A,this.gl.uniform4f(this.location,A[0],A[1],A[2],A[3]))}}class En extends Si{constructor(A,F){super(A,F),this.current=nr.transparent}set(A){A.r===this.current.r&&A.g===this.current.g&&A.b===this.current.b&&A.a===this.current.a||(this.current=A,this.gl.uniform4f(this.location,A.r,A.g,A.b,A.a))}}let Un=new Float32Array(16);function ia(R){return[kt(255*R.r,255*R.g),kt(255*R.b,255*R.a)]}class Ea{constructor(A,F,W){this.value=A,this.uniformNames=F.map(re=>`u_${re}`),this.type=W}setUniform(A,F,W){A.set(W.constantOr(this.value))}getBinding(A,F,W){return this.type==="color"?new En(A,F):new ei(A,F)}}class Ia{constructor(A,F){this.uniformNames=F.map(W=>`u_${W}`),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1}setConstantPatternPositions(A,F){this.pixelRatioFrom=F.pixelRatio,this.pixelRatioTo=A.pixelRatio,this.patternFrom=F.tlbr,this.patternTo=A.tlbr}setUniform(A,F,W,re){let fe=re==="u_pattern_to"?this.patternTo:re==="u_pattern_from"?this.patternFrom:re==="u_pixel_ratio_to"?this.pixelRatioTo:re==="u_pixel_ratio_from"?this.pixelRatioFrom:null;fe&&A.set(fe)}getBinding(A,F,W){return W.substr(0,9)==="u_pattern"?new Ln(A,F):new ei(A,F)}}class yo{constructor(A,F,W,re){this.expression=A,this.type=W,this.maxValue=0,this.paintVertexAttributes=F.map(fe=>({name:`a_${fe}`,type:"Float32",components:W==="color"?2:1,offset:0})),this.paintVertexArray=new re}populatePaintArray(A,F,W,re,fe){let pe=this.paintVertexArray.length,ze=this.expression.evaluate(new rs(0),F,{},re,[],fe);this.paintVertexArray.resize(A),this._setPaintValue(pe,A,ze)}updatePaintArray(A,F,W,re){let fe=this.expression.evaluate({zoom:0},W,re);this._setPaintValue(A,F,fe)}_setPaintValue(A,F,W){if(this.type==="color"){let re=ia(W);for(let fe=A;fe<F;fe++)this.paintVertexArray.emplace(fe,re[0],re[1])}else{for(let re=A;re<F;re++)this.paintVertexArray.emplace(re,W);this.maxValue=Math.max(this.maxValue,Math.abs(W))}}upload(A){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=A.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))}destroy(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()}}class Da{constructor(A,F,W,re,fe,pe){this.expression=A,this.uniformNames=F.map(ze=>`u_${ze}_t`),this.type=W,this.useIntegerZoom=re,this.zoom=fe,this.maxValue=0,this.paintVertexAttributes=F.map(ze=>({name:`a_${ze}`,type:"Float32",components:W==="color"?4:2,offset:0})),this.paintVertexArray=new pe}populatePaintArray(A,F,W,re,fe){let pe=this.expression.evaluate(new rs(this.zoom),F,{},re,[],fe),ze=this.expression.evaluate(new rs(this.zoom+1),F,{},re,[],fe),Ke=this.paintVertexArray.length;this.paintVertexArray.resize(A),this._setPaintValue(Ke,A,pe,ze)}updatePaintArray(A,F,W,re){let fe=this.expression.evaluate({zoom:this.zoom},W,re),pe=this.expression.evaluate({zoom:this.zoom+1},W,re);this._setPaintValue(A,F,fe,pe)}_setPaintValue(A,F,W,re){if(this.type==="color"){let fe=ia(W),pe=ia(re);for(let ze=A;ze<F;ze++)this.paintVertexArray.emplace(ze,fe[0],fe[1],pe[0],pe[1])}else{for(let fe=A;fe<F;fe++)this.paintVertexArray.emplace(fe,W,re);this.maxValue=Math.max(this.maxValue,Math.abs(W),Math.abs(re))}}upload(A){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=A.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))}destroy(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()}setUniform(A,F){let W=this.useIntegerZoom?Math.floor(F.zoom):F.zoom,re=E(this.expression.interpolationFactor(W,this.zoom,this.zoom+1),0,1);A.set(re)}getBinding(A,F,W){return new ei(A,F)}}class go{constructor(A,F,W,re,fe,pe){this.expression=A,this.type=F,this.useIntegerZoom=W,this.zoom=re,this.layerId=pe,this.zoomInPaintVertexArray=new fe,this.zoomOutPaintVertexArray=new fe}populatePaintArray(A,F,W){let re=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(A),this.zoomOutPaintVertexArray.resize(A),this._setPaintValues(re,A,F.patterns&&F.patterns[this.layerId],W)}updatePaintArray(A,F,W,re,fe){this._setPaintValues(A,F,W.patterns&&W.patterns[this.layerId],fe)}_setPaintValues(A,F,W,re){if(!re||!W)return;let{min:fe,mid:pe,max:ze}=W,Ke=re[fe],ct=re[pe],Lt=re[ze];if(Ke&&ct&&Lt)for(let $t=A;$t<F;$t++)this.zoomInPaintVertexArray.emplace($t,ct.tl[0],ct.tl[1],ct.br[0],ct.br[1],Ke.tl[0],Ke.tl[1],Ke.br[0],Ke.br[1],ct.pixelRatio,Ke.pixelRatio),this.zoomOutPaintVertexArray.emplace($t,ct.tl[0],ct.tl[1],ct.br[0],ct.br[1],Lt.tl[0],Lt.tl[1],Lt.br[0],Lt.br[1],ct.pixelRatio,Lt.pixelRatio)}upload(A){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=A.createVertexBuffer(this.zoomInPaintVertexArray,nt.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=A.createVertexBuffer(this.zoomOutPaintVertexArray,nt.members,this.expression.isStateDependent))}destroy(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()}}class Is{constructor(A,F,W){this.binders={},this._buffers=[];let re=[];for(let fe in A.paint._values){if(!W(fe))continue;let pe=A.paint.get(fe);if(!(pe instanceof xu&&Nl(pe.property.specification)))continue;let ze=Xs(fe,A.type),Ke=pe.value,ct=pe.property.specification.type,Lt=pe.property.useIntegerZoom,$t=pe.property.specification["property-type"],fr=$t==="cross-faded"||$t==="cross-faded-data-driven";if(Ke.kind==="constant")this.binders[fe]=fr?new Ia(Ke.value,ze):new Ea(Ke.value,ze,ct),re.push(`/u_${fe}`);else if(Ke.kind==="source"||fr){let mr=Gn(fe,ct,"source");this.binders[fe]=fr?new go(Ke,ct,Lt,F,mr,A.id):new yo(Ke,ze,ct,mr),re.push(`/a_${fe}`)}else{let mr=Gn(fe,ct,"composite");this.binders[fe]=new Da(Ke,ze,ct,Lt,F,mr),re.push(`/z_${fe}`)}}this.cacheKey=re.sort().join("")}getMaxValue(A){let F=this.binders[A];return F instanceof yo||F instanceof Da?F.maxValue:0}populatePaintArrays(A,F,W,re,fe){for(let pe in this.binders){let ze=this.binders[pe];(ze instanceof yo||ze instanceof Da||ze instanceof go)&&ze.populatePaintArray(A,F,W,re,fe)}}setConstantPatternPositions(A,F){for(let W in this.binders){let re=this.binders[W];re instanceof Ia&&re.setConstantPatternPositions(A,F)}}updatePaintArrays(A,F,W,re,fe){let pe=!1;for(let ze in A){let Ke=F.getPositions(ze);for(let ct of Ke){let Lt=W.feature(ct.index);for(let $t in this.binders){let fr=this.binders[$t];if((fr instanceof yo||fr instanceof Da||fr instanceof go)&&fr.expression.isStateDependent===!0){let mr=re.paint.get($t);fr.expression=mr.value,fr.updatePaintArray(ct.start,ct.end,Lt,A[ze],fe),pe=!0}}}}return pe}defines(){let A=[];for(let F in this.binders){let W=this.binders[F];(W instanceof Ea||W instanceof Ia)&&A.push(...W.uniformNames.map(re=>`#define HAS_UNIFORM_${re}`))}return A}getBinderAttributes(){let A=[];for(let F in this.binders){let W=this.binders[F];if(W instanceof yo||W instanceof Da)for(let re=0;re<W.paintVertexAttributes.length;re++)A.push(W.paintVertexAttributes[re].name);else if(W instanceof go)for(let re=0;re<nt.members.length;re++)A.push(nt.members[re].name)}return A}getBinderUniforms(){let A=[];for(let F in this.binders){let W=this.binders[F];if(W 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ct=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray,A.sortKey),Lt=ct.vertexLength;bu(this.layoutVertexArray,ze,Ke,-1,-1),bu(this.layoutVertexArray,ze,Ke,1,-1),bu(this.layoutVertexArray,ze,Ke,1,1),bu(this.layoutVertexArray,ze,Ke,-1,1),this.indexArray.emplaceBack(Lt,Lt+1,Lt+2),this.indexArray.emplaceBack(Lt,Lt+3,Lt+2),ct.vertexLength+=4,ct.primitiveLength+=2}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,A,W,{},re)}}function Sc(R,A){for(let F=0;F<R.length;F++)if(Ui(A,R[F]))return!0;for(let F=0;F<A.length;F++)if(Ui(R,A[F]))return!0;return!!Rr(R,A)}function Me(R,A,F){return!!Ui(R,A)||!!Nr(A,R,F)}function bt(R,A){if(R.length===1)return mi(A,R[0]);for(let F=0;F<A.length;F++){let W=A[F];for(let re=0;re<W.length;re++)if(Ui(R,W[re]))return!0}for(let F=0;F<R.length;F++)if(mi(A,R[F]))return!0;for(let F=0;F<A.length;F++)if(Rr(R,A[F]))return!0;return!1}function zt(R,A,F){if(R.length>1){if(Rr(R,A))return!0;for(let 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W=A[0],re=A[1],fe=A[2],pe=A[3],ze=A[4],Ke=A[5],ct=A[6],Lt=A[7],$t=A[8],fr=A[9],mr=A[10],Pr=A[11],zr=A[12],ui=A[13],yi=A[14],vn=A[15],zi=F[0],un=F[1],Tn=F[2],pa=F[3];return R[0]=zi*W+un*ze+Tn*$t+pa*zr,R[1]=zi*re+un*Ke+Tn*fr+pa*ui,R[2]=zi*fe+un*ct+Tn*mr+pa*yi,R[3]=zi*pe+un*Lt+Tn*Pr+pa*vn,R[4]=(zi=F[4])*W+(un=F[5])*ze+(Tn=F[6])*$t+(pa=F[7])*zr,R[5]=zi*re+un*Ke+Tn*fr+pa*ui,R[6]=zi*fe+un*ct+Tn*mr+pa*yi,R[7]=zi*pe+un*Lt+Tn*Pr+pa*vn,R[8]=(zi=F[8])*W+(un=F[9])*ze+(Tn=F[10])*$t+(pa=F[11])*zr,R[9]=zi*re+un*Ke+Tn*fr+pa*ui,R[10]=zi*fe+un*ct+Tn*mr+pa*yi,R[11]=zi*pe+un*Lt+Tn*Pr+pa*vn,R[12]=(zi=F[12])*W+(un=F[13])*ze+(Tn=F[14])*$t+(pa=F[15])*zr,R[13]=zi*re+un*Ke+Tn*fr+pa*ui,R[14]=zi*fe+un*ct+Tn*mr+pa*yi,R[15]=zi*pe+un*Lt+Tn*Pr+pa*vn,R}Math.hypot||(Math.hypot=function(){for(var R=0,A=arguments.length;A--;)R+=arguments[A]*arguments[A];return Math.sqrt(R)});var an,Zi=ci;function Bn(R,A,F){var W=A[0],re=A[1],fe=A[2],pe=A[3];return 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ui=$t?zr:li(zr,Ke),yi=mr,vn=Bn([],[zr.x,zr.y,0,1],Ke);if(this.paint.get("circle-pitch-scale")==="viewport"&&this.paint.get("circle-pitch-alignment")==="map"?yi*=vn[3]/pe.cameraToCenterDistance:this.paint.get("circle-pitch-scale")==="map"&&this.paint.get("circle-pitch-alignment")==="viewport"&&(yi*=pe.cameraToCenterDistance/vn[3]),Me(fr,ui,yi))return!0}return!1}}function li(R,A){let F=Bn([],[R.x,R.y,0,1],A);return new u(F[0]/F[3],F[1]/F[3])}class mn extends dl{}let Ji;Fi("HeatmapBucket",mn,{omit:["layers"]});var Vi={get paint(){return Ji=Ji||new ue({"heatmap-radius":new oo(ce.paint_heatmap["heatmap-radius"]),"heatmap-weight":new oo(ce.paint_heatmap["heatmap-weight"]),"heatmap-intensity":new Ua(ce.paint_heatmap["heatmap-intensity"]),"heatmap-color":new Ku(ce.paint_heatmap["heatmap-color"]),"heatmap-opacity":new Ua(ce.paint_heatmap["heatmap-opacity"])})}};function Ni(R,{width:A,height:F},W,re){if(re){if(re instanceof Uint8ClampedArray)re=new Uint8Array(re.buffer);else 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ct=((F.y+Ke)*R.width+F.x)*fe,Lt=((W.y+Ke)*A.width+W.x)*fe;for(let $t=0;$t<re.width*fe;$t++)ze[Lt+$t]=pe[ct+$t]}return A}class na{constructor(A,F){Ni(this,A,1,F)}resize(A){pn(this,A,1)}clone(){return new na({width:this.width,height:this.height},new Uint8Array(this.data))}static copy(A,F,W,re,fe){Vn(A,F,W,re,fe,1)}}class Ki{constructor(A,F){Ni(this,A,4,F)}resize(A){pn(this,A,4)}replace(A,F){F?this.data.set(A):this.data=A instanceof Uint8ClampedArray?new Uint8Array(A.buffer):A}clone(){return new Ki({width:this.width,height:this.height},new Uint8Array(this.data))}static copy(A,F,W,re,fe){Vn(A,F,W,re,fe,4)}}function kn(R){let A={},F=R.resolution||256,W=R.clips?R.clips.length:1,re=R.image||new Ki({width:F,height:W});if(Math.log(F)/Math.LN2%1!=0)throw new Error(`width is not a power of 2 - ${F}`);let fe=(pe,ze,Ke)=>{A[R.evaluationKey]=Ke;let 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Ke=R.readVarint();W=7&Ke,re=Ke>>3}if(re--,W===1||W===2)fe+=R.readSVarint(),pe+=R.readSVarint(),W===1&&(A&&ze.push(A),A=[]),A.push(new Ur(fe,pe));else{if(W!==7)throw new Error("unknown command "+W);A&&A.push(A[0].clone())}}return A&&ze.push(A),ze},fi.prototype.bbox=function(){var R=this._pbf;R.pos=this._geometry;for(var A=R.readVarint()+R.pos,F=1,W=0,re=0,fe=0,pe=1/0,ze=-1/0,Ke=1/0,ct=-1/0;R.pos<A;){if(W<=0){var Lt=R.readVarint();F=7&Lt,W=Lt>>3}if(W--,F===1||F===2)(re+=R.readSVarint())<pe&&(pe=re),re>ze&&(ze=re),(fe+=R.readSVarint())<Ke&&(Ke=fe),fe>ct&&(ct=fe);else if(F!==7)throw new Error("unknown command "+F)}return[pe,Ke,ze,ct]},fi.prototype.toGeoJSON=function(R,A,F){var W,re,fe=this.extent*Math.pow(2,F),pe=this.extent*R,ze=this.extent*A,Ke=this.loadGeometry(),ct=fi.types[this.type];function Lt(mr){for(var Pr=0;Pr<mr.length;Pr++){var zr=mr[Pr];mr[Pr]=[360*(zr.x+pe)/fe-180,360/Math.PI*Math.atan(Math.exp((180-360*(zr.y+ze)/fe)*Math.PI/180))-90]}}switch(this.type){case 1:var $t=[];for(W=0;W<Ke.length;W++)$t[W]=Ke[W][0];Lt(Ke=$t);break;case 2:for(W=0;W<Ke.length;W++)Lt(Ke[W]);break;case 3:for(Ke=function(mr){var Pr=mr.length;if(Pr<=1)return[mr];for(var zr,ui,yi=[],vn=0;vn<Pr;vn++){var zi=gn(mr[vn]);zi!==0&&(ui===void 0&&(ui=zi<0),ui===zi<0?(zr&&yi.push(zr),zr=[mr[vn]]):zr.push(mr[vn]))}return zr&&yi.push(zr),yi}(Ke),W=0;W<Ke.length;W++)for(re=0;re<Ke[W].length;re++)Lt(Ke[W][re])}Ke.length===1?Ke=Ke[0]:ct="Multi"+ct;var fr={type:"Feature",geometry:{type:ct,coordinates:Ke},properties:this.properties};return"id"in this&&(fr.id=this.id),fr};var rn=Di,Ci=Bi;function Bi(R,A){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=R,this._keys=[],this._values=[],this._features=[],R.readFields(Gi,this,A),this.length=this._features.length}function Gi(R,A,F){R===15?A.version=F.readVarint():R===1?A.name=F.readString():R===5?A.extent=F.readVarint():R===2?A._features.push(F.pos):R===3?A._keys.push(F.readString()):R===4&&A._values.push(function(W){for(var re=null,fe=W.readVarint()+W.pos;W.pos<fe;){var pe=W.readVarint()>>3;re=pe===1?W.readString():pe===2?W.readFloat():pe===3?W.readDouble():pe===4?W.readVarint64():pe===5?W.readVarint():pe===6?W.readSVarint():pe===7?W.readBoolean():null}return re}(F))}Bi.prototype.feature=function(R){if(R<0||R>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[R];var A=this._pbf.readVarint()+this._pbf.pos;return new rn(this._pbf,A,this.extent,this._keys,this._values)};var sn=Ci;function zn(R,A,F){if(R===3){var W=new sn(F,F.readVarint()+F.pos);W.length&&(A[W.name]=W)}}br.VectorTile=function(R,A){this.layers=R.readFields(zn,{},A)},br.VectorTileFeature=Di,br.VectorTileLayer=Ci;let Ja=br.VectorTileFeature.types,co=Math.pow(2,13);function ts(R,A,F,W,re,fe,pe,ze){R.emplaceBack(A,F,2*Math.floor(W*co)+pe,re*co*2,fe*co*2,Math.round(ze))}class so{constructor(A){this.zoom=A.zoom,this.overscaling=A.overscaling,this.layers=A.layers,this.layerIds=this.layers.map(F=>F.id),this.index=A.index,this.hasPattern=!1,this.layoutVertexArray=new Ul,this.centroidVertexArray=new wo,this.indexArray=new se,this.programConfigurations=new Ms(A.layers,A.zoom),this.segments=new Ye,this.stateDependentLayerIds=this.layers.filter(F=>F.isStateDependent()).map(F=>F.id)}populate(A,F,W){this.features=[],this.hasPattern=at("fill-extrusion",this.layers,F);for(let{feature:re,id:fe,index:pe,sourceLayerIndex:ze}of A){let Ke=this.layers[0]._featureFilter.needGeometry,ct=Sl(re,Ke);if(!this.layers[0]._featureFilter.filter(new rs(this.zoom),ct,W))continue;let Lt={id:fe,sourceLayerIndex:ze,index:pe,geometry:Ke?ct.geometry:$s(re),properties:re.properties,type:re.type,patterns:{}};this.hasPattern?this.features.push(Ct("fill-extrusion",this.layers,Lt,this.zoom,F)):this.addFeature(Lt,Lt.geometry,pe,W,{}),F.featureIndex.insert(re,Lt.geometry,pe,ze,this.index,!0)}}addFeatures(A,F,W){for(let re of this.features){let{geometry:fe}=re;this.addFeature(re,fe,re.index,F,W)}}update(A,F,W){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(A,F,this.stateDependentLayers,W)}isEmpty(){return this.layoutVertexArray.length===0&&this.centroidVertexArray.length===0}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(A){this.uploaded||(this.layoutVertexBuffer=A.createVertexBuffer(this.layoutVertexArray,ur),this.centroidVertexBuffer=A.createVertexBuffer(this.centroidVertexArray,ht.members,!0),this.indexBuffer=A.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(A),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.centroidVertexBuffer.destroy())}addFeature(A,F,W,re,fe){for(let pe of Cf(F,500)){let ze={x:0,y:0,vertexCount:0},Ke=0;for(let Pr of pe)Ke+=Pr.length;let ct=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray);for(let Pr of pe){if(Pr.length===0||ms(Pr))continue;let zr=0;for(let ui=0;ui<Pr.length;ui++){let yi=Pr[ui];if(ui>=1){let vn=Pr[ui-1];if(!Zo(yi,vn)){ct.vertexLength+4>Ye.MAX_VERTEX_ARRAY_LENGTH&&(ct=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));let zi=yi.sub(vn)._perp()._unit(),un=vn.dist(yi);zr+un>32768&&(zr=0),ts(this.layoutVertexArray,yi.x,yi.y,zi.x,zi.y,0,0,zr),ts(this.layoutVertexArray,yi.x,yi.y,zi.x,zi.y,0,1,zr),ze.x+=2*yi.x,ze.y+=2*yi.y,ze.vertexCount+=2,zr+=un,ts(this.layoutVertexArray,vn.x,vn.y,zi.x,zi.y,0,0,zr),ts(this.layoutVertexArray,vn.x,vn.y,zi.x,zi.y,0,1,zr),ze.x+=2*vn.x,ze.y+=2*vn.y,ze.vertexCount+=2;let Tn=ct.vertexLength;this.indexArray.emplaceBack(Tn,Tn+2,Tn+1),this.indexArray.emplaceBack(Tn+1,Tn+2,Tn+3),ct.vertexLength+=4,ct.primitiveLength+=2}}}}if(ct.vertexLength+Ke>Ye.MAX_VERTEX_ARRAY_LENGTH&&(ct=this.segments.prepareSegment(Ke,this.layoutVertexArray,this.indexArray)),Ja[A.type]!=="Polygon")continue;let Lt=[],$t=[],fr=ct.vertexLength;for(let Pr of pe)if(Pr.length!==0){Pr!==pe[0]&&$t.push(Lt.length/2);for(let zr=0;zr<Pr.length;zr++){let ui=Pr[zr];ts(this.layoutVertexArray,ui.x,ui.y,0,0,1,1,0),ze.x+=ui.x,ze.y+=ui.y,ze.vertexCount+=1,Lt.push(ui.x),Lt.push(ui.y)}}let mr=zo(Lt,$t);for(let Pr=0;Pr<mr.length;Pr+=3)this.indexArray.emplaceBack(fr+mr[Pr],fr+mr[Pr+2],fr+mr[Pr+1]);ct.primitiveLength+=mr.length/3,ct.vertexLength+=Ke;for(let Pr=0;Pr<ze.vertexCount;Pr++){let zr=Math.floor(ze.x/ze.vertexCount),ui=Math.floor(ze.y/ze.vertexCount);this.centroidVertexArray.emplaceBack(zr,ui)}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,A,W,fe,re)}}function Zo(R,A){return R.x===A.x&&(R.x<0||R.x>ja)||R.y===A.y&&(R.y<0||R.y>ja)}function ms(R){return R.every(A=>A.x<0)||R.every(A=>A.x>ja)||R.every(A=>A.y<0)||R.every(A=>A.y>ja)}let ou;Fi("FillExtrusionBucket",so,{omit:["layers","features"]});var Cv={get paint(){return ou=ou||new ue({"fill-extrusion-opacity":new Ua(ce["paint_fill-extrusion"]["fill-extrusion-opacity"]),"fill-extrusion-color":new oo(ce["paint_fill-extrusion"]["fill-extrusion-color"]),"fill-extrusion-translate":new Ua(ce["paint_fill-extrusion"]["fill-extrusion-translate"]),"fill-extrusion-translate-anchor":new Ua(ce["paint_fill-extrusion"]["fill-extrusion-translate-anchor"]),"fill-extrusion-pattern":new Vc(ce["paint_fill-extrusion"]["fill-extrusion-pattern"]),"fill-extrusion-height":new oo(ce["paint_fill-extrusion"]["fill-extrusion-height"]),"fill-extrusion-base":new oo(ce["paint_fill-extrusion"]["fill-extrusion-base"]),"fill-extrusion-vertical-gradient":new Ua(ce["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})}};class Lv extends B{constructor(A){super(A,Cv)}createBucket(A){return new so(A)}queryRadius(){return Hn(this.paint.get("fill-extrusion-translate"))}is3D(){return!0}queryIntersectsFeature(A,F,W,re,fe,pe,ze,Ke){let ct=en(A,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),pe.angle,ze),Lt=this.paint.get("fill-extrusion-height").evaluate(F,W),$t=this.paint.get("fill-extrusion-base").evaluate(F,W),fr=function(Pr,zr,ui,yi){let vn=[];for(let zi of Pr){let un=[zi.x,zi.y,0,1];Bn(un,un,zr),vn.push(new u(un[0]/un[3],un[1]/un[3]))}return vn}(ct,Ke),mr=function(Pr,zr,ui,yi){let vn=[],zi=[],un=yi[8]*zr,Tn=yi[9]*zr,pa=yi[10]*zr,ro=yi[11]*zr,Vo=yi[8]*ui,Xa=yi[9]*ui,sa=yi[10]*ui,Mo=yi[11]*ui;for(let fo of Pr){let lo=[],Xn=[];for(let Ro of fo){let uo=Ro.x,$o=Ro.y,Ju=yi[0]*uo+yi[4]*$o+yi[12],qu=yi[1]*uo+yi[5]*$o+yi[13],Mh=yi[2]*uo+yi[6]*$o+yi[14],Qv=yi[3]*uo+yi[7]*$o+yi[15],ld=Mh+pa,Eh=Qv+ro,Gd=Ju+Vo,Hd=qu+Xa,jd=Mh+sa,Af=Qv+Mo,kh=new u((Ju+un)/Eh,(qu+Tn)/Eh);kh.z=ld/Eh,lo.push(kh);let Ed=new u(Gd/Af,Hd/Af);Ed.z=jd/Af,Xn.push(Ed)}vn.push(lo),zi.push(Xn)}return[vn,zi]}(re,$t,Lt,Ke);return function(Pr,zr,ui){let yi=1/0;bt(ui,zr)&&(yi=Kv(ui,zr[0]));for(let vn=0;vn<zr.length;vn++){let zi=zr[vn],un=Pr[vn];for(let Tn=0;Tn<zi.length-1;Tn++){let pa=zi[Tn],ro=[pa,zi[Tn+1],un[Tn+1],un[Tn],pa];Sc(ui,ro)&&(yi=Math.min(yi,Kv(ui,ro)))}}return yi!==1/0&&yi}(mr[0],mr[1],fr)}}function wd(R,A){return R.x*A.x+R.y*A.y}function Kv(R,A){if(R.length===1){let F=0,W=A[F++],re;for(;!re||W.equals(re);)if(re=A[F++],!re)return 1/0;for(;F<A.length;F++){let fe=A[F],pe=R[0],ze=re.sub(W),Ke=fe.sub(W),ct=pe.sub(W),Lt=wd(ze,ze),$t=wd(ze,Ke),fr=wd(Ke,Ke),mr=wd(ct,ze),Pr=wd(ct,Ke),zr=Lt*fr-$t*$t,ui=(fr*mr-$t*Pr)/zr,yi=(Lt*Pr-$t*mr)/zr,vn=W.z*(1-ui-yi)+re.z*ui+fe.z*yi;if(isFinite(vn))return vn}return 1/0}{let F=1/0;for(let W of A)F=Math.min(F,W.z);return F}}let cg=qe([{name:"a_pos_normal",components:2,type:"Int16"},{name:"a_data",components:4,type:"Uint8"}],4),{members:pp}=cg,Td=qe([{name:"a_uv_x",components:1,type:"Float32"},{name:"a_split_index",components:1,type:"Float32"}]),{members:gp}=Td,Vd=br.VectorTileFeature.types,Ad=Math.cos(Math.PI/180*37.5),Pv=Math.pow(2,14)/.5;class Jv{constructor(A){this.zoom=A.zoom,this.overscaling=A.overscaling,this.layers=A.layers,this.layerIds=this.layers.map(F=>F.id),this.index=A.index,this.hasPattern=!1,this.patternFeatures=[],this.lineClipsArray=[],this.gradients={},this.layers.forEach(F=>{this.gradients[F.id]={}}),this.layoutVertexArray=new Lu,this.layoutVertexArray2=new au,this.indexArray=new se,this.programConfigurations=new Ms(A.layers,A.zoom),this.segments=new Ye,this.maxLineLength=0,this.stateDependentLayerIds=this.layers.filter(F=>F.isStateDependent()).map(F=>F.id)}populate(A,F,W){this.hasPattern=at("line",this.layers,F);let re=this.layers[0].layout.get("line-sort-key"),fe=!re.isConstant(),pe=[];for(let{feature:ze,id:Ke,index:ct,sourceLayerIndex:Lt}of A){let $t=this.layers[0]._featureFilter.needGeometry,fr=Sl(ze,$t);if(!this.layers[0]._featureFilter.filter(new rs(this.zoom),fr,W))continue;let mr=fe?re.evaluate(fr,{},W):void 0,Pr={id:Ke,properties:ze.properties,type:ze.type,sourceLayerIndex:Lt,index:ct,geometry:$t?fr.geometry:$s(ze),patterns:{},sortKey:mr};pe.push(Pr)}fe&&pe.sort((ze,Ke)=>ze.sortKey-Ke.sortKey);for(let ze of pe){let{geometry:Ke,index:ct,sourceLayerIndex:Lt}=ze;if(this.hasPattern){let $t=Ct("line",this.layers,ze,this.zoom,F);this.patternFeatures.push($t)}else this.addFeature(ze,Ke,ct,W,{});F.featureIndex.insert(A[ct].feature,Ke,ct,Lt,this.index)}}update(A,F,W){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(A,F,this.stateDependentLayers,W)}addFeatures(A,F,W){for(let re of this.patternFeatures)this.addFeature(re,re.geometry,re.index,F,W)}isEmpty(){return this.layoutVertexArray.length===0}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(A){this.uploaded||(this.layoutVertexArray2.length!==0&&(this.layoutVertexBuffer2=A.createVertexBuffer(this.layoutVertexArray2,gp)),this.layoutVertexBuffer=A.createVertexBuffer(this.layoutVertexArray,pp),this.indexBuffer=A.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(A),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())}lineFeatureClips(A){if(A.properties&&Object.prototype.hasOwnProperty.call(A.properties,"mapbox_clip_start")&&Object.prototype.hasOwnProperty.call(A.properties,"mapbox_clip_end"))return{start:+A.properties.mapbox_clip_start,end:+A.properties.mapbox_clip_end}}addFeature(A,F,W,re,fe){let pe=this.layers[0].layout,ze=pe.get("line-join").evaluate(A,{}),Ke=pe.get("line-cap"),ct=pe.get("line-miter-limit"),Lt=pe.get("line-round-limit");this.lineClips=this.lineFeatureClips(A);for(let $t of F)this.addLine($t,A,ze,Ke,ct,Lt);this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,A,W,fe,re)}addLine(A,F,W,re,fe,pe){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(let yi=0;yi<A.length-1;yi++)this.totalDistance+=A[yi].dist(A[yi+1]);this.updateScaledDistance(),this.maxLineLength=Math.max(this.maxLineLength,this.totalDistance)}let ze=Vd[F.type]==="Polygon",Ke=A.length;for(;Ke>=2&&A[Ke-1].equals(A[Ke-2]);)Ke--;let ct=0;for(;ct<Ke-1&&A[ct].equals(A[ct+1]);)ct++;if(Ke<(ze?3:2))return;W==="bevel"&&(fe=1.05);let Lt=this.overscaling<=16?15*ja/(512*this.overscaling):0,$t=this.segments.prepareSegment(10*Ke,this.layoutVertexArray,this.indexArray),fr,mr,Pr,zr,ui;this.e1=this.e2=-1,ze&&(fr=A[Ke-2],ui=A[ct].sub(fr)._unit()._perp());for(let yi=ct;yi<Ke;yi++){if(Pr=yi===Ke-1?ze?A[ct+1]:void 0:A[yi+1],Pr&&A[yi].equals(Pr))continue;ui&&(zr=ui),fr&&(mr=fr),fr=A[yi],ui=Pr?Pr.sub(fr)._unit()._perp():zr,zr=zr||ui;let vn=zr.add(ui);vn.x===0&&vn.y===0||vn._unit();let zi=zr.x*ui.x+zr.y*ui.y,un=vn.x*ui.x+vn.y*ui.y,Tn=un!==0?1/un:1/0,pa=2*Math.sqrt(2-2*un),ro=un<Ad&&mr&&Pr,Vo=zr.x*ui.y-zr.y*ui.x>0;if(ro&&yi>ct){let Mo=fr.dist(mr);if(Mo>2*Lt){let fo=fr.sub(fr.sub(mr)._mult(Lt/Mo)._round());this.updateDistance(mr,fo),this.addCurrentVertex(fo,zr,0,0,$t),mr=fo}}let Xa=mr&&Pr,sa=Xa?W:ze?"butt":re;if(Xa&&sa==="round"&&(Tn<pe?sa="miter":Tn<=2&&(sa="fakeround")),sa==="miter"&&Tn>fe&&(sa="bevel"),sa==="bevel"&&(Tn>2&&(sa="flipbevel"),Tn<fe&&(sa="miter")),mr&&this.updateDistance(mr,fr),sa==="miter")vn._mult(Tn),this.addCurrentVertex(fr,vn,0,0,$t);else if(sa==="flipbevel"){if(Tn>100)vn=ui.mult(-1);else{let Mo=Tn*zr.add(ui).mag()/zr.sub(ui).mag();vn._perp()._mult(Mo*(Vo?-1:1))}this.addCurrentVertex(fr,vn,0,0,$t),this.addCurrentVertex(fr,vn.mult(-1),0,0,$t)}else if(sa==="bevel"||sa==="fakeround"){let Mo=-Math.sqrt(Tn*Tn-1),fo=Vo?Mo:0,lo=Vo?0:Mo;if(mr&&this.addCurrentVertex(fr,zr,fo,lo,$t),sa==="fakeround"){let Xn=Math.round(180*pa/Math.PI/20);for(let Ro=1;Ro<Xn;Ro++){let uo=Ro/Xn;if(uo!==.5){let Ju=uo-.5;uo+=uo*Ju*(uo-1)*((1.0904+zi*(zi*(3.55645-1.43519*zi)-3.2452))*Ju*Ju+(.848013+zi*(.215638*zi-1.06021)))}let $o=ui.sub(zr)._mult(uo)._add(zr)._unit()._mult(Vo?-1:1);this.addHalfVertex(fr,$o.x,$o.y,!1,Vo,0,$t)}}Pr&&this.addCurrentVertex(fr,ui,-fo,-lo,$t)}else if(sa==="butt")this.addCurrentVertex(fr,vn,0,0,$t);else if(sa==="square"){let Mo=mr?1:-1;this.addCurrentVertex(fr,vn,Mo,Mo,$t)}else sa==="round"&&(mr&&(this.addCurrentVertex(fr,zr,0,0,$t),this.addCurrentVertex(fr,zr,1,1,$t,!0)),Pr&&(this.addCurrentVertex(fr,ui,-1,-1,$t,!0),this.addCurrentVertex(fr,ui,0,0,$t)));if(ro&&yi<Ke-1){let Mo=fr.dist(Pr);if(Mo>2*Lt){let fo=fr.add(Pr.sub(fr)._mult(Lt/Mo)._round());this.updateDistance(fr,fo),this.addCurrentVertex(fo,ui,0,0,$t),fr=fo}}}}addCurrentVertex(A,F,W,re,fe,pe=!1){let ze=F.y*re-F.x,Ke=-F.y-F.x*re;this.addHalfVertex(A,F.x+F.y*W,F.y-F.x*W,pe,!1,W,fe),this.addHalfVertex(A,ze,Ke,pe,!0,-re,fe),this.distance>Pv/2&&this.totalDistance===0&&(this.distance=0,this.updateScaledDistance(),this.addCurrentVertex(A,F,W,re,fe,pe))}addHalfVertex({x:A,y:F},W,re,fe,pe,ze,Ke){let ct=.5*(this.lineClips?this.scaledDistance*(Pv-1):this.scaledDistance);this.layoutVertexArray.emplaceBack((A<<1)+(fe?1:0),(F<<1)+(pe?1:0),Math.round(63*W)+128,Math.round(63*re)+128,1+(ze===0?0:ze<0?-1:1)|(63&ct)<<2,ct>>6),this.lineClips&&this.layoutVertexArray2.emplaceBack((this.scaledDistance-this.lineClips.start)/(this.lineClips.end-this.lineClips.start),this.lineClipsArray.length);let Lt=Ke.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,Lt),Ke.primitiveLength++),pe?this.e2=Lt:this.e1=Lt}updateScaledDistance(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance}updateDistance(A,F){this.distance+=A.dist(F),this.updateScaledDistance()}}let Iv,ay;Fi("LineBucket",Jv,{omit:["layers","patternFeatures"]});var fg={get paint(){return ay=ay||new ue({"line-opacity":new oo(ce.paint_line["line-opacity"]),"line-color":new oo(ce.paint_line["line-color"]),"line-translate":new Ua(ce.paint_line["line-translate"]),"line-translate-anchor":new Ua(ce.paint_line["line-translate-anchor"]),"line-width":new oo(ce.paint_line["line-width"]),"line-gap-width":new oo(ce.paint_line["line-gap-width"]),"line-offset":new oo(ce.paint_line["line-offset"]),"line-blur":new oo(ce.paint_line["line-blur"]),"line-dasharray":new hc(ce.paint_line["line-dasharray"]),"line-pattern":new Vc(ce.paint_line["line-pattern"]),"line-gradient":new Ku(ce.paint_line["line-gradient"])})},get layout(){return Iv=Iv||new ue({"line-cap":new Ua(ce.layout_line["line-cap"]),"line-join":new oo(ce.layout_line["line-join"]),"line-miter-limit":new Ua(ce.layout_line["line-miter-limit"]),"line-round-limit":new Ua(ce.layout_line["line-round-limit"]),"line-sort-key":new oo(ce.layout_line["line-sort-key"])})}};class oh extends oo{possiblyEvaluate(A,F){return F=new rs(Math.floor(F.zoom),{now:F.now,fadeDuration:F.fadeDuration,zoomHistory:F.zoomHistory,transition:F.transition}),super.possiblyEvaluate(A,F)}evaluate(A,F,W,re){return F=L({},F,{zoom:Math.floor(F.zoom)}),super.evaluate(A,F,W,re)}}let hg;class oy extends B{constructor(A){super(A,fg),this.gradientVersion=0,hg||(hg=new oh(fg.paint.properties["line-width"].specification),hg.useIntegerZoom=!0)}_handleSpecialPaintPropertyUpdate(A){if(A==="line-gradient"){let F=this.gradientExpression();this.stepInterpolant=!!function(W){return W._styleExpression!==void 0}(F)&&F._styleExpression.expression instanceof _n,this.gradientVersion=(this.gradientVersion+1)%Number.MAX_SAFE_INTEGER}}gradientExpression(){return this._transitionablePaint._values["line-gradient"].value.expression}recalculate(A,F){super.recalculate(A,F),this.paint._values["line-floorwidth"]=hg.possiblyEvaluate(this._transitioningPaint._values["line-width"].value,A)}createBucket(A){return new Jv(A)}queryRadius(A){let 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SC(R,A){let{expression:F}=A;if(F.kind==="constant")return{kind:"constant",layoutSize:F.evaluate(new rs(R+1))};if(F.kind==="source")return{kind:"source"};{let{zoomStops:W,interpolationType:re}=F,fe=0;for(;fe<W.length&&W[fe]<=R;)fe++;fe=Math.max(0,fe-1);let pe=fe;for(;pe<W.length&&W[pe]<R+1;)pe++;pe=Math.min(W.length-1,pe);let ze=W[fe],Ke=W[pe];return F.kind==="composite"?{kind:"composite",minZoom:ze,maxZoom:Ke,interpolationType:re}:{kind:"camera",minZoom:ze,maxZoom:Ke,minSize:F.evaluate(new rs(ze)),maxSize:F.evaluate(new rs(Ke)),interpolationType:re}}}function sS(R,A,F){let W="never",re=R.get(A);return re?W=re:R.get(F)&&(W="always"),W}let Y9=br.VectorTileFeature.types,K9=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function Fw(R,A,F,W,re,fe,pe,ze,Ke,ct,Lt,$t,fr){let mr=ze?Math.min(um,Math.round(ze[0])):0,Pr=ze?Math.min(um,Math.round(ze[1])):0;R.emplaceBack(A,F,Math.round(32*W),Math.round(32*re),fe,pe,(mr<<1)+(Ke?1:0),Pr,16*ct,16*Lt,256*$t,256*fr)}function lS(R,A,F){R.emplaceBack(A.x,A.y,F),R.emplaceBack(A.x,A.y,F),R.emplaceBack(A.x,A.y,F),R.emplaceBack(A.x,A.y,F)}function uS(R){for(let A of R.sections)if(no(A.text))return!0;return!1}class cS{constructor(A){this.layoutVertexArray=new Ql,this.indexArray=new se,this.programConfigurations=A,this.segments=new Ye,this.dynamicLayoutVertexArray=new dc,this.opacityVertexArray=new Tl,this.hasVisibleVertices=!1,this.placedSymbolArray=new Ta}isEmpty(){return this.layoutVertexArray.length===0&&this.indexArray.length===0&&this.dynamicLayoutVertexArray.length===0&&this.opacityVertexArray.length===0}upload(A,F,W,re){this.isEmpty()||(W&&(this.layoutVertexBuffer=A.createVertexBuffer(this.layoutVertexArray,im.members),this.indexBuffer=A.createIndexBuffer(this.indexArray,F),this.dynamicLayoutVertexBuffer=A.createVertexBuffer(this.dynamicLayoutVertexArray,b1.members,!0),this.opacityVertexBuffer=A.createVertexBuffer(this.opacityVertexArray,K9,!0),this.opacityVertexBuffer.itemSize=1),(W||re)&&this.programConfigurations.upload(A))}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())}}Fi("SymbolBuffers",cS);class cm{constructor(A,F,W){this.layoutVertexArray=new A,this.layoutAttributes=F,this.indexArray=new W,this.segments=new Ye,this.collisionVertexArray=new X}upload(A){this.layoutVertexBuffer=A.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=A.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=A.createVertexBuffer(this.collisionVertexArray,w1.members,!0)}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())}}Fi("CollisionBuffers",cm);class M1{constructor(A){this.collisionBoxArray=A.collisionBoxArray,this.zoom=A.zoom,this.overscaling=A.overscaling,this.layers=A.layers,this.layerIds=this.layers.map(pe=>pe.id),this.index=A.index,this.pixelRatio=A.pixelRatio,this.sourceLayerIndex=A.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=Ri([]),this.placementViewportMatrix=Ri([]);let F=this.layers[0]._unevaluatedLayout._values;this.textSizeData=SC(this.zoom,F["text-size"]),this.iconSizeData=SC(this.zoom,F["icon-size"]);let W=this.layers[0].layout,re=W.get("symbol-sort-key"),fe=W.get("symbol-z-order");this.canOverlap=sS(W,"text-overlap","text-allow-overlap")!=="never"||sS(W,"icon-overlap","icon-allow-overlap")!=="never"||W.get("text-ignore-placement")||W.get("icon-ignore-placement"),this.sortFeaturesByKey=fe!=="viewport-y"&&!re.isConstant(),this.sortFeaturesByY=(fe==="viewport-y"||fe==="auto"&&!this.sortFeaturesByKey)&&this.canOverlap,W.get("symbol-placement")==="point"&&(this.writingModes=W.get("text-writing-mode").map(pe=>i.ah[pe])),this.stateDependentLayerIds=this.layers.filter(pe=>pe.isStateDependent()).map(pe=>pe.id),this.sourceID=A.sourceID}createArrays(){this.text=new cS(new Ms(this.layers,this.zoom,A=>/^text/.test(A))),this.icon=new cS(new Ms(this.layers,this.zoom,A=>/^icon/.test(A))),this.glyphOffsetArray=new Co,this.lineVertexArray=new Qa,this.symbolInstances=new $a,this.textAnchorOffsets=new Bo}calculateGlyphDependencies(A,F,W,re,fe){for(let pe=0;pe<A.length;pe++)if(F[A.charCodeAt(pe)]=!0,(W||re)&&fe){let ze=vc[A.charAt(pe)];ze&&(F[ze.charCodeAt(0)]=!0)}}populate(A,F,W){let re=this.layers[0],fe=re.layout,pe=fe.get("text-font"),ze=fe.get("text-field"),Ke=fe.get("icon-image"),ct=(ze.value.kind!=="constant"||ze.value.value instanceof ri&&!ze.value.value.isEmpty()||ze.value.value.toString().length>0)&&(pe.value.kind!=="constant"||pe.value.value.length>0),Lt=Ke.value.kind!=="constant"||!!Ke.value.value||Object.keys(Ke.parameters).length>0,$t=fe.get("symbol-sort-key");if(this.features=[],!ct&&!Lt)return;let fr=F.iconDependencies,mr=F.glyphDependencies,Pr=F.availableImages,zr=new rs(this.zoom);for(let{feature:ui,id:yi,index:vn,sourceLayerIndex:zi}of A){let un=re._featureFilter.needGeometry,Tn=Sl(ui,un);if(!re._featureFilter.filter(zr,Tn,W))continue;let pa,ro;if(un||(Tn.geometry=$s(ui)),ct){let Xa=re.getValueAndResolveTokens("text-field",Tn,W,Pr),sa=ri.factory(Xa),Mo=this.hasRTLText=this.hasRTLText||uS(sa);(!Mo||ys.getRTLTextPluginStatus()==="unavailable"||Mo&&ys.isParsed())&&(pa=am(sa,re,Tn))}if(Lt){let Xa=re.getValueAndResolveTokens("icon-image",Tn,W,Pr);ro=Xa instanceof tn?Xa:tn.fromString(Xa)}if(!pa&&!ro)continue;let Vo=this.sortFeaturesByKey?$t.evaluate(Tn,{},W):void 0;if(this.features.push({id:yi,text:pa,icon:ro,index:vn,sourceLayerIndex:zi,geometry:Tn.geometry,properties:ui.properties,type:Y9[ui.type],sortKey:Vo}),ro&&(fr[ro.name]=!0),pa){let Xa=pe.evaluate(Tn,{},W).join(","),sa=fe.get("text-rotation-alignment")!=="viewport"&&fe.get("symbol-placement")!=="point";this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(i.ah.vertical)>=0;for(let Mo of pa.sections)if(Mo.image)fr[Mo.image.name]=!0;else{let fo=Ka(pa.toString()),lo=Mo.fontStack||Xa,Xn=mr[lo]=mr[lo]||{};this.calculateGlyphDependencies(Mo.text,Xn,sa,this.allowVerticalPlacement,fo)}}}fe.get("symbol-placement")==="line"&&(this.features=function(ui){let yi={},vn={},zi=[],un=0;function Tn(Xa){zi.push(ui[Xa]),un++}function pa(Xa,sa,Mo){let fo=vn[Xa];return delete vn[Xa],vn[sa]=fo,zi[fo].geometry[0].pop(),zi[fo].geometry[0]=zi[fo].geometry[0].concat(Mo[0]),fo}function ro(Xa,sa,Mo){let fo=yi[sa];return delete yi[sa],yi[Xa]=fo,zi[fo].geometry[0].shift(),zi[fo].geometry[0]=Mo[0].concat(zi[fo].geometry[0]),fo}function Vo(Xa,sa,Mo){let fo=Mo?sa[0][sa[0].length-1]:sa[0][0];return`${Xa}:${fo.x}:${fo.y}`}for(let Xa=0;Xa<ui.length;Xa++){let sa=ui[Xa],Mo=sa.geometry,fo=sa.text?sa.text.toString():null;if(!fo){Tn(Xa);continue}let lo=Vo(fo,Mo),Xn=Vo(fo,Mo,!0);if(lo in vn&&Xn in yi&&vn[lo]!==yi[Xn]){let Ro=ro(lo,Xn,Mo),uo=pa(lo,Xn,zi[Ro].geometry);delete yi[lo],delete vn[Xn],vn[Vo(fo,zi[uo].geometry,!0)]=uo,zi[Ro].geometry=null}else lo in vn?pa(lo,Xn,Mo):Xn in yi?ro(lo,Xn,Mo):(Tn(Xa),yi[lo]=un-1,vn[Xn]=un-1)}return zi.filter(Xa=>Xa.geometry)}(this.features)),this.sortFeaturesByKey&&this.features.sort((ui,yi)=>ui.sortKey-yi.sortKey)}update(A,F,W){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(A,F,this.layers,W),this.icon.programConfigurations.updatePaintArrays(A,F,this.layers,W))}isEmpty(){return this.symbolInstances.length===0&&!this.hasRTLText}uploadPending(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload}upload(A){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(A),this.iconCollisionBox.upload(A)),this.text.upload(A,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(A,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0}destroyDebugData(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()}destroy(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()}addToLineVertexArray(A,F){let W=this.lineVertexArray.length;if(A.segment!==void 0){let re=A.dist(F[A.segment+1]),fe=A.dist(F[A.segment]),pe={};for(let ze=A.segment+1;ze<F.length;ze++)pe[ze]={x:F[ze].x,y:F[ze].y,tileUnitDistanceFromAnchor:re},ze<F.length-1&&(re+=F[ze+1].dist(F[ze]));for(let ze=A.segment||0;ze>=0;ze--)pe[ze]={x:F[ze].x,y:F[ze].y,tileUnitDistanceFromAnchor:fe},ze>0&&(fe+=F[ze-1].dist(F[ze]));for(let ze=0;ze<F.length;ze++){let Ke=pe[ze];this.lineVertexArray.emplaceBack(Ke.x,Ke.y,Ke.tileUnitDistanceFromAnchor)}}return{lineStartIndex:W,lineLength:this.lineVertexArray.length-W}}addSymbols(A,F,W,re,fe,pe,ze,Ke,ct,Lt,$t,fr){let mr=A.indexArray,Pr=A.layoutVertexArray,zr=A.segments.prepareSegment(4*F.length,Pr,mr,this.canOverlap?pe.sortKey:void 0),ui=this.glyphOffsetArray.length,yi=zr.vertexLength,vn=this.allowVerticalPlacement&&ze===i.ah.vertical?Math.PI/2:0,zi=pe.text&&pe.text.sections;for(let un=0;un<F.length;un++){let{tl:Tn,tr:pa,bl:ro,br:Vo,tex:Xa,pixelOffsetTL:sa,pixelOffsetBR:Mo,minFontScaleX:fo,minFontScaleY:lo,glyphOffset:Xn,isSDF:Ro,sectionIndex:uo}=F[un],$o=zr.vertexLength,Ju=Xn[1];Fw(Pr,Ke.x,Ke.y,Tn.x,Ju+Tn.y,Xa.x,Xa.y,W,Ro,sa.x,sa.y,fo,lo),Fw(Pr,Ke.x,Ke.y,pa.x,Ju+pa.y,Xa.x+Xa.w,Xa.y,W,Ro,Mo.x,sa.y,fo,lo),Fw(Pr,Ke.x,Ke.y,ro.x,Ju+ro.y,Xa.x,Xa.y+Xa.h,W,Ro,sa.x,Mo.y,fo,lo),Fw(Pr,Ke.x,Ke.y,Vo.x,Ju+Vo.y,Xa.x+Xa.w,Xa.y+Xa.h,W,Ro,Mo.x,Mo.y,fo,lo),lS(A.dynamicLayoutVertexArray,Ke,vn),mr.emplaceBack($o,$o+1,$o+2),mr.emplaceBack($o+1,$o+2,$o+3),zr.vertexLength+=4,zr.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(Xn[0]),un!==F.length-1&&uo===F[un+1].sectionIndex||A.programConfigurations.populatePaintArrays(Pr.length,pe,pe.index,{},fr,zi&&zi[uo])}A.placedSymbolArray.emplaceBack(Ke.x,Ke.y,ui,this.glyphOffsetArray.length-ui,yi,ct,Lt,Ke.segment,W?W[0]:0,W?W[1]:0,re[0],re[1],ze,0,!1,0,$t)}_addCollisionDebugVertex(A,F,W,re,fe,pe){return F.emplaceBack(0,0),A.emplaceBack(W.x,W.y,re,fe,Math.round(pe.x),Math.round(pe.y))}addCollisionDebugVertices(A,F,W,re,fe,pe,ze){let Ke=fe.segments.prepareSegment(4,fe.layoutVertexArray,fe.indexArray),ct=Ke.vertexLength,Lt=fe.layoutVertexArray,$t=fe.collisionVertexArray,fr=ze.anchorX,mr=ze.anchorY;this._addCollisionDebugVertex(Lt,$t,pe,fr,mr,new u(A,F)),this._addCollisionDebugVertex(Lt,$t,pe,fr,mr,new u(W,F)),this._addCollisionDebugVertex(Lt,$t,pe,fr,mr,new u(W,re)),this._addCollisionDebugVertex(Lt,$t,pe,fr,mr,new u(A,re)),Ke.vertexLength+=4;let Pr=fe.indexArray;Pr.emplaceBack(ct,ct+1),Pr.emplaceBack(ct+1,ct+2),Pr.emplaceBack(ct+2,ct+3),Pr.emplaceBack(ct+3,ct),Ke.primitiveLength+=4}addDebugCollisionBoxes(A,F,W,re){for(let fe=A;fe<F;fe++){let pe=this.collisionBoxArray.get(fe);this.addCollisionDebugVertices(pe.x1,pe.y1,pe.x2,pe.y2,re?this.textCollisionBox:this.iconCollisionBox,pe.anchorPoint,W)}}generateCollisionDebugBuffers(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new cm(Al,sy.members,Te),this.iconCollisionBox=new cm(Al,sy.members,Te);for(let A=0;A<this.symbolInstances.length;A++){let F=this.symbolInstances.get(A);this.addDebugCollisionBoxes(F.textBoxStartIndex,F.textBoxEndIndex,F,!0),this.addDebugCollisionBoxes(F.verticalTextBoxStartIndex,F.verticalTextBoxEndIndex,F,!0),this.addDebugCollisionBoxes(F.iconBoxStartIndex,F.iconBoxEndIndex,F,!1),this.addDebugCollisionBoxes(F.verticalIconBoxStartIndex,F.verticalIconBoxEndIndex,F,!1)}}_deserializeCollisionBoxesForSymbol(A,F,W,re,fe,pe,ze,Ke,ct){let Lt={};for(let $t=F;$t<W;$t++){let fr=A.get($t);Lt.textBox={x1:fr.x1,y1:fr.y1,x2:fr.x2,y2:fr.y2,anchorPointX:fr.anchorPointX,anchorPointY:fr.anchorPointY},Lt.textFeatureIndex=fr.featureIndex;break}for(let $t=re;$t<fe;$t++){let fr=A.get($t);Lt.verticalTextBox={x1:fr.x1,y1:fr.y1,x2:fr.x2,y2:fr.y2,anchorPointX:fr.anchorPointX,anchorPointY:fr.anchorPointY},Lt.verticalTextFeatureIndex=fr.featureIndex;break}for(let $t=pe;$t<ze;$t++){let fr=A.get($t);Lt.iconBox={x1:fr.x1,y1:fr.y1,x2:fr.x2,y2:fr.y2,anchorPointX:fr.anchorPointX,anchorPointY:fr.anchorPointY},Lt.iconFeatureIndex=fr.featureIndex;break}for(let $t=Ke;$t<ct;$t++){let fr=A.get($t);Lt.verticalIconBox={x1:fr.x1,y1:fr.y1,x2:fr.x2,y2:fr.y2,anchorPointX:fr.anchorPointX,anchorPointY:fr.anchorPointY},Lt.verticalIconFeatureIndex=fr.featureIndex;break}return Lt}deserializeCollisionBoxes(A){this.collisionArrays=[];for(let F=0;F<this.symbolInstances.length;F++){let W=this.symbolInstances.get(F);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(A,W.textBoxStartIndex,W.textBoxEndIndex,W.verticalTextBoxStartIndex,W.verticalTextBoxEndIndex,W.iconBoxStartIndex,W.iconBoxEndIndex,W.verticalIconBoxStartIndex,W.verticalIconBoxEndIndex))}}hasTextData(){return this.text.segments.get().length>0}hasIconData(){return this.icon.segments.get().length>0}hasDebugData(){return this.textCollisionBox&&this.iconCollisionBox}hasTextCollisionBoxData(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0}hasIconCollisionBoxData(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0}addIndicesForPlacedSymbol(A,F){let W=A.placedSymbolArray.get(F),re=W.vertexStartIndex+4*W.numGlyphs;for(let fe=W.vertexStartIndex;fe<re;fe+=4)A.indexArray.emplaceBack(fe,fe+1,fe+2),A.indexArray.emplaceBack(fe+1,fe+2,fe+3)}getSortedSymbolIndexes(A){if(this.sortedAngle===A&&this.symbolInstanceIndexes!==void 0)return this.symbolInstanceIndexes;let F=Math.sin(A),W=Math.cos(A),re=[],fe=[],pe=[];for(let ze=0;ze<this.symbolInstances.length;++ze){pe.push(ze);let Ke=this.symbolInstances.get(ze);re.push(0|Math.round(F*Ke.anchorX+W*Ke.anchorY)),fe.push(Ke.featureIndex)}return pe.sort((ze,Ke)=>re[ze]-re[Ke]||fe[Ke]-fe[ze]),pe}addToSortKeyRanges(A,F){let W=this.sortKeyRanges[this.sortKeyRanges.length-1];W&&W.sortKey===F?W.symbolInstanceEnd=A+1:this.sortKeyRanges.push({sortKey:F,symbolInstanceStart:A,symbolInstanceEnd:A+1})}sortFeatures(A){if(this.sortFeaturesByY&&this.sortedAngle!==A&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(A),this.sortedAngle=A,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(let F of this.symbolInstanceIndexes){let W=this.symbolInstances.get(F);this.featureSortOrder.push(W.featureIndex),[W.rightJustifiedTextSymbolIndex,W.centerJustifiedTextSymbolIndex,W.leftJustifiedTextSymbolIndex].forEach((re,fe,pe)=>{re>=0&&pe.indexOf(re)===fe&&this.addIndicesForPlacedSymbol(this.text,re)}),W.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,W.verticalPlacedTextSymbolIndex),W.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,W.placedIconSymbolIndex),W.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,W.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}}}let Tf,Xx;Fi("SymbolBucket",M1,{omit:["layers","collisionBoxArray","features","compareText"]}),M1.MAX_GLYPHS=65535,M1.addDynamicAttributes=lS;var zw={get paint(){return Xx=Xx||new ue({"icon-opacity":new oo(ce.paint_symbol["icon-opacity"]),"icon-color":new oo(ce.paint_symbol["icon-color"]),"icon-halo-color":new oo(ce.paint_symbol["icon-halo-color"]),"icon-halo-width":new oo(ce.paint_symbol["icon-halo-width"]),"icon-halo-blur":new oo(ce.paint_symbol["icon-halo-blur"]),"icon-translate":new Ua(ce.paint_symbol["icon-translate"]),"icon-translate-anchor":new Ua(ce.paint_symbol["icon-translate-anchor"]),"text-opacity":new oo(ce.paint_symbol["text-opacity"]),"text-color":new oo(ce.paint_symbol["text-color"],{runtimeType:er,getOverride:R=>R.textColor,hasOverride:R=>!!R.textColor}),"text-halo-color":new oo(ce.paint_symbol["text-halo-color"]),"text-halo-width":new oo(ce.paint_symbol["text-halo-width"]),"text-halo-blur":new oo(ce.paint_symbol["text-halo-blur"]),"text-translate":new Ua(ce.paint_symbol["text-translate"]),"text-translate-anchor":new Ua(ce.paint_symbol["text-translate-anchor"])})},get layout(){return Tf=Tf||new ue({"symbol-placement":new Ua(ce.layout_symbol["symbol-placement"]),"symbol-spacing":new Ua(ce.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new Ua(ce.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new oo(ce.layout_symbol["symbol-sort-key"]),"symbol-z-order":new Ua(ce.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new Ua(ce.layout_symbol["icon-allow-overlap"]),"icon-overlap":new Ua(ce.layout_symbol["icon-overlap"]),"icon-ignore-placement":new Ua(ce.layout_symbol["icon-ignore-placement"]),"icon-optional":new Ua(ce.layout_symbol["icon-optional"]),"icon-rotation-alignment":new Ua(ce.layout_symbol["icon-rotation-alignment"]),"icon-size":new oo(ce.layout_symbol["icon-size"]),"icon-text-fit":new Ua(ce.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new Ua(ce.layout_symbol["icon-text-fit-padding"]),"icon-image":new oo(ce.layout_symbol["icon-image"]),"icon-rotate":new oo(ce.layout_symbol["icon-rotate"]),"icon-padding":new oo(ce.layout_symbol["icon-padding"]),"icon-keep-upright":new Ua(ce.layout_symbol["icon-keep-upright"]),"icon-offset":new oo(ce.layout_symbol["icon-offset"]),"icon-anchor":new oo(ce.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new Ua(ce.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new Ua(ce.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new Ua(ce.layout_symbol["text-rotation-alignment"]),"text-field":new oo(ce.layout_symbol["text-field"]),"text-font":new oo(ce.layout_symbol["text-font"]),"text-size":new oo(ce.layout_symbol["text-size"]),"text-max-width":new oo(ce.layout_symbol["text-max-width"]),"text-line-height":new Ua(ce.layout_symbol["text-line-height"]),"text-letter-spacing":new oo(ce.layout_symbol["text-letter-spacing"]),"text-justify":new oo(ce.layout_symbol["text-justify"]),"text-radial-offset":new oo(ce.layout_symbol["text-radial-offset"]),"text-variable-anchor":new Ua(ce.layout_symbol["text-variable-anchor"]),"text-variable-anchor-offset":new oo(ce.layout_symbol["text-variable-anchor-offset"]),"text-anchor":new oo(ce.layout_symbol["text-anchor"]),"text-max-angle":new Ua(ce.layout_symbol["text-max-angle"]),"text-writing-mode":new Ua(ce.layout_symbol["text-writing-mode"]),"text-rotate":new oo(ce.layout_symbol["text-rotate"]),"text-padding":new Ua(ce.layout_symbol["text-padding"]),"text-keep-upright":new Ua(ce.layout_symbol["text-keep-upright"]),"text-transform":new oo(ce.layout_symbol["text-transform"]),"text-offset":new oo(ce.layout_symbol["text-offset"]),"text-allow-overlap":new Ua(ce.layout_symbol["text-allow-overlap"]),"text-overlap":new Ua(ce.layout_symbol["text-overlap"]),"text-ignore-placement":new Ua(ce.layout_symbol["text-ignore-placement"]),"text-optional":new Ua(ce.layout_symbol["text-optional"])})}};class Zx{constructor(A){if(A.property.overrides===void 0)throw new Error("overrides must be provided to instantiate FormatSectionOverride class");this.type=A.property.overrides?A.property.overrides.runtimeType:Vt,this.defaultValue=A}evaluate(A){if(A.formattedSection){let F=this.defaultValue.property.overrides;if(F&&F.hasOverride(A.formattedSection))return F.getOverride(A.formattedSection)}return A.feature&&A.featureState?this.defaultValue.evaluate(A.feature,A.featureState):this.defaultValue.property.specification.default}eachChild(A){this.defaultValue.isConstant()||A(this.defaultValue.value._styleExpression.expression)}outputDefined(){return!1}serialize(){return null}}Fi("FormatSectionOverride",Zx,{omit:["defaultValue"]});class cy extends B{constructor(A){super(A,zw)}recalculate(A,F){if(super.recalculate(A,F),this.layout.get("icon-rotation-alignment")==="auto"&&(this.layout._values["icon-rotation-alignment"]=this.layout.get("symbol-placement")!=="point"?"map":"viewport"),this.layout.get("text-rotation-alignment")==="auto"&&(this.layout._values["text-rotation-alignment"]=this.layout.get("symbol-placement")!=="point"?"map":"viewport"),this.layout.get("text-pitch-alignment")==="auto"&&(this.layout._values["text-pitch-alignment"]=this.layout.get("text-rotation-alignment")==="map"?"map":"viewport"),this.layout.get("icon-pitch-alignment")==="auto"&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment")),this.layout.get("symbol-placement")==="point"){let W=this.layout.get("text-writing-mode");if(W){let re=[];for(let fe of W)re.indexOf(fe)<0&&re.push(fe);this.layout._values["text-writing-mode"]=re}else this.layout._values["text-writing-mode"]=["horizontal"]}this._setPaintOverrides()}getValueAndResolveTokens(A,F,W,re){let fe=this.layout.get(A).evaluate(F,{},W,re),pe=this._unevaluatedLayout._values[A];return pe.isDataDriven()||Dc(pe.value)||!fe?fe:function(ze,Ke){return Ke.replace(/{([^{}]+)}/g,(ct,Lt)=>ze&&Lt in ze?String(ze[Lt]):"")}(F.properties,fe)}createBucket(A){return new M1(A)}queryRadius(){return 0}queryIntersectsFeature(){throw new Error("Should take a different path in FeatureIndex")}_setPaintOverrides(){for(let A of zw.paint.overridableProperties){if(!cy.hasPaintOverride(this.layout,A))continue;let F=this.paint.get(A),W=new Zx(F),re=new Eu(W,F.property.specification),fe=null;fe=F.value.kind==="constant"||F.value.kind==="source"?new bc("source",re):new hu("composite",re,F.value.zoomStops),this.paint._values[A]=new xu(F.property,fe,F.parameters)}}_handleOverridablePaintPropertyUpdate(A,F,W){return!(!this.layout||F.isDataDriven()||W.isDataDriven())&&cy.hasPaintOverride(this.layout,A)}static hasPaintOverride(A,F){let W=A.get("text-field"),re=zw.paint.properties[F],fe=!1,pe=ze=>{for(let Ke of ze)if(re.overrides&&re.overrides.hasOverride(Ke))return void(fe=!0)};if(W.value.kind==="constant"&&W.value.value instanceof ri)pe(W.value.value.sections);else if(W.value.kind==="source"){let ze=ct=>{fe||(ct instanceof ua&&Sn(ct.value)===ti?pe(ct.value.sections):ct instanceof Zl?pe(ct.sections):ct.eachChild(ze))},Ke=W.value;Ke._styleExpression&&ze(Ke._styleExpression.expression)}return fe}}let MC;var Yx={get paint(){return MC=MC||new ue({"background-color":new Ua(ce.paint_background["background-color"]),"background-pattern":new hc(ce.paint_background["background-pattern"]),"background-opacity":new Ua(ce.paint_background["background-opacity"])})}};class J9 extends B{constructor(A){super(A,Yx)}}let fS;var EC={get paint(){return fS=fS||new ue({"raster-opacity":new Ua(ce.paint_raster["raster-opacity"]),"raster-hue-rotate":new Ua(ce.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new Ua(ce.paint_raster["raster-brightness-min"]),"raster-brightness-max":new Ua(ce.paint_raster["raster-brightness-max"]),"raster-saturation":new Ua(ce.paint_raster["raster-saturation"]),"raster-contrast":new Ua(ce.paint_raster["raster-contrast"]),"raster-resampling":new Ua(ce.paint_raster["raster-resampling"]),"raster-fade-duration":new Ua(ce.paint_raster["raster-fade-duration"])})}};class Kx extends B{constructor(A){super(A,EC)}}class hS extends B{constructor(A){super(A,{}),this.onAdd=F=>{this.implementation.onAdd&&this.implementation.onAdd(F,F.painter.context.gl)},this.onRemove=F=>{this.implementation.onRemove&&this.implementation.onRemove(F,F.painter.context.gl)},this.implementation=A}is3D(){return this.implementation.renderingMode==="3d"}hasOffscreenPass(){return this.implementation.prerender!==void 0}recalculate(){}updateTransitions(){}hasTransition(){return!1}serialize(){throw new Error("Custom layers cannot be serialized")}}class dS{constructor(A){this._methodToThrottle=A,this._triggered=!1,typeof MessageChannel!="undefined"&&(this._channel=new MessageChannel,this._channel.port2.onmessage=()=>{this._triggered=!1,this._methodToThrottle()})}trigger(){this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout(()=>{this._triggered=!1,this._methodToThrottle()},0))}remove(){delete this._channel,this._methodToThrottle=()=>{}}}let vS=63710088e-1;class dg{constructor(A,F){if(isNaN(A)||isNaN(F))throw new Error(`Invalid LngLat object: (${A}, ${F})`);if(this.lng=+A,this.lat=+F,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")}wrap(){return new dg(S(this.lng,-180,180),this.lat)}toArray(){return[this.lng,this.lat]}toString(){return`LngLat(${this.lng}, ${this.lat})`}distanceTo(A){let F=Math.PI/180,W=this.lat*F,re=A.lat*F,fe=Math.sin(W)*Math.sin(re)+Math.cos(W)*Math.cos(re)*Math.cos((A.lng-this.lng)*F);return vS*Math.acos(Math.min(fe,1))}static convert(A){if(A instanceof dg)return A;if(Array.isArray(A)&&(A.length===2||A.length===3))return new dg(Number(A[0]),Number(A[1]));if(!Array.isArray(A)&&typeof A=="object"&&A!==null)return new dg(Number("lng"in A?A.lng:A.lon),Number(A.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")}}let E1=2*Math.PI*vS;function kC(R){return E1*Math.cos(R*Math.PI/180)}function Ow(R){return(180+R)/360}function CC(R){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+R*Math.PI/360)))/360}function qw(R,A){return R/kC(A)}function Jx(R){return 360/Math.PI*Math.atan(Math.exp((180-360*R)*Math.PI/180))-90}class $x{constructor(A,F,W=0){this.x=+A,this.y=+F,this.z=+W}static fromLngLat(A,F=0){let W=dg.convert(A);return new $x(Ow(W.lng),CC(W.lat),qw(F,W.lat))}toLngLat(){return new dg(360*this.x-180,Jx(this.y))}toAltitude(){return this.z*kC(Jx(this.y))}meterInMercatorCoordinateUnits(){return 1/E1*(A=Jx(this.y),1/Math.cos(A*Math.PI/180));var A}}function mp(R,A,F){var W=2*Math.PI*6378137/256/Math.pow(2,F);return[R*W-2*Math.PI*6378137/2,A*W-2*Math.PI*6378137/2]}class pS{constructor(A,F,W){if(!function(re,fe,pe){return!(re<0||re>25||pe<0||pe>=Math.pow(2,re)||fe<0||fe>=Math.pow(2,re))}(A,F,W))throw new Error(`x=${F}, y=${W}, z=${A} outside of bounds. 0<=x<${Math.pow(2,A)}, 0<=y<${Math.pow(2,A)} 0<=z<=25 `);this.z=A,this.x=F,this.y=W,this.key=Qx(0,A,A,F,W)}equals(A){return this.z===A.z&&this.x===A.x&&this.y===A.y}url(A,F,W){let re=(pe=this.y,ze=this.z,Ke=mp(256*(fe=this.x),256*(pe=Math.pow(2,ze)-pe-1),ze),ct=mp(256*(fe+1),256*(pe+1),ze),Ke[0]+","+Ke[1]+","+ct[0]+","+ct[1]);var fe,pe,ze,Ke,ct;let Lt=function($t,fr,mr){let Pr,zr="";for(let ui=$t;ui>0;ui--)Pr=1<<ui-1,zr+=(fr&Pr?1:0)+(mr&Pr?2:0);return zr}(this.z,this.x,this.y);return A[(this.x+this.y)%A.length].replace(/{prefix}/g,(this.x%16).toString(16)+(this.y%16).toString(16)).replace(/{z}/g,String(this.z)).replace(/{x}/g,String(this.x)).replace(/{y}/g,String(W==="tms"?Math.pow(2,this.z)-this.y-1:this.y)).replace(/{ratio}/g,F>1?"@2x":"").replace(/{quadkey}/g,Lt).replace(/{bbox-epsg-3857}/g,re)}isChildOf(A){let F=this.z-A.z;return F>0&&A.x===this.x>>F&&A.y===this.y>>F}getTilePoint(A){let F=Math.pow(2,this.z);return new u((A.x*F-this.x)*ja,(A.y*F-this.y)*ja)}toString(){return`${this.z}/${this.x}/${this.y}`}}class LC{constructor(A,F){this.wrap=A,this.canonical=F,this.key=Qx(A,F.z,F.z,F.x,F.y)}}class $v{constructor(A,F,W,re,fe){if(A<W)throw new Error(`overscaledZ should be >= z; overscaledZ = ${A}; z = ${W}`);this.overscaledZ=A,this.wrap=F,this.canonical=new pS(W,+re,+fe),this.key=Qx(F,A,W,re,fe)}clone(){return new $v(this.overscaledZ,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)}equals(A){return this.overscaledZ===A.overscaledZ&&this.wrap===A.wrap&&this.canonical.equals(A.canonical)}scaledTo(A){if(A>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${A}; overscaledZ = ${this.overscaledZ}`);let F=this.canonical.z-A;return A>this.canonical.z?new $v(A,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new $v(A,this.wrap,A,this.canonical.x>>F,this.canonical.y>>F)}calculateScaledKey(A,F){if(A>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${A}; overscaledZ = ${this.overscaledZ}`);let W=this.canonical.z-A;return A>this.canonical.z?Qx(this.wrap*+F,A,this.canonical.z,this.canonical.x,this.canonical.y):Qx(this.wrap*+F,A,A,this.canonical.x>>W,this.canonical.y>>W)}isChildOf(A){if(A.wrap!==this.wrap)return!1;let F=this.canonical.z-A.canonical.z;return A.overscaledZ===0||A.overscaledZ<this.overscaledZ&&A.canonical.x===this.canonical.x>>F&&A.canonical.y===this.canonical.y>>F}children(A){if(this.overscaledZ>=A)return[new $v(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];let F=this.canonical.z+1,W=2*this.canonical.x,re=2*this.canonical.y;return[new $v(F,this.wrap,F,W,re),new $v(F,this.wrap,F,W+1,re),new $v(F,this.wrap,F,W,re+1),new $v(F,this.wrap,F,W+1,re+1)]}isLessThan(A){return this.wrap<A.wrap||!(this.wrap>A.wrap)&&(this.overscaledZ<A.overscaledZ||!(this.overscaledZ>A.overscaledZ)&&(this.canonical.x<A.canonical.x||!(this.canonical.x>A.canonical.x)&&this.canonical.y<A.canonical.y))}wrapped(){return new $v(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)}unwrapTo(A){return new $v(this.overscaledZ,A,this.canonical.z,this.canonical.x,this.canonical.y)}overscaleFactor(){return Math.pow(2,this.overscaledZ-this.canonical.z)}toUnwrapped(){return new LC(this.wrap,this.canonical)}toString(){return`${this.overscaledZ}/${this.canonical.x}/${this.canonical.y}`}getTilePoint(A){return this.canonical.getTilePoint(new $x(A.x-this.wrap,A.y))}}function Qx(R,A,F,W,re){(R*=2)<0&&(R=-1*R-1);let fe=1<<F;return(fe*fe*R+fe*re+W).toString(36)+F.toString(36)+A.toString(36)}Fi("CanonicalTileID",pS),Fi("OverscaledTileID",$v,{omit:["posMatrix"]});class PC{constructor(A,F,W,re=1,fe=1,pe=1,ze=0){if(this.uid=A,F.height!==F.width)throw new RangeError("DEM tiles must be square");if(W&&!["mapbox","terrarium","custom"].includes(W))return void T(`"${W}" is not a valid encoding type. Valid types include "mapbox", "terrarium" and "custom".`);this.stride=F.height;let Ke=this.dim=F.height-2;switch(this.data=new Uint32Array(F.data.buffer),W){case"terrarium":this.redFactor=256,this.greenFactor=1,this.blueFactor=1/256,this.baseShift=32768;break;case"custom":this.redFactor=re,this.greenFactor=fe,this.blueFactor=pe,this.baseShift=ze;break;default:this.redFactor=6553.6,this.greenFactor=25.6,this.blueFactor=.1,this.baseShift=1e4}for(let ct=0;ct<Ke;ct++)this.data[this._idx(-1,ct)]=this.data[this._idx(0,ct)],this.data[this._idx(Ke,ct)]=this.data[this._idx(Ke-1,ct)],this.data[this._idx(ct,-1)]=this.data[this._idx(ct,0)],this.data[this._idx(ct,Ke)]=this.data[this._idx(ct,Ke-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(Ke,-1)]=this.data[this._idx(Ke-1,0)],this.data[this._idx(-1,Ke)]=this.data[this._idx(0,Ke-1)],this.data[this._idx(Ke,Ke)]=this.data[this._idx(Ke-1,Ke-1)],this.min=Number.MAX_SAFE_INTEGER,this.max=Number.MIN_SAFE_INTEGER;for(let ct=0;ct<Ke;ct++)for(let Lt=0;Lt<Ke;Lt++){let $t=this.get(ct,Lt);$t>this.max&&(this.max=$t),$t<this.min&&(this.min=$t)}}get(A,F){let W=new Uint8Array(this.data.buffer),re=4*this._idx(A,F);return this.unpack(W[re],W[re+1],W[re+2])}getUnpackVector(){return[this.redFactor,this.greenFactor,this.blueFactor,this.baseShift]}_idx(A,F){if(A<-1||A>=this.dim+1||F<-1||F>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(F+1)*this.stride+(A+1)}unpack(A,F,W){return A*this.redFactor+F*this.greenFactor+W*this.blueFactor-this.baseShift}getPixels(){return new Ki({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))}backfillBorder(A,F,W){if(this.dim!==A.dim)throw new Error("dem dimension mismatch");let re=F*this.dim,fe=F*this.dim+this.dim,pe=W*this.dim,ze=W*this.dim+this.dim;switch(F){case-1:re=fe-1;break;case 1:fe=re+1}switch(W){case-1:pe=ze-1;break;case 1:ze=pe+1}let Ke=-F*this.dim,ct=-W*this.dim;for(let Lt=pe;Lt<ze;Lt++)for(let $t=re;$t<fe;$t++)this.data[this._idx($t,Lt)]=A.data[this._idx($t+Ke,Lt+ct)]}}Fi("DEMData",PC);class IC{constructor(A){this._stringToNumber={},this._numberToString=[];for(let F=0;F<A.length;F++){let W=A[F];this._stringToNumber[W]=F,this._numberToString[F]=W}}encode(A){return this._stringToNumber[A]}decode(A){if(A>=this._numberToString.length)throw new Error(`Out of bounds. Index requested n=${A} can't be >= this._numberToString.length ${this._numberToString.length}`);return this._numberToString[A]}}class gS{constructor(A,F,W,re,fe){this.type="Feature",this._vectorTileFeature=A,A._z=F,A._x=W,A._y=re,this.properties=A.properties,this.id=fe}get geometry(){return this._geometry===void 0&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry}set geometry(A){this._geometry=A}toJSON(){let A={geometry:this.geometry};for(let F in this)F!=="_geometry"&&F!=="_vectorTileFeature"&&(A[F]=this[F]);return A}}class fy{constructor(A,F){this.tileID=A,this.x=A.canonical.x,this.y=A.canonical.y,this.z=A.canonical.z,this.grid=new Qi(ja,16,0),this.grid3D=new Qi(ja,16,0),this.featureIndexArray=new Ts,this.promoteId=F}insert(A,F,W,re,fe,pe){let ze=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(W,re,fe);let Ke=pe?this.grid3D:this.grid;for(let ct=0;ct<F.length;ct++){let Lt=F[ct],$t=[1/0,1/0,-1/0,-1/0];for(let fr=0;fr<Lt.length;fr++){let mr=Lt[fr];$t[0]=Math.min($t[0],mr.x),$t[1]=Math.min($t[1],mr.y),$t[2]=Math.max($t[2],mr.x),$t[3]=Math.max($t[3],mr.y)}$t[0]<ja&&$t[1]<ja&&$t[2]>=0&&$t[3]>=0&&Ke.insert(ze,$t[0],$t[1],$t[2],$t[3])}}loadVTLayers(){return this.vtLayers||(this.vtLayers=new br.VectorTile(new nS(this.rawTileData)).layers,this.sourceLayerCoder=new IC(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers}query(A,F,W,re){this.loadVTLayers();let fe=A.params||{},pe=ja/A.tileSize/A.scale,ze=Fc(fe.filter),Ke=A.queryGeometry,ct=A.queryPadding*pe,Lt=DC(Ke),$t=this.grid.query(Lt.minX-ct,Lt.minY-ct,Lt.maxX+ct,Lt.maxY+ct),fr=DC(A.cameraQueryGeometry),mr=this.grid3D.query(fr.minX-ct,fr.minY-ct,fr.maxX+ct,fr.maxY+ct,(ui,yi,vn,zi)=>function(un,Tn,pa,ro,Vo){for(let sa of un)if(Tn<=sa.x&&pa<=sa.y&&ro>=sa.x&&Vo>=sa.y)return!0;let Xa=[new u(Tn,pa),new u(Tn,Vo),new u(ro,Vo),new u(ro,pa)];if(un.length>2){for(let sa of Xa)if(Ui(un,sa))return!0}for(let sa=0;sa<un.length-1;sa++)if(qi(un[sa],un[sa+1],Xa))return!0;return!1}(A.cameraQueryGeometry,ui-ct,yi-ct,vn+ct,zi+ct));for(let ui of mr)$t.push(ui);$t.sort($9);let Pr={},zr;for(let ui=0;ui<$t.length;ui++){let yi=$t[ui];if(yi===zr)continue;zr=yi;let vn=this.featureIndexArray.get(yi),zi=null;this.loadMatchingFeature(Pr,vn.bucketIndex,vn.sourceLayerIndex,vn.featureIndex,ze,fe.layers,fe.availableImages,F,W,re,(un,Tn,pa)=>(zi||(zi=$s(un)),Tn.queryIntersectsFeature(Ke,un,pa,zi,this.z,A.transform,pe,A.pixelPosMatrix)))}return Pr}loadMatchingFeature(A,F,W,re,fe,pe,ze,Ke,ct,Lt,$t){let fr=this.bucketLayerIDs[F];if(pe&&!function(ui,yi){for(let vn=0;vn<ui.length;vn++)if(yi.indexOf(ui[vn])>=0)return!0;return!1}(pe,fr))return;let mr=this.sourceLayerCoder.decode(W),Pr=this.vtLayers[mr].feature(re);if(fe.needGeometry){let ui=Sl(Pr,!0);if(!fe.filter(new rs(this.tileID.overscaledZ),ui,this.tileID.canonical))return}else if(!fe.filter(new rs(this.tileID.overscaledZ),Pr))return;let zr=this.getId(Pr,mr);for(let ui=0;ui<fr.length;ui++){let yi=fr[ui];if(pe&&pe.indexOf(yi)<0)continue;let vn=Ke[yi];if(!vn)continue;let zi={};zr&&Lt&&(zi=Lt.getState(vn.sourceLayer||"_geojsonTileLayer",zr));let un=L({},ct[yi]);un.paint=RC(un.paint,vn.paint,Pr,zi,ze),un.layout=RC(un.layout,vn.layout,Pr,zi,ze);let Tn=!$t||$t(Pr,vn,zi);if(!Tn)continue;let pa=new gS(Pr,this.z,this.x,this.y,zr);pa.layer=un;let ro=A[yi];ro===void 0&&(ro=A[yi]=[]),ro.push({featureIndex:re,feature:pa,intersectionZ:Tn})}}lookupSymbolFeatures(A,F,W,re,fe,pe,ze,Ke){let ct={};this.loadVTLayers();let Lt=Fc(fe);for(let $t of A)this.loadMatchingFeature(ct,W,re,$t,Lt,pe,ze,Ke,F);return ct}hasLayer(A){for(let F of this.bucketLayerIDs)for(let W of F)if(A===W)return!0;return!1}getId(A,F){let W=A.id;return this.promoteId&&(W=A.properties[typeof this.promoteId=="string"?this.promoteId:this.promoteId[F]],typeof W=="boolean"&&(W=Number(W))),W}}function RC(R,A,F,W,re){return C(R,(fe,pe)=>{let ze=A instanceof Ac?A.get(pe):null;return ze&&ze.evaluate?ze.evaluate(F,W,re):ze})}function DC(R){let A=1/0,F=1/0,W=-1/0,re=-1/0;for(let fe of R)A=Math.min(A,fe.x),F=Math.min(F,fe.y),W=Math.max(W,fe.x),re=Math.max(re,fe.y);return{minX:A,minY:F,maxX:W,maxY:re}}function $9(R,A){return A-R}function FC(R,A,F,W,re){let fe=[];for(let pe=0;pe<R.length;pe++){let ze=R[pe],Ke;for(let ct=0;ct<ze.length-1;ct++){let Lt=ze[ct],$t=ze[ct+1];Lt.x<A&&$t.x<A||(Lt.x<A?Lt=new u(A,Lt.y+(A-Lt.x)/($t.x-Lt.x)*($t.y-Lt.y))._round():$t.x<A&&($t=new u(A,Lt.y+(A-Lt.x)/($t.x-Lt.x)*($t.y-Lt.y))._round()),Lt.y<F&&$t.y<F||(Lt.y<F?Lt=new u(Lt.x+(F-Lt.y)/($t.y-Lt.y)*($t.x-Lt.x),F)._round():$t.y<F&&($t=new u(Lt.x+(F-Lt.y)/($t.y-Lt.y)*($t.x-Lt.x),F)._round()),Lt.x>=W&&$t.x>=W||(Lt.x>=W?Lt=new u(W,Lt.y+(W-Lt.x)/($t.x-Lt.x)*($t.y-Lt.y))._round():$t.x>=W&&($t=new u(W,Lt.y+(W-Lt.x)/($t.x-Lt.x)*($t.y-Lt.y))._round()),Lt.y>=re&&$t.y>=re||(Lt.y>=re?Lt=new 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s{constructor(Ge){this.tileID=new i.S(Ge.tileID.overscaledZ,Ge.tileID.wrap,Ge.tileID.canonical.z,Ge.tileID.canonical.x,Ge.tileID.canonical.y),this.uid=Ge.uid,this.zoom=Ge.zoom,this.pixelRatio=Ge.pixelRatio,this.tileSize=Ge.tileSize,this.source=Ge.source,this.overscaling=this.tileID.overscaleFactor(),this.showCollisionBoxes=Ge.showCollisionBoxes,this.collectResourceTiming=!!Ge.collectResourceTiming,this.returnDependencies=!!Ge.returnDependencies,this.promoteId=Ge.promoteId,this.inFlightDependencies=[]}parse(Ge,Je,je,tt){return i._(this,void 0,void 0,function*(){this.status="parsing",this.data=Ge,this.collisionBoxArray=new i.a5;let xt=new i.bm(Object.keys(Ge.layers).sort()),Ie=new i.bn(this.tileID,this.promoteId);Ie.bucketLayerIDs=[];let xe={},ke={featureIndex:Ie,iconDependencies:{},patternDependencies:{},glyphDependencies:{},availableImages:je},vt=Je.familiesBySource[this.source];for(let qn in vt){let Fn=Ge.layers[qn];if(!Fn)continue;Fn.version===1&&i.w(`Vector tile source 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0:qn.abort()),this.inFlightDependencies=[];let ar=Promise.resolve({});if(Object.keys(ir).length){let qn=new AbortController;this.inFlightDependencies.push(qn),ar=tt.sendAsync({type:"GG",data:{stacks:ir,source:this.source,tileID:this.tileID,type:"glyphs"}},qn)}let vr=Object.keys(ke.iconDependencies),ii=Promise.resolve({});if(vr.length){let qn=new AbortController;this.inFlightDependencies.push(qn),ii=tt.sendAsync({type:"GI",data:{icons:vr,source:this.source,tileID:this.tileID,type:"icons"}},qn)}let pi=Object.keys(ke.patternDependencies),$r=Promise.resolve({});if(pi.length){let qn=new AbortController;this.inFlightDependencies.push(qn),$r=tt.sendAsync({type:"GI",data:{icons:pi,source:this.source,tileID:this.tileID,type:"patterns"}},qn)}let[di,ji,In]=yield Promise.all([ar,ii,$r]),wi=new o(di),On=new i.bo(ji,In);for(let qn in xe){let Fn=xe[qn];Fn instanceof i.a6?(l(Fn.layers,this.zoom,je),i.bp({bucket:Fn,glyphMap:di,glyphPositions:wi.positions,imageMap:ji,imagePositions:On.iconPositions,showCollisionBoxes:this.showCollisionBoxes,canonical:this.tileID.canonical})):Fn.hasPattern&&(Fn instanceof i.bq||Fn instanceof i.br||Fn instanceof i.bs)&&(l(Fn.layers,this.zoom,je),Fn.addFeatures(ke,this.tileID.canonical,On.patternPositions))}return this.status="done",{buckets:Object.values(xe).filter(qn=>!qn.isEmpty()),featureIndex:Ie,collisionBoxArray:this.collisionBoxArray,glyphAtlasImage:wi.image,imageAtlas:On,glyphMap:this.returnDependencies?di:null,iconMap:this.returnDependencies?ji:null,glyphPositions:this.returnDependencies?wi.positions:null}})}}function l(dt,Ge,Je){let je=new i.z(Ge);for(let tt of dt)tt.recalculate(je,Je)}class u{constructor(Ge,Je,je){this.actor=Ge,this.layerIndex=Je,this.availableImages=je,this.fetching={},this.loading={},this.loaded={}}loadVectorTile(Ge,Je){return i._(this,void 0,void 0,function*(){let je=yield i.l(Ge.request,Je);try{return{vectorTile:new i.bt.VectorTile(new i.bu(je.data)),rawData:je.data,cacheControl:je.cacheControl,expires:je.expires}}catch(tt){let xt=new Uint8Array(je.data),Ie=`Unable to parse the tile at ${Ge.request.url}, `;throw Ie+=xt[0]===31&&xt[1]===139?"please make sure the data is not gzipped and that you have configured the relevant header in the server":`got error: ${tt.message}`,new Error(Ie)}})}loadTile(Ge){return i._(this,void 0,void 0,function*(){let Je=Ge.uid,je=!!(Ge&&Ge.request&&Ge.request.collectResourceTiming)&&new i.bv(Ge.request),tt=new s(Ge);this.loading[Je]=tt;let xt=new AbortController;tt.abort=xt;try{let Ie=yield this.loadVectorTile(Ge,xt);if(delete this.loading[Je],!Ie)return null;let xe=Ie.rawData,ke={};Ie.expires&&(ke.expires=Ie.expires),Ie.cacheControl&&(ke.cacheControl=Ie.cacheControl);let vt={};if(je){let ar=je.finish();ar&&(vt.resourceTiming=JSON.parse(JSON.stringify(ar)))}tt.vectorTile=Ie.vectorTile;let ir=tt.parse(Ie.vectorTile,this.layerIndex,this.availableImages,this.actor);this.loaded[Je]=tt,this.fetching[Je]={rawTileData:xe,cacheControl:ke,resourceTiming:vt};try{let ar=yield ir;return i.e({rawTileData:xe.slice(0)},ar,ke,vt)}finally{delete this.fetching[Je]}}catch(Ie){throw delete this.loading[Je],tt.status="done",this.loaded[Je]=tt,Ie}})}reloadTile(Ge){return i._(this,void 0,void 0,function*(){let Je=Ge.uid;if(!this.loaded||!this.loaded[Je])throw new Error("Should not be trying to reload a tile that was never loaded or has been removed");let je=this.loaded[Je];if(je.showCollisionBoxes=Ge.showCollisionBoxes,je.status==="parsing"){let tt=yield je.parse(je.vectorTile,this.layerIndex,this.availableImages,this.actor),xt;if(this.fetching[Je]){let{rawTileData:Ie,cacheControl:xe,resourceTiming:ke}=this.fetching[Je];delete this.fetching[Je],xt=i.e({rawTileData:Ie.slice(0)},tt,xe,ke)}else xt=tt;return xt}if(je.status==="done"&&je.vectorTile)return je.parse(je.vectorTile,this.layerIndex,this.availableImages,this.actor)})}abortTile(Ge){return i._(this,void 0,void 0,function*(){let Je=this.loading,je=Ge.uid;Je&&Je[je]&&Je[je].abort&&(Je[je].abort.abort(),delete Je[je])})}removeTile(Ge){return i._(this,void 0,void 0,function*(){this.loaded&&this.loaded[Ge.uid]&&delete this.loaded[Ge.uid]})}}class c{constructor(){this.loaded={}}loadTile(Ge){return i._(this,void 0,void 0,function*(){let{uid:Je,encoding:je,rawImageData:tt,redFactor:xt,greenFactor:Ie,blueFactor:xe,baseShift:ke}=Ge,vt=tt.width+2,ir=tt.height+2,ar=i.b(tt)?new i.R({width:vt,height:ir},yield i.bw(tt,-1,-1,vt,ir)):tt,vr=new i.bx(Je,ar,je,xt,Ie,xe,ke);return this.loaded=this.loaded||{},this.loaded[Je]=vr,vr})}removeTile(Ge){let Je=this.loaded,je=Ge.uid;Je&&Je[je]&&delete Je[je]}}function f(dt,Ge){if(dt.length!==0){h(dt[0],Ge);for(var Je=1;Je<dt.length;Je++)h(dt[Je],!Ge)}}function h(dt,Ge){for(var Je=0,je=0,tt=0,xt=dt.length,Ie=xt-1;tt<xt;Ie=tt++){var xe=(dt[tt][0]-dt[Ie][0])*(dt[Ie][1]+dt[tt][1]),ke=Je+xe;je+=Math.abs(Je)>=Math.abs(xe)?Je-ke+xe:xe-ke+Je,Je=ke}Je+je>=0!=!!Ge&&dt.reverse()}var d=i.by(function dt(Ge,Je){var je,tt=Ge&&Ge.type;if(tt==="FeatureCollection")for(je=0;je<Ge.features.length;je++)dt(Ge.features[je],Je);else if(tt==="GeometryCollection")for(je=0;je<Ge.geometries.length;je++)dt(Ge.geometries[je],Je);else if(tt==="Feature")dt(Ge.geometry,Je);else if(tt==="Polygon")f(Ge.coordinates,Je);else if(tt==="MultiPolygon")for(je=0;je<Ge.coordinates.length;je++)f(Ge.coordinates[je],Je);return Ge});let v=i.bt.VectorTileFeature.prototype.toGeoJSON;var _={exports:{}},b=i.bz,p=i.bt.VectorTileFeature,k=E;function E(dt,Ge){this.options=Ge||{},this.features=dt,this.length=dt.length}function S(dt,Ge){this.id=typeof dt.id=="number"?dt.id:void 0,this.type=dt.type,this.rawGeometry=dt.type===1?[dt.geometry]:dt.geometry,this.properties=dt.tags,this.extent=Ge||4096}E.prototype.feature=function(dt){return new S(this.features[dt],this.options.extent)},S.prototype.loadGeometry=function(){var dt=this.rawGeometry;this.geometry=[];for(var Ge=0;Ge<dt.length;Ge++){for(var Je=dt[Ge],je=[],tt=0;tt<Je.length;tt++)je.push(new b(Je[tt][0],Je[tt][1]));this.geometry.push(je)}return this.geometry},S.prototype.bbox=function(){this.geometry||this.loadGeometry();for(var dt=this.geometry,Ge=1/0,Je=-1/0,je=1/0,tt=-1/0,xt=0;xt<dt.length;xt++)for(var Ie=dt[xt],xe=0;xe<Ie.length;xe++){var ke=Ie[xe];Ge=Math.min(Ge,ke.x),Je=Math.max(Je,ke.x),je=Math.min(je,ke.y),tt=Math.max(tt,ke.y)}return[Ge,je,Je,tt]},S.prototype.toGeoJSON=p.prototype.toGeoJSON;var L=i.bA,x=k;function C(dt){var Ge=new L;return function(Je,je){for(var tt in Je.layers)je.writeMessage(3,M,Je.layers[tt])}(dt,Ge),Ge.finish()}function M(dt,Ge){var Je;Ge.writeVarintField(15,dt.version||1),Ge.writeStringField(1,dt.name||""),Ge.writeVarintField(5,dt.extent||4096);var je={keys:[],values:[],keycache:{},valuecache:{}};for(Je=0;Je<dt.length;Je++)je.feature=dt.feature(Je),Ge.writeMessage(2,g,je);var tt=je.keys;for(Je=0;Je<tt.length;Je++)Ge.writeStringField(3,tt[Je]);var xt=je.values;for(Je=0;Je<xt.length;Je++)Ge.writeMessage(4,V,xt[Je])}function g(dt,Ge){var Je=dt.feature;Je.id!==void 0&&Ge.writeVarintField(1,Je.id),Ge.writeMessage(2,P,dt),Ge.writeVarintField(3,Je.type),Ge.writeMessage(4,O,Je)}function P(dt,Ge){var Je=dt.feature,je=dt.keys,tt=dt.values,xt=dt.keycache,Ie=dt.valuecache;for(var xe in Je.properties){var ke=Je.properties[xe],vt=xt[xe];if(ke!==null){vt===void 0&&(je.push(xe),xt[xe]=vt=je.length-1),Ge.writeVarint(vt);var ir=typeof ke;ir!=="string"&&ir!=="boolean"&&ir!=="number"&&(ke=JSON.stringify(ke));var ar=ir+":"+ke,vr=Ie[ar];vr===void 0&&(tt.push(ke),Ie[ar]=vr=tt.length-1),Ge.writeVarint(vr)}}}function T(dt,Ge){return(Ge<<3)+(7&dt)}function z(dt){return dt<<1^dt>>31}function O(dt,Ge){for(var Je=dt.loadGeometry(),je=dt.type,tt=0,xt=0,Ie=Je.length,xe=0;xe<Ie;xe++){var ke=Je[xe],vt=1;je===1&&(vt=ke.length),Ge.writeVarint(T(1,vt));for(var ir=je===3?ke.length-1:ke.length,ar=0;ar<ir;ar++){ar===1&&je!==1&&Ge.writeVarint(T(2,ir-1));var vr=ke[ar].x-tt,ii=ke[ar].y-xt;Ge.writeVarint(z(vr)),Ge.writeVarint(z(ii)),tt+=vr,xt+=ii}je===3&&Ge.writeVarint(T(7,1))}}function V(dt,Ge){var Je=typeof dt;Je==="string"?Ge.writeStringField(1,dt):Je==="boolean"?Ge.writeBooleanField(7,dt):Je==="number"&&(dt%1!=0?Ge.writeDoubleField(3,dt):dt<0?Ge.writeSVarintField(6,dt):Ge.writeVarintField(5,dt))}_.exports=C,_.exports.fromVectorTileJs=C,_.exports.fromGeojsonVt=function(dt,Ge){Ge=Ge||{};var Je={};for(var je in dt)Je[je]=new x(dt[je].features,Ge),Je[je].name=je,Je[je].version=Ge.version,Je[je].extent=Ge.extent;return C({layers:Je})},_.exports.GeoJSONWrapper=x;var G=i.by(_.exports);let Z={minZoom:0,maxZoom:16,minPoints:2,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:dt=>dt},j=Math.fround||(N=new Float32Array(1),dt=>(N[0]=+dt,N[0]));var N;let H=3,te=5,oe=6;class _e{constructor(Ge){this.options=Object.assign(Object.create(Z),Ge),this.trees=new Array(this.options.maxZoom+1),this.stride=this.options.reduce?7:6,this.clusterProps=[]}load(Ge){let{log:Je,minZoom:je,maxZoom:tt}=this.options;Je&&console.time("total time");let xt=`prepare ${Ge.length} points`;Je&&console.time(xt),this.points=Ge;let Ie=[];for(let ke=0;ke<Ge.length;ke++){let vt=Ge[ke];if(!vt.geometry)continue;let[ir,ar]=vt.geometry.coordinates,vr=j(me(ir)),ii=j(ie(ar));Ie.push(vr,ii,1/0,ke,-1,1),this.options.reduce&&Ie.push(0)}let xe=this.trees[tt+1]=this._createTree(Ie);Je&&console.timeEnd(xt);for(let ke=tt;ke>=je;ke--){let vt=+Date.now();xe=this.trees[ke]=this._createTree(this._cluster(xe,ke)),Je&&console.log("z%d: %d clusters in %dms",ke,xe.numItems,+Date.now()-vt)}return Je&&console.timeEnd("total time"),this}getClusters(Ge,Je){let je=((Ge[0]+180)%360+360)%360-180,tt=Math.max(-90,Math.min(90,Ge[1])),xt=Ge[2]===180?180:((Ge[2]+180)%360+360)%360-180,Ie=Math.max(-90,Math.min(90,Ge[3]));if(Ge[2]-Ge[0]>=360)je=-180,xt=180;else if(je>xt){let ar=this.getClusters([je,tt,180,Ie],Je),vr=this.getClusters([-180,tt,xt,Ie],Je);return ar.concat(vr)}let xe=this.trees[this._limitZoom(Je)],ke=xe.range(me(je),ie(Ie),me(xt),ie(tt)),vt=xe.data,ir=[];for(let ar of ke){let vr=this.stride*ar;ir.push(vt[vr+te]>1?Ee(vt,vr,this.clusterProps):this.points[vt[vr+H]])}return ir}getChildren(Ge){let Je=this._getOriginId(Ge),je=this._getOriginZoom(Ge),tt="No cluster with the specified id.",xt=this.trees[je];if(!xt)throw new Error(tt);let Ie=xt.data;if(Je*this.stride>=Ie.length)throw new Error(tt);let xe=this.options.radius/(this.options.extent*Math.pow(2,je-1)),ke=xt.within(Ie[Je*this.stride],Ie[Je*this.stride+1],xe),vt=[];for(let ir of ke){let ar=ir*this.stride;Ie[ar+4]===Ge&&vt.push(Ie[ar+te]>1?Ee(Ie,ar,this.clusterProps):this.points[Ie[ar+H]])}if(vt.length===0)throw new Error(tt);return vt}getLeaves(Ge,Je,je){let tt=[];return this._appendLeaves(tt,Ge,Je=Je||10,je=je||0,0),tt}getTile(Ge,Je,je){let tt=this.trees[this._limitZoom(Ge)],xt=Math.pow(2,Ge),{extent:Ie,radius:xe}=this.options,ke=xe/Ie,vt=(je-ke)/xt,ir=(je+1+ke)/xt,ar={features:[]};return this._addTileFeatures(tt.range((Je-ke)/xt,vt,(Je+1+ke)/xt,ir),tt.data,Je,je,xt,ar),Je===0&&this._addTileFeatures(tt.range(1-ke/xt,vt,1,ir),tt.data,xt,je,xt,ar),Je===xt-1&&this._addTileFeatures(tt.range(0,vt,ke/xt,ir),tt.data,-1,je,xt,ar),ar.features.length?ar:null}getClusterExpansionZoom(Ge){let Je=this._getOriginZoom(Ge)-1;for(;Je<=this.options.maxZoom;){let je=this.getChildren(Ge);if(Je++,je.length!==1)break;Ge=je[0].properties.cluster_id}return Je}_appendLeaves(Ge,Je,je,tt,xt){let Ie=this.getChildren(Je);for(let xe of Ie){let ke=xe.properties;if(ke&&ke.cluster?xt+ke.point_count<=tt?xt+=ke.point_count:xt=this._appendLeaves(Ge,ke.cluster_id,je,tt,xt):xt<tt?xt++:Ge.push(xe),Ge.length===je)break}return xt}_createTree(Ge){let Je=new i.av(Ge.length/this.stride|0,this.options.nodeSize,Float32Array);for(let je=0;je<Ge.length;je+=this.stride)Je.add(Ge[je],Ge[je+1]);return Je.finish(),Je.data=Ge,Je}_addTileFeatures(Ge,Je,je,tt,xt,Ie){for(let xe of Ge){let ke=xe*this.stride,vt=Je[ke+te]>1,ir,ar,vr;if(vt)ir=Ce(Je,ke,this.clusterProps),ar=Je[ke],vr=Je[ke+1];else{let $r=this.points[Je[ke+H]];ir=$r.properties;let[di,ji]=$r.geometry.coordinates;ar=me(di),vr=ie(ji)}let ii={type:1,geometry:[[Math.round(this.options.extent*(ar*xt-je)),Math.round(this.options.extent*(vr*xt-tt))]],tags:ir},pi;pi=vt||this.options.generateId?Je[ke+H]:this.points[Je[ke+H]].id,pi!==void 0&&(ii.id=pi),Ie.features.push(ii)}}_limitZoom(Ge){return Math.max(this.options.minZoom,Math.min(Math.floor(+Ge),this.options.maxZoom+1))}_cluster(Ge,Je){let{radius:je,extent:tt,reduce:xt,minPoints:Ie}=this.options,xe=je/(tt*Math.pow(2,Je)),ke=Ge.data,vt=[],ir=this.stride;for(let ar=0;ar<ke.length;ar+=ir){if(ke[ar+2]<=Je)continue;ke[ar+2]=Je;let vr=ke[ar],ii=ke[ar+1],pi=Ge.within(ke[ar],ke[ar+1],xe),$r=ke[ar+te],di=$r;for(let ji of pi){let In=ji*ir;ke[In+2]>Je&&(di+=ke[In+te])}if(di>$r&&di>=Ie){let ji,In=vr*$r,wi=ii*$r,On=-1,qn=((ar/ir|0)<<5)+(Je+1)+this.points.length;for(let Fn of pi){let ra=Fn*ir;if(ke[ra+2]<=Je)continue;ke[ra+2]=Je;let la=ke[ra+te];In+=ke[ra]*la,wi+=ke[ra+1]*la,ke[ra+4]=qn,xt&&(ji||(ji=this._map(ke,ar,!0),On=this.clusterProps.length,this.clusterProps.push(ji)),xt(ji,this._map(ke,ra)))}ke[ar+4]=qn,vt.push(In/di,wi/di,1/0,qn,-1,di),xt&&vt.push(On)}else{for(let ji=0;ji<ir;ji++)vt.push(ke[ar+ji]);if(di>1)for(let ji of pi){let In=ji*ir;if(!(ke[In+2]<=Je)){ke[In+2]=Je;for(let wi=0;wi<ir;wi++)vt.push(ke[In+wi])}}}}return vt}_getOriginId(Ge){return Ge-this.points.length>>5}_getOriginZoom(Ge){return(Ge-this.points.length)%32}_map(Ge,Je,je){if(Ge[Je+te]>1){let Ie=this.clusterProps[Ge[Je+oe]];return je?Object.assign({},Ie):Ie}let tt=this.points[Ge[Je+H]].properties,xt=this.options.map(tt);return je&&xt===tt?Object.assign({},xt):xt}}function Ee(dt,Ge,Je){return{type:"Feature",id:dt[Ge+H],properties:Ce(dt,Ge,Je),geometry:{type:"Point",coordinates:[(je=dt[Ge],360*(je-.5)),Se(dt[Ge+1])]}};var je}function Ce(dt,Ge,Je){let je=dt[Ge+te],tt=je>=1e4?`${Math.round(je/1e3)}k`:je>=1e3?Math.round(je/100)/10+"k":je,xt=dt[Ge+oe],Ie=xt===-1?{}:Object.assign({},Je[xt]);return Object.assign(Ie,{cluster:!0,cluster_id:dt[Ge+H],point_count:je,point_count_abbreviated:tt})}function me(dt){return dt/360+.5}function ie(dt){let Ge=Math.sin(dt*Math.PI/180),Je=.5-.25*Math.log((1+Ge)/(1-Ge))/Math.PI;return Je<0?0:Je>1?1:Je}function Se(dt){let Ge=(180-360*dt)*Math.PI/180;return 360*Math.atan(Math.exp(Ge))/Math.PI-90}function Le(dt,Ge,Je,je){let tt=je,xt=Ge+(Je-Ge>>1),Ie,xe=Je-Ge,ke=dt[Ge],vt=dt[Ge+1],ir=dt[Je],ar=dt[Je+1];for(let vr=Ge+3;vr<Je;vr+=3){let ii=Ae(dt[vr],dt[vr+1],ke,vt,ir,ar);if(ii>tt)Ie=vr,tt=ii;else if(ii===tt){let pi=Math.abs(vr-xt);pi<xe&&(Ie=vr,xe=pi)}}tt>je&&(Ie-Ge>3&&Le(dt,Ge,Ie,je),dt[Ie+2]=tt,Je-Ie>3&&Le(dt,Ie,Je,je))}function Ae(dt,Ge,Je,je,tt,xt){let Ie=tt-Je,xe=xt-je;if(Ie!==0||xe!==0){let ke=((dt-Je)*Ie+(Ge-je)*xe)/(Ie*Ie+xe*xe);ke>1?(Je=tt,je=xt):ke>0&&(Je+=Ie*ke,je+=xe*ke)}return Ie=dt-Je,xe=Ge-je,Ie*Ie+xe*xe}function Fe(dt,Ge,Je,je){let tt={id:dt==null?null:dt,type:Ge,geometry:Je,tags:je,minX:1/0,minY:1/0,maxX:-1/0,maxY:-1/0};if(Ge==="Point"||Ge==="MultiPoint"||Ge==="LineString")Pe(tt,Je);else if(Ge==="Polygon")Pe(tt,Je[0]);else if(Ge==="MultiLineString")for(let xt of Je)Pe(tt,xt);else if(Ge==="MultiPolygon")for(let xt of Je)Pe(tt,xt[0]);return tt}function Pe(dt,Ge){for(let Je=0;Je<Ge.length;Je+=3)dt.minX=Math.min(dt.minX,Ge[Je]),dt.minY=Math.min(dt.minY,Ge[Je+1]),dt.maxX=Math.max(dt.maxX,Ge[Je]),dt.maxY=Math.max(dt.maxY,Ge[Je+1])}function ge(dt,Ge,Je,je){if(!Ge.geometry)return;let tt=Ge.geometry.coordinates;if(tt&&tt.length===0)return;let xt=Ge.geometry.type,Ie=Math.pow(Je.tolerance/((1<<Je.maxZoom)*Je.extent),2),xe=[],ke=Ge.id;if(Je.promoteId?ke=Ge.properties[Je.promoteId]:Je.generateId&&(ke=je||0),xt==="Point")Re(tt,xe);else if(xt==="MultiPoint")for(let vt of tt)Re(vt,xe);else if(xt==="LineString")ce(tt,xe,Ie,!1);else if(xt==="MultiLineString"){if(Je.lineMetrics){for(let vt of tt)xe=[],ce(vt,xe,Ie,!1),dt.push(Fe(ke,"LineString",xe,Ge.properties));return}Ze(tt,xe,Ie,!1)}else if(xt==="Polygon")Ze(tt,xe,Ie,!0);else{if(xt!=="MultiPolygon"){if(xt==="GeometryCollection"){for(let vt of Ge.geometry.geometries)ge(dt,{id:ke,geometry:vt,properties:Ge.properties},Je,je);return}throw new Error("Input data is not a valid GeoJSON object.")}for(let vt of tt){let ir=[];Ze(vt,ir,Ie,!0),xe.push(ir)}}dt.push(Fe(ke,xt,xe,Ge.properties))}function Re(dt,Ge){Ge.push(ut(dt[0]),pt(dt[1]),0)}function ce(dt,Ge,Je,je){let tt,xt,Ie=0;for(let ke=0;ke<dt.length;ke++){let vt=ut(dt[ke][0]),ir=pt(dt[ke][1]);Ge.push(vt,ir,0),ke>0&&(Ie+=je?(tt*ir-vt*xt)/2:Math.sqrt(Math.pow(vt-tt,2)+Math.pow(ir-xt,2))),tt=vt,xt=ir}let xe=Ge.length-3;Ge[2]=1,Le(Ge,0,xe,Je),Ge[xe+2]=1,Ge.size=Math.abs(Ie),Ge.start=0,Ge.end=Ge.size}function Ze(dt,Ge,Je,je){for(let tt=0;tt<dt.length;tt++){let xt=[];ce(dt[tt],xt,Je,je),Ge.push(xt)}}function ut(dt){return dt/360+.5}function pt(dt){let Ge=Math.sin(dt*Math.PI/180),Je=.5-.25*Math.log((1+Ge)/(1-Ge))/Math.PI;return Je<0?0:Je>1?1:Je}function Zt(dt,Ge,Je,je,tt,xt,Ie,xe){if(je/=Ge,xt>=(Je/=Ge)&&Ie<je)return dt;if(Ie<Je||xt>=je)return null;let ke=[];for(let vt of dt){let ir=vt.geometry,ar=vt.type,vr=tt===0?vt.minX:vt.minY,ii=tt===0?vt.maxX:vt.maxY;if(vr>=Je&&ii<je){ke.push(vt);continue}if(ii<Je||vr>=je)continue;let pi=[];if(ar==="Point"||ar==="MultiPoint")st(ir,pi,Je,je,tt);else if(ar==="LineString")lt(ir,pi,Je,je,tt,!1,xe.lineMetrics);else if(ar==="MultiLineString")Nt(ir,pi,Je,je,tt,!1);else if(ar==="Polygon")Nt(ir,pi,Je,je,tt,!0);else if(ar==="MultiPolygon")for(let $r of ir){let di=[];Nt($r,di,Je,je,tt,!0),di.length&&pi.push(di)}if(pi.length){if(xe.lineMetrics&&ar==="LineString"){for(let $r of pi)ke.push(Fe(vt.id,ar,$r,vt.tags));continue}ar!=="LineString"&&ar!=="MultiLineString"||(pi.length===1?(ar="LineString",pi=pi[0]):ar="MultiLineString"),ar!=="Point"&&ar!=="MultiPoint"||(ar=pi.length===3?"Point":"MultiPoint"),ke.push(Fe(vt.id,ar,pi,vt.tags))}}return ke.length?ke:null}function st(dt,Ge,Je,je,tt){for(let xt=0;xt<dt.length;xt+=3){let Ie=dt[xt+tt];Ie>=Je&&Ie<=je&&Jt(Ge,dt[xt],dt[xt+1],dt[xt+2])}}function lt(dt,Ge,Je,je,tt,xt,Ie){let xe=Gt(dt),ke=tt===0?sr:wr,vt,ir,ar=dt.start;for(let di=0;di<dt.length-3;di+=3){let ji=dt[di],In=dt[di+1],wi=dt[di+2],On=dt[di+3],qn=dt[di+4],Fn=tt===0?ji:In,ra=tt===0?On:qn,la=!1;Ie&&(vt=Math.sqrt(Math.pow(ji-On,2)+Math.pow(In-qn,2))),Fn<Je?ra>Je&&(ir=ke(xe,ji,In,On,qn,Je),Ie&&(xe.start=ar+vt*ir)):Fn>je?ra<je&&(ir=ke(xe,ji,In,On,qn,je),Ie&&(xe.start=ar+vt*ir)):Jt(xe,ji,In,wi),ra<Je&&Fn>=Je&&(ir=ke(xe,ji,In,On,qn,Je),la=!0),ra>je&&Fn<=je&&(ir=ke(xe,ji,In,On,qn,je),la=!0),!xt&&la&&(Ie&&(xe.end=ar+vt*ir),Ge.push(xe),xe=Gt(dt)),Ie&&(ar+=vt)}let vr=dt.length-3,ii=dt[vr],pi=dt[vr+1],$r=tt===0?ii:pi;$r>=Je&&$r<=je&&Jt(xe,ii,pi,dt[vr+2]),vr=xe.length-3,xt&&vr>=3&&(xe[vr]!==xe[0]||xe[vr+1]!==xe[1])&&Jt(xe,xe[0],xe[1],xe[2]),xe.length&&Ge.push(xe)}function Gt(dt){let Ge=[];return Ge.size=dt.size,Ge.start=dt.start,Ge.end=dt.end,Ge}function Nt(dt,Ge,Je,je,tt,xt){for(let Ie of dt)lt(Ie,Ge,Je,je,tt,xt,!1)}function Jt(dt,Ge,Je,je){dt.push(Ge,Je,je)}function sr(dt,Ge,Je,je,tt,xt){let Ie=(xt-Ge)/(je-Ge);return Jt(dt,xt,Je+(tt-Je)*Ie,1),Ie}function wr(dt,Ge,Je,je,tt,xt){let Ie=(xt-Je)/(tt-Je);return Jt(dt,Ge+(je-Ge)*Ie,xt,1),Ie}function cr(dt,Ge){let Je=[];for(let je=0;je<dt.length;je++){let tt=dt[je],xt=tt.type,Ie;if(xt==="Point"||xt==="MultiPoint"||xt==="LineString")Ie=$e(tt.geometry,Ge);else if(xt==="MultiLineString"||xt==="Polygon"){Ie=[];for(let xe of tt.geometry)Ie.push($e(xe,Ge))}else if(xt==="MultiPolygon"){Ie=[];for(let xe of tt.geometry){let ke=[];for(let vt of xe)ke.push($e(vt,Ge));Ie.push(ke)}}Je.push(Fe(tt.id,xt,Ie,tt.tags))}return Je}function $e(dt,Ge){let Je=[];Je.size=dt.size,dt.start!==void 0&&(Je.start=dt.start,Je.end=dt.end);for(let je=0;je<dt.length;je+=3)Je.push(dt[je]+Ge,dt[je+1],dt[je+2]);return Je}function St(dt,Ge){if(dt.transformed)return dt;let Je=1<<dt.z,je=dt.x,tt=dt.y;for(let xt of dt.features){let Ie=xt.geometry,xe=xt.type;if(xt.geometry=[],xe===1)for(let ke=0;ke<Ie.length;ke+=2)xt.geometry.push(Qt(Ie[ke],Ie[ke+1],Ge,Je,je,tt));else for(let ke=0;ke<Ie.length;ke++){let vt=[];for(let ir=0;ir<Ie[ke].length;ir+=2)vt.push(Qt(Ie[ke][ir],Ie[ke][ir+1],Ge,Je,je,tt));xt.geometry.push(vt)}}return dt.transformed=!0,dt}function Qt(dt,Ge,Je,je,tt,xt){return[Math.round(Je*(dt*je-tt)),Math.round(Je*(Ge*je-xt))]}function Vt(dt,Ge,Je,je,tt){let xt=Ge===tt.maxZoom?0:tt.tolerance/((1<<Ge)*tt.extent),Ie={features:[],numPoints:0,numSimplified:0,numFeatures:dt.length,source:null,x:Je,y:je,z:Ge,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0};for(let xe of dt)_t(Ie,xe,xt,tt);return Ie}function _t(dt,Ge,Je,je){let tt=Ge.geometry,xt=Ge.type,Ie=[];if(dt.minX=Math.min(dt.minX,Ge.minX),dt.minY=Math.min(dt.minY,Ge.minY),dt.maxX=Math.max(dt.maxX,Ge.maxX),dt.maxY=Math.max(dt.maxY,Ge.maxY),xt==="Point"||xt==="MultiPoint")for(let xe=0;xe<tt.length;xe+=3)Ie.push(tt[xe],tt[xe+1]),dt.numPoints++,dt.numSimplified++;else if(xt==="LineString")It(Ie,tt,dt,Je,!1,!1);else if(xt==="MultiLineString"||xt==="Polygon")for(let xe=0;xe<tt.length;xe++)It(Ie,tt[xe],dt,Je,xt==="Polygon",xe===0);else if(xt==="MultiPolygon")for(let xe=0;xe<tt.length;xe++){let ke=tt[xe];for(let vt=0;vt<ke.length;vt++)It(Ie,ke[vt],dt,Je,!0,vt===0)}if(Ie.length){let xe=Ge.tags||null;if(xt==="LineString"&&je.lineMetrics){xe={};for(let vt in Ge.tags)xe[vt]=Ge.tags[vt];xe.mapbox_clip_start=tt.start/tt.size,xe.mapbox_clip_end=tt.end/tt.size}let ke={geometry:Ie,type:xt==="Polygon"||xt==="MultiPolygon"?3:xt==="LineString"||xt==="MultiLineString"?2:1,tags:xe};Ge.id!==null&&(ke.id=Ge.id),dt.features.push(ke)}}function It(dt,Ge,Je,je,tt,xt){let Ie=je*je;if(je>0&&Ge.size<(tt?Ie:je))return void(Je.numPoints+=Ge.length/3);let xe=[];for(let ke=0;ke<Ge.length;ke+=3)(je===0||Ge[ke+2]>Ie)&&(Je.numSimplified++,xe.push(Ge[ke],Ge[ke+1])),Je.numPoints++;tt&&function(ke,vt){let ir=0;for(let ar=0,vr=ke.length,ii=vr-2;ar<vr;ii=ar,ar+=2)ir+=(ke[ar]-ke[ii])*(ke[ar+1]+ke[ii+1]);if(ir>0===vt)for(let ar=0,vr=ke.length;ar<vr/2;ar+=2){let ii=ke[ar],pi=ke[ar+1];ke[ar]=ke[vr-2-ar],ke[ar+1]=ke[vr-1-ar],ke[vr-2-ar]=ii,ke[vr-1-ar]=pi}}(xe,xt),dt.push(xe)}let mt={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0};class er{constructor(Ge,Je){let je=(Je=this.options=function(xt,Ie){for(let xe in Ie)xt[xe]=Ie[xe];return xt}(Object.create(mt),Je)).debug;if(je&&console.time("preprocess data"),Je.maxZoom<0||Je.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(Je.promoteId&&Je.generateId)throw new Error("promoteId and generateId cannot be used together.");let tt=function(xt,Ie){let xe=[];if(xt.type==="FeatureCollection")for(let ke=0;ke<xt.features.length;ke++)ge(xe,xt.features[ke],Ie,ke);else ge(xe,xt.type==="Feature"?xt:{geometry:xt},Ie);return xe}(Ge,Je);this.tiles={},this.tileCoords=[],je&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",Je.indexMaxZoom,Je.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),tt=function(xt,Ie){let xe=Ie.buffer/Ie.extent,ke=xt,vt=Zt(xt,1,-1-xe,xe,0,-1,2,Ie),ir=Zt(xt,1,1-xe,2+xe,0,-1,2,Ie);return(vt||ir)&&(ke=Zt(xt,1,-xe,1+xe,0,-1,2,Ie)||[],vt&&(ke=cr(vt,1).concat(ke)),ir&&(ke=ke.concat(cr(ir,-1)))),ke}(tt,Je),tt.length&&this.splitTile(tt,0,0,0),je&&(tt.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}splitTile(Ge,Je,je,tt,xt,Ie,xe){let ke=[Ge,Je,je,tt],vt=this.options,ir=vt.debug;for(;ke.length;){tt=ke.pop(),je=ke.pop(),Je=ke.pop(),Ge=ke.pop();let ar=1<<Je,vr=lr(Je,je,tt),ii=this.tiles[vr];if(!ii&&(ir>1&&console.time("creation"),ii=this.tiles[vr]=Vt(Ge,Je,je,tt,vt),this.tileCoords.push({z:Je,x:je,y:tt}),ir)){ir>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",Je,je,tt,ii.numFeatures,ii.numPoints,ii.numSimplified),console.timeEnd("creation"));let la=`z${Je}`;this.stats[la]=(this.stats[la]||0)+1,this.total++}if(ii.source=Ge,xt==null){if(Je===vt.indexMaxZoom||ii.numPoints<=vt.indexMaxPoints)continue}else{if(Je===vt.maxZoom||Je===xt)continue;if(xt!=null){let la=xt-Je;if(je!==Ie>>la||tt!==xe>>la)continue}}if(ii.source=null,Ge.length===0)continue;ir>1&&console.time("clipping");let pi=.5*vt.buffer/vt.extent,$r=.5-pi,di=.5+pi,ji=1+pi,In=null,wi=null,On=null,qn=null,Fn=Zt(Ge,ar,je-pi,je+di,0,ii.minX,ii.maxX,vt),ra=Zt(Ge,ar,je+$r,je+ji,0,ii.minX,ii.maxX,vt);Ge=null,Fn&&(In=Zt(Fn,ar,tt-pi,tt+di,1,ii.minY,ii.maxY,vt),wi=Zt(Fn,ar,tt+$r,tt+ji,1,ii.minY,ii.maxY,vt),Fn=null),ra&&(On=Zt(ra,ar,tt-pi,tt+di,1,ii.minY,ii.maxY,vt),qn=Zt(ra,ar,tt+$r,tt+ji,1,ii.minY,ii.maxY,vt),ra=null),ir>1&&console.timeEnd("clipping"),ke.push(In||[],Je+1,2*je,2*tt),ke.push(wi||[],Je+1,2*je,2*tt+1),ke.push(On||[],Je+1,2*je+1,2*tt),ke.push(qn||[],Je+1,2*je+1,2*tt+1)}}getTile(Ge,Je,je){Ge=+Ge,Je=+Je,je=+je;let tt=this.options,{extent:xt,debug:Ie}=tt;if(Ge<0||Ge>24)return null;let xe=1<<Ge,ke=lr(Ge,Je=Je+xe&xe-1,je);if(this.tiles[ke])return St(this.tiles[ke],xt);Ie>1&&console.log("drilling down to z%d-%d-%d",Ge,Je,je);let vt,ir=Ge,ar=Je,vr=je;for(;!vt&&ir>0;)ir--,ar>>=1,vr>>=1,vt=this.tiles[lr(ir,ar,vr)];return vt&&vt.source?(Ie>1&&(console.log("found parent tile z%d-%d-%d",ir,ar,vr),console.time("drilling down")),this.splitTile(vt.source,ir,ar,vr,Ge,Je,je),Ie>1&&console.timeEnd("drilling down"),this.tiles[ke]?St(this.tiles[ke],xt):null):null}}function lr(dt,Ge,Je){return 32*((1<<dt)*Je+Ge)+dt}function Tr(dt,Ge){return Ge?dt.properties[Ge]:dt.id}function Lr(dt,Ge){if(dt==null)return!0;if(dt.type==="Feature")return Tr(dt,Ge)!=null;if(dt.type==="FeatureCollection"){let Je=new Set;for(let je of dt.features){let tt=Tr(je,Ge);if(tt==null||Je.has(tt))return!1;Je.add(tt)}return!0}return!1}function ti(dt,Ge){let Je=new Map;if(dt!=null)if(dt.type==="Feature")Je.set(Tr(dt,Ge),dt);else for(let je of dt.features)Je.set(Tr(je,Ge),je);return Je}class Br extends u{constructor(){super(...arguments),this._dataUpdateable=new Map}loadVectorTile(Ge,Je){return i._(this,void 0,void 0,function*(){let je=Ge.tileID.canonical;if(!this._geoJSONIndex)throw new Error("Unable to parse the data into a cluster or geojson");let tt=this._geoJSONIndex.getTile(je.z,je.x,je.y);if(!tt)return null;let xt=new class{constructor(xe){this.layers={_geojsonTileLayer:this},this.name="_geojsonTileLayer",this.extent=i.X,this.length=xe.length,this._features=xe}feature(xe){return new class{constructor(ke){this._feature=ke,this.extent=i.X,this.type=ke.type,this.properties=ke.tags,"id"in ke&&!isNaN(ke.id)&&(this.id=parseInt(ke.id,10))}loadGeometry(){if(this._feature.type===1){let ke=[];for(let vt of this._feature.geometry)ke.push([new i.P(vt[0],vt[1])]);return ke}{let ke=[];for(let vt of this._feature.geometry){let ir=[];for(let ar of vt)ir.push(new i.P(ar[0],ar[1]));ke.push(ir)}return ke}}toGeoJSON(ke,vt,ir){return v.call(this,ke,vt,ir)}}(this._features[xe])}}(tt.features),Ie=G(xt);return Ie.byteOffset===0&&Ie.byteLength===Ie.buffer.byteLength||(Ie=new Uint8Array(Ie)),{vectorTile:xt,rawData:Ie.buffer}})}loadData(Ge){return i._(this,void 0,void 0,function*(){var Je;(Je=this._pendingRequest)===null||Je===void 0||Je.abort();let je=!!(Ge&&Ge.request&&Ge.request.collectResourceTiming)&&new i.bv(Ge.request);this._pendingRequest=new AbortController;try{this._pendingData=this.loadAndProcessGeoJSON(Ge,this._pendingRequest),this._geoJSONIndex=Ge.cluster?new _e(function({superclusterOptions:Ie,clusterProperties:xe}){if(!xe||!Ie)return Ie;let ke={},vt={},ir={accumulated:null,zoom:0},ar={properties:null},vr=Object.keys(xe);for(let ii of vr){let[pi,$r]=xe[ii],di=i.bC($r),ji=i.bC(typeof pi=="string"?[pi,["accumulated"],["get",ii]]:pi);ke[ii]=di.value,vt[ii]=ji.value}return Ie.map=ii=>{ar.properties=ii;let pi={};for(let $r of vr)pi[$r]=ke[$r].evaluate(ir,ar);return pi},Ie.reduce=(ii,pi)=>{ar.properties=pi;for(let $r of vr)ir.accumulated=ii[$r],ii[$r]=vt[$r].evaluate(ir,ar)},Ie}(Ge)).load((yield this._pendingData).features):(tt=yield this._pendingData,new er(tt,Ge.geojsonVtOptions)),this.loaded={};let xt={};if(je){let Ie=je.finish();Ie&&(xt.resourceTiming={},xt.resourceTiming[Ge.source]=JSON.parse(JSON.stringify(Ie)))}return xt}catch(xt){if(delete this._pendingRequest,i.bB(xt))return{abandoned:!0};throw xt}var tt})}getData(){return i._(this,void 0,void 0,function*(){return this._pendingData})}reloadTile(Ge){let Je=this.loaded;return Je&&Je[Ge.uid]?super.reloadTile(Ge):this.loadTile(Ge)}loadAndProcessGeoJSON(Ge,Je){return i._(this,void 0,void 0,function*(){let je=yield this.loadGeoJSON(Ge,Je);if(delete this._pendingRequest,typeof je!="object")throw new Error(`Input data given to '${Ge.source}' is not a valid GeoJSON object.`);if(d(je,!0),Ge.filter){let tt=i.bC(Ge.filter,{type:"boolean","property-type":"data-driven",overridable:!1,transition:!1});if(tt.result==="error")throw new Error(tt.value.map(Ie=>`${Ie.key}: ${Ie.message}`).join(", "));je={type:"FeatureCollection",features:je.features.filter(Ie=>tt.value.evaluate({zoom:0},Ie))}}return je})}loadGeoJSON(Ge,Je){return i._(this,void 0,void 0,function*(){let{promoteId:je}=Ge;if(Ge.request){let tt=yield i.h(Ge.request,Je);return this._dataUpdateable=Lr(tt.data,je)?ti(tt.data,je):void 0,tt.data}if(typeof Ge.data=="string")try{let tt=JSON.parse(Ge.data);return this._dataUpdateable=Lr(tt,je)?ti(tt,je):void 0,tt}catch(tt){throw new Error(`Input data given to '${Ge.source}' is not a valid GeoJSON object.`)}if(!Ge.dataDiff)throw new Error(`Input data given to '${Ge.source}' is not a valid GeoJSON object.`);if(!this._dataUpdateable)throw new Error(`Cannot update existing geojson data in ${Ge.source}`);return function(tt,xt,Ie){var xe,ke,vt,ir;if(xt.removeAll&&tt.clear(),xt.remove)for(let ar of xt.remove)tt.delete(ar);if(xt.add)for(let ar of xt.add){let vr=Tr(ar,Ie);vr!=null&&tt.set(vr,ar)}if(xt.update)for(let ar of xt.update){let vr=tt.get(ar.id);if(vr==null)continue;let ii=!ar.removeAllProperties&&(((xe=ar.removeProperties)===null||xe===void 0?void 0:xe.length)>0||((ke=ar.addOrUpdateProperties)===null||ke===void 0?void 0:ke.length)>0);if((ar.newGeometry||ar.removeAllProperties||ii)&&(vr=Object.assign({},vr),tt.set(ar.id,vr),ii&&(vr.properties=Object.assign({},vr.properties))),ar.newGeometry&&(vr.geometry=ar.newGeometry),ar.removeAllProperties)vr.properties={};else if(((vt=ar.removeProperties)===null||vt===void 0?void 0:vt.length)>0)for(let pi of ar.removeProperties)Object.prototype.hasOwnProperty.call(vr.properties,pi)&&delete vr.properties[pi];if(((ir=ar.addOrUpdateProperties)===null||ir===void 0?void 0:ir.length)>0)for(let{key:pi,value:$r}of ar.addOrUpdateProperties)vr.properties[pi]=$r}}(this._dataUpdateable,Ge.dataDiff,je),{type:"FeatureCollection",features:Array.from(this._dataUpdateable.values())}})}removeSource(Ge){return i._(this,void 0,void 0,function*(){this._pendingRequest&&this._pendingRequest.abort()})}getClusterExpansionZoom(Ge){return this._geoJSONIndex.getClusterExpansionZoom(Ge.clusterId)}getClusterChildren(Ge){return this._geoJSONIndex.getChildren(Ge.clusterId)}getClusterLeaves(Ge){return this._geoJSONIndex.getLeaves(Ge.clusterId,Ge.limit,Ge.offset)}}class Vr{constructor(Ge){this.self=Ge,this.actor=new i.F(Ge),this.layerIndexes={},this.availableImages={},this.workerSources={},this.demWorkerSources={},this.externalWorkerSourceTypes={},this.self.registerWorkerSource=(Je,je)=>{if(this.externalWorkerSourceTypes[Je])throw new Error(`Worker source with name "${Je}" already registered.`);this.externalWorkerSourceTypes[Je]=je},this.self.addProtocol=i.bi,this.self.removeProtocol=i.bj,this.self.registerRTLTextPlugin=Je=>{if(i.bD.isParsed())throw new Error("RTL text plugin already registered.");i.bD.setMethods(Je)},this.actor.registerMessageHandler("LDT",(Je,je)=>this._getDEMWorkerSource(Je,je.source).loadTile(je)),this.actor.registerMessageHandler("RDT",(Je,je)=>i._(this,void 0,void 0,function*(){this._getDEMWorkerSource(Je,je.source).removeTile(je)})),this.actor.registerMessageHandler("GCEZ",(Je,je)=>i._(this,void 0,void 0,function*(){return this._getWorkerSource(Je,je.type,je.source).getClusterExpansionZoom(je)})),this.actor.registerMessageHandler("GCC",(Je,je)=>i._(this,void 0,void 0,function*(){return this._getWorkerSource(Je,je.type,je.source).getClusterChildren(je)})),this.actor.registerMessageHandler("GCL",(Je,je)=>i._(this,void 0,void 0,function*(){return this._getWorkerSource(Je,je.type,je.source).getClusterLeaves(je)})),this.actor.registerMessageHandler("LD",(Je,je)=>this._getWorkerSource(Je,je.type,je.source).loadData(je)),this.actor.registerMessageHandler("GD",(Je,je)=>this._getWorkerSource(Je,je.type,je.source).getData()),this.actor.registerMessageHandler("LT",(Je,je)=>this._getWorkerSource(Je,je.type,je.source).loadTile(je)),this.actor.registerMessageHandler("RT",(Je,je)=>this._getWorkerSource(Je,je.type,je.source).reloadTile(je)),this.actor.registerMessageHandler("AT",(Je,je)=>this._getWorkerSource(Je,je.type,je.source).abortTile(je)),this.actor.registerMessageHandler("RMT",(Je,je)=>this._getWorkerSource(Je,je.type,je.source).removeTile(je)),this.actor.registerMessageHandler("RS",(Je,je)=>i._(this,void 0,void 0,function*(){if(!this.workerSources[Je]||!this.workerSources[Je][je.type]||!this.workerSources[Je][je.type][je.source])return;let tt=this.workerSources[Je][je.type][je.source];delete this.workerSources[Je][je.type][je.source],tt.removeSource!==void 0&&tt.removeSource(je)})),this.actor.registerMessageHandler("RM",Je=>i._(this,void 0,void 0,function*(){delete this.layerIndexes[Je],delete this.availableImages[Je],delete this.workerSources[Je],delete this.demWorkerSources[Je]})),this.actor.registerMessageHandler("SR",(Je,je)=>i._(this,void 0,void 0,function*(){this.referrer=je})),this.actor.registerMessageHandler("SRPS",(Je,je)=>this._syncRTLPluginState(Je,je)),this.actor.registerMessageHandler("IS",(Je,je)=>i._(this,void 0,void 0,function*(){this.self.importScripts(je)})),this.actor.registerMessageHandler("SI",(Je,je)=>this._setImages(Je,je)),this.actor.registerMessageHandler("UL",(Je,je)=>i._(this,void 0,void 0,function*(){this._getLayerIndex(Je).update(je.layers,je.removedIds)})),this.actor.registerMessageHandler("SL",(Je,je)=>i._(this,void 0,void 0,function*(){this._getLayerIndex(Je).replace(je)}))}_setImages(Ge,Je){return i._(this,void 0,void 0,function*(){this.availableImages[Ge]=Je;for(let je in this.workerSources[Ge]){let tt=this.workerSources[Ge][je];for(let xt in tt)tt[xt].availableImages=Je}})}_syncRTLPluginState(Ge,Je){return i._(this,void 0,void 0,function*(){if(i.bD.isParsed())return i.bD.getState();if(Je.pluginStatus!=="loading")return i.bD.setState(Je),Je;let je=Je.pluginURL;if(this.self.importScripts(je),i.bD.isParsed()){let tt={pluginStatus:"loaded",pluginURL:je};return i.bD.setState(tt),tt}throw i.bD.setState({pluginStatus:"error",pluginURL:""}),new Error(`RTL Text Plugin failed to import scripts from ${je}`)})}_getAvailableImages(Ge){let Je=this.availableImages[Ge];return Je||(Je=[]),Je}_getLayerIndex(Ge){let Je=this.layerIndexes[Ge];return Je||(Je=this.layerIndexes[Ge]=new a),Je}_getWorkerSource(Ge,Je,je){if(this.workerSources[Ge]||(this.workerSources[Ge]={}),this.workerSources[Ge][Je]||(this.workerSources[Ge][Je]={}),!this.workerSources[Ge][Je][je]){let tt={sendAsync:(xt,Ie)=>(xt.targetMapId=Ge,this.actor.sendAsync(xt,Ie))};switch(Je){case"vector":this.workerSources[Ge][Je][je]=new u(tt,this._getLayerIndex(Ge),this._getAvailableImages(Ge));break;case"geojson":this.workerSources[Ge][Je][je]=new Br(tt,this._getLayerIndex(Ge),this._getAvailableImages(Ge));break;default:this.workerSources[Ge][Je][je]=new this.externalWorkerSourceTypes[Je](tt,this._getLayerIndex(Ge),this._getAvailableImages(Ge))}}return this.workerSources[Ge][Je][je]}_getDEMWorkerSource(Ge,Je){return this.demWorkerSources[Ge]||(this.demWorkerSources[Ge]={}),this.demWorkerSources[Ge][Je]||(this.demWorkerSources[Ge][Je]=new c),this.demWorkerSources[Ge][Je]}}return i.i(self)&&(self.worker=new Vr(self)),Vr}),r("index",["exports","./shared"],function(i,a){"use strict";var o="4.7.1";let s,l,u={now:typeof performance!="undefined"&&performance&&performance.now?performance.now.bind(performance):Date.now.bind(Date),frameAsync:ue=>new Promise((w,B)=>{let Q=requestAnimationFrame(w);ue.signal.addEventListener("abort",()=>{cancelAnimationFrame(Q),B(a.c())})}),getImageData(ue,w=0){return this.getImageCanvasContext(ue).getImageData(-w,-w,ue.width+2*w,ue.height+2*w)},getImageCanvasContext(ue){let w=window.document.createElement("canvas"),B=w.getContext("2d",{willReadFrequently:!0});if(!B)throw new Error("failed to create canvas 2d context");return w.width=ue.width,w.height=ue.height,B.drawImage(ue,0,0,ue.width,ue.height),B},resolveURL:ue=>(s||(s=document.createElement("a")),s.href=ue,s.href),hardwareConcurrency:typeof navigator!="undefined"&&navigator.hardwareConcurrency||4,get prefersReducedMotion(){return!!matchMedia&&(l==null&&(l=matchMedia("(prefers-reduced-motion: reduce)")),l.matches)}};class c{static testProp(w){if(!c.docStyle)return w[0];for(let B=0;B<w.length;B++)if(w[B]in c.docStyle)return w[B];return w[0]}static create(w,B,Q){let ee=window.document.createElement(w);return B!==void 0&&(ee.className=B),Q&&Q.appendChild(ee),ee}static createNS(w,B){return window.document.createElementNS(w,B)}static disableDrag(){c.docStyle&&c.selectProp&&(c.userSelect=c.docStyle[c.selectProp],c.docStyle[c.selectProp]="none")}static enableDrag(){c.docStyle&&c.selectProp&&(c.docStyle[c.selectProp]=c.userSelect)}static setTransform(w,B){w.style[c.transformProp]=B}static addEventListener(w,B,Q,ee={}){w.addEventListener(B,Q,"passive"in ee?ee:ee.capture)}static removeEventListener(w,B,Q,ee={}){w.removeEventListener(B,Q,"passive"in ee?ee:ee.capture)}static suppressClickInternal(w){w.preventDefault(),w.stopPropagation(),window.removeEventListener("click",c.suppressClickInternal,!0)}static suppressClick(){window.addEventListener("click",c.suppressClickInternal,!0),window.setTimeout(()=>{window.removeEventListener("click",c.suppressClickInternal,!0)},0)}static getScale(w){let B=w.getBoundingClientRect();return{x:B.width/w.offsetWidth||1,y:B.height/w.offsetHeight||1,boundingClientRect:B}}static getPoint(w,B,Q){let ee=B.boundingClientRect;return new a.P((Q.clientX-ee.left)/B.x-w.clientLeft,(Q.clientY-ee.top)/B.y-w.clientTop)}static mousePos(w,B){let Q=c.getScale(w);return c.getPoint(w,Q,B)}static touchPos(w,B){let Q=[],ee=c.getScale(w);for(let le=0;le<B.length;le++)Q.push(c.getPoint(w,ee,B[le]));return Q}static mouseButton(w){return w.button}static remove(w){w.parentNode&&w.parentNode.removeChild(w)}}c.docStyle=typeof window!="undefined"&&window.document&&window.document.documentElement.style,c.selectProp=c.testProp(["userSelect","MozUserSelect","WebkitUserSelect","msUserSelect"]),c.transformProp=c.testProp(["transform","WebkitTransform"]);let f={supported:!1,testSupport:function(ue){!v&&d&&(_?b(ue):h=ue)}},h,d,v=!1,_=!1;function b(ue){let w=ue.createTexture();ue.bindTexture(ue.TEXTURE_2D,w);try{if(ue.texImage2D(ue.TEXTURE_2D,0,ue.RGBA,ue.RGBA,ue.UNSIGNED_BYTE,d),ue.isContextLost())return;f.supported=!0}catch(B){}ue.deleteTexture(w),v=!0}var p;typeof document!="undefined"&&(d=document.createElement("img"),d.onload=()=>{h&&b(h),h=null,_=!0},d.onerror=()=>{v=!0,h=null},d.src="data:image/webp;base64,UklGRh4AAABXRUJQVlA4TBEAAAAvAQAAAAfQ//73v/+BiOh/AAA="),function(ue){let w,B,Q,ee;ue.resetRequestQueue=()=>{w=[],B=0,Q=0,ee={}},ue.addThrottleControl=ot=>{let Tt=Q++;return ee[Tt]=ot,Tt},ue.removeThrottleControl=ot=>{delete ee[ot],qe()},ue.getImage=(ot,Tt,Yt=!0)=>new Promise((Kt,xr)=>{f.supported&&(ot.headers||(ot.headers={}),ot.headers.accept="image/webp,*/*"),a.e(ot,{type:"image"}),w.push({abortController:Tt,requestParameters:ot,supportImageRefresh:Yt,state:"queued",onError:Ir=>{xr(Ir)},onSuccess:Ir=>{Kt(Ir)}}),qe()});let le=ot=>a._(this,void 0,void 0,function*(){ot.state="running";let{requestParameters:Tt,supportImageRefresh:Yt,onError:Kt,onSuccess:xr,abortController:Ir}=ot,ve=Yt===!1&&!a.i(self)&&!a.g(Tt.url)&&(!Tt.headers||Object.keys(Tt.headers).reduce((Be,et)=>Be&&et==="accept",!0));B++;let be=ve?Xe(Tt,Ir):a.m(Tt,Ir);try{let Be=yield be;delete ot.abortController,ot.state="completed",Be.data instanceof HTMLImageElement||a.b(Be.data)?xr(Be):Be.data&&xr({data:yield(De=Be.data,typeof createImageBitmap=="function"?a.d(De):a.f(De)),cacheControl:Be.cacheControl,expires:Be.expires})}catch(Be){delete ot.abortController,Kt(Be)}finally{B--,qe()}var De}),qe=()=>{let ot=(()=>{for(let Tt of Object.keys(ee))if(ee[Tt]())return!0;return!1})()?a.a.MAX_PARALLEL_IMAGE_REQUESTS_PER_FRAME:a.a.MAX_PARALLEL_IMAGE_REQUESTS;for(let Tt=B;Tt<ot&&w.length>0;Tt++){let Yt=w.shift();Yt.abortController.signal.aborted?Tt--:le(Yt)}},Xe=(ot,Tt)=>new Promise((Yt,Kt)=>{let xr=new Image,Ir=ot.url,ve=ot.credentials;ve&&ve==="include"?xr.crossOrigin="use-credentials":(ve&&ve==="same-origin"||!a.s(Ir))&&(xr.crossOrigin="anonymous"),Tt.signal.addEventListener("abort",()=>{xr.src="",Kt(a.c())}),xr.fetchPriority="high",xr.onload=()=>{xr.onerror=xr.onload=null,Yt({data:xr})},xr.onerror=()=>{xr.onerror=xr.onload=null,Tt.signal.aborted||Kt(new Error("Could not load image. Please make sure to use a supported image type such as PNG or JPEG. Note that SVGs are not supported."))},xr.src=Ir})}(p||(p={})),p.resetRequestQueue();class k{constructor(w){this._transformRequestFn=w}transformRequest(w,B){return this._transformRequestFn&&this._transformRequestFn(w,B)||{url:w}}setTransformRequest(w){this._transformRequestFn=w}}function E(ue){var w=new a.A(3);return w[0]=ue[0],w[1]=ue[1],w[2]=ue[2],w}var S,L=function(ue,w,B){return ue[0]=w[0]-B[0],ue[1]=w[1]-B[1],ue[2]=w[2]-B[2],ue};S=new a.A(3),a.A!=Float32Array&&(S[0]=0,S[1]=0,S[2]=0);var x=function(ue){var w=ue[0],B=ue[1];return w*w+B*B};function C(ue){let w=[];if(typeof ue=="string")w.push({id:"default",url:ue});else if(ue&&ue.length>0){let B=[];for(let{id:Q,url:ee}of ue){let le=`${Q}${ee}`;B.indexOf(le)===-1&&(B.push(le),w.push({id:Q,url:ee}))}}return w}function M(ue,w,B){let Q=ue.split("?");return Q[0]+=`${w}${B}`,Q.join("?")}(function(){var ue=new a.A(2);a.A!=Float32Array&&(ue[0]=0,ue[1]=0)})();class g{constructor(w,B,Q,ee){this.context=w,this.format=Q,this.texture=w.gl.createTexture(),this.update(B,ee)}update(w,B,Q){let{width:ee,height:le}=w,qe=!(this.size&&this.size[0]===ee&&this.size[1]===le||Q),{context:Xe}=this,{gl:ot}=Xe;if(this.useMipmap=!!(B&&B.useMipmap),ot.bindTexture(ot.TEXTURE_2D,this.texture),Xe.pixelStoreUnpackFlipY.set(!1),Xe.pixelStoreUnpack.set(1),Xe.pixelStoreUnpackPremultiplyAlpha.set(this.format===ot.RGBA&&(!B||B.premultiply!==!1)),qe)this.size=[ee,le],w instanceof HTMLImageElement||w instanceof HTMLCanvasElement||w instanceof HTMLVideoElement||w instanceof ImageData||a.b(w)?ot.texImage2D(ot.TEXTURE_2D,0,this.format,this.format,ot.UNSIGNED_BYTE,w):ot.texImage2D(ot.TEXTURE_2D,0,this.format,ee,le,0,this.format,ot.UNSIGNED_BYTE,w.data);else{let{x:Tt,y:Yt}=Q||{x:0,y:0};w instanceof HTMLImageElement||w instanceof HTMLCanvasElement||w instanceof HTMLVideoElement||w instanceof 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a.E{constructor(){super(),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new a.R({width:1,height:1}),this.dirty=!0}isLoaded(){return this.loaded}setLoaded(w){if(this.loaded!==w&&(this.loaded=w,w)){for(let{ids:B,promiseResolve:Q}of this.requestors)Q(this._getImagesForIds(B));this.requestors=[]}}getImage(w){let B=this.images[w];if(B&&!B.data&&B.spriteData){let Q=B.spriteData;B.data=new a.R({width:Q.width,height:Q.height},Q.context.getImageData(Q.x,Q.y,Q.width,Q.height).data),B.spriteData=null}return B}addImage(w,B){if(this.images[w])throw new Error(`Image id ${w} already exist, use updateImage instead`);this._validate(w,B)&&(this.images[w]=B)}_validate(w,B){let Q=!0,ee=B.data||B.spriteData;return this._validateStretch(B.stretchX,ee&&ee.width)||(this.fire(new a.j(new Error(`Image "${w}" has invalid "stretchX" value`))),Q=!1),this._validateStretch(B.stretchY,ee&&ee.height)||(this.fire(new a.j(new Error(`Image "${w}" has invalid "stretchY" value`))),Q=!1),this._validateContent(B.content,B)||(this.fire(new a.j(new Error(`Image "${w}" has invalid "content" value`))),Q=!1),Q}_validateStretch(w,B){if(!w)return!0;let Q=0;for(let ee of w){if(ee[0]<Q||ee[1]<ee[0]||B<ee[1])return!1;Q=ee[1]}return!0}_validateContent(w,B){if(!w)return!0;if(w.length!==4)return!1;let Q=B.spriteData,ee=Q&&Q.width||B.data.width,le=Q&&Q.height||B.data.height;return!(w[0]<0||ee<w[0]||w[1]<0||le<w[1]||w[2]<0||ee<w[2]||w[3]<0||le<w[3]||w[2]<w[0]||w[3]<w[1])}updateImage(w,B,Q=!0){let ee=this.getImage(w);if(Q&&(ee.data.width!==B.data.width||ee.data.height!==B.data.height))throw new Error(`size mismatch between old image (${ee.data.width}x${ee.data.height}) and new image (${B.data.width}x${B.data.height}).`);B.version=ee.version+1,this.images[w]=B,this.updatedImages[w]=!0}removeImage(w){let B=this.images[w];delete this.images[w],delete this.patterns[w],B.userImage&&B.userImage.onRemove&&B.userImage.onRemove()}listImages(){return Object.keys(this.images)}getImages(w){return new Promise((B,Q)=>{let ee=!0;if(!this.isLoaded())for(let le of w)this.images[le]||(ee=!1);this.isLoaded()||ee?B(this._getImagesForIds(w)):this.requestors.push({ids:w,promiseResolve:B})})}_getImagesForIds(w){let B={};for(let Q of w){let ee=this.getImage(Q);ee||(this.fire(new a.k("styleimagemissing",{id:Q})),ee=this.getImage(Q)),ee?B[Q]={data:ee.data.clone(),pixelRatio:ee.pixelRatio,sdf:ee.sdf,version:ee.version,stretchX:ee.stretchX,stretchY:ee.stretchY,content:ee.content,textFitWidth:ee.textFitWidth,textFitHeight:ee.textFitHeight,hasRenderCallback:!!(ee.userImage&&ee.userImage.render)}:a.w(`Image "${Q}" could not be loaded. Please make sure you have added the image with map.addImage() or a "sprite" property in your style. You can provide missing images by listening for the "styleimagemissing" map event.`)}return B}getPixelSize(){let{width:w,height:B}=this.atlasImage;return{width:w,height:B}}getPattern(w){let B=this.patterns[w],Q=this.getImage(w);if(!Q)return null;if(B&&B.position.version===Q.version)return B.position;if(B)B.position.version=Q.version;else{let ee={w:Q.data.width+2,h:Q.data.height+2,x:0,y:0},le=new a.I(ee,Q);this.patterns[w]={bin:ee,position:le}}return this._updatePatternAtlas(),this.patterns[w].position}bind(w){let B=w.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new g(w,this.atlasImage,B.RGBA),this.atlasTexture.bind(B.LINEAR,B.CLAMP_TO_EDGE)}_updatePatternAtlas(){let w=[];for(let le in this.patterns)w.push(this.patterns[le].bin);let{w:B,h:Q}=a.p(w),ee=this.atlasImage;ee.resize({width:B||1,height:Q||1});for(let le in this.patterns){let{bin:qe}=this.patterns[le],Xe=qe.x+1,ot=qe.y+1,Tt=this.getImage(le).data,Yt=Tt.width,Kt=Tt.height;a.R.copy(Tt,ee,{x:0,y:0},{x:Xe,y:ot},{width:Yt,height:Kt}),a.R.copy(Tt,ee,{x:0,y:Kt-1},{x:Xe,y:ot-1},{width:Yt,height:1}),a.R.copy(Tt,ee,{x:0,y:0},{x:Xe,y:ot+Kt},{width:Yt,height:1}),a.R.copy(Tt,ee,{x:Yt-1,y:0},{x:Xe-1,y:ot},{width:1,height:Kt}),a.R.copy(Tt,ee,{x:0,y:0},{x:Xe+Yt,y:ot},{width:1,height:Kt})}this.dirty=!0}beginFrame(){this.callbackDispatchedThisFrame={}}dispatchRenderCallbacks(w){for(let B of w){if(this.callbackDispatchedThisFrame[B])continue;this.callbackDispatchedThisFrame[B]=!0;let Q=this.getImage(B);Q||a.w(`Image with ID: "${B}" was not found`),P(Q)&&this.updateImage(B,Q)}}}let z=1e20;function O(ue,w,B,Q,ee,le,qe,Xe,ot){for(let Tt=w;Tt<w+Q;Tt++)V(ue,B*le+Tt,le,ee,qe,Xe,ot);for(let Tt=B;Tt<B+ee;Tt++)V(ue,Tt*le+w,1,Q,qe,Xe,ot)}function V(ue,w,B,Q,ee,le,qe){le[0]=0,qe[0]=-z,qe[1]=z,ee[0]=ue[w];for(let Xe=1,ot=0,Tt=0;Xe<Q;Xe++){ee[Xe]=ue[w+Xe*B];let Yt=Xe*Xe;do{let Kt=le[ot];Tt=(ee[Xe]-ee[Kt]+Yt-Kt*Kt)/(Xe-Kt)/2}while(Tt<=qe[ot]&&--ot>-1);ot++,le[ot]=Xe,qe[ot]=Tt,qe[ot+1]=z}for(let Xe=0,ot=0;Xe<Q;Xe++){for(;qe[ot+1]<Xe;)ot++;let Tt=le[ot],Yt=Xe-Tt;ue[w+Xe*B]=ee[Tt]+Yt*Yt}}class G{constructor(w,B){this.requestManager=w,this.localIdeographFontFamily=B,this.entries={}}setURL(w){this.url=w}getGlyphs(w){return a._(this,void 0,void 0,function*(){let B=[];for(let le in w)for(let qe of w[le])B.push(this._getAndCacheGlyphsPromise(le,qe));let Q=yield Promise.all(B),ee={};for(let{stack:le,id:qe,glyph:Xe}of Q)ee[le]||(ee[le]={}),ee[le][qe]=Xe&&{id:Xe.id,bitmap:Xe.bitmap.clone(),metrics:Xe.metrics};return ee})}_getAndCacheGlyphsPromise(w,B){return a._(this,void 0,void 0,function*(){let Q=this.entries[w];Q||(Q=this.entries[w]={glyphs:{},requests:{},ranges:{}});let ee=Q.glyphs[B];if(ee!==void 0)return{stack:w,id:B,glyph:ee};if(ee=this._tinySDF(Q,w,B),ee)return Q.glyphs[B]=ee,{stack:w,id:B,glyph:ee};let le=Math.floor(B/256);if(256*le>65535)throw new Error("glyphs > 65535 not supported");if(Q.ranges[le])return{stack:w,id:B,glyph:ee};if(!this.url)throw new Error("glyphsUrl is not set");if(!Q.requests[le]){let Xe=G.loadGlyphRange(w,le,this.url,this.requestManager);Q.requests[le]=Xe}let qe=yield Q.requests[le];for(let Xe in qe)this._doesCharSupportLocalGlyph(+Xe)||(Q.glyphs[+Xe]=qe[+Xe]);return Q.ranges[le]=!0,{stack:w,id:B,glyph:qe[B]||null}})}_doesCharSupportLocalGlyph(w){return!!this.localIdeographFontFamily&&new RegExp("\\p{Ideo}|\\p{sc=Hang}|\\p{sc=Hira}|\\p{sc=Kana}","u").test(String.fromCodePoint(w))}_tinySDF(w,B,Q){let ee=this.localIdeographFontFamily;if(!ee||!this._doesCharSupportLocalGlyph(Q))return;let le=w.tinySDF;if(!le){let Xe="400";/bold/i.test(B)?Xe="900":/medium/i.test(B)?Xe="500":/light/i.test(B)&&(Xe="200"),le=w.tinySDF=new G.TinySDF({fontSize:48,buffer:6,radius:16,cutoff:.25,fontFamily:ee,fontWeight:Xe})}let qe=le.draw(String.fromCharCode(Q));return{id:Q,bitmap:new a.o({width:qe.width||60,height:qe.height||60},qe.data),metrics:{width:qe.glyphWidth/2||24,height:qe.glyphHeight/2||24,left:qe.glyphLeft/2+.5||0,top:qe.glyphTop/2-27.5||-8,advance:qe.glyphAdvance/2||24,isDoubleResolution:!0}}}}G.loadGlyphRange=function(ue,w,B,Q){return a._(this,void 0,void 0,function*(){let ee=256*w,le=ee+255,qe=Q.transformRequest(B.replace("{fontstack}",ue).replace("{range}",`${ee}-${le}`),"Glyphs"),Xe=yield a.l(qe,new AbortController);if(!Xe||!Xe.data)throw new Error(`Could not load glyph range. range: ${w}, ${ee}-${le}`);let ot={};for(let Tt of a.n(Xe.data))ot[Tt.id]=Tt;return ot})},G.TinySDF=class{constructor({fontSize:ue=24,buffer:w=3,radius:B=8,cutoff:Q=.25,fontFamily:ee="sans-serif",fontWeight:le="normal",fontStyle:qe="normal"}={}){this.buffer=w,this.cutoff=Q,this.radius=B;let Xe=this.size=ue+4*w,ot=this._createCanvas(Xe),Tt=this.ctx=ot.getContext("2d",{willReadFrequently:!0});Tt.font=`${qe} ${le} ${ue}px ${ee}`,Tt.textBaseline="alphabetic",Tt.textAlign="left",Tt.fillStyle="black",this.gridOuter=new Float64Array(Xe*Xe),this.gridInner=new Float64Array(Xe*Xe),this.f=new Float64Array(Xe),this.z=new Float64Array(Xe+1),this.v=new Uint16Array(Xe)}_createCanvas(ue){let w=document.createElement("canvas");return w.width=w.height=ue,w}draw(ue){let{width:w,actualBoundingBoxAscent:B,actualBoundingBoxDescent:Q,actualBoundingBoxLeft:ee,actualBoundingBoxRight:le}=this.ctx.measureText(ue),qe=Math.ceil(B),Xe=Math.max(0,Math.min(this.size-this.buffer,Math.ceil(le-ee))),ot=Math.min(this.size-this.buffer,qe+Math.ceil(Q)),Tt=Xe+2*this.buffer,Yt=ot+2*this.buffer,Kt=Math.max(Tt*Yt,0),xr=new Uint8ClampedArray(Kt),Ir={data:xr,width:Tt,height:Yt,glyphWidth:Xe,glyphHeight:ot,glyphTop:qe,glyphLeft:0,glyphAdvance:w};if(Xe===0||ot===0)return Ir;let{ctx:ve,buffer:be,gridInner:De,gridOuter:Be}=this;ve.clearRect(be,be,Xe,ot),ve.fillText(ue,be,be+qe);let et=ve.getImageData(be,be,Xe,ot);Be.fill(z,0,Kt),De.fill(0,0,Kt);for(let We=0;We<ot;We++)for(let it=0;it<Xe;it++){let Ft=et.data[4*(We*Xe+it)+3]/255;if(Ft===0)continue;let Ht=(We+be)*Tt+it+be;if(Ft===1)Be[Ht]=0,De[Ht]=z;else{let tr=.5-Ft;Be[Ht]=tr>0?tr*tr:0,De[Ht]=tr<0?tr*tr:0}}O(Be,0,0,Tt,Yt,Tt,this.f,this.v,this.z),O(De,be,be,Xe,ot,Tt,this.f,this.v,this.z);for(let We=0;We<Kt;We++){let it=Math.sqrt(Be[We])-Math.sqrt(De[We]);xr[We]=Math.round(255-255*(it/this.radius+this.cutoff))}return Ir}};class Z{constructor(){this.specification=a.v.light.position}possiblyEvaluate(w,B){return a.x(w.expression.evaluate(B))}interpolate(w,B,Q){return{x:a.y.number(w.x,B.x,Q),y:a.y.number(w.y,B.y,Q),z:a.y.number(w.z,B.z,Q)}}}let j;class N extends a.E{constructor(w){super(),j=j||new a.q({anchor:new a.D(a.v.light.anchor),position:new Z,color:new a.D(a.v.light.color),intensity:new a.D(a.v.light.intensity)}),this._transitionable=new a.T(j),this.setLight(w),this._transitioning=this._transitionable.untransitioned()}getLight(){return this._transitionable.serialize()}setLight(w,B={}){if(!this._validate(a.r,w,B))for(let Q in w){let ee=w[Q];Q.endsWith("-transition")?this._transitionable.setTransition(Q.slice(0,-11),ee):this._transitionable.setValue(Q,ee)}}updateTransitions(w){this._transitioning=this._transitionable.transitioned(w,this._transitioning)}hasTransition(){return this._transitioning.hasTransition()}recalculate(w){this.properties=this._transitioning.possiblyEvaluate(w)}_validate(w,B,Q){return(!Q||Q.validate!==!1)&&a.t(this,w.call(a.u,{value:B,style:{glyphs:!0,sprite:!0},styleSpec:a.v}))}}let H=new a.q({"sky-color":new a.D(a.v.sky["sky-color"]),"horizon-color":new a.D(a.v.sky["horizon-color"]),"fog-color":new a.D(a.v.sky["fog-color"]),"fog-ground-blend":new a.D(a.v.sky["fog-ground-blend"]),"horizon-fog-blend":new a.D(a.v.sky["horizon-fog-blend"]),"sky-horizon-blend":new a.D(a.v.sky["sky-horizon-blend"]),"atmosphere-blend":new a.D(a.v.sky["atmosphere-blend"])});class te extends a.E{constructor(w){super(),this._transitionable=new a.T(H),this.setSky(w),this._transitioning=this._transitionable.untransitioned(),this.recalculate(new a.z(0))}setSky(w,B={}){if(!this._validate(a.B,w,B)){w||(w={"sky-color":"transparent","horizon-color":"transparent","fog-color":"transparent","fog-ground-blend":1,"atmosphere-blend":0});for(let Q in w){let ee=w[Q];Q.endsWith("-transition")?this._transitionable.setTransition(Q.slice(0,-11),ee):this._transitionable.setValue(Q,ee)}}}getSky(){return this._transitionable.serialize()}updateTransitions(w){this._transitioning=this._transitionable.transitioned(w,this._transitioning)}hasTransition(){return this._transitioning.hasTransition()}recalculate(w){this.properties=this._transitioning.possiblyEvaluate(w)}_validate(w,B,Q={}){return(Q==null?void 0:Q.validate)!==!1&&a.t(this,w.call(a.u,a.e({value:B,style:{glyphs:!0,sprite:!0},styleSpec:a.v})))}calculateFogBlendOpacity(w){return w<60?0:w<70?(w-60)/10:1}}class oe{constructor(w,B){this.width=w,this.height=B,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}}getDash(w,B){let Q=w.join(",")+String(B);return this.dashEntry[Q]||(this.dashEntry[Q]=this.addDash(w,B)),this.dashEntry[Q]}getDashRanges(w,B,Q){let ee=[],le=w.length%2==1?-w[w.length-1]*Q:0,qe=w[0]*Q,Xe=!0;ee.push({left:le,right:qe,isDash:Xe,zeroLength:w[0]===0});let ot=w[0];for(let Tt=1;Tt<w.length;Tt++){Xe=!Xe;let Yt=w[Tt];le=ot*Q,ot+=Yt,qe=ot*Q,ee.push({left:le,right:qe,isDash:Xe,zeroLength:Yt===0})}return ee}addRoundDash(w,B,Q){let ee=B/2;for(let le=-Q;le<=Q;le++){let qe=this.width*(this.nextRow+Q+le),Xe=0,ot=w[Xe];for(let Tt=0;Tt<this.width;Tt++){Tt/ot.right>1&&(ot=w[++Xe]);let Yt=Math.abs(Tt-ot.left),Kt=Math.abs(Tt-ot.right),xr=Math.min(Yt,Kt),Ir,ve=le/Q*(ee+1);if(ot.isDash){let be=ee-Math.abs(ve);Ir=Math.sqrt(xr*xr+be*be)}else Ir=ee-Math.sqrt(xr*xr+ve*ve);this.data[qe+Tt]=Math.max(0,Math.min(255,Ir+128))}}}addRegularDash(w){for(let Xe=w.length-1;Xe>=0;--Xe){let ot=w[Xe],Tt=w[Xe+1];ot.zeroLength?w.splice(Xe,1):Tt&&Tt.isDash===ot.isDash&&(Tt.left=ot.left,w.splice(Xe,1))}let B=w[0],Q=w[w.length-1];B.isDash===Q.isDash&&(B.left=Q.left-this.width,Q.right=B.right+this.width);let ee=this.width*this.nextRow,le=0,qe=w[le];for(let Xe=0;Xe<this.width;Xe++){Xe/qe.right>1&&(qe=w[++le]);let ot=Math.abs(Xe-qe.left),Tt=Math.abs(Xe-qe.right),Yt=Math.min(ot,Tt);this.data[ee+Xe]=Math.max(0,Math.min(255,(qe.isDash?Yt:-Yt)+128))}}addDash(w,B){let Q=B?7:0,ee=2*Q+1;if(this.nextRow+ee>this.height)return a.w("LineAtlas out of space"),null;let le=0;for(let Xe=0;Xe<w.length;Xe++)le+=w[Xe];if(le!==0){let Xe=this.width/le,ot=this.getDashRanges(w,this.width,Xe);B?this.addRoundDash(ot,Xe,Q):this.addRegularDash(ot)}let qe={y:(this.nextRow+Q+.5)/this.height,height:2*Q/this.height,width:le};return this.nextRow+=ee,this.dirty=!0,qe}bind(w){let B=w.gl;this.texture?(B.bindTexture(B.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,B.texSubImage2D(B.TEXTURE_2D,0,0,0,this.width,this.height,B.ALPHA,B.UNSIGNED_BYTE,this.data))):(this.texture=B.createTexture(),B.bindTexture(B.TEXTURE_2D,this.texture),B.texParameteri(B.TEXTURE_2D,B.TEXTURE_WRAP_S,B.REPEAT),B.texParameteri(B.TEXTURE_2D,B.TEXTURE_WRAP_T,B.REPEAT),B.texParameteri(B.TEXTURE_2D,B.TEXTURE_MIN_FILTER,B.LINEAR),B.texParameteri(B.TEXTURE_2D,B.TEXTURE_MAG_FILTER,B.LINEAR),B.texImage2D(B.TEXTURE_2D,0,B.ALPHA,this.width,this.height,0,B.ALPHA,B.UNSIGNED_BYTE,this.data))}}let _e="maplibre_preloaded_worker_pool";class Ee{constructor(){this.active={}}acquire(w){if(!this.workers)for(this.workers=[];this.workers.length<Ee.workerCount;)this.workers.push(new Worker(a.a.WORKER_URL));return this.active[w]=!0,this.workers.slice()}release(w){delete this.active[w],this.numActive()===0&&(this.workers.forEach(B=>{B.terminate()}),this.workers=null)}isPreloaded(){return!!this.active[_e]}numActive(){return Object.keys(this.active).length}}let Ce=Math.floor(u.hardwareConcurrency/2),me,ie;function Se(){return me||(me=new Ee),me}Ee.workerCount=a.C(globalThis)?Math.max(Math.min(Ce,3),1):1;class Le{constructor(w,B){this.workerPool=w,this.actors=[],this.currentActor=0,this.id=B;let Q=this.workerPool.acquire(B);for(let ee=0;ee<Q.length;ee++){let le=new a.F(Q[ee],B);le.name=`Worker ${ee}`,this.actors.push(le)}if(!this.actors.length)throw new Error("No actors found")}broadcast(w,B){let Q=[];for(let ee of this.actors)Q.push(ee.sendAsync({type:w,data:B}));return Promise.all(Q)}getActor(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]}remove(w=!0){this.actors.forEach(B=>{B.remove()}),this.actors=[],w&&this.workerPool.release(this.id)}registerMessageHandler(w,B){for(let Q of this.actors)Q.registerMessageHandler(w,B)}}function Ae(){return ie||(ie=new Le(Se(),a.G),ie.registerMessageHandler("GR",(ue,w,B)=>a.m(w,B))),ie}function Fe(ue,w){let B=a.H();return a.J(B,B,[1,1,0]),a.K(B,B,[.5*ue.width,.5*ue.height,1]),a.L(B,B,ue.calculatePosMatrix(w.toUnwrapped()))}function Pe(ue,w,B,Q,ee,le){let qe=function(Kt,xr,Ir){if(Kt)for(let ve of Kt){let be=xr[ve];if(be&&be.source===Ir&&be.type==="fill-extrusion")return!0}else for(let ve in xr){let be=xr[ve];if(be.source===Ir&&be.type==="fill-extrusion")return!0}return!1}(ee&&ee.layers,w,ue.id),Xe=le.maxPitchScaleFactor(),ot=ue.tilesIn(Q,Xe,qe);ot.sort(ge);let Tt=[];for(let Kt of ot)Tt.push({wrappedTileID:Kt.tileID.wrapped().key,queryResults:Kt.tile.queryRenderedFeatures(w,B,ue._state,Kt.queryGeometry,Kt.cameraQueryGeometry,Kt.scale,ee,le,Xe,Fe(ue.transform,Kt.tileID))});let Yt=function(Kt){let xr={},Ir={};for(let ve of Kt){let be=ve.queryResults,De=ve.wrappedTileID,Be=Ir[De]=Ir[De]||{};for(let et in be){let We=be[et],it=Be[et]=Be[et]||{},Ft=xr[et]=xr[et]||[];for(let Ht of We)it[Ht.featureIndex]||(it[Ht.featureIndex]=!0,Ft.push(Ht))}}return xr}(Tt);for(let Kt in Yt)Yt[Kt].forEach(xr=>{let Ir=xr.feature,ve=ue.getFeatureState(Ir.layer["source-layer"],Ir.id);Ir.source=Ir.layer.source,Ir.layer["source-layer"]&&(Ir.sourceLayer=Ir.layer["source-layer"]),Ir.state=ve});return Yt}function ge(ue,w){let B=ue.tileID,Q=w.tileID;return B.overscaledZ-Q.overscaledZ||B.canonical.y-Q.canonical.y||B.wrap-Q.wrap||B.canonical.x-Q.canonical.x}function Re(ue,w,B){return a._(this,void 0,void 0,function*(){let Q=ue;if(ue.url?Q=(yield a.h(w.transformRequest(ue.url,"Source"),B)).data:yield u.frameAsync(B),!Q)return null;let ee=a.M(a.e(Q,ue),["tiles","minzoom","maxzoom","attribution","bounds","scheme","tileSize","encoding"]);return"vector_layers"in Q&&Q.vector_layers&&(ee.vectorLayerIds=Q.vector_layers.map(le=>le.id)),ee})}class ce{constructor(w,B){w&&(B?this.setSouthWest(w).setNorthEast(B):Array.isArray(w)&&(w.length===4?this.setSouthWest([w[0],w[1]]).setNorthEast([w[2],w[3]]):this.setSouthWest(w[0]).setNorthEast(w[1])))}setNorthEast(w){return this._ne=w instanceof a.N?new a.N(w.lng,w.lat):a.N.convert(w),this}setSouthWest(w){return this._sw=w instanceof a.N?new a.N(w.lng,w.lat):a.N.convert(w),this}extend(w){let B=this._sw,Q=this._ne,ee,le;if(w instanceof a.N)ee=w,le=w;else{if(!(w instanceof ce))return Array.isArray(w)?w.length===4||w.every(Array.isArray)?this.extend(ce.convert(w)):this.extend(a.N.convert(w)):w&&("lng"in w||"lon"in w)&&"lat"in w?this.extend(a.N.convert(w)):this;if(ee=w._sw,le=w._ne,!ee||!le)return this}return B||Q?(B.lng=Math.min(ee.lng,B.lng),B.lat=Math.min(ee.lat,B.lat),Q.lng=Math.max(le.lng,Q.lng),Q.lat=Math.max(le.lat,Q.lat)):(this._sw=new a.N(ee.lng,ee.lat),this._ne=new a.N(le.lng,le.lat)),this}getCenter(){return new a.N((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)}getSouthWest(){return this._sw}getNorthEast(){return this._ne}getNorthWest(){return new a.N(this.getWest(),this.getNorth())}getSouthEast(){return new a.N(this.getEast(),this.getSouth())}getWest(){return this._sw.lng}getSouth(){return this._sw.lat}getEast(){return this._ne.lng}getNorth(){return this._ne.lat}toArray(){return[this._sw.toArray(),this._ne.toArray()]}toString(){return`LngLatBounds(${this._sw.toString()}, ${this._ne.toString()})`}isEmpty(){return!(this._sw&&this._ne)}contains(w){let{lng:B,lat:Q}=a.N.convert(w),ee=this._sw.lng<=B&&B<=this._ne.lng;return this._sw.lng>this._ne.lng&&(ee=this._sw.lng>=B&&B>=this._ne.lng),this._sw.lat<=Q&&Q<=this._ne.lat&&ee}static convert(w){return w instanceof ce?w:w&&new ce(w)}static fromLngLat(w,B=0){let Q=360*B/40075017,ee=Q/Math.cos(Math.PI/180*w.lat);return new ce(new a.N(w.lng-ee,w.lat-Q),new a.N(w.lng+ee,w.lat+Q))}adjustAntiMeridian(){let w=new a.N(this._sw.lng,this._sw.lat),B=new a.N(this._ne.lng,this._ne.lat);return new ce(w,w.lng>B.lng?new a.N(B.lng+360,B.lat):B)}}class Ze{constructor(w,B,Q){this.bounds=ce.convert(this.validateBounds(w)),this.minzoom=B||0,this.maxzoom=Q||24}validateBounds(w){return Array.isArray(w)&&w.length===4?[Math.max(-180,w[0]),Math.max(-90,w[1]),Math.min(180,w[2]),Math.min(90,w[3])]:[-180,-90,180,90]}contains(w){let B=Math.pow(2,w.z),Q=Math.floor(a.O(this.bounds.getWest())*B),ee=Math.floor(a.Q(this.bounds.getNorth())*B),le=Math.ceil(a.O(this.bounds.getEast())*B),qe=Math.ceil(a.Q(this.bounds.getSouth())*B);return w.x>=Q&&w.x<le&&w.y>=ee&&w.y<qe}}class ut extends a.E{constructor(w,B,Q,ee){if(super(),this.id=w,this.dispatcher=Q,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,a.e(this,a.M(B,["url","scheme","tileSize","promoteId"])),this._options=a.e({type:"vector"},B),this._collectResourceTiming=B.collectResourceTiming,this.tileSize!==512)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(ee)}load(){return a._(this,void 0,void 0,function*(){this._loaded=!1,this.fire(new a.k("dataloading",{dataType:"source"})),this._tileJSONRequest=new AbortController;try{let w=yield Re(this._options,this.map._requestManager,this._tileJSONRequest);this._tileJSONRequest=null,this._loaded=!0,this.map.style.sourceCaches[this.id].clearTiles(),w&&(a.e(this,w),w.bounds&&(this.tileBounds=new Ze(w.bounds,this.minzoom,this.maxzoom)),this.fire(new a.k("data",{dataType:"source",sourceDataType:"metadata"})),this.fire(new a.k("data",{dataType:"source",sourceDataType:"content"})))}catch(w){this._tileJSONRequest=null,this.fire(new a.j(w))}})}loaded(){return this._loaded}hasTile(w){return!this.tileBounds||this.tileBounds.contains(w.canonical)}onAdd(w){this.map=w,this.load()}setSourceProperty(w){this._tileJSONRequest&&this._tileJSONRequest.abort(),w(),this.load()}setTiles(w){return this.setSourceProperty(()=>{this._options.tiles=w}),this}setUrl(w){return this.setSourceProperty(()=>{this.url=w,this._options.url=w}),this}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}serialize(){return a.e({},this._options)}loadTile(w){return a._(this,void 0,void 0,function*(){let B=w.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),Q={request:this.map._requestManager.transformRequest(B,"Tile"),uid:w.uid,tileID:w.tileID,zoom:w.tileID.overscaledZ,tileSize:this.tileSize*w.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};Q.request.collectResourceTiming=this._collectResourceTiming;let ee="RT";if(w.actor&&w.state!=="expired"){if(w.state==="loading")return new Promise((le,qe)=>{w.reloadPromise={resolve:le,reject:qe}})}else w.actor=this.dispatcher.getActor(),ee="LT";w.abortController=new AbortController;try{let le=yield w.actor.sendAsync({type:ee,data:Q},w.abortController);if(delete w.abortController,w.aborted)return;this._afterTileLoadWorkerResponse(w,le)}catch(le){if(delete w.abortController,w.aborted)return;if(le&&le.status!==404)throw le;this._afterTileLoadWorkerResponse(w,null)}})}_afterTileLoadWorkerResponse(w,B){if(B&&B.resourceTiming&&(w.resourceTiming=B.resourceTiming),B&&this.map._refreshExpiredTiles&&w.setExpiryData(B),w.loadVectorData(B,this.map.painter),w.reloadPromise){let Q=w.reloadPromise;w.reloadPromise=null,this.loadTile(w).then(Q.resolve).catch(Q.reject)}}abortTile(w){return a._(this,void 0,void 0,function*(){w.abortController&&(w.abortController.abort(),delete w.abortController),w.actor&&(yield w.actor.sendAsync({type:"AT",data:{uid:w.uid,type:this.type,source:this.id}}))})}unloadTile(w){return a._(this,void 0,void 0,function*(){w.unloadVectorData(),w.actor&&(yield w.actor.sendAsync({type:"RMT",data:{uid:w.uid,type:this.type,source:this.id}}))})}hasTransition(){return!1}}class pt extends a.E{constructor(w,B,Q,ee){super(),this.id=w,this.dispatcher=Q,this.setEventedParent(ee),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=a.e({type:"raster"},B),a.e(this,a.M(B,["url","scheme","tileSize"]))}load(){return a._(this,void 0,void 0,function*(){this._loaded=!1,this.fire(new a.k("dataloading",{dataType:"source"})),this._tileJSONRequest=new AbortController;try{let w=yield Re(this._options,this.map._requestManager,this._tileJSONRequest);this._tileJSONRequest=null,this._loaded=!0,w&&(a.e(this,w),w.bounds&&(this.tileBounds=new Ze(w.bounds,this.minzoom,this.maxzoom)),this.fire(new a.k("data",{dataType:"source",sourceDataType:"metadata"})),this.fire(new a.k("data",{dataType:"source",sourceDataType:"content"})))}catch(w){this._tileJSONRequest=null,this.fire(new a.j(w))}})}loaded(){return this._loaded}onAdd(w){this.map=w,this.load()}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}setSourceProperty(w){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null),w(),this.load()}setTiles(w){return this.setSourceProperty(()=>{this._options.tiles=w}),this}setUrl(w){return this.setSourceProperty(()=>{this.url=w,this._options.url=w}),this}serialize(){return a.e({},this._options)}hasTile(w){return!this.tileBounds||this.tileBounds.contains(w.canonical)}loadTile(w){return a._(this,void 0,void 0,function*(){let B=w.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme);w.abortController=new AbortController;try{let Q=yield p.getImage(this.map._requestManager.transformRequest(B,"Tile"),w.abortController,this.map._refreshExpiredTiles);if(delete w.abortController,w.aborted)return void(w.state="unloaded");if(Q&&Q.data){this.map._refreshExpiredTiles&&Q.cacheControl&&Q.expires&&w.setExpiryData({cacheControl:Q.cacheControl,expires:Q.expires});let ee=this.map.painter.context,le=ee.gl,qe=Q.data;w.texture=this.map.painter.getTileTexture(qe.width),w.texture?w.texture.update(qe,{useMipmap:!0}):(w.texture=new g(ee,qe,le.RGBA,{useMipmap:!0}),w.texture.bind(le.LINEAR,le.CLAMP_TO_EDGE,le.LINEAR_MIPMAP_NEAREST)),w.state="loaded"}}catch(Q){if(delete w.abortController,w.aborted)w.state="unloaded";else if(Q)throw w.state="errored",Q}})}abortTile(w){return a._(this,void 0,void 0,function*(){w.abortController&&(w.abortController.abort(),delete w.abortController)})}unloadTile(w){return a._(this,void 0,void 0,function*(){w.texture&&this.map.painter.saveTileTexture(w.texture)})}hasTransition(){return!1}}class Zt extends pt{constructor(w,B,Q,ee){super(w,B,Q,ee),this.type="raster-dem",this.maxzoom=22,this._options=a.e({type:"raster-dem"},B),this.encoding=B.encoding||"mapbox",this.redFactor=B.redFactor,this.greenFactor=B.greenFactor,this.blueFactor=B.blueFactor,this.baseShift=B.baseShift}loadTile(w){return a._(this,void 0,void 0,function*(){let B=w.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),Q=this.map._requestManager.transformRequest(B,"Tile");w.neighboringTiles=this._getNeighboringTiles(w.tileID),w.abortController=new AbortController;try{let ee=yield p.getImage(Q,w.abortController,this.map._refreshExpiredTiles);if(delete w.abortController,w.aborted)return void(w.state="unloaded");if(ee&&ee.data){let le=ee.data;this.map._refreshExpiredTiles&&ee.cacheControl&&ee.expires&&w.setExpiryData({cacheControl:ee.cacheControl,expires:ee.expires});let qe=a.b(le)&&a.U()?le:yield this.readImageNow(le),Xe={type:this.type,uid:w.uid,source:this.id,rawImageData:qe,encoding:this.encoding,redFactor:this.redFactor,greenFactor:this.greenFactor,blueFactor:this.blueFactor,baseShift:this.baseShift};if(!w.actor||w.state==="expired"){w.actor=this.dispatcher.getActor();let ot=yield w.actor.sendAsync({type:"LDT",data:Xe});w.dem=ot,w.needsHillshadePrepare=!0,w.needsTerrainPrepare=!0,w.state="loaded"}}}catch(ee){if(delete w.abortController,w.aborted)w.state="unloaded";else if(ee)throw w.state="errored",ee}})}readImageNow(w){return a._(this,void 0,void 0,function*(){if(typeof VideoFrame!="undefined"&&a.V()){let B=w.width+2,Q=w.height+2;try{return new a.R({width:B,height:Q},yield a.W(w,-1,-1,B,Q))}catch(ee){}}return u.getImageData(w,1)})}_getNeighboringTiles(w){let B=w.canonical,Q=Math.pow(2,B.z),ee=(B.x-1+Q)%Q,le=B.x===0?w.wrap-1:w.wrap,qe=(B.x+1+Q)%Q,Xe=B.x+1===Q?w.wrap+1:w.wrap,ot={};return ot[new a.S(w.overscaledZ,le,B.z,ee,B.y).key]={backfilled:!1},ot[new a.S(w.overscaledZ,Xe,B.z,qe,B.y).key]={backfilled:!1},B.y>0&&(ot[new a.S(w.overscaledZ,le,B.z,ee,B.y-1).key]={backfilled:!1},ot[new a.S(w.overscaledZ,w.wrap,B.z,B.x,B.y-1).key]={backfilled:!1},ot[new a.S(w.overscaledZ,Xe,B.z,qe,B.y-1).key]={backfilled:!1}),B.y+1<Q&&(ot[new a.S(w.overscaledZ,le,B.z,ee,B.y+1).key]={backfilled:!1},ot[new a.S(w.overscaledZ,w.wrap,B.z,B.x,B.y+1).key]={backfilled:!1},ot[new a.S(w.overscaledZ,Xe,B.z,qe,B.y+1).key]={backfilled:!1}),ot}unloadTile(w){return a._(this,void 0,void 0,function*(){w.demTexture&&this.map.painter.saveTileTexture(w.demTexture),w.fbo&&(w.fbo.destroy(),delete w.fbo),w.dem&&delete w.dem,delete w.neighboringTiles,w.state="unloaded",w.actor&&(yield w.actor.sendAsync({type:"RDT",data:{type:this.type,uid:w.uid,source:this.id}}))})}}class st extends a.E{constructor(w,B,Q,ee){super(),this.id=w,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._pendingLoads=0,this.actor=Q.getActor(),this.setEventedParent(ee),this._data=B.data,this._options=a.e({},B),this._collectResourceTiming=B.collectResourceTiming,B.maxzoom!==void 0&&(this.maxzoom=B.maxzoom),B.type&&(this.type=B.type),B.attribution&&(this.attribution=B.attribution),this.promoteId=B.promoteId;let le=a.X/this.tileSize;B.clusterMaxZoom!==void 0&&this.maxzoom<=B.clusterMaxZoom&&a.w(`The maxzoom value "${this.maxzoom}" is expected to be greater than the clusterMaxZoom value "${B.clusterMaxZoom}".`),this.workerOptions=a.e({source:this.id,cluster:B.cluster||!1,geojsonVtOptions:{buffer:(B.buffer!==void 0?B.buffer:128)*le,tolerance:(B.tolerance!==void 0?B.tolerance:.375)*le,extent:a.X,maxZoom:this.maxzoom,lineMetrics:B.lineMetrics||!1,generateId:B.generateId||!1},superclusterOptions:{maxZoom:B.clusterMaxZoom!==void 0?B.clusterMaxZoom:this.maxzoom-1,minPoints:Math.max(2,B.clusterMinPoints||2),extent:a.X,radius:(B.clusterRadius||50)*le,log:!1,generateId:B.generateId||!1},clusterProperties:B.clusterProperties,filter:B.filter},B.workerOptions),typeof this.promoteId=="string"&&(this.workerOptions.promoteId=this.promoteId)}load(){return a._(this,void 0,void 0,function*(){yield this._updateWorkerData()})}onAdd(w){this.map=w,this.load()}setData(w){return this._data=w,this._updateWorkerData(),this}updateData(w){return this._updateWorkerData(w),this}getData(){return a._(this,void 0,void 0,function*(){let w=a.e({type:this.type},this.workerOptions);return this.actor.sendAsync({type:"GD",data:w})})}setClusterOptions(w){return this.workerOptions.cluster=w.cluster,w&&(w.clusterRadius!==void 0&&(this.workerOptions.superclusterOptions.radius=w.clusterRadius),w.clusterMaxZoom!==void 0&&(this.workerOptions.superclusterOptions.maxZoom=w.clusterMaxZoom)),this._updateWorkerData(),this}getClusterExpansionZoom(w){return this.actor.sendAsync({type:"GCEZ",data:{type:this.type,clusterId:w,source:this.id}})}getClusterChildren(w){return this.actor.sendAsync({type:"GCC",data:{type:this.type,clusterId:w,source:this.id}})}getClusterLeaves(w,B,Q){return this.actor.sendAsync({type:"GCL",data:{type:this.type,source:this.id,clusterId:w,limit:B,offset:Q}})}_updateWorkerData(w){return a._(this,void 0,void 0,function*(){let B=a.e({type:this.type},this.workerOptions);w?B.dataDiff=w:typeof this._data=="string"?(B.request=this.map._requestManager.transformRequest(u.resolveURL(this._data),"Source"),B.request.collectResourceTiming=this._collectResourceTiming):B.data=JSON.stringify(this._data),this._pendingLoads++,this.fire(new a.k("dataloading",{dataType:"source"}));try{let Q=yield this.actor.sendAsync({type:"LD",data:B});if(this._pendingLoads--,this._removed||Q.abandoned)return void this.fire(new a.k("dataabort",{dataType:"source"}));let ee=null;Q.resourceTiming&&Q.resourceTiming[this.id]&&(ee=Q.resourceTiming[this.id].slice(0));let le={dataType:"source"};this._collectResourceTiming&&ee&&ee.length>0&&a.e(le,{resourceTiming:ee}),this.fire(new a.k("data",Object.assign(Object.assign({},le),{sourceDataType:"metadata"}))),this.fire(new a.k("data",Object.assign(Object.assign({},le),{sourceDataType:"content"})))}catch(Q){if(this._pendingLoads--,this._removed)return void this.fire(new a.k("dataabort",{dataType:"source"}));this.fire(new a.j(Q))}})}loaded(){return this._pendingLoads===0}loadTile(w){return a._(this,void 0,void 0,function*(){let B=w.actor?"RT":"LT";w.actor=this.actor;let Q={type:this.type,uid:w.uid,tileID:w.tileID,zoom:w.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};w.abortController=new AbortController;let ee=yield this.actor.sendAsync({type:B,data:Q},w.abortController);delete w.abortController,w.unloadVectorData(),w.aborted||w.loadVectorData(ee,this.map.painter,B==="RT")})}abortTile(w){return a._(this,void 0,void 0,function*(){w.abortController&&(w.abortController.abort(),delete w.abortController),w.aborted=!0})}unloadTile(w){return a._(this,void 0,void 0,function*(){w.unloadVectorData(),yield this.actor.sendAsync({type:"RMT",data:{uid:w.uid,type:this.type,source:this.id}})})}onRemove(){this._removed=!0,this.actor.sendAsync({type:"RS",data:{type:this.type,source:this.id}})}serialize(){return a.e({},this._options,{type:this.type,data:this._data})}hasTransition(){return!1}}var lt=a.Y([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]);class Gt extends a.E{constructor(w,B,Q,ee){super(),this.id=w,this.dispatcher=Q,this.coordinates=B.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(ee),this.options=B}load(w){return a._(this,void 0,void 0,function*(){this._loaded=!1,this.fire(new a.k("dataloading",{dataType:"source"})),this.url=this.options.url,this._request=new AbortController;try{let B=yield p.getImage(this.map._requestManager.transformRequest(this.url,"Image"),this._request);this._request=null,this._loaded=!0,B&&B.data&&(this.image=B.data,w&&(this.coordinates=w),this._finishLoading())}catch(B){this._request=null,this._loaded=!0,this.fire(new a.j(B))}})}loaded(){return this._loaded}updateImage(w){return w.url?(this._request&&(this._request.abort(),this._request=null),this.options.url=w.url,this.load(w.coordinates).finally(()=>{this.texture=null}),this):this}_finishLoading(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new a.k("data",{dataType:"source",sourceDataType:"metadata"})))}onAdd(w){this.map=w,this.load()}onRemove(){this._request&&(this._request.abort(),this._request=null)}setCoordinates(w){this.coordinates=w;let B=w.map(a.Z.fromLngLat);this.tileID=function(ee){let le=1/0,qe=1/0,Xe=-1/0,ot=-1/0;for(let xr of ee)le=Math.min(le,xr.x),qe=Math.min(qe,xr.y),Xe=Math.max(Xe,xr.x),ot=Math.max(ot,xr.y);let Tt=Math.max(Xe-le,ot-qe),Yt=Math.max(0,Math.floor(-Math.log(Tt)/Math.LN2)),Kt=Math.pow(2,Yt);return new a.a1(Yt,Math.floor((le+Xe)/2*Kt),Math.floor((qe+ot)/2*Kt))}(B),this.minzoom=this.maxzoom=this.tileID.z;let Q=B.map(ee=>this.tileID.getTilePoint(ee)._round());return this._boundsArray=new a.$,this._boundsArray.emplaceBack(Q[0].x,Q[0].y,0,0),this._boundsArray.emplaceBack(Q[1].x,Q[1].y,a.X,0),this._boundsArray.emplaceBack(Q[3].x,Q[3].y,0,a.X),this._boundsArray.emplaceBack(Q[2].x,Q[2].y,a.X,a.X),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new a.k("data",{dataType:"source",sourceDataType:"content"})),this}prepare(){if(Object.keys(this.tiles).length===0||!this.image)return;let w=this.map.painter.context,B=w.gl;this.boundsBuffer||(this.boundsBuffer=w.createVertexBuffer(this._boundsArray,lt.members)),this.boundsSegments||(this.boundsSegments=a.a0.simpleSegment(0,0,4,2)),this.texture||(this.texture=new g(w,this.image,B.RGBA),this.texture.bind(B.LINEAR,B.CLAMP_TO_EDGE));let Q=!1;for(let ee in this.tiles){let le=this.tiles[ee];le.state!=="loaded"&&(le.state="loaded",le.texture=this.texture,Q=!0)}Q&&this.fire(new a.k("data",{dataType:"source",sourceDataType:"idle",sourceId:this.id}))}loadTile(w){return a._(this,void 0,void 0,function*(){this.tileID&&this.tileID.equals(w.tileID.canonical)?(this.tiles[String(w.tileID.wrap)]=w,w.buckets={}):w.state="errored"})}serialize(){return{type:"image",url:this.options.url,coordinates:this.coordinates}}hasTransition(){return!1}}class Nt extends Gt{constructor(w,B,Q,ee){super(w,B,Q,ee),this.roundZoom=!0,this.type="video",this.options=B}load(){return a._(this,void 0,void 0,function*(){this._loaded=!1;let w=this.options;this.urls=[];for(let B of w.urls)this.urls.push(this.map._requestManager.transformRequest(B,"Source").url);try{let B=yield a.a3(this.urls);if(this._loaded=!0,!B)return;this.video=B,this.video.loop=!0,this.video.addEventListener("playing",()=>{this.map.triggerRepaint()}),this.map&&this.video.play(),this._finishLoading()}catch(B){this.fire(new a.j(B))}})}pause(){this.video&&this.video.pause()}play(){this.video&&this.video.play()}seek(w){if(this.video){let B=this.video.seekable;w<B.start(0)||w>B.end(0)?this.fire(new a.j(new a.a2(`sources.${this.id}`,null,`Playback for this video can be set only between the ${B.start(0)} and ${B.end(0)}-second mark.`))):this.video.currentTime=w}}getVideo(){return this.video}onAdd(w){this.map||(this.map=w,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))}prepare(){if(Object.keys(this.tiles).length===0||this.video.readyState<2)return;let w=this.map.painter.context,B=w.gl;this.boundsBuffer||(this.boundsBuffer=w.createVertexBuffer(this._boundsArray,lt.members)),this.boundsSegments||(this.boundsSegments=a.a0.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(B.LINEAR,B.CLAMP_TO_EDGE),B.texSubImage2D(B.TEXTURE_2D,0,0,0,B.RGBA,B.UNSIGNED_BYTE,this.video)):(this.texture=new g(w,this.video,B.RGBA),this.texture.bind(B.LINEAR,B.CLAMP_TO_EDGE));let Q=!1;for(let ee in this.tiles){let le=this.tiles[ee];le.state!=="loaded"&&(le.state="loaded",le.texture=this.texture,Q=!0)}Q&&this.fire(new a.k("data",{dataType:"source",sourceDataType:"idle",sourceId:this.id}))}serialize(){return{type:"video",urls:this.urls,coordinates:this.coordinates}}hasTransition(){return this.video&&!this.video.paused}}class Jt extends Gt{constructor(w,B,Q,ee){super(w,B,Q,ee),B.coordinates?Array.isArray(B.coordinates)&&B.coordinates.length===4&&!B.coordinates.some(le=>!Array.isArray(le)||le.length!==2||le.some(qe=>typeof qe!="number"))||this.fire(new a.j(new a.a2(`sources.${w}`,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new a.j(new a.a2(`sources.${w}`,null,'missing required property "coordinates"'))),B.animate&&typeof B.animate!="boolean"&&this.fire(new a.j(new a.a2(`sources.${w}`,null,'optional "animate" property must be a boolean value'))),B.canvas?typeof B.canvas=="string"||B.canvas instanceof HTMLCanvasElement||this.fire(new a.j(new a.a2(`sources.${w}`,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new a.j(new a.a2(`sources.${w}`,null,'missing required property "canvas"'))),this.options=B,this.animate=B.animate===void 0||B.animate}load(){return a._(this,void 0,void 0,function*(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof HTMLCanvasElement?this.options.canvas:document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new a.j(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())})}getCanvas(){return this.canvas}onAdd(w){this.map=w,this.load(),this.canvas&&this.animate&&this.play()}onRemove(){this.pause()}prepare(){let w=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,w=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,w=!0),this._hasInvalidDimensions()||Object.keys(this.tiles).length===0)return;let B=this.map.painter.context,Q=B.gl;this.boundsBuffer||(this.boundsBuffer=B.createVertexBuffer(this._boundsArray,lt.members)),this.boundsSegments||(this.boundsSegments=a.a0.simpleSegment(0,0,4,2)),this.texture?(w||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new g(B,this.canvas,Q.RGBA,{premultiply:!0});let ee=!1;for(let le in this.tiles){let qe=this.tiles[le];qe.state!=="loaded"&&(qe.state="loaded",qe.texture=this.texture,ee=!0)}ee&&this.fire(new a.k("data",{dataType:"source",sourceDataType:"idle",sourceId:this.id}))}serialize(){return{type:"canvas",coordinates:this.coordinates}}hasTransition(){return this._playing}_hasInvalidDimensions(){for(let w of[this.canvas.width,this.canvas.height])if(isNaN(w)||w<=0)return!0;return!1}}let sr={},wr=ue=>{switch(ue){case"geojson":return st;case"image":return Gt;case"raster":return pt;case"raster-dem":return Zt;case"vector":return ut;case"video":return Nt;case"canvas":return Jt}return sr[ue]},cr="RTLPluginLoaded";class $e extends a.E{constructor(){super(...arguments),this.status="unavailable",this.url=null,this.dispatcher=Ae()}_syncState(w){return this.status=w,this.dispatcher.broadcast("SRPS",{pluginStatus:w,pluginURL:this.url}).catch(B=>{throw this.status="error",B})}getRTLTextPluginStatus(){return this.status}clearRTLTextPlugin(){this.status="unavailable",this.url=null}setRTLTextPlugin(w){return a._(this,arguments,void 0,function*(B,Q=!1){if(this.url)throw new Error("setRTLTextPlugin cannot be called multiple times.");if(this.url=u.resolveURL(B),!this.url)throw new Error(`requested url ${B} is invalid`);if(this.status==="unavailable"){if(!Q)return this._requestImport();this.status="deferred",this._syncState(this.status)}else if(this.status==="requested")return this._requestImport()})}_requestImport(){return a._(this,void 0,void 0,function*(){yield this._syncState("loading"),this.status="loaded",this.fire(new a.k(cr))})}lazyLoad(){this.status==="unavailable"?this.status="requested":this.status==="deferred"&&this._requestImport()}}let St=null;function Qt(){return St||(St=new $e),St}class Vt{constructor(w,B){this.timeAdded=0,this.fadeEndTime=0,this.tileID=w,this.uid=a.a4(),this.uses=0,this.tileSize=B,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.rtt=[],this.rttCoords={},this.expiredRequestCount=0,this.state="loading"}registerFadeDuration(w){let B=w+this.timeAdded;B<this.fadeEndTime||(this.fadeEndTime=B)}wasRequested(){return this.state==="errored"||this.state==="loaded"||this.state==="reloading"}clearTextures(w){this.demTexture&&w.saveTileTexture(this.demTexture),this.demTexture=null}loadVectorData(w,B,Q){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",w){w.featureIndex&&(this.latestFeatureIndex=w.featureIndex,w.rawTileData?(this.latestRawTileData=w.rawTileData,this.latestFeatureIndex.rawTileData=w.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=w.collisionBoxArray,this.buckets=function(ee,le){let qe={};if(!le)return qe;for(let Xe of ee){let ot=Xe.layerIds.map(Tt=>le.getLayer(Tt)).filter(Boolean);if(ot.length!==0){Xe.layers=ot,Xe.stateDependentLayerIds&&(Xe.stateDependentLayers=Xe.stateDependentLayerIds.map(Tt=>ot.filter(Yt=>Yt.id===Tt)[0]));for(let Tt of ot)qe[Tt.id]=Xe}}return qe}(w.buckets,B.style),this.hasSymbolBuckets=!1;for(let ee in this.buckets){let le=this.buckets[ee];if(le instanceof a.a6){if(this.hasSymbolBuckets=!0,!Q)break;le.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(let ee in this.buckets){let le=this.buckets[ee];if(le instanceof a.a6&&le.hasRTLText){this.hasRTLText=!0,Qt().lazyLoad();break}}this.queryPadding=0;for(let ee in this.buckets){let le=this.buckets[ee];this.queryPadding=Math.max(this.queryPadding,B.style.getLayer(ee).queryRadius(le))}w.imageAtlas&&(this.imageAtlas=w.imageAtlas),w.glyphAtlasImage&&(this.glyphAtlasImage=w.glyphAtlasImage)}else this.collisionBoxArray=new a.a5}unloadVectorData(){for(let w in this.buckets)this.buckets[w].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"}getBucket(w){return this.buckets[w.id]}upload(w){for(let Q in this.buckets){let ee=this.buckets[Q];ee.uploadPending()&&ee.upload(w)}let B=w.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new g(w,this.imageAtlas.image,B.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new g(w,this.glyphAtlasImage,B.ALPHA),this.glyphAtlasImage=null)}prepare(w){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(w,this.imageAtlasTexture)}queryRenderedFeatures(w,B,Q,ee,le,qe,Xe,ot,Tt,Yt){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:ee,cameraQueryGeometry:le,scale:qe,tileSize:this.tileSize,pixelPosMatrix:Yt,transform:ot,params:Xe,queryPadding:this.queryPadding*Tt},w,B,Q):{}}querySourceFeatures(w,B){let Q=this.latestFeatureIndex;if(!Q||!Q.rawTileData)return;let ee=Q.loadVTLayers(),le=B&&B.sourceLayer?B.sourceLayer:"",qe=ee._geojsonTileLayer||ee[le];if(!qe)return;let Xe=a.a7(B&&B.filter),{z:ot,x:Tt,y:Yt}=this.tileID.canonical,Kt={z:ot,x:Tt,y:Yt};for(let xr=0;xr<qe.length;xr++){let Ir=qe.feature(xr);if(Xe.needGeometry){let De=a.a8(Ir,!0);if(!Xe.filter(new a.z(this.tileID.overscaledZ),De,this.tileID.canonical))continue}else if(!Xe.filter(new a.z(this.tileID.overscaledZ),Ir))continue;let ve=Q.getId(Ir,le),be=new a.a9(Ir,ot,Tt,Yt,ve);be.tile=Kt,w.push(be)}}hasData(){return this.state==="loaded"||this.state==="reloading"||this.state==="expired"}patternsLoaded(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length}setExpiryData(w){let B=this.expirationTime;if(w.cacheControl){let Q=a.aa(w.cacheControl);Q["max-age"]&&(this.expirationTime=Date.now()+1e3*Q["max-age"])}else w.expires&&(this.expirationTime=new Date(w.expires).getTime());if(this.expirationTime){let Q=Date.now(),ee=!1;if(this.expirationTime>Q)ee=!1;else if(B)if(this.expirationTime<B)ee=!0;else{let le=this.expirationTime-B;le?this.expirationTime=Q+Math.max(le,3e4):ee=!0}else ee=!0;ee?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}}getExpiryTimeout(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-new Date().getTime(),Math.pow(2,31)-1)}setFeatureState(w,B){if(!this.latestFeatureIndex||!this.latestFeatureIndex.rawTileData||Object.keys(w).length===0)return;let Q=this.latestFeatureIndex.loadVTLayers();for(let ee in this.buckets){if(!B.style.hasLayer(ee))continue;let le=this.buckets[ee],qe=le.layers[0].sourceLayer||"_geojsonTileLayer",Xe=Q[qe],ot=w[qe];if(!Xe||!ot||Object.keys(ot).length===0)continue;le.update(ot,Xe,this.imageAtlas&&this.imageAtlas.patternPositions||{});let Tt=B&&B.style&&B.style.getLayer(ee);Tt&&(this.queryPadding=Math.max(this.queryPadding,Tt.queryRadius(le)))}}holdingForFade(){return this.symbolFadeHoldUntil!==void 0}symbolFadeFinished(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<u.now()}clearFadeHold(){this.symbolFadeHoldUntil=void 0}setHoldDuration(w){this.symbolFadeHoldUntil=u.now()+w}setDependencies(w,B){let Q={};for(let ee of B)Q[ee]=!0;this.dependencies[w]=Q}hasDependency(w,B){for(let Q of w){let ee=this.dependencies[Q];if(ee){for(let le of B)if(ee[le])return!0}}return!1}}class _t{constructor(w,B){this.max=w,this.onRemove=B,this.reset()}reset(){for(let w in this.data)for(let B of this.data[w])B.timeout&&clearTimeout(B.timeout),this.onRemove(B.value);return this.data={},this.order=[],this}add(w,B,Q){let ee=w.wrapped().key;this.data[ee]===void 0&&(this.data[ee]=[]);let le={value:B,timeout:void 0};if(Q!==void 0&&(le.timeout=setTimeout(()=>{this.remove(w,le)},Q)),this.data[ee].push(le),this.order.push(ee),this.order.length>this.max){let qe=this._getAndRemoveByKey(this.order[0]);qe&&this.onRemove(qe)}return this}has(w){return w.wrapped().key in this.data}getAndRemove(w){return this.has(w)?this._getAndRemoveByKey(w.wrapped().key):null}_getAndRemoveByKey(w){let B=this.data[w].shift();return B.timeout&&clearTimeout(B.timeout),this.data[w].length===0&&delete this.data[w],this.order.splice(this.order.indexOf(w),1),B.value}getByKey(w){let B=this.data[w];return B?B[0].value:null}get(w){return this.has(w)?this.data[w.wrapped().key][0].value:null}remove(w,B){if(!this.has(w))return this;let Q=w.wrapped().key,ee=B===void 0?0:this.data[Q].indexOf(B),le=this.data[Q][ee];return this.data[Q].splice(ee,1),le.timeout&&clearTimeout(le.timeout),this.data[Q].length===0&&delete this.data[Q],this.onRemove(le.value),this.order.splice(this.order.indexOf(Q),1),this}setMaxSize(w){for(this.max=w;this.order.length>this.max;){let B=this._getAndRemoveByKey(this.order[0]);B&&this.onRemove(B)}return this}filter(w){let B=[];for(let Q in this.data)for(let ee of this.data[Q])w(ee.value)||B.push(ee);for(let Q of B)this.remove(Q.value.tileID,Q)}}class It{constructor(){this.state={},this.stateChanges={},this.deletedStates={}}updateState(w,B,Q){let ee=String(B);if(this.stateChanges[w]=this.stateChanges[w]||{},this.stateChanges[w][ee]=this.stateChanges[w][ee]||{},a.e(this.stateChanges[w][ee],Q),this.deletedStates[w]===null){this.deletedStates[w]={};for(let le in this.state[w])le!==ee&&(this.deletedStates[w][le]=null)}else if(this.deletedStates[w]&&this.deletedStates[w][ee]===null){this.deletedStates[w][ee]={};for(let le in this.state[w][ee])Q[le]||(this.deletedStates[w][ee][le]=null)}else for(let le in Q)this.deletedStates[w]&&this.deletedStates[w][ee]&&this.deletedStates[w][ee][le]===null&&delete this.deletedStates[w][ee][le]}removeFeatureState(w,B,Q){if(this.deletedStates[w]===null)return;let ee=String(B);if(this.deletedStates[w]=this.deletedStates[w]||{},Q&&B!==void 0)this.deletedStates[w][ee]!==null&&(this.deletedStates[w][ee]=this.deletedStates[w][ee]||{},this.deletedStates[w][ee][Q]=null);else if(B!==void 0)if(this.stateChanges[w]&&this.stateChanges[w][ee])for(Q in this.deletedStates[w][ee]={},this.stateChanges[w][ee])this.deletedStates[w][ee][Q]=null;else this.deletedStates[w][ee]=null;else this.deletedStates[w]=null}getState(w,B){let Q=String(B),ee=a.e({},(this.state[w]||{})[Q],(this.stateChanges[w]||{})[Q]);if(this.deletedStates[w]===null)return{};if(this.deletedStates[w]){let le=this.deletedStates[w][B];if(le===null)return{};for(let qe in le)delete ee[qe]}return ee}initializeTileState(w,B){w.setFeatureState(this.state,B)}coalesceChanges(w,B){let Q={};for(let ee in this.stateChanges){this.state[ee]=this.state[ee]||{};let le={};for(let qe in this.stateChanges[ee])this.state[ee][qe]||(this.state[ee][qe]={}),a.e(this.state[ee][qe],this.stateChanges[ee][qe]),le[qe]=this.state[ee][qe];Q[ee]=le}for(let ee in this.deletedStates){this.state[ee]=this.state[ee]||{};let le={};if(this.deletedStates[ee]===null)for(let qe in this.state[ee])le[qe]={},this.state[ee][qe]={};else for(let qe in this.deletedStates[ee]){if(this.deletedStates[ee][qe]===null)this.state[ee][qe]={};else for(let Xe of Object.keys(this.deletedStates[ee][qe]))delete this.state[ee][qe][Xe];le[qe]=this.state[ee][qe]}Q[ee]=Q[ee]||{},a.e(Q[ee],le)}if(this.stateChanges={},this.deletedStates={},Object.keys(Q).length!==0)for(let ee in w)w[ee].setFeatureState(Q,B)}}class mt extends a.E{constructor(w,B,Q){super(),this.id=w,this.dispatcher=Q,this.on("data",ee=>this._dataHandler(ee)),this.on("dataloading",()=>{this._sourceErrored=!1}),this.on("error",()=>{this._sourceErrored=this._source.loaded()}),this._source=((ee,le,qe,Xe)=>{let ot=new(wr(le.type))(ee,le,qe,Xe);if(ot.id!==ee)throw new Error(`Expected Source id to be ${ee} instead of ${ot.id}`);return ot})(w,B,Q,this),this._tiles={},this._cache=new _t(0,ee=>this._unloadTile(ee)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._maxTileCacheZoomLevels=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new It,this._didEmitContent=!1,this._updated=!1}onAdd(w){this.map=w,this._maxTileCacheSize=w?w._maxTileCacheSize:null,this._maxTileCacheZoomLevels=w?w._maxTileCacheZoomLevels:null,this._source&&this._source.onAdd&&this._source.onAdd(w)}onRemove(w){this.clearTiles(),this._source&&this._source.onRemove&&this._source.onRemove(w)}loaded(){if(this._sourceErrored)return!0;if(!this._sourceLoaded||!this._source.loaded())return!1;if(!(this.used===void 0&&this.usedForTerrain===void 0||this.used||this.usedForTerrain))return!0;if(!this._updated)return!1;for(let w in this._tiles){let B=this._tiles[w];if(B.state!=="loaded"&&B.state!=="errored")return!1}return!0}getSource(){return this._source}pause(){this._paused=!0}resume(){if(!this._paused)return;let w=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,w&&this.reload(),this.transform&&this.update(this.transform,this.terrain)}_loadTile(w,B,Q){return a._(this,void 0,void 0,function*(){try{yield this._source.loadTile(w),this._tileLoaded(w,B,Q)}catch(ee){w.state="errored",ee.status!==404?this._source.fire(new a.j(ee,{tile:w})):this.update(this.transform,this.terrain)}})}_unloadTile(w){this._source.unloadTile&&this._source.unloadTile(w)}_abortTile(w){this._source.abortTile&&this._source.abortTile(w),this._source.fire(new a.k("dataabort",{tile:w,coord:w.tileID,dataType:"source"}))}serialize(){return this._source.serialize()}prepare(w){this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null);for(let B in this._tiles){let Q=this._tiles[B];Q.upload(w),Q.prepare(this.map.style.imageManager)}}getIds(){return Object.values(this._tiles).map(w=>w.tileID).sort(er).map(w=>w.key)}getRenderableIds(w){let B=[];for(let Q in this._tiles)this._isIdRenderable(Q,w)&&B.push(this._tiles[Q]);return w?B.sort((Q,ee)=>{let le=Q.tileID,qe=ee.tileID,Xe=new a.P(le.canonical.x,le.canonical.y)._rotate(this.transform.angle),ot=new a.P(qe.canonical.x,qe.canonical.y)._rotate(this.transform.angle);return le.overscaledZ-qe.overscaledZ||ot.y-Xe.y||ot.x-Xe.x}).map(Q=>Q.tileID.key):B.map(Q=>Q.tileID).sort(er).map(Q=>Q.key)}hasRenderableParent(w){let B=this.findLoadedParent(w,0);return!!B&&this._isIdRenderable(B.tileID.key)}_isIdRenderable(w,B){return this._tiles[w]&&this._tiles[w].hasData()&&!this._coveredTiles[w]&&(B||!this._tiles[w].holdingForFade())}reload(){if(this._paused)this._shouldReloadOnResume=!0;else{this._cache.reset();for(let w in this._tiles)this._tiles[w].state!=="errored"&&this._reloadTile(w,"reloading")}}_reloadTile(w,B){return a._(this,void 0,void 0,function*(){let Q=this._tiles[w];Q&&(Q.state!=="loading"&&(Q.state=B),yield this._loadTile(Q,w,B))})}_tileLoaded(w,B,Q){w.timeAdded=u.now(),Q==="expired"&&(w.refreshedUponExpiration=!0),this._setTileReloadTimer(B,w),this.getSource().type==="raster-dem"&&w.dem&&this._backfillDEM(w),this._state.initializeTileState(w,this.map?this.map.painter:null),w.aborted||this._source.fire(new a.k("data",{dataType:"source",tile:w,coord:w.tileID}))}_backfillDEM(w){let B=this.getRenderableIds();for(let ee=0;ee<B.length;ee++){let le=B[ee];if(w.neighboringTiles&&w.neighboringTiles[le]){let qe=this.getTileByID(le);Q(w,qe),Q(qe,w)}}function Q(ee,le){ee.needsHillshadePrepare=!0,ee.needsTerrainPrepare=!0;let qe=le.tileID.canonical.x-ee.tileID.canonical.x,Xe=le.tileID.canonical.y-ee.tileID.canonical.y,ot=Math.pow(2,ee.tileID.canonical.z),Tt=le.tileID.key;qe===0&&Xe===0||Math.abs(Xe)>1||(Math.abs(qe)>1&&(Math.abs(qe+ot)===1?qe+=ot:Math.abs(qe-ot)===1&&(qe-=ot)),le.dem&&ee.dem&&(ee.dem.backfillBorder(le.dem,qe,Xe),ee.neighboringTiles&&ee.neighboringTiles[Tt]&&(ee.neighboringTiles[Tt].backfilled=!0)))}}getTile(w){return this.getTileByID(w.key)}getTileByID(w){return this._tiles[w]}_retainLoadedChildren(w,B,Q,ee){for(let le in this._tiles){let qe=this._tiles[le];if(ee[le]||!qe.hasData()||qe.tileID.overscaledZ<=B||qe.tileID.overscaledZ>Q)continue;let Xe=qe.tileID;for(;qe&&qe.tileID.overscaledZ>B+1;){let Tt=qe.tileID.scaledTo(qe.tileID.overscaledZ-1);qe=this._tiles[Tt.key],qe&&qe.hasData()&&(Xe=Tt)}let ot=Xe;for(;ot.overscaledZ>B;)if(ot=ot.scaledTo(ot.overscaledZ-1),w[ot.key]){ee[Xe.key]=Xe;break}}}findLoadedParent(w,B){if(w.key in this._loadedParentTiles){let Q=this._loadedParentTiles[w.key];return Q&&Q.tileID.overscaledZ>=B?Q:null}for(let Q=w.overscaledZ-1;Q>=B;Q--){let ee=w.scaledTo(Q),le=this._getLoadedTile(ee);if(le)return le}}findLoadedSibling(w){return this._getLoadedTile(w)}_getLoadedTile(w){let B=this._tiles[w.key];return B&&B.hasData()?B:this._cache.getByKey(w.wrapped().key)}updateCacheSize(w){let B=Math.ceil(w.width/this._source.tileSize)+1,Q=Math.ceil(w.height/this._source.tileSize)+1,ee=Math.floor(B*Q*(this._maxTileCacheZoomLevels===null?a.a.MAX_TILE_CACHE_ZOOM_LEVELS:this._maxTileCacheZoomLevels)),le=typeof this._maxTileCacheSize=="number"?Math.min(this._maxTileCacheSize,ee):ee;this._cache.setMaxSize(le)}handleWrapJump(w){let B=Math.round((w-(this._prevLng===void 0?w:this._prevLng))/360);if(this._prevLng=w,B){let Q={};for(let ee in this._tiles){let le=this._tiles[ee];le.tileID=le.tileID.unwrapTo(le.tileID.wrap+B),Q[le.tileID.key]=le}this._tiles=Q;for(let ee in this._timers)clearTimeout(this._timers[ee]),delete this._timers[ee];for(let ee in this._tiles)this._setTileReloadTimer(ee,this._tiles[ee])}}_updateCoveredAndRetainedTiles(w,B,Q,ee,le,qe){let Xe={},ot={},Tt=Object.keys(w),Yt=u.now();for(let Kt of Tt){let xr=w[Kt],Ir=this._tiles[Kt];if(!Ir||Ir.fadeEndTime!==0&&Ir.fadeEndTime<=Yt)continue;let ve=this.findLoadedParent(xr,B),be=this.findLoadedSibling(xr),De=ve||be||null;De&&(this._addTile(De.tileID),Xe[De.tileID.key]=De.tileID),ot[Kt]=xr}this._retainLoadedChildren(ot,ee,Q,w);for(let Kt in Xe)w[Kt]||(this._coveredTiles[Kt]=!0,w[Kt]=Xe[Kt]);if(qe){let Kt={},xr={};for(let Ir of le)this._tiles[Ir.key].hasData()?Kt[Ir.key]=Ir:xr[Ir.key]=Ir;for(let Ir in xr){let ve=xr[Ir].children(this._source.maxzoom);this._tiles[ve[0].key]&&this._tiles[ve[1].key]&&this._tiles[ve[2].key]&&this._tiles[ve[3].key]&&(Kt[ve[0].key]=w[ve[0].key]=ve[0],Kt[ve[1].key]=w[ve[1].key]=ve[1],Kt[ve[2].key]=w[ve[2].key]=ve[2],Kt[ve[3].key]=w[ve[3].key]=ve[3],delete xr[Ir])}for(let Ir in xr){let ve=xr[Ir],be=this.findLoadedParent(ve,this._source.minzoom),De=this.findLoadedSibling(ve),Be=be||De||null;if(Be){Kt[Be.tileID.key]=w[Be.tileID.key]=Be.tileID;for(let et in Kt)Kt[et].isChildOf(Be.tileID)&&delete Kt[et]}}for(let Ir in this._tiles)Kt[Ir]||(this._coveredTiles[Ir]=!0)}}update(w,B){if(!this._sourceLoaded||this._paused)return;let Q;this.transform=w,this.terrain=B,this.updateCacheSize(w),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used||this.usedForTerrain?this._source.tileID?Q=w.getVisibleUnwrappedCoordinates(this._source.tileID).map(Yt=>new a.S(Yt.canonical.z,Yt.wrap,Yt.canonical.z,Yt.canonical.x,Yt.canonical.y)):(Q=w.coveringTiles({tileSize:this.usedForTerrain?this.tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:!this.usedForTerrain&&this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled,terrain:B}),this._source.hasTile&&(Q=Q.filter(Yt=>this._source.hasTile(Yt)))):Q=[];let ee=w.coveringZoomLevel(this._source),le=Math.max(ee-mt.maxOverzooming,this._source.minzoom),qe=Math.max(ee+mt.maxUnderzooming,this._source.minzoom);if(this.usedForTerrain){let Yt={};for(let Kt of Q)if(Kt.canonical.z>this._source.minzoom){let xr=Kt.scaledTo(Kt.canonical.z-1);Yt[xr.key]=xr;let Ir=Kt.scaledTo(Math.max(this._source.minzoom,Math.min(Kt.canonical.z,5)));Yt[Ir.key]=Ir}Q=Q.concat(Object.values(Yt))}let Xe=Q.length===0&&!this._updated&&this._didEmitContent;this._updated=!0,Xe&&this.fire(new a.k("data",{sourceDataType:"idle",dataType:"source",sourceId:this.id}));let ot=this._updateRetainedTiles(Q,ee);lr(this._source.type)&&this._updateCoveredAndRetainedTiles(ot,le,qe,ee,Q,B);for(let Yt in ot)this._tiles[Yt].clearFadeHold();let Tt=a.ab(this._tiles,ot);for(let Yt of Tt){let Kt=this._tiles[Yt];Kt.hasSymbolBuckets&&!Kt.holdingForFade()?Kt.setHoldDuration(this.map._fadeDuration):Kt.hasSymbolBuckets&&!Kt.symbolFadeFinished()||this._removeTile(Yt)}this._updateLoadedParentTileCache(),this._updateLoadedSiblingTileCache()}releaseSymbolFadeTiles(){for(let w in this._tiles)this._tiles[w].holdingForFade()&&this._removeTile(w)}_updateRetainedTiles(w,B){var Q;let ee={},le={},qe=Math.max(B-mt.maxOverzooming,this._source.minzoom),Xe=Math.max(B+mt.maxUnderzooming,this._source.minzoom),ot={};for(let Tt of w){let Yt=this._addTile(Tt);ee[Tt.key]=Tt,Yt.hasData()||B<this._source.maxzoom&&(ot[Tt.key]=Tt)}this._retainLoadedChildren(ot,B,Xe,ee);for(let Tt of w){let Yt=this._tiles[Tt.key];if(Yt.hasData())continue;if(B+1>this._source.maxzoom){let xr=Tt.children(this._source.maxzoom)[0],Ir=this.getTile(xr);if(Ir&&Ir.hasData()){ee[xr.key]=xr;continue}}else{let xr=Tt.children(this._source.maxzoom);if(ee[xr[0].key]&&ee[xr[1].key]&&ee[xr[2].key]&&ee[xr[3].key])continue}let Kt=Yt.wasRequested();for(let xr=Tt.overscaledZ-1;xr>=qe;--xr){let Ir=Tt.scaledTo(xr);if(le[Ir.key])break;if(le[Ir.key]=!0,Yt=this.getTile(Ir),!Yt&&Kt&&(Yt=this._addTile(Ir)),Yt){let ve=Yt.hasData();if((ve||!(!((Q=this.map)===null||Q===void 0)&&Q.cancelPendingTileRequestsWhileZooming)||Kt)&&(ee[Ir.key]=Ir),Kt=Yt.wasRequested(),ve)break}}}return ee}_updateLoadedParentTileCache(){this._loadedParentTiles={};for(let w in this._tiles){let B=[],Q,ee=this._tiles[w].tileID;for(;ee.overscaledZ>0;){if(ee.key in this._loadedParentTiles){Q=this._loadedParentTiles[ee.key];break}B.push(ee.key);let le=ee.scaledTo(ee.overscaledZ-1);if(Q=this._getLoadedTile(le),Q)break;ee=le}for(let le of B)this._loadedParentTiles[le]=Q}}_updateLoadedSiblingTileCache(){this._loadedSiblingTiles={};for(let w in this._tiles){let B=this._tiles[w].tileID,Q=this._getLoadedTile(B);this._loadedSiblingTiles[B.key]=Q}}_addTile(w){let B=this._tiles[w.key];if(B)return B;B=this._cache.getAndRemove(w),B&&(this._setTileReloadTimer(w.key,B),B.tileID=w,this._state.initializeTileState(B,this.map?this.map.painter:null),this._cacheTimers[w.key]&&(clearTimeout(this._cacheTimers[w.key]),delete this._cacheTimers[w.key],this._setTileReloadTimer(w.key,B)));let Q=B;return B||(B=new Vt(w,this._source.tileSize*w.overscaleFactor()),this._loadTile(B,w.key,B.state)),B.uses++,this._tiles[w.key]=B,Q||this._source.fire(new a.k("dataloading",{tile:B,coord:B.tileID,dataType:"source"})),B}_setTileReloadTimer(w,B){w in this._timers&&(clearTimeout(this._timers[w]),delete this._timers[w]);let Q=B.getExpiryTimeout();Q&&(this._timers[w]=setTimeout(()=>{this._reloadTile(w,"expired"),delete this._timers[w]},Q))}_removeTile(w){let B=this._tiles[w];B&&(B.uses--,delete this._tiles[w],this._timers[w]&&(clearTimeout(this._timers[w]),delete this._timers[w]),B.uses>0||(B.hasData()&&B.state!=="reloading"?this._cache.add(B.tileID,B,B.getExpiryTimeout()):(B.aborted=!0,this._abortTile(B),this._unloadTile(B))))}_dataHandler(w){let B=w.sourceDataType;w.dataType==="source"&&B==="metadata"&&(this._sourceLoaded=!0),this._sourceLoaded&&!this._paused&&w.dataType==="source"&&B==="content"&&(this.reload(),this.transform&&this.update(this.transform,this.terrain),this._didEmitContent=!0)}clearTiles(){this._shouldReloadOnResume=!1,this._paused=!1;for(let w in this._tiles)this._removeTile(w);this._cache.reset()}tilesIn(w,B,Q){let ee=[],le=this.transform;if(!le)return ee;let qe=Q?le.getCameraQueryGeometry(w):w,Xe=w.map(ve=>le.pointCoordinate(ve,this.terrain)),ot=qe.map(ve=>le.pointCoordinate(ve,this.terrain)),Tt=this.getIds(),Yt=1/0,Kt=1/0,xr=-1/0,Ir=-1/0;for(let ve of ot)Yt=Math.min(Yt,ve.x),Kt=Math.min(Kt,ve.y),xr=Math.max(xr,ve.x),Ir=Math.max(Ir,ve.y);for(let ve=0;ve<Tt.length;ve++){let be=this._tiles[Tt[ve]];if(be.holdingForFade())continue;let De=be.tileID,Be=Math.pow(2,le.zoom-be.tileID.overscaledZ),et=B*be.queryPadding*a.X/be.tileSize/Be,We=[De.getTilePoint(new a.Z(Yt,Kt)),De.getTilePoint(new a.Z(xr,Ir))];if(We[0].x-et<a.X&&We[0].y-et<a.X&&We[1].x+et>=0&&We[1].y+et>=0){let it=Xe.map(Ht=>De.getTilePoint(Ht)),Ft=ot.map(Ht=>De.getTilePoint(Ht));ee.push({tile:be,tileID:De,queryGeometry:it,cameraQueryGeometry:Ft,scale:Be})}}return ee}getVisibleCoordinates(w){let B=this.getRenderableIds(w).map(Q=>this._tiles[Q].tileID);for(let Q of B)Q.posMatrix=this.transform.calculatePosMatrix(Q.toUnwrapped());return B}hasTransition(){if(this._source.hasTransition())return!0;if(lr(this._source.type)){let w=u.now();for(let B in this._tiles)if(this._tiles[B].fadeEndTime>=w)return!0}return!1}setFeatureState(w,B,Q){this._state.updateState(w=w||"_geojsonTileLayer",B,Q)}removeFeatureState(w,B,Q){this._state.removeFeatureState(w=w||"_geojsonTileLayer",B,Q)}getFeatureState(w,B){return this._state.getState(w=w||"_geojsonTileLayer",B)}setDependencies(w,B,Q){let ee=this._tiles[w];ee&&ee.setDependencies(B,Q)}reloadTilesForDependencies(w,B){for(let Q in this._tiles)this._tiles[Q].hasDependency(w,B)&&this._reloadTile(Q,"reloading");this._cache.filter(Q=>!Q.hasDependency(w,B))}}function er(ue,w){let B=Math.abs(2*ue.wrap)-+(ue.wrap<0),Q=Math.abs(2*w.wrap)-+(w.wrap<0);return ue.overscaledZ-w.overscaledZ||Q-B||w.canonical.y-ue.canonical.y||w.canonical.x-ue.canonical.x}function lr(ue){return ue==="raster"||ue==="image"||ue==="video"}mt.maxOverzooming=10,mt.maxUnderzooming=3;class Tr{constructor(w,B){this.reset(w,B)}reset(w,B){this.points=w||[],this._distances=[0];for(let Q=1;Q<this.points.length;Q++)this._distances[Q]=this._distances[Q-1]+this.points[Q].dist(this.points[Q-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(B||0,.5*this.length),this.paddedLength=this.length-2*this.padding}lerp(w){if(this.points.length===1)return this.points[0];w=a.ac(w,0,1);let B=1,Q=this._distances[B],ee=w*this.paddedLength+this.padding;for(;Q<ee&&B<this._distances.length;)Q=this._distances[++B];let le=B-1,qe=this._distances[le],Xe=Q-qe,ot=Xe>0?(ee-qe)/Xe:0;return this.points[le].mult(1-ot).add(this.points[B].mult(ot))}}function Lr(ue,w){let B=!0;return ue==="always"||ue!=="never"&&w!=="never"||(B=!1),B}class ti{constructor(w,B,Q){let ee=this.boxCells=[],le=this.circleCells=[];this.xCellCount=Math.ceil(w/Q),this.yCellCount=Math.ceil(B/Q);for(let qe=0;qe<this.xCellCount*this.yCellCount;qe++)ee.push([]),le.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=w,this.height=B,this.xScale=this.xCellCount/w,this.yScale=this.yCellCount/B,this.boxUid=0,this.circleUid=0}keysLength(){return this.boxKeys.length+this.circleKeys.length}insert(w,B,Q,ee,le){this._forEachCell(B,Q,ee,le,this._insertBoxCell,this.boxUid++),this.boxKeys.push(w),this.bboxes.push(B),this.bboxes.push(Q),this.bboxes.push(ee),this.bboxes.push(le)}insertCircle(w,B,Q,ee){this._forEachCell(B-ee,Q-ee,B+ee,Q+ee,this._insertCircleCell,this.circleUid++),this.circleKeys.push(w),this.circles.push(B),this.circles.push(Q),this.circles.push(ee)}_insertBoxCell(w,B,Q,ee,le,qe){this.boxCells[le].push(qe)}_insertCircleCell(w,B,Q,ee,le,qe){this.circleCells[le].push(qe)}_query(w,B,Q,ee,le,qe,Xe){if(Q<0||w>this.width||ee<0||B>this.height)return[];let ot=[];if(w<=0&&B<=0&&this.width<=Q&&this.height<=ee){if(le)return[{key:null,x1:w,y1:B,x2:Q,y2:ee}];for(let Tt=0;Tt<this.boxKeys.length;Tt++)ot.push({key:this.boxKeys[Tt],x1:this.bboxes[4*Tt],y1:this.bboxes[4*Tt+1],x2:this.bboxes[4*Tt+2],y2:this.bboxes[4*Tt+3]});for(let Tt=0;Tt<this.circleKeys.length;Tt++){let Yt=this.circles[3*Tt],Kt=this.circles[3*Tt+1],xr=this.circles[3*Tt+2];ot.push({key:this.circleKeys[Tt],x1:Yt-xr,y1:Kt-xr,x2:Yt+xr,y2:Kt+xr})}}else this._forEachCell(w,B,Q,ee,this._queryCell,ot,{hitTest:le,overlapMode:qe,seenUids:{box:{},circle:{}}},Xe);return ot}query(w,B,Q,ee){return this._query(w,B,Q,ee,!1,null)}hitTest(w,B,Q,ee,le,qe){return this._query(w,B,Q,ee,!0,le,qe).length>0}hitTestCircle(w,B,Q,ee,le){let qe=w-Q,Xe=w+Q,ot=B-Q,Tt=B+Q;if(Xe<0||qe>this.width||Tt<0||ot>this.height)return!1;let Yt=[];return this._forEachCell(qe,ot,Xe,Tt,this._queryCellCircle,Yt,{hitTest:!0,overlapMode:ee,circle:{x:w,y:B,radius:Q},seenUids:{box:{},circle:{}}},le),Yt.length>0}_queryCell(w,B,Q,ee,le,qe,Xe,ot){let{seenUids:Tt,hitTest:Yt,overlapMode:Kt}=Xe,xr=this.boxCells[le];if(xr!==null){let ve=this.bboxes;for(let be of xr)if(!Tt.box[be]){Tt.box[be]=!0;let De=4*be,Be=this.boxKeys[be];if(w<=ve[De+2]&&B<=ve[De+3]&&Q>=ve[De+0]&&ee>=ve[De+1]&&(!ot||ot(Be))&&(!Yt||!Lr(Kt,Be.overlapMode))&&(qe.push({key:Be,x1:ve[De],y1:ve[De+1],x2:ve[De+2],y2:ve[De+3]}),Yt))return!0}}let Ir=this.circleCells[le];if(Ir!==null){let ve=this.circles;for(let be of Ir)if(!Tt.circle[be]){Tt.circle[be]=!0;let De=3*be,Be=this.circleKeys[be];if(this._circleAndRectCollide(ve[De],ve[De+1],ve[De+2],w,B,Q,ee)&&(!ot||ot(Be))&&(!Yt||!Lr(Kt,Be.overlapMode))){let et=ve[De],We=ve[De+1],it=ve[De+2];if(qe.push({key:Be,x1:et-it,y1:We-it,x2:et+it,y2:We+it}),Yt)return!0}}}return!1}_queryCellCircle(w,B,Q,ee,le,qe,Xe,ot){let{circle:Tt,seenUids:Yt,overlapMode:Kt}=Xe,xr=this.boxCells[le];if(xr!==null){let ve=this.bboxes;for(let be of xr)if(!Yt.box[be]){Yt.box[be]=!0;let De=4*be,Be=this.boxKeys[be];if(this._circleAndRectCollide(Tt.x,Tt.y,Tt.radius,ve[De+0],ve[De+1],ve[De+2],ve[De+3])&&(!ot||ot(Be))&&!Lr(Kt,Be.overlapMode))return qe.push(!0),!0}}let Ir=this.circleCells[le];if(Ir!==null){let ve=this.circles;for(let be of Ir)if(!Yt.circle[be]){Yt.circle[be]=!0;let De=3*be,Be=this.circleKeys[be];if(this._circlesCollide(ve[De],ve[De+1],ve[De+2],Tt.x,Tt.y,Tt.radius)&&(!ot||ot(Be))&&!Lr(Kt,Be.overlapMode))return qe.push(!0),!0}}}_forEachCell(w,B,Q,ee,le,qe,Xe,ot){let Tt=this._convertToXCellCoord(w),Yt=this._convertToYCellCoord(B),Kt=this._convertToXCellCoord(Q),xr=this._convertToYCellCoord(ee);for(let Ir=Tt;Ir<=Kt;Ir++)for(let ve=Yt;ve<=xr;ve++)if(le.call(this,w,B,Q,ee,this.xCellCount*ve+Ir,qe,Xe,ot))return}_convertToXCellCoord(w){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(w*this.xScale)))}_convertToYCellCoord(w){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(w*this.yScale)))}_circlesCollide(w,B,Q,ee,le,qe){let Xe=ee-w,ot=le-B,Tt=Q+qe;return Tt*Tt>Xe*Xe+ot*ot}_circleAndRectCollide(w,B,Q,ee,le,qe,Xe){let ot=(qe-ee)/2,Tt=Math.abs(w-(ee+ot));if(Tt>ot+Q)return!1;let Yt=(Xe-le)/2,Kt=Math.abs(B-(le+Yt));if(Kt>Yt+Q)return!1;if(Tt<=ot||Kt<=Yt)return!0;let xr=Tt-ot,Ir=Kt-Yt;return xr*xr+Ir*Ir<=Q*Q}}function Br(ue,w,B,Q,ee){let le=a.H();return w?(a.K(le,le,[1/ee,1/ee,1]),B||a.ad(le,le,Q.angle)):a.L(le,Q.labelPlaneMatrix,ue),le}function Vr(ue,w,B,Q,ee){if(w){let le=a.ae(ue);return a.K(le,le,[ee,ee,1]),B||a.ad(le,le,-Q.angle),le}return Q.glCoordMatrix}function dt(ue,w,B,Q){let ee;Q?(ee=[ue,w,Q(ue,w),1],a.af(ee,ee,B)):(ee=[ue,w,0,1],$r(ee,ee,B));let le=ee[3];return{point:new a.P(ee[0]/le,ee[1]/le),signedDistanceFromCamera:le,isOccluded:!1}}function Ge(ue,w){return .5+ue/w*.5}function Je(ue,w){return ue.x>=-w[0]&&ue.x<=w[0]&&ue.y>=-w[1]&&ue.y<=w[1]}function je(ue,w,B,Q,ee,le,qe,Xe,ot,Tt,Yt,Kt,xr,Ir,ve){let be=Q?ue.textSizeData:ue.iconSizeData,De=a.ag(be,B.transform.zoom),Be=[256/B.width*2+1,256/B.height*2+1],et=Q?ue.text.dynamicLayoutVertexArray:ue.icon.dynamicLayoutVertexArray;et.clear();let We=ue.lineVertexArray,it=Q?ue.text.placedSymbolArray:ue.icon.placedSymbolArray,Ft=B.transform.width/B.transform.height,Ht=!1;for(let tr=0;tr<it.length;tr++){let dr=it.get(tr);if(dr.hidden||dr.writingMode===a.ah.vertical&&!Ht){pi(dr.numGlyphs,et);continue}Ht=!1;let Sr=dt(dr.anchorX,dr.anchorY,w,ve);if(!Je(Sr.point,Be)){pi(dr.numGlyphs,et);continue}let Or=Ge(B.transform.cameraToCenterDistance,Sr.signedDistanceFromCamera),Wr=a.ai(be,De,dr),ni=qe?Wr/Or:Wr*Or,Pi={getElevation:ve,labelPlaneMatrix:ee,lineVertexArray:We,pitchWithMap:qe,projectionCache:{projections:{},offsets:{},cachedAnchorPoint:void 0,anyProjectionOccluded:!1},projection:Tt,tileAnchorPoint:new a.P(dr.anchorX,dr.anchorY),unwrappedTileID:Yt,width:Kt,height:xr,translation:Ir},cn=Ie(Pi,dr,ni,!1,Xe,w,le,ue.glyphOffsetArray,et,Ft,ot);Ht=cn.useVertical,(cn.notEnoughRoom||Ht||cn.needsFlipping&&Ie(Pi,dr,ni,!0,Xe,w,le,ue.glyphOffsetArray,et,Ft,ot).notEnoughRoom)&&pi(dr.numGlyphs,et)}Q?ue.text.dynamicLayoutVertexBuffer.updateData(et):ue.icon.dynamicLayoutVertexBuffer.updateData(et)}function tt(ue,w,B,Q,ee,le,qe,Xe){let ot=le.glyphStartIndex+le.numGlyphs,Tt=le.lineStartIndex,Yt=le.lineStartIndex+le.lineLength,Kt=w.getoffsetX(le.glyphStartIndex),xr=w.getoffsetX(ot-1),Ir=vr(ue*Kt,B,Q,ee,le.segment,Tt,Yt,Xe,qe);if(!Ir)return null;let ve=vr(ue*xr,B,Q,ee,le.segment,Tt,Yt,Xe,qe);return ve?Xe.projectionCache.anyProjectionOccluded?null:{first:Ir,last:ve}:null}function xt(ue,w,B,Q){return ue===a.ah.horizontal&&Math.abs(B.y-w.y)>Math.abs(B.x-w.x)*Q?{useVertical:!0}:(ue===a.ah.vertical?w.y<B.y:w.x>B.x)?{needsFlipping:!0}:null}function Ie(ue,w,B,Q,ee,le,qe,Xe,ot,Tt,Yt){let Kt=B/24,xr=w.lineOffsetX*Kt,Ir=w.lineOffsetY*Kt,ve;if(w.numGlyphs>1){let be=w.glyphStartIndex+w.numGlyphs,De=w.lineStartIndex,Be=w.lineStartIndex+w.lineLength,et=tt(Kt,Xe,xr,Ir,Q,w,Yt,ue);if(!et)return{notEnoughRoom:!0};let We=dt(et.first.point.x,et.first.point.y,qe,ue.getElevation).point,it=dt(et.last.point.x,et.last.point.y,qe,ue.getElevation).point;if(ee&&!Q){let Ft=xt(w.writingMode,We,it,Tt);if(Ft)return Ft}ve=[et.first];for(let Ft=w.glyphStartIndex+1;Ft<be-1;Ft++)ve.push(vr(Kt*Xe.getoffsetX(Ft),xr,Ir,Q,w.segment,De,Be,ue,Yt));ve.push(et.last)}else{if(ee&&!Q){let De=dt(ue.tileAnchorPoint.x,ue.tileAnchorPoint.y,le,ue.getElevation).point,Be=w.lineStartIndex+w.segment+1,et=new a.P(ue.lineVertexArray.getx(Be),ue.lineVertexArray.gety(Be)),We=dt(et.x,et.y,le,ue.getElevation),it=We.signedDistanceFromCamera>0?We.point:function(Ht,tr,dr,Sr,Or,Wr){return xe(Ht,tr,dr,1,Or,Wr)}(ue.tileAnchorPoint,et,De,0,le,ue),Ft=xt(w.writingMode,De,it,Tt);if(Ft)return Ft}let be=vr(Kt*Xe.getoffsetX(w.glyphStartIndex),xr,Ir,Q,w.segment,w.lineStartIndex,w.lineStartIndex+w.lineLength,ue,Yt);if(!be||ue.projectionCache.anyProjectionOccluded)return{notEnoughRoom:!0};ve=[be]}for(let be of ve)a.aj(ot,be.point,be.angle);return{}}function xe(ue,w,B,Q,ee,le){let qe=ue.add(ue.sub(w)._unit()),Xe=ee!==void 0?dt(qe.x,qe.y,ee,le.getElevation).point:vt(qe.x,qe.y,le).point,ot=B.sub(Xe);return B.add(ot._mult(Q/ot.mag()))}function ke(ue,w,B){let Q=w.projectionCache;if(Q.projections[ue])return Q.projections[ue];let ee=new a.P(w.lineVertexArray.getx(ue),w.lineVertexArray.gety(ue)),le=vt(ee.x,ee.y,w);if(le.signedDistanceFromCamera>0)return Q.projections[ue]=le.point,Q.anyProjectionOccluded=Q.anyProjectionOccluded||le.isOccluded,le.point;let qe=ue-B.direction;return function(Xe,ot,Tt,Yt,Kt){return xe(Xe,ot,Tt,Yt,void 0,Kt)}(B.distanceFromAnchor===0?w.tileAnchorPoint:new a.P(w.lineVertexArray.getx(qe),w.lineVertexArray.gety(qe)),ee,B.previousVertex,B.absOffsetX-B.distanceFromAnchor+1,w)}function vt(ue,w,B){let Q=ue+B.translation[0],ee=w+B.translation[1],le;return!B.pitchWithMap&&B.projection.useSpecialProjectionForSymbols?(le=B.projection.projectTileCoordinates(Q,ee,B.unwrappedTileID,B.getElevation),le.point.x=(.5*le.point.x+.5)*B.width,le.point.y=(.5*-le.point.y+.5)*B.height):(le=dt(Q,ee,B.labelPlaneMatrix,B.getElevation),le.isOccluded=!1),le}function ir(ue,w,B){return ue._unit()._perp()._mult(w*B)}function ar(ue,w,B,Q,ee,le,qe,Xe,ot){if(Xe.projectionCache.offsets[ue])return Xe.projectionCache.offsets[ue];let Tt=B.add(w);if(ue+ot.direction<Q||ue+ot.direction>=ee)return Xe.projectionCache.offsets[ue]=Tt,Tt;let Yt=ke(ue+ot.direction,Xe,ot),Kt=ir(Yt.sub(B),qe,ot.direction),xr=B.add(Kt),Ir=Yt.add(Kt);return Xe.projectionCache.offsets[ue]=a.ak(le,Tt,xr,Ir)||Tt,Xe.projectionCache.offsets[ue]}function vr(ue,w,B,Q,ee,le,qe,Xe,ot){let Tt=Q?ue-w:ue+w,Yt=Tt>0?1:-1,Kt=0;Q&&(Yt*=-1,Kt=Math.PI),Yt<0&&(Kt+=Math.PI);let xr,Ir=Yt>0?le+ee:le+ee+1;Xe.projectionCache.cachedAnchorPoint?xr=Xe.projectionCache.cachedAnchorPoint:(xr=vt(Xe.tileAnchorPoint.x,Xe.tileAnchorPoint.y,Xe).point,Xe.projectionCache.cachedAnchorPoint=xr);let ve,be,De=xr,Be=xr,et=0,We=0,it=Math.abs(Tt),Ft=[],Ht;for(;et+We<=it;){if(Ir+=Yt,Ir<le||Ir>=qe)return null;et+=We,Be=De,be=ve;let Sr={absOffsetX:it,direction:Yt,distanceFromAnchor:et,previousVertex:Be};if(De=ke(Ir,Xe,Sr),B===0)Ft.push(Be),Ht=De.sub(Be);else{let Or,Wr=De.sub(Be);Or=Wr.mag()===0?ir(ke(Ir+Yt,Xe,Sr).sub(De),B,Yt):ir(Wr,B,Yt),be||(be=Be.add(Or)),ve=ar(Ir,Or,De,le,qe,be,B,Xe,Sr),Ft.push(be),Ht=ve.sub(be)}We=Ht.mag()}let tr=Ht._mult((it-et)/We)._add(be||Be),dr=Kt+Math.atan2(De.y-Be.y,De.x-Be.x);return Ft.push(tr),{point:tr,angle:ot?dr:0,path:Ft}}let ii=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function pi(ue,w){for(let B=0;B<ue;B++){let Q=w.length;w.resize(Q+4),w.float32.set(ii,3*Q)}}function $r(ue,w,B){let Q=w[0],ee=w[1];return ue[0]=B[0]*Q+B[4]*ee+B[12],ue[1]=B[1]*Q+B[5]*ee+B[13],ue[3]=B[3]*Q+B[7]*ee+B[15],ue}let di=100;class ji{constructor(w,B,Q=new ti(w.width+200,w.height+200,25),ee=new ti(w.width+200,w.height+200,25)){this.transform=w,this.mapProjection=B,this.grid=Q,this.ignoredGrid=ee,this.pitchFactor=Math.cos(w._pitch)*w.cameraToCenterDistance,this.screenRightBoundary=w.width+di,this.screenBottomBoundary=w.height+di,this.gridRightBoundary=w.width+200,this.gridBottomBoundary=w.height+200,this.perspectiveRatioCutoff=.6}placeCollisionBox(w,B,Q,ee,le,qe,Xe,ot,Tt,Yt,Kt){let xr=w.anchorPointX+ot[0],Ir=w.anchorPointY+ot[1],ve=this.projectAndGetPerspectiveRatio(ee,xr,Ir,le,Yt),be=Q*ve.perspectiveRatio,De;if(qe||Xe)De=this._projectCollisionBox(w,be,ee,le,qe,Xe,ot,ve,Yt,Kt);else{let Ft=ve.point.x+(Kt?Kt.x*be:0),Ht=ve.point.y+(Kt?Kt.y*be:0);De={allPointsOccluded:!1,box:[Ft+w.x1*be,Ht+w.y1*be,Ft+w.x2*be,Ht+w.y2*be]}}let[Be,et,We,it]=De.box;return this.mapProjection.useSpecialProjectionForSymbols&&(qe?De.allPointsOccluded:this.mapProjection.isOccluded(xr,Ir,le))||ve.perspectiveRatio<this.perspectiveRatioCutoff||!this.isInsideGrid(Be,et,We,it)||B!=="always"&&this.grid.hitTest(Be,et,We,it,B,Tt)?{box:[Be,et,We,it],placeable:!1,offscreen:!1}:{box:[Be,et,We,it],placeable:!0,offscreen:this.isOffscreen(Be,et,We,it)}}placeCollisionCircles(w,B,Q,ee,le,qe,Xe,ot,Tt,Yt,Kt,xr,Ir,ve,be,De){let Be=[],et=new a.P(B.anchorX,B.anchorY),We=this.getPerspectiveRatio(qe,et.x,et.y,Xe,De),it=(Kt?le/We:le*We)/a.ap,Ft={getElevation:De,labelPlaneMatrix:ot,lineVertexArray:Q,pitchWithMap:Kt,projectionCache:{projections:{},offsets:{},cachedAnchorPoint:void 0,anyProjectionOccluded:!1},projection:this.mapProjection,tileAnchorPoint:et,unwrappedTileID:Xe,width:this.transform.width,height:this.transform.height,translation:be},Ht=tt(it,ee,B.lineOffsetX*it,B.lineOffsetY*it,!1,B,!1,Ft),tr=!1,dr=!1,Sr=!0;if(Ht){let Or=.5*Ir*We+ve,Wr=new a.P(-100,-100),ni=new a.P(this.screenRightBoundary,this.screenBottomBoundary),Pi=new Tr,cn=Ht.first,ln=Ht.last,Cn=[];for(let fa=cn.path.length-1;fa>=1;fa--)Cn.push(cn.path[fa]);for(let fa=1;fa<ln.path.length;fa++)Cn.push(ln.path[fa]);let Kn=2.5*Or;if(Tt){let fa=this.projectPathToScreenSpace(Cn,Ft,Tt);Cn=fa.some($a=>$a.signedDistanceFromCamera<=0)?[]:fa.map($a=>$a.point)}let Ta=[];if(Cn.length>0){let fa=Cn[0].clone(),$a=Cn[0].clone();for(let Co=1;Co<Cn.length;Co++)fa.x=Math.min(fa.x,Cn[Co].x),fa.y=Math.min(fa.y,Cn[Co].y),$a.x=Math.max($a.x,Cn[Co].x),$a.y=Math.max($a.y,Cn[Co].y);Ta=fa.x>=Wr.x&&$a.x<=ni.x&&fa.y>=Wr.y&&$a.y<=ni.y?[Cn]:$a.x<Wr.x||fa.x>ni.x||$a.y<Wr.y||fa.y>ni.y?[]:a.al([Cn],Wr.x,Wr.y,ni.x,ni.y)}for(let fa of Ta){Pi.reset(fa,.25*Or);let $a=0;$a=Pi.length<=.5*Or?1:Math.ceil(Pi.paddedLength/Kn)+1;for(let Co=0;Co<$a;Co++){let Qa=Co/Math.max($a-1,1),mo=Pi.lerp(Qa),Bo=mo.x+di,Ps=mo.y+di;Be.push(Bo,Ps,Or,0);let Ts=Bo-Or,wo=Ps-Or,To=Bo+Or,hl=Ps+Or;if(Sr=Sr&&this.isOffscreen(Ts,wo,To,hl),dr=dr||this.isInsideGrid(Ts,wo,To,hl),w!=="always"&&this.grid.hitTestCircle(Bo,Ps,Or,w,xr)&&(tr=!0,!Yt))return{circles:[],offscreen:!1,collisionDetected:tr}}}}return{circles:!Yt&&tr||!dr||We<this.perspectiveRatioCutoff?[]:Be,offscreen:Sr,collisionDetected:tr}}projectPathToScreenSpace(w,B,Q){return w.map(ee=>dt(ee.x,ee.y,Q,B.getElevation))}queryRenderedSymbols(w){if(w.length===0||this.grid.keysLength()===0&&this.ignoredGrid.keysLength()===0)return{};let B=[],Q=1/0,ee=1/0,le=-1/0,qe=-1/0;for(let Yt of w){let Kt=new a.P(Yt.x+di,Yt.y+di);Q=Math.min(Q,Kt.x),ee=Math.min(ee,Kt.y),le=Math.max(le,Kt.x),qe=Math.max(qe,Kt.y),B.push(Kt)}let Xe=this.grid.query(Q,ee,le,qe).concat(this.ignoredGrid.query(Q,ee,le,qe)),ot={},Tt={};for(let Yt of Xe){let Kt=Yt.key;if(ot[Kt.bucketInstanceId]===void 0&&(ot[Kt.bucketInstanceId]={}),ot[Kt.bucketInstanceId][Kt.featureIndex])continue;let xr=[new a.P(Yt.x1,Yt.y1),new a.P(Yt.x2,Yt.y1),new a.P(Yt.x2,Yt.y2),new a.P(Yt.x1,Yt.y2)];a.am(B,xr)&&(ot[Kt.bucketInstanceId][Kt.featureIndex]=!0,Tt[Kt.bucketInstanceId]===void 0&&(Tt[Kt.bucketInstanceId]=[]),Tt[Kt.bucketInstanceId].push(Kt.featureIndex))}return Tt}insertCollisionBox(w,B,Q,ee,le,qe){(Q?this.ignoredGrid:this.grid).insert({bucketInstanceId:ee,featureIndex:le,collisionGroupID:qe,overlapMode:B},w[0],w[1],w[2],w[3])}insertCollisionCircles(w,B,Q,ee,le,qe){let Xe=Q?this.ignoredGrid:this.grid,ot={bucketInstanceId:ee,featureIndex:le,collisionGroupID:qe,overlapMode:B};for(let Tt=0;Tt<w.length;Tt+=4)Xe.insertCircle(ot,w[Tt],w[Tt+1],w[Tt+2])}projectAndGetPerspectiveRatio(w,B,Q,ee,le){let qe;le?(qe=[B,Q,le(B,Q),1],a.af(qe,qe,w)):(qe=[B,Q,0,1],$r(qe,qe,w));let Xe=qe[3];return{point:new a.P((qe[0]/Xe+1)/2*this.transform.width+di,(-qe[1]/Xe+1)/2*this.transform.height+di),perspectiveRatio:.5+this.transform.cameraToCenterDistance/Xe*.5,isOccluded:!1,signedDistanceFromCamera:Xe}}getPerspectiveRatio(w,B,Q,ee,le){let qe=this.mapProjection.useSpecialProjectionForSymbols?this.mapProjection.projectTileCoordinates(B,Q,ee,le):dt(B,Q,w,le);return .5+this.transform.cameraToCenterDistance/qe.signedDistanceFromCamera*.5}isOffscreen(w,B,Q,ee){return Q<di||w>=this.screenRightBoundary||ee<di||B>this.screenBottomBoundary}isInsideGrid(w,B,Q,ee){return Q>=0&&w<this.gridRightBoundary&&ee>=0&&B<this.gridBottomBoundary}getViewportMatrix(){let w=a.an([]);return a.J(w,w,[-100,-100,0]),w}_projectCollisionBox(w,B,Q,ee,le,qe,Xe,ot,Tt,Yt){let Kt=new a.P(1,0),xr=new a.P(0,1),Ir=new a.P(w.anchorPointX+Xe[0],w.anchorPointY+Xe[1]);if(qe&&!le){let Sr=this.projectAndGetPerspectiveRatio(Q,Ir.x+1,Ir.y,ee,Tt).point.sub(ot.point).unit(),Or=Math.atan(Sr.y/Sr.x)+(Sr.x<0?Math.PI:0),Wr=Math.sin(Or),ni=Math.cos(Or);Kt=new a.P(ni,Wr),xr=new a.P(-Wr,ni)}else if(!qe&&le){let Sr=-this.transform.angle,Or=Math.sin(Sr),Wr=Math.cos(Sr);Kt=new a.P(Wr,Or),xr=new a.P(-Or,Wr)}let ve=ot.point,be=B;if(le){ve=Ir;let Sr=this.transform.zoom-Math.floor(this.transform.zoom);be=Math.pow(2,-Sr),be*=this.mapProjection.getPitchedTextCorrection(this.transform,Ir,ee),Yt||(be*=a.ac(.5+ot.signedDistanceFromCamera/this.transform.cameraToCenterDistance*.5,0,4))}Yt&&(ve=ve.add(Kt.mult(Yt.x*be)).add(xr.mult(Yt.y*be)));let De=w.x1*be,Be=w.x2*be,et=(De+Be)/2,We=w.y1*be,it=w.y2*be,Ft=(We+it)/2,Ht=[{offsetX:De,offsetY:We},{offsetX:et,offsetY:We},{offsetX:Be,offsetY:We},{offsetX:Be,offsetY:Ft},{offsetX:Be,offsetY:it},{offsetX:et,offsetY:it},{offsetX:De,offsetY:it},{offsetX:De,offsetY:Ft}],tr=[];for(let{offsetX:Sr,offsetY:Or}of Ht)tr.push(new a.P(ve.x+Kt.x*Sr+xr.x*Or,ve.y+Kt.y*Sr+xr.y*Or));let dr=!1;if(le){let Sr=tr.map(Or=>this.projectAndGetPerspectiveRatio(Q,Or.x,Or.y,ee,Tt));dr=Sr.some(Or=>!Or.isOccluded),tr=Sr.map(Or=>Or.point)}else dr=!0;return{box:a.ao(tr),allPointsOccluded:!dr}}}function In(ue,w,B){return w*(a.X/(ue.tileSize*Math.pow(2,B-ue.tileID.overscaledZ)))}class wi{constructor(w,B,Q,ee){this.opacity=w?Math.max(0,Math.min(1,w.opacity+(w.placed?B:-B))):ee&&Q?1:0,this.placed=Q}isHidden(){return this.opacity===0&&!this.placed}}class On{constructor(w,B,Q,ee,le){this.text=new wi(w?w.text:null,B,Q,le),this.icon=new wi(w?w.icon:null,B,ee,le)}isHidden(){return this.text.isHidden()&&this.icon.isHidden()}}class qn{constructor(w,B,Q){this.text=w,this.icon=B,this.skipFade=Q}}class Fn{constructor(){this.invProjMatrix=a.H(),this.viewportMatrix=a.H(),this.circles=[]}}class ra{constructor(w,B,Q,ee,le){this.bucketInstanceId=w,this.featureIndex=B,this.sourceLayerIndex=Q,this.bucketIndex=ee,this.tileID=le}}class la{constructor(w){this.crossSourceCollisions=w,this.maxGroupID=0,this.collisionGroups={}}get(w){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[w]){let B=++this.maxGroupID;this.collisionGroups[w]={ID:B,predicate:Q=>Q.collisionGroupID===B}}return this.collisionGroups[w]}}function Ut(ue,w,B,Q,ee){let{horizontalAlign:le,verticalAlign:qe}=a.au(ue);return new a.P(-(le-.5)*w+Q[0]*ee,-(qe-.5)*B+Q[1]*ee)}class wt{constructor(w,B,Q,ee,le,qe){this.transform=w.clone(),this.terrain=Q,this.collisionIndex=new ji(this.transform,B),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=ee,this.retainedQueryData={},this.collisionGroups=new la(le),this.collisionCircleArrays={},this.collisionBoxArrays=new Map,this.prevPlacement=qe,qe&&(qe.prevPlacement=void 0),this.placedOrientations={}}_getTerrainElevationFunc(w){let B=this.terrain;return B?(Q,ee)=>B.getElevation(w,Q,ee):null}getBucketParts(w,B,Q,ee){let le=Q.getBucket(B),qe=Q.latestFeatureIndex;if(!le||!qe||B.id!==le.layerIds[0])return;let Xe=Q.collisionBoxArray,ot=le.layers[0].layout,Tt=le.layers[0].paint,Yt=Math.pow(2,this.transform.zoom-Q.tileID.overscaledZ),Kt=Q.tileSize/a.X,xr=Q.tileID.toUnwrapped(),Ir=this.transform.calculatePosMatrix(xr),ve=ot.get("text-pitch-alignment")==="map",be=ot.get("text-rotation-alignment")==="map",De=In(Q,1,this.transform.zoom),Be=this.collisionIndex.mapProjection.translatePosition(this.transform,Q,Tt.get("text-translate"),Tt.get("text-translate-anchor")),et=this.collisionIndex.mapProjection.translatePosition(this.transform,Q,Tt.get("icon-translate"),Tt.get("icon-translate-anchor")),We=Br(Ir,ve,be,this.transform,De),it=null;if(ve){let Ht=Vr(Ir,ve,be,this.transform,De);it=a.L([],this.transform.labelPlaneMatrix,Ht)}this.retainedQueryData[le.bucketInstanceId]=new ra(le.bucketInstanceId,qe,le.sourceLayerIndex,le.index,Q.tileID);let Ft={bucket:le,layout:ot,translationText:Be,translationIcon:et,posMatrix:Ir,unwrappedTileID:xr,textLabelPlaneMatrix:We,labelToScreenMatrix:it,scale:Yt,textPixelRatio:Kt,holdingForFade:Q.holdingForFade(),collisionBoxArray:Xe,partiallyEvaluatedTextSize:a.ag(le.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(le.sourceID)};if(ee)for(let Ht of le.sortKeyRanges){let{sortKey:tr,symbolInstanceStart:dr,symbolInstanceEnd:Sr}=Ht;w.push({sortKey:tr,symbolInstanceStart:dr,symbolInstanceEnd:Sr,parameters:Ft})}else w.push({symbolInstanceStart:0,symbolInstanceEnd:le.symbolInstances.length,parameters:Ft})}attemptAnchorPlacement(w,B,Q,ee,le,qe,Xe,ot,Tt,Yt,Kt,xr,Ir,ve,be,De,Be,et,We){let it=a.aq[w.textAnchor],Ft=[w.textOffset0,w.textOffset1],Ht=Ut(it,Q,ee,Ft,le),tr=this.collisionIndex.placeCollisionBox(B,xr,ot,Tt,Yt,Xe,qe,De,Kt.predicate,We,Ht);if((!et||this.collisionIndex.placeCollisionBox(et,xr,ot,Tt,Yt,Xe,qe,Be,Kt.predicate,We,Ht).placeable)&&tr.placeable){let dr;if(this.prevPlacement&&this.prevPlacement.variableOffsets[Ir.crossTileID]&&this.prevPlacement.placements[Ir.crossTileID]&&this.prevPlacement.placements[Ir.crossTileID].text&&(dr=this.prevPlacement.variableOffsets[Ir.crossTileID].anchor),Ir.crossTileID===0)throw new Error("symbolInstance.crossTileID can't be 0");return this.variableOffsets[Ir.crossTileID]={textOffset:Ft,width:Q,height:ee,anchor:it,textBoxScale:le,prevAnchor:dr},this.markUsedJustification(ve,it,Ir,be),ve.allowVerticalPlacement&&(this.markUsedOrientation(ve,be,Ir),this.placedOrientations[Ir.crossTileID]=be),{shift:Ht,placedGlyphBoxes:tr}}}placeLayerBucketPart(w,B,Q){let{bucket:ee,layout:le,translationText:qe,translationIcon:Xe,posMatrix:ot,unwrappedTileID:Tt,textLabelPlaneMatrix:Yt,labelToScreenMatrix:Kt,textPixelRatio:xr,holdingForFade:Ir,collisionBoxArray:ve,partiallyEvaluatedTextSize:be,collisionGroup:De}=w.parameters,Be=le.get("text-optional"),et=le.get("icon-optional"),We=a.ar(le,"text-overlap","text-allow-overlap"),it=We==="always",Ft=a.ar(le,"icon-overlap","icon-allow-overlap"),Ht=Ft==="always",tr=le.get("text-rotation-alignment")==="map",dr=le.get("text-pitch-alignment")==="map",Sr=le.get("icon-text-fit")!=="none",Or=le.get("symbol-z-order")==="viewport-y",Wr=it&&(Ht||!ee.hasIconData()||et),ni=Ht&&(it||!ee.hasTextData()||Be);!ee.collisionArrays&&ve&&ee.deserializeCollisionBoxes(ve);let Pi=this._getTerrainElevationFunc(this.retainedQueryData[ee.bucketInstanceId].tileID),cn=(ln,Cn,Kn)=>{var Ta,fa;if(B[ln.crossTileID])return;if(Ir)return void(this.placements[ln.crossTileID]=new qn(!1,!1,!1));let $a=!1,Co=!1,Qa=!0,mo=null,Bo={box:null,placeable:!1,offscreen:null},Ps={box:null,placeable:!1,offscreen:null},Ts=null,wo=null,To=null,hl=0,Ul=0,Lu=0;Cn.textFeatureIndex?hl=Cn.textFeatureIndex:ln.useRuntimeCollisionCircles&&(hl=ln.featureIndex),Cn.verticalTextFeatureIndex&&(Ul=Cn.verticalTextFeatureIndex);let au=Cn.textBox;if(au){let Tl=Te=>{let Ne=a.ah.horizontal;if(ee.allowVerticalPlacement&&!Te&&this.prevPlacement){let He=this.prevPlacement.placedOrientations[ln.crossTileID];He&&(this.placedOrientations[ln.crossTileID]=He,Ne=He,this.markUsedOrientation(ee,Ne,ln))}return Ne},Al=(Te,Ne)=>{if(ee.allowVerticalPlacement&&ln.numVerticalGlyphVertices>0&&Cn.verticalTextBox){for(let He of ee.writingModes)if(He===a.ah.vertical?(Bo=Ne(),Ps=Bo):Bo=Te(),Bo&&Bo.placeable)break}else Bo=Te()},X=ln.textAnchorOffsetStartIndex,se=ln.textAnchorOffsetEndIndex;if(se===X){let Te=(Ne,He)=>{let Ye=this.collisionIndex.placeCollisionBox(Ne,We,xr,ot,Tt,dr,tr,qe,De.predicate,Pi);return Ye&&Ye.placeable&&(this.markUsedOrientation(ee,He,ln),this.placedOrientations[ln.crossTileID]=He),Ye};Al(()=>Te(au,a.ah.horizontal),()=>{let Ne=Cn.verticalTextBox;return ee.allowVerticalPlacement&&ln.numVerticalGlyphVertices>0&&Ne?Te(Ne,a.ah.vertical):{box:null,offscreen:null}}),Tl(Bo&&Bo.placeable)}else{let Te=a.aq[(fa=(Ta=this.prevPlacement)===null||Ta===void 0?void 0:Ta.variableOffsets[ln.crossTileID])===null||fa===void 0?void 0:fa.anchor],Ne=(Ye,kt,nt)=>{let jt=Ye.x2-Ye.x1,gr=Ye.y2-Ye.y1,yr=ln.textBoxScale,Hr=Sr&&Ft==="never"?kt:null,qr=null,_i=We==="never"?1:2,bi="never";Te&&_i++;for(let Zr=0;Zr<_i;Zr++){for(let ai=X;ai<se;ai++){let gi=ee.textAnchorOffsets.get(ai);if(Te&&gi.textAnchor!==Te)continue;let Ii=this.attemptAnchorPlacement(gi,Ye,jt,gr,yr,tr,dr,xr,ot,Tt,De,bi,ln,ee,nt,qe,Xe,Hr,Pi);if(Ii&&(qr=Ii.placedGlyphBoxes,qr&&qr.placeable))return $a=!0,mo=Ii.shift,qr}Te?Te=null:bi=We}return Q&&!qr&&(qr={box:this.collisionIndex.placeCollisionBox(au,"always",xr,ot,Tt,dr,tr,qe,De.predicate,Pi,new a.P(0,0)).box,offscreen:!1,placeable:!1}),qr};Al(()=>Ne(au,Cn.iconBox,a.ah.horizontal),()=>{let Ye=Cn.verticalTextBox;return ee.allowVerticalPlacement&&(!Bo||!Bo.placeable)&&ln.numVerticalGlyphVertices>0&&Ye?Ne(Ye,Cn.verticalIconBox,a.ah.vertical):{box:null,occluded:!0,offscreen:null}}),Bo&&($a=Bo.placeable,Qa=Bo.offscreen);let He=Tl(Bo&&Bo.placeable);if(!$a&&this.prevPlacement){let Ye=this.prevPlacement.variableOffsets[ln.crossTileID];Ye&&(this.variableOffsets[ln.crossTileID]=Ye,this.markUsedJustification(ee,Ye.anchor,ln,He))}}}if(Ts=Bo,$a=Ts&&Ts.placeable,Qa=Ts&&Ts.offscreen,ln.useRuntimeCollisionCircles){let Tl=ee.text.placedSymbolArray.get(ln.centerJustifiedTextSymbolIndex),Al=a.ai(ee.textSizeData,be,Tl),X=le.get("text-padding");wo=this.collisionIndex.placeCollisionCircles(We,Tl,ee.lineVertexArray,ee.glyphOffsetArray,Al,ot,Tt,Yt,Kt,Q,dr,De.predicate,ln.collisionCircleDiameter,X,qe,Pi),wo.circles.length&&wo.collisionDetected&&!Q&&a.w("Collisions detected, but collision boxes are not shown"),$a=it||wo.circles.length>0&&!wo.collisionDetected,Qa=Qa&&wo.offscreen}if(Cn.iconFeatureIndex&&(Lu=Cn.iconFeatureIndex),Cn.iconBox){let Tl=Al=>this.collisionIndex.placeCollisionBox(Al,Ft,xr,ot,Tt,dr,tr,Xe,De.predicate,Pi,Sr&&mo?mo:void 0);Ps&&Ps.placeable&&Cn.verticalIconBox?(To=Tl(Cn.verticalIconBox),Co=To.placeable):(To=Tl(Cn.iconBox),Co=To.placeable),Qa=Qa&&To.offscreen}let Js=Be||ln.numHorizontalGlyphVertices===0&&ln.numVerticalGlyphVertices===0,Ql=et||ln.numIconVertices===0;Js||Ql?Ql?Js||(Co=Co&&$a):$a=Co&&$a:Co=$a=Co&&$a;let dc=Co&&To.placeable;if($a&&Ts.placeable&&this.collisionIndex.insertCollisionBox(Ts.box,We,le.get("text-ignore-placement"),ee.bucketInstanceId,Ps&&Ps.placeable&&Ul?Ul:hl,De.ID),dc&&this.collisionIndex.insertCollisionBox(To.box,Ft,le.get("icon-ignore-placement"),ee.bucketInstanceId,Lu,De.ID),wo&&$a&&this.collisionIndex.insertCollisionCircles(wo.circles,We,le.get("text-ignore-placement"),ee.bucketInstanceId,hl,De.ID),Q&&this.storeCollisionData(ee.bucketInstanceId,Kn,Cn,Ts,To,wo),ln.crossTileID===0)throw new Error("symbolInstance.crossTileID can't be 0");if(ee.bucketInstanceId===0)throw new Error("bucket.bucketInstanceId can't be 0");this.placements[ln.crossTileID]=new qn($a||Wr,Co||ni,Qa||ee.justReloaded),B[ln.crossTileID]=!0};if(Or){if(w.symbolInstanceStart!==0)throw new Error("bucket.bucketInstanceId should be 0");let ln=ee.getSortedSymbolIndexes(this.transform.angle);for(let Cn=ln.length-1;Cn>=0;--Cn){let Kn=ln[Cn];cn(ee.symbolInstances.get(Kn),ee.collisionArrays[Kn],Kn)}}else for(let ln=w.symbolInstanceStart;ln<w.symbolInstanceEnd;ln++)cn(ee.symbolInstances.get(ln),ee.collisionArrays[ln],ln);if(Q&&ee.bucketInstanceId in this.collisionCircleArrays){let ln=this.collisionCircleArrays[ee.bucketInstanceId];a.as(ln.invProjMatrix,ot),ln.viewportMatrix=this.collisionIndex.getViewportMatrix()}ee.justReloaded=!1}storeCollisionData(w,B,Q,ee,le,qe){if(Q.textBox||Q.iconBox){let Xe,ot;this.collisionBoxArrays.has(w)?Xe=this.collisionBoxArrays.get(w):(Xe=new Map,this.collisionBoxArrays.set(w,Xe)),Xe.has(B)?ot=Xe.get(B):(ot={text:null,icon:null},Xe.set(B,ot)),Q.textBox&&(ot.text=ee.box),Q.iconBox&&(ot.icon=le.box)}if(qe){let Xe=this.collisionCircleArrays[w];Xe===void 0&&(Xe=this.collisionCircleArrays[w]=new Fn);for(let ot=0;ot<qe.circles.length;ot+=4)Xe.circles.push(qe.circles[ot+0]),Xe.circles.push(qe.circles[ot+1]),Xe.circles.push(qe.circles[ot+2]),Xe.circles.push(qe.collisionDetected?1:0)}}markUsedJustification(w,B,Q,ee){let le;le=ee===a.ah.vertical?Q.verticalPlacedTextSymbolIndex:{left:Q.leftJustifiedTextSymbolIndex,center:Q.centerJustifiedTextSymbolIndex,right:Q.rightJustifiedTextSymbolIndex}[a.at(B)];let qe=[Q.leftJustifiedTextSymbolIndex,Q.centerJustifiedTextSymbolIndex,Q.rightJustifiedTextSymbolIndex,Q.verticalPlacedTextSymbolIndex];for(let Xe of qe)Xe>=0&&(w.text.placedSymbolArray.get(Xe).crossTileID=le>=0&&Xe!==le?0:Q.crossTileID)}markUsedOrientation(w,B,Q){let ee=B===a.ah.horizontal||B===a.ah.horizontalOnly?B:0,le=B===a.ah.vertical?B:0,qe=[Q.leftJustifiedTextSymbolIndex,Q.centerJustifiedTextSymbolIndex,Q.rightJustifiedTextSymbolIndex];for(let Xe of qe)w.text.placedSymbolArray.get(Xe).placedOrientation=ee;Q.verticalPlacedTextSymbolIndex&&(w.text.placedSymbolArray.get(Q.verticalPlacedTextSymbolIndex).placedOrientation=le)}commit(w){this.commitTime=w,this.zoomAtLastRecencyCheck=this.transform.zoom;let B=this.prevPlacement,Q=!1;this.prevZoomAdjustment=B?B.zoomAdjustment(this.transform.zoom):0;let ee=B?B.symbolFadeChange(w):1,le=B?B.opacities:{},qe=B?B.variableOffsets:{},Xe=B?B.placedOrientations:{};for(let ot in this.placements){let Tt=this.placements[ot],Yt=le[ot];Yt?(this.opacities[ot]=new On(Yt,ee,Tt.text,Tt.icon),Q=Q||Tt.text!==Yt.text.placed||Tt.icon!==Yt.icon.placed):(this.opacities[ot]=new On(null,ee,Tt.text,Tt.icon,Tt.skipFade),Q=Q||Tt.text||Tt.icon)}for(let ot in le){let Tt=le[ot];if(!this.opacities[ot]){let Yt=new On(Tt,ee,!1,!1);Yt.isHidden()||(this.opacities[ot]=Yt,Q=Q||Tt.text.placed||Tt.icon.placed)}}for(let ot in qe)this.variableOffsets[ot]||!this.opacities[ot]||this.opacities[ot].isHidden()||(this.variableOffsets[ot]=qe[ot]);for(let ot in Xe)this.placedOrientations[ot]||!this.opacities[ot]||this.opacities[ot].isHidden()||(this.placedOrientations[ot]=Xe[ot]);if(B&&B.lastPlacementChangeTime===void 0)throw new Error("Last placement time for previous placement is not defined");Q?this.lastPlacementChangeTime=w:typeof this.lastPlacementChangeTime!="number"&&(this.lastPlacementChangeTime=B?B.lastPlacementChangeTime:w)}updateLayerOpacities(w,B){let Q={};for(let ee of B){let le=ee.getBucket(w);le&&ee.latestFeatureIndex&&w.id===le.layerIds[0]&&this.updateBucketOpacities(le,ee.tileID,Q,ee.collisionBoxArray)}}updateBucketOpacities(w,B,Q,ee){w.hasTextData()&&(w.text.opacityVertexArray.clear(),w.text.hasVisibleVertices=!1),w.hasIconData()&&(w.icon.opacityVertexArray.clear(),w.icon.hasVisibleVertices=!1),w.hasIconCollisionBoxData()&&w.iconCollisionBox.collisionVertexArray.clear(),w.hasTextCollisionBoxData()&&w.textCollisionBox.collisionVertexArray.clear();let le=w.layers[0],qe=le.layout,Xe=new On(null,0,!1,!1,!0),ot=qe.get("text-allow-overlap"),Tt=qe.get("icon-allow-overlap"),Yt=le._unevaluatedLayout.hasValue("text-variable-anchor")||le._unevaluatedLayout.hasValue("text-variable-anchor-offset"),Kt=qe.get("text-rotation-alignment")==="map",xr=qe.get("text-pitch-alignment")==="map",Ir=qe.get("icon-text-fit")!=="none",ve=new On(null,0,ot&&(Tt||!w.hasIconData()||qe.get("icon-optional")),Tt&&(ot||!w.hasTextData()||qe.get("text-optional")),!0);!w.collisionArrays&&ee&&(w.hasIconCollisionBoxData()||w.hasTextCollisionBoxData())&&w.deserializeCollisionBoxes(ee);let be=(Be,et,We)=>{for(let it=0;it<et/4;it++)Be.opacityVertexArray.emplaceBack(We);Be.hasVisibleVertices=Be.hasVisibleVertices||We!==fn},De=this.collisionBoxArrays.get(w.bucketInstanceId);for(let Be=0;Be<w.symbolInstances.length;Be++){let et=w.symbolInstances.get(Be),{numHorizontalGlyphVertices:We,numVerticalGlyphVertices:it,crossTileID:Ft}=et,Ht=this.opacities[Ft];Q[Ft]?Ht=Xe:Ht||(Ht=ve,this.opacities[Ft]=Ht),Q[Ft]=!0;let tr=et.numIconVertices>0,dr=this.placedOrientations[et.crossTileID],Sr=dr===a.ah.vertical,Or=dr===a.ah.horizontal||dr===a.ah.horizontalOnly;if(We>0||it>0){let ni=tn(Ht.text);be(w.text,We,Sr?fn:ni),be(w.text,it,Or?fn:ni);let Pi=Ht.text.isHidden();[et.rightJustifiedTextSymbolIndex,et.centerJustifiedTextSymbolIndex,et.leftJustifiedTextSymbolIndex].forEach(Cn=>{Cn>=0&&(w.text.placedSymbolArray.get(Cn).hidden=Pi||Sr?1:0)}),et.verticalPlacedTextSymbolIndex>=0&&(w.text.placedSymbolArray.get(et.verticalPlacedTextSymbolIndex).hidden=Pi||Or?1:0);let cn=this.variableOffsets[et.crossTileID];cn&&this.markUsedJustification(w,cn.anchor,et,dr);let ln=this.placedOrientations[et.crossTileID];ln&&(this.markUsedJustification(w,"left",et,ln),this.markUsedOrientation(w,ln,et))}if(tr){let ni=tn(Ht.icon),Pi=!(Ir&&et.verticalPlacedIconSymbolIndex&&Sr);et.placedIconSymbolIndex>=0&&(be(w.icon,et.numIconVertices,Pi?ni:fn),w.icon.placedSymbolArray.get(et.placedIconSymbolIndex).hidden=Ht.icon.isHidden()),et.verticalPlacedIconSymbolIndex>=0&&(be(w.icon,et.numVerticalIconVertices,Pi?fn:ni),w.icon.placedSymbolArray.get(et.verticalPlacedIconSymbolIndex).hidden=Ht.icon.isHidden())}let Wr=De&&De.has(Be)?De.get(Be):{text:null,icon:null};if(w.hasIconCollisionBoxData()||w.hasTextCollisionBoxData()){let ni=w.collisionArrays[Be];if(ni){let Pi=new a.P(0,0);if(ni.textBox||ni.verticalTextBox){let cn=!0;if(Yt){let ln=this.variableOffsets[Ft];ln?(Pi=Ut(ln.anchor,ln.width,ln.height,ln.textOffset,ln.textBoxScale),Kt&&Pi._rotate(xr?this.transform.angle:-this.transform.angle)):cn=!1}if(ni.textBox||ni.verticalTextBox){let ln;ni.textBox&&(ln=Sr),ni.verticalTextBox&&(ln=Or),rr(w.textCollisionBox.collisionVertexArray,Ht.text.placed,!cn||ln,Wr.text,Pi.x,Pi.y)}}if(ni.iconBox||ni.verticalIconBox){let cn=!!(!Or&&ni.verticalIconBox),ln;ni.iconBox&&(ln=cn),ni.verticalIconBox&&(ln=!cn),rr(w.iconCollisionBox.collisionVertexArray,Ht.icon.placed,ln,Wr.icon,Ir?Pi.x:0,Ir?Pi.y:0)}}}}if(w.sortFeatures(this.transform.angle),this.retainedQueryData[w.bucketInstanceId]&&(this.retainedQueryData[w.bucketInstanceId].featureSortOrder=w.featureSortOrder),w.hasTextData()&&w.text.opacityVertexBuffer&&w.text.opacityVertexBuffer.updateData(w.text.opacityVertexArray),w.hasIconData()&&w.icon.opacityVertexBuffer&&w.icon.opacityVertexBuffer.updateData(w.icon.opacityVertexArray),w.hasIconCollisionBoxData()&&w.iconCollisionBox.collisionVertexBuffer&&w.iconCollisionBox.collisionVertexBuffer.updateData(w.iconCollisionBox.collisionVertexArray),w.hasTextCollisionBoxData()&&w.textCollisionBox.collisionVertexBuffer&&w.textCollisionBox.collisionVertexBuffer.updateData(w.textCollisionBox.collisionVertexArray),w.text.opacityVertexArray.length!==w.text.layoutVertexArray.length/4)throw new Error(`bucket.text.opacityVertexArray.length (= ${w.text.opacityVertexArray.length}) !== bucket.text.layoutVertexArray.length (= ${w.text.layoutVertexArray.length}) / 4`);if(w.icon.opacityVertexArray.length!==w.icon.layoutVertexArray.length/4)throw new Error(`bucket.icon.opacityVertexArray.length (= ${w.icon.opacityVertexArray.length}) !== bucket.icon.layoutVertexArray.length (= ${w.icon.layoutVertexArray.length}) / 4`);if(w.bucketInstanceId in this.collisionCircleArrays){let Be=this.collisionCircleArrays[w.bucketInstanceId];w.placementInvProjMatrix=Be.invProjMatrix,w.placementViewportMatrix=Be.viewportMatrix,w.collisionCircleArray=Be.circles,delete this.collisionCircleArrays[w.bucketInstanceId]}}symbolFadeChange(w){return this.fadeDuration===0?1:(w-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment}zoomAdjustment(w){return Math.max(0,(this.transform.zoom-w)/1.5)}hasTransitions(w){return this.stale||w-this.lastPlacementChangeTime<this.fadeDuration}stillRecent(w,B){let Q=this.zoomAtLastRecencyCheck===B?1-this.zoomAdjustment(B):1;return this.zoomAtLastRecencyCheck=B,this.commitTime+this.fadeDuration*Q>w}setStale(){this.stale=!0}}function rr(ue,w,B,Q,ee,le){Q&&Q.length!==0||(Q=[0,0,0,0]);let qe=Q[0]-di,Xe=Q[1]-di,ot=Q[2]-di,Tt=Q[3]-di;ue.emplaceBack(w?1:0,B?1:0,ee||0,le||0,qe,Xe),ue.emplaceBack(w?1:0,B?1:0,ee||0,le||0,ot,Xe),ue.emplaceBack(w?1:0,B?1:0,ee||0,le||0,ot,Tt),ue.emplaceBack(w?1:0,B?1:0,ee||0,le||0,qe,Tt)}let nr=Math.pow(2,25),Er=Math.pow(2,24),Xr=Math.pow(2,17),ri=Math.pow(2,16),Qr=Math.pow(2,9),Oi=Math.pow(2,8),$i=Math.pow(2,1);function tn(ue){if(ue.opacity===0&&!ue.placed)return 0;if(ue.opacity===1&&ue.placed)return 4294967295;let w=ue.placed?1:0,B=Math.floor(127*ue.opacity);return B*nr+w*Er+B*Xr+w*ri+B*Qr+w*Oi+B*$i+w}let fn=0;function yn(){return{isOccluded:(ue,w,B)=>!1,getPitchedTextCorrection:(ue,w,B)=>1,get useSpecialProjectionForSymbols(){return!1},projectTileCoordinates(ue,w,B,Q){throw new Error("Not implemented.")},translatePosition:(ue,w,B,Q)=>function(ee,le,qe,Xe,ot=!1){if(!qe[0]&&!qe[1])return[0,0];let Tt=ot?Xe==="map"?ee.angle:0:Xe==="viewport"?-ee.angle:0;if(Tt){let Yt=Math.sin(Tt),Kt=Math.cos(Tt);qe=[qe[0]*Kt-qe[1]*Yt,qe[0]*Yt+qe[1]*Kt]}return[ot?qe[0]:In(le,qe[0],ee.zoom),ot?qe[1]:In(le,qe[1],ee.zoom)]}(ue,w,B,Q),getCircleRadiusCorrection:ue=>1}}class Sn{constructor(w){this._sortAcrossTiles=w.layout.get("symbol-z-order")!=="viewport-y"&&!w.layout.get("symbol-sort-key").isConstant(),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]}continuePlacement(w,B,Q,ee,le){let qe=this._bucketParts;for(;this._currentTileIndex<w.length;)if(B.getBucketParts(qe,ee,w[this._currentTileIndex],this._sortAcrossTiles),this._currentTileIndex++,le())return!0;for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,qe.sort((Xe,ot)=>Xe.sortKey-ot.sortKey));this._currentPartIndex<qe.length;)if(B.placeLayerBucketPart(qe[this._currentPartIndex],this._seenCrossTileIDs,Q),this._currentPartIndex++,le())return!0;return!1}}class Ba{constructor(w,B,Q,ee,le,qe,Xe,ot){this.placement=new wt(w,yn(),B,qe,Xe,ot),this._currentPlacementIndex=Q.length-1,this._forceFullPlacement=ee,this._showCollisionBoxes=le,this._done=!1}isDone(){return this._done}continuePlacement(w,B,Q){let ee=u.now(),le=()=>!this._forceFullPlacement&&u.now()-ee>2;for(;this._currentPlacementIndex>=0;){let qe=B[w[this._currentPlacementIndex]],Xe=this.placement.collisionIndex.transform.zoom;if(qe.type==="symbol"&&(!qe.minzoom||qe.minzoom<=Xe)&&(!qe.maxzoom||qe.maxzoom>Xe)){if(this._inProgressLayer||(this._inProgressLayer=new Sn(qe)),this._inProgressLayer.continuePlacement(Q[qe.source],this.placement,this._showCollisionBoxes,qe,le))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0}commit(w){return this.placement.commit(w),this.placement}}let ua=512/a.X/2;class ma{constructor(w,B,Q){this.tileID=w,this.bucketInstanceId=Q,this._symbolsByKey={};let ee=new Map;for(let le=0;le<B.length;le++){let qe=B.get(le),Xe=qe.key,ot=ee.get(Xe);ot?ot.push(qe):ee.set(Xe,[qe])}for(let[le,qe]of ee){let Xe={positions:qe.map(ot=>({x:Math.floor(ot.anchorX*ua),y:Math.floor(ot.anchorY*ua)})),crossTileIDs:qe.map(ot=>ot.crossTileID)};if(Xe.positions.length>128){let ot=new a.av(Xe.positions.length,16,Uint16Array);for(let{x:Tt,y:Yt}of Xe.positions)ot.add(Tt,Yt);ot.finish(),delete Xe.positions,Xe.index=ot}this._symbolsByKey[le]=Xe}}getScaledCoordinates(w,B){let{x:Q,y:ee,z:le}=this.tileID.canonical,{x:qe,y:Xe,z:ot}=B.canonical,Tt=ua/Math.pow(2,ot-le),Yt=(Xe*a.X+w.anchorY)*Tt,Kt=ee*a.X*ua;return{x:Math.floor((qe*a.X+w.anchorX)*Tt-Q*a.X*ua),y:Math.floor(Yt-Kt)}}findMatches(w,B,Q){let ee=this.tileID.canonical.z<B.canonical.z?1:Math.pow(2,this.tileID.canonical.z-B.canonical.z);for(let le=0;le<w.length;le++){let qe=w.get(le);if(qe.crossTileID)continue;let Xe=this._symbolsByKey[qe.key];if(!Xe)continue;let ot=this.getScaledCoordinates(qe,B);if(Xe.index){let Tt=Xe.index.range(ot.x-ee,ot.y-ee,ot.x+ee,ot.y+ee).sort();for(let Yt of Tt){let Kt=Xe.crossTileIDs[Yt];if(!Q[Kt]){Q[Kt]=!0,qe.crossTileID=Kt;break}}}else if(Xe.positions)for(let Tt=0;Tt<Xe.positions.length;Tt++){let Yt=Xe.positions[Tt],Kt=Xe.crossTileIDs[Tt];if(Math.abs(Yt.x-ot.x)<=ee&&Math.abs(Yt.y-ot.y)<=ee&&!Q[Kt]){Q[Kt]=!0,qe.crossTileID=Kt;break}}}}getCrossTileIDsLists(){return Object.values(this._symbolsByKey).map(({crossTileIDs:w})=>w)}}class Wa{constructor(){this.maxCrossTileID=0}generate(){return++this.maxCrossTileID}}class Fa{constructor(){this.indexes={},this.usedCrossTileIDs={},this.lng=0}handleWrapJump(w){let B=Math.round((w-this.lng)/360);if(B!==0)for(let Q in this.indexes){let ee=this.indexes[Q],le={};for(let qe in ee){let Xe=ee[qe];Xe.tileID=Xe.tileID.unwrapTo(Xe.tileID.wrap+B),le[Xe.tileID.key]=Xe}this.indexes[Q]=le}this.lng=w}addBucket(w,B,Q){if(this.indexes[w.overscaledZ]&&this.indexes[w.overscaledZ][w.key]){if(this.indexes[w.overscaledZ][w.key].bucketInstanceId===B.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(w.overscaledZ,this.indexes[w.overscaledZ][w.key])}for(let le=0;le<B.symbolInstances.length;le++)B.symbolInstances.get(le).crossTileID=0;this.usedCrossTileIDs[w.overscaledZ]||(this.usedCrossTileIDs[w.overscaledZ]={});let ee=this.usedCrossTileIDs[w.overscaledZ];for(let le in this.indexes){let qe=this.indexes[le];if(Number(le)>w.overscaledZ)for(let Xe in qe){let ot=qe[Xe];ot.tileID.isChildOf(w)&&ot.findMatches(B.symbolInstances,w,ee)}else{let Xe=qe[w.scaledTo(Number(le)).key];Xe&&Xe.findMatches(B.symbolInstances,w,ee)}}for(let le=0;le<B.symbolInstances.length;le++){let qe=B.symbolInstances.get(le);qe.crossTileID||(qe.crossTileID=Q.generate(),ee[qe.crossTileID]=!0)}return this.indexes[w.overscaledZ]===void 0&&(this.indexes[w.overscaledZ]={}),this.indexes[w.overscaledZ][w.key]=new ma(w,B.symbolInstances,B.bucketInstanceId),!0}removeBucketCrossTileIDs(w,B){for(let Q of B.getCrossTileIDsLists())for(let ee of Q)delete this.usedCrossTileIDs[w][ee]}removeStaleBuckets(w){let B=!1;for(let Q in this.indexes){let ee=this.indexes[Q];for(let le in ee)w[ee[le].bucketInstanceId]||(this.removeBucketCrossTileIDs(Q,ee[le]),delete ee[le],B=!0)}return B}}class Wo{constructor(){this.layerIndexes={},this.crossTileIDs=new Wa,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}}addLayer(w,B,Q){let ee=this.layerIndexes[w.id];ee===void 0&&(ee=this.layerIndexes[w.id]=new Fa);let le=!1,qe={};ee.handleWrapJump(Q);for(let Xe of B){let ot=Xe.getBucket(w);ot&&w.id===ot.layerIds[0]&&(ot.bucketInstanceId||(ot.bucketInstanceId=++this.maxBucketInstanceId),ee.addBucket(Xe.tileID,ot,this.crossTileIDs)&&(le=!0),qe[ot.bucketInstanceId]=!0)}return ee.removeStaleBuckets(qe)&&(le=!0),le}pruneUnusedLayers(w){let B={};w.forEach(Q=>{B[Q]=!0});for(let Q in this.layerIndexes)B[Q]||delete this.layerIndexes[Q]}}let da=(ue,w)=>a.t(ue,w&&w.filter(B=>B.identifier!=="source.canvas")),Wn=a.aw();class Ha extends a.E{constructor(w,B={}){super(),this._rtlPluginLoaded=()=>{for(let Q in this.sourceCaches){let ee=this.sourceCaches[Q].getSource().type;ee!=="vector"&&ee!=="geojson"||this.sourceCaches[Q].reload()}},this.map=w,this.dispatcher=new Le(Se(),w._getMapId()),this.dispatcher.registerMessageHandler("GG",(Q,ee)=>this.getGlyphs(Q,ee)),this.dispatcher.registerMessageHandler("GI",(Q,ee)=>this.getImages(Q,ee)),this.imageManager=new T,this.imageManager.setEventedParent(this),this.glyphManager=new G(w._requestManager,B.localIdeographFontFamily),this.lineAtlas=new oe(256,512),this.crossTileSymbolIndex=new Wo,this._spritesImagesIds={},this._layers={},this._order=[],this.sourceCaches={},this.zoomHistory=new a.ax,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("SR",a.ay()),Qt().on(cr,this._rtlPluginLoaded),this.on("data",Q=>{if(Q.dataType!=="source"||Q.sourceDataType!=="metadata")return;let ee=this.sourceCaches[Q.sourceId];if(!ee)return;let le=ee.getSource();if(le&&le.vectorLayerIds)for(let qe in this._layers){let Xe=this._layers[qe];Xe.source===le.id&&this._validateLayer(Xe)}})}loadURL(w,B={},Q){this.fire(new a.k("dataloading",{dataType:"style"})),B.validate=typeof B.validate!="boolean"||B.validate;let ee=this.map._requestManager.transformRequest(w,"Style");this._loadStyleRequest=new AbortController;let le=this._loadStyleRequest;a.h(ee,this._loadStyleRequest).then(qe=>{this._loadStyleRequest=null,this._load(qe.data,B,Q)}).catch(qe=>{this._loadStyleRequest=null,qe&&!le.signal.aborted&&this.fire(new a.j(qe))})}loadJSON(w,B={},Q){this.fire(new a.k("dataloading",{dataType:"style"})),this._frameRequest=new AbortController,u.frameAsync(this._frameRequest).then(()=>{this._frameRequest=null,B.validate=B.validate!==!1,this._load(w,B,Q)}).catch(()=>{})}loadEmpty(){this.fire(new a.k("dataloading",{dataType:"style"})),this._load(Wn,{validate:!1})}_load(w,B,Q){var ee;let le=B.transformStyle?B.transformStyle(Q,w):w;if(!B.validate||!da(this,a.u(le))){this._loaded=!0,this.stylesheet=le;for(let qe in le.sources)this.addSource(qe,le.sources[qe],{validate:!1});le.sprite?this._loadSprite(le.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(le.glyphs),this._createLayers(),this.light=new N(this.stylesheet.light),this.sky=new te(this.stylesheet.sky),this.map.setTerrain((ee=this.stylesheet.terrain)!==null&&ee!==void 0?ee:null),this.fire(new a.k("data",{dataType:"style"})),this.fire(new a.k("style.load"))}}_createLayers(){let w=a.az(this.stylesheet.layers);this.dispatcher.broadcast("SL",w),this._order=w.map(B=>B.id),this._layers={},this._serializedLayers=null;for(let B of w){let Q=a.aA(B);Q.setEventedParent(this,{layer:{id:B.id}}),this._layers[B.id]=Q}}_loadSprite(w,B=!1,Q=void 0){let ee;this.imageManager.setLoaded(!1),this._spriteRequest=new AbortController,function(le,qe,Xe,ot){return a._(this,void 0,void 0,function*(){let Tt=C(le),Yt=Xe>1?"@2x":"",Kt={},xr={};for(let{id:Ir,url:ve}of Tt){let be=qe.transformRequest(M(ve,Yt,".json"),"SpriteJSON");Kt[Ir]=a.h(be,ot);let De=qe.transformRequest(M(ve,Yt,".png"),"SpriteImage");xr[Ir]=p.getImage(De,ot)}return yield Promise.all([...Object.values(Kt),...Object.values(xr)]),function(Ir,ve){return a._(this,void 0,void 0,function*(){let be={};for(let De in Ir){be[De]={};let Be=u.getImageCanvasContext((yield ve[De]).data),et=(yield Ir[De]).data;for(let We in et){let{width:it,height:Ft,x:Ht,y:tr,sdf:dr,pixelRatio:Sr,stretchX:Or,stretchY:Wr,content:ni,textFitWidth:Pi,textFitHeight:cn}=et[We];be[De][We]={data:null,pixelRatio:Sr,sdf:dr,stretchX:Or,stretchY:Wr,content:ni,textFitWidth:Pi,textFitHeight:cn,spriteData:{width:it,height:Ft,x:Ht,y:tr,context:Be}}}}return be})}(Kt,xr)})}(w,this.map._requestManager,this.map.getPixelRatio(),this._spriteRequest).then(le=>{if(this._spriteRequest=null,le)for(let qe in le){this._spritesImagesIds[qe]=[];let Xe=this._spritesImagesIds[qe]?this._spritesImagesIds[qe].filter(ot=>!(ot in le)):[];for(let ot of Xe)this.imageManager.removeImage(ot),this._changedImages[ot]=!0;for(let ot in le[qe]){let Tt=qe==="default"?ot:`${qe}:${ot}`;this._spritesImagesIds[qe].push(Tt),Tt in this.imageManager.images?this.imageManager.updateImage(Tt,le[qe][ot],!1):this.imageManager.addImage(Tt,le[qe][ot]),B&&(this._changedImages[Tt]=!0)}}}).catch(le=>{this._spriteRequest=null,ee=le,this.fire(new a.j(ee))}).finally(()=>{this.imageManager.setLoaded(!0),this._availableImages=this.imageManager.listImages(),B&&(this._changed=!0),this.dispatcher.broadcast("SI",this._availableImages),this.fire(new a.k("data",{dataType:"style"})),Q&&Q(ee)})}_unloadSprite(){for(let w of Object.values(this._spritesImagesIds).flat())this.imageManager.removeImage(w),this._changedImages[w]=!0;this._spritesImagesIds={},this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast("SI",this._availableImages),this.fire(new a.k("data",{dataType:"style"}))}_validateLayer(w){let B=this.sourceCaches[w.source];if(!B)return;let Q=w.sourceLayer;if(!Q)return;let ee=B.getSource();(ee.type==="geojson"||ee.vectorLayerIds&&ee.vectorLayerIds.indexOf(Q)===-1)&&this.fire(new a.j(new Error(`Source layer "${Q}" does not exist on source "${ee.id}" as specified by style layer "${w.id}".`)))}loaded(){if(!this._loaded||Object.keys(this._updatedSources).length)return!1;for(let w in this.sourceCaches)if(!this.sourceCaches[w].loaded())return!1;return!!this.imageManager.isLoaded()}_serializeByIds(w,B=!1){let Q=this._serializedAllLayers();if(!w||w.length===0)return Object.values(B?a.aB(Q):Q);let ee=[];for(let le of w)if(Q[le]){let qe=B?a.aB(Q[le]):Q[le];ee.push(qe)}return ee}_serializedAllLayers(){let w=this._serializedLayers;if(w)return w;w=this._serializedLayers={};let B=Object.keys(this._layers);for(let Q of B){let ee=this._layers[Q];ee.type!=="custom"&&(w[Q]=ee.serialize())}return w}hasTransitions(){if(this.light&&this.light.hasTransition()||this.sky&&this.sky.hasTransition())return!0;for(let w in this.sourceCaches)if(this.sourceCaches[w].hasTransition())return!0;for(let w in this._layers)if(this._layers[w].hasTransition())return!0;return!1}_checkLoaded(){if(!this._loaded)throw new Error("Style is not done loading.")}update(w){if(!this._loaded)return;let B=this._changed;if(B){let ee=Object.keys(this._updatedLayers),le=Object.keys(this._removedLayers);(ee.length||le.length)&&this._updateWorkerLayers(ee,le);for(let qe in this._updatedSources){let Xe=this._updatedSources[qe];if(Xe==="reload")this._reloadSource(qe);else{if(Xe!=="clear")throw new Error(`Invalid action ${Xe}`);this._clearSource(qe)}}this._updateTilesForChangedImages(),this._updateTilesForChangedGlyphs();for(let qe in this._updatedPaintProps)this._layers[qe].updateTransitions(w);this.light.updateTransitions(w),this.sky.updateTransitions(w),this._resetUpdates()}let Q={};for(let ee in this.sourceCaches){let le=this.sourceCaches[ee];Q[ee]=le.used,le.used=!1}for(let ee of this._order){let le=this._layers[ee];le.recalculate(w,this._availableImages),!le.isHidden(w.zoom)&&le.source&&(this.sourceCaches[le.source].used=!0)}for(let ee in Q){let le=this.sourceCaches[ee];!!Q[ee]!=!!le.used&&le.fire(new a.k("data",{sourceDataType:"visibility",dataType:"source",sourceId:ee}))}this.light.recalculate(w),this.sky.recalculate(w),this.z=w.zoom,B&&this.fire(new a.k("data",{dataType:"style"}))}_updateTilesForChangedImages(){let w=Object.keys(this._changedImages);if(w.length){for(let B in this.sourceCaches)this.sourceCaches[B].reloadTilesForDependencies(["icons","patterns"],w);this._changedImages={}}}_updateTilesForChangedGlyphs(){if(this._glyphsDidChange){for(let w in this.sourceCaches)this.sourceCaches[w].reloadTilesForDependencies(["glyphs"],[""]);this._glyphsDidChange=!1}}_updateWorkerLayers(w,B){this.dispatcher.broadcast("UL",{layers:this._serializeByIds(w,!1),removedIds:B})}_resetUpdates(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={},this._glyphsDidChange=!1}setState(w,B={}){var Q;this._checkLoaded();let ee=this.serialize();if(w=B.transformStyle?B.transformStyle(ee,w):w,((Q=B.validate)===null||Q===void 0||Q)&&da(this,a.u(w)))return!1;(w=a.aB(w)).layers=a.az(w.layers);let le=a.aC(ee,w),qe=this._getOperationsToPerform(le);if(qe.unimplemented.length>0)throw new Error(`Unimplemented: ${qe.unimplemented.join(", ")}.`);if(qe.operations.length===0)return!1;for(let Xe of qe.operations)Xe();return this.stylesheet=w,this._serializedLayers=null,!0}_getOperationsToPerform(w){let B=[],Q=[];for(let ee of w)switch(ee.command){case"setCenter":case"setZoom":case"setBearing":case"setPitch":continue;case"addLayer":B.push(()=>this.addLayer.apply(this,ee.args));break;case"removeLayer":B.push(()=>this.removeLayer.apply(this,ee.args));break;case"setPaintProperty":B.push(()=>this.setPaintProperty.apply(this,ee.args));break;case"setLayoutProperty":B.push(()=>this.setLayoutProperty.apply(this,ee.args));break;case"setFilter":B.push(()=>this.setFilter.apply(this,ee.args));break;case"addSource":B.push(()=>this.addSource.apply(this,ee.args));break;case"removeSource":B.push(()=>this.removeSource.apply(this,ee.args));break;case"setLayerZoomRange":B.push(()=>this.setLayerZoomRange.apply(this,ee.args));break;case"setLight":B.push(()=>this.setLight.apply(this,ee.args));break;case"setGeoJSONSourceData":B.push(()=>this.setGeoJSONSourceData.apply(this,ee.args));break;case"setGlyphs":B.push(()=>this.setGlyphs.apply(this,ee.args));break;case"setSprite":B.push(()=>this.setSprite.apply(this,ee.args));break;case"setSky":B.push(()=>this.setSky.apply(this,ee.args));break;case"setTerrain":B.push(()=>this.map.setTerrain.apply(this,ee.args));break;case"setTransition":B.push(()=>{});break;default:Q.push(ee.command)}return{operations:B,unimplemented:Q}}addImage(w,B){if(this.getImage(w))return this.fire(new a.j(new Error(`An image named "${w}" already exists.`)));this.imageManager.addImage(w,B),this._afterImageUpdated(w)}updateImage(w,B){this.imageManager.updateImage(w,B)}getImage(w){return this.imageManager.getImage(w)}removeImage(w){if(!this.getImage(w))return this.fire(new a.j(new Error(`An image named "${w}" does not exist.`)));this.imageManager.removeImage(w),this._afterImageUpdated(w)}_afterImageUpdated(w){this._availableImages=this.imageManager.listImages(),this._changedImages[w]=!0,this._changed=!0,this.dispatcher.broadcast("SI",this._availableImages),this.fire(new a.k("data",{dataType:"style"}))}listImages(){return this._checkLoaded(),this.imageManager.listImages()}addSource(w,B,Q={}){if(this._checkLoaded(),this.sourceCaches[w]!==void 0)throw new Error(`Source "${w}" already exists.`);if(!B.type)throw new Error(`The type property must be defined, but only the following properties were given: ${Object.keys(B).join(", ")}.`);if(["vector","raster","geojson","video","image"].indexOf(B.type)>=0&&this._validate(a.u.source,`sources.${w}`,B,null,Q))return;this.map&&this.map._collectResourceTiming&&(B.collectResourceTiming=!0);let ee=this.sourceCaches[w]=new mt(w,B,this.dispatcher);ee.style=this,ee.setEventedParent(this,()=>({isSourceLoaded:ee.loaded(),source:ee.serialize(),sourceId:w})),ee.onAdd(this.map),this._changed=!0}removeSource(w){if(this._checkLoaded(),this.sourceCaches[w]===void 0)throw new Error("There is no source with this ID");for(let Q in this._layers)if(this._layers[Q].source===w)return this.fire(new a.j(new Error(`Source "${w}" cannot be removed while layer "${Q}" is using it.`)));let B=this.sourceCaches[w];delete this.sourceCaches[w],delete this._updatedSources[w],B.fire(new a.k("data",{sourceDataType:"metadata",dataType:"source",sourceId:w})),B.setEventedParent(null),B.onRemove(this.map),this._changed=!0}setGeoJSONSourceData(w,B){if(this._checkLoaded(),this.sourceCaches[w]===void 0)throw new Error(`There is no source with this ID=${w}`);let Q=this.sourceCaches[w].getSource();if(Q.type!=="geojson")throw new Error(`geojsonSource.type is ${Q.type}, which is !== 'geojson`);Q.setData(B),this._changed=!0}getSource(w){return this.sourceCaches[w]&&this.sourceCaches[w].getSource()}addLayer(w,B,Q={}){this._checkLoaded();let ee=w.id;if(this.getLayer(ee))return void this.fire(new a.j(new Error(`Layer "${ee}" already exists on this map.`)));let le;if(w.type==="custom"){if(da(this,a.aD(w)))return;le=a.aA(w)}else{if("source"in w&&typeof w.source=="object"&&(this.addSource(ee,w.source),w=a.aB(w),w=a.e(w,{source:ee})),this._validate(a.u.layer,`layers.${ee}`,w,{arrayIndex:-1},Q))return;le=a.aA(w),this._validateLayer(le),le.setEventedParent(this,{layer:{id:ee}})}let qe=B?this._order.indexOf(B):this._order.length;if(B&&qe===-1)this.fire(new a.j(new Error(`Cannot add layer "${ee}" before non-existing layer "${B}".`)));else{if(this._order.splice(qe,0,ee),this._layerOrderChanged=!0,this._layers[ee]=le,this._removedLayers[ee]&&le.source&&le.type!=="custom"){let Xe=this._removedLayers[ee];delete this._removedLayers[ee],Xe.type!==le.type?this._updatedSources[le.source]="clear":(this._updatedSources[le.source]="reload",this.sourceCaches[le.source].pause())}this._updateLayer(le),le.onAdd&&le.onAdd(this.map)}}moveLayer(w,B){if(this._checkLoaded(),this._changed=!0,!this._layers[w])return void this.fire(new a.j(new Error(`The layer '${w}' does not exist in the map's style and cannot be moved.`)));if(w===B)return;let Q=this._order.indexOf(w);this._order.splice(Q,1);let ee=B?this._order.indexOf(B):this._order.length;B&&ee===-1?this.fire(new a.j(new Error(`Cannot move layer "${w}" before non-existing layer "${B}".`))):(this._order.splice(ee,0,w),this._layerOrderChanged=!0)}removeLayer(w){this._checkLoaded();let B=this._layers[w];if(!B)return void this.fire(new a.j(new Error(`Cannot remove non-existing layer "${w}".`)));B.setEventedParent(null);let Q=this._order.indexOf(w);this._order.splice(Q,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[w]=B,delete this._layers[w],this._serializedLayers&&delete this._serializedLayers[w],delete this._updatedLayers[w],delete this._updatedPaintProps[w],B.onRemove&&B.onRemove(this.map)}getLayer(w){return this._layers[w]}getLayersOrder(){return[...this._order]}hasLayer(w){return w in this._layers}setLayerZoomRange(w,B,Q){this._checkLoaded();let ee=this.getLayer(w);ee?ee.minzoom===B&&ee.maxzoom===Q||(B!=null&&(ee.minzoom=B),Q!=null&&(ee.maxzoom=Q),this._updateLayer(ee)):this.fire(new a.j(new Error(`Cannot set the zoom range of non-existing layer "${w}".`)))}setFilter(w,B,Q={}){this._checkLoaded();let ee=this.getLayer(w);if(ee){if(!a.aE(ee.filter,B))return B==null?(ee.filter=void 0,void this._updateLayer(ee)):void(this._validate(a.u.filter,`layers.${ee.id}.filter`,B,null,Q)||(ee.filter=a.aB(B),this._updateLayer(ee)))}else this.fire(new a.j(new Error(`Cannot filter non-existing layer "${w}".`)))}getFilter(w){return a.aB(this.getLayer(w).filter)}setLayoutProperty(w,B,Q,ee={}){this._checkLoaded();let le=this.getLayer(w);le?a.aE(le.getLayoutProperty(B),Q)||(le.setLayoutProperty(B,Q,ee),this._updateLayer(le)):this.fire(new a.j(new Error(`Cannot style non-existing layer "${w}".`)))}getLayoutProperty(w,B){let Q=this.getLayer(w);if(Q)return Q.getLayoutProperty(B);this.fire(new a.j(new Error(`Cannot get style of non-existing layer "${w}".`)))}setPaintProperty(w,B,Q,ee={}){this._checkLoaded();let le=this.getLayer(w);le?a.aE(le.getPaintProperty(B),Q)||(le.setPaintProperty(B,Q,ee)&&this._updateLayer(le),this._changed=!0,this._updatedPaintProps[w]=!0,this._serializedLayers=null):this.fire(new a.j(new Error(`Cannot style non-existing layer "${w}".`)))}getPaintProperty(w,B){return this.getLayer(w).getPaintProperty(B)}setFeatureState(w,B){this._checkLoaded();let Q=w.source,ee=w.sourceLayer,le=this.sourceCaches[Q];if(le===void 0)return void this.fire(new a.j(new Error(`The source '${Q}' does not exist in the map's style.`)));let qe=le.getSource().type;qe==="geojson"&&ee?this.fire(new a.j(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):qe!=="vector"||ee?(w.id===void 0&&this.fire(new a.j(new Error("The feature id parameter must be provided."))),le.setFeatureState(ee,w.id,B)):this.fire(new a.j(new Error("The sourceLayer parameter must be provided for vector source types.")))}removeFeatureState(w,B){this._checkLoaded();let Q=w.source,ee=this.sourceCaches[Q];if(ee===void 0)return void this.fire(new a.j(new Error(`The source '${Q}' does not exist in the map's style.`)));let le=ee.getSource().type,qe=le==="vector"?w.sourceLayer:void 0;le!=="vector"||qe?B&&typeof w.id!="string"&&typeof w.id!="number"?this.fire(new a.j(new Error("A feature id is required to remove its specific state property."))):ee.removeFeatureState(qe,w.id,B):this.fire(new a.j(new Error("The sourceLayer parameter must be provided for vector source types.")))}getFeatureState(w){this._checkLoaded();let B=w.source,Q=w.sourceLayer,ee=this.sourceCaches[B];if(ee!==void 0)return ee.getSource().type!=="vector"||Q?(w.id===void 0&&this.fire(new a.j(new Error("The feature id parameter must be provided."))),ee.getFeatureState(Q,w.id)):void this.fire(new a.j(new Error("The sourceLayer parameter must be provided for vector source types.")));this.fire(new a.j(new Error(`The source '${B}' does not exist in the map's style.`)))}getTransition(){return a.e({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)}serialize(){if(!this._loaded)return;let w=a.aF(this.sourceCaches,le=>le.serialize()),B=this._serializeByIds(this._order,!0),Q=this.map.getTerrain()||void 0,ee=this.stylesheet;return a.aG({version:ee.version,name:ee.name,metadata:ee.metadata,light:ee.light,sky:ee.sky,center:ee.center,zoom:ee.zoom,bearing:ee.bearing,pitch:ee.pitch,sprite:ee.sprite,glyphs:ee.glyphs,transition:ee.transition,sources:w,layers:B,terrain:Q},le=>le!==void 0)}_updateLayer(w){this._updatedLayers[w.id]=!0,w.source&&!this._updatedSources[w.source]&&this.sourceCaches[w.source].getSource().type!=="raster"&&(this._updatedSources[w.source]="reload",this.sourceCaches[w.source].pause()),this._serializedLayers=null,this._changed=!0}_flattenAndSortRenderedFeatures(w){let B=qe=>this._layers[qe].type==="fill-extrusion",Q={},ee=[];for(let qe=this._order.length-1;qe>=0;qe--){let Xe=this._order[qe];if(B(Xe)){Q[Xe]=qe;for(let ot of w){let Tt=ot[Xe];if(Tt)for(let Yt of Tt)ee.push(Yt)}}}ee.sort((qe,Xe)=>Xe.intersectionZ-qe.intersectionZ);let le=[];for(let qe=this._order.length-1;qe>=0;qe--){let Xe=this._order[qe];if(B(Xe))for(let ot=ee.length-1;ot>=0;ot--){let Tt=ee[ot].feature;if(Q[Tt.layer.id]<qe)break;le.push(Tt),ee.pop()}else for(let ot of w){let Tt=ot[Xe];if(Tt)for(let Yt of Tt)le.push(Yt.feature)}}return le}queryRenderedFeatures(w,B,Q){B&&B.filter&&this._validate(a.u.filter,"queryRenderedFeatures.filter",B.filter,null,B);let ee={};if(B&&B.layers){if(!Array.isArray(B.layers))return this.fire(new a.j(new Error("parameters.layers must be an Array."))),[];for(let Xe of B.layers){let ot=this._layers[Xe];if(!ot)return this.fire(new a.j(new Error(`The layer '${Xe}' does not exist in the map's style and cannot be queried for features.`))),[];ee[ot.source]=!0}}let le=[];B.availableImages=this._availableImages;let qe=this._serializedAllLayers();for(let Xe in this.sourceCaches)B.layers&&!ee[Xe]||le.push(Pe(this.sourceCaches[Xe],this._layers,qe,w,B,Q));return this.placement&&le.push(function(Xe,ot,Tt,Yt,Kt,xr,Ir){let ve={},be=xr.queryRenderedSymbols(Yt),De=[];for(let Be of Object.keys(be).map(Number))De.push(Ir[Be]);De.sort(ge);for(let Be of De){let et=Be.featureIndex.lookupSymbolFeatures(be[Be.bucketInstanceId],ot,Be.bucketIndex,Be.sourceLayerIndex,Kt.filter,Kt.layers,Kt.availableImages,Xe);for(let We in et){let it=ve[We]=ve[We]||[],Ft=et[We];Ft.sort((Ht,tr)=>{let dr=Be.featureSortOrder;if(dr){let Sr=dr.indexOf(Ht.featureIndex);return dr.indexOf(tr.featureIndex)-Sr}return tr.featureIndex-Ht.featureIndex});for(let Ht of Ft)it.push(Ht)}}for(let Be in ve)ve[Be].forEach(et=>{let We=et.feature,it=Tt[Xe[Be].source].getFeatureState(We.layer["source-layer"],We.id);We.source=We.layer.source,We.layer["source-layer"]&&(We.sourceLayer=We.layer["source-layer"]),We.state=it});return ve}(this._layers,qe,this.sourceCaches,w,B,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(le)}querySourceFeatures(w,B){B&&B.filter&&this._validate(a.u.filter,"querySourceFeatures.filter",B.filter,null,B);let Q=this.sourceCaches[w];return Q?function(ee,le){let qe=ee.getRenderableIds().map(Tt=>ee.getTileByID(Tt)),Xe=[],ot={};for(let Tt=0;Tt<qe.length;Tt++){let Yt=qe[Tt],Kt=Yt.tileID.canonical.key;ot[Kt]||(ot[Kt]=!0,Yt.querySourceFeatures(Xe,le))}return Xe}(Q,B):[]}getLight(){return this.light.getLight()}setLight(w,B={}){this._checkLoaded();let Q=this.light.getLight(),ee=!1;for(let qe in w)if(!a.aE(w[qe],Q[qe])){ee=!0;break}if(!ee)return;let le={now:u.now(),transition:a.e({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(w,B),this.light.updateTransitions(le)}getSky(){var w;return(w=this.stylesheet)===null||w===void 0?void 0:w.sky}setSky(w,B={}){let Q=this.getSky(),ee=!1;if(!w&&!Q)return;if(w&&!Q)ee=!0;else if(!w&&Q)ee=!0;else for(let qe in w)if(!a.aE(w[qe],Q[qe])){ee=!0;break}if(!ee)return;let le={now:u.now(),transition:a.e({duration:300,delay:0},this.stylesheet.transition)};this.stylesheet.sky=w,this.sky.setSky(w,B),this.sky.updateTransitions(le)}_validate(w,B,Q,ee,le={}){return(!le||le.validate!==!1)&&da(this,w.call(a.u,a.e({key:B,style:this.serialize(),value:Q,styleSpec:a.v},ee)))}_remove(w=!0){this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._loadStyleRequest&&(this._loadStyleRequest.abort(),this._loadStyleRequest=null),this._spriteRequest&&(this._spriteRequest.abort(),this._spriteRequest=null),Qt().off(cr,this._rtlPluginLoaded);for(let B in this._layers)this._layers[B].setEventedParent(null);for(let B in this.sourceCaches){let Q=this.sourceCaches[B];Q.setEventedParent(null),Q.onRemove(this.map)}this.imageManager.setEventedParent(null),this.setEventedParent(null),w&&this.dispatcher.broadcast("RM",void 0),this.dispatcher.remove(w)}_clearSource(w){this.sourceCaches[w].clearTiles()}_reloadSource(w){this.sourceCaches[w].resume(),this.sourceCaches[w].reload()}_updateSources(w){for(let B in this.sourceCaches)this.sourceCaches[B].update(w,this.map.terrain)}_generateCollisionBoxes(){for(let w in this.sourceCaches)this._reloadSource(w)}_updatePlacement(w,B,Q,ee,le=!1){let qe=!1,Xe=!1,ot={};for(let Tt of this._order){let Yt=this._layers[Tt];if(Yt.type!=="symbol")continue;if(!ot[Yt.source]){let xr=this.sourceCaches[Yt.source];ot[Yt.source]=xr.getRenderableIds(!0).map(Ir=>xr.getTileByID(Ir)).sort((Ir,ve)=>ve.tileID.overscaledZ-Ir.tileID.overscaledZ||(Ir.tileID.isLessThan(ve.tileID)?-1:1))}let Kt=this.crossTileSymbolIndex.addLayer(Yt,ot[Yt.source],w.center.lng);qe=qe||Kt}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((le=le||this._layerOrderChanged||Q===0)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(u.now(),w.zoom))&&(this.pauseablePlacement=new Ba(w,this.map.terrain,this._order,le,B,Q,ee,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,ot),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(u.now()),Xe=!0),qe&&this.pauseablePlacement.placement.setStale()),Xe||qe)for(let Tt of this._order){let Yt=this._layers[Tt];Yt.type==="symbol"&&this.placement.updateLayerOpacities(Yt,ot[Yt.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(u.now())}_releaseSymbolFadeTiles(){for(let w in this.sourceCaches)this.sourceCaches[w].releaseSymbolFadeTiles()}getImages(w,B){return a._(this,void 0,void 0,function*(){let Q=yield this.imageManager.getImages(B.icons);this._updateTilesForChangedImages();let ee=this.sourceCaches[B.source];return ee&&ee.setDependencies(B.tileID.key,B.type,B.icons),Q})}getGlyphs(w,B){return a._(this,void 0,void 0,function*(){let Q=yield this.glyphManager.getGlyphs(B.stacks),ee=this.sourceCaches[B.source];return ee&&ee.setDependencies(B.tileID.key,B.type,[""]),Q})}getGlyphsUrl(){return this.stylesheet.glyphs||null}setGlyphs(w,B={}){this._checkLoaded(),w&&this._validate(a.u.glyphs,"glyphs",w,null,B)||(this._glyphsDidChange=!0,this.stylesheet.glyphs=w,this.glyphManager.entries={},this.glyphManager.setURL(w))}addSprite(w,B,Q={},ee){this._checkLoaded();let le=[{id:w,url:B}],qe=[...C(this.stylesheet.sprite),...le];this._validate(a.u.sprite,"sprite",qe,null,Q)||(this.stylesheet.sprite=qe,this._loadSprite(le,!0,ee))}removeSprite(w){this._checkLoaded();let B=C(this.stylesheet.sprite);if(B.find(Q=>Q.id===w)){if(this._spritesImagesIds[w])for(let Q of this._spritesImagesIds[w])this.imageManager.removeImage(Q),this._changedImages[Q]=!0;B.splice(B.findIndex(Q=>Q.id===w),1),this.stylesheet.sprite=B.length>0?B:void 0,delete this._spritesImagesIds[w],this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast("SI",this._availableImages),this.fire(new a.k("data",{dataType:"style"}))}else this.fire(new a.j(new Error(`Sprite "${w}" doesn't exists on this map.`)))}getSprite(){return C(this.stylesheet.sprite)}setSprite(w,B={},Q){this._checkLoaded(),w&&this._validate(a.u.sprite,"sprite",w,null,B)||(this.stylesheet.sprite=w,w?this._loadSprite(w,!0,Q):(this._unloadSprite(),Q&&Q(null)))}}var vo=a.Y([{name:"a_pos",type:"Int16",components:2}]);let jn={prelude:Mt(`#ifdef GL_ES
+precision mediump float;
+#else
+#if !defined(lowp)
+#define lowp
+#endif
+#if !defined(mediump)
+#define mediump
+#endif
+#if !defined(highp)
+#define highp
+#endif
+#endif
+`,`#ifdef GL_ES
+precision highp float;
+#else
+#if !defined(lowp)
+#define lowp
+#endif
+#if !defined(mediump)
+#define mediump
+#endif
+#if !defined(highp)
+#define highp
+#endif
+#endif
+vec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0
+);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}
+#ifdef TERRAIN3D
+uniform sampler2D u_terrain;uniform float u_terrain_dim;uniform mat4 u_terrain_matrix;uniform vec4 u_terrain_unpack;uniform float u_terrain_exaggeration;uniform highp sampler2D u_depth;
+#endif
+const highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitShifts=vec4(1.)/bitSh;highp float unpack(highp vec4 color) {return dot(color,bitShifts);}highp float depthOpacity(vec3 frag) {
+#ifdef TERRAIN3D
+highp float d=unpack(texture2D(u_depth,frag.xy*0.5+0.5))+0.0001-frag.z;return 1.0-max(0.0,min(1.0,-d*500.0));
+#else
+return 1.0;
+#endif
+}float calculate_visibility(vec4 pos) {
+#ifdef TERRAIN3D
+vec3 frag=pos.xyz/pos.w;highp float d=depthOpacity(frag);if (d > 0.95) return 1.0;return (d+depthOpacity(frag+vec3(0.0,0.01,0.0)))/2.0;
+#else
+return 1.0;
+#endif
+}float ele(vec2 pos) {
+#ifdef TERRAIN3D
+vec4 rgb=(texture2D(u_terrain,pos)*255.0)*u_terrain_unpack;return rgb.r+rgb.g+rgb.b-u_terrain_unpack.a;
+#else
+return 0.0;
+#endif
+}float get_elevation(vec2 pos) {
+#ifdef TERRAIN3D
+vec2 coord=(u_terrain_matrix*vec4(pos,0.0,1.0)).xy*u_terrain_dim+1.0;vec2 f=fract(coord);vec2 c=(floor(coord)+0.5)/(u_terrain_dim+2.0);float d=1.0/(u_terrain_dim+2.0);float tl=ele(c);float tr=ele(c+vec2(d,0.0));float bl=ele(c+vec2(0.0,d));float br=ele(c+vec2(d,d));float elevation=mix(mix(tl,tr,f.x),mix(bl,br,f.x),f.y);return elevation*u_terrain_exaggeration;
+#else
+return 0.0;
+#endif
+}`),background:Mt(`uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),backgroundPattern:Mt(`uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),circle:Mt(`varying vec3 v_data;varying float v_visibility;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define mediump float radius
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define highp vec4 stroke_color
+#pragma mapbox: define mediump float stroke_width
+#pragma mapbox: define lowp float stroke_opacity
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize mediump float radius
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize highp vec4 stroke_color
+#pragma mapbox: initialize mediump float stroke_width
+#pragma mapbox: initialize lowp float stroke_opacity
+vec2 extrude=v_data.xy;float extrude_length=length(extrude);float antialiased_blur=v_data.z;float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=v_visibility*opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;varying float v_visibility;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define mediump float radius
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define highp vec4 stroke_color
+#pragma mapbox: define mediump float stroke_width
+#pragma mapbox: define lowp float stroke_opacity
+void main(void) {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize mediump float radius
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize highp vec4 stroke_color
+#pragma mapbox: initialize mediump float stroke_width
+#pragma mapbox: initialize lowp float stroke_opacity
+vec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);float ele=get_elevation(circle_center);v_visibility=calculate_visibility(u_matrix*vec4(circle_center,ele,1.0));if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,ele,1);} else {gl_Position=u_matrix*vec4(circle_center,ele,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}float antialiasblur=-max(1.0/u_device_pixel_ratio/(radius+stroke_width),blur);v_data=vec3(extrude.x,extrude.y,antialiasblur);}`),clippingMask:Mt("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),heatmap:Mt(`uniform highp float u_intensity;varying vec2 v_extrude;
+#pragma mapbox: define highp float weight
+#define GAUSS_COEF 0.3989422804014327
+void main() {
+#pragma mapbox: initialize highp float weight
+float d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;
+#pragma mapbox: define highp float weight
+#pragma mapbox: define mediump float radius
+const highp float ZERO=1.0/255.0/16.0;
+#define GAUSS_COEF 0.3989422804014327
+void main(void) {
+#pragma mapbox: initialize highp float weight
+#pragma mapbox: initialize mediump float radius
+vec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,get_elevation(floor(a_pos*0.5)),1);gl_Position=u_matrix*pos;}`),heatmapTexture:Mt(`uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(0.0);
+#endif
+}`,"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),collisionBox:Mt("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_anchor_pos;attribute vec2 a_placed;attribute vec2 a_box_real;uniform mat4 u_matrix;uniform vec2 u_pixel_extrude_scale;varying float v_placed;varying float v_notUsed;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}void main() {gl_Position=projectTileWithElevation(a_anchor_pos,get_elevation(a_anchor_pos));gl_Position.xy=((a_box_real+0.5)*u_pixel_extrude_scale*2.0-1.0)*vec2(1.0,-1.0)*gl_Position.w;if (gl_Position.z/gl_Position.w < 1.1) {gl_Position.z=0.5;}v_placed=a_placed.x;v_notUsed=a_placed.y;}"),collisionCircle:Mt("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),debug:Mt("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,get_elevation(a_pos),1);}"),fill:Mt(`#pragma mapbox: define highp vec4 color
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize lowp float opacity
+gl_FragColor=color*opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`attribute vec2 a_pos;uniform mat4 u_matrix;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize lowp float opacity
+gl_Position=u_matrix*vec4(a_pos,0,1);}`),fillOutline:Mt(`varying vec2 v_pos;
+#pragma mapbox: define highp vec4 outline_color
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize highp vec4 outline_color
+#pragma mapbox: initialize lowp float opacity
+float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;
+#pragma mapbox: define highp vec4 outline_color
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize highp vec4 outline_color
+#pragma mapbox: initialize lowp float opacity
+gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`),fillOutlinePattern:Mt(`uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+void main() {
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+#pragma mapbox: define lowp float pixel_ratio_from
+#pragma mapbox: define lowp float pixel_ratio_to
+void main() {
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+#pragma mapbox: initialize lowp float pixel_ratio_from
+#pragma mapbox: initialize lowp float pixel_ratio_to
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`),fillPattern:Mt(`#ifdef GL_ES
+precision highp float;
+#endif
+uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+void main() {
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+#pragma mapbox: define lowp float pixel_ratio_from
+#pragma mapbox: define lowp float pixel_ratio_to
+void main() {
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+#pragma mapbox: initialize lowp float pixel_ratio_from
+#pragma mapbox: initialize lowp float pixel_ratio_to
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}`),fillExtrusion:Mt(`varying vec4 v_color;void main() {gl_FragColor=v_color;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;
+#ifdef TERRAIN3D
+attribute vec2 a_centroid;
+#endif
+varying vec4 v_color;
+#pragma mapbox: define highp float base
+#pragma mapbox: define highp float height
+#pragma mapbox: define highp vec4 color
+void main() {
+#pragma mapbox: initialize highp float base
+#pragma mapbox: initialize highp float height
+#pragma mapbox: initialize highp vec4 color
+vec3 normal=a_normal_ed.xyz;
+#ifdef TERRAIN3D
+float height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);
+#else
+float height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;
+#endif
+base=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}`),fillExtrusionPattern:Mt(`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;
+#pragma mapbox: define lowp float base
+#pragma mapbox: define lowp float height
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+#pragma mapbox: define lowp float pixel_ratio_from
+#pragma mapbox: define lowp float pixel_ratio_to
+void main() {
+#pragma mapbox: initialize lowp float base
+#pragma mapbox: initialize lowp float height
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+#pragma mapbox: initialize lowp float pixel_ratio_from
+#pragma mapbox: initialize lowp float pixel_ratio_to
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;
+#ifdef TERRAIN3D
+attribute vec2 a_centroid;
+#endif
+varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;
+#pragma mapbox: define lowp float base
+#pragma mapbox: define lowp float height
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+#pragma mapbox: define lowp float pixel_ratio_from
+#pragma mapbox: define lowp float pixel_ratio_to
+void main() {
+#pragma mapbox: initialize lowp float base
+#pragma mapbox: initialize lowp float height
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+#pragma mapbox: initialize lowp float pixel_ratio_from
+#pragma mapbox: initialize lowp float pixel_ratio_to
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;
+#ifdef TERRAIN3D
+float height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);
+#else
+float height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;
+#endif
+base=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0
+? a_pos
+: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}`),hillshadePrepare:Mt(`#ifdef GL_ES
+precision highp float;
+#endif
+uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),hillshade:Mt(`uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;
+#define PI 3.141592653589793
+void main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),line:Mt(`uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`
+#define scale 0.015873016
+attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define mediump float gapwidth
+#pragma mapbox: define lowp float offset
+#pragma mapbox: define mediump float width
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump float gapwidth
+#pragma mapbox: initialize lowp float offset
+#pragma mapbox: initialize mediump float width
+float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;
+#ifdef TERRAIN3D
+v_gamma_scale=1.0;
+#else
+float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;
+#endif
+v_width2=vec2(outset,inset);}`),lineGradient:Mt(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`
+#define scale 0.015873016
+attribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define mediump float gapwidth
+#pragma mapbox: define lowp float offset
+#pragma mapbox: define mediump float width
+void main() {
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump float gapwidth
+#pragma mapbox: initialize lowp float offset
+#pragma mapbox: initialize mediump float width
+float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;
+#ifdef TERRAIN3D
+v_gamma_scale=1.0;
+#else
+float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;
+#endif
+v_width2=vec2(outset,inset);}`),linePattern:Mt(`#ifdef GL_ES
+precision highp float;
+#endif
+uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+#pragma mapbox: define lowp float pixel_ratio_from
+#pragma mapbox: define lowp float pixel_ratio_to
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+#pragma mapbox: initialize lowp float pixel_ratio_from
+#pragma mapbox: initialize lowp float pixel_ratio_to
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`
+#define scale 0.015873016
+#define LINE_DISTANCE_SCALE 2.0
+attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp float offset
+#pragma mapbox: define mediump float gapwidth
+#pragma mapbox: define mediump float width
+#pragma mapbox: define lowp float floorwidth
+#pragma mapbox: define lowp vec4 pattern_from
+#pragma mapbox: define lowp vec4 pattern_to
+#pragma mapbox: define lowp float pixel_ratio_from
+#pragma mapbox: define lowp float pixel_ratio_to
+void main() {
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize lowp float offset
+#pragma mapbox: initialize mediump float gapwidth
+#pragma mapbox: initialize mediump float width
+#pragma mapbox: initialize lowp float floorwidth
+#pragma mapbox: initialize mediump vec4 pattern_from
+#pragma mapbox: initialize mediump vec4 pattern_to
+#pragma mapbox: initialize lowp float pixel_ratio_from
+#pragma mapbox: initialize lowp float pixel_ratio_to
+float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;
+#ifdef TERRAIN3D
+v_gamma_scale=1.0;
+#else
+float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;
+#endif
+v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}`),lineSDF:Mt(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define mediump float width
+#pragma mapbox: define lowp float floorwidth
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump float width
+#pragma mapbox: initialize lowp float floorwidth
+float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`
+#define scale 0.015873016
+#define LINE_DISTANCE_SCALE 2.0
+attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;
+#pragma mapbox: define highp vec4 color
+#pragma mapbox: define lowp float blur
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define mediump float gapwidth
+#pragma mapbox: define lowp float offset
+#pragma mapbox: define mediump float width
+#pragma mapbox: define lowp float floorwidth
+void main() {
+#pragma mapbox: initialize highp vec4 color
+#pragma mapbox: initialize lowp float blur
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize mediump float gapwidth
+#pragma mapbox: initialize lowp float offset
+#pragma mapbox: initialize mediump float width
+#pragma mapbox: initialize lowp float floorwidth
+float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;
+#ifdef TERRAIN3D
+v_gamma_scale=1.0;
+#else
+float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;
+#endif
+v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}`),raster:Mt(`uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),symbolIcon:Mt(`uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize lowp float opacity
+lowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec2 v_tex;varying float v_fade_opacity;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}
+#pragma mapbox: define lowp float opacity
+void main() {
+#pragma mapbox: initialize lowp float opacity
+vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?
+camera_to_anchor_distance/u_camera_to_center_distance :
+u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}gl_Position=finalPos;v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float visibility=calculate_visibility(projectedPoint);v_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));}`),symbolSDF:Mt(`#define SDF_PX 8.0
+uniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;
+#pragma mapbox: define highp vec4 fill_color
+#pragma mapbox: define highp vec4 halo_color
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp float halo_width
+#pragma mapbox: define lowp float halo_blur
+void main() {
+#pragma mapbox: initialize highp vec4 fill_color
+#pragma mapbox: initialize highp vec4 halo_color
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize lowp float halo_width
+#pragma mapbox: initialize lowp float halo_blur
+float EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float inner_edge=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);inner_edge=inner_edge+gamma*gamma_scale;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(inner_edge-gamma_scaled,inner_edge+gamma_scaled,dist);if (u_is_halo) {lowp float halo_edge=(6.0-halo_width/fontScale)/SDF_PX;alpha=min(smoothstep(halo_edge-gamma_scaled,halo_edge+gamma_scaled,dist),1.0-alpha);}gl_FragColor=color*(alpha*opacity*fade_opacity);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec2 v_data0;varying vec3 v_data1;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}
+#pragma mapbox: define highp vec4 fill_color
+#pragma mapbox: define highp vec4 halo_color
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp float halo_width
+#pragma mapbox: define lowp float halo_blur
+void main() {
+#pragma mapbox: initialize highp vec4 fill_color
+#pragma mapbox: initialize highp vec4 halo_color
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize lowp float halo_width
+#pragma mapbox: initialize lowp float halo_blur
+vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?
+camera_to_anchor_distance/u_camera_to_center_distance :
+u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}`),symbolTextAndIcon:Mt(`#define SDF_PX 8.0
+#define SDF 1.0
+#define ICON 0.0
+uniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;
+#pragma mapbox: define highp vec4 fill_color
+#pragma mapbox: define highp vec4 halo_color
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp float halo_width
+#pragma mapbox: define lowp float halo_blur
+void main() {
+#pragma mapbox: initialize highp vec4 fill_color
+#pragma mapbox: initialize highp vec4 halo_color
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize lowp float halo_width
+#pragma mapbox: initialize lowp float halo_blur
+float fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+return;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);
+#ifdef OVERDRAW_INSPECTOR
+gl_FragColor=vec4(1.0);
+#endif
+}`,`attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec4 v_data0;varying vec4 v_data1;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}
+#pragma mapbox: define highp vec4 fill_color
+#pragma mapbox: define highp vec4 halo_color
+#pragma mapbox: define lowp float opacity
+#pragma mapbox: define lowp float halo_width
+#pragma mapbox: define lowp float halo_blur
+void main() {
+#pragma mapbox: initialize highp vec4 fill_color
+#pragma mapbox: initialize highp vec4 halo_color
+#pragma mapbox: initialize lowp float opacity
+#pragma mapbox: initialize lowp float halo_width
+#pragma mapbox: initialize lowp float halo_blur
+vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?
+camera_to_anchor_distance/u_camera_to_center_distance :
+u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}`),terrain:Mt("uniform sampler2D u_texture;uniform vec4 u_fog_color;uniform vec4 u_horizon_color;uniform float u_fog_ground_blend;uniform float u_fog_ground_blend_opacity;uniform float u_horizon_fog_blend;varying vec2 v_texture_pos;varying float v_fog_depth;const float gamma=2.2;vec4 gammaToLinear(vec4 color) {return pow(color,vec4(gamma));}vec4 linearToGamma(vec4 color) {return pow(color,vec4(1.0/gamma));}void main() {vec4 surface_color=texture2D(u_texture,v_texture_pos);if (v_fog_depth > u_fog_ground_blend) {vec4 surface_color_linear=gammaToLinear(surface_color);float blend_color=smoothstep(0.0,1.0,max((v_fog_depth-u_horizon_fog_blend)/(1.0-u_horizon_fog_blend),0.0));vec4 fog_horizon_color_linear=mix(gammaToLinear(u_fog_color),gammaToLinear(u_horizon_color),blend_color);float factor_fog=max(v_fog_depth-u_fog_ground_blend,0.0)/(1.0-u_fog_ground_blend);gl_FragColor=linearToGamma(mix(surface_color_linear,fog_horizon_color_linear,pow(factor_fog,2.0)*u_fog_ground_blend_opacity));} else {gl_FragColor=surface_color;}}","attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform mat4 u_fog_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;varying float v_fog_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);vec4 pos=u_fog_matrix*vec4(a_pos3d.xy,ele,1.0);v_fog_depth=pos.z/pos.w*0.5+0.5;}"),terrainDepth:Mt("varying float v_depth;const highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitMsk=vec4(0.,vec3(1./256.0));highp vec4 pack(highp float value) {highp vec4 comp=fract(value*bitSh);comp-=comp.xxyz*bitMsk;return comp;}void main() {gl_FragColor=pack(v_depth);}","attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying float v_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);v_depth=gl_Position.z/gl_Position.w;}"),terrainCoords:Mt("precision mediump float;uniform sampler2D u_texture;uniform float u_terrain_coords_id;varying vec2 v_texture_pos;void main() {vec4 rgba=texture2D(u_texture,v_texture_pos);gl_FragColor=vec4(rgba.r,rgba.g,rgba.b,u_terrain_coords_id);}","attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);}"),sky:Mt("uniform vec4 u_sky_color;uniform vec4 u_horizon_color;uniform float u_horizon;uniform float u_sky_horizon_blend;void main() {float y=gl_FragCoord.y;if (y > u_horizon) {float blend=y-u_horizon;if (blend < u_sky_horizon_blend) {gl_FragColor=mix(u_sky_color,u_horizon_color,pow(1.0-blend/u_sky_horizon_blend,2.0));} else {gl_FragColor=u_sky_color;}}}","attribute vec2 a_pos;void main() {gl_Position=vec4(a_pos,1.0,1.0);}")};function Mt(ue,w){let B=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,Q=w.match(/attribute ([\w]+) ([\w]+)/g),ee=ue.match(/uniform ([\w]+) ([\w]+)([\s]*)([\w]*)/g),le=w.match(/uniform ([\w]+) ([\w]+)([\s]*)([\w]*)/g),qe=le?le.concat(ee):ee,Xe={};return{fragmentSource:ue=ue.replace(B,(ot,Tt,Yt,Kt,xr)=>(Xe[xr]=!0,Tt==="define"?`
+#ifndef HAS_UNIFORM_u_${xr}
+varying ${Yt} ${Kt} ${xr};
+#else
+uniform ${Yt} ${Kt} u_${xr};
+#endif
+`:`
+#ifdef HAS_UNIFORM_u_${xr}
+    ${Yt} ${Kt} ${xr} = u_${xr};
+#endif
+`)),vertexSource:w=w.replace(B,(ot,Tt,Yt,Kt,xr)=>{let Ir=Kt==="float"?"vec2":"vec4",ve=xr.match(/color/)?"color":Ir;return Xe[xr]?Tt==="define"?`
+#ifndef HAS_UNIFORM_u_${xr}
+uniform lowp float u_${xr}_t;
+attribute ${Yt} ${Ir} a_${xr};
+varying ${Yt} ${Kt} ${xr};
+#else
+uniform ${Yt} ${Kt} u_${xr};
+#endif
+`:ve==="vec4"?`
+#ifndef HAS_UNIFORM_u_${xr}
+    ${xr} = a_${xr};
+#else
+    ${Yt} ${Kt} ${xr} = u_${xr};
+#endif
+`:`
+#ifndef HAS_UNIFORM_u_${xr}
+    ${xr} = unpack_mix_${ve}(a_${xr}, u_${xr}_t);
+#else
+    ${Yt} ${Kt} ${xr} = u_${xr};
+#endif
+`:Tt==="define"?`
+#ifndef HAS_UNIFORM_u_${xr}
+uniform lowp float u_${xr}_t;
+attribute ${Yt} ${Ir} a_${xr};
+#else
+uniform ${Yt} ${Kt} u_${xr};
+#endif
+`:ve==="vec4"?`
+#ifndef HAS_UNIFORM_u_${xr}
+    ${Yt} ${Kt} ${xr} = a_${xr};
+#else
+    ${Yt} ${Kt} ${xr} = u_${xr};
+#endif
+`:`
+#ifndef HAS_UNIFORM_u_${xr}
+    ${Yt} ${Kt} ${xr} = unpack_mix_${ve}(a_${xr}, u_${xr}_t);
+#else
+    ${Yt} ${Kt} ${xr} = u_${xr};
+#endif
+`}),staticAttributes:Q,staticUniforms:qe}}class kr{constructor(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null}bind(w,B,Q,ee,le,qe,Xe,ot,Tt){this.context=w;let Yt=this.boundPaintVertexBuffers.length!==ee.length;for(let Kt=0;!Yt&&Kt<ee.length;Kt++)this.boundPaintVertexBuffers[Kt]!==ee[Kt]&&(Yt=!0);!this.vao||this.boundProgram!==B||this.boundLayoutVertexBuffer!==Q||Yt||this.boundIndexBuffer!==le||this.boundVertexOffset!==qe||this.boundDynamicVertexBuffer!==Xe||this.boundDynamicVertexBuffer2!==ot||this.boundDynamicVertexBuffer3!==Tt?this.freshBind(B,Q,ee,le,qe,Xe,ot,Tt):(w.bindVertexArray.set(this.vao),Xe&&Xe.bind(),le&&le.dynamicDraw&&le.bind(),ot&&ot.bind(),Tt&&Tt.bind())}freshBind(w,B,Q,ee,le,qe,Xe,ot){let Tt=w.numAttributes,Yt=this.context,Kt=Yt.gl;this.vao&&this.destroy(),this.vao=Yt.createVertexArray(),Yt.bindVertexArray.set(this.vao),this.boundProgram=w,this.boundLayoutVertexBuffer=B,this.boundPaintVertexBuffers=Q,this.boundIndexBuffer=ee,this.boundVertexOffset=le,this.boundDynamicVertexBuffer=qe,this.boundDynamicVertexBuffer2=Xe,this.boundDynamicVertexBuffer3=ot,B.enableAttributes(Kt,w);for(let xr of Q)xr.enableAttributes(Kt,w);qe&&qe.enableAttributes(Kt,w),Xe&&Xe.enableAttributes(Kt,w),ot&&ot.enableAttributes(Kt,w),B.bind(),B.setVertexAttribPointers(Kt,w,le);for(let xr of Q)xr.bind(),xr.setVertexAttribPointers(Kt,w,le);qe&&(qe.bind(),qe.setVertexAttribPointers(Kt,w,le)),ee&&ee.bind(),Xe&&(Xe.bind(),Xe.setVertexAttribPointers(Kt,w,le)),ot&&(ot.bind(),ot.setVertexAttribPointers(Kt,w,le)),Yt.currentNumAttributes=Tt}destroy(){this.vao&&(this.context.deleteVertexArray(this.vao),this.vao=null)}}let Jr=(ue,w,B,Q,ee)=>({u_matrix:ue,u_texture:0,u_ele_delta:w,u_fog_matrix:B,u_fog_color:Q?Q.properties.get("fog-color"):a.aM.white,u_fog_ground_blend:Q?Q.properties.get("fog-ground-blend"):1,u_fog_ground_blend_opacity:Q?Q.calculateFogBlendOpacity(ee):0,u_horizon_color:Q?Q.properties.get("horizon-color"):a.aM.white,u_horizon_fog_blend:Q?Q.properties.get("horizon-fog-blend"):1});function vi(ue){let w=[];for(let B=0;B<ue.length;B++){if(ue[B]===null)continue;let Q=ue[B].split(" ");w.push(Q.pop())}return w}class hn{constructor(w,B,Q,ee,le,qe){let Xe=w.gl;this.program=Xe.createProgram();let ot=vi(B.staticAttributes),Tt=Q?Q.getBinderAttributes():[],Yt=ot.concat(Tt),Kt=jn.prelude.staticUniforms?vi(jn.prelude.staticUniforms):[],xr=B.staticUniforms?vi(B.staticUniforms):[],Ir=Q?Q.getBinderUniforms():[],ve=Kt.concat(xr).concat(Ir),be=[];for(let Ht of ve)be.indexOf(Ht)<0&&be.push(Ht);let De=Q?Q.defines():[];le&&De.push("#define OVERDRAW_INSPECTOR;"),qe&&De.push("#define TERRAIN3D;");let Be=De.concat(jn.prelude.fragmentSource,B.fragmentSource).join(`
+`),et=De.concat(jn.prelude.vertexSource,B.vertexSource).join(`
+`),We=Xe.createShader(Xe.FRAGMENT_SHADER);if(Xe.isContextLost())return void(this.failedToCreate=!0);if(Xe.shaderSource(We,Be),Xe.compileShader(We),!Xe.getShaderParameter(We,Xe.COMPILE_STATUS))throw new Error(`Could not compile fragment shader: ${Xe.getShaderInfoLog(We)}`);Xe.attachShader(this.program,We);let it=Xe.createShader(Xe.VERTEX_SHADER);if(Xe.isContextLost())return void(this.failedToCreate=!0);if(Xe.shaderSource(it,et),Xe.compileShader(it),!Xe.getShaderParameter(it,Xe.COMPILE_STATUS))throw new Error(`Could not compile vertex shader: ${Xe.getShaderInfoLog(it)}`);Xe.attachShader(this.program,it),this.attributes={};let Ft={};this.numAttributes=Yt.length;for(let Ht=0;Ht<this.numAttributes;Ht++)Yt[Ht]&&(Xe.bindAttribLocation(this.program,Ht,Yt[Ht]),this.attributes[Yt[Ht]]=Ht);if(Xe.linkProgram(this.program),!Xe.getProgramParameter(this.program,Xe.LINK_STATUS))throw new Error(`Program failed to link: ${Xe.getProgramInfoLog(this.program)}`);Xe.deleteShader(it),Xe.deleteShader(We);for(let Ht=0;Ht<be.length;Ht++){let tr=be[Ht];if(tr&&!Ft[tr]){let dr=Xe.getUniformLocation(this.program,tr);dr&&(Ft[tr]=dr)}}this.fixedUniforms=ee(w,Ft),this.terrainUniforms=((Ht,tr)=>({u_depth:new a.aH(Ht,tr.u_depth),u_terrain:new a.aH(Ht,tr.u_terrain),u_terrain_dim:new a.aI(Ht,tr.u_terrain_dim),u_terrain_matrix:new a.aJ(Ht,tr.u_terrain_matrix),u_terrain_unpack:new a.aK(Ht,tr.u_terrain_unpack),u_terrain_exaggeration:new a.aI(Ht,tr.u_terrain_exaggeration)}))(w,Ft),this.binderUniforms=Q?Q.getUniforms(w,Ft):[]}draw(w,B,Q,ee,le,qe,Xe,ot,Tt,Yt,Kt,xr,Ir,ve,be,De,Be,et){let We=w.gl;if(this.failedToCreate)return;if(w.program.set(this.program),w.setDepthMode(Q),w.setStencilMode(ee),w.setColorMode(le),w.setCullFace(qe),ot){w.activeTexture.set(We.TEXTURE2),We.bindTexture(We.TEXTURE_2D,ot.depthTexture),w.activeTexture.set(We.TEXTURE3),We.bindTexture(We.TEXTURE_2D,ot.texture);for(let Ft in this.terrainUniforms)this.terrainUniforms[Ft].set(ot[Ft])}for(let Ft in this.fixedUniforms)this.fixedUniforms[Ft].set(Xe[Ft]);be&&be.setUniforms(w,this.binderUniforms,Ir,{zoom:ve});let it=0;switch(B){case We.LINES:it=2;break;case We.TRIANGLES:it=3;break;case We.LINE_STRIP:it=1}for(let Ft of xr.get()){let Ht=Ft.vaos||(Ft.vaos={});(Ht[Tt]||(Ht[Tt]=new kr)).bind(w,this,Yt,be?be.getPaintVertexBuffers():[],Kt,Ft.vertexOffset,De,Be,et),We.drawElements(B,Ft.primitiveLength*it,We.UNSIGNED_SHORT,Ft.primitiveOffset*it*2)}}}function An(ue,w,B){let Q=1/In(B,1,w.transform.tileZoom),ee=Math.pow(2,B.tileID.overscaledZ),le=B.tileSize*Math.pow(2,w.transform.tileZoom)/ee,qe=le*(B.tileID.canonical.x+B.tileID.wrap*ee),Xe=le*B.tileID.canonical.y;return{u_image:0,u_texsize:B.imageAtlasTexture.size,u_scale:[Q,ue.fromScale,ue.toScale],u_fade:ue.t,u_pixel_coord_upper:[qe>>16,Xe>>16],u_pixel_coord_lower:[65535&qe,65535&Xe]}}let Mn=(ue,w,B,Q)=>{let ee=w.style.light,le=ee.properties.get("position"),qe=[le.x,le.y,le.z],Xe=function(){var Tt=new a.A(9);return a.A!=Float32Array&&(Tt[1]=0,Tt[2]=0,Tt[3]=0,Tt[5]=0,Tt[6]=0,Tt[7]=0),Tt[0]=1,Tt[4]=1,Tt[8]=1,Tt}();ee.properties.get("anchor")==="viewport"&&function(Tt,Yt){var Kt=Math.sin(Yt),xr=Math.cos(Yt);Tt[0]=xr,Tt[1]=Kt,Tt[2]=0,Tt[3]=-Kt,Tt[4]=xr,Tt[5]=0,Tt[6]=0,Tt[7]=0,Tt[8]=1}(Xe,-w.transform.angle),function(Tt,Yt,Kt){var xr=Yt[0],Ir=Yt[1],ve=Yt[2];Tt[0]=xr*Kt[0]+Ir*Kt[3]+ve*Kt[6],Tt[1]=xr*Kt[1]+Ir*Kt[4]+ve*Kt[7],Tt[2]=xr*Kt[2]+Ir*Kt[5]+ve*Kt[8]}(qe,qe,Xe);let ot=ee.properties.get("color");return{u_matrix:ue,u_lightpos:qe,u_lightintensity:ee.properties.get("intensity"),u_lightcolor:[ot.r,ot.g,ot.b],u_vertical_gradient:+B,u_opacity:Q}},Li=(ue,w,B,Q,ee,le,qe)=>a.e(Mn(ue,w,B,Q),An(le,w,qe),{u_height_factor:-Math.pow(2,ee.overscaledZ)/qe.tileSize/8}),_n=ue=>({u_matrix:ue}),ya=(ue,w,B,Q)=>a.e(_n(ue),An(B,w,Q)),Jn=(ue,w)=>({u_matrix:ue,u_world:w}),Ma=(ue,w,B,Q,ee)=>a.e(ya(ue,w,B,Q),{u_world:ee}),_o=(ue,w,B,Q)=>{let ee=ue.transform,le,qe;if(Q.paint.get("circle-pitch-alignment")==="map"){let Xe=In(B,1,ee.zoom);le=!0,qe=[Xe,Xe]}else le=!1,qe=ee.pixelsToGLUnits;return{u_camera_to_center_distance:ee.cameraToCenterDistance,u_scale_with_map:+(Q.paint.get("circle-pitch-scale")==="map"),u_matrix:ue.translatePosMatrix(w.posMatrix,B,Q.paint.get("circle-translate"),Q.paint.get("circle-translate-anchor")),u_pitch_with_map:+le,u_device_pixel_ratio:ue.pixelRatio,u_extrude_scale:qe}},No=(ue,w,B)=>({u_matrix:ue,u_inv_matrix:w,u_camera_to_center_distance:B.cameraToCenterDistance,u_viewport_size:[B.width,B.height]}),po=(ue,w,B=1)=>({u_matrix:ue,u_color:w,u_overlay:0,u_overlay_scale:B}),Lo=ue=>({u_matrix:ue}),ko=(ue,w,B,Q)=>({u_matrix:ue,u_extrude_scale:In(w,1,B),u_intensity:Q}),Ds=(ue,w,B,Q)=>{let ee=a.H();a.aP(ee,0,ue.width,ue.height,0,0,1);let le=ue.context.gl;return{u_matrix:ee,u_world:[le.drawingBufferWidth,le.drawingBufferHeight],u_image:B,u_color_ramp:Q,u_opacity:w.paint.get("heatmap-opacity")}};function Fs(ue,w){let B=Math.pow(2,w.canonical.z),Q=w.canonical.y;return[new a.Z(0,Q/B).toLngLat().lat,new a.Z(0,(Q+1)/B).toLngLat().lat]}let ll=(ue,w,B,Q)=>{let ee=ue.transform;return{u_matrix:As(ue,w,B,Q),u_ratio:1/In(w,1,ee.zoom),u_device_pixel_ratio:ue.pixelRatio,u_units_to_pixels:[1/ee.pixelsToGLUnits[0],1/ee.pixelsToGLUnits[1]]}},ul=(ue,w,B,Q,ee)=>a.e(ll(ue,w,B,ee),{u_image:0,u_image_height:Q}),zl=(ue,w,B,Q,ee)=>{let le=ue.transform,qe=il(w,le);return{u_matrix:As(ue,w,B,ee),u_texsize:w.imageAtlasTexture.size,u_ratio:1/In(w,1,le.zoom),u_device_pixel_ratio:ue.pixelRatio,u_image:0,u_scale:[qe,Q.fromScale,Q.toScale],u_fade:Q.t,u_units_to_pixels:[1/le.pixelsToGLUnits[0],1/le.pixelsToGLUnits[1]]}},us=(ue,w,B,Q,ee,le)=>{let qe=ue.lineAtlas,Xe=il(w,ue.transform),ot=B.layout.get("line-cap")==="round",Tt=qe.getDash(Q.from,ot),Yt=qe.getDash(Q.to,ot),Kt=Tt.width*ee.fromScale,xr=Yt.width*ee.toScale;return a.e(ll(ue,w,B,le),{u_patternscale_a:[Xe/Kt,-Tt.height/2],u_patternscale_b:[Xe/xr,-Yt.height/2],u_sdfgamma:qe.width/(256*Math.min(Kt,xr)*ue.pixelRatio)/2,u_image:0,u_tex_y_a:Tt.y,u_tex_y_b:Yt.y,u_mix:ee.t})};function il(ue,w){return 1/In(ue,1,w.tileZoom)}function As(ue,w,B,Q){return ue.translatePosMatrix(Q?Q.posMatrix:w.tileID.posMatrix,w,B.paint.get("line-translate"),B.paint.get("line-translate-anchor"))}let cl=(ue,w,B,Q,ee)=>{return{u_matrix:ue,u_tl_parent:w,u_scale_parent:B,u_buffer_scale:1,u_fade_t:Q.mix,u_opacity:Q.opacity*ee.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:ee.paint.get("raster-brightness-min"),u_brightness_high:ee.paint.get("raster-brightness-max"),u_saturation_factor:(qe=ee.paint.get("raster-saturation"),qe>0?1-1/(1.001-qe):-qe),u_contrast_factor:(le=ee.paint.get("raster-contrast"),le>0?1/(1-le):1+le),u_spin_weights:Ks(ee.paint.get("raster-hue-rotate"))};var le,qe};function Ks(ue){ue*=Math.PI/180;let w=Math.sin(ue),B=Math.cos(ue);return[(2*B+1)/3,(-Math.sqrt(3)*w-B+1)/3,(Math.sqrt(3)*w-B+1)/3]}let zs=(ue,w,B,Q,ee,le,qe,Xe,ot,Tt,Yt,Kt,xr,Ir)=>{let ve=qe.transform;return{u_is_size_zoom_constant:+(ue==="constant"||ue==="source"),u_is_size_feature_constant:+(ue==="constant"||ue==="camera"),u_size_t:w?w.uSizeT:0,u_size:w?w.uSize:0,u_camera_to_center_distance:ve.cameraToCenterDistance,u_pitch:ve.pitch/360*2*Math.PI,u_rotate_symbol:+B,u_aspect_ratio:ve.width/ve.height,u_fade_change:qe.options.fadeDuration?qe.symbolFadeChange:1,u_matrix:Xe,u_label_plane_matrix:ot,u_coord_matrix:Tt,u_is_text:+Kt,u_pitch_with_map:+Q,u_is_along_line:ee,u_is_variable_anchor:le,u_texsize:xr,u_texture:0,u_translation:Yt,u_pitched_scale:Ir}},Io=(ue,w,B,Q,ee,le,qe,Xe,ot,Tt,Yt,Kt,xr,Ir,ve)=>{let be=qe.transform;return a.e(zs(ue,w,B,Q,ee,le,qe,Xe,ot,Tt,Yt,Kt,xr,ve),{u_gamma_scale:Q?Math.cos(be._pitch)*be.cameraToCenterDistance:1,u_device_pixel_ratio:qe.pixelRatio,u_is_halo:+Ir})},ls=(ue,w,B,Q,ee,le,qe,Xe,ot,Tt,Yt,Kt,xr,Ir)=>a.e(Io(ue,w,B,Q,ee,le,qe,Xe,ot,Tt,Yt,!0,Kt,!0,Ir),{u_texsize_icon:xr,u_texture_icon:1}),Zl=(ue,w,B)=>({u_matrix:ue,u_opacity:w,u_color:B}),Su=(ue,w,B,Q,ee,le)=>a.e(function(qe,Xe,ot,Tt){let Yt=ot.imageManager.getPattern(qe.from.toString()),Kt=ot.imageManager.getPattern(qe.to.toString()),{width:xr,height:Ir}=ot.imageManager.getPixelSize(),ve=Math.pow(2,Tt.tileID.overscaledZ),be=Tt.tileSize*Math.pow(2,ot.transform.tileZoom)/ve,De=be*(Tt.tileID.canonical.x+Tt.tileID.wrap*ve),Be=be*Tt.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:Yt.tl,u_pattern_br_a:Yt.br,u_pattern_tl_b:Kt.tl,u_pattern_br_b:Kt.br,u_texsize:[xr,Ir],u_mix:Xe.t,u_pattern_size_a:Yt.displaySize,u_pattern_size_b:Kt.displaySize,u_scale_a:Xe.fromScale,u_scale_b:Xe.toScale,u_tile_units_to_pixels:1/In(Tt,1,ot.transform.tileZoom),u_pixel_coord_upper:[De>>16,Be>>16],u_pixel_coord_lower:[65535&De,65535&Be]}}(Q,le,B,ee),{u_matrix:ue,u_opacity:w}),nc={fillExtrusion:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_lightpos:new a.aN(ue,w.u_lightpos),u_lightintensity:new a.aI(ue,w.u_lightintensity),u_lightcolor:new a.aN(ue,w.u_lightcolor),u_vertical_gradient:new a.aI(ue,w.u_vertical_gradient),u_opacity:new a.aI(ue,w.u_opacity)}),fillExtrusionPattern:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_lightpos:new a.aN(ue,w.u_lightpos),u_lightintensity:new a.aI(ue,w.u_lightintensity),u_lightcolor:new a.aN(ue,w.u_lightcolor),u_vertical_gradient:new a.aI(ue,w.u_vertical_gradient),u_height_factor:new a.aI(ue,w.u_height_factor),u_image:new a.aH(ue,w.u_image),u_texsize:new a.aO(ue,w.u_texsize),u_pixel_coord_upper:new a.aO(ue,w.u_pixel_coord_upper),u_pixel_coord_lower:new a.aO(ue,w.u_pixel_coord_lower),u_scale:new a.aN(ue,w.u_scale),u_fade:new a.aI(ue,w.u_fade),u_opacity:new a.aI(ue,w.u_opacity)}),fill:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix)}),fillPattern:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_image:new a.aH(ue,w.u_image),u_texsize:new a.aO(ue,w.u_texsize),u_pixel_coord_upper:new a.aO(ue,w.u_pixel_coord_upper),u_pixel_coord_lower:new a.aO(ue,w.u_pixel_coord_lower),u_scale:new a.aN(ue,w.u_scale),u_fade:new a.aI(ue,w.u_fade)}),fillOutline:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_world:new 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a.aO(ue,w.u_viewport_size)}),debug:(ue,w)=>({u_color:new a.aL(ue,w.u_color),u_matrix:new a.aJ(ue,w.u_matrix),u_overlay:new a.aH(ue,w.u_overlay),u_overlay_scale:new a.aI(ue,w.u_overlay_scale)}),clippingMask:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix)}),heatmap:(ue,w)=>({u_extrude_scale:new a.aI(ue,w.u_extrude_scale),u_intensity:new a.aI(ue,w.u_intensity),u_matrix:new a.aJ(ue,w.u_matrix)}),heatmapTexture:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_world:new a.aO(ue,w.u_world),u_image:new a.aH(ue,w.u_image),u_color_ramp:new a.aH(ue,w.u_color_ramp),u_opacity:new a.aI(ue,w.u_opacity)}),hillshade:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_image:new a.aH(ue,w.u_image),u_latrange:new a.aO(ue,w.u_latrange),u_light:new a.aO(ue,w.u_light),u_shadow:new a.aL(ue,w.u_shadow),u_highlight:new a.aL(ue,w.u_highlight),u_accent:new a.aL(ue,w.u_accent)}),hillshadePrepare:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_image:new a.aH(ue,w.u_image),u_dimension:new a.aO(ue,w.u_dimension),u_zoom:new 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a.aO(ue,w.u_units_to_pixels),u_patternscale_a:new a.aO(ue,w.u_patternscale_a),u_patternscale_b:new a.aO(ue,w.u_patternscale_b),u_sdfgamma:new a.aI(ue,w.u_sdfgamma),u_image:new a.aH(ue,w.u_image),u_tex_y_a:new a.aI(ue,w.u_tex_y_a),u_tex_y_b:new a.aI(ue,w.u_tex_y_b),u_mix:new a.aI(ue,w.u_mix)}),raster:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_tl_parent:new a.aO(ue,w.u_tl_parent),u_scale_parent:new a.aI(ue,w.u_scale_parent),u_buffer_scale:new a.aI(ue,w.u_buffer_scale),u_fade_t:new a.aI(ue,w.u_fade_t),u_opacity:new a.aI(ue,w.u_opacity),u_image0:new a.aH(ue,w.u_image0),u_image1:new a.aH(ue,w.u_image1),u_brightness_low:new a.aI(ue,w.u_brightness_low),u_brightness_high:new a.aI(ue,w.u_brightness_high),u_saturation_factor:new a.aI(ue,w.u_saturation_factor),u_contrast_factor:new a.aI(ue,w.u_contrast_factor),u_spin_weights:new a.aN(ue,w.u_spin_weights)}),symbolIcon:(ue,w)=>({u_is_size_zoom_constant:new a.aH(ue,w.u_is_size_zoom_constant),u_is_size_feature_constant:new 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a.aI(ue,w.u_size_t),u_size:new a.aI(ue,w.u_size),u_camera_to_center_distance:new a.aI(ue,w.u_camera_to_center_distance),u_pitch:new a.aI(ue,w.u_pitch),u_rotate_symbol:new a.aH(ue,w.u_rotate_symbol),u_aspect_ratio:new a.aI(ue,w.u_aspect_ratio),u_fade_change:new a.aI(ue,w.u_fade_change),u_matrix:new a.aJ(ue,w.u_matrix),u_label_plane_matrix:new a.aJ(ue,w.u_label_plane_matrix),u_coord_matrix:new a.aJ(ue,w.u_coord_matrix),u_is_text:new a.aH(ue,w.u_is_text),u_pitch_with_map:new a.aH(ue,w.u_pitch_with_map),u_is_along_line:new a.aH(ue,w.u_is_along_line),u_is_variable_anchor:new a.aH(ue,w.u_is_variable_anchor),u_texsize:new a.aO(ue,w.u_texsize),u_texture:new a.aH(ue,w.u_texture),u_gamma_scale:new a.aI(ue,w.u_gamma_scale),u_device_pixel_ratio:new a.aI(ue,w.u_device_pixel_ratio),u_is_halo:new a.aH(ue,w.u_is_halo),u_translation:new a.aO(ue,w.u_translation),u_pitched_scale:new a.aI(ue,w.u_pitched_scale)}),symbolTextAndIcon:(ue,w)=>({u_is_size_zoom_constant:new a.aH(ue,w.u_is_size_zoom_constant),u_is_size_feature_constant:new a.aH(ue,w.u_is_size_feature_constant),u_size_t:new a.aI(ue,w.u_size_t),u_size:new a.aI(ue,w.u_size),u_camera_to_center_distance:new a.aI(ue,w.u_camera_to_center_distance),u_pitch:new a.aI(ue,w.u_pitch),u_rotate_symbol:new a.aH(ue,w.u_rotate_symbol),u_aspect_ratio:new a.aI(ue,w.u_aspect_ratio),u_fade_change:new a.aI(ue,w.u_fade_change),u_matrix:new a.aJ(ue,w.u_matrix),u_label_plane_matrix:new a.aJ(ue,w.u_label_plane_matrix),u_coord_matrix:new a.aJ(ue,w.u_coord_matrix),u_is_text:new a.aH(ue,w.u_is_text),u_pitch_with_map:new a.aH(ue,w.u_pitch_with_map),u_is_along_line:new a.aH(ue,w.u_is_along_line),u_is_variable_anchor:new a.aH(ue,w.u_is_variable_anchor),u_texsize:new a.aO(ue,w.u_texsize),u_texsize_icon:new a.aO(ue,w.u_texsize_icon),u_texture:new a.aH(ue,w.u_texture),u_texture_icon:new a.aH(ue,w.u_texture_icon),u_gamma_scale:new a.aI(ue,w.u_gamma_scale),u_device_pixel_ratio:new a.aI(ue,w.u_device_pixel_ratio),u_is_halo:new a.aH(ue,w.u_is_halo),u_translation:new a.aO(ue,w.u_translation),u_pitched_scale:new a.aI(ue,w.u_pitched_scale)}),background:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_opacity:new a.aI(ue,w.u_opacity),u_color:new a.aL(ue,w.u_color)}),backgroundPattern:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_opacity:new a.aI(ue,w.u_opacity),u_image:new a.aH(ue,w.u_image),u_pattern_tl_a:new a.aO(ue,w.u_pattern_tl_a),u_pattern_br_a:new a.aO(ue,w.u_pattern_br_a),u_pattern_tl_b:new a.aO(ue,w.u_pattern_tl_b),u_pattern_br_b:new a.aO(ue,w.u_pattern_br_b),u_texsize:new a.aO(ue,w.u_texsize),u_mix:new a.aI(ue,w.u_mix),u_pattern_size_a:new a.aO(ue,w.u_pattern_size_a),u_pattern_size_b:new a.aO(ue,w.u_pattern_size_b),u_scale_a:new a.aI(ue,w.u_scale_a),u_scale_b:new a.aI(ue,w.u_scale_b),u_pixel_coord_upper:new a.aO(ue,w.u_pixel_coord_upper),u_pixel_coord_lower:new a.aO(ue,w.u_pixel_coord_lower),u_tile_units_to_pixels:new a.aI(ue,w.u_tile_units_to_pixels)}),terrain:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_texture:new a.aH(ue,w.u_texture),u_ele_delta:new a.aI(ue,w.u_ele_delta),u_fog_matrix:new a.aJ(ue,w.u_fog_matrix),u_fog_color:new a.aL(ue,w.u_fog_color),u_fog_ground_blend:new a.aI(ue,w.u_fog_ground_blend),u_fog_ground_blend_opacity:new a.aI(ue,w.u_fog_ground_blend_opacity),u_horizon_color:new a.aL(ue,w.u_horizon_color),u_horizon_fog_blend:new a.aI(ue,w.u_horizon_fog_blend)}),terrainDepth:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_ele_delta:new a.aI(ue,w.u_ele_delta)}),terrainCoords:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_texture:new a.aH(ue,w.u_texture),u_terrain_coords_id:new a.aI(ue,w.u_terrain_coords_id),u_ele_delta:new a.aI(ue,w.u_ele_delta)}),sky:(ue,w)=>({u_sky_color:new a.aL(ue,w.u_sky_color),u_horizon_color:new a.aL(ue,w.u_horizon_color),u_horizon:new a.aI(ue,w.u_horizon),u_sky_horizon_blend:new a.aI(ue,w.u_sky_horizon_blend)})};class bs{constructor(w,B,Q){this.context=w;let ee=w.gl;this.buffer=ee.createBuffer(),this.dynamicDraw=!!Q,this.context.unbindVAO(),w.bindElementBuffer.set(this.buffer),ee.bufferData(ee.ELEMENT_ARRAY_BUFFER,B.arrayBuffer,this.dynamicDraw?ee.DYNAMIC_DRAW:ee.STATIC_DRAW),this.dynamicDraw||delete B.arrayBuffer}bind(){this.context.bindElementBuffer.set(this.buffer)}updateData(w){let B=this.context.gl;if(!this.dynamicDraw)throw new Error("Attempted to update data while not in dynamic mode.");this.context.unbindVAO(),this.bind(),B.bufferSubData(B.ELEMENT_ARRAY_BUFFER,0,w.arrayBuffer)}destroy(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)}}let Rn={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"};class _a{constructor(w,B,Q,ee){this.length=B.length,this.attributes=Q,this.itemSize=B.bytesPerElement,this.dynamicDraw=ee,this.context=w;let le=w.gl;this.buffer=le.createBuffer(),w.bindVertexBuffer.set(this.buffer),le.bufferData(le.ARRAY_BUFFER,B.arrayBuffer,this.dynamicDraw?le.DYNAMIC_DRAW:le.STATIC_DRAW),this.dynamicDraw||delete B.arrayBuffer}bind(){this.context.bindVertexBuffer.set(this.buffer)}updateData(w){if(w.length!==this.length)throw new Error(`Length of new data is ${w.length}, which doesn't match current length of ${this.length}`);let B=this.context.gl;this.bind(),B.bufferSubData(B.ARRAY_BUFFER,0,w.arrayBuffer)}enableAttributes(w,B){for(let Q=0;Q<this.attributes.length;Q++){let ee=B.attributes[this.attributes[Q].name];ee!==void 0&&w.enableVertexAttribArray(ee)}}setVertexAttribPointers(w,B,Q){for(let ee=0;ee<this.attributes.length;ee++){let le=this.attributes[ee],qe=B.attributes[le.name];qe!==void 0&&w.vertexAttribPointer(qe,le.components,w[Rn[le.type]],!1,this.itemSize,le.offset+this.itemSize*(Q||0))}}destroy(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)}}let Vu=new WeakMap;function Ol(ue){var w;if(Vu.has(ue))return Vu.get(ue);{let B=(w=ue.getParameter(ue.VERSION))===null||w===void 0?void 0:w.startsWith("WebGL 2.0");return Vu.set(ue,B),B}}class xo{constructor(w){this.gl=w.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1}get(){return this.current}set(w){}getDefault(){return this.default}setDefault(){this.set(this.default)}}class Yl extends xo{getDefault(){return a.aM.transparent}set(w){let B=this.current;(w.r!==B.r||w.g!==B.g||w.b!==B.b||w.a!==B.a||this.dirty)&&(this.gl.clearColor(w.r,w.g,w.b,w.a),this.current=w,this.dirty=!1)}}class Ns extends xo{getDefault(){return 1}set(w){(w!==this.current||this.dirty)&&(this.gl.clearDepth(w),this.current=w,this.dirty=!1)}}class Hl extends xo{getDefault(){return 0}set(w){(w!==this.current||this.dirty)&&(this.gl.clearStencil(w),this.current=w,this.dirty=!1)}}class ac extends xo{getDefault(){return[!0,!0,!0,!0]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||w[2]!==B[2]||w[3]!==B[3]||this.dirty)&&(this.gl.colorMask(w[0],w[1],w[2],w[3]),this.current=w,this.dirty=!1)}}class aa extends xo{getDefault(){return!0}set(w){(w!==this.current||this.dirty)&&(this.gl.depthMask(w),this.current=w,this.dirty=!1)}}class Oo extends xo{getDefault(){return 255}set(w){(w!==this.current||this.dirty)&&(this.gl.stencilMask(w),this.current=w,this.dirty=!1)}}class qo extends xo{getDefault(){return{func:this.gl.ALWAYS,ref:0,mask:255}}set(w){let B=this.current;(w.func!==B.func||w.ref!==B.ref||w.mask!==B.mask||this.dirty)&&(this.gl.stencilFunc(w.func,w.ref,w.mask),this.current=w,this.dirty=!1)}}class ql extends xo{getDefault(){let w=this.gl;return[w.KEEP,w.KEEP,w.KEEP]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||w[2]!==B[2]||this.dirty)&&(this.gl.stencilOp(w[0],w[1],w[2]),this.current=w,this.dirty=!1)}}class Pc extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;w?B.enable(B.STENCIL_TEST):B.disable(B.STENCIL_TEST),this.current=w,this.dirty=!1}}class Do extends xo{getDefault(){return[0,1]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||this.dirty)&&(this.gl.depthRange(w[0],w[1]),this.current=w,this.dirty=!1)}}class rf extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;w?B.enable(B.DEPTH_TEST):B.disable(B.DEPTH_TEST),this.current=w,this.dirty=!1}}class Uf extends xo{getDefault(){return this.gl.LESS}set(w){(w!==this.current||this.dirty)&&(this.gl.depthFunc(w),this.current=w,this.dirty=!1)}}class pl extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;w?B.enable(B.BLEND):B.disable(B.BLEND),this.current=w,this.dirty=!1}}class Zc extends xo{getDefault(){let w=this.gl;return[w.ONE,w.ZERO]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||this.dirty)&&(this.gl.blendFunc(w[0],w[1]),this.current=w,this.dirty=!1)}}class Kl extends xo{getDefault(){return a.aM.transparent}set(w){let B=this.current;(w.r!==B.r||w.g!==B.g||w.b!==B.b||w.a!==B.a||this.dirty)&&(this.gl.blendColor(w.r,w.g,w.b,w.a),this.current=w,this.dirty=!1)}}class Os extends xo{getDefault(){return this.gl.FUNC_ADD}set(w){(w!==this.current||this.dirty)&&(this.gl.blendEquation(w),this.current=w,this.dirty=!1)}}class yu extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;w?B.enable(B.CULL_FACE):B.disable(B.CULL_FACE),this.current=w,this.dirty=!1}}class oc extends xo{getDefault(){return this.gl.BACK}set(w){(w!==this.current||this.dirty)&&(this.gl.cullFace(w),this.current=w,this.dirty=!1)}}class Cf extends xo{getDefault(){return this.gl.CCW}set(w){(w!==this.current||this.dirty)&&(this.gl.frontFace(w),this.current=w,this.dirty=!1)}}class sc extends xo{getDefault(){return null}set(w){(w!==this.current||this.dirty)&&(this.gl.useProgram(w),this.current=w,this.dirty=!1)}}class Vh extends xo{getDefault(){return this.gl.TEXTURE0}set(w){(w!==this.current||this.dirty)&&(this.gl.activeTexture(w),this.current=w,this.dirty=!1)}}class Lf extends xo{getDefault(){let w=this.gl;return[0,0,w.drawingBufferWidth,w.drawingBufferHeight]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||w[2]!==B[2]||w[3]!==B[3]||this.dirty)&&(this.gl.viewport(w[0],w[1],w[2],w[3]),this.current=w,this.dirty=!1)}}class cs extends xo{getDefault(){return null}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.bindFramebuffer(B.FRAMEBUFFER,w),this.current=w,this.dirty=!1}}class nf extends xo{getDefault(){return null}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.bindRenderbuffer(B.RENDERBUFFER,w),this.current=w,this.dirty=!1}}class Vf extends xo{getDefault(){return null}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.bindTexture(B.TEXTURE_2D,w),this.current=w,this.dirty=!1}}class Jl extends xo{getDefault(){return null}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.bindBuffer(B.ARRAY_BUFFER,w),this.current=w,this.dirty=!1}}class fl extends xo{getDefault(){return null}set(w){let B=this.gl;B.bindBuffer(B.ELEMENT_ARRAY_BUFFER,w),this.current=w,this.dirty=!1}}class lc extends xo{getDefault(){return null}set(w){var B;if(w===this.current&&!this.dirty)return;let Q=this.gl;Ol(Q)?Q.bindVertexArray(w):(B=Q.getExtension("OES_vertex_array_object"))===null||B===void 0||B.bindVertexArrayOES(w),this.current=w,this.dirty=!1}}class Fu extends xo{getDefault(){return 4}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.pixelStorei(B.UNPACK_ALIGNMENT,w),this.current=w,this.dirty=!1}}class Es extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.pixelStorei(B.UNPACK_PREMULTIPLY_ALPHA_WEBGL,w),this.current=w,this.dirty=!1}}class Hs extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.pixelStorei(B.UNPACK_FLIP_Y_WEBGL,w),this.current=w,this.dirty=!1}}class Go extends xo{constructor(w,B){super(w),this.context=w,this.parent=B}getDefault(){return null}}class ps extends Go{setDirty(){this.dirty=!0}set(w){if(w===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);let B=this.gl;B.framebufferTexture2D(B.FRAMEBUFFER,B.COLOR_ATTACHMENT0,B.TEXTURE_2D,w,0),this.current=w,this.dirty=!1}}class uc extends Go{set(w){if(w===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);let B=this.gl;B.framebufferRenderbuffer(B.FRAMEBUFFER,B.DEPTH_ATTACHMENT,B.RENDERBUFFER,w),this.current=w,this.dirty=!1}}class xl extends Go{set(w){if(w===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);let B=this.gl;B.framebufferRenderbuffer(B.FRAMEBUFFER,B.DEPTH_STENCIL_ATTACHMENT,B.RENDERBUFFER,w),this.current=w,this.dirty=!1}}class Gu{constructor(w,B,Q,ee,le){this.context=w,this.width=B,this.height=Q;let qe=w.gl,Xe=this.framebuffer=qe.createFramebuffer();if(this.colorAttachment=new ps(w,Xe),ee)this.depthAttachment=le?new xl(w,Xe):new uc(w,Xe);else if(le)throw new Error("Stencil cannot be set without depth");if(qe.checkFramebufferStatus(qe.FRAMEBUFFER)!==qe.FRAMEBUFFER_COMPLETE)throw new Error("Framebuffer is not complete")}destroy(){let w=this.context.gl,B=this.colorAttachment.get();if(B&&w.deleteTexture(B),this.depthAttachment){let Q=this.depthAttachment.get();Q&&w.deleteRenderbuffer(Q)}w.deleteFramebuffer(this.framebuffer)}}class qs{constructor(w,B,Q){this.blendFunction=w,this.blendColor=B,this.mask=Q}}qs.Replace=[1,0],qs.disabled=new qs(qs.Replace,a.aM.transparent,[!1,!1,!1,!1]),qs.unblended=new qs(qs.Replace,a.aM.transparent,[!0,!0,!0,!0]),qs.alphaBlended=new qs([1,771],a.aM.transparent,[!0,!0,!0,!0]);class ad{constructor(w){var B,Q;if(this.gl=w,this.clearColor=new Yl(this),this.clearDepth=new Ns(this),this.clearStencil=new Hl(this),this.colorMask=new ac(this),this.depthMask=new aa(this),this.stencilMask=new Oo(this),this.stencilFunc=new qo(this),this.stencilOp=new ql(this),this.stencilTest=new Pc(this),this.depthRange=new Do(this),this.depthTest=new rf(this),this.depthFunc=new Uf(this),this.blend=new pl(this),this.blendFunc=new Zc(this),this.blendColor=new Kl(this),this.blendEquation=new Os(this),this.cullFace=new yu(this),this.cullFaceSide=new oc(this),this.frontFace=new Cf(this),this.program=new sc(this),this.activeTexture=new Vh(this),this.viewport=new Lf(this),this.bindFramebuffer=new cs(this),this.bindRenderbuffer=new nf(this),this.bindTexture=new Vf(this),this.bindVertexBuffer=new Jl(this),this.bindElementBuffer=new fl(this),this.bindVertexArray=new lc(this),this.pixelStoreUnpack=new Fu(this),this.pixelStoreUnpackPremultiplyAlpha=new Es(this),this.pixelStoreUnpackFlipY=new Hs(this),this.extTextureFilterAnisotropic=w.getExtension("EXT_texture_filter_anisotropic")||w.getExtension("MOZ_EXT_texture_filter_anisotropic")||w.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=w.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.maxTextureSize=w.getParameter(w.MAX_TEXTURE_SIZE),Ol(w)){this.HALF_FLOAT=w.HALF_FLOAT;let ee=w.getExtension("EXT_color_buffer_half_float");this.RGBA16F=(B=w.RGBA16F)!==null&&B!==void 0?B:ee==null?void 0:ee.RGBA16F_EXT,this.RGB16F=(Q=w.RGB16F)!==null&&Q!==void 0?Q:ee==null?void 0:ee.RGB16F_EXT,w.getExtension("EXT_color_buffer_float")}else{w.getExtension("EXT_color_buffer_half_float"),w.getExtension("OES_texture_half_float_linear");let ee=w.getExtension("OES_texture_half_float");this.HALF_FLOAT=ee==null?void 0:ee.HALF_FLOAT_OES}}setDefault(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()}setDirty(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.bindVertexArray.dirty=!0,this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0}createIndexBuffer(w,B){return new bs(this,w,B)}createVertexBuffer(w,B,Q){return new _a(this,w,B,Q)}createRenderbuffer(w,B,Q){let ee=this.gl,le=ee.createRenderbuffer();return this.bindRenderbuffer.set(le),ee.renderbufferStorage(ee.RENDERBUFFER,w,B,Q),this.bindRenderbuffer.set(null),le}createFramebuffer(w,B,Q,ee){return new Gu(this,w,B,Q,ee)}clear({color:w,depth:B,stencil:Q}){let ee=this.gl,le=0;w&&(le|=ee.COLOR_BUFFER_BIT,this.clearColor.set(w),this.colorMask.set([!0,!0,!0,!0])),B!==void 0&&(le|=ee.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(B),this.depthMask.set(!0)),Q!==void 0&&(le|=ee.STENCIL_BUFFER_BIT,this.clearStencil.set(Q),this.stencilMask.set(255)),ee.clear(le)}setCullFace(w){w.enable===!1?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(w.mode),this.frontFace.set(w.frontFace))}setDepthMode(w){w.func!==this.gl.ALWAYS||w.mask?(this.depthTest.set(!0),this.depthFunc.set(w.func),this.depthMask.set(w.mask),this.depthRange.set(w.range)):this.depthTest.set(!1)}setStencilMode(w){w.test.func!==this.gl.ALWAYS||w.mask?(this.stencilTest.set(!0),this.stencilMask.set(w.mask),this.stencilOp.set([w.fail,w.depthFail,w.pass]),this.stencilFunc.set({func:w.test.func,ref:w.ref,mask:w.test.mask})):this.stencilTest.set(!1)}setColorMode(w){a.aE(w.blendFunction,qs.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(w.blendFunction),this.blendColor.set(w.blendColor)),this.colorMask.set(w.mask)}createVertexArray(){var w;return Ol(this.gl)?this.gl.createVertexArray():(w=this.gl.getExtension("OES_vertex_array_object"))===null||w===void 0?void 0:w.createVertexArrayOES()}deleteVertexArray(w){var B;return Ol(this.gl)?this.gl.deleteVertexArray(w):(B=this.gl.getExtension("OES_vertex_array_object"))===null||B===void 0?void 0:B.deleteVertexArrayOES(w)}unbindVAO(){this.bindVertexArray.set(null)}}class Po{constructor(w,B,Q){this.func=w,this.mask=B,this.range=Q}}Po.ReadOnly=!1,Po.ReadWrite=!0,Po.disabled=new Po(519,Po.ReadOnly,[0,1]);let od=7680;class Yo{constructor(w,B,Q,ee,le,qe){this.test=w,this.ref=B,this.mask=Q,this.fail=ee,this.depthFail=le,this.pass=qe}}Yo.disabled=new Yo({func:519,mask:0},0,0,od,od,od);class Pa{constructor(w,B,Q){this.enable=w,this.mode=B,this.frontFace=Q}}let af;function Hu(ue,w,B,Q,ee){let le=ue.context,qe=le.gl,Xe=ue.useProgram("collisionBox"),ot=[],Tt=0,Yt=0;for(let Be=0;Be<Q.length;Be++){let et=Q[Be],We=w.getTile(et).getBucket(B);if(!We)continue;let it=ee?We.textCollisionBox:We.iconCollisionBox,Ft=We.collisionCircleArray;if(Ft.length>0){let Ht=a.H();a.aQ(Ht,We.placementInvProjMatrix,ue.transform.glCoordMatrix),a.aQ(Ht,Ht,We.placementViewportMatrix),ot.push({circleArray:Ft,circleOffset:Yt,transform:et.posMatrix,invTransform:Ht,coord:et}),Tt+=Ft.length/4,Yt=Tt}it&&Xe.draw(le,qe.LINES,Po.disabled,Yo.disabled,ue.colorModeForRenderPass(),Pa.disabled,{u_matrix:et.posMatrix,u_pixel_extrude_scale:[1/(Kt=ue.transform).width,1/Kt.height]},ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(et),B.id,it.layoutVertexBuffer,it.indexBuffer,it.segments,null,ue.transform.zoom,null,null,it.collisionVertexBuffer)}var Kt;if(!ee||!ot.length)return;let xr=ue.useProgram("collisionCircle"),Ir=new a.aR;Ir.resize(4*Tt),Ir._trim();let ve=0;for(let Be of ot)for(let et=0;et<Be.circleArray.length/4;et++){let We=4*et,it=Be.circleArray[We+0],Ft=Be.circleArray[We+1],Ht=Be.circleArray[We+2],tr=Be.circleArray[We+3];Ir.emplace(ve++,it,Ft,Ht,tr,0),Ir.emplace(ve++,it,Ft,Ht,tr,1),Ir.emplace(ve++,it,Ft,Ht,tr,2),Ir.emplace(ve++,it,Ft,Ht,tr,3)}(!af||af.length<2*Tt)&&(af=function(Be){let et=2*Be,We=new a.aT;We.resize(et),We._trim();for(let it=0;it<et;it++){let Ft=6*it;We.uint16[Ft+0]=4*it+0,We.uint16[Ft+1]=4*it+1,We.uint16[Ft+2]=4*it+2,We.uint16[Ft+3]=4*it+2,We.uint16[Ft+4]=4*it+3,We.uint16[Ft+5]=4*it+0}return We}(Tt));let be=le.createIndexBuffer(af,!0),De=le.createVertexBuffer(Ir,a.aS.members,!0);for(let Be of ot){let et=No(Be.transform,Be.invTransform,ue.transform);xr.draw(le,qe.TRIANGLES,Po.disabled,Yo.disabled,ue.colorModeForRenderPass(),Pa.disabled,et,ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(Be.coord),B.id,De,be,a.a0.simpleSegment(0,2*Be.circleOffset,Be.circleArray.length,Be.circleArray.length/2),null,ue.transform.zoom,null,null,null)}De.destroy(),be.destroy()}Pa.disabled=new Pa(!1,1029,2305),Pa.backCCW=new Pa(!0,1029,2305);let bl=a.an(new Float32Array(16));function Gf(ue,w,B,Q,ee,le){let{horizontalAlign:qe,verticalAlign:Xe}=a.au(ue);return new a.P((-(qe-.5)*w/ee+Q[0])*le,(-(Xe-.5)*B/ee+Q[1])*le)}function Ic(ue,w,B,Q,ee,le){let qe=w.tileAnchorPoint.add(new a.P(w.translation[0],w.translation[1]));if(w.pitchWithMap){let Xe=Q.mult(le);B||(Xe=Xe.rotate(-ee));let ot=qe.add(Xe);return dt(ot.x,ot.y,w.labelPlaneMatrix,w.getElevation).point}if(B){let Xe=vt(w.tileAnchorPoint.x+1,w.tileAnchorPoint.y,w).point.sub(ue),ot=Math.atan(Xe.y/Xe.x)+(Xe.x<0?Math.PI:0);return ue.add(Q.rotate(ot))}return ue.add(Q)}function yf(ue,w,B,Q,ee,le,qe,Xe,ot,Tt,Yt,Kt,xr,Ir){let ve=ue.text.placedSymbolArray,be=ue.text.dynamicLayoutVertexArray,De=ue.icon.dynamicLayoutVertexArray,Be={};be.clear();for(let et=0;et<ve.length;et++){let We=ve.get(et),it=We.hidden||!We.crossTileID||ue.allowVerticalPlacement&&!We.placedOrientation?null:Q[We.crossTileID];if(it){let Ft=new a.P(We.anchorX,We.anchorY),Ht={getElevation:Ir,width:ee.width,height:ee.height,labelPlaneMatrix:le,lineVertexArray:null,pitchWithMap:B,projection:Yt,projectionCache:null,tileAnchorPoint:Ft,translation:Kt,unwrappedTileID:xr},tr=B?dt(Ft.x,Ft.y,qe,Ir):vt(Ft.x,Ft.y,Ht),dr=Ge(ee.cameraToCenterDistance,tr.signedDistanceFromCamera),Sr=a.ai(ue.textSizeData,ot,We)*dr/a.ap;B&&(Sr*=ue.tilePixelRatio/Xe);let{width:Or,height:Wr,anchor:ni,textOffset:Pi,textBoxScale:cn}=it,ln=Gf(ni,Or,Wr,Pi,cn,Sr),Cn=Yt.getPitchedTextCorrection(ee,Ft.add(new a.P(Kt[0],Kt[1])),xr),Kn=Ic(tr.point,Ht,w,ln,ee.angle,Cn),Ta=ue.allowVerticalPlacement&&We.placedOrientation===a.ah.vertical?Math.PI/2:0;for(let fa=0;fa<We.numGlyphs;fa++)a.aj(be,Kn,Ta);Tt&&We.associatedIconIndex>=0&&(Be[We.associatedIconIndex]={shiftedAnchor:Kn,angle:Ta})}else pi(We.numGlyphs,be)}if(Tt){De.clear();let et=ue.icon.placedSymbolArray;for(let We=0;We<et.length;We++){let it=et.get(We);if(it.hidden)pi(it.numGlyphs,De);else{let Ft=Be[We];if(Ft)for(let Ht=0;Ht<it.numGlyphs;Ht++)a.aj(De,Ft.shiftedAnchor,Ft.angle);else pi(it.numGlyphs,De)}}ue.icon.dynamicLayoutVertexBuffer.updateData(De)}ue.text.dynamicLayoutVertexBuffer.updateData(be)}function Bl(ue,w,B){return B.iconsInText&&w?"symbolTextAndIcon":ue?"symbolSDF":"symbolIcon"}function wh(ue,w,B,Q,ee,le,qe,Xe,ot,Tt,Yt,Kt){let xr=ue.context,Ir=xr.gl,ve=ue.transform,be=yn(),De=Xe==="map",Be=ot==="map",et=Xe!=="viewport"&&B.layout.get("symbol-placement")!=="point",We=De&&!Be&&!et,it=!Be&&et,Ft=!B.layout.get("symbol-sort-key").isConstant(),Ht=!1,tr=ue.depthModeForSublayer(0,Po.ReadOnly),dr=B._unevaluatedLayout.hasValue("text-variable-anchor")||B._unevaluatedLayout.hasValue("text-variable-anchor-offset"),Sr=[],Or=be.getCircleRadiusCorrection(ve);for(let Wr of Q){let ni=w.getTile(Wr),Pi=ni.getBucket(B);if(!Pi)continue;let cn=ee?Pi.text:Pi.icon;if(!cn||!cn.segments.get().length||!cn.hasVisibleVertices)continue;let ln=cn.programConfigurations.get(B.id),Cn=ee||Pi.sdfIcons,Kn=ee?Pi.textSizeData:Pi.iconSizeData,Ta=Be||ve.pitch!==0,fa=ue.useProgram(Bl(Cn,ee,Pi),ln),$a=a.ag(Kn,ve.zoom),Co=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(Wr),Qa,mo,Bo,Ps,Ts=[0,0],wo=null;if(ee)mo=ni.glyphAtlasTexture,Bo=Ir.LINEAR,Qa=ni.glyphAtlasTexture.size,Pi.iconsInText&&(Ts=ni.imageAtlasTexture.size,wo=ni.imageAtlasTexture,Ps=Ta||ue.options.rotating||ue.options.zooming||Kn.kind==="composite"||Kn.kind==="camera"?Ir.LINEAR:Ir.NEAREST);else{let Ye=B.layout.get("icon-size").constantOr(0)!==1||Pi.iconsNeedLinear;mo=ni.imageAtlasTexture,Bo=Cn||ue.options.rotating||ue.options.zooming||Ye||Ta?Ir.LINEAR:Ir.NEAREST,Qa=ni.imageAtlasTexture.size}let To=In(ni,1,ue.transform.zoom),hl=it?Wr.posMatrix:bl,Ul=Br(hl,Be,De,ue.transform,To),Lu=Vr(hl,Be,De,ue.transform,To),au=Vr(Wr.posMatrix,Be,De,ue.transform,To),Js=be.translatePosition(ue.transform,ni,le,qe),Ql=dr&&Pi.hasTextData(),dc=B.layout.get("icon-text-fit")!=="none"&&Ql&&Pi.hasIconData();if(et){let Ye=ue.style.map.terrain?(nt,jt)=>ue.style.map.terrain.getElevation(Wr,nt,jt):null,kt=B.layout.get("text-rotation-alignment")==="map";je(Pi,Wr.posMatrix,ue,ee,Ul,au,Be,Tt,kt,be,Wr.toUnwrapped(),ve.width,ve.height,Js,Ye)}let Tl=Wr.posMatrix,Al=ee&&dr||dc,X=et||Al?bl:Ul,se=Lu,Te=Cn&&B.paint.get(ee?"text-halo-width":"icon-halo-width").constantOr(1)!==0,Ne;Ne=Cn?Pi.iconsInText?ls(Kn.kind,$a,We,Be,et,Al,ue,Tl,X,se,Js,Qa,Ts,Or):Io(Kn.kind,$a,We,Be,et,Al,ue,Tl,X,se,Js,ee,Qa,!0,Or):zs(Kn.kind,$a,We,Be,et,Al,ue,Tl,X,se,Js,ee,Qa,Or);let He={program:fa,buffers:cn,uniformValues:Ne,atlasTexture:mo,atlasTextureIcon:wo,atlasInterpolation:Bo,atlasInterpolationIcon:Ps,isSDF:Cn,hasHalo:Te};if(Ft&&Pi.canOverlap){Ht=!0;let Ye=cn.segments.get();for(let kt of Ye)Sr.push({segments:new a.a0([kt]),sortKey:kt.sortKey,state:He,terrainData:Co})}else Sr.push({segments:cn.segments,sortKey:0,state:He,terrainData:Co})}Ht&&Sr.sort((Wr,ni)=>Wr.sortKey-ni.sortKey);for(let Wr of Sr){let ni=Wr.state;if(xr.activeTexture.set(Ir.TEXTURE0),ni.atlasTexture.bind(ni.atlasInterpolation,Ir.CLAMP_TO_EDGE),ni.atlasTextureIcon&&(xr.activeTexture.set(Ir.TEXTURE1),ni.atlasTextureIcon&&ni.atlasTextureIcon.bind(ni.atlasInterpolationIcon,Ir.CLAMP_TO_EDGE)),ni.isSDF){let Pi=ni.uniformValues;ni.hasHalo&&(Pi.u_is_halo=1,Qf(ni.buffers,Wr.segments,B,ue,ni.program,tr,Yt,Kt,Pi,Wr.terrainData)),Pi.u_is_halo=0}Qf(ni.buffers,Wr.segments,B,ue,ni.program,tr,Yt,Kt,ni.uniformValues,Wr.terrainData)}}function Qf(ue,w,B,Q,ee,le,qe,Xe,ot,Tt){let Yt=Q.context;ee.draw(Yt,Yt.gl.TRIANGLES,le,qe,Xe,Pa.disabled,ot,Tt,B.id,ue.layoutVertexBuffer,ue.indexBuffer,w,B.paint,Q.transform.zoom,ue.programConfigurations.get(B.id),ue.dynamicLayoutVertexBuffer,ue.opacityVertexBuffer)}function _f(ue,w,B,Q){let ee=ue.context,le=ee.gl,qe=Yo.disabled,Xe=new qs([le.ONE,le.ONE],a.aM.transparent,[!0,!0,!0,!0]),ot=w.getBucket(B);if(!ot)return;let Tt=Q.key,Yt=B.heatmapFbos.get(Tt);Yt||(Yt=eh(ee,w.tileSize,w.tileSize),B.heatmapFbos.set(Tt,Yt)),ee.bindFramebuffer.set(Yt.framebuffer),ee.viewport.set([0,0,w.tileSize,w.tileSize]),ee.clear({color:a.aM.transparent});let Kt=ot.programConfigurations.get(B.id),xr=ue.useProgram("heatmap",Kt),Ir=ue.style.map.terrain.getTerrainData(Q);xr.draw(ee,le.TRIANGLES,Po.disabled,qe,Xe,Pa.disabled,ko(Q.posMatrix,w,ue.transform.zoom,B.paint.get("heatmap-intensity")),Ir,B.id,ot.layoutVertexBuffer,ot.indexBuffer,ot.segments,B.paint,ue.transform.zoom,Kt)}function Yc(ue,w,B){let Q=ue.context,ee=Q.gl;Q.setColorMode(ue.colorModeForRenderPass());let le=th(Q,w),qe=B.key,Xe=w.heatmapFbos.get(qe);Xe&&(Q.activeTexture.set(ee.TEXTURE0),ee.bindTexture(ee.TEXTURE_2D,Xe.colorAttachment.get()),Q.activeTexture.set(ee.TEXTURE1),le.bind(ee.LINEAR,ee.CLAMP_TO_EDGE),ue.useProgram("heatmapTexture").draw(Q,ee.TRIANGLES,Po.disabled,Yo.disabled,ue.colorModeForRenderPass(),Pa.disabled,Ds(ue,w,0,1),null,w.id,ue.rasterBoundsBuffer,ue.quadTriangleIndexBuffer,ue.rasterBoundsSegments,w.paint,ue.transform.zoom),Xe.destroy(),w.heatmapFbos.delete(qe))}function eh(ue,w,B){var Q,ee;let le=ue.gl,qe=le.createTexture();le.bindTexture(le.TEXTURE_2D,qe),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_WRAP_S,le.CLAMP_TO_EDGE),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_WRAP_T,le.CLAMP_TO_EDGE),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_MIN_FILTER,le.LINEAR),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_MAG_FILTER,le.LINEAR);let Xe=(Q=ue.HALF_FLOAT)!==null&&Q!==void 0?Q:le.UNSIGNED_BYTE,ot=(ee=ue.RGBA16F)!==null&&ee!==void 0?ee:le.RGBA;le.texImage2D(le.TEXTURE_2D,0,ot,w,B,0,le.RGBA,Xe,null);let Tt=ue.createFramebuffer(w,B,!1,!1);return Tt.colorAttachment.set(qe),Tt}function th(ue,w){return w.colorRampTexture||(w.colorRampTexture=new g(ue,w.colorRamp,ue.gl.RGBA)),w.colorRampTexture}function ju(ue,w,B,Q,ee){if(!B||!Q||!Q.imageAtlas)return;let le=Q.imageAtlas.patternPositions,qe=le[B.to.toString()],Xe=le[B.from.toString()];if(!qe&&Xe&&(qe=Xe),!Xe&&qe&&(Xe=qe),!qe||!Xe){let ot=ee.getPaintProperty(w);qe=le[ot],Xe=le[ot]}qe&&Xe&&ue.setConstantPatternPositions(qe,Xe)}function Hf(ue,w,B,Q,ee,le,qe){let Xe=ue.context.gl,ot="fill-pattern",Tt=B.paint.get(ot),Yt=Tt&&Tt.constantOr(1),Kt=B.getCrossfadeParameters(),xr,Ir,ve,be,De;qe?(Ir=Yt&&!B.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",xr=Xe.LINES):(Ir=Yt?"fillPattern":"fill",xr=Xe.TRIANGLES);let Be=Tt.constantOr(null);for(let et of Q){let We=w.getTile(et);if(Yt&&!We.patternsLoaded())continue;let it=We.getBucket(B);if(!it)continue;let Ft=it.programConfigurations.get(B.id),Ht=ue.useProgram(Ir,Ft),tr=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(et);Yt&&(ue.context.activeTexture.set(Xe.TEXTURE0),We.imageAtlasTexture.bind(Xe.LINEAR,Xe.CLAMP_TO_EDGE),Ft.updatePaintBuffers(Kt)),ju(Ft,ot,Be,We,B);let dr=tr?et:null,Sr=ue.translatePosMatrix(dr?dr.posMatrix:et.posMatrix,We,B.paint.get("fill-translate"),B.paint.get("fill-translate-anchor"));if(qe){be=it.indexBuffer2,De=it.segments2;let Or=[Xe.drawingBufferWidth,Xe.drawingBufferHeight];ve=Ir==="fillOutlinePattern"&&Yt?Ma(Sr,ue,Kt,We,Or):Jn(Sr,Or)}else be=it.indexBuffer,De=it.segments,ve=Yt?ya(Sr,ue,Kt,We):_n(Sr);Ht.draw(ue.context,xr,ee,ue.stencilModeForClipping(et),le,Pa.disabled,ve,tr,B.id,it.layoutVertexBuffer,be,De,B.paint,ue.transform.zoom,Ft)}}function cc(ue,w,B,Q,ee,le,qe){let Xe=ue.context,ot=Xe.gl,Tt="fill-extrusion-pattern",Yt=B.paint.get(Tt),Kt=Yt.constantOr(1),xr=B.getCrossfadeParameters(),Ir=B.paint.get("fill-extrusion-opacity"),ve=Yt.constantOr(null);for(let be of Q){let De=w.getTile(be),Be=De.getBucket(B);if(!Be)continue;let et=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(be),We=Be.programConfigurations.get(B.id),it=ue.useProgram(Kt?"fillExtrusionPattern":"fillExtrusion",We);Kt&&(ue.context.activeTexture.set(ot.TEXTURE0),De.imageAtlasTexture.bind(ot.LINEAR,ot.CLAMP_TO_EDGE),We.updatePaintBuffers(xr)),ju(We,Tt,ve,De,B);let Ft=ue.translatePosMatrix(be.posMatrix,De,B.paint.get("fill-extrusion-translate"),B.paint.get("fill-extrusion-translate-anchor")),Ht=B.paint.get("fill-extrusion-vertical-gradient"),tr=Kt?Li(Ft,ue,Ht,Ir,be,xr,De):Mn(Ft,ue,Ht,Ir);it.draw(Xe,Xe.gl.TRIANGLES,ee,le,qe,Pa.backCCW,tr,et,B.id,Be.layoutVertexBuffer,Be.indexBuffer,Be.segments,B.paint,ue.transform.zoom,We,ue.style.map.terrain&&Be.centroidVertexBuffer)}}function of(ue,w,B,Q,ee,le,qe){let Xe=ue.context,ot=Xe.gl,Tt=B.fbo;if(!Tt)return;let Yt=ue.useProgram("hillshade"),Kt=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(w);Xe.activeTexture.set(ot.TEXTURE0),ot.bindTexture(ot.TEXTURE_2D,Tt.colorAttachment.get()),Yt.draw(Xe,ot.TRIANGLES,ee,le,qe,Pa.disabled,((xr,Ir,ve,be)=>{let De=ve.paint.get("hillshade-shadow-color"),Be=ve.paint.get("hillshade-highlight-color"),et=ve.paint.get("hillshade-accent-color"),We=ve.paint.get("hillshade-illumination-direction")*(Math.PI/180);ve.paint.get("hillshade-illumination-anchor")==="viewport"&&(We-=xr.transform.angle);let it=!xr.options.moving;return{u_matrix:be?be.posMatrix:xr.transform.calculatePosMatrix(Ir.tileID.toUnwrapped(),it),u_image:0,u_latrange:Fs(0,Ir.tileID),u_light:[ve.paint.get("hillshade-exaggeration"),We],u_shadow:De,u_highlight:Be,u_accent:et}})(ue,B,Q,Kt?w:null),Kt,Q.id,ue.rasterBoundsBuffer,ue.quadTriangleIndexBuffer,ue.rasterBoundsSegments)}function Nl(ue,w,B,Q,ee,le){let qe=ue.context,Xe=qe.gl,ot=w.dem;if(ot&&ot.data){let Tt=ot.dim,Yt=ot.stride,Kt=ot.getPixels();if(qe.activeTexture.set(Xe.TEXTURE1),qe.pixelStoreUnpackPremultiplyAlpha.set(!1),w.demTexture=w.demTexture||ue.getTileTexture(Yt),w.demTexture){let Ir=w.demTexture;Ir.update(Kt,{premultiply:!1}),Ir.bind(Xe.NEAREST,Xe.CLAMP_TO_EDGE)}else w.demTexture=new g(qe,Kt,Xe.RGBA,{premultiply:!1}),w.demTexture.bind(Xe.NEAREST,Xe.CLAMP_TO_EDGE);qe.activeTexture.set(Xe.TEXTURE0);let xr=w.fbo;if(!xr){let Ir=new g(qe,{width:Tt,height:Tt,data:null},Xe.RGBA);Ir.bind(Xe.LINEAR,Xe.CLAMP_TO_EDGE),xr=w.fbo=qe.createFramebuffer(Tt,Tt,!0,!1),xr.colorAttachment.set(Ir.texture)}qe.bindFramebuffer.set(xr.framebuffer),qe.viewport.set([0,0,Tt,Tt]),ue.useProgram("hillshadePrepare").draw(qe,Xe.TRIANGLES,Q,ee,le,Pa.disabled,((Ir,ve)=>{let be=ve.stride,De=a.H();return a.aP(De,0,a.X,-a.X,0,0,1),a.J(De,De,[0,-a.X,0]),{u_matrix:De,u_image:1,u_dimension:[be,be],u_zoom:Ir.overscaledZ,u_unpack:ve.getUnpackVector()}})(w.tileID,ot),null,B.id,ue.rasterBoundsBuffer,ue.quadTriangleIndexBuffer,ue.rasterBoundsSegments),w.needsHillshadePrepare=!1}}function Kc(ue,w,B,Q,ee,le){let qe=Q.paint.get("raster-fade-duration");if(!le&&qe>0){let Xe=u.now(),ot=(Xe-ue.timeAdded)/qe,Tt=w?(Xe-w.timeAdded)/qe:-1,Yt=B.getSource(),Kt=ee.coveringZoomLevel({tileSize:Yt.tileSize,roundZoom:Yt.roundZoom}),xr=!w||Math.abs(w.tileID.overscaledZ-Kt)>Math.abs(ue.tileID.overscaledZ-Kt),Ir=xr&&ue.refreshedUponExpiration?1:a.ac(xr?ot:1-Tt,0,1);return ue.refreshedUponExpiration&&ot>=1&&(ue.refreshedUponExpiration=!1),w?{opacity:1,mix:1-Ir}:{opacity:Ir,mix:0}}return{opacity:1,mix:0}}let Rc=new a.aM(1,0,0,1),gs=new a.aM(0,1,0,1),jf=new a.aM(0,0,1,1),Gh=new a.aM(1,0,1,1),rh=new a.aM(0,1,1,1);function sf(ue,w,B,Q){Mu(ue,0,w+B/2,ue.transform.width,B,Q)}function Th(ue,w,B,Q){Mu(ue,w-B/2,0,B,ue.transform.height,Q)}function Mu(ue,w,B,Q,ee,le){let qe=ue.context,Xe=qe.gl;Xe.enable(Xe.SCISSOR_TEST),Xe.scissor(w*ue.pixelRatio,B*ue.pixelRatio,Q*ue.pixelRatio,ee*ue.pixelRatio),qe.clear({color:le}),Xe.disable(Xe.SCISSOR_TEST)}function ih(ue,w,B){let Q=ue.context,ee=Q.gl,le=B.posMatrix,qe=ue.useProgram("debug"),Xe=Po.disabled,ot=Yo.disabled,Tt=ue.colorModeForRenderPass(),Yt="$debug",Kt=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(B);Q.activeTexture.set(ee.TEXTURE0);let xr=w.getTileByID(B.key).latestRawTileData,Ir=Math.floor((xr&&xr.byteLength||0)/1024),ve=w.getTile(B).tileSize,be=512/Math.min(ve,512)*(B.overscaledZ/ue.transform.zoom)*.5,De=B.canonical.toString();B.overscaledZ!==B.canonical.z&&(De+=` => ${B.overscaledZ}`),function(Be,et){Be.initDebugOverlayCanvas();let We=Be.debugOverlayCanvas,it=Be.context.gl,Ft=Be.debugOverlayCanvas.getContext("2d");Ft.clearRect(0,0,We.width,We.height),Ft.shadowColor="white",Ft.shadowBlur=2,Ft.lineWidth=1.5,Ft.strokeStyle="white",Ft.textBaseline="top",Ft.font="bold 36px Open Sans, sans-serif",Ft.fillText(et,5,5),Ft.strokeText(et,5,5),Be.debugOverlayTexture.update(We),Be.debugOverlayTexture.bind(it.LINEAR,it.CLAMP_TO_EDGE)}(ue,`${De} ${Ir}kB`),qe.draw(Q,ee.TRIANGLES,Xe,ot,qs.alphaBlended,Pa.disabled,po(le,a.aM.transparent,be),null,Yt,ue.debugBuffer,ue.quadTriangleIndexBuffer,ue.debugSegments),qe.draw(Q,ee.LINE_STRIP,Xe,ot,Tt,Pa.disabled,po(le,a.aM.red),Kt,Yt,ue.debugBuffer,ue.tileBorderIndexBuffer,ue.debugSegments)}function js(ue,w,B){let Q=ue.context,ee=Q.gl,le=ue.colorModeForRenderPass(),qe=new Po(ee.LEQUAL,Po.ReadWrite,ue.depthRangeFor3D),Xe=ue.useProgram("terrain"),ot=w.getTerrainMesh();Q.bindFramebuffer.set(null),Q.viewport.set([0,0,ue.width,ue.height]);for(let Tt of B){let Yt=ue.renderToTexture.getTexture(Tt),Kt=w.getTerrainData(Tt.tileID);Q.activeTexture.set(ee.TEXTURE0),ee.bindTexture(ee.TEXTURE_2D,Yt.texture);let xr=ue.transform.calculatePosMatrix(Tt.tileID.toUnwrapped()),Ir=w.getMeshFrameDelta(ue.transform.zoom),ve=ue.transform.calculateFogMatrix(Tt.tileID.toUnwrapped()),be=Jr(xr,Ir,ve,ue.style.sky,ue.transform.pitch);Xe.draw(Q,ee.TRIANGLES,qe,Yo.disabled,le,Pa.backCCW,be,Kt,"terrain",ot.vertexBuffer,ot.indexBuffer,ot.segments)}}class Eu{constructor(w,B,Q){this.vertexBuffer=w,this.indexBuffer=B,this.segments=Q}destroy(){this.vertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.vertexBuffer=null,this.indexBuffer=null,this.segments=null}}class Dc{constructor(w,B){this.context=new ad(w),this.transform=B,this._tileTextures={},this.terrainFacilitator={dirty:!0,matrix:a.an(new Float64Array(16)),renderTime:0},this.setup(),this.numSublayers=mt.maxUnderzooming+mt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Wo}resize(w,B,Q){if(this.width=Math.floor(w*Q),this.height=Math.floor(B*Q),this.pixelRatio=Q,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(let ee of this.style._order)this.style._layers[ee].resize()}setup(){let w=this.context,B=new a.aX;B.emplaceBack(0,0),B.emplaceBack(a.X,0),B.emplaceBack(0,a.X),B.emplaceBack(a.X,a.X),this.tileExtentBuffer=w.createVertexBuffer(B,vo.members),this.tileExtentSegments=a.a0.simpleSegment(0,0,4,2);let Q=new a.aX;Q.emplaceBack(0,0),Q.emplaceBack(a.X,0),Q.emplaceBack(0,a.X),Q.emplaceBack(a.X,a.X),this.debugBuffer=w.createVertexBuffer(Q,vo.members),this.debugSegments=a.a0.simpleSegment(0,0,4,5);let ee=new a.$;ee.emplaceBack(0,0,0,0),ee.emplaceBack(a.X,0,a.X,0),ee.emplaceBack(0,a.X,0,a.X),ee.emplaceBack(a.X,a.X,a.X,a.X),this.rasterBoundsBuffer=w.createVertexBuffer(ee,lt.members),this.rasterBoundsSegments=a.a0.simpleSegment(0,0,4,2);let le=new a.aX;le.emplaceBack(0,0),le.emplaceBack(1,0),le.emplaceBack(0,1),le.emplaceBack(1,1),this.viewportBuffer=w.createVertexBuffer(le,vo.members),this.viewportSegments=a.a0.simpleSegment(0,0,4,2);let qe=new a.aZ;qe.emplaceBack(0),qe.emplaceBack(1),qe.emplaceBack(3),qe.emplaceBack(2),qe.emplaceBack(0),this.tileBorderIndexBuffer=w.createIndexBuffer(qe);let Xe=new a.aY;Xe.emplaceBack(0,1,2),Xe.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=w.createIndexBuffer(Xe);let ot=this.context.gl;this.stencilClearMode=new Yo({func:ot.ALWAYS,mask:0},0,255,ot.ZERO,ot.ZERO,ot.ZERO)}clearStencil(){let w=this.context,B=w.gl;this.nextStencilID=1,this.currentStencilSource=void 0;let Q=a.H();a.aP(Q,0,this.width,this.height,0,0,1),a.K(Q,Q,[B.drawingBufferWidth,B.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(w,B.TRIANGLES,Po.disabled,this.stencilClearMode,qs.disabled,Pa.disabled,Lo(Q),null,"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)}_renderTileClippingMasks(w,B){if(this.currentStencilSource===w.source||!w.isTileClipped()||!B||!B.length)return;this.currentStencilSource=w.source;let Q=this.context,ee=Q.gl;this.nextStencilID+B.length>256&&this.clearStencil(),Q.setColorMode(qs.disabled),Q.setDepthMode(Po.disabled);let le=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(let qe of B){let Xe=this._tileClippingMaskIDs[qe.key]=this.nextStencilID++,ot=this.style.map.terrain&&this.style.map.terrain.getTerrainData(qe);le.draw(Q,ee.TRIANGLES,Po.disabled,new Yo({func:ee.ALWAYS,mask:0},Xe,255,ee.KEEP,ee.KEEP,ee.REPLACE),qs.disabled,Pa.disabled,Lo(qe.posMatrix),ot,"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}stencilModeFor3D(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();let w=this.nextStencilID++,B=this.context.gl;return new Yo({func:B.NOTEQUAL,mask:255},w,255,B.KEEP,B.KEEP,B.REPLACE)}stencilModeForClipping(w){let B=this.context.gl;return new Yo({func:B.EQUAL,mask:255},this._tileClippingMaskIDs[w.key],0,B.KEEP,B.KEEP,B.REPLACE)}stencilConfigForOverlap(w){let B=this.context.gl,Q=w.sort((qe,Xe)=>Xe.overscaledZ-qe.overscaledZ),ee=Q[Q.length-1].overscaledZ,le=Q[0].overscaledZ-ee+1;if(le>1){this.currentStencilSource=void 0,this.nextStencilID+le>256&&this.clearStencil();let qe={};for(let Xe=0;Xe<le;Xe++)qe[Xe+ee]=new Yo({func:B.GEQUAL,mask:255},Xe+this.nextStencilID,255,B.KEEP,B.KEEP,B.REPLACE);return this.nextStencilID+=le,[qe,Q]}return[{[ee]:Yo.disabled},Q]}colorModeForRenderPass(){let w=this.context.gl;return this._showOverdrawInspector?new qs([w.CONSTANT_COLOR,w.ONE],new a.aM(.125,.125,.125,0),[!0,!0,!0,!0]):this.renderPass==="opaque"?qs.unblended:qs.alphaBlended}depthModeForSublayer(w,B,Q){if(!this.opaquePassEnabledForLayer())return Po.disabled;let ee=1-((1+this.currentLayer)*this.numSublayers+w)*this.depthEpsilon;return new Po(Q||this.context.gl.LEQUAL,B,[ee,ee])}opaquePassEnabledForLayer(){return this.currentLayer<this.opaquePassCutoff}render(w,B){this.style=w,this.options=B,this.lineAtlas=w.lineAtlas,this.imageManager=w.imageManager,this.glyphManager=w.glyphManager,this.symbolFadeChange=w.placement.symbolFadeChange(u.now()),this.imageManager.beginFrame();let Q=this.style._order,ee=this.style.sourceCaches,le={},qe={},Xe={};for(let ot in ee){let Tt=ee[ot];Tt.used&&Tt.prepare(this.context),le[ot]=Tt.getVisibleCoordinates(),qe[ot]=le[ot].slice().reverse(),Xe[ot]=Tt.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(let ot=0;ot<Q.length;ot++)if(this.style._layers[Q[ot]].is3D()){this.opaquePassCutoff=ot;break}this.maybeDrawDepthAndCoords(!1),this.renderToTexture&&(this.renderToTexture.prepareForRender(this.style,this.transform.zoom),this.opaquePassCutoff=0),this.renderPass="offscreen";for(let ot of Q){let Tt=this.style._layers[ot];if(!Tt.hasOffscreenPass()||Tt.isHidden(this.transform.zoom))continue;let Yt=qe[Tt.source];(Tt.type==="custom"||Yt.length)&&this.renderLayer(this,ee[Tt.source],Tt,Yt)}if(this.context.bindFramebuffer.set(null),this.context.clear({color:B.showOverdrawInspector?a.aM.black:a.aM.transparent,depth:1}),this.clearStencil(),this.style.sky&&function(ot,Tt){let Yt=ot.context,Kt=Yt.gl,xr=((Be,et,We)=>({u_sky_color:Be.properties.get("sky-color"),u_horizon_color:Be.properties.get("horizon-color"),u_horizon:(et.height/2+et.getHorizon())*We,u_sky_horizon_blend:Be.properties.get("sky-horizon-blend")*et.height/2*We}))(Tt,ot.style.map.transform,ot.pixelRatio),Ir=new Po(Kt.LEQUAL,Po.ReadWrite,[0,1]),ve=Yo.disabled,be=ot.colorModeForRenderPass(),De=ot.useProgram("sky");if(!Tt.mesh){let Be=new a.aX;Be.emplaceBack(-1,-1),Be.emplaceBack(1,-1),Be.emplaceBack(1,1),Be.emplaceBack(-1,1);let et=new a.aY;et.emplaceBack(0,1,2),et.emplaceBack(0,2,3),Tt.mesh=new Eu(Yt.createVertexBuffer(Be,vo.members),Yt.createIndexBuffer(et),a.a0.simpleSegment(0,0,Be.length,et.length))}De.draw(Yt,Kt.TRIANGLES,Ir,ve,be,Pa.disabled,xr,void 0,"sky",Tt.mesh.vertexBuffer,Tt.mesh.indexBuffer,Tt.mesh.segments)}(this,this.style.sky),this._showOverdrawInspector=B.showOverdrawInspector,this.depthRangeFor3D=[0,1-(w._order.length+2)*this.numSublayers*this.depthEpsilon],!this.renderToTexture)for(this.renderPass="opaque",this.currentLayer=Q.length-1;this.currentLayer>=0;this.currentLayer--){let ot=this.style._layers[Q[this.currentLayer]],Tt=ee[ot.source],Yt=le[ot.source];this._renderTileClippingMasks(ot,Yt),this.renderLayer(this,Tt,ot,Yt)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<Q.length;this.currentLayer++){let ot=this.style._layers[Q[this.currentLayer]],Tt=ee[ot.source];if(this.renderToTexture&&this.renderToTexture.renderLayer(ot))continue;let Yt=(ot.type==="symbol"?Xe:qe)[ot.source];this._renderTileClippingMasks(ot,le[ot.source]),this.renderLayer(this,Tt,ot,Yt)}if(this.options.showTileBoundaries){let ot=function(Tt,Yt){let Kt=null,xr=Object.values(Tt._layers).flatMap(De=>De.source&&!De.isHidden(Yt)?[Tt.sourceCaches[De.source]]:[]),Ir=xr.filter(De=>De.getSource().type==="vector"),ve=xr.filter(De=>De.getSource().type!=="vector"),be=De=>{(!Kt||Kt.getSource().maxzoom<De.getSource().maxzoom)&&(Kt=De)};return Ir.forEach(De=>be(De)),Kt||ve.forEach(De=>be(De)),Kt}(this.style,this.transform.zoom);ot&&function(Tt,Yt,Kt){for(let xr=0;xr<Kt.length;xr++)ih(Tt,Yt,Kt[xr])}(this,ot,ot.getVisibleCoordinates())}this.options.showPadding&&function(ot){let Tt=ot.transform.padding;sf(ot,ot.transform.height-(Tt.top||0),3,Rc),sf(ot,Tt.bottom||0,3,gs),Th(ot,Tt.left||0,3,jf),Th(ot,ot.transform.width-(Tt.right||0),3,Gh);let Yt=ot.transform.centerPoint;(function(Kt,xr,Ir,ve){Mu(Kt,xr-1,Ir-10,2,20,ve),Mu(Kt,xr-10,Ir-1,20,2,ve)})(ot,Yt.x,ot.transform.height-Yt.y,rh)}(this),this.context.setDefault()}maybeDrawDepthAndCoords(w){if(!this.style||!this.style.map||!this.style.map.terrain)return;let B=this.terrainFacilitator.matrix,Q=this.transform.modelViewProjectionMatrix,ee=this.terrainFacilitator.dirty;ee||(ee=w?!a.a_(B,Q):!a.a$(B,Q)),ee||(ee=this.style.map.terrain.sourceCache.tilesAfterTime(this.terrainFacilitator.renderTime).length>0),ee&&(a.b0(B,Q),this.terrainFacilitator.renderTime=Date.now(),this.terrainFacilitator.dirty=!1,function(le,qe){let Xe=le.context,ot=Xe.gl,Tt=qs.unblended,Yt=new Po(ot.LEQUAL,Po.ReadWrite,[0,1]),Kt=qe.getTerrainMesh(),xr=qe.sourceCache.getRenderableTiles(),Ir=le.useProgram("terrainDepth");Xe.bindFramebuffer.set(qe.getFramebuffer("depth").framebuffer),Xe.viewport.set([0,0,le.width/devicePixelRatio,le.height/devicePixelRatio]),Xe.clear({color:a.aM.transparent,depth:1});for(let ve of xr){let be=qe.getTerrainData(ve.tileID),De={u_matrix:le.transform.calculatePosMatrix(ve.tileID.toUnwrapped()),u_ele_delta:qe.getMeshFrameDelta(le.transform.zoom)};Ir.draw(Xe,ot.TRIANGLES,Yt,Yo.disabled,Tt,Pa.backCCW,De,be,"terrain",Kt.vertexBuffer,Kt.indexBuffer,Kt.segments)}Xe.bindFramebuffer.set(null),Xe.viewport.set([0,0,le.width,le.height])}(this,this.style.map.terrain),function(le,qe){let Xe=le.context,ot=Xe.gl,Tt=qs.unblended,Yt=new Po(ot.LEQUAL,Po.ReadWrite,[0,1]),Kt=qe.getTerrainMesh(),xr=qe.getCoordsTexture(),Ir=qe.sourceCache.getRenderableTiles(),ve=le.useProgram("terrainCoords");Xe.bindFramebuffer.set(qe.getFramebuffer("coords").framebuffer),Xe.viewport.set([0,0,le.width/devicePixelRatio,le.height/devicePixelRatio]),Xe.clear({color:a.aM.transparent,depth:1}),qe.coordsIndex=[];for(let be of Ir){let De=qe.getTerrainData(be.tileID);Xe.activeTexture.set(ot.TEXTURE0),ot.bindTexture(ot.TEXTURE_2D,xr.texture);let Be={u_matrix:le.transform.calculatePosMatrix(be.tileID.toUnwrapped()),u_terrain_coords_id:(255-qe.coordsIndex.length)/255,u_texture:0,u_ele_delta:qe.getMeshFrameDelta(le.transform.zoom)};ve.draw(Xe,ot.TRIANGLES,Yt,Yo.disabled,Tt,Pa.backCCW,Be,De,"terrain",Kt.vertexBuffer,Kt.indexBuffer,Kt.segments),qe.coordsIndex.push(be.tileID.key)}Xe.bindFramebuffer.set(null),Xe.viewport.set([0,0,le.width,le.height])}(this,this.style.map.terrain))}renderLayer(w,B,Q,ee){if(!Q.isHidden(this.transform.zoom)&&(Q.type==="background"||Q.type==="custom"||(ee||[]).length))switch(this.id=Q.id,Q.type){case"symbol":(function(le,qe,Xe,ot,Tt){if(le.renderPass!=="translucent")return;let Yt=Yo.disabled,Kt=le.colorModeForRenderPass();(Xe._unevaluatedLayout.hasValue("text-variable-anchor")||Xe._unevaluatedLayout.hasValue("text-variable-anchor-offset"))&&function(xr,Ir,ve,be,De,Be,et,We,it){let Ft=Ir.transform,Ht=yn(),tr=De==="map",dr=Be==="map";for(let Sr of xr){let Or=be.getTile(Sr),Wr=Or.getBucket(ve);if(!Wr||!Wr.text||!Wr.text.segments.get().length)continue;let ni=a.ag(Wr.textSizeData,Ft.zoom),Pi=In(Or,1,Ir.transform.zoom),cn=Br(Sr.posMatrix,dr,tr,Ir.transform,Pi),ln=ve.layout.get("icon-text-fit")!=="none"&&Wr.hasIconData();if(ni){let Cn=Math.pow(2,Ft.zoom-Or.tileID.overscaledZ),Kn=Ir.style.map.terrain?(fa,$a)=>Ir.style.map.terrain.getElevation(Sr,fa,$a):null,Ta=Ht.translatePosition(Ft,Or,et,We);yf(Wr,tr,dr,it,Ft,cn,Sr.posMatrix,Cn,ni,ln,Ht,Ta,Sr.toUnwrapped(),Kn)}}}(ot,le,Xe,qe,Xe.layout.get("text-rotation-alignment"),Xe.layout.get("text-pitch-alignment"),Xe.paint.get("text-translate"),Xe.paint.get("text-translate-anchor"),Tt),Xe.paint.get("icon-opacity").constantOr(1)!==0&&wh(le,qe,Xe,ot,!1,Xe.paint.get("icon-translate"),Xe.paint.get("icon-translate-anchor"),Xe.layout.get("icon-rotation-alignment"),Xe.layout.get("icon-pitch-alignment"),Xe.layout.get("icon-keep-upright"),Yt,Kt),Xe.paint.get("text-opacity").constantOr(1)!==0&&wh(le,qe,Xe,ot,!0,Xe.paint.get("text-translate"),Xe.paint.get("text-translate-anchor"),Xe.layout.get("text-rotation-alignment"),Xe.layout.get("text-pitch-alignment"),Xe.layout.get("text-keep-upright"),Yt,Kt),qe.map.showCollisionBoxes&&(Hu(le,qe,Xe,ot,!0),Hu(le,qe,Xe,ot,!1))})(w,B,Q,ee,this.style.placement.variableOffsets);break;case"circle":(function(le,qe,Xe,ot){if(le.renderPass!=="translucent")return;let Tt=Xe.paint.get("circle-opacity"),Yt=Xe.paint.get("circle-stroke-width"),Kt=Xe.paint.get("circle-stroke-opacity"),xr=!Xe.layout.get("circle-sort-key").isConstant();if(Tt.constantOr(1)===0&&(Yt.constantOr(1)===0||Kt.constantOr(1)===0))return;let Ir=le.context,ve=Ir.gl,be=le.depthModeForSublayer(0,Po.ReadOnly),De=Yo.disabled,Be=le.colorModeForRenderPass(),et=[];for(let We=0;We<ot.length;We++){let it=ot[We],Ft=qe.getTile(it),Ht=Ft.getBucket(Xe);if(!Ht)continue;let tr=Ht.programConfigurations.get(Xe.id),dr=le.useProgram("circle",tr),Sr=Ht.layoutVertexBuffer,Or=Ht.indexBuffer,Wr=le.style.map.terrain&&le.style.map.terrain.getTerrainData(it),ni={programConfiguration:tr,program:dr,layoutVertexBuffer:Sr,indexBuffer:Or,uniformValues:_o(le,it,Ft,Xe),terrainData:Wr};if(xr){let Pi=Ht.segments.get();for(let cn of Pi)et.push({segments:new a.a0([cn]),sortKey:cn.sortKey,state:ni})}else et.push({segments:Ht.segments,sortKey:0,state:ni})}xr&&et.sort((We,it)=>We.sortKey-it.sortKey);for(let We of et){let{programConfiguration:it,program:Ft,layoutVertexBuffer:Ht,indexBuffer:tr,uniformValues:dr,terrainData:Sr}=We.state;Ft.draw(Ir,ve.TRIANGLES,be,De,Be,Pa.disabled,dr,Sr,Xe.id,Ht,tr,We.segments,Xe.paint,le.transform.zoom,it)}})(w,B,Q,ee);break;case"heatmap":(function(le,qe,Xe,ot){if(Xe.paint.get("heatmap-opacity")===0)return;let Tt=le.context;if(le.style.map.terrain){for(let Yt of ot){let Kt=qe.getTile(Yt);qe.hasRenderableParent(Yt)||(le.renderPass==="offscreen"?_f(le,Kt,Xe,Yt):le.renderPass==="translucent"&&Yc(le,Xe,Yt))}Tt.viewport.set([0,0,le.width,le.height])}else le.renderPass==="offscreen"?function(Yt,Kt,xr,Ir){let ve=Yt.context,be=ve.gl,De=Yo.disabled,Be=new qs([be.ONE,be.ONE],a.aM.transparent,[!0,!0,!0,!0]);(function(et,We,it){let Ft=et.gl;et.activeTexture.set(Ft.TEXTURE1),et.viewport.set([0,0,We.width/4,We.height/4]);let Ht=it.heatmapFbos.get(a.aU);Ht?(Ft.bindTexture(Ft.TEXTURE_2D,Ht.colorAttachment.get()),et.bindFramebuffer.set(Ht.framebuffer)):(Ht=eh(et,We.width/4,We.height/4),it.heatmapFbos.set(a.aU,Ht))})(ve,Yt,xr),ve.clear({color:a.aM.transparent});for(let et=0;et<Ir.length;et++){let We=Ir[et];if(Kt.hasRenderableParent(We))continue;let it=Kt.getTile(We),Ft=it.getBucket(xr);if(!Ft)continue;let Ht=Ft.programConfigurations.get(xr.id),tr=Yt.useProgram("heatmap",Ht),{zoom:dr}=Yt.transform;tr.draw(ve,be.TRIANGLES,Po.disabled,De,Be,Pa.disabled,ko(We.posMatrix,it,dr,xr.paint.get("heatmap-intensity")),null,xr.id,Ft.layoutVertexBuffer,Ft.indexBuffer,Ft.segments,xr.paint,Yt.transform.zoom,Ht)}ve.viewport.set([0,0,Yt.width,Yt.height])}(le,qe,Xe,ot):le.renderPass==="translucent"&&function(Yt,Kt){let xr=Yt.context,Ir=xr.gl;xr.setColorMode(Yt.colorModeForRenderPass());let ve=Kt.heatmapFbos.get(a.aU);ve&&(xr.activeTexture.set(Ir.TEXTURE0),Ir.bindTexture(Ir.TEXTURE_2D,ve.colorAttachment.get()),xr.activeTexture.set(Ir.TEXTURE1),th(xr,Kt).bind(Ir.LINEAR,Ir.CLAMP_TO_EDGE),Yt.useProgram("heatmapTexture").draw(xr,Ir.TRIANGLES,Po.disabled,Yo.disabled,Yt.colorModeForRenderPass(),Pa.disabled,Ds(Yt,Kt,0,1),null,Kt.id,Yt.viewportBuffer,Yt.quadTriangleIndexBuffer,Yt.viewportSegments,Kt.paint,Yt.transform.zoom))}(le,Xe)})(w,B,Q,ee);break;case"line":(function(le,qe,Xe,ot){if(le.renderPass!=="translucent")return;let Tt=Xe.paint.get("line-opacity"),Yt=Xe.paint.get("line-width");if(Tt.constantOr(1)===0||Yt.constantOr(1)===0)return;let Kt=le.depthModeForSublayer(0,Po.ReadOnly),xr=le.colorModeForRenderPass(),Ir=Xe.paint.get("line-dasharray"),ve=Xe.paint.get("line-pattern"),be=ve.constantOr(1),De=Xe.paint.get("line-gradient"),Be=Xe.getCrossfadeParameters(),et=be?"linePattern":Ir?"lineSDF":De?"lineGradient":"line",We=le.context,it=We.gl,Ft=!0;for(let Ht of ot){let tr=qe.getTile(Ht);if(be&&!tr.patternsLoaded())continue;let dr=tr.getBucket(Xe);if(!dr)continue;let Sr=dr.programConfigurations.get(Xe.id),Or=le.context.program.get(),Wr=le.useProgram(et,Sr),ni=Ft||Wr.program!==Or,Pi=le.style.map.terrain&&le.style.map.terrain.getTerrainData(Ht),cn=ve.constantOr(null);if(cn&&tr.imageAtlas){let Kn=tr.imageAtlas,Ta=Kn.patternPositions[cn.to.toString()],fa=Kn.patternPositions[cn.from.toString()];Ta&&fa&&Sr.setConstantPatternPositions(Ta,fa)}let ln=Pi?Ht:null,Cn=be?zl(le,tr,Xe,Be,ln):Ir?us(le,tr,Xe,Ir,Be,ln):De?ul(le,tr,Xe,dr.lineClipsArray.length,ln):ll(le,tr,Xe,ln);if(be)We.activeTexture.set(it.TEXTURE0),tr.imageAtlasTexture.bind(it.LINEAR,it.CLAMP_TO_EDGE),Sr.updatePaintBuffers(Be);else if(Ir&&(ni||le.lineAtlas.dirty))We.activeTexture.set(it.TEXTURE0),le.lineAtlas.bind(We);else if(De){let Kn=dr.gradients[Xe.id],Ta=Kn.texture;if(Xe.gradientVersion!==Kn.version){let fa=256;if(Xe.stepInterpolant){let $a=qe.getSource().maxzoom,Co=Ht.canonical.z===$a?Math.ceil(1<<le.transform.maxZoom-Ht.canonical.z):1;fa=a.ac(a.aV(dr.maxLineLength/a.X*1024*Co),256,We.maxTextureSize)}Kn.gradient=a.aW({expression:Xe.gradientExpression(),evaluationKey:"lineProgress",resolution:fa,image:Kn.gradient||void 0,clips:dr.lineClipsArray}),Kn.texture?Kn.texture.update(Kn.gradient):Kn.texture=new g(We,Kn.gradient,it.RGBA),Kn.version=Xe.gradientVersion,Ta=Kn.texture}We.activeTexture.set(it.TEXTURE0),Ta.bind(Xe.stepInterpolant?it.NEAREST:it.LINEAR,it.CLAMP_TO_EDGE)}Wr.draw(We,it.TRIANGLES,Kt,le.stencilModeForClipping(Ht),xr,Pa.disabled,Cn,Pi,Xe.id,dr.layoutVertexBuffer,dr.indexBuffer,dr.segments,Xe.paint,le.transform.zoom,Sr,dr.layoutVertexBuffer2),Ft=!1}})(w,B,Q,ee);break;case"fill":(function(le,qe,Xe,ot){let Tt=Xe.paint.get("fill-color"),Yt=Xe.paint.get("fill-opacity");if(Yt.constantOr(1)===0)return;let Kt=le.colorModeForRenderPass(),xr=Xe.paint.get("fill-pattern"),Ir=le.opaquePassEnabledForLayer()&&!xr.constantOr(1)&&Tt.constantOr(a.aM.transparent).a===1&&Yt.constantOr(0)===1?"opaque":"translucent";if(le.renderPass===Ir){let ve=le.depthModeForSublayer(1,le.renderPass==="opaque"?Po.ReadWrite:Po.ReadOnly);Hf(le,qe,Xe,ot,ve,Kt,!1)}if(le.renderPass==="translucent"&&Xe.paint.get("fill-antialias")){let ve=le.depthModeForSublayer(Xe.getPaintProperty("fill-outline-color")?2:0,Po.ReadOnly);Hf(le,qe,Xe,ot,ve,Kt,!0)}})(w,B,Q,ee);break;case"fill-extrusion":(function(le,qe,Xe,ot){let Tt=Xe.paint.get("fill-extrusion-opacity");if(Tt!==0&&le.renderPass==="translucent"){let Yt=new Po(le.context.gl.LEQUAL,Po.ReadWrite,le.depthRangeFor3D);if(Tt!==1||Xe.paint.get("fill-extrusion-pattern").constantOr(1))cc(le,qe,Xe,ot,Yt,Yo.disabled,qs.disabled),cc(le,qe,Xe,ot,Yt,le.stencilModeFor3D(),le.colorModeForRenderPass());else{let Kt=le.colorModeForRenderPass();cc(le,qe,Xe,ot,Yt,Yo.disabled,Kt)}}})(w,B,Q,ee);break;case"hillshade":(function(le,qe,Xe,ot){if(le.renderPass!=="offscreen"&&le.renderPass!=="translucent")return;let Tt=le.context,Yt=le.depthModeForSublayer(0,Po.ReadOnly),Kt=le.colorModeForRenderPass(),[xr,Ir]=le.renderPass==="translucent"?le.stencilConfigForOverlap(ot):[{},ot];for(let ve of Ir){let be=qe.getTile(ve);be.needsHillshadePrepare!==void 0&&be.needsHillshadePrepare&&le.renderPass==="offscreen"?Nl(le,be,Xe,Yt,Yo.disabled,Kt):le.renderPass==="translucent"&&of(le,ve,be,Xe,Yt,xr[ve.overscaledZ],Kt)}Tt.viewport.set([0,0,le.width,le.height])})(w,B,Q,ee);break;case"raster":(function(le,qe,Xe,ot){if(le.renderPass!=="translucent"||Xe.paint.get("raster-opacity")===0||!ot.length)return;let Tt=le.context,Yt=Tt.gl,Kt=qe.getSource(),xr=le.useProgram("raster"),Ir=le.colorModeForRenderPass(),[ve,be]=Kt instanceof Gt?[{},ot]:le.stencilConfigForOverlap(ot),De=be[be.length-1].overscaledZ,Be=!le.options.moving;for(let et of be){let We=le.depthModeForSublayer(et.overscaledZ-De,Xe.paint.get("raster-opacity")===1?Po.ReadWrite:Po.ReadOnly,Yt.LESS),it=qe.getTile(et);it.registerFadeDuration(Xe.paint.get("raster-fade-duration"));let Ft=qe.findLoadedParent(et,0),Ht=qe.findLoadedSibling(et),tr=Kc(it,Ft||Ht||null,qe,Xe,le.transform,le.style.map.terrain),dr,Sr,Or=Xe.paint.get("raster-resampling")==="nearest"?Yt.NEAREST:Yt.LINEAR;Tt.activeTexture.set(Yt.TEXTURE0),it.texture.bind(Or,Yt.CLAMP_TO_EDGE,Yt.LINEAR_MIPMAP_NEAREST),Tt.activeTexture.set(Yt.TEXTURE1),Ft?(Ft.texture.bind(Or,Yt.CLAMP_TO_EDGE,Yt.LINEAR_MIPMAP_NEAREST),dr=Math.pow(2,Ft.tileID.overscaledZ-it.tileID.overscaledZ),Sr=[it.tileID.canonical.x*dr%1,it.tileID.canonical.y*dr%1]):it.texture.bind(Or,Yt.CLAMP_TO_EDGE,Yt.LINEAR_MIPMAP_NEAREST),it.texture.useMipmap&&Tt.extTextureFilterAnisotropic&&le.transform.pitch>20&&Yt.texParameterf(Yt.TEXTURE_2D,Tt.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,Tt.extTextureFilterAnisotropicMax);let Wr=le.style.map.terrain&&le.style.map.terrain.getTerrainData(et),ni=Wr?et:null,Pi=ni?ni.posMatrix:le.transform.calculatePosMatrix(et.toUnwrapped(),Be),cn=cl(Pi,Sr||[0,0],dr||1,tr,Xe);Kt instanceof Gt?xr.draw(Tt,Yt.TRIANGLES,We,Yo.disabled,Ir,Pa.disabled,cn,Wr,Xe.id,Kt.boundsBuffer,le.quadTriangleIndexBuffer,Kt.boundsSegments):xr.draw(Tt,Yt.TRIANGLES,We,ve[et.overscaledZ],Ir,Pa.disabled,cn,Wr,Xe.id,le.rasterBoundsBuffer,le.quadTriangleIndexBuffer,le.rasterBoundsSegments)}})(w,B,Q,ee);break;case"background":(function(le,qe,Xe,ot){let Tt=Xe.paint.get("background-color"),Yt=Xe.paint.get("background-opacity");if(Yt===0)return;let Kt=le.context,xr=Kt.gl,Ir=le.transform,ve=Ir.tileSize,be=Xe.paint.get("background-pattern");if(le.isPatternMissing(be))return;let De=!be&&Tt.a===1&&Yt===1&&le.opaquePassEnabledForLayer()?"opaque":"translucent";if(le.renderPass!==De)return;let Be=Yo.disabled,et=le.depthModeForSublayer(0,De==="opaque"?Po.ReadWrite:Po.ReadOnly),We=le.colorModeForRenderPass(),it=le.useProgram(be?"backgroundPattern":"background"),Ft=ot||Ir.coveringTiles({tileSize:ve,terrain:le.style.map.terrain});be&&(Kt.activeTexture.set(xr.TEXTURE0),le.imageManager.bind(le.context));let Ht=Xe.getCrossfadeParameters();for(let tr of Ft){let dr=ot?tr.posMatrix:le.transform.calculatePosMatrix(tr.toUnwrapped()),Sr=be?Su(dr,Yt,le,be,{tileID:tr,tileSize:ve},Ht):Zl(dr,Yt,Tt),Or=le.style.map.terrain&&le.style.map.terrain.getTerrainData(tr);it.draw(Kt,xr.TRIANGLES,et,Be,We,Pa.disabled,Sr,Or,Xe.id,le.tileExtentBuffer,le.quadTriangleIndexBuffer,le.tileExtentSegments)}})(w,0,Q,ee);break;case"custom":(function(le,qe,Xe){let ot=le.context,Tt=Xe.implementation;if(le.renderPass==="offscreen"){let Yt=Tt.prerender;Yt&&(le.setCustomLayerDefaults(),ot.setColorMode(le.colorModeForRenderPass()),Yt.call(Tt,ot.gl,le.transform.customLayerMatrix()),ot.setDirty(),le.setBaseState())}else if(le.renderPass==="translucent"){le.setCustomLayerDefaults(),ot.setColorMode(le.colorModeForRenderPass()),ot.setStencilMode(Yo.disabled);let Yt=Tt.renderingMode==="3d"?new Po(le.context.gl.LEQUAL,Po.ReadWrite,le.depthRangeFor3D):le.depthModeForSublayer(0,Po.ReadOnly);ot.setDepthMode(Yt),Tt.render(ot.gl,le.transform.customLayerMatrix(),{farZ:le.transform.farZ,nearZ:le.transform.nearZ,fov:le.transform._fov,modelViewProjectionMatrix:le.transform.modelViewProjectionMatrix,projectionMatrix:le.transform.projectionMatrix}),ot.setDirty(),le.setBaseState(),ot.bindFramebuffer.set(null)}})(w,0,Q)}}translatePosMatrix(w,B,Q,ee,le){if(!Q[0]&&!Q[1])return w;let qe=le?ee==="map"?this.transform.angle:0:ee==="viewport"?-this.transform.angle:0;if(qe){let Tt=Math.sin(qe),Yt=Math.cos(qe);Q=[Q[0]*Yt-Q[1]*Tt,Q[0]*Tt+Q[1]*Yt]}let Xe=[le?Q[0]:In(B,Q[0],this.transform.zoom),le?Q[1]:In(B,Q[1],this.transform.zoom),0],ot=new Float32Array(16);return a.J(ot,w,Xe),ot}saveTileTexture(w){let B=this._tileTextures[w.size[0]];B?B.push(w):this._tileTextures[w.size[0]]=[w]}getTileTexture(w){let B=this._tileTextures[w];return B&&B.length>0?B.pop():null}isPatternMissing(w){if(!w)return!1;if(!w.from||!w.to)return!0;let B=this.imageManager.getPattern(w.from.toString()),Q=this.imageManager.getPattern(w.to.toString());return!B||!Q}useProgram(w,B){this.cache=this.cache||{};let Q=w+(B?B.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"")+(this.style.map.terrain?"/terrain":"");return this.cache[Q]||(this.cache[Q]=new hn(this.context,jn[w],B,nc[w],this._showOverdrawInspector,this.style.map.terrain)),this.cache[Q]}setCustomLayerDefaults(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()}setBaseState(){let w=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(w.FUNC_ADD)}initDebugOverlayCanvas(){this.debugOverlayCanvas==null&&(this.debugOverlayCanvas=document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512,this.debugOverlayTexture=new g(this.context,this.debugOverlayCanvas,this.context.gl.RGBA))}destroy(){this.debugOverlayTexture&&this.debugOverlayTexture.destroy()}overLimit(){let{drawingBufferWidth:w,drawingBufferHeight:B}=this.context.gl;return this.width!==w||this.height!==B}}class ks{constructor(w,B){this.points=w,this.planes=B}static fromInvProjectionMatrix(w,B,Q){let ee=Math.pow(2,Q),le=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map(Xe=>{let ot=1/(Xe=a.af([],Xe,w))[3]/B*ee;return a.b1(Xe,Xe,[ot,ot,1/Xe[3],ot])}),qe=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map(Xe=>{let ot=function(xr,Ir){var ve=Ir[0],be=Ir[1],De=Ir[2],Be=ve*ve+be*be+De*De;return Be>0&&(Be=1/Math.sqrt(Be)),xr[0]=Ir[0]*Be,xr[1]=Ir[1]*Be,xr[2]=Ir[2]*Be,xr}([],function(xr,Ir,ve){var be=Ir[0],De=Ir[1],Be=Ir[2],et=ve[0],We=ve[1],it=ve[2];return xr[0]=De*it-Be*We,xr[1]=Be*et-be*it,xr[2]=be*We-De*et,xr}([],L([],le[Xe[0]],le[Xe[1]]),L([],le[Xe[2]],le[Xe[1]]))),Tt=-((Yt=ot)[0]*(Kt=le[Xe[1]])[0]+Yt[1]*Kt[1]+Yt[2]*Kt[2]);var Yt,Kt;return ot.concat(Tt)});return new ks(le,qe)}}class bc{constructor(w,B){this.min=w,this.max=B,this.center=function(Q,ee,le){return Q[0]=.5*ee[0],Q[1]=.5*ee[1],Q[2]=.5*ee[2],Q}([],function(Q,ee,le){return Q[0]=ee[0]+le[0],Q[1]=ee[1]+le[1],Q[2]=ee[2]+le[2],Q}([],this.min,this.max))}quadrant(w){let B=[w%2==0,w<2],Q=E(this.min),ee=E(this.max);for(let le=0;le<B.length;le++)Q[le]=B[le]?this.min[le]:this.center[le],ee[le]=B[le]?this.center[le]:this.max[le];return ee[2]=this.max[2],new bc(Q,ee)}distanceX(w){return Math.max(Math.min(this.max[0],w[0]),this.min[0])-w[0]}distanceY(w){return Math.max(Math.min(this.max[1],w[1]),this.min[1])-w[1]}intersects(w){let B=[[this.min[0],this.min[1],this.min[2],1],[this.max[0],this.min[1],this.min[2],1],[this.max[0],this.max[1],this.min[2],1],[this.min[0],this.max[1],this.min[2],1],[this.min[0],this.min[1],this.max[2],1],[this.max[0],this.min[1],this.max[2],1],[this.max[0],this.max[1],this.max[2],1],[this.min[0],this.max[1],this.max[2],1]],Q=!0;for(let ee=0;ee<w.planes.length;ee++){let le=w.planes[ee],qe=0;for(let Xe=0;Xe<B.length;Xe++)a.b2(le,B[Xe])>=0&&qe++;if(qe===0)return 0;qe!==B.length&&(Q=!1)}if(Q)return 2;for(let ee=0;ee<3;ee++){let le=Number.MAX_VALUE,qe=-Number.MAX_VALUE;for(let Xe=0;Xe<w.points.length;Xe++){let ot=w.points[Xe][ee]-this.min[ee];le=Math.min(le,ot),qe=Math.max(qe,ot)}if(qe<0||le>this.max[ee]-this.min[ee])return 0}return 1}}class hu{constructor(w=0,B=0,Q=0,ee=0){if(isNaN(w)||w<0||isNaN(B)||B<0||isNaN(Q)||Q<0||isNaN(ee)||ee<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=w,this.bottom=B,this.left=Q,this.right=ee}interpolate(w,B,Q){return B.top!=null&&w.top!=null&&(this.top=a.y.number(w.top,B.top,Q)),B.bottom!=null&&w.bottom!=null&&(this.bottom=a.y.number(w.bottom,B.bottom,Q)),B.left!=null&&w.left!=null&&(this.left=a.y.number(w.left,B.left,Q)),B.right!=null&&w.right!=null&&(this.right=a.y.number(w.right,B.right,Q)),this}getCenter(w,B){let Q=a.ac((this.left+w-this.right)/2,0,w),ee=a.ac((this.top+B-this.bottom)/2,0,B);return new a.P(Q,ee)}equals(w){return this.top===w.top&&this.bottom===w.bottom&&this.left===w.left&&this.right===w.right}clone(){return new hu(this.top,this.bottom,this.left,this.right)}toJSON(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}}}let _u=85.051129;class nl{constructor(w,B,Q,ee,le){this.tileSize=512,this._renderWorldCopies=le===void 0||!!le,this._minZoom=w||0,this._maxZoom=B||22,this._minPitch=Q==null?0:Q,this._maxPitch=ee==null?60:ee,this.setMaxBounds(),this.width=0,this.height=0,this._center=new a.N(0,0),this._elevation=0,this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new hu,this._posMatrixCache={},this._alignedPosMatrixCache={},this._fogMatrixCache={},this.minElevationForCurrentTile=0}clone(){let w=new nl(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return w.apply(this),w}apply(w){this.tileSize=w.tileSize,this.latRange=w.latRange,this.lngRange=w.lngRange,this.width=w.width,this.height=w.height,this._center=w._center,this._elevation=w._elevation,this.minElevationForCurrentTile=w.minElevationForCurrentTile,this.zoom=w.zoom,this.angle=w.angle,this._fov=w._fov,this._pitch=w._pitch,this._unmodified=w._unmodified,this._edgeInsets=w._edgeInsets.clone(),this._calcMatrices()}get minZoom(){return this._minZoom}set minZoom(w){this._minZoom!==w&&(this._minZoom=w,this.zoom=Math.max(this.zoom,w))}get maxZoom(){return this._maxZoom}set maxZoom(w){this._maxZoom!==w&&(this._maxZoom=w,this.zoom=Math.min(this.zoom,w))}get minPitch(){return this._minPitch}set minPitch(w){this._minPitch!==w&&(this._minPitch=w,this.pitch=Math.max(this.pitch,w))}get maxPitch(){return this._maxPitch}set maxPitch(w){this._maxPitch!==w&&(this._maxPitch=w,this.pitch=Math.min(this.pitch,w))}get renderWorldCopies(){return this._renderWorldCopies}set renderWorldCopies(w){w===void 0?w=!0:w===null&&(w=!1),this._renderWorldCopies=w}get worldSize(){return this.tileSize*this.scale}get centerOffset(){return this.centerPoint._sub(this.size._div(2))}get size(){return new a.P(this.width,this.height)}get bearing(){return-this.angle/Math.PI*180}set bearing(w){let B=-a.b3(w,-180,180)*Math.PI/180;this.angle!==B&&(this._unmodified=!1,this.angle=B,this._calcMatrices(),this.rotationMatrix=function(){var Q=new a.A(4);return a.A!=Float32Array&&(Q[1]=0,Q[2]=0),Q[0]=1,Q[3]=1,Q}(),function(Q,ee,le){var qe=ee[0],Xe=ee[1],ot=ee[2],Tt=ee[3],Yt=Math.sin(le),Kt=Math.cos(le);Q[0]=qe*Kt+ot*Yt,Q[1]=Xe*Kt+Tt*Yt,Q[2]=qe*-Yt+ot*Kt,Q[3]=Xe*-Yt+Tt*Kt}(this.rotationMatrix,this.rotationMatrix,this.angle))}get pitch(){return this._pitch/Math.PI*180}set pitch(w){let B=a.ac(w,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==B&&(this._unmodified=!1,this._pitch=B,this._calcMatrices())}get fov(){return this._fov/Math.PI*180}set fov(w){w=Math.max(.01,Math.min(60,w)),this._fov!==w&&(this._unmodified=!1,this._fov=w/180*Math.PI,this._calcMatrices())}get zoom(){return this._zoom}set zoom(w){let B=Math.min(Math.max(w,this.minZoom),this.maxZoom);this._zoom!==B&&(this._unmodified=!1,this._zoom=B,this.tileZoom=Math.max(0,Math.floor(B)),this.scale=this.zoomScale(B),this._constrain(),this._calcMatrices())}get center(){return this._center}set center(w){w.lat===this._center.lat&&w.lng===this._center.lng||(this._unmodified=!1,this._center=w,this._constrain(),this._calcMatrices())}get elevation(){return this._elevation}set elevation(w){w!==this._elevation&&(this._elevation=w,this._constrain(),this._calcMatrices())}get padding(){return this._edgeInsets.toJSON()}set padding(w){this._edgeInsets.equals(w)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,w,1),this._calcMatrices())}get centerPoint(){return this._edgeInsets.getCenter(this.width,this.height)}isPaddingEqual(w){return this._edgeInsets.equals(w)}interpolatePadding(w,B,Q){this._unmodified=!1,this._edgeInsets.interpolate(w,B,Q),this._constrain(),this._calcMatrices()}coveringZoomLevel(w){let B=(w.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/w.tileSize));return Math.max(0,B)}getVisibleUnwrappedCoordinates(w){let B=[new a.b4(0,w)];if(this._renderWorldCopies){let Q=this.pointCoordinate(new a.P(0,0)),ee=this.pointCoordinate(new a.P(this.width,0)),le=this.pointCoordinate(new a.P(this.width,this.height)),qe=this.pointCoordinate(new a.P(0,this.height)),Xe=Math.floor(Math.min(Q.x,ee.x,le.x,qe.x)),ot=Math.floor(Math.max(Q.x,ee.x,le.x,qe.x)),Tt=1;for(let Yt=Xe-Tt;Yt<=ot+Tt;Yt++)Yt!==0&&B.push(new a.b4(Yt,w))}return B}coveringTiles(w){var B,Q;let ee=this.coveringZoomLevel(w),le=ee;if(w.minzoom!==void 0&&ee<w.minzoom)return[];w.maxzoom!==void 0&&ee>w.maxzoom&&(ee=w.maxzoom);let qe=this.pointCoordinate(this.getCameraPoint()),Xe=a.Z.fromLngLat(this.center),ot=Math.pow(2,ee),Tt=[ot*qe.x,ot*qe.y,0],Yt=[ot*Xe.x,ot*Xe.y,0],Kt=ks.fromInvProjectionMatrix(this.invModelViewProjectionMatrix,this.worldSize,ee),xr=w.minzoom||0;!w.terrain&&this.pitch<=60&&this._edgeInsets.top<.1&&(xr=ee);let Ir=w.terrain?2/Math.min(this.tileSize,w.tileSize)*this.tileSize:3,ve=We=>({aabb:new bc([We*ot,0,0],[(We+1)*ot,ot,0]),zoom:0,x:0,y:0,wrap:We,fullyVisible:!1}),be=[],De=[],Be=ee,et=w.reparseOverscaled?le:ee;if(this._renderWorldCopies)for(let We=1;We<=3;We++)be.push(ve(-We)),be.push(ve(We));for(be.push(ve(0));be.length>0;){let We=be.pop(),it=We.x,Ft=We.y,Ht=We.fullyVisible;if(!Ht){let Wr=We.aabb.intersects(Kt);if(Wr===0)continue;Ht=Wr===2}let tr=w.terrain?Tt:Yt,dr=We.aabb.distanceX(tr),Sr=We.aabb.distanceY(tr),Or=Math.max(Math.abs(dr),Math.abs(Sr));if(We.zoom===Be||Or>Ir+(1<<Be-We.zoom)-2&&We.zoom>=xr){let Wr=Be-We.zoom,ni=Tt[0]-.5-(it<<Wr),Pi=Tt[1]-.5-(Ft<<Wr);De.push({tileID:new a.S(We.zoom===Be?et:We.zoom,We.wrap,We.zoom,it,Ft),distanceSq:x([Yt[0]-.5-it,Yt[1]-.5-Ft]),tileDistanceToCamera:Math.sqrt(ni*ni+Pi*Pi)})}else for(let Wr=0;Wr<4;Wr++){let ni=(it<<1)+Wr%2,Pi=(Ft<<1)+(Wr>>1),cn=We.zoom+1,ln=We.aabb.quadrant(Wr);if(w.terrain){let Cn=new a.S(cn,We.wrap,cn,ni,Pi),Kn=w.terrain.getMinMaxElevation(Cn),Ta=(B=Kn.minElevation)!==null&&B!==void 0?B:this.elevation,fa=(Q=Kn.maxElevation)!==null&&Q!==void 0?Q:this.elevation;ln=new bc([ln.min[0],ln.min[1],Ta],[ln.max[0],ln.max[1],fa])}be.push({aabb:ln,zoom:cn,x:ni,y:Pi,wrap:We.wrap,fullyVisible:Ht})}}return De.sort((We,it)=>We.distanceSq-it.distanceSq).map(We=>We.tileID)}resize(w,B){this.width=w,this.height=B,this.pixelsToGLUnits=[2/w,-2/B],this._constrain(),this._calcMatrices()}get unmodified(){return this._unmodified}zoomScale(w){return Math.pow(2,w)}scaleZoom(w){return Math.log(w)/Math.LN2}project(w){let B=a.ac(w.lat,-85.051129,_u);return new a.P(a.O(w.lng)*this.worldSize,a.Q(B)*this.worldSize)}unproject(w){return new a.Z(w.x/this.worldSize,w.y/this.worldSize).toLngLat()}get point(){return this.project(this.center)}getCameraPosition(){return{lngLat:this.pointLocation(this.getCameraPoint()),altitude:Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter+this.elevation}}recalculateZoom(w){let B=this.elevation,Q=Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter,ee=this.pointLocation(this.centerPoint,w),le=w.getElevationForLngLatZoom(ee,this.tileZoom);if(!(this.elevation-le))return;let qe=Q+B-le,Xe=Math.cos(this._pitch)*this.cameraToCenterDistance/qe/a.b5(1,ee.lat),ot=this.scaleZoom(Xe/this.tileSize);this._elevation=le,this._center=ee,this.zoom=ot}setLocationAtPoint(w,B){let Q=this.pointCoordinate(B),ee=this.pointCoordinate(this.centerPoint),le=this.locationCoordinate(w),qe=new a.Z(le.x-(Q.x-ee.x),le.y-(Q.y-ee.y));this.center=this.coordinateLocation(qe),this._renderWorldCopies&&(this.center=this.center.wrap())}locationPoint(w,B){return B?this.coordinatePoint(this.locationCoordinate(w),B.getElevationForLngLatZoom(w,this.tileZoom),this.pixelMatrix3D):this.coordinatePoint(this.locationCoordinate(w))}pointLocation(w,B){return this.coordinateLocation(this.pointCoordinate(w,B))}locationCoordinate(w){return a.Z.fromLngLat(w)}coordinateLocation(w){return w&&w.toLngLat()}pointCoordinate(w,B){if(B){let xr=B.pointCoordinate(w);if(xr!=null)return xr}let Q=[w.x,w.y,0,1],ee=[w.x,w.y,1,1];a.af(Q,Q,this.pixelMatrixInverse),a.af(ee,ee,this.pixelMatrixInverse);let le=Q[3],qe=ee[3],Xe=Q[1]/le,ot=ee[1]/qe,Tt=Q[2]/le,Yt=ee[2]/qe,Kt=Tt===Yt?0:(0-Tt)/(Yt-Tt);return new a.Z(a.y.number(Q[0]/le,ee[0]/qe,Kt)/this.worldSize,a.y.number(Xe,ot,Kt)/this.worldSize)}coordinatePoint(w,B=0,Q=this.pixelMatrix){let ee=[w.x*this.worldSize,w.y*this.worldSize,B,1];return a.af(ee,ee,Q),new a.P(ee[0]/ee[3],ee[1]/ee[3])}getBounds(){let w=Math.max(0,this.height/2-this.getHorizon());return new ce().extend(this.pointLocation(new a.P(0,w))).extend(this.pointLocation(new a.P(this.width,w))).extend(this.pointLocation(new a.P(this.width,this.height))).extend(this.pointLocation(new a.P(0,this.height)))}getMaxBounds(){return this.latRange&&this.latRange.length===2&&this.lngRange&&this.lngRange.length===2?new ce([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null}getHorizon(){return Math.tan(Math.PI/2-this._pitch)*this.cameraToCenterDistance*.85}setMaxBounds(w){w?(this.lngRange=[w.getWest(),w.getEast()],this.latRange=[w.getSouth(),w.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-85.051129,_u])}calculateTileMatrix(w){let B=w.canonical,Q=this.worldSize/this.zoomScale(B.z),ee=B.x+Math.pow(2,B.z)*w.wrap,le=a.an(new Float64Array(16));return a.J(le,le,[ee*Q,B.y*Q,0]),a.K(le,le,[Q/a.X,Q/a.X,1]),le}calculatePosMatrix(w,B=!1){let Q=w.key,ee=B?this._alignedPosMatrixCache:this._posMatrixCache;if(ee[Q])return ee[Q];let le=this.calculateTileMatrix(w);return a.L(le,B?this.alignedModelViewProjectionMatrix:this.modelViewProjectionMatrix,le),ee[Q]=new Float32Array(le),ee[Q]}calculateFogMatrix(w){let B=w.key,Q=this._fogMatrixCache;if(Q[B])return Q[B];let ee=this.calculateTileMatrix(w);return a.L(ee,this.fogMatrix,ee),Q[B]=new Float32Array(ee),Q[B]}customLayerMatrix(){return this.mercatorMatrix.slice()}getConstrained(w,B){B=a.ac(+B,this.minZoom,this.maxZoom);let Q={center:new a.N(w.lng,w.lat),zoom:B},ee=this.lngRange;if(!this._renderWorldCopies&&ee===null){let We=179.9999999999;ee=[-We,We]}let le=this.tileSize*this.zoomScale(Q.zoom),qe=0,Xe=le,ot=0,Tt=le,Yt=0,Kt=0,{x:xr,y:Ir}=this.size;if(this.latRange){let We=this.latRange;qe=a.Q(We[1])*le,Xe=a.Q(We[0])*le,Xe-qe<Ir&&(Yt=Ir/(Xe-qe))}ee&&(ot=a.b3(a.O(ee[0])*le,0,le),Tt=a.b3(a.O(ee[1])*le,0,le),Tt<ot&&(Tt+=le),Tt-ot<xr&&(Kt=xr/(Tt-ot)));let{x:ve,y:be}=this.project.call({worldSize:le},w),De,Be,et=Math.max(Kt||0,Yt||0);if(et){let We=new a.P(Kt?(Tt+ot)/2:ve,Yt?(Xe+qe)/2:be);return Q.center=this.unproject.call({worldSize:le},We).wrap(),Q.zoom+=this.scaleZoom(et),Q}if(this.latRange){let We=Ir/2;be-We<qe&&(Be=qe+We),be+We>Xe&&(Be=Xe-We)}if(ee){let We=(ot+Tt)/2,it=ve;this._renderWorldCopies&&(it=a.b3(ve,We-le/2,We+le/2));let Ft=xr/2;it-Ft<ot&&(De=ot+Ft),it+Ft>Tt&&(De=Tt-Ft)}if(De!==void 0||Be!==void 0){let We=new a.P(De!=null?De:ve,Be!=null?Be:be);Q.center=this.unproject.call({worldSize:le},We).wrap()}return Q}_constrain(){if(!this.center||!this.width||!this.height||this._constraining)return;this._constraining=!0;let w=this._unmodified,{center:B,zoom:Q}=this.getConstrained(this.center,this.zoom);this.center=B,this.zoom=Q,this._unmodified=w,this._constraining=!1}_calcMatrices(){if(!this.height)return;let w=this.centerOffset,B=this.point.x,Q=this.point.y;this.cameraToCenterDistance=.5/Math.tan(this._fov/2)*this.height,this._pixelPerMeter=a.b5(1,this.center.lat)*this.worldSize;let ee=a.an(new Float64Array(16));a.K(ee,ee,[this.width/2,-this.height/2,1]),a.J(ee,ee,[1,-1,0]),this.labelPlaneMatrix=ee,ee=a.an(new Float64Array(16)),a.K(ee,ee,[1,-1,1]),a.J(ee,ee,[-1,-1,0]),a.K(ee,ee,[2/this.width,2/this.height,1]),this.glCoordMatrix=ee;let le=this.cameraToCenterDistance+this._elevation*this._pixelPerMeter/Math.cos(this._pitch),qe=Math.min(this.elevation,this.minElevationForCurrentTile),Xe=le-qe*this._pixelPerMeter/Math.cos(this._pitch),ot=qe<0?Xe:le,Tt=Math.PI/2+this._pitch,Yt=this._fov*(.5+w.y/this.height),Kt=Math.sin(Yt)*ot/Math.sin(a.ac(Math.PI-Tt-Yt,.01,Math.PI-.01)),xr=this.getHorizon(),Ir=2*Math.atan(xr/this.cameraToCenterDistance)*(.5+w.y/(2*xr)),ve=Math.sin(Ir)*ot/Math.sin(a.ac(Math.PI-Tt-Ir,.01,Math.PI-.01)),be=Math.min(Kt,ve);this.farZ=1.01*(Math.cos(Math.PI/2-this._pitch)*be+ot),this.nearZ=this.height/50,ee=new Float64Array(16),a.b6(ee,this._fov,this.width/this.height,this.nearZ,this.farZ),ee[8]=2*-w.x/this.width,ee[9]=2*w.y/this.height,this.projectionMatrix=a.ae(ee),a.K(ee,ee,[1,-1,1]),a.J(ee,ee,[0,0,-this.cameraToCenterDistance]),a.b7(ee,ee,this._pitch),a.ad(ee,ee,this.angle),a.J(ee,ee,[-B,-Q,0]),this.mercatorMatrix=a.K([],ee,[this.worldSize,this.worldSize,this.worldSize]),a.K(ee,ee,[1,1,this._pixelPerMeter]),this.pixelMatrix=a.L(new Float64Array(16),this.labelPlaneMatrix,ee),a.J(ee,ee,[0,0,-this.elevation]),this.modelViewProjectionMatrix=ee,this.invModelViewProjectionMatrix=a.as([],ee),this.fogMatrix=new Float64Array(16),a.b6(this.fogMatrix,this._fov,this.width/this.height,le,this.farZ),this.fogMatrix[8]=2*-w.x/this.width,this.fogMatrix[9]=2*w.y/this.height,a.K(this.fogMatrix,this.fogMatrix,[1,-1,1]),a.J(this.fogMatrix,this.fogMatrix,[0,0,-this.cameraToCenterDistance]),a.b7(this.fogMatrix,this.fogMatrix,this._pitch),a.ad(this.fogMatrix,this.fogMatrix,this.angle),a.J(this.fogMatrix,this.fogMatrix,[-B,-Q,0]),a.K(this.fogMatrix,this.fogMatrix,[1,1,this._pixelPerMeter]),a.J(this.fogMatrix,this.fogMatrix,[0,0,-this.elevation]),this.pixelMatrix3D=a.L(new Float64Array(16),this.labelPlaneMatrix,ee);let De=this.width%2/2,Be=this.height%2/2,et=Math.cos(this.angle),We=Math.sin(this.angle),it=B-Math.round(B)+et*De+We*Be,Ft=Q-Math.round(Q)+et*Be+We*De,Ht=new Float64Array(ee);if(a.J(Ht,Ht,[it>.5?it-1:it,Ft>.5?Ft-1:Ft,0]),this.alignedModelViewProjectionMatrix=Ht,ee=a.as(new Float64Array(16),this.pixelMatrix),!ee)throw new Error("failed to invert matrix");this.pixelMatrixInverse=ee,this._posMatrixCache={},this._alignedPosMatrixCache={},this._fogMatrixCache={}}maxPitchScaleFactor(){if(!this.pixelMatrixInverse)return 1;let w=this.pointCoordinate(new a.P(0,0)),B=[w.x*this.worldSize,w.y*this.worldSize,0,1];return a.af(B,B,this.pixelMatrix)[3]/this.cameraToCenterDistance}getCameraPoint(){let w=Math.tan(this._pitch)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new a.P(0,w))}getCameraQueryGeometry(w){let B=this.getCameraPoint();if(w.length===1)return[w[0],B];{let Q=B.x,ee=B.y,le=B.x,qe=B.y;for(let Xe of w)Q=Math.min(Q,Xe.x),ee=Math.min(ee,Xe.y),le=Math.max(le,Xe.x),qe=Math.max(qe,Xe.y);return[new a.P(Q,ee),new a.P(le,ee),new a.P(le,qe),new a.P(Q,qe),new a.P(Q,ee)]}}lngLatToCameraDepth(w,B){let Q=this.locationCoordinate(w),ee=[Q.x*this.worldSize,Q.y*this.worldSize,B,1];return a.af(ee,ee,this.modelViewProjectionMatrix),ee[2]/ee[3]}}function nh(ue,w){let B,Q=!1,ee=null,le=null,qe=()=>{ee=null,Q&&(ue.apply(le,B),ee=setTimeout(qe,w),Q=!1)};return(...Xe)=>(Q=!0,le=this,B=Xe,ee||qe(),ee)}class Ah{constructor(w){this._getCurrentHash=()=>{let B=window.location.hash.replace("#","");if(this._hashName){let Q;return B.split("&").map(ee=>ee.split("=")).forEach(ee=>{ee[0]===this._hashName&&(Q=ee)}),(Q&&Q[1]||"").split("/")}return B.split("/")},this._onHashChange=()=>{let B=this._getCurrentHash();if(B.length>=3&&!B.some(Q=>isNaN(Q))){let Q=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(B[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+B[2],+B[1]],zoom:+B[0],bearing:Q,pitch:+(B[4]||0)}),!0}return!1},this._updateHashUnthrottled=()=>{let B=window.location.href.replace(/(#.*)?$/,this.getHashString());window.history.replaceState(window.history.state,null,B)},this._removeHash=()=>{let B=this._getCurrentHash();if(B.length===0)return;let Q=B.join("/"),ee=Q;ee.split("&").length>0&&(ee=ee.split("&")[0]),this._hashName&&(ee=`${this._hashName}=${Q}`);let le=window.location.hash.replace(ee,"");le.startsWith("#&")?le=le.slice(0,1)+le.slice(2):le==="#"&&(le="");let qe=window.location.href.replace(/(#.+)?$/,le);qe=qe.replace("&&","&"),window.history.replaceState(window.history.state,null,qe)},this._updateHash=nh(this._updateHashUnthrottled,300),this._hashName=w&&encodeURIComponent(w)}addTo(w){return this._map=w,addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this}remove(){return removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),this._removeHash(),delete this._map,this}getHashString(w){let B=this._map.getCenter(),Q=Math.round(100*this._map.getZoom())/100,ee=Math.ceil((Q*Math.LN2+Math.log(512/360/.5))/Math.LN10),le=Math.pow(10,ee),qe=Math.round(B.lng*le)/le,Xe=Math.round(B.lat*le)/le,ot=this._map.getBearing(),Tt=this._map.getPitch(),Yt="";if(Yt+=w?`/${qe}/${Xe}/${Q}`:`${Q}/${Xe}/${qe}`,(ot||Tt)&&(Yt+="/"+Math.round(10*ot)/10),Tt&&(Yt+=`/${Math.round(Tt)}`),this._hashName){let Kt=this._hashName,xr=!1,Ir=window.location.hash.slice(1).split("&").map(ve=>{let be=ve.split("=")[0];return be===Kt?(xr=!0,`${be}=${Yt}`):ve}).filter(ve=>ve);return xr||Ir.push(`${Kt}=${Yt}`),`#${Ir.join("&")}`}return`#${Yt}`}}let zu={linearity:.3,easing:a.b8(0,0,.3,1)},Fc=a.e({deceleration:2500,maxSpeed:1400},zu),wc=a.e({deceleration:20,maxSpeed:1400},zu),bd=a.e({deceleration:1e3,maxSpeed:360},zu),xf=a.e({deceleration:1e3,maxSpeed:90},zu);class Pf{constructor(w){this._map=w,this.clear()}clear(){this._inertiaBuffer=[]}record(w){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:u.now(),settings:w})}_drainInertiaBuffer(){let w=this._inertiaBuffer,B=u.now();for(;w.length>0&&B-w[0].time>160;)w.shift()}_onMoveEnd(w){if(this._drainInertiaBuffer(),this._inertiaBuffer.length<2)return;let B={zoom:0,bearing:0,pitch:0,pan:new a.P(0,0),pinchAround:void 0,around:void 0};for(let{settings:le}of this._inertiaBuffer)B.zoom+=le.zoomDelta||0,B.bearing+=le.bearingDelta||0,B.pitch+=le.pitchDelta||0,le.panDelta&&B.pan._add(le.panDelta),le.around&&(B.around=le.around),le.pinchAround&&(B.pinchAround=le.pinchAround);let Q=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,ee={};if(B.pan.mag()){let le=bf(B.pan.mag(),Q,a.e({},Fc,w||{}));ee.offset=B.pan.mult(le.amount/B.pan.mag()),ee.center=this._map.transform.center,Ou(ee,le)}if(B.zoom){let le=bf(B.zoom,Q,wc);ee.zoom=this._map.transform.zoom+le.amount,Ou(ee,le)}if(B.bearing){let le=bf(B.bearing,Q,bd);ee.bearing=this._map.transform.bearing+a.ac(le.amount,-179,179),Ou(ee,le)}if(B.pitch){let le=bf(B.pitch,Q,xf);ee.pitch=this._map.transform.pitch+le.amount,Ou(ee,le)}if(ee.zoom||ee.bearing){let le=B.pinchAround===void 0?B.around:B.pinchAround;ee.around=le?this._map.unproject(le):this._map.getCenter()}return this.clear(),a.e(ee,{noMoveStart:!0})}}function Ou(ue,w){(!ue.duration||ue.duration<w.duration)&&(ue.duration=w.duration,ue.easing=w.easing)}function bf(ue,w,B){let{maxSpeed:Q,linearity:ee,deceleration:le}=B,qe=a.ac(ue*ee/(w/1e3),-Q,Q),Xe=Math.abs(qe)/(le*ee);return{easing:B.easing,duration:1e3*Xe,amount:qe*(Xe/2)}}class jl extends a.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(w,B,Q,ee={}){let le=c.mousePos(B.getCanvas(),Q),qe=B.unproject(le);super(w,a.e({point:le,lngLat:qe,originalEvent:Q},ee)),this._defaultPrevented=!1,this.target=B}}class lf extends a.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(w,B,Q){let ee=w==="touchend"?Q.changedTouches:Q.touches,le=c.touchPos(B.getCanvasContainer(),ee),qe=le.map(ot=>B.unproject(ot)),Xe=le.reduce((ot,Tt,Yt,Kt)=>ot.add(Tt.div(Kt.length)),new a.P(0,0));super(w,{points:le,point:Xe,lngLats:qe,lngLat:B.unproject(Xe),originalEvent:Q}),this._defaultPrevented=!1}}class Hh extends a.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(w,B,Q){super(w,{originalEvent:Q}),this._defaultPrevented=!1}}class If{constructor(w,B){this._map=w,this._clickTolerance=B.clickTolerance}reset(){delete this._mousedownPos}wheel(w){return this._firePreventable(new Hh(w.type,this._map,w))}mousedown(w,B){return this._mousedownPos=B,this._firePreventable(new jl(w.type,this._map,w))}mouseup(w){this._map.fire(new jl(w.type,this._map,w))}click(w,B){this._mousedownPos&&this._mousedownPos.dist(B)>=this._clickTolerance||this._map.fire(new jl(w.type,this._map,w))}dblclick(w){return this._firePreventable(new jl(w.type,this._map,w))}mouseover(w){this._map.fire(new jl(w.type,this._map,w))}mouseout(w){this._map.fire(new jl(w.type,this._map,w))}touchstart(w){return this._firePreventable(new lf(w.type,this._map,w))}touchmove(w){this._map.fire(new lf(w.type,this._map,w))}touchend(w){this._map.fire(new lf(w.type,this._map,w))}touchcancel(w){this._map.fire(new lf(w.type,this._map,w))}_firePreventable(w){if(this._map.fire(w),w.defaultPrevented)return{}}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class Cs{constructor(w){this._map=w}reset(){this._delayContextMenu=!1,this._ignoreContextMenu=!0,delete this._contextMenuEvent}mousemove(w){this._map.fire(new jl(w.type,this._map,w))}mousedown(){this._delayContextMenu=!0,this._ignoreContextMenu=!1}mouseup(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new jl("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)}contextmenu(w){this._delayContextMenu?this._contextMenuEvent=w:this._ignoreContextMenu||this._map.fire(new jl(w.type,this._map,w)),this._map.listens("contextmenu")&&w.preventDefault()}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class du{constructor(w){this._map=w}get transform(){return this._map._requestedCameraState||this._map.transform}get center(){return{lng:this.transform.center.lng,lat:this.transform.center.lat}}get zoom(){return this.transform.zoom}get pitch(){return this.transform.pitch}get bearing(){return this.transform.bearing}unproject(w){return this.transform.pointLocation(a.P.convert(w),this._map.terrain)}}class ku{constructor(w,B){this._map=w,this._tr=new du(w),this._el=w.getCanvasContainer(),this._container=w.getContainer(),this._clickTolerance=B.clickTolerance||1}isEnabled(){return!!this._enabled}isActive(){return!!this._active}enable(){this.isEnabled()||(this._enabled=!0)}disable(){this.isEnabled()&&(this._enabled=!1)}mousedown(w,B){this.isEnabled()&&w.shiftKey&&w.button===0&&(c.disableDrag(),this._startPos=this._lastPos=B,this._active=!0)}mousemoveWindow(w,B){if(!this._active)return;let Q=B;if(this._lastPos.equals(Q)||!this._box&&Q.dist(this._startPos)<this._clickTolerance)return;let ee=this._startPos;this._lastPos=Q,this._box||(this._box=c.create("div","maplibregl-boxzoom",this._container),this._container.classList.add("maplibregl-crosshair"),this._fireEvent("boxzoomstart",w));let le=Math.min(ee.x,Q.x),qe=Math.max(ee.x,Q.x),Xe=Math.min(ee.y,Q.y),ot=Math.max(ee.y,Q.y);c.setTransform(this._box,`translate(${le}px,${Xe}px)`),this._box.style.width=qe-le+"px",this._box.style.height=ot-Xe+"px"}mouseupWindow(w,B){if(!this._active||w.button!==0)return;let Q=this._startPos,ee=B;if(this.reset(),c.suppressClick(),Q.x!==ee.x||Q.y!==ee.y)return this._map.fire(new a.k("boxzoomend",{originalEvent:w})),{cameraAnimation:le=>le.fitScreenCoordinates(Q,ee,this._tr.bearing,{linear:!0})};this._fireEvent("boxzoomcancel",w)}keydown(w){this._active&&w.keyCode===27&&(this.reset(),this._fireEvent("boxzoomcancel",w))}reset(){this._active=!1,this._container.classList.remove("maplibregl-crosshair"),this._box&&(c.remove(this._box),this._box=null),c.enableDrag(),delete this._startPos,delete this._lastPos}_fireEvent(w,B){return this._map.fire(new a.k(w,{originalEvent:B}))}}function Wf(ue,w){if(ue.length!==w.length)throw new Error(`The number of touches and points are not equal - touches ${ue.length}, points ${w.length}`);let B={};for(let Q=0;Q<ue.length;Q++)B[ue[Q].identifier]=w[Q];return B}class Us{constructor(w){this.reset(),this.numTouches=w.numTouches}reset(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1}touchstart(w,B,Q){(this.centroid||Q.length>this.numTouches)&&(this.aborted=!0),this.aborted||(this.startTime===void 0&&(this.startTime=w.timeStamp),Q.length===this.numTouches&&(this.centroid=function(ee){let le=new a.P(0,0);for(let qe of ee)le._add(qe);return le.div(ee.length)}(B),this.touches=Wf(Q,B)))}touchmove(w,B,Q){if(this.aborted||!this.centroid)return;let ee=Wf(Q,B);for(let le in this.touches){let qe=ee[le];(!qe||qe.dist(this.touches[le])>30)&&(this.aborted=!0)}}touchend(w,B,Q){if((!this.centroid||w.timeStamp-this.startTime>500)&&(this.aborted=!0),Q.length===0){let ee=!this.aborted&&this.centroid;if(this.reset(),ee)return ee}}}class wf{constructor(w){this.singleTap=new Us(w),this.numTaps=w.numTaps,this.reset()}reset(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()}touchstart(w,B,Q){this.singleTap.touchstart(w,B,Q)}touchmove(w,B,Q){this.singleTap.touchmove(w,B,Q)}touchend(w,B,Q){let ee=this.singleTap.touchend(w,B,Q);if(ee){let le=w.timeStamp-this.lastTime<500,qe=!this.lastTap||this.lastTap.dist(ee)<30;if(le&&qe||this.reset(),this.count++,this.lastTime=w.timeStamp,this.lastTap=ee,this.count===this.numTaps)return this.reset(),ee}}}class zc{constructor(w){this._tr=new du(w),this._zoomIn=new wf({numTouches:1,numTaps:2}),this._zoomOut=new wf({numTouches:2,numTaps:1}),this.reset()}reset(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()}touchstart(w,B,Q){this._zoomIn.touchstart(w,B,Q),this._zoomOut.touchstart(w,B,Q)}touchmove(w,B,Q){this._zoomIn.touchmove(w,B,Q),this._zoomOut.touchmove(w,B,Q)}touchend(w,B,Q){let ee=this._zoomIn.touchend(w,B,Q),le=this._zoomOut.touchend(w,B,Q),qe=this._tr;return ee?(this._active=!0,w.preventDefault(),setTimeout(()=>this.reset(),0),{cameraAnimation:Xe=>Xe.easeTo({duration:300,zoom:qe.zoom+1,around:qe.unproject(ee)},{originalEvent:w})}):le?(this._active=!0,w.preventDefault(),setTimeout(()=>this.reset(),0),{cameraAnimation:Xe=>Xe.easeTo({duration:300,zoom:qe.zoom-1,around:qe.unproject(le)},{originalEvent:w})}):void 0}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class Wu{constructor(w){this._enabled=!!w.enable,this._moveStateManager=w.moveStateManager,this._clickTolerance=w.clickTolerance||1,this._moveFunction=w.move,this._activateOnStart=!!w.activateOnStart,w.assignEvents(this),this.reset()}reset(w){this._active=!1,this._moved=!1,delete this._lastPoint,this._moveStateManager.endMove(w)}_move(...w){let B=this._moveFunction(...w);if(B.bearingDelta||B.pitchDelta||B.around||B.panDelta)return this._active=!0,B}dragStart(w,B){this.isEnabled()&&!this._lastPoint&&this._moveStateManager.isValidStartEvent(w)&&(this._moveStateManager.startMove(w),this._lastPoint=B.length?B[0]:B,this._activateOnStart&&this._lastPoint&&(this._active=!0))}dragMove(w,B){if(!this.isEnabled())return;let Q=this._lastPoint;if(!Q)return;if(w.preventDefault(),!this._moveStateManager.isValidMoveEvent(w))return void this.reset(w);let ee=B.length?B[0]:B;return!this._moved&&ee.dist(Q)<this._clickTolerance?void 0:(this._moved=!0,this._lastPoint=ee,this._move(Q,ee))}dragEnd(w){this.isEnabled()&&this._lastPoint&&this._moveStateManager.isValidEndEvent(w)&&(this._moved&&c.suppressClick(),this.reset(w))}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}getClickTolerance(){return this._clickTolerance}}let Rf={0:1,2:2};class Xu{constructor(w){this._correctEvent=w.checkCorrectEvent}startMove(w){let B=c.mouseButton(w);this._eventButton=B}endMove(w){delete this._eventButton}isValidStartEvent(w){return this._correctEvent(w)}isValidMoveEvent(w){return!function(B,Q){let ee=Rf[Q];return B.buttons===void 0||(B.buttons&ee)!==ee}(w,this._eventButton)}isValidEndEvent(w){return c.mouseButton(w)===this._eventButton}}class uf{constructor(){this._firstTouch=void 0}_isOneFingerTouch(w){return w.targetTouches.length===1}_isSameTouchEvent(w){return w.targetTouches[0].identifier===this._firstTouch}startMove(w){this._firstTouch=w.targetTouches[0].identifier}endMove(w){delete this._firstTouch}isValidStartEvent(w){return this._isOneFingerTouch(w)}isValidMoveEvent(w){return this._isOneFingerTouch(w)&&this._isSameTouchEvent(w)}isValidEndEvent(w){return this._isOneFingerTouch(w)&&this._isSameTouchEvent(w)}}let Xf=ue=>{ue.mousedown=ue.dragStart,ue.mousemoveWindow=ue.dragMove,ue.mouseup=ue.dragEnd,ue.contextmenu=w=>{w.preventDefault()}},Wl=({enable:ue,clickTolerance:w,bearingDegreesPerPixelMoved:B=.8})=>{let Q=new Xu({checkCorrectEvent:ee=>c.mouseButton(ee)===0&&ee.ctrlKey||c.mouseButton(ee)===2});return new Wu({clickTolerance:w,move:(ee,le)=>({bearingDelta:(le.x-ee.x)*B}),moveStateManager:Q,enable:ue,assignEvents:Xf})},ah=({enable:ue,clickTolerance:w,pitchDegreesPerPixelMoved:B=-.5})=>{let Q=new Xu({checkCorrectEvent:ee=>c.mouseButton(ee)===0&&ee.ctrlKey||c.mouseButton(ee)===2});return new Wu({clickTolerance:w,move:(ee,le)=>({pitchDelta:(le.y-ee.y)*B}),moveStateManager:Q,enable:ue,assignEvents:Xf})};class Zu{constructor(w,B){this._clickTolerance=w.clickTolerance||1,this._map=B,this.reset()}reset(){this._active=!1,this._touches={},this._sum=new a.P(0,0)}_shouldBePrevented(w){return w<(this._map.cooperativeGestures.isEnabled()?2:1)}touchstart(w,B,Q){return this._calculateTransform(w,B,Q)}touchmove(w,B,Q){if(this._active){if(!this._shouldBePrevented(Q.length))return w.preventDefault(),this._calculateTransform(w,B,Q);this._map.cooperativeGestures.notifyGestureBlocked("touch_pan",w)}}touchend(w,B,Q){this._calculateTransform(w,B,Q),this._active&&this._shouldBePrevented(Q.length)&&this.reset()}touchcancel(){this.reset()}_calculateTransform(w,B,Q){Q.length>0&&(this._active=!0);let ee=Wf(Q,B),le=new a.P(0,0),qe=new a.P(0,0),Xe=0;for(let Tt in ee){let Yt=ee[Tt],Kt=this._touches[Tt];Kt&&(le._add(Yt),qe._add(Yt.sub(Kt)),Xe++,ee[Tt]=Yt)}if(this._touches=ee,this._shouldBePrevented(Xe)||!qe.mag())return;let ot=qe.div(Xe);return this._sum._add(ot),this._sum.mag()<this._clickTolerance?void 0:{around:le.div(Xe),panDelta:ot}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class Oc{constructor(){this.reset()}reset(){this._active=!1,delete this._firstTwoTouches}touchstart(w,B,Q){this._firstTwoTouches||Q.length<2||(this._firstTwoTouches=[Q[0].identifier,Q[1].identifier],this._start([B[0],B[1]]))}touchmove(w,B,Q){if(!this._firstTwoTouches)return;w.preventDefault();let[ee,le]=this._firstTwoTouches,qe=Tc(Q,B,ee),Xe=Tc(Q,B,le);if(!qe||!Xe)return;let ot=this._aroundCenter?null:qe.add(Xe).div(2);return this._move([qe,Xe],ot,w)}touchend(w,B,Q){if(!this._firstTwoTouches)return;let[ee,le]=this._firstTwoTouches,qe=Tc(Q,B,ee),Xe=Tc(Q,B,le);qe&&Xe||(this._active&&c.suppressClick(),this.reset())}touchcancel(){this.reset()}enable(w){this._enabled=!0,this._aroundCenter=!!w&&w.around==="center"}disable(){this._enabled=!1,this.reset()}isEnabled(){return!!this._enabled}isActive(){return!!this._active}}function Tc(ue,w,B){for(let Q=0;Q<ue.length;Q++)if(ue[Q].identifier===B)return w[Q]}function wl(ue,w){return Math.log(ue/w)/Math.LN2}class vu extends Oc{reset(){super.reset(),delete this._distance,delete this._startDistance}_start(w){this._startDistance=this._distance=w[0].dist(w[1])}_move(w,B){let Q=this._distance;if(this._distance=w[0].dist(w[1]),this._active||!(Math.abs(wl(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:wl(this._distance,Q),pinchAround:B}}}function qc(ue,w){return 180*ue.angleWith(w)/Math.PI}class cf extends Oc{reset(){super.reset(),delete this._minDiameter,delete this._startVector,delete this._vector}_start(w){this._startVector=this._vector=w[0].sub(w[1]),this._minDiameter=w[0].dist(w[1])}_move(w,B,Q){let ee=this._vector;if(this._vector=w[0].sub(w[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:qc(this._vector,ee),pinchAround:B}}_isBelowThreshold(w){this._minDiameter=Math.min(this._minDiameter,w.mag());let B=25/(Math.PI*this._minDiameter)*360,Q=qc(w,this._startVector);return Math.abs(Q)<B}}function fc(ue){return Math.abs(ue.y)>Math.abs(ue.x)}class Bc extends Oc{constructor(w){super(),this._currentTouchCount=0,this._map=w}reset(){super.reset(),this._valid=void 0,delete this._firstMove,delete this._lastPoints}touchstart(w,B,Q){super.touchstart(w,B,Q),this._currentTouchCount=Q.length}_start(w){this._lastPoints=w,fc(w[0].sub(w[1]))&&(this._valid=!1)}_move(w,B,Q){if(this._map.cooperativeGestures.isEnabled()&&this._currentTouchCount<3)return;let ee=w[0].sub(this._lastPoints[0]),le=w[1].sub(this._lastPoints[1]);return this._valid=this.gestureBeginsVertically(ee,le,Q.timeStamp),this._valid?(this._lastPoints=w,this._active=!0,{pitchDelta:(ee.y+le.y)/2*-.5}):void 0}gestureBeginsVertically(w,B,Q){if(this._valid!==void 0)return this._valid;let ee=w.mag()>=2,le=B.mag()>=2;if(!ee&&!le)return;if(!ee||!le)return this._firstMove===void 0&&(this._firstMove=Q),Q-this._firstMove<100&&void 0;let qe=w.y>0==B.y>0;return fc(w)&&fc(B)&&qe}}let At={panStep:100,bearingStep:15,pitchStep:10};class Wt{constructor(w){this._tr=new du(w);let B=At;this._panStep=B.panStep,this._bearingStep=B.bearingStep,this._pitchStep=B.pitchStep,this._rotationDisabled=!1}reset(){this._active=!1}keydown(w){if(w.altKey||w.ctrlKey||w.metaKey)return;let B=0,Q=0,ee=0,le=0,qe=0;switch(w.keyCode){case 61:case 107:case 171:case 187:B=1;break;case 189:case 109:case 173:B=-1;break;case 37:w.shiftKey?Q=-1:(w.preventDefault(),le=-1);break;case 39:w.shiftKey?Q=1:(w.preventDefault(),le=1);break;case 38:w.shiftKey?ee=1:(w.preventDefault(),qe=-1);break;case 40:w.shiftKey?ee=-1:(w.preventDefault(),qe=1);break;default:return}return this._rotationDisabled&&(Q=0,ee=0),{cameraAnimation:Xe=>{let ot=this._tr;Xe.easeTo({duration:300,easeId:"keyboardHandler",easing:Cr,zoom:B?Math.round(ot.zoom)+B*(w.shiftKey?2:1):ot.zoom,bearing:ot.bearing+Q*this._bearingStep,pitch:ot.pitch+ee*this._pitchStep,offset:[-le*this._panStep,-qe*this._panStep],center:ot.center},{originalEvent:w})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}disableRotation(){this._rotationDisabled=!0}enableRotation(){this._rotationDisabled=!1}}function Cr(ue){return ue*(2-ue)}let Ar=4.000244140625;class Kr{constructor(w,B){this._onTimeout=Q=>{this._type="wheel",this._delta-=this._lastValue,this._active||this._start(Q)},this._map=w,this._tr=new du(w),this._triggerRenderFrame=B,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222}setZoomRate(w){this._defaultZoomRate=w}setWheelZoomRate(w){this._wheelZoomRate=w}isEnabled(){return!!this._enabled}isActive(){return!!this._active||this._finishTimeout!==void 0}isZooming(){return!!this._zooming}enable(w){this.isEnabled()||(this._enabled=!0,this._aroundCenter=!!w&&w.around==="center")}disable(){this.isEnabled()&&(this._enabled=!1)}_shouldBePrevented(w){return!!this._map.cooperativeGestures.isEnabled()&&!(w.ctrlKey||this._map.cooperativeGestures.isBypassed(w))}wheel(w){if(!this.isEnabled())return;if(this._shouldBePrevented(w))return void this._map.cooperativeGestures.notifyGestureBlocked("wheel_zoom",w);let B=w.deltaMode===WheelEvent.DOM_DELTA_LINE?40*w.deltaY:w.deltaY,Q=u.now(),ee=Q-(this._lastWheelEventTime||0);this._lastWheelEventTime=Q,B!==0&&B%Ar==0?this._type="wheel":B!==0&&Math.abs(B)<4?this._type="trackpad":ee>400?(this._type=null,this._lastValue=B,this._timeout=setTimeout(this._onTimeout,40,w)):this._type||(this._type=Math.abs(ee*B)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,B+=this._lastValue)),w.shiftKey&&B&&(B/=4),this._type&&(this._lastWheelEvent=w,this._delta-=B,this._active||this._start(w)),w.preventDefault()}_start(w){if(!this._delta)return;this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);let B=c.mousePos(this._map.getCanvas(),w),Q=this._tr;this._around=B.y>Q.transform.height/2-Q.transform.getHorizon()?a.N.convert(this._aroundCenter?Q.center:Q.unproject(B)):a.N.convert(Q.center),this._aroundPoint=Q.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._triggerRenderFrame())}renderFrame(){if(!this._frameId||(this._frameId=null,!this.isActive()))return;let w=this._tr.transform;if(this._delta!==0){let ot=this._type==="wheel"&&Math.abs(this._delta)>Ar?this._wheelZoomRate:this._defaultZoomRate,Tt=2/(1+Math.exp(-Math.abs(this._delta*ot)));this._delta<0&&Tt!==0&&(Tt=1/Tt);let Yt=typeof this._targetZoom=="number"?w.zoomScale(this._targetZoom):w.scale;this._targetZoom=Math.min(w.maxZoom,Math.max(w.minZoom,w.scaleZoom(Yt*Tt))),this._type==="wheel"&&(this._startZoom=w.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}let B=typeof this._targetZoom=="number"?this._targetZoom:w.zoom,Q=this._startZoom,ee=this._easing,le,qe=!1,Xe=u.now()-this._lastWheelEventTime;if(this._type==="wheel"&&Q&&ee&&Xe){let ot=Math.min(Xe/200,1),Tt=ee(ot);le=a.y.number(Q,B,Tt),ot<1?this._frameId||(this._frameId=!0):qe=!0}else le=B,qe=!0;return this._active=!0,qe&&(this._active=!1,this._finishTimeout=setTimeout(()=>{this._zooming=!1,this._triggerRenderFrame(),delete this._targetZoom,delete this._finishTimeout},200)),{noInertia:!0,needsRenderFrame:!qe,zoomDelta:le-w.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}_smoothOutEasing(w){let B=a.b9;if(this._prevEase){let Q=this._prevEase,ee=(u.now()-Q.start)/Q.duration,le=Q.easing(ee+.01)-Q.easing(ee),qe=.27/Math.sqrt(le*le+1e-4)*.01,Xe=Math.sqrt(.0729-qe*qe);B=a.b8(qe,Xe,.25,1)}return this._prevEase={start:u.now(),duration:w,easing:B},B}reset(){this._active=!1,this._zooming=!1,delete this._targetZoom,this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout)}}class ki{constructor(w,B){this._clickZoom=w,this._tapZoom=B}enable(){this._clickZoom.enable(),this._tapZoom.enable()}disable(){this._clickZoom.disable(),this._tapZoom.disable()}isEnabled(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()}isActive(){return this._clickZoom.isActive()||this._tapZoom.isActive()}}class Xi{constructor(w){this._tr=new du(w),this.reset()}reset(){this._active=!1}dblclick(w,B){return w.preventDefault(),{cameraAnimation:Q=>{Q.easeTo({duration:300,zoom:this._tr.zoom+(w.shiftKey?-1:1),around:this._tr.unproject(B)},{originalEvent:w})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class dn{constructor(){this._tap=new wf({numTouches:1,numTaps:1}),this.reset()}reset(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,delete this._tapPoint,this._tap.reset()}touchstart(w,B,Q){if(!this._swipePoint)if(this._tapTime){let ee=B[0],le=w.timeStamp-this._tapTime<500,qe=this._tapPoint.dist(ee)<30;le&&qe?Q.length>0&&(this._swipePoint=ee,this._swipeTouch=Q[0].identifier):this.reset()}else this._tap.touchstart(w,B,Q)}touchmove(w,B,Q){if(this._tapTime){if(this._swipePoint){if(Q[0].identifier!==this._swipeTouch)return;let ee=B[0],le=ee.y-this._swipePoint.y;return this._swipePoint=ee,w.preventDefault(),this._active=!0,{zoomDelta:le/128}}}else this._tap.touchmove(w,B,Q)}touchend(w,B,Q){if(this._tapTime)this._swipePoint&&Q.length===0&&this.reset();else{let ee=this._tap.touchend(w,B,Q);ee&&(this._tapTime=w.timeStamp,this._tapPoint=ee)}}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class wn{constructor(w,B,Q){this._el=w,this._mousePan=B,this._touchPan=Q}enable(w){this._inertiaOptions=w||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("maplibregl-touch-drag-pan")}disable(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("maplibregl-touch-drag-pan")}isEnabled(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()}isActive(){return this._mousePan.isActive()||this._touchPan.isActive()}}class Nn{constructor(w,B,Q){this._pitchWithRotate=w.pitchWithRotate,this._mouseRotate=B,this._mousePitch=Q}enable(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()}disable(){this._mouseRotate.disable(),this._mousePitch.disable()}isEnabled(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())}isActive(){return this._mouseRotate.isActive()||this._mousePitch.isActive()}}class Yi{constructor(w,B,Q,ee){this._el=w,this._touchZoom=B,this._touchRotate=Q,this._tapDragZoom=ee,this._rotationDisabled=!1,this._enabled=!0}enable(w){this._touchZoom.enable(w),this._rotationDisabled||this._touchRotate.enable(w),this._tapDragZoom.enable(),this._el.classList.add("maplibregl-touch-zoom-rotate")}disable(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("maplibregl-touch-zoom-rotate")}isEnabled(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()}isActive(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()}disableRotation(){this._rotationDisabled=!0,this._touchRotate.disable()}enableRotation(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()}}class Qi{constructor(w,B){this._bypassKey=navigator.userAgent.indexOf("Mac")!==-1?"metaKey":"ctrlKey",this._map=w,this._options=B,this._enabled=!1}isActive(){return!1}reset(){}_setupUI(){if(this._container)return;let w=this._map.getCanvasContainer();w.classList.add("maplibregl-cooperative-gestures"),this._container=c.create("div","maplibregl-cooperative-gesture-screen",w);let B=this._map._getUIString("CooperativeGesturesHandler.WindowsHelpText");this._bypassKey==="metaKey"&&(B=this._map._getUIString("CooperativeGesturesHandler.MacHelpText"));let Q=this._map._getUIString("CooperativeGesturesHandler.MobileHelpText"),ee=document.createElement("div");ee.className="maplibregl-desktop-message",ee.textContent=B,this._container.appendChild(ee);let le=document.createElement("div");le.className="maplibregl-mobile-message",le.textContent=Q,this._container.appendChild(le),this._container.setAttribute("aria-hidden","true")}_destroyUI(){this._container&&(c.remove(this._container),this._map.getCanvasContainer().classList.remove("maplibregl-cooperative-gestures")),delete this._container}enable(){this._setupUI(),this._enabled=!0}disable(){this._enabled=!1,this._destroyUI()}isEnabled(){return this._enabled}isBypassed(w){return w[this._bypassKey]}notifyGestureBlocked(w,B){this._enabled&&(this._map.fire(new a.k("cooperativegestureprevented",{gestureType:w,originalEvent:B})),this._container.classList.add("maplibregl-show"),setTimeout(()=>{this._container.classList.remove("maplibregl-show")},100))}}let on=ue=>ue.zoom||ue.drag||ue.pitch||ue.rotate;class Fi extends a.k{}function $n(ue){return ue.panDelta&&ue.panDelta.mag()||ue.zoomDelta||ue.bearingDelta||ue.pitchDelta}class Ca{constructor(w,B){this.handleWindowEvent=ee=>{this.handleEvent(ee,`${ee.type}Window`)},this.handleEvent=(ee,le)=>{if(ee.type==="blur")return void this.stop(!0);this._updatingCamera=!0;let qe=ee.type==="renderFrame"?void 0:ee,Xe={needsRenderFrame:!1},ot={},Tt={},Yt=ee.touches,Kt=Yt?this._getMapTouches(Yt):void 0,xr=Kt?c.touchPos(this._map.getCanvas(),Kt):c.mousePos(this._map.getCanvas(),ee);for(let{handlerName:be,handler:De,allowed:Be}of this._handlers){if(!De.isEnabled())continue;let et;this._blockedByActive(Tt,Be,be)?De.reset():De[le||ee.type]&&(et=De[le||ee.type](ee,xr,Kt),this.mergeHandlerResult(Xe,ot,et,be,qe),et&&et.needsRenderFrame&&this._triggerRenderFrame()),(et||De.isActive())&&(Tt[be]=De)}let Ir={};for(let be in this._previousActiveHandlers)Tt[be]||(Ir[be]=qe);this._previousActiveHandlers=Tt,(Object.keys(Ir).length||$n(Xe))&&(this._changes.push([Xe,ot,Ir]),this._triggerRenderFrame()),(Object.keys(Tt).length||$n(Xe))&&this._map._stop(!0),this._updatingCamera=!1;let{cameraAnimation:ve}=Xe;ve&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],ve(this._map))},this._map=w,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Pf(w),this._bearingSnap=B.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(B);let Q=this._el;this._listeners=[[Q,"touchstart",{passive:!0}],[Q,"touchmove",{passive:!1}],[Q,"touchend",void 0],[Q,"touchcancel",void 0],[Q,"mousedown",void 0],[Q,"mousemove",void 0],[Q,"mouseup",void 0],[document,"mousemove",{capture:!0}],[document,"mouseup",void 0],[Q,"mouseover",void 0],[Q,"mouseout",void 0],[Q,"dblclick",void 0],[Q,"click",void 0],[Q,"keydown",{capture:!1}],[Q,"keyup",void 0],[Q,"wheel",{passive:!1}],[Q,"contextmenu",void 0],[window,"blur",void 0]];for(let[ee,le,qe]of this._listeners)c.addEventListener(ee,le,ee===document?this.handleWindowEvent:this.handleEvent,qe)}destroy(){for(let[w,B,Q]of this._listeners)c.removeEventListener(w,B,w===document?this.handleWindowEvent:this.handleEvent,Q)}_addDefaultHandlers(w){let B=this._map,Q=B.getCanvasContainer();this._add("mapEvent",new If(B,w));let ee=B.boxZoom=new ku(B,w);this._add("boxZoom",ee),w.interactive&&w.boxZoom&&ee.enable();let le=B.cooperativeGestures=new Qi(B,w.cooperativeGestures);this._add("cooperativeGestures",le),w.cooperativeGestures&&le.enable();let qe=new zc(B),Xe=new Xi(B);B.doubleClickZoom=new ki(Xe,qe),this._add("tapZoom",qe),this._add("clickZoom",Xe),w.interactive&&w.doubleClickZoom&&B.doubleClickZoom.enable();let ot=new dn;this._add("tapDragZoom",ot);let Tt=B.touchPitch=new Bc(B);this._add("touchPitch",Tt),w.interactive&&w.touchPitch&&B.touchPitch.enable(w.touchPitch);let Yt=Wl(w),Kt=ah(w);B.dragRotate=new Nn(w,Yt,Kt),this._add("mouseRotate",Yt,["mousePitch"]),this._add("mousePitch",Kt,["mouseRotate"]),w.interactive&&w.dragRotate&&B.dragRotate.enable();let xr=(({enable:et,clickTolerance:We})=>{let it=new Xu({checkCorrectEvent:Ft=>c.mouseButton(Ft)===0&&!Ft.ctrlKey});return new Wu({clickTolerance:We,move:(Ft,Ht)=>({around:Ht,panDelta:Ht.sub(Ft)}),activateOnStart:!0,moveStateManager:it,enable:et,assignEvents:Xf})})(w),Ir=new Zu(w,B);B.dragPan=new wn(Q,xr,Ir),this._add("mousePan",xr),this._add("touchPan",Ir,["touchZoom","touchRotate"]),w.interactive&&w.dragPan&&B.dragPan.enable(w.dragPan);let ve=new cf,be=new vu;B.touchZoomRotate=new Yi(Q,be,ve,ot),this._add("touchRotate",ve,["touchPan","touchZoom"]),this._add("touchZoom",be,["touchPan","touchRotate"]),w.interactive&&w.touchZoomRotate&&B.touchZoomRotate.enable(w.touchZoomRotate);let De=B.scrollZoom=new Kr(B,()=>this._triggerRenderFrame());this._add("scrollZoom",De,["mousePan"]),w.interactive&&w.scrollZoom&&B.scrollZoom.enable(w.scrollZoom);let Be=B.keyboard=new Wt(B);this._add("keyboard",Be),w.interactive&&w.keyboard&&B.keyboard.enable(),this._add("blockableMapEvent",new Cs(B))}_add(w,B,Q){this._handlers.push({handlerName:w,handler:B,allowed:Q}),this._handlersById[w]=B}stop(w){if(!this._updatingCamera){for(let{handler:B}of this._handlers)B.reset();this._inertia.clear(),this._fireEvents({},{},w),this._changes=[]}}isActive(){for(let{handler:w}of this._handlers)if(w.isActive())return!0;return!1}isZooming(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()}isRotating(){return!!this._eventsInProgress.rotate}isMoving(){return!!on(this._eventsInProgress)||this.isZooming()}_blockedByActive(w,B,Q){for(let ee in w)if(ee!==Q&&(!B||B.indexOf(ee)<0))return!0;return!1}_getMapTouches(w){let B=[];for(let Q of w)this._el.contains(Q.target)&&B.push(Q);return B}mergeHandlerResult(w,B,Q,ee,le){if(!Q)return;a.e(w,Q);let qe={handlerName:ee,originalEvent:Q.originalEvent||le};Q.zoomDelta!==void 0&&(B.zoom=qe),Q.panDelta!==void 0&&(B.drag=qe),Q.pitchDelta!==void 0&&(B.pitch=qe),Q.bearingDelta!==void 0&&(B.rotate=qe)}_applyChanges(){let w={},B={},Q={};for(let[ee,le,qe]of this._changes)ee.panDelta&&(w.panDelta=(w.panDelta||new a.P(0,0))._add(ee.panDelta)),ee.zoomDelta&&(w.zoomDelta=(w.zoomDelta||0)+ee.zoomDelta),ee.bearingDelta&&(w.bearingDelta=(w.bearingDelta||0)+ee.bearingDelta),ee.pitchDelta&&(w.pitchDelta=(w.pitchDelta||0)+ee.pitchDelta),ee.around!==void 0&&(w.around=ee.around),ee.pinchAround!==void 0&&(w.pinchAround=ee.pinchAround),ee.noInertia&&(w.noInertia=ee.noInertia),a.e(B,le),a.e(Q,qe);this._updateMapTransform(w,B,Q),this._changes=[]}_updateMapTransform(w,B,Q){let ee=this._map,le=ee._getTransformForUpdate(),qe=ee.terrain;if(!($n(w)||qe&&this._terrainMovement))return this._fireEvents(B,Q,!0);let{panDelta:Xe,zoomDelta:ot,bearingDelta:Tt,pitchDelta:Yt,around:Kt,pinchAround:xr}=w;xr!==void 0&&(Kt=xr),ee._stop(!0),Kt=Kt||ee.transform.centerPoint;let Ir=le.pointLocation(Xe?Kt.sub(Xe):Kt);Tt&&(le.bearing+=Tt),Yt&&(le.pitch+=Yt),ot&&(le.zoom+=ot),qe?this._terrainMovement||!B.drag&&!B.zoom?B.drag&&this._terrainMovement?le.center=le.pointLocation(le.centerPoint.sub(Xe)):le.setLocationAtPoint(Ir,Kt):(this._terrainMovement=!0,this._map._elevationFreeze=!0,le.setLocationAtPoint(Ir,Kt)):le.setLocationAtPoint(Ir,Kt),ee._applyUpdatedTransform(le),this._map._update(),w.noInertia||this._inertia.record(w),this._fireEvents(B,Q,!0)}_fireEvents(w,B,Q){let ee=on(this._eventsInProgress),le=on(w),qe={};for(let Kt in w){let{originalEvent:xr}=w[Kt];this._eventsInProgress[Kt]||(qe[`${Kt}start`]=xr),this._eventsInProgress[Kt]=w[Kt]}!ee&&le&&this._fireEvent("movestart",le.originalEvent);for(let Kt in qe)this._fireEvent(Kt,qe[Kt]);le&&this._fireEvent("move",le.originalEvent);for(let Kt in w){let{originalEvent:xr}=w[Kt];this._fireEvent(Kt,xr)}let Xe={},ot;for(let Kt in this._eventsInProgress){let{handlerName:xr,originalEvent:Ir}=this._eventsInProgress[Kt];this._handlersById[xr].isActive()||(delete this._eventsInProgress[Kt],ot=B[xr]||Ir,Xe[`${Kt}end`]=ot)}for(let Kt in Xe)this._fireEvent(Kt,Xe[Kt]);let Tt=on(this._eventsInProgress),Yt=(ee||le)&&!Tt;if(Yt&&this._terrainMovement){this._map._elevationFreeze=!1,this._terrainMovement=!1;let Kt=this._map._getTransformForUpdate();Kt.recalculateZoom(this._map.terrain),this._map._applyUpdatedTransform(Kt)}if(Q&&Yt){this._updatingCamera=!0;let Kt=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),xr=Ir=>Ir!==0&&-this._bearingSnap<Ir&&Ir<this._bearingSnap;!Kt||!Kt.essential&&u.prefersReducedMotion?(this._map.fire(new a.k("moveend",{originalEvent:ot})),xr(this._map.getBearing())&&this._map.resetNorth()):(xr(Kt.bearing||this._map.getBearing())&&(Kt.bearing=0),Kt.freezeElevation=!0,this._map.easeTo(Kt,{originalEvent:ot})),this._updatingCamera=!1}}_fireEvent(w,B){this._map.fire(new a.k(w,B?{originalEvent:B}:{}))}_requestFrame(){return this._map.triggerRepaint(),this._map._renderTaskQueue.add(w=>{delete this._frameId,this.handleEvent(new Fi("renderFrame",{timeStamp:w})),this._applyChanges()})}_triggerRenderFrame(){this._frameId===void 0&&(this._frameId=this._requestFrame())}}class Ra extends a.E{constructor(w,B){super(),this._renderFrameCallback=()=>{let Q=Math.min((u.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(Q)),Q<1&&this._easeFrameId?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},this._moving=!1,this._zooming=!1,this.transform=w,this._bearingSnap=B.bearingSnap,this.on("moveend",()=>{delete this._requestedCameraState})}getCenter(){return new a.N(this.transform.center.lng,this.transform.center.lat)}setCenter(w,B){return this.jumpTo({center:w},B)}panBy(w,B,Q){return w=a.P.convert(w).mult(-1),this.panTo(this.transform.center,a.e({offset:w},B),Q)}panTo(w,B,Q){return this.easeTo(a.e({center:w},B),Q)}getZoom(){return this.transform.zoom}setZoom(w,B){return this.jumpTo({zoom:w},B),this}zoomTo(w,B,Q){return this.easeTo(a.e({zoom:w},B),Q)}zoomIn(w,B){return this.zoomTo(this.getZoom()+1,w,B),this}zoomOut(w,B){return this.zoomTo(this.getZoom()-1,w,B),this}getBearing(){return this.transform.bearing}setBearing(w,B){return this.jumpTo({bearing:w},B),this}getPadding(){return this.transform.padding}setPadding(w,B){return this.jumpTo({padding:w},B),this}rotateTo(w,B,Q){return this.easeTo(a.e({bearing:w},B),Q)}resetNorth(w,B){return this.rotateTo(0,a.e({duration:1e3},w),B),this}resetNorthPitch(w,B){return this.easeTo(a.e({bearing:0,pitch:0,duration:1e3},w),B),this}snapToNorth(w,B){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(w,B):this}getPitch(){return this.transform.pitch}setPitch(w,B){return this.jumpTo({pitch:w},B),this}cameraForBounds(w,B){w=ce.convert(w).adjustAntiMeridian();let Q=B&&B.bearing||0;return this._cameraForBoxAndBearing(w.getNorthWest(),w.getSouthEast(),Q,B)}_cameraForBoxAndBearing(w,B,Q,ee){let le={top:0,bottom:0,right:0,left:0};if(typeof(ee=a.e({padding:le,offset:[0,0],maxZoom:this.transform.maxZoom},ee)).padding=="number"){let Wr=ee.padding;ee.padding={top:Wr,bottom:Wr,right:Wr,left:Wr}}ee.padding=a.e(le,ee.padding);let qe=this.transform,Xe=qe.padding,ot=new ce(w,B),Tt=qe.project(ot.getNorthWest()),Yt=qe.project(ot.getNorthEast()),Kt=qe.project(ot.getSouthEast()),xr=qe.project(ot.getSouthWest()),Ir=a.ba(-Q),ve=Tt.rotate(Ir),be=Yt.rotate(Ir),De=Kt.rotate(Ir),Be=xr.rotate(Ir),et=new a.P(Math.max(ve.x,be.x,Be.x,De.x),Math.max(ve.y,be.y,Be.y,De.y)),We=new a.P(Math.min(ve.x,be.x,Be.x,De.x),Math.min(ve.y,be.y,Be.y,De.y)),it=et.sub(We),Ft=(qe.width-(Xe.left+Xe.right+ee.padding.left+ee.padding.right))/it.x,Ht=(qe.height-(Xe.top+Xe.bottom+ee.padding.top+ee.padding.bottom))/it.y;if(Ht<0||Ft<0)return void a.w("Map cannot fit within canvas with the given bounds, padding, and/or offset.");let tr=Math.min(qe.scaleZoom(qe.scale*Math.min(Ft,Ht)),ee.maxZoom),dr=a.P.convert(ee.offset),Sr=new a.P((ee.padding.left-ee.padding.right)/2,(ee.padding.top-ee.padding.bottom)/2).rotate(a.ba(Q)),Or=dr.add(Sr).mult(qe.scale/qe.zoomScale(tr));return{center:qe.unproject(Tt.add(Kt).div(2).sub(Or)),zoom:tr,bearing:Q}}fitBounds(w,B,Q){return this._fitInternal(this.cameraForBounds(w,B),B,Q)}fitScreenCoordinates(w,B,Q,ee,le){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(a.P.convert(w)),this.transform.pointLocation(a.P.convert(B)),Q,ee),ee,le)}_fitInternal(w,B,Q){return w?(delete(B=a.e(w,B)).padding,B.linear?this.easeTo(B,Q):this.flyTo(B,Q)):this}jumpTo(w,B){this.stop();let Q=this._getTransformForUpdate(),ee=!1,le=!1,qe=!1;return"zoom"in w&&Q.zoom!==+w.zoom&&(ee=!0,Q.zoom=+w.zoom),w.center!==void 0&&(Q.center=a.N.convert(w.center)),"bearing"in w&&Q.bearing!==+w.bearing&&(le=!0,Q.bearing=+w.bearing),"pitch"in w&&Q.pitch!==+w.pitch&&(qe=!0,Q.pitch=+w.pitch),w.padding==null||Q.isPaddingEqual(w.padding)||(Q.padding=w.padding),this._applyUpdatedTransform(Q),this.fire(new a.k("movestart",B)).fire(new a.k("move",B)),ee&&this.fire(new a.k("zoomstart",B)).fire(new a.k("zoom",B)).fire(new a.k("zoomend",B)),le&&this.fire(new a.k("rotatestart",B)).fire(new a.k("rotate",B)).fire(new a.k("rotateend",B)),qe&&this.fire(new a.k("pitchstart",B)).fire(new a.k("pitch",B)).fire(new a.k("pitchend",B)),this.fire(new a.k("moveend",B))}calculateCameraOptionsFromTo(w,B,Q,ee=0){let le=a.Z.fromLngLat(w,B),qe=a.Z.fromLngLat(Q,ee),Xe=qe.x-le.x,ot=qe.y-le.y,Tt=qe.z-le.z,Yt=Math.hypot(Xe,ot,Tt);if(Yt===0)throw new Error("Can't calculate camera options with same From and To");let Kt=Math.hypot(Xe,ot),xr=this.transform.scaleZoom(this.transform.cameraToCenterDistance/Yt/this.transform.tileSize),Ir=180*Math.atan2(Xe,-ot)/Math.PI,ve=180*Math.acos(Kt/Yt)/Math.PI;return ve=Tt<0?90-ve:90+ve,{center:qe.toLngLat(),zoom:xr,pitch:ve,bearing:Ir}}easeTo(w,B){var Q;this._stop(!1,w.easeId),((w=a.e({offset:[0,0],duration:500,easing:a.b9},w)).animate===!1||!w.essential&&u.prefersReducedMotion)&&(w.duration=0);let ee=this._getTransformForUpdate(),le=ee.zoom,qe=ee.bearing,Xe=ee.pitch,ot=ee.padding,Tt="bearing"in w?this._normalizeBearing(w.bearing,qe):qe,Yt="pitch"in w?+w.pitch:Xe,Kt="padding"in w?w.padding:ee.padding,xr=a.P.convert(w.offset),Ir=ee.centerPoint.add(xr),ve=ee.pointLocation(Ir),{center:be,zoom:De}=ee.getConstrained(a.N.convert(w.center||ve),(Q=w.zoom)!==null&&Q!==void 0?Q:le);this._normalizeCenter(be,ee);let Be=ee.project(ve),et=ee.project(be).sub(Be),We=ee.zoomScale(De-le),it,Ft;w.around&&(it=a.N.convert(w.around),Ft=ee.locationPoint(it));let Ht={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||De!==le,this._rotating=this._rotating||qe!==Tt,this._pitching=this._pitching||Yt!==Xe,this._padding=!ee.isPaddingEqual(Kt),this._easeId=w.easeId,this._prepareEase(B,w.noMoveStart,Ht),this.terrain&&this._prepareElevation(be),this._ease(tr=>{if(this._zooming&&(ee.zoom=a.y.number(le,De,tr)),this._rotating&&(ee.bearing=a.y.number(qe,Tt,tr)),this._pitching&&(ee.pitch=a.y.number(Xe,Yt,tr)),this._padding&&(ee.interpolatePadding(ot,Kt,tr),Ir=ee.centerPoint.add(xr)),this.terrain&&!w.freezeElevation&&this._updateElevation(tr),it)ee.setLocationAtPoint(it,Ft);else{let dr=ee.zoomScale(ee.zoom-le),Sr=De>le?Math.min(2,We):Math.max(.5,We),Or=Math.pow(Sr,1-tr),Wr=ee.unproject(Be.add(et.mult(tr*Or)).mult(dr));ee.setLocationAtPoint(ee.renderWorldCopies?Wr.wrap():Wr,Ir)}this._applyUpdatedTransform(ee),this._fireMoveEvents(B)},tr=>{this.terrain&&w.freezeElevation&&this._finalizeElevation(),this._afterEase(B,tr)},w),this}_prepareEase(w,B,Q={}){this._moving=!0,B||Q.moving||this.fire(new a.k("movestart",w)),this._zooming&&!Q.zooming&&this.fire(new a.k("zoomstart",w)),this._rotating&&!Q.rotating&&this.fire(new a.k("rotatestart",w)),this._pitching&&!Q.pitching&&this.fire(new a.k("pitchstart",w))}_prepareElevation(w){this._elevationCenter=w,this._elevationStart=this.transform.elevation,this._elevationTarget=this.terrain.getElevationForLngLatZoom(w,this.transform.tileZoom),this._elevationFreeze=!0}_updateElevation(w){this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);let B=this.terrain.getElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);if(w<1&&B!==this._elevationTarget){let Q=this._elevationTarget-this._elevationStart;this._elevationStart+=w*(Q-(B-(Q*w+this._elevationStart))/(1-w)),this._elevationTarget=B}this.transform.elevation=a.y.number(this._elevationStart,this._elevationTarget,w)}_finalizeElevation(){this._elevationFreeze=!1,this.transform.recalculateZoom(this.terrain)}_getTransformForUpdate(){return this.transformCameraUpdate||this.terrain?(this._requestedCameraState||(this._requestedCameraState=this.transform.clone()),this._requestedCameraState):this.transform}_elevateCameraIfInsideTerrain(w){let B=w.getCameraPosition(),Q=this.terrain.getElevationForLngLatZoom(B.lngLat,w.zoom);if(B.altitude<Q){let ee=this.calculateCameraOptionsFromTo(B.lngLat,Q,w.center,w.elevation);return{pitch:ee.pitch,zoom:ee.zoom}}return{}}_applyUpdatedTransform(w){let B=[];if(this.terrain&&B.push(ee=>this._elevateCameraIfInsideTerrain(ee)),this.transformCameraUpdate&&B.push(ee=>this.transformCameraUpdate(ee)),!B.length)return;let Q=w.clone();for(let ee of B){let le=Q.clone(),{center:qe,zoom:Xe,pitch:ot,bearing:Tt,elevation:Yt}=ee(le);qe&&(le.center=qe),Xe!==void 0&&(le.zoom=Xe),ot!==void 0&&(le.pitch=ot),Tt!==void 0&&(le.bearing=Tt),Yt!==void 0&&(le.elevation=Yt),Q.apply(le)}this.transform.apply(Q)}_fireMoveEvents(w){this.fire(new a.k("move",w)),this._zooming&&this.fire(new a.k("zoom",w)),this._rotating&&this.fire(new a.k("rotate",w)),this._pitching&&this.fire(new a.k("pitch",w))}_afterEase(w,B){if(this._easeId&&B&&this._easeId===B)return;delete this._easeId;let Q=this._zooming,ee=this._rotating,le=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,Q&&this.fire(new a.k("zoomend",w)),ee&&this.fire(new a.k("rotateend",w)),le&&this.fire(new a.k("pitchend",w)),this.fire(new a.k("moveend",w))}flyTo(w,B){var Q;if(!w.essential&&u.prefersReducedMotion){let Cn=a.M(w,["center","zoom","bearing","pitch","around"]);return this.jumpTo(Cn,B)}this.stop(),w=a.e({offset:[0,0],speed:1.2,curve:1.42,easing:a.b9},w);let ee=this._getTransformForUpdate(),le=ee.zoom,qe=ee.bearing,Xe=ee.pitch,ot=ee.padding,Tt="bearing"in w?this._normalizeBearing(w.bearing,qe):qe,Yt="pitch"in w?+w.pitch:Xe,Kt="padding"in w?w.padding:ee.padding,xr=a.P.convert(w.offset),Ir=ee.centerPoint.add(xr),ve=ee.pointLocation(Ir),{center:be,zoom:De}=ee.getConstrained(a.N.convert(w.center||ve),(Q=w.zoom)!==null&&Q!==void 0?Q:le);this._normalizeCenter(be,ee);let Be=ee.zoomScale(De-le),et=ee.project(ve),We=ee.project(be).sub(et),it=w.curve,Ft=Math.max(ee.width,ee.height),Ht=Ft/Be,tr=We.mag();if("minZoom"in w){let Cn=a.ac(Math.min(w.minZoom,le,De),ee.minZoom,ee.maxZoom),Kn=Ft/ee.zoomScale(Cn-le);it=Math.sqrt(Kn/tr*2)}let dr=it*it;function Sr(Cn){let Kn=(Ht*Ht-Ft*Ft+(Cn?-1:1)*dr*dr*tr*tr)/(2*(Cn?Ht:Ft)*dr*tr);return Math.log(Math.sqrt(Kn*Kn+1)-Kn)}function Or(Cn){return(Math.exp(Cn)-Math.exp(-Cn))/2}function Wr(Cn){return(Math.exp(Cn)+Math.exp(-Cn))/2}let ni=Sr(!1),Pi=function(Cn){return Wr(ni)/Wr(ni+it*Cn)},cn=function(Cn){return Ft*((Wr(ni)*(Or(Kn=ni+it*Cn)/Wr(Kn))-Or(ni))/dr)/tr;var Kn},ln=(Sr(!0)-ni)/it;if(Math.abs(tr)<1e-6||!isFinite(ln)){if(Math.abs(Ft-Ht)<1e-6)return this.easeTo(w,B);let Cn=Ht<Ft?-1:1;ln=Math.abs(Math.log(Ht/Ft))/it,cn=()=>0,Pi=Kn=>Math.exp(Cn*it*Kn)}return w.duration="duration"in w?+w.duration:1e3*ln/("screenSpeed"in w?+w.screenSpeed/it:+w.speed),w.maxDuration&&w.duration>w.maxDuration&&(w.duration=0),this._zooming=!0,this._rotating=qe!==Tt,this._pitching=Yt!==Xe,this._padding=!ee.isPaddingEqual(Kt),this._prepareEase(B,!1),this.terrain&&this._prepareElevation(be),this._ease(Cn=>{let Kn=Cn*ln,Ta=1/Pi(Kn);ee.zoom=Cn===1?De:le+ee.scaleZoom(Ta),this._rotating&&(ee.bearing=a.y.number(qe,Tt,Cn)),this._pitching&&(ee.pitch=a.y.number(Xe,Yt,Cn)),this._padding&&(ee.interpolatePadding(ot,Kt,Cn),Ir=ee.centerPoint.add(xr)),this.terrain&&!w.freezeElevation&&this._updateElevation(Cn);let fa=Cn===1?be:ee.unproject(et.add(We.mult(cn(Kn))).mult(Ta));ee.setLocationAtPoint(ee.renderWorldCopies?fa.wrap():fa,Ir),this._applyUpdatedTransform(ee),this._fireMoveEvents(B)},()=>{this.terrain&&w.freezeElevation&&this._finalizeElevation(),this._afterEase(B)},w),this}isEasing(){return!!this._easeFrameId}stop(){return this._stop()}_stop(w,B){var Q;if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){let ee=this._onEaseEnd;delete this._onEaseEnd,ee.call(this,B)}return w||(Q=this.handlers)===null||Q===void 0||Q.stop(!1),this}_ease(w,B,Q){Q.animate===!1||Q.duration===0?(w(1),B()):(this._easeStart=u.now(),this._easeOptions=Q,this._onEaseFrame=w,this._onEaseEnd=B,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))}_normalizeBearing(w,B){w=a.b3(w,-180,180);let Q=Math.abs(w-B);return Math.abs(w-360-B)<Q&&(w-=360),Math.abs(w+360-B)<Q&&(w+=360),w}_normalizeCenter(w,B){if(!B.renderWorldCopies||B.lngRange)return;let Q=w.lng-B.center.lng;w.lng+=Q>180?-360:Q<-180?360:0}queryTerrainElevation(w){return this.terrain?this.terrain.getElevationForLngLatZoom(a.N.convert(w),this.transform.tileZoom)-this.transform.elevation:null}}let La={compact:!0,customAttribution:'<a href="https://maplibre.org/" target="_blank">MapLibre</a>'};class Na{constructor(w=La){this._toggleAttribution=()=>{this._container.classList.contains("maplibregl-compact")&&(this._container.classList.contains("maplibregl-compact-show")?(this._container.setAttribute("open",""),this._container.classList.remove("maplibregl-compact-show")):(this._container.classList.add("maplibregl-compact-show"),this._container.removeAttribute("open")))},this._updateData=B=>{!B||B.sourceDataType!=="metadata"&&B.sourceDataType!=="visibility"&&B.dataType!=="style"&&B.type!=="terrain"||this._updateAttributions()},this._updateCompact=()=>{this._map.getCanvasContainer().offsetWidth<=640||this._compact?this._compact===!1?this._container.setAttribute("open",""):this._container.classList.contains("maplibregl-compact")||this._container.classList.contains("maplibregl-attrib-empty")||(this._container.setAttribute("open",""),this._container.classList.add("maplibregl-compact","maplibregl-compact-show")):(this._container.setAttribute("open",""),this._container.classList.contains("maplibregl-compact")&&this._container.classList.remove("maplibregl-compact","maplibregl-compact-show"))},this._updateCompactMinimize=()=>{this._container.classList.contains("maplibregl-compact")&&this._container.classList.contains("maplibregl-compact-show")&&this._container.classList.remove("maplibregl-compact-show")},this.options=w}getDefaultPosition(){return"bottom-right"}onAdd(w){return this._map=w,this._compact=this.options.compact,this._container=c.create("details","maplibregl-ctrl maplibregl-ctrl-attrib"),this._compactButton=c.create("summary","maplibregl-ctrl-attrib-button",this._container),this._compactButton.addEventListener("click",this._toggleAttribution),this._setElementTitle(this._compactButton,"ToggleAttribution"),this._innerContainer=c.create("div","maplibregl-ctrl-attrib-inner",this._container),this._updateAttributions(),this._updateCompact(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("terrain",this._updateData),this._map.on("resize",this._updateCompact),this._map.on("drag",this._updateCompactMinimize),this._container}onRemove(){c.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("terrain",this._updateData),this._map.off("resize",this._updateCompact),this._map.off("drag",this._updateCompactMinimize),this._map=void 0,this._compact=void 0,this._attribHTML=void 0}_setElementTitle(w,B){let Q=this._map._getUIString(`AttributionControl.${B}`);w.title=Q,w.setAttribute("aria-label",Q)}_updateAttributions(){if(!this._map.style)return;let w=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?w=w.concat(this.options.customAttribution.map(ee=>typeof ee!="string"?"":ee)):typeof this.options.customAttribution=="string"&&w.push(this.options.customAttribution)),this._map.style.stylesheet){let ee=this._map.style.stylesheet;this.styleOwner=ee.owner,this.styleId=ee.id}let B=this._map.style.sourceCaches;for(let ee in B){let le=B[ee];if(le.used||le.usedForTerrain){let qe=le.getSource();qe.attribution&&w.indexOf(qe.attribution)<0&&w.push(qe.attribution)}}w=w.filter(ee=>String(ee).trim()),w.sort((ee,le)=>ee.length-le.length),w=w.filter((ee,le)=>{for(let qe=le+1;qe<w.length;qe++)if(w[qe].indexOf(ee)>=0)return!1;return!0});let Q=w.join(" | ");Q!==this._attribHTML&&(this._attribHTML=Q,w.length?(this._innerContainer.innerHTML=Q,this._container.classList.remove("maplibregl-attrib-empty")):this._container.classList.add("maplibregl-attrib-empty"),this._updateCompact(),this._editLink=null)}}class Yn{constructor(w={}){this._updateCompact=()=>{let B=this._container.children;if(B.length){let Q=B[0];this._map.getCanvasContainer().offsetWidth<=640||this._compact?this._compact!==!1&&Q.classList.add("maplibregl-compact"):Q.classList.remove("maplibregl-compact")}},this.options=w}getDefaultPosition(){return"bottom-left"}onAdd(w){this._map=w,this._compact=this.options&&this.options.compact,this._container=c.create("div","maplibregl-ctrl");let B=c.create("a","maplibregl-ctrl-logo");return B.target="_blank",B.rel="noopener nofollow",B.href="https://maplibre.org/",B.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),B.setAttribute("rel","noopener nofollow"),this._container.appendChild(B),this._container.style.display="block",this._map.on("resize",this._updateCompact),this._updateCompact(),this._container}onRemove(){c.remove(this._container),this._map.off("resize",this._updateCompact),this._map=void 0,this._compact=void 0}}class Dn{constructor(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1}add(w){let B=++this._id;return this._queue.push({callback:w,id:B,cancelled:!1}),B}remove(w){let B=this._currentlyRunning,Q=B?this._queue.concat(B):this._queue;for(let ee of Q)if(ee.id===w)return void(ee.cancelled=!0)}run(w=0){if(this._currentlyRunning)throw new Error("Attempting to run(), but is already running.");let B=this._currentlyRunning=this._queue;this._queue=[];for(let Q of B)if(!Q.cancelled&&(Q.callback(w),this._cleared))break;this._cleared=!1,this._currentlyRunning=!1}clear(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]}}var Ka=a.Y([{name:"a_pos3d",type:"Int16",components:3}]);class bo extends a.E{constructor(w){super(),this.sourceCache=w,this._tiles={},this._renderableTilesKeys=[],this._sourceTileCache={},this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.deltaZoom=1,w.usedForTerrain=!0,w.tileSize=this.tileSize*2**this.deltaZoom}destruct(){this.sourceCache.usedForTerrain=!1,this.sourceCache.tileSize=null}update(w,B){this.sourceCache.update(w,B),this._renderableTilesKeys=[];let Q={};for(let ee of w.coveringTiles({tileSize:this.tileSize,minzoom:this.minzoom,maxzoom:this.maxzoom,reparseOverscaled:!1,terrain:B}))Q[ee.key]=!0,this._renderableTilesKeys.push(ee.key),this._tiles[ee.key]||(ee.posMatrix=new Float64Array(16),a.aP(ee.posMatrix,0,a.X,0,a.X,0,1),this._tiles[ee.key]=new Vt(ee,this.tileSize));for(let ee in this._tiles)Q[ee]||delete this._tiles[ee]}freeRtt(w){for(let B in this._tiles){let Q=this._tiles[B];(!w||Q.tileID.equals(w)||Q.tileID.isChildOf(w)||w.isChildOf(Q.tileID))&&(Q.rtt=[])}}getRenderableTiles(){return this._renderableTilesKeys.map(w=>this.getTileByID(w))}getTileByID(w){return this._tiles[w]}getTerrainCoords(w){let B={};for(let Q of this._renderableTilesKeys){let ee=this._tiles[Q].tileID;if(ee.canonical.equals(w.canonical)){let le=w.clone();le.posMatrix=new Float64Array(16),a.aP(le.posMatrix,0,a.X,0,a.X,0,1),B[Q]=le}else if(ee.canonical.isChildOf(w.canonical)){let le=w.clone();le.posMatrix=new Float64Array(16);let qe=ee.canonical.z-w.canonical.z,Xe=ee.canonical.x-(ee.canonical.x>>qe<<qe),ot=ee.canonical.y-(ee.canonical.y>>qe<<qe),Tt=a.X>>qe;a.aP(le.posMatrix,0,Tt,0,Tt,0,1),a.J(le.posMatrix,le.posMatrix,[-Xe*Tt,-ot*Tt,0]),B[Q]=le}else if(w.canonical.isChildOf(ee.canonical)){let le=w.clone();le.posMatrix=new Float64Array(16);let qe=w.canonical.z-ee.canonical.z,Xe=w.canonical.x-(w.canonical.x>>qe<<qe),ot=w.canonical.y-(w.canonical.y>>qe<<qe),Tt=a.X>>qe;a.aP(le.posMatrix,0,a.X,0,a.X,0,1),a.J(le.posMatrix,le.posMatrix,[Xe*Tt,ot*Tt,0]),a.K(le.posMatrix,le.posMatrix,[1/2**qe,1/2**qe,0]),B[Q]=le}}return B}getSourceTile(w,B){let Q=this.sourceCache._source,ee=w.overscaledZ-this.deltaZoom;if(ee>Q.maxzoom&&(ee=Q.maxzoom),ee<Q.minzoom)return null;this._sourceTileCache[w.key]||(this._sourceTileCache[w.key]=w.scaledTo(ee).key);let le=this.sourceCache.getTileByID(this._sourceTileCache[w.key]);if((!le||!le.dem)&&B)for(;ee>=Q.minzoom&&(!le||!le.dem);)le=this.sourceCache.getTileByID(w.scaledTo(ee--).key);return le}tilesAfterTime(w=Date.now()){return Object.values(this._tiles).filter(B=>B.timeAdded>=w)}}class Xo{constructor(w,B,Q){this.painter=w,this.sourceCache=new bo(B),this.options=Q,this.exaggeration=typeof Q.exaggeration=="number"?Q.exaggeration:1,this.qualityFactor=2,this.meshSize=128,this._demMatrixCache={},this.coordsIndex=[],this._coordsTextureSize=1024}getDEMElevation(w,B,Q,ee=a.X){var le;if(!(B>=0&&B<ee&&Q>=0&&Q<ee))return 0;let qe=this.getTerrainData(w),Xe=(le=qe.tile)===null||le===void 0?void 0:le.dem;if(!Xe)return 0;let ot=function(ve,be,De){var Be=be[0],et=be[1];return ve[0]=De[0]*Be+De[4]*et+De[12],ve[1]=De[1]*Be+De[5]*et+De[13],ve}([],[B/ee*a.X,Q/ee*a.X],qe.u_terrain_matrix),Tt=[ot[0]*Xe.dim,ot[1]*Xe.dim],Yt=Math.floor(Tt[0]),Kt=Math.floor(Tt[1]),xr=Tt[0]-Yt,Ir=Tt[1]-Kt;return Xe.get(Yt,Kt)*(1-xr)*(1-Ir)+Xe.get(Yt+1,Kt)*xr*(1-Ir)+Xe.get(Yt,Kt+1)*(1-xr)*Ir+Xe.get(Yt+1,Kt+1)*xr*Ir}getElevationForLngLatZoom(w,B){if(!a.bb(B,w.wrap()))return 0;let{tileID:Q,mercatorX:ee,mercatorY:le}=this._getOverscaledTileIDFromLngLatZoom(w,B);return this.getElevation(Q,ee%a.X,le%a.X,a.X)}getElevation(w,B,Q,ee=a.X){return this.getDEMElevation(w,B,Q,ee)*this.exaggeration}getTerrainData(w){if(!this._emptyDemTexture){let ee=this.painter.context,le=new a.R({width:1,height:1},new Uint8Array(4));this._emptyDepthTexture=new g(ee,le,ee.gl.RGBA,{premultiply:!1}),this._emptyDemUnpack=[0,0,0,0],this._emptyDemTexture=new g(ee,new a.R({width:1,height:1}),ee.gl.RGBA,{premultiply:!1}),this._emptyDemTexture.bind(ee.gl.NEAREST,ee.gl.CLAMP_TO_EDGE),this._emptyDemMatrix=a.an([])}let B=this.sourceCache.getSourceTile(w,!0);if(B&&B.dem&&(!B.demTexture||B.needsTerrainPrepare)){let ee=this.painter.context;B.demTexture=this.painter.getTileTexture(B.dem.stride),B.demTexture?B.demTexture.update(B.dem.getPixels(),{premultiply:!1}):B.demTexture=new g(ee,B.dem.getPixels(),ee.gl.RGBA,{premultiply:!1}),B.demTexture.bind(ee.gl.NEAREST,ee.gl.CLAMP_TO_EDGE),B.needsTerrainPrepare=!1}let Q=B&&B+B.tileID.key+w.key;if(Q&&!this._demMatrixCache[Q]){let ee=this.sourceCache.sourceCache._source.maxzoom,le=w.canonical.z-B.tileID.canonical.z;w.overscaledZ>w.canonical.z&&(w.canonical.z>=ee?le=w.canonical.z-ee:a.w("cannot calculate elevation if elevation maxzoom > source.maxzoom"));let qe=w.canonical.x-(w.canonical.x>>le<<le),Xe=w.canonical.y-(w.canonical.y>>le<<le),ot=a.bc(new Float64Array(16),[1/(a.X<<le),1/(a.X<<le),0]);a.J(ot,ot,[qe*a.X,Xe*a.X,0]),this._demMatrixCache[w.key]={matrix:ot,coord:w}}return{u_depth:2,u_terrain:3,u_terrain_dim:B&&B.dem&&B.dem.dim||1,u_terrain_matrix:Q?this._demMatrixCache[w.key].matrix:this._emptyDemMatrix,u_terrain_unpack:B&&B.dem&&B.dem.getUnpackVector()||this._emptyDemUnpack,u_terrain_exaggeration:this.exaggeration,texture:(B&&B.demTexture||this._emptyDemTexture).texture,depthTexture:(this._fboDepthTexture||this._emptyDepthTexture).texture,tile:B}}getFramebuffer(w){let B=this.painter,Q=B.width/devicePixelRatio,ee=B.height/devicePixelRatio;return!this._fbo||this._fbo.width===Q&&this._fbo.height===ee||(this._fbo.destroy(),this._fboCoordsTexture.destroy(),this._fboDepthTexture.destroy(),delete this._fbo,delete this._fboDepthTexture,delete this._fboCoordsTexture),this._fboCoordsTexture||(this._fboCoordsTexture=new g(B.context,{width:Q,height:ee,data:null},B.context.gl.RGBA,{premultiply:!1}),this._fboCoordsTexture.bind(B.context.gl.NEAREST,B.context.gl.CLAMP_TO_EDGE)),this._fboDepthTexture||(this._fboDepthTexture=new g(B.context,{width:Q,height:ee,data:null},B.context.gl.RGBA,{premultiply:!1}),this._fboDepthTexture.bind(B.context.gl.NEAREST,B.context.gl.CLAMP_TO_EDGE)),this._fbo||(this._fbo=B.context.createFramebuffer(Q,ee,!0,!1),this._fbo.depthAttachment.set(B.context.createRenderbuffer(B.context.gl.DEPTH_COMPONENT16,Q,ee))),this._fbo.colorAttachment.set(w==="coords"?this._fboCoordsTexture.texture:this._fboDepthTexture.texture),this._fbo}getCoordsTexture(){let w=this.painter.context;if(this._coordsTexture)return this._coordsTexture;let B=new Uint8Array(this._coordsTextureSize*this._coordsTextureSize*4);for(let le=0,qe=0;le<this._coordsTextureSize;le++)for(let Xe=0;Xe<this._coordsTextureSize;Xe++,qe+=4)B[qe+0]=255&Xe,B[qe+1]=255&le,B[qe+2]=Xe>>8<<4|le>>8,B[qe+3]=0;let Q=new a.R({width:this._coordsTextureSize,height:this._coordsTextureSize},new Uint8Array(B.buffer)),ee=new g(w,Q,w.gl.RGBA,{premultiply:!1});return ee.bind(w.gl.NEAREST,w.gl.CLAMP_TO_EDGE),this._coordsTexture=ee,ee}pointCoordinate(w){this.painter.maybeDrawDepthAndCoords(!0);let B=new Uint8Array(4),Q=this.painter.context,ee=Q.gl,le=Math.round(w.x*this.painter.pixelRatio/devicePixelRatio),qe=Math.round(w.y*this.painter.pixelRatio/devicePixelRatio),Xe=Math.round(this.painter.height/devicePixelRatio);Q.bindFramebuffer.set(this.getFramebuffer("coords").framebuffer),ee.readPixels(le,Xe-qe-1,1,1,ee.RGBA,ee.UNSIGNED_BYTE,B),Q.bindFramebuffer.set(null);let ot=B[0]+(B[2]>>4<<8),Tt=B[1]+((15&B[2])<<8),Yt=this.coordsIndex[255-B[3]],Kt=Yt&&this.sourceCache.getTileByID(Yt);if(!Kt)return null;let xr=this._coordsTextureSize,Ir=(1<<Kt.tileID.canonical.z)*xr;return new a.Z((Kt.tileID.canonical.x*xr+ot)/Ir+Kt.tileID.wrap,(Kt.tileID.canonical.y*xr+Tt)/Ir,this.getElevation(Kt.tileID,ot,Tt,xr))}depthAtPoint(w){let B=new Uint8Array(4),Q=this.painter.context,ee=Q.gl;return Q.bindFramebuffer.set(this.getFramebuffer("depth").framebuffer),ee.readPixels(w.x,this.painter.height/devicePixelRatio-w.y-1,1,1,ee.RGBA,ee.UNSIGNED_BYTE,B),Q.bindFramebuffer.set(null),(B[0]/16777216+B[1]/65536+B[2]/256+B[3])/256}getTerrainMesh(){if(this._mesh)return this._mesh;let w=this.painter.context,B=new a.bd,Q=new a.aY,ee=this.meshSize,le=a.X/ee,qe=ee*ee;for(let Kt=0;Kt<=ee;Kt++)for(let xr=0;xr<=ee;xr++)B.emplaceBack(xr*le,Kt*le,0);for(let Kt=0;Kt<qe;Kt+=ee+1)for(let xr=0;xr<ee;xr++)Q.emplaceBack(xr+Kt,ee+xr+Kt+1,ee+xr+Kt+2),Q.emplaceBack(xr+Kt,ee+xr+Kt+2,xr+Kt+1);let Xe=B.length,ot=Xe+2*(ee+1);for(let Kt of[0,1])for(let xr=0;xr<=ee;xr++)for(let Ir of[0,1])B.emplaceBack(xr*le,Kt*a.X,Ir);for(let Kt=0;Kt<2*ee;Kt+=2)Q.emplaceBack(ot+Kt,ot+Kt+1,ot+Kt+3),Q.emplaceBack(ot+Kt,ot+Kt+3,ot+Kt+2),Q.emplaceBack(Xe+Kt,Xe+Kt+3,Xe+Kt+1),Q.emplaceBack(Xe+Kt,Xe+Kt+2,Xe+Kt+3);let Tt=B.length,Yt=Tt+2*(ee+1);for(let Kt of[0,1])for(let xr=0;xr<=ee;xr++)for(let Ir of[0,1])B.emplaceBack(Kt*a.X,xr*le,Ir);for(let Kt=0;Kt<2*ee;Kt+=2)Q.emplaceBack(Tt+Kt,Tt+Kt+1,Tt+Kt+3),Q.emplaceBack(Tt+Kt,Tt+Kt+3,Tt+Kt+2),Q.emplaceBack(Yt+Kt,Yt+Kt+3,Yt+Kt+1),Q.emplaceBack(Yt+Kt,Yt+Kt+2,Yt+Kt+3);return this._mesh=new Eu(w.createVertexBuffer(B,Ka.members),w.createIndexBuffer(Q),a.a0.simpleSegment(0,0,B.length,Q.length)),this._mesh}getMeshFrameDelta(w){return 2*Math.PI*a.be/Math.pow(2,w)/5}getMinTileElevationForLngLatZoom(w,B){var Q;let{tileID:ee}=this._getOverscaledTileIDFromLngLatZoom(w,B);return(Q=this.getMinMaxElevation(ee).minElevation)!==null&&Q!==void 0?Q:0}getMinMaxElevation(w){let B=this.getTerrainData(w).tile,Q={minElevation:null,maxElevation:null};return B&&B.dem&&(Q.minElevation=B.dem.min*this.exaggeration,Q.maxElevation=B.dem.max*this.exaggeration),Q}_getOverscaledTileIDFromLngLatZoom(w,B){let Q=a.Z.fromLngLat(w.wrap()),ee=(1<<B)*a.X,le=Q.x*ee,qe=Q.y*ee,Xe=Math.floor(le/a.X),ot=Math.floor(qe/a.X);return{tileID:new a.S(B,0,B,Xe,ot),mercatorX:le,mercatorY:qe}}}class Ss{constructor(w,B,Q){this._context=w,this._size=B,this._tileSize=Q,this._objects=[],this._recentlyUsed=[],this._stamp=0}destruct(){for(let w of this._objects)w.texture.destroy(),w.fbo.destroy()}_createObject(w){let B=this._context.createFramebuffer(this._tileSize,this._tileSize,!0,!0),Q=new g(this._context,{width:this._tileSize,height:this._tileSize,data:null},this._context.gl.RGBA);return Q.bind(this._context.gl.LINEAR,this._context.gl.CLAMP_TO_EDGE),B.depthAttachment.set(this._context.createRenderbuffer(this._context.gl.DEPTH_STENCIL,this._tileSize,this._tileSize)),B.colorAttachment.set(Q.texture),{id:w,fbo:B,texture:Q,stamp:-1,inUse:!1}}getObjectForId(w){return this._objects[w]}useObject(w){w.inUse=!0,this._recentlyUsed=this._recentlyUsed.filter(B=>w.id!==B),this._recentlyUsed.push(w.id)}stampObject(w){w.stamp=++this._stamp}getOrCreateFreeObject(){for(let B of this._recentlyUsed)if(!this._objects[B].inUse)return this._objects[B];if(this._objects.length>=this._size)throw new Error("No free RenderPool available, call freeAllObjects() required!");let w=this._createObject(this._objects.length);return this._objects.push(w),w}freeObject(w){w.inUse=!1}freeAllObjects(){for(let w of this._objects)this.freeObject(w)}isFull(){return!(this._objects.length<this._size)&&this._objects.some(w=>!w.inUse)===!1}}let as={background:!0,fill:!0,line:!0,raster:!0,hillshade:!0};class ws{constructor(w,B){this.painter=w,this.terrain=B,this.pool=new Ss(w.context,30,B.sourceCache.tileSize*B.qualityFactor)}destruct(){this.pool.destruct()}getTexture(w){return this.pool.getObjectForId(w.rtt[this._stacks.length-1].id).texture}prepareForRender(w,B){this._stacks=[],this._prevType=null,this._rttTiles=[],this._renderableTiles=this.terrain.sourceCache.getRenderableTiles(),this._renderableLayerIds=w._order.filter(Q=>!w._layers[Q].isHidden(B)),this._coordsDescendingInv={};for(let Q in w.sourceCaches){this._coordsDescendingInv[Q]={};let ee=w.sourceCaches[Q].getVisibleCoordinates();for(let le of ee){let qe=this.terrain.sourceCache.getTerrainCoords(le);for(let Xe in qe)this._coordsDescendingInv[Q][Xe]||(this._coordsDescendingInv[Q][Xe]=[]),this._coordsDescendingInv[Q][Xe].push(qe[Xe])}}this._coordsDescendingInvStr={};for(let Q of w._order){let ee=w._layers[Q],le=ee.source;if(as[ee.type]&&!this._coordsDescendingInvStr[le]){this._coordsDescendingInvStr[le]={};for(let qe in this._coordsDescendingInv[le])this._coordsDescendingInvStr[le][qe]=this._coordsDescendingInv[le][qe].map(Xe=>Xe.key).sort().join()}}for(let Q of this._renderableTiles)for(let ee in this._coordsDescendingInvStr){let le=this._coordsDescendingInvStr[ee][Q.tileID.key];le&&le!==Q.rttCoords[ee]&&(Q.rtt=[])}}renderLayer(w){if(w.isHidden(this.painter.transform.zoom))return!1;let B=w.type,Q=this.painter,ee=this._renderableLayerIds[this._renderableLayerIds.length-1]===w.id;if(as[B]&&(this._prevType&&as[this._prevType]||this._stacks.push([]),this._prevType=B,this._stacks[this._stacks.length-1].push(w.id),!ee))return!0;if(as[this._prevType]||as[B]&&ee){this._prevType=B;let le=this._stacks.length-1,qe=this._stacks[le]||[];for(let Xe of this._renderableTiles){if(this.pool.isFull()&&(js(this.painter,this.terrain,this._rttTiles),this._rttTiles=[],this.pool.freeAllObjects()),this._rttTiles.push(Xe),Xe.rtt[le]){let Tt=this.pool.getObjectForId(Xe.rtt[le].id);if(Tt.stamp===Xe.rtt[le].stamp){this.pool.useObject(Tt);continue}}let ot=this.pool.getOrCreateFreeObject();this.pool.useObject(ot),this.pool.stampObject(ot),Xe.rtt[le]={id:ot.id,stamp:ot.stamp},Q.context.bindFramebuffer.set(ot.fbo.framebuffer),Q.context.clear({color:a.aM.transparent,stencil:0}),Q.currentStencilSource=void 0;for(let Tt=0;Tt<qe.length;Tt++){let Yt=Q.style._layers[qe[Tt]],Kt=Yt.source?this._coordsDescendingInv[Yt.source][Xe.tileID.key]:[Xe.tileID];Q.context.viewport.set([0,0,ot.fbo.width,ot.fbo.height]),Q._renderTileClippingMasks(Yt,Kt),Q.renderLayer(Q,Q.style.sourceCaches[Yt.source],Yt,Kt),Yt.source&&(Xe.rttCoords[Yt.source]=this._coordsDescendingInvStr[Yt.source][Xe.tileID.key])}}return js(this.painter,this.terrain,this._rttTiles),this._rttTiles=[],this.pool.freeAllObjects(),as[B]}return!1}}let Ho={"AttributionControl.ToggleAttribution":"Toggle attribution","AttributionControl.MapFeedback":"Map feedback","FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"MapLibre logo","Map.Title":"Map","Marker.Title":"Map marker","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","Popup.Close":"Close popup","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm","TerrainControl.Enable":"Enable terrain","TerrainControl.Disable":"Disable terrain","CooperativeGesturesHandler.WindowsHelpText":"Use Ctrl + scroll to zoom the map","CooperativeGesturesHandler.MacHelpText":"Use \u2318 + scroll to zoom the map","CooperativeGesturesHandler.MobileHelpText":"Use two fingers to move the map"},ml=o,Ws={hash:!1,interactive:!0,bearingSnap:7,attributionControl:La,maplibreLogo:!1,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,refreshExpiredTiles:!0,scrollZoom:!0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,cooperativeGestures:!1,trackResize:!0,center:[0,0],zoom:0,bearing:0,pitch:0,renderWorldCopies:!0,maxTileCacheSize:null,maxTileCacheZoomLevels:a.a.MAX_TILE_CACHE_ZOOM_LEVELS,transformRequest:null,transformCameraUpdate:null,fadeDuration:300,crossSourceCollisions:!0,clickTolerance:3,localIdeographFontFamily:"sans-serif",pitchWithRotate:!0,validateStyle:!0,maxCanvasSize:[4096,4096],cancelPendingTileRequestsWhileZooming:!0},Ls=ue=>{ue.touchstart=ue.dragStart,ue.touchmoveWindow=ue.dragMove,ue.touchend=ue.dragEnd},va={showCompass:!0,showZoom:!0,visualizePitch:!1};class no{constructor(w,B,Q=!1){this.mousedown=qe=>{this.startMouse(a.e({},qe,{ctrlKey:!0,preventDefault:()=>qe.preventDefault()}),c.mousePos(this.element,qe)),c.addEventListener(window,"mousemove",this.mousemove),c.addEventListener(window,"mouseup",this.mouseup)},this.mousemove=qe=>{this.moveMouse(qe,c.mousePos(this.element,qe))},this.mouseup=qe=>{this.mouseRotate.dragEnd(qe),this.mousePitch&&this.mousePitch.dragEnd(qe),this.offTemp()},this.touchstart=qe=>{qe.targetTouches.length!==1?this.reset():(this._startPos=this._lastPos=c.touchPos(this.element,qe.targetTouches)[0],this.startTouch(qe,this._startPos),c.addEventListener(window,"touchmove",this.touchmove,{passive:!1}),c.addEventListener(window,"touchend",this.touchend))},this.touchmove=qe=>{qe.targetTouches.length!==1?this.reset():(this._lastPos=c.touchPos(this.element,qe.targetTouches)[0],this.moveTouch(qe,this._lastPos))},this.touchend=qe=>{qe.targetTouches.length===0&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),delete this._startPos,delete this._lastPos,this.offTemp()},this.reset=()=>{this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),this.touchRotate.reset(),this.touchPitch&&this.touchPitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()},this._clickTolerance=10;let ee=w.dragRotate._mouseRotate.getClickTolerance(),le=w.dragRotate._mousePitch.getClickTolerance();this.element=B,this.mouseRotate=Wl({clickTolerance:ee,enable:!0}),this.touchRotate=(({enable:qe,clickTolerance:Xe,bearingDegreesPerPixelMoved:ot=.8})=>{let Tt=new uf;return new Wu({clickTolerance:Xe,move:(Yt,Kt)=>({bearingDelta:(Kt.x-Yt.x)*ot}),moveStateManager:Tt,enable:qe,assignEvents:Ls})})({clickTolerance:ee,enable:!0}),this.map=w,Q&&(this.mousePitch=ah({clickTolerance:le,enable:!0}),this.touchPitch=(({enable:qe,clickTolerance:Xe,pitchDegreesPerPixelMoved:ot=-.5})=>{let Tt=new uf;return new Wu({clickTolerance:Xe,move:(Yt,Kt)=>({pitchDelta:(Kt.y-Yt.y)*ot}),moveStateManager:Tt,enable:qe,assignEvents:Ls})})({clickTolerance:le,enable:!0})),c.addEventListener(B,"mousedown",this.mousedown),c.addEventListener(B,"touchstart",this.touchstart,{passive:!1}),c.addEventListener(B,"touchcancel",this.reset)}startMouse(w,B){this.mouseRotate.dragStart(w,B),this.mousePitch&&this.mousePitch.dragStart(w,B),c.disableDrag()}startTouch(w,B){this.touchRotate.dragStart(w,B),this.touchPitch&&this.touchPitch.dragStart(w,B),c.disableDrag()}moveMouse(w,B){let Q=this.map,{bearingDelta:ee}=this.mouseRotate.dragMove(w,B)||{};if(ee&&Q.setBearing(Q.getBearing()+ee),this.mousePitch){let{pitchDelta:le}=this.mousePitch.dragMove(w,B)||{};le&&Q.setPitch(Q.getPitch()+le)}}moveTouch(w,B){let Q=this.map,{bearingDelta:ee}=this.touchRotate.dragMove(w,B)||{};if(ee&&Q.setBearing(Q.getBearing()+ee),this.touchPitch){let{pitchDelta:le}=this.touchPitch.dragMove(w,B)||{};le&&Q.setPitch(Q.getPitch()+le)}}off(){let w=this.element;c.removeEventListener(w,"mousedown",this.mousedown),c.removeEventListener(w,"touchstart",this.touchstart,{passive:!1}),c.removeEventListener(window,"touchmove",this.touchmove,{passive:!1}),c.removeEventListener(window,"touchend",this.touchend),c.removeEventListener(w,"touchcancel",this.reset),this.offTemp()}offTemp(){c.enableDrag(),c.removeEventListener(window,"mousemove",this.mousemove),c.removeEventListener(window,"mouseup",this.mouseup),c.removeEventListener(window,"touchmove",this.touchmove,{passive:!1}),c.removeEventListener(window,"touchend",this.touchend)}}let ys;function rs(ue,w,B){let Q=new a.N(ue.lng,ue.lat);if(ue=new a.N(ue.lng,ue.lat),w){let ee=new a.N(ue.lng-360,ue.lat),le=new a.N(ue.lng+360,ue.lat),qe=B.locationPoint(ue).distSqr(w);B.locationPoint(ee).distSqr(w)<qe?ue=ee:B.locationPoint(le).distSqr(w)<qe&&(ue=le)}for(;Math.abs(ue.lng-B.center.lng)>180;){let ee=B.locationPoint(ue);if(ee.x>=0&&ee.y>=0&&ee.x<=B.width&&ee.y<=B.height)break;ue.lng>B.center.lng?ue.lng-=360:ue.lng+=360}return ue.lng!==Q.lng&&B.locationPoint(ue).y>B.height/2-B.getHorizon()?ue:Q}let $l={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Cu(ue,w,B){let Q=ue.classList;for(let ee in $l)Q.remove(`maplibregl-${B}-anchor-${ee}`);Q.add(`maplibregl-${B}-anchor-${w}`)}class Yu extends a.E{constructor(w){if(super(),this._onKeyPress=B=>{let Q=B.code,ee=B.charCode||B.keyCode;Q!=="Space"&&Q!=="Enter"&&ee!==32&&ee!==13||this.togglePopup()},this._onMapClick=B=>{let Q=B.originalEvent.target,ee=this._element;this._popup&&(Q===ee||ee.contains(Q))&&this.togglePopup()},this._update=B=>{var Q;if(!this._map)return;let ee=this._map.loaded()&&!this._map.isMoving();((B==null?void 0:B.type)==="terrain"||(B==null?void 0:B.type)==="render"&&!ee)&&this._map.once("render",this._update),this._lngLat=this._map.transform.renderWorldCopies?rs(this._lngLat,this._flatPos,this._map.transform):(Q=this._lngLat)===null||Q===void 0?void 0:Q.wrap(),this._flatPos=this._pos=this._map.project(this._lngLat)._add(this._offset),this._map.terrain&&(this._flatPos=this._map.transform.locationPoint(this._lngLat)._add(this._offset));let le="";this._rotationAlignment==="viewport"||this._rotationAlignment==="auto"?le=`rotateZ(${this._rotation}deg)`:this._rotationAlignment==="map"&&(le=`rotateZ(${this._rotation-this._map.getBearing()}deg)`);let qe="";this._pitchAlignment==="viewport"||this._pitchAlignment==="auto"?qe="rotateX(0deg)":this._pitchAlignment==="map"&&(qe=`rotateX(${this._map.getPitch()}deg)`),this._subpixelPositioning||B&&B.type!=="moveend"||(this._pos=this._pos.round()),c.setTransform(this._element,`${$l[this._anchor]} 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a.k("dragend")),this._state="inactive"},this._addDragHandler=B=>{this._element.contains(B.originalEvent.target)&&(B.preventDefault(),this._positionDelta=B.point.sub(this._pos).add(this._offset),this._pointerdownPos=B.point,this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},this._anchor=w&&w.anchor||"center",this._color=w&&w.color||"#3FB1CE",this._scale=w&&w.scale||1,this._draggable=w&&w.draggable||!1,this._clickTolerance=w&&w.clickTolerance||0,this._subpixelPositioning=w&&w.subpixelPositioning||!1,this._isDragging=!1,this._state="inactive",this._rotation=w&&w.rotation||0,this._rotationAlignment=w&&w.rotationAlignment||"auto",this._pitchAlignment=w&&w.pitchAlignment&&w.pitchAlignment!=="auto"?w.pitchAlignment:this._rotationAlignment,this.setOpacity(),this.setOpacity(w==null?void 0:w.opacity,w==null?void 0:w.opacityWhenCovered),w&&w.element)this._element=w.element,this._offset=a.P.convert(w&&w.offset||[0,0]);else{this._defaultMarker=!0,this._element=c.create("div");let B=c.createNS("http://www.w3.org/2000/svg","svg"),Q=41,ee=27;B.setAttributeNS(null,"display","block"),B.setAttributeNS(null,"height",`${Q}px`),B.setAttributeNS(null,"width",`${ee}px`),B.setAttributeNS(null,"viewBox",`0 0 ${ee} ${Q}`);let le=c.createNS("http://www.w3.org/2000/svg","g");le.setAttributeNS(null,"stroke","none"),le.setAttributeNS(null,"stroke-width","1"),le.setAttributeNS(null,"fill","none"),le.setAttributeNS(null,"fill-rule","evenodd");let qe=c.createNS("http://www.w3.org/2000/svg","g");qe.setAttributeNS(null,"fill-rule","nonzero");let Xe=c.createNS("http://www.w3.org/2000/svg","g");Xe.setAttributeNS(null,"transform","translate(3.0, 29.0)"),Xe.setAttributeNS(null,"fill","#000000");let ot=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];for(let Be of ot){let et=c.createNS("http://www.w3.org/2000/svg","ellipse");et.setAttributeNS(null,"opacity","0.04"),et.setAttributeNS(null,"cx","10.5"),et.setAttributeNS(null,"cy","5.80029008"),et.setAttributeNS(null,"rx",Be.rx),et.setAttributeNS(null,"ry",Be.ry),Xe.appendChild(et)}let Tt=c.createNS("http://www.w3.org/2000/svg","g");Tt.setAttributeNS(null,"fill",this._color);let Yt=c.createNS("http://www.w3.org/2000/svg","path");Yt.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),Tt.appendChild(Yt);let Kt=c.createNS("http://www.w3.org/2000/svg","g");Kt.setAttributeNS(null,"opacity","0.25"),Kt.setAttributeNS(null,"fill","#000000");let xr=c.createNS("http://www.w3.org/2000/svg","path");xr.setAttributeNS(null,"d","M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z"),Kt.appendChild(xr);let Ir=c.createNS("http://www.w3.org/2000/svg","g");Ir.setAttributeNS(null,"transform","translate(6.0, 7.0)"),Ir.setAttributeNS(null,"fill","#FFFFFF");let ve=c.createNS("http://www.w3.org/2000/svg","g");ve.setAttributeNS(null,"transform","translate(8.0, 8.0)");let be=c.createNS("http://www.w3.org/2000/svg","circle");be.setAttributeNS(null,"fill","#000000"),be.setAttributeNS(null,"opacity","0.25"),be.setAttributeNS(null,"cx","5.5"),be.setAttributeNS(null,"cy","5.5"),be.setAttributeNS(null,"r","5.4999962");let De=c.createNS("http://www.w3.org/2000/svg","circle");De.setAttributeNS(null,"fill","#FFFFFF"),De.setAttributeNS(null,"cx","5.5"),De.setAttributeNS(null,"cy","5.5"),De.setAttributeNS(null,"r","5.4999962"),ve.appendChild(be),ve.appendChild(De),qe.appendChild(Xe),qe.appendChild(Tt),qe.appendChild(Kt),qe.appendChild(Ir),qe.appendChild(ve),B.appendChild(qe),B.setAttributeNS(null,"height",Q*this._scale+"px"),B.setAttributeNS(null,"width",ee*this._scale+"px"),this._element.appendChild(B),this._offset=a.P.convert(w&&w.offset||[0,-14])}if(this._element.classList.add("maplibregl-marker"),this._element.addEventListener("dragstart",B=>{B.preventDefault()}),this._element.addEventListener("mousedown",B=>{B.preventDefault()}),Cu(this._element,this._anchor,"marker"),w&&w.className)for(let B of w.className.split(" "))this._element.classList.add(B);this._popup=null}addTo(w){return this.remove(),this._map=w,this._element.setAttribute("aria-label",w._getUIString("Marker.Title")),w.getCanvasContainer().appendChild(this._element),w.on("move",this._update),w.on("moveend",this._update),w.on("terrain",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this}remove(){return this._opacityTimeout&&(clearTimeout(this._opacityTimeout),delete this._opacityTimeout),this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("terrain",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),c.remove(this._element),this._popup&&this._popup.remove(),this}getLngLat(){return this._lngLat}setLngLat(w){return this._lngLat=a.N.convert(w),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this}getElement(){return this._element}setPopup(w){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),w){if(!("offset"in w.options)){let ee=Math.abs(13.5)/Math.SQRT2;w.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[ee,-1*(38.1-13.5+ee)],"bottom-right":[-ee,-1*(38.1-13.5+ee)],left:[13.5,-1*(38.1-13.5)],right:[-13.5,-1*(38.1-13.5)]}:this._offset}this._popup=w,this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this}setSubpixelPositioning(w){return this._subpixelPositioning=w,this}getPopup(){return this._popup}togglePopup(){let w=this._popup;return this._element.style.opacity===this._opacityWhenCovered?this:w?(w.isOpen()?w.remove():(w.setLngLat(this._lngLat),w.addTo(this._map)),this):this}_updateOpacity(w=!1){var B,Q;if(!(!((B=this._map)===null||B===void 0)&&B.terrain))return void(this._element.style.opacity!==this._opacity&&(this._element.style.opacity=this._opacity));if(w)this._opacityTimeout=null;else{if(this._opacityTimeout)return;this._opacityTimeout=setTimeout(()=>{this._opacityTimeout=null},100)}let ee=this._map,le=ee.terrain.depthAtPoint(this._pos),qe=ee.terrain.getElevationForLngLatZoom(this._lngLat,ee.transform.tileZoom);if(ee.transform.lngLatToCameraDepth(this._lngLat,qe)-le<.006)return void(this._element.style.opacity=this._opacity);let Xe=-this._offset.y/ee.transform._pixelPerMeter,ot=Math.sin(ee.getPitch()*Math.PI/180)*Xe,Tt=ee.terrain.depthAtPoint(new a.P(this._pos.x,this._pos.y-this._offset.y)),Yt=ee.transform.lngLatToCameraDepth(this._lngLat,qe+ot)-Tt>.006;!((Q=this._popup)===null||Q===void 0)&&Q.isOpen()&&Yt&&this._popup.remove(),this._element.style.opacity=Yt?this._opacityWhenCovered:this._opacity}getOffset(){return this._offset}setOffset(w){return this._offset=a.P.convert(w),this._update(),this}addClassName(w){this._element.classList.add(w)}removeClassName(w){this._element.classList.remove(w)}toggleClassName(w){return this._element.classList.toggle(w)}setDraggable(w){return this._draggable=!!w,this._map&&(w?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this}isDraggable(){return this._draggable}setRotation(w){return this._rotation=w||0,this._update(),this}getRotation(){return this._rotation}setRotationAlignment(w){return this._rotationAlignment=w||"auto",this._update(),this}getRotationAlignment(){return this._rotationAlignment}setPitchAlignment(w){return this._pitchAlignment=w&&w!=="auto"?w:this._rotationAlignment,this._update(),this}getPitchAlignment(){return this._pitchAlignment}setOpacity(w,B){return w===void 0&&B===void 0&&(this._opacity="1",this._opacityWhenCovered="0.2"),w!==void 0&&(this._opacity=w),B!==void 0&&(this._opacityWhenCovered=B),this._map&&this._updateOpacity(!0),this}}let Nc={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},pu=0,Uc=!1,xu={maxWidth:100,unit:"metric"};function Ac(ue,w,B){let Q=B&&B.maxWidth||100,ee=ue._container.clientHeight/2,le=ue.unproject([0,ee]),qe=ue.unproject([Q,ee]),Xe=le.distanceTo(qe);if(B&&B.unit==="imperial"){let ot=3.2808*Xe;ot>5280?Ua(w,Q,ot/5280,ue._getUIString("ScaleControl.Miles")):Ua(w,Q,ot,ue._getUIString("ScaleControl.Feet"))}else B&&B.unit==="nautical"?Ua(w,Q,Xe/1852,ue._getUIString("ScaleControl.NauticalMiles")):Xe>=1e3?Ua(w,Q,Xe/1e3,ue._getUIString("ScaleControl.Kilometers")):Ua(w,Q,Xe,ue._getUIString("ScaleControl.Meters"))}function Ua(ue,w,B,Q){let ee=function(le){let qe=Math.pow(10,`${Math.floor(le)}`.length-1),Xe=le/qe;return Xe=Xe>=10?10:Xe>=5?5:Xe>=3?3:Xe>=2?2:Xe>=1?1:function(ot){let Tt=Math.pow(10,Math.ceil(-Math.log(ot)/Math.LN10));return Math.round(ot*Tt)/Tt}(Xe),qe*Xe}(B);ue.style.width=w*(ee/B)+"px",ue.innerHTML=`${ee}&nbsp;${Q}`}let oo={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:"",maxWidth:"240px",subpixelPositioning:!1},Vc=["a[href]","[tabindex]:not([tabindex='-1'])","[contenteditable]:not([contenteditable='false'])","button:not([disabled])","input:not([disabled])","select:not([disabled])","textarea:not([disabled])"].join(", ");function hc(ue){if(ue){if(typeof ue=="number"){let w=Math.round(Math.abs(ue)/Math.SQRT2);return{center:new a.P(0,0),top:new a.P(0,ue),"top-left":new a.P(w,w),"top-right":new a.P(-w,w),bottom:new a.P(0,-ue),"bottom-left":new a.P(w,-w),"bottom-right":new a.P(-w,-w),left:new a.P(ue,0),right:new a.P(-ue,0)}}if(ue instanceof a.P||Array.isArray(ue)){let w=a.P.convert(ue);return{center:w,top:w,"top-left":w,"top-right":w,bottom:w,"bottom-left":w,"bottom-right":w,left:w,right:w}}return{center:a.P.convert(ue.center||[0,0]),top:a.P.convert(ue.top||[0,0]),"top-left":a.P.convert(ue["top-left"]||[0,0]),"top-right":a.P.convert(ue["top-right"]||[0,0]),bottom:a.P.convert(ue.bottom||[0,0]),"bottom-left":a.P.convert(ue["bottom-left"]||[0,0]),"bottom-right":a.P.convert(ue["bottom-right"]||[0,0]),left:a.P.convert(ue.left||[0,0]),right:a.P.convert(ue.right||[0,0])}}return hc(new a.P(0,0))}let Ku=o;i.AJAXError=a.bh,i.Evented=a.E,i.LngLat=a.N,i.MercatorCoordinate=a.Z,i.Point=a.P,i.addProtocol=a.bi,i.config=a.a,i.removeProtocol=a.bj,i.AttributionControl=Na,i.BoxZoomHandler=ku,i.CanvasSource=Jt,i.CooperativeGesturesHandler=Qi,i.DoubleClickZoomHandler=ki,i.DragPanHandler=wn,i.DragRotateHandler=Nn,i.EdgeInsets=hu,i.FullscreenControl=class extends a.E{constructor(ue={}){super(),this._onFullscreenChange=()=>{var w;let B=window.document.fullscreenElement||window.document.mozFullScreenElement||window.document.webkitFullscreenElement||window.document.msFullscreenElement;for(;!((w=B==null?void 0:B.shadowRoot)===null||w===void 0)&&w.fullscreenElement;)B=B.shadowRoot.fullscreenElement;B===this._container!==this._fullscreen&&this._handleFullscreenChange()},this._onClickFullscreen=()=>{this._isFullscreen()?this._exitFullscreen():this._requestFullscreen()},this._fullscreen=!1,ue&&ue.container&&(ue.container instanceof HTMLElement?this._container=ue.container:a.w("Full screen control 'container' must be a DOM element.")),"onfullscreenchange"in document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in document&&(this._fullscreenchange="MSFullscreenChange")}onAdd(ue){return this._map=ue,this._container||(this._container=this._map.getContainer()),this._controlContainer=c.create("div","maplibregl-ctrl maplibregl-ctrl-group"),this._setupUI(),this._controlContainer}onRemove(){c.remove(this._controlContainer),this._map=null,window.document.removeEventListener(this._fullscreenchange,this._onFullscreenChange)}_setupUI(){let ue=this._fullscreenButton=c.create("button","maplibregl-ctrl-fullscreen",this._controlContainer);c.create("span","maplibregl-ctrl-icon",ue).setAttribute("aria-hidden","true"),ue.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),window.document.addEventListener(this._fullscreenchange,this._onFullscreenChange)}_updateTitle(){let ue=this._getTitle();this._fullscreenButton.setAttribute("aria-label",ue),this._fullscreenButton.title=ue}_getTitle(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")}_isFullscreen(){return this._fullscreen}_handleFullscreenChange(){this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle("maplibregl-ctrl-shrink"),this._fullscreenButton.classList.toggle("maplibregl-ctrl-fullscreen"),this._updateTitle(),this._fullscreen?(this.fire(new a.k("fullscreenstart")),this._prevCooperativeGesturesEnabled=this._map.cooperativeGestures.isEnabled(),this._map.cooperativeGestures.disable()):(this.fire(new a.k("fullscreenend")),this._prevCooperativeGesturesEnabled&&this._map.cooperativeGestures.enable())}_exitFullscreen(){window.document.exitFullscreen?window.document.exitFullscreen():window.document.mozCancelFullScreen?window.document.mozCancelFullScreen():window.document.msExitFullscreen?window.document.msExitFullscreen():window.document.webkitCancelFullScreen?window.document.webkitCancelFullScreen():this._togglePseudoFullScreen()}_requestFullscreen(){this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen?this._container.webkitRequestFullscreen():this._togglePseudoFullScreen()}_togglePseudoFullScreen(){this._container.classList.toggle("maplibregl-pseudo-fullscreen"),this._handleFullscreenChange(),this._map.resize()}},i.GeoJSONSource=st,i.GeolocateControl=class extends a.E{constructor(ue){super(),this._onSuccess=w=>{if(this._map){if(this._isOutOfMapMaxBounds(w))return this._setErrorState(),this.fire(new a.k("outofmaxbounds",w)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=w,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-background");break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}this.options.showUserLocation&&this._watchState!=="OFF"&&this._updateMarker(w),this.options.trackUserLocation&&this._watchState!=="ACTIVE_LOCK"||this._updateCamera(w),this.options.showUserLocation&&this._dotElement.classList.remove("maplibregl-user-location-dot-stale"),this.fire(new a.k("geolocate",w)),this._finish()}},this._updateCamera=w=>{let B=new a.N(w.coords.longitude,w.coords.latitude),Q=w.coords.accuracy,ee=this._map.getBearing(),le=a.e({bearing:ee},this.options.fitBoundsOptions),qe=ce.fromLngLat(B,Q);this._map.fitBounds(qe,le,{geolocateSource:!0})},this._updateMarker=w=>{if(w){let B=new a.N(w.coords.longitude,w.coords.latitude);this._accuracyCircleMarker.setLngLat(B).addTo(this._map),this._userLocationDotMarker.setLngLat(B).addTo(this._map),this._accuracy=w.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},this._onZoom=()=>{this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},this._onError=w=>{if(this._map){if(this.options.trackUserLocation)if(w.code===1){this._watchState="OFF",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;let B=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=B,this._geolocateButton.setAttribute("aria-label",B),this._geolocationWatchID!==void 0&&this._clearWatch()}else{if(w.code===3&&Uc)return;this._setErrorState()}this._watchState!=="OFF"&&this.options.showUserLocation&&this._dotElement.classList.add("maplibregl-user-location-dot-stale"),this.fire(new a.k("error",w)),this._finish()}},this._finish=()=>{this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},this._setupUI=()=>{this._map&&(this._container.addEventListener("contextmenu",w=>w.preventDefault()),this._geolocateButton=c.create("button","maplibregl-ctrl-geolocate",this._container),c.create("span","maplibregl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden","true"),this._geolocateButton.type="button",this._geolocateButton.disabled=!0)},this._finishSetupUI=w=>{if(this._map){if(w===!1){a.w("Geolocation support is not available so the GeolocateControl will be disabled.");let B=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=B,this._geolocateButton.setAttribute("aria-label",B)}else{let B=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.disabled=!1,this._geolocateButton.title=B,this._geolocateButton.setAttribute("aria-label",B)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=c.create("div","maplibregl-user-location-dot"),this._userLocationDotMarker=new Yu({element:this._dotElement}),this._circleElement=c.create("div","maplibregl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Yu({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",()=>this.trigger()),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",B=>{B.geolocateSource||this._watchState!=="ACTIVE_LOCK"||B.originalEvent&&B.originalEvent.type==="resize"||(this._watchState="BACKGROUND",this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active"),this.fire(new a.k("trackuserlocationend")),this.fire(new a.k("userlocationlostfocus")))})}},this.options=a.e({},Nc,ue)}onAdd(ue){return this._map=ue,this._container=c.create("div","maplibregl-ctrl maplibregl-ctrl-group"),this._setupUI(),function(){return a._(this,arguments,void 0,function*(w=!1){if(ys!==void 0&&!w)return ys;if(window.navigator.permissions===void 0)return ys=!!window.navigator.geolocation,ys;try{ys=(yield window.navigator.permissions.query({name:"geolocation"})).state!=="denied"}catch(B){ys=!!window.navigator.geolocation}return ys})}().then(w=>this._finishSetupUI(w)),this._container}onRemove(){this._geolocationWatchID!==void 0&&(window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),c.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,pu=0,Uc=!1}_isOutOfMapMaxBounds(ue){let w=this._map.getMaxBounds(),B=ue.coords;return w&&(B.longitude<w.getWest()||B.longitude>w.getEast()||B.latitude<w.getSouth()||B.latitude>w.getNorth())}_setErrorState(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-waiting");break;case"ACTIVE_ERROR":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}}_updateCircleRadius(){let ue=this._map.getBounds(),w=ue.getSouthEast(),B=ue.getNorthEast(),Q=w.distanceTo(B),ee=Math.ceil(this._accuracy/(Q/this._map._container.clientHeight)*2);this._circleElement.style.width=`${ee}px`,this._circleElement.style.height=`${ee}px`}trigger(){if(!this._setup)return a.w("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new a.k("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":pu--,Uc=!1,this._watchState="OFF",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background-error"),this.fire(new a.k("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new a.k("trackuserlocationstart")),this.fire(new a.k("userlocationfocus"));break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-active");break;case"OFF":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}if(this._watchState==="OFF"&&this._geolocationWatchID!==void 0)this._clearWatch();else if(this._geolocationWatchID===void 0){let ue;this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),pu++,pu>1?(ue={maximumAge:6e5,timeout:0},Uc=!0):(ue=this.options.positionOptions,Uc=!1),this._geolocationWatchID=window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,ue)}}else window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0}_clearWatch(){window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)}},i.Hash=Ah,i.ImageSource=Gt,i.KeyboardHandler=Wt,i.LngLatBounds=ce,i.LogoControl=Yn,i.Map=class extends Ra{constructor(ue){a.bf.mark(a.bg.create);let w=Object.assign(Object.assign({},Ws),ue);if(w.minZoom!=null&&w.maxZoom!=null&&w.minZoom>w.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(w.minPitch!=null&&w.maxPitch!=null&&w.minPitch>w.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(w.minPitch!=null&&w.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(w.maxPitch!=null&&w.maxPitch>85)throw new Error("maxPitch must be less than or equal to 85");if(super(new nl(w.minZoom,w.maxZoom,w.minPitch,w.maxPitch,w.renderWorldCopies),{bearingSnap:w.bearingSnap}),this._idleTriggered=!1,this._crossFadingFactor=1,this._renderTaskQueue=new Dn,this._controls=[],this._mapId=a.a4(),this._contextLost=B=>{B.preventDefault(),this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this.fire(new a.k("webglcontextlost",{originalEvent:B}))},this._contextRestored=B=>{this._setupPainter(),this.resize(),this._update(),this.fire(new a.k("webglcontextrestored",{originalEvent:B}))},this._onMapScroll=B=>{if(B.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},this._onWindowOnline=()=>{this._update()},this._interactive=w.interactive,this._maxTileCacheSize=w.maxTileCacheSize,this._maxTileCacheZoomLevels=w.maxTileCacheZoomLevels,this._failIfMajorPerformanceCaveat=w.failIfMajorPerformanceCaveat===!0,this._preserveDrawingBuffer=w.preserveDrawingBuffer===!0,this._antialias=w.antialias===!0,this._trackResize=w.trackResize===!0,this._bearingSnap=w.bearingSnap,this._refreshExpiredTiles=w.refreshExpiredTiles===!0,this._fadeDuration=w.fadeDuration,this._crossSourceCollisions=w.crossSourceCollisions===!0,this._collectResourceTiming=w.collectResourceTiming===!0,this._locale=Object.assign(Object.assign({},Ho),w.locale),this._clickTolerance=w.clickTolerance,this._overridePixelRatio=w.pixelRatio,this._maxCanvasSize=w.maxCanvasSize,this.transformCameraUpdate=w.transformCameraUpdate,this.cancelPendingTileRequestsWhileZooming=w.cancelPendingTileRequestsWhileZooming===!0,this._imageQueueHandle=p.addThrottleControl(()=>this.isMoving()),this._requestManager=new k(w.transformRequest),typeof w.container=="string"){if(this._container=document.getElementById(w.container),!this._container)throw new Error(`Container '${w.container}' not found.`)}else{if(!(w.container instanceof HTMLElement))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=w.container}if(w.maxBounds&&this.setMaxBounds(w.maxBounds),this._setupContainer(),this._setupPainter(),this.on("move",()=>this._update(!1)).on("moveend",()=>this._update(!1)).on("zoom",()=>this._update(!0)).on("terrain",()=>{this.painter.terrainFacilitator.dirty=!0,this._update(!0)}).once("idle",()=>{this._idleTriggered=!0}),typeof window!="undefined"){addEventListener("online",this._onWindowOnline,!1);let B=!1,Q=nh(ee=>{this._trackResize&&!this._removed&&(this.resize(ee),this.redraw())},50);this._resizeObserver=new ResizeObserver(ee=>{B?Q(ee):B=!0}),this._resizeObserver.observe(this._container)}this.handlers=new Ca(this,w),this._hash=w.hash&&new Ah(typeof w.hash=="string"&&w.hash||void 0).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:w.center,zoom:w.zoom,bearing:w.bearing,pitch:w.pitch}),w.bounds&&(this.resize(),this.fitBounds(w.bounds,a.e({},w.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=w.localIdeographFontFamily,this._validateStyle=w.validateStyle,w.style&&this.setStyle(w.style,{localIdeographFontFamily:w.localIdeographFontFamily}),w.attributionControl&&this.addControl(new Na(typeof w.attributionControl=="boolean"?void 0:w.attributionControl)),w.maplibreLogo&&this.addControl(new Yn,w.logoPosition),this.on("style.load",()=>{this.transform.unmodified&&this.jumpTo(this.style.stylesheet)}),this.on("data",B=>{this._update(B.dataType==="style"),this.fire(new a.k(`${B.dataType}data`,B))}),this.on("dataloading",B=>{this.fire(new a.k(`${B.dataType}dataloading`,B))}),this.on("dataabort",B=>{this.fire(new a.k("sourcedataabort",B))})}_getMapId(){return this._mapId}addControl(ue,w){if(w===void 0&&(w=ue.getDefaultPosition?ue.getDefaultPosition():"top-right"),!ue||!ue.onAdd)return this.fire(new a.j(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));let B=ue.onAdd(this);this._controls.push(ue);let Q=this._controlPositions[w];return w.indexOf("bottom")!==-1?Q.insertBefore(B,Q.firstChild):Q.appendChild(B),this}removeControl(ue){if(!ue||!ue.onRemove)return this.fire(new a.j(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));let w=this._controls.indexOf(ue);return w>-1&&this._controls.splice(w,1),ue.onRemove(this),this}hasControl(ue){return this._controls.indexOf(ue)>-1}calculateCameraOptionsFromTo(ue,w,B,Q){return Q==null&&this.terrain&&(Q=this.terrain.getElevationForLngLatZoom(B,this.transform.tileZoom)),super.calculateCameraOptionsFromTo(ue,w,B,Q)}resize(ue){var w;let B=this._containerDimensions(),Q=B[0],ee=B[1],le=this._getClampedPixelRatio(Q,ee);if(this._resizeCanvas(Q,ee,le),this.painter.resize(Q,ee,le),this.painter.overLimit()){let Xe=this.painter.context.gl;this._maxCanvasSize=[Xe.drawingBufferWidth,Xe.drawingBufferHeight];let ot=this._getClampedPixelRatio(Q,ee);this._resizeCanvas(Q,ee,ot),this.painter.resize(Q,ee,ot)}this.transform.resize(Q,ee),(w=this._requestedCameraState)===null||w===void 0||w.resize(Q,ee);let qe=!this._moving;return qe&&(this.stop(),this.fire(new a.k("movestart",ue)).fire(new a.k("move",ue))),this.fire(new a.k("resize",ue)),qe&&this.fire(new a.k("moveend",ue)),this}_getClampedPixelRatio(ue,w){let{0:B,1:Q}=this._maxCanvasSize,ee=this.getPixelRatio(),le=ue*ee,qe=w*ee;return Math.min(le>B?B/le:1,qe>Q?Q/qe:1)*ee}getPixelRatio(){var ue;return(ue=this._overridePixelRatio)!==null&&ue!==void 0?ue:devicePixelRatio}setPixelRatio(ue){this._overridePixelRatio=ue,this.resize()}getBounds(){return this.transform.getBounds()}getMaxBounds(){return this.transform.getMaxBounds()}setMaxBounds(ue){return this.transform.setMaxBounds(ce.convert(ue)),this._update()}setMinZoom(ue){if((ue=ue==null?-2:ue)>=-2&&ue<=this.transform.maxZoom)return this.transform.minZoom=ue,this._update(),this.getZoom()<ue&&this.setZoom(ue),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")}getMinZoom(){return this.transform.minZoom}setMaxZoom(ue){if((ue=ue==null?22:ue)>=this.transform.minZoom)return this.transform.maxZoom=ue,this._update(),this.getZoom()>ue&&this.setZoom(ue),this;throw new Error("maxZoom must be greater than the current minZoom")}getMaxZoom(){return this.transform.maxZoom}setMinPitch(ue){if((ue=ue==null?0:ue)<0)throw new Error("minPitch must be greater than or equal to 0");if(ue>=0&&ue<=this.transform.maxPitch)return this.transform.minPitch=ue,this._update(),this.getPitch()<ue&&this.setPitch(ue),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")}getMinPitch(){return this.transform.minPitch}setMaxPitch(ue){if((ue=ue==null?60:ue)>85)throw new Error("maxPitch must be less than or equal to 85");if(ue>=this.transform.minPitch)return this.transform.maxPitch=ue,this._update(),this.getPitch()>ue&&this.setPitch(ue),this;throw new Error("maxPitch must be greater than the current minPitch")}getMaxPitch(){return this.transform.maxPitch}getRenderWorldCopies(){return this.transform.renderWorldCopies}setRenderWorldCopies(ue){return this.transform.renderWorldCopies=ue,this._update()}project(ue){return this.transform.locationPoint(a.N.convert(ue),this.style&&this.terrain)}unproject(ue){return this.transform.pointLocation(a.P.convert(ue),this.terrain)}isMoving(){var ue;return this._moving||((ue=this.handlers)===null||ue===void 0?void 0:ue.isMoving())}isZooming(){var ue;return this._zooming||((ue=this.handlers)===null||ue===void 0?void 0:ue.isZooming())}isRotating(){var ue;return this._rotating||((ue=this.handlers)===null||ue===void 0?void 0:ue.isRotating())}_createDelegatedListener(ue,w,B){if(ue==="mouseenter"||ue==="mouseover"){let Q=!1;return{layers:w,listener:B,delegates:{mousemove:le=>{let qe=w.filter(ot=>this.getLayer(ot)),Xe=qe.length!==0?this.queryRenderedFeatures(le.point,{layers:qe}):[];Xe.length?Q||(Q=!0,B.call(this,new jl(ue,this,le.originalEvent,{features:Xe}))):Q=!1},mouseout:()=>{Q=!1}}}}if(ue==="mouseleave"||ue==="mouseout"){let Q=!1;return{layers:w,listener:B,delegates:{mousemove:qe=>{let Xe=w.filter(ot=>this.getLayer(ot));(Xe.length!==0?this.queryRenderedFeatures(qe.point,{layers:Xe}):[]).length?Q=!0:Q&&(Q=!1,B.call(this,new jl(ue,this,qe.originalEvent)))},mouseout:qe=>{Q&&(Q=!1,B.call(this,new jl(ue,this,qe.originalEvent)))}}}}{let Q=ee=>{let le=w.filter(Xe=>this.getLayer(Xe)),qe=le.length!==0?this.queryRenderedFeatures(ee.point,{layers:le}):[];qe.length&&(ee.features=qe,B.call(this,ee),delete ee.features)};return{layers:w,listener:B,delegates:{[ue]:Q}}}}_saveDelegatedListener(ue,w){this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[ue]=this._delegatedListeners[ue]||[],this._delegatedListeners[ue].push(w)}_removeDelegatedListener(ue,w,B){if(!this._delegatedListeners||!this._delegatedListeners[ue])return;let Q=this._delegatedListeners[ue];for(let ee=0;ee<Q.length;ee++){let le=Q[ee];if(le.listener===B&&le.layers.length===w.length&&le.layers.every(qe=>w.includes(qe))){for(let qe in le.delegates)this.off(qe,le.delegates[qe]);return void Q.splice(ee,1)}}}on(ue,w,B){if(B===void 0)return super.on(ue,w);let Q=this._createDelegatedListener(ue,typeof w=="string"?[w]:w,B);this._saveDelegatedListener(ue,Q);for(let ee in Q.delegates)this.on(ee,Q.delegates[ee]);return this}once(ue,w,B){if(B===void 0)return super.once(ue,w);let Q=typeof w=="string"?[w]:w,ee=this._createDelegatedListener(ue,Q,B);for(let le in ee.delegates){let qe=ee.delegates[le];ee.delegates[le]=(...Xe)=>{this._removeDelegatedListener(ue,Q,B),qe(...Xe)}}this._saveDelegatedListener(ue,ee);for(let le in ee.delegates)this.once(le,ee.delegates[le]);return this}off(ue,w,B){return B===void 0?super.off(ue,w):(this._removeDelegatedListener(ue,typeof w=="string"?[w]:w,B),this)}queryRenderedFeatures(ue,w){if(!this.style)return[];let B,Q=ue instanceof a.P||Array.isArray(ue),ee=Q?ue:[[0,0],[this.transform.width,this.transform.height]];if(w=w||(Q?{}:ue)||{},ee instanceof a.P||typeof ee[0]=="number")B=[a.P.convert(ee)];else{let le=a.P.convert(ee[0]),qe=a.P.convert(ee[1]);B=[le,new a.P(qe.x,le.y),qe,new a.P(le.x,qe.y),le]}return this.style.queryRenderedFeatures(B,w,this.transform)}querySourceFeatures(ue,w){return this.style.querySourceFeatures(ue,w)}setStyle(ue,w){return(w=a.e({},{localIdeographFontFamily:this._localIdeographFontFamily,validate:this._validateStyle},w)).diff!==!1&&w.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&ue?(this._diffStyle(ue,w),this):(this._localIdeographFontFamily=w.localIdeographFontFamily,this._updateStyle(ue,w))}setTransformRequest(ue){return this._requestManager.setTransformRequest(ue),this}_getUIString(ue){let w=this._locale[ue];if(w==null)throw new Error(`Missing UI string '${ue}'`);return w}_updateStyle(ue,w){if(w.transformStyle&&this.style&&!this.style._loaded)return void this.style.once("style.load",()=>this._updateStyle(ue,w));let B=this.style&&w.transformStyle?this.style.serialize():void 0;return this.style&&(this.style.setEventedParent(null),this.style._remove(!ue)),ue?(this.style=new Ha(this,w||{}),this.style.setEventedParent(this,{style:this.style}),typeof ue=="string"?this.style.loadURL(ue,w,B):this.style.loadJSON(ue,w,B),this):(delete this.style,this)}_lazyInitEmptyStyle(){this.style||(this.style=new Ha(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())}_diffStyle(ue,w){if(typeof ue=="string"){let B=this._requestManager.transformRequest(ue,"Style");a.h(B,new AbortController).then(Q=>{this._updateDiff(Q.data,w)}).catch(Q=>{Q&&this.fire(new a.j(Q))})}else typeof ue=="object"&&this._updateDiff(ue,w)}_updateDiff(ue,w){try{this.style.setState(ue,w)&&this._update(!0)}catch(B){a.w(`Unable to perform style diff: ${B.message||B.error||B}.  Rebuilding the style from scratch.`),this._updateStyle(ue,w)}}getStyle(){if(this.style)return this.style.serialize()}isStyleLoaded(){return this.style?this.style.loaded():a.w("There is no style added to the map.")}addSource(ue,w){return this._lazyInitEmptyStyle(),this.style.addSource(ue,w),this._update(!0)}isSourceLoaded(ue){let w=this.style&&this.style.sourceCaches[ue];if(w!==void 0)return w.loaded();this.fire(new a.j(new Error(`There is no source with ID '${ue}'`)))}setTerrain(ue){if(this.style._checkLoaded(),this._terrainDataCallback&&this.style.off("data",this._terrainDataCallback),ue){let w=this.style.sourceCaches[ue.source];if(!w)throw new Error(`cannot load terrain, because there exists no source with ID: ${ue.source}`);this.terrain===null&&w.reload();for(let B in this.style._layers){let Q=this.style._layers[B];Q.type==="hillshade"&&Q.source===ue.source&&a.w("You are using the same source for a hillshade layer and for 3D terrain. Please consider using two separate sources to improve rendering quality.")}this.terrain=new Xo(this.painter,w,ue),this.painter.renderToTexture=new ws(this.painter,this.terrain),this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._terrainDataCallback=B=>{B.dataType==="style"?this.terrain.sourceCache.freeRtt():B.dataType==="source"&&B.tile&&(B.sourceId!==ue.source||this._elevationFreeze||(this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom)),this.terrain.sourceCache.freeRtt(B.tile.tileID))},this.style.on("data",this._terrainDataCallback)}else this.terrain&&this.terrain.sourceCache.destruct(),this.terrain=null,this.painter.renderToTexture&&this.painter.renderToTexture.destruct(),this.painter.renderToTexture=null,this.transform.minElevationForCurrentTile=0,this.transform.elevation=0;return this.fire(new a.k("terrain",{terrain:ue})),this}getTerrain(){var ue,w;return(w=(ue=this.terrain)===null||ue===void 0?void 0:ue.options)!==null&&w!==void 0?w:null}areTilesLoaded(){let ue=this.style&&this.style.sourceCaches;for(let w in ue){let B=ue[w]._tiles;for(let Q in B){let ee=B[Q];if(ee.state!=="loaded"&&ee.state!=="errored")return!1}}return!0}removeSource(ue){return this.style.removeSource(ue),this._update(!0)}getSource(ue){return this.style.getSource(ue)}addImage(ue,w,B={}){let{pixelRatio:Q=1,sdf:ee=!1,stretchX:le,stretchY:qe,content:Xe,textFitWidth:ot,textFitHeight:Tt}=B;if(this._lazyInitEmptyStyle(),!(w instanceof HTMLImageElement||a.b(w))){if(w.width===void 0||w.height===void 0)return this.fire(new a.j(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));{let{width:Yt,height:Kt,data:xr}=w,Ir=w;return this.style.addImage(ue,{data:new a.R({width:Yt,height:Kt},new Uint8Array(xr)),pixelRatio:Q,stretchX:le,stretchY:qe,content:Xe,textFitWidth:ot,textFitHeight:Tt,sdf:ee,version:0,userImage:Ir}),Ir.onAdd&&Ir.onAdd(this,ue),this}}{let{width:Yt,height:Kt,data:xr}=u.getImageData(w);this.style.addImage(ue,{data:new a.R({width:Yt,height:Kt},xr),pixelRatio:Q,stretchX:le,stretchY:qe,content:Xe,textFitWidth:ot,textFitHeight:Tt,sdf:ee,version:0})}}updateImage(ue,w){let B=this.style.getImage(ue);if(!B)return this.fire(new a.j(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));let Q=w instanceof HTMLImageElement||a.b(w)?u.getImageData(w):w,{width:ee,height:le,data:qe}=Q;if(ee===void 0||le===void 0)return this.fire(new a.j(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(ee!==B.data.width||le!==B.data.height)return this.fire(new a.j(new Error("The width and height of the updated image must be that same as the previous version of the image")));let Xe=!(w instanceof HTMLImageElement||a.b(w));return B.data.replace(qe,Xe),this.style.updateImage(ue,B),this}getImage(ue){return this.style.getImage(ue)}hasImage(ue){return ue?!!this.style.getImage(ue):(this.fire(new a.j(new Error("Missing required image id"))),!1)}removeImage(ue){this.style.removeImage(ue)}loadImage(ue){return p.getImage(this._requestManager.transformRequest(ue,"Image"),new AbortController)}listImages(){return this.style.listImages()}addLayer(ue,w){return this._lazyInitEmptyStyle(),this.style.addLayer(ue,w),this._update(!0)}moveLayer(ue,w){return this.style.moveLayer(ue,w),this._update(!0)}removeLayer(ue){return this.style.removeLayer(ue),this._update(!0)}getLayer(ue){return this.style.getLayer(ue)}getLayersOrder(){return this.style.getLayersOrder()}setLayerZoomRange(ue,w,B){return this.style.setLayerZoomRange(ue,w,B),this._update(!0)}setFilter(ue,w,B={}){return this.style.setFilter(ue,w,B),this._update(!0)}getFilter(ue){return this.style.getFilter(ue)}setPaintProperty(ue,w,B,Q={}){return this.style.setPaintProperty(ue,w,B,Q),this._update(!0)}getPaintProperty(ue,w){return this.style.getPaintProperty(ue,w)}setLayoutProperty(ue,w,B,Q={}){return this.style.setLayoutProperty(ue,w,B,Q),this._update(!0)}getLayoutProperty(ue,w){return this.style.getLayoutProperty(ue,w)}setGlyphs(ue,w={}){return this._lazyInitEmptyStyle(),this.style.setGlyphs(ue,w),this._update(!0)}getGlyphs(){return this.style.getGlyphsUrl()}addSprite(ue,w,B={}){return this._lazyInitEmptyStyle(),this.style.addSprite(ue,w,B,Q=>{Q||this._update(!0)}),this}removeSprite(ue){return this._lazyInitEmptyStyle(),this.style.removeSprite(ue),this._update(!0)}getSprite(){return this.style.getSprite()}setSprite(ue,w={}){return this._lazyInitEmptyStyle(),this.style.setSprite(ue,w,B=>{B||this._update(!0)}),this}setLight(ue,w={}){return this._lazyInitEmptyStyle(),this.style.setLight(ue,w),this._update(!0)}getLight(){return this.style.getLight()}setSky(ue){return this._lazyInitEmptyStyle(),this.style.setSky(ue),this._update(!0)}getSky(){return this.style.getSky()}setFeatureState(ue,w){return this.style.setFeatureState(ue,w),this._update()}removeFeatureState(ue,w){return this.style.removeFeatureState(ue,w),this._update()}getFeatureState(ue){return this.style.getFeatureState(ue)}getContainer(){return this._container}getCanvasContainer(){return this._canvasContainer}getCanvas(){return this._canvas}_containerDimensions(){let ue=0,w=0;return this._container&&(ue=this._container.clientWidth||400,w=this._container.clientHeight||300),[ue,w]}_setupContainer(){let ue=this._container;ue.classList.add("maplibregl-map");let w=this._canvasContainer=c.create("div","maplibregl-canvas-container",ue);this._interactive&&w.classList.add("maplibregl-interactive"),this._canvas=c.create("canvas","maplibregl-canvas",w),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex",this._interactive?"0":"-1"),this._canvas.setAttribute("aria-label",this._getUIString("Map.Title")),this._canvas.setAttribute("role","region");let B=this._containerDimensions(),Q=this._getClampedPixelRatio(B[0],B[1]);this._resizeCanvas(B[0],B[1],Q);let ee=this._controlContainer=c.create("div","maplibregl-control-container",ue),le=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach(qe=>{le[qe]=c.create("div",`maplibregl-ctrl-${qe} `,ee)}),this._container.addEventListener("scroll",this._onMapScroll,!1)}_resizeCanvas(ue,w,B){this._canvas.width=Math.floor(B*ue),this._canvas.height=Math.floor(B*w),this._canvas.style.width=`${ue}px`,this._canvas.style.height=`${w}px`}_setupPainter(){let ue={alpha:!0,stencil:!0,depth:!0,failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1},w=null;this._canvas.addEventListener("webglcontextcreationerror",Q=>{w={requestedAttributes:ue},Q&&(w.statusMessage=Q.statusMessage,w.type=Q.type)},{once:!0});let B=this._canvas.getContext("webgl2",ue)||this._canvas.getContext("webgl",ue);if(!B){let Q="Failed to initialize WebGL";throw w?(w.message=Q,new Error(JSON.stringify(w))):new Error(Q)}this.painter=new Dc(B,this.transform),f.testSupport(B)}loaded(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()}_update(ue){return this.style&&this.style._loaded?(this._styleDirty=this._styleDirty||ue,this._sourcesDirty=!0,this.triggerRepaint(),this):this}_requestRenderFrame(ue){return this._update(),this._renderTaskQueue.add(ue)}_cancelRenderFrame(ue){this._renderTaskQueue.remove(ue)}_render(ue){let w=this._idleTriggered?this._fadeDuration:0;if(this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(ue),this._removed)return;let B=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;let ee=this.transform.zoom,le=u.now();this.style.zoomHistory.update(ee,le);let qe=new a.z(ee,{now:le,fadeDuration:w,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),Xe=qe.crossFadingFactor();Xe===1&&Xe===this._crossFadingFactor||(B=!0,this._crossFadingFactor=Xe),this.style.update(qe)}this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this.terrain?(this.terrain.sourceCache.update(this.transform,this.terrain),this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._elevationFreeze||(this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom))):(this.transform.minElevationForCurrentTile=0,this.transform.elevation=0),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,w,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:w,showPadding:this.showPadding}),this.fire(new a.k("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,a.bf.mark(a.bg.load),this.fire(new a.k("load"))),this.style&&(this.style.hasTransitions()||B)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles();let Q=this._sourcesDirty||this._styleDirty||this._placementDirty;return Q||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new a.k("idle")),!this._loaded||this._fullyLoaded||Q||(this._fullyLoaded=!0,a.bf.mark(a.bg.fullLoad)),this}redraw(){return this.style&&(this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._render(0)),this}remove(){var ue;this._hash&&this._hash.remove();for(let B of this._controls)B.onRemove(this);this._controls=[],this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),typeof window!="undefined"&&removeEventListener("online",this._onWindowOnline,!1),p.removeThrottleControl(this._imageQueueHandle),(ue=this._resizeObserver)===null||ue===void 0||ue.disconnect();let w=this.painter.context.gl.getExtension("WEBGL_lose_context");w!=null&&w.loseContext&&w.loseContext(),this._canvas.removeEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.removeEventListener("webglcontextlost",this._contextLost,!1),c.remove(this._canvasContainer),c.remove(this._controlContainer),this._container.classList.remove("maplibregl-map"),a.bf.clearMetrics(),this._removed=!0,this.fire(new a.k("remove"))}triggerRepaint(){this.style&&!this._frameRequest&&(this._frameRequest=new AbortController,u.frameAsync(this._frameRequest).then(ue=>{a.bf.frame(ue),this._frameRequest=null,this._render(ue)}).catch(()=>{}))}get showTileBoundaries(){return!!this._showTileBoundaries}set showTileBoundaries(ue){this._showTileBoundaries!==ue&&(this._showTileBoundaries=ue,this._update())}get showPadding(){return!!this._showPadding}set showPadding(ue){this._showPadding!==ue&&(this._showPadding=ue,this._update())}get showCollisionBoxes(){return!!this._showCollisionBoxes}set showCollisionBoxes(ue){this._showCollisionBoxes!==ue&&(this._showCollisionBoxes=ue,ue?this.style._generateCollisionBoxes():this._update())}get showOverdrawInspector(){return!!this._showOverdrawInspector}set showOverdrawInspector(ue){this._showOverdrawInspector!==ue&&(this._showOverdrawInspector=ue,this._update())}get repaint(){return!!this._repaint}set repaint(ue){this._repaint!==ue&&(this._repaint=ue,this.triggerRepaint())}get vertices(){return!!this._vertices}set vertices(ue){this._vertices=ue,this._update()}get version(){return ml}getCameraTargetElevation(){return this.transform.elevation}},i.MapMouseEvent=jl,i.MapTouchEvent=lf,i.MapWheelEvent=Hh,i.Marker=Yu,i.NavigationControl=class{constructor(ue){this._updateZoomButtons=()=>{let w=this._map.getZoom(),B=w===this._map.getMaxZoom(),Q=w===this._map.getMinZoom();this._zoomInButton.disabled=B,this._zoomOutButton.disabled=Q,this._zoomInButton.setAttribute("aria-disabled",B.toString()),this._zoomOutButton.setAttribute("aria-disabled",Q.toString())},this._rotateCompassArrow=()=>{let w=this.options.visualizePitch?`scale(${1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)}) rotateX(${this._map.transform.pitch}deg) rotateZ(${this._map.transform.angle*(180/Math.PI)}deg)`:`rotate(${this._map.transform.angle*(180/Math.PI)}deg)`;this._compassIcon.style.transform=w},this._setButtonTitle=(w,B)=>{let Q=this._map._getUIString(`NavigationControl.${B}`);w.title=Q,w.setAttribute("aria-label",Q)},this.options=a.e({},va,ue),this._container=c.create("div","maplibregl-ctrl maplibregl-ctrl-group"),this._container.addEventListener("contextmenu",w=>w.preventDefault()),this.options.showZoom&&(this._zoomInButton=this._createButton("maplibregl-ctrl-zoom-in",w=>this._map.zoomIn({},{originalEvent:w})),c.create("span","maplibregl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden","true"),this._zoomOutButton=this._createButton("maplibregl-ctrl-zoom-out",w=>this._map.zoomOut({},{originalEvent:w})),c.create("span","maplibregl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden","true")),this.options.showCompass&&(this._compass=this._createButton("maplibregl-ctrl-compass",w=>{this.options.visualizePitch?this._map.resetNorthPitch({},{originalEvent:w}):this._map.resetNorth({},{originalEvent:w})}),this._compassIcon=c.create("span","maplibregl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden","true"))}onAdd(ue){return this._map=ue,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new no(this._map,this._compass,this.options.visualizePitch)),this._container}onRemove(){c.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map}_createButton(ue,w){let B=c.create("button",ue,this._container);return B.type="button",B.addEventListener("click",w),B}},i.Popup=class extends a.E{constructor(ue){super(),this.remove=()=>(this._content&&c.remove(this._content),this._container&&(c.remove(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("move",this._onClose),this._map.off("click",this._onClose),this._map.off("remove",this.remove),this._map.off("mousemove",this._onMouseMove),this._map.off("mouseup",this._onMouseUp),this._map.off("drag",this._onDrag),this._map._canvasContainer.classList.remove("maplibregl-track-pointer"),delete this._map,this.fire(new a.k("close"))),this),this._onMouseUp=w=>{this._update(w.point)},this._onMouseMove=w=>{this._update(w.point)},this._onDrag=w=>{this._update(w.point)},this._update=w=>{var B;if(!this._map||!this._lngLat&&!this._trackPointer||!this._content)return;if(!this._container){if(this._container=c.create("div","maplibregl-popup",this._map.getContainer()),this._tip=c.create("div","maplibregl-popup-tip",this._container),this._container.appendChild(this._content),this.options.className)for(let Xe of this.options.className.split(" "))this._container.classList.add(Xe);this._closeButton&&this._closeButton.setAttribute("aria-label",this._map._getUIString("Popup.Close")),this._trackPointer&&this._container.classList.add("maplibregl-popup-track-pointer")}if(this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._lngLat=this._map.transform.renderWorldCopies&&!this._trackPointer?rs(this._lngLat,this._flatPos,this._map.transform):(B=this._lngLat)===null||B===void 0?void 0:B.wrap(),this._trackPointer&&!w)return;let Q=this._flatPos=this._pos=this._trackPointer&&w?w:this._map.project(this._lngLat);this._map.terrain&&(this._flatPos=this._trackPointer&&w?w:this._map.transform.locationPoint(this._lngLat));let ee=this.options.anchor,le=hc(this.options.offset);if(!ee){let Xe=this._container.offsetWidth,ot=this._container.offsetHeight,Tt;Tt=Q.y+le.bottom.y<ot?["top"]:Q.y>this._map.transform.height-ot?["bottom"]:[],Q.x<Xe/2?Tt.push("left"):Q.x>this._map.transform.width-Xe/2&&Tt.push("right"),ee=Tt.length===0?"bottom":Tt.join("-")}let qe=Q.add(le[ee]);this.options.subpixelPositioning||(qe=qe.round()),c.setTransform(this._container,`${$l[ee]} translate(${qe.x}px,${qe.y}px)`),Cu(this._container,ee,"popup")},this._onClose=()=>{this.remove()},this.options=a.e(Object.create(oo),ue)}addTo(ue){return this._map&&this.remove(),this._map=ue,this.options.closeOnClick&&this._map.on("click",this._onClose),this.options.closeOnMove&&this._map.on("move",this._onClose),this._map.on("remove",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on("mousemove",this._onMouseMove),this._map.on("mouseup",this._onMouseUp),this._container&&this._container.classList.add("maplibregl-popup-track-pointer"),this._map._canvasContainer.classList.add("maplibregl-track-pointer")):this._map.on("move",this._update),this.fire(new a.k("open")),this}isOpen(){return!!this._map}getLngLat(){return this._lngLat}setLngLat(ue){return this._lngLat=a.N.convert(ue),this._pos=null,this._flatPos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on("move",this._update),this._map.off("mousemove",this._onMouseMove),this._container&&this._container.classList.remove("maplibregl-popup-track-pointer"),this._map._canvasContainer.classList.remove("maplibregl-track-pointer")),this}trackPointer(){return this._trackPointer=!0,this._pos=null,this._flatPos=null,this._update(),this._map&&(this._map.off("move",this._update),this._map.on("mousemove",this._onMouseMove),this._map.on("drag",this._onDrag),this._container&&this._container.classList.add("maplibregl-popup-track-pointer"),this._map._canvasContainer.classList.add("maplibregl-track-pointer")),this}getElement(){return this._container}setText(ue){return this.setDOMContent(document.createTextNode(ue))}setHTML(ue){let w=document.createDocumentFragment(),B=document.createElement("body"),Q;for(B.innerHTML=ue;Q=B.firstChild,Q;)w.appendChild(Q);return this.setDOMContent(w)}getMaxWidth(){var ue;return(ue=this._container)===null||ue===void 0?void 0:ue.style.maxWidth}setMaxWidth(ue){return this.options.maxWidth=ue,this._update(),this}setDOMContent(ue){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=c.create("div","maplibregl-popup-content",this._container);return this._content.appendChild(ue),this._createCloseButton(),this._update(),this._focusFirstElement(),this}addClassName(ue){return this._container&&this._container.classList.add(ue),this}removeClassName(ue){return this._container&&this._container.classList.remove(ue),this}setOffset(ue){return this.options.offset=ue,this._update(),this}toggleClassName(ue){if(this._container)return this._container.classList.toggle(ue)}setSubpixelPositioning(ue){this.options.subpixelPositioning=ue}_createCloseButton(){this.options.closeButton&&(this._closeButton=c.create("button","maplibregl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClose))}_focusFirstElement(){if(!this.options.focusAfterOpen||!this._container)return;let ue=this._container.querySelector(Vc);ue&&ue.focus()}},i.RasterDEMTileSource=Zt,i.RasterTileSource=pt,i.ScaleControl=class{constructor(ue){this._onMove=()=>{Ac(this._map,this._container,this.options)},this.setUnit=w=>{this.options.unit=w,Ac(this._map,this._container,this.options)},this.options=Object.assign(Object.assign({},xu),ue)}getDefaultPosition(){return"bottom-left"}onAdd(ue){return this._map=ue,this._container=c.create("div","maplibregl-ctrl maplibregl-ctrl-scale",ue.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container}onRemove(){c.remove(this._container),this._map.off("move",this._onMove),this._map=void 0}},i.ScrollZoomHandler=Kr,i.Style=Ha,i.TerrainControl=class{constructor(ue){this._toggleTerrain=()=>{this._map.getTerrain()?this._map.setTerrain(null):this._map.setTerrain(this.options),this._updateTerrainIcon()},this._updateTerrainIcon=()=>{this._terrainButton.classList.remove("maplibregl-ctrl-terrain"),this._terrainButton.classList.remove("maplibregl-ctrl-terrain-enabled"),this._map.terrain?(this._terrainButton.classList.add("maplibregl-ctrl-terrain-enabled"),this._terrainButton.title=this._map._getUIString("TerrainControl.Disable")):(this._terrainButton.classList.add("maplibregl-ctrl-terrain"),this._terrainButton.title=this._map._getUIString("TerrainControl.Enable"))},this.options=ue}onAdd(ue){return this._map=ue,this._container=c.create("div","maplibregl-ctrl maplibregl-ctrl-group"),this._terrainButton=c.create("button","maplibregl-ctrl-terrain",this._container),c.create("span","maplibregl-ctrl-icon",this._terrainButton).setAttribute("aria-hidden","true"),this._terrainButton.type="button",this._terrainButton.addEventListener("click",this._toggleTerrain),this._updateTerrainIcon(),this._map.on("terrain",this._updateTerrainIcon),this._container}onRemove(){c.remove(this._container),this._map.off("terrain",this._updateTerrainIcon),this._map=void 0}},i.TwoFingersTouchPitchHandler=Bc,i.TwoFingersTouchRotateHandler=cf,i.TwoFingersTouchZoomHandler=vu,i.TwoFingersTouchZoomRotateHandler=Yi,i.VectorTileSource=ut,i.VideoSource=Nt,i.addSourceType=(ue,w)=>a._(void 0,void 0,void 0,function*(){if(wr(ue))throw new Error(`A source type called "${ue}" already exists.`);((B,Q)=>{sr[B]=Q})(ue,w)}),i.clearPrewarmedResources=function(){let ue=me;ue&&(ue.isPreloaded()&&ue.numActive()===1?(ue.release(_e),me=null):console.warn("Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()"))},i.getMaxParallelImageRequests=function(){return a.a.MAX_PARALLEL_IMAGE_REQUESTS},i.getRTLTextPluginStatus=function(){return Qt().getRTLTextPluginStatus()},i.getVersion=function(){return Ku},i.getWorkerCount=function(){return Ee.workerCount},i.getWorkerUrl=function(){return a.a.WORKER_URL},i.importScriptInWorkers=function(ue){return Ae().broadcast("IS",ue)},i.prewarm=function(){Se().acquire(_e)},i.setMaxParallelImageRequests=function(ue){a.a.MAX_PARALLEL_IMAGE_REQUESTS=ue},i.setRTLTextPlugin=function(ue,w){return Qt().setRTLTextPlugin(ue,w)},i.setWorkerCount=function(ue){Ee.workerCount=ue},i.setWorkerUrl=function(ue){a.a.WORKER_URL=ue}});var n=e;return n})});var fje=ye((Ebr,cje)=>{"use strict";var aw=Dr(),AXt=ru().sanitizeHTML,SXt=SJ(),sje=wx();function lje(e,t){this.subplot=e,this.uid=e.uid+"-"+t,this.index=t,this.idSource="source-"+this.uid,this.idLayer=sje.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var ag=lje.prototype;ag.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=c7(t)};ag.needsNewImage=function(e){var t=this.subplot.map;return t.getSource(this.idSource)&&this.sourceType==="image"&&e.sourcetype==="image"&&(this.source!==e.source||JSON.stringify(this.coordinates)!==JSON.stringify(e.coordinates))};ag.needsNewSource=function(e){return this.sourceType!==e.sourcetype||JSON.stringify(this.source)!==JSON.stringify(e.source)||this.layerType!==e.type};ag.needsNewLayer=function(e){return this.layerType!==e.type||this.below!==this.subplot.belowLookup["layout-"+this.index]};ag.lookupBelow=function(){return this.subplot.belowLookup["layout-"+this.index]};ag.updateImage=function(e){var t=this.subplot.map;t.getSource(this.idSource).updateImage({url:e.source,coordinates:e.coordinates});var r=this.findFollowingMapLayerId(this.lookupBelow());r!==null&&this.subplot.map.moveLayer(this.idLayer,r)};ag.updateSource=function(e){var t=this.subplot.map;if(t.getSource(this.idSource)&&t.removeSource(this.idSource),this.sourceType=e.sourcetype,this.source=e.source,!!c7(e)){var r=MXt(e);t.addSource(this.idSource,r)}};ag.findFollowingMapLayerId=function(e){if(e==="traces")for(var t=this.subplot.getMapLayers(),r=0;r<t.length;r++){var n=t[r].id;if(typeof n=="string"&&n.indexOf(sje.traceLayerPrefix)===0){e=n;break}}return e};ag.updateLayer=function(e){var t=this.subplot,r=uje(e),n=this.lookupBelow(),i=this.findFollowingMapLayerId(n);this.removeLayer(),c7(e)&&t.addLayer({id:this.idLayer,source:this.idSource,"source-layer":e.sourcelayer||"",type:e.type,minzoom:e.minzoom,maxzoom:e.maxzoom,layout:r.layout,paint:r.paint},i),this.layerType=e.type,this.below=n};ag.updateStyle=function(e){if(c7(e)){var t=uje(e);this.subplot.setOptions(this.idLayer,"setLayoutProperty",t.layout),this.subplot.setOptions(this.idLayer,"setPaintProperty",t.paint)}};ag.removeLayer=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer)};ag.dispose=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer),e.getSource(this.idSource)&&e.removeSource(this.idSource)};function c7(e){if(!e.visible)return!1;var t=e.source;if(Array.isArray(t)&&t.length>0){for(var r=0;r<t.length;r++)if(typeof t[r]!="string"||t[r].length===0)return!1;return!0}return aw.isPlainObject(t)||typeof t=="string"&&t.length>0}function uje(e){var t={},r={};switch(e.type){case"circle":aw.extendFlat(r,{"circle-radius":e.circle.radius,"circle-color":e.color,"circle-opacity":e.opacity});break;case"line":aw.extendFlat(r,{"line-width":e.line.width,"line-color":e.color,"line-opacity":e.opacity,"line-dasharray":e.line.dash});break;case"fill":aw.extendFlat(r,{"fill-color":e.color,"fill-outline-color":e.fill.outlinecolor,"fill-opacity":e.opacity});break;case"symbol":var n=e.symbol,i=SXt(n.textposition,n.iconsize);aw.extendFlat(t,{"icon-image":n.icon+"-15","icon-size":n.iconsize/10,"text-field":n.text,"text-size":n.textfont.size,"text-anchor":i.anchor,"text-offset":i.offset,"symbol-placement":n.placement}),aw.extendFlat(r,{"icon-color":e.color,"text-color":n.textfont.color,"text-opacity":e.opacity});break;case"raster":aw.extendFlat(r,{"raster-fade-duration":0,"raster-opacity":e.opacity});break}return{layout:t,paint:r}}function MXt(e){var t=e.sourcetype,r=e.source,n={type:t},i;return t==="geojson"?i="data":t==="vector"?i=typeof r=="string"?"url":"tiles":t==="raster"?(i="tiles",n.tileSize=256):t==="image"&&(i="url",n.coordinates=e.coordinates),n[i]=r,e.sourceattribution&&(n.attribution=AXt(e.sourceattribution)),n}cje.exports=function(t,r,n){var i=new lje(t,r);return i.update(n),i}});var _je=ye((kbr,yje)=>{"use strict";var PJ=oje(),IJ=Dr(),vje=ix(),hje=qa(),EXt=ho(),kXt=yv(),f7=vf(),pje=Eg(),CXt=pje.drawMode,LXt=pje.selectMode,PXt=Of().prepSelect,IXt=Of().clearOutline,RXt=Of().clearSelectionsCache,DXt=Of().selectOnClick,ow=wx(),FXt=fje();function gje(e,t){this.id=t,this.gd=e;var r=e._fullLayout,n=e._context;this.container=r._glcontainer.node(),this.isStatic=n.staticPlot,this.uid=r._uid+"-"+this.id,this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(r),this.map=null,this.styleObj=null,this.traceHash={},this.layerList=[],this.belowLookup={},this.dragging=!1,this.wheeling=!1}var Uh=gje.prototype;Uh.plot=function(e,t,r){var n=this,i;n.map?i=new Promise(function(a,o){n.updateMap(e,t,a,o)}):i=new Promise(function(a,o){n.createMap(e,t,a,o)}),r.push(i)};Uh.createMap=function(e,t,r,n){var i=this,a=t[i.id],o=i.styleObj=mje(a.style),s=a.bounds,l=s?[[s.west,s.south],[s.east,s.north]]:null,u=i.map=new PJ.Map({container:i.div,style:o.style,center:RJ(a.center),zoom:a.zoom,bearing:a.bearing,pitch:a.pitch,maxBounds:l,interactive:!i.isStatic,preserveDrawingBuffer:i.isStatic,doubleClickZoom:!1,boxZoom:!1,attributionControl:!1}).addControl(new PJ.AttributionControl({compact:!0})),c={};u.on("styleimagemissing",function(h){var d=h.id;if(!c[d]&&d.includes("-15")){c[d]=!0;var v=new Image(15,15);v.onload=function(){u.addImage(d,v)},v.crossOrigin="Anonymous",v.src="https://unpkg.com/maki@2.1.0/icons/"+d+".svg"}}),u.setTransformRequest(function(h){return h=h.replace("https://fonts.openmaptiles.org/Open Sans Extrabold","https://fonts.openmaptiles.org/Open Sans Extra Bold"),h=h.replace("https://tiles.basemaps.cartocdn.com/fonts/Open Sans Extrabold","https://fonts.openmaptiles.org/Open Sans Extra Bold"),h=h.replace("https://fonts.openmaptiles.org/Open Sans Regular,Arial Unicode MS Regular","https://fonts.openmaptiles.org/Klokantech Noto Sans Regular"),{url:h}}),u._canvas.style.left="0px",u._canvas.style.top="0px",i.rejectOnError(n),i.isStatic||i.initFx(e,t);var f=[];f.push(new Promise(function(h){u.once("load",h)})),f=f.concat(vje.fetchTraceGeoData(e)),Promise.all(f).then(function(){i.fillBelowLookup(e,t),i.updateData(e),i.updateLayout(t),i.resolveOnRender(r)}).catch(n)};Uh.updateMap=function(e,t,r,n){var i=this,a=i.map,o=t[this.id];i.rejectOnError(n);var s=[],l=mje(o.style);JSON.stringify(i.styleObj)!==JSON.stringify(l)&&(i.styleObj=l,a.setStyle(l.style),i.traceHash={},s.push(new Promise(function(u){a.once("styledata",u)}))),s=s.concat(vje.fetchTraceGeoData(e)),Promise.all(s).then(function(){i.fillBelowLookup(e,t),i.updateData(e),i.updateLayout(t),i.resolveOnRender(r)}).catch(n)};Uh.fillBelowLookup=function(e,t){var r=t[this.id],n=r.layers,i,a,o=this.belowLookup={},s=!1;for(i=0;i<e.length;i++){var l=e[i][0].trace,u=l._module;typeof l.below=="string"?a=l.below:u.getBelow&&(a=u.getBelow(l,this)),a===""&&(s=!0),o["trace-"+l.uid]=a||""}for(i=0;i<n.length;i++){var c=n[i];typeof c.below=="string"?a=c.below:s?a="traces":a="",o["layout-"+i]=a}var f={},h,d;for(h in o)a=o[h],f[a]?f[a].push(h):f[a]=[h];for(a in f){var v=f[a];if(v.length>1)for(i=0;i<v.length;i++)h=v[i],h.indexOf("trace-")===0?(d=h.split("trace-")[1],this.traceHash[d]&&(this.traceHash[d].below=null)):h.indexOf("layout-")===0&&(d=h.split("layout-")[1],this.layerList[d]&&(this.layerList[d].below=null))}};var dje={choroplethmap:0,densitymap:1,scattermap:2};Uh.updateData=function(e){var t=this.traceHash,r,n,i,a,o=e.slice().sort(function(f,h){return dje[f[0].trace.type]-dje[h[0].trace.type]});for(i=0;i<o.length;i++){var s=o[i];n=s[0].trace,r=t[n.uid];var l=!1;r&&(r.type===n.type?(r.update(s),l=!0):r.dispose()),!l&&n._module&&(t[n.uid]=n._module.plot(this,s))}var u=Object.keys(t);e:for(i=0;i<u.length;i++){var c=u[i];for(a=0;a<e.length;a++)if(n=e[a][0].trace,c===n.uid)continue e;r=t[c],r.dispose(),delete t[c]}};Uh.updateLayout=function(e){var t=this.map,r=e[this.id];!this.dragging&&!this.wheeling&&(t.setCenter(RJ(r.center)),t.setZoom(r.zoom),t.setBearing(r.bearing),t.setPitch(r.pitch)),this.updateLayers(e),this.updateFramework(e),this.updateFx(e),this.map.resize(),this.gd._context._scrollZoom.map?t.scrollZoom.enable():t.scrollZoom.disable()};Uh.resolveOnRender=function(e){var t=this.map;t.on("render",function r(){t.loaded()&&(t.off("render",r),setTimeout(e,10))})};Uh.rejectOnError=function(e){var t=this.map;function r(){e(new Error(ow.mapOnErrorMsg))}t.once("error",r),t.once("style.error",r),t.once("source.error",r),t.once("tile.error",r),t.once("layer.error",r)};Uh.createFramework=function(e){var t=this,r=t.div=document.createElement("div");r.id=t.uid,r.style.position="absolute",t.container.appendChild(r),t.xaxis={_id:"x",c2p:function(n){return t.project(n).x}},t.yaxis={_id:"y",c2p:function(n){return t.project(n).y}},t.updateFramework(e),t.mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},EXt.setConvert(t.mockAxis,e)};Uh.initFx=function(e,t){var r=this,n=r.gd,i=r.map;i.on("moveend",function(s){if(r.map){var l=n._fullLayout;if(s.originalEvent||r.wheeling){var u=l[r.id];hje.call("_storeDirectGUIEdit",n.layout,l._preGUI,r.getViewEdits(u));var c=r.getView();u._input.center=u.center=c.center,u._input.zoom=u.zoom=c.zoom,u._input.bearing=u.bearing=c.bearing,u._input.pitch=u.pitch=c.pitch,n.emit("plotly_relayout",r.getViewEditsWithDerived(c))}s.originalEvent&&s.originalEvent.type==="mouseup"?r.dragging=!1:r.wheeling&&(r.wheeling=!1),l&&l._rehover&&l._rehover()}}),i.on("wheel",function(){r.wheeling=!0}),i.on("mousemove",function(s){var l=r.div.getBoundingClientRect(),u=[s.originalEvent.offsetX,s.originalEvent.offsetY];s.target.getBoundingClientRect=function(){return l},r.xaxis.p2c=function(){return i.unproject(u).lng},r.yaxis.p2c=function(){return i.unproject(u).lat},n._fullLayout._rehover=function(){n._fullLayout._hoversubplot===r.id&&n._fullLayout[r.id]&&f7.hover(n,s,r.id)},f7.hover(n,s,r.id),n._fullLayout._hoversubplot=r.id});function a(){f7.loneUnhover(t._hoverlayer)}i.on("dragstart",function(){r.dragging=!0,a()}),i.on("zoomstart",a),i.on("mouseout",function(){n._fullLayout._hoversubplot=null});function o(){var s=r.getView();n.emit("plotly_relayouting",r.getViewEditsWithDerived(s))}i.on("drag",o),i.on("zoom",o),i.on("dblclick",function(){var s=n._fullLayout[r.id];hje.call("_storeDirectGUIEdit",n.layout,n._fullLayout._preGUI,r.getViewEdits(s));var l=r.viewInitial;i.setCenter(RJ(l.center)),i.setZoom(l.zoom),i.setBearing(l.bearing),i.setPitch(l.pitch);var u=r.getView();s._input.center=s.center=u.center,s._input.zoom=s.zoom=u.zoom,s._input.bearing=s.bearing=u.bearing,s._input.pitch=s.pitch=u.pitch,n.emit("plotly_doubleclick",null),n.emit("plotly_relayout",r.getViewEditsWithDerived(u))}),r.clearOutline=function(){RXt(r.dragOptions),IXt(r.dragOptions.gd)},r.onClickInPanFn=function(s){return function(l){var u=n._fullLayout.clickmode;u.indexOf("select")>-1&&DXt(l.originalEvent,n,[r.xaxis],[r.yaxis],r.id,s),u.indexOf("event")>-1&&f7.click(n,l.originalEvent)}}};Uh.updateFx=function(e){var t=this,r=t.map,n=t.gd;if(t.isStatic)return;function i(l){var u=t.map.unproject(l);return[u.lng,u.lat]}var a=e.dragmode,o;o=function(l,u){if(u.isRect){var c=l.range={};c[t.id]=[i([u.xmin,u.ymin]),i([u.xmax,u.ymax])]}else{var f=l.lassoPoints={};f[t.id]=u.map(i)}};var s=t.dragOptions;t.dragOptions=IJ.extendDeep(s||{},{dragmode:e.dragmode,element:t.div,gd:n,plotinfo:{id:t.id,domain:e[t.id].domain,xaxis:t.xaxis,yaxis:t.yaxis,fillRangeItems:o},xaxes:[t.xaxis],yaxes:[t.yaxis],subplot:t.id}),r.off("click",t.onClickInPanHandler),LXt(a)||CXt(a)?(r.dragPan.disable(),r.on("zoomstart",t.clearOutline),t.dragOptions.prepFn=function(l,u,c){PXt(l,u,c,t.dragOptions,a)},kXt.init(t.dragOptions)):(r.dragPan.enable(),r.off("zoomstart",t.clearOutline),t.div.onmousedown=null,t.div.ontouchstart=null,t.div.removeEventListener("touchstart",t.div._ontouchstart),t.onClickInPanHandler=t.onClickInPanFn(t.dragOptions),r.on("click",t.onClickInPanHandler))};Uh.updateFramework=function(e){var t=e[this.id].domain,r=e._size,n=this.div.style;n.width=r.w*(t.x[1]-t.x[0])+"px",n.height=r.h*(t.y[1]-t.y[0])+"px",n.left=r.l+t.x[0]*r.w+"px",n.top=r.t+(1-t.y[1])*r.h+"px",this.xaxis._offset=r.l+t.x[0]*r.w,this.xaxis._length=r.w*(t.x[1]-t.x[0]),this.yaxis._offset=r.t+(1-t.y[1])*r.h,this.yaxis._length=r.h*(t.y[1]-t.y[0])};Uh.updateLayers=function(e){var t=e[this.id],r=t.layers,n=this.layerList,i;if(r.length!==n.length){for(i=0;i<n.length;i++)n[i].dispose();for(n=this.layerList=[],i=0;i<r.length;i++)n.push(FXt(this,i,r[i]))}else for(i=0;i<r.length;i++)n[i].update(r[i])};Uh.destroy=function(){this.map&&(this.map.remove(),this.map=null,this.container.removeChild(this.div))};Uh.toImage=function(){return this.map.stop(),this.map.getCanvas().toDataURL()};Uh.setOptions=function(e,t,r){for(var n in r)this.map[t](e,n,r[n])};Uh.getMapLayers=function(){return this.map.getStyle().layers};Uh.addLayer=function(e,t){var r=this.map;if(typeof t=="string"){if(t===""){r.addLayer(e,t);return}for(var n=this.getMapLayers(),i=0;i<n.length;i++)if(t===n[i].id){r.addLayer(e,t);return}IJ.warn(["Trying to add layer with *below* value",t,"referencing a layer that does not exist","or that does not yet exist."].join(" "))}r.addLayer(e)};Uh.project=function(e){return this.map.project(new PJ.LngLat(e[0],e[1]))};Uh.getView=function(){var e=this.map,t=e.getCenter(),r=t.lng,n=t.lat,i={lon:r,lat:n},a=e.getCanvas(),o=parseInt(a.style.width),s=parseInt(a.style.height);return{center:i,zoom:e.getZoom(),bearing:e.getBearing(),pitch:e.getPitch(),_derived:{coordinates:[e.unproject([0,0]).toArray(),e.unproject([o,0]).toArray(),e.unproject([o,s]).toArray(),e.unproject([0,s]).toArray()]}}};Uh.getViewEdits=function(e){for(var t=this.id,r=["center","zoom","bearing","pitch"],n={},i=0;i<r.length;i++){var a=r[i];n[t+"."+a]=e[a]}return n};Uh.getViewEditsWithDerived=function(e){var t=this.id,r=this.getViewEdits(e);return r[t+"._derived"]=e._derived,r};function mje(e){var t={};return IJ.isPlainObject(e)?(t.id=e.id,t.style=e):typeof e=="string"?(t.id=e,ow.stylesMap[e]?t.style=ow.stylesMap[e]:t.style=e):(t.id=ow.styleValueDflt,t.style=zXt(ow.styleValueDflt)),t.transition={duration:0,delay:0},t}function zXt(e){return ow.styleUrlPrefix+e+"-"+ow.styleUrlSuffix}function RJ(e){return[e.lon,e.lat]}yje.exports=gje});var wje=ye((Cbr,bje)=>{"use strict";var DJ=Dr(),OXt=k_(),qXt=Yd(),xje=jk();bje.exports=function(t,r,n){OXt(t,r,n,{type:"map",attributes:xje,handleDefaults:BXt,partition:"y"})};function BXt(e,t,r){r("style"),r("center.lon"),r("center.lat"),r("zoom"),r("bearing"),r("pitch");var n=r("bounds.west"),i=r("bounds.east"),a=r("bounds.south"),o=r("bounds.north");(n===void 0||i===void 0||a===void 0||o===void 0)&&delete t.bounds,qXt(e,t,{name:"layers",handleItemDefaults:NXt}),t._input=e}function NXt(e,t){function r(l,u){return DJ.coerce(e,t,xje.layers,l,u)}var n=r("visible");if(n){var i=r("sourcetype"),a=i==="raster"||i==="image";r("source"),r("sourceattribution"),i==="vector"&&r("sourcelayer"),i==="image"&&r("coordinates");var o;a&&(o="raster");var s=r("type",o);a&&s!=="raster"&&(s=t.type="raster",DJ.log("Source types *raster* and *image* must drawn *raster* layer type.")),r("below"),r("color"),r("opacity"),r("minzoom"),r("maxzoom"),s==="circle"&&r("circle.radius"),s==="line"&&(r("line.width"),r("line.dash")),s==="fill"&&r("fill.outlinecolor"),s==="symbol"&&(r("symbol.icon"),r("symbol.iconsize"),r("symbol.text"),DJ.coerceFont(r,"symbol.textfont",void 0,{noFontVariant:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0}),r("symbol.textposition"),r("symbol.placement"))}}});var d7=ye(l0=>{"use strict";var h7=Dr(),Tje=h7.strTranslate,UXt=h7.strScale,VXt=Id().getSubplotCalcData,GXt=Wp(),HXt=Oa(),Aje=So(),jXt=ru(),WXt=_je(),Tx="map";l0.name=Tx;l0.attr="subplot";l0.idRoot=Tx;l0.idRegex=l0.attrRegex=h7.counterRegex(Tx);l0.attributes={subplot:{valType:"subplotid",dflt:"map",editType:"calc"}};l0.layoutAttributes=jk();l0.supplyLayoutDefaults=wje();l0.plot=function(t){for(var r=t._fullLayout,n=t.calcdata,i=r._subplots[Tx],a=0;a<i.length;a++){var o=i[a],s=VXt(n,Tx,o),l=r[o],u=l._subplot;u||(u=new WXt(t,o),r[o]._subplot=u),u.viewInitial||(u.viewInitial={center:h7.extendFlat({},l.center),zoom:l.zoom,bearing:l.bearing,pitch:l.pitch}),u.plot(s,r,t._promises)}};l0.clean=function(e,t,r,n){for(var i=n._subplots[Tx]||[],a=0;a<i.length;a++){var o=i[a];!t[o]&&n[o]._subplot&&n[o]._subplot.destroy()}};l0.toSVG=function(e){for(var t=e._fullLayout,r=t._subplots[Tx],n=t._size,i=0;i<r.length;i++){var a=t[r[i]],o=a.domain,s=a._subplot,l=s.toImage("png"),u=t._glimages.append("svg:image");u.attr({xmlns:GXt.svg,"xlink:href":l,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"});var c=HXt.select(a._subplot.div),f=c.select(".maplibregl-ctrl-attrib").text().replace("Improve this map",""),h=t._glimages.append("g"),d=h.append("text");d.text(f).classed("static-attribution",!0).attr({"font-size":12,"font-family":"Arial",color:"rgba(0, 0, 0, 0.75)","text-anchor":"end","data-unformatted":f});var v=Aje.bBox(d.node()),_=n.w*(o.x[1]-o.x[0]);if(v.width>_/2){var b=f.split("|").join("<br>");d.text(b).attr("data-unformatted",b).call(jXt.convertToTspans,e),v=Aje.bBox(d.node())}d.attr("transform",Tje(-3,-v.height+8)),h.insert("rect",".static-attribution").attr({x:-v.width-6,y:-v.height-3,width:v.width+6,height:v.height+3,fill:"rgba(255, 255, 255, 0.75)"});var p=1;v.width+6>_&&(p=_/(v.width+6));var k=[n.l+n.w*o.x[1],n.t+n.h*(1-o.y[0])];h.attr("transform",Tje(k[0],k[1])+UXt(p))}};l0.updateFx=function(e){for(var t=e._fullLayout,r=t._subplots[Tx],n=0;n<r.length;n++){var i=t[r[n]]._subplot;i.updateFx(t)}}});var Mje=ye((Pbr,Sje)=>{"use strict";Sje.exports={attributes:n7(),supplyDefaults:qHe(),colorbar:$d(),formatLabels:AJ(),calc:pF(),plot:$He(),hoverPoints:u7().hoverPoints,eventData:rje(),selectPoints:nje(),styleOnSelect:function(e,t){if(t){var r=t[0].trace;r._glTrace.update(t)}},moduleType:"trace",name:"scattermap",basePlotModule:d7(),categories:["map","gl","symbols","showLegend","scatter-like"],meta:{}}});var kje=ye((Ibr,Eje)=>{"use strict";Eje.exports=Mje()});var FJ=ye((Rbr,Cje)=>{"use strict";var d1=t5(),XXt=Tu(),{hovertemplateAttrs:ZXt,templatefallbackAttrs:YXt}=Ll(),KXt=Gl(),Ax=Ao().extendFlat;Cje.exports=Ax({locations:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},geojson:{valType:"any",editType:"calc"},featureidkey:Ax({},d1.featureidkey,{}),below:{valType:"string",editType:"plot"},text:d1.text,hovertext:d1.hovertext,marker:{line:{color:Ax({},d1.marker.line.color,{editType:"plot"}),width:Ax({},d1.marker.line.width,{editType:"plot"}),editType:"calc"},opacity:Ax({},d1.marker.opacity,{editType:"plot"}),editType:"calc"},selected:{marker:{opacity:Ax({},d1.selected.marker.opacity,{editType:"plot"}),editType:"plot"},editType:"plot"},unselected:{marker:{opacity:Ax({},d1.unselected.marker.opacity,{editType:"plot"}),editType:"plot"},editType:"plot"},hoverinfo:d1.hoverinfo,hovertemplate:ZXt({},{keys:["properties"]}),hovertemplatefallback:YXt(),showlegend:Ax({},KXt.showlegend,{dflt:!1})},XXt("",{cLetter:"z",editTypeOverride:"calc"}))});var Pje=ye((Dbr,Lje)=>{"use strict";var Yk=Dr(),JXt=Qh(),$Xt=FJ();Lje.exports=function(t,r,n,i){function a(c,f){return Yk.coerce(t,r,$Xt,c,f)}var o=a("locations"),s=a("z"),l=a("geojson");if(!Yk.isArrayOrTypedArray(o)||!o.length||!Yk.isArrayOrTypedArray(s)||!s.length||!(typeof l=="string"&&l!==""||Yk.isPlainObject(l))){r.visible=!1;return}a("featureidkey"),r._length=Math.min(o.length,s.length),a("below"),a("text"),a("hovertext"),a("hovertemplate"),a("hovertemplatefallback");var u=a("marker.line.width");u&&a("marker.line.color"),a("marker.opacity"),JXt(t,r,i,a,{prefix:"",cLetter:"z"}),Yk.coerceSelectionMarkerOpacity(r,a)}});var zJ=ye((Fbr,Dje)=>{"use strict";var QXt=Eo(),v1=Dr(),eZt=tc(),tZt=So(),rZt=tx().makeBlank,Ije=ix();function iZt(e){var t=e[0].trace,r=t.visible===!0&&t._length!==0,n={layout:{visibility:"none"},paint:{}},i={layout:{visibility:"none"},paint:{}},a=t._opts={fill:n,line:i,geojson:rZt()};if(!r)return a;var o=Ije.extractTraceFeature(e);if(!o)return a;var s=eZt.makeColorScaleFuncFromTrace(t),l=t.marker,u=l.line||{},c;v1.isArrayOrTypedArray(l.opacity)&&(c=function(k){var E=k.mo;return QXt(E)?+v1.constrain(E,0,1):0});var f;v1.isArrayOrTypedArray(u.color)&&(f=function(k){return k.mlc});var h;v1.isArrayOrTypedArray(u.width)&&(h=function(k){return k.mlw});for(var d=0;d<e.length;d++){var v=e[d],_=v.fOut;if(_){var b=_.properties;b.fc=s(v.z),c&&(b.mo=c(v)),f&&(b.mlc=f(v)),h&&(b.mlw=h(v)),v.ct=b.ct,v._polygons=Ije.feature2polygons(_)}}var p=c?{type:"identity",property:"mo"}:l.opacity;return v1.extendFlat(n.paint,{"fill-color":{type:"identity",property:"fc"},"fill-opacity":p}),v1.extendFlat(i.paint,{"line-color":f?{type:"identity",property:"mlc"}:u.color,"line-width":h?{type:"identity",property:"mlw"}:u.width,"line-opacity":p}),n.layout.visibility="visible",i.layout.visibility="visible",a.geojson={type:"FeatureCollection",features:o},Rje(e),a}function Rje(e){var t=e[0].trace,r=t._opts,n;if(t.selectedpoints){for(var i=tZt.makeSelectedPointStyleFns(t),a=0;a<e.length;a++){var o=e[a];o.fOut&&(o.fOut.properties.mo2=i.selectedOpacityFn(o))}n={type:"identity",property:"mo2"}}else n=v1.isArrayOrTypedArray(t.marker.opacity)?{type:"identity",property:"mo"}:t.marker.opacity;return v1.extendFlat(r.fill.paint,{"fill-opacity":n}),v1.extendFlat(r.line.paint,{"line-opacity":n}),r}Dje.exports={convert:iZt,convertOnSelect:Rje}});var Bje=ye((zbr,qje)=>{"use strict";var zje=zJ().convert,nZt=zJ().convertOnSelect,Fje=wx().traceLayerPrefix;function Oje(e,t){this.type="choroplethmap",this.subplot=e,this.uid=t,this.sourceId="source-"+t,this.layerList=[["fill",Fje+t+"-fill"],["line",Fje+t+"-line"]],this.below=null}var L5=Oje.prototype;L5.update=function(e){this._update(zje(e)),e[0].trace._glTrace=this};L5.updateOnSelect=function(e){this._update(nZt(e))};L5._update=function(e){var t=this.subplot,r=this.layerList,n=t.belowLookup["trace-"+this.uid];t.map.getSource(this.sourceId).setData(e.geojson),n!==this.below&&(this._removeLayers(),this._addLayers(e,n),this.below=n);for(var i=0;i<r.length;i++){var a=r[i],o=a[0],s=a[1],l=e[o];t.setOptions(s,"setLayoutProperty",l.layout),l.layout.visibility==="visible"&&t.setOptions(s,"setPaintProperty",l.paint)}};L5._addLayers=function(e,t){for(var r=this.subplot,n=this.layerList,i=this.sourceId,a=0;a<n.length;a++){var o=n[a],s=o[0],l=e[s];r.addLayer({type:s,id:o[1],source:i,layout:l.layout,paint:l.paint},t)}};L5._removeLayers=function(){for(var e=this.subplot.map,t=this.layerList,r=t.length-1;r>=0;r--)e.removeLayer(t[r][1])};L5.dispose=function(){var e=this.subplot.map;this._removeLayers(),e.removeSource(this.sourceId)};qje.exports=function(t,r){var n=r[0].trace,i=new Oje(t,n.uid),a=i.sourceId,o=zje(r),s=i.below=t.belowLookup["trace-"+n.uid];return t.map.addSource(a,{type:"geojson",data:o.geojson}),i._addLayers(o,s),r[0].trace._glTrace=i,i}});var Uje=ye((Obr,Nje)=>{"use strict";Nje.exports={attributes:FJ(),supplyDefaults:Pje(),colorbar:S_(),calc:zF(),plot:Bje(),hoverPoints:qF(),eventData:BF(),selectPoints:NF(),styleOnSelect:function(e,t){if(t){var r=t[0].trace;r._glTrace.updateOnSelect(t)}},getBelow:function(e,t){for(var r=t.getMapLayers(),n=r.length-2;n>=0;n--){var i=r[n].id;if(typeof i=="string"&&i.indexOf("water")===0){for(var a=n+1;a<r.length;a++)if(i=r[a].id,typeof i=="string"&&i.indexOf("plotly-")===-1)return i}}},moduleType:"trace",name:"choroplethmap",basePlotModule:d7(),categories:["map","gl","noOpacity","showLegend"],meta:{hr_name:"choropleth_map"}}});var Gje=ye((qbr,Vje)=>{"use strict";Vje.exports=Uje()});var qJ=ye((Bbr,jje)=>{"use strict";var aZt=Tu(),{hovertemplateAttrs:oZt,templatefallbackAttrs:sZt}=Ll(),Hje=Gl(),v7=n7(),OJ=Ao().extendFlat;jje.exports=OJ({lon:v7.lon,lat:v7.lat,z:{valType:"data_array",editType:"calc"},radius:{valType:"number",editType:"plot",arrayOk:!0,min:1,dflt:30},below:{valType:"string",editType:"plot"},text:v7.text,hovertext:v7.hovertext,hoverinfo:OJ({},Hje.hoverinfo,{flags:["lon","lat","z","text","name"]}),hovertemplate:oZt(),hovertemplatefallback:sZt(),showlegend:OJ({},Hje.showlegend,{dflt:!1})},aZt("",{cLetter:"z",editTypeOverride:"calc"}))});var Xje=ye((Nbr,Wje)=>{"use strict";var lZt=Dr(),uZt=Qh(),cZt=qJ();Wje.exports=function(t,r,n,i){function a(u,c){return lZt.coerce(t,r,cZt,u,c)}var o=a("lon")||[],s=a("lat")||[],l=Math.min(o.length,s.length);if(!l){r.visible=!1;return}r._length=l,a("z"),a("radius"),a("below"),a("text"),a("hovertext"),a("hovertemplate"),a("hovertemplatefallback"),uZt(t,r,i,a,{prefix:"",cLetter:"z"})}});var Kje=ye((Ubr,Yje)=>{"use strict";var BJ=Eo(),fZt=Dr().isArrayOrTypedArray,NJ=fs().BADNUM,hZt=gv(),Zje=Dr()._;Yje.exports=function(t,r){for(var n=r._length,i=new Array(n),a=r.z,o=fZt(a)&&a.length,s=0;s<n;s++){var l=i[s]={},u=r.lon[s],c=r.lat[s];if(l.lonlat=BJ(u)&&BJ(c)?[+u,+c]:[NJ,NJ],o){var f=a[s];l.z=BJ(f)?f:NJ}}return hZt(t,r,{vals:o?a:[0,1],containerStr:"",cLetter:"z"}),n&&(i[0].t={labels:{lat:Zje(t,"lat:")+" ",lon:Zje(t,"lon:")+" "}}),i}});var tWe=ye((Vbr,eWe)=>{"use strict";var dZt=Eo(),UJ=Dr(),Jje=ka(),$je=tc(),Qje=fs().BADNUM,vZt=tx().makeBlank;eWe.exports=function(t){var r=t[0].trace,n=r.visible===!0&&r._length!==0,i={layout:{visibility:"none"},paint:{}},a=r._opts={heatmap:i,geojson:vZt()};if(!n)return a;var o=[],s,l=r.z,u=r.radius,c=UJ.isArrayOrTypedArray(l)&&l.length,f=UJ.isArrayOrTypedArray(u);for(s=0;s<t.length;s++){var h=t[s],d=h.lonlat;if(d[0]!==Qje){var v={};if(c){var _=h.z;v.z=_!==Qje?_:0}f&&(v.r=dZt(u[s])&&u[s]>0?+u[s]:0),o.push({type:"Feature",geometry:{type:"Point",coordinates:d},properties:v})}}var b=$je.extractOpts(r),p=b.reversescale?$je.flipScale(b.colorscale):b.colorscale,k=p[0][1],E=Jje.opacity(k)<1?k:Jje.addOpacity(k,0),S=["interpolate",["linear"],["heatmap-density"],0,E];for(s=1;s<p.length;s++)S.push(p[s][0],p[s][1]);var L=["interpolate",["linear"],["get","z"],b.min,0,b.max,1];return UJ.extendFlat(a.heatmap.paint,{"heatmap-weight":c?L:1/(b.max-b.min),"heatmap-color":S,"heatmap-radius":f?{type:"identity",property:"r"}:r.radius,"heatmap-opacity":r.opacity}),a.geojson={type:"FeatureCollection",features:o},a.heatmap.layout.visibility="visible",a}});var aWe=ye((Gbr,nWe)=>{"use strict";var rWe=tWe(),pZt=wx().traceLayerPrefix;function iWe(e,t){this.type="densitymap",this.subplot=e,this.uid=t,this.sourceId="source-"+t,this.layerList=[["heatmap",pZt+t+"-heatmap"]],this.below=null}var p7=iWe.prototype;p7.update=function(e){var t=this.subplot,r=this.layerList,n=rWe(e),i=t.belowLookup["trace-"+this.uid];t.map.getSource(this.sourceId).setData(n.geojson),i!==this.below&&(this._removeLayers(),this._addLayers(n,i),this.below=i);for(var a=0;a<r.length;a++){var o=r[a],s=o[0],l=o[1],u=n[s];t.setOptions(l,"setLayoutProperty",u.layout),u.layout.visibility==="visible"&&t.setOptions(l,"setPaintProperty",u.paint)}};p7._addLayers=function(e,t){for(var r=this.subplot,n=this.layerList,i=this.sourceId,a=0;a<n.length;a++){var o=n[a],s=o[0],l=e[s];r.addLayer({type:s,id:o[1],source:i,layout:l.layout,paint:l.paint},t)}};p7._removeLayers=function(){for(var e=this.subplot.map,t=this.layerList,r=t.length-1;r>=0;r--)e.removeLayer(t[r][1])};p7.dispose=function(){var e=this.subplot.map;this._removeLayers(),e.removeSource(this.sourceId)};nWe.exports=function(t,r){var n=r[0].trace,i=new iWe(t,n.uid),a=i.sourceId,o=rWe(r),s=i.below=t.belowLookup["trace-"+n.uid];return t.map.addSource(a,{type:"geojson",data:o.geojson}),i._addLayers(o,s),i}});var sWe=ye((Hbr,oWe)=>{"use strict";var gZt=ho(),mZt=u7().hoverPoints,yZt=u7().getExtraText;oWe.exports=function(t,r,n){var i=mZt(t,r,n);if(i){var a=i[0],o=a.cd,s=o[0].trace,l=o[a.index];if(delete a.color,"z"in l){var u=a.subplot.mockAxis;a.z=l.z,a.zLabel=gZt.tickText(u,u.c2l(l.z),"hover").text}return a.extraText=yZt(s,l,o[0].t.labels),[a]}}});var uWe=ye((jbr,lWe)=>{"use strict";lWe.exports=function(t,r){return t.lon=r.lon,t.lat=r.lat,t.z=r.z,t}});var fWe=ye((Wbr,cWe)=>{"use strict";cWe.exports={attributes:qJ(),supplyDefaults:Xje(),colorbar:S_(),formatLabels:AJ(),calc:Kje(),plot:aWe(),hoverPoints:sWe(),eventData:uWe(),getBelow:function(e,t){for(var r=t.getMapLayers(),n=0;n<r.length;n++){var i=r[n],a=i.id;if(i.type==="symbol"&&typeof a=="string"&&a.indexOf("plotly-")===-1)return a}},moduleType:"trace",name:"densitymap",basePlotModule:d7(),categories:["map","gl","showLegend"],meta:{hr_name:"density_map"}}});var dWe=ye((Xbr,hWe)=>{"use strict";hWe.exports=fWe()});var GJ=ye((Ybr,yWe)=>{"use strict";var _Zt=ec(),xZt=Gl(),vWe=Lh(),VJ=a3(),bZt=Cc().attributes,{hovertemplateAttrs:pWe,templatefallbackAttrs:gWe}=Ll(),wZt=Tu(),TZt=vl().templatedArray,AZt=df().descriptionOnlyNumbers,mWe=Ao().extendFlat,SZt=mc().overrideAll,Zbr=yWe.exports=SZt({hoverinfo:mWe({},xZt.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:VJ.hoverlabel,domain:bZt({name:"sankey",trace:!0}),orientation:{valType:"enumerated",values:["v","h"],dflt:"h"},valueformat:{valType:"string",dflt:".3s",description:AZt("value")},valuesuffix:{valType:"string",dflt:""},arrangement:{valType:"enumerated",values:["snap","perpendicular","freeform","fixed"],dflt:"snap"},textfont:_Zt({autoShadowDflt:!0}),customdata:void 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2:this._point=3,this._x4=Mt,this._y4=kr,this._context.moveTo((this._x0+4*this._x1+Mt)/6,(this._y0+4*this._y1+kr)/6);break;default:lr(this,Mt,kr);break}this._x0=this._x1,this._x1=Mt,this._y0=this._y1,this._y1=kr}};function Br(Mt){return new ti(Mt)}function Vr(Mt){this._context=Mt}Vr.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=NaN,this._point=0},lineEnd:function(){(this._line||this._line!==0&&this._point===3)&&this._context.closePath(),this._line=1-this._line},point:function(Mt,kr){switch(Mt=+Mt,kr=+kr,this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3;var Jr=(this._x0+4*this._x1+Mt)/6,vi=(this._y0+4*this._y1+kr)/6;this._line?this._context.lineTo(Jr,vi):this._context.moveTo(Jr,vi);break;case 3:this._point=4;default:lr(this,Mt,kr);break}this._x0=this._x1,this._x1=Mt,this._y0=this._y1,this._y1=kr}};function dt(Mt){return new Vr(Mt)}function Ge(Mt,kr){this._basis=new Tr(Mt),this._beta=kr}Ge.prototype={lineStart:function(){this._x=[],this._y=[],this._basis.lineStart()},lineEnd:function(){var Mt=this._x,kr=this._y,Jr=Mt.length-1;if(Jr>0)for(var vi=Mt[0],hn=kr[0],An=Mt[Jr]-vi,Mn=kr[Jr]-hn,Li=-1,_n;++Li<=Jr;)_n=Li/Jr,this._basis.point(this._beta*Mt[Li]+(1-this._beta)*(vi+_n*An),this._beta*kr[Li]+(1-this._beta)*(hn+_n*Mn));this._x=this._y=null,this._basis.lineEnd()},point:function(Mt,kr){this._x.push(+Mt),this._y.push(+kr)}};var Je=function Mt(kr){function Jr(vi){return kr===1?new Tr(vi):new Ge(vi,kr)}return Jr.beta=function(vi){return Mt(+vi)},Jr}(.85);function je(Mt,kr,Jr){Mt._context.bezierCurveTo(Mt._x1+Mt._k*(Mt._x2-Mt._x0),Mt._y1+Mt._k*(Mt._y2-Mt._y0),Mt._x2+Mt._k*(Mt._x1-kr),Mt._y2+Mt._k*(Mt._y1-Jr),Mt._x2,Mt._y2)}function tt(Mt,kr){this._context=Mt,this._k=(1-kr)/6}tt.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._point=0},lineEnd:function(){switch(this._point){case 2:this._context.lineTo(this._x2,this._y2);break;case 3:je(this,this._x1,this._y1);break}(this._line||this._line!==0&&this._point===1)&&this._context.closePath(),this._line=1-this._line},point:function(Mt,kr){switch(Mt=+Mt,kr=+kr,this._point){case 0:this._point=1,this._line?this._context.lineTo(Mt,kr):this._context.moveTo(Mt,kr);break;case 1:this._point=2,this._x1=Mt,this._y1=kr;break;case 2:this._point=3;default:je(this,Mt,kr);break}this._x0=this._x1,this._x1=this._x2,this._x2=Mt,this._y0=this._y1,this._y1=this._y2,this._y2=kr}};var xt=function Mt(kr){function Jr(vi){return new tt(vi,kr)}return Jr.tension=function(vi){return Mt(+vi)},Jr}(0);function 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Mt(+vi)},Jr}(0);function ke(Mt,kr){this._context=Mt,this._k=(1-kr)/6}ke.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._point=0},lineEnd:function(){(this._line||this._line!==0&&this._point===3)&&this._context.closePath(),this._line=1-this._line},point:function(Mt,kr){switch(Mt=+Mt,kr=+kr,this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3,this._line?this._context.lineTo(this._x2,this._y2):this._context.moveTo(this._x2,this._y2);break;case 3:this._point=4;default:je(this,Mt,kr);break}this._x0=this._x1,this._x1=this._x2,this._x2=Mt,this._y0=this._y1,this._y1=this._y2,this._y2=kr}};var vt=function Mt(kr){function Jr(vi){return new ke(vi,kr)}return Jr.tension=function(vi){return Mt(+vi)},Jr}(0);function ir(Mt,kr,Jr){var vi=Mt._x1,hn=Mt._y1,An=Mt._x2,Mn=Mt._y2;if(Mt._l01_a>c){var Li=2*Mt._l01_2a+3*Mt._l01_a*Mt._l12_a+Mt._l12_2a,_n=3*Mt._l01_a*(Mt._l01_a+Mt._l12_a);vi=(vi*Li-Mt._x0*Mt._l12_2a+Mt._x2*Mt._l01_2a)/_n,hn=(hn*Li-Mt._y0*Mt._l12_2a+Mt._y2*Mt._l01_2a)/_n}if(Mt._l23_a>c){var ya=2*Mt._l23_2a+3*Mt._l23_a*Mt._l12_a+Mt._l12_2a,Jn=3*Mt._l23_a*(Mt._l23_a+Mt._l12_a);An=(An*ya+Mt._x1*Mt._l23_2a-kr*Mt._l12_2a)/Jn,Mn=(Mn*ya+Mt._y1*Mt._l23_2a-Jr*Mt._l12_2a)/Jn}Mt._context.bezierCurveTo(vi,hn,An,Mn,Mt._x2,Mt._y2)}function ar(Mt,kr){this._context=Mt,this._alpha=kr}ar.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._l01_a=this._l12_a=this._l23_a=this._l01_2a=this._l12_2a=this._l23_2a=this._point=0},lineEnd:function(){switch(this._point){case 2:this._context.lineTo(this._x2,this._y2);break;case 3:this.point(this._x2,this._y2);break}(this._line||this._line!==0&&this._point===1)&&this._context.closePath(),this._line=1-this._line},point:function(Mt,kr){if(Mt=+Mt,kr=+kr,this._point){var Jr=this._x2-Mt,vi=this._y2-kr;this._l23_a=Math.sqrt(this._l23_2a=Math.pow(Jr*Jr+vi*vi,this._alpha))}switch(this._point){case 0:this._point=1,this._line?this._context.lineTo(Mt,kr):this._context.moveTo(Mt,kr);break;case 1:this._point=2;break;case 2:this._point=3;default:ir(this,Mt,kr);break}this._l01_a=this._l12_a,this._l12_a=this._l23_a,this._l01_2a=this._l12_2a,this._l12_2a=this._l23_2a,this._x0=this._x1,this._x1=this._x2,this._x2=Mt,this._y0=this._y1,this._y1=this._y2,this._y2=kr}};var vr=function Mt(kr){function Jr(vi){return kr?new ar(vi,kr):new tt(vi,0)}return Jr.alpha=function(vi){return Mt(+vi)},Jr}(.5);function ii(Mt,kr){this._context=Mt,this._alpha=kr}ii.prototype={areaStart:er,areaEnd:er,lineStart:function(){this._x0=this._x1=this._x2=this._x3=this._x4=this._x5=this._y0=this._y1=this._y2=this._y3=this._y4=this._y5=NaN,this._l01_a=this._l12_a=this._l23_a=this._l01_2a=this._l12_2a=this._l23_2a=this._point=0},lineEnd:function(){switch(this._point){case 1:{this._context.moveTo(this._x3,this._y3),this._context.closePath();break}case 2:{this._context.lineTo(this._x3,this._y3),this._context.closePath();break}case 3:{this.point(this._x3,this._y3),this.point(this._x4,this._y4),this.point(this._x5,this._y5);break}}},point:function(Mt,kr){if(Mt=+Mt,kr=+kr,this._point){var Jr=this._x2-Mt,vi=this._y2-kr;this._l23_a=Math.sqrt(this._l23_2a=Math.pow(Jr*Jr+vi*vi,this._alpha))}switch(this._point){case 0:this._point=1,this._x3=Mt,this._y3=kr;break;case 1:this._point=2,this._context.moveTo(this._x4=Mt,this._y4=kr);break;case 2:this._point=3,this._x5=Mt,this._y5=kr;break;default:ir(this,Mt,kr);break}this._l01_a=this._l12_a,this._l12_a=this._l23_a,this._l01_2a=this._l12_2a,this._l12_2a=this._l23_2a,this._x0=this._x1,this._x1=this._x2,this._x2=Mt,this._y0=this._y1,this._y1=this._y2,this._y2=kr}};var pi=function Mt(kr){function Jr(vi){return kr?new ii(vi,kr):new Ie(vi,0)}return Jr.alpha=function(vi){return Mt(+vi)},Jr}(.5);function $r(Mt,kr){this._context=Mt,this._alpha=kr}$r.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._l01_a=this._l12_a=this._l23_a=this._l01_2a=this._l12_2a=this._l23_2a=this._point=0},lineEnd:function(){(this._line||this._line!==0&&this._point===3)&&this._context.closePath(),this._line=1-this._line},point:function(Mt,kr){if(Mt=+Mt,kr=+kr,this._point){var Jr=this._x2-Mt,vi=this._y2-kr;this._l23_a=Math.sqrt(this._l23_2a=Math.pow(Jr*Jr+vi*vi,this._alpha))}switch(this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3,this._line?this._context.lineTo(this._x2,this._y2):this._context.moveTo(this._x2,this._y2);break;case 3:this._point=4;default:ir(this,Mt,kr);break}this._l01_a=this._l12_a,this._l12_a=this._l23_a,this._l01_2a=this._l12_2a,this._l12_2a=this._l23_2a,this._x0=this._x1,this._x1=this._x2,this._x2=Mt,this._y0=this._y1,this._y1=this._y2,this._y2=kr}};var di=function Mt(kr){function Jr(vi){return kr?new $r(vi,kr):new ke(vi,0)}return Jr.alpha=function(vi){return Mt(+vi)},Jr}(.5);function ji(Mt){this._context=Mt}ji.prototype={areaStart:er,areaEnd:er,lineStart:function(){this._point=0},lineEnd:function(){this._point&&this._context.closePath()},point:function(Mt,kr){Mt=+Mt,kr=+kr,this._point?this._context.lineTo(Mt,kr):(this._point=1,this._context.moveTo(Mt,kr))}};function In(Mt){return new ji(Mt)}function wi(Mt){return Mt<0?-1:1}function On(Mt,kr,Jr){var vi=Mt._x1-Mt._x0,hn=kr-Mt._x1,An=(Mt._y1-Mt._y0)/(vi||hn<0&&-0),Mn=(Jr-Mt._y1)/(hn||vi<0&&-0),Li=(An*hn+Mn*vi)/(vi+hn);return(wi(An)+wi(Mn))*Math.min(Math.abs(An),Math.abs(Mn),.5*Math.abs(Li))||0}function qn(Mt,kr){var Jr=Mt._x1-Mt._x0;return Jr?(3*(Mt._y1-Mt._y0)/Jr-kr)/2:kr}function Fn(Mt,kr,Jr){var vi=Mt._x0,hn=Mt._y0,An=Mt._x1,Mn=Mt._y1,Li=(An-vi)/3;Mt._context.bezierCurveTo(vi+Li,hn+Li*kr,An-Li,Mn-Li*Jr,An,Mn)}function ra(Mt){this._context=Mt}ra.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=this._t0=NaN,this._point=0},lineEnd:function(){switch(this._point){case 2:this._context.lineTo(this._x1,this._y1);break;case 3:Fn(this,this._t0,qn(this,this._t0));break}(this._line||this._line!==0&&this._point===1)&&this._context.closePath(),this._line=1-this._line},point:function(Mt,kr){var Jr=NaN;if(Mt=+Mt,kr=+kr,!(Mt===this._x1&&kr===this._y1)){switch(this._point){case 0:this._point=1,this._line?this._context.lineTo(Mt,kr):this._context.moveTo(Mt,kr);break;case 1:this._point=2;break;case 2:this._point=3,Fn(this,qn(this,Jr=On(this,Mt,kr)),Jr);break;default:Fn(this,this._t0,Jr=On(this,Mt,kr));break}this._x0=this._x1,this._x1=Mt,this._y0=this._y1,this._y1=kr,this._t0=Jr}}};function la(Mt){this._context=new Ut(Mt)}(la.prototype=Object.create(ra.prototype)).point=function(Mt,kr){ra.prototype.point.call(this,kr,Mt)};function Ut(Mt){this._context=Mt}Ut.prototype={moveTo:function(Mt,kr){this._context.moveTo(kr,Mt)},closePath:function(){this._context.closePath()},lineTo:function(Mt,kr){this._context.lineTo(kr,Mt)},bezierCurveTo:function(Mt,kr,Jr,vi,hn,An){this._context.bezierCurveTo(kr,Mt,vi,Jr,An,hn)}};function wt(Mt){return new ra(Mt)}function rr(Mt){return new la(Mt)}function 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Array(Jr);for(hn[0]=0,An[0]=2,Mn[0]=Mt[0]+2*Mt[1],kr=1;kr<Jr-1;++kr)hn[kr]=1,An[kr]=4,Mn[kr]=4*Mt[kr]+2*Mt[kr+1];for(hn[Jr-1]=2,An[Jr-1]=7,Mn[Jr-1]=8*Mt[Jr-1]+Mt[Jr],kr=1;kr<Jr;++kr)vi=hn[kr]/An[kr-1],An[kr]-=vi,Mn[kr]-=vi*Mn[kr-1];for(hn[Jr-1]=Mn[Jr-1]/An[Jr-1],kr=Jr-2;kr>=0;--kr)hn[kr]=(Mn[kr]-hn[kr+1])/An[kr];for(An[Jr-1]=(Mt[Jr]+hn[Jr-1])/2,kr=0;kr<Jr-1;++kr)An[kr]=2*Mt[kr+1]-hn[kr+1];return[hn,An]}function Xr(Mt){return new nr(Mt)}function ri(Mt,kr){this._context=Mt,this._t=kr}ri.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x=this._y=NaN,this._point=0},lineEnd:function(){0<this._t&&this._t<1&&this._point===2&&this._context.lineTo(this._x,this._y),(this._line||this._line!==0&&this._point===1)&&this._context.closePath(),this._line>=0&&(this._t=1-this._t,this._line=1-this._line)},point:function(Mt,kr){switch(Mt=+Mt,kr=+kr,this._point){case 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st.y0+lt>=Gt&&st.y1+lt<=Nt&&(st.y0=st.y0+lt,st.y1=st.y1+lt,st.targetLinks.forEach(function(Jt){Jt.y1=Jt.y1+lt}),st.sourceLinks.forEach(function(Jt){Jt.y0=Jt.y0+lt})),st}function Re(st,lt,Gt,Nt){st.nodes.forEach(function(Jt){Nt&&Jt.y+(Jt.y1-Jt.y0)>lt&&(Jt.y=Jt.y-(Jt.y+(Jt.y1-Jt.y0)-lt));var sr=st.links.filter(function($e){return C($e.source,Gt)==C(Jt,Gt)}),wr=sr.length;wr>1&&sr.sort(function($e,St){if(!$e.circular&&!St.circular){if($e.target.column==St.target.column)return $e.y1-St.y1;if(Ze($e,St)){if($e.target.column>St.target.column){var Qt=Ae(St,$e);return $e.y1-Qt}if(St.target.column>$e.target.column){var Vt=Ae($e,St);return Vt-St.y1}}else return $e.y1-St.y1}if($e.circular&&!St.circular)return $e.circularLinkType=="top"?-1:1;if(St.circular&&!$e.circular)return St.circularLinkType=="top"?1:-1;if($e.circular&&St.circular)return 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Nt=st.nodes,Jt=st.links,sr=!1,wr=!1;if(Jt.forEach(function(_t){_t.circularLinkType=="top"?sr=!0:_t.circularLinkType=="bottom"&&(wr=!0)}),sr==!1||wr==!1){var cr=t.min(Nt,function(_t){return _t.y0}),$e=t.max(Nt,function(_t){return _t.y1}),St=$e-cr,Qt=Gt-lt,Vt=Qt/St;Nt.forEach(function(_t){var It=(_t.y1-_t.y0)*Vt;_t.y0=(_t.y0-cr)*Vt,_t.y1=_t.y0+It}),Jt.forEach(function(_t){_t.y0=(_t.y0-cr)*Vt,_t.y1=(_t.y1-cr)*Vt,_t.width=_t.width*Vt})}}e.sankeyCircular=T,e.sankeyCenter=u,e.sankeyLeft=o,e.sankeyRight=s,e.sankeyJustify=l,Object.defineProperty(e,"__esModule",{value:!0})})});var WJ=ye((e2r,XWe)=>{"use strict";XWe.exports={nodeTextOffsetHorizontal:4,nodeTextOffsetVertical:3,nodePadAcross:10,sankeyIterations:50,forceIterations:5,forceTicksPerFrame:10,duration:500,ease:"linear",cn:{sankey:"sankey",sankeyLinks:"sankey-links",sankeyLink:"sankey-link",sankeyNodeSet:"sankey-node-set",sankeyNode:"sankey-node",nodeRect:"node-rect",nodeLabel:"node-label"}}});var oXe=ye((t2r,aXe)=>{"use strict";var ZWe=OWe(),zZt=(F2(),ob(D2)).interpolateNumber,R5=Oa(),Jk=VWe(),OZt=WWe(),Nu=WJ(),D5=cd(),sw=ka(),qZt=So(),p1=Dr(),YJ=p1.strTranslate,BZt=p1.strRotate,KJ=Jm(),$k=KJ.keyFun,E7=KJ.repeat,eXe=KJ.unwrap,YWe=ru(),NZt=qa(),tXe=$h(),UZt=tXe.CAP_SHIFT,VZt=tXe.LINE_SPACING,GZt=3;function HZt(e,t,r){var n=eXe(t),i=n.trace,a=i.domain,o=i.orientation==="h",s=i.node.pad,l=i.node.thickness,u={justify:Jk.sankeyJustify,left:Jk.sankeyLeft,right:Jk.sankeyRight,center:Jk.sankeyCenter}[i.node.align],c=e.width*(a.x[1]-a.x[0]),f=e.height*(a.y[1]-a.y[0]),h=n._nodes,d=n._links,v=n.circular,_;v?_=OZt.sankeyCircular().circularLinkGap(0):_=Jk.sankey(),_.iterations(Nu.sankeyIterations).size(o?[c,f]:[f,c]).nodeWidth(l).nodePadding(s).nodeId(function(V){return V.pointNumber}).nodeAlign(u).nodes(h).links(d);var b=_();_.nodePadding()<s&&p1.warn("node.pad was reduced to ",_.nodePadding()," to fit within the figure.");var p,k,E;for(var S in n._groupLookup){var L=parseInt(n._groupLookup[S]),x;for(p=0;p<b.nodes.length;p++)if(b.nodes[p].pointNumber===L){x=b.nodes[p];break}if(x){var C={pointNumber:parseInt(S),x0:x.x0,x1:x.x1,y0:x.y0,y1:x.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};b.nodes.unshift(C),x.childrenNodes.unshift(C)}}function M(){for(p=0;p<b.nodes.length;p++){var V=b.nodes[p],G={},Z,j;for(k=0;k<V.targetLinks.length;k++)j=V.targetLinks[k],Z=j.source.pointNumber+":"+j.target.pointNumber,G.hasOwnProperty(Z)||(G[Z]=[]),G[Z].push(j);var N=Object.keys(G);for(k=0;k<N.length;k++){Z=N[k];var H=G[Z],te=0,oe={};for(E=0;E<H.length;E++)j=H[E],oe[j.label]||(oe[j.label]=0),oe[j.label]+=j.value,te+=j.value;for(E=0;E<H.length;E++)j=H[E],j.flow={value:te,labelConcentration:oe[j.label]/te,concentration:j.value/te,links:H},j.concentrationscale&&(j.color=D5(j.concentrationscale(j.flow.labelConcentration)))}var _e=0;for(k=0;k<V.sourceLinks.length;k++)_e+=V.sourceLinks[k].value;for(k=0;k<V.sourceLinks.length;k++)j=V.sourceLinks[k],j.concentrationOut=j.value/_e;var Ee=0;for(k=0;k<V.targetLinks.length;k++)Ee+=V.targetLinks[k].value;for(k=0;k<V.targetLinks.length;k++)j=V.targetLinks[k],j.concenrationIn=j.value/Ee}}M();function g(V){V.forEach(function(G){var Z,j,N=0,H=G.length,te;for(G.sort(function(oe,_e){return oe.y0-_e.y0}),te=0;te<H;++te)Z=G[te],Z.y0>=N||(j=N-Z.y0,j>1e-6&&(Z.y0+=j,Z.y1+=j)),N=Z.y1+s})}function P(V){var G=V.map(function(_e,Ee){return{x0:_e.x0,index:Ee}}).sort(function(_e,Ee){return _e.x0-Ee.x0}),Z=[],j=-1,N,H=-1/0,te;for(p=0;p<G.length;p++){var oe=V[G[p].index];oe.x0>H+l&&(j+=1,N=oe.x0),H=oe.x0,Z[j]||(Z[j]=[]),Z[j].push(oe),te=N-oe.x0,oe.x0+=te,oe.x1+=te}return Z}if(i.node.x.length&&i.node.y.length){for(p=0;p<Math.min(i.node.x.length,i.node.y.length,b.nodes.length);p++)if(i.node.x[p]&&i.node.y[p]){var T=[i.node.x[p]*c,i.node.y[p]*f];b.nodes[p].x0=T[0]-l/2,b.nodes[p].x1=T[0]+l/2;var z=b.nodes[p].y1-b.nodes[p].y0;b.nodes[p].y0=T[1]-z/2,b.nodes[p].y1=T[1]+z/2}if(i.arrangement==="snap"){h=b.nodes;var O=P(h);g(O)}_.update(b)}return{circular:v,key:r,trace:i,guid:p1.randstr(),horizontal:o,width:c,height:f,nodePad:i.node.pad,nodeLineColor:i.node.line.color,nodeLineWidth:i.node.line.width,linkLineColor:i.link.line.color,linkLineWidth:i.link.line.width,linkArrowLength:i.link.arrowlen,valueFormat:i.valueformat,valueSuffix:i.valuesuffix,textFont:i.textfont,translateX:a.x[0]*e.width+e.margin.l,translateY:e.height-a.y[1]*e.height+e.margin.t,dragParallel:o?f:c,dragPerpendicular:o?c:f,arrangement:i.arrangement,sankey:_,graph:b,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function jZt(e,t,r){var n=D5(t.color),i=D5(t.hovercolor),a=t.source.label+"|"+t.target.label,o=a+"__"+r;return t.trace=e.trace,t.curveNumber=e.trace.index,{circular:e.circular,key:o,traceId:e.key,pointNumber:t.pointNumber,link:t,tinyColorHue:sw.tinyRGB(n),tinyColorAlpha:n.getAlpha(),tinyColorHoverHue:sw.tinyRGB(i),tinyColorHoverAlpha:i.getAlpha(),linkPath:JJ,linkLineColor:e.linkLineColor,linkLineWidth:e.linkLineWidth,linkArrowLength:e.linkArrowLength,valueFormat:e.valueFormat,valueSuffix:e.valueSuffix,sankey:e.sankey,parent:e,interactionState:e.interactionState,flow:t.flow}}function WZt(e,t){var r="",n=e.width/2,i=e.circularPathData,a=i.sourceX+i.verticalBuffer<i.targetX,o=i.rightFullExtent-i.rightLargeArcRadius-t<=i.leftFullExtent-n,s=Math.abs(i.rightFullExtent-i.leftFullExtent-n)<n;return e.circularLinkType==="top"?(r="M "+(i.targetX-t)+" "+(i.targetY+n)+" L "+(i.rightInnerExtent-t)+" "+(i.targetY+n)+"A "+(i.rightLargeArcRadius+n)+" "+(i.rightSmallArcRadius+n)+" 0 0 1 "+(i.rightFullExtent-n-t)+" "+(i.targetY-i.rightSmallArcRadius)+"L "+(i.rightFullExtent-n-t)+" 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"+(i.verticalFullExtent-n)+" L "+i.leftInnerExtent+" "+(i.verticalFullExtent-n)+" A "+(i.leftLargeArcRadius+n)+" "+(i.leftLargeArcRadius+n)+" 0 0 1 "+(i.leftFullExtent+n)+" "+i.verticalLeftInnerExtent,r+=" L "+(i.leftFullExtent+n)+" "+(i.sourceY-i.leftSmallArcRadius)+" A "+(i.leftLargeArcRadius+n)+" "+(i.leftSmallArcRadius+n)+" 0 0 1 "+i.leftInnerExtent+" "+(i.sourceY+n)+" L "+i.sourceX+" "+(i.sourceY+n)+" L "+i.sourceX+" "+(i.sourceY-n)+" L "+i.leftInnerExtent+" "+(i.sourceY-n)+" A "+(i.leftLargeArcRadius-n)+" "+(i.leftSmallArcRadius-n)+" 0 0 0 "+(i.leftFullExtent-n)+" "+(i.sourceY-i.leftSmallArcRadius)+" L "+(i.leftFullExtent-n)+" "+i.verticalLeftInnerExtent,a&&o?r+=" A "+(i.leftLargeArcRadius+n)+" "+(i.leftSmallArcRadius+n)+" 0 0 0 "+(i.leftFullExtent-n)+" "+(i.verticalFullExtent+n)+"L"+(i.rightFullExtent+n-t)+" "+(i.verticalFullExtent+n)+" A "+(i.leftLargeArcRadius+n)+" "+(i.leftSmallArcRadius+n)+" 0 0 0 "+(i.rightFullExtent+n-t)+" "+i.verticalRightInnerExtent:a?r+=" A "+(i.leftLargeArcRadius+n)+" "+(i.leftSmallArcRadius+n)+" 0 0 1 "+(i.leftFullExtent+n)+" "+(i.verticalFullExtent-n)+" L "+(i.rightFullExtent-n-t)+" "+(i.verticalFullExtent-n)+" A "+(i.leftLargeArcRadius+n)+" "+(i.leftSmallArcRadius+n)+" 0 0 1 "+(i.rightFullExtent+n-t)+" "+i.verticalRightInnerExtent:r+=" A "+(i.leftLargeArcRadius-n)+" "+(i.leftLargeArcRadius-n)+" 0 0 0 "+i.leftInnerExtent+" "+(i.verticalFullExtent+n)+" L "+(i.rightInnerExtent-t)+" "+(i.verticalFullExtent+n)+" A "+(i.rightLargeArcRadius-n)+" "+(i.rightLargeArcRadius-n)+" 0 0 0 "+(i.rightFullExtent+n-t)+" "+i.verticalRightInnerExtent,r+=" L "+(i.rightFullExtent+n-t)+" "+(i.targetY-i.rightSmallArcRadius)+" A "+(i.rightLargeArcRadius-n)+" "+(i.rightSmallArcRadius-n)+" 0 0 0 "+(i.rightInnerExtent-t)+" "+(i.targetY-n)+" L "+(i.targetX-t)+" "+(i.targetY-n)+(t>0?" L "+i.targetX+" "+i.targetY:"")+"Z"):(r="M "+(i.targetX-t)+" "+(i.targetY-n)+"  L "+(i.rightInnerExtent-t)+" "+(i.targetY-n)+" A "+(i.rightLargeArcRadius+n)+" "+(i.rightSmallArcRadius+n)+" 0 0 0 "+(i.rightFullExtent-n-t)+" "+(i.targetY+i.rightSmallArcRadius)+" L "+(i.rightFullExtent-n-t)+" "+i.verticalRightInnerExtent,a&&o?r+=" A "+(i.rightLargeArcRadius+n)+" "+(i.rightLargeArcRadius+n)+" 0 0 0 "+(i.rightInnerExtent-n-t)+" "+(i.verticalFullExtent+n)+" L "+(i.rightFullExtent+n-t-(i.rightLargeArcRadius-n))+" "+(i.verticalFullExtent+n)+" A "+(i.rightLargeArcRadius+n)+" "+(i.rightLargeArcRadius+n)+" 0 0 0 "+(i.leftFullExtent+n)+" "+i.verticalLeftInnerExtent:a?r+=" A "+(i.rightLargeArcRadius-n)+" "+(i.rightSmallArcRadius-n)+" 0 0 1 "+(i.rightFullExtent-t-n-(i.rightLargeArcRadius-n))+" "+(i.verticalFullExtent-n)+" L "+(i.leftFullExtent+n+(i.rightLargeArcRadius-n))+" "+(i.verticalFullExtent-n)+" A "+(i.rightLargeArcRadius-n)+" "+(i.rightSmallArcRadius-n)+" 0 0 1 "+(i.leftFullExtent+n)+" "+i.verticalLeftInnerExtent:r+=" A "+(i.rightLargeArcRadius+n)+" "+(i.rightLargeArcRadius+n)+" 0 0 0 "+(i.rightInnerExtent-t)+" "+(i.verticalFullExtent+n)+" L "+i.leftInnerExtent+" "+(i.verticalFullExtent+n)+" A "+(i.leftLargeArcRadius+n)+" "+(i.leftLargeArcRadius+n)+" 0 0 0 "+(i.leftFullExtent+n)+" "+i.verticalLeftInnerExtent,r+=" L "+(i.leftFullExtent+n)+" "+(i.sourceY+i.leftSmallArcRadius)+" A "+(i.leftLargeArcRadius+n)+" "+(i.leftSmallArcRadius+n)+" 0 0 0 "+i.leftInnerExtent+" "+(i.sourceY-n)+" L "+i.sourceX+" "+(i.sourceY-n)+" L "+i.sourceX+" "+(i.sourceY+n)+" L "+i.leftInnerExtent+" "+(i.sourceY+n)+" A "+(i.leftLargeArcRadius-n)+" "+(i.leftSmallArcRadius-n)+" 0 0 1 "+(i.leftFullExtent-n)+" "+(i.sourceY+i.leftSmallArcRadius)+" L "+(i.leftFullExtent-n)+" "+i.verticalLeftInnerExtent,a&&o?r+=" A "+(i.rightLargeArcRadius-n)+" "+(i.rightSmallArcRadius-n)+" 0 0 1 "+(i.leftFullExtent-n-(i.rightLargeArcRadius-n))+" "+(i.verticalFullExtent-n)+" L "+(i.rightFullExtent+n-t+(i.rightLargeArcRadius-n))+" "+(i.verticalFullExtent-n)+" A "+(i.rightLargeArcRadius-n)+" "+(i.rightSmallArcRadius-n)+" 0 0 1 "+(i.rightFullExtent+n-t)+" "+i.verticalRightInnerExtent:a?r+=" A "+(i.rightLargeArcRadius+n)+" "+(i.rightLargeArcRadius+n)+" 0 0 0 "+(i.leftFullExtent+n)+" "+(i.verticalFullExtent+n)+" L "+(i.rightFullExtent-t-n)+" "+(i.verticalFullExtent+n)+" A "+(i.rightLargeArcRadius+n)+" "+(i.rightLargeArcRadius+n)+" 0 0 0 "+(i.rightFullExtent+n-t)+" "+i.verticalRightInnerExtent:r+=" A "+(i.leftLargeArcRadius-n)+" "+(i.leftLargeArcRadius-n)+" 0 0 1 "+i.leftInnerExtent+" "+(i.verticalFullExtent-n)+" L "+(i.rightInnerExtent-t)+" "+(i.verticalFullExtent-n)+" A "+(i.rightLargeArcRadius-n)+" "+(i.rightLargeArcRadius-n)+" 0 0 1 "+(i.rightFullExtent+n-t)+" "+i.verticalRightInnerExtent,r+=" L "+(i.rightFullExtent+n-t)+" "+(i.targetY+i.rightSmallArcRadius)+" A "+(i.rightLargeArcRadius-n)+" "+(i.rightSmallArcRadius-n)+" 0 0 1 "+(i.rightInnerExtent-t)+" "+(i.targetY+n)+" L "+(i.targetX-t)+" "+(i.targetY+n)+(t>0?" L "+i.targetX+" "+i.targetY:"")+"Z"),r}function JJ(){var e=.5;function t(r){var n=r.linkArrowLength;if(r.link.circular)return WZt(r.link,n);var i=Math.abs((r.link.target.x0-r.link.source.x1)/2);n>i&&(n=i);var a=r.link.source.x1,o=r.link.target.x0-n,s=zZt(a,o),l=s(e),u=s(1-e),c=r.link.y0-r.link.width/2,f=r.link.y0+r.link.width/2,h=r.link.y1-r.link.width/2,d=r.link.y1+r.link.width/2,v="M"+a+","+c,_="C"+l+","+c+" "+u+","+h+" "+o+","+h,b="C"+u+","+d+" "+l+","+f+" "+a+","+f,p=n>0?"L"+(o+n)+","+(h+r.link.width/2):"";return p+="L"+o+","+d,v+_+p+b+"Z"}return t}function XZt(e,t){var r=D5(t.color),n=Nu.nodePadAcross,i=e.nodePad/2;t.dx=t.x1-t.x0,t.dy=t.y1-t.y0;var a=t.dx,o=Math.max(.5,t.dy),s="node_"+t.pointNumber;return t.group&&(s=p1.randstr()),t.trace=e.trace,t.curveNumber=e.trace.index,{index:t.pointNumber,key:s,partOfGroup:t.partOfGroup||!1,group:t.group,traceId:e.key,trace:e.trace,node:t,nodePad:e.nodePad,nodeLineColor:e.nodeLineColor,nodeLineWidth:e.nodeLineWidth,textFont:e.textFont,size:e.horizontal?e.height:e.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:e.horizontal?t.dy/2+1:t.dx/2+1,left:t.originalLayer===1,sizeAcross:e.width,forceLayouts:e.forceLayouts,horizontal:e.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:sw.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:e.valueFormat,valueSuffix:e.valueSuffix,sankey:e.sankey,graph:e.graph,arrangement:e.arrangement,uniqueNodeLabelPathId:[e.guid,e.key,s].join("_"),interactionState:e.interactionState,figure:e}}function ZJ(e){e.attr("transform",function(t){return YJ(t.node.x0.toFixed(3),t.node.y0.toFixed(3))})}function ZZt(e){e.call(ZJ)}function rXe(e,t){e.call(ZZt),t.attr("d",JJ())}function KWe(e){e.attr("width",function(t){return t.node.x1-t.node.x0}).attr("height",function(t){return t.visibleHeight})}function XJ(e){return e.link.width>1||e.linkLineWidth>0}function JWe(e){var t=YJ(e.translateX,e.translateY);return t+(e.horizontal?"matrix(1 0 0 1 0 0)":"matrix(0 1 1 0 0 0)")}function $We(e,t,r){e.on(".basic",null).on("mouseover.basic",function(n){!n.interactionState.dragInProgress&&!n.partOfGroup&&(r.hover(this,n,t),n.interactionState.hovered=[this,n])}).on("mousemove.basic",function(n){!n.interactionState.dragInProgress&&!n.partOfGroup&&(r.follow(this,n),n.interactionState.hovered=[this,n])}).on("mouseout.basic",function(n){!n.interactionState.dragInProgress&&!n.partOfGroup&&(r.unhover(this,n,t),n.interactionState.hovered=!1)}).on("click.basic",function(n){n.interactionState.hovered&&(r.unhover(this,n,t),n.interactionState.hovered=!1),!n.interactionState.dragInProgress&&!n.partOfGroup&&r.select(this,n,t)})}function YZt(e,t,r,n){var i=R5.behavior.drag().origin(function(a){return{x:a.node.x0+a.visibleWidth/2,y:a.node.y0+a.visibleHeight/2}}).on("dragstart",function(a){if(a.arrangement!=="fixed"&&(p1.ensureSingle(n._fullLayout._infolayer,"g","dragcover",function(s){n._fullLayout._dragCover=s}),p1.raiseToTop(this),a.interactionState.dragInProgress=a.node,QWe(a.node),a.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,a.interactionState.hovered),a.interactionState.hovered=!1),a.arrangement==="snap")){var o=a.traceId+"|"+a.key;a.forceLayouts[o]?a.forceLayouts[o].alpha(1):KZt(e,o,a,n),JZt(e,t,a,o,n)}}).on("drag",function(a){if(a.arrangement!=="fixed"){var o=R5.event.x,s=R5.event.y;a.arrangement==="snap"?(a.node.x0=o-a.visibleWidth/2,a.node.x1=o+a.visibleWidth/2,a.node.y0=s-a.visibleHeight/2,a.node.y1=s+a.visibleHeight/2):(a.arrangement==="freeform"&&(a.node.x0=o-a.visibleWidth/2,a.node.x1=o+a.visibleWidth/2),s=Math.max(0,Math.min(a.size-a.visibleHeight/2,s)),a.node.y0=s-a.visibleHeight/2,a.node.y1=s+a.visibleHeight/2),QWe(a.node),a.arrangement!=="snap"&&(a.sankey.update(a.graph),rXe(e.filter(nXe(a)),t))}}).on("dragend",function(a){if(a.arrangement!=="fixed"){a.interactionState.dragInProgress=!1;for(var o=0;o<a.node.childrenNodes.length;o++)a.node.childrenNodes[o].x=a.node.x,a.node.childrenNodes[o].y=a.node.y;a.arrangement!=="snap"&&iXe(a,n)}});e.on(".drag",null).call(i)}function KZt(e,t,r,n){eYt(r.graph.nodes);var i=r.graph.nodes.filter(function(a){return 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Mx=Ao().extendFlat,MXe=Ao().extendDeep,dYt=mc().overrideAll,EXe=ec(),kXe=Lh(),vYt=Cc().attributes,Bf=Rd(),pYt=vl().templatedArray,L7=XT(),SXe=df().descriptionOnlyNumbers,r$=EXe({editType:"plot",colorEditType:"plot"}),eC={color:{valType:"color",editType:"plot"},line:{color:{valType:"color",dflt:kXe.defaultLine,editType:"plot"},width:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"calc"},thickness:{valType:"number",min:0,max:1,dflt:1,editType:"plot"},editType:"calc"},CXe={valType:"info_array",items:[{valType:"number",editType:"plot"},{valType:"number",editType:"plot"}],editType:"plot"},gYt=pYt("step",MXe({},eC,{range:CXe}));LXe.exports={mode:{valType:"flaglist",editType:"calc",flags:["number","delta","gauge"],dflt:"number"},value:{valType:"number",editType:"calc",anim:!0},align:{valType:"enumerated",values:["left","center","right"],editType:"plot"},domain:vYt({name:"indicator",trace:!0,editType:"calc"}),title:{text:{valType:"string",editType:"plot"},align:{valType:"enumerated",values:["left","center","right"],editType:"plot"},font:Mx({},r$,{}),editType:"plot"},number:{valueformat:{valType:"string",dflt:"",editType:"plot",description:SXe("value")},font:Mx({},r$,{}),prefix:{valType:"string",dflt:"",editType:"plot"},suffix:{valType:"string",dflt:"",editType:"plot"},editType:"plot"},delta:{reference:{valType:"number",editType:"calc"},position:{valType:"enumerated",values:["top","bottom","left","right"],dflt:"bottom",editType:"plot"},relative:{valType:"boolean",editType:"plot",dflt:!1},valueformat:{valType:"string",editType:"plot",description:SXe("value")},increasing:{symbol:{valType:"string",dflt:L7.INCREASING.SYMBOL,editType:"plot"},color:{valType:"color",dflt:L7.INCREASING.COLOR,editType:"plot"},editType:"plot"},decreasing:{symbol:{valType:"string",dflt:L7.DECREASING.SYMBOL,editType:"plot"},color:{valType:"color",dflt:L7.DECREASING.COLOR,editType:"plot"},editType:"plot"},font:Mx({},r$,{}),prefix:{valType:"string",dflt:"",editType:"plot"},suffix:{valType:"string",dflt:"",editType:"plot"},editType:"calc"},gauge:{shape:{valType:"enumerated",editType:"plot",dflt:"angular",values:["angular","bullet"]},bar:MXe({},eC,{color:{dflt:"green"}}),bgcolor:{valType:"color",editType:"plot"},bordercolor:{valType:"color",dflt:kXe.defaultLine,editType:"plot"},borderwidth:{valType:"number",min:0,dflt:1,editType:"plot"},axis:dYt({range:CXe,visible:Mx({},Bf.visible,{dflt:!0}),tickmode:Bf.minor.tickmode,nticks:Bf.nticks,tick0:Bf.tick0,dtick:Bf.dtick,tickvals:Bf.tickvals,ticktext:Bf.ticktext,ticks:Mx({},Bf.ticks,{dflt:"outside"}),ticklen:Bf.ticklen,tickwidth:Bf.tickwidth,tickcolor:Bf.tickcolor,ticklabelstep:Bf.ticklabelstep,showticklabels:Bf.showticklabels,labelalias:Bf.labelalias,tickfont:EXe({}),tickangle:Bf.tickangle,tickformat:Bf.tickformat,tickformatstops:Bf.tickformatstops,tickprefix:Bf.tickprefix,showtickprefix:Bf.showtickprefix,ticksuffix:Bf.ticksuffix,showticksuffix:Bf.showticksuffix,separatethousands:Bf.separatethousands,exponentformat:Bf.exponentformat,minexponent:Bf.minexponent,showexponent:Bf.showexponent,editType:"plot"},"plot"),steps:gYt,threshold:{line:{color:Mx({},eC.line.color,{}),width:Mx({},eC.line.width,{dflt:1}),editType:"plot"},thickness:Mx({},eC.thickness,{dflt:.85}),value:{valType:"number",editType:"calc",dflt:!1},editType:"plot"},editType:"plot"}}});var n$=ye((u2r,PXe)=>{"use strict";PXe.exports={defaultNumberFontSize:80,bulletNumberDomainSize:.25,bulletPadding:.025,innerRadius:.75,valueThickness:.5,titlePadding:5,horizontalPadding:10}});var DXe=ye((c2r,RXe)=>{"use strict";var ty=Dr(),I7=i$(),mYt=Cc().defaults,IXe=vl(),yYt=Yd(),P7=n$(),_Yt=bb(),xYt=M3(),bYt=e_(),wYt=t_();function TYt(e,t,r,n){function i(x,C){return ty.coerce(e,t,I7,x,C)}mYt(t,n,i),i("mode"),t._hasNumber=t.mode.indexOf("number")!==-1,t._hasDelta=t.mode.indexOf("delta")!==-1,t._hasGauge=t.mode.indexOf("gauge")!==-1;var a=i("value");t._range=[0,typeof a=="number"?1.5*a:1];var o=new Array(2),s;if(t._hasNumber){i("number.valueformat");var l=ty.extendFlat({},n.font);l.size=void 0,ty.coerceFont(i,"number.font",l),t.number.font.size===void 0&&(t.number.font.size=P7.defaultNumberFontSize,o[0]=!0),i("number.prefix"),i("number.suffix"),s=t.number.font.size}var u;if(t._hasDelta){var c=ty.extendFlat({},n.font);c.size=void 0,ty.coerceFont(i,"delta.font",c),t.delta.font.size===void 0&&(t.delta.font.size=(t._hasNumber?.5:1)*(s||P7.defaultNumberFontSize),o[1]=!0),i("delta.reference",t.value),i("delta.relative"),i("delta.valueformat",t.delta.relative?"2%":""),i("delta.increasing.symbol"),i("delta.increasing.color"),i("delta.decreasing.symbol"),i("delta.decreasing.color"),i("delta.position"),i("delta.prefix"),i("delta.suffix"),u=t.delta.font.size}t._scaleNumbers=(!t._hasNumber||o[0])&&(!t._hasDelta||o[1])||!1;var f=ty.extendFlat({},n.font);f.size=.25*(s||u||P7.defaultNumberFontSize),ty.coerceFont(i,"title.font",f),i("title.text");var h,d,v,_;function b(x,C){return ty.coerce(h,d,I7.gauge,x,C)}function p(x,C){return ty.coerce(v,_,I7.gauge.axis,x,C)}if(t._hasGauge){h=e.gauge,h||(h={}),d=IXe.newContainer(t,"gauge"),b("shape");var k=t._isBullet=t.gauge.shape==="bullet";k||i("title.align","center");var E=t._isAngular=t.gauge.shape==="angular";E||i("align","center"),b("bgcolor",n.paper_bgcolor),b("borderwidth"),b("bordercolor"),b("bar.color"),b("bar.line.color"),b("bar.line.width");var S=P7.valueThickness*(t.gauge.shape==="bullet"?.5:1);b("bar.thickness",S),yYt(h,d,{name:"steps",handleItemDefaults:AYt}),b("threshold.value"),b("threshold.thickness"),b("threshold.line.width"),b("threshold.line.color"),v={},h&&(v=h.axis||{}),_=IXe.newContainer(d,"axis"),p("visible"),t._range=p("range",t._range);var L={font:n.font,noAutotickangles:!0,outerTicks:!0,noTicklabelshift:!0,noTicklabelstandoff:!0};_Yt(v,_,p,"linear"),wYt(v,_,p,"linear",L),bYt(v,_,p,"linear",L),xYt(v,_,p,L)}else i("title.align","center"),i("align","center"),t._isAngular=t._isBullet=!1;t._length=null}function AYt(e,t){function r(n,i){return ty.coerce(e,t,I7.gauge.steps,n,i)}r("color"),r("line.color"),r("line.width"),r("range"),r("thickness")}RXe.exports={supplyDefaults:TYt}});var zXe=ye((f2r,FXe)=>{"use strict";function SYt(e,t){var r=[],n=t.value;typeof t._lastValue!="number"&&(t._lastValue=t.value);var i=t._lastValue,a=i;return t._hasDelta&&typeof t.delta.reference=="number"&&(a=t.delta.reference),r[0]={y:n,lastY:i,delta:n-a,relativeDelta:(n-a)/a},r}FXe.exports={calc:SYt}});var VXe=ye((h2r,UXe)=>{"use strict";var dw=Oa(),MYt=(F2(),ob(D2)).interpolate,OXe=(F2(),ob(D2)).interpolateNumber,Ex=Dr(),EYt=Ex.strScale,rC=Ex.strTranslate,kYt=Ex.rad2deg,CYt=$h().MID_SHIFT,hw=So(),uw=n$(),D7=ru(),sv=ho(),LYt=i4(),PYt=uI(),IYt=Rd(),q5=ka(),a$={left:"start",center:"middle",right:"end"},cw={left:0,center:.5,right:1},qXe=/[yzafpnµmkMGTPEZY]/;function iC(e){return e&&e.duration>0}UXe.exports=function(t,r,n,i){var a=t._fullLayout,o;iC(n)&&i&&(o=i()),Ex.makeTraceGroups(a._indicatorlayer,r,"trace").each(function(s){var l=s[0],u=l.trace,c=dw.select(this),f=u._hasGauge,h=u._isAngular,d=u._isBullet,v=u.domain,_={w:a._size.w*(v.x[1]-v.x[0]),h:a._size.h*(v.y[1]-v.y[0]),l:a._size.l+a._size.w*v.x[0],r:a._size.r+a._size.w*(1-v.x[1]),t:a._size.t+a._size.h*(1-v.y[1]),b:a._size.b+a._size.h*v.y[0]},b=_.l+_.w/2,p=_.t+_.h/2,k=Math.min(_.w/2,_.h),E=uw.innerRadius*k,S,L,x,C=u.align||"center";if(L=p,!f)S=_.l+cw[C]*_.w,x=function(j){return BXe(j,_.w,_.h)};else if(h&&(S=b,L=p+k/2,x=function(j){return OYt(j,.9*E)}),d){var M=uw.bulletPadding,g=1-uw.bulletNumberDomainSize+M;S=_.l+(g+(1-g)*cw[C])*_.w,x=function(j){return BXe(j,(uw.bulletNumberDomainSize-M)*_.w,_.h)}}FYt(t,c,s,{numbersX:S,numbersY:L,numbersScaler:x,transitionOpts:n,onComplete:o});var P,T;f&&(P={range:u.gauge.axis.range,color:u.gauge.bgcolor,line:{color:u.gauge.bordercolor,width:0},thickness:1},T={range:u.gauge.axis.range,color:"rgba(0, 0, 0, 0)",line:{color:u.gauge.bordercolor,width:u.gauge.borderwidth},thickness:1});var z=c.selectAll("g.angular").data(h?s:[]);z.exit().remove();var O=c.selectAll("g.angularaxis").data(h?s:[]);O.exit().remove(),h&&DYt(t,c,s,{radius:k,innerRadius:E,gauge:z,layer:O,size:_,gaugeBg:P,gaugeOutline:T,transitionOpts:n,onComplete:o});var V=c.selectAll("g.bullet").data(d?s:[]);V.exit().remove();var G=c.selectAll("g.bulletaxis").data(d?s:[]);G.exit().remove(),d&&RYt(t,c,s,{gauge:V,layer:G,size:_,gaugeBg:P,gaugeOutline:T,transitionOpts:n,onComplete:o});var Z=c.selectAll("text.title").data(s);Z.exit().remove(),Z.enter().append("text").classed("title",!0),Z.attr("text-anchor",function(){return d?a$.right:a$[u.title.align]}).text(u.title.text).call(hw.font,u.title.font).call(D7.convertToTspans,t),Z.attr("transform",function(){var j=_.l+_.w*cw[u.title.align],N,H=uw.titlePadding,te=hw.bBox(Z.node());if(f){if(h)if(u.gauge.axis.visible){var oe=hw.bBox(O.node());N=oe.top-H-te.bottom}else N=_.t+_.h/2-k/2-te.bottom-H;d&&(N=L-(te.top+te.bottom)/2,j=_.l-uw.bulletPadding*_.w)}else N=u._numbersTop-H-te.bottom;return rC(j,N)})})};function RYt(e,t,r,n){var i=r[0].trace,a=n.gauge,o=n.layer,s=n.gaugeBg,l=n.gaugeOutline,u=n.size,c=i.domain,f=n.transitionOpts,h=n.onComplete,d,v,_,b,p;a.enter().append("g").classed("bullet",!0),a.attr("transform",rC(u.l,u.t)),o.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),o.selectAll("g.xbulletaxistick,path,text").remove();var k=u.h,E=i.gauge.bar.thickness*k,S=c.x[0],L=c.x[0]+(c.x[1]-c.x[0])*(i._hasNumber||i._hasDelta?1-uw.bulletNumberDomainSize:1);d=tC(e,i.gauge.axis),d._id="xbulletaxis",d.domain=[S,L],d.setScale(),v=sv.calcTicks(d),_=sv.makeTransTickFn(d),b=sv.getTickSigns(d)[2],p=u.t+u.h,d.visible&&(sv.drawTicks(e,d,{vals:d.ticks==="inside"?sv.clipEnds(d,v):v,layer:o,path:sv.makeTickPath(d,p,b),transFn:_}),sv.drawLabels(e,d,{vals:v,layer:o,transFn:_,labelFns:sv.makeLabelFns(d,p)}));function x(O){O.attr("width",function(V){return Math.max(0,d.c2p(V.range[1])-d.c2p(V.range[0]))}).attr("x",function(V){return d.c2p(V.range[0])}).attr("y",function(V){return .5*(1-V.thickness)*k}).attr("height",function(V){return V.thickness*k})}var C=[s].concat(i.gauge.steps),M=a.selectAll("g.bg-bullet").data(C);M.enter().append("g").classed("bg-bullet",!0).append("rect"),M.select("rect").call(x).call(fw),M.exit().remove();var g=a.selectAll("g.value-bullet").data([i.gauge.bar]);g.enter().append("g").classed("value-bullet",!0).append("rect"),g.select("rect").attr("height",E).attr("y",(k-E)/2).call(fw),iC(f)?g.select("rect").transition().duration(f.duration).ease(f.easing).each("end",function(){h&&h()}).each("interrupt",function(){h&&h()}).attr("width",Math.max(0,d.c2p(Math.min(i.gauge.axis.range[1],r[0].y)))):g.select("rect").attr("width",typeof r[0].y=="number"?Math.max(0,d.c2p(Math.min(i.gauge.axis.range[1],r[0].y))):0),g.exit().remove();var P=r.filter(function(){return i.gauge.threshold.value||i.gauge.threshold.value===0}),T=a.selectAll("g.threshold-bullet").data(P);T.enter().append("g").classed("threshold-bullet",!0).append("line"),T.select("line").attr("x1",d.c2p(i.gauge.threshold.value)).attr("x2",d.c2p(i.gauge.threshold.value)).attr("y1",(1-i.gauge.threshold.thickness)/2*k).attr("y2",(1-(1-i.gauge.threshold.thickness)/2)*k).call(q5.stroke,i.gauge.threshold.line.color).style("stroke-width",i.gauge.threshold.line.width),T.exit().remove();var z=a.selectAll("g.gauge-outline").data([l]);z.enter().append("g").classed("gauge-outline",!0).append("rect"),z.select("rect").call(x).call(fw),z.exit().remove()}function DYt(e,t,r,n){var i=r[0].trace,a=n.size,o=n.radius,s=n.innerRadius,l=n.gaugeBg,u=n.gaugeOutline,c=[a.l+a.w/2,a.t+a.h/2+o/2],f=n.gauge,h=n.layer,d=n.transitionOpts,v=n.onComplete,_=Math.PI/2;function b(_e){var Ee=i.gauge.axis.range[0],Ce=i.gauge.axis.range[1],me=(_e-Ee)/(Ce-Ee)*Math.PI-_;return me<-_?-_:me>_?_:me}function p(_e){return dw.svg.arc().innerRadius((s+o)/2-_e/2*(o-s)).outerRadius((s+o)/2+_e/2*(o-s)).startAngle(-_)}function k(_e){_e.attr("d",function(Ee){return p(Ee.thickness).startAngle(b(Ee.range[0])).endAngle(b(Ee.range[1]))()})}var E,S,L,x;f.enter().append("g").classed("angular",!0),f.attr("transform",rC(c[0],c[1])),h.enter().append("g").classed("angularaxis",!0).classed("crisp",!0),h.selectAll("g.xangularaxistick,path,text").remove(),E=tC(e,i.gauge.axis),E.type="linear",E.range=i.gauge.axis.range,E._id="xangularaxis",E.ticklabeloverflow="allow",E.setScale();var C=function(_e){return(E.range[0]-_e.x)/(E.range[1]-E.range[0])*Math.PI+Math.PI},M={},g=sv.makeLabelFns(E,0),P=g.labelStandoff;M.xFn=function(_e){var Ee=C(_e);return Math.cos(Ee)*P},M.yFn=function(_e){var Ee=C(_e),Ce=Math.sin(Ee)>0?.2:1;return-Math.sin(Ee)*(P+_e.fontSize*Ce)+Math.abs(Math.cos(Ee))*(_e.fontSize*CYt)},M.anchorFn=function(_e){var Ee=C(_e),Ce=Math.cos(Ee);return Math.abs(Ce)<.1?"middle":Ce>0?"start":"end"},M.heightFn=function(_e,Ee,Ce){var me=C(_e);return-.5*(1+Math.sin(me))*Ce};var T=function(_e){return rC(c[0]+o*Math.cos(_e),c[1]-o*Math.sin(_e))};L=function(_e){return T(C(_e))};var z=function(_e){var Ee=C(_e);return T(Ee)+"rotate("+-kYt(Ee)+")"};if(S=sv.calcTicks(E),x=sv.getTickSigns(E)[2],E.visible){x=E.ticks==="inside"?-1:1;var O=(E.linewidth||1)/2;sv.drawTicks(e,E,{vals:S,layer:h,path:"M"+x*O+",0h"+x*E.ticklen,transFn:z}),sv.drawLabels(e,E,{vals:S,layer:h,transFn:L,labelFns:M})}var V=[l].concat(i.gauge.steps),G=f.selectAll("g.bg-arc").data(V);G.enter().append("g").classed("bg-arc",!0).append("path"),G.select("path").call(k).call(fw),G.exit().remove();var Z=p(i.gauge.bar.thickness),j=f.selectAll("g.value-arc").data([i.gauge.bar]);j.enter().append("g").classed("value-arc",!0).append("path");var N=j.select("path");iC(d)?(N.transition().duration(d.duration).ease(d.easing).each("end",function(){v&&v()}).each("interrupt",function(){v&&v()}).attrTween("d",zYt(Z,b(r[0].lastY),b(r[0].y))),i._lastValue=r[0].y):N.attr("d",typeof r[0].y=="number"?Z.endAngle(b(r[0].y)):"M0,0Z"),N.call(fw),j.exit().remove(),V=[];var H=i.gauge.threshold.value;(H||H===0)&&V.push({range:[H,H],color:i.gauge.threshold.color,line:{color:i.gauge.threshold.line.color,width:i.gauge.threshold.line.width},thickness:i.gauge.threshold.thickness});var te=f.selectAll("g.threshold-arc").data(V);te.enter().append("g").classed("threshold-arc",!0).append("path"),te.select("path").call(k).call(fw),te.exit().remove();var oe=f.selectAll("g.gauge-outline").data([u]);oe.enter().append("g").classed("gauge-outline",!0).append("path"),oe.select("path").call(k).call(fw),oe.exit().remove()}function FYt(e,t,r,n){var i=r[0].trace,a=n.numbersX,o=n.numbersY,s=i.align||"center",l=a$[s],u=n.transitionOpts,c=n.onComplete,f=Ex.ensureSingle(t,"g","numbers"),h,d,v,_=[];i._hasNumber&&_.push("number"),i._hasDelta&&(_.push("delta"),i.delta.position==="left"&&_.reverse());var b=f.selectAll("text").data(_);b.enter().append("text"),b.attr("text-anchor",function(){return l}).attr("class",function(T){return T}).attr("x",null).attr("y",null).attr("dx",null).attr("dy",null),b.exit().remove();function p(T,z,O,V){if(T.match("s")&&O>=0!=V>=0&&!z(O).slice(-1).match(qXe)&&!z(V).slice(-1).match(qXe)){var G=T.slice().replace("s","f").replace(/\d+/,function(j){return parseInt(j)-1}),Z=tC(e,{tickformat:G});return function(j){return Math.abs(j)<1?sv.tickText(Z,j).text:z(j)}}else return z}function k(){var T=tC(e,{tickformat:i.number.valueformat},i._range);T.setScale(),sv.prepTicks(T);var z=function(j){return sv.tickText(T,j).text},O=i.number.suffix,V=i.number.prefix,G=f.select("text.number");function Z(){var j=typeof 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z=t.gd._fullLayout,O=z[t.id],V=p+P*e._invScaleX,G=k+T*e._invScaleY,Z=d(V,G),j=mw(Z-x);if(S=E+j,n.frontplot.attr("transform",xd(t.xOffset2,t.yOffset2)+iy([-j,l,u])),t.vangles){L=t.radialAxisAngle+j;var N=xd(o,s)+iy(-j),H=xd(o,s)+iy(-L);n.bg.attr("transform",N),n["radial-grid"].attr("transform",N),n["radial-axis"].attr("transform",H),n["radial-line"].select("line").attr("transform",H),t.updateRadialAxisTitle(z,O,L)}else t.clipPaths.forTraces.select("path").attr("transform",xd(l,u)+iy(j));_.each(function(){var ie=gw.select(this),Se=cC.getTranslate(ie);ie.attr("transform",xd(Se.x,Se.y)+iy([j]))}),b.each(function(){var ie=gw.select(this),Se=ie.select("text"),Le=cC.getTranslate(ie);ie.attr("transform",iy([j,Se.attr("x"),Se.attr("y")])+xd(Le.x,Le.y))}),a.rotation=Xc.modHalf(S,360),t.updateAngularAxis(z,O),t._hasClipOnAxisFalse&&!Xc.isFullCircle(t.sectorInRad)&&v.call(cC.hideOutsideRangePoints,t);var te=!1;for(var oe in t.traceHash)if(yw.traceIs(oe,"gl")){var 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lQ(e,t,r,n){if(this._calendar=e,this._year=t,this._month=r,this._day=n,this._calendar._validateLevel===0&&!this._calendar.isValid(this._year,this._month,this._day))throw(Gs.local.invalidDate||Gs.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function C9(e,t){return e=""+e,"000000".substring(0,t-e.length)+e}L9(lQ.prototype,{newDate:function(e,t,r){return this._calendar.newDate(e==null?this:e,t,r)},year:function(e){return arguments.length===0?this._year:this.set(e,"y")},month:function(e){return arguments.length===0?this._month:this.set(e,"m")},day:function(e){return arguments.length===0?this._day:this.set(e,"d")},date:function(e,t,r){if(!this._calendar.isValid(e,t,r))throw(Gs.local.invalidDate||Gs.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=e,this._month=t,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(e,t){return this._calendar.add(this,e,t)},set:function(e,t){return this._calendar.set(this,e,t)},compareTo:function(e){if(this._calendar.name!==e._calendar.name)throw(Gs.local.differentCalendars||Gs.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,e._calendar.local.name);var t=this._year!==e._year?this._year-e._year:this._month!==e._month?this.monthOfYear()-e.monthOfYear():this._day-e._day;return t===0?0:t<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(e){return this._calendar.fromJD(e)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(e){return this._calendar.fromJSDate(e)},toString:function(){return(this.year()<0?"-":"")+C9(Math.abs(this.year()),4)+"-"+C9(this.month(),2)+"-"+C9(this.day(),2)}});function uQ(){this.shortYearCutoff="+10"}L9(uQ.prototype,{_validateLevel:0,newDate:function(e,t,r){return e==null?this.today():(e.year&&(this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[""].invalidDate),r=e.day(),t=e.month(),e=e.year()),new lQ(this,e,t,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(e){var t=this._validate(e,this.minMonth,this.minDay,Gs.local.invalidYear||Gs.regionalOptions[""].invalidYear);return t.year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Gs.local.invalidYear||Gs.regionalOptions[""].invalidYear);return(t.year()<0?"-":"")+C9(Math.abs(t.year()),4)},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,Gs.local.invalidYear||Gs.regionalOptions[""].invalidYear),12},monthOfYear:function(e,t){var r=this._validate(e,t,this.minDay,Gs.local.invalidMonth||Gs.regionalOptions[""].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(e,t){var r=(t+this.firstMonth-2*this.minMonth)%this.monthsInYear(e)+this.minMonth;return this._validate(e,r,this.minDay,Gs.local.invalidMonth||Gs.regionalOptions[""].invalidMonth),r},daysInYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Gs.local.invalidYear||Gs.regionalOptions[""].invalidYear);return this.leapYear(t)?366:365},dayOfYear:function(e,t,r){var n=this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[""].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(e,t,r){var n=this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[""].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(e,t,r){return this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[""].invalidDate),{}},add:function(e,t,r){return this._validate(e,this.minMonth,this.minDay,Gs.local.invalidDate||Gs.regionalOptions[""].invalidDate),this._correctAdd(e,this._add(e,t,r),t,r)},_add:function(e,t,r){if(this._validateLevel++,r==="d"||r==="w"){var n=e.toJD()+t*(r==="w"?this.daysInWeek():1),i=e.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=e.year()+(r==="y"?t:0),o=e.monthOfYear()+(r==="m"?t:0),i=e.day(),s=function(c){for(;o<c.minMonth;)a--,o+=c.monthsInYear(a);for(var f=c.monthsInYear(a);o>f-1+c.minMonth;)a++,o-=f,f=c.monthsInYear(a)};r==="y"?(e.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,e.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):r==="m"&&(s(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var l=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,l}catch(u){throw this._validateLevel--,u}},_correctAdd:function(e,t,r,n){if(!this.hasYearZero&&(n==="y"||n==="m")&&(t[0]===0||e.year()>0!=t[0]>0)){var i={y:[1,1,"y"],m:[1,this.monthsInYear(-1),"m"],w:[this.daysInWeek(),this.daysInYear(-1),"d"],d:[1,this.daysInYear(-1),"d"]}[n],a=r<0?-1:1;t=this._add(e,r*i[0]+a*i[1],i[2])}return e.date(t[0],t[1],t[2])},set:function(e,t,r){this._validate(e,this.minMonth,this.minDay,Gs.local.invalidDate||Gs.regionalOptions[""].invalidDate);var n=r==="y"?t:e.year(),i=r==="m"?t:e.month(),a=r==="d"?t:e.day();return(r==="y"||r==="m")&&(a=Math.min(a,this.daysInMonth(n,i))),e.date(n,i,a)},isValid:function(e,t,r){this._validateLevel++;var n=this.hasYearZero||e!==0;if(n){var i=this.newDate(e,t,this.minDay);n=t>=this.minMonth&&t-this.minMonth<this.monthsInYear(i)&&r>=this.minDay&&r-this.minDay<this.daysInMonth(i)}return this._validateLevel--,n},toJSDate:function(e,t,r){var n=this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[""].invalidDate);return Gs.instance().fromJD(this.toJD(n)).toJSDate()},fromJSDate:function(e){return this.fromJD(Gs.instance().fromJSDate(e).toJD())},_validate:function(e,t,r,n){if(e.year){if(this._validateLevel===0&&this.name!==e.calendar().name)throw(Gs.local.differentCalendars||Gs.regionalOptions[""].differentCalendars).replace(/\{0\}/,this.local.name).replace(/\{1\}/,e.calendar().local.name);return e}try{if(this._validateLevel++,this._validateLevel===1&&!this.isValid(e,t,r))throw n.replace(/\{0\}/,this.local.name);var i=this.newDate(e,t,r);return this._validateLevel--,i}catch(a){throw this._validateLevel--,a}}});function cQ(e){this.local=this.regionalOptions[e]||this.regionalOptions[""]}cQ.prototype=new uQ;L9(cQ.prototype,{name:"Gregorian",jdEpoch:17214255e-1,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Gregorian",epochs:["BCE","CE"],monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],digits:null,dateFormat:"mm/dd/yyyy",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,Gs.local.invalidYear||Gs.regionalOptions[""].invalidYear),r=t.year()+(t.year()<0?1:0);return r%4===0&&(r%100!==0||r%400===0)},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(4-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Gs.local.invalidMonth||Gs.regionalOptions[""].invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[""].invalidDate);e=n.year(),t=n.month(),r=n.day(),e<0&&e++,t<3&&(t+=12,e--);var i=Math.floor(e/100),a=2-i+Math.floor(i/4);return Math.floor(365.25*(e+4716))+Math.floor(30.6001*(t+1))+r+a-1524.5},fromJD:function(e){var t=Math.floor(e+.5),r=Math.floor((t-186721625e-2)/36524.25);r=t+1+r-Math.floor(r/4);var n=r+1524,i=Math.floor((n-122.1)/365.25),a=Math.floor(365.25*i),o=Math.floor((n-a)/30.6001),s=n-a-Math.floor(o*30.6001),l=o-(o>13.5?13:1),u=i-(l>2.5?4716:4715);return u<=0&&u--,this.newDate(u,l,s)},toJSDate:function(e,t,r){var n=this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[""].invalidDate),i=new Date(n.year(),n.month()-1,n.day());return i.setHours(0),i.setMinutes(0),i.setSeconds(0),i.setMilliseconds(0),i.setHours(i.getHours()>12?i.getHours()+2:0),i},fromJSDate:function(e){return this.newDate(e.getFullYear(),e.getMonth()+1,e.getDate())}});var Gs=WQe.exports=new jQe;Gs.cdate=lQ;Gs.baseCalendar=uQ;Gs.calendars.gregorian=cQ});var XQe=ye(()=>{var fQ=Oh(),Ud=kv();fQ(Ud.regionalOptions[""],{invalidArguments:"Invalid arguments",invalidFormat:"Cannot format a date from another calendar",missingNumberAt:"Missing number at position {0}",unknownNameAt:"Unknown name at position {0}",unexpectedLiteralAt:"Unexpected literal at position {0}",unexpectedText:"Additional text found at end"});Ud.local=Ud.regionalOptions[""];fQ(Ud.cdate.prototype,{formatDate:function(e,t){return typeof e!="string"&&(t=e,e=""),this._calendar.formatDate(e||"",this,t)}});fQ(Ud.baseCalendar.prototype,{UNIX_EPOCH:Ud.instance().newDate(1970,1,1).toJD(),SECS_PER_DAY:24*60*60,TICKS_EPOCH:Ud.instance().jdEpoch,TICKS_PER_DAY:24*60*60*1e7,ATOM:"yyyy-mm-dd",COOKIE:"D, dd M yyyy",FULL:"DD, MM d, yyyy",ISO_8601:"yyyy-mm-dd",JULIAN:"J",RFC_822:"D, d M yy",RFC_850:"DD, dd-M-yy",RFC_1036:"D, d M yy",RFC_1123:"D, d M yyyy",RFC_2822:"D, d M yyyy",RSS:"D, d M yy",TICKS:"!",TIMESTAMP:"@",W3C:"yyyy-mm-dd",formatDate:function(e,t,r){if(typeof e!="string"&&(r=t,t=e,e=""),!t)return"";if(t.calendar()!==this)throw Ud.local.invalidFormat||Ud.regionalOptions[""].invalidFormat;e=e||this.local.dateFormat,r=r||{};for(var n=r.dayNamesShort||this.local.dayNamesShort,i=r.dayNames||this.local.dayNames,a=r.monthNumbers||this.local.monthNumbers,o=r.monthNamesShort||this.local.monthNamesShort,s=r.monthNames||this.local.monthNames,l=r.calculateWeek||this.local.calculateWeek,u=function(S,L){for(var x=1;E+x<e.length&&e.charAt(E+x)===S;)x++;return E+=x-1,Math.floor(x/(L||1))>1},c=function(S,L,x,C){var M=""+L;if(u(S,C))for(;M.length<x;)M="0"+M;return M},f=function(S,L,x,C){return u(S)?C[L]:x[L]},h=this,d=function(S){return typeof a=="function"?a.call(h,S,u("m")):b(c("m",S.month(),2))},v=function(S,L){return L?typeof s=="function"?s.call(h,S):s[S.month()-h.minMonth]:typeof o=="function"?o.call(h,S):o[S.month()-h.minMonth]},_=this.local.digits,b=function(S){return r.localNumbers&&_?_(S):S},p="",k=!1,E=0;E<e.length;E++)if(k)e.charAt(E)==="'"&&!u("'")?k=!1:p+=e.charAt(E);else switch(e.charAt(E)){case"d":p+=b(c("d",t.day(),2));break;case"D":p+=f("D",t.dayOfWeek(),n,i);break;case"o":p+=c("o",t.dayOfYear(),3);break;case"w":p+=c("w",t.weekOfYear(),2);break;case"m":p+=d(t);break;case"M":p+=v(t,u("M"));break;case"y":p+=u("y",2)?t.year():(t.year()%100<10?"0":"")+t.year()%100;break;case"Y":u("Y",2),p+=t.formatYear();break;case"J":p+=t.toJD();break;case"@":p+=(t.toJD()-this.UNIX_EPOCH)*this.SECS_PER_DAY;break;case"!":p+=(t.toJD()-this.TICKS_EPOCH)*this.TICKS_PER_DAY;break;case"'":u("'")?p+="'":k=!0;break;default:p+=e.charAt(E)}return p},parseDate:function(e,t,r){if(t==null)throw Ud.local.invalidArguments||Ud.regionalOptions[""].invalidArguments;if(t=typeof t=="object"?t.toString():t+"",t==="")return null;e=e||this.local.dateFormat,r=r||{};var n=r.shortYearCutoff||this.shortYearCutoff;n=typeof n!="string"?n:this.today().year()%100+parseInt(n,10);for(var i=r.dayNamesShort||this.local.dayNamesShort,a=r.dayNames||this.local.dayNames,o=r.parseMonth||this.local.parseMonth,s=r.monthNumbers||this.local.monthNumbers,l=r.monthNamesShort||this.local.monthNamesShort,u=r.monthNames||this.local.monthNames,c=-1,f=-1,h=-1,d=-1,v=-1,_=!1,b=!1,p=function(z,O){for(var V=1;g+V<e.length&&e.charAt(g+V)===z;)V++;return g+=V-1,Math.floor(V/(O||1))>1},k=function(z,O){var V=p(z,O),G=[2,3,V?4:2,V?4:2,10,11,20]["oyYJ@!".indexOf(z)+1],Z=new RegExp("^-?\\d{1,"+G+"}"),j=t.substring(M).match(Z);if(!j)throw(Ud.local.missingNumberAt||Ud.regionalOptions[""].missingNumberAt).replace(/\{0\}/,M);return M+=j[0].length,parseInt(j[0],10)},E=this,S=function(){if(typeof s=="function"){p("m");var z=s.call(E,t.substring(M));return M+=z.length,z}return k("m")},L=function(z,O,V,G){for(var Z=p(z,G)?V:O,j=0;j<Z.length;j++)if(t.substr(M,Z[j].length).toLowerCase()===Z[j].toLowerCase())return M+=Z[j].length,j+E.minMonth;throw(Ud.local.unknownNameAt||Ud.regionalOptions[""].unknownNameAt).replace(/\{0\}/,M)},x=function(){if(typeof u=="function"){var z=p("M")?u.call(E,t.substring(M)):l.call(E,t.substring(M));return M+=z.length,z}return L("M",l,u)},C=function(){if(t.charAt(M)!==e.charAt(g))throw(Ud.local.unexpectedLiteralAt||Ud.regionalOptions[""].unexpectedLiteralAt).replace(/\{0\}/,M);M++},M=0,g=0;g<e.length;g++)if(b)e.charAt(g)==="'"&&!p("'")?b=!1:C();else switch(e.charAt(g)){case"d":d=k("d");break;case"D":L("D",i,a);break;case"o":v=k("o");break;case"w":k("w");break;case"m":h=S();break;case"M":h=x();break;case"y":var P=g;_=!p("y",2),g=P,f=k("y",2);break;case"Y":f=k("Y",2);break;case"J":c=k("J")+.5,t.charAt(M)==="."&&(M++,k("J"));break;case"@":c=k("@")/this.SECS_PER_DAY+this.UNIX_EPOCH;break;case"!":c=k("!")/this.TICKS_PER_DAY+this.TICKS_EPOCH;break;case"*":M=t.length;break;case"'":p("'")?C():b=!0;break;default:C()}if(M<t.length)throw Ud.local.unexpectedText||Ud.regionalOptions[""].unexpectedText;if(f===-1?f=this.today().year():f<100&&_&&(f+=n===-1?1900:this.today().year()-this.today().year()%100-(f<=n?0:100)),typeof h=="string"&&(h=o.call(this,f,h)),v>-1){h=1,d=v;for(var T=this.daysInMonth(f,h);d>T;T=this.daysInMonth(f,h))h++,d-=T}return c>-1?this.fromJD(c):this.newDate(f,h,d)},determineDate:function(e,t,r,n,i){r&&typeof r!="object"&&(i=n,n=r,r=null),typeof n!="string"&&(i=n,n="");var a=this,o=function(s){try{return a.parseDate(n,s,i)}catch(f){}s=s.toLowerCase();for(var l=(s.match(/^c/)&&r?r.newDate():null)||a.today(),u=/([+-]?[0-9]+)\s*(d|w|m|y)?/g,c=u.exec(s);c;)l.add(parseInt(c[1],10),c[2]||"d"),c=u.exec(s);return l};return t=t?t.newDate():null,e=e==null?t:typeof e=="string"?o(e):typeof e=="number"?isNaN(e)||e===1/0||e===-1/0?t:a.today().add(e,"d"):a.newDate(e),e}})});var ZQe=ye(()=>{var Fx=kv(),Etr=Oh(),hQ=Fx.instance();function P9(e){this.local=this.regionalOptions[e||""]||this.regionalOptions[""]}P9.prototype=new Fx.baseCalendar;Etr(P9.prototype,{name:"Chinese",jdEpoch:17214255e-1,hasYearZero:!1,minMonth:0,firstMonth:0,minDay:1,regionalOptions:{"":{name:"Chinese",epochs:["BEC","EC"],monthNumbers:function(e,t){if(typeof e=="string"){var r=e.match(Ctr);return r?r[0]:""}var n=this._validateYear(e),i=e.month(),a=""+this.toChineseMonth(n,i);return t&&a.length<2&&(a="0"+a),this.isIntercalaryMonth(n,i)&&(a+="i"),a},monthNames:function(e){if(typeof e=="string"){var t=e.match(Ltr);return t?t[0]:""}var r=this._validateYear(e),n=e.month(),i=this.toChineseMonth(r,n),a=["\u4E00\u6708","\u4E8C\u6708","\u4E09\u6708","\u56DB\u6708","\u4E94\u6708","\u516D\u6708","\u4E03\u6708","\u516B\u6708","\u4E5D\u6708","\u5341\u6708","\u5341\u4E00\u6708","\u5341\u4E8C\u6708"][i-1];return this.isIntercalaryMonth(r,n)&&(a="\u95F0"+a),a},monthNamesShort:function(e){if(typeof e=="string"){var t=e.match(Ptr);return t?t[0]:""}var r=this._validateYear(e),n=e.month(),i=this.toChineseMonth(r,n),a=["\u4E00","\u4E8C","\u4E09","\u56DB","\u4E94","\u516D","\u4E03","\u516B","\u4E5D","\u5341","\u5341\u4E00","\u5341\u4E8C"][i-1];return this.isIntercalaryMonth(r,n)&&(a="\u95F0"+a),a},parseMonth:function(e,t){e=this._validateYear(e);var r=parseInt(t),n;if(isNaN(r))t[0]==="\u95F0"&&(n=!0,t=t.substring(1)),t[t.length-1]==="\u6708"&&(t=t.substring(0,t.length-1)),r=1+["\u4E00","\u4E8C","\u4E09","\u56DB","\u4E94","\u516D","\u4E03","\u516B","\u4E5D","\u5341","\u5341\u4E00","\u5341\u4E8C"].indexOf(t);else{var i=t[t.length-1];n=i==="i"||i==="I"}var a=this.toMonthIndex(e,r,n);return a},dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:1,isRTL:!1}},_validateYear:function(e,t){if(e.year&&(e=e.year()),typeof e!="number"||e<1888||e>2111)throw t.replace(/\{0\}/,this.local.name);return e},toMonthIndex:function(e,t,r){var n=this.intercalaryMonth(e),i=r&&t!==n;if(i||t<1||t>12)throw Fx.local.invalidMonth.replace(/\{0\}/,this.local.name);var a;return n?!r&&t<=n?a=t-1:a=t:a=t-1,a},toChineseMonth:function(e,t){e.year&&(e=e.year(),t=e.month());var r=this.intercalaryMonth(e),n=r?12:11;if(t<0||t>n)throw Fx.local.invalidMonth.replace(/\{0\}/,this.local.name);var i;return r?t<r?i=t+1:i=t:i=t+1,i},intercalaryMonth:function(e){e=this._validateYear(e);var t=zx[e-zx[0]],r=t>>13;return r},isIntercalaryMonth:function(e,t){e.year&&(e=e.year(),t=e.month());var r=this.intercalaryMonth(e);return!!r&&r===t},leapYear:function(e){return this.intercalaryMonth(e)!==0},weekOfYear:function(e,t,r){var n=this._validateYear(e,Fx.local.invalidyear),i=Ox[n-Ox[0]],a=i>>9&4095,o=i>>5&15,s=i&31,l;l=hQ.newDate(a,o,s),l.add(4-(l.dayOfWeek()||7),"d");var u=this.toJD(e,t,r)-l.toJD();return 1+Math.floor(u/7)},monthsInYear:function(e){return this.leapYear(e)?13:12},daysInMonth:function(e,t){e.year&&(t=e.month(),e=e.year()),e=this._validateYear(e);var r=zx[e-zx[0]],n=r>>13,i=n?12:11;if(t>i)throw Fx.local.invalidMonth.replace(/\{0\}/,this.local.name);var a=r&1<<12-t?30:29;return a},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,a,r,Fx.local.invalidDate);e=this._validateYear(n.year()),t=n.month(),r=n.day();var i=this.isIntercalaryMonth(e,t),a=this.toChineseMonth(e,t),o=Rtr(e,a,r,i);return hQ.toJD(o.year,o.month,o.day)},fromJD:function(e){var t=hQ.fromJD(e),r=Itr(t.year(),t.month(),t.day()),n=this.toMonthIndex(r.year,r.month,r.isIntercalary);return this.newDate(r.year,n,r.day)},fromString:function(e){var t=e.match(ktr),r=this._validateYear(+t[1]),n=+t[2],i=!!t[3],a=this.toMonthIndex(r,n,i),o=+t[4];return this.newDate(r,a,o)},add:function(e,t,r){var n=e.year(),i=e.month(),a=this.isIntercalaryMonth(n,i),o=this.toChineseMonth(n,i),s=Object.getPrototypeOf(P9.prototype).add.call(this,e,t,r);if(r==="y"){var l=s.year(),u=s.month(),c=this.isIntercalaryMonth(l,o),f=a&&c?this.toMonthIndex(l,o,!0):this.toMonthIndex(l,o,!1);f!==u&&s.month(f)}return s}});var ktr=/^\s*(-?\d\d\d\d|\d\d)[-/](\d?\d)([iI]?)[-/](\d?\d)/m,Ctr=/^\d?\d[iI]?/m,Ltr=/^闰?十?[一二三四五六七八九]?月/m,Ptr=/^闰?十?[一二三四五六七八九]?/m;Fx.calendars.chinese=P9;var zx=[1887,5780,5802,19157,2742,50359,1198,2646,46378,7466,3412,30122,5482,67949,2396,5294,43597,6732,6954,36181,2772,4954,18781,2396,54427,5274,6730,47781,5800,6868,21210,4790,59703,2350,5270,46667,3402,3496,38325,1388,4782,18735,2350,52374,6804,7498,44457,2906,1388,29294,4700,63789,6442,6804,56138,5802,2772,38235,1210,4698,22827,5418,63125,3476,5802,43701,2484,5302,27223,2646,70954,7466,3412,54698,5482,2412,38062,5294,2636,32038,6954,60245,2772,4826,43357,2394,5274,39501,6730,72357,5800,5844,53978,4790,2358,38039,5270,87627,3402,3496,54708,5484,4782,43311,2350,3222,27978,7498,68965,2904,5484,45677,4700,6444,39573,6804,6986,19285,2772,62811,1210,4698,47403,5418,5780,38570,5546,76469,2420,5302,51799,2646,5414,36501,3412,5546,18869,2412,54446,5276,6732,48422,6822,2900,28010,4826,92509,2394,5274,55883,6730,6820,47956,5812,2778,18779,2358,62615,5270,5450,46757,3492,5556,27318,4718,67887,2350,3222,52554,7498,3428,38252,5468,4700,31022,6444,64149,6804,6986,43861,2772,5338,35421,2650,70955,5418,5780,54954,5546,2740,38074,5302,2646,29991,3366,61011,3412,5546,43445,2412,5294,35406,6732,72998,6820,6996,52586,2778,2396,38045,5274,6698,23333,6820,64338,5812,2746,43355,2358,5270,39499,5450,79525,3492,5548],Ox=[1887,966732,967231,967733,968265,968766,969297,969798,970298,970829,971330,971830,972362,972863,973395,973896,974397,974928,975428,975929,976461,976962,977462,977994,978494,979026,979526,980026,980558,981059,981559,982091,982593,983124,983624,984124,984656,985157,985656,986189,986690,987191,987722,988222,988753,989254,989754,990286,990788,991288,991819,992319,992851,993352,993851,994383,994885,995385,995917,996418,996918,997450,997949,998481,998982,999483,1000014,1000515,1001016,1001548,1002047,1002578,1003080,1003580,1004111,1004613,1005113,1005645,1006146,1006645,1007177,1007678,1008209,1008710,1009211,1009743,1010243,1010743,1011275,1011775,1012306,1012807,1013308,1013840,1014341,1014841,1015373,1015874,1016404,1016905,1017405,1017937,1018438,1018939,1019471,1019972,1020471,1021002,1021503,1022035,1022535,1023036,1023568,1024069,1024568,1025100,1025601,1026102,1026633,1027133,1027666,1028167,1028666,1029198,1029699,1030199,1030730,1031231,1031763,1032264,1032764,1033296,1033797,1034297,1034828,1035329,1035830,1036362,1036861,1037393,1037894,1038394,1038925,1039427,1039927,1040459,1040959,1041491,1041992,1042492,1043023,1043524,1044024,1044556,1045057,1045558,1046090,1046590,1047121,1047622,1048122,1048654,1049154,1049655,1050187,1050689,1051219,1051720,1052220,1052751,1053252,1053752,1054284,1054786,1055285,1055817,1056317,1056849,1057349,1057850,1058382,1058883,1059383,1059915,1060415,1060947,1061447,1061947,1062479,1062981,1063480,1064012,1064514,1065014,1065545,1066045,1066577,1067078,1067578,1068110,1068611,1069112,1069642,1070142,1070674,1071175,1071675,1072207,1072709,1073209,1073740,1074241,1074741,1075273,1075773,1076305,1076807,1077308,1077839,1078340,1078840,1079372,1079871,1080403,1080904];function Itr(e,t,r,n){var i,a;if(typeof e=="object")i=e,a=t||{};else{var o=typeof e=="number"&&e>=1888&&e<=2111;if(!o)throw new Error("Solar year outside range 1888-2111");var s=typeof t=="number"&&t>=1&&t<=12;if(!s)throw new Error("Solar month outside range 1 - 12");var l=typeof r=="number"&&r>=1&&r<=31;if(!l)throw new Error("Solar day outside range 1 - 31");i={year:e,month:t,day:r},a=n||{}}var u=Ox[i.year-Ox[0]],c=i.year<<9|i.month<<5|i.day;a.year=c>=u?i.year:i.year-1,u=Ox[a.year-Ox[0]];var f=u>>9&4095,h=u>>5&15,d=u&31,v,_=new Date(f,h-1,d),b=new Date(i.year,i.month-1,i.day);v=Math.round((b-_)/(24*3600*1e3));var p=zx[a.year-zx[0]],k;for(k=0;k<13;k++){var E=p&1<<12-k?30:29;if(v<E)break;v-=E}var S=p>>13;return!S||k<S?(a.isIntercalary=!1,a.month=1+k):k===S?(a.isIntercalary=!0,a.month=k):(a.isIntercalary=!1,a.month=k),a.day=1+v,a}function Rtr(e,t,r,n,i){var a,o;if(typeof e=="object")o=e,a=t||{};else{var s=typeof e=="number"&&e>=1888&&e<=2111;if(!s)throw new Error("Lunar year outside range 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bw.baseCalendar;Dtr(dQ.prototype,{name:"Coptic",jdEpoch:18250295e-1,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Coptic",epochs:["BAM","AM"],monthNames:["Thout","Paopi","Hathor","Koiak","Tobi","Meshir","Paremhat","Paremoude","Pashons","Paoni","Epip","Mesori","Pi Kogi Enavot"],monthNamesShort:["Tho","Pao","Hath","Koi","Tob","Mesh","Pat","Pad","Pash","Pao","Epi","Meso","PiK"],dayNames:["Tkyriaka","Pesnau","Pshoment","Peftoou","Ptiou","Psoou","Psabbaton"],dayNamesShort:["Tky","Pes","Psh","Pef","Pti","Pso","Psa"],dayNamesMin:["Tk","Pes","Psh","Pef","Pt","Pso","Psa"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,bw.local.invalidYear),r=t.year()+(t.year()<0?1:0);return r%4===3||r%4===-1},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,bw.local.invalidYear||bw.regionalOptions[""].invalidYear),13},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,bw.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===13&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,bw.local.invalidDate);return e=n.year(),e<0&&e++,n.day()+(n.month()-1)*30+(e-1)*365+Math.floor(e/4)+this.jdEpoch-1},fromJD:function(e){var t=Math.floor(e)+.5-this.jdEpoch,r=Math.floor((t-Math.floor((t+366)/1461))/365)+1;r<=0&&r--,t=Math.floor(e)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(t/30)+1,i=t-(n-1)*30+1;return this.newDate(r,n,i)}});bw.calendars.coptic=dQ});var KQe=ye(()=>{var x1=kv(),Ftr=Oh();function vQ(e){this.local=this.regionalOptions[e||""]||this.regionalOptions[""]}vQ.prototype=new x1.baseCalendar;Ftr(vQ.prototype,{name:"Discworld",jdEpoch:17214255e-1,daysPerMonth:[16,32,32,32,32,32,32,32,32,32,32,32,32],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Discworld",epochs:["BUC","UC"],monthNames:["Ick","Offle","February","March","April","May","June","Grune","August","Spune","Sektober","Ember","December"],monthNamesShort:["Ick","Off","Feb","Mar","Apr","May","Jun","Gru","Aug","Spu","Sek","Emb","Dec"],dayNames:["Sunday","Octeday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Oct","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Oc","Mo","Tu","We","Th","Fr","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:2,isRTL:!1}},leapYear:function(e){return this._validate(e,this.minMonth,this.minDay,x1.local.invalidYear),!1},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,x1.local.invalidYear),13},daysInYear:function(e){return this._validate(e,this.minMonth,this.minDay,x1.local.invalidYear),400},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,x1.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(e,t,r){var n=this._validate(e,t,r,x1.local.invalidDate);return(n.day()+1)%8},weekDay:function(e,t,r){var n=this.dayOfWeek(e,t,r);return n>=2&&n<=6},extraInfo:function(e,t,r){var n=this._validate(e,t,r,x1.local.invalidDate);return{century:ztr[Math.floor((n.year()-1)/100)+1]||""}},toJD:function(e,t,r){var n=this._validate(e,t,r,x1.local.invalidDate);return e=n.year()+(n.year()<0?1:0),t=n.month(),r=n.day(),r+(t>1?16:0)+(t>2?(t-2)*32:0)+(e-1)*400+this.jdEpoch-1},fromJD:function(e){e=Math.floor(e+.5)-Math.floor(this.jdEpoch)-1;var t=Math.floor(e/400)+1;e-=(t-1)*400,e+=e>15?16:0;var r=Math.floor(e/32)+1,n=e-(r-1)*32+1;return this.newDate(t<=0?t-1:t,r,n)}});var ztr={20:"Fruitbat",21:"Anchovy"};x1.calendars.discworld=vQ});var JQe=ye(()=>{var ww=kv(),Otr=Oh();function pQ(e){this.local=this.regionalOptions[e||""]||this.regionalOptions[""]}pQ.prototype=new ww.baseCalendar;Otr(pQ.prototype,{name:"Ethiopian",jdEpoch:17242205e-1,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Ethiopian",epochs:["BEE","EE"],monthNames:["Meskerem","Tikemet","Hidar","Tahesas","Tir","Yekatit","Megabit","Miazia","Genbot","Sene","Hamle","Nehase","Pagume"],monthNamesShort:["Mes","Tik","Hid","Tah","Tir","Yek","Meg","Mia","Gen","Sen","Ham","Neh","Pag"],dayNames:["Ehud","Segno","Maksegno","Irob","Hamus","Arb","Kidame"],dayNamesShort:["Ehu","Seg","Mak","Iro","Ham","Arb","Kid"],dayNamesMin:["Eh","Se","Ma","Ir","Ha","Ar","Ki"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,ww.local.invalidYear),r=t.year()+(t.year()<0?1:0);return r%4===3||r%4===-1},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,ww.local.invalidYear||ww.regionalOptions[""].invalidYear),13},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,ww.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===13&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,ww.local.invalidDate);return e=n.year(),e<0&&e++,n.day()+(n.month()-1)*30+(e-1)*365+Math.floor(e/4)+this.jdEpoch-1},fromJD:function(e){var t=Math.floor(e)+.5-this.jdEpoch,r=Math.floor((t-Math.floor((t+366)/1461))/365)+1;r<=0&&r--,t=Math.floor(e)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(t/30)+1,i=t-(n-1)*30+1;return this.newDate(r,n,i)}});ww.calendars.ethiopian=pQ});var $Qe=ye(()=>{var qx=kv(),qtr=Oh();function gQ(e){this.local=this.regionalOptions[e||""]||this.regionalOptions[""]}gQ.prototype=new qx.baseCalendar;qtr(gQ.prototype,{name:"Hebrew",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{"":{name:"Hebrew",epochs:["BAM","AM"],monthNames:["Nisan","Iyar","Sivan","Tammuz","Av","Elul","Tishrei","Cheshvan","Kislev","Tevet","Shevat","Adar","Adar II"],monthNamesShort:["Nis","Iya","Siv","Tam","Av","Elu","Tis","Che","Kis","Tev","She","Ada","Ad2"],dayNames:["Yom Rishon","Yom Sheni","Yom Shlishi","Yom Revi'i","Yom Chamishi","Yom Shishi","Yom Shabbat"],dayNamesShort:["Ris","She","Shl","Rev","Cha","Shi","Sha"],dayNamesMin:["Ri","She","Shl","Re","Ch","Shi","Sha"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:0,isRTL:!1}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,qx.local.invalidYear);return this._leapYear(t.year())},_leapYear:function(e){return e=e<0?e+1:e,I9(e*7+1,19)<7},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,qx.local.invalidYear),this._leapYear(e.year?e.year():e)?13:12},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,qx.local.invalidYear);return e=t.year(),this.toJD(e===-1?1:e+1,7,1)-this.toJD(e,7,1)},daysInMonth:function(e,t){return e.year&&(t=e.month(),e=e.year()),this._validate(e,t,this.minDay,qx.local.invalidMonth),t===12&&this.leapYear(e)||t===8&&I9(this.daysInYear(e),10)===5?30:t===9&&I9(this.daysInYear(e),10)===3?29:this.daysPerMonth[t-1]},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==6},extraInfo:function(e,t,r){var n=this._validate(e,t,r,qx.local.invalidDate);return{yearType:(this.leapYear(n)?"embolismic":"common")+" "+["deficient","regular","complete"][this.daysInYear(n)%10-3]}},toJD:function(e,t,r){var n=this._validate(e,t,r,qx.local.invalidDate);e=n.year(),t=n.month(),r=n.day();var i=e<=0?e+1:e,a=this.jdEpoch+this._delay1(i)+this._delay2(i)+r+1;if(t<7){for(var o=7;o<=this.monthsInYear(e);o++)a+=this.daysInMonth(e,o);for(var o=1;o<t;o++)a+=this.daysInMonth(e,o)}else for(var o=7;o<t;o++)a+=this.daysInMonth(e,o);return a},_delay1:function(e){var t=Math.floor((235*e-234)/19),r=12084+13753*t,n=t*29+Math.floor(r/25920);return I9(3*(n+1),7)<3&&n++,n},_delay2:function(e){var t=this._delay1(e-1),r=this._delay1(e),n=this._delay1(e+1);return n-r===356?2:r-t===382?1:0},fromJD:function(e){e=Math.floor(e)+.5;for(var t=Math.floor((e-this.jdEpoch)*98496/35975351)-1;e>=this.toJD(t===-1?1:t+1,7,1);)t++;for(var r=e<this.toJD(t,1,1)?7:1;e>this.toJD(t,r,this.daysInMonth(t,r));)r++;var n=e-this.toJD(t,r,1)+1;return this.newDate(t,r,n)}});function I9(e,t){return e-t*Math.floor(e/t)}qx.calendars.hebrew=gQ});var QQe=ye(()=>{var dC=kv(),Btr=Oh();function mQ(e){this.local=this.regionalOptions[e||""]||this.regionalOptions[""]}mQ.prototype=new dC.baseCalendar;Btr(mQ.prototype,{name:"Islamic",jdEpoch:19484395e-1,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Islamic",epochs:["BH","AH"],monthNames:["Muharram","Safar","Rabi' al-awwal","Rabi' al-thani","Jumada al-awwal","Jumada al-thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-ahad","Yawm al-ithnayn","Yawm ath-thulaathaa'","Yawm al-arbi'aa'","Yawm al-kham\u012Bs","Yawm al-jum'a","Yawm as-sabt"],dayNamesShort:["Aha","Ith","Thu","Arb","Kha","Jum","Sab"],dayNamesMin:["Ah","It","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!1}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,dC.local.invalidYear);return(t.year()*11+14)%30<11},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(e){return this.leapYear(e)?355:354},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,dC.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===12&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==5},toJD:function(e,t,r){var n=this._validate(e,t,r,dC.local.invalidDate);return e=n.year(),t=n.month(),r=n.day(),e=e<=0?e+1:e,r+Math.ceil(29.5*(t-1))+(e-1)*354+Math.floor((3+11*e)/30)+this.jdEpoch-1},fromJD:function(e){e=Math.floor(e)+.5;var t=Math.floor((30*(e-this.jdEpoch)+10646)/10631);t=t<=0?t-1:t;var r=Math.min(12,Math.ceil((e-29-this.toJD(t,1,1))/29.5)+1),n=e-this.toJD(t,r,1)+1;return this.newDate(t,r,n)}});dC.calendars.islamic=mQ});var eet=ye(()=>{var vC=kv(),Ntr=Oh();function yQ(e){this.local=this.regionalOptions[e||""]||this.regionalOptions[""]}yQ.prototype=new vC.baseCalendar;Ntr(yQ.prototype,{name:"Julian",jdEpoch:17214235e-1,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Julian",epochs:["BC","AD"],monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],digits:null,dateFormat:"mm/dd/yyyy",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,vC.local.invalidYear),r=t.year()<0?t.year()+1:t.year();return r%4===0},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(4-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,vC.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,vC.local.invalidDate);return e=n.year(),t=n.month(),r=n.day(),e<0&&e++,t<=2&&(e--,t+=12),Math.floor(365.25*(e+4716))+Math.floor(30.6001*(t+1))+r-1524.5},fromJD:function(e){var t=Math.floor(e+.5),r=t+1524,n=Math.floor((r-122.1)/365.25),i=Math.floor(365.25*n),a=Math.floor((r-i)/30.6001),o=a-Math.floor(a<14?1:13),s=n-Math.floor(o>2?4716:4715),l=r-i-Math.floor(30.6001*a);return s<=0&&s--,this.newDate(s,o,l)}});vC.calendars.julian=yQ});var ret=ye(()=>{var ug=kv(),Utr=Oh();function xQ(e){this.local=this.regionalOptions[e||""]||this.regionalOptions[""]}xQ.prototype=new ug.baseCalendar;Utr(xQ.prototype,{name:"Mayan",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{"":{name:"Mayan",epochs:["",""],monthNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],monthNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],dayNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesMin:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],digits:null,dateFormat:"YYYY.m.d",firstDay:0,isRTL:!1,haabMonths:["Pop","Uo","Zip","Zotz","Tzec","Xul","Yaxkin","Mol","Chen","Yax","Zac","Ceh","Mac","Kankin","Muan","Pax","Kayab","Cumku","Uayeb"],tzolkinMonths:["Imix","Ik","Akbal","Kan","Chicchan","Cimi","Manik","Lamat","Muluc","Oc","Chuen","Eb","Ben","Ix","Men","Cib","Caban","Etznab","Cauac","Ahau"]}},leapYear:function(e){return this._validate(e,this.minMonth,this.minDay,ug.local.invalidYear),!1},formatYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,ug.local.invalidYear);e=t.year();var r=Math.floor(e/400);e=e%400,e+=e<0?400:0;var n=Math.floor(e/20);return r+"."+n+"."+e%20},forYear:function(e){if(e=e.split("."),e.length<3)throw"Invalid Mayan year";for(var t=0,r=0;r<e.length;r++){var n=parseInt(e[r],10);if(Math.abs(n)>19||r>0&&n<0)throw"Invalid Mayan year";t=t*20+n}return t},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,ug.local.invalidYear),18},weekOfYear:function(e,t,r){return this._validate(e,t,r,ug.local.invalidDate),0},daysInYear:function(e){return this._validate(e,this.minMonth,this.minDay,ug.local.invalidYear),360},daysInMonth:function(e,t){return this._validate(e,t,this.minDay,ug.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(e,t,r){var n=this._validate(e,t,r,ug.local.invalidDate);return n.day()},weekDay:function(e,t,r){return this._validate(e,t,r,ug.local.invalidDate),!0},extraInfo:function(e,t,r){var n=this._validate(e,t,r,ug.local.invalidDate),i=n.toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(e){e-=this.jdEpoch;var t=_Q(e+8+17*20,365);return[Math.floor(t/20)+1,_Q(t,20)]},_toTzolkin:function(e){return e-=this.jdEpoch,[tet(e+20,20),tet(e+4,13)]},toJD:function(e,t,r){var n=this._validate(e,t,r,ug.local.invalidDate);return n.day()+n.month()*20+n.year()*360+this.jdEpoch},fromJD:function(e){e=Math.floor(e)+.5-this.jdEpoch;var t=Math.floor(e/360);e=e%360,e+=e<0?360:0;var r=Math.floor(e/20),n=e%20;return this.newDate(t,r,n)}});function _Q(e,t){return e-t*Math.floor(e/t)}function tet(e,t){return _Q(e-1,t)+1}ug.calendars.mayan=xQ});var net=ye(()=>{var Tw=kv(),Vtr=Oh();function bQ(e){this.local=this.regionalOptions[e||""]||this.regionalOptions[""]}bQ.prototype=new Tw.baseCalendar;var iet=Tw.instance("gregorian");Vtr(bQ.prototype,{name:"Nanakshahi",jdEpoch:22576735e-1,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Nanakshahi",epochs:["BN","AN"],monthNames:["Chet","Vaisakh","Jeth","Harh","Sawan","Bhadon","Assu","Katak","Maghar","Poh","Magh","Phagun"],monthNamesShort:["Che","Vai","Jet","Har","Saw","Bha","Ass","Kat","Mgr","Poh","Mgh","Pha"],dayNames:["Somvaar","Mangalvar","Budhvaar","Veervaar","Shukarvaar","Sanicharvaar","Etvaar"],dayNamesShort:["Som","Mangal","Budh","Veer","Shukar","Sanichar","Et"],dayNamesMin:["So","Ma","Bu","Ve","Sh","Sa","Et"],digits:null,dateFormat:"dd-mm-yyyy",firstDay:0,isRTL:!1}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Tw.local.invalidYear||Tw.regionalOptions[""].invalidYear);return iet.leapYear(t.year()+(t.year()<1?1:0)+1469)},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(1-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Tw.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===12&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(i,t,r){var n=this._validate(i,t,r,Tw.local.invalidMonth),i=n.year();i<0&&i++;for(var a=n.day(),o=1;o<n.month();o++)a+=this.daysPerMonth[o-1];return a+iet.toJD(i+1468,3,13)},fromJD:function(e){e=Math.floor(e+.5);for(var t=Math.floor((e-(this.jdEpoch-1))/366);e>=this.toJD(t+1,1,1);)t++;for(var r=e-Math.floor(this.toJD(t,1,1)+.5)+1,n=1;r>this.daysInMonth(t,n);)r-=this.daysInMonth(t,n),n++;return this.newDate(t,n,r)}});Tw.calendars.nanakshahi=bQ});var aet=ye(()=>{var Aw=kv(),Gtr=Oh();function wQ(e){this.local=this.regionalOptions[e||""]||this.regionalOptions[""]}wQ.prototype=new Aw.baseCalendar;Gtr(wQ.prototype,{name:"Nepali",jdEpoch:17007095e-1,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{"":{name:"Nepali",epochs:["BBS","ABS"],monthNames:["Baisakh","Jestha","Ashadh","Shrawan","Bhadra","Ashwin","Kartik","Mangsir","Paush","Mangh","Falgun","Chaitra"],monthNamesShort:["Bai","Je","As","Shra","Bha","Ash","Kar","Mang","Pau","Ma","Fal","Chai"],dayNames:["Aaitabaar","Sombaar","Manglbaar","Budhabaar","Bihibaar","Shukrabaar","Shanibaar"],dayNamesShort:["Aaita","Som","Mangl","Budha","Bihi","Shukra","Shani"],dayNamesMin:["Aai","So","Man","Bu","Bi","Shu","Sha"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:1,isRTL:!1}},leapYear:function(e){return this.daysInYear(e)!==this.daysPerYear},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Aw.local.invalidYear);if(e=t.year(),typeof this.NEPALI_CALENDAR_DATA[e]=="undefined")return this.daysPerYear;for(var r=0,n=this.minMonth;n<=12;n++)r+=this.NEPALI_CALENDAR_DATA[e][n];return r},daysInMonth:function(e,t){return e.year&&(t=e.month(),e=e.year()),this._validate(e,t,this.minDay,Aw.local.invalidMonth),typeof this.NEPALI_CALENDAR_DATA[e]=="undefined"?this.daysPerMonth[t-1]:this.NEPALI_CALENDAR_DATA[e][t]},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==6},toJD:function(e,t,r){var n=this._validate(e,t,r,Aw.local.invalidDate);e=n.year(),t=n.month(),r=n.day();var i=Aw.instance(),a=0,o=t,s=e;this._createMissingCalendarData(e);var l=e-(o>9||o===9&&r>=this.NEPALI_CALENDAR_DATA[s][0]?56:57);for(t!==9&&(a=r,o--);o!==9;)o<=0&&(o=12,s--),a+=this.NEPALI_CALENDAR_DATA[s][o],o--;return t===9?(a+=r-this.NEPALI_CALENDAR_DATA[s][0],a<0&&(a+=i.daysInYear(l))):a+=this.NEPALI_CALENDAR_DATA[s][9]-this.NEPALI_CALENDAR_DATA[s][0],i.newDate(l,1,1).add(a,"d").toJD()},fromJD:function(e){var t=Aw.instance(),r=t.fromJD(e),n=r.year(),i=r.dayOfYear(),a=n+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)o++,o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var u=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,u)},_createMissingCalendarData:function(e){var t=this.daysPerMonth.slice(0);t.unshift(17);for(var r=e-1;r<e+2;r++)typeof this.NEPALI_CALENDAR_DATA[r]=="undefined"&&(this.NEPALI_CALENDAR_DATA[r]=t)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2e3:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30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oet=ye(()=>{var rS=kv(),Htr=Oh();function D9(e){this.local=this.regionalOptions[e||""]||this.regionalOptions[""]}function R9(e){var t=e-475;e<0&&t++;var r=.242197,n=r*t,i=r*(t+1),a=n-Math.floor(n),o=i-Math.floor(i);return a>o}D9.prototype=new rS.baseCalendar;Htr(D9.prototype,{name:"Persian",jdEpoch:19483205e-1,daysPerMonth:[31,31,31,31,31,31,30,30,30,30,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Persian",epochs:["BP","AP"],monthNames:["Farvardin","Ordibehesht","Khordad","Tir","Mordad","Shahrivar","Mehr","Aban","Azar","Dey","Bahman","Esfand"],monthNamesShort:["Far","Ord","Kho","Tir","Mor","Sha","Meh","Aba","Aza","Dey","Bah","Esf"],dayNames:["Yekshanbeh","Doshanbeh","Seshanbeh","Chah\u0101rshanbeh","Panjshanbeh","Jom'eh","Shanbeh"],dayNamesShort:["Yek","Do","Se","Cha","Panj","Jom","Sha"],dayNamesMin:["Ye","Do","Se","Ch","Pa","Jo","Sh"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!1}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,rS.local.invalidYear);return R9(t.year())},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-((n.dayOfWeek()+1)%7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,rS.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===12&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==5},toJD:function(e,t,r){var n=this._validate(e,t,r,rS.local.invalidDate);e=n.year(),t=n.month(),r=n.day();var i=0;if(e>0)for(var a=1;a<e;a++)R9(a)&&i++;else if(e<0)for(var a=e;a<0;a++)R9(a)&&i--;return r+(t<=7?(t-1)*31:(t-1)*30+6)+(e>0?e-1:e)*365+i+this.jdEpoch-1},fromJD:function(e){e=Math.floor(e)+.5;var t=475+(e-this.toJD(475,1,1))/365.242197,r=Math.floor(t);r<=0&&r--,e>this.toJD(r,12,R9(r)?30:29)&&(r++,r===0&&r++);var n=e-this.toJD(r,1,1)+1,i=n<=186?Math.ceil(n/31):Math.ceil((n-6)/30),a=e-this.toJD(r,i,1)+1;return this.newDate(r,i,a)}});rS.calendars.persian=D9;rS.calendars.jalali=D9});var set=ye(()=>{var Sw=kv(),jtr=Oh(),F9=Sw.instance();function TQ(e){this.local=this.regionalOptions[e||""]||this.regionalOptions[""]}TQ.prototype=new Sw.baseCalendar;jtr(TQ.prototype,{name:"Taiwan",jdEpoch:24194025e-1,yearsOffset:1911,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Taiwan",epochs:["BROC","ROC"],monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:1,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,Sw.local.invalidYear),r=this._t2gYear(t.year());return F9.leapYear(r)},weekOfYear:function(i,t,r){var n=this._validate(i,this.minMonth,this.minDay,Sw.local.invalidYear),i=this._t2gYear(n.year());return F9.weekOfYear(i,n.month(),n.day())},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Sw.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(i,t,r){var n=this._validate(i,t,r,Sw.local.invalidDate),i=this._t2gYear(n.year());return F9.toJD(i,n.month(),n.day())},fromJD:function(e){var t=F9.fromJD(e),r=this._g2tYear(t.year());return this.newDate(r,t.month(),t.day())},_t2gYear:function(e){return e+this.yearsOffset+(e>=-this.yearsOffset&&e<=-1?1:0)},_g2tYear:function(e){return e-this.yearsOffset-(e>=1&&e<=this.yearsOffset?1:0)}});Sw.calendars.taiwan=TQ});var uet=ye(()=>{var Mw=kv(),Wtr=Oh(),z9=Mw.instance();function AQ(e){this.local=this.regionalOptions[e||""]||this.regionalOptions[""]}AQ.prototype=new Mw.baseCalendar;Wtr(AQ.prototype,{name:"Thai",jdEpoch:15230985e-1,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Thai",epochs:["BBE","BE"],monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,Mw.local.invalidYear),r=this._t2gYear(t.year());return z9.leapYear(r)},weekOfYear:function(i,t,r){var n=this._validate(i,this.minMonth,this.minDay,Mw.local.invalidYear),i=this._t2gYear(n.year());return z9.weekOfYear(i,n.month(),n.day())},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Mw.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(i,t,r){var n=this._validate(i,t,r,Mw.local.invalidDate),i=this._t2gYear(n.year());return z9.toJD(i,n.month(),n.day())},fromJD:function(e){var t=z9.fromJD(e),r=this._g2tYear(t.year());return this.newDate(r,t.month(),t.day())},_t2gYear:function(e){return e-this.yearsOffset-(e>=1&&e<=this.yearsOffset?1:0)},_g2tYear:function(e){return e+this.yearsOffset+(e>=-this.yearsOffset&&e<=-1?1:0)}});Mw.calendars.thai=AQ});var cet=ye(()=>{var Ew=kv(),Xtr=Oh();function SQ(e){this.local=this.regionalOptions[e||""]||this.regionalOptions[""]}SQ.prototype=new Ew.baseCalendar;Xtr(SQ.prototype,{name:"UmmAlQura",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Umm al-Qura",epochs:["BH","AH"],monthNames:["Al-Muharram","Safar","Rabi' al-awwal","Rabi' Al-Thani","Jumada Al-Awwal","Jumada Al-Thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-Ahad","Yawm al-Ithnain","Yawm al-Thal\u0101th\u0101\u2019","Yawm al-Arba\u2018\u0101\u2019","Yawm al-Kham\u012Bs","Yawm al-Jum\u2018a","Yawm al-Sabt"],dayNamesMin:["Ah","Ith","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!0}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Ew.local.invalidYear);return this.daysInYear(t.year())===355},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(e){for(var t=0,r=1;r<=12;r++)t+=this.daysInMonth(e,r);return t},daysInMonth:function(e,t){for(var r=this._validate(e,t,this.minDay,Ew.local.invalidMonth),n=r.toJD()-24e5+.5,i=0,a=0;a<Bx.length;a++){if(Bx[a]>n)return Bx[i]-Bx[i-1];i++}return 30},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==5},toJD:function(e,t,r){var n=this._validate(e,t,r,Ew.local.invalidDate),i=12*(n.year()-1)+n.month()-15292,a=n.day()+Bx[i-1]-1;return a+24e5-.5},fromJD:function(e){for(var t=e-24e5+.5,r=0,n=0;n<Bx.length&&!(Bx[n]>t);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),o=a+1,s=i-12*a,l=t-Bx[r-1]+1;return this.newDate(o,s,l)},isValid:function(e,t,r){var n=Ew.baseCalendar.prototype.isValid.apply(this,arguments);return n&&(e=e.year!=null?e.year:e,n=e>=1276&&e<=1500),n},_validate:function(e,t,r,n){var i=Ew.baseCalendar.prototype._validate.apply(this,arguments);if(i.year<1276||i.year>1500)throw n.replace(/\{0\}/,this.local.name);return i}});Ew.calendars.ummalqura=SQ;var 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het=ye((rTr,fet)=>{"use strict";fet.exports=kv();XQe();ZQe();YQe();KQe();JQe();$Qe();QQe();eet();ret();net();aet();oet();set();uet();cet()});var _et=ye((iTr,yet)=>{"use strict";var vet=het(),pC=Dr(),pet=fs(),Ztr=pet.EPOCHJD,Ytr=pet.ONEDAY,kQ={valType:"enumerated",values:pC.sortObjectKeys(vet.calendars),editType:"calc",dflt:"gregorian"},get=function(e,t,r,n){var i={};return i[r]=kQ,pC.coerce(e,t,i,r,n)},Ktr=function(e,t,r,n){for(var i=0;i<r.length;i++)get(e,t,r[i]+"calendar",n.calendar)},Jtr={chinese:"2000-01-01",coptic:"2000-01-01",discworld:"2000-01-01",ethiopian:"2000-01-01",hebrew:"5000-01-01",islamic:"1000-01-01",julian:"2000-01-01",mayan:"5000-01-01",nanakshahi:"1000-01-01",nepali:"2000-01-01",persian:"1000-01-01",jalali:"1000-01-01",taiwan:"1000-01-01",thai:"2000-01-01",ummalqura:"1400-01-01"},$tr={chinese:"2000-01-02",coptic:"2000-01-03",discworld:"2000-01-03",ethiopian:"2000-01-05",hebrew:"5000-01-01",islamic:"1000-01-02",julian:"2000-01-03",mayan:"5000-01-01",nanakshahi:"1000-01-05",nepali:"2000-01-05",persian:"1000-01-01",jalali:"1000-01-01",taiwan:"1000-01-04",thai:"2000-01-04",ummalqura:"1400-01-06"},Qtr={chinese:["2000-01-01","2001-01-01"],coptic:["1700-01-01","1701-01-01"],discworld:["1800-01-01","1801-01-01"],ethiopian:["2000-01-01","2001-01-01"],hebrew:["5700-01-01","5701-01-01"],islamic:["1400-01-01","1401-01-01"],julian:["2000-01-01","2001-01-01"],mayan:["5200-01-01","5201-01-01"],nanakshahi:["0500-01-01","0501-01-01"],nepali:["2000-01-01","2001-01-01"],persian:["1400-01-01","1401-01-01"],jalali:["1400-01-01","1401-01-01"],taiwan:["0100-01-01","0101-01-01"],thai:["2500-01-01","2501-01-01"],ummalqura:["1400-01-01","1401-01-01"]},O9="##",err={d:{0:"dd","-":"d"},e:{0:"d","-":"d"},a:{0:"D","-":"D"},A:{0:"DD","-":"DD"},j:{0:"oo","-":"o"},W:{0:"ww","-":"w"},m:{0:"mm","-":"m"},b:{0:"M","-":"M"},B:{0:"MM","-":"MM"},y:{0:"yy","-":"yy"},Y:{0:"yyyy","-":"yyyy"},U:O9,w:O9,c:{0:"D M d %X yyyy","-":"D M d %X yyyy"},x:{0:"mm/dd/yyyy","-":"mm/dd/yyyy"}};function trr(e,t,r){for(var n=Math.floor((t+.05)/Ytr)+Ztr,i=met(r).fromJD(n),a=0,o,s,l,u,c;(a=e.indexOf("%",a))!==-1;)o=e.charAt(a+1),o==="0"||o==="-"||o==="_"?(l=3,s=e.charAt(a+2),o==="_"&&(o="-")):(s=o,o="0",l=2),u=err[s],u?(u===O9?c=O9:c=i.formatDate(u[o]),e=e.slice(0,a)+c+e.slice(a+l),a+=c.length):a+=l;return e}var det={};function met(e){var t=det[e];return t||(t=det[e]=vet.instance(e),t)}function gC(e){return pC.extendFlat({},kQ,{description:e})}function CQ(e){return"Sets the calendar system to use with `"+e+"` date data."}var EQ={xcalendar:gC(CQ("x"))},ny=pC.extendFlat({},EQ,{ycalendar:gC(CQ("y"))}),MQ=pC.extendFlat({},ny,{zcalendar:gC(CQ("z"))}),iS=gC(["Sets the calendar system to use for `range` and `tick0`","if this is a date axis. This does not set the calendar for","interpreting data on this axis, that's specified in the trace","or via the global `layout.calendar`"].join(" "));yet.exports={moduleType:"component",name:"calendars",schema:{traces:{scatter:ny,bar:ny,box:ny,heatmap:ny,contour:ny,histogram:ny,histogram2d:ny,histogram2dcontour:ny,scatter3d:MQ,surface:MQ,mesh3d:MQ,scattergl:ny,ohlc:EQ,candlestick:EQ},layout:{calendar:gC(["Sets the default calendar system to use for interpreting and","displaying dates throughout the plot."].join(" "))},subplots:{xaxis:{calendar:iS},yaxis:{calendar:iS},scene:{xaxis:{calendar:iS},yaxis:{calendar:iS},zaxis:{calendar:iS}},polar:{radialaxis:{calendar:iS}}}},layoutAttributes:kQ,handleDefaults:get,handleTraceDefaults:Ktr,CANONICAL_SUNDAY:$tr,CANONICAL_TICK:Jtr,DFLTRANGE:Qtr,getCal:met,worldCalFmt:trr}});var bet=ye((nTr,xet)=>{"use strict";xet.exports=_et()});var rrr=ye((aTr,Tet)=>{var wet=iye();wet.register([o1e(),X1e(),sxe(),kxe(),Vxe(),qbe(),Jbe(),q2e(),dwe(),Ywe(),z3e(),ZEe(),Oke(),k6e(),gLe(),XLe(),mPe(),VIe(),s8e(),S8e(),O8e(),J8e(),hRe(),kRe(),rFe(),wFe(),BBe(),BNe(),YUe(),bVe(),PGe(),WGe(),gHe(),kje(),Gje(),dWe(),wXe(),WXe(),EZe(),YYe(),_Ke(),NKe(),hJe(),MJe(),S$e(),H$e(),uQe(),HQe(),bet()]);Tet.exports=wet});return rrr();})();
+/*!
+ * pad-left <https://github.com/jonschlinkert/pad-left>
+ *
+ * Copyright (c) 2014-2015, Jon Schlinkert.
+ * Licensed under the MIT license.
+ */
+/*!
+ * repeat-string <https://github.com/jonschlinkert/repeat-string>
+ *
+ * Copyright (c) 2014-2015, Jon Schlinkert.
+ * Licensed under the MIT License.
+ */
+/*! ieee754. BSD-3-Clause License. Feross Aboukhadijeh <https://feross.org/opensource> */
+/*!
+ * The buffer module from node.js, for the browser.
+ *
+ * @author   Feross Aboukhadijeh <https://feross.org>
+ * @license  MIT
+ */
+/*!
+ * Determine if an object is a Buffer
+ *
+ * @author   Feross Aboukhadijeh <https://feross.org>
+ * @license  MIT
+ */
+/*! Bundled license information:
+
+native-promise-only/lib/npo.src.js:
+  (*! Native Promise Only
+      v0.8.1 (c) Kyle Simpson
+      MIT License: http://getify.mit-license.org
+  *)
+
+polybooljs/index.js:
+  (*
+   * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc
+   * @license MIT
+   * @preserve Project Home: https://github.com/voidqk/polybooljs
+   *)
+
+ieee754/index.js:
+  (*! ieee754. BSD-3-Clause License. Feross Aboukhadijeh <https://feross.org/opensource> *)
+
+buffer/index.js:
+  (*!
+   * The buffer module from node.js, for the browser.
+   *
+   * @author   Feross Aboukhadijeh <https://feross.org>
+   * @license  MIT
+   *)
+
+safe-buffer/index.js:
+  (*! safe-buffer. MIT License. Feross Aboukhadijeh <https://feross.org/opensource> *)
+
+assert/build/internal/util/comparisons.js:
+  (*!
+   * The buffer module from node.js, for the browser.
+   *
+   * @author   Feross Aboukhadijeh <feross@feross.org> <http://feross.org>
+   * @license  MIT
+   *)
+
+object-assign/index.js:
+  (*
+  object-assign
+  (c) Sindre Sorhus
+  @license MIT
+  *)
+
+maplibre-gl/dist/maplibre-gl.js:
+  (**
+   * MapLibre GL JS
+   * @license 3-Clause BSD. Full text of license: https://github.com/maplibre/maplibre-gl-js/blob/v4.7.1/LICENSE.txt
+   *)
+*/
+
+window.Plotly = Plotly;
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2ms","orientation":"h","showlegend":true,"textposition":"auto","x":{"dtype":"i1","bdata":"                                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                      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+</body>
+</html>
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diff --git a/docs/assets/training/layerwise.png b/docs/assets/training/layerwise.png
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diff --git a/docs/assets/training/layerwise_good_loading.png b/docs/assets/training/layerwise_good_loading.png
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diff --git a/docs/benchmarking/cli.md b/docs/benchmarking/cli.md
index f78ae8a95366..4f0c933c4ac8 100644
--- a/docs/benchmarking/cli.md
+++ b/docs/benchmarking/cli.md
@@ -37,6 +37,7 @@ th {
 | HuggingFace-Blazedit | ✅ | ✅ | `vdaita/edit_5k_char`, `vdaita/edit_10k_char` |
 | HuggingFace-ASR | ✅ | ✅ | `openslr/librispeech_asr`, `facebook/voxpopuli`,  `LIUM/tedlium`, `edinburghcstr/ami`,        `speechcolab/gigaspeech`,        `kensho/spgispeech` |
 | Spec Bench | ✅ | ✅ | `wget https://raw.githubusercontent.com/hemingkx/Spec-Bench/refs/heads/main/data/spec_bench/question.jsonl` |
+| SPEED-Bench | ✅ | ✅ | `curl -LsSf https://raw.githubusercontent.com/NVIDIA-NeMo/Skills/refs/heads/main/nemo_skills/dataset/speed-bench/prepare.py \| python3 -` |
 | Custom | ✅ | ✅ | Local file: `data.jsonl` |
 | Custom MM | ✅ | ✅ | Local file: `mm_data.jsonl` |
 
@@ -107,6 +108,38 @@ P99 ITL (ms):                            8.39
 ==================================================
 ```
 
+#### Results Visualization
+
+The `--plot-timeline` and `--plot-dataset-stats` can be used to generate respectively the requests completion timeline and dataset prompt and output tokens statistics, which can be useful for debugging purpose or for deeper analysis.
+
+```bash
+vllm bench serve \
+    --backend vllm \
+    --model meta-llama/Llama-3.1-8B-Instruct \
+    --endpoint /v1/completions \
+    --dataset-name sharegpt \
+    --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \
+    --num-prompts 100 \
+    --plot-timeline \
+    --timeline-itl-thresholds 2,5 \
+    --plot-dataset-stats \
+    --save-result
+```
+
+##### Interactive Timeline
+
+The generated timeline is an interactive visualization in the form of an HTML file that can be rendered in most browsers. To customize the ITL color thresholds, one can use `--timeline-itl-thresholds` flag (default: 25ms, 50ms)
+
+Example output:
+
+<iframe src="../assets/contributing/vllm_bench_serve_timeline.html" width="100%" height="600" frameborder="0"></iframe>
+
+##### Dataset statistics
+
+The generated figure shows the input prompt and output tokens distribution.
+
+Example output: ![Dataset Statistics](../assets/contributing/vllm_bench_serve_dataset_stats.png)
+
 #### Custom Dataset
 
 If the dataset you want to benchmark is not supported yet in vLLM, even then you can benchmark on it using `CustomDataset`. Your data needs to be in `.jsonl` format and needs to have "prompt" field per entry, e.g., data.jsonl
@@ -239,6 +272,69 @@ vllm bench serve \
     --spec-bench-category "summarization"
 ```
 
+#### SPEED-Bench Benchmark with Speculative Decoding
+
+[SPEED-Bench](https://huggingface.co/datasets/nvidia/SPEED-Bench) is a unified and diverse dataset for speculative decoding, supporting acceptance rate and length measurements using the Qualitative split and throughput measurements using the Throughput splits in 5 configuration of input sequence length (1k, 2k, 8k, 16k, 32k).
+
+!!! note
+    This dataset is governed by the [NVIDIA Evaluation Dataset License Agreement](https://huggingface.co/datasets/nvidia/SPEED-Bench/blob/main/License.pdf). For each dataset a user elects to use, the user is responsible for checking if the dataset license is fit for the intended purpose. The `prepare.py` script automatically fetches data from all the source datasets.
+
+First, download the dataset to a folder, using this one liner:
+
+```bash
+curl -LsSf https://raw.githubusercontent.com/NVIDIA-NeMo/Skills/refs/heads/main/nemo_skills/dataset/speed-bench/prepare.py \| python3 -
+```
+
+The command supports also the following arguments:
+
+- `--config`: download only a subset of the dataset: `qualitative`, `throughput_1k`, `throughput_2k`, `throughput_8k`, `throughput_16k` and `throughput_32k`. By default, it will download all subsets.
+- `--output_dir`: download to a specified folder. By default, it will download to the current directory.
+
+Start a server with speculative decoding:
+
+```bash
+vllm serve meta-llama/Llama-3.3-70B-Instruct \
+    --speculative-config $'{"method": "eagle3",
+    "num_speculative_tokens": 3,
+    "model": "nvidia/Llama-3.3-70B-Instruct-Eagle3"}'
+```
+
+Run all categories in the Qualitative split:
+
+```bash
+vllm bench serve \
+    --model meta-llama/Llama-3.3-70B-Instruct \
+    --dataset-name speed_bench \
+    --dataset-path "<YOUR_DOWNLOADED_PATH>/data/speed_bench" \
+    --num-prompts -1
+```
+
+Available categories include `[writing, roleplay, reasoning, math, coding, stem, humanities, multilingual, summarization, qa, rag]`.
+
+Run only a specific category like "multilingual":
+
+```bash
+vllm bench serve \
+    --model meta-llama/Llama-3.3-70B-Instruct \
+    --dataset-name speed_bench \
+    --dataset-path "<YOUR_DOWNLOADED_PATH>/data/speed_bench" \
+    --num-prompts -1
+    --speed-bench-category "multilingual"
+```
+
+Run all categories in the Throughput split (2k ISL):
+
+```bash
+vllm bench serve \
+    --model meta-llama/Llama-3.3-70B-Instruct \
+    --dataset-name speed_bench \
+    --speed-bench-dataset-subset throughput_2k
+    --dataset-path "<YOUR_DOWNLOADED_PATH>/data/speed_bench/" \
+    --num-prompts -1
+```
+
+Available categories include `[high_entropy, mixed, low_entropy]`, where high entropy data contains unstructued data such as creative writing while low entropy data contains more structured data such as coding, more details are in the dataset card.
+
 #### Other HuggingFaceDataset Examples
 
 ```bash
diff --git a/docs/cli/README.md b/docs/cli/README.md
index c708eb795898..b27bd3b647b5 100644
--- a/docs/cli/README.md
+++ b/docs/cli/README.md
@@ -163,7 +163,7 @@ Running with a local file:
 
 ```bash
 vllm run-batch \
-    -i offline_inference/openai_batch/openai_example_batch.jsonl \
+    -i features/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
     --model meta-llama/Meta-Llama-3-8B-Instruct
 ```
@@ -172,7 +172,7 @@ Using remote file:
 
 ```bash
 vllm run-batch \
-    -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl \
+    -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/features/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
     --model meta-llama/Meta-Llama-3-8B-Instruct
 ```
diff --git a/docs/configuration/conserving_memory.md b/docs/configuration/conserving_memory.md
index 8ea241c582e5..2c098118dbb1 100644
--- a/docs/configuration/conserving_memory.md
+++ b/docs/configuration/conserving_memory.md
@@ -23,7 +23,7 @@ llm = LLM(model="ibm-granite/granite-3.1-8b-instruct", tensor_parallel_size=2)
 !!! note
     With tensor parallelism enabled, each process will read the whole model and split it into chunks, which makes the disk reading time even longer (proportional to the size of tensor parallelism).
 
-    You can convert the model checkpoint to a sharded checkpoint using [examples/offline_inference/save_sharded_state.py](../../examples/offline_inference/save_sharded_state.py). The conversion process might take some time, but later you can load the sharded checkpoint much faster. The model loading time should remain constant regardless of the size of tensor parallelism.
+    You can convert the model checkpoint to a sharded checkpoint using [examples/features/sharded_state/load_sharded_state_offline.py](../../examples/features/sharded_state/load_sharded_state_offline.py). The conversion process might take some time, but later you can load the sharded checkpoint much faster. The model loading time should remain constant regardless of the size of tensor parallelism.
 
 ## Quantization
 
diff --git a/docs/configuration/optimization.md b/docs/configuration/optimization.md
index 26eda1246b1f..bbfa4beb0860 100644
--- a/docs/configuration/optimization.md
+++ b/docs/configuration/optimization.md
@@ -155,9 +155,9 @@ switch to `--physcpubind=<cpu-list> --membind=<node>`.
 
 These `--numa-bind*` options only apply to GPU execution processes. They do not
 configure the CPU backend's separate thread-affinity controls. Automatic
-GPU-to-NUMA detection is currently implemented for CUDA/NVML-based platforms;
-other GPU backends must provide explicit binding lists if they use these
-options.
+GPU-to-NUMA detection is currently implemented for CUDA/NVML-based as well as
+ROCM-based platforms; other GPU backends must provide explicit binding lists if
+they use these options.
 
 `--numa-bind-nodes` takes one non-negative NUMA node index per visible GPU, in
 the same order as the GPU indices.
@@ -270,6 +270,36 @@ Known supported models (with corresponding benchmarks):
 
 ## Input Processing
 
+### fastokens Tokenizer Mode
+
+By default vLLM uses the standard Hugging Face `tokenizers` library to power
+the fast tokenizer (`--tokenizer-mode hf`). For BPE tokenizers (Qwen, Llama,
+DeepSeek, GPT-OSS, etc.) you can switch to the
+[fastokens](https://github.com/crusoecloud/fastokens) Rust backend, a drop-in
+replacement that's substantially faster on encode/decode and on streaming
+detokenization:
+
+```console
+vllm serve Qwen/Qwen3-8B --tokenizer-mode fastokens
+```
+
+Equivalent in the offline API:
+
+```python
+from vllm import LLM
+llm = LLM(model="Qwen/Qwen3-8B", tokenizer_mode="fastokens")
+```
+
+The `fastokens` Python package must be installed; if it isn't, vLLM raises
+a clear `ImportError` at tokenizer load. `fastokens` loads a Hugging Face
+fast tokenizer with its inner Rust tokenizer replaced by the fastokens shim,
+so it is mutually exclusive with non-HF modes such as `mistral` or
+`deepseek_v32`.
+
+Tokenizer-bound workloads — long shared prefixes, bursty short prompts,
+batch detokenization — see the largest wins. If your bottleneck is GPU
+prefill/decode, the tokenizer change is unlikely to be visible end-to-end.
+
 ### Parallel Processing
 
 You can run input processing in parallel via [API server scale-out](../serving/data_parallel_deployment.md#internal-load-balancing).
diff --git a/docs/contributing/ci/failures.md b/docs/contributing/ci/failures.md
index dad04e75fbb6..a0038f461a04 100644
--- a/docs/contributing/ci/failures.md
+++ b/docs/contributing/ci/failures.md
@@ -60,9 +60,19 @@ the failure?
 
 ## Logs Wrangling
 
-Download the full log file from Buildkite locally.
+Download a job's log (no Buildkite login required):
 
-Strip timestamps and colorization:
+[.buildkite/scripts/ci-fetch-log.sh](../../../.buildkite/scripts/ci-fetch-log.sh)
+
+```bash
+# Find the failing job. Each row's URL is .../builds/<N>#<job_uuid>:
+gh pr checks <PR> --repo vllm-project/vllm
+
+# Download + strip timestamps/ANSI in one step:
+.buildkite/scripts/ci-fetch-log.sh "https://buildkite.com/vllm/ci/builds/<N>#<job_uuid>"
+```
+
+To clean an already-downloaded log:
 
 [.buildkite/scripts/ci-clean-log.sh](../../../.buildkite/scripts/ci-clean-log.sh)
 
diff --git a/docs/contributing/model/transcription.md b/docs/contributing/model/transcription.md
index a23de100da39..b076ef84a46c 100644
--- a/docs/contributing/model/transcription.md
+++ b/docs/contributing/model/transcription.md
@@ -66,7 +66,7 @@ This is for controlling general behavior of the API when serving your model:
 
 See [Audio preprocessing and chunking](#audio-preprocessing-and-chunking) for what each field controls.
 
-Implement the prompt construction via [get_generation_prompt][vllm.model_executor.models.interfaces.SupportsTranscription.get_generation_prompt]. The server passes you the resampled waveform and task parameters; you return a valid [PromptType][vllm.inputs.llm.PromptType]. There are two common patterns:
+Implement the prompt construction via [get_generation_prompt][vllm.model_executor.models.interfaces.SupportsTranscription.get_generation_prompt]. The server builds a [SpeechToTextParams][vllm.config.speech_to_text.SpeechToTextParams] object that bundles the resampled waveform, task parameters, and request-specific options. Your model receives this single object and returns a valid [PromptType][vllm.inputs.llm.PromptType]. There are two common patterns:
 
 #### Multimodal LLM with audio embeddings (e.g., Voxtral, Gemma3n)
 
@@ -75,21 +75,20 @@ Return a dict containing `multi_modal_data` with the audio, and either a `prompt
 ??? code "get_generation_prompt()"
 
     ```python
+    from vllm.config.speech_to_text import SpeechToTextParams
+
     class YourASRModel(nn.Module, SupportsTranscription):
         ...
 
         @classmethod
         def get_generation_prompt(
             cls,
-            audio: np.ndarray,
-            stt_config: SpeechToTextConfig,
-            model_config: ModelConfig,
-            language: str | None,
-            task_type: Literal["transcribe", "translate"],
-            request_prompt: str,
-            to_language: str | None,
+            stt_params: SpeechToTextParams,
         ) -> PromptType:
-            # Example with a free-form instruction prompt
+            audio = stt_params.audio
+            stt_config = stt_params.stt_config
+            task_type = stt_params.task_type
+
             task_word = "Transcribe" if task_type == "transcribe" else "Translate"
             prompt = (
                 "<start_of_turn>user\n"
@@ -112,20 +111,22 @@ Return a dict with separate `encoder_prompt` and `decoder_prompt` entries:
 ??? code "get_generation_prompt()"
 
     ```python
+    from vllm.config.speech_to_text import SpeechToTextParams
+
     class YourASRModel(nn.Module, SupportsTranscription):
         ...
 
         @classmethod
         def get_generation_prompt(
             cls,
-            audio: np.ndarray,
-            stt_config: SpeechToTextConfig,
-            model_config: ModelConfig,
-            language: str | None,
-            task_type: Literal["transcribe", "translate"],
-            request_prompt: str,
-            to_language: str | None,
+            stt_params: SpeechToTextParams,
         ) -> PromptType:
+            audio = stt_params.audio
+            stt_config = stt_params.stt_config
+            language = stt_params.language
+            task_type = stt_params.task_type
+            request_prompt = stt_params.request_prompt
+
             if language is None:
                 raise ValueError("Language must be specified")
 
@@ -193,7 +194,7 @@ Provide a fast duration→token estimate to improve streaming usage statistics:
 
 The API server takes care of basic audio I/O and optional chunking before building prompts:
 
-- Resampling: Input audio is resampled to `SpeechToTextConfig.sample_rate` using `librosa`.
+- Resampling: Input audio is resampled to `SpeechToTextConfig.sample_rate` using `AudioResampler`.
 - Chunking: If `SpeechToTextConfig.allow_audio_chunking` is True and the duration exceeds `max_audio_clip_s`, the server splits the audio into overlapping chunks and generates a prompt per chunk. Overlap is controlled by `overlap_chunk_second`.
 - Energy-aware splitting: When `min_energy_split_window_size` is set, the server finds low-energy regions to minimize cutting within words.
 
@@ -206,22 +207,20 @@ Relevant server logic:
     async def _preprocess_speech_to_text(...):
         language = self.model_cls.validate_language(request.language)
         ...
-        y, sr = librosa.load(bytes_, sr=self.asr_config.sample_rate)
-        duration = librosa.get_duration(y=y, sr=sr)
+        y, sr = load_audio(bytes_, sr=self.asr_config.sample_rate)
+        duration = get_audio_duration(y=y, sr=sr)
         do_split_audio = (self.asr_config.allow_audio_chunking
                         and duration > self.asr_config.max_audio_clip_s)
         chunks = [y] if not do_split_audio else self._split_audio(y, int(sr))
         prompts = []
         for chunk in chunks:
-            prompt = self.model_cls.get_generation_prompt(
+            stt_params = request.build_stt_params(
                 audio=chunk,
                 stt_config=self.asr_config,
                 model_config=self.model_config,
-                language=language,
                 task_type=self.task_type,
-                request_prompt=request.prompt,
-                to_language=to_language,
             )
+            prompt = self.model_cls.get_generation_prompt(stt_params)
             prompts.append(prompt)
         return prompts, duration
     ```
@@ -279,7 +278,7 @@ Once your model implements `SupportsTranscription`, you can test the endpoints (
       http://localhost:8000/v1/audio/translations
     ```
 
-Or check out more examples in [examples/online_serving](../../../examples/online_serving).
+Or check out more examples in [examples/speech_to_text](../../../examples/speech_to_text).
 
 !!! note
     - If your model handles chunking internally (e.g., via its processor or encoder), set `min_energy_split_window_size=None` in the returned `SpeechToTextConfig` to disable server-side chunking.
diff --git a/docs/contributing/profiling.md b/docs/contributing/profiling.md
index 1d12d63549a0..91757c40e4f8 100644
--- a/docs/contributing/profiling.md
+++ b/docs/contributing/profiling.md
@@ -42,7 +42,7 @@ Traces can be visualized using <https://ui.perfetto.dev/>.
 
 #### Offline Inference
 
-Refer to [examples/offline_inference/simple_profiling.py](../../examples/offline_inference/simple_profiling.py) for an example.
+Refer to [examples/features/profiling/simple_profiling_offline.py](../../examples/features/profiling/simple_profiling_offline.py) for an example.
 
 #### OpenAI Server
 
@@ -206,8 +206,8 @@ Both the `vllm.utils.profiling.cprofile` and `vllm.utils.profiling.cprofile_cont
 used to profile a section of code.
 
 !!! note
-    The legacy import paths `vllm.utils.cprofile` and `vllm.utils.cprofile_context` are deprecated.
-    Please use `vllm.utils.profiling.cprofile` and `vllm.utils.profiling.cprofile_context` instead.
+    The `vllm.utils.profiling` helpers are deprecated and will be removed in
+    `v0.21`. Please use Python's `cProfile` module directly instead.
 
 ### Example usage - decorator
 
diff --git a/docs/deployment/frameworks/anyscale.md b/docs/deployment/frameworks/anyscale.md
index 965742ec0726..6888e4dbf0b6 100644
--- a/docs/deployment/frameworks/anyscale.md
+++ b/docs/deployment/frameworks/anyscale.md
@@ -3,7 +3,7 @@
 [Anyscale](https://www.anyscale.com) is a managed, multi-cloud platform developed by the creators of Ray.
 
 Anyscale automates the entire lifecycle of Ray clusters in your AWS, GCP, or Azure account, delivering the flexibility of open-source Ray
-without the operational overhead of maintaining Kubernetes control planes, configuring autoscalers, managing observability stacks, or manually managing head and worker nodes with helper scripts like [examples/online_serving/run_cluster.sh](../../../examples/online_serving/run_cluster.sh).
+without the operational overhead of maintaining Kubernetes control planes, configuring autoscalers, managing observability stacks, or manually managing head and worker nodes with helper scripts like [examples/ray_serving/run_cluster.sh](../../../examples/ray_serving/run_cluster.sh).
 
 When serving large language models with vLLM, Anyscale can rapidly provision [production-ready HTTPS endpoints](https://docs.anyscale.com/examples/deploy-ray-serve-llms) or [fault-tolerant batch inference jobs](https://docs.anyscale.com/examples/ray-data-llm).
 
diff --git a/docs/deployment/frameworks/helm.md b/docs/deployment/frameworks/helm.md
index a0aee70b1b32..45924dd6f7c3 100644
--- a/docs/deployment/frameworks/helm.md
+++ b/docs/deployment/frameworks/helm.md
@@ -17,7 +17,7 @@ Before you begin, ensure that you have the following:
 
 ## Installing the chart
 
-This guide uses the Helm chart at [examples/online_serving/chart-helm](../../../examples/online_serving/chart-helm).
+This guide uses the Helm chart at [examples/deployment/chart-helm](../../../examples/deployment/chart-helm).
 
 To install the chart with the release name `test-vllm`:
 
diff --git a/docs/deployment/frameworks/lws.md b/docs/deployment/frameworks/lws.md
index 14710a8dc333..47586bcd7003 100644
--- a/docs/deployment/frameworks/lws.md
+++ b/docs/deployment/frameworks/lws.md
@@ -40,7 +40,7 @@ Deploy the following yaml file `lws.yaml`
                 command:
                   - sh
                   - -c
-                  - "bash /vllm-workspace/examples/online_serving/multi-node-serving.sh leader --ray_cluster_size=$(LWS_GROUP_SIZE); 
+                  - "bash /vllm-workspace/examples/ray_serving/multi-node-serving.sh leader --ray_cluster_size=$(LWS_GROUP_SIZE); 
                     vllm serve meta-llama/Meta-Llama-3.1-405B-Instruct --port 8080 --tensor-parallel-size 8 --pipeline_parallel_size 2"
                 resources:
                   limits:
@@ -73,7 +73,7 @@ Deploy the following yaml file `lws.yaml`
                 command:
                   - sh
                   - -c
-                  - "bash /vllm-workspace/examples/online_serving/multi-node-serving.sh worker --ray_address=$(LWS_LEADER_ADDRESS)"
+                  - "bash /vllm-workspace/examples/ray_serving/multi-node-serving.sh worker --ray_address=$(LWS_LEADER_ADDRESS)"
                 resources:
                   limits:
                     nvidia.com/gpu: "8"
diff --git a/docs/deployment/frameworks/retrieval_augmented_generation.md b/docs/deployment/frameworks/retrieval_augmented_generation.md
index 8a5d18807d06..c23862cde72f 100644
--- a/docs/deployment/frameworks/retrieval_augmented_generation.md
+++ b/docs/deployment/frameworks/retrieval_augmented_generation.md
@@ -36,7 +36,7 @@ pip install -U vllm \
     vllm serve qwen/Qwen1.5-0.5B-Chat --port 8001
     ```
 
-1. Use the script: [examples/online_serving/retrieval_augmented_generation_with_langchain.py](../../../examples/online_serving/retrieval_augmented_generation_with_langchain.py)
+1. Use the script: [examples/applications/rag/retrieval_augmented_generation_with_langchain.py](../../../examples/applications/rag/retrieval_augmented_generation_with_langchain.py)
 
 1. Run the script
 
@@ -74,7 +74,7 @@ pip install vllm \
     vllm serve qwen/Qwen1.5-0.5B-Chat --port 8001
     ```
 
-1. Use the script: [examples/online_serving/retrieval_augmented_generation_with_llamaindex.py](../../../examples/online_serving/retrieval_augmented_generation_with_llamaindex.py)
+1. Use the script: [examples/applications/rag/retrieval_augmented_generation_with_llamaindex.py](../../../examples/applications/rag/retrieval_augmented_generation_with_llamaindex.py)
 
 1. Run the script:
 
diff --git a/docs/deployment/frameworks/runpod.md b/docs/deployment/frameworks/runpod.md
index 61ca3c4e68ce..b00350386e48 100644
--- a/docs/deployment/frameworks/runpod.md
+++ b/docs/deployment/frameworks/runpod.md
@@ -12,8 +12,7 @@ vLLM can be deployed on [RunPod](https://www.runpod.io/), a cloud GPU platform t
 SSH into your RunPod pod and launch the vLLM OpenAI-compatible server:
 
 ```bash
-python -m vllm.entrypoints.openai.api_server \
-    --model <model-name> \
+vllm serve <model-name> \
     --host 0.0.0.0 \
     --port 8000
 ```
diff --git a/docs/deployment/frameworks/skypilot.md b/docs/deployment/frameworks/skypilot.md
index e9b0d5f0671c..e032769f15e8 100644
--- a/docs/deployment/frameworks/skypilot.md
+++ b/docs/deployment/frameworks/skypilot.md
@@ -59,7 +59,7 @@ See the vLLM SkyPilot YAML for serving, [serving.yaml](https://github.com/skypil
 
       echo 'Starting gradio server...'
       git clone https://github.com/vllm-project/vllm.git || true
-      python vllm/examples/online_serving/gradio_openai_chatbot_webserver.py \
+      python vllm/examples/applications/chatbot/gradio_openai_chatbot_webserver.py \
         -m $MODEL_NAME \
         --port 8811 \
         --model-url http://localhost:8081/v1 \
@@ -305,7 +305,7 @@ It is also possible to access the Llama-3 service with a separate GUI frontend,
 
       echo 'Starting gradio server...'
       git clone https://github.com/vllm-project/vllm.git || true
-      python vllm/examples/online_serving/gradio_openai_chatbot_webserver.py \
+      python vllm/examples/applications/api_client/gradio_openai_chatbot_webserver.py \
         -m $MODEL_NAME \
         --port 8811 \
         --model-url http://$ENDPOINT/v1 \
diff --git a/docs/deployment/frameworks/streamlit.md b/docs/deployment/frameworks/streamlit.md
index 1b214e1a32aa..6c0c8c6c1430 100644
--- a/docs/deployment/frameworks/streamlit.md
+++ b/docs/deployment/frameworks/streamlit.md
@@ -20,7 +20,7 @@ pip install vllm streamlit openai
     vllm serve Qwen/Qwen1.5-0.5B-Chat
     ```
 
-1. Use the script: [examples/online_serving/streamlit_openai_chatbot_webserver.py](../../../examples/online_serving/streamlit_openai_chatbot_webserver.py)
+1. Use the script: [examples/applications/chatbot/streamlit_openai_chatbot_webserver.py](../../../examples/applications/chatbot/streamlit_openai_chatbot_webserver.py)
 
 1. Start the streamlit web UI and start to chat:
 
diff --git a/docs/deployment/integrations/kthena.md b/docs/deployment/integrations/kthena.md
index 483dd7474440..03ef190e558c 100644
--- a/docs/deployment/integrations/kthena.md
+++ b/docs/deployment/integrations/kthena.md
@@ -78,10 +78,9 @@ Key points from the example YAML:
     - sh
     - -c
     - >
-      bash /vllm-workspace/examples/online_serving/multi-node-serving.sh leader --ray_cluster_size=2;
-      python3 -m vllm.entrypoints.openai.api_server
+      bash /vllm-workspace/examples/ray_serving/multi-node-serving.sh leader --ray_cluster_size=2;
+      vllm serve meta-llama/Llama-3.1-405B-Instruct
         --port 8080
-        --model meta-llama/Llama-3.1-405B-Instruct
         --tensor-parallel-size 8
         --pipeline-parallel-size 2
   ```
@@ -93,7 +92,7 @@ Key points from the example YAML:
     - sh
     - -c
     - >
-      bash /vllm-workspace/examples/online_serving/multi-node-serving.sh worker --ray_address=$(ENTRY_ADDRESS)
+      bash /vllm-workspace/examples/ray_serving/multi-node-serving.sh worker --ray_address=$(ENTRY_ADDRESS)
   ```
 
 ---
@@ -144,8 +143,8 @@ spec:
                 command:
                   - sh
                   - -c
-                  - "bash /vllm-workspace/examples/online_serving/multi-node-serving.sh leader --ray_cluster_size=2; 
-                    python3 -m vllm.entrypoints.openai.api_server --port 8080 --model meta-llama/Llama-3.1-405B-Instruct --tensor-parallel-size 8 --pipeline-parallel-size 2"
+                  - "bash /vllm-workspace/examples/ray_serving/multi-node-serving.sh leader --ray_cluster_size=2; 
+                    vllm serve meta-llama/Llama-3.1-405B-Instruct --port 8080 --tensor-parallel-size 8 --pipeline-parallel-size 2"
                 resources:
                   limits:
                     nvidia.com/gpu: "8"
@@ -178,7 +177,7 @@ spec:
                 command:
                   - sh
                   - -c
-                  - "bash /vllm-workspace/examples/online_serving/multi-node-serving.sh worker --ray_address=$(ENTRY_ADDRESS)"
+                  - "bash /vllm-workspace/examples/ray_serving/multi-node-serving.sh worker --ray_address=$(ENTRY_ADDRESS)"
                 resources:
                   limits:
                     nvidia.com/gpu: "8"
diff --git a/docs/deployment/k8s.md b/docs/deployment/k8s.md
index dbcb277278c9..7a92c99b2c4a 100644
--- a/docs/deployment/k8s.md
+++ b/docs/deployment/k8s.md
@@ -4,6 +4,7 @@ Deploying vLLM on Kubernetes is a scalable and efficient way to serve machine le
 
 - [Deployment with CPUs](#deployment-with-cpus)
 - [Deployment with GPUs](#deployment-with-gpus)
+- [Serving with gRPC](#serving-with-grpc)
 - [Troubleshooting](#troubleshooting)
     - [Startup Probe or Readiness Probe Failure, container log contains "KeyboardInterrupt: terminated"](#startup-probe-or-readiness-probe-failure-container-log-contains-keyboardinterrupt-terminated)
 - [Conclusion](#conclusion)
@@ -387,6 +388,49 @@ INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
 
       If the service is correctly deployed, you should receive a response from the vLLM model.
 
+## Serving with gRPC
+
+vLLM can serve models over gRPC instead of HTTP by passing the `--grpc` flag. This requires the optional gRPC dependencies:
+
+```bash
+pip install vllm[grpc]
+```
+
+When using `--grpc`, the server exposes the standard [gRPC Health Checking Protocol](https://github.com/grpc/grpc/blob/master/doc/health-checking.md) (`grpc.health.v1.Health`), which integrates with Kubernetes [native gRPC probes](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/#define-a-grpc-liveness-probe) (available since Kubernetes 1.24).
+
+To deploy with gRPC, change the `vllm serve` command to include `--grpc` and replace `httpGet` probes with `grpc` probes:
+
+```yaml
+containers:
+- name: mistral-7b
+  image: vllm/vllm-openai:latest
+  command: ["/bin/sh", "-c"]
+  args: [
+    "pip install vllm[grpc] && vllm serve mistralai/Mistral-7B-Instruct-v0.3 --grpc --port 50051 --trust-remote-code"
+  ]
+  ports:
+  - containerPort: 50051
+  livenessProbe:
+    grpc:
+      port: 50051
+    initialDelaySeconds: 120
+    periodSeconds: 10
+  readinessProbe:
+    grpc:
+      port: 50051
+    initialDelaySeconds: 120
+    periodSeconds: 5
+```
+
+!!! note
+    The gRPC health service checks the engine status on every probe. If the engine is unhealthy or the server is shutting down, the probe returns `NOT_SERVING`.
+
+You can also verify the health service manually with `grpcurl`:
+
+```bash
+grpcurl -plaintext localhost:50051 grpc.health.v1.Health/Check
+```
+
 ## Troubleshooting
 
 ### Startup Probe or Readiness Probe Failure, container log contains "KeyboardInterrupt: terminated"
diff --git a/docs/design/attention_backends.md b/docs/design/attention_backends.md
index 242cc6b3b1ed..3f6feecf5f78 100644
--- a/docs/design/attention_backends.md
+++ b/docs/design/attention_backends.md
@@ -106,6 +106,7 @@ Priority is **1 = highest** (tried first).
 | 2 | `FLASH_ATTN` |
 | 3 | `TRITON_ATTN` |
 | 4 | `FLEX_ATTENTION` |
+| 5 | `TURBOQUANT` |
 
 **Ampere/Hopper (SM 8.x-9.x):**
 
@@ -115,6 +116,7 @@ Priority is **1 = highest** (tried first).
 | 2 | `FLASHINFER` |
 | 3 | `TRITON_ATTN` |
 | 4 | `FLEX_ATTENTION` |
+| 5 | `TURBOQUANT` |
 
 ### MLA Attention (DeepSeek-style)
 
@@ -153,6 +155,7 @@ Priority is **1 = highest** (tried first).
 | **Block Sizes** | Supported KV cache block sizes (%N means multiples of N) |
 | **Head Sizes** | Supported attention head sizes |
 | **Sink** | Attention sink support (for StreamingLLM) |
+| **Non-Causal** | Non-causal (bidirectional) attention support for decoder models |
 | **Sparse** | Sparse attention support (MLA only) |
 | **MM Prefix** | Multimodal prefix full attention support |
 | **DCP** | Decode Context Parallelism support (`--decode-context-parallel-size`) |
@@ -163,21 +166,21 @@ Priority is **1 = highest** (tried first).
 
 ## Standard Attention (MHA, MQA, GQA) Backends
 
-| Backend | Version | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | MM Prefix | DCP | Attention Types | Compute Cap. |
-| ------- | ------- | ------ | --------- | ----------- | ---------- | ---- | --------- | --- | --------------- | ------------ |
-| `CPU_ATTN` | | fp16, bf16, fp32 | `auto` | Any | 32, 64, 80, 96, 112, 128, 160, 192, 224, 256, 512 | ❌ | ❌ | ❌ | All | N/A |
-| `FLASHINFER` | Native† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64 | 64, 128, 256 | ❌ | ❌ | ✅ | Decoder | 7.x-9.x |
-| `FLASHINFER` | TRTLLM† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64 | 64, 128, 256 | ✅ | ❌ | ✅ | Decoder | 10.0 |
-| `FLASH_ATTN` | FA2* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ❌ | ✅ | All | ≥8.0 |
-| `FLASH_ATTN` | FA3* | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | Any | ✅ | ❌ | ✅ | All | 9.x |
-| `FLASH_ATTN` | FA4* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ❌ | ✅ | All | ≥10.0 |
-| `FLASH_ATTN_DIFFKV` | | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ✅ | Decoder | Any |
-| `FLEX_ATTENTION` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16` | Any | Any | ❌ | ✅ | ❌ | Decoder, Encoder Only | Any |
-| `ROCM_AITER_FA` | | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32 | 64, 128, 256 | ❌ | ❌ | ❌ | Decoder | N/A |
-| `ROCM_AITER_UNIFIED_ATTN` | | fp16, bf16 | `auto` | %16 | Any | ✅ | ✅ | ❌ | All | N/A |
-| `ROCM_ATTN` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | 32, 64, 80, 96, 128, 160, 192, 224, 256 | ❌ | ✅ | ❌ | Decoder, Encoder, Encoder Only | N/A |
-| `TREE_ATTN` | | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | 32, 64, 96, 128, 160, 192, 224, 256 | ❌ | ❌ | ❌ | Decoder | Any |
-| `TRITON_ATTN` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `int8_per_token_head`, `fp8_per_token_head` | %16 | Any | ✅ | ✅ | ❌ | All | Any |
+| Backend | Version | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | Non-Causal | MM Prefix | DCP | Attention Types | Compute Cap. |
+| ------- | ------- | ------ | --------- | ----------- | ---------- | ---- | ---------- | --------- | --- | --------------- | ------------ |
+| `CPU_ATTN` | | fp16, bf16, fp32 | `auto`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | 32, 64, 80, 96, 112, 128, 160, 192, 224, 256, 512 | ❌ | ❌ | ❌ | ❌ | All | N/A |
+| `FLASHINFER` | Native† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64 | 64, 128, 256 | ❌ | ❌ | ❌ | ✅ | Decoder | 7.x-9.x |
+| `FLASHINFER` | TRTLLM† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `nvfp4` | 16, 32, 64 | 64, 128, 256 | ✅ | ❌ | ❌ | ✅ | Decoder | 10.x |
+| `FLASH_ATTN` | FA2* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ✅ | ❌ | ✅ | All | ≥8.0 |
+| `FLASH_ATTN` | FA3* | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | 9.x |
+| `FLASH_ATTN` | FA4* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | ≥10.0 |
+| `FLASH_ATTN_DIFFKV` | | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ❌ | ✅ | Decoder | Any |
+| `FLEX_ATTENTION` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ✅ | ✅ | ❌ | Decoder, Encoder Only | Any |
+| `ROCM_AITER_FA` | | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32 | 64, 128, 256 | ❌ | ✅ | ❌ | ❌ | Decoder | N/A |
+| `ROCM_AITER_UNIFIED_ATTN` | | fp16, bf16 | `auto` | %16 | Any | ✅ | ❌ | ✅ | ❌ | All | N/A |
+| `ROCM_ATTN` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | 32, 64, 80, 96, 128, 160, 192, 224, 256 | ❌ | ✅ | ✅ | ❌ | Decoder, Encoder, Encoder Only | N/A |
+| `TRITON_ATTN` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `int8_per_token_head`, `fp8_per_token_head` | %16 | Any | ✅ | ❌ | ✅ | ❌ | All | Any |
+| `TURBOQUANT` | | fp16, bf16 | `turboquant_k8v4`, `turboquant_4bit_nc`, `turboquant_k3v4_nc`, `turboquant_3bit_nc` | 16, 32, 64, 128 | Any | ❌ | ❌ | ❌ | ❌ | Decoder | Any |
 
 > **†** FlashInfer uses TRTLLM attention on Blackwell (SM100), which supports sinks. Disable via `--attention-config.use_trtllm_attention=0`.
 >
@@ -189,31 +192,35 @@ MLA uses separate backends for prefill and decode phases.
 
 ### Prefill Backends
 
-The prefill backend is selected at runtime based on hardware and
-configuration.
+To explicitly select a prefill backend, use
+`-ac.mla_prefill_backend=<BACKEND>` (e.g., `FLASH_ATTN`, `FLASHINFER`).
+Otherwise, the prefill backend is selected automatically at runtime based on
+hardware and configuration.
 
-| Backend | Description | Compute Cap. | Enable | Disable | Notes |
-| ------- | ----------- | ------------ | ------ | ------- | ----- |
-| TRT-LLM Ragged‡ | TensorRT-LLM ragged attention | 10.x | Default on SM100 | `-ac.use_trtllm_ragged_deepseek_prefill=0` | DeepSeek R1 dims only |
-| FlashInfer | FlashInfer CUTLASS backend | 10.x | `-ac.disable_flashinfer_prefill=0` | `-ac.disable_flashinfer_prefill=1` | DeepSeek R1 dims only |
-| cuDNN | cuDNN-based attention | 10.x | `-ac.use_cudnn_prefill=1` | `-ac.use_cudnn_prefill=0` | |
-| FlashAttention | FlashAttention varlen (FA2/FA3) | Any | Default fallback | Use other backends | FA3 on SM90, FA2 otherwise |
+| Backend | Description | Dtypes | Compute Cap. | Notes |
+| ------- | ----------- | ------ | ------------ | ----- |
+| `FLASH_ATTN`‡ | FlashAttention varlen (FA2/FA3/FA4) | fp16, bf16 | Any | FA4 on SM100+, FA3 on SM90, FA2 otherwise |
+| `TRTLLM_RAGGED` | TensorRT-LLM ragged attention | fp16, bf16 | 10.x | DeepSeek R1 dims only |
+| `FLASHINFER` | FlashInfer CUTLASS backend | fp16, bf16 | 10.x | DeepSeek R1 dims only |
 
 > **‡** TRT-LLM Ragged is the default on Blackwell (SM100).
 > On other GPUs, FlashAttention is used as the default.
 
 ### Decode Backends
 
-| Backend | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | Sparse | MM Prefix | DCP | Attention Types | Compute Cap. |
-| ------- | ------ | --------- | ----------- | ---------- | ---- | ------ | --------- | --- | --------------- | ------------ |
-| `CUTLASS_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 128 | Any | ❌ | ❌ | ❌ | ✅ | Decoder | 10.x |
-| `FLASHINFER_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 32, 64 | Any | ❌ | ❌ | ❌ | ❌ | Decoder | 10.x |
-| `FLASHINFER_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 32, 64 | 576 | ❌ | ✅ | ❌ | ❌ | Decoder | 10.x |
-| `FLASHMLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 64 | Any | ❌ | ❌ | ❌ | ✅ | Decoder | 9.x-10.x |
-| `FLASHMLA_SPARSE` | bf16 | `auto`, `bfloat16`, `fp8_ds_mla` | 64 | 576 | ❌ | ✅ | ❌ | ❌ | Decoder | 9.x-10.x |
-| `FLASH_ATTN_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ❌ | ❌ | ✅ | Decoder | 9.x |
-| `ROCM_AITER_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 1 | Any | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
-| `ROCM_AITER_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16` | 1 | Any | ❌ | ✅ | ❌ | ❌ | Decoder | N/A |
-| `ROCM_AITER_TRITON_MLA` | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
-| `TRITON_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | %16 | Any | ❌ | ❌ | ❌ | ✅ | Decoder | Any |
-| `XPU_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16` | Any | 576 | ❌ | ✅ | ❌ | ❌ | Decoder | Any |
+MLA decode backends are selected using the standard
+`-ac.backend=<BACKEND>` argument (e.g., `FLASHMLA`, `TRITON_MLA`).
+
+| Backend | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | Non-Causal | Sparse | MM Prefix | DCP | Attention Types | Compute Cap. |
+| ------- | ------ | --------- | ----------- | ---------- | ---- | ---------- | ------ | --------- | --- | --------------- | ------------ |
+| `CUTLASS_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 128 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | 10.x |
+| `FLASHINFER_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 32, 64 | Any | ❌ | ❌ | ❌ | ❌ | ❌ | Decoder | 10.x |
+| `FLASHINFER_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 32, 64 | 576 | ❌ | ❌ | ✅ | ❌ | ❌ | Decoder | 10.x |
+| `FLASHMLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 64 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | 9.x-10.x |
+| `FLASHMLA_SPARSE` | bf16 | `auto`, `bfloat16`, `fp8_ds_mla` | 64 | 512, 576 | ❌ | ❌ | ✅ | ❌ | ❌ | Decoder | 9.x-10.x |
+| `FLASH_ATTN_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | 9.x |
+| `ROCM_AITER_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %1 | Any | ❌ | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
+| `ROCM_AITER_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 1, 64 | Any | ❌ | ❌ | ✅ | ❌ | ❌ | Decoder | N/A |
+| `ROCM_AITER_TRITON_MLA` | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
+| `TRITON_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | %16 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | Any |
+| `XPU_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16` | Any | 576 | ❌ | ❌ | ✅ | ❌ | ❌ | Decoder | Any |
diff --git a/docs/design/cuda_graphs_multimodal.md b/docs/design/cuda_graphs_multimodal.md
index 9c15f02858c2..f44ef359df38 100644
--- a/docs/design/cuda_graphs_multimodal.md
+++ b/docs/design/cuda_graphs_multimodal.md
@@ -52,14 +52,14 @@ For each graph replay:
 
 When `mm_encoder_tp_mode="data"`, the manager distributes images across TP ranks using load-balanced assignment via `get_load_balance_assignment`, executes locally on each rank, then gathers results back in the original order via `tensor_model_parallel_all_gather`.
 
-### Video inference support (experimental)
+### Video inference support
 
 Following <https://github.com/vllm-project/vllm/pull/35963> (ViT full CUDA graph support for image inference), <https://github.com/vllm-project/vllm/pull/38061> extends the encoder CUDA graph framework to support video inference for Qwen3-VL. Previously, the CUDA graph capture/replay path only handled image inputs (`pixel_values` + `image_grid_thw`). Video inputs use different keys (`pixel_values_videos` + `video_grid_thw`) and require larger `cu_seqlens` buffers because each video item contributes multiple frames (`T` attention sequences). This PR generalizes the protocol and manager to handle both modalities through a single shared graph manager.
 
 !!! note
     Video CUDA graphs are automatically disabled when EVS (Efficient Video Sampling) pruning is enabled, since EVS makes the token count data-dependent and incompatible with CUDA graph capture.
 
-    Currently, we only support image-only or video-only inputs when enabling CUDA graph, mixed inputs (image + video) are not supported yet (we will work on it in the near future). Thus, it's recommended to turn off the image modality by `--limit-mm-per-prompt '{"image": 0}'` for video-only inputs.
+    Mixed inputs (image+video) per prompt are also supported now.
 
 ## Model integration via `SupportsEncoderCudaGraph`
 
@@ -76,6 +76,7 @@ Models opt-in to encoder CUDA Graphs by implementing the [SupportsEncoderCudaGra
 * `encoder_cudagraph_forward(...)` — forward pass using precomputed buffers (called during capture and replay).
 * `encoder_eager_forward(...)` — fallback eager forward when no graph fits.
 * `get_input_modality(...)` - return the modality of the inputs.
+* `get_max_frames_per_video()` - return model-specific max frames per video.
 
 !!! note
     The `SupportsEncoderCudaGraph` protocol is designed to be model-agnostic. New vision encoder models can opt-in by implementing the protocol methods without modifying the manager.
@@ -85,9 +86,11 @@ Models opt-in to encoder CUDA Graphs by implementing the [SupportsEncoderCudaGra
 | Architecture | Models | CG for Image | CG for Video |
 | ------------ | ------ | ------------ | ------------ |
 | `Qwen3VLForConditionalGeneration` | `Qwen3-VL` | ✅︎ | ✅︎ |
+| `Qwen2_5_VLForConditionalGeneration` | `Qwen2.5-VL` | ✅︎ | ✅︎ |
 
 !!! note
     Encoder CUDA Graphs have currently been tested with `--mm-encoder-attn-backend=FLASH_ATTN` and `--mm-encoder-attn-backend=FLASHINFER` on Blackwell GPUs.
+    For Qwen2.5-VL only FA2 and FA3 has been tested.
 
 ## Configuration
 
@@ -96,7 +99,7 @@ Three fields in `CompilationConfig` control encoder CUDA Graphs:
 * `cudagraph_mm_encoder` (`bool`, default `False`) — enable CUDA Graph capture for multimodal encoder. When enabled, captures the full encoder forward as a CUDA Graph for each token budget level.
 * `encoder_cudagraph_token_budgets` (`list[int]`, default `[]`) — token budget levels for capture. If empty (default), auto-inferred from model architecture as power-of-2 levels. User-provided values override auto-inference.
 * `encoder_cudagraph_max_vision_items_per_batch` (`int`, default `0`) — maximum number of images/videos per batch during capture. If 0 (default), auto-inferred as `max_budget // min_budget`.
-* `encoder_cudagraph_max_frames_per_batch` (`int`, default `0`) — maximum number of video frames per batch during capture. If 0 (default), auto-inferred as `encoder_cudagraph_max_vision_items_per_batch * 2` (to be optimized).
+* `encoder_cudagraph_max_frames_per_batch` (`int`, default `None`) — maximum number of video frames per batch during capture. If `None` (default), auto-inferred as `encoder_cudagraph_max_vision_items_per_batch * max_frames_per_video` (`max_frames_per_video` is a model-specific value according to its `processing_info`). If we limit the video count per prompt to `0`, it will also be set to `0` (i.e., fall back to image-only mode).
 
 ## Usage guide
 
@@ -142,7 +145,6 @@ Enable encoder CUDA Graphs via `compilation_config`:
 
 ```bash
 vllm serve Qwen/Qwen3-VL-32B \
-  --limit-mm-per-prompt '{"image": 0}' \
   --compilation-config '{"cudagraph_mm_encoder": true}'
 ```
 
@@ -150,7 +152,6 @@ With explicit budgets:
 
 ```bash
 vllm serve Qwen/Qwen3-VL-32B \
-  --limit-mm-per-prompt '{"image": 0}' \
   --compilation-config '{"cudagraph_mm_encoder": true, "encoder_cudagraph_token_budgets": [2048, 4096, 8192, 13824], "encoder_cudagraph_max_vision_items_per_batch": 8, "encoder_cudagraph_max_frames_per_batch": 64}'
 ```
 
@@ -169,7 +170,6 @@ compilation_config = {
 
 model = vllm.LLM(
     model="Qwen/Qwen3-VL-32B",
-    limit_mm_per_prompt='{"image": 0}',
     compilation_config=compilation_config,
 )
 ```
diff --git a/docs/design/custom_op.md b/docs/design/custom_op.md
index 4aefeb5585fe..d2557a2281cf 100644
--- a/docs/design/custom_op.md
+++ b/docs/design/custom_op.md
@@ -138,7 +138,7 @@ For example:
 
 --8<-- "vllm/model_executor/models/transformers/moe.py:transformers_fused_moe"
 
---8<-- "vllm/model_executor/layers/fused_moe/fused_moe.py:grouped_topk"
+--8<-- "vllm/model_executor/layers/fused_moe/router/grouped_topk_router.py:grouped_topk"
 ```
 
 **9. Norm:**
diff --git a/docs/design/debug_vllm_compile.md b/docs/design/debug_vllm_compile.md
index fbee9f4c3e3e..7edda6fa6476 100644
--- a/docs/design/debug_vllm_compile.md
+++ b/docs/design/debug_vllm_compile.md
@@ -5,12 +5,14 @@ TL;DR:
 - use tlparse to acquire torch.compile logs. Include these logs in bug reports and/or support asks.
 - The vLLM-torch.compile integration is multiple pieces. vLLM exposes flags to turn off each piece:
 
-| Online Flag | Offline Flag | Result |
-| ----------- | ------------ | ------ |
-| --enforce-eager | enforce_eager=True | Turn off torch.compile and CUDAGraphs |
-| -cc.mode=0 | mode=CompilationMode.NONE | Turn off torch.compile only |
-| -cc.cudagraph_mode=NONE | compilation_config=CompilationConfig(cudagraph_mode=CUDAGraphMode.NONE) | Turn off CUDAGraphs only |
-| -cc.backend=eager | compilation_config=CompilationConfig(backend='eager') | Turn off TorchInductor |
+| Online Flag                    | Offline Flag                                                                   | Result                                               |
+|--------------------------------|--------------------------------------------------------------------------------|------------------------------------------------------|
+| --enforce-eager                | enforce_eager=True                                                             | Turn off torch.compile and CUDAGraphs                |
+| -cc.mode=0                     | compilation_config=CompilationConfig(mode=CompilationMode.NONE)                | Turn off torch.compile only                          |
+| -cc.mode=1                     | compilation_config=CompilationConfig(mode=CompilationMode.STOCK_TORCH_COMPILE) | Turn off vLLM-compile modifications to torch.compile |
+| -cc.cudagraph_mode=NONE        | compilation_config=CompilationConfig(cudagraph_mode=CUDAGraphMode.NONE)        | Turn off CUDAGraphs only                             |
+| -cc.backend=eager              | compilation_config=CompilationConfig(backend='eager')                          | Turn off TorchInductor                               |
+| -cc.ir_enable_torch_wrap=False | compilation_config=CompilationConfig(ir_enable_torch_wrap=False)               | Turn off vLLM IR wrapping                            |
 
 ## vLLM-torch.compile overview
 
@@ -22,7 +24,7 @@ Most notably, vLLM-compile is NOT torch.compile, it is a custom compiler built u
 
 - Given a model, we do a full graph capture via TorchDynamo that is dynamic on the batch size (number of tokens)
 - vLLM then optionally splits and/or specializes this graph and then uses TorchInductor to compile each graph into a compiled artifact.
-This step may use vLLM custom Inductor passes to further optimize the graph.
+This step may use vLLM custom Inductor passes to further optimize the graph. This includes vLLM IR lowering to remove dispatch overhead.
 - The compiled artifact is saved to vLLM's compile cache so that it can be loaded in the future.
 - vLLM applies CUDAGraphs to reduce CPU overheads.
 
@@ -34,6 +36,7 @@ For more details on the design, please see the following resources:
 
 - [Introduction to vLLM-torch.compile blogpost](https://blog.vllm.ai/2025/08/20/torch-compile.html)
 - [vLLM-torch.compile integration design](./torch_compile.md)
+- [vLLM IR design](./vllm_ir.md)
 - [vLLM Office Hours #26](https://www.youtube.com/live/xLyxc7hxCJc?si=Xulo9pe53C6ywf0V&t=561)
 - [Talk at PyTorch Conference 2025](https://youtu.be/1wV1ESbGrVQ?si=s1GqymUfwiwOrDTg&t=725)
 
@@ -117,6 +120,21 @@ from vllm.config.compilation import CompilationConfig, CUDAGraphMode
 LLM(model, compilation_config=CompilationConfig(cudagraph_mode=CUDAGraphMode.NONE))
 ```
 
+vLLM IR makes heavy use of the compilation pipeline, from functionalization, custom fusions, and lowering.
+To turn that off and capture eager-mode dispatching behavior of vLLM IR, run with `ir_enable_torch_wrap=False`.
+IR torch wrap is only enabled by default when using `mode=VLLM_COMPILE` and `backend="inductor"` (default).
+
+```sh
+# Online
+vllm serve -cc.ir_enable_torch_wrap=False
+```
+
+```py
+# Offline
+from vllm.config.compilation import CompilationConfig
+LLM(model, compilation_config=CompilationConfig(ir_enable_torch_wrap=False))
+```
+
 ## Debugging TorchDynamo
 
 vLLM requires model code be capturable into a full graph via TorchDynamo (torch.compile's frontend).
diff --git a/docs/design/fusions.md b/docs/design/fusions.md
index 3fe1f769bb79..046c509d4b82 100644
--- a/docs/design/fusions.md
+++ b/docs/design/fusions.md
@@ -21,6 +21,7 @@ or just on the low or high end.
 | Fusion                                                                         | `PassConfig` flag            | Fused operations                               | Default at                     | E2E Speedup        | Fullgraph | `num_tokens` |
 | ------------------------------------------------------------------------------ | ---------------------------- | ---------------------------------------------- | ------------------------------ | ------------------ | --------- | ------------ |
 | [AllReduce + RMSNorm](#allreduce--rmsnorm-fuse_allreduce_rms)                  | `fuse_allreduce_rms`         | All-reduce → RMSNorm (+residual_add) (→ quant) | O2 (Hopper/Blackwell + TP > 1) | 5-20%              | No        | Low          |
+| [MiniMax QK Norm](#minimax-qk-norm-fuse_minimax_qk_norm)                       | `fuse_minimax_qk_norm`       | Q/K variance all-reduce → Q/K RMSNorm          | Off by default                 | 2-3%               | No        | Low          |
 | [Attention + Quant](#attention--quantization-fuse_attn_quant)                  | `fuse_attn_quant`            | Attention output → FP8/NVFP4 quant             | Off by default                 | 3-7%               | Yes       | Always       |
 | [MLA Attention + Quant](#attention--quantization-fuse_attn_quant)              | `fuse_attn_quant`            | MLA Attention output → FP8/NVFP4 quant         | Off by default                 | TBD                | Yes       | Always       |
 | [RoPE + KV-Cache Update](#rope--kv-cache-update-fuse_rope_kvcache)             | `fuse_rope_kvcache`          | Rotary embedding → KV cache write              | O2 (ROCm/AITER only)           | 2-4%               | No        | Low          |
@@ -30,6 +31,7 @@ or just on the low or high end.
 | [RMSNorm + Quant](#rmsnorm--quantization-fuse_norm_quant)                      | `fuse_norm_quant`            | RMSNorm (+residual add) → FP8/FP4 quant        | O1 (conditional)               | 1-4%               | No        | Always       |
 | [SiLU+Mul + Quant](#silumul--quantization-fuse_act_quant)                      | `fuse_act_quant`             | SiLU+Mul activation → FP8/FP4 quant            | O1 (conditional)               | 1-4%               | No        | Always       |
 | [RMSNorm + Padding](#rmsnorm--padding-fuse_act_padding)                        | `fuse_act_padding`           | Residual add + RMSNorm → padding               | O1 (ROCm/AITER only)           | TBD                | No        | Always       |
+| [MLA Dual RMSNorm](#mla-dual-rmsnorm-fuse_mla_dual_rms_norm)                   | `fuse_mla_dual_rms_norm`     | Paired Q + KV RMSNorm → single kernel          | O1 (ROCm/AITER only)           | ~2%                | No        | Always       |
 
 ## Support Matrix
 
@@ -40,8 +42,9 @@ The table below lists the quantization schemes supported by each fusion on each
 | Fusion                       | SM100 (Blackwell)                        | SM90 (Hopper)                            | SM89 (Ada)                               | SM80 (Ampere) | ROCm                                     |
 | ---------------------------- | ---------------------------------------- | ---------------------------------------- | ---------------------------------------- | ------------- | ---------------------------------------- |
 | `fuse_allreduce_rms`         | FP16/BF16, FP8 static, NVFP4             | FP16/BF16, FP8 static                    | —                                        | —             | —                                        |
+| `fuse_minimax_qk_norm`\*     | FP16/BF16                                | FP16/BF16                                | FP16/BF16                                | FP16/BF16     | —                                        |
 | `fuse_attn_quant`\*          | FP8 static\*, NVFP4\*                    | FP8 static\*                             | FP8 static\*                             | —             | FP8 static\*                             |
-| `fuse_attn_quant` (MLA)\*    | FP8 static\*, NVFP4\*                    | FP8 static\*                             | FP8 static\*                             | —             | FP8 static(untested)\*                   |
+| `fuse_attn_quant` (MLA)\*    | FP8 static\*, FP8 per-group\*, NVFP4\*   | FP8 static\*, FP8 per-group\*            | FP8 static\*, FP8 per-group\*            | —             | FP8 static\* (untested)                  |
 | `fuse_rope_kvcache`          | —                                        | —                                        | —                                        | —             | FP16/BF16                                |
 | `enable_qk_norm_rope_fusion` | FP16/BF16                                | FP16/BF16                                | FP16/BF16†                               | FP16/BF16†    | —                                        |
 | `enable_sp`                  | FP16/BF16, FP8 static†                   | FP16/BF16, FP8 static                    | FP16/BF16†                               | FP16/BF16†    | —                                        |
@@ -49,11 +52,15 @@ The table below lists the quantization schemes supported by each fusion on each
 | `fuse_norm_quant`            | FP8 static, FP8 per-token, FP8 per-group | FP8 static, FP8 per-token, FP8 per-group | FP8 static, FP8 per-token, FP8 per-group | —             | FP8 static, FP8 per-token, FP8 per-group |
 | `fuse_act_quant`             | FP8 static, NVFP4                        | FP8 static, FP8 per-group (128/64)       | FP8 static, FP8 per-group (128/64)       | —             | FP8 per-group                            |
 | `fuse_act_padding`           | —                                        | —                                        | —                                        | —             | FP16/BF16                                |
+| `fuse_mla_dual_rms_norm`     | —                                        | —                                        | —                                        | —             | BF16                                     |
 
 \* `fuse_attn_quant` support depends on the attention backend in use; not all backends support
 fused quantization output. See the [`fuse_attn_quant` section](#attention--quantization-fuse_attn_quant)
 for per-backend details.
 
+\* `fuse_minimax_qk_norm` is a model-specific pass for `MiniMaxM2ForCausalLM`. It also requires
+tensor parallelism (`tp_size > 1`) and the CUDA custom op `minimax_allreduce_rms_qk`.
+
 † `enable_sp` and `fuse_gemm_comms` are only autoconfigured for SM90 today;
 other architectures support requires setting `PassConfig.sp_min_token_num` explicitly.
 SM100 support also requires setting `VLLM_DISABLED_KERNELS=FlashInferFP8ScaledMMLinearKernel`.
@@ -145,7 +152,7 @@ standard `Attention` and `MLAAttention` (used by DeepSeek-V2/V3/R1 models). Patt
 
 - `FLASHINFER`: CUDA sm100+ with FlashInfer installed
 
-`MLAAttention → FP8 static quant` / `MLAAttention → NVFP4 dynamic quant`:
+`MLAAttention → FP8 static, FP8 per-group, NVFP4 dynamic quant`
 
 The MLA fusion operates at the graph level on the `unified_mla_attention_with_output` op and works
 with all MLA decode and prefill backend combinations. Unlike standard `Attention` backends (where
@@ -184,6 +191,35 @@ If these conditions are set, the fusion is enabled automatically for optimizatio
 
 - Pass: [`vllm/compilation/passes/fusion/rope_kvcache_fusion.py`](https://github.com/vllm-project/vllm/blob/main/vllm/compilation/passes/fusion/rope_kvcache_fusion.py)
 
+### MiniMax QK Norm (`fuse_minimax_qk_norm`)
+
+!!! info
+    This is a MiniMax-specific compile pass. It is currently only enabled when all of the following hold:
+    the model architecture is `MiniMaxM2ForCausalLM`, tensor parallelism is enabled (`tp_size > 1`),
+    and the CUDA custom op `minimax_allreduce_rms_qk` is available. It is not enabled by default at any
+    optimization level.
+
+**What it fuses.** Fuses the MiniMax M2 Q/K normalization path that performs an all-reduce over the
+per-token Q/K variances before applying RMS normalization to Q and K.
+
+This pass is distinct from [`enable_qk_norm_rope_fusion`](#qk-norm--rope-enable_qk_norm_rope_fusion):
+`fuse_minimax_qk_norm` targets MiniMax M2's tensor-parallel all-reduce + RMSNorm sequence, while
+`enable_qk_norm_rope_fusion` targets the later Q/K RMSNorm + RoPE sequence used by several other models.
+
+Example:
+
+```bash
+vllm serve MiniMaxAI/MiniMax-M2.5 \
+  --tensor-parallel-size 4 \
+  --compilation-config '{"mode": 3, "pass_config": {"fuse_minimax_qk_norm": true}}'
+```
+
+**Code locations.**
+
+- Pass: [`vllm/compilation/passes/fusion/minimax_qk_norm_fusion.py`](https://github.com/vllm-project/vllm/blob/main/vllm/compilation/passes/fusion/minimax_qk_norm_fusion.py)
+- CUDA op: [`csrc/minimax_reduce_rms_kernel.cu`](https://github.com/vllm-project/vllm/blob/main/csrc/minimax_reduce_rms_kernel.cu) (`minimax_allreduce_rms_qk`)
+- Workspace helper: [`vllm/model_executor/layers/mamba/lamport_workspace.py`](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/mamba/lamport_workspace.py)
+
 ### Sequence Parallelism (`enable_sp`)
 
 **What it fuses.** Replaces all-reduce collectives with reduce-scatter + local RMSNorm + all-gather,
@@ -347,6 +383,44 @@ when the hidden size is 2880 and AITER Triton GEMMs *not* enabled.
 
 - Pass: [`vllm/compilation/passes/fusion/rocm_aiter_fusion.py`](https://github.com/vllm-project/vllm/blob/main/vllm/compilation/passes/fusion/rocm_aiter_fusion.py) (`RocmAiterTritonAddRMSNormPadFusionPass`)
 
+### MLA Dual RMSNorm (`fuse_mla_dual_rms_norm`)
+
+!!! info
+    ROCm/AITER-only. Targeted at DeepSeek-V3 / Kimi-K2 MLA attention.
+
+!!! note
+    When the native implementation of `rms_norm` is used (the default on CUDA and
+    ROCm for now), Inductor's built-in fusion already handles merging these norms
+    automatically. This explicit pass targets the case where AITER's custom
+    `rms_norm` op is active, which Inductor cannot fuse on its own.
+
+**What it fuses.** Fuses the paired `q_a_layernorm` and `kv_a_layernorm` RMS norm
+operations in MLA attention into a single `fused_qk_rmsnorm` HIP kernel call via AITER,
+reducing kernel launch overhead from 2 launches to 1 per MLA layer.
+
+```text
+# Unfused:
+q_c, kv_lora = split(projected, [q_dim, kv_dim])
+kv_c, k_pe   = split(kv_lora,  [kv_c_dim, k_pe_dim])
+q_c  = rms_norm(q_c,  q_weight,  eps)
+kv_c = rms_norm(kv_c, kv_weight, eps)
+
+# Fused:
+q_c, kv_lora = split(projected, [q_dim, kv_dim])
+kv_c, k_pe   = split(kv_lora,  [kv_c_dim, k_pe_dim])
+q_normed, kv_normed = fused_mla_dual_rms_norm(
+    q_c, q_weight, kv_c, kv_weight, eps1, eps2)
+```
+
+Requires: AMD ROCm with AITER enabled. Enabled by default at optimization level O1 and above
+when AITER is available.
+
+**Code locations.**
+
+- Pass: [`vllm/compilation/passes/fusion/rocm_aiter_fusion.py`](https://github.com/vllm-project/vllm/blob/main/vllm/compilation/passes/fusion/rocm_aiter_fusion.py) (`MLADualRMSNormFusionPass`)
+- Custom op: [`vllm/_aiter_ops.py`](https://github.com/vllm-project/vllm/blob/main/vllm/_aiter_ops.py) (`fused_mla_dual_rms_norm`)
+- AITER kernel: [`fused_qk_rmsnorm`](https://github.com/ROCm/aiter/pull/2442)
+
 ## See Also
 
 - [Optimization Levels](optimization_levels.md) — high-level presets that set
diff --git a/docs/design/huggingface_integration.md b/docs/design/huggingface_integration.md
index 1109abf6cb93..07a2f41cdb71 100644
--- a/docs/design/huggingface_integration.md
+++ b/docs/design/huggingface_integration.md
@@ -21,7 +21,7 @@ Let's say we want to serve the popular Qwen model by running `vllm serve Qwen/Qw
 
 Beyond that, there are two more things vLLM depends on Hugging Face for.
 
-1. **Tokenizer**: vLLM uses the tokenizer from Hugging Face to tokenize the input text. The tokenizer is loaded using [AutoTokenizer.from_pretrained](https://huggingface.co/docs/transformers/en/model_doc/auto#transformers.AutoTokenizer.from_pretrained) with the `model` argument as the model name and the `--revision` argument as the revision. It is also possible to use a tokenizer from another model by specifying the `--tokenizer` argument in the `vllm serve` command. Other relevant arguments are `--tokenizer-revision` and `--tokenizer-mode`. Please check Hugging Face's documentation for the meaning of these arguments. This part of the logic can be found in the [get_tokenizer](https://github.com/vllm-project/vllm/blob/127c07480ecea15e4c2990820c457807ff78a057/vllm/transformers_utils/tokenizer.py#L87) function. After obtaining the tokenizer, notably, vLLM will cache some expensive attributes of the tokenizer in [vllm.tokenizers.hf.get_cached_tokenizer][].
+1. **Tokenizer**: vLLM uses the tokenizer from Hugging Face to tokenize the input text. The tokenizer is loaded using [AutoTokenizer.from_pretrained](https://huggingface.co/docs/transformers/en/model_doc/auto#transformers.AutoTokenizer.from_pretrained) with the `model` argument as the model name and the `--revision` argument as the revision. It is also possible to use a tokenizer from another model by specifying the `--tokenizer` argument in the `vllm serve` command. Other relevant arguments are `--tokenizer-revision` and `--tokenizer-mode`. Setting `--tokenizer-mode fastokens` swaps in a drop-in Rust BPE backend for the HF fast tokenizer (see [fastokens Tokenizer Mode](../configuration/optimization.md#fastokens-tokenizer-mode)). Please check Hugging Face's documentation for the meaning of these arguments. This part of the logic can be found in the [get_tokenizer](https://github.com/vllm-project/vllm/blob/127c07480ecea15e4c2990820c457807ff78a057/vllm/transformers_utils/tokenizer.py#L87) function. After obtaining the tokenizer, notably, vLLM will cache some expensive attributes of the tokenizer in [vllm.tokenizers.hf.get_cached_tokenizer][].
 
 2. **Model weight**: vLLM downloads the model weight from the Hugging Face model hub using the `model` argument as the model name and the `--revision` argument as the revision. vLLM provides the argument `--load-format` to control what files to download from the model hub. By default, it will try to load the weights in the safetensors format and fall back to the PyTorch bin format if the safetensors format is not available. We can also pass `--load-format dummy` to skip downloading the weights.
     - It is recommended to use the safetensors format, as it is efficient for loading in distributed inference and also safe from arbitrary code execution. See the [documentation](https://huggingface.co/docs/safetensors/en/index) for more information on the safetensors format. This part of the logic can be found [here](https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/model_executor/model_loader/loader.py#L385). Please note that:
diff --git a/docs/design/lora_resolver_plugins.md b/docs/design/lora_resolver_plugins.md
index ad644cbc50aa..019c4eef93d9 100644
--- a/docs/design/lora_resolver_plugins.md
+++ b/docs/design/lora_resolver_plugins.md
@@ -62,8 +62,7 @@ The filesystem resolver is installed with vLLM by default and enables loading Lo
 3. **Start vLLM server**:
    Your base model can be `meta-llama/Llama-2-7b-hf`. Please make sure you set up the Hugging Face token in your env var `export HF_TOKEN=xxx235`.
    ```bash
-   python -m vllm.entrypoints.openai.api_server \
-       --model your-base-model \
+   vllm serve your-base-model \
        --enable-lora
    ```
 
diff --git a/docs/design/metrics.md b/docs/design/metrics.md
index be917c0dc614..0ae420399767 100644
--- a/docs/design/metrics.md
+++ b/docs/design/metrics.md
@@ -42,7 +42,7 @@ These are documented under [Inferencing and Serving -> Production Metrics](../us
 
 ### Grafana Dashboard
 
-vLLM also provides [a reference example](../../examples/online_serving/prometheus_grafana/README.md) for how to collect and store these metrics using Prometheus and visualize them using a Grafana dashboard.
+vLLM also provides [a reference example](../../examples/observability/prometheus_grafana/README.md) for how to collect and store these metrics using Prometheus and visualize them using a Grafana dashboard.
 
 The subset of metrics exposed in the Grafana dashboard gives us an indication of which metrics are especially important:
 
@@ -657,7 +657,7 @@ vLLM has support for OpenTelemetry tracing:
 - Added by <https://github.com/vllm-project/vllm/pull/4687> and reinstated by <https://github.com/vllm-project/vllm/pull/20372>
 - Configured with `--oltp-traces-endpoint` and `--collect-detailed-traces`
 - [OpenTelemetry blog post](https://opentelemetry.io/blog/2024/llm-observability/)
-- [User-facing docs](../../examples/online_serving/opentelemetry/README.md)
+- [User-facing docs](../../examples/observability/opentelemetry/README.md)
 - [Blog post](https://medium.com/@ronen.schaffer/follow-the-trail-supercharging-vllm-with-opentelemetry-distributed-tracing-aa655229b46f)
 - [IBM product docs](https://www.ibm.com/docs/en/instana-observability/current?topic=mgaa-monitoring-large-language-models-llms-vllm-public-preview)
 
diff --git a/docs/design/moe_kernel_features.md b/docs/design/moe_kernel_features.md
index 231bca3646f9..2bbadd8ff813 100644
--- a/docs/design/moe_kernel_features.md
+++ b/docs/design/moe_kernel_features.md
@@ -36,7 +36,7 @@ th {
 | deepep_high_throughput | standard | fp8 | G(128),A,T<sup>2</sup> | Y | Y | [`DeepEPHTPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.prepare_finalize.deepep_ht.DeepEPHTPrepareAndFinalize] |
 | deepep_low_latency | batched | fp8 | G(128),A,T<sup>3</sup> | Y | Y | [`DeepEPLLPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.prepare_finalize.deepep_ll.DeepEPLLPrepareAndFinalize] |
 | flashinfer_nvlink_two_sided | standard | nvfp4,fp8 | G,A,T | N | N | [`FlashInferNVLinkTwoSidedPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.prepare_finalize.flashinfer_nvlink_two_sided.FlashInferNVLinkTwoSidedPrepareAndFinalize] |
-| flashinfer_nvlink_one_sided | standard | nvfp4 | G,A,T | N | N | [`FlashInferNVLinkOneSidedPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.prepare_finalize.flashinfer_nvlink_one_sided.FlashInferNVLinkOneSidedPrepareAndFinalize] |
+| flashinfer_nvlink_one_sided | standard | nvfp4,bf16,mxfp8 | G,A,T | N | N | [`FlashInferNVLinkOneSidedPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.prepare_finalize.flashinfer_nvlink_one_sided.FlashInferNVLinkOneSidedPrepareAndFinalize] |
 
 !!! info "Table key"
     1. All types: mxfp4, nvfp4, int4, int8, fp8
@@ -80,14 +80,14 @@ To be used with a particular `FusedMoEPrepareAndFinalizeModular` subclass, MoE k
 
 | Kernel | Input act. format | Quant. types | Quant. format | Activation function | Apply Weight On Input | Modular | Source |
 | ------ | ----------------- | ------------ | ------------- | ------------------- | --------------------- | ------- | ------ |
-| triton | standard | all<sup>1</sup> | G,A,T | silu, gelu,</br>swigluoai,</br>silu_no_mul,</br>gelu_no_mul | Y | Y | [`fused_experts`][vllm.model_executor.layers.fused_moe.fused_moe.fused_experts],</br>[`TritonExperts`][vllm.model_executor.layers.fused_moe.fused_moe.TritonExperts] |
+| triton | standard | all<sup>1</sup> | G,A,T | silu, gelu,</br>swigluoai,</br>silu_no_mul,</br>gelu_no_mul | Y | Y | [`fused_experts`][vllm.model_executor.layers.fused_moe.fused_moe.fused_experts],</br>[`TritonExperts`][vllm.model_executor.layers.fused_moe.experts.triton_moe.TritonExperts] |
 | triton (batched) | batched | all<sup>1</sup> | G,A,T | silu, gelu | <sup>6</sup> | Y | [`BatchedTritonExperts`][vllm.model_executor.layers.fused_moe.fused_batched_moe.BatchedTritonExperts] |
 | deep gemm | standard,</br>batched | fp8 | G(128),A,T | silu, gelu | <sup>6</sup> | Y | </br>[`DeepGemmExperts`][vllm.model_executor.layers.fused_moe.experts.deep_gemm_moe.DeepGemmExperts],</br>[`BatchedDeepGemmExperts`][vllm.model_executor.layers.fused_moe.experts.batched_deep_gemm_moe.BatchedDeepGemmExperts] |
-| cutlass_fp4 | standard,</br>batched | nvfp4 | A,T | silu | Y | Y | [`CutlassExpertsFp4`][vllm.model_executor.layers.fused_moe.cutlass_moe.CutlassExpertsFp4] |
-| cutlass_fp8 | standard,</br>batched | fp8 | A,T | silu, gelu | Y | Y | [`CutlassExpertsFp8`][vllm.model_executor.layers.fused_moe.cutlass_moe.CutlassExpertsFp8],</br>[`CutlasBatchedExpertsFp8`][vllm.model_executor.layers.fused_moe.cutlass_moe.CutlassBatchedExpertsFp8] |
-| flashinfer | standard | nvfp4,</br>fp8 | T | <sup>5</sup> | N | Y | [`FlashInferExperts`][vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe.FlashInferExperts] |
+| cutlass_fp4 | standard,</br>batched | nvfp4 | A,T | silu | Y | Y | [`CutlassExpertsFp4`][vllm.model_executor.layers.fused_moe.experts.cutlass_moe.CutlassExpertsFp4] |
+| cutlass_fp8 | standard,</br>batched | fp8 | A,T | silu, gelu | Y | Y | [`CutlassExpertsFp8`][vllm.model_executor.layers.fused_moe.experts.cutlass_moe.CutlassExpertsFp8],</br>[`CutlasBatchedExpertsFp8`][vllm.model_executor.layers.fused_moe.experts.cutlass_moe.CutlassBatchedExpertsFp8] |
+| flashinfer | standard | nvfp4,</br>fp8 | T | <sup>5</sup> | N | Y | [`FlashInferExperts`][vllm.model_executor.layers.fused_moe.experts.flashinfer_cutlass_moe.FlashInferExperts] |
 | gpt oss triton | standard | N/A | N/A | <sup>5</sup> | Y | Y | [`triton_kernel_fused_experts`][vllm.model_executor.layers.fused_moe.experts.gpt_oss_triton_kernels_moe.triton_kernel_fused_experts],</br>[`OAITritonExperts`][vllm.model_executor.layers.fused_moe.experts.gpt_oss_triton_kernels_moe.OAITritonExperts] |
-| marlin | standard,</br>batched | <sup>3</sup> / N/A | <sup>3</sup> / N/A | silu,</br>swigluoai | Y | Y | [`fused_marlin_moe`][vllm.model_executor.layers.fused_moe.fused_marlin_moe.fused_marlin_moe],</br>[`MarlinExperts`][vllm.model_executor.layers.fused_moe.fused_marlin_moe.MarlinExperts],</br>[`BatchedMarlinExperts`][vllm.model_executor.layers.fused_moe.fused_marlin_moe.BatchedMarlinExperts] |
+| marlin | standard,</br>batched | <sup>3</sup> / N/A | <sup>3</sup> / N/A | silu,</br>swigluoai | Y | Y | [`fused_marlin_moe`][vllm.model_executor.layers.fused_moe.experts.marlin_moe.fused_marlin_moe],</br>[`MarlinExperts`][vllm.model_executor.layers.fused_moe.experts.marlin_moe.MarlinExperts],</br>[`BatchedMarlinExperts`][vllm.model_executor.layers.fused_moe.experts.marlin_moe.BatchedMarlinExperts] |
 | trtllm | standard | mxfp4,</br>nvfp4 | G(16),G(32) | <sup>5</sup> | N | Y | [`TrtLlmMxfp4ExpertsMonolithic`][vllm.model_executor.layers.fused_moe.experts.trtllm_mxfp4_moe.TrtLlmMxfp4ExpertsMonolithic],</br>[`TrtLlmMxfp4ExpertsModular`][vllm.model_executor.layers.fused_moe.experts.trtllm_mxfp4_moe.TrtLlmMxfp4ExpertsModular],</br>[`TrtLlmNvFp4ExpertsMonolithic`][vllm.model_executor.layers.fused_moe.experts.trtllm_nvfp4_moe.TrtLlmNvFp4ExpertsMonolithic],</br>[`TrtLlmNvfp4ExpertsModular`][vllm.model_executor.layers.fused_moe.experts.trtllm_nvfp4_moe.TrtLlmNvFp4ExpertsModular] |
 | rocm aiter moe | standard | mxfp4,</br>fp8 | G(32),G(128),A,T | silu, gelu,</br>swigluoai | Y | N | `rocm_aiter_fused_experts`,</br>`AiterExperts` |
 | cpu_fused_moe | standard | N/A | N/A | silu | N | N | [`CPUFusedMOE`][vllm.model_executor.layers.fused_moe.cpu_fused_moe.CPUFusedMOE] |
diff --git a/docs/design/optimization_levels.md b/docs/design/optimization_levels.md
index 591978b542e6..3261aed74794 100644
--- a/docs/design/optimization_levels.md
+++ b/docs/design/optimization_levels.md
@@ -16,7 +16,7 @@ User-set flags take precedence over optimization level defaults.
 
 ```bash
 # CLI usage
-python -m vllm.entrypoints.api_server --model RedHatAI/Llama-3.2-1B-FP8 -O1
+vllm serve RedHatAI/Llama-3.2-1B-FP8 -O1
 
 # Python API usage
 from vllm.entrypoints.llm import LLM
@@ -56,6 +56,7 @@ Fusions:
 - `-cc.pass_config.fuse_norm_quant=True`*
 - `-cc.pass_config.fuse_act_quant=True`*
 - `-cc.pass_config.fuse_act_padding=True`†
+- `-cc.pass_config.fuse_mla_dual_rms_norm=True`†
 
 \* These fusions are only enabled when either op is using a custom kernel, otherwise Inductor fusion is better.</br>
 † These fusions are ROCm-only and require AITER.
diff --git a/docs/design/p2p_nccl_connector.md b/docs/design/p2p_nccl_connector.md
index 4674bef8d2b6..c1de955b6ffe 100644
--- a/docs/design/p2p_nccl_connector.md
+++ b/docs/design/p2p_nccl_connector.md
@@ -88,7 +88,7 @@ pip install "vllm>=0.9.2"
 #### Proxy (e.g. 10.0.1.1)
 
 ```shell
-cd {your vllm directory}/examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/
+cd {your vllm directory}/examples/disaggregated/p2p_nccl_xpyd/
 python3 disagg_proxy_p2p_nccl_xpyd.py &
 ```
 
@@ -181,7 +181,7 @@ python3 disagg_proxy_p2p_nccl_xpyd.py &
 #### Proxy (e.g. 10.0.1.1)
 
 ```shell
-cd {your vllm directory}/examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/
+cd {your vllm directory}/examples/disaggregated/p2p_nccl_xpyd/
 python3 disagg_proxy_p2p_nccl_xpyd.py &
 ```
 
diff --git a/docs/design/vllm_ir.md b/docs/design/vllm_ir.md
new file mode 100644
index 000000000000..82628f3762fe
--- /dev/null
+++ b/docs/design/vllm_ir.md
@@ -0,0 +1,615 @@
+# vLLM IR: Functional Intermediate Representation
+
+## Motivation
+
+vLLM IR is a **functional intermediate representation (IR)** that fills the gap between
+low-level `torch` ops and vLLM layers like `RMSNorm` and quantization operators,
+By separating operator **semantics** from the **implementation** and **dispatching**,
+vLLM IR simplifies both compilation and kernel registration & dispatching simultaneously.
+It operates as a **dialect** in the torch FX representation, allowing full interoperability
+with “regular” torch ops & custom torch ops/kernels, as well as a piecewise migration from
+the previous `CustomOp` approach.
+
+Key design principles:
+
+- **Eager-compile consistency**: identical behavior (barring minor numerics) in eager and compiled modes
+- **Simple, transparent, yet powerful kernel selection**: good visibility and control allowing easy debugging
+- **Convention over configuration**: near-zero boilerplate required to register ops and implementations
+- **Extensibility**: ops and implementations can be registered anywhere, in-tree or out-of-tree
+- **Interoperability**: fully compatible with “regular” torch ops & custom torch ops/kernels,
+reducing developer friction and allowing piecewise migration
+
+The clean semantics/implementation separation enables a unified and extensible dispatching mechanism,
+allowing multiple kernels per-platform and powerful kernel selection. The separation also facilitates
+cleaner testing and benchmarking, removing much of the boilerplate standard for legacy approaches.
+
+By delaying kernel selection until late in the compilation process, the compiler can operate on
+a higher-level representation, which has the following main benefits:
+
+- Pattern matching in fusion/transformation passes only requires a single, simple pattern per op
+- OOT compiler backends can lower from the higher-level representation (in-progress)
+- The compiler can autotune over available implementations (future feature)
+
+## Quick Overview
+
+### Declaring an IR Operation
+
+IR operations are declared using the `@register_op` decorator with a native PyTorch implementation that defines the op's semantics:
+
+```python
+# vllm/ir/ops/layernorm.py
+from torch import Tensor
+from vllm.ir import register_op
+
+@register_op
+def rms_norm(x: Tensor, weight: Tensor | None, epsilon: float, variance_size: int | None = None) -> Tensor:
+    """Weighted root-mean-square layer normalization"""
+    orig_dtype = x.dtype
+    x = x.to(torch.float32)
+    x_var = x if variance_size is None else x[..., :variance_size]
+    variance = x_var.pow(2).mean(dim=-1, keepdim=True)
+    x = x * torch.rsqrt(variance + epsilon)
+    x = x.to(orig_dtype)
+    if weight is not None:
+        x = x * weight
+    return x
+```
+
+The native implementation serves three purposes:
+
+1. **Semantic definition**: Specifies the exact semantics of the operation, including shapes and strides
+2. **Default implementation**: Used when no other (better) implementation is available
+3. **Reference for testing**: Other implementations must match these semantics
+
+### Registering Implementations
+
+Kernel implementations are registered using the `register_impl` decorator on the IR op object:
+
+```python
+# vllm/kernels/vllm_c.py
+from vllm import ir
+
+rms_norm_no_var = lambda x, weight, epsilon, variance_size=None: variance_size is None
+
+@ir.ops.rms_norm.register_impl("vllm_c", supports_args=rms_norm_no_var, supported=current_platform.is_cuda_alike())
+def rms_norm(x: Tensor, weight: Tensor | None, epsilon: float, variance_size: int | None = None) -> Tensor:
+    output = torch.empty_like(x)
+    torch.ops._C.rms_norm(output, x, weight, epsilon)
+    return output
+```
+
+Implementations can specify:
+
+- `supported`: Static boolean indicating if this implementation is available
+- `supports_args`: Function checking if the implementation supports specific arguments
+- `inplace`: Whether this implementation reuses input memory for outputs
+
+### Using IR Operations in Models
+
+IR operations are imported and called directly in model code:
+
+```python
+# vllm/model_executor/layers/layernorm.py
+from vllm import ir
+
+class RMSNorm(nn.Module):
+    def __init__(self, hidden_size: int, eps: float = 1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, x: Tensor, residual: Tensor | None = None):
+        if residual is None:
+            return ir.ops.rms_norm(x, self.weight, self.variance_epsilon)
+
+        # Use maybe_inplace overload to allow implementation to reuse input memory for outputs
+        # (using x or residual after this call is undefined behavior)
+        return ir.ops.fused_add_rms_norm.maybe_inplace(
+            x, residual, self.weight, self.variance_epsilon
+        )
+```
+
+### Configuring Kernel Selection
+
+Kernel selection is controlled via priority lists in the configuration.
+Priority lists specify the order in which implementations are considered,
+with the first supported implementation being selected.
+This includes the static support check (`supported=...`) and
+the dynamic arg support check (`supports_args=...`).
+
+#### Command Line Configuration
+
+Use `--ir-op-priority.<op_name>=<provider1>,<provider2>,...`:
+
+```bash
+# CUDA: Use vllm_c implementation for rms_norm
+vllm serve meta-llama/Llama-3.2-1B \
+  --ir-op-priority.rms_norm=vllm_c
+
+# ROCm: Try aiter first, fall back to vllm_c, then native
+vllm serve meta-llama/Llama-3.2-1B \
+  --ir-op-priority.rms_norm=aiter,vllm_c,native
+
+# Configure multiple operations
+vllm serve meta-llama/Llama-3.2-1B \
+  --ir-op-priority.rms_norm=vllm_c \
+  --ir-op-priority.fused_add_rms_norm=vllm_c
+```
+
+#### Python Configuration
+
+```python
+from vllm import LLM
+from vllm.config import VllmConfig, KernelConfig
+
+llm = LLM(
+    model="meta-llama/Llama-3.2-1B",
+    vllm_config=VllmConfig(
+        kernel_config=KernelConfig(
+            ir_op_priority={
+                "rms_norm": ["vllm_c", "native"],
+                "fused_add_rms_norm": ["vllm_c", "native"],
+            }
+        )
+    )
+)
+```
+
+#### Platform Defaults
+
+Each platform provides default priority lists that are automatically applied:
+
+```python
+# CUDA/XPU/ROCm platform defaults (when compiling with Inductor)
+{
+  "rms_norm": ["native"],  # Native torch is default
+  "fused_add_rms_norm": ["native"],
+}
+
+# CUDA platform defaults (eager or Dynamo-only)
+{
+  "rms_norm": ["vllm_c", "native"],
+  "fused_add_rms_norm": ["vllm_c", "native"],
+}
+
+# ROCm platform defaults (future - currently same as CUDA)
+{
+    "rms_norm": ["aiter", "vllm_c", "native"],
+    "fused_add_rms_norm": ["aiter", "vllm_c", "native"],
+}
+
+# XPU platform defaults (eager or Dynamo-only)
+{
+    "rms_norm": ["xpu_kernels", "native"],
+    "fused_add_rms_norm": ["xpu_kernels", "native"],
+}
+```
+
+User-specified priorities are prepended to platform defaults,
+so you only need to specify the out-of-order implementations,
+other implementations are appended automatically.
+
+## Compilation Pipeline
+
+vLLM IR heavily customizes the `torch.compile`-based compilation process to allow custom compile
+passes to operate on high-level IR while still producing efficient low-level code at the end.
+The compilation pipeline consists of several stages:
+
+### 1. Dynamo Tracing
+
+When `torch.compile` traces the model's forward pass, vLLM IR operations appear as custom operations
+in the `vllm_ir` torch library. These operations are opaque to Dynamo, meaning they appear directly
+in the FX graph without decomposition:
+
+```python
+# Python code (epsilon=1e-5)
+x1 = ir.ops.rms_norm(x, weight, epsilon)
+x2, residual_out = ir.ops.fused_add_rms_norm.maybe_inplace(x1, residual, weight, epsilon)
+
+# FX graph after Dynamo tracing
+x1 = torch.ops.vllm_ir.rms_norm.default(x, weight, 1e-5); x = None
+out = torch.ops.vllm_ir.fused_add_rms_norm.maybe_inplace(x1, residual, weight, 1e-5); x1 = residual = None
+x2 = out[0]
+residual_out = out[1]
+```
+
+### 2. AOTAutograd and Functionalization
+
+AOTAutograd functionalizes the graph, converting any mutating operations to functional equivalents.
+For vLLM IR operations with `maybe_inplace` overloads, we perform this manually before AOTAutograd,
+converting them to the functional `default` overload using the pre-grad custom pass hook.
+
+```python
+# After functionalization
+x1 = torch.ops.vllm_ir.rms_norm.default(x, weight, 1e-5); x = None
+out = torch.ops.vllm_ir.fused_add_rms_norm.default(x1, residual, weight, 1e-5); x1 = residual = None
+x2 = out[0]
+residual_out = out[1]
+```
+
+The pass also tracks which inputs were "donated" (passed to `maybe_inplace`),
+storing this information in vLLM's `PassContext` for later use in clone elimination.
+
+### 3. IR Fusion and Transformation Passes
+
+After functionalization, custom vLLM passes operate on the functional FX graph containing high-level IR operations.
+These passes can perform fusion, distribute operations for sequence parallelism, and other transformations:
+
+```python
+# Example: Sequence Parallelism (see SequenceParallelismPass)
+# Before SP pass
+
+all_reduce = torch.ops.vllm.all_reduce(x, "tp:0")
+rms_norm = torch.ops.vllm_ir.rms_norm(all_reduce, weight, 1e-5)
+
+# after SP pass
+reduce_scatter = torch.ops.vllm.reduce_scatter(x, "tp:0")
+rms_norm = torch.ops.vllm_ir.rms_norm(all_reduce, weight, 1e-5)
+all_gather = torch.ops.vllm.all_gather(x, "tp:0")
+```
+
+Fusion passes benefit from the high-level representation: they don't need to match against low-level PyTorch operations,
+handle different kernel implementations separately, or deal with functionalization of custom kernels.
+
+### 4. IR Lowering
+
+The lowering pass (`VllmIRLoweringPass`) replaces each vLLM IR operation with its selected implementation.
+The implementation is chosen based on the priority list and support predicates,
+using the **fake tensors** in the graph's metadata in place of op arguments:
+
+```python
+# Implementation selection, same in eager dispatch and compile lowering
+def dispatch(*args) -> IrOpImpl:
+  for provider in priority_list:  # e.g., ["vllm_c", "native"]
+    impl = ir_op.impls[provider]
+    if not impl.supported:
+      continue
+    if impl.supports_args and not impl.supports_args(*args):
+      continue
+    return impl
+
+# make_fx uses torch.fx.symbolic_trace
+impl_graph = make_fx(selected_impl.impl_fn)
+# Replace IR op node with impl_graph's nodes
+match.replace_by_example(selected_impl.impl_fn, node.args)
+```
+
+For example, lowering `rms_norm` with the `vllm_c` implementation:
+
+```python
+# Before lowering (IR op)
+rms_norm = torch.ops.vllm_ir.rms_norm.default(x, weight, 1e-5)
+
+# After lowering (vllm_c implementation traced)
+# Note: Lowering does not currently functionalize, this will likely change in the future.
+empty =  torch.ops.aten.empty.memory_format(x.shape, ...)
+rms_norm = torch.ops._C.rms_norm(empty, x, weight, 1e-5)
+```
+
+When lowering an implementation that mutates inputs (`inplace=True`),
+the lowering pass inserts clones to preserve functional semantics:
+
+```python
+# vllm_c implementation for fused_add_rms_norm mutates its first two arguments
+# Lowered with clones for safety
+clone_default = torch.ops.aten.clone.default(x)
+clone_default_1 = torch.ops.aten.clone.default(residual)
+fused_add_rms_norm = torch.ops._C.fused_add_rms_norm.default(clone_default, clone_default_1, weight, 1e-5)
+```
+
+### 5. Clone Cleanup
+
+After lowering, the clone elimination pass (`UnsafeCloneEliminationPass`) removes unnecessary clones introduced during lowering.
+This pass is essential for achieving zero-copy behavior when using in-place kernels with `maybe_inplace`.
+The pass removes a clone if:
+
+- the cloned input is created in the graph and not used again in the graph
+- the cloned input is a graph parameter, marked as donated
+
+```python
+# After cleanup (donated inputs, no subsequent uses)
+fused_add_rms_norm = torch.ops._C.fused_add_rms_norm.default(x, residual, weight, 1e-5)
+```
+
+The combination of inplace functionalization (tracking donated inputs) and clone cleanup enables the compiler to safely
+use in-place kernels without adding redundant copies or increasing the memory usage.
+
+### 6. Inductor Optimization and Codegen
+
+After IR lowering and cleanup, the graph contains only standard PyTorch operations and platform-specific custom ops.
+Inductor then performs its standard codegen:
+
+- **Inductor lowering and pointwise fusion**: Fusing element-wise operations, reductions, etc.
+- **Memory planning**: Determining buffer allocation and reuse
+- **Kernel generation**: Generating Triton or C++ code for fused operations
+- **Autotuning**: Selecting the best kernel configurations
+
+### Pipeline Summary
+
+```text
+Model Forward Pass
+    ↓
+[Dynamo Tracing] → FX Graph with vllm_ir.* ops
+    ↓
+[Pre-grad: Inplace Functionalization] → maybe_inplace → default, track donated inputs
+    ↓
+[AOTAutograd] → Functionalization
+    ↓
+[Post-grad: IR Fusion Passes] → Fuse high-level IR ops (e.g., rms_norm + quant)
+    ↓
+[Post-grad: IR Lowering] → vllm_ir.* ops → impl ops (with clones if needed)
+    ↓
+[Post-grad: Clone Cleanup] → Remove unnecessary clones using donated input info
+    ↓
+[Inductor] → Pattern matching, fusion, memory planning, codegen
+    ↓
+Compiled Code
+```
+
+## Core vLLM IR Concepts
+
+### Operation Declaration
+
+Operations are declared with the `@register_op` decorator, which creates an `IrOp` object:
+
+```python
+@register_op(
+    name=None,           # Operation name (defaults to function name)
+    activations=None,    # List of activation parameters (defaults to params starting with 'x')
+    allow_inplace=False, # Whether to create a maybe_inplace overload
+)
+def op_name(...):
+    ...
+```
+
+**Parameters:**
+
+- `activations`: List of parameter names considered "activations" (typically consumed by `maybe_inplace`). Defaults to parameters starting with `x`.
+- `allow_inplace`: Creates a `maybe_inplace` overload for memory-efficient execution (see below).
+
+### The `maybe_inplace` Overload
+
+The `maybe_inplace` overload is a critical feature for memory efficiency in LLM inference.
+It signals that the caller doesn't need to preserve the activation inputs after the operation,
+allowing in-place implementations to reuse input memory for outputs.
+
+#### Semantics and Usage
+
+```python
+# Standard usage: inputs are preserved
+out, res_out = ir.ops.fused_add_rms_norm(x, residual, weight, epsilon)
+# x and residual are unchanged, out and res_out are new tensors
+
+# maybe_inplace: inputs may be modified
+out, res_out = ir.ops.fused_add_rms_norm.maybe_inplace(x, residual, weight, epsilon)
+# x and residual may be modified (undefined behavior to use them after this)
+# out and res_out may alias x and residual
+```
+
+Using an activation input after passing it to `maybe_inplace` is **undefined behavior**:
+
+```python
+# WRONG: Using x after donating it
+out, res_out = ir.ops.fused_add_rms_norm.maybe_inplace(x, residual, weight, epsilon)
+result = out + x  # ERROR: x was donated!
+```
+
+If you need to preserve an input, either use the default overload or clone manually:
+
+```python
+# Option 1: Use default overload
+out, res_out = ir.ops.fused_add_rms_norm(x, residual, weight, epsilon)
+result = out + x  # OK: x is preserved
+
+# Option 2: Clone before maybe_inplace
+out, res_out = ir.ops.fused_add_rms_norm.maybe_inplace(x.clone(), residual, weight, epsilon)
+result = out + x  # OK: x is preserved, clone was donated
+```
+
+#### Compilation Behavior
+
+During compilation, the inplace functionalization pass validates that donated inputs are
+not used again and converts `maybe_inplace` to the functional `default` overload:
+
+```python
+# Inplace functionalization pass (pre-grad)
+for node in graph.nodes:
+    if node.target == torch.ops.vllm_ir.fused_add_rms_norm.maybe_inplace:
+        # Check that activation inputs aren't used after this node
+        for activation_arg in activation_inputs:
+            for user in activation_arg.users:
+                if user appears after node:
+                    raise ValueError(f"Input {activation_arg} donated but used again")
+
+        # Convert to default overload
+        node.target = torch.ops.vllm_ir.fused_add_rms_norm.default
+
+        # Track donated graph inputs for later clone elimination
+        for i, arg in enumerate(node.args):
+            if arg.op == "placeholder" and i in activation_indices:
+                pass_context.donated_input_ids.add(node_to_idx[arg])
+```
+
+The donated input information is then used by the clone cleanup pass to eliminate
+unnecessary copies when in-place kernels are lowered.
+
+#### Eager Mode Behavior
+
+In eager mode (without `torch.compile`), `maybe_inplace` enables **maximally memory-efficient**
+execution by allowing the IR operation to dispatch directly to in-place implementations:
+
+```python
+# Eager dispatch logic for maybe_inplace
+impl: IrOpImpl = ir_op.dispatch(*args)
+return impl.impl_fn(*args)
+
+# Eager dispatch logic for default:
+impl: IrOpImpl = ir_op.dispatch(*args)
+if impl.inplace:
+  args = [
+    arg.clone() if i in ir_op.activations else arg
+    for i, arg in enumerate(args)
+  ]
+return impl.impl_fn(*args)
+```
+
+The combination of `maybe_inplace` in model code and in-place kernel implementations provides optimal memory efficiency
+in both eager and compiled modes, with identical semantics in both cases.
+
+#### Memory Savings Example
+
+Consider a transformer layer with residual connections:
+
+```python
+# Without maybe_inplace (2 allocations per layer)
+hidden_states = self.attention(input)
+normed, residual = ir.ops.fused_add_rms_norm(hidden_states, input, weight, eps)
+# Memory: input (preserved), hidden_states (preserved), normed (new), residual (new)
+
+# With maybe_inplace (0 allocations per layer when using in-place kernel)
+hidden_states = self.attention(input)
+normed, residual = ir.ops.fused_add_rms_norm.maybe_inplace(hidden_states, input, weight, eps)
+# Memory: normed (reuses hidden_states), residual (reuses input)
+```
+
+### Implementation Registration
+
+Implementations are registered using the `register_impl` method:
+
+```python
+@ir.ops.op_name.register_impl(
+    provider="provider_name",  # Unique identifier (e.g., "vllm_c", "aiter", "triton")
+    supported=True,            # Static availability check
+    supports_args=None,        # Dynamic argument support check
+)
+def impl_fn(...):
+    ...
+```
+
+**Provider naming conventions:**
+
+- `native`: Reserved for the native torch implementation (declared with `@register_op`)
+- `vllm_c`: C++/CUDA kernels via `torch.ops._C`
+- `aiter`: AMD AITER library
+- `xpu_kernels`: SYCL/SYCLTLA kernels implemented in `vllm-xpu-kernels`
+- `triton_*`: Triton kernels
+- Platform/library names for other implementations
+
+**Support checking:**
+
+- `supported`: Static boolean, checked once at import time (e.g., `HAS_TRITON`, `is_cuda_alike()`)
+- `supports_args`: Function `(*args, **kwargs) -> bool` checking argument compatibility
+    - Called with **fake tensors** during compilation for zero-cost checking
+    - Called with **real tensors** during eager mode dispatch
+    - Should NOT check batch sizes or add guards based on values
+
+Example support predicate:
+
+```python
+def aiter_rms_norm_supports(x, weight, epsilon, variance_size=None):
+    # Check dtype (OK: doesn't depend on batch size)
+    if x.dtype not in [torch.float16, torch.bfloat16]:
+        return False
+    # Check optional parameter (OK: static check)
+    if variance_size is not None:
+        return False
+    return True
+
+@ir.ops.rms_norm.register_impl("aiter", supports_args=aiter_rms_norm_supports)
+def rms_norm(...):
+    ...
+```
+
+Batch-invariant kernels are automatically selected when `VLLM_BATCH_INVARIANT=1` is set.
+
+### Eager Mode vs Compile Mode
+
+vLLM IR operations behave identically in eager and compile modes:
+
+**Eager mode:**
+
+- Direct dispatch to implementation based on priority list
+- Support checked with real tensor arguments
+- Minimal overhead (can be optimized further if needed)
+
+**Compile mode:**
+
+- IR ops appear in FX graph as `torch.ops.vllm_ir.*` custom ops
+- Lowering selects implementation using fake tensors
+- Full integration with Inductor optimizations
+
+This consistency enables:
+
+- Prototyping in eager mode with confidence
+- Debugging by disabling compilation
+- Gradual migration from eager to compiled execution
+
+## Other Topics
+
+### Out-of-Tree Implementations
+
+External platforms can register implementations without modifying vLLM:
+
+```python
+# In external package
+from vllm import ir
+
+@ir.ops.rms_norm.register_impl("my_platform", supported=is_my_platform())
+def rms_norm(x, weight, epsilon, variance_size=None):
+    return my_platform.rms_norm(x, weight, epsilon)
+```
+
+Then configure priority to use your implementation:
+
+```python
+class MyPlatform(Platform):
+  def get_default_ir_op_priority(self):
+    return IrOpPriorityConfig(rms_norm=['my_platform', 'native'])
+
+# Users can still override priority in the same way
+llm = LLM(ir_op_priority=IrOpPriorityConfig(rms_norm=['custom_oot_kernel']))
+```
+
+### Debugging and Observability
+
+!!! note
+    Please let us know how observability can be improved for your use-case!
+
+Enable debug logging to see kernel selection:
+
+```bash
+VLLM_LOGGING_LEVEL=DEBUG vllm serve ...
+```
+
+This logs:
+
+- Which implementations are selected for each operation
+- Why implementations were rejected (unsupported, args not supported)
+- Compilation cache hits/misses
+- IR lowering statistics
+
+Check selected implementations in compiled graphs:
+
+```python
+# After compilation, inspect the lowering pass
+lowering_pass = backend.lowering_pass
+print(lowering_pass.selected_impls)
+# Output: {'rms_norm': {'node_123': 'vllm_c', 'node_456': 'vllm_c'}}
+```
+
+## Migration from CustomOp
+
+vLLM IR is designed to coexist with and gradually replace `CustomOp`:
+
+1. **Op declaration**: Convert `CustomOp` class `PluggableLayer` and move `forward_native` to `@register_op` function
+2. **Implementation registration**: Use `@ir.ops.op_name.register_impl` instead of overriding methods
+3. **Layer usage**: Replace `self.op(...)` with `ir.ops.op_name(...)`
+4. **Configuration**: Migrate `--compilation-config.custom-ops` to `--ir-op-priority`
+
+The migration can be done incrementally, one operation at a time.
+
+## See Also
+
+- [torch.compile Integration](torch_compile.md) - General compilation infrastructure
+- [Fusions](fusions.md) - Custom fusion and transformation passes in vLLM
+- [Custom Operations](custom_op.md) - Legacy custom op system
diff --git a/docs/examples/README.md b/docs/examples/README.md
index f5707ab6eeed..9d6126a65c41 100644
--- a/docs/examples/README.md
+++ b/docs/examples/README.md
@@ -1,7 +1,17 @@
 # Examples
 
-vLLM's examples are split into three categories:
+vLLM's examples are organized into the following categories:
 
-- If you are using vLLM from within Python code, see the [Offline Inference](../../examples/offline_inference) section.
-- If you are using vLLM from an HTTP application or client, see the [Online Serving](../../examples/online_serving) section.
-- For examples of using some of vLLM's advanced features (e.g. LMCache or Tensorizer) which are not specific to either of the above use cases, see the [Others](../../examples/others) section.
+- **[`basic/`](../../examples/basic)** – Minimal examples for offline inference and online serving.
+- **[`generate/`](../../examples/generate)** – Text generation examples, including multimodal models.
+- **[`pooling/`](../../examples/pooling)** – Examples for embedding, classification, scoring, reward, etc.
+- **[`speech_to_text/`](../../examples/speech_to_text)** – Speech transcription, translation and real-time audio examples.
+- **[`features/`](../../examples/features)** – Demonstrations of individual vLLM features: automatic prefix caching, speculative decoding, LoRA, structured outputs, prompt embedding, pause/resume, batch invariance, KV events, data parallelism, and more.
+- **[`reasoning/`](../../examples/reasoning)** – Examples for reasoning with vLLM.
+- **[`tool_calling/`](../../examples/tool_calling)** – Examples for function/tool calling with vLLM.
+- **[`applications/`](../../examples/applications)** – Application examples such as chatbots and RAG (Retrieval-Augmented Generation).
+- **[`rl/`](../../examples/rl)** – Reinforcement learning examples.
+- **[`deployment/`](../../examples/deployment)** – Examples for deploying vLLM in production.
+- **[`ray_serving/`](../../examples/ray_serving)** – Scalable serving using Ray.
+- **[`disaggregated/`](../../examples/disaggregated)** – Examples for disaggregated serving (separate prefill and decode), including various kv cache connectors (LMCache, Mooncake, FlexKV, P2P NCCL) and failure recovery.
+- **[`observability/`](../../examples/observability)** – Metrics, logging, tracing (OpenTelemetry), and dashboards (Grafana, Perses).
diff --git a/docs/features/README.md b/docs/features/README.md
index e62d9cddee76..28362f401477 100644
--- a/docs/features/README.md
+++ b/docs/features/README.md
@@ -52,10 +52,10 @@ th:not(:first-child) {
 | [mm](multimodal_inputs.md) | ✅ | ✅ | [🟠](https://github.com/vllm-project/vllm/pull/4194)<sup>^</sup> | ❔ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❔ | ✅ | | | |
 | best-of | ✅ | ✅ | ✅ | [❌](https://github.com/vllm-project/vllm/issues/6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](https://github.com/vllm-project/vllm/issues/7968) | ✅ | ✅ | | |
 | beam-search | ✅ | ✅ | ✅ | [❌](https://github.com/vllm-project/vllm/issues/6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](https://github.com/vllm-project/vllm/issues/7968) | ❔ | ✅ | ✅ | |
-| [prompt-embeds](prompt_embeds.md) | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❔ | ❔ | ❌ | ❔ | ❔ | ✅ |
+| [prompt-embeds](prompt_embeds.md) | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❔ | ❔ | ✅ | ❔ | ❔ | ✅ |
 
 \* Chunked prefill and prefix caching are only applicable to last-token or all pooling with causal attention.  
-<sup>^</sup> LoRA is only applicable to the language backbone of multimodal models.
+<sup>^</sup> LoRA is only applicable to the language backbone of multimodal models.  
 
 ### Feature x Hardware
 
diff --git a/docs/features/automatic_prefix_caching.md b/docs/features/automatic_prefix_caching.md
index 3718a4b74eb2..fe7977ee23d0 100644
--- a/docs/features/automatic_prefix_caching.md
+++ b/docs/features/automatic_prefix_caching.md
@@ -11,7 +11,7 @@ Automatic Prefix Caching (APC in short) caches the KV cache of existing queries,
 
 Set `enable_prefix_caching=True` in vLLM engine to enable APC. Here is an example:
 
-[examples/offline_inference/automatic_prefix_caching.py](../../examples/offline_inference/automatic_prefix_caching.py)
+[examples/features/automatic_prefix_caching/automatic_prefix_caching_offline.py](../../examples/features/automatic_prefix_caching/automatic_prefix_caching_offline.py)
 
 ## Example workloads
 
diff --git a/docs/features/batch_invariance.md b/docs/features/batch_invariance.md
index 85487697fd37..b23631484508 100644
--- a/docs/features/batch_invariance.md
+++ b/docs/features/batch_invariance.md
@@ -104,8 +104,8 @@ for output in outputs:
 Batch invariance has been tested and verified on the following models:
 
 - **DeepSeek series**: `deepseek-ai/DeepSeek-V3`, `deepseek-ai/DeepSeek-V3-0324`, `deepseek-ai/DeepSeek-R1`, `deepseek-ai/DeepSeek-V3.1`
-- **Qwen3 (Dense)**: `Qwen/Qwen3-1.7B`, `Qwen/Qwen3-8B`
-- **Qwen3 (MoE)**: `Qwen/Qwen3-30B-A3B`, `Qwen/Qwen3-Next-80B-A3B-Instruct`
+- **Qwen3 (Dense)**: `Qwen/Qwen3-1.7B`, `Qwen/Qwen3-8B`, `Qwen/Qwen3-4B-AWQ`, `Qwen/Qwen3-8B-AWQ`
+- **Qwen3 (MoE)**: `Qwen/Qwen3-30B-A3B`, `Qwen/Qwen3-Next-80B-A3B-Instruct`, `Qwen/Qwen3-30B-A3B-Thinking-2507-FP8`
 - **Qwen2.5**: `Qwen/Qwen2.5-0.5B-Instruct`, `Qwen/Qwen2.5-1.5B-Instruct`, `Qwen/Qwen2.5-3B-Instruct`, `Qwen/Qwen2.5-7B-Instruct`, `Qwen/Qwen2.5-14B-Instruct`, `Qwen/Qwen2.5-32B-Instruct`
 - **Llama 3**: `meta-llama/Llama-3.1-8B-Instruct`, `meta-llama/Llama-3.2-1B-Instruct`
 - **GPT-OSS**: `openai/gpt-oss-20b`, `openai/gpt-oss-120b`
diff --git a/docs/features/context_extension.md b/docs/features/context_extension.md
new file mode 100644
index 000000000000..f96340c3183f
--- /dev/null
+++ b/docs/features/context_extension.md
@@ -0,0 +1,70 @@
+# Context Extension
+
+!!! note
+    The `--rope-scaling` parameter used in older versions of vLLM is no longer supported. Please use the `--hf-overrides` method with `rope_parameters` instead.
+This directory contains examples for extending the context length of models using vLLM.
+
+## Offline Inference Example
+
+The [`context_extension.py`](../../examples/features/context_extension/context_extension_offline.py) script demonstrates how to extend the context length of a Qwen model using the YARN method (rope_parameters) and run a simple chat example.
+
+### Usage
+
+```bash
+python examples/features/context_extension/context_extension_offline.py
+```
+
+## OpenAI Online Method
+
+You can also use vLLM's OpenAI-compatible API to serve models with extended context length.
+
+### Usage
+
+Run the vLLM server with the following command to extend the context length using YARN:
+
+```bash
+vllm serve Qwen/Qwen3-0.6B \
+  --hf-overrides '{"rope_parameters": {"factor": 4.0, "original_max_position_embeddings": 32768, "rope_theta": 1000000, "rope_type": "yarn"}}' \
+  --max-model-len 131072
+```
+
+### Client Example
+
+After starting the server, you can use the OpenAI Python client to interact with it:
+
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:8000/v1",
+    api_key="token-abc123"  # Dummy API key, required by the client
+)
+
+response = client.chat.completions.create(
+    model="Qwen/Qwen3-0.6B",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant"},
+        {"role": "user", "content": "Hello"}
+    ],
+    max_tokens=128,
+    temperature=0.8,
+    top_p=0.95
+)
+
+print(response.choices[0].message.content)
+```
+
+### Key Parameters
+
+The available parameters depend on the `rope_type` you choose. For detailed information about all supported RoPE types and their specific parameters, please refer to the [Hugging Face Transformers RoPE documentation](https://huggingface.co/docs/transformers/main/en/internal/rope_utils#transformers.RopeParameters).
+
+Common parameters include:
+
+- `rope_type`: The type of RoPE implementation (e.g., "yarn", "linear", "dynamic")
+- `factor`: The factor by which to extend the context length
+- `original_max_position_embeddings`: The original maximum position embeddings of the model
+
+The following parameters are specific to vLLM:
+
+- `max_model_len`: The new maximum sequence length after extension (original * factor).
+  Used for KV cache pre‑allocation and request limit at serving time.
diff --git a/docs/features/disagg_encoder.md b/docs/features/disagg_encoder.md
index af6da94aa622..c27d6b277284 100644
--- a/docs/features/disagg_encoder.md
+++ b/docs/features/disagg_encoder.md
@@ -36,10 +36,10 @@ The current reference pathway is **ExampleConnector**.
 Below ready-to-run scripts shows the workflow:
 
 1 Encoder instance + 1 PD instance:
-`examples/online_serving/disaggregated_encoder/disagg_1e1pd_example.sh`
+`examples/disaggregated/disaggregated_encoder/disagg_1e1pd_example.sh`
 
 1 Encoder instance + 1 Prefill instance + 1 Decode instance:
-`examples/online_serving/disaggregated_encoder/disagg_1e1p1d_example.sh`
+`examples/disaggregated/disaggregated_encoder/disagg_1e1p1d_example.sh`
 
 ---
 
diff --git a/docs/features/disagg_prefill.md b/docs/features/disagg_prefill.md
index 5167d612391d..9ad005be3fed 100644
--- a/docs/features/disagg_prefill.md
+++ b/docs/features/disagg_prefill.md
@@ -17,15 +17,15 @@ Two main reasons:
 
 ## Usage example
 
-Please refer to [examples/online_serving/disaggregated_prefill.sh](../../examples/online_serving/disaggregated_prefill.sh) for the example usage of disaggregated prefilling.
+Please refer to [examples/disaggregated/disaggregated_prefill.sh](../../examples/disaggregated/disaggregated_prefill.sh) for the example usage of disaggregated prefilling.
 
 Now supports 6 types of connectors:
 
-- **ExampleConnector**: refer to [examples/offline_inference/disaggregated-prefill-v1/run.sh](../../examples/offline_inference/disaggregated-prefill-v1/run.sh) for the example usage of ExampleConnector disaggregated prefilling.
-- **LMCacheConnectorV1**: refer to [examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh](../../examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh) for the example usage of LMCacheConnectorV1 disaggregated prefilling which uses NIXL as the underlying KV transmission.
+- **ExampleConnector**: refer to [examples/disaggregated/example_connector/run.sh](../../examples/disaggregated/example_connector/run.sh) for the example usage of ExampleConnector disaggregated prefilling.
+- **LMCacheConnectorV1**: refer to [examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh](../../examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh) for the example usage of LMCacheConnectorV1 disaggregated prefilling which uses NIXL as the underlying KV transmission.
 - **NixlConnector**: refer to [tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh](../../tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh) for the example usage of NixlConnector disaggregated prefilling which support fully async send/recv. For detailed usage guide, see [NixlConnector Usage Guide](nixl_connector_usage.md). For feature compatibility details, see [NixlConnector Compatibility Matrix](nixl_connector_compatibility.md).
-- **P2pNcclConnector**: refer to [examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh](../../examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh) for the example usage of P2pNcclConnector disaggregated prefilling.
-- **MooncakeConnector**: refer to [examples/online_serving/disaggregated_serving/mooncake_connector/run_mooncake_connector.sh](../../examples/online_serving/disaggregated_serving/mooncake_connector/run_mooncake_connector.sh) for the example usage of ExampleConnector disaggregated prefilling. For detailed usage guide, see [MooncakeConnector Usage Guide](mooncake_connector_usage.md).
+- **P2pNcclConnector**: refer to [examples/disaggregated/p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh](../../examples/disaggregated/p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh) for the example usage of P2pNcclConnector disaggregated prefilling.
+- **MooncakeConnector**: refer to [examples/disaggregated/mooncake_connector/run_mooncake_connector.sh](../../examples/disaggregated/mooncake_connector/run_mooncake_connector.sh) for the example usage of MooncakeConnector disaggregated prefilling. For detailed usage guide, see [MooncakeConnector Usage Guide](mooncake_connector_usage.md).
 - **MultiConnector**: take advantage of the kv_connector_extra_config: dict[str, Any] already present in KVTransferConfig to stash all the connectors we want in an ordered list of kwargs.such as:
 
   ```bash
@@ -44,7 +44,7 @@ For NixlConnector, you may also specify one or multiple NIXL_Backend. Such as:
   --kv-transfer-config '{"kv_connector":"OffloadingConnector","kv_role":"kv_both","kv_connector_extra_config":{"block_size": 64, "cpu_bytes_to_use": 1000000000}}'
   ```
 
-- **FlexKVConnectorV1**: refer to [examples/offline_inference/prefix_caching_flexkv.py](../../examples/offline_inference/prefix_caching_flexkv.py) for the example usage of FlexKVConnectorV1. FlexKV is a distributed KV Store and multi-level cache management system for ultra-large-scale LLM inference.
+- **FlexKVConnectorV1**: refer to [examples/disaggregated/flexkv_connector/prefix_caching_flexkv.py](../../examples/disaggregated/flexkv_connector/prefix_caching_flexkv.py) for the example usage of FlexKVConnectorV1. FlexKV is a distributed KV Store and multi-level cache management system for ultra-large-scale LLM inference.
 
   ```bash
   --kv-transfer-config '{"kv_connector":"FlexKVConnectorV1","kv_role":"kv_both"}'
diff --git a/docs/features/index_cache.md b/docs/features/index_cache.md
new file mode 100644
index 000000000000..7e73d0949554
--- /dev/null
+++ b/docs/features/index_cache.md
@@ -0,0 +1,54 @@
+# IndexCache
+
+IndexCache reduces redundant top-k computation in DeepSeek-V3.2 (DSA) models by caching and reusing top-k indices across layers.
+
+## Background
+
+DeepSeek-V3.2 uses a DeepSeek Sparse Attention (DSA) mechanism where top-k token selection is computed per layer. For deep models with many layers, this computation can be expensive. IndexCache allows skipping redundant top-k computations by reusing indices from previous layers.
+
+See: [IndexCache Paper](https://arxiv.org/abs/2603.12201)
+
+## Usage
+
+### CLI
+
+```bash
+vllm serve deepseek-ai/DeepSeek-V3.2 \
+    --hf-overrides '{"use_index_cache": true, "index_topk_freq": 4}' ...
+```
+
+### Configuration Reference
+
+| Parameter            | Type | Default | Description                                                                                                                                      |
+|----------------------|------|---------|--------------------------------------------------------------------------------------------------------------------------------------------------|
+| `use_index_cache`    | bool | false   | Enable IndexCache. Must be set to true to use this feature                                                                                       |
+| `index_topk_freq`    | int  | 1       | Frequency (in layers) at which top-k is computed. 1 = compute on every layer (disabled), 4 = compute on 1/4 of layers                            |
+| `index_topk_pattern` | str  | null    | Per-layer F/S pattern. Overrides index_topk_freq if set. Each character maps to one DSA layer: F = Full, S = Shared                              |
+
+### Configuration Examples
+
+**Using `index_topk_freq`** (compute every N layers):
+
+```bash
+vllm serve deepseek-ai/DeepSeek-V3.2 \
+    --hf-overrides '{"use_index_cache": true, "index_topk_freq": 4}' ...
+```
+
+**Using `index_topk_pattern`** (explicit per-layer control):
+
+```bash
+# custom pattern for 61 layers: F = compute, S = reuse
+vllm serve deepseek-ai/DeepSeek-V3.2 \
+    --hf-overrides '{"use_index_cache": true, "index_topk_pattern": "FFSFSSSFSSFFFSSSFFFSFSSSSSSFFSFFSFFSSFFFFFFSFFFFFSFFSSSSSSFSF"}'
+```
+
+## How It Works
+
+1. When IndexCache is enabled, layers marked with `"F"` (Full) calculate and store top-k indices
+2. Subsequent layers marked with `"S"` (Shared) receive the cached indices from the previous layer instead of recomputing
+3. The cached indices are passed through the layer stack, reducing total computation
+
+## Requirements
+
+- DeepSeek-V3.2 or compatible DSA model
+- `use_index_cache: true` via `--hf-overrides`
diff --git a/docs/features/lora.md b/docs/features/lora.md
index 2e7b36545d46..d78fdc05792e 100644
--- a/docs/features/lora.md
+++ b/docs/features/lora.md
@@ -47,7 +47,7 @@ the third parameter is the path to the LoRA adapter.
     )
     ```
 
-Check out [examples/offline_inference/multilora_inference.py](../../examples/offline_inference/multilora_inference.py) for an example of how to use LoRA adapters with the async engine and how to use more advanced configuration options.
+Check out [examples/features/lora/multilora_offline.py](../../examples/features/lora/multilora_offline.py) for an example of how to use LoRA adapters with the async engine and how to use more advanced configuration options.
 
 ## Serving LoRA Adapters
 
diff --git a/docs/features/mooncake_connector_usage.md b/docs/features/mooncake_connector_usage.md
index 0e2478924ead..cc8c8ecff258 100644
--- a/docs/features/mooncake_connector_usage.md
+++ b/docs/features/mooncake_connector_usage.md
@@ -31,7 +31,7 @@ vllm serve Qwen/Qwen2.5-7B-Instruct --port 8020 --kv-transfer-config '{"kv_conne
 ### Proxy
 
 ```bash
-python examples/online_serving/disaggregated_serving/mooncake_connector/mooncake_connector_proxy.py --prefill http://192.168.0.2:8010 --decode http://192.168.0.3:8020
+python examples/disaggregated/disaggregated_serving/mooncake_connector/mooncake_connector_proxy.py --prefill http://192.168.0.2:8010 --decode http://192.168.0.3:8020
 ```
 
 Now you can send requests to the proxy server through port 8000.
@@ -65,5 +65,5 @@ Now you can send requests to the proxy server through port 8000.
 
 Refer to these example scripts in the vLLM repository:
 
-- [run_mooncake_connector.sh](../../examples/online_serving/disaggregated_serving/mooncake_connector/run_mooncake_connector.sh)
-- [mooncake_connector_proxy.py](../../examples/online_serving/disaggregated_serving/mooncake_connector/mooncake_connector_proxy.py)
+- [run_mooncake_connector.sh](../../examples/disaggregated/mooncake_connector/run_mooncake_connector.sh)
+- [mooncake_connector_proxy.py](../../examples/disaggregated/mooncake_connector/mooncake_connector_proxy.py)
diff --git a/docs/features/multimodal_inputs.md b/docs/features/multimodal_inputs.md
index ee82c34fa0eb..f6d4f3f86d80 100644
--- a/docs/features/multimodal_inputs.md
+++ b/docs/features/multimodal_inputs.md
@@ -68,7 +68,7 @@ You can pass a single image to the `'image'` field of the multi-modal dictionary
         print(generated_text)
     ```
 
-Full example: [examples/offline_inference/vision_language.py](../../examples/offline_inference/vision_language.py)
+Full example: [examples/generate/multimodal/vision_language_offline.py](../../examples/generate/multimodal/vision_language_offline.py)
 
 To substitute multiple images inside the same text prompt, you can pass in a list of images instead:
 
@@ -101,7 +101,7 @@ To substitute multiple images inside the same text prompt, you can pass in a lis
         print(generated_text)
     ```
 
-Full example: [examples/offline_inference/vision_language_multi_image.py](../../examples/offline_inference/vision_language_multi_image.py)
+Full example: [examples/generate/multimodal/vision_language_multi_image_offline.py](../../examples/generate/multimodal/vision_language_multi_image_offline.py)
 
 If using the [LLM.chat](../models/generative_models.md#llmchat) method, you can pass images directly in the message content using various formats: image URLs, PIL Image objects, or pre-computed embeddings:
 
@@ -215,6 +215,67 @@ When loading RGBA images (images with transparency), vLLM converts them to RGB f
     - This setting only affects RGBA images with transparency; RGB images are unchanged
     - If not specified, the default white background `(255, 255, 255)` is used for backward compatibility
 
+#### Moondream3 Prompt Recipes { #moondream3-prompt-recipes }
+
+`Moondream3ForCausalLM` supports two task-specific prompt formats:
+
+- `query`: ask a question about the image.
+- `caption`: generate a caption for the image.
+
+```python
+from vllm import LLM, SamplingParams
+from vllm.assets.image import ImageAsset
+
+llm = LLM(
+    model="moondream/moondream3-preview",
+    tokenizer="moondream/starmie-v1",
+    trust_remote_code=True,
+    max_model_len=2048,
+    limit_mm_per_prompt={"image": 1},
+)
+
+image = ImageAsset("stop_sign").pil_image
+
+
+def make_query_prompt(question: str) -> str:
+    return (
+        "<|endoftext|><image><|md_reserved_0|>query<|md_reserved_1|>"
+        f"{question}<|md_reserved_2|>"
+    )
+
+
+def make_caption_prompt(length: str = "normal") -> str:
+    return (
+        "<|endoftext|><image><|md_reserved_0|>"
+        f"describe<|md_reserved_1|>{length}<|md_reserved_2|>"
+    )
+
+
+query_out = llm.generate(
+    {
+        "prompt": make_query_prompt("What is shown in this image?"),
+        "multi_modal_data": {"image": image},
+    },
+    SamplingParams(max_tokens=64, temperature=0),
+)[0].outputs[0].text
+
+caption_out = llm.generate(
+    {
+        "prompt": make_caption_prompt(),
+        "multi_modal_data": {"image": image},
+    },
+    SamplingParams(max_tokens=100, temperature=0),
+)[0].outputs[0].text
+
+print("query:", query_out)
+print("caption:", caption_out)
+```
+
+!!! note
+    The native Moondream3 model also has `detect` and `point` skills. Those
+    require custom coordinate decoding and are not exposed by this vLLM
+    implementation.
+
 ### Video Inputs
 
 You can pass a list of NumPy arrays directly to the `'video'` field of the multi-modal dictionary
@@ -287,25 +348,25 @@ Instead of NumPy arrays, you can also pass `'torch.Tensor'` instances, as shown
     !!! note
         'process_vision_info' is only applicable to Qwen2.5-VL and similar models.
 
-Full example: [examples/offline_inference/vision_language.py](../../examples/offline_inference/vision_language.py)
+Full example: [examples/generate/multimodal/vision_language_offline.py](../../examples/generate/multimodal/vision_language_offline.py)
 
 ### Audio Inputs
 
 You can pass a tuple `(array, sampling_rate)` to the `'audio'` field of the multi-modal dictionary.
 
-Full example: [examples/offline_inference/audio_language.py](../../examples/offline_inference/audio_language.py)
+Full example: [examples/generate/multimodal/audio_language_offline.py](../../examples/generate/multimodal/audio_language_offline.py)
 
 #### Chunking Long Audio for Transcription
 
 Speech-to-text models like Whisper have a maximum audio length they can process (typically 30 seconds). For longer audio files, vLLM provides a utility to intelligently split audio into chunks at quiet points to minimize cutting through speech.
 
 ```python
-import librosa
 from vllm import LLM, SamplingParams
 from vllm.multimodal.audio import split_audio
+from vllm.multimodal.media.audio import load_audio
 
 # Load long audio file
-audio, sr = librosa.load("long_audio.wav", sr=16000)
+audio, sr = load_audio("long_audio.wav", sr=16000)
 
 # Split into chunks at low-energy (quiet) regions
 chunks = split_audio(
@@ -674,7 +735,7 @@ Then, you can use the OpenAI client as follows:
     print("Chat completion output:", chat_response.choices[0].message.content)
     ```
 
-Full example: [examples/online_serving/openai_chat_completion_client_for_multimodal.py](../../examples/online_serving/openai_chat_completion_client_for_multimodal.py)
+Full example: [examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py](../../examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py)
 
 !!! tip
     Loading from local file paths is also supported on vLLM: You can specify the allowed local media path via `--allowed-local-media-path` when launching the API server/engine,
@@ -745,7 +806,7 @@ Then, you can use the OpenAI client as follows:
     print("Chat completion output from image url:", result)
     ```
 
-Full example: [examples/online_serving/openai_chat_completion_client_for_multimodal.py](../../examples/online_serving/openai_chat_completion_client_for_multimodal.py)
+Full example: [examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py](../../examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py)
 
 !!! note
     By default, the timeout for fetching videos through HTTP URL is `30` seconds.
@@ -780,6 +841,70 @@ vllm serve Qwen/Qwen3-VL-30B-A3B-Instruct \
 
 Works with common video formats like MP4 when using OpenCV backends.
 
+#### Pre-extracted Frame Sequences with `media_io_kwargs`
+
+When you extract video frames on the client side and send them as `video/jpeg` (base64-concatenated JPEG frames), you can preserve the original video metadata by using `media_io_kwargs` in your request. This enables more accurate video understanding by preserving temporal information that would otherwise be lost during client-side frame extraction.
+
+**Supported Parameters:**
+
+| Parameter | Type | Description |
+| --------- | ---- | ----------- |
+| `fps` | float | Frame rate of the original video |
+| `frames_indices` | list[int] | Indices of the actually sampled frames |
+| `total_num_frames` | int | Total frame count of the original video |
+| `duration` | float | Duration of the original video in seconds |
+| `do_sample_frames` | bool | Whether to perform frame sampling |
+
+??? code
+
+    ```python
+    from openai import OpenAI
+
+    client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
+
+    # Client-side frame extraction
+    frames = extract_frames(video_path, num_frames=32)
+    frames_b64 = ",".join([encode_image(f) for f in frames])
+    video_url = f"data:video/jpeg;base64,{frames_b64}"
+
+    # Pass video metadata via media_io_kwargs
+    response = client.chat.completions.create(
+        model="your-multimodal-model",
+        messages=[{
+            "role": "user",
+            "content": [
+                {"type": "video_url", "video_url": {"url": video_url}},
+                {"type": "text", "text": "Describe what happens in this video."}
+            ]
+        }],
+        extra_body={
+            "media_io_kwargs": {
+                "video": {
+                    "fps": 30.0,
+                    "frames_indices": [0, 10, 20, 30, 40, 50, 60, 70, 80, 90,
+                                       100, 110, 120, 130, 140, 150, 160, 170,
+                                       180, 190, 200, 210, 220, 230, 240, 250,
+                                       260, 270, 280, 290, 300, 310],
+                    "total_num_frames": 900,
+                    "duration": 30.0,
+                }
+            }
+        },
+    )
+
+    print(response.choices[0].message.content)
+    ```
+
+**Why use `media_io_kwargs`?**
+
+When extracting frames client-side, the server loses important context about the original video:
+
+- **Temporal information**: Which frames were sampled and their positions in the original timeline
+- **Video duration**: How long the original video was
+- **Frame rate**: The original playback speed
+
+By passing this metadata, the model can better understand the temporal distribution of the sampled frames and whether important moments might have been skipped.
+
 #### Custom RGBA Background Color
 
 To use a custom background color for RGBA images, pass the `rgba_background_color` parameter via `--media-io-kwargs`:
@@ -832,7 +957,7 @@ Then, you can use the OpenAI client as follows:
         base_url=openai_api_base,
     )
 
-    # Any format supported by librosa is supported
+    # Any format supported by soundfile/PyAV is supported
     audio_url = AudioAsset("winning_call").url
     audio_base64 = encode_base64_content_from_url(audio_url)
 
@@ -894,7 +1019,7 @@ Alternatively, you can pass `audio_url`, which is the audio counterpart of `imag
     print("Chat completion output from audio url:", result)
     ```
 
-Full example: [examples/online_serving/openai_chat_completion_client_for_multimodal.py](../../examples/online_serving/openai_chat_completion_client_for_multimodal.py)
+Full example: [examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py](../../examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py)
 
 !!! note
     By default, the timeout for fetching audios through HTTP URL is `10` seconds.
diff --git a/docs/features/nixl_connector_usage.md b/docs/features/nixl_connector_usage.md
index ea668615153c..e7ee3a64008c 100644
--- a/docs/features/nixl_connector_usage.md
+++ b/docs/features/nixl_connector_usage.md
@@ -13,7 +13,7 @@ Install the NIXL library: `uv pip install nixl`, as a quick start on Nvidia plat
 - Refer to [NIXL official repository](https://github.com/ai-dynamo/nixl) for more installation instructions
 - The specified required NIXL version can be found in [requirements/kv_connectors.txt](../../requirements/kv_connectors.txt) and other relevant config files
 
-For ROCm platform, the [base ROCm docker file](../../docker/Dockerfile.rocm_base) includes RIXL and ucx already.
+For ROCm platform, the [ROCm docker file](../../docker/Dockerfile.rocm) includes RIXL and ucx already.
 
 - Refer to [RIXL official repository](https://github.com/rocm/rixl) for more information
 - The supportive libraries for RIXL can be found in [requirements/kv_connectors_rocm.txt](../../requirements/kv_connectors_rocm.txt)
diff --git a/docs/features/prompt_embeds.md b/docs/features/prompt_embeds.md
index b81d2f28e3b9..dd0b4d62c423 100644
--- a/docs/features/prompt_embeds.md
+++ b/docs/features/prompt_embeds.md
@@ -16,16 +16,51 @@ To input multi-modal data, follow this schema in [vllm.inputs.EmbedsPrompt][]:
 
 You can pass prompt embeddings from Hugging Face Transformers models to the  `'prompt_embeds'` field of the prompt embedding dictionary, as shown in the following examples:
 
-[examples/offline_inference/prompt_embed_inference.py](../../examples/offline_inference/prompt_embed_inference.py)
+[examples/features/prompt_embed/prompt_embed_offline.py](../../examples/features/prompt_embed/prompt_embed_offline.py)
 
 ## Online Serving
 
-Our OpenAI-compatible server accepts prompt embeddings inputs via the [Completions API](https://platform.openai.com/docs/api-reference/completions). Prompt embeddings inputs are added via a new `'prompt_embeds'` key in the JSON package and are enabled by the `--enable-prompt-embeds` flag in `vllm serve`.
+Our OpenAI-compatible server accepts prompt embeddings inputs via both the [Completions API](https://platform.openai.com/docs/api-reference/completions) and the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat). Both are enabled by the `--enable-prompt-embeds` flag in `vllm serve`.
+
+### Completions API
+
+Prompt embeddings inputs are added via a `'prompt_embeds'` key in the JSON request body.
 
 When a mixture of `'prompt_embeds'` and `'prompt'` inputs are provided in a single request, the prompt embeds are always returned first.
 
 Prompt embeddings are passed in as base64 encoded torch tensors.
 
+The Completions endpoint does **not** apply a chat template to `prompt_embeds`. If the model assumes some chat template, the caller is responsible for producing embeddings for the full, already-templated prompt: apply the chat template, then embed the resulting token IDs. Anything the model would normally need (system prompt, role markers, generation prompt, etc.) must already be baked into the embedded tokens.
+
+### Chat Completions API
+
+Prompt embeddings can be included as content parts in chat messages, interleaved with text:
+
+```json
+{
+  "messages": [
+    {
+      "role": "system",
+      "content": [
+        {"type": "text", "text": "You are a helpful assistant."},
+        {"type": "prompt_embeds", "data": "<base64_encoded_tensor>"}
+      ]
+    },
+    {
+      "role": "user",
+      "content": [
+        {"type": "prompt_embeds", "data": "<base64_encoded_tensor>"},
+        {"type": "text", "text": "Summarize the above."}
+      ]
+    }
+  ]
+}
+```
+
+Each `prompt_embeds` content part contains a `data` field with a base64-encoded `torch.Tensor` of shape `(num_tokens, hidden_size)`. Multiple `prompt_embeds` parts can appear in any message, in any position relative to text parts. The server expands each part into the correct number of placeholder tokens during chat template rendering, then splices the pre-computed embeddings into the model's input at the corresponding positions.
+
+Unlike the Completions API, a `prompt_embeds` content part should encode **only** the content, not a templated conversation. The server wraps the chat template around the embedded content at request time, the same way it would for a plain text `content` string. Embedding a full templated conversation here would double-apply the template and produce incorrect inputs to the model.
+
 !!! warning
     The vLLM engine may crash if incorrect shape of embeddings is passed.
     Only enable this flag for trusted users!
@@ -41,4 +76,4 @@ vllm serve meta-llama/Llama-3.2-1B-Instruct --runner generate \
 
 Then, you can use the OpenAI client as follows:
 
-[examples/online_serving/prompt_embed_inference_with_openai_client.py](../../examples/online_serving/prompt_embed_inference_with_openai_client.py)
+[examples/features/prompt_embed/prompt_embed_inference_with_openai_client.py](../../examples/features/prompt_embed/prompt_embed_inference_with_openai_client.py)
diff --git a/docs/features/quantization/README.md b/docs/features/quantization/README.md
index 0b8fc71d3f30..6c4aa7d8aaac 100644
--- a/docs/features/quantization/README.md
+++ b/docs/features/quantization/README.md
@@ -16,9 +16,11 @@ The following are the supported quantization formats for vLLM:
 - [INT8 W8A8](int8.md)
 - [FP8 W8A8](fp8.md)
 - [NVIDIA Model Optimizer](modelopt.md)
+- [Online Quantization](online.md)
 - [AMD Quark](quark.md)
 - [Quantized KV Cache](quantized_kvcache.md)
 - [TorchAO](torchao.md)
+- [FP8 ViT Encoder Attention](fp8_vit_attn.md)
 
 ## Supported Hardware
 
diff --git a/docs/features/quantization/auto_awq.md b/docs/features/quantization/auto_awq.md
index e77e8b5a1f41..e93005f26321 100644
--- a/docs/features/quantization/auto_awq.md
+++ b/docs/features/quantization/auto_awq.md
@@ -47,7 +47,7 @@ After installing AutoAWQ, you are ready to quantize a model. Please refer to the
 To run an AWQ model with vLLM, you can use [TheBloke/Llama-2-7b-Chat-AWQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-AWQ) with the following command:
 
 ```bash
-python examples/offline_inference/llm_engine_example.py \
+python examples/deployment/llm_engine_example.py \
     --model TheBloke/Llama-2-7b-Chat-AWQ \
     --quantization awq
 ```
diff --git a/docs/features/quantization/fp8_vit_attn.md b/docs/features/quantization/fp8_vit_attn.md
new file mode 100644
index 000000000000..bf628cd8a72a
--- /dev/null
+++ b/docs/features/quantization/fp8_vit_attn.md
@@ -0,0 +1,109 @@
+# FP8 ViT Encoder Attention
+
+For visual understanding workloads with large images (e.g. QHD, 4K) and relatively
+short text prompts/generation, the ViT encoder attention can become a significant
+bottleneck, especially when the text model is quantized (e.g. NVFP4). vLLM
+supports optional FP8 quantization for the ViT encoder attention via the
+FlashInfer cuDNN backend. Q/K/V are quantized on-the-fly to FP8 before the
+cuDNN attention call.
+
+!!! note
+    - Currently supports Qwen3-VL family models only (`qwen3_vl`, `qwen3_vl_moe`,
+      `qwen3_5`, `qwen3_5_moe`, and other models using Qwen3 ViT).
+    - Dynamic scaling is not compatible with ViT full CUDA graphs.
+    - Performance gains are mostly visible at QHD/4K resolutions or multi-image
+      requests. Smaller images may see no speedup due to quantization overhead
+      (3 quantization kernel launches + un-padding).
+    - FP8 tensor-core speedup is more pronounced on GB300 than GB200.
+
+## Requirements
+
+- FlashInfer cuDNN backend with cuDNN >= 9.17.1.
+
+## Usage
+
+Enable FP8 ViT attention by passing `--mm-encoder-attn-dtype fp8` together
+with `--mm-encoder-attn-backend FLASHINFER`:
+
+```bash
+vllm serve $MODEL \
+    --mm-encoder-attn-backend FLASHINFER \
+    --mm-encoder-attn-dtype fp8
+```
+
+By default (no scale file), **dynamic scaling** is used: a 16-entry circular
+buffer of observed Q/K/V amax values drives per-forward scale updates. This
+matches BF16 accuracy without any calibration but adds a small per-forward
+overhead.
+
+## Calibrate-Once, Reuse Workflow (Recommended)
+
+For production, calibrate static scales on a representative dataset once and
+reuse them to avoid the dynamic overhead:
+
+```bash
+# Step 1: calibrate and save scales (runs dynamic scaling for 16 passes,
+# then dumps the learned scales to JSON).
+vllm bench mm-processor \
+    --model $MODEL --mm-encoder-attn-backend FLASHINFER \
+    --mm-encoder-attn-dtype fp8 \
+    --mm-encoder-fp8-scale-save-path /path/to/scales.json \
+    --dataset-name hf --dataset-path lmarena-ai/VisionArena-Chat \
+    --num-prompts 100
+
+# Step 2: serve with static scales (no dynamic overhead).
+vllm serve $MODEL \
+    --mm-encoder-attn-backend FLASHINFER \
+    --mm-encoder-attn-dtype fp8 \
+    --mm-encoder-fp8-scale-path /path/to/scales.json
+```
+
+Saved scales are multiplied by `--mm-encoder-fp8-scale-save-margin` (default
+`1.5`) to leave headroom against activation outliers not present in the
+calibration set. The default has been validated to generalize across datasets
+(e.g. VisionArena-Chat calibration maintains BF16 accuracy on ChartQA).
+
+## Scale File Format
+
+```json
+{
+    "visual.blocks.0.attn.attn": {"q": 224.0, "k": 198.0, "v": 210.0},
+    "visual.blocks.1.attn.attn": {"q": 218.0, "k": 195.0, "v": 207.0}
+}
+```
+
+Keys `q_scale` / `k_scale` / `v_scale` are accepted as aliases.
+
+## Performance
+
+**Core cuDNN attention kernel** (PyTorch profiler, `cudnn_generated_fort_native_sdpa_sm100_flash_fprop`, head_dim=128, seq_len=8192):
+
+| Hardware | BF16 | FP8 | Speedup |
+| -------- | ---- | ---- | ------- |
+| GB200 | 350 us | 312 us | **1.12x** |
+| GB300 | 300 us | 211 us | **1.42x** |
+
+**End-to-end encoder forward time** (Qwen3-VL-30B-A3B-Instruct on GB200, 3 images/request):
+
+| Resolution | BF16 median | FP8 median | Speedup |
+| ---------- | ----------- | ---------- | ------- |
+| HD (720x1280) | 31.77 ms | 36.39 ms | 0.87x |
+| FullHD (1080x1920) | 57.99 ms | 58.73 ms | ~same |
+| QHD (1440x2560) | 131.83 ms | 122.30 ms | **1.08x** |
+| 4K (2160x3840) | 543.44 ms | 460.31 ms | **1.18x** |
+
+Crossover is around FullHD with 3 images/request. At QHD and above, FP8 wins.
+
+## Accuracy
+
+ChartQA, Qwen3-VL-8B-Instruct, 500 samples. FP8 static uses scales calibrated
+on VisionArena-Chat (with default 1.5x margin):
+
+| Metric | BF16 | FP8 dynamic | FP8 static |
+| ------ | ---- | ----------- | ---------- |
+| relaxed_accuracy | 0.780 | 0.776 | 0.780 |
+| anywhere_accuracy | 0.806 | 0.816 | 0.814 |
+| exact_match | 0.584 | 0.582 | 0.578 |
+
+All three configurations match within statistical noise, confirming that
+static scales calibrated on one dataset generalize to another.
diff --git a/docs/features/quantization/gptqmodel.md b/docs/features/quantization/gptqmodel.md
index f14a931725da..636a952b6551 100644
--- a/docs/features/quantization/gptqmodel.md
+++ b/docs/features/quantization/gptqmodel.md
@@ -58,7 +58,7 @@ Here is an example of how to quantize `meta-llama/Llama-3.2-1B-Instruct`:
 To run an GPTQModel quantized model with vLLM, you can use [DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2](https://huggingface.co/ModelCloud/DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2) with the following command:
 
 ```bash
-python examples/offline_inference/llm_engine_example.py \
+python examples/deployment/llm_engine_example.py \
     --model ModelCloud/DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2
 ```
 
diff --git a/docs/features/quantization/online.md b/docs/features/quantization/online.md
new file mode 100644
index 000000000000..913da250ee67
--- /dev/null
+++ b/docs/features/quantization/online.md
@@ -0,0 +1,97 @@
+# Online Quantization
+
+Online quantization lets you take a BF16/FP16 model and quantize its Linear
+and MoE weights to lower precision (such as FP8) at load time, without needing
+a pre-quantized checkpoint or calibration data. Weights are converted during
+model loading and activations are dynamically scaled during each forward pass.
+
+## Quick Start
+
+Pass a scheme name to the `quantization` parameter:
+
+```python
+from vllm import LLM
+
+# Per-tensor FP8 quantization (one scale per weight tensor)
+llm = LLM("meta-llama/Llama-3.1-8B", quantization="fp8_per_tensor")
+
+# Per-block FP8 quantization (128x128 block scaling for weights and 1x128 block scaling for activations)
+llm = LLM("meta-llama/Llama-3.1-8B", quantization="fp8_per_block")
+
+# MXFP8 quantization for weights and activations
+llm = LLM("meta-llama/Llama-3.1-8B", quantization="mxfp8")
+```
+
+Or with the CLI:
+
+```bash
+vllm serve meta-llama/Llama-3.1-8B --quantization fp8_per_tensor
+vllm serve meta-llama/Llama-3.1-8B --quantization fp8_per_block
+vllm serve meta-llama/Llama-3.1-8B --quantization mxfp8
+```
+
+## Supported Schemes
+
+| Scheme | Weight recipe | Activation recipe | Notes |
+| ------ | ------------- | ------------------ | ----- |
+| `fp8_per_tensor` | fp8_e4m3 data, fp32 per-tensor scale | fp8_e4m3 data, fp32 per-tensor scale | On some GPUs (Ada, Hopper) linear activations use per-token scaling for better performance |
+| `fp8_per_block` | fp8_e4m3 data, fp32 per-128x128-block scale | fp8_e4m3 data, fp32 per-1x128-block scale | |
+| `mxfp8` | fp8_e4m3 data, e8m0 per-1x32-block scale | fp8_e4m3 data, e8m0 per-1x32-block scale | Requires SM 100+ (Blackwell or newer) for w8a8, other GPUs use a w8a16 fallback |
+
+## Advanced Configuration
+
+For fine-grained control, use a `quantization_config` dictionary.
+
+### Separate Schemes for Dense and MoE Layers
+
+You can apply different quantization schemes to dense linear layers and MoE expert layers:
+
+```python
+from vllm import LLM
+
+llm = LLM(
+    "ibm-granite/granite-3.0-1b-a400m-base",
+    quantization="fp8_per_tensor",
+    quantization_config={
+        "linear_scheme_override": "fp8_per_block",
+    },
+)
+```
+
+Or,
+
+```python
+from vllm import LLM
+
+llm = LLM(
+    "ibm-granite/granite-3.0-1b-a400m-base",
+    quantization="fp8_per_tensor",
+    quantization_config={
+        "moe_scheme_override": "fp8_per_block",
+    },
+)
+```
+
+### Excluding Layers from Quantization
+
+Use the `ignore` parameter to skip specific layers. It accepts exact layer names and regex patterns (prefixed with `re:`):
+
+```python
+from vllm import LLM
+
+llm = LLM(
+    "ibm-granite/granite-3.0-1b-a400m-base",
+    quantization="fp8_per_tensor",
+    quantization_config={
+        "ignore": [
+            # exact layer name
+            "model.layers.1.self_attn.o_proj",
+            # regex: skip all QKV projections
+            "re:.*[qkv]_proj",
+        ],
+    },
+)
+```
+
+!!! note
+    For fused layers (e.g., `qkv_proj` which fuses `q_proj`, `k_proj`, `v_proj`), the ignore pattern must match the **unfused** shard names (`q_proj`, `k_proj`, `v_proj`), not the fused name.
diff --git a/docs/features/reasoning_outputs.md b/docs/features/reasoning_outputs.md
index 5c99eb20bc7d..f1cc18a25cbb 100644
--- a/docs/features/reasoning_outputs.md
+++ b/docs/features/reasoning_outputs.md
@@ -13,6 +13,7 @@ vLLM currently supports the following reasoning models:
 
 | Model Series | Parser Name | Structured Output Support | Tool Calling |
 | ------------ | ----------- | ---------------- | ----------- |
+| [Cohere Command A Reasoning](https://huggingface.co/CohereLabs/command-a-reasoning-08-2025) | `cohere_command3` | `json`, `regex` | ✅ |
 | [DeepSeek R1 series](https://huggingface.co/collections/deepseek-ai/deepseek-r1-678e1e131c0169c0bc89728d) | `deepseek_r1` | `json`, `regex` | ❌ |
 | [DeepSeek-V3.1](https://huggingface.co/collections/deepseek-ai/deepseek-v31-68a491bed32bd77e7fca048f) | `deepseek_v3` | `json`, `regex` | ❌ |
 | [ERNIE-4.5-VL series](https://huggingface.co/baidu/ERNIE-4.5-VL-28B-A3B-PT) | `ernie45` | `json`, `regex` | ❌ |
@@ -156,7 +157,7 @@ OpenAI Python client library does not officially support `reasoning` attribute f
             print(content, end="", flush=True)
     ```
 
-Remember to check whether the `reasoning` exists in the response before accessing it. You could check out the [example](https://github.com/vllm-project/vllm/blob/main/examples/online_serving/openai_chat_completion_with_reasoning_streaming.py).
+Remember to check whether the `reasoning` exists in the response before accessing it. You could check out the [example](https://github.com/vllm-project/vllm/blob/main/examples/reasoning/openai_chat_completion_with_reasoning_streaming.py).
 
 ## Tool Calling
 
@@ -202,7 +203,7 @@ The reasoning content is also available when both tool calling and the reasoning
     print(f"Arguments: {tool_call.arguments}")
     ```
 
-For more examples, please refer to [examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py](../../examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py).
+For more examples, please refer to [examples/reasoning/openai_chat_completion_tool_calls_with_reasoning.py](../../examples/reasoning/openai_chat_completion_tool_calls_with_reasoning.py).
 
 ## Server-Level Default Chat Template Kwargs
 
@@ -283,9 +284,7 @@ curl http://localhost:8000/v1/chat/completions \
     "messages": [
       { "role": "user", "content": "9.11 and 9.8, which is greater?" }
     ],
-    "extra_body": {
-      "thinking_token_budget": 10
-    }
+    "thinking_token_budget": 10
   }'
 ```
 
diff --git a/docs/features/speculative_decoding/README.md b/docs/features/speculative_decoding/README.md
index 9793de3f4c35..ad01ee3d74bc 100644
--- a/docs/features/speculative_decoding/README.md
+++ b/docs/features/speculative_decoding/README.md
@@ -32,9 +32,113 @@ depend on your model family, traffic pattern, hardware, and sampling settings.
 | Suffix decoding | Low to medium gain | Medium gain | No extra draft model; dynamic speculation depth. |
 
 For reproducible measurements in your environment, use
-[`examples/offline_inference/spec_decode.py`](../../../examples/offline_inference/spec_decode.py)
+[`examples/features/speculative_decoding/spec_decode_offline.py`](../../../examples/features/speculative_decoding/spec_decode_offline.py)
 or the [benchmark CLI guide](../../benchmarking/cli.md).
 
+## `--speculative-config` schema
+
+Use `--speculative-config` to pass speculative decoding settings as a JSON
+object on the CLI:
+
+```bash
+vllm serve <target-model> \
+  --speculative-config '{
+    "method": "draft_model",
+    "model": "<draft-model>",
+    "num_speculative_tokens": 5
+  }'
+```
+
+The same keys are accepted from Python via `LLM(..., speculative_config={...})`.
+The tables below highlight common user-facing keys accepted in this JSON
+object; they are not an exhaustive schema reference.
+For more details, see the generated [engine arguments reference](../../configuration/engine_args.md)
+and the API docs for [vllm.config.SpeculativeConfig][].
+
+### Common keys
+
+These keys are commonly used across speculative decoding setups, though some
+only apply to model-based methods such as `draft_model`, `mtp`, `eagle3`, and
+`dflash`.
+
+| Key | Type | Default | Allowed values / meaning |
+| --- | --- | --- | --- |
+| `method` | `string` | `None` | Speculation method. Common values include `draft_model`, `ngram`, `suffix`, `mtp`, `eagle3`, and `dflash`. If omitted, vLLM infers the method from the provided configuration when possible. |
+| `model` | `string` | `None` | Draft model, EAGLE head, or auxiliary model identifier. For `ngram`, `ngram_gpu`, `suffix`, and `mtp`, this can often be omitted. |
+| `num_speculative_tokens` | `integer > 0` | `None` | Number of speculative tokens to propose per step. Required for methods that do not infer it from model metadata. |
+| `draft_tensor_parallel_size` | `integer >= 1` | `None` | Tensor parallel size for the draft model. |
+| `max_model_len` | `integer >= 1` | `None` | Maximum context length for the draft model. |
+| `parallel_drafting` | `boolean` | `false` | Enable parallel draft token generation. Only compatible with EAGLE and draft-model methods. |
+| `rejection_sample_method` | `string` | `strict` | `strict`, `probabilistic`, or `synthetic`. |
+| `synthetic_acceptance_rate` | `float` | `None` | Average acceptance rate to target when `rejection_sample_method` is `synthetic`. Valid range is `[0, 1]`. |
+
+!!! note
+    Gemma 4 assistant checkpoints are handled as Gemma 4 MTP speculators, not
+    as generic draft models. Use `"method": "mtp"` with the assistant
+    checkpoint in `model`, as shown in the [MTP guide](mtp.md#gemma-4-assistant-models).
+
+    If startup logs show `SpeculativeConfig(method='draft_model', ...)` for a
+    Gemma 4 assistant checkpoint, the installed vLLM version does not include
+    Gemma 4 MTP support for that path. Upgrade to a version that includes
+    Gemma 4 MTP support instead of forcing the assistant checkpoint through
+    generic draft-model speculative decoding.
+
+### Method-specific keys
+
+#### N-gram
+
+| Key | Type | Default | Meaning |
+| --- | --- | --- | --- |
+| `prompt_lookup_max` | `integer >= 1` | `5` if both lookup bounds are omitted; otherwise mirrors `prompt_lookup_min` when omitted | Maximum n-gram window size. |
+| `prompt_lookup_min` | `integer >= 1` | `5` if both lookup bounds are omitted; otherwise mirrors `prompt_lookup_max` when omitted | Minimum n-gram window size. |
+
+Example:
+
+```bash
+vllm serve <target-model> \
+  --speculative-config '{
+    "method": "ngram",
+    "num_speculative_tokens": 4,
+    "prompt_lookup_min": 2,
+    "prompt_lookup_max": 5
+  }'
+```
+
+#### Suffix decoding
+
+| Key | Type | Default | Meaning |
+| --- | --- | --- | --- |
+| `suffix_decoding_max_tree_depth` | `integer` | `24` | Maximum combined prefix-match and speculation tree depth. |
+| `suffix_decoding_max_cached_requests` | `integer` | `10000` | Maximum number of requests cached in the global suffix tree. Set `0` to disable the global cache. |
+| `suffix_decoding_max_spec_factor` | `float` | `1.0` | Caps speculative length as a multiple of prefix-match length. |
+| `suffix_decoding_min_token_prob` | `float` | `0.1` | Minimum estimated token probability required to speculate a token. |
+
+Example:
+
+```bash
+vllm serve <target-model> \
+  --speculative-config '{
+    "method": "suffix",
+    "num_speculative_tokens": 8,
+    "suffix_decoding_max_tree_depth": 24,
+    "suffix_decoding_max_cached_requests": 10000,
+    "suffix_decoding_max_spec_factor": 1.0,
+    "suffix_decoding_min_token_prob": 0.1
+  }'
+```
+
+### Notes
+
+- `--speculative-config` expects a JSON object on the CLI. In YAML config
+  files, use a nested mapping instead of an escaped JSON string.
+- `tensor_parallel_size` is not a valid key in `speculative_config`. Use
+  `draft_tensor_parallel_size` instead.
+- Keys such as `temperature` and `top_p` are sampling parameters, not
+  `--speculative-config` fields.
+- Internal fields such as `target_model_config`, `draft_model_config`,
+  `target_parallel_config`, `draft_parallel_config`, and `draft_load_config`
+  are populated by vLLM and are not intended to be set by users.
+
 ## Lossless guarantees of Speculative Decoding
 
 In vLLM, speculative decoding aims to enhance inference efficiency while maintaining accuracy. This section addresses the lossless guarantees of
diff --git a/docs/features/speculative_decoding/draft_model.md b/docs/features/speculative_decoding/draft_model.md
index ee0eaf176e76..b4662e6438f9 100644
--- a/docs/features/speculative_decoding/draft_model.md
+++ b/docs/features/speculative_decoding/draft_model.md
@@ -33,9 +33,9 @@ vllm serve Qwen/Qwen3-4B-Thinking-2507 \
     --port 8000 \
     --seed 42 \
     -tp 1 \
-    --max_model_len 2048 \
-    --gpu_memory_utilization 0.8 \
-    --speculative_config '{"model": "Qwen/Qwen3-0.6B", "num_speculative_tokens": 5, "method": "draft_model"}'
+    --max-model-len 2048 \
+    --gpu-memory-utilization 0.8 \
+    --speculative-config '{"model": "Qwen/Qwen3-0.6B", "num_speculative_tokens": 5, "method": "draft_model"}'
 ```
 
 The code used to request as completions as a client remains unchanged:
@@ -77,4 +77,8 @@ The code used to request as completions as a client remains unchanged:
     ```
 
 !!! warning
-    Note: Please use `--speculative_config` to set all configurations related to speculative decoding. The previous method of specifying the model through `--speculative_model` and adding related parameters (e.g., `--num_speculative_tokens`) separately has been deprecated.
+    Note: Please use `--speculative-config` to set all configurations related
+    to speculative decoding. The previous method of specifying the model
+    through `--speculative-model` and adding related parameters such as
+    `--num-speculative-tokens` separately has been deprecated. For supported
+    keys and examples, see the [`--speculative-config` schema](README.md#--speculative-config-schema).
diff --git a/docs/features/speculative_decoding/eagle.md b/docs/features/speculative_decoding/eagle.md
index 3e0f3add416e..cc9e4fd4c0c1 100644
--- a/docs/features/speculative_decoding/eagle.md
+++ b/docs/features/speculative_decoding/eagle.md
@@ -1,6 +1,6 @@
 # EAGLE Draft Models
 
-The following code configures vLLM to use speculative decoding where proposals are generated by an [EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency)](https://arxiv.org/pdf/2401.15077) based draft model. A more detailed example for offline mode, including how to extract request level acceptance rate, can be found in [examples/offline_inference/spec_decode.py](../../../examples/offline_inference/spec_decode.py)
+The following code configures vLLM to use speculative decoding where proposals are generated by an [EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency)](https://arxiv.org/pdf/2401.15077) based draft model. A more detailed example for offline mode, including how to extract request level acceptance rate, can be found in [examples/features/speculative_decoding/spec_decode_offline.py](../../../examples/features/speculative_decoding/spec_decode_offline.py)
 
 ## Eagle Drafter Example
 
diff --git a/docs/features/speculative_decoding/mtp.md b/docs/features/speculative_decoding/mtp.md
index bcd7153deb51..d60f8ff27ba2 100644
--- a/docs/features/speculative_decoding/mtp.md
+++ b/docs/features/speculative_decoding/mtp.md
@@ -9,6 +9,31 @@ MTP is useful when:
 - Your model natively supports MTP.
 - You want model-based speculative decoding with minimal extra configuration.
 
+## Gemma 4 Assistant Models
+
+Gemma 4 assistant checkpoints use vLLM's Gemma 4 MTP path. They are not generic
+draft models, even though they are passed through the `model` field in
+`--speculative-config`.
+
+Use `"method": "mtp"` when serving Gemma 4 with an assistant checkpoint:
+
+```bash
+vllm serve google/gemma-4-E2B-it \
+    --tensor-parallel-size 1 \
+    --max-model-len 8192 \
+    --speculative-config '{"method":"mtp","model":"gg-hf-am/gemma-4-E2B-it-assistant","num_speculative_tokens":1}'
+```
+
+The E2B, E4B, 26B-A4B, and 31B Gemma 4 IT assistant checkpoints are supported
+when their configuration uses `model_type: gemma4_assistant`. vLLM maps those
+checkpoints to `Gemma4MTPModel` internally and wires the assistant layers to
+share KV cache with the target model.
+
+If an older vLLM release logs `SpeculativeConfig(method='draft_model', ...)`
+for a Gemma 4 assistant checkpoint, that release is treating the assistant as a
+generic draft model and may fail during initialization for multimodal Gemma 4
+targets. Upgrade to a version with Gemma 4 MTP support instead.
+
 ## Offline Example
 
 ```python
@@ -38,7 +63,7 @@ for output in outputs:
 ```bash
 vllm serve XiaomiMiMo/MiMo-7B-Base \
     --tensor-parallel-size 1 \
-    --speculative_config '{"method":"mtp","num_speculative_tokens":1}'
+    --speculative-config '{"method":"mtp","num_speculative_tokens":1}'
 ```
 
 ## Notes
diff --git a/docs/features/speculative_decoding/parallel_draft_model.md b/docs/features/speculative_decoding/parallel_draft_model.md
index 2a3f11a302d3..c31b8e2d2f40 100644
--- a/docs/features/speculative_decoding/parallel_draft_model.md
+++ b/docs/features/speculative_decoding/parallel_draft_model.md
@@ -36,9 +36,9 @@ vllm serve Qwen/Qwen3-4B \
     --port 8000 \
     --seed 42 \
     -tp 1 \
-    --max_model_len 2048 \
-    --gpu_memory_utilization 0.8 \
-    --speculative_config '{"model": "amd/PARD-Qwen3-0.6B", "num_speculative_tokens": 12, "method": "draft_model", "parallel_drafting": true}'
+    --max-model-len 2048 \
+    --gpu-memory-utilization 0.8 \
+    --speculative-config '{"model": "amd/PARD-Qwen3-0.6B", "num_speculative_tokens": 12, "method": "draft_model", "parallel_drafting": true}'
 ```
 
 ## Pre-trained PARD weights
diff --git a/docs/features/structured_outputs.md b/docs/features/structured_outputs.md
index 41cf7be89291..fa39f7ae6e48 100644
--- a/docs/features/structured_outputs.md
+++ b/docs/features/structured_outputs.md
@@ -165,7 +165,7 @@ As an example, we can use to define a specific format of simplified SQL queries:
     print(completion.choices[0].message.content)
     ```
 
-See also: [full example](../examples/online_serving/structured_outputs.md)
+See also: [full example](../../examples/features/structured_outputs/README.md)
 
 ## Reasoning Outputs
 
@@ -208,7 +208,7 @@ Note that you can use reasoning with any provided structured outputs feature. Th
     print("content: ", completion.choices[0].message.content)
     ```
 
-See also: [full example](../examples/online_serving/structured_outputs.md)
+See also: [full example](../../examples/features/structured_outputs/README.md)
 
 !!! note
     When using Qwen3 Coder models with reasoning enabled, structured outputs might become disabled if the reasoning content does not get parsed into the `reasoning` field separately (v0.11.2+).
@@ -304,7 +304,7 @@ Step #2: explanation="Next, let's isolate 'x' by dividing both sides of the equa
 Answer: x = -29/8
 ```
 
-An example of using `structural_tag` can be found here: [examples/online_serving/structured_outputs](../../examples/online_serving/structured_outputs)
+An example of using `structural_tag` can be found here: [examples/features/structured_outputs](../../examples/features/structured_outputs/README.md)
 
 ## Offline Inference
 
@@ -339,4 +339,4 @@ shown below:
     print(outputs[0].outputs[0].text)
     ```
 
-See also: [full example](../examples/online_serving/structured_outputs.md)
+See also: [full example](../../examples/features/structured_outputs/structured_outputs_offline.py)
diff --git a/docs/features/tool_calling.md b/docs/features/tool_calling.md
index e9aa87a69647..9c60255d6928 100644
--- a/docs/features/tool_calling.md
+++ b/docs/features/tool_calling.md
@@ -369,6 +369,16 @@ Flags:
 * For non-reasoning: `--tool-call-parser hunyuan_a13b`
 * For reasoning: `--tool-call-parser hunyuan_a13b --reasoning-parser hunyuan_a13b`
 
+### Cohere Command A Reasoning (`cohere_command3`)
+
+Supported models:
+
+* [`CohereLabs/command-a-reasoning-08-2025`](https://huggingface.co/CohereLabs/command-a-reasoning-08-2025)
+
+Flags: `--tool-call-parser cohere_command3 --reasoning-parser cohere_command3`
+
+Note: the Cohere tool parser requires the `cohere_melody` package, which is not installed by default. Before using this parser please install the [cohere_melody](https://pypi.org/project/cohere-melody/) package.
+
 ### LongCat-Flash-Chat Models (`longcat`)
 
 Supported models:
diff --git a/docs/getting_started/installation/cpu.s390x.inc.md b/docs/getting_started/installation/cpu.s390x.inc.md
index 3078c1537c09..1e36b4317647 100644
--- a/docs/getting_started/installation/cpu.s390x.inc.md
+++ b/docs/getting_started/installation/cpu.s390x.inc.md
@@ -3,15 +3,15 @@
 
 vLLM has experimental support for s390x architecture on IBM Z platform. For now, users must build from source to natively run on IBM Z platform.
 
-Currently, the CPU implementation for s390x architecture supports FP32 datatype only.
+Currently, the CPU implementation for s390x architecture supports FP32, BF16 and FP16.
 
 --8<-- [end:installation]
 --8<-- [start:requirements]
 
 - OS: `Linux`
-- SDK: `gcc/g++ >= 12.3.0` or later with Command Line Tools
+- SDK: `gcc/g++ >= 14.0.0` or later with Command Line Tools
 - Instruction Set Architecture (ISA): VXE support is required. Works with Z14 and above.
-- Build install python packages: `pyarrow`, `torch` and `torchvision`
+- Build install python packages: `torchvision`, `llvmlite`, `numba`, `pyarrow (for testing)`, `opencv-headless`
 
 --8<-- [end:requirements]
 --8<-- [start:set-up-using-python]
@@ -24,13 +24,14 @@ Currently, there are no pre-built IBM Z CPU wheels.
 --8<-- [end:pre-built-wheels]
 --8<-- [start:build-wheel-from-source]
 
-Install the following packages from the package manager before building the vLLM. For example on RHEL 9.4:
+Install the following packages from the package manager before building the vLLM. For example on RHEL 9.6:
 
 ```bash
 dnf install -y \
-    which procps findutils tar vim git gcc g++ make patch make cython zlib-devel \
+    which procps findutils tar vim git gcc-toolset-14 gcc-toolset-14-binutils gcc-toolset-14-libatomic-devel zlib-devel \
     libjpeg-turbo-devel libtiff-devel libpng-devel libwebp-devel freetype-devel harfbuzz-devel \
-    openssl-devel openblas openblas-devel wget autoconf automake libtool cmake numactl-devel
+    openssl-devel openblas openblas-devel autoconf automake libtool cmake numpy libsndfile \
+    clang llvm-devel llvm-static clang-devel
 ```
 
 Install rust>=1.80 which is needed for `outlines-core` and `uvloop` python packages installation.
@@ -43,13 +44,13 @@ curl https://sh.rustup.rs -sSf | sh -s -- -y && \
 Execute the following commands to build and install vLLM from source.
 
 !!! tip
-    Please build the following dependencies, `torchvision`, `pyarrow` from source before building vLLM.
+    Please build the following dependencies, `torchvision`, `llvmlite`, `numba`, `llguidance`, `pyarrow`, `opencv-headless` from source before building vLLM.
 
 ```bash
-    sed -i '/^torch/d' requirements/build/cuda.txt    # remove torch from requirements/build/cuda.txt since we use nightly builds
     uv pip install -v \
+        --extra-index-url https://download.pytorch.org/whl/cpu \
         --torch-backend auto \
-        -r requirements/build/cuda.txt \
+        -r requirements/build/cpu.txt \
         -r requirements/cpu.txt \
     VLLM_TARGET_DEVICE=cpu python setup.py bdist_wheel && \
         uv pip install dist/*.whl
@@ -57,10 +58,9 @@ Execute the following commands to build and install vLLM from source.
 
 ??? console "pip"
     ```bash
-        sed -i '/^torch/d' requirements/build/cuda.txt    # remove torch from requirements/build/cuda.txt since we use nightly builds
         pip install -v \
-            --extra-index-url https://download.pytorch.org/whl/nightly/cpu \
-            -r requirements/build/cuda.txt \
+            --extra-index-url https://download.pytorch.org/whl/cpu \
+            -r requirements/build/cpu.txt \
             -r requirements/cpu.txt \
         VLLM_TARGET_DEVICE=cpu python setup.py bdist_wheel && \
             pip install dist/*.whl
diff --git a/docs/getting_started/installation/gpu.cuda.inc.md b/docs/getting_started/installation/gpu.cuda.inc.md
index db181c60e771..309dd671251e 100644
--- a/docs/getting_started/installation/gpu.cuda.inc.md
+++ b/docs/getting_started/installation/gpu.cuda.inc.md
@@ -375,8 +375,8 @@ For (G)B300, we recommend using CUDA 13, as shown in the following command.
 
     ```bash
     DOCKER_BUILDKIT=1 docker build \
-    --build-arg CUDA_VERSION=13.0.1 \
-    --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 \
+    --build-arg CUDA_VERSION=13.0.2 \
+    --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 \
     --build-arg max_jobs=256 \
     --build-arg nvcc_threads=2 \
     --build-arg RUN_WHEEL_CHECK=false \
diff --git a/docs/getting_started/installation/gpu.rocm.inc.md b/docs/getting_started/installation/gpu.rocm.inc.md
index 4ab01ee8c687..f8385997eea3 100644
--- a/docs/getting_started/installation/gpu.rocm.inc.md
+++ b/docs/getting_started/installation/gpu.rocm.inc.md
@@ -240,7 +240,7 @@ uv pip install vllm==${VLLM_VERSION} \
         # Install dependencies
         pip install --upgrade numba \
             scipy \
-            huggingface-hub[cli,hf_transfer] \
+            huggingface-hub[cli] \
             setuptools_scm
         pip install -r requirements/rocm.txt
 
diff --git a/docs/getting_started/installation/gpu.xpu.inc.md b/docs/getting_started/installation/gpu.xpu.inc.md
index 9e71860d62fd..e8b74a06f079 100644
--- a/docs/getting_started/installation/gpu.xpu.inc.md
+++ b/docs/getting_started/installation/gpu.xpu.inc.md
@@ -46,7 +46,7 @@ pip install -v -r requirements/xpu.txt
 
     !!! note
         - `triton` (without suffix) is for NVIDIA GPUs only. On XPU, using it instead of `triton-xpu` can cause correctness or runtime issues.
-        - For torch 2.10 (the version used in `requirements/xpu.txt`), the matching package is `triton-xpu==3.6.0`. If you use a different version of torch, check the corresponding `triton-xpu` version in [docker/Dockerfile.xpu](https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile.xpu).
+        - For torch 2.11 (the version used in `requirements/xpu.txt`), the matching package is `triton-xpu==3.7.0`. If you use a different version of torch, check the corresponding `triton-xpu` version in [docker/Dockerfile.xpu](https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile.xpu).
 
 - Finally, build and install vLLM XPU backend:
 
@@ -88,7 +88,7 @@ vllm serve facebook/opt-13b \
      -tp=8
 ```
 
-By default, a ray instance will be launched automatically if no existing one is detected in the system, with `num-gpus` equals to `parallel_config.world_size`. We recommend properly starting a ray cluster before execution, referring to the [examples/online_serving/run_cluster.sh](https://github.com/vllm-project/vllm/blob/main/examples/online_serving/run_cluster.sh) helper script.
+By default, a ray instance will be launched automatically if no existing one is detected in the system, with `num-gpus` equals to `parallel_config.world_size`. We recommend properly starting a ray cluster before execution, referring to the [examples/ray_serving/run_cluster.sh](https://github.com/vllm-project/vllm/blob/main/examples/ray_serving/run_cluster.sh) helper script.
 
 --8<-- [end:supported-features]
 --8<-- [start:distributed-backend]
diff --git a/docs/governance/committers.md b/docs/governance/committers.md
index e6f8f317ebb3..cff041866b29 100644
--- a/docs/governance/committers.md
+++ b/docs/governance/committers.md
@@ -14,6 +14,7 @@ Sorted alphabetically by GitHub handle:
 - [@aarnphm](https://github.com/aarnphm): Structured output
 - [@alexm-redhat](https://github.com/alexm-redhat): Performance
 - [@ApostaC](https://github.com/ApostaC): Connectors, offloading
+- [@bbrowning](https://github.com/bbrowning): Tool use and reasoning parser
 - [@benchislett](https://github.com/benchislett): Engine core and spec decode
 - [@bigPYJ1151](https://github.com/bigPYJ1151): Intel CPU/XPU integration
 - [@chaunceyjiang](https://github.com/chaunceyjiang): Tool use and reasoning parser
@@ -31,6 +32,7 @@ Sorted alphabetically by GitHub handle:
 - [@LucasWilkinson](https://github.com/LucasWilkinson): Kernels and performance
 - [@luccafong](https://github.com/luccafong): Llama models, speculative decoding, distributed
 - [@markmc](https://github.com/markmc): Observability
+- [@MatthewBonanni](https://github.com/MatthewBonanni): Kernels and performance
 - [@mgoin](https://github.com/mgoin): Quantization and performance
 - [@NickLucche](https://github.com/NickLucche): KV connector
 - [@njhill](https://github.com/njhill): Distributed, API server, engine core
@@ -41,6 +43,7 @@ Sorted alphabetically by GitHub handle:
 - [@robertgshaw2-redhat](https://github.com/robertgshaw2-redhat): Core, distributed, disagg
 - [@ruisearch42](https://github.com/ruisearch42): Pipeline parallelism, Ray Support
 - [@russellb](https://github.com/russellb): Structured output, engine core, security
+- [@sfeng33](https://github.com/sfeng33): Tool use and reasoning parser
 - [@sighingnow](https://github.com/sighingnow): Qwen models, new model support
 - [@simon-mo](https://github.com/simon-mo): Project lead, API entrypoints, community
 - [@tdoublep](https://github.com/tdoublep): State space models
@@ -86,7 +89,7 @@ If you have PRs touching the area, please feel free to ping the area owner for r
 - AsyncLLM: the zmq based protocol hosting engine core and making it accessible for entrypoints
     - @robertgshaw2-redhat, @njhill, @russellb
 - ModelRunner, Executor, Worker: the abstractions for engine wrapping model implementation
-    - @WoosukKwon, @tlrmchlsmth, @heheda12345, @LucasWilkinson, @ProExpertProg
+    - @WoosukKwon, @tlrmchlsmth, @heheda12345, @LucasWilkinson, @ProExpertProg, @MatthewBonanni
 - KV Connector: Connector interface and implementation for KV cache offload and transfer
     - @robertgshaw2-redhat, @njhill, @KuntaiDu, @NickLucche, @ApostaC
 - Distributed, Parallelism, Process Management: Process launchers managing each worker, and assign them to the right DP/TP/PP/EP ranks
@@ -105,7 +108,7 @@ If you have PRs touching the area, please feel free to ping the area owner for r
 - Custom Layers: Utility layers in vLLM such as rotary embedding and rms norms
     - @ProExpertProg
 - Attention: Attention interface for paged attention
-    - @WoosukKwon, @LucasWilkinson, @heheda12345
+    - @WoosukKwon, @LucasWilkinson, @heheda12345, @MatthewBonanni
 - FusedMoE: FusedMoE kernel, Modular kernel framework, EPLB
     - @tlrmchlsmth
 - Quantization: Various quantization config, weight loading, and kernel.
@@ -119,7 +122,7 @@ If you have PRs touching the area, please feel free to ping the area owner for r
 - State space models: The state space models implementation in vLLM
     - @tdoublep, @tlrmchlsmth
 - Reasoning and tool calling parsers
-    - @chaunceyjiang, @aarnphm
+    - @chaunceyjiang, @aarnphm, @sfeng33, @bbrowning
 
 ### Entrypoints
 
@@ -133,7 +136,7 @@ If you have PRs touching the area, please feel free to ping the area owner for r
 ### Features
 
 - Spec Decode: Covers model definition, attention, sampler, and scheduler related to n-grams, EAGLE, and MTP.
-    - @WoosukKwon, @benchislett, @luccafong
+    - @WoosukKwon, @benchislett, @luccafong, @MatthewBonanni
 - Structured Output: The structured output implementation
     - @russellb, @aarnphm
 - RL: The RL related features such as collective rpc, sleep mode, etc.
@@ -153,8 +156,8 @@ If you have PRs touching the area, please feel free to ping the area owner for r
 
 ### External Kernels Integration
 
-- FlashAttention: @LucasWilkinson
-- FlashInfer: @LucasWilkinson, @mgoin, @WoosukKwon
+- FlashAttention: @LucasWilkinson, @MatthewBonanni
+- FlashInfer: @LucasWilkinson, @mgoin, @WoosukKwon, @MatthewBonanni
 - Blackwell Kernels: @mgoin, @yewentao256
 - DeepEP/DeepGEMM: @mgoin, @yewentao256
 
diff --git a/docs/maybe_skip_pr_build.sh b/docs/maybe_skip_pr_build.sh
deleted file mode 100755
index 2a0b338a0198..000000000000
--- a/docs/maybe_skip_pr_build.sh
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# Skip PR builds unless the PR has the "documentation" or "ready" label.
-# Used by Read the Docs (see .readthedocs.yaml).
-
-if [[ "$READTHEDOCS_VERSION_TYPE" != "external" ]]; then
-  exit 0
-fi
-
-PR_URL="https://api.github.com/repos/vllm-project/vllm/pulls/${READTHEDOCS_VERSION}"
-CURL_ARGS=(-s -o /tmp/pr_response.json -w "%{http_code}")
-if [[ -n "$GITHUB_TOKEN" ]]; then
-  CURL_ARGS+=(-H "Authorization: token ${GITHUB_TOKEN}")
-fi
-HTTP_CODE=$(curl "${CURL_ARGS[@]}" "$PR_URL")
-
-if [[ "$HTTP_CODE" -ne 200 ]]; then
-  echo "GitHub API returned HTTP ${HTTP_CODE}, proceeding with build."
-elif grep -qE '"name": *"(documentation|ready)"' /tmp/pr_response.json; then
-  echo "Found required label, proceeding with build."
-else
-  echo "PR #${READTHEDOCS_VERSION} lacks 'documentation' or 'ready' label, cancelling build."
-  exit 1
-fi
diff --git a/docs/mkdocs/hooks/generate_argparse.py b/docs/mkdocs/hooks/generate_argparse.py
index a798b8e34530..2c19dc1763f6 100644
--- a/docs/mkdocs/hooks/generate_argparse.py
+++ b/docs/mkdocs/hooks/generate_argparse.py
@@ -7,7 +7,7 @@
 import textwrap
 import traceback
 from argparse import SUPPRESS, Action, HelpFormatter
-from collections.abc import Iterable
+from collections.abc import Callable, Iterable
 from importlib.machinery import ModuleSpec
 from pathlib import Path
 from typing import TYPE_CHECKING, Literal
@@ -38,8 +38,21 @@ def decorator(cls):
         return decorator
 
 
+class MockPluggableLayer:
+    @staticmethod
+    def register(name):
+        def decorator(cls):
+            return cls
+
+        return decorator
+
+
 mock_if_no_torch("vllm._C", MagicMock())
-mock_if_no_torch("vllm.model_executor.custom_op", MagicMock(CustomOp=MockCustomOp))
+mock_if_no_torch("vllm._C_stable_libtorch", MagicMock())
+mock_if_no_torch(
+    "vllm.model_executor.custom_op",
+    MagicMock(CustomOp=MockCustomOp, PluggableLayer=MockPluggableLayer),
+)
 mock_if_no_torch(
     "vllm.utils.torch_utils", MagicMock(direct_register_custom_op=lambda *a, **k: None)
 )
@@ -55,6 +68,31 @@ def decorator(cls):
 mock_if_no_torch("torch.nn", MagicMock(Parameter=object))
 
 
+# Mock torch.library.infer_schema for vllm.ir.ops.IrOpInplaceOverload.__init__
+# We need to return the corresponding number of inputs, as IR infra will assert it
+def get_outputs(native_fn: Callable) -> str:
+    """
+    Extract output schema from function's return type annotation,
+    e.g. 'Tensor' or 'Tensor, Tensor'.
+    """
+    import typing
+
+    return_type = typing.get_type_hints(native_fn)["return"]
+    origin = typing.get_origin(return_type)
+    arg_name = lambda a: a.__name__ if hasattr(a, "__name__") else str(a)
+    if origin is tuple:
+        args = typing.get_args(return_type)
+        return ", ".join(arg_name(arg) for arg in args)
+    else:
+        return f"{arg_name(return_type)}"
+
+
+mock_if_no_torch(
+    "torch.library",
+    MagicMock(infer_schema=lambda fn, **k: f"(Tensor x) -> {get_outputs(fn)}"),
+)
+
+
 class PydanticMagicMock(MagicMock):
     """`MagicMock` that's able to generate pydantic-core schemas."""
 
diff --git a/docs/models/extensions/runai_model_streamer.md b/docs/models/extensions/runai_model_streamer.md
index 38c603b46e10..965b2932ffaa 100644
--- a/docs/models/extensions/runai_model_streamer.md
+++ b/docs/models/extensions/runai_model_streamer.md
@@ -101,7 +101,7 @@ vllm serve /path/to/sharded/model \
     --model-loader-extra-config '{"pattern":"custom-model-rank-{rank}-part-{part}.safetensors"}'
 ```
 
-To create sharded model files, you can use the script provided in [examples/offline_inference/save_sharded_state.py](../../../examples/offline_inference/save_sharded_state.py). This script demonstrates how to save a model in the sharded format that is compatible with the Run:ai Model Streamer sharded loader.
+To create sharded model files, you can use the script provided in [examples/features/sharded_state/save_sharded_state_offline.py](../../../examples/features/sharded_state/save_sharded_state_offline.py). This script demonstrates how to save a model in the sharded format that is compatible with the Run:ai Model Streamer sharded loader.
 
 The sharded loader supports all the same tunable parameters as the regular Run:ai Model Streamer, including `concurrency` and `memory_limit`. These can be configured in the same way:
 
diff --git a/docs/models/extensions/tensorizer.md b/docs/models/extensions/tensorizer.md
index 3df80d5af6c4..0f3ee1ede436 100644
--- a/docs/models/extensions/tensorizer.md
+++ b/docs/models/extensions/tensorizer.md
@@ -14,7 +14,7 @@ To install `tensorizer`, run `pip install vllm[tensorizer]`.
 ## The basics
 
 To load a model using Tensorizer, the model first needs to be serialized by
-Tensorizer. [The example script](../../examples/others/tensorize_vllm_model.md) takes care of this process.
+Tensorizer. [The example script](../../../examples/features/tensorize_vllm_model.py) takes care of this process.
 
 Let's walk through a basic example by serializing `facebook/opt-125m` using the script, and then loading it for inference.
 
@@ -25,7 +25,7 @@ CLI arguments. The docstring for the script itself explains the CLI args
 and how to use it properly in great detail, and we'll use one of the examples from the docstring directly, assuming we want to serialize and save our model at our S3 bucket example `s3://my-bucket`:
 
 ```bash
-python examples/others/tensorize_vllm_model.py \
+python examples/features/tensorize_vllm_model.py \
    --model facebook/opt-125m \
    serialize \
    --serialized-directory s3://my-bucket \
@@ -35,7 +35,7 @@ python examples/others/tensorize_vllm_model.py \
 This saves the model tensors at `s3://my-bucket/vllm/facebook/opt-125m/v1`. If you intend on applying a LoRA adapter to your tensorized model, you can pass the HF id of the LoRA adapter in the above command, and the artifacts will be saved there too:
 
 ```bash
-python examples/others/tensorize_vllm_model.py \
+python examples/features/tensorize_vllm_model.py \
    --model facebook/opt-125m \
    --lora-path <lora_id> \
    serialize \
@@ -71,7 +71,7 @@ llm = LLM(
 As an example, CPU concurrency can be limited when serializing with `tensorizer` via the `limit_cpu_concurrency` parameter in the initializer for `TensorSerializer`. To set `limit_cpu_concurrency` to some arbitrary value, you would do so like this when serializing:
 
 ```bash
-python examples/others/tensorize_vllm_model.py \
+python examples/features/tensorize_vllm_model.py \
    --model facebook/opt-125m \
    --lora-path <lora_id> \
    serialize \
diff --git a/docs/models/hardware_supported_models/xpu.md b/docs/models/hardware_supported_models/xpu.md
index 2857d80a7698..cfda6c76f05b 100644
--- a/docs/models/hardware_supported_models/xpu.md
+++ b/docs/models/hardware_supported_models/xpu.md
@@ -10,39 +10,40 @@
 
 ### Text-only Language Models
 
-| Model                                     | Architecture                                         | FP16 | Dynamic FP8 | MXFP4 |
-| ----------------------------------------- | ---------------------------------------------------- | ---- | ----------- | ----- |
-| openai/gpt-oss-20b                        | GPTForCausalLM                                       |      |             | ✅    |
-| openai/gpt-oss-120b                       | GPTForCausalLM                                       |      |             | ✅    |
-| deepseek-ai/DeepSeek-R1-Distill-Llama-8B  | LlamaForCausalLM                                     | ✅   | ✅          |       |
-| deepseek-ai/DeepSeek-R1-Distill-Qwen-14B  | QwenForCausalLM                                      | ✅   | ✅          |       |
-| deepseek-ai/DeepSeek-R1-Distill-Qwen-32B  | QwenForCausalLM                                      | ✅   | ✅          |       |
-| deepseek-ai/DeepSeek-R1-Distill-Llama-70B | LlamaForCausalLM                                     | ✅   | ✅          |       |
-| Qwen/Qwen2.5-72B-Instruct                 | Qwen2ForCausalLM                                     | ✅   | ✅          |       |
-| Qwen/Qwen3-14B                            | Qwen3ForCausalLM                                     | ✅   | ✅          |       |
-| Qwen/Qwen3-32B                            | Qwen3ForCausalLM                                     | ✅   | ✅          |       |
-| Qwen/Qwen3-30B-A3B                        | Qwen3ForCausalLM                                     | ✅   | ✅          |       |
-| Qwen/Qwen3-30B-A3B-GPTQ-Int4              | Qwen3ForCausalLM                                     | ✅   | ✅          |       |
-| Qwen/Qwen3-coder-30B-A3B-Instruct         | Qwen3ForCausalLM                                     | ✅   | ✅          |       |
-| Qwen/QwQ-32B                              | QwenForCausalLM                                      | ✅   | ✅          |       |
-| deepseek-ai/DeepSeek-V2-Lite              | DeepSeekForCausalLM                                  | ✅   | ✅          |       |
-| meta-llama/Llama-3.1-8B-Instruct          | LlamaForCausalLM                                     | ✅   | ✅          |       |
-| baichuan-inc/Baichuan2-13B-Chat           | BaichuanForCausalLM                                  | ✅   | ✅          |       |
-| THUDM/GLM-4-9B-chat                       | GLMForCausalLM                                       | ✅   | ✅          |       |
-| THUDM/CodeGeex4-All-9B                    | CodeGeexForCausalLM                                  | ✅   | ✅          |       |
-| chuhac/TeleChat2-35B                      | LlamaForCausalLM (TeleChat2 based on Llama arch)     | ✅   | ✅          |       |
-| 01-ai/Yi1.5-34B-Chat                      | YiForCausalLM                                        | ✅   | ✅          |       |
-| THUDM/CodeGeex4-All-9B                    | CodeGeexForCausalLM                                  | ✅   | ✅          |       |
-| deepseek-ai/DeepSeek-Coder-33B-base       | DeepSeekCoderForCausalLM                             | ✅   | ✅          |       |
-| baichuan-inc/Baichuan2-13B-Chat           | BaichuanForCausalLM                                  | ✅   | ✅          |       |
-| meta-llama/Llama-2-13b-chat-hf            | LlamaForCausalLM                                     | ✅   | ✅          |       |
-| THUDM/CodeGeex4-All-9B                    | CodeGeexForCausalLM                                  | ✅   | ✅          |       |
-| Qwen/Qwen1.5-14B-Chat                     | QwenForCausalLM                                      | ✅   | ✅          |       |
-| Qwen/Qwen1.5-32B-Chat                     | QwenForCausalLM                                      | ✅   | ✅          |       |
+| Model                                              | Architecture                                     | BF16/FP16/Dynamic FP8 | Compressed_tensors FP8 | MXFP4 |
+| -------------------------------------------------- | ------------------------------------------------ | --------------------- | ---------------------- | ----- |
+| openai/gpt-oss-20b                                 | GPTForCausalLM                                   |                       |                        | ✅    |
+| openai/gpt-oss-120b                                | GPTForCausalLM                                   |                       |                        | ✅    |
+| deepseek-ai/DeepSeek-R1-Distill-Llama-8B           | LlamaForCausalLM                                 | ✅                    |                        |       |
+| deepseek-ai/DeepSeek-R1-Distill-Qwen-14B           | QwenForCausalLM                                  | ✅                    |                        |       |
+| deepseek-ai/DeepSeek-R1-Distill-Qwen-32B           | QwenForCausalLM                                  | ✅                    |                        |       |
+| deepseek-ai/DeepSeek-R1-Distill-Llama-70B          | LlamaForCausalLM                                 | ✅                    |                        |       |
+| Qwen/Qwen2.5-72B-Instruct                          | Qwen2ForCausalLM                                 | ✅                    |                        |       |
+| Qwen/Qwen3-14B                                     | Qwen3ForCausalLM                                 | ✅                    |                        |       |
+| Qwen/Qwen3-32B                                     | Qwen3ForCausalLM                                 | ✅                    |                        |       |
+| Qwen/Qwen3-30B-A3B                                 | Qwen3ForCausalLM                                 | ✅                    |                        |       |
+| Qwen/Qwen3-30B-A3B-GPTQ-Int4                       | Qwen3ForCausalLM                                 | ✅                    |                        |       |
+| Qwen/Qwen3-coder-30B-A3B-Instruct                  | Qwen3ForCausalLM                                 | ✅                    |                        |       |
+| Qwen/QwQ-32B                                       | QwenForCausalLM                                  | ✅                    |                        |       |
+| deepseek-ai/DeepSeek-V2-Lite                       | DeepSeekForCausalLM                              | ✅                    |                        |       |
+| meta-llama/Llama-3.1-8B-Instruct                   | LlamaForCausalLM                                 | ✅                    |                        |       |
+| baichuan-inc/Baichuan2-13B-Chat                    | BaichuanForCausalLM                              | ✅                    |                        |       |
+| THUDM/GLM-4-9B-chat                                | GLMForCausalLM                                   | ✅                    |                        |       |
+| THUDM/CodeGeex4-All-9B                             | CodeGeexForCausalLM                              | ✅                    |                        |       |
+| chuhac/TeleChat2-35B                               | LlamaForCausalLM (TeleChat2 based on Llama arch) | ✅                    |                        |       |
+| 01-ai/Yi1.5-34B-Chat                               | YiForCausalLM                                    | ✅                    |                        |       |
+| THUDM/CodeGeex4-All-9B                             | CodeGeexForCausalLM                              | ✅                    |                        |       |
+| deepseek-ai/DeepSeek-Coder-33B-base                | DeepSeekCoderForCausalLM                         | ✅                    |                        |       |
+| baichuan-inc/Baichuan2-13B-Chat                    | BaichuanForCausalLM                              | ✅                    |                        |       |
+| meta-llama/Llama-2-13b-chat-hf                     | LlamaForCausalLM                                 | ✅                    |                        |       |
+| THUDM/CodeGeex4-All-9B                             | CodeGeexForCausalLM                              | ✅                    |                        |       |
+| Qwen/Qwen1.5-14B-Chat                              | QwenForCausalLM                                  | ✅                    |                        |       |
+| Qwen/Qwen1.5-32B-Chat                              | QwenForCausalLM                                  | ✅                    |                        |       |
+| RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8-dynamic    | LlamaForCausalLM                                 |                       | ✅                     |       |
 
 ### Multimodal Language Models
 
-| Model                        | Architecture                     | FP16 | Dynamic FP8 | MXFP4 |
+| Model                        | Architecture                     | BF16 | Dynamic FP8 | MXFP4 |
 | ---------------------------- | -------------------------------- | ---- | ----------- | ----- |
 | OpenGVLab/InternVL3_5-8B     | InternVLForConditionalGeneration | ✅   | ✅          |       |
 | OpenGVLab/InternVL3_5-14B    | InternVLForConditionalGeneration | ✅   | ✅          |       |
@@ -55,7 +56,7 @@
 
 ### Embedding and Reranker Language Models
 
-| Model                   | Architecture                   | FP16 | Dynamic FP8 | MXFP4 |
+| Model                   | Architecture                   | BF16 | Dynamic FP8 | MXFP4 |
 | ----------------------- | ------------------------------ | ---- | ----------- | ----- |
 | Qwen/Qwen3-Embedding-8B | Qwen3ForTextEmbedding          | ✅   | ✅          |       |
 | Qwen/Qwen3-Reranker-8B  | Qwen3ForSequenceClassification | ✅   | ✅          |       |
diff --git a/docs/models/pooling_models/README.md b/docs/models/pooling_models/README.md
index 2cf721f5eefe..7c8d6187148e 100644
--- a/docs/models/pooling_models/README.md
+++ b/docs/models/pooling_models/README.md
@@ -59,6 +59,16 @@ please refer to [IO Processor Plugins](../../design/io_processor_plugins.md).
     Within classification tasks, there is a specialized subcategory: Cross-encoder (aka reranker) models. These models
 are a subset of classification models that accept two prompts as input and output num_labels equal to 1.
 
+### Pooling Types
+
+| Pooling Tasks  | Granularity   | Description                                                                                                                                                                                       |
+|----------------|---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `CLS` pooling  | Sequence-wise | For BERT‑like (bidirectional self‑attention) models, CLS pooling is used by default. This means the last_hidden_states corresponding to the first token (the [CLS] token) is taken as the output. |
+| `LAST` pooling | Sequence-wise | For GPT‑like (causal self‑attention) models, LAST pooling is used by default. This means the last_hidden_states corresponding to the last token is taken as the output.                           |
+| `MEAN` pooling | Sequence-wise | Many studies have shown that averaging the last_hidden_states over all input tokens performs better on certain downstream tasks. Therefore, more and more models are using MEAN pooling.          |
+| `ALL` pooling  | Token-wise    | Outputs the last_hidden_states for all input tokens.                                                                                                                                              |
+| `STEP` pooling | Token-wise    | Filters and outputs the last_hidden_states corresponding to the token IDs returned by returned_token_ids.                                                                                         |
+
 ### Score Types
 
 The scoring models is designed to compute similarity scores between two input prompts. It supports three model types
@@ -68,7 +78,7 @@ The scoring models is designed to compute similarity scores between two input pr
 |-----------------------|---------------|----------------------------------------------|--------------------|--------------------------|
 | `classify` (see note) | Sequence-wise | reranker score for each sequence             | `cross-encoder`    | linear classifier        |
 | `embed`               | Sequence-wise | vector representations for each sequence     | `bi-encoder`       | cosine similarity        |
-| `token_classify`      | Token-wise    | probability vector of classes for each token | nan                | nan                      |
+| `token_classify`      | Token-wise    | probability vector of classes for each token | N/A                | N/A                      |
 | `token_embed`         | Token-wise    | vector representations for each token        | `late-interaction` | late interaction(MaxSim) |
 
 !!! note
@@ -76,14 +86,15 @@ The scoring models is designed to compute similarity scores between two input pr
 
 ### Pooling Usages
 
-| Pooling Usages              | Description                                                                                                                                             |
-|-----------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Classification Usages       | Predicting which predefined category, class, or label best corresponds to a given input.                                                                |
-| Embedding Usages            | Converts unstructured data (text, images, audio, etc.) into structured numerical vectors (embeddings).                                                  |
-| Token Classification Usages | Token-wise classification                                                                                                                               |
-| Token Embedding Usages      | Token-wise embedding                                                                                                                                    |
-| Scoring Usages              | Computes similarity scores between two inputs. It supports three model types (aka `score_type`): `cross-encoder`, `late-interaction`, and `bi-encoder`. |
-| Reward Usages               | Evaluates the quality of outputs generated by a language model, acting as a proxy for human preferences.                                                |
+| Pooling Usages              | Description                                                                                                                                               |
+|-----------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Classification Usages       | Predicting which predefined category, class, or label best corresponds to a given input.                                                                  |
+| Embedding Usages            | Converts unstructured data (text, images, audio, etc.) into structured numerical vectors (embeddings).                                                    |
+| Token Classification Usages | Token-wise classification                                                                                                                                 |
+| Token Embedding Usages      | Token-wise embedding                                                                                                                                      |
+| Reward Usages               | Evaluates the quality of outputs generated by a language model, acting as a proxy for human preferences.                                                  |
+| Scoring Usages              | Computes similarity scores between two inputs. It supports three model types (aka `score_type`): `cross-encoder`, `late-interaction`, and `bi-encoder`.   |
+| Plugins Usages              | Allow users to customize input and output processors. For more information, please refer to [IO Processor Plugins](../../design/io_processor_plugins.md). |
 
 We also have some special models that support multiple pooling tasks, or have specific usage scenarios, or support special inputs and outputs.
 
@@ -91,9 +102,9 @@ For more detailed information, please refer to the link below.
 
 - [Classification Usages](classify.md)
 - [Embedding Usages](embed.md)
-- [Reward Usages](reward.md)
 - [Token Classification Usages](token_classify.md)
 - [Token Embedding Usages](token_embed.md)
+- [Reward Usages](reward.md)
 - [Scoring Usages](scoring.md)
 - [Specific Model Examples](specific_models.md)
 
@@ -103,15 +114,17 @@ Each pooling model in vLLM supports one or more of these tasks according to
 [Pooler.get_supported_tasks][vllm.model_executor.layers.pooler.Pooler.get_supported_tasks],
 enabling the corresponding APIs.
 
-### Offline APIs corresponding to pooling tasks
+### Offline APIs corresponding to pooling usages
 
-| Task             | APIs                                                                                  |
-|------------------|---------------------------------------------------------------------------------------|
-| `embed`          | `LLM.embed(...)`, `LLM.encode(..., pooling_task="embed")`, `LLM.score(...)`(see note) |
-| `classify`       | `LLM.classify(...)`, `LLM.encode(..., pooling_task="classify")`, `LLM.score(...)`     |
-| `token_classify` | `LLM.reward(...)`, `LLM.encode(..., pooling_task="token_classify")`                   |
-| `token_embed`    | `LLM.encode(..., pooling_task="token_embed")`, `LLM.score(...)`                       |
-| `plugin`         | `LLM.encode(..., pooling_task="plugin")`                                              |
+| Pooling Usages              | Dedicated API       | Pooling task for `LLM.encode` API | Score Types                | scoring function         |
+|-----------------------------|---------------------|-----------------------------------|----------------------------|--------------------------|
+| Classification Usages       | `LLM.classify(...)` | `classify`                        | `cross-encoder` (see note) | linear classifier        |
+| Embedding Usages            | `LLM.embed(...)`    | `embed`                           | `bi-encoder`               | cosine similarity        |
+| Token Classification Usages | N/A                 | `token_classify`                  | N/A                        | N/A                      |
+| Token Embedding Usages      | N/A                 | `token_embed`                     | `late-interaction`         | late interaction(MaxSim) |
+| Reward Usages               | N/A                 | `classify` & `token_classify`     | N/A                        | N/A                      |
+| Scoring Usages              | `LLM.score(...)`    | N/A                               | N/A                        | N/A                      |
+| Plugins Usages              | N/A                 | `plugin`                          | N/A                        | N/A                      |
 
 !!! note
     Only when a classification model outputs num_labels equal to 1 can it be used as a scoring model and have its scoring API enabled.
@@ -137,7 +150,7 @@ It is primarily designed for [score models](scoring.md).
 
 The [encode][vllm.LLM.encode] method is available to all pooling models in vLLM.
 
-Please use one of the more specific methods or set the task directly when using `LLM.encode`, refer to the [table above](#offline-apis-corresponding-to-pooling-tasks).
+Please use one of the more specific methods or set the task directly when using `LLM.encode`, refer to the [table above](#offline-apis-corresponding-to-pooling-usages).
 
 ### Examples
 
@@ -173,9 +186,12 @@ Our Pooling API (`/pooling`) is similar to `LLM.encode`, being applicable to all
 
 The input format is the same as [Embeddings API](embed.md#openai-compatible-embeddings-api), but the output data can contain an arbitrary nested list, not just a 1-D list of floats.
 
-Please use one of the more specific APIs or set the task directly when using the Pooling API, refer to the [table above](#offline-apis-corresponding-to-pooling-tasks).
+Please use one of the more specific APIs or set the task directly when using the Pooling API, refer to the [table above](#offline-apis-corresponding-to-pooling-usages).
+
+Code examples:
 
-Code example: [examples/pooling/pooling/pooling_online.py](../../../examples/pooling/pooling/pooling_online.py)
+- [Online example](../../../examples/pooling/reward/token_reward_online.py)
+- [Offline example](../../../examples/pooling/reward/token_reward_offline.py)
 
 ### Examples
 
@@ -276,10 +292,10 @@ Pooling models now support token-wise task.
 
 ### Score task
 
-`score` task is deprecated and will be removed in v0.20. Please use `classify` instead. Only when a
-classification model outputs num_labels equal to 1 can it be used as a scoring model and have its scoring API enabled.
+`score` task have has been removed in v0.21, use `classify` instead. Only when a classification model outputs num_labels
+equal to 1 can it be used as a scoring model and have its scoring API enabled.
 
 ### Pooling multitask support
 
-Pooling multitask support is deprecated and will be removed in v0.20. When the default pooling task is not what you want,
+Pooling multitask support has been removed in v0.21. When the default pooling task is not what you want,
 you need to manually specify it via `PoolerConfig(task=<task>)` offline or `--pooler-config.task <task>` online.
diff --git a/docs/models/pooling_models/embed.md b/docs/models/pooling_models/embed.md
index 8b3632a9f33c..2f5d1a3fbe09 100644
--- a/docs/models/pooling_models/embed.md
+++ b/docs/models/pooling_models/embed.md
@@ -45,6 +45,7 @@ You can compute pairwise similarity scores to build a similarity matrix using th
 | `GritLM` | GritLM | `parasail-ai/GritLM-7B-vllm`. | ✅︎ | ✅︎ |
 | `GteModel` | Arctic-Embed-2.0-M | `Snowflake/snowflake-arctic-embed-m-v2.0`. | | |
 | `GteNewModel` | mGTE-TRM (see note) | `Alibaba-NLP/gte-multilingual-base`, etc. | | |
+| `JinaEmbeddingsV5Model`<sup>C</sup> | Qwen3-based with task-specific LoRA adapters | `jinaai/jina-embeddings-v5-text-small` (see note) | ✅︎ | ✅︎ |
 | `LlamaBidirectionalModel`<sup>C</sup> | Llama-based with bidirectional attention | `nvidia/llama-nemotron-embed-1b-v2`, etc. | ✅︎ | ✅︎ |
 | `LlamaModel`<sup>C</sup>, `LlamaForCausalLM`<sup>C</sup>, `MistralModel`<sup>C</sup>, etc. | Llama-based | `intfloat/e5-mistral-7b-instruct`, etc. | ✅︎ | ✅︎ |
 | `ModernBertModel` | ModernBERT-based | `Alibaba-NLP/gte-modernbert-base`, etc. | | |
@@ -73,6 +74,12 @@ You can compute pairwise similarity scores to build a similarity matrix using th
 !!! note
     `jinaai/jina-embeddings-v3` supports multiple tasks through LoRA, while vllm temporarily only supports text-matching tasks by merging LoRA weights.
 
+!!! note
+    `jinaai/jina-embeddings-v5-text-small` ships with four task-specific LoRA adapters
+    (`retrieval`, `text-matching`, `classification`, `clustering`). vLLM merges the
+    selected adapter into the base weights at load time. Choose the task with
+    `--hf-overrides '{"jina_task": "<task>"}'`; the default is `retrieval`.
+
 ### Multimodal Models
 
 !!! note
diff --git a/docs/models/pooling_models/reward.md b/docs/models/pooling_models/reward.md
index 8555060e66be..7cb6e8b5bb65 100644
--- a/docs/models/pooling_models/reward.md
+++ b/docs/models/pooling_models/reward.md
@@ -134,3 +134,13 @@ print(f"Data: {data!r}")
 ## Online Serving
 
 Please refer to the [pooling API](README.md#pooling-api). Pooling task corresponding to reward model types refer to the [table above](#summary).
+
+## More examples
+
+More examples can be found here: [examples/pooling/reward](../../../examples/pooling/reward)
+
+## Deprecated Features
+
+### `LLM.reward`
+
+`llm.reward` api is deprecated and will be removed in v0.23. Please use `LLM.encode` with `pooling_task="classify"` or `pooling_task="token_classify"` instead.
diff --git a/docs/models/pooling_models/scoring.md b/docs/models/pooling_models/scoring.md
index ac94a0cd76bc..baaf15f14fb4 100644
--- a/docs/models/pooling_models/scoring.md
+++ b/docs/models/pooling_models/scoring.md
@@ -41,6 +41,7 @@ The score models is designed to compute similarity scores between two input prom
 | `GemmaForSequenceClassification` | Gemma-based | `BAAI/bge-reranker-v2-gemma`(see note), etc. | [bge-reranker-v2-gemma.jinja](../../../examples/pooling/score/template/bge-reranker-v2-gemma.jinja) | ✅︎ | ✅︎ |
 | `GteNewForSequenceClassification` | mGTE-TRM (see note) | `Alibaba-NLP/gte-multilingual-reranker-base`, etc. | N/A | | |
 | `LlamaBidirectionalForSequenceClassification`<sup>C</sup> | Llama-based with bidirectional attention | `nvidia/llama-nemotron-rerank-1b-v2`, etc. | [nemotron-rerank.jinja](../../../examples/pooling/score/template/nemotron-rerank.jinja) | ✅︎ | ✅︎ |
+| `ModernBertForSequenceClassification` | ModernBERT-based | `Alibaba-NLP/gte-reranker-modernbert-base`, etc. | N/A | | |
 | `Qwen2ForSequenceClassification`<sup>C</sup> | Qwen2-based | `mixedbread-ai/mxbai-rerank-base-v2`(see note), etc. | [mxbai_rerank_v2.jinja](../../../examples/pooling/score/template/mxbai_rerank_v2.jinja) | ✅︎ | ✅︎ |
 | `Qwen3ForSequenceClassification`<sup>C</sup> | Qwen3-based | `tomaarsen/Qwen3-Reranker-0.6B-seq-cls`, `Qwen/Qwen3-Reranker-0.6B`(see note), etc. | [qwen3_reranker.jinja](../../../examples/pooling/score/template/qwen3_reranker.jinja) | ✅︎ | ✅︎ |
 | `RobertaForSequenceClassification` | RoBERTa-based | `cross-encoder/quora-roberta-base`, etc. | N/A | | |
@@ -160,6 +161,8 @@ The following Score API parameters are supported:
 --8<-- "vllm/entrypoints/pooling/base/protocol.py:pooling-common-params"
 --8<-- "vllm/entrypoints/pooling/base/protocol.py:pooling-common-extra-params"
 --8<-- "vllm/entrypoints/pooling/base/protocol.py:classify-extra-params"
+--8<-- "vllm/entrypoints/pooling/scoring/protocol.py:scoring-common-params"
+--8<-- "vllm/entrypoints/pooling/scoring/protocol.py:score-request-params"
 ```
 
 #### Examples
@@ -370,6 +373,8 @@ The following rerank api parameters are supported:
 --8<-- "vllm/entrypoints/pooling/base/protocol.py:pooling-common-params"
 --8<-- "vllm/entrypoints/pooling/base/protocol.py:pooling-common-extra-params"
 --8<-- "vllm/entrypoints/pooling/base/protocol.py:classify-extra-params"
+--8<-- "vllm/entrypoints/pooling/scoring/protocol.py:scoring-common-params"
+--8<-- "vllm/entrypoints/pooling/scoring/protocol.py:rerank-request-params"
 ```
 
 #### Examples
diff --git a/docs/models/pooling_models/token_classify.md b/docs/models/pooling_models/token_classify.md
index 201ce4ea6dcb..945a710df96b 100644
--- a/docs/models/pooling_models/token_classify.md
+++ b/docs/models/pooling_models/token_classify.md
@@ -68,7 +68,7 @@ If your model is not in the above list, we will try to automatically convert the
     Forced alignment usage requires `--hf-overrides '{"architectures": ["Qwen3ASRForcedAlignerForTokenClassification"]}'`.
     Please refer to [examples/pooling/token_classify/forced_alignment_offline.py](../../../examples/pooling/token_classify/forced_alignment_offline.py).
 
-### As Reward Models
+### Reward Models
 
 Using token classification models as reward models. For details on reward models, see [Reward Models](reward.md).
 
diff --git a/docs/models/pooling_models/token_embed.md b/docs/models/pooling_models/token_embed.md
index b0e094267db1..c20ae7cf1646 100644
--- a/docs/models/pooling_models/token_embed.md
+++ b/docs/models/pooling_models/token_embed.md
@@ -9,14 +9,14 @@
 - Online APIs:
     - Pooling API (`/pooling`)
 
-The difference between the (sequence) embedding task and the token embedding task is that (sequence) embedding outputs one embedding for each sequence, while token embedding outputs a embedding for each token.
+The difference between the (sequence) embedding task and the token embedding task is that (sequence) embedding outputs one embedding for each sequence, while token embedding outputs an embedding for each token.
 
 Many embedding models support both (sequence) embedding and token embedding. For further details on (sequence) embedding, please refer to [this page](embed.md).
 
 !!! note
 
     Pooling multitask support is deprecated and will be removed in v0.20. When the default pooling task (embed) is not 
-    what you want, you need to manually specify it via via `PoolerConfig(task="token_embed")` offline or
+    what you want, you need to manually specify it via `PoolerConfig(task="token_embed")` offline or
     `--pooler-config.task token_embed` online.
 
 ## Typical Use Cases
diff --git a/docs/models/supported_models.md b/docs/models/supported_models.md
index ef1f5901ed03..08f4c2b14b3f 100644
--- a/docs/models/supported_models.md
+++ b/docs/models/supported_models.md
@@ -378,12 +378,14 @@ th {
 | `BloomForCausalLM` | BLOOM, BLOOMZ, BLOOMChat | `bigscience/bloom`, `bigscience/bloomz`, etc. | | ✅︎ |
 | `ChatGLMModel`, `ChatGLMForConditionalGeneration` | ChatGLM | `zai-org/chatglm2-6b`, `zai-org/chatglm3-6b`, `thu-coai/ShieldLM-6B-chatglm3`, etc. | ✅︎ | ✅︎ |
 | `CohereForCausalLM`, `Cohere2ForCausalLM` | Command-R, Command-A | `CohereLabs/c4ai-command-r-v01`, `CohereLabs/c4ai-command-r7b-12-2024`, `CohereLabs/c4ai-command-a-03-2025`, `CohereLabs/command-a-reasoning-08-2025`, etc. | ✅︎ | ✅︎ |
+| `Cohere2MoeForCausalLM` | Command (MoE) | (model checkpoints loaded with `trust_remote_code=True`) | ✅︎ | ✅︎ |
 | `CwmForCausalLM` | CWM | `facebook/cwm`, etc. | ✅︎ | ✅︎ |
 | `DbrxForCausalLM` | DBRX | `databricks/dbrx-base`, `databricks/dbrx-instruct`, etc. | | ✅︎ |
 | `DeciLMForCausalLM` | DeciLM | `nvidia/Llama-3_3-Nemotron-Super-49B-v1`, etc. | ✅︎ | ✅︎ |
 | `DeepseekForCausalLM` | DeepSeek | `deepseek-ai/deepseek-llm-67b-base`, `deepseek-ai/deepseek-llm-7b-chat`, etc. | ✅︎ | ✅︎ |
 | `DeepseekV2ForCausalLM` | DeepSeek-V2 | `deepseek-ai/DeepSeek-V2`, `deepseek-ai/DeepSeek-V2-Chat`, etc. | ✅︎ | ✅︎ |
 | `DeepseekV3ForCausalLM` | DeepSeek-V3 | `deepseek-ai/DeepSeek-V3`, `deepseek-ai/DeepSeek-R1`, `deepseek-ai/DeepSeek-V3.1`, etc. | ✅︎ | ✅︎ |
+| `DeepseekV4ForCausalLM` | DeepSeek-V4 | `deepseek-ai/DeepSeek-V4-Flash`, `deepseek-ai/DeepSeek-V4-Pro`, etc. | | ✅︎ |
 | `Dots1ForCausalLM` | dots.llm1 | `rednote-hilab/dots.llm1.base`, `rednote-hilab/dots.llm1.inst`, etc. | | ✅︎ |
 | `DotsOCRForCausalLM` | dots_ocr | `rednote-hilab/dots.ocr` | ✅︎ | ✅︎ |
 | `Ernie4_5ForCausalLM` | Ernie4.5 | `baidu/ERNIE-4.5-0.3B-PT`, etc. | ✅︎ | ✅︎ |
@@ -400,6 +402,7 @@ th {
 | `Gemma2ForCausalLM` | Gemma 2 | `google/gemma-2-9b`, `google/gemma-2-27b`, etc. | ✅︎ | ✅︎ |
 | `Gemma3ForCausalLM` | Gemma 3 | `google/gemma-3-1b-it`, etc. | ✅︎ | ✅︎ |
 | `Gemma3nForCausalLM` | Gemma 3n | `google/gemma-3n-E2B-it`, `google/gemma-3n-E4B-it`, etc. | | |
+| `Gemma4ForCausalLM` | Gemma 4 | `google/gemma-4-E2B-it`, etc. | ✅︎ | ✅︎ |
 | `GlmForCausalLM` | GLM-4 | `zai-org/glm-4-9b-chat-hf`, etc. | ✅︎ | ✅︎ |
 | `Glm4ForCausalLM` | GLM-4-0414 | `zai-org/GLM-4-32B-0414`, etc. | ✅︎ | ✅︎ |
 | `Glm4MoeForCausalLM` | GLM-4.5, GLM-4.6, GLM-4.7 | `zai-org/GLM-4.5`, etc. | ✅︎ | ✅︎ |
@@ -418,6 +421,7 @@ th {
 | `Grok1ForCausalLM` | Grok2 | `xai-org/grok-2` | ✅︎ | ✅︎ |
 | `HunYuanDenseV1ForCausalLM` | Hunyuan Dense | `tencent/Hunyuan-7B-Instruct` | ✅︎ | ✅︎ |
 | `HunYuanMoEV1ForCausalLM` | Hunyuan-A13B | `tencent/Hunyuan-A13B-Instruct`, `tencent/Hunyuan-A13B-Pretrain`, `tencent/Hunyuan-A13B-Instruct-FP8`, etc. | ✅︎ | ✅︎ |
+| `HYV3ForCausalLM` | HY3 | `tencent/Hy3-preview-Base`, `tencent/Hy3-preview` | ✅︎ | ✅︎ |
 | `HyperCLOVAXForCausalLM` | HyperCLOVAX-SEED-Think-14B | `naver-hyperclovax/HyperCLOVAX-SEED-Think-14B` | ✅︎ | ✅︎ |
 | `InternLMForCausalLM` | InternLM | `internlm/internlm-7b`, `internlm/internlm-chat-7b`, etc. | ✅︎ | ✅︎ |
 | `InternLM2ForCausalLM` | InternLM2 | `internlm/internlm2-7b`, `internlm/internlm2-chat-7b`, etc. | ✅︎ | ✅︎ |
@@ -436,6 +440,7 @@ th {
 | `Mamba2ForCausalLM` | Mamba2 | `mistralai/Mamba-Codestral-7B-v0.1`, etc. | | ✅︎ |
 | `MiMoForCausalLM` | MiMo | `XiaomiMiMo/MiMo-7B-RL`, etc. | ✅︎ | ✅︎ |
 | `MiMoV2FlashForCausalLM` | MiMoV2Flash | `XiaomiMiMo/MiMo-V2-Flash`, etc. | | ✅︎ |
+| `MiMoV2ForCausalLM` | MiMoV2Pro | `XiaomiMiMo/MiMo-V2.5-Pro`, etc. | | ✅︎ |
 | `MiniCPMForCausalLM` | MiniCPM | `openbmb/MiniCPM-2B-sft-bf16`, `openbmb/MiniCPM-2B-dpo-bf16`, `openbmb/MiniCPM-S-1B-sft`, etc. | ✅︎ | ✅︎ |
 | `MiniCPM3ForCausalLM` | MiniCPM3 | `openbmb/MiniCPM3-4B`, etc. | ✅︎ | ✅︎ |
 | `MiniMaxForCausalLM` | MiniMax-Text | `MiniMaxAI/MiniMax-Text-01-hf`, etc. | | |
@@ -471,6 +476,7 @@ th {
 | `Qwen3MoeForCausalLM` | Qwen3MoE | `Qwen/Qwen3-30B-A3B`, etc. | ✅︎ | ✅︎ |
 | `Qwen3NextForCausalLM` | Qwen3NextMoE | `Qwen/Qwen3-Next-80B-A3B-Instruct`, etc. | ✅︎ | ✅︎ |
 | `RWForCausalLM` | Falcon RW | `tiiuae/falcon-40b`, etc. | | ✅︎ |
+| `Rnj1ForCausalLM` | Rnj1 | `EssentialAI/rnj-1-instruct`, etc. | | |
 | `SarvamMoEForCausalLM` | Sarvam 2 | `sarvamai/sarvam2-30b-a3b`, etc. | ✅︎ | ✅︎ |
 | `SarvamMLAForCausalLM` | Sarvam 2 | `sarvamai/sarvam2-105b-a9b`, etc. | | ✅︎ |
 | `SeedOssForCausalLM` | SeedOss | `ByteDance-Seed/Seed-OSS-36B-Instruct`, etc. | ✅︎ | ✅︎ |
@@ -554,10 +560,12 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `FuyuForCausalLM` | Fuyu | T + I | `adept/fuyu-8b`, etc. | | ✅︎ |
 | `Gemma3ForConditionalGeneration` | Gemma 3 | T + I<sup>E+</sup> | `google/gemma-3-4b-it`, `google/gemma-3-27b-it`, etc. | ✅︎ | ✅︎ |
 | `Gemma3nForConditionalGeneration` | Gemma 3n | T + I + A | `google/gemma-3n-E2B-it`, `google/gemma-3n-E4B-it`, etc. | | |
+| `Gemma4ForConditionalGeneration` | Gemma 4 | T + I<sup>+</sup> + V + A<sup>*</sup> | `google/gemma-4-E2B-it`, etc. | | ✅︎ |
 | `GLM4VForCausalLM`<sup>^</sup> | GLM-4V | T + I | `zai-org/glm-4v-9b`, `zai-org/cogagent-9b-20241220`, etc. | ✅︎ | ✅︎ |
 | `Glm4vForConditionalGeneration` | GLM-4.1V-Thinking | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.1V-9B-Thinking`, etc. | ✅︎ | ✅︎ |
 | `Glm4vMoeForConditionalGeneration` | GLM-4.5V | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.5V`, etc. | ✅︎ | ✅︎ |
 | `GlmOcrForConditionalGeneration` | GLM-OCR | T + I<sup>E+</sup> | `zai-org/GLM-OCR`, etc. | ✅︎ | ✅︎ |
+| `Granite4VisionForConditionalGeneration` | Granite 4 Vision | T + I<sup>E+</sup> | `ibm-granite/granite-4.1-3b-vision`, etc. | ✅︎ | ✅︎ |
 | `GraniteSpeechForConditionalGeneration` | Granite Speech | T + A | `ibm-granite/granite-speech-3.3-8b` | ✅︎ | ✅︎ |
 | `HCXVisionForCausalLM` | HyperCLOVAX-SEED-Vision-Instruct-3B | T + I<sup>+</sup> + V<sup>+</sup> | `naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B` | | |
 | `HCXVisionV2ForCausalLM` | HyperCLOVAX-SEED-Think-32B | T + I<sup>+</sup> + V<sup>+</sup> | `naver-hyperclovax/HyperCLOVAX-SEED-Think-32B` | | |
@@ -584,12 +592,14 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `LlavaNextVideoForConditionalGeneration` | LLaVA-NeXT-Video | T + V | `llava-hf/LLaVA-NeXT-Video-7B-hf`, etc. | | ✅︎ |
 | `LlavaOnevisionForConditionalGeneration` | LLaVA-Onevision | T + I<sup>+</sup> + V<sup>+</sup> | `llava-hf/llava-onevision-qwen2-7b-ov-hf`, `llava-hf/llava-onevision-qwen2-0.5b-ov-hf`, etc. | | ✅︎ |
 | `MiDashengLMModel` | MiDashengLM | T + A<sup>+</sup> | `mispeech/midashenglm-7b` | | ✅︎ |
+| `MiMoV2OmniForCausalLM` | MiMo-V2.5-Omni | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup> | `XiaomiMiMo/MiMo-V2.5-Omni` | | ✅︎ |
 | `MiniCPMO` | MiniCPM-O | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>E+</sup> | `openbmb/MiniCPM-o-2_6`, etc. | ✅︎ | ✅︎ |
 | `MiniCPMV` | MiniCPM-V | T + I<sup>E+</sup> + V<sup>E+</sup> | `openbmb/MiniCPM-V-2` (see note), `openbmb/MiniCPM-Llama3-V-2_5`, `openbmb/MiniCPM-V-2_6`, `openbmb/MiniCPM-V-4`, `openbmb/MiniCPM-V-4_5`, etc. | ✅︎ | |
 | `MiniMaxVL01ForConditionalGeneration` | MiniMax-VL | T + I<sup>E+</sup> | `MiniMaxAI/MiniMax-VL-01`, etc. | | ✅︎ |
 | `Mistral3ForConditionalGeneration` | Mistral3 (HF Transformers) | T + I<sup>+</sup> | `mistralai/Mistral-Small-3.1-24B-Instruct-2503`, etc. | ✅︎ | ✅︎ |
 | `MolmoForCausalLM` | Molmo | T + I<sup>+</sup> | `allenai/Molmo-7B-D-0924`, `allenai/Molmo-7B-O-0924`, etc. | ✅︎ | ✅︎ |
 | `Molmo2ForConditionalGeneration` | Molmo2 | T + I<sup>+</sup> / V | `allenai/Molmo2-4B`, `allenai/Molmo2-8B`, `allenai/Molmo2-O-7B` | ✅︎ | ✅︎ |
+| `Moondream3ForCausalLM` | Moondream3 | T + I | `moondream/moondream3-preview` | | ✅︎ |
 | `MusicFlamingoForConditionalGeneration` | MusicFlamingo | T + A | `nvidia/music-flamingo-2601-hf`, `nvidia/music-flamingo-think-2601-hf` | ✅︎ | ✅︎ |
 | `NVLM_D_Model` | NVLM-D 1.0 | T + I<sup>+</sup> | `nvidia/NVLM-D-72B`, etc. | | ✅︎ |
 | `OpenCUAForConditionalGeneration` | OpenCUA-7B | T + I<sup>E+</sup> | `xlangai/OpenCUA-7B` | ✅︎ | ✅︎ |
@@ -604,6 +614,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `Phi4MMForCausalLM` | Phi-4-multimodal | T + I<sup>+</sup> / T + A<sup>+</sup> / I<sup>+</sup> + A<sup>+</sup> | `microsoft/Phi-4-multimodal-instruct`, etc. | ✅︎ | ✅︎ |
 | `Phi4ForCausalLMV` | Phi-4-reasoning-vision | T + I<sup>+</sup> | `microsoft/Phi-4-reasoning-vision-15B`, etc. | | ✅︎ |
 | `PixtralForConditionalGeneration` | Ministral 3 (Mistral format), Mistral 3 (Mistral format), Mistral Large 3 (Mistral format), Pixtral (Mistral format) | T + I<sup>+</sup> | `mistralai/Ministral-3-3B-Instruct-2512`, `mistralai/Mistral-Small-3.1-24B-Instruct-2503`, `mistralai/Mistral-Large-3-675B-Instruct-2512` `mistralai/Pixtral-12B-2409` etc. | ✅︎ | ✅︎ |
+| `QianfanOCRForConditionalGeneration` | QianfanOCR | T + I<sup>E+</sup> | `baidu/Qianfan-OCR`, etc. | ✅︎ | ✅︎ |
 | `QwenVLForConditionalGeneration`<sup>^</sup> | Qwen-VL | T + I<sup>E+</sup> | `Qwen/Qwen-VL`, `Qwen/Qwen-VL-Chat`, etc. | ✅︎ | ✅︎ |
 | `Qwen2AudioForConditionalGeneration` | Qwen2-Audio | T + A<sup>+</sup> | `Qwen/Qwen2-Audio-7B-Instruct` | | ✅︎ |
 | `Qwen2VLForConditionalGeneration` | QVQ, Qwen2-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/QVQ-72B-Preview`, `Qwen/Qwen2-VL-7B-Instruct`, `Qwen/Qwen2-VL-72B-Instruct`, etc. | ✅︎ | ✅︎ |
@@ -633,19 +644,34 @@ Some models are supported only via the [Transformers modeling backend](#transfor
 <sup>^</sup> You need to set the architecture name via `--hf-overrides` to match the one in vLLM.</br>
 <sup>E</sup> Pre-computed embeddings can be inputted for this modality.</br>
 <sup>+</sup> Multiple items can be inputted per text prompt for this modality.
+<sup>*</sup> Only specific variants of the model support this modality (see notes below).</br>
 
 !!! note
     `Gemma3nForConditionalGeneration` is only supported on V1 due to shared KV caching and it depends on `timm>=1.0.17` to make use of its
     MobileNet-v5 vision backbone.
-  
+
     Performance is not yet fully optimized mainly due to:
-  
-    - Both audio and vision MM encoders use `transformers.AutoModel` implementation.  
+
+    - Both audio and vision MM encoders use `transformers.AutoModel` implementation.
     - There's no PLE caching or out-of-memory swapping support, as described in [Google's blog](https://developers.googleblog.com/en/introducing-gemma-3n/). These features might be too model-specific for vLLM, and swapping in particular may be better suited for constrained setups.
 
+!!! note
+    For `Gemma4ForConditionalGeneration`:
+    - audio input is only supported by the `gemma-4-E2B` and `gemma-4-E4B` variants.
+    - The model does not ingest videos directly. However, vLLM’s Gemma 4 implementation supports video inputs by handling video processing internally. Users can send videos directly in the message structure to vLLM, where they are converted into text and image frames before being passed to the model.
+    - Gemma 4 assistant checkpoints for speculative decoding use vLLM's Gemma
+      4 MTP path, not generic draft-model speculative decoding. See the
+      [Gemma 4 assistant model MTP example](../features/speculative_decoding/mtp.md#gemma-4-assistant-models).
+
 !!! note
     For `InternVLChatModel`, only InternVL2.5 with Qwen2.5 text backbone (`OpenGVLab/InternVL2.5-1B` etc.), InternVL3 and InternVL3.5 have video inputs support currently.
 
+!!! note
+    `Moondream3ForCausalLM` uses task-specific prompt templates for `query`
+    and `caption`. The native `detect` and `point` skills require custom
+    coordinate decoding and are not exposed by this vLLM implementation.
+    See [Moondream3 prompt recipes](../features/multimodal_inputs.md#moondream3-prompt-recipes).
+
 !!! note
     To use `TIGER-Lab/Mantis-8B-siglip-llama3`, you have to pass `--hf_overrides '{"architectures": ["MantisForConditionalGeneration"]}'` when running vLLM.
 
@@ -674,6 +700,24 @@ Speech2Text models trained specifically for Automatic Speech Recognition.
 !!! note
     `VoxtralForConditionalGeneration` requires `mistral-common[audio]` to be installed.
 
+#### Realtime Transcription
+
+Speech models that support streaming transcription via the
+[`/v1/realtime`](../serving/openai_compatible_server.md#realtime-api)
+WebSocket endpoint.
+
+| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
+| ------------ | ------ | ----------------- | -------------------- | ------------------------- |
+| `VoxtralRealtimeGeneration` | Voxtral Realtime | `mistralai/Voxtral-Mini-4B-Realtime-2602` | | |
+| `Qwen3ASRRealtimeGeneration` | Qwen3-ASR Realtime | `Qwen/Qwen3-ASR-0.6B` | | |
+
+!!! note
+    `VoxtralRealtimeGeneration` requires `mistral-common[audio]` to be installed, and must be served with `--tokenizer-mode mistral`.
+
+    `Qwen3ASRRealtimeGeneration` is not auto-detected from `config.json`.
+    You must pass `--hf-overrides '{"architectures":["Qwen3ASRRealtimeGeneration"]}'`
+    when serving.
+
 ## Pooling Models
 
 See [this page](pooling_models/README.md) for more information on how to use pooling models.
diff --git a/docs/serving/data_parallel_deployment.md b/docs/serving/data_parallel_deployment.md
index f0946eaf407a..1f18b92f95b4 100644
--- a/docs/serving/data_parallel_deployment.md
+++ b/docs/serving/data_parallel_deployment.md
@@ -16,7 +16,7 @@ For MoE models, when any requests are in progress in any rank, we must ensure th
 
 In all cases, it is beneficial to load-balance requests between DP ranks. For online deployments, this balancing can be optimized by taking into account the state of each DP engine - in particular its currently scheduled and waiting (queued) requests, and KV cache state. Each DP engine has an independent KV cache, and the benefit of prefix caching can be maximized by directing prompts intelligently.
 
-This document focuses on online deployments (with the API server). DP + EP is also supported for offline usage (via the LLM class), for an example see [examples/offline_inference/data_parallel.py](../../examples/offline_inference/data_parallel.py).
+This document focuses on online deployments (with the API server). DP + EP is also supported for offline usage (via the LLM class), for an example see [examples/features/data_parallel/data_parallel_offline.py](../../examples/features/data_parallel/data_parallel_offline.py).
 
 There are two distinct modes supported for online deployments - self-contained with internal load balancing, or externally per-rank process deployment and load balancing.
 
@@ -98,7 +98,7 @@ For larger scale deployments especially, it can make sense to handle the orchest
 
 In this case, it's more convenient to treat each DP rank like a separate vLLM deployment, with its own endpoint, and have an external router balance HTTP requests between them, making use of appropriate real-time telemetry from each server for routing decisions.
 
-This can already be done trivially for non-MoE models, since each deployed server is fully independent. No data parallel CLI options need to be used for this.
+This can already be done trivially for non-MoE models, since each deployed server is fully independent. In that case, launch independent vLLM instances without any `--data-parallel-*` arguments; external DP CLI options are only supported for MoE deployments.
 
 We support an equivalent topology for MoE DP+EP which can be configured via the following CLI arguments.
 
diff --git a/docs/serving/distributed_troubleshooting.md b/docs/serving/distributed_troubleshooting.md
index b5354a7e55d5..e6dde4944284 100644
--- a/docs/serving/distributed_troubleshooting.md
+++ b/docs/serving/distributed_troubleshooting.md
@@ -4,11 +4,11 @@ For general troubleshooting, see [Troubleshooting](../usage/troubleshooting.md).
 
 ## Verify inter-node GPU communication
 
-After you start the Ray cluster, verify GPU-to-GPU communication across nodes. Proper configuration can be non-trivial. For more information, see [troubleshooting script](../usage/troubleshooting.md#incorrect-hardwaredriver). If you need additional environment variables for communication configuration, append them to [examples/online_serving/run_cluster.sh](../../examples/online_serving/run_cluster.sh), for example `-e NCCL_SOCKET_IFNAME=eth0`. Setting environment variables during cluster creation is recommended because the variables propagate to all nodes. In contrast, setting environment variables in the shell affects only the local node. For more information, see <https://github.com/vllm-project/vllm/issues/6803>.
+After you start the Ray cluster, verify GPU-to-GPU communication across nodes. Proper configuration can be non-trivial. For more information, see [troubleshooting script](../usage/troubleshooting.md#incorrect-hardwaredriver). If you need additional environment variables for communication configuration, append them to [examples/ray_serving/run_cluster.sh](../../examples/ray_serving/run_cluster.sh), for example `-e NCCL_SOCKET_IFNAME=eth0`. Setting environment variables during cluster creation is recommended because the variables propagate to all nodes. In contrast, setting environment variables in the shell affects only the local node. For more information, see <https://github.com/vllm-project/vllm/issues/6803>.
 
 ## No available node types can fulfill resource request
 
-The error message `Error: No available node types can fulfill resource request` can appear even when the cluster has enough GPUs. The issue often occurs when nodes have multiple IP addresses and vLLM can't select the correct one. Ensure that vLLM and Ray use the same IP address by setting `VLLM_HOST_IP` in [examples/online_serving/run_cluster.sh](../../examples/online_serving/run_cluster.sh) (with a different value on each node). Use `ray status` and `ray list nodes` to verify the chosen IP address. For more information, see <https://github.com/vllm-project/vllm/issues/7815>.
+The error message `Error: No available node types can fulfill resource request` can appear even when the cluster has enough GPUs. The issue often occurs when nodes have multiple IP addresses and vLLM can't select the correct one. Ensure that vLLM and Ray use the same IP address by setting `VLLM_HOST_IP` in [examples/ray_serving/run_cluster.sh](../../examples/ray_serving/run_cluster.sh) (with a different value on each node). Use `ray status` and `ray list nodes` to verify the chosen IP address. For more information, see <https://github.com/vllm-project/vllm/issues/7815>.
 
 ## Ray observability
 
diff --git a/docs/serving/expert_parallel_deployment.md b/docs/serving/expert_parallel_deployment.md
index d75ae7feb49e..fef4df770fa3 100644
--- a/docs/serving/expert_parallel_deployment.md
+++ b/docs/serving/expert_parallel_deployment.md
@@ -153,6 +153,7 @@ Configure EPLB with the `--eplb-config` argument, which accepts a JSON string. T
 | `num_redundant_experts` | Additional global experts per EP rank beyond equal distribution | `0` |
 | `use_async` | Use non-blocking EPLB for reduced latency overhead | `false` |
 | `policy` | The policy type for expert parallel load balancing | `"default"` |
+| `communicator` | Backend for expert weight transfers: `"torch_nccl"`, `"torch_gloo"`, `"pynccl"`, `"nixl"`,  or `null` (auto) | `null` |
 
 For example:
 
diff --git a/docs/serving/integrations/codex.md b/docs/serving/integrations/codex.md
new file mode 100644
index 000000000000..48148acfd51f
--- /dev/null
+++ b/docs/serving/integrations/codex.md
@@ -0,0 +1,88 @@
+# Codex
+
+[Codex](https://github.com/openai/codex) is OpenAI's official agentic coding tool that lives in your terminal. It can understand your codebase, edit files, run commands, and help you write code more efficiently.
+
+By pointing Codex at a vLLM server, you can use your own models as the backend instead of the OpenAI API. This is useful for:
+
+- Running fully local/private coding assistance
+- Using open-weight models with tool calling capabilities
+- Testing and developing with custom models
+
+## How It Works
+
+vLLM implements the OpenAI-Responses API, which is the same API that Codex uses to communicate with OpenAI's servers. By configuring Codex to point at your vLLM server, Codex sends its requests to vLLM instead of OpenAI. vLLM then translates these requests to work with your local model and returns responses in the format Codex expects.
+
+This means any model served by vLLM with proper tool calling support can act as a drop-in replacement for OpenAI models in Codex.
+
+## Requirements
+
+Codex requires a model with strong tool calling capabilities. The model must support the OpenAI-Responses tool calling API. See [Tool Calling](../../features/tool_calling.md) for details on enabling tool calling for your model.
+
+## Installation
+
+First, install Codex by following the [official installation guide](https://github.com/openai/codex).
+
+## Starting the vLLM Server
+
+Start vLLM with a tool-calling capable model - here's an example using `Qwen/Qwen3-27B`:
+
+```bash
+vllm serve Qwen/Qwen3.6-27B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
+
+```
+
+For other models, you'll need to enable tool calling explicitly with `--enable-auto-tool-choice` and the right `--tool-call-parser`. Refer to the [Tool Calling documentation](../../features/tool_calling.md) for the correct flags for your model.
+
+## Configuring Codex
+
+Codex is configured via a TOML file located at `~/.codex/config.toml`. Create or edit this file to point Codex at your vLLM server:
+
+```toml
+model = "my-model"
+model_provider = "vllm"
+
+[model_providers.vllm]
+name = "vLLM"
+env_key = "VLLM_API_KEY"
+base_url = "http://localhost:8000/v1"
+wire_api = "responses"
+```
+
+The configuration fields:
+
+| Field | Description |
+| ----- | ----------- |
+| `model` | The model name to use. Must match the `--served-model-name` you passed to vLLM. |
+| `model_provider` | Set to `"vllm"` to use your local vLLM server. |
+| `[model_providers.vllm]` | Configuration section for the vLLM provider. |
+| `name` | A display name for your vLLM provider. |
+| `env_key` | The name of an environment variable that Codex will read for the API key. vLLM does not require authentication by default, so this can be any value. |
+| `base_url` | The URL of your vLLM server's OpenAI-compatible API endpoint (default is `http://localhost:8000/v1`). |
+| `wire_api` | The API style to use. Set to `"responses"` for the OpenAI Responses API |
+
+!!! tip
+    You can set the `env_key` to any dummy environment variable since vLLM doesn't require authentication by default:
+    ```bash
+    export VLLM_API_KEY=dummy
+    ```
+
+!!! warning
+    When using the `responses` API, ensure your vLLM version supports the OpenAI Responses API.
+
+## Testing the Setup
+
+Once Codex is configured, launch it in your project directory:
+
+```bash
+codex
+```
+
+Try a simple prompt to verify the connection, such as asking it to explain a file in your project. If the model responds correctly, your setup is working. You can now use Codex with your vLLM-served model for coding tasks.
+
+## Troubleshooting
+
+**Connection refused**: Ensure vLLM is running and accessible at the specified URL. Check that the port matches and that `base_url` includes the `/v1` path suffix.
+
+**Tool calls not working**: Verify that your model supports tool calling and that you've enabled it with the correct `--tool-call-parser` flag. See [Tool Calling](../../features/tool_calling.md).
+
+**Model not found**: Ensure the `model` field in `~/.codex/config.toml` matches the `--served-model-name` you passed to vLLM.
diff --git a/docs/serving/openai_compatible_server.md b/docs/serving/openai_compatible_server.md
index 28e643ea5f07..973c2ecdcb52 100644
--- a/docs/serving/openai_compatible_server.md
+++ b/docs/serving/openai_compatible_server.md
@@ -60,7 +60,7 @@ We currently support the following OpenAI APIs:
 - [Translation API](#translations-api) (`/v1/audio/translations`)
     - Only applicable to [Automatic Speech Recognition (ASR) models](../models/supported_models.md#transcription).
 - [Realtime API](#realtime-api) (`/v1/realtime`)
-    - Only applicable to [Automatic Speech Recognition (ASR) models](../models/supported_models.md#transcription).
+    - Only applicable to [Automatic Speech Recognition (ASR) models](../models/supported_models.md#realtime-transcription).
 
 In addition, we have the following custom APIs:
 
@@ -251,7 +251,7 @@ The following extra parameters are supported:
 Our Responses API is compatible with [OpenAI's Responses API](https://platform.openai.com/docs/api-reference/responses);
 you can use the [official OpenAI Python client](https://github.com/openai/openai-python) to interact with it.
 
-Code example: [examples/online_serving/openai_responses_client_with_tools.py](../../examples/online_serving/openai_responses_client_with_tools.py)
+Code example: [examples/tool_calling/openai_responses_client_with_tools.py](../../examples/tool_calling/openai_responses_client_with_tools.py)
 
 #### Extra parameters
 
@@ -279,7 +279,7 @@ you can use the [official OpenAI Python client](https://github.com/openai/openai
 !!! note
     To use the Transcriptions API, please install with extra audio dependencies using `pip install vllm[audio]`.
 
-Code example: [examples/online_serving/openai_transcription_client.py](../../examples/online_serving/openai_transcription_client.py)
+Code example: [examples/speech_to_text/openai/openai_transcription_client.py](../../examples/speech_to_text/openai/openai_transcription_client.py)
 
 NOTE: beam search is currently supported in the transcriptions endpoint for encoder-decoder multimodal models, e.g., whisper, but highly inefficient as work for handling the encoder/decoder cache is actively ongoing. This is an active point of ongoing optimization and will be handled properly in the very near future.
 
@@ -397,7 +397,7 @@ Please mind that the popular `openai/whisper-large-v3-turbo` model does not supp
 !!! note
     To use the Translation API, please install with extra audio dependencies using `pip install vllm[audio]`.
 
-Code example: [examples/online_serving/openai_translation_client.py](../../examples/online_serving/openai_translation_client.py)
+Code example: [examples/speech_to_text/openai/openai_translation_client.py](../../examples/speech_to_text/openai/openai_translation_client.py)
 
 #### Extra Parameters
 
@@ -456,8 +456,8 @@ Audio must be sent as base64-encoded PCM16 audio at 16kHz sample rate, mono chan
 
 #### Example Clients
 
-- [openai_realtime_client.py](https://github.com/vllm-project/vllm/tree/main/examples/online_serving/openai_realtime_client.py) - Upload and transcribe an audio file
-- [openai_realtime_microphone_client.py](https://github.com/vllm-project/vllm/tree/main/examples/online_serving/openai_realtime_microphone_client.py) - Gradio demo for live microphone transcription
+- [openai_realtime_client.py](https://github.com/vllm-project/vllm/tree/main/examples/speech_to_text/realtime/openai_realtime_client.py) - Upload and transcribe an audio file
+- [openai_realtime_microphone_client.py](https://github.com/vllm-project/vllm/tree/main/examples/speech_to_text/realtime/openai_realtime_microphone_client.py) - Gradio demo for live microphone transcription
 
 ### Tokenizer API
 
@@ -467,28 +467,11 @@ It consists of two endpoints:
 - `/tokenize` corresponds to calling `tokenizer.encode()`.
 - `/detokenize` corresponds to calling `tokenizer.decode()`.
 
-### Score API
-
-#### Score Template
-
-Some scoring models require a specific prompt format to work correctly. You can specify a custom score template using the `--chat-template` parameter (see [Chat Template](#chat-template)).
-
-Score templates are supported for **cross-encoder** models only. If you are using an **embedding** model for scoring, vLLM does not apply a score template.
-
-Like chat templates, the score template receives a `messages` list. For scoring, each message has a `role` attribute—either `"query"` or `"document"`. For the usual kind of point-wise cross-encoder, you can expect exactly two messages: one query and one document. To access the query and document content, use Jinja's `selectattr` filter:
-
-- **Query**: `{{ (messages | selectattr("role", "eq", "query") | first).content }}`
-- **Document**: `{{ (messages | selectattr("role", "eq", "document") | first).content }}`
-
-This approach is more robust than index-based access (`messages[0]`, `messages[1]`) because it selects messages by their semantic role. It also avoids assumptions about message ordering if additional message types are added to `messages` in the future.
-
-Example template file: [examples/pooling/score/template/nemotron-rerank.jinja](../../examples/pooling/score/template/nemotron-rerank.jinja)
-
 ### Generative Scoring API
 
 The `/generative_scoring` endpoint uses a CausalLM model (e.g., Llama, Qwen, Mistral) to compute the probability of specified token IDs appearing as the next token. Each item (document) is concatenated with the query to form a prompt, and the model predicts how likely each label token is as the next token after that prompt. This lets you score items against a query — for example, asking "Is this the capital of France?" and scoring each city by how likely the model is to answer "Yes".
 
-This endpoint is automatically available when the server is started with a generative model (task `"generate"`). It is separate from the pooling-based [Score API](#score-api), which uses cross-encoder, bi-encoder, or late-interaction models.
+This endpoint is automatically available when the server is started with a generative model (task `"generate"`). It is separate from the pooling-based [Score API](../models/pooling_models/scoring.md#score-api), which uses cross-encoder, bi-encoder, or late-interaction models.
 
 **Requirements:**
 
@@ -559,6 +542,6 @@ Key capabilities:
 - Scales from a single GPU to a multi-node cluster without code changes.
 - Provides observability and autoscaling policies through Ray dashboards and metrics.
 
-The following example shows how to deploy a large model like DeepSeek R1 with Ray Serve LLM: [examples/online_serving/ray_serve_deepseek.py](../../examples/online_serving/ray_serve_deepseek.py).
+The following example shows how to deploy a large model like DeepSeek R1 with Ray Serve LLM: [examples/ray_serving/ray_serve_deepseek.py](../../examples/ray_serving/ray_serve_deepseek.py).
 
 Learn more about Ray Serve LLM with the official [Ray Serve LLM documentation](https://docs.ray.io/en/latest/serve/llm/index.html).
diff --git a/docs/serving/parallelism_scaling.md b/docs/serving/parallelism_scaling.md
index b69ca17e8334..0f86a256727c 100644
--- a/docs/serving/parallelism_scaling.md
+++ b/docs/serving/parallelism_scaling.md
@@ -78,7 +78,7 @@ For details, see the [Ray documentation](https://docs.ray.io/en/latest/index.htm
 
 ### Ray cluster setup with containers
 
-The helper script [examples/online_serving/run_cluster.sh](../../examples/online_serving/run_cluster.sh) starts containers across nodes and initializes Ray. By default, the script runs Docker without administrative privileges, which prevents access to the GPU performance counters when profiling or tracing. To enable admin privileges, add the `--cap-add=CAP_SYS_ADMIN` flag to the Docker command.
+The helper script [examples/ray_serving/run_cluster.sh](../../examples/ray_serving/run_cluster.sh) starts containers across nodes and initializes Ray. By default, the script runs Docker without administrative privileges, which prevents access to the GPU performance counters when profiling or tracing. To enable admin privileges, add the `--cap-add=CAP_SYS_ADMIN` flag to the Docker command.
 
 Choose one node as the head node and run:
 
@@ -162,7 +162,7 @@ vllm serve /path/to/the/model/in/the/container \
 
 Efficient tensor parallelism requires fast internode communication, preferably through high-speed network adapters such as InfiniBand.
 To set up the cluster to use InfiniBand, append additional arguments like `--privileged -e NCCL_IB_HCA=mlx5` to the
-[examples/online_serving/run_cluster.sh](../../examples/online_serving/run_cluster.sh) helper script.
+[examples/ray_serving/run_cluster.sh](../../examples/ray_serving/run_cluster.sh) helper script.
 Contact your system administrator for more information about the required flags.
 
 ## Enabling GPUDirect RDMA
diff --git a/docs/training/async_rl.md b/docs/training/async_rl.md
index 172466f89039..d3be23fe698d 100644
--- a/docs/training/async_rl.md
+++ b/docs/training/async_rl.md
@@ -60,4 +60,4 @@ The key insight is that requests paused with `mode="keep"` will produce tokens f
 
 ## Example
 
-The [async RLHF example](../examples/rl/rlhf_async_new_apis.md) demonstrates this pattern with `vllm.AsyncLLMEngine`, NCCL weight transfer, and mid-flight pause/resume with validation.
+The [async RLHF example](../../examples/rl/rlhf_async_new_apis.py) demonstrates this pattern with `vllm.AsyncLLMEngine`, NCCL weight transfer, and mid-flight pause/resume with validation.
diff --git a/docs/training/layerwise.md b/docs/training/layerwise.md
new file mode 100644
index 000000000000..d304c4a8425d
--- /dev/null
+++ b/docs/training/layerwise.md
@@ -0,0 +1,146 @@
+# What is Layerwise (Re)loading?
+
+Layerwise reloading is the system used to handle the loading of new weight data into existing weight data destinations without triggering recompilation of the cuda graph and other runtime artifacts. This system is used to enable [QeRL](https://arxiv.org/pdf/2510.11696)-style post training flows, where full-precision trainer weights are quantized and loaded into a target vLLM instance for fast, high-exploration rollouts. The core implementation can be found in [layerwise.py](../../vllm/model_executor/model_loader/reload/layerwise.py).
+
+![Layerwise](../assets/training/layerwise.png)
+
+## Layerwise Reloading for QeRL
+
+In order to load new weights into existing weight data destinations, a weight must undergo the following operations:
+
+- Transfer: weights must be transferred from trainer model to target node/device
+- Fuse: weight partitions must be fused, for example qkv/gate_up
+- Process: this typically means online quantization and kernel-specific padding or striding
+- Shard: weights must be sharded according to the selected parallelism strategy
+- Copy: weights must be copied into the existing weight data destinations
+
+Layerwise reloading achieves this using the following steps:
+
+1. Weights are **transferred** from the trainer to the target (see [weight_transfer](weight_transfer/README.md))
+2. Weights loaded via `model.load_weights`, during which they are **sharded** and **fused**
+3. Weights are **processed** in an online fashion as soon as all of a layer's weights are loaded
+4. Weights are **copied** into the existing weight data destinations
+
+For more information on implementation, see [Low Level `layerwise` API](#low-level-layerwise-api).
+
+## Layerwise Loading with Online Quantization
+
+Online quantization refers to when a user provides full precision weights and those weights are quantized on-the-fly as they are loaded into the model. The layerwise reloading system handles this by treating online quantization as a **processing** step, which is then handled in an online way both during first-time load and during reload. A typical online quantization method implementation should look like this:
+
+```python
+class Fp8OnlineLinearMethod(Fp8LinearMethod):
+    """Online version of Fp8LinearMethod which loads a full precision checkpoint
+    and quantizes weights during loading."""
+
+    uses_meta_device: bool = True
+
+    def create_weights(self, layer: torch.nn.Module, ...):
+        # weight is materialized and processed during loading
+        layer.weight = ModelWeightParameter(
+            data=torch.empty(..., device="meta"),
+            weight_loader=weight_loader,
+        )
+
+        # set up online processing
+        initialize_online_processing(layer)
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+        if getattr(layer, "_already_called_process_weights_after_loading", False):
+            return
+
+        layer.weight, layer.weight_scale = ops.scaled_fp8_quant(layer.weight)
+
+        # Prevent duplicate processing (e.g., during weight reload)
+        layer._already_called_process_weights_after_loading = True
+```
+
+## Example Usages
+
+### High Level Weight Transfer API
+
+The layerwise reloading system is integrated with the post-training weight transfer system. To use layerwise reloading in conjunction to the weight transfer system, follow the examples found [here](../../examples/rl/). Layerwise reloading is controlled by the `WeightTransferUpdateInfo.is_checkpoint_format` flag and is set to `True` by default.
+
+### Mid Level `reload_weights` API
+
+Layerwise reloading is also exposed via the `reload_weights` API. This interface can be called using the following code:
+
+```python
+from vllm import LLM
+
+llm = LLM("Qwen/Qwen3-0.6B")
+llm.collective_rpc("reload_weights")
+```
+
+This interface also allows specifying a `weights_path` which can be used to select a checkpoint path to load from:
+
+```python
+from vllm import LLM
+
+# fine tuned model checkpoints for testing
+mul_path = "inference-optimization/Qwen3-0.6B-debug-multiply"
+add_path = "inference-optimization/Qwen3-0.6B-debug-add"
+
+llm = LLM("Qwen/Qwen3-0.6B")
+llm.collective_rpc("reload_weights", kwargs={"weights_path": mul_path})
+llm.generate("3 4 = ")  # 12
+
+llm.collective_rpc("reload_weights", kwargs={"weights_path": add_path})
+llm.generate("3 4 = ")  # 7
+```
+
+Finally, a `weights_iterator` can be provided directly. This iterator can be lazy or eagerly defined.
+
+```python
+from vllm import LLM
+
+weights_iterator = [("q_proj", ...), ("k_proj", ...), ...]
+
+llm = LLM("Qwen/Qwen3-0.6B")
+llm.collective_rpc("reload_weights", kwargs={"weights_iterator": weights_iterator})
+```
+
+### Low Level `layerwise` API
+
+[layerwise.py](../../vllm/model_executor/model_loader/reload/layerwise.py) Implements the following functions to execute its lifecycle:
+
+| Function | Purpose | Quantized Reload | Online Quantization |
+| - | - | - | - |
+| `record_metadata_for_reloading` | Record tensor metadata so that layers can be restored on the meta device | Called by `BaseModelLoader` | Called by `BaseModelLoader` |
+| `restore_layer_on_meta` | Restore layer to model format at start of reload | Called by `initialize_layerwise_reload` | Not called. Online quantized weights already start on meta device via `...OnlineLinearMethod.create_weights` |
+| `initialize_online_processing` | Wrap weight loaders with the `online_process_loader` wrapper, which buffers weights until all layer weights have been loaded | Called by `initialize_layerwise_reload` | Called by `...OnlineLinearMethod.create_weights` |
+| `_layerwise_process` | Process layer once all weights are loaded | Called by `online_process_loader` during loading | Called by `online_process_loader` during loading |
+| `_copy_and_restore_kernel_tensors` | Copy processed weights into original tensor locations to affect compiled cuda graphs, etc. | Called by `_layerwise_process` after `process_weights_after_loading` | Not called. There is no compiled cuda graph yet |
+| `finalize_layerwise_processing` | Catch any layers which did not load all weights (for example attention weights or weights with padding) | Called by `BaseModelLoader` | Called by `BaseModelLoader` |
+
+You can plug into this lifecycle directly by calling the `initialize_layerwise_reload`, loading weights, then calling `finalize_layerwise_processing`:
+
+```python
+from vllm import LLM
+from vllm.model_executor.model_loader.reload import initialize_layerwise_reload, finalize_layerwise_processing
+
+llm = LLM("Qwen/Qwen3-0.6B")
+
+# this model path requires `VLLM_ENABLE_V1_MULTIPROCESSING=0` and is not stable
+model = llm.llm_engine.engine_core.engine_core.model_executor.driver_worker.worker.get_model()
+
+# layerwise reload
+initialize_layerwise_reload(model)
+model.load_weights(...)
+finalize_layerwise_processing(model, llm.model_config)
+```
+
+## Troubleshooting Excessive Memory Usage
+
+Layerwise reloading allows users to incrementally load and process weights as they are loaded into the model. This system relies on buffering layer weights on device until all weights of a layer have been loaded. However, without offloading, this approach necessarily causes excessive buffering if weights are loaded out of order.
+
+For this reason, users must take care as to the order of weights when they are reloading into the model. Weight should be loaded "in order", meaning that each layer's weights are fully loaded before beginning to load the next layer's weights. "Out of order" loading can cause layer weights to stay buffered while other layer weights are loading, leading to excessive memory usage. In the example below, q_proj, k_proj, v_proj, and up_proj are all buffered at the same time, using more memory than if up_proj was loaded after q_proj, k_proj and v_proj.
+
+| Correct Loading | Incorrect Loading |
+| - | - |
+| ![Layerwise](../assets/training/layerwise_good_loading.png) | ![Layerwise](../assets/training/layerwise_bad_loading.png) |
+
+Users will see a warning like the one below if weights are loaded out-of-order.
+
+```console
+WARNING [layerwise.py:198] Allocating 28.5 MB of device memory to buffers to load ["QKVParallelLinear", "MergedColumnParallelLinear"] layers. This extra memory usage can be avoided by ordering weights by their parent layer when reloading.
+```
diff --git a/docs/training/routed_experts_replay.md b/docs/training/routed_experts_replay.md
new file mode 100644
index 000000000000..60eae33b848e
--- /dev/null
+++ b/docs/training/routed_experts_replay.md
@@ -0,0 +1,289 @@
+# Routed Experts Replay
+
+## Overview
+
+Routed experts replay captures which MoE (Mixture of Experts) experts process each token during inference and returns this information alongside the generated text. This is essential for **reinforcement learning (RL) training pipelines** (such as GRPO and RLHF) where the training step needs to reconstruct expert routing decisions from the inference pass.
+
+When enabled, each API response includes:
+
+- **`prompt_routed_experts`**: A `[prompt_len, num_moe_layers, top_k]` array of expert IDs for the prompt tokens (at the response level, shared across completions).
+- **`routed_experts`**: A `[gen_len, num_moe_layers, top_k]` array of expert IDs for the generated tokens (per completion).
+
+For example, a model with 40 MoE layers and top-22 routing that processes a 100-token prompt and generates 50 tokens would return:
+
+- `prompt_routed_experts`: shape `[100, 40, 22]`
+- `routed_experts`: shape `[50, 40, 22]`
+
+Each value is an int16 expert ID in the range `[0, num_experts)`.
+
+## Quickstart
+
+### OpenAI API Server
+
+```bash
+vllm serve <MODEL> \
+    --enable-return-routed-experts \
+    --tensor-parallel-size 4 \
+    --enable-expert-parallel
+```
+
+Then query the `/v1/completions` endpoint as usual. The response includes routing data:
+
+```python
+import requests
+
+resp = requests.post("http://localhost:8000/v1/completions", json={
+    "model": "<MODEL>",
+    "prompt": "Explain quantum computing.",
+    "max_tokens": 64,
+    "temperature": 0.0,
+}).json()
+
+# Generation routing (per completion choice)
+gen_routing = resp["choices"][0]["routed_experts"]  # [gen_len, layers, top_k]
+
+# Prompt routing (shared across all choices)
+prompt_routing = resp["prompt_routed_experts"]      # [prompt_len, layers, top_k]
+
+print(f"Prompt routing shape: [{len(prompt_routing)}, "
+      f"{len(prompt_routing[0])}, {len(prompt_routing[0][0])}]")
+print(f"Gen routing shape: [{len(gen_routing)}, "
+      f"{len(gen_routing[0])}, {len(gen_routing[0][0])}]")
+```
+
+### Python SDK (Offline Inference)
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(
+    model="<MODEL>",
+    enable_return_routed_experts=True,
+    tensor_parallel_size=4,
+    enable_expert_parallel=True,
+)
+
+outputs = llm.generate(
+    ["Explain quantum computing."],
+    SamplingParams(temperature=0, max_tokens=64),
+)
+
+result = outputs[0]
+
+# Prompt routing: numpy array, shape [prompt_len, num_moe_layers, top_k]
+prompt_routing = result.prompt_routed_experts
+print(f"Prompt routing: {prompt_routing.shape}, dtype={prompt_routing.dtype}")
+
+# Generation routing: numpy array, shape [gen_len, num_moe_layers, top_k]
+gen_routing = result.outputs[0].routed_experts
+print(f"Gen routing: {gen_routing.shape}, dtype={gen_routing.dtype}")
+```
+
+## Output Format
+
+### `CompletionOutput.routed_experts`
+
+- **Type**: `numpy.ndarray` (Python SDK) or `list[list[list[int]]]` (JSON API)
+- **Shape**: `[gen_len, num_moe_layers, top_k]`
+- **Dtype**: `int16`
+- **Content**: Expert IDs for **generated tokens only**. `gen_len` matches the number of generated tokens (i.e., `usage.completion_tokens` or fewer).
+
+### `RequestOutput.prompt_routed_experts`
+
+- **Type**: `numpy.ndarray` (Python SDK) or `list[list[list[int]]]` (JSON API)
+- **Shape**: `[prompt_len, num_moe_layers, top_k]`
+- **Dtype**: `int16`
+- **Content**: Expert IDs for **prompt tokens only**. `prompt_len` matches `usage.prompt_tokens`. This field lives on the request-level response (not per-choice), because prompt routing is shared across all completions when `n > 1`.
+
+### Why Separate Prompt and Generation Routing?
+
+When a request has multiple completions (`n > 1`), each completion shares the same prompt but produces different generated text. Storing prompt routing once on the `RequestOutput` (rather than duplicating it on every `CompletionOutput`) avoids redundant data. For RL training, the consumer typically needs:
+
+1. The prompt routing (once) to reconstruct the forward pass for the shared prefix.
+2. The per-completion generation routing to reconstruct each completion's forward pass.
+
+## Architecture
+
+### Data Flow
+
+```text
+Forward Pass                    Async D2H Pipeline              Output
+─────────────                   ──────────────────              ──────
+FusedMoE layer                  After forward pass:             On request finish:
+writes topk_ids    ──────►     D2H copy to pinned     ──────►  Extract from host cache
+to device buffer               staging buffer                   Split at prompt_len
+(L, N, K) int16                 (via CUDA stream)                Trim gen to output len
+                                Scatter to per-request           Serialize to API response
+                                host cache (numpy)
+```
+
+### Device Cache
+
+A pre-allocated GPU buffer with layout `(L, N, K)` where:
+
+- `L` = number of MoE layers
+- `N` = `max_num_batched_tokens`
+- `K` = `num_experts_per_tok` (top-k)
+
+The `(L, N, K)` layout ensures that `buffer[layer_id]` gives a contiguous `(N, K)` view per layer. Each `FusedMoE` layer gets a persistent reference to its slice via `module._routing_replay_out = buffer[layer_id]`.
+
+**Dtype**: `int16` — sufficient for expert IDs (max ~512 experts in practice) and half the memory of `int32`.
+
+### Host Cache
+
+Per-request numpy arrays for accumulating routing data across decode steps. Each request gets a lazily allocated `(seq_len, L, K)` int16 buffer that grows as the sequence lengthens. Buffers are freed when a request completes.
+
+### Async D2H Pipeline
+
+After each forward pass, the model runner issues a non-blocking device-to-host copy on a dedicated CUDA stream:
+
+1. **Copy**: `pinned_staging[:, :total_tokens, :].copy_(device_buffer[:, :total_tokens, :])` on a separate stream, recorded with a CUDA event.
+2. **Scatter** (deferred to next step): On the *next* forward pass, synchronize the event (effectively free — an entire forward pass has elapsed) and scatter the staging data into per-request host cache buffers using the token positions.
+
+This design ensures the D2H copy overlaps with the next forward pass, minimizing GPU stall time.
+
+### CUDA Graph Compatibility
+
+CUDA graph compatibility requires two mechanisms:
+
+1. **Persistent tensor attribute**: Each `FusedMoE` layer stores a reference to its buffer slice as `module._routing_replay_out`. Because `torch.compile` captures module attributes by reference, graph replay always writes to the live buffer — not a stale snapshot.
+
+2. **Static marking**: Both the full `(L, N, K)` buffer and each per-layer `(N, K)` view are marked with `cudagraph_mark_tensor_static()`. This prevents CUDA graphs from snapshot/restore behavior that would zero the buffer on replay.
+
+### Multi-Node Support
+
+On multi-node tensor-parallel setups, all TP ranks allocate a device buffer (required for symmetric CUDA graph structure), but only TP rank 0 runs the D2H pipeline and host cache. Routing data flows from the model runner through `ModelRunnerOutput` via Ray DAG to the scheduler — no shared memory or file locks needed.
+
+### Routing Capture Path
+
+For the **non-monolithic (Triton) kernel path** (e.g., BF16 MoE), routing is captured after `select_experts()` in the MoE runner:
+
+```python
+routing_replay_out = getattr(layer, "_routing_replay_out", None)
+topk_weights, topk_ids = self.router.select_experts(...)
+
+if routing_replay_out is not None:
+    routing_replay_out[:topk_ids.shape[0]].copy_(topk_ids.to(torch.int16))
+```
+
+For the **monolithic kernel path** (e.g., FP8/MXFP8 via FlashInfer), `routing_replay_out` is threaded through the `apply_monolithic()` call chain and FlashInfer writes expert IDs directly during routing inside the fused kernel.
+
+### MTP (Multi-Token Prediction) Handling
+
+With MTP speculative decoding, the model captures routing for all tokens including speculative ones that may later be rejected. When a request finishes, the generation routing is trimmed to match the actual number of accepted output tokens:
+
+```python
+num_gen = self.detokenizer.num_output_tokens()
+if gen_routed_experts.shape[0] > num_gen and num_gen > 0:
+    gen_routed_experts = gen_routed_experts[:num_gen]
+```
+
+This ensures the routing array length always matches the token IDs in the response.
+
+## Design Decisions
+
+### Why Replace SharedMemory with Device Cache?
+
+The previous implementation used `multiprocessing.SharedMemory` with `fcntl` file locking to transfer routing data from GPU workers to the scheduler. This approach had fundamental problems:
+
+- **Multi-node**: `SharedMemory` is node-local. On multi-node TP setups (required for 400B+ parameter models), the scheduler on node 0 cannot read shared memory from workers on other nodes.
+- **Performance**: Synchronous `.cpu().numpy()` D2H transfers block the GPU. File-based locking adds further overhead.
+- **CUDA graphs**: The callback-based capture mechanism bakes tensor references at trace time, causing stale data on graph replay.
+
+The device cache approach solves all three: data flows through Ray DAG (works multi-node), D2H is async (non-blocking), and persistent tensor attributes work with CUDA graphs.
+
+### Why `(L, N, K)` Layout Instead of `(N, L, K)`?
+
+FlashInfer's `routing_replay_out` parameter expects a contiguous `(N, K)` tensor per layer. With `(L, N, K)` layout, `buffer[layer_id]` gives a contiguous `(N, K)` view with zero-copy slicing. The previous `(N, L, K)` layout would require non-contiguous indexing or an explicit copy.
+
+### Why int16 Instead of int32?
+
+Expert IDs are small integers (typically 0-255 for models with up to 256 experts). `int16` supports up to 32,767 experts — far more than any current model — while halving GPU memory usage and D2H bandwidth compared to `int32`.
+
+### Why Split Prompt and Generation Routing?
+
+RL training pipelines process prompt and generation routing separately:
+
+- Prompt routing reconstructs the shared forward pass for the input.
+- Generation routing reconstructs each sampled trajectory.
+
+With `n > 1` completions, all completions share the same prompt routing. Duplicating it per completion would waste memory proportional to `n * prompt_len * L * K`. Instead, `prompt_routed_experts` is stored once on `RequestOutput` and shared.
+
+### Why Async D2H Instead of Synchronous Copy?
+
+A synchronous `.cpu()` call forces the GPU to drain its command queue before the copy can begin, stalling the pipeline. The async approach:
+
+1. Issues the copy on a separate CUDA stream (non-blocking to the main compute stream).
+2. Defers the host-side scatter to the *next* step, by which time the copy has finished.
+
+This means the D2H transfer overlaps entirely with the next forward pass, adding near-zero latency to the critical path.
+
+### Why All TP Ranks Get a Device Buffer?
+
+CUDA graph capture records the exact sequence of kernel calls and their arguments. If only rank 0 had a device buffer, the `FusedMoE` layer would take a different code path on rank 0 vs. other ranks (one writes to a buffer, others don't). This asymmetry causes different CUDA graph structures across ranks, which can lead to NCCL deadlocks during collective operations inside the graph. Giving all ranks a real buffer ensures symmetric graph structure. Only rank 0 does the D2H copy and host cache management.
+
+## Performance
+
+Routing replay adds a small overhead from the device buffer writes and async D2H copies. On tested configurations:
+
+- **Throughput overhead** (random data, ISL=1024, OSL=1024): **~2%**
+- **Memory overhead** (int16 buffer, 40 layers, 8192 tokens, top-22): **~14 MB per GPU**
+- **Accuracy impact** (GSM8K): **Zero** (pass@1 identical with and without routing replay)
+
+The overhead is dominated by the per-layer `.copy_()` during the forward pass. The async D2H pipeline runs entirely in the background.
+
+## Supported Configurations
+
+| Configuration                            | Supported                                                 |
+|------------------------------------------|-----------------------------------------------------------|
+| BF16 Triton MoE (non-monolithic)         | Yes                                                       |
+| FP8/MXFP8 FlashInfer MoE (monolithic)    | Yes (requires FlashInfer with `routing_replay_out`)       |
+| CUDA graphs                              | Yes                                                       |
+| Multi-node tensor parallelism            | Yes                                                       |
+| Data parallelism (DP)                    | Yes                                                       |
+| Expert parallelism (EP)                  | Yes                                                       |
+| Prefix caching                           | Yes (cached positions marked with `-1` sentinel)          |
+| MTP speculative decoding                 | Yes (gen routing trimmed to accepted tokens)              |
+| `n > 1` (multiple completions)           | Yes (prompt routing shared, gen routing per-completion)   |
+
+## Limitations
+
+- **Streaming**: Routing data is only available when the request finishes (not streamed incrementally).
+- **V1 engine only**: Routing replay is implemented for the vLLM V1 engine.
+- **Preempted requests**: When a request is preempted by the scheduler (and later resumed via re-prefill), any routing already accumulated in the worker's host cache for that request is dropped without being emitted. The consumer sees `routed_experts=None` for the resumed request with no other signal. Partial-rollout and async-RL pipelines that rely on routing for preempted requests should either disable preemption (`--no-enable-chunked-prefill` / sufficient KV headroom) or reconstruct routing on the resumed prefill.
+- **Async scheduling**: Not supported; rejected at config time. The worker-side stop predicate reads `req_state.output_token_ids[-1]`, which under async scheduling is the placeholder `-1` until `AsyncGPUModelRunnerOutput` resolves the real sampled token, so EOS / stop-token finishes would silently drop routing. Use sync scheduling (the default when `--enable-return-routed-experts` is set, or set explicitly with the appropriate scheduler config).
+- **Sequence parallelism / naive DP MoE dispatch**: Not supported on the FusedMoE layer; rejected at bind time. SP shards `topk_ids` along dim 0 across the TP group so each rank only captures `1/sp_size` of the rows; naive DP dispatch all-gathers tokens across DP ranks before routing, so `topk_ids.shape[0]` exceeds the per-rank buffer size. Both raise `NotImplementedError` from `bind_routing_capture_to_model`.
+- **Pipeline / prefill-context / decode-context parallelism**: Not yet validated; rejected at config time.
+
+## CLI Reference
+
+| Flag                               | Description                                                            |
+|------------------------------------|------------------------------------------------------------------------|
+| `--enable-return-routed-experts`   | Enable routing replay capture and return expert IDs in API responses.  |
+
+## API Reference
+
+### Completions (`/v1/completions`)
+
+**Response-level field:**
+
+| Field                     | Type                                | Description                                                                 |
+|---------------------------|-------------------------------------|-----------------------------------------------------------------------------|
+| `prompt_routed_experts`   | `list[list[list[int]]]` or `null`   | Expert IDs for prompt tokens. Shape: `[prompt_len, num_moe_layers, top_k]`. |
+
+**Choice-level field:**
+
+| Field              | Type                                | Description                                                                   |
+|--------------------|-------------------------------------|-------------------------------------------------------------------------------|
+| `routed_experts`   | `list[list[list[int]]]` or `null`   | Expert IDs for generated tokens. Shape: `[gen_len, num_moe_layers, top_k]`.   |
+
+### Chat Completions (`/v1/chat/completions`)
+
+Same fields as above on `ChatCompletionResponse` and `ChatCompletionResponseChoice`.
+
+### Python SDK
+
+| Object               | Field                     | Type                     | Description                 |
+|----------------------|---------------------------|--------------------------|-----------------------------|
+| `RequestOutput`      | `prompt_routed_experts`   | `np.ndarray` or `None`   | `[prompt_len, L, K]` i16    |
+| `CompletionOutput`   | `routed_experts`          | `np.ndarray` or `None`   | `[gen_len, L, K]` int16     |
diff --git a/docs/training/weight_transfer/README.md b/docs/training/weight_transfer/README.md
index 17afd2bc8965..4368483e8ba2 100644
--- a/docs/training/weight_transfer/README.md
+++ b/docs/training/weight_transfer/README.md
@@ -4,10 +4,12 @@ vLLM provides a pluggable weight transfer system for synchronizing model weights
 
 ## Architecture
 
-The weight transfer system follows a **two-phase protocol** with a pluggable backend design:
+The weight transfer system follows a **four-phase protocol** with a pluggable backend design:
 
 1. **Initialization** (`init_weight_transfer_engine`): Establishes the communication channel between the trainer and inference workers. Called once before the training loop begins.
-2. **Weight Update** (`update_weights`): Transfers updated weights from the trainer to the inference engine. Called after each training step (or batch of steps).
+2. **Start** (`start_weight_update`): Prepares the inference engine for a weight update.
+3. **Weight Update** (`update_weights`): Transfers updated weights from the trainer to the inference engine. May be called one or more times (e.g., for chunked transfers).
+4. **Finish** (`finish_weight_update`): Finalizes the weight update (e.g., runs post-processing for checkpoint-format weights). Called once after all weights have been transferred.
 
 ## Available Backends
 
@@ -48,7 +50,9 @@ When running vLLM as an HTTP server, the following endpoints are available for w
 | Endpoint | Method | Description |
 | -------- | ------ | ----------- |
 | `/init_weight_transfer_engine` | POST | Initialize the weight transfer engine with backend-specific info |
-| `/update_weights` | POST | Trigger a weight update with backend-specific metadata |
+| `/start_weight_update` | POST | Start a weight update |
+| `/update_weights` | POST | Transfer a batch of weights with backend-specific metadata |
+| `/finish_weight_update` | POST | Finish the weight update and run post-processing |
 | `/pause` | POST | Pause generation before weight sync to handle inflight requests |
 | `/resume` | POST | Resume generation after weight sync |
 | `/get_world_size` | GET | Get the number of inference workers (useful for NCCL world size calculation) |
@@ -64,11 +68,17 @@ Both backends provide static methods that the trainer calls to send weights. The
 # 1. Initialize the transfer engine (backend-specific)
 EngineClass.trainer_init(init_info)
 
-# 2. Send weights to inference workers
+# 2. Start weight update on inference side
+llm.start_weight_update(is_checkpoint_format=True)
+
+# 3. Send weights to inference workers
 EngineClass.trainer_send_weights(
     iterator=model.named_parameters(),
     trainer_args=backend_specific_args,
 )
+
+# 4. Finish weight update on inference side
+llm.finish_weight_update()
 ```
 
 See the [NCCL](nccl.md) and [IPC](ipc.md) pages for backend-specific trainer APIs and full examples.
diff --git a/docs/training/weight_transfer/base.md b/docs/training/weight_transfer/base.md
index 973ec8ad9f55..6c768c87fd90 100644
--- a/docs/training/weight_transfer/base.md
+++ b/docs/training/weight_transfer/base.md
@@ -43,16 +43,14 @@ update_request = WeightTransferUpdateRequest(
 
 ### WeightTransferUpdateInfo
 
-The base `WeightTransferUpdateInfo` includes an `is_checkpoint_format` flag:
+The base `WeightTransferUpdateInfo` is a marker class for backend-specific update info:
 
 ```python
 @dataclass
 class WeightTransferUpdateInfo(ABC):
-    is_checkpoint_format: bool = True
+    pass
 ```
 
-When `is_checkpoint_format=True` (the default), vLLM applies layerwise weight processing (repacking, renaming, etc.) on the received weights before loading them. Set to `False` if the trainer has already converted weights to the kernel format expected by the model.
-
 ## Implementing a Custom Engine
 
 To create a custom weight transfer backend:
diff --git a/docs/training/weight_transfer/ipc.md b/docs/training/weight_transfer/ipc.md
index 8e19fa7b429b..69e8adce6629 100644
--- a/docs/training/weight_transfer/ipc.md
+++ b/docs/training/weight_transfer/ipc.md
@@ -38,11 +38,15 @@ trainer_args = IPCTrainerSendWeightsArgs(
     mode="ray",
     llm_handle=llm_actor_handle,
 )
-
+# start
+ray.get(llm_actor_handle.start_weight_update.remote(is_checkpoint_format=True))
+# send weights
 IPCWeightTransferEngine.trainer_send_weights(
     iterator=model.named_parameters(),
     trainer_args=trainer_args,
 )
+# finish
+ray.get(llm_actor_handle.finish_weight_update.remote())
 ```
 
 In Ray mode, the engine calls `llm_handle.update_weights.remote(...)` directly, passing the IPC handles via Ray's serialization.
@@ -57,17 +61,27 @@ trainer_args = IPCTrainerSendWeightsArgs(
     url="http://localhost:8000",
 )
 
+# start
+base_url = "http://localhost:8000"
+url = f"{base_url}/start_weight_update"
+response = requests.post(url, json={"is_checkpoint_format": True}, timeout=60)
+response.raise_for_status()
+# send weights
 IPCWeightTransferEngine.trainer_send_weights(
     iterator=model.named_parameters(),
     trainer_args=trainer_args,
 )
+# finish
+url = f"{base_url}/finish_weight_update"
+response = requests.post(url, json={}, timeout=60)
+response.raise_for_status()
 ```
 
-In HTTP mode, IPC handles are pickled, base64-encoded, and sent as JSON to the `/update_weights` endpoint.
+In HTTP mode, IPC handles are pickled, base64-encoded, and sent as JSON to the `/update_weights` endpoint. As with Ray mode, you must call `start_weight_update` before and `finish_weight_update` after.
 
 See [`IPCTrainerSendWeightsArgs`](https://github.com/vllm-project/vllm/blob/main/vllm/distributed/weight_transfer/ipc_engine.py) for the full list of configurable fields.
 
 ## Examples
 
-- [RLHF with IPC weight syncing (offline, Ray)](../../examples/rl/rlhf_ipc.md) - Colocated training and inference on a single GPU using Ray placement groups and CUDA IPC handles
-- [RLHF with IPC weight syncing (online serving, HTTP)](../../examples/rl/rlhf_http_ipc.md) - Weight transfer with a vLLM HTTP server where both server and trainer share the same GPU
+- [RLHF with IPC weight syncing (offline, Ray)](../../../examples/rl/rlhf_ipc.py) - Colocated training and inference on a single GPU using Ray placement groups and CUDA IPC handles
+- [RLHF with IPC weight syncing (online serving, HTTP)](../../../examples/rl/rlhf_http_ipc.py) - Weight transfer with a vLLM HTTP server where both server and trainer share the same GPU
diff --git a/docs/training/weight_transfer/nccl.md b/docs/training/weight_transfer/nccl.md
index a50b3664d89d..bfde1ee2ae37 100644
--- a/docs/training/weight_transfer/nccl.md
+++ b/docs/training/weight_transfer/nccl.md
@@ -84,11 +84,15 @@ Both the trainer (`NCCLTrainerSendWeightsArgs`) and inference side (`NCCLWeightT
 
 ## Receiving Weights (Inference Side)
 
-The inference side triggers weight reception by calling `update_weights`:
+The inference side triggers weight reception using the four-phase protocol — `init_weight_transfer_engine`, `start_weight_update`, `update_weights`, `finish_weight_update`. The init phase is shown [above](#initialization). The remaining three steps are:
 
 ```python
 from vllm.distributed.weight_transfer.base import WeightTransferUpdateRequest
 
+# 1. Start the weight update
+llm.start_weight_update(is_checkpoint_format=True)
+
+# 2. Receive weights (can be called multiple times for chunked transfers)
 llm.update_weights(
     WeightTransferUpdateRequest(
         update_info=dict(
@@ -99,12 +103,17 @@ llm.update_weights(
         )
     )
 )
+
+# 3. Finish the weight update
+llm.finish_weight_update()
 ```
 
 The `names`, `dtype_names`, and `shapes` lists describe each parameter. These must match the order in which the trainer iterates over its parameters.
 
+`start_weight_update` must be called before `update_weights`, and `finish_weight_update` must be called after all weight chunks have been transferred. The `is_checkpoint_format` flag controls whether layerwise reload processing is applied (`True` for checkpoint-format weights, `False` for pre-processed kernel-format weights).
+
 ## Examples
 
-- [RLHF with NCCL weight syncing (offline, Ray)](../../examples/rl/rlhf_nccl.md) - Trainer on one GPU, 2x tensor-parallel vLLM engine on two others, with packed NCCL weight broadcast
-- [RLHF with async weight syncing (offline, Ray)](../../examples/rl/rlhf_async_new_apis.md) - Async generation with mid-flight pause, weight sync, resume, and validation against a fresh model
-- [RLHF with NCCL weight syncing (online serving, HTTP)](../../examples/rl/rlhf_http_nccl.md) - Weight transfer with a running vLLM HTTP server using HTTP control plane and NCCL data plane
+- [RLHF with NCCL weight syncing (offline, Ray)](../../../examples/rl/rlhf_nccl.py) - Trainer on one GPU, 2x tensor-parallel vLLM engine on two others, with packed NCCL weight broadcast
+- [RLHF with async weight syncing (offline, Ray)](../../../examples/rl/rlhf_async_new_apis.py) - Async generation with mid-flight pause, weight sync, resume, and validation against a fresh model
+- [RLHF with NCCL weight syncing (online serving, HTTP)](../../../examples/rl/rlhf_http_nccl.py) - Weight transfer with a running vLLM HTTP server using HTTP control plane and NCCL data plane
diff --git a/docs/usage/reproducibility.md b/docs/usage/reproducibility.md
index a8e49d0a3398..680791bbe24a 100644
--- a/docs/usage/reproducibility.md
+++ b/docs/usage/reproducibility.md
@@ -7,7 +7,7 @@ reproducible results:
   or enable [batch invariance](../features/batch_invariance.md) to make the outputs insensitive to scheduling.
 - In online mode, you can only enable [batch invariance](../features/batch_invariance.md).
 
-Example: [examples/offline_inference/reproducibility.py](../../examples/offline_inference/reproducibility.py)
+Example: [examples/features/batch_invariance/reproducibility_offline.py](../../examples/features/batch_invariance/reproducibility_offline.py)
 
 !!! warning
 
diff --git a/docs/usage/security.md b/docs/usage/security.md
index 4879ddbf64ef..e548899abbf1 100644
--- a/docs/usage/security.md
+++ b/docs/usage/security.md
@@ -138,14 +138,22 @@ When `--api-key` is configured, the following `/v1` endpoints require Bearer tok
 
 - `/v1/models` - List available models
 - `/v1/chat/completions` - Chat completions
+- `/v1/chat/completions/batch` - Batch chat completions
+- `/v1/chat/completions/render` - Render chat completion requests
 - `/v1/completions` - Text completions
+- `/v1/completions/render` - Render completion requests
 - `/v1/embeddings` - Generate embeddings
 - `/v1/audio/transcriptions` - Audio transcription
 - `/v1/audio/translations` - Audio translation
 - `/v1/messages` - Anthropic-compatible messages API
-- `/v1/responses` - Response management
+- `/v1/messages/count_tokens` - Count tokens for Anthropic messages
+- `/v1/responses` - Create a response
+- `/v1/responses/{response_id}` - Retrieve a response
+- `/v1/responses/{response_id}/cancel` - Cancel a response
 - `/v1/score` - Scoring API
 - `/v1/rerank` - Reranking API
+- `/v1/load_lora_adapter` - Load a LoRA adapter (can alter model behavior; only available when `--enable-lora` is set and `VLLM_ALLOW_RUNTIME_LORA_UPDATING=True`)
+- `/v1/unload_lora_adapter` - Unload a LoRA adapter (can alter model behavior; only available when `--enable-lora` is set and `VLLM_ALLOW_RUNTIME_LORA_UPDATING=True`)
 
 ### Unprotected Endpoints (No API Key Required)
 
@@ -155,16 +163,23 @@ The following endpoints **do not require authentication** even when `--api-key`
 
 - `/invocations` - SageMaker-compatible endpoint (routes to the same inference functions as `/v1` endpoints)
 - `/inference/v1/generate` - Generate completions
+- `/generative_scoring` - Generative scoring API
 - `/pooling` - Pooling API
 - `/classify` - Classification API
 - `/score` - Scoring API (non-`/v1` variant)
 - `/rerank` - Reranking API (non-`/v1` variant)
 
-**Operational control endpoints (always enabled):**
+**Operational control endpoints (only when `"generate"` task is supported):**
 
 - `/pause` - Pause generation (causes denial of service)
 - `/resume` - Resume generation
+- `/is_paused` - Check if generation is paused
 - `/scale_elastic_ep` - Trigger scaling operations
+- `/is_scaling_elastic_ep` - Check if scaling is in progress
+- `/init_weight_transfer_engine` - Initialize weight transfer engine for RLHF
+- `/update_weights` - Update model weights (can alter model behavior)
+- `/get_world_size` - Get distributed world size
+- `/abort_requests` - Abort in-flight requests (only when `--tokens-only` is also set)
 
 **Utility endpoints:**
 
@@ -207,9 +222,9 @@ These endpoints are only available when profiling is enabled and should only be
 
 An attacker who can reach the vLLM HTTP server can:
 
-1. **Bypass authentication** by using non-`/v1` endpoints like `/invocations`, `/inference/v1/generate`, `/pooling`, `/classify`, `/score`, or `/rerank` to run arbitrary inference without credentials
-2. **Cause denial of service** by calling `/pause` or `/scale_elastic_ep` without a token
-3. **Access operational controls** to manipulate server state (e.g., pausing generation)
+1. **Bypass authentication** by using non-`/v1` endpoints like `/invocations`, `/inference/v1/generate`, `/generative_scoring`, `/pooling`, `/classify`, `/score`, or `/rerank` to run arbitrary inference without credentials
+2. **Cause denial of service** by calling `/pause`, `/scale_elastic_ep`, or `/abort_requests` without a token
+3. **Access operational controls** to manipulate server state (e.g., pausing generation, updating model weights via `/update_weights`)
 4. **If `--enable-tokenizer-info-endpoint` is set:** Access sensitive tokenizer configuration including chat templates, which may reveal prompt engineering strategies or other implementation details
 5. **If `VLLM_SERVER_DEV_MODE=1` is set:** Execute arbitrary RPC commands via `/collective_rpc`, reset caches, put the engine to sleep, and access detailed server configuration
 
@@ -288,6 +303,36 @@ To disable the Python code interpreter specifically, omit `code_interpreter` fro
 
 **Consider a custom implementation**: The GPT-OSS Python tool is a reference implementation. For production deployments, consider implementing a custom code execution sandbox with stricter isolation guarantees. See the [GPT-OSS documentation](https://github.com/openai/gpt-oss?tab=readme-ov-file#python) for guidance.
 
+## Dynamic LoRA Loading
+
+vLLM supports dynamically loading and unloading LoRA adapters at runtime via the `/v1/load_lora_adapter` and `/v1/unload_lora_adapter` API endpoints. This functionality is **not enabled by default** — it requires both `--enable-lora` and the environment variable `VLLM_ALLOW_RUNTIME_LORA_UPDATING=True` to be set.
+
+**Warning:** Dynamic LoRA loading is not a secure operation and should not be enabled in deployments exposed to untrusted clients. If you must enable dynamic LoRA loading, restrict access to the `/v1/load_lora_adapter` and `/v1/unload_lora_adapter` endpoints to trusted administrators only, using a reverse proxy or network-level access controls. Do not expose these endpoints to end users. For details on configuring LoRA adapters, see the [LoRA Adapters documentation](../features/lora.md).
+
+## Cache Directory Security
+
+vLLM assumes that its cache directories are **private and trusted**. Cache contents are loaded without cryptographic integrity verification, including formats that support arbitrary code execution. If an untrusted user or process can write to vLLM's cache directories, they may be able to crash vLLM or cause it to execute arbitrary code.
+
+**Do not share vLLM cache directories with untrusted users or mount them from untrusted storage.** Treat the cache directory with the same care as the vLLM installation itself.
+
+### Cache Directory Configuration
+
+Most cache paths default to subdirectories under a single root. Changing `VLLM_CACHE_ROOT` changes the default location for all features that inherit from it. When `torch.compile` caching is enabled (the default), vLLM also redirects `TRITON_CACHE_DIR` into this tree. If compile caching is disabled, Triton falls back to its own default location (`~/.triton/cache`).
+
+| Environment Variable | Default | Description |
+| --- | --- | --- |
+| `VLLM_CACHE_ROOT` | `~/.cache/vllm` | Base cache directory. Respects `XDG_CACHE_HOME` if set. All paths below inherit from this unless explicitly overridden. |
+| *(torch.compile)* | `$VLLM_CACHE_ROOT/torch_compile_cache/` | Compilation cache for AOT-compiled models, Inductor graphs, and Triton kernels. Controlled by `VLLM_DISABLE_COMPILE_CACHE` (set to `1` to disable). |
+| `VLLM_ASSETS_CACHE` | `$VLLM_CACHE_ROOT/assets/` | Downloaded assets (e.g., tokenizer files). |
+| `VLLM_XLA_CACHE_PATH` | `$VLLM_CACHE_ROOT/xla_cache/` | XLA/TPU compilation cache. |
+| `VLLM_MEDIA_CACHE` | *(disabled)* | Optional cache for downloaded media (images, video, audio). Not enabled unless explicitly set. |
+
+### Recommendations
+
+- **Restrict file permissions** on `VLLM_CACHE_ROOT` (and any other cache directories used by dependencies, such as `~/.triton` if compile caching is disabled) so that only the vLLM process owner can read and write to them.
+- **Do not copy cache contents from untrusted sources.** If you distribute cache artifacts between environments, ensure they originate from a trusted build pipeline.
+- **Container deployments:** If mounting cache directories into containers, ensure the volume source is trusted.
+
 ## Reporting Security Vulnerabilities
 
 If you believe you have found a security vulnerability in vLLM, please report it following the project's security policy. For more information on how to report security issues and the project's security policy, please see the [vLLM Security Policy](https://github.com/vllm-project/vllm/blob/main/SECURITY.md).
diff --git a/examples/online_serving/api_client.py b/examples/applications/chatbot/api_client.py
similarity index 100%
rename from examples/online_serving/api_client.py
rename to examples/applications/chatbot/api_client.py
diff --git a/examples/online_serving/gradio_openai_chatbot_webserver.py b/examples/applications/chatbot/gradio_openai_chatbot_webserver.py
similarity index 97%
rename from examples/online_serving/gradio_openai_chatbot_webserver.py
rename to examples/applications/chatbot/gradio_openai_chatbot_webserver.py
index c76c60cc4472..2a67aefc0278 100644
--- a/examples/online_serving/gradio_openai_chatbot_webserver.py
+++ b/examples/applications/chatbot/gradio_openai_chatbot_webserver.py
@@ -5,7 +5,7 @@
     vllm serve meta-llama/Llama-2-7b-chat-hf
 
 Start Gradio OpenAI Chatbot Webserver:
-    python examples/online_serving/gradio_openai_chatbot_webserver.py \
+    python examples/applications/chatbot/gradio_openai_chatbot_webserver.py \
                     -m meta-llama/Llama-2-7b-chat-hf
 
 Note that `pip install --upgrade gradio` is needed to run this example.
diff --git a/examples/online_serving/gradio_webserver.py b/examples/applications/chatbot/gradio_webserver.py
similarity index 97%
rename from examples/online_serving/gradio_webserver.py
rename to examples/applications/chatbot/gradio_webserver.py
index 86d9ceb48bb0..f75636409c2f 100644
--- a/examples/online_serving/gradio_webserver.py
+++ b/examples/applications/chatbot/gradio_webserver.py
@@ -6,7 +6,7 @@
         --model meta-llama/Llama-2-7b-chat-hf
 
 Start Webserver:
-    python examples/online_serving/gradio_webserver.py
+    python examples/applications/chatbot/gradio_webserver.py
 
 Note that `pip install --upgrade gradio` is needed to run this example.
 More details: https://github.com/gradio-app/gradio
diff --git a/examples/online_serving/streamlit_openai_chatbot_webserver.py b/examples/applications/chatbot/streamlit_openai_chatbot_webserver.py
similarity index 100%
rename from examples/online_serving/streamlit_openai_chatbot_webserver.py
rename to examples/applications/chatbot/streamlit_openai_chatbot_webserver.py
diff --git a/examples/online_serving/retrieval_augmented_generation_with_langchain.py b/examples/applications/rag/retrieval_augmented_generation_with_langchain.py
similarity index 100%
rename from examples/online_serving/retrieval_augmented_generation_with_langchain.py
rename to examples/applications/rag/retrieval_augmented_generation_with_langchain.py
diff --git a/examples/online_serving/retrieval_augmented_generation_with_llamaindex.py b/examples/applications/rag/retrieval_augmented_generation_with_llamaindex.py
similarity index 100%
rename from examples/online_serving/retrieval_augmented_generation_with_llamaindex.py
rename to examples/applications/rag/retrieval_augmented_generation_with_llamaindex.py
diff --git a/examples/offline_inference/async_llm_streaming.py b/examples/deployment/async_llm_streaming.py
similarity index 98%
rename from examples/offline_inference/async_llm_streaming.py
rename to examples/deployment/async_llm_streaming.py
index b876d536e3a1..ef69089a0460 100644
--- a/examples/offline_inference/async_llm_streaming.py
+++ b/examples/deployment/async_llm_streaming.py
@@ -8,7 +8,7 @@
 streaming where you receive new tokens as they are generated.
 
 Usage:
-    python examples/offline_inference/async_llm_streaming.py
+    python examples/deployment/async_llm_streaming.py
 """
 
 import asyncio
diff --git a/examples/online_serving/chart-helm/.helmignore b/examples/deployment/chart-helm/.helmignore
similarity index 100%
rename from examples/online_serving/chart-helm/.helmignore
rename to examples/deployment/chart-helm/.helmignore
diff --git a/examples/online_serving/chart-helm/Chart.yaml b/examples/deployment/chart-helm/Chart.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/Chart.yaml
rename to examples/deployment/chart-helm/Chart.yaml
diff --git a/examples/online_serving/chart-helm/README.md b/examples/deployment/chart-helm/README.md
similarity index 100%
rename from examples/online_serving/chart-helm/README.md
rename to examples/deployment/chart-helm/README.md
diff --git a/examples/online_serving/chart-helm/ct.yaml b/examples/deployment/chart-helm/ct.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/ct.yaml
rename to examples/deployment/chart-helm/ct.yaml
diff --git a/examples/online_serving/chart-helm/lintconf.yaml b/examples/deployment/chart-helm/lintconf.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/lintconf.yaml
rename to examples/deployment/chart-helm/lintconf.yaml
diff --git a/examples/online_serving/chart-helm/templates/_helpers.tpl b/examples/deployment/chart-helm/templates/_helpers.tpl
similarity index 100%
rename from examples/online_serving/chart-helm/templates/_helpers.tpl
rename to examples/deployment/chart-helm/templates/_helpers.tpl
diff --git a/examples/online_serving/chart-helm/templates/configmap.yaml b/examples/deployment/chart-helm/templates/configmap.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/templates/configmap.yaml
rename to examples/deployment/chart-helm/templates/configmap.yaml
diff --git a/examples/online_serving/chart-helm/templates/custom-objects.yaml b/examples/deployment/chart-helm/templates/custom-objects.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/templates/custom-objects.yaml
rename to examples/deployment/chart-helm/templates/custom-objects.yaml
diff --git a/examples/online_serving/chart-helm/templates/deployment.yaml b/examples/deployment/chart-helm/templates/deployment.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/templates/deployment.yaml
rename to examples/deployment/chart-helm/templates/deployment.yaml
diff --git a/examples/online_serving/chart-helm/templates/hpa.yaml b/examples/deployment/chart-helm/templates/hpa.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/templates/hpa.yaml
rename to examples/deployment/chart-helm/templates/hpa.yaml
diff --git a/examples/online_serving/chart-helm/templates/job.yaml b/examples/deployment/chart-helm/templates/job.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/templates/job.yaml
rename to examples/deployment/chart-helm/templates/job.yaml
diff --git a/examples/online_serving/chart-helm/templates/poddisruptionbudget.yaml b/examples/deployment/chart-helm/templates/poddisruptionbudget.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/templates/poddisruptionbudget.yaml
rename to examples/deployment/chart-helm/templates/poddisruptionbudget.yaml
diff --git a/examples/online_serving/chart-helm/templates/pvc.yaml b/examples/deployment/chart-helm/templates/pvc.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/templates/pvc.yaml
rename to examples/deployment/chart-helm/templates/pvc.yaml
diff --git a/examples/online_serving/chart-helm/templates/secrets.yaml b/examples/deployment/chart-helm/templates/secrets.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/templates/secrets.yaml
rename to examples/deployment/chart-helm/templates/secrets.yaml
diff --git a/examples/online_serving/chart-helm/templates/service.yaml b/examples/deployment/chart-helm/templates/service.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/templates/service.yaml
rename to examples/deployment/chart-helm/templates/service.yaml
diff --git a/examples/online_serving/chart-helm/tests/deployment_test.yaml b/examples/deployment/chart-helm/tests/deployment_test.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/tests/deployment_test.yaml
rename to examples/deployment/chart-helm/tests/deployment_test.yaml
diff --git a/examples/online_serving/chart-helm/tests/job_test.yaml b/examples/deployment/chart-helm/tests/job_test.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/tests/job_test.yaml
rename to examples/deployment/chart-helm/tests/job_test.yaml
diff --git a/examples/online_serving/chart-helm/tests/pvc_test.yaml b/examples/deployment/chart-helm/tests/pvc_test.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/tests/pvc_test.yaml
rename to examples/deployment/chart-helm/tests/pvc_test.yaml
diff --git a/examples/online_serving/chart-helm/values.schema.json b/examples/deployment/chart-helm/values.schema.json
similarity index 100%
rename from examples/online_serving/chart-helm/values.schema.json
rename to examples/deployment/chart-helm/values.schema.json
diff --git a/examples/online_serving/chart-helm/values.yaml b/examples/deployment/chart-helm/values.yaml
similarity index 100%
rename from examples/online_serving/chart-helm/values.yaml
rename to examples/deployment/chart-helm/values.yaml
diff --git a/examples/offline_inference/llm_engine_example.py b/examples/deployment/llm_engine_example.py
similarity index 100%
rename from examples/offline_inference/llm_engine_example.py
rename to examples/deployment/llm_engine_example.py
diff --git a/examples/online_serving/sagemaker-entrypoint.sh b/examples/deployment/sagemaker-entrypoint.sh
similarity index 100%
rename from examples/online_serving/sagemaker-entrypoint.sh
rename to examples/deployment/sagemaker-entrypoint.sh
diff --git a/examples/online_serving/disaggregated_encoder/README.md b/examples/disaggregated/disaggregated_encoder/README.md
similarity index 100%
rename from examples/online_serving/disaggregated_encoder/README.md
rename to examples/disaggregated/disaggregated_encoder/README.md
diff --git a/examples/online_serving/disaggregated_encoder/disagg_1e1p1d_example.sh b/examples/disaggregated/disaggregated_encoder/disagg_1e1p1d_example.sh
similarity index 100%
rename from examples/online_serving/disaggregated_encoder/disagg_1e1p1d_example.sh
rename to examples/disaggregated/disaggregated_encoder/disagg_1e1p1d_example.sh
diff --git a/examples/online_serving/disaggregated_encoder/disagg_1e1pd_example.sh b/examples/disaggregated/disaggregated_encoder/disagg_1e1pd_example.sh
similarity index 100%
rename from examples/online_serving/disaggregated_encoder/disagg_1e1pd_example.sh
rename to examples/disaggregated/disaggregated_encoder/disagg_1e1pd_example.sh
diff --git a/examples/online_serving/disaggregated_encoder/disagg_epd_proxy.py b/examples/disaggregated/disaggregated_encoder/disagg_epd_proxy.py
similarity index 100%
rename from examples/online_serving/disaggregated_encoder/disagg_epd_proxy.py
rename to examples/disaggregated/disaggregated_encoder/disagg_epd_proxy.py
diff --git a/examples/offline_inference/disaggregated_prefill.py b/examples/disaggregated/disaggregated_prefill.py
similarity index 100%
rename from examples/offline_inference/disaggregated_prefill.py
rename to examples/disaggregated/disaggregated_prefill.py
diff --git a/examples/online_serving/disaggregated_prefill.sh b/examples/disaggregated/disaggregated_prefill.sh
similarity index 100%
rename from examples/online_serving/disaggregated_prefill.sh
rename to examples/disaggregated/disaggregated_prefill.sh
diff --git a/examples/online_serving/disaggregated_serving/README.md b/examples/disaggregated/disaggregated_serving/README.md
similarity index 100%
rename from examples/online_serving/disaggregated_serving/README.md
rename to examples/disaggregated/disaggregated_serving/README.md
diff --git a/examples/online_serving/disaggregated_serving/disagg_proxy_demo.py b/examples/disaggregated/disaggregated_serving/disagg_proxy_demo.py
similarity index 99%
rename from examples/online_serving/disaggregated_serving/disagg_proxy_demo.py
rename to examples/disaggregated/disaggregated_serving/disagg_proxy_demo.py
index 763361a30e02..57deef6a15d2 100644
--- a/examples/online_serving/disaggregated_serving/disagg_proxy_demo.py
+++ b/examples/disaggregated/disaggregated_serving/disagg_proxy_demo.py
@@ -5,7 +5,7 @@
 example usage of XpYd disaggregated prefilling.
 We can launch multiple vllm instances (2 for prefill and 2 for decode), and
 launch this proxy demo through:
-  python3 examples/online_serving/disaggregated_serving/disagg_proxy_demo.py  \
+  python3 examples/disaggregated/disaggregated_serving/disagg_proxy_demo.py  \
        --model $model_name  \
        --prefill localhost:8100 localhost:8101   \
        --decode localhost:8200 localhost:8201   \
diff --git a/examples/disaggregated/disaggregated_serving/disagg_proxy_multiturn.py b/examples/disaggregated/disaggregated_serving/disagg_proxy_multiturn.py
new file mode 100644
index 000000000000..24d90eab0292
--- /dev/null
+++ b/examples/disaggregated/disaggregated_serving/disagg_proxy_multiturn.py
@@ -0,0 +1,562 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Disaggregated Prefill/Decode Proxy with Bidirectional KV Transfer
+
+This proxy sits between clients and a vLLM Prefill/Decode (P/D) deployment,
+routing multi-turn chat requests so that each turn reuses KV cache blocks
+from the previous turn's Decode node via bidirectional KV transfer.
+
+Architecture:
+    Client  ──►  Proxy  ──►  Prefill (P)  ──►  Decode (D)
+                   │              │                 │
+                   │   kv_transfer_params flow:     │
+                   │   D finish ──► proxy caches    │
+                   │   next turn ──► proxy sends    │
+                   │   cached D blocks to P ──►     │
+                   │   P reads D blocks (bidir)     │
+                   │   P sends its blocks to D      │
+
+Per-request flow:
+    1. Client sends chat/completions request to proxy.
+    2. Proxy looks up cached D block info from the previous turn
+       (keyed by conversation_id).
+    3. If cache hit, proxy attaches D's block info to the request
+       so P can read D's KV blocks instead of recomputing.
+    4. Proxy sends request to P (max_tokens=1, non-streaming).
+    5. P returns kv_transfer_params with its own block info.
+    6. Proxy forwards request + P's block info to D (streaming).
+    7. D streams the response. The final chunk includes D's
+       kv_transfer_params, which the proxy caches for the next turn.
+    8. Proxy returns D's response to the client.
+
+Conversation isolation:
+    Each request must include a ``conversation_id`` field (top-level in
+    the JSON body) to scope the KV cache across turns. Without it, the
+    proxy cannot link turns and falls back to no-cache behavior.
+
+Usage:
+    python disagg_proxy_multiturn.py \\
+        --host 0.0.0.0 --port 8000 \\
+        --prefiller-host 10.0.0.1 --prefiller-port 8100 \\
+        --decoder-host 10.0.0.2 --decoder-port 8200
+
+Dependencies:
+    pip install fastapi uvicorn httpx
+"""
+
+from __future__ import annotations
+
+import argparse
+import itertools
+import json
+import logging
+import os
+import time
+import uuid
+from contextlib import asynccontextmanager
+from dataclasses import dataclass, field
+from typing import Any
+
+import httpx
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse, StreamingResponse
+
+# Logging
+logging.basicConfig(
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=logging.INFO,
+)
+logger = logging.getLogger("disagg_proxy")
+
+
+# Data structures
+@dataclass
+class CachedKVEntry:
+    """KV transfer parameters cached from D's response for one turn."""
+
+    kv_transfer_params: dict[str, Any]
+    timestamp: float = field(default_factory=time.time)
+
+
+class ConversationKVCache:
+    """Per-conversation KV block cache.
+
+    Each conversation is identified by a ``conversation_id`` supplied by
+    the client. After D finishes a turn, its ``kv_transfer_params`` are
+    stored here. On the next turn, the proxy retrieves them so P can
+    read D's blocks via bidirectional KV transfer.
+    """
+
+    def __init__(self, ttl_seconds: float = 600.0) -> None:
+        self._store: dict[str, CachedKVEntry] = {}
+        self._ttl = ttl_seconds
+
+    def get(self, conversation_id: str) -> dict[str, Any] | None:
+        """Retrieve and consume cached KV params for a conversation.
+
+        Returns a *copy* of the kv_transfer_params dict, or None.
+        The entry is removed after retrieval (single-use).
+        """
+        entry = self._store.pop(conversation_id, None)
+        if entry is None:
+            return None
+        age = time.time() - entry.timestamp
+        if age > self._ttl:
+            logger.info(
+                "conv=%s: stale cache entry (age=%.1fs > ttl=%.1fs), discarding",
+                conversation_id,
+                age,
+                self._ttl,
+            )
+            return None
+        logger.info(
+            "conv=%s: cache HIT (age=%.1fs)",
+            conversation_id,
+            age,
+        )
+        return dict(entry.kv_transfer_params)
+
+    def put(self, conversation_id: str, kv_params: dict[str, Any]) -> None:
+        """Store D's kv_transfer_params for a conversation."""
+        self._store[conversation_id] = CachedKVEntry(
+            kv_transfer_params=dict(kv_params),  # defensive copy
+        )
+        logger.info(
+            "conv=%s: cached D blocks (remote_request_id=%s, blocks=%d)",
+            conversation_id,
+            kv_params.get("remote_request_id", "?"),
+            len(kv_params.get("remote_block_ids", [[]])[0])
+            if kv_params.get("remote_block_ids")
+            else 0,
+        )
+
+    def evict_stale(self) -> int:
+        """Remove entries older than TTL. Returns count of evicted entries."""
+        now = time.time()
+        stale = [
+            cid
+            for cid, entry in self._store.items()
+            if now - entry.timestamp > self._ttl
+        ]
+        for cid in stale:
+            del self._store[cid]
+        return len(stale)
+
+    @property
+    def size(self) -> int:
+        return len(self._store)
+
+
+# Global state
+kv_cache = ConversationKVCache(
+    ttl_seconds=450.0
+)  # Must be < VLLM_NIXL_ABORT_REQUEST_TIMEOUT (480s)
+
+
+# Service client helpers
+@dataclass
+class ServiceClient:
+    """Wrapper around an httpx.AsyncClient for a P or D instance."""
+
+    client: httpx.AsyncClient
+    host: str
+    port: int
+    id: int
+
+
+def _make_headers(request_id: str) -> dict[str, str]:
+    """Build HTTP headers for upstream requests."""
+    headers = {"X-Request-Id": request_id}
+    api_key = os.environ.get("OPENAI_API_KEY")
+    if api_key:
+        headers["Authorization"] = f"Bearer {api_key}"
+    return headers
+
+
+async def _send_to_prefill(
+    client: ServiceClient,
+    endpoint: str,
+    req_data: dict[str, Any],
+    request_id: str,
+) -> dict[str, Any]:
+    """Send a non-streaming prefill request (max_tokens=1).
+
+    Returns the JSON response from P, which includes kv_transfer_params.
+    """
+    payload = req_data.copy()
+    payload["stream"] = False
+    payload["max_tokens"] = 1
+    payload.pop("max_completion_tokens", None)
+    payload.pop("min_tokens", None)
+    payload.pop("stream_options", None)
+
+    resp = await client.client.post(
+        endpoint,
+        json=payload,
+        headers=_make_headers(request_id),
+    )
+    resp.raise_for_status()
+    return resp.json()
+
+
+async def _stream_from_decode(
+    client: ServiceClient,
+    endpoint: str,
+    req_data: dict[str, Any],
+    request_id: str,
+    conversation_id: str,
+) -> tuple[str, str | None, dict[str, Any] | None, str, str | None, int | None]:
+    """Stream response from D, capturing text and kv_transfer_params.
+
+    Returns (collected_text, finish_reason, kv_params, response_id, created).
+    Also stores kv_params in the conversation cache.
+    """
+    payload = req_data.copy()
+    payload["stream"] = True
+
+    collected_text = ""
+    finish_reason: str | None = None
+    response_id: str | None = None
+    model_name: str | None = None
+    created: int | None = None
+    captured_kv: dict[str, Any] | None = None
+
+    async with client.client.stream(
+        "POST",
+        endpoint,
+        json=payload,
+        headers=_make_headers(request_id),
+    ) as resp:
+        resp.raise_for_status()
+        async for line in resp.aiter_lines():
+            if not line or not line.startswith("data: "):
+                continue
+            if line == "data: [DONE]":
+                break
+            try:
+                chunk = json.loads(line[6:])
+            except json.JSONDecodeError:
+                continue
+
+            if response_id is None:
+                response_id = chunk.get("id")
+                model_name = chunk.get("model")
+                created = chunk.get("created")
+
+            for choice in chunk.get("choices", []):
+                collected_text += choice.get("text", "")
+                delta = choice.get("delta", {})
+                collected_text += delta.get("content", "")
+                if choice.get("finish_reason"):
+                    finish_reason = choice["finish_reason"]
+
+            kv_params = chunk.get("kv_transfer_params")
+            if kv_params:
+                kv_params["remote_host"] = client.host
+                captured_kv = kv_params
+                if conversation_id:
+                    kv_cache.put(conversation_id, kv_params)
+
+    return (
+        collected_text,
+        finish_reason,
+        captured_kv,
+        response_id or request_id,
+        model_name,
+        created,
+    )
+
+
+async def _stream_from_decode_sse(
+    client: ServiceClient,
+    endpoint: str,
+    req_data: dict[str, Any],
+    request_id: str,
+    conversation_id: str,
+):
+    """Yield SSE chunks from D to the client, capturing kv_transfer_params."""
+    payload = req_data.copy()
+    payload["stream"] = True
+
+    async with client.client.stream(
+        "POST",
+        endpoint,
+        json=payload,
+        headers=_make_headers(request_id),
+    ) as resp:
+        resp.raise_for_status()
+        async for line in resp.aiter_lines():
+            if not line:
+                yield "\n"
+                continue
+
+            if line.startswith("data: ") and line != "data: [DONE]":
+                try:
+                    chunk = json.loads(line[6:])
+                    kv_params = chunk.get("kv_transfer_params")
+                    if kv_params and conversation_id:
+                        kv_params["remote_host"] = client.host
+                        kv_cache.put(conversation_id, kv_params)
+                except json.JSONDecodeError:
+                    pass
+
+            yield line + "\n"
+
+
+# FastAPI application
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Initialize HTTP clients for P and D instances."""
+    app.state.prefill_clients: list[ServiceClient] = []
+    app.state.decode_clients: list[ServiceClient] = []
+
+    for i, (host, port) in enumerate(global_args.prefiller_instances):
+        app.state.prefill_clients.append(
+            ServiceClient(
+                client=httpx.AsyncClient(
+                    timeout=None,
+                    base_url=f"http://{host}:{port}/v1",
+                ),
+                host=host,
+                port=port,
+                id=i,
+            )
+        )
+
+    for i, (host, port) in enumerate(global_args.decoder_instances):
+        app.state.decode_clients.append(
+            ServiceClient(
+                client=httpx.AsyncClient(
+                    timeout=None,
+                    base_url=f"http://{host}:{port}/v1",
+                ),
+                host=host,
+                port=port,
+                id=i,
+            )
+        )
+
+    app.state.prefill_iter = itertools.cycle(range(len(app.state.prefill_clients)))
+    app.state.decode_iter = itertools.cycle(range(len(app.state.decode_clients)))
+
+    logger.info(
+        "Ready: %d prefill, %d decode instances",
+        len(app.state.prefill_clients),
+        len(app.state.decode_clients),
+    )
+    yield
+
+    for sc in app.state.prefill_clients + app.state.decode_clients:
+        await sc.client.aclose()
+
+
+app = FastAPI(title="Disaggregated P/D Proxy (Multi-turn)", lifespan=lifespan)
+
+
+def _next_client(app_state, role: str) -> ServiceClient:
+    if role == "prefill":
+        return app_state.prefill_clients[next(app_state.prefill_iter)]
+    return app_state.decode_clients[next(app_state.decode_iter)]
+
+
+# Request handler
+async def _handle_request(api_path: str, request: Request):
+    """Core request handler for both /v1/chat/completions and /v1/completions."""
+    req_data = await request.json()
+    request_id = str(uuid.uuid4())
+    conversation_id: str = req_data.pop("conversation_id", "")
+    client_wants_stream = req_data.get("stream", False)
+
+    if not conversation_id:
+        logger.warning(
+            "[%s] No conversation_id provided — KV cache reuse disabled "
+            "for this request. Add a 'conversation_id' field to enable "
+            "cross-turn KV sharing.",
+            request_id,
+        )
+
+    # Step 1: Look up cached D blocks from the previous turn
+    cached_kv = kv_cache.get(conversation_id) if conversation_id else None
+
+    if cached_kv:
+        # Tell P to read D's blocks (bidirectional transfer)
+        cached_kv["do_remote_decode"] = True
+        cached_kv["do_remote_prefill"] = False
+        req_data["kv_transfer_params"] = cached_kv
+        logger.info(
+            "[%s] conv=%s: sending D's cached blocks to P (remote_request_id=%s)",
+            request_id,
+            conversation_id,
+            cached_kv.get("remote_request_id"),
+        )
+    else:
+        # No cached blocks — P recomputes from scratch
+        req_data["kv_transfer_params"] = {
+            "do_remote_decode": True,
+            "do_remote_prefill": False,
+            "remote_engine_id": None,
+            "remote_block_ids": None,
+            "remote_host": None,
+            "remote_port": None,
+        }
+        logger.info("[%s] conv=%s: cache MISS", request_id, conversation_id)
+
+    # Step 2: Send to Prefill node (non-streaming, max_tokens=1)
+    prefill_client = _next_client(request.app.state, "prefill")
+    t0 = time.time()
+    prefill_resp = await _send_to_prefill(
+        prefill_client,
+        api_path,
+        req_data,
+        request_id,
+    )
+    logger.info(
+        "[%s] Prefill done in %.0fms",
+        request_id,
+        (time.time() - t0) * 1000,
+    )
+
+    # Attach P's kv_transfer_params for D to read P's blocks
+    p_kv_params = prefill_resp.get("kv_transfer_params", {})
+    if p_kv_params:
+        p_kv_params["remote_host"] = prefill_client.host
+        req_data["kv_transfer_params"] = p_kv_params
+
+    # Step 3: Stream from Decode node, capturing kv_transfer_params
+    decode_client = _next_client(request.app.state, "decode")
+
+    if client_wants_stream:
+        return StreamingResponse(
+            _stream_from_decode_sse(
+                decode_client,
+                api_path,
+                req_data,
+                request_id,
+                conversation_id,
+            ),
+            media_type="text/event-stream",
+        )
+
+    text, finish_reason, _, resp_id, model, created = await _stream_from_decode(
+        decode_client,
+        api_path,
+        req_data,
+        request_id,
+        conversation_id,
+    )
+
+    # Build OpenAI-compatible response
+    is_chat = "messages" in req_data
+    if is_chat:
+        body = {
+            "id": resp_id,
+            "object": "chat.completion",
+            "created": created or int(time.time()),
+            "model": model or req_data.get("model", ""),
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {"role": "assistant", "content": text},
+                    "finish_reason": finish_reason,
+                }
+            ],
+            "usage": None,
+        }
+    else:
+        body = {
+            "id": resp_id,
+            "object": "text_completion",
+            "created": created or int(time.time()),
+            "model": model or req_data.get("model", ""),
+            "choices": [
+                {
+                    "index": 0,
+                    "text": text,
+                    "logprobs": None,
+                    "finish_reason": finish_reason,
+                }
+            ],
+            "usage": None,
+        }
+    return JSONResponse(content=body)
+
+
+# Routes
+@app.post("/v1/chat/completions")
+async def chat_completions(request: Request):
+    return await _handle_request("/chat/completions", request)
+
+
+@app.post("/v1/completions")
+async def completions(request: Request):
+    return await _handle_request("/completions", request)
+
+
+@app.get("/health")
+async def health():
+    evicted = kv_cache.evict_stale()
+    return {
+        "status": "ok",
+        "cached_conversations": kv_cache.size,
+        "evicted_stale": evicted,
+    }
+
+
+# CLI
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(
+        description="Disaggregated P/D proxy with bidirectional KV transfer",
+    )
+    p.add_argument("--host", default="0.0.0.0")
+    p.add_argument("--port", type=int, default=8000)
+    p.add_argument(
+        "--prefiller-host",
+        "--prefiller-hosts",
+        dest="prefiller_hosts",
+        nargs="+",
+        default=["localhost"],
+    )
+    p.add_argument(
+        "--prefiller-port",
+        "--prefiller-ports",
+        dest="prefiller_ports",
+        type=int,
+        nargs="+",
+        default=[8100],
+    )
+    p.add_argument(
+        "--decoder-host",
+        "--decoder-hosts",
+        dest="decoder_hosts",
+        nargs="+",
+        default=["localhost"],
+    )
+    p.add_argument(
+        "--decoder-port",
+        "--decoder-ports",
+        dest="decoder_ports",
+        type=int,
+        nargs="+",
+        default=[8200],
+    )
+    args = p.parse_args()
+
+    if len(args.prefiller_hosts) != len(args.prefiller_ports):
+        p.error("Number of prefiller hosts must match ports")
+    if len(args.decoder_hosts) != len(args.decoder_ports):
+        p.error("Number of decoder hosts must match ports")
+
+    args.prefiller_instances = list(zip(args.prefiller_hosts, args.prefiller_ports))
+    args.decoder_instances = list(zip(args.decoder_hosts, args.decoder_ports))
+    return args
+
+
+if __name__ == "__main__":
+    global global_args
+    global_args = parse_args()
+
+    import uvicorn
+
+    uvicorn.run(app, host=global_args.host, port=global_args.port)
diff --git a/examples/disaggregated/disaggregated_serving/example_mm_serve.py b/examples/disaggregated/disaggregated_serving/example_mm_serve.py
new file mode 100644
index 000000000000..11d81236c577
--- /dev/null
+++ b/examples/disaggregated/disaggregated_serving/example_mm_serve.py
@@ -0,0 +1,117 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Disaggregated multimodal serving: render → generate round-trip.
+
+Demonstrates the two-phase disaggregated flow:
+  1. /v1/chat/completions/render  – preprocesses a multimodal chat request
+     into token IDs and serialized tensor features.
+  2. /inference/v1/generate       – runs inference on the preprocessed tokens.
+
+The render response is passed *directly* to generate with only
+``sampling_params`` added, showing that the two endpoints compose with
+zero client-side transformation.
+
+Launch the server first:
+
+    vllm serve Qwen/Qwen3-VL-2B-Instruct \
+        --dtype bfloat16 --max-model-len 4096 --enforce-eager
+
+Then run this script:
+
+    python example_mm_serve.py
+"""
+
+import io
+
+import pybase64 as base64
+import requests
+from PIL import Image
+from transformers import AutoTokenizer
+
+BASE_URL = "http://localhost:8000"
+MODEL_NAME = "Qwen/Qwen3-VL-2B-Instruct"
+
+
+def make_data_url(image: Image.Image) -> str:
+    """Encode a PIL image as a base64 data URL."""
+    buf = io.BytesIO()
+    image.save(buf, format="PNG")
+    b64 = base64.b64encode(buf.getvalue()).decode()
+    return f"data:image/png;base64,{b64}"
+
+
+def main():
+    # -- Step 1: Create a test image (solid red) -------------------------
+    image = Image.new("RGB", (224, 224), color=(255, 0, 0))
+    data_url = make_data_url(image)
+    print("Created 224x224 red test image")
+
+    # -- Step 2: Render (preprocess) -------------------------------------
+    render_payload = {
+        "model": MODEL_NAME,
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image_url", "image_url": {"url": data_url}},
+                    {
+                        "type": "text",
+                        "text": "What color is this image? Answer in one word.",
+                    },
+                ],
+            }
+        ],
+    }
+
+    print("\n--- Render ---")
+    render_resp = requests.post(
+        f"{BASE_URL}/v1/chat/completions/render", json=render_payload
+    )
+    render_resp.raise_for_status()
+    render_data = render_resp.json()
+
+    print(f"Response keys: {list(render_data.keys())}")
+    print(f"Number of token_ids: {len(render_data['token_ids'])}")
+
+    features = render_data.get("features")
+    if features and features.get("kwargs_data"):
+        print(f"kwargs_data modalities: {list(features['kwargs_data'].keys())}")
+        for modality, items in features["kwargs_data"].items():
+            print(
+                f"  {modality}: {len(items)} item(s), "
+                f"first item type: {type(items[0])} length: {len(items[0])}"
+                if items
+                else "First item: (empty)"
+            )
+    else:
+        print("WARNING: no kwargs_data in render response")
+
+    # -- Step 3: Generate (inference) ------------------------------------
+    # Pass the render output directly — only add sampling_params.
+    generate_payload = render_data
+    generate_payload["sampling_params"] = {
+        "max_tokens": 20,
+        "temperature": 0.0,
+    }
+
+    print("\n--- Generate ---")
+    gen_resp = requests.post(f"{BASE_URL}/inference/v1/generate", json=generate_payload)
+    gen_resp.raise_for_status()
+    gen_data = gen_resp.json()
+
+    # -- Step 4: Decode & print ------------------------------------------
+    output_ids = gen_data["choices"][0]["token_ids"]
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    text = tokenizer.decode(output_ids, skip_special_tokens=True)
+
+    print(f"Output token count: {len(output_ids)}")
+    print(f"Generated text: {text!r}")
+
+    if "red" in text.lower():
+        print("\nModel correctly identified the red image.")
+    else:
+        print(f"\nWARNING: Expected 'red' in output, got: {text!r}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/online_serving/disaggregated_serving/kv_events.sh b/examples/disaggregated/disaggregated_serving/kv_events.sh
similarity index 100%
rename from examples/online_serving/disaggregated_serving/kv_events.sh
rename to examples/disaggregated/disaggregated_serving/kv_events.sh
diff --git a/examples/disaggregated/disaggregated_serving/moriio_toy_proxy_server.py b/examples/disaggregated/disaggregated_serving/moriio_toy_proxy_server.py
new file mode 100644
index 000000000000..aceb7a9b81c3
--- /dev/null
+++ b/examples/disaggregated/disaggregated_serving/moriio_toy_proxy_server.py
@@ -0,0 +1,418 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import argparse
+import asyncio
+import copy
+import logging
+import os
+import socket
+import threading
+import uuid
+from urllib.parse import urlparse
+
+import aiohttp
+import msgpack
+import zmq
+from quart import Quart, Request, make_response, request
+
+from vllm.distributed.kv_transfer.kv_connector.v1.moriio.moriio_common import (
+    MoRIIOConstants,
+)
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+prefill_instances: list[dict] = []
+decode_instances: list[dict] = []
+request_nums = 0
+app = Quart(__name__)
+
+
+TRANSFER_TYPE = None
+
+
+_list_lock = threading.RLock()
+
+
+def _listen_for_register(hostname, port):
+    context = zmq.Context()
+    router_socket = context.socket(zmq.ROUTER)
+    router_socket.bind(f"tcp://{hostname}:{port}")
+    poller = zmq.Poller()
+    poller.register(router_socket, zmq.POLLIN)
+    global prefill_instances
+    global decode_instances
+
+    while True:
+        socks = dict(poller.poll())
+        if router_socket in socks:
+            remote_addr, msg = router_socket.recv_multipart()
+            data = msgpack.loads(msg)
+            if data.get("type") == "HELLO":
+                pass
+            elif data.get("type") in ("P", "D"):
+                role = data["type"]
+                required_keys = {
+                    "http_address",
+                    "zmq_address",
+                    "dp_size",
+                    "tp_size",
+                    "transfer_mode",
+                }
+                missing = required_keys - data.keys()
+                if missing:
+                    logger.error(
+                        "Registration message missing required keys %s; skipping",
+                        missing,
+                    )
+                    continue
+                # Derive request_address from http_address
+                # api path suffix is appended at request time
+                instance = {
+                    "role": role,
+                    "request_address": f"http://{data['http_address']}/v1",
+                    "http_address": data["http_address"],
+                    "zmq_address": data["zmq_address"],
+                    "dp_size": data["dp_size"],
+                    "tp_size": data["tp_size"],
+                    "transfer_mode": data["transfer_mode"],
+                }
+                # zmq_address format: "host:IP,handshake:PORT,notify:PORT"
+                # Stored verbatim; embedded into the request_id by handle_request.
+
+                global TRANSFER_TYPE
+                transfer_mode = instance["transfer_mode"]
+                target_list = prefill_instances if role == "P" else decode_instances
+                with _list_lock:
+                    if TRANSFER_TYPE is None:
+                        TRANSFER_TYPE = transfer_mode
+                        logger.info("SET TRANSFER TYPE TO %s", TRANSFER_TYPE)
+                    elif transfer_mode != TRANSFER_TYPE:
+                        logger.error(
+                            "Mismatched transfer mode: expected %s, got %s;"
+                            " skipping registration of %s",
+                            TRANSFER_TYPE,
+                            transfer_mode,
+                            data["http_address"],
+                        )
+                        continue
+                    existing_idx = next(
+                        (
+                            idx
+                            for idx, i in enumerate(target_list)
+                            if i.get("http_address") == data["http_address"]
+                        ),
+                        None,
+                    )
+                    if existing_idx is not None:
+                        target_list[existing_idx] = instance
+                        logger.info(
+                            "Updated existing %s instance: %s",
+                            "Prefill" if role == "P" else "Decode",
+                            instance,
+                        )
+                    else:
+                        target_list.append(instance)
+                        logger.info(
+                            "Registered %s instance: %s",
+                            "Prefill" if role == "P" else "Decode",
+                            instance,
+                        )
+            else:
+                logger.warning(
+                    "Received message with unrecognized type %r; ignoring",
+                    data.get("type"),
+                )
+
+
+def start_service_discovery(hostname, port):
+    if not hostname:
+        hostname = socket.gethostname()
+    if port == 0:
+        raise ValueError("Port cannot be 0")
+
+    _listener_thread = threading.Thread(
+        target=_listen_for_register, args=(hostname, port), daemon=True
+    )
+    _listener_thread.start()
+    return _listener_thread
+
+
+async def send_request_to_prefill(
+    endpoint, req_data, request_id, selected_prefill_dp_rank
+):
+    req_data_copy = req_data
+
+    req_data_copy["kv_transfer_params"].update(
+        {
+            "do_remote_decode": True,
+            "do_remote_prefill": False,
+            "remote_engine_id": None,
+            "remote_block_ids": None,
+        }
+    )
+    req_data_copy["stream"] = False
+    req_data_copy["max_tokens"] = 1
+    if "max_completion_tokens" in req_data_copy:
+        req_data_copy["max_completion_tokens"] = 1
+    if "stream_options" in req_data_copy:
+        del req_data_copy["stream_options"]
+    async with aiohttp.ClientSession(
+        timeout=aiohttp.ClientTimeout(total=6 * 6000 * 6000)
+    ) as session:
+        headers = {
+            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
+            "X-Request-Id": request_id,
+        }
+        if selected_prefill_dp_rank is not None:
+            headers["X-data-parallel-rank"] = str(selected_prefill_dp_rank)
+        async with session.post(
+            url=endpoint, json=req_data_copy, headers=headers
+        ) as response:
+            if response.status == 200:
+                return await response.json()
+
+            else:
+                error_message = (
+                    f"send_request_to_prefill response ={response},"
+                    f"reason={response.reason}, status={response.status},"
+                    f"method={response.method}, url={response.url},"
+                    f"real_url={response.real_url}"
+                )
+                raise RuntimeError(error_message)
+
+
+async def start_decode_request(endpoint, req_data, request_id):
+    session = aiohttp.ClientSession(
+        timeout=aiohttp.ClientTimeout(total=6 * 6000 * 6000)
+    )
+    headers = {
+        "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
+        "X-Request-Id": request_id,
+    }
+    response = await session.post(url=endpoint, json=req_data, headers=headers)
+    return session, response
+
+
+async def stream_decode_response(session, response, request_id):
+    try:
+        if response.status == 200:
+            async for chunk_bytes in response.content.iter_chunked(1024):
+                yield chunk_bytes
+        else:
+            error_message = (
+                f"stream_decode_response response ={response},"
+                f"reason={response.reason}, status={response.status},"
+                f"method={response.method}, url={response.url},"
+                f"real_url={response.real_url}"
+            )
+            raise RuntimeError(error_message)
+    finally:
+        await session.close()
+
+
+def example_round_robin_dp_loader(request_number, dp_size):
+    return request_nums % dp_size
+
+
+@app.route("/v1/completions", methods=["POST"])
+async def handle_completions_request():
+    return await handle_request("/completions", request)
+
+
+@app.route("/v1/chat/completions", methods=["POST"])
+async def handle_chat_completions_request():
+    return await handle_request("/chat/completions", request)
+
+
+async def handle_request(api: str, request: Request):
+    try:
+        with _list_lock:
+            global request_nums
+            request_nums += 1
+
+        req_data = await request.get_json()
+
+        prefill_instance_endpoint = None
+        decode_instance_endpoint = None
+        error_msg = (
+            "Service Unavailable: No prefill or decode instances are registered."
+        )
+        if not prefill_instances or not decode_instances:
+            return await make_response(
+                (
+                    error_msg,
+                    503,
+                )
+            )
+        pid = request_nums % len(prefill_instances)
+        did = request_nums % len(decode_instances)
+        prefill_instance_endpoint = prefill_instances[pid]
+        decode_instance_endpoint = decode_instances[did]
+
+        selected_prefill_dp_rank = None
+        if prefill_instance_endpoint["dp_size"] > 1:
+            selected_prefill_dp_rank = example_round_robin_dp_loader(
+                request_nums // len(prefill_instance_endpoint),
+                prefill_instance_endpoint["dp_size"],
+            )
+
+        # Embed both zmq_addresses in the request_id so the connector can parse
+        # the peer's host/ports from it, similar to P2P-NCCL
+        uid = str(uuid.uuid4()).replace("-", "")
+        request_id = (
+            f"___prefill_addr_{prefill_instance_endpoint['zmq_address']}"
+            f"___decode_addr_{decode_instance_endpoint['zmq_address']}"
+            f"_{uid}"
+        )
+
+        transfer_id = f"{MoRIIOConstants.TRANSFER_PREFIX}-{str(uuid.uuid4())}"
+
+        req_data_to_prefill = copy.deepcopy(req_data)
+        req_data_to_prefill["kv_transfer_params"] = {}
+        req_data["kv_transfer_params"] = {}
+        req_data_to_prefill["kv_transfer_params"]["remote_dp_size"] = (
+            decode_instance_endpoint["dp_size"]
+        )
+        req_data_to_prefill["kv_transfer_params"]["remote_tp_size"] = (
+            decode_instance_endpoint["tp_size"]
+        )
+        req_data_to_prefill["kv_transfer_params"]["transfer_id"] = transfer_id
+
+        prefill_request_url = prefill_instance_endpoint["request_address"] + api
+        send_prefill_task = asyncio.create_task(
+            send_request_to_prefill(
+                prefill_request_url,
+                req_data_to_prefill,
+                request_id,
+                selected_prefill_dp_rank,
+            )
+        )
+
+        req_data["max_tokens"] -= 1
+
+        req_data["kv_transfer_params"] = {
+            "do_remote_decode": False,
+            "do_remote_prefill": True,
+            "remote_engine_id": None,
+            "remote_block_ids": None,
+            "transfer_id": transfer_id,
+        }
+        if TRANSFER_TYPE == "READ":
+            # In read mode, prefill and decode are executed serially.
+            prefill_response = await send_prefill_task
+            prefill_kv = prefill_response["kv_transfer_params"]
+            req_data["kv_transfer_params"]["remote_engine_id"] = prefill_kv[
+                "remote_engine_id"
+            ]
+            req_data["kv_transfer_params"]["remote_block_ids"] = prefill_kv[
+                "remote_block_ids"
+            ]
+            req_data["kv_transfer_params"]["transfer_id"] = prefill_kv["transfer_id"]
+
+        req_data["kv_transfer_params"]["remote_dp_size"] = prefill_instance_endpoint[
+            "dp_size"
+        ]
+        req_data["kv_transfer_params"]["remote_tp_size"] = prefill_instance_endpoint[
+            "tp_size"
+        ]
+
+        if selected_prefill_dp_rank is not None:
+            req_data["kv_transfer_params"]["remote_dp_rank"] = selected_prefill_dp_rank
+
+        decode_request_url = decode_instance_endpoint["request_address"] + api
+        decode_request_task = asyncio.create_task(
+            start_decode_request(decode_request_url, req_data, request_id)
+        )
+
+        session, decode_response = await decode_request_task
+        stream_generator = stream_decode_response(session, decode_response, request_id)
+        response = await make_response(stream_generator)
+        return response
+    except Exception as e:
+        logger.exception("An error occurred while handling the request: %s", e)
+        return await make_response(
+            (
+                f"Internal Server Error: {e!s}",
+                500,
+            )
+        )
+
+
+async def send_profile_cmd(req_data: dict, profiler_cmd: str):
+    assert profiler_cmd in {"start", "stop"}
+
+    with _list_lock:
+        p_instances = list(prefill_instances)
+        d_instances = list(decode_instances)
+
+    if not p_instances and not d_instances:
+        raise RuntimeError(
+            "Service Unavailable: No prefill or decode instances are registered."
+        )
+
+    headers = {
+        "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
+    }
+
+    tasks = []
+
+    async with aiohttp.ClientSession(
+        timeout=aiohttp.ClientTimeout(total=60)
+    ) as session:
+        for instances in (p_instances, d_instances):
+            for inst in instances:
+                _p = urlparse(inst["request_address"])
+                url = f"http://{_p.hostname}:{_p.port}/{profiler_cmd}_profile"
+
+                tasks.append(
+                    session.post(
+                        url,
+                        json=req_data,
+                        headers=headers,
+                    )
+                )
+
+        responses = await asyncio.gather(*tasks, return_exceptions=True)
+
+        for r in responses:
+            if isinstance(r, Exception):
+                raise r
+            if r.status >= 400:
+                msg = await r.text()
+                raise RuntimeError(f"{profiler_cmd}_profile failed: {r.status}, {msg}")
+
+        return await responses[0].json()
+
+
+@app.post("/start_profile")
+async def start_profile():
+    try:
+        req_data = await request.get_json()
+        return await send_profile_cmd(req_data, "start")
+    except Exception as e:
+        logger.exception("start_profile failed: %s", e)
+        return await make_response((str(e), 500))
+
+
+@app.post("/stop_profile")
+async def stop_profile():
+    try:
+        req_data = await request.get_json()
+        return await send_profile_cmd(req_data, "stop")
+    except Exception as e:
+        logger.exception("stop_profile failed: %s", e)
+        return await make_response((str(e), 500))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--port", type=int, default=10001)
+    args = parser.parse_args()
+
+    t = start_service_discovery("0.0.0.0", 36367)
+    app.debug = True
+    app.config["BODY_TIMEOUT"] = 360000
+    app.config["RESPONSE_TIMEOUT"] = 360000
+
+    app.run(host="0.0.0.0", port=args.port)
+    t.join()
diff --git a/examples/online_serving/ec_both_encoder/ec_both_encoder.sh b/examples/disaggregated/ec_both_encoder/ec_both_encoder.sh
old mode 100755
new mode 100644
similarity index 100%
rename from examples/online_serving/ec_both_encoder/ec_both_encoder.sh
rename to examples/disaggregated/ec_both_encoder/ec_both_encoder.sh
diff --git a/examples/offline_inference/disaggregated-prefill-v1/README.md b/examples/disaggregated/example_connector/README.md
similarity index 81%
rename from examples/offline_inference/disaggregated-prefill-v1/README.md
rename to examples/disaggregated/example_connector/README.md
index abf6883f8d3e..43f16223896c 100644
--- a/examples/offline_inference/disaggregated-prefill-v1/README.md
+++ b/examples/disaggregated/example_connector/README.md
@@ -5,6 +5,6 @@ This example contains scripts that demonstrate disaggregated prefill in the offl
 ## Files
 
 - `run.sh` - A helper script that will run `prefill_example.py` and `decode_example.py` sequentially.
-    - Make sure you are in the `examples/offline_inference/disaggregated-prefill-v1` directory before running `run.sh`.
+    - Make sure you are in the `examples/disaggregated/example_connector` directory before running `run.sh`.
 - `prefill_example.py` - A script which performs prefill only, saving the KV state to the `local_storage` directory and the prompts to `output.txt`.
 - `decode_example.py` - A script which performs decode only, loading the KV state from the `local_storage` directory and the prompts from `output.txt`.
diff --git a/examples/offline_inference/disaggregated-prefill-v1/decode_example.py b/examples/disaggregated/example_connector/decode_example.py
similarity index 100%
rename from examples/offline_inference/disaggregated-prefill-v1/decode_example.py
rename to examples/disaggregated/example_connector/decode_example.py
diff --git a/examples/offline_inference/disaggregated-prefill-v1/prefill_example.py b/examples/disaggregated/example_connector/prefill_example.py
similarity index 100%
rename from examples/offline_inference/disaggregated-prefill-v1/prefill_example.py
rename to examples/disaggregated/example_connector/prefill_example.py
diff --git a/examples/offline_inference/disaggregated-prefill-v1/run.sh b/examples/disaggregated/example_connector/run.sh
similarity index 100%
rename from examples/offline_inference/disaggregated-prefill-v1/run.sh
rename to examples/disaggregated/example_connector/run.sh
diff --git a/examples/offline_inference/prefix_caching_flexkv.py b/examples/disaggregated/flexkv_connector/prefix_caching_flexkv.py
similarity index 98%
rename from examples/offline_inference/prefix_caching_flexkv.py
rename to examples/disaggregated/flexkv_connector/prefix_caching_flexkv.py
index f2ffb75ef845..b67c2459319f 100644
--- a/examples/offline_inference/prefix_caching_flexkv.py
+++ b/examples/disaggregated/flexkv_connector/prefix_caching_flexkv.py
@@ -14,7 +14,7 @@
 
 Usage:
     1. Run this script:
-       python examples/offline_inference/prefix_caching_flexkv.py \
+       python examples/disaggregated/flexkv_connector/prefix_caching_flexkv.py \
            --model /path/to/your/model
 
     2. Arguments:
diff --git a/examples/offline_inference/kv_load_failure_recovery/README.md b/examples/disaggregated/kv_load_failure_recovery_offline/README.md
similarity index 91%
rename from examples/offline_inference/kv_load_failure_recovery/README.md
rename to examples/disaggregated/kv_load_failure_recovery_offline/README.md
index 176141b5de4a..7205b2135ebb 100644
--- a/examples/offline_inference/kv_load_failure_recovery/README.md
+++ b/examples/disaggregated/kv_load_failure_recovery_offline/README.md
@@ -1,12 +1,12 @@
 # KV Load Failure Recovery Test
 
-This example builds upon the `disaggregated-prefill-v1` example in `examples/offline_inference`.
+This example builds upon the `example_connector` example in `examples/disaggregated`.
 
 It demonstrates vLLM's ability to recover from KV load failures in both synchronous and asynchronous loading modes. The goal is to verify that vLLM correctly identifies invalid KV blocks, reschedules the affected requests, and ensures successful and consistent output.
 
 ## Files
 
-- `prefill_example.py` – performs the prefill stage and saves KV data (same as in `disaggregated-prefill-v1`).
+- `prefill_example.py` – performs the prefill stage and saves KV data (same as in `example_connector`).
 - `decode_example.py` – performs the decode stage. Accepts:
     - `--simulate-failure`: simulates KV load failure using a custom connector.
     - `--async-load`: enables asynchronous KV loading mode.
diff --git a/examples/offline_inference/kv_load_failure_recovery/decode_example.py b/examples/disaggregated/kv_load_failure_recovery_offline/decode_example.py
similarity index 100%
rename from examples/offline_inference/kv_load_failure_recovery/decode_example.py
rename to examples/disaggregated/kv_load_failure_recovery_offline/decode_example.py
diff --git a/examples/offline_inference/kv_load_failure_recovery/load_recovery_example_connector.py b/examples/disaggregated/kv_load_failure_recovery_offline/load_recovery_example_connector.py
similarity index 93%
rename from examples/offline_inference/kv_load_failure_recovery/load_recovery_example_connector.py
rename to examples/disaggregated/kv_load_failure_recovery_offline/load_recovery_example_connector.py
index 7aab07f8a2c3..5bae04426746 100644
--- a/examples/offline_inference/kv_load_failure_recovery/load_recovery_example_connector.py
+++ b/examples/disaggregated/kv_load_failure_recovery_offline/load_recovery_example_connector.py
@@ -20,6 +20,7 @@
 
 if TYPE_CHECKING:
     from vllm.v1.core.sched.output import SchedulerOutput
+    from vllm.v1.kv_cache_interface import KVCacheConfig
 
 logger = logging.getLogger()
 logging.basicConfig(level=logging.INFO)
@@ -35,8 +36,17 @@ def from_base(cls, base: ExampleConnectorMetadata):
 
 
 class LoadRecoveryExampleConnector(ExampleConnector):
-    def __init__(self, vllm_config: "VllmConfig", role: KVConnectorRole):
-        super().__init__(vllm_config=vllm_config, role=role)
+    def __init__(
+        self,
+        vllm_config: "VllmConfig",
+        role: KVConnectorRole,
+        kv_cache_config: "KVCacheConfig",
+    ):
+        super().__init__(
+            vllm_config=vllm_config,
+            role=role,
+            kv_cache_config=kv_cache_config,
+        )
         self._async_load = vllm_config.kv_transfer_config.get_from_extra_config(
             "async_load", False
         )
diff --git a/examples/offline_inference/kv_load_failure_recovery/prefill_example.py b/examples/disaggregated/kv_load_failure_recovery_offline/prefill_example.py
similarity index 100%
rename from examples/offline_inference/kv_load_failure_recovery/prefill_example.py
rename to examples/disaggregated/kv_load_failure_recovery_offline/prefill_example.py
diff --git a/examples/offline_inference/kv_load_failure_recovery/run.sh b/examples/disaggregated/kv_load_failure_recovery_offline/run.sh
old mode 100755
new mode 100644
similarity index 100%
rename from examples/offline_inference/kv_load_failure_recovery/run.sh
rename to examples/disaggregated/kv_load_failure_recovery_offline/run.sh
diff --git a/examples/others/lmcache/README.md b/examples/disaggregated/lmcache/README.md
similarity index 100%
rename from examples/others/lmcache/README.md
rename to examples/disaggregated/lmcache/README.md
diff --git a/examples/others/lmcache/cpu_offload_lmcache.py b/examples/disaggregated/lmcache/cpu_offload_lmcache.py
similarity index 100%
rename from examples/others/lmcache/cpu_offload_lmcache.py
rename to examples/disaggregated/lmcache/cpu_offload_lmcache.py
diff --git a/examples/others/lmcache/disagg_prefill_lmcache_v0.py b/examples/disaggregated/lmcache/disagg_prefill_lmcache_v0.py
similarity index 100%
rename from examples/others/lmcache/disagg_prefill_lmcache_v0.py
rename to examples/disaggregated/lmcache/disagg_prefill_lmcache_v0.py
diff --git a/examples/others/lmcache/disagg_prefill_lmcache_v1/configs/lmcache-decoder-config.yaml b/examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/configs/lmcache-decoder-config.yaml
similarity index 100%
rename from examples/others/lmcache/disagg_prefill_lmcache_v1/configs/lmcache-decoder-config.yaml
rename to examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/configs/lmcache-decoder-config.yaml
diff --git a/examples/others/lmcache/disagg_prefill_lmcache_v1/configs/lmcache-prefiller-config.yaml b/examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/configs/lmcache-prefiller-config.yaml
similarity index 100%
rename from examples/others/lmcache/disagg_prefill_lmcache_v1/configs/lmcache-prefiller-config.yaml
rename to examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/configs/lmcache-prefiller-config.yaml
diff --git a/examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh b/examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh
similarity index 100%
rename from examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh
rename to examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh
diff --git a/examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_proxy_server.py b/examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/disagg_proxy_server.py
similarity index 100%
rename from examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_proxy_server.py
rename to examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/disagg_proxy_server.py
diff --git a/examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_vllm_launcher.sh b/examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/disagg_vllm_launcher.sh
similarity index 100%
rename from examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_vllm_launcher.sh
rename to examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/disagg_vllm_launcher.sh
diff --git a/examples/others/lmcache/kv_cache_sharing_lmcache_v1.py b/examples/disaggregated/lmcache/kv_cache_sharing_lmcache_v1.py
similarity index 100%
rename from examples/others/lmcache/kv_cache_sharing_lmcache_v1.py
rename to examples/disaggregated/lmcache/kv_cache_sharing_lmcache_v1.py
diff --git a/examples/online_serving/disaggregated_serving/mooncake_connector/mooncake_connector_proxy.py b/examples/disaggregated/mooncake_connector/mooncake_connector_proxy.py
similarity index 100%
rename from examples/online_serving/disaggregated_serving/mooncake_connector/mooncake_connector_proxy.py
rename to examples/disaggregated/mooncake_connector/mooncake_connector_proxy.py
diff --git a/examples/online_serving/disaggregated_serving/mooncake_connector/run_mooncake_connector.sh b/examples/disaggregated/mooncake_connector/run_mooncake_connector.sh
similarity index 100%
rename from examples/online_serving/disaggregated_serving/mooncake_connector/run_mooncake_connector.sh
rename to examples/disaggregated/mooncake_connector/run_mooncake_connector.sh
diff --git a/examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh b/examples/disaggregated/p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh
similarity index 100%
rename from examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh
rename to examples/disaggregated/p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh
diff --git a/examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_proxy_p2p_nccl_xpyd.py b/examples/disaggregated/p2p_nccl_xpyd/disagg_proxy_p2p_nccl_xpyd.py
similarity index 100%
rename from examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_proxy_p2p_nccl_xpyd.py
rename to examples/disaggregated/p2p_nccl_xpyd/disagg_proxy_p2p_nccl_xpyd.py
diff --git a/examples/offline_inference/automatic_prefix_caching.py b/examples/features/automatic_prefix_caching/automatic_prefix_caching_offline.py
similarity index 98%
rename from examples/offline_inference/automatic_prefix_caching.py
rename to examples/features/automatic_prefix_caching/automatic_prefix_caching_offline.py
index 2d3c28d9dd4f..801b4b769792 100644
--- a/examples/offline_inference/automatic_prefix_caching.py
+++ b/examples/features/automatic_prefix_caching/automatic_prefix_caching_offline.py
@@ -15,7 +15,7 @@
 but ask different questions.
 
 Run:
-python examples/offline_inference/automatic_prefix_caching.py
+python examples/features/automatic_prefix_caching/automatic_prefix_caching_offline.py
 """
 
 import time
diff --git a/examples/offline_inference/prefix_caching.py b/examples/features/automatic_prefix_caching/prefix_caching_offline.py
similarity index 100%
rename from examples/offline_inference/prefix_caching.py
rename to examples/features/automatic_prefix_caching/prefix_caching_offline.py
diff --git a/examples/offline_inference/reproducibility.py b/examples/features/batch_invariance/reproducibility_offline.py
similarity index 100%
rename from examples/offline_inference/reproducibility.py
rename to examples/features/batch_invariance/reproducibility_offline.py
diff --git a/examples/offline_inference/context_extension.py b/examples/features/context_extension/context_extension_offline.py
similarity index 96%
rename from examples/offline_inference/context_extension.py
rename to examples/features/context_extension/context_extension_offline.py
index fae8590f914e..3874288b5e11 100644
--- a/examples/offline_inference/context_extension.py
+++ b/examples/features/context_extension/context_extension_offline.py
@@ -6,7 +6,7 @@
 and run a simple chat example.
 
 Usage:
-    python examples/offline_inference/context_extension.py
+    python examples/features/context_extension/context_extension_offline.py
 """
 
 from vllm import LLM, RequestOutput, SamplingParams
diff --git a/examples/offline_inference/data_parallel.py b/examples/features/data_parallel/data_parallel_offline.py
similarity index 96%
rename from examples/offline_inference/data_parallel.py
rename to examples/features/data_parallel/data_parallel_offline.py
index 287409fa2b5c..c38ff7297afc 100644
--- a/examples/offline_inference/data_parallel.py
+++ b/examples/features/data_parallel/data_parallel_offline.py
@@ -3,14 +3,14 @@
 """
 Usage:
 Single node:
-    python examples/offline_inference/data_parallel.py \
+    python examples/features/data_parallel/data_parallel_offline.py \
             --model="ibm-research/PowerMoE-3b" \
             -dp=2 \
             -tp=2
 
 Multi-node:
     Node 0 (assume the node has ip of 10.99.48.128):
-            python examples/offline_inference/data_parallel.py \
+            python examples/features/data_parallel/data_parallel_offline.py \
                     --model="ibm-research/PowerMoE-3b" \
                     -dp=2 \
                     -tp=2 \
@@ -19,7 +19,7 @@
                     --dp-master-addr=10.99.48.128 \
                     --dp-master-port=13345
     Node 1:
-            python examples/offline_inference/data_parallel.py \
+            python examples/features/data_parallel/data_parallel_offline.py \
                     --model="ibm-research/PowerMoE-3b" \
                     -dp=2 \
                     -tp=2 \
diff --git a/examples/online_serving/multi_instance_data_parallel.py b/examples/features/data_parallel/multi_instance_data_parallel.py
similarity index 97%
rename from examples/online_serving/multi_instance_data_parallel.py
rename to examples/features/data_parallel/multi_instance_data_parallel.py
index 04d21e048940..66fcd3d24644 100644
--- a/examples/online_serving/multi_instance_data_parallel.py
+++ b/examples/features/data_parallel/multi_instance_data_parallel.py
@@ -12,7 +12,7 @@
 """
 To run this example, run the following commands simultaneously with
 different CUDA_VISIBLE_DEVICES:
-    python examples/online_serving/multi_instance_data_parallel.py
+    python examples/features/data_parallel/multi_instance_data_parallel.py
 
     vllm serve ibm-research/PowerMoE-3b -dp 2 -dpr 1 \
         --data-parallel-address 127.0.0.1 --data-parallel-rpc-port 62300 \
diff --git a/examples/online_serving/kv_events_subscriber.py b/examples/features/kv_events/kv_events_subscriber.py
similarity index 100%
rename from examples/online_serving/kv_events_subscriber.py
rename to examples/features/kv_events/kv_events_subscriber.py
diff --git a/examples/others/logging_configuration.md b/examples/features/logging_configuration.md
similarity index 100%
rename from examples/others/logging_configuration.md
rename to examples/features/logging_configuration.md
diff --git a/examples/offline_inference/logits_processor/README.md b/examples/features/logits_processor/README.md
similarity index 90%
rename from examples/offline_inference/logits_processor/README.md
rename to examples/features/logits_processor/README.md
index 6b6e16942f85..07ca07dc71ed 100644
--- a/examples/offline_inference/logits_processor/README.md
+++ b/examples/features/logits_processor/README.md
@@ -9,7 +9,7 @@ This directory contains examples demonstrating how to use custom logits processo
 Demonstrates how to instantiate vLLM with a custom logits processor class that operates at the batch level. The example uses a `DummyLogitsProcessor` that masks out all tokens except a specified `target_token` when passed via `SamplingParams.extra_args`.
 
 ```bash
-python examples/offline_inference/logits_processor/custom.py
+python examples/features/logits_processor/custom.py
 ```
 
 ### `custom_req.py` — Request-level logits processor wrapper
@@ -17,7 +17,7 @@ python examples/offline_inference/logits_processor/custom.py
 Shows how to wrap a request-level logits processor (which operates on individual requests) to be compatible with vLLM's batch-level logits processing interface.
 
 ```bash
-python examples/offline_inference/logits_processor/custom_req.py
+python examples/features/logits_processor/custom_req.py
 ```
 
 ### `custom_req_init.py` — Request-level processor with engine config
@@ -25,7 +25,7 @@ python examples/offline_inference/logits_processor/custom_req.py
 A special case of wrapping a request-level logits processor where the processor needs access to engine configuration or model metadata during initialization (e.g., vocabulary size, tokenizer info).
 
 ```bash
-python examples/offline_inference/logits_processor/custom_req_init.py
+python examples/features/logits_processor/custom_req_init.py
 ```
 
 ## Key Concepts
diff --git a/examples/offline_inference/logits_processor/custom.py b/examples/features/logits_processor/custom.py
similarity index 100%
rename from examples/offline_inference/logits_processor/custom.py
rename to examples/features/logits_processor/custom.py
diff --git a/examples/offline_inference/logits_processor/custom_req.py b/examples/features/logits_processor/custom_req.py
similarity index 100%
rename from examples/offline_inference/logits_processor/custom_req.py
rename to examples/features/logits_processor/custom_req.py
diff --git a/examples/offline_inference/logits_processor/custom_req_init.py b/examples/features/logits_processor/custom_req_init.py
similarity index 100%
rename from examples/offline_inference/logits_processor/custom_req_init.py
rename to examples/features/logits_processor/custom_req_init.py
diff --git a/examples/offline_inference/lora_with_quantization_inference.py b/examples/features/lora/lora_with_quantization_offline.py
similarity index 100%
rename from examples/offline_inference/lora_with_quantization_inference.py
rename to examples/features/lora/lora_with_quantization_offline.py
diff --git a/examples/offline_inference/multilora_inference.py b/examples/features/lora/multilora_offline.py
similarity index 100%
rename from examples/offline_inference/multilora_inference.py
rename to examples/features/lora/multilora_offline.py
diff --git a/examples/offline_inference/openai_batch/README.md b/examples/features/openai_batch/README.md
similarity index 93%
rename from examples/offline_inference/openai_batch/README.md
rename to examples/features/openai_batch/README.md
index ef4e438d6b72..d010e04e10f9 100644
--- a/examples/offline_inference/openai_batch/README.md
+++ b/examples/features/openai_batch/README.md
@@ -8,7 +8,7 @@ This is a guide to performing batch inference using the OpenAI batch file format
 
 The OpenAI batch file format consists of a series of json objects on new lines.
 
-[See here for an example file.](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl)
+[See here for an example file.](https://github.com/vllm-project/vllm/blob/main/examples/features/openai_batch/openai_example_batch.jsonl)
 
 Each line represents a separate request. See the [OpenAI package reference](https://platform.openai.com/docs/api-reference/batch/requestInput) for more details.
 
@@ -30,13 +30,13 @@ We currently support `/v1/chat/completions`, `/v1/embeddings`, and `/v1/score` e
 To follow along with this example, you can download the example batch, or create your own batch file in your working directory.
 
 ```bash
-wget https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl
+wget https://raw.githubusercontent.com/vllm-project/vllm/main/examples/features/openai_batch/openai_example_batch.jsonl
 ```
 
 Once you've created your batch file it should look like this
 
 ```bash
-cat offline_inference/openai_batch/openai_example_batch.jsonl
+cat features/openai_batch/openai_example_batch.jsonl
 {"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
 {"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
 ```
@@ -49,7 +49,7 @@ You can run the batch with the following command, which will write its results t
 
 ```bash
 python -m vllm.entrypoints.openai.run_batch \
-    -i offline_inference/openai_batch/openai_example_batch.jsonl \
+    -i features/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
     --model meta-llama/Meta-Llama-3-8B-Instruct
 ```
@@ -58,7 +58,7 @@ or use command-line:
 
 ```bash
 vllm run-batch \
-    -i offline_inference/openai_batch/openai_example_batch.jsonl \
+    -i features/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
     --model meta-llama/Meta-Llama-3-8B-Instruct
 ```
@@ -77,11 +77,11 @@ cat results.jsonl
 
 The batch runner supports remote input and output urls that are accessible via http/https.
 
-For example, to run against our example input file located at `https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl`, you can run
+For example, to run against our example input file located at `https://raw.githubusercontent.com/vllm-project/vllm/main/examples/features/openai_batch/openai_example_batch.jsonl`, you can run
 
 ```bash
 python -m vllm.entrypoints.openai.run_batch \
-    -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl \
+    -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/features/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
     --model meta-llama/Meta-Llama-3-8B-Instruct
 ```
@@ -90,7 +90,7 @@ or use command-line:
 
 ```bash
 vllm run-batch \
-    -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl \
+    -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/features/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
     --model meta-llama/Meta-Llama-3-8B-Instruct
 ```
@@ -113,13 +113,13 @@ To integrate with cloud blob storage, we recommend using presigned urls.
 To follow along with this example, you can download the example batch, or create your own batch file in your working directory.
 
 ```bash
-wget https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl
+wget https://raw.githubusercontent.com/vllm-project/vllm/main/examples/features/openai_batch/openai_example_batch.jsonl
 ```
 
 Once you've created your batch file it should look like this
 
 ```bash
-cat offline_inference/openai_batch/openai_example_batch.jsonl
+cat features/openai_batch/openai_example_batch.jsonl
 {"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
 {"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
 ```
@@ -127,7 +127,7 @@ cat offline_inference/openai_batch/openai_example_batch.jsonl
 Now upload your batch file to your S3 bucket.
 
 ```bash
-aws s3 cp offline_inference/openai_batch/openai_example_batch.jsonl s3://MY_BUCKET/MY_INPUT_FILE.jsonl
+aws s3 cp features/openai_batch/openai_example_batch.jsonl s3://MY_BUCKET/MY_INPUT_FILE.jsonl
 ```
 
 ### Step 2: Generate your presigned urls
@@ -193,7 +193,7 @@ You can now run the batch runner, using the urls generated in the previous secti
 python -m vllm.entrypoints.openai.run_batch \
     -i "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_INPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
     -o "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
-    --model --model meta-llama/Meta-Llama-3-8B-Instruct
+    --model meta-llama/Meta-Llama-3-8B-Instruct
 ```
 
 or use command-line:
@@ -202,7 +202,7 @@ or use command-line:
 vllm run-batch \
     -i "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_INPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
     -o "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
-    --model --model meta-llama/Meta-Llama-3-8B-Instruct
+    --model meta-llama/Meta-Llama-3-8B-Instruct
 ```
 
 ### Step 4: View your results
diff --git a/examples/offline_inference/openai_batch/openai_example_batch.jsonl b/examples/features/openai_batch/openai_example_batch.jsonl
similarity index 100%
rename from examples/offline_inference/openai_batch/openai_example_batch.jsonl
rename to examples/features/openai_batch/openai_example_batch.jsonl
diff --git a/examples/online_serving/data_parallel_pause_resume.py b/examples/features/pause_resume/data_parallel_pause_resume.py
similarity index 96%
rename from examples/online_serving/data_parallel_pause_resume.py
rename to examples/features/pause_resume/data_parallel_pause_resume.py
index e94de22a1271..1f11536e5366 100644
--- a/examples/online_serving/data_parallel_pause_resume.py
+++ b/examples/features/pause_resume/data_parallel_pause_resume.py
@@ -1,135 +1,135 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-Test pause/resume with Data Parallel (DP) via HTTP API.
-
-This example demonstrates coordinated pause/resume across multiple DP ranks.
-The pause synchronizes across all DP engines via all-reduce.
-
-Prerequisites:
-    Start a vLLM server with data parallelism:
-
-    $ VLLM_SERVER_DEV_MODE=1 vllm serve facebook/opt-125m \
-        --enforce-eager \
-        --data-parallel-size 4 \
-        --tensor-parallel-size 1
-
-    Then run this script:
-
-    $ python data_parallel_pause_resume.py
-
-The test verifies pause works by:
-1. Starting a streaming generation request
-2. Pausing the server mid-generation
-3. Sleeping for PAUSE_DURATION seconds
-4. Resuming the server
-5. Verifying there was a gap in token generation matching the pause duration
-"""
-
-import argparse
-import threading
-import time
-
-import requests
-from openai import OpenAI
-
-BASE_URL = "http://localhost:8000"
-MODEL_NAME = "facebook/opt-125m"
-PAUSE_DURATION = 3.0
-
-
-def pause_generation(base_url: str, mode: str = "keep") -> None:
-    """Pause generation via HTTP endpoint."""
-    url = f"{base_url}/pause"
-    response = requests.post(url, params={"mode": mode}, timeout=60)
-    response.raise_for_status()
-    print("Server paused")
-
-
-def resume_generation(base_url: str) -> None:
-    """Resume generation via HTTP endpoint."""
-    url = f"{base_url}/resume"
-    response = requests.post(url, timeout=60)
-    response.raise_for_status()
-    print("Server resumed")
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--base-url", default=BASE_URL)
-    parser.add_argument("--model", default=MODEL_NAME)
-    args = parser.parse_args()
-
-    client = OpenAI(
-        base_url=f"{args.base_url}/v1",
-        api_key="EMPTY",
-    )
-
-    prompt = "Write a long story about a dragon. Once upon a time"
-    token_times: list[float] = []
-    pause_token_idx = 0
-    pause_triggered = threading.Event()
-
-    def generator_thread():
-        """Stream tokens and record timestamps."""
-        stream = client.completions.create(
-            model=args.model,
-            prompt=prompt,
-            max_tokens=50,
-            stream=True,
-        )
-        for chunk in stream:
-            if chunk.choices[0].text:
-                token_times.append(time.monotonic())
-                token_count = len(token_times)
-                print(f"Token {token_count}: {chunk.choices[0].text!r}")
-
-                # Signal controller after some tokens
-                if token_count >= 5 and not pause_triggered.is_set():
-                    pause_triggered.set()
-
-    def controller_thread():
-        """Pause and resume the server."""
-        nonlocal pause_token_idx
-
-        # Wait for some tokens
-        pause_triggered.wait()
-
-        print(f"\nPausing server (keep mode) at token {len(token_times)}...")
-        pause_generation(args.base_url, mode="keep")
-        pause_token_idx = len(token_times)
-        print(f"Sleeping for {PAUSE_DURATION}s...")
-
-        time.sleep(PAUSE_DURATION)
-
-        print("Resuming server...")
-        resume_generation(args.base_url)
-        print("Resumed!\n")
-
-    # Run both threads
-    gen_thread = threading.Thread(target=generator_thread)
-    ctrl_thread = threading.Thread(target=controller_thread)
-
-    gen_thread.start()
-    ctrl_thread.start()
-
-    gen_thread.join()
-    ctrl_thread.join()
-
-    # Check gap at the pause point
-    if pause_token_idx < len(token_times):
-        pause_gap = token_times[pause_token_idx] - token_times[pause_token_idx - 1]
-        print(
-            f"\nGap after pause (token {pause_token_idx} -> "
-            f"{pause_token_idx + 1}): {pause_gap:.3f}s"
-        )
-        if pause_gap >= PAUSE_DURATION * 0.9:
-            print("Test passed! Pause synchronized across DP ranks.")
-        else:
-            print(f"Test failed! Expected ~{PAUSE_DURATION}s gap, got {pause_gap:.3f}s")
-    else:
-        print("Test failed! No tokens were generated after resuming.")
-
-
-if __name__ == "__main__":
-    main()
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Test pause/resume with Data Parallel (DP) via HTTP API.
+
+This example demonstrates coordinated pause/resume across multiple DP ranks.
+The pause synchronizes across all DP engines via all-reduce.
+
+Prerequisites:
+    Start a vLLM server with data parallelism:
+
+    $ VLLM_SERVER_DEV_MODE=1 vllm serve facebook/opt-125m \
+        --enforce-eager \
+        --data-parallel-size 4 \
+        --tensor-parallel-size 1
+
+    Then run this script:
+
+    $ python data_parallel_pause_resume.py
+
+The test verifies pause works by:
+1. Starting a streaming generation request
+2. Pausing the server mid-generation
+3. Sleeping for PAUSE_DURATION seconds
+4. Resuming the server
+5. Verifying there was a gap in token generation matching the pause duration
+"""
+
+import argparse
+import threading
+import time
+
+import requests
+from openai import OpenAI
+
+BASE_URL = "http://localhost:8000"
+MODEL_NAME = "facebook/opt-125m"
+PAUSE_DURATION = 3.0
+
+
+def pause_generation(base_url: str, mode: str = "keep") -> None:
+    """Pause generation via HTTP endpoint."""
+    url = f"{base_url}/pause"
+    response = requests.post(url, params={"mode": mode}, timeout=60)
+    response.raise_for_status()
+    print("Server paused")
+
+
+def resume_generation(base_url: str) -> None:
+    """Resume generation via HTTP endpoint."""
+    url = f"{base_url}/resume"
+    response = requests.post(url, timeout=60)
+    response.raise_for_status()
+    print("Server resumed")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--base-url", default=BASE_URL)
+    parser.add_argument("--model", default=MODEL_NAME)
+    args = parser.parse_args()
+
+    client = OpenAI(
+        base_url=f"{args.base_url}/v1",
+        api_key="EMPTY",
+    )
+
+    prompt = "Write a long story about a dragon. Once upon a time"
+    token_times: list[float] = []
+    pause_token_idx = 0
+    pause_triggered = threading.Event()
+
+    def generator_thread():
+        """Stream tokens and record timestamps."""
+        stream = client.completions.create(
+            model=args.model,
+            prompt=prompt,
+            max_tokens=50,
+            stream=True,
+        )
+        for chunk in stream:
+            if chunk.choices[0].text:
+                token_times.append(time.monotonic())
+                token_count = len(token_times)
+                print(f"Token {token_count}: {chunk.choices[0].text!r}")
+
+                # Signal controller after some tokens
+                if token_count >= 5 and not pause_triggered.is_set():
+                    pause_triggered.set()
+
+    def controller_thread():
+        """Pause and resume the server."""
+        nonlocal pause_token_idx
+
+        # Wait for some tokens
+        pause_triggered.wait()
+
+        print(f"\nPausing server (keep mode) at token {len(token_times)}...")
+        pause_generation(args.base_url, mode="keep")
+        pause_token_idx = len(token_times)
+        print(f"Sleeping for {PAUSE_DURATION}s...")
+
+        time.sleep(PAUSE_DURATION)
+
+        print("Resuming server...")
+        resume_generation(args.base_url)
+        print("Resumed!\n")
+
+    # Run both threads
+    gen_thread = threading.Thread(target=generator_thread)
+    ctrl_thread = threading.Thread(target=controller_thread)
+
+    gen_thread.start()
+    ctrl_thread.start()
+
+    gen_thread.join()
+    ctrl_thread.join()
+
+    # Check gap at the pause point
+    if pause_token_idx < len(token_times):
+        pause_gap = token_times[pause_token_idx] - token_times[pause_token_idx - 1]
+        print(
+            f"\nGap after pause (token {pause_token_idx} -> "
+            f"{pause_token_idx + 1}): {pause_gap:.3f}s"
+        )
+        if pause_gap >= PAUSE_DURATION * 0.9:
+            print("Test passed! Pause synchronized across DP ranks.")
+        else:
+            print(f"Test failed! Expected ~{PAUSE_DURATION}s gap, got {pause_gap:.3f}s")
+    else:
+        print("Test failed! No tokens were generated after resuming.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/offline_inference/pause_resume.py b/examples/features/pause_resume/pause_resume_offline.py
similarity index 100%
rename from examples/offline_inference/pause_resume.py
rename to examples/features/pause_resume/pause_resume_offline.py
diff --git a/examples/offline_inference/run_one_batch.py b/examples/features/profiling/run_one_batch_offline.py
similarity index 100%
rename from examples/offline_inference/run_one_batch.py
rename to examples/features/profiling/run_one_batch_offline.py
diff --git a/examples/offline_inference/simple_profiling.py b/examples/features/profiling/simple_profiling_offline.py
similarity index 100%
rename from examples/offline_inference/simple_profiling.py
rename to examples/features/profiling/simple_profiling_offline.py
diff --git a/examples/features/prompt_embed/prompt_embed_inference_with_openai_client.py b/examples/features/prompt_embed/prompt_embed_inference_with_openai_client.py
new file mode 100644
index 000000000000..f3204645d0a0
--- /dev/null
+++ b/examples/features/prompt_embed/prompt_embed_inference_with_openai_client.py
@@ -0,0 +1,158 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""vLLM OpenAI-Compatible Client with Prompt Embeddings.
+
+This script demonstrates how to:
+1. Generate prompt embeddings using Hugging Face Transformers.
+2. Encode them in base64 format.
+3. Send them to a vLLM server for inference via both:
+    - OpenAI-compatible Chat Completions API
+    - OpenAI-compatible Completions API
+
+Important distinction between the two APIs:
+
+- Chat Completions API: `prompt_embeds` content parts should encode ONLY
+  the user-provided content, not a templated conversation. The server
+  renders the surrounding chat template around the embedded content at
+  request time, the same way it would for a plain text `content` string.
+  Embedding a full templated conversation here would double-apply the
+  template and likely produce undesirable results.
+
+- Completions API: the server does NOT apply a chat template to
+  `prompt_embeds`. The caller is responsible for producing embeddings for
+  the full, already-templated prompt (i.e. apply the chat template first, 
+  then embed the resulting token IDs). Anything the model would normally
+  need (system prompt, role markers, generation prompt, etc.) must already
+  be baked into the embedded tokens.
+
+Run the vLLM server first:
+vllm serve meta-llama/Llama-3.2-1B-Instruct \
+  --runner generate \
+  --max-model-len 4096 \
+  --enable-prompt-embeds
+
+Run the client:
+python examples/features/prompt_embed/prompt_embed_inference_with_openai_client.py
+
+Model: meta-llama/Llama-3.2-1B-Instruct
+Note: This model is gated on Hugging Face Hub.
+      You must request access to use it:
+      https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct
+
+Dependencies:
+- transformers
+- torch
+- openai
+"""
+
+import transformers
+from openai import OpenAI
+
+from vllm.utils.serial_utils import tensor2base64
+
+
+def run_chat_completion_prompt_embeds(
+    client: OpenAI,
+    model_name: str,
+    tokenizer: transformers.PreTrainedTokenizerBase,
+    embedding_layer,
+    messages: list[dict],
+) -> None:
+    """Run a Chat Completions API request using prompt_embeds content parts.
+
+    This example embeds ONLY the user-provided content of the final user turn, the
+    vLLM server applies the chat template around it at request time.
+    """
+    user_content = messages[-1]["content"]
+    content_token_ids = tokenizer(
+        user_content, return_tensors="pt", add_special_tokens=False
+    ).input_ids
+    content_prompt_embeds = embedding_layer(content_token_ids).squeeze(0)
+    encoded_embeds = tensor2base64(content_prompt_embeds)
+
+    api_messages = [
+        *messages[:-1],
+        {
+            "role": messages[-1]["role"],
+            "content": [{"type": "prompt_embeds", "data": encoded_embeds}],
+        },
+    ]
+
+    chat_completion = client.chat.completions.create(
+        model=model_name,
+        max_tokens=6,
+        temperature=0.0,
+        messages=api_messages,
+    )
+
+    print("-" * 30)
+    print("Chat Completions API")
+    print(chat_completion.choices[0].message.content)
+    print("-" * 30)
+
+
+def run_completion_prompt_embeds(
+    client: OpenAI,
+    model_name: str,
+    tokenizer: transformers.PreTrainedTokenizerBase,
+    embedding_layer,
+    messages: list[dict],
+) -> None:
+    """Run a Completions API request using prompt embeddings.
+
+    The Completions endpoint does not apply a chat template,
+    so the caller must apply it and embed the full templated prompt.
+    """
+    templated_token_ids = tokenizer.apply_chat_template(
+        messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
+    ).input_ids
+    templated_prompt_embeds = embedding_layer(templated_token_ids).squeeze(0)
+    encoded_embeds = tensor2base64(templated_prompt_embeds)
+
+    completion = client.completions.create(
+        model=model_name,
+        prompt=None,
+        max_tokens=6,
+        temperature=0.0,
+        # NOTE: The OpenAI client allows passing in extra JSON body via the
+        # `extra_body` argument.
+        extra_body={"prompt_embeds": encoded_embeds},
+    )
+
+    print("-" * 30)
+    print("Completions API")
+    print(completion.choices[0].text)
+    print("-" * 30)
+
+
+def main() -> None:
+    client = OpenAI(
+        api_key="EMPTY",
+        base_url="http://localhost:8000/v1",
+    )
+
+    model_name = "meta-llama/Llama-3.2-1B-Instruct"
+
+    tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+    transformers_model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
+    embedding_layer = transformers_model.get_input_embeddings()
+
+    messages = [
+        {"role": "user", "content": "Please tell me about the capital of France."}
+    ]
+
+    # Chat Completions API: embed ONLY the user content. The server wraps
+    # the embedding in the chat template when it renders the messages.
+    run_chat_completion_prompt_embeds(
+        client, model_name, tokenizer, embedding_layer, messages
+    )
+
+    # Completions API: embed the FULL templated prompt. The caller must
+    # apply the chat template up-front.
+    run_completion_prompt_embeds(
+        client, model_name, tokenizer, embedding_layer, messages
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/offline_inference/prompt_embed_inference.py b/examples/features/prompt_embed/prompt_embed_offline.py
similarity index 97%
rename from examples/offline_inference/prompt_embed_inference.py
rename to examples/features/prompt_embed/prompt_embed_offline.py
index a0eaeb6810a2..29853bce9673 100644
--- a/examples/offline_inference/prompt_embed_inference.py
+++ b/examples/features/prompt_embed/prompt_embed_offline.py
@@ -15,7 +15,7 @@
 - transformers
 
 Run:
-    python examples/offline_inference/prompt_embed_inference.py
+    python examples/features/prompt_embed/prompt_embed_offline.py
 """
 
 import torch
diff --git a/examples/offline_inference/llm_engine_reset_kv.py b/examples/features/reset_kv/reset_kv_offline.py
similarity index 100%
rename from examples/offline_inference/llm_engine_reset_kv.py
rename to examples/features/reset_kv/reset_kv_offline.py
diff --git a/examples/offline_inference/load_sharded_state.py b/examples/features/sharded_state/load_sharded_state_offline.py
similarity index 94%
rename from examples/offline_inference/load_sharded_state.py
rename to examples/features/sharded_state/load_sharded_state_offline.py
index 0085e8e8e32b..e867db5d12fe 100644
--- a/examples/offline_inference/load_sharded_state.py
+++ b/examples/features/sharded_state/load_sharded_state_offline.py
@@ -3,16 +3,16 @@
 """
 Validates the loading of a model saved with the sharded_state format.
 This script demonstrates how to load a model that was previously saved
-using save_sharded_state.py and validates it by running inference.
+using save_sharded_state_offline.py and validates it by running inference.
 Example usage:
 (First need to save a sharded_state mode)
 
-python save_sharded_state.py \
+python save_sharded_state_offline.py \
     --model /path/to/load \
     --tensor-parallel-size 8 \
     --output /path/to/save/sharded/model
 
-python load_sharded_state.py \
+python load_sharded_state_offline.py \
     --model /path/to/saved/sharded/model \
     --load-format sharded_state \
     --tensor-parallel-size 8 \
diff --git a/examples/offline_inference/save_sharded_state.py b/examples/features/sharded_state/save_sharded_state_offline.py
similarity index 98%
rename from examples/offline_inference/save_sharded_state.py
rename to examples/features/sharded_state/save_sharded_state_offline.py
index 14d472ee3f23..675f2e35a53f 100644
--- a/examples/offline_inference/save_sharded_state.py
+++ b/examples/features/sharded_state/save_sharded_state_offline.py
@@ -7,7 +7,7 @@
 
 Example usage:
 
-python save_sharded_state.py \
+python save_sharded_state_offline.py \
     --model /path/to/load \
     --tensor-parallel-size 8 \
     --output /path/to/save
diff --git a/examples/offline_inference/extract_hidden_states.py b/examples/features/speculative_decoding/extract_hidden_states_offline.py
similarity index 100%
rename from examples/offline_inference/extract_hidden_states.py
rename to examples/features/speculative_decoding/extract_hidden_states_offline.py
diff --git a/examples/offline_inference/mlpspeculator.py b/examples/features/speculative_decoding/mlpspeculator_offline.py
similarity index 100%
rename from examples/offline_inference/mlpspeculator.py
rename to examples/features/speculative_decoding/mlpspeculator_offline.py
diff --git a/examples/offline_inference/spec_decode.py b/examples/features/speculative_decoding/spec_decode_offline.py
similarity index 100%
rename from examples/offline_inference/spec_decode.py
rename to examples/features/speculative_decoding/spec_decode_offline.py
diff --git a/examples/online_serving/structured_outputs/README.md b/examples/features/structured_outputs/README.md
similarity index 85%
rename from examples/online_serving/structured_outputs/README.md
rename to examples/features/structured_outputs/README.md
index 7f539716ecf8..f2863eb0cbcf 100644
--- a/examples/online_serving/structured_outputs/README.md
+++ b/examples/features/structured_outputs/README.md
@@ -20,7 +20,7 @@ vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-7B \
 If you want to run this script standalone with `uv`, you can use the following:
 
 ```bash
-uvx --from git+https://github.com/vllm-project/vllm#subdirectory=examples/online_serving/structured_outputs \
+uvx --from git+https://github.com/vllm-project/vllm#subdirectory=examples/features/structured_outputs \
     structured-outputs
 ```
 
@@ -34,19 +34,19 @@ See [feature docs](https://docs.vllm.ai/en/latest/features/structured_outputs.ht
 Run all constraints, non-streaming:
 
 ```bash
-uv run structured_outputs.py
+uv run structured_outputs_offline.py
 ```
 
 Run all constraints, streaming:
 
 ```bash
-uv run structured_outputs.py --stream
+uv run structured_outputs_offline.py --stream
 ```
 
 Run certain constraints, for example `structural_tag` and `regex`, streaming:
 
 ```bash
-uv run structured_outputs.py \
+uv run structured_outputs_offline.py \
     --constraint structural_tag regex \
     --stream
 ```
@@ -54,5 +54,5 @@ uv run structured_outputs.py \
 Run all constraints, with reasoning models and streaming:
 
 ```bash
-uv run structured_outputs.py --reasoning --stream
+uv run structured_outputs_offline.py --reasoning --stream
 ```
diff --git a/examples/online_serving/structured_outputs/pyproject.toml b/examples/features/structured_outputs/pyproject.toml
similarity index 100%
rename from examples/online_serving/structured_outputs/pyproject.toml
rename to examples/features/structured_outputs/pyproject.toml
diff --git a/examples/online_serving/structured_outputs/structured_outputs.py b/examples/features/structured_outputs/structured_outputs_client.py
similarity index 100%
rename from examples/online_serving/structured_outputs/structured_outputs.py
rename to examples/features/structured_outputs/structured_outputs_client.py
diff --git a/examples/offline_inference/structured_outputs.py b/examples/features/structured_outputs/structured_outputs_offline.py
similarity index 100%
rename from examples/offline_inference/structured_outputs.py
rename to examples/features/structured_outputs/structured_outputs_offline.py
diff --git a/examples/others/tensorize_vllm_model.py b/examples/features/tensorize_vllm_model.py
similarity index 97%
rename from examples/others/tensorize_vllm_model.py
rename to examples/features/tensorize_vllm_model.py
index 3644a03b32ed..a89b1781264d 100644
--- a/examples/others/tensorize_vllm_model.py
+++ b/examples/features/tensorize_vllm_model.py
@@ -33,7 +33,7 @@
 To serialize a model, install vLLM from source, then run something 
 like this from the root level of this repository:
 
-python examples/others/tensorize_vllm_model.py \
+python examples/features/tensorize_vllm_model.py \
    --model facebook/opt-125m \
    serialize \
    --serialized-directory s3://my-bucket \
@@ -53,7 +53,7 @@
 To deserialize a model, you can run something like this from the root 
 level of this repository:
 
-python examples/others/tensorize_vllm_model.py \
+python examples/features/tensorize_vllm_model.py \
    --model EleutherAI/gpt-j-6B \
    --dtype float16 \
    deserialize \
@@ -71,11 +71,11 @@
 model-rank-%03d.tensors
 
 For more information on the available arguments for serializing, run 
-`python -m examples.others.tensorize_vllm_model serialize --help`.
+`python -m examples.features.tensorize_vllm_model serialize --help`.
 
 Or for deserializing:
 
-`python examples/others/tensorize_vllm_model.py deserialize --help`.
+`python examples/features/tensorize_vllm_model.py deserialize --help`.
 
 Once a model is serialized, tensorizer can be invoked with the `LLM` class 
 directly to load models:
@@ -100,7 +100,7 @@
 In order to see all of the available arguments usable to configure 
 loading with tensorizer that are given to `TensorizerConfig`, run:
 
-`python examples/others/tensorize_vllm_model.py deserialize --help`
+`python examples/features/tensorize_vllm_model.py deserialize --help`
 
 under the `tensorizer options` section. These can also be used for
 deserialization in this example script, although `--tensorizer-uri` and
diff --git a/examples/offline_inference/torchrun_dp_example.py b/examples/features/torchrun/torchrun_dp_example_offline.py
similarity index 95%
rename from examples/offline_inference/torchrun_dp_example.py
rename to examples/features/torchrun/torchrun_dp_example_offline.py
index eb7ed969ea4b..f18f6042e9c6 100644
--- a/examples/offline_inference/torchrun_dp_example.py
+++ b/examples/features/torchrun/torchrun_dp_example_offline.py
@@ -7,15 +7,15 @@
 
 To run this example:
 ```bash
-$ torchrun --nproc-per-node=2 examples/offline_inference/torchrun_dp_example.py
+$ torchrun --nproc-per-node=2 examples/features/torchrun/torchrun_dp_example_offline.py
 ```
 
 With custom parallelism settings:
 ```bash
-$ torchrun --nproc-per-node=8 examples/offline_inference/torchrun_dp_example.py \
+$ torchrun --nproc-per-node=8 examples/features/torchrun/torchrun_dp_example_offline.py \
     --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 ```
-"""
+"""  # noqa: E501
 
 import argparse
 
diff --git a/examples/offline_inference/torchrun_example.py b/examples/features/torchrun/torchrun_example_offline.py
similarity index 99%
rename from examples/offline_inference/torchrun_example.py
rename to examples/features/torchrun/torchrun_example_offline.py
index 2960d329968a..e41bcd420c20 100644
--- a/examples/offline_inference/torchrun_example.py
+++ b/examples/features/torchrun/torchrun_example_offline.py
@@ -4,7 +4,7 @@
 experimental support for tensor-parallel inference with torchrun,
 see https://github.com/vllm-project/vllm/issues/11400 for
 the motivation and use case for this example.
-run the script with `torchrun --nproc-per-node=4 torchrun_example.py`,
+run the script with `torchrun --nproc-per-node=4 torchrun_example_offline.py`,
 the argument `4` should match the product of `tensor_parallel_size` and
 `pipeline_parallel_size` below. see `tests/distributed/test_torchrun_example.py`
 for the unit test.
diff --git a/examples/online_serving/batched_chat_completions.py b/examples/generate/batched_chat_completions_online.py
similarity index 100%
rename from examples/online_serving/batched_chat_completions.py
rename to examples/generate/batched_chat_completions_online.py
diff --git a/examples/offline_inference/audio_language.py b/examples/generate/multimodal/audio_language_offline.py
old mode 100755
new mode 100644
similarity index 100%
rename from examples/offline_inference/audio_language.py
rename to examples/generate/multimodal/audio_language_offline.py
diff --git a/examples/offline_inference/encoder_decoder_multimodal.py b/examples/generate/multimodal/encoder_decoder_multimodal_offline.py
similarity index 100%
rename from examples/offline_inference/encoder_decoder_multimodal.py
rename to examples/generate/multimodal/encoder_decoder_multimodal_offline.py
diff --git a/examples/offline_inference/mistral-small.py b/examples/generate/multimodal/mistral-small_offline.py
similarity index 100%
rename from examples/offline_inference/mistral-small.py
rename to examples/generate/multimodal/mistral-small_offline.py
diff --git a/examples/online_serving/openai_chat_completion_client_for_multimodal.py b/examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py
similarity index 97%
rename from examples/online_serving/openai_chat_completion_client_for_multimodal.py
rename to examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py
index c4407923ed2d..3a007731c74d 100644
--- a/examples/online_serving/openai_chat_completion_client_for_multimodal.py
+++ b/examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py
@@ -25,7 +25,6 @@
 import pybase64 as base64
 import requests
 from openai import OpenAI
-from utils import get_first_model
 
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 
@@ -267,7 +266,7 @@ def run_audio(model: str, max_completion_tokens: int) -> None:
                     {
                         "type": "input_audio",
                         "input_audio": {
-                            # Any format supported by librosa is supported
+                            # Any format supported by soundfile/PyAV is supported
                             "data": audio_base64,
                             "format": "wav",
                         },
@@ -292,7 +291,7 @@ def run_audio(model: str, max_completion_tokens: int) -> None:
                     {
                         "type": "audio_url",
                         "audio_url": {
-                            # Any format supported by librosa is supported
+                            # Any format supported by soundfile/PyAV is supported
                             "url": audio_url
                         },
                     },
@@ -316,7 +315,7 @@ def run_audio(model: str, max_completion_tokens: int) -> None:
                     {
                         "type": "audio_url",
                         "audio_url": {
-                            # Any format supported by librosa is supported
+                            # Any format supported by soundfile/PyAV is supported
                             "url": f"data:audio/ogg;base64,{audio_base64}"
                         },
                     },
@@ -407,7 +406,7 @@ def parse_args():
 
 def main(args) -> None:
     chat_type = args.chat_type
-    model = get_first_model(client)
+    model = client.models.list().data[0].id
     example_function_map[chat_type](model, args.max_completion_tokens)
 
 
diff --git a/examples/offline_inference/qwen2_5_omni/README.md b/examples/generate/multimodal/qwen2_5_omni/README.md
similarity index 63%
rename from examples/offline_inference/qwen2_5_omni/README.md
rename to examples/generate/multimodal/qwen2_5_omni/README.md
index 409ac0223b55..bd96b080f67d 100644
--- a/examples/offline_inference/qwen2_5_omni/README.md
+++ b/examples/generate/multimodal/qwen2_5_omni/README.md
@@ -6,15 +6,15 @@ This folder provides several example scripts on how to inference Qwen2.5-Omni of
 
 ```bash
 # Audio + image + video
-python examples/offline_inference/qwen2_5_omni/only_thinker.py \
+python examples/generate/multimodal/qwen2_5_omni/only_thinker.py \
     -q mixed_modalities
 
 # Read vision and audio inputs from a single video file
-python examples/offline_inference/qwen2_5_omni/only_thinker.py \
+python examples/generate/multimodal/qwen2_5_omni/only_thinker.py \
     -q use_audio_in_video
 
 # Multiple audios
-python examples/offline_inference/qwen2_5_omni/only_thinker.py \
+python examples/generate/multimodal/qwen2_5_omni/only_thinker.py \
     -q multi_audios
 ```
 
@@ -24,16 +24,16 @@ You can also test Qwen2.5-Omni on a single modality:
 
 ```bash
 # Process audio inputs
-python examples/offline_inference/audio_language.py \
+python examples/generate/multimodal/audio_language_offline.py \
     --model-type qwen2_5_omni
 
 # Process image inputs
-python examples/offline_inference/vision_language.py \
+python examples/generate/multimodal/vision_language_offline.py \
     --modality image \
     --model-type qwen2_5_omni
 
 # Process video inputs
-python examples/offline_inference/vision_language.py \
+python examples/generate/multimodal/vision_language_offline.py \
     --modality video \
     --model-type qwen2_5_omni
 ```
diff --git a/examples/offline_inference/qwen2_5_omni/only_thinker.py b/examples/generate/multimodal/qwen2_5_omni/only_thinker.py
similarity index 100%
rename from examples/offline_inference/qwen2_5_omni/only_thinker.py
rename to examples/generate/multimodal/qwen2_5_omni/only_thinker.py
diff --git a/examples/offline_inference/qwen3_omni/only_thinker.py b/examples/generate/multimodal/qwen3_omni/only_thinker.py
similarity index 100%
rename from examples/offline_inference/qwen3_omni/only_thinker.py
rename to examples/generate/multimodal/qwen3_omni/only_thinker.py
diff --git a/examples/offline_inference/vision_language_multi_image.py b/examples/generate/multimodal/vision_language_multi_image_offline.py
old mode 100755
new mode 100644
similarity index 98%
rename from examples/offline_inference/vision_language_multi_image.py
rename to examples/generate/multimodal/vision_language_multi_image_offline.py
index 1a3b7fd954cf..1b68a23b3bd0
--- a/examples/offline_inference/vision_language_multi_image.py
+++ b/examples/generate/multimodal/vision_language_multi_image_offline.py
@@ -310,6 +310,38 @@ def load_gemma3(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
+def load_granite4_vision(question: str, image_urls: list[str]) -> ModelRequestData:
+    model_name = "ibm-granite/granite-vision-4.1-4b"
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=16,
+        limit_mm_per_prompt={"image": len(image_urls)},
+    )
+
+    placeholders = [{"type": "image", "image": url} for url in image_urls]
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                *placeholders,
+                {"type": "text", "text": question},
+            ],
+        }
+    ]
+
+    processor = AutoProcessor.from_pretrained(model_name)
+    prompt = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+        image_data=[fetch_image(url) for url in image_urls],
+    )
+
+
 def load_h2ovl(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "h2oai/h2ovl-mississippi-800m"
 
@@ -1487,6 +1519,7 @@ def load_molmo2(question: str, image_urls: list[str]) -> ModelRequestData:
     "deepseek_ocr": load_deepseek_ocr,
     "exaone4_5": load_exaone4_5,
     "gemma3": load_gemma3,
+    "granite4_vision": load_granite4_vision,
     "h2ovl_chat": load_h2ovl,
     "hunyuan_vl": load_hunyuan_vl,
     "hyperclovax_seed_vision": load_hyperclovax_seed_vision,
diff --git a/examples/offline_inference/vision_language.py b/examples/generate/multimodal/vision_language_offline.py
old mode 100755
new mode 100644
similarity index 83%
rename from examples/offline_inference/vision_language.py
rename to examples/generate/multimodal/vision_language_offline.py
index 2c0bd52c0e3e..794f20dd0a52
--- a/examples/offline_inference/vision_language.py
+++ b/examples/generate/multimodal/vision_language_offline.py
@@ -394,18 +394,24 @@ def run_eagle2_5(questions: list[str], modality: str) -> ModelRequestData:
 def run_ernie45_vl(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "baidu/ERNIE-4.5-VL-28B-A3B-PT"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
         max_num_seqs=5,
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
         trust_remote_code=True,
     )
 
+    image_placeholder = "Picture 1:<|IMAGE_START|><|image@placeholder|><|IMAGE_END|>"
+    video_placeholder = "Video 1:<|VIDEO_START|><|video@placeholder|><|VIDEO_END|>"
+
     if modality == "image":
-        placeholder = "Picture 1:<|IMAGE_START|><|image@placeholder|><|IMAGE_END|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "Video 1:<|VIDEO_START|><|video@placeholder|><|VIDEO_END|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
@@ -425,6 +431,7 @@ def run_ernie45_vl(questions: list[str], modality: str) -> ModelRequestData:
 def run_exaone4_5(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "LGAI-EXAONE/EXAONE-4.5-33B"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
@@ -434,18 +441,23 @@ def run_exaone4_5(questions: list[str], modality: str) -> ModelRequestData:
             "max_pixels": 1280 * 28 * 28,
             "fps": 1,
         },
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
 
+    image_placeholder = "<vision><|image_pad|></vision>"
+    video_placeholder = "<vision><|video_pad|></vision>"
+
     if modality == "image":
-        placeholder = "<|image_pad|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|video_pad|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
             "<|system|>\nYou are a helpful assistant.<|endofturn|>\n"
-            f"<|user|>\n<vision>{placeholder}</vision>"
+            f"<|user|>\n{placeholder}"
             f"{question}<|endofturn|>\n"
             "<|assistant|>\n"
         )
@@ -566,6 +578,7 @@ def run_glm4v(questions: list[str], modality: str) -> ModelRequestData:
 def run_glm4_1v(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "zai-org/GLM-4.1V-9B-Thinking"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
@@ -574,14 +587,19 @@ def run_glm4_1v(questions: list[str], modality: str) -> ModelRequestData:
             "size": {"shortest_edge": 12544, "longest_edge": 47040000},
             "fps": 1,
         },
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
         enforce_eager=True,
     )
 
+    image_placeholder = "<|begin_of_image|><|image|><|end_of_image|>"
+    video_placeholder = "<|begin_of_video|><|video|><|end_of_video|>"
+
     if modality == "image":
-        placeholder = "<|begin_of_image|><|image|><|end_of_image|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|begin_of_video|><|video|><|end_of_video|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
@@ -602,6 +620,7 @@ def run_glm4_1v(questions: list[str], modality: str) -> ModelRequestData:
 def run_glm4_5v(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "zai-org/GLM-4.5V"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
@@ -610,15 +629,20 @@ def run_glm4_5v(questions: list[str], modality: str) -> ModelRequestData:
             "size": {"shortest_edge": 12544, "longest_edge": 47040000},
             "fps": 1,
         },
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
         enforce_eager=True,
         tensor_parallel_size=4,
     )
 
+    image_placeholder = "<|begin_of_image|><|image|><|end_of_image|>"
+    video_placeholder = "<|begin_of_video|><|video|><|end_of_video|>"
+
     if modality == "image":
-        placeholder = "<|begin_of_image|><|image|><|end_of_image|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|begin_of_video|><|video|><|end_of_video|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
@@ -639,6 +663,7 @@ def run_glm4_5v(questions: list[str], modality: str) -> ModelRequestData:
 def run_glm4_5v_fp8(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "zai-org/GLM-4.5V-FP8"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
@@ -647,15 +672,20 @@ def run_glm4_5v_fp8(questions: list[str], modality: str) -> ModelRequestData:
             "size": {"shortest_edge": 12544, "longest_edge": 47040000},
             "fps": 1,
         },
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
         enforce_eager=True,
         tensor_parallel_size=4,
     )
 
+    image_placeholder = "<|begin_of_image|><|image|><|end_of_image|>"
+    video_placeholder = "<|begin_of_video|><|video|><|end_of_video|>"
+
     if modality == "image":
-        placeholder = "<|begin_of_image|><|image|><|end_of_image|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|begin_of_video|><|video|><|end_of_video|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
@@ -676,6 +706,7 @@ def run_glm4_5v_fp8(questions: list[str], modality: str) -> ModelRequestData:
 def run_glm_ocr(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "zai-org/GLM-OCR"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
@@ -684,14 +715,19 @@ def run_glm_ocr(questions: list[str], modality: str) -> ModelRequestData:
             "size": {"shortest_edge": 12544, "longest_edge": 47040000},
             "fps": 1,
         },
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
         enforce_eager=True,
     )
 
+    image_placeholder = "<|begin_of_image|><|image|><|end_of_image|>"
+    video_placeholder = "<|begin_of_video|><|video|><|end_of_video|>"
+
     if modality == "image":
-        placeholder = "<|begin_of_image|><|image|><|end_of_image|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|begin_of_video|><|video|><|end_of_video|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
@@ -772,11 +808,12 @@ def run_hyperclovax_seed_vision(
     model_name = "naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B"
     tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         trust_remote_code=True,
-        max_model_len=8192 if modality == "image" else 16384,
-        limit_mm_per_prompt={modality: 1},
+        max_model_len=16384 if modality in ("video", "image+video") else 8192,
+        limit_mm_per_prompt=mm_limit,
     )
 
     messages = list()
@@ -828,6 +865,29 @@ def run_hyperclovax_seed_vision(
                     }
                 ]
             )
+        elif modality == "image+video":
+            messages.append(
+                [
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "image",
+                                "ocr": "",
+                                "lens_keywords": "",
+                                "lens_local_keywords": "",
+                            },
+                            {
+                                "type": "video",
+                            },
+                            {
+                                "type": "text",
+                                "text": question,
+                            },
+                        ],
+                    }
+                ]
+            )
         else:
             raise ValueError(f"Unsupported modality: {modality}")
 
@@ -876,19 +936,25 @@ def run_idefics3(questions: list[str], modality: str) -> ModelRequestData:
 def run_interns1(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "internlm/Intern-S1-mini"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         trust_remote_code=True,
         max_model_len=8192,
         max_num_seqs=2,
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
         enforce_eager=True,
     )
 
+    image_placeholder = "<IMG_CONTEXT>"
+    video_placeholder = "<video>"
+
     if modality == "image":
-        placeholder = "<IMG_CONTEXT>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<video>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + "\n" + video_placeholder
 
     tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
     messages = [
@@ -909,20 +975,26 @@ def run_interns1(questions: list[str], modality: str) -> ModelRequestData:
 def run_interns1_pro(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "internlm/Intern-S1-Pro"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         trust_remote_code=True,
         max_model_len=8192,
         max_num_seqs=2,
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
         enforce_eager=True,
         tensor_parallel_size=4,
     )
 
+    image_placeholder = "<|vision_start|><|image_pad|><|vision_end|>"
+    video_placeholder = "<|vision_start|><|video_pad|><|vision_end|>"
+
     if modality == "image":
-        placeholder = "<|vision_start|><|image_pad|><|vision_end|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|vision_start|><|video_pad|><|vision_end|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
     messages = [
@@ -943,17 +1015,23 @@ def run_interns1_pro(questions: list[str], modality: str) -> ModelRequestData:
 def run_internvl(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "OpenGVLab/InternVL3-2B"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         trust_remote_code=True,
         max_model_len=8192,
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
 
+    image_placeholder = "<image>"
+    video_placeholder = "<video>"
+
     if modality == "image":
-        placeholder = "<image>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<video>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + "\n" + video_placeholder
 
     tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
     messages = [
@@ -1010,21 +1088,27 @@ def run_kanana_v(questions: list[str], modality: str) -> ModelRequestData:
 def run_keye_vl(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "Kwai-Keye/Keye-VL-8B-Preview"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=8192,
         trust_remote_code=True,
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
 
+    image_placeholder = "<|vision_start|><|image_pad|><|vision_end|>"
+    video_placeholder = "<|vision_start|><|video_pad|><|vision_end|>"
+
     if modality == "image":
-        placeholder = "<|image_pad|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|video_pad|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
-            f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>"
+            f"<|im_start|>user\n{placeholder}"
             f"{question}<|im_end|>\n"
             "<|im_start|>assistant\n"
         )
@@ -1041,21 +1125,27 @@ def run_keye_vl(questions: list[str], modality: str) -> ModelRequestData:
 def run_keye_vl1_5(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "Kwai-Keye/Keye-VL-1.5-8B"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=8192,
         trust_remote_code=True,
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
 
+    image_placeholder = "<|vision_start|><|image_pad|><|vision_end|>"
+    video_placeholder = "<|vision_start|><|video_pad|><|vision_end|>"
+
     if modality == "image":
-        placeholder = "<|image_pad|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|video_pad|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
-            f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>"
+            f"<|im_start|>user\n{placeholder}"
             f"{question}<|im_end|>\n"
             "<|im_start|>assistant\n"
         )
@@ -1259,22 +1349,26 @@ def run_llava_next_video(questions: list[str], modality: str) -> ModelRequestDat
 
 # LLaVA-OneVision
 def run_llava_onevision(questions: list[str], modality: str) -> ModelRequestData:
-    if modality == "video":
-        prompts = [
-            f"<|im_start|>user <video>\n{question}<|im_end|><|im_start|>assistant\n"
-            for question in questions
-        ]
+    image_placeholder = "<image>"
+    video_placeholder = "<video>"
 
-    elif modality == "image":
-        prompts = [
-            f"<|im_start|>user <image>\n{question}<|im_end|><|im_start|>assistant\n"
-            for question in questions
-        ]
+    if modality == "image":
+        placeholder = image_placeholder
+    elif modality == "video":
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + "\n" + video_placeholder
+
+    prompts = [
+        (f"<|im_start|>user {placeholder}\n{question}<|im_end|><|im_start|>assistant\n")
+        for question in questions
+    ]
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model="llava-hf/llava-onevision-qwen2-7b-ov-hf",
         max_model_len=16384,
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
 
     return ModelRequestData(
@@ -1307,8 +1401,8 @@ def run_mantis(questions: list[str], modality: str) -> ModelRequestData:
 
 # MiniCPM-V
 def run_minicpmv_base(questions: list[str], modality: str, model_name):
-    assert modality in ["image", "video"]
-    # If you want to use `MiniCPM-o-2_6` with audio inputs, check `audio_language.py` # noqa
+    assert modality in ["image", "video", "image+video"]
+    # If you want to use `MiniCPM-o-2_6` with audio inputs, check `audio_language_offline.py` # noqa
 
     # 2.0
     # The official repo doesn't work yet, so we need to use a fork for now
@@ -1329,12 +1423,13 @@ def run_minicpmv_base(questions: list[str], modality: str, model_name):
     # o2.6: image, video, audio
     # model_name = "openbmb/MiniCPM-o-2_6"
     tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
         max_num_seqs=2,
         trust_remote_code=True,
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
     # NOTE The stop_token_ids are different for various versions of MiniCPM-V
     # 2.0
@@ -1347,17 +1442,22 @@ def run_minicpmv_base(questions: list[str], modality: str, model_name):
     stop_tokens = ["<|im_end|>", "<|endoftext|>"]
     stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
 
-    modality_placeholder = {
-        "image": "(<image>./</image>)",
-        "video": "(<video>./</video>)",
-    }
+    image_placeholder = "(<image>./</image>)"
+    video_placeholder = "(<video>./</video>)"
+
+    if modality == "image":
+        placeholder = image_placeholder
+    elif modality == "video":
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + "\n" + video_placeholder
 
     prompts = [
         tokenizer.apply_chat_template(
             [
                 {
                     "role": "user",
-                    "content": f"{modality_placeholder[modality]}\n{question}",
+                    "content": f"{placeholder}\n{question}",
                 }
             ],
             tokenize=False,
@@ -1466,20 +1566,24 @@ def run_molmo(questions: list[str], modality: str) -> ModelRequestData:
 def run_molmo2(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "allenai/Molmo2-8B"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         trust_remote_code=True,
         dtype="bfloat16",
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
         max_num_batched_tokens=36864,
     )
 
+    image_placeholder = "<|image|>"
+    video_placeholder = "<|video|>"
+
     if modality == "image":
-        placeholder = "<|image|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|video|>"
-    else:
-        raise ValueError(f"Unsupported modality for molmo2: {modality}")
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         f"{placeholder}<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant\n"
@@ -1563,19 +1667,25 @@ def run_nvlm_d(questions: list[str], modality: str) -> ModelRequestData:
 def run_openpangu_vl(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "FreedomIntelligence/openPangu-VL-7B"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
         max_num_seqs=4,
         trust_remote_code=True,
         enforce_eager=True,
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
 
+    image_placeholder = "[unused19]"
+    video_placeholder = "[unused32]"
+
     if modality == "image":
-        placeholder = "[unused19]"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "[unused32]"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
@@ -1623,18 +1733,25 @@ def run_ovis(questions: list[str], modality: str) -> ModelRequestData:
 def run_ovis2_5(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "AIDC-AI/Ovis2.5-2B"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
         max_num_seqs=2,
         trust_remote_code=True,
         dtype="half",
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
+
+    image_placeholder = "<image>"
+    video_placeholder = "<video>"
+
     if modality == "image":
-        placeholder = "<image>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<video>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + "\n" + video_placeholder
 
     prompts = [
         f"<|im_start|>user\n\n{placeholder}\n{question}<|im_end|>\n<|im_start|>assistant\n"
@@ -1846,6 +1963,7 @@ def run_qwen_vl(questions: list[str], modality: str) -> ModelRequestData:
 def run_qwen2_vl(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "Qwen/Qwen2-VL-7B-Instruct"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
@@ -1855,18 +1973,23 @@ def run_qwen2_vl(questions: list[str], modality: str) -> ModelRequestData:
             "min_pixels": 28 * 28,
             "max_pixels": 1280 * 28 * 28,
         },
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
 
+    image_placeholder = "<|vision_start|><|image_pad|><|vision_end|>"
+    video_placeholder = "<|vision_start|><|video_pad|><|vision_end|>"
+
     if modality == "image":
-        placeholder = "<|image_pad|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|video_pad|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
             "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
-            f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>"
+            f"<|im_start|>user\n{placeholder}"
             f"{question}<|im_end|>\n"
             "<|im_start|>assistant\n"
         )
@@ -1883,6 +2006,7 @@ def run_qwen2_vl(questions: list[str], modality: str) -> ModelRequestData:
 def run_qwen2_5_vl(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "Qwen/Qwen2.5-VL-3B-Instruct"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
@@ -1892,18 +2016,23 @@ def run_qwen2_5_vl(questions: list[str], modality: str) -> ModelRequestData:
             "max_pixels": 1280 * 28 * 28,
             "fps": 1,
         },
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
 
+    image_placeholder = "<|vision_start|><|image_pad|><|vision_end|>"
+    video_placeholder = "<|vision_start|><|video_pad|><|vision_end|>"
+
     if modality == "image":
-        placeholder = "<|image_pad|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|video_pad|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
             "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
-            f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>"
+            f"<|im_start|>user\n{placeholder}"
             f"{question}<|im_end|>\n"
             "<|im_start|>assistant\n"
         )
@@ -1920,6 +2049,7 @@ def run_qwen2_5_vl(questions: list[str], modality: str) -> ModelRequestData:
 def run_qwen2_5_omni(questions: list[str], modality: str):
     model_name = "Qwen/Qwen2.5-Omni-7B"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
@@ -1929,13 +2059,18 @@ def run_qwen2_5_omni(questions: list[str], modality: str):
             "max_pixels": 1280 * 28 * 28,
             "fps": 1,
         },
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
 
+    image_placeholder = "<|vision_bos|><|IMAGE|><|vision_eos|>"
+    video_placeholder = "<|vision_bos|><|VIDEO|><|vision_eos|>"
+
     if modality == "image":
-        placeholder = "<|IMAGE|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|VIDEO|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     default_system = (
         "You are Qwen, a virtual human developed by the Qwen Team, Alibaba "
@@ -1946,7 +2081,7 @@ def run_qwen2_5_omni(questions: list[str], modality: str):
     prompts = [
         (
             f"<|im_start|>system\n{default_system}<|im_end|>\n"
-            f"<|im_start|>user\n<|vision_bos|>{placeholder}<|vision_eos|>"
+            f"<|im_start|>user\n{placeholder}"
             f"{question}<|im_end|>\n"
             "<|im_start|>assistant\n"
         )
@@ -1962,6 +2097,7 @@ def run_qwen2_5_omni(questions: list[str], modality: str):
 def run_qwen3_vl(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "Qwen/Qwen3-VL-4B-Instruct"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
@@ -1971,18 +2107,23 @@ def run_qwen3_vl(questions: list[str], modality: str) -> ModelRequestData:
             "max_pixels": 1280 * 28 * 28,
             "fps": 1,
         },
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
 
+    image_placeholder = "<|vision_start|><|image_pad|><|vision_end|>"
+    video_placeholder = "<|vision_start|><|video_pad|><|vision_end|>"
+
     if modality == "image":
-        placeholder = "<|image_pad|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|video_pad|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
             "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
-            f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>"
+            f"<|im_start|>user\n{placeholder}"
             f"{question}<|im_end|>\n"
             "<|im_start|>assistant\n"
         )
@@ -1999,6 +2140,7 @@ def run_qwen3_vl(questions: list[str], modality: str) -> ModelRequestData:
 def run_qwen3_vl_moe(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "Qwen/Qwen3-VL-30B-A3B-Instruct"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
@@ -2008,18 +2150,23 @@ def run_qwen3_vl_moe(questions: list[str], modality: str) -> ModelRequestData:
             "max_pixels": 1280 * 28 * 28,
             "fps": 1,
         },
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
 
+    image_placeholder = "<|vision_start|><|image_pad|><|vision_end|>"
+    video_placeholder = "<|vision_start|><|video_pad|><|vision_end|>"
+
     if modality == "image":
-        placeholder = "<|image_pad|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|video_pad|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
             "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
-            f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>"
+            f"<|im_start|>user\n{placeholder}"
             f"{question}<|im_end|>\n"
             "<|im_start|>assistant\n"
         )
@@ -2190,6 +2337,7 @@ def run_tarsier(questions: list[str], modality: str) -> ModelRequestData:
 def run_tarsier2(questions: list[str], modality: str) -> ModelRequestData:
     model_name = "omni-research/Tarsier2-Recap-7b"
 
+    mm_limit = {"image": 1, "video": 1} if modality == "image+video" else {modality: 1}
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
@@ -2197,18 +2345,23 @@ def run_tarsier2(questions: list[str], modality: str) -> ModelRequestData:
             "architectures": ["Tarsier2ForConditionalGeneration"],
             "model_type": "tarsier2",
         },
-        limit_mm_per_prompt={modality: 1},
+        limit_mm_per_prompt=mm_limit,
     )
 
+    image_placeholder = "<|vision_start|><|image_pad|><|vision_end|>"
+    video_placeholder = "<|vision_start|><|video_pad|><|vision_end|>"
+
     if modality == "image":
-        placeholder = "<|image_pad|>"
+        placeholder = image_placeholder
     elif modality == "video":
-        placeholder = "<|video_pad|>"
+        placeholder = video_placeholder
+    elif modality == "image+video":
+        placeholder = image_placeholder + video_placeholder
 
     prompts = [
         (
             "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
-            f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>"
+            f"<|im_start|>user\n{placeholder}"
             f"{question}<|im_end|>\n"
             "<|im_start|>assistant\n"
         )
@@ -2310,6 +2463,13 @@ def run_tarsier2(questions: list[str], modality: str) -> ModelRequestData:
 ]
 
 
+MODELS_SUPPORT_VIT_CUDA_GRAPH = [
+    "qwen3_vl",
+    "qwen3_vl_moe",
+    "qwen2_5_vl",
+]
+
+
 def get_multi_modal_input(args):
     """
     return {
@@ -2357,6 +2517,24 @@ def get_multi_modal_input(args):
             "questions": vision_chunk_questions,
         }
 
+    if args.modality == "image+video":
+        image = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")
+        needs_metadata = args.model_type in MODELS_NEED_VIDEO_METADATA
+        video = VideoAsset(name="baby_reading", num_frames=args.num_frames).np_ndarrays
+        metadata = VideoAsset(name="baby_reading", num_frames=args.num_frames).metadata
+        img_video_questions = [
+            "What is shown in the image? What happens in the video?",
+            "Describe both the image and the video content.",
+        ]
+
+        return {
+            "data": {
+                "image": image,
+                "video": ([(video, metadata)] if needs_metadata else video),
+            },
+            "questions": img_video_questions,
+        }
+
     msg = f"Modality {args.modality} is not supported."
     raise ValueError(msg)
 
@@ -2404,6 +2582,29 @@ def apply_image_repeat(
     return inputs, inputs_with_empty_media
 
 
+def maybe_add_vit_cuda_graph_compilation_config(args, engine_args):
+    model = args.model_type
+    modality = args.modality
+    enable_vit_cuda_graph = args.enable_vit_cuda_graph
+
+    if enable_vit_cuda_graph and model in MODELS_SUPPORT_VIT_CUDA_GRAPH:
+        if modality == "image" or modality == "video":
+            vision_items_per_batch = 1
+        elif modality == "image+video":
+            vision_items_per_batch = 2
+        else:
+            raise ValueError(
+                f"modality={modality} is not supported for vit cuda graph."
+            )
+
+        engine_args.compilation_config = {
+            "cudagraph_mm_encoder": True,
+            "encoder_cudagraph_max_vision_items_per_batch": vision_items_per_batch,
+        }
+
+    return engine_args
+
+
 @contextmanager
 def time_counter(enable: bool):
     if enable:
@@ -2439,7 +2640,7 @@ def parse_args():
         "--modality",
         type=str,
         default="image",
-        choices=["image", "video", "vision_chunk"],
+        choices=["image", "video", "image+video", "vision_chunk"],
         help="Modality of the input.",
     )
     parser.add_argument(
@@ -2454,33 +2655,28 @@ def parse_args():
         default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
-
     parser.add_argument(
         "--image-repeat-prob",
         type=float,
         default=None,
         help="Simulates the hit-ratio for multi-modal preprocessor cache (if enabled)",
     )
-
     parser.add_argument(
         "--disable-mm-processor-cache",
         action="store_true",
         help="If True, disables caching of multi-modal processor.",
     )
-
     parser.add_argument(
         "--time-generate",
         action="store_true",
         help="If True, then print the total generate() call time",
     )
-
     parser.add_argument(
         "--use-different-prompt-per-request",
         action="store_true",
         help="If True, then use different prompt (with the same multi-modal "
         "data) for each request.",
     )
-
     parser.add_argument(
         "--verify-mm-cache-hit-with-uuids",
         action="store_true",
@@ -2494,6 +2690,11 @@ def parse_args():
         default=None,
         help="Tensor parallel size to override the model's default setting. ",
     )
+    parser.add_argument(
+        "--enable-vit-cuda-graph",
+        action="store_true",
+        help="If True, will enable vit cuda graph capture and replay for the model.",
+    )
     return parser.parse_args()
 
 
@@ -2527,6 +2728,7 @@ def main(args):
     engine_args.mm_processor_cache_gb = mm_processor_cache_gb
     if args.tensor_parallel_size is not None:
         engine_args.tensor_parallel_size = args.tensor_parallel_size
+    engine_args = maybe_add_vit_cuda_graph_compilation_config(args, engine_args)
     llm = LLM.from_engine_args(engine_args)
 
     # Don't want to check the flag multiple times, so just hijack `prompts`.
@@ -2546,23 +2748,42 @@ def main(args):
         else req_data.sampling_params
     )
 
+    def _mm_data(data, modality):
+        if modality == "image+video":
+            return {"image": data["image"], "video": data["video"]}
+        return {modality: data}
+
+    def _mm_uuid(uuid, modality):
+        if modality == "image+video":
+            return {"image": uuid, "video": uuid + "v"}
+        return {modality: uuid}
+
+    def _mm_empty(modality):
+        if modality == "image+video":
+            return {"image": None, "video": None}
+        return {modality: None}
+
     assert args.num_prompts > 0
     if args.num_prompts == 1:
         # Single inference
         uuid = "uuid_0"
         inputs = {
             "prompt": prompts[0],
-            "multi_modal_data": {modality: data},
-            "multi_modal_uuids": {modality: uuid},
+            "multi_modal_data": _mm_data(data, modality),
+            "multi_modal_uuids": _mm_uuid(uuid, modality),
         }
         inputs_with_empty_media = {
             "prompt": prompts[0],
-            "multi_modal_data": {modality: None},
-            "multi_modal_uuids": {modality: uuid},
+            "multi_modal_data": _mm_empty(modality),
+            "multi_modal_uuids": _mm_uuid(uuid, modality),
         }
     else:
         # Batch inference
         if args.image_repeat_prob is not None:
+            if modality == "image+video":
+                raise ValueError(
+                    "--image-repeat-prob is not supported for 'image+video' modality"
+                )
             # Repeat images with specified probability of "image_repeat_prob"
             inputs, inputs_with_empty_media = apply_image_repeat(
                 args.image_repeat_prob,
@@ -2572,7 +2793,7 @@ def main(args):
                 modality,
             )
         else:
-            # Use the same image for all prompts
+            # Use the same image/video for all prompts
             inputs = []
             inputs_with_empty_media = []
             for i in range(args.num_prompts):
@@ -2580,15 +2801,15 @@ def main(args):
                 inputs.append(
                     {
                         "prompt": prompts[i % len(prompts)],
-                        "multi_modal_data": {modality: data},
-                        "multi_modal_uuids": {modality: uuid},
+                        "multi_modal_data": _mm_data(data, modality),
+                        "multi_modal_uuids": _mm_uuid(uuid, modality),
                     }
                 )
                 inputs_with_empty_media.append(
                     {
                         "prompt": prompts[i % len(prompts)],
-                        "multi_modal_data": {modality: None},
-                        "multi_modal_uuids": {modality: uuid},
+                        "multi_modal_data": _mm_empty(modality),
+                        "multi_modal_uuids": _mm_uuid(uuid, modality),
                     }
                 )
 
diff --git a/examples/offline_inference/qwen_1m.py b/examples/generate/qwen_1m_offline.py
similarity index 100%
rename from examples/offline_inference/qwen_1m.py
rename to examples/generate/qwen_1m_offline.py
diff --git a/examples/online_serving/token_generation_client.py b/examples/generate/token_generation_client.py
similarity index 100%
rename from examples/online_serving/token_generation_client.py
rename to examples/generate/token_generation_client.py
diff --git a/examples/online_serving/dashboards/README.md b/examples/observability/dashboards/README.md
similarity index 91%
rename from examples/online_serving/dashboards/README.md
rename to examples/observability/dashboards/README.md
index 10b9a864f572..29ec932cef24 100644
--- a/examples/online_serving/dashboards/README.md
+++ b/examples/observability/dashboards/README.md
@@ -43,7 +43,7 @@ Both platforms provide equivalent monitoring capabilities:
 First, navigate to this example's directory:
 
 ```bash
-cd examples/online_serving/dashboards
+cd examples/observability/dashboards
 ```
 
 ### Grafana
@@ -74,8 +74,8 @@ percli apply -f perses/performance_statistics.yaml
 
 For detailed deployment instructions and platform-specific options, see:
 
-- **[Grafana Documentation](./grafana)** - JSON dashboards, operator usage, manual import
-- **[Perses Documentation](./perses)** - YAML specs, CLI usage, operator wrapping
+- **[Grafana Documentation](grafana)** - JSON dashboards, operator usage, manual import
+- **[Perses Documentation](perses)** - YAML specs, CLI usage, operator wrapping
 
 ## Contributing
 
diff --git a/examples/online_serving/dashboards/grafana/README.md b/examples/observability/dashboards/grafana/README.md
similarity index 100%
rename from examples/online_serving/dashboards/grafana/README.md
rename to examples/observability/dashboards/grafana/README.md
diff --git a/examples/online_serving/dashboards/grafana/performance_statistics.json b/examples/observability/dashboards/grafana/performance_statistics.json
similarity index 100%
rename from examples/online_serving/dashboards/grafana/performance_statistics.json
rename to examples/observability/dashboards/grafana/performance_statistics.json
diff --git a/examples/online_serving/dashboards/grafana/query_statistics.json b/examples/observability/dashboards/grafana/query_statistics.json
similarity index 100%
rename from examples/online_serving/dashboards/grafana/query_statistics.json
rename to examples/observability/dashboards/grafana/query_statistics.json
diff --git a/examples/online_serving/dashboards/perses/README.md b/examples/observability/dashboards/perses/README.md
similarity index 100%
rename from examples/online_serving/dashboards/perses/README.md
rename to examples/observability/dashboards/perses/README.md
diff --git a/examples/online_serving/dashboards/perses/performance_statistics.yaml b/examples/observability/dashboards/perses/performance_statistics.yaml
similarity index 100%
rename from examples/online_serving/dashboards/perses/performance_statistics.yaml
rename to examples/observability/dashboards/perses/performance_statistics.yaml
diff --git a/examples/online_serving/dashboards/perses/query_statistics.yaml b/examples/observability/dashboards/perses/query_statistics.yaml
similarity index 100%
rename from examples/online_serving/dashboards/perses/query_statistics.yaml
rename to examples/observability/dashboards/perses/query_statistics.yaml
diff --git a/examples/offline_inference/metrics.py b/examples/observability/metrics/offline.py
similarity index 100%
rename from examples/offline_inference/metrics.py
rename to examples/observability/metrics/offline.py
diff --git a/examples/online_serving/opentelemetry/README.md b/examples/observability/opentelemetry/README.md
similarity index 100%
rename from examples/online_serving/opentelemetry/README.md
rename to examples/observability/opentelemetry/README.md
diff --git a/examples/online_serving/opentelemetry/dummy_client.py b/examples/observability/opentelemetry/dummy_client.py
similarity index 100%
rename from examples/online_serving/opentelemetry/dummy_client.py
rename to examples/observability/opentelemetry/dummy_client.py
diff --git a/examples/online_serving/prometheus_grafana/README.md b/examples/observability/prometheus_grafana/README.md
similarity index 100%
rename from examples/online_serving/prometheus_grafana/README.md
rename to examples/observability/prometheus_grafana/README.md
diff --git a/examples/online_serving/prometheus_grafana/docker-compose.yaml b/examples/observability/prometheus_grafana/docker-compose.yaml
similarity index 100%
rename from examples/online_serving/prometheus_grafana/docker-compose.yaml
rename to examples/observability/prometheus_grafana/docker-compose.yaml
diff --git a/examples/online_serving/prometheus_grafana/grafana.json b/examples/observability/prometheus_grafana/grafana.json
similarity index 100%
rename from examples/online_serving/prometheus_grafana/grafana.json
rename to examples/observability/prometheus_grafana/grafana.json
diff --git a/examples/online_serving/prometheus_grafana/prometheus.yaml b/examples/observability/prometheus_grafana/prometheus.yaml
similarity index 100%
rename from examples/online_serving/prometheus_grafana/prometheus.yaml
rename to examples/observability/prometheus_grafana/prometheus.yaml
diff --git a/examples/online_serving/disaggregated_serving/moriio_toy_proxy_server.py b/examples/online_serving/disaggregated_serving/moriio_toy_proxy_server.py
deleted file mode 100644
index 33fb56c88020..000000000000
--- a/examples/online_serving/disaggregated_serving/moriio_toy_proxy_server.py
+++ /dev/null
@@ -1,306 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import asyncio
-import copy
-import logging
-import os
-import socket
-import threading
-import uuid
-
-import aiohttp
-import msgpack
-import regex as re
-import zmq
-from quart import Quart, make_response, request
-
-from vllm.distributed.kv_transfer.kv_connector.v1.moriio.moriio_common import (
-    MoRIIOConstants,
-)
-
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-prefill_instances: list[dict] = []
-decode_instances: list[dict] = []
-request_nums = 0
-app = Quart(__name__)
-
-IP_PORT_PATTERN = re.compile(r"//(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}):(\d+)")
-
-
-TRANSFER_TYPE = None
-
-
-def _append_whole_dict_unique(target_list, data_dict):
-    new_filtered = {k: v for k, v in data_dict.items() if k != "index"}
-    for existed in target_list:
-        existed_filtered = {k: v for k, v in existed.items() if k != "index"}
-        if existed_filtered == new_filtered:
-            return False
-    print("!!APPEND!!", data_dict)
-    target_list.append(data_dict)
-    transfer_mode = data_dict.get("transfer_mode", "unknown")
-    global TRANSFER_TYPE
-
-    if TRANSFER_TYPE is None:
-        TRANSFER_TYPE = transfer_mode
-        logger.info("SET TRANSFER TYPE TO %s", TRANSFER_TYPE)
-    elif transfer_mode != TRANSFER_TYPE:
-        raise ValueError(f"mismatched transfer mode {TRANSFER_TYPE} vs {transfer_mode}")
-
-    return True
-
-
-_list_lock = threading.RLock()
-
-
-def _listen_for_register(hostname, port):
-    context = zmq.Context()
-    router_socket = context.socket(zmq.ROUTER)
-    router_socket.bind(f"tcp://{hostname}:{port}")
-    poller = zmq.Poller()
-    poller.register(router_socket, zmq.POLLIN)
-    global prefill_instances
-    global decode_instances
-
-    while True:
-        socks = dict(poller.poll())
-        if router_socket in socks:
-            remote_addr, msg = router_socket.recv_multipart()
-            data = msgpack.loads(msg)
-            if data["type"] == "HELLO":
-                pass
-            elif (
-                data["type"] == "register"
-                and data["role"] == "P"
-                and data["request_address"] not in prefill_instances
-            ):
-                with _list_lock:
-                    _append_whole_dict_unique(prefill_instances, data)
-
-            elif (
-                data["type"] == "register"
-                and data["role"] == "D"
-                and data["request_address"] not in decode_instances
-            ):
-                with _list_lock:
-                    _append_whole_dict_unique(decode_instances, data)
-
-
-def start_service_discovery(hostname, port):
-    if not hostname:
-        hostname = socket.gethostname()
-    if port == 0:
-        raise ValueError("Port cannot be 0")
-
-    _listener_thread = threading.Thread(
-        target=_listen_for_register, args=(hostname, port), daemon=True
-    )
-    _listener_thread.start()
-    return _listener_thread
-
-
-async def send_request_to_prefill(
-    endpoint, req_data, request_id, d_endpoint, dip, dport, selected_prefill_dp_rank
-):
-    req_data_copy = req_data
-
-    req_data_copy["kv_transfer_params"].update(
-        {
-            "do_remote_decode": True,
-            "do_remote_prefill": False,
-            "remote_handshake_port": d_endpoint["handshake_port"],
-            "remote_notify_port": d_endpoint["notify_port"],
-            "remote_engine_id": None,
-            "remote_block_ids": None,
-            "remote_host": dip,
-            "remote_port": dport,
-        }
-    )
-    req_data_copy["stream"] = False
-    req_data_copy["max_tokens"] = 1
-    if "max_completion_tokens" in req_data_copy:
-        req_data_copy["max_completion_tokens"] = 1
-    if "stream_options" in req_data_copy:
-        del req_data_copy["stream_options"]
-    async with aiohttp.ClientSession(
-        timeout=aiohttp.ClientTimeout(total=6 * 6000 * 6000)
-    ) as session:
-        headers = {
-            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
-            "X-Request-Id": request_id,
-        }
-        if selected_prefill_dp_rank is not None:
-            headers["X-data-parallel-rank"] = str(selected_prefill_dp_rank)
-        async with session.post(
-            url=endpoint, json=req_data_copy, headers=headers
-        ) as response:
-            if response.status == 200:
-                return await response.json()
-
-            else:
-                raise RuntimeError(
-                    "send_request_to_prefill response.status != 200response.status = ",
-                    response.status,
-                )
-
-
-async def start_decode_request(endpoint, req_data, request_id):
-    session = aiohttp.ClientSession(
-        timeout=aiohttp.ClientTimeout(total=6 * 6000 * 6000)
-    )
-    headers = {
-        "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
-        "X-Request-Id": request_id,
-    }
-    response = await session.post(url=endpoint, json=req_data, headers=headers)
-    return session, response
-
-
-async def stream_decode_response(session, response, request_id):
-    try:
-        if response.status == 200:
-            async for chunk_bytes in response.content.iter_chunked(1024):
-                yield chunk_bytes
-        else:
-            raise RuntimeError(
-                f"decode response.status != 200, status = {response.status}"
-            )
-    finally:
-        await session.close()
-
-
-def example_round_robin_dp_loader(request_number, dp_size):
-    return request_nums % dp_size
-
-
-@app.route("/v1/completions", methods=["POST"])
-@app.route("/v1/chat/completions", methods=["POST"])
-async def handle_request():
-    try:
-        with _list_lock:
-            global request_nums
-            request_nums += 1
-
-        def extract_ip_port_fast(url):
-            match = IP_PORT_PATTERN.search(url)
-            if not match:
-                raise ValueError(f"Invalid URL format: {url}")
-            return match.groups()
-
-        req_data = await request.get_json()
-        request_id = str(uuid.uuid4())
-
-        prefill_instance_endpoint = None
-        decode_instance_endpoint = None
-        error_msg = (
-            "Service Unavailable: No prefill or decode instances are registered."
-        )
-        if not prefill_instances or not decode_instances:
-            return await make_response(
-                (
-                    error_msg,
-                    503,
-                )
-            )
-        pid = request_nums % len(prefill_instances)
-        did = request_nums % len(decode_instances)
-        prefill_instance_endpoint = prefill_instances[pid]
-        decode_instance_endpoint = decode_instances[did]
-
-        selected_prefill_dp_rank = None
-        if prefill_instance_endpoint["dp_size"] > 1:
-            selected_prefill_dp_rank = example_round_robin_dp_loader(
-                request_nums // len(prefill_instance_endpoint),
-                prefill_instance_endpoint["dp_size"],
-            )
-
-        dip, dport = extract_ip_port_fast(decode_instance_endpoint["request_address"])
-
-        transfer_id = f"{MoRIIOConstants.TRANSFER_PREFIX}-{str(uuid.uuid4())}"
-
-        req_data_to_prefill = copy.deepcopy(req_data)
-        req_data_to_prefill["kv_transfer_params"] = {}
-        req_data["kv_transfer_params"] = {}
-        req_data_to_prefill["kv_transfer_params"]["remote_dp_size"] = (
-            decode_instance_endpoint["dp_size"]
-        )
-        req_data_to_prefill["kv_transfer_params"]["remote_tp_size"] = (
-            decode_instance_endpoint["tp_size"]
-        )
-        req_data_to_prefill["kv_transfer_params"]["transfer_id"] = transfer_id
-
-        send_prefill_task = asyncio.create_task(
-            send_request_to_prefill(
-                prefill_instance_endpoint["request_address"],
-                req_data_to_prefill,
-                request_id,
-                decode_instance_endpoint,
-                dip,
-                dport,
-                selected_prefill_dp_rank,
-            )
-        )
-        ip, port = extract_ip_port_fast(prefill_instance_endpoint["request_address"])
-
-        req_data["max_tokens"] -= 1
-
-        req_data["kv_transfer_params"] = {
-            "do_remote_decode": False,
-            "do_remote_prefill": True,
-            "remote_handshake_port": prefill_instance_endpoint["handshake_port"],
-            "remote_notify_port": prefill_instance_endpoint["notify_port"],
-            "remote_engine_id": None,
-            "remote_block_ids": None,
-            "remote_host": ip,
-            "remote_port": port,
-        }
-        if TRANSFER_TYPE == "READ":
-            # In read mode, prefill and decode are executed serially.
-            prefill_response = await send_prefill_task
-            req_data["kv_transfer_params"]["remote_engine_id"] = prefill_response[
-                "kv_transfer_params"
-            ]["remote_engine_id"]
-            req_data["kv_transfer_params"]["remote_block_ids"] = prefill_response[
-                "kv_transfer_params"
-            ]["remote_block_ids"]
-
-        req_data["kv_transfer_params"]["remote_dp_size"] = prefill_instance_endpoint[
-            "dp_size"
-        ]
-        req_data["kv_transfer_params"]["remote_tp_size"] = prefill_instance_endpoint[
-            "tp_size"
-        ]
-
-        if selected_prefill_dp_rank is not None:
-            req_data["kv_transfer_params"]["remote_dp_rank"] = selected_prefill_dp_rank
-        req_data["kv_transfer_params"]["transfer_id"] = transfer_id
-
-        decode_request_task = asyncio.create_task(
-            start_decode_request(
-                decode_instance_endpoint["request_address"], req_data, request_id
-            )
-        )
-
-        session, decode_response = await decode_request_task
-        stream_generator = stream_decode_response(session, decode_response, request_id)
-        response = await make_response(stream_generator)
-        return response
-    except Exception as e:
-        logger.exception("An error occurred while handling the request: %s", e)
-        return await make_response(
-            (
-                f"Internal Server Error: {e!s}",
-                500,
-            )
-        )
-
-
-if __name__ == "__main__":
-    t = start_service_discovery("0.0.0.0", 36367)
-    app.debug = True
-    app.config["BODY_TIMEOUT"] = 360000
-    app.config["RESPONSE_TIMEOUT"] = 360000
-
-    app.run(host="0.0.0.0", port=10001)
-    t.join()
diff --git a/examples/online_serving/prompt_embed_inference_with_openai_client.py b/examples/online_serving/prompt_embed_inference_with_openai_client.py
deleted file mode 100644
index fa4b64c00703..000000000000
--- a/examples/online_serving/prompt_embed_inference_with_openai_client.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-vLLM OpenAI-Compatible Client with Prompt Embeddings
-
-This script demonstrates how to:
-1. Generate prompt embeddings using Hugging Face Transformers
-2. Encode them in base64 format
-3. Send them to a vLLM server via the OpenAI-compatible Completions API
-
-Run the vLLM server first:
-vllm serve meta-llama/Llama-3.2-1B-Instruct \
-  --runner generate \
-  --max-model-len 4096 \
-  --enable-prompt-embeds
-
-Run the client:
-python examples/online_serving/prompt_embed_inference_with_openai_client.py
-
-Model: meta-llama/Llama-3.2-1B-Instruct
-Note: This model is gated on Hugging Face Hub.
-      You must request access to use it:
-      https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct
-
-Dependencies:
-- transformers
-- torch
-- openai
-"""
-
-import transformers
-from openai import OpenAI
-
-from vllm.utils.serial_utils import tensor2base64
-
-
-def main():
-    client = OpenAI(
-        api_key="EMPTY",
-        base_url="http://localhost:8000/v1",
-    )
-
-    model_name = "meta-llama/Llama-3.2-1B-Instruct"
-
-    # Transformers
-    tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
-    transformers_model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
-
-    # Refer to the HuggingFace repo for the correct format to use
-    chat = [{"role": "user", "content": "Please tell me about the capital of France."}]
-    token_ids = tokenizer.apply_chat_template(
-        chat, add_generation_prompt=True, return_tensors="pt", return_dict=True
-    ).input_ids
-
-    embedding_layer = transformers_model.get_input_embeddings()
-    prompt_embeds = embedding_layer(token_ids).squeeze(0)
-
-    # Prompt embeddings
-    encoded_embeds = tensor2base64(prompt_embeds)
-
-    completion = client.completions.create(
-        model=model_name,
-        prompt=None,
-        max_tokens=5,
-        temperature=0.0,
-        # NOTE: The OpenAI client allows passing in extra JSON body via the
-        # `extra_body` argument.
-        extra_body={"prompt_embeds": encoded_embeds},
-    )
-
-    print("-" * 30)
-    print(completion.choices[0].text)
-    print("-" * 30)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/online_serving/utils.py b/examples/online_serving/utils.py
deleted file mode 100644
index a512d8a31b53..000000000000
--- a/examples/online_serving/utils.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from openai import APIConnectionError, OpenAI
-from openai.pagination import SyncPage
-from openai.types.model import Model
-
-
-def get_first_model(client: OpenAI) -> str:
-    """
-    Get the first model from the vLLM server.
-    """
-    try:
-        models: SyncPage[Model] = client.models.list()
-    except APIConnectionError as e:
-        raise RuntimeError(
-            "Failed to get the list of models from the vLLM server at "
-            f"{client.base_url} with API key {client.api_key}. Check\n"
-            "1. the server is running\n"
-            "2. the server URL is correct\n"
-            "3. the API key is correct"
-        ) from e
-
-    if len(models.data) == 0:
-        raise RuntimeError(f"No models found on the vLLM server at {client.base_url}")
-
-    return models.data[0].id
diff --git a/examples/pooling/embed/openai_embedding_long_text/service.sh b/examples/pooling/embed/openai_embedding_long_text/service.sh
index 37a8b625b7f9..68950d04ee14 100644
--- a/examples/pooling/embed/openai_embedding_long_text/service.sh
+++ b/examples/pooling/embed/openai_embedding_long_text/service.sh
@@ -119,7 +119,7 @@ echo "   - API Key: $API_KEY"
 echo "   - Native Pooling: $POOLING_TYPE | Cross-chunk: MEAN"
 echo ""
 echo "🧪 Test the server with:"
-echo "   python examples/online_serving/openai_embedding_long_text/client.py"
+echo "   python examples/pooling/embed/openai_embedding_long_text/client.py"
 echo ""
 echo "📚 Enhanced features enabled:"
 echo "   ✅ Intelligent native pooling type detection"
diff --git a/examples/pooling/reward/sequence_reward_offline.py b/examples/pooling/reward/sequence_reward_offline.py
new file mode 100644
index 000000000000..0727bceee111
--- /dev/null
+++ b/examples/pooling/reward/sequence_reward_offline.py
@@ -0,0 +1,62 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""
+Example offline usage of sequence reward models.
+
+The key distinction between sequence classification and token classification
+lies in their output granularity: sequence classification produces a single
+result for an entire input sequence, whereas token classification yields a
+result for each individual token within the sequence.
+"""
+
+from argparse import Namespace
+
+from vllm import LLM, EngineArgs
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.print_utils import print_embeddings
+
+
+def parse_args():
+    parser = FlexibleArgumentParser()
+    parser = EngineArgs.add_cli_args(parser)
+    # Set example specific arguments
+    parser.set_defaults(
+        model="Skywork/Skywork-Reward-V2-Qwen3-0.6B",
+        runner="pooling",
+        enforce_eager=True,
+        max_model_len=1024,
+        trust_remote_code=True,
+    )
+    return parser.parse_args()
+
+
+def main(args: Namespace):
+    # Sample prompts.
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    # Create an LLM.
+    # You should pass runner="pooling" for reward models
+    llm = LLM(**vars(args))
+
+    # Generate rewards. The output is a list of PoolingRequestOutput.
+    # Use pooling_task="classify" for sequence reward models.
+    outputs = llm.encode(prompts, pooling_task="classify")
+
+    # Print the outputs.
+    print("\nGenerated Outputs:\n" + "-" * 60)
+    for prompt, output in zip(prompts, outputs):
+        rewards = output.outputs.data
+        print(f"Prompt: {prompt!r}")
+        print_embeddings(rewards.tolist(), prefix="Reward")
+        print("-" * 60)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)
diff --git a/examples/pooling/reward/sequence_reward_online.py b/examples/pooling/reward/sequence_reward_online.py
new file mode 100644
index 000000000000..40d8d28e3908
--- /dev/null
+++ b/examples/pooling/reward/sequence_reward_online.py
@@ -0,0 +1,71 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Example online usage of sequence reward models.
+
+Run `vllm serve <model> --runner pooling`
+to start up the server in vLLM. e.g.
+
+vllm serve Skywork/Skywork-Reward-V2-Qwen3-0.6B
+
+The key distinction between sequence classification and token classification
+lies in their output granularity: sequence classification produces a single
+result for an entire input sequence, whereas token classification yields a
+result for each individual token within the sequence.
+"""
+
+import argparse
+import pprint
+
+import requests
+
+
+def post_http_request(prompt: dict, api_url: str) -> requests.Response:
+    headers = {"User-Agent": "Test Client"}
+    response = requests.post(api_url, headers=headers, json=prompt)
+    return response
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--host", type=str, default="localhost")
+    parser.add_argument("--port", type=int, default=8000)
+
+    return parser.parse_args()
+
+
+def main(args):
+    base_url = f"http://{args.host}:{args.port}"
+    models_url = base_url + "/v1/models"
+    pooing_url = base_url + "/pooling"
+
+    response = requests.get(models_url)
+    model = response.json()["data"][0]["id"]
+
+    # Input like Completions API
+    prompt = {"model": model, "input": "vLLM is great!"}
+    pooling_response = post_http_request(prompt=prompt, api_url=pooing_url)
+    print("-" * 50)
+    print("Pooling Response:")
+    pprint.pprint(pooling_response.json())
+    print("-" * 50)
+
+    # Input like Chat API
+    prompt = {
+        "model": model,
+        "messages": [
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": "vLLM is great!"}],
+            }
+        ],
+    }
+    pooling_response = post_http_request(prompt=prompt, api_url=pooing_url)
+    print("Pooling Response:")
+    pprint.pprint(pooling_response.json())
+    print("-" * 50)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)
diff --git a/examples/basic/offline_inference/reward.py b/examples/pooling/reward/token_reward_offline.py
similarity index 74%
rename from examples/basic/offline_inference/reward.py
rename to examples/pooling/reward/token_reward_offline.py
index b6aece26ace1..4705c0491241 100644
--- a/examples/basic/offline_inference/reward.py
+++ b/examples/pooling/reward/token_reward_offline.py
@@ -1,6 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+"""
+Example offline usage of token reward models.
+
+The key distinction between sequence classification and token classification
+lies in their output granularity: sequence classification produces a single
+result for an entire input sequence, whereas token classification yields a
+result for each individual token within the sequence.
+"""
+
 from argparse import Namespace
 
 from vllm import LLM, EngineArgs
@@ -36,14 +45,14 @@ def main(args: Namespace):
     llm = LLM(**vars(args))
 
     # Generate rewards. The output is a list of PoolingRequestOutput.
-    outputs = llm.reward(prompts)
+    outputs = llm.encode(prompts, pooling_task="token_classify")
 
     # Print the outputs.
     print("\nGenerated Outputs:\n" + "-" * 60)
     for prompt, output in zip(prompts, outputs):
         rewards = output.outputs.data
         print(f"Prompt: {prompt!r}")
-        print_embeddings(rewards, prefix="Reward")
+        print_embeddings(rewards.tolist(), prefix="Reward")
         print("-" * 60)
 
 
diff --git a/examples/pooling/pooling/pooling_online.py b/examples/pooling/reward/token_reward_online.py
similarity index 83%
rename from examples/pooling/pooling/pooling_online.py
rename to examples/pooling/reward/token_reward_online.py
index e8ff38889a16..64ee0c9dfdcc 100644
--- a/examples/pooling/pooling/pooling_online.py
+++ b/examples/pooling/reward/token_reward_online.py
@@ -1,12 +1,17 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
-Example online usage of Pooling API.
+Example online usage of token reward models.
 
 Run `vllm serve <model> --runner pooling`
 to start up the server in vLLM. e.g.
 
 vllm serve internlm/internlm2-1_8b-reward --trust-remote-code
+
+The key distinction between sequence classification and token classification
+lies in their output granularity: sequence classification produces a single
+result for an entire input sequence, whereas token classification yields a
+result for each individual token within the sequence.
 """
 
 import argparse
diff --git a/examples/pooling/token_embed/jina_embeddings_v4_offline.py b/examples/pooling/token_embed/jina_embeddings_v4_offline.py
index 3822d2b42dcd..8aa14fda2e5e 100644
--- a/examples/pooling/token_embed/jina_embeddings_v4_offline.py
+++ b/examples/pooling/token_embed/jina_embeddings_v4_offline.py
@@ -4,68 +4,74 @@
 import torch
 
 from vllm import LLM
+from vllm.config import PoolerConfig
 from vllm.inputs import TextPrompt
 from vllm.multimodal.utils import fetch_image
 
-# Initialize model
-model = LLM(
-    model="jinaai/jina-embeddings-v4-vllm-text-matching",
-    runner="pooling",
-    max_model_len=1024,
-    gpu_memory_utilization=0.8,
-)
 
-# Create text prompts
-text1 = "Ein wunderschöner Sonnenuntergang am Strand"
-text1_prompt = TextPrompt(prompt=f"Query: {text1}")
+def main():
+    # Initialize model
+    model = LLM(
+        model="jinaai/jina-embeddings-v4-vllm-text-matching",
+        pooler_config=PoolerConfig(task="token_embed"),
+        runner="pooling",
+        max_model_len=1024,
+        gpu_memory_utilization=0.8,
+    )
 
-text2 = "浜辺に沈む美しい夕日"
-text2_prompt = TextPrompt(prompt=f"Query: {text2}")
+    # Create text prompts
+    text1 = "Ein wunderschöner Sonnenuntergang am Strand"
+    text1_prompt = TextPrompt(prompt=f"Query: {text1}")
 
-# Create image prompt
-image = fetch_image(
-    "https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/eskimo.jpg"  # noqa: E501
-)
-image_prompt = TextPrompt(
-    prompt="<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe the image.<|im_end|>\n",  # noqa: E501
-    multi_modal_data={"image": image},
-)
+    text2 = "浜辺に沈む美しい夕日"
+    text2_prompt = TextPrompt(prompt=f"Query: {text2}")
 
-# Encode all prompts
-prompts = [text1_prompt, text2_prompt, image_prompt]
-outputs = model.encode(prompts, pooling_task="token_embed")
+    # Create image prompt
+    image = fetch_image(
+        "https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/eskimo.jpg"  # noqa: E501
+    )
+    image_prompt = TextPrompt(
+        prompt="<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe the image.<|im_end|>\n",  # noqa: E501
+        multi_modal_data={"image": image},
+    )
 
+    # Encode all prompts
+    prompts = [text1_prompt, text2_prompt, image_prompt]
+    outputs = model.encode(prompts, pooling_task="token_embed")
 
-def get_embeddings(outputs):
-    VISION_START_TOKEN_ID, VISION_END_TOKEN_ID = 151652, 151653
+    def get_embeddings(outputs):
+        VISION_START_TOKEN_ID, VISION_END_TOKEN_ID = 151652, 151653
 
-    embeddings = []
-    for output in outputs:
-        if VISION_START_TOKEN_ID in output.prompt_token_ids:
-            # Gather only vision tokens
-            img_start_pos = torch.where(
-                torch.tensor(output.prompt_token_ids) == VISION_START_TOKEN_ID
-            )[0][0]
-            img_end_pos = torch.where(
-                torch.tensor(output.prompt_token_ids) == VISION_END_TOKEN_ID
-            )[0][0]
-            embeddings_tensor = output.outputs.data.detach().clone()[
-                img_start_pos : img_end_pos + 1
-            ]
-        else:
-            # Use all tokens for text-only prompts
-            embeddings_tensor = output.outputs.data.detach().clone()
+        embeddings = []
+        for output in outputs:
+            if VISION_START_TOKEN_ID in output.prompt_token_ids:
+                # Gather only vision tokens
+                img_start_pos = torch.where(
+                    torch.tensor(output.prompt_token_ids) == VISION_START_TOKEN_ID
+                )[0][0]
+                img_end_pos = torch.where(
+                    torch.tensor(output.prompt_token_ids) == VISION_END_TOKEN_ID
+                )[0][0]
+                embeddings_tensor = output.outputs.data.detach().clone()[
+                    img_start_pos : img_end_pos + 1
+                ]
+            else:
+                # Use all tokens for text-only prompts
+                embeddings_tensor = output.outputs.data.detach().clone()
 
-        # Pool and normalize embeddings
-        pooled_output = (
-            embeddings_tensor.sum(dim=0, dtype=torch.float32)
-            / embeddings_tensor.shape[0]
-        )
-        embeddings.append(torch.nn.functional.normalize(pooled_output, dim=-1))
-    return embeddings
+            # Pool and normalize embeddings
+            pooled_output = (
+                embeddings_tensor.sum(dim=0, dtype=torch.float32)
+                / embeddings_tensor.shape[0]
+            )
+            embeddings.append(torch.nn.functional.normalize(pooled_output, dim=-1))
+        return embeddings
 
+    embeddings = get_embeddings(outputs)
 
-embeddings = get_embeddings(outputs)
+    for embedding in embeddings:
+        print(embedding.shape)
 
-for embedding in embeddings:
-    print(embedding.shape)
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/pooling/token_embed/multi_vector_retrieval_offline.py b/examples/pooling/token_embed/multi_vector_retrieval_offline.py
index fa7d1c3ba216..5456936111cf 100644
--- a/examples/pooling/token_embed/multi_vector_retrieval_offline.py
+++ b/examples/pooling/token_embed/multi_vector_retrieval_offline.py
@@ -4,6 +4,7 @@
 from argparse import Namespace
 
 from vllm import LLM, EngineArgs
+from vllm.config import PoolerConfig
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 
@@ -13,6 +14,7 @@ def parse_args():
     # Set example specific arguments
     parser.set_defaults(
         model="BAAI/bge-m3",
+        pooler_config=PoolerConfig(task="token_embed"),
         runner="pooling",
         enforce_eager=True,
     )
@@ -32,15 +34,6 @@ def main(args: Namespace):
     # You should pass runner="pooling" for embedding models
     llm = LLM(**vars(args))
 
-    # Generate embedding. The output is a list of EmbeddingRequestOutputs.
-    outputs = llm.embed(prompts)
-
-    # Print the outputs.
-    print("\nGenerated Outputs:\n" + "-" * 60)
-    for prompt, output in zip(prompts, outputs):
-        embeds = output.outputs.embedding
-        print(len(embeds))
-
     # Generate embedding for each token. The output is a list of PoolingRequestOutput.
     outputs = llm.encode(prompts, pooling_task="token_embed")
 
@@ -50,6 +43,20 @@ def main(args: Namespace):
         multi_vector = output.outputs.data
         print(multi_vector.shape)
 
+    query = "What is the capital of France?"
+    documents = [
+        "The capital of Brazil is Brasilia.",
+        "The capital of France is Paris.",
+    ]
+    # Generate scores.
+    outputs = llm.score(query, documents)
+    # Print the outputs.
+    print("\nGenerated Outputs:\n" + "-" * 60)
+    for document, output in zip(documents, outputs):
+        score = output.outputs.score
+        print(f"Pair: {[query, document]!r} \nScore: {score}")
+        print("-" * 60)
+
 
 if __name__ == "__main__":
     args = parse_args()
diff --git a/examples/pooling/token_embed/multi_vector_retrieval_online.py b/examples/pooling/token_embed/multi_vector_retrieval_online.py
index ef8c4745aa53..c2aae88294e8 100644
--- a/examples/pooling/token_embed/multi_vector_retrieval_online.py
+++ b/examples/pooling/token_embed/multi_vector_retrieval_online.py
@@ -7,10 +7,11 @@
 Run `vllm serve <model> --runner pooling`
 to start up the server in vLLM. e.g.
 
-vllm serve BAAI/bge-m3
+vllm serve BAAI/bge-m3 --pooler-config.task token_embed
 """
 
 import argparse
+import pprint
 
 import requests
 import torch
@@ -32,7 +33,8 @@ def parse_args():
 
 
 def main(args):
-    api_url = f"http://{args.host}:{args.port}/pooling"
+    pooling_url = f"http://{args.host}:{args.port}/pooling"
+    score_url = f"http://{args.host}:{args.port}/score"
     model_name = args.model
 
     prompts = [
@@ -43,11 +45,23 @@ def main(args):
     ]
     prompt = {"model": model_name, "input": prompts}
 
-    pooling_response = post_http_request(prompt=prompt, api_url=api_url)
+    pooling_response = post_http_request(prompt=prompt, api_url=pooling_url)
     for output in pooling_response.json()["data"]:
         multi_vector = torch.tensor(output["data"])
         print(multi_vector.shape)
 
+    queries = "What is the capital of France?"
+    documents = [
+        "The capital of Brazil is Brasilia.",
+        "The capital of France is Paris.",
+    ]
+    prompt = {"model": model_name, "queries": queries, "documents": documents}
+    score_response = post_http_request(prompt=prompt, api_url=score_url)
+    print("\nPrompt when queries is string and documents is a list:")
+    pprint.pprint(prompt)
+    print("\nScore Response:")
+    pprint.pprint(score_response.json())
+
 
 if __name__ == "__main__":
     args = parse_args()
diff --git a/examples/offline_inference/batch_llm_inference.py b/examples/ray_serving/batch_llm_inference.py
similarity index 100%
rename from examples/offline_inference/batch_llm_inference.py
rename to examples/ray_serving/batch_llm_inference.py
diff --git a/examples/online_serving/elastic_ep/bench.sh b/examples/ray_serving/elastic_ep/bench.sh
similarity index 100%
rename from examples/online_serving/elastic_ep/bench.sh
rename to examples/ray_serving/elastic_ep/bench.sh
diff --git a/examples/online_serving/elastic_ep/scale.py b/examples/ray_serving/elastic_ep/scale.py
similarity index 100%
rename from examples/online_serving/elastic_ep/scale.py
rename to examples/ray_serving/elastic_ep/scale.py
diff --git a/examples/online_serving/elastic_ep/serve_deepseek_v2.sh b/examples/ray_serving/elastic_ep/serve_deepseek_v2.sh
similarity index 100%
rename from examples/online_serving/elastic_ep/serve_deepseek_v2.sh
rename to examples/ray_serving/elastic_ep/serve_deepseek_v2.sh
diff --git a/examples/online_serving/multi-node-serving.sh b/examples/ray_serving/multi-node-serving.sh
similarity index 100%
rename from examples/online_serving/multi-node-serving.sh
rename to examples/ray_serving/multi-node-serving.sh
diff --git a/examples/online_serving/ray_serve_deepseek.py b/examples/ray_serving/ray_serve_deepseek.py
similarity index 100%
rename from examples/online_serving/ray_serve_deepseek.py
rename to examples/ray_serving/ray_serve_deepseek.py
diff --git a/examples/online_serving/run_cluster.sh b/examples/ray_serving/run_cluster.sh
similarity index 100%
rename from examples/online_serving/run_cluster.sh
rename to examples/ray_serving/run_cluster.sh
diff --git a/examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py b/examples/reasoning/openai_chat_completion_tool_calls_with_reasoning.py
similarity index 100%
rename from examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py
rename to examples/reasoning/openai_chat_completion_tool_calls_with_reasoning.py
diff --git a/examples/online_serving/openai_chat_completion_with_reasoning.py b/examples/reasoning/openai_chat_completion_with_reasoning.py
similarity index 100%
rename from examples/online_serving/openai_chat_completion_with_reasoning.py
rename to examples/reasoning/openai_chat_completion_with_reasoning.py
diff --git a/examples/online_serving/openai_chat_completion_with_reasoning_streaming.py b/examples/reasoning/openai_chat_completion_with_reasoning_streaming.py
similarity index 100%
rename from examples/online_serving/openai_chat_completion_with_reasoning_streaming.py
rename to examples/reasoning/openai_chat_completion_with_reasoning_streaming.py
diff --git a/examples/online_serving/openai_responses_client.py b/examples/reasoning/openai_responses_client.py
similarity index 100%
rename from examples/online_serving/openai_responses_client.py
rename to examples/reasoning/openai_responses_client.py
diff --git a/examples/rl/rlhf_async_new_apis.py b/examples/rl/rlhf_async_new_apis.py
index 1d264d779859..41e9ec673fb8 100644
--- a/examples/rl/rlhf_async_new_apis.py
+++ b/examples/rl/rlhf_async_new_apis.py
@@ -131,16 +131,9 @@ def __init__(self, model_name: str):
         from vllm.model_executor.layers.batch_invariant import (
             init_batch_invariance,
         )
-        from vllm.platforms import current_platform
-        from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
         # need to init all env vars for batch invariance which affect nccl ops
-        attn_backend = (
-            AttentionBackendEnum.TRITON_ATTN
-            if current_platform.is_rocm()
-            else AttentionBackendEnum.FLASH_ATTN
-        )
-        init_batch_invariance(attn_backend)
+        init_batch_invariance()
 
         self.model = AutoModelForCausalLM.from_pretrained(
             model_name, dtype=torch.bfloat16
@@ -234,6 +227,9 @@ def generate(self, token_ids: list[int], max_new_tokens: int) -> list[int]:
     attention_backend=ATTN_BACKEND,
     gpu_memory_utilization=0.75,
     weight_transfer_config=WeightTransferConfig(backend="nccl"),
+    # TODO(haosdent): re-enable once #42043 is fixed. Both LLM
+    # instances must match.
+    async_scheduling=False,
 )
 llm_kwargs.update(rocm_determinism_kwargs)
 
@@ -314,6 +310,8 @@ def generate(self, token_ids: list[int], max_new_tokens: int) -> list[int]:
 
 ray.get(llm.pause_after_n_tokens.remote())
 
+ray.get(llm.start_weight_update.remote(is_checkpoint_format=True))
+
 inference_handle = llm.update_weights.remote(
     WeightTransferUpdateRequest(
         update_info=asdict(
@@ -329,6 +327,8 @@ def generate(self, token_ids: list[int], max_new_tokens: int) -> list[int]:
 train_handle = train_model.broadcast_weights.remote(packed=True)
 ray.get([train_handle, inference_handle])
 
+ray.get(llm.finish_weight_update.remote())
+
 ray.get(llm.resume_generation.remote())
 results = ray.get(gen_futures)
 
@@ -371,6 +371,9 @@ def generate(self, token_ids: list[int], max_new_tokens: int) -> list[int]:
     gpu_memory_utilization=0.75,
     distributed_executor_backend="ray",
     attention_backend=ATTN_BACKEND,
+    # TODO(haosdent): re-enable once #42043 is fixed. Both LLM
+    # instances must match.
+    async_scheduling=False,
 )
 llm_v2_kwargs.update(rocm_determinism_kwargs)
 
diff --git a/examples/rl/rlhf_http_ipc.py b/examples/rl/rlhf_http_ipc.py
index 1a6a96d9c092..7e179ba04aa2 100644
--- a/examples/rl/rlhf_http_ipc.py
+++ b/examples/rl/rlhf_http_ipc.py
@@ -80,6 +80,24 @@ def init_weight_transfer_engine(base_url: str) -> None:
     response.raise_for_status()
 
 
+def start_weight_update(
+    base_url: str,
+    is_checkpoint_format: bool = True,
+) -> None:
+    """Start a weight update via HTTP endpoint."""
+    url = f"{base_url}/start_weight_update"
+    payload = {"is_checkpoint_format": is_checkpoint_format}
+    response = requests.post(url, json=payload, timeout=60)
+    response.raise_for_status()
+
+
+def finish_weight_update(base_url: str) -> None:
+    """Finish a weight update via HTTP endpoint."""
+    url = f"{base_url}/finish_weight_update"
+    response = requests.post(url, json={}, timeout=60)
+    response.raise_for_status()
+
+
 def pause_generation(base_url: str) -> None:
     """Pause generation via HTTP endpoint."""
     url = f"{base_url}/pause"
@@ -151,7 +169,9 @@ def main():
     # Pause generation before weight sync
     pause_generation(BASE_URL)
 
-    # Broadcast weights via IPC handles using HTTP mode
+    # Start weight update, broadcast via IPC, then finish
+    start_weight_update(BASE_URL, is_checkpoint_format=False)
+
     print("Broadcasting weights via CUDA IPC (HTTP)...")
     trainer_args = IPCTrainerSendWeightsArgs(mode="http", url=BASE_URL)
     IPCWeightTransferEngine.trainer_send_weights(
@@ -159,6 +179,8 @@ def main():
         trainer_args=trainer_args,
     )
 
+    finish_weight_update(BASE_URL)
+
     # Resume generation after weight sync
     resume_generation(BASE_URL)
 
diff --git a/examples/rl/rlhf_http_nccl.py b/examples/rl/rlhf_http_nccl.py
index afc4cda2e306..01aafe43f160 100644
--- a/examples/rl/rlhf_http_nccl.py
+++ b/examples/rl/rlhf_http_nccl.py
@@ -83,6 +83,17 @@ def init_weight_transfer_engine(
     response.raise_for_status()
 
 
+def start_weight_update(
+    base_url: str,
+    is_checkpoint_format: bool = True,
+) -> None:
+    """Start a weight update via HTTP endpoint."""
+    url = f"{base_url}/start_weight_update"
+    payload = {"is_checkpoint_format": is_checkpoint_format}
+    response = requests.post(url, json=payload, timeout=60)
+    response.raise_for_status()
+
+
 def update_weights(
     base_url: str,
     names: list[str],
@@ -104,6 +115,13 @@ def update_weights(
     response.raise_for_status()
 
 
+def finish_weight_update(base_url: str) -> None:
+    """Finish a weight update via HTTP endpoint."""
+    url = f"{base_url}/finish_weight_update"
+    response = requests.post(url, json={}, timeout=60)
+    response.raise_for_status()
+
+
 def pause_generation(base_url: str) -> None:
     """Pause generation via HTTP endpoint."""
     url = f"{base_url}/pause"
@@ -204,6 +222,9 @@ def main():
         dtype_names.append(str(p.dtype).split(".")[-1])
         shapes.append(list(p.shape))
 
+    # Start weight update
+    start_weight_update(BASE_URL, is_checkpoint_format=True)
+
     # Start the update_weights call in a separate thread since it will block
     # waiting for NCCL broadcasts
     # packed=True enables efficient batched tensor broadcasting
@@ -227,6 +248,9 @@ def main():
     # Wait for update_weights to complete
     update_thread.join()
 
+    # Finish weight update
+    finish_weight_update(BASE_URL)
+
     # Resume generation after weight sync
     resume_generation(BASE_URL)
 
diff --git a/examples/rl/rlhf_ipc.py b/examples/rl/rlhf_ipc.py
index 169b1026ad4a..a284fcbf0933 100644
--- a/examples/rl/rlhf_ipc.py
+++ b/examples/rl/rlhf_ipc.py
@@ -134,8 +134,10 @@ def broadcast_weights(self, llm_handle: ray.actor.ActorHandle):
 ray.get(llm.sleep.remote(level=0))
 
 ray.get(train_model.init_weight_transfer.remote())
-# Synchronize the updated weights to the inference engine using batched API.
+# Start weight update, sync weights, then finish
+ray.get(llm.start_weight_update.remote(is_checkpoint_format=True))
 ray.get(train_model.broadcast_weights.remote(llm))
+ray.get(llm.finish_weight_update.remote())
 
 ray.get(llm.wake_up.remote(tags=["scheduling"]))
 
diff --git a/examples/rl/rlhf_nccl.py b/examples/rl/rlhf_nccl.py
index 5d5f24a93f35..b94d5e4db827 100644
--- a/examples/rl/rlhf_nccl.py
+++ b/examples/rl/rlhf_nccl.py
@@ -186,6 +186,9 @@ def broadcast_weights(self, packed: bool = True):
 # Collect all weight metadata from the training actor
 names, dtype_names, shapes = ray.get(train_model.get_weight_metadata.remote())
 
+# Start weight update
+ray.get(llm.start_weight_update.remote(is_checkpoint_format=True))
+
 # Issue update_weights call with NCCL-specific update info
 # packed=True enables efficient batched tensor broadcasting
 inference_handle = llm.update_weights.remote(
@@ -203,6 +206,9 @@ def broadcast_weights(self, packed: bool = True):
 train_handle = train_model.broadcast_weights.remote(packed=True)
 ray.get([train_handle, inference_handle])
 
+# Finish weight update
+ray.get(llm.finish_weight_update.remote())
+
 ray.get(llm.wake_up.remote(tags=["scheduling"]))
 
 # Generate text with the updated model. The output is expected to be normal
diff --git a/examples/rl/rlhf_nccl_fsdp_ep.py b/examples/rl/rlhf_nccl_fsdp_ep.py
index 5b1eda3f4610..4337e6fea5ac 100644
--- a/examples/rl/rlhf_nccl_fsdp_ep.py
+++ b/examples/rl/rlhf_nccl_fsdp_ep.py
@@ -298,6 +298,9 @@ async def main():
     names, dtype_names, shapes = ray.get(fsdp_workers[0].get_weight_metadata.remote())
     print(f"[sync] Got metadata for {len(names)} parameters.")
 
+    print("[sync] Starting weight update...")
+    await engine.start_weight_update(is_checkpoint_format=True)
+
     print("[sync] Broadcasting weights from FSDP → vLLM...")
     broadcast_handles = [
         w.gather_and_broadcast_weights.remote(packed=True) for w in fsdp_workers
@@ -315,6 +318,8 @@ async def main():
         )
     )
     ray.get(broadcast_handles)
+
+    await engine.finish_weight_update()
     print("[sync] Weight broadcast complete.")
 
     print("[sync] Resuming generation...")
diff --git a/examples/offline_inference/routed_experts_e2e.py b/examples/rl/routed_experts_e2e.py
similarity index 99%
rename from examples/offline_inference/routed_experts_e2e.py
rename to examples/rl/routed_experts_e2e.py
index bb1d7b411f99..1666bc3ffe16 100644
--- a/examples/offline_inference/routed_experts_e2e.py
+++ b/examples/rl/routed_experts_e2e.py
@@ -9,7 +9,7 @@
 3. Results are deterministic across runs (baseline vs reference).
 
 Usage:
-    python examples/offline_inference/routed_experts_e2e.py \
+    python examples/rl/routed_experts_e2e.py \
         --model Qwen/Qwen3-30B-A3B \
         --tp 4 \
         --max-model-len 4096 \
diff --git a/examples/offline_inference/skip_loading_weights_in_engine_init.py b/examples/rl/skip_loading_weights_in_engine_init.py
similarity index 100%
rename from examples/offline_inference/skip_loading_weights_in_engine_init.py
rename to examples/rl/skip_loading_weights_in_engine_init.py
diff --git a/examples/online_serving/openai_lid_client.py b/examples/speech_to_text/lid/openai_lid_client.py
similarity index 97%
rename from examples/online_serving/openai_lid_client.py
rename to examples/speech_to_text/lid/openai_lid_client.py
index 0ce0fbc92250..d91df3298c22 100644
--- a/examples/online_serving/openai_lid_client.py
+++ b/examples/speech_to_text/lid/openai_lid_client.py
@@ -15,14 +15,14 @@
 Then run this script:
 
     # Use the built-in sample audio
-    python examples/online_serving/openai_lid_client.py
+    python examples/speech_to_text/lid/openai_lid_client.py
 
     # Use your own audio file(s)
-    python examples/online_serving/openai_lid_client.py \
+    python examples/speech_to_text/lid/openai_lid_client.py \
         --audio_paths audio_en.wav audio_zh.wav audio_fr.wav
 
     # Batch-identify multiple files in one run
-    python examples/online_serving/openai_lid_client.py \
+    python examples/speech_to_text/lid/openai_lid_client.py \
         --audio_paths /path/to/dir/*.wav
 
 Requirements:
diff --git a/examples/online_serving/openai_transcription_client.py b/examples/speech_to_text/openai/openai_transcription_client.py
similarity index 90%
rename from examples/online_serving/openai_transcription_client.py
rename to examples/speech_to_text/openai/openai_transcription_client.py
index 478a0a7ea9e8..396edba1155d 100644
--- a/examples/online_serving/openai_transcription_client.py
+++ b/examples/speech_to_text/openai/openai_transcription_client.py
@@ -27,7 +27,12 @@
 
 
 def sync_openai(
-    audio_path: str, client: OpenAI, model: str, *, repetition_penalty: float = 1.3
+    audio_path: str,
+    client: OpenAI,
+    model: str,
+    *,
+    repetition_penalty: float = 1.3,
+    hotwords: str = None,
 ):
     """
     Perform synchronous transcription using OpenAI-compatible API.
@@ -43,12 +48,15 @@ def sync_openai(
             extra_body=dict(
                 seed=4419,
                 repetition_penalty=repetition_penalty,
+                hotwords=hotwords,
             ),
         )
         print("transcription result [sync]:", transcription.text)
 
 
-async def stream_openai_response(audio_path: str, client: AsyncOpenAI, model: str):
+async def stream_openai_response(
+    audio_path: str, client: AsyncOpenAI, model: str, hotwords: str = None
+):
     """
     Perform asynchronous transcription using OpenAI-compatible API.
     """
@@ -64,6 +72,7 @@ async def stream_openai_response(audio_path: str, client: AsyncOpenAI, model: st
             extra_body=dict(
                 seed=420,
                 top_p=0.6,
+                hotwords=hotwords,
             ),
             stream=True,
         )
@@ -136,6 +145,7 @@ def main(args):
         client=client,
         model=model,
         repetition_penalty=args.repetition_penalty,
+        hotwords=args.hotwords,
     )
 
     # Run the asynchronous function
@@ -146,7 +156,10 @@ def main(args):
         )
         asyncio.run(
             stream_openai_response(
-                args.audio_path if args.audio_path else winning_call, client, model
+                args.audio_path if args.audio_path else winning_call,
+                client,
+                model,
+                hotwords=args.hotwords,
             )
         )
     else:
@@ -174,5 +187,11 @@ def main(args):
         default=1.3,
         help="repetition penalty",
     )
+    parser.add_argument(
+        "--hotwords",
+        type=str,
+        default=None,
+        help="hotwords",
+    )
     args = parser.parse_args()
     main(args)
diff --git a/examples/online_serving/openai_translation_client.py b/examples/speech_to_text/openai/openai_translation_client.py
similarity index 100%
rename from examples/online_serving/openai_translation_client.py
rename to examples/speech_to_text/openai/openai_translation_client.py
diff --git a/examples/online_serving/openai_realtime_client.py b/examples/speech_to_text/realtime/openai_realtime_client.py
similarity index 97%
rename from examples/online_serving/openai_realtime_client.py
rename to examples/speech_to_text/realtime/openai_realtime_client.py
index 2bd3c7e60d55..fda3d7cb4564 100644
--- a/examples/online_serving/openai_realtime_client.py
+++ b/examples/speech_to_text/realtime/openai_realtime_client.py
@@ -12,7 +12,6 @@
 Requirements:
 - vllm with audio support
 - websockets
-- librosa
 - numpy
 
 The script:
@@ -26,12 +25,12 @@
 import asyncio
 import json
 
-import librosa
 import numpy as np
 import pybase64 as base64
 import websockets
 
 from vllm.assets.audio import AudioAsset
+from vllm.multimodal.media.audio import load_audio
 
 
 def audio_to_pcm16_base64(audio_path: str) -> str:
@@ -39,7 +38,7 @@ def audio_to_pcm16_base64(audio_path: str) -> str:
     Load an audio file and convert it to base64-encoded PCM16 @ 16kHz.
     """
     # Load audio and resample to 16kHz mono
-    audio, _ = librosa.load(audio_path, sr=16000, mono=True)
+    audio, _ = load_audio(audio_path, sr=16000, mono=True)
     # Convert to PCM16
     pcm16 = (audio * 32767).astype(np.int16)
     # Encode as base64
diff --git a/examples/online_serving/openai_realtime_microphone_client.py b/examples/speech_to_text/realtime/openai_realtime_microphone_client.py
similarity index 100%
rename from examples/online_serving/openai_realtime_microphone_client.py
rename to examples/speech_to_text/realtime/openai_realtime_microphone_client.py
diff --git a/examples/offline_inference/chat_with_tools.py b/examples/tool_calling/chat_with_tools_offline.py
similarity index 100%
rename from examples/offline_inference/chat_with_tools.py
rename to examples/tool_calling/chat_with_tools_offline.py
diff --git a/examples/online_serving/openai_chat_completion_client_with_tools.py b/examples/tool_calling/openai_chat_completion_client_with_tools.py
similarity index 100%
rename from examples/online_serving/openai_chat_completion_client_with_tools.py
rename to examples/tool_calling/openai_chat_completion_client_with_tools.py
diff --git a/examples/online_serving/openai_chat_completion_client_with_tools_required.py b/examples/tool_calling/openai_chat_completion_client_with_tools_required.py
similarity index 100%
rename from examples/online_serving/openai_chat_completion_client_with_tools_required.py
rename to examples/tool_calling/openai_chat_completion_client_with_tools_required.py
diff --git a/examples/online_serving/openai_chat_completion_client_with_tools_xlam.py b/examples/tool_calling/openai_chat_completion_client_with_tools_xlam.py
similarity index 100%
rename from examples/online_serving/openai_chat_completion_client_with_tools_xlam.py
rename to examples/tool_calling/openai_chat_completion_client_with_tools_xlam.py
diff --git a/examples/online_serving/openai_chat_completion_client_with_tools_xlam_streaming.py b/examples/tool_calling/openai_chat_completion_client_with_tools_xlam_streaming.py
similarity index 100%
rename from examples/online_serving/openai_chat_completion_client_with_tools_xlam_streaming.py
rename to examples/tool_calling/openai_chat_completion_client_with_tools_xlam_streaming.py
diff --git a/examples/online_serving/openai_responses_client_with_mcp_tools.py b/examples/tool_calling/openai_responses_client_with_mcp_tools.py
similarity index 97%
rename from examples/online_serving/openai_responses_client_with_mcp_tools.py
rename to examples/tool_calling/openai_responses_client_with_mcp_tools.py
index cafe19a2d195..1e685a4f3440 100644
--- a/examples/online_serving/openai_responses_client_with_mcp_tools.py
+++ b/examples/tool_calling/openai_responses_client_with_mcp_tools.py
@@ -19,7 +19,6 @@
 """
 
 from openai import OpenAI
-from utils import get_first_model
 
 
 def example_no_filter():
@@ -30,7 +29,7 @@ def example_no_filter():
 
     base_url = "http://0.0.0.0:8000/v1"
     client = OpenAI(base_url=base_url, api_key="empty")
-    model = get_first_model(client)
+    model = client.models.list().data[0].id
 
     response = client.responses.create(
         model=model,
@@ -59,7 +58,7 @@ def example_wildcard():
 
     base_url = "http://0.0.0.0:8000/v1"
     client = OpenAI(base_url=base_url, api_key="empty")
-    model = get_first_model(client)
+    model = client.models.list().data[0].id
 
     response = client.responses.create(
         model=model,
@@ -95,7 +94,7 @@ def example_specific_tools():
 
     base_url = "http://0.0.0.0:8000/v1"
     client = OpenAI(base_url=base_url, api_key="empty")
-    model = get_first_model(client)
+    model = client.models.list().data[0].id
 
     response = client.responses.create(
         model=model,
@@ -126,7 +125,7 @@ def example_object_format():
 
     base_url = "http://0.0.0.0:8000/v1"
     client = OpenAI(base_url=base_url, api_key="empty")
-    model = get_first_model(client)
+    model = client.models.list().data[0].id
 
     response = client.responses.create(
         model=model,
diff --git a/examples/online_serving/openai_responses_client_with_tools.py b/examples/tool_calling/openai_responses_client_with_tools.py
similarity index 97%
rename from examples/online_serving/openai_responses_client_with_tools.py
rename to examples/tool_calling/openai_responses_client_with_tools.py
index c85c8cf807b4..a47210f60020 100644
--- a/examples/online_serving/openai_responses_client_with_tools.py
+++ b/examples/tool_calling/openai_responses_client_with_tools.py
@@ -14,7 +14,6 @@
 import json
 
 from openai import OpenAI
-from utils import get_first_model
 
 
 def get_weather(latitude: float, longitude: float) -> str:
@@ -51,7 +50,7 @@ def get_weather(latitude: float, longitude: float) -> str:
 def main():
     base_url = "http://0.0.0.0:8000/v1"
     client = OpenAI(base_url=base_url, api_key="empty")
-    model = get_first_model(client)
+    model = client.models.list().data[0].id
     response = client.responses.create(
         model=model, input=input_messages, tools=tools, tool_choice="required"
     )
diff --git a/examples/tool_chat_template_gemma4.jinja b/examples/tool_chat_template_gemma4.jinja
index 15c5238ac332..f62ca843a405 100644
--- a/examples/tool_chat_template_gemma4.jinja
+++ b/examples/tool_chat_template_gemma4.jinja
@@ -1,9 +1,9 @@
-{%- macro format_parameters(properties, required) -%}
+{%- macro format_parameters(properties, required, filter_keys=false) -%}
     {%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%}
     {%- set ns = namespace(found_first=false) -%}
     {%- for key, value in properties | dictsort -%}
         {%- set add_comma = false -%}
-        {%- if key not in standard_keys -%}
+        {%- if not filter_keys or key not in standard_keys -%}
             {%- if ns.found_first %},{% endif -%}
             {%- set ns.found_first = true -%}
             {{ key }}:{
@@ -11,34 +11,15 @@
                 description:<|"|>{{ value['description'] }}<|"|>
                 {%- set add_comma = true -%}
             {%- endif -%}
-            {%- if value['nullable'] %}
-                {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
-                nullable:true
-            {%- endif -%}
             {%- if value['type'] | upper == 'STRING' -%}
                 {%- if value['enum'] -%}
                     {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
                     enum:{{ format_argument(value['enum']) }}
                 {%- endif -%}
-            {%- elif value['type'] | upper == 'OBJECT' -%}
-                ,properties:{
-                {%- if value['properties'] is defined and value['properties'] is mapping -%}
-                    {{- format_parameters(value['properties'], value['required'] | default([])) -}}
-                {%- elif value is mapping -%}
-                    {{- format_parameters(value, value['required'] | default([])) -}}
-                {%- endif -%}
-                }
-                {%- if value['required'] -%}
-                    ,required:[
-                    {%- for item in value['required'] | default([]) -%}
-                        <|"|>{{- item -}}<|"|>
-                        {%- if not loop.last %},{% endif -%}
-                    {%- endfor -%}
-                    ]
-                {%- endif -%}
             {%- elif value['type'] | upper == 'ARRAY' -%}
                 {%- if value['items'] is mapping and value['items'] -%}
-                    ,items:{
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    items:{
                     {%- set ns_items = namespace(found_first=false) -%}
                     {%- for item_key, item_value in value['items'] | dictsort -%}
                         {%- if item_value is not none -%}
@@ -71,6 +52,32 @@
                     }
                 {%- endif -%}
             {%- endif -%}
+            {%- if value['nullable'] %}
+                {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                nullable:true
+            {%- endif -%}
+            {%- if value['type'] | upper == 'OBJECT' -%}
+                {%- if value['properties'] is defined and value['properties'] is mapping -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    properties:{
+                    {{- format_parameters(value['properties'], value['required'] | default([])) -}}
+                    }
+                {%- elif value is mapping -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    properties:{
+                    {{- format_parameters(value, value['required'] | default([]), filter_keys=true) -}}
+                    }
+                {%- endif -%}
+                {%- if value['required'] -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    required:[
+                    {%- for item in value['required'] | default([]) -%}
+                        <|"|>{{- item -}}<|"|>
+                        {%- if not loop.last %},{% endif -%}
+                    {%- endfor -%}
+                    ]
+                {%- endif -%}
+            {%- endif -%}
             {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
             type:<|"|>{{ value['type'] | upper }}<|"|>}
         {%- endif -%}
@@ -167,20 +174,25 @@
 
 {%- set ns = namespace(prev_message_type=None) -%}
 {%- set loop_messages = messages -%}
-{{ bos_token }}
+{{- bos_token -}}
+{#- Handle System/Tool Definitions Block -#}
 {%- if (enable_thinking is defined and enable_thinking) or tools or messages[0]['role'] in ['system', 'developer'] -%}
     {{- '<|turn>system\n' -}}
-
+    {#- Inject Thinking token at the very top of the FIRST system turn -#}
     {%- if enable_thinking is defined and enable_thinking -%}
-        {{- '<|think|>' -}}
+        {{- '<|think|>\n' -}}
         {%- set ns.prev_message_type = 'think' -%}
     {%- endif -%}
-
     {%- if messages[0]['role'] in ['system', 'developer'] -%}
-        {{- messages[0]['content'] | trim -}}
+        {%- if messages[0]['content'] is string -%}
+            {{- messages[0]['content'] | trim -}}
+        {%- elif messages[0]['content'] is sequence -%}
+            {%- for item in messages[0]['content'] -%}
+                {{- item['text'] | trim + ' '-}}
+            {%- endfor -%}
+        {%- endif -%}
         {%- set loop_messages = messages[1:] -%}
     {%- endif -%}
-
     {%- if tools -%}
         {%- for tool in tools %}
             {{- '<|tool>' -}}
@@ -189,10 +201,10 @@
         {%- endfor %}
         {%- set ns.prev_message_type = 'tool' -%}
     {%- endif -%}
-
     {{- '<turn|>\n' -}}
 {%- endif %}
 
+{#- Pre-scan: find last user message index for reasoning guard -#}
 {%- set ns_turn = namespace(last_user_idx=-1) -%}
 {%- for i in range(loop_messages | length) -%}
     {%- if loop_messages[i]['role'] == 'user' -%}
@@ -200,12 +212,12 @@
     {%- endif -%}
 {%- endfor -%}
 
+{#- Loop through messages -#}
 {%- for message in loop_messages -%}
     {%- if message['role'] != 'tool' -%}
     {%- set ns.prev_message_type = None -%}
     {%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}
-    {#- OpenAI may emit multiple assistant messages in one tool loop (user → asst → tool → asst → tool).
-        Only the first of those should open <|turn>model; later ones continue the same model turn. -#}
+    {#- Detect continuation: suppress duplicate <|turn>model when previous non-tool message was also assistant -#}
     {%- set prev_nt = namespace(role=None, found=false) -%}
     {%- if loop.index0 > 0 -%}
         {%- for j in range(loop.index0 - 1, -1, -1) -%}
@@ -222,8 +234,10 @@
         {{- '<|turn>' + role + '\n' }}
     {%- endif -%}
 
-    {%- if message.get('reasoning') and loop.index0 > ns_turn.last_user_idx and message.get('tool_calls') -%}
-        {{- '<|channel>thought\n' + message['reasoning'] + '\n<channel|>'}}
+    {#- Render reasoning/reasoning_content as thinking channel -#}
+    {%- set thinking_text = message.get('reasoning') or message.get('reasoning_content') -%}
+    {%- if thinking_text and loop.index0 > ns_turn.last_user_idx and message.get('tool_calls') -%}
+        {{- '<|channel>thought\n' + thinking_text + '\n<channel|>' -}}
     {%- endif -%}
 
             {%- if message['tool_calls'] -%}
@@ -247,14 +261,14 @@
 
             {%- set ns_tr_out = namespace(flag=false) -%}
             {%- if message.get('tool_responses') -%}
-                {#- Legacy: tool_responses embedded on the assistant message -#}
+                {#- Legacy: tool_responses embedded on the assistant message (Google/Gemma native) -#}
                 {%- for tool_response in message['tool_responses'] -%}
                     {{- format_tool_response_block(tool_response['name'] | default('unknown'), tool_response['response']) -}}
                     {%- set ns_tr_out.flag = true -%}
                     {%- set ns.prev_message_type = 'tool_response' -%}
                 {%- endfor -%}
             {%- elif message.get('tool_calls') -%}
-                {#- OpenAI Chat Completions: consecutive following messages with role "tool" (no break/continue; range scan) -#}
+                {#- OpenAI Chat Completions: forward-scan consecutive role:tool messages -#}
                 {%- set ns_tool_scan = namespace(stopped=false) -%}
                 {%- for k in range(loop.index0 + 1, loop_messages | length) -%}
                     {%- if ns_tool_scan.stopped -%}
@@ -262,12 +276,14 @@
                         {%- set ns_tool_scan.stopped = true -%}
                     {%- else -%}
                         {%- set follow = loop_messages[k] -%}
+                        {#- Resolve tool_call_id to function name -#}
                         {%- set ns_tname = namespace(name=follow.get('name') | default('unknown')) -%}
                         {%- for tc in message['tool_calls'] -%}
                             {%- if tc.get('id') == follow.get('tool_call_id') -%}
                                 {%- set ns_tname.name = tc['function']['name'] -%}
                             {%- endif -%}
                         {%- endfor -%}
+                        {#- Handle content as string or content-parts array -#}
                         {%- set tool_body = follow.get('content') -%}
                         {%- if tool_body is string -%}
                             {{- format_tool_response_block(ns_tname.name, tool_body) -}}
@@ -288,6 +304,7 @@
                 {%- endfor -%}
             {%- endif -%}
 
+            {%- set captured_content -%}
             {%- if message['content'] is string -%}
                 {%- if role == 'model' -%}
                     {{- strip_thinking(message['content']) -}}
@@ -303,29 +320,35 @@
                             {{- item['text'] | trim -}}
                         {%- endif -%}
                     {%- elif item['type'] == 'image' -%}
-                        {{- '\n\n<|image|>\n\n' -}}
+                        {{- '<|image|>' -}}
                         {%- set ns.prev_message_type = 'image' -%}
                     {%- elif item['type'] == 'audio' -%}
                         {{- '<|audio|>' -}}
                         {%- set ns.prev_message_type = 'audio' -%}
                     {%- elif item['type'] == 'video' -%}
-                        {{- '\n\n<|video|>\n\n' -}}
+                        {{- '<|video|>' -}}
                         {%- set ns.prev_message_type = 'video' -%}
                     {%- endif -%}
                 {%- endfor -%}
             {%- endif -%}
+            {%- endset -%}
+
+            {{- captured_content -}}
+            {%- set has_content = captured_content | trim | length > 0 -%}
 
-        {%- if not (ns_tr_out.flag and not message.get('content')) -%}
+        {%- if ns.prev_message_type == 'tool_call' and not ns_tr_out.flag -%}
+            {{- '<|tool_response>' -}}
+        {%- elif not (ns_tr_out.flag and not has_content) -%}
             {{- '<turn|>\n' -}}
         {%- endif -%}
     {%- endif -%}
 {%- endfor -%}
 
 {%- if add_generation_prompt -%}
-    {%- if ns.prev_message_type != 'tool_response' -%}
+    {%- if ns.prev_message_type != 'tool_response' and ns.prev_message_type != 'tool_call' -%}
         {{- '<|turn>model\n' -}}
+        {%- if not enable_thinking | default(false) -%}
+            {{- '<|channel>thought\n<channel|>' -}}
+        {%- endif -%}
     {%- endif -%}
-    {%- if not enable_thinking | default(false) -%}
-        {{- '<|channel>thought\n<channel|>' -}}
-    {%- endif -%}
-{%- endif -%}
+{%- endif -%}
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index c4951b1a4ed3..6a0ae2fea6eb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,13 +24,14 @@ classifiers = [
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
     "Intended Audience :: Developers",
     "Intended Audience :: Information Technology",
     "Intended Audience :: Science/Research",
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
     "Topic :: Scientific/Engineering :: Information Analysis",
 ]
-requires-python = ">=3.10,<3.14"
+requires-python = ">=3.10,<3.15"
 dynamic = [ "version", "dependencies", "optional-dependencies"]
 
 [project.urls]
@@ -123,7 +124,8 @@ extend-exclude = ["tests/models/fixtures/*", "tests/prompts/*", "tests/tokenizer
     "benchmarks/sonnet.txt", "tests/lora/data/*", "build/*",
     "examples/pooling/token_embed/*", "tests/models/language/pooling/*",
     "vllm/third_party/*", "vllm/entrypoints/serve/instrumentator/static/*", "tests/entrypoints/openai/speech_to_text/test_transcription_validation.py",
-    "docs/governance/process.md", "tests/v1/engine/test_fast_incdec_prefix_err.py", ".git/*"]
+    "docs/governance/process.md", "docs/assets/contributing/vllm_bench_serve_timeline.html", 
+    "tests/v1/engine/test_fast_incdec_prefix_err.py", ".git/*", "csrc/cpu/sgl-kernels/*"]
 ignore-hidden = false
 
 [tool.typos.default]
@@ -170,6 +172,10 @@ eles = "eles"
 datas = "datas"
 ser = "ser"
 ure = "ure"
+VALU = "VALU"
+# Walsh-Hadamard Transform
+wht = "wht"
+WHT = "WHT"
 
 [tool.uv]
 no-build-isolation-package = ["torch"]
diff --git a/requirements/common.txt b/requirements/common.txt
index b610fd678687..843170325240 100644
--- a/requirements/common.txt
+++ b/requirements/common.txt
@@ -7,7 +7,7 @@ requests >= 2.26.0
 tqdm
 blake3
 py-cpuinfo
-transformers >= 4.56.0, < 5
+transformers >= 4.56.0, != 5.0.*, != 5.1.*, != 5.2.*, != 5.3.*, != 5.4.*, != 5.5.0
 tokenizers >= 0.21.1  # Required for fast incremental detokenization.
 protobuf >= 5.29.6, !=6.30.*, !=6.31.*, !=6.32.*, !=6.33.0.*, !=6.33.1.*, !=6.33.2.*, !=6.33.3.*, !=6.33.4.* # Required by LlamaTokenizer, gRPC. CVE-2026-0994
 fastapi[standard] >= 0.115.0 # Required by FastAPI's form models in the OpenAI API server's audio transcriptions endpoint.
@@ -19,29 +19,29 @@ pillow  # Required for image processing
 prometheus-fastapi-instrumentator >= 7.0.0
 tiktoken >= 0.6.0  # Required for DBRX tokenizer
 lm-format-enforcer == 0.11.3
-llguidance >= 1.3.0, < 1.4.0; platform_machine == "x86_64" or platform_machine == "arm64" or platform_machine == "aarch64" or platform_machine == "s390x" or platform_machine == "ppc64le"
-outlines_core == 0.2.11
+llguidance >= 1.3.0, < 1.4.0; platform_machine == "x86_64" or platform_machine == "arm64" or platform_machine == "aarch64" or platform_machine == "ppc64le"
+outlines_core == 0.2.14
 # required for outlines backend disk cache
 diskcache == 5.6.3
 lark == 1.2.2
-xgrammar >= 0.1.32, < 1.0.0; platform_machine == "x86_64" or platform_machine == "aarch64" or platform_machine == "arm64" or platform_machine == "s390x" or platform_machine == "ppc64le"
+xgrammar >= 0.2.0, < 1.0.0; platform_machine == "x86_64" or platform_machine == "aarch64" or platform_machine == "arm64" or platform_machine == "s390x" or platform_machine == "ppc64le"
 typing_extensions >= 4.10
 filelock >= 3.16.1 # need to contain https://github.com/tox-dev/filelock/pull/317
 partial-json-parser # used for parsing partial JSON outputs
 pyzmq >= 25.0.0
 msgspec
 gguf >= 0.17.0
-mistral_common[image] >= 1.11.0
+mistral_common[image] >= 1.11.2
 opencv-python-headless >= 4.13.0    # required for video IO
 pyyaml
 six>=1.16.0; python_version > '3.11' # transitive dependency of pandas that needs to be the latest version for python 3.12
 setuptools>=77.0.3,<81.0.0; python_version > '3.11' # Setuptools is used by triton, we need to ensure a modern version is installed for 3.12+ so that it does not try to import distutils, which was removed in 3.12
 einops # Required for Qwen2-VL.
-compressed-tensors == 0.14.0.1 # required for compressed-tensors
+compressed-tensors == 0.15.0.1 # required for compressed-tensors
 depyf==0.20.0 # required for profiling and debugging with compilation config
 cloudpickle # allows pickling lambda functions in model_executor/models/registry.py
 watchfiles # required for http server to monitor the updates of TLS files
-python-json-logger # Used by logging as per examples/others/logging_configuration.md
+python-json-logger # Used by logging as per examples/features/logging_configuration.md
 ninja # Required for xgrammar, rocm, tpu, xpu
 pybase64 # fast base64 implementation
 cbor2 # Required for cross-language serialization of hashable objects
@@ -49,7 +49,7 @@ ijson # Required for mistral streaming tool parser
 setproctitle # Used to set process names for better debugging and monitoring
 openai-harmony >= 0.0.3  # Required for gpt-oss
 anthropic >= 0.71.0
-model-hosting-container-standards >= 0.1.13, < 1.0.0
+model-hosting-container-standards >= 0.1.14, < 1.0.0
 mcp
 opentelemetry-sdk >= 1.27.0
 opentelemetry-api >= 1.27.0
diff --git a/requirements/cpu.txt b/requirements/cpu.txt
index 26a23ba628e2..5ec338af7362 100644
--- a/requirements/cpu.txt
+++ b/requirements/cpu.txt
@@ -4,7 +4,7 @@
 
 setuptools==77.0.3 # this version can reuse CMake build dir
 
-numba == 0.61.2; platform_machine != "s390x" # Required for N-gram speculative decoding
+numba == 0.65.0; platform_machine != "s390x" # Required for N-gram speculative decoding
 
 # Dependencies for CPUs
 torch==2.11.0+cpu; platform_machine == "x86_64" or platform_machine == "s390x" or platform_machine == "aarch64"
diff --git a/requirements/cuda.txt b/requirements/cuda.txt
index c6b8a82eac65..abff2525af9e 100644
--- a/requirements/cuda.txt
+++ b/requirements/cuda.txt
@@ -1,7 +1,7 @@
 # Common dependencies
 -r common.txt
 
-numba == 0.61.2 # Required for N-gram speculative decoding
+numba == 0.65.0 # Required for N-gram speculative decoding
 
 # Dependencies for NVIDIA GPUs
 torch==2.11.0
@@ -9,8 +9,10 @@ torchaudio==2.11.0
 # These must be updated alongside torch
 torchvision==0.26.0 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
 # FlashInfer should be updated together with the Dockerfile
-flashinfer-python==0.6.7
-flashinfer-cubin==0.6.7
+flashinfer-python==0.6.8.post1
+flashinfer-cubin==0.6.8.post1
+apache-tvm-ffi==0.1.9
+tilelang==0.1.9
 # Cap nvidia-cudnn-frontend (transitive dep of flashinfer) due to
 # breaking changes in 1.19.0
 nvidia-cudnn-frontend>=1.13.0,<1.19.0
diff --git a/requirements/docs.in b/requirements/docs.in
new file mode 100644
index 000000000000..952e7c09bae9
--- /dev/null
+++ b/requirements/docs.in
@@ -0,0 +1,17 @@
+mkdocs<2.0.0
+mkdocs-api-autonav
+mkdocs-material
+mkdocstrings-python
+mkdocs-gen-files
+mkdocs-awesome-nav
+mkdocs-glightbox
+mkdocs-git-revision-date-localized-plugin
+mkdocs-minify-plugin
+mkdocs-redirects
+regex
+ruff
+pydantic
+
+# For generating argparse docs.
+# Adding requirements here should only be used as a last resort.
+msgspec  # Need for multiple inheritance involving msgspec.Struct
\ No newline at end of file
diff --git a/requirements/docs.txt b/requirements/docs.txt
index 952e7c09bae9..5331a3b79b7d 100644
--- a/requirements/docs.txt
+++ b/requirements/docs.txt
@@ -1,17 +1,182 @@
-mkdocs<2.0.0
-mkdocs-api-autonav
-mkdocs-material
-mkdocstrings-python
-mkdocs-gen-files
-mkdocs-awesome-nav
-mkdocs-glightbox
-mkdocs-git-revision-date-localized-plugin
-mkdocs-minify-plugin
-mkdocs-redirects
-regex
-ruff
-pydantic
-
-# For generating argparse docs.
-# Adding requirements here should only be used as a last resort.
-msgspec  # Need for multiple inheritance involving msgspec.Struct
\ No newline at end of file
+# This file was autogenerated by uv via the following command:
+#    uv pip compile requirements/docs.in -o requirements/docs.txt --python-platform x86_64-manylinux_2_28 --python-version 3.12
+annotated-types==0.7.0
+    # via pydantic
+babel==2.18.0
+    # via
+    #   mkdocs-git-revision-date-localized-plugin
+    #   mkdocs-material
+backrefs==7.0
+    # via mkdocs-material
+bracex==2.6
+    # via wcmatch
+certifi==2026.4.22
+    # via requests
+charset-normalizer==3.4.7
+    # via requests
+click==8.3.3
+    # via
+    #   mkdocs
+    #   properdocs
+colorama==0.4.6
+    # via mkdocs-material
+csscompressor==0.9.5
+    # via mkdocs-minify-plugin
+ghp-import==2.1.0
+    # via
+    #   mkdocs
+    #   properdocs
+gitdb==4.0.12
+    # via gitpython
+gitpython==3.1.50
+    # via mkdocs-git-revision-date-localized-plugin
+griffelib==2.0.2
+    # via mkdocstrings-python
+htmlmin2==0.1.13
+    # via mkdocs-minify-plugin
+idna==3.13
+    # via requests
+jinja2==3.1.6
+    # via
+    #   mkdocs
+    #   mkdocs-material
+    #   mkdocstrings
+    #   properdocs
+jsmin==3.0.1
+    # via mkdocs-minify-plugin
+markdown==3.10.2
+    # via
+    #   mkdocs
+    #   mkdocs-autorefs
+    #   mkdocs-material
+    #   mkdocstrings
+    #   properdocs
+    #   pymdown-extensions
+markupsafe==3.0.3
+    # via
+    #   jinja2
+    #   mkdocs
+    #   mkdocs-autorefs
+    #   mkdocstrings
+    #   properdocs
+mergedeep==1.3.4
+    # via
+    #   mkdocs
+    #   mkdocs-get-deps
+mkdocs==1.6.1
+    # via
+    #   -r requirements/docs.in
+    #   mkdocs-api-autonav
+    #   mkdocs-autorefs
+    #   mkdocs-awesome-nav
+    #   mkdocs-gen-files
+    #   mkdocs-git-revision-date-localized-plugin
+    #   mkdocs-material
+    #   mkdocs-minify-plugin
+    #   mkdocs-redirects
+    #   mkdocstrings
+mkdocs-api-autonav==0.4.0
+    # via -r requirements/docs.in
+mkdocs-autorefs==1.4.4
+    # via
+    #   mkdocstrings
+    #   mkdocstrings-python
+mkdocs-awesome-nav==3.3.0
+    # via -r requirements/docs.in
+mkdocs-gen-files==0.6.1
+    # via -r requirements/docs.in
+mkdocs-get-deps==0.2.2
+    # via mkdocs
+mkdocs-git-revision-date-localized-plugin==1.5.1
+    # via -r requirements/docs.in
+mkdocs-glightbox==0.5.2
+    # via -r requirements/docs.in
+mkdocs-material==9.7.6
+    # via -r requirements/docs.in
+mkdocs-material-extensions==1.3.1
+    # via mkdocs-material
+mkdocs-minify-plugin==0.8.0
+    # via -r requirements/docs.in
+mkdocs-redirects==1.2.3
+    # via -r requirements/docs.in
+mkdocstrings==1.0.4
+    # via mkdocstrings-python
+mkdocstrings-python==2.0.3
+    # via
+    #   -r requirements/docs.in
+    #   mkdocs-api-autonav
+msgspec==0.21.1
+    # via -r requirements/docs.in
+natsort==8.4.0
+    # via mkdocs-awesome-nav
+packaging==26.2
+    # via
+    #   mkdocs
+    #   properdocs
+paginate==0.5.7
+    # via mkdocs-material
+pathspec==1.1.1
+    # via
+    #   mkdocs
+    #   properdocs
+platformdirs==4.9.6
+    # via
+    #   mkdocs-get-deps
+    #   properdocs
+properdocs==1.6.7
+    # via
+    #   mkdocs-gen-files
+    #   mkdocs-redirects
+pydantic==2.13.4
+    # via
+    #   -r requirements/docs.in
+    #   mkdocs-awesome-nav
+pydantic-core==2.46.4
+    # via pydantic
+pygments==2.20.0
+    # via mkdocs-material
+pymdown-extensions==10.21.2
+    # via
+    #   mkdocs-material
+    #   mkdocstrings
+python-dateutil==2.9.0.post0
+    # via ghp-import
+pyyaml==6.0.3
+    # via
+    #   mkdocs
+    #   mkdocs-api-autonav
+    #   mkdocs-get-deps
+    #   properdocs
+    #   pymdown-extensions
+    #   pyyaml-env-tag
+pyyaml-env-tag==1.1
+    # via
+    #   mkdocs
+    #   properdocs
+regex==2026.4.4
+    # via -r requirements/docs.in
+requests==2.33.1
+    # via mkdocs-material
+ruff==0.15.12
+    # via -r requirements/docs.in
+selectolax==0.4.8
+    # via mkdocs-glightbox
+six==1.17.0
+    # via python-dateutil
+smmap==5.0.3
+    # via gitdb
+typing-extensions==4.15.0
+    # via
+    #   pydantic
+    #   pydantic-core
+    #   typing-inspection
+typing-inspection==0.4.2
+    # via pydantic
+urllib3==2.6.3
+    # via requests
+watchdog==6.0.0
+    # via
+    #   mkdocs
+    #   properdocs
+wcmatch==10.1
+    # via mkdocs-awesome-nav
diff --git a/requirements/kv_connectors.txt b/requirements/kv_connectors.txt
index c0b24d99ee14..ae2173a9e8d1 100644
--- a/requirements/kv_connectors.txt
+++ b/requirements/kv_connectors.txt
@@ -1,3 +1,5 @@
 lmcache >= 0.3.9
-nixl[cu13] >= 0.7.1, < 0.10.0 # Required for disaggregated prefill
+nixl[cu13] >= 0.7.1, <= 0.10.1 # Required for disaggregated prefill
+nixl-cu12 >= 0.7.1, <= 0.10.1
+nixl-cu13 >= 0.7.1, <= 0.10.1
 mooncake-transfer-engine >= 0.3.8
diff --git a/requirements/rocm.txt b/requirements/rocm.txt
index 011d0e53fd18..037b20874b52 100644
--- a/requirements/rocm.txt
+++ b/requirements/rocm.txt
@@ -5,7 +5,7 @@
 grpcio==1.78.0
 grpcio-reflection==1.78.0
 
-numba == 0.61.2 # Required for N-gram speculative decoding
+numba == 0.65.0 # Required for N-gram speculative decoding
 
 # Dependencies for AMD GPUs
 datasets
@@ -21,5 +21,6 @@ timm>=1.0.17
 # amd-quark: required for Quark quantization on ROCm 
 # To be consistent with test_quark.py
 amd-quark>=0.8.99
-# Required for faster safetensors model loading
-fastsafetensors >= 0.2.2
\ No newline at end of file
+# tilelang has to be installed for mhc module to be
+# imported correctly.
+tilelang==0.1.9
diff --git a/requirements/test/cuda.in b/requirements/test/cuda.in
index 378ecf94222e..71e496ccf650 100644
--- a/requirements/test/cuda.in
+++ b/requirements/test/cuda.in
@@ -18,10 +18,9 @@ httpx
 librosa # required for audio tests
 vector_quantize_pytorch # required for minicpmo_26 test
 vocos # required for minicpmo_26 test
-peft>=0.15.0 # required for phi-4-mm test
+peft>=0.18.1 # required for phi-4-mm test
 pqdm
 ray[cgraph,default]>=2.48.0 # Ray Compiled Graph, required by pipeline parallelism tests
-resampy # required for audio tests
 sentence-transformers>=5.2.0 # required for embedding tests
 soundfile # required for audio tests
 jiwer # required for audio tests
@@ -32,15 +31,15 @@ torchaudio==2.11.0
 torchvision==0.26.0
 transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
-mistral_common[image,audio] >= 1.11.0 # required for voxtral test
+mistral_common[image,audio] >= 1.11.2 # required for voxtral test
 num2words # required for smolvlm test
 open_clip_torch==2.32.0 # Required for nemotron_vl test, Nemotron Parse in test_common.py
 opencv-python-headless >= 4.13.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]>=0.4.11 # required for model evaluation test
 mteb[bm25s]>=2, <3 # required for mteb test
-transformers==4.57.5
-tokenizers==0.22.0
+transformers==5.5.3
+tokenizers==0.22.2
 schemathesis>=3.39.15 # Required for openai schema test.
 # quantization
 bitsandbytes==0.49.2
@@ -55,14 +54,18 @@ grpcio==1.78.0
 grpcio-reflection==1.78.0
 
 arctic-inference == 0.1.1 # Required for suffix decoding test
-numba == 0.61.2 # Required for N-gram speculative decoding
+numba == 0.65.0 # Required for N-gram speculative decoding
 numpy
 runai-model-streamer[s3,gcs,azure]==0.15.7
 fastsafetensors>=0.2.2 # 0.2.2 contains important fixes for multi-GPU mem usage
 instanttensor>=0.1.5
 pydantic>=2.12 # 2.11 leads to error on python 3.13
 decord==0.6.0; platform_machine == "x86_64"
-terratorch >= 1.2.2 # Required for Prithvi tests
+# terratorch is temporarily disabled while PyPI has the `lightning` package
+# in `quarantined` status (every published terratorch version transitively
+# requires `lightning`, so the resolver fails with "no versions of lightning").
+# Re-enable once PyPI lifts the quarantine. Tracked in #41376.
+# terratorch >= 1.2.2 # Required for Prithvi tests
 imagehash # Required for Prithvi tests
 segmentation-models-pytorch > 0.4.0 # Required for Prithvi tests
 
diff --git a/requirements/test/cuda.txt b/requirements/test/cuda.txt
index 548ca9310ff8..8544f7b70969 100644
--- a/requirements/test/cuda.txt
+++ b/requirements/test/cuda.txt
@@ -1,15 +1,9 @@
 # This file was autogenerated by uv via the following command:
 #    uv pip compile requirements/test/cuda.in -c requirements/cuda.txt -o requirements/test/cuda.txt --index-strategy unsafe-best-match --torch-backend cu130 --python-platform x86_64-manylinux_2_28 --python-version 3.12
 absl-py==2.1.0
-    # via
-    #   rouge-score
-    #   tensorboard
-accelerate==1.0.1
+    # via rouge-score
+accelerate==1.13.0
     # via peft
-aenum==3.1.16
-    # via lightly
-affine==2.4.0
-    # via rasterio
 aiohappyeyeballs==2.6.1
     # via aiohttp
 aiohttp==3.13.3
@@ -25,22 +19,14 @@ aiohttp-cors==0.8.1
     # via ray
 aiosignal==1.4.0
     # via aiohttp
-albucore==0.0.16
-    # via terratorch
 albumentations==1.4.6
-    # via
-    #   -r requirements/test/cuda.in
-    #   terratorch
+    # via -r requirements/test/cuda.in
 alembic==1.16.4
     # via optuna
 annotated-doc==0.0.4
     # via fastapi
 annotated-types==0.7.0
     # via pydantic
-antlr4-python3-runtime==4.9.3
-    # via
-    #   hydra-core
-    #   omegaconf
 anyio==4.6.2.post1
     # via
     #   httpx
@@ -54,12 +40,10 @@ arrow==1.3.0
 attrs==24.2.0
     # via
     #   aiohttp
-    #   fiona
     #   hypothesis
     #   jsonlines
     #   jsonschema
     #   pytest-subtests
-    #   rasterio
     #   referencing
 audioread==3.0.1
     # via librosa
@@ -78,9 +62,7 @@ backoff==2.2.1
     #   -r requirements/test/cuda.in
     #   schemathesis
 bitsandbytes==0.49.2
-    # via
-    #   -r requirements/test/cuda.in
-    #   lightning
+    # via -r requirements/test/cuda.in
 black==24.10.0
     # via datamodel-code-generator
 blobfile==3.0.0
@@ -103,15 +85,9 @@ cachetools==5.5.2
     # via google-auth
 certifi==2024.8.30
     # via
-    #   fiona
     #   httpcore
     #   httpx
-    #   lightly
-    #   pyogrio
-    #   pyproj
-    #   rasterio
     #   requests
-    #   sentry-sdk
 cffi==2.0.0
     # via
     #   cryptography
@@ -125,25 +101,12 @@ chz==0.3.0
 click==8.1.7
     # via
     #   black
-    #   click-plugins
-    #   cligj
-    #   fiona
     #   jiwer
     #   nltk
-    #   rasterio
     #   ray
     #   schemathesis
     #   typer
     #   uvicorn
-    #   wandb
-click-plugins==1.1.1.2
-    # via
-    #   fiona
-    #   rasterio
-cligj==0.7.2
-    # via
-    #   fiona
-    #   rasterio
 colorama==0.4.6
     # via
     #   perceptron
@@ -191,8 +154,6 @@ decorator==5.1.1
     # via librosa
 decord==0.6.0
     # via -r requirements/test/cuda.in
-diffusers==0.36.0
-    # via terratorch
 dill==0.3.8
     # via
     #   datasets
@@ -207,14 +168,10 @@ docker==7.1.0
     # via gpt-oss
 docopt==0.6.2
     # via num2words
-docstring-parser==0.17.0
-    # via jsonargparse
 einops==0.8.1
     # via
     #   -r requirements/test/cuda.in
     #   encodec
-    #   terratorch
-    #   torchgeo
     #   vector-quantize-pytorch
     #   vocos
 einx==0.3.0
@@ -244,14 +201,10 @@ filelock==3.16.1
     #   -c requirements/common.txt
     #   blobfile
     #   datasets
-    #   diffusers
     #   huggingface-hub
     #   ray
     #   torch
-    #   transformers
     #   virtualenv
-fiona==1.10.1
-    # via torchgeo
 fonttools==4.55.0
     # via matplotlib
 fqdn==1.5.1
@@ -268,9 +221,6 @@ fsspec==2024.12.0
     #   evaluate
     #   fastparquet
     #   huggingface-hub
-    #   lightning
-    #   pytorch-lightning
-    #   tacoreader
     #   torch
 ftfy==6.3.1
     # via open-clip-torch
@@ -278,12 +228,6 @@ genai-perf==0.0.16
     # via -r requirements/test/cuda.in
 genson==1.3.0
     # via datamodel-code-generator
-geopandas==1.0.1
-    # via terratorch
-gitdb==4.0.12
-    # via gitpython
-gitpython==3.1.44
-    # via wandb
 google-api-core==2.24.2
     # via
     #   google-cloud-core
@@ -318,7 +262,6 @@ grpcio==1.78.0
     #   -r requirements/test/cuda.in
     #   grpcio-reflection
     #   ray
-    #   tensorboard
 grpcio-reflection==1.78.0
     # via -r requirements/test/cuda.in
 h11==0.14.0
@@ -327,11 +270,9 @@ h11==0.14.0
     #   uvicorn
 h2==4.3.0
     # via httpx
-h5py==3.13.0
-    # via terratorch
 harfile==0.3.0
     # via schemathesis
-hf-xet==1.1.7
+hf-xet==1.4.3
     # via huggingface-hub
 hiredis==3.0.0
     # via tensorizer
@@ -344,30 +285,24 @@ httpcore==1.0.6
 httpx==0.27.2
     # via
     #   -r requirements/test/cuda.in
-    #   diffusers
+    #   huggingface-hub
     #   perceptron
     #   schemathesis
-huggingface-hub==0.36.2
+huggingface-hub==1.10.2
     # via
     #   accelerate
     #   datasets
-    #   diffusers
     #   evaluate
     #   open-clip-torch
     #   peft
     #   segmentation-models-pytorch
     #   sentence-transformers
-    #   terratorch
     #   timm
     #   tokenizers
     #   transformers
     #   vocos
 humanize==4.11.0
     # via runai-model-streamer
-hydra-core==1.3.2
-    # via
-    #   lightly
-    #   lightning
 hyperframe==6.1.0
     # via h2
 hypothesis==6.131.0
@@ -392,11 +327,7 @@ imagehash==4.3.2
 imageio==2.37.0
     # via scikit-image
 importlib-metadata==8.7.0
-    # via
-    #   diffusers
-    #   opentelemetry-api
-importlib-resources==6.5.2
-    # via typeshed-client
+    # via opentelemetry-api
 inflect==5.6.2
     # via datamodel-code-generator
 iniconfig==2.0.0
@@ -426,14 +357,8 @@ joblib==1.4.2
     #   librosa
     #   nltk
     #   scikit-learn
-jsonargparse==4.46.0
-    # via
-    #   lightning
-    #   terratorch
 jsonlines==4.0.0
     # via lm-eval
-jsonnet==0.21.0
-    # via jsonargparse
 jsonpointer==3.0.0
     # via jsonschema
 jsonschema==4.23.0
@@ -452,10 +377,6 @@ kaleido==0.2.1
     # via genai-perf
 kiwisolver==1.4.7
     # via matplotlib
-kornia==0.8.1
-    # via torchgeo
-kornia-rs==0.1.9
-    # via kornia
 lazy-loader==0.4
     # via
     #   librosa
@@ -464,22 +385,7 @@ libnacl==2.1.0
     # via tensorizer
 librosa==0.10.2.post1
     # via -r requirements/test/cuda.in
-lightly==1.5.22
-    # via
-    #   terratorch
-    #   torchgeo
-lightly-utils==0.0.2
-    # via lightly
-lightning==2.6.1
-    # via
-    #   terratorch
-    #   torchgeo
-lightning-utilities==0.14.3
-    # via
-    #   lightning
-    #   pytorch-lightning
-    #   torchmetrics
-llvmlite==0.44.0
+llvmlite==0.47.0
     # via numba
 lm-eval==0.4.11
     # via -r requirements/test/cuda.in
@@ -490,8 +396,6 @@ lxml==5.3.0
     #   sacrebleu
 mako==1.3.10
     # via alembic
-markdown==3.8.2
-    # via tensorboard
 markdown-it-py==3.0.0
     # via rich
 markupsafe==3.0.1
@@ -500,11 +404,7 @@ markupsafe==3.0.1
     #   mako
     #   werkzeug
 matplotlib==3.9.2
-    # via
-    #   -r requirements/test/cuda.in
-    #   lightning
-    #   pycocotools
-    #   torchgeo
+    # via -r requirements/test/cuda.in
 mbstrdecoder==1.1.3
     # via
     #   dataproperty
@@ -512,7 +412,7 @@ mbstrdecoder==1.1.3
     #   typepy
 mdurl==0.1.2
     # via markdown-it-py
-mistral-common==1.11.0
+mistral-common==1.11.2
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/cuda.in
@@ -550,17 +450,15 @@ nltk==3.9.1
     # via rouge-score
 num2words==0.5.14
     # via -r requirements/test/cuda.in
-numba==0.61.2
+numba==0.65.0
     # via
     #   -c requirements/cuda.txt
     #   -r requirements/test/cuda.in
     #   librosa
-    #   resampy
 numpy==2.2.6
     # via
     #   -r requirements/test/cuda.in
     #   accelerate
-    #   albucore
     #   albumentations
     #   bitsandbytes
     #   bm25s
@@ -568,19 +466,14 @@ numpy==2.2.6
     #   cupy-cuda12x
     #   datasets
     #   decord
-    #   diffusers
     #   einx
     #   encodec
     #   evaluate
     #   fastparquet
     #   genai-perf
-    #   geopandas
-    #   h5py
     #   imagehash
     #   imageio
     #   librosa
-    #   lightly
-    #   lightly-utils
     #   lm-eval
     #   matplotlib
     #   mistral-common
@@ -592,12 +485,7 @@ numpy==2.2.6
     #   patsy
     #   peft
     #   perceptron
-    #   pycocotools
-    #   pyogrio
     #   pywavelets
-    #   rasterio
-    #   resampy
-    #   rioxarray
     #   rouge-score
     #   runai-model-streamer
     #   sacrebleu
@@ -605,21 +493,14 @@ numpy==2.2.6
     #   scikit-learn
     #   scipy
     #   segmentation-models-pytorch
-    #   shapely
     #   soxr
     #   statsmodels
-    #   tensorboard
-    #   tensorboardx
     #   tensorizer
-    #   terratorch
     #   tifffile
-    #   torchgeo
-    #   torchmetrics
     #   torchvision
     #   transformers
     #   tritonclient
     #   vocos
-    #   xarray
 nvidia-cublas==13.1.0.3
     # via
     #   cuda-toolkit
@@ -659,10 +540,6 @@ nvidia-nvshmem-cu13==3.4.5
     # via torch
 nvidia-nvtx==13.0.85
     # via cuda-toolkit
-omegaconf==2.3.0
-    # via
-    #   hydra-core
-    #   lightning
 open-clip-torch==2.32.0
     # via -r requirements/test/cuda.in
 openai-harmony==0.0.4
@@ -677,7 +554,6 @@ opencv-python-headless==4.13.0.90
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/cuda.in
-    #   albucore
     #   albumentations
     #   mistral-common
 openpyxl==3.1.5
@@ -712,51 +588,34 @@ packaging==24.2
     #   datasets
     #   evaluate
     #   fastparquet
-    #   geopandas
     #   huggingface-hub
-    #   hydra-core
-    #   kornia
     #   lazy-loader
-    #   lightning
-    #   lightning-utilities
     #   matplotlib
     #   optuna
     #   peft
     #   plotly
     #   pooch
-    #   pyogrio
     #   pytest
     #   pytest-rerunfailures
-    #   pytorch-lightning
     #   ray
-    #   rioxarray
     #   scikit-image
     #   statsmodels
-    #   tensorboard
-    #   tensorboardx
-    #   torchmetrics
     #   transformers
     #   typepy
-    #   wandb
-    #   xarray
 pandas==2.2.3
     # via
     #   datasets
     #   evaluate
     #   fastparquet
     #   genai-perf
-    #   geopandas
     #   statsmodels
-    #   tacoreader
-    #   torchgeo
-    #   xarray
 pathspec==0.12.1
     # via black
 pathvalidate==3.2.1
     # via pytablewriter
 patsy==1.0.1
     # via statsmodels
-peft==0.16.0
+peft==0.18.1
     # via -r requirements/test/cuda.in
 perceptron==0.1.4
     # via -r requirements/test/cuda.in
@@ -764,25 +623,20 @@ perf-analyzer==0.1.0
     # via genai-perf
 pillow==10.4.0
     # via
-    #   diffusers
     #   genai-perf
     #   imagehash
     #   imageio
-    #   lightly-utils
     #   matplotlib
     #   mistral-common
     #   perceptron
     #   scikit-image
     #   segmentation-models-pytorch
-    #   tensorboard
-    #   torchgeo
     #   torchvision
 platformdirs==4.3.6
     # via
     #   black
     #   pooch
     #   virtualenv
-    #   wandb
 plotly==5.24.1
     # via
     #   -r requirements/test/cuda.in
@@ -819,10 +673,7 @@ protobuf==6.33.6
     #   opentelemetry-proto
     #   proto-plus
     #   ray
-    #   tensorboard
-    #   tensorboardx
     #   tensorizer
-    #   wandb
 psutil==6.1.0
     # via
     #   accelerate
@@ -836,16 +687,12 @@ pyarrow==23.0.0
     # via
     #   datasets
     #   genai-perf
-    #   tacoreader
-    #   terratorch
 pyasn1==0.6.1
     # via
     #   pyasn1-modules
     #   rsa
 pyasn1-modules==0.4.2
     # via google-auth
-pycocotools==2.0.8
-    # via terratorch
 pycountry==24.6.1
     # via pydantic-extra-types
 pycparser==2.22
@@ -860,13 +707,11 @@ pydantic==2.12.0
     #   datamodel-code-generator
     #   fastapi
     #   gpt-oss
-    #   lightly
     #   mistral-common
     #   mteb
     #   openai-harmony
     #   pydantic-extra-types
     #   ray
-    #   wandb
 pydantic-core==2.41.1
     # via pydantic
 pydantic-extra-types==2.10.5
@@ -875,17 +720,8 @@ pygments==2.18.0
     # via rich
 pyjwt==2.11.0
     # via msal
-pyogrio==0.11.0
-    # via geopandas
 pyparsing==3.2.0
-    # via
-    #   matplotlib
-    #   rasterio
-pyproj==3.7.1
-    # via
-    #   geopandas
-    #   rioxarray
-    #   torchgeo
+    # via matplotlib
 pyrate-limiter==3.7.0
     # via schemathesis
 pystemmer==3.0.0
@@ -922,22 +758,15 @@ pytest-subtests==0.14.1
     # via schemathesis
 pytest-timeout==2.3.1
     # via -r requirements/test/cuda.in
-python-box==7.3.2
-    # via terratorch
 python-dateutil==2.9.0.post0
     # via
     #   arrow
     #   botocore
-    #   lightly
     #   matplotlib
     #   pandas
     #   typepy
 python-rapidjson==1.20
     # via tritonclient
-pytorch-lightning==2.5.2
-    # via
-    #   lightly
-    #   lightning
 pytrec-eval-terrier==0.5.7
     # via mteb
 pytz==2024.2
@@ -954,26 +783,16 @@ pyyaml==6.0.2
     #   datasets
     #   genai-perf
     #   huggingface-hub
-    #   jsonargparse
-    #   lightning
-    #   omegaconf
     #   optuna
     #   peft
-    #   pytorch-lightning
     #   ray
     #   responses
     #   schemathesis
     #   timm
     #   transformers
     #   vocos
-    #   wandb
 rapidfuzz==3.12.1
     # via jiwer
-rasterio==1.4.3
-    # via
-    #   rioxarray
-    #   terratorch
-    #   torchgeo
 ray==2.48.0
     # via -r requirements/test/cuda.in
 redis==5.2.0
@@ -982,9 +801,8 @@ referencing==0.35.1
     # via
     #   jsonschema
     #   jsonschema-specifications
-regex==2024.9.11
+regex==2026.2.28
     # via
-    #   diffusers
     #   nltk
     #   open-clip-torch
     #   sacrebleu
@@ -996,14 +814,11 @@ requests==2.32.3
     #   azure-core
     #   buildkite-test-collector
     #   datasets
-    #   diffusers
     #   docker
     #   evaluate
     #   google-api-core
     #   google-cloud-storage
     #   gpt-oss
-    #   huggingface-hub
-    #   lightly
     #   lm-eval
     #   mistral-common
     #   msal
@@ -1013,12 +828,7 @@ requests==2.32.3
     #   responses
     #   schemathesis
     #   starlette-testclient
-    #   tacoreader
     #   tiktoken
-    #   transformers
-    #   wandb
-resampy==0.4.3
-    # via -r requirements/test/cuda.in
 responses==0.25.3
     # via genai-perf
 rfc3339-validator==0.1.4
@@ -1028,13 +838,9 @@ rfc3987==1.3.8
 rich==13.9.4
     # via
     #   genai-perf
-    #   lightning
     #   mteb
     #   perceptron
-    #   terratorch
     #   typer
-rioxarray==0.19.0
-    # via terratorch
 rouge-score==0.1.2
     # via lm-eval
 rpds-py==0.20.1
@@ -1043,8 +849,6 @@ rpds-py==0.20.1
     #   referencing
 rsa==4.9.1
     # via google-auth
-rtree==1.4.0
-    # via torchgeo
 runai-model-streamer==0.15.7
     # via -r requirements/test/cuda.in
 runai-model-streamer-azure==0.15.7
@@ -1060,7 +864,6 @@ sacrebleu==2.4.3
 safetensors==0.4.5
     # via
     #   accelerate
-    #   diffusers
     #   open-clip-torch
     #   peft
     #   segmentation-models-pytorch
@@ -1069,9 +872,7 @@ safetensors==0.4.5
 schemathesis==3.39.15
     # via -r requirements/test/cuda.in
 scikit-image==0.25.2
-    # via
-    #   albumentations
-    #   terratorch
+    # via albumentations
 scikit-learn==1.5.2
     # via
     #   albumentations
@@ -1079,7 +880,6 @@ scikit-learn==1.5.2
     #   lm-eval
     #   mteb
     #   sentence-transformers
-    #   terratorch
 scipy==1.13.1
     # via
     #   albumentations
@@ -1093,27 +893,16 @@ scipy==1.13.1
     #   statsmodels
     #   vocos
 segmentation-models-pytorch==0.5.0
-    # via
-    #   -r requirements/test/cuda.in
-    #   terratorch
-    #   torchgeo
+    # via -r requirements/test/cuda.in
 sentence-transformers==5.2.0
     # via
     #   -r requirements/test/cuda.in
     #   mteb
-sentry-sdk==2.52.0
-    # via wandb
 setuptools==77.0.3
     # via
     #   -c requirements/common.txt
-    #   lightning-utilities
     #   pytablewriter
-    #   tensorboard
     #   torch
-shapely==2.1.1
-    # via
-    #   geopandas
-    #   torchgeo
 shellingham==1.5.4
     # via
     #   perceptron
@@ -1122,15 +911,12 @@ six==1.16.0
     # via
     #   -c requirements/common.txt
     #   junit-xml
-    #   lightly
     #   opencensus
     #   python-dateutil
     #   rfc3339-validator
     #   rouge-score
 smart-open==7.1.0
     # via ray
-smmap==5.0.2
-    # via gitdb
 sniffio==1.3.1
     # via
     #   anyio
@@ -1172,8 +958,6 @@ tabledata==1.3.3
     # via pytablewriter
 tabulate==0.9.0
     # via sacrebleu
-tacoreader==0.5.6
-    # via terratorch
 tblib==3.1.0
     # via -r requirements/test/cuda.in
 tcolorpy==0.1.6
@@ -1183,26 +967,14 @@ tenacity==9.1.2
     #   gpt-oss
     #   lm-eval
     #   plotly
-tensorboard==2.20.0
-    # via terratorch
-tensorboard-data-server==0.7.2
-    # via tensorboard
-tensorboardx==2.6.4
-    # via lightning
 tensorizer==2.10.1
     # via -r requirements/test/cuda.in
 termcolor==3.1.0
-    # via
-    #   gpt-oss
-    #   terratorch
-terratorch==1.2.2
-    # via -r requirements/test/cuda.in
+    # via gpt-oss
 threadpoolctl==3.5.0
     # via scikit-learn
 tifffile==2025.3.30
-    # via
-    #   scikit-image
-    #   terratorch
+    # via scikit-image
 tiktoken==0.12.0
     # via
     #   -c requirements/common.txt
@@ -1214,9 +986,7 @@ timm==1.0.17
     #   -r requirements/test/cuda.in
     #   open-clip-torch
     #   segmentation-models-pytorch
-    #   terratorch
-    #   torchgeo
-tokenizers==0.22.0
+tokenizers==0.22.2
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/cuda.in
@@ -1233,21 +1003,14 @@ torch==2.11.0+cu130
     #   bitsandbytes
     #   encodec
     #   instanttensor
-    #   kornia
-    #   lightly
-    #   lightning
     #   mteb
     #   open-clip-torch
     #   peft
-    #   pytorch-lightning
     #   runai-model-streamer
     #   segmentation-models-pytorch
     #   sentence-transformers
     #   tensorizer
-    #   terratorch
     #   timm
-    #   torchgeo
-    #   torchmetrics
     #   torchvision
     #   vector-quantize-pytorch
     #   vocos
@@ -1257,31 +1020,18 @@ torchaudio==2.11.0+cu130
     #   -r requirements/test/cuda.in
     #   encodec
     #   vocos
-torchgeo==0.7.0
-    # via terratorch
-torchmetrics==1.7.4
-    # via
-    #   lightning
-    #   pytorch-lightning
-    #   terratorch
-    #   torchgeo
 torchvision==0.26.0+cu130
     # via
     #   -c requirements/cuda.txt
     #   -r requirements/test/cuda.in
-    #   lightly
     #   open-clip-torch
     #   segmentation-models-pytorch
-    #   terratorch
     #   timm
-    #   torchgeo
 tqdm==4.67.3
     # via
     #   datasets
     #   evaluate
     #   huggingface-hub
-    #   lightly
-    #   lightning
     #   lm-eval
     #   mteb
     #   nltk
@@ -1289,13 +1039,10 @@ tqdm==4.67.3
     #   optuna
     #   peft
     #   pqdm
-    #   pytorch-lightning
     #   segmentation-models-pytorch
     #   sentence-transformers
-    #   tacoreader
-    #   terratorch
     #   transformers
-transformers==4.57.5
+transformers==5.5.3
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/cuda.in
@@ -1317,11 +1064,11 @@ typepy==1.3.2
 typer==0.15.2
     # via
     #   fastsafetensors
+    #   huggingface-hub
     #   perceptron
+    #   transformers
 types-python-dateutil==2.9.0.20241206
     # via arrow
-typeshed-client==2.8.2
-    # via jsonargparse
 typing-extensions==4.15.0
     # via
     #   -c requirements/common.txt
@@ -1336,8 +1083,6 @@ typing-extensions==4.15.0
     #   grpcio
     #   huggingface-hub
     #   librosa
-    #   lightning
-    #   lightning-utilities
     #   lm-eval
     #   mistral-common
     #   mteb
@@ -1348,16 +1093,12 @@ typing-extensions==4.15.0
     #   pydantic
     #   pydantic-core
     #   pydantic-extra-types
-    #   pytorch-lightning
     #   sentence-transformers
     #   sqlalchemy
     #   starlette
     #   torch
-    #   torchgeo
     #   typer
-    #   typeshed-client
     #   typing-inspection
-    #   wandb
 typing-inspection==0.4.2
     # via pydantic
 tzdata==2024.2
@@ -1369,10 +1110,8 @@ urllib3==2.2.3
     #   blobfile
     #   botocore
     #   docker
-    #   lightly
     #   requests
     #   responses
-    #   sentry-sdk
     #   tritonclient
 uvicorn==0.35.0
     # via gpt-oss
@@ -1382,22 +1121,16 @@ virtualenv==20.31.2
     # via ray
 vocos==0.1.0
     # via -r requirements/test/cuda.in
-wandb==0.24.2
-    # via terratorch
 wcwidth==0.2.13
     # via ftfy
 webcolors==24.11.1
     # via jsonschema
 werkzeug==3.1.3
-    # via
-    #   schemathesis
-    #   tensorboard
+    # via schemathesis
 word2number==1.1
     # via lm-eval
 wrapt==1.17.2
     # via smart-open
-xarray==2025.7.1
-    # via rioxarray
 xxhash==3.5.0
     # via
     #   datasets
diff --git a/requirements/test/nightly-torch.txt b/requirements/test/nightly-torch.txt
index e0eb7e114116..75928e088dab 100644
--- a/requirements/test/nightly-torch.txt
+++ b/requirements/test/nightly-torch.txt
@@ -23,14 +23,14 @@ jiwer # required for audio tests
 timm # required for internvl test
 transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
-mistral_common[image,audio] >= 1.11.0 # required for voxtral test
+mistral_common[image,audio] >= 1.11.2 # required for voxtral test
 num2words # required for smolvlm test
 opencv-python-headless >= 4.13.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]>=0.4.11 # required for model evaluation test
 mteb[bm25s]>=2, <3 # required for mteb test
-transformers==4.57.5
-tokenizers==0.22.0
+transformers==5.5.3
+tokenizers==0.22.2
 schemathesis>=3.39.15 # Required for openai schema test.
 # quantization
 bitsandbytes>=0.49.2
@@ -40,7 +40,7 @@ buildkite-test-collector==0.1.9
 genai_perf>=0.0.8
 tritonclient>=2.51.0
 
-numba == 0.61.2 # Required for N-gram speculative decoding
+numba == 0.65.0 # Required for N-gram speculative decoding
 numpy
 runai-model-streamer[s3,gcs,azure]==0.15.7
 fastsafetensors>=0.2.2
diff --git a/requirements/test/rocm.in b/requirements/test/rocm.in
index b5a9451b36f7..105c9c3527d2 100644
--- a/requirements/test/rocm.in
+++ b/requirements/test/rocm.in
@@ -23,7 +23,6 @@ vocos # required for minicpmo_26 test
 peft>=0.15.0 # required for phi-4-mm test
 pqdm
 ray[cgraph,default]>=2.48.0 # Ray Compiled Graph, required by pipeline parallelism tests
-resampy # required for audio tests
 sentence-transformers>=5.2.0 # required for embedding tests
 soundfile # required for audio tests
 jiwer # required for audio tests
@@ -31,15 +30,15 @@ tblib # for pickling test exceptions
 timm>=1.0.17 # required for internvl and gemma3n-mm test
 transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
-mistral_common[image,audio]>=1.11.0 # required for voxtral test
+mistral_common[image,audio]>=1.11.2 # required for voxtral test
 num2words # required for smolvlm test
 open_clip_torch==2.32.0 # Required for nemotron_vl test, Nemotron Parse in test_common.py
 opencv-python-headless>=4.13.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]>=0.4.11 # required for model evaluation test
 mteb[bm25s]>=2, <3 # required for mteb test
-transformers==4.57.5
-tokenizers==0.22.0
+transformers==5.5.3
+tokenizers==0.22.2
 schemathesis>=3.39.15 # Required for openai schema test
 # quantization
 bitsandbytes==0.49.2
@@ -53,16 +52,20 @@ grpcio==1.78.0
 grpcio-reflection==1.78.0
 
 arctic-inference==0.1.1 # Required for suffix decoding test
-numba==0.61.2 # Required for N-gram speculative decoding
+numba==0.65.0 # Required for N-gram speculative decoding
 numpy
 runai-model-streamer[s3,gcs,azure]==0.15.7
-fastsafetensors>=0.2.2 # 0.2.2 contains important fixes for multi-GPU mem usage
+fastsafetensors @ git+https://github.com/foundation-model-stack/fastsafetensors.git@0.2.2 # PyPI only ships CUDA wheels
 instanttensor>=0.1.5
 pydantic>=2.12 # 2.11 leads to error on python 3.13
 decord==0.6.0
 
 # Prithvi tests
-terratorch>=1.2.2
+# terratorch is temporarily disabled while PyPI has the `lightning` package
+# in `quarantined` status (every published terratorch version transitively
+# requires `lightning`, so the resolver fails with "no versions of lightning").
+# Re-enable once PyPI lifts the quarantine. Tracked in #41376.
+# terratorch>=1.2.2
 imagehash # Required for Prithvi tests
 segmentation-models-pytorch>0.4.0 # Required for Prithvi tests
 
@@ -80,6 +83,7 @@ plotly # required for perf comparison html report
 
 # ROCm-specific extras (not in CUDA cuda.in)
 rapidfuzz
-torchgeo==0.7.0
+# torchgeo also pulled in `lightning` transitively; disabled for the same
+# quarantine reason as terratorch above. Restore once the quarantine clears.
+# torchgeo==0.7.0
 multiprocess==0.70.16
-huggingface-hub==0.36.2
diff --git a/requirements/test/rocm.txt b/requirements/test/rocm.txt
index e1efae912ee4..6c0f8accdeb1 100644
--- a/requirements/test/rocm.txt
+++ b/requirements/test/rocm.txt
@@ -1,15 +1,9 @@
 # This file was autogenerated by uv via the following command:
 #    uv pip compile requirements/test/rocm.in -c requirements/rocm.txt -o requirements/test/rocm.txt --index-strategy unsafe-best-match --python-platform x86_64-manylinux_2_28 --python-version 3.12 --no-emit-package torch --no-emit-package torchvision --no-emit-package torchaudio --no-emit-package triton --no-emit-package cuda-bindings --no-emit-package cuda-pathfinder --no-emit-package cuda-toolkit --no-emit-package cupy-cuda12x --no-emit-package nvidia-cublas --no-emit-package nvidia-cuda-cupti --no-emit-package nvidia-cuda-nvrtc --no-emit-package nvidia-cuda-runtime --no-emit-package nvidia-cudnn --no-emit-package nvidia-cufft --no-emit-package nvidia-cufile --no-emit-package nvidia-curand --no-emit-package nvidia-cusolver --no-emit-package nvidia-cusparse --no-emit-package nvidia-cusparselt --no-emit-package nvidia-nccl --no-emit-package nvidia-nvjitlink --no-emit-package nvidia-nvshmem --no-emit-package nvidia-nvtx --no-emit-package nvidia-cublas-cu12 --no-emit-package nvidia-cuda-cupti-cu12 --no-emit-package nvidia-cuda-nvrtc-cu12 --no-emit-package nvidia-cuda-runtime-cu12 --no-emit-package nvidia-cudnn-cu12 --no-emit-package nvidia-cufft-cu12 --no-emit-package nvidia-cufile-cu12 --no-emit-package nvidia-curand-cu12 --no-emit-package nvidia-cusolver-cu12 --no-emit-package nvidia-cusparse-cu12 --no-emit-package nvidia-cusparselt-cu12 --no-emit-package nvidia-nccl-cu12 --no-emit-package nvidia-nvjitlink-cu12 --no-emit-package nvidia-nvshmem-cu12 --no-emit-package nvidia-nvtx-cu12 --no-emit-package nvidia-cublas-cu13 --no-emit-package nvidia-cuda-cupti-cu13 --no-emit-package nvidia-cuda-nvrtc-cu13 --no-emit-package nvidia-cuda-runtime-cu13 --no-emit-package nvidia-cudnn-cu13 --no-emit-package nvidia-cufft-cu13 --no-emit-package nvidia-cufile-cu13 --no-emit-package nvidia-curand-cu13 --no-emit-package nvidia-cusolver-cu13 --no-emit-package nvidia-cusparse-cu13 --no-emit-package nvidia-cusparselt-cu13 --no-emit-package nvidia-nccl-cu13 --no-emit-package nvidia-nvjitlink-cu13 --no-emit-package nvidia-nvshmem-cu13 --no-emit-package nvidia-nvtx-cu13
 absl-py==2.4.0
-    # via
-    #   rouge-score
-    #   tensorboard
+    # via rouge-score
 accelerate==1.13.0
     # via peft
-aenum==3.1.17
-    # via lightly
-affine==2.4.0
-    # via rasterio
 aiohappyeyeballs==2.6.1
     # via aiohttp
 aiohttp==3.13.3
@@ -25,12 +19,8 @@ aiohttp-cors==0.8.1
     # via ray
 aiosignal==1.4.0
     # via aiohttp
-albucore==0.1.2
-    # via terratorch
 albumentations==1.4.6
-    # via
-    #   -r requirements/test/rocm.in
-    #   terratorch
+    # via -r requirements/test/rocm.in
 alembic==1.18.4
     # via optuna
 annotated-doc==0.0.4
@@ -39,14 +29,10 @@ annotated-doc==0.0.4
     #   typer
 annotated-types==0.7.0
     # via pydantic
-anthropic==0.89.0
+anthropic==0.93.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
-antlr4-python3-runtime==4.9.3
-    # via
-    #   hydra-core
-    #   omegaconf
 anyio==4.13.0
     # via
     #   anthropic
@@ -56,6 +42,8 @@ anyio==4.13.0
     #   sse-starlette
     #   starlette
     #   watchfiles
+apache-tvm-ffi==0.1.10
+    # via xgrammar
 arctic-inference==0.1.1
     # via -r requirements/test/rocm.in
 argcomplete==3.6.3
@@ -67,11 +55,9 @@ astor==0.8.1
 attrs==26.1.0
     # via
     #   aiohttp
-    #   fiona
     #   jsonlines
     #   jsonschema
     #   pytest-subtests
-    #   rasterio
     #   referencing
 audioread==3.0.1
     # via librosa
@@ -90,9 +76,7 @@ backoff==2.2.1
     #   -r requirements/test/rocm.in
     #   schemathesis
 bitsandbytes==0.49.2
-    # via
-    #   -r requirements/test/rocm.in
-    #   lightning
+    # via -r requirements/test/rocm.in
 black==26.3.1
     # via datamodel-code-generator
 blake3==1.0.8
@@ -119,13 +103,8 @@ cbor2==5.9.0
     # via -r requirements/test/../common.txt
 certifi==2026.2.25
     # via
-    #   fiona
     #   httpcore
     #   httpx
-    #   lightly
-    #   pyogrio
-    #   pyproj
-    #   rasterio
     #   requests
     #   sentry-sdk
 cffi==1.17.1
@@ -143,24 +122,13 @@ chz==0.4.0
 click==8.3.1
     # via
     #   black
-    #   click-plugins
-    #   cligj
-    #   fiona
     #   jiwer
     #   nltk
-    #   rasterio
     #   ray
     #   rich-toolkit
     #   schemathesis
     #   typer
     #   uvicorn
-    #   wandb
-click-plugins==1.1.1.2
-    # via fiona
-cligj==0.7.2
-    # via
-    #   fiona
-    #   rasterio
 cloudpickle==3.1.2
     # via -r requirements/test/../common.txt
 colorama==0.4.6
@@ -172,7 +140,7 @@ colorful==0.5.8
     # via ray
 colorlog==6.10.1
     # via optuna
-compressed-tensors==0.14.0.1
+compressed-tensors==0.15.0.1
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -211,8 +179,6 @@ depyf==0.20.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
-diffusers==0.37.0
-    # via terratorch
 dill==0.3.8
     # via
     #   datasets
@@ -237,16 +203,12 @@ docker==7.1.0
 docopt==0.6.2
     # via num2words
 docstring-parser==0.17.0
-    # via
-    #   anthropic
-    #   jsonargparse
+    # via anthropic
 einops==0.8.2
     # via
     #   -r requirements/test/../common.txt
     #   -r requirements/test/rocm.in
     #   encodec
-    #   terratorch
-    #   torchgeo
     #   vector-quantize-pytorch
     #   vocos
 einx==0.4.2
@@ -269,31 +231,25 @@ fastapi==0.135.2
     #   model-hosting-container-standards
 fastapi-cli==0.0.24
     # via fastapi
-fastapi-cloud-cli==0.15.1
+fastapi-cloud-cli==0.16.1
     # via fastapi-cli
-fastar==0.9.0
+fastar==0.10.0
     # via fastapi-cloud-cli
 fastparquet==2026.3.0
     # via genai-perf
-fastsafetensors==0.2.2
-    # via
-    #   -c requirements/rocm.txt
-    #   -r requirements/test/rocm.in
+fastsafetensors @ git+https://github.com/foundation-model-stack/fastsafetensors.git@65d80088fca7a8f567fba30415fbcc80f7d2259c
+    # via -r requirements/test/rocm.in
 filelock==3.25.2
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
     #   blobfile
     #   datasets
-    #   diffusers
     #   huggingface-hub
     #   python-discovery
     #   ray
     #   torch
-    #   transformers
     #   virtualenv
-fiona==1.10.1
-    # via torchgeo
 fonttools==4.62.1
     # via matplotlib
 fqdn==1.5.1
@@ -310,9 +266,6 @@ fsspec==2025.3.0
     #   evaluate
     #   fastparquet
     #   huggingface-hub
-    #   lightning
-    #   pytorch-lightning
-    #   tacoreader
     #   torch
 ftfy==6.3.1
     # via open-clip-torch
@@ -320,16 +273,10 @@ genai-perf==0.0.16
     # via -r requirements/test/rocm.in
 genson==1.3.0
     # via datamodel-code-generator
-geopandas==1.1.3
-    # via terratorch
 gguf==0.18.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
-gitdb==4.0.12
-    # via gitpython
-gitpython==3.1.46
-    # via wandb
 google-api-core==2.30.0
     # via
     #   google-cloud-core
@@ -369,7 +316,6 @@ grpcio==1.78.0
     #   grpcio-reflection
     #   opentelemetry-exporter-otlp-proto-grpc
     #   ray
-    #   tensorboard
 grpcio-reflection==1.78.0
     # via
     #   -c requirements/rocm.txt
@@ -380,11 +326,9 @@ h11==0.16.0
     #   uvicorn
 h2==4.3.0
     # via httpx
-h5py==3.16.0
-    # via terratorch
 harfile==0.4.0
     # via schemathesis
-hf-xet==1.4.2
+hf-xet==1.4.3
     # via huggingface-hub
 hiredis==3.3.1
     # via tensorizer
@@ -400,9 +344,9 @@ httpx==0.27.2
     # via
     #   -r requirements/test/rocm.in
     #   anthropic
-    #   diffusers
     #   fastapi
     #   fastapi-cloud-cli
+    #   huggingface-hub
     #   mcp
     #   model-hosting-container-standards
     #   openai
@@ -410,28 +354,21 @@ httpx==0.27.2
     #   schemathesis
 httpx-sse==0.4.3
     # via mcp
-huggingface-hub==0.36.2
+huggingface-hub==1.10.2
     # via
-    #   -r requirements/test/rocm.in
     #   accelerate
     #   datasets
-    #   diffusers
     #   evaluate
     #   open-clip-torch
     #   peft
     #   segmentation-models-pytorch
     #   sentence-transformers
-    #   terratorch
     #   timm
     #   tokenizers
     #   transformers
     #   vocos
 humanize==4.15.0
     # via runai-model-streamer
-hydra-core==1.3.2
-    # via
-    #   lightly
-    #   lightning
 hyperframe==6.1.0
     # via h2
 hypothesis==6.151.9
@@ -458,11 +395,7 @@ imagehash==4.3.2
 imageio==2.37.3
     # via scikit-image
 importlib-metadata==8.7.1
-    # via
-    #   diffusers
-    #   opentelemetry-api
-importlib-resources==6.5.2
-    # via typeshed-client
+    # via opentelemetry-api
 inflect==7.5.0
     # via datamodel-code-generator
 iniconfig==2.3.0
@@ -484,7 +417,7 @@ jinja2==3.1.6
     #   genai-perf
     #   lm-eval
     #   torch
-jiter==0.13.0
+jiter==0.14.0
     # via
     #   anthropic
     #   openai
@@ -500,14 +433,8 @@ joblib==1.5.3
     #   librosa
     #   nltk
     #   scikit-learn
-jsonargparse==4.47.0
-    # via
-    #   lightning
-    #   terratorch
 jsonlines==4.0.0
     # via lm-eval
-jsonnet==0.21.0
-    # via jsonargparse
 jsonpointer==3.1.0
     # via jsonschema
 jsonschema==4.26.0
@@ -527,10 +454,6 @@ kaleido==1.0.0
     # via genai-perf
 kiwisolver==1.5.0
     # via matplotlib
-kornia==0.8.2
-    # via torchgeo
-kornia-rs==0.1.10
-    # via kornia
 lark==1.2.2
     # via
     #   -c requirements/common.txt
@@ -543,26 +466,11 @@ libnacl==2.1.0
     # via tensorizer
 librosa==0.10.2.post1
     # via -r requirements/test/rocm.in
-lightly==1.5.22
-    # via
-    #   terratorch
-    #   torchgeo
-lightly-utils==0.0.2
-    # via lightly
-lightning==2.6.1
-    # via
-    #   terratorch
-    #   torchgeo
-lightning-utilities==0.15.3
-    # via
-    #   lightning
-    #   pytorch-lightning
-    #   torchmetrics
 llguidance==1.3.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
-llvmlite==0.44.0
+llvmlite==0.47.0
     # via numba
 lm-eval==0.4.11
     # via -r requirements/test/rocm.in
@@ -583,8 +491,6 @@ lxml==6.0.2
     #   sacrebleu
 mako==1.3.10
     # via alembic
-markdown==3.10.2
-    # via tensorboard
 markdown-it-py==4.0.0
     # via rich
 markupsafe==3.0.3
@@ -593,10 +499,7 @@ markupsafe==3.0.3
     #   mako
     #   werkzeug
 matplotlib==3.10.8
-    # via
-    #   -r requirements/test/rocm.in
-    #   lightning
-    #   torchgeo
+    # via -r requirements/test/rocm.in
 mbstrdecoder==1.1.4
     # via
     #   dataproperty
@@ -606,7 +509,7 @@ mcp==1.27.0
     # via -r requirements/test/../common.txt
 mdurl==0.1.2
     # via markdown-it-py
-mistral-common==1.11.0
+mistral-common==1.11.2
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -631,7 +534,7 @@ msgpack==1.1.2
     # via
     #   librosa
     #   ray
-msgspec==0.20.0
+msgspec==0.21.0
     # via -r requirements/test/../common.txt
 mteb==2.11.5
     # via -r requirements/test/rocm.in
@@ -658,20 +561,16 @@ nltk==3.9.3
     # via rouge-score
 num2words==0.5.14
     # via -r requirements/test/rocm.in
-numba==0.61.2
+numba==0.65.0
     # via
     #   -c requirements/rocm.txt
     #   -r requirements/test/rocm.in
     #   librosa
-    #   resampy
-numkong==7.1.1
-    # via albucore
 numpy==2.2.6
     # via
     #   -r requirements/test/../common.txt
     #   -r requirements/test/rocm.in
     #   accelerate
-    #   albucore
     #   albumentations
     #   bitsandbytes
     #   bm25s
@@ -679,20 +578,15 @@ numpy==2.2.6
     #   cupy-cuda12x
     #   datasets
     #   decord
-    #   diffusers
     #   einx
     #   encodec
     #   evaluate
     #   fastparquet
     #   genai-perf
-    #   geopandas
     #   gguf
-    #   h5py
     #   imagehash
     #   imageio
     #   librosa
-    #   lightly
-    #   lightly-utils
     #   lm-eval
     #   matplotlib
     #   mistral-common
@@ -704,13 +598,8 @@ numpy==2.2.6
     #   patsy
     #   peft
     #   perceptron
-    #   pycocotools
-    #   pyogrio
     #   pytrec-eval-terrier
     #   pywavelets
-    #   rasterio
-    #   resampy
-    #   rioxarray
     #   rouge-score
     #   runai-model-streamer
     #   sacrebleu
@@ -719,30 +608,19 @@ numpy==2.2.6
     #   scipy
     #   segmentation-models-pytorch
     #   sentence-transformers
-    #   shapely
     #   soundfile
     #   soxr
     #   statsmodels
-    #   tensorboard
-    #   tensorboardx
     #   tensorizer
-    #   terratorch
     #   tifffile
-    #   torchgeo
-    #   torchmetrics
     #   torchvision
     #   transformers
     #   tritonclient
     #   vocos
-    #   xarray
     #   xgrammar
-omegaconf==2.3.0
-    # via
-    #   hydra-core
-    #   lightning
 open-clip-torch==2.32.0
     # via -r requirements/test/rocm.in
-openai==2.30.0
+openai==2.31.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -816,7 +694,7 @@ orjson==3.11.7
     # via
     #   genai-perf
     #   kaleido
-outlines-core==0.2.11
+outlines-core==0.2.14
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -829,46 +707,29 @@ packaging==26.0
     #   datasets
     #   evaluate
     #   fastparquet
-    #   geopandas
     #   huggingface-hub
-    #   hydra-core
     #   kaleido
-    #   kornia
     #   lazy-loader
-    #   lightning
-    #   lightning-utilities
     #   lm-format-enforcer
     #   matplotlib
     #   optuna
     #   peft
     #   plotly
     #   pooch
-    #   pyogrio
     #   pytest
     #   pytest-rerunfailures
-    #   pytorch-lightning
     #   ray
-    #   rioxarray
     #   scikit-image
     #   statsmodels
-    #   tensorboard
-    #   tensorboardx
-    #   torchmetrics
     #   transformers
     #   typepy
-    #   wandb
-    #   xarray
 pandas==3.0.1
     # via
     #   datasets
     #   evaluate
     #   fastparquet
     #   genai-perf
-    #   geopandas
     #   statsmodels
-    #   tacoreader
-    #   torchgeo
-    #   xarray
 partial-json-parser==0.2.1.1.post7
     # via -r requirements/test/../common.txt
 pathspec==1.0.4
@@ -886,18 +747,14 @@ perf-analyzer==0.1.0
 pillow==12.1.1
     # via
     #   -r requirements/test/../common.txt
-    #   diffusers
     #   genai-perf
     #   imagehash
     #   imageio
-    #   lightly-utils
     #   matplotlib
     #   mistral-common
     #   perceptron
     #   scikit-image
     #   segmentation-models-pytorch
-    #   tensorboard
-    #   torchgeo
     #   torchvision
 platformdirs==4.3.6
     # via
@@ -905,7 +762,6 @@ platformdirs==4.3.6
     #   pooch
     #   python-discovery
     #   virtualenv
-    #   wandb
 plotly==6.6.0
     # via
     #   -r requirements/test/rocm.in
@@ -951,10 +807,7 @@ protobuf==6.33.6
     #   opentelemetry-proto
     #   proto-plus
     #   ray
-    #   tensorboard
-    #   tensorboardx
     #   tensorizer
-    #   wandb
 psutil==7.2.2
     # via
     #   -r requirements/test/../common.txt
@@ -971,16 +824,12 @@ pyarrow==23.0.1
     # via
     #   datasets
     #   genai-perf
-    #   tacoreader
-    #   terratorch
 pyasn1==0.6.3
     # via pyasn1-modules
 pyasn1-modules==0.4.2
     # via google-auth
 pybase64==1.4.3
     # via -r requirements/test/../common.txt
-pycocotools==2.0.11
-    # via terratorch
 pycountry==26.2.16
     # via pydantic-extra-types
 pycparser==3.0
@@ -999,7 +848,6 @@ pydantic==2.12.5
     #   fastapi
     #   fastapi-cloud-cli
     #   gpt-oss
-    #   lightly
     #   lm-format-enforcer
     #   mcp
     #   mistral-common
@@ -1010,7 +858,6 @@ pydantic==2.12.5
     #   pydantic-extra-types
     #   pydantic-settings
     #   ray
-    #   wandb
     #   xgrammar
 pydantic-core==2.41.5
     # via pydantic
@@ -1028,17 +875,8 @@ pyjwt==2.12.1
     # via
     #   mcp
     #   msal
-pyogrio==0.12.1
-    # via geopandas
 pyparsing==3.3.2
-    # via
-    #   matplotlib
-    #   rasterio
-pyproj==3.7.2
-    # via
-    #   geopandas
-    #   rioxarray
-    #   torchgeo
+    # via matplotlib
 pyrate-limiter==3.9.0
     # via schemathesis
 pystemmer==3.0.0
@@ -1075,13 +913,10 @@ pytest-subtests==0.14.2
     # via schemathesis
 pytest-timeout==2.3.1
     # via -r requirements/test/rocm.in
-python-box==7.4.1
-    # via terratorch
 python-dateutil==2.9.0.post0
     # via
     #   arrow
     #   botocore
-    #   lightly
     #   matplotlib
     #   pandas
     #   typepy
@@ -1093,7 +928,7 @@ python-dotenv==1.2.2
     #   uvicorn
 python-json-logger==4.1.0
     # via -r requirements/test/../common.txt
-python-multipart==0.0.22
+python-multipart==0.0.26
     # via
     #   fastapi
     #   mcp
@@ -1101,10 +936,6 @@ python-rapidjson==1.23
     # via tritonclient
 pytokens==0.4.1
     # via black
-pytorch-lightning==2.6.1
-    # via
-    #   lightly
-    #   lightning
 pytrec-eval-terrier==0.5.10
     # via mteb
 pytz==2026.1.post1
@@ -1121,13 +952,9 @@ pyyaml==6.0.3
     #   genai-perf
     #   gguf
     #   huggingface-hub
-    #   jsonargparse
-    #   lightning
     #   lm-format-enforcer
-    #   omegaconf
     #   optuna
     #   peft
-    #   pytorch-lightning
     #   ray
     #   responses
     #   schemathesis
@@ -1135,7 +962,6 @@ pyyaml==6.0.3
     #   transformers
     #   uvicorn
     #   vocos
-    #   wandb
 pyzmq==27.1.0
     # via
     #   -c requirements/common.txt
@@ -1144,11 +970,6 @@ rapidfuzz==3.12.1
     # via
     #   -r requirements/test/rocm.in
     #   jiwer
-rasterio==1.5.0
-    # via
-    #   rioxarray
-    #   terratorch
-    #   torchgeo
 ray==2.54.0
     # via -r requirements/test/rocm.in
 redis==7.3.0
@@ -1160,7 +981,6 @@ referencing==0.37.0
 regex==2026.2.28
     # via
     #   -r requirements/test/../common.txt
-    #   diffusers
     #   nltk
     #   open-clip-torch
     #   sacrebleu
@@ -1173,15 +993,12 @@ requests==2.32.5
     #   azure-core
     #   buildkite-test-collector
     #   datasets
-    #   diffusers
     #   docker
     #   evaluate
     #   gguf
     #   google-api-core
     #   google-cloud-storage
     #   gpt-oss
-    #   huggingface-hub
-    #   lightly
     #   lm-eval
     #   mistral-common
     #   msal
@@ -1192,12 +1009,7 @@ requests==2.32.5
     #   responses
     #   schemathesis
     #   starlette-testclient
-    #   tacoreader
     #   tiktoken
-    #   transformers
-    #   wandb
-resampy==0.4.3
-    # via -r requirements/test/rocm.in
 responses==0.26.0
     # via genai-perf
 rfc3339-validator==0.1.4
@@ -1207,11 +1019,9 @@ rfc3987==1.3.8
 rich==14.3.3
     # via
     #   genai-perf
-    #   lightning
     #   mteb
     #   perceptron
     #   rich-toolkit
-    #   terratorch
     #   typer
 rich-toolkit==0.19.7
     # via
@@ -1219,16 +1029,12 @@ rich-toolkit==0.19.7
     #   fastapi-cloud-cli
 rignore==0.7.6
     # via fastapi-cloud-cli
-rioxarray==0.22.0
-    # via terratorch
 rouge-score==0.1.2
     # via lm-eval
 rpds-py==0.30.0
     # via
     #   jsonschema
     #   referencing
-rtree==1.4.1
-    # via torchgeo
 runai-model-streamer==0.15.7
     # via
     #   -c requirements/rocm.txt
@@ -1246,7 +1052,6 @@ sacrebleu==2.6.0
 safetensors==0.7.0
     # via
     #   accelerate
-    #   diffusers
     #   open-clip-torch
     #   peft
     #   segmentation-models-pytorch
@@ -1255,9 +1060,7 @@ safetensors==0.7.0
 schemathesis==3.39.15
     # via -r requirements/test/rocm.in
 scikit-image==0.26.0
-    # via
-    #   albumentations
-    #   terratorch
+    # via albumentations
 scikit-learn==1.8.0
     # via
     #   albumentations
@@ -1265,7 +1068,6 @@ scikit-learn==1.8.0
     #   lm-eval
     #   mteb
     #   sentence-transformers
-    #   terratorch
 scipy==1.17.1
     # via
     #   albumentations
@@ -1280,10 +1082,7 @@ scipy==1.17.1
     #   statsmodels
     #   vocos
 segmentation-models-pytorch==0.5.0
-    # via
-    #   -r requirements/test/rocm.in
-    #   terratorch
-    #   torchgeo
+    # via -r requirements/test/rocm.in
 sentence-transformers==5.3.0
     # via
     #   -r requirements/test/rocm.in
@@ -1291,9 +1090,7 @@ sentence-transformers==5.3.0
 sentencepiece==0.2.1
     # via -r requirements/test/../common.txt
 sentry-sdk==2.55.0
-    # via
-    #   fastapi-cloud-cli
-    #   wandb
+    # via fastapi-cloud-cli
 setproctitle==1.3.7
     # via -r requirements/test/../common.txt
 setuptools==79.0.1
@@ -1303,12 +1100,7 @@ setuptools==79.0.1
     #   -r requirements/test/../common.txt
     #   model-hosting-container-standards
     #   pytablewriter
-    #   tensorboard
     #   torch
-shapely==2.1.2
-    # via
-    #   geopandas
-    #   torchgeo
 shellingham==1.5.4
     # via
     #   perceptron
@@ -1320,15 +1112,12 @@ six==1.17.0
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
     #   junit-xml
-    #   lightly
     #   opencensus
     #   python-dateutil
     #   rfc3339-validator
     #   rouge-score
 smart-open==7.5.1
     # via ray
-smmap==5.0.3
-    # via gitdb
 sniffio==1.3.1
     # via
     #   anthropic
@@ -1367,8 +1156,6 @@ starlette-testclient==0.4.1
     # via schemathesis
 statsmodels==0.14.6
     # via genai-perf
-stringzilla==4.6.0
-    # via albucore
 structlog==25.5.0
     # via gpt-oss
 supervisor==4.3.0
@@ -1381,8 +1168,6 @@ tabledata==1.3.4
     # via pytablewriter
 tabulate==0.10.0
     # via sacrebleu
-tacoreader==0.5.6
-    # via terratorch
 tblib==3.1.0
     # via -r requirements/test/rocm.in
 tcolorpy==0.1.7
@@ -1391,28 +1176,16 @@ tenacity==9.1.4
     # via
     #   gpt-oss
     #   lm-eval
-tensorboard==2.20.0
-    # via terratorch
-tensorboard-data-server==0.7.2
-    # via tensorboard
-tensorboardx==2.6.4
-    # via lightning
 tensorizer==2.10.1
     # via
     #   -c requirements/rocm.txt
     #   -r requirements/test/rocm.in
 termcolor==3.3.0
-    # via
-    #   gpt-oss
-    #   terratorch
-terratorch==1.2.2
-    # via -r requirements/test/rocm.in
+    # via gpt-oss
 threadpoolctl==3.6.0
     # via scikit-learn
 tifffile==2026.3.3
-    # via
-    #   scikit-image
-    #   terratorch
+    # via scikit-image
 tiktoken==0.12.0
     # via
     #   -c requirements/common.txt
@@ -1426,9 +1199,7 @@ timm==1.0.17
     #   -r requirements/test/rocm.in
     #   open-clip-torch
     #   segmentation-models-pytorch
-    #   terratorch
-    #   torchgeo
-tokenizers==0.22.0
+tokenizers==0.22.2
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -1438,16 +1209,6 @@ tomli==2.4.0
     # via schemathesis
 tomli-w==1.2.0
     # via schemathesis
-torchgeo==0.7.0
-    # via
-    #   -r requirements/test/rocm.in
-    #   terratorch
-torchmetrics==1.9.0
-    # via
-    #   lightning
-    #   pytorch-lightning
-    #   terratorch
-    #   torchgeo
 tqdm==4.67.3
     # via
     #   -r requirements/test/../common.txt
@@ -1455,8 +1216,6 @@ tqdm==4.67.3
     #   evaluate
     #   gguf
     #   huggingface-hub
-    #   lightly
-    #   lightning
     #   lm-eval
     #   mteb
     #   nltk
@@ -1465,13 +1224,10 @@ tqdm==4.67.3
     #   optuna
     #   peft
     #   pqdm
-    #   pytorch-lightning
     #   segmentation-models-pytorch
     #   sentence-transformers
-    #   tacoreader
-    #   terratorch
     #   transformers
-transformers==4.57.5
+transformers==5.5.3
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -1498,9 +1254,9 @@ typer==0.24.1
     #   fastapi-cli
     #   fastapi-cloud-cli
     #   fastsafetensors
+    #   huggingface-hub
     #   perceptron
-typeshed-client==2.9.0
-    # via jsonargparse
+    #   transformers
 typing-extensions==4.15.0
     # via
     #   -c requirements/common.txt
@@ -1510,6 +1266,7 @@ typing-extensions==4.15.0
     #   alembic
     #   anthropic
     #   anyio
+    #   apache-tvm-ffi
     #   azure-core
     #   azure-identity
     #   azure-storage-blob
@@ -1518,8 +1275,6 @@ typing-extensions==4.15.0
     #   grpcio
     #   huggingface-hub
     #   librosa
-    #   lightning
-    #   lightning-utilities
     #   lm-eval
     #   mcp
     #   mistral-common
@@ -1534,18 +1289,14 @@ typing-extensions==4.15.0
     #   pydantic
     #   pydantic-core
     #   pydantic-extra-types
-    #   pytorch-lightning
     #   referencing
     #   rich-toolkit
     #   sentence-transformers
     #   sqlalchemy
     #   starlette
     #   torch
-    #   torchgeo
     #   typeguard
-    #   typeshed-client
     #   typing-inspection
-    #   wandb
     #   xgrammar
 typing-inspection==0.4.2
     # via
@@ -1562,7 +1313,6 @@ urllib3==2.6.3
     #   blobfile
     #   botocore
     #   docker
-    #   lightly
     #   requests
     #   responses
     #   sentry-sdk
@@ -1582,8 +1332,6 @@ virtualenv==21.2.0
     # via ray
 vocos==0.1.0
     # via -r requirements/test/rocm.in
-wandb==0.25.1
-    # via terratorch
 watchfiles==1.1.1
     # via
     #   -r requirements/test/../common.txt
@@ -1595,16 +1343,12 @@ webcolors==25.10.0
 websockets==16.0
     # via uvicorn
 werkzeug==3.1.6
-    # via
-    #   schemathesis
-    #   tensorboard
+    # via schemathesis
 word2number==1.1
     # via lm-eval
 wrapt==2.1.2
     # via smart-open
-xarray==2026.2.0
-    # via rioxarray
-xgrammar==0.1.33
+xgrammar==0.2.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
diff --git a/requirements/test/xpu.in b/requirements/test/xpu.in
index 0e4ca1d99dca..94ffc249395a 100644
--- a/requirements/test/xpu.in
+++ b/requirements/test/xpu.in
@@ -13,7 +13,6 @@ pytest-shard
 absl-py
 accelerate
 arctic-inference
-hf_transfer
 lm_eval[api]
 modelscope
 
diff --git a/requirements/test/xpu.txt b/requirements/test/xpu.txt
index 51810592c46f..80b0c148116d 100644
--- a/requirements/test/xpu.txt
+++ b/requirements/test/xpu.txt
@@ -19,7 +19,9 @@ aiosignal==1.4.0
 albumentations==1.4.6
     # via -r requirements/test/xpu.in
 annotated-doc==0.0.4
-    # via fastapi
+    # via
+    #   fastapi
+    #   typer
 annotated-types==0.7.0
     # via pydantic
 anyio==4.13.0
@@ -64,6 +66,7 @@ click==8.3.1
     #   jiwer
     #   nltk
     #   schemathesis
+    #   typer
     #   uvicorn
 colorama==0.4.6
     # via sacrebleu
@@ -90,7 +93,7 @@ docker==7.1.0
     # via gpt-oss
 docopt==0.6.2
     # via num2words
-dpcpp-cpp-rt==2025.3.1
+dpcpp-cpp-rt==2025.3.2
     # via
     #   onemkl-sycl-blas
     #   onemkl-sycl-dft
@@ -112,7 +115,6 @@ filelock==3.25.2
     #   huggingface-hub
     #   modelscope
     #   torch
-    #   transformers
 frozenlist==1.8.0
     # via
     #   aiohttp
@@ -133,9 +135,7 @@ h11==0.16.0
     #   uvicorn
 harfile==0.4.0
     # via schemathesis
-hf-transfer==0.1.9
-    # via -r requirements/test/xpu.in
-hf-xet==1.4.2
+hf-xet==1.4.3
     # via huggingface-hub
 html2text==2025.4.15
     # via gpt-oss
@@ -144,8 +144,9 @@ httpcore==1.0.9
 httpx==0.28.1
     # via
     #   datasets
+    #   huggingface-hub
     #   schemathesis
-huggingface-hub==0.36.2
+huggingface-hub==1.10.2
     # via
     #   accelerate
     #   datasets
@@ -171,27 +172,27 @@ idna==3.11
     #   yarl
 imageio==2.37.3
     # via scikit-image
-impi-rt==2021.17.0
+impi-rt==2021.17.2
     # via
     #   oneccl
     #   torch
 iniconfig==2.3.0
     # via pytest
-intel-cmplr-lib-rt==2025.3.1
+intel-cmplr-lib-rt==2025.3.2
     # via
     #   intel-sycl-rt
     #   torch
-intel-cmplr-lib-ur==2025.3.1
+intel-cmplr-lib-ur==2025.3.2
     # via
     #   intel-openmp
     #   intel-sycl-rt
     #   torch
-intel-cmplr-lic-rt==2025.3.1
+intel-cmplr-lic-rt==2025.3.2
     # via
     #   intel-opencl-rt
     #   intel-sycl-rt
     #   torch
-intel-opencl-rt==2025.3.1
+intel-opencl-rt==2025.3.2
     # via
     #   dpcpp-cpp-rt
     #   onemkl-sycl-blas
@@ -200,14 +201,14 @@ intel-opencl-rt==2025.3.1
     #   onemkl-sycl-rng
     #   onemkl-sycl-sparse
     #   torch
-intel-openmp==2025.3.1
+intel-openmp==2025.3.2
     # via
     #   dpcpp-cpp-rt
     #   mkl
     #   torch
-intel-pti==0.15.0
+intel-pti==0.16.0
     # via torch
-intel-sycl-rt==2025.3.1
+intel-sycl-rt==2025.3.2
     # via
     #   dpcpp-cpp-rt
     #   oneccl
@@ -243,7 +244,7 @@ lazy-loader==0.5
     #   scikit-image
 librosa==0.10.2.post1
     # via -r requirements/test/xpu.in
-llvmlite==0.44.0
+llvmlite==0.47.0
     # via numba
 lm-eval==0.4.11
     # via -r requirements/test/xpu.in
@@ -265,11 +266,11 @@ mbstrdecoder==1.1.4
     #   typepy
 mdurl==0.1.2
     # via markdown-it-py
-mistral-common==1.11.0
+mistral-common==1.11.2
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/xpu.in
-mkl==2025.3.0
+mkl==2025.3.1
     # via
     #   onemkl-sycl-blas
     #   onemkl-sycl-dft
@@ -303,7 +304,7 @@ nltk==3.9.4
     # via rouge-score
 num2words==0.5.14
     # via -r requirements/test/xpu.in
-numba==0.61.2
+numba==0.65.0
     # via
     #   -c requirements/xpu.txt
     #   librosa
@@ -334,28 +335,28 @@ numpy==2.2.6
     #   tifffile
     #   torchvision
     #   transformers
-oneccl==2021.17.1
+oneccl==2021.17.2
     # via
     #   oneccl-devel
     #   torch
-oneccl-devel==2021.17.1
+oneccl-devel==2021.17.2
     # via torch
-onemkl-license==2025.3.0
+onemkl-license==2025.3.1
     # via
     #   mkl
     #   torch
-onemkl-sycl-blas==2025.3.0
+onemkl-sycl-blas==2025.3.1
     # via
     #   onemkl-sycl-lapack
     #   onemkl-sycl-sparse
     #   torch
-onemkl-sycl-dft==2025.3.0
+onemkl-sycl-dft==2025.3.1
     # via torch
-onemkl-sycl-lapack==2025.3.0
+onemkl-sycl-lapack==2025.3.1
     # via torch
-onemkl-sycl-rng==2025.3.0
+onemkl-sycl-rng==2025.3.1
     # via torch
-onemkl-sycl-sparse==2025.3.0
+onemkl-sycl-sparse==2025.3.1
     # via torch
 openai-harmony==0.0.8
     # via
@@ -515,7 +516,6 @@ requests==2.33.1
     #   docker
     #   evaluate
     #   gpt-oss
-    #   huggingface-hub
     #   lm-eval
     #   mistral-common
     #   modelscope
@@ -524,11 +524,11 @@ requests==2.33.1
     #   schemathesis
     #   starlette-testclient
     #   tiktoken
-    #   transformers
 rich==14.3.3
     # via
     #   mteb
     #   schemathesis
+    #   typer
 rouge-score==0.1.2
     # via lm-eval
 rpds-py==0.30.0
@@ -572,6 +572,8 @@ setuptools==80.10.2
     #   modelscope
     #   pytablewriter
     #   torch
+shellingham==1.5.4
+    # via typer
 six==1.17.0
     # via
     #   -c requirements/common.txt
@@ -606,7 +608,7 @@ tabledata==1.3.4
     # via pytablewriter
 tabulate==0.10.0
     # via sacrebleu
-tbb==2022.3.0
+tbb==2022.3.1
     # via
     #   intel-opencl-rt
     #   mkl
@@ -643,7 +645,7 @@ tokenizers==0.22.2
     # via
     #   -c requirements/common.txt
     #   transformers
-torch==2.10.0+xpu
+torch==2.11.0+xpu
     # via
     #   -c requirements/xpu.txt
     #   accelerate
@@ -651,7 +653,7 @@ torch==2.10.0+xpu
     #   sentence-transformers
     #   timm
     #   torchvision
-torchvision==0.25.0+xpu
+torchvision==0.26.0+xpu
     # via timm
 tqdm==4.67.3
     # via
@@ -665,17 +667,21 @@ tqdm==4.67.3
     #   pqdm
     #   sentence-transformers
     #   transformers
-transformers==4.57.6
+transformers==5.5.3
     # via
     #   -c requirements/common.txt
     #   sentence-transformers
-triton-xpu==3.6.0
+triton-xpu==3.7.0
     # via torch
 typepy==1.3.4
     # via
     #   dataproperty
     #   pytablewriter
     #   tabledata
+typer==0.24.1
+    # via
+    #   huggingface-hub
+    #   transformers
 typing-extensions==4.15.0
     # via
     #   -c requirements/common.txt
@@ -704,7 +710,7 @@ typing-inspection==0.4.2
     # via
     #   fastapi
     #   pydantic
-umf==1.0.2
+umf==1.0.3
     # via
     #   intel-cmplr-lib-ur
     #   torch
diff --git a/requirements/tpu.txt b/requirements/tpu.txt
index 7695b4ba2f4c..f1074e4a6f2f 100644
--- a/requirements/tpu.txt
+++ b/requirements/tpu.txt
@@ -11,4 +11,4 @@ ray[default]
 ray[data]
 setuptools==78.1.0
 nixl==0.3.0
-tpu-inference==0.12.0
+tpu-inference==0.19.0
diff --git a/requirements/xpu.txt b/requirements/xpu.txt
index 26ba38f3efae..9b147bf69ebe 100644
--- a/requirements/xpu.txt
+++ b/requirements/xpu.txt
@@ -9,10 +9,10 @@ setuptools>=77.0.3,<81.0.0
 wheel
 jinja2>=3.1.6
 datasets # for benchmark scripts
-numba == 0.61.2 # Required for N-gram speculative decoding
+numba == 0.65.0 # Required for N-gram speculative decoding
 --extra-index-url=https://download.pytorch.org/whl/xpu
-torch==2.10.0+xpu
+torch==2.11.0+xpu
 torchaudio
 torchvision
 
-vllm_xpu_kernels @ https://github.com/vllm-project/vllm-xpu-kernels/releases/download/v0.1.5/vllm_xpu_kernels-0.1.5-cp38-abi3-manylinux_2_28_x86_64.whl
+vllm_xpu_kernels @ https://github.com/vllm-project/vllm-xpu-kernels/releases/download/v0.1.7/vllm_xpu_kernels-0.1.7-cp38-abi3-manylinux_2_28_x86_64.whl
diff --git a/setup.py b/setup.py
index 1748781985f2..7c226a72425f 100644
--- a/setup.py
+++ b/setup.py
@@ -693,6 +693,12 @@ def extract_precompiled_and_patch_package(
                 flash_attn_regex = re.compile(
                     r"vllm/vllm_flash_attn/(?:[^/.][^/]*/)*(?!\.)[^/]*\.py"
                 )
+                # __init__.py and flash_attn_interface.py are source-controlled
+                # in vllm and should not be overwritten (matches cmake exclusions)
+                flash_attn_files_to_skip = {
+                    "vllm/vllm_flash_attn/__init__.py",
+                    "vllm/vllm_flash_attn/flash_attn_interface.py",
+                }
                 triton_kernels_regex = re.compile(
                     r"vllm/third_party/triton_kernels/(?:[^/.][^/]*/)*(?!\.)[^/]*\.py"
                 )
@@ -705,7 +711,11 @@ def extract_precompiled_and_patch_package(
                     filter(lambda x: x.filename in files_to_copy, wheel.filelist)
                 )
                 file_members += list(
-                    filter(lambda x: flash_attn_regex.match(x.filename), wheel.filelist)
+                    filter(
+                        lambda x: flash_attn_regex.match(x.filename)
+                        and x.filename not in flash_attn_files_to_skip,
+                        wheel.filelist,
+                    )
                 )
                 file_members += list(
                     filter(
@@ -917,7 +927,9 @@ def get_vllm_version() -> str:
     elif _is_tpu():
         version += f"{sep}tpu"
     elif _is_cpu():
-        if envs.VLLM_TARGET_DEVICE == "cpu":
+        # Check the local VLLM_TARGET_DEVICE (may be set by auto-detect above),
+        # not envs.VLLM_TARGET_DEVICE, so CPU-only hosts still get `+cpu`.
+        if VLLM_TARGET_DEVICE == "cpu":
             version += f"{sep}cpu"
     elif _is_xpu():
         version += f"{sep}xpu"
@@ -1075,7 +1087,9 @@ def _read_requirements(filename: str) -> list[str]:
     install_requires=get_requirements(),
     extras_require={
         # AMD Zen CPU optimizations via zentorch
-        "zen": ["zentorch"],
+        "zen": [
+            "zentorch-weekly==5.2.1.dev20260408"
+        ],  # Zentorch has weekly releases. This pulls the known-good version.
         "bench": ["pandas", "matplotlib", "seaborn", "datasets", "scipy", "plotly"],
         "tensorizer": ["tensorizer==2.10.1"],
         "fastsafetensors": ["fastsafetensors >= 0.2.2"],
@@ -1083,7 +1097,6 @@ def _read_requirements(filename: str) -> list[str]:
         "runai": ["runai-model-streamer[s3,gcs,azure] >= 0.15.7"],
         "audio": [
             "av",
-            "resampy",
             "scipy",
             "soundfile",
             "mistral_common[audio]",
@@ -1094,9 +1107,9 @@ def _read_requirements(filename: str) -> list[str]:
         # NOTE: When updating helion version, also update CI files:
         #   - .buildkite/test_areas/kernels.yaml
         #   - .buildkite/test-amd.yaml
-        "helion": ["helion==0.3.3"],
+        "helion": ["helion==1.0.0"],
         # Optional deps for gRPC server (vllm serve --grpc)
-        "grpc": ["smg-grpc-servicer[vllm] >= 0.5.0"],
+        "grpc": ["smg-grpc-servicer[vllm] >= 0.5.2"],
         # Optional deps for OpenTelemetry tracing
         "otel": [
             "opentelemetry-sdk>=1.26.0",
diff --git a/tests/basic_correctness/test_cumem.py b/tests/basic_correctness/test_cumem.py
index b1a16cfcaba4..8d8f87f0a3c6 100644
--- a/tests/basic_correctness/test_cumem.py
+++ b/tests/basic_correctness/test_cumem.py
@@ -13,6 +13,8 @@
 
 from ..utils import create_new_process_for_each_test, requires_fp8
 
+DEVICE_TYPE = current_platform.device_type
+
 
 @create_new_process_for_each_test("fork" if not current_platform.is_rocm() else "spawn")
 def test_python_error():
@@ -26,13 +28,13 @@ def test_python_error():
     tensors = []
     with allocator.use_memory_pool():
         # allocate 70% of the total memory
-        x = torch.empty(alloc_bytes, dtype=torch.uint8, device="cuda")
+        x = torch.empty(alloc_bytes, dtype=torch.uint8, device=DEVICE_TYPE)
         tensors.append(x)
     # release the memory
     allocator.sleep()
 
     # allocate more memory than the total memory
-    y = torch.empty(alloc_bytes, dtype=torch.uint8, device="cuda")
+    y = torch.empty(alloc_bytes, dtype=torch.uint8, device=DEVICE_TYPE)
     tensors.append(y)
     with pytest.raises(RuntimeError):
         # when the allocator is woken up, it should raise an error
@@ -44,17 +46,17 @@ def test_python_error():
 def test_basic_cumem():
     # some tensors from default memory pool
     shape = (1024, 1024)
-    x = torch.empty(shape, device="cuda")
+    x = torch.empty(shape, device=DEVICE_TYPE)
     x.zero_()
 
     # some tensors from custom memory pool
     allocator = CuMemAllocator.get_instance()
     with allocator.use_memory_pool():
         # custom memory pool
-        y = torch.empty(shape, device="cuda")
+        y = torch.empty(shape, device=DEVICE_TYPE)
         y.zero_()
         y += 1
-        z = torch.empty(shape, device="cuda")
+        z = torch.empty(shape, device=DEVICE_TYPE)
         z.zero_()
         z += 2
 
@@ -77,16 +79,16 @@ def test_basic_cumem():
 def test_cumem_with_cudagraph():
     allocator = CuMemAllocator.get_instance()
     with allocator.use_memory_pool():
-        weight = torch.eye(1024, device="cuda")
+        weight = torch.eye(1024, device=DEVICE_TYPE)
     with allocator.use_memory_pool(tag="discard"):
-        cache = torch.empty(1024, 1024, device="cuda")
+        cache = torch.empty(1024, 1024, device=DEVICE_TYPE)
 
     def model(x):
         out = x @ weight
         cache[: out.size(0)].copy_(out)
         return out + 1
 
-    x = torch.empty(128, 1024, device="cuda")
+    x = torch.empty(128, 1024, device=DEVICE_TYPE)
 
     # warmup
     model(x)
diff --git a/tests/benchmarks/test_custom_dataset_seed.py b/tests/benchmarks/test_custom_dataset_seed.py
new file mode 100644
index 000000000000..dac87e6e6d98
--- /dev/null
+++ b/tests/benchmarks/test_custom_dataset_seed.py
@@ -0,0 +1,77 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import argparse
+import json
+from pathlib import Path
+
+import pytest
+from transformers import AutoTokenizer, PreTrainedTokenizerBase
+
+from vllm.benchmarks.datasets import get_samples
+
+
+@pytest.fixture(scope="session")
+def hf_tokenizer() -> PreTrainedTokenizerBase:
+    return AutoTokenizer.from_pretrained("gpt2")
+
+
+def _write_jsonl(path: Path, n_rows: int) -> None:
+    with path.open("w") as f:
+        for i in range(n_rows):
+            f.write(json.dumps({"prompt": f"row {i}: unique prompt content."}) + "\n")
+
+
+def _args_for_custom(dataset_path: str, seed: int) -> argparse.Namespace:
+    return argparse.Namespace(
+        dataset_name="custom",
+        dataset_path=dataset_path,
+        disable_shuffle=False,
+        num_prompts=30,
+        custom_output_len=32,
+        skip_chat_template=True,
+        no_oversample=False,
+        seed=seed,
+        request_id_prefix="",
+    )
+
+
+@pytest.mark.benchmark
+def test_custom_dataset_seed_propagates(
+    hf_tokenizer: PreTrainedTokenizerBase, tmp_path: Path
+) -> None:
+    """--seed must control the CustomDataset shuffle used by get_samples.
+
+    Without the fix, CustomDataset was instantiated without random_seed,
+    so its load-time shuffle always used DEFAULT_SEED=0 regardless of
+    args.seed, causing every run with --dataset-name custom to pick the
+    same subset of rows from a larger file.
+    """
+    jsonl = tmp_path / "data.jsonl"
+    _write_jsonl(jsonl, n_rows=60)
+
+    samples_a = get_samples(_args_for_custom(str(jsonl), seed=0), hf_tokenizer)
+    samples_b = get_samples(_args_for_custom(str(jsonl), seed=42), hf_tokenizer)
+
+    prompts_a = {s.prompt for s in samples_a}
+    prompts_b = {s.prompt for s in samples_b}
+
+    assert len(prompts_a) == 30
+    assert len(prompts_b) == 30
+    assert prompts_a != prompts_b
+
+
+@pytest.mark.benchmark
+def test_custom_dataset_same_seed_is_deterministic(
+    hf_tokenizer: PreTrainedTokenizerBase, tmp_path: Path
+) -> None:
+    """Same --seed must yield the same CustomDataset subset."""
+    jsonl = tmp_path / "data.jsonl"
+    _write_jsonl(jsonl, n_rows=60)
+
+    samples_a = get_samples(_args_for_custom(str(jsonl), seed=7), hf_tokenizer)
+    samples_b = get_samples(_args_for_custom(str(jsonl), seed=7), hf_tokenizer)
+
+    prompts_a = [s.prompt for s in samples_a]
+    prompts_b = [s.prompt for s in samples_b]
+
+    assert prompts_a == prompts_b
diff --git a/tests/compile/backend.py b/tests/compile/backend.py
index d61c128a59b6..87f98946a8ad 100644
--- a/tests/compile/backend.py
+++ b/tests/compile/backend.py
@@ -12,10 +12,17 @@
 from torch.fx._utils import lazy_format_graph_code
 
 from vllm.compilation.passes.fx_utils import find_op_nodes
-from vllm.compilation.passes.inductor_pass import InductorPass
+from vllm.compilation.passes.inductor_pass import (
+    InductorPass,
+    pass_context,
+)
+from vllm.compilation.passes.ir.inplace_functionalization import (
+    VllmIRInplaceFunctionalizationPass,
+)
 from vllm.compilation.passes.pass_manager import with_pattern_match_debug
 from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
 from vllm.config import VllmConfig, get_current_vllm_config
+from vllm.config.utils import Range
 from vllm.logger import init_logger
 
 logger = init_logger("vllm.tests.compile.backend")
@@ -53,11 +60,17 @@ def __init__(self, *passes: InductorPass | Callable[[fx.Graph], None]):
         self.custom_passes = list(passes)
         vllm_config = get_current_vllm_config()
         compile_config = vllm_config.compilation_config
+        self.range = Range(1, vllm_config.scheduler_config.max_num_batched_tokens)
         # Deepcopy to allow multiple TestBackend instances to use the same VllmConfig
         self.inductor_config = deepcopy(compile_config.inductor_compile_config)
         self.inductor_config["force_disable_caches"] = True
         self.inductor_config["post_grad_custom_post_pass"] = self.post_pass
 
+        # Add VllmIRInplaceFunctionalizationPass as pre-grad pass by default
+        self.inductor_config["pre_grad_custom_pass"] = (
+            VllmIRInplaceFunctionalizationPass(vllm_config)
+        )
+
         if debug_dump_path := vllm_config.compile_debug_dump_path():
             logger.debug("Dumping depyf output to %s", debug_dump_path)
             self.debug_ctx = depyf.prepare_debug(debug_dump_path.as_posix())
@@ -68,7 +81,7 @@ def __call__(self, graph: fx.GraphModule, example_inputs):
         self.graph_pre_compile = deepcopy(graph)
         from torch._inductor.compile_fx import compile_fx
 
-        with self.debug_ctx:
+        with self.debug_ctx, pass_context(self.range):
             return compile_fx(
                 graph, example_inputs, config_patches=self.inductor_config
             )
diff --git a/tests/compile/conftest.py b/tests/compile/conftest.py
index 6aafac7bcad3..1263cce04c6c 100644
--- a/tests/compile/conftest.py
+++ b/tests/compile/conftest.py
@@ -24,10 +24,24 @@ def test_something(mock_cuda_platform):
     def _mock_platform(is_cuda: bool = True, capability: tuple[int, int] | None = None):
         mock_platform = MagicMock()
         mock_platform.is_cuda.return_value = is_cuda
-        if capability is not None:
-            mock_platform.get_device_capability.return_value = DeviceCapability(
-                *capability
+        device_capability = (
+            DeviceCapability(*capability) if capability is not None else None
+        )
+        mock_platform.get_device_capability.return_value = device_capability
+
+        def is_device_capability_family(
+            requested_capability: int, device_id: int = 0
+        ) -> bool:
+            current_capability = mock_platform.get_device_capability(
+                device_id=device_id
             )
+            if current_capability is None:
+                return False
+            return current_capability.major == (requested_capability // 10)
+
+        mock_platform.is_device_capability_family.side_effect = (
+            is_device_capability_family
+        )
         with patch("vllm.platforms.current_platform", mock_platform):
             yield mock_platform
 
diff --git a/tests/compile/correctness_e2e/test_async_tp.py b/tests/compile/correctness_e2e/test_async_tp.py
index 3539e4d5abb4..932e513258d0 100644
--- a/tests/compile/correctness_e2e/test_async_tp.py
+++ b/tests/compile/correctness_e2e/test_async_tp.py
@@ -13,6 +13,17 @@
 from vllm.config import (
     CompilationMode,
 )
+from vllm.platforms import current_platform
+from vllm.utils.flashinfer import has_flashinfer
+
+NVFP4_MODEL_ID = "nvidia/Llama-3.1-8B-Instruct-NVFP4"
+NVFP4_HF_OVERRIDES = {
+    "num_hidden_layers": 4,
+    "hidden_size": 512,
+    "intermediate_size": 800,
+    "num_attention_heads": 4,
+    "num_key_value_heads": 1,
+}
 
 
 @create_new_process_for_each_test()
@@ -82,3 +93,65 @@ def test_async_tp_pass_correctness(
     ]
 
     compare_two_settings(model_id, async_tp_args, tp_args, method="generate")
+
+
+@create_new_process_for_each_test()
+def test_async_tp_pass_nvfp4_correctness(num_gpus_available: int, monkeypatch):
+    if (
+        not current_platform.is_cuda()
+        or not current_platform.is_device_capability_family(100)
+    ):
+        pytest.skip("NVFP4 requires Blackwell")
+    if not has_flashinfer():
+        pytest.skip("FlashInfer is required for the NVFP4 AsyncTP path")
+
+    monkeypatch.setenv("VLLM_NVFP4_GEMM_BACKEND", "flashinfer-cutlass")
+
+    tp_size = 2
+    if num_gpus_available < tp_size:
+        pytest.skip(f"Need at least {tp_size} GPUs")
+
+    common_args = [
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "2048",
+        "--max-num-seqs",
+        "8",
+        "--load-format",
+        "dummy",
+        "--hf-overrides",
+        json.dumps(NVFP4_HF_OVERRIDES),
+    ]
+
+    compilation_config = {
+        "mode": CompilationMode.VLLM_COMPILE,
+        "compile_sizes": [2, 4, 8],
+        "splitting_ops": [],
+        "pass_config": {
+            "enable_sp": True,
+            "fuse_gemm_comms": True,
+            "fuse_allreduce_rms": False,
+            "sp_min_token_num": 1,
+        },
+    }
+
+    async_tp_args = [
+        *common_args,
+        "--tensor-parallel-size",
+        str(tp_size),
+        "--distributed-executor-backend",
+        "mp",
+        "--compilation_config",
+        json.dumps(compilation_config),
+    ]
+
+    tp_args = [
+        *common_args,
+        "--tensor-parallel-size",
+        str(tp_size),
+        "--distributed-executor-backend",
+        "mp",
+    ]
+
+    compare_two_settings(NVFP4_MODEL_ID, async_tp_args, tp_args, method="generate")
diff --git a/tests/compile/correctness_e2e/test_sequence_parallel.py b/tests/compile/correctness_e2e/test_sequence_parallel.py
index 281ffbfd2ec8..295277ddacf7 100644
--- a/tests/compile/correctness_e2e/test_sequence_parallel.py
+++ b/tests/compile/correctness_e2e/test_sequence_parallel.py
@@ -21,12 +21,14 @@
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import is_torch_equal_or_newer
 
-from ...models.registry import HF_EXAMPLE_MODELS
+from ...models.registry import HF_EXAMPLE_MODELS, _HfExamplesInfo
 from ...utils import compare_two_settings, create_new_process_for_each_test
 
 logger = init_logger("test_sequence_parallel")
 
 VLLM_MULTI_NODE = os.getenv("VLLM_MULTI_NODE", "0") == "1"
+NVFP4_MODEL_ID = "nvidia/Llama-3.1-8B-Instruct-NVFP4"
+NVFP4_MODEL_INFO = _HfExamplesInfo(NVFP4_MODEL_ID)
 
 
 class ParallelSetup(NamedTuple):
@@ -41,6 +43,7 @@ class ParallelSetup(NamedTuple):
 class SPTestOptions(NamedTuple):
     multi_node_only: bool
     load_format: str | None = None
+    model_info: _HfExamplesInfo | None = None
 
 
 @dataclass
@@ -167,9 +170,11 @@ def _compare_sp(
     num_gpus_available: int,
     use_inductor_graph_partition: bool,
     fuse_gemm_comms: bool,
+    enable_prompt_embeds: bool,
     *,
     method: Literal["generate", "encode"],
     is_multimodal: bool,
+    dtype: str = "float16",
 ):
     (
         tp_size,
@@ -180,14 +185,15 @@ def _compare_sp(
         chunked_prefill,
     ) = parallel_setup
 
-    multi_node_only, load_format = test_options
+    multi_node_only = test_options.multi_node_only
+    load_format = test_options.load_format
 
-    model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
+    model_info = test_options.model_info or HF_EXAMPLE_MODELS.find_hf_info(model_id)
     model_info.check_transformers_version(on_fail="skip")
 
     trust_remote_code = model_info.trust_remote_code
     tokenizer_mode = model_info.tokenizer_mode
-    hf_overrides = model_info.hf_overrides
+    hf_overrides = dict(model_info.hf_overrides)
     require_embed_inputs = model_info.require_embed_inputs
 
     if load_format == "dummy":
@@ -220,7 +226,7 @@ def _compare_sp(
     common_args = [
         # use half precision for speed and memory savings in CI environment
         "--dtype",
-        "float16",
+        dtype,
         "--max-model-len",
         "2048",
         "--max-num-seqs",
@@ -248,6 +254,8 @@ def _compare_sp(
                 "--enable-mm-embeds",
             ]
         )
+    elif enable_prompt_embeds:
+        common_args.append("--enable-prompt-embeds")
 
     compilation_config = {
         "mode": CompilationMode.VLLM_COMPILE,
@@ -257,10 +265,14 @@ def _compare_sp(
             "fuse_gemm_comms": fuse_gemm_comms,
             "fuse_norm_quant": fuse_norm_quant,
             "fuse_act_quant": fuse_act_quant,
+            "fuse_allreduce_rms": False,
             "eliminate_noops": True,
+            "sp_min_token_num": 0,
         },
         "use_inductor_graph_partition": use_inductor_graph_partition,
     }
+    if not use_inductor_graph_partition:
+        compilation_config["splitting_ops"] = []
 
     tp_sp_args = [
         *common_args,
@@ -347,6 +359,84 @@ def test_tp_sp_generation(
         num_gpus_available,
         use_inductor_graph_partition,
         fuse_gemm_comms=fuse_gemm_comms,
+        enable_prompt_embeds=False,
         method="generate",
         is_multimodal=False,
     )
+
+
+# Focused regression test for the SP + prompt_embeds graph-rewrite path.
+# Covers pp_size=1 (SP only) and pp_size=2 (SP + PP); kept small on purpose so
+# we don't double the matrix of `test_tp_sp_generation` above.
+SP_PROMPT_EMBEDS_PARALLEL_SETUPS = [
+    ParallelSetup(
+        tp_size=2,
+        pp_size=pp_size,
+        fuse_norm_quant=False,
+        fuse_act_quant=False,
+        eager_mode=False,
+        chunked_prefill=False,
+    )
+    for pp_size in [1, 2]
+]
+
+
+@pytest.mark.parametrize("parallel_setup", SP_PROMPT_EMBEDS_PARALLEL_SETUPS)
+@pytest.mark.parametrize("use_inductor_graph_partition", [True, False])
+@create_new_process_for_each_test()
+def test_tp_sp_generation_prompt_embeds(
+    parallel_setup: ParallelSetup,
+    num_gpus_available,
+    use_inductor_graph_partition: bool,
+):
+    if use_inductor_graph_partition and not is_torch_equal_or_newer("2.9.0.dev"):
+        pytest.skip("inductor graph partition is only available in PyTorch 2.9+")
+
+    _compare_sp(
+        "hmellor/tiny-random-LlamaForCausalLM",
+        parallel_setup,
+        distributed_backend="mp",
+        runner="auto",
+        test_options=SPTestOptions(multi_node_only=False, load_format=None),
+        num_gpus_available=num_gpus_available,
+        use_inductor_graph_partition=use_inductor_graph_partition,
+        fuse_gemm_comms=False,
+        enable_prompt_embeds=True,
+        method="generate",
+        is_multimodal=False,
+    )
+
+
+@create_new_process_for_each_test()
+def test_tp_sp_nvfp4_generation(num_gpus_available: int):
+    if (
+        not current_platform.is_cuda()
+        or not current_platform.is_device_capability_family(100)
+    ):
+        pytest.skip("NVFP4 requires Blackwell")
+
+    _compare_sp(
+        NVFP4_MODEL_ID,
+        ParallelSetup(
+            tp_size=2,
+            pp_size=1,
+            fuse_norm_quant=True,
+            fuse_act_quant=True,
+            eager_mode=True,
+            chunked_prefill=False,
+        ),
+        "mp",
+        "auto",
+        SPTestOptions(
+            multi_node_only=False,
+            load_format="dummy",
+            model_info=NVFP4_MODEL_INFO,
+        ),
+        num_gpus_available,
+        use_inductor_graph_partition=False,
+        fuse_gemm_comms=False,
+        enable_prompt_embeds=False,
+        method="generate",
+        is_multimodal=False,
+        dtype="bfloat16",
+    )
diff --git a/tests/compile/fullgraph/test_simple.py b/tests/compile/fullgraph/test_simple.py
index ed9c7a351e42..f1ea0e414d76 100644
--- a/tests/compile/fullgraph/test_simple.py
+++ b/tests/compile/fullgraph/test_simple.py
@@ -161,7 +161,14 @@ def _run_simple_model(
 @pytest.mark.parametrize("intermediate_unbacked", [True, False])
 @torch.inference_mode()
 @create_new_process_for_each_test("spawn")
-def test_simple_piecewise_compile(backend, intermediate_unbacked):
+def test_simple_piecewise_compile(backend, intermediate_unbacked, monkeypatch):
+    # `intermediate_unbacked` flips a control-flow branch inside
+    # `SillyModel.forward`, but the AOT-compile cache key only hashes the
+    # forward function's qualname + line number, so both parametrize variants
+    # share the same cache slot. Disabling the cache forces each variant to
+    # compile fresh; otherwise the second-running variant loads the first's
+    # artifact and segfaults with an illegal memory access.
+    monkeypatch.setenv("VLLM_DISABLE_COMPILE_CACHE", "1")
     _run_simple_model(
         splitting_ops=["silly::attention"],
         use_inductor_graph_partition=False,
diff --git a/tests/compile/fullgraph/test_toy_llama.py b/tests/compile/fullgraph/test_toy_llama.py
index 915fbc6ce7f3..69c758702e8a 100644
--- a/tests/compile/fullgraph/test_toy_llama.py
+++ b/tests/compile/fullgraph/test_toy_llama.py
@@ -17,7 +17,6 @@
 import torch
 from torch import nn
 
-from vllm.compilation.counter import compilation_counter
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import (
     CompilationConfig,
@@ -340,6 +339,8 @@ def run_model(llama_config, compile_config: CompilationConfig) -> torch.Tensor:
 def test_toy_llama(
     backend: str, use_inductor_graph_partition: bool, monkeypatch, tmp_path
 ):
+    from vllm.compilation.counter import compilation_counter
+
     # We disable the vLLM compile cache into a new tmp dir for 1 reason:
     # 1. To make sure we can properly track the number of Inductor compilations.
     monkeypatch.setenv("VLLM_DISABLE_COMPILE_CACHE", "1")
diff --git a/tests/compile/fusions_e2e/conftest.py b/tests/compile/fusions_e2e/conftest.py
index adc569192d19..b4922a3fdc5e 100644
--- a/tests/compile/fusions_e2e/conftest.py
+++ b/tests/compile/fusions_e2e/conftest.py
@@ -97,6 +97,12 @@ def run(
                 f"attention backend '{attn_backend.backend.name}'"
             )
 
+        if attn_backend.backend.name == "FLASHINFER":
+            from vllm.utils.flashinfer import supports_trtllm_attention
+
+            if not supports_trtllm_attention():
+                matches = matches._replace(attn_quant_fusion=0)
+
         # TODO: remove this after finishing migration from envs to model kwargs
         if model_name == "openai/gpt-oss-20b":
             from .common import is_blackwell
@@ -116,6 +122,27 @@ def run(
         model_kwargs["attention_config"] = {"backend": attn_backend.backend.name}
         model_kwargs["tensor_parallel_size"] = tp_size
 
+        # Cap warmup memory: tests use small max_model_len (1024) but the
+        # engine default max_num_batched_tokens is 16384. Warming up large
+        # models (e.g. Llama-4-Scout-FP8) at 16384 tokens may trigger OOM.
+        model_kwargs.setdefault("max_num_batched_tokens", 8192)
+
+        # Sparse MLA models (DSv3.2) hit an over-strict inductor assertion in
+        # decompose_auto_functionalized when +rotary_embedding is forced into
+        # the compile graph. Disable qk_norm+rope fusion (which auto-enables
+        # +rotary_embedding) for this combo to avoid the known torch bug.
+        # TODO: remove once upstream torch fix lands.
+        if requires_sparse:
+            if "pass_config" in compilation_config:
+                compilation_config["pass_config"].enable_qk_norm_rope_fusion = False
+                matches_check = [m for m in matches_check if m != "norm_rope_fusion"]
+            # DSv3.2 sparse indexer uses persistent_topk with k=config.index_topk
+            # (2048 for the default config). max_model_len must be >= index_topk
+            # or the topk kernel raises "k out of range" at runtime.
+            model_kwargs["max_model_len"] = max(
+                model_kwargs.get("max_model_len", 0), 2048
+            )
+
         # Always compile the full graph instead of piecewise
         if not compilation_config["use_inductor_graph_partition"]:
             compilation_config["splitting_ops"] = []
@@ -173,7 +200,7 @@ def run(
             # TODO: Remove log counting in unit tests
             # once all matchers implement VllmFusionPatternMatcherPass
             n_expected = tp_size * num_ranges_activated
-            if match_name != "attn_quant_fusion":
+            if match_name not in ("attn_quant_fusion", "act_quant_fusion"):
                 assert len(log_matches) == n_expected, (
                     f"Could not find {n_expected} {match_name} "
                     f"(found {len(log_matches)}) in:\n {log_holder.text}"
@@ -234,6 +261,12 @@ def run(
                     f"entries (SP took precedence), found: {log_matches}"
                 )
 
+            elif match_name == "act_quant_fusion":
+                actual_match = match_table.get("activation_quant_fusion_pass", 0)
+                assert actual_match == expected_matches * n_expected, (
+                    f"Could not find {expected_matches * n_expected} "
+                    f"{match_name} (found {actual_match})."
+                )
             elif match_name == "attn_quant_fusion":
                 actual_match = match_table.get(
                     "attn_quant_fusion", 0
diff --git a/tests/compile/fusions_e2e/models.py b/tests/compile/fusions_e2e/models.py
index 8d830e884063..3d73373cb91f 100644
--- a/tests/compile/fusions_e2e/models.py
+++ b/tests/compile/fusions_e2e/models.py
@@ -59,7 +59,10 @@
 )
 
 FLASHMLA_SPARSE_ATTN = pytest.param(
-    AttentionBackendCase(backend=AttentionBackendEnum.FLASHMLA_SPARSE),
+    AttentionBackendCase(
+        backend=AttentionBackendEnum.FLASHMLA_SPARSE,
+        model_kwargs=dict(kv_cache_dtype="fp8_ds_mla"),
+    ),
     id="FLASHMLA_SPARSE",
     marks=pytest.mark.skipif(
         not is_blackwell(),
@@ -173,9 +176,8 @@
         rms_quant_fusion=n_layers * 2 + min(3, n_layers),  # add for 3 dense layers
         # silu+block quant
         act_quant_fusion=min(3, n_layers),  # dense layers only
-        # MLA attn + per-group FP8 quant not supported yet:
-        # https://github.com/vllm-project/vllm/issues/35792
-        attn_quant_fusion=0,
+        # MLA attn + per-group FP8 quant
+        attn_quant_fusion=n_layers,
         ar_rms_fusion=n_layers * 2 + 1,
         # TODO
         # sequence_parallel= n_layers * 2 + 1,
@@ -183,11 +185,23 @@
     ),
 )
 
+deepseek_r1_fp4 = ModelFusionInfo(
+    model_name="nvidia/DeepSeek-R1-0528-NVFP4-v2",
+    matches=lambda n_layers: Matches(
+        rms_quant_fusion=0,
+        act_quant_fusion=min(3, n_layers),
+        attn_quant_fusion=n_layers,
+        ar_rms_fusion=n_layers * 2 + 1,
+    ),
+)
+
 deepseek_v32_fp4 = ModelFusionInfo(
     model_name="nvidia/DeepSeek-V3.2-NVFP4",
     matches=lambda n_layers: Matches(
         rms_quant_fusion=0,
-        act_quant_fusion=0,
+        # silu+quant on dense layers only; MoE hides the act+quant site
+        act_quant_fusion=min(3, n_layers),
+        # MLA attn + NVFP4 output quant fuses on sparse MLA output path
         attn_quant_fusion=n_layers,
         ar_rms_fusion=n_layers * 2 + 1,
     ),
diff --git a/tests/compile/fusions_e2e/test_tp1_quant.py b/tests/compile/fusions_e2e/test_tp1_quant.py
index ded39939e167..fbb382b4458d 100644
--- a/tests/compile/fusions_e2e/test_tp1_quant.py
+++ b/tests/compile/fusions_e2e/test_tp1_quant.py
@@ -24,6 +24,7 @@
     TRITON_ATTN,
     TRITON_MLA_ATTN,
     deepseek_coder_v2_lite_fp8,
+    deepseek_r1_fp4,
     deepseek_v3_fp8,
     deepseek_v32_fp4,
     llama3_8b_fp4,
@@ -148,11 +149,11 @@ def test_tp1_fp8_fusions(
 
 @pytest.mark.parametrize(
     "model_name, matches_fn, model_kwargs, hf_overrides",
-    [llama3_8b_fp4, llama4_scout_fp4, deepseek_v32_fp4],
+    [llama3_8b_fp4, llama4_scout_fp4, deepseek_r1_fp4, deepseek_v32_fp4],
 )
 @pytest.mark.parametrize(
     "attn_backend",
-    [FLASHINFER_ATTN, FLASHMLA_SPARSE_ATTN],
+    [FLASHINFER_ATTN, FLASHINFER_MLA_ATTN, FLASHMLA_SPARSE_ATTN],
 )
 @pytest.mark.parametrize("n_layers", [6])
 @pytest.mark.parametrize("custom_ops", custom_ops_combos("rms_norm"))
diff --git a/tests/compile/fusions_e2e/test_tp2_ar_rms.py b/tests/compile/fusions_e2e/test_tp2_ar_rms.py
index 4b0a0859b023..b5e2b2dc07ea 100644
--- a/tests/compile/fusions_e2e/test_tp2_ar_rms.py
+++ b/tests/compile/fusions_e2e/test_tp2_ar_rms.py
@@ -19,8 +19,11 @@
     FLASHINFER_ATTN,
     FLASHINFER_MLA_ATTN,
     FLASHMLA_SPARSE_ATTN,
+    ROCM_AITER_UNIFIED_ATTN,
+    ROCM_ATTN,
     TRITON_ATTN,
     deepseek_coder_v2_lite_fp8,
+    deepseek_r1_fp4,
     deepseek_v3_fp8,
     deepseek_v32_fp4,
     gpt_oss_20b,
@@ -33,7 +36,9 @@
     qwen3_a3b_fp8,
 )
 
-pytestmark = pytest.mark.skipif(not current_platform.is_cuda(), reason="Only test CUDA")
+pytestmark = pytest.mark.skipif(
+    not current_platform.is_cuda_alike(), reason="Only test CUDA/ROCm"
+)
 
 
 @multi_gpu_test(num_gpus=2)
@@ -54,6 +59,7 @@
 @pytest.mark.parametrize("n_layers", [4])
 @pytest.mark.parametrize("custom_ops", custom_ops_combos("quant_fp8", "rms_norm"))
 @pytest.mark.parametrize("inductor_graph_partition", INDUCTOR_GRAPH_PARTITION)
+@pytest.mark.skipif(not current_platform.is_cuda(), reason="Only test CUDA")
 def test_tp2_ar_rms_fp8_fusions(
     model_name: str,
     matches_fn: Callable[[int], Matches],
@@ -113,16 +119,17 @@ def test_tp2_ar_rms_fp8_fusions(
 @multi_gpu_test(num_gpus=2)
 @pytest.mark.parametrize(
     "model_name, matches_fn, model_kwargs, hf_overrides",
-    [llama3_8b_fp4, llama4_scout_fp4, deepseek_v32_fp4],
+    [llama3_8b_fp4, llama4_scout_fp4, deepseek_r1_fp4, deepseek_v32_fp4],
 )
 @pytest.mark.parametrize(
     "attn_backend",
-    [FLASHINFER_ATTN, FLASHMLA_SPARSE_ATTN],
+    [FLASHINFER_ATTN, FLASHINFER_MLA_ATTN, FLASHMLA_SPARSE_ATTN],
 )
 @pytest.mark.parametrize("n_layers", [4])
 @pytest.mark.parametrize("custom_ops", custom_ops_combos("rms_norm"))
 @pytest.mark.parametrize("inductor_graph_partition", INDUCTOR_GRAPH_PARTITION)
 @pytest.mark.skipif(not is_blackwell(), reason="Blackwell required for fp4")
+@pytest.mark.skipif(not current_platform.is_cuda(), reason="Only test CUDA")
 def test_tp2_ar_rms_fp4_fusions(
     model_name: str,
     matches_fn: Callable[[int], Matches],
@@ -175,10 +182,19 @@ def test_tp2_ar_rms_fp4_fusions(
     "model_name, matches_fn, model_kwargs, hf_overrides",
     [llama3_8b, qwen3_a3b, gpt_oss_20b],
 )
-@pytest.mark.parametrize("attn_backend", [TRITON_ATTN])
+@pytest.mark.parametrize(
+    "attn_backend",
+    [
+        TRITON_ATTN,
+        FLASHINFER_ATTN,
+        ROCM_ATTN,
+        ROCM_AITER_UNIFIED_ATTN,
+    ],
+)
 @pytest.mark.parametrize("n_layers", [4])
-@pytest.mark.parametrize("custom_ops", custom_ops_combos("rms_norm"))
+@pytest.mark.parametrize("custom_ops", tuple(custom_ops_combos("rms_norm")))
 @pytest.mark.parametrize("inductor_graph_partition", INDUCTOR_GRAPH_PARTITION)
+@pytest.mark.skipif(not current_platform.is_cuda_alike(), reason="Only test CUDA/ROCm")
 def test_tp2_ar_rms_fusions(
     model_name: str,
     matches_fn: Callable[[int], Matches],
@@ -220,4 +236,5 @@ def test_tp2_ar_rms_fusions(
         compilation_config,
         matches_check,
         tp_size=2,
+        use_aiter=current_platform.is_rocm(),
     )
diff --git a/tests/compile/fusions_e2e/test_tp2_async_tp.py b/tests/compile/fusions_e2e/test_tp2_async_tp.py
index 609377e68958..a22c68f4bf92 100644
--- a/tests/compile/fusions_e2e/test_tp2_async_tp.py
+++ b/tests/compile/fusions_e2e/test_tp2_async_tp.py
@@ -19,6 +19,7 @@
     FLASHINFER_ATTN,
     TRITON_ATTN,
     llama3_8b,
+    llama3_8b_fp4,
     llama3_8b_fp8,
     llama4_scout_fp8,
     qwen3_a3b,
@@ -46,14 +47,9 @@ def test_tp2_async_tp_fp8_fusions(
     custom_ops: str,
     inductor_graph_partition: bool,
     run_e2e_fusion_test,
-    monkeypatch,
 ):
     matches = matches_fn(n_layers)
 
-    if is_blackwell():
-        # Disable FlashInfer scaled_mm FP8 as it's not supported in async tp patterns
-        monkeypatch.setenv("VLLM_DISABLED_KERNELS", "FlashInferFP8ScaledMMLinearKernel")
-
     # Reduce size of model and skip weight loading time
     model_kwargs["hf_overrides"] = hf_overrides(n_layers)
     model_kwargs["load_format"] = "dummy"
@@ -96,6 +92,69 @@ def test_tp2_async_tp_fp8_fusions(
     )
 
 
+@multi_gpu_test(num_gpus=2)
+@pytest.mark.parametrize(
+    "model_name, matches_fn, model_kwargs, hf_overrides",
+    [llama3_8b_fp4],
+)
+@pytest.mark.parametrize("attn_backend", [FLASHINFER_ATTN])
+@pytest.mark.parametrize("n_layers", [4])
+@pytest.mark.parametrize("custom_ops", custom_ops_combos("rms_norm"))
+@pytest.mark.parametrize("inductor_graph_partition", INDUCTOR_GRAPH_PARTITION)
+@pytest.mark.skipif(not is_blackwell(), reason="Blackwell required for fp4")
+@pytest.mark.skipif(not current_platform.is_cuda(), reason="Only test CUDA")
+def test_tp2_async_tp_nvfp4_fusions(
+    model_name: str,
+    matches_fn: Callable[[int], Matches],
+    model_kwargs: dict,
+    hf_overrides: Callable[[int], dict],
+    attn_backend: AttentionBackendCase,
+    n_layers: int,
+    custom_ops: str,
+    inductor_graph_partition: bool,
+    run_e2e_fusion_test,
+):
+    # NVFP4 currently wires the all-gather + GEMM path only.
+    matches = matches_fn(n_layers)._replace(async_tp=n_layers * 2)
+
+    # Reduce size of model and skip weight loading time
+    model_kwargs["hf_overrides"] = hf_overrides(n_layers)
+    model_kwargs["load_format"] = "dummy"
+    model_kwargs["max_model_len"] = 1024
+    model_kwargs["kernel_config"] = {"enable_flashinfer_autotune": False}
+
+    compilation_config = dict(
+        use_inductor_graph_partition=inductor_graph_partition,
+        custom_ops=custom_ops.split(","),
+        pass_config=PassConfig(
+            fuse_act_quant=True,
+            fuse_attn_quant=True,
+            enable_sp=True,
+            fuse_gemm_comms=True,
+            fuse_allreduce_rms=False,
+            # Override threshold for testing (models have small hidden_size)
+            sp_min_token_num=512,
+        ),
+    )
+
+    matches_check = [
+        "act_quant_fusion",
+        "attn_quant_fusion",
+        "sequence_parallel",
+        "async_tp",
+    ]
+
+    run_e2e_fusion_test(
+        model_name,
+        matches,
+        model_kwargs,
+        attn_backend,
+        compilation_config,
+        matches_check,
+        tp_size=2,
+    )
+
+
 @multi_gpu_test(num_gpus=2)
 @pytest.mark.parametrize(
     "model_name, matches_fn, model_kwargs, hf_overrides",
@@ -173,14 +232,9 @@ def test_tp2_sp_ar_rms_fp8_fusions(
     custom_ops: str,
     inductor_graph_partition: bool,
     run_e2e_fusion_test,
-    monkeypatch,
 ):
     matches = matches_fn(n_layers)
 
-    if is_blackwell():
-        # Disable FlashInfer scaled_mm FP8 as it's not supported in async tp patterns
-        monkeypatch.setenv("VLLM_DISABLED_KERNELS", "FlashInferFP8ScaledMMLinearKernel")
-
     # Reduce size of model and skip weight loading time
     model_kwargs["hf_overrides"] = hf_overrides(n_layers)
     model_kwargs["load_format"] = "dummy"
diff --git a/tests/compile/h100/test_startup.py b/tests/compile/h100/test_startup.py
index 6a94322b1b61..78554a3e93da 100644
--- a/tests/compile/h100/test_startup.py
+++ b/tests/compile/h100/test_startup.py
@@ -34,7 +34,10 @@ def _run_vllm(vllm_runner):
             mode=CompilationMode.VLLM_COMPILE,
             cudagraph_mode=CUDAGraphMode.NONE,
         ),
-        num_gpu_blocks_override=8,
+        # Phi-tiny-MoE uses SWA, whose admission cap is `cdiv(L, block_size) + 1`
+        # at default block_size=16 — i.e. 17 blocks for max_model_len=256. Use
+        # 32 for headroom.
+        num_gpu_blocks_override=32,
     ):
         pass
 
@@ -56,6 +59,7 @@ def _cold_start(vllm_runner):
 def test_moe_startup(monkeypatch, vllm_runner, fresh_vllm_cache, mega_aot_artifact):
     monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
     monkeypatch.setenv("VLLM_USE_MEGA_AOT_ARTIFACT", mega_aot_artifact)
+    monkeypatch.setenv("VLLM_DEEP_GEMM_WARMUP", "skip")
 
     # Cold start in a forked child (must fork before CUDA init).
     # This model has 32 identical transformer layers which produce
@@ -135,10 +139,9 @@ class ModelStartupSpec(NamedTuple):
             model="deepseek-ai/DeepSeek-V3.2",
             hf_overrides=_SMALL_MOE_OVERRIDES,
             cold_artifacts_saved=4,
-            # TODO: https://github.com/vllm-project/vllm/issues/38051
-            # We shouldn't be saving any artifacts on warm start.
-            warm_artifacts_saved=4,
-            warm_artifacts_loaded=0,
+            # https://github.com/vllm-project/vllm/issues/38051
+            warm_artifacts_saved=0 if is_torch_equal_or_newer("2.12.0") else 4,
+            warm_artifacts_loaded=4 if is_torch_equal_or_newer("2.12.0") else 0,
         ),
         id="deepseek_v3.2",
     ),
@@ -147,10 +150,9 @@ class ModelStartupSpec(NamedTuple):
             model="moonshotai/Kimi-K2.5",
             hf_overrides={"text_config": _SMALL_MOE_OVERRIDES},
             cold_artifacts_saved=4,
-            # TODO: https://github.com/vllm-project/vllm/issues/38051
-            # We shouldn't be saving any artifacts on warm start.
-            warm_artifacts_saved=4,
-            warm_artifacts_loaded=0,
+            # https://github.com/vllm-project/vllm/issues/38051
+            warm_artifacts_saved=0 if is_torch_equal_or_newer("2.12.0") else 4,
+            warm_artifacts_loaded=4 if is_torch_equal_or_newer("2.12.0") else 0,
         ),
         id="kimi_k2.5",
     ),
@@ -191,7 +193,7 @@ def _run_model(vllm_runner, spec: ModelStartupSpec):
             cudagraph_mode=CUDAGraphMode.NONE,
             pass_config=PassConfig(fuse_allreduce_rms=False),
         ),
-        num_gpu_blocks_override=8,
+        num_gpu_blocks_override=16,
     ):
         pass
 
@@ -237,6 +239,7 @@ def _cold_start_model(vllm_runner, spec: ModelStartupSpec):
 @fork_new_process_for_each_test
 def test_model_startup(monkeypatch, vllm_runner, fresh_vllm_cache, spec):
     monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
+    monkeypatch.setenv("VLLM_DEEP_GEMM_WARMUP", "skip")
 
     # Cold start in a forked child (must fork before CUDA init).
     ctx = mp.get_context("fork")
diff --git a/tests/compile/passes/distributed/test_async_tp.py b/tests/compile/passes/distributed/test_async_tp.py
index 7edceee9811e..33e050d776e9 100644
--- a/tests/compile/passes/distributed/test_async_tp.py
+++ b/tests/compile/passes/distributed/test_async_tp.py
@@ -19,6 +19,7 @@
     VllmConfig,
     set_current_vllm_config,
 )
+from vllm.config.utils import Range
 from vllm.distributed import (
     tensor_model_parallel_all_gather,
     tensor_model_parallel_reduce_scatter,
@@ -31,6 +32,7 @@
 from vllm.utils.system_utils import update_environment_variables
 from vllm.utils.torch_utils import set_random_seed
 
+DEVICE_TYPE = current_platform.device_type
 FP8_DTYPE = current_platform.fp8_dtype()
 
 prompts = [
@@ -287,6 +289,22 @@ def run_torch_spawn(fn, nprocs):
     run_torch_spawn(async_tp_pass_on_test_model, num_processes)
 
 
+def test_async_tp_pass_requires_full_graph_compilation():
+    vllm_config = VllmConfig()
+    vllm_config.compilation_config.use_inductor_graph_partition = False
+    vllm_config.compilation_config.splitting_ops = [
+        "vllm::unified_attention_with_output"
+    ]
+
+    async_tp_pass = object.__new__(AsyncTPPass)
+    async_tp_pass.compilation_config = vllm_config.compilation_config
+
+    with pytest.raises(
+        AssertionError, match="AsyncTPPass requires full-graph compilation"
+    ):
+        async_tp_pass.is_applicable_for_range(Range(start=8, end=8))
+
+
 def async_tp_pass_on_test_model(
     local_rank: int,
     world_size: int,
@@ -299,7 +317,7 @@ def async_tp_pass_on_test_model(
 ):
     set_random_seed(0)
 
-    device = torch.device(f"cuda:{local_rank}")
+    device = torch.device(f"{DEVICE_TYPE}:{local_rank}")
     torch.accelerator.set_device_index(device)
     torch.set_default_device(device)
     torch.set_default_dtype(dtype)
@@ -324,7 +342,7 @@ def async_tp_pass_on_test_model(
             fuse_gemm_comms=True,
         ),
     )
-    vllm_config.device_config = DeviceConfig(device=torch.device("cuda"))
+    vllm_config.device_config = DeviceConfig(device=torch.device(DEVICE_TYPE))
 
     # this is a fake model name to construct the model config
     # in the vllm_config, it's not really used.
diff --git a/tests/compile/passes/distributed/test_fusion_all_reduce.py b/tests/compile/passes/distributed/test_fusion_all_reduce.py
index a50d3ca8e3e1..1a175b8dd335 100644
--- a/tests/compile/passes/distributed/test_fusion_all_reduce.py
+++ b/tests/compile/passes/distributed/test_fusion_all_reduce.py
@@ -8,8 +8,12 @@
 import vllm.envs as envs
 from tests.compile.backend import TestBackend
 from tests.utils import TestFP8Layer, has_module_attribute, multi_gpu_test
+from vllm._aiter_ops import IS_AITER_FOUND, rocm_aiter_ops
 from vllm._custom_ops import cutlass_scaled_fp4_mm, scaled_fp4_quant
-from vllm.compilation.passes.fusion.allreduce_rms_fusion import AllReduceFusionPass
+from vllm.compilation.passes.fusion.allreduce_rms_fusion import (
+    AllReduceFusionPass,
+    RocmAiterAllReduceFusionPass,
+)
 from vllm.compilation.passes.utility.fix_functionalization import (
     FixFunctionalizationPass,
 )
@@ -37,16 +41,24 @@
 from vllm.utils.system_utils import update_environment_variables
 from vllm.utils.torch_utils import set_random_seed
 
+DEVICE_TYPE = current_platform.device_type
+
 
 class TestAllReduceRMSNormModel(torch.nn.Module):
     def __init__(
-        self, hidden_size=16, token_num=16, eps=1e-6, dtype: torch.dtype = torch.float16
+        self,
+        hidden_size=16,
+        token_num=16,
+        eps=1e-6,
+        dtype: torch.dtype = torch.float16,
+        use_aiter: bool = False,
     ):
         super().__init__()
         self.hidden_size = hidden_size
         self.eps = eps
         self.norm = [RMSNorm(hidden_size, eps) for i in range(4)]
         self.w = [torch.rand(hidden_size, hidden_size) for _ in range(3)]
+        self.use_aiter = use_aiter
 
     def forward(self, x):
         # avoid having graph input be an arg to a pattern directly
@@ -74,6 +86,8 @@ def ops_in_model_before(self):
         return [torch.ops.vllm.all_reduce.default]
 
     def ops_in_model_after(self):
+        if self.use_aiter:
+            return [rocm_aiter_ops.get_fused_allreduce_rmsnorm_op()]
         return [torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default]
 
 
@@ -192,12 +206,36 @@ def ops_in_model_before(self):
 
 @multi_gpu_test(num_gpus=2)
 @pytest.mark.parametrize(
-    "test_model, enable_quant_fp8_custom_op",
+    "test_model, enable_quant_fp8_custom_op, use_aiter",
     [
-        (TestAllReduceRMSNormModel, False),
-        (TestAllReduceRMSNormStaticQuantFP8Model, True),
-        (TestAllReduceRMSNormStaticQuantFP8Model, False),
-        (TestAllReduceFusedAddRMSNormStaticQuantFP4Model, False),
+        (TestAllReduceRMSNormModel, False, IS_AITER_FOUND),
+        pytest.param(
+            TestAllReduceRMSNormStaticQuantFP8Model,
+            True,
+            False,
+            marks=pytest.mark.skipif(
+                current_platform.is_rocm(),
+                reason="Not supported on ROCm platform",
+            ),
+        ),
+        pytest.param(
+            TestAllReduceRMSNormStaticQuantFP8Model,
+            False,
+            False,
+            marks=pytest.mark.skipif(
+                current_platform.is_rocm(),
+                reason="Not supported on ROCm platform",
+            ),
+        ),
+        pytest.param(
+            TestAllReduceFusedAddRMSNormStaticQuantFP4Model,
+            False,
+            False,
+            marks=pytest.mark.skipif(
+                current_platform.is_rocm(),
+                reason="Not supported on ROCm platform",
+            ),
+        ),
     ],
 )
 @pytest.mark.parametrize("batch_size", [8])
@@ -208,9 +246,18 @@ def ops_in_model_before(self):
 @pytest.mark.parametrize("flashinfer_allreduce_backend", ["trtllm", "mnnvl"])
 @pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda"], reason="Only test on CUDA")
 @pytest.mark.skipif(
-    not find_spec("flashinfer")
-    or not has_module_attribute("flashinfer.comm", "allreduce_fusion")
-    or not has_module_attribute("flashinfer.comm", "create_allreduce_fusion_workspace"),
+    current_platform.is_rocm() and not IS_AITER_FOUND,
+    reason="aiter is not found",
+)
+@pytest.mark.skipif(
+    current_platform.is_cuda()
+    and (
+        not find_spec("flashinfer")
+        or not has_module_attribute("flashinfer.comm", "allreduce_fusion")
+        or not has_module_attribute(
+            "flashinfer.comm", "create_allreduce_fusion_workspace"
+        )
+    ),
     reason="flashinfer is not found or flashinfer "
     "is not compiled with allreduce_fusion",
 )
@@ -223,7 +270,14 @@ def test_all_reduce_fusion_pass_replace(
     enable_rms_norm_custom_op,
     enable_quant_fp8_custom_op,
     flashinfer_allreduce_backend,
+    use_aiter: bool,
+    monkeypatch: pytest.MonkeyPatch,
 ):
+    if use_aiter:
+        with monkeypatch.context() as m:
+            m.setenv("VLLM_ROCM_USE_AITER", str(use_aiter))
+            rocm_aiter_ops.refresh_env_variables()
+
     num_processes = 2
     if (
         test_model == TestAllReduceFusedAddRMSNormStaticQuantFP4Model
@@ -247,6 +301,8 @@ def run_torch_spawn(fn, nprocs):
                 enable_rms_norm_custom_op,
                 enable_quant_fp8_custom_op,
                 flashinfer_allreduce_backend,
+                use_aiter,
+                monkeypatch,
             ),
             nprocs=nprocs,
         )
@@ -265,10 +321,12 @@ def all_reduce_fusion_pass_on_test_model(
     enable_rms_norm_custom_op,
     enable_quant_fp8_custom_op,
     flashinfer_allreduce_backend,
+    use_aiter: bool,
+    monkeypatch: pytest.MonkeyPatch,
 ):
     set_random_seed(0)
 
-    device = torch.device(f"cuda:{local_rank}")
+    device = torch.device(f"{DEVICE_TYPE}:{local_rank}")
     torch.accelerator.set_device_index(device)
     torch.set_default_device(device)
     torch.set_default_dtype(dtype)
@@ -300,7 +358,7 @@ def all_reduce_fusion_pass_on_test_model(
     vllm_config.compilation_config.pass_config = PassConfig(
         fuse_allreduce_rms=True, eliminate_noops=True
     )
-    vllm_config.device_config = DeviceConfig(device=torch.device("cuda"))
+    vllm_config.device_config = DeviceConfig(device=torch.device(DEVICE_TYPE))
     vllm_config.parallel_config.rank = local_rank  # Setup rank for debug path
 
     # this is a fake model name to construct the model config
@@ -311,7 +369,11 @@ def all_reduce_fusion_pass_on_test_model(
     )
     with set_current_vllm_config(vllm_config):
         initialize_model_parallel(tensor_model_parallel_size=world_size)
-        all_reduce_fusion_pass = AllReduceFusionPass(vllm_config)
+        all_reduce_fusion_pass = (
+            RocmAiterAllReduceFusionPass(vllm_config)
+            if use_aiter
+            else AllReduceFusionPass(vllm_config)
+        )
         noop_pass = NoOpEliminationPass(vllm_config)
         func_pass = FixFunctionalizationPass(vllm_config)
         cleanup_pass = PostCleanupPass(vllm_config)
@@ -321,7 +383,12 @@ def all_reduce_fusion_pass_on_test_model(
         )
 
         token_num = batch_size * seq_len
-        model = test_model_cls(hidden_size, token_num, dtype=dtype)
+        if test_model_cls is TestAllReduceRMSNormModel:
+            model = test_model_cls(
+                hidden_size, token_num, dtype=dtype, use_aiter=use_aiter
+            )
+        else:
+            model = test_model_cls(hidden_size, token_num, dtype=dtype)
 
         hidden_states = torch.randn((token_num, hidden_size), requires_grad=False)
 
diff --git a/tests/compile/passes/distributed/test_sequence_parallelism.py b/tests/compile/passes/distributed/test_sequence_parallelism.py
index 667ef4e04fbe..c40d75f6754a 100644
--- a/tests/compile/passes/distributed/test_sequence_parallelism.py
+++ b/tests/compile/passes/distributed/test_sequence_parallelism.py
@@ -22,6 +22,7 @@
     get_current_vllm_config,
     set_current_vllm_config,
 )
+from vllm.config.utils import Range
 from vllm.distributed import tensor_model_parallel_all_reduce
 from vllm.distributed.parallel_state import (
     init_distributed_environment,
@@ -35,6 +36,8 @@
 from vllm.utils.system_utils import update_environment_variables
 from vllm.utils.torch_utils import set_random_seed
 
+DEVICE_TYPE = current_platform.device_type
+
 pytestmark = pytest.mark.skipif(not current_platform.is_cuda(), reason="Only test CUDA")
 
 FP8_DTYPE = current_platform.fp8_dtype()
@@ -85,14 +88,10 @@ def ops_in_model_after(self):
         ]
 
     def ops_in_model(self):
-        return (
-            [torch.ops.vllm_ir.rms_norm]
-            + [
-                torch.ops._C.fused_add_rms_norm.default,
-            ]
-            if RMSNorm.enabled()
-            else []
-        )
+        return [
+            torch.ops.vllm_ir.rms_norm,
+            torch.ops.vllm_ir.fused_add_rms_norm,
+        ]
 
 
 class TestAllReduceRMSNormStaticQuantFP8Model(torch.nn.Module):
@@ -149,16 +148,17 @@ def ops_in_model_before(self):
     def ops_in_model(self):
         if self.vllm_config.compilation_config.pass_config.fuse_norm_quant:
             return [torch.ops._C.fused_add_rms_norm_static_fp8_quant.default]
-        elif RMSNorm.enabled():
-            return [
-                torch.ops._C.fused_add_rms_norm.default,
-            ]
-        elif any(layer.is_quant_fp8_enabled() for layer in self.fp8_linear_layers):
+        else:
+            quant_ops = (
+                [torch.ops._C.static_scaled_fp8_quant.default]
+                if any(layer.is_quant_fp8_enabled() for layer in self.fp8_linear_layers)
+                else [torch.ops.aten.reciprocal]
+            )
             return [
-                torch.ops._C.static_scaled_fp8_quant.default,
+                torch.ops.vllm_ir.rms_norm,
+                torch.ops.vllm_ir.fused_add_rms_norm,
+                *quant_ops,
             ]
-        else:
-            return []
 
 
 @multi_gpu_test(num_gpus=2)
@@ -214,6 +214,24 @@ def run_torch_spawn(fn, nprocs):
     run_torch_spawn(sequence_parallelism_pass_on_test_model, num_processes)
 
 
+def test_sequence_parallelism_pass_requires_full_graph_compilation():
+    vllm_config = VllmConfig()
+    vllm_config.compilation_config.use_inductor_graph_partition = False
+    vllm_config.compilation_config.splitting_ops = [
+        "vllm::unified_attention_with_output"
+    ]
+
+    sequence_parallelism_pass = object.__new__(SequenceParallelismPass)
+    sequence_parallelism_pass.compilation_config = vllm_config.compilation_config
+    sequence_parallelism_pass.min_token_num = 1
+
+    with pytest.raises(
+        AssertionError,
+        match="SequenceParallelismPass requires full-graph compilation",
+    ):
+        sequence_parallelism_pass.is_applicable_for_range(Range(start=8, end=8))
+
+
 def sequence_parallelism_pass_on_test_model(
     local_rank: int,
     world_size: int,
@@ -228,7 +246,7 @@ def sequence_parallelism_pass_on_test_model(
 ):
     set_random_seed(0)
 
-    device = torch.device(f"cuda:{local_rank}")
+    device = torch.device(f"{DEVICE_TYPE}:{local_rank}")
     torch.accelerator.set_device_index(device)
     torch.set_default_device(device)
     torch.set_default_dtype(dtype)
@@ -258,7 +276,7 @@ def sequence_parallelism_pass_on_test_model(
             eliminate_noops=True,
         ),
     )  # NoOp needed for fusion
-    device_config = DeviceConfig(device=torch.device("cuda"))
+    device_config = DeviceConfig(device=torch.device(DEVICE_TYPE))
 
     # this is a fake model name to construct the model config
     # in the vllm_config, it's not really used.
diff --git a/tests/compile/passes/ir/test_clone_cleanup.py b/tests/compile/passes/ir/test_clone_cleanup.py
new file mode 100644
index 000000000000..9fedb5fc9177
--- /dev/null
+++ b/tests/compile/passes/ir/test_clone_cleanup.py
@@ -0,0 +1,412 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Comprehensive tests for UnsafeCloneEliminationPass.
+
+This test suite exercises all possible valid FX graph patterns involving clones:
+1. Clone with no users (dead code)
+2. Clone with read-only users
+3. Clone with mutation users
+4. Clone of graph input
+5. Clone with original used after mutation
+6. Clone chains
+"""
+
+import pytest
+import torch
+from torch import fx
+from torch.fx.experimental.proxy_tensor import make_fx
+
+from vllm.compilation.passes.fx_utils import find_op_nodes
+from vllm.compilation.passes.inductor_pass import get_pass_context, pass_context
+from vllm.compilation.passes.ir.clone_elimination import (
+    UnsafeCloneEliminationPass,
+    user_writes_to_node,
+)
+from vllm.config import VllmConfig
+from vllm.config.utils import Range
+
+
+def count_clones(graph: fx.Graph) -> int:
+    """Count clone nodes in a graph."""
+    return len(list(find_op_nodes(torch.ops.aten.clone.default, graph)))
+
+
+@pytest.fixture(scope="function")
+def clone_cleanup_pass():
+    return UnsafeCloneEliminationPass(VllmConfig())
+
+
+@pytest.fixture(autouse=True)
+def setup_pass_context():
+    """Set up pass context for each test."""
+    with pass_context(compile_range=Range(1, 8192)):
+        yield
+
+
+class TestCloneCleanup:
+    """Test UnsafeCloneEliminationPass behavior on various graph patterns."""
+
+    def test_remove_clone_readonly_users(self, clone_cleanup_pass):
+        """Clone with only read-only users should be removed."""
+
+        def f(x: torch.Tensor) -> torch.Tensor:
+            x_clone = x.clone()
+            return x_clone + 1
+
+        inp = torch.randn(2, 3)
+        graph_module = make_fx(f)(inp)
+        assert count_clones(graph_module.graph) == 1
+
+        expected = graph_module(inp)
+        clone_cleanup_pass(graph_module.graph)
+        graph_module.recompile()
+        actual = graph_module(inp)
+
+        assert count_clones(graph_module.graph) == 0
+        torch.testing.assert_close(actual, expected)
+
+    def test_keep_clone_with_mutation_and_original_used_after(self, clone_cleanup_pass):
+        """Clone must be kept if it's mutated AND original is used after mutation."""
+
+        def f(x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+            x = x.relu()  # not a graph param
+            x_clone = x.clone()
+            x_clone.add_(1)
+            return x, x_clone
+
+        inp = torch.randn(2, 3)
+        graph_module = make_fx(f)(inp)
+        assert count_clones(graph_module.graph) == 1
+
+        expected = graph_module(inp)
+        clone_cleanup_pass(graph_module.graph)
+        graph_module.recompile()
+        actual = graph_module(inp)
+
+        # Clone should be KEPT because original is used after mutation
+        assert count_clones(graph_module.graph) == 1
+        torch.testing.assert_close(actual[0], expected[0])
+        torch.testing.assert_close(actual[1], expected[1])
+
+    def test_remove_clone_with_mutation_no_original_use(self, clone_cleanup_pass):
+        """Clone can be removed if it's mutated but original is not used after."""
+
+        def f(x: torch.Tensor) -> torch.Tensor:
+            x = x.relu()  # not a graph param
+            x_clone = x.clone()
+            x_clone.add_(1)
+            return x_clone
+
+        inp = torch.randn(2, 3)
+        graph_module = make_fx(f)(inp)
+        assert count_clones(graph_module.graph) == 1
+
+        expected = graph_module(inp)
+        clone_cleanup_pass(graph_module.graph)
+        graph_module.recompile()
+        actual = graph_module(inp)
+
+        assert count_clones(graph_module.graph) == 0
+        torch.testing.assert_close(actual, expected)
+
+    def test_clone_chain(self, clone_cleanup_pass):
+        """Test handling of clone chains: x -> clone1 -> clone2."""
+
+        def f(x: torch.Tensor) -> torch.Tensor:
+            x = x.relu()  # not a graph param
+            x1 = x.clone()
+            x2 = x1.clone()
+            return x2 + 1
+
+        inp = torch.randn(2, 3)
+        graph_module = make_fx(f)(inp)
+        assert count_clones(graph_module.graph) == 2
+
+        expected = graph_module(inp)
+        clone_cleanup_pass(graph_module.graph)
+        graph_module.recompile()
+        actual = graph_module(inp)
+
+        # Both clones should be removed
+        assert count_clones(graph_module.graph) == 0
+        torch.testing.assert_close(actual, expected)
+
+    def test_multiple_clones_of_same_input(self, clone_cleanup_pass):
+        """Test multiple independent clones of the same input."""
+
+        def f(x: torch.Tensor) -> torch.Tensor:
+            x1 = x.clone()
+            x2 = x.clone()
+            return x1 + x2
+
+        inp = torch.randn(2, 3)
+        graph_module = make_fx(f)(inp)
+        assert count_clones(graph_module.graph) == 2
+
+        expected = graph_module(inp)
+        clone_cleanup_pass(graph_module.graph)
+        graph_module.recompile()
+        actual = graph_module(inp)
+
+        # Both clones should be removed (only readonly uses)
+        assert count_clones(graph_module.graph) == 0
+        torch.testing.assert_close(actual, expected)
+
+    def test_no_clones_in_graph(self, clone_cleanup_pass):
+        """Test pass behavior when graph has no clones."""
+
+        def f(x: torch.Tensor) -> torch.Tensor:
+            return x + 1
+
+        inp = torch.randn(2, 3)
+        graph_module = make_fx(f)(inp)
+        assert count_clones(graph_module.graph) == 0
+
+        expected = graph_module(inp)
+        clone_cleanup_pass(graph_module.graph)
+        graph_module.recompile()
+        actual = graph_module(inp)
+
+        assert count_clones(graph_module.graph) == 0
+        torch.testing.assert_close(actual, expected)
+
+    def test_multiple_passes(self, clone_cleanup_pass):
+        """Test running the pass multiple times (should be idempotent)."""
+
+        def f(x: torch.Tensor) -> torch.Tensor:
+            x1 = x.clone()
+            return x1 + 1
+
+        inp = torch.randn(2, 3)
+        graph_module = make_fx(f)(inp)
+        assert count_clones(graph_module.graph) == 1
+
+        expected = graph_module(inp)
+
+        clone_cleanup_pass(graph_module.graph)
+        assert count_clones(graph_module.graph) == 0
+        graph_module.recompile()
+        actual = graph_module(inp)
+        torch.testing.assert_close(actual, expected)
+
+        clone_cleanup_pass(graph_module.graph)
+        assert count_clones(graph_module.graph) == 0
+        graph_module.recompile()
+        actual = graph_module(inp)
+        torch.testing.assert_close(actual, expected)
+
+    def test_output_node_no_write(self):
+        """Output nodes never write to their inputs."""
+
+        def f(x: torch.Tensor) -> torch.Tensor:
+            return x
+
+        graph_module = make_fx(f)(torch.randn(2, 3))
+        x_node = [n for n in graph_module.graph.nodes if n.op == "placeholder"][0]
+        output_node = [n for n in graph_module.graph.nodes if n.op == "output"][0]
+
+        assert not user_writes_to_node(output_node, x_node)
+
+    def test_readonly_op_no_write(self):
+        """Readonly operations don't write to inputs."""
+
+        def f(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+            return x + y
+
+        graph_module = make_fx(f)(torch.randn(2, 3), torch.randn(2, 3))
+        placeholders = [n for n in graph_module.graph.nodes if n.op == "placeholder"]
+        add_node = [
+            n
+            for n in graph_module.graph.nodes
+            if n.op == "call_function" and n.target == torch.ops.aten.add.Tensor
+        ][0]
+
+        assert not user_writes_to_node(add_node, placeholders[0])
+        assert not user_writes_to_node(add_node, placeholders[1])
+
+    def test_inplace_op_writes(self):
+        """Inplace operations write to first argument."""
+
+        def f(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+            x.add_(y)
+            return x
+
+        graph_module = make_fx(f)(torch.randn(2, 3), torch.randn(2, 3))
+        placeholders = [n for n in graph_module.graph.nodes if n.op == "placeholder"]
+        add_node = [
+            n
+            for n in graph_module.graph.nodes
+            if n.op == "call_function" and "add_" in str(n.target)
+        ][0]
+
+        # add_ writes to first arg but not second
+        assert user_writes_to_node(add_node, placeholders[0])
+        assert not user_writes_to_node(add_node, placeholders[1])
+
+    def test_copy_writes(self):
+        """copy_ operation writes to first argument."""
+
+        def f(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+            x.copy_(y)
+            return x
+
+        graph_module = make_fx(f)(torch.randn(2, 3), torch.randn(2, 3))
+        placeholders = [n for n in graph_module.graph.nodes if n.op == "placeholder"]
+        copy_node = [
+            n
+            for n in graph_module.graph.nodes
+            if n.op == "call_function" and "copy_" in str(n.target)
+        ][0]
+
+        assert user_writes_to_node(copy_node, placeholders[0])
+        assert not user_writes_to_node(copy_node, placeholders[1])
+
+    def test_auto_functionalized_not_a_write(self):
+        """auto_functionalized ops are follow-up uses, not writes."""
+        from torch._higher_order_ops.auto_functionalize import auto_functionalized
+
+        def f(x: torch.Tensor) -> torch.Tensor:
+            return x
+
+        graph_module = make_fx(f)(torch.randn(2, 3))
+        x_node = [n for n in graph_module.graph.nodes if n.op == "placeholder"][0]
+
+        # Create an auto_functionalized node in the graph
+        with graph_module.graph.inserting_before(None):
+            af_node = graph_module.graph.call_function(
+                auto_functionalized, kwargs={"input": x_node}
+            )
+
+        # auto_functionalized should not be treated as a write
+        assert not user_writes_to_node(af_node, x_node)
+
+    def test_higher_order_op_conservatively_writes(self):
+        """Other higher-order operators are conservatively treated as writes."""
+        from torch._ops import HigherOrderOperator
+
+        def f(x: torch.Tensor) -> torch.Tensor:
+            return x
+
+        graph_module = make_fx(f)(torch.randn(2, 3))
+        x_node = [n for n in graph_module.graph.nodes if n.op == "placeholder"][0]
+
+        # Create a concrete higher-order operator subclass
+        class MockHigherOrderOp(HigherOrderOperator):
+            def __call__(self, *args, **kwargs):
+                return args[0] if args else None
+
+        mock_hoo = MockHigherOrderOp("mock_higher_order_op")
+
+        with graph_module.graph.inserting_before(None):
+            hoo_node = graph_module.graph.call_function(mock_hoo, args=(x_node,))
+
+        # Should be conservative and assume it could write
+        assert user_writes_to_node(hoo_node, x_node)
+
+
+class TestCloneCleanupWithDonatedInputs:
+    """Test UnsafeCloneEliminationPass with donated input tracking via PassContext."""
+
+    @pytest.fixture(autouse=True)
+    def setup_pass_context(self):
+        """Set up pass context for each test."""
+        with pass_context(compile_range=Range(1, 8192)):
+            yield
+
+    def test_donated_input_clone_removed(self, clone_cleanup_pass):
+        """Clone of donated input should be removed."""
+
+        def f(x: torch.Tensor) -> torch.Tensor:
+            x_clone = x.clone()
+            x_clone.add_(1)
+            return x_clone
+
+        inp = torch.randn(2, 3)
+        graph_module = make_fx(f)(inp)
+        assert count_clones(graph_module.graph) == 1
+
+        # Mark first parameter as donated
+        get_pass_context().donated_input_ids = {0}
+
+        expected = graph_module(inp.clone())
+        clone_cleanup_pass(graph_module.graph)
+        graph_module.recompile()
+
+        # Clone should be removed since input is donated
+        assert count_clones(graph_module.graph) == 0
+
+        # Input can be mutated (donated)
+        inp_copy = inp.clone()
+        actual = graph_module(inp_copy)
+        torch.testing.assert_close(actual, expected)
+
+    def test_non_donated_input_clone_kept(self, clone_cleanup_pass):
+        """Clone of non-donated input with mutation should be kept."""
+
+        def f(x: torch.Tensor, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+            x_clone = x.clone()
+            x_clone.add_(1)
+            return x, x_clone
+
+        inp_x = torch.randn(2, 3)
+        inp_y = torch.randn(2, 3)
+        graph_module = make_fx(f)(inp_x, inp_y)
+        assert count_clones(graph_module.graph) == 1
+
+        # No donated inputs
+        get_pass_context().donated_input_ids = set()
+
+        expected = graph_module(inp_x.clone(), inp_y.clone())
+        clone_cleanup_pass(graph_module.graph)
+        graph_module.recompile()
+
+        # Clone should be kept since input is not donated and original is used
+        assert count_clones(graph_module.graph) == 1
+
+        # Verify inputs are not mutated
+        inp_x_before = inp_x.clone()
+        inp_y_before = inp_y.clone()
+        actual = graph_module(inp_x, inp_y)
+        torch.testing.assert_close(
+            inp_x, inp_x_before, msg="Input x should not be mutated"
+        )
+        torch.testing.assert_close(
+            inp_y, inp_y_before, msg="Input y should not be mutated"
+        )
+        torch.testing.assert_close(actual[0], expected[0])
+        torch.testing.assert_close(actual[1], expected[1])
+
+    def test_mixed_donated_inputs(self, clone_cleanup_pass):
+        """Test with some inputs donated and some not."""
+
+        def f(x: torch.Tensor, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+            x_clone = x.clone()
+            x_clone.add_(1)
+            y_clone = y.clone()
+            y_clone.add_(2)
+            return x_clone, y_clone
+
+        inp_x = torch.randn(2, 3)
+        inp_y = torch.randn(2, 3)
+        graph_module = make_fx(f)(inp_x, inp_y)
+        assert count_clones(graph_module.graph) == 2
+
+        # Only x is donated
+        get_pass_context().donated_input_ids = {0}
+
+        expected = graph_module(inp_x.clone(), inp_y.clone())
+        clone_cleanup_pass(graph_module.graph)
+        graph_module.recompile()
+
+        # x_clone removed (x is donated), y_clone kept (y is not donated)
+        assert count_clones(graph_module.graph) == 1
+
+        # Verify y is not mutated (x can be mutated since it's donated)
+        inp_y_before = inp_y.clone()
+        actual = graph_module(inp_x.clone(), inp_y)
+        torch.testing.assert_close(
+            inp_y, inp_y_before, msg="Input y should not be mutated"
+        )
+        torch.testing.assert_close(actual[0], expected[0])
+        torch.testing.assert_close(actual[1], expected[1])
diff --git a/tests/compile/passes/ir/test_inplace_functionalization.py b/tests/compile/passes/ir/test_inplace_functionalization.py
new file mode 100644
index 000000000000..1e8d5662162f
--- /dev/null
+++ b/tests/compile/passes/ir/test_inplace_functionalization.py
@@ -0,0 +1,465 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Tests for IR inplace functionalization pass integration.
+
+This test suite verifies that the inplace functionalization pass, lowering pass,
+and clone cleanup pass work together correctly with donated buffer tracking.
+"""
+
+from collections.abc import Callable
+
+import pytest
+import torch
+import torch._dynamo.exc
+from torch import nn
+
+import vllm.kernels  # noqa: F401 to register kernels
+from vllm.compilation.passes.inductor_pass import InductorPass, get_pass_context
+from vllm.compilation.passes.ir.clone_elimination import (
+    UnsafeCloneEliminationPass,
+)
+from vllm.compilation.passes.ir.inplace_functionalization import (
+    VllmIRInplaceFunctionalizationPass,
+)
+from vllm.compilation.passes.ir.lowering_pass import VllmIRLoweringPass
+from vllm.config import VllmConfig
+from vllm.ir import ops
+from vllm.platforms import current_platform
+from vllm.triton_utils import HAS_TRITON, tl, triton
+
+from ...backend import TestBackend
+
+
+class StoreDonationInfoPass(InductorPass):
+    def __init__(self):
+        self.donated_input_ids_sets: list[set[int]] = []
+
+    def __call__(self, *args, **kwargs):
+        ctx = get_pass_context()
+        self.donated_input_ids_sets += [ctx.donated_input_ids]
+
+
+class MaybeInplaceModel(nn.Module):
+    """Model using only maybe_inplace variants."""
+
+    def __init__(self, hidden_size=16):
+        super().__init__()
+        self.weight1 = nn.Parameter(torch.ones(hidden_size, dtype=torch.bfloat16))
+        self.weight2 = nn.Parameter(torch.ones(hidden_size, dtype=torch.bfloat16))
+
+    def forward(
+        self, x: torch.Tensor, residual1: torch.Tensor, residual2: torch.Tensor
+    ):
+        # First maybe_inplace - x & residual1 are donated
+        x_normed1, residual_out1 = ops.fused_add_rms_norm.maybe_inplace(
+            x, residual1, self.weight1, 1e-5
+        )
+        # Second maybe_inplace - residual2 is donated
+        x_normed2, residual_out2 = ops.fused_add_rms_norm.maybe_inplace(
+            x_normed1, residual2, self.weight2, 1e-5
+        )
+        return x_normed2, residual_out1, residual_out2
+
+
+class FunctionalModel(nn.Module):
+    """Model using only functional (default) variants."""
+
+    def __init__(self, hidden_size=16):
+        super().__init__()
+        self.weight1 = nn.Parameter(torch.ones(hidden_size, dtype=torch.bfloat16))
+        self.weight2 = nn.Parameter(torch.ones(hidden_size, dtype=torch.bfloat16))
+
+    def forward(
+        self, x: torch.Tensor, residual1: torch.Tensor, residual2: torch.Tensor
+    ):
+        # First functional - no donation
+        x_normed1, residual_out1 = ops.fused_add_rms_norm(
+            x, residual1, self.weight1, 1e-5
+        )
+        # Second functional - no donation
+        x_normed2, residual_out2 = ops.fused_add_rms_norm(
+            x_normed1, residual2, self.weight2, 1e-5
+        )
+        return x_normed2, residual_out1, residual_out2
+
+
+class MixedModel(nn.Module):
+    """Model mixing maybe_inplace and functional variants."""
+
+    def __init__(self, hidden_size=16):
+        super().__init__()
+        self.weight1 = nn.Parameter(torch.ones(hidden_size, dtype=torch.bfloat16))
+        self.weight2 = nn.Parameter(torch.ones(hidden_size, dtype=torch.bfloat16))
+
+    def forward(
+        self, x: torch.Tensor, residual1: torch.Tensor, residual2: torch.Tensor
+    ):
+        # First maybe_inplace - x & residual1 are donated
+        x_normed1, residual_out1 = ops.fused_add_rms_norm.maybe_inplace(
+            x, residual1, self.weight1, 1e-5
+        )
+        # Second functional - no donation, x_normed1 must be preserved as it's returned
+        x_normed2, residual_out2 = ops.fused_add_rms_norm(
+            x_normed1, residual2, self.weight2, 1e-5
+        )
+        # Return both to prevent x_normed1 from being optimized away
+        return x_normed1, x_normed2, residual_out1, residual_out2
+
+
+class ModelWithTritonAfterMaybeInplace(nn.Module):
+    """
+    Model using maybe_inplace followed by a Triton kernel.
+    Test clone elimination can handle Triton in the graph
+    """
+
+    def __init__(self, hidden_size=16):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size, dtype=torch.bfloat16))
+
+        @triton.jit
+        def _triton_add_kernel(
+            x_ptr,
+            y_ptr,
+            n_elements,
+            BLOCK_SIZE: tl.constexpr,
+        ):
+            pid = tl.program_id(axis=0)
+            block_start = pid * BLOCK_SIZE
+            offsets = block_start + tl.arange(0, BLOCK_SIZE)
+            mask = offsets < n_elements
+            x = tl.load(x_ptr + offsets, mask=mask)
+            y = x + 0.1
+            tl.store(y_ptr + offsets, y, mask=mask)
+
+        def triton_add(x: torch.Tensor) -> torch.Tensor:
+            """Simple Triton add kernel."""
+            y = torch.empty_like(x)
+            n_elements = x.numel()
+            grid = (triton.cdiv(n_elements, 256),)
+            _triton_add_kernel[grid](x, y, n_elements, BLOCK_SIZE=256)
+            return y
+
+        self.triton_add = triton_add
+
+    def forward(self, x: torch.Tensor, residual: torch.Tensor, residual2: torch.Tensor):
+        x_normed, residual_out = ops.fused_add_rms_norm.maybe_inplace(
+            x, residual, self.weight, 1e-5
+        )
+
+        x_processed = self.triton_add(x_normed)
+
+        # x_processed does not need to be cloned, residual2 does
+        x_normed2, residual_out2 = ops.fused_add_rms_norm(
+            x_processed, residual2, self.weight, 1e-5
+        )
+        return x_normed2, residual_out2
+
+
+skipif_no_triton = pytest.mark.skipif(not HAS_TRITON, reason="Requires Triton")
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="Only test on cuda and rocm platform",
+)
+@pytest.mark.parametrize(
+    "model_class,expected_functionalized,expected_donated,expected_clones",
+    [
+        # 2 inplace calls, all activations donated, all clones eliminated
+        (MaybeInplaceModel, 2, 3, 0),
+        # No inplace calls, no donations, 3 clones (one eliminated)
+        (FunctionalModel, 0, 0, 3),
+        # One inplace call, two donated activations, 2 clones
+        (MixedModel, 1, 2, 2),
+        # One inplace call, two donated, 1 clone remaining
+        pytest.param(ModelWithTritonAfterMaybeInplace, 1, 2, 1, marks=skipif_no_triton),
+    ],
+)
+def test_inplace_functionalization(
+    default_vllm_config: VllmConfig,
+    model_class,
+    expected_functionalized: int,
+    expected_clones: int,
+    expected_donated: int,
+):
+    """Test inplace functionalization, lowering, and clone cleanup."""
+    torch.set_default_device(current_platform.device_type)
+
+    # Use vllm_c so inplace path is triggered
+    default_vllm_config.kernel_config.ir_op_priority.fused_add_rms_norm = [
+        "vllm_c",
+        "native",
+    ]
+
+    # Create passes in order they run during compilation
+    functionalization_pass = VllmIRInplaceFunctionalizationPass(default_vllm_config)
+    lowering_pass = VllmIRLoweringPass(default_vllm_config)
+    donated_info_pass = StoreDonationInfoPass()
+    cleanup_pass = UnsafeCloneEliminationPass(default_vllm_config)
+
+    # Set up backend with pre-grad pass
+    backend = TestBackend(lowering_pass, donated_info_pass, cleanup_pass)
+    backend.inductor_config["pre_grad_custom_pass"] = functionalization_pass
+
+    model = model_class()
+    x = torch.randn(8, 16, dtype=torch.bfloat16)
+    residual1 = torch.randn(8, 16, dtype=torch.bfloat16)
+    residual2 = torch.randn(8, 16, dtype=torch.bfloat16)
+
+    with default_vllm_config.kernel_config.ir_op_priority.set_priority():
+        # Reference output without optimization
+        ref_output = model(x.clone(), residual1.clone(), residual2.clone())
+
+        # Compile with inplace optimization
+        compiled_model = torch.compile(model, backend=backend, fullgraph=True)
+        output = compiled_model(x.clone(), residual1.clone(), residual2.clone())
+
+    # Verify correctness (relaxed tolerance for bfloat16)
+    for i in range(len(ref_output)):
+        torch.testing.assert_close(output[i], ref_output[i], rtol=1e-2, atol=1e-2)
+
+    # Verify expected number of ops were functionalized
+    func_ops = functionalization_pass.functionalized_ops
+    assert len(func_ops) == int(bool(expected_functionalized))
+    if expected_functionalized > 0:
+        assert "fused_add_rms_norm" in func_ops
+        assert func_ops["fused_add_rms_norm"] == expected_functionalized
+
+    # Verify lowering happened (2 ops in all cases)
+    assert "fused_add_rms_norm" in lowering_pass.selected_impls
+    assert len(lowering_pass.selected_impls["fused_add_rms_norm"]) == 2
+    assert all(
+        provider == "vllm_c"
+        for node, provider in lowering_pass.selected_impls["fused_add_rms_norm"].items()
+    ), lowering_pass.selected_impls
+
+    # Verify correct number of donated IDs
+    assert len(donated_info_pass.donated_input_ids_sets) == 1
+    assert len(donated_info_pass.donated_input_ids_sets[0]) == expected_donated
+
+    # Verify expected number of clones after cleanup
+    actual_clones = backend.op_count(torch.ops.aten.clone.default, before=False)
+    assert actual_clones == expected_clones, (
+        f"Expected {expected_clones} clones, got {actual_clones}:"
+        f"{backend.print_graphs()}"
+    )
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="Only test on cuda and rocm platform",
+)
+def test_donated_buffer_context_propagation(default_vllm_config):
+    """Test that donated_input_ids propagates correctly through pass_context."""
+    torch.set_default_device(current_platform.device_type)
+
+    # Create a custom backend that inspects pass_context in cleanup pass
+    functionalization_pass = VllmIRInplaceFunctionalizationPass(default_vllm_config)
+    lowering_pass = VllmIRLoweringPass(default_vllm_config)
+
+    donation_info_pass = StoreDonationInfoPass()
+    cleanup_pass = UnsafeCloneEliminationPass(default_vllm_config)
+
+    backend = TestBackend(lowering_pass, donation_info_pass, cleanup_pass)
+    backend.inductor_config["pre_grad_custom_pass"] = functionalization_pass
+
+    model = MaybeInplaceModel()
+    x = torch.randn(8, 16, dtype=torch.bfloat16)
+    residual1 = torch.randn(8, 16, dtype=torch.bfloat16)
+    residual2 = torch.randn(8, 16, dtype=torch.bfloat16)
+
+    compiled_model = torch.compile(model, backend=backend, fullgraph=True)
+    compiled_model(x.clone(), residual1.clone(), residual2.clone())
+
+    donated_ids_seen = donation_info_pass.donated_input_ids_sets
+    # Verify donated_input_ids was set and propagated
+    assert len(donated_ids_seen) == 1
+    # Should have donated inputs (exact indices depend on AOTAutograd)
+    assert len(donated_ids_seen[0]) == 3
+    # All donated ids should be valid non-negative integers
+    for idx in donated_ids_seen[0]:
+        assert isinstance(idx, int) and idx >= 0, f"Invalid donated index: {idx}"
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="Only test on cuda and rocm platform",
+)
+def test_maybe_inplace_reuse_error(default_vllm_config):
+    """Test that reusing a donated activation input raises ValueError."""
+    torch.set_default_device(current_platform.device_type)
+
+    class ReuseModel(nn.Module):
+        """Model that incorrectly reuses a donated activation input."""
+
+        def __init__(self, hidden_size=16):
+            super().__init__()
+            self.weight = nn.Parameter(torch.ones(hidden_size, dtype=torch.bfloat16))
+
+        def forward(self, x: torch.Tensor, residual: torch.Tensor):
+            # x is donated to maybe_inplace
+            x_normed, residual_out = ops.fused_add_rms_norm.maybe_inplace(
+                x, residual, self.weight, 1e-5
+            )
+            # ERROR: x is used again after being donated
+            return x_normed + x  # This should raise ValueError
+
+    functionalization_pass = VllmIRInplaceFunctionalizationPass(default_vllm_config)
+    lowering_pass = VllmIRLoweringPass(default_vllm_config)
+    cleanup_pass = UnsafeCloneEliminationPass(default_vllm_config)
+
+    backend = TestBackend(lowering_pass, cleanup_pass)
+    backend.inductor_config["pre_grad_custom_pass"] = functionalization_pass
+
+    model = ReuseModel()
+    x = torch.randn(8, 16, dtype=torch.bfloat16)
+    residual = torch.randn(8, 16, dtype=torch.bfloat16)
+
+    # Compilation should raise BackendCompilerFailed wrapping ValueError
+    with pytest.raises(
+        torch._dynamo.exc.BackendCompilerFailed,
+        match="is used again after the node",
+    ):
+        compiled_model = torch.compile(model, backend=backend, fullgraph=True)
+        compiled_model(x.clone(), residual.clone())
+
+
+# Piecewise compilation tests with graph splitting
+
+
+@torch.library.custom_op("vllm::test_split_marker", mutates_args=())
+def test_split_marker(x: torch.Tensor) -> torch.Tensor:
+    """Identity op that marks a split point for piecewise compilation."""
+    return x.clone()
+
+
+@test_split_marker.register_fake
+def _fake_split_marker(x: torch.Tensor) -> torch.Tensor:
+    return torch.empty_like(x)
+
+
+class TransformerBlockWithSplits(nn.Module):
+    """Transformer block with explicit split points for piecewise compilation."""
+
+    def __init__(self, hidden_size=32, intermediate_size=128):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+
+        # Attention-like projection
+        self.attn_proj = nn.Linear(
+            hidden_size, hidden_size, bias=False, dtype=torch.bfloat16
+        )
+
+        # Post-attention norm
+        self.post_attn_norm = nn.Parameter(
+            torch.ones(hidden_size, dtype=torch.bfloat16)
+        )
+
+        # MLP
+        self.gate_proj = nn.Linear(
+            hidden_size, intermediate_size, bias=False, dtype=torch.bfloat16
+        )
+        self.up_proj = nn.Linear(
+            hidden_size, intermediate_size, bias=False, dtype=torch.bfloat16
+        )
+        self.down_proj = nn.Linear(
+            intermediate_size, hidden_size, bias=False, dtype=torch.bfloat16
+        )
+
+        # Post-MLP norm
+        self.post_mlp_norm = nn.Parameter(torch.ones(hidden_size, dtype=torch.bfloat16))
+
+    def forward(self, x: torch.Tensor):
+        # Attention block with residual
+        residual1 = x
+        attn_out = self.attn_proj(x)
+
+        # Fused add + norm (maybe_inplace: residual1 is donated)
+        normed1, residual1 = ops.fused_add_rms_norm.maybe_inplace(
+            attn_out, residual1, self.post_attn_norm, 1e-5
+        )
+
+        # Force a graph split here
+        normed1 = torch.ops.vllm.test_split_marker(normed1)
+
+        # MLP block
+        gate = self.gate_proj(normed1)
+        up = self.up_proj(normed1)
+        mlp_out = self.down_proj(gate * torch.nn.functional.silu(up))
+
+        # Fused add + norm (maybe_inplace: residual1 is donated)
+        normed2, residual2 = ops.fused_add_rms_norm.maybe_inplace(
+            mlp_out, residual1, self.post_mlp_norm, 1e-5
+        )
+
+        return normed2, residual2
+
+
+def with_dyn_arg(fn: Callable, arg_index: int, dim_index: int):
+    def inner(*args):
+        torch._dynamo.mark_dynamic(args[arg_index], dim_index)
+        return fn(*args)
+
+    return inner
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="Only test on cuda and rocm platform",
+)
+def test_piecewise_compilation_with_donated_buffers(monkeypatch, fresh_vllm_cache):
+    """
+    Test piecewise compilation with donated buffers across graph splits.
+    Utilizes a custom splitting op. Uses fresh cache to avoid compilation caching.
+    """
+    torch.set_default_device(current_platform.device_type)
+
+    # Disable compilation cache to avoid serialization issues
+    monkeypatch.setenv("VLLM_DISABLE_COMPILE_CACHE", "1")
+
+    from vllm.compilation.backends import VllmBackend
+    from vllm.config import CompilationConfig, VllmConfig
+
+    # Create config with custom splitting op
+    store_donation_info = StoreDonationInfoPass()
+    vllm_config = VllmConfig(
+        compilation_config=CompilationConfig(
+            custom_ops=["all"],
+            splitting_ops=["vllm::test_split_marker"],
+            inductor_compile_config={"post_grad_custom_post_pass": store_donation_info},
+        )
+    )
+
+    backend = VllmBackend(vllm_config)
+
+    model = TransformerBlockWithSplits()
+    x = torch.randn(8, 32, dtype=torch.bfloat16)
+
+    # Reference output
+    ref_output = with_dyn_arg(model, 0, 0)(x.clone())
+
+    # Compile with piecewise compilation (graph will split at split_marker)
+    compiled_model = torch.compile(model, backend=backend, fullgraph=False)
+    output = with_dyn_arg(compiled_model, 0, 0)(x.clone())
+
+    # Verify correctness (relaxed tolerance for bfloat16)
+    torch.testing.assert_close(output[0], ref_output[0], rtol=1e-2, atol=1e-2)
+    torch.testing.assert_close(output[1], ref_output[1], rtol=1e-2, atol=1e-2)
+
+    # Verify the model was split into multiple submodules
+    assert hasattr(backend, "split_gm"), "Backend should have split graph module"
+
+    # Should have at least 2 submodules (split by test_split_marker op)
+    submodules = list(backend.split_gm.named_children())
+    num_submodules = len(submodules)
+    assert num_submodules >= 2, (
+        f"Expected at least 2 submodules (split), got {num_submodules}"
+    )
+
+    # Check that donation info was propagated correctly
+    donated_inputs_sets = store_donation_info.donated_input_ids_sets
+    assert len(donated_inputs_sets) == 2
+    assert len(donated_inputs_sets[0]) == 1
+    assert len(donated_inputs_sets[1]) == 1
diff --git a/tests/compile/passes/test_functionalization.py b/tests/compile/passes/test_functionalization.py
index 0e1d3b3a5d64..31bf225d4135 100644
--- a/tests/compile/passes/test_functionalization.py
+++ b/tests/compile/passes/test_functionalization.py
@@ -117,16 +117,16 @@ def forward(self, hidden_states, residual):
         else:
             return norm_output, residual_output
 
-    def example_inputs(self, batch_size=8, hidden_size=16, seq_len=16):
-        hidden_states = torch.randn((batch_size * seq_len, hidden_size))
-        residual = torch.randn((batch_size * seq_len, hidden_size))
+    def example_inputs(self, batch_size=8, seq_len=16):
+        hidden_states = torch.randn((batch_size * seq_len, self.hidden_size))
+        residual = torch.randn((batch_size * seq_len, self.intermediate_size))
         return (hidden_states, residual)
 
     def ops_in_model(self, do_fusion):
         if TEST_FP8 and do_fusion:
             return [torch.ops._C.fused_add_rms_norm_static_fp8_quant.default]
         else:
-            return [torch.ops._C.fused_add_rms_norm.default]
+            return []
 
     def ops_not_in_model(self):
         return []
diff --git a/tests/compile/passes/test_fuse_act_padding.py b/tests/compile/passes/test_fuse_act_padding.py
index f3f3bda47277..bfbe762abdb6 100644
--- a/tests/compile/passes/test_fuse_act_padding.py
+++ b/tests/compile/passes/test_fuse_act_padding.py
@@ -59,7 +59,7 @@ def forward(self, x):
 
     def ops_in_model_before(self):
         return [
-            rocm_aiter_ops.get_rmsnorm_fused_add_op(),
+            torch.ops.vllm_ir.fused_add_rms_norm,
             torch.ops.aten.constant_pad_nd,
         ]
 
diff --git a/tests/compile/passes/test_fuse_mla_dual_rms_norm.py b/tests/compile/passes/test_fuse_mla_dual_rms_norm.py
new file mode 100644
index 000000000000..080417c98966
--- /dev/null
+++ b/tests/compile/passes/test_fuse_mla_dual_rms_norm.py
@@ -0,0 +1,148 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Unit test for the MLADualRMSNormFusionPass.
+
+The pass fuses paired q/kv RMS norms in MLA attention into a single
+fused_mla_dual_rms_norm op backed by AITER's fused_qk_rmsnorm kernel.
+"""
+
+import pytest
+import torch
+
+import vllm.config
+from tests.compile.backend import TestBackend
+from vllm._aiter_ops import is_aiter_found_and_supported, rocm_aiter_ops
+from vllm.compilation.passes.utility.noop_elimination import NoOpEliminationPass
+from vllm.compilation.passes.utility.post_cleanup import PostCleanupPass
+from vllm.config import (
+    CompilationConfig,
+    CompilationMode,
+    ModelConfig,
+    PassConfig,
+    VllmConfig,
+)
+from vllm.model_executor.layers.layernorm import RMSNorm
+
+# MLA attention geometry for DeepSeek-V3 / Kimi-K2
+Q_DIM = 1536
+KV_C_DIM = 512
+K_PE_DIM = 64
+EPS = 1e-6
+
+
+class MLADualRMSNormTestModel(torch.nn.Module):
+    """
+    Minimal model reproducing the MLA dual RMS norm pattern:
+        linear -> split([q_dim, kv_dim])
+            +-- q_c (getitem 0) -> rms_norm(q_w, eps) -> linear
+            +-- kv_lora (getitem 1) -> split([kv_c_dim, k_pe_dim])
+                    +-- kv_c (getitem 0) -> rms_norm(kv_w, eps)
+                    +-- k_pe
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        q_dim: int = Q_DIM,
+        kv_c_dim: int = KV_C_DIM,
+        k_pe_dim: int = K_PE_DIM,
+        eps: float = EPS,
+    ):
+        super().__init__()
+        self.q_dim = q_dim
+        self.kv_dim = kv_c_dim + k_pe_dim
+        self.kv_c_dim = kv_c_dim
+        self.k_pe_dim = k_pe_dim
+
+        self.proj = torch.nn.Linear(hidden_size, q_dim + self.kv_dim, bias=False)
+        self.q_norm = RMSNorm(q_dim, eps=eps)
+        self.kv_norm = RMSNorm(kv_c_dim, eps=eps)
+        self.q_b_proj = torch.nn.Linear(q_dim, hidden_size, bias=False)
+
+    def forward(
+        self, x: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        # Avoid graph input being a direct arg to a matched pattern node
+        x = torch.relu(x)
+
+        projected = self.proj(x)
+
+        q_c, kv_lora = projected.split([self.q_dim, self.kv_dim], dim=-1)
+        kv_c, k_pe = kv_lora.split([self.kv_c_dim, self.k_pe_dim], dim=-1)
+
+        q_normed = self.q_norm(q_c)
+        kv_normed = self.kv_norm(kv_c)
+
+        q_out = self.q_b_proj(q_normed)
+        return q_out, kv_normed, k_pe
+
+    def ops_in_model_before(self):
+        return [torch.ops.vllm_ir.rms_norm.default]
+
+    def ops_in_model_after(self):
+        return [torch.ops.vllm.fused_mla_dual_rms_norm.default]
+
+
+@pytest.mark.parametrize("dtype", [torch.bfloat16])
+@pytest.mark.parametrize("hidden_size", [7168])
+@pytest.mark.skipif(
+    not is_aiter_found_and_supported(),
+    reason="Only test on ROCm with AITER installed and supported",
+)
+def test_fuse_mla_dual_rms_norm(
+    dtype: torch.dtype,
+    hidden_size: int,
+    monkeypatch: pytest.MonkeyPatch,
+):
+    torch._dynamo.reset()
+
+    vllm_config = VllmConfig(
+        model_config=ModelConfig(dtype=dtype),
+        compilation_config=CompilationConfig(
+            mode=CompilationMode.VLLM_COMPILE,
+            custom_ops=["+rms_norm"],
+            pass_config=PassConfig(
+                fuse_mla_dual_rms_norm=True,
+                eliminate_noops=True,
+            ),
+        ),
+    )
+
+    with vllm.config.set_current_vllm_config(vllm_config), monkeypatch.context() as m:
+        from vllm.compilation.passes.fusion.rocm_aiter_fusion import (
+            MLADualRMSNormFusionPass,
+        )
+
+        torch.set_default_device("cuda")
+        torch.set_default_dtype(dtype)
+        torch.manual_seed(42)
+
+        m.setenv("VLLM_ROCM_USE_AITER", "1")
+        rocm_aiter_ops.refresh_env_variables()
+
+        fusion_pass = MLADualRMSNormFusionPass(vllm_config)
+        passes = [
+            NoOpEliminationPass(vllm_config),
+            fusion_pass,
+            PostCleanupPass(vllm_config),
+        ]
+        backend = TestBackend(*passes)
+        model = MLADualRMSNormTestModel(hidden_size)
+
+        x = torch.randn(1, hidden_size)
+        torch._dynamo.mark_dynamic(x, 0)
+
+        outputs_unfused = model(x)
+
+        model_fused = torch.compile(model, backend=backend)
+        outputs_fused = model_fused(x)
+
+        torch.testing.assert_close(outputs_unfused, outputs_fused, atol=1e-2, rtol=1e-2)
+
+        assert fusion_pass.matched_count == 1, (
+            f"Expected 1 fused pair, got {fusion_pass.matched_count}"
+        )
+
+        backend.check_before_ops(model.ops_in_model_before())
+        backend.check_after_ops(model.ops_in_model_after())
diff --git a/tests/compile/passes/test_fusion.py b/tests/compile/passes/test_fusion.py
index 79e63efdfe40..2feb0bc4f787 100644
--- a/tests/compile/passes/test_fusion.py
+++ b/tests/compile/passes/test_fusion.py
@@ -17,7 +17,6 @@
     FusedRMSQuantKey,
     RMSNormQuantFusionPass,
 )
-from vllm.compilation.passes.fx_utils import find_op_nodes
 from vllm.compilation.passes.utility.noop_elimination import NoOpEliminationPass
 from vllm.compilation.passes.utility.post_cleanup import PostCleanupPass
 from vllm.config import (
@@ -51,6 +50,7 @@
 )
 from vllm.platforms import current_platform
 from vllm.utils.deep_gemm import (
+    is_deep_gemm_e8m0_used,
     is_deep_gemm_supported,
 )
 
@@ -242,9 +242,10 @@ def ops_in_model_after(self):
         ]
 
     def ops_in_model_before_partial(self):
-        return [torch.ops.vllm_ir.rms_norm] + (
-            [RMS_ADD_OP] if self.enable_rms_norm_custom_op else [torch.ops.aten.rsqrt]
-        )
+        return [
+            torch.ops.vllm_ir.rms_norm,
+            torch.ops.vllm_ir.fused_add_rms_norm.default,
+        ]
 
 
 def _run_fusion_test(
@@ -317,6 +318,26 @@ def test_fusion_rmsnorm_quant(
     ):
         pytest.skip("Unsupported group shape 64 for CUTLASS/DeepGemm")
 
+    # TODO(quant-rms-fusion): DeepGEMM UE8M0 activation quant on B200 lowers
+    # to a packed int32-scale op (per_token_group_quant_fp8_packed_for_deepgemm),
+    # but the rms+quant fusion pattern only matches the fp32-scale variant, so
+    # the fused output gets a mismatched scale layout and produces NaN. Only
+    # reproduces on bf16 (DeepGEMM UE8M0 on B200 is bf16-only).
+    # To re-enable: make rms_norm_per_block_quant emit packed UE8M0 scales
+    # and extend the fusion pattern to rewrite the packed activation quant.
+    deepgemm_kernels = (
+        DeepGemmFp8BlockScaledMMKernel,
+        FlashInferFp8DeepGEMMDynamicBlockScaledKernel,
+    )
+    if (
+        dtype == torch.bfloat16
+        and force_kernel in deepgemm_kernels
+        and is_deep_gemm_e8m0_used()
+    ):
+        pytest.skip(
+            "rms+quant fusion does not yet match the packed UE8M0 DeepGEMM path"
+        )
+
     custom_ops = []
     if enable_rms_norm_custom_op:
         custom_ops.append("+rms_norm")
@@ -362,17 +383,6 @@ def test_fusion_rmsnorm_quant(
             model.ops_in_model_before_partial(), fully_replaced=False
         )
 
-        # If RMSNorm custom op is disabled (native/torch impl used),
-        # there's a risk that the fused add doesn't get included in the
-        # replacement and only the rms part gets fused with quant.
-        # Hence, we check only 2 add nodes are left (final fused rmsnorm add).
-        if not enable_rms_norm_custom_op:
-            n_add_nodes = lambda g: sum(1 for _ in find_op_nodes(torch.ops.aten.add, g))
-            # rms_norm is IR, not included
-            # 6 = 3x2 (3xRMS_ADD, 2 each)
-            assert n_add_nodes(backend.graph_pre_pass) == 6
-            assert n_add_nodes(backend.graph_post_pass) == 2
-
 
 @pytest.mark.parametrize("dtype", [torch.bfloat16])
 @pytest.mark.parametrize("hidden_size", [256])
diff --git a/tests/compile/passes/test_fusion_attn.py b/tests/compile/passes/test_fusion_attn.py
index 40904f2db11b..b776f6af98a1 100644
--- a/tests/compile/passes/test_fusion_attn.py
+++ b/tests/compile/passes/test_fusion_attn.py
@@ -41,6 +41,7 @@
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
 from vllm.v1.kv_cache_interface import AttentionSpec, get_kv_quant_mode
 
+DEVICE_TYPE = current_platform.device_type
 FP8_DTYPE = current_platform.fp8_dtype()
 FP4_DTYPE = torch.uint8
 
@@ -300,7 +301,7 @@ def test_attention_quant_pattern(
 
     custom_ops_list = custom_ops.split(",") if custom_ops else []
 
-    device = torch.device("cuda:0")
+    device = torch.device(f"{DEVICE_TYPE}:0")
     torch.set_default_dtype(dtype)
     torch.manual_seed(42)
 
diff --git a/tests/compile/passes/test_mla_attn_quant_fusion.py b/tests/compile/passes/test_mla_attn_quant_fusion.py
index ce1fa642ad51..0a38ffca483a 100644
--- a/tests/compile/passes/test_mla_attn_quant_fusion.py
+++ b/tests/compile/passes/test_mla_attn_quant_fusion.py
@@ -29,12 +29,18 @@
     set_current_vllm_config,
 )
 from vllm.forward_context import get_forward_context, set_forward_context
+from vllm.model_executor.kernels.linear.scaled_mm.cutlass import (
+    CutlassFp8BlockScaledMMKernel,
+)
 from vllm.model_executor.layers.attention import MLAAttention
 from vllm.model_executor.layers.linear import ColumnParallelLinear
 from vllm.model_executor.layers.quantization.fp8 import Fp8Config
 from vllm.model_executor.layers.quantization.modelopt import ModelOptNvFp4Config
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape,
     QuantKey,
+    create_fp8_quant_key,
+    kFp8Dynamic128Sym,
     kFp8StaticTensorSym,
     kNvfp4Dynamic,
 )
@@ -45,6 +51,7 @@
 
 FP8_DTYPE = current_platform.fp8_dtype()
 FP4_DTYPE = torch.uint8
+DEVICE_TYPE = current_platform.device_type
 
 
 class MLAAttentionQuantPatternModel(torch.nn.Module):
@@ -76,10 +83,6 @@ def __init__(
         self.vllm_config = vllm_config
         self.dtype = vllm_config.model_config.dtype
 
-        # Create kv_b_proj (ColumnParallelLinear) on device.
-        # Reuse weights from prior model instance when available, because
-        # ColumnParallelLinear may get NaN from recycled CUDA memory after
-        # torch.compile runs in the same process.
         kv_b_proj = ColumnParallelLinear(
             input_size=kv_lora_rank,
             output_size=num_heads * (qk_nope_head_dim + v_head_dim),
@@ -89,8 +92,7 @@ def __init__(
         kv_b_proj_weight = kwargs.get("kv_b_proj_weight")
         if kv_b_proj_weight is not None:
             kv_b_proj.weight.data.copy_(kv_b_proj_weight)
-        elif kv_b_proj.weight.data.isnan().any():
-            # Sanitize NaN from recycled CUDA memory
+        else:
             kv_b_proj.weight.data.normal_()
 
         # Create MLAAttention
@@ -278,6 +280,67 @@ def forward(
         )
 
 
+class TestMLAAttentionFp8GroupQuantPatternModel(MLAAttentionQuantPatternModel):
+    """Test model for MLA Attention + per-group FP8 (block quant) fusion."""
+
+    quant_key = kFp8Dynamic128Sym
+    quant_config = Fp8Config(
+        is_checkpoint_fp8_serialized=True,
+        weight_block_size=[128, 128],
+    )
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        weight_quant_key = create_fp8_quant_key(
+            static=True, group_shape=GroupShape(128, 128)
+        )
+        device = kwargs.get("device", torch.device("cuda:0"))
+
+        # Subclass to set weight_block_size before process_weights_after_loading
+        class _BlockFP8Layer(TestFP8Layer):
+            def __init__(self, *a, **kw):
+                self.weight_block_size = [128, 128]
+                super().__init__(*a, **kw)
+
+        # Force CutlassFp8BlockScaledMMKernel to ensure the graph uses
+        # per_token_group_fp8_quant (not the deepgemm packed variant).
+        self.block_fp8_linear = _BlockFP8Layer(
+            weight_shape=(self.output_dim, self.output_dim),
+            activation_quant_key=self.quant_key,
+            weight_quant_key=weight_quant_key,
+            input_dtype=self.dtype,
+            device=device,
+            force_kernel=CutlassFp8BlockScaledMMKernel,
+        )
+
+        w = kwargs.get("w")
+        if w is not None:
+            self.block_fp8_linear.weight = w["weight"]
+            # Block-wise uses weight_scale_inv, not weight_scale
+            self.block_fp8_linear.weight_scale_inv = w["wscale"]
+
+        self.w = {
+            "weight": self.block_fp8_linear.weight,
+            "wscale": self.block_fp8_linear.weight_scale_inv,
+        }
+
+    def forward(
+        self,
+        q: torch.Tensor,
+        kv_c_normed: torch.Tensor,
+        k_pe: torch.Tensor,
+    ):
+        """Forward pass: MLA attention -> block FP8 linear (group quant)."""
+        attn_output = self.mla_attn(
+            q,
+            kv_c_normed,
+            k_pe,
+            output_shape=(q.shape[0], self.output_dim),
+        )
+        return self.block_fp8_linear(attn_output)
+
+
 def is_nvfp4_supported():
     return current_platform.has_device_capability(100)
 
@@ -285,6 +348,7 @@ def is_nvfp4_supported():
 # MLA test configuration
 MLA_DIMS: list[tuple[int, int, int, int, int]] = []
 PATTERN_TEST_MODELS_MLA_FP8: list[tuple[str, type]] = []
+PATTERN_TEST_MODELS_MLA_GROUP_FP8: list[tuple[str, type]] = []
 PATTERN_TEST_MODELS_MLA_FP4: list[tuple[str, type]] = []
 BACKENDS_MLA_FP8: list[AttentionBackendEnum] = []
 BACKENDS_MLA_FP4: list[AttentionBackendEnum] = []
@@ -298,6 +362,12 @@ def is_nvfp4_supported():
             TestMLAAttentionFp8StaticQuantPatternModel,
         )
     ]
+    PATTERN_TEST_MODELS_MLA_GROUP_FP8 = [
+        (
+            "deepseek-ai/DeepSeek-V3",
+            TestMLAAttentionFp8GroupQuantPatternModel,
+        )
+    ]
     PATTERN_TEST_MODELS_MLA_FP4 = [
         (
             "deepseek-ai/DeepSeek-V2-Lite",
@@ -323,6 +393,13 @@ def is_nvfp4_supported():
             ["+quant_fp8", "-quant_fp8"],
         )
     )
+    + list(
+        flat_product(
+            BACKENDS_MLA_FP8,
+            PATTERN_TEST_MODELS_MLA_GROUP_FP8,
+            ["+quant_fp8"],
+        )
+    )
     + list(flat_product(BACKENDS_MLA_FP4, PATTERN_TEST_MODELS_MLA_FP4, [""])),
 )
 @pytest.mark.skipif(
@@ -356,7 +433,7 @@ def test_mla_attention_quant_pattern(
 
     custom_ops_list = custom_ops.split(",") if custom_ops else []
 
-    device = torch.device("cuda:0")
+    device = torch.device(f"{DEVICE_TYPE}:0")
     torch.set_default_dtype(dtype)
     torch.manual_seed(42)
 
@@ -469,12 +546,13 @@ def test_mla_attention_quant_pattern(
     )
 
     # Check quantization ops in the graph
+    is_per_group = quant_key.scale.group_shape.is_per_group()
     quant_op = (
         torch.ops.aten.reciprocal
         if "-quant_fp8" in custom_ops_list
         else QUANT_OPS[quant_key]
     )
-    test_backend.check_before_ops([quant_op], fully_replaced=quant_key is kNvfp4Dynamic)
+    test_backend.check_before_ops([quant_op], fully_replaced=is_per_group)
 
     assert attn_pass.pass_.matched_count == sum(attn_fusion_supported)
 
@@ -486,25 +564,24 @@ def test_mla_attention_quant_pattern(
     assert len(attn_nodes_pre) == len(attn_nodes_post), (
         "Should have same number of MLA attention nodes before and after fusion"
     )
-    assert attn_nodes_pre[0].kwargs.get("output_scale") is None, (
-        "MLA attention should not have output_scale before fusion"
-    )
-    assert attn_nodes_post[0].kwargs.get("output_scale") is not None, (
-        "MLA attention should have output_scale after fusion"
-    )
 
-    assert attn_nodes_pre[0].kwargs.get("output_block_scale") is None, (
-        "MLA attention should not have output_block_scale before fusion"
-    )
+    # Before fusion: neither scale should be set
+    assert attn_nodes_pre[0].kwargs.get("output_scale") is None
+    assert attn_nodes_pre[0].kwargs.get("output_block_scale") is None
 
-    if quant_key.dtype == FP8_DTYPE:
-        assert attn_nodes_post[0].kwargs.get("output_block_scale") is None, (
-            "MLA attention should not have output_block_scale after FP8 fusion"
-        )
-    elif quant_key.dtype == FP4_DTYPE:
-        assert attn_nodes_post[0].kwargs.get("output_block_scale") is not None, (
-            "MLA attention should have output_block_scale after FP4 fusion"
-        )
+    # After fusion: derive expected scale presence from quant_key properties.
+    # - output_scale: present for static quant or non-FP8 (NVFP4 carries input_scale)
+    # - output_block_scale: present when quant uses per-group/block scaling
+    has_output_scale = attn_nodes_post[0].kwargs.get("output_scale") is not None
+    has_block_scale = attn_nodes_post[0].kwargs.get("output_block_scale") is not None
+
+    expects_output_scale = quant_key.scale.static or quant_key.dtype != FP8_DTYPE
+    assert has_output_scale == expects_output_scale, (
+        f"output_scale: expected present={expects_output_scale}, got {has_output_scale}"
+    )
+    assert has_block_scale == is_per_group, (
+        f"output_block_scale: expected present={is_per_group}, got {has_block_scale}"
+    )
 
     # Check numerical correctness
     torch.testing.assert_close(result_unfused, result_fused, atol=1e-2, rtol=1e-2)
diff --git a/tests/compile/passes/test_noop_elimination.py b/tests/compile/passes/test_noop_elimination.py
index 412e8056f9cc..c31acfaf7238 100644
--- a/tests/compile/passes/test_noop_elimination.py
+++ b/tests/compile/passes/test_noop_elimination.py
@@ -8,6 +8,9 @@
 from tests.compile.backend import TestBackend
 from vllm.compilation.passes.utility.noop_elimination import NoOpEliminationPass
 from vllm.config import CompilationConfig, CompilationMode, PassConfig, VllmConfig
+from vllm.platforms import current_platform
+
+DEVICE_TYPE = current_platform.device_type
 
 
 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32])
@@ -17,7 +20,7 @@
 )
 @pytest.mark.parametrize("hidden_size", [64, 4096])
 def test_noop_elimination(dtype, num_tokens, hidden_size, buffer_size):
-    torch.set_default_device("cuda")
+    torch.set_default_device(DEVICE_TYPE)
     torch.set_default_dtype(dtype)
     torch.manual_seed(1)
 
@@ -88,7 +91,7 @@ def test_non_noop_slice_preserved():
     Regression test for a bug where end=-1 was treated like an inferred
     dimension (reshape semantics) leading to incorrect elimination.
     """
-    torch.set_default_device("cuda")
+    torch.set_default_device(DEVICE_TYPE)
     x = torch.randn(16, 16)
 
     class SliceModel(torch.nn.Module):
diff --git a/tests/compile/passes/test_scatter_split_replace.py b/tests/compile/passes/test_scatter_split_replace.py
index 659960896403..e85fd9f9efcd 100644
--- a/tests/compile/passes/test_scatter_split_replace.py
+++ b/tests/compile/passes/test_scatter_split_replace.py
@@ -13,6 +13,9 @@
 from vllm.compilation.passes.utility.split_coalescing import SplitCoalescingPass
 from vllm.config import CompilationConfig, CompilationMode, VllmConfig
 from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
+from vllm.platforms import current_platform
+
+DEVICE_TYPE = current_platform.device_type
 
 
 class ScatterSplitReplacementModel(nn.Module):
@@ -61,7 +64,7 @@ def ops_in_model_after(self) -> list[torch._ops.OpOverload]:
 
 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
 def test_scatter_split_replace(dtype):
-    torch.set_default_device("cuda")
+    torch.set_default_device(DEVICE_TYPE)
     torch.set_default_dtype(dtype)
     torch.manual_seed(0)
 
diff --git a/tests/compile/passes/test_silu_mul_quant_fusion.py b/tests/compile/passes/test_silu_mul_quant_fusion.py
index f3d800b2815c..bc134ed427a1 100644
--- a/tests/compile/passes/test_silu_mul_quant_fusion.py
+++ b/tests/compile/passes/test_silu_mul_quant_fusion.py
@@ -10,7 +10,7 @@
 from tests.compile.backend import TestBackend
 from tests.kernels.quantization.nvfp4_utils import quant_nvfp4_tensor
 from tests.utils import TestFP8Layer
-from vllm._aiter_ops import IS_AITER_FOUND
+from vllm._aiter_ops import IS_AITER_FOUND, rocm_aiter_ops
 from vllm._custom_ops import cutlass_scaled_fp4_mm, scaled_fp4_quant
 from vllm.compilation.passes.fusion.act_quant_fusion import (
     FUSED_OPS,
@@ -157,23 +157,27 @@ def __init__(self, hidden_size: int, dtype: torch.dtype, **kwargs):
             activation_quant_key=self.act_quant_key,
             input_dtype=dtype,
         )
-        self.w = torch.rand(hidden_size, hidden_size).to(dtype=FP8_DTYPE).t()
 
-        scale_hidden_size = (hidden_size + 128 - 1) // 128
-        self.wscale = torch.rand(
-            (scale_hidden_size, scale_hidden_size), dtype=torch.float32
-        )
+        if not current_platform.is_fp8_fnuz():
+            kernel = self.w8a8_block_fp8_linear.kernel
+            orig_quant = kernel.quant_fp8
+            kernel.quant_fp8 = lambda *a, use_triton=False, **kw: orig_quant(
+                *a, use_triton=True, **kw
+            )
 
         self.enable_silu_mul_custom_op = self.silu_and_mul.enabled()
 
     def forward(self, x):
         y = self.silu_and_mul(x)
-        x2 = self.w8a8_block_fp8_linear(y, self.w, self.wscale)
+        x2 = self.w8a8_block_fp8_linear(y)
         return x2
 
     def ops_in_model_before(self):
         return [
             SILU_MUL_OP if self.enable_silu_mul_custom_op else torch.ops.aten.mul,
+            rocm_aiter_ops.get_group_quant_op()
+            if current_platform.is_fp8_fnuz()
+            else torch.ops.vllm.triton_per_token_group_quant_fp8.default,
         ]
 
     def ops_in_model_after(self):
@@ -324,7 +328,6 @@ def test_fusion_silu_and_mul_quant(
     with set_current_vllm_config(config), monkeypatch.context() as m:
         fusion_passes = [ActivationQuantFusionPass(config)]
         if IS_AITER_FOUND and model_class is TestSiluMulGroupFp8QuantModel:
-            from vllm._aiter_ops import rocm_aiter_ops
             from vllm.compilation.passes.fusion.rocm_aiter_fusion import (
                 RocmAiterSiluMulFp8GroupQuantFusionPass,
             )
@@ -352,10 +355,16 @@ def test_fusion_silu_and_mul_quant(
             atol, rtol = 1e-3, 1e-3
         elif isinstance(model, TestSiluMulNvfp4QuantModel):
             atol, rtol = 1e-1, 1e-1
-        elif isinstance(
-            model, (TestSiluMulGroupFp8QuantModel, TestSiluMulBlockQuantModel)
-        ):
+        elif isinstance(model, TestSiluMulGroupFp8QuantModel):
             atol, rtol = 5e-2, 5e-2
+        elif isinstance(model, TestSiluMulBlockQuantModel):
+            if current_platform.is_rocm():
+                atol, rtol = 1e-3, 1e-3
+            else:
+                # CUDA fused kernel computes silu*mul in fp32 while the reference
+                # goes through bf16/fp16 storage, so group maxima (and thus scales)
+                # can shift by one FP8-e4m3 code (~1/8 relative step).
+                atol, rtol = 5e-2, 5e-2
 
         torch.testing.assert_close(
             result[0].to(dtype=dtype), result2[0].to(dtype=dtype), atol=atol, rtol=rtol
diff --git a/tests/compile/passes/test_split_coalescing.py b/tests/compile/passes/test_split_coalescing.py
index a217a4af9f29..ab7a0be1a215 100644
--- a/tests/compile/passes/test_split_coalescing.py
+++ b/tests/compile/passes/test_split_coalescing.py
@@ -8,6 +8,9 @@
 from tests.compile.backend import TestBackend
 from vllm.compilation.passes.utility.split_coalescing import SplitCoalescingPass
 from vllm.config import CompilationConfig, CompilationMode, PassConfig, VllmConfig
+from vllm.platforms import current_platform
+
+DEVICE_TYPE = current_platform.device_type
 
 
 class SplitCoalescingModel(torch.nn.Module):
@@ -28,7 +31,7 @@ def forward(self, qkv: torch.Tensor):
 
 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
 def test_split_coalescing(dtype):
-    torch.set_default_device("cuda")
+    torch.set_default_device(DEVICE_TYPE)
     torch.set_default_dtype(dtype)
     torch.manual_seed(0)
 
diff --git a/tests/compile/passes/test_vllm_fusion_pattern_matcher_pass.py b/tests/compile/passes/test_vllm_fusion_pattern_matcher_pass.py
new file mode 100644
index 000000000000..381c215a9ae1
--- /dev/null
+++ b/tests/compile/passes/test_vllm_fusion_pattern_matcher_pass.py
@@ -0,0 +1,126 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+import vllm.config
+from tests.compile.backend import TestBackend
+from vllm.compilation.passes.vllm_inductor_pass import (
+    VllmFusionPatternMatcherPass,
+    VllmPatternMatcherPass,
+    VllmPatternReplacement,
+)
+from vllm.config import CompilationConfig, CompilationMode, VllmConfig
+from vllm.platforms import current_platform
+
+
+class ReluToAbsPattern(VllmPatternReplacement):
+    """Replaces relu(x) with abs(x) — a minimal test fixture."""
+
+    @property
+    def pattern(self):
+        def _pattern(x: torch.Tensor) -> torch.Tensor:
+            return torch.ops.aten.relu.default(x)
+
+        return _pattern
+
+    @property
+    def replacement(self):
+        def _replacement(x: torch.Tensor) -> torch.Tensor:
+            return torch.ops.aten.abs.default(x)
+
+        return _replacement
+
+    def get_inputs(self) -> list[torch.Tensor]:
+        return [self.empty_fp32(4)]
+
+
+class ExpToSqrtPattern(VllmPatternReplacement):
+    """A second distinct pattern type — used to test uuid differentiation."""
+
+    @property
+    def pattern(self):
+        def _pattern(x: torch.Tensor) -> torch.Tensor:
+            return torch.ops.aten.exp.default(x)
+
+        return _pattern
+
+    @property
+    def replacement(self):
+        def _replacement(x: torch.Tensor) -> torch.Tensor:
+            return torch.ops.aten.sqrt.default(x)
+
+        return _replacement
+
+    def get_inputs(self) -> list[torch.Tensor]:
+        return [self.empty_fp32(4)]
+
+
+class ReluFusionPass(VllmFusionPatternMatcherPass):
+    def __init__(self, config: VllmConfig) -> None:
+        super().__init__(config, "test_relu_fusion")
+        self.register(ReluToAbsPattern())
+
+
+class TwoPatternFusionPass(VllmFusionPatternMatcherPass):
+    def __init__(self, config: VllmConfig) -> None:
+        super().__init__(config, "test_two_pattern_fusion")
+        self.register(ReluToAbsPattern())
+        self.register(ExpToSqrtPattern())
+
+
+@pytest.fixture
+def vllm_config():
+    return VllmConfig(
+        compilation_config=CompilationConfig(mode=CompilationMode.VLLM_COMPILE),
+    )
+
+
+@pytest.mark.skipif(not current_platform.is_cuda_alike(), reason="Requires CUDA")
+def test_register_tracks_patterns(vllm_config):
+    """register() appends each VllmPatternReplacement to _pattern_replacements."""
+    with vllm.config.set_current_vllm_config(vllm_config):
+        single = ReluFusionPass(vllm_config)
+        two = TwoPatternFusionPass(vllm_config)
+
+    assert len(single._pattern_replacements) == 1
+    assert len(two._pattern_replacements) == 2
+
+
+@pytest.mark.skipif(not current_platform.is_cuda_alike(), reason="Requires CUDA")
+def test_uuid_stable(vllm_config):
+    """Two instances of the same pass class produce identical uuids."""
+    with vllm.config.set_current_vllm_config(vllm_config):
+        p1 = ReluFusionPass(vllm_config)
+        p2 = ReluFusionPass(vllm_config)
+        p3 = TwoPatternFusionPass(vllm_config)
+
+    assert p1.uuid() == p2.uuid()
+    assert p1.uuid() != p3.uuid()
+    assert p2.uuid() != p3.uuid()
+
+
+@pytest.mark.skipif(not current_platform.is_cuda_alike(), reason="Requires CUDA")
+@pytest.mark.parametrize("N", [1, 2, 4])
+def test_matched_count_and_match_table(vllm_config, N):
+    """matched_count and match_table reflect the number of matched patterns."""
+
+    class Model(torch.nn.Module):
+        def forward(self, *inputs):
+            # N independent relus
+            return sum(torch.relu(x) for x in inputs)
+
+    with vllm.config.set_current_vllm_config(vllm_config):
+        torch.set_default_device("cuda")
+        torch.set_default_dtype(torch.float32)
+
+        fusion_pass = ReluFusionPass(vllm_config)
+        backend = TestBackend(fusion_pass)
+        model = torch.compile(Model(), backend=backend)
+
+        inputs = [torch.rand(8) for _ in range(N)]
+        model(*inputs)
+
+    assert fusion_pass.matched_count == N
+    assert VllmPatternMatcherPass.match_table["test_relu_fusion"] >= N
diff --git a/tests/compile/test_aot_compile.py b/tests/compile/test_aot_compile.py
index c3a065c56142..13e988307047 100644
--- a/tests/compile/test_aot_compile.py
+++ b/tests/compile/test_aot_compile.py
@@ -1,9 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import functools
 import hashlib
-import multiprocessing
 import os
 import pickle
 import tempfile
@@ -15,7 +13,6 @@
 import torch
 
 import vllm.envs as envs
-import vllm.model_executor.layers.activation
 from vllm.compilation.backends import VllmBackend
 from vllm.compilation.caching import (
     StandaloneCompiledArtifacts,
@@ -476,64 +473,57 @@ def test_standalone_compile_correctness():
 @create_new_process_for_each_test("spawn")
 def test_gpt2_cache_hit(monkeypatch: pytest.MonkeyPatch):
     """
-    Test that compiling gpt2 twice results in a cache hit and
-    capture torch dynamic symbol creations to ensure make_symbol
-    not called on cache hit.
-    """
+    Test that compiling gpt2 twice results in a cache hit.
 
-    import torch.fx.experimental.symbolic_shapes as symbolic_shapes_module
-    from torch.utils._sympy.symbol import make_symbol
+    Counter values are read from the EngineCore subprocess via
+    ``LLM.collective_rpc`` so the test works under default V1
+    multiprocessing (no shared memory between test and engine).
+    """
 
     from vllm import LLM
 
-    create_symbol_counter = multiprocessing.Value("i", 0)
-    original_make_symbol = make_symbol
+    def _snap(self):
+        from vllm.compilation.counter import compilation_counter
 
-    @functools.wraps(original_make_symbol)
-    def counting_make_symbol(prefix, idx, **kwargs):
-        with create_symbol_counter.get_lock():
-            create_symbol_counter.value += 1
-        return original_make_symbol(prefix, idx, **kwargs)
-
-    symbolic_shapes_module.make_symbol = counting_make_symbol
-    try:
-        with monkeypatch.context() as m, tempfile.TemporaryDirectory() as tmpdirname:
-            m.setenv("VLLM_CACHE_ROOT", tmpdirname)
-            m.setenv("VLLM_USE_AOT_COMPILE", "1")
-            # First compilation - initialize model and generate
-            llm_model = LLM(
-                model="gpt2",
-                compilation_config=CompilationConfig(
-                    mode=CompilationMode.VLLM_COMPILE,
-                ),
-                max_model_len=256,
-            )
+        return (
+            compilation_counter.num_aot_compiles,
+            compilation_counter.num_aot_artifacts_saved,
+            compilation_counter.num_aot_artifacts_loaded,
+        )
 
-            llm_model.generate("Hello, my name is")
-            assert create_symbol_counter.value == 2
-            create_symbol_counter.value = 0
+    # collective_rpc(callable) requires pickle-based serialization.
+    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
 
-            # Clean up first model
-            del llm_model
-            disable_envs_cache()
-            vllm.model_executor.layers.activation._ACTIVATION_REGISTRY._dict.clear()
+    with monkeypatch.context() as m, tempfile.TemporaryDirectory() as tmpdirname:
+        m.setenv("VLLM_CACHE_ROOT", tmpdirname)
+        m.setenv("VLLM_USE_AOT_COMPILE", "1")
+        # First compilation - initialize model and generate
+        llm_model = LLM(
+            model="gpt2",
+            compilation_config=CompilationConfig(
+                mode=CompilationMode.VLLM_COMPILE,
+            ),
+            max_model_len=256,
+        )
 
-            # Second compilation - should hit cache
-            m.setenv("VLLM_FORCE_AOT_LOAD", "1")
-            llm_model = LLM(
-                model="gpt2",
-                compilation_config=CompilationConfig(
-                    mode=CompilationMode.VLLM_COMPILE,
-                ),
-                max_model_len=256,
-            )
-            llm_model.generate("Hello, my name is")
+        llm_model.generate("Hello, my name is")
+        assert llm_model.collective_rpc(_snap)[0] == (1, 1, 0)
 
-            assert create_symbol_counter.value == 0
+        # Clean up first model
+        del llm_model
+        disable_envs_cache()
 
-    finally:
-        # Restore original method
-        symbolic_shapes_module.make_symbol = original_make_symbol
+        # Second compilation - should hit cache
+        m.setenv("VLLM_FORCE_AOT_LOAD", "1")
+        llm_model = LLM(
+            model="gpt2",
+            compilation_config=CompilationConfig(
+                mode=CompilationMode.VLLM_COMPILE,
+            ),
+            max_model_len=256,
+        )
+        llm_model.generate("Hello, my name is")
+        assert llm_model.collective_rpc(_snap)[0] == (0, 0, 1)
 
 
 @pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
diff --git a/tests/compile/test_codegen.py b/tests/compile/test_codegen.py
new file mode 100644
index 000000000000..21db287a2478
--- /dev/null
+++ b/tests/compile/test_codegen.py
@@ -0,0 +1,376 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for vllm.compilation.codegen — execution code generation.
+
+Each test runs a real Python function through the same pipeline vLLM uses
+in production: ``make_fx`` to obtain an aten-level fx graph, ``split_graph``
+to split it into the stitching layer + submodules, and then
+``generate_execution_code``/``compile_execution_fn`` for codegen.
+"""
+
+from collections.abc import Callable
+
+import pytest
+import regex as re
+import torch
+import torch.fx as fx
+from torch.fx.experimental.proxy_tensor import make_fx
+
+from vllm.compilation.backends import split_graph
+from vllm.compilation.codegen import (
+    _node_ref,
+    compile_execution_fn,
+    generate_execution_code,
+    generate_execution_code_with_name,
+)
+from vllm.utils.torch_utils import is_torch_equal_or_newer
+
+
+def _trace_and_split(
+    model_fn: Callable[..., torch.Tensor],
+    example_inputs: tuple[torch.Tensor, ...],
+    split_ops: list[str],
+) -> fx.GraphModule:
+    """Trace ``model_fn`` with make_fx, then split on the named aten ops."""
+    gm = make_fx(model_fn)(*example_inputs)
+    split_gm, _ = split_graph(gm, split_ops)
+    return split_gm
+
+
+def _to_copy_model(x: torch.Tensor) -> torch.Tensor:
+    """Traces to ``aten._to_copy.default`` with device + dtype kwargs."""
+    return x.to(device=torch.device("cpu"), dtype=torch.float16)
+
+
+def _empty_model(x: torch.Tensor) -> torch.Tensor:
+    """Traces to ``aten.empty.memory_format`` with device + dtype kwargs."""
+    buf = torch.empty(x.shape, device=torch.device("cpu"), dtype=torch.float16)
+    return buf.fill_(0).add(x.to(dtype=torch.float16))
+
+
+@pytest.fixture
+def x() -> torch.Tensor:
+    return torch.zeros(2, 3)
+
+
+@pytest.mark.parametrize(
+    "model_fn,split_ops",
+    [
+        (_to_copy_model, ["aten::_to_copy.default"]),
+        (_empty_model, []),
+    ],
+    ids=["aten::_to_copy.default", "aten::empty.memory_format"],
+)
+def test_non_primitive_kwargs_lifted_to_consts(
+    model_fn: Callable[[torch.Tensor], torch.Tensor],
+    split_ops: list[str],
+    x: torch.Tensor,
+) -> None:
+    """Regression: arguments whose ``repr()`` is not a valid Python
+    expression in the generated function's namespace (notably
+    ``torch.device``) used to be inlined via ``repr()``, producing source
+    like
+
+        out = torch.ops.aten._to_copy.default(x, device=device(type='cpu'))
+
+    which fails at call time — only ``torch`` and ``operator`` are imported
+    into the namespace, so ``device`` is unbound. The fix collects such
+    objects into ``__vllm_consts__`` and references them by index. The
+    unqualified ``device(type=...)`` form must never appear in the
+    generated source."""
+    split_gm = _trace_and_split(model_fn, (x,), split_ops)
+    code, submod_names, consts = generate_execution_code(split_gm)
+
+    assert "device(type=" not in code, (
+        "Generated code contains unqualified `device(type=...)` from repr(); "
+        "torch.device should be lifted into __vllm_consts__"
+    )
+    assert torch.device("cpu") in consts, "torch.device kwarg not lifted to consts"
+    assert torch.float16 in consts, "torch.dtype kwarg not lifted to consts"
+
+    fn = compile_execution_fn(code, {}, submod_names, consts)
+    out = fn(x)
+    expected = model_fn(x)
+    assert torch.equal(out, expected), "Compiled output does not match reference"
+
+
+def test_dtype_singleton_deduped(x: torch.Tensor) -> None:
+    """``torch.float16`` is a process-wide singleton, so two ops referring
+    to it in the traced graph share a single consts slot via ``id()``-based
+    dedup. Distinct expressions (``x.to(...)`` vs ``(x*2).to(...)``) ensure
+    the tracer can't CSE the two ops into a single node."""
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        return x.to(dtype=torch.float16) + (x * 2).to(dtype=torch.float16)
+
+    split_gm = _trace_and_split(model_fn, (x,), [])
+    code, submod_names, consts = generate_execution_code(split_gm)
+
+    # The traced graph must have two distinct _to_copy nodes (otherwise the
+    # dedup assertion below is trivially satisfied).
+    n_to_copy = sum(
+        1
+        for n in split_gm.graph.nodes
+        if n.op == "call_module"
+        for sn in getattr(split_gm, n.target).graph.nodes
+        if sn.op == "call_function" and "to_copy" in sn.name
+    )
+    assert n_to_copy >= 2, (
+        f"Test setup failed: expected ≥2 _to_copy nodes, got {n_to_copy}"
+    )
+
+    assert consts.count(torch.float16) == 1, (
+        f"torch.float16 should occupy exactly one slot, got consts={consts}"
+    )
+    assert code.count("__vllm_consts__[0]") >= 2, (
+        "Deduped const slot should be referenced from both _to_copy nodes"
+    )
+
+    fn = compile_execution_fn(code, {}, submod_names, consts)
+    assert torch.equal(fn(x), model_fn(x))
+
+
+def test_distinct_dtypes_get_distinct_slots(x: torch.Tensor) -> None:
+    """Distinct dtype singletons in the traced graph occupy distinct slots."""
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        return x.to(dtype=torch.float16) + x.to(dtype=torch.bfloat16)
+
+    split_gm = _trace_and_split(model_fn, (x,), [])
+    _, _, consts = generate_execution_code(split_gm)
+
+    assert torch.float16 in consts
+    assert torch.bfloat16 in consts
+    assert len(consts) == 2, f"Expected 2 distinct dtype slots, got {consts}"
+
+
+def test_consts_ordering_deterministic(x: torch.Tensor) -> None:
+    """Two independent traces of the same model must produce equal consts
+    lists *in the same order*. Cache artifacts identify const slots by
+    index, so a non-deterministic order would invalidate cached code."""
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        # Multiple distinct non-primitives encountered in a fixed graph order.
+        a = x.to(device=torch.device("cpu"), dtype=torch.float16)
+        return a.to(dtype=torch.bfloat16)
+
+    _, _, consts1 = generate_execution_code(_trace_and_split(model_fn, (x,), []))
+    _, _, consts2 = generate_execution_code(_trace_and_split(model_fn, (x,), []))
+
+    assert len(consts1) >= 2, "Test setup: model should produce ≥2 const slots"
+    assert consts1 == consts2, (
+        f"consts ordering must be reproducible across traces; "
+        f"got {consts1} vs {consts2}"
+    )
+
+
+def test_primitive_args_inlined(x: torch.Tensor) -> None:
+    """Primitive args (int dim, etc.) stay inline as repr — no consts."""
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        return torch.transpose(x, 0, 1).relu()
+
+    split_gm = _trace_and_split(model_fn, (x,), [])
+    code, submod_names, consts = generate_execution_code(split_gm)
+
+    assert consts == [], "Primitive-only graph must produce empty consts"
+
+    fn = compile_execution_fn(code, {}, submod_names, consts)
+    assert torch.equal(fn(x), model_fn(x))
+
+
+def test_consts_shared_across_split_submods(x: torch.Tensor) -> None:
+    """Dedup must apply across inlined submodules, not just within one.
+
+    The function below splits into three inlined submods, two of which
+    independently reference ``torch.float16``. The shared ``const_index``
+    threaded through recursive ``generate_execution_code_with_name`` calls
+    must collapse the dtype to a single slot used from both submods."""
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        a = x.to(dtype=torch.float16)  # submod_0: _to_copy(fp16)
+        b = a.relu()  # submod_1: relu (split point)
+        c = b.to(dtype=torch.float32)  # submod_2: _to_copy(fp32)
+        return c.to(dtype=torch.float16) + 1  # submod_2: another _to_copy(fp16)
+
+    split_gm = _trace_and_split(model_fn, (x,), ["aten::relu.default"])
+
+    n_submods = sum(1 for _ in split_gm.named_children())
+    assert n_submods >= 3, (
+        f"Test setup failed: expected ≥3 submods after split, got {n_submods}"
+    )
+
+    code, submod_names, consts = generate_execution_code(split_gm)
+
+    assert consts.count(torch.float16) == 1, (
+        f"fp16 singleton must dedup across submods, got consts={consts}"
+    )
+
+    # Find the consts index for fp16 and confirm at least two distinct
+    # inlined submods reference it. This rules out the false-positive where
+    # one submod references it twice and the other not at all.
+    fp16_idx = consts.index(torch.float16)
+    submod_bodies = re.findall(
+        r"def __vllm_inlined_submods__(\d+)\([^)]*\):\n((?:    .*\n)+)", code
+    )
+    assert len(submod_bodies) >= 2
+    referencing_submods = [
+        name for name, body in submod_bodies if f"__vllm_consts__[{fp16_idx}]" in body
+    ]
+    assert len(referencing_submods) >= 2, (
+        f"fp16 slot should be referenced from ≥2 inlined submods, "
+        f"got {referencing_submods}"
+    )
+
+    fn = compile_execution_fn(code, {}, submod_names, consts)
+    assert torch.equal(fn(x), model_fn(x))
+
+
+def test_non_graphmodule_submod_uses_indexed_callable(x: torch.Tensor) -> None:
+    """When a child of split_gm is *not* a ``torch.fx.GraphModule`` — as
+    happens in production once ``PiecewiseBackend`` replaces submods —
+    codegen emits ``__vllm_submods__[idx](...)`` instead of inlining, and
+    the runtime callable is bound from ``submod_callables``."""
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        return x.relu().sigmoid()
+
+    split_gm = _trace_and_split(model_fn, (x,), ["aten::relu.default"])
+
+    # Find a GraphModule child and wrap it in a non-GraphModule nn.Module
+    # that delegates to the original — this is the structural shape vLLM
+    # produces after PiecewiseBackend takes over a submod.
+    child_names = [name for name, _ in split_gm.named_children()]
+    target_name = child_names[0]
+
+    class NonGMWrapper(torch.nn.Module):
+        def __init__(self, gm: fx.GraphModule) -> None:
+            super().__init__()
+            self.gm = gm
+
+        def forward(self, *args, **kwargs):
+            return self.gm(*args, **kwargs)
+
+    original = getattr(split_gm, target_name)
+    del split_gm._modules[target_name]
+    split_gm.add_module(target_name, NonGMWrapper(original))
+
+    code, submod_names, consts = generate_execution_code(split_gm)
+
+    assert "__vllm_submods__[" in code, (
+        "Non-GraphModule submod should produce an indexed callable reference"
+    )
+    assert target_name in submod_names
+
+    submod_callables = {
+        name: getattr(split_gm, name)
+        for name in submod_names
+        if not isinstance(getattr(split_gm, name), fx.GraphModule)
+    }
+    fn = compile_execution_fn(code, submod_callables, submod_names, consts)
+    assert torch.equal(fn(x), model_fn(x))
+
+
+# split_graph only passes tuple_return=True to split_module on PyTorch >= 2.12,
+# so getitem nodes only appear in the stitching graph from that version onward.
+@pytest.mark.skipif(
+    not is_torch_equal_or_newer("2.12.0.dev"),
+    reason="split_module tuple_return requires PyTorch >= 2.12",
+)
+def test_getitem_in_stitching_graph(x: torch.Tensor) -> None:
+    """``operator.getitem`` on submod tuple returns is the ``call_function``
+    special case at codegen.py — emitted as ``name = source[index]``
+    rather than a function call."""
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        return x.relu().sigmoid()
+
+    split_gm = _trace_and_split(model_fn, (x,), ["aten::relu.default"])
+    code, _, _ = generate_execution_code(split_gm)
+
+    # split_module wraps each submod return in a tuple, so the stitching
+    # graph unpacks via getitem. The codegen must emit it as indexing.
+    assert re.search(r"\b\w+ = \w+\[\d+\]\n", code), (
+        "Stitching graph should emit `name = source[N]` for getitem nodes"
+    )
+
+
+def test_del_emitted_for_intermediate_values(x: torch.Tensor) -> None:
+    """The codegen schedules ``del`` after a value's last use to free
+    memory early. Multi-submod splits naturally have intermediates whose
+    last use is not the output node."""
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        return x.relu().sigmoid().tanh()
+
+    split_gm = _trace_and_split(
+        model_fn, (x,), ["aten::relu.default", "aten::sigmoid.default"]
+    )
+    code, _, _ = generate_execution_code(split_gm)
+
+    assert re.search(r"^    del \w+", code, re.MULTILINE), (
+        "Liveness analysis should emit `del` for intermediates with "
+        "last-use before the output"
+    )
+
+
+def test_with_submod_false_rejects_call_module() -> None:
+    """``generate_execution_code_with_name(with_submod=False)`` is the
+    recursive entry for inlining a GraphModule into its parent. It must
+    refuse a graph that itself contains ``call_module`` nodes — the parent
+    is responsible for handling those."""
+    g = fx.Graph()
+    x_node = g.placeholder("x")
+    root = torch.nn.Module()
+    root.add_module("inner", torch.nn.Identity())
+    call = g.call_module("inner", args=(x_node,))
+    g.output(call)
+    gm = fx.GraphModule(root, g)
+
+    with pytest.raises(RuntimeError, match="call_module is not allowed"):
+        generate_execution_code_with_name(gm, "f", with_submod=False)
+
+
+def test_node_ref_recurses_through_containers() -> None:
+    """``_node_ref`` is the recursive walker that lifts non-primitives
+    nested inside list/tuple/dict args. Real aten ops rarely produce such
+    structures, but the path is needed for DTensor placement lists and
+    other future cases — unit-test the walker directly."""
+    consts: list = []
+    const_index: dict[int, int] = {}
+    cpu = torch.device("cpu")
+
+    # Non-primitive in a list, primitive alongside.
+    assert _node_ref([cpu, 1], consts, const_index) == "[__vllm_consts__[0], 1]"
+    assert consts == [cpu]
+
+    # Same object in a tuple — id-based dedup reuses the existing slot.
+    assert _node_ref((cpu, 2), consts, const_index) == "(__vllm_consts__[0], 2)"
+    assert consts == [cpu]
+
+    # Single-element tuple uses the trailing-comma form.
+    assert _node_ref((cpu,), consts, const_index) == "(__vllm_consts__[0],)"
+
+    # Dict value lifts the same way.
+    ref = _node_ref({"k": cpu}, consts, const_index)
+    assert ref == "{'k': __vllm_consts__[0]}"
+
+
+def test_legacy_code_without_consts() -> None:
+    """``compile_execution_fn(consts=None)`` must still load code that has
+    no ``__vllm_consts__`` reference, so older serialized cache artifacts
+    keep working."""
+    # Pre-consts codegen: no __vllm_consts__ reference, only torch/operator.
+    legacy_code = (
+        "import torch\n"
+        "def execution_fn(x, *, __vllm_submods__):\n"
+        "    return __vllm_submods__[0](x) + 1\n"
+    )
+
+    class AddOne(torch.nn.Module):
+        def forward(self, x: torch.Tensor) -> torch.Tensor:
+            return x + 1
+
+    fn = compile_execution_fn(legacy_code, {"sub": AddOne()}, ["sub"], consts=None)
+    out = fn(torch.zeros(3))
+    assert torch.equal(out, torch.full((3,), 2.0))
diff --git a/tests/compile/test_config.py b/tests/compile/test_config.py
index c0f0dcca8d71..d822b68c5036 100644
--- a/tests/compile/test_config.py
+++ b/tests/compile/test_config.py
@@ -31,6 +31,8 @@
 # This import automatically registers `torch.ops.silly.attention`
 from . import silly_attention  # noqa: F401
 
+DEVICE_TYPE = current_platform.device_type
+
 
 def test_version():
     # Test the version comparison logic using the private function
@@ -203,6 +205,22 @@ def test_enforce_eager(vllm_runner, monkeypatch):
         pass
 
 
+@pytest.mark.forked
+def test_torch_compile_disable(vllm_runner, monkeypatch):
+    monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
+    monkeypatch.setenv("TORCH_COMPILE_DISABLE", "1")
+    monkeypatch.setenv("VLLM_DISABLE_COMPILE_CACHE", "1")
+
+    with (
+        compilation_counter.expect(num_graphs_seen=0, stock_torch_compile_count=0),
+        vllm_runner(
+            "facebook/opt-125m",
+            gpu_memory_utilization=0.4,
+        ) as _,
+    ):
+        pass
+
+
 def test_splitting_ops_dynamic():
     # Default config
     config = VllmConfig()
@@ -387,9 +405,12 @@ def test_should_split():
         (None, 0, 1, False, 2048, CUDAGraphMode.NONE, 0),
         # truncated to nearest multiple of 8 or 16
         (None, 257, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 256),
+        # max_num_batched_tokens <= max_cudagraph_capture_size should always be
+        # captured even if not landing on a 16-stride step
+        (None, 2048, 1, False, 257, CUDAGraphMode.FULL_AND_PIECEWISE, 257),
         # max from list
         ([1, 2, 4, 15], None, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 15),
-        # filtered out 15 due to SP
+        # SP forces full-graph compilation, sizes are filtered by TP
         ([1, 2, 4, 15], None, 2, True, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 4),
         # limited by the max_tokens
         ([1, 2, 4, 15], None, 1, False, 8, CUDAGraphMode.FULL_AND_PIECEWISE, 4),
@@ -447,6 +468,123 @@ def test_cudagraph_sizes_post_init(
         )
 
 
+@pytest.mark.skipif(
+    not current_platform.support_static_graph_mode(),
+    reason="Skip if not cudagraph mode supported",
+)
+@pytest.mark.parametrize(
+    (
+        "cudagraph_mode",
+        "use_inductor_graph_partition",
+        "expected_enable_sp",
+        "expected_cudagraph_mode",
+        "expected_piecewise_compile",
+        "expected_capture_sizes",
+        "expected_max_size",
+    ),
+    [
+        (CUDAGraphMode.PIECEWISE, False, True, CUDAGraphMode.FULL, False, [2, 4], 4),
+        (
+            CUDAGraphMode.FULL_DECODE_ONLY,
+            False,
+            True,
+            CUDAGraphMode.FULL_DECODE_ONLY,
+            False,
+            [2, 4],
+            4,
+        ),
+        (
+            CUDAGraphMode.FULL_AND_PIECEWISE,
+            False,
+            True,
+            CUDAGraphMode.FULL,
+            False,
+            [2, 4],
+            4,
+        ),
+        (
+            CUDAGraphMode.FULL_AND_PIECEWISE,
+            True,
+            True,
+            CUDAGraphMode.FULL_AND_PIECEWISE,
+            True,
+            [2, 4],
+            4,
+        ),
+    ],
+)
+def test_sequence_parallelism_requires_full_graph_compilation(
+    cudagraph_mode: CUDAGraphMode,
+    use_inductor_graph_partition: bool,
+    expected_enable_sp: bool,
+    expected_cudagraph_mode: CUDAGraphMode,
+    expected_piecewise_compile: bool,
+    expected_capture_sizes: list[int],
+    expected_max_size: int,
+):
+    with patch.object(current_platform, "device_count", return_value=2):
+        vllm_config = VllmConfig(
+            parallel_config=ParallelConfig(tensor_parallel_size=2),
+            scheduler_config=SchedulerConfig(
+                max_num_seqs=128,
+                max_num_batched_tokens=2048,
+                max_model_len=2048,
+                is_encoder_decoder=False,
+            ),
+        )
+        vllm_config.model_config = MagicMock(
+            dtype=torch.float16,
+            enforce_eager=False,
+            is_moe=False,
+            disable_cascade_attn=False,
+            get_hidden_size=MagicMock(return_value=4096),
+        )
+        vllm_config.compilation_config = CompilationConfig(
+            mode=CompilationMode.VLLM_COMPILE,
+            cudagraph_capture_sizes=[1, 2, 4, 15],
+            max_cudagraph_capture_size=None,
+            compile_sizes=["cudagraph_capture_sizes"],
+            use_inductor_graph_partition=use_inductor_graph_partition,
+            pass_config=PassConfig(
+                enable_sp=True,
+                fuse_gemm_comms=True,
+                fuse_norm_quant=True,
+                fuse_act_quant=True,
+                eliminate_noops=True,
+                sp_min_token_num=512,
+            ),
+            cudagraph_mode=cudagraph_mode,
+        )
+        vllm_config.compilation_config.set_splitting_ops_for_v1(
+            all2all_backend=vllm_config.parallel_config.all2all_backend,
+            data_parallel_size=1,
+        )
+        vllm_config._set_compile_ranges()
+        vllm_config._set_cudagraph_sizes()
+
+    assert (
+        vllm_config.compilation_config.use_inductor_graph_partition
+        == use_inductor_graph_partition
+    )
+    assert (
+        bool(vllm_config.compilation_config.splitting_ops) == expected_piecewise_compile
+    )
+    assert vllm_config.compilation_config.pass_config.enable_sp == expected_enable_sp
+    assert (
+        vllm_config.compilation_config.pass_config.fuse_gemm_comms == expected_enable_sp
+    )
+    assert vllm_config.compilation_config.cudagraph_mode == expected_cudagraph_mode
+    assert (
+        vllm_config.compilation_config.cudagraph_capture_sizes == expected_capture_sizes
+    )
+    assert (
+        vllm_config.compilation_config.max_cudagraph_capture_size == expected_max_size
+    )
+    assert (
+        511 in vllm_config.compilation_config.compile_ranges_endpoints
+    ) == expected_enable_sp
+
+
 def test_cached_compilation_config(default_vllm_config):
     import torch
     from torch._inductor.utils import run_and_get_code
@@ -456,7 +594,7 @@ def test_cached_compilation_config(default_vllm_config):
     from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
 
     dtype = torch.bfloat16
-    device = torch.device("cuda:0")
+    device = torch.device(f"{DEVICE_TYPE}:0")
     batch_size, num_qo_heads, head_size = 8, 16, 128
 
     # access and cache default compilation config
@@ -478,7 +616,7 @@ def test_cached_compilation_config(default_vllm_config):
         query_quant = QuantFP8(static=True, group_shape=GroupShape.PER_TENSOR)
         query_quant = torch.compile(query_quant)
 
-        _q_scale = torch.tensor(1.0, dtype=torch.float32, device="cuda")
+        _q_scale = torch.tensor(1.0, dtype=torch.float32, device=DEVICE_TYPE)
         query = torch.randn(
             batch_size, num_qo_heads * head_size, dtype=dtype, device=device
         )
@@ -615,6 +753,24 @@ def test_inductor_asserts_enabled_in_debug(monkeypatch):
         assert config.inductor_compile_config.get("scalar_asserts") is True
 
 
+def test_get_inductor_factors_includes_configs():
+    """Changing inductor or functorch config must change the cache key factors."""
+    from torch._functorch import config as functorch_config
+    from torch._inductor import config as inductor_config
+
+    from vllm.compilation.compiler_interface import get_inductor_factors
+
+    baseline = get_inductor_factors()
+
+    with inductor_config.patch("max_autotune", not inductor_config.max_autotune):
+        patched = get_inductor_factors()
+    assert baseline != patched, "inductor config change was not reflected"
+
+    with functorch_config.patch("donated_buffer", not functorch_config.donated_buffer):
+        patched = get_inductor_factors()
+    assert baseline != patched, "functorch config change was not reflected"
+
+
 def test_inductor_asserts_user_override(monkeypatch):
     """Test that explicit inductor_compile_config overrides the
     debug-logging default."""
diff --git a/tests/compile/test_dynamic_shapes_compilation.py b/tests/compile/test_dynamic_shapes_compilation.py
index bbd62237c5e8..e45e5cf425fe 100644
--- a/tests/compile/test_dynamic_shapes_compilation.py
+++ b/tests/compile/test_dynamic_shapes_compilation.py
@@ -8,6 +8,7 @@
 import pytest
 import torch
 
+from tests.models.utils import check_logprobs_close
 from vllm import LLM, SamplingParams
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CompilationConfig, VllmConfig, set_current_vllm_config
@@ -17,7 +18,6 @@
     DynamicShapesType,
 )
 from vllm.forward_context import set_forward_context
-from vllm.tokenizers import get_tokenizer
 from vllm.utils.torch_utils import is_torch_equal_or_newer
 
 
@@ -28,7 +28,7 @@ def get_test_models():
         "Qwen/Qwen2-7B-Instruct",
         "meta-llama/Llama-3.1-8B",
     ]
-    if is_torch_equal_or_newer("2.12.0"):
+    if is_torch_equal_or_newer("2.12.0.dev"):
         models.append("Qwen/Qwen3-4B-Instruct-2507")
     return models
 
@@ -55,12 +55,10 @@ def test_dynamic_shapes_compilation(
     evaluate_guards,
 ):
     """Test that all dynamic shapes types compile successfully"""
-    if use_bytecode_hook and shapes_type == DynamicShapesType.UNBACKED:
-        pytest.skip("UNBACKED dynamic shapes require VLLM_USE_BYTECODE_HOOK=0")
-
     if evaluate_guards and shapes_type == DynamicShapesType.UNBACKED:
         pytest.skip("unbacked dynamic shapes do not add guards")
 
+    # TODO is this still a requirement?
     if evaluate_guards and use_aot_compile:
         pytest.skip("evaluate_guards requires use_aot_compile=0")
 
@@ -84,30 +82,37 @@ def test_dynamic_shapes_compilation(
         max_model_len=1024,
     )
 
-    output = model.generate(prompt)
-    result = output[0].outputs[0].text
-    # Example of setting the sampling parameters
-    tokenizer = get_tokenizer(model_name)
-    yes_tokens = tokenizer.encode("yes", add_special_tokens=False)
-    no_tokens = tokenizer.encode("no", add_special_tokens=False)
-    allowed_ids = list(set(yes_tokens + no_tokens))
-    sampling_params = SamplingParams(
-        max_tokens=1, temperature=0, allowed_token_ids=allowed_ids
-    )
+    sampling_params = SamplingParams(max_tokens=5, temperature=0, logprobs=10)
+    test_prompts = [prompt, "The capital of France is"]
 
-    output = model.generate(
-        "answer with yes or no is " + result + " rubbish for prompt " + prompt + "?",
-        sampling_params=sampling_params,
-    )
-    result = output[0].outputs[0].text
-    assert result == "yes"
+    compiled_outputs = []
+    for p in test_prompts:
+        output = model.generate(p, sampling_params)[0].outputs[0]
+        assert len(output.text.strip()) > 0, "Compiled model produced empty output"
+        compiled_outputs.append((output.token_ids, output.text, output.logprobs))
 
-    # Clean up GPU memory
     del model
     gc.collect()
     torch.accelerator.empty_cache()
     torch.accelerator.synchronize()
-    print("GPU memory cleared")
+
+    eager_model = LLM(model=model_name, enforce_eager=True, max_model_len=1024)
+    eager_outputs = []
+    for p in test_prompts:
+        output = eager_model.generate(p, sampling_params)[0].outputs[0]
+        assert len(output.text.strip()) > 0, "Eager model produced empty output"
+        eager_outputs.append((output.token_ids, output.text, output.logprobs))
+    del eager_model
+    gc.collect()
+    torch.accelerator.empty_cache()
+    torch.accelerator.synchronize()
+
+    check_logprobs_close(
+        outputs_0_lst=eager_outputs,
+        outputs_1_lst=compiled_outputs,
+        name_0="eager",
+        name_1="compiled",
+    )
 
 
 @pytest.mark.parametrize("use_aot_compile", ["0", "1"])
@@ -222,3 +227,47 @@ def test(model_class, input1, input2, is_01_specialization=False):
         torch.randn(1, 10).cuda(),
         is_01_specialization=True,
     )
+
+
+@pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
+def test_piecewise_backend_empty_sym_shape_indices():
+    """Test that PiecewiseBackend handles empty sym_shape_indices correctly.
+
+    When all inputs have static shapes (no torch.SymInt), sym_shape_indices
+    will be empty. The fix in PiecewiseBackend.__call__ handles this case
+    by using the first compiled range_entry.
+    """
+    gc.collect()
+    torch.accelerator.empty_cache()
+    torch.accelerator.synchronize()
+
+    # Use small max_model_len and max_num_batched_tokens to encourage
+    # static shape compilation with empty sym_shape_indices
+    llm = LLM(
+        model="Qwen/Qwen3-0.6B",
+        max_model_len=512,
+        max_num_batched_tokens=1,
+        compilation_config={
+            "mode": CompilationMode.VLLM_COMPILE,
+            "dynamic_shapes_config": {
+                "type": DynamicShapesType.BACKED.value,
+            },
+        },
+    )
+
+    sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=10)
+
+    # Generate with static shape inputs
+    output = llm.generate("Hello, my name is", sampling_params=sampling_params)
+    result = output[0].outputs[0].text
+    assert len(result) > 0, "Should generate non-empty output"
+
+    # Generate again to verify compilation works with empty sym_shape_indices
+    output = llm.generate("The capital of France is", sampling_params=sampling_params)
+    result = output[0].outputs[0].text
+    assert len(result) > 0, "Should generate non-empty output on second run"
+
+    del llm
+    gc.collect()
+    torch.accelerator.empty_cache()
+    torch.accelerator.synchronize()
diff --git a/tests/compile/test_graph_partition.py b/tests/compile/test_graph_partition.py
index 09c3bd0d10ea..4cb199b5897d 100644
--- a/tests/compile/test_graph_partition.py
+++ b/tests/compile/test_graph_partition.py
@@ -15,10 +15,13 @@
     split_graph,
 )
 from vllm.compilation.passes.fx_utils import find_op_nodes
+from vllm.platforms import current_platform
 
 # This import automatically registers `torch.ops.silly.attention`
 from . import silly_attention  # noqa: F401
 
+DEVICE_TYPE = current_platform.device_type
+
 
 def test_getitem_moved_to_producer_subgraph():
     """
@@ -151,7 +154,7 @@ def model_fn(x: torch.Tensor) -> torch.Tensor:
         final_result = torch.sigmoid(attn_inout)
         return final_result
 
-    torch.set_default_device("cuda")
+    torch.set_default_device(DEVICE_TYPE)
 
     # Create the traced FX graph for the model
     x = torch.randn(8, 4)
@@ -329,7 +332,7 @@ def model_fn(x: torch.Tensor) -> torch.Tensor:
         "Expected two builtin empty_like nodes in merged non-splitting subgraph"
     )
 
-    x = torch.randn(2, 3, device="cuda")
+    x = torch.randn(2, 3, device=DEVICE_TYPE)
     output_original = gm(x)
     output_split = split_gm(x)
     assert torch.allclose(output_original, output_split), "Output mismatch after split"
diff --git a/tests/compile/test_rotary_embedding_compile.py b/tests/compile/test_rotary_embedding_compile.py
index 76f5382534e1..69a4cc05084c 100644
--- a/tests/compile/test_rotary_embedding_compile.py
+++ b/tests/compile/test_rotary_embedding_compile.py
@@ -16,6 +16,8 @@
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.platforms import current_platform
 
+DEVICE_TYPE = current_platform.device_type
+
 
 @support_torch_compile
 class RotaryEmbeddingCompileModule(torch.nn.Module):
@@ -45,7 +47,7 @@ def test_rotary_embedding_torch_compile_with_custom_op(monkeypatch):
     monkeypatch.setenv("VLLM_USE_BYTECODE_HOOK", "1")
     monkeypatch.setenv("VLLM_USE_AOT_COMPILE", "0")
 
-    device = "cuda"
+    device = DEVICE_TYPE
     positions = torch.arange(16, device=device)
     query = torch.randn(16, 32, device=device, dtype=torch.bfloat16)
     key = torch.randn(16, 32, device=device, dtype=torch.bfloat16)
diff --git a/tests/compile/test_structured_logging.py b/tests/compile/test_structured_logging.py
index 7813b7429b1f..10b0ed139cfe 100644
--- a/tests/compile/test_structured_logging.py
+++ b/tests/compile/test_structured_logging.py
@@ -17,8 +17,10 @@
 )
 from vllm.config.scheduler import SchedulerConfig
 from vllm.forward_context import set_forward_context
+from vllm.platforms import current_platform
 
 MLP_SIZE = 64
+DEVICE_TYPE = current_platform.device_type
 
 
 @support_torch_compile
@@ -71,7 +73,7 @@ def get(self, event_type: str, name_pattern: str) -> list[dict]:
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
 def test_vllm_structured_logging_artifacts(use_fresh_inductor_cache):
     """Test that all expected vLLM artifacts are logged during compilation."""
-    torch.set_default_device("cuda")
+    torch.set_default_device(DEVICE_TYPE)
 
     capture = TraceStructuredCapture()
 
diff --git a/tests/config/base_model_arch_groundtruth.json b/tests/config/base_model_arch_groundtruth.json
index 81534886dcb6..14e3f4f49d46 100644
--- a/tests/config/base_model_arch_groundtruth.json
+++ b/tests/config/base_model_arch_groundtruth.json
@@ -356,6 +356,23 @@
         "is_multimodal_model": false,
         "dtype": "torch.float32"
     },
+    "stepfun-ai/Step-3.5-Flash": {
+        "architectures": [
+            "Step3p5ForCausalLM"
+        ],
+        "model_type": "step3p5",
+        "text_model_type": "step3p5",
+        "hidden_size": 4096,
+        "total_num_hidden_layers": 45,
+        "total_num_attention_heads": 64,
+        "head_size": 128,
+        "vocab_size": 128896,
+        "total_num_kv_heads": 8,
+        "num_experts": 288,
+        "is_deepseek_mla": false,
+        "is_multimodal_model": false,
+        "dtype": "torch.bfloat16"
+    },
     "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16": {
         "architectures": [
             "NemotronHForCausalLM"
diff --git a/tests/config/test_model_arch_config.py b/tests/config/test_model_arch_config.py
index fbae31331be8..e172983b54f4 100644
--- a/tests/config/test_model_arch_config.py
+++ b/tests/config/test_model_arch_config.py
@@ -16,6 +16,7 @@
     "nvidia/Llama-3_3-Nemotron-Super-49B-v1",
     "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
     "XiaomiMiMo/MiMo-7B-RL",
+    "stepfun-ai/Step-3.5-Flash",
     # Excluded: Not available online right now
     # "FreedomIntelligence/openPangu-Ultra-MoE-718B-V1.1",
     "meituan-longcat/LongCat-Flash-Chat",
diff --git a/tests/config/test_multimodal_config.py b/tests/config/test_multimodal_config.py
index e5c30f999a05..9720d84672fd 100644
--- a/tests/config/test_multimodal_config.py
+++ b/tests/config/test_multimodal_config.py
@@ -41,3 +41,21 @@ def test_language_model_only_affects_model_hash():
     base_hash = ModelConfig(model).compute_hash()
     lm_only_hash = ModelConfig(model, language_model_only=True).compute_hash()
     assert base_hash != lm_only_hash
+
+
+def test_mm_encoder_fp8_scale_path_requires_fp8():
+    with pytest.raises(ValueError, match="mm_encoder_attn_dtype"):
+        MultiModalConfig(mm_encoder_fp8_scale_path="/tmp/scales.json")
+
+
+def test_mm_encoder_attn_dtype_hash_updates(tmp_path):
+    scale_file = tmp_path / "scales.json"
+    scale_file.write_text("{}")
+    base_hash = MultiModalConfig().compute_hash()
+    fp8_hash = MultiModalConfig(mm_encoder_attn_dtype="fp8").compute_hash()
+    fp8_static_hash = MultiModalConfig(
+        mm_encoder_attn_dtype="fp8",
+        mm_encoder_fp8_scale_path=str(scale_file),
+    ).compute_hash()
+    assert base_hash != fp8_hash
+    assert fp8_hash != fp8_static_hash
diff --git a/tests/conftest.py b/tests/conftest.py
index a666c5a86637..779bd475f34b 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -364,12 +364,15 @@ def __init__(
         model_name: str,
         dtype: str = "auto",
         *,
+        revision: str | None = None,
         model_kwargs: dict[str, Any] | None = None,
         trust_remote_code: bool = True,
         is_sentence_transformer: bool = False,
         is_cross_encoder: bool = False,
         skip_tokenizer_init: bool = False,
         auto_cls: type[_BaseAutoModelClass] = AutoModelForCausalLM,
+        tokenizer_name: str | None = None,
+        processor: Any | None = None,
         # Set this to avoid hanging issue
         default_torch_num_threads: int | None = None,
     ) -> None:
@@ -383,12 +386,15 @@ def __init__(
             self._init(
                 model_name=model_name,
                 dtype=dtype,
+                revision=revision,
                 model_kwargs=model_kwargs,
                 trust_remote_code=trust_remote_code,
                 is_sentence_transformer=is_sentence_transformer,
                 is_cross_encoder=is_cross_encoder,
                 skip_tokenizer_init=skip_tokenizer_init,
                 auto_cls=auto_cls,
+                tokenizer_name=tokenizer_name,
+                processor=processor,
             )
 
     def _init(
@@ -396,12 +402,15 @@ def _init(
         model_name: str,
         dtype: str = "auto",
         *,
+        revision: str | None = None,
         model_kwargs: dict[str, Any] | None = None,
         trust_remote_code: bool = True,
         is_sentence_transformer: bool = False,
         is_cross_encoder: bool = False,
         skip_tokenizer_init: bool = False,
         auto_cls: type[_BaseAutoModelClass] = AutoModelForCausalLM,
+        tokenizer_name: str | None = None,
+        processor: Any | None = None,
     ) -> None:
         model_name = maybe_model_redirect(model_name)
         self.model_name = model_name
@@ -410,6 +419,15 @@ def _init(
             model_name,
             trust_remote_code=trust_remote_code,
         )
+        # HF runner should use the HF config so that it's consistent with the HF model
+        if self.config.__module__.startswith("vllm.transformers_utils.configs"):
+            from transformers.models.auto.configuration_auto import CONFIG_MAPPING
+
+            del CONFIG_MAPPING._extra_content[self.config.model_type]
+            self.config = AutoConfig.from_pretrained(
+                model_name,
+                trust_remote_code=trust_remote_code,
+            )
         self.device = self.get_default_device()
         self.dtype = dtype = _get_and_verify_dtype(
             self.model_name,
@@ -428,6 +446,7 @@ def _init(
 
             self.model = SentenceTransformer(
                 model_name,
+                revision=revision,
                 device=self.device,
                 model_kwargs=model_kwargs,
                 trust_remote_code=trust_remote_code,
@@ -438,6 +457,7 @@ def _init(
 
             self.model = CrossEncoder(
                 model_name,
+                revision=revision,
                 device=self.device,
                 automodel_args=model_kwargs,
                 trust_remote_code=trust_remote_code,
@@ -447,6 +467,7 @@ def _init(
                 nn.Module,
                 auto_cls.from_pretrained(
                     model_name,
+                    revision=revision,
                     trust_remote_code=trust_remote_code,
                     **model_kwargs,
                 ),
@@ -469,20 +490,27 @@ def _init(
         if not skip_tokenizer_init:
             self.tokenizer: "PreTrainedTokenizer | PreTrainedTokenizerFast" = (
                 AutoTokenizer.from_pretrained(
-                    model_name,
+                    tokenizer_name or model_name,
                     trust_remote_code=trust_remote_code,
                 )
             )
 
-        # don't put this import at the top level
-        # it will call torch.accelerator.device_count()
-        from transformers import AutoProcessor
+        if processor is not None:
+            self.processor = processor
+        else:
+            # don't put this import at the top level
+            # it will call torch.accelerator.device_count()
+            from transformers import AutoProcessor
 
-        self.processor = AutoProcessor.from_pretrained(
-            model_name,
-            trust_remote_code=trust_remote_code,
-        )
+            self.processor = AutoProcessor.from_pretrained(
+                model_name,
+                trust_remote_code=trust_remote_code,
+            )
         if skip_tokenizer_init:
+            if self.processor is None:
+                raise ValueError(
+                    "skip_tokenizer_init=True requires processor initialization."
+                )
             self.tokenizer = self.processor.tokenizer
 
     def get_inputs(
@@ -505,6 +533,12 @@ def get_inputs(
         all_inputs: list[BatchFeature | BatchEncoding | dict[str, torch.Tensor]] = []
         for i, prompt in enumerate(prompts):
             if isinstance(prompt, str):
+                if self.processor is None:
+                    raise RuntimeError(
+                        "HfRunner.processor is not initialized. "
+                        "Pass processor=... to HfRunner or set "
+                        "hf_model.processor before generation."
+                    )
                 # Create a copy to avoid modifying the original dict
                 processor_kwargs = (
                     tokenization_kwargs.copy()
@@ -602,6 +636,10 @@ def generate(
                 use_cache=True,
                 **kwargs,
             )
+            if self.processor is None:
+                raise RuntimeError(
+                    "HfRunner.processor is not initialized; cannot decode output."
+                )
             output_str = self.processor.batch_decode(
                 output_ids,
                 skip_special_tokens=True,
@@ -958,6 +996,8 @@ def generate(
             req_sample_output_ids: list[list[int]] = []
             req_sample_output_strs: list[str] = []
             req_logprobs = []
+            if req_output.prompt_logprobs:
+                req_logprobs.extend(req_output.prompt_logprobs)
             for sample in req_output.outputs:
                 output_str = sample.text
                 output_ids = list(sample.token_ids)
@@ -1168,7 +1208,7 @@ def token_classify(self, prompts: list[str]) -> list[list[float]]:
         return [req_output.outputs.data for req_output in req_outputs]
 
     def reward(self, prompts: list[str]) -> list[list[float]]:
-        req_outputs = self.llm.reward(prompts)
+        req_outputs = self.llm.encode(prompts, pooling_task="token_classify")
         return [req_output.outputs.data for req_output in req_outputs]
 
     def score(
diff --git a/tests/distributed/test_dcp_a2a.py b/tests/distributed/test_dcp_a2a.py
index 2f92413e58d9..d80ed36be650 100644
--- a/tests/distributed/test_dcp_a2a.py
+++ b/tests/distributed/test_dcp_a2a.py
@@ -10,10 +10,95 @@
 
 import math
 
+import multiprocess as mp
 import pytest
 import torch
+import torch.distributed as dist
 
 from vllm.config.parallel import ParallelConfig
+from vllm.utils.network_utils import get_open_port
+from vllm.utils.system_utils import update_environment_variables
+
+mp.set_start_method("spawn", force=True)
+
+
+class _FakeCPGroup:
+    def __init__(self, world_size: int, device_group: dist.ProcessGroup):
+        self.world_size = world_size
+        self.device_group = device_group
+
+
+def _dtype_from_name(dtype_name: str) -> torch.dtype:
+    return {
+        "float16": torch.float16,
+        "bfloat16": torch.bfloat16,
+        "float32": torch.float32,
+    }[dtype_name]
+
+
+def _packed_a2a_reference(
+    cp_attn_out: torch.Tensor,
+    cp_attn_lse: torch.Tensor,
+    world_size: int,
+    h_per_rank: int,
+    is_lse_base_on_e: bool,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    from vllm.v1.attention.ops.dcp_alltoall import _lse_weighted_combine
+
+    B, _H, D = cp_attn_out.shape
+    outputs = (
+        cp_attn_out.view(B, world_size, h_per_rank, D)
+        .permute(1, 0, 2, 3)
+        .contiguous()
+        .float()
+    )
+    lses = cp_attn_lse.view(B, world_size, h_per_rank).permute(1, 0, 2).contiguous()
+    return _lse_weighted_combine(
+        outputs,
+        lses,
+        return_lse=True,
+        is_lse_base_on_e=is_lse_base_on_e,
+    )
+
+
+def _assert_packed_a2a_close(
+    actual: torch.Tensor,
+    expected: torch.Tensor,
+    dtype: torch.dtype,
+) -> None:
+    if dtype == torch.float32:
+        torch.testing.assert_close(actual, expected, rtol=1e-5, atol=1e-5)
+    else:
+        torch.testing.assert_close(
+            actual.float(), expected.float(), rtol=3e-2, atol=3e-2
+        )
+
+
+def _distributed_run(fn, world_size: int, extra_env: dict[str, str]) -> None:
+    port = str(get_open_port())
+    processes: list[mp.Process] = []
+    for rank in range(world_size):
+        env = {
+            "RANK": str(rank),
+            "LOCAL_RANK": str(rank),
+            "WORLD_SIZE": str(world_size),
+            "LOCAL_WORLD_SIZE": str(world_size),
+            "MASTER_ADDR": "localhost",
+            "MASTER_PORT": port,
+            **extra_env,
+        }
+        process = mp.Process(target=fn, args=(env,))
+        processes.append(process)
+        process.start()
+
+    for process in processes:
+        process.join(timeout=120)
+
+    for process in processes:
+        if process.is_alive():
+            process.kill()
+            process.join()
+        assert process.exitcode == 0
 
 
 class TestDCPCommBackendConfig:
@@ -38,14 +123,14 @@ def test_a2a_with_dcp_valid(self):
         """A2A backend is valid when DCP > 1."""
         config = ParallelConfig(
             dcp_comm_backend="a2a",
-            tensor_parallel_size=8,
+            tensor_parallel_size=4,
             decode_context_parallel_size=4,
         )
         assert config.dcp_comm_backend == "a2a"
 
     def test_invalid_backend_rejected(self):
         """Invalid backend values are rejected."""
-        with pytest.raises(ValueError, match="must be one of"):
+        with pytest.raises(ValueError, match="must be one of|Input should be"):
             ParallelConfig(
                 dcp_comm_backend="invalid",
             )
@@ -134,7 +219,7 @@ def test_dominant_rank(self):
         result = _lse_weighted_combine(outputs, lses)
 
         assert result.shape == (B, H, D)
-        torch.testing.assert_close(result, outputs[1].squeeze(0), atol=1e-5, rtol=1e-5)
+        torch.testing.assert_close(result, outputs[1], atol=1e-5, rtol=1e-5)
 
     def test_mathematically_correct(self):
         """Verify mathematical correctness of LSE combination."""
@@ -187,6 +272,224 @@ def test_return_lse(self):
         assert global_lse.shape == (B, H)
         assert abs(global_lse.item() - expected_global_lse) < 1e-5
 
+    def test_base2_return_lse(self):
+        """Base-2 LSE mode returns log2-sum-exp2 global LSE."""
+        from vllm.v1.attention.ops.dcp_alltoall import _lse_weighted_combine
+
+        outputs = torch.tensor(
+            [
+                [[[1.0, 2.0]]],
+                [[[3.0, 4.0]]],
+            ]
+        )
+        lses = torch.tensor(
+            [
+                [[1.0]],
+                [[2.0]],
+            ]
+        )
+
+        result, global_lse = _lse_weighted_combine(
+            outputs,
+            lses,
+            return_lse=True,
+            is_lse_base_on_e=False,
+        )
+
+        expected_global_lse = math.log2(2**1 + 2**2)
+        w0 = 2**1 / (2**1 + 2**2)
+        w1 = 2**2 / (2**1 + 2**2)
+        expected = torch.tensor([[[w0 * 1.0 + w1 * 3.0, w0 * 2.0 + w1 * 4.0]]])
+
+        torch.testing.assert_close(result, expected, rtol=1e-5, atol=1e-5)
+        torch.testing.assert_close(
+            global_lse,
+            torch.tensor([[expected_global_lse]]),
+            rtol=1e-5,
+            atol=1e-5,
+        )
+
+    def test_lse_pack_dim(self):
+        """Packed A2A stores one fp32 LSE in output-dtype lanes."""
+        from vllm.v1.attention.ops.dcp_alltoall import _dcp_a2a_lse_pack_dim
+
+        assert _dcp_a2a_lse_pack_dim(torch.bfloat16) == 2
+        assert _dcp_a2a_lse_pack_dim(torch.float16) == 2
+        assert _dcp_a2a_lse_pack_dim(torch.float32) == 1
+
+
+class TestPackedA2AKernels:
+    @pytest.mark.skipif(
+        torch.accelerator.device_count() < 1, reason="CUDA is required."
+    )
+    @pytest.mark.parametrize("dtype_name", ["float16", "bfloat16", "float32"])
+    @pytest.mark.parametrize("return_lse", [False, True])
+    @pytest.mark.parametrize("is_lse_base_on_e", [False, True])
+    def test_pack_unpack_combine_matches_reference(
+        self,
+        dtype_name: str,
+        return_lse: bool,
+        is_lse_base_on_e: bool,
+    ):
+        from vllm.v1.attention.ops.dcp_alltoall import (
+            _dcp_a2a_lse_pack_dim,
+            _dcp_a2a_pack_send,
+            _dcp_a2a_unpack_combine,
+        )
+
+        torch.manual_seed(0)
+        dtype = _dtype_from_name(dtype_name)
+        device = torch.device("cuda")
+        world_size, B, h_per_rank, D = 4, 7, 2, 32
+        H = world_size * h_per_rank
+        cp_attn_out = torch.randn(B, H, D, device=device, dtype=dtype)
+        cp_attn_lse = torch.randn(B, H, device=device, dtype=torch.float32)
+        lse_pack_dim = _dcp_a2a_lse_pack_dim(dtype)
+        send_buffer = torch.empty(
+            (world_size, B, h_per_rank, D + lse_pack_dim),
+            device=device,
+            dtype=dtype,
+        )
+
+        _dcp_a2a_pack_send(
+            cp_attn_out,
+            cp_attn_lse,
+            send_buffer,
+            world_size,
+            h_per_rank,
+            D,
+            lse_pack_dim,
+        )
+        actual = _dcp_a2a_unpack_combine(
+            send_buffer, D, lse_pack_dim, return_lse, is_lse_base_on_e
+        )
+        expected_out, expected_lse = _packed_a2a_reference(
+            cp_attn_out, cp_attn_lse, world_size, h_per_rank, is_lse_base_on_e
+        )
+
+        if return_lse:
+            actual_out, actual_lse = actual
+            _assert_packed_a2a_close(actual_out, expected_out, dtype)
+            torch.testing.assert_close(actual_lse, expected_lse, rtol=1e-4, atol=1e-4)
+        else:
+            _assert_packed_a2a_close(actual, expected_out, dtype)
+
+
+def _distributed_packed_a2a_worker(env: dict[str, str]) -> None:
+    update_environment_variables(env)
+    local_rank = int(env["LOCAL_RANK"])
+    torch.accelerator.set_device_index(local_rank)
+    dist.init_process_group(backend="nccl")
+    use_workspace = env.get("USE_WORKSPACE") == "1"
+    if use_workspace:
+        from vllm.v1.worker.workspace import init_workspace_manager
+
+        init_workspace_manager(torch.device(f"cuda:{local_rank}"))
+    try:
+        from vllm.v1.attention.ops.dcp_alltoall import dcp_a2a_lse_reduce
+
+        dtype = _dtype_from_name(env["TEST_DTYPE"])
+        return_lse = env["RETURN_LSE"] == "1"
+        is_lse_base_on_e = env["LSE_BASE_E"] == "1"
+        rank = dist.get_rank()
+        world_size = dist.get_world_size()
+        B, h_per_rank, D = 5, 2, 32
+        H = world_size * h_per_rank
+
+        generator = torch.Generator(device=f"cuda:{local_rank}")
+        generator.manual_seed(1234 + rank)
+        cp_attn_out = torch.randn(
+            B,
+            H,
+            D,
+            device=f"cuda:{local_rank}",
+            dtype=dtype,
+            generator=generator,
+        )
+        cp_attn_lse = torch.randn(
+            B,
+            H,
+            device=f"cuda:{local_rank}",
+            dtype=torch.float32,
+            generator=generator,
+        )
+        actual = dcp_a2a_lse_reduce(
+            cp_attn_out,
+            cp_attn_lse,
+            _FakeCPGroup(world_size, dist.group.WORLD),
+            return_lse=return_lse,
+            is_lse_base_on_e=is_lse_base_on_e,
+        )
+
+        gathered_out = [torch.empty_like(cp_attn_out) for _ in range(world_size)]
+        gathered_lse = [torch.empty_like(cp_attn_lse) for _ in range(world_size)]
+        dist.all_gather(gathered_out, cp_attn_out)
+        dist.all_gather(gathered_lse, cp_attn_lse)
+        outputs = torch.stack(
+            [
+                t[:, rank * h_per_rank : (rank + 1) * h_per_rank, :]
+                for t in gathered_out
+            ],
+            dim=0,
+        ).float()
+        lses = torch.stack(
+            [t[:, rank * h_per_rank : (rank + 1) * h_per_rank] for t in gathered_lse],
+            dim=0,
+        )
+        from vllm.v1.attention.ops.dcp_alltoall import _lse_weighted_combine
+
+        expected_out, expected_lse = _lse_weighted_combine(
+            outputs,
+            lses,
+            return_lse=True,
+            is_lse_base_on_e=is_lse_base_on_e,
+        )
+
+        if return_lse:
+            actual_out, actual_lse = actual
+            _assert_packed_a2a_close(actual_out, expected_out, dtype)
+            torch.testing.assert_close(actual_lse, expected_lse, rtol=1e-4, atol=1e-4)
+        else:
+            _assert_packed_a2a_close(actual, expected_out, dtype)
+    finally:
+        if use_workspace:
+            from vllm.v1.worker.workspace import reset_workspace_manager
+
+            reset_workspace_manager()
+        dist.destroy_process_group()
+
+
+@pytest.mark.skipif(
+    torch.accelerator.device_count() < 4, reason="Need at least 4 GPUs."
+)
+@pytest.mark.parametrize("dtype_name", ["float16", "bfloat16", "float32"])
+def test_distributed_packed_a2a_matches_reference(dtype_name: str):
+    _distributed_run(
+        _distributed_packed_a2a_worker,
+        world_size=4,
+        extra_env={
+            "TEST_DTYPE": dtype_name,
+            "RETURN_LSE": "1",
+            "LSE_BASE_E": "1",
+        },
+    )
+
+
+@pytest.mark.skipif(
+    torch.accelerator.device_count() < 4, reason="Need at least 4 GPUs."
+)
+def test_distributed_packed_a2a_with_workspace_matches_reference():
+    _distributed_run(
+        _distributed_packed_a2a_worker,
+        world_size=4,
+        extra_env={
+            "TEST_DTYPE": "bfloat16",
+            "RETURN_LSE": "1",
+            "LSE_BASE_E": "1",
+            "USE_WORKSPACE": "1",
+        },
+    )
+
 
 if __name__ == "__main__":
     pytest.main([__file__, "-v"])
diff --git a/tests/distributed/test_eplb_events.py b/tests/distributed/test_eplb_events.py
new file mode 100644
index 000000000000..40323dc89809
--- /dev/null
+++ b/tests/distributed/test_eplb_events.py
@@ -0,0 +1,98 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import threading
+import time
+
+import torch
+
+from vllm.distributed.eplb.eplb_utils import CpuGpuEvent
+
+
+def test_wait_blocks_until_record():
+    event = CpuGpuEvent()
+    record_stream = torch.cuda.Stream()
+    wait_stream = torch.cuda.Stream()
+    wait_returned = threading.Event()
+
+    def waiter():
+        event.wait(stream=wait_stream)
+        wait_returned.set()
+
+    t = threading.Thread(target=waiter)
+    t.start()
+
+    time.sleep(0.05)
+    assert not wait_returned.is_set(), "wait() returned before record() was called"
+
+    event.record(stream=record_stream)
+    t.join(timeout=5.0)
+
+    assert not event._recorded.is_set()
+
+
+def test_reuse_across_multiple_cycles():
+    wrapper = CpuGpuEvent()
+    record_stream = torch.cuda.Stream()
+    wait_stream = torch.cuda.Stream()
+    NUM_CYCLES = 8
+    completed_cycles = []
+    barriers = [threading.Barrier(2) for _ in range(NUM_CYCLES)]
+
+    def waiter():
+        for i in range(NUM_CYCLES):
+            wrapper.wait(stream=wait_stream)
+            completed_cycles.append(True)
+            barriers[i].wait()
+
+    t = threading.Thread(target=waiter)
+    t.start()
+
+    for i in range(NUM_CYCLES):
+        wrapper.record(stream=record_stream)
+        barriers[i].wait()
+
+    t.join(timeout=10.0)
+    assert len(completed_cycles) == NUM_CYCLES
+
+
+def test_producer_consumer():
+    """
+    This test uses the CpuGpuEvent to synchronize reads and writes to/from a shared GPU
+    tensor on multiple CPU threads.
+    """
+    worker_stream = torch.cuda.Stream()
+    # Create a single element counter that will be shared between two threads
+    buf = torch.zeros(1, device="cuda")
+    NUM_ROUNDS = 5
+
+    ready_cpu = [threading.Event() for _ in range(NUM_ROUNDS)]
+    events = [CpuGpuEvent() for _ in range(NUM_ROUNDS)]
+    errors: list[str] = []
+
+    # For each round, the worker thread (writer) sets the counter in buf and waits for
+    # the main thread to read it.
+    def worker():
+        for i in range(NUM_ROUNDS):
+            if i > 0:
+                events[i - 1].wait(stream=worker_stream)
+
+            with torch.cuda.stream(worker_stream):
+                buf.fill_(float(i + 1))
+
+            worker_stream.synchronize()
+            ready_cpu[i].set()
+
+    t = threading.Thread(target=worker)
+    t.start()
+
+    for i in range(NUM_ROUNDS):
+        ready_cpu[i].wait()
+        snapshot = buf.clone()
+        events[i].record()
+        val = snapshot.item()
+        if val != float(i + 1):
+            errors.append(f"round {i}: expected {i + 1:.1f}, got {val:.1f}")
+
+    t.join(timeout=10.0)
+    assert not errors, f"Buffer ordering errors: {errors}"
diff --git a/tests/distributed/test_eplb_execute.py b/tests/distributed/test_eplb_execute.py
index 7256ad56a3fa..d9e6a739b01b 100644
--- a/tests/distributed/test_eplb_execute.py
+++ b/tests/distributed/test_eplb_execute.py
@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import asyncio
 import random
 
 import pytest
@@ -9,7 +8,10 @@
 import torch.distributed
 
 from vllm.config import VllmConfig, set_current_vllm_config
-from vllm.distributed.eplb.eplb_communicator import create_eplb_communicator
+from vllm.distributed.eplb.eplb_communicator import (
+    create_eplb_communicator,
+    has_nixl,
+)
 from vllm.distributed.eplb.rebalance_execute import (
     move_from_buffer,
     rearrange_expert_weights_inplace,
@@ -358,24 +360,20 @@ def _test_async_transfer_layer_without_mtp_worker(
         communicator.set_stream(cuda_stream)
 
         for layer_idx in range(num_layers):
-            is_unchanged, is_received_locally, recv_metadata = asyncio.run(
-                transfer_layer(
-                    old_layer_indices=old_indices_cpu[layer_idx],
-                    new_layer_indices=new_indices_cpu[layer_idx],
-                    expert_weights=expert_weights[layer_idx],
-                    expert_weights_buffer=expert_buffer,
-                    ep_group=ep_group,
-                    communicator=communicator,
-                    cuda_stream=cuda_stream,
-                )
+            transfer_metadata = transfer_layer(
+                old_layer_indices=old_indices_cpu[layer_idx],
+                new_layer_indices=new_indices_cpu[layer_idx],
+                expert_weights=expert_weights[layer_idx],
+                expert_weights_buffer=expert_buffer,
+                ep_group=ep_group,
+                communicator=communicator,
+                cuda_stream=cuda_stream,
             )
             cuda_stream.synchronize()
             move_from_buffer(
                 expert_weights=expert_weights[layer_idx],
                 expert_weights_buffers=expert_buffer,
-                is_unchanged=is_unchanged,
-                is_received_locally=is_received_locally,
-                recv_metadata=recv_metadata,
+                transfer_metadata=transfer_metadata,
                 new_indices=new_indices_cpu[layer_idx].numpy(),
                 ep_rank=ep_rank,
             )
@@ -527,7 +525,9 @@ def _test_rearrange_expert_weights_with_redundancy(
         (4, 8, 8, 16),
     ],
 )
-@pytest.mark.parametrize("eplb_communicator", ["torch_nccl", "torch_gloo", "pynccl"])
+@pytest.mark.parametrize(
+    "eplb_communicator", ["torch_nccl", "torch_gloo", "pynccl", "nixl"]
+)
 def test_rearrange_expert_weights_with_redundancy(
     world_size,
     num_layers,
@@ -537,6 +537,8 @@ def test_rearrange_expert_weights_with_redundancy(
 ):
     """Test the functionality of rearranging expert weights with redundancy."""
 
+    if eplb_communicator == "nixl" and not has_nixl():
+        pytest.skip("NIXL is not available")
     if torch.accelerator.device_count() < world_size:
         pytest.skip(f"Need at least {world_size} GPUs to run the test")
     distributed_run(
@@ -633,7 +635,9 @@ def _test_rearrange_expert_weights_no_change(env, world_size) -> None:
         (2, 2, 2, 3),
     ],
 )
-@pytest.mark.parametrize("eplb_communicator", ["torch_nccl", "torch_gloo", "pynccl"])
+@pytest.mark.parametrize(
+    "eplb_communicator", ["torch_nccl", "torch_gloo", "pynccl", "nixl"]
+)
 def test_async_transfer_layer_without_mtp(
     world_size: int,
     num_layers: int,
@@ -643,6 +647,8 @@ def test_async_transfer_layer_without_mtp(
 ):
     """Exercise async EPLB transfer path without MTP/spec decode."""
 
+    if eplb_communicator == "nixl" and not has_nixl():
+        pytest.skip("NIXL is not available")
     if torch.accelerator.device_count() < world_size:
         pytest.skip(f"Need at least {world_size} GPUs to run the test")
 
diff --git a/tests/distributed/test_eplb_utils.py b/tests/distributed/test_eplb_utils.py
index 8b287244b742..53a4ce21af2e 100644
--- a/tests/distributed/test_eplb_utils.py
+++ b/tests/distributed/test_eplb_utils.py
@@ -80,7 +80,7 @@ def test_commit_eplb_maps_for_layer_logical_padding():
         .contiguous()
     )
     layer = 0
-    _commit_eplb_maps_for_layer(model_state, new_phy2log, layer)
+    _commit_eplb_maps_for_layer(model_state, new_phy2log[layer], layer)
 
     assert torch.all(model_state.logical_to_physical_map[layer, :, 2] == -1)
 
@@ -143,7 +143,7 @@ def test_commit_eplb_maps_for_layer():
     )
     new_logcnt = torch.tensor([[2, 1, 1], [1, 2, 1]], dtype=torch.long)
 
-    _commit_eplb_maps_for_layer(model_state, new_phy2log, layer=0)
+    _commit_eplb_maps_for_layer(model_state, new_phy2log[0], layer=0)
 
     # Layer 0 updated
     assert torch.equal(model_state.physical_to_logical_map[0], new_phy2log[0])
diff --git a/tests/distributed/test_mnnvl_alltoall.py b/tests/distributed/test_mnnvl_alltoall.py
new file mode 100644
index 000000000000..f395c96a3d3a
--- /dev/null
+++ b/tests/distributed/test_mnnvl_alltoall.py
@@ -0,0 +1,774 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Tests for MNNVL AllToAll operations.
+
+Requires: docker run ... --cap-add=SYS_PTRACE ...
+Run: pytest tests/distributed/test_mnnvl_alltoall.py -v
+"""
+
+import os
+import traceback
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+
+from vllm.distributed import get_ep_group
+from vllm.utils.flashinfer import (
+    has_flashinfer_nvlink_one_sided,
+    has_flashinfer_nvlink_two_sided,
+)
+from vllm.utils.network_utils import get_open_port
+
+from ..utils import init_test_distributed_environment
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _has_sys_ptrace() -> bool:
+    """Check for SYS_PTRACE capability (bit 19 in CapEff)."""
+    try:
+        with open("/proc/self/status") as f:
+            for line in f:
+                if line.startswith("CapEff:"):
+                    return bool(int(line.split()[1], 16) & (1 << 19))
+    except Exception:
+        pass
+    return False
+
+
+def _spawn_workers(worker_fn, world_size, *, dp_size=None):
+    """Spawn one process per GPU, run worker_fn, assert all succeed.
+
+    Uses an mp.Queue to propagate worker tracebacks back to the parent
+    so pytest shows the actual failure, not just an exit code.
+    """
+    if mp.get_start_method(allow_none=True) is None:
+        mp.set_start_method("spawn")
+
+    port = str(get_open_port())
+    # Allocate a second port for DP master when dp_size is set, so the
+    # distributed init port and DP port can't collide even under xdist.
+    dp_port = str(get_open_port()) if dp_size is not None else None
+    err_queue: mp.Queue = mp.Queue()
+    procs = []
+    for rank in range(world_size):
+        p = mp.Process(
+            target=_run_worker,
+            args=(rank, world_size, port, worker_fn, dp_size, dp_port, err_queue),
+        )
+        p.start()
+        procs.append(p)
+    for p in procs:
+        p.join()
+
+    # Collect any errors from workers before asserting.
+    errors = []
+    while not err_queue.empty():
+        errors.append(err_queue.get_nowait())
+    err_queue.close()
+    err_queue.join_thread()
+    if errors:
+        pytest.fail("Worker(s) failed:\n" + "\n---\n".join(errors))
+
+
+def _run_worker(rank, world_size, port, worker_fn, dp_size, dp_port, err_queue):
+    """Per-process setup: device, distributed env, then call worker_fn.
+
+    Args:
+        dp_size: If set, initialize with tp=1 and data_parallel_size=dp_size.
+                 Otherwise use tp=world_size (default for EP-based tests).
+        dp_port: Separate port for the DP master (only used when dp_size is set).
+        err_queue: Queue for propagating tracebacks to the parent process.
+    """
+    try:
+        os.environ.pop("CUDA_VISIBLE_DEVICES", None)
+        torch.accelerator.set_device_index(rank)
+        if dp_size is not None:
+            _init_dp_environment(world_size, rank, port, dp_size, dp_port)
+        else:
+            init_test_distributed_environment(world_size, 1, rank, port)
+        worker_fn(rank, world_size)
+        torch.distributed.barrier()
+    except Exception:
+        err_queue.put(f"[Rank {rank}]\n{traceback.format_exc()}")
+        # Don't re-raise: the parent reads errors from err_queue.
+        # A non-zero exit from the re-raise would be redundant.
+        import sys
+
+        sys.exit(1)
+
+
+def _init_dp_environment(world_size, rank, port, dp_size, dp_port):
+    """Initialize distributed env with data parallelism.
+
+    Sets up tp=1, pp=1, dp=dp_size. Each process is one DP rank
+    with local rank 0 within its (trivial) tp*pp group.
+
+    Args:
+        port: Port for torch.distributed init.
+        dp_port: Separate port for the DP master group init.
+    """
+    from vllm.config import VllmConfig, set_current_vllm_config
+    from vllm.config.parallel import ParallelConfig
+    from vllm.distributed.parallel_state import (
+        ensure_model_parallel_initialized,
+        init_distributed_environment,
+    )
+
+    vllm_config = VllmConfig()
+    vllm_config.parallel_config = ParallelConfig(
+        data_parallel_size=dp_size,
+        data_parallel_rank=rank,
+        # Pre-populate port list so __post_init__ doesn't auto-generate
+        # random ports. All DP ranks must agree on the same port.
+        _data_parallel_master_port_list=[int(dp_port)],
+    )
+    with set_current_vllm_config(vllm_config):
+        # rank=0 here because each DP rank has a single (tp=1,pp=1) process,
+        # so the local rank within the tp*pp group is always 0.
+        # init_distributed_environment will offset by data_parallel_rank.
+        init_distributed_environment(
+            world_size=1,  # tp * pp = 1
+            rank=0,
+            distributed_init_method=f"tcp://localhost:{port}",
+            local_rank=rank,
+        )
+        ensure_model_parallel_initialized(1, 1)
+
+
+def _make_forward_context(rank, world_size, num_tokens_per_rank):
+    """Create a forward context with mock DP metadata for AgRs tests.
+
+    Returns a context manager suitable for ``with`` statements.
+    The real DPMetadata (with sp_local_sizes etc.) is created internally
+    by set_forward_context from num_tokens_across_dp; the attn_metadata
+    placeholder just satisfies the "attn_metadata is not None" guard.
+    """
+    from vllm.config.parallel import ParallelConfig
+    from vllm.config.vllm import VllmConfig
+    from vllm.forward_context import set_forward_context
+
+    class _AttnMeta:
+        """Minimal placeholder so set_forward_context's
+        ``attn_metadata is not None`` guard (forward_context.py:334)
+        is satisfied. The real DPMetadata is built from num_tokens_across_dp."""
+
+        dp_metadata = None
+
+    vllm_config = VllmConfig()
+    vllm_config.parallel_config = ParallelConfig(
+        data_parallel_size=world_size,
+        is_moe_model=True,
+        data_parallel_rank=rank,
+    )
+    return set_forward_context(
+        _AttnMeta(),
+        vllm_config,
+        num_tokens=num_tokens_per_rank,
+        num_tokens_across_dp=torch.tensor(
+            [num_tokens_per_rank] * world_size, dtype=torch.int
+        ),
+    )
+
+
+# ---------------------------------------------------------------------------
+# Skip conditions
+# ---------------------------------------------------------------------------
+
+requires_multi_gpu = pytest.mark.skipif(
+    torch.accelerator.device_count() < 2, reason="Need >= 2 GPUs"
+)
+requires_two_sided = pytest.mark.skipif(
+    not has_flashinfer_nvlink_two_sided(),
+    reason="FlashInfer NVLink two-sided not available",
+)
+requires_one_sided = pytest.mark.skipif(
+    not has_flashinfer_nvlink_one_sided(),
+    reason="FlashInfer NVLink one-sided not available",
+)
+requires_ptrace = pytest.mark.skipif(
+    not _has_sys_ptrace(),
+    reason="SYS_PTRACE required (docker run --cap-add=SYS_PTRACE)",
+)
+
+# NOTE: No module-level pytestmark here. The FlashInfer lifecycle tests have
+# their own @requires_two_sided / @requires_one_sided decorators, and
+# test_args_dispatch_combine uses only standard torch.distributed ops and
+# should run even when FlashInfer NVLink backends are not installed.
+
+
+# ---------------------------------------------------------------------------
+# Test 1: Two-sided manager lifecycle (init, cleanup, reinit, ensure_init)
+# ---------------------------------------------------------------------------
+#
+# Tests FlashInferNVLinkTwoSidedManager which wraps FlashInfer's MnnvlMoe.
+# initialize() allocates MNNVL shared workspaces via MnnvlMoe.get_moe_workspaces,
+# which uses pidfd_getfd() to share memory file descriptors across processes —
+# hence the SYS_PTRACE requirement.
+#
+# Uses EP group (get_ep_group) because the two-sided manager is constructed
+# with an EP-scoped communicator in production. With tp=world_size the EP
+# group spans all ranks, giving us a multi-rank group for testing.
+# ---------------------------------------------------------------------------
+
+
+def _two_sided_lifecycle_worker(rank, world_size):
+    from vllm.distributed.device_communicators.all2all import (
+        FlashInferNVLinkTwoSidedManager,
+    )
+
+    cpu_group = get_ep_group().cpu_group
+    num_gpus = torch.accelerator.device_count()
+    manager = FlashInferNVLinkTwoSidedManager(cpu_group)
+
+    # Not initialized yet
+    assert not manager.initialized
+    assert manager.rank == rank
+    assert manager.world_size == world_size
+
+    # Initialize
+    manager.initialize(world_size=world_size, rank=rank, gpus_per_node=num_gpus)
+    assert manager.initialized
+    assert manager.workspace_tensor is not None
+    assert manager.prepare_workspace_tensor is not None
+    assert manager.mapping is not None
+
+    torch.distributed.barrier()
+
+    # Cleanup
+    manager.cleanup()
+    assert not manager.initialized
+    assert manager.workspace_tensor is None
+    assert manager.prepare_workspace_tensor is None
+
+    torch.distributed.barrier()
+
+    # Reinitialize
+    manager.initialize(world_size=world_size, rank=rank, gpus_per_node=num_gpus)
+    assert manager.initialized
+
+    torch.distributed.barrier()
+
+    # ensure_alltoall_workspace_initialized is idempotent when already init'd
+    assert manager.ensure_alltoall_workspace_initialized()
+    assert manager.initialized
+
+    manager.cleanup()
+    assert not manager.initialized
+
+
+@requires_multi_gpu
+@requires_two_sided
+@requires_ptrace
+@pytest.mark.parametrize("world_size", [2])
+def test_two_sided_manager_lifecycle(world_size):
+    """Test init, cleanup, reinit, and ensure_initialized idempotency."""
+    _spawn_workers(_two_sided_lifecycle_worker, world_size)
+
+
+# ---------------------------------------------------------------------------
+# Test 2: One-sided manager lifecycle (init, cleanup, reinit)
+# ---------------------------------------------------------------------------
+#
+# Tests FlashInferNVLinkOneSidedManager which wraps FlashInfer's MoeAlltoAll.
+# initialize() creates MoeAlltoAll with an MnnvlConfig, which allocates MNNVL
+# shared workspaces — same cross-process memory sharing as two-sided, hence
+# the SYS_PTRACE requirement.
+#
+# Uses DP group (get_dp_group) because the one-sided manager's initialize()
+# internally calls get_dp_group() to set up the MnnvlConfig communicator.
+# We therefore need a real DP group with world_size > 1, which requires
+# dp_size=world_size via _init_dp_environment.
+# ---------------------------------------------------------------------------
+
+
+def _one_sided_lifecycle_worker(rank, world_size):
+    from vllm.distributed.device_communicators.all2all import (
+        FlashInferNVLinkOneSidedManager,
+    )
+    from vllm.distributed.parallel_state import get_dp_group
+
+    cpu_group = get_dp_group().cpu_group
+    manager = FlashInferNVLinkOneSidedManager(cpu_group)
+
+    assert not manager.initialized
+    assert manager.rank == rank
+    assert manager.world_size == world_size
+
+    init_kwargs = dict(
+        max_num_tokens=1024,
+        top_k=2,
+        num_experts=world_size * 8,
+        hidden_size=4096,
+    )
+
+    # Initialize
+    manager.initialize(**init_kwargs)
+    assert manager.initialized
+    assert manager.moe_alltoall is not None
+    assert manager.mapping is not None
+
+    torch.distributed.barrier()
+
+    # Cleanup
+    manager.cleanup()
+    assert not manager.initialized
+    assert manager.moe_alltoall is None
+
+    torch.distributed.barrier()
+
+    # Reinitialize with different token count
+    manager.initialize(**{**init_kwargs, "max_num_tokens": 2048})
+    assert manager.initialized
+
+    torch.distributed.barrier()
+    manager.cleanup()
+
+
+@requires_multi_gpu
+@requires_one_sided
+@requires_ptrace
+@pytest.mark.parametrize("world_size", [2])
+def test_one_sided_manager_lifecycle(world_size):
+    """Test init, cleanup, and reinit with different params."""
+    _spawn_workers(
+        _one_sided_lifecycle_worker,
+        world_size,
+        dp_size=world_size,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Test 3: AgRs dispatch/combine with value validation
+# ---------------------------------------------------------------------------
+#
+# Tests AgRsAll2AllManager which uses only standard torch.distributed
+# all_gatherv / reduce_scatterv — no FlashInfer or MNNVL dependency.
+# This test validates the reference all-to-all implementation that other
+# backends are compared against.
+# ---------------------------------------------------------------------------
+
+
+def _args_dispatch_combine_worker(rank, world_size):
+    from vllm.distributed.device_communicators.all2all import AgRsAll2AllManager
+    from vllm.forward_context import get_forward_context
+
+    cpu_group = get_ep_group().cpu_group
+    device = torch.device(f"cuda:{rank}")
+
+    hidden_size = 64
+    tokens_per_rank = 16
+    experts_per_token = 2
+    num_experts = world_size * 4
+    total_tokens = world_size * tokens_per_rank
+
+    # Deterministic per-rank data: rank r has value (r + 1)
+    hidden = torch.full(
+        (tokens_per_rank, hidden_size),
+        float(rank + 1),
+        device=device,
+        dtype=torch.float32,
+    )
+    router = torch.full(
+        (tokens_per_rank, num_experts),
+        float(rank + 1) * 10,
+        device=device,
+        dtype=torch.float32,
+    )
+    weights = torch.full(
+        (tokens_per_rank, experts_per_token),
+        float(rank + 1) * 100,
+        device=device,
+        dtype=torch.float32,
+    )
+    ids = torch.full(
+        (tokens_per_rank, experts_per_token),
+        rank,
+        device=device,
+        dtype=torch.long,
+    )
+
+    with _make_forward_context(rank, world_size, tokens_per_rank):
+        manager = AgRsAll2AllManager(cpu_group)
+        dp_metadata = get_forward_context().dp_metadata
+
+        with dp_metadata.sp_local_sizes(sequence_parallel_size=1):
+            # -- dispatch_router_logits --
+            d_hidden, d_router = manager.dispatch_router_logits(
+                hidden.clone(),
+                router.clone(),
+                is_sequence_parallel=True,
+            )
+            assert d_hidden.shape == (total_tokens, hidden_size)
+            assert d_router.shape == (total_tokens, num_experts)
+
+            for r in range(world_size):
+                s = r * tokens_per_rank
+                e = (r + 1) * tokens_per_rank
+                torch.testing.assert_close(
+                    d_hidden[s:e],
+                    torch.full_like(d_hidden[s:e], float(r + 1)),
+                )
+                torch.testing.assert_close(
+                    d_router[s:e],
+                    torch.full_like(d_router[s:e], float(r + 1) * 10),
+                )
+
+            # -- dispatch --
+            d_hidden2, d_weights, d_ids = manager.dispatch(
+                hidden.clone(),
+                weights.clone(),
+                ids.clone(),
+                is_sequence_parallel=True,
+            )
+            assert d_hidden2.shape == (total_tokens, hidden_size)
+            assert d_weights.shape == (total_tokens, experts_per_token)
+            assert d_ids.shape == (total_tokens, experts_per_token)
+
+            for r in range(world_size):
+                s = r * tokens_per_rank
+                e = (r + 1) * tokens_per_rank
+                torch.testing.assert_close(
+                    d_weights[s:e],
+                    torch.full_like(d_weights[s:e], float(r + 1) * 100),
+                )
+                assert (d_ids[s:e] == r).all()
+
+            # -- combine (reduce-scatter) --
+            # Each token i has value i in all columns; after reduce-scatter
+            # each rank gets its slice, summed across ranks.
+            expert_out = (
+                torch.arange(total_tokens, device=device, dtype=torch.float32)
+                .unsqueeze(1)
+                .expand(total_tokens, hidden_size)
+                .contiguous()
+            )
+
+            combined = manager.combine(expert_out, is_sequence_parallel=True)
+            assert combined.shape == (tokens_per_rank, hidden_size)
+
+            for i in range(tokens_per_rank):
+                expected_val = float(rank * tokens_per_rank + i) * world_size
+                torch.testing.assert_close(
+                    combined[i],
+                    torch.full_like(combined[i], expected_val),
+                )
+
+            torch.distributed.barrier()
+
+
+@requires_multi_gpu
+@pytest.mark.parametrize("world_size", [2])
+def test_args_dispatch_combine(world_size):
+    """Validate dispatch gathers all-rank data and combine reduces correctly."""
+    _spawn_workers(_args_dispatch_combine_worker, world_size)
+
+
+# ---------------------------------------------------------------------------
+# Test 4: FlashInfer two-sided dispatch/combine data communication
+# ---------------------------------------------------------------------------
+#
+# Tests actual data flow through the FlashInfer NVLink two-sided backend
+# by calling flashinfer_alltoall_dispatch (with defer_input_quant=True to
+# skip quantization) and flashinfer_alltoall_combine, then verifying exact
+# round-trip values. Dispatch sends each token once per distinct expert
+# rank, and combine performs an unweighted sum, so:
+#   dispatch(hidden) → identity → combine = hidden * num_distinct_ranks(i)
+# ---------------------------------------------------------------------------
+
+
+def _two_sided_data_worker(rank, world_size):
+    from vllm.distributed.device_communicators.all2all import (
+        FlashInferNVLinkTwoSidedManager,
+    )
+    from vllm.distributed.parallel_state import get_dp_group
+    from vllm.forward_context import get_forward_context
+    from vllm.model_executor.layers.fused_moe.config import (
+        FusedMoEQuantConfig,
+        FusedMoEQuantDesc,
+    )
+    from vllm.model_executor.layers.fused_moe.prepare_finalize.flashinfer_nvlink_two_sided import (  # noqa: E501
+        flashinfer_alltoall_combine,
+        flashinfer_alltoall_dispatch,
+    )
+
+    # Use DP group because MnnvlMoe workspace allocation calls get_dp_group()
+    # internally and requires dp_size == ep_size.
+    cpu_group = get_dp_group().cpu_group
+    device = torch.device(f"cuda:{rank}")
+    num_gpus = torch.accelerator.device_count()
+
+    hidden_size = 128
+    tokens_per_rank = 32
+    experts_per_token = 2
+    num_experts = world_size * 4
+
+    # Initialize the FlashInfer two-sided manager
+    manager = FlashInferNVLinkTwoSidedManager(cpu_group)
+    manager.initialize(world_size=world_size, rank=rank, gpus_per_node=num_gpus)
+    assert manager.initialized
+
+    torch.distributed.barrier()
+
+    # Create deterministic per-rank test data
+    torch.manual_seed(rank + 42)
+    hidden = torch.randn(
+        tokens_per_rank,
+        hidden_size,
+        device=device,
+        dtype=torch.bfloat16,
+    )
+    # Assign each token to experts spread across ranks so tokens move between GPUs
+    topk_ids = torch.randint(
+        0,
+        num_experts,
+        (tokens_per_rank, experts_per_token),
+        device=device,
+        dtype=torch.int32,
+    )
+    topk_weights = torch.rand(
+        tokens_per_rank,
+        experts_per_token,
+        device=device,
+        dtype=torch.float32,
+    )
+
+    # Unquantized config: quant_dtype=None means moe_kernel_quantize_input is a no-op
+    no_quant = FusedMoEQuantDesc()
+    quant_config = FusedMoEQuantConfig(
+        _a1=no_quant,
+        _a2=no_quant,
+        _w1=no_quant,
+        _w2=no_quant,
+    )
+    assert quant_config.quant_dtype is None  # sanity: no quantization
+
+    with _make_forward_context(rank, world_size, tokens_per_rank):
+        dp_metadata = get_forward_context().dp_metadata
+
+        with dp_metadata.sp_local_sizes(sequence_parallel_size=1):
+            local_sizes = dp_metadata.get_chunk_sizes_across_dp_rank()
+
+            # --- FlashInfer two-sided dispatch ---
+            alltoall_info, fi_topk_ids, fi_topk_weights, fi_hidden, fi_scale = (
+                flashinfer_alltoall_dispatch(
+                    manager,
+                    local_sizes,
+                    hidden.clone(),
+                    None,  # no global scale
+                    topk_ids.clone(),
+                    topk_weights.clone(),
+                    experts_per_token,
+                    num_experts,
+                    quant_config,
+                    defer_input_quant=True,
+                )
+            )
+            assert fi_scale is None  # deferred quant: no scale produced
+            assert fi_hidden is not None
+            assert fi_hidden.shape[1] == hidden_size
+            assert fi_hidden.numel() > 0
+
+            # --- Round-trip exact verification ---
+            # The all-to-all sends each token once per *distinct* expert
+            # rank. Combine performs an unweighted sum of the per-rank
+            # contributions. With identity expert (feeding dispatched
+            # hidden straight back):
+            #   result[i] = hidden[i] * num_distinct_expert_ranks(i)
+            combined = flashinfer_alltoall_combine(
+                manager,
+                fi_hidden,
+                top_k=experts_per_token,
+                token_count=tokens_per_rank,
+                alltoall_info=alltoall_info,
+            )
+            assert combined.shape == (tokens_per_rank, hidden_size)
+
+            experts_per_rank = num_experts // world_size
+            expert_ranks = topk_ids // experts_per_rank  # (tokens, top_k)
+            num_distinct = torch.tensor(
+                [len(set(row.tolist())) for row in expert_ranks],
+                device=device,
+                dtype=torch.float32,
+            ).unsqueeze(1)  # (tokens, 1)
+            expected = (hidden.float() * num_distinct).to(hidden.dtype)
+            torch.testing.assert_close(combined, expected)
+
+            # --- Linearity check with scaled expert output ---
+            # Scaling the expert output by a constant should scale the
+            # combined result by the same constant.
+            scale = 3.0
+            combined_scaled = flashinfer_alltoall_combine(
+                manager,
+                fi_hidden * scale,
+                top_k=experts_per_token,
+                token_count=tokens_per_rank,
+                alltoall_info=alltoall_info,
+            )
+            expected_scaled = (hidden.float() * num_distinct * scale).to(hidden.dtype)
+            torch.testing.assert_close(combined_scaled, expected_scaled)
+
+            torch.distributed.barrier()
+
+    manager.cleanup()
+
+
+@requires_multi_gpu
+@requires_two_sided
+@requires_ptrace
+@pytest.mark.parametrize("world_size", [2])
+def test_two_sided_dispatch_combine(world_size):
+    """Test FlashInfer two-sided dispatch/combine with exact value verification."""
+    _spawn_workers(_two_sided_data_worker, world_size, dp_size=world_size)
+
+
+# ---------------------------------------------------------------------------
+# Test 5: FlashInfer one-sided dispatch/combine data communication
+# ---------------------------------------------------------------------------
+#
+# Tests actual data flow through the FlashInfer NVLink one-sided backend
+# by calling MoeAlltoAll.dispatch() and MoeAlltoAll.combine() directly
+# with synthetic payloads, then verifying shapes and round-trip consistency.
+# ---------------------------------------------------------------------------
+
+
+def _one_sided_data_worker(rank, world_size):
+    from vllm.distributed.device_communicators.all2all import (
+        FlashInferNVLinkOneSidedManager,
+    )
+    from vllm.distributed.parallel_state import get_dp_group
+    from vllm.forward_context import get_forward_context
+
+    cpu_group = get_dp_group().cpu_group
+    device = torch.device(f"cuda:{rank}")
+
+    hidden_size = 256
+    tokens_per_rank = 32
+    experts_per_token = 2
+    num_experts = world_size * 8
+
+    # Initialize the one-sided manager
+    manager = FlashInferNVLinkOneSidedManager(cpu_group)
+    manager.initialize(
+        max_num_tokens=tokens_per_rank,
+        top_k=experts_per_token,
+        num_experts=num_experts,
+        hidden_size=hidden_size,
+    )
+    assert manager.initialized
+    assert manager.moe_alltoall is not None
+
+    with _make_forward_context(rank, world_size, tokens_per_rank):
+        dp_metadata = get_forward_context().dp_metadata
+
+        with dp_metadata.sp_local_sizes(sequence_parallel_size=1):
+            local_sizes = dp_metadata.get_chunk_sizes_across_dp_rank()
+            runtime_max_tokens = max(local_sizes)
+
+            # Create test data with raw tensors matching the nvfp4 payload
+            # sizes the workspace was allocated for:
+            #   a1q: (tokens, hidden_size // 2) — nvfp4 hidden states
+            #   a1q_scale: (tokens, hidden_size // 16) — fp8 scaling factors
+            torch.manual_seed(rank + 42)
+            a1q = torch.randint(
+                0,
+                256,
+                (tokens_per_rank, hidden_size // 2),
+                device=device,
+                dtype=torch.uint8,
+            )
+            a1q_scale = torch.randint(
+                0,
+                256,
+                (tokens_per_rank, hidden_size // 16),
+                device=device,
+                dtype=torch.uint8,
+            )
+            topk_ids = torch.randint(
+                0,
+                num_experts,
+                (tokens_per_rank, experts_per_token),
+                device=device,
+                dtype=torch.int32,
+            )
+            topk_weights = torch.rand(
+                tokens_per_rank,
+                experts_per_token,
+                device=device,
+                dtype=torch.float32,
+            )
+
+            # --- One-sided dispatch ---
+            payloads = [a1q, a1q_scale, topk_ids, topk_weights]
+            recv_payloads = manager.moe_alltoall.dispatch(
+                token_selected_experts=topk_ids,
+                input_payloads=payloads,
+                runtime_max_tokens_per_rank=runtime_max_tokens,
+            )
+            assert len(recv_payloads) == 4
+            recv_a1q, recv_scale, recv_ids, recv_weights = recv_payloads
+            assert recv_a1q.numel() > 0
+            assert recv_ids.numel() > 0
+
+            # --- Round-trip exact verification ---
+            # The dispatch routes each token once per *distinct* expert
+            # rank. Combine performs an unweighted sum of per-rank
+            # contributions. With constant expert output (all 1s):
+            #   result[i] = 1.0 * num_distinct_expert_ranks(i)
+            expert_output = torch.ones(
+                world_size,
+                runtime_max_tokens,
+                hidden_size,
+                device=device,
+                dtype=torch.bfloat16,
+            )
+            combined = manager.moe_alltoall.combine(
+                payload=expert_output,
+                runtime_max_tokens_per_rank=runtime_max_tokens,
+            )
+            assert combined.shape == (tokens_per_rank, hidden_size)
+
+            experts_per_rank = num_experts // world_size
+            expert_ranks = topk_ids // experts_per_rank  # (tokens, top_k)
+            num_distinct = torch.tensor(
+                [len(set(row.tolist())) for row in expert_ranks],
+                device=device,
+                dtype=torch.bfloat16,
+            ).unsqueeze(1)  # (tokens, 1)
+            expected = num_distinct.expand_as(combined)
+            torch.testing.assert_close(combined, expected)
+
+            # --- Linearity check with scaled expert output ---
+            # Scaling the expert output by a constant should scale the
+            # combined result by the same constant.
+            # Re-dispatch to reset internal state (one-sided requires a
+            # fresh dispatch before each combine).
+            manager.moe_alltoall.dispatch(
+                token_selected_experts=topk_ids,
+                input_payloads=payloads,
+                runtime_max_tokens_per_rank=runtime_max_tokens,
+            )
+            scale = 3.0
+            combined_scaled = manager.moe_alltoall.combine(
+                payload=expert_output * scale,
+                runtime_max_tokens_per_rank=runtime_max_tokens,
+            )
+            expected_scaled = (expected * scale).to(torch.bfloat16)
+            torch.testing.assert_close(combined_scaled, expected_scaled)
+
+            torch.distributed.barrier()
+
+    manager.cleanup()
+
+
+@requires_multi_gpu
+@requires_one_sided
+@requires_ptrace
+@pytest.mark.parametrize("world_size", [2])
+def test_one_sided_dispatch_combine(world_size):
+    """Test FlashInfer one-sided dispatch/combine with actual data flow."""
+    _spawn_workers(_one_sided_data_worker, world_size, dp_size=world_size)
diff --git a/tests/distributed/test_torchrun_example.py b/tests/distributed/test_torchrun_example.py
index 8c9898ca20f3..e72f00bc91e0 100644
--- a/tests/distributed/test_torchrun_example.py
+++ b/tests/distributed/test_torchrun_example.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-# unit test for `examples/offline_inference/torchrun_example.py`
+# unit test for `examples/features/torchrun/torchrun_example_offline.py`
 import os
 import random
 
@@ -10,7 +10,8 @@
 from vllm import LLM, SamplingParams
 from vllm.distributed.parallel_state import get_world_group
 
-dist.init_process_group(backend="gloo")
+# Let PyTorch choose the WORLD backend for the current device type.
+dist.init_process_group()
 
 # Create prompts
 prompts = [
@@ -29,7 +30,7 @@
     tensor_parallel_size=2,
     pipeline_parallel_size=int(os.getenv("PP_SIZE", 1)),
     distributed_executor_backend="external_launcher",
-    gpu_memory_utilization=random.uniform(0.7, 0.9),
+    gpu_memory_utilization=random.uniform(0.8, 0.92),
     seed=0,
 )
 
diff --git a/tests/distributed/test_torchrun_example_moe.py b/tests/distributed/test_torchrun_example_moe.py
index a6298d1b6739..969b5e92e3fc 100644
--- a/tests/distributed/test_torchrun_example_moe.py
+++ b/tests/distributed/test_torchrun_example_moe.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-# unit test for `examples/offline_inference/torchrun_example.py`
+# unit test for `examples/features/torchrun/torchrun_example_offline.py`
 import os
 import random
 
@@ -10,7 +10,8 @@
 from vllm import LLM, SamplingParams
 from vllm.distributed.parallel_state import get_tp_group, get_world_group
 
-dist.init_process_group(backend="gloo")
+# Let PyTorch choose the WORLD backend for the current device type.
+dist.init_process_group()
 
 # Create prompts
 prompts = [
@@ -36,8 +37,10 @@
     pipeline_parallel_size=int(os.getenv("PP_SIZE", "1")),
     enable_expert_parallel=int(os.getenv("ENABLE_EP", "0")) == 1,
     distributed_executor_backend="external_launcher",
-    gpu_memory_utilization=random.uniform(0.7, 0.9),
+    gpu_memory_utilization=random.uniform(0.8, 0.92),
     seed=0,
+    max_model_len=1024,
+    max_num_seqs=16,
 )
 
 outputs = llm.generate(prompts, sampling_params)
diff --git a/tests/distributed/test_weight_transfer.py b/tests/distributed/test_weight_transfer.py
index 1c9bc766ab1d..92b8100a1669 100644
--- a/tests/distributed/test_weight_transfer.py
+++ b/tests/distributed/test_weight_transfer.py
@@ -41,6 +41,7 @@ def create_mock_parallel_config(
     config.rank = rank
     config.world_size = world_size
     config.data_parallel_rank = dp_rank
+    config.data_parallel_index = dp_rank
     return config
 
 
@@ -283,6 +284,7 @@ def inference_receive_tensor(
     parallel_config.rank = 0
     parallel_config.world_size = 1
     parallel_config.data_parallel_rank = 0
+    parallel_config.data_parallel_index = 0
 
     engine = NCCLWeightTransferEngine(config, parallel_config)
 
@@ -666,6 +668,7 @@ def inference_receive_ipc_tensor(
     parallel_config.rank = 0
     parallel_config.world_size = 1
     parallel_config.data_parallel_rank = 0
+    parallel_config.data_parallel_index = 0
 
     engine = IPCWeightTransferEngine(config, parallel_config)
 
diff --git a/tests/engine/test_arg_utils.py b/tests/engine/test_arg_utils.py
index 9f03078a4596..2a2658016e24 100644
--- a/tests/engine/test_arg_utils.py
+++ b/tests/engine/test_arg_utils.py
@@ -9,9 +9,10 @@
 import pytest
 from pydantic import Field
 
-from vllm.config import AttentionConfig, CompilationConfig, config
+from vllm.config import AttentionConfig, CompilationConfig, ModelConfig, config
 from vllm.engine.arg_utils import (
     EngineArgs,
+    _expand_json_human_readable_numbers,
     contains_type,
     get_kwargs,
     get_type,
@@ -115,6 +116,10 @@ class DummyConfig:
     """Regular bool with default True"""
     optional_bool: bool | None = None
     """Optional bool with default None"""
+
+    optional_bool_or_str: bool | str | None = None
+    """Optional bool-or-str with default None"""
+
     optional_literal: Literal["x", "y"] | None = None
     """Optional literal with default None"""
     tuple_n: tuple[int, ...] = Field(default_factory=lambda: (1, 2, 3))
@@ -169,6 +174,11 @@ def test_get_kwargs():
     # bools should not have their type set
     assert kwargs["regular_bool"].get("type") is None
     assert kwargs["optional_bool"].get("type") is None
+    # optional bool-or-str should accept an optional string value
+    assert kwargs["optional_bool_or_str"]["type"] is str
+    assert kwargs["optional_bool_or_str"]["nargs"] == "?"
+    assert kwargs["optional_bool_or_str"]["const"] is True
+    assert "action" not in kwargs["optional_bool_or_str"]
     # optional literals should have None as a choice
     assert kwargs["optional_literal"]["choices"] == ["x", "y", "None"]
     # tuples should have the correct nargs
@@ -196,6 +206,32 @@ def test_get_kwargs():
     assert kwargs["nested_config"]["type"]('{"field": 2}') == NestedConfig(2)  # type: ignore[call-arg]
 
 
+def test_hf_token_get_kwargs():
+    kwargs = get_kwargs(ModelConfig)["hf_token"]
+
+    assert kwargs["type"] is str
+    assert kwargs["nargs"] == "?"
+    assert kwargs["const"] is True
+    assert "action" not in kwargs
+
+
+@pytest.mark.parametrize(
+    ("cli_args", "expected"),
+    [
+        ([], None),
+        (["--hf-token"], True),
+        (["--hf-token", "hf_secret"], "hf_secret"),
+        (["--hf-token", "None"], "None"),
+    ],
+)
+def test_hf_token_cli_arg(cli_args, expected):
+    parser = EngineArgs.add_cli_args(FlexibleArgumentParser())
+
+    args = parser.parse_args(cli_args)
+
+    assert args.hf_token == expected
+
+
 @pytest.mark.parametrize(
     ("arg", "expected"),
     [
@@ -332,8 +368,6 @@ def test_attention_config():
             "true",
             "--attention-config.flash_attn_max_num_splits_for_cuda_graph",
             "16",
-            "--attention-config.use_cudnn_prefill",
-            "true",
             "--attention-config.use_trtllm_ragged_deepseek_prefill",
             "true",
             "--attention-config.use_trtllm_attention",
@@ -351,7 +385,6 @@ def test_attention_config():
     assert engine_args.attention_config.flash_attn_version == 3
     assert engine_args.attention_config.use_prefill_decode_attention is True
     assert engine_args.attention_config.flash_attn_max_num_splits_for_cuda_graph == 16
-    assert engine_args.attention_config.use_cudnn_prefill is True
     assert engine_args.attention_config.use_trtllm_ragged_deepseek_prefill is True
     assert engine_args.attention_config.use_trtllm_attention is True
     assert engine_args.attention_config.disable_flashinfer_prefill is True
@@ -563,3 +596,32 @@ def test_ir_op_priority():
             ir_op_priority=ir_op_priority,
             kernel_config=KernelConfig(ir_op_priority=ir_op_priority),
         ).create_engine_config()
+
+
+@pytest.mark.parametrize(
+    ("input_json", "expected_json"),
+    [
+        # Decimal suffixes (lowercase)
+        ('{"x": 80g}', '{"x": 80000000000}'),
+        ('{"x": 1k}', '{"x": 1000}'),
+        ('{"x": 5m}', '{"x": 5000000}'),
+        ('{"x": 2t}', '{"x": 2000000000000}'),
+        # Binary suffixes (uppercase)
+        ('{"x": 1K}', f'{{"x": {2**10}}}'),
+        ('{"x": 1G}', f'{{"x": {2**30}}}'),
+        # Decimal values
+        ('{"x": 1.5g}', '{"x": 1500000000}'),
+        # Quoted strings must NOT be modified
+        ('{"my_key": 80g}', '{"my_key": 80000000000}'),
+        ('{"name": "80g"}', '{"name": "80g"}'),
+        ('{"model_name": "foo_bar"}', '{"model_name": "foo_bar"}'),
+        # Multiple values
+        ('{"a": 1k, "b": 2m}', '{"a": 1000, "b": 2000000}'),
+        # Plain numbers are untouched
+        ('{"x": 42}', '{"x": 42}'),
+        # Nested JSON
+        ('{"outer": {"inner": 10g}}', '{"outer": {"inner": 10000000000}}'),
+    ],
+)
+def test_expand_json_human_readable_numbers(input_json, expected_json):
+    assert _expand_json_human_readable_numbers(input_json) == expected_json
diff --git a/tests/entrypoints/llm/test_generate.py b/tests/entrypoints/llm/test_generate.py
index 34465b7d2708..82f38adfb772 100644
--- a/tests/entrypoints/llm/test_generate.py
+++ b/tests/entrypoints/llm/test_generate.py
@@ -91,6 +91,12 @@ def test_multiple_priority(llm: LLM):
         outputs = llm.generate(PROMPTS, sampling_params=None, priority=[])
 
 
+def test_single_prompt_priority(llm: LLM):
+    # Single string prompts should be normalized to one request.
+    outputs = llm.generate(PROMPTS[0], sampling_params=None, priority=[0])
+    assert len(outputs) == 1
+
+
 def test_max_model_len():
     max_model_len = 20
     llm = LLM(
diff --git a/tests/entrypoints/llm/test_mm_cache_external_injection.py b/tests/entrypoints/llm/test_mm_cache_external_injection.py
new file mode 100644
index 000000000000..3023457c5fed
--- /dev/null
+++ b/tests/entrypoints/llm/test_mm_cache_external_injection.py
@@ -0,0 +1,193 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Test that ``InputProcessor.inject_into_mm_cache()`` correctly injects
+pre-processed mm_kwargs into the processor cache and reports MM cache
+hit rate metrics accurately.
+
+This is used by frameworks like Dynamo that run the HF processor on a
+frontend and transfer pre-processed mm_kwargs to the backend, avoiding
+redundant processing.
+"""
+
+import logging
+
+import pytest
+import regex as re
+
+from tests.entrypoints.openai.chat_completion.test_vision import TEST_IMAGE_ASSETS
+from vllm import LLM, SamplingParams
+from vllm.renderers.params import ChatParams
+from vllm.v1.metrics import loggers as stat_loggers
+from vllm.v1.metrics.reader import Counter, Metric
+
+
+def _make_messages(image_url: str):
+    return [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {"url": image_url},
+                },
+            ],
+        }
+    ]
+
+
+def _get_counter_value(metrics: list[Metric], name: str):
+    metric = next(m for m in metrics if m.name == name)
+    assert isinstance(metric, Counter)
+    return metric.value
+
+
+def _get_mm_cache_stats(metrics: list[Metric]):
+    mm_cache_queries = _get_counter_value(metrics, "vllm:mm_cache_queries")
+    mm_cache_hits = _get_counter_value(metrics, "vllm:mm_cache_hits")
+    return mm_cache_queries, mm_cache_hits
+
+
+def _get_mm_cache_log(llm: LLM, caplog_vllm: pytest.LogCaptureFixture) -> float:
+    caplog_vllm.clear()
+    with caplog_vllm.at_level(logging.INFO, logger=stat_loggers.__name__):
+        llm.llm_engine.do_log_stats()
+
+    assert len(caplog_vllm.records) == 1
+    msg = caplog_vllm.records[0].getMessage()
+
+    assert "MM cache hit rate" in msg
+    match = re.search(r"MM cache hit rate: ([0-9.]+)%", msg)
+    assert match is not None
+    return float(match.group(1))
+
+
+@pytest.mark.parametrize("image_urls", [TEST_IMAGE_ASSETS[:2]], indirect=True)
+@pytest.mark.parametrize("mm_processor_cache_type", ["lru", "shm"])
+def test_inject_into_mm_cache(
+    num_gpus_available,
+    image_urls,
+    mm_processor_cache_type,
+    caplog_vllm,
+):
+    """Test that inject_into_mm_cache() injects pre-processed mm_kwargs into
+    the processor cache and MM cache hit metrics are updated correctly.
+
+    Steps:
+    1. Two normal requests (same image) -> cache miss then hit (baseline)
+    2. Extract cached kwargs, call inject_into_mm_cache with a new hash,
+       then generate with a pre-rendered input -> verifies injection works
+    """
+    llm = LLM(
+        model="llava-hf/llava-1.5-7b-hf",
+        max_model_len=4096,
+        max_num_seqs=5,
+        enforce_eager=True,
+        disable_log_stats=False,
+        limit_mm_per_prompt={"image": 2},
+        mm_processor_cache_type=mm_processor_cache_type,
+    )
+
+    # Step 1: Normal requests to populate the cache
+    llm.chat(_make_messages(image_urls[0]))
+    assert _get_mm_cache_stats(llm.get_metrics()) == (1, 0)
+
+    llm.chat(_make_messages(image_urls[0]))
+    assert _get_mm_cache_stats(llm.get_metrics()) == (2, 1)
+    assert _get_mm_cache_log(llm, caplog_vllm) == pytest.approx(50.0)
+
+    # Step 2: Use a second image to get valid expanded tokens and
+    # placeholder positions via the renderer.
+    llm.chat(_make_messages(image_urls[1]))
+    queries_before = _get_mm_cache_stats(llm.get_metrics())[0]  # 3
+
+    renderer = llm.llm_engine.renderer
+    cache = renderer.mm_processor_cache
+    assert cache is not None, "Processor cache should be enabled"
+
+    _, eng_prompts = renderer.render_chat(
+        [_make_messages(image_urls[1])],
+        ChatParams(),
+    )
+    eng_input = eng_prompts[0]
+
+    # Inject pre-processed mm_kwargs with a NEW hash via public API
+    new_mm_hash = "deadbeef" * 8
+    mm_hashes = {"image": [new_mm_hash]}
+    mm_kwargs = eng_input["mm_kwargs"]
+
+    llm.llm_engine.input_processor.inject_into_mm_cache(mm_hashes, mm_kwargs)
+
+    # Build pre-rendered input (no externally_processed flag needed)
+    pre_rendered_input = {
+        "type": "multimodal",
+        "prompt_token_ids": eng_input["prompt_token_ids"],
+        "mm_kwargs": mm_kwargs,
+        "mm_hashes": mm_hashes,
+        "mm_placeholders": eng_input["mm_placeholders"],
+    }
+
+    llm.generate(
+        pre_rendered_input,
+        sampling_params=SamplingParams(max_tokens=1),
+    )
+
+    # Verify cache was queried and injection happened
+    queries_after = _get_mm_cache_stats(llm.get_metrics())[0]
+    assert queries_after > queries_before, (
+        "Cache should have been queried for the injected item"
+    )
+    mm_rate = _get_mm_cache_log(llm, caplog_vllm)
+    assert mm_rate >= 0.0, "MM cache hit rate should be reported"
+
+
+@pytest.mark.parametrize("image_urls", [TEST_IMAGE_ASSETS[:1]], indirect=True)
+def test_inject_into_mm_cache_without_cache(
+    num_gpus_available,
+    image_urls,
+):
+    """Test that inject_into_mm_cache works gracefully when processor cache
+    is disabled (mm_processor_cache_gb=0). Should not crash.
+    """
+    llm = LLM(
+        model="llava-hf/llava-1.5-7b-hf",
+        max_model_len=4096,
+        max_num_seqs=5,
+        enforce_eager=True,
+        disable_log_stats=False,
+        limit_mm_per_prompt={"image": 2},
+        mm_processor_cache_gb=0,
+    )
+
+    # Run a normal chat request first to warm up the model.
+    llm.chat(_make_messages(image_urls[0]))
+
+    # Use the renderer to get a proper EngineInput with expanded tokens
+    renderer = llm.llm_engine.renderer
+    _, eng_prompts = renderer.render_chat(
+        [_make_messages(image_urls[0])],
+        ChatParams(),
+    )
+    eng_input = eng_prompts[0]
+
+    mm_hashes = {"image": ["abcd1234" * 8]}
+    mm_kwargs = eng_input["mm_kwargs"]
+
+    # inject_into_mm_cache should not crash even without cache
+    llm.llm_engine.input_processor.inject_into_mm_cache(mm_hashes, mm_kwargs)
+
+    # Build and generate with pre-rendered input
+    pre_rendered_input = {
+        "type": "multimodal",
+        "prompt_token_ids": eng_input["prompt_token_ids"],
+        "mm_kwargs": mm_kwargs,
+        "mm_hashes": mm_hashes,
+        "mm_placeholders": eng_input["mm_placeholders"],
+    }
+
+    result = llm.generate(
+        pre_rendered_input,
+        sampling_params=SamplingParams(max_tokens=1),
+    )
+    assert len(result) == 1, "Should produce one output"
+    assert len(result[0].outputs) >= 1, "Should have at least one output sequence"
diff --git a/tests/entrypoints/llm/test_mm_processor_kwargs.py b/tests/entrypoints/llm/test_mm_processor_kwargs.py
new file mode 100644
index 000000000000..1b0092df011d
--- /dev/null
+++ b/tests/entrypoints/llm/test_mm_processor_kwargs.py
@@ -0,0 +1,243 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pytest
+
+from vllm import LLM, SamplingParams
+
+
+def _make_mock_llm() -> LLM:
+    llm = object.__new__(LLM)
+    llm.model_config = SimpleNamespace(
+        runner_type="generate", enable_prompt_embeds=False
+    )
+    return llm
+
+
+def test_generate_forwards_mm_processor_kwargs() -> None:
+    llm = _make_mock_llm()
+    mm_processor_kwargs = {"num_crops": 4}
+    sampling_params = SamplingParams(max_tokens=1)
+
+    llm._run_completion = Mock(return_value=["ok"])
+
+    outputs = llm.generate(
+        "prompt",
+        sampling_params=sampling_params,
+        mm_processor_kwargs=mm_processor_kwargs,
+    )
+
+    assert outputs == ["ok"]
+    assert llm._run_completion.call_args.kwargs["mm_processor_kwargs"] == (
+        mm_processor_kwargs
+    )
+
+
+def test_enqueue_forwards_mm_processor_kwargs() -> None:
+    llm = _make_mock_llm()
+    mm_processor_kwargs = {"do_resize": False}
+    sampling_params = SamplingParams(max_tokens=1)
+
+    llm._add_completion_requests = Mock(return_value=["req-0"])
+
+    request_ids = llm.enqueue(
+        "prompt",
+        sampling_params=sampling_params,
+        use_tqdm=False,
+        mm_processor_kwargs=mm_processor_kwargs,
+    )
+
+    assert request_ids == ["req-0"]
+    assert llm._add_completion_requests.call_args.kwargs["mm_processor_kwargs"] == (
+        mm_processor_kwargs
+    )
+
+
+def test_chat_forwards_mm_processor_kwargs() -> None:
+    llm = _make_mock_llm()
+    mm_processor_kwargs = {"do_pan_and_scan": True}
+    sampling_params = SamplingParams(max_tokens=1)
+    messages = [{"role": "user", "content": "hello"}]
+
+    llm._run_chat = Mock(return_value=["ok"])
+
+    outputs = llm.chat(
+        messages,
+        sampling_params=sampling_params,
+        mm_processor_kwargs=mm_processor_kwargs,
+    )
+
+    assert outputs == ["ok"]
+    assert llm._run_chat.call_args.kwargs["mm_processor_kwargs"] == (
+        mm_processor_kwargs
+    )
+
+
+def test_run_completion_forwards_mm_processor_kwargs() -> None:
+    llm = _make_mock_llm()
+    mm_processor_kwargs = {"min_pixels": 4 * 28 * 28}
+    sampling_params = SamplingParams(max_tokens=1)
+    sentinel_output = ["done"]
+
+    llm._add_completion_requests = Mock()
+    llm._run_engine = Mock(return_value=sentinel_output)
+
+    outputs = llm._run_completion(
+        prompts=["prompt"],
+        params=sampling_params,
+        output_type=object,
+        use_tqdm=False,
+        mm_processor_kwargs=mm_processor_kwargs,
+    )
+
+    assert outputs == sentinel_output
+    assert llm._add_completion_requests.call_args.kwargs["mm_processor_kwargs"] == (
+        mm_processor_kwargs
+    )
+
+
+def test_add_completion_requests_forwards_mm_processor_kwargs() -> None:
+    llm = _make_mock_llm()
+    mm_processor_kwargs = {"max_dynamic_patch": 4}
+    sampling_params = SamplingParams(max_tokens=1)
+
+    llm._params_to_seq = Mock(return_value=[sampling_params])
+    llm._lora_request_to_seq = Mock(return_value=[None])
+    llm._priority_to_seq = Mock(return_value=[0])
+    llm._preprocess_cmpl_one = Mock(return_value={"prompt_token_ids": [1]})
+
+    captured_prompts = []
+
+    def fake_render_and_add_requests(*, prompts, **_kwargs):
+        captured_prompts.extend(prompts)
+        return ["req-0"]
+
+    llm._render_and_add_requests = Mock(side_effect=fake_render_and_add_requests)
+
+    request_ids = llm._add_completion_requests(
+        prompts=["prompt"],
+        params=sampling_params,
+        use_tqdm=False,
+        mm_processor_kwargs=mm_processor_kwargs,
+    )
+
+    assert request_ids == ["req-0"]
+    llm._preprocess_cmpl_one.assert_called_once_with(
+        "prompt",
+        None,
+        mm_processor_kwargs=mm_processor_kwargs,
+    )
+    assert captured_prompts == [{"prompt_token_ids": [1]}]
+
+
+def test_preprocess_cmpl_applies_mm_processor_kwargs_to_renderer(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    llm = _make_mock_llm()
+    mm_processor_kwargs = {"num_crops": 8}
+    prompt = {"prompt": "<image>", "multi_modal_data": {"image": object()}}
+
+    renderer = Mock()
+    renderer.default_cmpl_tok_params = Mock()
+    renderer.default_cmpl_tok_params.with_kwargs.return_value = "tok-params"
+    renderer.render_cmpl.return_value = ["engine-input"]
+    llm.renderer = renderer
+
+    monkeypatch.setattr(
+        "vllm.entrypoints.llm.parse_model_prompt",
+        lambda _model_config, parsed_prompt: parsed_prompt,
+    )
+
+    outputs = llm._preprocess_cmpl(
+        [prompt],
+        mm_processor_kwargs=mm_processor_kwargs,
+    )
+
+    assert outputs == ["engine-input"]
+    renderer.render_cmpl.assert_called_once_with(
+        [prompt],
+        "tok-params",
+        prompt_extras={"mm_processor_kwargs": mm_processor_kwargs},
+    )
+
+
+def test_preprocess_cmpl_keeps_prompt_mm_processor_kwargs_when_no_override(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    llm = _make_mock_llm()
+    prompt = {
+        "prompt": "<image>",
+        "multi_modal_data": {"image": object()},
+        "mm_processor_kwargs": {"num_crops": 2},
+    }
+
+    renderer = Mock()
+    renderer.default_cmpl_tok_params = Mock()
+    renderer.default_cmpl_tok_params.with_kwargs.return_value = "tok-params"
+    renderer.render_cmpl.return_value = ["engine-input"]
+    llm.renderer = renderer
+
+    monkeypatch.setattr(
+        "vllm.entrypoints.llm.parse_model_prompt",
+        lambda _model_config, parsed_prompt: parsed_prompt,
+    )
+
+    outputs = llm._preprocess_cmpl([prompt])
+
+    assert outputs == ["engine-input"]
+    renderer.render_cmpl.assert_called_once_with(
+        [prompt],
+        "tok-params",
+        prompt_extras=None,
+    )
+
+
+def test_preprocess_chat_applies_mm_processor_kwargs_to_renderer() -> None:
+    llm = _make_mock_llm()
+    mm_processor_kwargs = {"num_crops": 8}
+    messages = [[{"role": "user", "content": "Describe this image."}]]
+
+    renderer = Mock()
+    renderer.tokenizer = object()
+    renderer.default_chat_tok_params = Mock()
+    renderer.default_chat_tok_params.with_kwargs.return_value = "tok-params"
+    renderer.render_chat.return_value = (messages, ["engine-input"])
+    llm.renderer = renderer
+
+    outputs = llm._preprocess_chat(
+        messages,
+        mm_processor_kwargs=mm_processor_kwargs,
+    )
+
+    assert outputs == ["engine-input"]
+    call_args = renderer.render_chat.call_args
+    assert call_args.args[0] == messages
+    assert call_args.args[1].mm_processor_kwargs == mm_processor_kwargs
+    assert call_args.args[2] == "tok-params"
+    assert call_args.kwargs["prompt_extras"] == {
+        "mm_processor_kwargs": mm_processor_kwargs
+    }
+
+
+def test_preprocess_chat_omits_mm_processor_kwargs_when_no_override() -> None:
+    llm = _make_mock_llm()
+    messages = [[{"role": "user", "content": "Describe this image."}]]
+
+    renderer = Mock()
+    renderer.tokenizer = object()
+    renderer.default_chat_tok_params = Mock()
+    renderer.default_chat_tok_params.with_kwargs.return_value = "tok-params"
+    renderer.render_chat.return_value = (messages, ["engine-input"])
+    llm.renderer = renderer
+
+    outputs = llm._preprocess_chat(messages)
+
+    assert outputs == ["engine-input"]
+    call_args = renderer.render_chat.call_args
+    assert call_args.args[0] == messages
+    assert call_args.args[1].mm_processor_kwargs is None
+    assert call_args.args[2] == "tok-params"
+    assert call_args.kwargs["prompt_extras"] is None
diff --git a/tests/entrypoints/openai/chat_completion/test_chat.py b/tests/entrypoints/openai/chat_completion/test_chat.py
index 212839f78d5c..6703095aec4a 100644
--- a/tests/entrypoints/openai/chat_completion/test_chat.py
+++ b/tests/entrypoints/openai/chat_completion/test_chat.py
@@ -845,9 +845,10 @@ async def test_chat_completion_n_parameter_non_streaming(
     chat_completion = await client.chat.completions.create(
         model=model_name,
         messages=messages,
-        max_completion_tokens=20,
-        temperature=0.7,
+        max_completion_tokens=50,
+        temperature=1.0,
         n=3,
+        seed=42,
         stream=False,
     )
 
@@ -859,7 +860,6 @@ async def test_chat_completion_n_parameter_non_streaming(
         assert choice.message.content is not None
         assert len(choice.message.content) > 0
 
-    # Verify all responses are different (highly likely with temperature > 0)
     contents = [choice.message.content for choice in chat_completion.choices]
     assert len(set(contents)) > 1, "Expected different responses with n=3"
 
@@ -1002,6 +1002,31 @@ def test_chat_completion_request_n_parameter_default():
     assert sampling_params.n == 1, f"Expected n=1 (default), got n={sampling_params.n}"
 
 
+def test_chat_completion_request_accepts_model_specific_reasoning_effort():
+    request = ChatCompletionRequest(
+        model="test-model",
+        messages=[{"role": "user", "content": "Hello"}],
+        reasoning_effort="max",
+    )
+
+    chat_params = request.build_chat_params(
+        default_template=None,
+        default_template_content_format="auto",
+    )
+
+    assert request.reasoning_effort == "max"
+    assert chat_params.chat_template_kwargs["reasoning_effort"] == "max"
+
+
+def test_chat_completion_request_rejects_unknown_reasoning_effort():
+    with pytest.raises(ValueError, match="Input should be"):
+        ChatCompletionRequest(
+            model="test-model",
+            messages=[{"role": "user", "content": "Hello"}],
+            reasoning_effort="extra_high",
+        )
+
+
 def test_chat_completion_request_n_parameter_various_values():
     """Test n parameter with various values."""
     for n_value in [1, 2, 5, 10]:
diff --git a/tests/entrypoints/openai/chat_completion/test_chat_completion_with_mixed_audio_embeds.py b/tests/entrypoints/openai/chat_completion/test_chat_completion_with_mixed_audio_embeds.py
new file mode 100644
index 000000000000..d005edc950cc
--- /dev/null
+++ b/tests/entrypoints/openai/chat_completion/test_chat_completion_with_mixed_audio_embeds.py
@@ -0,0 +1,190 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""E2E test for mixing `prompt_embeds` with `audio_embeds` in a single
+Chat Completions request."""
+
+import json
+
+import openai
+import pytest
+import pytest_asyncio
+import safetensors
+import torch
+import torch.nn as nn
+from huggingface_hub import hf_hub_download
+from transformers import AutoConfig, AutoTokenizer
+
+from tests.utils import RemoteOpenAIServer
+from vllm.utils.serial_utils import tensor2base64
+
+QWEN2AUDIO_MODEL = "Qwen/Qwen2-Audio-7B-Instruct"
+
+# Use the model's native dtype to avoid an implicit cast inside
+# `safe_load_prompt_embeds` (mismatched floating-point dtypes are cast to the
+# model's dtype automatically, matching here just skips the conversion).
+QWEN2AUDIO_DTYPE = torch.bfloat16
+
+
+@pytest.fixture(scope="module")
+def qwen2audio_server_args() -> list[str]:
+    return [
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "2048",
+        "--max-num-seqs",
+        "4",
+        "--enforce-eager",
+        "--trust-remote-code",
+        "--gpu-memory-utilization",
+        "0.85",
+        "--limit-mm-per-prompt",
+        json.dumps({"audio": 1}),
+        "--enable-prompt-embeds",
+        "--enable-mm-embeds",
+    ]
+
+
+@pytest.fixture(scope="module")
+def qwen2audio_server(qwen2audio_server_args):
+    with RemoteOpenAIServer(
+        QWEN2AUDIO_MODEL,
+        qwen2audio_server_args,
+        max_wait_seconds=600,
+    ) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def qwen2audio_client(qwen2audio_server):
+    async with qwen2audio_server.get_async_client() as async_client:
+        yield async_client
+
+
+@pytest.fixture(scope="module")
+def qwen2audio_hidden_size() -> int:
+    config = AutoConfig.from_pretrained(QWEN2AUDIO_MODEL, trust_remote_code=True)
+    return config.text_config.hidden_size
+
+
+@pytest.fixture(scope="module")
+def qwen2audio_prompt_embeds_b64(qwen2audio_hidden_size: int) -> str:
+    tensor = torch.randn(4, qwen2audio_hidden_size, dtype=QWEN2AUDIO_DTYPE)
+    return tensor2base64(tensor)
+
+
+@pytest.fixture(scope="module")
+def qwen2audio_audio_embeds_b64(qwen2audio_hidden_size: int) -> str:
+    # Shape matches the `audio_embeds` unit-test fixture.
+    torch.manual_seed(0)
+    tensor = torch.randn(1, 128, qwen2audio_hidden_size, dtype=QWEN2AUDIO_DTYPE)
+    return tensor2base64(tensor)
+
+
+@pytest.mark.asyncio
+async def test_prompt_embeds_plus_audio_embeds(
+    qwen2audio_client: openai.AsyncOpenAI,
+    qwen2audio_prompt_embeds_b64: str,
+    qwen2audio_audio_embeds_b64: str,
+):
+    """Single user message carrying both prompt_embeds and audio_embeds parts."""
+    chat = await qwen2audio_client.chat.completions.create(
+        model=QWEN2AUDIO_MODEL,
+        max_tokens=5,
+        temperature=0.0,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "prompt_embeds",
+                        "data": qwen2audio_prompt_embeds_b64,
+                    },
+                    {
+                        "type": "audio_embeds",
+                        "audio_embeds": qwen2audio_audio_embeds_b64,
+                    },
+                    {"type": "text", "text": "Continue."},
+                ],
+            }
+        ],
+    )
+    assert chat.choices[0].message.content is not None
+    assert len(chat.choices[0].message.content) > 0
+
+
+@pytest.fixture(scope="module")
+def qwen2audio_aligned_content_and_embeds_b64() -> tuple[str, str]:
+    """Return `(content, base64_embeds)` where the embeddings are the model's
+    embedding of `content` tokenized WITHOUT special tokens.
+
+    Loads only the `embed_tokens` shard from disk on CPU (~1.1 GB of host
+    RAM) instead of the full 7B model on GPU.
+    """
+    content = "Describe this audio."
+    tokenizer = AutoTokenizer.from_pretrained(QWEN2AUDIO_MODEL, trust_remote_code=True)
+
+    index_path = hf_hub_download(QWEN2AUDIO_MODEL, "model.safetensors.index.json")
+    with open(index_path) as f:
+        weight_map = json.load(f)["weight_map"]
+    embed_key = next(k for k in weight_map if k.endswith("embed_tokens.weight"))
+    shard_path = hf_hub_download(QWEN2AUDIO_MODEL, weight_map[embed_key])
+    with safetensors.safe_open(shard_path, framework="pt", device="cpu") as f:
+        embed_weight = f.get_tensor(embed_key)
+    embed_layer = nn.Embedding.from_pretrained(embed_weight.to(QWEN2AUDIO_DTYPE))
+
+    ids = tokenizer(content, add_special_tokens=False, return_tensors="pt").input_ids
+    embeds = embed_layer(ids).squeeze(0)
+    return content, tensor2base64(embeds)
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "audio_first",
+    [True, False],
+    ids=["audio_embeds-then-text", "text-then-audio_embeds"],
+)
+async def test_text_content_and_prompt_embeds_match_with_audio_embeds(
+    qwen2audio_client: openai.AsyncOpenAI,
+    qwen2audio_audio_embeds_b64: str,
+    qwen2audio_aligned_content_and_embeds_b64: tuple[str, str],
+    audio_first: bool,
+):
+    """Same content as text vs `prompt_embeds` should yield identical Chat
+    Completions output when mixed with `audio_embeds` in the same message.
+    """
+    content, encoded_text_embeds = qwen2audio_aligned_content_and_embeds_b64
+
+    audio_part = {
+        "type": "audio_embeds",
+        "audio_embeds": qwen2audio_audio_embeds_b64,
+    }
+    text_part = {"type": "text", "text": content}
+    embeds_part = {"type": "prompt_embeds", "data": encoded_text_embeds}
+
+    if audio_first:
+        text_content = [audio_part, text_part]
+        embeds_content = [audio_part, embeds_part]
+    else:
+        text_content = [text_part, audio_part]
+        embeds_content = [embeds_part, audio_part]
+
+    text_resp = await qwen2audio_client.chat.completions.create(
+        model=QWEN2AUDIO_MODEL,
+        max_tokens=10,
+        temperature=0.0,
+        messages=[{"role": "user", "content": text_content}],
+    )
+    embeds_resp = await qwen2audio_client.chat.completions.create(
+        model=QWEN2AUDIO_MODEL,
+        max_tokens=10,
+        temperature=0.0,
+        messages=[{"role": "user", "content": embeds_content}],
+    )
+
+    text_out = text_resp.choices[0].message.content
+    embeds_out = embeds_resp.choices[0].message.content
+    assert text_out is not None and len(text_out) > 0
+    assert embeds_out is not None and len(embeds_out) > 0
+    assert text_out == embeds_out
diff --git a/tests/entrypoints/openai/chat_completion/test_chat_completion_with_mixed_image_embeds.py b/tests/entrypoints/openai/chat_completion/test_chat_completion_with_mixed_image_embeds.py
new file mode 100644
index 000000000000..dbbed3c47127
--- /dev/null
+++ b/tests/entrypoints/openai/chat_completion/test_chat_completion_with_mixed_image_embeds.py
@@ -0,0 +1,212 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""E2E tests for mixing `prompt_embeds` with image content parts in a single
+Chat Completions request.
+"""
+
+import json
+
+import openai
+import pytest
+import pytest_asyncio
+import safetensors
+import torch
+import torch.nn as nn
+from huggingface_hub import hf_hub_download
+from transformers import AutoTokenizer
+
+from tests.utils import RemoteOpenAIServer
+from vllm.assets.image import ImageAsset
+from vllm.multimodal.utils import encode_image_url
+from vllm.utils.serial_utils import tensor2base64
+
+MODEL_NAME = "Qwen/Qwen2-VL-2B-Instruct"
+
+# Use the model's native dtype to skip the implicit cast inside
+# `safe_load_prompt_embeds` (mismatched floating-point dtypes are cast to the
+# model's dtype automatically).
+MODEL_DTYPE = torch.bfloat16
+
+
+@pytest.fixture(scope="module")
+def server_args() -> list[str]:
+    return [
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "2048",
+        "--max-num-seqs",
+        "4",
+        "--enforce-eager",
+        "--gpu-memory-utilization",
+        "0.4",
+        "--limit-mm-per-prompt",
+        json.dumps({"image": 1}),
+        "--enable-prompt-embeds",
+        "--enable-mm-embeds",
+    ]
+
+
+@pytest.fixture(scope="module")
+def server(server_args):
+    with RemoteOpenAIServer(
+        MODEL_NAME,
+        server_args,
+        max_wait_seconds=600,
+    ) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client(server):
+    async with server.get_async_client() as async_client:
+        yield async_client
+
+
+@pytest.fixture(scope="module")
+def image_url() -> str:
+    """Stable real image as a data URL, kept identical across both the
+    text and prompt_embeds requests so any output difference must come from
+    how the text content is delivered."""
+    return encode_image_url(ImageAsset("stop_sign").pil_image)
+
+
+@pytest.fixture(scope="module")
+def aligned_content_and_embeds_b64() -> tuple[str, str]:
+    """`(content, base64_embeds)` where the embeddings are the model's
+    embedding of `content` tokenized WITHOUT special tokens.
+
+    Loads only the `embed_tokens` shard from disk on CPU instead of the full
+    model on GPU, so the fixture has zero VRAM footprint and won't contend
+    with the running vLLM server.
+    """
+    content = "Describe this image."
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
+
+    index_path = hf_hub_download(MODEL_NAME, "model.safetensors.index.json")
+    with open(index_path) as f:
+        weight_map = json.load(f)["weight_map"]
+    embed_key = next(k for k in weight_map if k.endswith("embed_tokens.weight"))
+    shard_path = hf_hub_download(MODEL_NAME, weight_map[embed_key])
+    with safetensors.safe_open(shard_path, framework="pt", device="cpu") as f:
+        embed_weight = f.get_tensor(embed_key)
+    embed_layer = nn.Embedding.from_pretrained(embed_weight.to(MODEL_DTYPE))
+
+    ids = tokenizer(content, add_special_tokens=False, return_tensors="pt").input_ids
+    embeds = embed_layer(ids).squeeze(0)
+    return content, tensor2base64(embeds)
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "image_first",
+    [True, False],
+    ids=["image_url-then-text", "text-then-image_url"],
+)
+async def test_text_content_and_prompt_embeds_match_with_image_url(
+    client: openai.AsyncOpenAI,
+    image_url: str,
+    aligned_content_and_embeds_b64: tuple[str, str],
+    image_first: bool,
+):
+    """Same content as text vs `prompt_embeds` should yield identical Chat
+    Completions output when mixed with an `image_url` part in the same
+    message under greedy decoding.
+    """
+    content, encoded_text_embeds = aligned_content_and_embeds_b64
+
+    image_part = {"type": "image_url", "image_url": {"url": image_url}}
+    text_part = {"type": "text", "text": content}
+    embeds_part = {"type": "prompt_embeds", "data": encoded_text_embeds}
+
+    if image_first:
+        text_content = [image_part, text_part]
+        embeds_content = [image_part, embeds_part]
+    else:
+        text_content = [text_part, image_part]
+        embeds_content = [embeds_part, image_part]
+
+    text_resp = await client.chat.completions.create(
+        model=MODEL_NAME,
+        max_tokens=10,
+        temperature=0.0,
+        messages=[{"role": "user", "content": text_content}],
+    )
+    embeds_resp = await client.chat.completions.create(
+        model=MODEL_NAME,
+        max_tokens=10,
+        temperature=0.0,
+        messages=[{"role": "user", "content": embeds_content}],
+    )
+
+    text_out = text_resp.choices[0].message.content
+    embeds_out = embeds_resp.choices[0].message.content
+    assert text_out is not None and len(text_out) > 0
+    assert embeds_out is not None and len(embeds_out) > 0
+    assert text_out == embeds_out
+
+
+@pytest.fixture(scope="module")
+def image_embeds_b64() -> dict[str, str]:
+    """Synthetic but stable `image_embeds` for Qwen2-VL."""
+    grid = (1, 4, 4)
+    spatial_merge_size = 2
+    num_patches = (grid[1] // spatial_merge_size) * (grid[2] // spatial_merge_size)
+    text_hidden_size = 1536  # Qwen2-VL-2B
+    torch.manual_seed(0)
+    return {
+        "image_embeds": tensor2base64(
+            torch.randn(num_patches, text_hidden_size, dtype=MODEL_DTYPE)
+        ),
+        "image_grid_thw": tensor2base64(torch.tensor(grid)),
+    }
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "image_first",
+    [True, False],
+    ids=["image_embeds-then-text", "text-then-image_embeds"],
+)
+async def test_text_content_and_prompt_embeds_match_with_image_embeds(
+    client: openai.AsyncOpenAI,
+    image_embeds_b64: dict[str, str],
+    aligned_content_and_embeds_b64: tuple[str, str],
+    image_first: bool,
+):
+    """Same content as text vs `prompt_embeds` should yield identical Chat
+    Completions output when mixed with a precomputed `image_embeds` part in
+    the same message under greedy decoding.
+    """
+    content, encoded_text_embeds = aligned_content_and_embeds_b64
+
+    image_part = {"type": "image_embeds", "image_embeds": image_embeds_b64}
+    text_part = {"type": "text", "text": content}
+    embeds_part = {"type": "prompt_embeds", "data": encoded_text_embeds}
+
+    if image_first:
+        text_content = [image_part, text_part]
+        embeds_content = [image_part, embeds_part]
+    else:
+        text_content = [text_part, image_part]
+        embeds_content = [embeds_part, image_part]
+
+    text_resp = await client.chat.completions.create(
+        model=MODEL_NAME,
+        max_tokens=10,
+        temperature=0.0,
+        messages=[{"role": "user", "content": text_content}],
+    )
+    embeds_resp = await client.chat.completions.create(
+        model=MODEL_NAME,
+        max_tokens=10,
+        temperature=0.0,
+        messages=[{"role": "user", "content": embeds_content}],
+    )
+
+    text_out = text_resp.choices[0].message.content
+    embeds_out = embeds_resp.choices[0].message.content
+    assert text_out is not None and len(text_out) > 0
+    assert embeds_out is not None and len(embeds_out) > 0
+    assert text_out == embeds_out
diff --git a/tests/entrypoints/openai/chat_completion/test_chat_completion_with_prompt_embeds.py b/tests/entrypoints/openai/chat_completion/test_chat_completion_with_prompt_embeds.py
new file mode 100644
index 000000000000..1813d74798de
--- /dev/null
+++ b/tests/entrypoints/openai/chat_completion/test_chat_completion_with_prompt_embeds.py
@@ -0,0 +1,293 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""E2E tests for `prompt_embeds` content parts in the Chat Completions API."""
+
+import asyncio
+import io
+
+import openai
+import pybase64 as base64
+import pytest
+import pytest_asyncio
+import torch
+from openai import BadRequestError
+
+from tests.utils import VLLM_PATH, RemoteOpenAIServer
+
+MODEL_NAME = "facebook/opt-125m"
+CHAT_TEMPLATE = VLLM_PATH / "examples/template_chatml.jinja"
+# Matches `--dtype` in `server_args` to avoid an implicit cast in
+# `safe_load_prompt_embeds` (mismatched floating-point dtypes are cast to the
+# model's dtype automatically, we match here just to skip the conversion).
+SERVER_DTYPE: torch.dtype = torch.bfloat16
+
+
+@pytest.fixture(scope="module")
+def server_args() -> list[str]:
+    return [
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "2048",
+        "--max-num-seqs",
+        "128",
+        "--enforce-eager",
+        "--chat-template",
+        str(CHAT_TEMPLATE),
+        # Prompt Embeds server args
+        "--enable-prompt-embeds",
+    ]
+
+
+@pytest.fixture(scope="module")
+def server(server_args):
+    with RemoteOpenAIServer(MODEL_NAME, server_args) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client(server):
+    async with server.get_async_client() as async_client:
+        yield async_client
+
+
+def _encode_embeds(embeds: torch.Tensor) -> str:
+    buf = io.BytesIO()
+    torch.save(embeds, buf)
+    return base64.b64encode(buf.getvalue()).decode("utf-8")
+
+
+@pytest.fixture(scope="module")
+def prompt_embeds_b64(hf_runner) -> list[str]:
+    """Pre-compute embeddings for two short prompts and return as base64."""
+    prompts = ["Hello, my name is", "What is an LLM?"]
+    with hf_runner(MODEL_NAME) as hf_model:
+        embeddings = hf_model.get_prompt_embeddings(prompts)
+    # Cast to the server's dtype so `safe_load_prompt_embeds` doesn't need to
+    # convert on its own, the function accepts any floating-point dtype and
+    # will cast to the model's dtype, but matching up front skips the work.
+    return [_encode_embeds(e.to(SERVER_DTYPE)) for e in embeddings]
+
+
+@pytest.mark.asyncio
+async def test_single_prompt_embeds_part(
+    client: openai.AsyncOpenAI,
+    prompt_embeds_b64: list[str],
+):
+    """A user message with one prompt_embeds part + text."""
+    b64 = prompt_embeds_b64[0]
+    chat = await client.chat.completions.create(
+        model=MODEL_NAME,
+        max_tokens=5,
+        temperature=0.0,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "prompt_embeds", "data": b64},
+                    {"type": "text", "text": "Continue:"},
+                ],
+            }
+        ],
+    )
+    assert chat.choices[0].message.content is not None
+    assert len(chat.choices[0].message.content) > 0
+
+
+@pytest.mark.asyncio
+async def test_multiple_prompt_embeds_parts(
+    client: openai.AsyncOpenAI,
+    prompt_embeds_b64: list[str],
+):
+    """Multiple prompt_embeds parts in a single message."""
+    b64_a, b64_b = prompt_embeds_b64
+    chat = await client.chat.completions.create(
+        model=MODEL_NAME,
+        max_tokens=5,
+        temperature=0.0,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "prompt_embeds", "data": b64_a},
+                    {"type": "text", "text": " and "},
+                    {"type": "prompt_embeds", "data": b64_b},
+                ],
+            }
+        ],
+    )
+    assert chat.choices[0].message.content is not None
+    assert len(chat.choices[0].message.content) > 0
+
+
+@pytest.mark.asyncio
+async def test_multi_message_conversation(
+    client: openai.AsyncOpenAI,
+    prompt_embeds_b64: list[str],
+):
+    """prompt_embeds in both system and user messages."""
+    b64_sys, b64_usr = prompt_embeds_b64
+    chat = await client.chat.completions.create(
+        model=MODEL_NAME,
+        max_tokens=5,
+        temperature=0.0,
+        messages=[
+            {
+                "role": "system",
+                "content": [
+                    {"type": "text", "text": "You are helpful."},
+                    {"type": "prompt_embeds", "data": b64_sys},
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {"type": "prompt_embeds", "data": b64_usr},
+                    {"type": "text", "text": "Summarize."},
+                ],
+            },
+        ],
+    )
+    assert chat.choices[0].message.content is not None
+    assert len(chat.choices[0].message.content) > 0
+
+
+@pytest.mark.asyncio
+async def test_streaming(
+    client: openai.AsyncOpenAI,
+    prompt_embeds_b64: list[str],
+):
+    """Streaming chat completion with prompt_embeds."""
+    b64 = prompt_embeds_b64[0]
+
+    # Non-streaming baseline.
+    baseline = await client.chat.completions.create(
+        model=MODEL_NAME,
+        max_tokens=5,
+        temperature=0.0,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "prompt_embeds", "data": b64},
+                    {"type": "text", "text": "Continue:"},
+                ],
+            }
+        ],
+    )
+    expected = baseline.choices[0].message.content
+
+    # Streaming.
+    stream = await client.chat.completions.create(
+        model=MODEL_NAME,
+        max_tokens=5,
+        temperature=0.0,
+        stream=True,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "prompt_embeds", "data": b64},
+                    {"type": "text", "text": "Continue:"},
+                ],
+            }
+        ],
+    )
+    chunks: list[str] = []
+    async for chunk in stream:
+        delta = chunk.choices[0].delta.content
+        if delta:
+            chunks.append(delta)
+    assert "".join(chunks) == expected
+
+
+@pytest.fixture(scope="module")
+def aligned_content_and_embeds_b64(hf_runner) -> tuple[str, str]:
+    """Return `(content, base64_embeds)` where the embeddings are the model's
+    embedding of `content` tokenized WITHOUT special tokens.
+    """
+    content = "Hello, my name is"
+    with hf_runner(MODEL_NAME) as hf_model:
+        ids = hf_model.tokenizer(
+            content, add_special_tokens=False, return_tensors="pt"
+        ).input_ids
+        ids = hf_model.wrap_device({"input_ids": ids})["input_ids"]
+        embed_layer = hf_model.model.get_input_embeddings()
+        embeds = embed_layer(ids).squeeze(0).to(SERVER_DTYPE).cpu()
+    return content, _encode_embeds(embeds)
+
+
+@pytest.mark.asyncio
+async def test_text_content_and_prompt_embeds_match(
+    client: openai.AsyncOpenAI,
+    aligned_content_and_embeds_b64: tuple[str, str],
+):
+    """Equal content in text and `prompt_embeds` should yield identical
+    Chat Completions output under greedy decoding.
+    """
+    content, encoded_embeds = aligned_content_and_embeds_b64
+
+    text_resp, embeds_resp = await asyncio.gather(
+        client.chat.completions.create(
+            model=MODEL_NAME,
+            max_tokens=10,
+            temperature=0.0,
+            messages=[{"role": "user", "content": content}],
+        ),
+        client.chat.completions.create(
+            model=MODEL_NAME,
+            max_tokens=10,
+            temperature=0.0,
+            messages=[
+                {
+                    "role": "user",
+                    "content": [{"type": "prompt_embeds", "data": encoded_embeds}],
+                }
+            ],
+        ),
+    )
+
+    text_out = text_resp.choices[0].message.content
+    embeds_out = embeds_resp.choices[0].message.content
+    assert text_out is not None and len(text_out) > 0
+    assert embeds_out is not None and len(embeds_out) > 0
+    assert text_out == embeds_out
+
+
+@pytest.mark.asyncio
+async def test_missing_data_field(
+    client: openai.AsyncOpenAI,
+):
+    """A prompt_embeds part without `data` should return a clear error."""
+    with pytest.raises(BadRequestError):
+        await client.chat.completions.create(
+            model=MODEL_NAME,
+            max_tokens=5,
+            messages=[
+                {
+                    "role": "user",
+                    "content": [{"type": "prompt_embeds"}],
+                }
+            ],
+        )
+
+
+@pytest.mark.asyncio
+async def test_invalid_base64(
+    client: openai.AsyncOpenAI,
+):
+    """Invalid base64 in the `data` field should return a clear error."""
+    with pytest.raises(BadRequestError):
+        await client.chat.completions.create(
+            model=MODEL_NAME,
+            max_tokens=5,
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "prompt_embeds", "data": "not_valid_base64!!"},
+                    ],
+                }
+            ],
+        )
diff --git a/tests/entrypoints/openai/chat_completion/test_chat_error.py b/tests/entrypoints/openai/chat_completion/test_chat_error.py
index f1fb7c7518b6..582e0792156c 100644
--- a/tests/entrypoints/openai/chat_completion/test_chat_error.py
+++ b/tests/entrypoints/openai/chat_completion/test_chat_error.py
@@ -164,6 +164,58 @@ async def mock_generate(*args, **kwargs):
         await serving_chat.create_chat_completion(request)
 
 
+@pytest.mark.asyncio
+async def test_openai_chat_keeps_mm_cache_for_engine_execution():
+    mock_engine = MagicMock(spec=AsyncLLM)
+    mock_engine.errored = False
+    mock_engine.model_config = MockModelConfig()
+    mock_engine.input_processor = MagicMock()
+    mock_engine.renderer = _build_renderer(mock_engine.model_config)
+
+    serving_chat = _build_serving_chat(mock_engine)
+
+    request = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=[{"role": "user", "content": "Test prompt"}],
+    )
+
+    result = await serving_chat.render_chat_request(request)
+
+    assert isinstance(result, tuple)
+    assert (
+        serving_chat.openai_serving_render.preprocess_chat.call_args.kwargs[
+            "skip_mm_cache"
+        ]
+        is False
+    )
+
+
+@pytest.mark.asyncio
+async def test_renderer_only_chat_request_skips_mm_cache():
+    mock_engine = MagicMock(spec=AsyncLLM)
+    mock_engine.errored = False
+    mock_engine.model_config = MockModelConfig()
+    mock_engine.input_processor = MagicMock()
+    mock_engine.renderer = _build_renderer(mock_engine.model_config)
+
+    serving_chat = _build_serving_chat(mock_engine)
+
+    request = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=[{"role": "user", "content": "Test prompt"}],
+    )
+
+    result = await serving_chat.openai_serving_render.render_chat_request(request)
+
+    assert result.token_ids == [1, 2, 3]
+    assert (
+        serving_chat.openai_serving_render.preprocess_chat.call_args.kwargs[
+            "skip_mm_cache"
+        ]
+        is True
+    )
+
+
 @pytest.mark.asyncio
 async def test_chat_error_stream():
     """test finish_reason='error' returns 500 InternalServerError (streaming)"""
diff --git a/tests/entrypoints/openai/chat_completion/test_completion_with_function_calling.py b/tests/entrypoints/openai/chat_completion/test_completion_with_function_calling.py
index 965b21351302..839793fde856 100644
--- a/tests/entrypoints/openai/chat_completion/test_completion_with_function_calling.py
+++ b/tests/entrypoints/openai/chat_completion/test_completion_with_function_calling.py
@@ -518,7 +518,13 @@ async def test_inconsistent_tool_choice_and_tools(
 
 
 @pytest.mark.asyncio
-async def test_max_tokens_with_tool_choice_required(client: openai.AsyncOpenAI):
+@pytest.mark.parametrize(
+    "tool_choice",
+    ["required", {"type": "function", "function": {"name": "get_current_weather"}}],
+)
+async def test_max_tokens_with_tool_choice_required(
+    client: openai.AsyncOpenAI, tool_choice
+):
     """ """
     models = await client.models.list()
     model_name: str = models.data[0].id
@@ -530,7 +536,7 @@ async def test_max_tokens_with_tool_choice_required(client: openai.AsyncOpenAI):
         max_completion_tokens=1,
         model=model_name,
         tools=tools,
-        tool_choice="required",
+        tool_choice=tool_choice,
     )
     # When `tool_choice="required"` and the tokens of `tools` exceed `max_tokens`,
     # both `tool_calls` and `content` should be empty.
@@ -538,4 +544,3 @@ async def test_max_tokens_with_tool_choice_required(client: openai.AsyncOpenAI):
     choice = chat_completion.choices[0]
     assert choice.finish_reason == "length"
     assert len(choice.message.tool_calls) == 0
-    assert choice.message.content == ""
diff --git a/tests/entrypoints/openai/chat_completion/test_serving_chat.py b/tests/entrypoints/openai/chat_completion/test_serving_chat.py
index 39d59d28f854..c44e07a4c10d 100644
--- a/tests/entrypoints/openai/chat_completion/test_serving_chat.py
+++ b/tests/entrypoints/openai/chat_completion/test_serving_chat.py
@@ -538,6 +538,7 @@ class MockModelConfig:
     is_encoder_decoder: bool = False
     is_multimodal_model: bool = False
     renderer_num_workers: int = 1
+    enable_prompt_embeds: bool = False
 
     def get_diff_sampling_param(self):
         return self.diff_sampling_param or {}
@@ -806,6 +807,57 @@ async def test_serving_chat_should_set_correct_max_tokens():
     assert mock_engine.generate.call_args.args[1].max_tokens == 5
 
 
+@pytest.mark.asyncio
+async def test_serving_chat_truncate_prompt_tokens_max_token_accounting():
+    """When truncate_prompt_tokens is set, max_tokens must be calculated using
+    the truncated prompt length, not the original prompt length.
+
+    Regression: without the fix, get_max_tokens received the untruncated prompt
+    length, causing the output budget to be underestimated.
+    """
+    mock_engine = MagicMock(spec=AsyncLLM)
+    mock_engine.errored = False
+    mock_engine.model_config = MockModelConfig()
+    mock_engine.input_processor = MagicMock()
+    mock_engine.renderer = _build_renderer(mock_engine.model_config)
+
+    serving_chat = _build_serving_chat(mock_engine)
+
+    # "what is 1+1?" tokenizes to 7 tokens with the test chat template
+    # (max_model_len=100 -> max_tokens = 93 without truncation, confirmed by
+    # test_serving_chat_should_set_correct_max_tokens above).
+    messages = [{"role": "user", "content": "what is 1+1?"}]
+
+    # Baseline: no truncation -> max_tokens = 100 - 7 = 93.
+    req = ChatCompletionRequest(model=MODEL_NAME, messages=messages)
+    with suppress(Exception):
+        await serving_chat.create_chat_completion(req)
+    assert mock_engine.generate.call_args.args[1].max_tokens == 93
+
+    # With truncate_prompt_tokens=5 (less than 7): the effective prompt length
+    # is 5, so max_tokens should be 100 - 5 = 95, not 93.
+    req = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=messages,
+        truncate_prompt_tokens=5,
+    )
+    with suppress(Exception):
+        await serving_chat.create_chat_completion(req)
+    assert mock_engine.generate.call_args.args[1].max_tokens == 95
+
+    # With truncate_prompt_tokens=-1 (meaning use full max_model_len as the
+    # truncation limit, i.e., no practical truncation vs the window): effective
+    # length = min(7, 100) = 7 -> max_tokens = 93 again.
+    req = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=messages,
+        truncate_prompt_tokens=-1,
+    )
+    with suppress(Exception):
+        await serving_chat.create_chat_completion(req)
+    assert mock_engine.generate.call_args.args[1].max_tokens == 93
+
+
 @pytest.mark.asyncio
 async def test_serving_chat_mistral_token_ids_prompt_is_validated():
     """Regression test: when the Mistral tokenizer path returns token IDs
diff --git a/tests/entrypoints/openai/chat_completion/test_thinking_token_budget.py b/tests/entrypoints/openai/chat_completion/test_thinking_token_budget.py
index d2db50082a55..ae2b597e13ac 100644
--- a/tests/entrypoints/openai/chat_completion/test_thinking_token_budget.py
+++ b/tests/entrypoints/openai/chat_completion/test_thinking_token_budget.py
@@ -1,18 +1,62 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-"""E2E tests for thinking_token_budget with reasoning models."""
+"""E2E tests for ``thinking_token_budget`` with reasoning models.
+
+Covers Qwen3-0.6B and Qwen3.5 FP8 + MTP.
+"""
+
+import asyncio
+import json
+from typing import Literal
 
 import openai
 import pytest
 import pytest_asyncio
 
-from tests.utils import RemoteOpenAIServer
+from tests.utils import RemoteOpenAIServer, multi_gpu_only, requires_fp8
+from vllm.platforms import current_platform
+from vllm.tokenizers import get_tokenizer
 
 MODEL_NAME = "Qwen/Qwen3-0.6B"
+QWEN35_FP8_MTP_MODEL = "Qwen/Qwen3.5-35B-A3B-FP8"
 MESSAGES = [{"role": "user", "content": "What is 1+1? Be concise."}]
 THINK_BUDGET = 5
 
+REASONING_START_STR = "<think>"
+REASONING_END_STR = "</think>"
+
+
+def _count_reasoning_decode_token_ids_between_markers(
+    full_token_ids: list[int],
+    reasoning_start_ids: list[int],
+    reasoning_end_ids: list[int],
+) -> int | None:
+    """Count decode tokens in the thinking span (after last start, before first end)."""
+
+    if not reasoning_start_ids or not reasoning_end_ids:
+        raise ValueError("reasoning marker token id lists must be non-empty")
+
+    def _last_subseq_index(haystack: list[int], needle: list[int]) -> int:
+        n = len(needle)
+        if n > len(haystack):
+            return -1
+        for i in range(len(haystack) - n, -1, -1):
+            if haystack[i : i + n] == needle:
+                return i
+        return -1
+
+    last_start = _last_subseq_index(full_token_ids, reasoning_start_ids)
+    if last_start < 0:
+        return None
+
+    pos_after_start = last_start + len(reasoning_start_ids)
+    end_n = len(reasoning_end_ids)
+    for j in range(pos_after_start, len(full_token_ids) - end_n + 1):
+        if full_token_ids[j : j + end_n] == reasoning_end_ids:
+            return j - pos_after_start
+    return len(full_token_ids) - pos_after_start
+
 
 @pytest.fixture(scope="module")
 def server():
@@ -48,6 +92,51 @@ def server_with_auto_reasoning_config():
         yield remote_server
 
 
+@pytest.fixture(scope="module")
+def server_qwen35_fp8_mtp_tp2():
+    """Qwen3.5-35B FP8 with MTP speculative decoding and tensor parallel size 2."""
+    if current_platform.device_count() < 2:
+        pytest.skip("Need at least 2 GPUs for --tensor-parallel-size 2")
+    if not current_platform.supports_fp8():
+        pytest.skip("FP8 is not supported on this platform")
+
+    spec_cfg = {
+        "method": "mtp",
+        "num_speculative_tokens": 2,
+        "max_model_len": 32768,
+    }
+    args = [
+        "--tensor-parallel-size",
+        "2",
+        "--max-model-len",
+        "32768",
+        "--speculative-config",
+        json.dumps(spec_cfg),
+        "--reasoning-parser",
+        "qwen3",
+        "--reasoning-config",
+        json.dumps(
+            {
+                "reasoning_start_str": REASONING_START_STR,
+                "reasoning_end_str": REASONING_END_STR,
+            }
+        ),
+    ]
+    # With 4+ GPUs, run TP=2 on physical devices 2,3 so module-scoped 0.6B servers
+    # on 0,1 do not exhaust memory on the same devices as this worker.
+    env_dict = None
+    if current_platform.device_count() >= 4:
+        env_dict = {"CUDA_VISIBLE_DEVICES": "2,3"}
+
+    with RemoteOpenAIServer(
+        QWEN35_FP8_MTP_MODEL,
+        args,
+        max_wait_seconds=3000,
+        env_dict=env_dict,
+    ) as remote_server:
+        yield remote_server
+
+
 @pytest_asyncio.fixture
 async def client(request, server, server_with_auto_reasoning_config):
     server_map = {
@@ -89,8 +178,10 @@ async def test_thinking_token_budget_mixed_requests(client: openai.AsyncOpenAI):
 async def test_thinking_token_budget_limits_reasoning(client: openai.AsyncOpenAI):
     """Test that thinking_token_budget limits the number of reasoning tokens.
 
-    In streaming mode each reasoning delta corresponds to one token, so
-    counting non-empty reasoning_content chunks gives the exact token count.
+    Counts non-empty streaming ``delta.reasoning`` chunks (coarse proxy; each
+    chunk may represent multiple decode tokens — see
+    ``_count_reasoning_decode_token_ids_between_markers`` and the Qwen3.5 MTP
+    test for id-based checks).
     """
 
     reasoning_token_count = 0
@@ -110,3 +201,133 @@ async def test_thinking_token_budget_limits_reasoning(client: openai.AsyncOpenAI
         f"reasoning tokens ({reasoning_token_count}) exceeded "
         f"thinking_token_budget ({THINK_BUDGET})"
     )
+
+
+@pytest.mark.asyncio
+@multi_gpu_only(num_gpus=2)
+@requires_fp8
+async def test_thinking_token_budget_qwen35_fp8_mtp_concurrent_mixed_budget_and_plain(
+    server_qwen35_fp8_mtp_tp2,
+):
+    """Concurrent chat requests: some with ``thinking_token_budget``, some without.
+
+    Exercises the scheduler / input processor under a mixed batch on the same
+    Qwen3.5 FP8 + MTP (TP=2) server. Budgeted calls are checked with
+    ``_count_reasoning_decode_token_ids_between_markers`` on full token ids.
+    """
+
+    _batch_spec: list[tuple[Literal["budget"], int] | tuple[Literal["plain"], None]] = [
+        ("budget", 1),
+        ("budget", 12),
+        ("plain", None),
+        ("budget", 20),
+        ("budget", 14),
+        ("plain", None),
+        ("plain", None),
+        ("budget", 12),
+        ("plain", None),
+    ]
+
+    tokenizer = get_tokenizer(tokenizer_name=QWEN35_FP8_MTP_MODEL)
+    start_ids = list(tokenizer.encode(REASONING_START_STR, add_special_tokens=False))
+    end_ids = list(tokenizer.encode(REASONING_END_STR, add_special_tokens=False))
+
+    async with server_qwen35_fp8_mtp_tp2.get_async_client() as client:
+
+        async def budgeted_call(expected_budget: int):
+            return await client.chat.completions.create(
+                model=QWEN35_FP8_MTP_MODEL,
+                messages=MESSAGES,
+                max_tokens=256,
+                stream=False,
+                extra_body={
+                    "thinking_token_budget": expected_budget,
+                    "return_token_ids": True,
+                },
+            )
+
+        async def plain_call():
+            return await client.chat.completions.create(
+                model=QWEN35_FP8_MTP_MODEL,
+                messages=MESSAGES,
+                max_tokens=256,
+                stream=False,
+            )
+
+        coros = []
+        for row in _batch_spec:
+            if row[0] == "budget":
+                b = row[1]
+                assert isinstance(b, int)
+                coros.append(budgeted_call(b))
+            else:
+                coros.append(plain_call())
+        results = await asyncio.gather(*coros)
+
+    for i, (response, (kind, expected_budget)) in enumerate(
+        zip(results, _batch_spec, strict=True)
+    ):
+        msg = response.choices[0].message
+        assert msg.content or getattr(msg, "reasoning", None), (
+            f"index {i} ({kind}): empty message"
+        )
+
+        if kind == "budget":
+            assert expected_budget is not None
+            assert response.prompt_token_ids is not None
+            assert response.choices[0].token_ids is not None
+            full_ids = list(response.prompt_token_ids) + list(
+                response.choices[0].token_ids
+            )
+            n_reason = _count_reasoning_decode_token_ids_between_markers(
+                full_ids, start_ids, end_ids
+            )
+            assert n_reason is not None, f"index {i}: missing reasoning start in ids"
+            assert n_reason == expected_budget, (
+                f"index {i}: reasoning decode token ids ({n_reason}) != "
+                f"thinking_token_budget ({expected_budget})"
+            )
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("client", ["default", "auto_config"], indirect=True)
+async def test_streaming_with_thinking_disabled_stays_in_content(
+    client: openai.AsyncOpenAI,
+):
+    request_kwargs = {
+        "model": MODEL_NAME,
+        "messages": [
+            {
+                "role": "user",
+                "content": "Which is larger, 4 or 12?"
+                " Output exactly one token: 4 or 12.",
+            }
+        ],
+        "max_tokens": 16,
+        "temperature": 0.0,
+        "extra_body": {"chat_template_kwargs": {"enable_thinking": False}},
+    }
+
+    response = await client.chat.completions.create(**request_kwargs)
+    message = response.choices[0].message
+    assert message.content is not None and message.content.strip() != ""
+    assert getattr(message, "reasoning", None) in (None, "")
+
+    stream = await client.chat.completions.create(
+        **request_kwargs,
+        stream=True,
+    )
+
+    content_chunks = []
+    reasoning_chunks = []
+    async for chunk in stream:
+        if not chunk.choices:
+            continue
+        delta = chunk.choices[0].delta
+        if getattr(delta, "content", None):
+            content_chunks.append(delta.content)
+        if getattr(delta, "reasoning", None):
+            reasoning_chunks.append(delta.reasoning)
+
+    assert "".join(content_chunks).strip() != ""
+    assert reasoning_chunks == []
diff --git a/tests/entrypoints/openai/chat_completion/test_vision_embeds.py b/tests/entrypoints/openai/chat_completion/test_vision_embeds.py
index 574a8f1c86a9..da9787e3f89e 100644
--- a/tests/entrypoints/openai/chat_completion/test_vision_embeds.py
+++ b/tests/entrypoints/openai/chat_completion/test_vision_embeds.py
@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import importlib.util
+
 import numpy as np
 import pybase64 as base64
 import pytest
@@ -10,7 +12,16 @@
 from tests.utils import RemoteOpenAIServer
 from vllm.utils.serial_utils import tensor2base64
 
+# Prithvi requires terratorch, which is temporarily unavailable while PyPI has
+# `lightning` quarantined (#41376). Skip just the Prithvi case; leave the
+# Qwen3-VL case in the same file untouched.
+_TERRATORCH_AVAILABLE = importlib.util.find_spec("terratorch") is not None
+
 
+@pytest.mark.skipif(
+    not _TERRATORCH_AVAILABLE,
+    reason="terratorch unavailable while PyPI has `lightning` quarantined; see #41376",
+)
 @pytest.mark.parametrize(
     "model_name", ["ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11"]
 )
diff --git a/tests/entrypoints/openai/completion/test_completion_error.py b/tests/entrypoints/openai/completion/test_completion_error.py
index 3349f4126bc8..c95e47fa1b16 100644
--- a/tests/entrypoints/openai/completion/test_completion_error.py
+++ b/tests/entrypoints/openai/completion/test_completion_error.py
@@ -3,7 +3,7 @@
 
 from dataclasses import dataclass, field
 from typing import Any
-from unittest.mock import MagicMock
+from unittest.mock import AsyncMock, MagicMock
 
 import pytest
 
@@ -148,6 +148,66 @@ async def mock_generate(*args, **kwargs):
         await serving_completion.create_completion(request)
 
 
+@pytest.mark.asyncio
+async def test_openai_completion_keeps_mm_cache_for_engine_execution():
+    mock_engine = MagicMock(spec=AsyncLLM)
+    mock_engine.errored = False
+    mock_engine.model_config = MockModelConfig()
+    mock_engine.input_processor = MagicMock()
+    mock_engine.renderer = _build_renderer(mock_engine.model_config)
+
+    serving_completion = _build_serving_completion(mock_engine)
+    serving_completion.openai_serving_render.preprocess_completion = AsyncMock(
+        return_value=[{"prompt_token_ids": [1, 2, 3]}]
+    )
+
+    request = CompletionRequest(
+        model=MODEL_NAME,
+        prompt="Test prompt",
+    )
+
+    result = await serving_completion.render_completion_request(request)
+
+    assert isinstance(result, list)
+    assert (
+        serving_completion.openai_serving_render.preprocess_completion.call_args.kwargs[
+            "skip_mm_cache"
+        ]
+        is False
+    )
+
+
+@pytest.mark.asyncio
+async def test_renderer_only_completion_request_skips_mm_cache():
+    mock_engine = MagicMock(spec=AsyncLLM)
+    mock_engine.errored = False
+    mock_engine.model_config = MockModelConfig()
+    mock_engine.input_processor = MagicMock()
+    mock_engine.renderer = _build_renderer(mock_engine.model_config)
+
+    serving_completion = _build_serving_completion(mock_engine)
+    serving_completion.openai_serving_render.preprocess_completion = AsyncMock(
+        return_value=[{"prompt_token_ids": [1, 2, 3]}]
+    )
+
+    request = CompletionRequest(
+        model=MODEL_NAME,
+        prompt="Test prompt",
+    )
+
+    result = await serving_completion.openai_serving_render.render_completion_request(
+        request
+    )
+
+    assert isinstance(result, list)
+    assert (
+        serving_completion.openai_serving_render.preprocess_completion.call_args.kwargs[
+            "skip_mm_cache"
+        ]
+        is True
+    )
+
+
 @pytest.mark.asyncio
 async def test_completion_error_stream():
     """test finish_reason='error' returns 500 InternalServerError (streaming)"""
diff --git a/tests/entrypoints/openai/completion/test_prompt_validation.py b/tests/entrypoints/openai/completion/test_prompt_validation.py
index f44d13c555c5..81204b27bc0b 100644
--- a/tests/entrypoints/openai/completion/test_prompt_validation.py
+++ b/tests/entrypoints/openai/completion/test_prompt_validation.py
@@ -62,6 +62,8 @@ def test_load_prompt_embeds(
 ):
     model_config = Mock(spec=ModelConfig)
     model_config.enable_prompt_embeds = True
+    model_config.get_hidden_size.return_value = hidden_size
+    model_config.dtype = dtype
 
     # construct arbitrary tensors of various dtypes, layouts, and sizes.
     # We need to check against different layouts to make sure that if a user
diff --git a/tests/entrypoints/openai/completion/test_shutdown.py b/tests/entrypoints/openai/completion/test_shutdown.py
index 80d00bd2397a..d7fba364ab71 100644
--- a/tests/entrypoints/openai/completion/test_shutdown.py
+++ b/tests/entrypoints/openai/completion/test_shutdown.py
@@ -299,19 +299,22 @@ async def test_abort_timeout_exits_quickly(wait_for_engine_idle: float):
         start_time = time.time()
         proc.send_signal(signal.SIGTERM)
 
-        # abort timeout (0) should exit promptly
-        for _ in range(20):
-            if proc.poll() is not None:
-                break
-            time.sleep(0.1)
+        # abort timeout (0) should stop the server promptly. On ROCm, process
+        # exit can spend extra time in HIP/RCCL/native extension teardown after
+        # the server and engine have already shut down.
+        max_exit_time = 4.0 if _IS_ROCM else 2.1
 
-        if proc.poll() is None:
+        try:
+            proc.wait(timeout=max_exit_time)
+        except subprocess.TimeoutExpired:
             proc.kill()
             proc.wait(timeout=5)
             pytest.fail("Process did not exit after SIGTERM with abort timeout")
 
         exit_time = time.time() - start_time
-        assert exit_time < 2, f"Default shutdown took too long: {exit_time:.1f}s"
+        assert exit_time < max_exit_time, (
+            f"Default shutdown took too long: {exit_time:.1f}s"
+        )
         assert proc.returncode in (0, -15, None), f"Unexpected: {proc.returncode}"
 
         await _assert_children_cleaned_up(child_pids)
diff --git a/tests/entrypoints/openai/correctness/test_transcription_api_correctness.py b/tests/entrypoints/openai/correctness/test_transcription_api_correctness.py
index 194c52eae35e..fedbd74795b5 100644
--- a/tests/entrypoints/openai/correctness/test_transcription_api_correctness.py
+++ b/tests/entrypoints/openai/correctness/test_transcription_api_correctness.py
@@ -13,7 +13,6 @@
 import time
 from statistics import mean, median
 
-import librosa
 import pytest
 import soundfile
 import torch
@@ -21,11 +20,16 @@
 from evaluate import load
 from transformers.models.whisper.english_normalizer import EnglishTextNormalizer
 
+from vllm.multimodal.audio import get_audio_duration
 from vllm.tokenizers import get_tokenizer
 
 from ....models.registry import HF_EXAMPLE_MODELS
 from ....utils import RemoteOpenAIServer
 
+# Tuned to prevent OOM on 18GB GPUs in transcription correctness tests.
+MAX_SEQS_FOR_TRANSCRIPTION_TEST = 8
+GPU_UTIL_FOR_TRANSCRIPTION_TEST = 0.5
+
 
 def to_bytes(y, sr):
     buffer = io.BytesIO()
@@ -84,7 +88,7 @@ async def process_dataset(model, client, data, concurrent_request):
         trust_remote_code=model_info.trust_remote_code,
     )
 
-    # Warmup call as the first `librosa.load` server-side is quite slow.
+    # Warmup call as the first `load_audio` server-side is quite slow.
     audio, sr = data[0]["audio"]["array"], data[0]["audio"]["sampling_rate"]
     _ = await bound_transcribe(sem, client, tokenizer, (audio, sr), "")
 
@@ -118,7 +122,7 @@ def print_performance_metrics(results, total_time):
 
 def add_duration(sample):
     y, sr = sample["audio"]["array"], sample["audio"]["sampling_rate"]
-    sample["duration_ms"] = librosa.get_duration(y=y, sr=sr) * 1000
+    sample["duration_ms"] = get_audio_duration(y=y, sr=sr) * 1000
     return sample
 
 
@@ -167,9 +171,8 @@ def run_evaluation(
     "model_config",
     [
         ("openai/whisper-large-v3", 12.744980),
-        # TODO (ekagra): turn on after asr release
         # CohereASR is used to test the variable encoder length code paths
-        # ("CohereLabs/cohere-transcribe-03-2026", 11.92),
+        ("CohereLabs/cohere-transcribe-03-2026", 11.92),
     ],
 )
 # Original dataset is 20GB+ in size, hence we use a pre-filtered slice.
@@ -185,6 +188,8 @@ def test_wer_correctness(
     server_args = [
         "--enforce-eager",
         f"--tokenizer_mode={model_info.tokenizer_mode}",
+        f"--max_num_seqs={MAX_SEQS_FOR_TRANSCRIPTION_TEST}",
+        f"--gpu_memory_utilization={GPU_UTIL_FOR_TRANSCRIPTION_TEST}",
     ]
     if model_info.trust_remote_code:
         server_args.append("--trust-remote-code")
diff --git a/tests/entrypoints/openai/parser/test_harmony_utils.py b/tests/entrypoints/openai/parser/test_harmony_utils.py
index 21b53dff1507..69b9f101ea28 100644
--- a/tests/entrypoints/openai/parser/test_harmony_utils.py
+++ b/tests/entrypoints/openai/parser/test_harmony_utils.py
@@ -843,6 +843,13 @@ def test_all_standard_channels_present(self) -> None:
                     f"{channel} missing when with_custom_tools={with_tools}"
                 )
 
+    def test_unsupported_reasoning_effort_raises_clear_error(self) -> None:
+        with pytest.raises(
+            ValueError,
+            match="reasoning_effort='max' is not supported by Harmony",
+        ):
+            get_system_message(reasoning_effort="max")
+
 
 class TestResponseInputToHarmonyReasoningItem:
     """Tests for response_input_to_harmony handling of reasoning input items.
diff --git a/tests/entrypoints/openai/realtime/test_realtime_validation.py b/tests/entrypoints/openai/realtime/test_realtime_validation.py
index bb6b02f5c99e..e317090fa543 100644
--- a/tests/entrypoints/openai/realtime/test_realtime_validation.py
+++ b/tests/entrypoints/openai/realtime/test_realtime_validation.py
@@ -5,7 +5,6 @@
 import json
 import warnings
 
-import librosa
 import numpy as np
 import pybase64 as base64
 import pytest
@@ -14,6 +13,7 @@
 from tests.entrypoints.openai.conftest import add_attention_backend
 from tests.utils import ROCM_ENV_OVERRIDES, ROCM_EXTRA_ARGS, RemoteOpenAIServer
 from vllm.assets.audio import AudioAsset
+from vllm.multimodal.media.audio import load_audio
 
 # Increase engine iteration timeout for ROCm where first-use JIT compilation
 # can exceed the default 60s, causing a silent deadlock in feed_tokens.
@@ -56,7 +56,7 @@ async def send_event(ws, event: dict) -> None:
 def mary_had_lamb_audio_chunks() -> list[str]:
     """Audio split into ~1 second chunks for streaming."""
     path = AudioAsset("mary_had_lamb").get_local_path()
-    audio, _ = librosa.load(str(path), sr=16000, mono=True)
+    audio, _ = load_audio(str(path), sr=16000, mono=True)
 
     # Split into ~0.1 second chunks (1600 samples at 16kHz)
     chunk_size = 1600
diff --git a/tests/entrypoints/openai/responses/test_errors.py b/tests/entrypoints/openai/responses/test_errors.py
index 0ef9bb901a64..e21f6aa2a42a 100644
--- a/tests/entrypoints/openai/responses/test_errors.py
+++ b/tests/entrypoints/openai/responses/test_errors.py
@@ -6,7 +6,9 @@
 
 import pytest
 
+import vllm.envs as envs
 from vllm.entrypoints.openai.engine.serving import GenerationError, OpenAIServing
+from vllm.envs import disable_envs_cache
 
 
 @pytest.mark.asyncio
@@ -60,3 +62,35 @@ async def test_convert_generation_error_to_streaming_response():
     assert isinstance(error_json, str)
     assert "Internal server error" in error_json
     assert "InternalServerError" in error_json
+
+
+def test_is_model_supported_skip_name_validation_env(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """When VLLM_SKIP_MODEL_NAME_VALIDATION is set, accept any model id."""
+    disable_envs_cache()
+    monkeypatch.delenv("VLLM_SKIP_MODEL_NAME_VALIDATION", raising=False)
+
+    mock_engine = MagicMock()
+    mock_engine.model_config = MagicMock()
+    mock_engine.model_config.max_model_len = 100
+    mock_models = MagicMock()
+    mock_models.is_base_model.return_value = False
+
+    serving = OpenAIServing(
+        engine_client=mock_engine,
+        models=mock_models,
+        request_logger=None,
+    )
+
+    assert serving._is_model_supported("not-a-registered-model") is False
+
+    monkeypatch.setenv("VLLM_SKIP_MODEL_NAME_VALIDATION", "1")
+    disable_envs_cache()
+    assert envs.VLLM_SKIP_MODEL_NAME_VALIDATION is True
+    assert serving._is_model_supported("not-a-registered-model") is True
+
+    monkeypatch.setenv("VLLM_SKIP_MODEL_NAME_VALIDATION", "true")
+    disable_envs_cache()
+    assert envs.VLLM_SKIP_MODEL_NAME_VALIDATION is True
+    assert serving._is_model_supported("another-alias") is True
diff --git a/tests/entrypoints/openai/responses/test_function_call.py b/tests/entrypoints/openai/responses/test_function_call.py
index f198e1cfa708..8ca43feaca4f 100644
--- a/tests/entrypoints/openai/responses/test_function_call.py
+++ b/tests/entrypoints/openai/responses/test_function_call.py
@@ -321,12 +321,100 @@ async def test_function_calling_with_streaming_expected_arguments(
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
+@pytest.mark.parametrize(
+    "tool_choice",
+    ["auto", "required", {"type": "function", "name": "get_current_weather"}],
+)
 async def test_function_calling_with_streaming_types(
-    client: openai.AsyncOpenAI, model_name: str
+    client: openai.AsyncOpenAI, model_name: str, tool_choice
 ):
     # this links the "done" type with the "start" type
     # so every "done" type should have a corresponding "start" type
     # and every open block should be closed by the end of the stream
+    #
+    # stream of events for a response with function call could look like this:
+    # option1: reasoning -> content(option) -> function_call
+    # response.created
+    # -> response.in_progress
+    # -> response.output_item.added
+    # -> response.reasoning_part.added
+    # -> response.reasoning_text.delta
+    # ....
+    # -> response.reasoning_text.delta
+    # -> response.reasoning_text.done
+    # -> response.reasoning_part.done
+    # -> response.output_item.done
+    # -> response.output_item.added
+    # -> response.content_part.added
+    # -> response.output_text.delta
+    # ...
+    # -> response.output_text.delta
+    # -> response.output_text.done
+    # -> response.content_part.done
+    # -> response.output_item.done
+    # -> response.output_item.added
+    # -> response.function_call_arguments.delta
+    # ...
+    # -> response.function_call_arguments.delta
+    # -> response.function_call_arguments.done
+    # -> response.output_item.done
+    # -> response.completed
+    #
+    #
+    # option2: reasoning -> content
+    # response.created
+    # -> response.in_progress
+    # -> response.output_item.added
+    # -> response.reasoning_part.added
+    # -> response.reasoning_text.delta
+    # ....
+    # -> response.reasoning_text.delta
+    # -> response.reasoning_text.done
+    # -> response.reasoning_part.done
+    # -> response.output_item.done
+    # -> response.output_item.added
+    # -> response.content_part.added
+    # -> response.output_text.delta
+    # ..
+    # -> response.output_text.delta
+    # -> response.output_text.done
+    # -> response.content_part.done
+    # -> response.output_item.done
+    # -> response.completed
+    #
+    # option3: content
+    #
+    # response.created
+    # -> response.in_progress
+    # -> response.output_item.added
+    # -> response.content_part.added
+    # -> response.output_text.delta
+    # ...
+    # -> response.output_text.delta
+    # -> response.output_text.done
+    # -> response.content_part.done
+    # -> response.output_item.done
+    # -> response.completed
+    #
+    # option4: content -> function_call
+    # response.created
+    # -> response.in_progress
+    # -> response.output_item.added
+    # -> response.content_part.added
+    # -> response.output_text.delta
+    # ...
+    # -> response.output_text.delta
+    # -> response.output_text.done
+    # -> response.content_part.done
+    # -> response.output_item.done
+    # -> response.output_item.added
+    # -> response.function_call_arguments.delta
+    # ...
+    # -> response.function_call_arguments.delta
+    # -> response.function_call_arguments.done
+    # -> response.output_item.done
+    # -> response.completed
+
     pairs_of_event_types = {
         "response.completed": "response.created",
         "response.output_item.done": "response.output_item.added",
@@ -347,6 +435,7 @@ async def test_function_calling_with_streaming_types(
         model=model_name,
         input=input_list,
         tools=tools,
+        tool_choice=tool_choice,
         stream=True,
     )
 
@@ -367,3 +456,69 @@ async def test_function_calling_with_streaming_types(
             assert stack_of_event_types[-1] == pairs_of_event_types[event.type]
             stack_of_event_types.pop()
     assert len(stack_of_event_types) == 0
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+@pytest.mark.parametrize(
+    "tool_choice",
+    ["required", "auto", {"type": "function", "name": "get_weather"}],
+)
+async def test_function_calling_with_streaming_forced_tool_choice(
+    client: openai.AsyncOpenAI, model_name: str, tool_choice: str
+):
+    tools = [
+        {
+            "type": "function",
+            "name": "get_weather",
+            "description": "Get current temperature for provided location in celsius.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {"type": "string"},
+                },
+                "required": ["location"],
+                "additionalProperties": False,
+            },
+            "strict": True,
+        }
+    ]
+
+    stream_response = await client.responses.create(
+        model=model_name,
+        input="Call the get_weather function for Berlin and do not answer directly.",
+        tools=tools,
+        tool_choice=tool_choice,
+        stream=True,
+    )
+
+    tool_call_item = None
+    completed_event = None
+    text_deltas = []
+    async for event in stream_response:
+        if (
+            event.type == "response.output_item.added"
+            and event.item.type == "function_call"
+        ):
+            tool_call_item = event.item
+        elif event.type == "response.output_text.delta":
+            text_deltas.append(event.delta)
+        elif event.type == "response.function_call_arguments.delta" and tool_call_item:
+            tool_call_item.arguments += event.delta
+        elif (
+            event.type == "response.output_item.done"
+            and event.item.type == "function_call"
+        ):
+            completed_event = event
+
+    assert tool_call_item is not None
+    assert tool_call_item.type == "function_call"
+    assert tool_call_item.name == "get_weather"
+    assert completed_event is not None
+    assert tool_call_item.arguments == completed_event.item.arguments
+    assert tool_call_item.name == completed_event.item.name
+    args = json.loads(tool_call_item.arguments)
+    assert "location" in args
+    assert args["location"] is not None
+    # Forced tool choice should not leak tool-call JSON via output_text delta.
+    assert "".join(text_deltas).strip() == ""
diff --git a/tests/entrypoints/openai/speech_to_text/test_speech_to_text_cancellation.py b/tests/entrypoints/openai/speech_to_text/test_speech_to_text_cancellation.py
new file mode 100644
index 000000000000..2c8c1229e840
--- /dev/null
+++ b/tests/entrypoints/openai/speech_to_text/test_speech_to_text_cancellation.py
@@ -0,0 +1,191 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import asyncio
+from types import SimpleNamespace
+from unittest.mock import AsyncMock, Mock
+
+import pytest
+
+from vllm.entrypoints.openai.speech_to_text.protocol import TranscriptionResponse
+from vllm.entrypoints.openai.speech_to_text.speech_to_text import OpenAISpeechToText
+
+
+async def _never_finishes():
+    await asyncio.Event().wait()
+    yield
+
+
+async def _records_start_then_never_finishes(started_request_ids, request_id):
+    started_request_ids.append(request_id)
+    await asyncio.Event().wait()
+    yield
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    ("engine_inputs", "expected_request_ids"),
+    [
+        ([{"prompt": "chunk"}], ["transcribe-outer-request"]),
+        (
+            [{"prompt": "chunk-0"}, {"prompt": "chunk-1"}],
+            ["transcribe-outer-request-0", "transcribe-outer-request-1"],
+        ),
+    ],
+)
+async def test_non_streaming_cancel_aborts_engine_requests(
+    engine_inputs, expected_request_ids
+):
+    engine_client = SimpleNamespace(
+        errored=False,
+        generate=Mock(side_effect=lambda *_args, **_kwargs: _never_finishes()),
+        abort=AsyncMock(),
+        is_tracing_enabled=AsyncMock(return_value=False),
+    )
+
+    server = OpenAISpeechToText.__new__(OpenAISpeechToText)
+    server.engine_client = engine_client
+    server.task_type = "transcribe"
+    server.models = SimpleNamespace(model_name=lambda: "audio")
+    server.model_config = SimpleNamespace(max_model_len=1024)
+    server.model_cls = SimpleNamespace(no_space_languages=set())
+    server.default_sampling_params = {}
+    server.asr_config = SimpleNamespace(max_audio_clip_s=30)
+    server._check_model = AsyncMock(return_value=None)
+    server._maybe_get_adapters = Mock(return_value=None)
+    server._preprocess_speech_to_text = AsyncMock(return_value=(engine_inputs, 40.0))
+    server._log_inputs = Mock()
+
+    request = SimpleNamespace(
+        model="audio",
+        response_format="json",
+        stream=False,
+        use_beam_search=False,
+        max_completion_tokens=None,
+        language="en",
+        prompt="",
+        to_sampling_params=Mock(return_value=object()),
+    )
+    raw_request = SimpleNamespace(
+        headers={"X-Request-Id": "outer-request"},
+        state=SimpleNamespace(),
+    )
+
+    task = asyncio.create_task(
+        server._create_speech_to_text(
+            audio_data=b"audio",
+            request=request,
+            raw_request=raw_request,
+            response_class=TranscriptionResponse,
+            stream_generator_method=Mock(),
+        )
+    )
+    await asyncio.sleep(0)
+
+    task.cancel()
+    with pytest.raises(asyncio.CancelledError):
+        await task
+
+    generated_request_ids = [
+        call.args[2] for call in engine_client.generate.call_args_list
+    ]
+    assert generated_request_ids == expected_request_ids
+    engine_client.abort.assert_awaited_once_with(expected_request_ids)
+
+
+@pytest.mark.asyncio
+async def test_non_streaming_cancel_advances_all_chunk_generators():
+    started_request_ids: list[str] = []
+    engine_client = SimpleNamespace(
+        errored=False,
+        generate=Mock(
+            side_effect=lambda *_args, **_kwargs: (
+                _records_start_then_never_finishes(started_request_ids, _args[2])
+            )
+        ),
+        abort=AsyncMock(),
+        is_tracing_enabled=AsyncMock(return_value=False),
+    )
+
+    engine_inputs = [
+        {"prompt": "chunk-0"},
+        {"prompt": "chunk-1"},
+        {"prompt": "chunk-2"},
+    ]
+    server = OpenAISpeechToText.__new__(OpenAISpeechToText)
+    server.engine_client = engine_client
+    server.task_type = "transcribe"
+    server.models = SimpleNamespace(model_name=lambda: "audio")
+    server.model_config = SimpleNamespace(max_model_len=1024)
+    server.model_cls = SimpleNamespace(no_space_languages=set())
+    server.default_sampling_params = {}
+    server.asr_config = SimpleNamespace(max_audio_clip_s=30)
+    server._check_model = AsyncMock(return_value=None)
+    server._maybe_get_adapters = Mock(return_value=None)
+    server._preprocess_speech_to_text = AsyncMock(return_value=(engine_inputs, 90.0))
+    server._log_inputs = Mock()
+
+    request = SimpleNamespace(
+        model="audio",
+        response_format="json",
+        stream=False,
+        use_beam_search=False,
+        max_completion_tokens=None,
+        language="en",
+        prompt="",
+        to_sampling_params=Mock(return_value=object()),
+    )
+    raw_request = SimpleNamespace(
+        headers={"X-Request-Id": "outer-request"},
+        state=SimpleNamespace(),
+    )
+
+    task = asyncio.create_task(
+        server._create_speech_to_text(
+            audio_data=b"audio",
+            request=request,
+            raw_request=raw_request,
+            response_class=TranscriptionResponse,
+            stream_generator_method=Mock(),
+        )
+    )
+    await asyncio.sleep(0.01)
+
+    expected_request_ids = [
+        "transcribe-outer-request-0",
+        "transcribe-outer-request-1",
+        "transcribe-outer-request-2",
+    ]
+    assert set(started_request_ids) == set(expected_request_ids)
+
+    task.cancel()
+    with pytest.raises(asyncio.CancelledError):
+        await task
+
+
+@pytest.mark.asyncio
+async def test_language_detection_cancel_aborts_engine_request():
+    engine_client = SimpleNamespace(
+        generate=Mock(return_value=_never_finishes()),
+        abort=AsyncMock(),
+    )
+
+    server = OpenAISpeechToText.__new__(OpenAISpeechToText)
+    server.engine_client = engine_client
+    server.asr_config = SimpleNamespace()
+    server.tokenizer = Mock()
+    server.model_cls = SimpleNamespace(
+        get_language_detection_prompt=Mock(return_value={"prompt": "detect"}),
+        get_language_token_ids=Mock(return_value=[1]),
+        parse_language_detection_output=Mock(),
+    )
+
+    request_id = "transcribe-outer-request-lang_detect"
+    task = asyncio.create_task(server._detect_language(Mock(), request_id))
+    await asyncio.sleep(0)
+
+    task.cancel()
+    with pytest.raises(asyncio.CancelledError):
+        await task
+
+    engine_client.abort.assert_awaited_once_with(request_id)
diff --git a/tests/entrypoints/openai/speech_to_text/test_transcription_validation_whisper.py b/tests/entrypoints/openai/speech_to_text/test_transcription_validation_whisper.py
index 8dba1b59742b..511179f7fcb1 100644
--- a/tests/entrypoints/openai/speech_to_text/test_transcription_validation_whisper.py
+++ b/tests/entrypoints/openai/speech_to_text/test_transcription_validation_whisper.py
@@ -6,7 +6,6 @@
 import io
 import json
 
-import librosa
 import numpy as np
 import openai
 import pytest
@@ -14,6 +13,7 @@
 import soundfile as sf
 
 from tests.utils import RemoteOpenAIServer
+from vllm.multimodal.media.audio import load_audio
 from vllm.platforms import current_platform
 
 MODEL_NAME = "openai/whisper-large-v3-turbo"
@@ -134,7 +134,7 @@ async def test_bad_requests(mary_had_lamb, whisper_client):
 @pytest.mark.asyncio
 async def test_long_audio_request(mary_had_lamb, whisper_client):
     mary_had_lamb.seek(0)
-    audio, sr = librosa.load(mary_had_lamb)
+    audio, sr = load_audio(mary_had_lamb)
     # Add small silence after each audio for repeatability in the split process
     audio = np.pad(audio, (0, 1600))
     repeated_audio = np.tile(audio, 10)
diff --git a/tests/entrypoints/openai/speech_to_text/test_translation_validation.py b/tests/entrypoints/openai/speech_to_text/test_translation_validation.py
index 16b9614d957e..a8b17bf34324 100644
--- a/tests/entrypoints/openai/speech_to_text/test_translation_validation.py
+++ b/tests/entrypoints/openai/speech_to_text/test_translation_validation.py
@@ -7,7 +7,6 @@
 import json
 
 import httpx
-import librosa
 import numpy as np
 import openai
 import pytest
@@ -17,6 +16,7 @@
 from tests.entrypoints.openai.conftest import add_attention_backend
 from tests.utils import RemoteOpenAIServer
 from vllm.logger import init_logger
+from vllm.multimodal.media.audio import load_audio
 
 logger = init_logger(__name__)
 
@@ -264,7 +264,7 @@ async def test_long_audio_request(foscolo, client_and_model):
     if model_name == "google/gemma-3n-E2B-it":
         pytest.skip("Gemma3n does not support long audio requests")
     foscolo.seek(0)
-    audio, sr = librosa.load(foscolo)
+    audio, sr = load_audio(foscolo)
     repeated_audio = np.tile(audio, 2)
     # Repeated audio to buffer
     buffer = io.BytesIO()
diff --git a/tests/entrypoints/openai/test_fingerprint.py b/tests/entrypoints/openai/test_fingerprint.py
new file mode 100644
index 000000000000..b78ed38636c5
--- /dev/null
+++ b/tests/entrypoints/openai/test_fingerprint.py
@@ -0,0 +1,76 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for ``system_fingerprint`` construction."""
+
+from types import SimpleNamespace
+
+import pytest
+
+from vllm.entrypoints.openai import fingerprint as fp
+
+
+def _cfg(tp=1, pp=1, dp=1, ep=False, digest="a3b21f94deadbeef"):
+    c = SimpleNamespace(
+        parallel_config=SimpleNamespace(
+            tensor_parallel_size=tp,
+            pipeline_parallel_size=pp,
+            data_parallel_size=dp,
+            enable_expert_parallel=ep,
+        )
+    )
+    c.compute_hash = lambda: digest  # type: ignore[attr-defined]
+    return c
+
+
+@pytest.fixture(autouse=True)
+def _reset():
+    fp.set_default_fingerprint_mode("full")
+    yield
+    fp.set_default_fingerprint_mode("full")
+
+
+def test_four_modes_produce_expected_shapes():
+    from vllm import __version__ as v
+
+    cfg = _cfg(tp=8, ep=True)
+
+    assert fp.build_system_fingerprint(cfg, "full") == (f"vllm-{v}-tp8-ep-a3b21f94")
+    assert fp.build_system_fingerprint(cfg, "hash") == f"vllm-{v}-a3b21f94"
+    assert fp.build_system_fingerprint(cfg, "custom", "my-fp") == "my-fp"
+    assert fp.build_system_fingerprint(cfg, "none") is None
+
+
+def test_full_mode_emits_only_non_trivial_parallelism():
+    from vllm import __version__ as v
+
+    # Single-GPU: nothing between version and hash.
+    assert fp.build_system_fingerprint(_cfg(), "full") == f"vllm-{v}-a3b21f94"
+    # All parallelism axes.
+    assert (
+        fp.build_system_fingerprint(_cfg(tp=8, pp=2, dp=4, ep=True), "full")
+        == f"vllm-{v}-tp8-pp2-dp4-ep-a3b21f94"
+    )
+
+
+def test_get_respects_set_default():
+    cfg = _cfg(tp=8)
+    full = fp.get_system_fingerprint(cfg)
+    assert full == fp.get_system_fingerprint(cfg)
+
+    fp.set_default_fingerprint_mode("hash")
+    hashed = fp.get_system_fingerprint(cfg)
+    assert hashed != full
+    assert "tp8" not in hashed
+
+    fp.set_default_fingerprint_mode("custom", "deploy-42")
+    assert fp.get_system_fingerprint(cfg) == "deploy-42"
+
+    fp.set_default_fingerprint_mode("none")
+    assert fp.get_system_fingerprint(cfg) is None
+
+
+def test_compute_hash_failure_does_not_raise():
+    cfg = _cfg()
+    cfg.compute_hash = lambda: (_ for _ in ()).throw(RuntimeError("boom"))
+    assert fp.build_system_fingerprint(cfg, "full").endswith("-nohash")
+    assert fp.build_system_fingerprint(cfg, "hash").endswith("-nohash")
diff --git a/tests/entrypoints/openai/test_mm_serde.py b/tests/entrypoints/openai/test_mm_serde.py
new file mode 100644
index 000000000000..c568d822e1c0
--- /dev/null
+++ b/tests/entrypoints/openai/test_mm_serde.py
@@ -0,0 +1,111 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Roundtrip tests for multimodal serde used by the disagg generate endpoint."""
+
+import torch
+
+from vllm.entrypoints.serve.disagg.mm_serde import (
+    decode_mm_kwargs_item,
+    encode_mm_kwargs_item,
+)
+from vllm.entrypoints.serve.disagg.protocol import (
+    MultiModalFeatures,
+    PlaceholderRangeInfo,
+)
+from vllm.multimodal.inputs import (
+    MultiModalBatchedField,
+    MultiModalFieldElem,
+    MultiModalFlatField,
+    MultiModalKwargsItem,
+    MultiModalSharedField,
+)
+
+
+def test_mm_kwargs_item_roundtrip():
+    """Full roundtrip test with all three field types and multiple dtypes."""
+    e1 = MultiModalFieldElem(
+        data=torch.zeros(1000, dtype=torch.bfloat16),
+        field=MultiModalBatchedField(),
+    )
+    e2 = MultiModalFieldElem(
+        data=torch.ones(100, dtype=torch.int32),
+        field=MultiModalSharedField(batch_size=4),
+    )
+    e3 = MultiModalFieldElem(
+        data=torch.randn(20, dtype=torch.float32),
+        field=MultiModalFlatField(slices=[slice(0, 10), slice(10, 20)], dim=0),
+    )
+
+    item = MultiModalKwargsItem({"pixel_values": e1, "grid_thw": e2, "embeds": e3})
+    encoded = encode_mm_kwargs_item(item)
+
+    # Encoded result is a base64 string
+    assert isinstance(encoded, str)
+
+    decoded = decode_mm_kwargs_item(encoded)
+
+    assert set(decoded.keys()) == {"pixel_values", "grid_thw", "embeds"}
+    assert torch.equal(item["pixel_values"].data, decoded["pixel_values"].data)
+    assert torch.equal(item["grid_thw"].data, decoded["grid_thw"].data)
+    assert torch.equal(item["embeds"].data, decoded["embeds"].data)
+    assert isinstance(decoded["pixel_values"].field, MultiModalBatchedField)
+    assert isinstance(decoded["grid_thw"].field, MultiModalSharedField)
+    assert isinstance(decoded["embeds"].field, MultiModalFlatField)
+
+
+def test_mm_kwargs_item_none_data():
+    """Roundtrip with None data field."""
+    elem = MultiModalFieldElem(
+        data=None,
+        field=MultiModalSharedField(batch_size=2),
+    )
+    item = MultiModalKwargsItem({"empty": elem})
+    encoded = encode_mm_kwargs_item(item)
+    decoded = decode_mm_kwargs_item(encoded)
+
+    assert decoded["empty"].data is None
+    assert isinstance(decoded["empty"].field, MultiModalSharedField)
+
+
+def test_mm_kwargs_item_nested_tensors():
+    """Roundtrip with nested tensor data."""
+    nested = [torch.randn(3, 4), torch.randn(5, 4)]
+    elem = MultiModalFieldElem(
+        data=nested,
+        field=MultiModalBatchedField(),
+    )
+    item = MultiModalKwargsItem({"nested": elem})
+    encoded = encode_mm_kwargs_item(item)
+    decoded = decode_mm_kwargs_item(encoded)
+
+    decoded_data = decoded["nested"].data
+    assert len(decoded_data) == 2
+    assert torch.equal(nested[0], decoded_data[0])
+    assert torch.equal(nested[1], decoded_data[1])
+
+
+def test_mm_features_with_kwargs_data():
+    """Test that MultiModalFeatures can carry serialized tensor data."""
+    elem = MultiModalFieldElem(
+        data=torch.randn(5, 3, dtype=torch.float32),
+        field=MultiModalBatchedField(),
+    )
+    item = MultiModalKwargsItem({"pixel_values": elem})
+    encoded = encode_mm_kwargs_item(item)
+
+    features = MultiModalFeatures(
+        mm_hashes={"image": ["abc123"]},
+        mm_placeholders={"image": [PlaceholderRangeInfo(offset=0, length=10)]},
+        kwargs_data={"image": [encoded]},
+    )
+
+    # JSON roundtrip
+    json_str = features.model_dump_json()
+    features2 = MultiModalFeatures.model_validate_json(json_str)
+
+    assert features2.mm_hashes == {"image": ["abc123"]}
+    assert features2.kwargs_data is not None
+    assert len(features2.kwargs_data["image"]) == 1
+
+    decoded = decode_mm_kwargs_item(features2.kwargs_data["image"][0])
+    assert torch.equal(elem.data, decoded["pixel_values"].data)
diff --git a/tests/entrypoints/openai/test_openai_schema.py b/tests/entrypoints/openai/test_openai_schema.py
index 083290ed5b3a..61ec8b5c50ef 100644
--- a/tests/entrypoints/openai/test_openai_schema.py
+++ b/tests/entrypoints/openai/test_openai_schema.py
@@ -1,18 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import json
-from http import HTTPStatus
 from typing import Final
 
 import pytest
 import schemathesis
-from httpx import URL
-from hypothesis import settings
+from hypothesis import HealthCheck, settings
 from schemathesis import GenerationConfig
-from schemathesis.checks import not_a_server_error
-from schemathesis.internal.checks import CheckContext
 from schemathesis.models import Case
-from schemathesis.transports.responses import GenericResponse
 
 from vllm.platforms import current_platform
 
@@ -65,20 +60,10 @@ def before_generate_case(context: schemathesis.hooks.HookContext, strategy):
 
     def no_invalid_types(case: schemathesis.models.Case):
         """
-        This filter skips test cases with invalid data that schemathesis
-        incorrectly generates due to permissive schema configurations.
-        
-        1. Skips `POST /tokenize` endpoint cases with `"type": "file"` in 
-           message content, which isn't implemented.
-        
-        2. Skips tool_calls with `"type": "custom"` which schemathesis 
-           incorrectly generates instead of the valid `"type": "function"`.
-
-        Example test cases that are skipped:
-        curl -X POST -H 'Content-Type: application/json' \
-            -d '{"messages": [{"content": [{"file": {}, "type": "file"}], "role": "user"}]}' \
-            http://localhost:8000/tokenize
+        Skips tool_calls with `"type": "custom"` which schemathesis incorrectly
+        generates instead of the valid `"type": "function"`.
 
+        Example test case that is skipped:
         curl -X POST -H 'Content-Type: application/json' \
             -d '{"messages": [{"role": "assistant", "tool_calls": [{"custom": {"input": "", "name": ""}, "id": "", "type": "custom"}]}]}' \
             http://localhost:8000/v1/chat/completions
@@ -93,20 +78,6 @@ def no_invalid_types(case: schemathesis.models.Case):
                     if not isinstance(message, dict):
                         continue
 
-                    # Check for invalid file type in tokenize endpoint
-                    if op.method.lower() == "post" and op.path == "/tokenize":
-                        content = message.get("content", [])
-                        if (
-                            isinstance(content, list)
-                            and len(content) > 0
-                            and any(
-                                isinstance(item, dict) and item.get("type") == "file"
-                                for item in content
-                            )
-                        ):
-                            return False
-
-                    # Check for invalid tool_calls with non-function types
                     tool_calls = message.get("tool_calls", [])
                     if isinstance(tool_calls, list):
                         for tool_call in tool_calls:
@@ -136,24 +107,19 @@ def no_invalid_types(case: schemathesis.models.Case):
     return strategy.filter(no_invalid_types)
 
 
-def customized_not_a_server_error(
-    ctx: CheckContext, response: GenericResponse, case: Case
-) -> bool | None:
-    try:
-        return not_a_server_error(ctx, response, case)
-    except Exception:
-        if (
-            URL(response.request.url).path
-            in ["/v1/chat/completions/render", "/v1/chat/completions"]
-            and response.status_code == HTTPStatus.NOT_IMPLEMENTED.value
-        ):
-            return True
-        raise
-
-
 @schema.parametrize()
 @schema.override(headers={"Content-Type": "application/json"})
-@settings(deadline=LONG_TIMEOUT_SECONDS * 1000, max_examples=50)
+@settings(
+    deadline=LONG_TIMEOUT_SECONDS * 1000,
+    max_examples=50,
+    # Under CI's derandomized hypothesis seed, the schemathesis strategy
+    # for /v1/chat/completions/batch's nested-message body, combined with
+    # the no_invalid_types filter (notably the grammar=="" rule), exceeds
+    # the default filtered-vs-good ratio. The filter is intentional, so
+    # suppress the health check rather than drop the filter — dropping it
+    # exposes pre-existing server bugs out of scope here.
+    suppress_health_check=[HealthCheck.filter_too_much],
+)
 def test_openapi_stateless(case: Case):
     key = (
         case.operation.method.upper(),
@@ -167,7 +133,9 @@ def test_openapi_stateless(case: Case):
     # (weight_transfer_config) and are meant to be stateful.
     if case.operation.path in (
         "/init_weight_transfer_engine",
+        "/start_weight_update",
         "/update_weights",
+        "/finish_weight_update",
     ):
         return
 
@@ -180,9 +148,4 @@ def test_openapi_stateless(case: Case):
     }.get(key, DEFAULT_TIMEOUT_SECONDS)
 
     # No need to verify SSL certificate for localhost
-    case.call_and_validate(
-        verify=False,
-        timeout=timeout,
-        additional_checks=(customized_not_a_server_error,),
-        excluded_checks=(not_a_server_error,),
-    )
+    case.call_and_validate(verify=False, timeout=timeout)
diff --git a/tests/entrypoints/openai/test_tool_calls_serialization.py b/tests/entrypoints/openai/test_tool_calls_serialization.py
new file mode 100644
index 000000000000..cedc2575b80e
--- /dev/null
+++ b/tests/entrypoints/openai/test_tool_calls_serialization.py
@@ -0,0 +1,150 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for tool_calls Iterable → list materialisation.
+
+Regression tests for https://github.com/vllm-project/vllm/issues/34792.
+
+Setting VLLM_LOGGING_LEVEL=debug caused tool calling to break for Mistral
+models because:
+  1. The OpenAI Python SDK types tool_calls as Iterable[...] in
+     ChatCompletionAssistantMessageParam.
+  2. Pydantic v2, when validating from Python objects (not from raw JSON),
+     wraps Iterable fields in a one-shot lazy iterator.
+  3. Debug logging called model_dump_json() which consumed that iterator.
+  4. The Mistral tokenizer then saw empty tool_calls and raised
+     "ValueError: Unexpected tool call id ...".
+"""
+
+import pytest
+
+from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
+
+
+def _make_tool_call(tc_id: str, name: str, args: str) -> dict:
+    return {
+        "id": tc_id,
+        "type": "function",
+        "function": {"name": name, "arguments": args},
+    }
+
+
+def _make_request(messages: list) -> ChatCompletionRequest:
+    return ChatCompletionRequest(
+        model="test-model",
+        messages=messages,
+    )
+
+
+def test_tool_calls_list_preserved_after_model_dump():
+    """tool_calls in assistant messages must be readable after model_dump_json.
+
+    When the request is built from Python dicts (as in the Anthropic → OpenAI
+    conversion path), Pydantic v2 previously wrapped the Iterable tool_calls
+    in a one-shot iterator.  model_dump_json() consumed it, leaving subsequent
+    readers (e.g. the Mistral tokenizer) with an empty sequence.
+    """
+    tool_call = _make_tool_call("call_abc123", "get_weather", '{"city": "Paris"}')
+    messages = [
+        {"role": "user", "content": "What is the weather in Paris?"},
+        {"role": "assistant", "content": None, "tool_calls": [tool_call]},
+        {
+            "role": "tool",
+            "tool_call_id": "call_abc123",
+            "content": '{"temperature": 20}',
+        },
+    ]
+
+    req = _make_request(messages)
+
+    # Simulate debug logging: serialize the model (this was the trigger)
+    _ = req.model_dump_json()
+
+    # The assistant message must still have accessible tool_calls afterwards
+    assistant_msg = req.messages[1]
+    assert isinstance(assistant_msg, dict)
+    tool_calls = assistant_msg.get("tool_calls")
+    assert tool_calls is not None, "tool_calls must not be None after model_dump_json"
+    assert isinstance(tool_calls, list), "tool_calls must be a list"
+    assert len(tool_calls) > 0, "tool_calls must not be empty after model_dump_json"
+
+
+def test_tool_calls_from_generator_are_materialised():
+    """tool_calls passed as a generator must be converted to list on validation."""
+    tool_call = _make_tool_call("call_gen1", "search", '{"query": "vllm"}')
+
+    def tool_calls_gen():
+        yield tool_call
+
+    messages = [
+        {"role": "user", "content": "Search for vllm"},
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": tool_calls_gen(),  # one-shot generator
+        },
+    ]
+
+    req = _make_request(messages)
+    assistant_msg = req.messages[1]
+    assert isinstance(assistant_msg, dict)
+
+    # Iterate twice — must not raise or return empty on second pass
+    tool_calls_first = list(assistant_msg.get("tool_calls", []))
+    tool_calls_second = list(assistant_msg.get("tool_calls", []))
+
+    assert len(tool_calls_first) == 1, "First read must return the tool call"
+    assert len(tool_calls_second) == 1, "Second read must also return the tool call"
+
+
+def test_tool_calls_list_passthrough():
+    """tool_calls already provided as a list must remain a list."""
+    tool_call = _make_tool_call("call_list1", "calculate", '{"expr": "2+2"}')
+    messages = [
+        {"role": "user", "content": "Calculate 2+2"},
+        {"role": "assistant", "content": None, "tool_calls": [tool_call]},
+    ]
+
+    req = _make_request(messages)
+    assistant_msg = req.messages[1]
+    assert isinstance(assistant_msg, dict)
+    assert isinstance(assistant_msg.get("tool_calls"), list)
+
+
+def test_messages_without_tool_calls_unaffected():
+    """Messages without tool_calls must be handled correctly."""
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hello!"},
+        {"role": "assistant", "content": "Hi there!"},
+    ]
+
+    req = _make_request(messages)
+    # None of the messages should have tool_calls injected
+    for msg in req.messages:
+        assert isinstance(msg, dict)
+        assert msg.get("tool_calls") is None or msg.get("tool_calls") == []
+
+
+@pytest.mark.parametrize("num_tool_calls", [1, 3])
+def test_multiple_tool_calls_materialised(num_tool_calls: int):
+    """Multiple tool calls in a single message are all preserved."""
+    tool_calls = [
+        _make_tool_call(f"call_{i}", f"func_{i}", f'{{"arg": {i}}}')
+        for i in range(num_tool_calls)
+    ]
+    messages = [
+        {"role": "user", "content": "Do things"},
+        {"role": "assistant", "content": None, "tool_calls": iter(tool_calls)},
+    ]
+
+    req = _make_request(messages)
+    assistant_msg = req.messages[1]
+    assert isinstance(assistant_msg, dict)
+
+    result_tool_calls = assistant_msg.get("tool_calls")
+    assert isinstance(result_tool_calls, list)
+    assert len(result_tool_calls) == num_tool_calls
+
+    # Verify after model_dump_json too
+    _ = req.model_dump_json()
+    assert len(assistant_msg.get("tool_calls", [])) == num_tool_calls
diff --git a/tests/entrypoints/openai/test_tool_choice_content_none.py b/tests/entrypoints/openai/test_tool_choice_content_none.py
new file mode 100644
index 000000000000..c1da5918697c
--- /dev/null
+++ b/tests/entrypoints/openai/test_tool_choice_content_none.py
@@ -0,0 +1,94 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
+from vllm.entrypoints.openai.engine.serving import OpenAIServing
+from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
+from vllm.parser.abstract_parser import DelegatingParser
+
+pytestmark = pytest.mark.skip_global_cleanup
+
+
+class _DummyDelegatingParser(DelegatingParser):
+    def is_reasoning_end(self, input_ids: list[int]) -> bool:
+        return False
+
+    def extract_content_ids(self, input_ids: list[int]) -> list[int]:
+        return input_ids
+
+    def extract_reasoning(self, model_output: str, request):
+        return None, model_output
+
+    def extract_reasoning_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: list[int],
+        current_token_ids: list[int],
+        delta_token_ids: list[int],
+    ):
+        return None
+
+    def extract_tool_calls(self, model_output: str, request):
+        return None
+
+
+def test_parse_tool_calls_from_content_allows_named_tool_choice_with_none_content():
+    request = ChatCompletionRequest.model_validate(
+        {
+            "model": "test-model",
+            "messages": [{"role": "user", "content": "test"}],
+            "tools": [
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "parameters": {"type": "object", "properties": {}},
+                    },
+                }
+            ],
+            "tool_choice": {"type": "function", "function": {"name": "get_weather"}},
+        }
+    )
+
+    tool_calls, content = OpenAIServing._parse_tool_calls_from_content(
+        request=request,
+        tokenizer=None,
+        enable_auto_tools=True,
+        tool_parser_cls=None,
+        content=None,
+    )
+
+    assert content is None
+    assert tool_calls is not None
+    assert tool_calls == []
+
+
+def test_responses_parser_allows_named_tool_choice_with_none_content():
+    request = ResponsesRequest.model_validate(
+        {
+            "model": "test-model",
+            "input": "test",
+            "tools": [
+                {
+                    "type": "function",
+                    "name": "get_weather",
+                    "parameters": {"type": "object", "properties": {}},
+                }
+            ],
+            "tool_choice": {"type": "function", "name": "get_weather"},
+        }
+    )
+    parser = _DummyDelegatingParser(tokenizer=None)
+
+    tool_calls, content = parser._parse_tool_calls(
+        request=request,
+        content=None,
+        enable_auto_tools=False,
+    )
+
+    assert content is None
+    assert tool_calls == []
diff --git a/tests/entrypoints/pooling/classify/test_offline.py b/tests/entrypoints/pooling/classify/test_offline.py
index 828443ebc419..2f6a9f1db4a9 100644
--- a/tests/entrypoints/pooling/classify/test_offline.py
+++ b/tests/entrypoints/pooling/classify/test_offline.py
@@ -1,13 +1,12 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import logging
 import weakref
 
 import pytest
 import torch
 
 from tests.models.utils import softmax
-from vllm import LLM, ClassificationRequestOutput, PoolingParams, PoolingRequestOutput
+from vllm import LLM, ClassificationRequestOutput, PoolingParams
 from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.tasks import PoolingTask
 
@@ -66,18 +65,6 @@ def test_list_prompts(llm: LLM):
         assert len(outputs[i].outputs.probs) == num_labels
 
 
-@pytest.mark.skip_global_cleanup
-def test_token_classify(llm: LLM, caplog_vllm):
-    with caplog_vllm.at_level(level=logging.WARNING, logger="vllm"):
-        outputs = llm.encode(prompt, pooling_task="token_classify", use_tqdm=False)
-        assert "deprecated" in caplog_vllm.text
-
-    assert len(outputs) == 1
-    assert isinstance(outputs[0], PoolingRequestOutput)
-    assert outputs[0].prompt_token_ids == prompt_token_ids
-    assert outputs[0].outputs.data.shape == (len(prompt_token_ids), num_labels)
-
-
 @pytest.mark.skip_global_cleanup
 def test_pooling_params(llm: LLM):
     def get_outputs(use_activation):
@@ -110,10 +97,12 @@ def test_score_api(llm: LLM):
         llm.score("ping", "pong", use_tqdm=False)
 
 
-@pytest.mark.parametrize("task", ["embed", "token_embed", "plugin"])
+@pytest.mark.parametrize("task", ["embed", "token_embed", "token_classify", "plugin"])
 def test_unsupported_tasks(llm: LLM, task: PoolingTask):
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "token_classify":
+        err_msg = "Try switching the model's pooling_task via.+"
     else:
         err_msg = "Embedding API is not supported by this model.+"
     with pytest.raises(ValueError, match=err_msg):
diff --git a/tests/entrypoints/pooling/classify/test_online.py b/tests/entrypoints/pooling/classify/test_online.py
index 848ce4083c50..c37da642b494 100644
--- a/tests/entrypoints/pooling/classify/test_online.py
+++ b/tests/entrypoints/pooling/classify/test_online.py
@@ -436,26 +436,7 @@ async def test_pooling_classify(server: RemoteOpenAIServer, model_name: str):
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_pooling_token_classify(server: RemoteOpenAIServer, model_name: str):
-    task = "token_classify"
-    response = requests.post(
-        server.url_for("pooling"),
-        json={
-            "model": model_name,
-            "input": input_text,
-            "encoding_format": "float",
-            "task": task,
-        },
-    )
-    poolings = PoolingResponse.model_validate(response.json())
-    assert len(poolings.data) == 1
-    assert len(poolings.data[0].data) == 8
-    assert len(poolings.data[0].data[0]) == 2
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize("model_name", [MODEL_NAME])
-@pytest.mark.parametrize("task", ["embed", "token_embed", "plugin"])
+@pytest.mark.parametrize("task", ["embed", "token_embed", "token_classify", "plugin"])
 async def test_pooling_not_supported(
     server: RemoteOpenAIServer, model_name: str, task: str
 ):
@@ -469,8 +450,11 @@ async def test_pooling_not_supported(
         },
     )
     assert response.json()["error"]["type"] == "BadRequestError"
+
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "token_classify":
+        err_msg = "Try switching the model's pooling_task via"
     else:
         err_msg = f"Unsupported task: {task!r}"
     assert response.json()["error"]["message"].startswith(err_msg)
diff --git a/tests/entrypoints/pooling/embed/test_offline.py b/tests/entrypoints/pooling/embed/test_offline.py
index 1ffeb027b489..c19e13075eb8 100644
--- a/tests/entrypoints/pooling/embed/test_offline.py
+++ b/tests/entrypoints/pooling/embed/test_offline.py
@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import logging
 import weakref
 
 import pytest
@@ -38,11 +37,11 @@ def llm():
         seed=0,
         attention_config=attention_config,
     )
+    assert embedding_size == llm.model_config.embedding_size
 
     yield weakref.proxy(llm)
 
     del llm
-
     cleanup_dist_env_and_memory()
 
 
@@ -74,16 +73,6 @@ def test_list_prompts(llm: LLM):
         assert len(outputs[i].outputs.embedding) == embedding_size
 
 
-@pytest.mark.skip_global_cleanup
-def test_token_embed(llm: LLM, caplog_vllm):
-    with caplog_vllm.at_level(level=logging.WARNING, logger="vllm"):
-        outputs = llm.encode(prompt, pooling_task="token_embed", use_tqdm=False)
-        assert "deprecated" in caplog_vllm.text
-
-    multi_vector = outputs[0].outputs.data
-    assert multi_vector.shape == (11, 384)
-
-
 @pytest.mark.skip_global_cleanup
 def test_pooling_params(llm: LLM):
     def get_outputs(normalize):
@@ -107,10 +96,14 @@ def get_outputs(normalize):
     )
 
 
-@pytest.mark.parametrize("task", ["token_classify", "classify", "plugin"])
+@pytest.mark.parametrize(
+    "task", ["token_classify", "classify", "token_embed", "plugin"]
+)
 def test_unsupported_tasks(llm: LLM, task: PoolingTask):
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "token_embed":
+        err_msg = "Try switching the model's pooling_task via.+"
     else:
         err_msg = "Classification API is not supported by this model.+"
     with pytest.raises(ValueError, match=err_msg):
diff --git a/tests/entrypoints/pooling/embed/test_online.py b/tests/entrypoints/pooling/embed/test_online.py
index 3032645c7b17..d5565f25d37c 100644
--- a/tests/entrypoints/pooling/embed/test_online.py
+++ b/tests/entrypoints/pooling/embed/test_online.py
@@ -732,28 +732,9 @@ async def test_pooling_embed(server: RemoteOpenAIServer, model_name: str):
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_pooling_token_embed(server: RemoteOpenAIServer, model_name: str):
-    task = "token_embed"
-    response = requests.post(
-        server.url_for("pooling"),
-        json={
-            "model": model_name,
-            "input": input_text,
-            "encoding_format": "float",
-            "task": task,
-        },
-    )
-
-    poolings = PoolingResponse.model_validate(response.json())
-
-    assert len(poolings.data) == 1
-    assert len(poolings.data[0].data) == len(input_tokens)
-    assert len(poolings.data[0].data[0]) == 384
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize("model_name", [MODEL_NAME])
-@pytest.mark.parametrize("task", ["classify", "token_classify", "plugin"])
+@pytest.mark.parametrize(
+    "task", ["classify", "token_classify", "token_embed", "plugin"]
+)
 async def test_pooling_not_supported(
     server: RemoteOpenAIServer, model_name: str, task: str
 ):
@@ -769,6 +750,8 @@ async def test_pooling_not_supported(
     assert response.json()["error"]["type"] == "BadRequestError"
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "token_embed":
+        err_msg = "Try switching the model's pooling_task via"
     else:
         err_msg = f"Unsupported task: {task!r}"
     assert response.json()["error"]["message"].startswith(err_msg)
diff --git a/tests/entrypoints/pooling/reward/test_offline.py b/tests/entrypoints/pooling/reward/test_token_reward_offline.py
similarity index 100%
rename from tests/entrypoints/pooling/reward/test_offline.py
rename to tests/entrypoints/pooling/reward/test_token_reward_offline.py
diff --git a/tests/entrypoints/pooling/pooling/test_online.py b/tests/entrypoints/pooling/reward/test_token_reward_online.py
similarity index 100%
rename from tests/entrypoints/pooling/pooling/test_online.py
rename to tests/entrypoints/pooling/reward/test_token_reward_online.py
diff --git a/tests/entrypoints/pooling/scoring/test_cross_encoder_online.py b/tests/entrypoints/pooling/scoring/test_cross_encoder_online.py
index 5eaa2d92805c..54b4fe075b47 100644
--- a/tests/entrypoints/pooling/scoring/test_cross_encoder_online.py
+++ b/tests/entrypoints/pooling/scoring/test_cross_encoder_online.py
@@ -452,25 +452,6 @@ async def test_pooling_classify(server: RemoteOpenAIServer):
     assert len(poolings.data[0].data) == 1
 
 
-@pytest.mark.asyncio
-async def test_pooling_token_classify(server: RemoteOpenAIServer):
-    response = requests.post(
-        server.url_for("pooling"),
-        json={
-            "model": MODEL_NAME,
-            "task": "token_classify",
-            "input": input_text,
-            "encoding_format": "float",
-        },
-    )
-
-    poolings = PoolingResponse.model_validate(response.json())
-
-    assert len(poolings.data) == 1
-    assert len(poolings.data[0].data) == len(input_tokens)
-    assert len(poolings.data[0].data[0]) == 1
-
-
 @pytest.mark.asyncio
 async def test_rerank_max_tokens_per_doc(
     server: RemoteOpenAIServer,
@@ -544,7 +525,7 @@ async def test_rerank_max_tokens_per_doc_validation(
 
 
 @pytest.mark.asyncio
-@pytest.mark.parametrize("task", ["embed", "token_embed", "plugin"])
+@pytest.mark.parametrize("task", ["embed", "token_embed", "token_classify", "plugin"])
 async def test_pooling_not_supported(server: RemoteOpenAIServer, task: str):
     response = requests.post(
         server.url_for("pooling"),
@@ -558,6 +539,8 @@ async def test_pooling_not_supported(server: RemoteOpenAIServer, task: str):
     assert response.json()["error"]["type"] == "BadRequestError"
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "token_classify":
+        err_msg = "Try switching the model's pooling_task via"
     else:
         err_msg = f"Unsupported task: {task!r}"
     assert response.json()["error"]["message"].startswith(err_msg)
diff --git a/tests/entrypoints/pooling/scoring/test_cross_encoder_online_vision.py b/tests/entrypoints/pooling/scoring/test_cross_encoder_online_vision.py
index d773802a9c24..a815e6e509c2 100644
--- a/tests/entrypoints/pooling/scoring/test_cross_encoder_online_vision.py
+++ b/tests/entrypoints/pooling/scoring/test_cross_encoder_online_vision.py
@@ -38,6 +38,15 @@
     "FLEX_ATTENTION": 0.045,  # gfx950:~3.25%, gfx942:~1.10%
 }
 
+# ROCm 7.2/gfx950 shows small absolute drift on the low text-vs-text
+# probability even though larger scores remain well inside the relative
+# tolerance. Keep the relative tolerances tight and add only a small floor.
+BACKEND_ABS_TOL: dict[str, float] = {
+    "default": 0.0,
+    "ROCM_AITER_FA": 0.005,
+    "FLEX_ATTENTION": 0.006,
+}
+
 # ROCm: disable skinny GEMM to avoid non-deterministic results from
 # atomic reductions in wvSplitKrc kernel.
 # See: https://github.com/vllm-project/vllm/pull/33493#issuecomment-3906083975
@@ -57,18 +66,23 @@ def get_tol(backend: str) -> float:
     return BACKEND_TOL.get(backend, BACKEND_TOL["default"])
 
 
+def get_abs_tol(backend: str) -> float:
+    return BACKEND_ABS_TOL.get(backend, BACKEND_ABS_TOL["default"])
+
+
 def assert_score(actual: float, expected: float, backend: str, label: str):
     tol = get_tol(backend)
+    abs_tol = get_abs_tol(backend)
     diff = abs(actual - expected)
     rel_diff = diff / abs(expected) if expected != 0 else diff
     print(
         f"[{backend}] {label}: actual={actual:.6f} expected={expected:.6f} "
-        f"diff={diff:.6f} rel_diff={rel_diff:.4f} tol={tol}"
+        f"diff={diff:.6f} rel_diff={rel_diff:.4f} tol={tol} abs_tol={abs_tol}"
     )
-    assert actual == pytest.approx(expected, rel=tol), (
+    assert actual == pytest.approx(expected, rel=tol, abs=abs_tol), (
         f"[{backend}] {label}: score mismatch — "
         f"actual={actual:.6f}, expected={expected:.6f}, "
-        f"rel_diff={rel_diff:.4f}, tol={tol}"
+        f"rel_diff={rel_diff:.4f}, tol={tol}, abs_tol={abs_tol}"
     )
 
 
diff --git a/tests/entrypoints/pooling/test_utils.py b/tests/entrypoints/pooling/test_utils.py
new file mode 100644
index 000000000000..13a89f2520ec
--- /dev/null
+++ b/tests/entrypoints/pooling/test_utils.py
@@ -0,0 +1,59 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import importlib
+import importlib.util
+import json
+import warnings
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+import torch
+
+from vllm.entrypoints.pooling.utils import encode_pooling_output_float_or_ndarray
+
+
+def _pooling_output(data):
+    return SimpleNamespace(outputs=SimpleNamespace(data=data))
+
+
+def test_encode_pooling_output_float_or_ndarray_returns_numpy_array():
+    output = _pooling_output(torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32))
+
+    encoded = encode_pooling_output_float_or_ndarray(output)
+
+    assert isinstance(encoded, np.ndarray)
+    np.testing.assert_allclose(encoded, [1.0, 2.0, 3.0])
+
+
+@pytest.mark.skipif(
+    importlib.util.find_spec("orjson") is None,
+    reason="orjson is not installed",
+)
+def test_orjson_serializes_numpy_array():
+    from fastapi.responses import ORJSONResponse
+
+    output = _pooling_output(torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32))
+    encoded = encode_pooling_output_float_or_ndarray(output)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", DeprecationWarning)
+        response = ORJSONResponse(content={"embedding": encoded})
+    assert json.loads(response.body)["embedding"] == pytest.approx([1.0, 2.0, 3.0])
+
+
+def test_encode_pooling_output_float_or_ndarray_falls_back_to_list():
+    class DataWithUnsupportedNumpy:
+        def is_contiguous(self):
+            return True
+
+        def numpy(self):
+            raise TypeError("unsupported dtype")
+
+        def tolist(self):
+            return [1.0, 2.0, 3.0]
+
+    output = _pooling_output(DataWithUnsupportedNumpy())
+
+    assert encode_pooling_output_float_or_ndarray(output) == [1.0, 2.0, 3.0]
diff --git a/tests/entrypoints/pooling/token_classify/test_offline.py b/tests/entrypoints/pooling/token_classify/test_offline.py
index d36761466dfe..3f59b375177f 100644
--- a/tests/entrypoints/pooling/token_classify/test_offline.py
+++ b/tests/entrypoints/pooling/token_classify/test_offline.py
@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import logging
 import weakref
 
 import pytest
@@ -60,22 +59,19 @@ def test_token_ids_prompts(llm: LLM):
 
 @pytest.mark.skip_global_cleanup
 def test_score_api(llm: LLM):
-    err_msg = "Scoring API is only enabled for num_labels == 1."
+    err_msg = "This model does not support the Scoring API."
     with pytest.raises(ValueError, match=err_msg):
         llm.score("ping", "pong", use_tqdm=False)
 
 
 @pytest.mark.parametrize("task", ["classify", "embed", "token_embed", "plugin"])
 def test_unsupported_tasks(llm: LLM, task: PoolingTask, caplog_vllm):
-    if task == "classify":
-        with caplog_vllm.at_level(level=logging.WARNING, logger="vllm"):
-            llm.encode(prompt, pooling_task=task, use_tqdm=False)
-        assert "deprecated" in caplog_vllm.text
+    if task == "plugin":
+        err_msg = "No IOProcessor plugin installed."
+    elif task == "classify":
+        err_msg = "Try switching the model's pooling_task via.+"
     else:
-        if task == "plugin":
-            err_msg = "No IOProcessor plugin installed."
-        else:
-            err_msg = "Embedding API is not supported by this model.+"
+        err_msg = "Embedding API is not supported by this model.+"
 
-        with pytest.raises(ValueError, match=err_msg):
-            llm.encode(prompt, pooling_task=task, use_tqdm=False)
+    with pytest.raises(ValueError, match=err_msg):
+        llm.encode(prompt, pooling_task=task, use_tqdm=False)
diff --git a/tests/entrypoints/pooling/token_classify/test_online.py b/tests/entrypoints/pooling/token_classify/test_online.py
index 39fd378336c1..a8e240ab1aeb 100644
--- a/tests/entrypoints/pooling/token_classify/test_online.py
+++ b/tests/entrypoints/pooling/token_classify/test_online.py
@@ -50,7 +50,7 @@ async def test_pooling_token_classify(server: RemoteOpenAIServer, model_name: st
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-@pytest.mark.parametrize("task", ["embed", "token_embed", "plugin"])
+@pytest.mark.parametrize("task", ["classify", "embed", "token_embed", "plugin"])
 async def test_pooling_not_supported(
     server: RemoteOpenAIServer, model_name: str, task: str
 ):
@@ -63,9 +63,12 @@ async def test_pooling_not_supported(
             "task": task,
         },
     )
+    assert response.json()["error"]["type"] == "BadRequestError"
 
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "classify":
+        err_msg = "Try switching the model's pooling_task via"
     else:
         err_msg = f"Unsupported task: {task!r}"
     assert response.json()["error"]["message"].startswith(err_msg)
diff --git a/tests/entrypoints/pooling/token_embed/test_offline.py b/tests/entrypoints/pooling/token_embed/test_offline.py
index d2e87fbf23e5..5bf15052d16a 100644
--- a/tests/entrypoints/pooling/token_embed/test_offline.py
+++ b/tests/entrypoints/pooling/token_embed/test_offline.py
@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import logging
 import weakref
 
 import pytest
@@ -64,15 +63,12 @@ def test_token_ids_prompts(llm: LLM):
 
 @pytest.mark.parametrize("task", ["embed", "classify", "token_classify", "plugin"])
 def test_unsupported_tasks(llm: LLM, task: PoolingTask, caplog_vllm):
-    if task == "embed":
-        with caplog_vllm.at_level(level=logging.WARNING, logger="vllm"):
-            llm.encode(prompt, pooling_task=task, use_tqdm=False)
-        assert "deprecated" in caplog_vllm.text
+    if task == "plugin":
+        err_msg = "No IOProcessor plugin installed."
+    elif task == "embed":
+        err_msg = "Try switching the model's pooling_task via.+"
     else:
-        if task == "plugin":
-            err_msg = "No IOProcessor plugin installed."
-        else:
-            err_msg = "Classification API is not supported by this model.+"
+        err_msg = "Classification API is not supported by this model.+"
 
-        with pytest.raises(ValueError, match=err_msg):
-            llm.encode(prompt, pooling_task=task, use_tqdm=False)
+    with pytest.raises(ValueError, match=err_msg):
+        llm.encode(prompt, pooling_task=task, use_tqdm=False)
diff --git a/tests/entrypoints/pooling/token_embed/test_online.py b/tests/entrypoints/pooling/token_embed/test_online.py
index 048491dac15d..42713ae852b2 100644
--- a/tests/entrypoints/pooling/token_embed/test_online.py
+++ b/tests/entrypoints/pooling/token_embed/test_online.py
@@ -73,7 +73,7 @@ async def test_pooling_token_embed(server: RemoteOpenAIServer, model_name: str):
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-@pytest.mark.parametrize("task", ["classify", "token_classify", "plugin"])
+@pytest.mark.parametrize("task", ["embed", "classify", "token_classify", "plugin"])
 async def test_pooling_not_supported(
     server: RemoteOpenAIServer, model_name: str, task: str
 ):
@@ -86,9 +86,12 @@ async def test_pooling_not_supported(
             "task": task,
         },
     )
+    assert response.json()["error"]["type"] == "BadRequestError"
 
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "embed":
+        err_msg = "Try switching the model's pooling_task via"
     else:
         err_msg = f"Unsupported task: {task!r}"
     assert response.json()["error"]["message"].startswith(err_msg)
diff --git a/tests/entrypoints/serve/disagg/test_generate_stream.py b/tests/entrypoints/serve/disagg/test_generate_stream.py
index 76a9df22f69b..49349bf4ba50 100644
--- a/tests/entrypoints/serve/disagg/test_generate_stream.py
+++ b/tests/entrypoints/serve/disagg/test_generate_stream.py
@@ -12,7 +12,10 @@
 from vllm.entrypoints.openai.engine.protocol import StreamOptions
 from vllm.entrypoints.openai.models.protocol import BaseModelPath
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
-from vllm.entrypoints.serve.disagg.protocol import GenerateRequest
+from vllm.entrypoints.serve.disagg.protocol import (
+    GenerateRequest,
+    GenerateResponse,
+)
 from vllm.entrypoints.serve.disagg.serving import ServingTokens
 from vllm.entrypoints.serve.render.serving import OpenAIServingRender
 from vllm.logprobs import Logprob
@@ -164,6 +167,36 @@ def _parse_sse_chunks(chunks: list[str]) -> list[Any]:
     return parsed
 
 
+@pytest.mark.asyncio
+async def test_serve_tokens_skips_mm_cache_for_remote_engine_execution():
+    engine = _mock_engine()
+
+    async def mock_generate(*args, **kwargs):
+        yield _make_request_output(
+            "req-1", token_ids=[10], finish_reason="stop", finished=True
+        )
+
+    engine.generate = MagicMock(side_effect=mock_generate)
+    serving = _build_serving_tokens(engine)
+
+    request = GenerateRequest(
+        token_ids=[1, 2, 3],
+        sampling_params=SamplingParams(max_tokens=1),
+        model=MODEL_NAME,
+        stream=False,
+    )
+
+    response = await serving.serve_tokens(request)
+
+    assert isinstance(response, GenerateResponse)
+    assert (
+        serving.openai_serving_render.preprocess_completion.call_args.kwargs[
+            "skip_mm_cache"
+        ]
+        is True
+    )
+
+
 @pytest.mark.asyncio
 async def test_stream_basic():
     """Streaming returns SSE chunks with correct token_ids and ends with [DONE]."""
diff --git a/tests/entrypoints/serve/disagg/test_serving_multimodal_tokens.py b/tests/entrypoints/serve/disagg/test_serving_multimodal_tokens.py
new file mode 100644
index 000000000000..e13dd425af14
--- /dev/null
+++ b/tests/entrypoints/serve/disagg/test_serving_multimodal_tokens.py
@@ -0,0 +1,158 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for multimodal features through the /inference/v1/generate endpoint.
+
+Mirrors test_serving_tokens.py but exercises the multimodal piping
+using Qwen/Qwen3-VL-2B-Instruct end-to-end via the server's /render ->
+/generate -> /detokenize path. Intentionally avoids running the HF
+processor in the pytest parent process to keep os.fork() in sibling
+tests (e.g. test_weight_transfer_llm.py) deadlock-free.
+"""
+
+import os
+
+import httpx
+import pytest
+import pytest_asyncio
+from PIL import Image
+
+from tests.utils import RemoteOpenAIServer
+from vllm.multimodal.utils import encode_image_url
+
+MODEL_NAME = "Qwen/Qwen3-VL-2B-Instruct"
+GEN_ENDPOINT = "/inference/v1/generate"
+RENDER_ENDPOINT = "/v1/chat/completions/render"
+DETOKENIZE_ENDPOINT = "/detokenize"
+
+
+@pytest.fixture(scope="module")
+def test_image():
+    return Image.new("RGB", (224, 224), color=(255, 0, 0))
+
+
+@pytest.fixture(scope="module")
+def server():
+    args = [
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "4096",
+        "--enforce-eager",
+        "--no-enable-prefix-caching",
+    ]
+
+    envs = os.environ.copy()
+    envs["VLLM_ROCM_USE_SKINNY_GEMM"] = "0"
+
+    with RemoteOpenAIServer(MODEL_NAME, args, env_dict=envs) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client(server: RemoteOpenAIServer):
+    transport = httpx.AsyncHTTPTransport(uds=server.uds) if server.uds else None
+    headers = {"Authorization": f"Bearer {server.DUMMY_API_KEY}"}
+    async with httpx.AsyncClient(
+        transport=transport,
+        base_url=server.url_root,
+        timeout=600,
+        headers=headers,
+    ) as c:
+        yield c
+
+
+@pytest.mark.asyncio
+async def test_render_to_generate_roundtrip(client, test_image):
+    """End-to-end: render a multimodal chat -> feed into generate -> decode.
+
+    All preprocessing and detokenization happens in the server subprocess;
+    the pytest parent never imports transformers or touches torch tensors.
+    """
+    data_url = encode_image_url(test_image, format="PNG")
+
+    render_payload = {
+        "model": MODEL_NAME,
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image_url", "image_url": {"url": data_url}},
+                    {
+                        "type": "text",
+                        "text": "What color is this image? Answer in one word.",
+                    },
+                ],
+            }
+        ],
+    }
+
+    render_resp = await client.post(RENDER_ENDPOINT, json=render_payload)
+    render_resp.raise_for_status()
+    render_data = render_resp.json()
+
+    # Validate render output structure: keys exist and values are non-empty
+    # and well-typed.
+    assert "token_ids" in render_data
+    assert isinstance(render_data["token_ids"], list)
+    assert len(render_data["token_ids"]) > 0
+    assert all(isinstance(t, int) for t in render_data["token_ids"])
+
+    assert "features" in render_data
+    features = render_data["features"]
+    assert features is not None
+    assert isinstance(features, dict)
+
+    assert "mm_hashes" in features
+    assert "image" in features["mm_hashes"]
+    image_hashes = features["mm_hashes"]["image"]
+    assert isinstance(image_hashes, list)
+    assert len(image_hashes) > 0
+    assert all(isinstance(h, str) and h for h in image_hashes)
+
+    assert "mm_placeholders" in features
+    assert "image" in features["mm_placeholders"]
+    image_placeholders = features["mm_placeholders"]["image"]
+    assert isinstance(image_placeholders, list)
+    assert len(image_placeholders) > 0
+    for p in image_placeholders:
+        assert isinstance(p.get("offset"), int)
+        assert isinstance(p.get("length"), int)
+        assert p["length"] > 0
+
+    assert "kwargs_data" in features
+    assert "image" in features["kwargs_data"]
+    assert len(features["kwargs_data"]["image"]) > 0
+
+    # Build generate request from render output
+    generate_payload = render_data
+    generate_payload["sampling_params"] = {
+        "max_tokens": 10,
+        "temperature": 0.0,
+    }
+
+    gen_resp = await client.post(GEN_ENDPOINT, json=generate_payload)
+    gen_resp.raise_for_status()
+    gen_data = gen_resp.json()
+
+    assert "choices" in gen_data
+    assert isinstance(gen_data["choices"], list)
+    assert len(gen_data["choices"]) >= 1
+    choice = gen_data["choices"][0]
+    assert "token_ids" in choice
+    assert isinstance(choice["token_ids"], list)
+    assert len(choice["token_ids"]) > 0
+    assert all(isinstance(t, int) for t in choice["token_ids"])
+
+    detok_resp = await client.post(
+        DETOKENIZE_ENDPOINT,
+        json={"model": MODEL_NAME, "tokens": choice["token_ids"]},
+    )
+    detok_resp.raise_for_status()
+    detok_data = detok_resp.json()
+    assert "prompt" in detok_data
+    text = detok_data["prompt"]
+    assert isinstance(text, str)
+    assert len(text) > 0
+    assert "red" in text.lower(), (
+        f"Expected model to identify the red image, got: {text!r}"
+    )
diff --git a/tests/entrypoints/serve/tokenize/test_serving_tokenization.py b/tests/entrypoints/serve/tokenize/test_serving_tokenization.py
new file mode 100644
index 000000000000..ba9d7989a865
--- /dev/null
+++ b/tests/entrypoints/serve/tokenize/test_serving_tokenization.py
@@ -0,0 +1,140 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass, field
+from typing import Any
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from vllm.config.multimodal import MultiModalConfig
+from vllm.entrypoints.openai.models.protocol import BaseModelPath
+from vllm.entrypoints.openai.models.serving import OpenAIServingModels
+from vllm.entrypoints.serve.render.serving import OpenAIServingRender
+from vllm.entrypoints.serve.tokenize.protocol import (
+    TokenizeChatRequest,
+    TokenizeCompletionRequest,
+)
+from vllm.entrypoints.serve.tokenize.serving import OpenAIServingTokenization
+from vllm.v1.engine.async_llm import AsyncLLM
+
+MODEL_NAME = "openai-community/gpt2"
+BASE_MODEL_PATHS = [
+    BaseModelPath(name=MODEL_NAME, model_path=MODEL_NAME),
+]
+
+
+@dataclass
+class MockHFConfig:
+    model_type: str = "any"
+
+
+@dataclass
+class MockModelConfig:
+    task = "generate"
+    runner_type = "generate"
+    model = MODEL_NAME
+    tokenizer = MODEL_NAME
+    trust_remote_code = False
+    tokenizer_mode = "auto"
+    max_model_len = 100
+    tokenizer_revision = None
+    multimodal_config = MultiModalConfig()
+    hf_config = MockHFConfig()
+    hf_text_config = MockHFConfig()
+    logits_processors: list[str] | None = None
+    diff_sampling_param: dict | None = None
+    allowed_local_media_path: str = ""
+    allowed_media_domains: list[str] | None = None
+    encoder_config = None
+    generation_config: str = "auto"
+    media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)
+    skip_tokenizer_init = False
+    is_encoder_decoder: bool = False
+    is_multimodal_model: bool = False
+    renderer_num_workers: int = 1
+
+    def get_diff_sampling_param(self):
+        return self.diff_sampling_param or {}
+
+
+def _build_serving_tokenization(engine: AsyncLLM) -> OpenAIServingTokenization:
+    models = OpenAIServingModels(
+        engine_client=engine,
+        base_model_paths=BASE_MODEL_PATHS,
+    )
+    serving_render = OpenAIServingRender(
+        model_config=engine.model_config,
+        renderer=engine.renderer,
+        model_registry=models.registry,
+        request_logger=None,
+        chat_template=None,
+        chat_template_content_format="auto",
+    )
+    return OpenAIServingTokenization(
+        engine,
+        models,
+        openai_serving_render=serving_render,
+        request_logger=None,
+        chat_template=None,
+        chat_template_content_format="auto",
+    )
+
+
+@pytest.mark.asyncio
+async def test_tokenize_chat_skips_mm_cache_for_renderer_only_path():
+    mock_engine = MagicMock(spec=AsyncLLM)
+    mock_engine.errored = False
+    mock_engine.model_config = MockModelConfig()
+    mock_engine.input_processor = MagicMock()
+    mock_engine.renderer = MagicMock()
+
+    serving = _build_serving_tokenization(mock_engine)
+    serving.openai_serving_render.preprocess_chat = AsyncMock(
+        return_value=(
+            [{"role": "user", "content": "Test"}],
+            [{"prompt_token_ids": [1, 2, 3]}],
+        )
+    )
+
+    request = TokenizeChatRequest(
+        model=MODEL_NAME,
+        messages=[{"role": "user", "content": "Test prompt"}],
+    )
+
+    response = await serving.create_tokenize(request, MagicMock(headers={}))
+
+    assert response.tokens == [1, 2, 3]
+    assert (
+        serving.openai_serving_render.preprocess_chat.call_args.kwargs["skip_mm_cache"]
+        is True
+    )
+
+
+@pytest.mark.asyncio
+async def test_tokenize_completion_skips_mm_cache_for_renderer_only_path():
+    mock_engine = MagicMock(spec=AsyncLLM)
+    mock_engine.errored = False
+    mock_engine.model_config = MockModelConfig()
+    mock_engine.input_processor = MagicMock()
+    mock_engine.renderer = MagicMock()
+
+    serving = _build_serving_tokenization(mock_engine)
+    serving.openai_serving_render.preprocess_completion = AsyncMock(
+        return_value=[{"prompt_token_ids": [1, 2, 3]}]
+    )
+
+    request = TokenizeCompletionRequest(
+        model=MODEL_NAME,
+        prompt="Test prompt",
+    )
+
+    response = await serving.create_tokenize(request, MagicMock(headers={}))
+
+    assert response.tokens == [1, 2, 3]
+    assert (
+        serving.openai_serving_render.preprocess_completion.call_args.kwargs[
+            "skip_mm_cache"
+        ]
+        is True
+    )
diff --git a/tests/entrypoints/test_grpc_health.py b/tests/entrypoints/test_grpc_health.py
new file mode 100644
index 000000000000..d63b8294c5f4
--- /dev/null
+++ b/tests/entrypoints/test_grpc_health.py
@@ -0,0 +1,143 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+grpc = pytest.importorskip("grpc")
+health_pb2 = pytest.importorskip("grpc_health.v1.health_pb2")
+VllmHealthServicer = pytest.importorskip(
+    "smg_grpc_servicer.vllm.health_servicer"
+).VllmHealthServicer
+
+SERVING = health_pb2.HealthCheckResponse.SERVING
+NOT_SERVING = health_pb2.HealthCheckResponse.NOT_SERVING
+SERVICE_UNKNOWN = health_pb2.HealthCheckResponse.SERVICE_UNKNOWN
+
+
+@pytest.fixture
+def async_llm():
+    mock = MagicMock()
+    mock.check_health = AsyncMock()
+    return mock
+
+
+@pytest.fixture
+def context():
+    return MagicMock(spec=grpc.aio.ServicerContext)
+
+
+@pytest.fixture
+def servicer(async_llm):
+    return VllmHealthServicer(async_llm)
+
+
+@pytest.fixture
+def request_msg():
+    msg = MagicMock()
+    msg.service = ""
+    return msg
+
+
+# -- Check() tests --
+
+
+@pytest.mark.asyncio
+async def test_check_serving_overall(servicer, request_msg, context, async_llm):
+    request_msg.service = ""
+    response = await servicer.Check(request_msg, context)
+    assert response.status == SERVING
+    async_llm.check_health.assert_awaited_once()
+
+
+@pytest.mark.asyncio
+async def test_check_serving_vllm_service(servicer, request_msg, context, async_llm):
+    request_msg.service = "vllm.grpc.engine.VllmEngine"
+    response = await servicer.Check(request_msg, context)
+    assert response.status == SERVING
+    async_llm.check_health.assert_awaited_once()
+
+
+@pytest.mark.asyncio
+async def test_check_not_serving_engine_errored(
+    servicer, request_msg, context, async_llm
+):
+    async_llm.check_health = AsyncMock(side_effect=Exception("engine dead"))
+    request_msg.service = ""
+    response = await servicer.Check(request_msg, context)
+    assert response.status == NOT_SERVING
+
+
+@pytest.mark.asyncio
+async def test_check_not_serving_shutting_down(
+    servicer, request_msg, context, async_llm
+):
+    servicer.set_not_serving()
+    request_msg.service = ""
+    response = await servicer.Check(request_msg, context)
+    assert response.status == NOT_SERVING
+    async_llm.check_health.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_check_unknown_service_status(servicer, request_msg, context):
+    request_msg.service = "nonexistent.Service"
+    response = await servicer.Check(request_msg, context)
+    assert response.status == SERVICE_UNKNOWN
+
+
+@pytest.mark.asyncio
+async def test_check_unknown_service_grpc_code(servicer, request_msg, context):
+    request_msg.service = "fake.Svc"
+    await servicer.Check(request_msg, context)
+    context.set_code.assert_called_once_with(grpc.StatusCode.NOT_FOUND)
+    context.set_details.assert_called_once()
+    details_arg = context.set_details.call_args[0][0]
+    assert "fake.Svc" in details_arg
+
+
+@pytest.mark.asyncio
+@patch("smg_grpc_servicer.vllm.health_servicer.logger")
+async def test_check_logs_exception_on_error(
+    mock_logger, servicer, request_msg, context, async_llm
+):
+    async_llm.check_health = AsyncMock(side_effect=Exception("engine exploded"))
+    request_msg.service = ""
+    await servicer.Check(request_msg, context)
+    mock_logger.exception.assert_called_once()
+    log_args = mock_logger.exception.call_args
+    assert "service" in str(log_args).lower()
+
+
+# -- Watch() tests --
+
+
+@pytest.mark.asyncio
+async def test_watch_yields_serving(servicer, request_msg, context, async_llm):
+    request_msg.service = ""
+    watch_iter = servicer.Watch(request_msg, context)
+    first = await anext(watch_iter.__aiter__())
+    assert first.status == SERVING
+
+
+@pytest.mark.asyncio
+async def test_watch_yields_not_serving(servicer, request_msg, context, async_llm):
+    async_llm.check_health = AsyncMock(side_effect=Exception("engine down"))
+    request_msg.service = ""
+    watch_iter = servicer.Watch(request_msg, context)
+    first = await anext(watch_iter.__aiter__())
+    assert first.status == NOT_SERVING
+
+
+@pytest.mark.asyncio
+async def test_watch_unknown_service(servicer, request_msg, context):
+    request_msg.service = "fake.Service"
+    results = []
+    async for response in servicer.Watch(request_msg, context):
+        results.append(response)
+    assert len(results) == 1
+    assert results[0].status == SERVICE_UNKNOWN
+    # Watch returns SERVICE_UNKNOWN in the response body (not as a gRPC error
+    # code) so the stream terminates normally -- unlike Check, which sets
+    # NOT_FOUND on the gRPC context for unknown services.
+    context.set_code.assert_not_called()
diff --git a/tests/entrypoints/weight_transfer/test_weight_transfer_llm.py b/tests/entrypoints/weight_transfer/test_weight_transfer_llm.py
index 7d6d330aa544..aea4c523eca8 100644
--- a/tests/entrypoints/weight_transfer/test_weight_transfer_llm.py
+++ b/tests/entrypoints/weight_transfer/test_weight_transfer_llm.py
@@ -199,6 +199,9 @@ def test_update_weights_calls_engine():
             WeightTransferInitRequest(init_info={"test_param": "init"})
         )
 
+        # Start weight update (required before update_weights)
+        llm.start_weight_update(is_checkpoint_format=True)
+
         # Call update_weights
         test_names = ["layer.weight", "layer.bias"]
         test_dtypes = ["float32", "float32"]
@@ -229,10 +232,14 @@ def check_update_called(self):
             assert dtypes == test_dtypes
             assert shapes == test_shapes
 
+        # Finish weight update
+        llm.finish_weight_update()
+
 
 @create_new_process_for_each_test()
 def test_full_weight_transfer_flow():
-    """Test the complete weight transfer flow: init -> update."""
+    """Test the complete weight transfer flow:
+    init -> start -> update -> finish."""
     if torch.accelerator.device_count() < 1:
         pytest.skip("Need at least 1 GPU for this test")
 
@@ -253,12 +260,15 @@ def test_full_weight_transfer_flow():
             weight_transfer_config=WeightTransferConfig(backend="nccl"),
         )
 
-        # Step 1: Initialize
+        # Step 1: Initialize weight transfer engine
         llm.init_weight_transfer_engine(
             WeightTransferInitRequest(init_info={"test_param": "flow_test"})
         )
 
-        # Step 2: Update weights
+        # Step 2: Start weight update
+        llm.start_weight_update(is_checkpoint_format=True)
+
+        # Step 3: Update weights
         llm.update_weights(
             WeightTransferUpdateRequest(
                 update_info={
@@ -269,6 +279,9 @@ def test_full_weight_transfer_flow():
             )
         )
 
+        # Step 4: Finish weight update
+        llm.finish_weight_update()
+
         # Verify the full flow completed
         def check_flow(self):
             engine = self.weight_transfer_engine
diff --git a/tests/evals/gsm8k/configs/Qwen3-4B-TQ-k3v4nc.yaml b/tests/evals/gsm8k/configs/Qwen3-4B-TQ-k3v4nc.yaml
new file mode 100644
index 000000000000..b9f9a7944f2f
--- /dev/null
+++ b/tests/evals/gsm8k/configs/Qwen3-4B-TQ-k3v4nc.yaml
@@ -0,0 +1,5 @@
+model_name: "Qwen/Qwen3-4B"
+accuracy_threshold: 0.78
+num_questions: 1319
+num_fewshot: 5
+server_args: "--kv-cache-dtype turboquant_k3v4_nc --max-model-len 4096"
diff --git a/tests/evals/gsm8k/configs/Qwen3-4B-TQ-k8v4.yaml b/tests/evals/gsm8k/configs/Qwen3-4B-TQ-k8v4.yaml
new file mode 100644
index 000000000000..200b570e23d5
--- /dev/null
+++ b/tests/evals/gsm8k/configs/Qwen3-4B-TQ-k8v4.yaml
@@ -0,0 +1,5 @@
+model_name: "Qwen/Qwen3-4B"
+accuracy_threshold: 0.80
+num_questions: 1319
+num_fewshot: 5
+server_args: "--kv-cache-dtype turboquant_k8v4 --max-model-len 4096"
diff --git a/tests/evals/gsm8k/configs/Qwen3-4B-TQ-t3nc.yaml b/tests/evals/gsm8k/configs/Qwen3-4B-TQ-t3nc.yaml
new file mode 100644
index 000000000000..1c833fe7bf2d
--- /dev/null
+++ b/tests/evals/gsm8k/configs/Qwen3-4B-TQ-t3nc.yaml
@@ -0,0 +1,5 @@
+model_name: "Qwen/Qwen3-4B"
+accuracy_threshold: 0.75
+num_questions: 1319
+num_fewshot: 5
+server_args: "--kv-cache-dtype turboquant_3bit_nc --max-model-len 4096"
diff --git a/tests/evals/gsm8k/configs/Qwen3-4B-TQ-t4nc.yaml b/tests/evals/gsm8k/configs/Qwen3-4B-TQ-t4nc.yaml
new file mode 100644
index 000000000000..6a7f82b66099
--- /dev/null
+++ b/tests/evals/gsm8k/configs/Qwen3-4B-TQ-t4nc.yaml
@@ -0,0 +1,5 @@
+model_name: "Qwen/Qwen3-4B"
+accuracy_threshold: 0.80
+num_questions: 1319
+num_fewshot: 5
+server_args: "--kv-cache-dtype turboquant_4bit_nc --max-model-len 4096"
diff --git a/tests/evals/gsm8k/configs/models-mi3xx.txt b/tests/evals/gsm8k/configs/models-mi3xx.txt
index 6cf833b64642..dfa4bc8eb53f 100644
--- a/tests/evals/gsm8k/configs/models-mi3xx.txt
+++ b/tests/evals/gsm8k/configs/models-mi3xx.txt
@@ -2,3 +2,5 @@ DeepSeek-R1-TP_MI325.yaml
 DeepSeek-R1-DP_MI325.yaml
 DeepSeek-V3.2-TP_MI325.yaml
 DeepSeek-V3.2-DP_MI325.yaml
+Qwen3-30B-A3B-NVFP4.yaml
+Qwen3.5-35B-A3B-MXFP4-TP2.yaml
\ No newline at end of file
diff --git a/tests/evals/gsm8k/configs/models-turboquant.txt b/tests/evals/gsm8k/configs/models-turboquant.txt
new file mode 100644
index 000000000000..518aac780b90
--- /dev/null
+++ b/tests/evals/gsm8k/configs/models-turboquant.txt
@@ -0,0 +1,4 @@
+Qwen3-4B-TQ-k8v4.yaml
+Qwen3-4B-TQ-t4nc.yaml
+Qwen3-4B-TQ-k3v4nc.yaml
+Qwen3-4B-TQ-t3nc.yaml
diff --git a/tests/evals/mrcr/README.md b/tests/evals/mrcr/README.md
new file mode 100644
index 000000000000..59acc11ac48c
--- /dev/null
+++ b/tests/evals/mrcr/README.md
@@ -0,0 +1,44 @@
+# MRCR Long-Context Accuracy Evaluation
+
+Smoke test for long-context behavior using OpenAI's public [`openai/mrcr`](https://huggingface.co/datasets/openai/mrcr) dataset. The model sees a long chat with several near-duplicate "needles" and must reproduce a specific earlier assistant turn verbatim, prepended with a random anti-guessing string.
+
+**Scoring:** if the response doesn't start with `random_string_to_prepend`, score is 0; otherwise the prefix is stripped and the mean `SequenceMatcher.ratio()` against the reference answer is reported.
+
+## Usage
+
+```bash
+# Pytest (spawns the server)
+pytest -s -v tests/evals/mrcr/test_mrcr_correctness.py \
+    --config-list-file=configs/models-small.txt
+
+# Standalone (server already running; model and context auto-discovered)
+vllm serve Qwen/Qwen3-0.6B --reasoning-parser qwen3 --port 8000
+python tests/evals/mrcr/mrcr_eval.py --port 8000
+```
+
+## Configuration
+
+```yaml
+model_name: "Qwen/Qwen3-0.6B"
+# Per-needle thresholds catch bucket-specific regressions (sliding window,
+# chunked prefill, prefix cache) that an aggregate can hide. A scalar
+# (e.g. `match_ratio_threshold: 0.20`) is also accepted and checked against
+# the mean match ratio.
+match_ratio_threshold:
+  2: 0.30
+  4: 0.15
+  8: 0.10
+num_samples: 30
+needles: [2, 4, 8]
+# max_prompt_tokens: 32768       # Optional; defaults to server max_model_len - max_tokens - 256
+max_tokens: 2048
+concurrency: 8
+server_args: "--max-model-len 32768 --reasoning-parser qwen3"
+```
+
+## Notes
+
+- Samples stream from three parquet shards (`{N}needle/{N}needle_0.parquet`); only the first few row groups are fetched, not the full 1.4 GB repo.
+- `max_prompt_tokens` defaults to `max_model_len - max_tokens - 256`, i.e. fills whatever context the server advertises. Set `--max-model-len` on the server to control the smoke-test context length; override `--max-prompt-tokens` on the client to cap below that.
+- Sample length is pre-filtered by `n_chars × 4 ≤ max_prompt_tokens`, then verified via the server's `/tokenize` endpoint under the actual chat template.
+- Reasoning models: start the server with `--reasoning-parser <name>` (e.g. `qwen3`, `deepseek_r1`) so `<think>` goes to `message.reasoning_content` and doesn't contaminate the scored answer.
diff --git a/tests/evals/mrcr/__init__.py b/tests/evals/mrcr/__init__.py
new file mode 100644
index 000000000000..208f01a7cb5e
--- /dev/null
+++ b/tests/evals/mrcr/__init__.py
@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
diff --git a/tests/evals/mrcr/configs/Qwen3.5-4B.yaml b/tests/evals/mrcr/configs/Qwen3.5-4B.yaml
new file mode 100644
index 000000000000..c2fd438dd377
--- /dev/null
+++ b/tests/evals/mrcr/configs/Qwen3.5-4B.yaml
@@ -0,0 +1,7 @@
+model_name: "Qwen/Qwen3.5-4B"
+needles: [2, 4, 8]
+match_ratio_threshold:
+  2: 0.99
+  4: 0.84
+  8: 0.76
+server_args: "--max-model-len 128K --reasoning-parser qwen3"
diff --git a/tests/evals/mrcr/configs/models-small.txt b/tests/evals/mrcr/configs/models-small.txt
new file mode 100644
index 000000000000..b78704fe539f
--- /dev/null
+++ b/tests/evals/mrcr/configs/models-small.txt
@@ -0,0 +1 @@
+Qwen3.5-4B.yaml
diff --git a/tests/evals/mrcr/conftest.py b/tests/evals/mrcr/conftest.py
new file mode 100644
index 000000000000..46f59a56c238
--- /dev/null
+++ b/tests/evals/mrcr/conftest.py
@@ -0,0 +1,54 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from pathlib import Path
+
+
+def pytest_addoption(parser):
+    """Add custom command line options."""
+    parser.addoption(
+        "--config-list-file",
+        default="configs/models-small.txt",
+        help="File containing list of config files to test",
+    )
+
+
+def pytest_generate_tests(metafunc):
+    """Generate test parameters from config files."""
+    if "config_filename" in metafunc.fixturenames:
+        config_list_file = metafunc.config.getoption("--config-list-file")
+
+        config_list_path = Path(config_list_file)
+        if not config_list_path.is_absolute():
+            test_dir_path = Path(__file__).parent / config_list_file
+            if test_dir_path.exists():
+                config_list_path = test_dir_path
+            else:
+                config_list_path = Path.cwd() / config_list_file
+
+        print(f"Looking for config list at: {config_list_path}")
+
+        config_files = []
+        if config_list_path.exists():
+            config_dir = config_list_path.parent
+            with open(config_list_path) as f:
+                for line in f:
+                    line = line.strip()
+                    if line and not line.startswith("#"):
+                        config_path = config_dir / line
+                        if config_path.exists():
+                            config_files.append(config_path)
+                            print(f"  ✓ Found: {config_path}")
+                        else:
+                            print(f"  ✗ Missing: {config_path}")
+        else:
+            print(f"Config list file not found: {config_list_path}")
+
+        if config_files:
+            metafunc.parametrize(
+                "config_filename",
+                config_files,
+                ids=[config_file.stem for config_file in config_files],
+            )
+        else:
+            print("No config files found, test will be skipped")
diff --git a/tests/evals/mrcr/mrcr_eval.py b/tests/evals/mrcr/mrcr_eval.py
new file mode 100644
index 000000000000..3ab87a57d122
--- /dev/null
+++ b/tests/evals/mrcr/mrcr_eval.py
@@ -0,0 +1,333 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""MRCR long-context evaluation for vLLM's OpenAI-compatible server.
+
+Streams samples from `openai/mrcr` on HuggingFace, sends chat completions to
+the server, and scores each response with a prefix-gated SequenceMatcher ratio
+against the reference answer.
+"""
+
+import argparse
+import asyncio
+import json
+import time
+from difflib import SequenceMatcher
+
+import aiohttp
+import numpy as np
+import requests
+from tqdm.asyncio import tqdm
+
+DATASET_REPO = "openai/mrcr"
+NEEDLE_SHARDS = {
+    2: "2needle/2needle_0.parquet",
+    4: "4needle/4needle_0.parquet",
+    8: "8needle/8needle_0.parquet",
+}
+# Reserve headroom for chat-template tokens on top of the messages.
+PROMPT_SAFETY_BUFFER = 256
+# Pre-filter heuristic before the authoritative /tokenize check.
+CHARS_PER_TOKEN = 4
+# Skip chain-of-thought on reasoning models; ignored by non-reasoning templates.
+DEFAULT_EXTRA_BODY: dict = {"chat_template_kwargs": {"enable_thinking": False}}
+
+
+def discover_server_model(base_url: str) -> tuple[str, int | None]:
+    """Return (model_id, max_model_len) from /v1/models."""
+    resp = requests.get(f"{base_url}/v1/models", timeout=30)
+    resp.raise_for_status()
+    data = resp.json().get("data", [])
+    if not data:
+        raise RuntimeError(f"No models advertised at {base_url}/v1/models")
+    entry = data[0]
+    return entry["id"], entry.get("max_model_len")
+
+
+def count_chat_tokens(base_url: str, model: str, messages: list[dict]) -> int:
+    """Return the chat-template-rendered token count via /tokenize."""
+    resp = requests.post(
+        f"{base_url}/tokenize",
+        json={"model": model, "messages": messages, "add_generation_prompt": True},
+        timeout=120,
+    )
+    resp.raise_for_status()
+    return int(resp.json()["count"])
+
+
+def _load_mrcr_samples(
+    needles: list[int],
+    max_prompt_tokens: int,
+    num_samples: int,
+    seed: int,
+    base_url: str,
+    model_name: str,
+) -> list[dict]:
+    """Stream MRCR samples balanced across needle buckets, token-verified."""
+    try:
+        from datasets import load_dataset
+    except ImportError as e:
+        raise ImportError(
+            "MRCR eval requires `datasets`. Install with: uv pip install datasets"
+        ) from e
+
+    max_chars = max_prompt_tokens * CHARS_PER_TOKEN
+    per_bucket = num_samples // len(needles)
+    leftover = num_samples - per_bucket * len(needles)
+
+    samples: list[dict] = []
+    for idx, n in enumerate(needles):
+        if n not in NEEDLE_SHARDS:
+            raise ValueError(f"Unsupported needle count {n}")
+        target = per_bucket + (1 if idx < leftover else 0)
+        if target == 0:
+            continue
+
+        ds = load_dataset(
+            DATASET_REPO,
+            data_files=NEEDLE_SHARDS[n],
+            split="train",
+            streaming=True,
+        ).shuffle(seed=seed + n, buffer_size=16)
+
+        taken = 0
+        for row in ds:
+            if int(row.get("n_chars", 0)) > max_chars:
+                continue
+            prompt = row["prompt"]
+            messages = json.loads(prompt) if isinstance(prompt, str) else list(prompt)
+            n_tokens = count_chat_tokens(base_url, model_name, messages)
+            if n_tokens > max_prompt_tokens:
+                continue
+            samples.append(
+                {
+                    "messages": messages,
+                    "answer": row["answer"],
+                    "random_string_to_prepend": row["random_string_to_prepend"],
+                    "n_needles": int(row["n_needles"]),
+                    "n_tokens": n_tokens,
+                }
+            )
+            taken += 1
+            if taken >= target:
+                break
+
+        if taken < target:
+            print(f"Warning: only {taken}/{target} samples for n_needles={n}")
+
+    if not samples:
+        raise RuntimeError("No MRCR samples fit; loosen max_prompt_tokens.")
+    return samples
+
+
+def score_mrcr(response: str, answer: str, random_prefix: str) -> float:
+    """Prefix-gated SequenceMatcher ratio; 0 if the prefix is missing."""
+    if not response.startswith(random_prefix):
+        return 0.0
+    stripped = response[len(random_prefix) :]
+    return SequenceMatcher(a=answer, b=stripped, autojunk=False).ratio()
+
+
+async def _call_chat(
+    session: aiohttp.ClientSession,
+    url: str,
+    model: str,
+    messages: list[dict],
+    max_tokens: int,
+    temperature: float,
+    seed: int | None,
+    extra_body: dict,
+) -> tuple[str, int]:
+    data = {
+        "model": model,
+        "messages": messages,
+        "temperature": temperature,
+        "max_tokens": max_tokens,
+        **extra_body,
+    }
+    if seed is not None:
+        data["seed"] = seed
+    try:
+        async with session.post(f"{url}/v1/chat/completions", json=data) as resp:
+            resp.raise_for_status()
+            result = await resp.json()
+            text = result["choices"][0]["message"]["content"] or ""
+            return text, result.get("usage", {}).get("completion_tokens", 0)
+    except Exception as e:
+        print(f"chat request failed: {e}")
+        return "", 0
+
+
+def evaluate_mrcr(
+    model_name: str | None = None,
+    num_samples: int = 40,
+    needles: list[int] | None = None,
+    max_prompt_tokens: int | None = None,
+    max_tokens: int = 2048,
+    host: str = "http://127.0.0.1",
+    port: int = 8000,
+    temperature: float = 0.0,
+    seed: int | None = 42,
+    concurrency: int = 8,
+    extra_body: dict | None = None,
+) -> dict:
+    """Run MRCR against a vLLM server; auto-discovers model and context."""
+    needles = needles or [2, 4, 8]
+    extra_body = DEFAULT_EXTRA_BODY if extra_body is None else extra_body
+    base_url = f"{host}:{port}"
+
+    discovered_model, server_max_len = discover_server_model(base_url)
+    if model_name is None:
+        model_name = discovered_model
+    if max_prompt_tokens is None:
+        if server_max_len is None:
+            raise RuntimeError(
+                "Server did not advertise max_model_len; pass --max-prompt-tokens."
+            )
+        max_prompt_tokens = max(512, server_max_len - max_tokens - PROMPT_SAFETY_BUFFER)
+    print(
+        f"Model: {model_name} | max_prompt_tokens={max_prompt_tokens} "
+        f"(server max_model_len={server_max_len}, max_tokens={max_tokens})"
+    )
+
+    samples = _load_mrcr_samples(
+        needles=needles,
+        max_prompt_tokens=max_prompt_tokens,
+        num_samples=num_samples,
+        seed=seed or 0,
+        base_url=base_url,
+        model_name=model_name,
+    )
+    tok_counts = [s["n_tokens"] for s in samples]
+    print(
+        f"Loaded {len(samples)} samples (needles={needles}, "
+        f"tokens={min(tok_counts)}-{max(tok_counts)})"
+    )
+
+    async def run():
+        sem = asyncio.Semaphore(concurrency)
+        responses = [""] * len(samples)
+        out_tokens = [0] * len(samples)
+
+        async def one(session, i):
+            async with sem:
+                text, toks = await _call_chat(
+                    session=session,
+                    url=base_url,
+                    model=model_name,
+                    messages=samples[i]["messages"],
+                    max_tokens=max_tokens,
+                    temperature=temperature,
+                    seed=seed,
+                    extra_body=extra_body,
+                )
+                responses[i] = text
+                out_tokens[i] = toks
+
+        timeout = aiohttp.ClientTimeout(total=1800)
+        async with aiohttp.ClientSession(timeout=timeout) as session:
+            await tqdm.gather(
+                *[one(session, i) for i in range(len(samples))], desc="MRCR"
+            )
+        return responses, out_tokens
+
+    tic = time.perf_counter()
+    responses, out_tokens = asyncio.run(run())
+    latency = time.perf_counter() - tic
+
+    scores = np.array(
+        [
+            score_mrcr(r, s["answer"], s["random_string_to_prepend"])
+            for r, s in zip(responses, samples)
+        ]
+    )
+    prefix_hits = np.array(
+        [
+            r.startswith(s["random_string_to_prepend"])
+            for r, s in zip(responses, samples)
+        ]
+    )
+    per_needle = {
+        f"match_ratio_n{n}": float(
+            scores[np.array([s["n_needles"] == n for s in samples])].mean()
+        )
+        for n in needles
+        if any(s["n_needles"] == n for s in samples)
+    }
+
+    total_out = int(sum(out_tokens))
+    return {
+        "model": model_name,
+        "match_ratio": float(scores.mean()),
+        "prefix_hit_rate": float(prefix_hits.mean()),
+        "per_needle": per_needle,
+        "num_samples": len(samples),
+        "latency": latency,
+        "total_output_tokens": total_out,
+        "tokens_per_second": total_out / latency if latency > 0 else 0.0,
+        "max_tokens": max_tokens,
+        "needles": needles,
+        "max_prompt_tokens": max_prompt_tokens,
+    }
+
+
+def main() -> None:
+    p = argparse.ArgumentParser(description="MRCR evaluation for vLLM serve")
+    p.add_argument("--model", default=None, help="Default: discovered from /v1/models")
+    p.add_argument("--num-samples", type=int, default=40)
+    p.add_argument(
+        "--needles", type=int, nargs="+", default=[2, 4, 8], choices=[2, 4, 8]
+    )
+    p.add_argument(
+        "--max-prompt-tokens",
+        type=int,
+        default=None,
+        help="Default: server max_model_len - max_tokens - buffer",
+    )
+    p.add_argument("--max-tokens", type=int, default=2048)
+    p.add_argument("--host", default="http://127.0.0.1")
+    p.add_argument("--port", type=int, default=8000)
+    p.add_argument("--temperature", type=float, default=0.0)
+    p.add_argument("--seed", type=int, default=42)
+    p.add_argument("--concurrency", type=int, default=8)
+    p.add_argument(
+        "--extra-body",
+        default=None,
+        help="JSON merged into each request. "
+        "Pass '{}' to disable the default enable_thinking=false.",
+    )
+    p.add_argument("--save-results", default=None)
+    args = p.parse_args()
+
+    extra_body = json.loads(args.extra_body) if args.extra_body else None
+
+    result = evaluate_mrcr(
+        model_name=args.model,
+        num_samples=args.num_samples,
+        needles=args.needles,
+        max_prompt_tokens=args.max_prompt_tokens,
+        max_tokens=args.max_tokens,
+        host=args.host,
+        port=args.port,
+        temperature=args.temperature,
+        seed=args.seed,
+        concurrency=args.concurrency,
+        extra_body=extra_body,
+    )
+
+    print("\nResults:")
+    print(f"  match_ratio:     {result['match_ratio']:.4f}")
+    print(f"  prefix_hit_rate: {result['prefix_hit_rate']:.4f}")
+    for k, v in result["per_needle"].items():
+        print(f"  {k}: {v:.4f}")
+    print(f"  samples:         {result['num_samples']}")
+    print(f"  latency:         {result['latency']:.1f}s")
+    print(f"  output tok/s:    {result['tokens_per_second']:.1f}")
+
+    if args.save_results:
+        with open(args.save_results, "w") as f:
+            json.dump(result, f, indent=2)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/evals/mrcr/test_mrcr_correctness.py b/tests/evals/mrcr/test_mrcr_correctness.py
new file mode 100644
index 000000000000..3adfd4dc8799
--- /dev/null
+++ b/tests/evals/mrcr/test_mrcr_correctness.py
@@ -0,0 +1,83 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+MRCR long-context accuracy test.
+
+Usage:
+    pytest -s -v tests/evals/mrcr/test_mrcr_correctness.py \
+        --config-list-file=configs/models-small.txt
+"""
+
+import shlex
+
+import yaml
+
+from tests.utils import RemoteOpenAIServer
+
+from .mrcr_eval import evaluate_mrcr
+
+
+def _split_host_port(url: str, default_port: int = 8000) -> tuple[str, int]:
+    if "://" in url:
+        url = url.split("://", 1)[1]
+    host_port = url.split("/", 1)[0]
+    if ":" in host_port:
+        host, p = host_port.split(":", 1)
+        return f"http://{host}", int(p)
+    return f"http://{host_port}", default_port
+
+
+def test_mrcr_correctness(config_filename):
+    cfg = yaml.safe_load(config_filename.read_text(encoding="utf-8"))
+
+    server_args = shlex.split(cfg.get("server_args", ""))
+    server_args += ["--trust-remote-code", "--disable-uvicorn-access-log"]
+
+    print(
+        f"MRCR eval for {cfg['model_name']} (threshold {cfg['match_ratio_threshold']})"
+    )
+
+    with RemoteOpenAIServer(
+        cfg["model_name"],
+        server_args,
+        env_dict=cfg.get("env"),
+        max_wait_seconds=cfg.get("startup_max_wait_seconds", 600),
+    ) as server:
+        host, port = _split_host_port(server.url_for("v1"))
+        results = evaluate_mrcr(
+            model_name=cfg.get("model_name"),
+            num_samples=cfg.get("num_samples", 40),
+            needles=cfg.get("needles", [2, 4, 8]),
+            max_prompt_tokens=cfg.get("max_prompt_tokens"),
+            max_tokens=cfg.get("max_tokens", 2048),
+            host=host,
+            port=port,
+            concurrency=cfg.get("concurrency", 8),
+            extra_body=cfg.get("extra_body"),
+        )
+
+    threshold = cfg["match_ratio_threshold"]
+    tol = cfg.get("tolerance", 0.05)
+
+    print(f"  match_ratio:     {results['match_ratio']:.4f}")
+    print(f"  prefix_hit_rate: {results['prefix_hit_rate']:.4f}")
+    for k, v in results["per_needle"].items():
+        print(f"  {k}: {v:.4f}")
+
+    failures: list[str] = []
+    if isinstance(threshold, dict):
+        for n, expected in threshold.items():
+            key = f"match_ratio_n{int(n)}"
+            measured = results["per_needle"].get(key)
+            if measured is None:
+                failures.append(f"{key}: no samples collected")
+            elif measured < expected - tol:
+                failures.append(f"{key}: {measured:.4f} < {expected:.4f} - {tol:.4f}")
+    else:
+        measured = results["match_ratio"]
+        if measured < threshold - tol:
+            failures.append(
+                f"match_ratio: {measured:.4f} < {threshold:.4f} - {tol:.4f}"
+            )
+
+    assert not failures, "MRCR thresholds failed: " + "; ".join(failures)
diff --git a/tests/ir/ir_test_utils.py b/tests/ir/ir_test_utils.py
new file mode 100644
index 000000000000..a82206b6f72e
--- /dev/null
+++ b/tests/ir/ir_test_utils.py
@@ -0,0 +1,46 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Shared test utilities for vLLM IR op correctness tests.
+"""
+
+import torch
+
+from vllm.ir.op import IrOp
+
+NUM_TOKENS = [1, 8, 17, 32, 512, 2048]
+COMMON_HIDDEN_SIZES = [
+    2048,  # Llama 3.2 1B, Qwen 3 MoE 30B-A3B, Gemma 3n
+    4096,  # Llama 3 8B, Qwen 3 8B
+    5120,  # Llama 4 Scout 17B-16E
+    7168,  # DeepSeek V3
+    8192,  # Llama 3 70B
+]
+
+
+def clone_args(args: tuple) -> tuple:
+    return tuple(a.clone() if isinstance(a, torch.Tensor) else a for a in args)
+
+
+def supported_providers(op: IrOp) -> list[str]:
+    return [
+        name for name, impl in op.impls.items() if name != "native" and impl.supported
+    ]
+
+
+def assert_close(op: IrOp, actual, expected):
+    if isinstance(actual, torch.Tensor):
+        tol = op.get_tolerance(actual.dtype)
+        try:
+            torch.testing.assert_close(actual, expected, **tol)
+        except AssertionError as e:
+            raise AssertionError(
+                f"{e}\n\nTo adjust tolerance, use:\n"
+                f"  ir.ops.{op.name}.override_tolerance("
+                f"{actual.dtype}, atol=..., rtol=...)"
+            ) from None
+    elif isinstance(actual, (tuple, list)):
+        for a, ex in zip(actual, expected):
+            assert_close(op, a, ex)
+    else:
+        assert actual == expected
diff --git a/tests/ir/test_inplace_op.py b/tests/ir/test_inplace_op.py
new file mode 100644
index 000000000000..decc4f51c777
--- /dev/null
+++ b/tests/ir/test_inplace_op.py
@@ -0,0 +1,91 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+from torch import Tensor
+from torch.fx.experimental.proxy_tensor import make_fx
+
+import vllm.ir.op
+from vllm.ir.op import IrOp, IrOpInplaceOverload
+
+
+@vllm.ir.register_op(allow_inplace=True)
+def _custom_mm2(x: Tensor, w: Tensor) -> Tensor:
+    return x @ w
+
+
+@_custom_mm2.register_impl("regular")
+def _custom_mm2_regular(x: Tensor, w: Tensor) -> Tensor:
+    return x @ w + 1
+
+
+@_custom_mm2.register_impl("inplace", inplace=True)
+def _custom_mm2_inplace(x: Tensor, w: Tensor) -> Tensor:
+    x.copy_(x @ w + 2)
+    return x
+
+
+class TestInplaceOp:
+    def test_registration(self):
+        # Test that the inplace op is registered correctly.
+        assert "_custom_mm2" in IrOp.registry
+        assert IrOp.registry["_custom_mm2"] is _custom_mm2
+        assert _custom_mm2.torch_op is torch.ops.vllm_ir._custom_mm2.default
+        assert isinstance(_custom_mm2.maybe_inplace, IrOpInplaceOverload)
+        assert (
+            _custom_mm2.maybe_inplace.torch_op
+            is torch.ops.vllm_ir._custom_mm2.maybe_inplace
+        )
+
+    def test_inplace_dispatching(self):
+        # check that the correct implementation is dispatched based on priority,
+        # and inplace semantics hold
+        w = torch.randn(3, 3)
+        x = torch.randn(2, 3)
+        x1 = x.clone()
+
+        with _custom_mm2.set_priority(["regular"]):
+            result_regular = _custom_mm2.maybe_inplace(x, w)
+
+        # check that the regular op does not modify x
+        torch.testing.assert_close(x, x1, atol=0, rtol=0)
+
+        with _custom_mm2.set_priority(["inplace"]):
+            result_inplace: Tensor = _custom_mm2.maybe_inplace(x, w)
+
+        # check that the inplace op returns x directly
+        assert result_inplace.data_ptr() == x.data_ptr()
+
+        torch.testing.assert_close(result_inplace, x1 @ w + 2)
+        torch.testing.assert_close(result_regular, x1 @ w + 1)
+
+    def test_default_dispatching(self):
+        # check that the correct implementation is dispatched,
+        # and ops do not modify inputs when using the default overload
+        w = torch.randn(3, 3)
+        x = torch.randn(2, 3)
+        x1 = x.clone()
+
+        with _custom_mm2.set_priority(["regular"]):
+            result_regular = _custom_mm2(x, w)
+
+        with _custom_mm2.set_priority(["inplace"]):
+            result_inplace = _custom_mm2(x, w)
+
+        # check that x was not modified by either impl
+        torch.testing.assert_close(x, x1, atol=0, rtol=0)
+
+        torch.testing.assert_close(result_inplace, x1 @ w + 2)
+        torch.testing.assert_close(result_regular, x1 @ w + 1)
+
+    def test_trace(self):
+        # Test that the inplace op can be used in a graph.
+        def func(x: Tensor, y: Tensor) -> Tensor:
+            return _custom_mm2.maybe_inplace(x, y)
+
+        x = torch.randn(2, 3)
+        y = torch.randn(3, 4)
+        graph = make_fx(func)(x, y)
+        assert any(
+            node.target == torch.ops.vllm_ir._custom_mm2.maybe_inplace
+            for node in graph.graph.nodes
+        )
diff --git a/tests/ir/test_op.py b/tests/ir/test_op.py
index 8d4245a04a98..3576e5aef8bd 100644
--- a/tests/ir/test_op.py
+++ b/tests/ir/test_op.py
@@ -21,7 +21,7 @@ class CustomError(Exception):
     pass
 
 
-@vllm.ir.register_op
+@vllm.ir.register_op(allow_inplace=True)
 def _custom_add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
     return x + y
 
@@ -129,11 +129,15 @@ def test_schema_contains_tensor_signature(self):
 
     @pytest.mark.parametrize("enable_torch_wrap", [True, False])
     @pytest.mark.parametrize("symbolic_trace", [True, False])
+    @pytest.mark.parametrize("overload", ["default", "maybe_inplace"])
     def test_trace_sees_single_custom_op(
-        self, symbolic_trace: bool, enable_torch_wrap: bool
+        self, symbolic_trace: bool, enable_torch_wrap: bool, overload: str
     ):
+        op_fn = _custom_add if overload == "default" else _custom_add.maybe_inplace
+        torch_op = getattr(torch.ops.vllm_ir._custom_add, overload)
+
         def fn(x, y):
-            return _custom_add(x, y)
+            return op_fn(x, y)
 
         def find_fn(target: Any, gm: fx.GraphModule):
             return gm.graph.find_nodes(op="call_function", target=target)
@@ -155,7 +159,7 @@ def find_fn(target: Any, gm: fx.GraphModule):
         torch.testing.assert_close(out_fx, out_eager)
 
         # check that IR nodes only appear if enable_torch_wrap=True
-        ir_nodes = find_fn(torch.ops.vllm_ir._custom_add.default, gm)
+        ir_nodes = find_fn(torch_op, gm)
         if enable_torch_wrap:
             assert len(ir_nodes) == 1, gm.code
         else:
@@ -167,7 +171,7 @@ def find_fn(target: Any, gm: fx.GraphModule):
         else:
             gm = make_fx(fn)(torch.randn(2, 2), torch.randn(2, 2))
 
-        ir_nodes = find_fn(torch.ops.vllm_ir._custom_add.default, gm)
+        ir_nodes = find_fn(torch_op, gm)
         assert len(ir_nodes) == 1, gm.code
 
 
@@ -176,9 +180,12 @@ def impl_a(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
     return x + y + 10
 
 
-@_custom_add.register_impl("impl_b")
+@_custom_add.register_impl("impl_b", inplace=True)
 def impl_b(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-    return x + y + 20
+    """Computes x+y+20"""
+    x.add_(y)
+    x.add_(20)
+    return x
 
 
 @_custom_add.register_impl("impl_even", supports_args=lambda x, y: x.size(1) % 2 == 0)
@@ -243,19 +250,23 @@ def test_set_priority_scoped(self):
         # Restored to empty
         assert _custom_add.get_priority() == []
 
-    def test_dispatch_priority_order(self):
+    @pytest.mark.parametrize("overload", ["default", "maybe_inplace"])
+    def test_dispatch_priority_order(self, overload: str):
+        op_fn = _custom_add if overload == "default" else _custom_add.maybe_inplace
+        torch_op = getattr(torch.ops.vllm_ir._custom_add, overload)
+
         x = torch.tensor(1, dtype=torch.int32)
         y = torch.tensor(2, dtype=torch.int32)
 
         with _custom_add.set_priority(["impl_b", "impl_a"]):
             assert _custom_add.dispatch(x, y) is impl_b
-            out1 = _custom_add(x, y)
-            out2 = torch.ops.vllm_ir._custom_add(x, y)
+            out1 = op_fn(x.clone(), y)
+            out2 = torch_op(x.clone(), y)
 
             with _custom_add.set_priority(["impl_a"]):
                 assert _custom_add.dispatch(x, y) is impl_a
-                out3 = _custom_add(x, y)
-                out4 = torch.ops.vllm_ir._custom_add(x, y)
+                out3 = op_fn(x.clone(), y)
+                out4 = torch_op(x.clone(), y)
 
         # impl_b
         assert out1.item() == 1 + 2 + 20
@@ -265,18 +276,18 @@ def test_dispatch_priority_order(self):
         assert out4.item() == 1 + 2 + 10
 
     def test_unsupported_impl_filtered(self):
-        @_custom_add.register_impl("unsupported", supported=False)
-        def impl_bad(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        @_custom_add.register_impl("impl_unsupported", supported=False)
+        def impl_unsupported(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
             return x + y + 999
 
         x = torch.tensor(1, dtype=torch.int32)
         y = torch.tensor(2, dtype=torch.int32)
 
-        with _custom_add.set_priority(["unsupported", "impl_a"]):
+        with _custom_add.set_priority(["impl_unsupported", "impl_a"]):
             assert _custom_add.get_priority() == ["impl_a"]
             out = _custom_add(x, y)
 
-        # impl_bad skipped → impl_a
+        # impl_unsupported skipped → impl_a
         assert out.item() == 1 + 2 + 10
 
     def test_supports_args_runtime_dispatch_and_warning(
@@ -495,3 +506,68 @@ def test_uuid_and_oot(tmp_path: Path):
     assert uuid2 == uuid
     assert uuid2 != uuid1
     del _custom_mm.impls["impl_mm_oot"]
+
+
+def _test_native(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+    return x + y
+
+
+def _make_op_with_generator(name: str = "_ig_test"):
+    op = IrOp(name, _test_native)
+
+    @op.register_input_generator
+    def _gen(n: int = 4):
+        x = torch.randn(n, 3)
+        y = torch.randn(n, 3)
+        return x, y
+
+    return op
+
+
+def _test_native_single(x: torch.Tensor) -> torch.Tensor:
+    return x
+
+
+class TestInputGenerator:
+    def test_no_input_generator_by_default(self):
+        op = IrOp("_ig_test_no_gen", _test_native_single)
+        assert not op.has_input_generator
+
+    def test_register_input_generator(self):
+        op = _make_op_with_generator("_ig_test_reg")
+        assert op.has_input_generator
+
+    def test_generate_inputs_returns_tuple(self):
+        op = _make_op_with_generator("_ig_test_tuple")
+        result = op.generate_inputs(n=2)
+        assert isinstance(result, tuple)
+        assert len(result) == 2
+        assert result[0].shape == (2, 3)
+        assert result[1].shape == (2, 3)
+
+    def test_generate_inputs_default_kwargs(self):
+        op = _make_op_with_generator("_ig_test_default")
+        result = op.generate_inputs()
+        assert result[0].shape == (4, 3)
+
+    def test_generate_inputs_without_registration_raises(self):
+        op = IrOp("_ig_test_no_gen_raises", _test_native_single)
+        with pytest.raises(RuntimeError, match="No input generator"):
+            op.generate_inputs()
+
+
+class TestTolerance:
+    def test_override_and_get_tolerance(self):
+        op = IrOp("_tol_test", _test_native)
+
+        tol = op.get_tolerance(torch.float32)
+        assert tol == {"atol": 1e-5, "rtol": 1.3e-6}
+
+        op.override_tolerance(torch.float32, atol=0.1, rtol=0.2)
+        assert op.get_tolerance(torch.float32) == {"atol": 0.1, "rtol": 0.2}
+        assert op.get_tolerance(torch.float16) == {"atol": 1e-3, "rtol": 1e-3}
+
+    def test_get_tolerance_raises_for_unknown_dtype(self):
+        op = IrOp("_tol_test_unknown", _test_native)
+        with pytest.raises(ValueError, match="No tolerance defined"):
+            op.get_tolerance(torch.complex64)
diff --git a/tests/kernels/attention/test_attention_selector.py b/tests/kernels/attention/test_attention_selector.py
index 3ebf9cc3713a..95561b500b22 100644
--- a/tests/kernels/attention/test_attention_selector.py
+++ b/tests/kernels/attention/test_attention_selector.py
@@ -427,3 +427,90 @@ def test_per_head_quant_scales_backend_selection(
                     use_per_head_quant_scales=True,
                 )
             assert backend_name in str(exc_info.value)
+
+
+@pytest.mark.parametrize(
+    "backend_name,use_non_causal,should_succeed",
+    [
+        ("FLASH_ATTN", True, True),  # FlashAttn supports non-causal
+        ("FLASH_ATTN", False, True),  # FlashAttn also works with causal
+        ("FLASHINFER", True, False),  # FlashInfer does not support non-causal
+        ("FLASHINFER", False, True),  # FlashInfer works with causal
+    ],
+)
+def test_non_causal_backend_selection(
+    backend_name: str, use_non_causal: bool, should_succeed: bool
+):
+    """Test that use_non_causal on AttentionConfig controls backend filtering.
+
+    DFlashProposer sets use_non_causal=True on the draft model's
+    AttentionConfig so only non-causal-capable backends are selected.
+    The target model keeps use_non_causal=False (default) and can use
+    any backend.
+    """
+    _cached_get_attn_backend.cache_clear()
+
+    attention_config = AttentionConfig(
+        backend=AttentionBackendEnum[backend_name],
+        use_non_causal=use_non_causal,
+    )
+    cache_config = CacheConfig(block_size=16)
+    vllm_config = VllmConfig(
+        attention_config=attention_config, cache_config=cache_config
+    )
+
+    if CudaPlatform is None:
+        pytest.skip("CudaPlatform not available")
+    with (
+        set_current_vllm_config(vllm_config),
+        patch("vllm.platforms.current_platform", CudaPlatform()),
+    ):
+        if should_succeed:
+            backend = get_attn_backend(
+                head_size=128,
+                dtype=torch.float16,
+                kv_cache_dtype=None,
+            )
+            assert backend.get_name() == backend_name
+        else:
+            with pytest.raises(ValueError) as exc_info:
+                get_attn_backend(
+                    head_size=128,
+                    dtype=torch.float16,
+                    kv_cache_dtype=None,
+                )
+            assert "non-causal" in str(exc_info.value).lower()
+
+
+def test_non_causal_autoselect_backend():
+    """Test that when backend=None with use_non_causal=True, auto-selection
+    picks a compatible backend.
+
+    This simulates the DFlash scenario where the user doesn't specify
+    --attention-backend or --speculative-config.attention_backend.
+    The drafter inherits backend=None and auto-selects a backend that
+    supports non-causal attention.
+    """
+    _cached_get_attn_backend.cache_clear()
+
+    attention_config = AttentionConfig(
+        backend=None,
+        use_non_causal=True,
+    )
+    cache_config = CacheConfig(block_size=16)
+    vllm_config = VllmConfig(
+        attention_config=attention_config, cache_config=cache_config
+    )
+
+    if CudaPlatform is None:
+        pytest.skip("CudaPlatform not available")
+    with (
+        set_current_vllm_config(vllm_config),
+        patch("vllm.platforms.current_platform", CudaPlatform()),
+    ):
+        backend = get_attn_backend(
+            head_size=128,
+            dtype=torch.float16,
+            kv_cache_dtype=None,
+        )
+        assert backend.supports_non_causal()
diff --git a/tests/kernels/attention/test_cache.py b/tests/kernels/attention/test_cache.py
index 0249461dd2fd..9b022a042c81 100644
--- a/tests/kernels/attention/test_cache.py
+++ b/tests/kernels/attention/test_cache.py
@@ -10,7 +10,7 @@
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.quant_utils import scaled_dequantize
 from vllm.platforms import current_platform
-from vllm.utils.torch_utils import set_random_seed
+from vllm.utils.torch_utils import nvfp4_kv_cache_split_views, set_random_seed
 
 COPYING_DIRECTION = [("cuda", "cpu"), ("cuda", "cuda"), ("cpu", "cuda")]
 DTYPES = [torch.bfloat16, torch.float]
@@ -172,7 +172,7 @@ def test_reshape_and_cache(
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("seed", SEEDS)
 @pytest.mark.parametrize("device", CUDA_DEVICES)
-@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
+@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE + ["nvfp4"])
 @pytest.mark.parametrize("kv_cache_layout", CACHE_LAYOUTS)
 @pytest.mark.parametrize("kv_scale_type", KV_SCALE_TYPES)
 @pytest.mark.parametrize("implementation", RESHAPE_FLASH_IMPLEMENTATIONS)
@@ -202,6 +202,25 @@ def test_reshape_and_cache_flash(
     if kv_scale_type == "attn_head" and implementation != "cuda":
         pytest.skip("Only CUDA implementation supports attn_head scaling.")
 
+    if kv_cache_dtype == "nvfp4":
+        if not current_platform.has_device_capability(100):
+            pytest.skip("NVFP4 requires compute capability >= 10.0 (Blackwell).")
+        if implementation != "cuda":
+            pytest.skip("NVFP4 only supports CUDA implementation.")
+        if kv_scale_type != "tensor":
+            pytest.skip("NVFP4 only supports per-tensor scaling.")
+        if head_size % 16 != 0:
+            pytest.skip("NVFP4 requires head_size divisible by 16.")
+        if (head_size // 16) % 4 != 0:
+            pytest.skip(
+                "NVFP4 requires (head_size // 16) divisible by 4 "
+                "for 4x4 block scale swizzle."
+            )
+        if block_size % 4 != 0:
+            pytest.skip("NVFP4 requires block_size divisible by 4.")
+        if dtype not in (torch.float16, torch.bfloat16):
+            pytest.skip("NVFP4 quantization only supports fp16/bf16 input.")
+
     # fp8 conversion requires continugous memory buffer. Reduce the number of
     # blocks and tokens to consume less memory.
     num_tokens = num_tokens // 2
@@ -229,7 +248,23 @@ def test_reshape_and_cache_flash(
     del key_caches
     del value_caches
 
-    if kv_scale_type == "tensor":
+    # For nvfp4, the factory returns kv[:, 0] and kv[:, 1] like all dtypes.
+    # Split views are still needed for dequant verification.
+    key_scale_cache = None
+    value_scale_cache = None
+    nvfp4_key_data = None
+    nvfp4_value_data = None
+    if kv_cache_dtype == "nvfp4":
+        (nvfp4_key_data,), (key_scale_cache,) = nvfp4_kv_cache_split_views(key_cache)
+        (nvfp4_value_data,), (value_scale_cache,) = nvfp4_kv_cache_split_views(
+            value_cache
+        )
+
+    if kv_cache_dtype == "nvfp4":
+        # Global scale = amax / 448 (per-tensor)
+        k_scale = (key.abs().amax() / 448.0).to(torch.float32)
+        v_scale = (value.abs().amax() / 448.0).to(torch.float32)
+    elif kv_scale_type == "tensor":
         k_scale = (key.amax() / 64.0).to(torch.float32)
         v_scale = (value.amax() / 64.0).to(torch.float32)
     else:  # "attn_head"
@@ -240,8 +275,9 @@ def permute_and_compact(x):
         y = x if kv_cache_layout == "NHD" else x.permute(0, 2, 1, 3)
         return y.contiguous()
 
-    key_cache_compact = permute_and_compact(key_cache)
-    value_cache_compact = permute_and_compact(value_cache)
+    if kv_cache_dtype != "nvfp4":
+        key_cache_compact = permute_and_compact(key_cache)
+        value_cache_compact = permute_and_compact(value_cache)
 
     def convert_fp8_local(output, input, scale, kv_dtype):
         fp8_input = input.view(current_platform.fp8_dtype())
@@ -257,7 +293,7 @@ def convert_fp8_local(output, input, scale, kv_dtype):
                 result = fp8_input.to(output.dtype) * scale.view(1, -1, 1, 1)
         output.copy_(result)
 
-    # Clone the KV caches.
+    # Clone the KV caches (for non-nvfp4, used as reference baseline).
     if kv_cache_dtype == "fp8":
         cloned_key_cache = torch.empty_like(key_cache_compact, dtype=torch.float16)
         convert_fp8_local(cloned_key_cache, key_cache_compact, k_scale, kv_cache_dtype)
@@ -265,25 +301,27 @@ def convert_fp8_local(output, input, scale, kv_dtype):
         convert_fp8_local(
             cloned_value_cache, value_cache_compact, v_scale, kv_cache_dtype
         )
-    else:
+    elif kv_cache_dtype != "nvfp4":
         cloned_key_cache = key_cache_compact.clone()
         cloned_value_cache = value_cache_compact.clone()
+
     # Call the reshape_and_cache kernel.
     if implementation == "cuda":
-        opcheck(
-            torch.ops._C_cache_ops.reshape_and_cache_flash,
-            (
-                key,
-                value,
-                key_cache,
-                value_cache,
-                slot_mapping,
-                kv_cache_dtype,
-                k_scale,
-                v_scale,
-            ),
-            cond=(head_size == HEAD_SIZES[0]),
-        )
+        if kv_cache_dtype != "nvfp4":
+            opcheck(
+                torch.ops._C_cache_ops.reshape_and_cache_flash,
+                (
+                    key,
+                    value,
+                    key_cache,
+                    value_cache,
+                    slot_mapping,
+                    kv_cache_dtype,
+                    k_scale,
+                    v_scale,
+                ),
+                cond=(head_size == HEAD_SIZES[0]),
+            )
         ops.reshape_and_cache_flash(
             key,
             value,
@@ -309,6 +347,46 @@ def convert_fp8_local(output, input, scale, kv_dtype):
             k_scale,
             v_scale,
         )
+
+    if kv_cache_dtype == "nvfp4":
+        # Verify NVFP4 by dequantizing the entire cache and comparing
+        # the written positions against original bf16 values.
+        # Same pattern as FP8: dequant whole cache, then extract and compare.
+        from tests.kernels.quantization.nvfp4_utils import (
+            dequant_nvfp4_kv_cache,
+        )
+
+        def dequant_nvfp4_cache_nhd(data_cache, scale_cache, global_scale):
+            # data_cache:  [N, T, H, data_dim]  NHD (contiguous inner dims)
+            # scale_cache: [N, T, H, scale_dim] NHD (contiguous inner dims)
+            # Permute to HND layout for the dequant utility.
+            data_hnd = data_cache.permute(0, 2, 1, 3)
+            scale_hnd = scale_cache.permute(0, 2, 1, 3)
+            result_hnd = dequant_nvfp4_kv_cache(
+                data_hnd, scale_hnd, global_scale, head_size, block_size
+            )
+            return result_hnd.permute(0, 2, 1, 3)  # back to [N, T, H, D]
+
+        result_key_cache = dequant_nvfp4_cache_nhd(
+            nvfp4_key_data, key_scale_cache, k_scale.item()
+        )
+        result_value_cache = dequant_nvfp4_cache_nhd(
+            nvfp4_value_data, value_scale_cache, v_scale.item()
+        )
+
+        # Flatten [num_blocks, block_size] → [num_slots] and index by slot_mapping.
+        num_slots = num_blocks * block_size
+        result_key_flat = result_key_cache.reshape(num_slots, num_heads, head_size)
+        result_value_flat = result_value_cache.reshape(num_slots, num_heads, head_size)
+
+        torch.testing.assert_close(
+            result_key_flat[slot_mapping], key.float(), atol=1.5, rtol=0.5
+        )
+        torch.testing.assert_close(
+            result_value_flat[slot_mapping], value.float(), atol=1.5, rtol=0.5
+        )
+        return
+
     key_cache_compact = permute_and_compact(key_cache)
     value_cache_compact = permute_and_compact(value_cache)
 
diff --git a/tests/kernels/attention/test_cpu_attn.py b/tests/kernels/attention/test_cpu_attn.py
index f7691a90ed8b..ce7eb3088ac0 100644
--- a/tests/kernels/attention/test_cpu_attn.py
+++ b/tests/kernels/attention/test_cpu_attn.py
@@ -20,6 +20,12 @@
     cpu_attn_reshape_and_cache,
 )
 
+# Enable AMX tile data registers so isolated runs (e.g. -k fp8_amx) don't rely
+# on ref_paged_attn's einsum to trigger oneDNN's _init_amx() first.
+if torch.cpu._is_amx_tile_supported():
+    torch.cpu._init_amx()
+
+
 NUM_HEADS = [
     (4, 4),
     (8, 2),
@@ -178,6 +184,10 @@ def ref_paged_attn(
     return torch.cat(outputs, dim=0)
 
 
+_FP8_ATOL = {"fp8_e4m3": 0.2, "fp8_e5m2": 0.3}
+_FP8_RTOL = 0.1
+
+
 @torch.inference_mode()
 def varlen_with_paged_kv(
     seq_lens: list[tuple[int, int]],
@@ -191,6 +201,9 @@ def varlen_with_paged_kv(
     use_alibi: bool,
     use_sink: bool,
     isa: str,
+    kv_cache_dtype: str = "auto",
+    k_scale: float = 1.0,
+    v_scale: float = 1.0,
 ) -> None:
     set_random_seed(0)
     num_seqs = len(seq_lens)
@@ -212,6 +225,10 @@ def varlen_with_paged_kv(
         15 * torch.rand((num_query_heads,), dtype=torch.bfloat16) if use_sink else None
     )
 
+    is_fp8 = kv_cache_dtype != "auto"
+    if is_fp8 and current_platform.get_cpu_architecture() != CpuArchEnum.X86:
+        pytest.skip("FP8 KV cache only supported on x86")
+
     query = tensor_cache(
         elem_num=token_num * num_query_heads * head_size,
         dtype=dtype,
@@ -233,11 +250,17 @@ def varlen_with_paged_kv(
         num_kv_heads,
         head_size,
     )
+    if is_fp8:
+        # Clamp KV to [-1, 1] so FP8 quantization error (<=12.5% for E4M3,
+        # <=25% for E5M2) stays within the test tolerances regardless of
+        # which tensor_cache values happen to be in use.
+        key_value = key_value.clamp(-1, 1)
     key_cache, value_cache = key_value.unbind(0)
 
     # KV cache for CPU attention
+    cache_dtype = torch.uint8 if is_fp8 else dtype
     packed_key_cache = torch.empty(
-        num_blocks, num_kv_heads, block_size, head_size, dtype=dtype
+        num_blocks, num_kv_heads, block_size, head_size, dtype=cache_dtype
     )
     packed_value_cache = torch.empty_like(packed_key_cache)
 
@@ -252,6 +275,11 @@ def varlen_with_paged_kv(
 
     # use reshape_and_cache to pack key_cache and value_cache
     slot_mapping = torch.arange(0, num_blocks * block_size, dtype=torch.int64)
+    fp8_kwargs: dict = (
+        dict(k_scale=k_scale, v_scale=v_scale, kv_cache_dtype=kv_cache_dtype)
+        if is_fp8
+        else {}
+    )
     cpu_attn_reshape_and_cache(
         key=key_cache.view(-1, num_kv_heads, head_size),
         value=value_cache.view(-1, num_kv_heads, head_size),
@@ -259,6 +287,7 @@ def varlen_with_paged_kv(
         value_cache=packed_value_cache,
         slot_mapping=slot_mapping,
         isa=isa,
+        **fp8_kwargs,
     )
 
     metadata = cpu_attn_get_scheduler_metadata(
@@ -291,6 +320,7 @@ def varlen_with_paged_kv(
         softcap=soft_cap if soft_cap is not None else 0,
         scheduler_metadata=metadata,
         s_aux=s_aux,
+        **fp8_kwargs,
     )
 
     metadata = cpu_attn_get_scheduler_metadata(
@@ -323,23 +353,59 @@ def varlen_with_paged_kv(
         softcap=soft_cap if soft_cap is not None else 0,
         scheduler_metadata=metadata,
         s_aux=s_aux,
+        **fp8_kwargs,
     )
 
-    ref_output = ref_paged_attn(
-        query=query,
-        key_cache=key_cache,
-        value_cache=value_cache,
-        query_lens=query_lens,
-        kv_lens=kv_lens,
-        block_tables=block_tables,
-        scale=scale,
-        sliding_window=sliding_window,
-        soft_cap=soft_cap,
-        alibi_slopes=alibi_slopes,
-        s_aux=s_aux,
-    )
+    if is_fp8:
+        # Build a float KV cache via the non-FP8 path and run float attention
+        # to use as the reference.
+        ref_key_cache = torch.empty(
+            num_blocks, num_kv_heads, block_size, head_size, dtype=dtype
+        )
+        ref_value_cache = torch.empty_like(ref_key_cache)
+        cpu_attn_reshape_and_cache(
+            key=key_cache.view(-1, num_kv_heads, head_size),
+            value=value_cache.view(-1, num_kv_heads, head_size),
+            key_cache=ref_key_cache,
+            value_cache=ref_value_cache,
+            slot_mapping=slot_mapping,
+            isa=isa,
+        )
+        ref_output = torch.empty_like(query)
+        cpu_attention_with_kv_cache(
+            query=query,
+            key_cache=ref_key_cache,
+            value_cache=ref_value_cache,
+            output=ref_output,
+            query_start_loc=cu_query_lens,
+            seq_lens=kv_lens_tensor,
+            scale=scale,
+            causal=True,
+            alibi_slopes=alibi_slopes,
+            sliding_window=window_size,
+            block_table=block_tables,
+            softcap=soft_cap if soft_cap is not None else 0,
+            scheduler_metadata=metadata,
+            s_aux=s_aux,
+        )
+        atol = _FP8_ATOL[kv_cache_dtype]
+        rtol = _FP8_RTOL
+    else:
+        ref_output = ref_paged_attn(
+            query=query,
+            key_cache=key_cache,
+            value_cache=value_cache,
+            query_lens=query_lens,
+            kv_lens=kv_lens,
+            block_tables=block_tables,
+            scale=scale,
+            sliding_window=sliding_window,
+            soft_cap=soft_cap,
+            alibi_slopes=alibi_slopes,
+            s_aux=s_aux,
+        )
+        atol, rtol = 1.5e-2, 1e-2
 
-    atol, rtol = 1.5e-2, 1e-2
     (
         torch.testing.assert_close(out_with_split, ref_output, atol=atol, rtol=rtol),
         f"{torch.max(torch.abs(out_with_split - ref_output))}",
@@ -350,6 +416,7 @@ def varlen_with_paged_kv(
     )
 
 
+@pytest.mark.parametrize("kv_cache_dtype", ["auto", "fp8_e4m3", "fp8_e5m2"])
 @pytest.mark.parametrize("seq_lens", SEQ_LENS)
 @pytest.mark.parametrize("num_heads", NUM_HEADS)
 @pytest.mark.parametrize("head_size", HEAD_SIZES)
@@ -373,6 +440,7 @@ def test_varlen_with_paged_kv_normal_vec(
     use_alibi: bool,
     use_sink: bool,
     isa: str,
+    kv_cache_dtype: str,
 ) -> None:
     varlen_with_paged_kv(
         seq_lens=seq_lens,
@@ -386,9 +454,11 @@ def test_varlen_with_paged_kv_normal_vec(
         use_alibi=use_alibi,
         use_sink=use_sink,
         isa=isa,
+        kv_cache_dtype=kv_cache_dtype,
     )
 
 
+@pytest.mark.parametrize("kv_cache_dtype", ["auto", "fp8_e4m3", "fp8_e5m2"])
 @pytest.mark.parametrize("seq_lens", SEQ_LENS)
 @pytest.mark.parametrize("num_heads", NUM_HEADS)
 @pytest.mark.parametrize("head_size", HEAD_SIZES)
@@ -413,6 +483,7 @@ def test_varlen_with_paged_kv_normal_amx(
     use_alibi: bool,
     use_sink: bool,
     isa: str,
+    kv_cache_dtype: str,
 ) -> None:
     varlen_with_paged_kv(
         seq_lens=seq_lens,
@@ -426,6 +497,7 @@ def test_varlen_with_paged_kv_normal_amx(
         use_alibi=use_alibi,
         use_sink=use_sink,
         isa=isa,
+        kv_cache_dtype=kv_cache_dtype,
     )
 
 
@@ -511,6 +583,7 @@ def test_varlen_with_paged_kv_normal_neon(
     )
 
 
+@pytest.mark.parametrize("kv_cache_dtype", ["auto", "fp8_e4m3"])
 @pytest.mark.parametrize("seq_lens", SEQ_LENS)
 @pytest.mark.parametrize("num_heads", NUM_HEADS)
 @pytest.mark.parametrize("head_size", [96])
@@ -534,6 +607,7 @@ def test_varlen_with_paged_kv_softcap(
     use_alibi: bool,
     use_sink: bool,
     isa: str,
+    kv_cache_dtype: str,
 ) -> None:
     varlen_with_paged_kv(
         seq_lens=seq_lens,
@@ -547,9 +621,11 @@ def test_varlen_with_paged_kv_softcap(
         use_alibi=use_alibi,
         use_sink=use_sink,
         isa=isa,
+        kv_cache_dtype=kv_cache_dtype,
     )
 
 
+@pytest.mark.parametrize("kv_cache_dtype", ["auto", "fp8_e4m3"])
 @pytest.mark.parametrize("seq_lens", SEQ_LENS)
 @pytest.mark.parametrize("num_heads", NUM_HEADS)
 @pytest.mark.parametrize("head_size", [96])
@@ -573,6 +649,7 @@ def test_varlen_with_paged_kv_alibi(
     use_alibi: bool,
     use_sink: bool,
     isa: str,
+    kv_cache_dtype: str,
 ) -> None:
     varlen_with_paged_kv(
         seq_lens=seq_lens,
@@ -586,9 +663,11 @@ def test_varlen_with_paged_kv_alibi(
         use_alibi=use_alibi,
         use_sink=use_sink,
         isa=isa,
+        kv_cache_dtype=kv_cache_dtype,
     )
 
 
+@pytest.mark.parametrize("kv_cache_dtype", ["auto", "fp8_e4m3"])
 @pytest.mark.parametrize("seq_lens", SEQ_LENS)
 @pytest.mark.parametrize("num_heads", NUM_HEADS)
 @pytest.mark.parametrize("head_size", [96])
@@ -612,6 +691,7 @@ def test_varlen_with_paged_kv_sink(
     use_alibi: bool,
     use_sink: bool,
     isa: str,
+    kv_cache_dtype: str,
 ) -> None:
     varlen_with_paged_kv(
         seq_lens=seq_lens,
@@ -625,4 +705,5 @@ def test_varlen_with_paged_kv_sink(
         use_alibi=use_alibi,
         use_sink=use_sink,
         isa=isa,
+        kv_cache_dtype=kv_cache_dtype,
     )
diff --git a/tests/kernels/attention/test_deepgemm_attention.py b/tests/kernels/attention/test_deepgemm_attention.py
index 2dc522598e4e..0cea46d6284f 100644
--- a/tests/kernels/attention/test_deepgemm_attention.py
+++ b/tests/kernels/attention/test_deepgemm_attention.py
@@ -9,8 +9,8 @@
 from vllm.utils.deep_gemm import (
     _ceil_to_ue8m0,
     calc_diff,
-    fp8_mqa_logits,
-    fp8_paged_mqa_logits,
+    fp8_fp4_mqa_logits,
+    fp8_fp4_paged_mqa_logits,
     get_num_sms,
     get_paged_mqa_logits_metadata,
 )
@@ -127,8 +127,8 @@ def test_deepgemm_fp8_mqa_logits(clean_logits: bool):
 
                 q_fp8 = q.to(torch.float8_e4m3fn)
                 kv_fp8 = per_custom_dims_cast_to_fp8(kv, (0,), False)
-                logits = fp8_mqa_logits(
-                    q_fp8, kv_fp8, weights, ks, ke, clean_logits=clean_logits
+                logits = fp8_fp4_mqa_logits(
+                    (q_fp8, None), kv_fp8, weights, ks, ke, clean_logits=clean_logits
                 )
 
                 ref_logits = _ref_fp8_mqa_logits(
@@ -150,7 +150,7 @@ def test_deepgemm_fp8_mqa_logits(clean_logits: bool):
                 assert diff < 1e-3, f"{diff=}"
 
 
-def _ref_fp8_paged_mqa_logits(
+def _ref_fp8_fp4_paged_mqa_logits(
     q: torch.Tensor,
     kv_cache: torch.Tensor,
     weights: torch.Tensor,
@@ -205,8 +205,10 @@ def _ref_fp8_paged_mqa_logits(
 @pytest.mark.skipif(
     not current_platform.has_device_capability(90), reason="SM90 and SM100 only"
 )
-@pytest.mark.parametrize("clean_logits", [True, False])
-def test_deepgemm_fp8_paged_mqa_logits(clean_logits: bool):
+def test_deepgemm_fp8_fp4_paged_mqa_logits():
+    # NOTE: clean_logits=True is incompatible with the 2D context_lens
+    # required by csrc/apis/attention.hpp; only the False path is exercised.
+    clean_logits = False
     torch.manual_seed(0)
     random.seed(0)
 
@@ -258,21 +260,29 @@ def test_deepgemm_fp8_paged_mqa_logits(clean_logits: bool):
                 q_fp8 = q.to(torch.float8_e4m3fn)
                 kv_cache_fp8 = kv_cache_cast_to_fp8(kv_cache)
 
+                # deep_gemm paged MQA logits requires 2D context_lens of
+                # shape (B, next_n) (csrc/apis/attention.hpp:332-335);
+                # see indexer.py:607-608. For each batch/next_n token, the
+                # effective context length is context_lens[b] - next_n + j + 1.
+                next_n_arange = torch.arange(next_n, device="cuda", dtype=torch.int32)
+                context_lens_2d = (
+                    context_lens.unsqueeze(-1) - next_n + 1 + next_n_arange
+                ).contiguous()
                 schedule_metadata = get_paged_mqa_logits_metadata(
-                    context_lens, blocksize, get_num_sms()
+                    context_lens_2d, blocksize, get_num_sms()
                 )
-                logits = fp8_paged_mqa_logits(
-                    q_fp8,
+                logits = fp8_fp4_paged_mqa_logits(
+                    (q_fp8, None),
                     kv_cache_fp8,
                     weights,
-                    context_lens,
+                    context_lens_2d,
                     block_tables,
                     schedule_metadata,
                     max_model_len,
                     clean_logits=clean_logits,
                 )
 
-                ref_logits = _ref_fp8_paged_mqa_logits(
+                ref_logits = _ref_fp8_fp4_paged_mqa_logits(
                     q,
                     kv_cache,
                     weights,
diff --git a/tests/kernels/attention/test_flashinfer_trtllm_attention.py b/tests/kernels/attention/test_flashinfer_trtllm_attention.py
index b5f8584015be..87a12c2ff395 100644
--- a/tests/kernels/attention/test_flashinfer_trtllm_attention.py
+++ b/tests/kernels/attention/test_flashinfer_trtllm_attention.py
@@ -5,12 +5,17 @@
 import torch
 
 from tests.kernels.quantization.nvfp4_utils import (
+    dequant_nvfp4_kv_cache,
     dequantize_nvfp4_to_dtype,
     get_nvfp4_global_scale,
 )
 from vllm.platforms import current_platform
 from vllm.utils.math_utils import round_up
-from vllm.utils.torch_utils import set_random_seed
+from vllm.utils.torch_utils import (
+    nvfp4_kv_cache_full_dim,
+    nvfp4_kv_cache_split_views,
+    set_random_seed,
+)
 
 if not current_platform.is_device_capability_family(100):
     pytest.skip(
@@ -33,6 +38,117 @@ def to_float8(x, dtype=torch.float8_e4m3fn):
     return x_scl_sat.to(dtype), scale.float().reciprocal()
 
 
+def build_paged_kv_metadata(
+    seq_lens: torch.Tensor,
+    block_tables: torch.Tensor,
+    block_size: int,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Build paged-KV indptr/indices/last_page_lens from seq_lens + block_tables."""
+    kv_indptr = [0]
+    kv_indices = []
+    kv_last_page_lens = []
+    for i in range(len(seq_lens)):
+        sl = int(seq_lens[i])
+        assert sl > 0
+        nb = (sl + block_size - 1) // block_size
+        kv_indices.extend(block_tables[i, :nb].tolist())
+        kv_indptr.append(kv_indptr[-1] + nb)
+        kv_last_page_lens.append(sl % block_size or block_size)
+    return (
+        torch.tensor(kv_indptr, dtype=torch.int32),
+        torch.tensor(kv_indices, dtype=torch.int32),
+        torch.tensor(kv_last_page_lens, dtype=torch.int32),
+    )
+
+
+def make_nvfp4_kv_cache(
+    kv_bf16_hnd: torch.Tensor, block_size: int, head_size: int
+) -> tuple:
+    """Quantize bf16 KV cache to nvfp4 via reshape_and_cache_flash.
+
+    Returns (k_data, v_data), (k_scales, v_scales), kv_scale, ref_kv_bf16.
+    """
+    num_blocks, _, num_kv_heads, _, _ = kv_bf16_hnd.shape
+    kv_scale_val = (kv_bf16_hnd.abs().amax() / 448.0).item()
+    kv_scale_tensor = torch.tensor(
+        kv_scale_val, dtype=torch.float32, device=kv_bf16_hnd.device
+    )
+
+    # Allocate in HND physical order, permute to NHD logical order.
+    # hnd_order swaps dims 2↔3; it is its own inverse.
+    full_dim = nvfp4_kv_cache_full_dim(head_size)
+    hnd_order = (0, 1, 3, 2, 4)
+    kv_cache = torch.zeros(
+        (num_blocks, 2, num_kv_heads, block_size, full_dim),
+        dtype=torch.uint8,
+        device=kv_bf16_hnd.device,
+    ).permute(*hnd_order)
+
+    # Flatten NHD [N, T, H, D] → token tensors [N*T, H, D] for the kernel.
+    num_tokens = num_blocks * block_size
+    k_tokens = (
+        kv_bf16_hnd[:, 0]
+        .permute(0, 2, 1, 3)
+        .reshape(num_tokens, num_kv_heads, head_size)
+    )
+    v_tokens = (
+        kv_bf16_hnd[:, 1]
+        .permute(0, 2, 1, 3)
+        .reshape(num_tokens, num_kv_heads, head_size)
+    )
+    slot_mapping = torch.arange(num_tokens, dtype=torch.long, device=kv_bf16_hnd.device)
+
+    # reshape_and_cache_flash: kernel receives kv_cache[:, 0] and [:, 1]
+    # (full K/V buffers containing both data and scale).
+    torch.ops._C_cache_ops.reshape_and_cache_flash(
+        k_tokens,
+        v_tokens,
+        kv_cache[:, 0],
+        kv_cache[:, 1],
+        slot_mapping,
+        "nvfp4",
+        kv_scale_tensor,
+        kv_scale_tensor,
+    )
+
+    # Split in HND order for trtllm kernel (expects HND numTokensPerPage).
+    kv_cache_hnd = kv_cache.permute(*hnd_order)
+    (k_data, v_data), (k_scales, v_scales) = nvfp4_kv_cache_split_views(kv_cache_hnd)
+
+    # Dequantize for the FA2 reference baseline.
+    ref_k = dequant_nvfp4_kv_cache(
+        k_data, k_scales, kv_scale_val, head_size, block_size
+    ).to(torch.bfloat16)
+    ref_v = dequant_nvfp4_kv_cache(
+        v_data, v_scales, kv_scale_val, head_size, block_size
+    ).to(torch.bfloat16)
+    ref_kv_bf16 = torch.stack([ref_k, ref_v], dim=1)  # [N, 2, H, T, D]
+
+    return (k_data, v_data), (k_scales, v_scales), kv_scale_val, ref_kv_bf16
+
+
+def make_quantized_kv_cache(
+    kv_cache: torch.Tensor,
+    kv_quant_dtype: torch.dtype,
+    block_size: int,
+    head_size: int,
+) -> tuple:
+    """Quantize kv_cache based on dtype. Returns (kv_cache, kv_cache_sf,
+    kv_scale, ref_kv_cache, is_nvfp4_kv)."""
+    is_nvfp4_kv = kv_quant_dtype == FP4_DTYPE
+    if is_nvfp4_kv:
+        data, scales, kv_scale, ref = make_nvfp4_kv_cache(
+            kv_cache, block_size, head_size
+        )
+        return data, scales, kv_scale, ref, True
+    elif kv_quant_dtype == FP8_DTYPE:
+        kv_fp8, kv_scale = to_float8(kv_cache)
+        ref = kv_fp8.to(kv_cache.dtype) * kv_scale
+        return kv_fp8, None, kv_scale, ref, False
+    else:
+        return kv_cache, None, 1.0, kv_cache, False
+
+
 DTYPE = [torch.bfloat16]
 QUANT_DTYPES = [
     # (q_quant_dtype, kv_quant_dtype, o_quant_dtype)
@@ -41,6 +157,7 @@ def to_float8(x, dtype=torch.float8_e4m3fn):
     (FP8_DTYPE, FP8_DTYPE, None),
     (FP8_DTYPE, FP8_DTYPE, FP8_DTYPE),
     (FP8_DTYPE, FP8_DTYPE, FP4_DTYPE),
+    (FP8_DTYPE, FP4_DTYPE, FP8_DTYPE),  # nvfp4 KV cache
 ]
 BATCH_SIZE = [4, 12]
 MAX_SEQ_LENS = [(1024, 4096)]
@@ -127,35 +244,19 @@ def test_flashinfer_trtllm_decode_with_baseline(
     max_seq_len = torch.max(seq_lens).item()
 
     kv_cache = torch.randn(kv_cache_shape, dtype=dtype)
-    if kv_quant_dtype == FP8_DTYPE:
-        kv_cache, kv_scale = to_float8(kv_cache)
-        ref_kv_cache = kv_cache.to(dtype) * kv_scale
-    else:
-        kv_scale = 1.0
-        ref_kv_cache = kv_cache
+    kv_cache, kv_cache_sf, kv_scale, ref_kv_cache, is_nvfp4_kv = (
+        make_quantized_kv_cache(kv_cache, kv_quant_dtype, block_size, head_size)
+    )
+
     k_scale = v_scale = kv_scale
 
     max_num_blocks_per_seq = (max_seq_len + block_size - 1) // block_size
     block_tables = torch.randint(
         0, NUM_BLOCKS, (batch_size, max_num_blocks_per_seq), dtype=torch.int32
     )
-    kv_indptr = [0]
-    kv_indices = []
-    kv_last_page_lens = []
-    for i in range(batch_size):
-        seq_len = seq_lens[i]
-        assert seq_len > 0
-        num_blocks = (seq_len + block_size - 1) // block_size
-        kv_indices.extend(block_tables[i, :num_blocks])
-        kv_indptr.append(kv_indptr[-1] + num_blocks)
-        kv_last_page_len = seq_len % block_size
-        if kv_last_page_len == 0:
-            kv_last_page_len = block_size
-        kv_last_page_lens.append(kv_last_page_len)
-
-    kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
-    kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
-    kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
+    kv_indptr, kv_indices, kv_last_page_lens = build_paged_kv_metadata(
+        seq_lens, block_tables, block_size
+    )
     workspace_buffer = torch.zeros(128 * 1024 * 1024, dtype=torch.int8)
 
     # Baseline Decode
@@ -225,6 +326,7 @@ def test_flashinfer_trtllm_decode_with_baseline(
         sinks=sinks,
         o_sf_scale=o_sf_scale_float,
         out=output_trtllm,
+        kv_cache_sf=kv_cache_sf,
     )
     if o_quant_dtype == FP8_DTYPE:
         output_trtllm = output_trtllm.to(dtype) * o_scale
@@ -237,7 +339,9 @@ def test_flashinfer_trtllm_decode_with_baseline(
         )
         output_trtllm = output_trtllm.reshape(-1, query.shape[1], query.shape[2])
 
-    if q_quant_dtype == FP8_DTYPE and o_quant_dtype == FP4_DTYPE:
+    if is_nvfp4_kv:
+        rtol, atol = 1.0, 1.0  # nvfp4 has higher quantization error
+    elif q_quant_dtype == FP8_DTYPE and o_quant_dtype == FP4_DTYPE:
         rtol, atol = 7e-2, 9e-2
     elif q_quant_dtype == FP8_DTYPE and o_quant_dtype == FP8_DTYPE:
         rtol, atol = 3e-2, 4e-2
@@ -287,7 +391,12 @@ def test_flashinfer_trtllm_prefill_with_baseline(
     kv_quant_dtype = kv_quant_dtype or dtype
     o_quant_dtype = o_quant_dtype or dtype
 
-    if q_quant_dtype != kv_quant_dtype:
+    # FP8 Q + nvfp4 KV is the required combination for the nvfp4 KV path.
+    # All other mixed Q/KV dtype combinations are unsupported.
+    is_nvfp4_kv = kv_quant_dtype == FP4_DTYPE
+    if q_quant_dtype != kv_quant_dtype and not (
+        q_quant_dtype == FP8_DTYPE and is_nvfp4_kv
+    ):
         pytest.skip("Skipped mixed QKV dtypes for prefill")
 
     max_q_len, max_kv_len = max_seq_lens
@@ -329,35 +438,19 @@ def test_flashinfer_trtllm_prefill_with_baseline(
     max_seq_len = torch.max(seq_lens).item()
 
     kv_cache = torch.randn(kv_cache_shape, dtype=dtype)
-    if kv_quant_dtype == FP8_DTYPE:
-        kv_cache, kv_scale = to_float8(kv_cache)
-        ref_kv_cache = kv_cache.to(dtype) * kv_scale
-    else:
-        kv_scale = 1.0
-        ref_kv_cache = kv_cache
+    kv_cache, kv_cache_sf, kv_scale, ref_kv_cache, is_nvfp4_kv = (
+        make_quantized_kv_cache(kv_cache, kv_quant_dtype, block_size, head_size)
+    )
+
     k_scale = v_scale = kv_scale
 
     max_num_blocks_per_seq = (max_seq_len + block_size - 1) // block_size
     block_tables = torch.randint(
         0, NUM_BLOCKS, (batch_size, max_num_blocks_per_seq), dtype=torch.int32
     )
-    kv_indptr = [0]
-    kv_indices = []
-    kv_last_page_lens = []
-    for i in range(batch_size):
-        seq_len = seq_lens[i]
-        assert seq_len > 0
-        num_blocks = (seq_len + block_size - 1) // block_size
-        kv_indices.extend(block_tables[i, :num_blocks])
-        kv_indptr.append(kv_indptr[-1] + num_blocks)
-        kv_last_page_len = seq_len % block_size
-        if kv_last_page_len == 0:
-            kv_last_page_len = block_size
-        kv_last_page_lens.append(kv_last_page_len)
-
-    kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
-    kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
-    kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
+    kv_indptr, kv_indices, kv_last_page_lens = build_paged_kv_metadata(
+        seq_lens, block_tables, block_size
+    )
     workspace_buffer = torch.zeros(128 * 1024 * 1024, dtype=torch.int8)
 
     # Baseline Prefill
@@ -431,6 +524,7 @@ def test_flashinfer_trtllm_prefill_with_baseline(
         sinks=sinks,
         o_sf_scale=o_sf_scale_float,
         out=output_trtllm,
+        kv_cache_sf=kv_cache_sf,
     )
     if o_quant_dtype == FP8_DTYPE:
         output_trtllm = output_trtllm.to(dtype) * o_scale
@@ -443,7 +537,9 @@ def test_flashinfer_trtllm_prefill_with_baseline(
         )
         output_trtllm = output_trtllm.reshape(-1, query.shape[1], query.shape[2])
 
-    if q_quant_dtype == FP8_DTYPE and o_quant_dtype == FP4_DTYPE:
+    if is_nvfp4_kv:
+        rtol, atol = 1.0, 1.5  # nvfp4 has higher quantization error
+    elif q_quant_dtype == FP8_DTYPE and o_quant_dtype == FP4_DTYPE:
         rtol, atol = 3e-1, 4e-1
     elif q_quant_dtype == FP8_DTYPE and o_quant_dtype == FP8_DTYPE:
         rtol, atol = 4e-2, 6e-2
diff --git a/tests/kernels/attention/test_triton_decode_attention.py b/tests/kernels/attention/test_triton_decode_attention.py
index a9b881629441..81e8bb17e7bc 100644
--- a/tests/kernels/attention/test_triton_decode_attention.py
+++ b/tests/kernels/attention/test_triton_decode_attention.py
@@ -4,9 +4,12 @@
 import pytest
 import torch
 
+from vllm.platforms import current_platform
 from vllm.utils.math_utils import cdiv
 from vllm.v1.attention.ops.triton_decode_attention import decode_attention_fwd
 
+DEVICE_TYPE = current_platform.device_type
+
 
 @pytest.mark.parametrize("B", [3, 5])
 @pytest.mark.parametrize("L", [1027, 1025])
@@ -25,33 +28,35 @@ def test_decode_attention(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE):
 
     num_pages_per_batch = cdiv(seq_len, PAGE_SIZE)
     req_to_page = torch.randint(
-        0, CACHE_SIZE // PAGE_SIZE, (B, num_pages_per_batch, 1), device="cuda"
+        0, CACHE_SIZE // PAGE_SIZE, (B, num_pages_per_batch, 1), device=DEVICE_TYPE
     )
     req_to_token = req_to_page * PAGE_SIZE
     req_to_token = req_to_token.expand(B, num_pages_per_batch, PAGE_SIZE)
-    req_to_token = req_to_token + torch.arange(PAGE_SIZE, device="cuda").view(1, 1, -1)
+    req_to_token = req_to_token + torch.arange(PAGE_SIZE, device=DEVICE_TYPE).view(
+        1, 1, -1
+    )
     req_to_token = req_to_token.view(B, -1)
     req_to_token = req_to_token[:, :seq_len].contiguous()
 
     # q represents the new token being generated, one per batch
-    q = torch.randn(B, H_Q, D_QK, dtype=dtype, device="cuda")
+    q = torch.randn(B, H_Q, D_QK, dtype=dtype, device=DEVICE_TYPE)
 
     # k_buffer and v_buffer represent all previous tokens
     # Page size is 1.
-    k_buffer = torch.randn(CACHE_SIZE, H_KV, D_QK, dtype=dtype, device="cuda")
-    v_buffer = torch.randn(CACHE_SIZE, H_KV, D_V, dtype=dtype, device="cuda")
+    k_buffer = torch.randn(CACHE_SIZE, H_KV, D_QK, dtype=dtype, device=DEVICE_TYPE)
+    v_buffer = torch.randn(CACHE_SIZE, H_KV, D_V, dtype=dtype, device=DEVICE_TYPE)
 
     # o will have the same shape as q
-    o = torch.zeros(B, H_Q, D_V, dtype=dtype, device="cuda")
+    o = torch.zeros(B, H_Q, D_V, dtype=dtype, device=DEVICE_TYPE)
 
-    lse = torch.zeros(B, H_Q, dtype=dtype, device="cuda")
+    lse = torch.zeros(B, H_Q, dtype=dtype, device=DEVICE_TYPE)
 
-    b_seq_len = torch.full((B,), seq_len, device="cuda")
+    b_seq_len = torch.full((B,), seq_len, device=DEVICE_TYPE)
 
     attn_logits = torch.empty(
         (B, H_Q, num_kv_splits, D_V + 1),
         dtype=torch.float32,
-        device="cuda",
+        device=DEVICE_TYPE,
     )
 
     # Call the original implementation.
@@ -127,25 +132,27 @@ def test_decode_attention_fp8(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE)
 
     num_pages_per_batch = cdiv(seq_len, PAGE_SIZE)
     req_to_page = torch.randint(
-        0, CACHE_SIZE // PAGE_SIZE, (B, num_pages_per_batch, 1), device="cuda"
+        0, CACHE_SIZE // PAGE_SIZE, (B, num_pages_per_batch, 1), device=DEVICE_TYPE
     )
     req_to_token = req_to_page * PAGE_SIZE
     req_to_token = req_to_token.expand(B, num_pages_per_batch, PAGE_SIZE)
-    req_to_token = req_to_token + torch.arange(PAGE_SIZE, device="cuda").view(1, 1, -1)
+    req_to_token = req_to_token + torch.arange(PAGE_SIZE, device=DEVICE_TYPE).view(
+        1, 1, -1
+    )
     req_to_token = req_to_token.view(B, -1)
     req_to_token = req_to_token[:, :seq_len].contiguous()
 
-    q = torch.randn(B, H_Q, D_QK, dtype=dtype, device="cuda")
+    q = torch.randn(B, H_Q, D_QK, dtype=dtype, device=DEVICE_TYPE)
 
     # Create BF16 K/V as reference
-    k_bf16 = torch.randn(CACHE_SIZE, H_KV, D_QK, dtype=dtype, device="cuda")
-    v_bf16 = torch.randn(CACHE_SIZE, H_KV, D_V, dtype=dtype, device="cuda")
+    k_bf16 = torch.randn(CACHE_SIZE, H_KV, D_QK, dtype=dtype, device=DEVICE_TYPE)
+    v_bf16 = torch.randn(CACHE_SIZE, H_KV, D_V, dtype=dtype, device=DEVICE_TYPE)
 
     # --- BF16 reference ---
-    o_ref = torch.zeros(B, H_Q, D_V, dtype=dtype, device="cuda")
-    lse_ref = torch.zeros(B, H_Q, dtype=dtype, device="cuda")
+    o_ref = torch.zeros(B, H_Q, D_V, dtype=dtype, device=DEVICE_TYPE)
+    lse_ref = torch.zeros(B, H_Q, dtype=dtype, device=DEVICE_TYPE)
     attn_logits = torch.empty(
-        (B, H_Q, num_kv_splits, D_V + 1), dtype=torch.float32, device="cuda"
+        (B, H_Q, num_kv_splits, D_V + 1), dtype=torch.float32, device=DEVICE_TYPE
     )
 
     if PAGE_SIZE == 1:
@@ -156,7 +163,7 @@ def test_decode_attention_fp8(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE)
             o_ref,
             lse_ref,
             req_to_token,
-            b_seq_len=torch.full((B,), seq_len, device="cuda"),
+            b_seq_len=torch.full((B,), seq_len, device=DEVICE_TYPE),
             attn_logits=attn_logits,
             num_kv_splits=num_kv_splits,
             sm_scale=sm_scale,
@@ -171,7 +178,7 @@ def test_decode_attention_fp8(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE)
             o_ref,
             lse_ref,
             req_to_page,
-            b_seq_len=torch.full((B,), seq_len, device="cuda"),
+            b_seq_len=torch.full((B,), seq_len, device=DEVICE_TYPE),
             attn_logits=attn_logits,
             num_kv_splits=num_kv_splits,
             sm_scale=sm_scale,
@@ -182,10 +189,10 @@ def test_decode_attention_fp8(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE)
     k_fp8, k_scale = _quantize_to_fp8(k_bf16)
     v_fp8, v_scale = _quantize_to_fp8(v_bf16)
 
-    o_fp8 = torch.zeros(B, H_Q, D_V, dtype=dtype, device="cuda")
-    lse_fp8 = torch.zeros(B, H_Q, dtype=dtype, device="cuda")
+    o_fp8 = torch.zeros(B, H_Q, D_V, dtype=dtype, device=DEVICE_TYPE)
+    lse_fp8 = torch.zeros(B, H_Q, dtype=dtype, device=DEVICE_TYPE)
     attn_logits_fp8 = torch.empty(
-        (B, H_Q, num_kv_splits, D_V + 1), dtype=torch.float32, device="cuda"
+        (B, H_Q, num_kv_splits, D_V + 1), dtype=torch.float32, device=DEVICE_TYPE
     )
 
     if PAGE_SIZE == 1:
@@ -196,7 +203,7 @@ def test_decode_attention_fp8(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE)
             o_fp8,
             lse_fp8,
             req_to_token,
-            b_seq_len=torch.full((B,), seq_len, device="cuda"),
+            b_seq_len=torch.full((B,), seq_len, device=DEVICE_TYPE),
             attn_logits=attn_logits_fp8,
             num_kv_splits=num_kv_splits,
             sm_scale=sm_scale,
@@ -213,7 +220,7 @@ def test_decode_attention_fp8(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE)
             o_fp8,
             lse_fp8,
             req_to_page,
-            b_seq_len=torch.full((B,), seq_len, device="cuda"),
+            b_seq_len=torch.full((B,), seq_len, device=DEVICE_TYPE),
             attn_logits=attn_logits_fp8,
             num_kv_splits=num_kv_splits,
             sm_scale=sm_scale,
diff --git a/tests/kernels/attention/test_triton_prefill_attention.py b/tests/kernels/attention/test_triton_prefill_attention.py
index f4505d91f5f7..6316a926ae33 100644
--- a/tests/kernels/attention/test_triton_prefill_attention.py
+++ b/tests/kernels/attention/test_triton_prefill_attention.py
@@ -5,8 +5,11 @@
 import torch
 import torch.nn.functional as F
 
+from vllm.platforms import current_platform
 from vllm.v1.attention.ops.triton_prefill_attention import context_attention_fwd
 
+DEVICE_TYPE = current_platform.device_type
+
 
 def ref_masked_attention(
     q: torch.Tensor,
@@ -92,17 +95,19 @@ def test_context_attention(
     torch.manual_seed(42)
 
     # Generate random sequence lengths for each batch
-    seq_lens = torch.randint(max_seq_len // 2, max_seq_len + 1, (B,), device="cuda")
+    seq_lens = torch.randint(
+        max_seq_len // 2, max_seq_len + 1, (B,), device=DEVICE_TYPE
+    )
     total_tokens = seq_lens.sum().item()
 
     # Create batch start locations
-    b_start_loc = torch.zeros(B, dtype=torch.int32, device="cuda")
+    b_start_loc = torch.zeros(B, dtype=torch.int32, device=DEVICE_TYPE)
     b_start_loc[1:] = torch.cumsum(seq_lens[:-1], dim=0)
 
     # Create input tensors
-    q = torch.randn(total_tokens, H_Q, D, dtype=dtype, device="cuda")
-    k = torch.randn(total_tokens, H_KV, D, dtype=dtype, device="cuda")
-    v = torch.randn(total_tokens, H_KV, D, dtype=dtype, device="cuda")
+    q = torch.randn(total_tokens, H_Q, D, dtype=dtype, device=DEVICE_TYPE)
+    k = torch.randn(total_tokens, H_KV, D, dtype=dtype, device=DEVICE_TYPE)
+    v = torch.randn(total_tokens, H_KV, D, dtype=dtype, device=DEVICE_TYPE)
     o = torch.zeros_like(q)
 
     # Call Triton kernel
@@ -169,17 +174,19 @@ def test_context_attention_sliding_window(
     torch.manual_seed(42)
 
     # Generate random sequence lengths for each batch
-    seq_lens = torch.randint(max_seq_len // 2, max_seq_len + 1, (B,), device="cuda")
+    seq_lens = torch.randint(
+        max_seq_len // 2, max_seq_len + 1, (B,), device=DEVICE_TYPE
+    )
     total_tokens = seq_lens.sum().item()
 
     # Create batch start locations
-    b_start_loc = torch.zeros(B, dtype=torch.int32, device="cuda")
+    b_start_loc = torch.zeros(B, dtype=torch.int32, device=DEVICE_TYPE)
     b_start_loc[1:] = torch.cumsum(seq_lens[:-1], dim=0)
 
     # Create input tensors
-    q = torch.randn(total_tokens, H_Q, D, dtype=dtype, device="cuda")
-    k = torch.randn(total_tokens, H_KV, D, dtype=dtype, device="cuda")
-    v = torch.randn(total_tokens, H_KV, D, dtype=dtype, device="cuda")
+    q = torch.randn(total_tokens, H_Q, D, dtype=dtype, device=DEVICE_TYPE)
+    k = torch.randn(total_tokens, H_KV, D, dtype=dtype, device=DEVICE_TYPE)
+    v = torch.randn(total_tokens, H_KV, D, dtype=dtype, device=DEVICE_TYPE)
     o = torch.zeros_like(q)
 
     # Call Triton kernel
diff --git a/tests/kernels/attention/test_triton_unified_attention.py b/tests/kernels/attention/test_triton_unified_attention.py
index 99cdc7ffa4a3..ef52d80929c2 100644
--- a/tests/kernels/attention/test_triton_unified_attention.py
+++ b/tests/kernels/attention/test_triton_unified_attention.py
@@ -10,6 +10,8 @@
 from vllm.utils.torch_utils import set_random_seed
 from vllm.v1.attention.ops.triton_unified_attention import unified_attention
 
+DEVICE_TYPE = current_platform.device_type
+
 NUM_HEADS = [(4, 4), (8, 2), (5, 1)]
 HEAD_SIZES = [128, 256]
 BLOCK_SIZES = [16]
@@ -114,7 +116,7 @@ def test_triton_unified_attn(
     q_dtype: torch.dtype | None,
     seq_threshold_3D: int,
 ) -> None:
-    torch.set_default_device("cuda")
+    torch.set_default_device(DEVICE_TYPE)
 
     set_random_seed(0)
     num_seqs = len(seq_lens)
@@ -249,7 +251,7 @@ def test_triton_unified_attn_fp16_input_fp8_output(
     seq_threshold_3D: int,
 ) -> None:
     """Test with fp16 input and fp8 output using output_scale."""
-    torch.set_default_device("cuda")
+    torch.set_default_device(DEVICE_TYPE)
 
     set_random_seed(0)
     num_seqs = len(seq_lens)
diff --git a/tests/kernels/attention/test_use_trtllm_attention.py b/tests/kernels/attention/test_use_trtllm_attention.py
index 12ab146a983f..fba18fe46e3d 100644
--- a/tests/kernels/attention/test_use_trtllm_attention.py
+++ b/tests/kernels/attention/test_use_trtllm_attention.py
@@ -62,7 +62,7 @@ def test_supports_batch_invariant_disables():
 
 @patch("vllm.envs.VLLM_BATCH_INVARIANT", False)
 @patch(
-    "vllm.utils.flashinfer.current_platform.is_device_capability",
+    "vllm.utils.flashinfer.current_platform.is_device_capability_family",
     return_value=True,
 )
 @patch("vllm.utils.flashinfer.has_nvidia_artifactory", return_value=True)
@@ -72,7 +72,7 @@ def test_supports_sm100_with_artifactory(_art, _cap):
 
 @patch("vllm.envs.VLLM_BATCH_INVARIANT", False)
 @patch(
-    "vllm.utils.flashinfer.current_platform.is_device_capability",
+    "vllm.utils.flashinfer.current_platform.is_device_capability_family",
     return_value=False,
 )
 def test_supports_non_sm100_platform(_cap):
@@ -81,7 +81,7 @@ def test_supports_non_sm100_platform(_cap):
 
 @patch("vllm.envs.VLLM_BATCH_INVARIANT", False)
 @patch(
-    "vllm.utils.flashinfer.current_platform.is_device_capability",
+    "vllm.utils.flashinfer.current_platform.is_device_capability_family",
     return_value=True,
 )
 @patch("vllm.utils.flashinfer.has_nvidia_artifactory", return_value=False)
diff --git a/tests/kernels/core/test_activation.py b/tests/kernels/core/test_activation.py
index e7de7731286f..3f1d45ba8e9e 100644
--- a/tests/kernels/core/test_activation.py
+++ b/tests/kernels/core/test_activation.py
@@ -16,6 +16,7 @@
     NewGELU,
     QuickGELU,
     SiluAndMul,
+    SiluAndMulWithClamp,
     SwigluOAIAndMul,
     SwigluStepAndMul,
     swiglustep_and_mul_triton,
@@ -116,6 +117,85 @@ def _get_rtol(output) -> float:
         opcheck(fn, (out, x))
 
 
+SWIGLU_LIMITS = [3.0, 7.0, 15.0]
+
+
+@pytest.mark.parametrize("swiglu_limit", SWIGLU_LIMITS)
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("d", D)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("device", CUDA_DEVICES)
+@torch.inference_mode()
+def test_silu_and_mul_with_clamp(
+    default_vllm_config,
+    swiglu_limit: float,
+    num_tokens: int,
+    d: int,
+    dtype: torch.dtype,
+    seed: int,
+    device: str,
+) -> None:
+    """SiluAndMulWithClamp: cuda kernel must match native reference."""
+    set_random_seed(seed)
+    torch.set_default_device(device)
+    # Use large values to ensure clamping is exercised.
+    x = torch.randn(num_tokens, 2 * d, dtype=dtype) * swiglu_limit * 2
+
+    layer = SiluAndMulWithClamp(swiglu_limit, compile_native=False)
+    out = layer(x)
+    ref_out = layer.forward_native(x)
+
+    rtol = {
+        torch.float16: 2e-3,
+        torch.bfloat16: 2e-2,
+        torch.float: 1.3e-6,
+    }
+    torch.testing.assert_close(
+        out, ref_out, atol=get_default_atol(out), rtol=rtol[out.dtype]
+    )
+
+    # Verify clamping is actually being applied: the clamped output should
+    # differ from the unclamped SiluAndMul output when inputs are large.
+    unclamped_out = SiluAndMul.forward_native(x)
+    assert not torch.equal(ref_out.float(), unclamped_out.float()), (
+        "Input was not large enough to exercise the clamp; increase scale"
+    )
+
+    # Verify gate clamping semantics with a controlled scalar case.
+    # gate=large_val is clamped to limit first, then silu(limit) * 1.0.
+    x_gate = torch.tensor(
+        [[swiglu_limit * 20.0, 1.0]], dtype=torch.float32, device=device
+    )
+    out_gate = SiluAndMulWithClamp(swiglu_limit, compile_native=False)(x_gate)
+    expected_gate = torch.nn.functional.silu(
+        torch.tensor(swiglu_limit, dtype=torch.float32)
+    ).item()
+    torch.testing.assert_close(
+        out_gate,
+        torch.tensor([[expected_gate]], dtype=torch.float32, device=device),
+        atol=1e-3,
+        rtol=1e-3,
+    )
+
+    # Verify up clamping semantics: up >> limit gets clamped to limit.
+    x_up = torch.tensor(
+        [[1.0, swiglu_limit * 20.0]], dtype=torch.float32, device=device
+    )
+    out_up = SiluAndMulWithClamp(swiglu_limit, compile_native=False)(x_up)
+    silu_1 = torch.nn.functional.silu(torch.tensor(1.0)).item()
+    torch.testing.assert_close(
+        out_up,
+        torch.tensor([[silu_1 * swiglu_limit]], dtype=torch.float32, device=device),
+        atol=1e-3,
+        rtol=1e-3,
+    )
+
+    # opcheck
+    out_buf = torch.empty(x.shape[:-1] + (d,), dtype=dtype, device=device)
+    opcheck(torch.ops._C.silu_and_mul_with_clamp, (out_buf, x, swiglu_limit))
+
+
 @pytest.mark.parametrize(
     "activation",
     [
diff --git a/tests/kernels/core/test_cpu_activation.py b/tests/kernels/core/test_cpu_activation.py
new file mode 100644
index 000000000000..40b5f0454683
--- /dev/null
+++ b/tests/kernels/core/test_cpu_activation.py
@@ -0,0 +1,111 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+from tests.kernels.allclose_default import get_default_atol, get_default_rtol
+from tests.kernels.utils import opcheck
+from vllm.platforms import CpuArchEnum, current_platform
+from vllm.utils.torch_utils import set_random_seed
+
+if not current_platform.is_cpu():
+    pytest.skip("skipping CPU-only tests", allow_module_level=True)
+
+from vllm.model_executor.layers.activation import (
+    GELU,
+    FastGELU,
+    GeluAndMul,
+    NewGELU,
+    QuickGELU,
+    SiluAndMul,
+)
+
+DTYPES = [torch.bfloat16, torch.float32]
+NUM_TOKENS = [7, 83]
+D = [512, 2048]
+SEEDS = [0]
+
+
+@pytest.mark.parametrize(
+    ("activation_cls", "fn"),
+    [
+        (SiluAndMul, torch.ops._C.silu_and_mul),
+        (GeluAndMul, torch.ops._C.gelu_and_mul),
+        (GeluAndMul, torch.ops._C.gelu_tanh_and_mul),
+    ],
+)
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("d", D)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@torch.inference_mode()
+def test_cpu_act_and_mul(
+    default_vllm_config,
+    activation_cls: type[torch.nn.Module],
+    fn: object,
+    num_tokens: int,
+    d: int,
+    dtype: torch.dtype,
+    seed: int,
+) -> None:
+    set_random_seed(seed)
+    x = torch.randn(num_tokens, 2 * d, dtype=dtype)
+
+    layer = activation_cls()
+    out = layer(x)
+    ref_out = layer.forward_native(x)
+
+    torch.testing.assert_close(
+        out, ref_out, atol=get_default_atol(out), rtol=get_default_rtol(out)
+    )
+
+    output_shape = x.shape[:-1] + (x.shape[-1] // 2,)
+    raw_out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+    opcheck(fn, (raw_out, x))
+
+
+@pytest.mark.parametrize(
+    ("activation_cls", "fn", "op_args"),
+    [
+        (NewGELU, torch.ops._C.gelu_new, ()),
+        (FastGELU, torch.ops._C.gelu_fast, ()),
+        (QuickGELU, torch.ops._C.gelu_quick, ()),
+        pytest.param(
+            GELU,
+            getattr(torch.ops._C, "activation_lut_bf16", None),
+            ("gelu",),
+            marks=pytest.mark.skipif(
+                current_platform.get_cpu_architecture() != CpuArchEnum.ARM,
+                reason="activation_lut_bf16 is only built on Arm CPU",
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("d", D)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@torch.inference_mode()
+def test_cpu_unary_activation(
+    default_vllm_config,
+    activation_cls: type[torch.nn.Module],
+    fn: object,
+    op_args: tuple[str, ...],
+    num_tokens: int,
+    d: int,
+    dtype: torch.dtype,
+    seed: int,
+) -> None:
+    set_random_seed(seed)
+    x = torch.randn(num_tokens, d, dtype=dtype)
+    layer = activation_cls()
+    out = layer(x)
+    ref_out = layer.forward_native(x)
+    torch.testing.assert_close(
+        out, ref_out, atol=get_default_atol(out), rtol=get_default_rtol(out)
+    )
+    # gelu with activation_lut_bf16 only makes sense for BF16
+    if not (activation_cls is GELU and dtype != torch.bfloat16):
+        raw_out = torch.empty_like(x)
+        opcheck(fn, (raw_out, x, *op_args))
diff --git a/tests/kernels/core/test_fused_q_kv_rmsnorm.py b/tests/kernels/core/test_fused_q_kv_rmsnorm.py
new file mode 100644
index 000000000000..1017dc52ff98
--- /dev/null
+++ b/tests/kernels/core/test_fused_q_kv_rmsnorm.py
@@ -0,0 +1,81 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Correctness + large-token-count launch tests for fused_q_kv_rmsnorm.
+
+Before the grid-dim fix the kernel used grid ``(2, num_tokens)``, which hit
+CUDA's 65535 grid-y cap for ``num_tokens >= 65536`` and failed with
+``Triton Error [CUDA]: invalid argument`` at every large chunked-prefill
+profile run. These tests pin the new grid layout.
+"""
+
+from __future__ import annotations
+
+import pytest
+import torch
+
+from vllm.platforms import current_platform
+from vllm.v1.attention.ops.deepseek_v4_ops import fused_q_kv_rmsnorm
+
+pytestmark = pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="fused_q_kv_rmsnorm requires a CUDA/ROCm device",
+)
+
+
+def _ref_rmsnorm(x: torch.Tensor, w: torch.Tensor, eps: float) -> torch.Tensor:
+    x_f32 = x.to(torch.float32)
+    variance = x_f32.pow(2).mean(dim=-1, keepdim=True)
+    y = x_f32 * torch.rsqrt(variance + eps) * w.to(torch.float32)
+    return y.to(x.dtype)
+
+
+@pytest.mark.parametrize("num_tokens", [1, 17, 1024, 8192])
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16])
+def test_fused_q_kv_rmsnorm_correctness(num_tokens: int, dtype: torch.dtype):
+    torch.manual_seed(0)
+    device = "cuda"
+    q_size, kv_size = 192, 576
+    qr = torch.randn(num_tokens, q_size, dtype=dtype, device=device)
+    kv = torch.randn(num_tokens, kv_size, dtype=dtype, device=device)
+    qw = torch.randn(q_size, dtype=dtype, device=device)
+    kvw = torch.randn(kv_size, dtype=dtype, device=device)
+    eps = 1e-6
+
+    qr_out, kv_out = fused_q_kv_rmsnorm(qr, kv, qw, kvw, eps)
+
+    qr_ref = _ref_rmsnorm(qr, qw, eps)
+    kv_ref = _ref_rmsnorm(kv, kvw, eps)
+
+    tol = dict(rtol=1e-2, atol=1e-2)
+    torch.testing.assert_close(qr_out, qr_ref, **tol)
+    torch.testing.assert_close(kv_out, kv_ref, **tol)
+
+
+@pytest.mark.parametrize("num_tokens", [65535, 65536, 131072])
+def test_fused_q_kv_rmsnorm_launches_past_grid_y_cap(num_tokens: int):
+    """Regression guard: grid used to be (2, num_tokens), hitting CUDA's
+    65535 grid-y cap at num_tokens >= 65536. The new grid (num_tokens, 2)
+    lifts that bound to 2**31-1."""
+    device = "cuda"
+    dtype = torch.bfloat16
+    q_size, kv_size = 192, 576
+    qr = torch.randn(num_tokens, q_size, dtype=dtype, device=device)
+    kv = torch.randn(num_tokens, kv_size, dtype=dtype, device=device)
+    qw = torch.randn(q_size, dtype=dtype, device=device)
+    kvw = torch.randn(kv_size, dtype=dtype, device=device)
+
+    qr_out, kv_out = fused_q_kv_rmsnorm(qr, kv, qw, kvw, 1e-6)
+    # spot-check a couple of rows against the torch reference
+    for row in (0, num_tokens // 2, num_tokens - 1):
+        torch.testing.assert_close(
+            qr_out[row],
+            _ref_rmsnorm(qr[row : row + 1], qw, 1e-6)[0],
+            rtol=1e-2,
+            atol=1e-2,
+        )
+        torch.testing.assert_close(
+            kv_out[row],
+            _ref_rmsnorm(kv[row : row + 1], kvw, 1e-6)[0],
+            rtol=1e-2,
+            atol=1e-2,
+        )
diff --git a/tests/kernels/core/test_rotary_embedding.py b/tests/kernels/core/test_rotary_embedding.py
index 6cdd94fdc865..1cbb5dbd1881 100644
--- a/tests/kernels/core/test_rotary_embedding.py
+++ b/tests/kernels/core/test_rotary_embedding.py
@@ -35,6 +35,7 @@ def rotary_embedding_opcheck(
 @pytest.mark.parametrize("seq_len", [11, 1024])
 @pytest.mark.parametrize("use_key", [True, False])
 @pytest.mark.parametrize("head_stride_is_contiguous", [True, False])
+@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
 def test_rotary_embedding_opcheck(
     default_vllm_config,
     dist_init,
@@ -46,19 +47,20 @@ def test_rotary_embedding_opcheck(
     seq_len,
     use_key,
     head_stride_is_contiguous,
+    dtype,
 ):
     batch_size = 1
     base = 10000
     num_heads = 7
     rot = RotaryEmbedding(
-        head_size, rotary_dim, max_position, base, is_neox_style, torch.float32
+        head_size, rotary_dim, max_position, base, is_neox_style, dtype
     )
 
     positions = torch.randint(0, max_position, (batch_size, seq_len), device=device)
     head_stride = head_size + (64 if head_stride_is_contiguous else 0)
 
     query = torch.randn(
-        batch_size, seq_len, num_heads, head_stride, dtype=torch.float32, device=device
+        batch_size, seq_len, num_heads, head_stride, dtype=dtype, device=device
     )
     key = torch.randn_like(query) if use_key else None
     query = query[..., :head_size]
diff --git a/tests/kernels/core/test_vit_fp8_attn.py b/tests/kernels/core/test_vit_fp8_attn.py
new file mode 100644
index 000000000000..ef1c44cada29
--- /dev/null
+++ b/tests/kernels/core/test_vit_fp8_attn.py
@@ -0,0 +1,279 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for the full FP8 ViT attention path (quantize -> cuDNN -> un-pad)."""
+
+import contextlib
+
+import pytest
+import torch
+
+from vllm.triton_utils import HAS_TRITON
+from vllm.utils.flashinfer import (
+    is_flashinfer_cudnn_fp8_prefill_attn_supported,
+)
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+
+def _has_flashinfer_cudnn() -> bool:
+    """Check if FlashInfer cuDNN backend is available."""
+    try:
+        from flashinfer.prefill import (
+            cudnn_batch_prefill_with_kv_cache,  # noqa: F401
+        )
+
+        return True
+    except ImportError:
+        return False
+
+
+HEAD_DIMS = [72, 80]
+SEQ_LENS = [256]
+NUM_HEADS = [16]
+
+
+@pytest.fixture
+def _fp8_attention():
+    """Create FP8-enabled MMEncoderAttention via config."""
+    from types import SimpleNamespace
+    from unittest.mock import patch
+
+    from vllm.config import VllmConfig, set_current_vllm_config
+    from vllm.config.multimodal import MultiModalConfig
+
+    if not is_flashinfer_cudnn_fp8_prefill_attn_supported():
+        pytest.skip("FlashInfer cuDNN FP8 prefill attention not supported")
+
+    mm_config = MultiModalConfig(mm_encoder_attn_dtype="fp8")
+    vllm_config = VllmConfig()
+    vllm_config.model_config = SimpleNamespace(multimodal_config=mm_config)
+
+    # MMEncoderAttention reads torch.get_default_dtype() during init
+    # to determine the output dtype. In real model loading this is bf16.
+    old_dtype = torch.get_default_dtype()
+    torch.set_default_dtype(torch.bfloat16)
+
+    with (
+        set_current_vllm_config(vllm_config),
+        patch(
+            "vllm.model_executor.layers.attention.mm_encoder_attention"
+            ".get_vit_attn_backend",
+            return_value=AttentionBackendEnum.FLASHINFER,
+        ),
+    ):
+        yield
+
+    torch.set_default_dtype(old_dtype)
+
+
+def _build_cu_seqlens_and_meta(
+    seq_len: int,
+    num_heads: int,
+    head_dim: int,
+    fp8_padded_hidden_size: int | None = None,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Build cu_seqlens, max_seqlen, sequence_lengths for a single sequence."""
+    import numpy as np
+
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+    )
+
+    cu_seqlens_np = np.array([0, seq_len], dtype=np.int32)
+
+    sequence_lengths = MMEncoderAttention.maybe_compute_seq_lens(
+        AttentionBackendEnum.FLASHINFER,
+        cu_seqlens_np,
+        torch.device("cuda"),
+    )
+
+    max_seqlen = torch.tensor(
+        MMEncoderAttention.compute_max_seqlen(
+            AttentionBackendEnum.FLASHINFER, cu_seqlens_np
+        ),
+        dtype=torch.int32,
+    )
+
+    cu_seqlens = MMEncoderAttention.maybe_recompute_cu_seqlens(
+        AttentionBackendEnum.FLASHINFER,
+        cu_seqlens_np,
+        num_heads * head_dim,
+        1,  # tp_size
+        torch.device("cuda"),
+        fp8_padded_hidden_size=fp8_padded_hidden_size,
+    )
+
+    return cu_seqlens, max_seqlen, sequence_lengths
+
+
+@pytest.mark.skipif(
+    not (HAS_TRITON and _has_flashinfer_cudnn()),
+    reason="Triton and FlashInfer cuDNN required",
+)
+@pytest.mark.parametrize("head_dim", HEAD_DIMS)
+@pytest.mark.parametrize("seq_len", SEQ_LENS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+def test_fp8_attn_output_shape(
+    head_dim: int,
+    seq_len: int,
+    num_heads: int,
+    _fp8_attention,
+) -> None:
+    """Verify FP8 attention produces correct output shape after un-padding."""
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+    )
+    from vllm.utils.math_utils import round_up
+
+    attn = None
+    with contextlib.suppress(ValueError, ImportError):
+        attn = MMEncoderAttention(
+            num_heads=num_heads,
+            head_size=head_dim,
+            prefix="visual.blocks.0.attn",
+        ).to("cuda")
+
+    if attn is None or not attn.fp8_enabled:
+        pytest.skip("FP8 MMEncoderAttention not available")
+    assert attn is not None  # mypy narrowing
+
+    # FP8 always needs fp8_padded_hidden_size for correct cu_seqlens
+    fp8_padded_hidden_size = num_heads * round_up(head_dim, 16)
+
+    cu_seqlens, max_seqlen, sequence_lengths = _build_cu_seqlens_and_meta(
+        seq_len, num_heads, head_dim, fp8_padded_hidden_size=fp8_padded_hidden_size
+    )
+
+    q = torch.randn(
+        seq_len,
+        num_heads,
+        head_dim,
+        device="cuda",
+        dtype=torch.bfloat16,
+    )
+    k = torch.randn_like(q)
+    v = torch.randn_like(q)
+
+    output = attn._forward_flashinfer(q, k, v, cu_seqlens, max_seqlen, sequence_lengths)
+
+    # Output should have original head_dim (un-padded)
+    assert output.shape[-1] == head_dim
+    assert output.dtype == torch.bfloat16
+
+
+@pytest.mark.skipif(
+    not (HAS_TRITON and _has_flashinfer_cudnn()),
+    reason="Triton and FlashInfer cuDNN required",
+)
+@pytest.mark.parametrize("head_dim", HEAD_DIMS)
+@pytest.mark.parametrize("seq_len", SEQ_LENS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+def test_fp8_vs_bf16_close(
+    head_dim: int, seq_len: int, num_heads: int, _fp8_attention
+) -> None:
+    """FP8 attention output should be reasonably close to BF16 baseline."""
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+    )
+    from vllm.utils.math_utils import round_up
+
+    torch.manual_seed(42)
+    q = torch.randn(
+        1,
+        seq_len,
+        num_heads,
+        head_dim,
+        device="cuda",
+        dtype=torch.bfloat16,
+    )
+    k = torch.randn_like(q)
+    v = torch.randn_like(q)
+
+    # FP8 path
+    attn_fp8 = None
+    with contextlib.suppress(ValueError, ImportError):
+        attn_fp8 = MMEncoderAttention(
+            num_heads=num_heads,
+            head_size=head_dim,
+            prefix="visual.blocks.0.attn",
+        ).to("cuda")
+
+    if attn_fp8 is None or not attn_fp8.fp8_enabled:
+        pytest.skip("FP8 MMEncoderAttention not available")
+    assert attn_fp8 is not None  # mypy narrowing
+
+    fp8_padded_hidden_size = num_heads * round_up(head_dim, 16)
+    cu_seqlens, max_seqlen, seq_lengths = _build_cu_seqlens_and_meta(
+        seq_len,
+        num_heads,
+        head_dim,
+        fp8_padded_hidden_size=fp8_padded_hidden_size,
+    )
+
+    out_fp8 = attn_fp8._forward_flashinfer(
+        q.clone(),
+        k.clone(),
+        v.clone(),
+        cu_seqlens,
+        max_seqlen,
+        seq_lengths,
+    )
+
+    # BF16 baseline (create non-FP8 attention by using scale=attn_fp8.scale
+    # and calling the wrapper directly without FP8 quantization)
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        _get_flashinfer_workspace_buffer,
+    )
+    from vllm.v1.attention.ops.vit_attn_wrappers import (
+        vit_flashinfer_wrapper,
+    )
+
+    out_bf16 = vit_flashinfer_wrapper(
+        q=q.clone(),
+        k=k.clone(),
+        v=v.clone(),
+        scale=attn_fp8.scale,
+        workspace_buffer=_get_flashinfer_workspace_buffer(),
+        cu_seqlens=cu_seqlens,
+        max_seqlen=max_seqlen,
+        sequence_lengths=seq_lengths,
+    )
+
+    out_fp8_f = out_fp8.float()
+    out_bf16_f = out_bf16.float()
+
+    abs_diff = (out_fp8_f - out_bf16_f).abs()
+    abs_diff_flat = abs_diff.flatten()
+
+    # Relative diff (avoid division by zero)
+    denom = out_bf16_f.abs().clamp(min=1e-6)
+    rel_diff_flat = (abs_diff / denom).flatten()
+
+    cosine_sim = torch.nn.functional.cosine_similarity(
+        out_fp8_f.flatten().unsqueeze(0),
+        out_bf16_f.flatten().unsqueeze(0),
+    ).item()
+
+    pcts = [50, 90, 95, 99, 99.9]
+    abs_pct = {p: torch.quantile(abs_diff_flat, p / 100).item() for p in pcts}
+    rel_pct = {p: torch.quantile(rel_diff_flat, p / 100).item() for p in pcts}
+
+    print(f"\nFP8 vs BF16 (head_dim={head_dim}, seq_len={seq_len}):")
+    print(f"  cosine_sim={cosine_sim:.6f}")
+    print(
+        f"  abs_diff: max={abs_diff_flat.max().item():.6f}, "
+        f"mean={abs_diff_flat.mean().item():.6f}, "
+        + ", ".join(f"p{p}={abs_pct[p]:.6f}" for p in pcts)
+    )
+    print(
+        f"  rel_diff: max={rel_diff_flat.max().item():.6f}, "
+        f"mean={rel_diff_flat.mean().item():.6f}, "
+        + ", ".join(f"p{p}={rel_pct[p]:.6f}" for p in pcts)
+    )
+
+    assert abs_diff_flat.max().item() < 0.3, (
+        f"FP8 vs BF16 max abs diff too large: {abs_diff_flat.max().item()}"
+    )
+    assert abs_diff_flat.mean().item() < 0.03, (
+        f"FP8 vs BF16 mean abs diff too large: {abs_diff_flat.mean().item()}"
+    )
+    assert cosine_sim > 0.99, f"Cosine similarity too low: {cosine_sim:.6f}"
diff --git a/tests/kernels/core/test_vit_fp8_quant.py b/tests/kernels/core/test_vit_fp8_quant.py
new file mode 100644
index 000000000000..0c63d0069f16
--- /dev/null
+++ b/tests/kernels/core/test_vit_fp8_quant.py
@@ -0,0 +1,124 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for the stride-aware FP8 quantization kernel with head_dim padding."""
+
+import pytest
+import torch
+
+from vllm.platforms import current_platform
+from vllm.triton_utils import HAS_TRITON
+
+if HAS_TRITON:
+    from vllm.kernels.triton.qkv_padded_fp8_quant import (
+        quantize_fp8_pad_head_dim_triton,
+    )
+
+HEAD_DIMS = [72, 80, 128]
+SEQ_LENS = [64, 256]
+NUM_HEADS = [16]
+SCALES = [0.01, 0.1, 1.0]
+
+
+def _naive_fp8_quantize(
+    tensor: torch.Tensor, scale: torch.Tensor, skip_scale: bool
+) -> torch.Tensor:
+    """Reference FP8 quantization in PyTorch."""
+    fp8_dtype = current_platform.fp8_dtype()
+    fp8_max = torch.finfo(fp8_dtype).max
+    fp8_min = -fp8_max
+
+    x = tensor.float()
+    if not skip_scale:
+        x = x / scale.item()
+    x = x.clamp(fp8_min, fp8_max)
+    return x.to(fp8_dtype)
+
+
+@pytest.mark.skipif(not HAS_TRITON, reason="Triton not available")
+@pytest.mark.parametrize("head_dim", HEAD_DIMS)
+@pytest.mark.parametrize("seq_len", SEQ_LENS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("scale_val", SCALES)
+def test_quantize_contiguous(
+    head_dim: int, seq_len: int, num_heads: int, scale_val: float
+) -> None:
+    """Test quantization of contiguous 3D tensors."""
+    torch.manual_seed(42)
+    tensor = torch.randn(
+        seq_len, num_heads, head_dim, device="cuda", dtype=torch.bfloat16
+    )
+    scale = torch.tensor([scale_val], dtype=torch.float32, device="cuda").view(
+        1, 1, 1, 1
+    )
+
+    result = quantize_fp8_pad_head_dim_triton(tensor, scale)
+
+    padded_dim = (head_dim + 15) // 16 * 16
+    assert result.shape == (seq_len, num_heads, padded_dim)
+    assert result.is_contiguous()
+    assert result.dtype == current_platform.fp8_dtype()
+
+    # Compare unpadded portion against reference
+    ref = _naive_fp8_quantize(tensor, scale, skip_scale=False)
+    torch.testing.assert_close(result[:, :, :head_dim].float(), ref.float())
+
+    # Padded region should be zero
+    if padded_dim > head_dim:
+        assert (result[:, :, head_dim:].float() == 0).all()
+
+
+@pytest.mark.skipif(not HAS_TRITON, reason="Triton not available")
+@pytest.mark.parametrize("head_dim", [72, 80])
+def test_quantize_non_contiguous(head_dim: int) -> None:
+    """Test quantization from non-contiguous QKV views (interleaved buffer)."""
+    seq_len, num_heads = 64, 16
+    # Simulate interleaved QKV buffer: shape (seq_len, 3 * num_heads, head_dim)
+    qkv = torch.randn(
+        seq_len, 3 * num_heads, head_dim, device="cuda", dtype=torch.bfloat16
+    )
+    # Q is every 3rd head slice - non-contiguous view
+    q = qkv[:, 0::3, :]
+    assert not q.is_contiguous()
+
+    scale = torch.tensor([0.1], dtype=torch.float32, device="cuda").view(1, 1, 1, 1)
+    result = quantize_fp8_pad_head_dim_triton(q, scale)
+
+    padded_dim = (head_dim + 15) // 16 * 16
+    assert result.shape == (seq_len, num_heads, padded_dim)
+    assert result.is_contiguous()
+
+    # Compare against contiguous reference
+    ref = _naive_fp8_quantize(q.contiguous(), scale, skip_scale=False)
+    torch.testing.assert_close(result[:, :, :head_dim].float(), ref.float())
+
+
+@pytest.mark.skipif(not HAS_TRITON, reason="Triton not available")
+def test_skip_scale() -> None:
+    """Test skip_scale=True produces cast-only output (no division)."""
+    seq_len, num_heads, head_dim = 32, 8, 80
+    tensor = torch.randn(
+        seq_len, num_heads, head_dim, device="cuda", dtype=torch.bfloat16
+    )
+    scale = torch.tensor([0.5], dtype=torch.float32, device="cuda").view(1, 1, 1, 1)
+
+    result_skip = quantize_fp8_pad_head_dim_triton(tensor, scale, skip_scale=True)
+    result_noskip = quantize_fp8_pad_head_dim_triton(tensor, scale, skip_scale=False)
+
+    # skip_scale should just cast, not divide
+    ref_cast = _naive_fp8_quantize(tensor, scale, skip_scale=True)
+    torch.testing.assert_close(result_skip[:, :, :head_dim].float(), ref_cast.float())
+
+    # With scale != 1.0, skip and no-skip should differ
+    assert not torch.equal(result_skip.float(), result_noskip.float())
+
+
+@pytest.mark.skipif(not HAS_TRITON, reason="Triton not available")
+def test_4d_input() -> None:
+    """Test that 4D input (B, S, H, D) is handled correctly."""
+    B, S, H, D = 2, 32, 8, 72
+    tensor = torch.randn(B, S, H, D, device="cuda", dtype=torch.bfloat16)
+    scale = torch.tensor([0.1], dtype=torch.float32, device="cuda").view(1, 1, 1, 1)
+
+    result = quantize_fp8_pad_head_dim_triton(tensor, scale)
+    padded_dim = (D + 15) // 16 * 16
+    assert result.shape == (B, S, H, padded_dim)
diff --git a/tests/kernels/core/test_vit_fp8_scaling.py b/tests/kernels/core/test_vit_fp8_scaling.py
new file mode 100644
index 000000000000..a197439237fb
--- /dev/null
+++ b/tests/kernels/core/test_vit_fp8_scaling.py
@@ -0,0 +1,251 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for FP8 scaling (dynamic and static) in MMEncoderAttention."""
+
+import contextlib
+import json
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import pytest
+import torch
+
+from vllm.model_executor.layers.attention.mm_encoder_attention import (
+    _FP8_AMAX_HISTORY_LEN,
+    _FP8_MAX,
+)
+from vllm.utils.flashinfer import (
+    is_flashinfer_cudnn_fp8_prefill_attn_supported,
+)
+
+LAYER_0 = "visual.blocks.0.attn.attn"
+LAYER_1 = "visual.blocks.1.attn.attn"
+NUM_HEADS = 16
+HEAD_DIM = 72
+
+
+@contextlib.contextmanager
+def _build_attention(mm_config):
+    """Yield an MMEncoderAttention with the given multimodal config.
+
+    The VllmConfig context stays active while the test runs so that
+    ``get_multimodal_config()`` calls during the forward path resolve. Also
+    invokes ``process_weights_after_loading`` to simulate the model loader's
+    auto-scan. Yields ``None`` if FlashInfer cuDNN is not available.
+    """
+    from vllm.config import VllmConfig, set_current_vllm_config
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+    )
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+    if not is_flashinfer_cudnn_fp8_prefill_attn_supported():
+        yield None
+        return
+
+    vllm_config = VllmConfig()
+    vllm_config.model_config = SimpleNamespace(multimodal_config=mm_config)
+
+    with (
+        set_current_vllm_config(vllm_config),
+        patch(
+            "vllm.model_executor.layers.attention.mm_encoder_attention"
+            ".get_vit_attn_backend",
+            return_value=AttentionBackendEnum.FLASHINFER,
+        ),
+    ):
+        attn = MMEncoderAttention(
+            num_heads=NUM_HEADS,
+            head_size=HEAD_DIM,
+            prefix=LAYER_0,
+        )
+        attn.process_weights_after_loading(torch.bfloat16)
+        yield attn
+
+
+@pytest.fixture
+def _make_attention():
+    """Create an MMEncoderAttention with dynamic FP8 scaling."""
+    from vllm.config.multimodal import MultiModalConfig
+
+    with _build_attention(MultiModalConfig(mm_encoder_attn_dtype="fp8")) as attn:
+        yield attn
+
+
+@pytest.fixture
+def _make_static_attention(tmp_path):
+    """Create an MMEncoderAttention with static FP8 scales from a file."""
+    from vllm.config.multimodal import MultiModalConfig
+
+    scale_file = tmp_path / "scales.json"
+    scale_file.write_text(
+        json.dumps(
+            {
+                LAYER_0: {"q": 224.0, "k": 198.0, "v": 210.0},
+                LAYER_1: {"q": 100.0, "k": 110.0, "v": 120.0},
+            }
+        )
+    )
+    with _build_attention(
+        MultiModalConfig(
+            mm_encoder_attn_dtype="fp8",
+            mm_encoder_fp8_scale_path=str(scale_file),
+        )
+    ) as attn:
+        yield attn
+
+
+def test_dynamic_scaling_updates_scales(_make_attention) -> None:
+    """Verify that _record_amax_and_update_scales updates scale buffers."""
+    attn = _make_attention
+    if attn is None or not attn.fp8_enabled:
+        pytest.skip("FP8 attention not available (FlashInfer backend required)")
+
+    attn = attn.to("cuda")
+
+    S, H, D = 32, NUM_HEADS, HEAD_DIM
+    q = torch.full((S, H, D), 2.0, device="cuda", dtype=torch.bfloat16)
+    k = torch.full((S, H, D), 3.0, device="cuda", dtype=torch.bfloat16)
+    v = torch.full((S, H, D), 4.0, device="cuda", dtype=torch.bfloat16)
+
+    attn._record_amax_and_update_scales(q, k, v)
+
+    expected_q_scale = 2.0 / _FP8_MAX
+    expected_k_scale = 3.0 / _FP8_MAX
+    expected_v_scale = 4.0 / _FP8_MAX
+
+    torch.testing.assert_close(attn._fp8_q_scale.item(), expected_q_scale)
+    torch.testing.assert_close(attn._fp8_k_scale.item(), expected_k_scale)
+    torch.testing.assert_close(attn._fp8_v_scale.item(), expected_v_scale)
+
+
+def test_circular_buffer_wraps(_make_attention) -> None:
+    """Verify the amax circular buffer wraps at HISTORY_LEN."""
+    attn = _make_attention
+    if attn is None or not attn.fp8_enabled:
+        pytest.skip("FP8 attention not available (FlashInfer backend required)")
+
+    attn = attn.to("cuda")
+    S, H, D = 16, NUM_HEADS, HEAD_DIM
+
+    for i in range(_FP8_AMAX_HISTORY_LEN + 2):
+        mag = float(i + 1)
+        q = torch.full((S, H, D), mag, device="cuda", dtype=torch.bfloat16)
+        k = torch.full((S, H, D), mag, device="cuda", dtype=torch.bfloat16)
+        v = torch.full((S, H, D), mag, device="cuda", dtype=torch.bfloat16)
+        attn._record_amax_and_update_scales(q, k, v)
+
+    assert attn._fp8_amax_pos == 2
+
+    expected_max = float(_FP8_AMAX_HISTORY_LEN + 2)
+    expected_scale = expected_max / _FP8_MAX
+    torch.testing.assert_close(attn._fp8_q_scale.item(), expected_scale)
+
+
+def test_static_scales_loaded(_make_static_attention) -> None:
+    """Verify static scales are loaded from the JSON file."""
+    attn = _make_static_attention
+    if attn is None or not attn.fp8_enabled:
+        pytest.skip("FP8 attention not available (FlashInfer backend required)")
+
+    assert attn.fp8_enabled
+    assert not attn._fp8_dynamic_scale
+
+    # Layer 0 scales (the layer this attention was created with).
+    assert attn._fp8_q_scale.item() == 224.0
+    assert attn._fp8_k_scale.item() == 198.0
+    assert attn._fp8_v_scale.item() == 210.0
+
+    assert not attn.skip_scale_q
+    assert not attn.skip_scale_k
+    assert not attn.skip_scale_v
+
+    # No amax history buffers for static scaling.
+    assert not hasattr(attn, "_fp8_q_amax")
+
+
+def test_static_scales_missing_layer(tmp_path) -> None:
+    """Verify error when requested layer is not in the scale file."""
+    from vllm.config import VllmConfig, set_current_vllm_config
+    from vllm.config.multimodal import MultiModalConfig
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+    if not is_flashinfer_cudnn_fp8_prefill_attn_supported():
+        pytest.skip("FlashInfer cuDNN not available")
+
+    scale_file = tmp_path / "wrong_layer.json"
+    scale_file.write_text(
+        json.dumps({"visual.blocks.99.attn": {"q": 1.0, "k": 1.0, "v": 1.0}})
+    )
+    mm_config = MultiModalConfig(
+        mm_encoder_attn_dtype="fp8",
+        mm_encoder_fp8_scale_path=str(scale_file),
+    )
+    vllm_config = VllmConfig()
+    vllm_config.model_config = SimpleNamespace(multimodal_config=mm_config)
+
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+    )
+
+    with (
+        set_current_vllm_config(vllm_config),
+        patch(
+            "vllm.model_executor.layers.attention.mm_encoder_attention"
+            ".get_vit_attn_backend",
+            return_value=AttentionBackendEnum.FLASHINFER,
+        ),
+    ):
+        attn = MMEncoderAttention(
+            num_heads=NUM_HEADS,
+            head_size=HEAD_DIM,
+            prefix=LAYER_0,
+        )
+        with pytest.raises(ValueError, match="scales not found for layer"):
+            attn.process_weights_after_loading(torch.bfloat16)
+
+
+def test_dynamic_scales_auto_save(tmp_path) -> None:
+    """Verify scales are saved to disk after the amax buffer fills."""
+    import vllm.model_executor.layers.attention.mm_encoder_attention as _mod
+    from vllm.config.multimodal import MultiModalConfig
+
+    if not is_flashinfer_cudnn_fp8_prefill_attn_supported():
+        pytest.skip("FlashInfer cuDNN not available")
+
+    # Reset module-level state between runs (other tests may have left
+    # state behind after triggering a save).
+    _mod._fp8_scale_save_path = None
+    _mod._fp8_saved_scale_refs.clear()
+
+    save_file = tmp_path / "auto_scales.json"
+    with _build_attention(
+        MultiModalConfig(
+            mm_encoder_attn_dtype="fp8",
+            mm_encoder_fp8_scale_save_path=str(save_file),
+        )
+    ) as attn:
+        if attn is None or not attn.fp8_enabled:
+            pytest.skip("FP8 attention not available")
+
+        attn = attn.to("cuda")
+        S, H, D = 16, NUM_HEADS, HEAD_DIM
+
+        # Run exactly _FP8_AMAX_HISTORY_LEN forward passes.
+        for i in range(_FP8_AMAX_HISTORY_LEN):
+            mag = float(i + 1)
+            q = torch.full((S, H, D), mag, device="cuda", dtype=torch.bfloat16)
+            k = torch.full((S, H, D), mag * 0.5, device="cuda", dtype=torch.bfloat16)
+            v = torch.full((S, H, D), mag * 0.3, device="cuda", dtype=torch.bfloat16)
+            attn._record_amax_and_update_scales(q, k, v)
+
+    # File should have been written on the 16th call (buffer wrap).
+    assert save_file.is_file(), "Scale file was not saved"
+    scales = json.loads(save_file.read_text())
+    assert LAYER_0 in scales
+    assert set(scales[LAYER_0].keys()) == {"q", "k", "v"}
+    for val in scales[LAYER_0].values():
+        assert isinstance(val, float) and val > 0
+
+    # Path is cleared after the one-shot save fires.
+    assert _mod._fp8_scale_save_path is None
diff --git a/tests/kernels/helion/helpers.py b/tests/kernels/helion/helpers.py
index dbe553be5589..f25c0a274aaa 100644
--- a/tests/kernels/helion/helpers.py
+++ b/tests/kernels/helion/helpers.py
@@ -6,24 +6,26 @@
 from collections.abc import Callable
 from contextlib import contextmanager
 from pathlib import Path
+from typing import Any
 from unittest.mock import patch
 
 import helion
 
+from vllm.kernels.helion.case_key import CaseKey
 from vllm.kernels.helion.config_manager import ConfigManager
 from vllm.kernels.helion.register import register_kernel
 from vllm.kernels.helion.utils import get_canonical_gpu_name
 
 GPU_PLATFORM = get_canonical_gpu_name()
 
-DEFAULT_CONFIGS: dict[str, helion.Config] = {
-    "default": helion.Config(block_sizes=[32]),
+DEFAULT_CONFIGS: dict[CaseKey, helion.Config] = {
+    CaseKey.default(): helion.Config(block_sizes=[32]),
 }
 
 
 @contextmanager
 def dummy_kernel_registry(
-    configs: dict[str, helion.Config] | None = None,
+    configs: dict[CaseKey, helion.Config] | None = None,
 ):
     """Context manager providing a register function with automatic config setup.
 
@@ -34,7 +36,13 @@ def dummy_kernel_registry(
     """
     if configs is None:
         configs = DEFAULT_CONFIGS
-    config_data = {k: v.__dict__["config"] for k, v in configs.items()}
+
+    def _to_config_entries(cfgs: dict) -> list[dict[str, Any]]:
+        pairs: list[dict[str, Any]] = []
+        for k, v in cfgs.items():
+            config_data = v.__dict__["config"]
+            pairs.append({"key": dict(k), "config": config_data})
+        return pairs
 
     with tempfile.TemporaryDirectory() as tmpdir:
         config_dir = Path(tmpdir)
@@ -55,7 +63,7 @@ def decorator(fn: Callable) -> Callable:
                     kernel_dir = config_dir / name
                     kernel_dir.mkdir(parents=True, exist_ok=True)
                     (kernel_dir / f"{GPU_PLATFORM}.json").write_text(
-                        json.dumps(config_data)
+                        json.dumps(_to_config_entries(configs))
                     )
                     return register_kernel(op_name, **kwargs)(fn)
 
diff --git a/tests/kernels/helion/test_autotune.py b/tests/kernels/helion/test_autotune.py
index 87f06c43581e..8b42e145d484 100644
--- a/tests/kernels/helion/test_autotune.py
+++ b/tests/kernels/helion/test_autotune.py
@@ -63,7 +63,7 @@ def test_autotune_disabled_kernel_produces_valid_config(self):
         with dummy_kernel_registry(configs={}) as register:
             wrapper = register(
                 "autotune_test_kernel",
-                config_picker=lambda args, keys: "default",
+                config_picker=lambda args, keys: None,
                 fake_impl=lambda *a, **kw: None,
                 input_generator=lambda: {
                     "small": (
diff --git a/tests/kernels/helion/test_case_key.py b/tests/kernels/helion/test_case_key.py
new file mode 100644
index 000000000000..335902fd9ef6
--- /dev/null
+++ b/tests/kernels/helion/test_case_key.py
@@ -0,0 +1,68 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from vllm.utils.import_utils import has_helion
+
+if not has_helion():
+    pytest.skip(
+        "Helion is not installed. Install with: pip install vllm[helion]",
+        allow_module_level=True,
+    )
+
+from vllm.kernels.helion.case_key import CaseKey
+
+
+class TestCaseKey:
+    """Test suite for CaseKey class."""
+
+    def test_construction_with_dict(self):
+        key = CaseKey({"intermediate": 2048, "numtokens": 256})
+        assert key["intermediate"] == 2048
+        assert key["numtokens"] == 256
+
+    def test_empty_construction_raises(self):
+        with pytest.raises(TypeError, match="at least one key-value pair"):
+            CaseKey()
+        with pytest.raises(TypeError, match="at least one key-value pair"):
+            CaseKey({})
+
+    def test_default_construction(self):
+        key = CaseKey.default()
+        assert len(key) == 0
+        assert key.is_default()
+
+    def test_non_default_is_not_default(self):
+        key = CaseKey({"intermediate": 2048})
+        assert not key.is_default()
+
+    def test_hashable_and_equality(self):
+        a = CaseKey({"intermediate": 2048, "numtokens": 256})
+        b = CaseKey({"numtokens": 256, "intermediate": 2048})
+        assert a == b
+        assert hash(a) == hash(b)
+        assert a != CaseKey({"intermediate": 4096})
+        assert CaseKey.default() == CaseKey.default()
+
+        configs = {
+            CaseKey.default(): "default_config",
+            a: "a_config",
+        }
+        assert configs[b] == "a_config"
+        assert configs[CaseKey.default()] == "default_config"
+
+    def test_str_is_sorted_json(self):
+        assert str(CaseKey({"z": 1, "a": 2})) == '{"a":2,"z":1}'
+        assert str(CaseKey.default()) == "{}"
+
+    def test_immutable(self):
+        key = CaseKey({"intermediate": 2048})
+        with pytest.raises(TypeError, match="immutable"):
+            key["intermediate"] = 4096
+        with pytest.raises(TypeError, match="immutable"):
+            del key["intermediate"]
+        with pytest.raises(TypeError, match="immutable"):
+            key.update({"numtokens": 256})
+        with pytest.raises(TypeError, match="immutable"):
+            key.clear()
diff --git a/tests/kernels/helion/test_config_manager.py b/tests/kernels/helion/test_config_manager.py
index 337696ee066b..f8e5eae6f106 100644
--- a/tests/kernels/helion/test_config_manager.py
+++ b/tests/kernels/helion/test_config_manager.py
@@ -23,6 +23,7 @@
 
 import helion
 
+from vllm.kernels.helion.case_key import CaseKey
 from vllm.kernels.helion.config_manager import (
     ConfigManager,
     ConfigSet,
@@ -49,22 +50,25 @@ def test_config_set_creation(self):
 
     def test_config_set_from_dict(self):
         """Test creating ConfigSet from dictionary data."""
-        # Use realistic config data that helion.Config can handle
         config_data = {
             "block_sizes": [32, 16],
             "num_warps": 4,
             "num_stages": 3,
             "pid_type": "persistent_interleaved",
         }
-        data = {"h100": {"batch_32_hidden_4096": config_data}}
+        data = {
+            "h100": [
+                {"key": {"batch": 32, "hidden": 4096}, "config": config_data},
+            ]
+        }
 
         config_set = ConfigSet.from_dict("test_kernel", data)
 
         assert config_set.kernel_name == "test_kernel"
         assert config_set.get_platforms() == ["h100"]
 
-        # Verify the config was created correctly
-        config = config_set.get_config("h100", "batch_32_hidden_4096")
+        internal_key = CaseKey({"batch": 32, "hidden": 4096})
+        config = config_set.get_config("h100", internal_key)
         assert isinstance(config, helion.Config)
         assert config.block_sizes == [32, 16]
         assert config.num_warps == 4
@@ -76,17 +80,19 @@ def test_config_set_get_config_keyerror(self):
         config_set = ConfigSet("test_kernel")
 
         with pytest.raises(KeyError, match="platform 'h100' not found"):
-            config_set.get_config("h100", "batch_32_hidden_4096")
+            config_set.get_config("h100", "nonexistent")
 
-        # Use realistic config data
         config_data = {"num_warps": 8, "num_stages": 4}
-        data = {"h100": {"batch_64_hidden_2048": config_data}}
+        data = {
+            "h100": [
+                {"key": {"batch": 64, "hidden": 2048}, "config": config_data},
+            ]
+        }
         config_set = ConfigSet.from_dict("test_kernel", data)
 
-        with pytest.raises(
-            KeyError, match="config_key 'batch_32_hidden_4096' not found"
-        ):
-            config_set.get_config("h100", "batch_32_hidden_4096")
+        nonexistent_key = CaseKey({"batch": 32, "hidden": 4096})
+        with pytest.raises(KeyError, match="config_key .* not found"):
+            config_set.get_config("h100", nonexistent_key)
 
     def test_config_set_get_platforms(self):
         """Test get_platforms method."""
@@ -95,8 +101,12 @@ def test_config_set_get_platforms(self):
         config2 = {"num_warps": 8, "num_stages": 5}
 
         data = {
-            "h100": {"batch_32_hidden_4096": config1},
-            "a100": {"batch_16_hidden_2048": config2},
+            "h100": [
+                {"key": {"batch": 32, "hidden": 4096}, "config": config1},
+            ],
+            "a100": [
+                {"key": {"batch": 16, "hidden": 2048}, "config": config2},
+            ],
         }
         config_set = ConfigSet.from_dict("test_kernel", data)
 
@@ -105,39 +115,49 @@ def test_config_set_get_platforms(self):
 
     def test_config_set_get_config_keys(self):
         """Test get_config_keys method."""
-        # Use realistic config data
         config1 = {"num_warps": 4, "num_stages": 3}
         config2 = {"num_warps": 8, "num_stages": 5}
 
         data = {
-            "h100": {
-                "batch_32_hidden_4096": config1,
-                "batch_64_hidden_2048": config2,
-            }
+            "h100": [
+                {"key": {"batch": 32, "hidden": 4096}, "config": config1},
+                {"key": {"batch": 64, "hidden": 2048}, "config": config2},
+            ]
         }
         config_set = ConfigSet.from_dict("test_kernel", data)
 
         config_keys = config_set.get_config_keys("h100")
-        assert config_keys == ["batch_32_hidden_4096", "batch_64_hidden_2048"]
+        expected_keys = sorted(
+            [
+                CaseKey({"batch": 32, "hidden": 4096}),
+                CaseKey({"batch": 64, "hidden": 2048}),
+            ],
+            key=lambda k: str(k) if k is not None else "",
+        )
+        assert config_keys == expected_keys
 
         assert config_set.get_config_keys("v100") == []
 
     def test_config_set_to_dict(self):
         """Test converting ConfigSet to dictionary."""
-        # Use realistic config data
         original_config = {
             "block_sizes": [64, 32],
             "num_warps": 16,
             "num_stages": 4,
             "pid_type": "persistent_blocked",
         }
-        original_data = {"h100": {"batch_32_hidden_4096": original_config}}
+        original_data = {
+            "h100": [
+                {"key": {"batch": 32, "hidden": 4096}, "config": original_config},
+            ]
+        }
 
         config_set = ConfigSet.from_dict("test_kernel", original_data)
         result_data = config_set.to_dict()
 
-        # The result should match the original (Config roundtrip should work)
-        assert result_data == original_data
+        internal_key = CaseKey({"batch": 32, "hidden": 4096})
+        assert internal_key in result_data["h100"]
+        assert result_data["h100"][internal_key] == original_config
 
 
 class TestConfigManager:
@@ -202,7 +222,10 @@ def test_load_config_set_valid_file(self):
             kernel_dir.mkdir()
             platform_file = kernel_dir / "h100.json"
             with open(platform_file, "w") as f:
-                json.dump({"batch_32_hidden_4096": kernel_config}, f)
+                json.dump(
+                    [{"key": {"batch": 32, "hidden": 4096}, "config": kernel_config}],
+                    f,
+                )
 
             manager = ConfigManager(base_dir=temp_dir)
             config_set = manager.load_config_set("test_kernel")
@@ -211,7 +234,8 @@ def test_load_config_set_valid_file(self):
             assert config_set.kernel_name == "test_kernel"
             assert config_set.get_platforms() == ["h100"]
 
-            config = config_set.get_config("h100", "batch_32_hidden_4096")
+            internal_key = CaseKey({"batch": 32, "hidden": 4096})
+            config = config_set.get_config("h100", internal_key)
             assert isinstance(config, helion.Config)
             assert config.block_sizes == [128, 64]
             assert config.num_warps == 8
@@ -241,7 +265,11 @@ def test_save_config_set(self):
                 "num_stages": 8,
                 "pid_type": "persistent_blocked",
             }
-            data = {"h100": {"batch_32_hidden_4096": kernel_config}}
+            data = {
+                "h100": [
+                    {"key": {"batch": 32, "hidden": 4096}, "config": kernel_config},
+                ]
+            }
             config_set = ConfigSet.from_dict("test_kernel", data)
 
             manager = ConfigManager(base_dir=temp_dir)
@@ -255,13 +283,21 @@ def test_save_config_set(self):
             assert platform_file.exists()
             with open(platform_file) as f:
                 loaded_data = json.load(f)
-            assert loaded_data == data["h100"]
+            assert isinstance(loaded_data, list)
+            assert len(loaded_data) == 1
+            entry = loaded_data[0]
+            assert entry["key"] == {"batch": 32, "hidden": 4096}
+            assert entry["config"] == kernel_config
 
     def test_save_config_set_creates_directory(self):
         """Test that save_config_set creates parent directories if needed."""
         with tempfile.TemporaryDirectory() as temp_dir:
             nested_dir = Path(temp_dir) / "nested" / "configs"
-            data = {"h100": {"default": {"num_warps": 4}}}
+            data = {
+                "h100": [
+                    {"key": {}, "config": {"num_warps": 4}},
+                ]
+            }
             config_set = ConfigSet.from_dict("test_kernel", data)
 
             manager = ConfigManager(base_dir=nested_dir)
@@ -288,34 +324,41 @@ def test_get_platform_configs(self):
             kernel_dir.mkdir()
             with open(kernel_dir / "h100.json", "w") as f:
                 json.dump(
-                    {
-                        "batch_32_hidden_4096": config_1,
-                        "batch_64_hidden_2048": config_2,
-                        "default": default_config,
-                    },
+                    [
+                        {"key": {"batch": 32, "hidden": 4096}, "config": config_1},
+                        {"key": {"batch": 64, "hidden": 2048}, "config": config_2},
+                        {"key": {}, "config": default_config},
+                    ],
                     f,
                 )
             with open(kernel_dir / "a100.json", "w") as f:
-                json.dump({"batch_16_hidden_1024": config_3}, f)
+                json.dump(
+                    [{"key": {"batch": 16, "hidden": 1024}, "config": config_3}],
+                    f,
+                )
 
             manager = ConfigManager(base_dir=temp_dir)
 
+            key_b32_h4096 = CaseKey({"batch": 32, "hidden": 4096})
+            key_b64_h2048 = CaseKey({"batch": 64, "hidden": 2048})
+            key_b16_h1024 = CaseKey({"batch": 16, "hidden": 1024})
+
             h100_configs = manager.get_platform_configs("test_kernel", "h100")
             assert len(h100_configs) == 3
-            assert "batch_32_hidden_4096" in h100_configs
-            assert "batch_64_hidden_2048" in h100_configs
-            assert "default" in h100_configs
+            assert key_b32_h4096 in h100_configs
+            assert key_b64_h2048 in h100_configs
+            assert CaseKey.default() in h100_configs
             for config in h100_configs.values():
                 assert isinstance(config, helion.Config)
 
-            assert h100_configs["batch_32_hidden_4096"].num_warps == 4
-            assert h100_configs["default"].num_stages == 7
+            assert h100_configs[key_b32_h4096].num_warps == 4
+            assert h100_configs[CaseKey.default()].num_stages == 7
 
             a100_configs = manager.get_platform_configs("test_kernel", "a100")
             assert len(a100_configs) == 1
-            assert "batch_16_hidden_1024" in a100_configs
-            assert isinstance(a100_configs["batch_16_hidden_1024"], helion.Config)
-            assert a100_configs["batch_16_hidden_1024"].num_warps == 2
+            assert key_b16_h1024 in a100_configs
+            assert isinstance(a100_configs[key_b16_h1024], helion.Config)
+            assert a100_configs[key_b16_h1024].num_warps == 2
 
             nonexistent_configs = manager.get_platform_configs("test_kernel", "v100")
             assert len(nonexistent_configs) == 0
diff --git a/tests/kernels/helion/test_register.py b/tests/kernels/helion/test_register.py
index c7f93993c574..c82c3c8358ed 100644
--- a/tests/kernels/helion/test_register.py
+++ b/tests/kernels/helion/test_register.py
@@ -24,6 +24,7 @@
 import helion.language as hl
 
 from tests.kernels.helion.helpers import dummy_kernel_registry
+from vllm.kernels.helion.case_key import CaseKey
 from vllm.kernels.helion.config_manager import ConfigManager
 from vllm.kernels.helion.register import (
     _HOP_AVAILABLE,
@@ -36,9 +37,11 @@
 )
 
 if _HOP_AVAILABLE:
+    from helion._compat import supports_torch_compile_fusion
     from helion._compiler._dynamo.higher_order_ops import (
         helion_kernel_wrapper_mutation,
     )
+    from torch._inductor.utils import run_and_get_code
 
 
 def _add_kernel(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
@@ -52,22 +55,22 @@ def _add_kernel(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
 def sample_configs():
     """Create real Helion config objects for testing."""
     return {
-        "hiddensize_4096_batchsize_32": helion.Config(
+        CaseKey({"batchsize": 32, "hiddensize": 4096}): helion.Config(
             block_sizes=[128],
             num_warps=4,
             num_stages=3,
         ),
-        "hiddensize_4096_batchsize_64": helion.Config(
+        CaseKey({"batchsize": 64, "hiddensize": 4096}): helion.Config(
             block_sizes=[256],
             num_warps=8,
             num_stages=4,
         ),
-        "hiddensize_4096_batchsize_128": helion.Config(
+        CaseKey({"batchsize": 128, "hiddensize": 4096}): helion.Config(
             block_sizes=[512],
             num_warps=16,
             num_stages=2,
         ),
-        "default": helion.Config(
+        CaseKey.default(): helion.Config(
             block_sizes=[64],
             num_warps=2,
             num_stages=2,
@@ -99,8 +102,7 @@ def configured_kernel(sample_kernel, sample_configs, config_manager_with_test_co
     """Create a ConfiguredHelionKernel for testing."""
 
     def test_config_picker(args, config_keys):
-        """Simple config picker that returns default."""
-        return "default"
+        return None
 
     with (
         patch(
@@ -113,7 +115,6 @@ def test_config_picker(args, config_keys):
         ),
         patch("vllm.kernels.helion.register.helion.kernel") as mock_kernel,
     ):
-        # Mock just the helion.kernel decorator to avoid actual kernel compilation
         mock_decorated = Mock()
         mock_kernel.return_value = Mock(return_value=mock_decorated)
 
@@ -197,7 +198,9 @@ class TestConfiguredHelionKernel:
 
     def test_init_raises_without_picker(self, sample_kernel, sample_configs):
         """Test that __init__ raises when no picker registered."""
-        configs = {"default": sample_configs["default"]}
+        configs: dict[CaseKey, helion.Config] = {
+            CaseKey.default(): sample_configs[CaseKey.default()]
+        }
         mock_config_manager = Mock(spec=ConfigManager)
         mock_config_manager.get_platform_configs = Mock(return_value=configs)
 
@@ -225,7 +228,7 @@ def test_config_selector_validates_picker_result(
         """Test that config selector validates picker returns valid key."""
 
         def invalid_picker(args, config_keys):
-            return "invalid_key"
+            return {"invalid": 999}
 
         kernel = create_configured_kernel_with_configs(
             op_name="test_kernel",
@@ -261,7 +264,7 @@ def none_picker(args, config_keys):
         selector = kernel._create_config_selector(key_computer)
 
         result = selector((torch.randn(32, 4096),))
-        assert result is kernel.configs["default"]
+        assert result is kernel.configs[CaseKey.default()]
 
     def test_create_decorated_kernel_passes_helion_settings(
         self, sample_kernel, sample_configs
@@ -269,7 +272,7 @@ def test_create_decorated_kernel_passes_helion_settings(
         """Test that _create_decorated_kernel passes helion_settings."""
 
         def default_picker(args, config_keys):
-            return "default"
+            return None
 
         settings = helion.Settings()
         settings.print_output_code = True
@@ -313,10 +316,10 @@ def tracking_picker(args, config_keys):
             x = args[0]
             batch_size = x.shape[0]
             if batch_size <= 32:
-                return "hiddensize_4096_batchsize_32"
+                return CaseKey({"batchsize": 32, "hiddensize": 4096})
             elif batch_size <= 64:
-                return "hiddensize_4096_batchsize_64"
-            return "hiddensize_4096_batchsize_128"
+                return CaseKey({"batchsize": 64, "hiddensize": 4096})
+            return CaseKey({"batchsize": 128, "hiddensize": 4096})
 
         mock_config_manager = Mock(spec=ConfigManager)
         mock_config_manager.get_platform_configs = Mock(return_value=sample_configs)
@@ -348,13 +351,13 @@ def tracking_picker(args, config_keys):
 
             tensor = torch.randn(50, 4096)  # batch=50, should select batchsize_64
 
-            # key receives unpacked args, autotuner receives args as tuple
             key_result = key_fn(tensor)
             autotuner = autotuner_fn(None, (tensor,))
             config = autotuner.autotune()
 
-            assert key_result == "hiddensize_4096_batchsize_64"
-            assert config is kernel.configs["hiddensize_4096_batchsize_64"]
+            expected_key = CaseKey({"batchsize": 64, "hiddensize": 4096})
+            assert key_result == str(expected_key)
+            assert config is kernel.configs[expected_key]
 
 
 class TestHelionKernelWrapper:
@@ -367,7 +370,7 @@ def fake_impl(*args, **kwargs):
             return torch.zeros_like(args[0])
 
         def default_picker(args, config_keys):
-            return "default"
+            return None
 
         mock_config_manager = Mock(spec=ConfigManager)
         mock_config_manager.get_platform_configs = Mock(
@@ -404,7 +407,7 @@ def fake_impl(*args, **kwargs):
             return torch.zeros_like(args[0])
 
         def default_picker(args, config_keys):
-            return "default"
+            return None
 
         mock_config_manager = Mock(spec=ConfigManager)
         mock_config_manager.get_platform_configs = Mock(return_value={})
@@ -439,7 +442,7 @@ def fake_impl(*args, **kwargs):
             return torch.zeros_like(args[0])
 
         def default_picker(args, config_keys):
-            return "default"
+            return None
 
         mock_config_manager = Mock(spec=ConfigManager)
         mock_config_manager.get_platform_configs = Mock(return_value={})
@@ -474,7 +477,7 @@ def fake_impl(*args, **kwargs):
             return torch.zeros_like(args[0])
 
         def default_picker(args, config_keys):
-            return "default"
+            return None
 
         expected_inputs = {"key1": (torch.randn(4),)}
         input_gen = Mock(return_value=expected_inputs)
@@ -514,7 +517,7 @@ def fake_impl(*args, **kwargs):
             return torch.zeros_like(args[0])
 
         def default_picker(args, config_keys):
-            return "default"
+            return None
 
         mock_config_manager = Mock(spec=ConfigManager)
         mock_config_manager.get_platform_configs = Mock(return_value={})
@@ -561,7 +564,7 @@ def fake_impl(*args, **kwargs):
             return torch.zeros_like(args[0])
 
         def default_picker(args, config_keys):
-            return "default"
+            return None
 
         mock_config_manager = Mock(spec=ConfigManager)
         mock_config_manager.get_platform_configs = Mock(return_value=sample_configs)
@@ -599,7 +602,9 @@ def test_init_eagerly_initializes_hop_path(self):
         on the HOP path (no custom op registration needed)."""
         from vllm.kernels.helion.utils import get_canonical_gpu_name
 
-        configs = {"default": helion.Config(block_sizes=[4, 4])}
+        configs: dict[CaseKey, helion.Config] = {
+            CaseKey.default(): helion.Config(block_sizes=[4, 4])
+        }
         with (
             dummy_kernel_registry(configs=configs) as register,
             patch(
@@ -608,7 +613,7 @@ def test_init_eagerly_initializes_hop_path(self):
             ) as mock_gpu,
         ):
             wrapper = register(
-                config_picker=lambda args, keys: "default",
+                config_picker=lambda args, keys: None,
             )(_add_kernel)
 
             mock_gpu.assert_called_once()
@@ -640,7 +645,7 @@ def test_init_eagerly_initializes(self):
             ) as mock_gpu,
         ):
             wrapper = register(
-                config_picker=lambda args, keys: "default",
+                config_picker=lambda args, keys: None,
             )(_add_kernel)
 
             # Init must have detected GPU and built the kernel
@@ -658,7 +663,7 @@ def fake_impl(*args, **kwargs):
             return torch.zeros_like(args[0])
 
         def default_picker(args, config_keys):
-            return "default"
+            return None
 
         mock_config_manager = Mock(spec=ConfigManager)
         mock_config_manager.get_platform_configs = Mock(return_value=sample_configs)
@@ -701,7 +706,7 @@ def fake_impl(*args, **kwargs):
             return torch.zeros_like(args[0])
 
         def default_picker(args, config_keys):
-            return "default"
+            return None
 
         mock_config_manager = Mock(spec=ConfigManager)
         mock_config_manager.get_platform_configs = Mock(return_value=sample_configs)
@@ -782,9 +787,9 @@ def test_get_registered_kernels_returns_copy(self):
     def test_get_kernel_by_name_returns_kernel(self):
         """Test get_kernel_by_name returns registered kernel."""
         with dummy_kernel_registry() as register:
-            wrapper = register(
-                "test_kernel", config_picker=lambda args, keys: "default"
-            )(_add_kernel)
+            wrapper = register("test_kernel", config_picker=lambda args, keys: None)(
+                _add_kernel
+            )
 
         from vllm.kernels.helion.register import _REGISTERED_KERNELS
 
@@ -807,7 +812,7 @@ def test_register_kernel_auto_generates_fake_impl(self):
             mock_fake = Mock()
             mock_infer.return_value = mock_fake
             wrapper = register(
-                config_picker=lambda args, keys: "default",
+                config_picker=lambda args, keys: None,
             )(_add_kernel)
 
         mock_infer.assert_called_once_with(_add_kernel, None)
@@ -816,7 +821,7 @@ def test_register_kernel_auto_generates_fake_impl(self):
     def test_register_kernel_creates_wrapper(self):
         """Test register_kernel creates HelionKernelWrapper."""
         with dummy_kernel_registry() as register:
-            result = register("test_name", config_picker=lambda args, keys: "default")(
+            result = register("test_name", config_picker=lambda args, keys: None)(
                 _add_kernel
             )
 
@@ -827,16 +832,16 @@ def test_register_kernel_creates_wrapper(self):
     def test_register_kernel_auto_detects_name(self):
         """Test register_kernel uses function name when no name provided."""
         with dummy_kernel_registry() as register:
-            wrapper = register(config_picker=lambda args, keys: "default")(_add_kernel)
+            wrapper = register(config_picker=lambda args, keys: None)(_add_kernel)
 
         assert wrapper.op_name == "_add_kernel"
 
     def test_register_kernel_registers_in_global_registry(self):
         """Test register_kernel adds wrapper to global registry."""
         with dummy_kernel_registry() as register:
-            wrapper = register(
-                "test_kernel", config_picker=lambda args, keys: "default"
-            )(_add_kernel)
+            wrapper = register("test_kernel", config_picker=lambda args, keys: None)(
+                _add_kernel
+            )
 
         registered_kernels = get_registered_kernels()
         assert "test_kernel" in registered_kernels
@@ -850,7 +855,7 @@ def test_register_kernel_passes_helion_settings(self):
         with dummy_kernel_registry() as register:
             result = register(
                 "test_name",
-                config_picker=lambda args, keys: "default",
+                config_picker=lambda args, keys: None,
                 helion_settings=settings,
             )(_add_kernel)
 
@@ -863,7 +868,7 @@ def test_register_kernel_supports_decorator_syntax(self):
         with dummy_kernel_registry() as register:
             result = register(
                 "custom_name",
-                config_picker=lambda args, keys: "default",
+                config_picker=lambda args, keys: None,
                 fake_impl=mock_fake,
             )(_add_kernel)
 
@@ -873,12 +878,12 @@ def test_register_kernel_supports_decorator_syntax(self):
     def test_register_kernel_raises_on_duplicate_registration(self):
         """Test register_kernel raises error on duplicate names."""
         with dummy_kernel_registry() as register:
-            register("duplicate_name", config_picker=lambda args, keys: "default")(
+            register("duplicate_name", config_picker=lambda args, keys: None)(
                 _add_kernel
             )
 
             with pytest.raises(ValueError, match="already registered"):
-                register("duplicate_name", config_picker=lambda args, keys: "default")(
+                register("duplicate_name", config_picker=lambda args, keys: None)(
                     _add_kernel
                 )
 
@@ -891,7 +896,7 @@ def test_register_kernel_rejects_autotuner_fn_in_settings(self):
 
             @register_kernel(
                 "test",
-                config_picker=lambda args, keys: "default",
+                config_picker=lambda args, keys: None,
                 helion_settings=mock_settings,
             )
             def test_kernel(x):
@@ -908,7 +913,7 @@ def test_register_kernel_no_warning_with_static_shapes_false(self):
         ):
             register(
                 "test",
-                config_picker=lambda args, keys: "default",
+                config_picker=lambda args, keys: None,
                 helion_settings=mock_settings,
             )(_add_kernel)
 
@@ -938,7 +943,7 @@ def fake_impl(*args, **kwargs):
 
             wrapper = register_kernel(
                 "disabled_kernel",
-                config_picker=lambda args, keys: "default",
+                config_picker=lambda args, keys: None,
                 fake_impl=fake_impl,
             )(_add_kernel)
 
@@ -955,12 +960,14 @@ class TestTorchCompileHOP:
     def test_compiled_graph_contains_helion_hop(self):
         """Verify torch.compile on a HelionKernelWrapper emits a
         helion_kernel_wrapper_mutation HOP node in the FX graph."""
-        configs = {"default": helion.Config(block_sizes=[4, 4])}
+        configs: dict[CaseKey, helion.Config] = {
+            CaseKey.default(): helion.Config(block_sizes=[4, 4])
+        }
 
         with dummy_kernel_registry(configs=configs) as register:
             add_helion_kernel = register(
                 op_name="test_torch_compile_add_kernel",
-                config_picker=lambda args, keys: "default",
+                config_picker=lambda args, keys: None,
             )(_add_kernel)
 
         captured_graph: torch.fx.GraphModule | None = None
@@ -1003,3 +1010,51 @@ def f(x, y):
             "Compiled execution result doesn't match eager execution. "
             f"Max difference: {torch.max(torch.abs(compiled_result - eager_result))}"
         )
+
+    @pytest.mark.skipif(
+        not (_HOP_AVAILABLE and supports_torch_compile_fusion()),
+        reason="Requires PyTorch with Helion inductor fusion support",
+    )
+    def test_inductor_backend_compiles_helion_hop(self):
+        """Test torch.compile with inductor backend and Helion fusion enabled."""
+
+        configs: dict[CaseKey, helion.Config] = {
+            CaseKey.default(): helion.Config(block_sizes=[4, 4])
+        }
+
+        with dummy_kernel_registry(configs=configs) as register:
+            add_helion_kernel = register(
+                op_name="test_inductor_add_kernel",
+                config_picker=lambda args, keys: None,
+                helion_settings=helion.Settings(
+                    torch_compile_fusion=True, static_shapes=False
+                ),
+            )(_add_kernel)
+
+        def f(x, y):
+            x = x * 2.0
+            y = y + 1.0
+            out = add_helion_kernel(x, y)
+            return out.relu()
+
+        torch._dynamo.reset()
+        compiled_f = torch.compile(f, backend="inductor", fullgraph=True)
+
+        x = torch.randn(4, 4, device="cuda")
+        y = torch.randn(4, 4, device="cuda")
+
+        compiled_result, source_codes = run_and_get_code(compiled_f, x, y)
+        eager_result = f(x, y)
+
+        assert torch.allclose(compiled_result, eager_result, atol=1e-5, rtol=1e-5), (
+            "Inductor-compiled result doesn't match eager execution. "
+            f"Max difference: {torch.max(torch.abs(compiled_result - eager_result))}"
+        )
+
+        # With fusion enabled, prologue/epilogue ops should be fused into
+        # a single triton kernel rather than generating separate kernels.
+        kernel_count = sum(code.count("@triton.jit") for code in source_codes)
+        assert kernel_count == 1, (
+            f"Expected 1 fused triton kernel, got {kernel_count}. "
+            "Prologue/epilogue ops were not fused into the Helion kernel."
+        )
diff --git a/tests/kernels/helion/test_silu_mul_fp8.py b/tests/kernels/helion/test_silu_mul_fp8.py
index 887f20b9f563..bd3131e08da2 100644
--- a/tests/kernels/helion/test_silu_mul_fp8.py
+++ b/tests/kernels/helion/test_silu_mul_fp8.py
@@ -13,8 +13,10 @@
         allow_module_level=True,
     )
 
+from vllm.kernels.helion.case_key import CaseKey
 from vllm.kernels.helion.config_manager import ConfigManager
 from vllm.kernels.helion.ops.silu_mul_fp8 import (
+    _pick_cache,
     pick_silu_mul_fp8_config,
     silu_mul_fp8,
     silu_mul_fp8_baseline,
@@ -52,10 +54,13 @@ def reset_config_manager_singleton():
 
 
 class TestSiluMulFp8ConfigPicker:
+    def setup_method(self):
+        _pick_cache.clear()
+
     def test_config_picker_exact_match(self):
         config_keys = [
-            "intermediate_2048_numtokens_256",
-            "intermediate_4096_numtokens_256",
+            CaseKey({"intermediate": 2048, "numtokens": 256}),
+            CaseKey({"intermediate": 4096, "numtokens": 256}),
         ]
 
         input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
@@ -63,33 +68,22 @@ def test_config_picker_exact_match(self):
         args = (input_tensor, scale)
 
         selected_key = pick_silu_mul_fp8_config(args, config_keys)
-        assert selected_key == "intermediate_2048_numtokens_256"
+        assert selected_key == CaseKey({"intermediate": 2048, "numtokens": 256})
 
     def test_config_picker_closest_match(self):
         config_keys = [
-            "intermediate_2048_numtokens_256",
-            "intermediate_4096_numtokens_256",
+            CaseKey({"intermediate": 2048, "numtokens": 256}),
+            CaseKey({"intermediate": 4096, "numtokens": 256}),
         ]
-        # Use 7000 (intermediate_size=3500) which is closer to 4096 than 2048
         input_tensor = torch.randn(32, 7000, dtype=torch.bfloat16, device="cuda")
         scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
         args = (input_tensor, scale)
 
         selected_key = pick_silu_mul_fp8_config(args, config_keys)
-        assert selected_key == "intermediate_4096_numtokens_256"
-
-    def test_config_picker_fallback_to_default(self):
-        config_keys = ["default"]
-
-        input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
-        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
-        args = (input_tensor, scale)
-
-        selected_key = pick_silu_mul_fp8_config(args, config_keys)
-        assert selected_key == "default"
+        assert selected_key == CaseKey({"intermediate": 4096, "numtokens": 256})
 
     def test_config_picker_no_configs(self):
-        config_keys: list[str] = []
+        config_keys: list[dict] = []
 
         input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
         scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
@@ -101,9 +95,9 @@ def test_config_picker_no_configs(self):
     @pytest.mark.parametrize("intermediate_size", [2048, 4096, 5120])
     def test_config_picker_different_sizes(self, intermediate_size):
         config_keys = [
-            "intermediate_2048_numtokens_256",
-            "intermediate_4096_numtokens_256",
-            "intermediate_5120_numtokens_256",
+            CaseKey({"intermediate": 2048, "numtokens": 256}),
+            CaseKey({"intermediate": 4096, "numtokens": 256}),
+            CaseKey({"intermediate": 5120, "numtokens": 256}),
         ]
 
         input_tensor = torch.randn(
@@ -113,72 +107,47 @@ def test_config_picker_different_sizes(self, intermediate_size):
         args = (input_tensor, scale)
 
         selected_key = pick_silu_mul_fp8_config(args, config_keys)
-        expected_key = f"intermediate_{intermediate_size}_numtokens_256"
-        assert selected_key == expected_key
+        assert selected_key == {
+            "intermediate": intermediate_size,
+            "numtokens": 256,
+        }
 
     def test_config_picker_numtokens_ceiling(self):
-        """Pick the smallest numtokens >= input num_tokens."""
         config_keys = [
-            "intermediate_4096_numtokens_8",
-            "intermediate_4096_numtokens_32",
-            "intermediate_4096_numtokens_128",
-            "intermediate_4096_numtokens_256",
+            CaseKey({"intermediate": 4096, "numtokens": 8}),
+            CaseKey({"intermediate": 4096, "numtokens": 32}),
+            CaseKey({"intermediate": 4096, "numtokens": 128}),
+            CaseKey({"intermediate": 4096, "numtokens": 256}),
         ]
-        # 20 tokens -> should pick numtokens_32 (smallest >= 20)
         input_tensor = torch.randn(20, 8192, dtype=torch.bfloat16, device="cuda")
         scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
 
         selected_key = pick_silu_mul_fp8_config((input_tensor, scale), config_keys)
-        assert selected_key == "intermediate_4096_numtokens_32"
+        assert selected_key == CaseKey({"intermediate": 4096, "numtokens": 32})
 
     def test_config_picker_numtokens_exact(self):
-        """Exact num_tokens match is preferred over ceiling."""
         config_keys = [
-            "intermediate_4096_numtokens_8",
-            "intermediate_4096_numtokens_32",
-            "intermediate_4096_numtokens_128",
+            CaseKey({"intermediate": 4096, "numtokens": 8}),
+            CaseKey({"intermediate": 4096, "numtokens": 32}),
+            CaseKey({"intermediate": 4096, "numtokens": 128}),
         ]
         input_tensor = torch.randn(32, 8192, dtype=torch.bfloat16, device="cuda")
         scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
 
         selected_key = pick_silu_mul_fp8_config((input_tensor, scale), config_keys)
-        assert selected_key == "intermediate_4096_numtokens_32"
+        assert selected_key == CaseKey({"intermediate": 4096, "numtokens": 32})
 
     def test_config_picker_numtokens_fallback_to_largest(self):
-        """Fall back to the largest numtokens when input exceeds all."""
         config_keys = [
-            "intermediate_4096_numtokens_8",
-            "intermediate_4096_numtokens_32",
-            "intermediate_4096_numtokens_128",
+            CaseKey({"intermediate": 4096, "numtokens": 8}),
+            CaseKey({"intermediate": 4096, "numtokens": 32}),
+            CaseKey({"intermediate": 4096, "numtokens": 128}),
         ]
-        # 512 tokens -> exceeds all available, should pick largest (128)
         input_tensor = torch.randn(512, 8192, dtype=torch.bfloat16, device="cuda")
         scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
 
         selected_key = pick_silu_mul_fp8_config((input_tensor, scale), config_keys)
-        assert selected_key == "intermediate_4096_numtokens_128"
-
-    def test_config_picker_malformed_key_raises(self):
-        """Malformed config keys should raise ValueError."""
-        config_keys = ["intermediate_4096_badformat_256"]
-        input_tensor = torch.randn(32, 8192, dtype=torch.bfloat16, device="cuda")
-        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
-
-        with pytest.raises(ValueError, match="Malformed config key"):
-            pick_silu_mul_fp8_config((input_tensor, scale), config_keys)
-
-    def test_config_picker_default_ignored_when_valid_keys_exist(self):
-        """'default' is skipped in favor of a real match."""
-        config_keys = [
-            "default",
-            "intermediate_4096_numtokens_32",
-            "intermediate_4096_numtokens_128",
-        ]
-        input_tensor = torch.randn(64, 8192, dtype=torch.bfloat16, device="cuda")
-        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
-
-        selected_key = pick_silu_mul_fp8_config((input_tensor, scale), config_keys)
-        assert selected_key == "intermediate_4096_numtokens_128"
+        assert selected_key == CaseKey({"intermediate": 4096, "numtokens": 128})
 
 
 class TestSiluMulFp8Correctness:
diff --git a/tests/kernels/ir/test_ir_ops.py b/tests/kernels/ir/test_ir_ops.py
new file mode 100644
index 000000000000..1ee36b8f4c90
--- /dev/null
+++ b/tests/kernels/ir/test_ir_ops.py
@@ -0,0 +1,20 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Meta-tests for vLLM IR op infrastructure.
+
+Ensures all registered ops have input generators defined.
+Per-op correctness tests live alongside their op definitions
+(e.g. tests/kernels/ir/test_layernorm.py).
+"""
+
+import vllm.kernels  # noqa: F401 — registers provider implementations
+from vllm.ir.op import IrOp
+
+
+def test_all_ops_have_input_generator():
+    missing = [name for name, op in IrOp.registry.items() if not op.has_input_generator]
+    assert not missing, (
+        f"IR ops without input generators: {missing}. "
+        f"Register one with @ir.ops.<name>.register_input_generator"
+    )
diff --git a/tests/kernels/ir/test_layernorm.py b/tests/kernels/ir/test_layernorm.py
index 3d21169098dc..e9661f5202f9 100644
--- a/tests/kernels/ir/test_layernorm.py
+++ b/tests/kernels/ir/test_layernorm.py
@@ -5,17 +5,17 @@
 
 # This registers op implementations
 import vllm.kernels  # noqa: F401
+from tests.ir.ir_test_utils import (
+    COMMON_HIDDEN_SIZES,
+    NUM_TOKENS,
+    assert_close,
+    clone_args,
+    supported_providers,
+)
 from tests.kernels.allclose_default import get_default_rtol
 from vllm import ir
 from vllm.platforms import current_platform
 
-
-def rms_norm_inputs(n_tokens: int, hidden_size: int, dtype: torch.dtype):
-    x = torch.randn(n_tokens, hidden_size, dtype=dtype)
-    weight = torch.rand(hidden_size, dtype=dtype)
-    return x, weight
-
-
 rms_norm_native = ir.ops.rms_norm.impls["native"].impl_fn
 
 
@@ -28,7 +28,9 @@ def test_rms_norm_registration():
         "native": True,
         "vllm_c": current_platform.is_cuda_alike(),
         "aiter": current_platform.is_rocm(),
-        "oink": False,
+        "oink": current_platform.has_device_capability(100)
+        and hasattr(torch.ops, "oink")
+        and hasattr(torch.ops.oink, "rmsnorm"),
         "xpu_kernels": current_platform.is_xpu(),
     }
 
@@ -40,8 +42,8 @@ def test_rms_norm_registration():
 
 
 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32])
-@pytest.mark.parametrize("n_tokens", [1, 8, 17])
-@pytest.mark.parametrize("hidden_size", [16, 4096, 8192])
+@pytest.mark.parametrize("n_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("hidden_size", COMMON_HIDDEN_SIZES)
 @pytest.mark.parametrize("epsilon", [1e-6, 1e-5])
 @pytest.mark.skipif(
     not current_platform.is_cuda_alike() and not current_platform.is_xpu(),
@@ -53,7 +55,9 @@ def setup_class(cls, **kwargs):
         torch.set_default_device(current_platform.device_type)
 
     def test_native_semantics(self, dtype, n_tokens, hidden_size, epsilon):
-        x, weight = rms_norm_inputs(4, 8, dtype)
+        x, weight, epsilon = ir.ops.rms_norm.generate_inputs(
+            num_tokens=4, hidden_size=8, dtype=dtype, epsilon=epsilon
+        )
         out = rms_norm_native(x, weight, epsilon=epsilon)
 
         # Check shape, dtype, device
@@ -65,65 +69,267 @@ def test_native_semantics(self, dtype, n_tokens, hidden_size, epsilon):
         out2 = rms_norm_native(x * 2.0, weight, epsilon=epsilon)
         torch.testing.assert_close(out2, out, rtol=get_default_rtol(out), atol=1e-3)
 
+        # Mean square should be approximately 1 (ignoring epsilon and weight scaling)
+        combined_norm = out.float() / weight.float()
+        variance = combined_norm.pow(2).mean(dim=-1)
+        # After RMS normalization, variance should be close to 1
+        torch.testing.assert_close(
+            variance, torch.ones_like(variance), rtol=1e-2, atol=1e-2
+        )
+
         # Check behavior with and without weight
         weight1 = torch.ones_like(weight)
         out3 = rms_norm_native(x, weight1, epsilon=epsilon)
         out4 = rms_norm_native(x, None, epsilon=epsilon)
         torch.testing.assert_close(out3, out4)
 
-    @pytest.mark.parametrize("provider", ["vllm_c", "aiter", "xpu_kernels"])
+    @pytest.mark.parametrize("provider", supported_providers(ir.ops.rms_norm))
     def test_impls(self, dtype, n_tokens, hidden_size, epsilon, provider):
         impl = ir.ops.rms_norm.impls[provider]
-        if not impl.supported:
+        x, weight, eps = ir.ops.rms_norm.generate_inputs(
+            num_tokens=n_tokens, hidden_size=hidden_size, dtype=dtype, epsilon=epsilon
+        )
+        args = (x, weight, eps)
+
+        if not impl.supports_args(*args):
+            pytest.skip(f"{provider} does not support args")
+
+        ref_output = rms_norm_native(*clone_args(args))
+        output = impl.impl_fn(*clone_args(args))
+        assert_close(ir.ops.rms_norm, output, ref_output)
+
+        # check that dispatched call matches direct call
+        with ir.ops.rms_norm.set_priority([provider, "native"]):
+            out_dispatched = ir.ops.rms_norm(*args)
+        out_direct = impl.impl_fn(*args)
+        torch.testing.assert_close(out_dispatched, out_direct, rtol=0.0, atol=0.0)
+
+        # none of these support variance_size override
+        assert not impl.supports_args(x, weight, eps, 4)
+        assert not impl.supports_args(x, weight, eps, variance_size=4)
+
+        # test weight=None behavior
+        out_no_weight = impl.impl_fn(x, None, eps)
+        out_unit_weight = impl.impl_fn(x, torch.ones_like(weight), eps)
+        assert_close(ir.ops.rms_norm, out_no_weight, out_unit_weight)
+
+    @pytest.mark.parametrize("provider", ["vllm_c", "aiter", "xpu_kernels", "native"])
+    def test_torch_opcheck(self, dtype, n_tokens, hidden_size, epsilon, provider):
+        if not ir.ops.rms_norm.impls[provider].supported:
             pytest.skip(f"{provider} impl not supported on this platform")
 
-        x, weight = rms_norm_inputs(n_tokens, hidden_size, dtype)
-        args = (x, weight, epsilon, None)
+        args = ir.ops.rms_norm.generate_inputs(
+            num_tokens=n_tokens, hidden_size=hidden_size, dtype=dtype, epsilon=epsilon
+        )
 
-        assert impl.supported
+        # When checking the torch op, we have to set priority and use dispatch
+        with ir.ops.rms_norm.set_priority([provider, "native"]):
+            torch.library.opcheck(torch.ops.vllm_ir.rms_norm, args)
 
-        if provider == "aiter" and dtype not in [torch.float16, torch.bfloat16]:
-            assert not impl.supports_args(*args)
-            return
 
-        assert impl.supports_args(*args)
+@pytest.mark.skipif(
+    not current_platform.is_rocm(),
+    reason="aiter is only supported on ROCm",
+)
+def test_aiter_rejects_unsupported_dtypes():
+    torch.set_default_device(current_platform.device_type)
+    impl = ir.ops.rms_norm.impls["aiter"]
+    for dtype in [torch.float32, torch.float64]:
+        args = ir.ops.rms_norm.generate_inputs(
+            num_tokens=8, hidden_size=4096, dtype=dtype, epsilon=1e-5
+        )
+        assert not impl.supports_args(*args), f"aiter should reject dtype={dtype}"
+
+
+fused_add_rms_norm_native = ir.ops.fused_add_rms_norm.impls["native"].impl_fn
 
-        out_impl = impl.impl_fn(*args)
-        out_native = rms_norm_native(*args)
 
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike() and not current_platform.is_xpu(),
+    reason="Currently only kernels on CUDA, ROCm and XPU",
+)
+def test_fused_add_rms_norm_registration():
+    expected = {
+        "native": True,
+        "vllm_c": current_platform.is_cuda_alike(),
+        "aiter": current_platform.is_rocm(),
+        "oink": current_platform.has_device_capability(100)
+        and hasattr(torch.ops, "oink")
+        and hasattr(torch.ops.oink, "fused_add_rms_norm"),
+        "xpu_kernels": current_platform.is_xpu(),
+    }
+
+    actual = {
+        provider: impl.supported
+        for provider, impl in ir.ops.fused_add_rms_norm.impls.items()
+    }
+
+    assert actual == expected
+
+
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32])
+@pytest.mark.parametrize("n_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("hidden_size", COMMON_HIDDEN_SIZES)
+@pytest.mark.parametrize("epsilon", [1e-6, 1e-5])
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike() and not current_platform.is_xpu(),
+    reason="Currently only kernels on CUDA, ROCm and XPU",
+)
+class TestFusedAddRMSNorm:
+    @classmethod
+    def setup_class(cls, **kwargs):
+        torch.set_default_device(current_platform.device_type)
+
+    def test_native_semantics(self, dtype, n_tokens, hidden_size, epsilon):
+        x, x_residual, weight, eps = ir.ops.fused_add_rms_norm.generate_inputs(
+            num_tokens=4, hidden_size=8, dtype=dtype, epsilon=epsilon
+        )
+        out, residual_out = fused_add_rms_norm_native(x, x_residual, weight, eps)
+
+        # Check shape, dtype, device
+        assert out.shape == x.shape
+        assert out.dtype == x.dtype
+        assert out.device == x.device
+        assert residual_out.shape == x_residual.shape
+        assert residual_out.dtype == x_residual.dtype
+        assert residual_out.device == x_residual.device
+
+        # Check that residual_out = x + x_residual
+        expected_residual = (x.float() + x_residual.float()).to(dtype)
         torch.testing.assert_close(
-            out_impl, out_native, rtol=get_default_rtol(out_impl), atol=1e-3
+            residual_out, expected_residual, rtol=1e-3, atol=1e-3
         )
 
-        # check that dispatched call matches direct call
-        with ir.ops.rms_norm.set_priority([provider, "native"]):
-            out_impl2 = ir.ops.rms_norm(*args)
+        # Verify that the output is RMS normalized version of (x + x_residual)
+        expected_out = rms_norm_native(expected_residual, weight, epsilon)
+        assert_close(
+            ir.ops.fused_add_rms_norm,
+            (out, residual_out),
+            (expected_out, expected_residual),
+        )
+
+        # Check the scaling property of rms norm
+        out1, _ = fused_add_rms_norm_native(
+            x, torch.zeros_like(x), weight, epsilon=epsilon
+        )
+        out2, _ = fused_add_rms_norm_native(
+            x * 2.0, torch.zeros_like(x), weight, epsilon=epsilon
+        )
+        torch.testing.assert_close(out2, out1, rtol=get_default_rtol(out), atol=1e-3)
+
+        # Check behavior with and without weight
+        weight1 = torch.ones_like(weight)
+        out3, _ = fused_add_rms_norm_native(x, x_residual, weight1, eps)
+        out4, _ = fused_add_rms_norm_native(x, x_residual, None, eps)
+        torch.testing.assert_close(out3, out4)
+
+    @pytest.mark.parametrize("provider", supported_providers(ir.ops.fused_add_rms_norm))
+    def test_impls(self, dtype, n_tokens, hidden_size, epsilon, provider):
+        impl = ir.ops.fused_add_rms_norm.impls[provider]
+        x, x_residual, weight, eps = ir.ops.fused_add_rms_norm.generate_inputs(
+            num_tokens=n_tokens, hidden_size=hidden_size, dtype=dtype, epsilon=epsilon
+        )
+        args = (x, x_residual, weight, eps, None)
+
+        if not impl.supports_args(*args):
+            pytest.skip(f"{provider} does not support args")
 
-        # exact match
-        torch.testing.assert_close(out_impl2, out_impl, rtol=0.0, atol=0.0)
+        ref_output, ref_residual = fused_add_rms_norm_native(*clone_args(args))
+        output, residual = impl.impl_fn(*clone_args(args))
+        assert_close(ir.ops.fused_add_rms_norm, output, ref_output)
+        assert_close(ir.ops.fused_add_rms_norm, residual, ref_residual)
+
+        # check that dispatched call matches direct call
+        with ir.ops.fused_add_rms_norm.set_priority([provider, "native"]):
+            out_dispatched, residual_dispatched = ir.ops.fused_add_rms_norm(*args[:4])
+        out_direct, residual_direct = impl.impl_fn(*clone_args(args))
+        torch.testing.assert_close(out_dispatched, out_direct, rtol=0.0, atol=0.0)
+        torch.testing.assert_close(
+            residual_dispatched, residual_direct, rtol=0.0, atol=0.0
+        )
 
         # none of these support variance_size override
-        assert not impl.supports_args(x, weight, epsilon, 4)
-        assert not impl.supports_args(x, weight, epsilon, variance_size=4)
+        assert not impl.supports_args(x, x_residual, weight, epsilon, 4)
+        assert not impl.supports_args(x, x_residual, weight, epsilon, variance_size=4)
 
         # test weight=None behavior
-        out_impl_no_weight = impl.impl_fn(x, None, epsilon)
-        out_impl_unit_weight = impl.impl_fn(x, torch.ones_like(weight), epsilon)
-        torch.testing.assert_close(
-            out_impl_no_weight,
-            out_impl_unit_weight,
-            rtol=get_default_rtol(out_impl_no_weight),
-            atol=2e-4,
+        out_no_weight, residual_no_weight = impl.impl_fn(
+            x.clone(), x_residual.clone(), None, epsilon
+        )
+        out_unit_weight, residual_unit_weight = impl.impl_fn(
+            x.clone(), x_residual.clone(), torch.ones_like(weight), epsilon
+        )
+        assert_close(ir.ops.fused_add_rms_norm, out_no_weight, out_unit_weight)
+        assert_close(
+            ir.ops.fused_add_rms_norm, residual_no_weight, residual_unit_weight
         )
 
-    @pytest.mark.parametrize("provider", ["vllm_c", "aiter", "xpu_kernels", "native"])
-    def test_torch_opcheck(self, dtype, n_tokens, hidden_size, epsilon, provider):
-        if not ir.ops.rms_norm.impls[provider].supported:
+    @pytest.mark.parametrize("provider", ["vllm_c"])
+    def test_inplace_semantics(self, dtype, n_tokens, hidden_size, epsilon, provider):
+        """Test that inplace implementations reuse inputs,
+        for maybe_inplace overload but not for default overload."""
+        impl = ir.ops.fused_add_rms_norm.impls[provider]
+        if not impl.supported:
             pytest.skip(f"{provider} impl not supported on this platform")
 
-        x, weight = rms_norm_inputs(n_tokens, hidden_size, dtype)
-        args = (x, weight, epsilon, None)
+        x, x_residual, weight, eps = ir.ops.fused_add_rms_norm.generate_inputs(
+            num_tokens=n_tokens, hidden_size=hidden_size, dtype=dtype, epsilon=epsilon
+        )
+
+        # Test default overload - should NOT modify inputs even with inplace impl
+        x_default = x.clone()
+        x_residual_default = x_residual.clone()
+        x_default_ptr = x_default.data_ptr()
+        x_residual_default_ptr = x_residual_default.data_ptr()
+
+        with ir.ops.fused_add_rms_norm.set_priority([provider, "native"]):
+            out_default, residual_default = ir.ops.fused_add_rms_norm(
+                x_default, x_residual_default, weight, eps
+            )
+
+        # Default should NOT be inplace (even with inplace implementation)
+        assert out_default.data_ptr() != x_default_ptr
+        assert residual_default.data_ptr() != x_residual_default_ptr
+        torch.testing.assert_close(x, x_default, rtol=0.0, atol=0.0)
+        torch.testing.assert_close(x_residual, x_residual_default, rtol=0.0, atol=0.0)
+
+        # Test maybe_inplace overload - should modify inputs with inplace impl
+        x_inplace = x.clone()
+        x_residual_inplace = x_residual.clone()
+        x_inplace_ptr = x_inplace.data_ptr()
+        x_residual_inplace_ptr = x_residual_inplace.data_ptr()
+
+        with ir.ops.fused_add_rms_norm.set_priority([provider, "native"]):
+            out_inplace, residual_inplace = ir.ops.fused_add_rms_norm.maybe_inplace(
+                x_inplace, x_residual_inplace, weight, eps
+            )
+
+        # maybe_inplace should be inplace
+        assert out_inplace.data_ptr() == x_inplace_ptr
+        assert residual_inplace.data_ptr() == x_residual_inplace_ptr
+
+        # Both should produce same results
+        torch.testing.assert_close(out_default, out_inplace, atol=0.0, rtol=0.0)
+        torch.testing.assert_close(
+            residual_default, residual_inplace, atol=0.0, rtol=0.0
+        )
+
+    @pytest.mark.parametrize("provider", supported_providers(ir.ops.fused_add_rms_norm))
+    def test_torch_opcheck(self, dtype, n_tokens, hidden_size, epsilon, provider):
+        args = ir.ops.fused_add_rms_norm.generate_inputs(
+            num_tokens=n_tokens, hidden_size=hidden_size, dtype=dtype, epsilon=epsilon
+        )
+        args = args + (None,)  # Add variance_size parameter
 
         # When checking the torch op, we have to set priority and use dispatch
-        with ir.ops.rms_norm.set_priority([provider, "native"]):
-            torch.library.opcheck(torch.ops.vllm_ir.rms_norm, args)
+        with ir.ops.fused_add_rms_norm.set_priority([provider, "native"]):
+            torch.library.opcheck(torch.ops.vllm_ir.fused_add_rms_norm.default, args)
+
+            # Only test maybe_inplace with non-inplace implementations
+            # Inplace implementations return aliases of inputs which is not allowed.
+            # We break this invariant, but we also convert maybe_inplace to the default
+            # overload during compilation, so maybe_inplace never reaches Inductor.
+            if not ir.ops.fused_add_rms_norm.impls[provider].inplace:
+                torch.library.opcheck(
+                    torch.ops.vllm_ir.fused_add_rms_norm.maybe_inplace, args
+                )
diff --git a/tests/kernels/mamba/test_ssu_dispatch.py b/tests/kernels/mamba/test_ssu_dispatch.py
index 96b04f44d220..887d60b27365 100644
--- a/tests/kernels/mamba/test_ssu_dispatch.py
+++ b/tests/kernels/mamba/test_ssu_dispatch.py
@@ -13,6 +13,11 @@
     selective_state_update,
 )
 from vllm.utils.torch_utils import set_random_seed
+from vllm.v1.kv_cache_interface import (
+    KVCacheConfig,
+    KVCacheGroupSpec,
+    MambaSpec,
+)
 
 try:
     import flashinfer.mamba  # noqa: F401
@@ -22,22 +27,40 @@
     HAS_FLASHINFER = False
 
 
+def _kv_cache_config_with_ssu(mamba_type: str = "mamba2") -> KVCacheConfig:
+    spec = MambaSpec(
+        block_size=16,
+        shapes=((16, 64),),
+        dtypes=(torch.float16,),
+        mamba_type=mamba_type,
+    )
+    return KVCacheConfig(
+        num_blocks=1,
+        kv_cache_tensors=[],
+        kv_cache_groups=[KVCacheGroupSpec(layer_names=["l0"], kv_cache_spec=spec)],
+    )
+
+
 def test_default_backend_is_triton():
-    initialize_mamba_ssu_backend(MambaConfig())
+    initialize_mamba_ssu_backend(MambaConfig(), _kv_cache_config_with_ssu())
     backend = get_mamba_ssu_backend()
     assert isinstance(backend, TritonSSUBackend)
     assert backend.name == "triton"
 
 
 def test_explicit_triton_backend():
-    initialize_mamba_ssu_backend(MambaConfig(backend=MambaBackendEnum.TRITON))
+    initialize_mamba_ssu_backend(
+        MambaConfig(backend=MambaBackendEnum.TRITON), _kv_cache_config_with_ssu()
+    )
     backend = get_mamba_ssu_backend()
     assert isinstance(backend, TritonSSUBackend)
 
 
 @pytest.mark.skipif(not HAS_FLASHINFER, reason="flashinfer not installed")
 def test_flashinfer_backend_init():
-    initialize_mamba_ssu_backend(MambaConfig(backend=MambaBackendEnum.FLASHINFER))
+    initialize_mamba_ssu_backend(
+        MambaConfig(backend=MambaBackendEnum.FLASHINFER), _kv_cache_config_with_ssu()
+    )
     backend = get_mamba_ssu_backend()
     assert isinstance(backend, FlashInferSSUBackend)
     assert backend.name == "flashinfer"
@@ -53,6 +76,25 @@ def test_uninitialized_backend_raises():
     mod._mamba_ssu_backend = old
 
 
+@pytest.mark.parametrize(
+    "mamba_type", ["linear_attention", "gdn_attention", "short_conv"]
+)
+def test_init_is_noop_for_non_ssu_mamba_type(mamba_type):
+    import vllm.model_executor.layers.mamba.ops.ssu_dispatch as mod
+
+    old = mod._mamba_ssu_backend
+    mod._mamba_ssu_backend = None
+    try:
+        initialize_mamba_ssu_backend(
+            MambaConfig(), _kv_cache_config_with_ssu(mamba_type)
+        )
+        assert mod._mamba_ssu_backend is None
+        with pytest.raises(RuntimeError, match="not been initialized"):
+            get_mamba_ssu_backend()
+    finally:
+        mod._mamba_ssu_backend = old
+
+
 @pytest.mark.skipif(HAS_FLASHINFER, reason="flashinfer is installed")
 def test_flashinfer_import_error():
     with pytest.raises(ImportError, match="FlashInfer is required"):
@@ -61,7 +103,9 @@ def test_flashinfer_import_error():
 
 def test_triton_basic_call():
     set_random_seed(0)
-    initialize_mamba_ssu_backend(MambaConfig(backend=MambaBackendEnum.TRITON))
+    initialize_mamba_ssu_backend(
+        MambaConfig(backend=MambaBackendEnum.TRITON), _kv_cache_config_with_ssu()
+    )
     device = "cuda"
     batch_size = 2
     dim = 64
diff --git a/tests/kernels/moe/modular_kernel_tools/mk_objects.py b/tests/kernels/moe/modular_kernel_tools/mk_objects.py
index a39e03abeb32..111975cf6bae 100644
--- a/tests/kernels/moe/modular_kernel_tools/mk_objects.py
+++ b/tests/kernels/moe/modular_kernel_tools/mk_objects.py
@@ -223,7 +223,7 @@ def expert_info(kind) -> ExpertInfo:
     )
 
 if has_mori():
-    from vllm.model_executor.layers.fused_moe.mori_prepare_finalize import (
+    from vllm.model_executor.layers.fused_moe.prepare_finalize.mori import (
         MoriPrepareAndFinalize,
     )
 
@@ -237,7 +237,7 @@ def expert_info(kind) -> ExpertInfo:
     )
 
 if has_flashinfer_cutlass_fused_moe() and current_platform.has_device_capability(100):
-    from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
+    from vllm.model_executor.layers.fused_moe.experts.flashinfer_cutlass_moe import (
         FlashInferExperts,
     )
     from vllm.model_executor.layers.fused_moe.prepare_finalize.flashinfer_nvlink_two_sided import (  # noqa: E501
@@ -298,7 +298,7 @@ def expert_info(kind) -> ExpertInfo:
     )
 
 if has_aiter():
-    from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+    from vllm.model_executor.layers.fused_moe.experts.rocm_aiter_moe import (
         AiterExperts,
     )
 
@@ -367,7 +367,9 @@ def expert_info(kind) -> ExpertInfo:
     CutlassExpertsFp8 = None
 
 if cutlass_fp4_supported():
-    from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp4
+    from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import (
+        CutlassExpertsFp4,
+    )
 
     register_experts(
         CutlassExpertsFp4,
diff --git a/tests/kernels/moe/modular_kernel_tools/parallel_utils.py b/tests/kernels/moe/modular_kernel_tools/parallel_utils.py
index 95004fa0ab4d..07f244451b45 100644
--- a/tests/kernels/moe/modular_kernel_tools/parallel_utils.py
+++ b/tests/kernels/moe/modular_kernel_tools/parallel_utils.py
@@ -77,8 +77,8 @@ def _worker_parallel_launch(
     *args: Any,
 ) -> None:
     rank = node_rank * world_local_size + local_rank
-    torch.accelerator.set_device_index(local_rank)
     device = torch.device("cuda", local_rank)
+    torch.accelerator.set_device_index(device)
     torch.distributed.init_process_group(
         backend="cpu:gloo,cuda:nccl",
         init_method=init_method,
diff --git a/tests/kernels/moe/test_batched_deepgemm.py b/tests/kernels/moe/test_batched_deepgemm.py
index b11098c820c1..4c8b2d87d61f 100644
--- a/tests/kernels/moe/test_batched_deepgemm.py
+++ b/tests/kernels/moe/test_batched_deepgemm.py
@@ -10,10 +10,12 @@
     BatchedDeepGemmExperts,
 )
 from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
-    BatchedPrepareAndFinalize,
     BatchedTritonExperts,
 )
 from vllm.model_executor.layers.fused_moe.modular_kernel import FusedMoEKernel
+from vllm.model_executor.layers.fused_moe.prepare_finalize.batched import (
+    BatchedPrepareAndFinalize,
+)
 from vllm.utils.deep_gemm import calc_diff, is_deep_gemm_supported
 
 from .test_deepgemm import make_block_quant_fp8_weights
diff --git a/tests/kernels/moe/test_cpu_fp8_fused_moe.py b/tests/kernels/moe/test_cpu_fp8_fused_moe.py
new file mode 100644
index 000000000000..dbc6cd198c23
--- /dev/null
+++ b/tests/kernels/moe/test_cpu_fp8_fused_moe.py
@@ -0,0 +1,256 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for CPU FP8 W8A16 block-scaled fused MoE kernel."""
+
+import math
+import sys
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from vllm.platforms import current_platform
+from vllm.utils.torch_utils import set_random_seed
+
+if not current_platform.is_cpu():
+    pytest.skip("skipping CPU-only tests", allow_module_level=True)
+
+import vllm._custom_ops as ops  # noqa: E402
+
+if not hasattr(torch.ops._C, "fused_experts_cpu"):
+    pytest.skip("fused_experts_cpu op not available", allow_module_level=True)
+
+
+BLOCK_SIZE = [128, 128]  # [block_n, block_k]
+
+# FP8 weight generation parameters
+_FP8_INFO = torch.finfo(torch.float8_e4m3fn)
+FP8_SCALE = _FP8_INFO.max  # 448.0
+FACTOR_FOR_SCALE = 1e-3
+
+# Tolerance for FP8 W8A16
+FP8_W8A16_ATOL = 1e-2
+FP8_W8A16_RTOL = 1e-2
+
+
+def _silu_and_mul(x: torch.Tensor) -> torch.Tensor:
+    d = x.shape[-1] // 2
+    return F.silu(x[..., :d]) * x[..., d:]
+
+
+def _block_dequant_weight(
+    weight: torch.Tensor,
+    scales: torch.Tensor,
+    block_size: list[int],
+) -> torch.Tensor:
+    """Block-dequantize FP8 weight [E, N, K] → float [E, N, K].
+
+    Each (block_n × block_k) tile is multiplied by its per-block scale.
+    """
+    E, N, K = weight.shape
+    block_n, block_k = block_size
+    pad_N = (block_n - N % block_n) % block_n
+    pad_K = (block_k - K % block_k) % block_k
+
+    if pad_N > 0 or pad_K > 0:
+        weight = F.pad(weight, (0, pad_K, 0, pad_N))
+
+    n_tiles = math.ceil(N / block_n)
+    k_tiles = math.ceil(K / block_k)
+
+    weight_block = (
+        weight.view(E, n_tiles, block_n, k_tiles, block_k)
+        .permute(0, 1, 3, 2, 4)
+        .float()
+        .contiguous()
+    )
+    weight_scaled = (
+        (weight_block * scales.view(E, n_tiles, k_tiles, 1, 1))
+        .permute(0, 1, 3, 2, 4)
+        .contiguous()
+    )
+    if pad_N > 0 or pad_K > 0:
+        weight_scaled = weight_scaled.view(E, N + pad_N, K + pad_K)
+        weight_scaled = weight_scaled[..., :N, :K].contiguous()
+    else:
+        weight_scaled = weight_scaled.view(E, N, K)
+    return weight_scaled
+
+
+def ref_w8a16_block_fp8_moe(
+    a: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    w1_s: torch.Tensor,
+    w2_s: torch.Tensor,
+    topk_weight: torch.Tensor,
+    topk_ids: torch.Tensor,
+    block_size: list[int],
+) -> torch.Tensor:
+    """Reference FP8 W8A16 block-scaled fused MoE in pure torch.
+
+    Steps:
+      1. Block-dequant FP8 weights → float
+      2. For each expert: matmul → SiLU+Mul → matmul
+      3. Weighted sum across top-k experts
+    """
+    B, D = a.shape
+    topk = topk_ids.size(1)
+
+    w1_dq = _block_dequant_weight(w1, w1_s, block_size)
+    w2_dq = _block_dequant_weight(w2, w2_s, block_size)
+
+    a_exp = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D).float()
+    out = torch.zeros(B * topk, w2_dq.shape[1], dtype=torch.float32)
+
+    topk_weight_flat = topk_weight.view(-1)
+    topk_ids_flat = topk_ids.view(-1)
+
+    for i in range(w1_dq.shape[0]):
+        mask = topk_ids_flat == i
+        if mask.sum():
+            ic0 = torch.matmul(a_exp[mask], w1_dq[i].transpose(0, 1))
+            ic1 = _silu_and_mul(ic0)
+            out[mask] = torch.matmul(ic1, w2_dq[i].transpose(0, 1))
+
+    return (
+        (out.view(B, -1, w2_dq.shape[1]) * topk_weight_flat.view(B, -1, 1))
+        .sum(dim=1)
+        .to(a.dtype)
+    )
+
+
+def _make_fp8_moe_weights(
+    E: int,
+    N: int,
+    K: int,
+    block_size: list[int],
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Generate random FP8 MoE weights with random block scales.
+
+    Weight generation follows SGLang: ``randn * FP8_SCALE`` → clamp → cast.
+    Scales are small random values (``FACTOR_FOR_SCALE``), independent of
+    the actual weight magnitudes — this is sufficient to test the kernel's
+    block-dequant + matmul correctness.
+
+    Returns: (w1, w2, w1_s, w2_s)
+    """
+    block_n, block_k = block_size
+
+    w1 = (
+        (torch.randn(E, 2 * N, K) * FP8_SCALE)
+        .clamp(min=-FP8_SCALE, max=FP8_SCALE)
+        .to(torch.float8_e4m3fn)
+    )
+    w2 = (
+        (torch.randn(E, K, N) * FP8_SCALE)
+        .clamp(min=-FP8_SCALE, max=FP8_SCALE)
+        .to(torch.float8_e4m3fn)
+    )
+
+    w1_s = (
+        torch.randn(E, math.ceil(2 * N / block_n), math.ceil(K / block_k))
+        * FACTOR_FOR_SCALE
+    )
+    w2_s = (
+        torch.randn(E, math.ceil(K / block_n), math.ceil(N / block_k))
+        * FACTOR_FOR_SCALE
+    )
+    return w1, w2, w1_s, w2_s
+
+
+def _prepack_experts(w: torch.Tensor) -> torch.Tensor:
+    """VNNI-prepack each expert's weight via ``convert_weight_packed``."""
+    return torch.stack(
+        [torch.ops._C.convert_weight_packed(w[e]) for e in range(w.shape[0])]
+    )
+
+
+NUM_TOKENS = [1, 2, 64, 121]
+# (M, intermediate_size N, hidden_size K, num_experts E, topk)
+MoE_CONFIGS = [
+    (256, 512, 8, 2),
+    (256, 512, 8, 4),
+    (512, 256, 8, 2),
+    (512, 256, 8, 4),
+    (512, 512, 8, 2),
+    (512, 512, 8, 4),
+    (768, 2048, 8, 2),
+    (768, 2048, 8, 4),
+    (768, 2048, 128, 8),
+]
+SEEDS = [0]
+
+
+@pytest.mark.parametrize("M", NUM_TOKENS)
+@pytest.mark.parametrize("N,K,E,topk", MoE_CONFIGS)
+@pytest.mark.parametrize("seed", SEEDS)
+def test_w8a16_block_fp8_cpu_fused_moe(M, N, K, E, topk, seed):
+    """Test fused_experts_cpu FP8 W8A16 against dequantised torch reference."""
+    set_random_seed(seed)
+
+    a = torch.randn(M, K, dtype=torch.bfloat16) / math.sqrt(K)
+    w1, w2, w1_s, w2_s = _make_fp8_moe_weights(E, N, K, BLOCK_SIZE)
+
+    score = torch.randn(M, E, dtype=torch.bfloat16)
+    score = torch.softmax(score, dim=-1, dtype=torch.float32)
+    topk_weight, topk_ids = torch.topk(score, topk)
+    topk_ids = topk_ids.to(torch.int32)
+
+    ref_out = ref_w8a16_block_fp8_moe(
+        a,
+        w1,
+        w2,
+        w1_s,
+        w2_s,
+        topk_weight,
+        topk_ids,
+        BLOCK_SIZE,
+    )
+
+    pw1, pw2 = _prepack_experts(w1), _prepack_experts(w2)
+
+    # Test inplace=False against reference
+    out = ops.fused_experts_cpu(
+        a.clone(),
+        pw1,
+        pw2,
+        topk_weight,
+        topk_ids,
+        False,
+        ops.CPUQuantMethod.FP8_W8A16,
+        w1_s,
+        w2_s,
+        None,
+        None,
+        BLOCK_SIZE,
+        True,
+    )
+    torch.testing.assert_close(
+        ref_out.bfloat16(),
+        out,
+        atol=FP8_W8A16_ATOL,
+        rtol=FP8_W8A16_RTOL,
+    )
+
+    # Test inplace=True produces identical output
+    out_inplace = ops.fused_experts_cpu(
+        a.clone(),
+        pw1,
+        pw2,
+        topk_weight,
+        topk_ids,
+        True,
+        ops.CPUQuantMethod.FP8_W8A16,
+        w1_s,
+        w2_s,
+        None,
+        None,
+        BLOCK_SIZE,
+        True,
+    )
+    torch.testing.assert_close(out_inplace, out, atol=0, rtol=0)
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__, "-v"]))
diff --git a/tests/kernels/moe/test_cutlass_moe.py b/tests/kernels/moe/test_cutlass_moe.py
index e06672f41d0c..a613e7d2e290 100644
--- a/tests/kernels/moe/test_cutlass_moe.py
+++ b/tests/kernels/moe/test_cutlass_moe.py
@@ -21,7 +21,7 @@
     FusedMoEQuantConfig,
     fp8_w8a8_moe_quant_config,
 )
-from vllm.model_executor.layers.fused_moe.cutlass_moe import (
+from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import (
     CutlassExpertsFp8,
     run_cutlass_moe_fp8,
 )
diff --git a/tests/kernels/moe/test_deepgemm.py b/tests/kernels/moe/test_deepgemm.py
index 47700f82a7b3..fd05759ac3df 100644
--- a/tests/kernels/moe/test_deepgemm.py
+++ b/tests/kernels/moe/test_deepgemm.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
-Unit-test DeepGEMM FP8 kernels (no DeepEP).
+Unit-test DeepGEMM FP8 and FP4 kernels (no DeepEP).
 Compare DeepGEMM path against the Triton fallback inside vLLM's fused_experts.
 """
 
@@ -21,6 +21,8 @@
     maybe_make_prepare_finalize,
 )
 from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEQuantConfig,
+    FusedMoEQuantDesc,
     fp8_w8a8_moe_quant_config,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
@@ -204,3 +206,195 @@ def _spy_apply(*args, **kwargs):
             f"DeepGEMM path was not executed during the test. "
             f"Call counter: {call_counter['cnt']}"
         )
+
+
+# ---------------------------------------------------------------------------
+# FP4 weight tests (DeepGEMM m_grouped_fp8_fp4_gemm_nt_contiguous)
+# ---------------------------------------------------------------------------
+
+
+def make_mxfp4_weights(
+    e: int,
+    n: int,
+    k: int,
+):
+    """
+    Generate (w1, w2) expert weights in MXFP4 packed format with float32 scales,
+    plus BF16 reference weights for validation.
+
+      w1 shape: (E, 2N, K//2) uint8    — packed FP4
+      w2 shape: (E, K, N//2)  uint8    — packed FP4
+      w1_s shape: (E, 2N, K//32) float32  — per-row block-32 scales
+      w2_s shape: (E, K, N//32)  float32  — per-row block-32 scales
+      w1_bf16: (E, 2N, K)   — original BF16 for reference
+      w2_bf16: (E, K, N)    — original BF16 for reference
+    """
+    from deep_gemm.utils.math import per_token_cast_to_fp4
+
+    dtype = torch.bfloat16
+    gran_k = 32  # MXFP4 block size
+
+    # bf16 reference weights — scale by 1/sqrt(dim) for numerical stability
+    w1_bf16 = torch.randn(e, 2 * n, k, device="cuda", dtype=dtype) * (k**-0.5)
+    w2_bf16 = torch.randn(e, k, n, device="cuda", dtype=dtype) * (n**-0.5)
+
+    # Quantize per-expert to FP4
+    w1 = torch.empty(e, 2 * n, k // 2, device="cuda", dtype=torch.uint8)
+    w2 = torch.empty(e, k, n // 2, device="cuda", dtype=torch.uint8)
+    w1_s = torch.empty(
+        e, 2 * n, math.ceil(k / gran_k), device="cuda", dtype=torch.float32
+    )
+    w2_s = torch.empty(e, k, math.ceil(n / gran_k), device="cuda", dtype=torch.float32)
+
+    for i in range(e):
+        w1[i], w1_s[i] = per_token_cast_to_fp4(
+            w1_bf16[i].float(), use_ue8m0=True, gran_k=gran_k
+        )
+        w2[i], w2_s[i] = per_token_cast_to_fp4(
+            w2_bf16[i].float(), use_ue8m0=True, gran_k=gran_k
+        )
+
+    return w1, w2, w1_s, w2_s, w1_bf16, w2_bf16
+
+
+def _bf16_moe_reference(x, w1, w2, topk_weights, topk_ids):
+    """BF16 token-loop MoE reference for correctness testing."""
+    import torch.nn.functional as F
+
+    num_tokens, hidden_size = x.shape
+    intermediate = w1.shape[1] // 2
+    top_k = topk_ids.shape[1]
+
+    output = torch.zeros(num_tokens, hidden_size, dtype=torch.float32, device=x.device)
+    for t in range(num_tokens):
+        for kk in range(top_k):
+            e = topk_ids[t, kk].item()
+            w = topk_weights[t, kk].item()
+            fc1 = x[t : t + 1].float() @ w1[e].float().T
+            linear = fc1[:, :intermediate]
+            gate = fc1[:, intermediate:]
+            act = F.silu(gate) * linear
+            fc2 = act @ w2[e].float().T
+            output[t] += w * fc2[0]
+    return output.to(torch.bfloat16)
+
+
+def run_single_fp4_case(m, n, k, topk, num_experts):
+    """
+    Run one (M,N,K) configuration with FP4 weights on DeepGEMM and assert
+    DeepGEMM FP4 == BF16 reference within tolerance.
+    """
+    tokens_bf16 = torch.randn(m, k, device="cuda", dtype=torch.bfloat16) * (k**-0.5)
+
+    # FP4 expert weight tensors + BF16 originals for reference
+    w1, w2, w1_s, w2_s, w1_bf16, w2_bf16 = make_mxfp4_weights(num_experts, n, k)
+
+    router_logits = torch.randn(m, num_experts, device="cuda", dtype=torch.float32)
+    topk_weights, topk_ids = torch.topk(router_logits, k=topk, dim=-1)
+    topk_weights = torch.nn.functional.softmax(topk_weights, dim=-1)
+
+    from vllm.model_executor.layers.quantization.utils.quant_utils import (
+        GroupShape,
+    )
+    from vllm.platforms import current_platform
+
+    _fp8_dtype = current_platform.fp8_dtype()
+    _block_shape = GroupShape(128, 128)
+    quant_config = FusedMoEQuantConfig(
+        _a1=FusedMoEQuantDesc(_fp8_dtype, _block_shape, None, None, None, None),
+        _a2=FusedMoEQuantDesc(_fp8_dtype, _block_shape, None, None, None, None),
+        _w1=FusedMoEQuantDesc("mxfp4", None, w1_s, None, None, None),
+        _w2=FusedMoEQuantDesc("mxfp4", None, w2_s, None, None, None),
+    )
+    moe_config = make_dummy_moe_config()
+
+    from vllm.model_executor.layers.fused_moe.experts.deep_gemm_moe import (
+        DeepGemmFP4Experts,
+    )
+
+    deep_gemm_fp4_experts = mk.FusedMoEKernel(
+        prepare_finalize=maybe_make_prepare_finalize(
+            moe=moe_config,
+            quant_config=quant_config,
+            allow_new_interface=True,
+            use_monolithic=False,
+        ),
+        fused_experts=DeepGemmFP4Experts(
+            moe_config=moe_config,
+            quant_config=quant_config,
+        ),
+        inplace=False,
+    )
+
+    # DeepGEMM FP4 path
+    out_deepgemm_fp4 = deep_gemm_fp4_experts.apply(
+        hidden_states=tokens_bf16,
+        w1=w1,
+        w2=w2,
+        topk_weights=topk_weights,
+        topk_ids=topk_ids,
+        global_num_experts=num_experts,
+        activation=MoEActivation.SILU,
+        apply_router_weight_on_input=False,
+        expert_map=None,
+    )
+
+    # BF16 reference using the same original weights
+    out_ref = _bf16_moe_reference(tokens_bf16, w1_bf16, w2_bf16, topk_weights, topk_ids)
+
+    # FP4 vs BF16 reference: quantization error from FP4 weights + FP8 activations
+    diff = calc_diff(out_deepgemm_fp4, out_ref)
+    assert diff < 0.05, f"FP4 diff exceeded 5%: {diff}"
+
+
+# DeepSeek V4 dims: H=4096, I=2048, so N=2*I=4096, K=H=4096.
+# FP4 quantization with block_k=32 needs large K for good accuracy.
+FP4_MNKs = [
+    (128, 4096, 4096),  # DeepSeek V4 shape
+    (256, 2048, 2048),  # Half-size variant
+]
+
+FP4_TOPKS = [2]
+FP4_NUM_EXPERTS = [8]
+
+
+@pytest.mark.parametrize(("m", "n", "k"), FP4_MNKs)
+@pytest.mark.parametrize("topk", FP4_TOPKS)
+@pytest.mark.parametrize("num_experts", FP4_NUM_EXPERTS)
+@pytest.mark.skipif(not is_deep_gemm_supported(), reason="Requires deep_gemm kernels")
+def test_deepgemm_fp4_vs_triton(
+    m, n, k, topk, num_experts, monkeypatch, workspace_init
+):
+    pytest.importorskip("deep_gemm.utils.math")
+    with monkeypatch.context() as mp:
+        mp.setenv("VLLM_USE_DEEP_GEMM", "1")
+
+        _DeepGemmFP4Experts = importlib.import_module(
+            "vllm.model_executor.layers.fused_moe.experts.deep_gemm_moe"
+        ).DeepGemmFP4Experts
+
+        call_counter = {"cnt": 0}
+
+        orig_fn = _DeepGemmFP4Experts.apply
+
+        def _spy_apply(*args, **kwargs):
+            call_counter["cnt"] += 1
+            return orig_fn(*args, **kwargs)
+
+        monkeypatch.setattr(_DeepGemmFP4Experts, "apply", _spy_apply)
+        if topk > num_experts:
+            pytest.skip(f"topk={topk} > num_experts={num_experts}")
+
+        run_single_fp4_case(
+            m=m,
+            n=n,
+            k=k,
+            topk=topk,
+            num_experts=num_experts,
+        )
+
+        # ensure that the DeepGEMM FP4 path was indeed taken.
+        assert call_counter["cnt"] == 1, (
+            f"DeepGEMM FP4 path was not executed during the test. "
+            f"Call counter: {call_counter['cnt']}"
+        )
diff --git a/tests/kernels/moe/test_flashinfer.py b/tests/kernels/moe/test_flashinfer.py
index dad25bc31959..9e35be0db801 100644
--- a/tests/kernels/moe/test_flashinfer.py
+++ b/tests/kernels/moe/test_flashinfer.py
@@ -18,12 +18,12 @@
     RoutingMethodType,
     fp8_w8a8_moe_quant_config,
 )
+from vllm.model_executor.layers.fused_moe.experts.flashinfer_cutlass_moe import (
+    FlashInferExperts,
+)
 from vllm.model_executor.layers.fused_moe.experts.trtllm_fp8_moe import (
     TrtLlmFp8ExpertsMonolithic,
 )
-from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
-    FlashInferExperts,
-)
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
 from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
     rotate_weights_for_fi_trtllm_fp8_per_tensor_moe,
diff --git a/tests/kernels/moe/test_flashinfer_moe.py b/tests/kernels/moe/test_flashinfer_moe.py
index d116a96f58bc..6ae67fa0e987 100644
--- a/tests/kernels/moe/test_flashinfer_moe.py
+++ b/tests/kernels/moe/test_flashinfer_moe.py
@@ -22,7 +22,7 @@
     FusedMoEParallelConfig,
     RoutingMethodType,
 )
-from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
+from vllm.model_executor.layers.fused_moe.experts.flashinfer_cutlass_moe import (
     FlashInferExperts,
     is_valid_flashinfer_cutlass_fused_moe,
 )
diff --git a/tests/kernels/moe/test_fused_topk.py b/tests/kernels/moe/test_fused_topk.py
index 5384d8964b58..825cd20263d7 100644
--- a/tests/kernels/moe/test_fused_topk.py
+++ b/tests/kernels/moe/test_fused_topk.py
@@ -135,3 +135,139 @@ def test_fused_topk_bias(
         topk_weights_ref.to(torch.float32), topk_weights, atol=1e-2, rtol=1e-2
     )
     torch.testing.assert_close(topk_ids_ref.to(torch.int32), topk_ids, atol=0, rtol=0)
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda(), reason="This test is skipped on non-CUDA platform."
+)
+@pytest.mark.parametrize("num_experts", [6, 8, 16])
+@pytest.mark.parametrize("topk", [3, 4])
+@pytest.mark.parametrize("scoring_func", ["softmax", "sigmoid"])
+@pytest.mark.parametrize("bad_value", [float("nan"), float("inf")])
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.half, torch.float32])
+def test_fused_topk_nan_inf_clamp(
+    num_experts: int,
+    topk: int,
+    scoring_func: str,
+    bad_value: float,
+    dtype: torch.dtype,
+):
+    """Regression test for the NaN/Inf clamp in topk_softmax_kernels.cu.
+
+    Degenerate hidden states (e.g., from CUDA graph padding) can produce
+    NaN/Inf gating logits. Without the clamp, softmax/sigmoid outputs are
+    NaN and the argmax loop picks expert 0 for every top-k slot (since
+    "NaN > NaN" is false per IEEE 754), yielding duplicate expert IDs that
+    crash downstream MoE sort kernels. The fix clamps NaN/Inf to 0 before
+    argmax so index tie-breaking selects unique experts [0, 1, ..., k-1].
+    """
+    torch.manual_seed(0)
+    num_tokens = 4
+    hidden_size = 1024
+    hidden_states = torch.randn((num_tokens, hidden_size), dtype=dtype, device="cuda")
+
+    # Row 0: all normal. Rows 1-3: fully poisoned with NaN or Inf.
+    gating_output = torch.randn((num_tokens, num_experts), dtype=dtype, device="cuda")
+    gating_output[1:, :] = bad_value
+
+    topk_weights, topk_ids, _ = fused_topk(
+        hidden_states=hidden_states,
+        gating_output=gating_output,
+        topk=topk,
+        renormalize=False,
+        scoring_func=scoring_func,
+    )
+
+    # Normal row must still match the torch reference.
+    ref_weights, ref_ids = torch_topk(
+        gating_output=gating_output[:1],
+        topk=topk,
+        renormalize=False,
+        scoring_func=scoring_func,
+    )
+    torch.testing.assert_close(
+        ref_weights.to(torch.float32), topk_weights[:1], atol=1e-2, rtol=1e-2
+    )
+    torch.testing.assert_close(ref_ids.to(torch.int32), topk_ids[:1], atol=0, rtol=0)
+
+    # Poisoned rows: IDs must be unique (no duplicates) and weights must be
+    # finite (no NaN/Inf propagation into downstream MoE kernels).
+    for row in range(1, num_tokens):
+        row_ids = topk_ids[row]
+        assert row_ids.unique().numel() == topk, (
+            f"Row {row} has duplicate expert IDs {row_ids.tolist()} "
+            f"(bad_value={bad_value}, scoring_func={scoring_func})"
+        )
+        assert torch.isfinite(topk_weights[row]).all(), (
+            f"Row {row} has non-finite weights {topk_weights[row].tolist()} "
+            f"(bad_value={bad_value}, scoring_func={scoring_func})"
+        )
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda(), reason="This test is skipped on non-CUDA platform."
+)
+@pytest.mark.parametrize("num_experts", [6, 8, 16])
+@pytest.mark.parametrize("topk", [3, 4])
+@pytest.mark.parametrize("scoring_func", ["softmax", "sigmoid"])
+@pytest.mark.parametrize("bad_value", [float("nan"), float("inf")])
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.half, torch.float32])
+def test_fused_topk_bias_nan_inf_clamp(
+    num_experts: int,
+    topk: int,
+    scoring_func: str,
+    bad_value: float,
+    dtype: torch.dtype,
+):
+    """Regression test: NaN/Inf in gating logits must not produce duplicate
+    expert IDs or non-finite weights when e_score_correction_bias is present.
+
+    Same scenario as test_fused_topk_nan_inf_clamp but exercising the bias
+    path (fused_topk_bias) so the fix in topk_softmax_kernels.cu is covered
+    for that entry point as well.
+    """
+    torch.manual_seed(0)
+    num_tokens = 4
+    hidden_size = 1024
+    hidden_states = torch.randn((num_tokens, hidden_size), dtype=dtype, device="cuda")
+    e_score_correction_bias = torch.randn(
+        (num_experts,), dtype=torch.float32, device="cuda"
+    )
+
+    gating_output = torch.randn((num_tokens, num_experts), dtype=dtype, device="cuda")
+    gating_output[1:, :] = bad_value
+
+    topk_weights, topk_ids = fused_topk_bias(
+        hidden_states=hidden_states,
+        gating_output=gating_output,
+        e_score_correction_bias=e_score_correction_bias,
+        topk=topk,
+        renormalize=False,
+        scoring_func=scoring_func,
+    )
+
+    # Normal row must still match the torch reference.
+    ref_weights, ref_ids = torch_topk(
+        gating_output=gating_output[:1],
+        topk=topk,
+        renormalize=False,
+        e_score_correction_bias=e_score_correction_bias,
+        scoring_func=scoring_func,
+    )
+    torch.testing.assert_close(
+        ref_weights.to(torch.float32), topk_weights[:1], atol=1e-2, rtol=1e-2
+    )
+    torch.testing.assert_close(ref_ids.to(torch.int32), topk_ids[:1], atol=0, rtol=0)
+
+    # Poisoned rows: IDs must be unique (no duplicates) and weights must be
+    # finite (no NaN/Inf propagation into downstream MoE kernels).
+    for row in range(1, num_tokens):
+        row_ids = topk_ids[row]
+        assert row_ids.unique().numel() == topk, (
+            f"Row {row} has duplicate expert IDs {row_ids.tolist()} "
+            f"(bad_value={bad_value}, scoring_func={scoring_func})"
+        )
+        assert torch.isfinite(topk_weights[row]).all(), (
+            f"Row {row} has non-finite weights {topk_weights[row].tolist()} "
+            f"(bad_value={bad_value}, scoring_func={scoring_func})"
+        )
diff --git a/tests/kernels/moe/test_gemma4router.py b/tests/kernels/moe/test_gemma4router.py
new file mode 100644
index 000000000000..ba69d6927495
--- /dev/null
+++ b/tests/kernels/moe/test_gemma4router.py
@@ -0,0 +1,57 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+
+from vllm.model_executor.models.gemma4 import (
+    gemma4_fused_routing_kernel_triton,
+    gemma4_routing_function_torch,
+)
+
+
+def sort_by_id(w, ids):
+    order = ids.argsort(dim=-1)
+    return w.gather(1, order), ids.gather(1, order)
+
+
+# Gemma4 MoE Model has context length of 250K
+# the minus 1 is to ensure that edge cases are tested
+@pytest.mark.parametrize("num_tokens", [1, 2, 2048, 250000])
+@pytest.mark.parametrize("num_experts", [128])  # gemma4 moe experts
+@pytest.mark.parametrize("topk", [8])  # gemma4 topk
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.half, torch.float32])
+def test_gemma4_routing_kernel_triton(
+    num_tokens: int,
+    num_experts: int,
+    topk: int,
+    dtype: torch.dtype,
+):
+    torch.manual_seed(0)
+
+    gating = torch.randn(num_tokens, num_experts, dtype=dtype, device="cuda")
+    scales = torch.rand(num_experts, dtype=torch.float32, device="cuda")
+
+    ref_w, ref_ids = gemma4_routing_function_torch(gating, topk, scales)
+    tri_w, tri_ids = gemma4_fused_routing_kernel_triton(gating, topk, scales)
+
+    # Sort by expert id — to remove tie-breaking differences
+    ref_ws, ref_is = sort_by_id(ref_w, ref_ids)
+    tri_ws, tri_is = sort_by_id(tri_w, tri_ids)
+
+    ids_match = (ref_is == tri_is).all().item()
+    weights_match = torch.allclose(ref_ws, tri_ws, atol=1e-2, rtol=1e-2)
+    all_match = ids_match and weights_match
+    max_err = (ref_ws - tri_ws).abs().max().item()
+    print(
+        f"T={num_tokens:5d} E={num_experts:4d} K={topk} "
+        f"{str(dtype).split('.')[-1]:7s} ids={ids_match} max_Δweight={max_err:.2e}"
+    )
+    if not all_match:
+        bad = (ref_is != tri_is).any(dim=-1).nonzero(as_tuple=True)[0]
+        if len(bad):
+            r = bad[0].item()
+            print(
+                f"  first bad row {r}: ref_ids={ref_ids[r].tolist()} "
+                f"tri_ids={tri_ids[r].tolist()}"
+            )
+        assert all_match
diff --git a/tests/kernels/moe/test_marlin_vs_trtllm_mxint4.py b/tests/kernels/moe/test_marlin_vs_trtllm_mxint4.py
index 0ad2cd06ee3c..eaeca6a8a5dc 100644
--- a/tests/kernels/moe/test_marlin_vs_trtllm_mxint4.py
+++ b/tests/kernels/moe/test_marlin_vs_trtllm_mxint4.py
@@ -5,7 +5,7 @@
 import pytest
 import torch
 
-from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+from vllm.model_executor.layers.fused_moe.experts.marlin_moe import (
     fused_marlin_moe,
 )
 from vllm.model_executor.layers.fused_moe.router.grouped_topk_router import (
diff --git a/tests/kernels/moe/test_moe.py b/tests/kernels/moe/test_moe.py
index 35b21320f826..544106282585 100644
--- a/tests/kernels/moe/test_moe.py
+++ b/tests/kernels/moe/test_moe.py
@@ -14,8 +14,6 @@
 import torch
 from torch.nn import Parameter
 from torch.nn import functional as F
-from transformers import MixtralConfig
-from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
 
 import vllm.model_executor.layers.fused_moe  # noqa
 from tests.kernels.moe.utils import (
@@ -24,10 +22,7 @@
     modular_triton_fused_moe,
 )
 from tests.kernels.utils import opcheck, stack_and_dev, torch_experts, torch_moe
-from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import VllmConfig, set_current_vllm_config
-from vllm.distributed.parallel_state import init_distributed_environment
-from vllm.forward_context import get_forward_context, set_forward_context
 from vllm.model_executor.layers.fused_moe import (
     MoEActivation,
     fused_topk,
@@ -37,7 +32,7 @@
     int4_w4a16_moe_quant_config,
     int8_w8a16_moe_quant_config,
 )
-from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+from vllm.model_executor.layers.fused_moe.experts.marlin_moe import (
     batched_fused_marlin_moe,
     fused_marlin_moe,
 )
@@ -56,12 +51,10 @@
     marlin_quantize,
 )
 from vllm.model_executor.layers.quantization.utils.quant_utils import quantize_weights
-from vllm.model_executor.models.mixtral import MixtralMoE
 from vllm.platforms import current_platform
 from vllm.scalar_type import ScalarType, scalar_types
 from vllm.utils.math_utils import next_power_of_2
 from vllm.utils.torch_utils import set_random_seed
-from vllm.v1.worker.workspace import init_workspace_manager
 
 
 def iterative_moe(
@@ -150,12 +143,14 @@ def iterative_moe(
     {
         "a_type": [scalar_types.bfloat16],
         "b_type": scalar_types.float4_e2m1f,
+        "c_type": [scalar_types.bfloat16],
         "group_blocks": [2],
     },
     # MXFP8
     {
         "a_type": [scalar_types.bfloat16],
         "b_type": scalar_types.float8_e4m3fn,
+        "c_type": [scalar_types.bfloat16],
         "group_blocks": [2],
     },
     # AWQ-INT4 with INT8 activation
@@ -681,154 +676,35 @@ def test_fused_moe_wn16(
     torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
 
 
-@pytest.mark.parametrize("dtype", [torch.bfloat16])
-@pytest.mark.parametrize("padding", [True, False])
-@pytest.mark.parametrize(
-    "use_rocm_aiter", [True, False] if current_platform.is_rocm() else [False]
-)
-@torch.inference_mode()
-def test_mixtral_moe(
-    default_vllm_config,
-    dist_init,
-    dtype: torch.dtype,
-    padding: bool,
-    use_rocm_aiter: bool,
-    monkeypatch,
-):
-    """Make sure our Mixtral MoE implementation agrees with the one from
-    huggingface."""
-
-    # Explicitly set AITER env var based on test parameter to ensure
-    # consistent behavior regardless of external environment
-    monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1" if use_rocm_aiter else "0")
-    rocm_aiter_ops.refresh_env_variables()
-
-    if use_rocm_aiter and dtype == torch.float32:
-        pytest.skip("AITER ROCm test skip for float32")
-
-    monkeypatch.setenv("RANK", "0")
-    monkeypatch.setenv("LOCAL_RANK", "0")
-    monkeypatch.setenv("WORLD_SIZE", "1")
-    monkeypatch.setenv("MASTER_ADDR", "localhost")
-    monkeypatch.setenv("MASTER_PORT", "12345")
-    init_distributed_environment()
-    init_workspace_manager(torch.accelerator.current_device_index())
-
-    # Instantiate our and huggingface's MoE blocks
-    vllm_config.compilation_config.static_forward_context = dict()
-    with set_current_vllm_config(vllm_config), set_forward_context(None, vllm_config):
-        config = MixtralConfig()
-        hf_moe = MixtralSparseMoeBlock(config).to(dtype).to("cuda")
-        vllm_moe = MixtralMoE(
-            num_experts=config.num_local_experts,
-            top_k=config.num_experts_per_tok,
-            hidden_size=config.hidden_size,
-            intermediate_size=config.intermediate_size,
-            params_dtype=dtype,
-            tp_size=1,
-            dp_size=1,
-        ).cuda()
-
-        # Load the weights
-        vllm_moe.gate.weight.data[:] = hf_moe.gate.weight.data
-        if isinstance(hf_moe.experts, torch.nn.ModuleList):
-            # Transformers v4
-            for i in range(config.num_local_experts):
-                weights = (
-                    hf_moe.experts[i].w1.weight.data,
-                    hf_moe.experts[i].w3.weight.data,
-                )
-                vllm_moe.experts.w13_weight[i][:] = torch.cat(weights, dim=0)
-                vllm_moe.experts.w2_weight[i][:] = hf_moe.experts[i].w2.weight.data
-        else:
-            # Transformers v5
-            vllm_moe.experts.w13_weight.data[:] = hf_moe.experts.gate_up_proj.data
-            vllm_moe.experts.w2_weight.data[:] = hf_moe.experts.down_proj.data
-            # TODO: remove this line after https://github.com/huggingface/transformers/pull/43622
-            hf_moe.experts.config._experts_implementation = "eager"
-
-        # Generate input batch of dimensions [batch_size, seq_len, hidden_dim]
-        hf_inputs = torch.randn((1, 64, config.hidden_size)).to(dtype).to("cuda")
-        # vLLM uses 1D query [num_tokens, hidden_dim]
-        vllm_inputs = hf_inputs.flatten(0, 1)
-
-        # Pad the weight if moe padding is enabled
-        if padding:
-            vllm_moe.experts.w13_weight = Parameter(
-                F.pad(vllm_moe.experts.w13_weight, (0, 128), "constant", 0)[
-                    ..., 0:-128
-                ],
-                requires_grad=False,
-            )
-            vllm_moe.experts.w2_weight = Parameter(
-                F.pad(vllm_moe.experts.w2_weight, (0, 128), "constant", 0)[..., 0:-128],
-                requires_grad=False,
-            )
-            torch.accelerator.synchronize()
-            torch.accelerator.empty_cache()
-
-        # FIXME (zyongye) fix this after we move self.kernel
-        # assignment in FusedMoE.__init__
-
-        vllm_moe.experts.quant_method.process_weights_after_loading(vllm_moe.experts)
-
-        # need to override the forward context for unittests, otherwise it assumes
-        # we're running the model forward pass (the model specified in vllm_config)
-        get_forward_context().all_moe_layers = None
-
-        # Run forward passes for both MoE blocks
-        hf_states = hf_moe.forward(hf_inputs)
-        if isinstance(hf_states, tuple):
-            # Transformers v4
-            hf_states = hf_states[0]
-        vllm_states = vllm_moe.forward(vllm_inputs)
-
-    mixtral_moe_tol = {
-        torch.float32: 1e-3,
-        torch.float16: 1e-3,
-        torch.bfloat16: 1e-2,
-    }
-
-    if use_rocm_aiter:
-        # The values of rtol and atol are set based on the tests in ROCM AITER package.
-        # https://github.com/ROCm/aiter/blob/dfed377f4be7da96ca2d75ac0761f569676f7240/op_tests/test_moe.py#L174
-        torch.testing.assert_close(
-            hf_states.flatten(0, 1), vllm_states, rtol=0.01, atol=100
-        )
-    else:
-        torch.testing.assert_close(
-            hf_states.flatten(0, 1),
-            vllm_states,
-            rtol=mixtral_moe_tol[dtype],
-            atol=mixtral_moe_tol[dtype],
-        )
+MARLIN_MOE_SCENARIOS = [
+    # (m, n, k, e, topk, ep_size, act_order, is_k_full)
+    # No act_order: is_k_full=True matches usual case (marlin_is_k_full).
+    # N>=256 required for Marlin kernel thread config for MXFP8.
+    # Single token, small matrices
+    (1, 128, 256, 5, 2, 1, False, True),
+    # Single token, large matrices
+    (1, 1024, 2048, 5, 2, 1, False, True),
+    # Unaligned m, small matrices
+    (133, 256, 256, 5, 2, 1, False, True),
+    # Unaligned m, large matrices
+    (133, 1024, 2048, 12, 3, 1, False, True),
+    # Aligned batch, small matrices
+    (128, 256, 256, 5, 2, 1, False, True),
+    # Aligned batch, large matrices
+    (128, 1024, 2048, 12, 3, 1, False, True),
+    # Expert parallelism
+    (64, 1024, 2048, 12, 3, 4, False, True),
+    # Act order with is_k_full=True (no tensor parallelism)
+    (1, 1024, 2048, 5, 2, 1, True, True),
+    # Act order with is_k_full=False (tensor parallelism)
+    (133, 256, 256, 5, 2, 1, True, False),
+]
 
 
 def marlin_moe_generate_valid_test_cases():
     import itertools
 
-    m_list = [1, 123, 666]
-    n_list = [128, 1024]
-    k_list = [256, 2048]
-    e_list = [5, 12]
-    topk_list = [2, 3]
-    ep_size_list = [1, 4]
-    act_order_list = [True, False]
-    is_k_full_list = [True, False]
-
-    all_combinations = itertools.product(
-        MOE_MARLIN_QUANT_TEST_CONFIGS,
-        m_list,
-        n_list,
-        k_list,
-        e_list,
-        topk_list,
-        ep_size_list,
-        act_order_list,
-        is_k_full_list,
-    )
-
-    def is_invalid(
+    def is_valid(
         a_type,
         b_type,
         c_type,
@@ -845,39 +721,42 @@ def is_invalid(
         group_size = group_blocks if group_blocks <= 0 else group_blocks * 16
         if group_size > 0 and k % group_size != 0:
             return False
-
         if act_order and group_size in [-1, k, n]:
             return False
         if group_size in [k, n]:
             return False
-        if not act_order and is_k_full:
+        if b_type == scalar_types.float8_e4m3fn and group_size == 32 and is_k_full:
             return False
-
         return a_type.size_bits < 16 or a_type is c_type
 
     cases = []
-    for case in all_combinations:
-        quant_test_config, m, n, k, _, _, _, act_order, *_ = case
-        if act_order and not quant_test_config.get("support_act_order", False):
-            continue
-
+    for quant_test_config in MOE_MARLIN_QUANT_TEST_CONFIGS:
         f16_types = [scalar_types.float16]
-        inner_combinations = itertools.product(
-            quant_test_config.get("a_type", f16_types),
-            [quant_test_config["b_type"]],
-            quant_test_config.get("c_type", f16_types),
-            quant_test_config["group_blocks"],
+        inner_combinations = list(
+            itertools.product(
+                quant_test_config.get("a_type", f16_types),
+                [quant_test_config["b_type"]],
+                quant_test_config.get("c_type", f16_types),
+                quant_test_config["group_blocks"],
+            )
         )
 
+        supports_act_order = quant_test_config.get("support_act_order", False)
+
         for sub_case in inner_combinations:
             if (
                 sub_case[0] == scalar_types.float8_e4m3fn
                 and current_platform.get_device_capability() not in [89, 120]
             ):
                 continue
-            args = sub_case + (m, n, k) + case[4:]
-            if is_invalid(*args):
-                cases.append(args)
+
+            for scenario in MARLIN_MOE_SCENARIOS:
+                m, n, k, e, topk, ep_size, act_order, is_k_full = scenario
+                if act_order and not supports_act_order:
+                    continue
+                args = sub_case + (m, n, k, e, topk, ep_size, act_order, is_k_full)
+                if is_valid(*args):
+                    cases.append(args)
     return cases
 
 
diff --git a/tests/kernels/moe/test_moe_layer.py b/tests/kernels/moe/test_moe_layer.py
index aa2948b8e989..89e28d950f9d 100644
--- a/tests/kernels/moe/test_moe_layer.py
+++ b/tests/kernels/moe/test_moe_layer.py
@@ -17,6 +17,7 @@
 import pytest
 import torch
 
+import vllm.model_executor.layers.quantization.utils.w8a8_utils
 from tests.kernels.moe.modular_kernel_tools.parallel_utils import (
     ProcessGroupInfo,
     _set_vllm_config,
@@ -37,7 +38,7 @@
     get_eplb_group,
 )
 from vllm.forward_context import set_forward_context
-from vllm.model_executor.layers.fused_moe import FusedMoE, SharedFusedMoE, fused_experts
+from vllm.model_executor.layers.fused_moe import FusedMoE, fused_experts
 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
 from vllm.model_executor.layers.fused_moe.router.router_factory import (
@@ -65,8 +66,8 @@
 
 SHAPE_COMBOS = [
     (1, 128, 256),
-    (32, 1024, 512),
-    (222, 2048, 2048),
+    (32, 512, 512),
+    (222, 1024, 2048),
 ]
 MAX_M = max([x[0] for x in SHAPE_COMBOS])
 
@@ -95,7 +96,7 @@
     BACKENDS += ["flashinfer_nvlink_one_sided"]
 
 if has_deep_ep():
-    BACKENDS += ["deepep_low_latency", "deepep_high_throughput"]
+    BACKENDS += ["deepep_high_throughput", "deepep_low_latency"]
 
 if has_nixl_ep():
     BACKENDS += ["nixl_ep"]
@@ -103,6 +104,7 @@
 QUANT_METHODS = [
     None,
     "fp8",
+    "fp8_blocked",
     "modelopt_fp8",
     "modelopt_fp4",
 ]
@@ -114,10 +116,21 @@
     "mori":                        {None, "fp8", "modelopt_fp8"},
     "flashinfer_nvlink_two_sided": {None,        "modelopt_fp8", "modelopt_fp4"},
     "flashinfer_nvlink_one_sided": {None,        "modelopt_fp8", "modelopt_fp4"},
-    "deepep_low_latency":          {None,        "modelopt_fp8", "modelopt_fp4"},
-    "deepep_high_throughput":      {None, "fp8", "modelopt_fp8", "modelopt_fp4"},
+    "deepep_low_latency":          {None, "fp8_blocked", "modelopt_fp4"},
+    "deepep_high_throughput":      {None, "fp8_blocked", "modelopt_fp8", "modelopt_fp4"}, # noqa: E501
     "nixl_ep":                     {None, "fp8", "modelopt_fp8"},
 }
+
+# Map from backend -> (DP/EP support, DP support, TP support)
+BACKEND_EP_DP_TP_SUPPORT: dict[str, tuple[bool, bool, bool]] = {
+    "allgather_reducescatter":     (True,  True,  True),
+    "mori":                        (True, False, False),
+    "flashinfer_nvlink_two_sided": (False, True, False),
+    "flashinfer_nvlink_one_sided": (False, True, False),
+    "deepep_low_latency":          (True, False, False),
+    "deepep_high_throughput":      (True, False, False),
+    "nixl_ep":                     (True, False, False),
+}
 # fmt: on
 
 # Which quantization methods support EPLB.
@@ -132,6 +145,24 @@
 EPLB_SUPPORTED_BACKENDS: list[str] = ["allgather_reducescatter"]
 
 
+def mock_normalize_e4m3fn_to_e4m3fnuz(
+    weight: torch.Tensor,
+    weight_scale: torch.Tensor,
+    input_scale: torch.Tensor | None = None,
+):
+    return weight, weight_scale, input_scale
+
+
+# Needed since weights will already be in e4m3fnuz format on platforms that
+# use the fnuz fp8 format and the normalize_e4m3fn_to_e4m3fnuz() function
+# is not being tested here.
+# NOTE: The weights are quantized by moe_quantize_weights_2d in
+# _quantize_fp8_halves.
+# NOTE: Not able to use monkeypatch because of the spawned parallel workers.
+def override_normalize_e4m3fn_to_e4m3fnuz():
+    vllm.model_executor.layers.quantization.utils.w8a8_utils.normalize_e4m3fn_to_e4m3fnuz = mock_normalize_e4m3fn_to_e4m3fnuz  # noqa: E501
+
+
 def maybe_roundup_layer_hidden_size(
     hidden_size: int,
     act_dtype: torch.dtype,
@@ -236,7 +267,6 @@ class MoETestConfig:
     use_gate: bool
     use_routed_input_transform: bool
     enable_eplb: bool = False
-    reduce_results: bool = False
     backend: str | None = None
     ep_size: int = 1
     dp_size: int = 1
@@ -295,7 +325,6 @@ def generate_valid_test_configs(
         use_shared_experts,
         use_gate,
         use_routed_input_transform,
-        reduce_results,
     ) in product(
         SHAPE_COMBOS,
         NUM_EXPERTS,
@@ -304,7 +333,6 @@ def generate_valid_test_configs(
         [False, True],  # shared
         [False, True],  # gate
         [False, True],  # routed input exform
-        [False, True],  # reduce results
     ):
         config = MoETestConfig(
             shape[0],  # m
@@ -318,7 +346,6 @@ def generate_valid_test_configs(
             use_gate,
             use_routed_input_transform,
             enable_eplb,
-            reduce_results,
             backend,
             ep_size,
             dp_size,
@@ -395,18 +422,7 @@ def is_valid_config(config: MoETestConfig) -> tuple[bool, str | None]:
         and config.backend.startswith("flashinfer_nvlink")
         and not current_platform.has_device_capability(90)
     ):
-        return False, "flashinfer_nvlink needs an H100+ GPUs"
-
-    # reduce_results incompatibilities
-    if config.reduce_results and config.use_shared_experts:
-        return False, "reduce_results=True is not compatible with shared_experts=True"
-
-    if config.reduce_results and config.quantization is not None:
-        return (
-            False,
-            "reduce_results=True only tested with unquantized data types in "
-            "order to limit number of tests run",
-        )
+        return False, "flashinfer_nvlink needs H100+ GPUs"
 
     # Backend-specific checks
     if config.backend is not None:
@@ -439,31 +455,43 @@ def is_valid_config(config: MoETestConfig) -> tuple[bool, str | None]:
                     f"Skipping unsupported K {config.k} in {config.backend} w/o EP.",
                 )
 
-    if config.enable_eplb and config.ep_size == 1:
-        return False, "EPLB requires EP."
+    if config.backend is not None:
+        supports_ep_dp, supports_dp, supports_tp = BACKEND_EP_DP_TP_SUPPORT[
+            config.backend
+        ]
 
-    if config.enable_eplb and config.quantization not in EPLB_SUPPORTED_QUANTS:
-        return False, f"EPLB not supported with {config.quantization} quantization."
+        if config.tp_size > 1 and not supports_tp:
+            return False, f"{config.backend} does not support TP."
 
-    if config.enable_eplb and config.backend not in EPLB_SUPPORTED_BACKENDS:
-        return False, f"EPLB not supported with {config.backend}."
+        if config.dp_size > 1 and config.ep_size == 1 and not supports_dp:
+            return False, f"{config.backend} does not support DP."
 
-    world_size = config.tp_size * config.dp_size
-    if config.reduce_results and world_size == 1:
-        return False, "reduce_results=True only makes sense for multi-GPU tests"
+        if config.dp_size > 1 and config.ep_size > 1 and not supports_ep_dp:
+            return False, f"{config.backend} does not support EP/DP."
+    else:
+        if config.tp_size > 1 or config.ep_size > 1 or config.dp_size > 1:
+            return False, "An all2all backend is required for parallelism."
 
-    if (
-        config.backend is not None
-        and config.backend.startswith("flashinfer_nvlink")
-        and config.ep_size > 1
-    ):
-        return False, "flashinfer_nvlink EP not yet supported."
+    if config.enable_eplb:
+        if config.ep_size == 1:
+            return False, "EPLB requires EP."
+
+        if config.quantization not in EPLB_SUPPORTED_QUANTS:
+            return False, f"EPLB not supported with {config.quantization} quantization."
+
+        if config.backend not in EPLB_SUPPORTED_BACKENDS:
+            return False, f"EPLB not supported with {config.backend}."
 
-    if config.enable_eplb and config.num_experts % config.dp_size != 0:
-        return False, "EPLB requires num_experts divisible by ep_size"
+        if config.num_experts % config.dp_size != 0:
+            return False, "EPLB requires num_experts divisible by ep_size"
 
-    if config.enable_eplb and config.ep_size == 1:
-        return False, "EPLB only works with EP+DP"
+    # Disable fp4 tests until flashinfer is updated or the Dockerfile is
+    # modified to install cublasLt.h. See #39525.
+    if (
+        config.quantization == "modelopt_fp4"
+        and current_platform.is_device_capability_family(100)
+    ):
+        return False, "Temporarily skip until #39525 is resolved"
 
     return True, None
 
@@ -518,27 +546,48 @@ class QuantizedWeights:
 def _quantize_fp8_halves(
     w1: torch.Tensor,
     w2: torch.Tensor,
+    block_shape: list[int] | None = None,
 ) -> QuantizedWeights:
     """Quantize w13 gate/up halves separately to FP8, producing per-shard scales."""
     half = w1.shape[1] // 2
     w1q_a, w1s_a, _ = moe_quantize_weights(
-        w1[:, :half, :], None, fp8_dtype, False, None
+        w1[:, :half, :],
+        None,
+        fp8_dtype,
+        False,
+        block_shape,
     )
     w1q_b, w1s_b, _ = moe_quantize_weights(
-        w1[:, half:, :], None, fp8_dtype, False, None
+        w1[:, half:, :],
+        None,
+        fp8_dtype,
+        False,
+        block_shape,
     )
     assert w1s_a is not None and w1s_b is not None
 
-    w2q, w2s, _ = moe_quantize_weights(w2, None, fp8_dtype, False, None)
+    w2q, w2s, _ = moe_quantize_weights(w2, None, fp8_dtype, False, block_shape)
     assert w2s is not None
 
+    if block_shape is not None:
+        # Blocked quantization: scales have shape (E, n_tiles, k_tiles)
+        # Concatenate gate and up scales along the n_tiles dimension (dim=1)
+        # to match the concatenation of gate and up weights
+        w13_weight_scale = torch.cat([w1s_a, w1s_b], dim=1)
+        # w2 scales keep their blocked shape (E, k_tiles, n_tiles)
+        w2_weight_scale = w2s
+    else:
+        # Non-blocked quantization: scales have shape (E, 1, 1)
+        # Each w1s_x is (E, 1, 1) -> reshape to (E, 1), cat to (E, 2)
+        w13_weight_scale = torch.cat([w1s_a.view(-1, 1), w1s_b.view(-1, 1)], dim=1)
+        # w2s is (E, 1, 1) -> reshape to (E,)
+        w2_weight_scale = w2s.view(-1)
+
     return QuantizedWeights(
         w13_weight=torch.cat([w1q_a, w1q_b], dim=1),
         w2_weight=w2q,
-        # Each w1s_x is (E, 1, 1) -> reshape to (E, 1), cat to (E, 2)
-        w13_weight_scale=torch.cat([w1s_a.view(-1, 1), w1s_b.view(-1, 1)], dim=1),
-        # w2s is (E, 1, 1) -> reshape to (E,)
-        w2_weight_scale=w2s.view(-1),
+        w13_weight_scale=w13_weight_scale,
+        w2_weight_scale=w2_weight_scale,
     )
 
 
@@ -547,7 +596,7 @@ def quantization_to_quant_dtype(
 ) -> torch.dtype | str | None:
     if quantization is None:
         return None
-    elif quantization in ["fp8", "modelopt_fp8"]:
+    elif quantization in ["fp8", "fp8_blocked", "modelopt_fp8"]:
         return fp8_dtype
     elif quantization in ["modelopt_fp4"]:
         return "nvfp4"
@@ -569,6 +618,12 @@ def make_quant_config(
     if quantization == "fp8":
         return Fp8Config(True), _quantize_fp8_halves(w1, w2)
 
+    if quantization == "fp8_blocked":
+        block_shape = [128, 128]
+        return Fp8Config(True, weight_block_size=block_shape), _quantize_fp8_halves(
+            w1, w2, block_shape
+        )
+
     if quantization == "modelopt_fp8":
         qw = _quantize_fp8_halves(w1, w2)
         # why?
@@ -838,7 +893,6 @@ def make_fused_moe_layer(
     tp_size: int,
     ep_size: int,
     dp_size: int,
-    reduce_results: bool,
     w1: torch.Tensor,
     w2: torch.Tensor,
     top_k: int,
@@ -866,29 +920,26 @@ def make_fused_moe_layer(
     routed_input_transform: torch.nn.Module | None = None,
     routed_output_transform: torch.nn.Module | None = None,
     pcp_size: int | None = 1,
-) -> tuple[Callable, FusedMoE]:
+) -> FusedMoE:
     quant_config, qw = make_quant_config(quantization, w1, w2, global_num_experts)
 
     kwargs = dict()
-    if shared_experts is None:
-        builder = FusedMoE
-    else:
-        builder = SharedFusedMoE
-        kwargs["shared_experts"] = shared_experts
+    kwargs["shared_experts"] = shared_experts
 
     # Add gate and routed_input_transform if provided
     if gate is not None:
         kwargs["gate"] = gate
+
     if routed_input_transform is not None:
         kwargs["routed_input_transform"] = routed_input_transform
+        kwargs["routed_output_transform"] = routed_output_transform
 
-    layer = builder(
+    layer = FusedMoE(
         num_experts=global_num_experts,
         top_k=top_k,
         hidden_size=hidden_size,
         intermediate_size=intermediate_size,
         params_dtype=in_dtype,
-        reduce_results=reduce_results,
         renormalize=renormalize,
         use_grouped_topk=use_grouped_topk,
         num_expert_group=num_expert_group,
@@ -911,11 +962,13 @@ def make_fused_moe_layer(
         **kwargs,
     )
 
+    weight_scale_name = getattr(layer.quant_method, "weight_scale_name", "weight_scale")
+
     for name, value in [
         ("w13_weight", qw.w13_weight),
         ("w2_weight", qw.w2_weight),
-        ("w13_weight_scale", qw.w13_weight_scale),
-        ("w2_weight_scale", qw.w2_weight_scale),
+        (f"w13_{weight_scale_name}", qw.w13_weight_scale),
+        (f"w2_{weight_scale_name}", qw.w2_weight_scale),
         ("w13_weight_scale_2", qw.w13_weight_scale_2),
         ("w2_weight_scale_2", qw.w2_weight_scale_2),
         ("w13_input_scale", qw.w13_input_scale),
@@ -928,36 +981,7 @@ def make_fused_moe_layer(
 
     layer.quant_method.process_weights_after_loading(layer)
 
-    def _moe(
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-    ) -> torch.Tensor:
-        if shared_experts is None:
-            final_shared_states = None
-            final_hidden_states = layer(hidden_states, router_logits)
-        else:
-            final_shared_states, final_hidden_states = layer(
-                hidden_states, router_logits
-            )
-
-        # Apply routed output transform if provided
-        # (e.g., latent space -> original space)
-        if routed_output_transform is not None:
-            final_hidden_states = routed_output_transform(final_hidden_states)
-
-        if shared_experts is not None:
-            assert not reduce_results
-            assert final_shared_states is not None
-            final_hidden_states += final_shared_states
-
-        if not reduce_results and layer.tp_size > 1:
-            final_hidden_states = layer.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
-
-        return final_hidden_states
-
-    return _moe, layer
+    return layer
 
 
 def make_fake_moe_layer(
@@ -966,7 +990,7 @@ def make_fake_moe_layer(
     top_k: int,
     global_num_experts: int,
     in_dtype: torch.dtype,
-    quant_dtype: torch.dtype | None,
+    quantization: str | None,
     renormalize: bool = False,
     shared_experts_config: SharedExpertsConfig | None = None,
     use_grouped_topk: bool = False,
@@ -991,8 +1015,8 @@ def make_fake_moe_layer(
     tp_size: int = 1,
     dp_size: int = 1,
     ep_size: int = 1,
-    reduce_results: bool = False,
 ) -> Callable:
+    quant_dtype = None
     activation = MoEActivation.from_str(activation)
 
     router = create_fused_moe_router(
@@ -1093,7 +1117,7 @@ def _moe(
 
 
 def _test_body_regular(
-    moe_fn: Callable,
+    moe_layer: Callable,
     hidden_states: torch.Tensor,
     router_logits: torch.Tensor,
     vllm_config: VllmConfig,
@@ -1110,13 +1134,12 @@ def _test_body_regular(
         num_tokens=num_tokens,
         num_tokens_across_dp=num_tokens_across_dp,
     ):
-        output = moe_fn(hidden_states, router_logits)
+        output = moe_layer(hidden_states, router_logits)
 
     return baseline_output, output
 
 
 def _test_body_eplb(
-    moe_fn: Callable,
     moe_layer: FusedMoE,
     hidden_states: torch.Tensor,
     router_logits: torch.Tensor,
@@ -1137,7 +1160,6 @@ def _test_body_eplb(
     n: int,
     top_k: int,
     shared_experts,
-    reduce_results: bool,
     gate: torch.nn.Module | None,
     routed_input_transform: torch.nn.Module | None,
     routed_output_transform: torch.nn.Module | None,
@@ -1153,7 +1175,7 @@ def _test_body_eplb(
         num_tokens=num_tokens,
         num_tokens_across_dp=num_tokens_across_dp,
     ):
-        output_before = moe_fn(hidden_states, router_logits)
+        output_before = moe_layer(hidden_states, router_logits)
 
     # Create a fresh FusedMoE layer with enable_eplb=True
     # Delete the original layer's registration so the constructor can
@@ -1166,7 +1188,7 @@ def _test_body_eplb(
     # When using routed_input_transform, experts operate in latent space
     hidden_size_for_layer = k // 2 if routed_input_transform is not None else k
 
-    moe_fn, moe_layer = make_fused_moe_layer(
+    eplb_moe_layer = make_fused_moe_layer(
         quantization=quantization,
         use_ep=use_ep,
         hidden_size=hidden_size_for_layer,
@@ -1175,7 +1197,6 @@ def _test_body_eplb(
         tp_size=tp_size,
         ep_size=ep_size,
         dp_size=dp_size,
-        reduce_results=reduce_results,
         w1=w1,
         w2=w2,
         top_k=top_k,
@@ -1187,15 +1208,14 @@ def _test_body_eplb(
         routed_output_transform=routed_output_transform,
     )
 
-    # Necessary?
-    if moe_layer._expert_map is not None:
-        moe_layer._expert_map = moe_layer._expert_map.to(device)
+    if eplb_moe_layer._expert_map is not None:
+        eplb_moe_layer._expert_map = eplb_moe_layer._expert_map.to(device)
 
     # All ranks must generate the same permutation
     initial_indices = torch.arange(num_experts, dtype=torch.long)
     shuffled_indices = initial_indices[torch.randperm(num_experts)]
 
-    expert_weights = [list(moe_layer.get_expert_weights())]
+    expert_weights = [list(eplb_moe_layer.get_expert_weights())]
 
     communicator = create_eplb_communicator(
         group_coordinator=get_eplb_group(),
@@ -1219,7 +1239,7 @@ def _test_body_eplb(
         num_experts, dtype=torch.int32, device=device
     )
 
-    moe_layer.set_eplb_state(
+    eplb_moe_layer.set_eplb_state(
         moe_layer_idx=0,
         expert_load_view=torch.zeros(
             (1, num_experts),
@@ -1236,7 +1256,7 @@ def _test_body_eplb(
         ),
     )
 
-    moe_layer.eplb_state.should_record_tensor = torch.ones(
+    eplb_moe_layer.eplb_state.should_record_tensor = torch.ones(
         (), dtype=torch.bool, device=device
     )
 
@@ -1247,7 +1267,7 @@ def _test_body_eplb(
         num_tokens=num_tokens,
         num_tokens_across_dp=num_tokens_across_dp,
     ):
-        output_after = moe_fn(hidden_states, router_logits)
+        output_after = eplb_moe_layer(hidden_states, router_logits)
 
     return output_before, output_after
 
@@ -1266,7 +1286,6 @@ def _run_one_config(
     num_experts: int,
     top_k: int,
     quantization: str | None,
-    reduce_results: bool,
     backend: str | None,
     test_body_fn: Callable,
     use_shared_experts: bool,
@@ -1316,6 +1335,7 @@ def _run_one_config(
     gate = test_data.gate
     routed_input_transform = test_data.routed_input_transform
     routed_output_transform = test_data.routed_output_transform
+    activation = "silu"
 
     baseline_layer = make_fake_moe_layer(
         w1=w1,
@@ -1323,7 +1343,7 @@ def _run_one_config(
         top_k=top_k,
         global_num_experts=num_experts,
         in_dtype=in_dtype,
-        quant_dtype=None,  # quantization_to_quant_dtype(quantization),
+        quantization=quantization,
         renormalize=False,
         shared_experts_config=shared_experts_config,
         gate=gate,
@@ -1333,7 +1353,7 @@ def _run_one_config(
         tp_size=tp_size,
         ep_size=ep_size,
         dp_size=dp_size,
-        reduce_results=reduce_results,
+        activation=activation,
     )
 
     baseline_output = baseline_layer(hidden_states, router_logits)
@@ -1361,7 +1381,7 @@ def _run_one_config(
         hidden_size_for_layer = k // 2 if routed_input_transform is not None else k
 
         # Create initial MoE layer
-        moe_fn, moe_layer = make_fused_moe_layer(
+        moe_layer = make_fused_moe_layer(
             quantization=quantization,
             use_ep=use_ep,
             hidden_size=hidden_size_for_layer,
@@ -1370,7 +1390,6 @@ def _run_one_config(
             tp_size=tp_size,
             ep_size=ep_size,
             dp_size=dp_size,
-            reduce_results=reduce_results,
             w1=w1,
             w2=w2,
             top_k=top_k,
@@ -1379,9 +1398,9 @@ def _run_one_config(
             gate=gate,
             routed_input_transform=routed_input_transform,
             routed_output_transform=routed_output_transform,
+            activation=activation,
         )
 
-        # Necessary?
         if moe_layer._expert_map is not None:
             moe_layer._expert_map = moe_layer._expert_map.to(device)
 
@@ -1394,7 +1413,6 @@ def _run_one_config(
 
         # Call the test body function with all necessary context
         expected, actual = test_body_fn(
-            moe_fn=moe_fn,
             moe_layer=moe_layer,
             hidden_states=hidden_states,
             router_logits=router_logits,
@@ -1415,7 +1433,6 @@ def _run_one_config(
             m=m,
             top_k=top_k,
             shared_experts=shared_experts,
-            reduce_results=reduce_results,
             gate=gate,
             routed_input_transform=routed_input_transform,
             routed_output_transform=routed_output_transform,
@@ -1430,13 +1447,17 @@ def _run_one_config(
             atol, rtol = 7.6e-2, 7.6e-2
         else:
             atol, rtol = 3.5e-2, 3.5e-2
-    elif quantization in ("fp8", "modelopt_fp8"):
+    elif quantization in ("fp8", "fp8_blocked", "modelopt_fp8"):
+        atol, rtol = 6e-2, 6e-2
+    elif quantization == "modelopt_fp4":
         if k >= 2048:
-            atol, rtol = 7.6e-2, 7.6e-2
+            atol = rtol = 1e-1 + (k * 1e-4)
         else:
-            atol, rtol = 6e-2, 6e-2
-    elif quantization == "modelopt_fp4":
-        atol = rtol = 1e-1 + k * 5e-4
+            atol = rtol = 1e-1
+
+        if backend == "allgather_reducescatter" and tp_size > 1:
+            atol += 2e-1
+            rtol += 2e-1
     else:
         atol, rtol = 6e-2, 6e-2
 
@@ -1469,6 +1490,11 @@ def test_moe_layer_no_parallel(
     if os.environ.get("VLLM_LOGGING_LEVEL") is None:
         monkeypatch.setenv("VLLM_LOGGING_LEVEL", "ERROR")
 
+    # Needed since weights will already be in e4m3fnuz format and the
+    # normalize_e4m3fn_to_e4m3fnuz() function is not being tested here.
+    if current_platform.is_fp8_fnuz():
+        override_normalize_e4m3fn_to_e4m3fnuz()
+
     test_config = MoETestConfig(
         m,
         n,
@@ -1512,7 +1538,6 @@ def test_moe_layer_no_parallel(
         test_config.num_experts,
         test_config.top_k,
         test_config.quantization,
-        test_config.reduce_results,
         test_config.backend,
         _test_body_regular,
         use_shared_experts=test_config.use_shared_experts,
@@ -1545,6 +1570,9 @@ def _parallel_worker(
 
     dp_rank = vllm_config.parallel_config.data_parallel_rank
 
+    if current_platform.is_fp8_fnuz():
+        override_normalize_e4m3fn_to_e4m3fnuz()
+
     for test_config in test_configs:
         cc = vllm_config.compilation_config
         if "from_forward_context" in cc.static_forward_context:
@@ -1570,7 +1598,6 @@ def _parallel_worker(
                 test_config.num_experts,
                 test_config.top_k,
                 test_config.quantization,
-                test_config.reduce_results,
                 test_config.backend,
                 functools.partial(
                     _test_body_config, test_config=test_config, cpu_group=cpu_group
@@ -1589,27 +1616,23 @@ def _parallel_worker(
             failed = failed + 1
             if verbosity > 0:
                 traceback.print_exc()
-                print(f"\n{str(ex)}\nFAILED {ex.__class__}")
+                print(f"\n{str(ex)}\nFAILED")
             else:
                 print("F", end="")
         finally:
-            # Note: for some reason DeepEP buffers don't seem to be
-            # entirely reusable on B200. In order to work around this
-            # we clear the all2all manager's cache after each testpoint.
-            cap = current_platform.get_device_capability()
-            if (
-                cap is not None
-                and cap.major == 10
-                and (
-                    test_config.backend == "deepep_low_latency"
-                    or test_config.backend == "deepep_high_throughput"
-                )
-            ):
+            # DeepEP managers are not reliably reusable across many subtests in
+            # a single worker process. Tear them down after each DeepEP case so
+            # later subtests do not inherit stale communication state.
+            if test_config.backend in {
+                "deepep_low_latency",
+                "deepep_high_throughput",
+            }:
                 torch.accelerator.synchronize()
                 all2all_manager = get_ep_group().device_communicator.all2all_manager
                 if all2all_manager is not None:
                     all2all_manager.destroy()
             total = total + 1
+            torch.distributed.barrier()
 
     skipped = total - (passed + failed)
 
diff --git a/tests/kernels/moe/test_mxfp4_moe.py b/tests/kernels/moe/test_mxfp4_moe.py
new file mode 100644
index 000000000000..11fd853f54f3
--- /dev/null
+++ b/tests/kernels/moe/test_mxfp4_moe.py
@@ -0,0 +1,248 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Tests for SM100 CUTLASS MXFP4 x MXFP4 grouped MoE kernels."""
+
+import random
+
+import pytest
+import torch
+
+from tests.kernels.utils import torch_moe_single
+from vllm import _custom_ops as ops
+from vllm.platforms import current_platform
+from vllm.utils.torch_utils import set_random_seed
+
+random.seed(42)
+set_random_seed(42)
+
+MXFP4_BLOCK_SIZE = 32
+
+
+def align(val: int, alignment: int = 128) -> int:
+    return int((val + alignment - 1) // alignment * alignment)
+
+
+def calc_diff(x, y):
+    x, y = x.double(), y.double()
+    denominator = (x * x + y * y).sum()
+    sim = 2 * (x * y).sum() / denominator
+    return 1 - sim
+
+
+def is_sm100_supported() -> bool:
+    return current_platform.is_cuda() and current_platform.is_device_capability_family(
+        100
+    )
+
+
+def compute_ref_output(
+    input_tensor: torch.Tensor,
+    weight_list: list[torch.Tensor],
+    expert_offsets: list[int],
+    expert_offset: int,
+    num_experts: int,
+) -> torch.Tensor:
+    """Reference output using torch_moe_single with top-1 routing."""
+    score = torch.full(
+        (expert_offset, num_experts),
+        -1e9,
+        device=input_tensor.device,
+        dtype=torch.float32,
+    )
+    for g in range(num_experts):
+        start = expert_offsets[g]
+        end = expert_offsets[g + 1] if g + 1 < num_experts else expert_offset
+        score[start:end, g] = 0.0
+
+    return torch_moe_single(
+        input_tensor, torch.stack(weight_list, dim=0), score, topk=1
+    )
+
+
+@pytest.mark.skipif(
+    not is_sm100_supported(),
+    reason="cutlass_mxfp4_group_mm requires CUDA SM100",
+)
+@pytest.mark.parametrize("num_experts", [8, 16, 32])
+@pytest.mark.parametrize("out_dtype", [torch.bfloat16])
+def test_cutlass_mxfp4_grouped_mm(num_experts, out_dtype):
+    """
+    Test the MXFP4 grouped GEMM kernel by:
+    1. Creating random per-expert inputs and weights
+    2. Quantizing both to MXFP4 using the CUDA kernel
+    3. Running the CUTLASS grouped GEMM
+    4. Comparing against BF16 reference
+    """
+    device = "cuda"
+    alignment = 128
+    # N and K must be multiples of 128 for clean swizzle layout
+    n_g = random.randint(1, 16) * alignment
+    k_g = random.randint(1, 16) * alignment
+
+    expert_offset = 0
+    expert_offsets_input = []
+    problem_sizes = []
+    input_list = []
+    weight_list = []
+
+    for g in range(num_experts):
+        m_g = random.randint(1, 256)
+        expert_offsets_input.append(expert_offset)
+        expert_offset += m_g
+        problem_sizes.append([m_g, n_g, k_g])
+
+        input_list.append(
+            torch.normal(0.0, std=0.5, size=(m_g, k_g), device=device, dtype=out_dtype)
+        )
+        weight_list.append(
+            torch.normal(0.0, std=0.5, size=(n_g, k_g), device=device, dtype=out_dtype)
+        )
+
+    input_tensor = torch.concat(input_list, dim=0)  # [M_total, K]
+
+    # --- Quantize INPUTS via mxfp4_experts_quant ---
+    input_bs_offsets = []
+    tot = 0
+    for g in range(num_experts):
+        input_bs_offsets.append(tot)
+        tot += align(problem_sizes[g][0], 128)
+    input_bs_offsets.append(tot)
+
+    _inp_expert_offsets = torch.tensor(
+        expert_offsets_input + [expert_offset], device=device, dtype=torch.int32
+    )
+    _inp_bs_offsets = torch.tensor(input_bs_offsets, device=device, dtype=torch.int32)
+
+    input_quant, input_sf = ops.mxfp4_experts_quant(
+        input_tensor,
+        _inp_expert_offsets,
+        _inp_bs_offsets,
+        num_experts,
+        topk=1,
+    )
+
+    # --- Quantize WEIGHTS via mxfp4_experts_quant ---
+    # Treat each expert's N weight rows as an "expert" with N tokens
+    weight_tensor = torch.concat(weight_list, dim=0)  # [E*N, K]
+    weight_expert_offsets = [g * n_g for g in range(num_experts)] + [num_experts * n_g]
+    # N is always multiple of 128, so blockscale offsets are clean
+    weight_bs_offsets = [g * n_g for g in range(num_experts)] + [num_experts * n_g]
+
+    _wt_expert_offsets = torch.tensor(
+        weight_expert_offsets, device=device, dtype=torch.int32
+    )
+    _wt_bs_offsets = torch.tensor(weight_bs_offsets, device=device, dtype=torch.int32)
+
+    weight_quant, weight_sf = ops.mxfp4_experts_quant(
+        weight_tensor,
+        _wt_expert_offsets,
+        _wt_bs_offsets,
+        num_experts,
+        topk=1,
+    )
+
+    # Reshape weight quantized data to [E, N, K//2]
+    weight_quant = weight_quant[: num_experts * n_g].view(num_experts, n_g, k_g // 2)
+
+    # Reshape weight scale factors to [E, N, K//32]
+    # The quant kernel produces uint8 SF buffer. Each row has K//32 SFs.
+    scales_per_row = k_g // MXFP4_BLOCK_SIZE
+    weight_sf_flat = weight_sf.view(-1)[: num_experts * n_g * scales_per_row]
+    weight_sf_3d = weight_sf_flat.view(num_experts, n_g, scales_per_row)
+
+    # Output
+    output = torch.empty((expert_offset, n_g), device=device, dtype=out_dtype)
+
+    _problem_sizes = torch.tensor(problem_sizes, device=device, dtype=torch.int32)
+    _expert_offsets = torch.tensor(
+        expert_offsets_input, device=device, dtype=torch.int32
+    )
+    _input_bs = torch.tensor(input_bs_offsets[:-1], device=device, dtype=torch.int32)
+
+    # Run the MXFP4 grouped GEMM
+    ops.cutlass_mxfp4_moe_mm(
+        output,
+        input_quant,
+        weight_quant,
+        input_sf,
+        weight_sf_3d,
+        _problem_sizes,
+        _expert_offsets,
+        _input_bs,
+    )
+
+    # Reference: BF16 matmul
+    ref_output = compute_ref_output(
+        input_tensor=input_tensor,
+        weight_list=weight_list,
+        expert_offsets=expert_offsets_input,
+        expert_offset=expert_offset,
+        num_experts=num_experts,
+    )
+
+    # Compare per-expert
+    for g in range(num_experts):
+        start = expert_offsets_input[g]
+        end = expert_offsets_input[g + 1] if g + 1 < num_experts else expert_offset
+        if start == end:
+            continue
+        baseline = ref_output[start:end]
+        actual = output[start:end]
+        diff = calc_diff(actual, baseline)
+        print(
+            f"m_g={end - start} n_g={n_g} k_g={k_g} "
+            f"num_experts={num_experts}, "
+            f"out_dtype={out_dtype}, diff={diff:.5f}"
+        )
+        # FP4 quantization is very lossy (~4 bits precision)
+        # Comparing quantized vs full-precision gives cosine diff of 0.05-0.15
+        assert diff < 0.15, f"Expert {g}: diff={diff:.5f} exceeds threshold"
+
+
+@pytest.mark.skipif(
+    not is_sm100_supported(),
+    reason="mxfp4_experts_quant requires CUDA SM100",
+)
+def test_mxfp4_experts_quant_basic():
+    """
+    Basic smoke test for the MXFP4 experts quantization kernel.
+    """
+    device = "cuda"
+    num_experts = 4
+    k = 256
+    tokens_per_expert = 16
+
+    total_tokens = tokens_per_expert * num_experts
+    input_tensor = torch.randn(total_tokens, k, device=device, dtype=torch.bfloat16) / 5
+
+    expert_offsets = [i * tokens_per_expert for i in range(num_experts + 1)]
+    blockscale_offsets = [
+        align(i * tokens_per_expert, 128) for i in range(num_experts + 1)
+    ]
+
+    _expert_offsets = torch.tensor(expert_offsets, device=device, dtype=torch.int32)
+    _blockscale_offsets = torch.tensor(
+        blockscale_offsets, device=device, dtype=torch.int32
+    )
+
+    output, output_sf = ops.mxfp4_experts_quant(
+        input_tensor,
+        _expert_offsets,
+        _blockscale_offsets,
+        num_experts,
+        topk=1,
+    )
+
+    assert output.shape == (total_tokens, k // 2)
+    assert output.dtype == torch.uint8
+    assert output_sf.dtype == torch.uint8
+    assert output.any(), "Quantized output is all zeros"
+    print(
+        f"MXFP4 experts quant: output shape={output.shape}, sf shape={output_sf.shape}"
+    )
+    print("PASSED")
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "-s"])
diff --git a/tests/kernels/moe/test_nvfp4_moe.py b/tests/kernels/moe/test_nvfp4_moe.py
index e12659729c9c..e2a6cd1a7dcc 100644
--- a/tests/kernels/moe/test_nvfp4_moe.py
+++ b/tests/kernels/moe/test_nvfp4_moe.py
@@ -19,7 +19,7 @@
     maybe_make_prepare_finalize,
 )
 from vllm.model_executor.layers.fused_moe.config import nvfp4_moe_quant_config
-from vllm.model_executor.layers.fused_moe.cutlass_moe import (
+from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import (
     CutlassExpertsFp4,
 )
 from vllm.model_executor.layers.fused_moe.prepare_finalize import (
diff --git a/tests/kernels/moe/test_ocp_mx_moe.py b/tests/kernels/moe/test_ocp_mx_moe.py
index e54e7a9cd18e..8ed7757f6553 100644
--- a/tests/kernels/moe/test_ocp_mx_moe.py
+++ b/tests/kernels/moe/test_ocp_mx_moe.py
@@ -28,6 +28,25 @@
     and has_flashinfer()
 )
 
+# ROCm platform and dependencies
+ROCM_AVAILABLE = current_platform.is_rocm()
+ROCM_TRITON_KERNELS_AVAILABLE = False
+ROCM_AITER_AVAILABLE = False
+ROCM_GFX950 = False
+
+if ROCM_AVAILABLE:
+    from vllm._aiter_ops import rocm_aiter_ops
+    from vllm.platforms.rocm import on_gfx950
+    from vllm.utils.import_utils import has_triton_kernels
+
+    ROCM_TRITON_KERNELS_AVAILABLE = has_triton_kernels()
+    ROCM_GFX950 = on_gfx950()
+    ROCM_AITER_AVAILABLE = rocm_aiter_ops.is_enabled()
+
+    if ROCM_AITER_AVAILABLE:
+        from aiter.ops.triton.moe.quant_moe import upcast_from_mxfp
+        from aiter.ops.triton.quant import dynamic_mxfp4_quant
+
 if TRTLLM_GEN_MXFP4_AVAILABLE:
     from flashinfer import (
         fp4_quantize,
@@ -111,6 +130,7 @@ def test_mxfp4_loading_and_execution_moe(vllm_runner, model_case: ModelCase):
 
 def swiglu(x, alpha: float = 1.702, beta: float = 1.0, limit: float | None = None):
     # Note we add an extra bias of 1 to the linear layer
+    # Uses chunked layout: first half is gate, second half is up
     x_glu, x_linear = torch.chunk(x, 2, dim=-1)
     if limit is not None:
         x_glu = x_glu.clamp(max=limit)
@@ -119,6 +139,16 @@ def swiglu(x, alpha: float = 1.702, beta: float = 1.0, limit: float | None = Non
     return out_glu * (x_linear + beta)
 
 
+def swigluoai(x, alpha: float = 1.702, limit: float = 7.0):
+    # OAI swiglu uses interleaved layout: gate/up alternating
+    # See SwigluOAIAndMul in vllm/model_executor/layers/activation.py
+    gate, up = x[..., ::2], x[..., 1::2]
+    gate = gate.clamp(max=limit)
+    up = up.clamp(min=-limit, max=limit)
+    glu = gate * torch.sigmoid(gate * alpha)
+    return (up + 1) * glu
+
+
 fp4_lookup_table = [0, 0.5, 1, 1.5, 2, 3, 4, 6, -0, -0.5, -1, -1.5, -2, -3, -4, -6]
 
 
@@ -168,8 +198,20 @@ def reference_moe(
     beta,
     limit,
     act_type,
-    is_gated,
+    activation: str = "swiglu",
+    use_interleaved_layout: bool = False,
 ):
+    """
+    Reference MoE implementation for accuracy testing.
+
+    Args:
+        activation: One of "swiglu", "silu", "relu2". Controls the activation
+            function used after the first MLP.
+        use_interleaved_layout: If True, uses interleaved gate/up layout
+            (gate=x[..., ::2], up=x[..., 1::2]) as used by SWIGLUOAI.
+            If False, uses chunked layout (gate, up = chunk(x, 2)) as used
+            by standard swiglu/silu.
+    """
     # renormalize routing
     experts = torch.topk(roouting_logits, k=topk, dim=-1, sorted=True)
     expert_weights = torch.nn.functional.softmax(experts.values, dim=1)
@@ -179,12 +221,21 @@ def reference_moe(
     mlp1_weight = w13[expert_indices, ...]
     mlp1_bias = bias13[expert_indices, ...]
     t = torch.einsum("beck,bk->bec", mlp1_weight, t) + mlp1_bias
-    if is_gated:
-        t = swiglu(t, alpha=alpha, beta=beta, limit=limit)
-    else:
+
+    # Apply activation
+    if activation in ("swiglu", "silu"):
+        if use_interleaved_layout:
+            # SWIGLUOAI: interleaved gate/up layout
+            t = swigluoai(t, alpha=alpha, limit=limit)
+        else:
+            # Standard swiglu/silu: chunked layout
+            t = swiglu(t, alpha=alpha, beta=beta, limit=limit)
+    elif activation == "relu2":
         # RELU2_NO_MUL: relu(x)^2
         t = torch.relu(t)
         t = t * t
+    else:
+        raise ValueError(f"Unknown activation: {activation}")
 
     if act_type == "mxfp8":
         t_quantized, t_scale = mxfp8_quantize(
@@ -548,7 +599,9 @@ def test_trtllm_gen_mxfp4_fused_moe(
         hidden_states, hidden_states_scale = mxfp8_quantize(
             hidden_states, is_sf_swizzled_layout=False
         )
-        hidden_states_scale = hidden_states_scale.view(torch.float8_e4m3fn).reshape(-1)
+        hidden_states_scale = hidden_states_scale.view(torch.float8_e4m3fn).reshape(
+            *hidden_states.shape[:-1], -1
+        )
     else:
         hidden_states_scale = None
 
@@ -583,7 +636,8 @@ def test_trtllm_gen_mxfp4_fused_moe(
             beta,
             limit,
             act_type,
-            is_gated=True,
+            activation="swiglu",
+            use_interleaved_layout=False,
         )
         ref_result[start_idx:end_idx].copy_(chunk_result)
 
@@ -595,20 +649,20 @@ def test_trtllm_gen_mxfp4_fused_moe(
     if beta is not None:
         beta = torch.full((num_experts,), beta, device=hidden_states.device)
     tg_result = tg_mxfp4_moe(
-        router_logits,
-        topk,
-        num_experts,
-        intermediate_size,
-        hidden_size,
-        hidden_states,
-        hidden_states_scale,
-        w13,
-        w13_scale,
-        bias13,
-        w2,
-        w2_scale,
-        bias2,
-        act_type,
+        router_logits=router_logits,
+        topk=topk,
+        num_experts=num_experts,
+        intermediate_size=intermediate_size,
+        hidden_size=hidden_size,
+        hidden_states=hidden_states,
+        hidden_states_scale=hidden_states_scale,
+        w13_weight=w13,
+        w13_weight_scale=w13_scale,
+        w13_bias=bias13,
+        w2_weight=w2,
+        w2_weight_scale=w2_scale,
+        w2_bias=bias2,
+        act_type=act_type,
         alpha=alpha,
         beta=beta,
         limit=limit,
@@ -720,7 +774,8 @@ def test_flashinfer_cutlass_mxfp4_fused_moe(
         beta,
         limit,
         "bf16",
-        is_gated=True,
+        activation="swiglu",
+        use_interleaved_layout=False,
     )
 
     from vllm.utils.flashinfer import flashinfer_cutlass_fused_moe
@@ -906,7 +961,8 @@ def dequant_mxfp4_batches(mat_fp4: torch.Tensor, scale_tensor: torch.Tensor):
         beta,
         limit,
         "mxfp8",
-        is_gated=True,
+        activation="swiglu",
+        use_interleaved_layout=False,
     )
 
     # Prepare inputs for FlashInfer CUTLASS fused MoE
@@ -1078,7 +1134,8 @@ def test_trtllm_gen_mxfp8_block_scale_moe(
         beta=0.0,
         limit=None,
         act_type="mxfp8",
-        is_gated=is_gated,
+        activation="swiglu" if is_gated else "relu2",
+        use_interleaved_layout=False,
     )
 
     # Shuffle weights/scales with the same indexed layout used by TRTLLM kernels.
@@ -1148,3 +1205,328 @@ def test_trtllm_gen_mxfp8_block_scale_moe(
 
     # Block-scale MXFP8 kernels are approximate; require majority close.
     check_accuracy(ref, out, atol=0.1, rtol=0.85, percent=0.8)
+
+
+# -----------------------------------------------------------------------------
+# ROCm Oracle-based kernel execution tests
+# -----------------------------------------------------------------------------
+# TODO: Further tighten the accuracy threshold.
+# - More accurate ref moe to include activation quantization
+# - Check aiter kernel accuracy. E.g., quant / dequant details.
+ROCM_BACKEND_CONFIGS = {
+    "TRITON": {
+        "activation": "SWIGLUOAI",
+        "rtol": 0.3,
+        "percent": 0.95,
+        "requires_aiter": False,
+        "requires_gfx950": False,
+    },
+    "TRITON_UNFUSED": {
+        "activation": "SWIGLUOAI",
+        "rtol": 0.3,
+        "percent": 0.95,
+        "requires_aiter": False,
+        "requires_gfx950": False,
+    },
+    "AITER_MXFP4_BF16": {
+        "activation": "SILU",
+        "rtol": 1.0,
+        "percent": 0.7,
+        "requires_aiter": True,
+        "requires_gfx950": True,
+    },
+    "AITER_MXFP4_FP8": {
+        "activation": "SWIGLUOAI",
+        "rtol": 0.5,
+        "percent": 0.9,
+        "requires_aiter": True,
+        "requires_gfx950": True,
+    },
+}
+
+
+@pytest.mark.parametrize("backend_name", list(ROCM_BACKEND_CONFIGS.keys()))
+@pytest.mark.parametrize("topk", [4])
+@pytest.mark.parametrize("num_experts", [8])
+@pytest.mark.parametrize("num_tokens,hidden_size,intermediate_size", [(16, 256, 256)])
+@pytest.mark.skipif(
+    not ROCM_AVAILABLE,
+    reason="ROCm is required for this test",
+)
+@torch.inference_mode()
+def test_rocm_mxfp4_moe_oracle(
+    backend_name: str,
+    topk: int,
+    num_experts: int,
+    num_tokens: int,
+    hidden_size: int,
+    intermediate_size: int,
+):
+    """
+    Test ROCm MXFP4 MoE using oracle functions.
+
+    This test validates that the oracle functions work end-to-end:
+    - select_mxfp4_moe_backend() selects a valid backend
+    - convert_to_mxfp4_moe_kernel_format() converts weights without error
+    - make_mxfp4_moe_quant_config() builds a valid quant config
+    - make_mxfp4_moe_kernel() creates a kernel that runs without error
+    - The kernel output is within accuracy tolerance of reference
+    """
+    config = ROCM_BACKEND_CONFIGS[backend_name]
+
+    # Check platform requirements
+    if not ROCM_TRITON_KERNELS_AVAILABLE:
+        pytest.skip("triton_kernels required for quantization")
+    if config["requires_aiter"] and not ROCM_AITER_AVAILABLE:
+        pytest.skip(f"Backend {backend_name} requires AITER")
+    if config["requires_gfx950"] and not ROCM_GFX950:
+        pytest.skip(f"Backend {backend_name} requires GFX950")
+
+    from vllm.config import VllmConfig, set_current_vllm_config
+    from vllm.model_executor.layers.fused_moe.activation import MoEActivation
+    from vllm.model_executor.layers.fused_moe.oracle.mxfp4 import (
+        Mxfp4MoeBackend,
+        backend_to_kernel_cls,
+        convert_to_mxfp4_moe_kernel_format,
+        make_mxfp4_moe_kernel,
+        make_mxfp4_moe_quant_config,
+    )
+    from vllm.v1.worker.workspace import init_workspace_manager
+
+    # Initialize workspace manager (needed for modular kernels)
+    init_workspace_manager(torch.accelerator.current_device_index())
+
+    # Map string to enum
+    backend = Mxfp4MoeBackend[backend_name]
+
+    # Get experts class from oracle
+    experts_cls_list = backend_to_kernel_cls(backend)
+    if experts_cls_list is None or len(experts_cls_list) == 0:
+        pytest.skip(f"Backend {backend_name} not available")
+
+    # Use first experts class
+    experts_cls = experts_cls_list[0]
+
+    torch.manual_seed(42)
+    dtype = torch.bfloat16
+    device = "cuda:0"
+
+    # Create MoE config with Renormalize routing (required by monolithic kernels)
+    from vllm.model_executor.layers.fused_moe import FusedMoEConfig
+    from vllm.model_executor.layers.fused_moe.config import (
+        FusedMoEParallelConfig,
+        RoutingMethodType,
+    )
+
+    moe_config = FusedMoEConfig(
+        num_experts=num_experts,
+        experts_per_token=topk,
+        hidden_dim=hidden_size,
+        intermediate_size_per_partition=intermediate_size,
+        num_local_experts=num_experts,
+        num_logical_experts=num_experts,
+        moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
+        activation=MoEActivation[config["activation"]],
+        in_dtype=dtype,
+        device="cuda",
+        routing_method=RoutingMethodType.Renormalize,
+    )
+
+    # Create float weights in checkpoint format:
+    # w13: [num_experts, 2*intermediate_size, hidden_size]
+    # w2: [num_experts, hidden_size, intermediate_size]
+    w13_float = torch.randn(
+        num_experts, 2 * intermediate_size, hidden_size, dtype=dtype, device=device
+    )
+    w2_float = torch.randn(
+        num_experts, hidden_size, intermediate_size, dtype=dtype, device=device
+    )
+
+    # dynamic_mxfp4_quant expects 2D input, so reshape 3D weights
+    # w13: [E, 2*I, H] -> [E*2*I, H] -> quantize -> [E, 2*I, H//2]
+    # w2: [E, H, I] -> [E*H, I] -> quantize -> [E, H, I//2]
+    w13_2d = w13_float.reshape(-1, hidden_size)
+    w13_quant_2d, w13_scale_2d = dynamic_mxfp4_quant(w13_2d)
+    w13_quant = w13_quant_2d.reshape(num_experts, 2 * intermediate_size, -1)
+    w13_scale = w13_scale_2d.reshape(num_experts, 2 * intermediate_size, -1)
+
+    w2_2d = w2_float.reshape(-1, intermediate_size)
+    w2_quant_2d, w2_scale_2d = dynamic_mxfp4_quant(w2_2d)
+    w2_quant = w2_quant_2d.reshape(num_experts, hidden_size, -1)
+    w2_scale = w2_scale_2d.reshape(num_experts, hidden_size, -1)
+
+    w13_bias = torch.randn(
+        num_experts, 2 * intermediate_size, dtype=dtype, device=device
+    )
+    w2_bias = torch.randn(num_experts, hidden_size, dtype=dtype, device=device)
+
+    # Create static input scales for W4A8 backend (AITER_MXFP4_FP8)
+    w13_input_scale: torch.Tensor | None = None
+    w2_input_scale: torch.Tensor | None = None
+    if backend_name == "AITER_MXFP4_FP8":
+        # Static FP8 scales: one scale per expert
+        w13_input_scale = torch.ones(num_experts, dtype=torch.float32, device=device)
+        w2_input_scale = torch.ones(num_experts, dtype=torch.float32, device=device)
+
+    # Create mock layer for oracle functions
+    class MockLayer:
+        w13_weight: torch.Tensor
+        w2_weight: torch.Tensor
+        w13_weight_scale: torch.Tensor
+        w2_weight_scale: torch.Tensor
+        w13_input_scale: torch.Tensor | None
+        w2_input_scale: torch.Tensor | None
+
+    layer = MockLayer()
+    layer.w13_weight = w13_quant
+    layer.w2_weight = w2_quant
+    layer.w13_weight_scale = w13_scale
+    layer.w2_weight_scale = w2_scale
+    layer.w13_input_scale = w13_input_scale
+    layer.w2_input_scale = w2_input_scale
+
+    # Convert weights using oracle
+    w13_conv, w2_conv, w13_scale_conv, w2_scale_conv, w13_bias_conv, w2_bias_conv = (
+        convert_to_mxfp4_moe_kernel_format(
+            mxfp4_backend=backend,
+            layer=layer,  # type: ignore[arg-type]
+            w13_weight=w13_quant,
+            w2_weight=w2_quant,
+            w13_weight_scale=w13_scale,
+            w2_weight_scale=w2_scale,
+            w13_bias=w13_bias,
+            w2_bias=w2_bias,
+        )
+    )
+
+    # Build quant config using oracle
+    quant_config = make_mxfp4_moe_quant_config(
+        mxfp4_backend=backend,
+        w1_scale=w13_scale_conv,
+        w2_scale=w2_scale_conv,
+        w1_bias=w13_bias_conv,
+        w2_bias=w2_bias_conv,
+        a1_scale=w13_input_scale,
+        a2_scale=w2_input_scale,
+    )
+
+    # Select activation based on backend
+    activation_name = str(config["activation"])
+    activation = MoEActivation[activation_name]
+
+    # Build kernel using oracle
+    assert quant_config is not None, "Failed to create quant config"
+    with set_current_vllm_config(VllmConfig()):
+        kernel = make_mxfp4_moe_kernel(
+            moe_quant_config=quant_config,
+            moe_config=moe_config,
+            mxfp4_backend=backend,
+            experts_cls=experts_cls,
+            routing_tables=None,
+            shared_experts=None,
+        )
+
+        # Create inputs
+        x = torch.randn(num_tokens, hidden_size, dtype=dtype, device=device)
+        router_logits = torch.randn(
+            num_tokens, num_experts, dtype=torch.float32, device=device
+        )
+        topk_weights, topk_ids = torch.topk(router_logits, k=topk, dim=-1, sorted=True)
+        topk_weights = torch.nn.functional.softmax(topk_weights, dim=-1)
+
+        # Run kernel - use appropriate method based on impl type
+        if kernel.is_monolithic:
+            # Monolithic impl uses router_logits
+            out = kernel.apply_monolithic(
+                hidden_states=x,
+                w1=w13_conv,
+                w2=w2_conv,
+                router_logits=router_logits,
+                activation=activation,
+                global_num_experts=num_experts,
+                expert_map=None,
+                apply_router_weight_on_input=False,
+            )
+        else:
+            # Modular impl uses topk_weights and topk_ids
+            out = kernel.apply(
+                hidden_states=x,
+                w1=w13_conv,
+                w2=w2_conv,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                activation=activation,
+                global_num_experts=num_experts,
+                expert_map=None,
+                apply_router_weight_on_input=False,
+            )
+
+    # Verify output is valid (no NaN/Inf) and has expected shape
+    assert out.shape == (num_tokens, hidden_size), f"Unexpected shape: {out.shape}"
+    assert not torch.any(torch.isnan(out)), "Output contains NaN"
+    assert not torch.any(torch.isinf(out)), "Output contains Inf"
+
+    # Verify output has reasonable magnitude (not all zeros)
+    assert out.abs().max() > 0.01, "Output is effectively zero"
+
+    # Dequantize weights for reference computation
+    w13_dq = upcast_from_mxfp(
+        w13_quant.view(torch.uint8), w13_scale, torch.bfloat16, axis=-1
+    )
+    w2_dq = upcast_from_mxfp(
+        w2_quant.view(torch.uint8), w2_scale, torch.bfloat16, axis=-1
+    )
+
+    # Determine activation type and layout
+    # SWIGLUOAI uses interleaved layout (gate/up alternating)
+    # SILU uses chunked layout (first half gate, second half up)
+    use_interleaved = activation == MoEActivation.SWIGLUOAI
+    if activation in [MoEActivation.SWIGLUOAI, MoEActivation.SILU]:
+        act_name = "swiglu"
+    else:
+        act_name = "relu2"
+
+    ref = reference_moe(
+        router_logits,
+        topk,
+        num_experts,
+        x.to(torch.float32),
+        w13_dq.to(torch.float32),
+        w13_bias.to(torch.float32),
+        w2_dq.to(torch.float32),
+        w2_bias.to(torch.float32),
+        alpha=1.702 if activation == MoEActivation.SWIGLUOAI else 1.0,
+        beta=1.0 if activation == MoEActivation.SWIGLUOAI else 0.0,
+        limit=7.0 if activation == MoEActivation.SWIGLUOAI else None,
+        act_type="bf16",
+        activation=act_name,
+        use_interleaved_layout=use_interleaved,
+    )
+
+    # Compute and print accuracy statistics
+    diff = (ref.float() - out.float()).abs()
+    rel_diff = diff / (ref.float().abs() + 1e-6)
+
+    print(f"\n[{backend_name}] Accuracy statistics:")
+    print(
+        f"  Reference: min={ref.min():.4f}, max={ref.max():.4f}, mean={ref.mean():.4f}"
+    )
+    print(
+        f"  Output:    min={out.min():.4f}, max={out.max():.4f}, mean={out.mean():.4f}"
+    )
+    print(
+        f"  Abs diff:  min={diff.min():.4f}, max={diff.max():.4f}, "
+        f"mean={diff.mean():.4f}"
+    )
+    print(
+        f"  Rel diff:  min={rel_diff.min():.4f}, max={rel_diff.max():.4f}, "
+        f"mean={rel_diff.mean():.4f}"
+    )
+
+    # Check what percentage of values are within various tolerances
+    for rtol in [0.1, 0.5, 1.0, 2.0]:
+        within_tol = (diff <= rtol * out.float().abs()).float().mean()
+        print(f"  Within rtol={rtol}: {within_tol * 100:.1f}%")
+
+    # Check accuracy using per-backend thresholds
+    check_accuracy(ref, out, atol=0.1, rtol=config["rtol"], percent=config["percent"])
diff --git a/tests/kernels/moe/test_rocm_aiter_topk.py b/tests/kernels/moe/test_rocm_aiter_topk.py
index b0ecc9ed71f6..11f9cf28ae87 100644
--- a/tests/kernels/moe/test_rocm_aiter_topk.py
+++ b/tests/kernels/moe/test_rocm_aiter_topk.py
@@ -20,7 +20,7 @@
     pytest.skip("This test can only run on ROCm.", allow_module_level=True)
 
 # this import statement is needed to ensure the ops are registered
-import vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe  # noqa: F401
+import vllm.model_executor.layers.fused_moe.experts.rocm_aiter_moe  # noqa: F401
 
 # need to import once to ensure the ops are registered
 # Check if aiter package is installed
diff --git a/tests/kernels/moe/test_routing.py b/tests/kernels/moe/test_routing.py
index 7b065cf152e0..90a6cd841efd 100644
--- a/tests/kernels/moe/test_routing.py
+++ b/tests/kernels/moe/test_routing.py
@@ -662,6 +662,52 @@ def test_eplb_map_no_redundancy(
         assert load.sum().item() == 0
 
 
+@pytest.mark.parametrize("top_k,R", [(2, 2), (4, 2), (8, 4), (8, 8)])
+def test_eplb_map_hot_expert_replica_balance(top_k, R):
+    """Hot logical expert with R replicas must be balanced across replicas
+    even when ``top_k`` is a multiple of ``R``. In that regime every top-k
+    offset for the hot expert lands on a multiple of ``top_k`` in the flat
+    ``topk_ids`` view, so per-replica assignment must not collapse onto a
+    single replica.
+    """
+    num_tokens = 8192
+    num_logical = 16
+    num_physical = R + (num_logical - 1)
+
+    l2p = torch.full((num_logical, R), -1, dtype=torch.int64, device="cuda")
+    l2p[0] = torch.arange(R, dtype=torch.int64, device="cuda")
+    for i in range(1, num_logical):
+        l2p[i, 0] = R + i - 1
+    rc = torch.tensor([R] + [1] * (num_logical - 1), dtype=torch.int64, device="cuda")
+
+    torch.manual_seed(0)
+    topk_ids = torch.randint(
+        1,
+        num_logical,
+        (num_tokens, top_k),
+        dtype=torch.int32,
+        device="cuda",
+    )
+    topk_ids[:, 0] = 0
+
+    load = torch.zeros(num_physical, dtype=torch.int32, device="cuda")
+    rec = torch.tensor(True, dtype=torch.bool, device="cuda")
+
+    eplb_map_to_physical_and_record(
+        topk_ids=topk_ids,
+        expert_load_view=load,
+        logical_to_physical_map=l2p,
+        logical_replica_count=rc,
+        record_enabled=rec,
+    )
+
+    hot_load = load[:R].float()
+    max_mean = (hot_load.max() / hot_load.mean()).item()
+    assert max_mean < 1.15, (
+        f"Hot expert replicas uneven: {hot_load.tolist()}, max/mean={max_mean:.3f}"
+    )
+
+
 @pytest.mark.parametrize("record_enabled", [True, False])
 @pytest.mark.parametrize(
     "l2p_map, replica_count, num_physical, topk_ids, expected_out, expected_load",
@@ -672,10 +718,12 @@ def test_eplb_map_no_redundancy(
             [2, 2, 1, 1],
             6,
             [[0, 1], [2, 3], [0, 2]],
-            # offs: 0→0%2=0→p0, 1→1%2=1→p5, 2→2%1=0→p2,
-            #        3→3%1=0→p3, 4→4%2=0→p0, 5→5%1=0→p2
-            [[0, 5], [2, 3], [0, 2]],
-            [2, 0, 2, 1, 0, 1],
+            # replica = (token_idx * KNUTH) & 0xFFFFFFFF % R.
+            # token 0 hash=0x00000000: %2=0, %1=0.
+            # token 1 hash=0x9E3779B9: %2=1, %1=0.
+            # token 2 hash=0x3C6EF372: %2=0, %1=0.
+            [[0, 1], [2, 3], [0, 2]],
+            [2, 1, 2, 1, 0, 0],
             id="partial",
         ),
         pytest.param(
@@ -684,10 +732,11 @@ def test_eplb_map_no_redundancy(
             [2, 2, 2, 2],
             8,
             [[0, 1], [2, 3], [0, 2]],
-            # offs: 0→0%2=0→p0, 1→1%2=1→p5, 2→2%2=0→p2,
-            #        3→3%2=1→p7, 4→4%2=0→p0, 5→5%2=1→p6
-            [[0, 5], [2, 7], [0, 6]],
-            [2, 0, 1, 0, 0, 1, 1, 1],
+            # token 0 hash=0x00000000: %2=0.
+            # token 1 hash=0x9E3779B9: %2=1.
+            # token 2 hash=0x3C6EF372: %2=0.
+            [[0, 1], [6, 7], [0, 2]],
+            [2, 1, 1, 0, 0, 0, 1, 1],
             id="full",
         ),
         pytest.param(
@@ -696,10 +745,11 @@ def test_eplb_map_no_redundancy(
             [4, 2, 2],
             8,
             [[0, 1], [2, 0], [1, 2]],
-            # offs: 0→0%4=0→p0, 1→1%2=1→p4, 2→2%2=0→p2,
-            #        3→3%4=3→p7, 4→4%2=0→p1, 5→5%2=1→p6
-            [[0, 4], [2, 7], [1, 6]],
-            [1, 1, 1, 0, 1, 0, 1, 1],
+            # token 0 hash=0x00000000: %4=0, %2=0.
+            # token 1 hash=0x9E3779B9: %4=1, %2=1.
+            # token 2 hash=0x3C6EF372: %4=2, %2=0.
+            [[0, 1], [6, 3], [1, 2]],
+            [1, 2, 1, 1, 0, 0, 1, 0],
             id="uneven",
         ),
     ],
diff --git a/tests/kernels/moe/test_shared_fused_moe_routed_transform.py b/tests/kernels/moe/test_shared_fused_moe_routed_transform.py
index 89e23eb0d741..4515021a4e91 100644
--- a/tests/kernels/moe/test_shared_fused_moe_routed_transform.py
+++ b/tests/kernels/moe/test_shared_fused_moe_routed_transform.py
@@ -1,9 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
-Tests for SharedFusedMoE with routed_input_transform.
+Tests for FusedMoE with routed_input_transform.
 
-Verifies that applying routed_input_transform inside SharedFusedMoE
+Verifies that applying routed_input_transform inside FusedMoE
 produces the same results as applying the transform manually outside.
 """
 
@@ -13,7 +13,7 @@
 
 from vllm.config import VllmConfig, set_current_vllm_config
 from vllm.forward_context import set_forward_context
-from vllm.model_executor.layers.fused_moe.shared_fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import is_torch_equal_or_newer, set_random_seed
 
@@ -133,9 +133,9 @@ def test_routed_input_transform_inside_vs_outside(
     workspace_init,
     monkeypatch,
 ):
-    """Compare SharedFusedMoE with transform inside vs manually applying outside.
-    Method A (inside): SharedFusedMoE with routed_input_transform
-    Method B (outside): Manually transform, then SharedFusedMoE without transform
+    """Compare FusedMoE with transform inside vs manually applying outside.
+    Method A (inside): FusedMoE with routed_input_transform
+    Method B (outside): Manually transform, then FusedMoE without transform
     """
     if current_platform.is_rocm():
         monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1" if use_rocm_aiter else "0")
@@ -157,15 +157,14 @@ def test_routed_input_transform_inside_vs_outside(
     routed_transform = SimpleLinear(hidden_size, latent_size, dtype)
 
     with set_current_vllm_config(vllm_config):
-        # Method A: SharedFusedMoE WITH routed_input_transform
-        moe_with_transform = SharedFusedMoE(
+        # Method A: FusedMoE WITH routed_input_transform
+        moe_with_transform = FusedMoE(
             shared_experts=shared_experts,
             routed_input_transform=routed_transform,
             num_experts=num_experts,
             top_k=top_k,
             hidden_size=latent_size,
             intermediate_size=intermediate_size,
-            reduce_results=False,
             renormalize=True,
             params_dtype=dtype,
             tp_size=1,
@@ -174,16 +173,15 @@ def test_routed_input_transform_inside_vs_outside(
             prefix="moe_with_transform",
         )
 
-        # Method B: SharedFusedMoE WITHOUT routed_input_transform
+        # Method B: FusedMoE WITHOUT routed_input_transform
         # Note: shared_experts=None because when transform is done outside,
-        moe_without_transform = SharedFusedMoE(
+        moe_without_transform = FusedMoE(
             shared_experts=None,
             routed_input_transform=None,
             num_experts=num_experts,
             top_k=top_k,
             hidden_size=latent_size,
             intermediate_size=intermediate_size,
-            reduce_results=False,
             renormalize=True,
             params_dtype=dtype,
             tp_size=1,
@@ -212,34 +210,20 @@ def test_routed_input_transform_inside_vs_outside(
         hidden_states = torch.randn(num_tokens, hidden_size, device="cuda", dtype=dtype)
         router_logits = torch.randn(num_tokens, num_experts, device="cuda", dtype=dtype)
 
-        # Clone inputs so any in-place modification by Method A
-        # cannot affect Method B's computation.
-        hidden_states_A = hidden_states.clone()
-        router_logits_A = router_logits.clone()
-
         with set_forward_context(None, vllm_config, num_tokens=num_tokens):
-            shared_out_A, routed_out_A = moe_with_transform(
-                hidden_states_A, router_logits_A
-            )
+            # Method A: combined output (shared + routed)
+            combined_A = moe_with_transform(hidden_states, router_logits)
 
+            # Method B: manually transform, get routed output, add shared
             transformed_hidden = routed_transform(hidden_states)
-            shared_out_B, routed_out_B = moe_without_transform(
-                transformed_hidden, router_logits
-            )
-
-        expected_shared_out = shared_experts(hidden_states)
+            routed_out_B = moe_without_transform(transformed_hidden, router_logits)
+            shared_out_B = shared_experts(hidden_states)
+            combined_B = shared_out_B + routed_out_B
 
-        _assert_close(
-            routed_out_A,
-            routed_out_B,
-            atol=1e-3,
-            rtol=1e-3,
-            label="Routed output: transform inside vs outside",
-        )
-        _assert_close(
-            shared_out_A,
-            expected_shared_out,
+        torch.testing.assert_close(
+            combined_A,
+            combined_B,
             atol=1e-3,
             rtol=1e-3,
-            label="Shared expert output",
+            msg="Combined output should match: transform inside vs outside",
         )
diff --git a/tests/kernels/moe/test_topk_softplus_sqrt.py b/tests/kernels/moe/test_topk_softplus_sqrt.py
new file mode 100644
index 000000000000..1b68213fafef
--- /dev/null
+++ b/tests/kernels/moe/test_topk_softplus_sqrt.py
@@ -0,0 +1,188 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from vllm.model_executor.layers.fused_moe.config import (
+    RoutingMethodType,
+    get_routing_method_type,
+)
+from vllm.model_executor.layers.fused_moe.router.fused_topk_bias_router import (
+    fused_topk_bias,
+)
+from vllm.platforms import current_platform
+
+
+def _torch_topk_softplus_sqrt(
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    routed_scaling_factor: float,
+    e_score_correction_bias: torch.Tensor | None = None,
+    input_ids: torch.Tensor | None = None,
+    hash_indices_table: torch.Tensor | None = None,
+):
+    scores = F.softplus(gating_output.float()).sqrt()
+    original_scores = scores
+    if e_score_correction_bias is not None:
+        scores_for_choice = scores + e_score_correction_bias.unsqueeze(0)
+    else:
+        scores_for_choice = scores
+
+    if hash_indices_table is not None:
+        assert input_ids is not None
+        topk_ids = hash_indices_table[input_ids.long()]
+    else:
+        topk_ids = torch.topk(scores_for_choice, k=topk, dim=-1, sorted=True)[1]
+
+    topk_weights = original_scores.gather(1, topk_ids.long())
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+    if routed_scaling_factor != 1.0:
+        topk_weights = topk_weights * routed_scaling_factor
+    return topk_weights.to(torch.float32), topk_ids.to(torch.int32)
+
+
+def test_sqrtsoftplus_bias_uses_deepseek_v4_routing_method():
+    assert (
+        get_routing_method_type(
+            scoring_func="sqrtsoftplus",
+            top_k=8,
+            renormalize=True,
+            num_expert_group=None,
+            has_e_score_bias=True,
+        )
+        == RoutingMethodType.DeepseekV4
+    )
+    assert (
+        get_routing_method_type(
+            scoring_func="sqrtsoftplus",
+            top_k=8,
+            renormalize=False,
+            num_expert_group=None,
+            has_e_score_bias=True,
+        )
+        == RoutingMethodType.Unspecified
+    )
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="This test is skipped on non-CUDA platform.",
+)
+@pytest.mark.parametrize("num_tokens", [1, 33, 128])
+@pytest.mark.parametrize("hidden_size", [1024, 2048])
+@pytest.mark.parametrize("num_experts", [128, 256, 384, 512])
+@pytest.mark.parametrize("topk", [6, 8, 16])
+@pytest.mark.parametrize("renormalize", [True, False])
+@pytest.mark.parametrize("routed_scaling_factor", [1.0, 1.5])
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.half, torch.float32])
+def test_fused_topk_softplus_sqrt(
+    num_tokens: int,
+    hidden_size: int,
+    num_experts: int,
+    topk: int,
+    renormalize: bool,
+    routed_scaling_factor: float,
+    dtype: torch.dtype,
+):
+    torch.manual_seed(0)
+    hidden_states = torch.randn((num_tokens, hidden_size), dtype=dtype, device="cuda")
+    gating_output = torch.randn((num_tokens, num_experts), dtype=dtype, device="cuda")
+    e_score_correction_bias = torch.randn(
+        (num_experts,), dtype=torch.float32, device="cuda"
+    )
+
+    topk_weights_ref, topk_ids_ref = _torch_topk_softplus_sqrt(
+        gating_output=gating_output,
+        topk=topk,
+        renormalize=renormalize,
+        routed_scaling_factor=routed_scaling_factor,
+        e_score_correction_bias=e_score_correction_bias,
+    )
+
+    topk_weights, topk_ids = fused_topk_bias(
+        hidden_states=hidden_states,
+        gating_output=gating_output,
+        scoring_func="sqrtsoftplus",
+        e_score_correction_bias=e_score_correction_bias,
+        topk=topk,
+        renormalize=renormalize,
+        routed_scaling_factor=routed_scaling_factor,
+    )
+
+    # Different kernels may return the topk experts in different orders when
+    # scores tie; sort by expert id before comparing.
+    sorted_ref_ids, idx_ref = topk_ids_ref.sort(dim=-1)
+    sorted_ids, idx_ops = topk_ids.sort(dim=-1)
+    torch.testing.assert_close(sorted_ref_ids, sorted_ids, atol=0, rtol=0)
+
+    sorted_w_ref = topk_weights_ref.gather(1, idx_ref)
+    sorted_w = topk_weights.gather(1, idx_ops)
+    torch.testing.assert_close(sorted_w_ref, sorted_w, atol=2e-2, rtol=1e-2)
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="This test is skipped on non-CUDA platform.",
+)
+@pytest.mark.parametrize("num_tokens", [1, 33, 128])
+@pytest.mark.parametrize("hidden_size", [1024, 2048])
+@pytest.mark.parametrize("num_experts", [256, 384, 512])
+@pytest.mark.parametrize("topk", [6, 8, 16])
+@pytest.mark.parametrize("renormalize", [True, False])
+@pytest.mark.parametrize("routed_scaling_factor", [1.0, 2.5])
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.half, torch.float32])
+def test_fused_topk_softplus_sqrt_hash(
+    num_tokens: int,
+    hidden_size: int,
+    num_experts: int,
+    topk: int,
+    renormalize: bool,
+    routed_scaling_factor: float,
+    dtype: torch.dtype,
+):
+    torch.manual_seed(0)
+    vocab_size = 1024
+    hidden_states = torch.randn((num_tokens, hidden_size), dtype=dtype, device="cuda")
+    gating_output = torch.randn((num_tokens, num_experts), dtype=dtype, device="cuda")
+    # Per-token fixed expert selection: for each vocab id pick `topk` distinct
+    # experts.
+    hash_indices_table = torch.stack(
+        [torch.randperm(num_experts)[:topk] for _ in range(vocab_size)]
+    ).to(device="cuda", dtype=torch.int32)
+    input_ids = torch.randint(
+        0, vocab_size, (num_tokens,), dtype=torch.int32, device="cuda"
+    )
+
+    topk_weights_ref, topk_ids_ref = _torch_topk_softplus_sqrt(
+        gating_output=gating_output,
+        topk=topk,
+        renormalize=renormalize,
+        routed_scaling_factor=routed_scaling_factor,
+        input_ids=input_ids,
+        hash_indices_table=hash_indices_table,
+    )
+
+    topk_weights, topk_ids = fused_topk_bias(
+        hidden_states=hidden_states,
+        gating_output=gating_output,
+        scoring_func="sqrtsoftplus",
+        e_score_correction_bias=None,
+        topk=topk,
+        renormalize=renormalize,
+        input_tokens=input_ids,
+        hash_indices_table=hash_indices_table,
+        routed_scaling_factor=routed_scaling_factor,
+    )
+
+    sorted_ref_ids, idx_ref = topk_ids_ref.sort(dim=-1)
+    sorted_ids, idx_ops = topk_ids.sort(dim=-1)
+    torch.testing.assert_close(sorted_ref_ids, sorted_ids, atol=0, rtol=0)
+
+    sorted_w_ref = topk_weights_ref.gather(1, idx_ref)
+    sorted_w = topk_weights.gather(1, idx_ops)
+    torch.testing.assert_close(sorted_w_ref, sorted_w, atol=2e-2, rtol=1e-2)
diff --git a/tests/kernels/moe/test_triton_moe_no_act_mul.py b/tests/kernels/moe/test_triton_moe_no_act_mul.py
index 1dfac3cf0fdc..9d16ae5b63db 100644
--- a/tests/kernels/moe/test_triton_moe_no_act_mul.py
+++ b/tests/kernels/moe/test_triton_moe_no_act_mul.py
@@ -15,7 +15,7 @@
 from vllm.model_executor.layers.fused_moe.config import (
     FUSED_MOE_UNQUANTIZED_CONFIG,
 )
-from vllm.model_executor.layers.fused_moe.fused_moe import TritonExperts
+from vllm.model_executor.layers.fused_moe.experts.triton_moe import TritonExperts
 from vllm.platforms import current_platform
 
 # Test parameters
@@ -151,7 +151,7 @@ def test_triton_experts_no_mul_activation(
 @torch.inference_mode()
 def test_workspace_shapes_no_mul_vs_gated():
     """Test that workspace shapes differ correctly between gated and non-gated."""
-    from vllm.model_executor.layers.fused_moe.fused_moe import TritonExperts
+    from vllm.model_executor.layers.fused_moe.experts.triton_moe import TritonExperts
 
     M, N, K, topk = 64, 256, 128, 2
 
@@ -192,7 +192,7 @@ def test_workspace_shapes_no_mul_vs_gated():
 @torch.inference_mode()
 def test_adjust_n_for_activation():
     """Test the adjust_N_for_activation method."""
-    from vllm.model_executor.layers.fused_moe.fused_moe import TritonExperts
+    from vllm.model_executor.layers.fused_moe.experts.triton_moe import TritonExperts
 
     experts = TritonExperts(
         moe_config=make_dummy_moe_config(),
diff --git a/tests/kernels/moe/test_trtllm_nvfp4_moe.py b/tests/kernels/moe/test_trtllm_nvfp4_moe.py
new file mode 100644
index 000000000000..e59519740516
--- /dev/null
+++ b/tests/kernels/moe/test_trtllm_nvfp4_moe.py
@@ -0,0 +1,207 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Tests for the FlashInfer TRTLLM NvFP4 MoE backend
+(`TrtLlmNvFp4ExpertsModular`).
+
+Covers the activations the wrapper claims to support — SiLU, RELU^2 (non-gated),
+and GELU — including a Gemma4-shaped case (128 experts, top-k 8,
+intermediate_size 704) that exercises the non-256-aligned padding path.
+"""
+
+import pytest
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from tests.kernels.moe.utils import make_test_quant_config
+from tests.kernels.quantization.nvfp4_utils import (
+    FLOAT4_E2M1_MAX,
+    FLOAT8_E4M3_MAX,
+    dequantize_nvfp4_to_dtype,
+)
+from tests.kernels.utils import torch_moe
+from vllm import _custom_ops as ops
+from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
+from vllm.model_executor.layers.fused_moe import fused_topk
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
+from vllm.model_executor.layers.fused_moe.all2all_utils import (
+    maybe_make_prepare_finalize,
+)
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEParallelConfig,
+    RoutingMethodType,
+)
+from vllm.model_executor.layers.fused_moe.experts.trtllm_nvfp4_moe import (
+    TrtLlmNvFp4ExpertsModular,
+)
+from vllm.platforms import current_platform
+from vllm.utils.flashinfer import has_flashinfer_trtllm_fused_moe
+from vllm.utils.math_utils import next_power_of_2
+from vllm.utils.torch_utils import set_random_seed
+
+if pytest and (
+    not has_flashinfer_trtllm_fused_moe()
+    or not current_platform.has_device_capability(100)
+):
+    pytest.skip(
+        "Requires flashinfer TRTLLM fused MoE and NvFP4 (SM100)",
+        allow_module_level=True,
+    )
+
+# (m, n, k) = (tokens, intermediate_size_per_partition, hidden_dim).
+# The (64, 704, 4096) row matches Gemma4's MoE shape and exercises the
+# non-256-aligned intermediate (padded inside the wrapper).
+MNK_FACTORS = [
+    (2, 1024, 1024),
+    (64, 2048, 1536),
+    (64, 704, 4096),
+]
+
+
+@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
+@pytest.mark.parametrize("e", [128])
+@pytest.mark.parametrize("topk", [8])
+@pytest.mark.parametrize("dtype", [torch.bfloat16])
+@pytest.mark.parametrize(
+    "activation",
+    [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL, MoEActivation.GELU],
+)
+@torch.inference_mode()
+def test_trtllm_fp4_moe_no_graph(
+    m: int,
+    n: int,
+    k: int,
+    e: int,
+    topk: int,
+    dtype: torch.dtype,
+    activation: MoEActivation,
+    workspace_init,
+):
+    # FlashInfer's trtllm_batched_gemm_runner has no precompiled tile
+    # config for non-gated RELU^2 at non-256-aligned intermediate_size
+    # (e.g. Gemma4's 704). Other activations (SiLU/GELU) work at the
+    # same shape. Tracked upstream in FlashInfer; unrelated to this
+    # PR's GELU enablement (Gemma4 uses GeGLU, not non-gated RELU^2).
+    if activation == MoEActivation.RELU2_NO_MUL and (m, n, k) == (64, 704, 4096):
+        pytest.skip(
+            "FlashInfer trtllm_batched_gemm_runner: no valid tile config "
+            "for non-gated RELU^2 at intermediate_size=704 "
+            "(getValidConfigIndices throws). Tracked upstream."
+        )
+
+    set_random_seed(7)
+    with set_current_vllm_config(
+        VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
+    ):
+        a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
+
+        quant_blocksize = 16
+        is_gated_act = activation.is_gated
+
+        w1_q, w2_q, quant_config = make_test_quant_config(
+            e,
+            n,
+            k,
+            in_dtype=dtype,
+            quant_dtype="nvfp4",
+            block_shape=None,
+            per_act_token_quant=False,
+            make_gate=is_gated_act,
+            # The TRT-LLM FP4 MoE kernel rejects swizzled (padded) activation
+            # scales — its numel-based vec_size check requires numel == M*K/16.
+            # Match what oracle/nvfp4.py does for this backend.
+            is_scale_swizzled=False,
+        )
+
+        score = torch.randn((m, e), device="cuda", dtype=dtype)
+        topk_weights, topk_ids, _ = fused_topk(a, score, topk, renormalize=False)
+
+        moe_config = FusedMoEConfig(
+            num_experts=e,
+            experts_per_token=topk,
+            hidden_dim=k,
+            intermediate_size_per_partition=n,
+            num_local_experts=e,
+            num_logical_experts=e,
+            activation=activation,
+            device="cuda",
+            moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
+            in_dtype=dtype,
+            is_act_and_mul=is_gated_act,
+            routing_method=RoutingMethodType.TopK,
+            max_num_tokens=next_power_of_2(m),
+        )
+
+        trtllm_experts = mk.FusedMoEKernel(
+            maybe_make_prepare_finalize(
+                moe=moe_config,
+                quant_config=quant_config,
+                allow_new_interface=True,
+                use_monolithic=False,
+            ),
+            TrtLlmNvFp4ExpertsModular(moe_config=moe_config, quant_config=quant_config),
+            inplace=False,
+        )
+
+        trtllm_output = trtllm_experts.apply(
+            hidden_states=a,
+            w1=w1_q,
+            w2=w2_q,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            activation=activation,
+            global_num_experts=e,
+            expert_map=None,
+            apply_router_weight_on_input=False,
+        )
+
+        # Reference: round-trip activations and weights through FP4
+        # quant/dequant so the comparison isolates kernel/activation behavior
+        # from quantization error.
+        a_global_scale = ((FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / a.abs().max()).to(
+            torch.float32
+        )
+        a_fp4, a_scale_interleaved = ops.scaled_fp4_quant(a, a_global_scale)
+        a_in_dtype = dequantize_nvfp4_to_dtype(
+            a_fp4,
+            a_scale_interleaved,
+            a_global_scale,
+            dtype=a.dtype,
+            device=a.device,
+            block_size=quant_blocksize,
+        )
+
+        w1_d = torch.empty(
+            (e, (2 if is_gated_act else 1) * n, k), device="cuda", dtype=dtype
+        )
+        w2_d = torch.empty((e, k, n), device="cuda", dtype=dtype)
+        for idx in range(e):
+            w1_d[idx] = dequantize_nvfp4_to_dtype(
+                w1_q[idx],
+                quant_config.w1_scale[idx],
+                (1 / quant_config.g1_alphas[idx]),
+                dtype=dtype,
+                device=w1_q.device,
+                block_size=quant_blocksize,
+            )
+            w2_d[idx] = dequantize_nvfp4_to_dtype(
+                w2_q[idx],
+                quant_config.w2_scale[idx],
+                (1 / quant_config.g2_alphas[idx]),
+                dtype=dtype,
+                device=w2_q.device,
+                block_size=quant_blocksize,
+            )
+
+        torch_output = torch_moe(
+            a_in_dtype, w1_d, w2_d, score, topk, activation=activation
+        )
+
+        torch.testing.assert_close(torch_output, trtllm_output, atol=2e-1, rtol=2e-1)
+
+
+if __name__ == "__main__":
+    test_trtllm_fp4_moe_no_graph(
+        64, 704, 4096, 128, 8, torch.bfloat16, MoEActivation.GELU, None
+    )
diff --git a/tests/kernels/moe/test_unquantized_backend_selection.py b/tests/kernels/moe/test_unquantized_backend_selection.py
index 469c45b2312c..bc322aed3903 100644
--- a/tests/kernels/moe/test_unquantized_backend_selection.py
+++ b/tests/kernels/moe/test_unquantized_backend_selection.py
@@ -11,6 +11,11 @@
 )
 from vllm.platforms import current_platform
 
+skipif_not_cuda_rocm = pytest.mark.skipif(
+    not (current_platform.is_cuda() or current_platform.is_rocm()),
+    reason="Only supported on CUDA/ROCm platforms.",
+)
+
 
 @pytest.mark.parametrize(
     "platform_method,expected_backend",
@@ -80,7 +85,7 @@ def test_select_default_backend_by_platform(
 
 
 @patch(
-    "vllm.model_executor.layers.fused_moe.oracle.unquantized.has_flashinfer",
+    "vllm.utils.flashinfer.has_flashinfer",
     return_value=False,
 )
 @patch(
@@ -153,7 +158,7 @@ def test_select_cuda_flashinfer_trtllm_backend(mock_is_supported_trtllm, monkeyp
     return_value=(False, None),
 )
 @patch(
-    "vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe.FlashInferExperts.is_supported_config",
+    "vllm.model_executor.layers.fused_moe.experts.flashinfer_cutlass_moe.FlashInferExperts.is_supported_config",
     return_value=(True, None),
 )
 @pytest.mark.skipif(
@@ -190,3 +195,83 @@ def test_select_cuda_flashinfer_cutlass_backend(
 
         assert selected_backend == UnquantizedMoeBackend.FLASHINFER_CUTLASS
         assert experts_cls is not None
+
+
+@skipif_not_cuda_rocm
+def test_select_lora_backend_prefers_triton():
+    """LoRA-enabled unquantized MoE should select Triton backend."""
+    moe_config = make_dummy_moe_config()
+    moe_config.is_lora_enabled = True
+    selected_backend, experts_cls = select_unquantized_moe_backend(
+        moe_config=moe_config
+    )
+
+    assert selected_backend == UnquantizedMoeBackend.TRITON
+    assert experts_cls is not None
+
+
+@skipif_not_cuda_rocm
+def test_select_lora_explicit_non_triton_backend():
+    """LoRA should override explicit non-Triton backend to Triton."""
+    moe_config = make_dummy_moe_config()
+    moe_config.is_lora_enabled = True
+
+    # Use string from mapping in function map_unquantized_backend()
+    moe_config.moe_backend = "flashinfer_cutlass"
+
+    selected_backend, experts_cls = select_unquantized_moe_backend(
+        moe_config=moe_config
+    )
+
+    assert selected_backend == UnquantizedMoeBackend.TRITON
+    assert experts_cls is not None
+
+
+@skipif_not_cuda_rocm
+@pytest.mark.parametrize("is_lora_enabled", [False, True])
+def test_select_explicit_triton_backend(is_lora_enabled):
+    """Explicit triton backend selection should return Triton."""
+    moe_config = make_dummy_moe_config()
+    moe_config.is_lora_enabled = is_lora_enabled
+    moe_config.moe_backend = "triton"
+
+    selected_backend, experts_cls = select_unquantized_moe_backend(
+        moe_config=moe_config
+    )
+
+    assert selected_backend == UnquantizedMoeBackend.TRITON
+    assert experts_cls is not None
+
+
+@skipif_not_cuda_rocm
+def test_select_explicit_triton_ignores_flashinfer_env(monkeypatch):
+    """Explicit triton backend should override FlashInfer env selection."""
+    monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP16", "1")
+    monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "throughput")
+
+    moe_config = make_dummy_moe_config()
+    moe_config.is_lora_enabled = False
+    moe_config.moe_backend = "triton"
+
+    selected_backend, experts_cls = select_unquantized_moe_backend(
+        moe_config=moe_config
+    )
+
+    assert selected_backend == UnquantizedMoeBackend.TRITON
+    assert experts_cls is not None
+
+
+@skipif_not_cuda_rocm
+def test_select_lora_ignores_flashinfer_env(monkeypatch):
+    """LoRA path should still choose Triton even if FlashInfer env is on."""
+    monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP16", "1")
+    monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "throughput")
+
+    moe_config = make_dummy_moe_config()
+    moe_config.is_lora_enabled = True
+    selected_backend, experts_cls = select_unquantized_moe_backend(
+        moe_config=moe_config
+    )
+
+    assert selected_backend == UnquantizedMoeBackend.TRITON
+    assert experts_cls is not None
diff --git a/tests/kernels/moe/utils.py b/tests/kernels/moe/utils.py
index c9c5c97b26d5..de3a456fb449 100644
--- a/tests/kernels/moe/utils.py
+++ b/tests/kernels/moe/utils.py
@@ -17,16 +17,20 @@
     FusedMoEQuantConfig,
     RoutingMethodType,
 )
+from vllm.model_executor.layers.fused_moe.experts.triton_moe import (
+    TritonExperts,
+)
 from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
-    BatchedPrepareAndFinalize,
     BatchedTritonExperts,
     NaiveBatchedExperts,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe import (
-    TritonExperts,
     fused_experts,
 )
 from vllm.model_executor.layers.fused_moe.modular_kernel import FusedMoEKernel
+from vllm.model_executor.layers.fused_moe.prepare_finalize.batched import (
+    BatchedPrepareAndFinalize,
+)
 from vllm.model_executor.layers.fused_moe.router.fused_topk_router import fused_topk
 from vllm.model_executor.layers.fused_moe.utils import moe_kernel_quantize_input
 from vllm.utils.deep_gemm import per_block_cast_to_fp8
@@ -400,6 +404,7 @@ def make_test_quant_config(
     per_act_token_quant: bool = False,
     block_shape: list[int] | None = None,
     make_gate: bool = True,
+    is_scale_swizzled: bool = True,
 ) -> tuple[torch.Tensor, torch.Tensor, FusedMoEQuantConfig]:
     (_, w1, w1_s, w1_gs), (_, w2, w2_s, w2_gs) = make_test_weights(
         e,
@@ -440,6 +445,7 @@ def make_test_quant_config(
             # TODO: make sure this is handled properly
             g1_alphas=(1 / w1_gs) if w1_gs is not None else None,
             g2_alphas=(1 / w2_gs) if w2_gs is not None else None,
+            is_scale_swizzled=is_scale_swizzled,
         ),
     )
 
diff --git a/tests/kernels/quantization/nvfp4_utils.py b/tests/kernels/quantization/nvfp4_utils.py
index 778895271432..df6513c131d4 100644
--- a/tests/kernels/quantization/nvfp4_utils.py
+++ b/tests/kernels/quantization/nvfp4_utils.py
@@ -88,6 +88,60 @@ def break_fp4_bytes(a, dtype):
     return values.reshape(m, n * 2).to(dtype=dtype)
 
 
+def dequant_nvfp4_kv_cache(
+    fp4_data: torch.Tensor,
+    block_scale: torch.Tensor,
+    global_scale: float,
+    head_size: int,
+    block_size: int,
+) -> torch.Tensor:
+    """Dequantize an NVFP4 KV cache with 4x4-swizzled block scales.
+
+    The input must be in HND layout so that the last two dims are
+    (block_size, last_dim).  For NHD caches, permute to HND first.
+
+    Args:
+        fp4_data: [..., num_heads, block_size, head_size//2] uint8 packed fp4.
+        block_scale: [..., num_heads, block_size, head_size//16] fp8 block
+            scales (as uint8 or float8_e4m3fn).
+        global_scale: checkpoint dequant scale (k_scale or v_scale).
+        head_size: head dimension.
+        block_size: page size.
+
+    Returns:
+        [..., num_heads, block_size, head_size] float32.
+    """
+    data_dim = head_size // 2
+    scale_dim = head_size // 16
+
+    fp4_packed = fp4_data
+    sf_swizzled = block_scale.view(torch.uint8)
+
+    # Unswizzle 4x4 block scales on (block_size, scale_dim) plane.
+    # [..., T, S] → [..., T//4, 4, sg, 4] → permute → [..., T, S]
+    batch_shape = sf_swizzled.shape[:-2]
+    T, S = block_size, scale_dim
+    sg = S // 4
+    sf_reshape = sf_swizzled.reshape(*batch_shape, T // 4, 4, sg, 4)
+    ndim = sf_reshape.ndim
+    # Swap the last four dims: (..., T//4, 4, sg, 4) → (..., T//4, 4, 4, sg)
+    perm = list(range(ndim - 4)) + [ndim - 4, ndim - 1, ndim - 3, ndim - 2]
+    sf_linear = sf_reshape.permute(*perm).reshape(*batch_shape, T, S)
+    sf_f32 = sf_linear.view(torch.float8_e4m3fn).to(torch.float32)
+
+    # Unpack fp4
+    shape = fp4_packed.shape  # [..., T, data_dim]
+    fp4_flat = fp4_packed.reshape(-1, data_dim)
+    fp4_vals = break_fp4_bytes(fp4_flat, torch.float32)
+    fp4_vals = fp4_vals.reshape(*shape[:-1], head_size)
+
+    # Dequant: fp4_val * block_scale * global_scale per 16-element group
+    return (
+        fp4_vals.reshape(*shape[:-1], scale_dim, 16)
+        * (sf_f32 * global_scale).unsqueeze(-1)
+    ).reshape(*shape[:-1], head_size)
+
+
 def get_nvfp4_global_scale(a: torch.Tensor):
     return (FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.abs(a).max().to(torch.float32)
 
diff --git a/tests/kernels/quantization/test_cpu_fp8_scaled_mm.py b/tests/kernels/quantization/test_cpu_fp8_scaled_mm.py
new file mode 100644
index 000000000000..3154e2cb98bb
--- /dev/null
+++ b/tests/kernels/quantization/test_cpu_fp8_scaled_mm.py
@@ -0,0 +1,162 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for CPU FP8 W8A16 block-scaled GEMM kernel (fp8_scaled_mm_cpu).
+
+Run `pytest tests/kernels/quantization/test_cpu_fp8_scaled_mm.py -v`.
+"""
+
+import pytest
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.platforms import current_platform
+
+if not current_platform.is_cpu():
+    pytest.skip("skipping CPU-only tests", allow_module_level=True)
+
+if not ops._supports_cpu_fp8_w8a16:
+    pytest.skip("fp8_scaled_mm_cpu op not available", allow_module_level=True)
+
+BLOCK_SIZE = [128, 128]
+
+
+def cdiv(a: int, b: int) -> int:
+    return -(a // -b)
+
+
+def quantize_weight_block_fp8(
+    weight: torch.Tensor,
+    block_size: list[int],
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Quantize weight [N, K] to FP8 with block scales.
+
+    Returns:
+        fp8_weight: [N, K] float8_e4m3fn
+        scales: [n_tiles, k_tiles] float32
+    """
+    N, K = weight.shape
+    block_n, block_k = block_size
+    fp8_max = torch.finfo(torch.float8_e4m3fn).max
+
+    n_tiles = cdiv(N, block_n)
+    k_tiles = cdiv(K, block_k)
+
+    # Pad for even blocking
+    pad_N = (block_n - (N % block_n)) % block_n
+    pad_K = (block_k - (K % block_k)) % block_k
+    if pad_N > 0 or pad_K > 0:
+        weight = torch.nn.functional.pad(weight, (0, pad_K, 0, pad_N))
+
+    # Reshape into blocks
+    w_blocks = weight.view(n_tiles, block_n, k_tiles, block_k)
+    w_blocks = w_blocks.permute(0, 2, 1, 3).contiguous()
+
+    # Per-block scale
+    abs_max = w_blocks.abs().amax(dim=(-2, -1), keepdim=True)
+    scales = abs_max / fp8_max
+    scales = torch.where(scales == 0, torch.ones_like(scales), scales)
+
+    # Quantize
+    q_fp8 = (w_blocks / scales).clamp(-fp8_max, fp8_max).to(torch.float8_e4m3fn)
+
+    # Reshape back
+    fp8_weight = (
+        q_fp8.permute(0, 2, 1, 3)
+        .contiguous()
+        .view(N + pad_N, K + pad_K)[:N, :K]
+        .contiguous()
+    )
+
+    scales = scales.view(n_tiles, k_tiles)
+    return fp8_weight, scales
+
+
+def dequant_weight_block_fp8(
+    fp8_weight: torch.Tensor,
+    scales: torch.Tensor,
+    block_size: list[int],
+    out_dtype: torch.dtype,
+) -> torch.Tensor:
+    """Dequantize FP8 weight back to float for reference computation."""
+    N, K = fp8_weight.shape
+    block_n, block_k = block_size
+    n_tiles, k_tiles = scales.shape
+
+    pad_N = (block_n - (N % block_n)) % block_n
+    pad_K = (block_k - (K % block_k)) % block_k
+    if pad_N > 0 or pad_K > 0:
+        fp8_padded = torch.nn.functional.pad(fp8_weight.float(), (0, pad_K, 0, pad_N))
+    else:
+        fp8_padded = fp8_weight.float()
+
+    w_blocks = fp8_padded.view(n_tiles, block_n, k_tiles, block_k)
+    w_blocks = w_blocks.permute(0, 2, 1, 3).contiguous()
+    dq = w_blocks * scales.view(n_tiles, k_tiles, 1, 1)
+    dq = dq.permute(0, 2, 1, 3).contiguous().view(N + pad_N, K + pad_K)
+    return dq[:N, :K].to(out_dtype)
+
+
+def ref_fp8_block_scaled_mm(
+    x: torch.Tensor,
+    fp8_weight: torch.Tensor,
+    scales: torch.Tensor,
+    block_size: list[int],
+    bias: torch.Tensor | None,
+    out_dtype: torch.dtype,
+) -> torch.Tensor:
+    """Reference: dequant FP8→float32, matmul in float32, cast to out_dtype."""
+    w_dq = dequant_weight_block_fp8(fp8_weight, scales, block_size, torch.float32)
+    out = torch.mm(x.float(), w_dq.t())
+    if bias is not None:
+        out = out + bias.float()
+    return out.to(out_dtype)
+
+
+# ---------------------------------------------------------------------------
+# Test parameters
+# ---------------------------------------------------------------------------
+M_SIZES = [1, 4, 16, 64, 128]
+# (N, K) — weight shape is [N, K], output has N columns.
+NK_SIZES = [
+    (128, 256),
+    (256, 512),
+    (512, 1024),
+    (1024, 2048),
+    (5120, 5120),
+    (17408, 5120),
+    (5120, 17408),
+]
+
+
+@pytest.mark.parametrize("M", M_SIZES)
+@pytest.mark.parametrize("N,K", NK_SIZES)
+@pytest.mark.parametrize("use_bias", [False, True])
+def test_cpu_fp8_scaled_mm(M: int, N: int, K: int, use_bias: bool):
+    """fp8_scaled_mm_cpu correctness against float reference."""
+    torch.manual_seed(42)
+    out_dtype = torch.bfloat16
+    block_size = BLOCK_SIZE
+
+    x = torch.randn(M, K, dtype=out_dtype) / (K**0.5)
+    w_f32 = torch.randn(N, K, dtype=torch.float32) / (K**0.5)
+    fp8_weight, scales = quantize_weight_block_fp8(w_f32, block_size)
+
+    bias = torch.randn(N, dtype=torch.float32) * 0.1 if use_bias else None
+
+    ref_out = ref_fp8_block_scaled_mm(
+        x, fp8_weight, scales, block_size, bias, out_dtype
+    )
+
+    packed_weight = torch.ops._C.convert_weight_packed(fp8_weight)
+    kernel_out = ops.fp8_scaled_mm_cpu(
+        x,
+        packed_weight,
+        scales,
+        block_size,
+        bias,
+        out_dtype,
+        True,
+    )
+
+    assert kernel_out.dtype == out_dtype
+    torch.testing.assert_close(kernel_out, ref_out, rtol=0.02, atol=0.01)
diff --git a/tests/kernels/quantization/test_cutlass_scaled_mm.py b/tests/kernels/quantization/test_cutlass_scaled_mm.py
index a8adec49a955..32eb65cfc5b6 100644
--- a/tests/kernels/quantization/test_cutlass_scaled_mm.py
+++ b/tests/kernels/quantization/test_cutlass_scaled_mm.py
@@ -40,6 +40,18 @@
     (512, 24576, 128),
 ]
 
+# Shapes with N or K not divisible by 16.  These exercise the padding path
+# inside CutlassFP8ScaledMMLinearKernel.apply_scaled_mm (e.g. Qwen2.5-VL
+# vision MLP dims).
+UNALIGNED_MNK_FACTORS = [
+    (32, 3420, 1280),
+    (32, 1280, 6840),
+    (1, 3420, 1280),
+    (64, 6840, 1280),
+    (16, 100, 200),
+    (33, 255, 513),
+]
+
 CUDA_DEVICES = [
     f"cuda:{i}" for i in range(1 if torch.accelerator.device_count() == 1 else 2)
 ]
@@ -152,6 +164,62 @@ def test_cutlass_fp8_gemm(
     cutlass_fp8_gemm_helper(m, n, k, a_scale_group_shape, b_scale_group_shape, use_bias)
 
 
+@pytest.mark.parametrize("m,n,k", UNALIGNED_MNK_FACTORS)
+@pytest.mark.parametrize(
+    "a_scale_group_shape", [PER_TOKEN_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE]
+)
+@pytest.mark.parametrize(
+    "b_scale_group_shape", [PER_OUT_CH_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE]
+)
+@pytest.mark.parametrize("use_bias", [True, False])
+@pytest.mark.skipif(
+    not current_platform.has_device_capability(89),
+    reason="FP8 is not supported on this GPU type.",
+)
+def test_cutlass_fp8_gemm_padded(
+    m: int, n: int, k: int, a_scale_group_shape, b_scale_group_shape, use_bias: bool
+):
+    """Test CUTLASS FP8 GEMM with padding for non-16-aligned N/K dims.
+
+    Exercises CutlassFP8ScaledMMLinearKernel.apply_scaled_mm which pads
+    inputs to satisfy CUTLASS alignment requirements — the path taken by
+    models like Qwen2.5-VL whose vision MLP has non-16-aligned dims.
+    """
+    from vllm.model_executor.kernels.linear.scaled_mm.cutlass import (
+        CutlassFP8ScaledMMLinearKernel,
+    )
+
+    a = to_fp8(torch.randn((m, k), device="cuda"))
+    b = to_fp8(torch.randn((n, k), device="cuda").t())
+
+    a_scales_shape = scale_shape(a.shape, a_scale_group_shape)
+    b_scales_shape = scale_shape(b.shape, b_scale_group_shape)
+
+    scale_a = torch.randn(a_scales_shape, device="cuda", dtype=torch.float32)
+    scale_b = torch.randn(b_scales_shape, device="cuda", dtype=torch.float32)
+
+    scale_a = scale_a.t().contiguous().t()
+    scale_b = scale_b.t().contiguous().t()
+
+    out_dtype = torch.bfloat16
+    bias = torch.rand((n,), device="cuda", dtype=out_dtype) * 10 if use_bias else None
+
+    baseline = baseline_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
+
+    kernel = object.__new__(CutlassFP8ScaledMMLinearKernel)
+    out = kernel.apply_scaled_mm(
+        A=a,
+        B=b,
+        out_dtype=out_dtype,
+        As=scale_a,
+        Bs=scale_b,
+        bias=bias,
+        output_shape=[m, n],
+    )
+
+    torch.testing.assert_close(out, baseline, rtol=5e-1, atol=1.5e-1)
+
+
 @pytest.mark.parametrize("m,n,k", MNK_FACTORS)
 @pytest.mark.parametrize(
     "a_scale_group_shape,b_scale_group_shape", [((1, 128), (128, 128))]
diff --git a/tests/kernels/quantization/test_nvfp4_emulation.py b/tests/kernels/quantization/test_nvfp4_emulation.py
new file mode 100644
index 000000000000..71072d9e9fff
--- /dev/null
+++ b/tests/kernels/quantization/test_nvfp4_emulation.py
@@ -0,0 +1,308 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import huggingface_hub
+import pytest
+import torch
+from safetensors import safe_open
+
+from vllm.model_executor.layers.quantization.utils import (
+    nvfp4_emulation_utils,
+)
+from vllm.model_executor.layers.quantization.utils.nvfp4_emulation_utils import (
+    dequantize_to_dtype,
+    ref_nvfp4_quant_dequant,
+)
+from vllm.platforms import current_platform
+from vllm.triton_utils import triton
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="Triton NVFP4 kernel requires CUDA.",
+)
+def test_triton_dequantize_nvfp4(monkeypatch) -> None:
+    """Test the Triton dequantization kernel against the CPU reference
+    using real NVFP4 weights from a checkpoint.
+
+    Tests both 2D (attention projection) and 3D (stacked MoE experts).
+    """
+    checkpoint_path = huggingface_hub.snapshot_download(
+        "nvidia/Qwen3-30B-A3B-NVFP4",
+        allow_patterns=["model-00001-of-00004.safetensors"],
+    )
+    shard_path = f"{checkpoint_path}/model-00001-of-00004.safetensors"
+    block_size = 16
+
+    with safe_open(shard_path, framework="pt", device="cpu") as f:
+        all_keys = list(f.keys())
+
+        # 2D case: attention projection
+        tensor_fp4_2d = f.get_tensor("model.layers.9.self_attn.k_proj.weight")
+        tensor_sf_2d = f.get_tensor("model.layers.9.self_attn.k_proj.weight_scale")
+        global_scale_2d = f.get_tensor("model.layers.9.self_attn.k_proj.weight_scale_2")
+
+        # 3D case: stack ALL experts for layer 9 up_proj
+        expert_prefix = "model.layers.9.mlp.experts."
+        expert_indices = sorted(
+            int(key.split(".")[5])
+            for key in all_keys
+            if key.startswith(expert_prefix) and key.endswith(".up_proj.weight")
+        )
+        assert len(expert_indices) > 0
+
+        all_fp4 = []
+        all_sf = []
+        all_global_scale = []
+        for index in expert_indices:
+            name = f"{expert_prefix}{index}.up_proj"
+            all_fp4.append(f.get_tensor(f"{name}.weight"))
+            all_sf.append(f.get_tensor(f"{name}.weight_scale"))
+            all_global_scale.append(f.get_tensor(f"{name}.weight_scale_2"))
+
+    tensor_fp4_3d = torch.stack(all_fp4)
+    tensor_sf_3d = torch.stack(all_sf)
+    global_scale_3d = torch.stack(all_global_scale)
+
+    test_cases = [
+        ("2D base", tensor_fp4_2d, tensor_sf_2d, global_scale_2d),
+        (
+            "2D 2x rows",
+            tensor_fp4_2d.repeat(2, 1),
+            tensor_sf_2d.repeat(2, 1),
+            global_scale_2d,
+        ),
+        (
+            "2D 4x rows",
+            tensor_fp4_2d.repeat(4, 1),
+            tensor_sf_2d.repeat(4, 1),
+            global_scale_2d,
+        ),
+        (
+            "2D 2x cols",
+            tensor_fp4_2d.repeat(1, 2),
+            tensor_sf_2d.repeat(1, 2),
+            global_scale_2d,
+        ),
+        ("3D base", tensor_fp4_3d, tensor_sf_3d, global_scale_3d),
+        (
+            "3D 2x experts",
+            tensor_fp4_3d.repeat(2, 1, 1),
+            tensor_sf_3d.repeat(2, 1, 1),
+            global_scale_3d.repeat(2),
+        ),
+        (
+            "3D 2x rows",
+            tensor_fp4_3d.repeat(1, 2, 1),
+            tensor_sf_3d.repeat(1, 2, 1),
+            global_scale_3d,
+        ),
+        (
+            "3D 2x cols",
+            tensor_fp4_3d.repeat(1, 1, 2),
+            tensor_sf_3d.repeat(1, 1, 2),
+            global_scale_3d,
+        ),
+    ]
+
+    quantiles = [0.5, 0.001, 0.999]
+
+    # Move the E2M1 lookup table to CUDA ahead of time, as would normally
+    # happen during model loading (process_weights_after_loading).  Both the
+    # Triton and PyTorch reference paths run on CUDA.
+    nvfp4_emulation_utils.kE2M1ToFloat_handle.val = (
+        nvfp4_emulation_utils.kE2M1ToFloat_handle.val.cuda()
+    )
+
+    for label, tensor_fp4, tensor_sf, global_scale in test_cases:
+        fp4_cuda = tensor_fp4.cuda()
+        sf_cuda = tensor_sf.cuda()
+        gs_cuda = global_scale.cuda()
+
+        # Triton path
+        triton_result = dequantize_to_dtype(
+            fp4_cuda,
+            sf_cuda,
+            gs_cuda,
+            torch.bfloat16,
+            block_size,
+            swizzle=False,
+        )
+
+        # Reference path (PyTorch ops on CUDA, Triton dispatch disabled)
+        with monkeypatch.context() as m:
+            m.setattr(
+                nvfp4_emulation_utils.current_platform,
+                "is_cuda_alike",
+                lambda: False,
+            )
+            reference = dequantize_to_dtype(
+                fp4_cuda,
+                sf_cuda,
+                gs_cuda,
+                torch.bfloat16,
+                block_size,
+                swizzle=False,
+            )
+
+        torch.testing.assert_close(triton_result, reference, atol=0, rtol=0)
+
+        # Benchmark
+        shape = list(tensor_fp4.shape)
+
+        def _triton_bench(
+            fp4_cuda=fp4_cuda,
+            scale_cuda=sf_cuda,
+            global_scale_cuda=gs_cuda,
+            block_size=block_size,
+        ):
+            return dequantize_to_dtype(
+                fp4_cuda,
+                scale_cuda,
+                global_scale_cuda,
+                torch.bfloat16,
+                block_size,
+                swizzle=False,
+            )
+
+        triton_ms, triton_min, triton_max = triton.testing.do_bench(
+            _triton_bench, quantiles=quantiles
+        )
+
+        def _reference_bench(
+            fp4_cuda=fp4_cuda,
+            scale_cuda=sf_cuda,
+            global_scale_cuda=gs_cuda,
+            block_size=block_size,
+        ):
+            with monkeypatch.context() as m2:
+                m2.setattr(
+                    nvfp4_emulation_utils.current_platform,
+                    "is_cuda_alike",
+                    lambda: False,
+                )
+                dequantize_to_dtype(
+                    fp4_cuda,
+                    scale_cuda,
+                    global_scale_cuda,
+                    torch.bfloat16,
+                    block_size,
+                    swizzle=False,
+                )
+
+        ref_ms, ref_min, ref_max = triton.testing.do_bench(
+            _reference_bench, quantiles=quantiles
+        )
+
+        speedup = ref_ms / triton_ms if triton_ms > 0 else float("inf")
+        print(f"  dequantize {label} {shape}:")
+        print(
+            f"    triton:    median={triton_ms:.3f}ms, "
+            f"min={triton_min:.3f}ms, max={triton_max:.3f}ms"
+        )
+        print(
+            f"    reference: median={ref_ms:.3f}ms, "
+            f"min={ref_min:.3f}ms, max={ref_max:.3f}ms"
+        )
+        print(f"    speedup:   {speedup:.2f}x")
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="Triton NVFP4 kernel requires CUDA.",
+)
+@pytest.mark.parametrize(
+    "m, k",
+    [
+        (1, 16),
+        (1, 4096),
+        (2, 4096),
+        (4, 4096),
+        (8, 4096),
+        (16, 4096),
+        (24, 4096),
+        (32, 4096),
+        (1, 8192),
+        (2, 8192),
+        (4, 8192),
+        (8, 8192),
+        (16, 8192),
+        (24, 8192),
+        (32, 8192),
+        (1, 32),
+        (2, 48),
+        (7, 64),
+        (16, 128),
+        (33, 160),
+        (128, 256),
+        (256, 512),
+        (1024, 1024),
+        (5120, 2048),
+        (2048, 4096),
+        (4096, 7168),
+        (8192, 8192),
+        (128, 16384),
+    ],
+)
+@pytest.mark.parametrize("global_scale_value", [0.5, 1.0, 0.001])
+def test_triton_nvfp4_quant_dequant(
+    monkeypatch, m: int, k: int, global_scale_value: float
+) -> None:
+    """Test the Triton quant-dequant kernel against the CPU reference."""
+    block_size = 16
+    x = torch.randn(m, k, dtype=torch.bfloat16, device="cuda")
+    global_scale = torch.tensor(global_scale_value, dtype=torch.float32, device="cuda")
+
+    # Triton path
+    triton_result = ref_nvfp4_quant_dequant(x, global_scale, block_size)
+
+    # CPU reference path
+    with monkeypatch.context() as mp:
+        mp.setattr(
+            nvfp4_emulation_utils.current_platform,
+            "is_cuda_alike",
+            lambda: False,
+        )
+        reference = ref_nvfp4_quant_dequant(x.cpu(), global_scale.cpu(), block_size)
+
+    torch.testing.assert_close(triton_result.cpu(), reference, atol=0, rtol=0)
+
+    # Benchmark (both paths on CUDA tensors for fair comparison)
+    quantiles = [0.5, 0.001, 0.999]
+
+    def _triton_bench(
+        input_tensor=x, input_global_scale=global_scale, input_block_size=block_size
+    ):
+        return ref_nvfp4_quant_dequant(
+            input_tensor, input_global_scale, input_block_size
+        )
+
+    triton_ms, triton_min, triton_max = triton.testing.do_bench(
+        _triton_bench, quantiles=quantiles
+    )
+
+    def _reference_bench(
+        input_tensor=x, input_global_scale=global_scale, input_block_size=block_size
+    ):
+        with monkeypatch.context() as mp2:
+            mp2.setattr(
+                nvfp4_emulation_utils.current_platform,
+                "is_cuda_alike",
+                lambda: False,
+            )
+            ref_nvfp4_quant_dequant(input_tensor, input_global_scale, input_block_size)
+
+    ref_ms, ref_min, ref_max = triton.testing.do_bench(
+        _reference_bench, quantiles=quantiles
+    )
+
+    speedup = ref_ms / triton_ms if triton_ms > 0 else float("inf")
+    print(f"  quant_dequant [{m}x{k}] gs={global_scale_value}:")
+    print(
+        f"    triton:    median={triton_ms:.3f}ms, "
+        f"min={triton_min:.3f}ms, max={triton_max:.3f}ms"
+    )
+    print(
+        f"    reference: median={ref_ms:.3f}ms, "
+        f"min={ref_min:.3f}ms, max={ref_max:.3f}ms"
+    )
+    print(f"    speedup:   {speedup:.2f}x")
diff --git a/tests/kernels/quantization/test_per_token_group_quant.py b/tests/kernels/quantization/test_per_token_group_quant.py
index 5447a43ffb98..4089e9bc4688 100644
--- a/tests/kernels/quantization/test_per_token_group_quant.py
+++ b/tests/kernels/quantization/test_per_token_group_quant.py
@@ -73,11 +73,6 @@ def test_per_token_group_quant_fp8(
         # Larger shapes with padding
         (127, 7168, 128),
         (253, 640, 128),
-        # Non-power-of-2 group size
-        (4, 768, 96),  # 768/96=8 groups, no padding
-        (3, 768, 96),  # 768/96=8 groups, MN padding
-        (4, 480, 96),  # 480/96=5 groups, K padding
-        (1, 480, 96),  # both MN and K padding
     ],
 )
 @pytest.mark.parametrize("poisoned_scales", [False, True])
@@ -161,6 +156,188 @@ def test_per_token_group_quant_fp8_packed(
     )
 
 
+@pytest.mark.skipif(
+    not current_platform.is_cuda(), reason="DeepGEMM not available on this platform"
+)
+def test_per_token_group_quant_fp8_packed_all_zero():
+    """All-zero input must produce well-defined UE8M0 scale bytes via the eps
+    floor in the kernel's UE8M0 path. Locks down the all-zero behavior before
+    optimization.
+
+    The CUDA kernel computes:
+        y_s = eps / fp8_max
+        y_s = exp2(ceil(log2(fmax(y_s, 1e-10))))
+    For all-zero input, eps/fp8_max < 1e-10, so the inner fmax clamps back to
+    1e-10, giving exp2(ceil(log2(1e-10))) = exp2(-33) => UE8M0 byte 0x5E (94).
+    """
+
+    device = "cuda"
+    num_tokens, hidden_dim, group_size = 4, 7168, 128
+    x = torch.zeros((num_tokens, hidden_dim), device=device, dtype=torch.bfloat16)
+
+    out_q, out_s_packed = fp8_utils.per_token_group_quant_fp8_packed_for_deepgemm(
+        x,
+        group_size=group_size,
+        use_ue8m0=True,
+    )
+
+    # Quantized values must be all zero.
+    assert torch.equal(
+        out_q.view(torch.uint8),
+        torch.zeros_like(out_q, dtype=torch.uint8),
+    ), "All-zero input should produce all-zero FP8 output"
+
+    # UE8M0 byte produced by the kernel for all-zero input.
+    # The kernel's inner fmax(y_s, 1e-10) clamps eps/fp8_max back to 1e-10.
+    # 1e-10 as float32 has biased exponent 0x5D and a non-zero mantissa, so
+    # the kernel's bit-twiddle (exp_bits + (mant_bits != 0)) rounds up to
+    # 0x5E. This matches exp2(ceil(log2(1e-10))) = exp2(-33).
+    expected_exp_byte = 0x5E
+
+    mn = num_tokens
+    groups_per_row = hidden_dim // group_size
+    k_num_packed = (groups_per_row + 3) // 4
+    tma_aligned_mn = ((mn + 3) // 4) * 4
+    num_scale_elems = mn + (k_num_packed - 1) * tma_aligned_mn
+
+    # All valid scale slots must contain the expected packed value.
+    # Padding slots must be zero.
+    actual = torch.as_strided(out_s_packed, (num_scale_elems,), (1,)).cpu()
+
+    expected = torch.zeros(num_scale_elems, dtype=torch.int32, device="cpu")
+    for row in range(mn):
+        for g in range(groups_per_row):
+            pack_col = g // 4
+            pos = g % 4
+            idx = pack_col * tma_aligned_mn + row
+            expected[idx] |= expected_exp_byte << (pos * 8)
+
+    assert torch.equal(actual, expected), "All-zero scale bytes mismatch"
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda(), reason="DeepGEMM not available on this platform"
+)
+def test_per_token_group_quant_fp8_packed_mantissa_rounds_up():
+    """Inputs whose absmax/max_8bit produces a non-power-of-2 force the
+    mantissa-rounding-up branch (exp_byte += 1). Locks down this behavior
+    before optimization."""
+
+    device = "cuda"
+    num_tokens, hidden_dim, group_size = 4, 7168, 128
+
+    # Build a tensor whose per-group absmax = 1.5 * fp8_max * 2^k for various k.
+    # fp8_max = torch.finfo(torch.float8_e4m3fn).max = 448.0.
+    # Then absmax/fp8_max = 1.5 * 2^k -> non-zero mantissa, triggers ceil
+    # rounding to 2^(k+1). Use k=0 for simplicity; the bf16 representation of
+    # 1.5*448=672.0 is exact.
+    x = torch.full(
+        (num_tokens, hidden_dim),
+        672.0,
+        device=device,
+        dtype=torch.bfloat16,
+    )
+
+    out_q, out_s_packed = fp8_utils.per_token_group_quant_fp8_packed_for_deepgemm(
+        x,
+        group_size=group_size,
+        use_ue8m0=True,
+    )
+
+    with patch("vllm.platforms.current_platform.is_cuda", return_value=False):
+        ref_q, ref_s = fp8_utils.per_token_group_quant_fp8(
+            x,
+            group_size,
+            use_ue8m0=True,
+        )
+
+    assert torch.equal(out_q, ref_q), "Quantized output mismatch"
+
+    mn = num_tokens
+    groups_per_row = hidden_dim // group_size
+    k_num_packed = (groups_per_row + 3) // 4
+    tma_aligned_mn = ((mn + 3) // 4) * 4
+    num_scale_elems = mn + (k_num_packed - 1) * tma_aligned_mn
+
+    ref_s_flat = ref_s.reshape(mn, groups_per_row)
+    ref_exponents = (ref_s_flat.view(torch.int32) >> 23) & 0xFF
+    expected = torch.zeros(num_scale_elems, dtype=torch.int32, device="cpu")
+    for row in range(mn):
+        for g in range(groups_per_row):
+            pack_col = g // 4
+            pos = g % 4
+            idx = pack_col * tma_aligned_mn + row
+            expected[idx] |= int(ref_exponents[row, g].item()) << (pos * 8)
+
+    actual = torch.as_strided(out_s_packed, (num_scale_elems,), (1,)).cpu()
+    assert torch.equal(actual, expected), "Scale bytes mismatch"
+
+
+@pytest.mark.parametrize(
+    "num_tokens,hidden_dim",
+    [
+        (1, 7168),  # mn padded 1 -> 4
+        (2, 7168),  # mn padded 2 -> 4
+        (3, 7168),  # mn padded 3 -> 4
+        (5, 7168),  # mn padded 5 -> 8
+        (127, 7168),  # mn padded 127 -> 128
+        (253, 640),  # both mn and groups padded
+        (1, 384),  # extreme: 1 group, 1 mn row -> both axes padded
+    ],
+)
+@pytest.mark.skipif(
+    not current_platform.is_cuda(), reason="DeepGEMM not available on this platform"
+)
+def test_per_token_group_quant_fp8_packed_zero_fills_padded_output_q(
+    num_tokens, hidden_dim
+):
+    """When output_q is allocated with shape (tma_aligned_mn, k) instead of
+    (mn, k), the kernel must overwrite the padded mn rows with zeros so
+    callers can use ``torch.empty`` instead of ``torch.zeros``."""
+
+    device = "cuda"
+    group_size = 128
+    torch.manual_seed(42)
+    x = torch.randn((num_tokens, hidden_dim), device=device, dtype=torch.bfloat16) * 8
+
+    mn = num_tokens
+    groups_per_row = hidden_dim // group_size
+    k_num_packed = (groups_per_row + 3) // 4
+    tma_aligned_mn = ((mn + 3) // 4) * 4
+
+    fp8_dtype = torch.float8_e4m3fn
+    finfo = torch.finfo(fp8_dtype)
+    # Allocate output_q with the padded mn extent and pre-fill with 0xFF
+    # so the kernel cannot rely on a clean buffer.
+    out_q = torch.empty((tma_aligned_mn, hidden_dim), device=device, dtype=fp8_dtype)
+    out_q.view(torch.uint8).fill_(0xFF)
+
+    out_s_packed = torch.empty_strided(
+        (mn, k_num_packed),
+        (1, tma_aligned_mn),
+        device=device,
+        dtype=torch.int32,
+    )
+
+    torch.ops._C.per_token_group_fp8_quant_packed(
+        x, out_q, out_s_packed, group_size, 1e-10, finfo.min, finfo.max
+    )
+
+    # Live rows must match the Triton reference.
+    with patch("vllm.platforms.current_platform.is_cuda", return_value=False):
+        ref_q, _ = fp8_utils.per_token_group_quant_fp8(x, group_size, use_ue8m0=True)
+    assert torch.equal(out_q[:mn], ref_q), "Live region mismatch"
+
+    # Padded rows must be all-zero; without this, downstream TMA loads would
+    # see uninitialised data.
+    if tma_aligned_mn > mn:
+        padded_bytes = out_q[mn:tma_aligned_mn].view(torch.uint8)
+        assert padded_bytes.eq(0).all(), (
+            f"Padded rows [{mn}, {tma_aligned_mn}) not zeroed; "
+            f"{padded_bytes.ne(0).sum().item()} non-zero bytes"
+        )
+
+
 @pytest.mark.parametrize("shape", [(32, 128), (64, 256), (16, 512)])
 @pytest.mark.parametrize("group_size", [64, 128])
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
diff --git a/tests/kernels/test_awq_int4_to_int8.py b/tests/kernels/test_awq_int4_to_int8.py
index 829c0d2277a7..f94214785d22 100644
--- a/tests/kernels/test_awq_int4_to_int8.py
+++ b/tests/kernels/test_awq_int4_to_int8.py
@@ -20,7 +20,11 @@
 import pytest
 import torch
 
-from vllm._custom_ops import _supports_cpu_w4a8_int8
+from vllm._custom_ops import (
+    CPUQuantAlgo,
+    convert_weight_packed_scale_zp,
+    int4_scaled_mm_cpu,
+)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     pack_cols,
 )
@@ -29,11 +33,6 @@
 if not current_platform.is_cpu():
     pytest.skip("skipping CPU-only tests", allow_module_level=True)
 
-requires_cpu_w4a8_int8 = pytest.mark.skipif(
-    not _supports_cpu_w4a8_int8,
-    reason="Requires vLLM CPU build with SGLang INT4 W4A8 kernels",
-)
-
 
 def make_awq_checkpoint_data(K, N, group_size, seed=42):
     """Create synthetic AWQ checkpoint data in packed int32 format.
@@ -87,7 +86,6 @@ def make_awq_checkpoint_data(K, N, group_size, seed=42):
 class TestConvertWeightPackedScaleZp:
     """Tests for convert_weight_packed_scale_zp weightpacking."""
 
-    @requires_cpu_w4a8_int8
     @pytest.mark.parametrize(
         "K,N,group_size",
         [
@@ -102,8 +100,11 @@ def test_packing_output_shapes(self, K, N, group_size):
             K, N, group_size
         )
 
-        blocked_w, blocked_zp, blocked_s = torch.ops._C.convert_weight_packed_scale_zp(
-            packed_qweight, packed_qzeros, scales
+        blocked_w, blocked_zp, blocked_s = convert_weight_packed_scale_zp(
+            packed_qweight,
+            packed_qzeros,
+            scales,
+            CPUQuantAlgo.AWQ,
         )
 
         block_n = 32
@@ -129,7 +130,6 @@ def test_packing_output_shapes(self, K, N, group_size):
 class TestInt4ScaledMmCpu:
     """Tests for int4_scaled_mm_cpu GEMM kernel."""
 
-    @requires_cpu_w4a8_int8
     @pytest.mark.parametrize(
         "M,K,N,group_size",
         [
@@ -146,12 +146,15 @@ def test_gemm_vs_float_reference(self, M, K, N, group_size):
             make_awq_checkpoint_data(K, N, group_size)
         )
 
-        blocked_w, blocked_zp, blocked_s = torch.ops._C.convert_weight_packed_scale_zp(
-            packed_qweight, packed_qzeros, scales
+        blocked_w, blocked_zp, blocked_s = convert_weight_packed_scale_zp(
+            packed_qweight,
+            packed_qzeros,
+            scales,
+            CPUQuantAlgo.AWQ,
         )
 
         x = torch.randn(M, K, dtype=torch.bfloat16)
-        out = torch.ops._C.int4_scaled_mm_cpu(x, blocked_w, blocked_zp, blocked_s, None)
+        out = int4_scaled_mm_cpu(x, blocked_w, blocked_zp, blocked_s, None)
 
         ref_out = torch.mm(x.float(), float_ref)
 
@@ -169,7 +172,6 @@ def test_gemm_vs_float_reference(self, M, K, N, group_size):
         )
         print(f"  [PASS] INT4 GEMM correct: M={M}, K={K}, N={N}")
 
-    @requires_cpu_w4a8_int8
     @pytest.mark.parametrize("M", [1, 8, 32])
     def test_gemm_with_bias(self, M):
         """INT4 W4A8 GEMM with bias should match reference."""
@@ -178,14 +180,17 @@ def test_gemm_with_bias(self, M):
             make_awq_checkpoint_data(K, N, group_size)
         )
 
-        blocked_w, blocked_zp, blocked_s = torch.ops._C.convert_weight_packed_scale_zp(
-            packed_qweight, packed_qzeros, scales
+        blocked_w, blocked_zp, blocked_s = convert_weight_packed_scale_zp(
+            packed_qweight,
+            packed_qzeros,
+            scales,
+            CPUQuantAlgo.AWQ,
         )
 
         bias = torch.randn(N, dtype=torch.float32)
         x = torch.randn(M, K, dtype=torch.bfloat16)
 
-        out = torch.ops._C.int4_scaled_mm_cpu(x, blocked_w, blocked_zp, blocked_s, bias)
+        out = int4_scaled_mm_cpu(x, blocked_w, blocked_zp, blocked_s, bias)
 
         ref_out = torch.mm(x.float(), float_ref) + bias
         abs_diff = (out.float() - ref_out).abs()
@@ -197,7 +202,6 @@ def test_gemm_with_bias(self, M):
         )
         print(f"  [PASS] INT4 GEMM with bias: M={M}")
 
-    @requires_cpu_w4a8_int8
     def test_gemm_3d_input(self):
         """apply() reshapes 3D input [B, S, K] -> [B*S, K] -> back to 3D."""
         K, N, group_size = 256, 128, 128
@@ -205,17 +209,18 @@ def test_gemm_3d_input(self):
             make_awq_checkpoint_data(K, N, group_size)
         )
 
-        blocked_w, blocked_zp, blocked_s = torch.ops._C.convert_weight_packed_scale_zp(
-            packed_qweight, packed_qzeros, scales
+        blocked_w, blocked_zp, blocked_s = convert_weight_packed_scale_zp(
+            packed_qweight,
+            packed_qzeros,
+            scales,
+            CPUQuantAlgo.AWQ,
         )
 
         B, S = 2, 8
         x_3d = torch.randn(B, S, K, dtype=torch.bfloat16)
         x_2d = x_3d.reshape(-1, K)
 
-        out_2d = torch.ops._C.int4_scaled_mm_cpu(
-            x_2d, blocked_w, blocked_zp, blocked_s, None
-        )
+        out_2d = int4_scaled_mm_cpu(x_2d, blocked_w, blocked_zp, blocked_s, None)
         out_3d = out_2d.reshape(B, S, N)
 
         ref_out = torch.mm(x_2d.float(), float_ref).reshape(B, S, N)
@@ -229,7 +234,6 @@ def test_gemm_3d_input(self):
         assert mean_rel < 0.05, f"Mean relative error {mean_rel:.4f} for 3D exceeds 5%"
         print(f"  [PASS] 3D input [{B},{S},{K}] -> output [{B},{S},{N}]")
 
-    @requires_cpu_w4a8_int8
     def test_gemm_fp16_input(self):
         """INT4 GEMM should also work with fp16 input."""
         K, N, group_size, M = 256, 256, 128, 8
@@ -237,12 +241,15 @@ def test_gemm_fp16_input(self):
             make_awq_checkpoint_data(K, N, group_size)
         )
 
-        blocked_w, blocked_zp, blocked_s = torch.ops._C.convert_weight_packed_scale_zp(
-            packed_qweight, packed_qzeros, scales
+        blocked_w, blocked_zp, blocked_s = convert_weight_packed_scale_zp(
+            packed_qweight,
+            packed_qzeros,
+            scales,
+            CPUQuantAlgo.AWQ,
         )
 
         x = torch.randn(M, K, dtype=torch.float16)
-        out = torch.ops._C.int4_scaled_mm_cpu(x, blocked_w, blocked_zp, blocked_s, None)
+        out = int4_scaled_mm_cpu(x, blocked_w, blocked_zp, blocked_s, None)
 
         ref_out = torch.mm(x.float(), float_ref)
         abs_diff = (out.float() - ref_out).abs()
diff --git a/tests/kernels/test_compressor_kv_cache.py b/tests/kernels/test_compressor_kv_cache.py
new file mode 100644
index 000000000000..122254bc3c41
--- /dev/null
+++ b/tests/kernels/test_compressor_kv_cache.py
@@ -0,0 +1,535 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Round-trip tests for compressor → FP8 quant + KV cache insert → gather + dequant.
+
+Four test functions cover five paths:
+  A) DeepseekV4 Attention: head_dim=512 (448 FP8 nope + 64 bf16 rope), quant_block=64
+  B) Indexer:       head_dim=128 (all FP8), quant_block=128
+  C) DeepseekV4 Attention magnitude range: correctness across small/large values
+  D) Indexer fused Triton kernel: compress+norm+rope+quant+insert
+"""
+
+import math
+
+import pytest
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.v1.attention.ops.deepseek_v4_ops import (
+    dequantize_and_gather_k_cache,
+    quantize_and_insert_k_cache,
+)
+from vllm.v1.attention.ops.deepseek_v4_ops.fused_compress_quant_cache import (
+    _fused_kv_compress_norm_rope_insert_indexer_attn,
+    _fused_kv_compress_norm_rope_insert_indexer_mxfp4_attn,
+)
+
+from .test_fused_indexer_q_rope_quant import quantize_to_mxfp4
+
+
+def _ue8m0_reference(x: torch.Tensor, block_size: int, fp8_max: float):
+    """PyTorch reference for UE8M0 FP8 quantization (per-block, power-of-2 scale).
+
+    Returns (x_fp8, scales) where x_fp8 is float8_e4m3fn and scales are float32.
+    """
+    assert x.dim() == 1
+    n = x.numel()
+    n_blocks = math.ceil(n / block_size)
+    x_fp8 = torch.zeros(n, dtype=torch.float8_e4m3fn, device=x.device)
+    scales = torch.zeros(n_blocks, dtype=torch.float32, device=x.device)
+
+    for i in range(n_blocks):
+        start = i * block_size
+        end = min(start + block_size, n)
+        block = x[start:end].float()
+        amax = block.abs().max().clamp(min=1e-4)
+        raw_scale = amax / fp8_max
+        exponent = math.ceil(math.log2(raw_scale.item()))
+        scale = 2.0**exponent
+        scales[i] = scale
+        quantized = (block / scale).clamp(-fp8_max, fp8_max)
+        x_fp8[start:end] = quantized.to(torch.float8_e4m3fn)
+
+    return x_fp8, scales
+
+
+# ── Test A: DeepseekV4 Attention path ──────────────────────────────────────────────
+
+
+@pytest.mark.parametrize("num_tokens", [1, 4, 8, 17])
+@pytest.mark.parametrize("block_size", [16, 64])
+def test_deepseek_v4_attention_quant_cache_roundtrip(num_tokens: int, block_size: int):
+    """compressed_kv → quantize_and_insert_k_cache → dequantize_and_gather_k_cache
+    → compare against original."""
+
+    HEAD_DIM = 512
+    NOPE_DIM = 448
+    HEAD_BYTES = 584  # 448 fp8 + 128 bf16 + 8 uint8 scale
+    FP8_MAX = 448.0
+    QUANT_BLOCK = 64
+
+    num_blocks = (num_tokens + block_size - 1) // block_size + 1
+    device = "cuda"
+
+    # Random compressed_kv (simulates compressor output)
+    compressed_kv = torch.randn(
+        num_tokens, HEAD_DIM, dtype=torch.bfloat16, device=device
+    )
+
+    # ── Quant + insert ──────────────────────────────────────────────────
+    k_cache = torch.zeros(
+        num_blocks, block_size, HEAD_BYTES, dtype=torch.uint8, device=device
+    )
+    k_cache_2d = k_cache.view(num_blocks, -1)
+
+    # Sequential slot mapping: token i → slot i
+    slot_mapping = torch.arange(num_tokens, dtype=torch.int64, device=device)
+
+    quantize_and_insert_k_cache(
+        compressed_kv, k_cache_2d, slot_mapping, block_size=block_size
+    )
+
+    # ── Gather + dequant ────────────────────────────────────────────────
+    num_reqs = 1
+    max_blocks_per_seq = num_blocks
+    out = torch.zeros(
+        num_reqs, num_tokens, HEAD_DIM, dtype=torch.bfloat16, device=device
+    )
+    seq_lens = torch.tensor([num_tokens], dtype=torch.int32, device=device)
+    # block_table: request 0 uses physical blocks 0, 1, ...
+    block_table = torch.arange(
+        max_blocks_per_seq, dtype=torch.int32, device=device
+    ).unsqueeze(0)
+
+    dequantize_and_gather_k_cache(
+        out, k_cache, seq_lens, None, block_table, block_size, offset=0
+    )
+
+    recovered = out[0, :num_tokens]
+
+    # ── NoPE portion (first 448): FP8 quantized, expect UE8M0 error ──
+    nope_orig = compressed_kv[:, :NOPE_DIM].float()
+    nope_recv = recovered[:, :NOPE_DIM].float()
+    nope_diff = (nope_recv - nope_orig).abs()
+
+    # Per-token check: FP8 e4m3 (3-bit mantissa) worst-case error is
+    # half-ULP at the largest representable value.  At y ≈ 448 (max),
+    # ULP = 2^(8-3) = 32, so error ≤ 16 * scale.
+    for t in range(num_tokens):
+        _, scales = _ue8m0_reference(
+            compressed_kv[t, :NOPE_DIM].float(), QUANT_BLOCK, FP8_MAX
+        )
+        max_allowed = 16.0 * scales.max().item()
+        token_diff = nope_diff[t].max().item()
+        assert token_diff <= max_allowed, (
+            f"Token {t} nope diff {token_diff} exceeds max_allowed "
+            f"{max_allowed} (scale={scales.max().item()})"
+        )
+
+    # ── RoPE portion (last 64): stored as bf16, should be exact ─────
+    rope_diff = (recovered[:, NOPE_DIM:] - compressed_kv[:, NOPE_DIM:]).abs()
+    assert rope_diff.max().item() == 0.0, (
+        f"RoPE portion should be exact but got max diff {rope_diff.max().item()}"
+    )
+
+
+# ── Test B: Indexer path ────────────────────────────────────────────────────
+
+
+@pytest.mark.parametrize("num_tokens", [1, 4, 8, 17])
+@pytest.mark.parametrize("block_size", [16, 64])
+def test_indexer_quant_cache_roundtrip(num_tokens: int, block_size: int):
+    """k → indexer_k_quant_and_cache → cp_gather_indexer_k_quant_cache
+    → manual dequant → compare against original."""
+
+    HEAD_DIM = 128
+    QUANT_BLOCK_SIZE = 128
+    # cache_stride = head_dim + (head_dim * 4 / quant_block_size) = 128 + 4 = 132
+    CACHE_STRIDE = HEAD_DIM + HEAD_DIM * 4 // QUANT_BLOCK_SIZE
+
+    num_blocks = (num_tokens + block_size - 1) // block_size + 1
+    device = "cuda"
+
+    # Random K (simulates compressor output for indexer)
+    k = torch.randn(num_tokens, HEAD_DIM, dtype=torch.bfloat16, device=device)
+
+    # ── Quant + insert ──────────────────────────────────────────────────
+    kv_cache = torch.zeros(
+        num_blocks, block_size, CACHE_STRIDE, dtype=torch.uint8, device=device
+    )
+    slot_mapping = torch.arange(num_tokens, dtype=torch.int64, device=device)
+
+    ops.indexer_k_quant_and_cache(k, kv_cache, slot_mapping, QUANT_BLOCK_SIZE, "ue8m0")
+
+    # ── Gather ──────────────────────────────────────────────────────────
+    max_blocks_per_seq = num_blocks
+    block_table = torch.arange(
+        max_blocks_per_seq, dtype=torch.int32, device=device
+    ).unsqueeze(0)
+    cu_seq_lens = torch.tensor([0, num_tokens], dtype=torch.int32, device=device)
+
+    # dst_k: [total_seq_len, head_dim] as uint8 (raw FP8 bytes)
+    dst_k = torch.zeros(num_tokens, HEAD_DIM, dtype=torch.uint8, device=device)
+    # dst_scale: [total_seq_len, head_dim/quant_block*4] as uint8 (raw float32 bytes)
+    num_scale_bytes = HEAD_DIM * 4 // QUANT_BLOCK_SIZE  # 4
+    dst_scale = torch.zeros(
+        num_tokens, num_scale_bytes, dtype=torch.uint8, device=device
+    )
+
+    ops.cp_gather_indexer_k_quant_cache(
+        kv_cache, dst_k, dst_scale, block_table, cu_seq_lens
+    )
+
+    # ── Manual dequant ──────────────────────────────────────────────────
+    k_fp8 = dst_k.view(torch.float8_e4m3fn).float()  # [num_tokens, 128]
+    scale = dst_scale.view(torch.float32)  # [num_tokens, 1]
+    k_recovered = k_fp8 * scale  # [num_tokens, 128]
+
+    # ── Compare ─────────────────────────────────────────────────────────
+    diff = (k_recovered - k.float()).abs()
+    k_abs = k.float().abs()
+
+    for t in range(num_tokens):
+        amax = k_abs[t].max().clamp(min=1e-4).item()
+        # UE8M0: scale = 2^ceil(log2(amax / 448))
+        exponent = math.ceil(math.log2(amax / 448.0))
+        ue8m0_scale = 2.0**exponent
+        # FP8 e4m3 (3-bit mantissa): worst-case error = 16 * scale
+        max_allowed = 16.0 * ue8m0_scale
+        token_diff = diff[t].max().item()
+        assert token_diff <= max_allowed, (
+            f"Token {t} diff {token_diff} exceeds max_allowed "
+            f"{max_allowed} (scale={ue8m0_scale})"
+        )
+
+
+def test_indexer_gather_accepts_upper_bound_output():
+    """Gather only exact cu_seq_lens even when dst is over-allocated."""
+
+    head_dim = 128
+    quant_block_size = 128
+    cache_stride = head_dim + head_dim * 4 // quant_block_size
+    valid_tokens = 9
+    upper_bound_tokens = 13
+    block_size = 16
+    num_blocks = 2
+    sentinel = 123
+    device = "cuda"
+
+    k = torch.randn(valid_tokens, head_dim, dtype=torch.bfloat16, device=device)
+    kv_cache = torch.zeros(
+        num_blocks, block_size, cache_stride, dtype=torch.uint8, device=device
+    )
+    slot_mapping = torch.arange(valid_tokens, dtype=torch.int64, device=device)
+    ops.indexer_k_quant_and_cache(k, kv_cache, slot_mapping, quant_block_size, "ue8m0")
+
+    block_table = torch.arange(num_blocks, dtype=torch.int32, device=device).unsqueeze(
+        0
+    )
+    cu_seq_lens = torch.tensor([0, valid_tokens], dtype=torch.int32, device=device)
+    dst_k = torch.full(
+        (upper_bound_tokens, head_dim), sentinel, dtype=torch.uint8, device=device
+    )
+    num_scale_bytes = head_dim * 4 // quant_block_size
+    dst_scale = torch.full(
+        (upper_bound_tokens, num_scale_bytes),
+        sentinel,
+        dtype=torch.uint8,
+        device=device,
+    )
+
+    ops.cp_gather_indexer_k_quant_cache(
+        kv_cache, dst_k, dst_scale, block_table, cu_seq_lens
+    )
+    torch.accelerator.synchronize()
+
+    k_recovered = dst_k[:valid_tokens].view(torch.float8_e4m3fn).float() * dst_scale[
+        :valid_tokens
+    ].view(torch.float32)
+    diff = (k_recovered - k.float()).abs()
+    max_allowed = (16.0 * dst_scale[:valid_tokens].view(torch.float32).max()).item()
+    assert diff.max().item() <= max_allowed
+    assert torch.all(dst_k[valid_tokens:] == sentinel)
+    assert torch.all(dst_scale[valid_tokens:] == sentinel)
+
+
+# ── Test C: DeepseekV4 attention with values at different magnitudes ───────────
+
+
+def test_deepseek_v4_quant_magnitude_range():
+    """Test that quantization handles a range of magnitudes correctly."""
+
+    HEAD_DIM = 512
+    NOPE_DIM = 448
+    HEAD_BYTES = 584
+    block_size = 16
+    num_tokens = 4
+    num_blocks = 2
+    device = "cuda"
+
+    # Create inputs with varying magnitudes: small, medium, large
+    compressed_kv = torch.zeros(
+        num_tokens, HEAD_DIM, dtype=torch.bfloat16, device=device
+    )
+    compressed_kv[0] = 0.001  # very small
+    compressed_kv[1] = 1.0  # unit scale
+    compressed_kv[2] = 100.0  # large
+    compressed_kv[3] = torch.randn(HEAD_DIM, dtype=torch.bfloat16, device=device)
+
+    k_cache = torch.zeros(
+        num_blocks, block_size, HEAD_BYTES, dtype=torch.uint8, device=device
+    )
+    slot_mapping = torch.arange(num_tokens, dtype=torch.int64, device=device)
+
+    quantize_and_insert_k_cache(
+        compressed_kv, k_cache.view(num_blocks, -1), slot_mapping, block_size
+    )
+
+    out = torch.zeros(1, num_tokens, HEAD_DIM, dtype=torch.bfloat16, device=device)
+    seq_lens = torch.tensor([num_tokens], dtype=torch.int32, device=device)
+    block_table = torch.arange(num_blocks, dtype=torch.int32, device=device).unsqueeze(
+        0
+    )
+
+    dequantize_and_gather_k_cache(
+        out, k_cache, seq_lens, None, block_table, block_size, offset=0
+    )
+
+    recovered = out[0, :num_tokens]
+
+    # RoPE portion must be exact
+    rope_diff = (recovered[:, NOPE_DIM:] - compressed_kv[:, NOPE_DIM:]).abs().max()
+    assert rope_diff.item() == 0.0, f"RoPE diff {rope_diff.item()}"
+
+    # NoPE: relative error should be reasonable
+    for t in range(num_tokens):
+        orig = compressed_kv[t, :NOPE_DIM].float()
+        recv = recovered[t, :NOPE_DIM].float()
+        abs_diff = (recv - orig).abs().max().item()
+        magnitude = orig.abs().max().item()
+        if magnitude > 0.01:
+            rel_err = abs_diff / magnitude
+            assert rel_err < 0.15, (
+                f"Token {t}: rel_err={rel_err:.4f}, abs_diff={abs_diff:.6f}, "
+                f"magnitude={magnitude:.4f}"
+            )
+
+
+# ── Test D: Indexer fused K-cache insert (Triton kernels) ────────────────────
+#
+# Both kernels share the same Triton signature; use_fp4 selects between them.
+# Full pipeline: state-cache gather → softmax-weighted compress → RMSNorm →
+#   GPT-J RoPE → quant (MXFP4 or FP8) → paged cache insert.
+
+
+def _reference_kv_compress_norm_rope(
+    state_cache: torch.Tensor,
+    block_table: torch.Tensor,
+    positions: torch.Tensor,
+    rms_weight: torch.Tensor,
+    cos_sin_cache: torch.Tensor,
+    compress_ratio: int = 1,
+    overlap: int = 0,
+    use_fp4: bool = False,
+    rms_eps: float = 1e-6,
+    fp8_max: float = 448.0,
+):
+    """Compress → RMSNorm → GPT-J RoPE → quantize.
+
+    Gathers (1+overlap)*compress_ratio state entries per output token, applies
+    per-element softmax over the scores, and computes the weighted kv sum.
+    Returns (quantized_values, scale) matching the kernel's output layout.
+    """
+    device = state_cache.device
+    head_dim = rms_weight.shape[0]
+    rope_dim = cos_sin_cache.shape[-1]
+    state_block_size = state_cache.shape[1]
+    state_width = state_cache.shape[-1] // 2
+    nope_dim = head_dim - rope_dim
+    total = (1 + overlap) * compress_ratio
+    results = []
+    for pos in positions.tolist():
+        src = torch.arange(pos - total + 1, pos + 1, dtype=torch.int64, device=device)
+        valid = src >= 0
+        idx = src.clamp(min=0)
+        pages = block_table[0, idx // state_block_size]
+        offsets = idx % state_block_size
+        raw = state_cache[pages, offsets].float()  # [total, state_dim]
+
+        # Group 0 (tokens 0..cr-1):   kv[:H],   score[SW:SW+H]
+        # Group 1 (tokens cr..2cr-1): kv[H:2H], score[SW+H:SW+2H]
+        if overlap:
+            sw = state_width
+            g0_kv = raw[:compress_ratio, :head_dim]
+            g1_kv = raw[compress_ratio:, head_dim : 2 * head_dim]
+            g0_scores = raw[:compress_ratio, sw : sw + head_dim]
+            g1_scores = raw[compress_ratio:, sw + head_dim : sw + 2 * head_dim]
+            kv = torch.cat([g0_kv, g1_kv])
+            scores = torch.cat([g0_scores, g1_scores])
+        else:
+            kv = raw[:, :head_dim]
+            scores = raw[:, state_width : state_width + head_dim]
+
+        scores[~valid] = float("-inf")
+        kv[~valid] = 0.0
+        weights = torch.softmax(scores, dim=0)
+        compressed = (kv * weights).sum(dim=0)  # [H]
+        var = (compressed * compressed).mean()
+        normed = compressed * torch.rsqrt(var + rms_eps) * rms_weight.float()
+        compressed_pos = (pos // compress_ratio) * compress_ratio
+        cos, sin = cos_sin_cache[compressed_pos].float().chunk(2)
+        nope, rope = normed.split([nope_dim, rope_dim])
+        rope = torch.stack(
+            [rope[0::2] * cos - rope[1::2] * sin, rope[1::2] * cos + rope[0::2] * sin],
+            dim=-1,
+        ).reshape(rope_dim)
+        results.append(torch.cat([nope, rope]).to(state_cache.dtype))
+    result = torch.stack(results)
+
+    if use_fp4:
+        return quantize_to_mxfp4(result)
+    else:
+        pairs = [
+            _ue8m0_reference(result[t], head_dim, fp8_max) for t in range(len(result))
+        ]
+        quants, scales = zip(*pairs)
+        return torch.stack(quants), torch.cat(scales)
+
+
+@pytest.mark.parametrize("num_tokens", [1, 7, 32])
+@pytest.mark.parametrize("kv_block_size", [16, 32])
+@pytest.mark.parametrize("use_fp4", [False, True])
+def test_fused_kv_insert_indexer(num_tokens: int, kv_block_size: int, use_fp4: bool):
+    """Fused K compress+norm+rope+quant+insert for the indexer KV cache."""
+    HEAD_DIM = 128
+    ROPE_DIM = 64
+    BLOCK_SIZE = 16
+    RMS_EPS = 1e-6
+    FP8_MAX = 448.0
+
+    device = "cuda"
+    torch.manual_seed(42)
+    compress_ratio = 4
+
+    if use_fp4:
+        TOKEN_STRIDE = HEAD_DIM // 2  # packed nibbles: 64 bytes
+        SCALE_DIM = HEAD_DIM // 32  # ue8m0 bytes: 4
+        QUANT_BLOCK = 32
+        kernel = _fused_kv_compress_norm_rope_insert_indexer_mxfp4_attn
+    else:
+        TOKEN_STRIDE = HEAD_DIM  # FP8 bytes: 128
+        SCALE_DIM = 4  # 1 float32: 4 bytes
+        QUANT_BLOCK = HEAD_DIM
+        kernel = _fused_kv_compress_norm_rope_insert_indexer_attn
+
+    # overlap=1 whenever compress_ratio==4, matching DeepseekCompressor logic.
+    overlap = 1 if compress_ratio == 4 else 0
+    coff = 1 + overlap  # multiplier for state_dim per entry
+
+    num_pages = (compress_ratio * num_tokens - 1) // BLOCK_SIZE + 2
+    state_cache = torch.randn(
+        num_pages,
+        BLOCK_SIZE,
+        2 * coff * HEAD_DIM,  # kv_state + score_state, each coff*HEAD_DIM wide
+        dtype=torch.bfloat16,
+        device=device,
+    )
+    block_table = torch.arange(num_pages, dtype=torch.int32, device=device).unsqueeze(0)
+    token_to_req = torch.zeros(num_tokens, dtype=torch.int32, device=device)
+    slot_mapping = torch.arange(num_tokens, dtype=torch.int64, device=device)
+    positions = torch.arange(
+        compress_ratio - 1,
+        compress_ratio * num_tokens,
+        compress_ratio,
+        dtype=torch.int64,
+        device=device,
+    )
+    rms_weight = torch.randn(HEAD_DIM, dtype=torch.bfloat16, device=device)
+    cos_sin_cache = torch.randn(compress_ratio * num_tokens, ROPE_DIM, device=device)
+
+    kv_n_blocks = (num_tokens + kv_block_size - 1) // kv_block_size + 1
+    kv_cache = torch.zeros(
+        kv_n_blocks,
+        kv_block_size * (TOKEN_STRIDE + SCALE_DIM),
+        dtype=torch.uint8,
+        device=device,
+    )
+
+    kernel[(num_tokens,)](
+        state_cache,
+        state_cache.stride(0),
+        state_cache.stride(1),
+        token_to_req,
+        positions,
+        slot_mapping,
+        block_table,
+        block_table.stride(0),
+        BLOCK_SIZE,
+        rms_weight,
+        RMS_EPS,
+        cos_sin_cache,
+        cos_sin_cache.stride(0),
+        kv_cache,
+        slot_mapping,
+        kv_block_size,
+        HEAD_SIZE=HEAD_DIM,
+        TRITON_BLOCK_SIZE=HEAD_DIM,
+        STATE_WIDTH=coff * HEAD_DIM,
+        COMPRESS_RATIO=compress_ratio,
+        OVERLAP=overlap,
+        ROPE_HEAD_DIM=ROPE_DIM,
+        FP8_MAX=FP8_MAX,
+        QUANT_BLOCK=QUANT_BLOCK,
+        TOKEN_STRIDE=TOKEN_STRIDE,
+        SCALE_DIM=SCALE_DIM,
+        KV_BLOCK_STRIDE=kv_cache.stride(0),
+        num_warps=1,
+    )
+
+    k_quant, scale = _reference_kv_compress_norm_rope(
+        state_cache,
+        block_table,
+        positions,
+        rms_weight,
+        cos_sin_cache,
+        compress_ratio,
+        overlap,
+        use_fp4,
+        rms_eps=RMS_EPS,
+        fp8_max=FP8_MAX,
+    )
+
+    if use_fp4:
+        for i in range(num_tokens):
+            blk, pos = i // kv_block_size, i % kv_block_size
+            val_off = pos * TOKEN_STRIDE
+            fp4_actual = kv_cache[blk, val_off : val_off + TOKEN_STRIDE]
+            assert torch.equal(k_quant[i], fp4_actual), (
+                f"token {i}: packed nibbles differ, "
+                f"{(k_quant[i] != fp4_actual).sum()} "
+                f"/ {TOKEN_STRIDE}"
+            )
+
+            scale_off = kv_block_size * TOKEN_STRIDE + pos * SCALE_DIM
+            scale_actual = kv_cache[blk, scale_off : scale_off + SCALE_DIM]
+            assert torch.equal(scale_actual, scale[i]), (
+                f"token {i}: ue8m0 {scale_actual.tolist()} != {scale[i].tolist()}"
+            )
+
+    else:
+        k_quant = k_quant.view(torch.uint8)
+        for i in range(num_tokens):
+            blk, pos = i // kv_block_size, i % kv_block_size
+            val_off = pos * TOKEN_STRIDE
+            assert torch.equal(
+                k_quant[i], kv_cache[blk, val_off : val_off + TOKEN_STRIDE]
+            ), f"token {i}: FP8 bytes differ"
+
+            scale_off = kv_block_size * TOKEN_STRIDE + pos * SCALE_DIM
+            actual_scale = kv_cache[blk, scale_off : scale_off + SCALE_DIM].view(
+                torch.float32
+            )
+            assert torch.equal(actual_scale, scale[i : i + 1]), (
+                f"token {i}: scale {actual_scale.item()} != {scale[i].item()}"
+            )
diff --git a/tests/kernels/test_fused_deepseek_v4_qnorm_rope_kv_insert.py b/tests/kernels/test_fused_deepseek_v4_qnorm_rope_kv_insert.py
new file mode 100644
index 000000000000..46d226e0f74e
--- /dev/null
+++ b/tests/kernels/test_fused_deepseek_v4_qnorm_rope_kv_insert.py
@@ -0,0 +1,359 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Standalone unit test for the horizontally-fused DeepseekV4-MLA kernel:
+
+  fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert
+    - Q side:  per-head RMSNorm (no weight) + GPT-J RoPE on last 64 dims
+    - KV side: GPT-J RoPE on last 64 + UE8M0 FP8 quant + paged cache insert
+
+We compare against:
+  - PyTorch reference for RMSNorm + GPT-J RoPE on Q
+  - Existing Triton `quantize_and_insert_k_cache` + round-trip via
+    `dequantize_and_gather_k_cache` for KV
+
+The kernel is imported via
+`torch.ops._C.fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert`.
+"""
+
+import pytest
+import torch
+
+from vllm.v1.attention.ops.deepseek_v4_ops import (
+    dequantize_and_gather_k_cache,
+    quantize_and_insert_k_cache,
+)
+
+# ── Constants matching the kernel ────────────────────────────────────────────
+HEAD_DIM = 512
+ROPE_DIM = 64
+NOPE_DIM = HEAD_DIM - ROPE_DIM  # 448
+QUANT_BLOCK = 64
+FP8_MAX = 448.0
+HEAD_BYTES = NOPE_DIM + ROPE_DIM * 2 + 8  # 448 + 128 + 8 = 584
+
+
+# ── PyTorch reference implementations ────────────────────────────────────────
+
+
+def make_cos_sin_cache(max_pos: int, rope_dim: int, dtype, device):
+    """Build a cos||sin cache matching DeepseekV4ScalingRotaryEmbedding layout.
+    cos_sin_cache[pos, :rope_dim/2] = cos(theta), [rope_dim/2:] = sin(theta).
+    """
+    base = 10000.0
+    inv_freq = 1.0 / (
+        base
+        ** (torch.arange(0, rope_dim, 2, dtype=torch.float32, device=device) / rope_dim)
+    )
+    t = torch.arange(max_pos, dtype=torch.float32, device=device)
+    freqs = torch.einsum("i,j -> ij", t, inv_freq)  # [max_pos, rope_dim/2]
+    cache = torch.cat((freqs.cos(), freqs.sin()), dim=-1)  # [max_pos, rope_dim]
+    return cache.to(dtype)
+
+
+def apply_rope_gptj_last_k(
+    x: torch.Tensor, positions: torch.Tensor, cos_sin_cache: torch.Tensor
+) -> torch.Tensor:
+    """GPT-J-style (interleaved-pair) RoPE on the LAST rope_dim elements.
+
+    x: [..., head_dim] float32
+    positions: [num_tokens] int64 (positions[i] corresponds to x[i, ...])
+    cos_sin_cache: [max_pos, rope_dim] float (cos|sin layout)
+
+    Returns rotated x (same shape/dtype).
+    """
+    rope_dim = cos_sin_cache.shape[-1]
+    half = rope_dim // 2
+    head_dim = x.shape[-1]
+    nope_dim = head_dim - rope_dim
+
+    # Gather cos/sin for each token position: [num_tokens, rope_dim]
+    cs = cos_sin_cache[positions].to(torch.float32)  # [N, rope_dim]
+    cos = cs[..., :half]  # [N, half]
+    sin = cs[..., half:]  # [N, half]
+
+    # Reshape leading dims so we can broadcast: x shape [..., head_dim].
+    # Bring token dim to front; assume x is [num_tokens, ..., head_dim].
+    # We rely on positions being per-token and all other dims sharing the same pos.
+    rope = x[..., nope_dim:].float()  # [..., rope_dim]
+    # Make rope pairs: reshape last dim to [half, 2]
+    shape = rope.shape
+    rope = rope.reshape(*shape[:-1], half, 2)
+    even = rope[..., 0]  # [..., half]
+    odd = rope[..., 1]
+
+    # Broadcast cos/sin over any heads dim in between.  cos/sin are [N, half].
+    # Add singleton dims for intermediate axes.
+    for _ in range(rope.ndim - 3):
+        cos = cos.unsqueeze(1)
+        sin = sin.unsqueeze(1)
+
+    new_even = even * cos - odd * sin
+    new_odd = even * sin + odd * cos
+    rope_rotated = torch.stack((new_even, new_odd), dim=-1).reshape(shape)
+
+    out = x.clone().float()
+    out[..., nope_dim:] = rope_rotated
+    return out.to(x.dtype)
+
+
+def rmsnorm_no_weight(x: torch.Tensor, eps: float) -> torch.Tensor:
+    """RMSNorm with no learnable weight, matching
+    `RMSNorm(head_dim, has_weight=False)`."""
+    orig_dtype = x.dtype
+    xf = x.float()
+    variance = xf.pow(2).mean(dim=-1, keepdim=True)
+    return (xf * torch.rsqrt(variance + eps)).to(orig_dtype)
+
+
+# ── Dispatch to the CUDA op (skip test cleanly if it isn't built in) ─────────
+
+
+def _op_available() -> bool:
+    return hasattr(torch.ops._C, "fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert")
+
+
+pytestmark = pytest.mark.skipif(
+    not torch.cuda.is_available() or not _op_available(),
+    reason="CUDA not available or fused DeepseekV4 op not built in",
+)
+
+
+def _call_fused(q, kv, k_cache, slot_mapping, positions, cos_sin_cache, eps, bs):
+    torch.ops._C.fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert(
+        q, kv, k_cache, slot_mapping, positions, cos_sin_cache, eps, bs
+    )
+
+
+# ── Test 1: Q path numerical parity ──────────────────────────────────────────
+
+
+@pytest.mark.parametrize("num_tokens", [1, 4, 17, 64])
+@pytest.mark.parametrize("n_heads", [8, 64])
+def test_q_path_matches_reference(num_tokens: int, n_heads: int):
+    torch.manual_seed(0)
+    device = "cuda"
+    dtype = torch.bfloat16
+    eps = 1e-6
+    max_pos = 4096
+
+    q = torch.randn(num_tokens, n_heads, HEAD_DIM, dtype=dtype, device=device)
+    positions = torch.arange(num_tokens, dtype=torch.int64, device=device)
+    cos_sin_cache = make_cos_sin_cache(max_pos, ROPE_DIM, torch.float32, device)
+
+    # Reference: RMSNorm (no weight) per head, then GPT-J RoPE on last 64.
+    q_ref = rmsnorm_no_weight(q, eps)
+    q_ref = apply_rope_gptj_last_k(q_ref, positions, cos_sin_cache)
+
+    # Fused call with dummy KV tensors (KV branch will write slot_mapping=-1 → noop).
+    num_blocks = 2
+    bs = 16
+    kv = torch.zeros(num_tokens, HEAD_DIM, dtype=dtype, device=device)
+    k_cache = torch.zeros(
+        num_blocks, bs, HEAD_BYTES, dtype=torch.uint8, device=device
+    ).view(num_blocks, -1)
+    slot_mapping = torch.full((num_tokens,), -1, dtype=torch.int64, device=device)
+    q_fused = q.clone()
+    _call_fused(q_fused, kv, k_cache, slot_mapping, positions, cos_sin_cache, eps, bs)
+
+    torch.testing.assert_close(q_fused, q_ref, rtol=1e-2, atol=1e-2)
+
+
+# ── Test 2: KV path round-trip byte/value parity ─────────────────────────────
+
+
+def _ue8m0_per_block_scales(kv_roped_nope_f32: torch.Tensor, qblock: int):
+    """Return per-token per-block max scale (used to bound FP8 error)."""
+    n_tok, nope = kv_roped_nope_f32.shape
+    n_blocks = nope // qblock
+    blocks = kv_roped_nope_f32.view(n_tok, n_blocks, qblock)
+    absmax = blocks.abs().amax(dim=-1).clamp(min=1e-4)
+    raw = absmax / FP8_MAX
+    exponent = torch.ceil(torch.log2(raw))
+    return torch.pow(2.0, exponent)  # [n_tok, n_blocks]
+
+
+@pytest.mark.parametrize("num_tokens", [1, 4, 17, 64])
+@pytest.mark.parametrize("block_size", [16, 64])
+def test_kv_path_matches_reference(num_tokens: int, block_size: int):
+    torch.manual_seed(1)
+    device = "cuda"
+    dtype = torch.bfloat16
+    eps = 1e-6
+    max_pos = 4096
+
+    kv = torch.randn(num_tokens, HEAD_DIM, dtype=dtype, device=device)
+    positions = torch.arange(num_tokens, dtype=torch.int64, device=device)
+    cos_sin_cache = make_cos_sin_cache(max_pos, ROPE_DIM, torch.float32, device)
+
+    num_blocks = (num_tokens + block_size - 1) // block_size + 1
+    slot_mapping = torch.arange(num_tokens, dtype=torch.int64, device=device)
+
+    # ── Reference path: RoPE on kv, then existing Triton quant+insert ──────
+    kv_ref = apply_rope_gptj_last_k(kv, positions, cos_sin_cache)
+    k_cache_ref = torch.zeros(
+        num_blocks, block_size * HEAD_BYTES, dtype=torch.uint8, device=device
+    )
+    quantize_and_insert_k_cache(
+        kv_ref, k_cache_ref, slot_mapping, block_size=block_size
+    )
+
+    # ── Fused path (dummy q, single head) ──────────────────────────────────
+    k_cache_fused = torch.zeros_like(k_cache_ref)
+    q_dummy = torch.zeros(num_tokens, 1, HEAD_DIM, dtype=dtype, device=device)
+    _call_fused(
+        q_dummy,
+        kv,
+        k_cache_fused,
+        slot_mapping,
+        positions,
+        cos_sin_cache,
+        eps,
+        block_size,
+    )
+
+    # ── Round-trip compare via dequant+gather ──────────────────────────────
+    def _dequant(k_cache_2d):
+        num_reqs = 1
+        max_blocks = num_blocks
+        out = torch.zeros(
+            num_reqs, num_tokens, HEAD_DIM, dtype=torch.bfloat16, device=device
+        )
+        seq_lens = torch.tensor([num_tokens], dtype=torch.int32, device=device)
+        block_table = torch.arange(
+            max_blocks, dtype=torch.int32, device=device
+        ).unsqueeze(0)
+        # gather_lens arg is None (use seq_lens)
+        k_cache_3d = k_cache_2d.view(num_blocks, block_size, HEAD_BYTES)
+        dequantize_and_gather_k_cache(
+            out, k_cache_3d, seq_lens, None, block_table, block_size, offset=0
+        )
+        return out[0, :num_tokens]
+
+    recovered_ref = _dequant(k_cache_ref)
+    recovered_fused = _dequant(k_cache_fused)
+
+    # NoPE: per-block UE8M0 FP8 error bound (half-ULP at max = 16 * scale).
+    scales = _ue8m0_per_block_scales(kv_ref[:, :NOPE_DIM].float(), QUANT_BLOCK)
+    for t in range(num_tokens):
+        max_allowed = 16.0 * scales[t].max().item()
+        diff_ref = (
+            (recovered_ref[t, :NOPE_DIM] - kv_ref[t, :NOPE_DIM]).abs().max().item()
+        )
+        diff_fused = (
+            (recovered_fused[t, :NOPE_DIM] - kv_ref[t, :NOPE_DIM]).abs().max().item()
+        )
+        assert diff_ref <= max_allowed, (
+            f"ref NoPE token {t} diff {diff_ref} > {max_allowed}"
+        )
+        assert diff_fused <= max_allowed, (
+            f"fused NoPE token {t} diff {diff_fused} > {max_allowed}"
+        )
+
+    # RoPE region: bf16 stored exactly → zero diff.
+    rope_diff = (recovered_fused[:, NOPE_DIM:] - kv_ref[:, NOPE_DIM:]).abs().max()
+    assert rope_diff.item() == 0.0, f"RoPE portion not exact: {rope_diff.item()}"
+
+    # Exact byte equality of the two cache buffers — strong parity.
+    torch.testing.assert_close(k_cache_fused, k_cache_ref, rtol=0, atol=0)
+
+
+# ── Test 2b: DP padding (slot_mapping shorter than q/kv) ─────────────────────
+
+
+@pytest.mark.parametrize("num_tokens", [4, 17])
+@pytest.mark.parametrize("pad", [1, 5])
+@pytest.mark.parametrize("block_size", [16, 64])
+def test_kv_path_with_dp_padding(num_tokens: int, pad: int, block_size: int):
+    """slot_mapping.size(0) < q.size(0): the kernel must skip padded
+    tokens in the KV branch while still running Q-norm+RoPE on all rows."""
+    torch.manual_seed(3)
+    device = "cuda"
+    dtype = torch.bfloat16
+    eps = 1e-6
+    max_pos = 4096
+    total = num_tokens + pad
+
+    kv = torch.randn(total, HEAD_DIM, dtype=dtype, device=device)
+    positions = torch.arange(total, dtype=torch.int64, device=device)
+    cos_sin_cache = make_cos_sin_cache(max_pos, ROPE_DIM, torch.float32, device)
+
+    num_blocks = (num_tokens + block_size - 1) // block_size + 1
+    slot_mapping = torch.arange(num_tokens, dtype=torch.int64, device=device)
+
+    # Reference: only the first num_tokens kv rows get inserted.
+    kv_ref = apply_rope_gptj_last_k(
+        kv[:num_tokens], positions[:num_tokens], cos_sin_cache
+    )
+    k_cache_ref = torch.zeros(
+        num_blocks, block_size * HEAD_BYTES, dtype=torch.uint8, device=device
+    )
+    quantize_and_insert_k_cache(
+        kv_ref, k_cache_ref, slot_mapping, block_size=block_size
+    )
+
+    # Fused: pass full-sized q/kv/positions, shorter slot_mapping.
+    q_dummy = torch.zeros(total, 1, HEAD_DIM, dtype=dtype, device=device)
+    k_cache_fused = torch.zeros_like(k_cache_ref)
+    _call_fused(
+        q_dummy,
+        kv,
+        k_cache_fused,
+        slot_mapping,
+        positions,
+        cos_sin_cache,
+        eps,
+        block_size,
+    )
+
+    torch.testing.assert_close(k_cache_fused, k_cache_ref, rtol=0, atol=0)
+
+
+# ── Test 3: combined single-call Q + KV parity ───────────────────────────────
+
+
+@pytest.mark.parametrize("num_tokens", [1, 4, 17])
+@pytest.mark.parametrize("n_heads", [8, 64])
+@pytest.mark.parametrize("block_size", [16, 64])
+def test_combined_q_and_kv(num_tokens: int, n_heads: int, block_size: int):
+    torch.manual_seed(2)
+    device = "cuda"
+    dtype = torch.bfloat16
+    eps = 1e-6
+    max_pos = 4096
+
+    q = torch.randn(num_tokens, n_heads, HEAD_DIM, dtype=dtype, device=device)
+    kv = torch.randn(num_tokens, HEAD_DIM, dtype=dtype, device=device)
+    positions = torch.arange(num_tokens, dtype=torch.int64, device=device)
+    cos_sin_cache = make_cos_sin_cache(max_pos, ROPE_DIM, torch.float32, device)
+
+    num_blocks = (num_tokens + block_size - 1) // block_size + 1
+    slot_mapping = torch.arange(num_tokens, dtype=torch.int64, device=device)
+
+    # Reference.
+    q_ref = rmsnorm_no_weight(q, eps)
+    q_ref = apply_rope_gptj_last_k(q_ref, positions, cos_sin_cache)
+    kv_ref = apply_rope_gptj_last_k(kv, positions, cos_sin_cache)
+    k_cache_ref = torch.zeros(
+        num_blocks, block_size * HEAD_BYTES, dtype=torch.uint8, device=device
+    )
+    quantize_and_insert_k_cache(
+        kv_ref, k_cache_ref, slot_mapping, block_size=block_size
+    )
+
+    # Fused single call.
+    q_fused = q.clone()
+    k_cache_fused = torch.zeros_like(k_cache_ref)
+    _call_fused(
+        q_fused,
+        kv,
+        k_cache_fused,
+        slot_mapping,
+        positions,
+        cos_sin_cache,
+        eps,
+        block_size,
+    )
+
+    torch.testing.assert_close(q_fused, q_ref, rtol=1e-2, atol=1e-2)
+    torch.testing.assert_close(k_cache_fused, k_cache_ref, rtol=0, atol=0)
diff --git a/tests/kernels/test_fused_indexer_q_rope_quant.py b/tests/kernels/test_fused_indexer_q_rope_quant.py
new file mode 100644
index 000000000000..41a4d0ed0905
--- /dev/null
+++ b/tests/kernels/test_fused_indexer_q_rope_quant.py
@@ -0,0 +1,171 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit test for fused_indexer_q_rope_quant.
+
+Compares the fused Triton kernel against the unfused reference flow used by
+the DeepseekV4 indexer in model_tracking:
+    q_rot = ops.rotary_embedding(positions, q, None, head_dim, cos_sin_cache,
+                                 is_neox_style=False,
+                                 rope_dim_offset=head_dim - rope_dim)
+    q_fp8, q_scale = per_token_group_quant_fp8(q_rot, head_dim, use_ue8m0=True)
+    weights_out = weights * q_scale * softmax_scale * head_scale
+
+Expects bit-exact equality on both q_fp8 and weights_out.
+"""
+
+import pytest
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+    per_token_group_quant_fp8,
+)
+from vllm.v1.attention.ops.deepseek_v4_ops.fused_indexer_q import (
+    fused_indexer_q_rope_quant,
+)
+
+HEAD_DIM = 128
+ROPE_DIM = 64
+N_HEAD = 64
+MAX_POS = 4096
+
+
+def quantize_to_mxfp4(
+    x: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Reference MXFP4 quantization.
+
+    Args:
+        x: [..., head_dim] where head_dim is divisible by 32
+    Returns:
+        packed: [..., head_dim//2]  uint8   2 E2M1 nibbles/byte, low nibble = even index
+        scales: [..., head_dim//32] uint8   1 ue8m0 byte
+    """
+    MXFP4_BLOCK_SIZE = 32
+    orig_shape = x.shape
+    head_dim = orig_shape[-1]
+    n_blocks = head_dim // MXFP4_BLOCK_SIZE
+
+    x_f32 = x.float().reshape(-1, n_blocks, MXFP4_BLOCK_SIZE)
+
+    # Per-block ue8m0 scale: 2^ceil(log2(amax / 6.0)), stored as byte = exp + 127
+    # 6 * 2^-126 is from https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/inference/kernel.py#L163
+    amax = x_f32.abs().amax(dim=-1, keepdim=True).clamp(min=6 * (2**-126))
+    log2_ratio = (amax * (1.0 / 6.0)).log2().ceil().clamp(-127.0, 127.0)
+    scale = log2_ratio.exp2()
+    ue8m0 = (log2_ratio + 127.0).to(torch.uint8)  # [*, n_blocks]
+
+    # E2M1 round-to-nearest-even: midpoints round to the even code.
+    # E2M1 values: [0.00, 0.50, 1.00, 1.50, 2.00, 3.00, 4.00, 6.00]
+    # boundaries:  [   0.25, 0.75, 1.25, 1.75, 2.50, 3.50, 5.00]
+    x_scaled = (x_f32 / scale).clamp(-6.0, 6.0)
+    abs_x = x_scaled.abs()
+    code = torch.zeros_like(abs_x, dtype=torch.int32)
+    code = torch.where(abs_x > 0.25, 1, code)
+    code = torch.where(abs_x >= 0.75, 2, code)
+    code = torch.where(abs_x > 1.25, 3, code)
+    code = torch.where(abs_x >= 1.75, 4, code)
+    code = torch.where(abs_x > 2.5, 5, code)
+    code = torch.where(abs_x >= 3.5, 6, code)
+    code = torch.where(abs_x > 5.0, 7, code)
+    sign = ((x_scaled.view(torch.int32) >> 31) & 1).to(torch.uint8)
+    nibble = code.to(torch.uint8) | (sign << 3)
+
+    # Pack: even-index element → low nibble, odd-index → high nibble
+    nibble_flat = nibble.reshape(-1, head_dim)
+    packed = (nibble_flat[:, 0::2] | (nibble_flat[:, 1::2] << 4)).contiguous()
+    packed = packed.reshape(*orig_shape[:-1], head_dim // 2)
+
+    scales = ue8m0.view(*orig_shape[:-1], n_blocks)
+    return packed, scales
+
+
+def _reference(
+    positions: torch.Tensor,
+    q: torch.Tensor,
+    cos_sin_cache: torch.Tensor,
+    weights: torch.Tensor,
+    softmax_scale: float,
+    head_scale: float,
+    use_fp4: bool = False,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    q_rot = q.clone()
+    ops.rotary_embedding(
+        positions,
+        q_rot,
+        None,
+        HEAD_DIM,
+        cos_sin_cache,
+        False,  # is_neox_style=False → GPT-J interleaved
+        HEAD_DIM - ROPE_DIM,  # rope_dim_offset → rotate the tail
+        False,
+    )
+
+    if use_fp4:
+        q_packed, ue8m0 = quantize_to_mxfp4(q_rot.view(-1, N_HEAD, HEAD_DIM))
+        # Pack 4 ue8m0 bytes into 1 int32
+        q_scale = ue8m0.view(torch.int32).squeeze(-1)
+        # FP4 path: q_scale stays separate (cannot be folded into a per-token scalar)
+        weights_out = weights.to(torch.float32) * softmax_scale * head_scale
+        return (q_packed, q_scale), weights_out
+
+    else:
+        q_fp8, q_scale = per_token_group_quant_fp8(
+            q_rot.view(-1, HEAD_DIM).contiguous(),
+            HEAD_DIM,
+            use_ue8m0=True,
+        )
+        q_fp8 = q_fp8.view(-1, N_HEAD, HEAD_DIM)
+        q_scale = q_scale.view(-1, N_HEAD)
+
+        weights_out = weights.to(torch.float32) * q_scale * softmax_scale * head_scale
+        return q_fp8, weights_out
+
+
+@pytest.mark.parametrize("num_tokens", [1, 7, 32, 257, 1023])
+@pytest.mark.parametrize("cache_dtype", [torch.float32, torch.bfloat16])
+@pytest.mark.parametrize("use_fp4", [False, True])
+@torch.inference_mode()
+def test_fused_indexer_q_rope_quant_matches_unfused(num_tokens, cache_dtype, use_fp4):
+    device = "cuda"
+    torch.manual_seed(0)
+
+    q = torch.randn(num_tokens, N_HEAD, HEAD_DIM, dtype=torch.bfloat16, device=device)
+    positions = torch.randint(
+        0, MAX_POS, (num_tokens,), dtype=torch.int64, device=device
+    )
+    cos_sin_cache = torch.randn(MAX_POS, ROPE_DIM, dtype=cache_dtype, device=device)
+    weights = torch.randn(num_tokens, N_HEAD, dtype=torch.bfloat16, device=device)
+    softmax_scale = HEAD_DIM**-0.5
+    head_scale = N_HEAD**-0.5
+
+    q_quant_ref, weights_ref = _reference(
+        positions, q, cos_sin_cache, weights, softmax_scale, head_scale, use_fp4
+    )
+    q_quant_fused, weights_fused = fused_indexer_q_rope_quant(
+        positions, q.clone(), cos_sin_cache, weights, softmax_scale, head_scale, use_fp4
+    )
+
+    if use_fp4:
+        q_quant_ref, q_scale_ref = q_quant_ref
+        q_quant_fused, q_scale_fused = q_quant_fused
+
+        assert torch.equal(q_scale_ref, q_scale_fused), (
+            f"q_scale mismatch: "
+            f"{(q_scale_ref != q_scale_fused).sum().item()} "
+            f"/ {q_scale_ref.numel()} bytes differ"
+        )
+
+    # fp8 tensors aren't directly comparable via torch.equal — reinterpret as int8.
+    ref_bits = q_quant_ref.view(torch.int8)
+    fused_bits = q_quant_fused.view(torch.int8)
+    assert torch.equal(ref_bits, fused_bits), (
+        f"q_quant_fused mismatch: "
+        f"{(ref_bits != fused_bits).sum().item()} / {ref_bits.numel()} bytes differ"
+    )
+
+    assert weights_fused.dtype == torch.float32
+    assert torch.equal(weights_ref, weights_fused), (
+        f"weights mismatch: max abs diff "
+        f"{(weights_ref - weights_fused).abs().max().item()}"
+    )
diff --git a/tests/kernels/test_fused_inv_rope_fp8_quant.py b/tests/kernels/test_fused_inv_rope_fp8_quant.py
new file mode 100644
index 000000000000..10561a8a0304
--- /dev/null
+++ b/tests/kernels/test_fused_inv_rope_fp8_quant.py
@@ -0,0 +1,908 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Unit tests for the fused inverse RoPE + block-scaled FP8 quantization kernel.
+
+Tests compare the fused kernel against a reference implementation built from
+the existing separate operations (inverse RoPE via rotate_neox + FP8 quant
+via per_token_group_quant_fp8).
+
+The reference faithfully reproduces the exact flow in deepseek_v4_attention.py:295-310:
+  1. Apply inverse RoPE (NeoX style, last rope_dim=64 dims of each head)
+  2. Reshape [T, H, head_dim] -> [T, G, D]
+  3. Transpose+flatten to [G*T, D], quantize, reshape back
+  4. Return o_fp8 and o_scale with strides (D, T*D, 1) and (S, T*S, 1)
+     (non-contiguous [T, G, ...] view backed by contiguous [G, T, ...] memory)
+
+Usage:
+    pytest tests/kernels/test_fused_inv_rope_fp8_quant.py -v
+"""
+
+import pytest
+import torch
+
+from vllm.v1.attention.ops.deepseek_v4_ops import fused_inv_rope_fp8_quant
+
+# -- Default dimensions matching DeepSeek V3/V4 --------------------------
+HEAD_DIM = 512
+NOPE_DIM = 448
+ROPE_DIM = 64
+QUANT_GROUP_SIZE = 128
+FP8_MAX = 448.0  # torch.finfo(torch.float8_e4m3fn).max
+FP8_DTYPE = torch.float8_e4m3fn
+EPS = 1e-10
+
+
+# =========================================================================
+# Helpers
+# =========================================================================
+
+
+def assert_dequant_close(
+    fp8_a: torch.Tensor,
+    scale_a: torch.Tensor,
+    fp8_b: torch.Tensor,
+    scale_b: torch.Tensor,
+    msg: str = "",
+):
+    """Compare two FP8-quantized tensors via their dequantized values.
+
+    Uses cosine-similarity-based diff (same as deep_gemm calc_diff).
+    Both fused and reference paths rotate in fp32 using an fp32
+    cos_sin_cache, so differences are only fp32 ordering ULPs that can
+    occasionally shift FP8 values at quantization boundaries.
+    """
+    S = scale_a.shape[-1]
+    shape = fp8_a.shape
+
+    dq_a = fp8_a.float() * scale_a.unsqueeze(-1).expand(
+        *shape[:-1], S, QUANT_GROUP_SIZE
+    ).reshape(shape)
+    dq_b = fp8_b.float() * scale_b.unsqueeze(-1).expand(
+        *shape[:-1], S, QUANT_GROUP_SIZE
+    ).reshape(shape)
+
+    # Cosine diff: 1 - cos_sim (0 = identical, higher = worse)
+    dq_a_flat = dq_a.flatten().float()
+    dq_b_flat = dq_b.flatten().float()
+    cos_sim = torch.nn.functional.cosine_similarity(
+        dq_a_flat.unsqueeze(0), dq_b_flat.unsqueeze(0)
+    ).item()
+    diff = 1.0 - cos_sim
+
+    assert diff < 1e-4, f"Dequant diff too large: {diff:.8f} (expected < 1e-4). {msg}"
+
+
+def rotate_gptj(x: torch.Tensor) -> torch.Tensor:
+    """GPT-J style rotation: interleaved pairs, negate-swap.
+
+    Matches vllm/model_executor/layers/rotary_embedding/common.py:23-27.
+    DeepseekV4 uses is_neox_style=False, so this is the correct rotation.
+    """
+    x1 = x[..., ::2]
+    x2 = x[..., 1::2]
+    x = torch.stack((-x2, x1), dim=-1)
+    return x.flatten(-2)
+
+
+def make_cos_sin_cache(
+    max_pos: int,
+    rope_dim: int = ROPE_DIM,
+    dtype: torch.dtype = torch.float32,
+    device: str = "cuda",
+) -> torch.Tensor:
+    """Create a synthetic cos_sin_cache matching the layout used by
+    DeepseekV4ScalingRotaryEmbedding._compute_cos_sin_cache.
+
+    Shape: [max_pos, rope_dim] where first half is cos, second half is sin.
+    The fused kernel requires fp32; callers can override dtype if passing
+    the cache into the bf16-only paths.
+    """
+    half = rope_dim // 2
+    # Use random but bounded frequencies so cos/sin are well-behaved
+    inv_freq = 1.0 / (
+        10000.0 ** (torch.arange(0, half, device=device, dtype=torch.float32) / half)
+    )
+    t = torch.arange(max_pos, device=device, dtype=torch.float32)
+    freqs = torch.outer(t, inv_freq)  # [max_pos, half]
+    cos = freqs.cos()
+    sin = freqs.sin()
+    cache = torch.cat((cos, sin), dim=-1)  # [max_pos, rope_dim]
+    return cache.to(dtype)
+
+
+def reference_inv_rope(
+    o: torch.Tensor,
+    positions: torch.Tensor,
+    cos_sin_cache: torch.Tensor,
+    nope_dim: int = NOPE_DIM,
+    rope_dim: int = ROPE_DIM,
+) -> torch.Tensor:
+    """Apply inverse RoPE to the last rope_dim dimensions of each head.
+
+    Matches the GPT-J inverse rotation in pos_encoding_kernels.cu, which
+    promotes the cache to fp32 and performs the rotation in fp32. The
+    result is cast back to the input dtype.
+
+    Args:
+        o: [T, H, head_dim] bf16
+        positions: [T] int64
+        cos_sin_cache: [max_pos, rope_dim] fp32
+
+    Returns:
+        o with inverse RoPE applied on the rope portion (bf16).
+    """
+    assert cos_sin_cache.dtype == torch.float32
+    cos_sin = cos_sin_cache[positions]  # [T, rope_dim] fp32
+    half = rope_dim // 2
+    cos = cos_sin[:, :half]
+    sin = cos_sin[:, half:]
+
+    # GPT-J style: repeat_interleave (not repeat) to match interleaved pairs
+    cos = cos.repeat_interleave(2, dim=-1).unsqueeze(1)
+    sin = sin.repeat_interleave(2, dim=-1).unsqueeze(1)
+    sin = -sin  # inverse
+
+    o_pass = o[..., :nope_dim]
+    o_rot_f32 = o[..., nope_dim:].float()
+    o_rot_f32 = o_rot_f32 * cos + rotate_gptj(o_rot_f32) * sin
+    o_rot = o_rot_f32.to(o.dtype)
+
+    return torch.cat([o_pass, o_rot], dim=-1)
+
+
+def _ref_ue8m0_quant_block(x_f32: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """Per-block UE8M0 FP8 quantization in pure float32.
+
+    Matches the Triton kernel logic exactly:
+      absmax -> 2^ceil(log2(absmax / fp8_max)) -> clamp(x / scale) -> fp8
+
+    Args:
+        x_f32: [..., quant_group_size] float32 — one or more 128-element blocks.
+
+    Returns:
+        x_fp8: same shape, float8_e4m3fn
+        scales: [...] float32, one scale per block
+    """
+    assert x_f32.shape[-1] == QUANT_GROUP_SIZE
+    absmax = x_f32.abs().amax(dim=-1, keepdim=True).clamp(min=EPS)
+    scale_raw = absmax * (1.0 / FP8_MAX)
+    scale = torch.exp2(torch.ceil(torch.log2(scale_raw)))
+    x_scaled = (x_f32 / scale).clamp(-FP8_MAX, FP8_MAX)
+    x_fp8 = x_scaled.to(FP8_DTYPE)
+    return x_fp8, scale.squeeze(-1)
+
+
+def reference_inv_rope_fp8_quant(
+    o: torch.Tensor,
+    positions: torch.Tensor,
+    cos_sin_cache: torch.Tensor,
+    n_groups: int,
+    heads_per_group: int,
+    nope_dim: int = NOPE_DIM,
+    rope_dim: int = ROPE_DIM,
+    quant_group_size: int = QUANT_GROUP_SIZE,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Full reference: inverse RoPE in fp32 + UE8M0 FP8 quant in fp32.
+
+    Mimics the Triton kernel's precision path exactly:
+      Load bf16 -> cast to fp32 -> apply inverse RoPE with fp32 cos/sin ->
+      UE8M0 quant in fp32 -> write fp8 + scale
+
+    Returns:
+        o_fp8: [T, G, D] FP8 with strides (D, T*D, 1)
+        o_scale: [T, G, S] FP32 with strides (S, T*S, 1)
+    """
+    assert cos_sin_cache.dtype == torch.float32
+    T, _H, head_dim = o.shape
+    d = heads_per_group * head_dim
+    S = d // quant_group_size
+    half_rope = rope_dim // 2
+    chunks_per_head = head_dim // quant_group_size
+
+    # Reshape [T, H, head_dim] -> [T, G, heads_per_group, head_dim]
+    o_4d = o.view(T, n_groups, heads_per_group, head_dim)
+
+    # Lookup cos/sin directly in fp32
+    cos_sin = cos_sin_cache[positions]  # [T, rope_dim] fp32
+    cos = cos_sin[:, :half_rope]  # [T, half_rope] fp32
+    sin = cos_sin[:, half_rope:]  # [T, half_rope] fp32
+
+    # Allocate outputs in [G, T, ...] contiguous layout
+    fp8_buf = torch.empty(n_groups, T, d, dtype=FP8_DTYPE, device=o.device)
+    scale_buf = torch.empty(n_groups, T, S, dtype=torch.float32, device=o.device)
+
+    # Process each quant block, matching the Triton kernel's per-program logic
+    for g in range(n_groups):
+        for qb in range(S):
+            head_in_group = qb // chunks_per_head
+            chunk_in_head = qb % chunks_per_head
+            offset = chunk_in_head * quant_group_size
+
+            # Load 128 bf16 elements and promote to fp32 for rotation+quant
+            block = o_4d[:, g, head_in_group, offset : offset + quant_group_size]
+            x = block.float()
+
+            # Apply inverse RoPE in fp32 if this is the last chunk
+            # GPT-J style: interleaved pairs (even=x, odd=y)
+            if chunk_in_head == chunks_per_head - 1:
+                rope_start = nope_dim % quant_group_size  # 64
+                rope_region = x[:, rope_start:].clone()
+                x_vals = rope_region[:, ::2]
+                y_vals = rope_region[:, 1::2]
+                x_new = x_vals * cos + y_vals * sin
+                y_new = y_vals * cos - x_vals * sin
+                x = x.clone()
+                x[:, rope_start::2] = x_new
+                x[:, rope_start + 1 :: 2] = y_new
+
+            # UE8M0 quant in fp32
+            x_fp8, scale = _ref_ue8m0_quant_block(x)
+
+            # Write to [G, T, D] contiguous memory
+            fp8_buf[g, :, qb * quant_group_size : (qb + 1) * quant_group_size] = x_fp8
+            scale_buf[g, :, qb] = scale
+
+    # Return transposed views
+    return fp8_buf.transpose(0, 1), scale_buf.transpose(0, 1)
+
+
+# =========================================================================
+# Tests
+# =========================================================================
+
+
+@pytest.mark.parametrize("num_tokens", [1, 7, 32, 128])
+@pytest.mark.parametrize(
+    "num_heads,n_groups",
+    [(64, 8), (32, 4), (128, 8)],
+    ids=["H64_G8", "H32_G4", "H128_G8"],
+)
+@pytest.mark.parametrize("seed", [0, 42])
+@torch.inference_mode()
+def test_correctness(num_tokens, num_heads, n_groups, seed):
+    """Compare fused kernel against reference for FP8 values and scales."""
+    torch.manual_seed(seed)
+
+    heads_per_group = num_heads // n_groups
+    max_pos = 4096
+    device = "cuda"
+
+    # Create inputs
+    o = torch.randn(
+        num_tokens, num_heads, HEAD_DIM, device=device, dtype=torch.bfloat16
+    )
+    positions = torch.randint(
+        0, max_pos, (num_tokens,), device=device, dtype=torch.long
+    )
+    cos_sin_cache = make_cos_sin_cache(
+        max_pos, ROPE_DIM, dtype=torch.float32, device=device
+    )
+
+    # Reference
+    ref_fp8, ref_scale = reference_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+
+    # Fused kernel
+    fused_fp8, fused_scale = fused_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+
+    # Check shapes
+    d = heads_per_group * HEAD_DIM
+    S = d // QUANT_GROUP_SIZE
+    assert ref_fp8.shape == (num_tokens, n_groups, d)
+    assert fused_fp8.shape == (num_tokens, n_groups, d)
+    assert ref_scale.shape == (num_tokens, n_groups, S)
+    assert fused_scale.shape == (num_tokens, n_groups, S)
+
+    # Scales: exact match (both use identical UE8M0 algorithm)
+    # Scales may differ by one UE8M0 step (factor of 2) if fp32 rotation
+    # ordering shifts absmax across a power-of-2 boundary. Check ratio is
+    # close to 1.
+    scale_ratio = fused_scale / ref_scale.clamp(min=1e-30)
+    assert scale_ratio.max() <= 2.0 and scale_ratio.min() >= 0.5, (
+        f"Scale ratio out of [0.5, 2]: min={scale_ratio.min():.4f} "
+        f"max={scale_ratio.max():.4f}"
+    )
+
+    # Compare via dequant (Triton vs PyTorch fp32 may differ by ULPs)
+    assert_dequant_close(ref_fp8, ref_scale, fused_fp8, fused_scale)
+
+
+@pytest.mark.parametrize("num_tokens", [1, 7, 32, 128])
+@pytest.mark.parametrize(
+    "num_heads,n_groups",
+    [(64, 8), (128, 8)],
+    ids=["H64_G8", "H128_G8"],
+)
+@torch.inference_mode()
+def test_output_strides(num_tokens, num_heads, n_groups):
+    """Verify fused output layout:
+    - FP8: logical [T, G, D] backed by contiguous [G, T, D].
+    - Scale: MN-major TMA-aligned (column-major: T-stride=1).
+    """
+
+    heads_per_group = num_heads // n_groups
+    max_pos = 4096
+    device = "cuda"
+
+    o = torch.randn(
+        num_tokens, num_heads, HEAD_DIM, device=device, dtype=torch.bfloat16
+    )
+    positions = torch.randint(
+        0, max_pos, (num_tokens,), device=device, dtype=torch.long
+    )
+    cos_sin_cache = make_cos_sin_cache(max_pos, device=device)
+
+    fused_fp8, fused_scale = fused_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+
+    # FP8: logical [T, G, D] backed by [G, T, D] row-major
+    d = heads_per_group * HEAD_DIM
+    expected_fp8_stride = (d, num_tokens * d, 1)
+    assert fused_fp8.stride() == expected_fp8_stride, (
+        f"FP8 stride mismatch: got {fused_fp8.stride()}, expected {expected_fp8_stride}"
+    )
+
+    # Scale: MN-major TMA-aligned layout. After fp8_einsum permutes
+    # [T,G,S] -> [G,T,S], T-dim should have stride 1.
+    # Our output is [T,G,S] = transpose of [G,T,S].
+    # So fused_scale.permute(1,0,2) should have T-stride=1.
+    perm = fused_scale.permute(1, 0, 2)  # [G, T, S]
+    assert perm.stride(1) == 1 or num_tokens == 1, (
+        f"Scale T-stride (after permute to [G,T,S]) should be 1, got {perm.stride(1)}"
+    )
+
+
+@pytest.mark.parametrize("num_tokens", [1, 7, 32, 128])
+@torch.inference_mode()
+def test_per_group_contiguity(num_tokens):
+    """FP8 per-group slices must be contiguous. Scale per-group slices
+    are column-major (T-stride=1) — not row-major contiguous, which is
+    correct for TMA loads."""
+    num_heads, n_groups = 64, 8
+    heads_per_group = num_heads // n_groups
+    max_pos = 4096
+    device = "cuda"
+
+    o = torch.randn(
+        num_tokens, num_heads, HEAD_DIM, device=device, dtype=torch.bfloat16
+    )
+    positions = torch.randint(
+        0, max_pos, (num_tokens,), device=device, dtype=torch.long
+    )
+    cos_sin_cache = make_cos_sin_cache(max_pos, device=device)
+
+    fused_fp8, fused_scale = fused_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+
+    for g in range(n_groups):
+        fp8_slice = fused_fp8[:, g, :]
+        assert fp8_slice.is_contiguous(), (
+            f"o_fp8[:, {g}, :] is not contiguous: "
+            f"shape={list(fp8_slice.shape)}, stride={list(fp8_slice.stride())}"
+        )
+
+
+@torch.inference_mode()
+def test_scales_are_power_of_two():
+    """Verify all scales are exact powers of 2 (UE8M0 property)."""
+    num_tokens, num_heads, n_groups = 32, 64, 8
+    heads_per_group = num_heads // n_groups
+    max_pos = 4096
+    device = "cuda"
+
+    o = torch.randn(
+        num_tokens, num_heads, HEAD_DIM, device=device, dtype=torch.bfloat16
+    )
+    positions = torch.randint(
+        0, max_pos, (num_tokens,), device=device, dtype=torch.long
+    )
+    cos_sin_cache = make_cos_sin_cache(max_pos, device=device)
+
+    _, fused_scale = fused_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+
+    # log2 of a power-of-two is an exact integer
+    log2_scales = torch.log2(fused_scale)
+    residual = (log2_scales - log2_scales.round()).abs()
+    assert residual.max() < 1e-5, (
+        f"Not all scales are powers of 2: max log2 residual = {residual.max().item()}"
+    )
+
+
+@torch.inference_mode()
+def test_nope_dims_unchanged():
+    """Nope dimensions (first 448 per head) should only be quantized,
+    not rotated. Verify by dequantizing and comparing against
+    quantize-only reference (no RoPE)."""
+    num_tokens, num_heads, n_groups = 16, 64, 8
+    heads_per_group = num_heads // n_groups
+    max_pos = 4096
+    device = "cuda"
+    torch.manual_seed(0)
+
+    o = torch.randn(
+        num_tokens, num_heads, HEAD_DIM, device=device, dtype=torch.bfloat16
+    )
+    positions = torch.randint(
+        0, max_pos, (num_tokens,), device=device, dtype=torch.long
+    )
+    cos_sin_cache = make_cos_sin_cache(max_pos, device=device)
+
+    # Fused kernel result
+    fused_fp8, fused_scale = fused_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+
+    # Reference: quantize without RoPE (identity rotation)
+    # Create a zero-sin cache so RoPE is identity
+    zero_cache = torch.zeros_like(cos_sin_cache)
+    half = ROPE_DIM // 2
+    zero_cache[:, :half] = 1.0  # cos = 1
+    # sin = 0 (already zero)
+
+    norope_fp8, norope_scale = fused_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        zero_cache,
+        n_groups,
+        heads_per_group,
+    )
+
+    # Extract nope quant blocks only (first 3 of every 4 blocks per head)
+    chunks_per_head = HEAD_DIM // QUANT_GROUP_SIZE  # 4
+
+    for h in range(heads_per_group):
+        for c in range(chunks_per_head - 1):  # skip last chunk (has rope)
+            qb = h * chunks_per_head + c
+            start = qb * QUANT_GROUP_SIZE
+            end = start + QUANT_GROUP_SIZE
+
+            fused_nope = fused_fp8[:, :, start:end].view(torch.uint8)
+            norope_nope = norope_fp8[:, :, start:end].view(torch.uint8)
+            assert torch.equal(fused_nope, norope_nope), (
+                f"Nope block (head={h}, chunk={c}) differs between "
+                f"fused and no-rope reference"
+            )
+
+            fused_s = fused_scale[:, :, qb]
+            norope_s = norope_scale[:, :, qb]
+            assert torch.equal(fused_s, norope_s), (
+                f"Nope scale (head={h}, chunk={c}) differs"
+            )
+
+
+@torch.inference_mode()
+def test_single_token():
+    """Edge case: single token."""
+    num_tokens, num_heads, n_groups = 1, 64, 8
+    heads_per_group = num_heads // n_groups
+    max_pos = 4096
+    device = "cuda"
+
+    o = torch.randn(
+        num_tokens, num_heads, HEAD_DIM, device=device, dtype=torch.bfloat16
+    )
+    positions = torch.tensor([42], device=device, dtype=torch.long)
+    cos_sin_cache = make_cos_sin_cache(max_pos, device=device)
+
+    ref_fp8, ref_scale = reference_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+    fused_fp8, fused_scale = fused_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+
+    assert_dequant_close(ref_fp8, ref_scale, fused_fp8, fused_scale)
+
+
+@torch.inference_mode()
+def test_zero_positions():
+    """Edge case: all positions are 0."""
+    num_tokens, num_heads, n_groups = 16, 64, 8
+    heads_per_group = num_heads // n_groups
+    max_pos = 4096
+    device = "cuda"
+
+    o = torch.randn(
+        num_tokens, num_heads, HEAD_DIM, device=device, dtype=torch.bfloat16
+    )
+    positions = torch.zeros(num_tokens, device=device, dtype=torch.long)
+    cos_sin_cache = make_cos_sin_cache(max_pos, device=device)
+
+    ref_fp8, ref_scale = reference_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+    fused_fp8, fused_scale = fused_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+
+    assert_dequant_close(ref_fp8, ref_scale, fused_fp8, fused_scale)
+
+
+@torch.inference_mode()
+def test_large_values():
+    """Edge case: values near FP8 saturation to test clamping."""
+    num_tokens, num_heads, n_groups = 8, 64, 8
+    heads_per_group = num_heads // n_groups
+    max_pos = 4096
+    device = "cuda"
+
+    # Create inputs with large values that will saturate FP8
+    o = torch.randn(
+        num_tokens, num_heads, HEAD_DIM, device=device, dtype=torch.bfloat16
+    )
+    o = o * 1000.0  # scale up to force saturation
+    positions = torch.randint(
+        0, max_pos, (num_tokens,), device=device, dtype=torch.long
+    )
+    cos_sin_cache = make_cos_sin_cache(max_pos, device=device)
+
+    ref_fp8, ref_scale = reference_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+    fused_fp8, fused_scale = fused_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+
+    assert_dequant_close(ref_fp8, ref_scale, fused_fp8, fused_scale)
+
+
+@torch.inference_mode()
+def test_dequant_numerical_accuracy():
+    """Verify dequantized values are close to the original (after inv RoPE)."""
+    num_tokens, num_heads, n_groups = 32, 64, 8
+    heads_per_group = num_heads // n_groups
+    max_pos = 4096
+    device = "cuda"
+    torch.manual_seed(0)
+
+    o = torch.randn(
+        num_tokens, num_heads, HEAD_DIM, device=device, dtype=torch.bfloat16
+    )
+    positions = torch.randint(
+        0, max_pos, (num_tokens,), device=device, dtype=torch.long
+    )
+    cos_sin_cache = make_cos_sin_cache(max_pos, device=device)
+
+    # Get the post-inv-RoPE values (ground truth before quantization)
+    o_after_rope = reference_inv_rope(o.clone(), positions, cos_sin_cache)
+    d = heads_per_group * HEAD_DIM
+    o_after_rope = o_after_rope.view(num_tokens, n_groups, d)
+
+    # Get fused quantized output
+    fused_fp8, fused_scale = fused_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+
+    # Dequantize: broadcast scale [T, G, S] to [T, G, D] via repeat
+    S = d // QUANT_GROUP_SIZE
+    scale_expanded = (
+        fused_scale.unsqueeze(-1)
+        .expand(num_tokens, n_groups, S, QUANT_GROUP_SIZE)
+        .reshape(num_tokens, n_groups, d)
+    )
+    dequant = fused_fp8.float() * scale_expanded
+
+    # Check relative error.
+    # FP8 e4m3 with UE8M0 (power-of-two scales that round UP) quantizes more
+    # coarsely than optimal scaling. Both paths rotate in fp32, so the bulk
+    # of the error comes from UE8M0 quantization itself (~10-12% typical).
+    o_gt = o_after_rope.transpose(0, 1).contiguous().transpose(0, 1)
+    dequant_contig = dequant.transpose(0, 1).contiguous().transpose(0, 1)
+
+    abs_err = (dequant_contig.float() - o_gt.float()).abs()
+    rel_err = abs_err / (o_gt.float().abs().clamp(min=1e-6))
+    mean_rel_err = rel_err.mean().item()
+
+    assert mean_rel_err < 0.15, (
+        f"Mean relative error too high: {mean_rel_err:.4f} (expected < 0.15)"
+    )
+
+
+def _unfused_inv_rope_fp8_quant(
+    o: torch.Tensor,
+    positions: torch.Tensor,
+    cos_sin_cache: torch.Tensor,
+    n_groups: int,
+    heads_per_group: int,
+    nope_dim: int = NOPE_DIM,
+    rope_dim: int = ROPE_DIM,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Unfused path matching deepseek_v4_attention.py:295-310.
+
+    Uses the production CUDA RoPE kernel + per_token_group_quant_fp8.
+    """
+    from vllm import _custom_ops as ops
+    from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+        per_token_group_quant_fp8,
+    )
+
+    head_dim = o.shape[-1]
+    rope_dim_offset = head_dim - rope_dim
+
+    # Step 1: In-place CUDA RoPE (same as production)
+    ops.rotary_embedding(
+        positions,
+        o,
+        None,
+        head_dim,
+        cos_sin_cache,
+        False,  # is_neox=False for DeepseekV4 (GPT-J style)
+        rope_dim_offset=rope_dim_offset,
+        inverse=True,
+    )
+
+    # Step 2: Reshape + quant + reshape (same as production)
+    T = o.shape[0]
+    d = heads_per_group * head_dim
+    o = o.view(T, n_groups, -1)
+    o_flat = o.transpose(0, 1).contiguous().reshape(-1, d)
+    o_fp8, o_scale = per_token_group_quant_fp8(
+        o_flat,
+        group_size=QUANT_GROUP_SIZE,
+        use_ue8m0=True,
+    )
+    o_fp8 = o_fp8.view(n_groups, T, d).transpose(0, 1)
+    o_scale = o_scale.view(n_groups, T, -1).transpose(0, 1)
+    return o_fp8, o_scale
+
+
+# =========================================================================
+# End-to-end test including fp8_einsum
+# =========================================================================
+
+
+@pytest.mark.parametrize("num_tokens", [1, 7, 32, 128, 1024])
+@pytest.mark.parametrize(
+    "num_heads,n_groups",
+    [(64, 8)],
+    ids=["H64_G8"],
+)
+@torch.inference_mode()
+def test_einsum_end_to_end(num_tokens, num_heads, n_groups):
+    """End-to-end: fused inv_rope+quant → fp8_einsum must match
+    unfused CUDA_rope+quant → fp8_einsum bitwise.
+
+    This catches stride/layout bugs that only manifest when the einsum
+    kernel actually consumes the quantized activations.
+    """
+    from deep_gemm.utils.math import ceil_div
+
+    from vllm.utils.deep_gemm import (
+        fp8_einsum,
+        per_block_cast_to_fp8,
+        transform_sf_into_required_layout,
+    )
+
+    heads_per_group = num_heads // n_groups
+    d = heads_per_group * HEAD_DIM
+    o_lora_rank = 1024
+    max_pos = 4096
+    device = "cuda"
+    torch.manual_seed(0)
+
+    o = torch.randn(
+        num_tokens, num_heads, HEAD_DIM, device=device, dtype=torch.bfloat16
+    )
+    positions = torch.randint(
+        0, max_pos, (num_tokens,), device=device, dtype=torch.long
+    )
+    cos_sin_cache = make_cos_sin_cache(max_pos, device=device)
+
+    # -- Weight quantization (shared between both paths) --
+    w = torch.randn(n_groups, o_lora_rank, d, device=device, dtype=torch.bfloat16)
+    w_fp8 = torch.empty_like(w, dtype=torch.float8_e4m3fn)
+    w_scale = torch.empty(
+        n_groups,
+        ceil_div(o_lora_rank, 128),
+        ceil_div(d, 128),
+        device=device,
+        dtype=torch.float32,
+    )
+    for g in range(n_groups):
+        w_fp8[g], w_scale[g] = per_block_cast_to_fp8(w[g], use_ue8m0=True)
+
+    recipe = (1, 1, 128)
+    w_scale_t = transform_sf_into_required_layout(
+        sf=w_scale,
+        mn=o_lora_rank,
+        k=d,
+        recipe=(1, 128, 128),
+        num_groups=n_groups,
+        is_sfa=False,
+    )
+
+    # -- UNFUSED path --
+    ref_fp8, ref_scale = _unfused_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+    z_ref = torch.empty(
+        num_tokens, n_groups, o_lora_rank, device=device, dtype=torch.bfloat16
+    )
+    fp8_einsum(
+        "bhr,hdr->bhd", (ref_fp8, ref_scale), (w_fp8, w_scale_t), z_ref, recipe=recipe
+    )
+
+    # -- FUSED path --
+    fused_fp8, fused_scale = fused_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+    z_fused = torch.empty(
+        num_tokens, n_groups, o_lora_rank, device=device, dtype=torch.bfloat16
+    )
+    fp8_einsum(
+        "bhr,hdr->bhd",
+        (fused_fp8, fused_scale),
+        (w_fp8, w_scale_t),
+        z_fused,
+        recipe=recipe,
+    )
+
+    # -- Checks --
+    # Einsum output: Triton and CUDA both rotate in fp32 now, so diffs
+    # come from fp32 ordering and UE8M0 boundary shifts only.
+    # Use relative diff (same metric as test_fp8_einsum.py).
+    from deep_gemm.testing import calc_diff
+
+    z_diff = calc_diff(z_fused, z_ref)
+    assert z_diff < 0.01, (
+        f"Einsum output diff too large: {z_diff:.6f} (expected < 0.01)"
+    )
+
+
+@pytest.mark.parametrize("num_tokens", [1, 32, 256])
+@torch.inference_mode()
+def test_with_real_deepseek_v4_rope(num_tokens, default_vllm_config):
+    """Test with real DeepseekV4ScalingRotaryEmbedding (GPT-J style,
+    mscale=0, YaRN scaling) matching the production config."""
+
+    num_heads = 64
+    n_groups = 8
+    heads_per_group = num_heads // n_groups
+    device = "cuda"
+    torch.manual_seed(0)
+
+    # Build YaRN-scaled cos_sin_cache matching real DeepSeek V3/V4 config
+    # (mscale=0 → mscale=1.0, so no magnitude scaling)
+    from vllm.model_executor.layers.rotary_embedding.common import (
+        yarn_find_correction_range,
+        yarn_linear_ramp_mask,
+    )
+
+    scaling_factor = 16
+    base = 10000.0
+    max_pos = 65536
+    beta_fast, beta_slow = 32, 1
+
+    pos_freqs = base ** (
+        torch.arange(0, ROPE_DIM, 2, dtype=torch.float32, device=device) / ROPE_DIM
+    )
+    inv_freq_extra = 1.0 / pos_freqs
+    inv_freq_interp = 1.0 / (scaling_factor * pos_freqs)
+    low, high = yarn_find_correction_range(
+        beta_fast, beta_slow, ROPE_DIM, base, max_pos
+    )
+    mask = 1 - yarn_linear_ramp_mask(low, high, ROPE_DIM // 2, dtype=torch.float32).to(
+        device
+    )
+    inv_freq = inv_freq_interp * (1 - mask) + inv_freq_extra * mask
+    t = torch.arange(max_pos * scaling_factor, device=device, dtype=torch.float32)
+    freqs = torch.outer(t, inv_freq)
+    # mscale=0 → yarn_get_mscale returns 1.0
+    cos_sin_cache = torch.cat([freqs.cos(), freqs.sin()], dim=-1)  # fp32
+
+    o = torch.randn(
+        num_tokens, num_heads, HEAD_DIM, device=device, dtype=torch.bfloat16
+    )
+    positions = torch.randint(0, 4096, (num_tokens,), device=device, dtype=torch.long)
+
+    # UNFUSED: CUDA RoPE with is_neox=False (GPT-J)
+    from vllm import _custom_ops as ops
+    from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+        per_token_group_quant_fp8,
+    )
+
+    o_unfused = o.clone()
+    ops.rotary_embedding(
+        positions,
+        o_unfused,
+        None,
+        HEAD_DIM,
+        cos_sin_cache,
+        False,  # is_neox=False (GPT-J style)
+        rope_dim_offset=NOPE_DIM,
+        inverse=True,
+    )
+    d = heads_per_group * HEAD_DIM
+    T = num_tokens
+    o_unfused = o_unfused.view(T, n_groups, d)
+    o_flat = o_unfused.transpose(0, 1).contiguous().reshape(-1, d)
+    ref_fp8, ref_scale = per_token_group_quant_fp8(
+        o_flat,
+        group_size=QUANT_GROUP_SIZE,
+        use_ue8m0=True,
+    )
+    ref_fp8 = ref_fp8.view(n_groups, T, d).transpose(0, 1)
+    ref_scale = ref_scale.view(n_groups, T, -1).transpose(0, 1)
+
+    # FUSED: use the real YaRN-scaled cos_sin_cache
+    fused_fp8, fused_scale = fused_inv_rope_fp8_quant(
+        o.clone(),
+        positions,
+        cos_sin_cache,
+        n_groups,
+        heads_per_group,
+    )
+
+    # Scales must match exactly (same UE8M0 algorithm)
+    # Compare via dequant (Triton bf16 rotation may differ from CUDA by 1 ULP)
+    assert_dequant_close(
+        ref_fp8, ref_scale, fused_fp8, fused_scale, msg="Real DeepSeek V4 rope"
+    )
diff --git a/tests/kernels/test_kda.py b/tests/kernels/test_kda.py
new file mode 100644
index 000000000000..18531fad999a
--- /dev/null
+++ b/tests/kernels/test_kda.py
@@ -0,0 +1,157 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Precision tests for vllm's chunk_kda Triton operator.
+
+Compares chunk_kda against a naive recurrent reference (float32).
+Uses torch.rand for q/k/v to match FLA's test pattern.
+"""
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from vllm.model_executor.layers.fla.ops.kda import chunk_kda
+from vllm.model_executor.layers.fla.ops.l2norm import l2norm_fwd
+
+DEVICE = "cuda"
+
+
+def naive_recurrent_kda(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    g: torch.Tensor,
+    beta: torch.Tensor,
+    scale: float | None = None,
+    initial_state: torch.Tensor | None = None,
+    output_final_state: bool = False,
+) -> tuple[torch.Tensor, torch.Tensor | None]:
+    """Naive recurrent KDA reference, ported from FLA's naive.py."""
+    dtype = v.dtype
+    B, T, H, K = q.shape
+    V = v.shape[-1]
+    if scale is None:
+        scale = K**-0.5
+
+    q, k, v, g, beta = (x.to(torch.float) for x in [q, k, v, g, beta])
+    q = q * scale
+
+    S = k.new_zeros(B, H, K, V).to(q)
+    if initial_state is not None:
+        S += initial_state
+    o = torch.zeros_like(v)
+    for i in range(T):
+        q_i, k_i, v_i, g_i, b_i = q[:, i], k[:, i], v[:, i], g[:, i], beta[:, i]
+        S = S * g_i[..., None].exp()
+        S = S + torch.einsum(
+            "bhk,bhv->bhkv",
+            b_i[..., None] * k_i,
+            v_i - (k_i[..., None] * S).sum(-2),
+        )
+        o[:, i] = torch.einsum("bhk,bhkv->bhv", q_i, S)
+    if not output_final_state:
+        S = None
+    return o.to(dtype), S
+
+
+def assert_close(
+    name: str,
+    ref: torch.Tensor,
+    tri: torch.Tensor,
+    ratio: float,
+    err_atol: float = 1e-6,
+):
+    """RMSE-based relative error comparison."""
+    abs_err = (ref.detach() - tri.detach()).flatten().abs().max().item()
+    rmse_diff = (ref.detach() - tri.detach()).flatten().square().mean().sqrt().item()
+    rmse_base = ref.detach().flatten().square().mean().sqrt().item()
+    rel_err = rmse_diff / (rmse_base + 1e-8)
+    print(f"{name:>4} | abs={abs_err:.6f} | rmse={rel_err:.6f} | thr={ratio}")
+    if abs_err <= err_atol:
+        return
+    assert not torch.isnan(ref).any(), f"{name}: NaN detected in ref"
+    assert not torch.isnan(tri).any(), f"{name}: NaN detected in tri"
+    assert rel_err < ratio, (
+        f"{name}: max abs err {abs_err:.6f}, rmse ratio {rel_err:.6f} >= {ratio}"
+    )
+
+
+@pytest.mark.parametrize(
+    ("H", "D", "cu_seqlens", "dtype"),
+    [
+        pytest.param(
+            *test,
+            id="H{}-D{}-cu{}-{}".format(*test),
+        )
+        for test in [
+            (32, 128, [0, 64], torch.float16),
+            (32, 128, [0, 1024], torch.float16),
+            (32, 128, [0, 15], torch.float16),
+            (32, 128, [0, 256, 512, 768, 1024], torch.float16),
+            (32, 128, [0, 15, 100, 300, 1200], torch.float16),
+            (64, 128, [0, 256, 500, 1000], torch.float16),
+            (32, 128, [0, 8192], torch.float16),
+            (32, 128, [0, 256, 500, 1000], torch.bfloat16),
+        ]
+    ],
+)
+@torch.inference_mode()
+def test_chunk_kda(
+    H: int,
+    D: int,
+    cu_seqlens: list[int],
+    dtype: torch.dtype,
+):
+    T = cu_seqlens[-1]
+    torch.manual_seed(42)
+    B = 1
+    cu_seqlens_t = torch.LongTensor(cu_seqlens).to(DEVICE)
+    N = len(cu_seqlens) - 1
+
+    q = torch.rand(B, T, H, D, dtype=dtype, device=DEVICE)
+    k = torch.rand(B, T, H, D, dtype=dtype, device=DEVICE)
+    v = torch.rand(B, T, H, D, dtype=dtype, device=DEVICE)
+    g = F.logsigmoid(torch.randn(B, T, H, D, dtype=torch.float32, device=DEVICE)).to(
+        dtype
+    )
+    beta = torch.rand(B, T, H, dtype=dtype, device=DEVICE).sigmoid()
+    h0 = torch.randn(N, H, D, D, dtype=torch.float32, device=DEVICE)
+
+    # Naive reference with l2norm_fwd (same kernel as chunk_kda)
+    ref_outputs = []
+    ref_states = []
+    for i in range(N):
+        s, e = cu_seqlens[i], cu_seqlens[i + 1]
+        q_i = l2norm_fwd(q[:, s:e].contiguous())
+        k_i = l2norm_fwd(k[:, s:e].contiguous())
+        o_i, ht_i = naive_recurrent_kda(
+            q_i,
+            k_i,
+            v[:, s:e],
+            g[:, s:e],
+            beta[:, s:e],
+            initial_state=h0[i],
+            output_final_state=True,
+        )
+        ref_outputs.append(o_i)
+        ref_states.append(ht_i)
+    ref_o = torch.cat(ref_outputs, dim=1)
+    ref_ht = torch.cat(ref_states, dim=0)
+
+    # h0 transposed to (V, K) layout for the kernel; naive uses (K, V)
+    tri_o, tri_ht = chunk_kda(
+        q=q.clone(),
+        k=k.clone(),
+        v=v.clone(),
+        g=g.clone(),
+        beta=beta.clone(),
+        initial_state=h0.transpose(-1, -2).contiguous().clone(),
+        output_final_state=True,
+        cu_seqlens=cu_seqlens_t,
+        use_qk_l2norm_in_kernel=True,
+    )
+
+    assert not torch.isnan(tri_o).any(), "Triton output o contains NaN"
+    assert not torch.isnan(tri_ht).any(), "Triton output ht contains NaN"
+    assert_close("o", ref_o, tri_o, 0.005)
+    assert_close("ht", ref_ht, tri_ht.transpose(-1, -2).contiguous(), 0.005)
diff --git a/tests/kernels/test_top_k_per_row.py b/tests/kernels/test_top_k_per_row.py
index 40bd2af65ea1..7b9c11495e8b 100644
--- a/tests/kernels/test_top_k_per_row.py
+++ b/tests/kernels/test_top_k_per_row.py
@@ -718,7 +718,6 @@ def test_persistent_topk_stress() -> None:
         pytest.param(
             {
                 "seq_lens": [2000, 6000, 30000, 80000],
-                "top_k": 2048,
                 "data_type": "random",
             },
             id="mixed_all_paths",
@@ -727,7 +726,6 @@ def test_persistent_topk_stress() -> None:
         pytest.param(
             {
                 "seq_lens": [2048, 4096, 8192, 16000],
-                "top_k": 2048,
                 "data_type": "random",
             },
             id="all_decode_medium",
@@ -736,7 +734,6 @@ def test_persistent_topk_stress() -> None:
         pytest.param(
             {
                 "seq_lens": [70000, 100000, 163840],
-                "top_k": 2048,
                 "data_type": "random",
             },
             id="all_large",
@@ -745,7 +742,6 @@ def test_persistent_topk_stress() -> None:
         pytest.param(
             {
                 "seq_lens": [32767, 32768, 32769, 32772],
-                "top_k": 2048,
                 "data_type": "random",
             },
             id="large_threshold_boundary",
@@ -754,7 +750,6 @@ def test_persistent_topk_stress() -> None:
         pytest.param(
             {
                 "seq_lens": [5000],
-                "top_k": 2048,
                 "data_type": "random",
             },
             id="single_row_medium",
@@ -772,15 +767,15 @@ def test_persistent_topk_stress() -> None:
         pytest.param(
             {
                 "seq_lens": [100, 2048, 10000, 80000],
-                "top_k": 2048,
                 "data_type": "random",
             },
             id="trivial_medium_large_mix",
         ),
     ],
 )
+@pytest.mark.parametrize("top_k", [512, 2048])
 @torch.inference_mode()
-def test_persistent_topk(test_config: dict) -> None:
+def test_persistent_topk(test_config: dict, top_k: int) -> None:
     """
     Tests specific to the persistent_topk kernel:
     - Mixed medium/large rows in the same batch (dynamic per-row dispatch)
@@ -790,14 +785,15 @@ def test_persistent_topk(test_config: dict) -> None:
     run_large_context_topk_test(
         batch_size=len(test_config["seq_lens"]),
         seq_lens=test_config["seq_lens"],
-        top_k=test_config["top_k"],
+        top_k=top_k,
         data_type=test_config.get("data_type", "random"),
     )
 
 
 @pytest.mark.skipif(not current_platform.is_cuda(), reason="This test requires CUDA")
+@pytest.mark.parametrize("top_k", [512, 2048])
 @torch.inference_mode()
-def test_persistent_topk_padded_stride() -> None:
+def test_persistent_topk_padded_stride(top_k: int) -> None:
     """
     Test persistent_topk with padded logits (large stride, small seq_len)
     to simulate the e2e CUDAGraph scenario where fp8_paged_mqa_logits
@@ -806,7 +802,6 @@ def test_persistent_topk_padded_stride() -> None:
     set_random_seed(42)
     torch.set_default_device("cuda:0")
 
-    top_k = 2048
     batch_size = 4
     padded_stride = 163840  # DeepSeek-V3.2 max_model_len
     actual_seq_lens = [3000, 5000, 8000, 12000]
diff --git a/tests/lora/conftest.py b/tests/lora/conftest.py
index 20944a9111e0..bfe796d66c3f 100644
--- a/tests/lora/conftest.py
+++ b/tests/lora/conftest.py
@@ -3,6 +3,7 @@
 
 import tempfile
 from collections import OrderedDict
+from importlib import reload
 from unittest.mock import MagicMock
 
 import pytest
@@ -47,6 +48,11 @@ def cleanup_fixture(should_do_global_cleanup_after_test: bool):
 def maybe_enable_lora_dual_stream(monkeypatch: pytest.MonkeyPatch):
     if current_platform.is_cuda():
         monkeypatch.setenv("VLLM_LORA_ENABLE_DUAL_STREAM", "1")
+        import vllm.lora.layers.base_linear
+
+        if not hasattr(vllm.lora.layers.base_linear, "lora_linear_async"):
+            # Reload the module to ensure the environment variable takes effect.
+            reload(vllm.lora.layers.base_linear)
     yield
 
 
@@ -56,9 +62,7 @@ def dist_init():
 
     temp_file = tempfile.mkstemp()[1]
 
-    backend = "nccl"
-    if current_platform.is_cpu() or current_platform.is_tpu():
-        backend = "gloo"
+    backend = "gloo" if current_platform.is_tpu() else current_platform.dist_backend
 
     with ensure_current_vllm_config():
         init_distributed_environment(
@@ -77,9 +81,7 @@ def dist_init():
 def dist_init_torch_only():
     if torch.distributed.is_initialized():
         return
-    backend = "nccl"
-    if current_platform.is_cpu():
-        backend = "gloo"
+    backend = current_platform.dist_backend
 
     temp_file = tempfile.mkstemp()[1]
     torch.distributed.init_process_group(
diff --git a/tests/lora/test_fused_moe_lora_kernel.py b/tests/lora/test_fused_moe_lora_kernel.py
index 8adc20865755..e50d7d5aacfe 100644
--- a/tests/lora/test_fused_moe_lora_kernel.py
+++ b/tests/lora/test_fused_moe_lora_kernel.py
@@ -647,6 +647,7 @@ def _get_shard_slice(shard_size):
         rank=local_rank,
         local_rank=local_rank,
         distributed_init_method=init_method,
+        backend=current_platform.dist_backend,
     )
     with ensure_current_vllm_config():
         initialize_model_parallel(world_size, 1)
diff --git a/tests/lora/test_gptoss_tp.py b/tests/lora/test_gptoss_tp.py
index 68dd87233ac0..648660734655 100644
--- a/tests/lora/test_gptoss_tp.py
+++ b/tests/lora/test_gptoss_tp.py
@@ -129,6 +129,7 @@ def test_gpt_oss_lora_tp2(
             tensor_parallel_size=2,
             gpu_memory_utilization=0.8,
             fully_sharded_loras=fully_sharded_loras,
+            enable_expert_parallel=not fully_sharded_loras,
             compilation_config=vllm.config.CompilationConfig(  # Avoid OOM
                 cudagraph_specialize_lora=False,
             ),
diff --git a/tests/lora/test_layers.py b/tests/lora/test_layers.py
index c2b4f5515644..c366b2cf2976 100644
--- a/tests/lora/test_layers.py
+++ b/tests/lora/test_layers.py
@@ -44,6 +44,7 @@
     VocabParallelEmbedding,
     get_masked_input_and_mask,
 )
+from vllm.model_executor.models.deepseek_v2 import DeepSeekV2FusedQkvAProjLinear
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import set_random_seed
 
@@ -56,7 +57,11 @@
 }
 
 pytestmark = pytest.mark.skipif(
-    not (current_platform.is_cuda_alike() or current_platform.is_cpu()),
+    not (
+        current_platform.is_cuda_alike()
+        or current_platform.is_cpu()
+        or current_platform.is_xpu()
+    ),
     reason="Backend not supported",
 )
 
@@ -66,7 +71,7 @@
         f"{DEVICE_TYPE}:{i}"
         for i in range(1 if torch.accelerator.device_count() == 1 else 2)
     ]
-    if current_platform.is_cuda_alike()
+    if (current_platform.is_cuda_alike() or current_platform.is_xpu())
     else ["cpu"]
 )
 
@@ -95,7 +100,7 @@ def skip_cuda_with_stage_false(request):
     On cuda-like platforms, we use the same kernels for prefill and decode
     stage, and 'stage' is generally ignored, so we only need to test once.
     """
-    if current_platform.is_cuda_alike():
+    if current_platform.is_cuda_alike() or current_platform.is_xpu():
         try:
             if hasattr(request.node, "callspec") and hasattr(
                 request.node.callspec, "params"
@@ -248,6 +253,10 @@ def check_punica_wrapper(punica_wrapper) -> bool:
         from vllm.lora.punica_wrapper.punica_cpu import PunicaWrapperCPU
 
         return type(punica_wrapper) is PunicaWrapperCPU
+    elif current_platform.is_xpu():
+        from vllm.lora.punica_wrapper.punica_xpu import PunicaWrapperXPU
+
+        return type(punica_wrapper) is PunicaWrapperXPU
     else:
         return False
 
@@ -263,7 +272,7 @@ def test_embeddings(
     # For multi-GPU testing of Triton kernel, we must explicitly set the CUDA
     # device, see: https://github.com/triton-lang/triton/issues/2925
     # Same below.
-    if current_platform.is_cuda_alike():
+    if current_platform.is_cuda_alike() or current_platform.is_xpu():
         torch.accelerator.set_device_index(device)
 
     torch.set_default_device(device)
@@ -362,7 +371,7 @@ def create_random_embedding_layer():
 def test_lm_head_logits_processor(
     default_vllm_config, dist_init, num_loras, device, vocab_size, stage
 ) -> None:
-    if current_platform.is_cuda_alike():
+    if current_platform.is_cuda_alike() or current_platform.is_xpu():
         torch.accelerator.set_device_index(device)
 
     torch.set_default_device(device)
@@ -479,7 +488,7 @@ def test_lm_head_logits_processor_invalid_vocab_size(
     default_vllm_config, dist_init, vocab_size, device
 ) -> None:
     """Test that LogitsProcessorWithLoRA raises ValueError for invalid vocab sizes."""
-    if current_platform.is_cuda_alike():
+    if current_platform.is_cuda_alike() or current_platform.is_xpu():
         torch.accelerator.set_device_index(device)
 
     torch.set_default_device(device)
@@ -508,7 +517,7 @@ def test_linear_replicated(
     device,
     stage,
 ) -> None:
-    if current_platform.is_cuda_alike():
+    if current_platform.is_cuda_alike() or current_platform.is_xpu():
         torch.accelerator.set_device_index(device)
 
     max_loras = 8
@@ -617,7 +626,7 @@ def create_random_linear_replicated_layer(idx: int = 0):
 def test_linear_parallel(
     default_vllm_config, dist_init, num_loras, orientation, fully_shard, device, stage
 ) -> None:
-    if current_platform.is_cuda_alike():
+    if current_platform.is_cuda_alike() or current_platform.is_xpu():
         torch.accelerator.set_device_index(device)
 
     max_loras = 8
@@ -750,7 +759,7 @@ def create_random_linear_parallel_layer(idx: int = 0):
 def test_column_parallel_packed(
     default_vllm_config, dist_init, num_loras, repeats, fully_shard, device, stage
 ) -> None:
-    if current_platform.is_cuda_alike():
+    if current_platform.is_cuda_alike() or current_platform.is_xpu():
         torch.accelerator.set_device_index(device)
 
     max_loras = 8
@@ -912,7 +921,7 @@ class FakeConfig:
 def test_merged_column_parallel_variable_slice(
     default_vllm_config, dist_init, num_loras, num_slices, device, stage
 ) -> None:
-    if current_platform.is_cuda_alike():
+    if current_platform.is_cuda_alike() or current_platform.is_xpu():
         torch.accelerator.set_device_index(device)
 
     max_loras = 8
@@ -1422,7 +1431,107 @@ def test_variable_slice_lora_class_selection(default_vllm_config, dist_init):
         f"for 2 packed modules, got {type(selected_layer_merged).__name__}"
     )
 
-    # Case 5: Plain ColumnParallelLinear (not merged) - common in many models
+    fully_sharded_tp_lora_config = LoRAConfig(
+        max_loras=8,
+        max_lora_rank=16,
+        lora_dtype=torch.float16,
+        fully_sharded_loras=True,
+    )
+    fully_sharded_tp_layer = MergedColumnParallelLinear(
+        4096, [2048, 2048], bias=False, params_dtype=torch.float16
+    )
+    fully_sharded_tp_layer.tp_size = 2
+
+    assert not MergedColumnParallelLinearWithLoRA.can_replace_layer(
+        source_layer=fully_sharded_tp_layer,
+        lora_config=fully_sharded_tp_lora_config,
+        packed_modules_list=packed_modules_two,
+    ), "Generic merged wrapper should reject fully sharded TP layers"
+
+    assert MergedColumnParallelLinearWithShardedLoRA.can_replace_layer(
+        source_layer=fully_sharded_tp_layer,
+        lora_config=fully_sharded_tp_lora_config,
+        packed_modules_list=packed_modules_two,
+    ), "Sharded merged wrapper should remain eligible for fully sharded TP layers"
+
+    selected_fully_sharded_tp_layer = from_layer(
+        fully_sharded_tp_layer,
+        max_loras=8,
+        lora_config=fully_sharded_tp_lora_config,
+        packed_modules_list=packed_modules_two,
+    )
+    assert isinstance(
+        selected_fully_sharded_tp_layer,
+        MergedColumnParallelLinearWithShardedLoRA,
+    ), (
+        "from_layer should select MergedColumnParallelLinearWithShardedLoRA "
+        "for fully sharded TP merged layers, got "
+        f"{type(selected_fully_sharded_tp_layer).__name__}"
+    )
+
+    # Case 5: DeepSeek's fused_qkv_a_proj should reuse the generic merged
+    # wrapper while preserving its custom base forward path.
+    deepseek_fused_layer = DeepSeekV2FusedQkvAProjLinear(
+        4096, [2048, 2048], prefix="model.layers.0.self_attn.fused_qkv_a_proj"
+    )
+    selected_deepseek_layer = from_layer(
+        deepseek_fused_layer,
+        max_loras=8,
+        lora_config=lora_config,
+        packed_modules_list=packed_modules_two,
+    )
+    assert isinstance(selected_deepseek_layer, MergedColumnParallelLinearWithLoRA), (
+        "from_layer should select MergedColumnParallelLinearWithLoRA "
+        f"for DeepSeek fused_qkv_a_proj, got {type(selected_deepseek_layer).__name__}"
+    )
+
+    fully_sharded_lora_config = LoRAConfig(
+        max_loras=8,
+        max_lora_rank=16,
+        lora_dtype=torch.float16,
+        fully_sharded_loras=True,
+    )
+    selected_fully_sharded_deepseek_layer = from_layer(
+        deepseek_fused_layer,
+        max_loras=8,
+        lora_config=fully_sharded_lora_config,
+        packed_modules_list=packed_modules_two,
+    )
+    assert isinstance(
+        selected_fully_sharded_deepseek_layer,
+        MergedColumnParallelLinearWithLoRA,
+    ), (
+        "from_layer should keep using MergedColumnParallelLinearWithLoRA "
+        "for fused_qkv_a_proj when the base layer is effectively unsharded, got "
+        f"{type(selected_fully_sharded_deepseek_layer).__name__}"
+    )
+
+    # Case 6: Generic subclass of MergedColumnParallelLinear with 2 packed
+    # modules should still use the generic merged wrapper.
+    class CustomMergedColumnParallelLinear(MergedColumnParallelLinear):
+        pass
+
+    custom_merged_layer = CustomMergedColumnParallelLinear(
+        4096, [2048, 2048], bias=False, params_dtype=torch.float16
+    )
+    assert MergedColumnParallelLinearWithLoRA.can_replace_layer(
+        source_layer=custom_merged_layer,
+        lora_config=lora_config,
+        packed_modules_list=packed_modules_two,
+    ), "MergedColumnParallelLinearWithLoRA should handle subclasses"
+
+    selected_custom_layer = from_layer(
+        custom_merged_layer,
+        max_loras=8,
+        lora_config=lora_config,
+        packed_modules_list=packed_modules_two,
+    )
+    assert isinstance(selected_custom_layer, MergedColumnParallelLinearWithLoRA), (
+        f"from_layer should select MergedColumnParallelLinearWithLoRA "
+        f"for subclassed merged layers, got {type(selected_custom_layer).__name__}"
+    )
+
+    # Case 7: Plain ColumnParallelLinear (not merged) - common in many models
     # -> ColumnParallelLinearWithLoRA should be selected
     plain_column_parallel = ColumnParallelLinear(
         4096, 4096, bias=False, params_dtype=torch.float16
@@ -1455,7 +1564,7 @@ def test_variable_slice_lora_class_selection(default_vllm_config, dist_init):
         f"for plain ColumnParallelLinear, got {type(selected_plain).__name__}"
     )
 
-    # Case 6: MergedColumnParallelLinear with exactly 2 output sizes
+    # Case 8: MergedColumnParallelLinear with exactly 2 output sizes
     # and empty packed_modules_list
     # -> ColumnParallelLinearWithLoRA should NOT match (packed_modules_list != 1)
     # -> MergedColumnParallelLinearVariableSliceWithLoRA should NOT match (< 3 slices)
@@ -1473,3 +1582,178 @@ def test_variable_slice_lora_class_selection(default_vllm_config, dist_init):
         "MergedColumnParallelLinearVariableSliceWithLoRA "
         "should NOT handle 2 slices even with empty packed_modules_list"
     )
+
+
+@pytest.mark.parametrize(
+    "wrapper_cls",
+    [ColumnParallelLinearWithLoRA, ColumnParallelLinearWithShardedLoRA],
+)
+def test_get_and_maybe_dequant_weights_accepts_lora_wrappers(dist_init, wrapper_cls):
+    from vllm.model_executor.layers.quantization.utils.quant_utils import (
+        get_and_maybe_dequant_weights,
+    )
+
+    linear = ColumnParallelLinear(4096, 4096, bias=False, params_dtype=torch.float16)
+    lora_linear = wrapper_cls(linear)
+
+    # Should work with LoRA wrappers and return [out, in] weights.
+    dequant_weight = get_and_maybe_dequant_weights(lora_linear, out_dtype=torch.float16)
+    assert dequant_weight.shape == linear.weight.shape
+
+
+@torch.inference_mode()
+@pytest.mark.parametrize("device", DEVICES)
+@pytest.mark.parametrize("stage", STAGES)
+@pytest.mark.parametrize("fully_sharded", [False, True])
+def test_deepseek_fused_qkv_a_proj_lora_preserves_base_forward(
+    default_vllm_config, dist_init, device, stage, fully_sharded
+):
+    if current_platform.is_cuda_alike() or current_platform.is_xpu():
+        torch.accelerator.set_device_index(device)
+
+    torch.set_default_device(device)
+    dtype = (
+        torch.float16
+        if (current_platform.is_cuda_alike() or current_platform.is_xpu())
+        else torch.float32
+    )
+    max_loras = 8
+    lora_config = LoRAConfig(
+        max_loras=max_loras,
+        max_lora_rank=8,
+        lora_dtype=dtype,
+        fully_sharded_loras=fully_sharded,
+    )
+    punica_wrapper = get_punica_wrapper(8192, 256, device, lora_config=lora_config)
+    assert check_punica_wrapper(punica_wrapper)
+
+    class OffsetDeepSeekFusedQkvAProjLinear(DeepSeekV2FusedQkvAProjLinear):
+        def forward(self, input_):
+            output, output_bias = super().forward(input_)
+            return output + 1, output_bias
+
+    layer = OffsetDeepSeekFusedQkvAProjLinear(
+        32, [16, 16], prefix="model.layers.0.self_attn.fused_qkv_a_proj"
+    )
+    layer.weight.data = torch.rand_like(layer.weight.data, dtype=dtype)
+
+    lora_layer = MergedColumnParallelLinearWithLoRA(layer)
+    lora_layer.create_lora_weights(max_loras, lora_config)
+    lora_layer.set_mapping(punica_wrapper)
+
+    id_to_index = get_random_id_to_index(1, max_loras, log=False)
+    active_slot = next(i for i, lora_id in enumerate(id_to_index) if lora_id == 1)
+    lora_a = [
+        torch.rand(8, 32, dtype=dtype, device=device),
+        torch.rand(8, 32, dtype=dtype, device=device),
+    ]
+    lora_b = [
+        torch.rand(16, 8, dtype=dtype, device=device),
+        torch.rand(16, 8, dtype=dtype, device=device),
+    ]
+    lora_layer.set_lora(active_slot, lora_a=lora_a, lora_b=lora_b)
+
+    inputs, index_mapping, prompt_mapping = create_random_inputs(
+        active_lora_ids=[1],
+        num_inputs=4,
+        input_size=(1, 32),
+        input_range=(0, 1),
+        input_type=dtype,
+        device=device,
+    )
+    lora_mapping = LoRAMapping(index_mapping, prompt_mapping, is_prefill=stage)
+    punica_wrapper.update_metadata(lora_mapping, id_to_index, max_loras, 512)
+
+    lora_result = lora_layer(torch.cat(inputs))[0]
+
+    expected_results = []
+    for input_ in inputs:
+        result = layer(input_)[0]
+        result[:, :16] += input_ @ lora_a[0].T @ lora_b[0].T
+        result[:, 16:] += input_ @ lora_a[1].T @ lora_b[1].T
+        expected_results.append(result)
+
+    rtol, atol = TOLERANCES[lora_result.dtype]
+    torch.testing.assert_close(
+        lora_result, torch.cat(expected_results), rtol=rtol, atol=atol
+    )
+
+    merged_layer = OffsetDeepSeekFusedQkvAProjLinear(
+        32, [16, 16], prefix="model.layers.0.self_attn.fused_qkv_a_proj"
+    )
+    merged_layer.weight.data = layer.weight.data.clone()
+    merged_layer.weight.data[:16].add_(lora_b[0] @ lora_a[0])
+    merged_layer.weight.data[16:].add_(lora_b[1] @ lora_a[1])
+    merged_result = merged_layer(torch.cat(inputs))[0]
+
+    torch.testing.assert_close(lora_result, merged_result, rtol=rtol, atol=atol)
+
+
+@torch.inference_mode()
+@pytest.mark.parametrize("device", DEVICES)
+@pytest.mark.parametrize("stage", STAGES)
+def test_replicated_lora_preserves_base_forward_for_subclasses(
+    default_vllm_config, dist_init, device, stage
+):
+    if current_platform.is_cuda_alike() or current_platform.is_xpu():
+        torch.accelerator.set_device_index(device)
+
+    torch.set_default_device(device)
+    dtype = (
+        torch.float16
+        if current_platform.is_cuda_alike() or current_platform.is_xpu()
+        else torch.float32
+    )
+    max_loras = 8
+    lora_config = LoRAConfig(max_loras=max_loras, max_lora_rank=8, lora_dtype=dtype)
+    punica_wrapper = get_punica_wrapper(8192, 256, device, lora_config=lora_config)
+    assert check_punica_wrapper(punica_wrapper)
+
+    class OffsetReplicatedLinear(ReplicatedLinear):
+        def forward(self, input_):
+            output, output_bias = super().forward(input_)
+            return output + 1, output_bias
+
+    layer = OffsetReplicatedLinear(32, 16, bias=False, params_dtype=dtype)
+    layer.weight.data = torch.rand_like(layer.weight.data, dtype=dtype)
+
+    lora_layer = ReplicatedLinearWithLoRA(layer)
+    lora_layer.create_lora_weights(max_loras, lora_config)
+    lora_layer.set_mapping(punica_wrapper)
+
+    id_to_index = get_random_id_to_index(1, max_loras, log=False)
+    active_slot = next(i for i, lora_id in enumerate(id_to_index) if lora_id == 1)
+    lora_a = torch.rand(8, 32, dtype=dtype, device=device)
+    lora_b = torch.rand(16, 8, dtype=dtype, device=device)
+    lora_layer.set_lora(active_slot, lora_a=lora_a, lora_b=lora_b)
+
+    inputs, index_mapping, prompt_mapping = create_random_inputs(
+        active_lora_ids=[1],
+        num_inputs=4,
+        input_size=(1, 32),
+        input_range=(0, 1),
+        input_type=dtype,
+        device=device,
+    )
+    lora_mapping = LoRAMapping(index_mapping, prompt_mapping, is_prefill=stage)
+    punica_wrapper.update_metadata(lora_mapping, id_to_index, max_loras, 512)
+
+    lora_result = lora_layer(torch.cat(inputs))[0]
+
+    expected_results = []
+    for input_ in inputs:
+        result = layer(input_)[0]
+        result += input_ @ lora_a.T @ lora_b.T
+        expected_results.append(result)
+
+    rtol, atol = TOLERANCES[lora_result.dtype]
+    torch.testing.assert_close(
+        lora_result, torch.cat(expected_results), rtol=rtol, atol=atol
+    )
+
+    merged_layer = OffsetReplicatedLinear(32, 16, bias=False, params_dtype=dtype)
+    merged_layer.weight.data = layer.weight.data.clone()
+    merged_layer.weight.data.add_(lora_b @ lora_a)
+    merged_result = merged_layer(torch.cat(inputs))[0]
+
+    torch.testing.assert_close(lora_result, merged_result, rtol=rtol, atol=atol)
diff --git a/tests/lora/test_llama_tp.py b/tests/lora/test_llama_tp.py
index 483235ff5129..99c823238ddb 100644
--- a/tests/lora/test_llama_tp.py
+++ b/tests/lora/test_llama_tp.py
@@ -184,7 +184,7 @@ def test_tp2_serialize_and_deserialize_lora(
         result = subprocess.run(
             [
                 sys.executable,
-                f"{VLLM_PATH}/examples/others/tensorize_vllm_model.py",
+                f"{VLLM_PATH}/examples/features/tensorize_vllm_model.py",
                 "--model",
                 MODEL_PATH,
                 "--lora-path",
@@ -224,6 +224,10 @@ def test_tp2_serialize_and_deserialize_lora(
         max_model_len=1024,
         tensor_parallel_size=2,
         max_loras=2,
+        # Leave headroom for LoRA adapter loading and Triton JIT
+        # compilation, which can allocate GPU memory concurrently
+        # during the first inference step.
+        gpu_memory_utilization=0.85,
     )
 
     tc_as_dict = tensorizer_config.to_serializable()
diff --git a/tests/lora/test_lora_manager.py b/tests/lora/test_lora_manager.py
index e80d96f00e74..85d079673df5 100644
--- a/tests/lora/test_lora_manager.py
+++ b/tests/lora/test_lora_manager.py
@@ -13,6 +13,7 @@
 from vllm.lora.layers import (
     ColumnParallelLinearWithLoRA,
     MergedColumnParallelLinearWithLoRA,
+    ReplicatedLinearWithLoRA,
     RowParallelLinearWithLoRA,
 )
 from vllm.lora.lora_model import LoRAModel
@@ -26,6 +27,7 @@
 from vllm.lora.peft_helper import PEFTHelper
 from vllm.lora.request import LoRARequest
 from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager, WorkerLoRAManager
+from vllm.model_executor.layers.fused_moe import GateLinear
 from vllm.platforms import current_platform
 
 from .utils import create_peft_lora
@@ -38,7 +40,7 @@
 DEVICE_TYPE = current_platform.device_type
 DEVICES = (
     [f"{DEVICE_TYPE}:{i}" for i in range(min(torch.accelerator.device_count(), 2))]
-    if current_platform.is_cuda_alike()
+    if (current_platform.is_cuda_alike() or current_platform.is_xpu())
     else ["cpu"]
 )
 
@@ -132,6 +134,135 @@ def test_replace_submodules(default_vllm_config, dist_init, dummy_model):
     assert isinstance(model.get_submodule("layer1.dense2"), RowParallelLinearWithLoRA)
 
 
+def test_wrap_replicated_linear_subclasses(default_vllm_config, dist_init, dummy_model):
+    from vllm.model_executor.layers.linear import ReplicatedLinear
+
+    class CustomReplicatedLinear(ReplicatedLinear):
+        pass
+
+    model = dummy_model
+    model.add_module("custom_gate", CustomReplicatedLinear(10, 10, bias=False))
+
+    manager = LoRAModelManager(
+        model,
+        1,
+        1,
+        1,
+        LoRAConfig(
+            max_lora_rank=8, max_cpu_loras=8, max_loras=8, lora_dtype=DEFAULT_DTYPE
+        ),
+        torch.device(DEVICES[0]),
+    )
+
+    assert isinstance(
+        manager.model.get_submodule("custom_gate"), ReplicatedLinearWithLoRA
+    )
+
+
+def test_wrap_gate_linear(default_vllm_config, dist_init, dummy_model):
+    model = dummy_model
+    model.add_module("router_gate", GateLinear(10, 4, bias=False))
+
+    manager = LoRAModelManager(
+        model,
+        1,
+        1,
+        1,
+        LoRAConfig(
+            max_lora_rank=8, max_cpu_loras=8, max_loras=8, lora_dtype=DEFAULT_DTYPE
+        ),
+        torch.device(DEVICES[0]),
+    )
+
+    assert isinstance(
+        manager.model.get_submodule("router_gate"), ReplicatedLinearWithLoRA
+    )
+
+
+def test_skip_unsupported_matched_modules(default_vllm_config, dist_init, dummy_model):
+    class UnsupportedContainer(nn.Module):
+        def __init__(self):
+            super().__init__()
+            # This name matches a supported target suffix ("dense1"),
+            # but nn.Linear is not currently a LoRA-wrappable layer type.
+            self.dense1 = nn.Linear(10, 10, bias=False)
+
+    model = dummy_model
+    model.add_module("unsupported", UnsupportedContainer())
+
+    manager = LoRAModelManager(
+        model,
+        1,
+        1,
+        1,
+        LoRAConfig(
+            max_lora_rank=8, max_cpu_loras=8, max_loras=8, lora_dtype=DEFAULT_DTYPE
+        ),
+        torch.device(DEVICES[0]),
+    )
+
+    # Should not crash and should keep unsupported matched modules unchanged.
+    assert isinstance(manager.model.get_submodule("unsupported.dense1"), nn.Linear)
+    assert "unsupported.dense1" not in manager.modules
+
+
+def test_target_modules_fail_closed_on_unsupported_matched_modules(
+    default_vllm_config, dist_init, dummy_model
+):
+    class UnsupportedContainer(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.dense1 = nn.Linear(10, 10, bias=False)
+
+    model = dummy_model
+    model.add_module("unsupported", UnsupportedContainer())
+
+    with pytest.raises(ValueError, match="unsupported.dense1"):
+        LoRAModelManager(
+            model,
+            1,
+            1,
+            1,
+            LoRAConfig(
+                max_lora_rank=8,
+                max_cpu_loras=8,
+                max_loras=8,
+                lora_dtype=DEFAULT_DTYPE,
+                target_modules=["dense1"],
+            ),
+            torch.device(DEVICES[0]),
+        )
+
+
+def test_get_dummy_lora_warmup_rank_for_fully_sharded_moe():
+    manager = LoRAModelManager.__new__(LoRAModelManager)
+    manager.lora_config = LoRAConfig(
+        max_lora_rank=64,
+        max_cpu_loras=1,
+        max_loras=1,
+        lora_dtype=DEFAULT_DTYPE,
+        fully_sharded_loras=True,
+    )
+
+    class DummyModule:
+        def __init__(self, tp_size: int, fully_sharded: bool):
+            self.tp_size = tp_size
+            self.fully_sharded = fully_sharded
+
+    manager.modules = {
+        "model.layers.0.self_attn.q_proj": DummyModule(
+            tp_size=32,
+            fully_sharded=True,
+        ),
+        "model.layers.0.mlp.experts": DummyModule(
+            tp_size=32,
+            fully_sharded=True,
+        ),
+    }
+
+    assert manager.get_dummy_lora_warmup_rank(8) == 32
+
+
 @pytest.mark.parametrize("device", DEVICES)
 def test_lora_model_manager(default_vllm_config, dist_init, dummy_model, device):
     model = dummy_model
@@ -796,107 +927,19 @@ def test_target_modules_none_uses_all(
 
 
 @pytest.mark.parametrize("device", DEVICES)
-def test_load_adapter_warns_on_unsupported_modules(
-    default_vllm_config, dist_init, dummy_model_gate_up, device, tmp_path
+def test_target_modules_match_packed_runtime_modules(
+    default_vllm_config, dist_init, dummy_model_gate_up, device
 ):
-    """Test that _load_adapter warns when a LoRA adapter contains modules
-    not in the model's supported LoRA target modules."""
-    from unittest.mock import patch
-
-    import vllm.lora.worker_manager as wm_module
-
-    lora_config = LoRAConfig(
-        max_lora_rank=8, max_cpu_loras=4, max_loras=4, lora_dtype=DEFAULT_DTYPE
-    )
-
-    dummy_lora_files = f"{tmp_path}/lora_adapter"
-    os.makedirs(dummy_lora_files, exist_ok=True)
-    create_peft_lora(
-        dummy_model_gate_up,
-        save_dir=dummy_lora_files,
-        target_modules=["layer1.dense1", "dense2"],
-        lora_dtype=DEFAULT_DTYPE,
-    )
-
-    model_config = ModelConfig(max_model_len=16)
-    vllm_config = VllmConfig(model_config=model_config, lora_config=lora_config)
-    vllm_config.scheduler_config.max_num_seqs = 4
-    vllm_config.scheduler_config.max_num_batched_tokens = 2
-
-    worker_manager = WorkerLoRAManager(vllm_config, device, EMBEDDING_MODULES)
-    worker_manager.vocab_size = dummy_model_gate_up.unpadded_vocab_size
-    worker_manager.create_lora_manager(dummy_model_gate_up)
-
-    # Patch from_local_checkpoint to inject an unsupported module
-    original_from_checkpoint = LoRAModel.from_local_checkpoint
-
-    def patched_from_checkpoint(*args, **kwargs):
-        lora = original_from_checkpoint(*args, **kwargs)
-        lora.loras["unsupported_module"] = LoRALayerWeights(
-            module_name="unsupported_module",
-            rank=8,
-            lora_alpha=16,
-            lora_a=torch.randn(8, 10),
-            lora_b=torch.randn(10, 8),
-        )
-        return lora
-
-    lora_request = LoRARequest("test", 1, dummy_lora_files)
-    with (
-        patch.object(LoRAModel, "from_local_checkpoint", patched_from_checkpoint),
-        patch.object(wm_module.logger, "warning_once") as mock_warning,
-    ):
-        worker_manager._load_adapter(lora_request)
-        warning_args = mock_warning.call_args_list
-        found = any("unsupported_module" in str(call) for call in warning_args)
-        assert found, (
-            f"Expected warning about 'unsupported_module', got: {warning_args}"
-        )
-
-
-@pytest.mark.parametrize("device", DEVICES)
-def test_load_adapter_warns_on_target_modules_restriction(
-    default_vllm_config, dist_init, dummy_model_gate_up, device, tmp_path
-):
-    """Test that _load_adapter warns when a LoRA adapter contains modules
-    excluded by the deployment-time target_modules restriction."""
-    from unittest.mock import patch
-
-    import vllm.lora.worker_manager as wm_module
-
-    # Restrict to only dense2 — adapter has dense1 which will be excluded
-    lora_config = LoRAConfig(
-        max_lora_rank=8,
-        max_cpu_loras=4,
-        max_loras=4,
-        lora_dtype=DEFAULT_DTYPE,
-        target_modules=["dense2"],
-    )
-
-    dummy_lora_files = f"{tmp_path}/lora_adapter"
-    os.makedirs(dummy_lora_files, exist_ok=True)
-    create_peft_lora(
+    """Packed runtime modules should be selected by their adapter-visible names."""
+    _test_target_modules(
         dummy_model_gate_up,
-        save_dir=dummy_lora_files,
-        target_modules=["layer1.dense1", "dense2"],
-        lora_dtype=DEFAULT_DTYPE,
+        ["gate_proj"],
+        device,
+        expected_lora=[("gate_up_proj", MergedColumnParallelLinearWithLoRA)],
+        expected_no_lora=[
+            ("dense1", ColumnParallelLinearWithLoRA),
+            ("dense2", RowParallelLinearWithLoRA),
+            ("layer1.dense1", ColumnParallelLinearWithLoRA),
+            ("layer1.dense2", RowParallelLinearWithLoRA),
+        ],
     )
-
-    model_config = ModelConfig(max_model_len=16)
-    vllm_config = VllmConfig(model_config=model_config, lora_config=lora_config)
-    vllm_config.scheduler_config.max_num_seqs = 4
-    vllm_config.scheduler_config.max_num_batched_tokens = 2
-
-    worker_manager = WorkerLoRAManager(vllm_config, device, EMBEDDING_MODULES)
-    worker_manager.vocab_size = dummy_model_gate_up.unpadded_vocab_size
-    worker_manager.create_lora_manager(dummy_model_gate_up)
-
-    lora_request = LoRARequest("test", 1, dummy_lora_files)
-    with patch.object(wm_module.logger, "warning_once") as mock_warning:
-        worker_manager._load_adapter(lora_request)
-        warning_args = mock_warning.call_args_list
-        # dense1 is supported by the model but excluded by target_modules
-        found = any("target_modules" in str(call) for call in warning_args)
-        assert found, (
-            f"Expected warning about target_modules restriction, got: {warning_args}"
-        )
diff --git a/tests/lora/test_lora_utils.py b/tests/lora/test_lora_utils.py
index da66aa60b0d8..603ec9297491 100644
--- a/tests/lora/test_lora_utils.py
+++ b/tests/lora/test_lora_utils.py
@@ -58,3 +58,24 @@ def test_exact_name_match(self):
 
     def test_exact_name_no_match(self):
         assert not is_in_target_modules("dense3", ["dense1", "dense2"])
+
+    def test_packed_parent_matches_child_target_modules(self):
+        assert is_in_target_modules(
+            "model.layers.0.mlp.gate_up_proj",
+            ["gate_proj", "up_proj"],
+            {"gate_up_proj": ["gate_proj", "up_proj"]},
+        )
+
+    def test_packed_child_matches_parent_target_modules(self):
+        assert is_in_target_modules(
+            "model.layers.0.mlp.gate_proj",
+            ["gate_up_proj"],
+            {"gate_up_proj": ["gate_proj", "up_proj"]},
+        )
+
+    def test_fused_parent_matches_child_target_modules(self):
+        assert is_in_target_modules(
+            "model.layers.0.self_attn.fused_qkv_a_proj",
+            ["q_a_proj", "kv_a_proj_with_mqa"],
+            {"fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"]},
+        )
diff --git a/tests/lora/test_minicpmv_tp.py b/tests/lora/test_minicpmv_tp.py
index e430826461a1..3d6484a710a6 100644
--- a/tests/lora/test_minicpmv_tp.py
+++ b/tests/lora/test_minicpmv_tp.py
@@ -1,7 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from importlib.metadata import version
+
 import pytest
+from packaging.version import Version
 
 import vllm
 from vllm.assets.image import ImageAsset
@@ -10,6 +13,14 @@
 
 from ..utils import multi_gpu_test
 
+pytestmark = pytest.mark.skipif(
+    Version("5.0") <= Version(version("transformers")),
+    reason=(
+        "MiniCPMV custom processor uses tokenizer.im_start_id which is not "
+        "available on TokenizersBackend in transformers v5.0+"
+    ),
+)
+
 MODEL_PATH = "openbmb/MiniCPM-Llama3-V-2_5"
 
 PROMPT_TEMPLATE = (
diff --git a/tests/lora/test_qwen3moe_tp.py b/tests/lora/test_qwen3moe_tp.py
index fcac4275cc40..9af142f6f388 100644
--- a/tests/lora/test_qwen3moe_tp.py
+++ b/tests/lora/test_qwen3moe_tp.py
@@ -5,6 +5,8 @@
 # NOTE To avoid overloading the CI pipeline, this test script will not
 # be triggered on CI and is primarily intended for local testing and verification.
 
+import pytest
+
 import vllm
 from vllm.lora.request import LoRARequest
 
@@ -82,15 +84,15 @@ def test_qwen3moe_lora(qwen3moe_lora_files):
 
 
 @multi_gpu_test(num_gpus=2)
-def test_qwen3moe_lora_tp2(qwen3moe_lora_files):
+@pytest.mark.parametrize("ep", [False, True])
+def test_qwen3moe_lora_tp2(ep, qwen3moe_lora_files):
     llm = vllm.LLM(
         MODEL_PATH,
         max_model_len=1024,
         enable_lora=True,
         max_loras=4,
-        enforce_eager=True,
         trust_remote_code=True,
-        enable_chunked_prefill=True,
+        enable_expert_parallel=ep,
         tensor_parallel_size=2,
     )
 
@@ -99,15 +101,15 @@ def test_qwen3moe_lora_tp2(qwen3moe_lora_files):
 
 
 @multi_gpu_test(num_gpus=4)
-def test_qwen3moe_lora_tp4(qwen3moe_lora_files):
+@pytest.mark.parametrize("ep", [False, True])
+def test_qwen3moe_lora_tp4(ep, qwen3moe_lora_files):
     llm = vllm.LLM(
         MODEL_PATH,
         max_model_len=1024,
         enable_lora=True,
         max_loras=4,
-        enforce_eager=True,
         trust_remote_code=True,
-        enable_chunked_prefill=True,
+        enable_expert_parallel=ep,
         tensor_parallel_size=4,
     )
 
diff --git a/tests/lora/test_worker.py b/tests/lora/test_worker.py
index 88763551cf1b..e929fcad2896 100644
--- a/tests/lora/test_worker.py
+++ b/tests/lora/test_worker.py
@@ -20,7 +20,11 @@
 from vllm.lora.model_manager import LoRAMapping
 from vllm.lora.request import LoRARequest
 from vllm.platforms import current_platform
-from vllm.v1.worker.gpu_worker import Worker
+
+if current_platform.is_xpu():
+    from vllm.v1.worker.xpu_worker import XPUWorker as Worker
+else:
+    from vllm.v1.worker.gpu_worker import Worker
 
 MODEL_PATH = "Qwen/Qwen3-0.6B"
 NUM_LORAS = 16
diff --git a/tests/model_executor/model_loader/tensorizer_loader/test_tensorizer.py b/tests/model_executor/model_loader/tensorizer_loader/test_tensorizer.py
index 3b950c843c56..a15a624c905d 100644
--- a/tests/model_executor/model_loader/tensorizer_loader/test_tensorizer.py
+++ b/tests/model_executor/model_loader/tensorizer_loader/test_tensorizer.py
@@ -460,7 +460,7 @@ async def test_serialize_and_serve_entrypoints(tmp_path):
         result = subprocess.run(
             [
                 sys.executable,
-                f"{VLLM_PATH}/examples/others/tensorize_vllm_model.py",
+                f"{VLLM_PATH}/examples/features/tensorize_vllm_model.py",
                 "--model",
                 model_ref,
                 "serialize",
diff --git a/tests/model_executor/model_loader/test_reload.py b/tests/model_executor/model_loader/test_reload.py
index fc79f6a02281..cf3553bd57de 100644
--- a/tests/model_executor/model_loader/test_reload.py
+++ b/tests/model_executor/model_loader/test_reload.py
@@ -59,6 +59,34 @@ def test_reload_lifecycle():
         assert tensor.__dict__ == materialized_tensor.__dict__
 
 
+def test_materialize_layer_preserves_non_meta_tensors():
+    """Ensure that materialize_layer does not overwrite non meta tensors."""
+    layer = torch.nn.Linear(2, 3, bias=True)
+
+    # Create a non meta bias tensor and meta weight, which can happen with FP8
+    bias_values = torch.ones(3)
+    layer.bias.data.copy_(bias_values)
+    layer.weight = torch.nn.Parameter(layer.weight.data.to("meta"))
+
+    assert layer.weight.is_meta
+    assert not layer.bias.is_meta
+
+    # materialize the layer weights after the bias is initialized
+    info = LayerReloadingInfo(
+        restore_metadata=({}, {}),
+        restore_device=torch.device("cpu"),
+    )
+    materialize_layer(layer, info)
+
+    # Ensure the weight materialized off meta
+    assert not layer.weight.is_meta
+    assert layer.weight.device.type == "cpu"
+
+    # Ensure that the bias is (still) not meta and values are unchanged
+    assert not layer.bias.is_meta
+    assert torch.equal(layer.bias.data, bias_values)
+
+
 def test_model_cleanup(dist_init, default_vllm_config):
     layer = QKVParallelLinear(2, 3, 4)
     assert layer.weight.weight_loader.__self__ is layer
@@ -164,6 +192,34 @@ def test_reload_weights(base_model, mul_model, add_model, tp_size, vllm_runner):
         assert add_perp < mul_perp
 
 
+def test_kv_scale_reload(vllm_runner):
+    """Test reloading a checkpoint that contains k_scale/v_scale weights."""
+    if not current_platform.supports_fp8():
+        pytest.skip(reason="Requires FP8 support")
+
+    model = "nm-testing/Llama-3.2-1B-Instruct-FP8-KV"
+
+    # Load dummy weights, then reload real checkpoint
+    with vllm_runner(
+        model_name=model,
+        load_format="dummy",
+        enable_prefix_caching=False,
+        max_model_len=16,
+        max_num_seqs=1,
+    ) as llm:
+        llm.collective_rpc(
+            "update_config",
+            kwargs={"overrides": {"load_config": {"load_format": "auto"}}},
+        )
+        llm.collective_rpc("reload_weights", kwargs={"weights_path": model})
+        reloaded_perp = llm.generate_prompt_perplexity(
+            ["The capital of France is the city of Paris"],
+            mask=["The capital of France is"],
+        )[0]
+
+    assert reloaded_perp < 10
+
+
 @pytest.mark.parametrize(
     "tp_size", [pytest.param(1), pytest.param(2, marks=[pytest.mark.slow_test])]
 )
diff --git a/tests/model_executor/test_eagle_quantization.py b/tests/model_executor/test_eagle_quantization.py
index 519a48cae52e..481715da9cd7 100644
--- a/tests/model_executor/test_eagle_quantization.py
+++ b/tests/model_executor/test_eagle_quantization.py
@@ -10,9 +10,10 @@
 from vllm.model_executor.models.utils import get_draft_quant_config
 from vllm.platforms import current_platform
 
+DEVICE_TYPE = current_platform.device_type
 DEVICES = (
-    [f"cuda:{i}" for i in range(1 if torch.accelerator.device_count() == 1 else 2)]
-    if current_platform.is_cuda_alike()
+    [f"{DEVICE_TYPE}:{i}" for i in range(min(torch.accelerator.device_count(), 2))]
+    if not current_platform.is_cpu()
     else ["cpu"]
 )
 
diff --git a/tests/model_executor/test_enabled_custom_ops.py b/tests/model_executor/test_enabled_custom_ops.py
index fc4f6f6b63f9..490284f43954 100644
--- a/tests/model_executor/test_enabled_custom_ops.py
+++ b/tests/model_executor/test_enabled_custom_ops.py
@@ -23,11 +23,7 @@
     vllm_topk_sigmoid,
     vllm_topk_softmax,
 )
-from vllm.model_executor.layers.layernorm import (
-    RMSNorm,
-    dispatch_rocm_rmsnorm_func,
-    fused_add_rms_norm,
-)
+from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.platforms import current_platform
 
 RMS_NORM_SUPPORTED_DTYPES = [torch.float16, torch.bfloat16]
@@ -153,26 +149,3 @@ def test_topk_sigmoid_dispatch(use_rocm_aiter: bool):
         assert topk_func == rocm_aiter_ops.topk_sigmoid
     else:
         assert topk_func == vllm_topk_sigmoid
-
-
-@pytest.mark.parametrize("add_residual", [False])
-@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16])
-@pytest.mark.parametrize("use_rocm_aiter", [True, False])
-@pytest.mark.skipif(
-    not current_platform.is_rocm(), reason="AITER is a feature exclusive for ROCm"
-)
-def test_rms_norm_dispatch(
-    add_residual: bool, dtype: torch.dtype, use_rocm_aiter: bool
-):
-    rms_norm_func = dispatch_rocm_rmsnorm_func(dtype, use_rocm_aiter)
-
-    should_use_rocm_aiter = (
-        current_platform.is_rocm()
-        and use_rocm_aiter
-        and dtype in RMS_NORM_SUPPORTED_DTYPES
-    )
-
-    if should_use_rocm_aiter:
-        assert rms_norm_func == rocm_aiter_ops.rms_norm2d_with_add
-    else:
-        assert rms_norm_func == fused_add_rms_norm
diff --git a/tests/model_executor/test_gemma_hidden_act.py b/tests/model_executor/test_gemma_hidden_act.py
new file mode 100644
index 000000000000..d34851a6f107
--- /dev/null
+++ b/tests/model_executor/test_gemma_hidden_act.py
@@ -0,0 +1,60 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+from vllm.model_executor.layers.activation import (
+    GeluAndMul,
+    SiluAndMul,
+    get_act_and_mul_fn,
+    get_act_fn,
+)
+from vllm.model_executor.models.gemma3 import Gemma3MLP
+from vllm.model_executor.models.gemma4 import Gemma4MLP
+
+
+@pytest.mark.parametrize(
+    ("activation_name", "expected_type"),
+    [
+        ("gelu_pytorch_tanh", GeluAndMul),
+        ("silu", SiluAndMul),
+        ("swish", SiluAndMul),
+    ],
+)
+def test_get_act_and_mul_fn_supports_gemma_hidden_act_aliases(
+    activation_name: str,
+    expected_type: type[torch.nn.Module],
+    default_vllm_config,
+) -> None:
+    assert isinstance(get_act_and_mul_fn(activation_name), expected_type)
+
+
+def test_get_act_fn_supports_swish_alias() -> None:
+    assert isinstance(get_act_fn("swish"), torch.nn.SiLU)
+
+
+@pytest.mark.parametrize("mlp_cls", [Gemma3MLP, Gemma4MLP])
+@pytest.mark.parametrize(
+    ("activation_name", "expected_type"),
+    [
+        ("gelu_pytorch_tanh", GeluAndMul),
+        ("silu", SiluAndMul),
+        ("swish", SiluAndMul),
+    ],
+)
+def test_gemma_mlp_supports_hidden_act_variants(
+    mlp_cls: type[torch.nn.Module],
+    activation_name: str,
+    expected_type: type[torch.nn.Module],
+    default_vllm_config,
+    dist_init,
+) -> None:
+    mlp = mlp_cls(
+        hidden_size=16,
+        intermediate_size=32,
+        hidden_activation=activation_name,
+    )
+
+    assert isinstance(mlp.act_fn, expected_type)
+    assert mlp(torch.randn(3, 16)).shape == (3, 16)
diff --git a/tests/model_executor/test_keye_mrope.py b/tests/model_executor/test_keye_mrope.py
new file mode 100644
index 000000000000..2289a59e2bf9
--- /dev/null
+++ b/tests/model_executor/test_keye_mrope.py
@@ -0,0 +1,145 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass, field
+
+import pytest
+import torch
+
+from vllm.model_executor.models.keye import KeyeForConditionalGeneration
+from vllm.multimodal.inputs import (
+    MultiModalFeatureSpec,
+    MultiModalFieldElem,
+    MultiModalKwargsItem,
+    PlaceholderRange,
+)
+
+pytestmark = pytest.mark.skip_global_cleanup
+
+
+@pytest.fixture(autouse=True, scope="module")
+def _force_cpu_default_device():
+    original = torch.get_default_device()
+    torch.set_default_device("cpu")
+    yield
+    torch.set_default_device(original)
+
+
+@dataclass
+class DummyVisionConfig:
+    spatial_merge_size: int = 2
+
+
+@dataclass
+class DummyConfig:
+    vision_config: DummyVisionConfig = field(default_factory=DummyVisionConfig)
+
+
+def make_model(config: DummyConfig) -> KeyeForConditionalGeneration:
+    model = object.__new__(KeyeForConditionalGeneration)
+    model.config = config
+    return model
+
+
+def make_mm_feature(
+    *,
+    modality: str,
+    offset: int,
+    length: int,
+    grid_thw: tuple[int, int, int] | list[tuple[int, int, int]],
+) -> MultiModalFeatureSpec:
+    field_name = "image_grid_thw" if modality == "image" else "video_grid_thw"
+    return MultiModalFeatureSpec(
+        data=MultiModalKwargsItem(
+            {
+                field_name: MultiModalFieldElem(
+                    data=torch.tensor(grid_thw),
+                    field=None,  # HACK.
+                ),
+            }
+        ),
+        modality=modality,
+        identifier="DUMMY",
+        mm_position=PlaceholderRange(offset=offset, length=length),
+    )
+
+
+def test_get_mrope_input_positions_text_only():
+    model = make_model(DummyConfig())
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=[11, 12, 13, 14, 15],
+        mm_features=[],
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    assert delta == 0
+
+
+def test_get_mrope_input_positions_single_image():
+    model = make_model(DummyConfig())
+    mm_features = [
+        make_mm_feature(
+            modality="image",
+            offset=1,
+            length=4,
+            grid_thw=(1, 4, 4),
+        )
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=[10, 20, 21, 22, 23, 30, 31],
+        mm_features=mm_features,
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 1, 1, 1, 3, 4],
+            [0, 1, 1, 2, 2, 3, 4],
+            [0, 1, 2, 1, 2, 3, 4],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    assert delta == -2
+
+
+def test_get_mrope_input_positions_interleaved_image_and_video():
+    model = make_model(DummyConfig())
+    mm_features = [
+        make_mm_feature(
+            modality="image",
+            offset=1,
+            length=4,
+            grid_thw=(1, 4, 4),
+        ),
+        make_mm_feature(
+            modality="video",
+            offset=7,
+            length=4,
+            grid_thw=[(2, 4, 2)],
+        ),
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=[10, 20, 21, 22, 23, 30, 31, 40, 41, 42, 43, 50, 51],
+        mm_features=mm_features,
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 1, 1, 1, 3, 4, 5, 5, 7, 7, 9, 10],
+            [0, 1, 1, 2, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            [0, 1, 2, 1, 2, 3, 4, 5, 5, 7, 7, 9, 10],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    assert delta == -2
diff --git a/tests/model_executor/test_keye_vl1_5_mrope.py b/tests/model_executor/test_keye_vl1_5_mrope.py
new file mode 100644
index 000000000000..de3488bc922e
--- /dev/null
+++ b/tests/model_executor/test_keye_vl1_5_mrope.py
@@ -0,0 +1,145 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass, field
+
+import pytest
+import torch
+
+from vllm.model_executor.models.keye_vl1_5 import KeyeVL1_5ForConditionalGeneration
+from vllm.multimodal.inputs import (
+    MultiModalFeatureSpec,
+    MultiModalFieldElem,
+    MultiModalKwargsItem,
+    PlaceholderRange,
+)
+
+pytestmark = pytest.mark.skip_global_cleanup
+
+
+@pytest.fixture(autouse=True, scope="module")
+def _force_cpu_default_device():
+    original = torch.get_default_device()
+    torch.set_default_device("cpu")
+    yield
+    torch.set_default_device(original)
+
+
+@dataclass
+class DummyVisionConfig:
+    spatial_merge_size: int = 2
+
+
+@dataclass
+class DummyConfig:
+    vision_config: DummyVisionConfig = field(default_factory=DummyVisionConfig)
+
+
+def make_model(config: DummyConfig) -> KeyeVL1_5ForConditionalGeneration:
+    model = object.__new__(KeyeVL1_5ForConditionalGeneration)
+    model.config = config
+    return model
+
+
+def make_mm_feature(
+    *,
+    modality: str,
+    offset: int,
+    length: int,
+    grid_thw: tuple[int, int, int] | list[tuple[int, int, int]],
+    is_embed: list[bool] | None = None,
+) -> MultiModalFeatureSpec:
+    field_name = "image_grid_thw" if modality == "image" else "video_grid_thw"
+    return MultiModalFeatureSpec(
+        data=MultiModalKwargsItem(
+            {
+                field_name: MultiModalFieldElem(
+                    data=torch.tensor(grid_thw),
+                    field=None,  # HACK.
+                ),
+            }
+        ),
+        modality=modality,
+        identifier="DUMMY",
+        mm_position=PlaceholderRange(
+            offset=offset,
+            length=length,
+            is_embed=None if is_embed is None else torch.tensor(is_embed),
+        ),
+    )
+
+
+def test_get_mrope_input_positions_text_only():
+    model = make_model(DummyConfig())
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=[11, 12, 13, 14, 15],
+        mm_features=[],
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    assert delta == 0
+
+
+def test_get_mrope_input_positions_single_image():
+    model = make_model(DummyConfig())
+    mm_features = [
+        make_mm_feature(
+            modality="image",
+            offset=1,
+            length=4,
+            grid_thw=(1, 4, 4),
+        )
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=[10, 20, 21, 22, 23, 30, 31],
+        mm_features=mm_features,
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 1, 1, 1, 3, 4],
+            [0, 1, 1, 2, 2, 3, 4],
+            [0, 1, 2, 1, 2, 3, 4],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    assert delta == -2
+
+
+def test_get_mrope_input_positions_video_uses_embed_ranges():
+    model = make_model(DummyConfig())
+    mm_features = [
+        make_mm_feature(
+            modality="video",
+            offset=1,
+            length=8,
+            grid_thw=[(2, 4, 2)],
+            is_embed=[False, False, True, True, False, False, True, True],
+        )
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=[10, 101, 102, 20, 21, 103, 104, 30, 31, 40, 41],
+        mm_features=mm_features,
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 2, 3, 3, 5, 6, 7, 7, 9, 10],
+            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            [0, 1, 2, 3, 3, 5, 6, 7, 7, 9, 10],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    assert delta == 0
diff --git a/tests/model_executor/test_oink_integration.py b/tests/model_executor/test_oink_integration.py
index d7f38fdd5158..2f37472b73ef 100644
--- a/tests/model_executor/test_oink_integration.py
+++ b/tests/model_executor/test_oink_integration.py
@@ -1,60 +1,97 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
+import multiprocessing
 import types
 
 import pytest
-import torch
-
-
-def _load_oink_ops_module():
-    # Import the module normally (vllm is installed as an editable package in CI).
-    from vllm import _oink_ops
-
-    return _oink_ops
-
 
-def test_oink_availability_checks(monkeypatch: pytest.MonkeyPatch):
-    _oink_ops = _load_oink_ops_module()
-
-    # Ensure the ops namespace exists and is mutable for tests.
-    monkeypatch.setattr(
-        torch.ops,
-        "oink",
-        types.SimpleNamespace(rmsnorm=lambda x, w, eps: x),
-        raising=False,
-    )
-
-    # Case 1: CUDA not available.
-    monkeypatch.setattr(torch.cuda, "is_available", lambda: False)
-    assert _oink_ops.is_oink_available_for_device(0) is False
-
-    # Case 2: CUDA available but < SM100.
-    monkeypatch.setattr(torch.cuda, "is_available", lambda: True)
-    monkeypatch.setattr(torch.cuda, "get_device_capability", lambda idx: (9, 0))
-    assert _oink_ops.is_oink_available_for_device(0) is False
-
-    # Case 3: CUDA available and SM100, rmsnorm op registered.
-    monkeypatch.setattr(torch.cuda, "get_device_capability", lambda idx: (10, 0))
-    assert _oink_ops.is_oink_available_for_device(0) is True
-
-    # fused op presence probe
-    assert _oink_ops.has_fused_add_rms_norm() is False
-    monkeypatch.setattr(
-        torch.ops,
-        "oink",
-        types.SimpleNamespace(
-            rmsnorm=lambda x, w, eps: x,
-            fused_add_rms_norm=lambda x, residual, w, eps: None,
+from vllm.platforms import current_platform
+
+
+def _test_oink_availability_impl(
+    device_capability: tuple[int, int],
+    has_rmsnorm: bool,
+    has_fused_add_rms_norm: bool,
+    expected_available: bool,
+    expected_fused: bool,
+) -> None:
+    """Test OINK support detection with mocked state."""
+    import torch
+
+    from vllm import platforms
+
+    # Mock device capability (class method, override on class)
+    dc = platforms.interface.DeviceCapability(*device_capability)
+    platforms.current_platform.__class__.get_device_capability = lambda device_id=0: dc
+
+    # Mock oink ops
+    oink_ops = types.SimpleNamespace()
+    if has_rmsnorm:
+        oink_ops.rmsnorm = lambda x, w, eps: x
+    if has_fused_add_rms_norm:
+        oink_ops.fused_add_rms_norm = lambda x, residual, w, eps: None
+
+    torch.ops.oink = oink_ops
+
+    # Now import vllm modules with mocks in place (fresh import with mocked platform)
+    import vllm.kernels.oink_ops  # noqa: F401
+    from vllm.ir.ops import fused_add_rms_norm, rms_norm
+
+    # Verify support checks
+    assert rms_norm.impls["oink"].supported is expected_available
+    assert fused_add_rms_norm.impls["oink"].supported is expected_fused
+
+
+@pytest.mark.parametrize(
+    "device_capability,has_rmsnorm,has_fused_add_rms_norm,expected_available,expected_fused",
+    [
+        # Case 1: < SM100, ops not supported
+        ((9, 0), True, False, False, False),
+        # Case 2: CUDA available and SM100, rmsnorm op registered
+        ((10, 0), True, False, True, False),
+        # Case 3: SM100 with both rmsnorm and fused_add_rms_norm
+        ((10, 0), True, True, True, True),
+    ],
+)
+@pytest.mark.skipif(not current_platform.is_cuda(), reason="Only test on CUDA")
+def test_oink_availability_checks(
+    device_capability: tuple[int, int],
+    has_rmsnorm: bool,
+    has_fused_add_rms_norm: bool,
+    expected_available: bool,
+    expected_fused: bool,
+):
+    """Test OINK support detection with clean import state for each parameter set."""
+
+    # Use spawn to run function in fresh process with clean imports
+    # TODO migrate to spawn utility:
+    # https://github.com/vllm-project/vllm/issues/41415
+    ctx = multiprocessing.get_context("spawn")
+    process = ctx.Process(
+        target=_test_oink_availability_impl,
+        args=(
+            device_capability,
+            has_rmsnorm,
+            has_fused_add_rms_norm,
+            expected_available,
+            expected_fused,
         ),
-        raising=False,
     )
-    assert _oink_ops.has_fused_add_rms_norm() is True
+    process.start()
+    process.join()
+
+    if process.exitcode != 0:
+        raise AssertionError(
+            f"Subprocess test failed with exit code {process.exitcode}"
+        )
 
 
 def test_can_view_as_2d_stride_guard():
-    # Import the helper from the layernorm module.
-    from vllm.model_executor.layers.layernorm import _can_view_as_2d
+    # No global import
+    import torch
+
+    # Import the helper from the kernels module.
+    from vllm.kernels.oink_ops import _can_view_as_2d
 
     x = torch.zeros((2, 3, 4))
     assert _can_view_as_2d(x) is True
diff --git a/tests/model_executor/test_paddleocr_vl_mrope.py b/tests/model_executor/test_paddleocr_vl_mrope.py
new file mode 100644
index 000000000000..9090a9254d00
--- /dev/null
+++ b/tests/model_executor/test_paddleocr_vl_mrope.py
@@ -0,0 +1,210 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass, field
+
+import pytest
+import torch
+
+from vllm.model_executor.models.paddleocr_vl import (
+    PaddleOCRVLForConditionalGeneration,
+)
+from vllm.multimodal.inputs import (
+    MultiModalFeatureSpec,
+    MultiModalFieldElem,
+    MultiModalKwargsItem,
+    PlaceholderRange,
+)
+
+pytestmark = pytest.mark.skip_global_cleanup
+
+
+@pytest.fixture(autouse=True, scope="module")
+def _force_cpu_default_device():
+    original = torch.get_default_device()
+    torch.set_default_device("cpu")
+    yield
+    torch.set_default_device(original)
+
+
+@dataclass
+class DummyVisionConfig:
+    spatial_merge_size: int = 2
+    patch_size: int = 14
+
+
+@dataclass
+class DummyConfig:
+    image_token_id: int = 151655
+    video_token_id: int = 151654
+    vision_start_token_id: int = 151652
+    vision_end_token_id: int = 151653
+    vision_config: DummyVisionConfig = field(default_factory=DummyVisionConfig)
+
+
+def make_model(config: DummyConfig) -> PaddleOCRVLForConditionalGeneration:
+    model = object.__new__(PaddleOCRVLForConditionalGeneration)
+    model.config = config
+    return model
+
+
+def make_mm_feature(
+    *,
+    offset: int,
+    length: int,
+    image_grid_thw: tuple[int, int, int],
+) -> MultiModalFeatureSpec:
+    return MultiModalFeatureSpec(
+        data=MultiModalKwargsItem(
+            {
+                "image_grid_thw": MultiModalFieldElem(
+                    data=torch.tensor(image_grid_thw),
+                    field=None,
+                ),
+            }
+        ),
+        modality="image",
+        identifier="DUMMY",
+        mm_position=PlaceholderRange(offset=offset, length=length),
+    )
+
+
+def test_get_mrope_input_positions_text_only():
+    model = make_model(DummyConfig())
+    input_tokens = [11, 12, 13, 14, 15]
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=input_tokens,
+        mm_features=[],
+    )
+    expected = torch.tensor(
+        [
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+        ]
+    )
+    assert torch.equal(positions, expected)
+    assert delta == 0
+
+
+def test_get_mrope_input_positions_single_image():
+    model = make_model(DummyConfig())
+    spatial_merge_size = model.config.vision_config.spatial_merge_size
+
+    t, h, w = 1, 2, 2
+    num_image_tokens = t * h * w
+
+    input_tokens = (
+        [10]
+        + [model.config.vision_start_token_id]
+        + [model.config.image_token_id] * num_image_tokens
+        + [model.config.vision_end_token_id]
+        + [30, 31]
+    )
+
+    mm_features = [
+        make_mm_feature(
+            offset=2,  # 1 (text) + 1 (vision_start)
+            length=num_image_tokens,
+            image_grid_thw=(t, h * spatial_merge_size, w * spatial_merge_size),
+        )
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=input_tokens,
+        mm_features=mm_features,
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 2, 2, 2, 2, 4, 5, 6],
+            [0, 1, 2, 2, 3, 3, 4, 5, 6],
+            [0, 1, 2, 3, 2, 3, 4, 5, 6],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    expected_delta = (positions.max().item() + 1) - len(input_tokens)
+    assert delta == expected_delta
+
+
+def test_get_mrope_input_positions_multiple_images():
+    model = make_model(DummyConfig())
+    spatial_merge_size = model.config.vision_config.spatial_merge_size
+
+    t1, h1, w1 = 1, 2, 2
+    num1 = t1 * h1 * w1
+
+    t2, h2, w2 = 1, 1, 3
+    num2 = t2 * h2 * w2
+
+    input_tokens = (
+        [10]
+        + [model.config.vision_start_token_id]
+        + [model.config.image_token_id] * num1
+        + [model.config.vision_end_token_id]
+        + [20, 21]
+        + [model.config.vision_start_token_id]
+        + [model.config.image_token_id] * num2
+        + [model.config.vision_end_token_id]
+        + [30]
+    )
+
+    mm_features = [
+        make_mm_feature(
+            offset=2,
+            length=num1,
+            image_grid_thw=(t1, h1 * spatial_merge_size, w1 * spatial_merge_size),
+        ),
+        make_mm_feature(
+            offset=2 + num1 + 1 + 2 + 1,
+            length=num2,
+            image_grid_thw=(t2, h2 * spatial_merge_size, w2 * spatial_merge_size),
+        ),
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=input_tokens,
+        mm_features=mm_features,
+    )
+
+    assert positions.shape == (3, 15)
+    assert not torch.equal(positions[:, 2:6], torch.arange(4).expand(3, 4) + 2)
+    assert not torch.equal(positions[:, 10:13], torch.arange(3).expand(3, 3) + 10)
+
+
+def test_get_mrope_input_positions_image_at_start():
+    model = make_model(DummyConfig())
+    spatial_merge_size = model.config.vision_config.spatial_merge_size
+
+    t, h, w = 1, 2, 2
+    num_tokens = t * h * w
+
+    input_tokens = (
+        [model.config.vision_start_token_id]
+        + [model.config.image_token_id] * num_tokens
+        + [model.config.vision_end_token_id]
+        + [10, 11]
+    )
+
+    mm_features = [
+        make_mm_feature(
+            offset=1,  # start token at index 0
+            length=num_tokens,
+            image_grid_thw=(t, h * spatial_merge_size, w * spatial_merge_size),
+        )
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=input_tokens,
+        mm_features=mm_features,
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 1, 1, 1, 3, 4, 5],
+            [0, 1, 1, 2, 2, 3, 4, 5],
+            [0, 1, 2, 1, 2, 3, 4, 5],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
diff --git a/tests/model_executor/test_routed_experts_capture.py b/tests/model_executor/test_routed_experts_capture.py
index 770a3fa53850..d33d716c1d14 100644
--- a/tests/model_executor/test_routed_experts_capture.py
+++ b/tests/model_executor/test_routed_experts_capture.py
@@ -1,243 +1,162 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import types
-from types import SimpleNamespace
-from unittest.mock import patch
 
 import pytest
 import torch
 
-from vllm.distributed.eplb.eplb_state import EplbLayerState
-from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
-from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
-    RoutedExpertsCapturer,
-)
-from vllm.model_executor.layers.fused_moe.router.base_router import BaseRouter
-
 pytestmark = pytest.mark.cpu_test
 
-_REC_MODULE = "vllm.model_executor.layers.fused_moe.routed_experts_capturer"
-
-
-def _capturer_with_buffer(
-    *,
-    max_tokens: int = 8,
-    num_layers: int = 4,
-    num_experts_per_tok: int = 2,
-    dp_rank: int = 0,
-) -> RoutedExpertsCapturer:
-    c = RoutedExpertsCapturer()
-    c.dp_rank = dp_rank
-    c._device_buffer = torch.full(
-        (max_tokens, num_layers, num_experts_per_tok),
-        -1,
-        dtype=torch.int32,
-    )
-    return c
-
 
-class DummyRouter(BaseRouter):
-    @property
-    def routing_method_type(self) -> RoutingMethodType:
-        return RoutingMethodType.FUSED_TOPK
+def test_bind_routing_capture_to_model_sets_layer_view(monkeypatch):
+    import vllm.model_executor.layers.fused_moe.layer as fused_moe_layer
+    import vllm.model_executor.layers.fused_moe.routed_experts_capturer as rec_mod
 
-    def _compute_routing(self, hidden_states, router_logits, indices_type):
-        topk_ids = torch.tensor([[1, 2], [3, 4]], dtype=torch.int64)
-        topk_weights = torch.ones_like(topk_ids, dtype=torch.float32)
-        return topk_weights, topk_ids
+    class _DummyMoEConfig:
+        is_sequence_parallel = False
+        dp_size = 1
 
-    def _apply_eplb_mapping(self, topk_ids: torch.Tensor) -> torch.Tensor:
-        # Make mapping observable without requiring CUDA EPLB path.
-        return topk_ids + 10
+    class _DummyQuantMethod:
+        supports_internal_mk = True
 
+    class DummyFusedMoE:
+        _routing_replay_out: torch.Tensor
 
-def _make_router() -> DummyRouter:
-    return DummyRouter(
-        top_k=2,
-        global_num_experts=16,
-        eplb_state=EplbLayerState(),
-        enable_eplb=False,
-        indices_type_getter=None,
-    )
+        def __init__(self, moe_layer_id):
+            self.moe_layer_id = moe_layer_id
+            self.moe_config = _DummyMoEConfig()
+            self.quant_method = _DummyQuantMethod()
 
+    monkeypatch.setattr(fused_moe_layer, "FusedMoE", DummyFusedMoE)
 
-def test_base_router_capture_pre_eplb_mapping():
-    router = _make_router()
-    captured = []
+    num_layers, num_tokens, top_k = 4, 8, 2
+    buffer = torch.zeros((num_layers, num_tokens, top_k), dtype=torch.int16)
 
-    def capture_fn(ids):
-        captured.append(ids.clone())
+    class DummyDeviceCache:
+        def __init__(self, buf):
+            self.buffer = buf
 
-    router.set_capture_fn(capture_fn)
-    topk_weights, topk_ids = router.select_experts(
-        hidden_states=torch.empty(1),
-        router_logits=torch.empty(1),
-    )
+    class DummyCapturer:
+        def get_device_cache(self):
+            return DummyDeviceCache(buffer)
 
-    assert topk_weights.shape == topk_ids.shape
-    assert len(captured) == 1
-    assert torch.equal(captured[0], torch.tensor([[1, 2], [3, 4]]))
-    assert torch.equal(topk_ids, torch.tensor([[11, 12], [13, 14]]))
+    monkeypatch.setattr(rec_mod, "get_global_experts_capturer", lambda: DummyCapturer())
 
+    m0 = DummyFusedMoE(moe_layer_id=0)
+    m2 = DummyFusedMoE(moe_layer_id=2)
 
-def test_base_router_capture_with_eplb_enabled():
-    router = _make_router()
-    router.enable_eplb = True
-    router.eplb_state.expert_load_view = torch.zeros(32, dtype=torch.int64)
-    router.eplb_state.logical_to_physical_map = torch.arange(32).view(32, 1)
-    router.eplb_state.logical_replica_count = torch.ones(32, dtype=torch.int64)
-    router.eplb_state.should_record_tensor = torch.ones((), dtype=torch.bool)
+    class DummyModel:
+        def modules(self):
+            return iter([m0, m2])
 
-    captured = []
+    rec_mod.bind_routing_capture_to_model(DummyModel())
 
-    def capture_fn(ids):
-        captured.append(ids.clone())
+    assert torch.equal(m0._routing_replay_out, buffer[0])
+    assert torch.equal(m2._routing_replay_out, buffer[2])
 
-    router.set_capture_fn(capture_fn)
-    _, topk_ids = router.select_experts(
-        hidden_states=torch.empty(1),
-        router_logits=torch.empty(1),
-    )
 
-    assert len(captured) == 1
-    # Capture should see logical ids pre-EPLB mapping.
-    assert torch.equal(captured[0], torch.tensor([[1, 2], [3, 4]]))
-    # Our DummyRouter mapping adds +10.
-    assert torch.equal(topk_ids, torch.tensor([[11, 12], [13, 14]]))
+def test_bind_routing_capture_to_model_noop_when_disabled(monkeypatch):
+    import vllm.model_executor.layers.fused_moe.routed_experts_capturer as rec_mod
 
+    class DummyCapturer:
+        def get_device_cache(self):
+            return None
 
-def test_gpu_model_runner_binds_router_capture(monkeypatch):
-    from vllm.v1.worker import gpu_model_runner as gmr
+    monkeypatch.setattr(rec_mod, "get_global_experts_capturer", lambda: DummyCapturer())
 
-    class DummyFusedMoE:
-        def __init__(self):
-            self.layer_id = 7
-            self.router = _make_router()
+    class DummyModel:
+        def modules(self):
+            return iter([])
 
-    class DummyCapturer:
-        def __init__(self):
-            self.calls = []
+    rec_mod.bind_routing_capture_to_model(DummyModel())
 
-        def capture(self, layer_id, topk_ids):
-            self.calls.append((layer_id, topk_ids))
 
-    dummy_module = DummyFusedMoE()
+# =========================================================================
+# Tests for device-cache routing replay architecture
+# =========================================================================
 
-    # Patch the runtime import inside _bind_routed_experts_capturer.
-    import vllm.model_executor.layers.fused_moe.layer as fused_moe_layer
 
-    monkeypatch.setattr(fused_moe_layer, "FusedMoE", DummyFusedMoE)
+class TestRoutedExpertsDeviceCache:
+    """Tests for _RoutedExpertsDeviceCache (GPU buffer for routing data)."""
 
-    dummy_self = types.SimpleNamespace(
-        compilation_config=types.SimpleNamespace(
-            static_forward_context={"dummy": dummy_module}
+    def test_allocation_shape_and_dtype(self):
+        """Device cache allocates (L, N, K) int16 buffer."""
+        from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
+            _RoutedExpertsDeviceCache,
         )
-    )
-
-    capturer = DummyCapturer()
-    gmr.GPUModelRunner._bind_routed_experts_capturer(dummy_self, capturer)
 
-    assert dummy_module.router.capture_fn is not None
-    dummy_module.router.capture_fn(torch.tensor([[5, 6]]))
+        cache = _RoutedExpertsDeviceCache(
+            num_hidden_layers=40,
+            max_num_batched_tokens=8192,
+            num_experts_per_tok=8,
+            device="cpu",
+        )
+        assert cache.buffer.shape == (40, 8192, 8)
+        assert cache.buffer.dtype == torch.int16
 
-    assert len(capturer.calls) == 1
-    layer_id, topk_ids = capturer.calls[0]
-    assert layer_id == 7
-    assert torch.equal(topk_ids, torch.tensor([[5, 6]]))
+    def test_per_layer_view_is_contiguous(self):
+        """buffer[layer_id] gives contiguous (N, K) view for FlashInfer."""
+        from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
+            _RoutedExpertsDeviceCache,
+        )
 
+        cache = _RoutedExpertsDeviceCache(
+            num_hidden_layers=40,
+            max_num_batched_tokens=8192,
+            num_experts_per_tok=8,
+            device="cpu",
+        )
+        layer_view = cache.buffer[0]
+        assert layer_view.is_contiguous()
+        assert layer_view.shape == (8192, 8)
 
-def test_gpu_model_runner_binding_stage(monkeypatch):
-    from vllm.v1.worker import gpu_model_runner as gmr
 
-    class DummyFusedMoE:
-        def __init__(self):
-            self.layer_id = 11
-            self.router = _make_router()
+class TestRoutedExpertsHostCache:
+    """Tests for _RoutedExpertsHostCache (per-request numpy buffer)."""
 
-    class DummyCapturer:
-        def __init__(self):
-            self.calls = []
-
-        def capture(self, layer_id, topk_ids):
-            self.calls.append((layer_id, topk_ids))
+    def test_sentinel_initialization(self):
+        """Host cache initializes with zeros by default."""
+        import numpy as np
 
-    dummy_module = DummyFusedMoE()
+        from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
+            _RoutedExpertsHostCache,
+        )
 
-    import vllm.model_executor.layers.fused_moe.layer as fused_moe_layer
+        cache = _RoutedExpertsHostCache(
+            num_hidden_layers=40,
+            num_experts_per_tok=8,
+            max_model_len=1024,
+        )
+        buf = cache.get_or_grow_buffer("req1", max_pos=100)
+        assert buf.dtype == np.int16
+        assert (buf == 0).all(), "Host cache must initialize with zeros"
+
+    def test_grow_preserves_existing_data(self):
+        """Growing the buffer preserves previously written data."""
+        from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
+            _RoutedExpertsHostCache,
+        )
 
-    monkeypatch.setattr(fused_moe_layer, "FusedMoE", DummyFusedMoE)
+        cache = _RoutedExpertsHostCache(
+            num_hidden_layers=40,
+            num_experts_per_tok=8,
+            max_model_len=1024,
+        )
+        buf = cache.get_or_grow_buffer("req1", max_pos=50)
+        buf[0, 0, 0] = 42
+        buf2 = cache.get_or_grow_buffer("req1", max_pos=200)
+        assert buf2[0, 0, 0] == 42, "Data lost during buffer grow"
+
+    def test_free_request_removes_buffer(self):
+        """Freeing a request removes its buffer."""
+        from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
+            _RoutedExpertsHostCache,
+        )
 
-    dummy_self = types.SimpleNamespace(
-        compilation_config=types.SimpleNamespace(
-            static_forward_context={"dummy": dummy_module}
+        cache = _RoutedExpertsHostCache(
+            num_hidden_layers=40,
+            num_experts_per_tok=8,
+            max_model_len=1024,
         )
-    )
-
-    # Before binding, no capture hook.
-    assert dummy_module.router.capture_fn is None
-
-    capturer = DummyCapturer()
-    gmr.GPUModelRunner._bind_routed_experts_capturer(dummy_self, capturer)
-
-    # After binding, hook should exist and be callable.
-    assert callable(dummy_module.router.capture_fn)
-    dummy_module.router.capture_fn(torch.tensor([[9, 10]]))
-    assert len(capturer.calls) == 1
-
-
-def test_routed_experts_capturer_single_dp_no_metadata():
-    """dp_metadata is None: capture writes the full topk_ids rows."""
-    capturer = _capturer_with_buffer(dp_rank=0)
-    topk = torch.tensor([[1, 2], [3, 4], [5, 6]], dtype=torch.int32)
-    ctx = SimpleNamespace(dp_metadata=None)
-    with patch(f"{_REC_MODULE}.get_forward_context", return_value=ctx):
-        capturer.capture(layer_id=0, topk_ids=topk)
-    assert torch.equal(capturer._device_buffer[:3, 0, :], topk)
-    assert capturer._device_buffer[3, 0, 0].item() == -1
-
-
-def test_routed_experts_capturer_dp_naive_concatenated_all_ranks():
-    """n == sum(num_tokens_dp): slice this rank's segment from concatenated topk."""
-    capturer = _capturer_with_buffer(dp_rank=1)
-    num_tokens_dp = torch.tensor([2, 3], dtype=torch.int32)
-    ctx = SimpleNamespace(
-        dp_metadata=SimpleNamespace(num_tokens_across_dp_cpu=num_tokens_dp)
-    )
-    # Concatenated order: rank0 rows then rank1 rows.
-    topk = torch.tensor(
-        [[0, 1], [2, 3], [10, 11], [12, 13], [14, 15]], dtype=torch.int32
-    )
-    with patch(f"{_REC_MODULE}.get_forward_context", return_value=ctx):
-        capturer.capture(layer_id=0, topk_ids=topk)
-    want = topk[2:5]
-    assert torch.equal(capturer._device_buffer[:3, 0, :], want)
-
-
-def test_routed_experts_capturer_dp_modular_local_tokens():
-    """n == token_num_per_dp: topk is already local to this DP rank."""
-    capturer = _capturer_with_buffer(dp_rank=1)
-    num_tokens_dp = torch.tensor([2, 3], dtype=torch.int32)
-    ctx = SimpleNamespace(
-        dp_metadata=SimpleNamespace(num_tokens_across_dp_cpu=num_tokens_dp)
-    )
-    topk = torch.tensor([[10, 11], [12, 13], [14, 15]], dtype=torch.int32)
-    with patch(f"{_REC_MODULE}.get_forward_context", return_value=ctx):
-        capturer.capture(layer_id=0, topk_ids=topk)
-    assert torch.equal(capturer._device_buffer[:3, 0, :], topk)
-
-
-def test_routed_experts_capturer_dp_unexpected_batch_raises():
-    """Mismatch between topk batch dim and DP layout: fail fast."""
-    capturer = _capturer_with_buffer(dp_rank=0)
-    num_tokens_dp = torch.tensor([2, 3], dtype=torch.int32)
-    ctx = SimpleNamespace(
-        dp_metadata=SimpleNamespace(num_tokens_across_dp_cpu=num_tokens_dp)
-    )
-    # total=5, local=2: n=1 matches neither naive (5) nor modular (2).
-    topk = torch.tensor([[1, 2]], dtype=torch.int32)
-    with (
-        patch(f"{_REC_MODULE}.get_forward_context", return_value=ctx),
-        pytest.raises(AssertionError, match="unexpected topk_ids batch dim"),
-    ):
-        capturer.capture(layer_id=0, topk_ids=topk)
-    assert capturer._device_buffer[0, 0, 0].item() == -1
+        cache.get_or_grow_buffer("req1", max_pos=50)
+        cache.free_request("req1")
+        assert cache.get_buffer("req1") is None
diff --git a/tests/model_executor/test_weight_utils.py b/tests/model_executor/test_weight_utils.py
index 93535ae0aacd..260ebdcefb3b 100644
--- a/tests/model_executor/test_weight_utils.py
+++ b/tests/model_executor/test_weight_utils.py
@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import os
 import tempfile
 
 import huggingface_hub.constants
@@ -10,26 +9,10 @@
 
 from vllm.model_executor.model_loader.weight_utils import (
     download_weights_from_hf,
-    enable_hf_transfer,
     maybe_remap_kv_scale_name,
 )
 
 
-def test_hf_transfer_auto_activation():
-    if "HF_HUB_ENABLE_HF_TRANSFER" in os.environ:
-        # in case it is already set, we can't test the auto activation
-        pytest.skip("HF_HUB_ENABLE_HF_TRANSFER is set, can't test auto activation")
-    enable_hf_transfer()
-    try:
-        # enable hf hub transfer if available
-        import hf_transfer  # type: ignore # noqa
-
-        HF_TRANSFER_ACTIVE = True
-    except ImportError:
-        HF_TRANSFER_ACTIVE = False
-    assert huggingface_hub.constants.HF_HUB_ENABLE_HF_TRANSFER == HF_TRANSFER_ACTIVE
-
-
 def test_download_weights_from_hf():
     with tempfile.TemporaryDirectory() as tmpdir:
         # assert LocalEntryNotFoundError error is thrown
@@ -178,5 +161,4 @@ def test_missing_target_returns_none(self):
 
 
 if __name__ == "__main__":
-    test_hf_transfer_auto_activation()
     test_download_weights_from_hf()
diff --git a/tests/models/fixtures/ministral_3b_chat.json b/tests/models/fixtures/ministral_3b_chat.json
new file mode 100644
index 000000000000..22dd9527adca
--- /dev/null
+++ b/tests/models/fixtures/ministral_3b_chat.json
@@ -0,0 +1 @@
+[[[4380, 3937, 6122, 1261, 7244, 10575, 18970, 41132, 3923, 1408, 1261, 32656, 4691, 1454, 2246, 22131, 15179, 11521, 17277, 1046, 2], "This image shows a black dog sitting attentively on a wooden surface with its gaze directed straight ahead.", [{"4380": {"logprob": -1.0445597171783447, "rank": 1, "decoded_token": "This"}, "1784": {"logprob": -1.5445597171783447, "rank": 2, "decoded_token": "The"}, "1065": {"logprob": -1.7945597171783447, "rank": 3, "decoded_token": "A"}, "1785": {"logprob": -2.2945597171783447, "rank": 4, "decoded_token": "In"}, "4051": {"logprob": -4.357059478759766, "rank": 5, "decoded_token": "An"}}, {"3937": {"logprob": -0.012832445092499256, "rank": 1, "decoded_token": " image"}, "13083": {"logprob": -6.8253326416015625, "rank": 2, "decoded_token": " picture"}, "16649": {"logprob": -6.8878326416015625, "rank": 3, "decoded_token": " photo"}, "1395": {"logprob": -7.2003326416015625, "rank": 4, "decoded_token": " is"}, "7244": {"logprob": -7.5128326416015625, "rank": 5, "decoded_token": " black"}}, {"6122": {"logprob": -0.2629571557044983, "rank": 1, "decoded_token": " shows"}, "51948": {"logprob": -2.2629570960998535, "rank": 2, "decoded_token": " depicts"}, "6971": {"logprob": -2.5129570960998535, "rank": 3, "decoded_token": " features"}, "25981": {"logprob": -3.6379570960998535, "rank": 4, "decoded_token": " displays"}, "89995": {"logprob": -4.7004570960998535, "rank": 5, "decoded_token": " showc"}}, {"1261": {"logprob": -0.00752826826646924, "rank": 1, "decoded_token": " a"}, "1420": {"logprob": -5.132528305053711, "rank": 2, "decoded_token": " an"}, "1278": {"logprob": -6.632528305053711, "rank": 3, "decoded_token": " the"}, "7244": {"logprob": -11.195028305053711, "rank": 4, "decoded_token": " black"}, "44056": {"logprob": -11.757528305053711, "rank": 5, "decoded_token": "\ta"}}, {"7244": {"logprob": -0.19620661437511444, "rank": 1, "decoded_token": " black"}, "8500": {"logprob": -3.446206569671631, "rank": 2, "decoded_token": " dark"}, "4329": {"logprob": -3.446206569671631, "rank": 3, "decoded_token": " large"}, "6231": {"logprob": -3.446206569671631, "rank": 4, "decoded_token": " close"}, "85596": {"logprob": -4.196206569671631, "rank": 5, "decoded_token": " solemn"}}, {"10575": {"logprob": -0.08389955013990402, "rank": 1, "decoded_token": " dog"}, "119075": {"logprob": -3.14639949798584, "rank": 2, "decoded_token": " Labrador"}, "116572": {"logprob": -4.83389949798584, "rank": 3, "decoded_token": " puppy"}, "1044": {"logprob": -5.14639949798584, "rank": 4, "decoded_token": ","}, "9566": {"logprob": -5.58389949798584, "rank": 5, "decoded_token": " medium"}}, {"18970": {"logprob": -1.0850104093551636, "rank": 1, "decoded_token": " sitting"}, "1454": {"logprob": -1.6475104093551636, "rank": 2, "decoded_token": " with"}, "28528": {"logprob": -2.022510528564453, "rank": 3, "decoded_token": " lying"}, "7283": {"logprob": -2.085010528564453, "rank": 4, "decoded_token": " looking"}, "38235": {"logprob": -2.460010528564453, "rank": 5, "decoded_token": " resting"}}, {"41132": {"logprob": -1.2341952323913574, "rank": 1, "decoded_token": " attent"}, "106534": {"logprob": -1.4841952323913574, "rank": 2, "decoded_token": " calmly"}, "1505": {"logprob": -2.2966952323913574, "rank": 3, "decoded_token": " or"}, "17558": {"logprob": -2.3591952323913574, "rank": 4, "decoded_token": " closely"}, "1408": {"logprob": -2.5466952323913574, "rank": 5, "decoded_token": " on"}}, {"3923": {"logprob": -0.005889324937015772, "rank": 1, "decoded_token": "ively"}, "1556": {"logprob": -6.693389415740967, "rank": 2, "decoded_token": "ive"}, "3929": {"logprob": -6.880889415740967, "rank": 3, "decoded_token": "ently"}, "10980": {"logprob": -7.193389415740967, "rank": 4, "decoded_token": "ibly"}, "14194": {"logprob": -7.943389415740967, "rank": 5, "decoded_token": "antly"}}, {"1408": {"logprob": -0.5112090110778809, "rank": 1, "decoded_token": " on"}, "1454": {"logprob": -1.7612090110778809, "rank": 2, "decoded_token": " with"}, "1321": {"logprob": -2.386209011077881, "rank": 3, "decoded_token": " and"}, "3675": {"logprob": -2.761209011077881, "rank": 4, "decoded_token": " against"}, "1505": {"logprob": -4.198709011077881, "rank": 5, "decoded_token": " or"}}, {"1261": {"logprob": -0.189262256026268, "rank": 1, "decoded_token": " a"}, "2549": {"logprob": -2.1892621517181396, "rank": 2, "decoded_token": " what"}, "32656": {"logprob": -3.8767621517181396, "rank": 3, "decoded_token": " wooden"}, "17253": {"logprob": -4.626762390136719, "rank": 4, "decoded_token": " weather"}, "3403": {"logprob": -4.751762390136719, "rank": 5, "decoded_token": " text"}}, {"32656": {"logprob": -0.9549442529678345, "rank": 1, "decoded_token": " wooden"}, "3403": {"logprob": -1.2049442529678345, "rank": 2, "decoded_token": " text"}, "17253": {"logprob": -1.8924442529678345, "rank": 3, "decoded_token": " weather"}, "44130": {"logprob": -2.267444133758545, "rank": 4, "decoded_token": " rust"}, "16673": {"logprob": -4.142444133758545, "rank": 5, "decoded_token": " rough"}}, {"4691": {"logprob": -0.2555857002735138, "rank": 1, "decoded_token": " surface"}, "3403": {"logprob": -2.6930856704711914, "rank": 2, "decoded_token": " text"}, "1615": {"logprob": -2.8805856704711914, "rank": 3, "decoded_token": " pl"}, "1044": {"logprob": -4.193085670471191, "rank": 4, "decoded_token": ","}, "26228": {"logprob": -4.380585670471191, "rank": 5, "decoded_token": " texture"}}, {"1454": {"logprob": -0.5042062997817993, "rank": 1, "decoded_token": " with"}, "1044": {"logprob": -1.6292062997817993, "rank": 2, "decoded_token": ","}, "1046": {"logprob": -2.2542061805725098, "rank": 3, "decoded_token": "."}, "7283": {"logprob": -3.7542061805725098, "rank": 4, "decoded_token": " looking"}, "1338": {"logprob": -4.44170618057251, "rank": 5, "decoded_token": ".\n\n"}}, {"2246": {"logprob": -1.1512703895568848, "rank": 1, "decoded_token": " its"}, "1261": {"logprob": -1.2137703895568848, "rank": 2, "decoded_token": " a"}, "1420": {"logprob": -2.3387703895568848, "rank": 3, "decoded_token": " an"}, "9924": {"logprob": -2.4637703895568848, "rank": 4, "decoded_token": " wide"}, "12593": {"logprob": -3.7762703895568848, "rank": 5, "decoded_token": " slightly"}}, {"22131": {"logprob": -1.0600039958953857, "rank": 1, "decoded_token": " gaze"}, "5731": {"logprob": -1.4350039958953857, "rank": 2, "decoded_token": " eyes"}, "9924": {"logprob": -2.7475039958953857, "rank": 3, "decoded_token": " wide"}, "14781": {"logprob": -2.9350039958953857, "rank": 4, "decoded_token": " focused"}, "3518": {"logprob": -3.1225039958953857, "rank": 5, "decoded_token": " head"}}, {"15179": {"logprob": -1.0535285472869873, "rank": 1, "decoded_token": " directed"}, "9247": {"logprob": -1.5535285472869873, "rank": 2, "decoded_token": " fixed"}, "14781": {"logprob": -2.4285285472869873, "rank": 3, "decoded_token": " focused"}, "7283": {"logprob": -2.6785285472869873, "rank": 4, "decoded_token": " looking"}, "12593": {"logprob": -2.7410285472869873, "rank": 5, "decoded_token": " slightly"}}, {"11521": {"logprob": -1.4163023233413696, "rank": 1, "decoded_token": " straight"}, "40022": {"logprob": -1.6038023233413696, "rank": 2, "decoded_token": " upward"}, "74606": {"logprob": -1.6663023233413696, "rank": 3, "decoded_token": " upwards"}, "12593": {"logprob": -2.16630220413208, "rank": 4, "decoded_token": " slightly"}, "8848": {"logprob": -2.16630220413208, "rank": 5, "decoded_token": " forward"}}, {"17277": {"logprob": -0.20115239918231964, "rank": 1, "decoded_token": " ahead"}, "8848": {"logprob": -2.4511523246765137, "rank": 2, "decoded_token": " forward"}, "1513": {"logprob": -2.9511523246765137, "rank": 3, "decoded_token": " at"}, "1046": {"logprob": -4.576152324676514, "rank": 4, "decoded_token": "."}, "8994": {"logprob": -4.826152324676514, "rank": 5, "decoded_token": " towards"}}, {"1046": {"logprob": -0.1819322109222412, "rank": 1, "decoded_token": "."}, "1338": {"logprob": -2.619432210922241, "rank": 2, "decoded_token": ".\n\n"}, "1321": {"logprob": -3.119432210922241, "rank": 3, "decoded_token": " and"}, "1044": {"logprob": -4.05693244934082, "rank": 4, "decoded_token": ","}, "1626": {"logprob": -5.18193244934082, "rank": 5, "decoded_token": ".\n"}}, {"2": {"logprob": -2.0367233753204346, "rank": 1, "decoded_token": "</s>"}, "35": {"logprob": -4.6617231369018555, "rank": 2, "decoded_token": "[/THINK]"}, "9": {"logprob": -4.9742231369018555, "rank": 3, "decoded_token": "[TOOL_CALLS]"}, "108349": {"logprob": -5.4742231369018555, "rank": 4, "decoded_token": "\u305d\u306e\u4e00\u65b9\u3067"}, "32": {"logprob": -5.8492231369018555, "rank": 5, "decoded_token": "[ARGS]"}}]], [[1784, 2158, 3937, 6122, 1261, 7244, 10575, 18970, 41132, 3923, 1408, 1261, 32656, 4691, 1454, 1261, 26517, 1321, 14781, 4818, 1338, 1784, 2667, 3937, 51948, 1261, 10726, 1290, 3719, 1307, 122203, 1044, 23745, 3591, 13194, 24361, 27469, 1294, 1278, 7786, 1454, 1295, 3506, 11223, 47260, 1408, 1278, 61263, 1046, 2], "The first image shows a black dog sitting attentively on a wooden surface with a calm and focused expression.\n\nThe second image depicts a scenic view of rugged, snow-capped mountain peaks in the distance with lush green vegetation on the slopes.", [{"1784": {"logprob": -0.6781838536262512, "rank": 1, "decoded_token": "The"}, "1049": {"logprob": -1.1781837940216064, "rank": 2, "decoded_token": "1"}, "69957": {"logprob": -4.0531840324401855, "rank": 3, "decoded_token": "Sure"}, "11745": {"logprob": -4.6781840324401855, "rank": 4, "decoded_token": "Here"}, "1785": {"logprob": -5.1781840324401855, "rank": 5, "decoded_token": "In"}}, {"2158": {"logprob": -0.17460788786411285, "rank": 1, "decoded_token": " first"}, "3937": {"logprob": -2.612107992172241, "rank": 2, "decoded_token": " image"}, "8061": {"logprob": -4.299607753753662, "rank": 3, "decoded_token": " images"}, "7244": {"logprob": -5.487107753753662, "rank": 4, "decoded_token": " black"}, "5662": {"logprob": -5.549607753753662, "rank": 5, "decoded_token": " provided"}}, {"3937": {"logprob": -0.014750940725207329, "rank": 1, "decoded_token": " image"}, "13083": {"logprob": -5.889750957489014, "rank": 2, "decoded_token": " picture"}, "2016": {"logprob": -7.264750957489014, "rank": 3, "decoded_token": " set"}, "16649": {"logprob": -7.764750957489014, "rank": 4, "decoded_token": " photo"}, "1877": {"logprob": -7.764750957489014, "rank": 5, "decoded_token": ":\n"}}, {"6122": {"logprob": -0.29441142082214355, "rank": 1, "decoded_token": " shows"}, "51948": {"logprob": -2.2944114208221436, "rank": 2, "decoded_token": " depicts"}, "1877": {"logprob": -3.4194114208221436, "rank": 3, "decoded_token": ":\n"}, "1395": {"logprob": -3.5444114208221436, "rank": 4, "decoded_token": " is"}, "25981": {"logprob": -3.7319114208221436, "rank": 5, "decoded_token": " displays"}}, {"1261": {"logprob": -0.05616755038499832, "rank": 1, "decoded_token": " a"}, "1420": {"logprob": -4.0561676025390625, 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The first image shows a black dog sitting attentively on a wooden surface.\n   - The second image (not shown in your question) appears to be a mountain landscape with rugged terrain and distant peaks.\n\n2. The third image depicts a serene beach scene with gentle waves meeting the shore and a lone person walking along the shore.\n\n3. The fourth image features a winding gravel path bordered by grass and trees in a picturesque outdoor setting.\n\n4. The fifth image (not applicable as per the initial request) would be the one with the dog again, but since it's already described, here\u2019s a different focus if we consider the following hypothetical:\n   - The initial provided image is a black dog looking up with a calm expression on a rustic wooden plank background.\n\nTo clarify, here\u2019s the correct description for the provided black dog image:\nA black dog gazes intently upward with a calm expression on a rustic wooden plank background.", [{"1049": {"logprob": -0.3098178803920746, "rank": 1, "decoded_token": "1"}, "1784": {"logprob": -2.1848177909851074, "rank": 2, "decoded_token": "The"}, "11745": {"logprob": -2.6223177909851074, "rank": 3, "decoded_token": "Here"}, "2757": {"logprob": -6.059817790985107, "rank": 4, "decoded_token": "It"}, "69957": {"logprob": -6.122317790985107, "rank": 5, "decoded_token": "Sure"}}, {"1046": {"logprob": -0.16265615820884705, "rank": 1, "decoded_token": "."}, "1626": {"logprob": 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"rank": 1, "decoded_token": " dog"}, "3937": {"logprob": -3.8450632095336914, "rank": 2, "decoded_token": " image"}, "63524": {"logprob": -4.470063209533691, "rank": 3, "decoded_token": "dog"}, "3028": {"logprob": -4.970063209533691, "rank": 4, "decoded_token": "-d"}, "1321": {"logprob": -5.220063209533691, "rank": 5, "decoded_token": " and"}}, {"3937": {"logprob": -0.6087309122085571, "rank": 1, "decoded_token": " image"}, "2100": {"logprob": -2.7337307929992676, "rank": 2, "decoded_token": ":\n\n"}, "1294": {"logprob": -3.2337307929992676, "rank": 3, "decoded_token": " in"}, "13083": {"logprob": -3.3587307929992676, "rank": 4, "decoded_token": " picture"}, "1877": {"logprob": -3.3587307929992676, "rank": 5, "decoded_token": ":\n"}}, {"1877": {"logprob": -1.969537615776062, "rank": 1, "decoded_token": ":\n"}, "2100": {"logprob": -2.0945377349853516, "rank": 2, "decoded_token": ":\n\n"}, "2342": {"logprob": -2.4695377349853516, "rank": 3, "decoded_token": " only"}, "13703": {"logprob": -3.0945377349853516, "rank": 4, "decoded_token": " specifically"}, "9412": {"logprob": -3.1570377349853516, "rank": 5, "decoded_token": " alone"}}, {"1065": {"logprob": -1.341880202293396, "rank": 1, "decoded_token": "A"}, "1784": {"logprob": -1.841880202293396, "rank": 2, "decoded_token": "The"}, "1256": {"logprob": -1.966880202293396, "rank": 3, "decoded_token": "  "}, "1438": {"logprob": -2.4668803215026855, "rank": 4, "decoded_token": "**"}, "1045": {"logprob": -2.5293803215026855, "rank": 5, "decoded_token": "-"}}, {"7244": {"logprob": -0.6517431139945984, "rank": 1, "decoded_token": " black"}, "6231": {"logprob": -2.714243173599243, "rank": 2, "decoded_token": " close"}, "85596": {"logprob": -3.026743173599243, "rank": 3, "decoded_token": " solemn"}, "14781": {"logprob": -3.401743173599243, "rank": 4, "decoded_token": " focused"}, "26517": {"logprob": -3.526743173599243, "rank": 5, "decoded_token": " calm"}}, {"10575": {"logprob": -0.10892026871442795, "rank": 1, "decoded_token": " dog"}, "119075": {"logprob": -3.7964203357696533, "rank": 2, "decoded_token": " Labrador"}, "116572": {"logprob": -3.9839203357696533, "rank": 3, "decoded_token": " puppy"}, "1044": {"logprob": -4.983920097351074, "rank": 4, "decoded_token": ","}, "94057": {"logprob": -5.171420097351074, "rank": 5, "decoded_token": " canine"}}, {"11589": {"logprob": -1.534816861152649, "rank": 1, "decoded_token": " gaz"}, "1395": {"logprob": -1.722316861152649, "rank": 2, "decoded_token": " is"}, "1454": {"logprob": -2.0348167419433594, "rank": 3, "decoded_token": " with"}, "53048": {"logprob": -2.2848167419433594, "rank": 4, "decoded_token": " sits"}, "10637": {"logprob": -2.6598167419433594, "rank": 5, "decoded_token": " looks"}}, {"1264": {"logprob": -0.4754399061203003, "rank": 1, "decoded_token": "es"}, "1302": {"logprob": -0.9754399061203003, "rank": 2, "decoded_token": "ing"}, "1944": {"logprob": -8.16294002532959, "rank": 3, "decoded_token": "ely"}, "47885": {"logprob": -8.16294002532959, "rank": 4, "decoded_token": "edly"}, "15006": {"logprob": -8.41294002532959, "rank": 5, "decoded_token": "ingly"}}, {"1935": {"logprob": -1.7025537490844727, "rank": 1, "decoded_token": " int"}, "7655": {"logprob": -2.0775537490844727, "rank": 2, "decoded_token": " directly"}, "40022": {"logprob": -2.2650537490844727, "rank": 3, "decoded_token": " upward"}, "2015": {"logprob": -2.4525537490844727, "rank": 4, "decoded_token": " up"}, "74606": {"logprob": -2.7025537490844727, "rank": 5, "decoded_token": " upwards"}}, {"3929": {"logprob": -0.0234219990670681, "rank": 1, "decoded_token": "ently"}, "2749": {"logprob": -5.335921764373779, "rank": 2, "decoded_token": "ros"}, "1533": {"logprob": -6.398421764373779, "rank": 3, "decoded_token": "ang"}, "3923": {"logprob": -6.523421764373779, "rank": 4, "decoded_token": "ively"}, "20626": {"logprob": -6.773421764373779, "rank": 5, "decoded_token": "rep"}}, {"40022": {"logprob": -0.8856239318847656, "rank": 1, "decoded_token": " upward"}, "74606": {"logprob": -1.5106239318847656, "rank": 2, "decoded_token": " upwards"}, "1454": {"logprob": -2.2606239318847656, "rank": 3, "decoded_token": " with"}, "8848": {"logprob": -2.7606239318847656, "rank": 4, "decoded_token": " forward"}, "1513": {"logprob": -3.5106239318847656, "rank": 5, "decoded_token": " at"}}, {"1454": {"logprob": -1.170392632484436, "rank": 1, "decoded_token": " with"}, "1408": {"logprob": -1.295392632484436, "rank": 2, "decoded_token": " on"}, "1562": {"logprob": -2.1703925132751465, "rank": 3, "decoded_token": " from"}, "3016": {"logprob": -2.4828925132751465, "rank": 4, "decoded_token": " while"}, "3675": {"logprob": -3.1703925132751465, "rank": 5, "decoded_token": " against"}}, {"1261": {"logprob": -0.8887192010879517, "rank": 1, "decoded_token": " a"}, "1420": {"logprob": -1.7637192010879517, "rank": 2, "decoded_token": " an"}, "2246": {"logprob": -2.138719081878662, "rank": 3, "decoded_token": " its"}, "9924": {"logprob": -2.888719081878662, "rank": 4, "decoded_token": " wide"}, "26517": {"logprob": -3.888719081878662, "rank": 5, "decoded_token": " calm"}}, {"26517": {"logprob": -1.2183846235275269, "rank": 1, "decoded_token": " calm"}, "2169": {"logprob": -2.8433847427368164, "rank": 2, "decoded_token": " ser"}, "16318": {"logprob": -3.0933847427368164, "rank": 3, "decoded_token": " neutral"}, "14781": {"logprob": -3.2183847427368164, "rank": 4, "decoded_token": " focused"}, "6444": {"logprob": -3.2183847427368164, "rank": 5, "decoded_token": " soft"}}, {"4818": {"logprob": -0.4964141249656677, "rank": 1, "decoded_token": " expression"}, "1321": {"logprob": -1.9339141845703125, "rank": 2, "decoded_token": " and"}, "1311": {"logprob": -2.4339141845703125, "rank": 3, "decoded_token": " de"}, "1044": {"logprob": -3.3089141845703125, "rank": 4, "decoded_token": ","}, "2985": {"logprob": -3.4964141845703125, "rank": 5, "decoded_token": " look"}}, {"1408": {"logprob": -0.8785426616668701, "rank": 1, "decoded_token": " on"}, "1562": {"logprob": -2.06604266166687, "rank": 2, "decoded_token": " from"}, "18970": {"logprob": -2.31604266166687, "rank": 3, "decoded_token": " sitting"}, "3675": {"logprob": -2.62854266166687, "rank": 4, "decoded_token": " against"}, "38235": {"logprob": -3.06604266166687, "rank": 5, "decoded_token": " resting"}}, {"1261": {"logprob": -0.3605937659740448, "rank": 1, "decoded_token": " a"}, "32656": {"logprob": -2.923093795776367, "rank": 2, "decoded_token": " wooden"}, "17253": {"logprob": -3.110593795776367, "rank": 3, "decoded_token": " weather"}, "3403": {"logprob": -3.173093795776367, "rank": 4, "decoded_token": " text"}, "44130": {"logprob": -3.298093795776367, "rank": 5, "decoded_token": " rust"}}, {"44130": {"logprob": -1.4187138080596924, "rank": 1, "decoded_token": " rust"}, "32656": {"logprob": -1.4812138080596924, "rank": 2, "decoded_token": " wooden"}, "3403": {"logprob": -1.6687138080596924, "rank": 3, "decoded_token": " text"}, "17253": {"logprob": -2.4812138080596924, "rank": 4, "decoded_token": " weather"}, "8500": {"logprob": -4.168713569641113, "rank": 5, "decoded_token": " dark"}}, {"1290": {"logprob": -0.28350138664245605, "rank": 1, "decoded_token": "ic"}, "1970": {"logprob": -2.283501386642456, "rank": 2, "decoded_token": "led"}, "1121": {"logprob": -2.971001386642456, "rank": 3, "decoded_token": "y"}, "2981": {"logprob": -4.283501625061035, "rank": 4, "decoded_token": "ically"}, "11395": {"logprob": -4.471001625061035, "rank": 5, "decoded_token": "iced"}}, {"32656": {"logprob": -0.7727869749069214, "rank": 1, "decoded_token": " wooden"}, "1044": {"logprob": -1.4602869749069214, "rank": 2, "decoded_token": ","}, "3403": {"logprob": -2.585287094116211, "rank": 3, "decoded_token": " text"}, "1615": {"logprob": -2.835287094116211, "rank": 4, "decoded_token": " pl"}, "12603": {"logprob": -3.335287094116211, "rank": 5, "decoded_token": " wood"}}, {"1615": {"logprob": -0.7352191805839539, "rank": 1, "decoded_token": " pl"}, "4691": {"logprob": -2.1102192401885986, "rank": 2, "decoded_token": " surface"}, "9710": {"logprob": -2.6102192401885986, "rank": 3, "decoded_token": " board"}, "7042": {"logprob": -2.9852192401885986, "rank": 4, "decoded_token": " background"}, "92504": {"logprob": -3.1102192401885986, "rank": 5, "decoded_token": " backdrop"}}, {"2395": {"logprob": -0.13373544812202454, "rank": 1, "decoded_token": "ank"}, "5933": {"logprob": -2.133735418319702, "rank": 2, "decoded_token": "anks"}, "122370": {"logprob": -6.883735656738281, "rank": 3, "decoded_token": "anking"}, "11847": {"logprob": -8.071235656738281, "rank": 4, "decoded_token": "anned"}, "2077": {"logprob": -8.071235656738281, "rank": 5, "decoded_token": "ink"}}, {"7042": {"logprob": -1.197163701057434, "rank": 1, "decoded_token": " background"}, "4691": {"logprob": -1.572163701057434, "rank": 2, "decoded_token": " surface"}, "92504": {"logprob": -1.884663701057434, "rank": 3, "decoded_token": " backdrop"}, "26228": {"logprob": -3.3221635818481445, "rank": 4, "decoded_token": " texture"}, "9436": {"logprob": -3.3846635818481445, "rank": 5, "decoded_token": " setting"}}, {"1046": {"logprob": -0.13945358991622925, "rank": 1, "decoded_token": "."}, "1338": {"logprob": -2.576953649520874, "rank": 2, "decoded_token": ".\n\n"}, "1294": {"logprob": -4.764453411102295, "rank": 3, "decoded_token": " in"}, "1044": {"logprob": -5.701953411102295, "rank": 4, "decoded_token": ","}, "1319": {"logprob": -5.889453411102295, "rank": 5, "decoded_token": " ("}}, {"2": {"logprob": -0.5447223782539368, "rank": 1, "decoded_token": "</s>"}, "1319": {"logprob": -2.607222318649292, "rank": 2, "decoded_token": " ("}, "9380": {"logprob": -3.669722318649292, "rank": 3, "decoded_token": " Here"}, "1531": {"logprob": -3.919722318649292, "rank": 4, "decoded_token": " The"}, "2898": {"logprob": -4.107222557067871, "rank": 5, "decoded_token": " For"}}]]]
\ No newline at end of file
diff --git a/tests/models/language/generation/test_common.py b/tests/models/language/generation/test_common.py
index c524480839bc..6e5f1e328431 100644
--- a/tests/models/language/generation/test_common.py
+++ b/tests/models/language/generation/test_common.py
@@ -46,7 +46,7 @@
         ),
         pytest.param(
             "openai-community/gpt2",  # gpt2
-            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
+            marks=[pytest.mark.core_model],
         ),
         pytest.param("Milos/slovak-gpt-j-405M"),  # gptj
         pytest.param("bigcode/tiny_starcoder_py"),  # gpt_bigcode
@@ -100,7 +100,7 @@
         pytest.param("bigcode/starcoder2-3b"),  # starcoder2
         pytest.param(
             "TitanML/tiny-mixtral",  # mixtral
-            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
+            marks=[pytest.mark.core_model],
         ),
         pytest.param("swiss-ai/Apertus-8B-Instruct-2509"),  # apertus
         pytest.param(
diff --git a/tests/models/language/generation/test_granite.py b/tests/models/language/generation/test_granite.py
index e569e75ff3a8..c0498b2f7de1 100644
--- a/tests/models/language/generation/test_granite.py
+++ b/tests/models/language/generation/test_granite.py
@@ -15,6 +15,7 @@
 @pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("max_tokens", [64])
 @pytest.mark.parametrize("num_logprobs", [5])
+@pytest.mark.cpu_model
 def test_models(
     hf_runner,
     vllm_runner,
diff --git a/tests/models/language/generation/test_hybrid.py b/tests/models/language/generation/test_hybrid.py
index 01d395b1e0d8..e410daf2fcdd 100644
--- a/tests/models/language/generation/test_hybrid.py
+++ b/tests/models/language/generation/test_hybrid.py
@@ -881,7 +881,7 @@ def test_apc_common_prefix_same_batch(
         "hello what is one plus one what is one plus one what is one plus one the answer is",  # noqa: E501
         "hello what is one plus one what is one plus one what is one plus one the answer is",  # noqa: E501
     ]
-    sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=20)
+    sampling_params = SamplingParams(temperature=0.0, max_tokens=20)
     outputs = llm.generate(prompts, sampling_params)
     for output in outputs:
         assert "two" in output.outputs[0].text
diff --git a/tests/models/language/pooling/test_all_pooling_plus_chunked_prefill.py b/tests/models/language/pooling/test_all_pooling_plus_chunked_prefill.py
index c259c532220b..4119e1d5e00d 100644
--- a/tests/models/language/pooling/test_all_pooling_plus_chunked_prefill.py
+++ b/tests/models/language/pooling/test_all_pooling_plus_chunked_prefill.py
@@ -6,6 +6,7 @@
 
 from tests.models.utils import check_embeddings_close
 from vllm import TokensPrompt
+from vllm.config import PoolerConfig
 
 
 @pytest.mark.parametrize(
@@ -21,6 +22,7 @@ def test_embed_models(hf_runner, vllm_runner, model: str):
     with vllm_runner(
         model,
         runner="pooling",
+        pooler_config=PoolerConfig(task="token_embed"),
         max_model_len=128,
         max_num_batched_tokens=chunk_size,
         enforce_eager=True,
diff --git a/tests/models/language/pooling/test_bge_m3.py b/tests/models/language/pooling/test_bge_m3.py
index c0ef263c7781..772f60197fb5 100644
--- a/tests/models/language/pooling/test_bge_m3.py
+++ b/tests/models/language/pooling/test_bge_m3.py
@@ -3,7 +3,6 @@
 import httpx
 import openai
 import pytest
-import pytest_asyncio
 import torch
 
 from ....utils import RemoteOpenAIServer
@@ -25,29 +24,42 @@
 similarity_reference = [[0.6259, 0.3474], [0.3309, 0.6734]]
 lexical_score_reference = [0.19554901123046875, 0.0]
 colbert_score_reference = [0.7797, 0.4620]
+SUPPORTED_TASKS = ["embed", "token_embed", "token_classify"]
+
+
+@pytest.fixture(scope="module", params=SUPPORTED_TASKS)
+def pooling_task(request):
+    yield request.param
 
 
 @pytest.fixture(scope="module")
-def server():
+def server(pooling_task):
     args = [
         "--max-model-len",
         str(MAX_MODEL_LEN),
         "--hf-overrides",
         '{"architectures": ["BgeM3EmbeddingModel"]}',
+        "--pooler-config.task",
+        pooling_task,
     ]
 
     with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
         yield remote_server
 
 
-@pytest_asyncio.fixture
-async def client(server):
-    async with server.get_async_client() as async_client:
-        yield async_client
-
-
 @pytest.mark.asyncio
-async def test_bge_m3_api_server_embedding(client: openai.AsyncOpenAI):
+async def test_bge_m3_api_server_embedding(server, pooling_task):
+    client = server.get_async_client()
+
+    if pooling_task != "embed":
+        with pytest.raises(openai.InternalServerError):
+            await run_client_embeddings(
+                client,
+                MODEL_NAME,
+                sentences_1,
+            )
+        return
+
     embeddings_list_1 = await run_client_embeddings(
         client,
         MODEL_NAME,
@@ -117,7 +129,14 @@ def compute_lexical_matching_score(
 
 
 @pytest.mark.asyncio
-async def test_bge_m3_api_server_sparse_embedding(client: openai.AsyncOpenAI):
+async def test_bge_m3_api_server_sparse_embedding(server, pooling_task):
+    client = server.get_async_client()
+
+    if pooling_task != "token_classify":
+        with pytest.raises(openai.BadRequestError):
+            await sparse_embeddings(client, sentences_1)
+        return
+
     embeddings_1 = await sparse_embeddings(client, sentences_1)
     embeddings_2 = await sparse_embeddings(client, sentences_2)
 
@@ -137,9 +156,11 @@ async def test_bge_m3_api_server_sparse_embedding(client: openai.AsyncOpenAI):
 
 
 @pytest.mark.asyncio
-async def test_bge_m3_api_server_sparse_embedding_corner_case(
-    client: openai.AsyncOpenAI,
-):
+async def test_bge_m3_api_server_sparse_embedding_corner_case(server, pooling_task):
+    if pooling_task != "token_classify":
+        return
+
+    client = server.get_async_client()
     embeddings = await sparse_embeddings(client, ["Hi"])
     assert len(embeddings) == 1
     assert 2673 in embeddings[0]
@@ -155,7 +176,18 @@ def colbert_score(q_reps: torch.Tensor, p_reps: torch.Tensor) -> torch.Tensor:
 
 
 @pytest.mark.asyncio
-async def test_bge_m3_api_server_multi_vector(client: openai.AsyncOpenAI):
+async def test_bge_m3_api_server_multi_vector(server, pooling_task):
+    client = server.get_async_client()
+
+    if pooling_task != "token_embed":
+        with pytest.raises(openai.BadRequestError):
+            await client.post(
+                "../pooling",
+                body={"model": MODEL_NAME, "input": sentences_1, "task": "token_embed"},
+                cast_to=httpx.Response,
+            )
+        return
+
     result_1 = await client.post(
         "../pooling",
         body={"model": MODEL_NAME, "input": sentences_1, "task": "token_embed"},
diff --git a/tests/models/language/pooling/test_extract_hidden_states.py b/tests/models/language/pooling/test_extract_hidden_states.py
index 488b27e2da0f..40bbeb1c39f6 100644
--- a/tests/models/language/pooling/test_extract_hidden_states.py
+++ b/tests/models/language/pooling/test_extract_hidden_states.py
@@ -4,6 +4,7 @@
 import torch
 
 from vllm import TokensPrompt
+from vllm.config import PoolerConfig
 
 
 @pytest.mark.parametrize(
@@ -20,6 +21,7 @@ def test_extract_hidden_states(hf_runner, vllm_runner, model: str):
         max_model_len=128,
         enforce_eager=True,
         runner="pooling",
+        pooler_config=PoolerConfig(task="token_embed"),
         enable_prefix_caching=True,
     ) as vllm_model:
         pooling_outputs = vllm_model.llm.encode(
@@ -44,14 +46,3 @@ def test_extract_hidden_states(hf_runner, vllm_runner, model: str):
             assert len(output.prompt_token_ids) == n
             assert len(output.outputs.data) == n
             assert output.num_cached_tokens == 0
-
-        # skip_reading_prefix_cache can still write to cache
-        # to accelerate following requests
-        pooling_outputs = vllm_model.llm.encode(
-            [TokensPrompt(prompt_token_ids=t) for t in token_prompts],
-            pooling_task="embed",
-        )
-
-        for n, output in zip(n_prompt_tokens, pooling_outputs):
-            assert len(output.prompt_token_ids) == n
-            assert output.num_cached_tokens > 0
diff --git a/tests/models/language/pooling/test_multi_vector_retrieval.py b/tests/models/language/pooling/test_multi_vector_retrieval.py
index 302f2df13557..3161271be091 100644
--- a/tests/models/language/pooling/test_multi_vector_retrieval.py
+++ b/tests/models/language/pooling/test_multi_vector_retrieval.py
@@ -5,6 +5,7 @@
 from transformers import AutoModel
 
 from tests.models.utils import check_embeddings_close
+from vllm.config import PoolerConfig
 
 
 @pytest.mark.parametrize(
@@ -17,6 +18,7 @@ def test_embed_models(hf_runner, vllm_runner, example_prompts, model: str, dtype
     with vllm_runner(
         model,
         runner="pooling",
+        pooler_config=PoolerConfig(task="token_embed"),
         max_model_len=None,
     ) as vllm_model:
         vllm_outputs = vllm_model.token_embed(example_prompts)
diff --git a/tests/models/language/pooling/test_pooler_config_init_behaviour.py b/tests/models/language/pooling/test_pooler_config_init_behaviour.py
index a5a0c07e0c5d..2f6fb9c873f8 100644
--- a/tests/models/language/pooling/test_pooler_config_init_behaviour.py
+++ b/tests/models/language/pooling/test_pooler_config_init_behaviour.py
@@ -146,7 +146,7 @@ def test_multi_vector_retrieval_models_using_normalize(
         model,
         max_model_len=512,
         dtype=dtype,
-        pooler_config=PoolerConfig(use_activation=False),
+        pooler_config=PoolerConfig(use_activation=False, task="token_embed"),
     ) as vllm_model:
         wo_normalize = vllm_model.token_embed(example_prompts)
 
@@ -154,7 +154,7 @@ def test_multi_vector_retrieval_models_using_normalize(
         model,
         max_model_len=512,
         dtype=dtype,
-        pooler_config=PoolerConfig(use_activation=True),
+        pooler_config=PoolerConfig(use_activation=True, task="token_embed"),
     ) as vllm_model:
         w_normalize = vllm_model.token_embed(example_prompts)
 
diff --git a/tests/models/language/pooling_mteb_test/mteb_embed_utils.py b/tests/models/language/pooling_mteb_test/mteb_embed_utils.py
index 34b758d22ac8..fc575c399d04 100644
--- a/tests/models/language/pooling_mteb_test/mteb_embed_utils.py
+++ b/tests/models/language/pooling_mteb_test/mteb_embed_utils.py
@@ -74,10 +74,25 @@ def similarity_pairwise(
         return sim
 
 
+class HfMtebEncoder(MtebEmbedMixin):
+    def __init__(self, model):
+        self.model = model
+
+    def encode(
+        self,
+        inputs: DataLoader[mteb.types.BatchedInput],
+        *args,
+        **kwargs,
+    ) -> np.ndarray:
+        sentences = [text for batch in inputs for text in batch["text"]]
+        return self.model.encode(sentences)
+
+
 class VllmMtebEncoder(MtebEmbedMixin):
-    def __init__(self, vllm_model):
+    def __init__(self, vllm_model, prompt_prefix: str | None = None):
         self.llm = vllm_model
         self.rng = np.random.default_rng(seed=42)
+        self.prompt_prefix = prompt_prefix
 
     def encode(
         self,
@@ -87,7 +102,11 @@ def encode(
     ) -> np.ndarray:
         # Hoping to discover potential scheduling
         # issues by randomizing the order.
-        sentences = [text for batch in inputs for text in batch["text"]]
+        sentences = [
+            self.prompt_prefix + text if self.prompt_prefix else text
+            for batch in inputs
+            for text in batch["text"]
+        ]
         r = self.rng.permutation(len(sentences))
         sentences = [sentences[i] for i in r]
         outputs = self.llm.embed(sentences, use_tqdm=False)
@@ -143,6 +162,7 @@ def mteb_test_embed_models(
     vllm_extra_kwargs=None,
     hf_model_callback=None,
     atol=MTEB_EMBED_TOL,
+    prompt_prefix: str | None = None,
 ):
     vllm_extra_kwargs = get_vllm_extra_kwargs(model_info, vllm_extra_kwargs)
 
@@ -182,7 +202,7 @@ def mteb_test_embed_models(
             )
 
         vllm_main_score = run_mteb_embed_task(
-            VllmMtebEncoder(vllm_model), MTEB_EMBED_TASKS
+            VllmMtebEncoder(vllm_model, prompt_prefix=prompt_prefix), MTEB_EMBED_TASKS
         )
         vllm_dtype = vllm_model.llm.llm_engine.model_config.dtype
         head_dtype = model_config.head_dtype
@@ -210,7 +230,9 @@ def mteb_test_embed_models(
             if hf_model_callback is not None:
                 hf_model_callback(hf_model)
 
-            st_main_score = run_mteb_embed_task(hf_model, MTEB_EMBED_TASKS)
+            st_main_score = run_mteb_embed_task(
+                HfMtebEncoder(hf_model), MTEB_EMBED_TASKS
+            )
             st_dtype = next(hf_model.model.parameters()).dtype
 
             # Check embeddings close to hf outputs
diff --git a/tests/models/language/pooling_mteb_test/test_baai.py b/tests/models/language/pooling_mteb_test/test_baai.py
index 1199393d4b74..ec11960fda07 100644
--- a/tests/models/language/pooling_mteb_test/test_baai.py
+++ b/tests/models/language/pooling_mteb_test/test_baai.py
@@ -69,7 +69,10 @@
         attn_type="decoder",
         is_prefix_caching_supported=True,
         is_chunked_prefill_supported=True,
-        enable_test=True,
+        # Skip: model's custom tokenizer on HF hub is incompatible with
+        # transformers v5 (sets attrs before super().__init__, triggering
+        # AttributeError on 'verbose' in __getattr__).
+        enable_test=False,
     ),
 ]
 
diff --git a/tests/models/language/pooling_mteb_test/test_gte.py b/tests/models/language/pooling_mteb_test/test_gte.py
index 0c35d66c3667..0a54262e124f 100644
--- a/tests/models/language/pooling_mteb_test/test_gte.py
+++ b/tests/models/language/pooling_mteb_test/test_gte.py
@@ -72,7 +72,8 @@
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
-        enable_test=True,
+        # Skip: numerical regression with transformers v5.
+        enable_test=False,
     ),
     ########## ModernBertModel
     EmbedModelInfo(
diff --git a/tests/models/language/pooling_mteb_test/test_jina.py b/tests/models/language/pooling_mteb_test/test_jina.py
index 627cc0431943..24aa3188f8be 100644
--- a/tests/models/language/pooling_mteb_test/test_jina.py
+++ b/tests/models/language/pooling_mteb_test/test_jina.py
@@ -28,7 +28,16 @@
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
-    )
+    ),
+    EmbedModelInfo(
+        "jinaai/jina-embeddings-v5-text-small",
+        mteb_score=0.794535707854956,
+        architecture="JinaEmbeddingsV5Model",
+        seq_pooling_type="LAST",
+        attn_type="decoder",
+        is_prefix_caching_supported=True,
+        is_chunked_prefill_supported=True,
+    ),
 ]
 
 RERANK_MODELS = [
@@ -46,11 +55,18 @@
 
 @pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
 def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
+    task = "retrieval" if "v5" in model_info.name else "text-matching"
+    prompt_prefix: str | None = "Document: " if "v5" in model_info.name else None
+
     def hf_model_callback(model):
-        model.encode = partial(model.encode, task="text-matching")
+        model.encode = partial(model.encode, task=task)
 
     mteb_test_embed_models(
-        hf_runner, vllm_runner, model_info, hf_model_callback=hf_model_callback
+        hf_runner,
+        vllm_runner,
+        model_info,
+        hf_model_callback=hf_model_callback,
+        prompt_prefix=prompt_prefix,
     )
 
 
@@ -58,8 +74,10 @@ def hf_model_callback(model):
 def test_embed_models_correctness(
     hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
 ) -> None:
+    task = "retrieval" if "v5" in model_info.name else "text-matching"
+
     def hf_model_callback(model):
-        model.encode = partial(model.encode, task="text-matching")
+        model.encode = partial(model.encode, task=task)
 
     correctness_test_embed_models(
         hf_runner,
@@ -75,6 +93,10 @@ def test_rerank_models_mteb(vllm_runner, model_info: RerankModelInfo) -> None:
     mteb_test_rerank_models(vllm_runner, model_info)
 
 
+@pytest.mark.skip(
+    reason="jinaai/jina-embeddings-v3 custom XLMRobertaLoRA model on HF hub "
+    "is incompatible with transformers v5 (missing all_tied_weights_keys)"
+)
 @pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("dimensions", [16, 32])
@@ -93,12 +115,14 @@ def test_matryoshka(
     # ST will strip the input texts, see test_embedding.py
     example_prompts = [str(s).strip() for s in example_prompts]
 
+    task = "retrieval" if "v5" in model_info.name else "text-matching"
+
     with hf_runner(
         model_info.name,
         dtype=dtype,
         is_sentence_transformer=True,
     ) as hf_model:
-        hf_outputs = hf_model.encode(example_prompts, task="text-matching")
+        hf_outputs = hf_model.encode(example_prompts, task=task)
         hf_outputs = matryoshka_fy(hf_outputs, dimensions)
 
     with vllm_runner(
diff --git a/tests/models/multimodal/generation/test_common.py b/tests/models/multimodal/generation/test_common.py
index bf5119cf44f4..45e693598f1b 100644
--- a/tests/models/multimodal/generation/test_common.py
+++ b/tests/models/multimodal/generation/test_common.py
@@ -86,6 +86,29 @@
 # which cases would be selected and deselected by pytest. In general,
 # this is a good idea for checking your command first, since tests are slow.
 
+
+def _granite4_vision_vllm_to_hf_output(vllm_output, model):
+    """Post-processor for granite4_vision vLLM output.
+
+    Self-contained to avoid calling AutoConfig/AutoTokenizer without
+    trust_remote_code (needed while the model is not in upstream HF).
+    """
+    output_ids, output_str, out_logprobs = vllm_output
+    mm_token_id = 100352
+    hf_output_ids = [
+        token_id
+        for idx, token_id in enumerate(output_ids)
+        if token_id != mm_token_id or idx == 0 or output_ids[idx - 1] != mm_token_id
+    ]
+    hf_output_str = (
+        output_str[1:] if output_str and output_str[0] == " " else output_str
+    )
+    eos_token_id = 100257
+    if hf_output_ids and hf_output_ids[-1] == eos_token_id:
+        hf_output_str = hf_output_str + "<|end_of_text|>"
+    return hf_output_ids, hf_output_str, out_logprobs
+
+
 VLM_TEST_SETTINGS = {
     #### Core tests to always run in the CI
     "llava": VLMTestInfo(
@@ -186,7 +209,14 @@
         max_num_seqs=2,
         auto_cls=AutoModel,
         hf_output_post_proc=model_utils.ultravox_trunc_hf_output,
-        marks=[pytest.mark.core_model, pytest.mark.cpu_model],
+        marks=[
+            pytest.mark.core_model,
+            pytest.mark.cpu_model,
+            # TODO: Remove skip once model has been upstreamed to Transformers
+            pytest.mark.skip(
+                reason="Custom model code is not compatible with Transformers v5"
+            ),
+        ],
     ),
     #### Transformers fallback to test
     ## To reduce test burden, we only test batching arbitrary image size
@@ -397,14 +427,14 @@
     "gemma4": VLMTestInfo(
         models=["google/gemma-4-E2B-it"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
-        prompt_formatter=lambda img_prompt: f"<bos><start_of_turn>user\n{img_prompt}<end_of_turn>\n<start_of_turn>model\n",  # noqa: E501
+        prompt_formatter=lambda img_prompt: f"<bos><|turn>user\n{img_prompt}<turn|>\n<|turn>model\n",  # noqa: E501
         single_image_prompts=IMAGE_ASSETS.prompts(
             {
-                "stop_sign": "What's the content in the center of the image?",
-                "cherry_blossom": "What is the season?",
+                "stop_sign": "<|image|>What's the content in the center of the image?",  # noqa: E501
+                "cherry_blossom": "<|image|>What is the season?",
             }
         ),
-        multi_image_prompt="Describe the two images in detail.",
+        multi_image_prompt="<|image|><|image|>Describe the two images in detail.",  # noqa: E501
         max_model_len=4096,
         max_num_seqs=2,
         auto_cls=AutoModelForImageTextToText,
@@ -485,6 +515,20 @@
         auto_cls=AutoModelForImageTextToText,
         marks=[large_gpu_mark(min_gb=32)],
     ),
+    "granite4_vision": VLMTestInfo(
+        models=["ibm-granite/granite-vision-4.1-4b"],
+        test_type=(VLMTestType.IMAGE),
+        prompt_formatter=lambda img_prompt: f"<|user|>\n{img_prompt}\n<|assistant|>\n",
+        max_model_len=8192,
+        auto_cls=AutoModelForImageTextToText,
+        vllm_output_post_proc=_granite4_vision_vllm_to_hf_output,
+        image_size_factors=[(1.0,)],
+        vllm_runner_kwargs={
+            "enable_lora": True,
+            "max_lora_rank": 256,
+            "default_mm_loras": {"image": "ibm-granite/granite-vision-4.1-4b"},
+        },
+    ),
     "h2ovl": VLMTestInfo(
         models=[
             "h2oai/h2ovl-mississippi-800m",
@@ -533,6 +577,12 @@
         max_model_len=4096,
         use_tokenizer_eos=True,
         patch_hf_runner=model_utils.internvl_patch_hf_runner,
+        # TODO: Remove skip once model has been upstreamed to Transformers
+        marks=[
+            pytest.mark.skip(
+                reason="Custom model code tries to access data from meta-tensor"
+            )
+        ],
     ),
     "intern_vl-video": VLMTestInfo(
         models=[
@@ -545,6 +595,12 @@
         use_tokenizer_eos=True,
         patch_hf_runner=model_utils.internvl_patch_hf_runner,
         num_logprobs=10 if current_platform.is_rocm() else 5,
+        # TODO: Remove skip once model has been upstreamed to Transformers
+        marks=[
+            pytest.mark.skip(
+                reason="Custom model code tries to access data from meta-tensor"
+            )
+        ],
     ),
     "intern_vl-hf": VLMTestInfo(
         models=["OpenGVLab/InternVL3-1B-hf"],
@@ -591,6 +647,8 @@
         hf_model_kwargs={"device_map": "auto"},
         patch_hf_runner=model_utils.isaac_patch_hf_runner,
         image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
+        # TODO: Remove skip once model has been upstreamed to Transformers
+        marks=[pytest.mark.skip(reason="Custom model imports deleted object")],  # noqa: E501
     ),
     "kimi_vl": VLMTestInfo(
         models=["moonshotai/Kimi-VL-A3B-Instruct"],
@@ -747,6 +805,32 @@
         max_num_seqs=2,
         patch_hf_runner=model_utils.molmo_patch_hf_runner,
     ),
+    "moondream3": VLMTestInfo(
+        models=["moondream/moondream3-preview"],
+        test_type=VLMTestType.IMAGE,
+        prompt_formatter=identity,
+        img_idx_to_prompt=lambda idx: "<|endoftext|><image>",
+        # Common-image coverage here targets query/caption. The native
+        # detect/point skills are not exposed by vLLM.
+        single_image_prompts=IMAGE_ASSETS.prompts(
+            {
+                "stop_sign": "<vlm_image><|md_reserved_0|>query<|md_reserved_1|>What is this sign?<|md_reserved_2|>",  # noqa: E501
+                "cherry_blossom": (
+                    "<vlm_image><|md_reserved_0|>query<|md_reserved_1|>What season is shown?<|md_reserved_2|>"  # noqa: E501
+                ),
+            }
+        ),
+        max_model_len=4096,
+        max_num_seqs=2,
+        dtype="bfloat16",
+        hf_processor=model_utils.moondream3_processor,
+        patch_hf_runner=model_utils.moondream3_patch_hf_runner,
+        # Single size factor to avoid GPU OOM when running multiple test
+        # cases sequentially (9B MoE model uses ~18 GiB per instance).
+        image_size_factors=[(1.0,)],
+        # Moondream3 is 9B params with MoE, needs significant GPU memory
+        marks=[large_gpu_mark(min_gb=48)],
+    ),
     "ovis1_6-gemma2": VLMTestInfo(
         models=["AIDC-AI/Ovis1.6-Gemma2-9B"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
@@ -806,7 +890,12 @@
             pytest.mark.skipif(
                 Version(TRANSFORMERS_VERSION) == Version("4.57.3"),
                 reason="This model is broken in Transformers v4.57.3",
-            )
+            ),
+            pytest.mark.skipif(
+                Version(TRANSFORMERS_VERSION) >= Version("5.0.0"),
+                reason="Model's custom code uses ROPE_INIT_FUNCTIONS"
+                "['default'] which was removed in transformers v5",
+            ),
         ],
     ),
     "phi3v": VLMTestInfo(
@@ -839,6 +928,16 @@
             ),
         ],
     ),
+    "qianfan_ocr": VLMTestInfo(
+        models=["baidu/Qianfan-OCR"],
+        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
+        prompt_formatter=lambda img_prompt: f"<|im_start|>user\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n",  # noqa: E501
+        img_idx_to_prompt=lambda idx: "<image>",
+        max_model_len=4096,
+        use_tokenizer_eos=True,
+        auto_cls=AutoModelForImageTextToText,
+        hf_model_kwargs=model_utils.qianfan_ocr_hf_model_kwargs("baidu/Qianfan-OCR"),
+    ),
     "qwen_vl": VLMTestInfo(
         models=["Qwen/Qwen-VL"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
@@ -858,6 +957,7 @@
         multi_image_prompt="Picture 1: <vlm_image>\nPicture 2: <vlm_image>\nDescribe these two images with one paragraph respectively.",  # noqa: E501
         max_model_len=4096,
         max_num_seqs=2,
+        num_logprobs=10,
         auto_cls=AutoModelForImageTextToText,
         vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
         image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
@@ -960,6 +1060,12 @@
             )
             for inp in custom_inputs.different_patch_input_cases_internvl()
         ],
+        # TODO: Remove skip once model has been upstreamed to Transformers
+        marks=[
+            pytest.mark.skip(
+                reason="Custom model code tries to access data from meta-tensor"
+            )
+        ],
     ),
     "llava_onevision-multiple-images": VLMTestInfo(
         models=["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"],
diff --git a/tests/models/multimodal/generation/test_moondream3.py b/tests/models/multimodal/generation/test_moondream3.py
new file mode 100644
index 000000000000..e4aaa5a55b55
--- /dev/null
+++ b/tests/models/multimodal/generation/test_moondream3.py
@@ -0,0 +1,176 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Generation tests for Moondream3 query and caption support."""
+
+import pytest
+
+from tests.models.registry import HF_EXAMPLE_MODELS
+from vllm.platforms import current_platform
+
+from ....conftest import IMAGE_ASSETS, ImageTestAssets
+from ....utils import large_gpu_mark, multi_gpu_test
+
+MOONDREAM3_MODEL_ID = "moondream/moondream3-preview"
+MOONDREAM3_TOKENIZER = "moondream/starmie-v1"
+
+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts(
+    {
+        "stop_sign": "<|endoftext|><image><|md_reserved_0|>query<|md_reserved_1|>What color is the stop sign?<|md_reserved_2|>",  # noqa: E501
+        "cherry_blossom": "<|endoftext|><image><|md_reserved_0|>query<|md_reserved_1|>What color are the flowers?<|md_reserved_2|>",  # noqa: E501
+    }
+)
+
+
+def make_query_prompt(question: str) -> str:
+    """Create a direct-answer query prompt for Moondream3."""
+    return (
+        "<|endoftext|><image><|md_reserved_0|>query<|md_reserved_1|>"
+        f"{question}<|md_reserved_2|>"
+    )
+
+
+def make_caption_prompt(length: str = "normal") -> str:
+    """Create a caption prompt for Moondream3."""
+    return (
+        "<|endoftext|><image><|md_reserved_0|>"
+        f"describe<|md_reserved_1|>{length}<|md_reserved_2|>"
+    )
+
+
+@multi_gpu_test(num_gpus=2)
+@large_gpu_mark(min_gb=80)
+def test_tensor_parallel(image_assets: ImageTestAssets):
+    import gc
+
+    from vllm import LLM, SamplingParams
+    from vllm.distributed.parallel_state import destroy_model_parallel
+
+    destroy_model_parallel()
+    gc.collect()
+    current_platform.empty_cache()
+
+    llm = LLM(
+        model=MOONDREAM3_MODEL_ID,
+        tokenizer=MOONDREAM3_TOKENIZER,
+        trust_remote_code=True,
+        dtype="bfloat16",
+        tensor_parallel_size=2,
+        max_model_len=1024,
+        enforce_eager=True,
+        limit_mm_per_prompt={"image": 1},
+        gpu_memory_utilization=0.45,
+    )
+
+    image = image_assets[0].pil_image
+    prompt = make_query_prompt("What color is the stop sign?")
+
+    try:
+        outputs = llm.generate(
+            {"prompt": prompt, "multi_modal_data": {"image": image}},
+            SamplingParams(max_tokens=20, temperature=0),
+        )
+
+        assert len(outputs) > 0
+        assert outputs[0].outputs[0].text is not None
+    finally:
+        del llm
+        gc.collect()
+        current_platform.empty_cache()
+
+
+@pytest.fixture(scope="module")
+def llm():
+    model_info = HF_EXAMPLE_MODELS.get_hf_info("Moondream3ForCausalLM")
+    model_info.check_transformers_version(on_fail="skip")
+
+    from vllm import LLM
+
+    try:
+        return LLM(
+            model=MOONDREAM3_MODEL_ID,
+            tokenizer=MOONDREAM3_TOKENIZER,
+            trust_remote_code=True,
+            dtype="bfloat16",
+            max_model_len=2048,
+            enforce_eager=True,
+            limit_mm_per_prompt={"image": 1},
+            gpu_memory_utilization=0.45,
+        )
+    except Exception as exc:
+        pytest.skip(f"Failed to load {MOONDREAM3_MODEL_ID}: {exc}")
+
+
+@large_gpu_mark(min_gb=48)
+def test_model_loading(llm):
+    assert llm is not None
+
+
+@large_gpu_mark(min_gb=48)
+def test_query_skill(llm, image_assets: ImageTestAssets):
+    from vllm import SamplingParams
+
+    image = image_assets[0].pil_image
+    prompt = make_query_prompt("What color is the stop sign?")
+
+    outputs = llm.generate(
+        {"prompt": prompt, "multi_modal_data": {"image": image}},
+        SamplingParams(max_tokens=50, temperature=0),
+    )
+
+    output_text = outputs[0].outputs[0].text
+    assert output_text is not None
+    assert len(output_text) > 0
+
+
+@large_gpu_mark(min_gb=48)
+def test_caption_skill(llm, image_assets: ImageTestAssets):
+    from vllm import SamplingParams
+
+    image = image_assets[1].pil_image
+    prompt = make_caption_prompt()
+
+    outputs = llm.generate(
+        {"prompt": prompt, "multi_modal_data": {"image": image}},
+        SamplingParams(max_tokens=100, temperature=0),
+    )
+
+    output_text = outputs[0].outputs[0].text
+    assert output_text is not None
+    assert len(output_text) > 0
+
+
+@large_gpu_mark(min_gb=48)
+def test_batched_inference(llm, image_assets: ImageTestAssets):
+    from vllm import SamplingParams
+
+    images = [asset.pil_image for asset in image_assets]
+    prompts = [
+        {"prompt": prompt, "multi_modal_data": {"image": img}}
+        for img, prompt in zip(images, HF_IMAGE_PROMPTS)
+    ]
+
+    outputs = llm.generate(prompts, SamplingParams(max_tokens=50, temperature=0))
+
+    assert len(outputs) == len(images)
+    for output in outputs:
+        assert output.outputs[0].text is not None
+        assert len(output.outputs[0].text) > 0
+
+
+@pytest.mark.parametrize("asset_name", ["stop_sign", "cherry_blossom"])
+@large_gpu_mark(min_gb=48)
+def test_image_assets(llm, image_assets: ImageTestAssets, asset_name: str):
+    from vllm import SamplingParams
+
+    asset_idx = 0 if asset_name == "stop_sign" else 1
+    image = image_assets[asset_idx].pil_image
+    prompt = HF_IMAGE_PROMPTS[asset_idx]
+
+    outputs = llm.generate(
+        {"prompt": prompt, "multi_modal_data": {"image": image}},
+        SamplingParams(max_tokens=50, temperature=0),
+    )
+
+    output_text = outputs[0].outputs[0].text
+    assert output_text is not None
+    assert len(output_text) > 0
diff --git a/tests/models/multimodal/generation/test_nemotron_parse.py b/tests/models/multimodal/generation/test_nemotron_parse.py
index e224f31e6df9..8159cc9a8dae 100644
--- a/tests/models/multimodal/generation/test_nemotron_parse.py
+++ b/tests/models/multimodal/generation/test_nemotron_parse.py
@@ -103,6 +103,10 @@ def run_test(
         )
 
 
+@pytest.mark.skip(
+    reason="Model's custom MBart decoder has head count mismatch with "
+    "transformers v5's GQA-aware cross-attention (8 vs 16 heads)"
+)
 @pytest.mark.parametrize("model", ["nvidia/NVIDIA-Nemotron-Parse-v1.1"])
 @pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("num_logprobs", [5])
diff --git a/tests/models/multimodal/generation/test_phi4mm.py b/tests/models/multimodal/generation/test_phi4mm.py
index 7f1a12f04474..1a4fb35a28aa 100644
--- a/tests/models/multimodal/generation/test_phi4mm.py
+++ b/tests/models/multimodal/generation/test_phi4mm.py
@@ -4,7 +4,6 @@
 import os
 from collections.abc import Sequence
 
-import librosa
 import pytest
 import regex as re
 from huggingface_hub import snapshot_download
@@ -14,6 +13,7 @@
 from vllm.logprobs import SampleLogprobs
 from vllm.lora.request import LoRARequest
 from vllm.multimodal.image import convert_image_mode, rescale_image_size
+from vllm.multimodal.media.audio import load_audio
 
 from ....conftest import (
     IMAGE_ASSETS,
@@ -290,7 +290,7 @@ def test_vision_speech_models(
     num_logprobs: int,
 ) -> None:
     # use the example speech question so that the model outputs are reasonable
-    audio = librosa.load(speech_question, sr=None)
+    audio = load_audio(speech_question, sr=None)
     image = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")
 
     inputs_vision_speech = [
diff --git a/tests/models/multimodal/generation/test_phi4siglip.py b/tests/models/multimodal/generation/test_phi4siglip.py
index e8f4ba829250..f80b16c341b6 100644
--- a/tests/models/multimodal/generation/test_phi4siglip.py
+++ b/tests/models/multimodal/generation/test_phi4siglip.py
@@ -2,9 +2,11 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from collections.abc import Sequence
+from importlib.metadata import version
 
 import pytest
 import regex as re
+from packaging.version import Version
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from vllm.logprobs import SampleLogprobs
@@ -19,6 +21,15 @@
 from ....utils import multi_gpu_test
 from ...utils import check_logprobs_close
 
+pytestmark = pytest.mark.skipif(
+    Version("5.0") <= Version(version("transformers")),
+    reason=(
+        "vllm upgraded transformers above v5.4 where HF model custom code uses siglip2 "
+        "internals (filter_out_non_signature_kwargs) removed by "
+        "huggingface/transformers#43514"
+    ),
+)
+
 MODEL_ID = "microsoft/Phi-4-reasoning-vision-15B"
 
 HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts(
diff --git a/tests/models/multimodal/generation/test_pixtral.py b/tests/models/multimodal/generation/test_pixtral.py
index 48329d9aea3d..2ce732342bdb 100644
--- a/tests/models/multimodal/generation/test_pixtral.py
+++ b/tests/models/multimodal/generation/test_pixtral.py
@@ -25,6 +25,7 @@
 
 PIXTRAL_ID = "mistralai/Pixtral-12B-2409"
 MISTRAL_SMALL_3_1_ID = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
+MINISTRAL_3B_ID = "mistralai/Ministral-3-3B-Instruct-2512"
 
 MODELS = [PIXTRAL_ID, MISTRAL_SMALL_3_1_ID]
 
@@ -116,6 +117,7 @@ def _create_engine_inputs_hf(urls: list[str]) -> TextPrompt:
 FIXTURE_LOGPROBS_CHAT = {
     PIXTRAL_ID: FIXTURES_PATH / "pixtral_chat.json",
     MISTRAL_SMALL_3_1_ID: FIXTURES_PATH / "mistral_small_3_chat.json",
+    MINISTRAL_3B_ID: FIXTURES_PATH / "ministral_3b_chat.json",
 }
 
 OutputsLogprobs = list[tuple[list[int], str, SampleLogprobs | None]]
@@ -209,3 +211,41 @@ def test_chat(
         name_0="h100_ref",
         name_1="output",
     )
+
+
+@large_gpu_test(min_gb=16)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+def test_chat_consolidated(vllm_runner, dtype: str, local_asset_server) -> None:
+    EXPECTED_CHAT_LOGPROBS = load_outputs_w_logprobs(
+        FIXTURE_LOGPROBS_CHAT[MINISTRAL_3B_ID]
+    )
+    with vllm_runner(
+        MINISTRAL_3B_ID,
+        dtype=dtype,
+        tokenizer_mode="mistral",
+        load_format="mistral",
+        config_format="mistral",
+        max_model_len=8192,
+        limit_mm_per_prompt=LIMIT_MM_PER_PROMPT,
+    ) as vllm_model:
+        outputs = []
+        urls_all = [local_asset_server.url_for(u) for u in IMG_URLS]
+        msgs = [
+            _create_msg_format(urls_all[:1]),
+            _create_msg_format(urls_all[:2]),
+            _create_msg_format(urls_all),
+        ]
+        for msg in msgs:
+            output = vllm_model.llm.chat(msg, sampling_params=SAMPLING_PARAMS)
+            outputs.extend(output)
+
+    logprobs = vllm_runner._final_steps_generate_w_logprobs(outputs)
+    for i in range(len(logprobs)):
+        assert logprobs[i][-1] is None
+        logprobs[i] = logprobs[i][:-1]
+    check_logprobs_close(
+        outputs_0_lst=EXPECTED_CHAT_LOGPROBS,
+        outputs_1_lst=logprobs,
+        name_0="h100_ref",
+        name_1="output",
+    )
diff --git a/tests/models/multimodal/generation/test_qwen2_5_vl.py b/tests/models/multimodal/generation/test_qwen2_5_vl.py
index 3ba665710af4..791bb3b3088f 100644
--- a/tests/models/multimodal/generation/test_qwen2_5_vl.py
+++ b/tests/models/multimodal/generation/test_qwen2_5_vl.py
@@ -3,6 +3,7 @@
 
 import pytest
 
+from vllm.assets.image import ImageAsset
 from vllm.multimodal.video import sample_frames_from_video
 
 from ....conftest import VIDEO_ASSETS
@@ -11,6 +12,7 @@
 target_dtype = "bfloat16"
 
 VIDEO_PLACEHOLDER = "<|vision_start|><|video_pad|><|vision_end|>"
+IMAGE_PLACEHOLDER = "<|vision_start|><|image_pad|><|vision_end|>"
 
 
 def qwen2_5_vl_chat_template(*query):
@@ -28,6 +30,25 @@ def qwen2_5_vl_chat_template(*query):
 )
 
 
+WINDOW_ATTN_IMAGE_PROMPT = qwen2_5_vl_chat_template(
+    IMAGE_PLACEHOLDER,
+    "Describe the image.",
+)
+
+
+def _window_attention_regression_image():
+    # image from regression issue: https://github.com/vllm-project/vllm/issues/15122
+    image = ImageAsset("hato").pil_image
+    return image.resize((image.width // 2, image.height // 2))
+
+
+def _encoder_cudagraph_config(*, max_vision_items: int) -> dict:
+    return {
+        "cudagraph_mm_encoder": True,
+        "encoder_cudagraph_max_vision_items_per_batch": max_vision_items,
+    }
+
+
 @pytest.mark.core_model
 @pytest.mark.parametrize("model", models)
 @pytest.mark.parametrize("video_pruning_rate", [0.0, 0.75])
@@ -146,3 +167,77 @@ def test_qwen2_5_vl_evs_batched_videos(
 
             # Ensure the output is a string
             assert isinstance(output_text, str)
+
+
+@pytest.mark.core_model
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize("dtype", [target_dtype])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("use_bytecode_hook", [True, False])
+def test_qwen2_5_vl_window_attention_image(
+    vllm_runner,
+    model,
+    dtype: str,
+    max_tokens: int,
+    use_bytecode_hook: bool,
+    monkeypatch,
+) -> None:
+    """Regression test for Qwen2.5 window-attention image path."""
+    monkeypatch.setenv("VLLM_USE_BYTECODE_HOOK", "1" if use_bytecode_hook else "0")
+
+    prompt = [WINDOW_ATTN_IMAGE_PROMPT]
+    images = [[_window_attention_regression_image()]]
+
+    with vllm_runner(
+        model,
+        runner="generate",
+        max_model_len=4096,
+        dtype=dtype,
+        limit_mm_per_prompt={"image": 1},
+        compilation_config=_encoder_cudagraph_config(max_vision_items=1),
+    ) as vllm_model:
+        outputs = vllm_model.generate_greedy(prompt, max_tokens, images=images)
+
+        assert len(outputs) == 1
+        output_ids, output_text = outputs[0]
+        assert len(output_ids) > 0
+        assert len(output_text) > 0
+        assert isinstance(output_text, str)
+
+
+@pytest.mark.core_model
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize("dtype", [target_dtype])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("use_bytecode_hook", [True, False])
+def test_qwen2_5_vl_window_attention_image_batch(
+    vllm_runner,
+    model,
+    dtype: str,
+    max_tokens: int,
+    use_bytecode_hook: bool,
+    monkeypatch,
+) -> None:
+    """Regression test window-attention with a small image batch."""
+    monkeypatch.setenv("VLLM_USE_BYTECODE_HOOK", "1" if use_bytecode_hook else "0")
+
+    image = _window_attention_regression_image()
+    prompts = [WINDOW_ATTN_IMAGE_PROMPT, WINDOW_ATTN_IMAGE_PROMPT]
+    images = [[image], [image]]
+
+    with vllm_runner(
+        model,
+        runner="generate",
+        max_model_len=4096,
+        max_num_seqs=2,
+        dtype=dtype,
+        limit_mm_per_prompt={"image": 1},
+        compilation_config=_encoder_cudagraph_config(max_vision_items=2),
+    ) as vllm_model:
+        outputs = vllm_model.generate_greedy(prompts, max_tokens, images=images)
+
+        assert len(outputs) == 2
+        for output_ids, output_text in outputs:
+            assert len(output_ids) > 0
+            assert len(output_text) > 0
+            assert isinstance(output_text, str)
diff --git a/tests/models/multimodal/generation/test_vit_cudagraph.py b/tests/models/multimodal/generation/test_vit_cudagraph.py
new file mode 100644
index 000000000000..fb7bdfc8625d
--- /dev/null
+++ b/tests/models/multimodal/generation/test_vit_cudagraph.py
@@ -0,0 +1,177 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass, field
+
+import pytest
+
+from vllm.multimodal.video import sample_frames_from_video
+from vllm.platforms import current_platform
+
+from ....conftest import IMAGE_ASSETS, VIDEO_ASSETS
+from ....utils import create_new_process_for_each_test
+from .vlm_utils.builders import sample_frames_with_video_metadata
+
+
+@dataclass
+class VitCudagraphTestConfig:
+    model: str
+    modalities: list[str] = field(default_factory=lambda: ["image", "video"])
+    image_prompt: str | None = None
+    video_prompt: str | None = None
+    dtype: str = "bfloat16"
+    max_model_len: int = 4096
+    max_tokens: int = 64
+    max_num_seqs: int = 2
+    num_video_frames: int = 16
+    needs_video_metadata: bool = False
+    vllm_runner_kwargs: dict = field(default_factory=dict)
+    marks: list = field(default_factory=list)
+
+
+def params_with_marks(
+    configs: dict[str, VitCudagraphTestConfig],
+) -> list[pytest.param]:
+    return [
+        pytest.param(model_id, marks=cfg.marks) for model_id, cfg in configs.items()
+    ]
+
+
+def qwen_vl_chat_template(content: str) -> str:
+    return f"<|im_start|>user\n{content}<|im_end|>\n<|im_start|>assistant\n"
+
+
+MODEL_CONFIGS: dict[str, VitCudagraphTestConfig] = {
+    "qwen3_vl": VitCudagraphTestConfig(
+        model="Qwen/Qwen3-VL-2B-Instruct",
+        image_prompt=qwen_vl_chat_template(
+            "<|vision_start|><|image_pad|><|vision_end|>What is in this image?"
+        ),
+        video_prompt=qwen_vl_chat_template(
+            "<|vision_start|><|video_pad|><|vision_end|>"
+            "Describe this video in one sentence."
+        ),
+        needs_video_metadata=True,
+        marks=[pytest.mark.core_model],
+    ),
+    "qwen2_5_vl": VitCudagraphTestConfig(
+        model="Qwen/Qwen2.5-VL-3B-Instruct",
+        image_prompt=qwen_vl_chat_template(
+            "<|vision_start|><|image_pad|><|vision_end|>What is in this image?"
+        ),
+        video_prompt=qwen_vl_chat_template(
+            "<|vision_start|><|video_pad|><|vision_end|>"
+            "Describe this video in one sentence."
+        ),
+        needs_video_metadata=False,
+        marks=[pytest.mark.core_model],
+    ),
+}
+
+
+def get_compilation_config():
+    return {
+        "cudagraph_mm_encoder": True,
+        "encoder_cudagraph_max_vision_items_per_batch": 1,
+        "encoder_cudagraph_max_frames_per_batch": 16,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize("model_id", params_with_marks(MODEL_CONFIGS))
+@pytest.mark.skipif(not current_platform.is_cuda(), reason="Requires CUDA")
+@create_new_process_for_each_test()
+def test_vit_cudagraph_image(model_id, vllm_runner, image_assets):
+    config = MODEL_CONFIGS[model_id]
+
+    if "image" not in config.modalities:
+        pytest.skip(f"{model_id} does not support the image modality.")
+
+    image_prompts = IMAGE_ASSETS.prompts(
+        {
+            "stop_sign": config.image_prompt,  # type: ignore[typeddict-item]
+            "cherry_blossom": config.image_prompt,  # type: ignore[typeddict-item]
+        }
+    )
+    images = [[asset.pil_image] for asset in image_assets]
+
+    with vllm_runner(
+        config.model,
+        dtype=config.dtype,
+        max_model_len=config.max_model_len,
+        max_num_seqs=config.max_num_seqs,
+        limit_mm_per_prompt={"image": 1},
+        compilation_config=get_compilation_config(),
+        **config.vllm_runner_kwargs,
+    ) as vllm_model:
+        outputs = vllm_model.generate_greedy(
+            image_prompts, config.max_tokens, images=images
+        )
+
+        # Basic validation that we got a response
+        assert len(outputs) == 2
+        output_ids, output_text = outputs[0]
+
+        # Ensure we got some output
+        assert len(output_ids) > 0
+        assert len(output_text) > 0
+
+        # Ensure the output is a string
+        assert isinstance(output_text, str)
+
+
+@pytest.mark.parametrize("model_id", params_with_marks(MODEL_CONFIGS))
+@pytest.mark.skipif(not current_platform.is_cuda(), reason="Requires CUDA")
+@create_new_process_for_each_test()
+def test_vit_cudagraph_video(model_id, vllm_runner, video_assets):
+    config = MODEL_CONFIGS[model_id]
+
+    if "video" not in config.modalities:
+        pytest.skip(f"{model_id} does not support the video modality")
+
+    video_prompts = VIDEO_ASSETS.prompts(
+        {
+            "baby_reading": config.video_prompt,  # type: ignore[typeddict-item]
+        }
+    )
+    if config.needs_video_metadata:
+        sampled_vids = [
+            sample_frames_with_video_metadata(
+                (asset.np_ndarrays, asset.metadata), config.num_video_frames
+            )
+            for asset in video_assets
+        ]
+    else:
+        sampled_vids = [
+            sample_frames_from_video(asset.np_ndarrays, config.num_video_frames)
+            for asset in video_assets
+        ]
+    videos = [sampled_vids[0]]
+
+    with vllm_runner(
+        config.model,
+        dtype=config.dtype,
+        max_model_len=config.max_model_len,
+        max_num_seqs=config.max_num_seqs,
+        limit_mm_per_prompt={"video": 1},
+        compilation_config=get_compilation_config(),
+        **config.vllm_runner_kwargs,
+    ) as vllm_model:
+        outputs = vllm_model.generate_greedy(
+            video_prompts, config.max_tokens, videos=videos
+        )
+
+        # Basic validation that we got a response
+        assert len(outputs) == 1
+        output_ids, output_text = outputs[0]
+
+        # Ensure we got some output
+        assert len(output_ids) > 0
+        assert len(output_text) > 0
+
+        # Ensure the output is a string
+        assert isinstance(output_text, str)
diff --git a/tests/models/multimodal/generation/test_voxtral.py b/tests/models/multimodal/generation/test_voxtral.py
index 590b549dcf59..82db1dc6812c 100644
--- a/tests/models/multimodal/generation/test_voxtral.py
+++ b/tests/models/multimodal/generation/test_voxtral.py
@@ -149,6 +149,10 @@ def _asset_to_openai_chunk(asset):
     )
 
 
+@pytest.mark.skip(
+    reason="VoxtralProcessor.apply_chat_template() in transformers v5 "
+    "doesn't resolve chat_template=None to the default template"
+)
 def test_hf_reference(hf_runner, vllm_runner, audio_assets: AudioTestAssets):
     """Compare vLLM Mistral-format output against HF Transformers reference.
 
diff --git a/tests/models/multimodal/generation/test_whisper.py b/tests/models/multimodal/generation/test_whisper.py
index babf7e7a4978..93634760a576 100644
--- a/tests/models/multimodal/generation/test_whisper.py
+++ b/tests/models/multimodal/generation/test_whisper.py
@@ -4,11 +4,11 @@
 from collections.abc import Sequence
 from typing import Any
 
-import librosa
 import pytest
 from transformers import AutoModelForSpeechSeq2Seq
 
 from vllm.assets.audio import AudioAsset
+from vllm.multimodal.audio import AudioResampler
 from vllm.platforms import current_platform
 
 from ....conftest import HfRunner, PromptAudioInput, VllmRunner
@@ -41,6 +41,7 @@ def run_test(
     tensor_parallel_size: int,
     distributed_executor_backend: str | None = None,
     enforce_eager: bool = True,
+    gpu_memory_utilization: float = 0.9,
 ) -> None:
     """Inference result should be the same between hf and vllm.
 
@@ -57,6 +58,7 @@ def run_test(
         distributed_executor_backend=distributed_executor_backend,
         limit_mm_per_prompt={"audio": 2},
         enforce_eager=enforce_eager,
+        gpu_memory_utilization=gpu_memory_utilization,
         disable_custom_all_reduce=True,
     ) as vllm_model:
         vllm_outputs_per_case = [
@@ -93,13 +95,12 @@ def run_test(
 def resampled_assets() -> list[tuple[Any, int]]:
     audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
     sampled_assets = []
+    resampler = AudioResampler(target_sr=WHISPER_SAMPLE_RATE)
     for asset in audio_assets:
         audio, orig_sr = asset.audio_and_sample_rate
         # Resample to Whisper's expected sample rate (16kHz)
         if orig_sr != WHISPER_SAMPLE_RATE:
-            audio = librosa.resample(
-                audio, orig_sr=orig_sr, target_sr=WHISPER_SAMPLE_RATE
-            )
+            audio = resampler.resample(audio, orig_sr=orig_sr)
         sampled_assets.append(
             (audio, WHISPER_SAMPLE_RATE),
         )
@@ -320,6 +321,7 @@ def test_models_distributed(
         tensor_parallel_size=2,
         distributed_executor_backend=distributed_executor_backend,
         enforce_eager=False,
+        gpu_memory_utilization=0.65,
     )
 
 
diff --git a/tests/models/multimodal/generation/vlm_utils/core.py b/tests/models/multimodal/generation/vlm_utils/core.py
index 3de4ca209a6f..207d3a3202a1 100644
--- a/tests/models/multimodal/generation/vlm_utils/core.py
+++ b/tests/models/multimodal/generation/vlm_utils/core.py
@@ -38,6 +38,7 @@ def run_test(
     limit_mm_per_prompt: dict[str, int],
     vllm_runner_kwargs: dict[str, Any] | None,
     hf_model_kwargs: dict[str, Any] | None,
+    hf_processor: Callable[[str], Any] | None,
     patch_hf_runner: Callable[[HfRunner], HfRunner] | None,
     runner: RunnerOption = "auto",
     distributed_executor_backend: str | None = None,
@@ -80,6 +81,11 @@ def run_test(
     if vllm_runner_kwargs:
         vllm_runner_kwargs_.update(vllm_runner_kwargs)
 
+    # Avoid passing limit_mm_per_prompt twice when vllm_runner_kwargs
+    # already contains it (e.g. gemma4 sets it via vllm_runner_kwargs).
+    if "limit_mm_per_prompt" in vllm_runner_kwargs_:
+        limit_mm_per_prompt = vllm_runner_kwargs_.pop("limit_mm_per_prompt")
+
     with vllm_runner(
         model,
         max_model_len=max_model_len,
@@ -111,8 +117,18 @@ def run_test(
             )
             vllm_outputs_per_mm.append(vllm_output)
 
+    hf_runner_kwargs: dict[str, Any] = {}
+    if model_info.tokenizer:
+        hf_runner_kwargs["tokenizer_name"] = model_info.tokenizer
+    if hf_processor is not None:
+        hf_runner_kwargs["processor"] = hf_processor(model)
+
     hf_model = hf_runner(
-        model, dtype=dtype, auto_cls=auto_cls, model_kwargs=hf_model_kwargs
+        model,
+        dtype=dtype,
+        auto_cls=auto_cls,
+        model_kwargs=hf_model_kwargs,
+        **hf_runner_kwargs,
     )
 
     # Some models need to patch things like the model processor, e.g., internvl
diff --git a/tests/models/multimodal/generation/vlm_utils/model_utils.py b/tests/models/multimodal/generation/vlm_utils/model_utils.py
index 0a692387cffc..62ea36061c9c 100644
--- a/tests/models/multimodal/generation/vlm_utils/model_utils.py
+++ b/tests/models/multimodal/generation/vlm_utils/model_utils.py
@@ -1336,3 +1336,312 @@ def patched_generate(*args, **kwargs):
     hf_model.get_inputs = patched_get_inputs  # type: ignore[method-assign, assignment]
     hf_model.model.generate = patched_generate  # type: ignore[method-assign]
     return hf_model
+
+
+def moondream3_processor(model: str):
+    from vllm.transformers_utils.processors.moondream3 import Moondream3Processor
+
+    return Moondream3Processor.from_pretrained(model, trust_remote_code=True)
+
+
+def moondream3_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
+    """Patch HfRunner for Moondream3."""
+    moondream_processor = hf_model.processor
+
+    def processor(*args, text="", images=None, **kwargs):
+        if images is None:
+            return moondream_processor(text=text, **kwargs)
+
+        images_list = [images] if isinstance(images, Image) else images
+        return moondream_processor(images=images_list, text=text, **kwargs)
+
+    hf_model.processor = processor
+
+    # Expose the LM head for logprob extraction.
+    hf_model.model.get_output_embeddings = lambda: hf_model.model.model.text.lm_head
+
+    native_model = hf_model.model.model  # MoondreamModel instance
+
+    from torch.nn import functional as F
+
+    from vllm.model_executor.models.moondream3 import reconstruct_from_crops
+
+    # Resolve the placeholder tokens from the tokenizer instead of hard-coding.
+    image_placeholder_ids = moondream_processor.tokenizer.encode(
+        "<image>", add_special_tokens=False
+    )
+
+    def _normalize_tiling(tilings):
+        """Extract (h, w) tuple from various tiling container formats."""
+        tiling = tilings
+        if isinstance(tiling, torch.Tensor):
+            tiling = tuple(tiling.squeeze().tolist())
+        elif isinstance(tiling, (list, tuple)):
+            t0 = tiling[0]
+            if isinstance(t0, torch.Tensor):
+                tiling = tuple(t0.tolist())
+            elif isinstance(t0, (list, tuple)):
+                tiling = tuple(t0)
+        return tiling
+
+    def _encode_vision(pixel_values, tilings):
+        """Run preprocessed crops through vision encoder + projection."""
+        device = native_model.device
+        dtype = native_model.vision.pos_emb.dtype
+        config = native_model.config
+
+        pv = pixel_values
+        while pv.dim() > 4:
+            pv = pv.squeeze(0)
+        pv = pv.to(device=device, dtype=dtype)
+
+        features = native_model._vis_enc(pv)
+        grid_size = config.vision.crop_size // config.vision.enc_patch_size
+        global_feat = features[0]
+
+        if features.shape[0] > 1 and tilings is not None:
+            tiling = _normalize_tiling(tilings)
+            local = features[1:].view(-1, grid_size, grid_size, config.vision.enc_dim)
+            reconstructed = reconstruct_from_crops(
+                local,
+                tiling,
+                config.vision.overlap_margin,
+                patch_size=1,
+            )
+        else:
+            reconstructed = global_feat.view(
+                grid_size, grid_size, config.vision.enc_dim
+            )
+
+        return native_model._vis_proj(global_feat, reconstructed)
+
+    def _find_subsequence(seq, subseq):
+        """Find start index of subseq in seq, or None."""
+        n = len(subseq)
+        for i in range(len(seq) - n + 1):
+            if seq[i : i + n] == subseq:
+                return i
+        return None
+
+    def _generate(
+        self,
+        input_ids=None,
+        pixel_values=None,
+        tilings=None,
+        attention_mask=None,
+        **kwargs,
+    ):
+        max_new_tokens = kwargs.get("max_new_tokens", 128)
+        return_dict = kwargs.get("return_dict_in_generate", False)
+        output_hs = kwargs.get("output_hidden_states", False)
+
+        if pixel_values is None:
+            sequences = input_ids
+            if return_dict:
+                return types.SimpleNamespace(
+                    sequences=sequences,
+                    hidden_states=() if output_hs else None,
+                )
+            return sequences
+
+        # Processor may return lists; extract the single element.
+        if isinstance(pixel_values, (list, tuple)):
+            pixel_values = pixel_values[0]
+        if (
+            isinstance(tilings, (list, tuple))
+            and tilings
+            and not isinstance(tilings[0], int)
+        ):
+            tilings = tilings[0]
+
+        hf_model.model._setup_caches()
+        native_model.use_flex_decoding = False
+
+        device = native_model.device
+        config = native_model.config
+
+        with torch.inference_mode():
+            for block in native_model.text.blocks:
+                block.kv_cache.k_cache.zero_()
+                block.kv_cache.v_cache.zero_()
+
+            img_emb = _encode_vision(pixel_values, tilings)
+
+            bos_emb = F.embedding(
+                torch.tensor([[config.tokenizer.bos_id]], device=device),
+                native_model.text.wte,
+            )
+            img_input = torch.cat([bos_emb, img_emb.unsqueeze(0)], dim=1)
+            prefix_len = img_input.size(1)
+
+            mask = native_model.attn_mask[:, :, :prefix_len, :]
+            pos_ids = torch.arange(prefix_len, dtype=torch.long, device=device)
+            native_model._prefill(img_input, mask, pos_ids, None)
+
+            ids = input_ids.squeeze(0).tolist()
+            img_start = _find_subsequence(ids, image_placeholder_ids)
+
+            if img_start is None:
+                sequences = input_ids
+                if return_dict:
+                    return types.SimpleNamespace(
+                        sequences=sequences,
+                        hidden_states=() if output_hs else None,
+                    )
+                return sequences
+
+            prompt_tokens = ids[img_start + len(image_placeholder_ids) :]
+
+            if not prompt_tokens:
+                sequences = input_ids
+                if return_dict:
+                    return types.SimpleNamespace(
+                        sequences=sequences,
+                        hidden_states=() if output_hs else None,
+                    )
+                return sequences
+
+            prompt_tensor = torch.tensor([prompt_tokens], device=device)
+            prompt_emb = F.embedding(prompt_tensor, native_model.text.wte)
+            prompt_len = prompt_emb.size(1)
+
+            mask = native_model.attn_mask[:, :, prefix_len : prefix_len + prompt_len, :]
+            pos_ids = torch.arange(
+                prefix_len,
+                prefix_len + prompt_len,
+                dtype=torch.long,
+                device=device,
+            )
+            hidden = native_model._prefill(prompt_emb, mask, pos_ids, None)
+            pos = prefix_len + prompt_len
+
+            hidden_last = native_model.text.post_ln(hidden[:, -1:, :])
+            logits = native_model.text.lm_head(hidden_last.squeeze(1))
+
+            generated = []
+            all_hidden_states = []
+            # Record the hidden state that predicted each generated token.
+            prev_hs = hidden_last
+            for _ in range(max_new_tokens):
+                next_token = logits.argmax(dim=-1).item()
+                if next_token == 0:
+                    break
+                generated.append(next_token)
+                if output_hs:
+                    all_hidden_states.append((prev_hs,))
+
+                next_emb = F.embedding(
+                    torch.tensor([[next_token]], device=device),
+                    native_model.text.wte,
+                )
+                mask = native_model.attn_mask[:, :, pos : pos + 1, :]
+                pos_ids_step = torch.tensor([pos], dtype=torch.long, device=device)
+                hidden = native_model._prefill(next_emb, mask, pos_ids_step, None)
+                hidden_last = native_model.text.post_ln(hidden[:, -1:, :])
+                prev_hs = hidden_last
+                logits = native_model.text.lm_head(hidden_last.squeeze(1))
+                pos += 1
+
+            result_ids = ids + generated
+            sequences = torch.tensor([result_ids], device=device)
+
+            if return_dict:
+                return types.SimpleNamespace(
+                    sequences=sequences,
+                    hidden_states=tuple(all_hidden_states) if output_hs else None,
+                )
+            return sequences
+
+    hf_model.model.generate = types.MethodType(_generate, hf_model.model)
+    return hf_model
+
+
+def qianfan_ocr_hf_model_kwargs(model_name: str) -> dict:
+    """Return hf_model_kwargs with a patched config for QianfanOCR."""
+    from vllm.transformers_utils.configs.qianfan_ocr import QianfanOCRConfig
+
+    config = QianfanOCRConfig.from_pretrained(model_name)
+    vc = config.vision_config
+    if isinstance(vc.image_size, int):
+        vc.image_size = (vc.image_size, vc.image_size)
+    if isinstance(vc.patch_size, int):
+        vc.patch_size = (vc.patch_size, vc.patch_size)
+    return {"config": config}
+
+
+def qianfan_ocr_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
+    """Patches an HfRunner instance to run QianfanOCR model inference.
+
+    QianfanOCR shares the same architecture as InternVLChatModel, so the
+    patching logic mirrors ``internvl_patch_hf_runner``.  The only difference
+    is that we load the config via vllm's registered ``QianfanOCRConfig``
+    instead of relying on ``trust_remote_code``.
+    """
+
+    class QianfanOCRProcessor:
+        def __init__(self, hf_runner: HfRunner):
+            self.tokenizer = hf_runner.tokenizer
+
+            from vllm.transformers_utils.configs.qianfan_ocr import QianfanOCRConfig
+
+            self.config = QianfanOCRConfig.from_pretrained(hf_runner.model_name)
+            self.vision_config = self.config.vision_config
+            self.use_thumbnail = self.config.use_thumbnail
+            self.min_num = self.config.min_dynamic_patch
+            self.max_num = self.config.max_dynamic_patch
+            self.image_size = self.vision_config.image_size
+
+            # Compute num_image_token from config instead of model attribute,
+            # since the transformers-native model doesn't expose it.
+            image_size = self.config.force_image_size or self.vision_config.image_size
+            patch_size = self.vision_config.patch_size
+            downsample_ratio = self.config.downsample_ratio
+            self.num_image_token = int(
+                (image_size // patch_size) ** 2 * (downsample_ratio**2)
+            )
+
+        def __call__(
+            self,
+            text: str,
+            images: PIL.Image.Image | list[PIL.Image.Image] = None,
+            **kwargs,
+        ):
+            from vllm.transformers_utils.processors.internvl import (
+                image_to_pixel_values_internvl,
+            )
+
+            IMG_START = "<img>"
+            IMG_END = "</img>"
+            IMG_CONTEXT = "<IMG_CONTEXT>"
+
+            images = [images] if isinstance(images, PIL.Image.Image) else images
+            pixel_values_list = [
+                image_to_pixel_values_internvl(
+                    image,
+                    input_size=self.image_size,
+                    min_num=self.min_num,
+                    max_num=self.max_num,
+                    use_thumbnail=self.use_thumbnail,
+                )
+                for image in images
+            ]
+            num_patches_list = [pv.shape[0] for pv in pixel_values_list]
+            pixel_values = torch.cat(pixel_values_list, dim=0)
+
+            for num_patches in num_patches_list:
+                context_tokens = IMG_CONTEXT * self.num_image_token * num_patches
+                image_tokens = IMG_START + context_tokens + IMG_END
+                text = text.replace("<image>", image_tokens, 1)
+
+            prompt = self.tokenizer(text, return_tensors="pt")
+            prompt.update({"pixel_values": pixel_values})
+            return prompt
+
+    img_context_token_id = hf_model.tokenizer.convert_tokens_to_ids("<IMG_CONTEXT>")
+    hf_model.model.img_context_token_id = img_context_token_id
+    hf_model.processor = QianfanOCRProcessor(hf_model)
+    hf_model.model.get_output_embeddings = (
+        lambda: hf_model.model.language_model.get_output_embeddings()
+    )
+    hf_model.model.generate = types.MethodType(_internvl_generate, hf_model.model)
+    return hf_model
diff --git a/tests/models/multimodal/generation/vlm_utils/types.py b/tests/models/multimodal/generation/vlm_utils/types.py
index ae2f75481359..af48a1479bad 100644
--- a/tests/models/multimodal/generation/vlm_utils/types.py
+++ b/tests/models/multimodal/generation/vlm_utils/types.py
@@ -133,6 +133,7 @@ class VLMTestInfo(NamedTuple):
 
     # Exposed options for HF runner
     hf_model_kwargs: dict[str, Any] | None = None
+    hf_processor: Callable[[str], Any] | None = None
     # Indicates we should explicitly pass the EOS from the tokenizer
     use_tokenizer_eos: bool = False
     auto_cls: type[_BaseAutoModelClass] = AutoModelForCausalLM
@@ -196,6 +197,7 @@ def get_non_parametrized_runner_kwargs(self):
             "comparator": self.comparator,
             "get_stop_token_ids": self.get_stop_token_ids,
             "hf_model_kwargs": self.hf_model_kwargs,
+            "hf_processor": self.hf_processor,
             "stop_str": self.stop_str,
             "patch_hf_runner": self.patch_hf_runner,
         }
diff --git a/tests/models/multimodal/pooling/test_colqwen3.py b/tests/models/multimodal/pooling/test_colqwen3.py
index 2faac7fbfb61..9eefedc153c2 100644
--- a/tests/models/multimodal/pooling/test_colqwen3.py
+++ b/tests/models/multimodal/pooling/test_colqwen3.py
@@ -22,6 +22,11 @@
 
 from ....conftest import VllmRunner
 
+pytestmark = pytest.mark.skip(
+    reason="ColQwen3 model's weight tying is incompatible with "
+    "transformers v5 (missing all_tied_weights_keys)"
+)
+
 MODELS = [
     "TomoroAI/tomoro-colqwen3-embed-4b",
     "OpenSearch-AI/Ops-Colqwen3-4B",
diff --git a/tests/models/multimodal/pooling/test_intern_vit.py b/tests/models/multimodal/pooling/test_intern_vit.py
index cd457c62c0af..d7b67b8bdb6a 100644
--- a/tests/models/multimodal/pooling/test_intern_vit.py
+++ b/tests/models/multimodal/pooling/test_intern_vit.py
@@ -7,14 +7,22 @@
 from transformers import AutoConfig, AutoModel, CLIPImageProcessor
 
 from vllm.distributed import cleanup_dist_env_and_memory
+from vllm.platforms import current_platform
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 
 from ....conftest import ImageTestAssets
 
+pytestmark = pytest.mark.skip(
+    reason="InternVisionModel's custom code is incompatible with "
+    "transformers v5 (missing all_tied_weights_keys)"
+)
+
 # we use snapshot_download to prevent conflicts between
 # dynamic_module and trust_remote_code for hf_runner
 DOWNLOAD_PATTERN = ["*.json", "*.py", "*.safetensors", "*.txt", "*.model"]
 
+DEVICE_TYPE = current_platform.device_type
+
 
 @torch.inference_mode()
 def run_intern_vit_test(
@@ -39,9 +47,9 @@ def run_intern_vit_test(
 
     hf_model = AutoModel.from_pretrained(
         model, dtype=torch_dtype, trust_remote_code=True
-    ).to("cuda")
+    ).to(DEVICE_TYPE)
     hf_outputs_per_image = [
-        hf_model(pixel_value.to("cuda")).last_hidden_state
+        hf_model(pixel_value.to(DEVICE_TYPE)).last_hidden_state
         for pixel_value in pixel_values
     ]
 
@@ -53,9 +61,10 @@ def run_intern_vit_test(
     del hf_model
     cleanup_dist_env_and_memory()
 
-    vllm_model = vllm_model.to("cuda", torch_dtype)
+    vllm_model = vllm_model.to(DEVICE_TYPE, torch_dtype)
     vllm_outputs_per_image = [
-        vllm_model(pixel_values=pixel_value.to("cuda")) for pixel_value in pixel_values
+        vllm_model(pixel_values=pixel_value.to(DEVICE_TYPE))
+        for pixel_value in pixel_values
     ]
     del vllm_model
     cleanup_dist_env_and_memory()
diff --git a/tests/models/multimodal/pooling/test_jinavl_reranker.py b/tests/models/multimodal/pooling/test_jinavl_reranker.py
index 035ca62058a8..18a02625ea44 100644
--- a/tests/models/multimodal/pooling/test_jinavl_reranker.py
+++ b/tests/models/multimodal/pooling/test_jinavl_reranker.py
@@ -15,6 +15,11 @@
 
 from ....conftest import HfRunner, VllmRunner
 
+pytestmark = pytest.mark.skip(
+    reason="jinaai/jina-reranker-m0 custom code is incompatible with "
+    "transformers v5 (missing all_tied_weights_keys)"
+)
+
 MODELS = ["jinaai/jina-reranker-m0"]
 
 MM_PROCESSOR_KWARGS = {
diff --git a/tests/models/multimodal/pooling/test_prithvi_mae.py b/tests/models/multimodal/pooling/test_prithvi_mae.py
index 19154c27da9a..1a466931a0e5 100644
--- a/tests/models/multimodal/pooling/test_prithvi_mae.py
+++ b/tests/models/multimodal/pooling/test_prithvi_mae.py
@@ -1,11 +1,18 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import importlib.util
+
 import pytest
 import torch
 
 from ....conftest import VllmRunner
 
+pytestmark = pytest.mark.skipif(
+    importlib.util.find_spec("terratorch") is None,
+    reason="terratorch unavailable while PyPI has `lightning` quarantined; see #41376",
+)
+
 
 def _run_test(
     vllm_runner: type[VllmRunner],
diff --git a/tests/models/multimodal/pooling/test_radio.py b/tests/models/multimodal/pooling/test_radio.py
index 86b5b1b5d1f9..fcab077fbba8 100644
--- a/tests/models/multimodal/pooling/test_radio.py
+++ b/tests/models/multimodal/pooling/test_radio.py
@@ -8,6 +8,7 @@
 
 from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.model_executor.models.radio import RadioModel
+from vllm.platforms import current_platform
 from vllm.transformers_utils.configs.radio import RadioConfig
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 
@@ -17,6 +18,8 @@
 # dynamic_module and trust_remote_code for hf_runner
 DOWNLOAD_PATTERN = ["*.json", "*.py", "*.safetensors", "*.txt", "*.model"]
 
+DEVICE_TYPE = current_platform.device_type
+
 
 @torch.inference_mode()
 def run_radio_test(
@@ -51,7 +54,7 @@ def run_radio_test(
         config=hf_config,
         dtype=torch_dtype,
         trust_remote_code=True,
-    ).to("cuda")
+    ).to(DEVICE_TYPE)
     hf_model.eval()
 
     # A HF model has image normalization as a part of model's forward
@@ -62,7 +65,7 @@ def run_radio_test(
     hf_model.make_preprocessor_external()
 
     hf_outputs_per_image = [
-        hf_model(pixel_value.to("cuda")) for pixel_value in pixel_values
+        hf_model(pixel_value.to(DEVICE_TYPE)) for pixel_value in pixel_values
     ]
 
     vllm_config = RadioConfig(
@@ -71,10 +74,11 @@ def run_radio_test(
     )
     vllm_model = RadioModel(vllm_config)
     vllm_model.load_weights(hf_model.state_dict())
-    vllm_model = vllm_model.to("cuda", torch_dtype)
+    vllm_model = vllm_model.to(DEVICE_TYPE, torch_dtype)
 
     vllm_outputs_per_image = [
-        vllm_model(pixel_values=pixel_value.to("cuda")) for pixel_value in pixel_values
+        vllm_model(pixel_values=pixel_value.to(DEVICE_TYPE))
+        for pixel_value in pixel_values
     ]
     del vllm_model, hf_model
     cleanup_dist_env_and_memory()
diff --git a/tests/models/multimodal/processing/test_audioflamingo3.py b/tests/models/multimodal/processing/test_audioflamingo3.py
index 24311e5212b2..3a6da75ed5b6 100644
--- a/tests/models/multimodal/processing/test_audioflamingo3.py
+++ b/tests/models/multimodal/processing/test_audioflamingo3.py
@@ -140,88 +140,3 @@ def test_audio_token_count_matches_hf_processor_math():
         _count_audio_tokens_from_mask(feature_attention_mask, chunk_counts, 0) == 1499
     )
     assert _count_audio_tokens_from_mask(feature_attention_mask, chunk_counts, 1) == 375
-
-
-def test_audio_feature_pipeline_matches_hf_small_config():
-    from transformers.models.audioflamingo3 import (
-        modeling_audioflamingo3 as hf_audioflamingo3_modeling,
-    )
-    from transformers.models.audioflamingo3.configuration_audioflamingo3 import (
-        AudioFlamingo3Config,
-    )
-
-    from vllm.model_executor.models.audioflamingo3 import (
-        AudioFlamingo3Encoder,
-        AudioFlamingo3MultiModalProjector,
-        _build_audio_encoder_attention_mask,
-        _flatten_valid_audio_embeddings,
-    )
-
-    text_config = {
-        "model_type": "qwen2",
-        "intermediate_size": 64,
-        "initializer_range": 0.02,
-        "hidden_size": 32,
-        "max_position_embeddings": 1024,
-        "num_hidden_layers": 2,
-        "num_attention_heads": 4,
-        "num_key_value_heads": 2,
-        "vocab_size": 128,
-        "pad_token_id": 1,
-        "use_mrope": False,
-    }
-    audio_config = {
-        "hidden_size": 16,
-        "num_attention_heads": 4,
-        "intermediate_size": 32,
-        "num_hidden_layers": 2,
-        "num_mel_bins": 80,
-        "max_source_positions": 1500,
-        "dropout": 0.0,
-        "attention_dropout": 0.0,
-        "activation_dropout": 0.0,
-        "encoder_layerdrop": 0.0,
-    }
-
-    torch.manual_seed(0)
-    config = AudioFlamingo3Config(
-        text_config=text_config,
-        audio_config=audio_config,
-        audio_token_id=0,
-    )
-    hf_model = hf_audioflamingo3_modeling.AudioFlamingo3ForConditionalGeneration(
-        config
-    ).eval()
-
-    vllm_encoder = AudioFlamingo3Encoder(config.audio_config).eval()
-    vllm_encoder.load_state_dict(hf_model.audio_tower.state_dict())
-
-    vllm_projector = AudioFlamingo3MultiModalProjector(config).eval()
-    vllm_projector.load_state_dict(hf_model.multi_modal_projector.state_dict())
-
-    input_features = torch.randn(3, 80, 3000)
-    feature_attention_mask = torch.zeros(3, 3000, dtype=torch.bool)
-    feature_attention_mask[0, :3000] = True
-    feature_attention_mask[1, :2500] = True
-    feature_attention_mask[2, :1500] = True
-
-    hf_output = hf_model.get_audio_features(
-        input_features,
-        feature_attention_mask,
-        return_dict=True,
-    ).pooler_output
-    vllm_attention_mask = _build_audio_encoder_attention_mask(
-        feature_attention_mask,
-        dtype=vllm_encoder.conv1.weight.dtype,
-        device=vllm_encoder.conv1.weight.device,
-    )
-    vllm_hidden_states = vllm_encoder(
-        input_features,
-        attention_mask=vllm_attention_mask,
-    )
-    vllm_output, _ = _flatten_valid_audio_embeddings(
-        vllm_projector(vllm_hidden_states),
-        feature_attention_mask,
-    )
-
-    torch.testing.assert_close(vllm_output, hf_output)
diff --git a/tests/models/multimodal/processing/test_common.py b/tests/models/multimodal/processing/test_common.py
index cce69e15b00b..914e1a38ee80 100644
--- a/tests/models/multimodal/processing/test_common.py
+++ b/tests/models/multimodal/processing/test_common.py
@@ -311,6 +311,9 @@ def _to_dummy_options(modality: str, count: int) -> BaseDummyOptions:
             baseline_processor,
             cached_processor,
             batch_idx,
+            hit_rate,
+            num_batches,
+            simplify_rate,
         )
 
 
@@ -320,6 +323,9 @@ def _test_processing_correctness_one(
     baseline_processor: BaseMultiModalProcessor,
     cached_processor: BaseMultiModalProcessor,
     batch_idx: int,
+    hit_rate: float,
+    num_batches: int,
+    simplify_rate: float,
 ):
     model_type = model_config.hf_config.model_type
 
@@ -343,7 +349,11 @@ def _test_processing_correctness_one(
         baseline_tokenized_result,
         cached_tokenized_result,
         ignore_mm_keys=ignore_mm_keys,
-        msg=f"Failed ({batch_idx=}, {token_prompt=}, {mm_data=})",
+        msg=(
+            f"Failed ({batch_idx=}, {hit_rate=}, "
+            f"{num_batches=}, {simplify_rate=}, "
+            f"{text_prompt=}, {token_prompt=}, {mm_data=})"
+        ),
     )
 
     if text_prompt is not None:
@@ -362,21 +372,33 @@ def _test_processing_correctness_one(
             baseline_text_result,
             cached_text_result,
             ignore_mm_keys=ignore_mm_keys,
-            msg=f"Failed ({batch_idx=}, {text_prompt=}, {mm_data=})",
+            msg=(
+                f"Failed ({batch_idx=}, {hit_rate=}, "
+                f"{num_batches=}, {simplify_rate=}, "
+                f"{text_prompt=}, {token_prompt=}, {mm_data=})"
+            ),
         )
 
         _assert_inputs_equal(
             baseline_text_result,
             baseline_tokenized_result,
             ignore_mm_keys=ignore_mm_keys,
-            msg=f"Failed ({batch_idx=}, {text_prompt=}, {token_prompt=}, {mm_data=})",
+            msg=(
+                f"Failed ({batch_idx=}, {hit_rate=}, "
+                f"{num_batches=}, {simplify_rate=}, "
+                f"{text_prompt=}, {token_prompt=}, {mm_data=})"
+            ),
         )
 
         _assert_inputs_equal(
             cached_text_result,
             cached_tokenized_result,
             ignore_mm_keys=ignore_mm_keys,
-            msg=f"Failed ({batch_idx=}, {text_prompt=}, {token_prompt=}, {mm_data=})",
+            msg=(
+                f"Failed ({batch_idx=}, {hit_rate=}, "
+                f"{num_batches=}, {simplify_rate=}, "
+                f"{text_prompt=}, {token_prompt=}, {mm_data=})"
+            ),
         )
 
 
@@ -408,6 +430,8 @@ def test_processing_correctness(
             "correctness test as is. Let's revisit adapting this "
             "test once more realtime models exist."
         )
+    if model_id == "CohereLabs/cohere-transcribe-03-2026":
+        pytest.skip("Fix later")
 
     _test_processing_correctness(
         model_id,
diff --git a/tests/models/multimodal/processing/test_gemma4.py b/tests/models/multimodal/processing/test_gemma4.py
index 808fab6a030f..8541701ae101 100644
--- a/tests/models/multimodal/processing/test_gemma4.py
+++ b/tests/models/multimodal/processing/test_gemma4.py
@@ -1,7 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from collections.abc import Mapping
+
 import pytest
+from PIL import Image as PILImage
 
 from vllm.multimodal import MULTIMODAL_REGISTRY
 
@@ -12,6 +15,152 @@
 GEMMA4_MODEL_ID = "google/gemma-4-E2B-it"
 
 
+@pytest.mark.parametrize(
+    "image_width,image_height,max_soft_tokens",
+    [
+        # Production repro: a 3x900 image (extreme aspect ratio) made the
+        # prompt-side estimator return 289 while the HF Gemma 4 image
+        # processor's vision tower output capped at 280, producing the
+        # "Attempted to assign 280 multimodal tokens to 289 placeholders"
+        # mismatch that crashed EngineCore.
+        (900, 3, 280),
+        (3, 900, 280),
+        # Same pathology should hold for the video-frame budget (70 tokens).
+        (900, 3, 70),
+        # And for any other supported budget.
+        (4000, 2, 1120),
+    ],
+)
+@pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
+def test_compute_num_soft_tokens_does_not_exceed_max_soft_tokens(
+    model_id: str,
+    image_width: int,
+    image_height: int,
+    max_soft_tokens: int,
+):
+    """Regression for the Gemma 3/4 multimodal crash.
+
+    `_compute_num_soft_tokens` must never return a value larger than
+    `max_soft_tokens`. The HF Gemma 4 image processor clamps its vision
+    tower output to that value; if the prompt-side estimator returns more,
+    the prompt has more `image` placeholder tokens than the encoder will
+    fill, and `_merge_multimodal_embeddings` raises `ValueError` deep in
+    the model forward.
+    """
+    ctx = build_model_context(
+        model_id,
+        mm_processor_kwargs={"do_pan_and_scan": True},
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+
+    num_soft_tokens = processor.info._compute_num_soft_tokens(
+        image_width=image_width,
+        image_height=image_height,
+        max_soft_tokens=max_soft_tokens,
+    )
+
+    assert num_soft_tokens <= max_soft_tokens, (
+        f"_compute_num_soft_tokens returned {num_soft_tokens} for "
+        f"image_width={image_width}, image_height={image_height}, "
+        f"max_soft_tokens={max_soft_tokens} — exceeds the cap that the HF "
+        f"image processor enforces on its vision tower output. This is "
+        f"the placeholder/encoder count mismatch that crashes EngineCore."
+    )
+
+
+@pytest.mark.parametrize(
+    ("mm_processor_kwargs", "expected_image_tokens"),
+    [
+        ({}, 280),
+        ({"max_soft_tokens": 70}, 70),
+        ({"max_soft_tokens": 280}, 280),
+        ({"max_soft_tokens": 1120}, 1120),
+        ({"images_kwargs": {"max_soft_tokens": 560}}, 560),
+        ({"images_kwargs": None}, 280),
+        ({"images_kwargs": "not-a-dict"}, 280),
+    ],
+)
+@pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
+def test_get_mm_max_tokens_per_item_respects_configured_max_soft_tokens(
+    model_id: str,
+    mm_processor_kwargs: dict[str, object],
+    expected_image_tokens: int,
+):
+    ctx = build_model_context(
+        model_id,
+        mm_processor_kwargs=mm_processor_kwargs,
+        limit_mm_per_prompt={"image": 1, "video": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+
+    tokens = processor.info.get_mm_max_tokens_per_item(
+        seq_len=ctx.model_config.max_model_len,
+        mm_counts={"image": 1, "video": 1},
+    )
+
+    assert tokens is not None
+    assert tokens["image"] == expected_image_tokens
+    assert tokens["video"] == 32 * (70 + 2 + 6)
+
+
+@pytest.mark.parametrize(
+    ("limit_mm_per_prompt", "expected_video_tokens"),
+    [
+        ({"video": 1}, 32 * (70 + 2 + 6)),
+        ({"video": {"count": 1}}, 32 * (70 + 2 + 6)),
+        ({"video": {"count": 1, "num_frames": 1}}, 1 * (70 + 2 + 6)),
+        ({"video": {"count": 1, "num_frames": 8}}, 8 * (70 + 2 + 6)),
+        ({"video": {"count": 1, "num_frames": 32}}, 32 * (70 + 2 + 6)),
+        ({"video": {"count": 1, "num_frames": 40}}, 32 * (70 + 2 + 6)),
+    ],
+)
+@pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
+def test_get_mm_max_tokens_per_item_respects_configured_video_num_frames(
+    model_id: str,
+    limit_mm_per_prompt: Mapping[str, int | Mapping[str, int]],
+    expected_video_tokens: int,
+):
+    ctx = build_model_context(
+        model_id,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+
+    tokens = processor.info.get_mm_max_tokens_per_item(
+        seq_len=ctx.model_config.max_model_len,
+        mm_counts={"video": 1},
+    )
+
+    assert tokens is not None
+    assert tokens["image"] == 280
+    assert tokens["video"] == expected_video_tokens
+
+
+@pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
+def test_get_prompt_updates_respects_nested_max_soft_tokens(model_id: str):
+    ctx = build_model_context(
+        model_id,
+        mm_processor_kwargs={"images_kwargs": {"max_soft_tokens": 560}},
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+    image = PILImage.new("RGB", (1000, 1000), color="white")
+    image_size = image.size
+    mm_items = processor.info.parse_mm_data({"image": image})
+
+    prompt_update = processor._get_prompt_updates(mm_items, {}, {})[0]
+    replacement = prompt_update.resolve(0).content.full
+    expected = processor.info.get_image_repl(
+        image_width=image_size[0],
+        image_height=image_size[1],
+        processor=processor.info.get_hf_processor(),
+        max_soft_tokens=560,
+    ).full
+
+    assert replacement == expected
+
+
 @pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
 def test_limit_mm_per_prompt(
     image_assets: ImageTestAssets,
diff --git a/tests/models/multimodal/processing/test_glm4_1v.py b/tests/models/multimodal/processing/test_glm4_1v.py
index f70d00524275..5798c5663472 100644
--- a/tests/models/multimodal/processing/test_glm4_1v.py
+++ b/tests/models/multimodal/processing/test_glm4_1v.py
@@ -6,7 +6,7 @@
 from vllm.assets.video import VideoAsset
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import batched_tensors_equal
-from vllm.multimodal.video import OpenCVDynamicVideoBackend, OpenCVVideoBackend
+from vllm.multimodal.video import DynamicVideoBackend, VideoBackend
 
 from ...utils import build_model_context
 
@@ -70,9 +70,11 @@ def test_processor_override(
 
 @pytest.mark.parametrize("model_id", ["zai-org/GLM-4.1V-9B-Thinking"])
 @pytest.mark.parametrize("fps", [2])
+@pytest.mark.parametrize("backend", ["opencv", "pyav"])
 def test_video_loader_consistency(
     model_id: str,
     fps: int,
+    backend: str,
 ):
     """
     Ensure dynamic video loader (pre-sampled by loader) and normal video
@@ -93,9 +95,11 @@ def test_video_loader_consistency(
     with open(video_path, "rb") as f:
         video_bytes = f.read()
 
-    static_video, static_metadata = OpenCVVideoBackend.load_bytes(video_bytes)
-    dynamic_video, dynamic_metadata = OpenCVDynamicVideoBackend.load_bytes(
-        video_bytes, fps=fps
+    static_video, static_metadata = VideoBackend.load_bytes(
+        video_bytes, backend=backend
+    )
+    dynamic_video, dynamic_metadata = DynamicVideoBackend.load_bytes(
+        video_bytes, fps=fps, backend=backend
     )
 
     # pre-sampled loader shouldn't read all frames
diff --git a/tests/models/multimodal/processing/test_molmo2.py b/tests/models/multimodal/processing/test_molmo2.py
new file mode 100644
index 000000000000..c12f70c2765a
--- /dev/null
+++ b/tests/models/multimodal/processing/test_molmo2.py
@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from types import SimpleNamespace
+
+import torch
+
+from vllm.model_executor.models.molmo2 import build_flat_image_bool_length
+
+
+def test_build_flat_image_bool_length_matches_molmoweb_processor_tokens():
+    hf_config = SimpleNamespace(
+        image_patch_id=151938,
+        low_res_image_start_token_id=151940,
+        image_start_token_id=151936,
+        image_col_id=151939,
+        image_end_token_id=151937,
+    )
+    image_grids = torch.tensor([[14, 14, 14, 23]], dtype=torch.long)
+
+    image_tokens, num_image_tokens = build_flat_image_bool_length(
+        image_grids,
+        hf_config,
+        image_use_col_tokens=True,
+        use_single_crop_col_tokens=None,
+        use_single_crop_start_token=False,
+    )
+
+    assert num_image_tokens.tolist() == [550]
+    assert len(image_tokens) == 550
+    assert image_tokens[0].item() == hf_config.image_start_token_id
+    assert (image_tokens == hf_config.image_col_id).sum().item() == 28
+
+
+def test_build_flat_image_bool_length_respects_disabled_col_tokens():
+    hf_config = SimpleNamespace(
+        image_patch_id=151938,
+        low_res_image_start_token_id=151940,
+        image_start_token_id=151936,
+        image_col_id=151939,
+        image_end_token_id=151937,
+    )
+    image_grids = torch.tensor([[2, 3, 5, 7]], dtype=torch.long)
+
+    image_tokens, num_image_tokens = build_flat_image_bool_length(
+        image_grids,
+        hf_config,
+        image_use_col_tokens=False,
+        use_single_crop_col_tokens=False,
+        use_single_crop_start_token=True,
+    )
+
+    assert num_image_tokens.tolist() == [45]
+    assert len(image_tokens) == 45
+    assert image_tokens[0].item() == hf_config.low_res_image_start_token_id
+    assert (image_tokens == hf_config.image_col_id).sum().item() == 0
diff --git a/tests/models/multimodal/processing/test_moondream3.py b/tests/models/multimodal/processing/test_moondream3.py
new file mode 100644
index 000000000000..a6284ae45fd1
--- /dev/null
+++ b/tests/models/multimodal/processing/test_moondream3.py
@@ -0,0 +1,553 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for Moondream3 multimodal processing.
+
+Includes:
+- Processor creation and application tests
+- Image tokenization and placeholder expansion tests
+- Tiling and cropping logic tests (CPU-based)
+- Pixel normalization tests
+"""
+
+import numpy as np
+import pytest
+import torch
+
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+from ....conftest import ImageTestAssets
+from ...utils import build_model_context
+
+MOONDREAM3_MODEL_ID = "moondream/moondream3-preview"
+# Expected multimodal prefix: BOS + 729 image tokens.
+EXPECTED_IMAGE_TOKENS = 730
+# Vision encoder constants
+CROP_SIZE = 378
+PATCH_SIZE = 14
+MAX_CROPS = 12
+
+
+@pytest.mark.parametrize("model_id", [MOONDREAM3_MODEL_ID])
+def test_processor_creation(model_id: str):
+    """Test that Moondream3 processor can be created."""
+    ctx = build_model_context(
+        model_id,
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+    assert processor is not None
+
+
+@pytest.mark.parametrize("model_id", [MOONDREAM3_MODEL_ID])
+def test_processor_apply(
+    image_assets: ImageTestAssets,
+    model_id: str,
+):
+    """Test that Moondream3 processor can process inputs.
+
+    NOTE: The prompt includes the leading BOS token because Moondream3
+    pre-fills BOS and image embeddings together.
+    """
+    ctx = build_model_context(
+        model_id,
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+
+    prompt = "<|endoftext|><image><|md_reserved_0|>query<|md_reserved_1|>What is this?<|md_reserved_2|>"  # noqa: E501
+    mm_data = {"image": [image_assets[0].pil_image]}
+
+    processed_inputs = processor(
+        prompt,
+        mm_items=processor.info.parse_mm_data(mm_data),
+        hf_processor_mm_kwargs={},
+    )
+
+    assert "prompt_token_ids" in processed_inputs
+    image_placeholders = processed_inputs["mm_placeholders"]["image"]
+    assert len(image_placeholders) == 1
+    assert image_placeholders[0].length == EXPECTED_IMAGE_TOKENS
+
+
+@pytest.mark.parametrize("model_id", [MOONDREAM3_MODEL_ID])
+def test_processor_pixel_values(
+    image_assets: ImageTestAssets,
+    model_id: str,
+):
+    """Test that pixel values are correctly produced."""
+    ctx = build_model_context(
+        model_id,
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+
+    prompt = "<|endoftext|><image><|md_reserved_0|>query<|md_reserved_1|>What is this?<|md_reserved_2|>"  # noqa: E501
+    mm_data = {"image": [image_assets[0].pil_image]}
+
+    processed_inputs = processor(
+        prompt,
+        mm_items=processor.info.parse_mm_data(mm_data),
+        hf_processor_mm_kwargs={},
+    )
+
+    # Check mm_kwargs contains pixel_values
+    mm_kwargs = processed_inputs.get("mm_kwargs")
+    assert mm_kwargs is not None
+    mm_data_result = mm_kwargs.get_data()
+    assert "pixel_values" in mm_data_result
+
+    # Verify pixel_values shape
+    pixel_values = mm_data_result["pixel_values"]
+    assert pixel_values.dim() == 5  # [batch, num_crops, C, H, W]
+    assert pixel_values.shape[2] == 3  # RGB channels
+    assert pixel_values.shape[3] == 378  # crop height
+    assert pixel_values.shape[4] == 378  # crop width
+
+
+@pytest.mark.parametrize("model_id", [MOONDREAM3_MODEL_ID])
+def test_processor_image_token_expansion(
+    image_assets: ImageTestAssets,
+    model_id: str,
+):
+    """Test that <image> placeholder is expanded to correct number of tokens."""
+    ctx = build_model_context(
+        model_id,
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+
+    prompt = "<|endoftext|><image><|md_reserved_0|>query<|md_reserved_1|>Describe.<|md_reserved_2|>"  # noqa: E501
+    mm_data = {"image": [image_assets[0].pil_image]}
+
+    processed_inputs = processor(
+        prompt,
+        mm_items=processor.info.parse_mm_data(mm_data),
+        hf_processor_mm_kwargs={},
+    )
+    image_placeholders = processed_inputs["mm_placeholders"]["image"]
+    assert len(image_placeholders) == 1
+    assert image_placeholders[0].length == EXPECTED_IMAGE_TOKENS
+
+
+@pytest.mark.parametrize("model_id", [MOONDREAM3_MODEL_ID])
+def test_multi_crop_tiling(
+    model_id: str,
+):
+    """Test that large images produce correct multi-crop tiling."""
+    from PIL import Image
+
+    from vllm.transformers_utils.processors.moondream3 import Moondream3Processor
+
+    processor = Moondream3Processor.from_pretrained(model_id, trust_remote_code=True)
+
+    # Create a large image that requires multiple crops
+    large_image = Image.new("RGB", (1000, 1000), color="blue")
+    pixel_values, tiling = processor.preprocess_image(large_image)
+
+    # Large images should produce more than 1x1 tiling
+    assert tiling[0] >= 1 and tiling[1] >= 1
+    # Check that we have global crop + local crops
+    expected_crops = tiling[0] * tiling[1] + 1
+    assert pixel_values.shape[0] == expected_crops
+
+
+@pytest.mark.parametrize(
+    "image_size",
+    [
+        (500, 500),
+        (800, 600),
+        (1920, 1080),
+    ],
+)
+@pytest.mark.parametrize("model_id", [MOONDREAM3_MODEL_ID])
+def test_tiling_various_sizes(
+    image_size: tuple[int, int],
+    model_id: str,
+):
+    """Test tiling with various image sizes."""
+    from PIL import Image
+
+    from vllm.transformers_utils.processors.moondream3 import Moondream3Processor
+
+    processor = Moondream3Processor.from_pretrained(model_id, trust_remote_code=True)
+
+    width, height = image_size
+    image = Image.new("RGB", (width, height), color="red")
+    pixel_values, tiling = processor.preprocess_image(image)
+
+    # Basic shape checks
+    assert pixel_values.dim() == 4  # [num_crops, C, H, W]
+    assert pixel_values.shape[1] == 3  # RGB
+    assert pixel_values.shape[2] == 378  # crop height
+    assert pixel_values.shape[3] == 378  # crop width
+
+    # Tiling should respect max_crops (12)
+    assert tiling[0] * tiling[1] <= 12
+
+
+@pytest.mark.parametrize("model_id", [MOONDREAM3_MODEL_ID])
+def test_pixel_normalization(
+    model_id: str,
+):
+    """Test that pixel values are normalized to [-1, 1] range."""
+    from PIL import Image
+
+    from vllm.transformers_utils.processors.moondream3 import Moondream3Processor
+
+    processor = Moondream3Processor.from_pretrained(model_id, trust_remote_code=True)
+
+    # Create test image
+    image = Image.new("RGB", (378, 378), color="green")
+    pixel_values, _ = processor.preprocess_image(image)
+
+    # Normalization: (x - 0.5) / 0.5 = 2*x - 1
+    # For input [0, 1], output should be [-1, 1]
+    assert pixel_values.min() >= -1.0
+    assert pixel_values.max() <= 1.0
+
+
+@pytest.mark.parametrize("model_id", [MOONDREAM3_MODEL_ID])
+def test_chat_template_with_image(
+    image_assets: ImageTestAssets,
+    model_id: str,
+):
+    """Test that chat template correctly formats BOS + image + prompt."""
+    ctx = build_model_context(
+        model_id,
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+    tokenizer = ctx.tokenizer
+
+    # Use the chat template format
+    prompt = "<|endoftext|><image><|md_reserved_0|>query<|md_reserved_1|>What is this?<|md_reserved_2|>"  # noqa: E501
+    mm_data = {"image": [image_assets[0].pil_image]}
+
+    processed_inputs = processor(
+        prompt,
+        mm_items=processor.info.parse_mm_data(mm_data),
+        hf_processor_mm_kwargs={},
+    )
+    token_ids = processed_inputs["prompt_token_ids"]
+
+    # BOS token (<|endoftext|>) should be token ID 0
+    bos_token_id = tokenizer.encode("<|endoftext|>", add_special_tokens=False)[0]
+    assert bos_token_id == 0
+
+    # First token should be BOS
+    assert token_ids[0] == bos_token_id
+
+
+@pytest.mark.parametrize(
+    "content",
+    [
+        pytest.param(
+            [
+                {
+                    "type": "image_url",
+                    "image_url": {"url": "https://example.invalid/image.png"},
+                },
+                {"type": "text", "text": "What is in this image?"},
+            ],
+            id="image-first",
+        ),
+        pytest.param(
+            [
+                {"type": "text", "text": "What is in this image?"},
+                {
+                    "type": "image_url",
+                    "image_url": {"url": "https://example.invalid/image.png"},
+                },
+            ],
+            id="text-first",
+        ),
+    ],
+)
+@pytest.mark.parametrize("model_id", [MOONDREAM3_MODEL_ID])
+def test_chat_template_content_list_uses_moondream_image_prefix(
+    image_assets: ImageTestAssets,
+    content: list[dict[str, object]],
+    model_id: str,
+):
+    ctx = build_model_context(
+        model_id,
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+    hf_processor = processor.info.get_hf_processor()
+
+    prompt = hf_processor.tokenizer.apply_chat_template(
+        [{"role": "user", "content": content}],
+        chat_template=hf_processor.chat_template,
+        tokenize=False,
+    )
+
+    expected_prompt = (
+        "<|endoftext|><image><|md_reserved_0|>query<|md_reserved_1|>"
+        "What is in this image?<|md_reserved_2|>"
+    )
+    assert prompt == expected_prompt
+
+    processed_inputs = processor(
+        prompt,
+        mm_items=processor.info.parse_mm_data({"image": [image_assets[0].pil_image]}),
+        hf_processor_mm_kwargs={},
+    )
+    image_placeholders = processed_inputs["mm_placeholders"]["image"]
+    assert len(image_placeholders) == 1
+    assert image_placeholders[0].length == EXPECTED_IMAGE_TOKENS
+
+
+@pytest.mark.parametrize("model_id", [MOONDREAM3_MODEL_ID])
+def test_bos_token_always_first(
+    image_assets: ImageTestAssets,
+    model_id: str,
+):
+    """Test that BOS token (ID 0) is always at position 0."""
+    ctx = build_model_context(
+        model_id,
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+
+    # Start with BOS token explicitly
+    prompt = "<|endoftext|><image><|md_reserved_0|>query<|md_reserved_1|>Describe this image.<|md_reserved_2|>"  # noqa: E501
+    mm_data = {"image": [image_assets[0].pil_image]}
+
+    processed_inputs = processor(
+        prompt,
+        mm_items=processor.info.parse_mm_data(mm_data),
+        hf_processor_mm_kwargs={},
+    )
+    token_ids = processed_inputs["prompt_token_ids"]
+
+    # Token ID 0 (<|endoftext|>) should be the first token
+    assert token_ids[0] == 0, (
+        f"Expected BOS token (0) at position 0, got {token_ids[0]}"
+    )
+
+
+@pytest.mark.parametrize("model_id", [MOONDREAM3_MODEL_ID])
+def test_processor_with_small_image(
+    model_id: str,
+):
+    """Test processor with image smaller than crop size."""
+    from PIL import Image
+
+    from vllm.transformers_utils.processors.moondream3 import Moondream3Processor
+
+    processor = Moondream3Processor.from_pretrained(model_id, trust_remote_code=True)
+
+    # Small image (smaller than crop size)
+    small_image = Image.new("RGB", (100, 100), color="yellow")
+    pixel_values, tiling = processor.preprocess_image(small_image)
+
+    # Small images should use 1x1 tiling
+    assert tiling == (1, 1)
+    # Should have 2 crops (global + 1 local)
+    assert pixel_values.shape[0] == 2
+
+
+@pytest.mark.parametrize(
+    "image_kind",
+    [
+        pytest.param("numpy_hwc", id="numpy-hwc"),
+        pytest.param("numpy_chw", id="numpy-chw"),
+        pytest.param("torch_chw", id="torch-chw"),
+    ],
+)
+@pytest.mark.parametrize("model_id", [MOONDREAM3_MODEL_ID])
+def test_preprocess_image_accepts_non_pil_inputs(
+    image_assets: ImageTestAssets,
+    image_kind: str,
+    model_id: str,
+):
+    from vllm.transformers_utils.processors.moondream3 import Moondream3Processor
+
+    processor = Moondream3Processor.from_pretrained(model_id, trust_remote_code=True)
+    pil_image = image_assets[0].pil_image.convert("RGB")
+    hwc_array = np.asarray(pil_image)
+    expected_pixel_values, expected_tiling = processor.preprocess_image(pil_image)
+
+    if image_kind == "numpy_hwc":
+        image = hwc_array
+    elif image_kind == "numpy_chw":
+        image = np.transpose(hwc_array, (2, 0, 1))
+    else:
+        image = torch.from_numpy(np.transpose(hwc_array, (2, 0, 1)).copy())
+
+    pixel_values, tiling = processor.preprocess_image(image)
+
+    assert tiling == expected_tiling
+    assert pixel_values.shape == expected_pixel_values.shape
+    assert pixel_values.dtype == torch.bfloat16
+    assert torch.equal(pixel_values, expected_pixel_values)
+
+
+@pytest.mark.parametrize("image_kind", ["numpy_chw", "torch_chw"])
+@pytest.mark.parametrize("model_id", [MOONDREAM3_MODEL_ID])
+def test_processor_apply_accepts_non_pil_image_inputs(
+    image_assets: ImageTestAssets,
+    image_kind: str,
+    model_id: str,
+):
+    ctx = build_model_context(
+        model_id,
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+
+    prompt = "<|endoftext|><image><|md_reserved_0|>query<|md_reserved_1|>What is this?<|md_reserved_2|>"  # noqa: E501
+    hwc_array = np.asarray(image_assets[0].pil_image.convert("RGB"))
+    chw_array = np.transpose(hwc_array, (2, 0, 1)).copy()
+    image = chw_array if image_kind == "numpy_chw" else torch.from_numpy(chw_array)
+
+    processed_inputs = processor(
+        prompt,
+        mm_items=processor.info.parse_mm_data({"image": [image]}),
+        hf_processor_mm_kwargs={},
+    )
+
+    image_placeholders = processed_inputs["mm_placeholders"]["image"]
+    assert len(image_placeholders) == 1
+    assert image_placeholders[0].length == EXPECTED_IMAGE_TOKENS
+
+    mm_kwargs = processed_inputs["mm_kwargs"].get_data()
+    assert mm_kwargs["pixel_values"].shape[2:] == (3, 378, 378)
+
+
+class TestMoondream3TilingLogic:
+    """CPU-based tests for Moondream3 tiling selection logic.
+
+    These tests validate the select_tiling() function which determines
+    how images are divided into crops for the vision encoder.
+    """
+
+    def test_small_image_no_tiling(self):
+        """Small images should use 1x1 tiling."""
+        from vllm.transformers_utils.processors.moondream3 import select_tiling
+
+        tiling = select_tiling(
+            height=300, width=300, crop_size=CROP_SIZE, max_crops=MAX_CROPS
+        )
+        assert tiling == (1, 1)
+
+    def test_exact_crop_size(self):
+        """Image exactly at crop size should use 1x1."""
+        from vllm.transformers_utils.processors.moondream3 import select_tiling
+
+        tiling = select_tiling(
+            height=CROP_SIZE, width=CROP_SIZE, crop_size=CROP_SIZE, max_crops=MAX_CROPS
+        )
+        assert tiling == (1, 1)
+
+    def test_large_square_image(self):
+        """Large square image should use multiple tiles."""
+        from vllm.transformers_utils.processors.moondream3 import select_tiling
+
+        tiling = select_tiling(
+            height=800, width=800, crop_size=CROP_SIZE, max_crops=MAX_CROPS
+        )
+        h_tiles, w_tiles = tiling
+        assert h_tiles >= 2
+        assert w_tiles >= 2
+        assert h_tiles * w_tiles <= MAX_CROPS
+
+    def test_wide_image(self):
+        """Wide image should have more width tiles."""
+        from vllm.transformers_utils.processors.moondream3 import select_tiling
+
+        tiling = select_tiling(
+            height=400, width=1200, crop_size=CROP_SIZE, max_crops=MAX_CROPS
+        )
+        h_tiles, w_tiles = tiling
+        assert w_tiles >= h_tiles
+
+    def test_tall_image(self):
+        """Tall image should have more height tiles."""
+        from vllm.transformers_utils.processors.moondream3 import select_tiling
+
+        tiling = select_tiling(
+            height=1200, width=400, crop_size=CROP_SIZE, max_crops=MAX_CROPS
+        )
+        h_tiles, w_tiles = tiling
+        assert h_tiles >= w_tiles
+
+    def test_respects_max_crops(self):
+        """Tiling should not exceed max_crops."""
+        from vllm.transformers_utils.processors.moondream3 import select_tiling
+
+        tiling = select_tiling(
+            height=2000, width=2000, crop_size=CROP_SIZE, max_crops=4
+        )
+        h_tiles, w_tiles = tiling
+        assert h_tiles * w_tiles <= 4
+
+
+class TestMoondream3VisionShapes:
+    """CPU-based tests for vision encoder expected shapes.
+
+    These tests verify the mathematical relationships between
+    crop size, patch size, and token counts.
+    """
+
+    def test_expected_patch_count(self):
+        """Test 378/14 = 27 patches per side, 729 total."""
+        patches_per_side = CROP_SIZE // PATCH_SIZE
+        total_patches = patches_per_side**2
+
+        assert patches_per_side == 27
+        assert total_patches == EXPECTED_IMAGE_TOKENS - 1
+
+    def test_patch_embedding_input_dim(self):
+        """Test patch embedding input dimension."""
+        channels = 3
+        input_dim = PATCH_SIZE * PATCH_SIZE * channels
+
+        assert input_dim == 14 * 14 * 3
+        assert input_dim == 588
+
+
+class TestMoondream3TauAttention:
+    """CPU-based tests for tau attention scaling components.
+
+    These tests validate the tau attention formula used in Moondream3:
+    - Token-based: tok_q = tanh(gelu(qkv) @ tau_wq.T)
+    - Position-based: tau_pos = 1 + (sigmoid(alpha * log(pos+1)) - 0.5)
+    """
+
+    def test_tau_position_range(self):
+        """Test tau position scaling produces values in valid range."""
+        num_heads = 32
+        seq_len = 100
+
+        tau_alpha = torch.randn(num_heads)
+        positions = torch.arange(seq_len)
+
+        pos_float = (positions.float() + 1.0).clamp(min=1e-6)
+        pos_log = pos_float.log()
+        tau_pos = 1.0 + (torch.sigmoid(tau_alpha[:, None] * pos_log[None, :]) - 0.5)
+
+        assert tau_pos.shape == (num_heads, seq_len)
+        # tau_pos should be between 0.5 and 1.5
+        assert tau_pos.min() >= 0.5
+        assert tau_pos.max() <= 1.5
+
+    def test_tau_token_output_range(self):
+        """Test tau token scaling output is bounded by tanh."""
+        import torch.nn.functional as F
+
+        seq_len = 100
+        qkv_dim = 6144  # 2048 * 3
+        num_heads = 32
+
+        qkv = torch.randn(seq_len, qkv_dim)
+        tau_wq = torch.randn(num_heads, qkv_dim)
+
+        tok_feat = F.gelu(qkv)
+        tok_q = torch.tanh(tok_feat @ tau_wq.t())
+
+        assert tok_q.shape == (seq_len, num_heads)
+        # tanh output is bounded by [-1, 1]
+        assert tok_q.min() >= -1.0
+        assert tok_q.max() <= 1.0
diff --git a/tests/models/multimodal/processing/test_musicflamingo.py b/tests/models/multimodal/processing/test_musicflamingo.py
index 625e1ad8d29b..ba14b7760299 100644
--- a/tests/models/multimodal/processing/test_musicflamingo.py
+++ b/tests/models/multimodal/processing/test_musicflamingo.py
@@ -17,11 +17,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from importlib.metadata import version
 from unittest.mock import MagicMock
 
 import numpy as np
 import pytest
 import torch
+from packaging.version import Version
 from transformers import PretrainedConfig
 
 from tests.models.registry import HF_EXAMPLE_MODELS
@@ -122,6 +124,11 @@ def test_musicflamingo_dummy_text_uses_plain_audio_tokens(mock_ctx):
     assert builder.get_dummy_text({"audio": 2}) == "<sound><sound>"
 
 
+@pytest.mark.skipif(
+    Version(version("transformers")) >= Version("5.5"),
+    reason="transformers v5.5 added native MusicFlamingoForConditionalGeneration "
+    "with a different get_audio_features signature (requires input_ids)",
+)
 def test_musicflamingo_audio_feature_pipeline_matches_hf_small_config():
     from transformers.models.musicflamingo import (
         modeling_musicflamingo as hf_musicflamingo_modeling,
diff --git a/tests/models/multimodal/test_nano_nemotron_vl.py b/tests/models/multimodal/test_nano_nemotron_vl.py
new file mode 100644
index 000000000000..6922af79c08e
--- /dev/null
+++ b/tests/models/multimodal/test_nano_nemotron_vl.py
@@ -0,0 +1,114 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from vllm.model_executor.models.nano_nemotron_vl import NemotronH_Nano_VL_V2
+
+
+class _TextOnlyMultiModalConfig:
+    def get_limit_per_prompt(self, modality: str) -> int:
+        return 0
+
+
+class _ImageOnlyMultiModalConfig:
+    def get_limit_per_prompt(self, modality: str) -> int:
+        return 1 if modality == "image" else 0
+
+
+class _ModelConfig:
+    multimodal_config = _TextOnlyMultiModalConfig()
+
+
+class _ImageOnlyModelConfig:
+    multimodal_config = _ImageOnlyMultiModalConfig()
+
+
+class _LanguageModel:
+    def __init__(self) -> None:
+        self.loaded_weights: list[tuple[str, object]] = []
+
+    def load_weights(self, weights):
+        self.loaded_weights = list(weights)
+
+
+class _MissingMultiModalModule:
+    def named_parameters(self):
+        raise AssertionError("multimodal weights should not be inspected")
+
+    def load_weights(self, weights):
+        raise AssertionError("multimodal weights should not be loaded")
+
+
+class _AdapterModule:
+    def named_parameters(self):
+        return []
+
+
+class _VisionModel:
+    def __init__(self) -> None:
+        self.loaded_weights: list[tuple[str, object]] = []
+
+    def load_weights(self, weights):
+        self.loaded_weights = list(weights)
+
+
+def test_nano_nemotron_vl_skips_multimodal_weights_in_text_only_mode():
+    model = object.__new__(NemotronH_Nano_VL_V2)
+    language_model = _LanguageModel()
+    object.__setattr__(model, "model_config", _ModelConfig())
+    object.__setattr__(model, "language_model", language_model)
+    object.__setattr__(model, "mlp1", _AdapterModule())
+    object.__setattr__(model, "vision_model", _MissingMultiModalModule())
+    object.__setattr__(model, "sound_encoder", None)
+
+    language_weight = object()
+    model.load_weights(
+        [
+            ("language_model.layers.0.weight", language_weight),
+            ("mlp1.0.weight", object()),
+            ("vision_model.radio_model.encoder.weight", object()),
+            ("sound_encoder.encoder.weight", object()),
+        ]
+    )
+
+    assert language_model.loaded_weights == [("layers.0.weight", language_weight)]
+
+
+def test_nano_nemotron_vl_loads_vision_weights_without_sound_encoder():
+    model = object.__new__(NemotronH_Nano_VL_V2)
+    language_model = _LanguageModel()
+    vision_model = _VisionModel()
+    object.__setattr__(model, "model_config", _ImageOnlyModelConfig())
+    object.__setattr__(model, "language_model", language_model)
+    object.__setattr__(model, "mlp1", _AdapterModule())
+    object.__setattr__(model, "vision_model", vision_model)
+    object.__setattr__(model, "sound_encoder", None)
+
+    language_weight = object()
+    vision_weight = object()
+    model.load_weights(
+        [
+            ("language_model.layers.0.weight", language_weight),
+            ("vision_model.radio_model.encoder.weight", vision_weight),
+        ]
+    )
+
+    assert language_model.loaded_weights == [("layers.0.weight", language_weight)]
+    assert vision_model.loaded_weights == [
+        ("radio_model.encoder.weight", vision_weight)
+    ]
+
+
+def test_nano_nemotron_vl_requires_sound_encoder_for_sound_weights():
+    model = object.__new__(NemotronH_Nano_VL_V2)
+    language_model = _LanguageModel()
+    vision_model = _VisionModel()
+    object.__setattr__(model, "model_config", _ImageOnlyModelConfig())
+    object.__setattr__(model, "language_model", language_model)
+    object.__setattr__(model, "mlp1", _AdapterModule())
+    object.__setattr__(model, "vision_model", vision_model)
+    object.__setattr__(model, "sound_encoder", None)
+
+    with pytest.raises(AssertionError):
+        model.load_weights([("sound_encoder.encoder.weight", object())])
diff --git a/tests/models/quantization/test_awq.py b/tests/models/quantization/test_awq.py
index 6b34262d3e9e..25a63f6bd907 100644
--- a/tests/models/quantization/test_awq.py
+++ b/tests/models/quantization/test_awq.py
@@ -93,6 +93,37 @@ def run_awq_test(
         )
 
 
+@pytest.mark.parametrize(
+    ("model", "quantization", "dtype"),
+    [
+        ("mattbucci/gemma-4-26B-AWQ", "awq", "float16"),
+        ("cyankiwi/gemma-4-26B-A4B-it-AWQ-4bit", "compressed-tensors", "bfloat16"),
+    ],
+    ids=[
+        "gemma4-moe-standard-awq-dot-suffix",
+        "gemma4-moe-compressed-tensors-underscore-suffix",
+    ],
+)
+@torch.inference_mode()
+def test_awq_load(
+    vllm_runner: type[VllmRunner],
+    example_prompts: list[str],
+    model: str,
+    quantization: str,
+    dtype: str,
+) -> None:
+    """Regression test: AWQ weight loading must not KeyError."""
+    with vllm_runner(
+        model,
+        quantization=quantization,
+        dtype=dtype,
+        max_model_len=128,
+        enforce_eager=True,
+    ) as vllm_model:
+        outputs = vllm_model.generate_greedy(example_prompts[:2], max_tokens=32)
+    assert len(outputs) == 2
+
+
 @pytest.mark.parametrize(
     ("source_model", "quant_model"),
     [("OpenGVLab/InternVL2-2B", "OpenGVLab/InternVL2-2B-AWQ")],
diff --git a/tests/models/quantization/test_nvfp4.py b/tests/models/quantization/test_nvfp4.py
index 30f69f62130f..afbcb1e5aeca 100644
--- a/tests/models/quantization/test_nvfp4.py
+++ b/tests/models/quantization/test_nvfp4.py
@@ -120,3 +120,26 @@ def test_nvfp4(vllm_runner, model, eager, backend, monkeypatch):
     with vllm_runner(model, enforce_eager=eager) as llm:
         output = llm.generate_greedy(["1 2 3 4 5"], max_tokens=2)
     assert output[0][1] == "1 2 3 4 5 6"
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        "nvidia/Qwen3-30B-A3B-NVFP4",
+        "RedHatAI/Qwen3-30B-A3B-NVFP4",
+    ],
+)
+@pytest.mark.parametrize("backend", ["emulation"])
+@pytest.mark.skipif(
+    not current_platform.is_rocm(),
+    reason="NVFP4 MOE emulation is only useful on AMD Instinct MI3xx",
+)
+def test_nvfp4_moe(vllm_runner, model, backend, monkeypatch):
+    monkeypatch.setenv("VLLM_NVFP4_GEMM_BACKEND", backend)
+    with vllm_runner(
+        model,
+        moe_backend=backend,
+        load_format="dummy",
+        hf_overrides={"num_hidden_layers": 2},
+    ) as llm:
+        _ = llm.generate_greedy(["1 2 3 4 5"], max_tokens=2)
diff --git a/tests/models/registry.py b/tests/models/registry.py
index 92ebac018412..3465ce9b9734 100644
--- a/tests/models/registry.py
+++ b/tests/models/registry.py
@@ -238,6 +238,11 @@ def check_available_online(
         "CohereLabs/c4ai-command-r7b-12-2024",
         trust_remote_code=True,
     ),
+    "Cohere2MoeForCausalLM": _HfExamplesInfo(
+        "/host/engines/cohere-moe",
+        trust_remote_code=True,
+        is_available_online=False,
+    ),
     "CwmForCausalLM": _HfExamplesInfo("facebook/cwm", min_transformers_version="4.58"),
     # FIXME: databricks/dbrx-instruct has been deleted
     "DbrxForCausalLM": _HfExamplesInfo(
@@ -260,6 +265,9 @@ def check_available_online(
         trust_remote_code=True,
     ),
     "DeepseekV32ForCausalLM": _HfExamplesInfo("deepseek-ai/DeepSeek-V3.2-Exp"),
+    "DeepseekV4ForCausalLM": _HfExamplesInfo(
+        "deepseek-ai/DeepSeek-V4-Flash", is_available_online=False
+    ),
     "Ernie4_5ForCausalLM": _HfExamplesInfo("baidu/ERNIE-4.5-0.3B-PT"),
     "Ernie4_5_MoeForCausalLM": _HfExamplesInfo("baidu/ERNIE-4.5-21B-A3B-PT"),
     "ExaoneForCausalLM": _HfExamplesInfo(
@@ -324,6 +332,7 @@ def check_available_online(
     "HunYuanMoEV1ForCausalLM": _HfExamplesInfo(
         "tencent/Hunyuan-A13B-Instruct", trust_remote_code=True
     ),
+    "HYV3ForCausalLM": _HfExamplesInfo("tencent/Hy3-preview", trust_remote_code=True),
     "HyperCLOVAXForCausalLM": _HfExamplesInfo(
         "naver-hyperclovax/HyperCLOVAX-SEED-Think-14B",
         trust_remote_code=True,
@@ -335,7 +344,15 @@ def check_available_online(
         "internlm/internlm2-chat-7b", trust_remote_code=True
     ),
     "InternLM2VEForCausalLM": _HfExamplesInfo(
-        "OpenGVLab/Mono-InternVL-2B", trust_remote_code=True
+        "OpenGVLab/Mono-InternVL-2B",
+        trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": (
+                "Custom config cannot be loaded with Transformers "
+                "v5 because `vision_config` is not always set"
+            )
+        },
     ),
     "InternLM3ForCausalLM": _HfExamplesInfo(
         "internlm/internlm3-8b-instruct", trust_remote_code=True
@@ -360,6 +377,7 @@ def check_available_online(
     "KimiLinearForCausalLM": _HfExamplesInfo(
         "moonshotai/Kimi-Linear-48B-A3B-Instruct", trust_remote_code=True
     ),
+    "LagunaForCausalLM": _HfExamplesInfo("poolside/Laguna-XS.2"),
     "Lfm2ForCausalLM": _HfExamplesInfo("LiquidAI/LFM2-1.2B"),
     "Lfm2MoeForCausalLM": _HfExamplesInfo(
         "LiquidAI/LFM2-8B-A1B",
@@ -475,6 +493,13 @@ def check_available_online(
     "Plamo2ForCausalLM": _HfExamplesInfo(
         "pfnet/plamo-2-1b",
         trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "hf": (
+                "Custom model code uses `_tied_weight_keys: list[str]` but "
+                "Transformers v5 now expects `_tied_weight_keys: dict[str, str]`"
+            )
+        },
     ),
     "Plamo3ForCausalLM": _HfExamplesInfo(
         "pfnet/plamo-3-nict-2b-base",
@@ -503,6 +528,10 @@ def check_available_online(
         extras={"tiny-random": "tiny-random/qwen3-next-moe"},
         min_transformers_version="4.56.3",
     ),
+    "Rnj1ForCausalLM": _HfExamplesInfo(
+        "EssentialAI/rnj-1-instruct",
+        is_available_online=False,
+    ),
     "RWForCausalLM": _HfExamplesInfo("tiiuae/falcon-40b"),
     "SarvamMoEForCausalLM": _HfExamplesInfo(
         "sarvamai/sarvam-30b",
@@ -515,6 +544,13 @@ def check_available_online(
         trust_remote_code=True,
         max_model_len=4096,
         is_available_online=True,
+        max_transformers_version="5.3",
+        transformers_version_reason={
+            "vllm": (
+                "vllm upgraded transformers above v5.4 where "
+                "validate_rope() no longer accepts ignore_keys param"
+            )
+        },
     ),
     "SeedOssForCausalLM": _HfExamplesInfo(
         "ByteDance-Seed/Seed-OSS-36B-Instruct",
@@ -553,12 +589,20 @@ def check_available_online(
         "xverse/XVERSE-7B-Chat",
         tokenizer="meta-llama/Llama-2-7b",
         trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": "XVERSE tokenizer is incompatible with transformers v5 "
+            "(add_prefix_space / prepend_scheme mismatch).",
+        },
     ),
     "Zamba2ForCausalLM": _HfExamplesInfo("Zyphra/Zamba2-7B-instruct"),
     "MiMoForCausalLM": _HfExamplesInfo("XiaomiMiMo/MiMo-7B-RL", trust_remote_code=True),
     "MiMoV2FlashForCausalLM": _HfExamplesInfo(
         "XiaomiMiMo/MiMo-V2-Flash", trust_remote_code=True
     ),
+    "MiMoV2ForCausalLM": _HfExamplesInfo(
+        "XiaomiMiMo/MiMo-V2.5-Pro", trust_remote_code=True
+    ),
     "Dots1ForCausalLM": _HfExamplesInfo("rednote-hilab/dots.llm1.inst"),
 }
 
@@ -582,6 +626,10 @@ def check_available_online(
         trust_remote_code=True,
         hf_overrides={"architectures": ["GteNewModel"]},
     ),
+    "JinaEmbeddingsV5Model": _HfExamplesInfo(
+        "jinaai/jina-embeddings-v5-text-small",
+        trust_remote_code=True,
+    ),
     "LlamaModel": _HfExamplesInfo("llama", is_available_online=False),
     "LlamaBidirectionalModel": _HfExamplesInfo(
         "nvidia/llama-nemotron-embed-1b-v2", trust_remote_code=True
@@ -763,10 +811,18 @@ def check_available_online(
     # [Decoder-only]
     "AriaForConditionalGeneration": _HfExamplesInfo("rhymes-ai/Aria"),
     "AudioFlamingo3ForConditionalGeneration": _HfExamplesInfo(
-        "nvidia/audio-flamingo-3-hf", min_transformers_version="5.0.0"
+        "nvidia/audio-flamingo-3-hf",
+        min_transformers_version="5.3.0",
+        transformers_version_reason={
+            "vllm": "Needs https://github.com/huggingface/transformers/pull/43538"
+        },
     ),
     "MusicFlamingoForConditionalGeneration": _HfExamplesInfo(
-        "nvidia/music-flamingo-2601-hf", min_transformers_version="5.3.0"
+        "nvidia/music-flamingo-2601-hf",
+        min_transformers_version="5.3.0",
+        transformers_version_reason={
+            "vllm": "Needs https://github.com/huggingface/transformers/pull/43538"
+        },
     ),
     "AyaVisionForConditionalGeneration": _HfExamplesInfo("CohereLabs/aya-vision-8b"),
     "BagelForConditionalGeneration": _HfExamplesInfo("ByteDance-Seed/BAGEL-7B-MoT"),
@@ -821,12 +877,30 @@ def check_available_online(
     ),
     "FireRedASR2ForConditionalGeneration": _HfExamplesInfo(
         "allendou/FireRedASR2-LLM-vllm",
+        trust_remote_code=True,
+        max_transformers_version="5.1",
+        transformers_version_reason={
+            "vllm": "Incompatible with transformers v5.2+ "
+            "(dict object has no attribute '__name__').",
+        },
     ),
     "FireRedLIDForConditionalGeneration": _HfExamplesInfo(
         "PatchyTisa/FireRedLID-vllm",
+        trust_remote_code=True,
+        max_transformers_version="5.1",
+        transformers_version_reason={
+            "vllm": "Incompatible with transformers v5.2+ "
+            "(dict object has no attribute '__name__').",
+        },
     ),
     "FunASRForConditionalGeneration": _HfExamplesInfo(
         "allendou/Fun-ASR-Nano-2512-vllm",
+        trust_remote_code=True,
+        max_transformers_version="5.1",
+        transformers_version_reason={
+            "vllm": "Incompatible with transformers v5.2+ "
+            "(dict object has no attribute '__name__').",
+        },
     ),
     "FunAudioChatForConditionalGeneration": _HfExamplesInfo(
         "funaudiochat", is_available_online=False
@@ -842,6 +916,10 @@ def check_available_online(
         "zai-org/GLM-ASR-Nano-2512",
         min_transformers_version="5.0.0",
     ),
+    "Granite4VisionForConditionalGeneration": _HfExamplesInfo(
+        "ibm-granite/granite-vision-4.1-4b",
+        is_available_online=False,
+    ),
     "GraniteVision": _HfExamplesInfo("ibm-granite/granite-vision-3.3-2b"),
     "GraniteSpeechForConditionalGeneration": _HfExamplesInfo(
         "ibm-granite/granite-speech-3.3-2b",
@@ -885,9 +963,26 @@ def check_available_online(
         "PerceptronAI/Isaac-0.1",
         trust_remote_code=True,
         extras={"0.2-2B-Preview": "PerceptronAI/Isaac-0.2-2B-Preview"},
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": (
+                "Custom Isaac code is not compatible with Transformers v5. "
+                "The model should be upstreamed to Transformers for "
+                "long-term support."
+            ),
+            "hf": (
+                "Isaac's remote model and processor code import or configure "
+                "APIs that changed in Transformers v5."
+            ),
+        },
     ),
     "InternS1ForConditionalGeneration": _HfExamplesInfo(
-        "internlm/Intern-S1", trust_remote_code=True
+        "internlm/Intern-S1",
+        trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": "Custom tokenizer code is not compatible with Transformers v5."
+        },
     ),
     "InternS1ProForConditionalGeneration": _HfExamplesInfo(
         "internlm/Intern-S1-Pro",
@@ -976,7 +1071,17 @@ def check_available_online(
     "MiDashengLMModel": _HfExamplesInfo(
         "mispeech/midashenglm-7b", trust_remote_code=True
     ),
-    "MiniCPMO": _HfExamplesInfo("openbmb/MiniCPM-o-2_6", trust_remote_code=True),
+    "MiMoV2OmniForCausalLM": _HfExamplesInfo(
+        "XiaomiMiMo/MiMo-V2.5-Omni", trust_remote_code=True, is_available_online=False
+    ),
+    "MiniCPMO": _HfExamplesInfo(
+        "openbmb/MiniCPM-o-2_6",
+        trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "hf": "Custom processor code is not compatible with Transformers v5."
+        },
+    ),
     "MiniCPMV": _HfExamplesInfo(
         "openbmb/MiniCPM-Llama3-V-2_5",
         extras={
@@ -984,6 +1089,13 @@ def check_available_online(
             "4.0": "openbmb/MiniCPM-V-4",
             "4.5": "openbmb/MiniCPM-V-4_5",
         },
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": (
+                "MiniCPMVBatchFeature is incompatible with its base class in "
+                "Transformers v5. See https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/discussions/78"
+            )
+        },
         trust_remote_code=True,
     ),
     "MiniMaxVL01ForConditionalGeneration": _HfExamplesInfo(
@@ -1003,6 +1115,16 @@ def check_available_online(
         extras={"olmo": "allenai/Molmo-7B-O-0924"},
         trust_remote_code=True,
     ),
+    "Moondream3ForCausalLM": _HfExamplesInfo(
+        "moondream/moondream3-preview",
+        tokenizer="moondream/starmie-v1",
+        trust_remote_code=True,
+    ),
+    "HfMoondream": _HfExamplesInfo(
+        "moondream/moondream3-preview",
+        tokenizer="moondream/starmie-v1",
+        trust_remote_code=True,
+    ),
     "Molmo2ForConditionalGeneration": _HfExamplesInfo(
         "allenai/Molmo2-8B",
         extras={"olmo": "allenai/Molmo2-O-7B"},
@@ -1019,33 +1141,58 @@ def check_available_online(
     "NemotronH_Nano_VL_V2": _HfExamplesInfo(
         "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16",
         max_model_len=4096,
-        # NemotronH layers are constructed via `hybrid_override_pattern`:
+        # NemotronH layers are constructed via `hybrid_override_pattern`
+        use_original_num_layers=True,
+        hf_overrides={
+            "text_config": {"num_hidden_layers": 2, "hybrid_override_pattern": "M*"},
+        },
+        trust_remote_code=True,
+    ),
+    "NemotronH_Nano_Omni_Reasoning_V3": _HfExamplesInfo(
+        "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
+        max_model_len=4096,
+        # NemotronH layers are constructed via `hybrid_override_pattern`
         use_original_num_layers=True,
         hf_overrides={
             "vision_config": PretrainedConfig(
                 args={
-                    "min_num_patches": 1,  # Trigger image dynamic res
+                    "min_num_patches": 1,
                     "max_num_patches": 12,
                     "model": "vit_huge_patch16_224",
                 },
-                # Trigger conv3d:
                 video_temporal_patch_size=2,
+                # TODO(nhaber): This is `true` in the official `config.json`,
+                # but this causes a processor exception in the tests due to a known bug
+                # with mixed-resolution video when `true`. To be resolved.
+                video_maintain_aspect_ratio=False,
             ),
-            "text_config": {
-                "num_hidden_layers": 2,
-                "hybrid_override_pattern": "M*",
-            },
+            "text_config": {"num_hidden_layers": 2, "hybrid_override_pattern": "M*"},
         },
         trust_remote_code=True,
     ),
+    "NemotronH_Super_Omni_Reasoning_V3": _HfExamplesInfo(
+        "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", is_available_online=False
+    ),
     "OpenCUAForConditionalGeneration": _HfExamplesInfo(
-        "xlangai/OpenCUA-7B", trust_remote_code=True
+        "xlangai/OpenCUA-7B",
+        trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": "Tokenizer cannot be initialised in Transformers v5."
+        },
     ),
     "OpenPanguVLForConditionalGeneration": _HfExamplesInfo(
         "FreedomIntelligence/openPangu-VL-7B",
         trust_remote_code=True,
         max_model_len=4096,
         enforce_eager=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": (
+                "OpenPanguVLVideoProcessorInitKwargs does not specify total=False, "
+                "making all kwargs required. See https://huggingface.co/FreedomIntelligence/openPangu-VL-7B/discussions/2"
+            )
+        },
     ),
     "Ovis": _HfExamplesInfo(
         "AIDC-AI/Ovis2-1B",
@@ -1057,12 +1204,24 @@ def check_available_online(
             "1.6-gemma": "AIDC-AI/Ovis1.6-Gemma2-9B",
         },
     ),
-    "Ovis2_5": _HfExamplesInfo("AIDC-AI/Ovis2.5-2B", trust_remote_code=True),
+    "Ovis2_5": _HfExamplesInfo(
+        "AIDC-AI/Ovis2.5-2B",
+        trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": "Custom processor code is not compatible with Transformers v5."
+        },
+    ),
     "Ovis2_6ForCausalLM": _HfExamplesInfo(
         "AIDC-AI/Ovis2.6-2B", is_available_online=False, trust_remote_code=True
     ),
     "Ovis2_6_MoeForCausalLM": _HfExamplesInfo(
-        "AIDC-AI/Ovis2.6-30B-A3B", trust_remote_code=True
+        "AIDC-AI/Ovis2.6-30B-A3B",
+        trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": "Custom processor code is not compatible with Transformers v5."
+        },
     ),
     "PaddleOCRVLForConditionalGeneration": _HfExamplesInfo(
         "PaddlePaddle/PaddleOCR-VL",
@@ -1082,7 +1241,17 @@ def check_available_online(
         extras={"phi3.5": "microsoft/Phi-3.5-vision-instruct"},
     ),
     "Phi4ForCausalLMV": _HfExamplesInfo(
-        "microsoft/Phi-4-reasoning-vision-15B", trust_remote_code=True
+        "microsoft/Phi-4-reasoning-vision-15B",
+        trust_remote_code=True,
+        max_transformers_version="5.3",
+        transformers_version_reason={
+            "vllm": (
+                "vllm upgraded transformers above v5.4 where HF model "
+                "custom code uses siglip2 internals "
+                "(filter_out_non_signature_kwargs) removed "
+                "by huggingface/transformers#43514"
+            )
+        },
     ),
     "Phi4MMForCausalLM": _HfExamplesInfo(
         "microsoft/Phi-4-multimodal-instruct", trust_remote_code=True
@@ -1095,6 +1264,10 @@ def check_available_online(
         },
         tokenizer_mode="mistral",
     ),
+    "QianfanOCRForConditionalGeneration": _HfExamplesInfo(
+        "baidu/Qianfan-OCR",
+        min_transformers_version="5.6.0",
+    ),
     "QwenVLForConditionalGeneration": _HfExamplesInfo(
         "Qwen/Qwen-VL",
         extras={"chat": "Qwen/Qwen-VL-Chat"},
@@ -1179,6 +1352,14 @@ def check_available_online(
             "architectures": ["Tarsier2ForConditionalGeneration"],
             "model_type": "tarsier2",
         },
+        max_transformers_version="5.3",
+        transformers_version_reason={
+            "vllm": (
+                "Qwen2VLConfig was split into Qwen2VLConfig + "
+                "Qwen2VLTextConfig in transformers v5, breaking "
+                "attribute access (num_attention_heads, hidden_size, etc.)"
+            )
+        },
     ),
     "VoxtralForConditionalGeneration": _HfExamplesInfo(
         "mistralai/Voxtral-Mini-3B-2507",
@@ -1191,9 +1372,7 @@ def check_available_online(
     ),
     # [Encoder-decoder]
     "CohereAsrForConditionalGeneration": _HfExamplesInfo(
-        "CohereLabs/cohere-transcribe-03-2026",
-        trust_remote_code=True,
-        is_available_online=False,  # TODO (ekagra): revert after asr release
+        "CohereLabs/cohere-transcribe-03-2026", trust_remote_code=True
     ),
     "NemotronParseForConditionalGeneration": _HfExamplesInfo(
         "nvidia/NVIDIA-Nemotron-Parse-v1.1", trust_remote_code=True
@@ -1227,6 +1406,13 @@ def check_available_online(
         max_num_seqs=32,
     ),
     # [Eagle]
+    "EagleCohereForCausalLM": _HfExamplesInfo(
+        "/host/engines/cohere-moe",
+        speculative_model="/host/engines/cohere-moe/eagle",
+        tokenizer="/host/engines/cohere-moe",
+        trust_remote_code=True,
+        is_available_online=False,
+    ),
     "EagleDeepSeekMTPModel": _HfExamplesInfo(
         "eagle618/deepseek-v3-random",
         speculative_model="eagle618/eagle-deepseek-v3-random",
@@ -1264,6 +1450,11 @@ def check_available_online(
         speculative_model="yuhuili/EAGLE3-LLaMA3.1-Instruct-8B",
         tokenizer="MiniMaxAI/MiniMax-M2",
     ),
+    "EagleMistralForCausalLM": _HfExamplesInfo(
+        "mistralai/Mistral-Medium-3.5-128B",
+        speculative_model="mistralai/Mistral-Medium-3.5-128B-EAGLE",
+        is_available_online=False,
+    ),
     "EagleMistralLarge3ForCausalLM": _HfExamplesInfo(
         "mistralai/Mistral-Large-3-675B-Instruct-2512",
         speculative_model="mistralai/Mistral-Large-3-675B-Instruct-2512-Eagle",
@@ -1304,6 +1495,18 @@ def check_available_online(
         speculative_model="luccafong/deepseek_mtp_draft_random",
         trust_remote_code=True,
     ),
+    "DeepSeekV4MTPModel": _HfExamplesInfo(
+        "deepseek-ai/DeepSeek-V4-Flash",
+        speculative_model="deepseek-ai/DeepSeek-V4-Flash",
+        trust_remote_code=True,
+        is_available_online=False,
+    ),
+    "Gemma4MTPModel": _HfExamplesInfo(
+        "google/gemma-4-E4B-it",
+        speculative_model="google/gemma-4-E4B-it-assistant",
+        trust_remote_code=True,
+        min_transformers_version="5.8.0",
+    ),
     "ErnieMTPModel": _HfExamplesInfo(
         "baidu/ERNIE-4.5-21B-A3B-PT",
         trust_remote_code=True,
@@ -1339,6 +1542,10 @@ def check_available_online(
         is_available_online=False,
         min_transformers_version="5.1.0",
     ),
+    "HYV3MTPModel": _HfExamplesInfo(
+        "tencent/Hy3-preview",
+        speculative_model="tencent/Hy3-preview",
+    ),
     "LongCatFlashMTPModel": _HfExamplesInfo(
         "meituan-longcat/LongCat-Flash-Chat",
         trust_remote_code=True,
@@ -1349,6 +1556,18 @@ def check_available_online(
         trust_remote_code=True,
         speculative_model="XiaomiMiMo/MiMo-7B-RL",
     ),
+    "MiMoV2MTPModel": _HfExamplesInfo(
+        "XiaomiMiMo/MiMo-V2.5-Pro",
+        trust_remote_code=True,
+        speculative_model="XiaomiMiMo/MiMo-V2.5-Pro",
+        is_available_online=False,
+    ),
+    "MiMoV2OmniMTPModel": _HfExamplesInfo(
+        "XiaomiMiMo/MiMo-V2.5-Omni",
+        trust_remote_code=True,
+        speculative_model="XiaomiMiMo/MiMo-V2.5-Omni",
+        is_available_online=False,
+    ),
     "NemotronHMTPModel": _HfExamplesInfo(
         "nvidia/Nemotron-Super-Placeholder",
         speculative_model="nvidia/Nemotron-Super-Placeholder",
diff --git a/tests/models/test_deepseek_v4_mega_moe.py b/tests/models/test_deepseek_v4_mega_moe.py
new file mode 100644
index 000000000000..304f044868a3
--- /dev/null
+++ b/tests/models/test_deepseek_v4_mega_moe.py
@@ -0,0 +1,184 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from vllm.model_executor.models.deepseek_v4 import (
+    DeepseekV4MegaMoEExperts,
+    _stage_deepseek_v4_mega_moe_inputs,
+    make_deepseek_v4_expert_params_mapping,
+)
+from vllm.platforms import current_platform
+
+pytestmark = pytest.mark.skipif(
+    not current_platform.is_cuda(),
+    reason="DeepSeek V4 MegaMoE requires CUDA",
+)
+
+
+def test_deepseek_v4_mega_moe_expert_mapping():
+    mapping = make_deepseek_v4_expert_params_mapping(2)
+
+    assert mapping == [
+        ("experts.w13_", "experts.0.w1.", 0, "w1"),
+        ("experts.w2_", "experts.0.w2.", 0, "w2"),
+        ("experts.w13_", "experts.0.w3.", 0, "w3"),
+        ("experts.w13_", "experts.1.w1.", 1, "w1"),
+        ("experts.w2_", "experts.1.w2.", 1, "w2"),
+        ("experts.w13_", "experts.1.w3.", 1, "w3"),
+    ]
+
+
+def test_deepseek_v4_mega_moe_ue8m0_uint8_to_float():
+    raw = torch.tensor([0, 126, 127, 128], dtype=torch.uint8)
+
+    decoded = DeepseekV4MegaMoEExperts._ue8m0_uint8_to_float(raw)
+
+    assert torch.equal(decoded.view(torch.int32), raw.to(torch.int32) << 23)
+    assert decoded[0].item() == 0.0
+    assert decoded[1].item() == 0.5
+    assert decoded[2].item() == 1.0
+    assert decoded[3].item() == 2.0
+
+
+def test_deepseek_v4_mega_moe_weight_loader_uses_ep_expert_ownership():
+    vllm_config = SimpleNamespace(
+        scheduler_config=SimpleNamespace(max_num_batched_tokens=4)
+    )
+    experts = DeepseekV4MegaMoEExperts(
+        vllm_config,
+        num_experts=4,
+        num_local_experts=2,
+        experts_start_idx=2,
+        top_k=2,
+        hidden_size=128,
+        intermediate_size=128,
+    )
+
+    nonlocal_weight = torch.ones(128, 64, dtype=torch.uint8)
+    assert (
+        experts.weight_loader(
+            experts.w13_weight,
+            nonlocal_weight,
+            "experts.w13_weight",
+            shard_id="w1",
+            expert_id=1,
+            return_success=True,
+        )
+        is False
+    )
+
+    w1 = torch.full((128, 64), 3, dtype=torch.uint8)
+    w3 = torch.full((128, 64), 7, dtype=torch.uint8)
+    w2 = torch.full((128, 64), 11, dtype=torch.uint8)
+
+    assert experts.weight_loader(
+        experts.w13_weight,
+        w1,
+        "experts.w13_weight",
+        shard_id="w1",
+        expert_id=2,
+        return_success=True,
+    )
+    assert experts.weight_loader(
+        experts.w13_weight,
+        w3,
+        "experts.w13_weight",
+        shard_id="w3",
+        expert_id=2,
+        return_success=True,
+    )
+    assert experts.weight_loader(
+        experts.w2_weight,
+        w2,
+        "experts.w2_weight",
+        shard_id="w2",
+        expert_id=2,
+        return_success=True,
+    )
+
+    assert torch.equal(experts.w13_weight[0, :128], w1)
+    assert torch.equal(experts.w13_weight[0, 128:], w3)
+    assert torch.equal(experts.w2_weight[0], w2)
+    assert torch.count_nonzero(experts.w13_weight[1]) == 0
+
+
+@pytest.mark.skipif(
+    not torch.cuda.is_available(),
+    reason="DeepSeek V4 MegaMoE fused input staging requires CUDA.",
+)
+def test_deepseek_v4_mega_moe_fused_input_staging_is_bitwise_exact():
+    from vllm.third_party.deep_gemm.utils import per_token_cast_to_fp8
+
+    device = torch.device("cuda")
+    num_tokens = 7
+    hidden_size = 256
+    top_k = 8
+
+    generator = torch.Generator(device=device)
+    generator.manual_seed(0)
+    hidden_states = (
+        torch.randn(
+            num_tokens,
+            hidden_size,
+            device=device,
+            dtype=torch.float32,
+            generator=generator,
+        )
+        * 17.0
+    ).to(torch.bfloat16)
+    hidden_states[0, :32] = 0
+    hidden_states[1, 32:64] = 1.0e-6
+    hidden_states[2, 64:96] = -1.0e-6
+
+    topk_ids = torch.randint(
+        0,
+        256,
+        (num_tokens, top_k),
+        device=device,
+        dtype=torch.int32,
+        generator=generator,
+    )
+    topk_weights = torch.randn(
+        num_tokens,
+        top_k,
+        device=device,
+        dtype=torch.float32,
+        generator=generator,
+    )
+
+    ref_x, ref_x_sf = per_token_cast_to_fp8(
+        hidden_states,
+        use_ue8m0=True,
+        gran_k=32,
+        use_packed_ue8m0=True,
+    )
+    ref_topk_idx = topk_ids.to(torch.int64)
+    ref_topk_weights = topk_weights.clone()
+
+    fused_x = torch.empty_like(ref_x)
+    fused_x_sf = torch.empty_like(ref_x_sf)
+    fused_topk_idx = torch.empty_like(ref_topk_idx)
+    fused_topk_weights = torch.empty_like(ref_topk_weights)
+
+    _stage_deepseek_v4_mega_moe_inputs(
+        hidden_states,
+        topk_weights,
+        topk_ids,
+        fused_x,
+        fused_x_sf,
+        fused_topk_idx,
+        fused_topk_weights,
+    )
+    torch.accelerator.synchronize()
+
+    assert torch.equal(fused_x.view(torch.uint8), ref_x.view(torch.uint8))
+    assert torch.equal(fused_x_sf, ref_x_sf)
+    assert torch.equal(fused_topk_idx, ref_topk_idx)
+    assert torch.equal(
+        fused_topk_weights.view(torch.uint8),
+        ref_topk_weights.view(torch.uint8),
+    )
diff --git a/tests/models/test_initialization.py b/tests/models/test_initialization.py
index 979c8d31775c..476ad1c7c17f 100644
--- a/tests/models/test_initialization.py
+++ b/tests/models/test_initialization.py
@@ -109,6 +109,16 @@ def _initialize_kv_caches_v1(self, vllm_config):
             "which is not configured in test environment"
         )
 
+    if model_arch in ("PrithviGeoSpatialMAE", "Terratorch"):
+        import importlib.util
+
+        if importlib.util.find_spec("terratorch") is None:
+            pytest.skip(
+                "terratorch is not installed; "
+                "temporarily skipped while PyPI has `lightning` quarantined "
+                "(see #41376)"
+            )
+
     if model_arch in ["DeepseekV32ForCausalLM", "GlmMoeDsaForCausalLM"]:
         from vllm.platforms import current_platform
 
diff --git a/tests/models/test_registry.py b/tests/models/test_registry.py
index 81fae02efda1..0715409abda6 100644
--- a/tests/models/test_registry.py
+++ b/tests/models/test_registry.py
@@ -36,6 +36,17 @@ def test_registry_imports(model_arch):
         check_max_version=False,
         check_version_reason="vllm",
     )
+
+    if model_arch in ("PrithviGeoSpatialMAE", "Terratorch"):
+        import importlib.util
+
+        if importlib.util.find_spec("terratorch") is None:
+            pytest.skip(
+                "terratorch is not installed; "
+                "temporarily skipped while PyPI has `lightning` quarantined "
+                "(see #41376)"
+            )
+
     # Ensure all model classes can be imported successfully
     model_cls = ModelRegistry._try_load_model_cls(model_arch)
     assert model_cls is not None
diff --git a/tests/models/test_terratorch.py b/tests/models/test_terratorch.py
index 71125dbe94f8..6d4d1921a88d 100644
--- a/tests/models/test_terratorch.py
+++ b/tests/models/test_terratorch.py
@@ -1,12 +1,19 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import importlib.util
+
 import pytest
 import torch
 
 from tests.conftest import VllmRunner
 from tests.utils import create_new_process_for_each_test
 
+pytestmark = pytest.mark.skipif(
+    importlib.util.find_spec("terratorch") is None,
+    reason="terratorch unavailable while PyPI has `lightning` quarantined; see #41376",
+)
+
 
 @create_new_process_for_each_test()  # Hangs otherwise
 @pytest.mark.parametrize(
diff --git a/tests/models/test_utils.py b/tests/models/test_utils.py
index 3d719940e3ae..8d47b4436575 100644
--- a/tests/models/test_utils.py
+++ b/tests/models/test_utils.py
@@ -10,6 +10,8 @@
 )
 from vllm.platforms import current_platform
 
+DEVICE_TYPE = current_platform.device_type
+
 
 class ModuleWithBatchNorm(torch.nn.Module):
     def __init__(self):
@@ -174,8 +176,12 @@ def __exit__(self, exception_type, exception_value, traceback):
 
 @pytest.mark.skipif(not current_platform.is_cuda(), reason="Skip if not cuda")
 def test_merge_multimodal_embeddings_no_sync():
-    inputs_embeds = torch.zeros([5, 10], dtype=torch.bfloat16, device="cuda:0")
-    multimodal_embeddings = [torch.ones([3, 10], dtype=torch.bfloat16, device="cuda:0")]
+    inputs_embeds = torch.zeros(
+        [5, 10], dtype=torch.bfloat16, device=f"{DEVICE_TYPE}:0"
+    )
+    multimodal_embeddings = [
+        torch.ones([3, 10], dtype=torch.bfloat16, device=f"{DEVICE_TYPE}:0")
+    ]
     is_multimodal = torch.tensor([True, False, True, True, False], device="cpu")
     with raise_if_cuda_sync():
         _merge_multimodal_embeddings(
diff --git a/tests/models/utils.py b/tests/models/utils.py
index 3b94f34fab08..a5d1844a3071 100644
--- a/tests/models/utils.py
+++ b/tests/models/utils.py
@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import warnings
-from collections.abc import Sequence
+from collections.abc import Mapping, Sequence
 from dataclasses import dataclass
 from typing import Any
 
@@ -277,7 +277,7 @@ def build_model_context(
     dtype: ModelDType = "auto",
     model_config_kwargs: dict[str, Any] | None = None,
     mm_processor_kwargs: dict[str, Any] | None = None,
-    limit_mm_per_prompt: dict[str, int] | None = None,
+    limit_mm_per_prompt: Mapping[str, int | Mapping[str, int]] | None = None,
     mm_processor_cache_gb: int = 0,
 ):
     """Creates an InputProcessingContext for a given model.
@@ -300,7 +300,10 @@ def build_model_context(
     )
 
     model_config_kwargs = model_config_kwargs or {}
-    limit_mm_per_prompt = limit_mm_per_prompt or {}
+    limit_mm_per_prompt = {
+        modality: dict(limit) if isinstance(limit, Mapping) else limit
+        for modality, limit in (limit_mm_per_prompt or {}).items()
+    }
     model_config = ModelConfig(
         model_id,
         runner=runner,
@@ -476,7 +479,16 @@ def dummy_hf_overrides(
     else:
         # Use minimal layers for testing
         num_layers = 1
-        num_hidden_layers = 3 if model_arch == "Gemma3nForConditionalGeneration" else 1
+        num_hidden_layers = (
+            3
+            if model_arch
+            in (
+                "Gemma3nForConditionalGeneration",
+                "Gemma4ForCausalLM",
+                "Gemma4ForConditionalGeneration",
+            )
+            else 1
+        )
 
     update_dict = {
         "num_layers": num_layers,
@@ -514,6 +526,17 @@ class DummyConfig:
 
     text_config.update(update_dict)
 
+    # Update n_layers and moe configs for Moondream3 model
+    if model_arch in ("Moondream3ForCausalLM", "HfMoondream"):
+        text_config.update(
+            {
+                "n_layers": num_hidden_layers,
+                "moe_num_experts": num_experts,
+                "moe_experts_per_token": 2,
+                "moe_start_layer": num_hidden_layers,
+            }
+        )
+
     if hasattr(hf_config, "vision_config"):
         hf_config.vision_config.update(
             {
@@ -522,6 +545,9 @@ class DummyConfig:
             }
         )
 
+        if model_arch in ("Moondream3ForCausalLM", "HfMoondream"):
+            hf_config.vision_config.update({"enc_n_layers": 1})
+
     # e.g.: ibm-granite/granite-speech-3.3-2b
     if hasattr(hf_config, "encoder_config"):
         hf_config.encoder_config.update(
diff --git a/tests/multimodal/media/test_audio.py b/tests/multimodal/media/test_audio.py
index 4361066ab885..3729e71f24e7 100644
--- a/tests/multimodal/media/test_audio.py
+++ b/tests/multimodal/media/test_audio.py
@@ -3,12 +3,12 @@
 from pathlib import Path
 from unittest.mock import patch
 
-import librosa
 import numpy as np
 import pybase64 as base64
 import pytest
 
 from vllm.multimodal.media import AudioMediaIO
+from vllm.multimodal.media.audio import load_audio
 
 from ...conftest import AudioTestAssets
 
@@ -73,6 +73,6 @@ def test_audio_media_io_from_video(video_assets):
     video_path = video_assets[0].video_path
     with open(video_path, "rb") as f:
         audio, sr = audio_io.load_bytes(f.read())
-    audio_ref, sr_ref = librosa.load(video_path, sr=None)
+    audio_ref, sr_ref = load_audio(video_path, sr=None)
     assert sr == sr_ref
     np.testing.assert_allclose(audio_ref, audio, atol=1e-4)
diff --git a/tests/multimodal/test_inputs.py b/tests/multimodal/test_inputs.py
index d6bdf76a6f79..7752a543f429 100644
--- a/tests/multimodal/test_inputs.py
+++ b/tests/multimodal/test_inputs.py
@@ -26,11 +26,8 @@ def test_placeholder_range_get_num_embeds(is_embed, expected):
     "is_embed,expected",
     [
         (None, None),
-        (
-            torch.tensor([False, True, False, True, True]),
-            torch.tensor([0, 1, 1, 2, 3]),
-        ),
-        (torch.tensor([True, True, True]), torch.tensor([1, 2, 3])),
+        (torch.tensor([False, True, False, True, True]), [0, 1, 1, 2, 3]),
+        (torch.tensor([True, True, True]), [1, 2, 3]),
     ],
 )
 def test_placeholder_range_embeds_cumsum(is_embed, expected):
@@ -41,6 +38,6 @@ def test_placeholder_range_embeds_cumsum(is_embed, expected):
         assert pr.embeds_cumsum is None
         return
 
-    assert torch.equal(pr.embeds_cumsum, expected)
+    assert pr.embeds_cumsum == expected
     # cached_property should return the same object on repeated access
     assert pr.embeds_cumsum is pr.embeds_cumsum
diff --git a/tests/multimodal/test_registry.py b/tests/multimodal/test_registry.py
index 3b01bda7f54c..7ee83cc4f99b 100644
--- a/tests/multimodal/test_registry.py
+++ b/tests/multimodal/test_registry.py
@@ -5,6 +5,8 @@
 Qwen2.5-VL visual component loading behavior.
 """
 
+from types import SimpleNamespace
+
 import pytest
 
 from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -32,3 +34,17 @@ def test_supports_multimodal_inputs(model_id, limit_mm_per_prompt, expected):
         limit_mm_per_prompt=limit_mm_per_prompt,
     )
     assert MULTIMODAL_REGISTRY.supports_multimodal_inputs(ctx.model_config) is expected
+
+
+def test_create_processor_error_uses_served_model_name():
+    model_config = SimpleNamespace(
+        is_multimodal_model=False,
+        model="/path/to/model/weights",
+        served_model_name="friendly-model-name",
+    )
+
+    with pytest.raises(
+        ValueError,
+        match="friendly-model-name is not a multimodal model",
+    ):
+        MULTIMODAL_REGISTRY.create_processor(model_config)
diff --git a/tests/multimodal/test_video.py b/tests/multimodal/test_video.py
index 3ece384348bc..e82883ece338 100644
--- a/tests/multimodal/test_video.py
+++ b/tests/multimodal/test_video.py
@@ -71,7 +71,9 @@ def test_video_backend_handles_broken_frames(monkeypatch: pytest.MonkeyPatch):
             video_data = f.read()
 
         loader = VIDEO_LOADER_REGISTRY.load("opencv")
-        frames, metadata = loader.load_bytes(video_data, num_frames=-1)
+        frames, metadata = loader.load_bytes(
+            video_data, num_frames=-1, backend="opencv"
+        )
 
         # Verify metadata consistency:
         # frames_indices must match actual loaded frames
@@ -158,12 +160,12 @@ def release(self):
 
         # Test WITHOUT recovery - should have fewer frames due to failures
         frames_no_recovery, meta_no = loader.load_bytes(
-            video_data, num_frames=8, frame_recovery=False
+            video_data, num_frames=8, frame_recovery=False, backend="opencv"
         )
 
         # Test WITH recovery - should recover using next valid frames
         frames_with_recovery, meta_yes = loader.load_bytes(
-            video_data, num_frames=8, frame_recovery=True
+            video_data, num_frames=8, frame_recovery=True, backend="opencv"
         )
 
         # With recovery should have MORE frames than without
@@ -214,12 +216,12 @@ def test_video_recovery_with_corrupted_file(monkeypatch: pytest.MonkeyPatch):
 
         # Test without recovery - frame 17 will be skipped
         frames_no_recovery, meta_no_recovery = loader.load_bytes(
-            video_data, num_frames=8, frame_recovery=False
+            video_data, num_frames=8, frame_recovery=False, backend="opencv"
         )
 
         # Test with recovery - frame 18 should fill in for frame 17
         frames_with_recovery, meta_with_recovery = loader.load_bytes(
-            video_data, num_frames=8, frame_recovery=True
+            video_data, num_frames=8, frame_recovery=True, backend="opencv"
         )
 
         # Verify metadata consistency for both modes
@@ -271,12 +273,16 @@ def test_video_recovery_dynamic_backend(monkeypatch: pytest.MonkeyPatch):
 
         # Test without recovery
         frames_no_recovery, meta_no = loader.load_bytes(
-            video_data, fps=2, max_duration=10, frame_recovery=False
+            video_data,
+            fps=2,
+            max_duration=10,
+            frame_recovery=False,
+            backend="opencv",
         )
 
         # Test with frame_recovery enabled
         frames_with_recovery, meta_with = loader.load_bytes(
-            video_data, fps=2, max_duration=10, frame_recovery=True
+            video_data, fps=2, max_duration=10, frame_recovery=True, backend="opencv"
         )
 
         # Verify basic properties
@@ -310,27 +316,81 @@ def dummy_video_path(tmp_path):
     return video_path
 
 
+# ============================================================================
+# PyAV Backend Tests
+# ============================================================================
+
+
+def test_pyav_backend_loads_frames(dummy_video_path, monkeypatch: pytest.MonkeyPatch):
+    """Test that the pyav codec backend can load frames from a valid video."""
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_VIDEO_LOADER_BACKEND", "opencv")
+
+        with open(dummy_video_path, "rb") as f:
+            video_data = f.read()
+
+        loader = VIDEO_LOADER_REGISTRY.load("opencv")
+        frames, metadata = loader.load_bytes(video_data, num_frames=8, backend="pyav")
+
+        assert frames.ndim == 4
+        assert frames.shape[3] == 3  # RGB
+        assert frames.shape[0] == 8
+        assert frames.shape[0] == len(metadata["frames_indices"])
+        assert metadata["video_backend"] == "pyav"
+        assert "total_num_frames" in metadata
+        assert "fps" in metadata
+        assert "duration" in metadata
+
+
+def test_pyav_dynamic_backend_loads_frames(
+    dummy_video_path, monkeypatch: pytest.MonkeyPatch
+):
+    """Test that the pyav codec with dynamic sampling can load frames."""
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_VIDEO_LOADER_BACKEND", "opencv_dynamic")
+
+        with open(dummy_video_path, "rb") as f:
+            video_data = f.read()
+
+        loader = VIDEO_LOADER_REGISTRY.load("opencv_dynamic")
+        frames, metadata = loader.load_bytes(
+            video_data, fps=2, max_duration=10, backend="pyav"
+        )
+
+        assert frames.ndim == 4
+        assert frames.shape[3] == 3  # RGB
+        assert frames.shape[0] > 0
+        assert frames.shape[0] == len(metadata["frames_indices"])
+        assert metadata["video_backend"] == "pyav_dynamic"
+
+
 @pytest.mark.parametrize(
-    "backend, kwargs, expected_num_frames",
+    "loader_key, kwargs, expected_num_frames",
     [
-        # opencv: num_frames directly controls count
-        pytest.param("opencv", {"num_frames": 32}, 32, id="opencv-num_frames"),
-        pytest.param("opencv", {"fps": 2}, 120, id="opencv-fps"),
+        # uniform sampling + opencv codec
+        pytest.param(
+            "opencv",
+            {"num_frames": 32, "backend": "opencv"},
+            32,
+            id="opencv-num_frames",
+        ),
+        pytest.param("opencv", {"fps": 2, "backend": "opencv"}, 120, id="opencv-fps"),
         pytest.param(
             "opencv",
-            {"num_frames": 500, "fps": 2},
+            {"num_frames": 500, "fps": 2, "backend": "opencv"},
             120,
             id="opencv-num_frames_wins_fps",
         ),
+        # dynamic sampling + opencv codec
         pytest.param(
             "opencv_dynamic",
-            {"fps": 1, "max_duration": 60},
+            {"fps": 1, "max_duration": 60, "backend": "opencv"},
             60,
             id="opencv_dynamic-within_max_duration",
         ),
         pytest.param(
             "opencv_dynamic",
-            {"fps": 2, "max_duration": 30},
+            {"fps": 2, "max_duration": 30, "backend": "opencv"},
             60,
             id="opencv_dynamic-exceeds_max_duration",
         ),
@@ -349,18 +409,45 @@ def dummy_video_path(tmp_path):
             119,
             id="molmo2-fps",
         ),
+        # uniform sampling + pyav codec (same frame counts as opencv)
+        pytest.param(
+            "opencv",
+            {"num_frames": 32, "backend": "pyav"},
+            32,
+            id="pyav-num_frames",
+        ),
+        pytest.param("opencv", {"fps": 2, "backend": "pyav"}, 120, id="pyav-fps"),
+        pytest.param(
+            "opencv",
+            {"num_frames": 500, "fps": 2, "backend": "pyav"},
+            120,
+            id="pyav-num_frames_wins_fps",
+        ),
+        # dynamic sampling + pyav codec
+        pytest.param(
+            "opencv_dynamic",
+            {"fps": 1, "max_duration": 60, "backend": "pyav"},
+            60,
+            id="pyav_dynamic-within_max_duration",
+        ),
+        pytest.param(
+            "opencv_dynamic",
+            {"fps": 2, "max_duration": 30, "backend": "pyav"},
+            60,
+            id="pyav_dynamic-exceeds_max_duration",
+        ),
     ],
 )
 def test_video_loader_frames_sampling(
     dummy_video_path,
     monkeypatch: pytest.MonkeyPatch,
-    backend: str,
+    loader_key: str,
     kwargs: dict,
     expected_num_frames: int,
 ):
     """Test video loader frames sampling functionality."""
-    monkeypatch.setenv("VLLM_VIDEO_LOADER_BACKEND", backend)
-    loader = VIDEO_LOADER_REGISTRY.load(backend)
+    monkeypatch.setenv("VLLM_VIDEO_LOADER_BACKEND", loader_key)
+    loader = VIDEO_LOADER_REGISTRY.load(loader_key)
 
     with open(dummy_video_path, "rb") as f:
         long_video_bytes = f.read()
diff --git a/tests/parser/__init__.py b/tests/parser/__init__.py
new file mode 100644
index 000000000000..208f01a7cb5e
--- /dev/null
+++ b/tests/parser/__init__.py
@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
diff --git a/tests/parser/test_streaming.py b/tests/parser/test_streaming.py
new file mode 100644
index 000000000000..d9194d48ed5a
--- /dev/null
+++ b/tests/parser/test_streaming.py
@@ -0,0 +1,165 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import json
+
+import pytest
+
+from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
+from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.parser.abstract_parser import _WrappedParser
+from vllm.reasoning.basic_parsers import BaseThinkingReasoningParser
+from vllm.tool_parsers.hermes_tool_parser import Hermes2ProToolParser
+
+
+class ThinkReasoningParser(BaseThinkingReasoningParser):
+    @property
+    def start_token(self) -> str:
+        return "<think>"
+
+    @property
+    def end_token(self) -> str:
+        return "</think>"
+
+
+MODEL_OUTPUT = (
+    "<think>let me think about this</think>"
+    '<tool_call>\n{"name": "get_weather", '
+    '"arguments": {"city": "Dallas"}}\n</tool_call>'
+)
+
+
+@pytest.fixture(scope="module")
+def tokenizer():
+    from vllm.tokenizers import get_tokenizer
+
+    return get_tokenizer("Qwen/Qwen3-32B")
+
+
+@pytest.fixture
+def request_obj():
+    return ChatCompletionRequest(
+        model="test-model",
+        messages=[{"role": "user", "content": "hi"}],
+    )
+
+
+def make_parser(tokenizer, reasoning=False, tool=False):
+    _WrappedParser.reasoning_parser_cls = ThinkReasoningParser if reasoning else None
+    _WrappedParser.tool_parser_cls = Hermes2ProToolParser if tool else None
+    return _WrappedParser(tokenizer)
+
+
+def stream_text(parser, tokenizer, text, request, prompt_token_ids=None):
+    token_ids = tokenizer.encode(text, add_special_tokens=False)
+    results: list[DeltaMessage | None] = []
+    for tid in token_ids:
+        delta_text = tokenizer.decode([tid])
+        result = parser.parse_delta(
+            delta_text, [tid], request, prompt_token_ids=prompt_token_ids
+        )
+        prompt_token_ids = None
+        results.append(result)
+    return results
+
+
+def collect_fields(results):
+    all_reasoning = "".join(r.reasoning for r in results if r and r.reasoning)
+    all_content = "".join(r.content for r in results if r and r.content)
+    all_tool_calls = [tc for r in results if r and r.tool_calls for tc in r.tool_calls]
+    return all_reasoning, all_content, all_tool_calls
+
+
+def test_parse_delta_neither_parser(tokenizer, request_obj):
+    parser = make_parser(tokenizer, reasoning=False, tool=False)
+    results = stream_text(
+        parser, tokenizer, MODEL_OUTPUT, request_obj, prompt_token_ids=[]
+    )
+    reasoning, content, tool_calls = collect_fields(results)
+
+    assert reasoning == ""
+    assert len(tool_calls) == 0
+    assert "<think>" in content
+    assert "let me think about this" in content
+    assert "<tool_call>" in content
+    assert "get_weather" in content
+
+
+def test_parse_delta_tool_parser_only(tokenizer, request_obj):
+    parser = make_parser(tokenizer, reasoning=False, tool=True)
+    results = stream_text(
+        parser, tokenizer, MODEL_OUTPUT, request_obj, prompt_token_ids=[]
+    )
+    reasoning, content, tool_calls = collect_fields(results)
+
+    assert reasoning == ""
+    assert "<think>" in content
+    assert "let me think about this" in content
+    assert "</think>" in content
+
+    assert len(tool_calls) > 0
+    assert tool_calls[0].function.name == "get_weather"
+    tool_args = "".join(
+        tc.function.arguments for tc in tool_calls if tc.function.arguments
+    )
+    assert json.loads(tool_args) == {"city": "Dallas"}
+
+
+def test_parse_delta_reasoning_parser_only(tokenizer, request_obj):
+    parser = make_parser(tokenizer, reasoning=True, tool=False)
+    results = stream_text(
+        parser, tokenizer, MODEL_OUTPUT, request_obj, prompt_token_ids=[]
+    )
+    reasoning, content, tool_calls = collect_fields(results)
+
+    assert "let me think about this" in reasoning
+    assert len(tool_calls) == 0
+    assert "<tool_call>" in content
+    assert "get_weather" in content
+    assert "</tool_call>" in content
+
+
+def test_parse_delta_both_parsers(tokenizer, request_obj):
+    parser = make_parser(tokenizer, reasoning=True, tool=True)
+    results = stream_text(
+        parser, tokenizer, MODEL_OUTPUT, request_obj, prompt_token_ids=[]
+    )
+    reasoning, content, tool_calls = collect_fields(results)
+
+    assert "let me think about this" in reasoning
+    assert content == ""
+
+    assert len(tool_calls) > 0
+    assert tool_calls[0].function.name == "get_weather"
+    tool_args = "".join(
+        tc.function.arguments for tc in tool_calls if tc.function.arguments
+    )
+    assert json.loads(tool_args) == {"city": "Dallas"}
+
+
+def test_parse_delta_reasoning_only_thinking_disabled(tokenizer, request_obj):
+    """Regression test for vllm-project/vllm#40466.
+
+    When enable_thinking=False, the chat template places <think>\\n\\n</think>
+    in the prompt. The model then generates pure content (no think tokens).
+    All streaming output must go to delta.content, not delta.reasoning.
+    """
+    parser = make_parser(tokenizer, reasoning=True, tool=False)
+
+    end_token_id = parser._reasoning_parser.end_token_id
+    prompt_token_ids = [1, 2, end_token_id, 3]
+
+    content_text = "Hello! How can I assist you today?"
+    results = stream_text(
+        parser,
+        tokenizer,
+        content_text,
+        request_obj,
+        prompt_token_ids=prompt_token_ids,
+    )
+    reasoning, content, tool_calls = collect_fields(results)
+
+    assert reasoning == "", f"Expected no reasoning, got: {reasoning!r}"
+    assert "Hello" in content
+    assert "assist" in content
+    assert len(tool_calls) == 0
diff --git a/tests/plugins_tests/test_terratorch_io_processor_plugins.py b/tests/plugins_tests/test_terratorch_io_processor_plugins.py
index 34799b3c42c0..b4c84b30d2ca 100644
--- a/tests/plugins_tests/test_terratorch_io_processor_plugins.py
+++ b/tests/plugins_tests/test_terratorch_io_processor_plugins.py
@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import importlib.util
 import io
 
 import imagehash
@@ -11,6 +12,11 @@
 from tests.utils import RemoteOpenAIServer
 from vllm.entrypoints.pooling.pooling.protocol import IOProcessorResponse
 
+pytestmark = pytest.mark.skipif(
+    importlib.util.find_spec("terratorch") is None,
+    reason="terratorch unavailable while PyPI has `lightning` quarantined; see #41376",
+)
+
 models_config = {
     "ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11": {
         "image_url": "https://huggingface.co/christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM/resolve/main/valencia_example_2024-10-26.tiff",  # noqa: E501
diff --git a/tests/quantization/test_compressed_tensors.py b/tests/quantization/test_compressed_tensors.py
index 6b95d9e346db..7e80b0ecfa61 100644
--- a/tests/quantization/test_compressed_tensors.py
+++ b/tests/quantization/test_compressed_tensors.py
@@ -32,6 +32,9 @@
     CompressedTensorsW8A16Fp8,
     CompressedTensorsWNA16,
 )
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    find_matched_target,
+)
 from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.utils.nvfp4_utils import (
     cutlass_fp4_supported,
@@ -635,6 +638,24 @@ def test_get_quant_method_returns_none_for_unmatched_parallel_lm_head():
     )
 
 
+def test_find_matched_target_returns_none_on_no_match():
+    result = find_matched_target(
+        layer_name="model.layers.0.self_attn.qkv_proj",
+        module=Mock(spec=torch.nn.Linear),
+        targets=["no_match_target"],
+    )
+    assert result is None
+
+
+def test_get_scheme_dict_returns_none_on_no_match():
+    config = _make_ct_config(target="matched_layer")
+    result = config.get_scheme_dict(
+        layer=Mock(spec=torch.nn.Linear),
+        layer_name="model.layers.0.unmatched_layer",
+    )
+    assert result is None
+
+
 @pytest.mark.skipif(
     not current_platform.is_cuda() or not current_platform.has_device_capability(75),
     reason="MXFP8 requires Turing (sm_75+) or newer.",
diff --git a/tests/quantization/test_configs.py b/tests/quantization/test_configs.py
index 40ca587bc6ef..581ee2efd766 100644
--- a/tests/quantization/test_configs.py
+++ b/tests/quantization/test_configs.py
@@ -54,13 +54,13 @@ class ModelPair:
     (
         "TheBloke/OpenHermes-2.5-Mistral-7B-AWQ",
         None,
-        "awq_marlin" if current_platform.is_cuda() else "awq",
+        "awq_marlin" if current_platform.is_cuda_alike() else "awq",
     ),
     ("TheBloke/OpenHermes-2.5-Mistral-7B-AWQ", "awq", "awq"),
     (
         "TheBloke/OpenHermes-2.5-Mistral-7B-AWQ",
         "marlin",
-        "awq_marlin" if current_platform.is_cuda() else "ERROR",
+        "awq_marlin" if current_platform.is_cuda_alike() else "ERROR",
     ),
     ("TheBloke/OpenHermes-2.5-Mistral-7B-AWQ", "gptq", "ERROR"),
 ]
diff --git a/tests/quantization/test_cpu_offload.py b/tests/quantization/test_cpu_offload.py
index 3b58614e58d4..151b5d97ddf3 100644
--- a/tests/quantization/test_cpu_offload.py
+++ b/tests/quantization/test_cpu_offload.py
@@ -70,4 +70,5 @@ def test_cpu_offload_compressed_tensors(monkeypatch):
         ["--enforce_eager"],
         ["--enforce_eager", "--cpu-offload-gb", "1"],
         max_wait_seconds=480,
+        include_seeded_sampling=False,
     )
diff --git a/tests/quantization/test_cpu_wna16.py b/tests/quantization/test_cpu_wna16.py
index 650bf714a071..1fbe22201952 100644
--- a/tests/quantization/test_cpu_wna16.py
+++ b/tests/quantization/test_cpu_wna16.py
@@ -12,6 +12,9 @@
     "TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ",  # with g_idx
     "Qwen/Qwen1.5-0.5B-Chat-GPTQ-Int4",  # without g_idx
     "RedHatAI/Qwen3-1.7B-quantized.w4a16",  # with zp
+    "OPEA/Qwen2.5-0.5B-Instruct-int4-sym-inc",
+    "Qwen/Qwen3-0.6B-FP8",  # FP8 W8A16 block-quantized linear
+    "Qwen/Qwen3-30B-A3B-FP8",  # FP8 W8A16 block-quantized MoE
 ]
 DTYPE = ["bfloat16"]
 
diff --git a/tests/quantization/test_cutlass_w4a16.py b/tests/quantization/test_cutlass_w4a16.py
new file mode 100644
index 000000000000..7557e6fc8f32
--- /dev/null
+++ b/tests/quantization/test_cutlass_w4a16.py
@@ -0,0 +1,185 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for Cutlass W4A16 (Machete) kernel on Hopper.
+
+Verifies that W4A16 quantized models loaded through vllm select the
+MacheteLinearKernel on sm_90 GPUs, that weights are correctly repacked,
+and that inference produces valid output.
+
+Run `pytest tests/quantization/test_cutlass_w4a16.py`.
+"""
+
+import pytest
+import torch
+
+from vllm.platforms import current_platform
+
+if not current_platform.has_device_capability(90):
+    pytest.skip(
+        "Machete W4A16 requires Hopper (sm_90).",
+        allow_module_level=True,
+    )
+
+from vllm.model_executor.kernels.linear import (
+    MPLinearLayerConfig,
+    choose_mp_linear_kernel,
+)
+from vllm.model_executor.kernels.linear.mixed_precision import (
+    MacheteLinearKernel,
+)
+from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import (  # noqa: E501
+    CompressedTensorsLinearMethod,
+    CompressedTensorsWNA16,
+)
+from vllm.scalar_type import scalar_types
+
+
+@pytest.fixture(scope="function", autouse=True)
+def enable_pickle(monkeypatch):
+    """`LLM.apply_model` requires pickling a function."""
+    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
+
+
+@pytest.mark.parametrize(
+    "act_type,weight_type,group_size,zero_points",
+    [
+        (torch.float16, scalar_types.uint4b8, 128, False),
+        (torch.bfloat16, scalar_types.uint4b8, 128, False),
+        (torch.float16, scalar_types.uint4, 128, True),
+        (torch.float16, scalar_types.uint4b8, -1, False),
+    ],
+    ids=[
+        "fp16-gptq-g128",
+        "bf16-gptq-g128",
+        "fp16-awq-g128",
+        "fp16-channelwise",
+    ],
+)
+def test_machete_kernel_selected(act_type, weight_type, group_size, zero_points):
+    """Verify choose_mp_linear_kernel picks MacheteLinearKernel."""
+    config = MPLinearLayerConfig(
+        full_weight_shape=(4096, 4096),
+        partition_weight_shape=(4096, 4096),
+        act_type=act_type,
+        weight_type=weight_type,
+        group_size=group_size,
+        zero_points=zero_points,
+        has_g_idx=False,
+    )
+    kernel = choose_mp_linear_kernel(config)
+    assert kernel is MacheteLinearKernel, (
+        f"Expected MacheteLinearKernel, got {kernel.__name__}"
+    )
+
+
+@pytest.mark.parametrize(
+    "full_shape,part_shape,weight_type,group_size,has_g_idx,expected_reason",
+    [
+        ((4096, 4096), (2048, 4096), scalar_types.uint4b8, 128, True, "Act reordering"),
+        (
+            (4096, 4096),
+            (4096, 4096),
+            scalar_types.float6_e3m2f,
+            128,
+            False,
+            "Quant type",
+        ),
+        ((4096, 4096), (4096, 4096), scalar_types.uint4b8, 32, False, "Group size"),
+    ],
+    ids=["partitioned-g_idx", "unsupported-quant-type", "unsupported-group-size"],
+)
+def test_machete_rejects_invalid_config(
+    full_shape, part_shape, weight_type, group_size, has_g_idx, expected_reason
+):
+    """Verify Machete rejects unsupported configurations."""
+    config = MPLinearLayerConfig(
+        full_weight_shape=full_shape,
+        partition_weight_shape=part_shape,
+        act_type=torch.float16,
+        weight_type=weight_type,
+        group_size=group_size,
+        zero_points=False,
+        has_g_idx=has_g_idx,
+    )
+    can_impl, reason = MacheteLinearKernel.can_implement(config)
+    assert not can_impl
+    assert expected_reason in reason
+
+
+def test_kernel_selection_with_disabled_machete(monkeypatch):
+    """Verify kernel selection falls back when Machete is disabled."""
+    monkeypatch.setattr("vllm.envs.VLLM_DISABLED_KERNELS", ["MacheteLinearKernel"])
+
+    config = MPLinearLayerConfig(
+        full_weight_shape=(4096, 4096),
+        partition_weight_shape=(4096, 4096),
+        act_type=torch.float16,
+        weight_type=scalar_types.uint4b8,
+        group_size=128,
+        zero_points=False,
+        has_g_idx=False,
+    )
+    kernel = choose_mp_linear_kernel(config)
+    assert kernel is not MacheteLinearKernel, "MacheteLinearKernel should be disabled"
+
+
+@pytest.mark.parametrize(
+    "model_name",
+    [
+        "nm-testing/tinyllama-oneshot-w4a16-channel-v2",
+        "nm-testing/TinyLlama-1.1B-Chat-v1.0-W4A16-G128-Asym-Updated-ActOrder",
+    ],
+)
+def test_w4a16_machete_e2e(vllm_runner, model_name):
+    """Load a W4A16 model, verify Machete kernel is used, and generate."""
+    with vllm_runner(model_name, enforce_eager=True, gpu_memory_utilization=0.5) as llm:
+
+        def check_model(model):
+            layer = model.model.layers[0]
+            qkv_proj = layer.self_attn.qkv_proj
+
+            assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod)
+            assert isinstance(qkv_proj.scheme, CompressedTensorsWNA16)
+            assert isinstance(qkv_proj.scheme.kernel, MacheteLinearKernel), (
+                f"Expected MacheteLinearKernel on Hopper, "
+                f"got {type(qkv_proj.scheme.kernel).__name__}"
+            )
+
+            assert hasattr(qkv_proj, "weight_packed")
+            assert hasattr(qkv_proj, "weight_scale")
+            assert qkv_proj.weight_packed.dtype == torch.int32
+
+        llm.apply_model(check_model)
+
+        output = llm.generate_greedy("Hello my name is", max_tokens=10)
+        assert output
+        assert len(output[0][1]) > 0
+
+
+def test_w4a16_machete_bfloat16_deterministic(vllm_runner):
+    """Verify Machete works with bf16 activations and is deterministic."""
+    model_name = "nm-testing/tinyllama-oneshot-w4a16-channel-v2"
+    prompt = "The capital of France is"
+
+    with vllm_runner(
+        model_name,
+        enforce_eager=True,
+        dtype="bfloat16",
+        gpu_memory_utilization=0.5,
+    ) as llm:
+
+        def check_kernel_type(model):
+            layer = model.model.layers[0]
+            scheme = layer.self_attn.qkv_proj.scheme
+            assert isinstance(scheme.kernel, MacheteLinearKernel), (
+                f"Expected MacheteLinearKernel with bf16, "
+                f"got {type(scheme.kernel).__name__}"
+            )
+
+        llm.apply_model(check_kernel_type)
+
+        out1 = llm.generate_greedy(prompt, max_tokens=10)
+        out2 = llm.generate_greedy(prompt, max_tokens=10)
+        assert out1[0][1] == out2[0][1], (
+            f"Non-deterministic: '{out1[0][1]}' vs '{out2[0][1]}'"
+        )
diff --git a/tests/quantization/test_experts_int8.py b/tests/quantization/test_experts_int8.py
index 22edb9c58daf..7cdb135fa077 100644
--- a/tests/quantization/test_experts_int8.py
+++ b/tests/quantization/test_experts_int8.py
@@ -38,6 +38,5 @@ def test_model_experts_int8_startup(
         dtype=dtype,
         enforce_eager=True,
         quantization="experts_int8",
-        allow_deprecated_quantization=True,
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
diff --git a/tests/quantization/test_fp8.py b/tests/quantization/test_fp8.py
index 4209d59ba286..4ba0e5d3dadc 100644
--- a/tests/quantization/test_fp8.py
+++ b/tests/quantization/test_fp8.py
@@ -24,6 +24,8 @@
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.platforms import current_platform
 
+DEVICE_TYPE = current_platform.device_type
+
 MODELS = [
     "neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV",
     # The checkpoint below was removed from the HF.
@@ -314,7 +316,7 @@ def per_tensor_dequantize(tensor, inv_scale, dtype):
 
     # Note that we use a shape % 4 != 0 to cover edge cases,
     # because scaled_fp8_quant is vectorized by 4.
-    x = (torch.randn(size=(11, 11), device="cuda") * 13).to(dtype)
+    x = (torch.randn(size=(11, 11), device=DEVICE_TYPE) * 13).to(dtype)
 
     # Dynamic quantization
     ref_y, inv_scale = ops.scaled_fp8_quant(x, None)
@@ -338,7 +340,9 @@ def per_tensor_dequantize(tensor, inv_scale, dtype):
 
     # non-contiguous input with padding
     m, n, padded_stride = 975, 512, 576
-    padded_tensor = (torch.randn(size=(m, padded_stride), device="cuda") * 13).to(dtype)
+    padded_tensor = (torch.randn(size=(m, padded_stride), device=DEVICE_TYPE) * 13).to(
+        dtype
+    )
     x_nc = padded_tensor[:, :n]  # shape (m, n) with stride (padded_stride, 1)
 
     assert not x_nc.is_contiguous()
@@ -409,7 +413,7 @@ def test_fp8_reloading(
 
     # Set model config as model_config.dtype is required in Fp8LinearMethod.
     default_vllm_config.model_config = ModelConfig()
-    with torch.device("cuda:0"):
+    with torch.device(f"{DEVICE_TYPE}:0"):
         config = Fp8Config(
             is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized,
             weight_block_size=weight_block_size,
diff --git a/tests/quantization/test_mi3xx_moe.py b/tests/quantization/test_gfx950_moe.py
similarity index 58%
rename from tests/quantization/test_mi3xx_moe.py
rename to tests/quantization/test_gfx950_moe.py
index 2f8dfde68477..9cb94086f733 100644
--- a/tests/quantization/test_mi3xx_moe.py
+++ b/tests/quantization/test_gfx950_moe.py
@@ -2,5 +2,5 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 
-def test_mi3xx_moe():
-    print("TODO: add tests for Mi3xx MoE quantization")
+def test_mi355_moe():
+    print("TODO: add tests for Mi355 MoE quantization")
diff --git a/tests/quantization/test_mixed_precision.py b/tests/quantization/test_mixed_precision.py
index 5087f9049cc5..d04692046340 100755
--- a/tests/quantization/test_mixed_precision.py
+++ b/tests/quantization/test_mixed_precision.py
@@ -43,7 +43,7 @@ def get_model_args(self) -> str:
     "amd/Qwen3-8B-WMXFP4FP8-AMXFP4FP8-AMP-KVFP8": {"arc_challenge": 0.52, "mmlu": 0.72},
     # Non-mixed-precision (PTQ) model
     # - Reference for pipeline compatibility verification -> No conflicts or breakings
-    "amd/Llama-2-70b-chat-hf-FP8-MLPerf-fp8_attn_quark_format": {
+    "amd/Llama-2-70b-chat-hf_FP8_MLPerf_V2": {
         "arc_challenge": 0.53,
         "mmlu": 0.61,
     },
diff --git a/tests/quantization/test_modelopt.py b/tests/quantization/test_modelopt.py
index 120b2cde0f35..593075e9d491 100644
--- a/tests/quantization/test_modelopt.py
+++ b/tests/quantization/test_modelopt.py
@@ -239,3 +239,49 @@ def check_model(model):
         output = llm.generate_greedy(["Hello my name is"], max_tokens=4)
         assert output
         print(f"ModelOpt FP8_PB_WO output: {output}")
+
+
+def test_modelopt_nvfp4_config_dispatches_w4a4_method():
+    """``quant_method="NVFP4"`` (W4A4 default) routes to the existing
+    ``ModelOptNvFp4LinearMethod``."""
+    from vllm.model_executor.layers.quantization.modelopt import (
+        ModelOptNvFp4Config,
+        ModelOptNvFp4LinearMethod,
+    )
+
+    config = ModelOptNvFp4Config(
+        quant_method="NVFP4",
+        is_checkpoint_nvfp4_serialized=True,
+        kv_cache_quant_algo=None,
+        exclude_modules=[],
+    )
+    assert config.LinearMethodCls is ModelOptNvFp4LinearMethod
+    assert config.quant_method == "NVFP4"
+
+
+def test_modelopt_nvfp4_config_dispatches_w4a16_method():
+    """``quant_method="W4A16_NVFP4"`` routes to the new
+    ``ModelOptNvFp4W4A16LinearMethod`` instead of the W4A4 sibling.
+
+    Mirrors the FP8 dispatch precedent (``ModelOptFp8Config`` selects
+    one of three FP8 LinearMethods on ``quant_method``); a regression
+    here would mean a W4A16 NVFP4 checkpoint silently loaded under the
+    W4A4 method, which would try to register an ``input_scale`` runtime
+    parameter and (more importantly) call the cutlass W4A4 NVFP4 GEMM
+    instead of FP4 Marlin.
+    """
+    from vllm.model_executor.layers.quantization.modelopt import (
+        ModelOptNvFp4Config,
+        ModelOptNvFp4LinearMethod,
+        ModelOptNvFp4W4A16LinearMethod,
+    )
+
+    config = ModelOptNvFp4Config(
+        quant_method="W4A16_NVFP4",
+        is_checkpoint_nvfp4_serialized=True,
+        kv_cache_quant_algo=None,
+        exclude_modules=[],
+    )
+    assert config.LinearMethodCls is ModelOptNvFp4W4A16LinearMethod
+    assert config.LinearMethodCls is not ModelOptNvFp4LinearMethod
+    assert config.quant_method == "W4A16_NVFP4"
diff --git a/tests/quantization/test_per_token_kv_cache.py b/tests/quantization/test_per_token_kv_cache.py
index 3e660e6b00d0..254e284efb51 100644
--- a/tests/quantization/test_per_token_kv_cache.py
+++ b/tests/quantization/test_per_token_kv_cache.py
@@ -25,11 +25,13 @@
 from vllm.utils.torch_utils import set_random_seed
 from vllm.v1.kv_cache_interface import KVQuantMode, is_quantized_kv_cache
 
+DEVICE_TYPE = current_platform.device_type
+
 # Skip entire module if no CUDA/ROCm GPU available
 pytestmark = [
     pytest.mark.skipif(
-        not current_platform.is_cuda_alike(),
-        reason="Per-token-head KV cache tests require CUDA or ROCm GPU.",
+        current_platform.is_cpu(),
+        reason="Per-token-head KV cache tests require GPU.",
     ),
 ]
 
@@ -166,7 +168,7 @@ def test_reshape_and_cache_per_token_head(
     )
 
     set_random_seed(seed)
-    torch.set_default_device("cuda")
+    torch.set_default_device(DEVICE_TYPE)
 
     num_blocks = (num_tokens + block_size - 1) // block_size + 4
 
@@ -260,7 +262,7 @@ def test_per_token_head_round_trip_accuracy(
         triton_reshape_and_cache_flash_per_token_head_quant,
     )
 
-    torch.set_default_device("cuda")
+    torch.set_default_device(DEVICE_TYPE)
     set_random_seed(42)
 
     num_blocks = (num_tokens + block_size - 1) // block_size + 2
@@ -323,7 +325,7 @@ def test_per_token_head_negative_slot_skipped(qcfg: QuantConfig):
         triton_reshape_and_cache_flash_per_token_head_quant,
     )
 
-    torch.set_default_device("cuda")
+    torch.set_default_device(DEVICE_TYPE)
     num_tokens = 4
     num_heads = 2
     head_size = 64
@@ -430,7 +432,7 @@ def test_triton_unified_attention_per_token_head_scale(
     from vllm.utils.math_utils import next_power_of_2
     from vllm.v1.attention.ops.triton_unified_attention import unified_attention
 
-    torch.set_default_device("cuda")
+    torch.set_default_device(DEVICE_TYPE)
     set_random_seed(0)
 
     num_seqs = len(seq_lens)
diff --git a/tests/quantization/test_quark.py b/tests/quantization/test_quark.py
index c51b3461c63b..9eca6cda0837 100644
--- a/tests/quantization/test_quark.py
+++ b/tests/quantization/test_quark.py
@@ -36,6 +36,8 @@
     importlib.metadata.version("amd-quark")
 ) >= version.parse(QUARK_MXFP4_MIN_VERSION)
 
+DEVICE_TYPE = current_platform.device_type
+
 if QUARK_MXFP4_AVAILABLE:
     from quark.torch.export.nn.modules.realquantizer import StaticScaledRealQuantizer
     from quark.torch.kernel import mx as mx_kernel
@@ -309,7 +311,7 @@ def test_mxfp4_fused_qdq_match_quark(float_dtype: torch.dtype, scalings: list[in
     torch.manual_seed(0)
 
     hidden_size = 64 * 32
-    inp = (torch.rand(1, hidden_size, dtype=float_dtype, device="cuda") - 0.5) * 2
+    inp = (torch.rand(1, hidden_size, dtype=float_dtype, device=DEVICE_TYPE) - 0.5) * 2
     for i in range(hidden_size // 32):
         inp[:, i * 32 : (i + 1) * 32] = (
             inp[:, i * 32 : (i + 1) * 32] * scalings[i % len(scalings)]
@@ -353,15 +355,15 @@ def test_mxfp4_dequant_kernel_match_quark(
         reorder=False,
         real_quantized=True,
         float_dtype=float_dtype,
-        device="cuda",
+        device=DEVICE_TYPE,
     )
 
-    observer = qspec.observer_cls(qspec, device="cuda")
+    observer = qspec.observer_cls(qspec, device=DEVICE_TYPE)
 
     hidden_size = 512
     shape = (11008, hidden_size)
 
-    w = (torch.rand(shape, device="cuda", dtype=float_dtype) - 0.5) * 2
+    w = (torch.rand(shape, device=DEVICE_TYPE, dtype=float_dtype) - 0.5) * 2
 
     # Make it so that different groups have different scales.
     for i in range(hidden_size // 32):
@@ -373,7 +375,7 @@ def test_mxfp4_dequant_kernel_match_quark(
     scale, _ = observer._calculate_qparams()
     weight_quantizer.scale = scale
 
-    w_mxfp4 = weight_quantizer.to_real_quantize_params(w).to("cuda")
+    w_mxfp4 = weight_quantizer.to_real_quantize_params(w).to(DEVICE_TYPE)
     weight_quantizer.maybe_convert_and_transpose_scale()
 
     scale = weight_quantizer.scale
diff --git a/tests/quantization/test_quark_maybe_update_config.py b/tests/quantization/test_quark_maybe_update_config.py
deleted file mode 100644
index 0142e869c22c..000000000000
--- a/tests/quantization/test_quark_maybe_update_config.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Unit tests for QuarkConfig.maybe_update_config.
-
-Fetches real HF configs (metadata only, no model weights) to verify
-that dynamic_mxfp4_quant is only enabled for DeepSeek-V3-family models.
-
-Run: pytest tests/quantization/test_quark_maybe_update_config.py -v
-"""
-
-import pytest
-from transformers import AutoConfig
-
-from vllm.model_executor.layers.quantization.quark.quark import QuarkConfig
-
-
-def _make_quark_config() -> QuarkConfig:
-    """Create a minimal QuarkConfig for testing."""
-    return QuarkConfig(quant_config={}, kv_cache_group=[], pack_method="reorder")
-
-
-# ---------------------------------------------------------------------------
-# Non-deepseek models must not flip dynamic_mxfp4_quant
-# ---------------------------------------------------------------------------
-@pytest.mark.parametrize(
-    "model_name",
-    ["amd/MiniMax-M2.1-MXFP4"],
-)
-def test_non_deepseek_model_stays_false(model_name: str):
-    """Non-deepseek_v3 models must not enable dynamic_mxfp4_quant."""
-    hf_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
-    qcfg = _make_quark_config()
-
-    qcfg.maybe_update_config(model_name, hf_config=hf_config)
-
-    assert qcfg.dynamic_mxfp4_quant is False
-
-
-# ---------------------------------------------------------------------------
-# DeepSeek-V3 family + fp4 must enable dynamic_mxfp4_quant
-# ---------------------------------------------------------------------------
-@pytest.mark.parametrize(
-    "model_name",
-    ["amd/DeepSeek-R1-MXFP4-ASQ"],
-)
-def test_deepseek_family_fp4_enables_flag(model_name: str):
-    hf_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
-    qcfg = _make_quark_config()
-
-    qcfg.maybe_update_config(model_name, hf_config=hf_config)
-
-    assert qcfg.dynamic_mxfp4_quant is True
-
-
-# ---------------------------------------------------------------------------
-# Missing hf_config → warn and stay False
-# ---------------------------------------------------------------------------
-def test_missing_hf_config_stays_false():
-    qcfg = _make_quark_config()
-
-    qcfg.maybe_update_config("some/model")
-
-    assert qcfg.dynamic_mxfp4_quant is False
diff --git a/tests/quantization/test_torchao.py b/tests/quantization/test_torchao.py
index fb794baa53f0..8efc6742a2d9 100644
--- a/tests/quantization/test_torchao.py
+++ b/tests/quantization/test_torchao.py
@@ -8,6 +8,7 @@
 from vllm.model_executor.model_loader import get_model_loader
 from vllm.platforms import current_platform
 
+DEVICE_TYPE = current_platform.device_type
 DTYPE = ["bfloat16"]
 
 TORCHAO_AVAILABLE = importlib.util.find_spec("torchao") is not None
@@ -33,7 +34,7 @@ def test_pre_quantized_model(vllm_runner):
 @pytest.mark.parametrize(
     "pt_load_map_location",
     [
-        "cuda:0",
+        f"{DEVICE_TYPE}:0",
         # {"": "cuda"},
     ],
 )
@@ -60,7 +61,7 @@ def test_qwenvl_int8wo_model_loading_with_params(vllm_runner):
         model_name=model_name,
         quantization="torchao",
         dtype="bfloat16",
-        pt_load_map_location="cuda:0",
+        pt_load_map_location=f"{DEVICE_TYPE}:0",
         enforce_eager=True,
     ) as llm:
         output = llm.generate_greedy(["The capital of France is"], max_tokens=4)
@@ -81,7 +82,7 @@ def test_opt_125m_awq_int4wo_model_loading_with_params(vllm_runner):
         model_name=model_name,
         quantization="torchao",
         dtype="bfloat16",
-        pt_load_map_location="cuda:0",
+        pt_load_map_location=f"{DEVICE_TYPE}:0",
     ) as llm:
         output = llm.generate_greedy(["The capital of France is"], max_tokens=4)
 
@@ -112,7 +113,7 @@ def test_online_quant_config_dict_json(vllm_runner, enable_pickle):
     with vllm_runner(
         model_name=model_name,
         dtype="bfloat16",
-        pt_load_map_location="cuda:0",
+        pt_load_map_location=f"{DEVICE_TYPE}:0",
         quantization="torchao",
         hf_overrides=hf_overrides,
         enforce_eager=True,
@@ -158,7 +159,7 @@ def test_online_quant_config_file(vllm_runner):
         with vllm_runner(
             model_name=model_name,
             dtype="bfloat16",
-            pt_load_map_location="cuda:0",
+            pt_load_map_location=f"{DEVICE_TYPE}:0",
             quantization="torchao",
             hf_overrides=hf_overrides,
             enforce_eager=True,
@@ -248,7 +249,7 @@ def test_opt_125m_module_fqn_to_config_regex_model(vllm_runner):
     torch._dynamo.reset()
     model_name = "torchao-testing/opt-125m-ModuleFqnToConfig-v1-regex-0.14.0.dev"
     with vllm_runner(
-        model_name=model_name, dtype="bfloat16", pt_load_map_location="cuda:0"
+        model_name=model_name, dtype="bfloat16", pt_load_map_location=f"{DEVICE_TYPE}:0"
     ) as llm:
         output = llm.generate_greedy(["The capital of France is"], max_tokens=4)
 
@@ -278,7 +279,7 @@ def test_opt_125m_int4wo_model_running_preshuffled_kernel(vllm_runner, monkeypat
         model_name=model_name,
         quantization="torchao",
         dtype="bfloat16",
-        pt_load_map_location="cuda:0",
+        pt_load_map_location=f"{DEVICE_TYPE}:0",
         enforce_eager=True,
     ) as llm:
 
@@ -357,7 +358,7 @@ def test_opt_125m_int4wo_model_running_preshuffled_kernel_online_quant(
         model_name=model_name,
         quantization="torchao",
         dtype="bfloat16",
-        pt_load_map_location="cuda:0",
+        pt_load_map_location=f"{DEVICE_TYPE}:0",
         hf_overrides=hf_overrides,
         enforce_eager=True,
     ) as llm:
diff --git a/tests/quantization/test_turboquant.py b/tests/quantization/test_turboquant.py
new file mode 100644
index 000000000000..b9567195b3a8
--- /dev/null
+++ b/tests/quantization/test_turboquant.py
@@ -0,0 +1,625 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for TurboQuant KV-cache quantization.
+
+Run: .venv/bin/python -m pytest tests/quantization/test_turboquant.py -v
+"""
+
+import math
+
+import pytest
+import torch
+
+from vllm.model_executor.layers.quantization.turboquant.centroids import (
+    get_centroids,
+    solve_lloyd_max,
+)
+from vllm.model_executor.layers.quantization.turboquant.config import (
+    TQ_PRESETS,
+    TurboQuantConfig,
+)
+from vllm.platforms import current_platform
+from vllm.utils.math_utils import next_power_of_2
+
+# ============================================================================
+# Helpers
+# ============================================================================
+
+ALL_PRESETS = list(TQ_PRESETS.keys())
+
+
+def _assert_strictly_sorted(seq, name="sequence"):
+    for i in range(len(seq) - 1):
+        assert seq[i] < seq[i + 1], f"{name} not sorted at index {i}"
+
+
+def _is_power_of_2(n: int) -> bool:
+    return n > 0 and next_power_of_2(n) == n
+
+
+# Expected concrete values for each preset at head_dim=128.
+# fmt: off
+PRESET_EXPECTED = {
+    "turboquant_k8v4": dict(
+        key_fp8=True,  key_quant_bits=8,
+        key_mse_bits=0, value_quant_bits=4,
+        mse_bits=4, n_centroids=16, centroid_bits=4,
+        norm_correction=False,
+        key_packed_size=128, value_packed_size=68,
+        slot_size=196, slot_size_aligned=196,
+    ),
+    "turboquant_4bit_nc": dict(
+        key_fp8=False, key_quant_bits=4,
+        key_mse_bits=4, value_quant_bits=4,
+        mse_bits=4, n_centroids=16, centroid_bits=4,
+        norm_correction=True,
+        key_packed_size=66, value_packed_size=68,
+        slot_size=134, slot_size_aligned=134,
+    ),
+    "turboquant_k3v4_nc": dict(
+        key_fp8=False, key_quant_bits=3,
+        key_mse_bits=3, value_quant_bits=4,
+        mse_bits=3, n_centroids=8, centroid_bits=3,
+        norm_correction=True,
+        key_packed_size=50, value_packed_size=68,
+        slot_size=118, slot_size_aligned=118,
+    ),
+    "turboquant_3bit_nc": dict(
+        key_fp8=False, key_quant_bits=3,
+        key_mse_bits=3, value_quant_bits=3,
+        mse_bits=3, n_centroids=8, centroid_bits=3,
+        norm_correction=True,
+        key_packed_size=50, value_packed_size=52,
+        slot_size=102, slot_size_aligned=102,
+    ),
+}
+# fmt: on
+
+
+# ============================================================================
+# Config tests (CPU-only, no dependencies beyond config.py)
+# ============================================================================
+
+
+class TestTurboQuantConfig:
+    @pytest.mark.parametrize("preset", ALL_PRESETS)
+    def test_preset_parses(self, preset):
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=128)
+        assert isinstance(cfg, TurboQuantConfig)
+
+    def test_invalid_preset_raises(self):
+        with pytest.raises(ValueError, match="Unknown TurboQuant"):
+            TurboQuantConfig.from_cache_dtype("turboquant_invalid", head_dim=128)
+
+    # ---- Per-preset concrete value checks (table-driven) ----
+
+    @pytest.mark.parametrize("preset", ALL_PRESETS)
+    def test_key_mode(self, preset):
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=128)
+        exp = PRESET_EXPECTED[preset]
+        assert cfg.key_fp8 is exp["key_fp8"]
+        assert cfg.key_quant_bits == exp["key_quant_bits"]
+        assert cfg.key_mse_bits == exp["key_mse_bits"]
+
+    @pytest.mark.parametrize("preset", ALL_PRESETS)
+    def test_value_mode(self, preset):
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=128)
+        exp = PRESET_EXPECTED[preset]
+        assert cfg.value_quant_bits == exp["value_quant_bits"]
+
+    @pytest.mark.parametrize("preset", ALL_PRESETS)
+    def test_bits_and_centroids(self, preset):
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=128)
+        exp = PRESET_EXPECTED[preset]
+        assert cfg.mse_bits == exp["mse_bits"]
+        assert cfg.n_centroids == exp["n_centroids"]
+        assert cfg.centroid_bits == exp["centroid_bits"]
+
+    @pytest.mark.parametrize("preset", ALL_PRESETS)
+    def test_norm_correction(self, preset):
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=128)
+        assert cfg.norm_correction is PRESET_EXPECTED[preset]["norm_correction"]
+
+    @pytest.mark.parametrize("preset", ALL_PRESETS)
+    def test_packed_sizes(self, preset):
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=128)
+        exp = PRESET_EXPECTED[preset]
+        assert cfg.key_packed_size == exp["key_packed_size"]
+        assert cfg.value_packed_size == exp["value_packed_size"]
+        assert cfg.slot_size == exp["slot_size"]
+        assert cfg.slot_size_aligned == exp["slot_size_aligned"]
+
+    # ---- Cross-preset structural invariants ----
+
+    @pytest.mark.parametrize("preset", ALL_PRESETS)
+    def test_slot_equals_key_plus_value(self, preset):
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=128)
+        assert cfg.slot_size == cfg.key_packed_size + cfg.value_packed_size
+
+    @pytest.mark.parametrize("preset", ALL_PRESETS)
+    def test_padded_slot_is_even(self, preset):
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=128)
+        assert cfg.slot_size_aligned >= cfg.slot_size
+        assert cfg.slot_size_aligned % 2 == 0, (
+            f"slot_size_aligned={cfg.slot_size_aligned} is not even"
+        )
+
+    @pytest.mark.parametrize("preset", ALL_PRESETS)
+    def test_key_value_packed_sizes_positive(self, preset):
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=128)
+        assert cfg.key_packed_size > 0
+        assert cfg.value_packed_size > 0
+
+    @pytest.mark.parametrize("preset", ALL_PRESETS)
+    def test_n_centroids_is_2_to_mse_bits(self, preset):
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=128)
+        assert cfg.n_centroids == 2**cfg.mse_bits
+
+    @pytest.mark.parametrize("preset", ALL_PRESETS)
+    def test_centroid_bits_always_positive(self, preset):
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=128)
+        assert cfg.centroid_bits > 0
+
+    @pytest.mark.parametrize("preset", ALL_PRESETS)
+    def test_mse_key_or_fp8_exclusive(self, preset):
+        """Each preset is either FP8 keys or MSE keys, never both."""
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=128)
+        if cfg.key_fp8:
+            assert cfg.key_mse_bits == 0
+            assert cfg.key_quant_bits == 8
+        else:
+            assert cfg.key_mse_bits > 0
+            assert cfg.key_quant_bits in (3, 4)
+
+    @pytest.mark.parametrize("preset", ALL_PRESETS)
+    @pytest.mark.parametrize("head_dim", [64, 96, 128, 256])
+    def test_all_presets_all_head_dims(self, preset, head_dim):
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=head_dim)
+        assert cfg.head_dim == head_dim
+        assert cfg.slot_size == cfg.key_packed_size + cfg.value_packed_size
+        assert cfg.slot_size_aligned >= cfg.slot_size
+        assert cfg.slot_size_aligned % 2 == 0
+
+    # ---- Boundary skip layers ----
+
+    @staticmethod
+    def _dense_model_config(num_layers):
+        from types import SimpleNamespace
+
+        return SimpleNamespace(
+            is_hybrid=False,
+            hf_text_config=SimpleNamespace(num_hidden_layers=num_layers),
+        )
+
+    def test_boundary_skip_layers_basic(self):
+        mc = self._dense_model_config(32)
+        layers = TurboQuantConfig.get_boundary_skip_layers(mc)
+        assert layers == ["0", "1", "30", "31"]
+
+    def test_boundary_skip_layers_zero(self):
+        mc = self._dense_model_config(32)
+        assert TurboQuantConfig.get_boundary_skip_layers(mc, 0) == []
+
+    def test_boundary_skip_layers_small_model(self):
+        mc = self._dense_model_config(4)
+        layers = TurboQuantConfig.get_boundary_skip_layers(mc)
+        assert layers == ["0", "1", "2", "3"]
+
+    def test_boundary_skip_layers_cap_at_half(self):
+        mc = self._dense_model_config(8)
+        layers = TurboQuantConfig.get_boundary_skip_layers(mc, 10)
+        assert len(layers) == 8
+
+
+class TestHybridAttentionIndices:
+    """Regression tests for boundary protection on hybrid models.
+
+    Hybrid models (attention + Mamba / linear-attention) identify KV-carrying
+    layers via layer_types / layers_block_type / attn_type_list. The helper
+    must return the *global* layer indices of the full-attention layers so
+    that kv_cache_dtype_skip_layers matches what extract_layer_index(prefix)
+    reports on the Attention layers at runtime.
+    """
+
+    @staticmethod
+    def _fake_model_config(text_cfg=None, hf_cfg=None):
+        from types import SimpleNamespace
+
+        return SimpleNamespace(
+            hf_text_config=text_cfg if text_cfg is not None else SimpleNamespace(),
+            hf_config=hf_cfg if hf_cfg is not None else SimpleNamespace(),
+        )
+
+    def test_layer_types_full_attention(self):
+        from vllm.model_executor.layers.quantization.turboquant.config import (
+            _get_full_attention_layer_indices,
+        )
+
+        cfg = type("C", (), {})()
+        cfg.layer_types = [
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "full_attention",
+            "full_attention",
+        ]
+        mc = self._fake_model_config(text_cfg=cfg)
+        assert _get_full_attention_layer_indices(mc) == [2, 4, 5]
+
+    def test_layers_block_type_jamba(self):
+        from vllm.model_executor.layers.quantization.turboquant.config import (
+            _get_full_attention_layer_indices,
+        )
+
+        cfg = type("C", (), {})()
+        cfg.layers_block_type = ["mamba", "attention", "mamba", "attention"]
+        mc = self._fake_model_config(text_cfg=cfg)
+        assert _get_full_attention_layer_indices(mc) == [1, 3]
+
+    def test_attn_type_list_minimax(self):
+        from vllm.model_executor.layers.quantization.turboquant.config import (
+            _get_full_attention_layer_indices,
+        )
+
+        hf = type("C", (), {})()
+        hf.attn_type_list = [0, 1, 0, 1, 1]
+        mc = self._fake_model_config(hf_cfg=hf)
+        assert _get_full_attention_layer_indices(mc) == [1, 3, 4]
+
+    def test_no_hybrid_hints_returns_empty(self):
+        from vllm.model_executor.layers.quantization.turboquant.config import (
+            _get_full_attention_layer_indices,
+        )
+
+        mc = self._fake_model_config()
+        assert _get_full_attention_layer_indices(mc) == []
+
+
+# ============================================================================
+# Centroids tests (CPU-only)
+# ============================================================================
+
+
+class TestCentroids:
+    @pytest.mark.parametrize("bits,expected_n", [(2, 4), (3, 8), (4, 16)])
+    def test_centroids_shape(self, bits, expected_n):
+        c = get_centroids(128, bits)
+        assert c.shape == (expected_n,)
+
+    @pytest.mark.parametrize("bits", [2, 3, 4])
+    def test_centroids_sorted(self, bits):
+        _assert_strictly_sorted(get_centroids(128, bits), "centroids")
+
+    def test_centroids_cached(self):
+        c1 = get_centroids(128, 3)
+        c2 = get_centroids(128, 3)
+        assert c1 is c2, "get_centroids should return cached object"
+
+    def test_centroids_different_dims_not_identical(self):
+        c64 = get_centroids(64, 3)
+        c128 = get_centroids(128, 3)
+        assert not torch.equal(c64, c128)
+
+    @pytest.mark.parametrize("bits", [2, 3, 4])
+    def test_centroids_symmetric_around_zero(self, bits):
+        """N(0, 1/d) is symmetric, so centroids should be ~symmetric."""
+        c = get_centroids(128, bits)
+        assert abs(c.mean().item()) < 0.01, "Centroids not centered near 0"
+        assert abs(c[0].item() + c[-1].item()) < 0.01
+
+    @pytest.mark.parametrize("bits", [2, 3, 4])
+    def test_centroids_within_4sigma(self, bits):
+        """All centroids should be within ~4 sigma of N(0, 1/d)."""
+        sigma = math.sqrt(1.0 / 128)
+        c = get_centroids(128, bits)
+        for i, val in enumerate(c):
+            assert abs(val.item()) < 4 * sigma, (
+                f"Centroid {i}={val:.6f} outside 4*sigma={4 * sigma:.6f}"
+            )
+
+
+class TestLloydMax:
+    @pytest.mark.parametrize("bits,expected_n", [(2, 4), (3, 8), (4, 16)])
+    def test_solve_shapes(self, bits, expected_n):
+        centroids, boundaries = solve_lloyd_max(128, bits)
+        assert centroids.shape == (expected_n,)
+        assert boundaries.shape == (expected_n - 1,)
+
+    @pytest.mark.parametrize("bits", [2, 3, 4])
+    def test_centroids_sorted(self, bits):
+        centroids, _ = solve_lloyd_max(128, bits)
+        _assert_strictly_sorted(centroids, "centroids")
+
+    @pytest.mark.parametrize("bits", [2, 3, 4])
+    def test_boundaries_sorted(self, bits):
+        _, boundaries = solve_lloyd_max(128, bits)
+        _assert_strictly_sorted(boundaries, "boundaries")
+
+    @pytest.mark.parametrize("bits", [2, 3, 4])
+    def test_boundaries_between_centroids(self, bits):
+        """Each boundary must lie between its adjacent centroids."""
+        centroids, boundaries = solve_lloyd_max(128, bits)
+        for i in range(len(boundaries)):
+            assert centroids[i] < boundaries[i] < centroids[i + 1], (
+                f"Boundary {i}={boundaries[i]:.6f} not between "
+                f"c[{i}]={centroids[i]:.6f} and c[{i + 1}]={centroids[i + 1]:.6f}"
+            )
+
+    @pytest.mark.parametrize("bits", [2, 3, 4])
+    def test_boundaries_are_midpoints(self, bits):
+        """Lloyd-Max boundaries are midpoints of adjacent centroids."""
+        centroids, boundaries = solve_lloyd_max(128, bits)
+        for i in range(len(boundaries)):
+            expected = (centroids[i] + centroids[i + 1]) / 2.0
+            assert abs(boundaries[i].item() - expected.item()) < 1e-6
+
+    def test_solve_deterministic(self):
+        c1, b1 = solve_lloyd_max(128, 3)
+        c2, b2 = solve_lloyd_max(128, 3)
+        assert torch.equal(c1, c2)
+        assert torch.equal(b1, b2)
+
+    def test_solve_dtype_float32(self):
+        centroids, boundaries = solve_lloyd_max(128, 3)
+        assert centroids.dtype == torch.float32
+        assert boundaries.dtype == torch.float32
+
+    @pytest.mark.parametrize("bits", [3, 4])
+    def test_centroids_match_scipy_reference(self, bits):
+        """Verify _trapz(n=200) centroids match scipy.integrate.quad reference.
+
+        This ensures our scipy-free trapezoid integration doesn't silently
+        drift from the published Lloyd-Max quality.
+        """
+        pytest.importorskip("scipy")
+        from scipy.integrate import quad
+
+        d = 128
+        sigma2 = 1.0 / d
+        sigma = math.sqrt(sigma2)
+
+        def pdf(x):
+            return (1.0 / math.sqrt(2 * math.pi * sigma2)) * math.exp(
+                -x * x / (2 * sigma2)
+            )
+
+        n_levels = 2**bits
+        lo, hi = -3.5 * sigma, 3.5 * sigma
+        ref_centroids = [lo + (hi - lo) * (i + 0.5) / n_levels for i in range(n_levels)]
+        for _ in range(200):
+            boundaries = [
+                (ref_centroids[i] + ref_centroids[i + 1]) / 2.0
+                for i in range(n_levels - 1)
+            ]
+            edges = [lo * 3] + boundaries + [hi * 3]
+            new_centroids = []
+            for i in range(n_levels):
+                a, b = edges[i], edges[i + 1]
+                num, _ = quad(lambda x: x * pdf(x), a, b)
+                den, _ = quad(pdf, a, b)
+                new_centroids.append(num / den if den > 1e-15 else ref_centroids[i])
+            if (
+                max(abs(new_centroids[i] - ref_centroids[i]) for i in range(n_levels))
+                < 1e-10
+            ):
+                break
+            ref_centroids = new_centroids
+
+        # Compare our _trapz centroids against scipy reference
+        our_centroids, _ = solve_lloyd_max(d, bits)
+        ref_t = torch.tensor(ref_centroids, dtype=torch.float32)
+        max_err = (our_centroids - ref_t).abs().max().item()
+        # _trapz(n=200) has ~O(h^2) error vs adaptive quad; 1e-3 is tight
+        # enough to catch regression while allowing trapezoid approximation.
+        assert max_err < 1e-3, (
+            f"d={d}, bits={bits}: max centroid error vs scipy = {max_err:.2e}"
+        )
+
+
+# ============================================================================
+# Rotation matrix tests (GPU required)
+# ============================================================================
+
+GPGPU_AVAILABLE = torch.cuda.is_available() or torch.xpu.is_available()
+DEVICE_TYPE = current_platform.device_type
+
+
+def generate_rotation_matrix(d: int, seed: int, device: str = "cpu") -> torch.Tensor:
+    """Haar-distributed random orthogonal matrix via QR (test/benchmark only)."""
+    gen = torch.Generator(device="cpu")
+    gen.manual_seed(seed)
+    G = torch.randn(d, d, generator=gen, device="cpu", dtype=torch.float32)
+    # torch.linalg.qr on CPU requires LAPACK, which some torch wheels
+    # (ROCm) ship without. Run QR on accelerator instead
+    qr_device = "cuda" if torch.cuda.is_available() else "cpu"
+    Q, R = torch.linalg.qr(G.to(qr_device))
+    diag_sign = torch.sign(torch.diag(R))
+    diag_sign[diag_sign == 0] = 1.0
+    Q = Q * diag_sign.unsqueeze(0)
+    return Q.to(device)
+
+
+@pytest.mark.skipif(not GPGPU_AVAILABLE, reason="GPGPU not available")
+class TestRotationMatrix:
+    """Tests for the QR-based rotation (standalone benchmarks only)."""
+
+    @pytest.mark.parametrize("dim", [64, 96, 128, 256])
+    def test_rotation_matrix_shape_and_orthogonal(self, dim):
+        Pi = generate_rotation_matrix(dim, seed=42, device=DEVICE_TYPE)
+        assert Pi.shape == (dim, dim)
+        eye = Pi @ Pi.T
+        assert torch.allclose(eye, torch.eye(dim, device=DEVICE_TYPE), atol=1e-5), (
+            f"Pi not orthogonal for dim={dim}"
+        )
+
+    def test_rotation_matrix_deterministic(self):
+        Pi1 = generate_rotation_matrix(128, seed=42)
+        Pi2 = generate_rotation_matrix(128, seed=42)
+        assert torch.equal(Pi1, Pi2)
+
+    def test_rotation_matrix_different_seeds(self):
+        Pi1 = generate_rotation_matrix(128, seed=42)
+        Pi2 = generate_rotation_matrix(128, seed=99)
+        assert not torch.equal(Pi1, Pi2)
+
+    def test_rotation_matrix_det_is_pm1(self):
+        """Orthogonal matrix determinant must be +1 or -1."""
+        Pi = generate_rotation_matrix(128, seed=42, device=DEVICE_TYPE)
+        det = torch.linalg.det(Pi)
+        assert abs(abs(det.item()) - 1.0) < 1e-4
+
+
+# ============================================================================
+# Hadamard rotation tests (serving path: _build_hadamard)
+# ============================================================================
+
+
+def _build_hadamard(d: int, device: str = "cpu") -> torch.Tensor:
+    """Reproduce the serving-path Hadamard construction."""
+    H = torch.tensor([[1.0]])
+    while H.shape[0] < d:
+        H = torch.cat([torch.cat([H, H], 1), torch.cat([H, -H], 1)], 0)
+    return (H / math.sqrt(d)).to(torch.device(device))
+
+
+@pytest.mark.skipif(not GPGPU_AVAILABLE, reason="GPGPU not available")
+class TestHadamardRotation:
+    """Tests for the Hadamard rotation used in serving."""
+
+    @pytest.mark.parametrize("dim", [64, 128, 256])
+    def test_hadamard_orthonormal(self, dim):
+        """H must be orthonormal: H @ H^T = I."""
+        H = _build_hadamard(dim, DEVICE_TYPE)
+        eye = H @ H.T
+        assert torch.allclose(eye, torch.eye(dim, device=DEVICE_TYPE), atol=1e-5), (
+            f"Hadamard not orthonormal for dim={dim}"
+        )
+
+    @pytest.mark.parametrize("dim", [64, 128, 256])
+    def test_hadamard_symmetric(self, dim):
+        """Sylvester Hadamard must be symmetric: H = H^T."""
+        H = _build_hadamard(dim, DEVICE_TYPE)
+        assert torch.allclose(H, H.T, atol=1e-6), (
+            f"Hadamard not symmetric for dim={dim}"
+        )
+
+
+# ============================================================================
+# Store → Decode round-trip test (GPU + Triton required)
+# ============================================================================
+
+
+@pytest.mark.skipif(not GPGPU_AVAILABLE, reason="GPGPU not available")
+class TestStoreDecodeRoundTrip:
+    """End-to-end: store KV into TQ cache, decode, compare vs fp16 ref."""
+
+    @pytest.mark.parametrize(
+        "preset",
+        ["turboquant_k8v4", "turboquant_4bit_nc"],
+    )
+    def test_single_token_roundtrip(self, preset):
+        """Store 1 token, decode with query=key, check attention output.
+
+        For a single token with query=key, attention output should equal
+        the value (softmax over single key = 1.0). Quantization error
+        means we check cosine similarity rather than exact equality.
+        """
+        from vllm.model_executor.layers.quantization.turboquant.centroids import (
+            solve_lloyd_max,
+        )
+        from vllm.v1.attention.ops.triton_turboquant_decode import (
+            triton_turboquant_decode_attention,
+        )
+        from vllm.v1.attention.ops.triton_turboquant_store import (
+            triton_turboquant_store,
+        )
+
+        cfg = TurboQuantConfig.from_cache_dtype(preset, head_dim=128)
+        D = 128
+        Hk = 4  # num_kv_heads
+        Hq = 4  # num_q_heads (no GQA for simplicity)
+        B = 1  # single token
+        block_size = 16
+        num_blocks = 1
+
+        device = torch.device(DEVICE_TYPE)
+
+        # Pure Hadamard rotation (symmetric: H = H^T, so Pi = PiT = H)
+        H = _build_hadamard(D, DEVICE_TYPE)
+        PiT = H
+        Pi = H
+
+        # Generate centroids
+        centroids, _ = solve_lloyd_max(D, cfg.centroid_bits)
+        centroids = centroids.float().to(device)
+        c_sorted, _ = centroids.sort()
+        midpoints = ((c_sorted[:-1] + c_sorted[1:]) / 2).to(device)
+
+        # Random K, V
+        torch.manual_seed(123)
+        key = torch.randn(B, Hk, D, device=device, dtype=torch.float16)
+        value = torch.randn(B, Hk, D, device=device, dtype=torch.float16)
+
+        # Allocate KV cache
+        padded_slot = cfg.slot_size_aligned
+        kv_cache = torch.zeros(
+            num_blocks,
+            block_size,
+            Hk,
+            padded_slot,
+            device=device,
+            dtype=torch.uint8,
+        )
+        slot_mapping = torch.tensor([0], device=device, dtype=torch.int32)
+
+        # Store
+        triton_turboquant_store(
+            key,
+            value,
+            kv_cache,
+            slot_mapping,
+            PiT,
+            midpoints,
+            mse_bits=cfg.key_mse_bits,
+            key_packed_size=cfg.key_packed_size,
+            value_quant_bits=cfg.effective_value_quant_bits,
+            key_fp8=cfg.key_fp8,
+        )
+
+        # Decode: use key as query so attention = softmax([1]) * V = V
+        query = key.expand(B, Hq, D).contiguous().to(torch.float16)
+        block_table = torch.tensor([[0]], device=device, dtype=torch.int32)
+        seq_lens = torch.tensor([1], device=device, dtype=torch.int32)
+
+        output = triton_turboquant_decode_attention(
+            query=query,
+            kv_cache=kv_cache,
+            block_table=block_table,
+            seq_lens=seq_lens,
+            Pi=Pi,
+            centroids=centroids,
+            scale=1.0 / math.sqrt(D),
+            mse_bits=cfg.key_mse_bits,
+            key_packed_size=cfg.key_packed_size,
+            value_quant_bits=cfg.effective_value_quant_bits,
+            key_fp8=cfg.key_fp8,
+            norm_correction=cfg.norm_correction,
+            PiT=PiT,
+            max_num_kv_splits=4,
+        )
+
+        # With single KV, output should approximate the stored value.
+        # Check per-head cosine similarity > threshold.
+        out_fp32 = output.float()
+        val_fp32 = value.expand(B, Hq, D).float()
+        for h in range(Hq):
+            cos_sim = torch.nn.functional.cosine_similarity(
+                out_fp32[0, h].unsqueeze(0),
+                val_fp32[0, h].unsqueeze(0),
+            ).item()
+            # FP8 keys should be very accurate; MSE keys have more error
+            threshold = 0.95 if cfg.key_fp8 else 0.85
+            assert cos_sim > threshold, (
+                f"Preset {preset} head {h}: cosine_sim={cos_sim:.4f} < {threshold}"
+            )
diff --git a/tests/quantization/utils.py b/tests/quantization/utils.py
index e5eea5772934..8ab9c310dcad 100644
--- a/tests/quantization/utils.py
+++ b/tests/quantization/utils.py
@@ -80,14 +80,13 @@ def _test_online_quant_peak_mem_impl(
     print(f"GPU memory used after loading weights: {model_memory_gib} GiB")
     print(f"Peak GPU memory usage while loading weights: {peak_memory_gib} GiB")
 
-    # model specific, allenai/OLMoE-1B-7B-0125-Instruct fp8 online quant
-    # uses 6.65 GiB for weight loading (bf16 checkpoint is ~12.89 GiB)
     expected_model_memory_gib = 6.7
 
     # for allenai/OLMoE-1B-7B-0125-Instruct the number we see today is 9.06
-    # GiB, which is 1.36x above model_memory_gib. A slightly higher number is
-    # expected as when we load and quantize weights in a streaming fashion we
-    # need to have individual weights in bf16 + fp8 alive at the same time.
+    # GiB on CUDA, which is 1.36x above model_memory_gib. A slightly higher
+    # number is expected as when we load and quantize weights in a streaming
+    # fashion we need to have individual weights in bf16 + fp8 alive at the
+    # same time.
     expected_peak_memory_gib = expected_model_memory_gib * 1.4
 
     assert model_memory_gib < expected_model_memory_gib, (
diff --git a/tests/reasoning/test_deepseekv3_reasoning_parser.py b/tests/reasoning/test_deepseekv3_reasoning_parser.py
index 4b0938d15520..f5b37194f927 100644
--- a/tests/reasoning/test_deepseekv3_reasoning_parser.py
+++ b/tests/reasoning/test_deepseekv3_reasoning_parser.py
@@ -6,6 +6,7 @@
 
 from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
 from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.reasoning import ReasoningParserManager
 from vllm.reasoning.deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser
 from vllm.reasoning.deepseek_v3_reasoning_parser import DeepSeekV3ReasoningParser
 from vllm.reasoning.identity_reasoning_parser import IdentityReasoningParser
@@ -33,6 +34,12 @@ def test_parser_selection(tokenizer, thinking, expected_parser_type):
     assert isinstance(parser._parser, expected_parser_type)
 
 
+def test_deepseek_v4_reasoning_parser_alias():
+    parser_cls = ReasoningParserManager.get_reasoning_parser("deepseek_v4")
+
+    assert parser_cls is DeepSeekV3ReasoningParser
+
+
 def test_identity_reasoning_parser_basic(tokenizer):
     parser = IdentityReasoningParser(tokenizer)
 
diff --git a/tests/reasoning/test_gptoss_reasoning_parser.py b/tests/reasoning/test_gptoss_reasoning_parser.py
index 3b1327acb688..a6f815b6ae5c 100644
--- a/tests/reasoning/test_gptoss_reasoning_parser.py
+++ b/tests/reasoning/test_gptoss_reasoning_parser.py
@@ -280,3 +280,72 @@ def test_tag_format_consistency(self, reasoning_parser):
             assert tag["content"]["type"] == "any_text"
             assert tag["end"] == "<|end|>"
             assert tag["begin"].startswith("<|channel|>")
+
+
+@pytest.mark.parametrize(
+    "output, is_reasoning_end",
+    [(t["output"], t["is_reasoning_end"]) for t in TEST_CASES],
+)
+def test_gptoss_is_reasoning_end_streaming(
+    output,
+    is_reasoning_end,
+    gpt_oss_tokenizer,
+):
+    """Streaming override must agree with is_reasoning_end for all cases."""
+    tokens = gpt_oss_tokenizer.tokenize(output)
+    parser: ReasoningParser = GptOssReasoningParser(gpt_oss_tokenizer)
+    output_ids = gpt_oss_tokenizer.convert_tokens_to_ids(tokens)
+    delta_ids = output_ids[-1:] if output_ids else []
+    actual = parser.is_reasoning_end_streaming(output_ids, delta_ids)
+    assert is_reasoning_end == actual
+
+
+@pytest.mark.parametrize(
+    "output, is_reasoning_end",
+    [(t["output"], t["is_reasoning_end"]) for t in TEST_CASES],
+)
+def test_gptoss_is_reasoning_end_streaming_long_prefix(
+    output,
+    is_reasoning_end,
+    gpt_oss_tokenizer,
+):
+    """Windowing must produce correct results even with a long prefix."""
+    tokens = gpt_oss_tokenizer.tokenize(output)
+    parser: ReasoningParser = GptOssReasoningParser(gpt_oss_tokenizer)
+    output_ids = gpt_oss_tokenizer.convert_tokens_to_ids(tokens)
+    # Prepend 10k dummy reasoning tokens to simulate a long generation
+    long_prefix = [1] * 10_000
+    padded_ids = long_prefix + list(output_ids)
+    delta_ids = output_ids[-1:] if output_ids else []
+    actual = parser.is_reasoning_end_streaming(padded_ids, delta_ids)
+    assert is_reasoning_end == actual
+
+
+@pytest.mark.parametrize(
+    "output, is_reasoning_end",
+    [(t["output"], t["is_reasoning_end"]) for t in TEST_CASES],
+)
+def test_gptoss_is_reasoning_end_streaming_large_delta(
+    output,
+    is_reasoning_end,
+    gpt_oss_tokenizer,
+):
+    """Simulate speculative decoding where the entire test sequence arrives
+    as a single large delta appended after a long prefix.  The window must
+    expand to cover delta_ids so the end pattern is never missed."""
+    tokens = gpt_oss_tokenizer.tokenize(output)
+    parser: ReasoningParser = GptOssReasoningParser(gpt_oss_tokenizer)
+    output_ids = gpt_oss_tokenizer.convert_tokens_to_ids(tokens)
+    long_prefix = [1] * 10_000
+    padded_ids = long_prefix + list(output_ids)
+    # delta_ids = the entire test sequence (as if accepted in one spec step)
+    delta_ids = list(output_ids)
+    actual = parser.is_reasoning_end_streaming(padded_ids, delta_ids)
+    assert is_reasoning_end == actual
+
+
+def test_gptoss_is_reasoning_end_streaming_signature(gpt_oss_tokenizer):
+    """Verify the method is callable with the expected signature."""
+    parser = GptOssReasoningParser(gpt_oss_tokenizer)
+    result = parser.is_reasoning_end_streaming([], [])
+    assert result is False
diff --git a/tests/reasoning/test_hy_v3_reasoning_parser.py b/tests/reasoning/test_hy_v3_reasoning_parser.py
new file mode 100644
index 000000000000..e527c979f6e2
--- /dev/null
+++ b/tests/reasoning/test_hy_v3_reasoning_parser.py
@@ -0,0 +1,274 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+
+from tests.reasoning.utils import run_reasoning_extraction
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
+from vllm.reasoning.hy_v3_reasoning_parser import HYV3ReasoningParser
+from vllm.tokenizers import get_tokenizer
+
+parser_name = "hy_v3"
+MODEL = "tencent/Hy3-preview"
+
+
+@pytest.fixture(scope="module")
+def hy_v3_tokenizer():
+    return get_tokenizer(tokenizer_name=MODEL)
+
+
+WITH_THINK = {
+    "output": "This is a reasoning section</think>This is the rest",
+    "reasoning": "This is a reasoning section",
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+    "reasoning_effort": "high",
+}
+
+WITH_THINK_STREAM = {
+    "output": "This is a reasoning section</think>This is the rest",
+    "reasoning": "This is a reasoning section",
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+    "reasoning_effort": "high",
+}
+
+WITHOUT_THINK = {
+    "output": "This is the rest",
+    "reasoning": None,
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+    "reasoning_effort": "no_think",
+}
+
+WITHOUT_THINK_STREAM = {
+    "output": "This is the rest",
+    "reasoning": None,
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+    "reasoning_effort": "no_think",
+}
+
+WITH_REASONING_EFFORT_NONE = {
+    "output": "This is the rest",
+    "reasoning": None,
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+
+WITH_REASONING_EFFORT_NONE_STREAM = {
+    "output": "This is the rest",
+    "reasoning": None,
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+
+COMPLETE_REASONING = {
+    "output": "This is a reasoning section</think>",
+    "reasoning": "This is a reasoning section",
+    "content": None,
+    "is_reasoning_end": True,
+    "reasoning_effort": "high",
+}
+MULTILINE_REASONING = {
+    "output": "This is a reasoning\nsection</think>This is the rest\nThat",
+    "reasoning": "This is a reasoning\nsection",
+    "content": "This is the rest\nThat",
+    "is_reasoning_end": True,
+    "reasoning_effort": "high",
+}
+ONLY_OPEN_TAG = {
+    "output": "This is a reasoning section",
+    "reasoning": "This is a reasoning section",
+    "content": None,
+    "is_reasoning_end": False,
+    "reasoning_effort": "high",
+}
+
+ONLY_OPEN_TAG_STREAM = {
+    "output": "This is a reasoning section",
+    "reasoning": "This is a reasoning section",
+    "content": None,
+    "is_reasoning_end": False,
+    "reasoning_effort": "high",
+}
+
+TEST_CASES = [
+    pytest.param(
+        False,
+        WITH_THINK,
+        id="with_think",
+    ),
+    pytest.param(
+        True,
+        WITH_THINK_STREAM,
+        id="with_think_stream",
+    ),
+    pytest.param(
+        False,
+        WITHOUT_THINK,
+        id="without_think",
+    ),
+    pytest.param(
+        True,
+        WITHOUT_THINK_STREAM,
+        id="without_think_stream",
+    ),
+    pytest.param(
+        False,
+        WITH_REASONING_EFFORT_NONE,
+        id="with_reasoning_effort_none",
+    ),
+    pytest.param(
+        True,
+        WITH_REASONING_EFFORT_NONE_STREAM,
+        id="with_reasoning_effort_none_stream",
+    ),
+    pytest.param(
+        False,
+        COMPLETE_REASONING,
+        id="complete_reasoning",
+    ),
+    pytest.param(
+        True,
+        COMPLETE_REASONING,
+        id="complete_reasoning_stream",
+    ),
+    pytest.param(
+        False,
+        MULTILINE_REASONING,
+        id="multiline_reasoning",
+    ),
+    pytest.param(
+        True,
+        MULTILINE_REASONING,
+        id="multiline_reasoning_stream",
+    ),
+    pytest.param(
+        False,
+        ONLY_OPEN_TAG,
+        id="only_open_tag",
+    ),
+    pytest.param(
+        True,
+        ONLY_OPEN_TAG_STREAM,
+        id="only_open_tag_stream",
+    ),
+]
+
+STILL_REASONING_PROMPT = """<｜hy_begin▁of▁sentence｜>
+You are a helpful assistant.
+<｜reasoning_mode｜>reasoning_effort:high<｜hy_User｜>
+What is the capital of France?<｜hy_Assistant｜>
+<think>The user is asking for the capital of"""
+
+DONE_REASONING_PROMPT = """<｜hy_begin▁of▁sentence｜>
+You are a helpful assistant.
+<｜reasoning_mode｜>reasoning_effort:high<｜hy_User｜>
+What is the capital of France?<｜hy_Assistant｜>
+<think>The user is asking for the capital of France.</think>
+The capital of France is Paris."""
+
+MULTI_TURN_STILL_REASONING_PROMPT = """<｜hy_begin▁of▁sentence｜>
+You are a helpful assistant.
+<｜reasoning_mode｜>reasoning_effort:high<｜hy_User｜>
+What is the capital of France?<｜hy_Assistant｜
+><think></think>The capital of France is Paris.<eos:6124c78e>
+<｜hy_User｜>What about Chile?<｜hy_Assistant｜>
+<think>The user is asking for the capital of"""
+
+MULTI_TURN_DONE_REASONING_PROMPT = """<｜hy_begin▁of▁sentence｜>
+You are a helpful assistant.
+<｜reasoning_mode｜>reasoning_effort:high<｜hy_User｜>
+What is the capital of France?<｜hy_Assistant｜
+><think></think>The capital of France is Paris.<eos:6124c78e>
+<｜hy_User｜>What about Chile?<｜hy_Assistant｜>
+<think>The user is asking for the capital of Chile.</think>
+The capital of Chile is Santiago."""
+
+REASONING_END_TEST_CASES = [
+    pytest.param(STILL_REASONING_PROMPT, False, id="still_reasoning"),
+    pytest.param(DONE_REASONING_PROMPT, True, id="done_reasoning"),
+    pytest.param(
+        MULTI_TURN_STILL_REASONING_PROMPT, False, id="multi_turn_still_reasoning"
+    ),
+    pytest.param(
+        MULTI_TURN_DONE_REASONING_PROMPT, True, id="multi_turn_done_reasoning"
+    ),
+]
+
+
+@pytest.mark.parametrize("streaming, param_dict", TEST_CASES)
+def test_reasoning(
+    streaming: bool,
+    param_dict: dict,
+    hy_v3_tokenizer,
+):
+    output = hy_v3_tokenizer.tokenize(param_dict["output"])
+    output_tokens: list[str] = [
+        hy_v3_tokenizer.convert_tokens_to_string([token]) for token in output
+    ]
+
+    parser_kwargs = {}
+    if "reasoning_effort" in param_dict:
+        parser_kwargs["chat_template_kwargs"] = {
+            "reasoning_effort": param_dict["reasoning_effort"]
+        }
+    parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(parser_name)(
+        hy_v3_tokenizer,
+        **parser_kwargs,
+    )
+
+    reasoning, content = run_reasoning_extraction(
+        parser, output_tokens, streaming=streaming
+    )
+
+    assert reasoning == param_dict["reasoning"]
+    assert content == param_dict["content"]
+
+    output_ids = hy_v3_tokenizer.convert_tokens_to_ids(output)
+    is_reasoning_end = parser.is_reasoning_end(output_ids)
+    assert is_reasoning_end == param_dict["is_reasoning_end"]
+
+
+@pytest.mark.parametrize("prompt, is_reasoning_end", REASONING_END_TEST_CASES)
+def test_is_reasoning_end_full_prompt(
+    prompt: str, is_reasoning_end: bool, hy_v3_tokenizer
+):
+    parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(parser_name)(
+        hy_v3_tokenizer,
+        chat_template_kwargs={"reasoning_effort": "high"},
+    )
+    tokens = hy_v3_tokenizer.tokenize(prompt)
+    token_ids = hy_v3_tokenizer.convert_tokens_to_ids(tokens)
+    check_is_reasoning_end = parser.is_reasoning_end(token_ids)
+    assert check_is_reasoning_end == is_reasoning_end
+
+
+def test_constructor_does_not_mutate_shared_chat_template_kwargs(hy_v3_tokenizer):
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    chat_template_kwargs = {"reasoning_effort": "low"}
+
+    first_parser: ReasoningParser = parser_cls(
+        hy_v3_tokenizer,
+        chat_template_kwargs=chat_template_kwargs,
+    )
+    second_parser: ReasoningParser = parser_cls(
+        hy_v3_tokenizer,
+        chat_template_kwargs=chat_template_kwargs,
+    )
+
+    assert chat_template_kwargs == {"reasoning_effort": "low"}
+    assert isinstance(first_parser, HYV3ReasoningParser)
+    assert isinstance(second_parser, HYV3ReasoningParser)
+    assert first_parser._identity_parser is None
+    assert second_parser._identity_parser is None
+
+
+def test_constructor_falls_back_to_outer_reasoning_effort(hy_v3_tokenizer):
+    parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(parser_name)(
+        hy_v3_tokenizer,
+        reasoning_effort="low",
+    )
+
+    assert isinstance(parser, HYV3ReasoningParser)
+    assert parser._identity_parser is None
diff --git a/tests/reasoning/test_kimi_k2_reasoning_parser.py b/tests/reasoning/test_kimi_k2_reasoning_parser.py
index 0f80bb8854a8..dfce2075c6a9 100644
--- a/tests/reasoning/test_kimi_k2_reasoning_parser.py
+++ b/tests/reasoning/test_kimi_k2_reasoning_parser.py
@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from unittest.mock import MagicMock
+
 import pytest
 
 from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
@@ -12,6 +14,20 @@
 REASONING_MODEL_NAME = "moonshotai/Kimi-K2.5"
 
 
+@pytest.fixture
+def mock_kimi_k2_tokenizer():
+    tokenizer = MagicMock()
+    tokenizer.get_vocab.return_value = {
+        "<think>": 100,
+        "</think>": 101,
+        "<|tool_calls_section_begin|>": 200,
+        "<|tool_calls_section_end|>": 201,
+        "<|tool_call_begin|>": 202,
+        "<|tool_call_end|>": 203,
+    }
+    return tokenizer
+
+
 @pytest.fixture(scope="module")
 def kimi_k2_tokenizer():
     return get_tokenizer(tokenizer_name=REASONING_MODEL_NAME, trust_remote_code=True)
@@ -153,3 +169,50 @@ def test_streaming_tool_section_ends_reasoning(kimi_k2_tokenizer):
     )
     assert isinstance(result, DeltaMessage)
     assert result.content == "<|tool_calls_section_begin|>"
+
+
+def test_streaming_end_token_id_buffered(mock_kimi_k2_tokenizer):
+    """When stop sequences buffer text, </think> ID arrives before its text.
+
+    The token ID is present in delta_token_ids but the actual string is not
+    yet in delta_text (still buffered). The parser must return None to wait
+    for the next delta, instead of calling find() which returns -1 and
+    silently corrupting the text split.
+    """
+    parser = KimiK2ReasoningParser(mock_kimi_k2_tokenizer)
+    think_id = parser._start_token_id
+    end_think_id = parser._end_token_id
+
+    # Simulate: </think> ID arrived but text not yet flushed.
+    # Two token IDs in delta to bypass the single-special-token guard.
+    result = parser.extract_reasoning_streaming(
+        previous_text="some reasoning",
+        current_text="some reasoning extra",
+        delta_text="extra",  # </think> text not yet flushed
+        previous_token_ids=[think_id],
+        current_token_ids=[think_id, end_think_id, 999],
+        delta_token_ids=[end_think_id, 999],
+    )
+    assert result is None
+
+
+def test_streaming_tool_section_id_buffered(mock_kimi_k2_tokenizer):
+    """When stop sequences buffer text, tool section start ID arrives before its text.
+
+    Same buffering scenario as above but for <|tool_calls_section_begin|>.
+    Without the guard, find() returns -1 and delta_text[:tool_index] silently
+    drops the last character of reasoning.
+    """
+    parser = KimiK2ReasoningParser(mock_kimi_k2_tokenizer)
+    think_id = parser._start_token_id
+    tool_begin_id = parser._tool_section_start_token_id
+
+    result = parser.extract_reasoning_streaming(
+        previous_text="some reasoning",
+        current_text="some reasoning extra",
+        delta_text="extra",  # tool section text not yet flushed
+        previous_token_ids=[think_id],
+        current_token_ids=[think_id, tool_begin_id, 999],
+        delta_token_ids=[tool_begin_id, 999],
+    )
+    assert result is None
diff --git a/tests/reasoning/test_olmo3_reasoning_parser.py b/tests/reasoning/test_olmo3_reasoning_parser.py
index bc0e72e2a456..a74ca50d11a4 100644
--- a/tests/reasoning/test_olmo3_reasoning_parser.py
+++ b/tests/reasoning/test_olmo3_reasoning_parser.py
@@ -41,6 +41,12 @@
     "content": "\n\n\nThis is the rest",
 }
 
+SIMPLE_REASONING_WITH_TRAILING_SPACE = {
+    "output": f"{START_REASONING}\nLook!\nI'm thinking... {END_REASONING}\nThis is the rest",  # noqa: E501
+    "reasoning": "\nLook!\nI'm thinking... ",
+    "content": "\nThis is the rest",
+}
+
 NO_REASONING_ONLY_END_THINK = {
     "output": f"{END_REASONING}\n\nNo thoughts, head empty!",
     "reasoning": None,
@@ -114,6 +120,11 @@
         SIMPLE_REASONING_WITH_MULTIPLE_NEWLINES,
         id="simple_reasoning_with_multiple_newlines_streaming",
     ),
+    pytest.param(
+        True,  # enable streaming
+        SIMPLE_REASONING_WITH_TRAILING_SPACE,
+        id="simple_reasoning_with_trailing_space_streaming",
+    ),
     pytest.param(
         True,  # enable streaming
         NO_REASONING_ONLY_END_THINK,
@@ -127,7 +138,7 @@
 ]
 
 # Global tokenizer initialization to avoid repeated loading
-tokenizer = AutoTokenizer.from_pretrained("allenai/dolma2-tokenizer")
+tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3-7B-Think")
 
 
 @pytest.mark.parametrize("streaming, param_dict", TEST_CASES)
diff --git a/tests/reasoning/test_qwen3_reasoning_parser.py b/tests/reasoning/test_qwen3_reasoning_parser.py
index 411c7ba485a8..f42458560f9f 100644
--- a/tests/reasoning/test_qwen3_reasoning_parser.py
+++ b/tests/reasoning/test_qwen3_reasoning_parser.py
@@ -78,6 +78,25 @@ def qwen3_tokenizer(request):
     "content": None,
 }
 
+# --- <tool_call> without </think> (implicit reasoning end) ---
+
+TOOL_CALL_BODY = (
+    "<tool_call>\n<function=bash>\n<parameter=command>"
+    "\ncat /etc/hosts\n</parameter>\n</function>\n</tool_call>"
+)
+
+TOOL_CALL_NO_THINK_END = {
+    "output": "I need to read the file.\n\n" + TOOL_CALL_BODY,
+    "reasoning": "I need to read the file.\n\n",
+    "content": TOOL_CALL_BODY,
+}
+
+TOOL_CALL_WITH_THINK_NO_END = {
+    "output": "<think>I need to read the file.\n\n" + TOOL_CALL_BODY,
+    "reasoning": "I need to read the file.\n\n",
+    "content": TOOL_CALL_BODY,
+}
+
 # --- Edge cases ---
 
 COMPLETE_REASONING = {
@@ -199,6 +218,26 @@ def qwen3_tokenizer(request):
         TRUNCATED_NO_START_TOKEN_STREAM,
         id="truncated_no_start_token_stream",
     ),
+    pytest.param(
+        False,
+        TOOL_CALL_NO_THINK_END,
+        id="tool_call_no_think_end",
+    ),
+    pytest.param(
+        True,
+        TOOL_CALL_NO_THINK_END,
+        id="tool_call_no_think_end_stream",
+    ),
+    pytest.param(
+        False,
+        TOOL_CALL_WITH_THINK_NO_END,
+        id="tool_call_with_think_no_end",
+    ),
+    pytest.param(
+        True,
+        TOOL_CALL_WITH_THINK_NO_END,
+        id="tool_call_with_think_no_end_stream",
+    ),
 ]
 
 
@@ -255,6 +294,13 @@ def test_reasoning(
         "content",
         id="no_start_end_grouped_with_content",
     ),
+    pytest.param(
+        # <tool_call> arrives in a separate delta after reasoning text
+        ["I need to read the file.\n\n", "<tool_call>\n<function=bash>"],
+        "I need to read the file.\n\n",
+        "<tool_call>\n<function=bash>",
+        id="tool_call_implicit_reasoning_end",
+    ),
 ]
 
 
@@ -296,6 +342,12 @@ def test_reasoning_streaming_multi_token_deltas(
         "Some output without think tokens",
         id="thinking_disabled_no_think_tokens",
     ),
+    pytest.param(
+        "I need to read the file.\n\n" + TOOL_CALL_BODY,
+        None,
+        "I need to read the file.\n\n" + TOOL_CALL_BODY,
+        id="thinking_disabled_with_tool_call",
+    ),
 ]
 
 
diff --git a/tests/reasoning/test_step3p5_reasoning_parser.py b/tests/reasoning/test_step3p5_reasoning_parser.py
index 2196d247cb45..8f62e7a2cb4d 100644
--- a/tests/reasoning/test_step3p5_reasoning_parser.py
+++ b/tests/reasoning/test_step3p5_reasoning_parser.py
@@ -2,10 +2,10 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import pytest
-from transformers import AutoTokenizer
 
 from tests.reasoning.utils import run_reasoning_extraction
 from vllm.reasoning import ReasoningParser, ReasoningParserManager
+from vllm.tokenizers import get_tokenizer
 
 parser_name = "step3p5"
 start_token = "<think>"
@@ -16,7 +16,7 @@
 
 @pytest.fixture(scope="module")
 def step3p5_tokenizer():
-    return AutoTokenizer.from_pretrained(REASONING_MODEL_NAME)
+    return get_tokenizer(tokenizer_name=REASONING_MODEL_NAME)
 
 
 SIMPLE_REASONING = {
diff --git a/tests/renderers/inputs/test_preprocess.py b/tests/renderers/inputs/test_preprocess.py
index 98219bb14382..9b120c03a301 100644
--- a/tests/renderers/inputs/test_preprocess.py
+++ b/tests/renderers/inputs/test_preprocess.py
@@ -1,6 +1,12 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from vllm.renderers.inputs.preprocess import prompt_to_seq
+import pytest
+
+from vllm.renderers.inputs.preprocess import (
+    parse_dec_only_prompt,
+    parse_enc_dec_prompt,
+    prompt_to_seq,
+)
 
 
 def test_empty_input():
@@ -39,3 +45,23 @@ def test_dict_input():
         {"prompt": "foo"},
         {"prompt_token_ids": [1, 2]},
     ]
+
+
+def test_parse_dec_only_prompt_rejects_non_string_prompt_field():
+    with pytest.raises(TypeError, match="Prompt text should be a string"):
+        parse_dec_only_prompt({"prompt": [1, 2, 3], "cache_salt": "abc"})
+
+
+def test_parse_dec_only_prompt_rejects_non_string_prompt_list():
+    with pytest.raises(TypeError, match="Prompt text should be a string"):
+        parse_dec_only_prompt({"prompt": [1, "x"]})
+
+
+def test_parse_enc_dec_prompt_rejects_nested_non_string_prompt_field():
+    with pytest.raises(TypeError, match="Prompt text should be a string"):
+        parse_enc_dec_prompt(
+            {
+                "encoder_prompt": {"prompt": [1, 2, 3]},
+                "decoder_prompt": {"prompt": [4, 5]},
+            }
+        )
diff --git a/tests/renderers/test_chat_utils_prompt_embeds.py b/tests/renderers/test_chat_utils_prompt_embeds.py
new file mode 100644
index 000000000000..e33cc304710d
--- /dev/null
+++ b/tests/renderers/test_chat_utils_prompt_embeds.py
@@ -0,0 +1,576 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Offline unit tests for `prompt_embeds` chat-completion content parts."""
+
+from __future__ import annotations
+
+import inspect
+import io
+from typing import Final
+from unittest import mock
+
+import pybase64 as base64
+import pytest
+import regex as re
+import torch
+from transformers import AutoTokenizer
+
+from vllm.entrypoints.chat_utils import (
+    _ENABLE_PROMPT_EMBEDS_ERROR,
+    _PROMPT_EMBEDS_MISSING_DATA_ERROR,
+    _RESERVED_PLACEHOLDER_IN_TEXT_ERROR,
+    MM_PARSER_MAP,
+    MODALITY_PLACEHOLDERS_MAP,
+    PROMPT_EMBEDS_PLACEHOLDER_TOKEN,
+    parse_chat_messages,
+    parse_chat_messages_async,
+)
+from vllm.renderers.hf import (
+    _PROMPT_EMBEDS_PLACEHOLDER_SPAN_MISMATCH_ERROR,
+    _build_mixed_prompt_embeds,
+    _build_prompt_embeds_positions,
+    _build_prompt_embeds_updates,
+    _ensure_prompt_embeds_placeholder_token,
+    _expand_prompt_embeds_placeholders,
+)
+
+# Cover distinct tokenizer families:
+#   GPT2TokenizerFast  (BPE, OpenAI-style)
+#   Qwen2TokenizerFast (SentencePiece BPE variant)
+#   BertTokenizerFast  (WordPiece)
+TOKENIZER_IDS: Final[list[str]] = [
+    "gpt2",
+    "Qwen/Qwen2.5-1.5B-Instruct",
+    "bert-base-uncased",
+]
+
+
+@pytest.fixture(params=TOKENIZER_IDS, ids=TOKENIZER_IDS)
+def tokenizer(request):
+    """A fresh tokenizer instance per tokenizer family."""
+    return AutoTokenizer.from_pretrained(request.param)
+
+
+# Minimal chat template that works with any tokenizer.  Iterates
+# `message.content` as either a string or a list of dicts (openai format).
+_SIMPLE_CHAT_TEMPLATE: Final[str] = (
+    "{% for m in messages %}"
+    "{% if m['content'] is string %}{{m['content']}}"
+    "{% else %}{% for p in m['content'] %}{{p['text']}}{% endfor %}"
+    "{% endif %}\n{% endfor %}"
+)
+
+
+async def _maybe_await(fn, *args, **kwargs):
+    """Call *fn* and `await` the result if it's a coroutine."""
+    result = fn(*args, **kwargs)
+    if inspect.iscoroutine(result):
+        result = await result
+    return result
+
+
+# Parametrize over sync / async parse paths so every end-to-end test
+# exercises both.
+_PARSE_FUNCTIONS = [parse_chat_messages, parse_chat_messages_async]
+
+
+@pytest.fixture(params=_PARSE_FUNCTIONS, ids=["sync", "async"])
+def parse_fn(request):
+    """Either the sync or async `parse_chat_messages` callable."""
+    return request.param
+
+
+def _encode_tensor(t: torch.Tensor) -> str:
+    buf = io.BytesIO()
+    torch.save(t, buf)
+    return base64.b64encode(buf.getvalue()).decode("utf-8")
+
+
+_MOCK_HIDDEN_SIZE: Final[int] = 8
+_MOCK_DTYPE: Final[torch.dtype] = torch.float32
+
+
+def _make_mock_model_config(*, enable_prompt_embeds: bool = True) -> mock.MagicMock:
+    mc = mock.MagicMock()
+    mc.enable_prompt_embeds = enable_prompt_embeds
+    mc.multimodal_config = None
+    mc.allowed_local_media_path = None
+    mc.allowed_media_domains = None
+    # Test text-only code path in `MultiModalItemTracker.resolve_items`.
+    mc.is_multimodal_model = False
+    # `safe_load_prompt_embeds` pins each tensor to the model's hidden_size
+    # and dtype, so the mock must return concrete values.
+    mc.get_hidden_size.return_value = _MOCK_HIDDEN_SIZE
+    mc.dtype = _MOCK_DTYPE
+    return mc
+
+
+def test_prompt_embeds_keys_registered():
+    assert "prompt_embeds" in MODALITY_PLACEHOLDERS_MAP
+    assert MODALITY_PLACEHOLDERS_MAP["prompt_embeds"] == "<##PROMPT_EMBEDS##>"
+    assert "prompt_embeds" in MM_PARSER_MAP
+
+
+def test_ensure_placeholder_token_is_single_token_and_idempotent(tokenizer):
+    """Ensure the placeholder token is a single token and that multiple calls to
+    "ensure" are idempotent, across all tokenizer families."""
+    tid1 = _ensure_prompt_embeds_placeholder_token(tokenizer)
+    tid2 = _ensure_prompt_embeds_placeholder_token(tokenizer)
+    assert tid1 == tid2
+
+    ids = tokenizer.encode(PROMPT_EMBEDS_PLACEHOLDER_TOKEN, add_special_tokens=False)
+    assert ids == [tid1]
+
+    # Repeating it in a string N times must produce exactly that many tokens.
+    N = 5
+    ids_rep = tokenizer.encode(
+        PROMPT_EMBEDS_PLACEHOLDER_TOKEN * N, add_special_tokens=False
+    )
+    assert ids_rep == [tid1] * N
+
+
+def test_parse_chat_messages_openai_format():
+    NUM_TOKENS = 3
+    t = torch.randn(NUM_TOKENS, _MOCK_HIDDEN_SIZE, dtype=_MOCK_DTYPE)
+    b64 = _encode_tensor(t)
+    mc = _make_mock_model_config()
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Hello "},
+                {"type": "prompt_embeds", "data": b64},
+                {"type": "text", "text": " world"},
+            ],
+        }
+    ]
+    conv, mm_data, _ = parse_chat_messages(
+        messages,
+        mc,
+        content_format="openai",
+    )
+    # The middle content part is rewritten to a single placeholder-token
+    # sentinel.
+    texts = [p["text"] for p in conv[0]["content"]]
+    assert texts == [
+        "Hello ",
+        PROMPT_EMBEDS_PLACEHOLDER_TOKEN,
+        " world",
+    ]
+    assert mm_data is not None and "prompt_embeds" in mm_data
+    assert torch.equal(mm_data["prompt_embeds"][0], t)
+
+
+# Each layout entry is one content part:
+#   ("text", "A")  -> {"type": "text", "text": "A"}
+#   ("embed", N)   -> {"type": "prompt_embeds", "data": <base64 of (N, H) tensor>}
+@pytest.mark.parametrize(
+    "layout",
+    [
+        # Case: Single embed only.
+        [("embed", 2)],
+        # Case: Embed at the start of the message.
+        [("embed", 3), ("text", "B")],
+        # Case: Embed at the end of the message.
+        [("text", "A"), ("embed", 1)],
+        # Case: Embed sandwiched between text spans.
+        [("text", "A"), ("embed", 2), ("text", "B")],
+        # Case: Multiple embeds with text in between.
+        [("text", "A"), ("embed", 2), ("text", "B"), ("embed", 3)],
+        # Case: Adjacent embeds with no separating text.
+        [("embed", 1), ("embed", 2)],
+        # Case: Multiple text spans before a trailing embed.
+        [("text", "A"), ("text", "B"), ("embed", 1)],
+        # Case: Long-ish run mixing both kinds.
+        [
+            ("text", "head"),
+            ("embed", 4),
+            ("text", "mid"),
+            ("embed", 1),
+            ("embed", 2),
+            ("text", "tail"),
+        ],
+    ],
+    ids=[
+        "single-embed",
+        "embed-then-text",
+        "text-then-embed",
+        "text-embed-text",
+        "text-embed-text-embed",
+        "adjacent-embeds",
+        "text-text-embed",
+        "long-mixed-run",
+    ],
+)
+@pytest.mark.parametrize(
+    "interleave_mm_strings",
+    # `None`: text-only path where `multimodal_config` is absent.
+    # `False`: non-interleave multimodal path (the common default).
+    # `True`: sentinel-substitution interleave path.
+    # All three must preserve the request ordering of prompt_embeds
+    # relative to surrounding text because prompt_embeds are spliced at the
+    # token offset during rendering.
+    [None, False, True],
+    ids=["text-only", "interleave-off", "interleave-on"],
+)
+def test_parse_chat_messages_string_format_preserves_position(
+    layout, interleave_mm_strings
+):
+    mc = _make_mock_model_config()
+    if interleave_mm_strings is not None:
+        mm_cfg = mock.MagicMock()
+        mm_cfg.interleave_mm_strings = interleave_mm_strings
+        mc.multimodal_config = mm_cfg
+
+    content: list[dict] = []
+    expected_parts: list[str] = []
+    expected_embeds: list[torch.Tensor] = []
+    for kind, value in layout:
+        if kind == "text":
+            content.append({"type": "text", "text": value})
+            expected_parts.append(value)
+        else:  # prompt embeds
+            num_tokens = value
+            t = torch.randn(num_tokens, _MOCK_HIDDEN_SIZE, dtype=_MOCK_DTYPE)
+            expected_embeds.append(t)
+            content.append({"type": "prompt_embeds", "data": _encode_tensor(t)})
+            # Parser emits ONE sentinel per part.
+            expected_parts.append(PROMPT_EMBEDS_PLACEHOLDER_TOKEN)
+
+    messages = [{"role": "user", "content": content}]
+    conv, mm_data, _ = parse_chat_messages(
+        messages,
+        mc,
+        content_format="string",
+    )
+
+    assert conv[0]["content"] == "\n".join(expected_parts)
+    assert mm_data is not None and "prompt_embeds" in mm_data
+    assert len(mm_data["prompt_embeds"]) == len(expected_embeds)
+    for got, want in zip(mm_data["prompt_embeds"], expected_embeds, strict=True):
+        assert torch.equal(got, want)
+
+
+def test_parse_chat_messages_requires_flag():
+    t = torch.randn(2, 4)
+    b64 = _encode_tensor(t)
+    mc = _make_mock_model_config(enable_prompt_embeds=False)
+
+    messages = [
+        {
+            "role": "user",
+            "content": [{"type": "prompt_embeds", "data": b64}],
+        }
+    ]
+    with pytest.raises(ValueError, match=_ENABLE_PROMPT_EMBEDS_ERROR):
+        parse_chat_messages(
+            messages,
+            mc,
+            content_format="openai",
+        )
+
+
+def test_parse_chat_messages_rejects_missing_data():
+    # `data` is marked `Required` on `ChatCompletionContentPartPromptEmbedsParam`;
+    # malformed requests without `data` must surface a clear validation error
+    # rather than being silently dropped.
+    mc = _make_mock_model_config()
+    messages = [
+        {
+            "role": "user",
+            "content": [{"type": "prompt_embeds"}],  # no `data`
+        }
+    ]
+    with pytest.raises(ValueError, match=_PROMPT_EMBEDS_MISSING_DATA_ERROR):
+        parse_chat_messages(
+            messages,
+            mc,
+            content_format="openai",
+        )
+
+
+# Reserved placeholder guard: when `enable_prompt_embeds=True` the tokenizer is
+# mutated to make `<prompt_embeds>` a single unsplittable token. Any user text
+# containing that literal sequence would tokenize to the same sentinel ID and
+# be mistaken for a splice point, so we reject it at parse time.
+_PLACEHOLDER_ERROR_PATTERN: Final[str] = re.sub(
+    r"\\{[^}]*\\}", ".*", re.escape(_RESERVED_PLACEHOLDER_IN_TEXT_ERROR)
+)
+
+
+@pytest.mark.parametrize(
+    "content",
+    [
+        # Case: Top-level string content (wrapped as a single text part).
+        f"hello {PROMPT_EMBEDS_PLACEHOLDER_TOKEN} world",
+        # Case: List with a typed text part containing the placeholder.
+        [{"type": "text", "text": f"leading {PROMPT_EMBEDS_PLACEHOLDER_TOKEN}"}],
+        # Case: List with a plain-string part (no wrapping dict).
+        [f"raw string {PROMPT_EMBEDS_PLACEHOLDER_TOKEN}"],
+    ],
+    ids=["top-level-string", "typed-text-part", "plain-string-part"],
+)
+def test_parse_chat_messages_rejects_placeholder_in_user_text(content):
+    mc = _make_mock_model_config()  # enable_prompt_embeds=True by default
+    messages = [{"role": "user", "content": content}]
+    with pytest.raises(ValueError, match=_PLACEHOLDER_ERROR_PATTERN):
+        parse_chat_messages(messages, mc, content_format="openai")
+
+
+def test_parse_chat_messages_allows_placeholder_in_text_when_feature_disabled():
+    # When `enable_prompt_embeds=False` the tokenizer is never mutated, so the
+    # literal `<prompt_embeds>` is just ordinary text and must pass through.
+    mc = _make_mock_model_config(enable_prompt_embeds=False)
+    messages = [
+        {
+            "role": "user",
+            "content": f"benign mention of {PROMPT_EMBEDS_PLACEHOLDER_TOKEN} here",
+        }
+    ]
+    conv, mm_data, _ = parse_chat_messages(messages, mc, content_format="openai")
+    assert mm_data is None or "prompt_embeds" not in mm_data
+    # Text reaches the rendered conversation unchanged.
+    texts = [p["text"] for p in conv[0]["content"]]
+    assert PROMPT_EMBEDS_PLACEHOLDER_TOKEN in "".join(texts)
+
+
+# Token-stream spec: ints are regular token IDs, tuples `(N,)` expand to
+# a placeholder span of length N (creates corresponding `(N, H)` tensor).
+# `expected` lists the `(start_idx, length)` pairs that
+# `_build_prompt_embeds_positions` should return.
+@pytest.mark.parametrize(
+    "stream, expected",
+    [
+        # Case: Single run in the middle.
+        ([10, 20, (3,), 30], [(2, 3)]),
+        # Case: Single run at the start.
+        ([(2,), 10, 20], [(0, 2)]),
+        # Case: Single run at the end.
+        ([10, 20, (4,)], [(2, 4)]),
+        # Case: Two runs with tokens between.
+        ([1, (2,), 2, 3, (3,), 4], [(1, 2), (5, 3)]),
+        # Case: Adjacent runs (no separating tokens).
+        ([(1,), (2,)], [(0, 1), (1, 2)]),
+        # Case: Three runs.
+        ([5, (2,), 6, (1,), 7, (3,), 8], [(1, 2), (4, 1), (6, 3)]),
+    ],
+    ids=[
+        "single-middle",
+        "single-start",
+        "single-end",
+        "two-runs-separated",
+        "two-runs-adjacent",
+        "three-runs",
+    ],
+)
+def test_build_positions(tokenizer, stream, expected):
+    H = 4
+    tid = _ensure_prompt_embeds_placeholder_token(tokenizer)
+    tensors: list[torch.Tensor] = []
+    token_ids: list[int] = []
+    for item in stream:
+        if isinstance(item, tuple):
+            length = item[0]
+            tensors.append(torch.randn(length, H))
+            token_ids.extend([tid] * length)
+        else:
+            token_ids.append(item)
+    mm_updates = _build_prompt_embeds_updates(tensors, tid)
+    positions = _build_prompt_embeds_positions(token_ids, len(tensors), mm_updates)
+    assert positions == expected
+
+
+def test_build_positions_length_mismatch(tokenizer):
+    N1, H1 = 2, 4
+    N2, H2 = 3, 4
+    tid = _ensure_prompt_embeds_placeholder_token(tokenizer)
+    # 2 tensors expected but only a single placeholder run in the token
+    # stream (simulating dropping the second one).
+    tensors = [torch.randn(N1, H1), torch.randn(N2, H2)]
+    token_ids = [1, tid, tid, 2, 3]
+    mm_updates = _build_prompt_embeds_updates(tensors, tid)
+    # The error constant is a `str.format` template, escape it and turn
+    # the `{field}` placeholders into `.*` so it matches any substitution.
+    pattern = re.sub(
+        r"\\{[^}]*\\}", ".*", re.escape(_PROMPT_EMBEDS_PLACEHOLDER_SPAN_MISMATCH_ERROR)
+    )
+    with pytest.raises(ValueError, match=pattern):
+        _build_prompt_embeds_positions(token_ids, len(tensors), mm_updates)
+
+
+# ints  = regular token IDs (any value)
+# (N,)  = embed span of length N
+@pytest.mark.parametrize(
+    "stream",
+    [
+        [10, 20, (3,), 30],
+        [(2,), 10, 20],
+        [10, 20, (4,)],
+        [1, (2,), 2, 3, (3,), 4],
+        [(1,), (2,)],
+        [5, (2,), 6, (1,), 7, (3,), 8],
+    ],
+    ids=[
+        "single-middle",
+        "single-start",
+        "single-end",
+        "two-spans-separated",
+        "two-spans-adjacent",
+        "three-spans",
+    ],
+)
+def test_build_mixed_prompt_embeds(stream):
+    H = 8
+    _PLACEHOLDER = 0  # sentinel for embed positions in token_ids
+
+    tensors: list[torch.Tensor] = []
+    token_ids: list[int] = []
+    positions: list[tuple[int, int]] = []
+    cursor = 0
+    for item in stream:
+        if isinstance(item, tuple):
+            length = item[0]
+            tensors.append(torch.randn(length, H))
+            positions.append((cursor, length))
+            token_ids.extend([_PLACEHOLDER] * length)
+            cursor += length
+        else:
+            token_ids.append(item)
+            cursor += 1
+
+    embeds, mask = _build_mixed_prompt_embeds(token_ids, tensors, positions)
+
+    assert embeds.shape == (len(token_ids), H)
+    assert len(mask) == len(token_ids)
+
+    # Mask: False exactly at embed positions, True everywhere else.
+    expected_mask = torch.ones(len(token_ids), dtype=torch.bool)
+    for start, length in positions:
+        expected_mask[start : start + length] = False
+    assert mask == expected_mask.tolist()
+
+    # Embed rows match input tensors at the right positions.
+    for tensor, (start, length) in zip(tensors, positions):
+        assert torch.equal(embeds[start : start + length], tensor)
+
+    # Non-embed positions remain zero-filled.
+    assert torch.all(embeds[expected_mask] == 0)
+
+
+# End-to-end tests: each runs both sync and async parse paths via the
+# `parse_fn` fixture.
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("role", ["user", "system"])
+async def test_end_to_end_expand_and_build(tokenizer, parse_fn, role):
+    """Full renderer pipeline: parse -> chat template -> expand -> locate
+    -> build mixed prompt, across tokenizers, roles, and sync/async."""
+    tokenizer.chat_template = _SIMPLE_CHAT_TEMPLATE
+    tid = _ensure_prompt_embeds_placeholder_token(tokenizer)
+
+    LEN_A, LEN_B = 3, 2
+    t_a = torch.randn(LEN_A, _MOCK_HIDDEN_SIZE, dtype=_MOCK_DTYPE)
+    t_b = torch.randn(LEN_B, _MOCK_HIDDEN_SIZE, dtype=_MOCK_DTYPE)
+    NUM_TENSORS = 2
+
+    mc = _make_mock_model_config()
+
+    messages = [
+        {
+            "role": role,
+            "content": [
+                {"type": "text", "text": "Hello "},
+                {"type": "prompt_embeds", "data": _encode_tensor(t_a)},
+                {"type": "text", "text": " world "},
+                {"type": "prompt_embeds", "data": _encode_tensor(t_b)},
+                {"type": "text", "text": "!"},
+            ],
+        }
+    ]
+
+    conv, mm_data, _ = await _maybe_await(
+        parse_fn, messages, mc, content_format="openai"
+    )
+    tensors = list(mm_data["prompt_embeds"])
+    assert len(tensors) == NUM_TENSORS
+
+    # Tokenize: each prompt_embeds part becomes 1 placeholder token.
+    # `return_dict=False` to get a flat `list[int]` on transformers v5
+    # (where the default flipped to True and yields a `BatchEncoding` dict).
+    token_ids = tokenizer.apply_chat_template(conv, tokenize=True, return_dict=False)
+    assert sum(t == tid for t in token_ids) == NUM_TENSORS
+
+    # Expand, locate, and build.
+    mm_updates = _build_prompt_embeds_updates(tensors, tid)
+    expanded = _expand_prompt_embeds_placeholders(token_ids, mm_updates)
+    assert len(expanded) == len(token_ids) + LEN_A + LEN_B - NUM_TENSORS
+
+    positions = _build_prompt_embeds_positions(expanded, len(tensors), mm_updates)
+    assert positions[0][1] == LEN_A
+    assert positions[1][1] == LEN_B
+
+    embeds, mask = _build_mixed_prompt_embeds(expanded, tensors, positions)
+    assert embeds.shape == (len(expanded), _MOCK_HIDDEN_SIZE)
+    assert mask.count(False) == LEN_A + LEN_B
+    assert torch.equal(embeds[positions[0][0] : positions[0][0] + LEN_A], t_a)
+    assert torch.equal(embeds[positions[1][0] : positions[1][0] + LEN_B], t_b)
+
+
+@pytest.mark.asyncio
+async def test_end_to_end_multi_message_conversation(tokenizer, parse_fn):
+    """Full pipeline with prompt_embeds spread across system + user messages,
+    verifying ordering and positioning in the final token stream."""
+    tokenizer.chat_template = _SIMPLE_CHAT_TEMPLATE
+    tid = _ensure_prompt_embeds_placeholder_token(tokenizer)
+
+    LEN_SYS, LEN_USR = 4, 3
+    t_sys = torch.randn(LEN_SYS, _MOCK_HIDDEN_SIZE, dtype=_MOCK_DTYPE)
+    t_usr = torch.randn(LEN_USR, _MOCK_HIDDEN_SIZE, dtype=_MOCK_DTYPE)
+    NUM_TENSORS = 2  # t_sys and t_usr.
+
+    mc = _make_mock_model_config()
+
+    messages = [
+        {
+            "role": "system",
+            "content": [
+                {"type": "text", "text": "You are helpful."},
+                {"type": "prompt_embeds", "data": _encode_tensor(t_sys)},
+            ],
+        },
+        {
+            "role": "user",
+            "content": [
+                {"type": "prompt_embeds", "data": _encode_tensor(t_usr)},
+                {"type": "text", "text": "Summarize."},
+            ],
+        },
+    ]
+
+    conv, mm_data, _ = await _maybe_await(
+        parse_fn, messages, mc, content_format="openai"
+    )
+    tensors = list(mm_data["prompt_embeds"])
+    assert len(tensors) == NUM_TENSORS
+
+    # Tokenize, expand, locate, and build.
+    # `return_dict=False` to get a flat `list[int]` on transformers v5
+    # (where the default flipped to True and yields a `BatchEncoding` dict).
+    token_ids = tokenizer.apply_chat_template(conv, tokenize=True, return_dict=False)
+    mm_updates = _build_prompt_embeds_updates(tensors, tid)
+    expanded = _expand_prompt_embeds_placeholders(token_ids, mm_updates)
+    positions = _build_prompt_embeds_positions(expanded, len(tensors), mm_updates)
+
+    assert positions[0][1] == LEN_SYS
+    assert positions[1][1] == LEN_USR
+    # System embed must appear before user embed in the token stream.
+    assert positions[0][0] < positions[1][0]
+
+    embeds, mask = _build_mixed_prompt_embeds(expanded, tensors, positions)
+    assert embeds.shape == (len(expanded), _MOCK_HIDDEN_SIZE)
+    assert mask.count(False) == LEN_SYS + LEN_USR
+    assert torch.equal(embeds[positions[0][0] : positions[0][0] + LEN_SYS], t_sys)
+    assert torch.equal(embeds[positions[1][0] : positions[1][0] + LEN_USR], t_usr)
diff --git a/tests/renderers/test_completions.py b/tests/renderers/test_completions.py
index ccc806ba137d..00d604afdcf9 100644
--- a/tests/renderers/test_completions.py
+++ b/tests/renderers/test_completions.py
@@ -39,6 +39,11 @@ class MockModelConfig:
     is_encoder_decoder: bool = False
     is_multimodal_model: bool = False
     renderer_num_workers: int = 1
+    hidden_size: int = 768
+    dtype: torch.dtype = torch.float32
+
+    def get_hidden_size(self) -> int:
+        return self.hidden_size
 
 
 @dataclass
@@ -384,12 +389,13 @@ def test_single_prompt_embed(self):
         assert torch.equal(results[0]["prompt_embeds"], tensor_input)
 
     def test_multiple_prompt_embeds(self):
-        renderer = _build_renderer(MockModelConfig())
+        hidden_size = 512
+        renderer = _build_renderer(MockModelConfig(hidden_size=hidden_size))
 
         # Create multiple test tensors
         tensor_inputs = [
-            torch.randn(8, 512, dtype=torch.float32),
-            torch.randn(12, 512, dtype=torch.float32),
+            torch.randn(8, hidden_size, dtype=torch.float32),
+            torch.randn(12, hidden_size, dtype=torch.float32),
         ]
 
         prompts = renderer.render_prompts(
@@ -432,13 +438,15 @@ def test_prompt_embed_truncation(self):
         assert torch.equal(results[0]["prompt_embeds"], expected)
 
     def test_prompt_embed_different_dtypes(self):
-        renderer = _build_renderer(MockModelConfig())
-
+        hidden_size = 256
         # Test different supported dtypes
         dtypes = [torch.float32, torch.float16, torch.bfloat16]
 
         for dtype in dtypes:
-            tensor_input = torch.randn(5, 256, dtype=dtype)
+            renderer = _build_renderer(
+                MockModelConfig(hidden_size=hidden_size, dtype=dtype)
+            )
+            tensor_input = torch.randn(5, hidden_size, dtype=dtype)
 
             prompts = renderer.render_prompts(
                 _preprocess_prompt(
@@ -474,10 +482,11 @@ def test_prompt_embed_squeeze_batch_dim(self):
         assert results[0]["prompt_embeds"].shape == (10, 768)
 
     def test_both_prompts_and_embeds(self):
-        renderer = _build_renderer(MockModelConfig())
+        hidden_size = 256
+        renderer = _build_renderer(MockModelConfig(hidden_size=hidden_size))
 
         text_input = "Hello world"
-        tensor_input = torch.randn(5, 256, dtype=torch.float32)
+        tensor_input = torch.randn(5, hidden_size, dtype=torch.float32)
 
         prompts = renderer.render_prompts(
             _preprocess_prompt(
diff --git a/tests/renderers/test_sparse_tensor_validation.py b/tests/renderers/test_sparse_tensor_validation.py
index 5c51cd30a336..642867086fc9 100644
--- a/tests/renderers/test_sparse_tensor_validation.py
+++ b/tests/renderers/test_sparse_tensor_validation.py
@@ -12,6 +12,7 @@
 import pytest
 import torch
 
+from vllm.exceptions import VLLMValidationError
 from vllm.multimodal.media import AudioEmbeddingMediaIO, ImageEmbeddingMediaIO
 from vllm.renderers.embed_utils import safe_load_prompt_embeds
 
@@ -53,8 +54,14 @@ def _create_malicious_sparse_tensor() -> torch.Tensor:
     values = torch.tensor([1.0])
     shape = (3, 3)
 
-    # Create sparse tensor (this will be invalid)
-    sparse_tensor = torch.sparse_coo_tensor(indices, values, shape, dtype=torch.float32)
+    # Create sparse tensor (this will be invalid). Pass `check_invariants=False`
+    # explicitly so this fixture is robust to process-wide invariant-check state
+    # left enabled by other tests (the global flag isn't thread-local, and
+    # concurrent users of the `check_sparse_tensor_invariants` context manager
+    # can leak the "enabled" state across tests).
+    sparse_tensor = torch.sparse_coo_tensor(
+        indices, values, shape, dtype=torch.float32, check_invariants=False
+    )
     return sparse_tensor
 
 
@@ -117,7 +124,7 @@ def test_extremely_large_indices_rejected(self, model_config):
         shape = (10, 10)
 
         malicious_tensor = torch.sparse_coo_tensor(
-            indices, values, shape, dtype=torch.float32
+            indices, values, shape, dtype=torch.float32, check_invariants=False
         )
         encoded = _encode_tensor(malicious_tensor)
 
@@ -132,13 +139,69 @@ def test_negative_indices_rejected(self, model_config):
         shape = (10, 10)
 
         malicious_tensor = torch.sparse_coo_tensor(
-            indices, values, shape, dtype=torch.float32
+            indices, values, shape, dtype=torch.float32, check_invariants=False
         )
         encoded = _encode_tensor(malicious_tensor)
 
         with pytest.raises((RuntimeError, ValueError)):
             safe_load_prompt_embeds(model_config, encoded)
 
+    def test_hidden_size_mismatch_rejected(self, model_config):
+        """Tensors whose trailing dim doesn't match the model's hidden_size
+        must be rejected at parse time."""
+        # opt-125m has hidden_size=768, passing 512 triggers the check.
+        wrong_hidden = torch.randn(10, 512, dtype=torch.float32)
+        encoded = _encode_tensor(wrong_hidden)
+
+        with pytest.raises(VLLMValidationError, match="hidden_size"):
+            safe_load_prompt_embeds(model_config, encoded)
+
+    def test_float_dtype_mismatch_cast_to_model_dtype(self, model_config):
+        """Tensors whose dtype doesn't match the model's dtype but are still
+        floating-point are cast, since API clients generally can't know the
+        server's `--dtype` setting ahead of time."""
+        # Fixture pins model dtype to float32, upload a bfloat16 tensor.
+        mismatched_float = torch.randn(10, 768, dtype=torch.bfloat16)
+        encoded = _encode_tensor(mismatched_float)
+
+        result = safe_load_prompt_embeds(model_config, encoded)
+
+        assert result.dtype == torch.float32
+        assert result.shape == mismatched_float.shape
+
+    def test_non_float_dtype_rejected(self, model_config):
+        """Non-floating-point dtypes cannot be safely cast for embeddings
+        (e.g. integer tensors almost certainly indicate caller confusion),
+        so they are rejected at parse time."""
+        non_float = torch.randint(0, 100, (10, 768), dtype=torch.int32)
+        encoded = _encode_tensor(non_float)
+
+        with pytest.raises(VLLMValidationError, match="floating-point"):
+            safe_load_prompt_embeds(model_config, encoded)
+
+    def test_non_2d_tensor_rejected(self, model_config):
+        """Tensors that aren't 2D (even after squeezing a leading dim)
+        must be rejected with a clear error."""
+        # A 1D tensor cannot be interpreted as (num_tokens, hidden_size).
+        bad = torch.randn(768, dtype=torch.float32)
+        encoded = _encode_tensor(bad)
+
+        with pytest.raises(VLLMValidationError, match="2D tensor"):
+            safe_load_prompt_embeds(model_config, encoded)
+
+    def test_non_tensor_payload_rejected(self, model_config):
+        """Deserializing to a non-Tensor object must raise a clear error
+        instead of propagating an AssertionError."""
+        # `torch.save` will serialize a plain dict; `weights_only=True` allows
+        # loading built-in containers, so this exercises the isinstance check.
+        buffer = io.BytesIO()
+        torch.save({"not": "a tensor"}, buffer)
+        buffer.seek(0)
+        encoded = base64.b64encode(buffer.read())
+
+        with pytest.raises(VLLMValidationError, match="torch.Tensor"):
+            safe_load_prompt_embeds(model_config, encoded)
+
 
 class TestImageEmbedsValidation:
     """Test sparse tensor validation in image embeddings (Chat API)."""
diff --git a/tests/renderers/test_warmup.py b/tests/renderers/test_warmup.py
new file mode 100644
index 000000000000..ac951aae4618
--- /dev/null
+++ b/tests/renderers/test_warmup.py
@@ -0,0 +1,135 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for BaseRenderer.warmup MM-warmup behavior.
+
+These tests exercise:
+  - Zero-limit modalities are filtered from mm_counts passed to
+    get_dummy_processor_inputs (e.g. --limit-mm-per-prompt image=0 ...)
+  - MM warmup is skipped entirely when mm_processor is None
+
+No model weights are required: warmup() is called directly on a MagicMock
+that acts as the renderer instance.
+"""
+
+from unittest.mock import MagicMock, patch
+
+from vllm.renderers.base import BaseRenderer
+from vllm.renderers.params import ChatParams
+
+
+def _make_renderer_mock(mm_limits: dict[str, int]) -> MagicMock:
+    """Return a MagicMock that quacks like a BaseRenderer instance.
+
+    render_chat is mocked to raise ChatTemplateResolutionError so the chat
+    warmup block is skipped cleanly, keeping the test focused on MM warmup.
+    """
+    from vllm.entrypoints.chat_utils import ChatTemplateResolutionError
+
+    renderer = MagicMock()
+
+    # chat warmup: make render_chat raise so we skip past it cleanly
+    renderer.render_chat.side_effect = ChatTemplateResolutionError("no template")
+
+    # MM processor with configurable limits
+    mm_processor = MagicMock()
+    mm_processor.info.allowed_mm_limits = mm_limits
+    renderer.mm_processor = mm_processor
+    renderer._readonly_mm_processor = None
+    renderer._warmup_mm_processor = BaseRenderer._warmup_mm_processor.__get__(
+        renderer, BaseRenderer
+    )
+    renderer._clear_processor_cache = BaseRenderer._clear_processor_cache
+    renderer.clear_mm_cache = MagicMock()
+    renderer.model_config.max_model_len = 128
+    renderer.model_config.get_multimodal_config.return_value.limit_per_prompt = {}
+
+    return renderer
+
+
+class TestMmWarmupZeroLimitFiltering:
+    """Zero-limit modalities must be excluded from mm_counts."""
+
+    def test_zero_limit_modality_excluded_from_mm_counts(self):
+        """A modality with limit=0 must not appear in mm_counts."""
+        renderer = _make_renderer_mock({"image": 1, "video": 0})
+
+        with patch("vllm.multimodal.processing.TimingContext", autospec=True):
+            BaseRenderer.warmup(renderer, ChatParams())
+
+        get_inputs = renderer.mm_processor.dummy_inputs.get_dummy_processor_inputs
+        get_inputs.assert_called_once()
+        _, kwargs = get_inputs.call_args
+        assert "video" not in kwargs["mm_counts"]
+        assert kwargs["mm_counts"]["image"] == 1
+
+    def test_all_zero_limits_passes_empty_mm_counts(self):
+        """When all limits are 0, mm_counts must be empty."""
+        renderer = _make_renderer_mock({"image": 0, "video": 0})
+
+        with patch("vllm.multimodal.processing.TimingContext", autospec=True):
+            BaseRenderer.warmup(renderer, ChatParams())
+
+        get_inputs = renderer.mm_processor.dummy_inputs.get_dummy_processor_inputs
+        get_inputs.assert_called_once()
+        _, kwargs = get_inputs.call_args
+        assert kwargs["mm_counts"] == {}
+
+    def test_positive_limits_all_included_in_mm_counts(self):
+        """All modalities with limit > 0 must be present in mm_counts."""
+        renderer = _make_renderer_mock({"image": 2, "video": 1})
+
+        with patch("vllm.multimodal.processing.TimingContext", autospec=True):
+            BaseRenderer.warmup(renderer, ChatParams())
+
+        get_inputs = renderer.mm_processor.dummy_inputs.get_dummy_processor_inputs
+        get_inputs.assert_called_once()
+        _, kwargs = get_inputs.call_args
+        assert kwargs["mm_counts"] == {"image": 1, "video": 1}
+
+
+class TestMmWarmupRunsNormally:
+    """MM warmup must run when mm_processor is set and limits > 0."""
+
+    def test_processor_apply_called(self):
+        renderer = _make_renderer_mock({"image": 1})
+
+        with patch("vllm.multimodal.processing.TimingContext", autospec=True):
+            BaseRenderer.warmup(renderer, ChatParams())
+
+        renderer.mm_processor.apply.assert_called_once()
+
+    def test_mm_cache_cleared_after_warmup(self):
+        renderer = _make_renderer_mock({"image": 1})
+
+        with patch("vllm.multimodal.processing.TimingContext", autospec=True):
+            BaseRenderer.warmup(renderer, ChatParams())
+
+        renderer.clear_mm_cache.assert_called_once()
+
+
+class TestMmWarmupSkippedWhenNoProcessor:
+    """MM warmup must be skipped when mm_processor is None (text-only model)."""
+
+    def test_no_warmup_without_processor(self):
+        renderer = _make_renderer_mock({})
+        renderer.mm_processor = None  # override to None
+
+        BaseRenderer.warmup(renderer, ChatParams())
+
+        renderer.model_config.get_multimodal_config.assert_not_called()
+
+
+class TestReadonlyMmWarmup:
+    """Readonly MM processor warmup must mirror the render path behavior."""
+
+    def test_readonly_processor_apply_called_and_cache_cleared(self):
+        renderer = _make_renderer_mock({"image": 1})
+        readonly_mm_processor = MagicMock()
+        readonly_mm_processor.info.allowed_mm_limits = {"image": 1}
+        renderer._readonly_mm_processor = readonly_mm_processor
+
+        with patch("vllm.multimodal.processing.TimingContext", autospec=True):
+            BaseRenderer.warmup(renderer, ChatParams())
+
+        readonly_mm_processor.apply.assert_called_once()
+        readonly_mm_processor.cache.clear_cache.assert_called_once()
diff --git a/tests/test_config.py b/tests/test_config.py
index 364285fbf528..57d1e1bc686b 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -34,6 +34,8 @@
 )
 from vllm.platforms import current_platform
 
+DEVICE_TYPE = current_platform.device_type
+
 
 def test_compile_config_repr_succeeds():
     # setup: VllmBackend mutates the config object
@@ -504,8 +506,8 @@ def test_generation_config_loading():
 @pytest.mark.parametrize(
     "pt_load_map_location",
     [
-        "cuda",
-        {"": "cuda"},
+        DEVICE_TYPE,
+        {"": DEVICE_TYPE},
     ],
 )
 def test_load_config_pt_load_map_location(pt_load_map_location):
@@ -1213,8 +1215,6 @@ def test_scheduler_config_init():
         ("facebook/opt-125m", 1, False, False),
         # Non-MoE model with DP>1 internal LB should need coordinator
         ("facebook/opt-125m", 2, False, True),
-        # Non-MoE model with DP>1 external LB should not need coordinator
-        ("facebook/opt-125m", 2, True, False),
         # MoE model with DP=1 should not need coordinator
         ("mistralai/Mixtral-8x7B-Instruct-v0.1", 1, False, False),
         # MoE model with DP>1 internal LB should need both coordinator
@@ -1293,11 +1293,14 @@ def test_ir_op_priority_default():
     # Assert default is applied to ops
     priority_config = IrOpPriorityConfig.with_default(["vllm_c", "native"])
     assert priority_config.rms_norm == ["vllm_c", "native"]
+    assert priority_config.fused_add_rms_norm == ["vllm_c", "native"]
 
     # Assert single ops override the default
-    assert IrOpPriorityConfig.with_default(
-        ["vllm_c", "native"], rms_norm=["oink", "native"]
-    ) == IrOpPriorityConfig(rms_norm=["oink", "native"])
+    priority_config = IrOpPriorityConfig.with_default(
+        ["native"], rms_norm=["oink", "native"]
+    )
+    assert priority_config.rms_norm == ["oink", "native"]
+    assert priority_config.fused_add_rms_norm == ["native"]
 
 
 def test_ir_op_priority_str():
@@ -1316,3 +1319,34 @@ def test_ir_op_priority_str():
     with pytest.raises(pydantic.ValidationError):
         # must be list of only strings
         priority_config = IrOpPriorityConfig(rms_norm=["vllm_c", 4, "native"])
+
+
+def test_ir_op_priority_ctx():
+    """Test that the priority-setting context sets priority correctly."""
+    from vllm import ir
+    from vllm.config.kernel import IrOpPriorityConfig
+
+    priority = IrOpPriorityConfig.with_default(["native"], rms_norm=["vllm_c"])
+    priority2 = IrOpPriorityConfig.with_default(
+        ["native"], fused_add_rms_norm=["vllm_c"]
+    )
+    with priority.set_priority():
+        assert ir.ops.rms_norm.get_priority() == ["vllm_c", "native"]
+        assert ir.ops.fused_add_rms_norm.get_priority() == ["native"]
+        with priority2.set_priority():
+            assert ir.ops.rms_norm.get_priority() == ["native"]
+            assert ir.ops.fused_add_rms_norm.get_priority() == ["vllm_c", "native"]
+
+        # context restored
+        assert ir.ops.rms_norm.get_priority() == ["vllm_c", "native"]
+        assert ir.ops.fused_add_rms_norm.get_priority() == ["native"]
+
+        with pytest.raises(ValueError), priority2.set_priority():
+            assert ir.ops.rms_norm.get_priority() == ["native"]
+            assert ir.ops.fused_add_rms_norm.get_priority() == ["vllm_c", "native"]
+
+            raise ValueError
+
+        # context restored even after exception
+        assert ir.ops.rms_norm.get_priority() == ["vllm_c", "native"]
+        assert ir.ops.fused_add_rms_norm.get_priority() == ["native"]
diff --git a/tests/test_jit_monitor.py b/tests/test_jit_monitor.py
new file mode 100644
index 000000000000..a463f4b5faa1
--- /dev/null
+++ b/tests/test_jit_monitor.py
@@ -0,0 +1,240 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import os
+import sys
+from types import SimpleNamespace
+from unittest import mock
+
+import pytest
+
+from vllm.triton_utils import jit_monitor
+
+
+@pytest.fixture(autouse=True)
+def _reset_monitor():
+    """Reset global monitor state between tests."""
+    jit_monitor._active = False
+    yield
+    jit_monitor._active = False
+
+
+# ------------------------------------------------------------------
+# Helpers — lightweight stand-ins for triton.knobs
+# ------------------------------------------------------------------
+
+
+def _make_fake_knobs(*, autotuning_print=False, jit_hook=None):
+    """Build a minimal fake ``triton.knobs`` namespace."""
+    autotuning = SimpleNamespace(print=autotuning_print)
+    runtime = SimpleNamespace(jit_post_compile_hook=jit_hook)
+    return SimpleNamespace(autotuning=autotuning, runtime=runtime)
+
+
+def _patch_triton_knobs(fake_knobs):
+    """Context manager that makes ``from triton import knobs`` return *fake_knobs*."""
+    fake_triton = SimpleNamespace(knobs=fake_knobs)
+    return mock.patch.dict(sys.modules, {"triton": fake_triton})
+
+
+# ------------------------------------------------------------------
+# Unit tests (no GPU required, triton is mocked)
+# ------------------------------------------------------------------
+
+
+class TestActivateBasic:
+    def test_sets_active(self):
+        assert not jit_monitor.is_active()
+        with _patch_triton_knobs(_make_fake_knobs()):
+            jit_monitor.activate()
+        assert jit_monitor.is_active()
+
+    def test_idempotent(self):
+        fake = _make_fake_knobs()
+        with _patch_triton_knobs(fake):
+            jit_monitor.activate()
+            first_hook = fake.runtime.jit_post_compile_hook
+            jit_monitor.activate()
+            assert fake.runtime.jit_post_compile_hook is first_hook
+
+    def test_logs_info_on_activation(self):
+        with (
+            mock.patch.object(jit_monitor.logger, "info") as m,
+            _patch_triton_knobs(_make_fake_knobs()),
+        ):
+            jit_monitor.activate()
+        m.assert_called_once()
+        assert "Kernel JIT monitor activated" in m.call_args[0][0]
+
+
+class TestAutotuningPrint:
+    def test_enables_autotuning_print(self):
+        fake = _make_fake_knobs(autotuning_print=False)
+        with _patch_triton_knobs(fake):
+            jit_monitor.activate()
+        assert fake.autotuning.print is True
+
+    def test_respects_user_opt_out(self):
+        fake = _make_fake_knobs(autotuning_print=False)
+        with (
+            mock.patch.dict(os.environ, {"TRITON_PRINT_AUTOTUNING": "0"}),
+            _patch_triton_knobs(fake),
+        ):
+            jit_monitor.activate()
+        assert fake.autotuning.print is False
+
+    def test_noop_when_user_already_enabled(self):
+        fake = _make_fake_knobs(autotuning_print=True)
+        with (
+            mock.patch.dict(os.environ, {"TRITON_PRINT_AUTOTUNING": "1"}),
+            _patch_triton_knobs(fake),
+        ):
+            jit_monitor.activate()
+        assert fake.autotuning.print is True
+
+
+class TestJitHook:
+    def test_hook_registered(self):
+        fake = _make_fake_knobs()
+        assert fake.runtime.jit_post_compile_hook is None
+        with _patch_triton_knobs(fake):
+            jit_monitor.activate()
+        assert fake.runtime.jit_post_compile_hook is not None
+
+    def test_hook_logs_warning(self):
+        fake = _make_fake_knobs()
+        with _patch_triton_knobs(fake):
+            jit_monitor.activate()
+
+        hook = fake.runtime.jit_post_compile_hook
+        mock_fn = SimpleNamespace(name="test_kernel")
+
+        with mock.patch.object(jit_monitor.logger, "warning") as m:
+            hook(
+                key="some_key",
+                repr="some_repr",
+                fn=mock_fn,
+                compile=lambda: None,
+                is_manual_warmup=False,
+                already_compiled=False,
+            )
+
+        m.assert_called_once()
+        msg = m.call_args[0][0] % m.call_args[0][1:]
+        assert "Triton kernel JIT compilation during inference" in msg
+        assert "test_kernel" in msg
+
+    def test_hook_chains_existing_hook(self):
+        existing = mock.MagicMock(return_value="existing_result")
+        fake = _make_fake_knobs(jit_hook=existing)
+        with _patch_triton_knobs(fake):
+            jit_monitor.activate()
+
+        hook = fake.runtime.jit_post_compile_hook
+        mock_fn = SimpleNamespace(name="chained_kernel")
+        kwargs = dict(
+            key="k",
+            repr="r",
+            fn=mock_fn,
+            compile=lambda: None,
+            is_manual_warmup=False,
+            already_compiled=False,
+        )
+        result = hook(**kwargs)
+
+        existing.assert_called_once()
+        assert result == "existing_result"
+
+    def test_hook_works_without_existing_hook(self):
+        fake = _make_fake_knobs(jit_hook=None)
+        with _patch_triton_knobs(fake):
+            jit_monitor.activate()
+
+        hook = fake.runtime.jit_post_compile_hook
+        mock_fn = SimpleNamespace(name="solo_kernel")
+        result = hook(
+            key="k",
+            repr="r",
+            fn=mock_fn,
+            compile=lambda: None,
+            is_manual_warmup=False,
+            already_compiled=False,
+        )
+        assert result is None
+
+
+class TestNoTritonFallback:
+    def test_activate_without_triton(self):
+        with mock.patch.object(jit_monitor, "HAS_TRITON", False):
+            jit_monitor.activate()
+        assert jit_monitor.is_active()
+
+
+# ------------------------------------------------------------------
+# Integration tests (real Triton + GPU)
+# ------------------------------------------------------------------
+
+try:
+    import torch
+
+    _HAS_CUDA = torch.cuda.is_available()
+except ImportError:
+    _HAS_CUDA = False
+
+try:
+    import triton
+    import triton.language as tl
+
+    _HAS_TRITON = True
+except ImportError:
+    _HAS_TRITON = False
+
+_skip_no_gpu = pytest.mark.skipif(
+    not (_HAS_CUDA and _HAS_TRITON),
+    reason="Requires CUDA GPU and Triton",
+)
+
+
+if _HAS_TRITON:
+
+    @triton.jit
+    def _add_kernel(x_ptr, y_ptr, out_ptr, n, BLOCK: tl.constexpr):
+        pid = tl.program_id(0)
+        offs = pid * BLOCK + tl.arange(0, BLOCK)
+        mask = offs < n
+        x = tl.load(x_ptr + offs, mask=mask)
+        y = tl.load(y_ptr + offs, mask=mask)
+        tl.store(out_ptr + offs, x + y, mask=mask)
+
+
+def _run_add_kernel(n: int, block: int = 256) -> None:
+    """Launch ``_add_kernel`` with vectors of length *n*."""
+    x = torch.randn(n, device="cuda")
+    y = torch.randn(n, device="cuda")
+    out = torch.empty(n, device="cuda")
+    grid = ((n + block - 1) // block,)
+    _add_kernel[grid](x, y, out, n, BLOCK=block)
+    torch.accelerator.synchronize()
+
+
+@_skip_no_gpu
+class TestTritonJitHookIntegration:
+    """End-to-end: real Triton kernel, real GPU, real hook."""
+
+    def test_no_warning_on_cached_shape(self):
+        _run_add_kernel(1024)
+
+        jit_monitor.activate()
+        with mock.patch.object(jit_monitor.logger, "warning") as w:
+            _run_add_kernel(1024)
+        w.assert_not_called()
+
+    def test_warning_on_new_constexpr(self):
+        _run_add_kernel(1024, block=256)
+
+        jit_monitor.activate()
+        with mock.patch.object(jit_monitor.logger, "warning") as w:
+            # Different BLOCK (a tl.constexpr) forces recompilation.
+            _run_add_kernel(1024, block=512)
+        w.assert_called()
+        msg = w.call_args[0][0] % w.call_args[0][1:]
+        assert "_add_kernel" in msg
diff --git a/tests/test_regression.py b/tests/test_regression.py
index a38b4428dea5..c38d974119aa 100644
--- a/tests/test_regression.py
+++ b/tests/test_regression.py
@@ -17,22 +17,6 @@
 from vllm.platforms import current_platform
 
 
-@pytest.mark.skip(reason="In V1, we reject tokens > max_seq_len")
-def test_duplicated_ignored_sequence_group():
-    """https://github.com/vllm-project/vllm/issues/1655"""
-
-    sampling_params = SamplingParams(temperature=0.01, top_p=0.1, max_tokens=256)
-    llm = LLM(
-        model="distilbert/distilgpt2",
-        max_num_batched_tokens=4096,
-        tensor_parallel_size=1,
-    )
-    prompts = ["This is a short prompt", "This is a very long prompt " * 1000]
-    outputs = llm.generate(prompts, sampling_params=sampling_params)
-
-    assert len(prompts) == len(outputs)
-
-
 @pytest.mark.parametrize(
     "model",
     [
@@ -72,9 +56,10 @@ def test_gc():
 
 
 def test_model_from_modelscope(monkeypatch: pytest.MonkeyPatch):
-    # model: https://modelscope.cn/models/qwen/Qwen1.5-0.5B-Chat/summary
+    # model: https://www.modelscope.ai/models/qwen/Qwen1.5-0.5B-Chat
     with monkeypatch.context() as m:
         m.setenv("VLLM_USE_MODELSCOPE", "True")
+        m.setenv("MODELSCOPE_DOMAIN", "www.modelscope.ai")
         # Don't use HF_TOKEN for ModelScope repos, otherwise it will fail
         # with 400 Client Error: Bad Request.
         m.setenv("HF_TOKEN", "")
diff --git a/tests/tokenizers_/fixtures/deepseek_v4/test_input_1.json b/tests/tokenizers_/fixtures/deepseek_v4/test_input_1.json
new file mode 100644
index 000000000000..35e49588dfa3
--- /dev/null
+++ b/tests/tokenizers_/fixtures/deepseek_v4/test_input_1.json
@@ -0,0 +1,81 @@
+{
+    "tools": [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_weather",
+                "description": "Get the weather for a specific location",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city name"
+                        },
+                        "unit": {
+                            "type": "string",
+                            "enum": ["celsius", "fahrenheit"],
+                            "description": "Temperature unit"
+                        }
+                    },
+                    "required": ["location"]
+                }
+            }
+        },
+        {
+            "type": "function",
+            "function": {
+                "name": "search",
+                "description": "Search the web for information",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "query": {
+                            "type": "string",
+                            "description": "Search query"
+                        },
+                        "num_results": {
+                            "type": "integer",
+                            "description": "Number of results to return"
+                        }
+                    },
+                    "required": ["query"]
+                }
+            }
+        }
+    ],
+    "messages": [
+        {
+            "role": "system",
+            "content": "You are a helpful assistant."
+        },
+        {
+            "role": "user",
+            "content": "What's the weather in Beijing?"
+        },
+        {
+            "role": "assistant",
+            "reasoning": "The user wants to know the weather in Beijing. I should use the get_weather tool.",
+            "tool_calls": [
+                {
+                    "id": "call_001",
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "arguments": "{\"location\": \"Beijing\", \"unit\": \"celsius\"}"
+                    }
+                }
+            ]
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_001",
+            "content": "{\"temperature\": 22, \"condition\": \"sunny\", \"humidity\": 45}"
+        },
+        {
+            "role": "assistant",
+            "reasoning": "Got the weather data. Let me format a nice response.",
+            "content": "The weather in Beijing is currently sunny with a temperature of 22°C and 45% humidity."
+        }
+    ]
+}
diff --git a/tests/tokenizers_/fixtures/deepseek_v4/test_input_2.json b/tests/tokenizers_/fixtures/deepseek_v4/test_input_2.json
new file mode 100644
index 000000000000..a301609ac2b7
--- /dev/null
+++ b/tests/tokenizers_/fixtures/deepseek_v4/test_input_2.json
@@ -0,0 +1,24 @@
+[
+  {
+    "role": "system",
+    "content": "You are a helpful assistant."
+  },
+  {
+    "role": "user",
+    "content": "Hello"
+  },
+  {
+    "role": "assistant",
+    "reasoning": "The user said hello, I should greet back.",
+    "content": "Hi there! How can I help you?"
+  },
+  {
+    "role": "user",
+    "content": "What is the capital of France?"
+  },
+  {
+    "role": "assistant",
+    "reasoning": "The user asks about the capital of France. It is Paris.",
+    "content": "The capital of France is Paris."
+  }
+]
\ No newline at end of file
diff --git a/tests/tokenizers_/fixtures/deepseek_v4/test_input_3.json b/tests/tokenizers_/fixtures/deepseek_v4/test_input_3.json
new file mode 100644
index 000000000000..d2dc42e3de20
--- /dev/null
+++ b/tests/tokenizers_/fixtures/deepseek_v4/test_input_3.json
@@ -0,0 +1,159 @@
+[
+  {
+    "role": "system",
+    "content": "该助手为DeepSeek，由深度求索公司创造。"
+  },
+  {
+    "role": "latest_reminder",
+    "content": "2026-02-21,星期六,广州,App,中文"
+  },
+  {
+    "role": "developer",
+    "content": "小柴胡冲剂和布洛芬能一起吃吗？\n\nCITATION FORMAT: 【{cursor_id}†L{start_line_id}(-L{end_line_id})?】",
+    "tools": [
+      {
+        "type": "function",
+        "function": {
+          "name": "search",
+          "description": "Web search. Split multiple queries with '||'.",
+          "parameters": {
+            "type": "object",
+            "properties": {
+              "queries": {
+                "type": "string",
+                "description": "query1||query2"
+              }
+            },
+            "required": [
+              "queries"
+            ],
+            "additionalProperties": false,
+            "$schema": "http://json-schema.org/draft-07/schema#"
+          }
+        }
+      },
+      {
+        "type": "function",
+        "function": {
+          "name": "open",
+          "description": "Batch open IDs (format 【{id}†...】) or URLs.",
+          "parameters": {
+            "type": "object",
+            "properties": {
+              "open_list": {
+                "type": "array",
+                "items": {
+                  "type": "object",
+                  "properties": {
+                    "id": {
+                      "description": "ID or URL",
+                      "anyOf": [
+                        {
+                          "type": "integer"
+                        },
+                        {
+                          "type": "string"
+                        }
+                      ],
+                      "default": -1
+                    },
+                    "cursor": {
+                      "type": "integer",
+                      "description": "",
+                      "default": -1
+                    },
+                    "loc": {
+                      "type": "integer",
+                      "description": "Start line",
+                      "default": -1
+                    },
+                    "num_lines": {
+                      "type": "integer",
+                      "description": "",
+                      "default": -1
+                    },
+                    "view_source": {
+                      "type": "boolean",
+                      "description": "",
+                      "default": false
+                    }
+                  },
+                  "additionalProperties": false
+                },
+                "description": ""
+              }
+            },
+            "required": [
+              "open_list"
+            ],
+            "additionalProperties": false,
+            "$schema": "http://json-schema.org/draft-07/schema#"
+          }
+        }
+      },
+      {
+        "type": "function",
+        "function": {
+          "name": "find",
+          "description": "Find exact text pattern in pages.",
+          "parameters": {
+            "type": "object",
+            "properties": {
+              "find_list": {
+                "type": "array",
+                "items": {
+                  "type": "object",
+                  "properties": {
+                    "pattern": {
+                      "type": "string",
+                      "description": ""
+                    },
+                    "cursor": {
+                      "type": "integer",
+                      "description": "",
+                      "default": -1
+                    }
+                  },
+                  "required": [
+                    "pattern"
+                  ],
+                  "additionalProperties": false
+                },
+                "description": ""
+              }
+            },
+            "required": [
+              "find_list"
+            ],
+            "additionalProperties": false,
+            "$schema": "http://json-schema.org/draft-07/schema#"
+          }
+        }
+      }
+    ]
+  },
+  {
+    "role": "assistant",
+    "content": "",
+    "reasoning": "用户想知道小柴胡冲剂和布洛芬能否一起服用。",
+    "tool_calls": [
+      {
+        "type": "function",
+        "function": {
+          "name": "search",
+          "arguments": "{\"queries\": \"小柴胡冲剂 布洛芬 相互作用 一起吃\"}"
+        }
+      }
+    ]
+  },
+  {
+    "role": "tool",
+    "content": "[0]"
+  },
+  {
+    "role": "assistant",
+    "content": "请及时就医。",
+    "reasoning": "现在开始组织回答。",
+    "tool_calls": []
+  }
+]
\ No newline at end of file
diff --git a/tests/tokenizers_/fixtures/deepseek_v4/test_input_4.json b/tests/tokenizers_/fixtures/deepseek_v4/test_input_4.json
new file mode 100644
index 000000000000..d5e0791dd699
--- /dev/null
+++ b/tests/tokenizers_/fixtures/deepseek_v4/test_input_4.json
@@ -0,0 +1,28 @@
+[
+  {
+    "role": "system",
+    "content": "该助手为DeepSeek-V3，由深度求索公司创造。\n今天是2025年10月17日，星期五。"
+  },
+  {
+    "role": "latest_reminder",
+    "content": "2024-11-15,上海市,App,中文"
+  },
+  {
+    "role": "user",
+    "content": "热海大滚锅是世界著名温泉吗"
+  },
+  {
+    "role": "assistant",
+    "content": "关于热海大滚锅是否是世界著名温泉，可以这样总结：它在**中国乃至全球的地热奇观中占有重要地位**，但“世界著名”的称号更侧重于它作为**独特的地质现象和旅游景点**，而非普遍的温泉疗养体验。\n\n为了让你快速了解，我整理了一个简要的表格：\n\n| 维度 | 热海大滚锅的地位与特点 |\n| :--- | :--- |\n| **地质奇观** | **世界罕见**的高温地热系统，泉眼多、水温高、形态多样。 |\n| **国内声誉** | **中国三大地热区之一**，**国家5A级旅游景区**，自古闻名（徐霞客曾游历并记载）。 |\n| **国际知名度** | 在一些旅游平台被提及为“世界六大温泉”之一，但此说法流传不广，其国际声誉更多建立在地质独特性上。 |\n| **核心体验** | **观赏地热奇观**（如97℃沸腾的“大滚锅”）、**体验温泉煮鸡蛋**。 |\n\n### 💡 游玩攻略与温馨提示\n\n如果你计划前往热海大滚锅，这里有一些实用信息供你参考：\n\n- **门票与开放时间**：\n    - **门票**：景区门票约为**50元/人**。如果选择包含温泉沐浴的套餐，价格会更高，例如约**288元**。\n    - **开放时间**：景区一般**08:00-18:00**开放，但具体时间可能变动，建议提前核实。\n\n- **特色体验**：\n    - **温泉煮鸡蛋**：这几乎是必试项目。可以在景区门口购买用草绳串起的生鸡蛋（约5-8元/串），然后到“大滚锅”旁的指定区域蒸煮，几分钟便可熟食，趣味十足。\n    - **金汤足浴**：可以直接用从“大滚锅”流出的温泉水泡脚，缓解旅途疲劳。\n\n- **注意事项**：\n    - **安全第一**：“大滚锅”水温极高，务必遵守游览规则，在指定区域内观赏，切勿随意触碰泉水。\n    - **规划行程**：建议为热海景区预留**3-4小时**的游览时间。景区内步道不走回头路，出入口有观光车接送。\n\n希望这些信息能帮助你更好地了解热海大滚锅。如果你对腾冲的其他景点或者行程规划有更多疑问，我很乐意提供进一步的信息。",
+    "mask": 1
+  },
+  {
+    "role": "user",
+    "content": "世界著名温泉有哪些",
+    "task": "action"
+  },
+  {
+    "role": "assistant",
+    "content": "Search"
+  }
+]
\ No newline at end of file
diff --git a/tests/tokenizers_/fixtures/deepseek_v4/test_output_1.txt b/tests/tokenizers_/fixtures/deepseek_v4/test_output_1.txt
new file mode 100644
index 000000000000..dbd823476c1c
--- /dev/null
+++ b/tests/tokenizers_/fixtures/deepseek_v4/test_output_1.txt
@@ -0,0 +1,36 @@
+<｜begin▁of▁sentence｜>
+
+## Tools
+
+You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<｜DSML｜tool_calls>" block like the following:
+
+<｜DSML｜tool_calls>
+<｜DSML｜invoke name="$TOOL_NAME">
+<｜DSML｜parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</｜DSML｜parameter>
+...
+</｜DSML｜invoke>
+<｜DSML｜invoke name="$TOOL_NAME2">
+...
+</｜DSML｜invoke>
+</｜DSML｜tool_calls>
+
+String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.
+
+If thinking_mode is enabled (triggered by <think>), you MUST output your complete reasoning inside <think>...</think> BEFORE any tool calls or final response.
+
+Otherwise, output directly after </think> with tool calls or final response.
+
+### Available Tool Schemas
+
+{"name": "get_weather", "description": "Get the weather for a specific location", "parameters": {"type": "object", "properties": {"location": {"type": "string", "description": "The city name"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"], "description": "Temperature unit"}}, "required": ["location"]}}
+{"name": "search", "description": "Search the web for information", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "Search query"}, "num_results": {"type": "integer", "description": "Number of results to return"}}, "required": ["query"]}}
+
+You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.
+You are a helpful assistant.<｜User｜>What's the weather in Beijing?<｜Assistant｜><think>The user wants to know the weather in Beijing. I should use the get_weather tool.</think>
+
+<｜DSML｜tool_calls>
+<｜DSML｜invoke name="get_weather">
+<｜DSML｜parameter name="location" string="true">Beijing</｜DSML｜parameter>
+<｜DSML｜parameter name="unit" string="true">celsius</｜DSML｜parameter>
+</｜DSML｜invoke>
+</｜DSML｜tool_calls><｜end▁of▁sentence｜><｜User｜><tool_result>{"temperature": 22, "condition": "sunny", "humidity": 45}</tool_result><｜Assistant｜><think>Got the weather data. Let me format a nice response.</think>The weather in Beijing is currently sunny with a temperature of 22°C and 45% humidity.<｜end▁of▁sentence｜>
\ No newline at end of file
diff --git a/tests/tokenizers_/fixtures/deepseek_v4/test_output_2.txt b/tests/tokenizers_/fixtures/deepseek_v4/test_output_2.txt
new file mode 100644
index 000000000000..fc397ef54972
--- /dev/null
+++ b/tests/tokenizers_/fixtures/deepseek_v4/test_output_2.txt
@@ -0,0 +1 @@
+<｜begin▁of▁sentence｜>You are a helpful assistant.<｜User｜>Hello<｜Assistant｜></think>Hi there! How can I help you?<｜end▁of▁sentence｜><｜User｜>What is the capital of France?<｜Assistant｜><think>The user asks about the capital of France. It is Paris.</think>The capital of France is Paris.<｜end▁of▁sentence｜>
\ No newline at end of file
diff --git a/tests/tokenizers_/fixtures/deepseek_v4/test_output_3.txt b/tests/tokenizers_/fixtures/deepseek_v4/test_output_3.txt
new file mode 100644
index 000000000000..edee563300d4
--- /dev/null
+++ b/tests/tokenizers_/fixtures/deepseek_v4/test_output_3.txt
@@ -0,0 +1,38 @@
+<｜begin▁of▁sentence｜>该助手为DeepSeek，由深度求索公司创造。<｜latest_reminder｜>2026-02-21,星期六,广州,App,中文<｜User｜>小柴胡冲剂和布洛芬能一起吃吗？
+
+CITATION FORMAT: 【{cursor_id}†L{start_line_id}(-L{end_line_id})?】
+
+## Tools
+
+You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<｜DSML｜tool_calls>" block like the following:
+
+<｜DSML｜tool_calls>
+<｜DSML｜invoke name="$TOOL_NAME">
+<｜DSML｜parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</｜DSML｜parameter>
+...
+</｜DSML｜invoke>
+<｜DSML｜invoke name="$TOOL_NAME2">
+...
+</｜DSML｜invoke>
+</｜DSML｜tool_calls>
+
+String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.
+
+If thinking_mode is enabled (triggered by <think>), you MUST output your complete reasoning inside <think>...</think> BEFORE any tool calls or final response.
+
+Otherwise, output directly after </think> with tool calls or final response.
+
+### Available Tool Schemas
+
+{"name": "search", "description": "Web search. Split multiple queries with '||'.", "parameters": {"type": "object", "properties": {"queries": {"type": "string", "description": "query1||query2"}}, "required": ["queries"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}}
+{"name": "open", "description": "Batch open IDs (format 【{id}†...】) or URLs.", "parameters": {"type": "object", "properties": {"open_list": {"type": "array", "items": {"type": "object", "properties": {"id": {"description": "ID or URL", "anyOf": [{"type": "integer"}, {"type": "string"}], "default": -1}, "cursor": {"type": "integer", "description": "", "default": -1}, "loc": {"type": "integer", "description": "Start line", "default": -1}, "num_lines": {"type": "integer", "description": "", "default": -1}, "view_source": {"type": "boolean", "description": "", "default": false}}, "additionalProperties": false}, "description": ""}}, "required": ["open_list"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}}
+{"name": "find", "description": "Find exact text pattern in pages.", "parameters": {"type": "object", "properties": {"find_list": {"type": "array", "items": {"type": "object", "properties": {"pattern": {"type": "string", "description": ""}, "cursor": {"type": "integer", "description": "", "default": -1}}, "required": ["pattern"], "additionalProperties": false}, "description": ""}}, "required": ["find_list"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}}
+
+You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.
+<｜Assistant｜><think>用户想知道小柴胡冲剂和布洛芬能否一起服用。</think>
+
+<｜DSML｜tool_calls>
+<｜DSML｜invoke name="search">
+<｜DSML｜parameter name="queries" string="true">小柴胡冲剂 布洛芬 相互作用 一起吃</｜DSML｜parameter>
+</｜DSML｜invoke>
+</｜DSML｜tool_calls><｜end▁of▁sentence｜><｜User｜><tool_result>[0]</tool_result><｜Assistant｜><think>现在开始组织回答。</think>请及时就医。<｜end▁of▁sentence｜>
\ No newline at end of file
diff --git a/tests/tokenizers_/fixtures/deepseek_v4/test_output_4.txt b/tests/tokenizers_/fixtures/deepseek_v4/test_output_4.txt
new file mode 100644
index 000000000000..d30bd5d06cf3
--- /dev/null
+++ b/tests/tokenizers_/fixtures/deepseek_v4/test_output_4.txt
@@ -0,0 +1,29 @@
+<｜begin▁of▁sentence｜>该助手为DeepSeek-V3，由深度求索公司创造。
+今天是2025年10月17日，星期五。<｜latest_reminder｜>2024-11-15,上海市,App,中文<｜User｜>热海大滚锅是世界著名温泉吗<｜Assistant｜></think>关于热海大滚锅是否是世界著名温泉，可以这样总结：它在**中国乃至全球的地热奇观中占有重要地位**，但“世界著名”的称号更侧重于它作为**独特的地质现象和旅游景点**，而非普遍的温泉疗养体验。
+
+为了让你快速了解，我整理了一个简要的表格：
+
+| 维度 | 热海大滚锅的地位与特点 |
+| :--- | :--- |
+| **地质奇观** | **世界罕见**的高温地热系统，泉眼多、水温高、形态多样。 |
+| **国内声誉** | **中国三大地热区之一**，**国家5A级旅游景区**，自古闻名（徐霞客曾游历并记载）。 |
+| **国际知名度** | 在一些旅游平台被提及为“世界六大温泉”之一，但此说法流传不广，其国际声誉更多建立在地质独特性上。 |
+| **核心体验** | **观赏地热奇观**（如97℃沸腾的“大滚锅”）、**体验温泉煮鸡蛋**。 |
+
+### 💡 游玩攻略与温馨提示
+
+如果你计划前往热海大滚锅，这里有一些实用信息供你参考：
+
+- **门票与开放时间**：
+    - **门票**：景区门票约为**50元/人**。如果选择包含温泉沐浴的套餐，价格会更高，例如约**288元**。
+    - **开放时间**：景区一般**08:00-18:00**开放，但具体时间可能变动，建议提前核实。
+
+- **特色体验**：
+    - **温泉煮鸡蛋**：这几乎是必试项目。可以在景区门口购买用草绳串起的生鸡蛋（约5-8元/串），然后到“大滚锅”旁的指定区域蒸煮，几分钟便可熟食，趣味十足。
+    - **金汤足浴**：可以直接用从“大滚锅”流出的温泉水泡脚，缓解旅途疲劳。
+
+- **注意事项**：
+    - **安全第一**：“大滚锅”水温极高，务必遵守游览规则，在指定区域内观赏，切勿随意触碰泉水。
+    - **规划行程**：建议为热海景区预留**3-4小时**的游览时间。景区内步道不走回头路，出入口有观光车接送。
+
+希望这些信息能帮助你更好地了解热海大滚锅。如果你对腾冲的其他景点或者行程规划有更多疑问，我很乐意提供进一步的信息。<｜end▁of▁sentence｜><｜User｜>世界著名温泉有哪些<｜Assistant｜></think><｜action｜>Search<｜end▁of▁sentence｜>
\ No newline at end of file
diff --git a/tests/tokenizers_/test_deepseek_v4.py b/tests/tokenizers_/test_deepseek_v4.py
new file mode 100644
index 000000000000..358732eabf40
--- /dev/null
+++ b/tests/tokenizers_/test_deepseek_v4.py
@@ -0,0 +1,290 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import json
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+
+from vllm.entrypoints.chat_utils import parse_chat_messages
+from vllm.renderers.registry import RENDERER_REGISTRY
+from vllm.tokenizers.deepseek_v4 import get_deepseek_v4_tokenizer
+from vllm.tokenizers.registry import TokenizerRegistry
+
+FIXTURES_DIR = Path(__file__).parent / "fixtures" / "deepseek_v4"
+
+
+class FakeHfTokenizer:
+    vocab_size = 100
+
+    def get_added_vocab(self) -> dict[str, int]:
+        return {"</think>": 100}
+
+    def encode(
+        self,
+        text: str,
+        add_special_tokens: bool = False,
+        **kwargs,
+    ) -> list[int]:
+        self.last_encode = (text, add_special_tokens, kwargs)
+        return [len(text)]
+
+
+def _tokenizer():
+    return get_deepseek_v4_tokenizer(FakeHfTokenizer())
+
+
+def _model_config():
+    return SimpleNamespace(
+        multimodal_config=None,
+        allowed_local_media_path="",
+        allowed_media_domains=None,
+        enable_prompt_embeds=False,
+    )
+
+
+def _load_reference_case(case_id: int):
+    data = json.loads((FIXTURES_DIR / f"test_input_{case_id}.json").read_text())
+    if isinstance(data, dict):
+        return data["messages"], data.get("tools")
+    return data, None
+
+
+def _render_reference_case(case_id: int, **kwargs):
+    messages, tools = _load_reference_case(case_id)
+    conversation, _, _ = parse_chat_messages(
+        messages,
+        _model_config(),
+        content_format="string",
+    )
+    return _tokenizer().apply_chat_template(
+        conversation=conversation,
+        messages=messages,
+        tools=tools,
+        tokenize=False,
+        **kwargs,
+    )
+
+
+def test_deepseek_v4_tokenizer_registered():
+    assert TokenizerRegistry.load_tokenizer_cls("deepseek_v4").__name__ == (
+        "DeepseekV4Tokenizer"
+    )
+    assert RENDERER_REGISTRY.load_renderer_cls("deepseek_v4").__name__ == (
+        "DeepseekV4Renderer"
+    )
+
+
+def test_deepseek_v4_defaults_to_chat_mode():
+    prompt = _tokenizer().apply_chat_template(
+        [{"role": "user", "content": "Hello"}],
+        tokenize=False,
+    )
+
+    assert prompt == ("<｜begin▁of▁sentence｜><｜User｜>Hello<｜Assistant｜></think>")
+
+
+@pytest.mark.parametrize("kwargs", [{"thinking": True}, {"enable_thinking": True}])
+def test_deepseek_v4_enables_thinking_with_compatible_kwargs(kwargs):
+    prompt = _tokenizer().apply_chat_template(
+        [{"role": "user", "content": "Hello"}],
+        tokenize=False,
+        **kwargs,
+    )
+
+    assert prompt == ("<｜begin▁of▁sentence｜><｜User｜>Hello<｜Assistant｜><think>")
+
+
+def test_deepseek_v4_uses_v4_tool_prompt_from_request_tools():
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_weather",
+                "description": "Get weather for a city",
+                "parameters": {
+                    "type": "object",
+                    "properties": {"city": {"type": "string"}},
+                    "required": ["city"],
+                },
+            },
+        }
+    ]
+
+    prompt = _tokenizer().apply_chat_template(
+        [{"role": "user", "content": "Weather?"}],
+        tools=tools,
+        tokenize=False,
+    )
+
+    assert "## Tools" in prompt
+    assert "<｜DSML｜tool_calls>" in prompt
+    assert "</｜DSML｜tool_calls>" in prompt
+    assert "function_calls" not in prompt
+    assert '"name": "get_weather"' in prompt
+    assert prompt.endswith("<｜User｜>Weather?<｜Assistant｜></think>")
+
+
+def test_deepseek_v4_renders_parsed_history_tool_arguments():
+    messages = [
+        {"role": "user", "content": "List the repo"},
+        {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "id": "call_1",
+                    "type": "function",
+                    "function": {
+                        "name": "str_replace_editor",
+                        "arguments": '{"command": "view", "path": "/testbed"}',
+                    },
+                }
+            ],
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_1",
+            "content": "file list",
+        },
+    ]
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "str_replace_editor",
+                "description": "Edit files",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "command": {"type": "string"},
+                        "path": {"type": "string"},
+                    },
+                    "required": ["command", "path"],
+                },
+            },
+        }
+    ]
+    conversation, _, _ = parse_chat_messages(
+        messages,
+        _model_config(),
+        content_format="string",
+    )
+
+    prompt = _tokenizer().apply_chat_template(
+        conversation=conversation,
+        messages=messages,
+        tools=tools,
+        tokenize=False,
+    )
+
+    assert '<｜DSML｜parameter name="command" string="true">view' in prompt
+    assert '<｜DSML｜parameter name="path" string="true">/testbed' in prompt
+    assert 'parameter name="arguments"' not in prompt
+
+
+@pytest.mark.parametrize("reasoning_effort", ["minimal", "low", "medium", "high"])
+def test_deepseek_v4_accepts_openai_reasoning_effort_values(reasoning_effort):
+    prompt = _tokenizer().apply_chat_template(
+        [{"role": "user", "content": "Hello"}],
+        tokenize=False,
+        enable_thinking=True,
+        reasoning_effort=reasoning_effort,
+    )
+
+    assert prompt.endswith("<｜Assistant｜><think>")
+    assert "Reasoning Effort: Absolute maximum" not in prompt
+
+
+def test_deepseek_v4_none_reasoning_effort_disables_thinking():
+    prompt = _tokenizer().apply_chat_template(
+        [{"role": "user", "content": "Hello"}],
+        tokenize=False,
+        enable_thinking=True,
+        reasoning_effort="none",
+    )
+
+    assert prompt == ("<｜begin▁of▁sentence｜><｜User｜>Hello<｜Assistant｜></think>")
+
+
+@pytest.mark.parametrize(
+    ("reasoning_effort", "expected_mode", "expected_effort"),
+    [
+        ("none", "chat", None),
+        ("minimal", "thinking", "high"),
+        ("low", "thinking", "high"),
+        ("medium", "thinking", "high"),
+        ("high", "thinking", "high"),
+        ("xhigh", "thinking", "max"),
+        ("max", "thinking", "max"),
+        ("unexpected", "thinking", "high"),
+    ],
+)
+def test_deepseek_v4_maps_compatible_thinking_reasoning_effort_values(
+    monkeypatch: pytest.MonkeyPatch,
+    reasoning_effort,
+    expected_mode,
+    expected_effort,
+):
+    captured_kwargs = []
+
+    def fake_encode_messages(messages, **kwargs):
+        captured_kwargs.append(kwargs)
+        return "prompt"
+
+    monkeypatch.setattr(
+        "vllm.tokenizers.deepseek_v4.encode_messages",
+        fake_encode_messages,
+    )
+
+    _tokenizer().apply_chat_template(
+        [{"role": "user", "content": "Hello"}],
+        tokenize=False,
+        enable_thinking=True,
+        reasoning_effort=reasoning_effort,
+    )
+
+    assert captured_kwargs[-1]["thinking_mode"] == expected_mode
+    assert captured_kwargs[-1]["reasoning_effort"] == expected_effort
+
+
+def test_deepseek_v4_preserves_reference_max_reasoning_effort():
+    prompt = _tokenizer().apply_chat_template(
+        [{"role": "user", "content": "Hello"}],
+        tokenize=False,
+        enable_thinking=True,
+        reasoning_effort="max",
+    )
+
+    assert prompt.startswith(
+        "<｜begin▁of▁sentence｜>Reasoning Effort: Absolute maximum"
+    )
+
+
+def test_deepseek_v4_maps_xhigh_to_reference_max_reasoning_effort():
+    prompt = _tokenizer().apply_chat_template(
+        [{"role": "user", "content": "Hello"}],
+        tokenize=False,
+        enable_thinking=True,
+        reasoning_effort="xhigh",
+    )
+
+    assert prompt.startswith(
+        "<｜begin▁of▁sentence｜>Reasoning Effort: Absolute maximum"
+    )
+
+
+@pytest.mark.parametrize(
+    ("case_id", "kwargs"),
+    [
+        (1, {"thinking": True}),
+        (2, {"thinking": True}),
+        (3, {"thinking": True}),
+        (4, {}),
+    ],
+)
+def test_deepseek_v4_matches_reference_golden_fixtures(case_id, kwargs):
+    prompt = _render_reference_case(case_id, **kwargs)
+
+    expected = (FIXTURES_DIR / f"test_output_{case_id}.txt").read_text()
+    assert prompt == expected
diff --git a/tests/tokenizers_/test_mistral.py b/tests/tokenizers_/test_mistral.py
index 2b101e8f98d9..2023337e8577 100644
--- a/tests/tokenizers_/test_mistral.py
+++ b/tests/tokenizers_/test_mistral.py
@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import copy
 from typing import Any
 
 import llguidance
@@ -11,353 +12,34 @@
 
 from vllm.tokenizers.mistral import (
     MistralTokenizer,
-    _prepare_apply_chat_template_tools_and_messages,
+    _validate_apply_chat_template_args,
 )
 
 
-@pytest.mark.parametrize(
-    "openai_request,expected_mistral_output",
-    [
-        (
-            {
-                "messages": [
-                    {
-                        "role": "user",
-                        "content": "What is the current local date and time?",
-                    }
-                ],
-                "tools": [
-                    {
-                        "type": "function",
-                        "function": {
-                            "description": "Fetch the current local date and time.",
-                            "name": "get_current_time",
-                        },
-                    }
-                ],
-            },
-            (
-                [
-                    {
-                        "role": "user",
-                        "content": "What is the current local date and time?",
-                    }
-                ],
-                [
-                    {
-                        "type": "function",
-                        "function": {
-                            "description": "Fetch the current local date and time.",
-                            "name": "get_current_time",
-                            "parameters": {},
-                        },
-                    }
-                ],
-            ),
-        ),
-        (
-            {
-                "messages": [
-                    {
-                        "role": "user",
-                        "content": "What is the current local date and time?",
-                    }
-                ],
-                "tools": [
-                    {
-                        "type": "function",
-                        "function": {
-                            "description": "Fetch the current local date and time.",
-                            "name": "get_current_time",
-                            "parameters": {},
-                        },
-                    }
-                ],
-            },
-            (
-                [
-                    {
-                        "role": "user",
-                        "content": "What is the current local date and time?",
-                    }
-                ],
-                [
-                    {
-                        "type": "function",
-                        "function": {
-                            "description": "Fetch the current local date and time.",
-                            "name": "get_current_time",
-                            "parameters": {},
-                        },
-                    }
-                ],
-            ),
-        ),
-        (
-            {
-                "messages": [
-                    {
-                        "role": "user",
-                        "content": "What is the current local date and time?",
-                    }
-                ],
-                "tools": [
-                    {
-                        "type": "function",
-                        "function": {
-                            "description": "Fetch the current local date and time.",
-                            "unsupported_field": False,
-                            "name": "get_current_time",
-                            "parameters": {},
-                        },
-                    },
-                    {
-                        "type": "function",
-                        "function": {
-                            "description": "Fetch the current local date and time.",
-                            "unsupported_field2": False,
-                            "name": "get_current_time",
-                            "parameters": {},
-                        },
-                    },
-                ],
-            },
-            (
-                [
-                    {
-                        "role": "user",
-                        "content": "What is the current local date and time?",
-                    }
-                ],
-                [
-                    {
-                        "type": "function",
-                        "function": {
-                            "description": "Fetch the current local date and time.",
-                            "name": "get_current_time",
-                            "parameters": {},
-                        },
-                    },
-                    {
-                        "type": "function",
-                        "function": {
-                            "description": "Fetch the current local date and time.",
-                            "name": "get_current_time",
-                            "parameters": {},
-                        },
-                    },
-                ],
-            ),
-        ),
-        (
-            {
-                "messages": [
-                    {
-                        "role": "user",
-                        "content": "What is the current local date and time?",
-                    }
-                ],
-                "tools": [
-                    {
-                        "type": "function",
-                        "unsupported_field": False,
-                        "function": {
-                            "description": "Fetch the current local date and time.",
-                            "name": "get_current_time",
-                            "parameters": {},
-                        },
-                    },
-                    {
-                        "type": "function",
-                        "unsupported_field2": False,
-                        "function": {
-                            "description": "Fetch the current local date and time 2.",
-                            "name": "get_current_time2",
-                            "parameters": {"a": "1"},
-                        },
-                    },
-                ],
-            },
-            (
-                [
-                    {
-                        "role": "user",
-                        "content": "What is the current local date and time?",
-                    }
-                ],
-                [
-                    {
-                        "type": "function",
-                        "function": {
-                            "description": "Fetch the current local date and time.",
-                            "name": "get_current_time",
-                            "parameters": {},
-                        },
-                    },
-                    {
-                        "type": "function",
-                        "function": {
-                            "description": "Fetch the current local date and time 2.",
-                            "name": "get_current_time2",
-                            "parameters": {"a": "1"},
-                        },
-                    },
-                ],
-            ),
-        ),
-    ],
-)
-def test_prepare_apply_chat_template_tools_and_messages(
-    openai_request, expected_mistral_output
-):
-    actual_request = _prepare_apply_chat_template_tools_and_messages(
-        openai_request["messages"], openai_request["tools"]
-    )
-    assert actual_request == expected_mistral_output
-
-
-# Tool use with list content and reasoning
-@pytest.mark.parametrize(
-    "openai_request,expected_mistral_output",
-    [
-        (
-            {
-                "messages": [
-                    {
-                        "role": "user",
-                        "content": "What's the weather in Paris?",
-                    },
-                    {
-                        "role": "assistant",
-                        "reasoning": None,
-                        "content": None,
-                        "tool_calls": [
-                            {
-                                "id": "call123",
-                                "type": "function",
-                                "function": {
-                                    "name": "get_weather",
-                                    "arguments": '{"city": "Paris"}',
-                                },
-                            }
-                        ],
-                    },
-                    {
-                        "role": "tool",
-                        "content": [{"type": "text", "text": "Rainy"}],
-                        "name": "get_weather",
-                        "tool_call_id": "call123",
-                    },
-                ],
-                "tools": [
-                    {
-                        "type": "function",
-                        "function": {
-                            "name": "get_weather",
-                            "description": "Gets the current weather in a city.",
-                            "parameters": {
-                                "type": "object",
-                                "properties": {
-                                    "city": {
-                                        "type": "string",
-                                        "description": "The city name",
-                                    }
-                                },
-                                "required": ["city"],
-                            },
-                        },
-                    }
-                ],
-            },
-            (
-                [
-                    {
-                        "role": "user",
-                        "content": "What's the weather in Paris?",
-                    },
-                    {
-                        "role": "assistant",
-                        "content": None,
-                        "tool_calls": [
-                            {
-                                "id": "call123",
-                                "type": "function",
-                                "function": {
-                                    "name": "get_weather",
-                                    "arguments": '{"city": "Paris"}',
-                                },
-                            }
-                        ],
-                    },
-                    {
-                        "role": "tool",
-                        "content": [{"type": "text", "text": "Rainy"}],
-                        "name": "get_weather",
-                        "tool_call_id": "call123",
-                    },
-                ],
-                [
-                    {
-                        "type": "function",
-                        "function": {
-                            "name": "get_weather",
-                            "description": "Gets the current weather in a city.",
-                            "parameters": {
-                                "type": "object",
-                                "properties": {
-                                    "city": {
-                                        "type": "string",
-                                        "description": "The city name",
-                                    }
-                                },
-                                "required": ["city"],
-                            },
-                        },
-                    }
-                ],
-            ),
-        )
-    ],
-)
-def test_prepare_apply_chat_template_tools_and_messages_list_content(
-    openai_request, expected_mistral_output
-):
-    actual_request = _prepare_apply_chat_template_tools_and_messages(
-        openai_request["messages"], openai_request["tools"]
-    )
-    assert actual_request == expected_mistral_output
-
-
-def test_prepare_apply_chat_template_generation_prompt_and_continue():
+def test_validate_apply_chat_template_args():
+    # add_generation_prompt with assistant last message → error
     messages = [{"role": "assistant", "content": "Hello"}]
-    tools: list[dict[str, Any]] = []
     with pytest.raises(ValueError):
-        _prepare_apply_chat_template_tools_and_messages(
-            messages, tools, add_generation_prompt=True
-        )
+        _validate_apply_chat_template_args(messages, add_generation_prompt=True)
 
+    # add_generation_prompt with user last message → ok
     messages = [{"role": "user", "content": "Hello"}]
-    out_messages, _ = _prepare_apply_chat_template_tools_and_messages(
-        messages, tools, add_generation_prompt=True
-    )
-    assert out_messages == [{"role": "user", "content": "Hello"}]
+    _validate_apply_chat_template_args(messages, add_generation_prompt=True)
 
+    # both add_generation_prompt and continue_final_message → error
     with pytest.raises(ValueError):
-        _prepare_apply_chat_template_tools_and_messages(
-            messages, tools, add_generation_prompt=True, continue_final_message=True
+        _validate_apply_chat_template_args(
+            messages, add_generation_prompt=True, continue_final_message=True
         )
 
+    # continue_final_message with assistant last message → ok
     messages = [{"role": "assistant", "content": "Hello"}]
-    out_messages, _ = _prepare_apply_chat_template_tools_and_messages(
-        messages, tools, add_generation_prompt=False, continue_final_message=True
-    )
-    assert out_messages == [{"role": "assistant", "content": "Hello"}]
+    _validate_apply_chat_template_args(messages, continue_final_message=True)
 
+    # continue_final_message with user last message → error
     messages = [{"role": "user", "content": "Hello"}]
     with pytest.raises(ValueError):
-        _prepare_apply_chat_template_tools_and_messages(
-            messages, tools, add_generation_prompt=False, continue_final_message=True
-        )
+        _validate_apply_chat_template_args(messages, continue_final_message=True)
 
 
 @pytest.fixture(scope="module")
@@ -2435,3 +2117,120 @@ def test_llg_tokenizer(self, mistral_tokenizer: MistralTokenizer) -> None:
         # Test caching
         llg_tokenizer_2 = mistral_tokenizer.llg_tokenizer
         assert llg_tokenizer is llg_tokenizer_2
+
+    @pytest.mark.parametrize(
+        "messages,tools,tekken_expected_substrings,spm_expected_substrings",
+        [
+            pytest.param(
+                [{"role": "user", "content": "Hello"}],
+                [{"type": "function", "function": {"name": "do_nothing"}}],
+                ["do_nothing", '"description": ""', '"parameters": {}'],
+                ["do_nothing", '"description":▁""', '"parameters":▁{}'],
+                id="tool_without_description_and_parameters",
+            ),
+            pytest.param(
+                [
+                    {"role": "user", "content": "Do nothing"},
+                    {
+                        "role": "assistant",
+                        "content": "",
+                        "tool_calls": [
+                            {
+                                "id": "123456789",
+                                "type": "function",
+                                "function": {
+                                    "name": "do_nothing",
+                                    "arguments": None,
+                                },
+                            }
+                        ],
+                    },
+                    {
+                        "role": "tool",
+                        "tool_call_id": "123456789",
+                        "content": "done",
+                    },
+                ],
+                [{"type": "function", "function": {"name": "do_nothing"}}],
+                ["do_nothing"],
+                ["do_nothing"],
+                id="tool_call_with_none_arguments",
+            ),
+        ],
+    )
+    def test_apply_chat_template_tool_optional_fields(
+        self,
+        mistral_tokenizer: MistralTokenizer,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]],
+        tekken_expected_substrings: list[str],
+        spm_expected_substrings: list[str],
+    ) -> None:
+        output = mistral_tokenizer.apply_chat_template(
+            messages, tools=tools, add_generation_prompt=True
+        )
+        decoded = mistral_tokenizer.tokenizer.decode(output, SpecialTokenPolicy.KEEP)
+
+        expected = (
+            tekken_expected_substrings
+            if mistral_tokenizer.is_tekken
+            else spm_expected_substrings
+        )
+        for substring in expected:
+            assert substring in decoded
+
+    def test_apply_chat_template_tools_not_mutated(
+        self, mistral_tokenizer: MistralTokenizer
+    ) -> None:
+        messages: list[dict[str, Any]] = [
+            {"role": "user", "content": "Hello"},
+        ]
+        tools: list[dict[str, Any]] = [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "Gets weather.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "city": {"type": "string"},
+                        },
+                    },
+                },
+            },
+        ]
+        original_tools = copy.deepcopy(tools)
+
+        mistral_tokenizer.apply_chat_template(
+            messages, tools=tools, add_generation_prompt=True
+        )
+
+        assert tools == original_tools
+
+    @pytest.mark.parametrize(
+        "reasoning_key",
+        ["reasoning", "reasoning_content"],
+    )
+    def test_apply_chat_template_reasoning_assistant(
+        self, mistral_tokenizer: MistralTokenizer, reasoning_key: str
+    ) -> None:
+        if not mistral_tokenizer.is_tekken:
+            pytest.skip("Reasoning tokens only supported on tekken tokenizers")
+
+        messages: list[dict[str, Any]] = [
+            {"role": "user", "content": "What is 2+2?"},
+            {
+                "role": "assistant",
+                "content": "4",
+                reasoning_key: "2+2 equals 4",
+            },
+            {"role": "user", "content": "Are you sure?"},
+        ]
+
+        output = mistral_tokenizer.apply_chat_template(
+            messages, add_generation_prompt=True
+        )
+        decoded = mistral_tokenizer.tokenizer.decode(output, SpecialTokenPolicy.KEEP)
+
+        assert "[THINK]2+2 equals 4[/THINK]" in decoded
diff --git a/tests/tool_parsers/test_deepseekv32_tool_parser.py b/tests/tool_parsers/test_deepseekv32_tool_parser.py
index 41bcd3a477fd..693cf5caddd5 100644
--- a/tests/tool_parsers/test_deepseekv32_tool_parser.py
+++ b/tests/tool_parsers/test_deepseekv32_tool_parser.py
@@ -188,6 +188,149 @@ def test_multiple_tools(self, parser):
             "location": "NYC"
         }
 
+    def test_type_conversion_in_non_streaming(self):
+        """Non-streaming extraction must convert params using the tool schema."""
+        tool = ChatCompletionToolsParam(
+            function=FunctionDefinition(
+                name="toggle",
+                parameters={
+                    "type": "object",
+                    "properties": {
+                        "enabled": {"type": "boolean"},
+                        "count": {"type": "integer"},
+                    },
+                },
+            ),
+        )
+        parser = make_parser(tools=[tool])
+        model_output = (
+            f"{FC_START}\n"
+            f'{INV_START}toggle">\n'
+            f'{PARAM_START}enabled" string="false">true{PARAM_END}\n'
+            f'{PARAM_START}count" string="false">42{PARAM_END}\n'
+            f"{INV_END}\n"
+            f"{FC_END}"
+        )
+        result = parser.extract_tool_calls(model_output, None)
+        assert result.tools_called
+        assert len(result.tool_calls) == 1
+        args = json.loads(result.tool_calls[0].function.arguments)
+        assert args == {"enabled": True, "count": 42}
+        assert isinstance(args["enabled"], bool)
+        assert isinstance(args["count"], int)
+
+    def test_string_attr_true_preserves_literal_despite_schema(self):
+        """string="true" must keep the value as a string even
+        if the schema says integer."""
+        tool = ChatCompletionToolsParam(
+            function=FunctionDefinition(
+                name="score",
+                parameters={
+                    "type": "object",
+                    "properties": {
+                        "value": {"type": "integer"},
+                    },
+                },
+            ),
+        )
+        parser = make_parser(tools=[tool])
+        model_output = (
+            f"{FC_START}\n"
+            f'{INV_START}score">\n'
+            f'{PARAM_START}value" string="true">42{PARAM_END}\n'
+            f"{INV_END}\n"
+            f"{FC_END}"
+        )
+        result = parser.extract_tool_calls(model_output, None)
+        assert result.tools_called
+        args = json.loads(result.tool_calls[0].function.arguments)
+        assert args == {"value": "42"}
+        assert isinstance(args["value"], str)
+
+    def test_string_attr_false_allows_schema_conversion(self):
+        """string="false" allows the parser to convert via the tool schema."""
+        tool = ChatCompletionToolsParam(
+            function=FunctionDefinition(
+                name="score",
+                parameters={
+                    "type": "object",
+                    "properties": {
+                        "value": {"type": "integer"},
+                    },
+                },
+            ),
+        )
+        parser = make_parser(tools=[tool])
+        model_output = (
+            f"{FC_START}\n"
+            f'{INV_START}score">\n'
+            f'{PARAM_START}value" string="false">42{PARAM_END}\n'
+            f"{INV_END}\n"
+            f"{FC_END}"
+        )
+        result = parser.extract_tool_calls(model_output, None)
+        assert result.tools_called
+        args = json.loads(result.tool_calls[0].function.arguments)
+        assert args == {"value": 42}
+        assert isinstance(args["value"], int)
+
+    def test_arguments_wrapper_repaired(self):
+        """A single 'arguments' wrapper parameter must be unwrapped when it
+        is not part of the tool schema and the inner object matches schema fields."""
+        tool = ChatCompletionToolsParam(
+            function=FunctionDefinition(
+                name="get_weather",
+                parameters={
+                    "type": "object",
+                    "properties": {
+                        "location": {"type": "string"},
+                    },
+                },
+            ),
+        )
+        parser = make_parser(tools=[tool])
+        model_output = (
+            f"{FC_START}\n"
+            f'{INV_START}get_weather">\n'
+            f'{PARAM_START}arguments" string="false">'
+            f'{{"location":"Beijing"}}'
+            f"{PARAM_END}\n"
+            f"{INV_END}\n"
+            f"{FC_END}"
+        )
+        result = parser.extract_tool_calls(model_output, None)
+        assert result.tools_called
+        args = json.loads(result.tool_calls[0].function.arguments)
+        assert args == {"location": "Beijing"}
+
+    def test_input_wrapper_repaired(self):
+        """A single 'input' wrapper parameter must be unwrapped similarly."""
+        tool = ChatCompletionToolsParam(
+            function=FunctionDefinition(
+                name="get_weather",
+                parameters={
+                    "type": "object",
+                    "properties": {
+                        "location": {"type": "string"},
+                    },
+                },
+            ),
+        )
+        parser = make_parser(tools=[tool])
+        model_output = (
+            f"{FC_START}\n"
+            f'{INV_START}get_weather">\n'
+            f'{PARAM_START}input" string="true">'
+            f'{{"location":"Beijing"}}'
+            f"{PARAM_END}\n"
+            f"{INV_END}\n"
+            f"{FC_END}"
+        )
+        result = parser.extract_tool_calls(model_output, None)
+        assert result.tools_called
+        args = json.loads(result.tool_calls[0].function.arguments)
+        assert args == {"location": "Beijing"}
+
 
 # ---------------------------------------------------------------------------
 # Tests: extract_tool_calls_streaming
@@ -295,11 +438,45 @@ def test_type_conversion_in_streaming(self):
             ),
         )
         parser = make_parser(tools=[tool])
-        full_text = build_tool_call("add", {"x": "3", "y": "4"})
+        full_text = (
+            f"{FC_START}\n"
+            f'{INV_START}add">\n'
+            f'{PARAM_START}x" string="false">3{PARAM_END}\n'
+            f'{PARAM_START}y" string="false">4{PARAM_END}\n'
+            f"{INV_END}\n"
+            f"{FC_END}"
+        )
         deltas = self._stream(parser, full_text)
         args_str = self._reconstruct_args(deltas)
         assert json.loads(args_str) == {"x": 3, "y": 4}
 
+    def test_string_attr_true_preserves_literal_in_streaming(self):
+        """Streaming: string='true' must keep the value literal despite schema."""
+        tool = ChatCompletionToolsParam(
+            function=FunctionDefinition(
+                name="score",
+                parameters={
+                    "type": "object",
+                    "properties": {
+                        "value": {"type": "integer"},
+                    },
+                },
+            ),
+        )
+        parser = make_parser(tools=[tool])
+        full_text = (
+            f"{FC_START}\n"
+            f'{INV_START}score">\n'
+            f'{PARAM_START}value" string="true">42{PARAM_END}\n'
+            f"{INV_END}\n"
+            f"{FC_END}"
+        )
+        deltas = self._stream(parser, full_text)
+        args_str = self._reconstruct_args(deltas)
+        args = json.loads(args_str)
+        assert args == {"value": "42"}
+        assert isinstance(args["value"], str)
+
     def test_multiple_tools_streaming(self, parser):
         full_text = (
             f"{FC_START}\n"
@@ -484,6 +661,58 @@ def test_no_emission_while_incomplete(self, parser):
         # Should have no tool call deltas yet
         assert all(not d.tool_calls for d in deltas)
 
+    def test_no_marker_leak_chunked(self, parser):
+        """Chunked streaming must NOT leak DSML start-marker fragments
+        as content (GitHub #40801)."""
+        full_text = build_tool_call("fn", {"k": "v"})
+        deltas = self._stream_chunked(parser, full_text, chunk_size=5)
+        content = "".join(d.content for d in deltas if d.content is not None)
+        assert content == ""
+        args_str = self._reconstruct_args(deltas)
+        assert json.loads(args_str) == {"k": "v"}
+
+    def test_no_marker_leak_with_prefix_chunked(self, parser):
+        """Content before a tool call must not include start-marker
+        fragments when chunked (GitHub #40801)."""
+        full_text = "Hello!" + build_tool_call("fn", {"a": "b"})
+        deltas = self._stream_chunked(parser, full_text, chunk_size=5)
+        content = "".join(d.content for d in deltas if d.content is not None)
+        assert content == "Hello!"
+        assert "DSML" not in content
+        assert "<｜" not in content
+        args_str = self._reconstruct_args(deltas)
+        assert json.loads(args_str) == {"a": "b"}
+
+    def test_no_marker_leak_char_by_char(self, parser):
+        """Character-by-character streaming must not leak marker
+        fragments (GitHub #40801)."""
+        full_text = build_tool_call("fn", {"k": "v"})
+        deltas = self._stream_chunked(parser, full_text, chunk_size=1)
+        content = "".join(d.content for d in deltas if d.content is not None)
+        assert content == ""
+        args_str = self._reconstruct_args(deltas)
+        assert json.loads(args_str) == {"k": "v"}
+
+    def test_no_marker_leak_all_split_points(self, parser):
+        """Start token split at every possible boundary must not
+        leak (GitHub #40801)."""
+        for chunk_size in range(1, len(FC_START) + 2):
+            p = make_parser()
+            full_text = build_tool_call("fn", {"k": "v"})
+            deltas = self._stream_chunked(p, full_text, chunk_size=chunk_size)
+            content = "".join(d.content for d in deltas if d.content is not None)
+            assert content == "", (
+                f"Leaked content {content!r} at chunk_size={chunk_size}"
+            )
+
+    def test_false_partial_marker_emitted(self, parser):
+        """Text ending with a prefix of the start token that turns out
+        NOT to be a marker must still be emitted as content."""
+        full_text = "<｜DSM some regular text"
+        deltas = self._stream_chunked(parser, full_text, chunk_size=3)
+        content = "".join(d.content for d in deltas if d.content is not None)
+        assert content == full_text
+
 
 class TestDelimiterPreservation:
     """Regression: fast detokenization skipping DSML delimiters (PR #33964)."""
diff --git a/tests/tool_parsers/test_deepseekv4_tool_parser.py b/tests/tool_parsers/test_deepseekv4_tool_parser.py
new file mode 100644
index 000000000000..afcd0573958b
--- /dev/null
+++ b/tests/tool_parsers/test_deepseekv4_tool_parser.py
@@ -0,0 +1,238 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Unit tests for DeepSeekV4ToolParser."""
+
+import json
+from unittest.mock import MagicMock
+
+import pytest
+from xgrammar import StructuralTag
+
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionNamedFunction,
+    ChatCompletionNamedToolChoiceParam,
+    ChatCompletionRequest,
+    ChatCompletionToolsParam,
+)
+from vllm.tool_parsers import ToolParserManager
+from vllm.tool_parsers.deepseekv4_tool_parser import DeepSeekV4ToolParser
+
+MOCK_TOKENIZER = MagicMock()
+MOCK_TOKENIZER.get_vocab.return_value = {}
+
+TC_START = "<｜DSML｜tool_calls>"
+TC_END = "</｜DSML｜tool_calls>"
+INV_START = '<｜DSML｜invoke name="'
+INV_END = "</｜DSML｜invoke>"
+PARAM_START = '<｜DSML｜parameter name="'
+PARAM_END = "</｜DSML｜parameter>"
+
+
+@pytest.fixture
+def sample_tools() -> list[ChatCompletionToolsParam]:
+    return [
+        ChatCompletionToolsParam(
+            type="function",
+            function={
+                "name": "get_current_weather",
+                "description": "Get the current weather",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "city": {"type": "string", "description": "The city name"},
+                        "state": {"type": "string", "description": "The state code"},
+                        "unit": {"type": "string", "enum": ["fahrenheit", "celsius"]},
+                    },
+                    "required": ["city", "state"],
+                },
+            },
+        ),
+        ChatCompletionToolsParam(
+            type="function",
+            function={
+                "name": "calculate_area",
+                "description": "Calculate area of a shape",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "shape": {"type": "string"},
+                        "dimensions": {"type": "object"},
+                        "precision": {"type": "integer"},
+                    },
+                },
+            },
+        ),
+    ]
+
+
+def make_parser(tools=None) -> DeepSeekV4ToolParser:
+    return DeepSeekV4ToolParser(MOCK_TOKENIZER, tools=tools)
+
+
+def make_request(tools=None) -> MagicMock:
+    req = MagicMock()
+    req.tools = tools
+    return req
+
+
+def build_tool_call(func_name: str, params: dict[str, str]) -> str:
+    param_strs = "".join(
+        f'{PARAM_START}{k}" string="true">{v}{PARAM_END}\n' for k, v in params.items()
+    )
+    return f'{TC_START}\n{INV_START}{func_name}">\n{param_strs}{INV_END}\n{TC_END}'
+
+
+def stream(parser: DeepSeekV4ToolParser, full_text: str, chunk_size: int = 7):
+    deltas = []
+    previous_text = ""
+    for start in range(0, len(full_text), chunk_size):
+        delta_text = full_text[start : start + chunk_size]
+        current_text = previous_text + delta_text
+        delta = parser.extract_tool_calls_streaming(
+            previous_text=previous_text,
+            current_text=current_text,
+            delta_text=delta_text,
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[1],
+            request=make_request(),
+        )
+        previous_text = current_text
+        if delta is not None:
+            deltas.append(delta)
+    return deltas
+
+
+def reconstruct_args(deltas, tool_index: int = 0) -> str:
+    fragments = []
+    for delta in deltas:
+        if delta.tool_calls:
+            for tool_call in delta.tool_calls:
+                if (
+                    tool_call.index == tool_index
+                    and tool_call.function
+                    and tool_call.function.arguments
+                ):
+                    fragments.append(tool_call.function.arguments)
+    return "".join(fragments)
+
+
+def test_registered():
+    assert ToolParserManager.get_tool_parser("deepseek_v4") is DeepSeekV4ToolParser
+
+
+def test_extract_tool_calls():
+    parser = make_parser()
+    model_output = "Let me check. " + build_tool_call(
+        "get_weather", {"location": "Beijing", "unit": "celsius"}
+    )
+
+    result = parser.extract_tool_calls(model_output, make_request())
+
+    assert result.tools_called
+    assert result.content == "Let me check. "
+    assert len(result.tool_calls) == 1
+    tool_call = result.tool_calls[0]
+    assert tool_call.function.name == "get_weather"
+    assert json.loads(tool_call.function.arguments) == {
+        "location": "Beijing",
+        "unit": "celsius",
+    }
+
+
+def test_function_calls_block_is_not_accepted():
+    parser = make_parser()
+    model_output = build_tool_call("search", {"query": "vllm"}).replace(
+        "tool_calls", "function_calls"
+    )
+
+    result = parser.extract_tool_calls(model_output, make_request())
+
+    assert not result.tools_called
+    assert result.content == model_output
+
+
+def test_streaming_extracts_complete_invokes():
+    parser = make_parser()
+    full_text = build_tool_call("search", {"query": "deepseek v4"})
+
+    deltas = stream(parser, full_text, chunk_size=5)
+
+    names = [
+        tool_call.function.name
+        for delta in deltas
+        if delta.tool_calls
+        for tool_call in delta.tool_calls
+    ]
+    assert names == ["search"]
+    assert json.loads(reconstruct_args(deltas)) == {"query": "deepseek v4"}
+
+
+def test_get_vllm_registry_structural_tag_returns_structural_tag(
+    sample_tools: list[ChatCompletionToolsParam],
+) -> None:
+    parser = make_parser()
+    req = ChatCompletionRequest(
+        messages=[],
+        model="m",
+        tools=sample_tools,
+        tool_choice="auto",
+    )
+    tag = parser.get_structural_tag(req)
+    assert isinstance(tag, StructuralTag)
+
+    req = ChatCompletionRequest(
+        messages=[],
+        model="m",
+        tools=sample_tools,
+        tool_choice="required",
+    )
+    tag = parser.get_structural_tag(req)
+    assert isinstance(tag, StructuralTag)
+
+    if sample_tools:
+        tool = sample_tools[0]
+        req = ChatCompletionRequest(
+            messages=[],
+            model="m",
+            tools=sample_tools,
+        )
+        req.tool_choice = ChatCompletionNamedToolChoiceParam(
+            function=ChatCompletionNamedFunction(name=tool.function.name)
+        )
+        tag = parser.get_structural_tag(req)
+        assert isinstance(tag, StructuralTag)
+
+
+def test_extract_tool_calls_arguments_wrapper():
+    mock_tokenizer = MagicMock()
+    mock_tokenizer.get_vocab.return_value = {}
+
+    tool = ChatCompletionToolsParam(
+        type="function",
+        function={
+            "name": "get_weather",
+            "parameters": {
+                "type": "object",
+                "properties": {"location": {"type": "string"}},
+            },
+        },
+    )
+
+    parser = DeepSeekV4ToolParser(mock_tokenizer, tools=[tool])
+    request = MagicMock()
+    request.tools = [tool]
+
+    model_output = (
+        f"{TC_START}"
+        f'{INV_START}get_weather">'
+        f'{PARAM_START}arguments" string="false">{{"location":"Beijing"}}{PARAM_END}'
+        f"{INV_END}"
+        f"{TC_END}"
+    )
+
+    result = parser.extract_tool_calls(model_output, request)
+    assert result.tools_called
+    args = json.loads(result.tool_calls[0].function.arguments)
+    assert args == {"location": "Beijing"}
diff --git a/tests/tool_parsers/test_ernie45_moe_tool_parser.py b/tests/tool_parsers/test_ernie45_moe_tool_parser.py
index a00e43894767..ffee62441c97 100644
--- a/tests/tool_parsers/test_ernie45_moe_tool_parser.py
+++ b/tests/tool_parsers/test_ernie45_moe_tool_parser.py
@@ -328,7 +328,7 @@ def test_extract_tool_calls_streaming_incremental(
     expected_content,
 ):
     """Verify the Ernie45 Parser streaming behavior by verifying each chunk is as expected."""  # noqa: E501
-    request = ChatCompletionRequest(model=MODEL, messages=[], tools=[])
+    request = ChatCompletionRequest(model=MODEL, messages=[])
 
     tool_calls_dict = {}
     for delta_message in stream_delta_message_generator(
diff --git a/tests/tool_parsers/test_gemma4_tool_parser.py b/tests/tool_parsers/test_gemma4_tool_parser.py
index 16450c6a8d01..bc2aebb7b23f 100644
--- a/tests/tool_parsers/test_gemma4_tool_parser.py
+++ b/tests/tool_parsers/test_gemma4_tool_parser.py
@@ -135,6 +135,11 @@ def test_empty_value_after_other_keys_partial_withheld(self):
         result = _parse_gemma4_args('name:<|"|>test<|"|>,flag:', partial=True)
         assert result == {"name": "test"}
 
+    @pytest.mark.timeout(5)
+    def test_malformed_partial_array(self):
+        result = _parse_gemma4_args(":[t:[]")
+        assert isinstance(result, dict)
+
 
 class TestParseGemma4Array:
     def test_string_array(self):
@@ -149,6 +154,16 @@ def test_bare_values(self):
         result = _parse_gemma4_array("42,true,3.14")
         assert result == [42, True, 3.14]
 
+    @pytest.mark.timeout(5)
+    def test_string_element_with_closing_bracket(self):
+        result = _parse_gemma4_array('[<|"|>a]b<|"|>,<|"|>c<|"|>],<|"|>tail<|"|>')
+        assert result == [["a]b", "c"], "tail"]
+
+    @pytest.mark.timeout(5)
+    def test_stray_closing_bracket(self):
+        result = _parse_gemma4_array("42,]trailing")
+        assert result == [42]
+
 
 # ---------------------------------------------------------------------------
 # Non-streaming extraction tests
diff --git a/tests/tool_parsers/test_glm47_moe_tool_parser.py b/tests/tool_parsers/test_glm47_moe_tool_parser.py
index 5e5501e4abff..51696c954788 100644
--- a/tests/tool_parsers/test_glm47_moe_tool_parser.py
+++ b/tests/tool_parsers/test_glm47_moe_tool_parser.py
@@ -91,6 +91,12 @@ def test_args_with_newlines(self, glm47_tool_parser, mock_request):
         assert r.tools_called
         assert json.loads(r.tool_calls[0].function.arguments) == {"city": "Beijing"}
 
+    def test_whitespace_preserved_in_arg_values(self, glm47_tool_parser, mock_request):
+        out = "<tool_call>get_weather<arg_key>city</arg_key><arg_value>  Beijing  </arg_value></tool_call>"
+        r = glm47_tool_parser.extract_tool_calls(out, request=mock_request)
+        assert r.tools_called
+        assert json.loads(r.tool_calls[0].function.arguments) == {"city": "  Beijing  "}
+
     def test_content_before(self, glm47_tool_parser, mock_request):
         out = "Checking.<tool_call>get_current_date</tool_call>"
         r = glm47_tool_parser.extract_tool_calls(out, request=mock_request)
diff --git a/tests/tool_parsers/test_glm4_moe_tool_parser.py b/tests/tool_parsers/test_glm4_moe_tool_parser.py
index 9f430b7814fc..c706d0a4c257 100644
--- a/tests/tool_parsers/test_glm4_moe_tool_parser.py
+++ b/tests/tool_parsers/test_glm4_moe_tool_parser.py
@@ -801,6 +801,36 @@ def test_extract_tool_calls_numeric_deserialization(glm4_moe_tool_parser, mock_r
     assert isinstance(args["enabled"], bool)
 
 
+def test_whitespace_preserved_in_arg_values(glm4_moe_tokenizer):
+    """Test that string arguments preserve leading and trailing whitespace."""
+    tools = [
+        ChatCompletionToolsParam(
+            function=FunctionDefinition(
+                name="apply_diff",
+                parameters={
+                    "type": "object",
+                    "properties": {
+                        "s": {"type": "string"},
+                    },
+                    "required": ["s"],
+                },
+            ),
+        ),
+    ]
+    parser = Glm4MoeModelToolParser(glm4_moe_tokenizer, tools=tools)
+    request = ChatCompletionRequest(model=MODEL, messages=[], tools=tools)
+
+    model_output = """<tool_call>apply_diff
+<arg_key>s</arg_key>
+<arg_value>    indented code    </arg_value>
+</tool_call>"""
+
+    extracted_tool_calls = parser.extract_tool_calls(model_output, request=request)
+    args = json.loads(extracted_tool_calls.tool_calls[0].function.arguments)
+
+    assert args["s"] == "    indented code    "
+
+
 def test_zero_argument_tool_call(glm4_moe_tool_parser, mock_request):
     """Regression: zero-argument tool call crash (PR #32321)."""
     model_output = """<tool_call>get_time
diff --git a/tests/tool_parsers/test_hy_v3_tool_parser.py b/tests/tool_parsers/test_hy_v3_tool_parser.py
new file mode 100644
index 000000000000..b5aaaf52988d
--- /dev/null
+++ b/tests/tool_parsers/test_hy_v3_tool_parser.py
@@ -0,0 +1,274 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# ruff: noqa: E501
+"""Tests for the HYV3 tool call parser."""
+
+import json
+from unittest.mock import Mock
+
+import pytest
+
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+    ChatCompletionToolsParam,
+    FunctionDefinition,
+)
+from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.tokenizers import get_tokenizer
+from vllm.tool_parsers.hy_v3_tool_parser import HYV3ToolParser
+
+parser_name = "hy_v3"
+MODEL = "tencent/Hy3-preview"
+
+
+@pytest.fixture(scope="module")
+def hy_v3_tokenizer():
+    return get_tokenizer(tokenizer_name=MODEL)
+
+
+@pytest.fixture
+def hy_v3_tool_parser(hy_v3_tokenizer):
+    return HYV3ToolParser(hy_v3_tokenizer)
+
+
+@pytest.fixture
+def mock_request() -> ChatCompletionRequest:
+    request = Mock(spec=ChatCompletionRequest)
+    request.tools = [
+        ChatCompletionToolsParam(
+            function=FunctionDefinition(name="get_current_date", parameters={}),
+        ),
+        ChatCompletionToolsParam(
+            function=FunctionDefinition(
+                name="get_weather",
+                parameters={
+                    "type": "object",
+                    "properties": {
+                        "city": {"type": "string"},
+                        "date": {"type": "string"},
+                    },
+                },
+            ),
+        ),
+    ]
+    request.tool_choice = "auto"
+    return request
+
+
+class TestHYV3ExtractToolCalls:
+    def test_no_tool_call(self, hy_v3_tool_parser, mock_request):
+        out = "This is a plain response."
+        r = hy_v3_tool_parser.extract_tool_calls(out, request=mock_request)
+        assert not r.tools_called
+        assert r.content == out
+
+    def test_zero_arg_inline(self, hy_v3_tool_parser, mock_request):
+        out = (
+            "<tool_calls><tool_call>get_current_date<tool_sep></tool_call></tool_calls>"
+        )
+        r = hy_v3_tool_parser.extract_tool_calls(out, request=mock_request)
+        assert r.tools_called
+        assert r.tool_calls[0].function.name == "get_current_date"
+        assert json.loads(r.tool_calls[0].function.arguments) == {}
+        assert r.content is None
+
+    def test_zero_arg_newline(self, hy_v3_tool_parser, mock_request):
+        out = "<tool_calls>\n<tool_call>get_current_date<tool_sep>\n</tool_call>\n</tool_calls>"
+        r = hy_v3_tool_parser.extract_tool_calls(out, request=mock_request)
+        assert r.tools_called
+        assert r.tool_calls[0].function.name == "get_current_date"
+
+    def test_args_same_line(self, hy_v3_tool_parser, mock_request):
+        out = (
+            "<tool_calls><tool_call>get_weather<tool_sep><arg_key>city</arg_key><arg_value>Beijing"
+            "</arg_value><arg_key>date</arg_key><arg_value>2026-03-30</arg_value></tool_call></tool_calls>"
+        )
+        r = hy_v3_tool_parser.extract_tool_calls(out, request=mock_request)
+        assert r.tools_called
+        assert json.loads(r.tool_calls[0].function.arguments) == {
+            "city": "Beijing",
+            "date": "2026-03-30",
+        }
+
+    def test_args_with_newlines(self, hy_v3_tool_parser, mock_request):
+        out = (
+            "<tool_calls>\n<tool_call>get_weather<tool_sep>\n<arg_key>city</arg_key>\n<arg_value>Beijing"
+            "</arg_value>\n<arg_key>date</arg_key>\n<arg_value>2026-03-30</arg_value>\n</tool_call>\n</tool_calls>"
+        )
+        r = hy_v3_tool_parser.extract_tool_calls(out, request=mock_request)
+        assert r.tools_called
+        assert json.loads(r.tool_calls[0].function.arguments) == {
+            "city": "Beijing",
+            "date": "2026-03-30",
+        }
+
+    def test_content_before(self, hy_v3_tool_parser, mock_request):
+        out = "Checking.<tool_calls>\n<tool_call>get_current_date<tool_sep>\n</tool_call>\n</tool_calls>"
+        r = hy_v3_tool_parser.extract_tool_calls(out, request=mock_request)
+        assert r.tools_called
+        assert r.content == "Checking."
+
+    def test_multiple(self, hy_v3_tool_parser, mock_request):
+        out = (
+            "<tool_calls>\n<tool_call>get_weather<tool_sep>\n<arg_key>city</arg_key>\n<arg_value>Beijing"
+            "</arg_value>\n<arg_key>date</arg_key>\n<arg_value>2026-03-30</arg_value>\n</tool_call>\n"
+            "<tool_call>get_weather<tool_sep>\n<arg_key>city</arg_key>\n<arg_value>Hangzhou</arg_value>\n"
+            "<arg_key>date</arg_key>\n<arg_value>2026-03-30</arg_value>\n</tool_call>\n</tool_calls>"
+        )
+        r = hy_v3_tool_parser.extract_tool_calls(out, request=mock_request)
+        assert len(r.tool_calls) == 2
+
+    def test_empty_content_none(self, hy_v3_tool_parser, mock_request):
+        out = "<tool_calls>\n<tool_call>get_current_date<tool_sep>\n</tool_call>\n</tool_calls>"
+        r = hy_v3_tool_parser.extract_tool_calls(out, request=mock_request)
+        assert r.content is None
+
+
+def _simulate_streaming(
+    parser: HYV3ToolParser,
+    deltas: list[str],
+    request: ChatCompletionRequest,
+) -> list[DeltaMessage | None]:
+    results: list[DeltaMessage | None] = []
+    previous_text = ""
+    previous_token_ids: list[int] = []
+    vocab = parser.vocab
+    for delta_text in deltas:
+        current_text = previous_text + delta_text
+        delta_token_ids = [tid for tok, tid in vocab.items() if tok in delta_text]
+        current_token_ids = previous_token_ids + delta_token_ids
+        result = parser.extract_tool_calls_streaming(
+            previous_text=previous_text,
+            current_text=current_text,
+            delta_text=delta_text,
+            previous_token_ids=previous_token_ids,
+            current_token_ids=current_token_ids,
+            delta_token_ids=delta_token_ids,
+            request=request,
+        )
+        results.append(result)
+        previous_text = current_text
+        previous_token_ids = current_token_ids
+    return results
+
+
+def _collect_streaming_tool_calls(results: list[DeltaMessage | None]) -> list[dict]:
+    tool_calls: dict[int, dict] = {}
+    for result in results:
+        if result is None or not result.tool_calls:
+            continue
+        for tc in result.tool_calls:
+            idx = tc.index
+            if idx not in tool_calls:
+                tool_calls[idx] = {
+                    "name": tc.function.name or "",
+                    "arguments": tc.function.arguments or "",
+                }
+            else:
+                if tc.function.name:
+                    tool_calls[idx]["name"] += tc.function.name
+                if tc.function.arguments:
+                    tool_calls[idx]["arguments"] += tc.function.arguments
+    return [tool_calls[i] for i in sorted(tool_calls.keys())]
+
+
+def _collect_streaming_content(results: list[DeltaMessage | None]) -> str:
+    parts = []
+    for result in results:
+        if result is not None and result.content:
+            parts.append(result.content)
+    return "".join(parts)
+
+
+class TestHYV3ExtractToolCallsStreaming:
+    def test_no_tool_call_streaming(self, hy_v3_tool_parser, mock_request):
+        deltas = ["This is ", "a plain ", "response."]
+        results = _simulate_streaming(hy_v3_tool_parser, deltas, mock_request)
+        content = _collect_streaming_content(results)
+        assert content == "This is a plain response."
+        assert len(_collect_streaming_tool_calls(results)) == 0
+
+    def test_zero_arg_streaming(self, hy_v3_tool_parser, mock_request):
+        deltas = [
+            "<tool_calls>",
+            "\n<tool_call>",
+            "get_current_date",
+            "<tool_sep>",
+            "\n</tool_call>",
+            "\n</tool_calls>",
+        ]
+        results = _simulate_streaming(hy_v3_tool_parser, deltas, mock_request)
+        tc = _collect_streaming_tool_calls(results)
+        assert len(tc) == 1
+        assert tc[0]["name"] == "get_current_date"
+        assert json.loads(tc[0]["arguments"]) == {}
+
+    def test_args_streaming(self, hy_v3_tool_parser, mock_request):
+        deltas = [
+            "<tool_calls>",
+            "\n<tool_call>",
+            "get_weather",
+            "<tool_sep>",
+            "\n<arg_key>city</arg_key>",
+            "\n<arg_value>Beijing</arg_value>",
+            "\n<arg_key>date</arg_key>",
+            "\n<arg_value>2026-03-30</arg_value>",
+            "\n</tool_call>",
+            "\n</tool_calls>",
+        ]
+        results = _simulate_streaming(hy_v3_tool_parser, deltas, mock_request)
+        tc = _collect_streaming_tool_calls(results)
+        assert len(tc) == 1 and tc[0]["name"] == "get_weather"
+        assert json.loads(tc[0]["arguments"]) == {
+            "city": "Beijing",
+            "date": "2026-03-30",
+        }
+
+    def test_content_before_streaming(self, hy_v3_tool_parser, mock_request):
+        deltas = [
+            "Checking.",
+            "<tool_calls>",
+            "\n<tool_call>",
+            "get_current_date",
+            "<tool_sep>",
+            "\n</tool_call>",
+            "\n</tool_calls>",
+        ]
+        results = _simulate_streaming(hy_v3_tool_parser, deltas, mock_request)
+        assert "Checking." in _collect_streaming_content(results)
+        tc = _collect_streaming_tool_calls(results)
+        assert len(tc) == 1 and tc[0]["name"] == "get_current_date"
+
+    def test_multiple_streaming(self, hy_v3_tool_parser, mock_request):
+        deltas = [
+            "<tool_calls>",
+            "\n<tool_call>",
+            "get_weather",
+            "<tool_sep>",
+            "\n<arg_key>city</arg_key>",
+            "\n<arg_value>Beijing</arg_value>",
+            "\n<arg_key>date</arg_key>",
+            "\n<arg_value>2026-03-30</arg_value>",
+            "\n</tool_call>",
+            "\n<tool_call>",
+            "get_weather",
+            "<tool_sep>",
+            "\n<arg_key>city</arg_key>",
+            "\n<arg_value>Hangzhou</arg_value>",
+            "\n<arg_key>date</arg_key>",
+            "\n<arg_value>2026-03-30</arg_value>",
+            "\n</tool_call>",
+            "\n</tool_calls>",
+        ]
+        results = _simulate_streaming(hy_v3_tool_parser, deltas, mock_request)
+        tc = _collect_streaming_tool_calls(results)
+        assert len(tc) == 2
+        assert json.loads(tc[0]["arguments"])["city"] == "Beijing"
+        assert json.loads(tc[1]["arguments"])["city"] == "Hangzhou"
+
+    def test_all_in_one_delta_streaming(self, hy_v3_tool_parser, mock_request):
+        out = "<tool_calls>\n<tool_call>get_current_date<tool_sep>\n</tool_call>\n</tool_calls>"
+        results = _simulate_streaming(hy_v3_tool_parser, [out], mock_request)
+        tc = _collect_streaming_tool_calls(results)
+        assert len(tc) == 1 and tc[0]["name"] == "get_current_date"
+        assert json.loads(tc[0]["arguments"]) == {}
diff --git a/tests/tool_parsers/test_kimi_k2_tool_parser.py b/tests/tool_parsers/test_kimi_k2_tool_parser.py
index 09c1c461c100..b56032b91c17 100644
--- a/tests/tool_parsers/test_kimi_k2_tool_parser.py
+++ b/tests/tool_parsers/test_kimi_k2_tool_parser.py
@@ -3,14 +3,20 @@
 # ruff: noqa: E501
 
 import json
+from unittest.mock import MagicMock
 
 import pytest
 
-from vllm.entrypoints.openai.engine.protocol import FunctionCall, ToolCall
+from tests.tool_parsers.utils import (
+    run_tool_extraction,
+    run_tool_extraction_streaming,
+)
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+)
 from vllm.tokenizers import get_tokenizer
 from vllm.tool_parsers.kimi_k2_tool_parser import KimiK2ToolParser
 
-# Use a common model that is likely to be available
 MODEL = "moonshotai/Kimi-K2-Instruct"
 
 
@@ -20,959 +26,557 @@ def kimi_k2_tokenizer():
 
 
 @pytest.fixture
-def kimi_k2_tool_parser(kimi_k2_tokenizer):
+def parser(kimi_k2_tokenizer):
     return KimiK2ToolParser(kimi_k2_tokenizer)
 
 
-def assert_tool_calls(
-    actual_tool_calls: list[ToolCall], expected_tool_calls: list[ToolCall]
-):
-    assert len(actual_tool_calls) == len(expected_tool_calls)
+SECTION_BEGIN = "<|tool_calls_section_begin|>"
+SECTION_END = "<|tool_calls_section_end|>"
+TOOL_BEGIN = "<|tool_call_begin|>"
+TOOL_END = "<|tool_call_end|>"
+ARG_BEGIN = "<|tool_call_argument_begin|>"
+
+
+def _tool(tool_id: str, args: str) -> str:
+    return f"{TOOL_BEGIN}{tool_id} {ARG_BEGIN}{args}{TOOL_END}"
 
-    for actual_tool_call, expected_tool_call in zip(
-        actual_tool_calls, expected_tool_calls
-    ):
-        assert actual_tool_call.type == "function"
-        assert actual_tool_call.function == expected_tool_call.function
-
-        # assert tool call id format: should contain function name and numeric index
-        # Format can be either "functions.func_name:0" or "func_name:0"
-        assert actual_tool_call.id.split(":")[-1].isdigit()
-        assert (
-            actual_tool_call.id.split(":")[0].split(".")[-1]
-            == expected_tool_call.function.name
-        )
 
+def _wrap(*tool_strs: str) -> str:
+    return SECTION_BEGIN + "".join(tool_strs) + SECTION_END
 
-def run_streaming_sequence(parser, deltas):
-    """Helper to simulate a streaming sequence and return results."""
-    previous_text = ""
-    previous_token_ids: list[int] = []
-    results = []
-
-    for delta_text, delta_token_ids in deltas:
-        current_text = previous_text + delta_text
-        current_token_ids = previous_token_ids + delta_token_ids
-
-        result = parser.extract_tool_calls_streaming(
-            previous_text=previous_text,
-            current_text=current_text,
-            delta_text=delta_text,
-            previous_token_ids=previous_token_ids,
-            current_token_ids=current_token_ids,
-            delta_token_ids=delta_token_ids,
-            request=None,
+
+class TestExtractToolCalls:
+    def test_no_tools(self, parser):
+        content, tool_calls = run_tool_extraction(
+            parser, "This is a test", streaming=False
         )
-        results.append(result)
-
-        previous_text = current_text
-        previous_token_ids = current_token_ids
-
-    return results
-
-
-def test_extract_tool_calls_no_tools(kimi_k2_tool_parser):
-    model_output = "This is a test"
-    extracted_tool_calls = kimi_k2_tool_parser.extract_tool_calls(
-        model_output, request=None
-    )  # type: ignore[arg-type]
-    assert not extracted_tool_calls.tools_called
-    assert extracted_tool_calls.tool_calls == []
-    assert extracted_tool_calls.content == model_output
-
-
-@pytest.mark.parametrize(
-    ids=[
-        "tool_call_with_content_before",
-        "multi_tool_call_with_content_before",
-        "concatenated_tool_calls_bug_fix",
-        "three_concatenated_tool_calls",
-        "mixed_spacing_tool_calls",
-        "angle_brackets_in_json",
-        "newlines_in_json",
-    ],
-    argnames=["model_output", "expected_tool_calls", "expected_content"],
-    argvalues=[
-        (
-            """I'll help you check the weather. <|tool_calls_section_begin|> <|tool_call_begin|>
-functions.get_weather:0 <|tool_call_argument_begin|> {"city": "Beijing"} <|tool_call_end|> <|tool_calls_section_end|>""",
-            [
-                ToolCall(
-                    id="functions.get_weather:0",
-                    function=FunctionCall(
-                        name="get_weather",
-                        arguments=json.dumps(
-                            {
-                                "city": "Beijing",
-                            },
-                        ),
-                    ),
-                    type="function",
-                )
-            ],
-            "I'll help you check the weather. ",
-        ),
-        (
-            """I'll help you check the weather. <|tool_calls_section_begin|> <|tool_call_begin|>
-functions.get_weather:0 <|tool_call_argument_begin|> {"city": "Beijing"} <|tool_call_end|> <|tool_call_begin|>
-functions.get_weather:1 <|tool_call_argument_begin|> {"city": "Shanghai"} <|tool_call_end|> <|tool_calls_section_end|>""",
-            [
-                ToolCall(
-                    id="functions.get_weather:0",
-                    function=FunctionCall(
-                        name="get_weather",
-                        arguments=json.dumps(
-                            {
-                                "city": "Beijing",
-                            },
-                        ),
-                    ),
-                    type="function",
-                ),
-                ToolCall(
-                    id="functions.get_weather:1",
-                    function=FunctionCall(
-                        name="get_weather",
-                        arguments=json.dumps(
-                            {
-                                "city": "Shanghai",
-                            },
-                        ),
-                    ),
-                    type="function",
-                ),
-            ],
-            "I'll help you check the weather. ",
-        ),
-        (
-            """I'll get the weather and news for LA today. First, let me get the weather using Los Angeles coordinates, and then get the latest news. <|tool_calls_section_begin|><|tool_call_begin|>functions.get_weather:0<|tool_call_argument_begin|>{"latitude": 34.0522, "longitude": -118.2437}<|tool_call_end|><|tool_call_begin|>functions.get_news:1<|tool_call_argument_begin|>{"content": "Los Angeles today"}<|tool_call_end|><|tool_calls_section_end|>""",
-            [
-                ToolCall(
-                    id="functions.get_weather:0",
-                    function=FunctionCall(
-                        name="get_weather",
-                        arguments=json.dumps(
-                            {"latitude": 34.0522, "longitude": -118.2437}
-                        ),
-                    ),
-                    type="function",
-                ),
-                ToolCall(
-                    id="functions.get_news:1",
-                    function=FunctionCall(
-                        name="get_news",
-                        arguments=json.dumps({"content": "Los Angeles today"}),
-                    ),
-                    type="function",
-                ),
-            ],
-            "I'll get the weather and news for LA today. First, let me get the weather using Los Angeles coordinates, and then get the latest news. ",
-        ),
-        (
-            """I'll help you with multiple tasks. <|tool_calls_section_begin|><|tool_call_begin|>functions.get_weather:0<|tool_call_argument_begin|>{"city": "New York"}<|tool_call_end|><|tool_call_begin|>functions.get_news:1<|tool_call_argument_begin|>{"topic": "technology"}<|tool_call_end|><|tool_call_begin|>functions.send_email:2<|tool_call_argument_begin|>{"to": "user@example.com", "subject": "Daily Update"}<|tool_call_end|><|tool_calls_section_end|>""",
-            [
-                ToolCall(
-                    id="functions.get_weather:0",
-                    function=FunctionCall(
-                        name="get_weather",
-                        arguments=json.dumps({"city": "New York"}),
-                    ),
-                    type="function",
+        assert content == "This is a test"
+        assert tool_calls == []
+
+    @pytest.mark.parametrize(
+        "model_output, expected_names, expected_args_list, expected_content",
+        [
+            pytest.param(
+                "I'll check. "
+                + _wrap(_tool("functions.get_weather:0", '{"city": "Beijing"}')),
+                ["get_weather"],
+                [{"city": "Beijing"}],
+                "I'll check. ",
+                id="single_tool_call",
+            ),
+            pytest.param(
+                "Compare weather. "
+                + _wrap(
+                    _tool("functions.get_weather:0", '{"city": "Beijing"}'),
+                    _tool("functions.get_weather:1", '{"city": "Shanghai"}'),
                 ),
-                ToolCall(
-                    id="functions.get_news:1",
-                    function=FunctionCall(
-                        name="get_news",
-                        arguments=json.dumps({"topic": "technology"}),
+                ["get_weather", "get_weather"],
+                [{"city": "Beijing"}, {"city": "Shanghai"}],
+                "Compare weather. ",
+                id="parallel_tool_calls",
+            ),
+            pytest.param(
+                "Multiple tasks. "
+                + _wrap(
+                    _tool("functions.get_weather:0", '{"city": "New York"}'),
+                    _tool("functions.get_news:1", '{"topic": "technology"}'),
+                    _tool(
+                        "functions.send_email:2",
+                        '{"to": "user@example.com", "subject": "Daily Update"}',
                     ),
-                    type="function",
                 ),
-                ToolCall(
-                    id="functions.send_email:2",
-                    function=FunctionCall(
-                        name="send_email",
-                        arguments=json.dumps(
-                            {"to": "user@example.com", "subject": "Daily Update"}
-                        ),
-                    ),
-                    type="function",
+                ["get_weather", "get_news", "send_email"],
+                [
+                    {"city": "New York"},
+                    {"topic": "technology"},
+                    {"to": "user@example.com", "subject": "Daily Update"},
+                ],
+                "Multiple tasks. ",
+                id="three_tool_calls",
+            ),
+            pytest.param(
+                "Process HTML. "
+                + _wrap(
+                    _tool("functions.process_html:0", '{"html": "<div>content</div>"}')
                 ),
-            ],
-            "I'll help you with multiple tasks. ",
-        ),
-        (
-            """Mixed spacing test. <|tool_calls_section_begin|> <|tool_call_begin|> functions.test:0 <|tool_call_argument_begin|> {} <|tool_call_end|><|tool_call_begin|>functions.test2:1<|tool_call_argument_begin|>{}<|tool_call_end|> <|tool_calls_section_end|>""",
-            [
-                ToolCall(
-                    id="functions.test:0",
-                    function=FunctionCall(
-                        name="test",
-                        arguments=json.dumps({}),
-                    ),
-                    type="function",
+                ["process_html"],
+                [{"html": "<div>content</div>"}],
+                "Process HTML. ",
+                id="angle_brackets_in_json",
+            ),
+            pytest.param(
+                "Formatted. "
+                + _wrap(
+                    _tool(
+                        "functions.process_data:0",
+                        '{\n  "name": "test",\n  "value": 123\n}',
+                    )
                 ),
-                ToolCall(
-                    id="functions.test2:1",
-                    function=FunctionCall(
-                        name="test2",
-                        arguments=json.dumps({}),
-                    ),
-                    type="function",
-                ),
-            ],
-            "Mixed spacing test. ",
-        ),
-        (
-            """I need to process HTML content. <|tool_calls_section_begin|><|tool_call_begin|>functions.process_html:0<|tool_call_argument_begin|>{"html": "<div>content</div>", "text": "normal text"}<|tool_call_end|><|tool_calls_section_end|>""",
-            [
-                ToolCall(
-                    id="functions.process_html:0",
-                    function=FunctionCall(
-                        name="process_html",
-                        arguments=json.dumps(
-                            {"html": "<div>content</div>", "text": "normal text"}
-                        ),
-                    ),
-                    type="function",
-                )
-            ],
-            "I need to process HTML content. ",
-        ),
-        (
-            """I need to process formatted JSON. <|tool_calls_section_begin|><|tool_call_begin|>functions.process_data:0<|tool_call_argument_begin|>{
-  "name": "test",
-  "value": 123,
-  "nested": {
-    "key": "value"
-  }
-}<|tool_call_end|><|tool_calls_section_end|>""",
-            [
-                ToolCall(
-                    id="functions.process_data:0",
-                    function=FunctionCall(
-                        name="process_data",
-                        arguments=json.dumps(
-                            {"name": "test", "value": 123, "nested": {"key": "value"}},
-                            indent=2,
-                        ),
-                    ),
-                    type="function",
-                )
-            ],
-            "I need to process formatted JSON. ",
-        ),
-    ],
-)
-def test_extract_tool_calls(
-    kimi_k2_tool_parser, model_output, expected_tool_calls, expected_content
-):
-    extracted_tool_calls = kimi_k2_tool_parser.extract_tool_calls(
-        model_output, request=None
-    )  # type: ignore[arg-type]
-    assert extracted_tool_calls.tools_called
-
-    assert_tool_calls(extracted_tool_calls.tool_calls, expected_tool_calls)
-
-    assert extracted_tool_calls.content == expected_content
-
-
-def test_extract_tool_calls_invalid_json(kimi_k2_tool_parser):
-    """we'll return every funcall result"""
-    model_output = """I'll help you check the weather. <|tool_calls_section_begin|> <|tool_call_begin|>
-functions.invalid_get_weather:0 <|tool_call_argument_begin|> {"city": "Beijing" <|tool_call_end|> <|tool_call_begin|>
-functions.valid_get_weather:1 <|tool_call_argument_begin|> {"city": "Shanghai"} <|tool_call_end|> <|tool_calls_section_end|>"""
-
-    extracted_tool_calls = kimi_k2_tool_parser.extract_tool_calls(
-        model_output, request=None
-    )  # type: ignore[arg-type]
-
-    assert extracted_tool_calls.tools_called
-    # Should extract only the valid JSON tool calls
-    assert len(extracted_tool_calls.tool_calls) == 2
-    assert extracted_tool_calls.tool_calls[0].function.name == "invalid_get_weather"
-    assert extracted_tool_calls.tool_calls[1].function.name == "valid_get_weather"
-
-
-def test_extract_tool_calls_invalid_funcall(kimi_k2_tool_parser):
-    """we'll return every funcall result"""
-    model_output = """I'll help you check the weather. <|tool_calls_section_begin|> <|tool_call_begin|>
-functions.invalid_get_weather.0 <|tool_call_argument_begin|> {"city": "Beijing"} <|tool_call_end|> <|tool_call_begin|>
-functions.valid_get_weather:1 <|tool_call_argument_begin|> {"city": "Shanghai"} <|tool_call_end|> <|tool_calls_section_end|>"""
-
-    extracted_tool_calls = kimi_k2_tool_parser.extract_tool_calls(
-        model_output, request=None
-    )  # type: ignore[arg-type]
-
-    assert extracted_tool_calls.tools_called
-    # Should extract only the valid JSON tool calls
-    assert len(extracted_tool_calls.tool_calls) == 1
-    assert extracted_tool_calls.tool_calls[0].function.name == "valid_get_weather"
-
-
-def test_streaming_basic_functionality(kimi_k2_tool_parser):
-    """Test basic streaming functionality."""
-    # Reset streaming state
-    kimi_k2_tool_parser.current_tool_name_sent = False
-    kimi_k2_tool_parser.prev_tool_call_arr = []
-    kimi_k2_tool_parser.current_tool_id = -1
-    kimi_k2_tool_parser.streamed_args_for_tool = []
-
-    # Test with a simple tool call
-    current_text = """ check the weather. <|tool_calls_section_begin|> <|tool_call_begin|>
-functions.get_weather:0 <|tool_call_argument_begin|> {"city": "Beijing"} <|tool_call_end|> <|tool_calls_section_end|>"""
-
-    # First call should handle the initial setup
-    result = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="I'll help you",
-        current_text=current_text,
-        delta_text="<|tool_calls_section_end|>",
-        previous_token_ids=[],
-        current_token_ids=[],
-        delta_token_ids=[],
-        request=None,
-    )
-
-    # The result might be None or contain tool call information
-    # This depends on the internal state management
-    if result is not None and hasattr(result, "tool_calls") and result.tool_calls:
-        assert len(result.tool_calls) >= 0
-
-
-def test_streaming_no_tool_calls(kimi_k2_tool_parser):
-    """Test streaming when there are no tool calls."""
-    current_text = "This is just regular text without any tool calls."
-
-    result = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="This is just regular text",
-        current_text=current_text,
-        delta_text=" without any tool calls.",
-        previous_token_ids=[],
-        current_token_ids=[],
-        delta_token_ids=[],
-        request=None,
-    )
-
-    # Should return the delta text as content
-    assert result is not None
-    assert hasattr(result, "content")
-    assert result.content == " without any tool calls."
-
-
-def test_token_leak_between_section_and_tool_begin(kimi_k2_tool_parser):
-    """
-    Test that text between <|tool_calls_section_begin|> and <|tool_call_begin|>
-    is suppressed and does not leak into reasoning_delta.
-    This is the main vulnerability being fixed.
-    """
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    # Get token IDs for the markers
-    section_begin_token_id = kimi_k2_tool_parser.vocab.get(
-        "<|tool_calls_section_begin|>"
-    )
-    tool_call_begin_token_id = kimi_k2_tool_parser.vocab.get("<|tool_call_begin|>")
-
-    # Simulate streaming sequence:
-    deltas = [
-        ("I'll help you with that. ", [1, 2, 3]),
-        ("<|tool_calls_section_begin|>", [section_begin_token_id]),
-        (" spurious text ", [4, 5]),
-        ("<|tool_call_begin|>", [tool_call_begin_token_id]),
-    ]
-
-    results = run_streaming_sequence(kimi_k2_tool_parser, deltas)
-
-    # Delta 1: "I'll help you with that. "
-    assert results[0] is not None
-    assert results[0].content == "I'll help you with that. "
-
-    # Delta 2: "<|tool_calls_section_begin|>"
-    # Section marker should be stripped and suppressed
-    assert results[1] is None or (
-        results[1].content is None or results[1].content == ""
-    )
-
-    # Delta 3: " spurious text or tokens " (THE LEAK SCENARIO)
-    # CRITICAL: This text should be suppressed, NOT returned as reasoning_delta
-    assert results[2] is None or (
-        results[2].content is None or results[2].content == ""
-    )
-
-    # Delta 4: "<|tool_call_begin|>..."
-    # Now we're in tool call mode, result depends on internal state
-    # The key is that the spurious text from Delta 3 was not leaked
-
-
-def test_split_markers_across_deltas(kimi_k2_tool_parser):
-    """
-    Test that markers split across delta chunks are correctly detected
-    via the rolling buffer mechanism.
-    """
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    section_begin_token_id = kimi_k2_tool_parser.vocab.get(
-        "<|tool_calls_section_begin|>"
+                ["process_data"],
+                [{"name": "test", "value": 123}],
+                "Formatted. ",
+                id="multiline_json",
+            ),
+            pytest.param(
+                "No prefix. " + _wrap(_tool("get_weather:0", '{"city": "Tokyo"}')),
+                ["get_weather"],
+                [{"city": "Tokyo"}],
+                "No prefix. ",
+                id="no_functions_prefix",
+            ),
+            pytest.param(
+                "Empty args. " + _wrap(_tool("functions.test:0", "{}")),
+                ["test"],
+                [{}],
+                "Empty args. ",
+                id="empty_arguments",
+            ),
+        ],
     )
-
-    # Delta 1: partial token, Delta 2: complete marker
-    deltas = [
-        ("<|tool_calls_sec", [3]),
-        ("tion_begin|> ", [section_begin_token_id, 4]),
-    ]
-
-    _results = run_streaming_sequence(kimi_k2_tool_parser, deltas)
-
-    # Now the complete marker should be detected via buffer
-    assert kimi_k2_tool_parser.in_tool_section is True
-
-
-def test_marker_variants(kimi_k2_tool_parser):
-    """Test that both singular and plural marker variants are recognized."""
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    # Test singular variant: <|tool_call_section_begin|> (note: singular "call")
-    singular_token_id = kimi_k2_tool_parser.vocab.get("<|tool_call_section_begin|>")
-
-    if singular_token_id is not None:  # Only test if tokenizer supports it
-        _result = kimi_k2_tool_parser.extract_tool_calls_streaming(
-            previous_text="Reasoning ",
-            current_text="Reasoning <|tool_call_section_begin|>",
-            delta_text="<|tool_call_section_begin|>",
-            previous_token_ids=[1, 2],
-            current_token_ids=[1, 2, singular_token_id],
-            delta_token_ids=[singular_token_id],
-            request=None,
+    def test_extract_tool_calls(
+        self, parser, model_output, expected_names, expected_args_list, expected_content
+    ):
+        content, tool_calls = run_tool_extraction(parser, model_output, streaming=False)
+        assert content == expected_content
+        assert len(tool_calls) == len(expected_names)
+        for tc, name, expected_args in zip(
+            tool_calls, expected_names, expected_args_list
+        ):
+            assert tc.type == "function"
+            assert tc.function.name == name
+            assert json.loads(tc.function.arguments) == expected_args
+            # id format: "something:digit"
+            assert tc.id.split(":")[-1].isdigit()
+
+    def test_invalid_json_still_extracted(self, parser):
+        """Tool calls with invalid JSON are still returned (arguments as-is)."""
+        model_output = (
+            "Help. "
+            + SECTION_BEGIN
+            + _tool("functions.bad:0", '{"city": "Beijing"')
+            + _tool("functions.good:1", '{"city": "Shanghai"}')
+            + SECTION_END
         )
-        # Should enter tool section mode with singular variant too
-        assert kimi_k2_tool_parser.in_tool_section is True
-
-
-def test_reentry_to_reasoning_after_tool_section(kimi_k2_tool_parser):
-    """
-    Test that after exiting a tool section with <|tool_calls_section_end|>,
-    subsequent text is correctly returned as reasoning content.
-    """
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    section_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_begin|>")
-    section_end_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_end|>")
-
-    deltas = [
-        ("<|tool_calls_section_begin|>", [section_begin_id]),
-        ("<|tool_calls_section_end|>", [section_end_id]),
-        (" More reasoning", [10, 11]),
-    ]
-
-    results = run_streaming_sequence(kimi_k2_tool_parser, deltas)
-
-    assert kimi_k2_tool_parser.in_tool_section is False
-    assert results[2] is not None
-    assert results[2].content == " More reasoning"
-
-
-def test_empty_tool_section(kimi_k2_tool_parser):
-    """Test an empty tool section (begin immediately followed by end)."""
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    section_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_begin|>")
-    section_end_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_end|>")
-
-    # Section begin
-    _result1 = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="Reasoning ",
-        current_text="Reasoning <|tool_calls_section_begin|>",
-        delta_text="<|tool_calls_section_begin|>",
-        previous_token_ids=[1],
-        current_token_ids=[1, section_begin_id],
-        delta_token_ids=[section_begin_id],
-        request=None,
-    )
-
-    # Immediate section end
-    _result2 = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="Reasoning <|tool_calls_section_begin|>",
-        current_text="Reasoning <|tool_calls_section_begin|><|tool_calls_section_end|>",
-        delta_text="<|tool_calls_section_end|>",
-        previous_token_ids=[1, section_begin_id],
-        current_token_ids=[1, section_begin_id, section_end_id],
-        delta_token_ids=[section_end_id],
-        request=None,
-    )
-    # Should exit cleanly without errors
-    assert kimi_k2_tool_parser.in_tool_section is False
-
-
-def test_malformed_tool_section_recovery(kimi_k2_tool_parser):
-    """
-    Test that the parser recovers from a malformed tool section
-    that never closes properly.
-    """
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    section_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_begin|>")
-
-    # Enter tool section
-    _result1 = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="",
-        current_text="<|tool_calls_section_begin|>",
-        delta_text="<|tool_calls_section_begin|>",
-        previous_token_ids=[],
-        current_token_ids=[section_begin_id],
-        delta_token_ids=[section_begin_id],
-        request=None,
-    )
-    assert kimi_k2_tool_parser.in_tool_section is True
-
-    # Simulate a lot of text without proper tool calls or section end
-    # This should trigger the error recovery mechanism
-    large_text = "x" * 10000  # Exceeds max_section_chars
-
-    result2 = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="<|tool_calls_section_begin|>",
-        current_text="<|tool_calls_section_begin|>" + large_text,
-        delta_text=large_text,
-        previous_token_ids=[section_begin_id],
-        current_token_ids=[section_begin_id] + list(range(100, 100 + len(large_text))),
-        delta_token_ids=list(range(100, 100 + len(large_text))),
-        request=None,
-    )
-
-    # Parser should have force-exited the tool section
-    assert kimi_k2_tool_parser.in_tool_section is False
-    # And returned the content as reasoning
-    assert result2 is not None
-    assert result2.content == large_text
-
-
-def test_state_reset(kimi_k2_tool_parser):
-    """Test that reset_streaming_state() properly clears all state."""
-    # Put parser in a complex state
-    kimi_k2_tool_parser.in_tool_section = True
-    kimi_k2_tool_parser.token_buffer = "some buffer"
-    kimi_k2_tool_parser.current_tool_id = 5
-    kimi_k2_tool_parser.prev_tool_call_arr = [{"id": "test"}]
-    kimi_k2_tool_parser.section_char_count = 1000
-
-    # Reset
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    # Verify all state is cleared
-    assert kimi_k2_tool_parser.in_tool_section is False
-    assert kimi_k2_tool_parser.token_buffer == ""
-    assert kimi_k2_tool_parser.current_tool_id == -1
-    assert kimi_k2_tool_parser.prev_tool_call_arr == []
-    assert kimi_k2_tool_parser.section_char_count == 0
-    assert kimi_k2_tool_parser.current_tool_name_sent is False
-    assert kimi_k2_tool_parser.streamed_args_for_tool == []
-
-
-def test_section_begin_noise_tool_begin_same_chunk(kimi_k2_tool_parser):
-    """
-    Test that begin→noise→tool_begin within the SAME chunk suppresses
-    the noise text correctly (not just across chunks).
-    """
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    section_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_begin|>")
-    tool_call_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_call_begin|>")
-
-    # Single delta containing: section_begin + spurious text + tool_call_begin
-    combined_text = "<|tool_calls_section_begin|> noise text <|tool_call_begin|>"
-
-    result = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="Reasoning ",
-        current_text="Reasoning " + combined_text,
-        delta_text=combined_text,
-        previous_token_ids=[1, 2],
-        current_token_ids=[1, 2, section_begin_id, 3, 4, tool_call_begin_id],
-        delta_token_ids=[section_begin_id, 3, 4, tool_call_begin_id],
-        request=None,
-    )
-
-    # The noise text should NOT leak into content
-    # Result should either be None/empty or start tool call parsing
-    if result is not None and result.content is not None:
-        # If content is returned, it should not contain the noise
-        assert "noise text" not in result.content
-        assert result.content == "" or result.content.strip() == ""
-
-
-def test_stream_ends_without_section_end_marker(kimi_k2_tool_parser):
-    """
-    Test that if the stream ends (EOF) without a proper section end marker,
-    the parser doesn't leak text, doesn't crash, and resets state cleanly.
-    """
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    section_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_begin|>")
-
-    # Enter tool section
-    _result1 = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="",
-        current_text="<|tool_calls_section_begin|>",
-        delta_text="<|tool_calls_section_begin|>",
-        previous_token_ids=[],
-        current_token_ids=[section_begin_id],
-        delta_token_ids=[section_begin_id],
-        request=None,
-    )
-    assert kimi_k2_tool_parser.in_tool_section is True
-
-    # Some content in tool section
-    result2 = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="<|tool_calls_section_begin|>",
-        current_text="<|tool_calls_section_begin|> partial content",
-        delta_text=" partial content",
-        previous_token_ids=[section_begin_id],
-        current_token_ids=[section_begin_id, 10, 11],
-        delta_token_ids=[10, 11],
-        request=None,
-    )
-    # Content should be suppressed
-    assert result2.content == "" or result2.content is None
-
-    # Stream ends (EOF) - no more deltas, no section_end marker
-    # Simulate this by manually checking state and resetting
-    # (In real usage, the request handler would call reset_streaming_state)
-    assert kimi_k2_tool_parser.in_tool_section is True  # Still in section
-
-    # Reset state (as would happen between requests)
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    # Verify clean slate
-    assert kimi_k2_tool_parser.in_tool_section is False
-    assert kimi_k2_tool_parser.token_buffer == ""
-
-    # Next request should work normally
-    result3 = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="",
-        current_text="New reasoning",
-        delta_text="New reasoning",
-        previous_token_ids=[],
-        current_token_ids=[20, 21],
-        delta_token_ids=[20, 21],
-        request=None,
-    )
-    assert result3 is not None
-    assert result3.content == "New reasoning"
-
-
-def test_same_chunk_begin_and_end_markers(kimi_k2_tool_parser):
-    """
-    CRITICAL TEST: Verify that when both section_begin and section_end
-    markers appear in the SAME chunk, the parser correctly:
-    1. Enters the tool section
-    2. Immediately exits the tool section
-    3. Does NOT get stuck in in_tool_section=True state
-
-    This tests the bug fix where elif was changed to if to handle
-    both state transitions in a single delta.
-    """
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    section_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_begin|>")
-    section_end_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_end|>")
-
-    # Single chunk with both markers (e.g., empty tool section)
-    combined_delta = "<|tool_calls_section_begin|><|tool_calls_section_end|>"
-
-    result = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="Some reasoning ",
-        current_text="Some reasoning " + combined_delta,
-        delta_text=combined_delta,
-        previous_token_ids=[1, 2],
-        current_token_ids=[1, 2, section_begin_id, section_end_id],
-        delta_token_ids=[section_begin_id, section_end_id],
-        request=None,
-    )
-
-    # CRITICAL: Parser should NOT be stuck in tool section
-    assert kimi_k2_tool_parser.in_tool_section is False, (
-        "Parser stuck in tool section after processing both begin/end in same chunk. "
-        "This indicates the elif bug was not fixed."
-    )
-
-    # Result should be empty or contain only stripped content
-    assert result is not None
-    assert result.content == "" or result.content is None
-
-    # Verify subsequent content streams correctly (not suppressed)
-    result2 = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="Some reasoning " + combined_delta,
-        current_text="Some reasoning " + combined_delta + " More reasoning",
-        delta_text=" More reasoning",
-        previous_token_ids=[1, 2, section_begin_id, section_end_id],
-        current_token_ids=[1, 2, section_begin_id, section_end_id, 10, 11],
-        delta_token_ids=[10, 11],
-        request=None,
-    )
-
-    # This content should NOT be suppressed (we're out of tool section)
-    assert result2 is not None
-    assert result2.content == " More reasoning"
-
-
-def test_same_chunk_begin_content_end_markers(kimi_k2_tool_parser):
-    """
-    Test the same-chunk scenario with actual content between markers.
-    Example: <|tool_calls_section_begin|> text <|tool_calls_section_end|>
-    all arriving in one delta. The key is that the state machine correctly
-    transitions in and out within the same chunk.
-    """
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    section_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_begin|>")
-    section_end_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_end|>")
-
-    # Chunk with begin, some whitespace/noise, and end all together
-    # This simulates a tool section that opens and closes in the same chunk
-    combined_delta = "<|tool_calls_section_begin|>   <|tool_calls_section_end|>"
-
-    _result = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="Reasoning ",
-        current_text="Reasoning " + combined_delta,
-        delta_text=combined_delta,
-        previous_token_ids=[1],
-        current_token_ids=[1, section_begin_id, 100, section_end_id],
-        delta_token_ids=[section_begin_id, 100, section_end_id],
-        request=None,
-    )
-
-    # Parser should exit cleanly (not stuck in tool section)
-    assert kimi_k2_tool_parser.in_tool_section is False
-
-    # Verify the fix: next content should stream normally, not be suppressed
-    result2 = kimi_k2_tool_parser.extract_tool_calls_streaming(
-        previous_text="Reasoning " + combined_delta,
-        current_text="Reasoning " + combined_delta + " Done",
-        delta_text=" Done",
-        previous_token_ids=[1, section_begin_id, 100, section_end_id],
-        current_token_ids=[1, section_begin_id, 100, section_end_id, 200],
-        delta_token_ids=[200],
-        request=None,
-    )
-
-    # Content after section should be returned (not suppressed)
-    assert result2 is not None
-    assert result2.content == " Done"
-
-
-def test_tool_call_end_and_section_end_same_chunk(kimi_k2_tool_parser):
-    """
-    CRITICAL TEST (P1): Verify that when both <|tool_call_end|> and
-    <|tool_calls_section_end|> appear in the SAME chunk, the parser:
-    1. Processes the tool_call_end first (emits final arguments)
-    2. THEN exits the section
-    3. Does NOT drop the final tool call update
-    4. Does NOT leak special tokens into reasoning
-
-    This tests the deferred section exit fix.
-    """
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    section_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_begin|>")
-    section_end_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_end|>")
-    tool_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_call_begin|>")
-    tool_end_id = kimi_k2_tool_parser.vocab.get("<|tool_call_end|>")
-
-    # Simulate a streaming sequence for a SHORT tool call (all in one chunk):
-    combined = (
-        '<|tool_call_begin|>get_weather:0 <|tool_call_argument_begin|> {"city": "Paris"} '
-        "<|tool_call_end|><|tool_calls_section_end|>"
-    )
-
-    deltas = [
-        ("Let me help. ", [1, 2]),
-        ("<|tool_calls_section_begin|>", [section_begin_id]),
-        (combined, [tool_begin_id, 10, 11, 12, tool_end_id, section_end_id]),
-        (" Done", [20]),
-    ]
-
-    results = run_streaming_sequence(kimi_k2_tool_parser, deltas)
-
-    # CRITICAL: Parser should have exited section AFTER processing tool
-    assert kimi_k2_tool_parser.in_tool_section is False
-
-    # Tool call should have been emitted (not dropped)
-    if results[2] is not None and results[2].content is not None:
-        # Verify no special tokens leaked into content
-        assert "<|tool_call_end|>" not in results[2].content
-        assert "<|tool_calls_section_end|>" not in results[2].content
+        content, tool_calls = run_tool_extraction(parser, model_output, streaming=False)
+        assert len(tool_calls) == 2
+        assert tool_calls[0].function.name == "bad"
+        assert tool_calls[1].function.name == "good"
+
+    def test_invalid_funcall_id_skipped(self, parser):
+        """Tool calls with malformed id (no colon+digit) are skipped."""
+        model_output = (
+            "Help. "
+            + SECTION_BEGIN
+            + _tool("functions.invalid.0", '{"city": "Beijing"}')
+            + _tool("functions.valid:1", '{"city": "Shanghai"}')
+            + SECTION_END
+        )
+        content, tool_calls = run_tool_extraction(parser, model_output, streaming=False)
+        assert len(tool_calls) == 1
+        assert tool_calls[0].function.name == "valid"
+
+    def test_native_id_extracted(self, parser):
+        """Regression: parser extracts native ID onto ToolCall (PR #32768)."""
+        model_output = "Checking weather. " + _wrap(
+            _tool("functions.get_weather:0", '{"city": "Tokyo"}')
+        )
+        content, tool_calls = run_tool_extraction(parser, model_output, streaming=False)
+        assert len(tool_calls) == 1
+        assert tool_calls[0].id == "functions.get_weather:0"
+        assert tool_calls[0].function.name == "get_weather"
+        assert json.loads(tool_calls[0].function.arguments) == {"city": "Tokyo"}
+
+    def test_multi_turn_native_id_continuity(self, kimi_k2_tokenizer):
+        """Regression: native IDs from turn 1 preserved across turns (PR #32768)."""
+        turn1_parser = KimiK2ToolParser(kimi_k2_tokenizer)
+        turn1_output = "Let me check. " + _wrap(
+            _tool("functions.get_weather:0", '{"city": "Beijing"}')
+        )
+        _, turn1_tools = run_tool_extraction(
+            turn1_parser, turn1_output, streaming=False
+        )
+        assert len(turn1_tools) == 1
+        assert turn1_tools[0].id == "functions.get_weather:0"
 
-    # Content after tool section should stream normally
-    assert results[3] is not None
-    assert results[3].content == " Done"
+        # Fresh parser for turn 2
+        turn2_parser = KimiK2ToolParser(kimi_k2_tokenizer)
+        turn2_output = "Now let me get news. " + _wrap(
+            _tool("functions.get_news:0", '{"topic": "weather in Beijing"}')
+        )
+        _, turn2_tools = run_tool_extraction(
+            turn2_parser, turn2_output, streaming=False
+        )
+        assert len(turn2_tools) == 1
+        assert turn2_tools[0].id == "functions.get_news:0"
 
 
-def test_streaming_tool_call_markers_not_leaked(kimi_k2_tool_parser):
-    """
-    CRITICAL TEST: Verify that tool call markers (<|tool_call_begin|>,
-    <|tool_call_end|>, <|tool_call_argument_begin|>) are NOT leaked
-    into the content field during streaming.
+def _split_tool_output_to_deltas(
+    content: str, tool_strs: list[tuple[str, str]]
+) -> list[str]:
+    """Build a list of string deltas with special tokens as separate chunks.
 
-    This reproduces the AWS Bedrock bug where tool call markers appeared
-    in the 'text' field of responses.
+    Args:
+        content: text before tool section
+        tool_strs: list of (tool_id, args_json)
     """
-    kimi_k2_tool_parser.reset_streaming_state()
-
-    section_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_begin|>")
-    section_end_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_end|>")
-    tool_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_call_begin|>")
-    tool_end_id = kimi_k2_tool_parser.vocab.get("<|tool_call_end|>")
-
-    # List of markers that should NEVER appear in content
-    forbidden_markers = [
-        "<|tool_call_begin|>",
-        "<|tool_call_end|>",
-        "<|tool_call_argument_begin|>",
-        "<|tool_calls_section_begin|>",
-        "<|tool_calls_section_end|>",
-    ]
-
-    all_content = []
-
-    # Steps: reasoning, section begin, tool call, section end, more reasoning
-    tool_chunk = (
-        "<|tool_call_begin|> functions.get_weather:0 "
-        '<|tool_call_argument_begin|> {"city": "Tokyo"} <|tool_call_end|>'
-    )
-    deltas = [
-        ("I'll check the weather. ", [1, 2, 3]),
-        ("<|tool_calls_section_begin|>", [section_begin_id]),
-        (tool_chunk, [tool_begin_id, 10, 11, tool_end_id]),
-        ("<|tool_calls_section_end|>", [section_end_id]),
-        (" Here's the result.", [20, 21]),
-    ]
-
-    results = run_streaming_sequence(kimi_k2_tool_parser, deltas)
-
-    for res in results:
-        if res and res.content:
-            all_content.append(res.content)
-
-    # CRITICAL ASSERTIONS: No forbidden markers in any content
-    full_content = "".join(all_content)
-    for marker in forbidden_markers:
-        assert marker not in full_content, (
-            f"MARKER LEAK DETECTED: '{marker}' found in content. "
-            f"Full content: {repr(full_content)}"
+    deltas = [content, SECTION_BEGIN]
+    for tool_id, args_json in tool_strs:
+        deltas.extend(
+            [
+                TOOL_BEGIN,
+                f"{tool_id} ",
+                ARG_BEGIN,
+                f"{args_json} ",
+                TOOL_END,
+            ]
         )
-
-    # Also check that tool call content (function name, arguments) is not leaked
-    assert "get_weather" not in full_content, (
-        f"TOOL CALL CONTENT LEAKED: 'get_weather' found in content. "
-        f"Full content: {repr(full_content)}"
-    )
-    assert "Tokyo" not in full_content, (
-        f"TOOL CALL CONTENT LEAKED: 'Tokyo' found in content. "
-        f"Full content: {repr(full_content)}"
-    )
-
-    # Verify that legitimate content was preserved
-    assert "I'll check the weather." in full_content or len(all_content) > 0
+    deltas.append(SECTION_END)
+    return deltas
 
 
-def test_native_id_extracted_and_placed_on_tool_call(kimi_k2_tool_parser):
-    """Regression: parser extracts native ID onto ToolCall (PR #32768)."""
-    model_output = (
-        "Checking weather. "
-        "<|tool_calls_section_begin|>"
-        "<|tool_call_begin|>functions.get_weather:0"
-        '<|tool_call_argument_begin|>{"city": "Tokyo"}'
-        "<|tool_call_end|>"
-        "<|tool_calls_section_end|>"
-    )
-
-    result = kimi_k2_tool_parser.extract_tool_calls(model_output, request=None)
-    assert result.tools_called
-    assert len(result.tool_calls) == 1
-
-    tc = result.tool_calls[0]
-    # Native ID from model output must be used as the tool call ID
-    assert tc.id == "functions.get_weather:0"
-    assert tc.function.name == "get_weather"
-    assert json.loads(tc.function.arguments) == {"city": "Tokyo"}
-
-
-def test_multi_turn_native_id_continuity(kimi_k2_tool_parser, kimi_k2_tokenizer):
-    """Regression: native IDs from turn 1 preserved across turns (PR #32768)."""
-    turn1_output = (
-        "Let me check. "
-        "<|tool_calls_section_begin|>"
-        "<|tool_call_begin|>functions.get_weather:0"
-        '<|tool_call_argument_begin|>{"city": "Beijing"}'
-        "<|tool_call_end|>"
-        "<|tool_calls_section_end|>"
+class TestStreamingHappyPath:
+    def test_single_tool_call(self, parser):
+        """Verify DeltaToolCall output: name, id, arguments for one tool."""
+        deltas = _split_tool_output_to_deltas(
+            "I'll help. ",
+            [("functions.get_weather:0", '{"city": "Beijing"}')],
+        )
+        rec = run_tool_extraction_streaming(parser, deltas)
+
+        assert len(rec.tool_calls) == 1
+        tc = rec.tool_calls[0]
+        assert tc.function.name == "get_weather"
+        assert tc.id == "functions.get_weather:0"
+        assert json.loads(tc.function.arguments) == {"city": "Beijing"}
+
+    def test_multiple_tool_calls(self, parser):
+        """Two tool calls emitted with correct indices, names, arguments."""
+        deltas = _split_tool_output_to_deltas(
+            "Compare weather. ",
+            [
+                ("functions.get_weather:0", '{"city": "Tokyo"}'),
+                ("functions.get_weather:1", '{"city": "NYC"}'),
+            ],
+        )
+        rec = run_tool_extraction_streaming(parser, deltas)
+
+        assert len(rec.tool_calls) == 2
+        assert rec.tool_calls[0].function.name == "get_weather"
+        assert rec.tool_calls[0].id == "functions.get_weather:0"
+        assert json.loads(rec.tool_calls[0].function.arguments) == {"city": "Tokyo"}
+
+        assert rec.tool_calls[1].function.name == "get_weather"
+        assert rec.tool_calls[1].id == "functions.get_weather:1"
+        assert json.loads(rec.tool_calls[1].function.arguments) == {"city": "NYC"}
+
+    def test_content_before_tools(self, parser):
+        """Content before section is streamed; markers/args don't leak."""
+        deltas = _split_tool_output_to_deltas(
+            "I'll check the weather. ",
+            [("functions.get_weather:0", '{"city": "Tokyo"}')],
+        )
+        rec = run_tool_extraction_streaming(parser, deltas)
+
+        assert "check the weather" in rec.other_content
+        # No markers or tool content leaked
+        for marker in [SECTION_BEGIN, SECTION_END, TOOL_BEGIN, TOOL_END, ARG_BEGIN]:
+            assert marker not in rec.other_content
+        assert "get_weather" not in rec.other_content
+        assert "Tokyo" not in rec.other_content
+
+    def test_no_tool_calls(self, parser):
+        """Plain text streaming returns content only."""
+        deltas = ["This is just ", "regular text ", "without tools."]
+        rec = run_tool_extraction_streaming(parser, deltas)
+
+        assert rec.other_content == "This is just regular text without tools."
+        assert rec.tool_calls == []
+
+    def test_incremental_arguments(self, parser):
+        """Arguments split across small chunks accumulate correctly."""
+        deltas = [
+            "Help. ",
+            SECTION_BEGIN,
+            TOOL_BEGIN,
+            "functions.get_weather:0 ",
+            ARG_BEGIN,
+            '{"ci',
+            'ty": "Be',
+            'ijing"}',
+            " ",
+            TOOL_END,
+            SECTION_END,
+        ]
+        rec = run_tool_extraction_streaming(parser, deltas)
+
+        assert len(rec.tool_calls) == 1
+        assert rec.tool_calls[0].function.name == "get_weather"
+        assert json.loads(rec.tool_calls[0].function.arguments) == {"city": "Beijing"}
+
+    @pytest.mark.parametrize(
+        "model_output",
+        [
+            pytest.param(
+                "Single. "
+                + _wrap(_tool("functions.get_weather:0", '{"city": "Beijing"}')),
+                id="single_tool",
+            ),
+            pytest.param(
+                "Multi. "
+                + _wrap(
+                    _tool("functions.get_weather:0", '{"city": "Tokyo"}'),
+                    _tool("functions.get_news:1", '{"topic": "tech"}'),
+                ),
+                id="parallel_tools",
+            ),
+            pytest.param(
+                "No prefix id. " + _wrap(_tool("get_weather:0", '{"city": "NYC"}')),
+                id="no_functions_prefix",
+            ),
+        ],
     )
+    def test_streaming_matches_nonstreaming(self, parser, model_output):
+        """Streaming reconstruction matches non-streaming extraction."""
+        content_non, tools_non = run_tool_extraction(
+            parser, model_output, streaming=False
+        )
+        content_stream, tools_stream = run_tool_extraction(
+            parser, model_output, streaming=True
+        )
 
-    turn1_result = kimi_k2_tool_parser.extract_tool_calls(turn1_output, request=None)
-    assert turn1_result.tools_called
-    assert turn1_result.tool_calls[0].id == "functions.get_weather:0"
-
-    # Fresh parser for turn 2
-    turn2_parser = KimiK2ToolParser(kimi_k2_tokenizer)
-    turn2_output = (
-        "Now let me get news. "
-        "<|tool_calls_section_begin|>"
-        "<|tool_call_begin|>functions.get_news:0"
-        '<|tool_call_argument_begin|>{"topic": "weather in Beijing"}'
-        "<|tool_call_end|>"
-        "<|tool_calls_section_end|>"
-    )
+        assert len(tools_non) == len(tools_stream)
+        for tc_non, tc_stream in zip(tools_non, tools_stream):
+            assert tc_non.function.name == tc_stream.function.name
+            assert json.loads(tc_non.function.arguments) == json.loads(
+                tc_stream.function.arguments
+            )
 
-    turn2_result = turn2_parser.extract_tool_calls(turn2_output, request=None)
-    assert turn2_result.tools_called
-    assert turn2_result.tool_calls[0].id == "functions.get_news:0"
 
+class TestStreamingEdgeCases:
+    def test_marker_suppression(self, parser):
+        """No special-token markers appear in reconstructed content."""
+        deltas = _split_tool_output_to_deltas(
+            "I'll check. ",
+            [("functions.get_weather:0", '{"city": "Tokyo"}')],
+        )
+        rec = run_tool_extraction_streaming(parser, deltas)
+
+        forbidden = [SECTION_BEGIN, SECTION_END, TOOL_BEGIN, TOOL_END, ARG_BEGIN]
+        for marker in forbidden:
+            assert marker not in rec.other_content, (
+                f"Marker leaked: {marker!r} in {rec.other_content!r}"
+            )
+
+    def test_noise_between_markers_suppressed(self, parser):
+        """Text between section_begin and tool_call_begin doesn't leak."""
+        deltas = [
+            "Reasoning. ",
+            SECTION_BEGIN,
+            " spurious noise ",
+            TOOL_BEGIN,
+            "functions.test:0 ",
+            ARG_BEGIN,
+            '{"k": "v"} ',
+            TOOL_END,
+            SECTION_END,
+        ]
+        rec = run_tool_extraction_streaming(parser, deltas)
+
+        assert "spurious" not in rec.other_content
+        assert "noise" not in rec.other_content
+
+    def test_empty_tool_section(self, parser):
+        """Empty section (begin immediately followed by end) doesn't crash."""
+        deltas = ["Reasoning. ", SECTION_BEGIN, SECTION_END]
+        rec = run_tool_extraction_streaming(parser, deltas)
+
+        assert rec.tool_calls == []
+
+    def test_three_different_tools(self, parser):
+        """Three tool calls with different functions stream correctly."""
+        deltas = _split_tool_output_to_deltas(
+            "Multiple tasks. ",
+            [
+                ("functions.get_weather:0", '{"city": "NYC"}'),
+                ("functions.get_news:1", '{"topic": "tech"}'),
+                ("functions.send_email:2", '{"to": "a@b.com"}'),
+            ],
+        )
+        rec = run_tool_extraction_streaming(parser, deltas)
+
+        assert len(rec.tool_calls) == 3
+        names = [tc.function.name for tc in rec.tool_calls]
+        assert names == ["get_weather", "get_news", "send_email"]
+        ids = [tc.id for tc in rec.tool_calls]
+        assert len(set(ids)) == 3  # unique ids
+
+    def test_truncated_tool_call_no_end_marker(self, parser):
+        """Stream ending mid-tool-call (max_tokens) doesn't crash."""
+        deltas = [
+            "I'll check. ",
+            SECTION_BEGIN,
+            TOOL_BEGIN,
+            "functions.get_weather:0 ",
+            ARG_BEGIN,
+            '{"city": "Bei',
+            # Stream ends here — no TOOL_END, no SECTION_END
+        ]
+        rec = run_tool_extraction_streaming(parser, deltas)
+
+        # Should not crash; tool name and partial args extracted
+        assert len(rec.tool_calls) == 1
+        assert rec.tool_calls[0].function.name == "get_weather"
+        assert rec.tool_calls[0].id == "functions.get_weather:0"
+        assert rec.tool_calls[0].function.arguments == '{"city": "Bei'
+        # No markers leaked into content
+        for marker in [SECTION_BEGIN, SECTION_END, TOOL_BEGIN, TOOL_END, ARG_BEGIN]:
+            assert marker not in rec.other_content
+
+    def test_content_after_tool_section(self, parser):
+        """Trailing text after section_end doesn't crash or leak markers."""
+        deltas = [
+            "Before. ",
+            SECTION_BEGIN,
+            TOOL_BEGIN,
+            "functions.get_weather:0 ",
+            ARG_BEGIN,
+            '{"city": "Tokyo"} ',
+            TOOL_END,
+            SECTION_END,
+            " After tools.",
+        ]
+        rec = run_tool_extraction_streaming(parser, deltas)
+
+        # Tool call extracted correctly
+        assert len(rec.tool_calls) == 1
+        assert rec.tool_calls[0].function.name == "get_weather"
+        assert json.loads(rec.tool_calls[0].function.arguments) == {"city": "Tokyo"}
+        # Trailing content after tool section is dropped
+        assert "After tools." not in rec.other_content
+        # No markers leaked into content
+        for marker in [SECTION_BEGIN, SECTION_END, TOOL_BEGIN, TOOL_END, ARG_BEGIN]:
+            assert marker not in rec.other_content
+
+
+class TestAdjustRequest:
+    def test_sets_skip_special_tokens_false(self, parser):
+        request = MagicMock(spec=ChatCompletionRequest)
+        request.tools = [{"type": "function", "function": {"name": "test"}}]
+        request.tool_choice = "auto"
+        request.skip_special_tokens = True
+
+        result = parser.adjust_request(request)
+        assert result.skip_special_tokens is False
+
+    def test_no_change_when_tool_choice_none(self, parser):
+        request = MagicMock(spec=ChatCompletionRequest)
+        request.tools = [{"type": "function", "function": {"name": "test"}}]
+        request.tool_choice = "none"
+        request.skip_special_tokens = True
+
+        result = parser.adjust_request(request)
+        assert result.skip_special_tokens is True
+
+    def test_no_change_when_no_tools(self, parser):
+        request = MagicMock(spec=ChatCompletionRequest)
+        request.tools = None
+        request.tool_choice = "auto"
+        request.skip_special_tokens = True
+
+        result = parser.adjust_request(request)
+        assert result.skip_special_tokens is True
+
+
+def _chunk_tokenized_deltas(tokenizer, text: str, stream_interval: int) -> list[str]:
+    """Encode text, group tokens into chunks of stream_interval, decode each."""
+    token_ids = tokenizer.encode(text, add_special_tokens=False)
+    deltas = []
+    prev = ""
+    for i in range(0, len(token_ids), stream_interval):
+        decoded = tokenizer.decode(
+            token_ids[: i + stream_interval], skip_special_tokens=False
+        )
+        deltas.append(decoded[len(prev) :])
+        prev = decoded
+    return deltas
 
-def test_streaming_multiple_tool_calls_not_leaked(kimi_k2_tool_parser):
-    """
-    Test that MULTIPLE tool calls in streaming mode do not leak into content.
-    This reproduces the AWS Bedrock scenario: "Compare weather in Tokyo and NYC".
-    """
-    kimi_k2_tool_parser.reset_streaming_state()
 
-    section_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_begin|>")
-    section_end_id = kimi_k2_tool_parser.vocab.get("<|tool_calls_section_end|>")
-    tool_begin_id = kimi_k2_tool_parser.vocab.get("<|tool_call_begin|>")
-    tool_end_id = kimi_k2_tool_parser.vocab.get("<|tool_call_end|>")
+class TestStreamingIntervals:
+    """Test streaming at various token-chunk sizes to catch boundary bugs."""
 
-    all_content = []
+    @pytest.mark.parametrize("stream_interval", [1, 2, 3, 5, 8])
+    def test_single_tool_call_at_interval(self, kimi_k2_tokenizer, stream_interval):
+        text = "Help. " + _wrap(_tool("functions.get_weather:0", '{"city": "Beijing"}'))
+        deltas = _chunk_tokenized_deltas(kimi_k2_tokenizer, text, stream_interval)
+        parser = KimiK2ToolParser(kimi_k2_tokenizer)
+        rec = run_tool_extraction_streaming(
+            parser, deltas, assert_one_tool_per_delta=False
+        )
 
-    tool1 = '<|tool_call_begin|> get_weather:0 <|tool_call_argument_begin|> {"city": "Tokyo"} <|tool_call_end|>'
-    tool2 = ' <|tool_call_begin|> get_weather:1 <|tool_call_argument_begin|> {"city": "New York"} <|tool_call_end|>'
+        assert len(rec.tool_calls) == 1
+        assert rec.tool_calls[0].function.name == "get_weather"
+        assert json.loads(rec.tool_calls[0].function.arguments) == {"city": "Beijing"}
 
-    deltas = [
-        ("I'll compare the weather. ", [1, 2, 3]),
-        ("<|tool_calls_section_begin|>", [section_begin_id]),
-        (tool1, [tool_begin_id, 10, tool_end_id]),
-        (tool2, [tool_begin_id, 20, tool_end_id]),
-        ("<|tool_calls_section_end|>", [section_end_id]),
-        (" Here's the comparison.", [30]),
-    ]
+    @pytest.mark.parametrize("stream_interval", [1, 2, 3, 5, 8])
+    def test_content_then_tool_call_at_interval(
+        self, kimi_k2_tokenizer, stream_interval
+    ):
+        text = "Sure, let me check. " + _wrap(
+            _tool("functions.get_weather:0", '{"city": "Tokyo"}')
+        )
+        deltas = _chunk_tokenized_deltas(kimi_k2_tokenizer, text, stream_interval)
+        parser = KimiK2ToolParser(kimi_k2_tokenizer)
+        rec = run_tool_extraction_streaming(
+            parser, deltas, assert_one_tool_per_delta=False
+        )
 
-    results = run_streaming_sequence(kimi_k2_tool_parser, deltas)
+        assert "let me check" in rec.other_content
+        assert "get_weather" not in rec.other_content
+        assert len(rec.tool_calls) == 1
+        assert rec.tool_calls[0].function.name == "get_weather"
+        assert json.loads(rec.tool_calls[0].function.arguments) == {"city": "Tokyo"}
+
+    @pytest.mark.parametrize("stream_interval", [1, 2, 3, 5, 8])
+    def test_multiple_tool_calls_at_interval(self, kimi_k2_tokenizer, stream_interval):
+        text = "Compare. " + _wrap(
+            _tool("functions.search:0", '{"q": "cats"}'),
+            _tool("functions.search:1", '{"q": "dogs"}'),
+        )
+        deltas = _chunk_tokenized_deltas(kimi_k2_tokenizer, text, stream_interval)
+        parser = KimiK2ToolParser(kimi_k2_tokenizer)
+        rec = run_tool_extraction_streaming(
+            parser, deltas, assert_one_tool_per_delta=False
+        )
 
-    for res in results:
-        if res and res.content:
-            all_content.append(res.content)
+        assert len(rec.tool_calls) == 2
+        assert rec.tool_calls[0].function.name == "search"
+        assert json.loads(rec.tool_calls[0].function.arguments) == {"q": "cats"}
+        assert rec.tool_calls[1].function.name == "search"
+        assert json.loads(rec.tool_calls[1].function.arguments) == {"q": "dogs"}
+
+    @pytest.mark.parametrize("stream_interval", [1, 2, 3, 5, 8])
+    def test_plain_text_at_interval(self, kimi_k2_tokenizer, stream_interval):
+        text = "This is plain text with no tool calling involved."
+        deltas = _chunk_tokenized_deltas(kimi_k2_tokenizer, text, stream_interval)
+        parser = KimiK2ToolParser(kimi_k2_tokenizer)
+        rec = run_tool_extraction_streaming(
+            parser, deltas, assert_one_tool_per_delta=False
+        )
 
-    # Assertions
-    full_content = "".join(all_content)
+        assert rec.other_content == text
+        assert rec.tool_calls == []
 
-    # Check no markers leaked
-    forbidden = ["<|tool_call", "<|tool_calls_section"]
-    for marker in forbidden:
-        assert marker not in full_content, (
-            f"MARKER LEAKED: {marker} in {repr(full_content)}"
+    def test_content_and_tool_call_in_single_chunk(self, kimi_k2_tokenizer):
+        """Content + complete tool call in one chunk must both be emitted."""
+        text = "Hi! " + _wrap(_tool("functions.get_weather:0", '{"city": "Beijing"}'))
+        deltas = _chunk_tokenized_deltas(kimi_k2_tokenizer, text, stream_interval=9999)
+        parser = KimiK2ToolParser(kimi_k2_tokenizer)
+        rec = run_tool_extraction_streaming(
+            parser, deltas, assert_one_tool_per_delta=False
         )
 
-    # Check no tool call content leaked (both tools)
-    assert "get_weather" not in full_content, f"TOOL NAME LEAKED: {repr(full_content)}"
-    assert "Tokyo" not in full_content, f"TOOL ARG LEAKED (Tokyo): {repr(full_content)}"
-    assert "New York" not in full_content, (
-        f"TOOL ARG LEAKED (NYC): {repr(full_content)}"
-    )
-
-    # Legitimate content preserved
-    assert "compare" in full_content.lower() or len(all_content) > 0
+        assert "Hi!" in rec.other_content
+        assert "get_weather" not in rec.other_content
+        assert len(rec.tool_calls) == 1
+        assert rec.tool_calls[0].function.name == "get_weather"
+        assert json.loads(rec.tool_calls[0].function.arguments) == {"city": "Beijing"}
diff --git a/tests/tool_parsers/test_lfm2_tool_parser.py b/tests/tool_parsers/test_lfm2_tool_parser.py
new file mode 100644
index 000000000000..9cb5b195f1a7
--- /dev/null
+++ b/tests/tool_parsers/test_lfm2_tool_parser.py
@@ -0,0 +1,468 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+from transformers import AutoTokenizer
+
+from tests.tool_parsers.utils import (
+    run_tool_extraction,
+    run_tool_extraction_streaming,
+)
+from vllm.entrypoints.openai.engine.protocol import FunctionCall
+from vllm.tokenizers import TokenizerLike
+from vllm.tool_parsers import ToolParser, ToolParserManager
+
+TOOL_CALL_START = "<|tool_call_start|>"
+TOOL_CALL_END = "<|tool_call_end|>"
+
+SIMPLE_FUNCTION_OUTPUT = "get_candidate_status(candidate_id='12345')"
+SIMPLE_FUNCTION_CALL = FunctionCall(
+    name="get_candidate_status",
+    arguments='{"candidate_id": "12345"}',
+)
+MORE_TYPES_FUNCTION_OUTPUT = (
+    "register_user(name='John Doe', "
+    "age=37, "
+    "address={'city': 'San Francisco', 'state': 'CA'}, "
+    "role=None, "
+    "passed_test=True, "
+    "aliases=['John', 'Johnny'])"
+)
+MORE_TYPES_FUNCTION_CALL = FunctionCall(
+    name="register_user",
+    arguments='{"name": "John Doe", '
+    '"age": 37, '
+    '"address": {"city": "San Francisco", "state": "CA"}, '
+    '"role": null, '
+    '"passed_test": true, '
+    '"aliases": ["John", "Johnny"]}',
+)
+PARAMETERLESS_FUNCTION_OUTPUT = "get_weather()"
+PARAMETERLESS_FUNCTION_CALL = FunctionCall(
+    name="get_weather",
+    arguments="{}",
+)
+EMPTY_DICT_FUNCTION_OUTPUT = "do_something_cool(additional_data={})"
+EMPTY_DICT_FUNCTION_CALL = FunctionCall(
+    name="do_something_cool",
+    arguments='{"additional_data": {}}',
+)
+EMPTY_LIST_FUNCTION_OUTPUT = "do_something_cool(steps=[])"
+EMPTY_LIST_FUNCTION_CALL = FunctionCall(
+    name="do_something_cool",
+    arguments='{"steps": []}',
+)
+ESCAPED_STRING_FUNCTION_OUTPUT = (
+    r"get_weather(city='Martha\'s Vineyard', metric='\"cool units\"')"
+)
+ESCAPED_STRING_FUNCTION_CALL = FunctionCall(
+    name="get_weather",
+    arguments='{"city": "Martha\'s Vineyard", "metric": "\\"cool units\\""}',
+)
+DOTTED_NAME_FUNCTION_OUTPUT = (
+    "grocery.orderIngredients("
+    "ingredientList=[{'name': 'Lasagna noodles', 'amount': 250, 'unit': 'g'}], "
+    "deliveryAddress='845 Willow Lane, Springfield, IL 62704')"
+)
+DOTTED_NAME_FUNCTION_CALL = FunctionCall(
+    name="grocery.orderIngredients",
+    arguments=(
+        '{"ingredientList": ['
+        '{"name": "Lasagna noodles", "amount": 250, "unit": "g"}], '
+        '"deliveryAddress": "845 Willow Lane, Springfield, IL 62704"}'
+    ),
+)
+
+
+@pytest.fixture(scope="module")
+def lfm2_tokenizer() -> TokenizerLike:
+    return AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-1.2B-Instruct")
+
+
+def _wrap(tool_text: str, content_after: str = "") -> str:
+    """Wrap pythonic tool call in LFM2.5 sentinel tokens."""
+    result = f"{TOOL_CALL_START}[{tool_text}]{TOOL_CALL_END}"
+    if content_after:
+        result += f"\n{content_after}"
+    return result
+
+
+@pytest.mark.parametrize("streaming", [True, False])
+def test_no_tool_call(streaming: bool, lfm2_tokenizer: TokenizerLike):
+    tool_parser: ToolParser = ToolParserManager.get_tool_parser("lfm2")(lfm2_tokenizer)
+    model_output = "How can I help you today?"
+
+    content, tool_calls = run_tool_extraction(
+        tool_parser, model_output, streaming=streaming
+    )
+
+    assert content == model_output
+    assert len(tool_calls) == 0
+
+
+TEST_CASES = [
+    pytest.param(
+        True,
+        _wrap(SIMPLE_FUNCTION_OUTPUT),
+        [SIMPLE_FUNCTION_CALL],
+        None,
+        id="simple_streaming",
+    ),
+    pytest.param(
+        False,
+        _wrap(SIMPLE_FUNCTION_OUTPUT),
+        [SIMPLE_FUNCTION_CALL],
+        None,
+        id="simple_nonstreaming",
+    ),
+    pytest.param(
+        True,
+        _wrap(MORE_TYPES_FUNCTION_OUTPUT),
+        [MORE_TYPES_FUNCTION_CALL],
+        None,
+        id="more_types_streaming",
+    ),
+    pytest.param(
+        False,
+        _wrap(MORE_TYPES_FUNCTION_OUTPUT),
+        [MORE_TYPES_FUNCTION_CALL],
+        None,
+        id="more_types_nonstreaming",
+    ),
+    pytest.param(
+        True,
+        _wrap(PARAMETERLESS_FUNCTION_OUTPUT),
+        [PARAMETERLESS_FUNCTION_CALL],
+        None,
+        id="parameterless_streaming",
+    ),
+    pytest.param(
+        False,
+        _wrap(PARAMETERLESS_FUNCTION_OUTPUT),
+        [PARAMETERLESS_FUNCTION_CALL],
+        None,
+        id="parameterless_nonstreaming",
+    ),
+    pytest.param(
+        True,
+        _wrap(EMPTY_DICT_FUNCTION_OUTPUT),
+        [EMPTY_DICT_FUNCTION_CALL],
+        None,
+        id="empty_dict_streaming",
+    ),
+    pytest.param(
+        False,
+        _wrap(EMPTY_DICT_FUNCTION_OUTPUT),
+        [EMPTY_DICT_FUNCTION_CALL],
+        None,
+        id="empty_dict_nonstreaming",
+    ),
+    pytest.param(
+        True,
+        _wrap(EMPTY_LIST_FUNCTION_OUTPUT),
+        [EMPTY_LIST_FUNCTION_CALL],
+        None,
+        id="empty_list_streaming",
+    ),
+    pytest.param(
+        False,
+        _wrap(EMPTY_LIST_FUNCTION_OUTPUT),
+        [EMPTY_LIST_FUNCTION_CALL],
+        None,
+        id="empty_list_nonstreaming",
+    ),
+    pytest.param(
+        True,
+        _wrap(ESCAPED_STRING_FUNCTION_OUTPUT),
+        [ESCAPED_STRING_FUNCTION_CALL],
+        None,
+        id="escaped_string_streaming",
+    ),
+    pytest.param(
+        False,
+        _wrap(ESCAPED_STRING_FUNCTION_OUTPUT),
+        [ESCAPED_STRING_FUNCTION_CALL],
+        None,
+        id="escaped_string_nonstreaming",
+    ),
+    pytest.param(
+        True,
+        _wrap(f"{SIMPLE_FUNCTION_OUTPUT}, {MORE_TYPES_FUNCTION_OUTPUT}"),
+        [SIMPLE_FUNCTION_CALL, MORE_TYPES_FUNCTION_CALL],
+        None,
+        id="parallel_calls_streaming",
+    ),
+    pytest.param(
+        False,
+        _wrap(f"{SIMPLE_FUNCTION_OUTPUT}, {MORE_TYPES_FUNCTION_OUTPUT}"),
+        [SIMPLE_FUNCTION_CALL, MORE_TYPES_FUNCTION_CALL],
+        None,
+        id="parallel_calls_nonstreaming",
+    ),
+    # LFM2.5 specific: content AFTER tool call
+    pytest.param(
+        False,
+        _wrap(
+            SIMPLE_FUNCTION_OUTPUT,
+            content_after="Checking the current status of candidate ID 12345.",
+        ),
+        [SIMPLE_FUNCTION_CALL],
+        "Checking the current status of candidate ID 12345.",
+        id="content_after_tool_call_nonstreaming",
+    ),
+    # Dotted / class-method function names: grocery.orderIngredients(...)
+    pytest.param(
+        True,
+        _wrap(DOTTED_NAME_FUNCTION_OUTPUT),
+        [DOTTED_NAME_FUNCTION_CALL],
+        None,
+        id="dotted_name_streaming",
+    ),
+    pytest.param(
+        False,
+        _wrap(DOTTED_NAME_FUNCTION_OUTPUT),
+        [DOTTED_NAME_FUNCTION_CALL],
+        None,
+        id="dotted_name_nonstreaming",
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    "streaming, model_output, expected_tool_calls, expected_content",
+    TEST_CASES,
+)
+def test_tool_call(
+    streaming: bool,
+    model_output: str,
+    expected_tool_calls: list[FunctionCall],
+    expected_content: str | None,
+    lfm2_tokenizer: TokenizerLike,
+):
+    tool_parser: ToolParser = ToolParserManager.get_tool_parser("lfm2")(lfm2_tokenizer)
+
+    content, tool_calls = run_tool_extraction(
+        tool_parser, model_output, streaming=streaming
+    )
+
+    if expected_content and not streaming:
+        assert content == expected_content
+    assert len(tool_calls) == len(expected_tool_calls)
+    for actual, expected in zip(tool_calls, expected_tool_calls):
+        assert actual.type == "function"
+        assert actual.function == expected
+
+
+def test_streaming_tool_call_with_large_steps(lfm2_tokenizer: TokenizerLike):
+    tool_parser: ToolParser = ToolParserManager.get_tool_parser("lfm2")(lfm2_tokenizer)
+    model_output_deltas = [
+        f"{TOOL_CALL_START}[get_candidate_status(candidate_id='12345'), "
+        f"{PARAMETERLESS_FUNCTION_OUTPUT}, "
+        f"{EMPTY_LIST_FUNCTION_OUTPUT}]{TOOL_CALL_END}",
+    ]
+
+    reconstructor = run_tool_extraction_streaming(
+        tool_parser, model_output_deltas, assert_one_tool_per_delta=False
+    )
+
+    assert len(reconstructor.tool_calls) == 3
+    assert reconstructor.tool_calls[0].function == SIMPLE_FUNCTION_CALL
+    assert reconstructor.tool_calls[1].function == PARAMETERLESS_FUNCTION_CALL
+    assert reconstructor.tool_calls[2].function == EMPTY_LIST_FUNCTION_CALL
+
+
+def test_streaming_full_block_and_trailing_in_single_delta(
+    lfm2_tokenizer: TokenizerLike,
+):
+    """The entire <|tool_call_start|>[...]<|tool_call_end|> block plus
+    trailing assistant text arrive in one delta. Trailing content must
+    still be emitted — not silently dropped."""
+    tool_parser: ToolParser = ToolParserManager.get_tool_parser("lfm2")(lfm2_tokenizer)
+    full_text = f"{TOOL_CALL_START}[{SIMPLE_FUNCTION_OUTPUT}]{TOOL_CALL_END}\nDone."
+
+    reconstructor = run_tool_extraction_streaming(tool_parser, [full_text])
+
+    assert len(reconstructor.tool_calls) == 1
+    assert reconstructor.tool_calls[0].function == SIMPLE_FUNCTION_CALL
+    assert "Done." in reconstructor.other_content
+
+
+def test_streaming_leading_content_and_full_block_in_single_delta(
+    lfm2_tokenizer: TokenizerLike,
+):
+    """Leading assistant text plus the entire tool block arrive in one
+    delta. Leading content must be emitted — not silently dropped."""
+    tool_parser: ToolParser = ToolParserManager.get_tool_parser("lfm2")(lfm2_tokenizer)
+    full_text = (
+        f"Let me check. {TOOL_CALL_START}[{SIMPLE_FUNCTION_OUTPUT}]{TOOL_CALL_END}"
+    )
+
+    reconstructor = run_tool_extraction_streaming(tool_parser, [full_text])
+
+    assert len(reconstructor.tool_calls) == 1
+    assert reconstructor.tool_calls[0].function == SIMPLE_FUNCTION_CALL
+    assert "Let me check." in reconstructor.other_content
+
+
+def test_streaming_leading_block_and_trailing_in_single_delta(
+    lfm2_tokenizer: TokenizerLike,
+):
+    """Leading text + complete tool block + trailing text in one delta.
+    Both leading and trailing content must be preserved."""
+    tool_parser: ToolParser = ToolParserManager.get_tool_parser("lfm2")(lfm2_tokenizer)
+    full_text = (
+        "Let me check. "
+        f"{TOOL_CALL_START}[{SIMPLE_FUNCTION_OUTPUT}]{TOOL_CALL_END}\nDone."
+    )
+
+    reconstructor = run_tool_extraction_streaming(tool_parser, [full_text])
+
+    assert len(reconstructor.tool_calls) == 1
+    assert reconstructor.tool_calls[0].function == SIMPLE_FUNCTION_CALL
+    assert "Let me check." in reconstructor.other_content
+    assert "Done." in reconstructor.other_content
+
+
+def test_echoed_tool_call_body_not_leaked_to_content(
+    lfm2_tokenizer: TokenizerLike,
+):
+    """LFM2 sometimes emits the tool call body again after the first
+    <|tool_call_end|>, capped with a second <|tool_call_end|>. The
+    echoed body must not surface as assistant content — neither in
+    streaming nor non-streaming paths."""
+    tool_parser: ToolParser = ToolParserManager.get_tool_parser("lfm2")(lfm2_tokenizer)
+    body = (
+        "[grocery.orderIngredients("
+        "ingredientList=[{'name': 'apple', 'quantity': '2'}], "
+        "deliveryAddress='123 Main St')]"
+    )
+    model_output = f"{TOOL_CALL_START}{body}{TOOL_CALL_END}{body}{TOOL_CALL_END}"
+
+    # Non-streaming
+    content_ns, tool_calls_ns = run_tool_extraction(
+        tool_parser, model_output, streaming=False
+    )
+    assert len(tool_calls_ns) == 1
+    assert tool_calls_ns[0].function.name == "grocery.orderIngredients"
+    assert content_ns in (None, "")
+
+    # Streaming: re-fetch a fresh parser since state was mutated above.
+    tool_parser2: ToolParser = ToolParserManager.get_tool_parser("lfm2")(lfm2_tokenizer)
+    content_s, tool_calls_s = run_tool_extraction(
+        tool_parser2, model_output, streaming=True
+    )
+    assert len(tool_calls_s) == 1
+    assert tool_calls_s[0].function.name == "grocery.orderIngredients"
+    # Echoed body must not leak as content.
+    assert content_s in (None, "")
+    assert "grocery.orderIngredients" not in (content_s or "")
+    assert TOOL_CALL_END not in (content_s or "")
+
+
+def test_streaming_char_by_char_multi_dict_list(lfm2_tokenizer: TokenizerLike):
+    """Stream a tool call containing a list of multiple dicts one
+    character at a time. Every prefix lands in some partial-parse state
+    (mid-key, mid-value, open quote inside dict, empty dict, etc.). The
+    parser must not raise — incomplete prefixes should silently wait for
+    more text instead of logging exceptions."""
+    tool_parser: ToolParser = ToolParserManager.get_tool_parser("lfm2")(lfm2_tokenizer)
+    full_text = (
+        f"{TOOL_CALL_START}[grocery.orderIngredients("
+        "ingredientList=["
+        '{"name": "apple", "quantity": "2"}, '
+        '{"name": "bread", "quantity": "1"}'
+        f"])]{TOOL_CALL_END}"
+    )
+    deltas = [c for c in full_text]
+
+    reconstructor = run_tool_extraction_streaming(
+        tool_parser, deltas, assert_one_tool_per_delta=False
+    )
+
+    assert len(reconstructor.tool_calls) == 1
+    assert reconstructor.tool_calls[0].function.name == "grocery.orderIngredients"
+    import json
+
+    args = json.loads(reconstructor.tool_calls[0].function.arguments)
+    assert args == {
+        "ingredientList": [
+            {"name": "apple", "quantity": "2"},
+            {"name": "bread", "quantity": "1"},
+        ]
+    }
+
+
+def test_streaming_dotted_name_in_single_delta(lfm2_tokenizer: TokenizerLike):
+    """A pythonic call with a dotted/attribute function name (e.g.
+    ``domain.method(arg=...)``) must be parsed correctly in streaming mode
+    just as in non-streaming mode."""
+    tool_parser: ToolParser = ToolParserManager.get_tool_parser("lfm2")(lfm2_tokenizer)
+    full_text = f"{TOOL_CALL_START}[{DOTTED_NAME_FUNCTION_OUTPUT}]{TOOL_CALL_END}"
+
+    reconstructor = run_tool_extraction_streaming(tool_parser, [full_text])
+
+    assert len(reconstructor.tool_calls) == 1
+    assert reconstructor.tool_calls[0].function == DOTTED_NAME_FUNCTION_CALL
+
+
+def test_adjust_request_disables_skip_special_tokens(
+    lfm2_tokenizer: TokenizerLike,
+):
+    """When tools are present, the parser must force
+    ``skip_special_tokens=False`` so the engine does not strip the
+    <|tool_call_start|>/<|tool_call_end|> sentinels before they reach the
+    parser."""
+    from vllm.entrypoints.openai.chat_completion.protocol import (
+        ChatCompletionRequest,
+    )
+
+    tool_parser: ToolParser = ToolParserManager.get_tool_parser("lfm2")(lfm2_tokenizer)
+
+    request_with_tools = ChatCompletionRequest(
+        messages=[],
+        model="test-model",
+        tools=[
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {"city": {"type": "string"}},
+                    },
+                },
+            }
+        ],
+    )
+    assert request_with_tools.skip_special_tokens is True
+    adjusted = tool_parser.adjust_request(request_with_tools)
+    assert adjusted.skip_special_tokens is False
+
+    # No tools → no override; default behaviour preserved.
+    request_no_tools = ChatCompletionRequest(messages=[], model="test-model")
+    assert request_no_tools.skip_special_tokens is True
+    adjusted_no_tools = tool_parser.adjust_request(request_no_tools)
+    assert adjusted_no_tools.skip_special_tokens is True
+
+
+@pytest.mark.parametrize("streaming", [False])
+def test_regex_timeout_handling(streaming: bool, lfm2_tokenizer: TokenizerLike):
+    """Test regex timeout is handled gracefully."""
+    tool_parser: ToolParser = ToolParserManager.get_tool_parser("lfm2")(lfm2_tokenizer)
+
+    fake_input = f"{TOOL_CALL_START}[A(A=" + "\t)A(A=,\t" * 2
+    fake_input += f"]{TOOL_CALL_END}"
+
+    mock_regex = MagicMock()
+    mock_regex.match.side_effect = TimeoutError("Regex timeout")
+
+    with patch.object(tool_parser, "TOOL_CALL_REGEX", mock_regex):
+        content, tool_calls = run_tool_extraction(
+            tool_parser, fake_input, streaming=streaming
+        )
+
+        assert content == fake_input
+        assert len(tool_calls) == 0
+        mock_regex.match.assert_called_once()
diff --git a/tests/tool_parsers/test_llama3_json_tool_parser.py b/tests/tool_parsers/test_llama3_json_tool_parser.py
index 53948d577c15..7040fe87d07a 100644
--- a/tests/tool_parsers/test_llama3_json_tool_parser.py
+++ b/tests/tool_parsers/test_llama3_json_tool_parser.py
@@ -4,15 +4,22 @@
 from unittest.mock import MagicMock, patch
 
 import pytest
+from transformers import AutoTokenizer
 
 from vllm.entrypoints.openai.engine.protocol import ExtractedToolCallInformation
-from vllm.tokenizers import TokenizerLike
 from vllm.tool_parsers.llama_tool_parser import Llama3JsonToolParser
 
+LLAMA_MODEL = "meta-llama/Llama-3.2-1B-Instruct"
+
+
+@pytest.fixture(scope="module")
+def llama_tokenizer():
+    return AutoTokenizer.from_pretrained(LLAMA_MODEL)
+
 
 @pytest.fixture
-def parser(default_tokenizer: TokenizerLike):
-    return Llama3JsonToolParser(default_tokenizer)
+def parser(llama_tokenizer):
+    return Llama3JsonToolParser(llama_tokenizer)
 
 
 def test_extract_tool_calls_simple(parser):
diff --git a/tests/tool_parsers/test_mistral_tool_parser.py b/tests/tool_parsers/test_mistral_tool_parser.py
index 064ccb39ef4b..f6a5c6bfb265 100644
--- a/tests/tool_parsers/test_mistral_tool_parser.py
+++ b/tests/tool_parsers/test_mistral_tool_parser.py
@@ -3,6 +3,7 @@
 
 import json
 from collections.abc import Generator
+from typing import Any
 from unittest.mock import MagicMock, patch
 
 import partial_json_parser
@@ -28,19 +29,27 @@
     ChatCompletionRequest,
 )
 from vllm.entrypoints.openai.engine.protocol import (
+    DeltaFunctionCall,
     DeltaMessage,
     DeltaToolCall,
+    ExtractedToolCallInformation,
     StructuralTagResponseFormat,
 )
+from vllm.entrypoints.openai.engine.protocol import FunctionCall as VllmFunctionCall
+from vllm.reasoning.mistral_reasoning_parser import MistralReasoningParser
 from vllm.sampling_params import StructuredOutputsParams
 from vllm.tokenizers import TokenizerLike, get_tokenizer
 from vllm.tokenizers.detokenizer_utils import detokenize_incrementally
 from vllm.tokenizers.mistral import MistralTokenizer
 from vllm.tool_parsers.mistral_tool_parser import (
     _DEFAULT_JSON_SCHEMA,
+    MistralStreamingResult,
+    MistralToolCall,
     MistralToolParser,
 )
 
+_DUMMY_REQUEST = ChatCompletionRequest(messages=[], model="test")
+
 
 @pytest.fixture(scope="module")
 def mistral_pre_v11_tokenizer():
@@ -205,7 +214,7 @@ def stream_delta_message_generator(
             previous_token_ids,
             current_token_ids,
             delta_token_ids,
-            request=None,  # type: ignore[arg-type]
+            request=_DUMMY_REQUEST,
         )
         if delta_message:
             yield delta_message
@@ -218,14 +227,18 @@ def stream_delta_message_generator(
         read_offset = new_read_offset
 
 
-def test_extract_tool_calls_no_tools(mistral_pre_v11_tool_parser):
+@pytest.mark.parametrize(
+    "parser_fixture",
+    ["mistral_pre_v11_tool_parser", "mistral_tool_parser"],
+    ids=["pre_v11", "v11"],
+)
+def test_extract_tool_calls_no_tools(parser_fixture, request):
+    parser = request.getfixturevalue(parser_fixture)
     model_output = "This is a test"
-    extracted_tool_calls = mistral_pre_v11_tool_parser.extract_tool_calls(
-        model_output, request=None
-    )  # type: ignore[arg-type]
-    assert not extracted_tool_calls.tools_called
-    assert extracted_tool_calls.tool_calls == []
-    assert extracted_tool_calls.content == model_output
+    result = parser.extract_tool_calls(model_output, request=_DUMMY_REQUEST)
+    assert result == ExtractedToolCallInformation(
+        tools_called=False, tool_calls=[], content=model_output
+    )
 
 
 @pytest.mark.parametrize(
@@ -234,6 +247,9 @@ def test_extract_tool_calls_no_tools(mistral_pre_v11_tool_parser):
         "single_tool_weather",
         "argument_before_name",
         "argument_before_name_and_name_in_argument",
+        "multiple_tools",
+        "content_before_tool",
+        "trailing_data_after_json",
     ],
     argnames=["model_output", "expected_tool_calls", "expected_content"],
     argvalues=[
@@ -292,14 +308,62 @@ def test_extract_tool_calls_no_tools(mistral_pre_v11_tool_parser):
             ],
             None,
         ),
+        (
+            """[TOOL_CALLS] [{"name": "add", "arguments": {"a": 3.5, "b": 4}}, {"name": "get_current_weather", "arguments":{"city": "San Francisco", "state": "CA", "unit": "celsius"}}]""",  # noqa: E501
+            [
+                ToolCall(
+                    function=FunctionCall(
+                        name="add", arguments=json.dumps({"a": 3.5, "b": 4})
+                    )
+                ),
+                ToolCall(
+                    function=FunctionCall(
+                        name="get_current_weather",
+                        arguments=json.dumps(
+                            {"city": "San Francisco", "state": "CA", "unit": "celsius"}
+                        ),
+                    )
+                ),
+            ],
+            None,
+        ),
+        (
+            """Hello[TOOL_CALLS] [{"name": "add", "arguments":{"a": 1, "b": 2}}]""",  # noqa: E501
+            [
+                ToolCall(
+                    function=FunctionCall(
+                        name="add", arguments=json.dumps({"a": 1, "b": 2})
+                    )
+                )
+            ],
+            "Hello",
+        ),
+        (
+            """[TOOL_CALLS] [{"name": "get_current_weather", "arguments":{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}]\nextra trailing data""",  # noqa: E501
+            [
+                ToolCall(
+                    function=FunctionCall(
+                        name="get_current_weather",
+                        arguments=json.dumps(
+                            {
+                                "city": "Dallas",
+                                "state": "TX",
+                                "unit": "fahrenheit",
+                            }
+                        ),
+                    )
+                )
+            ],
+            None,
+        ),
     ],
 )
 def test_extract_tool_calls_pre_v11_tokenizer(
     mistral_pre_v11_tool_parser, model_output, expected_tool_calls, expected_content
 ):
     extracted_tool_calls = mistral_pre_v11_tool_parser.extract_tool_calls(
-        model_output, request=None
-    )  # type: ignore[arg-type]
+        model_output, request=_DUMMY_REQUEST
+    )
     assert extracted_tool_calls.tools_called
 
     assert_tool_calls(extracted_tool_calls.tool_calls, expected_tool_calls)
@@ -307,6 +371,49 @@ def test_extract_tool_calls_pre_v11_tokenizer(
     assert extracted_tool_calls.content == expected_content
 
 
+def test_extract_tool_calls_pre_v11_multiple_bot_tokens_raises(
+    mistral_pre_v11_tool_parser,
+):
+    model_output = (
+        '[TOOL_CALLS] [{"name": "add", "arguments":{"a": 1}}]'
+        '[TOOL_CALLS] [{"name": "sub", "arguments":{"b": 2}}]'
+    )
+    with pytest.raises(ValueError, match="Only one BOT token"):
+        mistral_pre_v11_tool_parser.extract_tool_calls(
+            model_output, request=_DUMMY_REQUEST
+        )
+
+
+def test_extract_tool_calls_pre_v11_regex_fallback(
+    mistral_pre_v11_tool_parser,
+):
+    """The regex fallback path finds valid JSON via regex when the primary
+    raw_decode fails on leading junk. It should re-serialize arguments
+    and return a valid tool call."""
+    model_output = (
+        '[TOOL_CALLS]  junk [{"name": "add", "arguments":{"a": 1, "b": 2}}] trail'
+    )
+    result = mistral_pre_v11_tool_parser.extract_tool_calls(
+        model_output, request=_DUMMY_REQUEST
+    )
+    assert result.tools_called
+    assert len(result.tool_calls) == 1
+    assert result.tool_calls[0].function.name == "add"
+    assert result.tool_calls[0].function.arguments == json.dumps({"a": 1, "b": 2})
+
+
+def test_extract_tool_calls_pre_v11_regex_fallback_fails(
+    mistral_pre_v11_tool_parser,
+):
+    model_output = "[TOOL_CALLS] not json at all"
+    result = mistral_pre_v11_tool_parser.extract_tool_calls(
+        model_output, request=_DUMMY_REQUEST
+    )
+    assert result == ExtractedToolCallInformation(
+        tools_called=False, tool_calls=[], content="not json at all"
+    )
+
+
 @pytest.mark.parametrize(
     ids=[
         "single_tool_add",
@@ -395,8 +502,8 @@ def test_extract_tool_calls(
     mistral_tool_parser, model_output, expected_tool_calls, expected_content
 ):
     extracted_tool_calls = mistral_tool_parser.extract_tool_calls(
-        model_output, request=None
-    )  # type: ignore[arg-type]
+        model_output, request=_DUMMY_REQUEST
+    )
     assert extracted_tool_calls.tools_called
 
     assert_tool_calls(extracted_tool_calls.tool_calls, expected_tool_calls)
@@ -404,6 +511,16 @@ def test_extract_tool_calls(
     assert extracted_tool_calls.content == expected_content
 
 
+def test_extract_tool_calls_v11_without_args_skipped(mistral_tool_parser):
+    model_output = "[TOOL_CALLS]toolname_no_args"
+    result = mistral_tool_parser.extract_tool_calls(
+        model_output, request=_DUMMY_REQUEST
+    )
+    assert result == ExtractedToolCallInformation(
+        tools_called=True, tool_calls=[], content=None
+    )
+
+
 def _test_extract_tool_calls_streaming(
     tool_parser, tokenizer, model_output, tools, expected_tool_calls, expected_content
 ):
@@ -473,6 +590,33 @@ def _test_extract_tool_calls_streaming(
     ]
     assert_tool_calls(actual_tool_calls, expected_tool_calls)
 
+    if expected_tool_calls:
+        assert len(tool_parser.streamed_args_for_tool) == len(expected_tool_calls)
+        assert len(tool_parser.prev_tool_call_arr) == len(expected_tool_calls)
+        for i in range(len(expected_tool_calls)):
+            assert (
+                tool_parser.prev_tool_call_arr[i]["arguments"]
+                == tool_parser.streamed_args_for_tool[i]
+            )
+            assert tool_parser.streamed_args_for_tool[i] == function_args_strs[i]
+            assert (
+                tool_parser.prev_tool_call_arr[i]["name"]
+                == expected_tool_calls[i].function.name
+            )
+
+        # Simulate the serving layer's unstreamed-args check
+        index = len(tool_parser.prev_tool_call_arr) - 1
+        args = tool_parser.prev_tool_call_arr[index].get("arguments", {})
+        expected_call = (
+            args if isinstance(args, str) else json.dumps(args, ensure_ascii=False)
+        )
+        actual_call = tool_parser.streamed_args_for_tool[index]
+        remaining_call = expected_call.replace(actual_call, "", 1)
+        assert remaining_call == ""
+    else:
+        assert len(tool_parser.streamed_args_for_tool) == 0
+        assert len(tool_parser.prev_tool_call_arr) == 0
+
 
 @pytest.mark.parametrize(
     ids=[
@@ -483,6 +627,7 @@ def _test_extract_tool_calls_streaming(
         "argument_before_name",
         "argument_before_name_and_name_in_argument",
         "multiple_tools",
+        "trailing_data_after_json",
     ],
     argnames=["model_output", "expected_tool_calls", "expected_content"],
     argvalues=[
@@ -572,6 +717,24 @@ def _test_extract_tool_calls_streaming(
             ],
             "",
         ),
+        (
+            """[TOOL_CALLS] [{"name": "get_current_weather", "arguments":{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}]\nextra trailing data""",  # noqa: E501
+            [
+                ToolCall(
+                    function=FunctionCall(
+                        name="get_current_weather",
+                        arguments=json.dumps(
+                            {
+                                "city": "Dallas",
+                                "state": "TX",
+                                "unit": "fahrenheit",
+                            }
+                        ),
+                    )
+                )
+            ],
+            "\nextra trailing data",
+        ),
     ],
 )
 def test_extract_tool_calls_streaming_pre_v11_tokenizer(
@@ -669,17 +832,67 @@ def test_extract_tool_calls_streaming(
     )
 
 
+def test_extract_tool_calls_streaming_v11_no_tools(
+    mistral_tool_parser, mistral_tokenizer
+):
+    model_output = "This is a test"
+    if isinstance(mistral_tokenizer, MistralTokenizer):
+        all_token_ids = mistral_tokenizer.encode(model_output)
+    else:
+        all_token_ids = mistral_tokenizer.encode(model_output, add_special_tokens=False)
+    skip_special = isinstance(mistral_tokenizer, MistralTokenizer)
+    collected_content = ""
+    previous_text = ""
+    previous_tokens = None
+    prefix_offset = 0
+    read_offset = 0
+    for i in range(len(all_token_ids)):
+        current_token_ids = all_token_ids[: i + 1]
+        previous_token_ids = all_token_ids[:i]
+        delta_token_ids = [all_token_ids[i]]
+
+        new_tokens, delta_text, prefix_offset, read_offset = detokenize_incrementally(
+            tokenizer=mistral_tokenizer,
+            all_input_ids=current_token_ids,
+            prev_tokens=previous_tokens,
+            prefix_offset=prefix_offset,
+            read_offset=read_offset,
+            skip_special_tokens=skip_special,
+            spaces_between_special_tokens=True,
+        )
+        current_text = previous_text + delta_text
+        previous_tokens = (
+            previous_tokens + new_tokens if previous_tokens else new_tokens
+        )
+
+        delta_message = mistral_tool_parser.extract_tool_calls_streaming(
+            previous_text=previous_text,
+            current_text=current_text,
+            delta_text=delta_text,
+            previous_token_ids=previous_token_ids,
+            current_token_ids=current_token_ids,
+            delta_token_ids=delta_token_ids,
+            request=_DUMMY_REQUEST,
+        )
+        if delta_message and delta_message.content:
+            collected_content += delta_message.content
+        if delta_message:
+            assert not delta_message.tool_calls
+
+        previous_text = current_text
+
+    assert collected_content == model_output
+    assert len(mistral_tool_parser.streamed_args_for_tool) == 0
+    assert len(mistral_tool_parser.prev_tool_call_arr) == 0
+
+
 @pytest.mark.parametrize(
-    ids=[
-        "single_tool_add",
-        "single_tool_weather",
-        "multiple_tool_calls",
-        "content_before_tool",
-        "complex",
-    ],
-    argnames=["model_output", "expected_tool_calls", "expected_content"],
-    argvalues=[
-        (
+    "parser_fixture, tokenizer_fixture, model_output,"
+    " expected_tool_calls, expected_content",
+    [
+        pytest.param(
+            "mistral_tool_parser",
+            "mistral_tokenizer",
             """[TOOL_CALLS]add_this_and_that{"a": 3.5, "b": 4}""",  # noqa: E501
             [
                 ToolCall(
@@ -690,8 +903,11 @@ def test_extract_tool_calls_streaming(
                 )
             ],
             "",
+            id="v11-single_tool_add",
         ),
-        (
+        pytest.param(
+            "mistral_tool_parser",
+            "mistral_tokenizer",
             """[TOOL_CALLS]get_current_weather{"city": "San Francisco", "state": "CA", "unit": "celsius"}""",  # noqa: E501
             [
                 ToolCall(
@@ -704,8 +920,11 @@ def test_extract_tool_calls_streaming(
                 )
             ],
             "",
+            id="v11-single_tool_weather",
         ),
-        (
+        pytest.param(
+            "mistral_tool_parser",
+            "mistral_tokenizer",
             """[TOOL_CALLS]add{"a": 3.5, "b": 4}[TOOL_CALLS]multiply{"a": 3, "b": 6}""",  # noqa: E501
             [
                 ToolCall(
@@ -720,9 +939,11 @@ def test_extract_tool_calls_streaming(
                 ),
             ],
             "",
+            id="v11-multiple_tool_calls",
         ),
-        (
-            # Additional content should not be after the tool calls
+        pytest.param(
+            "mistral_tool_parser",
+            "mistral_tokenizer",
             """bla[TOOL_CALLS]add_this_and_that{"a": 3.5, "b": 4}""",  # noqa: E501
             [
                 ToolCall(
@@ -733,9 +954,11 @@ def test_extract_tool_calls_streaming(
                 )
             ],
             "bla",
+            id="v11-content_before_tool",
         ),
-        (
-            # Complex
+        pytest.param(
+            "mistral_tool_parser",
+            "mistral_tokenizer",
             """hi{hi[TOOL_CALLS]bash{"command": "print(\\"hello world!\\")\\nre.compile(r\'{}\')"}""",  # noqa: E501
             [
                 ToolCall(
@@ -748,58 +971,19 @@ def test_extract_tool_calls_streaming(
                 )
             ],
             "hi{hi",
+            id="v11-complex",
         ),
-    ],
-)
-def test_extract_tool_calls_streaming_one_chunk(
-    mistral_tool_parser,
-    mistral_tokenizer,
-    model_output,
-    expected_tool_calls,
-    expected_content,
-):
-    if isinstance(mistral_tokenizer, MistralTokenizer):
-        all_token_ids = mistral_tokenizer.encode(model_output)
-    else:
-        all_token_ids = mistral_tokenizer.encode(model_output, add_special_tokens=False)
-    all_token_ids = fix_tool_call_tokenization(
-        all_token_ids, mistral_tool_parser, mistral_tokenizer
-    )
-
-    delta_message = mistral_tool_parser.extract_tool_calls_streaming(
-        previous_text="",
-        current_text=model_output,
-        delta_text=model_output,
-        previous_token_ids=[],
-        current_token_ids=all_token_ids,
-        delta_token_ids=all_token_ids,
-        request=None,
-    )  # type: ignore[arg-type]
-    assert isinstance(delta_message, DeltaMessage)
-    assert len(delta_message.tool_calls) == len(expected_tool_calls)
-
-    assert_tool_calls(delta_message.tool_calls, expected_tool_calls)
-
-    if delta_message.content is None:
-        assert expected_content == ""
-    else:
-        assert delta_message.content == expected_content
-
-
-@pytest.mark.parametrize(
-    ids=[
-        "no_tools",
-        "single_tool_add",
-        "single_tool_add_strings",
-        "single_tool_weather",
-        "argument_before_name",
-        "argument_before_name_and_name_in_argument",
-        "multiple_tools",
-    ],
-    argnames=["model_output", "expected_tool_calls", "expected_content"],
-    argvalues=[
-        ("""This is a test""", [], """This is a test"""),
-        (
+        pytest.param(
+            "mistral_pre_v11_tool_parser",
+            "mistral_pre_v11_tokenizer",
+            """This is a test""",
+            [],
+            """This is a test""",
+            id="pre_v11-no_tools",
+        ),
+        pytest.param(
+            "mistral_pre_v11_tool_parser",
+            "mistral_pre_v11_tokenizer",
             """[TOOL_CALLS]  [ {"name":"add" , "arguments" : {"a": 3, "b": 4} } ]""",  # noqa: E501
             [
                 ToolCall(
@@ -809,8 +993,11 @@ def test_extract_tool_calls_streaming_one_chunk(
                 )
             ],
             "",
+            id="pre_v11-single_tool_add",
         ),
-        (
+        pytest.param(
+            "mistral_pre_v11_tool_parser",
+            "mistral_pre_v11_tokenizer",
             """[TOOL_CALLS] [{"name": "add", "arguments":{"a": "3", "b": "4"}}]""",  # noqa: E501
             [
                 ToolCall(
@@ -820,8 +1007,11 @@ def test_extract_tool_calls_streaming_one_chunk(
                 )
             ],
             "",
+            id="pre_v11-single_tool_add_strings",
         ),
-        (
+        pytest.param(
+            "mistral_pre_v11_tool_parser",
+            "mistral_pre_v11_tokenizer",
             """[TOOL_CALLS] [{"name": "get_current_weather", "arguments": {"city": "San Francisco", "state": "CA", "unit": "celsius"}}]""",  # noqa: E501
             [
                 ToolCall(
@@ -834,8 +1024,11 @@ def test_extract_tool_calls_streaming_one_chunk(
                 )
             ],
             "",
+            id="pre_v11-single_tool_weather",
         ),
-        (
+        pytest.param(
+            "mistral_pre_v11_tool_parser",
+            "mistral_pre_v11_tokenizer",
             """[TOOL_CALLS] [{"arguments": {"city": "San Francisco", "state": "CA", "unit": "celsius"}, "name": "get_current_weather"}]""",  # noqa: E501
             [
                 ToolCall(
@@ -848,8 +1041,11 @@ def test_extract_tool_calls_streaming_one_chunk(
                 )
             ],
             "",
+            id="pre_v11-argument_before_name",
         ),
-        (
+        pytest.param(
+            "mistral_pre_v11_tool_parser",
+            "mistral_pre_v11_tokenizer",
             """[TOOL_CALLS] [{"arguments": {"name": "John Doe"}, "name": "get_age"}]""",  # noqa: E501
             [
                 ToolCall(
@@ -864,8 +1060,11 @@ def test_extract_tool_calls_streaming_one_chunk(
                 )
             ],
             "",
+            id="pre_v11-argument_before_name_and_name_in_argument",
         ),
-        (
+        pytest.param(
+            "mistral_pre_v11_tool_parser",
+            "mistral_pre_v11_tokenizer",
             """[TOOL_CALLS] [{"arguments": {"a": 3.5, "b": 4}, "name": "add"}, {"arguments":{"city": "San Francisco", "state": "CA", "unit": "celsius"}, "name": "get_current_weather"}]""",  # noqa: E501
             [
                 ToolCall(
@@ -883,35 +1082,50 @@ def test_extract_tool_calls_streaming_one_chunk(
                 ),
             ],
             "",
+            id="pre_v11-multiple_tools",
+        ),
+        pytest.param(
+            "mistral_pre_v11_tool_parser",
+            "mistral_pre_v11_tokenizer",
+            """Some text[TOOL_CALLS] [{"name": "add", "arguments":{"a": 1, "b": 2}}]""",  # noqa: E501
+            [
+                ToolCall(
+                    function=FunctionCall(
+                        name="add", arguments=json.dumps({"a": 1, "b": 2})
+                    )
+                )
+            ],
+            "Some text",
+            id="pre_v11-content_before_tool",
         ),
     ],
 )
-def test_extract_tool_calls_streaming_pre_v11_tokenizer_one_chunk(
-    mistral_pre_v11_tool_parser,
-    mistral_pre_v11_tokenizer,
+def test_extract_tool_calls_streaming_one_chunk(
+    parser_fixture,
+    tokenizer_fixture,
     model_output,
     expected_tool_calls,
     expected_content,
+    request,
 ):
-    if isinstance(mistral_pre_v11_tokenizer, MistralTokenizer):
-        all_token_ids = mistral_pre_v11_tokenizer.encode(model_output)
+    tool_parser = request.getfixturevalue(parser_fixture)
+    tokenizer = request.getfixturevalue(tokenizer_fixture)
+
+    if isinstance(tokenizer, MistralTokenizer):
+        all_token_ids = tokenizer.encode(model_output)
     else:
-        all_token_ids = mistral_pre_v11_tokenizer.encode(
-            model_output, add_special_tokens=False
-        )
-    all_token_ids = fix_tool_call_tokenization(
-        all_token_ids, mistral_pre_v11_tool_parser, mistral_pre_v11_tokenizer
-    )
+        all_token_ids = tokenizer.encode(model_output, add_special_tokens=False)
+    all_token_ids = fix_tool_call_tokenization(all_token_ids, tool_parser, tokenizer)
 
-    delta_message = mistral_pre_v11_tool_parser.extract_tool_calls_streaming(
+    delta_message = tool_parser.extract_tool_calls_streaming(
         previous_text="",
         current_text=model_output,
         delta_text=model_output,
         previous_token_ids=[],
         current_token_ids=all_token_ids,
         delta_token_ids=all_token_ids,
-        request=None,
-    )  # type: ignore[arg-type]
+        request=_DUMMY_REQUEST,
+    )
     assert isinstance(delta_message, DeltaMessage)
     assert len(delta_message.tool_calls) == len(expected_tool_calls)
 
@@ -923,65 +1137,105 @@ def test_extract_tool_calls_streaming_pre_v11_tokenizer_one_chunk(
         assert delta_message.content == expected_content
 
 
-def test_fast_detokenization_text_detection(mistral_tool_parser):
+@pytest.mark.parametrize(
+    "parser_fixture, model_output, fake_count, two_phase",
+    [
+        pytest.param(
+            "mistral_tool_parser",
+            '[TOOL_CALLS]add{"a": 1, "b": 2}',
+            20,
+            True,
+            id="v11",
+        ),
+        pytest.param(
+            "mistral_pre_v11_tool_parser",
+            '[TOOL_CALLS] [{"name": "add", "arguments":{"a": 1, "b": 2}}]',
+            30,
+            False,
+            id="pre_v11",
+        ),
+    ],
+)
+def test_fast_detokenization_text_detection(
+    parser_fixture, model_output, fake_count, two_phase, request
+):
     """Regression: bot_token in text but not token_ids (PR #37209)."""
-    model_output = '[TOOL_CALLS]add{"a": 1, "b": 2}'
+    parser = request.getfixturevalue(parser_fixture)
     # Token IDs that do NOT contain bot_token_id.
-    fake_token_ids = list(range(99, 99 + 20))
-
-    # First delta: pure content, no bot token yet
-    delta_message_before = mistral_tool_parser.extract_tool_calls_streaming(
-        previous_text="",
-        current_text="Hello",
-        delta_text="Hello",
-        previous_token_ids=[],
-        current_token_ids=[99],
-        delta_token_ids=[99],
-        request=None,
-    )
-    assert delta_message_before is not None
-    assert delta_message_before.content == "Hello"
-    assert not delta_message_before.tool_calls
-
-    # Second delta: bot token in text but NOT in token_ids
-    delta_message = mistral_tool_parser.extract_tool_calls_streaming(
-        previous_text="Hello",
-        current_text="Hello" + model_output,
+    fake_token_ids = list(range(99, 99 + fake_count))
+
+    if two_phase:
+        # First delta: pure content, no bot token yet
+        delta_message_before = parser.extract_tool_calls_streaming(
+            previous_text="",
+            current_text="Hello",
+            delta_text="Hello",
+            previous_token_ids=[],
+            current_token_ids=[99],
+            delta_token_ids=[99],
+            request=_DUMMY_REQUEST,
+        )
+        assert delta_message_before is not None
+        assert delta_message_before.content == "Hello"
+        assert not delta_message_before.tool_calls
+
+        previous_text = "Hello"
+        current_text = "Hello" + model_output
+        previous_token_ids = [99]
+        delta_token_ids = fake_token_ids[1:]
+    else:
+        previous_text = ""
+        current_text = model_output
+        previous_token_ids = []
+        delta_token_ids = fake_token_ids
+
+    delta_message = parser.extract_tool_calls_streaming(
+        previous_text=previous_text,
+        current_text=current_text,
         delta_text=model_output,
-        previous_token_ids=[99],
+        previous_token_ids=previous_token_ids,
         current_token_ids=fake_token_ids,
-        delta_token_ids=fake_token_ids[1:],
-        request=None,
+        delta_token_ids=delta_token_ids,
+        request=_DUMMY_REQUEST,
     )
     assert delta_message is not None
     assert delta_message.tool_calls is not None
-    assert len(delta_message.tool_calls) > 0
+    assert len(delta_message.tool_calls) == 1
     assert delta_message.tool_calls[0].function is not None
     assert delta_message.tool_calls[0].function.name == "add"
 
 
-def test_fast_detokenization_text_detection_pre_v11(
-    mistral_pre_v11_tool_parser,
+@pytest.mark.parametrize(
+    "parser_fixture, patched_method, current_text",
+    [
+        (
+            "mistral_tool_parser",
+            "_extract_tool_calls_streaming",
+            "[TOOL_CALLS]add{}",
+        ),
+        (
+            "mistral_pre_v11_tool_parser",
+            "_extract_tool_calls_streaming_pre_v11_tokenizer",
+            '[TOOL_CALLS] [{"name":"a","arguments":{}}]',
+        ),
+    ],
+    ids=["v11", "pre_v11"],
+)
+def test_extract_tool_calls_streaming_exception_returns_none(
+    parser_fixture, patched_method, current_text, request
 ):
-    """Regression: bot_token text detection for pre-v11 tokenizer (PR #37209)."""
-    model_output = '[TOOL_CALLS] [{"name": "add", "arguments":{"a": 1, "b": 2}}]'
-
-    fake_token_ids = list(range(99, 99 + 30))
-
-    delta_message = mistral_pre_v11_tool_parser.extract_tool_calls_streaming(
-        previous_text="",
-        current_text=model_output,
-        delta_text=model_output,
-        previous_token_ids=[],
-        current_token_ids=fake_token_ids,
-        delta_token_ids=fake_token_ids,
-        request=None,
-    )
-    assert delta_message is not None
-    assert delta_message.tool_calls is not None
-    assert len(delta_message.tool_calls) > 0
-    assert delta_message.tool_calls[0].function is not None
-    assert delta_message.tool_calls[0].function.name == "add"
+    parser = request.getfixturevalue(parser_fixture)
+    with patch.object(parser, patched_method, side_effect=RuntimeError("boom")):
+        result = parser.extract_tool_calls_streaming(
+            previous_text="",
+            current_text=current_text,
+            delta_text=current_text,
+            previous_token_ids=[],
+            current_token_ids=[parser.bot_token_id],
+            delta_token_ids=[parser.bot_token_id],
+            request=_DUMMY_REQUEST,
+        )
+    assert result is None
 
 
 SAMPLE_TOOLS_DICTS = [
@@ -1238,57 +1492,444 @@ def test_adjust_request_response_format_generates_grammar(
     assert len(result.structured_outputs.grammar) > 0
 
 
-def test_adjust_request_tool_choice_none_with_json_schema_uses_json_schema_factory(
+@pytest.mark.parametrize(
+    "tool_choice, expected_method, not_called_method",
+    [
+        ("none", "get_lark_for_json_schema", None),
+        ("auto", "get_lark_from_jinja", "get_lark_for_json_schema"),
+    ],
+    ids=["none_uses_json_schema_factory", "auto_uses_jinja_factory"],
+)
+def test_adjust_request_tool_choice_with_json_schema_factory_routing(
     mistral_tool_parser: MistralToolParser,
+    tool_choice: str,
+    expected_method: str,
+    not_called_method: str | None,
 ) -> None:
     request = _make_request(
-        tool_choice="none",
+        tool_choice=tool_choice,
         structured_outputs=StructuredOutputsParams(json='{"type": "object"}'),
     )
     factory = mistral_tool_parser.model_tokenizer.grammar_factory
 
-    with patch.object(
-        factory,
-        "get_lark_for_json_schema",
-        wraps=factory.get_lark_for_json_schema,
-    ) as mock_json_schema:
-        result = mistral_tool_parser.adjust_request(request)
+    patches = {
+        expected_method: patch.object(
+            factory,
+            expected_method,
+            wraps=getattr(factory, expected_method),
+        ),
+    }
+    if not_called_method:
+        patches[not_called_method] = patch.object(
+            factory,
+            not_called_method,
+            wraps=getattr(factory, not_called_method),
+        )
 
-        mock_json_schema.assert_called_once()
-        assert mock_json_schema.call_args.kwargs["json_schema"] == {"type": "object"}
+    with patches[expected_method] as mock_expected:
+        ctx = patches[not_called_method] if not_called_method else None
+        if ctx:
+            with ctx as mock_not_called:
+                result = mistral_tool_parser.adjust_request(request)
+                mock_not_called.assert_not_called()
+        else:
+            result = mistral_tool_parser.adjust_request(request)
+
+        mock_expected.assert_called_once()
+        assert mock_expected.call_args.kwargs["json_schema"] == {"type": "object"}
 
     assert result.structured_outputs is not None
     assert isinstance(result.structured_outputs.grammar, str)
     assert len(result.structured_outputs.grammar) > 0
 
 
-def test_adjust_request_tool_choice_auto_with_json_schema_uses_jinja_factory(
+def test_grammar_from_tool_parser_default_false() -> None:
+    request = _make_request()
+    assert request._grammar_from_tool_parser is False
+
+
+def test_grammar_from_tool_parser_set_by_adjust_request(
     mistral_tool_parser: MistralToolParser,
 ) -> None:
-    request = _make_request(
-        tool_choice="auto",
-        structured_outputs=StructuredOutputsParams(json='{"type": "object"}'),
-    )
-    factory = mistral_tool_parser.model_tokenizer.grammar_factory
+    request = _make_request()
+    result = mistral_tool_parser.adjust_request(request)
+    assert result._grammar_from_tool_parser is True
 
-    with (
-        patch.object(
-            factory,
-            "get_lark_for_json_schema",
-            wraps=factory.get_lark_for_json_schema,
-        ) as mock_json_schema,
-        patch.object(
-            factory,
-            "get_lark_from_jinja",
-            wraps=factory.get_lark_from_jinja,
-        ) as mock_jinja,
-    ):
-        result = mistral_tool_parser.adjust_request(request)
 
-        mock_jinja.assert_called_once()
-        assert mock_jinja.call_args.kwargs["json_schema"] == {"type": "object"}
-        mock_json_schema.assert_not_called()
+@pytest.mark.parametrize(
+    "tool_calls, expected_len",
+    [
+        (None, 0),
+        ([], 0),
+        ([VllmFunctionCall(id="abc123xyz", name="f", arguments="{}")], 1),
+        ([VllmFunctionCall(name="f", arguments="{}")], 1),
+        (
+            [
+                VllmFunctionCall(id="fixed1234", name="a", arguments='{"x": 1}'),
+                VllmFunctionCall(name="b", arguments='{"y": 2}'),
+            ],
+            2,
+        ),
+    ],
+    ids=["none", "empty", "with_id", "without_id", "mixed"],
+)
+def test_build_non_streaming_tool_calls(
+    tool_calls: list[VllmFunctionCall] | None,
+    expected_len: int,
+) -> None:
+    result = MistralToolParser.build_non_streaming_tool_calls(tool_calls)
+    assert len(result) == expected_len
 
-    assert result.structured_outputs is not None
-    assert isinstance(result.structured_outputs.grammar, str)
-    assert len(result.structured_outputs.grammar) > 0
+    if tool_calls is None:
+        return
+
+    for i, tc in enumerate(result):
+        assert isinstance(tc, MistralToolCall)
+        assert tc.type == "function"
+
+        input_tc = tool_calls[i]
+        if input_tc.id:
+            assert tc.id == input_tc.id
+        else:
+            assert len(tc.id) == 9
+            assert tc.id.isalnum()
+
+        assert tc.function.name == input_tc.name
+        assert tc.function.arguments == input_tc.arguments
+
+
+class TestExtractMaybeReasoningAndToolStreaming:
+    r"""Tests for `MistralToolParser.extract_maybe_reasoning_and_tool_streaming`."""
+
+    @pytest.fixture
+    def parser(self) -> MistralToolParser:
+        mock_tokenizer = MagicMock()
+        mock_tokenizer.get_vocab.return_value = {"[TOOL_CALLS]": 1}
+        return MistralToolParser(mock_tokenizer)
+
+    @pytest.fixture
+    def request_obj(self) -> ChatCompletionRequest:
+        return _make_request()
+
+    @staticmethod
+    def _call(
+        parser: MistralToolParser,
+        request: ChatCompletionRequest,
+        *,
+        reasoning_parser: Any = None,
+        previous_text: str = "",
+        current_text: str = "hello",
+        delta_text: str = "hello",
+        previous_token_ids: list[int] | None = None,
+        current_token_ids: list[int] | None = None,
+        output_token_ids: list[int] | None = None,
+        reasoning_ended: bool = False,
+        prompt_is_reasoning_end: bool | None = None,
+    ) -> MistralStreamingResult:
+        return parser.extract_maybe_reasoning_and_tool_streaming(
+            reasoning_parser=reasoning_parser,
+            previous_text=previous_text,
+            current_text=current_text,
+            delta_text=delta_text,
+            previous_token_ids=previous_token_ids or [],
+            current_token_ids=current_token_ids or [1, 2, 3],
+            output_token_ids=output_token_ids or [1, 2, 3],
+            reasoning_ended=reasoning_ended,
+            prompt_is_reasoning_end=prompt_is_reasoning_end,
+            request=request,
+        )
+
+    def test_no_reasoning_tools_called(
+        self, parser: MistralToolParser, request_obj: ChatCompletionRequest
+    ) -> None:
+        tool_delta = DeltaMessage(
+            tool_calls=[
+                DeltaToolCall(
+                    index=0,
+                    function=DeltaFunctionCall(name="f", arguments="{}"),
+                )
+            ]
+        )
+        with patch.object(
+            parser, "extract_tool_calls_streaming", return_value=tool_delta
+        ):
+            result = self._call(parser, request_obj, reasoning_parser=None)
+
+        assert result == MistralStreamingResult(
+            delta_message=tool_delta,
+            reasoning_ended=False,
+            tools_called=True,
+            current_text="hello",
+            current_token_ids=[1, 2, 3],
+        )
+
+    def test_no_reasoning_no_tools(
+        self, parser: MistralToolParser, request_obj: ChatCompletionRequest
+    ) -> None:
+        content_delta = DeltaMessage(content="hello")
+        with patch.object(
+            parser, "extract_tool_calls_streaming", return_value=content_delta
+        ):
+            result = self._call(parser, request_obj, reasoning_parser=None)
+
+        assert result == MistralStreamingResult(
+            delta_message=content_delta,
+            reasoning_ended=False,
+            tools_called=False,
+            current_text="hello",
+            current_token_ids=[1, 2, 3],
+        )
+
+    def test_mistral_reasoning_parser_no_think_token(
+        self, parser: MistralToolParser, request_obj: ChatCompletionRequest
+    ) -> None:
+        mock_rp = MagicMock(spec=MistralReasoningParser)
+        mock_rp.start_token_id = 999
+        content_delta = DeltaMessage(content="direct")
+        with patch.object(
+            parser, "extract_tool_calls_streaming", return_value=content_delta
+        ):
+            result = self._call(
+                parser,
+                request_obj,
+                reasoning_parser=mock_rp,
+                reasoning_ended=False,
+                current_token_ids=[1, 2, 3],
+            )
+
+        mock_rp.extract_reasoning_streaming.assert_not_called()
+        assert result == MistralStreamingResult(
+            delta_message=content_delta,
+            reasoning_ended=False,
+            tools_called=False,
+            current_text="hello",
+            current_token_ids=[1, 2, 3],
+        )
+
+    def test_mistral_reasoning_parser_with_think_token(
+        self, parser: MistralToolParser, request_obj: ChatCompletionRequest
+    ) -> None:
+        mock_rp = MagicMock(spec=MistralReasoningParser)
+        mock_rp.start_token_id = 999
+        mock_rp.extract_reasoning_streaming.return_value = DeltaMessage(
+            reasoning="thinking..."
+        )
+        mock_rp.is_reasoning_end_streaming.return_value = False
+
+        result = self._call(
+            parser,
+            request_obj,
+            reasoning_parser=mock_rp,
+            reasoning_ended=False,
+            current_token_ids=[1, 999, 3],
+        )
+
+        mock_rp.extract_reasoning_streaming.assert_called_once()
+        assert result == MistralStreamingResult(
+            delta_message=DeltaMessage(reasoning="thinking..."),
+            reasoning_ended=False,
+            tools_called=False,
+            current_text="hello",
+            current_token_ids=[1, 999, 3],
+        )
+
+    def test_non_mistral_reasoning_parser_always_expects_thinking(
+        self, parser: MistralToolParser, request_obj: ChatCompletionRequest
+    ) -> None:
+        mock_rp = MagicMock()
+        mock_rp.start_token_id = 999
+        mock_rp.extract_reasoning_streaming.return_value = DeltaMessage(
+            reasoning="thinking..."
+        )
+        mock_rp.is_reasoning_end_streaming.return_value = False
+
+        result = self._call(
+            parser,
+            request_obj,
+            reasoning_parser=mock_rp,
+            reasoning_ended=False,
+            current_token_ids=[1, 2, 3],
+        )
+
+        mock_rp.extract_reasoning_streaming.assert_called_once()
+        assert result == MistralStreamingResult(
+            delta_message=DeltaMessage(reasoning="thinking..."),
+            reasoning_ended=False,
+            tools_called=False,
+            current_text="hello",
+            current_token_ids=[1, 2, 3],
+        )
+
+    def test_reasoning_already_ended_no_reset(
+        self, parser: MistralToolParser, request_obj: ChatCompletionRequest
+    ) -> None:
+        content_delta = DeltaMessage(content="content")
+        with patch.object(
+            parser, "extract_tool_calls_streaming", return_value=content_delta
+        ) as mock_extract:
+            result = self._call(
+                parser,
+                request_obj,
+                reasoning_parser=MagicMock(),
+                reasoning_ended=True,
+                previous_text="prior_tool_text",
+                previous_token_ids=[10, 20],
+                current_text="prior_tool_texthello",
+                current_token_ids=[10, 20, 1, 2, 3],
+            )
+
+            _, call_kwargs = mock_extract.call_args
+            assert call_kwargs["previous_text"] == "prior_tool_text"
+            assert call_kwargs["previous_token_ids"] == [10, 20]
+
+        assert result == MistralStreamingResult(
+            delta_message=content_delta,
+            reasoning_ended=True,
+            tools_called=False,
+            current_text="prior_tool_texthello",
+            current_token_ids=[10, 20, 1, 2, 3],
+        )
+
+    def test_pre_v15_ignores_prompt_reasoning_end(
+        self, parser: MistralToolParser, request_obj: ChatCompletionRequest
+    ) -> None:
+        mock_tokenizer = MagicMock(spec=MistralTokenizer)
+        mock_tokenizer.version = 13
+        parser.model_tokenizer = mock_tokenizer
+
+        mock_rp = MagicMock(spec=MistralReasoningParser)
+        mock_rp.start_token_id = 999
+        mock_rp.extract_reasoning_streaming.return_value = DeltaMessage(
+            reasoning="thinking..."
+        )
+        mock_rp.is_reasoning_end_streaming.return_value = False
+
+        result = self._call(
+            parser,
+            request_obj,
+            reasoning_parser=mock_rp,
+            reasoning_ended=False,
+            prompt_is_reasoning_end=True,
+            current_token_ids=[999, 1, 2],
+        )
+
+        mock_rp.extract_reasoning_streaming.assert_called_once()
+        assert result == MistralStreamingResult(
+            delta_message=DeltaMessage(reasoning="thinking..."),
+            reasoning_ended=False,
+            tools_called=False,
+            current_text="hello",
+            current_token_ids=[999, 1, 2],
+        )
+
+    def test_non_pre_v15_prompt_reasoning_end(
+        self, parser: MistralToolParser, request_obj: ChatCompletionRequest
+    ) -> None:
+        mock_tokenizer = MagicMock(spec=MistralTokenizer)
+        mock_tokenizer.version = 15
+        parser.model_tokenizer = mock_tokenizer
+
+        mock_rp = MagicMock(spec=MistralReasoningParser)
+        mock_rp.start_token_id = 999
+
+        content_delta = DeltaMessage(content="after reasoning")
+        with patch.object(
+            parser, "extract_tool_calls_streaming", return_value=content_delta
+        ):
+            result = self._call(
+                parser,
+                request_obj,
+                reasoning_parser=mock_rp,
+                reasoning_ended=False,
+                prompt_is_reasoning_end=True,
+                current_token_ids=[999, 1, 2],
+                output_token_ids=[10, 20, 30],
+            )
+
+        mock_rp.extract_reasoning_streaming.assert_not_called()
+        assert result == MistralStreamingResult(
+            delta_message=content_delta,
+            reasoning_ended=True,
+            tools_called=False,
+            current_text="hello",
+            current_token_ids=[10, 20, 30],
+        )
+
+    def test_reasoning_end_transition_with_content(
+        self, parser: MistralToolParser, request_obj: ChatCompletionRequest
+    ) -> None:
+        """When reasoning ends and the delta has content, that content is
+        cleared from delta_message and used as current_text for tool parsing."""
+        mock_rp = MagicMock()
+        mock_rp.start_token_id = 999
+        mock_rp.extract_reasoning_streaming.return_value = DeltaMessage(
+            reasoning="think", content="leftover"
+        )
+        mock_rp.is_reasoning_end_streaming.return_value = True
+        mock_rp.extract_content_ids.return_value = [50, 51]
+
+        content_delta = DeltaMessage(content="leftover")
+        with patch.object(
+            parser, "extract_tool_calls_streaming", return_value=content_delta
+        ) as mock_extract:
+            result = self._call(
+                parser,
+                request_obj,
+                reasoning_parser=mock_rp,
+                reasoning_ended=False,
+                current_token_ids=[999, 1, 2],
+                output_token_ids=[10, 20, 30],
+            )
+
+            mock_rp.extract_content_ids.assert_called_once_with([10, 20, 30])
+            _, call_kwargs = mock_extract.call_args
+            assert call_kwargs["previous_text"] == ""
+            assert call_kwargs["previous_token_ids"] == []
+            assert call_kwargs["delta_text"] == "leftover"
+            assert call_kwargs["current_token_ids"] == [50, 51]
+
+        assert result == MistralStreamingResult(
+            delta_message=content_delta,
+            reasoning_ended=True,
+            tools_called=False,
+            current_text="leftover",
+            current_token_ids=[50, 51],
+        )
+
+    def test_reasoning_end_transition_without_content(
+        self, parser: MistralToolParser, request_obj: ChatCompletionRequest
+    ) -> None:
+        """When reasoning ends but the delta has no content, current_text
+        is set to empty string."""
+        mock_rp = MagicMock()
+        mock_rp.start_token_id = 999
+        mock_rp.extract_reasoning_streaming.return_value = DeltaMessage(
+            reasoning="think"
+        )
+        mock_rp.is_reasoning_end_streaming.return_value = True
+        mock_rp.extract_content_ids.return_value = [50, 51]
+
+        empty_delta = DeltaMessage(content="")
+        with patch.object(
+            parser, "extract_tool_calls_streaming", return_value=empty_delta
+        ) as mock_extract:
+            result = self._call(
+                parser,
+                request_obj,
+                reasoning_parser=mock_rp,
+                reasoning_ended=False,
+                current_token_ids=[999, 1, 2],
+                output_token_ids=[10, 20, 30],
+            )
+
+            _, call_kwargs = mock_extract.call_args
+            assert call_kwargs["delta_text"] == ""
+            assert call_kwargs["current_token_ids"] == [50, 51]
+
+        assert result == MistralStreamingResult(
+            delta_message=empty_delta,
+            reasoning_ended=True,
+            tools_called=False,
+            current_text="",
+            current_token_ids=[50, 51],
+        )
diff --git a/tests/tool_parsers/test_qwen3coder_tool_parser.py b/tests/tool_parsers/test_qwen3coder_tool_parser.py
index c62e95830243..26bbf1a044bc 100644
--- a/tests/tool_parsers/test_qwen3coder_tool_parser.py
+++ b/tests/tool_parsers/test_qwen3coder_tool_parser.py
@@ -6,8 +6,11 @@
 
 import pytest
 from openai.types.responses.function_tool import FunctionTool
+from xgrammar import StructuralTag
 
 from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionNamedFunction,
+    ChatCompletionNamedToolChoiceParam,
     ChatCompletionRequest,
     ChatCompletionToolsParam,
 )
@@ -108,6 +111,27 @@ def sample_tools(request):
         ]
 
 
+def _as_chat_completion_tools(
+    tools: list[ChatCompletionToolsParam | FunctionTool],
+) -> list[ChatCompletionToolsParam]:
+    normalized: list[ChatCompletionToolsParam] = []
+    for tool in tools:
+        if isinstance(tool, ChatCompletionToolsParam):
+            normalized.append(tool)
+        else:
+            normalized.append(
+                ChatCompletionToolsParam(
+                    type="function",
+                    function={
+                        "name": tool.name,
+                        "description": tool.description,
+                        "parameters": tool.parameters,
+                    },
+                )
+            )
+    return normalized
+
+
 def assert_tool_calls(
     actual_tool_calls: list[ToolCall], expected_tool_calls: list[ToolCall]
 ):
@@ -1146,3 +1170,88 @@ def test_no_double_serialization_string_args(qwen3_tool_parser):
     args = json.loads(raw_arguments)
     assert args["message"] == "hello world"
     assert '\\"hello world\\"' not in raw_arguments
+
+
+def test_get_vllm_registry_structural_tag_returns_structural_tag(
+    qwen3_tool_parser: Qwen3CoderToolParser,
+    sample_tools: list[ChatCompletionToolsParam],
+) -> None:
+    request_tools = _as_chat_completion_tools(sample_tools)
+    req = ChatCompletionRequest(
+        messages=[],
+        model="m",
+        tools=request_tools,
+        tool_choice="auto",
+    )
+    tag = qwen3_tool_parser.get_structural_tag(req)
+    assert isinstance(tag, StructuralTag)
+
+    req = ChatCompletionRequest(
+        messages=[],
+        model="m",
+        tools=request_tools,
+        tool_choice="required",
+    )
+    tag = qwen3_tool_parser.get_structural_tag(req)
+    assert isinstance(tag, StructuralTag)
+
+    if request_tools:
+        tool = request_tools[0]
+        req = ChatCompletionRequest(
+            messages=[],
+            model="m",
+            tools=request_tools,
+        )
+        req.tool_choice = ChatCompletionNamedToolChoiceParam(
+            function=ChatCompletionNamedFunction(name=tool.function.name)
+        )
+        tag = qwen3_tool_parser.get_structural_tag(req)
+        assert isinstance(tag, StructuralTag)
+
+
+@pytest.mark.parametrize("include_reasoning", [True, False])
+def test_adjust_request_auto_uses_vllm_registry_structural_tag(
+    monkeypatch: pytest.MonkeyPatch,
+    qwen3_tool_parser: Qwen3CoderToolParser,
+    sample_tools: list[ChatCompletionToolsParam],
+    include_reasoning: bool,
+) -> None:
+    monkeypatch.setattr(
+        "vllm.tool_parsers.abstract_tool_parser.VLLM_ENFORCE_STRICT_TOOL_CALLING",
+        True,
+    )
+    request_tools = _as_chat_completion_tools(sample_tools)
+    req = ChatCompletionRequest(
+        messages=[],
+        model="m",
+        tools=request_tools,
+        tool_choice="auto",
+        include_reasoning=include_reasoning,
+    )
+    out = qwen3_tool_parser.adjust_request(req)
+    assert out.structured_outputs is not None
+    assert out.structured_outputs.structural_tag is not None
+    assert isinstance(out.structured_outputs.structural_tag, str)
+    loaded = json.loads(out.structured_outputs.structural_tag)
+    assert isinstance(loaded, dict)
+
+
+def test_adjust_request_required_prefers_structural_tag(
+    monkeypatch: pytest.MonkeyPatch,
+    qwen3_tool_parser: Qwen3CoderToolParser,
+    sample_tools: list[ChatCompletionToolsParam],
+) -> None:
+    monkeypatch.setattr(
+        "vllm.tool_parsers.abstract_tool_parser.VLLM_ENFORCE_STRICT_TOOL_CALLING",
+        True,
+    )
+    request_tools = _as_chat_completion_tools(sample_tools)
+    req = ChatCompletionRequest(
+        messages=[],
+        model="m",
+        tools=request_tools,
+        tool_choice="required",
+    )
+    out = qwen3_tool_parser.adjust_request(req)
+    assert out.structured_outputs is not None
+    assert out.structured_outputs.structural_tag is not None
diff --git a/tests/tool_parsers/test_qwen3xml_tool_parser.py b/tests/tool_parsers/test_qwen3xml_tool_parser.py
index 3771b8afd24c..1ea9a1d65c04 100644
--- a/tests/tool_parsers/test_qwen3xml_tool_parser.py
+++ b/tests/tool_parsers/test_qwen3xml_tool_parser.py
@@ -64,9 +64,6 @@ def test_config(self) -> ToolParserTestConfig:
                 "test_empty_arguments": "Qwen3XML streaming has systematic issues",
                 "test_surrounding_text": "Qwen3XML streaming has systematic issues",
                 "test_escaped_strings": "Qwen3XML streaming has systematic issues",
-                "test_malformed_input": (
-                    "Qwen3XML parser is lenient with malformed input"
-                ),
                 "test_streaming_reconstruction": (
                     "Qwen3XML streaming reconstruction has known issues"
                 ),
diff --git a/tests/tool_parsers/test_xlam_tool_parser.py b/tests/tool_parsers/test_xlam_tool_parser.py
index a5cab218f72b..3853d2039a72 100644
--- a/tests/tool_parsers/test_xlam_tool_parser.py
+++ b/tests/tool_parsers/test_xlam_tool_parser.py
@@ -484,7 +484,7 @@ def test_extract_tool_calls_streaming_incremental(
     expected_content,
 ):
     """Verify the XLAM Parser streaming behavior by verifying each chunk is as expected."""  # noqa: E501
-    request = ChatCompletionRequest(model=MODEL, messages=[], tools=[])
+    request = ChatCompletionRequest(model=MODEL, messages=[])
 
     chunks = []
     for delta_message in stream_delta_message_generator(
diff --git a/tests/tool_use/mistral/test_mistral_tool_calls.py b/tests/tool_use/mistral/test_mistral_tool_calls.py
index 3c4a543abe41..6dcfd43a9497 100644
--- a/tests/tool_use/mistral/test_mistral_tool_calls.py
+++ b/tests/tool_use/mistral/test_mistral_tool_calls.py
@@ -1,25 +1,198 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import json
+from dataclasses import dataclass, field
+
 import openai
 import pytest
 
-from tests.tool_use.utils import MESSAGES_ASKING_FOR_TOOLS, WEATHER_TOOL
+from tests.tool_use.utils import (
+    MESSAGES_ASKING_FOR_PARALLEL_TOOLS,
+    MESSAGES_ASKING_FOR_TOOLS,
+    MESSAGES_WITH_TOOL_RESPONSE,
+    MESSAGES_WITHOUT_TOOLS,
+    SEARCH_TOOL,
+    SEED,
+    WEATHER_TOOL,
+    ensure_system_prompt,
+)
+
+from .utils import ServerConfig
+
+
+def _requires_tool_parser(server_config: ServerConfig) -> None:
+    r"""Skip test if server was not started with --tool-call-parser."""
+    if "--tool-call-parser" not in server_config.get("arguments", []):
+        pytest.skip(
+            f"Skipping: {server_config['model']} not configured with --tool-call-parser"
+        )
+
+
+def _is_pre_v11(server_config: ServerConfig) -> bool:
+    r"""Pre-v11 Mistral models lack grammar-based tool call enforcement."""
+    return "7B" in server_config.get("model", "")
+
+
+@dataclass
+class StreamedToolCallResult:
+    r"""Accumulated result from streaming a single tool call."""
+
+    function_name: str | None = None
+    function_args_str: str = ""
+    tool_call_id: str | None = None
+    role_name: str | None = None
+    finish_reason_count: int = 0
+    finish_reason: str | None = None
+
+
+async def _collect_streamed_tool_call(
+    stream: openai.AsyncStream,
+    *,
+    expected_finish_reason: str = "tool_calls",
+) -> StreamedToolCallResult:
+    result = StreamedToolCallResult()
+
+    async for chunk in stream:
+        if chunk.choices[0].finish_reason:
+            result.finish_reason_count += 1
+            result.finish_reason = chunk.choices[0].finish_reason
+            assert chunk.choices[0].finish_reason == expected_finish_reason
+
+        if chunk.choices[0].delta.role:
+            assert not result.role_name or result.role_name == "assistant"
+            result.role_name = "assistant"
+
+        streamed_tool_calls = chunk.choices[0].delta.tool_calls
+        if streamed_tool_calls and len(streamed_tool_calls) > 0:
+            assert len(streamed_tool_calls) == 1
+            tool_call = streamed_tool_calls[0]
+
+            if tool_call.id:
+                assert not result.tool_call_id
+                result.tool_call_id = tool_call.id
+
+            if tool_call.function:
+                if tool_call.function.name:
+                    assert result.function_name is None
+                    result.function_name = tool_call.function.name
+                if tool_call.function.arguments:
+                    result.function_args_str += tool_call.function.arguments
+
+    return result
+
+
+@dataclass
+class StreamedContentResult:
+    r"""Accumulated result from streaming a content-only response."""
+
+    chunks: list[str] = field(default_factory=list)
+    finish_reason_count: int = 0
+    finish_reason: str | None = None
+    role_sent: bool = False
+
+
+async def _collect_streamed_content(
+    stream: openai.AsyncStream,
+    *,
+    expected_finish_reason: str | None = None,
+    no_tool_calls: bool = True,
+) -> StreamedContentResult:
+    r"""Consume a streaming response and collect text content."""
+    result = StreamedContentResult()
+
+    async for chunk in stream:
+        delta = chunk.choices[0].delta
+
+        if delta.role:
+            assert not result.role_sent
+            assert delta.role == "assistant"
+            result.role_sent = True
+
+        if delta.content:
+            result.chunks.append(delta.content)
+
+        if chunk.choices[0].finish_reason is not None:
+            result.finish_reason_count += 1
+            result.finish_reason = chunk.choices[0].finish_reason
+            if expected_finish_reason is not None:
+                assert result.finish_reason == expected_finish_reason
+
+        if no_tool_calls:
+            assert not delta.tool_calls or len(delta.tool_calls) == 0
+
+    return result
+
+
+@dataclass
+class StreamedParallelToolCallResult:
+    r"""Accumulated result from streaming parallel tool calls."""
+
+    function_names: list[str] = field(default_factory=list)
+    function_args_strs: list[str] = field(default_factory=list)
+    tool_call_ids: list[str] = field(default_factory=list)
+    role_name: str | None = None
+    finish_reason_count: int = 0
+
+
+async def _collect_streamed_parallel_tool_calls(
+    stream: openai.AsyncStream,
+) -> StreamedParallelToolCallResult:
+    r"""Consume a streaming response and collect parallel tool calls."""
+    result = StreamedParallelToolCallResult()
+    tool_call_idx: int = -1
+
+    async for chunk in stream:
+        if chunk.choices[0].finish_reason:
+            result.finish_reason_count += 1
+            assert chunk.choices[0].finish_reason == "tool_calls"
+
+        if chunk.choices[0].delta.role:
+            assert not result.role_name or result.role_name == "assistant"
+            result.role_name = "assistant"
+
+        streamed_tool_calls = chunk.choices[0].delta.tool_calls
+        if streamed_tool_calls and len(streamed_tool_calls) > 0:
+            assert len(streamed_tool_calls) == 1
+            tool_call = streamed_tool_calls[0]
+
+            if tool_call.index != tool_call_idx:
+                tool_call_idx = tool_call.index
+                result.function_args_strs.append("")
+                result.tool_call_ids.append("")
+
+            if tool_call.id:
+                result.tool_call_ids[tool_call.index] = tool_call.id
+
+            if tool_call.function:
+                if tool_call.function.name:
+                    result.function_names.append(tool_call.function.name)
+                if tool_call.function.arguments:
+                    result.function_args_strs[tool_call.index] += (
+                        tool_call.function.arguments
+                    )
+
+    return result
 
 
 # test: a tool_choice with mistral-tokenizer results in an ID of length 9
 @pytest.mark.asyncio
-async def test_tool_call_with_tool_choice(client: openai.AsyncOpenAI):
+async def test_tool_call_with_tool_choice(
+    client: openai.AsyncOpenAI, server_config: ServerConfig
+) -> None:
+    _requires_tool_parser(server_config)
+
     models = await client.models.list()
     model_name: str = models.data[0].id
     chat_completion = await client.chat.completions.create(
-        messages=MESSAGES_ASKING_FOR_TOOLS,
+        messages=ensure_system_prompt(MESSAGES_ASKING_FOR_TOOLS, server_config),
         temperature=0,
         max_completion_tokens=100,
         model=model_name,
         tools=[WEATHER_TOOL],
         tool_choice=WEATHER_TOOL,
         logprobs=False,
+        seed=SEED,
     )
 
     choice = chat_completion.choices[0]
@@ -28,3 +201,307 @@ async def test_tool_call_with_tool_choice(client: openai.AsyncOpenAI):
     assert choice.message.role == "assistant"
     assert choice.message.tool_calls is None or len(choice.message.tool_calls) == 1
     assert len(choice.message.tool_calls[0].id) == 9  # length of 9 for mistral
+
+
+_NOT_SET = object()
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "tools, tool_choice, streaming_id_len_pre_v11",
+    [
+        pytest.param(
+            [WEATHER_TOOL, SEARCH_TOOL],
+            _NOT_SET,
+            9,
+            id="auto",
+        ),
+        pytest.param(
+            [WEATHER_TOOL],
+            "required",
+            30,
+            id="required",
+        ),
+    ],
+)
+async def test_tool_call_auto_or_required(
+    client: openai.AsyncOpenAI,
+    server_config: ServerConfig,
+    tools: list,
+    tool_choice: object,
+    streaming_id_len_pre_v11: int,
+) -> None:
+    _requires_tool_parser(server_config)
+
+    models = await client.models.list()
+    model_name: str = models.data[0].id
+
+    create_kwargs: dict = {
+        "messages": ensure_system_prompt(MESSAGES_ASKING_FOR_TOOLS, server_config),
+        "temperature": 0,
+        "max_completion_tokens": 100,
+        "model": model_name,
+        "tools": tools,
+        "logprobs": False,
+        "seed": SEED,
+    }
+    if tool_choice is not _NOT_SET:
+        create_kwargs["tool_choice"] = tool_choice
+
+    # --- non-streaming ---
+    chat_completion = await client.chat.completions.create(**create_kwargs)
+
+    choice = chat_completion.choices[0]
+    tool_calls = choice.message.tool_calls
+
+    assert choice.finish_reason == "tool_calls"
+    assert tool_calls is not None and len(tool_calls) >= 1
+    assert tool_calls[0].function.name == "get_current_weather"
+    parsed_arguments = json.loads(tool_calls[0].function.arguments)
+    assert "city" in parsed_arguments
+    assert len(tool_calls[0].id) == 9
+
+    # --- streaming ---
+    stream = await client.chat.completions.create(**create_kwargs, stream=True)
+
+    result = await _collect_streamed_tool_call(stream)
+
+    assert result.finish_reason_count == 1
+    assert result.role_name == "assistant"
+    assert result.function_name == "get_current_weather"
+    streamed_args = json.loads(result.function_args_str)
+    assert isinstance(result.tool_call_id, str)
+    if _is_pre_v11(server_config):
+        assert len(result.tool_call_id) == streaming_id_len_pre_v11
+    else:
+        assert len(result.tool_call_id) == 9
+    assert parsed_arguments == streamed_args
+
+
+@pytest.mark.asyncio
+async def test_tool_call_none_with_tools(
+    client: openai.AsyncOpenAI, server_config: ServerConfig
+) -> None:
+    _requires_tool_parser(server_config)
+
+    models = await client.models.list()
+    model_name: str = models.data[0].id
+
+    # --- non-streaming ---
+    chat_completion = await client.chat.completions.create(
+        messages=ensure_system_prompt(MESSAGES_ASKING_FOR_TOOLS, server_config),
+        temperature=0,
+        max_completion_tokens=100,
+        model=model_name,
+        tools=[WEATHER_TOOL],
+        tool_choice="none",
+        logprobs=False,
+        seed=SEED,
+    )
+
+    choice = chat_completion.choices[0]
+
+    assert choice.finish_reason != "tool_calls"
+    assert choice.message.tool_calls is None or len(choice.message.tool_calls) == 0
+    assert choice.message.content is not None
+    # Without grammar enforcement, pre-v11 models may still emit [TOOL_CALLS]
+    if not _is_pre_v11(server_config):
+        assert "[TOOL_CALLS]" not in choice.message.content
+
+    non_streaming_content = choice.message.content
+
+    # --- streaming ---
+    stream = await client.chat.completions.create(
+        messages=ensure_system_prompt(MESSAGES_ASKING_FOR_TOOLS, server_config),
+        temperature=0,
+        max_completion_tokens=100,
+        model=model_name,
+        tools=[WEATHER_TOOL],
+        tool_choice="none",
+        logprobs=False,
+        seed=SEED,
+        stream=True,
+    )
+
+    # Pre-v11 models lack grammar enforcement, so the model may still
+    # emit tool calls even with tool_choice="none".
+    pre_v11 = _is_pre_v11(server_config)
+    result = await _collect_streamed_content(stream, no_tool_calls=not pre_v11)
+
+    assert result.finish_reason_count == 1
+    if not pre_v11:
+        assert result.finish_reason != "tool_calls"
+    streamed_content = "".join(result.chunks)
+    if not pre_v11:
+        assert "[TOOL_CALLS]" not in streamed_content
+        assert streamed_content == non_streaming_content
+
+
+@pytest.mark.asyncio
+async def test_chat_without_tools(
+    client: openai.AsyncOpenAI, server_config: ServerConfig
+) -> None:
+    models = await client.models.list()
+    model_name: str = models.data[0].id
+
+    # --- non-streaming ---
+    chat_completion = await client.chat.completions.create(
+        messages=ensure_system_prompt(MESSAGES_WITHOUT_TOOLS, server_config),
+        temperature=0,
+        max_completion_tokens=150,
+        model=model_name,
+        logprobs=False,
+        seed=SEED,
+    )
+
+    choice = chat_completion.choices[0]
+    output_text = choice.message.content
+
+    assert output_text is not None and len(output_text) > 0
+    assert choice.finish_reason != "tool_calls"
+    assert choice.message.tool_calls is None or len(choice.message.tool_calls) == 0
+
+    # --- streaming ---
+    stream = await client.chat.completions.create(
+        messages=ensure_system_prompt(MESSAGES_WITHOUT_TOOLS, server_config),
+        temperature=0,
+        max_completion_tokens=150,
+        model=model_name,
+        logprobs=False,
+        seed=SEED,
+        stream=True,
+    )
+
+    result = await _collect_streamed_content(
+        stream, expected_finish_reason=choice.finish_reason
+    )
+
+    assert result.role_sent
+    assert result.finish_reason_count == 1
+    assert len(result.chunks)
+    assert "".join(result.chunks) == output_text
+
+
+@pytest.mark.asyncio
+async def test_tool_call_with_results(
+    client: openai.AsyncOpenAI, server_config: ServerConfig
+) -> None:
+    _requires_tool_parser(server_config)
+
+    models = await client.models.list()
+    model_name: str = models.data[0].id
+
+    # --- non-streaming ---
+    chat_completion = await client.chat.completions.create(
+        messages=ensure_system_prompt(MESSAGES_WITH_TOOL_RESPONSE, server_config),
+        temperature=0,
+        max_completion_tokens=100,
+        model=model_name,
+        tools=[WEATHER_TOOL, SEARCH_TOOL],
+        logprobs=False,
+        seed=SEED,
+    )
+
+    choice = chat_completion.choices[0]
+
+    assert choice.finish_reason != "tool_calls"
+    assert choice.message.tool_calls is None or len(choice.message.tool_calls) == 0
+    assert choice.message.content is not None
+    assert "98" in choice.message.content
+
+    # --- streaming ---
+    stream = await client.chat.completions.create(
+        messages=ensure_system_prompt(MESSAGES_WITH_TOOL_RESPONSE, server_config),
+        temperature=0,
+        max_completion_tokens=100,
+        model=model_name,
+        tools=[WEATHER_TOOL, SEARCH_TOOL],
+        logprobs=False,
+        seed=SEED,
+        stream=True,
+    )
+
+    result = await _collect_streamed_content(
+        stream, expected_finish_reason=choice.finish_reason
+    )
+
+    assert result.role_sent
+    assert result.finish_reason_count == 1
+    assert len(result.chunks)
+    assert "".join(result.chunks) == choice.message.content
+
+
+def _requires_parallel(server_config: ServerConfig) -> None:
+    r"""Skip test if the model does not support parallel tool calls."""
+    if not server_config.get("supports_parallel"):
+        pytest.skip(
+            f"Skipping: {server_config['model']} does not support parallel tool calls"
+        )
+
+
+@pytest.mark.asyncio
+async def test_tool_call_parallel(
+    client: openai.AsyncOpenAI, server_config: ServerConfig
+) -> None:
+    _requires_tool_parser(server_config)
+    _requires_parallel(server_config)
+
+    models = await client.models.list()
+    model_name: str = models.data[0].id
+
+    # --- non-streaming ---
+    chat_completion = await client.chat.completions.create(
+        messages=ensure_system_prompt(
+            MESSAGES_ASKING_FOR_PARALLEL_TOOLS, server_config
+        ),
+        temperature=0,
+        max_completion_tokens=200,
+        model=model_name,
+        tools=[WEATHER_TOOL],
+        logprobs=False,
+        seed=SEED,
+    )
+
+    choice = chat_completion.choices[0]
+    tool_calls = choice.message.tool_calls
+
+    assert choice.finish_reason == "tool_calls"
+    assert tool_calls is not None and len(tool_calls) >= 2
+    for tc in tool_calls:
+        assert tc.type == "function"
+        assert tc.function.name == "get_current_weather"
+        assert isinstance(tc.function.arguments, str)
+        parsed = json.loads(tc.function.arguments)
+        assert "city" in parsed
+        assert len(tc.id) == 9
+
+    non_streaming_tool_calls = tool_calls
+
+    # --- streaming ---
+    stream = await client.chat.completions.create(
+        messages=ensure_system_prompt(
+            MESSAGES_ASKING_FOR_PARALLEL_TOOLS, server_config
+        ),
+        temperature=0,
+        max_completion_tokens=200,
+        model=model_name,
+        tools=[WEATHER_TOOL],
+        logprobs=False,
+        seed=SEED,
+        stream=True,
+    )
+
+    result = await _collect_streamed_parallel_tool_calls(stream)
+
+    assert result.finish_reason_count == 1
+    assert result.role_name == "assistant"
+    assert len(result.function_names) >= 2
+    assert all(name == "get_current_weather" for name in result.function_names)
+    assert len(result.tool_call_ids) >= 2
+    assert all(isinstance(tid, str) and len(tid) == 9 for tid in result.tool_call_ids)
+
+    for args_str in result.function_args_strs:
+        streamed_args = json.loads(args_str)
+        assert "city" in streamed_args
+
+    assert len(result.function_names) == len(non_streaming_tool_calls)
diff --git a/tests/tool_use/mistral/utils.py b/tests/tool_use/mistral/utils.py
index 4d772ba63793..01a2aaee6d2e 100644
--- a/tests/tool_use/mistral/utils.py
+++ b/tests/tool_use/mistral/utils.py
@@ -2,16 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 
-from typing_extensions import TypedDict
-
-
-class ServerConfig(TypedDict, total=False):
-    model: str
-    arguments: list[str]
-    system_prompt: str | None
-    supports_parallel: bool | None
-    supports_rocm: bool | None
-
+from tests.tool_use.utils import ServerConfig
 
 ARGS: list[str] = ["--max-model-len", "1024"]
 
@@ -21,6 +12,11 @@ class ServerConfig(TypedDict, total=False):
         "arguments": [
             "--tokenizer-mode",
             "mistral",
+            "--tool-call-parser",
+            "mistral",
+            "--enable-auto-tool-choice",
+            "--enforce-eager",
+            "--no-enable-prefix-caching",
             '--ignore-patterns="consolidated.safetensors"',
         ],
         "system_prompt": "You are a helpful assistant with access to tools. If a tool"
@@ -29,4 +25,22 @@ class ServerConfig(TypedDict, total=False):
         "without calling a tool. DO NOT CALL A TOOL THAT IS IRRELEVANT "
         "to the user's question - just respond to it normally.",
     },
+    "ministral-3b": {
+        "model": "mistralai/Ministral-3-3B-Instruct-2512",
+        "arguments": [
+            "--tokenizer-mode",
+            "mistral",
+            "--tool-call-parser",
+            "mistral",
+            "--enable-auto-tool-choice",
+            "--enforce-eager",
+            "--no-enable-prefix-caching",
+        ],
+        "system_prompt": "You are a helpful assistant with access to tools. If a tool"
+        " that you have would be helpful to answer a user query, "
+        "call the tool. Otherwise, answer the user's query directly "
+        "without calling a tool. DO NOT CALL A TOOL THAT IS IRRELEVANT "
+        "to the user's question - just respond to it normally.",
+        "supports_parallel": True,
+    },
 }
diff --git a/tests/tool_use/test_chat_completion_request_validations.py b/tests/tool_use/test_chat_completion_request_validations.py
index 69846f9adb12..70f4ac22541a 100644
--- a/tests/tool_use/test_chat_completion_request_validations.py
+++ b/tests/tool_use/test_chat_completion_request_validations.py
@@ -26,15 +26,15 @@ def test_chat_completion_request_with_no_tools():
     )
     assert request.tool_choice == "none"
 
-    # tools key present but empty
-    request = ChatCompletionRequest.model_validate(
-        {
-            "messages": [{"role": "user", "content": "Hello"}],
-            "model": "facebook/opt-125m",
-            "tools": [],
-        }
-    )
-    assert request.tool_choice == "none"
+    # tools key present but empty -- should be rejected
+    with pytest.raises(ValueError, match="must not be an empty array"):
+        ChatCompletionRequest.model_validate(
+            {
+                "messages": [{"role": "user", "content": "Hello"}],
+                "model": "facebook/opt-125m",
+                "tools": [],
+            }
+        )
 
 
 @pytest.mark.parametrize("tool_choice", ["auto", "required"])
diff --git a/tests/tool_use/test_gemma4_responses_adjust_request.py b/tests/tool_use/test_gemma4_responses_adjust_request.py
new file mode 100644
index 000000000000..e08896ee3237
--- /dev/null
+++ b/tests/tool_use/test_gemma4_responses_adjust_request.py
@@ -0,0 +1,116 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Regression tests for Responses API tool-calling request adjustment.
+
+Covers two bugs on the ``/v1/responses`` path that broke streaming tool
+calling for parsers relying on special-token delimiters (Gemma4):
+
+1. :class:`Gemma4ToolParser.adjust_request` used an
+   ``isinstance(request, ChatCompletionRequest)`` guard, so a
+   :class:`ResponsesRequest` with tools never had
+   ``skip_special_tokens`` flipped to ``False``. The default (``True``)
+   stripped ``<|tool_call>`` / ``<tool_call|>`` delimiters, causing
+   :meth:`Gemma4ToolParser.extract_tool_calls_streaming` to fall through
+   to the content branch and leak the raw ``call:fn{...}`` body via
+   ``response.output_text.delta``.
+
+2. :meth:`ToolParser.adjust_request` built
+   :class:`ResponseTextConfig` in two steps (bare constructor then
+   ``.format = ...``). Under Pydantic v2 the later assignment is not
+   tracked in ``__fields_set__``, which can drop the nested config from
+   ``model_dump``. It also passed a ``description`` kwarg carrying the
+   wrong-purpose string ``"Response format for tool calling"``.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from openai.types.responses.tool_param import FunctionToolParam
+
+from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
+from vllm.tool_parsers.abstract_tool_parser import ToolParser
+from vllm.tool_parsers.gemma4_tool_parser import Gemma4ToolParser
+
+
+def _get_weather_tool() -> FunctionToolParam:
+    return FunctionToolParam(
+        type="function",
+        name="get_weather",
+        description="Get current weather for a city",
+        parameters={
+            "type": "object",
+            "properties": {"city": {"type": "string"}},
+            "required": ["city"],
+        },
+        strict=True,
+    )
+
+
+def _build_responses_request(*, tool_choice: str) -> ResponsesRequest:
+    return ResponsesRequest(
+        model="gemma4-test",
+        input=[{"role": "user", "content": "What is the weather in Hanoi?"}],
+        tools=[_get_weather_tool()],
+        tool_choice=tool_choice,
+        stream=True,
+        max_output_tokens=200,
+    )
+
+
+class _StubTokenizer:
+    """Minimal tokenizer stub to satisfy ``Gemma4ToolParser.__init__``."""
+
+    def get_vocab(self) -> dict[str, int]:
+        return {"<|tool_call>": 256_000, "<tool_call|>": 256_001, '<|"|>': 52}
+
+
+def test_gemma4_adjust_request_sets_skip_special_tokens_on_responses() -> None:
+    """``Gemma4ToolParser.adjust_request`` must flip
+    ``skip_special_tokens=False`` for both ``ChatCompletionRequest`` and
+    ``ResponsesRequest`` so that ``<|tool_call>`` delimiters reach the
+    streaming extractor. The previous
+    ``isinstance(ChatCompletionRequest)`` guard omitted the Responses
+    path, causing raw ``call:fn{...}`` text to leak via
+    ``response.output_text.delta``.
+    """
+    parser = Gemma4ToolParser.__new__(Gemma4ToolParser)
+    parser.model_tokenizer = _StubTokenizer()
+
+    request = _build_responses_request(tool_choice="auto")
+    assert request.skip_special_tokens is True, (
+        "Precondition: ResponsesRequest.skip_special_tokens default is True"
+    )
+
+    Gemma4ToolParser.adjust_request(parser, request)
+
+    assert request.skip_special_tokens is False
+
+
+def test_tool_parser_adjust_request_builds_valid_response_text_config() -> None:
+    """``ToolParser.adjust_request`` must produce a ``ResponseTextConfig``
+    whose dumped form contains the JSON schema under the ``schema`` alias
+    and does not leak the unrelated ``"Response format for tool calling"``
+    description string that the previous two-step construction injected.
+    """
+    parser = ToolParser.__new__(ToolParser)
+    parser.model_tokenizer = None
+
+    request = _build_responses_request(tool_choice="required")
+    ToolParser.adjust_request(parser, request)
+
+    assert request.text is not None
+    assert request.text.format is not None
+    assert request.text.format.type == "json_schema"
+
+    dump: dict[str, Any] = request.text.model_dump(mode="json", by_alias=True)
+    fmt = dump.get("format") or {}
+    assert fmt.get("type") == "json_schema"
+    assert fmt.get("name") == "tool_calling_response"
+    assert fmt.get("strict") is True
+    # Nested config must be present under the alias. Two-step Pydantic v2
+    # construction could drop it from __fields_set__.
+    assert "schema" in fmt and isinstance(fmt["schema"], dict)
+    # The old code passed a wrong-purpose string; valid field should now
+    # either be absent or None (the openai-python default).
+    assert fmt.get("description") in (None, "")
diff --git a/tests/tool_use/test_responses_request_validations.py b/tests/tool_use/test_responses_request_validations.py
new file mode 100644
index 000000000000..63a1828c5009
--- /dev/null
+++ b/tests/tool_use/test_responses_request_validations.py
@@ -0,0 +1,184 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+from pydantic import ValidationError
+
+from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
+
+SAMPLE_TOOL = {
+    "type": "function",
+    "name": "get_weather",
+    "description": "Get current weather",
+    "parameters": {
+        "type": "object",
+        "properties": {"location": {"type": "string", "description": "City name"}},
+        "required": ["location"],
+    },
+}
+
+NAMED_TOOL_CHOICE = {
+    "type": "function",
+    "name": "get_weather",
+}
+
+
+def test_responses_request_with_no_tools():
+    # tools key is not present — defaults tool_choice to "none"
+    request = ResponsesRequest.model_validate({"input": "Hello", "model": "test-model"})
+    assert request.tool_choice == "none"
+
+    # tools key present but empty
+    request = ResponsesRequest.model_validate(
+        {"input": "Hello", "model": "test-model", "tools": []}
+    )
+    assert request.tool_choice == "none"
+
+
+def test_responses_request_no_tools_tool_choice_none():
+    request = ResponsesRequest.model_validate(
+        {"input": "Hello", "model": "test-model", "tool_choice": "none"}
+    )
+    assert request.tool_choice == "none"
+
+
+def test_responses_request_no_tools_tool_choice_auto():
+    request = ResponsesRequest.model_validate(
+        {"input": "Hello", "model": "test-model", "tool_choice": "auto"}
+    )
+    assert request.tool_choice == "none"
+
+
+@pytest.mark.parametrize("tools", [None, []])
+def test_responses_request_required_without_tools(tools):
+    kwargs = {"input": "Hello", "model": "test-model", "tool_choice": "required"}
+    if tools is not None:
+        kwargs["tools"] = tools
+    with pytest.raises(
+        ValidationError, match="Tool choice 'required' must be specified"
+    ):
+        ResponsesRequest.model_validate(kwargs)
+
+
+def test_responses_request_named_tool_choice_without_tools():
+    with pytest.raises(ValidationError, match="not found in 'tools' parameter"):
+        ResponsesRequest.model_validate(
+            {
+                "input": "Hello",
+                "model": "test-model",
+                "tool_choice": NAMED_TOOL_CHOICE,
+            }
+        )
+
+
+def test_responses_request_with_tools_default_tool_choice():
+    request = ResponsesRequest.model_validate(
+        {"input": "Hello", "model": "test-model", "tools": [SAMPLE_TOOL]}
+    )
+    assert request.tool_choice == "auto"
+
+
+def test_responses_request_with_tools_tool_choice_none():
+    request = ResponsesRequest.model_validate(
+        {
+            "input": "Hello",
+            "model": "test-model",
+            "tools": [SAMPLE_TOOL],
+            "tool_choice": "none",
+        }
+    )
+    assert request.tool_choice == "none"
+
+
+def test_responses_request_named_tool_choice_matching():
+    request = ResponsesRequest.model_validate(
+        {
+            "input": "Hello",
+            "model": "test-model",
+            "tools": [SAMPLE_TOOL],
+            "tool_choice": NAMED_TOOL_CHOICE,
+        }
+    )
+    assert request.tool_choice.type == "function"
+    assert request.tool_choice.name == "get_weather"
+
+
+def test_responses_request_named_tool_choice_not_matching():
+    with pytest.raises(ValidationError, match="not found in 'tools' parameter"):
+        ResponsesRequest.model_validate(
+            {
+                "input": "Hello",
+                "model": "test-model",
+                "tools": [SAMPLE_TOOL],
+                "tool_choice": {"type": "function", "name": "nonexistent"},
+            }
+        )
+
+
+def test_responses_request_with_tools_tool_choice_auto():
+    request = ResponsesRequest.model_validate(
+        {
+            "input": "Hello",
+            "model": "test-model",
+            "tools": [SAMPLE_TOOL],
+            "tool_choice": "auto",
+        }
+    )
+    assert request.tool_choice == "auto"
+
+
+def test_responses_request_with_tools_tool_choice_required():
+    request = ResponsesRequest.model_validate(
+        {
+            "input": "Hello",
+            "model": "test-model",
+            "tools": [SAMPLE_TOOL],
+            "tool_choice": "required",
+        }
+    )
+    assert request.tool_choice == "required"
+
+
+def test_responses_request_empty_tools_tool_choice_none():
+    request = ResponsesRequest.model_validate(
+        {"input": "Hello", "model": "test-model", "tools": [], "tool_choice": "none"}
+    )
+    assert request.tool_choice == "none"
+
+
+def test_responses_request_empty_tools_tool_choice_auto():
+    request = ResponsesRequest.model_validate(
+        {"input": "Hello", "model": "test-model", "tools": [], "tool_choice": "auto"}
+    )
+    assert request.tool_choice == "none"
+
+
+@pytest.mark.parametrize(
+    "tool_choice",
+    [
+        {"type": "function"},
+        {"type": "function", "name": ""},
+    ],
+)
+def test_responses_request_named_tool_choice_missing_name(tool_choice):
+    with pytest.raises(ValidationError, match="not found in 'tools' parameter"):
+        ResponsesRequest.model_validate(
+            {
+                "input": "Hello",
+                "model": "test-model",
+                "tools": [SAMPLE_TOOL],
+                "tool_choice": tool_choice,
+            }
+        )
+
+
+def test_responses_request_empty_tools_named_tool_choice():
+    with pytest.raises(ValidationError, match="not found in 'tools' parameter"):
+        ResponsesRequest.model_validate(
+            {
+                "input": "Hello",
+                "model": "test-model",
+                "tools": [],
+                "tool_choice": NAMED_TOOL_CHOICE,
+            }
+        )
diff --git a/tests/tool_use/test_tool_choice_required.py b/tests/tool_use/test_tool_choice_required.py
index 01c1360818eb..e99165f3569a 100644
--- a/tests/tool_use/test_tool_choice_required.py
+++ b/tests/tool_use/test_tool_choice_required.py
@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import json
 from copy import deepcopy
-from unittest.mock import MagicMock
 
 import pytest
 import regex as re
@@ -11,7 +10,7 @@
 from vllm.entrypoints.openai.chat_completion.protocol import (
     ChatCompletionToolsParam,
 )
-from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
+from vllm.tool_parsers.streaming import extract_required_tool_call_streaming
 from vllm.tool_parsers.utils import get_json_schema_from_tools
 
 pytestmark = pytest.mark.cpu_test
@@ -281,8 +280,6 @@ def test_structured_outputs_json_without_parameters(
 @pytest.mark.parametrize("empty_params", [False, True])
 @pytest.mark.parametrize("delta_len", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
 def test_streaming_output_valid(output, empty_params, delta_len):
-    self = MagicMock()
-
     output = deepcopy(output)
     if empty_params:
         output = [{"name": o["name"], "parameters": {}} for o in output]
@@ -295,14 +292,13 @@ def test_streaming_output_valid(output, empty_params, delta_len):
         delta_text = output_json[i : i + delta_len]
         current_text = previous_text + delta_text
 
-        delta_message, function_name_returned = (
-            OpenAIServingChat.extract_tool_call_required_streaming(
-                self,
-                previous_text=previous_text,
-                current_text=current_text,
-                delta_text=delta_text,
-                function_name_returned=function_name_returned,
-            )
+        delta_message, function_name_returned = extract_required_tool_call_streaming(
+            previous_text=previous_text,
+            current_text=current_text,
+            delta_text=delta_text,
+            function_name_returned=function_name_returned,
+            tool_call_idx=None,
+            tool_call_id_type="random",
         )
 
         if delta_message:
@@ -332,8 +328,6 @@ def test_streaming_output_valid(output, empty_params, delta_len):
 
 
 def test_streaming_output_valid_with_trailing_extra_data():
-    self = MagicMock()
-
     output = [{"name": "get_current_weather", "parameters": {"city": "Vienna"}}]
     output_json = json.dumps(output) + "\nDONE"
 
@@ -345,14 +339,13 @@ def test_streaming_output_valid_with_trailing_extra_data():
         delta_text = output_json[i : i + delta_len]
         current_text = previous_text + delta_text
 
-        delta_message, function_name_returned = (
-            OpenAIServingChat.extract_tool_call_required_streaming(
-                self,
-                previous_text=previous_text,
-                current_text=current_text,
-                delta_text=delta_text,
-                function_name_returned=function_name_returned,
-            )
+        delta_message, function_name_returned = extract_required_tool_call_streaming(
+            previous_text=previous_text,
+            current_text=current_text,
+            delta_text=delta_text,
+            function_name_returned=function_name_returned,
+            tool_call_idx=None,
+            tool_call_id_type="random",
         )
 
         if delta_message:
diff --git a/tests/tools/test_docker_build_metadata_args.py b/tests/tools/test_docker_build_metadata_args.py
new file mode 100644
index 000000000000..fa2eac558f53
--- /dev/null
+++ b/tests/tools/test_docker_build_metadata_args.py
@@ -0,0 +1,152 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+import shlex
+import subprocess
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+HELPER = REPO_ROOT / ".buildkite" / "scripts" / "docker-build-metadata-args.sh"
+
+
+def run_helper(
+    *args: str,
+    env: dict[str, str] | None = None,
+    path: str | None = None,
+) -> list[str]:
+    helper_env = {"PATH": path or os.environ["PATH"]}
+    if env:
+        helper_env.update(env)
+    result = subprocess.run(
+        ["bash", str(HELPER), *args],
+        check=True,
+        env=helper_env,
+        stdout=subprocess.PIPE,
+        text=True,
+    )
+    return shlex.split(result.stdout)
+
+
+def option_values(args: list[str], option: str) -> list[str]:
+    return [args[i + 1] for i, arg in enumerate(args[:-1]) if arg == option]
+
+
+def build_args(args: list[str]) -> dict[str, str]:
+    values = {}
+    for value in option_values(args, "--build-arg"):
+        key, arg_value = value.split("=", 1)
+        values[key] = arg_value
+    return values
+
+
+def test_release_metadata_args_prefer_pipeline_id() -> None:
+    args = run_helper(
+        "cu130-ubuntu2404",
+        env={
+            "BUILDKITE": "1",
+            "BUILDKITE_COMMIT": "abc123",
+            "BUILDKITE_PIPELINE_ID": "pipe-uuid",
+            "BUILDKITE_PIPELINE_SLUG": "release",
+            "BUILDKITE_BUILD_URL": "https://buildkite.example/vllm/builds/1",
+            "RELEASE_VERSION": "v0.20.0",
+        },
+    )
+
+    assert build_args(args) == {
+        "VLLM_BUILD_COMMIT": "abc123",
+        "VLLM_BUILD_PIPELINE": "pipe-uuid",
+        "VLLM_BUILD_URL": "https://buildkite.example/vllm/builds/1",
+        "VLLM_IMAGE_TAG": "vllm/vllm-openai:v0.20.0-cu130-ubuntu2404",
+    }
+    expected_tag = (
+        "public.ecr.aws/q9t5s3a7/vllm-release-repo:"
+        f"abc123-{os.uname().machine}-cu130-ubuntu2404"
+    )
+    assert option_values(args, "--tag") == [expected_tag]
+
+
+def test_nightly_metadata_args_fall_back_to_pipeline_slug() -> None:
+    args = run_helper(
+        "ubuntu2404",
+        env={
+            "BUILDKITE": "1",
+            "BUILDKITE_COMMIT": "def456",
+            "BUILDKITE_PIPELINE_SLUG": "release",
+            "BUILDKITE_BUILD_URL": "https://buildkite.example/vllm/builds/2",
+            "NIGHTLY": "1",
+        },
+    )
+
+    assert build_args(args) == {
+        "VLLM_BUILD_COMMIT": "def456",
+        "VLLM_BUILD_PIPELINE": "release",
+        "VLLM_BUILD_URL": "https://buildkite.example/vllm/builds/2",
+        "VLLM_IMAGE_TAG": "vllm/vllm-openai:nightly-def456-ubuntu2404",
+    }
+    expected_tag = (
+        "public.ecr.aws/q9t5s3a7/vllm-release-repo:"
+        f"def456-{os.uname().machine}-ubuntu2404"
+    )
+    assert option_values(args, "--tag") == [expected_tag]
+
+
+def test_local_metadata_args_use_local_overrides() -> None:
+    args = run_helper(
+        env={
+            "VLLM_IMAGE_TAG": "local/test:dev",
+            "VLLM_BUILD_COMMIT": "localsha",
+            "VLLM_BUILD_PIPELINE": "local-pipeline",
+            "VLLM_BUILD_URL": "https://buildkite.example/local",
+        },
+    )
+
+    assert build_args(args) == {
+        "VLLM_BUILD_COMMIT": "localsha",
+        "VLLM_BUILD_PIPELINE": "local-pipeline",
+        "VLLM_BUILD_URL": "https://buildkite.example/local",
+        "VLLM_IMAGE_TAG": "local/test:dev",
+    }
+    assert option_values(args, "--tag") == ["local/test:dev"]
+
+
+def test_release_version_lookup_failure_falls_back_to_commit(
+    tmp_path: Path,
+) -> None:
+    fake_bin = tmp_path / "bin"
+    fake_bin.mkdir()
+    buildkite_agent = fake_bin / "buildkite-agent"
+    buildkite_agent.write_text("#!/bin/sh\nexit 1\n")
+    buildkite_agent.chmod(0o755)
+
+    args = run_helper(
+        "cu129",
+        env={
+            "BUILDKITE": "1",
+            "BUILDKITE_COMMIT": "fallback123",
+            "BUILDKITE_PIPELINE_SLUG": "release",
+        },
+        path=f"{fake_bin}:{os.environ['PATH']}",
+    )
+
+    assert build_args(args)["VLLM_IMAGE_TAG"] == ("vllm/vllm-openai:vfallback123-cu129")
+
+
+def test_vllm_openai_image_embeds_metadata_contract() -> None:
+    dockerfile = (REPO_ROOT / "docker" / "Dockerfile").read_text()
+
+    for expected in (
+        "ARG VLLM_BUILD_COMMIT",
+        "ARG VLLM_BUILD_PIPELINE",
+        "ARG VLLM_BUILD_URL",
+        "ARG VLLM_IMAGE_TAG",
+        "VLLM_BUILD_COMMIT=${VLLM_BUILD_COMMIT:-unknown}",
+        "VLLM_BUILD_PIPELINE=${VLLM_BUILD_PIPELINE:-local}",
+        "VLLM_BUILD_URL=${VLLM_BUILD_URL:-}",
+        "VLLM_IMAGE_TAG=${VLLM_IMAGE_TAG:-local/vllm-openai:dev}",
+        'ai.vllm.build.commit="${VLLM_BUILD_COMMIT}"',
+        'ai.vllm.build.pipeline="${VLLM_BUILD_PIPELINE}"',
+        'ai.vllm.build.url="${VLLM_BUILD_URL}"',
+        'ai.vllm.image.tag="${VLLM_IMAGE_TAG}"',
+    ):
+        assert expected in dockerfile
diff --git a/tests/transformers_utils/test_hf_overrides_model_type.py b/tests/transformers_utils/test_hf_overrides_model_type.py
new file mode 100644
index 000000000000..cce10d3b084e
--- /dev/null
+++ b/tests/transformers_utils/test_hf_overrides_model_type.py
@@ -0,0 +1,81 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Test that hf_overrides model_type returns the correct config class."""
+
+import json
+import tempfile
+
+from transformers import PretrainedConfig
+
+from vllm.transformers_utils.config import _CONFIG_REGISTRY, get_config
+
+
+class _TestCustomConfig(PretrainedConfig):
+    model_type = "test_custom_model"
+
+    def __init__(self, custom_attr=42, **kw):
+        super().__init__(**kw)
+        self.custom_attr = custom_attr
+
+
+def test_hf_overrides_model_type_returns_correct_config_class():
+    """When hf_overrides sets model_type to a registered custom type whose
+    checkpoint has a *different* model_type on disk, get_config() must return
+    an instance of the registered config class — not the class that matches
+    the on-disk model_type."""
+
+    # Register the custom config
+    _CONFIG_REGISTRY["test_custom_model"] = _TestCustomConfig
+
+    try:
+        with tempfile.TemporaryDirectory() as tmpdir:
+            # Checkpoint says model_type="mixtral" on disk
+            cfg = {
+                "model_type": "mixtral",
+                "hidden_size": 128,
+                "num_hidden_layers": 2,
+                "num_attention_heads": 4,
+                "num_key_value_heads": 4,
+                "intermediate_size": 256,
+                "num_local_experts": 4,
+                "num_experts_per_tok": 2,
+            }
+            with open(f"{tmpdir}/config.json", "w") as f:
+                json.dump(cfg, f)
+
+            config = get_config(
+                tmpdir,
+                trust_remote_code=False,
+                hf_overrides_kw={
+                    "model_type": "test_custom_model",
+                },
+            )
+
+            from transformers import AutoConfig
+            from transformers.models.auto.configuration_auto import CONFIG_MAPPING
+
+            # get_config() returns the registered custom class
+            assert isinstance(config, _TestCustomConfig), (
+                f"Expected _TestCustomConfig, got {type(config).__name__}"
+            )
+
+            # AutoConfig has _TestCustomConfig registered under both
+            # the overridden model_type and the on-disk model_type
+            assert CONFIG_MAPPING["test_custom_model"] is _TestCustomConfig
+            assert CONFIG_MAPPING["mixtral"] is _TestCustomConfig
+
+            # AutoConfig.from_pretrained now returns _TestCustomConfig
+            # for this checkpoint (even though its on-disk model_type
+            # is "mixtral")
+            auto_config = AutoConfig.from_pretrained(tmpdir)
+            assert isinstance(auto_config, _TestCustomConfig), (
+                f"Expected _TestCustomConfig from AutoConfig, got "
+                f"{type(auto_config).__name__}"
+            )
+    finally:
+        _CONFIG_REGISTRY.pop("test_custom_model", None)
+        # Restore the original mixtral AutoConfig mapping to avoid
+        # side effects on other tests in the same process
+        from transformers import AutoConfig, MixtralConfig
+
+        AutoConfig.register("mixtral", MixtralConfig, exist_ok=True)
diff --git a/tests/utils.py b/tests/utils.py
index 7af72cb730b0..68a9031a2c4e 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import asyncio
+import atexit
 import contextlib
 import copy
 import functools
@@ -14,13 +15,14 @@
 import subprocess
 import sys
 import tempfile
+import threading
 import time
 import warnings
-from collections.abc import Callable, Iterable
-from contextlib import ExitStack, contextmanager, suppress
-from multiprocessing import Process
+from collections.abc import Callable, Iterable, Sequence
+from contextlib import ExitStack, contextmanager
+from multiprocessing import Process, get_context
 from pathlib import Path
-from typing import Any, Literal
+from typing import Any, Literal, cast
 from unittest.mock import patch
 
 import anthropic
@@ -62,8 +64,6 @@
 FP8_DTYPE = current_platform.fp8_dtype()
 
 if current_platform.is_rocm():
-    import threading
-
     from amdsmi import (
         amdsmi_get_gpu_vram_usage,
         amdsmi_get_processor_handles,
@@ -127,6 +127,21 @@ def _nvml():
 )
 
 
+def requires_spawn_multiprocessing() -> bool:
+    """Whether this platform requires spawn instead of fork for test processes."""
+    return current_platform.is_rocm() or current_platform.is_xpu()
+
+
+def _run_in_new_process_group(
+    child_process_fxn: Callable[[dict[str, str] | None, str, list[str]], None],
+    env_dict: dict[str, str] | None,
+    model: str,
+    vllm_serve_args: list[str],
+) -> None:
+    os.setsid()
+    child_process_fxn(env_dict, model, vllm_serve_args)
+
+
 class RemoteVLLMServer:
     """Base class for launching vLLM server subprocesses for testing.
 
@@ -135,6 +150,11 @@ class RemoteVLLMServer:
     """
 
     DUMMY_API_KEY = "token-abc123"  # vLLM's OpenAI server does not need API key
+    _active_servers: set["RemoteVLLMServer"] = set()
+    _active_servers_lock = threading.RLock()
+    _cleanup_hooks_registered = False
+    _signal_hooks_registered = False
+    _previous_signal_handlers: dict[int, Any] = {}
     proc: subprocess.Popen
 
     def _create_cli_subcommand(self):
@@ -210,6 +230,7 @@ def __init__(
         )
 
         self._pre_download_model(model, args)
+        self._shutdown_complete = False
 
         # Record GPU memory before server start so we know what
         # "released" looks like.
@@ -222,6 +243,7 @@ def __init__(
             )
 
         self._start_server(model, vllm_serve_args, env_dict)
+        self._register_active_server()
         max_wait_seconds = max_wait_seconds or 480
         try:
             self._wait_for_server(url=self.url_for("health"), timeout=max_wait_seconds)
@@ -247,6 +269,78 @@ def _shutdown(self) -> None:
         (when the server fails to start). Must be safe to call even if
         the process is already dead.
         """
+        if self._shutdown_complete:
+            return
+
+        self._shutdown_complete = True
+        try:
+            self._terminate_process_tree()
+            self._wait_for_gpu_memory_release()
+        finally:
+            self._unregister_active_server()
+
+    @classmethod
+    def _ensure_cleanup_hooks_registered(cls) -> None:
+        """Register process-exit cleanup for detached server subprocesses."""
+        root_cls = RemoteVLLMServer
+        with root_cls._active_servers_lock:
+            if not root_cls._cleanup_hooks_registered:
+                atexit.register(root_cls._shutdown_active_servers)
+                root_cls._cleanup_hooks_registered = True
+
+            if (
+                threading.current_thread() is threading.main_thread()
+                and not root_cls._signal_hooks_registered
+            ):
+                for signum in (signal.SIGTERM, signal.SIGINT):
+                    root_cls._previous_signal_handlers[signum] = signal.getsignal(
+                        signum
+                    )
+                    signal.signal(signum, root_cls._handle_parent_signal)
+                root_cls._signal_hooks_registered = True
+
+    def _register_active_server(self) -> None:
+        """Track this server so parent-process exits still clean it up."""
+        RemoteVLLMServer._ensure_cleanup_hooks_registered()
+        with RemoteVLLMServer._active_servers_lock:
+            RemoteVLLMServer._active_servers.add(self)
+
+    def _unregister_active_server(self) -> None:
+        with RemoteVLLMServer._active_servers_lock:
+            RemoteVLLMServer._active_servers.discard(self)
+
+    @classmethod
+    def _shutdown_active_servers(cls) -> None:
+        """Best-effort shutdown for all live RemoteVLLMServer instances."""
+        with cls._active_servers_lock:
+            servers = list(cls._active_servers)
+
+        for server in servers:
+            with contextlib.suppress(Exception):
+                server._shutdown()
+
+    @classmethod
+    def _handle_parent_signal(cls, signum, frame) -> None:
+        """Clean up detached servers before letting the signal terminate pytest."""
+        cls._shutdown_active_servers()
+
+        previous_handler = cls._previous_signal_handlers.get(signum, signal.SIG_DFL)
+        if callable(previous_handler):
+            previous_handler(signum, frame)
+        elif previous_handler == signal.SIG_IGN:
+            return
+        elif signum == signal.SIGINT:
+            raise KeyboardInterrupt
+        else:
+            raise SystemExit(128 + signum)
+
+    def _terminate_process_tree(self) -> None:
+        """Kill the server process tree without waiting for GPU memory release.
+
+        Split out from ``_shutdown`` so that ``shutdown_many`` can run this
+        phase in parallel for sibling servers and then wait for GPU memory
+        release once at the end.
+        """
         pid = self.proc.pid
 
         # Get the process group ID. Because we used
@@ -288,9 +382,56 @@ def _shutdown(self) -> None:
         # prevent VRAM from being reclaimed by the driver.
         self._kill_process_group_survivors(pgid)
 
-        # Wait for GPU memory to actually be freed, not just
-        # "stabilized at whatever level it's at".
-        self._wait_for_gpu_memory_release()
+    @classmethod
+    def shutdown_many(cls, servers: Sequence["RemoteVLLMServer"]) -> None:
+        """Shut down multiple sibling servers and wait for GPU memory once.
+
+        Test fixtures that hold several ``RemoteVLLMServer`` instances at
+        once must NOT shut them down by calling each server's ``__exit__``
+        sequentially: every server measures total GPU memory across all
+        visible devices in ``_wait_for_gpu_memory_release``, so the first
+        server's wait blocks the full timeout because later sibling
+        servers are still holding GPU memory.
+
+        Instead, this method terminates every server's process tree in
+        parallel, then runs the GPU-memory-release wait once against the
+        earliest recorded baseline (memory before any server started).
+        """
+        if not servers:
+            return
+
+        for server in servers:
+            server._shutdown_complete = True
+
+        threads = [
+            threading.Thread(
+                target=s._terminate_process_tree,
+                name=f"shutdown-{s.proc.pid}",
+                daemon=True,
+            )
+            for s in servers
+        ]
+        for t in threads:
+            t.start()
+        for t in threads:
+            t.join()
+
+        # Use the smallest pre-server baseline so the wait targets memory
+        # usage before *any* of these sibling servers started, not after
+        # earlier siblings had already allocated.
+        earliest = min(
+            servers,
+            key=lambda s: (
+                float("inf")
+                if s._pre_server_gpu_memory is None
+                else s._pre_server_gpu_memory
+            ),
+        )
+        try:
+            earliest._wait_for_gpu_memory_release()
+        finally:
+            for server in servers:
+                server._unregister_active_server()
 
     def _kill_process_group_survivors(
         self, pgid: int | None, timeout: float = 15.0
@@ -612,8 +753,11 @@ class RemoteOpenAIServerCustom(RemoteOpenAIServer):
     def _start_server(
         self, model: str, vllm_serve_args: list[str], env_dict: dict[str, str] | None
     ) -> None:
-        self.proc: Process = Process(
-            target=self.child_process_fxn, args=(env_dict, model, vllm_serve_args)
+        method = "spawn" if requires_spawn_multiprocessing() else "fork"
+        ctx = get_context(method)
+        self.proc: Process = cast(Any, ctx).Process(
+            target=_run_in_new_process_group,
+            args=(self.child_process_fxn, env_dict, model, vllm_serve_args),
         )  # type: ignore[assignment]
         self.proc.start()
 
@@ -643,12 +787,40 @@ def __init__(
     def _poll(self) -> int | None:
         return self.proc.exitcode
 
-    def __exit__(self, exc_type, exc_value, traceback):
-        self.proc.terminate()
-        self.proc.join(8)
+    def _terminate_process_tree(self) -> None:
+        pid = self.proc.pid
+        if pid is None:
+            return
+
+        pgid: int | None
+        try:
+            pgid = os.getpgid(pid)
+            # _run_in_new_process_group should make the child the group
+            # leader. Avoid signaling pytest's process group if startup failed
+            # before os.setsid() ran.
+            if pgid != pid:
+                pgid = None
+        except (ProcessLookupError, OSError):
+            pgid = None
+
+        with contextlib.suppress(ProcessLookupError, OSError):
+            self.proc.terminate()
+            print(f"[RemoteOpenAIServerCustom] Sent SIGTERM to process {pid}")
+
+        self.proc.join(15)
         if self.proc.is_alive():
-            # force kill if needed
-            self.proc.kill()
+            print(
+                f"[RemoteOpenAIServerCustom] Server {pid} did not respond "
+                "to SIGTERM, sending SIGKILL to process group"
+            )
+            if pgid is not None:
+                with contextlib.suppress(ProcessLookupError, OSError):
+                    os.killpg(pgid, signal.SIGKILL)
+            else:
+                self.proc.kill()
+            self.proc.join(10)
+
+        self._kill_process_group_survivors(pgid)
 
 
 def _test_completion(
@@ -656,6 +828,7 @@ def _test_completion(
     model: str,
     prompt: str,
     token_ids: list[int],
+    include_seeded_sampling: bool = True,
 ):
     results = []
 
@@ -690,33 +863,40 @@ def _test_completion(
         }
     )
 
-    # test seeded random sampling
-    completion = client.completions.create(
-        model=model, prompt=prompt, max_tokens=5, seed=33, temperature=1.0
-    )
+    if include_seeded_sampling:
+        # test seeded random sampling
+        completion = client.completions.create(
+            model=model, prompt=prompt, max_tokens=5, seed=33, temperature=1.0
+        )
 
-    results.append(
-        {
-            "test": "seeded_sampling",
-            "text": completion.choices[0].text,
-            "finish_reason": completion.choices[0].finish_reason,
-            "usage": completion.usage,
-        }
-    )
+        results.append(
+            {
+                "test": "seeded_sampling",
+                "text": completion.choices[0].text,
+                "finish_reason": completion.choices[0].finish_reason,
+                "usage": completion.usage,
+            }
+        )
 
-    # test seeded random sampling with multiple prompts
-    completion = client.completions.create(
-        model=model, prompt=[prompt, prompt], max_tokens=5, seed=33, temperature=1.0
-    )
+        # test seeded random sampling with multiple prompts
+        completion = client.completions.create(
+            model=model,
+            prompt=[prompt, prompt],
+            max_tokens=5,
+            seed=33,
+            temperature=1.0,
+        )
 
-    results.append(
-        {
-            "test": "seeded_sampling",
-            "text": [choice.text for choice in completion.choices],
-            "finish_reason": [choice.finish_reason for choice in completion.choices],
-            "usage": completion.usage,
-        }
-    )
+        results.append(
+            {
+                "test": "seeded_sampling",
+                "text": [choice.text for choice in completion.choices],
+                "finish_reason": [
+                    choice.finish_reason for choice in completion.choices
+                ],
+                "usage": completion.usage,
+            }
+        )
 
     # test simple list
     batch = client.completions.create(
@@ -911,6 +1091,7 @@ def compare_two_settings(
     *,
     method: str = "generate",
     max_wait_seconds: float | None = None,
+    include_seeded_sampling: bool = True,
 ) -> None:
     """
     Launch API server with two different sets of arguments/environments
@@ -922,6 +1103,8 @@ def compare_two_settings(
         arg2: The second set of arguments to pass to the API server.
         env1: The first set of environment variables to pass to the API server.
         env2: The second set of environment variables to pass to the API server.
+        include_seeded_sampling: Whether to include temperature=1.0 seeded
+            sampling checks in the default generate comparison.
     """
 
     compare_all_settings(
@@ -930,6 +1113,7 @@ def compare_two_settings(
         [env1, env2],
         method=method,
         max_wait_seconds=max_wait_seconds,
+        include_seeded_sampling=include_seeded_sampling,
     )
 
 
@@ -940,6 +1124,7 @@ def compare_all_settings(
     *,
     method: str = "generate",
     max_wait_seconds: float | None = None,
+    include_seeded_sampling: bool = True,
 ) -> None:
     """
     Launch API server with several different sets of arguments/environments
@@ -948,6 +1133,8 @@ def compare_all_settings(
         model: The model to test.
         all_args: A list of argument lists to pass to the API server.
         all_envs: A list of environment dictionaries to pass to the API server.
+        include_seeded_sampling: Whether to include temperature=1.0 seeded
+            sampling checks in the default generate comparison.
     """
 
     trust_remote_code = False
@@ -1008,7 +1195,13 @@ def compare_all_settings(
             )
 
             if method == "generate":
-                results += _test_completion(client, model, prompt, token_ids)
+                results += _test_completion(
+                    client,
+                    model,
+                    prompt,
+                    token_ids,
+                    include_seeded_sampling=include_seeded_sampling,
+                )
             elif method == "generate_close":
                 results += _test_completion_close(client, model, prompt)
             elif method == "generate_chat":
@@ -1261,9 +1454,6 @@ def fork_new_process_for_each_test(func: Callable[_P, None]) -> Callable[_P, Non
 
     @functools.wraps(func)
     def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> None:
-        # Make the process the leader of its own process group
-        # to avoid sending SIGTERM to the parent process
-        os.setpgrp()
         from _pytest.outcomes import Skipped
 
         # Create a unique temporary file to store exception info from child
@@ -1283,6 +1473,9 @@ def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> None:
             pid = os.fork()
             print(f"Fork a new process to run a test {pid}")
             if pid == 0:
+                # Make the child process the leader of its own process group
+                # to avoid sending SIGTERM to the parent process
+                os.setpgrp()
                 # Parent process responsible for deleting, don't delete
                 # in child.
                 delete_after.pop_all()
@@ -1322,14 +1515,12 @@ def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> None:
                 else:
                     os._exit(0)
             else:
-                pgid = os.getpgid(pid)
+                # After setpgrp(), the child's pgid equals its pid
+                pgid = pid
                 _pid, _exitcode = os.waitpid(pid, 0)
-                # ignore SIGTERM signal itself
-                old_signal_handler = signal.signal(signal.SIGTERM, signal.SIG_IGN)
-                # kill all child processes
-                os.killpg(pgid, signal.SIGTERM)
-                # restore the signal handler
-                signal.signal(signal.SIGTERM, old_signal_handler)
+                # kill all child processes - but they may already have exited cleanly
+                with contextlib.suppress(ProcessLookupError):
+                    os.killpg(pgid, signal.SIGTERM)
                 if _exitcode != 0:
                     # Try to read the exception from the child process
                     exc_info = {}
@@ -1366,53 +1557,110 @@ def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> None:
     return wrapper
 
 
+def _format_subprocess_exit(returncode: int) -> str:
+    """Render a subprocess exit code, naming the signal for negative codes."""
+    if returncode >= 0:
+        return f"exit code {returncode}"
+    try:
+        return f"killed by {signal.Signals(-returncode).name} ({returncode})"
+    except ValueError:
+        return f"exit code {returncode}"
+
+
+# Set on the spawn-child interpreter so the wrapper short-circuits when the
+# child resolves `module.qualname` back to its own decorated form, instead of
+# launching another subprocess.
+_SPAWN_CHILD_ENV = "VLLM_TEST_SPAWN_CHILD"
+
+
 def spawn_new_process_for_each_test(f: Callable[_P, None]) -> Callable[_P, None]:
-    """Decorator to spawn a new process for each test function."""
+    """Decorator to spawn a new process for each test function.
+
+    Uses subprocess to run each test in a fresh interpreter and propagates
+    exceptions back to the parent, so test failures are never silently
+    swallowed (fixes https://github.com/vllm-project/vllm/issues/41415).
+
+    The child resolves the test function by importing its module and looking
+    it up by qualified name, rather than reconstructing it from a cloudpickle
+    blob. Pickling the function by value would also pickle its ``__globals__``
+    by value — turning module-level singletons (e.g.
+    ``vllm.compilation.counter.compilation_counter``) into stale clones in
+    the child, so increments performed by the production code in the child
+    would never be observable to the test.
+
+    The child inherits the parent's stdout/stderr so its output (engine
+    cores, NCCL, CUDA, ...) reaches the test runner live; the Python-level
+    traceback is serialized to ``tb_file`` for structured re-raising. A
+    native crash leaves ``tb_file`` empty — the diagnostic is then only in
+    the inherited subprocess output.
+    """
 
     @functools.wraps(f)
     def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> None:
-        # Check if we're already in a subprocess
-        if os.environ.get("RUNNING_IN_SUBPROCESS") == "1":
-            # If we are, just run the function directly
+        if os.environ.get(_SPAWN_CHILD_ENV) == "1":
             return f(*args, **kwargs)
 
-        import torch.multiprocessing as mp
-
-        with suppress(RuntimeError):
-            mp.set_start_method("spawn")
-
-        # Get the module
-        module_name = f.__module__
-
-        # Create a process with environment variable set
-        env = os.environ.copy()
-        env["RUNNING_IN_SUBPROCESS"] = "1"
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".tb", mode="wb") as tmp:
+            tb_file = tmp.name
 
-        with tempfile.TemporaryDirectory() as tempdir:
-            output_filepath = os.path.join(tempdir, "new_process.tmp")
+        try:
+            payload = cloudpickle.dumps(
+                {
+                    "module": f.__module__,
+                    "qualname": f.__qualname__,
+                    "args": args,
+                    "kwargs": kwargs,
+                    "tb_file": tb_file,
+                }
+            )
 
-            # `cloudpickle` allows pickling complex functions directly
-            input_bytes = cloudpickle.dumps((f, output_filepath))
+            child_script = (
+                "import sys, importlib, cloudpickle, traceback\n"
+                "try:\n"
+                "    from _pytest.outcomes import Skipped\n"
+                "except ImportError:\n"
+                "    class Skipped(BaseException): pass\n"
+                "data = cloudpickle.loads(sys.stdin.buffer.read())\n"
+                "mod = importlib.import_module(data['module'])\n"
+                "target = mod\n"
+                "for name in data['qualname'].split('.'):\n"
+                "    target = getattr(target, name)\n"
+                "try:\n"
+                "    target(*data['args'], **data['kwargs'])\n"
+                "except Skipped:\n"
+                "    sys.exit(0)\n"
+                "except BaseException:\n"
+                "    with open(data['tb_file'], 'w') as fp:\n"
+                "        fp.write(traceback.format_exc())\n"
+                "    sys.exit(1)\n"
+            )
 
             repo_root = str(VLLM_PATH.resolve())
-
-            env = dict(env or os.environ)
+            env = os.environ.copy()
             env["PYTHONPATH"] = repo_root + os.pathsep + env.get("PYTHONPATH", "")
+            env[_SPAWN_CHILD_ENV] = "1"
 
-            cmd = [sys.executable, "-m", f"{module_name}"]
-
-            returned = subprocess.run(
-                cmd, input=input_bytes, capture_output=True, env=env
+            result = subprocess.run(
+                [sys.executable, "-c", child_script],
+                input=payload,
+                env=env,
             )
 
-            # check if the subprocess is successful
-            try:
-                returned.check_returncode()
-            except Exception as e:
-                # wrap raised exception to provide more information
+            if result.returncode != 0:
+                try:
+                    with open(tb_file) as fp:
+                        tb = fp.read()
+                except OSError:
+                    tb = ""
+                if not tb:
+                    tb = "<no Python traceback; see subprocess output above>"
                 raise RuntimeError(
-                    f"Error raised in subprocess:\n{returned.stderr.decode()}"
-                ) from e
+                    f"Test subprocess '{f.__name__}' failed "
+                    f"({_format_subprocess_exit(result.returncode)}):\n{tb}"
+                )
+        finally:
+            with contextlib.suppress(OSError):
+                os.remove(tb_file)
 
     return wrapper
 
@@ -1431,8 +1679,7 @@ def create_new_process_for_each_test(
         A decorator to run test functions in separate processes.
     """
     if method is None:
-        use_spawn = current_platform.is_rocm() or current_platform.is_xpu()
-        method = "spawn" if use_spawn else "fork"
+        method = "spawn" if requires_spawn_multiprocessing() else "fork"
 
     assert method in ["spawn", "fork"], "Method must be either 'spawn' or 'fork'"
 
@@ -1663,7 +1910,7 @@ def has_module_attribute(module_name, attribute_name):
 
 def get_attn_backend_list_based_on_platform() -> list[str]:
     if current_platform.is_cuda():
-        return ["FLASH_ATTN", "TRITON_ATTN", "TREE_ATTN"]
+        return ["FLASH_ATTN", "TRITON_ATTN"]
     elif current_platform.is_rocm():
         attn_backend_list = ["TRITON_ATTN"]
         try:
@@ -1828,6 +2075,7 @@ def __init__(
             self.weight = torch.rand(weight_shape).to(dtype=FP8_DTYPE)
             self.input_scale = None
             self.weight_scale = None
+            self.weight_block_size = [block_size, block_size]
             if transpose_weights:
                 self.weight = self.weight.t()
         else:
@@ -1858,6 +2106,7 @@ def __init__(
             out_dtype=out_dtype,
             force_kernel=force_kernel,
         )
+        self.kernel.process_weights_after_loading(self)
 
     def is_quant_fp8_enabled(self) -> bool:
         return self.kernel.quant_fp8.enabled()
diff --git a/tests/utils_/test_serial_utils.py b/tests/utils_/test_serial_utils.py
index 42e466709cbf..85661657d50f 100644
--- a/tests/utils_/test_serial_utils.py
+++ b/tests/utils_/test_serial_utils.py
@@ -7,17 +7,39 @@
 from vllm.utils.serial_utils import (
     EMBED_DTYPES,
     ENDIANNESS,
+    MM_METADATA_DTYPES,
     EmbedDType,
     Endianness,
+    MmMetadataDType,
     binary2tensor,
     tensor2binary,
 )
 
+FLOAT_EMBED_DTYPES = tuple(EMBED_DTYPES.keys())
+INTEGER_EMBED_DTYPES = tuple(MM_METADATA_DTYPES.keys())
+
+
+def _build_integer_tensor(
+    embed_dtype: MmMetadataDType, shape: tuple[int, ...]
+) -> torch.Tensor:
+    torch_dtype = MM_METADATA_DTYPES[embed_dtype].torch_dtype
+
+    if torch_dtype is torch.bool:
+        return torch.randint(0, 2, shape, dtype=torch.int32).to(torch.bool)
+    if torch_dtype is torch.uint8:
+        return torch.randint(0, 256, shape, dtype=torch.uint8)
+    if torch_dtype is torch.int32:
+        return torch.randint(-(2**20), 2**20, shape, dtype=torch.int32)
+    if torch_dtype is torch.int64:
+        return torch.randint(-(2**62), 2**62, shape, dtype=torch.int64)
+
+    raise AssertionError(f"Unsupported non-floating embed dtype: {embed_dtype}")
+
 
 @pytest.mark.parametrize("endianness", ENDIANNESS)
-@pytest.mark.parametrize("embed_dtype", EMBED_DTYPES.keys())
+@pytest.mark.parametrize("embed_dtype", FLOAT_EMBED_DTYPES)
 @torch.inference_mode()
-def test_encode_and_decode(embed_dtype: EmbedDType, endianness: Endianness):
+def test_encode_and_decode_floats(embed_dtype: EmbedDType, endianness: Endianness):
     for i in range(10):
         tensor = torch.rand(2, 3, 5, 7, 11, 13, device="cpu", dtype=torch.float32)
         shape = tensor.shape
@@ -40,3 +62,20 @@ def test_encode_and_decode(embed_dtype: EmbedDType, endianness: Endianness):
             name_1="new",
             tol=1e-2,
         )
+
+
+@pytest.mark.parametrize("endianness", ENDIANNESS)
+@pytest.mark.parametrize("embed_dtype", INTEGER_EMBED_DTYPES)
+@torch.inference_mode()
+def test_encode_and_decode_integers(
+    embed_dtype: MmMetadataDType, endianness: Endianness
+):
+    shape = (2, 3, 5, 7, 11, 13)
+
+    for i in range(10):
+        tensor = _build_integer_tensor(embed_dtype, shape)
+        binary = tensor2binary(tensor, embed_dtype, endianness)
+        new_tensor = binary2tensor(binary, shape, embed_dtype, endianness)
+
+        assert new_tensor.dtype == MM_METADATA_DTYPES[embed_dtype].torch_dtype
+        torch.testing.assert_close(tensor, new_tensor, atol=0, rtol=0)
diff --git a/tests/utils_/test_spawn_decorator.py b/tests/utils_/test_spawn_decorator.py
new file mode 100644
index 000000000000..1740ea30de94
--- /dev/null
+++ b/tests/utils_/test_spawn_decorator.py
@@ -0,0 +1,33 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for spawn_new_process_for_each_test decorator."""
+
+import pytest
+
+from tests.utils import spawn_new_process_for_each_test
+
+
+@spawn_new_process_for_each_test
+def test_spawn_decorator_passing():
+    """Passing function should complete normally."""
+    assert 1 + 1 == 2
+
+
+@pytest.mark.xfail(raises=RuntimeError, strict=True)
+@spawn_new_process_for_each_test
+def test_spawn_decorator_failure_is_caught():
+    """Failing function should raise RuntimeError, never silently pass."""
+    raise ValueError("intentional failure")
+
+
+@spawn_new_process_for_each_test
+def test_spawn_decorator_skip():
+    """pytest.skip inside subprocess should propagate correctly."""
+    pytest.skip("intentional skip")
+
+
+@spawn_new_process_for_each_test
+@pytest.mark.parametrize("x,y,expected", [(1, 2, 3), (0, 0, 0)])
+def test_spawn_decorator_parametrized(x, y, expected):
+    """Args and kwargs must be forwarded correctly to subprocess."""
+    assert x + y == expected
diff --git a/tests/v1/attention/test_attention_backends.py b/tests/v1/attention/test_attention_backends.py
index 06095b87e59e..46d25c017f27 100644
--- a/tests/v1/attention/test_attention_backends.py
+++ b/tests/v1/attention/test_attention_backends.py
@@ -36,7 +36,6 @@
     AttentionBackendEnum.FLASHINFER,
     AttentionBackendEnum.FLEX_ATTENTION,
     AttentionBackendEnum.TRITON_ATTN,
-    AttentionBackendEnum.TREE_ATTN,
     "FLEX_ATTENTION_SLOW",
 ]
 
@@ -315,6 +314,7 @@ def _test_backend_correctness(
     backend_to_test: list[AttentionBackendEnum | str],
     mask_mod,
     *,
+    causal: bool = True,
     attn_type: AttentionType = AttentionType.DECODER,
     block_size: int = 16,
     atol: float = 1e-2,
@@ -370,7 +370,7 @@ def _test_backend_correctness(
     )
     device = torch.device(f"{DEVICE_TYPE}:0")
 
-    kv_cache_spec = create_standard_kv_cache_spec(vllm_config)
+    kv_cache_spec = create_standard_kv_cache_spec(vllm_config, attn_type)
 
     # 1. Setup
     batch_size = batch_spec.batch_size
@@ -453,9 +453,7 @@ def _test_backend_correctness(
     common_attn_metadata = create_common_attn_metadata(
         batch_spec, vllm_config.cache_config.block_size, device
     )
-    if attn_type == AttentionType.ENCODER_ONLY:
-        # For encoder-only, all tokens are prefill tokens
-        common_attn_metadata.causal = False
+    common_attn_metadata.causal = causal
 
     # 3. Simulate Paged KV Cache and a realistic slot_mapping
     kv_cache = create_and_prepopulate_kv_cache(
@@ -736,6 +734,76 @@ def bidi_sliding_window_mask_mod(
         model,
         SLIDING_WINDOW_BACKENDS_TO_TEST,
         sliding_window_mask_mod_fn,
+        causal=False,
         attn_type=AttentionType.ENCODER_ONLY,
         tensor_parallel_size=tensor_parallel_size,
     )
+
+
+NON_CAUSAL_BACKENDS_TO_TEST = [
+    AttentionBackendEnum.FLASH_ATTN,
+    AttentionBackendEnum.FLEX_ATTENTION,
+    "FLEX_ATTENTION_SLOW",
+]
+
+if current_platform.is_rocm():
+    NON_CAUSAL_BACKENDS_TO_TEST = [
+        x
+        for x in NON_CAUSAL_BACKENDS_TO_TEST
+        if x is not AttentionBackendEnum.FLASH_ATTN
+    ]
+
+
+@pytest.mark.parametrize(
+    "batch_spec_name",
+    [
+        "small_decode",
+        "small_prefill",
+        "mixed_small",
+    ],
+)
+@pytest.mark.parametrize("model", ["meta-llama/Meta-Llama-3-8B"])
+def test_non_causal_backend_correctness(
+    default_vllm_config, batch_spec_name: str, model: str
+):
+    """Test backend's correctness with non-causal (bidirectional) decoder
+    attention, as used by DFlash speculative decoding."""
+
+    def bidirectional_mask_mod(
+        b: torch.Tensor,
+        h: torch.Tensor,
+        q_idx: torch.Tensor,
+        kv_idx: torch.Tensor,
+        *,
+        context_len: int,
+    ):
+        return q_idx >= 0  # Always True
+
+    batch_spec = BATCH_SPECS[batch_spec_name]
+    LARGE_BLOCK_BACKENDS = (
+        [AttentionBackendEnum.FLEX_ATTENTION]
+        if is_torch_equal_or_newer("2.9.0.dev0")
+        else []
+    )
+
+    SMALL_BLOCK_BACKENDS = [
+        x for x in NON_CAUSAL_BACKENDS_TO_TEST if x not in LARGE_BLOCK_BACKENDS
+    ]
+
+    _test_backend_correctness(
+        batch_spec,
+        model,
+        SMALL_BLOCK_BACKENDS,
+        bidirectional_mask_mod,
+        causal=False,
+    )
+
+    if LARGE_BLOCK_BACKENDS:
+        _test_backend_correctness(
+            batch_spec,
+            model,
+            LARGE_BLOCK_BACKENDS,
+            bidirectional_mask_mod,
+            causal=False,
+            block_size=128,
+        )
diff --git a/tests/v1/attention/test_indexer_deepseek_v4_slot_mapping.py b/tests/v1/attention/test_indexer_deepseek_v4_slot_mapping.py
new file mode 100644
index 000000000000..159bb8af3fb9
--- /dev/null
+++ b/tests/v1/attention/test_indexer_deepseek_v4_slot_mapping.py
@@ -0,0 +1,92 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+from tests.v1.attention.utils import create_vllm_config
+from vllm.v1.attention.backend import CommonAttentionMetadata
+from vllm.v1.attention.backends.mla.indexer import DeepseekV32IndexerMetadataBuilder
+from vllm.v1.kv_cache_interface import MLAAttentionSpec
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA")
+def test_indexer_builder_deepseek_v4_compressed_slot_mapping_uses_storage_block_size():
+    """Regression test: DeepseekV4 compression path must compute slot_mapping from
+    compressed positions, not reuse the uncompressed common metadata mapping.
+    """
+    device = torch.device("cuda")
+
+    # storage_block_size = block_size // compress_ratio = 256 // 4 = 64
+    kv_cache_spec = MLAAttentionSpec(
+        block_size=256,
+        num_kv_heads=1,
+        head_size=128,
+        dtype=torch.bfloat16,
+        compress_ratio=4,
+    )
+    vllm_config = create_vllm_config(max_model_len=1024)
+    builder = DeepseekV32IndexerMetadataBuilder(
+        kv_cache_spec=kv_cache_spec,
+        layer_names=["dummy"],
+        vllm_config=vllm_config,
+        device=device,
+    )
+
+    # Construct a single request where:
+    # - num_computed = 240 (=> compressed_pos_start = 60)
+    # - query_len = 40 (=> num_groups = 10)
+    # => compressed positions are 60..69 which cross the storage block boundary at 64.
+    query_start_loc = torch.tensor([0, 40], dtype=torch.int32, device=device)
+    query_start_loc_cpu = query_start_loc.cpu()
+    seq_lens = torch.tensor([280], dtype=torch.int32, device=device)  # 240 + 40
+
+    # Two blocks: compressed positions 0..63 map to block 5, 64..127 map to block 7.
+    block_table_tensor = torch.tensor([[5, 7]], dtype=torch.int32, device=device)
+
+    # Dummy uncompressed slot mapping (length == uncompressed num_actual_tokens).
+    slot_mapping = torch.full((40,), -123, dtype=torch.int64, device=device)
+
+    common = CommonAttentionMetadata(
+        query_start_loc=query_start_loc,
+        query_start_loc_cpu=query_start_loc_cpu,
+        seq_lens=seq_lens,
+        seq_lens_cpu_upper_bound=seq_lens.cpu(),
+        num_reqs=1,
+        num_actual_tokens=40,
+        max_query_len=40,
+        max_seq_len=280,
+        block_table_tensor=block_table_tensor,
+        slot_mapping=slot_mapping,
+        causal=True,
+    )
+
+    md = builder.build(common_prefix_len=0, common_attn_metadata=common)
+
+    # The compressed slot_mapping retains the original uncompressed size (40).
+    # Only every compress_ratio-th position gets a valid slot; the rest are -1.
+    assert md.slot_mapping.numel() == 40
+    valid_slots = md.slot_mapping[md.slot_mapping >= 0]
+    assert valid_slots.numel() == 10  # 40 tokens / compress_ratio 4
+
+    storage_bs = kv_cache_spec.storage_block_size  # 64
+    # Compressed positions 60..63 land in block 5, positions 64..69 in block 7.
+    expected = torch.tensor(
+        [
+            5 * storage_bs + 60,
+            5 * storage_bs + 61,
+            5 * storage_bs + 62,
+            5 * storage_bs + 63,
+        ]
+        + [
+            7 * storage_bs + 0,
+            7 * storage_bs + 1,
+            7 * storage_bs + 2,
+            7 * storage_bs + 3,
+            7 * storage_bs + 4,
+            7 * storage_bs + 5,
+        ],
+        dtype=torch.int64,
+        device=device,
+    )
+    torch.testing.assert_close(valid_slots, expected)
diff --git a/tests/v1/attention/test_kv_head_stride_canonicalization.py b/tests/v1/attention/test_kv_head_stride_canonicalization.py
new file mode 100644
index 000000000000..635f46390cfc
--- /dev/null
+++ b/tests/v1/attention/test_kv_head_stride_canonicalization.py
@@ -0,0 +1,162 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for canonicalize_singleton_dim_strides.
+
+Background
+----------
+When num_kv_heads_per_rank == 1 (e.g. Qwen3.5-397B with TP=8 → 1 KV head
+per rank), PyTorch's is_contiguous() returns True for *any* stride on the
+size-1 dimension.  The KV cache allocator can therefore produce a tensor
+where that singleton dim has stride = 1 element (2 bytes for bf16) instead
+of the canonical product-of-remaining-dims value.
+
+CUDA TMA (used by FlashInfer XQA SM90 and Flash-Attention 3/4 on H100+)
+requires all non-outermost strides to be multiples of 16 bytes.  A 2-byte
+stride triggers cudaErrorIllegalInstruction.
+
+canonicalize_singleton_dim_strides() patches degenerate strides on all
+size-1 dimensions via torch.as_strided — zero-copy.
+
+The degenerate stride manifests at different positions in different backends:
+- FlashInfer: stride(-3) after kv_cache.permute() → shape [..., 1, B, D]
+- FlashAttention: stride(-2) after kv_cache.unbind(0) → shape [N, B, 1, D]
+"""
+
+import torch
+
+from vllm.utils.torch_utils import canonicalize_singleton_dim_strides
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _inject_degenerate_stride(t: torch.Tensor, dim: int) -> torch.Tensor:
+    """Return a view of t with a degenerate (stride=1) on a size-1 dim."""
+    assert t.shape[dim] == 1, f"dim {dim} must have size 1"
+    strides = list(t.stride())
+    strides[dim] = 1  # inject the bug
+    return t.as_strided(t.shape, strides)
+
+
+# ---------------------------------------------------------------------------
+# Tests: canonicalize_singleton_dim_strides
+# ---------------------------------------------------------------------------
+
+
+class TestCanonicalizeSingletonDimStrides:
+    def test_flashinfer_layout_dim_neg3(self):
+        """FlashInfer path: degenerate stride at dim -3 (num_kv_heads)."""
+        # Shape after permute: [num_blocks, 2, num_kv_heads, block_size, head_size]
+        num_blocks, block_size, head_size = 64, 16, 128
+        t = torch.zeros(num_blocks, 2, 1, block_size, head_size, dtype=torch.bfloat16)
+        t_deg = _inject_degenerate_stride(t, dim=-3)
+
+        assert t_deg.stride(-3) == 1  # confirm degenerate
+        assert t_deg.is_contiguous()  # PyTorch doesn't notice
+
+        fixed = canonicalize_singleton_dim_strides(t_deg)
+
+        assert fixed.stride(-3) == block_size * head_size  # canonical = 2048
+        assert fixed.stride(-2) == head_size  # inner dims unchanged
+        assert fixed.stride(-1) == 1
+
+    def test_flash_attn_layout_dim_neg2(self):
+        """FlashAttention path: degenerate stride at dim -2 (num_kv_heads)."""
+        # Shape after unbind(0): [num_blocks, block_size, num_kv_heads, head_size]
+        num_blocks, block_size, head_size = 64, 16, 128
+        t = torch.zeros(num_blocks, block_size, 1, head_size, dtype=torch.bfloat16)
+        t_deg = _inject_degenerate_stride(t, dim=-2)
+
+        assert t_deg.stride(-2) == 1
+        assert t_deg.is_contiguous()
+
+        fixed = canonicalize_singleton_dim_strides(t_deg)
+
+        assert fixed.stride(-2) == head_size  # canonical = 128
+        assert fixed.stride(-1) == 1
+
+    def test_canonical_strides_returned_as_is(self):
+        """No degenerate strides → same object returned (no copy, no new view)."""
+        t = torch.zeros(64, 2, 1, 16, 128, dtype=torch.bfloat16)
+        result = canonicalize_singleton_dim_strides(t)
+        assert result is t
+
+    def test_multi_kv_heads_unchanged(self):
+        """num_kv_heads > 1 → strides are already canonical → unchanged."""
+        t = torch.zeros(16, 2, 4, 16, 128, dtype=torch.bfloat16)
+        original_strides = t.stride()
+        result = canonicalize_singleton_dim_strides(t)
+        assert result.stride() == original_strides
+
+    def test_data_pointer_preserved(self):
+        """Fix is zero-copy: same underlying storage."""
+        t = torch.zeros(8, 2, 1, 16, 128, dtype=torch.bfloat16)
+        t_deg = _inject_degenerate_stride(t, dim=-3)
+        fixed = canonicalize_singleton_dim_strides(t_deg)
+        assert fixed.data_ptr() == t_deg.data_ptr()
+        assert fixed.storage_offset() == t_deg.storage_offset()
+
+    def test_multiple_singleton_dims(self):
+        """All size-1 dims with degenerate strides are fixed."""
+        # Shape: [1, 1, 8, 32] — two size-1 dims
+        t = torch.zeros(1, 1, 8, 32, dtype=torch.float16)
+        # Both size-1 dims get degenerate strides
+        t_deg = t.as_strided(t.shape, (1, 1, 32, 1))  # both leading dims = 1
+
+        fixed = canonicalize_singleton_dim_strides(t_deg)
+
+        assert fixed.stride(0) == 1 * 8 * 32  # canonical: 256
+        assert fixed.stride(1) == 1 * 8 * 32  # canonical: 256 (same since size-1)
+        assert fixed.stride(2) == 32
+        assert fixed.stride(3) == 1
+
+    def test_various_shapes_flashinfer(self):
+        """Correctness across different block_size / head_size for FlashInfer layout."""
+        for block_size, head_size in [(16, 64), (16, 128), (32, 128), (16, 256)]:
+            t = torch.zeros(8, 2, 1, block_size, head_size, dtype=torch.bfloat16)
+            t_deg = _inject_degenerate_stride(t, dim=-3)
+            fixed = canonicalize_singleton_dim_strides(t_deg)
+            assert fixed.stride(-3) == block_size * head_size, (
+                f"Failed for block_size={block_size}, head_size={head_size}: "
+                f"got stride(-3)={fixed.stride(-3)}"
+            )
+
+    def test_various_shapes_flash_attn(self):
+        """Correctness across different shapes for FlashAttention layout."""
+        for block_size, head_size in [(16, 64), (16, 128), (32, 128)]:
+            t = torch.zeros(8, block_size, 1, head_size, dtype=torch.bfloat16)
+            t_deg = _inject_degenerate_stride(t, dim=-2)
+            fixed = canonicalize_singleton_dim_strides(t_deg)
+            assert fixed.stride(-2) == head_size, (
+                f"Failed for block_size={block_size}, head_size={head_size}: "
+                f"got stride(-2)={fixed.stride(-2)}"
+            )
+
+    def test_tma_alignment_satisfied_after_fix_bf16(self):
+        """After fix, all strides meet 16-byte TMA alignment for bf16."""
+        t = torch.zeros(64, 2, 1, 16, 128, dtype=torch.bfloat16)
+        t_deg = _inject_degenerate_stride(t, dim=-3)
+        fixed = canonicalize_singleton_dim_strides(t_deg)
+
+        element_size = fixed.element_size()  # 2 bytes for bf16
+        for i, s in enumerate(fixed.stride()):
+            assert (s * element_size) % 16 == 0 or i == len(fixed.stride()) - 1, (
+                f"dim {i} stride {s} * {element_size} bytes not 16-byte aligned"
+            )
+
+    def test_non_contiguous_outer_dims_preserved(self):
+        """Outer (non-size-1) non-contiguous strides are left unchanged."""
+        # Simulate cross-layer unified allocation: num_blocks stride is non-canonical
+        # but the inner dims should be fixed.
+        base = torch.zeros(200, 2, 1, 16, 128, dtype=torch.bfloat16)
+        # Slice every 2nd block → non-canonical outer stride
+        t_sliced = base[::2]  # shape [100, 2, 1, 16, 128], stride[0] = 2*canonical
+        t_deg = _inject_degenerate_stride(t_sliced, dim=-3)
+
+        fixed = canonicalize_singleton_dim_strides(t_deg)
+
+        # Outer stride should be unchanged (not a size-1 dim)
+        assert fixed.stride(0) == t_sliced.stride(0)
+        # Inner degenerate stride should be fixed
+        assert fixed.stride(-3) == 16 * 128
diff --git a/tests/v1/attention/test_mla_backends.py b/tests/v1/attention/test_mla_backends.py
index e65d1d604029..3807ee69ecfc 100644
--- a/tests/v1/attention/test_mla_backends.py
+++ b/tests/v1/attention/test_mla_backends.py
@@ -30,6 +30,7 @@
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 from vllm.v1.attention.backend import CommonAttentionMetadata
 from vllm.v1.attention.backends.fa_utils import flash_attn_supports_mla
+from vllm.v1.attention.backends.mla.prefill import get_mla_prefill_backend
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
 from vllm.v1.attention.ops.flashmla import is_flashmla_dense_supported
 from vllm.v1.kv_cache_interface import MLAAttentionSpec
@@ -621,6 +622,19 @@ def run_attention_backend(
             k_scale=k_scale,
         )
 
+        # Attach prefill backend (normally created by MLAAttention.__init__)
+        prefill_scale = (qk_nope_head_dim + qk_rope_head_dim) ** -0.5
+        prefill_backend_cls = get_mla_prefill_backend(vllm_config)
+        mock_layer.prefill_backend = prefill_backend_cls(
+            num_heads=num_heads,
+            scale=prefill_scale,
+            kv_lora_rank=kv_lora_rank,
+            qk_nope_head_dim=qk_nope_head_dim,
+            qk_rope_head_dim=qk_rope_head_dim,
+            v_head_dim=v_head_dim,
+            vllm_config=vllm_config,
+        )
+
         # Populate static_forward_context with mock attention layers
         for layer_name in layer_names:
             vllm_config.compilation_config.static_forward_context[layer_name] = (
@@ -672,6 +686,7 @@ def run_attention_backend(
 def test_backend_correctness(
     default_vllm_config,
     dist_init,
+    workspace_init,
     batch_spec_name: str,
     model: str,
     tensor_parallel_size: int,
@@ -784,7 +799,9 @@ def test_backend_correctness(
     assert kv_lora_rank + qk_rope_head_dim == head_size, (
         f"MLA dimensions don't match: {total_head_size} != {head_size}"
     )
-    scale = 1.0 / (total_head_size**0.5)
+    decode_scale = 1.0 / (total_head_size**0.5)
+    qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
+    prefill_scale = qk_head_dim**-0.5
 
     # 2. Generate data and compute SDPA reference output for MLA
     all_q_vllm, all_kv_c_vllm, all_k_pe_vllm = [], [], []
@@ -901,7 +918,7 @@ def test_backend_correctness(
         v_sdpa_in = v_mqa.unsqueeze(0).transpose(1, 2)
 
         sdpa_out_i_decode = torch.nn.functional.scaled_dot_product_attention(
-            q_sdpa_in, k_sdpa_in, v_sdpa_in, attn_mask=attn_mask, scale=scale
+            q_sdpa_in, k_sdpa_in, v_sdpa_in, attn_mask=attn_mask, scale=decode_scale
         )
         sdpa_out_i_decode = sdpa_out_i_decode.transpose(1, 2).squeeze(
             0
@@ -937,7 +954,7 @@ def test_backend_correctness(
 
         # Single attention call with custom mask
         sdpa_out_i_prefill = torch.nn.functional.scaled_dot_product_attention(
-            q_sdpa_in, k_sdpa_in, v_sdpa_in, attn_mask=attn_mask, scale=scale
+            q_sdpa_in, k_sdpa_in, v_sdpa_in, attn_mask=attn_mask, scale=prefill_scale
         )
         sdpa_out_i_prefill = sdpa_out_i_prefill.transpose(1, 2).squeeze(0)
         sdpa_out_i_prefill = sdpa_out_i_prefill.flatten(start_dim=-2)
diff --git a/tests/v1/attention/test_mla_prefill_selector.py b/tests/v1/attention/test_mla_prefill_selector.py
new file mode 100644
index 000000000000..068eb43faf40
--- /dev/null
+++ b/tests/v1/attention/test_mla_prefill_selector.py
@@ -0,0 +1,304 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for MLA prefill backend selector."""
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+import torch
+
+from vllm.config import AttentionConfig, ModelConfig, VllmConfig
+from vllm.platforms.interface import DeviceCapability
+from vllm.v1.attention.backends.mla.prefill.registry import MLAPrefillBackendEnum
+from vllm.v1.attention.backends.mla.prefill.selector import (
+    MLAPrefillSelectorConfig,
+    _auto_select_mla_prefill_backend,
+    get_mla_prefill_backend,
+    is_deepseek_r1_mla_compatible,
+)
+
+
+@pytest.fixture(autouse=True)
+def clear_cache():
+    """Clear lru cache to ensure each test case runs without caching."""
+    _auto_select_mla_prefill_backend.cache_clear()
+
+
+def _make_mock_model_config(
+    qk_nope_head_dim: int = 128,
+    qk_rope_head_dim: int = 64,
+    v_head_dim: int = 128,
+    dtype: torch.dtype = torch.bfloat16,
+) -> ModelConfig:
+    mock_config = MagicMock(spec=ModelConfig)
+    mock_config.dtype = dtype
+    mock_config.hf_text_config = MagicMock()
+    mock_config.hf_text_config.qk_nope_head_dim = qk_nope_head_dim
+    mock_config.hf_text_config.qk_rope_head_dim = qk_rope_head_dim
+    mock_config.hf_text_config.v_head_dim = v_head_dim
+    return mock_config
+
+
+def _make_vllm_config(
+    model_config: ModelConfig | None = None,
+    mla_prefill_backend: MLAPrefillBackendEnum | None = None,
+) -> VllmConfig:
+    if model_config is None:
+        model_config = _make_mock_model_config()
+
+    attention_config = AttentionConfig(mla_prefill_backend=mla_prefill_backend)
+    mock_vllm_config = MagicMock(spec=VllmConfig)
+    mock_vllm_config.model_config = model_config
+    mock_vllm_config.attention_config = attention_config
+    return mock_vllm_config
+
+
+class TestGetMLAPrefillBackend:
+    """Tests for get_mla_prefill_backend (public API)."""
+
+    def test_no_device_capability_returns_flash_attn(self):
+        vllm_config = _make_vllm_config()
+
+        with patch("vllm.platforms.current_platform") as mock_platform:
+            mock_platform.get_device_capability.return_value = None
+
+            backend = get_mla_prefill_backend(vllm_config)
+            assert backend.get_name() == "FLASH_ATTN"
+
+    def test_explicit_flash_attn_selection(self):
+        try:
+            flash_attn_cls = MLAPrefillBackendEnum.FLASH_ATTN.get_class()
+        except ImportError:
+            pytest.skip("FLASH_ATTN backend not available")
+            return
+
+        vllm_config = _make_vllm_config(
+            mla_prefill_backend=MLAPrefillBackendEnum.FLASH_ATTN,
+        )
+
+        with patch("vllm.platforms.current_platform") as mock_platform:
+            mock_platform.get_device_capability.return_value = DeviceCapability(
+                major=9, minor=0
+            )
+
+            with patch.object(
+                flash_attn_cls,
+                "validate_configuration",
+                return_value=[],
+            ):
+                backend = get_mla_prefill_backend(vllm_config)
+                assert backend.get_name() == "FLASH_ATTN"
+
+    def test_explicit_backend_invalid_raises_error(self):
+        vllm_config = _make_vllm_config(
+            mla_prefill_backend=MLAPrefillBackendEnum.FLASHINFER,
+        )
+
+        with patch("vllm.platforms.current_platform") as mock_platform:
+            mock_platform.get_device_capability.return_value = DeviceCapability(
+                major=9, minor=0
+            )
+
+            with pytest.raises(ValueError, match="is not valid"):
+                get_mla_prefill_backend(vllm_config)
+
+    def test_explicit_backend_import_error_raises(self):
+        vllm_config = _make_vllm_config(
+            mla_prefill_backend=MLAPrefillBackendEnum.TRTLLM_RAGGED,
+        )
+
+        with patch("vllm.platforms.current_platform") as mock_platform:
+            mock_platform.get_device_capability.return_value = DeviceCapability(
+                major=10, minor=0
+            )
+
+            with (
+                patch.object(
+                    MLAPrefillBackendEnum.TRTLLM_RAGGED,
+                    "get_class",
+                    side_effect=ImportError("trtllm not installed"),
+                ),
+                pytest.raises(ValueError, match="is not valid"),
+            ):
+                get_mla_prefill_backend(vllm_config)
+
+    def test_auto_selection_on_hopper(self):
+        try:
+            flash_attn_cls = MLAPrefillBackendEnum.FLASH_ATTN.get_class()
+        except ImportError:
+            pytest.skip("FLASH_ATTN backend not available")
+            return
+
+        vllm_config = _make_vllm_config()
+
+        with patch("vllm.platforms.current_platform") as mock_platform:
+            mock_platform.get_device_capability.return_value = DeviceCapability(
+                major=9, minor=0
+            )
+
+            with patch.object(
+                flash_attn_cls,
+                "validate_configuration",
+                return_value=[],
+            ):
+                backend = get_mla_prefill_backend(vllm_config)
+                assert backend.get_name() == "FLASH_ATTN"
+
+
+class TestAutoSelectMLAPrefillBackend:
+    """Tests for fallback and error paths in auto-selection."""
+
+    def test_blackwell_falls_back_to_trtllm(self):
+        vllm_config = _make_vllm_config()
+        capability = DeviceCapability(major=10, minor=0)
+        selector_config = MLAPrefillSelectorConfig(
+            dtype=torch.bfloat16,
+            is_r1_compatible=is_deepseek_r1_mla_compatible(vllm_config),
+        )
+
+        try:
+            trtllm_cls = MLAPrefillBackendEnum.TRTLLM_RAGGED.get_class()
+        except ImportError:
+            pytest.skip("TRTLLM_RAGGED backend not available")
+            return
+
+        with (
+            patch.object(
+                MLAPrefillBackendEnum.FLASH_ATTN,
+                "get_class",
+                side_effect=ImportError("FLASH_ATTN not available"),
+            ),
+            patch.object(trtllm_cls, "validate_configuration", return_value=[]),
+        ):
+            backend = _auto_select_mla_prefill_backend(
+                capability,
+                selector_config,
+            )
+            assert backend.get_name() == "TRTLLM_RAGGED"
+
+    def test_all_fail_raises_error(self):
+        vllm_config = _make_vllm_config()
+        capability = DeviceCapability(major=10, minor=0)
+        selector_config = MLAPrefillSelectorConfig(
+            dtype=torch.bfloat16,
+            is_r1_compatible=is_deepseek_r1_mla_compatible(vllm_config),
+        )
+
+        def mock_get_class(backend_enum):  # noqa: ARG001
+            cls = MagicMock()
+            cls.validate_configuration.return_value = ["not available"]
+            return cls
+
+        with patch.object(MLAPrefillBackendEnum, "get_class", mock_get_class):
+            _auto_select_mla_prefill_backend.cache_clear()
+            with pytest.raises(ValueError, match="No valid MLA"):
+                _auto_select_mla_prefill_backend(
+                    capability,
+                    selector_config,
+                )
+
+
+class TestBackendValidation:
+    """Tests for backend validation logic."""
+
+    def test_r1_dimension_requirement(self):
+        try:
+            from vllm.v1.attention.backends.mla.prefill.flashinfer import (
+                FlashInferPrefillBackend,
+            )
+        except ImportError:
+            pytest.skip("FlashInfer prefill backend not available")
+            return
+
+        assert FlashInferPrefillBackend.requires_r1_mla_dimensions is True
+
+        vllm_config = _make_vllm_config(
+            model_config=_make_mock_model_config(
+                qk_nope_head_dim=128,
+                qk_rope_head_dim=64,
+                v_head_dim=128,
+            )
+        )
+        capability = DeviceCapability(major=10, minor=0)
+        selector_config = MLAPrefillSelectorConfig(
+            dtype=torch.bfloat16,
+            is_r1_compatible=is_deepseek_r1_mla_compatible(vllm_config),
+        )
+
+        with patch.object(FlashInferPrefillBackend, "is_available", return_value=True):
+            invalid_reasons = FlashInferPrefillBackend.validate_configuration(
+                capability,
+                selector_config,
+            )
+            assert len(invalid_reasons) == 0
+
+        vllm_config_invalid = _make_vllm_config(
+            model_config=_make_mock_model_config(
+                qk_nope_head_dim=64,
+                qk_rope_head_dim=64,
+                v_head_dim=128,
+            )
+        )
+        selector_config_invalid = MLAPrefillSelectorConfig(
+            dtype=torch.bfloat16,
+            is_r1_compatible=is_deepseek_r1_mla_compatible(vllm_config_invalid),
+        )
+
+        with patch.object(FlashInferPrefillBackend, "is_available", return_value=True):
+            invalid_reasons = FlashInferPrefillBackend.validate_configuration(
+                capability,
+                selector_config_invalid,
+            )
+            assert len(invalid_reasons) == 1
+            assert "DeepSeek R1 MLA dimensions" in invalid_reasons[0]
+
+
+class TestMLAPrefillBackendParsing:
+    """Tests for string-based mla_prefill_backend parsing from CLI args."""
+
+    def test_valid_string_parses_to_enum(self):
+        config = AttentionConfig(
+            mla_prefill_backend="FLASH_ATTN",  # type: ignore[arg-type]
+        )
+        assert config.mla_prefill_backend == MLAPrefillBackendEnum.FLASH_ATTN
+
+    def test_invalid_string_raises_error(self):
+        with pytest.raises(ValueError, match="Unknown MLA prefill backend"):
+            AttentionConfig(
+                mla_prefill_backend="NONEXISTENT",  # type: ignore[arg-type]
+            )
+
+
+class TestDeprecatedFlagMigration:
+    """Tests for _migrate_deprecated_mla_prefill_flags in AttentionConfig."""
+
+    def test_no_deprecated_flags_leaves_backend_none(self):
+        config = AttentionConfig()
+        assert config.mla_prefill_backend is None
+
+    def test_use_trtllm_ragged_migrates_to_trtllm_ragged(self):
+        config = AttentionConfig(use_trtllm_ragged_deepseek_prefill=True)
+        assert config.mla_prefill_backend == MLAPrefillBackendEnum.TRTLLM_RAGGED
+
+    def test_disable_flashinfer_prefill_migrates_to_flash_attn(self):
+        config = AttentionConfig(disable_flashinfer_prefill=True)
+        assert config.mla_prefill_backend == MLAPrefillBackendEnum.FLASH_ATTN
+
+    def test_explicit_backend_ignores_deprecated_flags(self):
+        config = AttentionConfig(
+            mla_prefill_backend=MLAPrefillBackendEnum.FLASH_ATTN,
+            use_cudnn_prefill=True,
+        )
+        assert config.mla_prefill_backend == MLAPrefillBackendEnum.FLASH_ATTN
+
+    def test_cudnn_raises_error(self):
+        match = "cuDNN MLA prefill backend has been removed"
+        with pytest.raises(ValueError, match=match):
+            AttentionConfig(use_cudnn_prefill=True)
+
+    def test_trtllm_takes_priority_over_disable_flashinfer(self):
+        config = AttentionConfig(
+            use_trtllm_ragged_deepseek_prefill=True,
+            disable_flashinfer_prefill=True,
+        )
+        assert config.mla_prefill_backend == MLAPrefillBackendEnum.TRTLLM_RAGGED
diff --git a/tests/v1/attention/test_trtllm_attention_integration.py b/tests/v1/attention/test_trtllm_attention_integration.py
index 12af0773cb39..06c5844508f4 100644
--- a/tests/v1/attention/test_trtllm_attention_integration.py
+++ b/tests/v1/attention/test_trtllm_attention_integration.py
@@ -17,13 +17,13 @@
 from vllm.config import set_current_vllm_config
 from vllm.platforms import current_platform
 from vllm.utils.math_utils import cdiv
-from vllm.utils.torch_utils import set_random_seed
+from vllm.utils.torch_utils import nvfp4_kv_cache_full_dim, set_random_seed
 from vllm.v1.attention.backends.utils import (
     PerLayerParameters,
     get_kv_cache_layout,
     set_kv_cache_layout,
 )
-from vllm.v1.kv_cache_interface import FullAttentionSpec
+from vllm.v1.kv_cache_interface import FullAttentionSpec, KVQuantMode
 
 if not current_platform.is_device_capability_family(100):
     pytest.skip(
@@ -53,6 +53,7 @@ def __init__(self, device: torch.device):
 
 
 MODEL = "Qwen/Qwen2.5-0.5B"
+MODEL_NVFP4 = "Qwen/Qwen3-4B"  # nvfp4 needs head_dim >= 128 (or 80)
 BLOCK_SIZE = 16
 NUM_GPU_BLOCKS = 8192
 DEVICE_TYPE = current_platform.device_type
@@ -169,19 +170,129 @@ def _create_hnd_kv_cache(
     return kv_cache
 
 
-def _run_trtllm_integration(batch_spec):
+def _create_nvfp4_hnd_kv_cache(
+    k_contexts,
+    v_contexts,
+    block_size,
+    num_kv_heads,
+    head_size,
+    dtype,
+    device,
+    num_blocks,
+    common_attn_metadata,
+    kv_scale_val,
+):
+    """Create an nvfp4 KV cache by quantizing bf16 context via
+    reshape_and_cache_flash, using the same block-table layout as
+    _create_hnd_kv_cache.
+
+    The returned tensor is dtype ``uint8`` with shape
+    ``(num_blocks, 2, block_size, num_kv_heads, full_dim)`` in logical
+    (NHD) order, but physically permuted to HND layout via stride order
+    ``(0, 1, 3, 2, 4)`` (i.e. ``num_kv_heads`` before ``block_size``).
+
+    The last dimension ``full_dim = head_size // 2 + head_size // 16``
+    packs two regions contiguously:
+      - **FP4 data** (``head_size // 2`` bytes): pairs of E2M1 values,
+        two per byte.
+      - **FP8 block scales** (``head_size // 16`` bytes): one E4M3
+        scale per 16-element block.
+
+    Dimension 1 indexes K (``[:, 0]``) and V (``[:, 1]``).
+
+    Args:
+        k_contexts: List of key context tensors, one per sequence.
+        v_contexts: List of value context tensors, one per sequence.
+        block_size: Number of tokens per cache block.
+        num_kv_heads: Number of key/value heads.
+        head_size: Head dimension (must be divisible by 16).
+        dtype: Source data type for the bf16 intermediate cache.
+        device: Target device.
+        num_blocks: Total number of blocks to allocate.
+        common_attn_metadata: Metadata containing block tables and
+            sequence lengths.
+        kv_scale_val: Scalar float used as both k_scale and v_scale
+            during quantization.
+
+    Returns:
+        ``torch.Tensor``: The nvfp4 kv_cache tensor (uint8, HND-strided).
+    """
+    # First create a bf16 HND cache so block tables are populated.
+    bf16_cache = _create_hnd_kv_cache(
+        k_contexts,
+        v_contexts,
+        block_size,
+        num_kv_heads,
+        head_size,
+        dtype,
+        device,
+        num_blocks,
+        common_attn_metadata,
+    )
+
+    # Allocate nvfp4 cache: same shape but with full_dim (data + scale).
+    full_dim = nvfp4_kv_cache_full_dim(head_size)
+    hnd_order = (0, 1, 3, 2, 4)
+    nvfp4_cache = torch.zeros(
+        (num_blocks, 2, num_kv_heads, block_size, full_dim),
+        dtype=torch.uint8,
+        device=device,
+    ).permute(*hnd_order)
+
+    # Flatten bf16 context into tokens and quantize via reshape_and_cache_flash.
+    # bf16_cache is (num_blocks, 2, block_size, num_kv_heads, head_size) logical
+    # with HND physical strides.
+    block_table = common_attn_metadata.block_table_tensor
+    seq_lens = common_attn_metadata.seq_lens.cpu()
+    query_lens = (
+        common_attn_metadata.query_start_loc_cpu[1:]
+        - common_attn_metadata.query_start_loc_cpu[:-1]
+    )
+    kv_scale_t = torch.tensor(kv_scale_val, dtype=torch.float32, device=device)
+
+    for i in range(len(k_contexts)):
+        ctx_len = int(seq_lens[i]) - int(query_lens[i])
+        if ctx_len == 0:
+            continue
+        # Gather context tokens from the bf16 cache using block table.
+        n_ctx_blocks = (ctx_len + block_size - 1) // block_size
+        blocks = block_table[i, :n_ctx_blocks]
+        # bf16_cache[:, kv_idx] is (num_blocks, block_size, num_kv_heads, head_size)
+        k_ctx = bf16_cache[blocks, 0].reshape(-1, num_kv_heads, head_size)[:ctx_len]
+        v_ctx = bf16_cache[blocks, 1].reshape(-1, num_kv_heads, head_size)[:ctx_len]
+        # Build slot mapping for these context tokens.
+        token_offsets = torch.arange(ctx_len, device=device)
+        block_indices = token_offsets // block_size
+        intra_offsets = token_offsets % block_size
+        slots = block_table[i, block_indices] * block_size + intra_offsets
+        torch.ops._C_cache_ops.reshape_and_cache_flash(
+            k_ctx,
+            v_ctx,
+            nvfp4_cache[:, 0],
+            nvfp4_cache[:, 1],
+            slots,
+            "nvfp4",
+            kv_scale_t,
+            kv_scale_t,
+        )
+
+    return nvfp4_cache
+
+
+def _run_trtllm_integration(batch_spec, kv_cache_dtype="auto", model_name=MODEL):
     """Run TRTLLM attention through the full FlashInfer pipeline
     and compare against an SDPA reference."""
     set_random_seed(42)
     device = torch.device(f"{DEVICE_TYPE}:0")
 
     vllm_config = create_vllm_config(
-        model_name=MODEL,
+        model_name=model_name,
         max_model_len=max(batch_spec.seq_lens),
         block_size=BLOCK_SIZE,
         num_gpu_blocks=NUM_GPU_BLOCKS,
     )
     vllm_config.attention_config.use_trtllm_attention = True
+    vllm_config.cache_config.cache_dtype = kv_cache_dtype
 
     num_q_heads = vllm_config.model_config.get_num_attention_heads(
         vllm_config.parallel_config
@@ -248,28 +359,51 @@ def causal_mask_mod(b, h, q_idx, kv_idx, *, context_len):
     common_attn_metadata = create_common_attn_metadata(batch_spec, BLOCK_SIZE, device)
 
     # 2. Create HND KV cache
-    kv_cache = _create_hnd_kv_cache(
-        k_contexts,
-        v_contexts,
-        BLOCK_SIZE,
-        num_kv_heads,
-        head_size,
-        dtype,
-        device,
-        NUM_GPU_BLOCKS,
-        common_attn_metadata,
-    )
+    is_nvfp4 = kv_cache_dtype == "nvfp4"
+    if is_nvfp4:
+        # Compute a global scale from the context data.
+        all_ctx = torch.cat(k_contexts + v_contexts, dim=0)
+        kv_scale_val = (all_ctx.abs().amax() / 448.0).item()
+        kv_cache = _create_nvfp4_hnd_kv_cache(
+            k_contexts,
+            v_contexts,
+            BLOCK_SIZE,
+            num_kv_heads,
+            head_size,
+            dtype,
+            device,
+            NUM_GPU_BLOCKS,
+            common_attn_metadata,
+            kv_scale_val,
+        )
+    else:
+        kv_scale_val = 1.0
+        kv_cache = _create_hnd_kv_cache(
+            k_contexts,
+            v_contexts,
+            BLOCK_SIZE,
+            num_kv_heads,
+            head_size,
+            dtype,
+            device,
+            NUM_GPU_BLOCKS,
+            common_attn_metadata,
+        )
 
     # 3. Run through FlashInfer with TRTLLM enabled
     set_kv_cache_layout("HND")
     get_kv_cache_layout.cache_clear()
 
     try:
+        is_nvfp4 = kv_cache_dtype == "nvfp4"
+        kv_quant_mode = KVQuantMode.NVFP4 if is_nvfp4 else KVQuantMode.NONE
+        spec_dtype = torch.uint8 if is_nvfp4 else dtype
         kv_cache_spec = FullAttentionSpec(
             block_size=BLOCK_SIZE,
             num_kv_heads=num_kv_heads,
             head_size=head_size,
-            dtype=dtype,
+            dtype=spec_dtype,
+            kv_quant_mode=kv_quant_mode,
         )
         layer_names = ["test_layer_0"]
 
@@ -312,10 +446,20 @@ def causal_mask_mod(b, h, q_idx, kv_idx, *, context_len):
                 num_kv_heads=num_kv_heads,
                 alibi_slopes=None,
                 sliding_window=None,
-                kv_cache_dtype="auto",
+                kv_cache_dtype=kv_cache_dtype,
             )
 
             mock_layer = MockAttentionLayer(device)
+            if is_nvfp4:
+                # For nvfp4, k_scale/v_scale are the global quantization
+                # scales (amax/448) used by reshape_and_cache_flash.
+                kv_scale_t = torch.tensor(
+                    kv_scale_val, dtype=torch.float32, device=device
+                )
+                mock_layer._k_scale = kv_scale_t
+                mock_layer._v_scale = kv_scale_t
+                mock_layer._k_scale_float = kv_scale_val
+                mock_layer._v_scale_float = kv_scale_val
             output = torch.empty_like(query_vllm)
 
             impl.do_kv_cache_update(
@@ -326,6 +470,23 @@ def causal_mask_mod(b, h, q_idx, kv_idx, *, context_len):
                 attn_metadata.slot_mapping,
             )
 
+            # nvfp4 trtllm kernel requires FP8 queries. In the real
+            # pipeline the attention layer handles this; here we
+            # quantize manually.
+            if is_nvfp4:
+                finfo = torch.finfo(torch.float8_e4m3fn)
+                q_amax = query_vllm.abs().amax().clamp(min=1e-12)
+                q_s = (finfo.max / q_amax * 0.1).item()
+                query_vllm = (
+                    (query_vllm * q_s)
+                    .clamp(finfo.min, finfo.max)
+                    .to(torch.float8_e4m3fn)
+                )
+                mock_layer._q_scale = torch.tensor(
+                    1.0 / q_s, dtype=torch.float32, device=device
+                )
+                mock_layer._q_scale_float = 1.0 / q_s
+
             output = impl.forward(
                 mock_layer,
                 query_vllm,
@@ -337,12 +498,11 @@ def causal_mask_mod(b, h, q_idx, kv_idx, *, context_len):
             )
 
         # 4. Compare against SDPA reference
-        torch.testing.assert_close(
-            output,
-            sdpa_output,
-            atol=1e-2,
-            rtol=1e-2,
-        )
+        if is_nvfp4:
+            atol, rtol = 1.0, 1.0  # nvfp4 has higher quantization error
+        else:
+            atol, rtol = 1e-2, 1e-2
+        torch.testing.assert_close(output, sdpa_output, atol=atol, rtol=rtol)
 
     finally:
         set_kv_cache_layout(None)
@@ -359,3 +519,18 @@ def test_trtllm_gen_full_attention_integration(batch_spec_name: str):
     MetadataBuilder.build() -> FlashInferImpl.forward() pipeline,
     with real TRTLLM kernels on Blackwell."""
     _run_trtllm_integration(BATCH_SPECS[batch_spec_name])
+
+
+@pytest.mark.parametrize(
+    "batch_spec_name",
+    list(BATCH_SPECS.keys()),
+)
+@torch.inference_mode()
+def test_trtllm_gen_nvfp4_kv_integration(batch_spec_name: str):
+    """Test TRTLLM attention with nvfp4 KV cache through the full
+    FlashInfer MetadataBuilder.build() -> FlashInferImpl.forward() pipeline."""
+    _run_trtllm_integration(
+        BATCH_SPECS[batch_spec_name],
+        kv_cache_dtype="nvfp4",
+        model_name=MODEL_NVFP4,
+    )
diff --git a/tests/v1/attention/utils.py b/tests/v1/attention/utils.py
index 91decf6658a5..1d5eba74693a 100644
--- a/tests/v1/attention/utils.py
+++ b/tests/v1/attention/utils.py
@@ -21,10 +21,11 @@
 from vllm.v1.attention.backend import (
     AttentionImpl,
     AttentionMetadataBuilder,
+    AttentionType,
     CommonAttentionMetadata,
 )
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
-from vllm.v1.kv_cache_interface import FullAttentionSpec
+from vllm.v1.kv_cache_interface import EncoderOnlyAttentionSpec, FullAttentionSpec
 
 
 @dataclass
@@ -106,6 +107,7 @@ def create_common_attn_metadata(
         query_start_loc=query_start_loc,
         query_start_loc_cpu=query_start_loc_cpu,
         seq_lens=seq_lens,
+        seq_lens_cpu_upper_bound=seq_lens_cpu,
         _seq_lens_cpu=seq_lens_cpu,
         _num_computed_tokens_cpu=num_computed_tokens_cpu,
         num_reqs=batch_spec.batch_size,
@@ -142,8 +144,24 @@ def try_backend_includes_kv_cache_update(
         raise AssertionError("unreachable") from None
 
 
-def create_standard_kv_cache_spec(vllm_config: VllmConfig) -> FullAttentionSpec:
-    """Create a FullAttentionSpec from ModelParams only."""
+def create_standard_kv_cache_spec(
+    vllm_config: VllmConfig,
+    attn_type: AttentionType = AttentionType.DECODER,
+) -> FullAttentionSpec | EncoderOnlyAttentionSpec:
+    """Create an AttentionSpec from VllmConfig.
+
+    Returns an EncoderOnlyAttentionSpec for encoder-only attention (no KV
+    cache), and a FullAttentionSpec otherwise.
+    """
+    if attn_type == AttentionType.ENCODER_ONLY:
+        return EncoderOnlyAttentionSpec(
+            block_size=vllm_config.cache_config.block_size,
+            num_kv_heads=vllm_config.model_config.get_num_kv_heads(
+                vllm_config.parallel_config
+            ),
+            head_size=vllm_config.model_config.get_head_size(),
+            dtype=vllm_config.model_config.dtype,
+        )
     return FullAttentionSpec(
         block_size=vllm_config.cache_config.block_size,
         num_kv_heads=vllm_config.model_config.get_num_kv_heads(
diff --git a/tests/v1/core/test_kv_cache_utils.py b/tests/v1/core/test_kv_cache_utils.py
index 046f04e0c79a..985b97c69ca4 100644
--- a/tests/v1/core/test_kv_cache_utils.py
+++ b/tests/v1/core/test_kv_cache_utils.py
@@ -1855,10 +1855,11 @@ def test_generate_scheduler_kv_cache_config():
 
 
 def new_mla_spec(cache_dtype_str=None):
+    # head_size = kv_lora_rank(512) + qk_rope_head_dim(64) = 576
     return MLAAttentionSpec(
         block_size=16,
-        num_kv_heads=16,
-        head_size=64,
+        num_kv_heads=1,
+        head_size=576,
         dtype=torch.float32,
         cache_dtype_str=cache_dtype_str,
     )
@@ -2073,6 +2074,54 @@ def test_auto_fit_max_model_len_not_triggered():
     assert vllm_config.model_config.max_model_len == 16
 
 
+def test_auto_fit_max_model_len_respects_num_gpu_blocks_override():
+    """Auto-fit must size max_model_len against the override-clamped pool, not
+    the raw `available_memory`. Without this, auto-fit could pick a
+    max_model_len that no longer fits once `num_gpu_blocks_override` is applied.
+    """
+    model_config = ModelConfig(max_model_len=16384)
+    model_config.original_max_model_len = -1  # request auto-fit
+    vllm_config = VllmConfig(model_config=model_config)
+    # Cap the cache to 32 blocks regardless of available memory.
+    vllm_config.cache_config.num_gpu_blocks_override = 32
+
+    mem_per_block_per_layer = 16 * 2 * 64 * 4 * 2
+    kv_cache_specs = {
+        "layer_1": new_kv_cache_spec(),  # block_size=16
+        "layer_2": new_kv_cache_spec(),
+    }
+    # Plenty of raw memory (1024 blocks per layer would fit max_model_len=16384).
+    large_available_memory = mem_per_block_per_layer * 2 * 1024
+
+    get_kv_cache_configs(vllm_config, [kv_cache_specs], [large_available_memory])
+
+    # 32 blocks * block_size 16 = 512 token slots, so max_model_len must
+    # auto-fit at or below that.
+    assert 0 < vllm_config.model_config.max_model_len <= 32 * 16
+
+
+def test_check_enough_kv_cache_memory_respects_num_gpu_blocks_override():
+    """Admission check must use the override-clamped pool size, not raw
+    `available_memory`. Without this, startup could accept a max_model_len
+    that does not actually fit in `num_gpu_blocks_override` blocks.
+    """
+    model_config = ModelConfig(max_model_len=16384)
+    vllm_config = VllmConfig(model_config=model_config)
+    # 32 blocks is far too small for max_model_len=16384 (would need 1024).
+    vllm_config.cache_config.num_gpu_blocks_override = 32
+
+    mem_per_block_per_layer = 16 * 2 * 64 * 4 * 2
+    kv_cache_specs = {
+        "layer_1": new_kv_cache_spec(),
+        "layer_2": new_kv_cache_spec(),
+    }
+    # Plenty of raw memory: a bytes-only check against this would pass.
+    large_available_memory = mem_per_block_per_layer * 2 * 1024
+
+    with pytest.raises(ValueError, match="max seq len"):
+        get_kv_cache_configs(vllm_config, [kv_cache_specs], [large_available_memory])
+
+
 def test_unify_hybrid_kv_cache_specs():
     # 1. has_full_attention and has_sliding_window
     before_spec_1 = new_kv_cache_spec()
diff --git a/tests/v1/core/test_prefix_caching.py b/tests/v1/core/test_prefix_caching.py
index 22220599f158..c35c38911a1a 100644
--- a/tests/v1/core/test_prefix_caching.py
+++ b/tests/v1/core/test_prefix_caching.py
@@ -557,19 +557,19 @@ def test_prefill_hybrid_model_eagle():
     computed_blocks, num_computed_tokens = manager.get_computed_blocks(req1)
     assert len(req1.block_hashes) == num_full_blocks
     assert computed_blocks.get_block_ids() == (
-        [1, 2, 3, 4],
-        [0, 9, 10, 11],
-        [0, 16, 17, 18],
+        [1, 2, 3, 4, 5],
+        [0, 0, 10, 11, 12],
+        [0, 0, 17, 18, 19],
     )
-    assert num_computed_tokens == 4 * block_size
+    assert num_computed_tokens == 5 * block_size
     num_new_tokens = len(all_token_ids) - num_computed_tokens
     blocks = manager.allocate_slots(
         req1, num_new_tokens, num_computed_tokens, computed_blocks
     )
     assert blocks is not None and blocks.get_block_ids() == (
-        [22, 23, 24],
-        [25, 26, 27],
-        [28, 29, 30],
+        [22, 23],
+        [24, 25],
+        [26, 27],
     )
     for block_per_group in computed_blocks.blocks:
         for block in block_per_group:
@@ -591,7 +591,7 @@ def test_prefill_hybrid_model_eagle():
             make_block_hash_with_group_id(block_hashes[0], 1),
             make_block_hash_with_group_id(block_hashes[0], 2),
         ],
-        4,
+        5,
     )
 
     # Evict the first block of full attention, makes total cache miss.
@@ -605,7 +605,7 @@ def test_prefill_hybrid_model_eagle():
         0,
     )
 
-    # Evict the last block of all layers, reduces the hit length to 3.
+    # Evict the last block of all layers, reduces the hit length to 4.
     _test_partial_request_hit(
         manager,
         block_size,
@@ -617,10 +617,10 @@ def test_prefill_hybrid_model_eagle():
             make_block_hash_with_group_id(block_hashes[-1], 1),
             make_block_hash_with_group_id(block_hashes[-1], 2),
         ],
-        3,
+        4,
     )
 
-    # Evict the last block of full attention, reduces the hit length to 3.
+    # Evict the last block of full attention, reduces the hit length to 4.
     _test_partial_request_hit(
         manager,
         block_size,
@@ -628,7 +628,7 @@ def test_prefill_hybrid_model_eagle():
         "5",
         all_token_ids,
         [make_block_hash_with_group_id(block_hashes[-1], 0)],
-        3,
+        4,
     )
 
     # Since the last block of full attention is dropped for eagle, evict
@@ -655,12 +655,11 @@ def test_prefill_hybrid_model_eagle():
         3,
     )
 
-    # Evict different set of blocks for full attention and sliding window makes
-    # total cache miss.
-    # The cache hit length of full attention is 4 * block_size.
-    # The cache hit length of sliding window is 3 * block_size.
-    # Then it is cache miss as the two type of layers
-    # have different hit length.
+    # Evict different set of blocks for full attention and sliding window.
+    # Full loses its last block so it drops to 4 full blocks after the eagle
+    # pop; SWA lost block 0 (outside the sliding window of the final hit),
+    # which is not required for the K+1 anchor at position 4. Coordinated
+    # single-drop aligns both groups at hit=4.
     _test_partial_request_hit(
         manager,
         block_size,
@@ -672,7 +671,7 @@ def test_prefill_hybrid_model_eagle():
             make_block_hash_with_group_id(block_hashes[0], 1),
             make_block_hash_with_group_id(block_hashes[0], 2),
         ],
-        0,
+        4,
     )
 
 
@@ -893,7 +892,7 @@ def test_prefill_hybrid_model_combinations(spec_types: list[str]):
 # - 2 groups: 1 full + 1 other
 _EAGLE_HYBRID_MODEL_TEST_CASES = [
     # 2 groups: 1 full + 1 other
-    pytest.param(["full", "sliding_window"], 2, id="2g-full+sw"),
+    pytest.param(["full", "sliding_window"], 3, id="2g-full+sw"),
 ]
 
 
@@ -2513,3 +2512,113 @@ def test_block_lookup_cache_multi_blocks_per_key():
     assert cache.pop(key1, 11) is block11
     assert cache.get_one_block(key1) is None
     assert cache.pop(key1, 12) is None
+
+
+def test_can_fit_full_sequence_swa_cap_admits_long_prompt():
+    """Hybrid full+SWA model with a pool sized at the startup minimum should
+    admit a prompt longer than the SWA cap, because SlidingWindowManager
+    recycles blocks during chunked prefill (issue #39734)."""
+    block_size = 16
+    sliding_window = 4 * block_size  # 64 tokens
+    max_num_batched_tokens = 8 * block_size  # 128 tokens
+    max_model_len = 64 * block_size  # 1024 tokens — much larger than the SWA cap
+    # Startup pool sizing: full demands cdiv(max_model_len, bs) = 64 blocks,
+    # SWA demands cdiv(SW-1+max_batched, bs) + 1 = cdiv(191, 16) + 1 = 13.
+    # Pool minimum = 64 + 13 = 77; +1 for the null block.
+    num_blocks = 64 + 13 + 1
+
+    config = KVCacheConfig(
+        num_blocks=num_blocks,
+        kv_cache_tensors=[],
+        kv_cache_groups=[
+            KVCacheGroupSpec(
+                ["layer_full"],
+                FullAttentionSpec(
+                    block_size=block_size,
+                    num_kv_heads=1,
+                    head_size=1,
+                    dtype=torch.float32,
+                ),
+            ),
+            KVCacheGroupSpec(
+                ["layer_swa"],
+                SlidingWindowSpec(
+                    block_size=block_size,
+                    num_kv_heads=1,
+                    head_size=1,
+                    dtype=torch.float32,
+                    sliding_window=sliding_window,
+                ),
+            ),
+        ],
+    )
+
+    manager = KVCacheManager(
+        config,
+        max_model_len=max_model_len,
+        max_num_batched_tokens=max_num_batched_tokens,
+        enable_caching=True,
+        hash_block_size=block_size,
+    )
+
+    # A prompt that is shorter than max_model_len but longer than SW + chunk:
+    # cdiv(prompt_len, bs) = 32 blocks. Without the cap, admission would
+    # demand 32 (full) + 32 (SWA) = 64 blocks. With the cap, SWA contributes
+    # only 13, so total = 32 + 13 = 45 ≤ pool size.
+    prompt_len = 32 * block_size
+    req = make_request("long", list(range(prompt_len)), block_size, sha256)
+
+    assert (
+        manager.allocate_slots(req, block_size, full_sequence_must_fit=True) is not None
+    )
+
+
+def test_can_fit_full_sequence_full_attention_still_gates_oversized():
+    """The cap only loosens the SWA group; a prompt that exceeds the
+    full-attention pool capacity must still be rejected."""
+    block_size = 16
+    sliding_window = 4 * block_size
+    max_num_batched_tokens = 8 * block_size
+    max_model_len = 64 * block_size
+    # Provide a tiny pool — even a small prompt should be rejected.
+    num_blocks = 5
+
+    config = KVCacheConfig(
+        num_blocks=num_blocks,
+        kv_cache_tensors=[],
+        kv_cache_groups=[
+            KVCacheGroupSpec(
+                ["layer_full"],
+                FullAttentionSpec(
+                    block_size=block_size,
+                    num_kv_heads=1,
+                    head_size=1,
+                    dtype=torch.float32,
+                ),
+            ),
+            KVCacheGroupSpec(
+                ["layer_swa"],
+                SlidingWindowSpec(
+                    block_size=block_size,
+                    num_kv_heads=1,
+                    head_size=1,
+                    dtype=torch.float32,
+                    sliding_window=sliding_window,
+                ),
+            ),
+        ],
+    )
+
+    manager = KVCacheManager(
+        config,
+        max_model_len=max_model_len,
+        max_num_batched_tokens=max_num_batched_tokens,
+        enable_caching=True,
+        hash_block_size=block_size,
+    )
+
+    # 16 blocks of full attention demand alone exceeds the 5-block pool.
+    prompt_len = 16 * block_size
+    req = make_request("oversized", list(range(prompt_len)), block_size, sha256)
+
+    assert manager.allocate_slots(req, block_size, full_sequence_must_fit=True) is None
diff --git a/tests/v1/core/test_scheduler.py b/tests/v1/core/test_scheduler.py
index f825220800f3..42f4825e2b3b 100644
--- a/tests/v1/core/test_scheduler.py
+++ b/tests/v1/core/test_scheduler.py
@@ -1892,6 +1892,7 @@ def create_scheduler_with_priority(
         log_stats=True,
         structured_output_manager=StructuredOutputManager(vllm_config),
         block_size=block_size,
+        hash_block_size=block_size,
     )
 
 
@@ -4008,6 +4009,7 @@ def _create_encoder_decoder_scheduler(
         vllm_config=vllm_config,
         kv_cache_config=kv_cache_config,
         block_size=block_size,
+        hash_block_size=block_size,
         structured_output_manager=StructuredOutputManager(vllm_config),
     )
 
diff --git a/tests/v1/core/test_single_type_kv_cache_manager.py b/tests/v1/core/test_single_type_kv_cache_manager.py
index b05040ebe2a6..f59830dcd741 100644
--- a/tests/v1/core/test_single_type_kv_cache_manager.py
+++ b/tests/v1/core/test_single_type_kv_cache_manager.py
@@ -22,11 +22,13 @@
 
 
 def get_sliding_window_manager(sliding_window_spec, block_pool, enable_caching=True):
+    # Tests don't exercise admission gating; pass a large cap that is a no-op.
     return SlidingWindowManager(
         sliding_window_spec,
         block_pool=block_pool,
         enable_caching=enable_caching,
         kv_cache_group_id=0,
+        max_admission_blocks_per_request=10**9,
     )
 
 
@@ -38,6 +40,7 @@ def get_chunked_local_attention_manager(
         block_pool=block_pool,
         enable_caching=enable_caching,
         kv_cache_group_id=0,
+        max_admission_blocks_per_request=10**9,
     )
 
 
@@ -429,3 +432,52 @@ def test_chunked_local_attention_get_num_blocks_to_allocate():
         )
         == 15
     )
+
+
+def test_predictor_matches_allocator_blocks_calculation_with_admission_cap():
+    """In forward steps, `get_num_blocks_to_allocate` must return exactly what
+    `allocate_new_blocks` will pull; otherwise `block_pool.get_new_blocks`
+    raises `ValueError: Cannot get N free blocks from the pool`.
+    """
+    block_size = 2
+    sliding_window = 8  # 4-block live window
+    cap = sliding_window // block_size
+
+    spec = SlidingWindowSpec(
+        block_size=block_size,
+        num_kv_heads=1,
+        head_size=1,
+        dtype=torch.float32,
+        sliding_window=sliding_window,
+    )
+    block_pool = BlockPool(
+        num_gpu_blocks=100, enable_caching=True, hash_block_size=block_size
+    )
+    manager = SlidingWindowManager(
+        spec,
+        block_pool=block_pool,
+        enable_caching=False,
+        kv_cache_group_id=0,
+        max_admission_blocks_per_request=cap,
+    )
+
+    request_id = "req"
+    total_computed = 0
+    # Walk through request forward steps. Check num_blocks returned by
+    # `get_num_blocks_to_allocate` matches what `allocate_new_blocks` pulls
+    for num_tokens in (4, 8, 12, 16):
+        predicted = manager.get_num_blocks_to_allocate(
+            request_id=request_id,
+            num_tokens=num_tokens,
+            new_computed_blocks=[],
+            total_computed_tokens=total_computed,
+            num_tokens_main_model=num_tokens,
+        )
+        new_blocks = manager.allocate_new_blocks(
+            request_id, num_tokens=num_tokens, num_tokens_main_model=num_tokens
+        )
+        assert predicted == len(new_blocks), (
+            f"num_tokens={num_tokens}: predictor returned {predicted} "
+            f"but allocator pulled {len(new_blocks)}"
+        )
+        total_computed = num_tokens
diff --git a/tests/v1/cudagraph/test_cudagraph_dispatch.py b/tests/v1/cudagraph/test_cudagraph_dispatch.py
index 66e6d7dd4605..97b5fd46a2eb 100644
--- a/tests/v1/cudagraph/test_cudagraph_dispatch.py
+++ b/tests/v1/cudagraph/test_cudagraph_dispatch.py
@@ -371,193 +371,200 @@ def test_bypass_on_mode_none(self):
         assert not wrapper.concrete_cudagraph_entries
 
 
-@pytest.mark.skipif(not current_platform.is_cuda(), reason="Skip if not cuda")
-class TestCudagraphIntegration:
-    def setup_method(self):
-        # only FULL mode for non-uniform batches
-        self.comp_config = CompilationConfig(
-            mode=CompilationMode.VLLM_COMPILE,
-            cudagraph_mode="FULL",
-            cudagraph_capture_sizes=[10, 20],
-        )
-        self.vllm_config = _create_vllm_config(self.comp_config)
-        self.dispatcher = CudagraphDispatcher(self.vllm_config)
-        self.dispatcher.initialize_cudagraph_keys(
-            self.comp_config.cudagraph_mode, uniform_decode_query_len=1
-        )
-
-    def _run_and_monitor_call(
-        self, wrapper, input_tensor, runtime_mode, batch_descriptor
+def _run_and_monitor_call(
+    wrapper, input_tensor, runtime_mode, batch_descriptor, vllm_config
+):
+    """Helper to run a single call and monitor the action."""
+
+    with (
+        patch("torch.cuda.graph", wraps=torch.cuda.graph) as mock_graph_context,
+        patch.object(wrapper, "runnable", wraps=wrapper.runnable) as mock_runnable,
     ):
-        """Helper to run a single call and monitor the action."""
-
-        with (
-            patch("torch.cuda.graph", wraps=torch.cuda.graph) as mock_graph_context,
-            patch.object(wrapper, "runnable", wraps=wrapper.runnable) as mock_runnable,
-        ):
-            entry = wrapper.concrete_cudagraph_entries.get(batch_descriptor, None)
-
-            context = set_forward_context(
-                attn_metadata=None,
-                vllm_config=self.vllm_config,
-                cudagraph_runtime_mode=runtime_mode,
-                batch_descriptor=batch_descriptor,
-            )
-            mock_replay = MagicMock()
-            if entry and entry.cudagraph:
-                with (
-                    context,
-                    patch.object(
-                        entry.cudagraph, "replay", new_callable=MagicMock
-                    ) as mock_replay,
-                ):
-                    wrapper(input_tensor)
-            else:
-                with context:
-                    wrapper(input_tensor)
-
-            if mock_graph_context.called:
-                # note that this is globally mocked, so it will be detected
-                # even whether called by the inner or outer wrapper
-                return "capture_global"
-            if mock_replay.called:
-                # only for outer wrapper
-                return "replay"
-            if mock_runnable.call_count > 0:
-                # only for outer wrapper
-                return "bypass"
-            return "unknown"
-
-    @create_new_process_for_each_test("spawn")
-    def test_capture_replay_bypass_logic(self):
-        model = SimpleMLP().to(DEVICE_TYPE)
-        full_wrapper = CUDAGraphWrapper(model, self.vllm_config, CUDAGraphMode.FULL)
-        max_bs = 16
-        persistent_input_buffer = torch.zeros(max_bs, 10, device=DEVICE_TYPE)
-        input_1 = persistent_input_buffer[:1]
-        input_2 = persistent_input_buffer[:2]
-        input_3 = persistent_input_buffer[:3]
-
-        desc_1 = BatchDescriptor(num_tokens=1)
-        desc_2 = BatchDescriptor(num_tokens=2)
-        desc_3_unseen = BatchDescriptor(num_tokens=3)
+        entry = wrapper.concrete_cudagraph_entries.get(batch_descriptor, None)
 
-        # 0. global warmup
-        with set_forward_context(
+        context = set_forward_context(
             attn_metadata=None,
-            vllm_config=self.vllm_config,
-            cudagraph_runtime_mode=CUDAGraphMode.NONE,
-            batch_descriptor=None,
-        ):
-            full_wrapper(input_1)
+            vllm_config=vllm_config,
+            cudagraph_runtime_mode=runtime_mode,
+            batch_descriptor=batch_descriptor,
+        )
+        mock_replay = MagicMock()
+        if entry and entry.cudagraph:
+            with (
+                context,
+                patch.object(
+                    entry.cudagraph, "replay", new_callable=MagicMock
+                ) as mock_replay,
+            ):
+                wrapper(input_tensor)
+        else:
+            with context:
+                wrapper(input_tensor)
+
+        if mock_graph_context.called:
+            # note that this is globally mocked, so it will be detected
+            # even whether called by the inner or outer wrapper
+            return "capture_global"
+        if mock_replay.called:
+            # only for outer wrapper
+            return "replay"
+        if mock_runnable.call_count > 0:
+            # only for outer wrapper
+            return "bypass"
+        return "unknown"
+
+
+@create_new_process_for_each_test("spawn")
+@pytest.mark.skipif(not current_platform.is_cuda(), reason="Skip if not cuda")
+def test_capture_replay_bypass_logic():
+    comp_config = CompilationConfig(
+        mode=CompilationMode.VLLM_COMPILE,
+        cudagraph_mode="FULL",
+        cudagraph_capture_sizes=[1, 2],
+    )
+    vllm_config = _create_vllm_config(comp_config)
+    dispatcher = CudagraphDispatcher(vllm_config)
+    dispatcher.initialize_cudagraph_keys(
+        comp_config.cudagraph_mode, uniform_decode_query_len=1
+    )
+    model = SimpleMLP().to(DEVICE_TYPE)
+    full_wrapper = CUDAGraphWrapper(model, vllm_config, CUDAGraphMode.FULL)
+    max_bs = 16
+    persistent_input_buffer = torch.zeros(max_bs, 10, device=DEVICE_TYPE)
+    input_1 = persistent_input_buffer[:1]
+    input_2 = persistent_input_buffer[:2]
+    input_3 = persistent_input_buffer[:3]
+
+    desc_1 = BatchDescriptor(num_tokens=1)
+    desc_2 = BatchDescriptor(num_tokens=2)
+    desc_3_unseen = BatchDescriptor(num_tokens=3)
+
+    # 0. global warmup
+    with set_forward_context(
+        attn_metadata=None,
+        vllm_config=vllm_config,
+        cudagraph_runtime_mode=CUDAGraphMode.NONE,
+        batch_descriptor=None,
+    ):
+        full_wrapper(input_1)
 
-        rt_mode, key = self.dispatcher.dispatch(num_tokens=desc_1.num_tokens)
-        # 1. Capture first shape
-        action = self._run_and_monitor_call(full_wrapper, input_1, rt_mode, key)
-        assert action == "capture_global"
+    rt_mode, key = dispatcher.dispatch(num_tokens=desc_1.num_tokens)
+    # 1. Capture first shape
+    action = _run_and_monitor_call(full_wrapper, input_1, rt_mode, key, vllm_config)
+    assert action == "capture_global"
 
-        # 2. Replay first shape
-        action = self._run_and_monitor_call(full_wrapper, input_1, rt_mode, key)
-        assert action == "replay"
+    # 2. Replay first shape
+    action = _run_and_monitor_call(full_wrapper, input_1, rt_mode, key, vllm_config)
+    assert action == "replay"
 
-        rt_mode, key = self.dispatcher.dispatch(num_tokens=desc_2.num_tokens)
-        # 3. Capture second shape
-        action = self._run_and_monitor_call(full_wrapper, input_2, rt_mode, key)
-        assert action == "capture_global"
+    rt_mode, key = dispatcher.dispatch(num_tokens=desc_2.num_tokens)
+    # 3. Capture second shape
+    action = _run_and_monitor_call(full_wrapper, input_2, rt_mode, key, vllm_config)
+    assert action == "capture_global"
 
-        # 4. Replay second shape
-        action = self._run_and_monitor_call(
-            full_wrapper, input_2, CUDAGraphMode.FULL, desc_2
+    # 4. Replay second shape
+    action = _run_and_monitor_call(
+        full_wrapper, input_2, CUDAGraphMode.FULL, key, vllm_config
+    )
+    assert action == "replay"
+
+    # 5. Bypass if no key match
+    rt_mode, key = dispatcher.dispatch(num_tokens=desc_3_unseen.num_tokens)
+    assert rt_mode == CUDAGraphMode.NONE
+    action = _run_and_monitor_call(full_wrapper, input_3, rt_mode, key, vllm_config)
+    assert action == "bypass"
+
+    # capture unseen shape is not allowed after disable
+    set_cudagraph_capturing_enabled(False)
+    with pytest.raises(RuntimeError):
+        _run_and_monitor_call(
+            full_wrapper, input_3, CUDAGraphMode.FULL, desc_3_unseen, vllm_config
         )
-        assert action == "replay"
+    set_cudagraph_capturing_enabled(True)
 
-        # 5. Bypass if no key match
-        rt_mode, key = self.dispatcher.dispatch(num_tokens=desc_3_unseen.num_tokens)
-        assert rt_mode == CUDAGraphMode.NONE
-        action = self._run_and_monitor_call(full_wrapper, input_3, rt_mode, key)
-        assert action == "bypass"
-
-        # capture unseen shape is not allowed after disable
-        set_cudagraph_capturing_enabled(False)
-        with pytest.raises(RuntimeError):
-            self._run_and_monitor_call(
-                full_wrapper, input_3, CUDAGraphMode.FULL, desc_3_unseen
-            )
-        set_cudagraph_capturing_enabled(True)
-
-    @create_new_process_for_each_test("spawn")
-    def test_nested_wrappers(self):
-        """Tests a scenario with a PIECEWISE wrapper inside a FULL one."""
-        model = SimpleMLP().to(DEVICE_TYPE)
-        full_wrapper = CUDAGraphWrapper(model, self.vllm_config, CUDAGraphMode.FULL)
-        input_1 = torch.randn(1, 10, device=DEVICE_TYPE)
-
-        # Setup: Inner model is wrapped with PIECEWISE, outer with FULL
-        inner_model = SimpleMLP().to(DEVICE_TYPE)
-        piecewise_wrapper = CUDAGraphWrapper(
-            inner_model, self.vllm_config, CUDAGraphMode.PIECEWISE
-        )
-        inner_model.forward = MagicMock(wraps=inner_model.forward)
-        outer_model = SimpleMLP().to(DEVICE_TYPE)
-        # When outer model is called, it calls the piecewise_wrapper
-        outer_model.forward = MagicMock(
-            wraps=outer_model.forward, side_effect=piecewise_wrapper
-        )
-        full_wrapper = CUDAGraphWrapper(
-            outer_model, self.vllm_config, CUDAGraphMode.FULL
-        )
 
-        desc_1 = BatchDescriptor(num_tokens=1)
+@create_new_process_for_each_test("spawn")
+@pytest.mark.skipif(not current_platform.is_cuda(), reason="Skip if not cuda")
+def test_nested_wrappers():
+    """Tests a scenario with a PIECEWISE wrapper inside a FULL one."""
+    comp_config = CompilationConfig(
+        mode=CompilationMode.VLLM_COMPILE,
+        cudagraph_mode="FULL",
+        cudagraph_capture_sizes=[1],
+    )
+    vllm_config = _create_vllm_config(comp_config)
+    dispatcher = CudagraphDispatcher(vllm_config)
+    dispatcher.initialize_cudagraph_keys(
+        comp_config.cudagraph_mode, uniform_decode_query_len=1
+    )
+    model = SimpleMLP().to(DEVICE_TYPE)
+    full_wrapper = CUDAGraphWrapper(model, vllm_config, CUDAGraphMode.FULL)
+    input_1 = torch.randn(1, 10, device=DEVICE_TYPE)
+
+    # Setup: Inner model is wrapped with PIECEWISE, outer with FULL
+    inner_model = SimpleMLP().to(DEVICE_TYPE)
+    piecewise_wrapper = CUDAGraphWrapper(
+        inner_model, vllm_config, CUDAGraphMode.PIECEWISE
+    )
+    inner_model.forward = MagicMock(wraps=inner_model.forward)
+    outer_model = SimpleMLP().to(DEVICE_TYPE)
+    # When outer model is called, it calls the piecewise_wrapper
+    outer_model.forward = MagicMock(
+        wraps=outer_model.forward, side_effect=piecewise_wrapper
+    )
+    full_wrapper = CUDAGraphWrapper(outer_model, vllm_config, CUDAGraphMode.FULL)
 
-        # 0. global warmup
-        with set_forward_context(
-            attn_metadata=None,
-            vllm_config=self.vllm_config,
-            cudagraph_runtime_mode=CUDAGraphMode.NONE,
-            batch_descriptor=None,
-        ):
-            full_wrapper(input_1)
-
-        # --- Test runtime mode FULL---
-        # Run with FULL mode context. Expect outer wrapper to capture.
-        # The inner mock should be called once inside the graph capture.
-        outer_model.forward.reset_mock()
-        inner_model.forward.reset_mock()
-        action = self._run_and_monitor_call(
-            full_wrapper, input_1, CUDAGraphMode.FULL, desc_1
-        )
-        assert action == "capture_global"
-        assert outer_model.forward.call_count == 1
-        assert inner_model.forward.call_count == 1
-
-        # Run again. Expect outer wrapper to replay.
-        # The outer model should NOT be called because the whole graph
-        # is replayed.
-        action = self._run_and_monitor_call(
-            full_wrapper, input_1, CUDAGraphMode.FULL, desc_1
-        )
-        assert action == "replay"
-        assert outer_model.forward.call_count == 1  # No new call
-        assert inner_model.forward.call_count == 1
-
-        # --- Test runtime mode PIECEWISE ---
-        outer_model.forward.reset_mock()
-        inner_model.forward.reset_mock()
-        # Run with PIECEWISE mode context.
-        # Expect outer wrapper to bypass and call inner wrapper.
-        # Inner wrapper should capture.
-        action = self._run_and_monitor_call(
-            full_wrapper, input_1, CUDAGraphMode.PIECEWISE, desc_1
-        )
-        assert action == "capture_global"
-        assert outer_model.forward.call_count == 1
-        assert inner_model.forward.call_count == 1
-
-        # Run again with PIECEWISE.
-        # Outer bypasses, inner replays.
-        action = self._run_and_monitor_call(
-            full_wrapper, input_1, CUDAGraphMode.PIECEWISE, desc_1
-        )
-        assert action == "bypass"
-        assert outer_model.forward.call_count == 2
-        assert inner_model.forward.call_count == 1
+    desc_1 = BatchDescriptor(num_tokens=1)
+
+    # 0. global warmup
+    with set_forward_context(
+        attn_metadata=None,
+        vllm_config=vllm_config,
+        cudagraph_runtime_mode=CUDAGraphMode.NONE,
+        batch_descriptor=None,
+    ):
+        full_wrapper(input_1)
+
+    # --- Test runtime mode FULL---
+    # Run with FULL mode context. Expect outer wrapper to capture.
+    # The inner mock should be called once inside the graph capture.
+    outer_model.forward.reset_mock()
+    inner_model.forward.reset_mock()
+    action = _run_and_monitor_call(
+        full_wrapper, input_1, CUDAGraphMode.FULL, desc_1, vllm_config
+    )
+    assert action == "capture_global"
+    assert outer_model.forward.call_count == 1
+    assert inner_model.forward.call_count == 1
+
+    # Run again. Expect outer wrapper to replay.
+    # The outer model should NOT be called because the whole graph
+    # is replayed.
+    action = _run_and_monitor_call(
+        full_wrapper, input_1, CUDAGraphMode.FULL, desc_1, vllm_config
+    )
+    assert action == "replay"
+    assert outer_model.forward.call_count == 1  # No new call
+    assert inner_model.forward.call_count == 1
+
+    # --- Test runtime mode PIECEWISE ---
+    outer_model.forward.reset_mock()
+    inner_model.forward.reset_mock()
+    # Run with PIECEWISE mode context.
+    # Expect outer wrapper to bypass and call inner wrapper.
+    # Inner wrapper should capture.
+    action = _run_and_monitor_call(
+        full_wrapper, input_1, CUDAGraphMode.PIECEWISE, desc_1, vllm_config
+    )
+    assert action == "capture_global"
+    assert outer_model.forward.call_count == 1
+    assert inner_model.forward.call_count == 1
+
+    # Run again with PIECEWISE.
+    # Outer bypasses, inner replays.
+    action = _run_and_monitor_call(
+        full_wrapper, input_1, CUDAGraphMode.PIECEWISE, desc_1, vllm_config
+    )
+    assert action == "bypass"
+    assert outer_model.forward.call_count == 2
+    assert inner_model.forward.call_count == 1
diff --git a/tests/v1/determinism/test_batch_invariance.py b/tests/v1/determinism/test_batch_invariance.py
index b162b469bcda..41242da5c22b 100644
--- a/tests/v1/determinism/test_batch_invariance.py
+++ b/tests/v1/determinism/test_batch_invariance.py
@@ -36,10 +36,10 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
     using the high-level v1 LLM() API only (no manual batching).
 
     Strategy:
-    - Create two LLM engines with identical config except max_num_seqs: 1 vs N.
-    - Compute a baseline output for the needle prompt with the bs=1 engine.
-    - For many trials, generate a batch (size N) where the needle appears at a
-      random position among random filler prompts using the bs=N engine.
+    - Create a single LLM engine configured for the larger batch limit (N).
+    - Compute a baseline output for the needle prompt when it is run alone.
+    - For many trials, generate a mixed batch (size N) where the needle appears
+      at a random position among random filler prompts using the same engine.
     - Track how many trials match vs mismatch, and report totals at the end.
       The test fails if any mismatches occur, but we still dump pass/fail
       counts.
@@ -65,7 +65,7 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
     assert max_batch_size >= 2, "Batch size should be >= 2 to mix needle."
 
     # Keep GPU memory usage low to avoid startup allocation failures.
-    gpu_mem_util = float(os.getenv("VLLM_GPU_MEMORY_UTILIZATION", "0.4"))
+    gpu_mem_util = float(os.getenv("VLLM_GPU_MEMORY_UTILIZATION", "0.5"))
     max_model_len = int(os.getenv("VLLM_MAX_MODEL_LEN", "5120"))
 
     # Sampling parameters: longer outputs with a more random-sounding
@@ -83,11 +83,9 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
 
     needle_prompt = "There once was a "
 
-    llm_bs1 = None
-    llm_bsN = None
+    llm = None
     try:
-        # Engine with bs=1 behavior
-        llm_bs1 = LLM_with_max_seqs(
+        llm = LLM_with_max_seqs(
             model=model,
             max_num_seqs=max_batch_size,
             gpu_memory_utilization=gpu_mem_util,
@@ -96,20 +94,11 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
         )
 
         # Baseline generation for the needle prompt alone.
-        baseline_out = llm_bs1.generate([needle_prompt], sampling)
+        baseline_out = llm.generate([needle_prompt], sampling)
         assert len(baseline_out) == 1
         assert len(baseline_out[0].outputs) >= 1
         baseline_text = baseline_out[0].outputs[0].text
 
-        # Engine with larger batch limit (e.g., 64)
-        llm_bsN = LLM_with_max_seqs(
-            model=model,
-            max_num_seqs=max_batch_size,
-            gpu_memory_utilization=gpu_mem_util,
-            max_model_len=max_model_len,
-            attention_config=attention_config,
-        )
-
         mismatches = 0
 
         for trial in range(num_trials):
@@ -124,8 +113,8 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
                 else:
                     prompts.append(_random_prompt(min_random_prompt, max_random_prompt))
 
-            # Generate with the larger-batch engine
-            outputs = llm_bsN.generate(prompts, sampling)
+            # Generate with the same engine but in a larger batch.
+            outputs = llm.generate(prompts, sampling)
             # Find the needle output by position
             needle_output = outputs[needle_pos]
             assert needle_output.prompt == needle_prompt
@@ -151,12 +140,9 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
 
     finally:
         # Ensure engines are shutdown to free GPU/VRAM across test sessions
-        if llm_bs1 is not None:
-            with contextlib.suppress(Exception):
-                llm_bs1.shutdown()
-        if llm_bsN is not None:
+        if llm is not None:
             with contextlib.suppress(Exception):
-                llm_bsN.shutdown()
+                llm.shutdown()
 
 
 @skip_unsupported
diff --git a/tests/v1/determinism/test_matmul_batch_invariant.py b/tests/v1/determinism/test_matmul_batch_invariant.py
new file mode 100644
index 000000000000..8838b6be2705
--- /dev/null
+++ b/tests/v1/determinism/test_matmul_batch_invariant.py
@@ -0,0 +1,105 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Test batch-invariant matmul against torch.matmul for various shape combinations.
+
+Tests correctness (matches torch.matmul) and batch invariance (result for one
+item doesn't change based on other items in the batch).
+"""
+
+import pytest
+import torch
+from utils import skip_unsupported
+
+from vllm.model_executor.layers.batch_invariant import matmul_batch_invariant
+from vllm.platforms import current_platform
+
+DEVICE_TYPE = current_platform.device_type
+
+
+@skip_unsupported
+@pytest.mark.parametrize(
+    "a_shape,b_shape",
+    [
+        # 2D x 2D
+        ((32, 64), (64, 16)),
+        # 2D x 3D
+        ((64, 16), (4, 16, 32)),
+        # 3D x 2D
+        ((4, 32, 64), (64, 16)),
+        # 4D x 2D
+        ((1, 4, 32, 64), (64, 16)),
+        # 3D x 3D
+        ((4, 32, 64), (4, 64, 16)),
+        # 3D x 4D
+        ((2, 32, 64), (1, 2, 64, 16)),
+        # 4D x 3D (Gemma4 pattern)
+        ((1, 2, 32, 64), (2, 64, 16)),
+        # 4D x 4D
+        ((1, 2, 32, 64), (4, 2, 64, 16)),
+        # 2D x 4D
+        ((32, 64), (1, 2, 64, 16)),
+        # 2D x 5D
+        ((32, 64), (1, 2, 2, 64, 16)),
+        # 5D x 2D
+        ((1, 2, 2, 32, 64), (64, 16)),
+        # 5D x 5D
+        ((1, 2, 4, 32, 64), (1, 2, 4, 64, 16)),
+    ],
+)
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+def test_matmul_correctness(a_shape, b_shape, dtype):
+    """
+    Compare matmul_batch_invariant against torch.matmul for various shapes.
+    """
+    device = torch.device(DEVICE_TYPE)
+
+    torch.manual_seed(42)
+    a = torch.rand(a_shape, dtype=dtype, device=device)
+    b = torch.rand(b_shape, dtype=dtype, device=device)
+
+    # Standard implementation (CUDA ops)
+    standard_output = torch.matmul(a, b)
+
+    # Batch-invariant implementation (Triton)
+    triton_output = matmul_batch_invariant(a, b)
+
+    # Compare outputs
+    # Use looser tolerance for bfloat16 due to its lower precision
+    if dtype == torch.bfloat16:
+        rtol, atol = 1e-1, 1e-1  # 10% relative tolerance for bfloat16
+    else:
+        rtol, atol = 1e-2, 1e-2  # 1% for float16/float32
+
+    torch.testing.assert_close(
+        triton_output,
+        standard_output,
+        rtol=rtol,
+        atol=atol,
+        msg=f"matmul mismatch for a ndim={a.ndim}, b ndim={b.ndim},",
+    )
+
+
+@skip_unsupported
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+def test_matmul_batch_invariance(dtype):
+    """
+    Verify that the result for one item is bitwise identical regardless
+    of what other items are in the batch.
+    """
+
+    device = torch.device(DEVICE_TYPE)
+
+    torch.manual_seed(42)
+    a_single = torch.rand((1, 64, 32), dtype=dtype, device=device)
+    b = torch.rand((32, 128), dtype=dtype, device=device)
+
+    standard_output = matmul_batch_invariant(a_single, b)
+
+    a_batch = torch.rand((8, 64, 32), dtype=dtype, device=device)
+    a_batch[3] = a_single[0]
+
+    batch_output = matmul_batch_invariant(a_batch, b)
+    batch_output_a = batch_output[3]
+
+    assert torch.equal(standard_output[0], batch_output_a)
diff --git a/tests/v1/determinism/test_rms_norm_batch_invariant.py b/tests/v1/determinism/test_rms_norm_batch_invariant.py
index 7d3b8437a937..2e9f77881273 100644
--- a/tests/v1/determinism/test_rms_norm_batch_invariant.py
+++ b/tests/v1/determinism/test_rms_norm_batch_invariant.py
@@ -71,6 +71,99 @@ def test_rms_norm_batch_invariant_vs_standard(
     )
 
 
+@skip_unsupported
+@pytest.mark.parametrize("hidden_size", [512, 4096])
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("eps", [1e-6])
+def test_fused_add_rms_norm_batch_invariant_residual_path(
+    hidden_size: int,
+    dtype: torch.dtype,
+    eps: float,
+):
+    """
+    Test the batch-invariant fused residual-add + RMSNorm helper directly.
+    """
+    device = torch.device(DEVICE_TYPE)
+
+    torch.manual_seed(42)
+    x_single = torch.randn(1, hidden_size, dtype=dtype, device=device)
+    residual_single = torch.randn(1, hidden_size, dtype=dtype, device=device)
+    weight = torch.randn(hidden_size, dtype=dtype, device=device)
+
+    x_batch = torch.cat(
+        [
+            x_single,
+            torch.randn(3, hidden_size, dtype=dtype, device=device),
+        ],
+        dim=0,
+    )
+    residual_batch = torch.cat(
+        [
+            residual_single,
+            torch.randn(3, hidden_size, dtype=dtype, device=device),
+        ],
+        dim=0,
+    )
+
+    def fused_add_rms_norm(x, residual, w, e) -> tuple[torch.Tensor, torch.Tensor]:
+        import vllm._custom_ops as ops
+
+        ops.fused_add_rms_norm(x, residual, w, e)
+        return x, residual
+
+    out_single, residual_out_single = fused_add_rms_norm(
+        x_single.clone(),
+        residual_single.clone(),
+        weight,
+        eps,
+    )
+    out_batch, residual_out_batch = fused_add_rms_norm(
+        x_batch.clone(),
+        residual_batch.clone(),
+        weight,
+        eps,
+    )
+
+    merged_single = x_single + residual_single
+    ref_out = triton_rms_norm(merged_single, weight, eps=eps)
+
+    torch.testing.assert_close(
+        residual_out_single,
+        merged_single,
+        rtol=0.0,
+        atol=0.0,
+        msg="Residual output should equal x + residual exactly",
+    )
+    torch.testing.assert_close(
+        residual_out_batch[:1],
+        merged_single,
+        rtol=0.0,
+        atol=0.0,
+        msg="Residual output should be batch invariant",
+    )
+    torch.testing.assert_close(
+        out_single,
+        out_batch[:1],
+        rtol=0.0,
+        atol=0.0,
+        msg="Fused add RMSNorm output should be batch invariant",
+    )
+
+    if dtype == torch.bfloat16:
+        rtol, atol = 1e-1, 1e-1
+    else:
+        rtol, atol = 1e-2, 1e-2
+
+    torch.testing.assert_close(
+        out_single,
+        ref_out,
+        rtol=rtol,
+        atol=atol,
+        msg="Fused add RMSNorm output should stay numerically close to the "
+        "batch-invariant RMSNorm reference",
+    )
+
+
 @skip_unsupported
 @pytest.mark.parametrize("batch_size", [1, 16, 128])
 @pytest.mark.parametrize("seq_len", [1, 32, 512])
diff --git a/tests/v1/determinism/utils.py b/tests/v1/determinism/utils.py
index f9bebec98619..bbef61477232 100644
--- a/tests/v1/determinism/utils.py
+++ b/tests/v1/determinism/utils.py
@@ -26,6 +26,7 @@
 BACKENDS: list[str] = [
     "FLASH_ATTN",
     "TRITON_ATTN",
+    "FLEX_ATTENTION",
 ]
 
 # FlashInfer temporarily disabled due to invariant CTA sizes.
diff --git a/tests/v1/distributed/test_async_llm_dp.py b/tests/v1/distributed/test_async_llm_dp.py
index 1b7739d2f071..70a5136a57ce 100644
--- a/tests/v1/distributed/test_async_llm_dp.py
+++ b/tests/v1/distributed/test_async_llm_dp.py
@@ -365,10 +365,13 @@ async def test_dp_pause_keep_race_staggered_engines():
         async def staggered_pause_keep(method: str, *args) -> Any:
             if method != "pause_scheduler" or not args or args[0] != "keep":
                 return await original_call_utility(method, *args)
-            # Send pause(keep) to engine 0 first
-            await client._call_utility_async(
-                method, *args, engine=client.core_engines[0]
+            # Fire pause(keep) to engine 0 (don't await — with DP
+            # two-phase pause, consensus requires all ranks).
+            pause_0 = asyncio.create_task(
+                client._call_utility_async(method, *args, engine=client.core_engines[0])
             )
+            # Let the event loop send the message to engine 0.
+            await asyncio.sleep(0.5)
             # In the middle: send two requests (race window)
             sp = SamplingParams(max_tokens=5, ignore_eos=True)
 
@@ -384,11 +387,13 @@ async def consume_gen(req_id: str) -> None:
             t2 = asyncio.create_task(consume_gen("race-2"))
             mid_pause_tasks.extend([t1, t2])
             await asyncio.sleep(3)
-            # Then send pause(keep) to engine 1
-            result = await client._call_utility_async(
-                method, *args, engine=client.core_engines[1]
+            # Fire pause(keep) to engine 1, then await both so
+            # consensus can be reached.
+            pause_1 = asyncio.create_task(
+                client._call_utility_async(method, *args, engine=client.core_engines[1])
             )
-            return result
+            results = await asyncio.gather(pause_0, pause_1)
+            return results[0]
 
         client.call_utility_async = staggered_pause_keep
 
@@ -398,3 +403,113 @@ async def consume_gen(req_id: str) -> None:
         assert not await engine.is_paused()
         # Let the two requests we sent mid-pause complete
         await asyncio.gather(*mid_pause_tasks)
+
+
+@pytest.mark.asyncio
+async def test_dp_pause_barrier_request_deadlock():
+    """
+    Test that start_dp_wave is ignored while paused.
+
+    Sequence:
+      1. Pause all engines (PAUSED_ALL).
+      2. Send barrier to engine 0 only — blocks in dist.barrier(dp_group).
+      3. Send a request routed to engine 1.
+      4. Wait for any (buggy) START_DP_WAVE propagation.
+      5. Send barrier to engine 1 — completes in fixed code, deadlocks
+         in buggy code because engine 1 is stuck in EP all-to-all.
+    """
+    if DP_SIZE != 2:
+        pytest.skip("requires DP_SIZE=2")
+
+    with ExitStack() as after:
+        engine_args = _get_dp_pause_engine_args(expert_parallel=True)
+        engine = AsyncLLM.from_engine_args(engine_args)
+        after.callback(engine.shutdown)
+
+        client = engine.engine_core
+
+        # Cache get_supported_tasks so that generate() won't need to
+        # send a utility call to all engines (which would hang once
+        # engine 0 is blocked in the barrier).
+        await engine.get_supported_tasks()
+
+        # Pause all engines normally — no staggering.
+        await engine.pause_generation(mode="keep")
+        assert await engine.is_paused()
+
+        original_call_utility = client.call_utility_async
+        mid_barrier_tasks: list[asyncio.Task] = []
+
+        async def staggered_barrier(method: str, *args) -> Any:
+            if method != "barrier":
+                return await original_call_utility(method, *args)
+
+            # Send barrier to engine 0 only — it blocks in
+            # dist.barrier(dp_group) waiting for engine 1.
+            barrier_0 = asyncio.create_task(
+                client._call_utility_async(method, *args, engine=client.core_engines[0])
+            )
+            await asyncio.sleep(1)
+
+            # While engine 0 is blocked, send a request routed
+            # specifically to engine 1.
+            sp = SamplingParams(max_tokens=5, ignore_eos=True)
+
+            engine_1 = client.core_engines[1]
+            original_get_engine = client.get_core_engine_for_request
+
+            def route_to_engine_1(req):
+                client.reqs_in_flight[req.request_id] = engine_1
+                return engine_1
+
+            client.get_core_engine_for_request = route_to_engine_1
+
+            async def consume_gen(req_id: str) -> None:
+                async for _ in engine.generate(
+                    request_id=req_id,
+                    prompt=DP_PAUSE_PROMPT,
+                    sampling_params=sp,
+                ):
+                    pass
+
+            t1 = asyncio.create_task(consume_gen("race-1"))
+            mid_barrier_tasks.append(t1)
+
+            # Yield so generate() preprocessing completes and
+            # add_request_async is called (which, in buggy code,
+            # would send FIRST_REQ and wake engine 1).
+            for _ in range(200):
+                await asyncio.sleep(0)
+
+            client.get_core_engine_for_request = original_get_engine
+
+            # Wait for any START_DP_WAVE to propagate and for
+            # engine 1 to potentially enter execute_dummy_batch.
+            await asyncio.sleep(5)
+
+            # Now send barrier to engine 1.  In buggy code engine 1
+            # is stuck in execute_dummy_batch (EP all-to-all) while
+            # engine 0 is stuck in dist.barrier(dp_group) — deadlock.
+            result = await client._call_utility_async(
+                method, *args, engine=client.core_engines[1]
+            )
+            await barrier_0
+            return result
+
+        client.call_utility_async = staggered_barrier
+
+        # Drive the staggered barrier.  Old code deadlocks here.
+        try:
+            await asyncio.wait_for(client.call_utility_async("barrier"), timeout=30)
+        except asyncio.TimeoutError:
+            for t in mid_barrier_tasks:
+                t.cancel()
+            pytest.fail(
+                "Staggered barrier deadlocked — FIRST_REQ sent while "
+                "paused caused collective-op mismatch between engines"
+            )
+
+        await engine.resume_generation()
+        assert not await engine.is_paused()
+        # Let the two requests we sent mid-barrier complete.
+        await asyncio.gather(*mid_barrier_tasks)
diff --git a/tests/v1/distributed/test_external_lb_dp.py b/tests/v1/distributed/test_external_lb_dp.py
index 912f8cffe7f6..06e8e574a05d 100644
--- a/tests/v1/distributed/test_external_lb_dp.py
+++ b/tests/v1/distributed/test_external_lb_dp.py
@@ -14,7 +14,7 @@
 from tests.utils import RemoteOpenAIServer
 from vllm.platforms import current_platform
 
-MODEL_NAME = "ibm-research/PowerMoE-3b"
+MODEL_NAME = os.getenv("MODEL_NAME", "ibm-research/PowerMoE-3b")
 
 # Number of data parallel ranks for external LB testing
 DP_SIZE = int(os.getenv("DP_SIZE", "2"))
@@ -111,11 +111,12 @@ def start_server(r: int, sargs: list[str]):
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         """Stop all server instances."""
-        while self.servers:
-            try:
-                self.servers.pop()[0].__exit__(exc_type, exc_val, exc_tb)
-            except Exception as e:
-                print(f"Error stopping server: {e}")
+        servers = [s for s, _ in self.servers]
+        self.servers.clear()
+        try:
+            RemoteOpenAIServer.shutdown_many(servers)
+        except Exception as e:
+            print(f"Error stopping servers: {e}")
 
 
 @pytest.fixture(scope="module")
diff --git a/tests/v1/distributed/test_hybrid_lb_dp.py b/tests/v1/distributed/test_hybrid_lb_dp.py
index aa25130752a4..fcd3c69af54a 100644
--- a/tests/v1/distributed/test_hybrid_lb_dp.py
+++ b/tests/v1/distributed/test_hybrid_lb_dp.py
@@ -134,11 +134,12 @@ def start_server(node: int, sargs: list[str]):
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         """Stop all server instances."""
-        while self.servers:
-            try:
-                self.servers.pop()[0].__exit__(exc_type, exc_val, exc_tb)
-            except Exception as e:
-                print(f"Error stopping server: {e}")
+        servers = [s for s, _ in self.servers]
+        self.servers.clear()
+        try:
+            RemoteOpenAIServer.shutdown_many(servers)
+        except Exception as e:
+            print(f"Error stopping servers: {e}")
 
 
 @pytest.fixture(scope="module")
diff --git a/tests/v1/distributed/test_internal_lb_dp.py b/tests/v1/distributed/test_internal_lb_dp.py
index efd9fc607dbb..31859ffba4c9 100644
--- a/tests/v1/distributed/test_internal_lb_dp.py
+++ b/tests/v1/distributed/test_internal_lb_dp.py
@@ -228,13 +228,13 @@ def start_server(sidx: int, r: int, sargs: list[str]):
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         """Stop all server instances."""
-        while self.servers:
-            if server := self.servers.pop():
-                try:
-                    server[0].__exit__(exc_type, exc_val, exc_tb)
-                except Exception as e:
-                    print(f"Error stopping server: {e}")
-                    traceback.print_exc()
+        servers = [entry[0] for entry in self.servers if entry is not None]
+        self.servers.clear()
+        try:
+            RemoteOpenAIServer.shutdown_many(servers)
+        except Exception as e:
+            print(f"Error stopping servers: {e}")
+            traceback.print_exc()
 
 
 class APIOnlyServerManager:
@@ -370,13 +370,13 @@ def start_engines_server():
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         """Stop both server instances."""
-        while self.servers:
-            if server := self.servers.pop():
-                try:
-                    server[0].__exit__(exc_type, exc_val, exc_tb)
-                except Exception as e:
-                    print(f"Error stopping server: {e}")
-                    traceback.print_exc()
+        servers = [entry[0] for entry in self.servers if entry is not None]
+        self.servers.clear()
+        try:
+            RemoteOpenAIServer.shutdown_many(servers)
+        except Exception as e:
+            print(f"Error stopping servers: {e}")
+            traceback.print_exc()
 
 
 @pytest.fixture(scope="module")
diff --git a/tests/v1/e2e/general/test_async_scheduling.py b/tests/v1/e2e/general/test_async_scheduling.py
index 8e1eddb0f64e..c3c4970de382 100644
--- a/tests/v1/e2e/general/test_async_scheduling.py
+++ b/tests/v1/e2e/general/test_async_scheduling.py
@@ -57,6 +57,8 @@ def test_without_spec_decoding(
         dict(bad_words=["the", " the"]),
         dict(logprobs=2),
         dict(logprobs=2, frequency_penalty=-1.0),
+        dict(prompt_logprobs=2),
+        dict(prompt_logprobs=2, logprobs=2),
         dict(structured_outputs=struct_outputs),
         dict(
             structured_outputs=struct_outputs,
@@ -126,6 +128,8 @@ def test_with_eagle3_spec_decoding(sample_json_schema, monkeypatch: pytest.Monke
         dict(bad_words=["the", " the"]),
         dict(logprobs=2),
         dict(logprobs=2, frequency_penalty=-1.0),
+        dict(prompt_logprobs=2),
+        dict(prompt_logprobs=2, logprobs=2),
         dict(structured_outputs=struct_outputs),
         dict(
             structured_outputs=struct_outputs,
@@ -324,10 +328,13 @@ def run_test(
 ):
     spec_decoding = spec_config is not None
     cache_arg: dict[str, Any] = (
-        # Force preemptions
-        dict(num_gpu_blocks_override=32)
+        # Force preemptions: with 32 blocks the cache holds at most a single
+        # max-length request, so the ~34 concurrent prompts contend and trigger
+        # preemption. (Prompts here are << max_model_len, so dropping
+        # max_model_len from 4096 to 512 doesn't change generation behavior.)
+        dict(num_gpu_blocks_override=32, max_model_len=512)
         if test_preemption
-        else dict(gpu_memory_utilization=0.9)
+        else dict(gpu_memory_utilization=0.9, max_model_len=4096)
     )
     spec_mml = (spec_config or {}).get("max_model_len")
     spec_method = (spec_config or {}).get("method", "none")
@@ -343,7 +350,6 @@ def run_test(
 
     with VllmRunner(
         model,
-        max_model_len=4096,
         enable_chunked_prefill=test_prefill_chunking,
         # Force prefill chunking
         max_num_batched_tokens=48 if test_prefill_chunking else None,
@@ -411,7 +417,12 @@ def _all_logprobs_match(req_a, req_b) -> bool:
     )
 
 
-def _logprobs_match(lps_a: dict[int, Logprob], lps_b: dict[int, Logprob]) -> bool:
+def _logprobs_match(
+    lps_a: dict[int, Logprob] | None,
+    lps_b: dict[int, Logprob] | None,
+) -> bool:
+    if lps_a is None or lps_b is None:
+        return lps_a is lps_b
     rel_tol, abs_tol = 1e-3, 1e-6
     return (
         len(lps_a) == len(lps_b)
diff --git a/tests/v1/e2e/general/test_mamba_prefix_cache.py b/tests/v1/e2e/general/test_mamba_prefix_cache.py
index 8b9f7bb6c5ad..6ec9e7656e31 100644
--- a/tests/v1/e2e/general/test_mamba_prefix_cache.py
+++ b/tests/v1/e2e/general/test_mamba_prefix_cache.py
@@ -179,6 +179,7 @@ def fake_allocate_slots_fn(
         num_external_computed_tokens: int = 0,
         delay_cache_blocks: bool = False,
         num_encoder_tokens: int = 0,
+        full_sequence_must_fit: bool = False,
     ):
         ret = original_allocate_slots_fn(
             self,
@@ -190,6 +191,7 @@ def fake_allocate_slots_fn(
             num_external_computed_tokens,
             delay_cache_blocks,
             num_encoder_tokens,
+            full_sequence_must_fit,
         )
         if cur_step_action is not None:
             cur_block_ids = self.coordinator.single_type_managers[0].req_to_blocks[
diff --git a/tests/v1/e2e/spec_decode/test_async_spec_decode.py b/tests/v1/e2e/spec_decode/test_async_spec_decode.py
index c00dbd6d8197..b19f90e2cdc6 100644
--- a/tests/v1/e2e/spec_decode/test_async_spec_decode.py
+++ b/tests/v1/e2e/spec_decode/test_async_spec_decode.py
@@ -82,6 +82,13 @@ def assert_no_sync(self, msg: str = ""):
         2,
         id="eagle-mla-deepseek",
     ),
+    pytest.param(
+        "Qwen/Qwen3.5-0.8B-Base",
+        "Qwen/Qwen3.5-0.8B-Base",
+        "mtp",
+        1,
+        id="mtp-qwen3_5-hybrid",
+    ),
 ]
 
 
@@ -104,6 +111,14 @@ def test_no_sync_with_spec_decode(
     from vllm import LLM, SamplingParams
     from vllm.distributed import cleanup_dist_env_and_memory
 
+    # Qwen3.5 is a VLM; without this, profile_run runs the ViT warmup
+    # and peaks well above the 18GB MIG slice used by one of the CI lanes.
+    # This test only exercises text generation, so the vision tower is
+    # never needed.
+    extra_kwargs: dict = {}
+    if "Qwen3.5" in model:
+        extra_kwargs["limit_mm_per_prompt"] = {"image": 0, "video": 0}
+
     llm = LLM(
         model=model,
         max_model_len=256,
@@ -114,6 +129,7 @@ def test_no_sync_with_spec_decode(
         },
         enforce_eager=True,
         async_scheduling=True,
+        **extra_kwargs,
     )
 
     # Assert async scheduling is actually active before running inference.
diff --git a/tests/v1/e2e/spec_decode/test_spec_decode.py b/tests/v1/e2e/spec_decode/test_spec_decode.py
index c11bdbc50f70..e14f7681bf43 100644
--- a/tests/v1/e2e/spec_decode/test_spec_decode.py
+++ b/tests/v1/e2e/spec_decode/test_spec_decode.py
@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import os
 import random
 from collections.abc import Iterable
 from dataclasses import dataclass
@@ -321,7 +322,7 @@ def test_speculators_model_integration(
     test_prompts = get_test_prompts(mm_enabled=False)
 
     # First run: Direct speculator model (simplified integration)
-    spec_llm = LLM(model=model_path, max_model_len=4096)
+    spec_llm = LLM(model=model_path, max_model_len=4096, gpu_memory_utilization=0.92)
     evaluate_llm_for_gsm8k(
         spec_llm, expected_accuracy_threshold=expected_accuracy_threshold
     )
@@ -351,7 +352,7 @@ def test_speculators_model_integration(
     cleanup_dist_env_and_memory()
 
     # Second run: Reference without speculative decoding
-    ref_llm = LLM(model=verifier_model, max_model_len=4096)
+    ref_llm = LLM(model=verifier_model, max_model_len=4096, gpu_memory_utilization=0.92)
     ref_outputs = ref_llm.chat(test_prompts, sampling_config)
     del ref_llm
     torch.accelerator.empty_cache()
@@ -385,11 +386,6 @@ def _run_eagle_correctness(
     Compare the outputs of an original LLM and a speculative LLM
     which should be the same when using eagle speculative decoding.
     """
-    if attn_backend == "TREE_ATTN":
-        pytest.skip(
-            "TREE_ATTN is flaky in the test disable for now until it can be "
-            "resolved (see https://github.com/vllm-project/vllm/issues/22922)"
-        )
     if model_impl == "transformers":
         import transformers
         from packaging.version import Version
@@ -557,12 +553,16 @@ def test_eagle_correctness_light(
             "auto",
             0.8,
         ),
-        (
+        pytest.param(
             ("eagle3", "Qwen/Qwen3-8B", "AngelSlim/Qwen3-8B_eagle3", 1),
             False,
             False,
             "transformers",
             0.8,
+            # TODO(hmellor): figure out why memory usage is so high
+            marks=pytest.mark.skip(
+                reason="Feature is experimental and uses too much memory in CI",
+            ),
         ),
         pytest.param(
             (
@@ -719,15 +719,25 @@ def test_eagle_correctness_heavy(
     [
         (("mtp", "XiaomiMiMo/MiMo-7B-Base", 1), False, 0.5),  # ref: 65%-70%
         (("mtp", "ZixiQi/DeepSeek-V3-4layers-MTP-FP8", 1), False, 0.0),  # dummy model
+        (
+            ("mtp", "Qwen/Qwen3.5-0.8B-Base", 1),
+            False,
+            0.20,
+        ),  # hybrid + MTP, ref: ~34%-35%
+        (
+            ("mtp", "google/gemma-4-E4B-it", 1, "google/gemma-4-E4B-it-assistant"),
+            False,
+            0.50,
+        ),  # gemma4 MTP with assistant model, ref: ~62%
     ],
-    ids=["mimo", "deepseek"],
+    ids=["mimo", "deepseek", "qwen3_5-hybrid", "gemma4-e4b"],
 )
 @single_gpu_only
 @large_gpu_mark(min_gb=20)
 def test_mtp_correctness(
     monkeypatch: pytest.MonkeyPatch,
     sampling_config: SamplingParams,
-    model_setup: tuple[str, str, int],
+    model_setup: tuple[str, str, int] | tuple[str, str, int, str],
     mm_enabled: bool,
     expected_accuracy_threshold: float,
 ):
@@ -743,16 +753,45 @@ def test_mtp_correctness(
     with monkeypatch.context() as m:
         m.setenv("VLLM_MLA_DISABLE", "1")
 
-        method, model_name, tp_size = model_setup
+        if len(model_setup) == 4:
+            method, model_name, tp_size, draft_model = model_setup
+        else:
+            method, model_name, tp_size = model_setup
+            draft_model = None
         _skip_if_insufficient_gpus_for_tp(tp_size)
 
+        if "Qwen3.5" in model_name and os.environ.get("VLLM_USE_V2_MODEL_RUNNER"):
+            pytest.skip(
+                "Model Runner V2 does not yet support hybrid models "
+                "(Qwen3.5 mixes Mamba-style GDN with attention layers)."
+            )
+
         attn_backend = "TRITON_ATTN" if current_platform.is_rocm() else "auto"
+
+        # Skip multimodal profiling for models that don't need it in this test.
+        extra_kwargs: dict[str, Any] = {}
+        if "Qwen3.5" in model_name:
+            extra_kwargs["limit_mm_per_prompt"] = {"image": 0, "video": 0}
+        elif "gemma-4" in model_name:
+            extra_kwargs["limit_mm_per_prompt"] = {"image": 0, "audio": 0}
+
+        if draft_model is not None and "gemma-4" in draft_model:
+            import transformers
+            from packaging.version import Version
+
+            if Version(transformers.__version__) < Version("5.8.0"):
+                pytest.skip(
+                    "Gemma4 MTP assistant requires transformers>=5.8.0, "
+                    f"got {transformers.__version__}"
+                )
+
         ref_llm = LLM(
             model=model_name,
             max_model_len=2048,
             tensor_parallel_size=tp_size,
             trust_remote_code=True,
             attention_backend=attn_backend,
+            **extra_kwargs,
         )
         ref_outputs = ref_llm.chat(test_prompts, sampling_config)
         evaluate_llm_for_gsm8k(
@@ -762,17 +801,23 @@ def test_mtp_correctness(
         torch.accelerator.empty_cache()
         cleanup_dist_env_and_memory()
 
+        speculative_config: dict[str, Any] = {
+            "method": method,
+            "num_speculative_tokens": 1,
+            "max_model_len": 2048,
+        }
+        if draft_model is not None:
+            speculative_config["model"] = draft_model
+            speculative_config["num_speculative_tokens"] = 2
+
         spec_llm = LLM(
             model=model_name,
             trust_remote_code=True,
             tensor_parallel_size=tp_size,
-            speculative_config={
-                "method": method,
-                "num_speculative_tokens": 1,
-                "max_model_len": 2048,
-            },
+            speculative_config=speculative_config,
             max_model_len=2048,
             attention_backend=attn_backend,
+            **extra_kwargs,
         )
         # MTP supports async scheduling; assert it is active by default.
         assert spec_llm.llm_engine.vllm_config.scheduler_config.async_scheduling
@@ -1306,6 +1351,54 @@ def test_dflash_acceptance_rates(dflash_config):
     cleanup_dist_env_and_memory()
 
 
+@single_gpu_only
+def test_synthetic_acceptance_rate():
+    """Verify that synthetic rejection sampling produces an acceptance
+    length close to the requested mean acceptance length."""
+    num_spec_tokens = 3
+    expected_acceptance_len = 1.875
+    tolerance = 0.15
+
+    spec_llm = LLM(
+        model="meta-llama/Llama-3.2-1B-Instruct",
+        trust_remote_code=True,
+        speculative_config={
+            "method": "eagle3",
+            "model": "nm-testing/Llama3_2_1B_speculator.eagle3",
+            "num_speculative_tokens": num_spec_tokens,
+            "max_model_len": 2048,
+            "rejection_sample_method": "synthetic",
+            "synthetic_acceptance_length": expected_acceptance_len,
+        },
+        max_model_len=2048,
+        enforce_eager=True,
+        disable_log_stats=False,
+    )
+
+    test_prompts = get_test_prompts(mm_enabled=False, num_prompts=50)
+    spec_llm.chat(
+        test_prompts,
+        SamplingParams(temperature=0, max_tokens=64, ignore_eos=True),
+    )
+
+    metrics = spec_llm.get_metrics()
+    acceptance_len = compute_acceptance_len(metrics)
+
+    print(
+        f"Synthetic acceptance length: {acceptance_len:.3f}"
+        f" (expected={expected_acceptance_len:.3f},"
+        f" tolerance=±{tolerance})"
+    )
+    assert abs(acceptance_len - expected_acceptance_len) <= tolerance, (
+        f"Synthetic acceptance length {acceptance_len:.3f} is not within"
+        f" ±{tolerance} of expected {expected_acceptance_len:.3f}"
+    )
+
+    del spec_llm
+    torch.accelerator.empty_cache()
+    cleanup_dist_env_and_memory()
+
+
 def test_dflash_correctness(dflash_config):
     """
     E2E test for DFlash (block diffusion) speculative decoding.
diff --git a/tests/v1/ec_connector/integration/README.md b/tests/v1/ec_connector/integration/README.md
index 2dbcb307fda3..a7dab5d5d9d1 100644
--- a/tests/v1/ec_connector/integration/README.md
+++ b/tests/v1/ec_connector/integration/README.md
@@ -13,7 +13,7 @@ The test ensures that disaggregated encoding produces **identical** outputs to t
 
 Note that currently PD disaggregation set up may give slightly different results from a single instance. Therefore, we need the result from 1P+1D as the baseline for 1E+1P+1D
 
-Please refer to [Disaggregated Encoder Feature](../../../docs/features/disagg_encoder.md) for the detailed explanation for the EPD features.
+Please refer to [Disaggregated Encoder Feature](../../../../docs/features/disagg_encoder.md) for the detailed explanation for the EPD features.
 
 ## Files
 
@@ -122,7 +122,7 @@ Quick sanity check:
 - Encoder cache should enable exact output reproduction
 - Test cleans up all instances and cache files after completion
 - Safe to run multiple times (idempotent)
-- We setup the PD disagg part with NixlConnector. Please read details about EPD in `examples/online_serving/disaggregated_encoder/README.md`
+- We setup the PD disagg part with NixlConnector. Please read details about EPD in `examples/disaggregated/disaggregated_encoder/README.md`
 
 ## Requirements
 
diff --git a/tests/v1/ec_connector/integration/run_epd_correctness_test.sh b/tests/v1/ec_connector/integration/run_epd_correctness_test.sh
index e199a3ecea43..65716444a57c 100644
--- a/tests/v1/ec_connector/integration/run_epd_correctness_test.sh
+++ b/tests/v1/ec_connector/integration/run_epd_correctness_test.sh
@@ -185,7 +185,7 @@ run_epd_1e_1pd() {
 
     # Start proxy
     echo "Starting EPD proxy on port $PROXY_PORT"
-    python "${GIT_ROOT}/examples/online_serving/disaggregated_encoder/disagg_epd_proxy.py" \
+    python "${GIT_ROOT}/examples/disaggregated/disaggregated_encoder/disagg_epd_proxy.py" \
         --host "0.0.0.0" \
         --port "$PROXY_PORT" \
         --encode-servers-urls "http://localhost:$ENCODE_PORT" \
@@ -411,7 +411,7 @@ run_epd_1e_1p_1d() {
     
     # Start proxy
     echo "Starting EPD proxy on port $PROXY_PORT"
-    python "${GIT_ROOT}/examples/online_serving/disaggregated_encoder/disagg_epd_proxy.py" \
+    python "${GIT_ROOT}/examples/disaggregated/disaggregated_encoder/disagg_epd_proxy.py" \
         --host "0.0.0.0" \
         --port "$PROXY_PORT" \
         --encode-servers-urls "http://localhost:$ENCODE_PORT" \
diff --git a/tests/v1/engine/test_abort_final_step.py b/tests/v1/engine/test_abort_final_step.py
index 81a120d151d6..8f1e8029955f 100644
--- a/tests/v1/engine/test_abort_final_step.py
+++ b/tests/v1/engine/test_abort_final_step.py
@@ -66,7 +66,7 @@ def __init__(
         self,
         vllm_config: VllmConfig,
         role: KVConnectorRole,
-        kv_cache_config: KVCacheConfig | None = None,
+        kv_cache_config: KVCacheConfig,
     ):
         super().__init__(vllm_config, role, kv_cache_config)
         # Get the status file path from extra config
diff --git a/tests/v1/engine/test_async_llm.py b/tests/v1/engine/test_async_llm.py
index 21a651c62ab3..92de5a7e9819 100644
--- a/tests/v1/engine/test_async_llm.py
+++ b/tests/v1/engine/test_async_llm.py
@@ -256,8 +256,10 @@ async def test_multi_abort(output_kind: RequestOutputKind):
                 )
             )
 
-        # Let requests start
-        await asyncio.sleep(0.5)
+        # Let requests start generating, use a longer sleep to ensure all
+        # requests have exited prefill and produced at least one
+        # decode token before we abort.
+        await asyncio.sleep(1.0)
 
         # Use multi-abort to abort multiple requests at once
         abort_request_ids = [request_ids[i] for i in REQUEST_IDS_TO_ABORT]
@@ -369,9 +371,10 @@ async def test_mid_stream_cancellation(
         # Wait for all tasks to complete
         results = await asyncio.gather(*tasks)
 
-        # Verify all tasks were cancelled at the expected point
+        # Verify all tasks were cancelled at the expected point.
+        # Uses >= because the cancel check is `count >= cancel_after`.
         for num_generated_tokens, request_id in results:
-            assert num_generated_tokens == NUM_EXPECTED_TOKENS, (
+            assert num_generated_tokens >= NUM_EXPECTED_TOKENS, (
                 f"{request_id} generated {num_generated_tokens} tokens but "
                 f"expected to cancel after {NUM_EXPECTED_TOKENS}"
             )
diff --git a/tests/v1/executor/test_ray_utils.py b/tests/v1/executor/test_ray_utils.py
new file mode 100644
index 000000000000..8da9d5459e73
--- /dev/null
+++ b/tests/v1/executor/test_ray_utils.py
@@ -0,0 +1,54 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import numpy as np
+
+from vllm.v1.executor.ray_utils import detach_zero_copy_from_model_runner_output
+from vllm.v1.outputs import LogprobsLists, LogprobsTensors, ModelRunnerOutput
+
+
+def _make_readonly(arr: np.ndarray) -> np.ndarray:
+    arr.setflags(write=False)
+    return arr
+
+
+def test_detach_zero_copy_from_model_runner_output_copies_only_numpy_views():
+    cu_num_generated_tokens = [0, 2]
+    prompt_logprobs = LogprobsTensors.empty_cpu(1, 2)
+    output = ModelRunnerOutput(
+        req_ids=["req-0"],
+        req_id_to_index={"req-0": 0},
+        logprobs=LogprobsLists(
+            logprob_token_ids=_make_readonly(
+                np.array([[1, 2], [3, 4]], dtype=np.int32)
+            ),
+            logprobs=_make_readonly(
+                np.array([[0.1, 0.2], [0.3, 0.4]], dtype=np.float32)
+            ),
+            sampled_token_ranks=_make_readonly(np.array([1, 2], dtype=np.int32)),
+            cu_num_generated_tokens=cu_num_generated_tokens,
+        ),
+        prompt_logprobs_dict={"req-0": prompt_logprobs},
+    )
+
+    original_logprobs = output.logprobs
+    assert original_logprobs is not None
+
+    detach_zero_copy_from_model_runner_output(output)
+
+    detached_logprobs = output.logprobs
+    assert detached_logprobs is not None
+    assert detached_logprobs is not original_logprobs
+    assert (
+        detached_logprobs.logprob_token_ids is not original_logprobs.logprob_token_ids
+    )
+    assert detached_logprobs.logprobs is not original_logprobs.logprobs
+    assert (
+        detached_logprobs.sampled_token_ranks
+        is not original_logprobs.sampled_token_ranks
+    )
+    assert detached_logprobs.logprob_token_ids.flags.writeable
+    assert detached_logprobs.logprobs.flags.writeable
+    assert detached_logprobs.sampled_token_ranks.flags.writeable
+    assert detached_logprobs.cu_num_generated_tokens is cu_num_generated_tokens
+    assert output.prompt_logprobs_dict["req-0"] is prompt_logprobs
diff --git a/tests/v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh b/tests/v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
index b0794bfa38a1..9bc0f3135edb 100755
--- a/tests/v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+++ b/tests/v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
@@ -12,7 +12,6 @@ tp_configs=(
   "GPU_MEMORY_UTILIZATION=0.8 MODEL_NAMES=deepseek-ai/deepseek-vl2-tiny" # MLA case
   "GPU_MEMORY_UTILIZATION=0.8 PREFILLER_TP_SIZE=1 DECODER_TP_SIZE=2 MODEL_NAMES=deepseek-ai/deepseek-vl2-tiny"
   "GPU_MEMORY_UTILIZATION=0.8 PREFILLER_TP_SIZE=2 DECODER_TP_SIZE=1 MODEL_NAMES=deepseek-ai/deepseek-vl2-tiny"
-  "GPU_MEMORY_UTILIZATION=0.8 MODEL_NAMES=google/gemma-3-4b-it VLLM_SERVE_EXTRA_ARGS=--max-model-len,8192" # SW model
 )
 dp_ep_configs=(
 "DP_EP=1 GPU_MEMORY_UTILIZATION=0.8 PREFILLER_TP_SIZE=1 DECODER_TP_SIZE=2 MODEL_NAMES=deepseek-ai/deepseek-vl2-tiny" # MLA+P-TP1, D-DPEP=2 (TP=1)
@@ -24,6 +23,8 @@ hybrid_ssm_configs=(
   "VLLM_SSM_CONV_STATE_LAYOUT=DS ENABLE_HMA_FLAG=1 PREFILLER_TP_SIZE=2 DECODER_TP_SIZE=2 GPU_MEMORY_UTILIZATION=0.8 MODEL_NAMES=ibm-granite/granite-4.0-h-tiny VLLM_SERVE_EXTRA_ARGS=--max-model-len,8192,--trust-remote-code,--no-async-scheduling"
 )
 sw_attn_configs=(
+  # NOTE: gemma3 does not work with FlashInfer
+  "GPU_MEMORY_UTILIZATION=0.8 MODEL_NAMES=google/gemma-3-4b-it VLLM_SERVE_EXTRA_ARGS=--max-model-len,8192" # SW model
   "ENABLE_HMA_FLAG=1 GPU_MEMORY_UTILIZATION=0.8 MODEL_NAMES=google/gemma-3-4b-it PREFILLER_TP_SIZE=1 DECODER_TP_SIZE=2 VLLM_SERVE_EXTRA_ARGS=--max-model-len,8192"
   "ENABLE_HMA_FLAG=1 GPU_MEMORY_UTILIZATION=0.8 MODEL_NAMES=google/gemma-3-4b-it PREFILLER_TP_SIZE=2 DECODER_TP_SIZE=1 VLLM_SERVE_EXTRA_ARGS=--max-model-len,8192"
 )
diff --git a/tests/v1/kv_connector/nixl_integration/nixl_side_channel_probe.py b/tests/v1/kv_connector/nixl_integration/nixl_side_channel_probe.py
new file mode 100644
index 000000000000..24ecbd795e41
--- /dev/null
+++ b/tests/v1/kv_connector/nixl_integration/nixl_side_channel_probe.py
@@ -0,0 +1,48 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Probe a NIXL side-channel socket for handshake metadata readiness."""
+
+import argparse
+import ipaddress
+
+import msgspec
+import zmq
+
+GET_META_MSG = b"get_meta_msg"
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--host", required=True)
+    parser.add_argument("--port", required=True, type=int)
+    parser.add_argument("--rank", default=0, type=int)
+    parser.add_argument("--timeout-ms", default=1000, type=int)
+    return parser.parse_args()
+
+
+def make_zmq_path(host: str, port: int) -> str:
+    try:
+        if isinstance(ipaddress.ip_address(host), ipaddress.IPv6Address):
+            return f"tcp://[{host}]:{port}"
+    except ValueError:
+        pass
+    return f"tcp://{host}:{port}"
+
+
+def main() -> None:
+    args = parse_args()
+    ctx = zmq.Context()
+    sock = ctx.socket(zmq.REQ)
+    sock.setsockopt(zmq.LINGER, 0)
+    sock.setsockopt(zmq.RCVTIMEO, args.timeout_ms)
+    try:
+        sock.connect(make_zmq_path(args.host, args.port))
+        sock.send(msgspec.msgpack.encode((GET_META_MSG, args.rank)))
+        sock.recv()
+    finally:
+        sock.close()
+        ctx.term()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh b/tests/v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
index a82dae2d5109..fdec5f40cbb0 100755
--- a/tests/v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
+++ b/tests/v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
@@ -51,6 +51,8 @@ PREFILLER_TP_SIZE=${PREFILLER_TP_SIZE:-1}
 DECODER_TP_SIZE=${DECODER_TP_SIZE:-1}
 GPU_MEMORY_UTILIZATION=${GPU_MEMORY_UTILIZATION:-0.7}
 BLOCK_SIZE=${BLOCK_SIZE:-16}
+SERVER_HOST="${SERVER_HOST:-127.0.0.1}"
+NIXL_SIDE_CHANNEL_HOST="${NIXL_SIDE_CHANNEL_HOST:-$SERVER_HOST}"
 
 # Resolve the repository root from the script location instead of `.git`.
 SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd -P)"
@@ -96,18 +98,57 @@ trap 'echo " Interrupted."; exit 130' INT TERM
 
 wait_for_server() {
   local port=$1
-  local deadline=600
+  local server_pid=$2
+  local server_name=$3
+  local endpoint=${4:-/v1/completions}
+  local deadline=${5:-600}
   local elapsed=0
-  echo "Waiting for server on port ${port}..."
+  echo "Waiting for ${server_name} on port ${port}..."
   while [ $elapsed -lt $deadline ]; do
-    if curl -s "localhost:${port}/v1/completions" > /dev/null 2>&1; then
-      echo "Server on port ${port} ready"
+    if ! ps -p "$server_pid" > /dev/null 2>&1; then
+      local status=0
+      wait "$server_pid" || status=$?
+      echo "FAIL: ${server_name} process ${server_pid} exited with status ${status} before port ${port} became ready"
+      exit 1
+    fi
+    if curl -s "http://${SERVER_HOST}:${port}${endpoint}" > /dev/null 2>&1; then
+      echo "${server_name} on port ${port} ready"
       return 0
     fi
     sleep 2
     elapsed=$((elapsed + 2))
   done
-  echo "FAIL: Server on port ${port} did not start within ${deadline}s"
+  echo "FAIL: ${server_name} on port ${port} did not start within ${deadline}s"
+  exit 1
+}
+
+wait_for_nixl_side_channel() {
+  local host=$1
+  local port=$2
+  local server_pid=$3
+  local server_name=$4
+  local deadline=120
+  local elapsed=0
+  echo "Waiting for ${server_name} NIXL side channel on ${host}:${port}..."
+  while [ $elapsed -lt $deadline ]; do
+    if ! ps -p "$server_pid" > /dev/null 2>&1; then
+      local status=0
+      wait "$server_pid" || status=$?
+      echo "FAIL: ${server_name} server process ${server_pid} exited with status ${status} before NIXL side channel ${host}:${port} became ready"
+      exit 1
+    fi
+    if python3 "${GIT_ROOT}/tests/v1/kv_connector/nixl_integration/nixl_side_channel_probe.py" \
+      --host "$host" \
+      --port "$port" \
+      --timeout-ms 1000 > /dev/null 2>&1
+    then
+      echo "${server_name} NIXL side channel on ${host}:${port} ready"
+      return 0
+    fi
+    sleep 2
+    elapsed=$((elapsed + 2))
+  done
+  echo "FAIL: ${server_name} NIXL side channel ${host}:${port} did not start within ${deadline}s"
   exit 1
 }
 
@@ -158,6 +199,8 @@ run_test_for_device() {
   echo "KV buffer device:   ${kv_device}"
   echo "Attention backend:  ${ATTENTION_BACKEND}"
   echo "GPU platform:       ${GPU_PLATFORM}"
+  echo "Server host:        ${SERVER_HOST}"
+  echo "NIXL side channel:  ${NIXL_SIDE_CHANNEL_HOST}"
   echo "GPUs available:     ${ALL_GPUS[*]}"
   echo "================================================================"
 
@@ -167,7 +210,8 @@ run_test_for_device() {
   local DECODE_PORTS=()
   local GPU_IDX=0
 
-  # Start prefill instances
+  # Start prefill instances and wait for each one before allocating the next
+  # server. This keeps failures from leaving extra model servers spinning.
   for i in $(seq 0 $((NUM_PREFILL_INSTANCES-1))); do
     local GPU_ID="${ALL_GPUS[$GPU_IDX]}"
     GPU_IDX=$((GPU_IDX + 1))
@@ -184,6 +228,7 @@ run_test_for_device() {
     ${GPU_DEVICE_VAR}=$GPU_ID \
     VLLM_KV_CACHE_LAYOUT='HND' \
     UCX_NET_DEVICES=all \
+    VLLM_NIXL_SIDE_CHANNEL_HOST=$NIXL_SIDE_CHANNEL_HOST \
     VLLM_NIXL_SIDE_CHANNEL_PORT=$SIDE_CHANNEL_PORT \
     vllm serve $MODEL_NAME \
       --port $PORT \
@@ -195,12 +240,15 @@ run_test_for_device() {
       --kv-transfer-config "$kv_config" \
       --speculative-config "$PREFILL_SPEC_CONFIG" \
       --attention-backend $ATTENTION_BACKEND &
+    local SERVER_PID=$!
 
-    PREFILL_HOSTS+=("localhost")
+    PREFILL_HOSTS+=("$SERVER_HOST")
     PREFILL_PORTS+=("$PORT")
+    wait_for_server "$PORT" "$SERVER_PID" "prefill"
+    wait_for_nixl_side_channel "$NIXL_SIDE_CHANNEL_HOST" "$SIDE_CHANNEL_PORT" "$SERVER_PID" "prefill"
   done
 
-  # Start decode instances
+  # Start decode instances after prefill is ready.
   for i in $(seq 0 $((NUM_DECODE_INSTANCES-1))); do
     local GPU_ID="${ALL_GPUS[$GPU_IDX]}"
     GPU_IDX=$((GPU_IDX + 1))
@@ -217,6 +265,7 @@ run_test_for_device() {
     ${GPU_DEVICE_VAR}=$GPU_ID \
     VLLM_KV_CACHE_LAYOUT='HND' \
     UCX_NET_DEVICES=all \
+    VLLM_NIXL_SIDE_CHANNEL_HOST=$NIXL_SIDE_CHANNEL_HOST \
     VLLM_NIXL_SIDE_CHANNEL_PORT=$SIDE_CHANNEL_PORT \
     vllm serve $MODEL_NAME \
       --port $PORT \
@@ -228,17 +277,12 @@ run_test_for_device() {
       --kv-transfer-config "$kv_config" \
       --speculative-config "$DECODE_SPEC_CONFIG" \
       --attention-backend $ATTENTION_BACKEND &
+    local SERVER_PID=$!
 
-    DECODE_HOSTS+=("localhost")
+    DECODE_HOSTS+=("$SERVER_HOST")
     DECODE_PORTS+=("$PORT")
-  done
-
-  # Wait for servers
-  for PORT in "${PREFILL_PORTS[@]}"; do
-    wait_for_server "$PORT"
-  done
-  for PORT in "${DECODE_PORTS[@]}"; do
-    wait_for_server "$PORT"
+    wait_for_server "$PORT" "$SERVER_PID" "decode"
+    wait_for_nixl_side_channel "$NIXL_SIDE_CHANNEL_HOST" "$SIDE_CHANNEL_PORT" "$SERVER_PID" "decode"
   done
 
   # Start proxy
@@ -250,12 +294,14 @@ run_test_for_device() {
     --prefiller-ports ${PREFILL_PORTS[*]} \
     --decoder-hosts ${DECODE_HOSTS[*]} \
     --decoder-ports ${DECODE_PORTS[*]} &
+  local PROXY_PID=$!
 
-  sleep 5
+  wait_for_server "$PROXY_PORT" "$PROXY_PID" "proxy" "/healthcheck" 60
 
   # Run test
   echo "Running spec decode acceptance test (kv_buffer_device=${kv_device}, backend=${ATTENTION_BACKEND})..."
   DECODE_PORT=${DECODE_PORTS[0]} \
+  SERVER_HOST=$SERVER_HOST \
   TEST_MODEL=$MODEL_NAME \
   python3 -m pytest -s -x "${GIT_ROOT}/tests/v1/kv_connector/nixl_integration/test_spec_decode_acceptance.py"
 
diff --git a/tests/v1/kv_connector/nixl_integration/test_spec_decode_acceptance.py b/tests/v1/kv_connector/nixl_integration/test_spec_decode_acceptance.py
index b747f953a220..67cb753a9887 100644
--- a/tests/v1/kv_connector/nixl_integration/test_spec_decode_acceptance.py
+++ b/tests/v1/kv_connector/nixl_integration/test_spec_decode_acceptance.py
@@ -27,7 +27,8 @@
 
 from vllm.benchmarks.datasets import get_samples
 
-PROXY_BASE_URL = "http://localhost:8192/v1"
+SERVER_HOST = os.environ.get("SERVER_HOST", "127.0.0.1")
+PROXY_BASE_URL = f"http://{SERVER_HOST}:8192/v1"
 DECODE_PORT = os.environ.get("DECODE_PORT", "8200")
 MODEL_NAME = os.environ.get("TEST_MODEL", "meta-llama/Llama-3.1-8B-Instruct")
 
@@ -101,7 +102,7 @@ def _get_mt_bench_prompts() -> list[str]:
 
 def _fetch_metric(metric_name: str) -> float:
     """Fetch a single counter metric from the decode server's /metrics."""
-    url = f"http://localhost:{DECODE_PORT}/metrics"
+    url = f"http://{SERVER_HOST}:{DECODE_PORT}/metrics"
     body = urlopen(url).read().decode()
     for line in body.split("\n"):
         if line.startswith(metric_name + "{") or line.startswith(metric_name + " "):
@@ -111,7 +112,7 @@ def _fetch_metric(metric_name: str) -> float:
 
 def _fetch_per_position_acceptance() -> dict[int, float]:
     """Fetch per-position acceptance counts from decode /metrics."""
-    url = f"http://localhost:{DECODE_PORT}/metrics"
+    url = f"http://{SERVER_HOST}:{DECODE_PORT}/metrics"
     body = urlopen(url).read().decode()
     counts: dict[int, float] = {}
     for line in body.split("\n"):
diff --git a/tests/v1/kv_connector/nixl_integration/toy_proxy_server.py b/tests/v1/kv_connector/nixl_integration/toy_proxy_server.py
index b92d3fcd6fb8..1a910c8d24dc 100644
--- a/tests/v1/kv_connector/nixl_integration/toy_proxy_server.py
+++ b/tests/v1/kv_connector/nixl_integration/toy_proxy_server.py
@@ -173,6 +173,9 @@ async def send_request_to_service(
         req_data["max_completion_tokens"] = 1
     if "stream_options" in req_data:
         del req_data["stream_options"]
+    # These args are not supported for P
+    min_tokens = req_data.pop("min_tokens", None)
+    min_completion_tokens = req_data.pop("min_completion_tokens", None)
     headers = {
         "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
         "X-Request-Id": request_id,
@@ -187,6 +190,10 @@ async def send_request_to_service(
     # otherwise, it would http.ReadError
     await response.aread()
 
+    # Add back the min_tokens and min_completion_tokens so D can use them
+    req_data["min_tokens"] = min_tokens
+    req_data["min_completion_tokens"] = min_completion_tokens
+
     return response
 
 
diff --git a/tests/v1/kv_connector/unit/offloading_connector/test_scheduler.py b/tests/v1/kv_connector/unit/offloading_connector/test_scheduler.py
index 291d0574e50f..22eb1a86d4fb 100644
--- a/tests/v1/kv_connector/unit/offloading_connector/test_scheduler.py
+++ b/tests/v1/kv_connector/unit/offloading_connector/test_scheduler.py
@@ -1,8 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from collections.abc import Iterable
+from unittest.mock import MagicMock
 
 import pytest
+import torch
 
 from tests.v1.kv_connector.unit.offloading_connector.utils import (
     generate_store_output,
@@ -10,56 +12,73 @@
 )
 from tests.v1.kv_connector.unit.utils import EOS_TOKEN_ID
 from vllm.distributed.kv_events import BlockRemoved, BlockStored
+from vllm.distributed.kv_transfer.kv_connector.v1.offloading.scheduler import (
+    OffloadingConnectorScheduler,
+)
 from vllm.v1.core.kv_cache_utils import BlockHash
-from vllm.v1.kv_offload.abstract import OffloadingEvent
+from vllm.v1.kv_cache_interface import (
+    FullAttentionSpec,
+    KVCacheGroupSpec,
+    SlidingWindowSpec,
+)
+from vllm.v1.kv_offload.base import (
+    OffloadingEvent,
+    OffloadingManager,
+    ReqContext,
+    get_offload_block_hash,
+)
 from vllm.v1.request import RequestStatus
 
 
 @pytest.mark.parametrize("async_scheduling", [True, False])
 def test_offloading_connector(request_runner, async_scheduling: bool):
-    offloaded_block_size = 12
-    gpu_block_size = 4
+    block_size = 4
+    block_size_factor = 3
+    offloaded_block_size = block_size * block_size_factor
     num_gpu_blocks = 100
-    block_size_factor = offloaded_block_size // gpu_block_size
 
     runner = request_runner(
-        offloaded_block_size=offloaded_block_size,
-        gpu_block_size=gpu_block_size,
+        block_size=block_size,
         num_gpu_blocks=num_gpu_blocks,
         async_scheduling=async_scheduling,
+        block_size_factor=block_size_factor,
     )
 
     # 3 blocks, store just the middle block (skip first and last)
     # blocks = [0, 1, 2], [3, 4, 5], [6, 7, 8]
     runner.new_request(token_ids=[0] * offloaded_block_size * 3)
-    runner.manager.prepare_store.side_effect = lambda keys: generate_store_output(
-        list(keys)[1:2]
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(list(keys)[1:2])
     )
     runner.run(decoded_tokens=[0])
 
     # add block missing 1 token -> no offload
     runner.run(
         decoded_tokens=[0] * (offloaded_block_size - 1),
-        expected_stored_gpu_block_indexes=(3, 4, 5),
+        expected_stored=(3, 4, 5),
     )
-    runner.manager.prepare_store.assert_not_called()
+    runner.manager.touch.assert_not_called()
 
     # +1 token -> single block, fail prepare_store
-    runner.manager.prepare_store.side_effect = lambda keys: None
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: None
     runner.run(decoded_tokens=[0])
     runner.manager.prepare_store.assert_called()
 
     # 1 more block (+ token for async scheduling)
     # now set block_hashes_to_store = []
-    runner.manager.prepare_store.side_effect = lambda keys: generate_store_output([])
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output([])
+    )
     runner.run(decoded_tokens=[0] * (offloaded_block_size + 1))
 
     # 1 more block (+ token for kicking off offloading)
     # now check touch was called with all 6 blocks
-    runner.manager.prepare_store.side_effect = lambda keys: generate_store_output(keys)
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
     runner.run(
         decoded_tokens=[0] * (offloaded_block_size + 1),
-        expected_stored_gpu_block_indexes=(15, 16, 17),
+        expected_stored=(15, 16, 17),
     )
     runner.manager.touch.assert_called()
     block_hashes1 = list(runner.manager.touch.call_args.args[0])
@@ -82,54 +101,53 @@ def test_offloading_connector(request_runner, async_scheduling: bool):
     # terminate request
     runner.run(
         decoded_tokens=[EOS_TOKEN_ID],
-        expected_stored_gpu_block_indexes=tuple(range(6 * block_size_factor)),
+        expected_stored=tuple(range(6 * block_size_factor)),
     )
 
     # full_block_tokens - num_computed_tokens < offloaded_block_size
     runner.new_request(
-        token_ids=[0] * gpu_block_size + [1] * (offloaded_block_size - gpu_block_size)
+        token_ids=[0] * block_size + [1] * (offloaded_block_size - block_size)
+    )
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output([])
     )
-    runner.manager.prepare_store.side_effect = lambda keys: generate_store_output([])
     runner.run(decoded_tokens=[EOS_TOKEN_ID])
     runner.manager.lookup.assert_not_called()
 
     # single block lookup with no hits
     runner.new_request(token_ids=[1] * offloaded_block_size)
-    runner.manager.prepare_store.side_effect = lambda keys: generate_store_output([])
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output([])
+    )
     runner.run(decoded_tokens=[EOS_TOKEN_ID])
-    runner.manager.lookup.assert_called()
-    assert len(list(runner.manager.lookup.call_args.args[0])) == 1
+    runner.manager.lookup.assert_called_once()
 
     # single block lookup with a hit
     runner.scheduler.reset_prefix_cache()
     runner.new_request(token_ids=[0] * offloaded_block_size)
-    runner.manager.prepare_store.side_effect = lambda keys: generate_store_output([])
-    runner.manager.lookup.return_value = 1
-    runner.run(
-        decoded_tokens=[EOS_TOKEN_ID], expected_loaded_gpu_block_indexes=(0, 1, 2)
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output([])
     )
+    runner.connector_scheduler._maximal_prefix_lookup = lambda key, req_context: 1
+    runner.run(decoded_tokens=[EOS_TOKEN_ID], expected_loaded=(0, 1, 2))
 
     # single block lookup with a hit in a middle block
     runner.new_request(
         token_ids=[0] * offloaded_block_size * 2 + [1] * offloaded_block_size
     )
-    runner.manager.prepare_store.side_effect = lambda keys: generate_store_output([])
-    runner.manager.lookup.return_value = 1
-    runner.run(
-        decoded_tokens=[EOS_TOKEN_ID], expected_loaded_gpu_block_indexes=(3, 4, 5)
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output([])
     )
+    runner.connector_scheduler._maximal_prefix_lookup = lambda key, req_context: 1
+    runner.run(decoded_tokens=[EOS_TOKEN_ID], expected_loaded=(3, 4, 5))
 
     # test take_events
     def to_hashes(int_hashes: list[int]) -> list[BlockHash]:
         return [BlockHash(str(i).encode()) for i in int_hashes]
 
     def take_events() -> Iterable[OffloadingEvent]:
-        yield OffloadingEvent(
-            keys=to_keys([1, 2, 3]), block_size=16, medium="A", removed=False
-        )
-        yield OffloadingEvent(
-            keys=to_keys([4, 5, 6]), block_size=32, medium="B", removed=True
-        )
+        yield OffloadingEvent(keys=to_keys([1, 2, 3]), medium="A", removed=False)
+        yield OffloadingEvent(keys=to_keys([4, 5, 6]), medium="B", removed=True)
 
     runner.manager.take_events.side_effect = take_events
     events = list(runner.scheduler_connector.take_events())
@@ -137,7 +155,7 @@ def take_events() -> Iterable[OffloadingEvent]:
     event = events[0]
     assert isinstance(event, BlockStored)
     assert event.block_hashes == to_hashes([1, 2, 3])
-    assert event.block_size == 16
+    assert event.block_size == 0
     assert event.medium == "A"
     assert event.token_ids == []
     assert event.parent_block_hash is None
@@ -151,15 +169,16 @@ def take_events() -> Iterable[OffloadingEvent]:
 
 @pytest.mark.parametrize("async_scheduling", [True, False])
 def test_request_preemption(request_runner, async_scheduling: bool):
-    offloaded_block_size = 12
-    gpu_block_size = 4
+    block_size = 4
+    block_size_factor = 3
+    offloaded_block_size = block_size * block_size_factor
     num_gpu_blocks = 100
 
     runner = request_runner(
-        offloaded_block_size=offloaded_block_size,
-        gpu_block_size=gpu_block_size,
+        block_size=block_size,
         num_gpu_blocks=num_gpu_blocks,
         async_scheduling=async_scheduling,
+        block_size_factor=block_size_factor,
     )
 
     free_block_queue = runner.scheduler.kv_cache_manager.block_pool.free_block_queue
@@ -168,16 +187,20 @@ def test_request_preemption(request_runner, async_scheduling: bool):
     # 2 blocks, store all, without flushing
     # blocks = [0, 1, 2], [3, 4, 5]
     runner.new_request(token_ids=[0] * offloaded_block_size * 2)
-    runner.manager.prepare_store.side_effect = lambda keys: generate_store_output(keys)
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
     runner.run(
         decoded_tokens=[0],
         complete_transfers=False,
     )
 
     # decode 2 more blocks - 1 gpu block, storing [6, 7, 8] (no flush)
-    runner.manager.prepare_store.side_effect = lambda keys: generate_store_output(keys)
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
     runner.run(
-        decoded_tokens=[0] * (2 * offloaded_block_size - gpu_block_size),
+        decoded_tokens=[0] * (2 * offloaded_block_size - block_size),
         complete_transfers=False,
     )
 
@@ -188,8 +211,8 @@ def test_request_preemption(request_runner, async_scheduling: bool):
     runner.run(
         decoded_tokens=[],
         complete_transfers=False,
-        expected_flushed_gpu_block_indexes=(0, 1, 2, 3, 4, 5, 6, 7, 8),
-        expected_stored_gpu_block_indexes=(0, 1, 2, 3, 4, 5, 6, 7, 8),
+        expected_flushed=(0, 1, 2, 3, 4, 5, 6, 7, 8),
+        expected_stored=(0, 1, 2, 3, 4, 5, 6, 7, 8),
     )
 
     # restore KV cache space and reset GPU prefix cache
@@ -198,44 +221,52 @@ def test_request_preemption(request_runner, async_scheduling: bool):
 
     # request should now return from preemption
     # re-load [0, ..., 8] from the CPU and store [9, 10, 11]
-    runner.manager.lookup.return_value = 3
-    runner.manager.prepare_store.side_effect = lambda keys: generate_store_output(keys)
+    runner.connector_scheduler._maximal_prefix_lookup = lambda key, req_context: 3
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
     runner.run(
-        decoded_tokens=[0] * gpu_block_size,
-        expected_loaded_gpu_block_indexes=(0, 1, 2, 3, 4, 5, 6, 7, 8),
+        decoded_tokens=[0] * block_size,
+        expected_loaded=(0, 1, 2, 3, 4, 5, 6, 7, 8),
     )
 
     runner.run(
         decoded_tokens=[EOS_TOKEN_ID],
-        expected_stored_gpu_block_indexes=(9, 10, 11),
+        expected_stored=(9, 10, 11),
     )
 
+    # All stores completed before request_finished -> fence index empty.
+    assert runner.connector_scheduler._block_id_to_pending_jobs == {}
+
 
 @pytest.mark.parametrize("async_scheduling", [True, False])
 def test_concurrent_lookups_of_the_same_prefix(request_runner, async_scheduling: bool):
-    offloaded_block_size = 12
-    gpu_block_size = 4
+    block_size = 4
+    block_size_factor = 3
+    offloaded_block_size = block_size * block_size_factor
     num_gpu_blocks = 100
 
     runner = request_runner(
-        offloaded_block_size=offloaded_block_size,
-        gpu_block_size=gpu_block_size,
+        block_size=block_size,
         num_gpu_blocks=num_gpu_blocks,
         async_scheduling=async_scheduling,
+        block_size_factor=block_size_factor,
     )
 
     # store 1 blocks
     runner.new_request(token_ids=[0] * offloaded_block_size)
-    runner.manager.prepare_store.side_effect = lambda keys: generate_store_output(keys)
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
     runner.run(
         decoded_tokens=[EOS_TOKEN_ID],
-        expected_stored_gpu_block_indexes=(0, 1, 2),
+        expected_stored=(0, 1, 2),
     )
 
     # start a request to load the first block, but don't complete
     runner.scheduler.reset_prefix_cache()
     runner.new_request(token_ids=[0] * offloaded_block_size)
-    runner.manager.lookup.return_value = 1
+    runner.connector_scheduler._maximal_prefix_lookup = lambda key, req_context: 1
     runner.run(
         decoded_tokens=[],
         complete_transfers=False,
@@ -247,7 +278,7 @@ def test_concurrent_lookups_of_the_same_prefix(request_runner, async_scheduling:
 
     # start a new request to load the same first block
     runner.new_request(token_ids=[0] * offloaded_block_size)
-    runner.manager.lookup.return_value = 1
+    runner.connector_scheduler._maximal_prefix_lookup = lambda key, req_context: 1
     runner.run(
         decoded_tokens=[],
         complete_transfers=False,
@@ -257,41 +288,49 @@ def test_concurrent_lookups_of_the_same_prefix(request_runner, async_scheduling:
     assert transfer_jobs == list(runner.offloading_spec.handler.transfer_specs)
 
     # complete transfers
-    runner.manager.prepare_store.side_effect = lambda keys: generate_store_output([])
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output([])
+    )
     runner.run(
         decoded_tokens=[EOS_TOKEN_ID],
-        expected_loaded_gpu_block_indexes=(0, 1, 2),
+        expected_loaded=(0, 1, 2),
     )
 
     # second request will use the GPU prefix cache
     assert transfer_jobs == list(runner.offloading_spec.handler.transfer_specs)
 
+    # Fence index drained: stores completed before request_finished ran.
+    assert runner.connector_scheduler._block_id_to_pending_jobs == {}
+
 
 @pytest.mark.parametrize("async_scheduling", [True, False])
 def test_abort_loading_requests(request_runner, async_scheduling: bool):
-    offloaded_block_size = 12
-    gpu_block_size = 4
+    block_size = 4
+    block_size_factor = 3
+    offloaded_block_size = block_size * block_size_factor
     num_gpu_blocks = 100
 
     runner = request_runner(
-        offloaded_block_size=offloaded_block_size,
-        gpu_block_size=gpu_block_size,
+        block_size=block_size,
         num_gpu_blocks=num_gpu_blocks,
         async_scheduling=async_scheduling,
+        block_size_factor=block_size_factor,
     )
 
     # store 1 blocks
     runner.new_request(token_ids=[0] * offloaded_block_size)
-    runner.manager.prepare_store.side_effect = lambda keys: generate_store_output(keys)
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
     runner.run(
         decoded_tokens=[EOS_TOKEN_ID],
-        expected_stored_gpu_block_indexes=(0, 1, 2),
+        expected_stored=(0, 1, 2),
     )
 
     # start a request to load the first block, but don't complete
     runner.scheduler.reset_prefix_cache()
     runner.new_request(token_ids=[0] * offloaded_block_size)
-    runner.manager.lookup.return_value = 1
+    runner.connector_scheduler._maximal_prefix_lookup = lambda key, req_context: 1
     runner.run(
         decoded_tokens=[],
         complete_transfers=False,
@@ -311,8 +350,572 @@ def test_abort_loading_requests(request_runner, async_scheduling: bool):
     # complete loading request
     runner.run(
         decoded_tokens=[],
-        expected_loaded_gpu_block_indexes=(0, 1, 2),
+        expected_loaded=(0, 1, 2),
     )
 
     # assert request is deleted
     assert req_id not in runner.scheduler.requests
+
+
+@pytest.mark.parametrize("async_scheduling", [True, False])
+def test_two_groups_full_and_sliding_window(request_runner, async_scheduling: bool):
+    block_size = 4
+    num_gpu_blocks = 100
+    # sliding_window=8 -> 2 offloaded blocks (block_size_factor=1)
+    sliding_window = 8
+
+    kv_cache_groups = [
+        KVCacheGroupSpec(
+            ["layer0"],
+            FullAttentionSpec(
+                block_size=block_size,
+                num_kv_heads=1,
+                head_size=1,
+                dtype=torch.float32,
+            ),
+        ),
+        KVCacheGroupSpec(
+            ["layer1"],
+            SlidingWindowSpec(
+                block_size=block_size,
+                num_kv_heads=1,
+                head_size=1,
+                dtype=torch.float32,
+                sliding_window=sliding_window,
+            ),
+        ),
+    ]
+
+    runner = request_runner(
+        block_size=block_size,
+        num_gpu_blocks=num_gpu_blocks,
+        async_scheduling=async_scheduling,
+        kv_cache_groups=kv_cache_groups,
+    )
+
+    # Verify group configs: group 0 = full attention, group 1 = sliding window
+    kv_group_configs = runner.connector_scheduler.config.kv_group_configs
+    assert len(kv_group_configs) == 2
+    assert kv_group_configs[0].sliding_window_size_in_blocks is None
+    assert kv_group_configs[1].sliding_window_size_in_blocks == 2
+
+    # Blocks [0, 1, 2] miss
+    runner.new_request(token_ids=[0] * block_size * 3)
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
+    runner.run(decoded_tokens=[0])
+    # _touch called from get_num_new_matched_tokens (2 groups) and
+    # _get_reqs_to_store (2 groups) → 4 touch calls total.
+    touch_calls = runner.manager.touch.call_args_list
+    assert len(touch_calls) == 4
+    assert len(touch_calls[0].args[0]) == 3
+    assert len(touch_calls[1].args[0]) == 3
+    assert len(touch_calls[2].args[0]) == 3
+    assert len(touch_calls[3].args[0]) == 3
+
+    # store 3 more block
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
+    runner.run(
+        decoded_tokens=[0] * (block_size * 3 + 2),
+        expected_stored=(0, 1, 2, 3, 4, 5),
+    )
+
+    # touch called from _get_reqs_to_store * 3 blocks, once for each group
+    touch_calls = runner.manager.touch.call_args_list
+    assert len(touch_calls) == 6
+
+    runner.run(decoded_tokens=[EOS_TOKEN_ID])
+
+    runner.scheduler.reset_prefix_cache()
+
+    # full 3 blocks hit [0, 1, 2]
+    runner.new_request(token_ids=[0] * (block_size * 3 + 1))
+    runner.manager.lookup.return_value = True
+    runner.run(
+        decoded_tokens=[EOS_TOKEN_ID],
+        # Group 0 (full attn): prefix lookup hits 3 → loads blocks 0,1,2
+        # Group 1 (sliding window, window=2): only the last 2 blocks
+        #   are within the window → loads blocks 1,2
+        expected_loaded=((0, 0), (0, 1), (0, 2), (1, 1), (1, 2)),
+    )
+
+    # one touch in get_num_new_matched_tokens x 2 groups
+    touch_calls = runner.manager.touch.call_args_list
+    assert len(touch_calls) == 2
+    # full attention group touched all 3 blocks
+    assert len(touch_calls[0].args[0]) == 3
+    # sliding window group touched just the last 2 blocks
+    assert len(touch_calls[1].args[0]) == 2
+
+    # 3 blocks are hit on GPU [0, 1, 2]
+    # 1 block loaded [3,]
+    runner.new_request(token_ids=[0] * (block_size * 4 + 1))
+    runner.manager.lookup.return_value = True
+    runner.run(
+        decoded_tokens=[EOS_TOKEN_ID],
+        # Group 0 (full attn): prefix lookup hits 3 → loads blocks 0,1,2
+        # Group 1 (sliding window, window=2): only the last 2 blocks
+        #   are within the window → loads blocks 1,2
+        expected_loaded=((0, 3), (1, 3)),
+    )
+
+
+@pytest.mark.parametrize("async_scheduling", [True, False])
+def test_two_groups_different_block_sizes(request_runner, async_scheduling: bool):
+    hash_block_size = 4
+    num_gpu_blocks = 100
+
+    # Group 0: block_size=12 (offloaded_block_size=12)
+    # Group 1: block_size=16 (offloaded_block_size=16)
+    kv_cache_groups = [
+        KVCacheGroupSpec(
+            ["layer0"],
+            FullAttentionSpec(
+                block_size=hash_block_size * 3,
+                num_kv_heads=1,
+                head_size=1,
+                dtype=torch.float32,
+            ),
+        ),
+        KVCacheGroupSpec(
+            ["layer1"],
+            FullAttentionSpec(
+                block_size=hash_block_size * 4,
+                num_kv_heads=1,
+                head_size=1,
+                dtype=torch.float32,
+            ),
+        ),
+    ]
+
+    runner = request_runner(
+        block_size=hash_block_size,
+        num_gpu_blocks=num_gpu_blocks,
+        async_scheduling=async_scheduling,
+        kv_cache_groups=kv_cache_groups,
+    )
+
+    # Verify group configs
+    kv_group_configs = runner.connector_scheduler.config.kv_group_configs
+    assert len(kv_group_configs) == 2
+    assert kv_group_configs[0].gpu_block_size == 12
+    assert kv_group_configs[0].offloaded_block_size == 12
+    assert kv_group_configs[1].gpu_block_size == 16
+    assert kv_group_configs[1].offloaded_block_size == 16
+
+    # Prompt: 25 tokens, unaligned to both block sizes.
+    # Group 0 blocks: [0, 1], ending_token_offset = 24
+    # Group 1 blocks: [0,], ending_token_offset = 16
+    runner.new_request(token_ids=[0] * 25)
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
+    runner.run(decoded_tokens=[0])
+    # _touch called from get_num_new_matched_tokens (2 groups) and
+    # _get_reqs_to_store (2 groups) → 4 touch calls total.
+    # Group 0 has 2 offload keys, group 1 has 1.
+    touch_calls = runner.manager.touch.call_args_list
+    assert len(touch_calls) == 4
+    assert len(touch_calls[0].args[0]) == 2
+    assert len(touch_calls[1].args[0]) == 1
+    assert len(touch_calls[2].args[0]) == 2
+    assert len(touch_calls[3].args[0]) == 1
+
+    # Get to 31 tokens
+    # No further blocks offloaded
+    runner.run(decoded_tokens=[0] * 6, expected_stored=((0, 0), (0, 1), (1, 0)))
+
+    # Get to 32 tokens
+    # Group 0 blocks: [0, 1], ending_token_offset = 24
+    # Group 1 blocks: [0, 1], ending_token_offset = 32
+    runner.run(decoded_tokens=[0])
+    # _get_reqs_to_store touch: only group 1 has a new block to store
+    touch_calls = runner.manager.touch.call_args_list
+    assert len(touch_calls) == 2
+    assert len(touch_calls[0].args[0]) == 2
+    assert len(touch_calls[1].args[0]) == 2
+
+    # Get to 35 tokens
+    # No further blocks offloaded
+    runner.run(decoded_tokens=[0] * 3, expected_stored=((1, 1),))
+
+    # Get to 36 tokens
+    # Group 0 blocks: [0, 1, 2], ending_token_offset = 36
+    # Group 1 blocks: [0, 1], ending_token_offset = 32
+    runner.run(decoded_tokens=[0])
+    # _get_reqs_to_store touch: only group 0 has a new block to store
+    touch_calls = runner.manager.touch.call_args_list
+    assert len(touch_calls) == 2
+    assert len(touch_calls[0].args[0]) == 3
+    assert len(touch_calls[1].args[0]) == 2
+
+    # Get to 47 tokens
+    # No further blocks offloaded
+    runner.run(decoded_tokens=[0] * 11, expected_stored=((0, 2),))
+
+    # Get to 48 tokens
+    # Group 0 blocks: [0, 1, 2, 3], ending_token_offset = 4
+    # Group 1 blocks: [0, 1, 2], ending_token_offset = 48
+    runner.run(decoded_tokens=[0])
+    # _get_reqs_to_store touch: both groups have a new block, each with 1 key
+    touch_calls = runner.manager.touch.call_args_list
+    assert len(touch_calls) == 2
+    assert len(touch_calls[0].args[0]) == 4
+    assert len(touch_calls[1].args[0]) == 3
+
+    runner.run(decoded_tokens=[0], expected_stored=((0, 3), (1, 2)))
+
+    # Get to 96 tokens
+    runner.run(
+        decoded_tokens=[0] * 47 + [EOS_TOKEN_ID],
+        expected_stored=((0, 4), (0, 5), (0, 6), (0, 7), (1, 3), (1, 4), (1, 5)),
+    )
+
+    runner.scheduler.reset_prefix_cache()
+
+    # Request with 48 matching tokens
+    # will match 48 tokens (4 block) from the first group
+    # 48 tokens (3 block) from the second group
+    # Total 48 tokens can be loaded
+    runner.new_request(token_ids=[0] * 48)
+    runner.manager.lookup.return_value = True
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output([])
+    )
+    runner.run(
+        decoded_tokens=[0],
+        expected_loaded=((0, 0), (0, 1), (0, 2), (0, 3), (1, 0), (1, 1), (1, 2)),
+    )
+    runner.run(decoded_tokens=[EOS_TOKEN_ID])
+
+    # Request with 48+37 matching tokens
+    # 48 tokens will be hit on GPU
+    # extra 32 tokens will be loaded
+    # extra tokens [0, 36] (blocks [4, 5, 6]) from the first group
+    # extra tokens [0, 32] (block [3, 4]) from the second group
+    runner.new_request(token_ids=[0] * (48 + 37))
+    runner.manager.lookup.return_value = True
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output([])
+    )
+    runner.run(
+        decoded_tokens=[0],
+        expected_loaded=((0, 4), (0, 5), (0, 6), (1, 3), (1, 4)),
+    )
+    runner.run(decoded_tokens=[EOS_TOKEN_ID])
+
+
+# ---------------------------------------------------------------------------
+# Unit tests for _maximal_prefix_lookup / _sliding_window_lookup
+# ---------------------------------------------------------------------------
+
+
+def _make_scheduler_with_lookup(
+    lookup_results: dict[int, bool | None],
+) -> OffloadingConnectorScheduler:
+    """Create an OffloadingConnectorScheduler with a mocked manager.lookup."""
+    manager = MagicMock(spec=OffloadingManager)
+    manager.lookup.side_effect = lambda key, req_context: lookup_results.get(
+        int(get_offload_block_hash(key).decode()), False
+    )
+
+    scheduler = object.__new__(OffloadingConnectorScheduler)
+    scheduler.manager = manager
+    return scheduler
+
+
+_EMPTY_REQ_CTX = ReqContext()
+
+
+class TestMaximalPrefixLookup:
+    def test_all_hit(self):
+        sched = _make_scheduler_with_lookup({1: True, 2: True})
+        assert sched._maximal_prefix_lookup(to_keys([1, 2]), _EMPTY_REQ_CTX) == 2
+
+    def test_all_miss(self):
+        sched = _make_scheduler_with_lookup({})
+        assert sched._maximal_prefix_lookup(to_keys([1, 2]), _EMPTY_REQ_CTX) == 0
+
+    def test_partial_prefix(self):
+        sched = _make_scheduler_with_lookup({1: True, 2: True})
+        assert sched._maximal_prefix_lookup(to_keys([1, 2, 3]), _EMPTY_REQ_CTX) == 2
+
+    def test_miss_then_hit(self):
+        sched = _make_scheduler_with_lookup({2: True})
+        assert sched._maximal_prefix_lookup(to_keys([1, 2]), _EMPTY_REQ_CTX) == 0
+
+    def test_single_hit(self):
+        sched = _make_scheduler_with_lookup({1: True})
+        assert sched._maximal_prefix_lookup(to_keys([1]), _EMPTY_REQ_CTX) == 1
+
+    def test_empty(self):
+        sched = _make_scheduler_with_lookup({})
+        assert sched._maximal_prefix_lookup([], _EMPTY_REQ_CTX) == 0
+
+    def test_none_defers(self):
+        sched = _make_scheduler_with_lookup({1: None, 2: True})
+        assert sched._maximal_prefix_lookup(to_keys([1, 2]), _EMPTY_REQ_CTX) is None
+
+    def test_none_after_hit_defers(self):
+        sched = _make_scheduler_with_lookup({1: True, 2: None})
+        assert sched._maximal_prefix_lookup(to_keys([1, 2]), _EMPTY_REQ_CTX) is None
+
+    def test_none_stops_at_miss(self):
+        """None is treated as hit for iteration, but miss stops the scan."""
+        sched = _make_scheduler_with_lookup({1: None, 2: False, 3: True})
+        assert sched._maximal_prefix_lookup(to_keys([1, 2, 3]), _EMPTY_REQ_CTX) is None
+        # lookup should have been called for blocks 1 and 2 (stops at miss)
+        assert sched.manager.lookup.call_count == 2
+
+
+class TestSlidingWindowLookup:
+    def test_all_hit_exact_window(self):
+        sched = _make_scheduler_with_lookup({1: True, 2: True})
+        assert sched._sliding_window_lookup(to_keys([1, 2]), 2, _EMPTY_REQ_CTX) == 2
+
+    def test_all_miss(self):
+        sched = _make_scheduler_with_lookup({})
+        assert sched._sliding_window_lookup(to_keys([1, 2, 3]), 1, _EMPTY_REQ_CTX) == 0
+
+    def test_window_at_end(self):
+        sched = _make_scheduler_with_lookup({2: True, 3: True})
+        assert sched._sliding_window_lookup(to_keys([1, 2, 3]), 2, _EMPTY_REQ_CTX) == 3
+
+    def test_window_in_middle(self):
+        sched = _make_scheduler_with_lookup({2: True, 3: True})
+        assert (
+            sched._sliding_window_lookup(to_keys([1, 2, 3, 4]), 2, _EMPTY_REQ_CTX) == 3
+        )
+
+    def test_no_full_window_falls_back_to_prefix(self):
+        sched = _make_scheduler_with_lookup({1: True, 2: True})
+        assert sched._sliding_window_lookup(to_keys([1, 2, 3]), 3, _EMPTY_REQ_CTX) == 2
+
+    def test_single_block_window(self):
+        sched = _make_scheduler_with_lookup({2: True, 3: True})
+        assert sched._sliding_window_lookup(to_keys([1, 2, 3]), 1, _EMPTY_REQ_CTX) == 3
+
+    def test_gap_resets_consecutive(self):
+        sched = _make_scheduler_with_lookup({2: True, 3: True, 4: True})
+        # [1, 2, 3, 0, 4] — gap at 0 resets, window of 2 found at [2,3]
+        assert (
+            sched._sliding_window_lookup(to_keys([1, 2, 3, 0, 4]), 2, _EMPTY_REQ_CTX)
+            == 3
+        )
+
+    def test_window_prefers_rightmost(self):
+        sched = _make_scheduler_with_lookup({1: True, 2: True, 4: True, 5: True})
+        # two valid windows: [1,2] at positions 0-1 and [4,5] at positions 3-4
+        # scans right-to-left, finds [4,5] first
+        assert (
+            sched._sliding_window_lookup(to_keys([1, 2, 3, 4, 5]), 2, _EMPTY_REQ_CTX)
+            == 5
+        )
+
+    def test_prefix_fallback_with_gap(self):
+        sched = _make_scheduler_with_lookup({2: True, 3: True, 4: True, 5: True})
+        # window of 4 not found contiguously (gap at 1)
+        assert (
+            sched._sliding_window_lookup(to_keys([2, 1, 3, 4, 5]), 4, _EMPTY_REQ_CTX)
+            == 1
+        )
+
+    def test_empty(self):
+        sched = _make_scheduler_with_lookup({})
+        assert sched._sliding_window_lookup([], 1, _EMPTY_REQ_CTX) == 0
+
+    def test_none_defers(self):
+        sched = _make_scheduler_with_lookup({1: True, 2: None})
+        assert sched._sliding_window_lookup(to_keys([1, 2]), 2, _EMPTY_REQ_CTX) is None
+
+    def test_none_with_full_window_still_defers(self):
+        """Even if a real window is found after a None, result is deferred."""
+        # Scan right-to-left: 4(True), 3(None) resets, 2(True), 1(True) = window
+        # but block 3 was None so defer_lookup is set
+        sched = _make_scheduler_with_lookup({1: True, 2: True, 3: None, 4: True})
+        assert (
+            sched._sliding_window_lookup(to_keys([1, 2, 3, 4]), 2, _EMPTY_REQ_CTX)
+            is None
+        )
+
+
+@pytest.mark.parametrize("async_scheduling", [True, False])
+def test_do_remote_decode_stores_all_blocks(request_runner, async_scheduling: bool):
+    """With do_remote_decode=True, after loading prefix blocks from CPU,
+    all blocks must be re-stored — not just the newly computed ones.
+
+    This supports P/D disaggregation where the prefill instance offloads the
+    complete KV cache so a remote decode node can consume it."""
+    gpu_block_size = 4
+    block_size_factor = 3
+    offloaded_block_size = gpu_block_size * block_size_factor
+    num_gpu_blocks = 100
+
+    runner = request_runner(
+        block_size_factor=block_size_factor,
+        block_size=gpu_block_size,
+        num_gpu_blocks=num_gpu_blocks,
+        async_scheduling=async_scheduling,
+    )
+
+    # Store 1 offloaded block (3 GPU blocks) via a normal request.
+    runner.new_request(token_ids=[0] * offloaded_block_size)
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
+    runner.run(decoded_tokens=[EOS_TOKEN_ID], expected_stored=(0, 1, 2))
+
+    # Reset GPU prefix cache so the next request must load from CPU.
+    runner.scheduler.reset_prefix_cache()
+
+    # New request with do_remote_decode=True and 2 offloaded blocks.
+    # The first offloaded block matches what we stored in CPU.
+    runner.new_request(
+        token_ids=[0] * offloaded_block_size * 2,
+        kv_transfer_params={"do_remote_decode": True},
+    )
+    runner.connector_scheduler._maximal_prefix_lookup = lambda key, req_context: 1
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
+
+    # Load the first offloaded block from CPU.
+    runner.run(decoded_tokens=[0], expected_loaded=(0, 1, 2))
+
+    # Store must include ALL 6 GPU blocks (both the loaded prefix and
+    # the newly computed block), not just the 3 new ones.
+    runner.run(decoded_tokens=[EOS_TOKEN_ID], expected_stored=(0, 1, 2, 3, 4, 5))
+
+    # All stores completed before request_finished -> fence index empty.
+    assert runner.connector_scheduler._block_id_to_pending_jobs == {}
+
+
+# ---------------------------------------------------------------------------
+# Tests for the per-job-store-completion design and fence invariants.
+# ---------------------------------------------------------------------------
+
+
+def test_loads_do_not_populate_fence_index(request_runner):
+    """Loads don't populate _block_id_to_pending_jobs (protected by
+    delay_free_blocks while in flight)."""
+    runner = request_runner(
+        block_size_factor=3,
+        block_size=4,
+        num_gpu_blocks=100,
+        async_scheduling=False,
+    )
+    runner.new_request(token_ids=[0] * 12)
+    runner.connector_scheduler._maximal_prefix_lookup = lambda key, req_context: 1
+    runner.run(decoded_tokens=[], complete_transfers=False)
+    assert runner.connector_scheduler._block_id_to_pending_jobs == {}
+
+
+def test_fence_at_update_state_after_alloc(request_runner):
+    """A load reusing a finished request's pending-store block triggers
+    a flush via update_state_after_alloc's fence.
+
+    num_gpu_blocks=2 forces the BlockPool to give req2 the same block
+    req1 just freed.
+    """
+    runner = request_runner(
+        block_size_factor=1,
+        block_size=4,
+        num_gpu_blocks=2,
+        async_scheduling=False,
+    )
+
+    runner.new_request(token_ids=[0] * 4)
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
+    runner.run(decoded_tokens=[EOS_TOKEN_ID], complete_transfers=False)
+    assert runner.connector_scheduler._block_id_to_pending_jobs
+
+    runner.scheduler.reset_prefix_cache()
+    runner.new_request(token_ids=[0] * 4)
+    runner.connector_scheduler._maximal_prefix_lookup = lambda key, req_context: 1
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output([])
+    )
+    runner.run(
+        decoded_tokens=[],
+        complete_transfers=False,
+        expected_stored=(0,),
+        expected_flushed=(0,),
+    )
+    assert runner.connector_scheduler._block_id_to_pending_jobs == {}
+
+
+def test_fence_at_build_store_jobs(request_runner):
+    """A new prefill (no load -> update_state_after_alloc returns early)
+    reusing a finished request's pending-store block is flushed by
+    _build_store_jobs's fence."""
+    runner = request_runner(
+        block_size_factor=1,
+        block_size=4,
+        num_gpu_blocks=2,
+        async_scheduling=False,
+    )
+
+    runner.new_request(token_ids=[0] * 4)
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
+    runner.run(decoded_tokens=[EOS_TOKEN_ID], complete_transfers=False)
+    assert runner.connector_scheduler._block_id_to_pending_jobs
+
+    runner.scheduler.reset_prefix_cache()
+    runner.new_request(token_ids=[1] * 4)
+    runner.connector_scheduler._maximal_prefix_lookup = lambda key, req_context: 0
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output([])
+    )
+    runner.run(
+        decoded_tokens=[EOS_TOKEN_ID],
+        expected_stored=(0,),
+        expected_flushed=(0,),
+    )
+    assert runner.connector_scheduler._block_id_to_pending_jobs == {}
+
+
+@pytest.mark.parametrize("async_scheduling", [True, False])
+def test_complete_store_called_per_job(request_runner, async_scheduling: bool):
+    """complete_store fires per-job, not deferred to request finish.
+    Each call carries only that store's keys."""
+    gpu_block_size = 4
+    block_size_factor = 3
+    offloaded_block_size = gpu_block_size * block_size_factor
+    runner = request_runner(
+        block_size_factor=block_size_factor,
+        block_size=gpu_block_size,
+        num_gpu_blocks=100,
+        async_scheduling=async_scheduling,
+    )
+    runner.new_request(token_ids=[0] * offloaded_block_size)
+    runner.manager.prepare_store.side_effect = lambda keys, req_context: (
+        generate_store_output(keys)
+    )
+
+    # First store: fires when block 0 is fully populated.
+    runner.run(decoded_tokens=[0, 0], expected_stored=(0, 1, 2))
+    assert runner.manager.complete_store.call_count == 1
+    first_call_keys = set(runner.manager.complete_store.call_args.args[0])
+    assert len(first_call_keys) == 1
+    runner.manager.complete_store.reset_mock()
+
+    # Second store: fires when block 1 is fully populated, with different keys.
+    runner.run(
+        decoded_tokens=[0] * (offloaded_block_size + 1),
+        expected_stored=(3, 4, 5),
+    )
+    assert runner.manager.complete_store.call_count == 1
+    second_call_keys = set(runner.manager.complete_store.call_args.args[0])
+    assert first_call_keys != second_call_keys
+    runner.manager.complete_store.reset_mock()
+
+    # Finish: no store pending -> no further call.
+    runner.run(decoded_tokens=[EOS_TOKEN_ID])
+    assert runner.manager.complete_store.call_count == 0
diff --git a/tests/v1/kv_connector/unit/offloading_connector/test_worker.py b/tests/v1/kv_connector/unit/offloading_connector/test_worker.py
index 3cfb32b3068f..b2fb0846d912 100644
--- a/tests/v1/kv_connector/unit/offloading_connector/test_worker.py
+++ b/tests/v1/kv_connector/unit/offloading_connector/test_worker.py
@@ -20,7 +20,7 @@
     MLAAttentionSpec,
     UniformTypeKVCacheSpecs,
 )
-from vllm.v1.kv_offload.spec import (
+from vllm.v1.kv_offload.base import (
     CanonicalKVCacheRef,
     CanonicalKVCaches,
     OffloadingSpec,
diff --git a/tests/v1/kv_connector/unit/offloading_connector/test_worker_metadata.py b/tests/v1/kv_connector/unit/offloading_connector/test_worker_metadata.py
new file mode 100644
index 000000000000..ab9d676cb4ae
--- /dev/null
+++ b/tests/v1/kv_connector/unit/offloading_connector/test_worker_metadata.py
@@ -0,0 +1,32 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from vllm.distributed.kv_transfer.kv_connector.v1.offloading.common import (
+    OffloadingWorkerMetadata,
+)
+
+pytestmark = pytest.mark.cpu_test
+
+
+def test_aggregate_sums_counts():
+    meta1 = OffloadingWorkerMetadata(completed_jobs={42: 1, 7: 1})
+    meta2 = OffloadingWorkerMetadata(completed_jobs={42: 1, 7: 1})
+    result = meta1.aggregate(meta2)
+    assert result.completed_jobs == {42: 2, 7: 2}
+
+
+def test_aggregate_disjoint_jobs():
+    meta1 = OffloadingWorkerMetadata(completed_jobs={42: 1, 7: 1})
+    meta2 = OffloadingWorkerMetadata(completed_jobs={43: 1, 8: 1})
+    result = meta1.aggregate(meta2)
+    assert result.completed_jobs == {42: 1, 7: 1, 43: 1, 8: 1}
+
+
+def test_aggregate_multiple_workers():
+    meta1 = OffloadingWorkerMetadata(completed_jobs={42: 1, 43: 1, 7: 1})
+    meta2 = OffloadingWorkerMetadata(completed_jobs={42: 1, 7: 1, 8: 1})
+    meta3 = OffloadingWorkerMetadata(completed_jobs={42: 1, 43: 1, 8: 1})
+    result = meta1.aggregate(meta2).aggregate(meta3)
+    assert result.completed_jobs == {42: 3, 43: 2, 7: 2, 8: 2}
diff --git a/tests/v1/kv_connector/unit/offloading_connector/utils.py b/tests/v1/kv_connector/unit/offloading_connector/utils.py
index d774d81def93..89f05f1053a1 100644
--- a/tests/v1/kv_connector/unit/offloading_connector/utils.py
+++ b/tests/v1/kv_connector/unit/offloading_connector/utils.py
@@ -1,10 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import copy
 from collections.abc import Iterable, Iterator
 from dataclasses import dataclass
 from typing import Any
-from unittest.mock import MagicMock
+from unittest.mock import MagicMock, patch
 
 import pytest
 import torch
@@ -19,6 +18,7 @@
 from vllm.distributed.kv_transfer.kv_connector.v1 import KVConnectorRole
 from vllm.distributed.kv_transfer.kv_connector.v1.offloading.common import (
     OffloadingConnectorMetadata,
+    OffloadingWorkerMetadata,
 )
 from vllm.distributed.kv_transfer.kv_connector.v1.offloading_connector import (
     OffloadingConnector,
@@ -37,27 +37,30 @@
     KVCacheConfig,
     KVCacheGroupSpec,
 )
-from vllm.v1.kv_offload.abstract import (
+from vllm.v1.kv_offload.base import (
+    GPULoadStoreSpec,
     LoadStoreSpec,
     OffloadingManager,
+    OffloadingSpec,
     OffloadKey,
     PrepareStoreOutput,
     make_offload_key,
 )
-from vllm.v1.kv_offload.mediums import GPULoadStoreSpec
-from vllm.v1.kv_offload.spec import OffloadingSpec
 from vllm.v1.kv_offload.worker.worker import (
     OffloadingHandler,
     TransferResult,
     TransferSpec,
 )
-from vllm.v1.outputs import EMPTY_MODEL_RUNNER_OUTPUT, KVConnectorOutput
 from vllm.v1.request import Request
 from vllm.v1.structured_output import StructuredOutputManager
 
 
-def to_keys(int_ids: list[int]) -> list[OffloadKey]:
-    return [make_offload_key(str(i).encode(), 0) for i in int_ids]
+def to_key(int_hash: int) -> OffloadKey:
+    return make_offload_key(str(int_hash).encode(), 0)
+
+
+def to_keys(int_hashes: list[int]) -> list[OffloadKey]:
+    return [to_key(i) for i in int_hashes]
 
 
 class MockLoadStoreSpec(LoadStoreSpec):
@@ -115,7 +118,8 @@ def __init__(self, vllm_config: VllmConfig, kv_cache_config: KVCacheConfig):
 
         self.manager = MagicMock(spec=OffloadingManager)
         self.manager.lookup.return_value = 0
-        self.manager.prepare_load = lambda keys: MockLoadStoreSpec(keys)
+        self.manager.prepare_load = lambda keys, req_context: MockLoadStoreSpec(keys)
+        self.manager.lookup.return_value = False
         self.handler = MockOffloadingHandler()
 
     def get_manager(self) -> OffloadingManager:
@@ -146,46 +150,61 @@ def get_flushed_transfers(self):
         return specs
 
 
+@dataclass(frozen=True)
+class GPUBlock:
+    group_idx: int
+    request_block_offset: int
+
+
 @dataclass
 class TransferSummary:
-    gpu_block_indices: list[int]
+    gpu_blocks: list[GPUBlock]
     offload_addresses: list[Any]
 
 
 class RequestRunner:
     def __init__(
         self,
-        offloaded_block_size: int,
-        gpu_block_size: int,
+        block_size: int,
         num_gpu_blocks: int,
+        block_size_factor: int = 1,
         async_scheduling: bool = True,
+        kv_cache_groups: list[KVCacheGroupSpec] | None = None,
     ):
-        self.offloaded_block_size: int = offloaded_block_size
-        self.gpu_block_size: int = gpu_block_size
+        assert block_size_factor == 1 or kv_cache_groups is None, (
+            "block_size_factor > 1 requires all groups to have the same "
+            "block size, so kv_cache_groups must be None (use default group)"
+        )
+
+        self.block_size_factor: int = block_size_factor
+        self.block_size: int = block_size
         self.num_gpu_blocks: int = num_gpu_blocks
         self.async_scheduling: bool = async_scheduling
 
         self.req_id: int = -1
 
         vllm_config = create_vllm_config(
-            block_size=gpu_block_size, max_num_batched_tokens=1000
+            block_size=block_size,
+            max_num_batched_tokens=1000,
+            disable_hybrid_kv_cache_manager=False,
         )
         vllm_config.scheduler_config.async_scheduling = async_scheduling
+
+        extra_config: dict[str, Any] = {
+            "spec_name": "MockOffloadingSpec",
+            "spec_module_path": "tests.v1.kv_connector.unit.offloading_connector.utils",  # noqa: E501
+        }
+        if block_size_factor > 1:
+            extra_config["block_size"] = block_size * block_size_factor
+
         vllm_config.kv_transfer_config = KVTransferConfig(
             kv_connector="OffloadingConnector",
             kv_role="kv_both",
-            kv_connector_extra_config={
-                "spec_name": "MockOffloadingSpec",
-                "spec_module_path": "tests.v1.kv_connector.unit.offloading_connector.utils",  # noqa: E501
-                "block_size": offloaded_block_size,
-            },
+            kv_connector_extra_config=extra_config,
         )
 
-        block_size = vllm_config.cache_config.block_size
-        kv_cache_config = KVCacheConfig(
-            num_blocks=num_gpu_blocks,
-            kv_cache_tensors=[],
-            kv_cache_groups=[
+        if kv_cache_groups is None:
+            kv_cache_groups = [
                 KVCacheGroupSpec(
                     ["layer"],
                     FullAttentionSpec(
@@ -195,7 +214,12 @@ def __init__(
                         dtype=torch.float32,
                     ),
                 )
-            ],
+            ]
+
+        kv_cache_config = KVCacheConfig(
+            num_blocks=num_gpu_blocks,
+            kv_cache_tensors=[],
+            kv_cache_groups=kv_cache_groups,
         )
         vllm_config.cache_config.num_gpu_blocks = num_gpu_blocks
         self.num_kv_groups = len(kv_cache_config.kv_cache_groups)
@@ -215,11 +239,38 @@ def __init__(
 
         # register worker kv_caches to enable OffloadingWorker creations
         # set_current_vllm_config is needed for get_kv_cache_layout() to work
-        with set_current_vllm_config(vllm_config):
-            self.worker_connector.register_cross_layers_kv_cache(
-                kv_cache=torch.empty(0),
-                attn_backend=FlashAttentionBackend,
-            )
+        # Mock get_layers_from_vllm_config so that mock layer names
+        # resolve to layers whose get_attn_backend() returns
+        # FlashAttentionBackend.
+        def _mock_get_layers(_vllm_config, _layer_type, layer_names):
+            mock_layer = MagicMock()
+            mock_layer.get_attn_backend.return_value = FlashAttentionBackend
+            return {name: mock_layer for name in layer_names}
+
+        kv_caches: dict[str, torch.Tensor] = {}
+        for group in kv_cache_groups:
+            spec = group.kv_cache_spec
+            for layer_name in group.layer_names:
+                # Shape follows FlashAttention layout:
+                # (2, num_blocks, block_size, num_kv_heads, head_size)
+                kv_caches[layer_name] = torch.empty(
+                    2,
+                    num_gpu_blocks,
+                    spec.block_size,
+                    spec.num_kv_heads,
+                    spec.head_size,
+                    dtype=spec.dtype,
+                )
+
+        with (
+            set_current_vllm_config(vllm_config),
+            patch(
+                "vllm.distributed.kv_transfer.kv_connector.v1"
+                ".offloading.worker.get_layers_from_vllm_config",
+                side_effect=_mock_get_layers,
+            ),
+        ):
+            self.worker_connector.register_kv_caches(kv_caches)
 
         # extract connector of scheduler
         scheduler_connector = self.scheduler.connector
@@ -228,16 +279,23 @@ def __init__(
         self.scheduler_connector: OffloadingConnector = scheduler_connector
 
         # extract mocked OffloadingManager of scheduler connector
-        connector_scheduler = scheduler_connector.connector_scheduler
-        assert connector_scheduler is not None
-        manager = connector_scheduler.manager
+        self.connector_scheduler = scheduler_connector.connector_scheduler
+        assert self.connector_scheduler is not None
+        manager = self.connector_scheduler.manager
         assert isinstance(manager, MagicMock)
         self.manager: MagicMock = manager
 
-        assert len(connector_scheduler.config.kv_group_configs) == 1
-        kv_group_config = connector_scheduler.config.kv_group_configs[0]
-        assert kv_group_config.gpu_block_size == gpu_block_size
-        assert kv_group_config.offloaded_block_size == offloaded_block_size
+        num_kv_groups = len(kv_cache_config.kv_cache_groups)
+        assert len(self.connector_scheduler.config.kv_group_configs) == num_kv_groups
+        for group_config, kv_cache_group in zip(
+            self.connector_scheduler.config.kv_group_configs,
+            kv_cache_config.kv_cache_groups,
+        ):
+            gpu_block_size = kv_cache_group.kv_cache_spec.block_size
+            assert group_config.gpu_block_size == gpu_block_size
+            assert (
+                group_config.offloaded_block_size == gpu_block_size * block_size_factor
+            )
 
         # extract OffloadingSpec of worker_connector
         connector_worker = self.worker_connector.connector_worker
@@ -246,18 +304,18 @@ def __init__(
         assert isinstance(offloading_spec, MockOffloadingSpec)
         self.offloading_spec: MockOffloadingSpec = offloading_spec
 
-        # mapping (offloading address) -> gpu_block_index
-        self.offloaded: dict[Any, int] = {}
+        # mapping (offloading address) -> GPUBlock
+        self.offloaded: dict[Any, GPUBlock] = {}
 
         self.completed_loads: list[TransferSummary] = []
         self.completed_stores: list[TransferSummary] = []
-        self.flushed_gpu_block_indexes: set[int] = set()
+        self.flushed_gpu_blocks: set[GPUBlock] = set()
 
-        # maps {block_id: block_offset}
-        self.gpu_block_index: dict[int, int] = {}
+        # block_id -> GPUBlock
+        self.gpu_blocks: dict[int, GPUBlock] = {}
 
         init_none_hash(sha256)
-        self._block_hasher = get_request_block_hasher(gpu_block_size, sha256)
+        self._block_hasher = get_request_block_hasher(block_size, sha256)
 
         self._dummy_ctx: ForwardContext = ForwardContext(
             no_compile_layers={},
@@ -265,7 +323,11 @@ def __init__(
             slot_mapping={},
         )
 
-    def new_request(self, token_ids: list[int]):
+    def new_request(
+        self,
+        token_ids: list[int],
+        kv_transfer_params: dict | None = None,
+    ):
         self.req_id += 1
 
         sampling_params = SamplingParams(max_tokens=1000)
@@ -278,6 +340,8 @@ def new_request(self, token_ids: list[int]):
             pooling_params=None,
             block_hasher=self._block_hasher,
         )
+        if kv_transfer_params is not None:
+            req.kv_transfer_params = kv_transfer_params
 
         self.scheduler.add_request(req)
 
@@ -287,11 +351,9 @@ def _parse_transfers(self):
             assert isinstance(src_spec, GPULoadStoreSpec)
 
             for block_id in src_spec.block_ids:
-                self.flushed_gpu_block_indexes.add(
-                    self.gpu_block_index[block_id.item()]
-                )
+                self.flushed_gpu_blocks.add(self.gpu_blocks[block_id.item()])
 
-        block_size_factor = self.offloaded_block_size // self.gpu_block_size
+        block_size_factor = self.block_size_factor
 
         for transfer_spec in self.offloading_spec.get_completed_transfers():
             src_spec, dst_spec = transfer_spec
@@ -307,10 +369,11 @@ def _parse_transfers(self):
 
             assert isinstance(offload_spec, MockLoadStoreSpec)
             assert isinstance(gpu_spec, GPULoadStoreSpec)
+            assert len(gpu_spec.group_sizes) == self.num_kv_groups
 
-            gpu_block_indices: list[int] = []
+            gpu_blocks: list[GPUBlock] = []
             for block_id in gpu_spec.block_ids:
-                gpu_block_indices.append(self.gpu_block_index[block_id.item()])
+                gpu_blocks.append(self.gpu_blocks[block_id.item()])
 
             # list of (offload_key, sub_block_offset)
             offload_addresses: list[Any] = []
@@ -318,30 +381,49 @@ def _parse_transfers(self):
                 for sub_block_idx in range(block_size_factor):
                     offload_addresses.append((offload_key, sub_block_idx))
 
-            if store:
-                assert len(gpu_block_indices) == len(offload_addresses)
+            assert gpu_spec.block_indices is not None
+            assert len(gpu_spec.block_indices) == self.num_kv_groups
 
-                self.completed_stores.append(
-                    TransferSummary(gpu_block_indices, offload_addresses)
-                )
-            else:
-                remainder_sub_block_count = len(offload_addresses) - len(
-                    gpu_block_indices
+            gpu_block_offset = 0
+            offload_address_offset = 0
+            for group_size, logical_offset in zip(
+                gpu_spec.group_sizes, gpu_spec.block_indices
+            ):
+                gpu_block_end_offset = gpu_block_offset + group_size
+                assert gpu_block_end_offset <= len(gpu_blocks)
+
+                offload_addresses_to_skip = logical_offset % block_size_factor
+                offload_addresses_end_offset = (
+                    offload_address_offset + offload_addresses_to_skip + group_size
                 )
-                assert remainder_sub_block_count >= 0
-                assert remainder_sub_block_count < block_size_factor
-                offload_addresses = offload_addresses[remainder_sub_block_count:]
+                assert offload_addresses_end_offset <= len(offload_addresses)
 
-                self.completed_loads.append(
-                    TransferSummary(gpu_block_indices, offload_addresses)
+                offload_addresses = (
+                    offload_addresses[:offload_address_offset]
+                    + offload_addresses[
+                        offload_address_offset + offload_addresses_to_skip :
+                    ]
                 )
 
-    def _update_gpu_block_idx(self):
-        for blocks in self.scheduler.kv_cache_manager.coordinator.single_type_managers[
-            0
-        ].req_to_blocks.values():
-            for block_idx, block in enumerate(blocks):
-                self.gpu_block_index[block.block_id] = block_idx
+                gpu_block_offset += group_size
+                offload_address_offset += group_size
+
+            assert gpu_block_offset == len(gpu_blocks)
+            assert offload_address_offset == len(offload_addresses)
+
+            transfer_summary = TransferSummary(gpu_blocks, offload_addresses)
+            if store:
+                self.completed_stores.append(transfer_summary)
+            else:
+                self.completed_loads.append(transfer_summary)
+
+    def _update_gpu_blocks(self):
+        for group_idx, manager in enumerate(
+            self.scheduler.kv_cache_manager.coordinator.single_type_managers
+        ):
+            for blocks in manager.req_to_blocks.values():
+                for block_idx, block in enumerate(blocks):
+                    self.gpu_blocks[block.block_id] = GPUBlock(group_idx, block_idx)
 
     def _run(self, decoded_tokens: list[int], complete_transfers: bool):
         """
@@ -358,10 +440,15 @@ def _run(self, decoded_tokens: list[int], complete_transfers: bool):
         prev_scheduler_output = None
         prev_model_runner_output = None
         while True:
-            assert self.scheduler.requests
+            # Strict-always-False frees the request immediately on EOS, but
+            # the worker may still have a deferred store queued. In production
+            # the next request's step drains it; in single-request tests we
+            # must keep stepping until the scheduler sees no in-flight jobs.
+            if not self.scheduler.requests and not self.connector_scheduler._jobs:
+                break
 
             scheduler_output = self.scheduler.schedule()
-            self._update_gpu_block_idx()
+            self._update_gpu_blocks()
 
             kv_connector_metadata = scheduler_output.kv_connector_metadata
             assert kv_connector_metadata is not None
@@ -381,6 +468,10 @@ def _run(self, decoded_tokens: list[int], complete_transfers: bool):
             finished_sending, finished_recving = self.worker_connector.get_finished(
                 scheduler_output.finished_req_ids
             )
+            worker_meta = (
+                self.worker_connector.build_connector_worker_meta()
+                or OffloadingWorkerMetadata()
+            )
 
             self.worker_connector.clear_connector_metadata()
 
@@ -389,6 +480,7 @@ def _run(self, decoded_tokens: list[int], complete_transfers: bool):
                 finished_sending=finished_sending,
                 finished_recving=finished_recving,
                 token_id=token_id or 0,
+                kv_connector_worker_meta=worker_meta,
             )
 
             prev_token_id = token_id
@@ -409,7 +501,7 @@ def _run(self, decoded_tokens: list[int], complete_transfers: bool):
             if (
                 prev_token_id == EOS_TOKEN_ID
                 and prev_token_id != token_id
-                and self.scheduler.requests
+                and (self.scheduler.requests or self.connector_scheduler._jobs)
             ):
                 # continue for one more step to allow offloading to kick off
                 continue
@@ -424,33 +516,33 @@ def _run(self, decoded_tokens: list[int], complete_transfers: bool):
 
         self._parse_transfers()
 
-        # run one more step to update finished stored
         if EOS_TOKEN_ID in decoded_tokens:
             assert not self.scheduler.running
 
-            while self.scheduler.requests:
-                scheduler_output = self.scheduler.schedule()
-
-                finished_sending, finished_recving = self.worker_connector.get_finished(
-                    scheduler_output.finished_req_ids
-                )
-
-                assert not finished_recving
-
-                model_runner_output = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
-                model_runner_output.kv_connector_output = KVConnectorOutput(
-                    finished_sending=finished_sending
+    def _to_gpu_blocks(
+        self, blocks: tuple[int | tuple[int, int], ...]
+    ) -> list[GPUBlock]:
+        gpu_blocks: list[GPUBlock] = []
+        for block in blocks:
+            if isinstance(block, int):
+                for group_idx in range(self.num_kv_groups):
+                    gpu_blocks.append(
+                        GPUBlock(group_idx=group_idx, request_block_offset=block)
+                    )
+            else:
+                group_idx, offset = block
+                gpu_blocks.append(
+                    GPUBlock(group_idx=group_idx, request_block_offset=offset)
                 )
-
-                self.scheduler.update_from_output(scheduler_output, model_runner_output)
+        return gpu_blocks
 
     def run(
         self,
         decoded_tokens: list[int],
         complete_transfers: bool = True,
-        expected_stored_gpu_block_indexes: tuple[int, ...] = (),
-        expected_loaded_gpu_block_indexes: tuple[int, ...] = (),
-        expected_flushed_gpu_block_indexes: tuple[int, ...] = (),
+        expected_stored: tuple[int | tuple[int, int], ...] = (),
+        expected_loaded: tuple[int | tuple[int, int], ...] = (),
+        expected_flushed: tuple[int | tuple[int, int], ...] = (),
     ):
         """
         Runs multiple engine (scheduler + worker) steps.
@@ -459,41 +551,49 @@ def run(
         Args:
             decoded_tokens: the tokens to yield at each step.
             complete_transfers: complete transfers immediately
-            expected_stored_gpu_block_indexes: GPU block indexes
+            expected_stored: GPU blocks
                 that are expected to be written during the run.
-            expected_loaded_gpu_block_indexes: GPU block indexes
+            expected_loaded: GPU blocks
                 that are expected to be loaded during the run.
-            expected_flushed_gpu_block_indexes: GPU block indexes
+            expected_flushed: GPU blocks
                 that are expected to be flushed during the run.
+
+            A GPU block is either a (group_idx: int, request_block_offset: int)
+            or just request_block_offset: int.
+            The latter case is a convenience for representing all groups.
         """
 
+        expected_stored_gpu_blocks = self._to_gpu_blocks(expected_stored)
+        expected_loaded_gpu_blocks = self._to_gpu_blocks(expected_loaded)
+        expected_flushed_gpu_blocks = self._to_gpu_blocks(expected_flushed)
+
         self.manager.reset_mock()
         self._run(decoded_tokens, complete_transfers)
 
-        loaded_gpu_block_indexes: set[int] = set()
+        loaded_gpu_blocks: set[GPUBlock] = set()
         for transfer in self.completed_loads:
-            for gpu_block_idx, offloaded_address in zip(
-                transfer.gpu_block_indices, transfer.offload_addresses
+            for gpu_block, offloaded_address in zip(
+                transfer.gpu_blocks, transfer.offload_addresses
             ):
-                loaded_gpu_block_indexes.add(gpu_block_idx)
-                assert gpu_block_idx == self.offloaded[offloaded_address]
+                loaded_gpu_blocks.add(gpu_block)
+                assert gpu_block == self.offloaded[offloaded_address]
 
-        assert set(expected_loaded_gpu_block_indexes) == loaded_gpu_block_indexes
+        assert set(expected_loaded_gpu_blocks) == loaded_gpu_blocks
         self.completed_loads.clear()
 
-        stored_gpu_block_indexes: set[int] = set()
+        stored_gpu_blocks: set[GPUBlock] = set()
         for transfer in self.completed_stores:
-            for gpu_block_idx, offloaded_address in zip(
-                transfer.gpu_block_indices, transfer.offload_addresses
+            for gpu_block, offloaded_address in zip(
+                transfer.gpu_blocks, transfer.offload_addresses
             ):
-                stored_gpu_block_indexes.add(gpu_block_idx)
-                self.offloaded[offloaded_address] = gpu_block_idx
+                stored_gpu_blocks.add(gpu_block)
+                self.offloaded[offloaded_address] = gpu_block
 
-        assert set(expected_stored_gpu_block_indexes) == stored_gpu_block_indexes
+        assert set(expected_stored_gpu_blocks) == stored_gpu_blocks
         self.completed_stores.clear()
 
-        assert set(expected_flushed_gpu_block_indexes) == self.flushed_gpu_block_indexes
-        self.flushed_gpu_block_indexes.clear()
+        assert set(expected_flushed_gpu_blocks) == self.flushed_gpu_blocks
+        self.flushed_gpu_blocks.clear()
 
 
 @pytest.fixture
@@ -501,13 +601,18 @@ def request_runner():
     runners = []
 
     def runner_factory(
-        offloaded_block_size, gpu_block_size, num_gpu_blocks, async_scheduling
+        block_size,
+        num_gpu_blocks,
+        async_scheduling,
+        block_size_factor=1,
+        kv_cache_groups=None,
     ):
         runner = RequestRunner(
-            offloaded_block_size=offloaded_block_size,
-            gpu_block_size=gpu_block_size,
+            block_size=block_size,
             num_gpu_blocks=num_gpu_blocks,
+            block_size_factor=block_size_factor,
             async_scheduling=async_scheduling,
+            kv_cache_groups=kv_cache_groups,
         )
         runners.append(runner)
         return runner
diff --git a/tests/v1/kv_connector/unit/test_backwards_compatibility.py b/tests/v1/kv_connector/unit/test_backwards_compatibility.py
deleted file mode 100644
index da6a5aadbc6d..000000000000
--- a/tests/v1/kv_connector/unit/test_backwards_compatibility.py
+++ /dev/null
@@ -1,275 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-Unit tests for backwards compatibility with external KV connector implementations.
-
-This test ensures that external connectors (loaded via kv_connector_module_path)
-implemented with the old signature continue to work:
-- Old signature: __init__(self, vllm_config, role)
-- New signature: __init__(self, vllm_config, role, kv_cache_config)
-"""
-
-from typing import TYPE_CHECKING
-from unittest.mock import patch
-
-import pytest
-
-from vllm.distributed.kv_transfer.kv_connector.factory import KVConnectorFactory
-from vllm.distributed.kv_transfer.kv_connector.v1 import (
-    KVConnectorBase_V1,
-    KVConnectorRole,
-)
-from vllm.v1.attention.backend import AttentionMetadata
-from vllm.v1.core.sched.output import SchedulerOutput
-
-from .utils import create_scheduler, create_vllm_config
-
-if TYPE_CHECKING:
-    from vllm.config import VllmConfig
-    from vllm.forward_context import ForwardContext
-    from vllm.v1.core.kv_cache_manager import KVCacheBlocks
-    from vllm.v1.kv_cache_interface import KVCacheConfig
-    from vllm.v1.request import Request
-
-
-class OldStyleTestConnector(KVConnectorBase_V1):
-    """
-    Test connector using the old signature with 2 required arguments.
-    This simulates external connectors that haven't been updated yet.
-    """
-
-    def __init__(self, vllm_config: "VllmConfig", role: KVConnectorRole):
-        # Old-style call to super().__init__ with only 2 arguments
-        super().__init__(vllm_config=vllm_config, role=role)
-
-    def get_num_new_matched_tokens(
-        self, request: "Request", num_computed_tokens: int
-    ) -> tuple[int | None, bool]:
-        return 0, False
-
-    def update_state_after_alloc(
-        self,
-        request: "Request",
-        blocks: "KVCacheBlocks",
-        num_external_tokens: int,
-    ):
-        pass
-
-    def build_connector_meta(self, scheduler_output: SchedulerOutput):
-        return None
-
-    def start_load_kv(self, forward_context: "ForwardContext", **kwargs) -> None:
-        pass
-
-    def wait_for_layer_load(self, layer_name: str) -> None:
-        pass
-
-    def save_kv_layer(
-        self,
-        layer_name: str,
-        kv_layer,
-        attn_metadata: AttentionMetadata,
-        **kwargs,
-    ) -> None:
-        pass
-
-    def wait_for_save(self):
-        pass
-
-
-class NewStyleTestConnector(KVConnectorBase_V1):
-    """
-    Test connector using the new signature with 3 required arguments.
-    """
-
-    def __init__(
-        self,
-        vllm_config: "VllmConfig",
-        role: KVConnectorRole,
-        kv_cache_config: "KVCacheConfig",
-    ):
-        # New-style call to super().__init__ with all 3 arguments
-        super().__init__(
-            vllm_config=vllm_config, role=role, kv_cache_config=kv_cache_config
-        )
-
-    def get_num_new_matched_tokens(
-        self, request: "Request", num_computed_tokens: int
-    ) -> tuple[int | None, bool]:
-        return 0, False
-
-    def update_state_after_alloc(
-        self,
-        request: "Request",
-        blocks: "KVCacheBlocks",
-        num_external_tokens: int,
-    ):
-        pass
-
-    def build_connector_meta(self, scheduler_output: SchedulerOutput):
-        return None
-
-    def start_load_kv(self, forward_context: "ForwardContext", **kwargs) -> None:
-        pass
-
-    def wait_for_layer_load(self, layer_name: str) -> None:
-        pass
-
-    def save_kv_layer(
-        self,
-        layer_name: str,
-        kv_layer,
-        attn_metadata: AttentionMetadata,
-        **kwargs,
-    ) -> None:
-        pass
-
-    def wait_for_save(self):
-        pass
-
-
-@pytest.mark.parametrize("role", [KVConnectorRole.SCHEDULER, KVConnectorRole.WORKER])
-def test_external_old_signature_factory_instantiation(role):
-    """
-    Test that external connectors with old signature (2 required args) loaded
-    via kv_connector_module_path are correctly instantiated with backwards
-    compatibility support.
-    """
-    vllm_config = create_vllm_config()
-    vllm_config.kv_transfer_config.kv_connector = "OldStyleTestConnector"
-    vllm_config.kv_transfer_config.kv_connector_module_path = (
-        "tests.v1.kv_connector.unit.test_backwards_compatibility"
-    )
-
-    scheduler = create_scheduler(vllm_config)
-    kv_cache_config = scheduler.kv_cache_config
-
-    connector = KVConnectorFactory.create_connector(vllm_config, role, kv_cache_config)
-
-    assert connector is not None
-    assert isinstance(connector, OldStyleTestConnector)
-    assert connector.role == role
-    assert connector._kv_cache_config is None
-
-
-@pytest.mark.parametrize("role", [KVConnectorRole.SCHEDULER, KVConnectorRole.WORKER])
-def test_external_new_signature_factory_instantiation(role):
-    """
-    Test that external connectors with new signature (3 required args) loaded
-    via kv_connector_module_path are correctly instantiated.
-    """
-    vllm_config = create_vllm_config()
-    vllm_config.kv_transfer_config.kv_connector = "NewStyleTestConnector"
-    vllm_config.kv_transfer_config.kv_connector_module_path = (
-        "tests.v1.kv_connector.unit.test_backwards_compatibility"
-    )
-
-    scheduler = create_scheduler(vllm_config)
-    kv_cache_config = scheduler.kv_cache_config
-
-    connector = KVConnectorFactory.create_connector(vllm_config, role, kv_cache_config)
-
-    assert connector is not None
-    assert isinstance(connector, NewStyleTestConnector)
-    assert connector.role == role
-    assert connector._kv_cache_config is not None
-    assert connector._kv_cache_config == kv_cache_config
-
-
-@pytest.mark.parametrize("role", [KVConnectorRole.SCHEDULER, KVConnectorRole.WORKER])
-def test_old_signature_super_init(role):
-    """
-    Test that old-style connectors can call super().__init__() without
-    kv_cache_config parameter.
-    """
-    vllm_config = create_vllm_config()
-
-    connector = OldStyleTestConnector(vllm_config, role)
-
-    assert connector is not None
-    assert connector.role == role
-    assert connector._kv_cache_config is None
-
-
-def test_old_signature_super_init_with_kwargs():
-    """
-    Test that old-style connectors can call super().__init__() with keyword
-    arguments in different orders.
-    """
-    vllm_config = create_vllm_config()
-
-    # Test with vllm_config= and role= kwargs
-    connector1 = OldStyleTestConnector(
-        vllm_config=vllm_config, role=KVConnectorRole.SCHEDULER
-    )
-    assert connector1 is not None
-    assert connector1._kv_cache_config is None
-
-    # Test with role= and vllm_config= in reversed order
-    connector2 = OldStyleTestConnector(
-        role=KVConnectorRole.WORKER, vllm_config=vllm_config
-    )
-    assert connector2 is not None
-    assert connector2._kv_cache_config is None
-
-
-def test_internal_connector_uses_new_signature():
-    """
-    Test that internal connectors (registered in factory) always use the new
-    signature and get kv_cache_config.
-    """
-    from vllm.distributed.kv_transfer.kv_connector.v1.example_connector import (
-        ExampleConnector,
-    )
-
-    vllm_config = create_vllm_config()
-    vllm_config.kv_transfer_config.kv_connector = "ExampleConnector"
-
-    scheduler = create_scheduler(vllm_config)
-    kv_cache_config = scheduler.kv_cache_config
-
-    connector = KVConnectorFactory.create_connector(
-        vllm_config, KVConnectorRole.SCHEDULER, kv_cache_config
-    )
-
-    assert connector is not None
-    assert isinstance(connector, ExampleConnector)
-    assert connector._kv_cache_config is not None
-    assert connector._kv_cache_config == kv_cache_config
-
-
-def test_signature_detection_with_mocking():
-    """
-    Test that the factory correctly applies compat_sig flag returned from
-    _get_connector_class_with_compat.
-    """
-    vllm_config = create_vllm_config()
-    scheduler = create_scheduler(vllm_config)
-    kv_cache_config = scheduler.kv_cache_config
-
-    # Mock _get_connector_class_with_compat to return old-style connector
-    with patch.object(
-        KVConnectorFactory,
-        "_get_connector_class_with_compat",
-        return_value=(OldStyleTestConnector, True),
-    ):
-        old_connector = KVConnectorFactory.create_connector(
-            vllm_config, KVConnectorRole.SCHEDULER, kv_cache_config
-        )
-        assert old_connector is not None
-        assert isinstance(old_connector, OldStyleTestConnector)
-        assert old_connector._kv_cache_config is None
-
-    # Mock _get_connector_class_with_compat to return new-style connector
-    with patch.object(
-        KVConnectorFactory,
-        "_get_connector_class_with_compat",
-        return_value=(NewStyleTestConnector, False),
-    ):
-        new_connector = KVConnectorFactory.create_connector(
-            vllm_config, KVConnectorRole.SCHEDULER, kv_cache_config
-        )
-        assert new_connector is not None
-        assert isinstance(new_connector, NewStyleTestConnector)
-        assert new_connector._kv_cache_config is not None
-        assert new_connector._kv_cache_config == kv_cache_config
diff --git a/tests/v1/kv_connector/unit/test_bidirectional_kv_transfer.py b/tests/v1/kv_connector/unit/test_bidirectional_kv_transfer.py
new file mode 100644
index 000000000000..dc76d61178d8
--- /dev/null
+++ b/tests/v1/kv_connector/unit/test_bidirectional_kv_transfer.py
@@ -0,0 +1,915 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for bi-directional KV cache transfer between P and D nodes.
+
+Tests cover the new behaviors added by the bi-directional KV transfer PR:
+1. P-node scheduler lifecycle: P pulls KV from D using remote_block_ids,
+   eliminating redundant prefill computation in multi-turn conversations.
+2. P-node metadata: NixlConnectorMetadata correctly populates recv metadata
+   when P pulls KV from D (do_remote_decode=True + remote_block_ids).
+3. P-node worker: start_load_kv processes reqs_to_recv for KV pull from D.
+4. D-node request_finished: returns kv_transfer_params with remote_block_ids
+   and remote_num_tokens so P can pull KV in future turns.
+5. Edge cases:
+   - No double read after reschedule (_remote_blocks_processed flag)
+   - remote_num_tokens bounded by block capacity (num_computed_tokens)
+   - kv_recompute_threshold skips small transfers
+   - P-node holds blocks for D after finishing
+   - Cache MISS first turn falls back to local prefill
+   - Partial remote coverage: P pulls partial, computes the rest
+   - _remote_blocks_processed flag persists across reschedules
+
+P-node flags: do_remote_prefill=False (prefill locally),
+do_remote_decode=True (don't decode locally, send KV to D).
+P pulls KV from D when remote_block_ids is not None and
+external tokens > 0.
+"""
+
+import copy
+import time
+from unittest.mock import patch
+
+import pytest
+
+from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorRole
+from vllm.distributed.kv_transfer.kv_connector.v1.nixl.connector import (
+    NixlConnector,
+    NixlConnectorMetadata,
+)
+from vllm.forward_context import ForwardContext
+from vllm.v1.outputs import (
+    EMPTY_MODEL_RUNNER_OUTPUT,
+    KVConnectorOutput,
+)
+from vllm.v1.request import RequestStatus
+
+from .test_nixl_connector import FakeNixlConnectorWorker, FakeNixlWrapper
+from .utils import (
+    assert_scheduler_empty,
+    create_model_runner_output,
+    create_request,
+    create_scheduler,
+    create_vllm_config,
+    make_kv_cache_config,
+)
+
+pytestmark = pytest.mark.cpu_test
+
+# Common extra config for all bi-directional KV transfer tests.
+BIDIR_KV_EXTRA_CONFIG = {"bidirectional_kv_xfer": True, "kv_recompute_threshold": 0}
+
+
+# Helpers
+
+
+def _make_p_node_turn2_request(
+    request_id, block_size, num_tokens, num_remote_blocks=3, remote_num_tokens=None
+):
+    """Create a P-node Turn 2 request with remote_block_ids from D."""
+    request = create_request(
+        request_id=request_id,
+        block_size=block_size,
+        num_tokens=num_tokens,
+        do_remote_decode=True,
+    )
+    if remote_num_tokens is None:
+        remote_num_tokens = num_remote_blocks * block_size
+    request.kv_transfer_params["remote_block_ids"] = [list(range(num_remote_blocks))]
+    request.kv_transfer_params["remote_num_tokens"] = remote_num_tokens
+    request.kv_transfer_params["remote_engine_id"] = "decode-engine"
+    request.kv_transfer_params["remote_request_id"] = f"decode-{request_id}"
+    request.kv_transfer_params["remote_host"] = "decode-host"
+    request.kv_transfer_params["remote_port"] = 5678
+    return request
+
+
+def _make_connector_with_fake_worker(
+    hand_shake_latency=0, cycles_before_done=0, do_handshake=True
+):
+    """Create a NixlConnector with FakeNixlConnectorWorker."""
+    vllm_config = create_vllm_config()
+    kv_cache_config = make_kv_cache_config(block_size=16, num_blocks=2)
+    connector = NixlConnector(vllm_config, KVConnectorRole.WORKER, kv_cache_config)
+    connector.connector_worker = FakeNixlConnectorWorker(
+        vllm_config,
+        connector.engine_id,
+        hand_shake_latency=hand_shake_latency,
+        kv_cache_config=kv_cache_config,
+    )
+    worker = connector.connector_worker
+    assert isinstance(worker.nixl_wrapper, FakeNixlWrapper)
+    worker.nixl_wrapper.set_cycles_before_xfer_done(cycles_before_done)
+    worker.kv_cache_layout = "HND"
+    if do_handshake:
+        remote_agents = worker._nixl_handshake(
+            host="localhost",
+            port=1234,
+            remote_tp_size=1,
+            expected_engine_id=FakeNixlConnectorWorker.REMOTE_ENGINE_ID,
+        )
+        worker._remote_agents[FakeNixlConnectorWorker.REMOTE_ENGINE_ID] = remote_agents
+    return connector, worker
+
+
+def _make_p_node_recv_metadata(request_id, local_blocks, remote_blocks):
+    """Build NixlConnectorMetadata for P-node pulling KV from D."""
+    meta = NixlConnectorMetadata()
+    meta.add_new_req_to_recv(
+        request_id=request_id,
+        local_block_ids=(local_blocks,),
+        kv_transfer_params={
+            "do_remote_prefill": False,
+            "do_remote_decode": True,
+            "remote_block_ids": (remote_blocks,),
+            "remote_engine_id": FakeNixlConnectorWorker.REMOTE_ENGINE_ID,
+            "remote_request_id": f"decode-{request_id}",
+            "remote_host": "localhost",
+            "remote_port": 1234,
+            "remote_tp_size": 1,
+        },
+    )
+    return meta
+
+
+def _do_load_kv(connector, metadata):
+    """Bind metadata and call start_load_kv."""
+    connector.bind_connector_metadata(metadata)
+    ctx = ForwardContext(no_compile_layers={}, attn_metadata={}, slot_mapping={})
+    connector.start_load_kv(ctx)
+
+
+# 1. P-node scheduler lifecycle tests
+
+
+def test_multiturn_lifecycle():
+    """Full two-turn lifecycle on the P node:
+    Turn 1: P prefills locally (do_remote_prefill=False), sends KV to D
+    (do_remote_decode=True). Finishes LENGTH_CAPPED with remote_block_ids.
+    Turn 2: P receives remote_block_ids from D. P pulls KV from D because
+    remote_block_ids is not None and external tokens > 0. Computes only
+    new tokens, finishes LENGTH_CAPPED."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+
+    t1 = create_request(
+        request_id=100, block_size=BS, num_tokens=int(BS * 2.5), do_remote_decode=True
+    )
+    scheduler.add_request(t1)
+    t1_id = t1.request_id
+    so = scheduler.schedule()
+    mro = create_model_runner_output(reqs=[t1])
+    eco = scheduler.update_from_output(so, mro)
+    assert t1.status == RequestStatus.FINISHED_LENGTH_CAPPED
+    kv = eco[0].outputs[0].kv_transfer_params
+    assert kv and sum(len(g) for g in kv["remote_block_ids"]) > 0
+    so = scheduler.schedule()
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+
+    t2 = _make_p_node_turn2_request(200, BS, int(BS * 2.5))
+    scheduler.add_request(t2)
+    t2_id = t2.request_id
+    so = scheduler.schedule()
+    assert t2.status == RequestStatus.WAITING_FOR_REMOTE_KVS
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_recving={t2_id})
+    scheduler.update_from_output(so, mro)
+    so = scheduler.schedule()
+    mro = create_model_runner_output(reqs=[t2])
+    scheduler.update_from_output(so, mro)
+    assert t2.status == RequestStatus.FINISHED_LENGTH_CAPPED
+    so = scheduler.schedule()
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_sending={t1_id, t2_id})
+    scheduler.update_from_output(so, mro)
+    assert_scheduler_empty(scheduler)
+
+
+def test_first_turn_no_remote_blocks():
+    """First turn: P has no remote_block_ids from D yet.
+    Standard local prefill, returns kv_transfer_params for future turns."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = create_request(
+        request_id=3, block_size=BS, num_tokens=int(BS * 2.5), do_remote_decode=True
+    )
+    scheduler.add_request(req)
+    req_id = req.request_id
+    so = scheduler.schedule()
+    assert req.status != RequestStatus.WAITING_FOR_REMOTE_KVS
+    mro = create_model_runner_output(reqs=[req])
+    eco = scheduler.update_from_output(so, mro)
+    assert req.status == RequestStatus.FINISHED_LENGTH_CAPPED
+    assert eco[0].outputs[0].kv_transfer_params is not None
+    so = scheduler.schedule()
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_sending={req_id})
+    scheduler.update_from_output(so, mro)
+    assert_scheduler_empty(scheduler)
+
+
+def test_abort_p_side_during_send():
+    """P-side do_remote_decode=True: blocks held until finished_sending."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = create_request(
+        request_id=42, block_size=BS, num_tokens=int(BS * 2.5), do_remote_decode=True
+    )
+    scheduler.add_request(req)
+    req_id = req.request_id
+    so = scheduler.schedule()
+    mro = create_model_runner_output(reqs=[req])
+    scheduler.update_from_output(so, mro)
+    assert req_id in scheduler.requests
+    so = scheduler.schedule()
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    assert req_id in scheduler.requests
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_sending={req_id})
+    scheduler.update_from_output(so, mro)
+    assert_scheduler_empty(scheduler)
+
+
+def test_abort_p_side_non_length_capped():
+    """P-side abort with non-LENGTH_CAPPED → immediate block free."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = create_request(
+        request_id=44, block_size=BS, num_tokens=int(BS * 2.5), do_remote_decode=True
+    )
+    req.sampling_params.max_tokens = 100
+    req.max_tokens = 100
+    scheduler.add_request(req)
+    req_id = req.request_id
+    so = scheduler.schedule()
+    mro = create_model_runner_output(reqs=[req])
+    scheduler.update_from_output(so, mro)
+    scheduler.finish_requests([req_id], RequestStatus.FINISHED_ABORTED)
+    conn = scheduler.connector.connector_scheduler
+    assert req_id in conn._reqs_not_processed
+    assert req_id not in scheduler.requests
+    so = scheduler.schedule()
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    assert_scheduler_empty(scheduler)
+
+
+def test_remote_blocks_exceed_prompt_tokens():
+    """D provides more remote tokens than P's prompt needs.
+    P caps external tokens to prompt length."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    NUM_TOKENS = int(BS * 2.5)
+    req = _make_p_node_turn2_request(
+        300, BS, NUM_TOKENS, num_remote_blocks=5, remote_num_tokens=5 * BS
+    )
+    scheduler.add_request(req)
+    req_id = req.request_id
+    so = scheduler.schedule()
+    assert req.status == RequestStatus.WAITING_FOR_REMOTE_KVS
+    assert req.num_computed_tokens == NUM_TOKENS
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_recving={req_id})
+    scheduler.update_from_output(so, mro)
+    so = scheduler.schedule()
+    mro = create_model_runner_output(reqs=[req])
+    scheduler.update_from_output(so, mro)
+    assert req.status == RequestStatus.FINISHED_LENGTH_CAPPED
+    so = scheduler.schedule()
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_sending={req_id})
+    scheduler.update_from_output(so, mro)
+    assert_scheduler_empty(scheduler)
+
+
+def test_p_node_pulls_partial_last_block_from_d():
+    """D sends remote_block_ids with partially filled last block.
+    remote_num_tokens < len(remote_block_ids) * block_size.
+    P pulls only remote_num_tokens worth of external tokens."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    num_remote_blocks = 3
+    remote_num_tokens = int(BS * 2.5)
+    assert remote_num_tokens < num_remote_blocks * BS
+    NUM_TOKENS = int(BS * 3.5)
+    req = _make_p_node_turn2_request(
+        400,
+        BS,
+        NUM_TOKENS,
+        num_remote_blocks=num_remote_blocks,
+        remote_num_tokens=remote_num_tokens,
+    )
+    scheduler.add_request(req)
+    req_id = req.request_id
+    so = scheduler.schedule()
+    assert req.status == RequestStatus.WAITING_FOR_REMOTE_KVS
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_recving={req_id})
+    scheduler.update_from_output(so, mro)
+    so = scheduler.schedule()
+    assert len(scheduler.running) == 1
+    mro = create_model_runner_output(reqs=[req])
+    scheduler.update_from_output(so, mro)
+    assert req.status == RequestStatus.FINISHED_LENGTH_CAPPED
+    so = scheduler.schedule()
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_sending={req_id})
+    scheduler.update_from_output(so, mro)
+    assert_scheduler_empty(scheduler)
+
+
+# 2. P-node metadata tests
+
+
+def test_add_new_req_to_recv_populates_remote_meta():
+    """add_new_req_to_recv correctly populates RemoteMeta for P-node
+    bi-directional KV pull from D."""
+    meta = NixlConnectorMetadata()
+    kv_params = {
+        "remote_block_ids": [[0, 1, 2]],
+        "remote_engine_id": "decode-engine",
+        "remote_request_id": "decode-req-123",
+        "remote_host": "decode-host",
+        "remote_port": 5678,
+    }
+    local_block_ids = ([10, 11, 12],)
+    meta.add_new_req_to_recv(
+        request_id="test-req",
+        local_block_ids=local_block_ids,
+        kv_transfer_params=kv_params,
+    )
+    assert "test-req" in meta.reqs_to_recv
+    rm = meta.reqs_to_recv["test-req"]
+    assert rm.remote is not None
+    assert rm.remote.block_ids == kv_params["remote_block_ids"]
+    assert rm.remote.engine_id == "decode-engine"
+    assert rm.remote.request_id == "decode-req-123"
+    assert rm.remote.host == "decode-host"
+    assert rm.remote.port == 5678
+    assert rm.local_block_ids == local_block_ids
+
+
+def test_build_connector_meta_recv_entries():
+    """P-node scheduler: do_remote_decode=True + remote_block_ids →
+    _reqs_need_recv populated, build_connector_meta produces reqs_to_recv."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = _make_p_node_turn2_request(1, BS, int(BS * 2.5))
+    scheduler.add_request(req)
+    req_id = req.request_id
+    so = scheduler.schedule()
+    assert req.status == RequestStatus.WAITING_FOR_REMOTE_KVS
+    meta = so.kv_connector_metadata
+    assert isinstance(meta, NixlConnectorMetadata)
+    assert req_id in meta.reqs_to_recv
+    rm = meta.reqs_to_recv[req_id]
+    assert rm.remote is not None
+    assert rm.remote.engine_id == "decode-engine"
+
+
+def test_build_connector_meta_clears_reqs_need_recv():
+    """After build_connector_meta, _reqs_need_recv is cleared."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = _make_p_node_turn2_request(2, BS, int(BS * 2.5))
+    scheduler.add_request(req)
+    conn = scheduler.connector.connector_scheduler
+    scheduler.schedule()
+    assert len(conn._reqs_need_recv) == 0
+
+
+def test_build_connector_meta_multiple_requests():
+    """Multiple P-node requests all included in reqs_to_recv."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    reqs = [_make_p_node_turn2_request(10 + i, BS, int(BS * 2.5)) for i in range(3)]
+    for r in reqs:
+        scheduler.add_request(r)
+    so = scheduler.schedule()
+    meta = so.kv_connector_metadata
+    assert isinstance(meta, NixlConnectorMetadata)
+    assert len(meta.reqs_to_recv) == 3
+    for r in reqs:
+        assert r.request_id in meta.reqs_to_recv
+
+
+# 3. P-node worker tests (FakeNixlWrapper)
+
+
+@patch(
+    "vllm.distributed.kv_transfer.kv_connector.v1.nixl.worker.NixlWrapper",
+    FakeNixlWrapper,
+)
+def test_p_node_pull_kv_from_d(dist_init):
+    """P node pulls KV from D via start_load_kv with reqs_to_recv."""
+    connector, worker = _make_connector_with_fake_worker()
+    meta = _make_p_node_recv_metadata("req-p1", [10, 11, 12], [20, 21, 22])
+    _do_load_kv(connector, meta)
+    assert "req-p1" in worker._recving_metadata
+    _, done_recving = connector.get_finished(finished_req_ids=set())
+    assert "req-p1" in done_recving
+
+
+@patch(
+    "vllm.distributed.kv_transfer.kv_connector.v1.nixl.worker.NixlWrapper",
+    FakeNixlWrapper,
+)
+def test_p_node_pull_then_send_kv(dist_init):
+    """Full P-node bi-directional: pull KV from D → prefill →
+    send KV back to D via notification."""
+    connector, worker = _make_connector_with_fake_worker()
+    meta = _make_p_node_recv_metadata("req-p2", [10, 11], [20, 21])
+    _do_load_kv(connector, meta)
+    _, done_recving = connector.get_finished(finished_req_ids=set())
+    assert "req-p2" in done_recving
+    worker._reqs_to_send["req-p2"] = time.perf_counter() + 60
+    worker._reqs_to_process.add("req-p2")
+    notif = f"req-p2:{worker.world_size}".encode()
+    orig = worker.nixl_wrapper.get_new_notifs
+    worker.nixl_wrapper.get_new_notifs = lambda: {"agent": [notif]}
+    done_sending, _ = connector.get_finished(finished_req_ids=set())
+    assert "req-p2" in done_sending
+    worker.nixl_wrapper.get_new_notifs = orig
+
+
+@patch(
+    "vllm.distributed.kv_transfer.kv_connector.v1.nixl.worker.NixlWrapper",
+    FakeNixlWrapper,
+)
+def test_p_node_deferred_pull_on_no_handshake(dist_init):
+    """P defers KV pull when no prior handshake exists."""
+    connector, worker = _make_connector_with_fake_worker(
+        hand_shake_latency=0, do_handshake=False
+    )
+    meta = _make_p_node_recv_metadata("req-p3", [10, 11], [20, 21])
+    _do_load_kv(connector, meta)
+    assert "req-p3" in worker._recving_metadata
+    timeout = 3.0
+    start = time.perf_counter()
+    while time.perf_counter() - start < timeout:
+        connector.bind_connector_metadata(NixlConnectorMetadata())
+        ctx = ForwardContext(no_compile_layers={}, attn_metadata={}, slot_mapping={})
+        connector.start_load_kv(ctx)
+        _, done = connector.get_finished(finished_req_ids=set())
+        if "req-p3" in done:
+            return
+        time.sleep(0.2)
+    raise AssertionError("Transfer did not complete after async handshake")
+
+
+# 4. D-node request_finished returns kv_transfer_params (new behavior)
+
+
+def test_d_node_request_finished_returns_kv_params():
+    """D-node request_finished returns kv_transfer_params with
+    do_remote_decode=True, remote_block_ids, remote_num_tokens
+    for P to pull. These params go directly to P node."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = create_request(
+        request_id=1, block_size=BS, num_tokens=int(BS * 2.5), do_remote_prefill=True
+    )
+    scheduler.add_request(req)
+    req_id = req.request_id
+    so = scheduler.schedule()
+    scheduler.update_from_output(
+        so, create_model_runner_output(reqs=[], finished_recving={req_id})
+    )
+    so = scheduler.schedule()
+    eco = scheduler.update_from_output(
+        so, create_model_runner_output(reqs=[req], use_eos=True)
+    )
+    assert req.status == RequestStatus.FINISHED_STOPPED
+    kv = eco[0].outputs[0].kv_transfer_params
+    assert kv is not None
+    assert kv["do_remote_decode"] is True
+    assert kv["do_remote_prefill"] is False
+    assert "remote_block_ids" in kv
+    assert "remote_num_tokens" in kv
+    assert kv["remote_num_tokens"] > 0
+
+
+def test_d_node_request_finished_delays_block_free():
+    """D-node holds blocks (delay_free=True) until P reads them."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = create_request(
+        request_id=2, block_size=BS, num_tokens=int(BS * 2.5), do_remote_prefill=True
+    )
+    scheduler.add_request(req)
+    req_id = req.request_id
+    so = scheduler.schedule()
+    scheduler.update_from_output(
+        so, create_model_runner_output(reqs=[], finished_recving={req_id})
+    )
+    so = scheduler.schedule()
+    scheduler.update_from_output(
+        so, create_model_runner_output(reqs=[req], use_eos=True)
+    )
+    assert req_id in scheduler.requests
+    conn = scheduler.connector.connector_scheduler
+    assert req_id in conn._reqs_need_send
+
+
+def test_d_node_request_finished_remote_num_tokens():
+    """D-node kv_transfer_params includes correct remote_num_tokens."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = create_request(
+        request_id=3, block_size=BS, num_tokens=int(BS * 2.5), do_remote_prefill=True
+    )
+    scheduler.add_request(req)
+    req_id = req.request_id
+    so = scheduler.schedule()
+    scheduler.update_from_output(
+        so, create_model_runner_output(reqs=[], finished_recving={req_id})
+    )
+    so = scheduler.schedule()
+    eco = scheduler.update_from_output(
+        so, create_model_runner_output(reqs=[req], use_eos=True)
+    )
+    kv = eco[0].outputs[0].kv_transfer_params
+    assert kv["remote_num_tokens"] > 0
+    assert sum(len(g) for g in kv["remote_block_ids"]) > 0
+
+
+def test_d_node_partial_last_block_remote_num_tokens():
+    """D-node: remote_num_tokens < len(remote_block_ids) * block_size
+    when last block is partially filled."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = create_request(
+        request_id=5, block_size=BS, num_tokens=int(BS * 2.5), do_remote_prefill=True
+    )
+    scheduler.add_request(req)
+    req_id = req.request_id
+    so = scheduler.schedule()
+    scheduler.update_from_output(
+        so, create_model_runner_output(reqs=[], finished_recving={req_id})
+    )
+    so = scheduler.schedule()
+    eco = scheduler.update_from_output(
+        so, create_model_runner_output(reqs=[req], use_eos=True)
+    )
+    kv = eco[0].outputs[0].kv_transfer_params
+    total_blocks = sum(len(g) for g in kv["remote_block_ids"])
+    assert total_blocks == 3
+    assert kv["remote_num_tokens"] < total_blocks * BS
+    assert kv["remote_num_tokens"] > 0
+
+
+# 5. Edge case tests
+
+
+def test_no_double_read_blocks_after_reschedule():
+    """Edge case 1: update_state_after_alloc called twice for the same
+    bidirectional request (once on initial schedule, once after
+    WAITING_FOR_REMOTE_KVS → reschedule). The _remote_blocks_processed
+    flag must prevent the request from being added to _reqs_need_recv
+    twice, which would cause P to read D's blocks twice."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = _make_p_node_turn2_request(500, BS, int(BS * 2.5))
+    scheduler.add_request(req)
+    req_id = req.request_id
+    conn = scheduler.connector.connector_scheduler
+
+    # First schedule: request enters WAITING_FOR_REMOTE_KVS,
+    # _reqs_need_recv populated then cleared by build_connector_meta.
+    so = scheduler.schedule()
+    assert req.status == RequestStatus.WAITING_FOR_REMOTE_KVS
+    meta = so.kv_connector_metadata
+    assert isinstance(meta, NixlConnectorMetadata)
+    assert req_id in meta.reqs_to_recv
+    # _reqs_need_recv should be cleared after build_connector_meta
+    assert len(conn._reqs_need_recv) == 0
+
+    # Simulate recv completion
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_recving={req_id})
+    scheduler.update_from_output(so, mro)
+
+    # Second schedule after recv: update_state_after_alloc called again.
+    # The _remote_blocks_processed flag should prevent re-entry.
+    so = scheduler.schedule()
+    meta2 = so.kv_connector_metadata
+    assert isinstance(meta2, NixlConnectorMetadata)
+    # Must NOT be in reqs_to_recv again
+    assert req_id not in meta2.reqs_to_recv
+
+    # Clean up
+    mro = create_model_runner_output(reqs=[req])
+    scheduler.update_from_output(so, mro)
+    assert req.status == RequestStatus.FINISHED_LENGTH_CAPPED
+    so = scheduler.schedule()
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_sending={req_id})
+    scheduler.update_from_output(so, mro)
+    assert_scheduler_empty(scheduler)
+
+
+def test_remote_num_tokens_bounded_by_blocks():
+    """Edge case 2: D-node request_finished must return
+    remote_num_tokens <= len(remote_block_ids) * block_size.
+    request.num_tokens includes the last sampled token which has no KV
+    in the cache, so remote_num_tokens must use num_computed_tokens."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = create_request(
+        request_id=501,
+        block_size=BS,
+        num_tokens=int(BS * 2.5),
+        do_remote_prefill=True,
+    )
+    scheduler.add_request(req)
+    req_id = req.request_id
+    so = scheduler.schedule()
+    scheduler.update_from_output(
+        so, create_model_runner_output(reqs=[], finished_recving={req_id})
+    )
+    so = scheduler.schedule()
+    eco = scheduler.update_from_output(
+        so, create_model_runner_output(reqs=[req], use_eos=True)
+    )
+    kv = eco[0].outputs[0].kv_transfer_params
+    assert kv is not None
+    total_blocks = sum(len(g) for g in kv["remote_block_ids"])
+    max_tokens_in_blocks = total_blocks * BS
+    assert kv["remote_num_tokens"] <= max_tokens_in_blocks, (
+        f"remote_num_tokens ({kv['remote_num_tokens']}) exceeds "
+        f"block capacity ({max_tokens_in_blocks})"
+    )
+    assert kv["remote_num_tokens"] > 0
+
+
+def test_kv_recompute_threshold_skips_small_transfer():
+    """Edge case 3: When remote tokens are below kv_recompute_threshold,
+    P should skip the remote pull and compute locally instead of
+    entering WAITING_FOR_REMOTE_KVS."""
+    threshold = 256
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config={
+            "bidirectional_kv_xfer": True,
+            "kv_recompute_threshold": threshold,
+        },
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+
+    # Create request where remote tokens (48) < threshold (256)
+    req = _make_p_node_turn2_request(
+        502,
+        BS,
+        int(BS * 2.5),
+        num_remote_blocks=3,
+        remote_num_tokens=3 * BS,
+    )
+    scheduler.add_request(req)
+    so = scheduler.schedule()
+    # Should NOT enter WAITING_FOR_REMOTE_KVS — threshold not met
+    assert req.status != RequestStatus.WAITING_FOR_REMOTE_KVS
+    assert req.status == RequestStatus.RUNNING
+
+    # Clean up
+    mro = create_model_runner_output(reqs=[req])
+    scheduler.update_from_output(so, mro)
+    assert req.status == RequestStatus.FINISHED_LENGTH_CAPPED
+    so = scheduler.schedule()
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_sending={req.request_id})
+    scheduler.update_from_output(so, mro)
+    assert_scheduler_empty(scheduler)
+
+
+def test_p_node_finished_holds_blocks_for_d():
+    """Edge case 4: P-node finishes with FINISHED_LENGTH_CAPPED and
+    do_remote_decode=True. P must hold blocks (delay_free=True) and
+    return kv_transfer_params with do_remote_prefill=True so D can
+    read P's blocks."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = create_request(
+        request_id=503,
+        block_size=BS,
+        num_tokens=int(BS * 2.5),
+        do_remote_decode=True,
+    )
+    scheduler.add_request(req)
+    req_id = req.request_id
+    so = scheduler.schedule()
+    mro = create_model_runner_output(reqs=[req])
+    eco = scheduler.update_from_output(so, mro)
+    assert req.status == RequestStatus.FINISHED_LENGTH_CAPPED
+    kv = eco[0].outputs[0].kv_transfer_params
+    assert kv is not None
+    # P-node finished: should tell D to pull (do_remote_prefill=True)
+    assert kv["do_remote_prefill"] is True
+    assert kv["do_remote_decode"] is False
+    assert "remote_block_ids" in kv
+    assert sum(len(g) for g in kv["remote_block_ids"]) > 0
+    # Blocks should be held (request still tracked)
+    assert req_id in scheduler.requests
+
+    # Clean up: simulate D reading and notifying
+    so = scheduler.schedule()
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_sending={req_id})
+    scheduler.update_from_output(so, mro)
+    assert_scheduler_empty(scheduler)
+
+
+def test_cache_miss_first_turn_no_remote_pull():
+    """Edge case 5: First turn with do_remote_decode=True but no
+    remote_block_ids (cache MISS). P should prefill locally with
+    num_external_tokens=0 and not enter WAITING_FOR_REMOTE_KVS."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = create_request(
+        request_id=504,
+        block_size=BS,
+        num_tokens=int(BS * 2.5),
+        do_remote_decode=True,
+    )
+    # No remote_block_ids set — this is a cache MISS
+    assert req.kv_transfer_params.get("remote_block_ids") is None
+    scheduler.add_request(req)
+    so = scheduler.schedule()
+    # Should NOT wait for remote KVs
+    assert req.status != RequestStatus.WAITING_FOR_REMOTE_KVS
+    assert req.status == RequestStatus.RUNNING
+
+    # Clean up
+    mro = create_model_runner_output(reqs=[req])
+    scheduler.update_from_output(so, mro)
+    so = scheduler.schedule()
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_sending={req.request_id})
+    scheduler.update_from_output(so, mro)
+    assert_scheduler_empty(scheduler)
+
+
+def test_partial_remote_tokens_less_than_prompt():
+    """Edge case 6: D's remote_num_tokens covers only part of P's
+    prompt. P should pull remote_num_tokens worth of external tokens
+    and compute the rest locally."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    NUM_TOKENS = int(BS * 4.5)  # 72 tokens
+    # D provides only 2 blocks (32 tokens) out of 72
+    req = _make_p_node_turn2_request(
+        505,
+        BS,
+        NUM_TOKENS,
+        num_remote_blocks=2,
+        remote_num_tokens=2 * BS,
+    )
+    scheduler.add_request(req)
+    req_id = req.request_id
+    so = scheduler.schedule()
+    assert req.status == RequestStatus.WAITING_FOR_REMOTE_KVS
+    # num_computed_tokens should reflect the external tokens pulled
+    # (capped to remote_num_tokens, not full prompt)
+    assert req.num_computed_tokens < NUM_TOKENS
+
+    # Complete the transfer and finish
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_recving={req_id})
+    scheduler.update_from_output(so, mro)
+    so = scheduler.schedule()
+    mro = create_model_runner_output(reqs=[req])
+    scheduler.update_from_output(so, mro)
+    assert req.status == RequestStatus.FINISHED_LENGTH_CAPPED
+    so = scheduler.schedule()
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_sending={req_id})
+    scheduler.update_from_output(so, mro)
+    assert_scheduler_empty(scheduler)
+
+
+def test_remote_blocks_processed_flag_persists():
+    """Edge case 7: After recv completes and request is rescheduled,
+    the _remote_blocks_processed flag in kv_transfer_params prevents
+    the bidirectional path from re-entering _reqs_need_recv."""
+    vllm_config = create_vllm_config(
+        kv_connector_extra_config=BIDIR_KV_EXTRA_CONFIG,
+    )
+    scheduler = create_scheduler(vllm_config)
+    BS = vllm_config.cache_config.block_size
+    req = _make_p_node_turn2_request(506, BS, int(BS * 2.5))
+    scheduler.add_request(req)
+    req_id = req.request_id
+    conn = scheduler.connector.connector_scheduler
+
+    # First schedule → WAITING_FOR_REMOTE_KVS
+    so = scheduler.schedule()
+    assert req.status == RequestStatus.WAITING_FOR_REMOTE_KVS
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+
+    # Recv completes
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_recving={req_id})
+    scheduler.update_from_output(so, mro)
+
+    # Verify the flag is set
+    assert req.kv_transfer_params.get("_remote_blocks_processed") is True
+
+    # Next schedule: update_state_after_alloc is called again.
+    # _reqs_need_recv must NOT contain this request.
+    so = scheduler.schedule()
+    assert req_id not in conn._reqs_need_recv
+    meta = so.kv_connector_metadata
+    assert isinstance(meta, NixlConnectorMetadata)
+    assert req_id not in meta.reqs_to_recv
+
+    # Clean up
+    mro = create_model_runner_output(reqs=[req])
+    scheduler.update_from_output(so, mro)
+    so = scheduler.schedule()
+    scheduler.update_from_output(so, EMPTY_MODEL_RUNNER_OUTPUT)
+    so = scheduler.schedule()
+    mro = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT)
+    mro.kv_connector_output = KVConnectorOutput(finished_sending={req_id})
+    scheduler.update_from_output(so, mro)
+    assert_scheduler_empty(scheduler)
diff --git a/tests/v1/kv_connector/unit/test_config.py b/tests/v1/kv_connector/unit/test_config.py
index 8a547c3f03f9..821807424575 100644
--- a/tests/v1/kv_connector/unit/test_config.py
+++ b/tests/v1/kv_connector/unit/test_config.py
@@ -65,6 +65,61 @@ def test_kv_connector(
         assert "existing_key" not in kv_connector_extra_config
 
 
+def _build_config(
+    *, kv_connector: str | None, enable_sleep_mode: bool = False
+) -> VllmConfig:
+    """Build a VllmConfig that exercises _verify_kv_transfer_compat without
+    requiring a real model (avoids HF downloads in CI)."""
+    from types import SimpleNamespace
+
+    kv_transfer_config = (
+        KVTransferConfig(kv_connector=kv_connector, kv_role="kv_both")
+        if kv_connector is not None
+        else None
+    )
+    cfg = VllmConfig.__new__(VllmConfig)
+    cfg.kv_transfer_config = kv_transfer_config
+    cfg.model_config = SimpleNamespace(enable_sleep_mode=enable_sleep_mode)
+    cfg._verify_kv_transfer_compat()
+    return cfg
+
+
+@pytest.mark.parametrize(
+    "kv_connector", ["NixlConnector", "MooncakeConnectorV1", "SomeOOTConnector"]
+)
+def test_kv_connector_rejects_expandable_segments(monkeypatch, kv_connector):
+    """KV connectors that pin KV cache memory (e.g. via ibv_reg_mr) are
+    invalidated when expandable_segments lets the CUDA VMM allocator remap
+    the underlying physical pages. We can't enumerate every connector that
+    does this (especially OOT ones), so reject the combination whenever any
+    connector is configured."""
+    monkeypatch.setenv("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")
+    with pytest.raises(ValueError, match="expandable_segments"):
+        _build_config(kv_connector=kv_connector)
+
+
+def test_kv_connector_allows_expandable_segments_with_sleep_mode(monkeypatch):
+    """Sleep mode routes KV allocations through CuMemAllocator's pool, which
+    auto-disables expandable_segments (see #40812)."""
+    monkeypatch.setenv("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")
+    _build_config(kv_connector="NixlConnector", enable_sleep_mode=True)
+
+
+def test_kv_connector_allows_other_alloc_conf(monkeypatch):
+    """Other PYTORCH_CUDA_ALLOC_CONF values must not be rejected."""
+    monkeypatch.setenv(
+        "PYTORCH_CUDA_ALLOC_CONF", "max_split_size_mb:512,expandable_segments:False"
+    )
+    _build_config(kv_connector="NixlConnector")
+
+
+def test_no_kv_connector_ignores_expandable_segments(monkeypatch):
+    """The expandable_segments check only applies when a KV connector is
+    configured."""
+    monkeypatch.setenv("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")
+    _build_config(kv_connector=None)
+
+
 def test_kv_offloading_size_only_uses_native_default():
     """Test that setting only kv_offloading_size enables native offloading."""
     vllm_config = VllmConfig(
diff --git a/tests/v1/kv_connector/unit/test_decode_bench_connector.py b/tests/v1/kv_connector/unit/test_decode_bench_connector.py
index 30652b3d5c51..3af58d63c9a1 100644
--- a/tests/v1/kv_connector/unit/test_decode_bench_connector.py
+++ b/tests/v1/kv_connector/unit/test_decode_bench_connector.py
@@ -58,7 +58,9 @@ def __init__(self, block_size: int, num_gpu_blocks: int):
 
         # Create worker-side connector
         self.worker_connector = DecodeBenchConnector(
-            vllm_config, KVConnectorRole.WORKER
+            vllm_config,
+            KVConnectorRole.WORKER,
+            self.scheduler.kv_cache_config,
         )
 
         # Create dummy KV caches for testing
diff --git a/tests/v1/kv_connector/unit/test_kv_connector_lifecycle.py b/tests/v1/kv_connector/unit/test_kv_connector_lifecycle.py
index 5121dcf90c13..a9a38a17b949 100644
--- a/tests/v1/kv_connector/unit/test_kv_connector_lifecycle.py
+++ b/tests/v1/kv_connector/unit/test_kv_connector_lifecycle.py
@@ -10,6 +10,7 @@
     get_kv_transfer_group,
 )
 from vllm.v1.core.sched.output import CachedRequestData, SchedulerOutput
+from vllm.v1.kv_cache_interface import KVCacheConfig
 from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
 
 # Importing utils registers TestExampleConnector with the factory
@@ -38,7 +39,10 @@ def test_kv_connector_mixin_clears_metadata():
     vllm_config.kv_transfer_config.kv_connector_extra_config["name"] = "unit"
 
     # Initialize the global connector instance
-    ensure_kv_transfer_initialized(vllm_config)
+    kv_cache_config = KVCacheConfig(
+        num_blocks=0, kv_cache_tensors=[], kv_cache_groups=[]
+    )
+    ensure_kv_transfer_initialized(vllm_config, kv_cache_config)
 
     try:
         # Minimal scheduler output with empty metadata; mixin should still
diff --git a/tests/v1/kv_connector/unit/test_mooncake_connector.py b/tests/v1/kv_connector/unit/test_mooncake_connector.py
index f21f8ecdc5c2..44292d94e147 100644
--- a/tests/v1/kv_connector/unit/test_mooncake_connector.py
+++ b/tests/v1/kv_connector/unit/test_mooncake_connector.py
@@ -26,11 +26,16 @@
 )
 from vllm.utils.network_utils import get_open_port
 from vllm.v1.attention.backends.flash_attn import FlashAttentionBackend
+from vllm.v1.kv_cache_interface import KVCacheConfig
 from vllm.v1.request import RequestStatus
 
 from .utils import create_request, create_scheduler, create_vllm_config
 
 
+def _make_test_kv_cache_config() -> KVCacheConfig:
+    return KVCacheConfig(num_blocks=0, kv_cache_tensors=[], kv_cache_groups=[])
+
+
 class FakeMooncakeWrapper:
     """Mock Mooncake TransferEngine for unit testing environments."""
 
@@ -91,8 +96,10 @@ def test_basic_interface():
     assert request_id in kv_connector_metadata.reqs_to_recv["my-engine-id"]
     req_meta = kv_connector_metadata.reqs_to_recv["my-engine-id"][request_id]
 
+    # local_block_ids is list[list[int]] (per-group); flatten for comparison.
+    all_block_ids = [bid for group in req_meta.local_block_ids for bid in group]
     for block_id, block in zip(
-        req_meta.local_block_ids,
+        all_block_ids,
         scheduler.kv_cache_manager.coordinator.single_type_managers[0].req_to_blocks[
             request_id
         ],
@@ -228,15 +235,15 @@ def test_scheduler_request_finished():
 
     # Case: Capped length (Successful prefill, need to send to decoder)
     request.status = RequestStatus.FINISHED_LENGTH_CAPPED
-    delay_free, _ = scheduler_connector.request_finished(request, block_ids=[10, 11])
+    delay_free, _ = scheduler_connector.request_finished(request, block_ids=([10, 11],))
     assert delay_free is True
     assert "id-1" in scheduler_connector._reqs_need_send
-    assert scheduler_connector._reqs_need_send["id-1"][1] == [10, 11]
+    assert scheduler_connector._reqs_need_send["id-1"][1] == [[10, 11]]
 
     # Case: Aborted (No need to transfer, free blocks immediately)
     scheduler_connector._reqs_need_send.clear()
     request.status = RequestStatus.FINISHED_ABORTED
-    delay_free, _ = scheduler_connector.request_finished(request, block_ids=[12])
+    delay_free, _ = scheduler_connector.request_finished(request, block_ids=([12],))
     assert delay_free is False
     assert len(scheduler_connector._reqs_need_send) == 0
     assert "id-1" in scheduler_connector._reqs_not_processed
@@ -319,7 +326,11 @@ async def test_kv_producer(monkeypatch):
     )
 
     with set_current_vllm_config(vllm_config), patch_worker_dependencies():
-        prefill_connector = MooncakeConnector(vllm_config, KVConnectorRole.WORKER)
+        prefill_connector = MooncakeConnector(
+            vllm_config,
+            KVConnectorRole.WORKER,
+            _make_test_kv_cache_config(),
+        )
         prefill_worker = prefill_connector.connector_worker
         prefill_worker.kv_caches_base_addr = [0x1000]
         block_len = 4096
@@ -334,7 +345,7 @@ async def test_kv_producer(monkeypatch):
         send_meta = SendBlockMeta(
             p_req_id="p-req-1",
             transfer_id=transfer_id,
-            local_block_ids=[10, 11],
+            local_block_ids=[[10, 11]],
             ready=asyncio.Event(),
         )
         prefill_worker.reqs_need_send[transfer_id] = send_meta
@@ -346,7 +357,7 @@ async def test_kv_producer(monkeypatch):
             remote_port=54321,
             remote_tp_size=1,
             remote_tp_rank=0,
-            req_blocks={"d-req-1": (transfer_id, [20, 21])},
+            req_blocks={"d-req-1": (transfer_id, [[20, 21]])},
             kv_caches_base_addr=[0x2000],
             block_lens=[block_len],
         )
@@ -389,7 +400,7 @@ async def test_kv_producer(monkeypatch):
             prefill_worker.reqs_need_send[transfer_id] = send_meta
             send_meta.sent = 0
             send_meta.ready.set()
-            xfer_meta.req_blocks["d-req-1"] = (transfer_id, [20])
+            xfer_meta.req_blocks["d-req-1"] = (transfer_id, [[20]])
             # Worker processes the consumer's request
             await prefill_worker.send_kv_to_decode(identity, mock_socket, xfer_meta)
             # Verify transfer parameters are correct: 11 to 20
@@ -407,7 +418,7 @@ async def test_kv_producer(monkeypatch):
             prefill_worker.reqs_need_send[transfer_id] = send_meta
             send_meta.sent = 0
             send_meta.ready.set()
-            xfer_meta.req_blocks["d-req-1"] = (transfer_id, [20, 21, 22])
+            xfer_meta.req_blocks["d-req-1"] = (transfer_id, [[20, 21, 22]])
             # Worker processes the consumer's request
             await prefill_worker.send_kv_to_decode(identity, mock_socket, xfer_meta)
             # This should not be called because error.
@@ -424,7 +435,7 @@ async def test_kv_producer(monkeypatch):
             prefill_worker.reqs_need_send[transfer_id] = send_meta
             send_meta.sent = 0
             send_meta.ready.clear()
-            xfer_meta.req_blocks["d-req-1"] = (transfer_id, [20, 21])
+            xfer_meta.req_blocks["d-req-1"] = (transfer_id, [[20, 21]])
             # Worker processes the consumer's request
             await prefill_worker.send_kv_to_decode(identity, mock_socket, xfer_meta)
             # This should not be called because timeout.
@@ -443,7 +454,7 @@ async def test_kv_producer(monkeypatch):
             prefill_worker.reqs_need_send[transfer_id] = send_meta
             send_meta.sent = 0
             send_meta.ready.set()
-            xfer_meta.req_blocks["d-req-1"] = (transfer_id, [20, 21])
+            xfer_meta.req_blocks["d-req-1"] = (transfer_id, [[20, 21]])
             # Worker processes the consumer's request
             await prefill_worker.send_kv_to_decode(identity, mock_socket, xfer_meta)
             mock_send_blocks.assert_called_once()
@@ -471,7 +482,11 @@ async def test_kv_consumuer(monkeypatch):
     )
 
     with set_current_vllm_config(vllm_config), patch_worker_dependencies() as mocks:
-        decode_connector = MooncakeConnector(vllm_config, KVConnectorRole.WORKER)
+        decode_connector = MooncakeConnector(
+            vllm_config,
+            KVConnectorRole.WORKER,
+            _make_test_kv_cache_config(),
+        )
         decode_worker = decode_connector.connector_worker
         decode_worker.kv_caches_base_addr = [0x1000]
         decode_worker.rpc_port = 54321
@@ -481,7 +496,7 @@ async def test_kv_consumuer(monkeypatch):
             "d-req-1": PullReqMeta(
                 d_req_id="d-req-1",
                 transfer_id="xfer-req-1",
-                local_block_ids=[100, 101],
+                local_block_ids=[[100, 101]],
                 remote_engine_id="p-engine",
                 remote_bootstrap_addr="http://bootstrap:33333",
                 pull_tasks_count=1,
@@ -514,7 +529,7 @@ async def test_kv_consumuer(monkeypatch):
 
         assert sent_meta.remote_hostname == "127.0.0.1"
         assert sent_meta.remote_port == 54321
-        assert sent_meta.req_blocks["d-req-1"] == ("xfer-req-1", [100, 101])
+        assert sent_meta.req_blocks["d-req-1"] == ("xfer-req-1", [[100, 101]])
 
         # Verify internal state is updated correctly.
         assert "d-req-1" in decode_worker.finished_recving_reqs
@@ -531,14 +546,18 @@ async def test_worker_get_finished_timeout(monkeypatch):
         kv_connector="MooncakeConnector", kv_role="kv_producer"
     )
     with set_current_vllm_config(vllm_config), patch_worker_dependencies():
-        prefill_connector = MooncakeConnector(vllm_config, KVConnectorRole.WORKER)
+        prefill_connector = MooncakeConnector(
+            vllm_config,
+            KVConnectorRole.WORKER,
+            _make_test_kv_cache_config(),
+        )
         prefill_worker = prefill_connector.connector_worker
 
         # Add an expired request (expire_time is in the past).
         prefill_worker.reqs_need_send["tx-expired"] = SendBlockMeta(
             p_req_id="p-req-expired",
             transfer_id="tx-expired",
-            local_block_ids=[1, 2],
+            local_block_ids=[[1, 2]],
             ready=MagicMock(),
             expire_time=time.perf_counter() - 100,
         )
@@ -547,7 +566,7 @@ async def test_worker_get_finished_timeout(monkeypatch):
         prefill_worker.reqs_need_send["tx-active"] = SendBlockMeta(
             p_req_id="p-req-active",
             transfer_id="tx-active",
-            local_block_ids=[3, 4],
+            local_block_ids=[[3, 4]],
             ready=MagicMock(),
             expire_time=time.perf_counter() + 100,
         )
@@ -577,7 +596,11 @@ def test_register_kv_caches():
             "vllm.distributed.kv_transfer.kv_connector.v1.mooncake.mooncake_connector.threading.Thread"
         ) as mock_thread,
     ):
-        connector = MooncakeConnector(vllm_config, KVConnectorRole.WORKER)
+        connector = MooncakeConnector(
+            vllm_config,
+            KVConnectorRole.WORKER,
+            _make_test_kv_cache_config(),
+        )
         worker = connector.connector_worker
         mock_thread.return_value.is_alive.return_value = False
 
@@ -609,6 +632,55 @@ def test_register_kv_caches():
                 assert bl == tensor1[0].nbytes // tensor1.shape[1]
 
 
+def test_register_kv_caches_supports_mixed_mla_and_eagle_shapes():
+    """Mixed MLA+Eagle caches should register by byte length, not shape."""
+
+    vllm_config = create_vllm_config(
+        kv_connector="MooncakeConnector", kv_role="kv_consumer"
+    )
+
+    with (
+        set_current_vllm_config(vllm_config),
+        patch_worker_dependencies(),
+        patch(
+            "vllm.distributed.kv_transfer.kv_connector.v1.mooncake.mooncake_connector.threading.Event"
+        ),
+        patch(
+            "vllm.distributed.kv_transfer.kv_connector.v1.mooncake.mooncake_connector.threading.Thread"
+        ) as mock_thread,
+    ):
+        connector = MooncakeConnector(
+            vllm_config,
+            KVConnectorRole.WORKER,
+            _make_test_kv_cache_config(),
+        )
+        worker = connector.connector_worker
+        mock_thread.return_value.is_alive.return_value = False
+
+        worker.use_mla = True
+        worker.transfer_topo.is_mla = True
+
+        # MLA cache tensor: shape[-2] is the block size.
+        mla_cache = torch.zeros((2, 16, 96), dtype=torch.float16)
+        # Eagle3/GQA-like cache tensor: shape[-2] is num_kv_heads, not block size.
+        eagle_cache = torch.zeros((2, 16, 8, 64), dtype=torch.float16)
+        kv_caches = {"mla_layer": mla_cache, "eagle_layer": eagle_cache}
+
+        with patch.object(
+            worker.engine, "batch_register_memory", return_value=0
+        ) as mock_batch_register:
+            connector.register_kv_caches(kv_caches)
+
+        mock_batch_register.assert_called_once()
+        registered_ptrs, registered_lens = mock_batch_register.call_args[0]
+        assert registered_ptrs == [mla_cache.data_ptr(), eagle_cache.data_ptr()]
+        assert registered_lens == [mla_cache.nbytes, eagle_cache.nbytes]
+        assert worker.block_len_per_layer == [
+            mla_cache.nbytes // mla_cache.shape[0],
+            eagle_cache.nbytes // eagle_cache.shape[0],
+        ]
+
+
 @pytest.mark.asyncio
 @patch(
     "vllm.distributed.kv_transfer.kv_connector.v1.mooncake."
@@ -641,15 +713,19 @@ async def test_kv_producer_heterogeneous_tp(monkeypatch, d_tp_size):
     )
 
     with set_current_vllm_config(vllm_config), patch_worker_dependencies():
-        prefill_connector = MooncakeConnector(vllm_config, KVConnectorRole.WORKER)
+        prefill_connector = MooncakeConnector(
+            vllm_config,
+            KVConnectorRole.WORKER,
+            _make_test_kv_cache_config(),
+        )
         prefill_worker = prefill_connector.connector_worker
 
         # Override TP rank/size to simulate P TP=2
         prefill_worker.tp_rank = P_TP_RANK
         prefill_worker.tp_size = P_TP_SIZE
-        # Update shared dict so kv_topo sees correct TP size
         prefill_worker._tp_size[prefill_worker.engine_id] = P_TP_SIZE
-        prefill_worker.kv_topo.tp_rank = P_TP_RANK
+        prefill_worker.transfer_topo.tp_rank = P_TP_RANK
+        prefill_worker.transfer_topo.tp_size = P_TP_SIZE
 
         prefill_worker.kv_caches_base_addr = [0x1000]
         prefill_worker.block_len_per_layer = [local_block_len]
@@ -658,7 +734,7 @@ async def test_kv_producer_heterogeneous_tp(monkeypatch, d_tp_size):
         prefill_worker.sender_loop = asyncio.get_event_loop()
 
         transfer_id = "xfer-hetero-1"
-        local_block_ids = [10, 11]
+        local_block_ids = [[10, 11]]
         send_meta = SendBlockMeta(
             p_req_id="p-req-h1",
             transfer_id=transfer_id,
@@ -669,15 +745,15 @@ async def test_kv_producer_heterogeneous_tp(monkeypatch, d_tp_size):
         send_meta.ready.set()
 
         # Compute target D ranks using the production code path
-        target_d_ranks = prefill_worker.kv_topo.get_target_remote_ranks(d_tp_size)
+        target_d_ranks = prefill_worker.transfer_topo.handshake_target_ranks(d_tp_size)
 
         mock_socket = AsyncMock(spec=zmq.asyncio.Socket)
         mock_socket.send_multipart = AsyncMock()
         identity = b"consumer-hetero"
 
-        # Assign different remote block IDs per D rank
+        # Assign different remote block IDs per D rank (nested per-group)
         d_rank_remote_blocks = {
-            rank: [20 + i * 10, 21 + i * 10] for i, rank in enumerate(target_d_ranks)
+            rank: [[20 + i * 10, 21 + i * 10]] for i, rank in enumerate(target_d_ranks)
         }
 
         with patch.object(
@@ -712,11 +788,15 @@ async def test_kv_producer_heterogeneous_tp(monkeypatch, d_tp_size):
                 dst_ptrs = call_args[2]
                 lengths = call_args[3]
 
+                # Flatten nested per-group block IDs for assertions
+                flat_local = [b for g in local_block_ids for b in g]
+                flat_remote = [b for g in remote_block_ids for b in g]
+
                 # Heterogeneous TP: blocks cannot be coalesced because
                 # local and remote block_lens differ
-                assert len(src_ptrs) == len(local_block_ids)
-                assert len(dst_ptrs) == len(local_block_ids)
-                assert len(lengths) == len(local_block_ids)
+                assert len(src_ptrs) == len(flat_local)
+                assert len(dst_ptrs) == len(flat_local)
+                assert len(lengths) == len(flat_local)
 
                 # Compute expected offsets based on TP ratio
                 if d_tp_size <= P_TP_SIZE:
@@ -730,9 +810,7 @@ async def test_kv_producer_heterogeneous_tp(monkeypatch, d_tp_size):
                     expected_dst_off = 0
                     expected_xfer_len = remote_block_len
 
-                for idx, (lblk, rblk) in enumerate(
-                    zip(local_block_ids, remote_block_ids)
-                ):
+                for idx, (lblk, rblk) in enumerate(zip(flat_local, flat_remote)):
                     assert src_ptrs[idx] == (
                         0x1000 + lblk * local_block_len + expected_src_off
                     )
diff --git a/tests/v1/kv_connector/unit/test_mooncake_connector_hma.py b/tests/v1/kv_connector/unit/test_mooncake_connector_hma.py
new file mode 100644
index 000000000000..8e25df7ca837
--- /dev/null
+++ b/tests/v1/kv_connector/unit/test_mooncake_connector_hma.py
@@ -0,0 +1,420 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for MooncakeConnector HMA (Hybrid Memory Architecture) support.
+
+Covers sliding-window clipping, multi-group metadata shape, multi-group
+send trimming, and group-count invariant checking in _build_transfer_params.
+"""
+
+import asyncio
+from unittest.mock import patch
+
+import pytest
+
+from vllm.config import set_current_vllm_config
+from vllm.distributed.kv_transfer.kv_connector.v1.mooncake.mooncake_connector import (
+    KVConnectorRole,
+    MooncakeConnector,
+    MooncakeConnectorMetadata,
+    MooncakeConnectorScheduler,
+    MooncakeXferMetadata,
+    SendBlockMeta,
+    TransferRegion,
+)
+
+from .test_mooncake_connector import FakeMooncakeWrapper, patch_worker_dependencies
+from .utils import create_request, create_vllm_config, make_kv_cache_config
+
+
+# ---------------------------------------------------------------------------
+#  test_sw_sizes: blocks_per_sw computed from KVCacheConfig
+# ---------------------------------------------------------------------------
+@pytest.mark.cpu_test
+@pytest.mark.parametrize(
+    "swa_enabled,expected_blocks_per_sw",
+    [
+        # SWA enabled: FullAttentionSpec (0) + SlidingWindowSpec (2048/16=128+1)
+        (True, [0, 128 + 1]),
+        # SWA disabled: only FullAttentionSpec (0)
+        (False, [0]),
+    ],
+)
+def test_sw_sizes(swa_enabled, expected_blocks_per_sw):
+    """blocks_per_sw is correctly computed based on SWA enabled/disabled."""
+    block_size = 16
+    vllm_config = create_vllm_config(
+        kv_connector="MooncakeConnector",
+        kv_role="kv_both",
+        block_size=block_size,
+    )
+    # Override so HMA detection works
+    vllm_config.scheduler_config.disable_hybrid_kv_cache_manager = False
+    kv_cache_config = make_kv_cache_config(
+        block_size=block_size, swa_enabled=swa_enabled, sw_size=2048
+    )
+
+    scheduler = MooncakeConnectorScheduler(
+        vllm_config=vllm_config,
+        engine_id="test-engine",
+        kv_cache_config=kv_cache_config,
+    )
+    assert scheduler.blocks_per_sw == expected_blocks_per_sw
+
+
+# ---------------------------------------------------------------------------
+#  test_is_hma_required: derived from kv_cache_config groups
+# ---------------------------------------------------------------------------
+@pytest.mark.cpu_test
+@pytest.mark.parametrize(
+    "swa_enabled,disable_hma,expected_is_hma",
+    [
+        (True, False, True),  # SWA group present, HMA enabled
+        (True, True, False),  # SWA group present, but HMA disabled
+        (False, False, False),  # FA only, HMA not needed
+    ],
+)
+def test_is_hma_required(swa_enabled, disable_hma, expected_is_hma):
+    """_is_hma_required is correctly derived from kv_cache_config."""
+    block_size = 16
+    vllm_config = create_vllm_config(
+        kv_connector="MooncakeConnector",
+        kv_role="kv_both",
+        block_size=block_size,
+    )
+    vllm_config.scheduler_config.disable_hybrid_kv_cache_manager = disable_hma
+    kv_cache_config = make_kv_cache_config(
+        block_size=block_size, swa_enabled=swa_enabled
+    )
+
+    scheduler = MooncakeConnectorScheduler(
+        vllm_config=vllm_config,
+        engine_id="test-engine",
+        kv_cache_config=kv_cache_config,
+    )
+    assert scheduler._is_hma_required is expected_is_hma
+
+
+# ---------------------------------------------------------------------------
+#  test_get_sw_clipped_blocks: sliding-window clipping logic
+# ---------------------------------------------------------------------------
+@pytest.mark.cpu_test
+def test_get_sw_clipped_blocks():
+    """get_sw_clipped_blocks clips SWA group but keeps FA group intact."""
+    block_size = 16
+    vllm_config = create_vllm_config(
+        kv_connector="MooncakeConnector",
+        kv_role="kv_both",
+        block_size=block_size,
+    )
+    vllm_config.scheduler_config.disable_hybrid_kv_cache_manager = False
+    # SW=128 tokens → 128/16 = 8 blocks + 1 = 9 blocks_per_sw
+    kv_cache_config = make_kv_cache_config(
+        block_size=block_size, swa_enabled=True, sw_size=128
+    )
+
+    scheduler = MooncakeConnectorScheduler(
+        vllm_config=vllm_config,
+        engine_id="test-engine",
+        kv_cache_config=kv_cache_config,
+    )
+    assert scheduler.blocks_per_sw == [0, 9]
+
+    # FA group: 20 blocks, SW group: 20 blocks (exceeds window)
+    fa_blocks = list(range(20))
+    sw_blocks = list(range(100, 120))
+    block_ids = (fa_blocks, sw_blocks)
+
+    clipped = scheduler.get_sw_clipped_blocks(block_ids)
+
+    # FA: untouched (blocks_per_sw[0] = 0)
+    assert clipped[0] == fa_blocks
+    # SW: clipped to last 9 blocks
+    assert clipped[1] == sw_blocks[-9:]
+    assert len(clipped[1]) == 9
+
+
+@pytest.mark.cpu_test
+def test_get_sw_clipped_blocks_noop_no_hma():
+    """get_sw_clipped_blocks is a no-op when HMA is not required."""
+    block_size = 16
+    vllm_config = create_vllm_config(
+        kv_connector="MooncakeConnector",
+        kv_role="kv_both",
+        block_size=block_size,
+    )
+    # FA only → _is_hma_required = False
+    kv_cache_config = make_kv_cache_config(block_size=block_size, swa_enabled=False)
+
+    scheduler = MooncakeConnectorScheduler(
+        vllm_config=vllm_config,
+        engine_id="test-engine",
+        kv_cache_config=kv_cache_config,
+    )
+    assert scheduler._is_hma_required is False
+
+    block_ids = ([1, 2, 3],)
+    clipped = scheduler.get_sw_clipped_blocks(block_ids)
+    assert clipped == [[1, 2, 3]]
+
+
+# ---------------------------------------------------------------------------
+#  test_metadata_hma_block_ids: MooncakeConnectorMetadata stores per-group IDs
+# ---------------------------------------------------------------------------
+@pytest.mark.cpu_test
+def test_metadata_hma_block_ids():
+    """MooncakeConnectorMetadata.add_new_req stores per-group block IDs."""
+    metadata = MooncakeConnectorMetadata()
+
+    # FA group: 6 blocks, SW group: 3 blocks (clipped)
+    fa_blocks = [0, 1, 2, 3, 4, 5]
+    sw_blocks = [10, 11, 12]
+
+    # Test recv path
+    metadata.add_new_req(
+        request_id="recv-req",
+        local_block_ids=[fa_blocks, sw_blocks],
+        kv_transfer_params={
+            "transfer_id": "recv-req",
+            "remote_engine_id": "remote-engine",
+            "remote_bootstrap_addr": "http://bootstrap:33333",
+        },
+        load_remote_cache=True,
+    )
+
+    assert "recv-req" in metadata.reqs_to_recv["remote-engine"]
+    req_meta = metadata.reqs_to_recv["remote-engine"]["recv-req"]
+    assert len(req_meta.local_block_ids) == 2
+    assert req_meta.local_block_ids[0] == fa_blocks
+    assert req_meta.local_block_ids[1] == sw_blocks
+
+    # Test send path
+    metadata.add_new_req(
+        request_id="send-req",
+        local_block_ids=[fa_blocks, sw_blocks],
+        kv_transfer_params={
+            "transfer_id": "send-req",
+        },
+        load_remote_cache=False,
+    )
+
+    assert "send-req" in metadata.reqs_to_send
+    transfer_id, stored_blocks = metadata.reqs_to_send["send-req"]
+    assert transfer_id == "send-req"
+    assert len(stored_blocks) == 2
+    assert stored_blocks[0] == fa_blocks
+    assert stored_blocks[1] == sw_blocks
+
+
+# ---------------------------------------------------------------------------
+#  test_build_transfer_params_multi_group_trimming
+# ---------------------------------------------------------------------------
+@pytest.mark.asyncio
+@patch(
+    "vllm.distributed.kv_transfer.kv_connector.v1.mooncake"
+    ".mooncake_connector.TransferEngine",
+    FakeMooncakeWrapper,
+)
+async def test_build_transfer_params_multi_group_trimming(monkeypatch):
+    """_build_transfer_params trims per-group blocks when local > remote."""
+
+    monkeypatch.setenv("VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT", "5")
+    vllm_config = create_vllm_config(
+        kv_connector="MooncakeConnector", kv_role="kv_producer"
+    )
+    kv_cache_config = make_kv_cache_config(
+        block_size=vllm_config.cache_config.block_size, swa_enabled=True
+    )
+
+    with set_current_vllm_config(vllm_config), patch_worker_dependencies():
+        connector = MooncakeConnector(
+            vllm_config, KVConnectorRole.WORKER, kv_cache_config
+        )
+        worker = connector.connector_worker
+
+        block_len = 4096
+        # Call _build_transfer_params directly (avoids send_kv_to_decode
+        # async event loop complexity).
+        transfer_id = "xfer-hma-trim"
+        send_meta = SendBlockMeta(
+            p_req_id="p-trim",
+            transfer_id=transfer_id,
+            # FA: 4 blocks, SW: 3 blocks (producer has more)
+            local_block_ids=[[10, 11, 12, 13], [20, 21, 22]],
+            ready=asyncio.Event(),
+        )
+
+        xfer_meta = MooncakeXferMetadata(
+            remote_hostname="consumer-host",
+            remote_port=54321,
+            remote_tp_size=1,
+            remote_tp_rank=0,
+            req_blocks={
+                "d-trim": (
+                    transfer_id,
+                    # FA: 2 blocks, SW: 2 blocks (consumer needs fewer)
+                    [[30, 31], [40, 41]],
+                )
+            },
+            kv_caches_base_addr=[0x2000],
+            block_lens=[block_len],
+        )
+
+        local_regions = [
+            TransferRegion(
+                base_addr=0x1000, block_len=block_len, kv_block_len=block_len
+            ),
+        ]
+        remote_regions = [
+            TransferRegion(
+                base_addr=0x2000, block_len=block_len, kv_block_len=block_len
+            ),
+        ]
+
+        ready_reqs = [("d-trim", send_meta)]
+        (
+            src_ptrs,
+            dst_ptrs,
+            lengths,
+            err_reqs,
+            err_msg,
+        ) = await worker._build_transfer_params(
+            ready_reqs, xfer_meta, local_regions, remote_regions
+        )
+
+        # No errors
+        assert err_reqs == []
+        assert err_msg is None
+        # After trimming: FA [10..13] → last 2 → [12,13]; SW [20..22] → last 2 → [21,22]
+        # Flattened: [12,13,21,22] = 4 blocks → coalesced into some transfers
+        assert len(src_ptrs) > 0
+        assert len(dst_ptrs) == len(src_ptrs)
+        assert len(lengths) == len(src_ptrs)
+
+        worker.shutdown()
+
+
+# ---------------------------------------------------------------------------
+#  test_build_transfer_params_group_count_mismatch
+# ---------------------------------------------------------------------------
+@pytest.mark.asyncio
+@patch(
+    "vllm.distributed.kv_transfer.kv_connector.v1.mooncake"
+    ".mooncake_connector.TransferEngine",
+    FakeMooncakeWrapper,
+)
+async def test_build_transfer_params_group_count_mismatch(monkeypatch):
+    """_build_transfer_params reports an error when group counts differ."""
+
+    monkeypatch.setenv("VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT", "5")
+    vllm_config = create_vllm_config(
+        kv_connector="MooncakeConnector", kv_role="kv_producer"
+    )
+    kv_cache_config = make_kv_cache_config(
+        block_size=vllm_config.cache_config.block_size, swa_enabled=True
+    )
+
+    with set_current_vllm_config(vllm_config), patch_worker_dependencies():
+        connector = MooncakeConnector(
+            vllm_config, KVConnectorRole.WORKER, kv_cache_config
+        )
+        worker = connector.connector_worker
+
+        block_len = 4096
+        transfer_id = "xfer-mismatch"
+        send_meta = SendBlockMeta(
+            p_req_id="p-mismatch",
+            transfer_id=transfer_id,
+            # Producer has 2 groups
+            local_block_ids=[[10, 11], [20, 21]],
+            ready=asyncio.Event(),
+        )
+
+        # Consumer has only 1 group — group count mismatch
+        xfer_meta = MooncakeXferMetadata(
+            remote_hostname="consumer-host",
+            remote_port=54321,
+            remote_tp_size=1,
+            remote_tp_rank=0,
+            req_blocks={
+                "d-mismatch": (transfer_id, [[30, 31]]),
+            },
+            kv_caches_base_addr=[0x2000],
+            block_lens=[block_len],
+        )
+
+        local_regions = [
+            TransferRegion(
+                base_addr=0x1000, block_len=block_len, kv_block_len=block_len
+            ),
+        ]
+        remote_regions = [
+            TransferRegion(
+                base_addr=0x2000, block_len=block_len, kv_block_len=block_len
+            ),
+        ]
+
+        ready_reqs = [("d-mismatch", send_meta)]
+        (
+            src_ptrs,
+            dst_ptrs,
+            lengths,
+            err_reqs,
+            err_msg,
+        ) = await worker._build_transfer_params(
+            ready_reqs, xfer_meta, local_regions, remote_regions
+        )
+
+        # Mismatched req is reported via err_reqs/err_msg with no transfers built.
+        assert err_reqs == ["d-mismatch"]
+        assert err_msg == "KV group count mismatch"
+        assert src_ptrs == []
+        assert dst_ptrs == []
+        assert lengths == []
+
+        worker.shutdown()
+
+
+# ---------------------------------------------------------------------------
+#  test_request_finished_with_hma_groups
+# ---------------------------------------------------------------------------
+@pytest.mark.cpu_test
+def test_request_finished_with_hma_groups():
+    """request_finished correctly handles per-group block_ids."""
+    block_size = 16
+    vllm_config = create_vllm_config(
+        kv_connector="MooncakeConnector",
+        kv_role="kv_producer",
+        block_size=block_size,
+    )
+    vllm_config.scheduler_config.disable_hybrid_kv_cache_manager = False
+    kv_cache_config = make_kv_cache_config(
+        block_size=block_size, swa_enabled=True, sw_size=128
+    )
+
+    scheduler = MooncakeConnectorScheduler(
+        vllm_config=vllm_config,
+        engine_id="test-engine",
+        kv_cache_config=kv_cache_config,
+    )
+
+    request = create_request(request_id=1, do_remote_decode=True)
+    request.kv_transfer_params["transfer_id"] = request.request_id
+
+    from vllm.v1.request import RequestStatus
+
+    request.status = RequestStatus.FINISHED_LENGTH_CAPPED
+
+    # 2 groups: FA with 10 blocks, SW with 20 blocks (will be clipped)
+    fa_blocks = list(range(10))
+    sw_blocks = list(range(100, 120))
+    block_ids = (fa_blocks, sw_blocks)
+
+    delay_free, _ = scheduler.request_finished(request, block_ids)
+    assert delay_free is True
+    assert request.request_id in scheduler._reqs_need_send
+
+    _, stored_blocks = scheduler._reqs_need_send[request.request_id]
+    # FA: untouched
+    assert stored_blocks[0] == fa_blocks
+    # SW: clipped to last 9 blocks (sw_size=128, block_size=16 → 8+1=9)
+    assert stored_blocks[1] == sw_blocks[-9:]
diff --git a/tests/v1/kv_connector/unit/test_mooncake_stats.py b/tests/v1/kv_connector/unit/test_mooncake_stats.py
new file mode 100644
index 000000000000..a20fcb505330
--- /dev/null
+++ b/tests/v1/kv_connector/unit/test_mooncake_stats.py
@@ -0,0 +1,281 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import threading
+from unittest.mock import MagicMock
+
+from vllm.distributed.kv_transfer.kv_connector.v1.mooncake.mooncake_connector import (
+    MooncakeConnector,
+    MooncakeConnectorWorker,
+    SendBlockMeta,
+)
+from vllm.distributed.kv_transfer.kv_connector.v1.mooncake.stats import (
+    MooncakeKVConnectorStats,
+)
+
+
+def test_is_empty_on_fresh_stats():
+    stats = MooncakeKVConnectorStats()
+    assert stats.is_empty()
+    assert stats.num_successful_transfers == 0
+
+
+def test_record_transfer_and_reduce():
+    stats = MooncakeKVConnectorStats()
+    # 1 MB transfer in 1 ms -> 1000 MB/s throughput
+    stats.record_transfer(duration_s=0.001, total_bytes=1 * 2**20, num_descs=4)
+    # 2 MB transfer in 2 ms
+    stats.record_transfer(duration_s=0.002, total_bytes=2 * 2**20, num_descs=6)
+    assert not stats.is_empty()
+    assert stats.num_successful_transfers == 2
+
+    reduced = stats.reduce()
+    assert reduced["Num successful transfers"] == 2
+    # avg = (1 + 2) / 2 = 1.5 ms
+    assert reduced["Avg xfer time (ms)"] == 1.5
+    assert reduced["Avg MB per transfer"] == 1.5
+    # 3 MB total / 3 ms total = 1000 MB/s
+    assert reduced["Throughput (MB/s)"] == 1000.0
+    assert reduced["Avg number of descriptors"] == 5.0
+    assert reduced["Num failed transfers"] == 0
+    assert reduced["Num failed recvs"] == 0
+    assert reduced["Num KV expired reqs"] == 0
+
+
+def test_record_failures_keeps_stats_non_empty():
+    stats = MooncakeKVConnectorStats()
+    stats.record_failed_transfer()
+    stats.record_failed_recv()
+    stats.record_kv_expired_req()
+    assert not stats.is_empty()
+
+    reduced = stats.reduce()
+    # No successful transfers -> latency/throughput all zero, but failure
+    # counters still surface.
+    assert reduced["Num successful transfers"] == 0
+    assert reduced["Num failed transfers"] == 1
+    assert reduced["Num failed recvs"] == 1
+    assert reduced["Num KV expired reqs"] == 1
+
+
+def test_aggregate_sums_observations():
+    a = MooncakeKVConnectorStats()
+    b = MooncakeKVConnectorStats()
+    a.record_transfer(duration_s=0.001, total_bytes=1 * 2**20, num_descs=1)
+    b.record_transfer(duration_s=0.002, total_bytes=2 * 2**20, num_descs=2)
+    b.record_failed_transfer()
+
+    a.aggregate(b)
+
+    assert a.num_successful_transfers == 2
+    reduced = a.reduce()
+    assert reduced["Num successful transfers"] == 2
+    assert reduced["Num failed transfers"] == 1
+
+
+def test_aggregate_with_empty_other_is_noop():
+    a = MooncakeKVConnectorStats()
+    a.record_transfer(duration_s=0.001, total_bytes=1, num_descs=1)
+    b = MooncakeKVConnectorStats()
+
+    a.aggregate(b)
+
+    assert a.num_successful_transfers == 1
+
+
+def test_getstate_drops_lock_and_setstate_recreates_it():
+    # KVConnectorStats subclasses must be picklable (worker→scheduler IPC),
+    # but threading.Lock isn't — so __getstate__ strips it and __setstate__
+    # rebuilds a fresh per-process lock.
+    original = MooncakeKVConnectorStats()
+    original.record_transfer(duration_s=0.01, total_bytes=2048, num_descs=3)
+
+    state = original.__getstate__()
+    assert "_lock" not in state
+
+    rebuilt = MooncakeKVConnectorStats.__new__(MooncakeKVConnectorStats)
+    rebuilt.__setstate__(state)
+    assert rebuilt.data == original.data
+    # Lock works on the receiver side.
+    rebuilt.record_transfer(duration_s=0.02, total_bytes=4096, num_descs=5)
+    assert rebuilt.num_successful_transfers == 2
+
+
+def test_concurrent_writers_keep_row_lengths_aligned():
+    # Multiple writers + a snapshot reader must never produce a snapshot
+    # with mismatched column lengths — reduce()'s
+    # len(descs) == num_successful_transfers assertion would fire.
+    stats = MooncakeKVConnectorStats()
+    stop = threading.Event()
+    writer_count = 4
+    snapshots: list[MooncakeKVConnectorStats] = []
+
+    def writer():
+        i = 0
+        while not stop.is_set():
+            stats.record_transfer(
+                duration_s=0.001 + i * 1e-9,
+                total_bytes=1024 + i,
+                num_descs=1 + (i % 8),
+            )
+            i += 1
+
+    def snapper():
+        while not stop.is_set():
+            snap = stats.clone_and_reset()
+            if not snap.is_empty():
+                # Force the same path the logger walks; reduce() will
+                # blow up on torn rows via its internal assert.
+                snap.reduce()
+                snapshots.append(snap)
+
+    threads = [threading.Thread(target=writer) for _ in range(writer_count)]
+    snapshotter = threading.Thread(target=snapper)
+    for t in threads:
+        t.start()
+    snapshotter.start()
+    # Short fixed window — long enough to interleave thousands of ops.
+    threading.Event().wait(0.2)
+    stop.set()
+    for t in threads:
+        t.join()
+    snapshotter.join()
+
+    # Final drain so we don't lose the in-flight tail.
+    final = stats.clone_and_reset()
+    if not final.is_empty():
+        final.reduce()
+        snapshots.append(final)
+
+    # Every snapshot's columns must have identical lengths (the invariant
+    # the lock protects), and the union must contain at least one row.
+    total_rows = 0
+    for snap in snapshots:
+        n = len(snap.data["transfer_duration"])
+        assert len(snap.data["bytes_transferred"]) == n
+        assert len(snap.data["num_descriptors"]) == n
+        total_rows += n
+    assert total_rows > 0
+
+
+def test_clone_and_reset_hands_off_old_data():
+    stats = MooncakeKVConnectorStats()
+    stats.record_transfer(duration_s=0.001, total_bytes=1, num_descs=1)
+    stats.record_failed_recv()
+
+    snapshot = stats.clone_and_reset()
+
+    assert snapshot.num_successful_transfers == 1
+    assert not snapshot.is_empty()
+    # Original is now empty.
+    assert stats.is_empty()
+    assert stats.num_successful_transfers == 0
+    # Recording on the original does not mutate the snapshot.
+    stats.record_transfer(duration_s=0.005, total_bytes=2, num_descs=2)
+    assert snapshot.num_successful_transfers == 1
+
+
+def test_build_kv_connector_stats_none_returns_empty_instance():
+    out = MooncakeConnector.build_kv_connector_stats()
+    assert isinstance(out, MooncakeKVConnectorStats)
+    assert out.is_empty()
+
+
+def test_build_kv_connector_stats_with_data_round_trips():
+    original = MooncakeKVConnectorStats()
+    original.record_transfer(duration_s=0.01, total_bytes=1024, num_descs=3)
+    original.record_failed_transfer()
+
+    # Serialized form is the .data dict; build should reconstruct an instance
+    # that behaves the same.
+    rebuilt = MooncakeConnector.build_kv_connector_stats(data=original.data)
+
+    assert isinstance(rebuilt, MooncakeKVConnectorStats)
+    assert rebuilt.num_successful_transfers == 1
+    assert rebuilt.reduce()["Num failed transfers"] == 1
+
+
+def _bare_worker() -> MooncakeConnectorWorker:
+    """Construct a MooncakeConnectorWorker skipping __init__ (full init requires
+    a live TransferEngine). Only the attributes touched by the methods under
+    test are populated; role flags and async_zmq_ctx keep __del__'s shutdown
+    path a no-op."""
+    worker = MooncakeConnectorWorker.__new__(MooncakeConnectorWorker)
+    worker.xfer_stats = MooncakeKVConnectorStats()
+    worker.engine = MagicMock()
+    worker.async_zmq_ctx = MagicMock()
+    worker.is_kv_consumer = True
+    worker.is_kv_producer = True
+    return worker
+
+
+def test_send_blocks_records_success():
+    worker = _bare_worker()
+    worker.engine.batch_transfer_sync_write.return_value = 0
+
+    ret = worker._send_blocks(
+        "host:1234",
+        src_ptrs=[0x1000, 0x2000],
+        dst_ptrs=[0x3000, 0x4000],
+        lengths=[1024, 2048],
+    )
+
+    assert ret == 0
+    assert worker.xfer_stats.num_successful_transfers == 1
+    data = worker.xfer_stats.data
+    assert data["bytes_transferred"] == [1024 + 2048]
+    assert data["num_descriptors"] == [2]
+    assert data["num_failed_transfers"] == []
+
+
+def test_send_blocks_records_failure():
+    worker = _bare_worker()
+    worker.engine.batch_transfer_sync_write.return_value = 1  # non-zero = fail
+
+    ret = worker._send_blocks("host:1234", [0x1000], [0x2000], [4096])
+
+    assert ret == 1
+    assert worker.xfer_stats.num_successful_transfers == 0
+    assert worker.xfer_stats.data["num_failed_transfers"] == [1]
+
+
+def test_get_kv_connector_stats_returns_none_when_empty():
+    worker = _bare_worker()
+
+    assert worker.get_kv_connector_stats() is None
+
+
+def test_get_kv_connector_stats_returns_and_resets():
+    worker = _bare_worker()
+    worker.engine.batch_transfer_sync_write.return_value = 0
+    worker._send_blocks("host:1234", [0x1000], [0x2000], [4096])
+
+    snapshot = worker.get_kv_connector_stats()
+    assert isinstance(snapshot, MooncakeKVConnectorStats)
+    assert snapshot.num_successful_transfers == 1
+
+    # Second call returns None because the worker's stats were reset.
+    assert worker.get_kv_connector_stats() is None
+
+
+def test_expired_request_bumps_counter():
+    import asyncio
+
+    worker = _bare_worker()
+    worker.reqs_need_send = {
+        "tid1": SendBlockMeta(
+            p_req_id="req1",
+            transfer_id="tid1",
+            local_block_ids=[0, 1],
+            ready=asyncio.Event(),
+            expire_time=-1.0,  # Already expired.
+            sending=0,
+        ),
+    }
+    worker.finished_sending_reqs = set()
+
+    asyncio.run(worker.fetch_finished_sending_reqs())
+
+    assert worker.xfer_stats.data["num_kv_expired_reqs"] == [1]
+    # Expired transfer also cleaned out of reqs_need_send.
+    assert "tid1" not in worker.reqs_need_send
diff --git a/tests/v1/kv_connector/unit/test_moriio_connector.py b/tests/v1/kv_connector/unit/test_moriio_connector.py
index 902957e18309..da78b62b9a03 100644
--- a/tests/v1/kv_connector/unit/test_moriio_connector.py
+++ b/tests/v1/kv_connector/unit/test_moriio_connector.py
@@ -3,6 +3,7 @@
 import importlib.util
 import os
 import subprocess
+import uuid
 from unittest.mock import MagicMock, patch
 
 import msgspec
@@ -36,9 +37,15 @@
     get_ip,
     make_zmq_path,
 )
+from vllm.v1.kv_cache_interface import KVCacheConfig
 
 from .utils import create_request, create_scheduler
 
+
+def _make_test_kv_cache_config() -> KVCacheConfig:
+    return KVCacheConfig(num_blocks=0, kv_cache_tensors=[], kv_cache_groups=[])
+
+
 aiter_available = importlib.util.find_spec("aiter") is not None
 mori_available = importlib.util.find_spec("mori") is not None
 
@@ -99,6 +106,11 @@ def _setup_kv_transfer_request(
             "remote_engine_id": "test_engine",
         }
     )
+    zmq_addr = f"host:{remote_host},handshake:{fake_port},notify:{fake_port}"
+    fake_uuid = uuid.uuid4().hex
+    request.request_id = (
+        f"___prefill_addr_{zmq_addr}___decode_addr_{zmq_addr}_{fake_uuid}"
+    )
     return request
 
 
@@ -254,13 +266,14 @@ def test_write_mode_saves_local_block_ids():
         do_remote_decode=True,
         do_remote_prefill=False,
     )
+
+    # Setup KV transfer params and embed ZMQ addrs in request_id before
+    # adding to scheduler so the ID is consistent everywhere.
+    request = _setup_kv_transfer_request(request)
     request_id = request.request_id
 
     scheduler.add_request(request)
 
-    # Fake Config
-    request = _setup_kv_transfer_request(request)
-
     # Remote Prefill, triggers MoRIIOConnectorMetadata.
     scheduler_output = scheduler.schedule()
     kv_connector_metadata = scheduler_output.kv_connector_metadata
@@ -312,13 +325,14 @@ def test_write_mode_with_chunked_prefill_saves_local_block_ids():
         do_remote_decode=True,
         do_remote_prefill=False,
     )
+
+    # Setup KV transfer params and embed ZMQ addrs in request_id before
+    # adding to scheduler so the ID is consistent everywhere.
+    request = _setup_kv_transfer_request(request)
     request_id = request.request_id
 
     scheduler.add_request(request)
 
-    # Fake Config
-    request = _setup_kv_transfer_request(request)
-
     # Remote Prefill with chunked prefill, triggers multiple schedules.
     expected_counts = [(0, 0, 0), (0, 0, 0), (1, 0, 0)]
     kv_connector_metadata = None
@@ -363,6 +377,10 @@ def test_read_mode_loads_remote_block_ids(moriio_read_mode):
         do_remote_decode=False,
         do_remote_prefill=True,
     )
+
+    # Setup KV transfer params and embed ZMQ addrs in request_id before
+    # adding to scheduler so the ID is consistent everywhere.
+    request = _setup_kv_transfer_request(request)
     request_id = request.request_id
 
     scheduler.add_request(request)
@@ -370,8 +388,6 @@ def test_read_mode_loads_remote_block_ids(moriio_read_mode):
         0
     ].req_to_blocks[request_id]
 
-    request = _setup_kv_transfer_request(request)
-
     # Set remote block ids to be fetched.
     request.kv_transfer_params["remote_block_ids"] = block_list
 
@@ -452,7 +468,11 @@ def test_register_kv_caches(mock_parallel_groups):
         )
 
         with set_current_vllm_config(vllm_config):
-            connector = MoRIIOConnector(vllm_config, KVConnectorRole.WORKER)
+            connector = MoRIIOConnector(
+                vllm_config,
+                KVConnectorRole.WORKER,
+                _make_test_kv_cache_config(),
+            )
             connector.connector_worker = FakeMoRIIOConnectorWorker(
                 vllm_config, connector.engine_id, hand_shake_latency=0
             )
@@ -544,7 +564,11 @@ def test_moriio_handshake_returns_metadata(mock_parallel_groups):
             }
         )
         with set_current_vllm_config(vllm_config):
-            connector = MoRIIOConnector(vllm_config, KVConnectorRole.WORKER)
+            connector = MoRIIOConnector(
+                vllm_config,
+                KVConnectorRole.WORKER,
+                _make_test_kv_cache_config(),
+            )
 
         # Execute register_kv_caches
         connector.register_kv_caches(kv_caches)
diff --git a/tests/v1/kv_connector/unit/test_multi_connector.py b/tests/v1/kv_connector/unit/test_multi_connector.py
index 855c34117137..51024fb92171 100644
--- a/tests/v1/kv_connector/unit/test_multi_connector.py
+++ b/tests/v1/kv_connector/unit/test_multi_connector.py
@@ -8,13 +8,18 @@
 from unittest.mock import MagicMock
 
 import pytest
+import torch
 
 from tests.v1.kv_connector.unit.utils import create_vllm_config
 from vllm import LLM, SamplingParams
 from vllm.config import KVTransferConfig
 from vllm.distributed.kv_transfer.kv_connector.factory import KVConnectorFactory
 from vllm.distributed.kv_transfer.kv_connector.v1 import KVConnectorRole
-from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorBase_V1
+from vllm.distributed.kv_transfer.kv_connector.v1.base import (
+    KVConnectorBase_V1,
+    SupportsHMA,
+    supports_hma,
+)
 from vllm.distributed.kv_transfer.kv_connector.v1.metrics import KVConnectorStats
 from vllm.distributed.kv_transfer.kv_connector.v1.multi_connector import (
     MultiConnector,
@@ -83,8 +88,43 @@ def update_state_after_alloc(self, request, blocks, num_tokens) -> None:
         pass
 
 
-# Register the mock connector
+class MockHMAConnector(KVConnectorBase_V1, SupportsHMA):
+    """Mock connector that supports HMA for testing."""
+
+    def __new__(cls, *args, **kwargs):
+        mock = MagicMock(spec_set=cls)
+        return mock
+
+    def start_load_kv(self, forward_context, **kwargs):
+        pass
+
+    def wait_for_layer_load(self, layer_name):
+        pass
+
+    def save_kv_layer(self, layer_name, kv_layer, attn_metadata, **kwargs):
+        pass
+
+    def wait_for_save(self):
+        pass
+
+    def build_connector_meta(self, scheduler_output):
+        return None
+
+    def get_num_new_matched_tokens(self, request, num_computed_tokens):
+        return (0, False)
+
+    def update_state_after_alloc(self, request, blocks, num_tokens) -> None:
+        pass
+
+    def request_finished_all_groups(self, request, block_ids):
+        return (False, None)
+
+
+# Register mock connectors
 KVConnectorFactory.register_connector("MockConnector", __name__, MockConnector.__name__)
+KVConnectorFactory.register_connector(
+    "MockHMAConnector", __name__, MockHMAConnector.__name__
+)
 
 
 @pytest.fixture
@@ -920,3 +960,133 @@ def assert_update_connector_output_called(mc: MultiConnector):
     mc.update_connector_output(kv_connector_output)
     assert_update_connector_output_called(mc)
     assert kv_connector_output.kv_connector_worker_meta == mc_worker_meta_01a_01b
+
+
+def _make_multi_connector(connector_names: list[str]) -> MultiConnector:
+    """Build a MultiConnector wrapping the given registered connectors."""
+    vllm_config = create_vllm_config()
+    connectors = [
+        {
+            "kv_connector": name,
+            "kv_role": "kv_both",
+            "kv_connector_module_path": "tests.v1.kv_connector.unit.test_multi_connector",  # noqa: E501
+        }
+        for name in connector_names
+    ]
+    vllm_config.kv_transfer_config = KVTransferConfig(
+        kv_connector="MultiConnector",
+        kv_role="kv_both",
+        kv_connector_extra_config={"connectors": connectors},
+    )
+    kv_cache_config = KVCacheConfig(
+        num_blocks=0,
+        kv_cache_tensors=[],
+        kv_cache_groups=[],
+    )
+    return MultiConnector(
+        vllm_config=vllm_config,
+        role=KVConnectorRole.WORKER,
+        kv_cache_config=kv_cache_config,
+    )
+
+
+def test_multi_connector_hma_opt_in():
+    """
+    MultiConnector currently assumes HMA is opt-in: it needs
+    --no-disable-hybrid-kv-cache-manager to be enabled.
+
+    At runtime, _all_support_hma is True only when every sub-connector
+    implements SupportsHMA. Test all combinations of HMA / non-HMA
+    sub-connectors.
+    """
+
+    assert supports_hma(MultiConnector)
+
+    # -- All non-HMA connectors => _all_support_hma is False --
+    mc_none = _make_multi_connector(["MockConnector", "MockConnector"])
+    assert not supports_hma(mc_none._connectors[0])
+    assert not supports_hma(mc_none._connectors[1])
+    assert mc_none._all_support_hma is False
+
+    # -- All HMA connectors => _all_support_hma is True --
+    mc_all = _make_multi_connector(["MockHMAConnector", "MockHMAConnector"])
+    assert supports_hma(mc_all._connectors[0])
+    assert supports_hma(mc_all._connectors[1])
+    assert mc_all._all_support_hma is True
+
+    # -- Mixed: first HMA, second non-HMA => _all_support_hma is False --
+    mc_mixed1 = _make_multi_connector(["MockHMAConnector", "MockConnector"])
+    assert supports_hma(mc_mixed1._connectors[0])
+    assert not supports_hma(mc_mixed1._connectors[1])
+    assert mc_mixed1._all_support_hma is False
+
+    # -- Mixed: first non-HMA, second HMA => _all_support_hma is False --
+    mc_mixed2 = _make_multi_connector(["MockConnector", "MockHMAConnector"])
+    assert not supports_hma(mc_mixed2._connectors[0])
+    assert supports_hma(mc_mixed2._connectors[1])
+    assert mc_mixed2._all_support_hma is False
+
+
+@pytest.mark.skipif(
+    not torch.cuda.is_available(), reason="Requires GPU to instantiate LLM"
+)
+def test_multi_connector_mixed_hma_disables_hybrid_kv_cache(monkeypatch):
+    """
+    When MultiConnector wraps a mix of HMA (NixlConnector) and non-HMA
+    (MockConnector) sub-connectors, verify that:
+    1. The scheduler's MultiConnector has _all_support_hma == False.
+    2. vLLM auto-disables the hybrid KV cache manager (no preference expressed by user)
+    """
+    from unittest.mock import patch
+
+    from tests.v1.kv_connector.unit.test_nixl_connector import FakeNixlWrapper
+
+    monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
+
+    kv_transfer_config = KVTransferConfig(
+        kv_connector="MultiConnector",
+        kv_role="kv_both",
+        kv_connector_extra_config={
+            "connectors": [
+                {
+                    "kv_connector": "NixlConnector",
+                    "kv_role": "kv_both",
+                },
+                {
+                    "kv_connector": "MockConnector",
+                    "kv_role": "kv_both",
+                    "kv_connector_module_path": (
+                        "tests.v1.kv_connector.unit.test_multi_connector"
+                    ),
+                },
+            ],
+        },
+    )
+
+    with patch(
+        "vllm.distributed.kv_transfer.kv_connector.v1.nixl.worker.NixlWrapper",
+        FakeNixlWrapper,
+    ):
+        llm = LLM(
+            model="Qwen/Qwen3-0.6B",
+            enforce_eager=True,
+            gpu_memory_utilization=0.3,
+            max_model_len=128,
+            max_num_seqs=1,
+            max_num_batched_tokens=128,
+            kv_transfer_config=kv_transfer_config,
+        )
+        try:
+            # HMA should be auto-disabled when user has not expressed a preference.
+            assert (
+                llm.llm_engine.vllm_config.scheduler_config.disable_hybrid_kv_cache_manager
+                is True
+            )
+            # The scheduler-side MultiConnector should detect the mixed
+            # HMA support among its sub-connectors.
+            scheduler = llm.llm_engine.engine_core.engine_core.scheduler
+            mc = scheduler.connector
+            assert isinstance(mc, MultiConnector)
+            assert mc._all_support_hma is False
+        finally:
+            llm.llm_engine.engine_core.shutdown()
diff --git a/tests/v1/kv_connector/unit/test_nixl_connector.py b/tests/v1/kv_connector/unit/test_nixl_connector.py
index d67b14e8dd4a..1a8612cd3fc4 100644
--- a/tests/v1/kv_connector/unit/test_nixl_connector.py
+++ b/tests/v1/kv_connector/unit/test_nixl_connector.py
@@ -21,7 +21,7 @@
 from vllm.config import KVTransferConfig, set_current_vllm_config
 from vllm.distributed.kv_transfer.kv_connector.utils import (
     KVOutputAggregator,
-    TpKVTopology,
+    TransferTopology,
     get_current_attn_backend,
 )
 from vllm.distributed.kv_transfer.kv_connector.v1 import nixl
@@ -321,6 +321,34 @@ def test_prompt_less_than_block_size():
     assert len(scheduler_output.scheduled_new_reqs) == 0
 
 
+def test_abort_immediately_remote_prefill_enqueues_empty_recv():
+    """A remote-prefill request added with abort_immediately=True should
+    be added to the scheduler's waiting queue then immediately aborted, so the
+    NIXL connector's request_finished hook enqueues an empty recv to notify
+    the prefill instance to free its blocks."""
+    from vllm.v1.request import RequestStatus
+
+    scheduler = create_scheduler(create_vllm_config())
+
+    request = create_request(request_id=42, num_tokens=10, do_remote_prefill=True)
+    assert request.kv_transfer_params is not None
+    assert request.kv_transfer_params["do_remote_prefill"] is True
+
+    # Mimic the EngineCore.add_request path for an abort-immediately req.
+    scheduler.add_request(request)
+    scheduler.finish_requests([request.request_id], RequestStatus.FINISHED_ABORTED)
+
+    scheduler_output = scheduler.schedule()
+    meta = scheduler_output.kv_connector_metadata
+    assert isinstance(meta, NixlConnectorMetadata)
+    assert set(meta.reqs_to_recv) == {request.request_id}
+    req_meta = meta.reqs_to_recv[request.request_id]
+    assert req_meta.local_block_ids == []
+    assert req_meta.remote.request_id == f"prefill-{42}"
+    # do_remote_prefill is consumed by request_finished to prevent re-issuing.
+    assert request.kv_transfer_params["do_remote_prefill"] is False
+
+
 @patch(
     "vllm.distributed.kv_transfer.kv_connector.v1.nixl.worker.NixlWrapper",
     FakeNixlWrapper,
@@ -463,19 +491,20 @@ def __init__(
         test_shape = self.attn_backends[0].get_kv_cache_shape(
             num_blocks=1, block_size=16, num_kv_heads=1, head_size=1
         )
-        self.kv_topo = TpKVTopology(
+        self.transfer_topo = TransferTopology(
             tp_rank=self.tp_rank,
+            tp_size=self.world_size,
+            block_size=self.block_size,
             engine_id=self.engine_id,
-            remote_tp_size=self._tp_size,  # shared state
-            remote_block_size=self._block_size,  # shared state
             is_mla=self.use_mla,
+            is_mamba=False,
             total_num_kv_heads=self.model_config.get_total_num_kv_heads(),
             attn_backends=self.attn_backends,
             tensor_shape=test_shape,
         )
 
         self.compat_hash = compute_nixl_compatibility_hash(
-            self.vllm_config, self.backend_name, self.kv_topo.cross_layers_blocks
+            self.vllm_config, self.backend_name, self.transfer_topo.cross_layers_blocks
         )
 
     def _nixl_handshake(
@@ -496,7 +525,7 @@ def _nixl_handshake(
         # Adjust remote block length metadata to satisfy heterogeneous TP
         # invariants enforced during handshake validation.
         remote_block_lens = list(self.block_len_per_layer)
-        tp_ratio = self.kv_topo.tp_ratio(remote_tp_size)
+        tp_ratio = self.transfer_topo.tp_ratio(remote_tp_size)
         if remote_tp_size > self.world_size:
             # P TP > D TP case, block_len of remote is smaller
             remote_block_lens = [
@@ -526,6 +555,7 @@ def _nixl_handshake(
                     block_size=self.block_size,
                     ssm_sizes=(0, 0),
                     attn_backend_name=self.backend_name,
+                    physical_blocks_per_logical_kv_block=1,
                 ),
                 remote_tp_rank=remote_tp_rank,
                 remote_tp_size=remote_tp_size,
@@ -725,14 +755,16 @@ def test_prefill_tp_size_greater_than_decode_tp_size(
         worker.num_blocks = 1
         worker.dst_num_blocks[worker.engine_id] = worker.num_blocks
         worker.src_blocks_data = [(0, worker.block_len_per_layer[0], worker.tp_rank)]
+        worker.num_descs = len(worker.src_blocks_data)
 
         def check_handshake(remote_tp_size: int):
             tp_ratio = remote_tp_size // local_tp_size
             assert set(remote_agents.keys()) == set(range(tp_ratio))
 
             remote_engine_id = worker.REMOTE_ENGINE_ID
-            assert worker._tp_size[remote_engine_id] == remote_tp_size
-            assert -tp_ratio == worker.kv_topo.tp_ratio_from_engine_id(remote_engine_id)
+            remote_info = worker.transfer_topo.get_engine_info(remote_engine_id)
+            assert remote_info.remote_tp_size == remote_tp_size
+            assert -tp_ratio == worker.transfer_topo.tp_ratio(remote_tp_size)
             # ensure src_xfer_handles_by_tp_ratio is populated with tpratio chunks
             assert -tp_ratio in worker.src_xfer_handles_by_tp_ratio
             assert len(worker.src_xfer_handles_by_tp_ratio[-tp_ratio]) == tp_ratio
@@ -796,7 +828,7 @@ def test_prefill_tp_size_greater_than_decode_tp_size_mla(
             (conn_p0.connector_worker, conn_p1.connector_worker)
         ):
             worker.world_size = p_tp_size
-            worker.kv_topo.remote_tp_size = {worker.engine_id: p_tp_size}
+            worker.transfer_topo.tp_size = p_tp_size
             worker.tp_rank = rank
             worker.use_mla = True
 
@@ -976,6 +1008,7 @@ def test_handshake_fails_on_kv_cache_layout_mismatch(
                 block_size=worker.block_size,
                 ssm_sizes=(0, 0),
                 attn_backend_name=worker.backend_name,
+                physical_blocks_per_logical_kv_block=1,
             )
 
             with pytest.raises(RuntimeError):
@@ -1033,6 +1066,7 @@ def test_handshake_succeed_on_kv_cache_layout_mismatch_with_experimental(
                 block_size=worker.block_size,
                 ssm_sizes=(0, 0),
                 attn_backend_name=worker.backend_name,
+                physical_blocks_per_logical_kv_block=1,
             )
 
             # We don't check layout for homogeneous TP and MLA for now, as the
@@ -2337,7 +2371,7 @@ def test_compatibility_hash_validation(
         remote_hash = compute_nixl_compatibility_hash(
             remote_vllm_config,
             decode_worker.backend_name,
-            decode_worker.kv_topo.cross_layers_blocks,
+            decode_worker.transfer_topo.cross_layers_blocks,
         )
 
     prefill_block_size = config_overrides.get("block_size", 16)
@@ -2352,6 +2386,7 @@ def test_compatibility_hash_validation(
         block_size=prefill_block_size,
         ssm_sizes=(0, 0),
         attn_backend_name=decode_worker.backend_name,
+        physical_blocks_per_logical_kv_block=1,
     )
     handshake_payload = NixlHandshakePayload(
         compatibility_hash=remote_hash,
@@ -2424,12 +2459,13 @@ def test_handshake_decode_errors(default_vllm_config, dist_init, error_scenario)
     test_shape = backend.get_kv_cache_shape(
         num_blocks=1, block_size=16, num_kv_heads=1, head_size=1
     )
-    decode_worker.kv_topo = TpKVTopology(
+    decode_worker.transfer_topo = TransferTopology(
         tp_rank=decode_worker.tp_rank,
+        tp_size=decode_worker.world_size,
+        block_size=decode_worker.block_size,
         engine_id=decode_worker.engine_id,
-        remote_tp_size=decode_worker._tp_size,  # shared state
-        remote_block_size=decode_worker._block_size,  # shared state
         is_mla=decode_worker.use_mla,
+        is_mamba=False,
         total_num_kv_heads=decode_worker.model_config.get_total_num_kv_heads(),
         attn_backends=[backend],
         tensor_shape=test_shape,
@@ -2438,7 +2474,7 @@ def test_handshake_decode_errors(default_vllm_config, dist_init, error_scenario)
     decode_worker.compat_hash = compute_nixl_compatibility_hash(
         decode_worker.vllm_config,
         decode_worker.backend_name,
-        decode_worker.kv_topo.cross_layers_blocks,
+        decode_worker.transfer_topo.cross_layers_blocks,
     )
 
     if error_scenario == "handshake_decode_error":
@@ -2476,3 +2512,122 @@ def test_handshake_decode_errors(default_vllm_config, dist_init, error_scenario)
                 remote_tp_size=1,
                 expected_engine_id=FakeNixlConnectorWorker.REMOTE_ENGINE_ID,
             )
+
+    @patch(
+        "vllm.distributed.kv_transfer.kv_connector.v1.nixl.worker.NixlWrapper",
+        FakeNixlWrapper,
+    )
+    def test_mla_broadcast_notif_uses_remote_request_id(
+        self, default_vllm_config, dist_init
+    ):
+        """MLA + remote TP > local TP: the broadcast notification sent to
+        non-read prefill ranks must be keyed by the prefill-side request
+        id (``meta.remote.request_id``), not the local decode request id.
+
+        Prefill ranks key ``_reqs_to_send`` by their own request id, so a
+        broadcast keyed by the decode id is rejected in
+        ``_get_new_notifs`` with "Potentially invalid KV blocks for
+        unrecognized request" and the blocks only release via the abort
+        timeout. See ``_read_blocks_for_req`` in
+        ``vllm/distributed/kv_transfer/kv_connector/v1/nixl/worker.py``.
+        """
+        decode_tp_size = 1
+        prefill_tp_size = 4
+
+        vllm_config = create_vllm_config()
+        vllm_config.parallel_config.tensor_parallel_size = decode_tp_size
+
+        connector = NixlConnector(
+            vllm_config, KVConnectorRole.WORKER, make_kv_cache_config(block_size=16)
+        )
+        connector.connector_worker = FakeNixlConnectorWorker(
+            vllm_config, connector.engine_id, hand_shake_latency=0
+        )
+        worker = connector.connector_worker
+
+        # Force the MLA path; only `self.use_mla` gates the branches we
+        # exercise inside `_read_blocks_for_req`.
+        worker.use_mla = True
+
+        # Manually register the remote (P) engine and pre-populate the
+        # per-rank state the handshake would normally fill in. The real
+        # `_nixl_handshake` is unnecessary here — we only need
+        # `transfer_topo` to know `remote_tp_size`, and `_remote_agents`
+        # / `dst_xfer_side_handles` to be keyed by remote rank.
+        remote_engine_id = "remote_engine"
+        worker.transfer_topo.register_remote_engine(
+            remote_engine_id=remote_engine_id,
+            remote_tp_size=prefill_tp_size,
+            remote_block_size=worker.block_size,
+            remote_block_len=worker.block_size * 4096,
+            remote_physical_blocks_per_logical=1,
+            local_block_len=worker.block_size * 4096,
+        )
+        worker._remote_agents[remote_engine_id] = {
+            rank: f"agent_p{rank}" for rank in range(prefill_tp_size)
+        }
+        worker.dst_xfer_side_handles = {
+            remote_engine_id: {rank: 100 + rank for rank in range(prefill_tp_size)}
+        }
+        # Sanity: D TP=1, P TP=4 => tp_ratio = -4 (P > D).
+        assert worker.transfer_topo.tp_ratio(prefill_tp_size) == -prefill_tp_size
+
+        # Distinct ids on each side — that's the whole point of the bug.
+        decode_req_id = "decode-req-AAAA"
+        prefill_req_id = "prefill-req-BBBB"
+        assert decode_req_id != prefill_req_id
+
+        metadata = NixlConnectorMetadata()
+        metadata.add_new_req_to_recv(
+            request_id=decode_req_id,
+            local_block_ids=([0, 1, 2],),
+            kv_transfer_params={
+                "remote_block_ids": ([10, 11, 12],),
+                "remote_engine_id": remote_engine_id,
+                "remote_request_id": prefill_req_id,
+                "remote_host": "localhost",
+                "remote_port": 1234,
+                "remote_tp_size": prefill_tp_size,
+            },
+        )
+        meta = metadata.reqs_to_recv[decode_req_id]
+
+        # Capture broadcast send_notif calls; stub `_read_blocks` so we
+        # don't need a working xfer path. Real `_read_blocks` emits its
+        # auto-notif via `make_prepped_xfer`, not via `send_notif`, so
+        # any captured `send_notif` here is a broadcast.
+        send_notif_calls: list[tuple[str, bytes]] = []
+        worker.nixl_wrapper.send_notif = (  # type: ignore[method-assign]
+            lambda agent_name, notif_msg: send_notif_calls.append(
+                (agent_name, notif_msg)
+            )
+        )
+        worker._read_blocks = MagicMock()  # type: ignore[method-assign]
+
+        worker._read_blocks_for_req(decode_req_id, meta)
+
+        # MLA: read once from rank 0 and broadcast to the other ranks.
+        worker._read_blocks.assert_called_once()
+        assert worker._read_blocks.call_args.kwargs["remote_rank"] == 0
+        assert (
+            worker._read_blocks.call_args.kwargs["remote_request_id"] == prefill_req_id
+        )
+
+        # Broadcast goes to ranks {1, 2, 3} only, never to the read target.
+        expected_recipients = {
+            worker._remote_agents[remote_engine_id][r]
+            for r in range(1, prefill_tp_size)
+        }
+        assert {agent for agent, _ in send_notif_calls} == expected_recipients
+
+        # Every broadcast notif must be keyed by the prefill request id.
+        # Pre-fix this used the *decode* request id, which prefill ranks
+        # didn't recognize.
+        expected_notif = f"{prefill_req_id}:{decode_tp_size}".encode()
+        bad_notif = f"{decode_req_id}:{decode_tp_size}".encode()
+        for agent, notif in send_notif_calls:
+            assert notif == expected_notif, (
+                f"Broadcast notif to {agent!r} must use prefill_req_id; "
+                f"got {notif!r} (expected {expected_notif!r}, "
+                f"buggy form would be {bad_notif!r})"
+            )
diff --git a/tests/v1/kv_connector/unit/test_nixl_connector_hma.py b/tests/v1/kv_connector/unit/test_nixl_connector_hma.py
index 17edd2069b34..edc591e06090 100644
--- a/tests/v1/kv_connector/unit/test_nixl_connector_hma.py
+++ b/tests/v1/kv_connector/unit/test_nixl_connector_hma.py
@@ -2,9 +2,11 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Unit tests for NixlConnectorScheduler with HMA and Mamba N-1 prefill."""
 
+import gc
 from unittest.mock import patch
 
 import pytest
+import torch
 
 from vllm import LLM, SamplingParams
 from vllm.config import KVTransferConfig
@@ -89,6 +91,107 @@ def test_logical_to_kernel_block_ids_with_hma():
     )
 
 
+@pytest.mark.cpu_test
+@pytest.mark.parametrize(
+    "group_spec_types,expansion_stride,remote_block_ids,expected_remote_block_ids",
+    [
+        pytest.param(
+            ("FullAttentionSpec", "SlidingWindowSpec"),
+            2,
+            ([0, 1, 2], [3, 4]),
+            [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9]],
+            id="dense_fa_swa",
+        ),
+        pytest.param(
+            ("FullAttentionSpec", "MambaSpec"),
+            261,
+            ([0, 1, 2], [10, 11]),
+            [[0, 1, 261, 262, 522, 523], [10, 11]],
+            id="mamba_fa_ssm",
+        ),
+    ],
+)
+def test_read_blocks_for_req_expands_remote_ids(
+    group_spec_types,
+    expansion_stride,
+    remote_block_ids,
+    expected_remote_block_ids,
+):
+    """_read_blocks_for_req must expand remote logical block IDs to kernel
+    block IDs when kernel block size != logical block size.
+
+    The hot path always calls _logical_to_remote_kernel_block_ids with
+    remote_info.remote_physical_blocks_per_logical (model-agnostic).
+    """
+    from unittest.mock import MagicMock
+
+    from vllm.distributed.kv_transfer.kv_connector.v1.nixl.metadata import (
+        NixlConnectorMetadata,
+    )
+    from vllm.distributed.kv_transfer.kv_connector.v1.nixl.tp_mapping import (
+        TPMapping,
+    )
+    from vllm.distributed.kv_transfer.kv_connector.v1.nixl.worker import (
+        NixlConnectorWorker,
+    )
+    from vllm.v1.kv_cache_interface import (
+        FullAttentionSpec,
+        MambaSpec,
+        SlidingWindowSpec,
+    )
+
+    spec_name_to_type = {
+        "FullAttentionSpec": FullAttentionSpec,
+        "SlidingWindowSpec": SlidingWindowSpec,
+        "MambaSpec": MambaSpec,
+    }
+    resolved_types = tuple(spec_name_to_type[n] for n in group_spec_types)
+
+    worker = object.__new__(NixlConnectorWorker)
+    worker._physical_blocks_per_logical_kv_block = 2
+
+    has_mamba = any(t is MambaSpec for t in resolved_types)
+    has_swa = any(t is SlidingWindowSpec for t in resolved_types)
+    worker.kv_cache_config = make_kv_cache_config(
+        block_size=16, swa_enabled=has_swa, mamba_enabled=has_mamba
+    )
+
+    remote_engine_id = "remote-engine"
+
+    worker.transfer_topo = MagicMock()
+    worker.transfer_topo.tp_ratio.return_value = 1
+    remote_info = MagicMock()
+    remote_info.remote_physical_blocks_per_logical = expansion_stride
+    worker.transfer_topo.get_engine_info.return_value = remote_info
+    worker.use_mla = False
+
+    mock_plan = MagicMock(spec=TPMapping)
+    mock_plan.all_source_ranks = ()
+    mock_plan.source_ranks_per_group = ()
+    worker.tp_mappings = {remote_engine_id: mock_plan}
+
+    metadata = NixlConnectorMetadata()
+    metadata.add_new_req_to_recv(
+        request_id="test-req",
+        local_block_ids=([0, 1], [2, 3]),
+        kv_transfer_params={
+            "remote_block_ids": remote_block_ids,
+            "remote_engine_id": remote_engine_id,
+            "remote_request_id": "prefill-test-req",
+            "remote_host": "localhost",
+            "remote_port": 1234,
+            "tp_size": 1,
+        },
+    )
+
+    meta = metadata.reqs_to_recv["test-req"]
+    worker._read_blocks_for_req("test-req", meta)
+
+    assert meta.remote.block_ids == expected_remote_block_ids, (
+        f"Expected {expected_remote_block_ids}, got {meta.remote.block_ids}"
+    )
+
+
 @pytest.mark.parametrize("model_name, sw_size", [("google/gemma-3-1b-it", 512)])
 def test_fewer_blocks_with_hma(monkeypatch, model_name, sw_size):
     """Test that a prefill instance returns fewer "remote blocks" for the SWA groups
@@ -102,12 +205,13 @@ def test_fewer_blocks_with_hma(monkeypatch, model_name, sw_size):
     llm_kwargs = {
         "model": model_name,
         "enforce_eager": True,
-        "gpu_memory_utilization": 0.5,
+        "gpu_memory_utilization": 0.3,
         "kv_transfer_config": kv_transfer_config,
         "max_model_len": 2048,
+        "max_num_seqs": 1,
         # NOTE: Make sure HMA is enabled
         "disable_hybrid_kv_cache_manager": False,
-        "max_num_batched_tokens": 1024,
+        "max_num_batched_tokens": 2048,
         "enable_prefix_caching": False,
         "block_size": block_size,
     }
@@ -154,6 +258,8 @@ def run_hma_test(llm: LLM):
             assert len(group_block_ids) == expected_num_remote_blocks
 
     def run_test_and_cleanup():
+        gc.collect()
+        torch.accelerator.empty_cache()
         llm = LLM(**llm_kwargs)
         try:
             run_hma_test(llm)
@@ -207,75 +313,82 @@ def test_nixl_metadata_hma_block_ids_structure():
     assert list(req_meta.remote.block_ids[1]) == [18, 19, 20, 21]
 
 
-@pytest.mark.cpu_test
-def test_get_block_descs_ids_hybrid_ssm():
-    """Test _get_block_descs_ids uses per-group strides for hybrid FA+SSM
-    when ratio=1 (no kernel block size mismatch)."""
+def _make_mock_worker_for_desc_ids(
+    num_regions: int,
+    has_mamba: bool,
+    group_spec_types: tuple,
+    block_len_per_layer: list[int] | None = None,
+):
+    """Build a mock NixlConnectorWorker with attrs needed by _compute_desc_ids."""
+    from unittest.mock import MagicMock
+
     from vllm.distributed.kv_transfer.kv_connector.v1.nixl.worker import (
         NixlConnectorWorker,
     )
 
-    worker = object.__new__(NixlConnectorWorker)
+    worker = MagicMock(spec=NixlConnectorWorker)
+    worker.num_regions = num_regions
+    worker._has_mamba = has_mamba
+    worker._group_spec_types = group_spec_types
+    worker.block_len_per_layer = block_len_per_layer or [100]
+    worker._compute_desc_ids = NixlConnectorWorker._compute_desc_ids.__get__(
+        worker, NixlConnectorWorker
+    )
+    return worker
+
 
-    num_blocks = 100
-    engine_id = "test-engine"
-    worker.num_regions = 2
-    worker.dst_num_blocks = {engine_id: num_blocks}
-    worker._has_mamba = True
-    worker._is_mamba_group = [False, True]
-    worker._physical_blocks_per_logical_kv_block = 1
-    worker._mamba_phys_ratio = {engine_id: 1}
-    worker.block_len_per_layer = [100]
-    # num_descs = num_regions * num_blocks (no blocks_first doubling)
-    worker.num_descs = 2 * num_blocks
+@pytest.mark.cpu_test
+def test_get_block_descs_ids_hybrid_ssm():
+    """Test _compute_desc_ids uses per-group strides for hybrid
+    FA+SSM when ratio=1 (no kernel block size mismatch)."""
+    from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec
+
+    worker = _make_mock_worker_for_desc_ids(
+        num_regions=2,
+        has_mamba=True,
+        group_spec_types=(FullAttentionSpec, MambaSpec),
+        block_len_per_layer=[100],
+    )
 
     fa_blocks = [3, 5]
     ssm_blocks = [1, 2]
-    result = worker._get_block_descs_ids(engine_id, (fa_blocks, ssm_blocks))
-
-    # FA group: stride=num_blocks=100, offset=0
-    #   region0: [3, 5],  region1: [103, 105]
-    # SSM group: stride=logical_blocks=100 (=num_blocks/ratio=100/1),
-    #   offset=num_fa_descs=200, 4 regions per Mamba layer (x, B, C, ssm)
-    #   region0: [201, 202], region1: [301, 302],
-    #   region2: [401, 402], region3: [501, 502]
+    result = worker._compute_desc_ids(
+        block_ids=(fa_blocks, ssm_blocks),
+        dst_num_blocks=100,
+        block_size_ratio=None,
+        physical_blocks_per_logical=1,
+    )
+
     expected = [3, 5, 103, 105, 201, 202, 301, 302, 401, 402, 501, 502]
     assert list(result) == expected, f"Expected {expected}, got {list(result)}"
 
 
 @pytest.mark.cpu_test
 def test_get_block_descs_ids_kernel_block_mismatch():
-    """Test _get_block_descs_ids uses different strides for FA (kernel blocks)
-    vs SSM (logical blocks) when ratio > 1."""
-    from vllm.distributed.kv_transfer.kv_connector.v1.nixl.worker import (
-        NixlConnectorWorker,
-    )
-
-    worker = object.__new__(NixlConnectorWorker)
+    """Test _compute_desc_ids uses different strides for FA
+    (kernel blocks) vs SSM (logical blocks) when ratio > 1."""
+    from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec
 
     ratio = 4
     logical_blocks = 100
     num_blocks = logical_blocks * ratio  # 400 kernel blocks
-    engine_id = "test-engine"
-    worker.num_regions = 2
-    worker.dst_num_blocks = {engine_id: num_blocks}
-    worker._has_mamba = True
-    worker._is_mamba_group = [False, True]
-    worker._physical_blocks_per_logical_kv_block = ratio
-    worker._mamba_phys_ratio = {engine_id: ratio}
-    worker.block_len_per_layer = [100]
-    worker.num_descs = 2 * num_blocks  # 800
-
-    fa_blocks = [3, 7]  # kernel-level block IDs
-    ssm_blocks = [1, 2]  # logical block IDs
-    result = worker._get_block_descs_ids(engine_id, (fa_blocks, ssm_blocks))
-
-    # FA group: stride=num_blocks=400, offset=0
-    #   region0: [3, 7],  region1: [403, 407]
-    # SSM group: stride=logical_blocks=400//4=100, offset=num_fa_descs=800,
-    #   4 regions per Mamba layer (x, B, C, ssm)
-    #   region0: [801, 802], region1: [901, 902],
-    #   region2: [1001, 1002], region3: [1101, 1102]
+
+    worker = _make_mock_worker_for_desc_ids(
+        num_regions=2,
+        has_mamba=True,
+        group_spec_types=(FullAttentionSpec, MambaSpec),
+        block_len_per_layer=[100],
+    )
+
+    fa_blocks = [3, 7]
+    ssm_blocks = [1, 2]
+    result = worker._compute_desc_ids(
+        block_ids=(fa_blocks, ssm_blocks),
+        dst_num_blocks=num_blocks,
+        block_size_ratio=None,
+        physical_blocks_per_logical=ratio,
+    )
+
     expected = [3, 7, 403, 407, 801, 802, 901, 902, 1001, 1002, 1101, 1102]
     assert list(result) == expected, f"Expected {expected}, got {list(result)}"
 
@@ -439,15 +552,15 @@ def test_has_mamba_init(
         ((9216, 524288), 4096, 131),
     ],
 )
-def test_compute_mamba_phys_ratio(ssm_sizes, block_len, expected_ratio):
-    """Verify that compute_mamba_phys_ratio is TP-dependent.
+def test_compute_physical_blocks_per_logical(ssm_sizes, block_len, expected_ratio):
+    """Verify that compute_physical_blocks_per_logical is TP-dependent.
 
     With dimension-sharded Mamba state, the ratio differs across TP sizes
     (e.g. TP=1 → 261, TP=4 → 131 for Nemotron 30B). This is why
-    _mamba_phys_ratio must be stored per-engine.
+    _physical_blocks_per_logical must be stored per-engine.
     """
     from vllm.distributed.kv_transfer.kv_connector.v1.ssm_conv_transfer_utils import (
-        compute_mamba_phys_ratio,
+        compute_physical_blocks_per_logical,
     )
 
-    assert compute_mamba_phys_ratio(ssm_sizes, block_len) == expected_ratio
+    assert compute_physical_blocks_per_logical(ssm_sizes, block_len) == expected_ratio
diff --git a/tests/v1/kv_offload/test_cpu_offloading.py b/tests/v1/kv_connector/unit/test_offloading_connector.py
similarity index 51%
rename from tests/v1/kv_offload/test_cpu_offloading.py
rename to tests/v1/kv_connector/unit/test_offloading_connector.py
index d3db828dc60e..555daea50e38 100644
--- a/tests/v1/kv_offload/test_cpu_offloading.py
+++ b/tests/v1/kv_connector/unit/test_offloading_connector.py
@@ -14,13 +14,36 @@
 from vllm.distributed.kv_events import BlockStored, KVEventBatch
 from vllm.platforms import current_platform
 
-CPU_BLOCK_SIZES = [48]
-ATTN_BACKENDS = []
-
+_ATTN_BACKENDS: list[str] = []
 if current_platform.is_cuda():
-    ATTN_BACKENDS = ["FLASH_ATTN", "FLASHINFER", "TRITON_ATTN"]
+    _ATTN_BACKENDS = ["FLASH_ATTN", "FLASHINFER", "TRITON_ATTN"]
 elif current_platform.is_rocm():
-    ATTN_BACKENDS = ["TRITON_ATTN"]
+    _ATTN_BACKENDS = ["TRITON_ATTN"]
+
+# (model, attn_backend | None, block_size | None, uses_hma)
+#
+# - Llama: tested with each attention backend and a custom block_size.
+# - Gemma-3: HMA (sliding window + full attention), default backend.
+# - Mamba-130m: HMA (attention-free, acts like sliding_window=1),
+#   default backend.  Prefix caching must be force-enabled.
+# - Falcon-H1-0.5B-Instruct: HMA (parallel SSM/attention in every layer).
+#   After page-size unification the mamba and attention groups have
+#   different block sizes.
+MODEL_PARAMS: list[tuple[str, str | None, int | None, bool]] = [
+    ("meta-llama/Llama-3.2-1B-Instruct", backend, 48, False)
+    for backend in _ATTN_BACKENDS
+]
+# HMA / Mamba models are only tested on CUDA (not ROCm).
+if current_platform.is_cuda():
+    MODEL_PARAMS += [
+        ("google/gemma-3-1b-it", None, 48, True),
+        ("state-spaces/mamba-130m-hf", None, 48, True),
+        # Falcon-H1: parallel hybrid (every layer has both attention and SSM).
+        # The mamba and attention groups end up with different GPU block sizes
+        # after page-size unification, so we leave cpu_block_size=None
+        # (block_size_factor stays 1).
+        ("tiiuae/Falcon-H1-0.5B-Instruct", None, None, True),
+    ]
 
 # Maximum time (seconds) to wait for the async CPU offload transfer
 # to complete before giving up.
@@ -87,10 +110,13 @@ def close(self):
 def _wait_for_prefix_cache_reset(llm: LLM) -> None:
     """Wait for async offload transfers to finish so prefix cache can reset.
 
-    The GPU-to-CPU offload runs on a CUDA stream asynchronously.  While blocks
+    The GPU-to-CPU offload runs on a CUDA stream asynchronously. While blocks
     are still held by the offload worker, ``reset_prefix_cache`` returns
-    ``False``.  Retry with a short sleep until it succeeds or we time out.
+    ``False``. Between retries we send a dummy single-token prefill to force
+    the engine to step, which polls the worker for completed transfers and
+    frees GPU blocks.
     """
+    _dummy_params = SamplingParams(max_tokens=1)
     deadline = time.monotonic() + _RESET_CACHE_TIMEOUT
     while not llm.reset_prefix_cache():
         if time.monotonic() > deadline:
@@ -98,10 +124,16 @@ def _wait_for_prefix_cache_reset(llm: LLM) -> None:
                 "reset_prefix_cache did not succeed within "
                 f"{_RESET_CACHE_TIMEOUT}s - async offload may be stuck"
             )
-        time.sleep(0.1)
+        # Force an engine step so the scheduler polls get_finished()
+        # and releases GPU blocks held by in-flight async stores.
+        llm.generate(
+            [TokensPrompt(prompt_token_ids=[0])],
+            _dummy_params,
+            use_tqdm=False,
+        )
 
 
-def _latency_test(llm: LLM, subscriber: MockSubscriber):
+def _latency_test(llm: LLM, subscriber: MockSubscriber | None):
     sampling_params = SamplingParams(max_tokens=1)
 
     num_times_cpu_better_than_cold = 0
@@ -109,7 +141,10 @@ def _latency_test(llm: LLM, subscriber: MockSubscriber):
     total_cold_time = 0.0
     total_gpu_hit_time = 0.0
     total_cpu_hit_time = 0.0
-    prompt_token_ids = [0] * 10001
+    max_model_len = llm.llm_engine.vllm_config.model_config.max_model_len
+    # Use a long prompt that fits within the model's context window.
+    prompt_len = min(10001, max_model_len - 1)
+    prompt_token_ids = [0] * prompt_len
     for i in tqdm(range(num_tests), desc="Running tests"):
         prompt_token_ids[0] = i
         prompts = [TokensPrompt(prompt_token_ids=prompt_token_ids)]
@@ -132,10 +167,11 @@ def _latency_test(llm: LLM, subscriber: MockSubscriber):
 
         # Verify CPU stored events arrived (offload is done before we
         # attempt to load from CPU).
-        assert subscriber.get_new_cpu_stored_events(), (
-            f"No CPU stored events received on iteration {i}; "
-            "async offload may not have completed in time"
-        )
+        if subscriber is not None:
+            assert subscriber.get_new_cpu_stored_events(), (
+                f"No CPU stored events received on iteration {i}; "
+                "async offload may not have completed in time"
+            )
 
         # run generation again - this should trigger loading from CPU
         start_time = time.time()
@@ -154,84 +190,105 @@ def _latency_test(llm: LLM, subscriber: MockSubscriber):
     assert num_times_cpu_better_than_cold >= 0.8 * num_tests
 
 
-def _accuracy_test(llm: LLM, subscriber: MockSubscriber):
+def _accuracy_test(llm: LLM, subscriber: MockSubscriber | None):
     sampling_params = SamplingParams(max_tokens=1)
-    cpu_block_size = (
-        llm.llm_engine.vllm_config.kv_transfer_config.kv_connector_extra_config[
-            "block_size"
-        ]
+    extra_config = (
+        llm.llm_engine.vllm_config.kv_transfer_config.kv_connector_extra_config
     )
-
-    subscriber.get_new_cpu_stored_events()
-
-    # prepend prompt to be cpu block aligned
+    cpu_block_size = extra_config.get("block_size")
+    if cpu_block_size is None:
+        # No custom offloaded block_size: offloaded blocks match GPU blocks.
+        # Use the hash block_size (cache_config.block_size) for alignment.
+        cpu_block_size = llm.llm_engine.vllm_config.cache_config.block_size
+
+    if subscriber is not None:
+        subscriber.get_new_cpu_stored_events()
+
+    # Pad prompt so its token count is a multiple of cpu_block_size.
+    # Use the tokenizer directly to avoid expensive llm.generate() calls.
+    tokenizer = llm.get_tokenizer()
     prompt = "Let's count to 10. One, two, three, four,"
-    while (
-        len(llm.generate(prompt, use_tqdm=False)[0].prompt_token_ids) % cpu_block_size
-        != 0
-    ):
+    while len(tokenizer.encode(prompt)) % cpu_block_size != 0:
         prompt = ". " + prompt
 
-    assert subscriber.get_new_cpu_stored_events()
+    # Seed the CPU cache with the prompt.
+    llm.generate(prompt, sampling_params, use_tqdm=False)
 
-    test_count = 100
-    success_count = 0
-    for i in range(test_count):
-        if (
-            llm.generate(prompt, sampling_params, use_tqdm=False)[0].outputs[0].text
-            == " five"
-        ):
-            success_count += 1
+    if subscriber is not None:
+        assert subscriber.get_new_cpu_stored_events()
 
+    test_count = 20
+    results = llm.generate([prompt] * test_count, sampling_params, use_tqdm=False)
+    success_count = sum(1 for r in results if r.outputs[0].text == " five")
     assert success_count >= 0.5 * test_count
 
 
-@pytest.mark.parametrize("cpu_block_size", CPU_BLOCK_SIZES)
-@pytest.mark.parametrize("attn_backend", ATTN_BACKENDS)
-def test_cpu_offloading(cpu_block_size: int, attn_backend: str) -> None:
+@pytest.mark.parametrize("model, attn_backend, cpu_block_size, uses_hma", MODEL_PARAMS)
+def test_cpu_offloading(
+    model: str,
+    attn_backend: str | None,
+    cpu_block_size: int | None,
+    uses_hma: bool,
+) -> None:
     """
     Tests OffloadingConnector with CPUOffloadingSpec.
     """
-
     # configure OffloadingConnector (spec_name=CPUOffloadingSpec by default)
+    extra_config: dict = {"cpu_bytes_to_use": 500 << 20}
+    if cpu_block_size is not None:
+        extra_config["block_size"] = cpu_block_size
     kv_transfer_config = KVTransferConfig(
         kv_connector="OffloadingConnector",
         kv_role="kv_both",
-        kv_connector_extra_config={
-            "cpu_bytes_to_use": 500 << 20,
-            "block_size": cpu_block_size,
-        },
+        kv_connector_extra_config=extra_config,
     )
 
-    port: int
-    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-        s.bind(("0.0.0.0", 0))
-        port = s.getsockname()[1]
-
-    events_endpoint = f"tcp://*:{port}"
-    kv_events_config = KVEventsConfig(
-        enable_kv_cache_events=True,
-        publisher="zmq",
-        endpoint=events_endpoint,
-        topic="test",
-    )
+    # KV events are incompatible with HMA (setting kv_events_config
+    # would force HMA off), so only enable them for non-HMA models.
+    subscriber: MockSubscriber | None = None
+    kv_events_config: KVEventsConfig | None = None
+    if not uses_hma:
+        port: int
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+            s.bind(("0.0.0.0", 0))
+            port = s.getsockname()[1]
+
+        events_endpoint = f"tcp://*:{port}"
+        kv_events_config = KVEventsConfig(
+            enable_kv_cache_events=True,
+            publisher="zmq",
+            endpoint=events_endpoint,
+            topic="test",
+        )
+
+    # Attention-free / hybrid models disable prefix caching by default
+    # (ModelConfig.is_prefix_caching_supported returns False).  Without it,
+    # mamba_block_size falls back to max_model_len, making GPU blocks too
+    # large for any reasonable offloaded block_size.  Force-enable it.
+    force_prefix_caching = uses_hma
 
     llm = LLM(
-        model="meta-llama/Llama-3.2-1B-Instruct",
+        model=model,
+        max_model_len=4096,
         gpu_memory_utilization=0.5,
         kv_events_config=kv_events_config,
         kv_transfer_config=kv_transfer_config,
-        attention_config={"backend": attn_backend},
+        **({"attention_config": {"backend": attn_backend}} if attn_backend else {}),
+        # HMA models need explicit opt-in when kv_transfer_config is set
+        **({"disable_hybrid_kv_cache_manager": False} if uses_hma else {}),
+        **({"enable_prefix_caching": True} if force_prefix_caching else {}),
         # ROCm: batch size 1 to reduce variability
         **({"max_num_seqs": 1} if current_platform.is_rocm() else {}),
     )
 
-    events_endpoint = events_endpoint.replace("*", "127.0.0.1")
-    subscriber = MockSubscriber(events_endpoint, topic=kv_events_config.topic)
+    if kv_events_config is not None:
+        events_endpoint = events_endpoint.replace("*", "127.0.0.1")
+        subscriber = MockSubscriber(events_endpoint, topic=kv_events_config.topic)
 
     try:
         _latency_test(llm, subscriber)
         _accuracy_test(llm, subscriber)
     finally:
-        subscriber.close()
+        if subscriber is not None:
+            subscriber.close()
         del llm
diff --git a/tests/v1/kv_connector/unit/test_rixl_gpu_mem_diag.py b/tests/v1/kv_connector/unit/test_rixl_gpu_mem_diag.py
new file mode 100644
index 000000000000..3a3ef2a88a68
--- /dev/null
+++ b/tests/v1/kv_connector/unit/test_rixl_gpu_mem_diag.py
@@ -0,0 +1,222 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Verify that GPU memory is fully released after RixlConnector shutdown on ROCm.
+
+Regression test for ROCm/ucx#33: UCX rocm_ipc transport permanently pinned
+GPU memory via hsa_amd_ipc_memory_create during ucp_mem_map, causing
+GPU memory to be unrecoverable after engine shutdown.
+"""
+
+import gc
+
+import pytest
+import torch
+
+from vllm.platforms import current_platform
+
+pytestmark = pytest.mark.skipif(
+    not current_platform.is_rocm(),
+    reason="ROCm platform required",
+)
+
+
+def _mb(b: int) -> float:
+    return b / (1024 * 1024)
+
+
+def _gpu_snapshot(tag: str, prev_alloc: float = 0.0) -> dict:
+    """Print and return current GPU memory stats."""
+    torch.accelerator.synchronize()
+    alloc = torch.accelerator.memory_allocated()
+    reserved = torch.accelerator.memory_reserved()
+    # mem_get_info is not available on torch.accelerator
+    try:
+        drv_free, drv_total = torch.cuda.mem_get_info()
+        drv_used = drv_total - drv_free
+        drv_pct = drv_used / drv_total * 100
+    except Exception:
+        drv_used = drv_total = drv_pct = 0
+    alloc_mb = _mb(alloc)
+    drv_used_mb = _mb(drv_used)
+    delta = alloc_mb - prev_alloc
+    print(
+        f"  {tag:<40s} | {alloc_mb:>9.1f} alloc | "
+        f"{_mb(reserved):>9.1f} rsrvd | "
+        f"{drv_used_mb:>9.1f} driver ({drv_pct:.1f}%) | "
+        f"delta {delta:>+9.1f}"
+    )
+    return {
+        "tag": tag,
+        "alloc_mb": alloc_mb,
+        "drv_used_mb": drv_used_mb,
+        "drv_pct": drv_pct,
+    }
+
+
+def _full_gpu_cleanup():
+    """gc.collect + torch empty_cache, multiple rounds."""
+    gc.unfreeze()
+    for _ in range(3):
+        if gc.collect() == 0:
+            break
+    torch.accelerator.empty_cache()
+
+
+@pytest.mark.parametrize("model_name, sw_size", [("google/gemma-3-1b-it", 512)])
+def test_gpu_memory_rixl_hma(model_name, sw_size):
+    """Track GPU memory through NixlConnector create/infer/shutdown cycle."""
+    from vllm import LLM, SamplingParams
+    from vllm.config import KVTransferConfig
+    from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
+
+    llm_kwargs = {
+        "model": model_name,
+        "enforce_eager": True,
+        "gpu_memory_utilization": 0.5,
+        "kv_transfer_config": KVTransferConfig(
+            kv_connector="NixlConnector",
+            kv_role="kv_both",
+        ),
+        "max_model_len": 2048,
+        "disable_hybrid_kv_cache_manager": False,
+        "max_num_batched_tokens": 1024,
+        "enable_prefix_caching": False,
+        "block_size": 16,
+    }
+
+    print("\n" + "=" * 90)
+    print("GPU MEMORY -- RIXL NixlConnector HMA (ROCm)")
+    print("=" * 90)
+    gc.collect()
+    torch.accelerator.empty_cache()
+    torch.accelerator.reset_peak_memory_stats()
+    snap0 = _gpu_snapshot("0. baseline", 0.0)
+
+    # create + infer
+    llm = LLM(**llm_kwargs)
+    snap1 = _gpu_snapshot("1. after LLM()", snap0["alloc_mb"])
+
+    llm.generate(
+        ["hi" * 1401],
+        SamplingParams(
+            temperature=0.0,
+            max_tokens=1,
+            extra_args={
+                "kv_transfer_params": {
+                    "do_remote_decode": True,
+                    "do_remote_prefill": False,
+                    "remote_engine_id": None,
+                    "remote_block_ids": None,
+                    "remote_host": None,
+                    "remote_port": None,
+                }
+            },
+        ),
+    )
+    snap2 = _gpu_snapshot("2. after generate()", snap1["alloc_mb"])
+
+    # shutdown + cleanup
+    print("\n--- shutdown ---")
+    llm.llm_engine.engine_core.shutdown()
+    _gpu_snapshot("3. after shutdown()", snap2["alloc_mb"])
+
+    del llm
+    _full_gpu_cleanup()
+    cleanup_dist_env_and_memory()
+    _full_gpu_cleanup()
+    torch._dynamo.reset()
+    gc.collect()
+    torch.accelerator.empty_cache()
+    snap_final = _gpu_snapshot("4. final", snap2["alloc_mb"])
+
+    # summary
+    print("\n" + "=" * 90)
+    baseline = snap0["alloc_mb"]
+    final = snap_final["alloc_mb"]
+    peak = snap2["alloc_mb"]
+    total_alloc = peak - baseline
+
+    print(
+        f"  PyTorch:  baseline={baseline:.0f}  peak={peak:.0f}  "
+        f"final={final:.0f}  "
+        f"leaked={final - baseline:.0f} MB"
+        + (
+            f" ({(final - baseline) / total_alloc * 100:.1f}%)"
+            if total_alloc > 0
+            else ""
+        )
+    )
+
+    drv_base = snap0["drv_used_mb"]
+    drv_final = snap_final["drv_used_mb"]
+    drv_leaked = drv_final - drv_base
+    print(
+        f"  Driver:   baseline={drv_base:.0f} ({snap0['drv_pct']:.1f}%)  "
+        f"peak={snap2['drv_used_mb']:.0f} ({snap2['drv_pct']:.1f}%)  "
+        f"final={drv_final:.0f} ({snap_final['drv_pct']:.1f}%)  "
+        f"leaked={drv_leaked:.0f} MB"
+    )
+    print("=" * 90)
+
+    # Peak driver memory used above baseline
+    drv_peak = snap2["drv_used_mb"] - drv_base
+    leak_pct = (drv_leaked / drv_peak * 100) if drv_peak > 0 else 0
+    max_leak_pct = 10
+    assert leak_pct <= max_leak_pct, (
+        f"{drv_leaked:.0f} MB ({leak_pct:.1f}%) of driver-level GPU memory "
+        f"not freed after NixlConnector shutdown "
+        f"(peak allocation: {drv_peak:.0f} MB, threshold: {max_leak_pct}%)"
+    )
+
+
+@pytest.mark.parametrize("model_name", ["google/gemma-3-1b-it"])
+def test_gpu_memory_no_rixl_baseline(model_name):
+    """Same workload without NixlConnector.  Comparing driver-level memory
+    between this and test_gpu_memory_rixl_hma isolates UCX/RIXL impact."""
+    from vllm import LLM, SamplingParams
+    from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
+
+    print("\n" + "=" * 90)
+    print("CONTROL -- same model, no RIXL connector")
+    print("=" * 90)
+    gc.collect()
+    torch.accelerator.empty_cache()
+    snap0 = _gpu_snapshot("baseline", 0.0)
+
+    llm = LLM(
+        model=model_name,
+        enforce_eager=True,
+        gpu_memory_utilization=0.5,
+        max_model_len=2048,
+        max_num_batched_tokens=1024,
+        enable_prefix_caching=False,
+        block_size=16,
+    )
+    _gpu_snapshot("after LLM()", snap0["alloc_mb"])
+
+    llm.generate(["hi " * 500], SamplingParams(max_tokens=1))
+    snap_peak = _gpu_snapshot("after generate()", snap0["alloc_mb"])
+
+    llm.llm_engine.engine_core.shutdown()
+    del llm
+    _full_gpu_cleanup()
+    cleanup_dist_env_and_memory()
+    _full_gpu_cleanup()
+    torch._dynamo.reset()
+    gc.collect()
+    torch.accelerator.empty_cache()
+    snap_final = _gpu_snapshot("final", snap0["alloc_mb"])
+
+    drv_base = snap0["drv_used_mb"]
+    drv_leaked = snap_final["drv_used_mb"] - drv_base
+    drv_peak = snap_peak["drv_used_mb"] - drv_base
+    print(f"\n  Driver leaked (no rixl): {drv_leaked:.0f} MB")
+    print("=" * 90)
+
+    leak_pct = (drv_leaked / drv_peak * 100) if drv_peak > 0 else 0
+    max_leak_pct = 10
+    assert leak_pct <= max_leak_pct, (
+        f"{drv_leaked:.0f} MB ({leak_pct:.1f}%) of driver-level GPU memory "
+        f"not freed after baseline shutdown "
+        f"(peak allocation: {drv_peak:.0f} MB, threshold: {max_leak_pct}%)"
+    )
diff --git a/tests/v1/kv_connector/unit/test_scheduler_kv_connector_override.py b/tests/v1/kv_connector/unit/test_scheduler_kv_connector_override.py
index 2834647fe1ff..164d9025dfef 100644
--- a/tests/v1/kv_connector/unit/test_scheduler_kv_connector_override.py
+++ b/tests/v1/kv_connector/unit/test_scheduler_kv_connector_override.py
@@ -17,6 +17,7 @@
 from vllm.v1.core.kv_cache_utils import BlockHash
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.core.sched.scheduler import Scheduler
+from vllm.v1.kv_cache_interface import KVCacheConfig
 from vllm.v1.request import Request
 
 
@@ -26,7 +27,7 @@ def __init__(self, block_hashes_by_req: dict[str, list[BlockHash]]):
 
 
 class DummyKVConnector(KVConnectorBase_V1):
-    def __init__(self, vllm_config, role, kv_cache_config=None):
+    def __init__(self, vllm_config, role, kv_cache_config: KVCacheConfig):
         super().__init__(vllm_config, role, kv_cache_config)
 
     def get_num_new_matched_tokens(
diff --git a/tests/v1/kv_connector/unit/test_tp_mapping.py b/tests/v1/kv_connector/unit/test_tp_mapping.py
new file mode 100644
index 000000000000..95d49faf042f
--- /dev/null
+++ b/tests/v1/kv_connector/unit/test_tp_mapping.py
@@ -0,0 +1,146 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for TP mapping and transfer plan utilities.
+
+These tests verify that TP mapping produces correct outputs
+(source ranks, split handles, desc IDs).
+No GPU or NIXL required.
+"""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+
+import pytest
+
+from vllm.distributed.kv_transfer.kv_connector.v1.nixl.tp_mapping import (
+    TPMapping,
+    compute_tp_mapping,
+)
+from vllm.distributed.kv_transfer.kv_connector.v1.nixl.worker import (
+    NixlConnectorWorker,
+)
+from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec
+
+# ======================================================================
+# Test fixtures / helpers
+# ======================================================================
+
+
+def _compute_mapping(
+    tp_rank: int = 0,
+    tp_size: int = 1,
+    remote_tp_size: int = 1,
+    is_mla: bool = False,
+    num_kv_heads: int = 8,
+    group_spec_types: tuple[type, ...] = (FullAttentionSpec,),
+) -> TPMapping:
+    transfer_topology = SimpleNamespace(
+        tp_rank=tp_rank,
+        tp_size=tp_size,
+        is_mla=is_mla,
+        total_num_kv_heads=num_kv_heads,
+    )
+    return compute_tp_mapping(
+        transfer_topology=transfer_topology,
+        remote_tp_size=remote_tp_size,
+        group_spec_types=group_spec_types,
+    )
+
+
+# ======================================================================
+# TP mapping structure tests
+# ======================================================================
+
+
+class TestTPMappingStructure:
+    def test_source_ranks_homogeneous(self):
+        m = _compute_mapping(tp_size=2, tp_rank=1, remote_tp_size=2)
+        assert m.all_source_ranks == (1,)
+
+    def test_source_ranks_d_gt_p(self):
+        m = _compute_mapping(tp_size=4, tp_rank=2, remote_tp_size=2)
+        assert m.all_source_ranks == (1,)
+
+    def test_source_ranks_p_gt_d(self):
+        m = _compute_mapping(tp_size=1, tp_rank=0, remote_tp_size=2)
+        assert m.all_source_ranks == (0, 1)
+
+
+# ======================================================================
+# Split handle tests
+# ======================================================================
+
+
+def _make_mock_worker_for_splits(group_spec_types):
+    """Build a mock NixlConnectorWorker with _group_spec_types for split tests."""
+    worker = object.__new__(NixlConnectorWorker)
+    worker._group_spec_types = group_spec_types
+    return worker
+
+
+class TestBuildSrcSplitHandles:
+    @pytest.mark.parametrize("remote_tp_size", [2, 4])
+    def test_build_src_split_handles(self, remote_tp_size):
+        tp_rank = 0
+        tp_size = 1
+
+        plan = _compute_mapping(
+            tp_rank=tp_rank,
+            tp_size=tp_size,
+            remote_tp_size=remote_tp_size,
+        )
+
+        worker = _make_mock_worker_for_splits((FullAttentionSpec,))
+        src_blocks_data = [(0x2000 + i * 1024, 1024, 0) for i in range(8)]
+        num_descs = len(src_blocks_data)
+        splits = list(
+            worker._build_local_splits_from_plan(
+                plan,
+                src_blocks_data,
+                num_descs,
+            )
+        )
+
+        assert len(splits) == remote_tp_size
+        for handle in splits:
+            assert len(handle) == len(src_blocks_data)
+            for _, length, _ in handle:
+                assert length == 1024 // remote_tp_size
+
+
+class TestMambaPlanSplitHandles:
+    """Verify split handles for Mamba with FA/SSM distinction."""
+
+    def test_fa_and_ssm_different_split_factors(self):
+        """Section 0 split by num_attn_reads, section 1 by abs_tp."""
+        fa_readers = (0,)
+        ssm_readers = (0, 1)
+        plan = TPMapping(
+            source_ranks_per_group=(fa_readers, ssm_readers),
+            all_source_ranks=(0, 1),
+            rank_to_attention_slot={0: 0, 1: 0},
+            rank_offset_factor=0,
+        )
+
+        worker = _make_mock_worker_for_splits((FullAttentionSpec, MambaSpec))
+        # 2 FA descs + 1 SSM desc
+        src_blocks_data = [
+            (1000, 200, 0),  # FA desc 0
+            (2000, 200, 0),  # FA desc 1
+            (3000, 400, 0),  # SSM desc 0
+        ]
+
+        splits = list(worker._build_local_splits_from_plan(plan, src_blocks_data, 2))
+
+        assert len(splits) == 2  # 2 source ranks
+
+        # Rank 0 (FA source, p_idx=0):
+        # FA: chunk=200//1=200, slot=0 → (1000, 200, 0), (2000, 200, 0)
+        # SSM: chunk=400//2=200, idx=0 → (3000, 200, 0)
+        assert splits[0] == [(1000, 200, 0), (2000, 200, 0), (3000, 200, 0)]
+
+        # Rank 1 (not FA source, p_idx=1):
+        # FA: chunk=200//1=200, slot=0 (skip_fa) → (1000, 200, 0), (2000, 200, 0)
+        # SSM: chunk=400//2=200, idx=1 → (3200, 200, 0)
+        assert splits[1] == [(1000, 200, 0), (2000, 200, 0), (3200, 200, 0)]
diff --git a/tests/v1/kv_connector/unit/utils.py b/tests/v1/kv_connector/unit/utils.py
index 5f0036807b0c..2c46538618ea 100644
--- a/tests/v1/kv_connector/unit/utils.py
+++ b/tests/v1/kv_connector/unit/utils.py
@@ -24,6 +24,7 @@
     KVConnectorBase_V1,
     KVConnectorMetadata,
     KVConnectorRole,
+    KVConnectorWorkerMetadata,
 )
 from vllm.distributed.kv_transfer.kv_connector.v1.example_connector import (  # noqa
     ExampleConnector,
@@ -102,6 +103,7 @@ def create_vllm_config(
     kv_load_failure_policy: Literal["recompute", "fail"] = "fail",
     kv_connector: str = "NixlConnector",
     kv_role: str = "kv_both",
+    disable_hybrid_kv_cache_manager: bool | None = None,
 ) -> VllmConfig:
     """Initialize VllmConfig For Testing."""
     model_config = ModelConfig(
@@ -117,6 +119,7 @@ def create_vllm_config(
         max_model_len=max_model_len,
         enable_chunked_prefill=enable_chunked_prefill,
         is_encoder_decoder=model_config.is_encoder_decoder,
+        disable_hybrid_kv_cache_manager=disable_hybrid_kv_cache_manager,
     )
     # Cache config, optionally force APC
     cache_config = CacheConfig(
@@ -249,6 +252,7 @@ def create_model_runner_output(
     invalid_block_ids: set[int] | None = None,
     use_eos: bool = False,
     token_id: int = 0,
+    kv_connector_worker_meta: KVConnectorWorkerMetadata | None = None,
 ) -> ModelRunnerOutput:
     """Make dummy model runner output for testing."""
 
@@ -266,11 +270,13 @@ def create_model_runner_output(
             finished_sending is None
             and finished_recving is None
             and invalid_block_ids is None
+            and kv_connector_worker_meta is None
         )
         else KVConnectorOutput(
             finished_sending=finished_sending,
             finished_recving=finished_recving,
             invalid_block_ids=invalid_block_ids or set(),
+            kv_connector_worker_meta=kv_connector_worker_meta,
         )
     )
 
@@ -287,9 +293,14 @@ def create_model_runner_output(
 
 
 class TestExampleConnector(ExampleConnector):
-    def __init__(self, config: VllmConfig, role, kv_cache_config):
+    def __init__(
+        self,
+        config: VllmConfig,
+        role: KVConnectorRole,
+        kv_cache_config: KVCacheConfig,
+    ):
         self.name = config.kv_transfer_config.kv_connector_extra_config["name"]
-        self._connector = ExampleConnector(config, role)
+        self._connector = ExampleConnector(config, role, kv_cache_config)
         self.call_record: dict[str, int] = defaultdict(int)
         # Use a unique temp file per connector
         self._event_file = (
@@ -362,7 +373,7 @@ def __init__(
         self,
         vllm_config: VllmConfig,
         role: KVConnectorRole,
-        kv_cache_config: KVCacheConfig | None = None,
+        kv_cache_config: KVCacheConfig,
     ):
         super().__init__(vllm_config, role, kv_cache_config)
         extra_config = self._kv_transfer_config.kv_connector_extra_config
diff --git a/tests/v1/kv_offload/cpu/test_gpu_worker.py b/tests/v1/kv_offload/cpu/test_gpu_worker.py
new file mode 100644
index 000000000000..e4ed635b9b70
--- /dev/null
+++ b/tests/v1/kv_offload/cpu/test_gpu_worker.py
@@ -0,0 +1,419 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import random
+import time
+import uuid
+
+import pytest
+import torch
+
+from vllm.platforms import current_platform
+from vllm.utils.torch_utils import set_random_seed
+from vllm.v1.kv_offload.base import (
+    CanonicalKVCacheRef,
+    CanonicalKVCaches,
+    CanonicalKVCacheTensor,
+    GPULoadStoreSpec,
+)
+from vllm.v1.kv_offload.cpu.common import CPULoadStoreSpec
+from vllm.v1.kv_offload.cpu.gpu_worker import CpuGpuOffloadingHandlers
+from vllm.v1.kv_offload.cpu.shared_offload_region import SharedOffloadRegion
+
+NUM_GPU_BLOCKS = [64]
+NUM_CPU_BLOCKS = [256]
+GPU_PAGE_SIZES = [512, 1024]
+BLOCK_SIZE_FACTORS = [1, 3]
+NUM_TENSORS = [4]
+SEEDS = [0]
+DEVICE_TYPE = current_platform.device_type
+DEVICES = [f"{DEVICE_TYPE}:0"]
+NUM_MAPPINGS = [3]
+NUM_MAPPINGS_PER_GROUP = [2]
+
+
+@pytest.mark.parametrize("gpu_to_cpu", [True, False])
+@pytest.mark.parametrize("num_mappings", NUM_MAPPINGS)
+@pytest.mark.parametrize("gpu_page_size_bytes", GPU_PAGE_SIZES)
+@pytest.mark.parametrize("block_size_factor", BLOCK_SIZE_FACTORS)
+@pytest.mark.parametrize("num_gpu_blocks", NUM_GPU_BLOCKS)
+@pytest.mark.parametrize("num_cpu_blocks", NUM_CPU_BLOCKS)
+@pytest.mark.parametrize("num_tensors", NUM_TENSORS)
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("device", DEVICES)
+@pytest.mark.parametrize("use_shared_memory", [False, True])
+@torch.inference_mode()
+def test_transfer(
+    default_vllm_config,
+    gpu_to_cpu: bool,
+    num_mappings: int,
+    gpu_page_size_bytes: int,
+    block_size_factor: int,
+    num_gpu_blocks: int,
+    num_cpu_blocks: int,
+    num_tensors: int,
+    seed: int,
+    device: str,
+    use_shared_memory: bool,
+) -> None:
+    set_random_seed(seed)
+
+    # build CanonicalKVCacheTensor list: one per tensor
+    kv_cache_tensors: list[CanonicalKVCacheTensor] = []
+    for i in range(num_tensors):
+        gpu_tensor = torch.zeros(
+            (num_gpu_blocks, gpu_page_size_bytes),
+            dtype=torch.int8,
+            device=device,
+        )
+        kv_cache_tensors.append(
+            CanonicalKVCacheTensor(
+                tensor=gpu_tensor,
+                page_size_bytes=gpu_page_size_bytes,
+            )
+        )
+
+    # one group containing all tensors, one data ref per tensor
+    kv_cache_groups_data_refs: list[list[CanonicalKVCacheRef]] = [
+        [
+            CanonicalKVCacheRef(
+                tensor_idx=i,
+                page_size_bytes=gpu_page_size_bytes,
+            )
+            for i in range(num_tensors)
+        ]
+    ]
+
+    kv_caches = CanonicalKVCaches(
+        tensors=kv_cache_tensors,
+        group_data_refs=kv_cache_groups_data_refs,
+    )
+
+    mmap_region: SharedOffloadRegion | None = None
+    if use_shared_memory:
+        cpu_page_size = gpu_page_size_bytes * num_tensors * block_size_factor
+        mmap_region = SharedOffloadRegion(
+            instance_id=str(uuid.uuid4()),
+            total_size_bytes=num_cpu_blocks * cpu_page_size,
+            num_blocks=num_cpu_blocks,
+            rank=0,
+            num_workers=1,
+            cpu_page_size=cpu_page_size,
+        )
+
+    handlers = CpuGpuOffloadingHandlers(
+        kv_caches=kv_caches,
+        block_size_factor=block_size_factor,
+        num_cpu_blocks=num_cpu_blocks,
+        mmap_region=mmap_region,
+    )
+
+    # select block mappings
+    gpu_blocks = random.sample(range(num_gpu_blocks), num_mappings * block_size_factor)
+    cpu_blocks = random.sample(range(num_cpu_blocks), num_mappings)
+
+    # expand cpu blocks to gpu-page granularity for uniform comparison:
+    # each cpu block maps to block_size_factor consecutive sub-blocks
+    cpu_blocks_expanded = [
+        cpu_block * block_size_factor + j
+        for cpu_block in cpu_blocks
+        for j in range(block_size_factor)
+    ]
+
+    # maybe skip some GPU blocks to test reading/writing from the middle of a CPU block
+    blocks_to_skip = block_size_factor - 1
+    if blocks_to_skip > 0:
+        gpu_blocks = gpu_blocks[blocks_to_skip:]
+        cpu_blocks_expanded = cpu_blocks_expanded[blocks_to_skip:]
+
+    # set transfer direction
+    if gpu_to_cpu:
+        handler = handlers.gpu_to_cpu_handler
+        src_spec = GPULoadStoreSpec(
+            gpu_blocks, group_sizes=(len(gpu_blocks),), block_indices=(blocks_to_skip,)
+        )
+        dst_spec = CPULoadStoreSpec(cpu_blocks)
+        dst_to_src = dict(zip(cpu_blocks_expanded, gpu_blocks))
+        num_dst_sub_blocks = num_gpu_blocks
+    else:
+        handler = handlers.cpu_to_gpu_handler
+        src_spec = CPULoadStoreSpec(cpu_blocks)
+        dst_spec = GPULoadStoreSpec(
+            gpu_blocks, group_sizes=(len(gpu_blocks),), block_indices=(blocks_to_skip,)
+        )
+        dst_to_src = dict(zip(gpu_blocks, cpu_blocks_expanded))
+        num_dst_sub_blocks = num_gpu_blocks
+
+    # randomize src and dst tensors before transfer
+    for tensor in handler.src_tensors:
+        tensor.random_()
+    for tensor in handler.dst_tensors:
+        tensor.random_()
+
+    # clone src and dst tensors before transfer
+    orig_src_tensors = [x.clone() for x in handler.src_tensors]
+    orig_dst_tensors = [x.clone() for x in handler.dst_tensors]
+
+    # call transfer function
+    start_time = time.time()
+    assert handler.transfer_async(1, (src_spec, dst_spec))
+    assert {x.job_id for x in handler._transfers} == {1}
+
+    # wait for transfer to complete
+    end_time = time.time() + 10
+    while time.time() < end_time:
+        finished = handler.get_finished()
+        if finished:
+            assert finished[0].job_id == 1
+            assert finished[0].success
+            assert (
+                finished[0].transfer_type == ("GPU", "CPU")
+                if gpu_to_cpu
+                else ("CPU", "GPU")
+            )
+            assert finished[0].transfer_size == (
+                len(gpu_blocks)
+                * sum([x.page_size_bytes for x in handler.kv_cache_groups_data_refs[0]])
+            )
+            assert finished[0].transfer_time > 0
+            assert finished[0].transfer_time < (time.time() - start_time)
+            break
+        time.sleep(0.1)
+
+    # verify src tensors did not change
+    for orig_tensor, tensor in zip(orig_src_tensors, handler.src_tensors):
+        assert torch.equal(orig_tensor, tensor)
+
+    # verify dst tensors at gpu-page granularity.
+    for src_tensor, dst_tensor, orig_dst_tensor in zip(
+        handler.src_tensors,
+        handler.dst_tensors,
+        orig_dst_tensors,
+    ):
+        # view both GPU and CPU tensors as (n, gpu_page_size_bytes) for comparison.
+        src_view = src_tensor.reshape(-1, gpu_page_size_bytes)
+        dst_view = dst_tensor.reshape(-1, gpu_page_size_bytes)
+        orig_dst_view = orig_dst_tensor.reshape(-1, gpu_page_size_bytes)
+        for dst_sub_block in range(num_dst_sub_blocks):
+            src_sub_block = dst_to_src.get(dst_sub_block)
+            if src_sub_block is not None:
+                expected = src_view[src_sub_block]
+            else:
+                expected = orig_dst_view[dst_sub_block]
+            torch.testing.assert_close(dst_view[dst_sub_block].cpu(), expected.cpu())
+
+    # Drop loop-variable refs so mmap_obj has no exported buffers at cleanup.
+    del orig_tensor, tensor, src_tensor, dst_tensor, orig_dst_tensor
+    del src_view, dst_view, orig_dst_view, expected
+
+    handlers.cpu_to_gpu_handler.shutdown()
+    handlers.gpu_to_cpu_handler.shutdown()
+    if mmap_region:
+        mmap_region.cleanup()
+
+
+@pytest.mark.parametrize("gpu_to_cpu", [True, False])
+@pytest.mark.parametrize("num_mappings_per_group", NUM_MAPPINGS_PER_GROUP)
+@pytest.mark.parametrize("gpu_page_size_bytes", GPU_PAGE_SIZES)
+@pytest.mark.parametrize("block_size_factor", BLOCK_SIZE_FACTORS)
+@pytest.mark.parametrize("num_gpu_blocks", NUM_GPU_BLOCKS)
+@pytest.mark.parametrize("num_cpu_blocks", NUM_CPU_BLOCKS)
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("device", DEVICES)
+@torch.inference_mode()
+def test_transfer_multi_group(
+    default_vllm_config,
+    gpu_to_cpu: bool,
+    num_mappings_per_group: int,
+    gpu_page_size_bytes: int,
+    block_size_factor: int,
+    num_gpu_blocks: int,
+    num_cpu_blocks: int,
+    seed: int,
+    device: str,
+) -> None:
+    """Test transfers with three KV cache groups:
+    - Group 0: aligned transfer with num_mappings_per_group blocks
+    - Group 1: zero blocks (empty group)
+    - Group 2: unaligned CPU->GPU transfer (logical_offset=block_size_factor-1,
+      causing the implementation to skip source sub-blocks) with
+      num_mappings_per_group blocks
+    """
+    set_random_seed(seed)
+
+    # 3 groups, each with 2 tensors
+    num_groups = 3
+    tensors_per_group = 2
+    num_tensors = num_groups * tensors_per_group
+    kv_cache_tensors: list[CanonicalKVCacheTensor] = []
+    for _ in range(num_tensors):
+        gpu_tensor = torch.zeros(
+            (num_gpu_blocks, gpu_page_size_bytes),
+            dtype=torch.int8,
+            device=device,
+        )
+        kv_cache_tensors.append(
+            CanonicalKVCacheTensor(
+                tensor=gpu_tensor,
+                page_size_bytes=gpu_page_size_bytes,
+            )
+        )
+
+    kv_cache_groups_data_refs: list[list[CanonicalKVCacheRef]] = [
+        [
+            CanonicalKVCacheRef(
+                tensor_idx=g * tensors_per_group + i,
+                page_size_bytes=gpu_page_size_bytes,
+            )
+            for i in range(tensors_per_group)
+        ]
+        for g in range(num_groups)
+    ]
+
+    canonical_kv_caches = CanonicalKVCaches(
+        tensors=kv_cache_tensors, group_data_refs=kv_cache_groups_data_refs
+    )
+
+    handlers = CpuGpuOffloadingHandlers(
+        kv_caches=canonical_kv_caches,
+        block_size_factor=block_size_factor,
+        num_cpu_blocks=num_cpu_blocks,
+    )
+
+    # group 0: aligned, group 1: empty, group 2: unaligned on CPU->GPU
+    group_sizes_in_cpu_blocks = [num_mappings_per_group, 0, num_mappings_per_group]
+
+    total_cpu_blocks = sum(group_sizes_in_cpu_blocks)
+    total_gpu_blocks_needed = total_cpu_blocks * block_size_factor
+    gpu_blocks_all = random.sample(range(num_gpu_blocks), total_gpu_blocks_needed)
+    cpu_blocks_all = random.sample(range(num_cpu_blocks), total_cpu_blocks)
+
+    # split gpu/cpu blocks per group
+    gpu_blocks_per_group: list[list[int]] = []
+    cpu_blocks_per_group: list[list[int]] = []
+    gpu_offset = 0
+    cpu_offset = 0
+    for size in group_sizes_in_cpu_blocks:
+        gpu_count = size * block_size_factor
+        gpu_blocks_per_group.append(gpu_blocks_all[gpu_offset : gpu_offset + gpu_count])
+        cpu_blocks_per_group.append(cpu_blocks_all[cpu_offset : cpu_offset + size])
+        gpu_offset += gpu_count
+        cpu_offset += size
+
+    # expand cpu blocks to gpu-page granularity
+    cpu_blocks_expanded_per_group = [
+        [
+            cpu_block * block_size_factor + j
+            for cpu_block in cpu_blocks
+            for j in range(block_size_factor)
+        ]
+        for cpu_blocks in cpu_blocks_per_group
+    ]
+
+    # skip sub-blocks from group 2 to test unaligned transfers.
+    sub_blocks_to_skip = block_size_factor - 1  # e.g. 2 when block_size_factor=3
+    if sub_blocks_to_skip > 0:
+        gpu_blocks_per_group[2] = gpu_blocks_per_group[2][
+            sub_blocks_to_skip:-sub_blocks_to_skip
+        ]
+        cpu_blocks_expanded_per_group[2] = cpu_blocks_expanded_per_group[2][
+            sub_blocks_to_skip:-sub_blocks_to_skip
+        ]
+
+    # build flat gpu_blocks list and group_sizes in GPU blocks
+    gpu_blocks: list[int] = []
+    group_sizes: list[int] = []
+    for gpu_blks in gpu_blocks_per_group:
+        gpu_blocks.extend(gpu_blks)
+        group_sizes.append(len(gpu_blks))
+
+    # build flat cpu_blocks list
+    cpu_blocks = []
+    for cpu_blks in cpu_blocks_per_group:
+        cpu_blocks.extend(cpu_blks)
+
+    # block_indices: only relevant for unaligned transfers
+    block_indices: list[int] = [0, 0, sub_blocks_to_skip]
+
+    if gpu_to_cpu:
+        handler = handlers.gpu_to_cpu_handler
+        src_spec = GPULoadStoreSpec(
+            gpu_blocks, group_sizes=group_sizes, block_indices=block_indices
+        )
+        dst_spec = CPULoadStoreSpec(cpu_blocks)
+        # per-group mapping: cpu sub-block -> gpu sub-block
+        dst_to_src_per_group = [
+            dict(zip(expanded, gpu_blks))
+            for expanded, gpu_blks in zip(
+                cpu_blocks_expanded_per_group, gpu_blocks_per_group
+            )
+        ]
+        num_dst_sub_blocks = num_cpu_blocks * block_size_factor
+    else:
+        handler = handlers.cpu_to_gpu_handler
+        src_spec = CPULoadStoreSpec(cpu_blocks)
+        dst_spec = GPULoadStoreSpec(
+            gpu_blocks, group_sizes=group_sizes, block_indices=block_indices
+        )
+        # per-group mapping: gpu sub-block -> cpu sub-block
+        dst_to_src_per_group = [
+            dict(zip(gpu_blks, expanded))
+            for gpu_blks, expanded in zip(
+                gpu_blocks_per_group, cpu_blocks_expanded_per_group
+            )
+        ]
+        num_dst_sub_blocks = num_gpu_blocks
+
+    # randomize src and dst tensors before transfer
+    for tensor in handler.src_tensors:
+        tensor.random_()
+    for tensor in handler.dst_tensors:
+        tensor.random_()
+
+    orig_src_tensors = [x.clone() for x in handler.src_tensors]
+    orig_dst_tensors = [x.clone() for x in handler.dst_tensors]
+
+    assert handler.transfer_async(1, (src_spec, dst_spec))
+    assert {x.job_id for x in handler._transfers} == {1}
+
+    end_time = time.time() + 10
+    while time.time() < end_time:
+        finished = handler.get_finished()
+        if finished:
+            assert finished[0].job_id == 1
+            assert finished[0].success
+            expected_bytes = sum(
+                group_size * sum([x.page_size_bytes for x in data_refs])
+                for group_size, data_refs in zip(
+                    group_sizes, handler.kv_cache_groups_data_refs
+                )
+            )
+            assert finished[0].transfer_size == expected_bytes
+            break
+        time.sleep(0.1)
+
+    # verify src tensors did not change
+    for orig_tensor, tensor in zip(orig_src_tensors, handler.src_tensors):
+        assert torch.equal(orig_tensor, tensor)
+
+    # verify dst tensors at gpu-page granularity
+    for group_idx, dst_to_src in enumerate(dst_to_src_per_group):
+        group_tensor_offset = group_idx * tensors_per_group
+        for tensor_idx in range(tensors_per_group):
+            src_tensor = handler.src_tensors[group_tensor_offset + tensor_idx]
+            dst_tensor = handler.dst_tensors[group_tensor_offset + tensor_idx]
+            orig_dst_tensor = orig_dst_tensors[group_tensor_offset + tensor_idx]
+            src_view = src_tensor.view(-1, gpu_page_size_bytes)
+            dst_view = dst_tensor.view(-1, gpu_page_size_bytes)
+            orig_dst_view = orig_dst_tensor.view(-1, gpu_page_size_bytes)
+            for dst_sub_block in range(num_dst_sub_blocks):
+                src_sub_block = dst_to_src.get(dst_sub_block)
+                if src_sub_block is not None:
+                    expected = src_view[src_sub_block]
+                else:
+                    expected = orig_dst_view[dst_sub_block]
+                torch.testing.assert_close(
+                    dst_view[dst_sub_block].cpu(), expected.cpu()
+                )
+
+    handlers.cpu_to_gpu_handler.shutdown()
+    handlers.gpu_to_cpu_handler.shutdown()
diff --git a/tests/v1/kv_offload/test_cpu_manager.py b/tests/v1/kv_offload/cpu/test_manager.py
similarity index 69%
rename from tests/v1/kv_offload/test_cpu_manager.py
rename to tests/v1/kv_offload/cpu/test_manager.py
index 85629c96cea1..86cec459c3e5 100644
--- a/tests/v1/kv_offload/test_cpu_manager.py
+++ b/tests/v1/kv_offload/cpu/test_manager.py
@@ -6,19 +6,28 @@
 import numpy as np
 import pytest
 
-from vllm.v1.kv_offload.abstract import (
+from vllm.v1.kv_offload.base import (
     LoadStoreSpec,
     OffloadingEvent,
     OffloadKey,
     PrepareStoreOutput,
+    ReqContext,
     make_offload_key,
 )
+from vllm.v1.kv_offload.cpu.common import CPULoadStoreSpec
 from vllm.v1.kv_offload.cpu.manager import CPUOffloadingManager
 from vllm.v1.kv_offload.cpu.policies.arc import ARCCachePolicy
-from vllm.v1.kv_offload.mediums import CPULoadStoreSpec
 from vllm.v1.kv_offload.reuse_manager import FilterReusedOffloadingManager
 
 
+def make_req_context(kv_transfer_params: dict | None = None) -> ReqContext:
+    """Create a ReqContext as production code would, from a request's params."""
+    return ReqContext(kv_transfer_params=kv_transfer_params)
+
+
+_EMPTY_REQ_CTX = make_req_context()
+
+
 @dataclass
 class ExpectedPrepareStoreOutput:
     keys_to_store: list[int]
@@ -26,8 +35,12 @@ class ExpectedPrepareStoreOutput:
     evicted_keys: list[int]
 
 
-def to_keys(int_ids: list[int]) -> list[OffloadKey]:
-    return [make_offload_key(str(i).encode(), 0) for i in int_ids]
+def to_key(int_hash: int) -> OffloadKey:
+    return make_offload_key(str(int_hash).encode(), 0)
+
+
+def to_keys(int_hashes: list[int]) -> list[OffloadKey]:
+    return [to_key(i) for i in int_hashes]
 
 
 def verify_store_output(
@@ -59,7 +72,6 @@ def verify_load_output(
 
 def verify_events(
     events: Iterable[OffloadingEvent],
-    block_size: int,
     expected_stores: tuple[set[int], ...] = (),
     expected_evictions: tuple[set[int], ...] = (),
 ):
@@ -67,7 +79,6 @@ def verify_events(
     evictions: list[set[OffloadKey]] = []
     for event in events:
         assert event.medium == CPULoadStoreSpec.medium()
-        assert event.block_size == block_size
         if event.removed:
             evictions.append(set(event.keys))
         else:
@@ -98,26 +109,24 @@ def test_already_stored_block_not_evicted_during_prepare_store(eviction_policy):
               candidate to make room for [3, 4, 5]
         - After complete_store([2, 3, 4, 5]), block 2 must still be present.
     """
-    block_size = 256
     manager = CPUOffloadingManager(
-        block_size=block_size,
         num_blocks=4,
         cache_policy=eviction_policy,
         enable_events=True,
     )
 
     # store [1, 2] and complete
-    manager.prepare_store(to_keys([1, 2]))
-    manager.complete_store(to_keys([1, 2]))
+    manager.prepare_store(to_keys([1, 2]), _EMPTY_REQ_CTX)
+    manager.complete_store(to_keys([1, 2]), _EMPTY_REQ_CTX)
 
     # touch [1] to make block 2 the LRU candidate
-    manager.touch(to_keys([1]))
+    manager.touch(to_keys([1]), _EMPTY_REQ_CTX)
 
     # prepare_store([2, 3, 4, 5]):
     #   - block 2 is already stored -> filtered out of keys_to_store
     #   - block 2 must NOT be evicted even though it is the LRU candidate
     #   - block 1 (ID 0) is evicted instead; new blocks [3,4,5] get IDs 2,3,0
-    prepare_store_output = manager.prepare_store(to_keys([2, 3, 4, 5]))
+    prepare_store_output = manager.prepare_store(to_keys([2, 3, 4, 5]), _EMPTY_REQ_CTX)
     verify_store_output(
         prepare_store_output,
         ExpectedPrepareStoreOutput(
@@ -128,24 +137,23 @@ def test_already_stored_block_not_evicted_during_prepare_store(eviction_policy):
     )
 
     # complete_store must not silently drop block 2
-    manager.complete_store(to_keys([2, 3, 4, 5]))
+    manager.complete_store(to_keys([2, 3, 4, 5]), _EMPTY_REQ_CTX)
 
     # block 2 must still be present in the cache
-    assert manager.lookup(to_keys([2])) == 1
+    assert manager.lookup(to_key(2), _EMPTY_REQ_CTX) is True
 
 
 def test_cpu_manager():
     """
     Tests CPUOffloadingManager with lru policy.
     """
-    # initialize a CPU backend with a capacity of 4 blocks
-    block_size = 256
+    # initialize a CPU manager with a capacity of 4 blocks
     cpu_manager = CPUOffloadingManager(
-        block_size=block_size, num_blocks=4, cache_policy="lru", enable_events=True
+        num_blocks=4, cache_policy="lru", enable_events=True
     )
 
     # prepare store [1, 2]
-    prepare_store_output = cpu_manager.prepare_store(to_keys([1, 2]))
+    prepare_store_output = cpu_manager.prepare_store(to_keys([1, 2]), _EMPTY_REQ_CTX)
     verify_store_output(
         prepare_store_output,
         ExpectedPrepareStoreOutput(
@@ -155,25 +163,26 @@ def test_cpu_manager():
         ),
     )
 
-    # lookup [1, 2] -> not ready
-    assert cpu_manager.lookup(to_keys([1, 2])) == 0
+    # lookup [1, 2] -> write in-flight, not yet ready
+    assert cpu_manager.lookup(to_key(1), _EMPTY_REQ_CTX) is None
+    assert cpu_manager.lookup(to_key(2), _EMPTY_REQ_CTX) is None
 
     # no events so far
     assert list(cpu_manager.take_events()) == []
 
     # complete store [1, 2]
-    cpu_manager.complete_store(to_keys([1, 2]))
-    verify_events(
-        cpu_manager.take_events(), block_size=block_size, expected_stores=({1, 2},)
-    )
+    cpu_manager.complete_store(to_keys([1, 2]), _EMPTY_REQ_CTX)
+    verify_events(cpu_manager.take_events(), expected_stores=({1, 2},))
 
     # lookup [1, 2]
-    assert cpu_manager.lookup(to_keys([1])) == 1
-    assert cpu_manager.lookup(to_keys([1, 2])) == 2
-    assert cpu_manager.lookup(to_keys([1, 2, 3])) == 2
+    assert cpu_manager.lookup(to_key(1), _EMPTY_REQ_CTX) is True
+    assert cpu_manager.lookup(to_key(2), _EMPTY_REQ_CTX) is True
+    assert cpu_manager.lookup(to_key(3), _EMPTY_REQ_CTX) is False
 
     # prepare store [2, 3, 4, 5] -> evicts [1]
-    prepare_store_output = cpu_manager.prepare_store(to_keys([2, 3, 4, 5]))
+    prepare_store_output = cpu_manager.prepare_store(
+        to_keys([2, 3, 4, 5]), _EMPTY_REQ_CTX
+    )
     verify_store_output(
         prepare_store_output,
         ExpectedPrepareStoreOutput(
@@ -184,28 +193,34 @@ def test_cpu_manager():
     )
 
     # verify eviction event
-    verify_events(
-        cpu_manager.take_events(), block_size=block_size, expected_evictions=({1},)
-    )
+    verify_events(cpu_manager.take_events(), expected_evictions=({1},))
 
     # prepare store with no space
-    assert cpu_manager.prepare_store(to_keys([1, 6])) is None
+    assert cpu_manager.prepare_store(to_keys([1, 6]), _EMPTY_REQ_CTX) is None
 
     # complete store [2, 3, 4, 5]
-    cpu_manager.complete_store(to_keys([2, 3, 4, 5]))
+    cpu_manager.complete_store(to_keys([2, 3, 4, 5]), _EMPTY_REQ_CTX)
+
+    # lookup (now that we have [2, 3, 4, 5])
+    assert cpu_manager.lookup(to_key(1), _EMPTY_REQ_CTX) is False
+    assert cpu_manager.lookup(to_key(2), _EMPTY_REQ_CTX) is True
+    assert cpu_manager.lookup(to_key(3), _EMPTY_REQ_CTX) is True
+    assert cpu_manager.lookup(to_key(4), _EMPTY_REQ_CTX) is True
+    assert cpu_manager.lookup(to_key(5), _EMPTY_REQ_CTX) is True
+    assert cpu_manager.lookup(to_key(0), _EMPTY_REQ_CTX) is False
 
     # prepare load [2, 3]
-    prepare_load_output = cpu_manager.prepare_load(to_keys([2, 3]))
+    prepare_load_output = cpu_manager.prepare_load(to_keys([2, 3]), _EMPTY_REQ_CTX)
     verify_load_output(prepare_load_output, [1, 2])
 
     # prepare store with no space ([2, 3] is being loaded)
-    assert cpu_manager.prepare_store(to_keys([6, 7, 8])) is None
+    assert cpu_manager.prepare_store(to_keys([6, 7, 8]), _EMPTY_REQ_CTX) is None
 
     # complete load [2, 3]
-    cpu_manager.complete_load(to_keys([2, 3]))
+    cpu_manager.complete_load(to_keys([2, 3]), _EMPTY_REQ_CTX)
 
     # prepare store [6, 7, 8] -> evicts [2, 3, 4] (oldest)
-    prepare_store_output = cpu_manager.prepare_store(to_keys([6, 7, 8]))
+    prepare_store_output = cpu_manager.prepare_store(to_keys([6, 7, 8]), _EMPTY_REQ_CTX)
     verify_store_output(
         prepare_store_output,
         ExpectedPrepareStoreOutput(
@@ -216,13 +231,13 @@ def test_cpu_manager():
     )
 
     # complete store [6, 7, 8]
-    cpu_manager.complete_store(to_keys([6, 7, 8]))
+    cpu_manager.complete_store(to_keys([6, 7, 8]), _EMPTY_REQ_CTX)
 
     # touch [5, 6, 7] (move to end of LRU order)
-    cpu_manager.touch(to_keys([5, 6, 7]))
+    cpu_manager.touch(to_keys([5, 6, 7]), _EMPTY_REQ_CTX)
 
     # prepare store [7, 9] -> evicts [8] (oldest following previous touch)
-    prepare_store_output = cpu_manager.prepare_store(to_keys([9]))
+    prepare_store_output = cpu_manager.prepare_store(to_keys([9]), _EMPTY_REQ_CTX)
     verify_store_output(
         prepare_store_output,
         ExpectedPrepareStoreOutput(
@@ -233,15 +248,14 @@ def test_cpu_manager():
     )
 
     # complete store [7, 9] with failure
-    cpu_manager.complete_store(to_keys([7, 9]), success=False)
+    cpu_manager.complete_store(to_keys([7, 9]), _EMPTY_REQ_CTX, success=False)
 
     # assert [7] is still stored, but [9] is not
-    assert cpu_manager.lookup(to_keys([7])) == 1
-    assert cpu_manager.lookup(to_keys([9])) == 0
+    assert cpu_manager.lookup(to_key(7), _EMPTY_REQ_CTX) is True
+    assert cpu_manager.lookup(to_key(9), _EMPTY_REQ_CTX) is False
 
     verify_events(
         cpu_manager.take_events(),
-        block_size=block_size,
         expected_stores=({3, 4, 5}, {6, 7, 8}),
         expected_evictions=({2, 3, 4}, {8}),
     )
@@ -254,7 +268,6 @@ def _make_manager(
         self, num_blocks: int = 4, enable_events: bool = True
     ) -> tuple[CPUOffloadingManager, ARCCachePolicy]:
         manager = CPUOffloadingManager(
-            block_size=256,
             num_blocks=num_blocks,
             cache_policy="arc",
             enable_events=enable_events,
@@ -271,7 +284,9 @@ def test_basic(self):
         cpu_manager, arc_policy = self._make_manager()
 
         # prepare store [1, 2]
-        prepare_store_output = cpu_manager.prepare_store(to_keys([1, 2]))
+        prepare_store_output = cpu_manager.prepare_store(
+            to_keys([1, 2]), _EMPTY_REQ_CTX
+        )
         verify_store_output(
             prepare_store_output,
             ExpectedPrepareStoreOutput(
@@ -281,22 +296,21 @@ def test_basic(self):
             ),
         )
 
-        # lookup [1, 2] -> not ready
-        assert cpu_manager.lookup(to_keys([1, 2])) == 0
+        # lookup [1, 2] -> write in-flight, not yet ready
+        assert cpu_manager.lookup(to_key(1), _EMPTY_REQ_CTX) is None
+        assert cpu_manager.lookup(to_key(2), _EMPTY_REQ_CTX) is None
 
         # no events so far
         assert list(cpu_manager.take_events()) == []
 
         # complete store [1, 2]
-        cpu_manager.complete_store(to_keys([1, 2]))
-        verify_events(
-            cpu_manager.take_events(), block_size=256, expected_stores=({1, 2},)
-        )
+        cpu_manager.complete_store(to_keys([1, 2]), _EMPTY_REQ_CTX)
+        verify_events(cpu_manager.take_events(), expected_stores=({1, 2},))
 
         # lookup [1, 2]
-        assert cpu_manager.lookup(to_keys([1])) == 1
-        assert cpu_manager.lookup(to_keys([1, 2])) == 2
-        assert cpu_manager.lookup(to_keys([1, 2, 3])) == 2
+        assert cpu_manager.lookup(to_key(1), _EMPTY_REQ_CTX) is True
+        assert cpu_manager.lookup(to_key(2), _EMPTY_REQ_CTX) is True
+        assert cpu_manager.lookup(to_key(3), _EMPTY_REQ_CTX) is False
 
         # blocks should be in T1 (recent)
         assert len(arc_policy.t1) == 2
@@ -310,15 +324,15 @@ def test_t1_to_t2_promotion(self):
         cpu_manager, arc_policy = self._make_manager(enable_events=False)
 
         # store and complete block 1
-        cpu_manager.prepare_store(to_keys([1]))
-        cpu_manager.complete_store(to_keys([1]))
+        cpu_manager.prepare_store(to_keys([1]), _EMPTY_REQ_CTX)
+        cpu_manager.complete_store(to_keys([1]), _EMPTY_REQ_CTX)
 
         # block 1 starts in T1 (recent)
         assert to_keys([1])[0] in arc_policy.t1
         assert to_keys([1])[0] not in arc_policy.t2
 
         # touch block 1 (simulate second access)
-        cpu_manager.touch(to_keys([1]))
+        cpu_manager.touch(to_keys([1]), _EMPTY_REQ_CTX)
 
         # block 1 should now be in T2 (frequent)
         assert to_keys([1])[0] not in arc_policy.t1
@@ -332,7 +346,9 @@ def test_eviction_with_load(self):
         cpu_manager, _ = self._make_manager()
 
         # prepare and complete store [1, 2, 3, 4]
-        prepare_store_output = cpu_manager.prepare_store(to_keys([1, 2, 3, 4]))
+        prepare_store_output = cpu_manager.prepare_store(
+            to_keys([1, 2, 3, 4]), _EMPTY_REQ_CTX
+        )
         verify_store_output(
             prepare_store_output,
             ExpectedPrepareStoreOutput(
@@ -341,22 +357,24 @@ def test_eviction_with_load(self):
                 evicted_keys=[],
             ),
         )
-        cpu_manager.complete_store(to_keys([1, 2, 3, 4]))
+        cpu_manager.complete_store(to_keys([1, 2, 3, 4]), _EMPTY_REQ_CTX)
 
         # prepare load [2, 3] (increases ref_cnt)
-        prepare_load_output = cpu_manager.prepare_load(to_keys([2, 3]))
+        prepare_load_output = cpu_manager.prepare_load(to_keys([2, 3]), _EMPTY_REQ_CTX)
         verify_load_output(prepare_load_output, [1, 2])
 
         # prepare store [5, 6, 7] with [2, 3] being loaded
         # should fail because [2, 3] have ref_cnt > 0
-        assert cpu_manager.prepare_store(to_keys([5, 6, 7])) is None
+        assert cpu_manager.prepare_store(to_keys([5, 6, 7]), _EMPTY_REQ_CTX) is None
 
         # complete load [2, 3]
-        cpu_manager.complete_load(to_keys([2, 3]))
+        cpu_manager.complete_load(to_keys([2, 3]), _EMPTY_REQ_CTX)
 
         # now prepare store [5, 6, 7] should succeed
         # ARC will evict blocks one at a time from T1 as needed
-        prepare_store_output = cpu_manager.prepare_store(to_keys([5, 6, 7]))
+        prepare_store_output = cpu_manager.prepare_store(
+            to_keys([5, 6, 7]), _EMPTY_REQ_CTX
+        )
         assert prepare_store_output is not None
         # Should successfully evict enough blocks to make room (at least 1)
         assert len(prepare_store_output.evicted_keys) >= 1
@@ -370,21 +388,21 @@ def test_adaptive_target(self):
         cpu_manager, arc_policy = self._make_manager(num_blocks=2, enable_events=False)
 
         # store blocks 1, 2 (fills cache)
-        cpu_manager.prepare_store(to_keys([1, 2]))
-        cpu_manager.complete_store(to_keys([1, 2]))
+        cpu_manager.prepare_store(to_keys([1, 2]), _EMPTY_REQ_CTX)
+        cpu_manager.complete_store(to_keys([1, 2]), _EMPTY_REQ_CTX)
 
         initial_target = arc_policy.target_t1_size
 
         # store block 3, evicting block 1 (moves to B1 ghost list)
-        cpu_manager.prepare_store(to_keys([3]))
-        cpu_manager.complete_store(to_keys([3]))
+        cpu_manager.prepare_store(to_keys([3]), _EMPTY_REQ_CTX)
+        cpu_manager.complete_store(to_keys([3]), _EMPTY_REQ_CTX)
 
         # block 1 should be in B1 (ghost list)
         assert to_keys([1])[0] in arc_policy.b1
 
         # touch block 1 (cache miss, but in B1)
         # this should increase target_t1_size (favor recency)
-        cpu_manager.touch(to_keys([1]))
+        cpu_manager.touch(to_keys([1]), _EMPTY_REQ_CTX)
 
         # target should have increased
         assert arc_policy.target_t1_size > initial_target
@@ -397,11 +415,11 @@ def test_t1_t2_eviction_policy(self):
         cpu_manager, arc_policy = self._make_manager(enable_events=False)
 
         # store blocks 1, 2, 3, 4
-        cpu_manager.prepare_store(to_keys([1, 2, 3, 4]))
-        cpu_manager.complete_store(to_keys([1, 2, 3, 4]))
+        cpu_manager.prepare_store(to_keys([1, 2, 3, 4]), _EMPTY_REQ_CTX)
+        cpu_manager.complete_store(to_keys([1, 2, 3, 4]), _EMPTY_REQ_CTX)
 
         # promote blocks 3, 4 to T2 by touching them
-        cpu_manager.touch(to_keys([3, 4]))
+        cpu_manager.touch(to_keys([3, 4]), _EMPTY_REQ_CTX)
 
         # now: T1 = {1, 2}, T2 = {3, 4}
         assert len(arc_policy.t1) == 2
@@ -412,11 +430,11 @@ def test_t1_t2_eviction_policy(self):
         arc_policy.target_t1_size = 1
 
         # store block 5, should evict from T1 (block 1, LRU in T1)
-        output = cpu_manager.prepare_store(to_keys([5]))
+        output = cpu_manager.prepare_store(to_keys([5]), _EMPTY_REQ_CTX)
         assert output is not None
         assert to_keys([1]) == output.evicted_keys
 
-        cpu_manager.complete_store(to_keys([5]))
+        cpu_manager.complete_store(to_keys([5]), _EMPTY_REQ_CTX)
 
         # block 1 should be in B1 (ghost list)
         assert to_keys([1])[0] in arc_policy.b1
@@ -431,13 +449,13 @@ def test_ghost_list_bounds(self):
         cpu_manager, arc_policy = self._make_manager(num_blocks=2, enable_events=False)
 
         # fill cache with blocks 1, 2
-        cpu_manager.prepare_store(to_keys([1, 2]))
-        cpu_manager.complete_store(to_keys([1, 2]))
+        cpu_manager.prepare_store(to_keys([1, 2]), _EMPTY_REQ_CTX)
+        cpu_manager.complete_store(to_keys([1, 2]), _EMPTY_REQ_CTX)
 
         # store many blocks to fill ghost lists
         for i in range(3, 20):
-            cpu_manager.prepare_store(to_keys([i]))
-            cpu_manager.complete_store(to_keys([i]))
+            cpu_manager.prepare_store(to_keys([i]), _EMPTY_REQ_CTX)
+            cpu_manager.complete_store(to_keys([i]), _EMPTY_REQ_CTX)
 
         # ghost lists should not exceed cache_capacity
         assert len(arc_policy.b1) <= arc_policy.cache_capacity
@@ -451,22 +469,22 @@ def test_touch_ordering(self):
         cpu_manager, arc_policy = self._make_manager()
 
         # store blocks 1, 2, 3, 4
-        cpu_manager.prepare_store(to_keys([1, 2, 3, 4]))
-        cpu_manager.complete_store(to_keys([1, 2, 3, 4]))
+        cpu_manager.prepare_store(to_keys([1, 2, 3, 4]), _EMPTY_REQ_CTX)
+        cpu_manager.complete_store(to_keys([1, 2, 3, 4]), _EMPTY_REQ_CTX)
 
         # promote 3, 4 to T2
-        cpu_manager.touch(to_keys([3, 4]))
+        cpu_manager.touch(to_keys([3, 4]), _EMPTY_REQ_CTX)
 
         # T1 = {1, 2}, T2 = {3, 4}
         # touch [1, 3, 4] - should promote 1 to T2, and move 3,4 to end of T2
-        cpu_manager.touch(to_keys([1, 3, 4]))
+        cpu_manager.touch(to_keys([1, 3, 4]), _EMPTY_REQ_CTX)
 
         # T1 = {2}, T2 = {1, 3, 4} (in that order, with 4 most recent)
         assert len(arc_policy.t1) == 1
         assert len(arc_policy.t2) == 3
 
         # store block 5, should evict from T1 (block 2, only one in T1)
-        prepare_store_output = cpu_manager.prepare_store(to_keys([5]))
+        prepare_store_output = cpu_manager.prepare_store(to_keys([5]), _EMPTY_REQ_CTX)
         verify_store_output(
             prepare_store_output,
             ExpectedPrepareStoreOutput(
@@ -484,19 +502,19 @@ def test_failed_store(self):
         cpu_manager, arc_policy = self._make_manager()
 
         # store blocks 1, 2, 3, 4
-        cpu_manager.prepare_store(to_keys([1, 2, 3, 4]))
-        cpu_manager.complete_store(to_keys([1, 2, 3, 4]))
+        cpu_manager.prepare_store(to_keys([1, 2, 3, 4]), _EMPTY_REQ_CTX)
+        cpu_manager.complete_store(to_keys([1, 2, 3, 4]), _EMPTY_REQ_CTX)
 
         # prepare store block 5 (will evict block 1)
-        prepare_store_output = cpu_manager.prepare_store(to_keys([5]))
+        prepare_store_output = cpu_manager.prepare_store(to_keys([5]), _EMPTY_REQ_CTX)
         assert prepare_store_output is not None
         assert len(prepare_store_output.evicted_keys) == 1
 
         # complete store with failure
-        cpu_manager.complete_store(to_keys([5]), success=False)
+        cpu_manager.complete_store(to_keys([5]), _EMPTY_REQ_CTX, success=False)
 
         # block 5 should not be in cache
-        assert cpu_manager.lookup(to_keys([5])) == 0
+        assert cpu_manager.lookup(to_key(5), _EMPTY_REQ_CTX) is False
         # block 5 should not be in T1 or T2
         assert to_keys([5])[0] not in arc_policy.t1
         assert to_keys([5])[0] not in arc_policy.t2
@@ -513,30 +531,32 @@ def test_full_scenario(self):
         cpu_manager, arc_policy = self._make_manager()
 
         # store [1, 2]
-        cpu_manager.prepare_store(to_keys([1, 2]))
-        cpu_manager.complete_store(to_keys([1, 2]))
+        cpu_manager.prepare_store(to_keys([1, 2]), _EMPTY_REQ_CTX)
+        cpu_manager.complete_store(to_keys([1, 2]), _EMPTY_REQ_CTX)
 
         # store [3, 4, 5] -> evicts [1]
-        prepare_store_output = cpu_manager.prepare_store(to_keys([3, 4, 5]))
+        prepare_store_output = cpu_manager.prepare_store(
+            to_keys([3, 4, 5]), _EMPTY_REQ_CTX
+        )
         assert prepare_store_output is not None
         assert len(prepare_store_output.evicted_keys) == 1
-        cpu_manager.complete_store(to_keys([3, 4, 5]))
+        cpu_manager.complete_store(to_keys([3, 4, 5]), _EMPTY_REQ_CTX)
 
         # promote some blocks to T2
-        cpu_manager.touch(to_keys([2, 3]))
+        cpu_manager.touch(to_keys([2, 3]), _EMPTY_REQ_CTX)
 
         # T1 has {4, 5}, T2 has {2, 3}
         assert len(arc_policy.t1) == 2
         assert len(arc_policy.t2) == 2
 
         # store [6] -> should evict from T1 (4 is oldest in T1)
-        prepare_store_output = cpu_manager.prepare_store(to_keys([6]))
+        prepare_store_output = cpu_manager.prepare_store(to_keys([6]), _EMPTY_REQ_CTX)
         assert prepare_store_output is not None
-        cpu_manager.complete_store(to_keys([6]))
+        cpu_manager.complete_store(to_keys([6]), _EMPTY_REQ_CTX)
 
         # verify blocks 2, 3 (in T2) are still present
-        assert cpu_manager.lookup(to_keys([2])) == 1
-        assert cpu_manager.lookup(to_keys([3])) == 1
+        assert cpu_manager.lookup(to_key(2), _EMPTY_REQ_CTX) is True
+        assert cpu_manager.lookup(to_key(3), _EMPTY_REQ_CTX) is True
 
         # verify events
         events = list(cpu_manager.take_events())
@@ -547,9 +567,8 @@ def test_filter_reused_manager():
     """
     Tests FilterReusedOffloadingManager with a CPUOffloadingManager.
     """
-    block_size = 256
     lru_manager = CPUOffloadingManager(
-        block_size=block_size, num_blocks=4, cache_policy="lru", enable_events=True
+        num_blocks=4, cache_policy="lru", enable_events=True
     )
 
     manager = FilterReusedOffloadingManager(
@@ -557,35 +576,37 @@ def test_filter_reused_manager():
     )
 
     # Lookup [1, 2] -> 1st time, added to tracker but not eligible for store yet
-    assert manager.lookup(to_keys([1, 2])) == 0
+    assert manager.lookup(to_key(1), _EMPTY_REQ_CTX) is False
+    assert manager.lookup(to_key(2), _EMPTY_REQ_CTX) is False
 
     # prepare store [1, 2] -> should be filtered
-    prepare_store_output = manager.prepare_store(to_keys([1, 2]))
+    prepare_store_output = manager.prepare_store(to_keys([1, 2]), _EMPTY_REQ_CTX)
     assert prepare_store_output is not None
     assert prepare_store_output.keys_to_store == []
 
     # Lookup [1] -> 2nd time, eligible now
-    assert manager.lookup(to_keys([1])) == 0
+    assert manager.lookup(to_key(1), _EMPTY_REQ_CTX) is False
 
     # prepare store [1, 2] -> [1] should be eligible, [2] should be filtered
-    prepare_store_output = manager.prepare_store(to_keys([1, 2]))
+    prepare_store_output = manager.prepare_store(to_keys([1, 2]), _EMPTY_REQ_CTX)
     assert prepare_store_output is not None
     assert prepare_store_output.keys_to_store == to_keys([1])
 
     # Lookup [3, 4] -> 1st time
     # (evicts [2] from tracker since max_size is 3 and tracker has [1])
-    assert manager.lookup(to_keys([3, 4])) == 0
+    assert manager.lookup(to_key(3), _EMPTY_REQ_CTX) is False
+    assert manager.lookup(to_key(4), _EMPTY_REQ_CTX) is False
     # Verify [2] was evicted from the tracker (tracker now has: [1], [3], [4])
     assert to_keys([2])[0] not in manager.counts
 
     # Lookup [2] again -> (this adds [2] back to the tracker as 1st time)
-    assert manager.lookup(to_keys([2])) == 0
+    assert manager.lookup(to_key(2), _EMPTY_REQ_CTX) is False
     # Verify [2] was re-added with count=1 (not eligible yet)
     assert manager.counts.get(to_keys([2])[0]) == 1
 
     # prepare store [2] -> should still be filtered out since count was reset
-    prepare_store_output = manager.prepare_store(to_keys([2]))
+    prepare_store_output = manager.prepare_store(to_keys([2]), _EMPTY_REQ_CTX)
     assert prepare_store_output is not None
     assert prepare_store_output.keys_to_store == []
 
-    manager.complete_store(to_keys([1]))
+    manager.complete_store(to_keys([1]), _EMPTY_REQ_CTX)
diff --git a/tests/v1/kv_offload/test_shared_offload_region.py b/tests/v1/kv_offload/cpu/test_shared_offload_region.py
similarity index 100%
rename from tests/v1/kv_offload/test_shared_offload_region.py
rename to tests/v1/kv_offload/cpu/test_shared_offload_region.py
diff --git a/tests/v1/kv_offload/test_cpu_gpu.py b/tests/v1/kv_offload/test_cpu_gpu.py
deleted file mode 100644
index de482aec4a42..000000000000
--- a/tests/v1/kv_offload/test_cpu_gpu.py
+++ /dev/null
@@ -1,198 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import random
-import time
-import uuid
-
-import pytest
-import torch
-
-from vllm.platforms import current_platform
-from vllm.utils.torch_utils import set_random_seed
-from vllm.v1.kv_offload.cpu.shared_offload_region import SharedOffloadRegion
-from vllm.v1.kv_offload.mediums import CPULoadStoreSpec, GPULoadStoreSpec
-from vllm.v1.kv_offload.spec import (
-    CanonicalKVCacheRef,
-    CanonicalKVCaches,
-    CanonicalKVCacheTensor,
-)
-from vllm.v1.kv_offload.worker.cpu_gpu import CpuGpuOffloadingHandlers
-
-NUM_GPU_BLOCKS = [64]
-NUM_CPU_BLOCKS = [256]
-GPU_PAGE_SIZES = [512, 1024]
-BLOCK_SIZE_FACTORS = [1, 3]
-NUM_TENSORS = [4]
-SEEDS = [0]
-DEVICE_TYPE = current_platform.device_type
-DEVICES = [f"{DEVICE_TYPE}:0"]
-NUM_MAPPINGS = [3]
-
-
-@pytest.mark.parametrize("gpu_to_cpu", [True, False])
-@pytest.mark.parametrize("num_mappings", NUM_MAPPINGS)
-@pytest.mark.parametrize("gpu_page_size_bytes", GPU_PAGE_SIZES)
-@pytest.mark.parametrize("block_size_factor", BLOCK_SIZE_FACTORS)
-@pytest.mark.parametrize("num_gpu_blocks", NUM_GPU_BLOCKS)
-@pytest.mark.parametrize("num_cpu_blocks", NUM_CPU_BLOCKS)
-@pytest.mark.parametrize("num_tensors", NUM_TENSORS)
-@pytest.mark.parametrize("seed", SEEDS)
-@pytest.mark.parametrize("device", DEVICES)
-@pytest.mark.parametrize("use_shared_memory", [False, True])
-@torch.inference_mode()
-def test_transfer(
-    default_vllm_config,
-    gpu_to_cpu: bool,
-    num_mappings: int,
-    gpu_page_size_bytes: int,
-    block_size_factor: int,
-    num_gpu_blocks: int,
-    num_cpu_blocks: int,
-    num_tensors: int,
-    seed: int,
-    device: str,
-    use_shared_memory: bool,
-) -> None:
-    set_random_seed(seed)
-
-    # build CanonicalKVCacheTensor list: one per tensor
-    kv_cache_tensors: list[CanonicalKVCacheTensor] = []
-    for i in range(num_tensors):
-        gpu_tensor = torch.randint(
-            -128,
-            127,
-            (num_gpu_blocks, gpu_page_size_bytes),
-            dtype=torch.int8,
-            device=device,
-        )
-        kv_cache_tensors.append(
-            CanonicalKVCacheTensor(
-                tensor=gpu_tensor,
-                page_size_bytes=gpu_page_size_bytes,
-            )
-        )
-
-    # one group containing all tensors, one data ref per tensor
-    kv_cache_groups_data_refs: list[list[CanonicalKVCacheRef]] = [
-        [
-            CanonicalKVCacheRef(
-                tensor_idx=i,
-                page_size_bytes=gpu_page_size_bytes,
-            )
-            for i in range(num_tensors)
-        ]
-    ]
-
-    kv_caches = CanonicalKVCaches(
-        tensors=kv_cache_tensors,
-        group_data_refs=kv_cache_groups_data_refs,
-    )
-
-    mmap_region: SharedOffloadRegion | None = None
-    if use_shared_memory:
-        cpu_page_size = gpu_page_size_bytes * num_tensors * block_size_factor
-        mmap_region = SharedOffloadRegion(
-            instance_id=str(uuid.uuid4()),
-            total_size_bytes=num_cpu_blocks * cpu_page_size,
-            num_blocks=num_cpu_blocks,
-            rank=0,
-            num_workers=1,
-            cpu_page_size=cpu_page_size,
-        )
-
-    handlers = CpuGpuOffloadingHandlers(
-        kv_caches=kv_caches,
-        block_size_factor=block_size_factor,
-        num_cpu_blocks=num_cpu_blocks,
-        mmap_region=mmap_region,
-    )
-
-    # select block mappings
-    gpu_blocks = random.sample(range(num_gpu_blocks), num_mappings * block_size_factor)
-    cpu_blocks = random.sample(range(num_cpu_blocks), num_mappings)
-
-    # expand cpu blocks to gpu-page granularity for uniform comparison:
-    # each cpu block maps to block_size_factor consecutive sub-blocks
-    cpu_blocks_expanded = [
-        cpu_block * block_size_factor + j
-        for cpu_block in cpu_blocks
-        for j in range(block_size_factor)
-    ]
-
-    # maybe skip some GPU blocks to test reading from the middle of a CPU block
-    if not gpu_to_cpu:
-        blocks_to_skip = block_size_factor - 1
-        gpu_blocks = gpu_blocks[blocks_to_skip:]
-        cpu_blocks_expanded = cpu_blocks_expanded[blocks_to_skip:]
-
-    # set transfer direction
-    if gpu_to_cpu:
-        handler = handlers.gpu_to_cpu_handler
-        src_spec = GPULoadStoreSpec(gpu_blocks, group_sizes=(len(gpu_blocks),))
-        dst_spec = CPULoadStoreSpec(cpu_blocks)
-        dst_to_src = dict(zip(cpu_blocks_expanded, gpu_blocks))
-        num_dst_sub_blocks = num_cpu_blocks * block_size_factor
-    else:
-        handler = handlers.cpu_to_gpu_handler
-        src_spec = CPULoadStoreSpec(cpu_blocks)
-        dst_spec = GPULoadStoreSpec(gpu_blocks, group_sizes=(len(gpu_blocks),))
-        dst_to_src = dict(zip(gpu_blocks, cpu_blocks_expanded))
-        num_dst_sub_blocks = num_gpu_blocks
-
-    # clone src and dst tensors before transfer
-    orig_src_tensors = [x.clone() for x in handler.src_tensors]
-    orig_dst_tensors = [x.clone() for x in handler.dst_tensors]
-
-    # call transfer function
-    start_time = time.time()
-    assert handler.transfer_async(1, (src_spec, dst_spec))
-    assert set({x.job_id for x in handler._transfers}) == {1}
-
-    # wait for transfer to complete
-    end_time = time.time() + 10
-    while time.time() < end_time:
-        finished = handler.get_finished()
-        if finished:
-            assert finished[0].job_id == 1
-            assert finished[0].success
-            assert finished[0].transfer_type == (
-                ("GPU", "CPU") if gpu_to_cpu else ("CPU", "GPU")
-            )
-            assert finished[0].transfer_size == (
-                len(gpu_blocks) * handler.group_block_size_in_bytes[0]
-            )
-            assert finished[0].transfer_time > 0
-            assert finished[0].transfer_time < (time.time() - start_time)
-            break
-        time.sleep(0.1)
-
-    # verify src tensors did not change
-    for orig_tensor, tensor in zip(orig_src_tensors, handler.src_tensors):
-        assert torch.equal(orig_tensor, tensor)
-
-    # verify dst tensors at gpu-page granularity.
-    for src_tensor, dst_tensor, orig_dst_tensor in zip(
-        handler.src_tensors,
-        handler.dst_tensors,
-        orig_dst_tensors,
-    ):
-        # view both GPU and CPU tensors as (n, gpu_page_size_bytes) for comparison.
-        src_view = src_tensor.reshape(-1, gpu_page_size_bytes)
-        dst_view = dst_tensor.reshape(-1, gpu_page_size_bytes)
-        orig_dst_view = orig_dst_tensor.reshape(-1, gpu_page_size_bytes)
-        for dst_sub_block in range(num_dst_sub_blocks):
-            src_sub_block = dst_to_src.get(dst_sub_block)
-            if src_sub_block is not None:
-                expected = src_view[src_sub_block]
-            else:
-                expected = orig_dst_view[dst_sub_block]
-            torch.testing.assert_close(dst_view[dst_sub_block].cpu(), expected.cpu())
-
-    # Drop loop-variable refs so mmap_obj has no exported buffers at cleanup.
-    del orig_tensor, tensor, src_tensor, dst_tensor, orig_dst_tensor
-    del src_view, dst_view, orig_dst_view, expected
-
-    handlers.cpu_to_gpu_handler.shutdown()
-    handlers.gpu_to_cpu_handler.shutdown()
-    if mmap_region:
-        mmap_region.cleanup()
diff --git a/tests/v1/kv_offload/test_worker.py b/tests/v1/kv_offload/test_worker.py
index fbdac5f9dc7c..b291fcf1b857 100644
--- a/tests/v1/kv_offload/test_worker.py
+++ b/tests/v1/kv_offload/test_worker.py
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from vllm.v1.kv_offload.abstract import LoadStoreSpec
+from vllm.v1.kv_offload.base import LoadStoreSpec
 from vllm.v1.kv_offload.worker.worker import (
     OffloadingHandler,
     OffloadingWorker,
diff --git a/tests/v1/logits_processors/test_correctness.py b/tests/v1/logits_processors/test_correctness.py
index 9ee6a70abe4c..1326b96346a7 100644
--- a/tests/v1/logits_processors/test_correctness.py
+++ b/tests/v1/logits_processors/test_correctness.py
@@ -30,10 +30,13 @@
     MinPLogitsProcessor,
     MinTokensLogitsProcessor,
     MoveDirectionality,
-    ThinkingTokenBudgetLogitsProcessor,
     build_logitsprocs,
 )
 from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.sample.thinking_budget_state import (
+    ThinkingBudgetStateHolder,
+    maybe_create_thinking_budget_state_holder,
+)
 
 PIN_MEMORY_AVAILABLE = is_pin_memory_available()
 MAX_NUM_REQS = 256
@@ -48,8 +51,10 @@
 MIN_TOKENS_LEN_THRESHOLD = 5
 REQS_PER_LOGITPROC = 50
 STR_NO_LOGITPROC = "none"
+# Thinking budget uses ``ThinkingBudgetStateHolder`` (not a logits processor).
+STR_THINKING_BUDGET = "thinking_budget"
 
-# ThinkingTokenBudgetLogitsProcessor testing constants
+# Thinking token budget testing constants
 THINKING_TOKEN_BUDGET = 5
 THINK_START_TOKEN_ID = 999
 THINK_END_TOKEN_ID = 998
@@ -80,15 +85,8 @@ def __init__(self, workload_index: int, logitproc_type: LogitprocType):
         if num_tokens > 0:
             # Use diverse random tokens
             self.out_tokens = [random.randint(1, 950) for _ in range(num_tokens)]
-            # Set first token for ThinkingTokenBudget testing
-            is_thinking_processor = (
-                logitproc_type is ThinkingTokenBudgetLogitsProcessor
-                or (
-                    hasattr(logitproc_type, "__name__")
-                    and logitproc_type.__name__ == "ThinkingTokenBudgetLogitsProcessor"
-                )
-            )
-            if is_thinking_processor:
+            # Think-start seed for ``STR_THINKING_BUDGET`` rows.
+            if logitproc_type == STR_THINKING_BUDGET:
                 self.out_tokens[0] = THINK_START_TOKEN_ID
         else:
             self.out_tokens = []
@@ -102,7 +100,7 @@ def __str__(self):
 
 
 class MockReasoningConfig:
-    """Mock reasoning config for testing ThinkingTokenBudgetLogitsProcessor."""
+    """Minimal reasoning config for ``ThinkingBudgetStateHolder`` tests."""
 
     reasoning_start_token_ids = [THINK_START_TOKEN_ID]
     reasoning_end_token_ids = [THINK_END_TOKEN_ID]
@@ -137,6 +135,18 @@ def _generate_fake_sampling_metadata(
         is_pin_memory=PIN_MEMORY_AVAILABLE,
         is_pooling_model=False,
     )
+    num_spec = (
+        vllm_config.speculative_config.num_speculative_tokens
+        if vllm_config.speculative_config
+        else 0
+    )
+    thinking_holder = maybe_create_thinking_budget_state_holder(
+        vllm_config.reasoning_config,
+        vllm_config.scheduler_config.max_num_seqs,
+        num_spec,
+        device,
+        PIN_MEMORY_AVAILABLE,
+    )
     fake_sampling_metadata = SamplingMetadata(
         temperature=torch.full((batch_size,), 0.0),
         all_greedy=True,
@@ -156,6 +166,7 @@ def _generate_fake_sampling_metadata(
         allowed_token_ids_mask=None,
         bad_words_token_ids={},
         logitsprocs=logitsprocs,
+        thinking_budget_state_holder=thinking_holder,
     )
     return fake_sampling_metadata
 
@@ -187,7 +198,7 @@ def _sampling_params_from_logitproc(logitproc_type: LogitprocType) -> SamplingPa
 
 def _generate_mixed_logitsprocs_batch_params(
     reqs_per_logitproc: int,
-    logitsprocs_types: list[str],
+    logitsprocs_types: list[LogitprocType],
 ) -> list[LogitsProcsRequestParams]:
     """Define key params for a batch of requests with a different
     logitproc enabled per request.
@@ -450,23 +461,21 @@ def _thinking_budget_validate(
     request_params: LogitsProcsRequestParams,
     step_idx: int,
 ) -> None:
-    """Validate thinking token budget processor behavior"""
-    # Get the ThinkingTokenBudgetLogitsProcessor instance
-    tb_processor: ThinkingTokenBudgetLogitsProcessor = next(
-        test_fakes.get_logitsprocs_by_cls(ThinkingTokenBudgetLogitsProcessor)
-    )
+    """Validate ``ThinkingBudgetStateHolder`` thinking-budget behavior.
 
-    # Get current request state
-    state = tb_processor._state.get(batch_index)
+    State is keyed by **batch slot** (same index space as logits rows), matching
+    ``sync_batch`` / sampler integration (see PR #34668 discussion).
+    """
+    holder = test_fakes.sampling_metadata.thinking_budget_state_holder
+    assert holder is not None
+    state = holder._state.get(batch_index)
     params = request_params.params
 
-    # Validate thinking token budget configuration
     if hasattr(params, "thinking_token_budget") and params.thinking_token_budget:
-        # State should exist for requests with thinking_token_budget
         if state is None:
             _raise_error_invalid(
                 msg_suffix=(
-                    f"Expected state for batch {batch_index} "
+                    f"Expected holder state for batch slot {batch_index} "
                     f"with thinking_token_budget={params.thinking_token_budget}"
                 ),
                 batch_index=batch_index,
@@ -474,10 +483,8 @@ def _thinking_budget_validate(
                 step_idx=step_idx,
             )
 
-        # Validate budget matches what was set
         expected_budget = params.thinking_token_budget
         actual_budget = state["thinking_token_budget"]
-
         if actual_budget != expected_budget:
             _raise_error_invalid(
                 msg_suffix=(
@@ -488,13 +495,9 @@ def _thinking_budget_validate(
                 step_idx=step_idx,
             )
 
-        # Check if we're in thinking mode and validate token counting
         output_tokens = request_params.out_tokens
-
-        # Find if thinking has started in output tokens
+        start_tokens = holder.think_start_token_ids
         thinking_started = False
-        start_tokens = tb_processor.reasoning_start_token_ids
-
         if len(start_tokens) > 0:
             for i in range(len(output_tokens) - len(start_tokens) + 1):
                 if output_tokens[i : i + len(start_tokens)] == start_tokens:
@@ -502,61 +505,42 @@ def _thinking_budget_validate(
                     break
 
         if thinking_started:
-            # If budget is exceeded, validate end token forcing
             think_count = state["think_count"]
             budget = state["thinking_token_budget"]
+            if think_count >= budget and not state["in_end"]:
+                _raise_error_invalid(
+                    msg_suffix=(
+                        f"Budget exceeded ({think_count} >= {budget}) but "
+                        "in_end is false"
+                    ),
+                    batch_index=batch_index,
+                    request_params=request_params,
+                    step_idx=step_idx,
+                )
 
-            if think_count >= budget:
-                if not state["in_end"]:
+            end_tokens = holder.think_end_token_ids
+            if (
+                think_count >= budget
+                and state["in_end"]
+                and len(end_tokens) > 0
+                and holder.has_tracked_requests()
+            ):
+                expected_end_token_id = end_tokens[
+                    min(state["end_count"], len(end_tokens) - 1)
+                ]
+                # Holder bumps forced vocab positions to 1e9 (does not -inf others).
+                forced_logit = float(logits_new[batch_index, expected_end_token_id])
+                if forced_logit < 1.0e8:
                     _raise_error_invalid(
                         msg_suffix=(
-                            f"Budget exceeded ({think_count} >= "
-                            f"{budget}) but not "
-                            "forcing end tokens"
+                            f"Expected forced end token {expected_end_token_id} "
+                            f"with large logit, got {forced_logit}"
                         ),
                         batch_index=batch_index,
                         request_params=request_params,
                         step_idx=step_idx,
                     )
 
-                # Validate that only end tokens are allowed
-                end_tokens = tb_processor.reasoning_end_token_ids
-                if len(end_tokens) > 0:
-                    expected_end_token_id = end_tokens[
-                        min(state["end_count"], len(end_tokens) - 1)
-                    ]
-
-                    # Check logits masking
-                    batch_logits = logits_new[batch_index]
-                    for token_id in range(len(batch_logits)):
-                        logit_value = batch_logits[token_id]
-
-                        if token_id == expected_end_token_id:
-                            # End token should not be masked
-                            if logit_value == -float("inf"):
-                                _raise_error_invalid(
-                                    msg_suffix=(
-                                        f"End token {token_id} should not be "
-                                        "masked but is"
-                                    ),
-                                    batch_index=batch_index,
-                                    request_params=request_params,
-                                    step_idx=step_idx,
-                                )
-                        else:
-                            # All other tokens should be masked when forcing end
-                            if logit_value != -float("inf"):
-                                _raise_error_invalid(
-                                    msg_suffix=(
-                                        f"Token {token_id} should be masked "
-                                        f"when forcing end tokens, but "
-                                        f"logit={logit_value}"
-                                    ),
-                                    batch_index=batch_index,
-                                    request_params=request_params,
-                                    step_idx=step_idx,
-                                )
-
 
 def _none_validate(
     test_fakes: LogitsprocsTestFakes,
@@ -604,7 +588,7 @@ class LogitsprocTestHelpers(NamedTuple):
     MinTokensLogitsProcessor: LogitsprocTestHelpers(
         gen_request_fxn=_min_tokens_params, eval_fxn=_min_tokens_validate
     ),
-    ThinkingTokenBudgetLogitsProcessor: LogitsprocTestHelpers(
+    STR_THINKING_BUDGET: LogitsprocTestHelpers(
         gen_request_fxn=_thinking_budget_params, eval_fxn=_thinking_budget_validate
     ),
 }
@@ -614,20 +598,17 @@ def _get_test_cases() -> list[list[str]]:
     """Each test case is a set of logitsprocs"""
     logitsprocs_types = list(logitsprocs_test_mapping.keys())
 
-    # Isolate ThinkingTokenBudgetLogitsProcessor from all other processors
-    # to avoid unexpected modification of logits interference
-    thinking_processor = ThinkingTokenBudgetLogitsProcessor
+    # Isolate thinking-budget handling from other processors to avoid cross-talk.
+    thinking_id: LogitprocType = STR_THINKING_BUDGET
     other_processors = [
-        p
-        for p in logitsprocs_types
-        if p != STR_NO_LOGITPROC and p != thinking_processor
+        p for p in logitsprocs_types if p != STR_NO_LOGITPROC and p != thinking_id
     ]
 
     return (
         [[STR_NO_LOGITPROC]]
         + [[logitproc_type, STR_NO_LOGITPROC] for logitproc_type in other_processors]
         + [other_processors]
-        + [[thinking_processor]]
+        + [[thinking_id]]
     )
 
 
@@ -802,12 +783,23 @@ def _assert_valid(
         )
 
 
+def _slot_outputs_for_metadata(
+    persistent_batch: list[LogitsProcsRequestParams], pad_len: int
+) -> list[list[int]]:
+    """Per-batch-slot output token ids aligned with ``SamplingMetadata`` rows."""
+    rows: list[list[int]] = [[] for _ in range(pad_len)]
+    for i, req in enumerate(persistent_batch):
+        if i < pad_len:
+            rows[i] = list(req.out_tokens)
+    return rows
+
+
 @create_new_process_for_each_test()
 @pytest.mark.parametrize("device", DEVICES)
 @pytest.mark.parametrize("reqs_per_logitproc", [REQS_PER_LOGITPROC])
 @pytest.mark.parametrize("logitsprocs_under_test", _get_test_cases())
 def test_logitsprocs(
-    device: str, reqs_per_logitproc: int, logitsprocs_under_test: list[str]
+    device: str, reqs_per_logitproc: int, logitsprocs_under_test: list[LogitprocType]
 ):
     random.seed(40)
     torch.set_default_device(device)
@@ -855,9 +847,10 @@ def test_logitsprocs(
         # Apply fake batch update to logitsprocs
         fake_update_logitsprocs_state(test_fakes, batch_update)
 
-        # Emulate application of logits processors in engine
+        # Emulate application of logits processors + thinking holder (sampler order).
         slice_idxs = [req.workload_index for req in persistent_batch]
-        logits_w_lp = fake_apply_logitsprocs(test_fakes, slice_idxs).cpu()
+        slot_rows = _slot_outputs_for_metadata(persistent_batch, workload_size)
+        logits_w_lp = fake_apply_logitsprocs(test_fakes, slice_idxs, slot_rows).cpu()
 
         _assert_valid(
             batch_size=batch_size,
@@ -869,3 +862,263 @@ def test_logitsprocs(
         )
 
         step_idx += 1
+
+
+class MockReasoningNoEndTokens:
+    """Reasoning config with no end token ids (disables enforcement in holder)."""
+
+    reasoning_start_token_ids = [THINK_START_TOKEN_ID]
+    reasoning_end_token_ids: list[int] = []
+
+
+def test_maybe_create_thinking_budget_holder_without_reasoning():
+    cfg = VllmConfig()
+    assert cfg.reasoning_config is None
+    assert (
+        maybe_create_thinking_budget_state_holder(
+            None,
+            cfg.scheduler_config.max_num_seqs,
+            0,
+            torch.device("cpu"),
+            False,
+        )
+        is None
+    )
+
+
+def test_thinking_budget_holder_has_tracked_after_sync_add():
+    vc = VllmConfig()
+    vc.reasoning_config = MockReasoningConfig()
+    h = ThinkingBudgetStateHolder(
+        vc.reasoning_config,
+        vc.scheduler_config.max_num_seqs,
+        0,
+        torch.device("cpu"),
+        False,
+    )
+    assert not h.has_tracked_requests()
+    h.sync_batch(
+        BatchUpdate(
+            batch_size=1,
+            removed=(),
+            added=[
+                (
+                    0,
+                    SamplingParams(thinking_token_budget=3),
+                    None,
+                    [THINK_START_TOKEN_ID],
+                )
+            ],
+            moved=(),
+        )
+    )
+    assert h.has_tracked_requests()
+    assert h._state[0]["thinking_token_budget"] == 3
+
+
+def test_thinking_budget_holder_sync_remove_clears_state():
+    vc = VllmConfig()
+    vc.reasoning_config = MockReasoningConfig()
+    h = ThinkingBudgetStateHolder(
+        vc.reasoning_config,
+        vc.scheduler_config.max_num_seqs,
+        0,
+        torch.device("cpu"),
+        False,
+    )
+    h.sync_batch(
+        BatchUpdate(
+            batch_size=1,
+            removed=(),
+            added=[
+                (
+                    0,
+                    SamplingParams(thinking_token_budget=3),
+                    None,
+                    [],
+                )
+            ],
+            moved=(),
+        )
+    )
+    assert h.has_tracked_requests()
+    h.sync_batch(BatchUpdate(batch_size=0, removed=(0,), added=(), moved=()))
+    assert not h.has_tracked_requests()
+
+
+def test_thinking_budget_holder_sync_add_without_budget_drops_row():
+    vc = VllmConfig()
+    vc.reasoning_config = MockReasoningConfig()
+    h = ThinkingBudgetStateHolder(
+        vc.reasoning_config,
+        vc.scheduler_config.max_num_seqs,
+        0,
+        torch.device("cpu"),
+        False,
+    )
+    h.sync_batch(
+        BatchUpdate(
+            batch_size=1,
+            removed=(),
+            added=[(0, SamplingParams(), None, [])],
+            moved=(),
+        )
+    )
+    assert not h.has_tracked_requests()
+
+
+def test_thinking_budget_holder_swap_exchanges_state():
+    vc = VllmConfig()
+    vc.reasoning_config = MockReasoningConfig()
+    h = ThinkingBudgetStateHolder(
+        vc.reasoning_config,
+        vc.scheduler_config.max_num_seqs,
+        0,
+        torch.device("cpu"),
+        False,
+    )
+    h.sync_batch(
+        BatchUpdate(
+            batch_size=2,
+            removed=(),
+            added=[
+                (
+                    0,
+                    SamplingParams(thinking_token_budget=3),
+                    None,
+                    [],
+                ),
+                (
+                    1,
+                    SamplingParams(thinking_token_budget=7),
+                    None,
+                    [],
+                ),
+            ],
+            moved=(),
+        )
+    )
+    b0, b1 = h._state[0]["thinking_token_budget"], h._state[1]["thinking_token_budget"]
+    h.sync_batch(
+        BatchUpdate(
+            batch_size=2,
+            removed=(),
+            added=(),
+            moved=[(0, 1, MoveDirectionality.SWAP)],
+        )
+    )
+    assert h._state[0]["thinking_token_budget"] == b1
+    assert h._state[1]["thinking_token_budget"] == b0
+
+
+def test_thinking_budget_holder_unidirectional_move():
+    vc = VllmConfig()
+    vc.reasoning_config = MockReasoningConfig()
+    h = ThinkingBudgetStateHolder(
+        vc.reasoning_config,
+        vc.scheduler_config.max_num_seqs,
+        0,
+        torch.device("cpu"),
+        False,
+    )
+    h.sync_batch(
+        BatchUpdate(
+            batch_size=2,
+            removed=(),
+            added=[
+                (
+                    1,
+                    SamplingParams(thinking_token_budget=4),
+                    None,
+                    [],
+                ),
+            ],
+            moved=(),
+        )
+    )
+    assert 1 in h._state and 0 not in h._state
+    h.sync_batch(
+        BatchUpdate(
+            batch_size=2,
+            removed=(),
+            added=(),
+            moved=[(1, 0, MoveDirectionality.UNIDIRECTIONAL)],
+        )
+    )
+    assert 0 in h._state and 1 not in h._state
+    assert h._state[0]["thinking_token_budget"] == 4
+
+
+def test_thinking_budget_holder_update_state_repeat_indices_last_row_wins():
+    vc = VllmConfig()
+    vc.reasoning_config = MockReasoningConfig()
+    h = ThinkingBudgetStateHolder(
+        vc.reasoning_config,
+        vc.scheduler_config.max_num_seqs,
+        0,
+        torch.device("cpu"),
+        False,
+    )
+    h.sync_batch(
+        BatchUpdate(
+            batch_size=1,
+            removed=(),
+            added=[
+                (
+                    0,
+                    SamplingParams(thinking_token_budget=5),
+                    None,
+                    [THINK_START_TOKEN_ID],
+                )
+            ],
+            moved=(),
+        )
+    )
+    out_lists = [[THINK_START_TOKEN_ID], [THINK_START_TOKEN_ID, 10, 11, 12, 13, 14]]
+    h.update_state(
+        out_lists,
+        None,
+        torch.tensor([0, 0], dtype=torch.long),
+    )
+    assert h._state[0]["output_tok_ids"] == out_lists[1]
+
+
+def test_thinking_budget_holder_spec_mode_tensor_layout():
+    h = ThinkingBudgetStateHolder(
+        MockReasoningConfig(),
+        8,
+        2,
+        torch.device("cpu"),
+        False,
+    )
+    assert h.in_spec_mode
+    assert h.mask.shape[0] == 8 * (2 + 1)
+
+
+def test_thinking_budget_holder_empty_end_tokens_disables_row():
+    vc = VllmConfig()
+    vc.reasoning_config = MockReasoningNoEndTokens()
+    h = ThinkingBudgetStateHolder(
+        vc.reasoning_config,
+        vc.scheduler_config.max_num_seqs,
+        0,
+        torch.device("cpu"),
+        False,
+    )
+    h.sync_batch(
+        BatchUpdate(
+            batch_size=1,
+            removed=(),
+            added=[
+                (
+                    0,
+                    SamplingParams(thinking_token_budget=5),
+                    None,
+                    [THINK_START_TOKEN_ID],
+                )
+            ],
+            moved=(),
+        )
+    )
+    h.update_state([[THINK_START_TOKEN_ID, 1]], None, None)
+    assert h._state[0]["thinking_token_budget"] == -1
diff --git a/tests/v1/logits_processors/test_custom_offline.py b/tests/v1/logits_processors/test_custom_offline.py
index 29ec72186b8d..325ca48b597e 100644
--- a/tests/v1/logits_processors/test_custom_offline.py
+++ b/tests/v1/logits_processors/test_custom_offline.py
@@ -16,8 +16,8 @@
     DummyLogitsProcessor,
     WrappedPerReqLogitsProcessor,
     prompts,
+    setup_fake_entrypoint,
 )
-from tests.v1.logits_processors.utils import entry_points as fake_entry_points
 from vllm import LLM, SamplingParams
 from vllm.v1.sample.logits_processor import (
     STR_POOLING_REJECTS_LOGITSPROCS,
@@ -145,13 +145,9 @@ def test_custom_logitsprocs(monkeypatch, logitproc_source: CustomLogitprocSource
 
     if logitproc_source == CustomLogitprocSource.LOGITPROC_SOURCE_ENTRYPOINT:
         # Scenario: vLLM loads a logitproc from a preconfigured entrypoint
-        # To that end, mock a dummy logitproc entrypoint
-        import importlib.metadata
-
-        importlib.metadata.entry_points = fake_entry_points  # type: ignore
-
-        # fork is required for workers to see entrypoint patch
-        monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "fork")
+        # To that end, register a real dist-info package so spawned
+        # workers can discover the entrypoint via PYTHONPATH
+        setup_fake_entrypoint(monkeypatch)
         _run_test({}, logitproc_loaded=True)
         return
 
@@ -266,14 +262,9 @@ def test_rejects_custom_logitsprocs(
         # Scenario: vLLM loads a model and ignores a logitproc that is
         # available at a preconfigured entrypoint
 
-        # Patch in dummy logitproc entrypoint
-        import importlib.metadata
-
-        importlib.metadata.entry_points = fake_entry_points  # type: ignore
-
-        # fork is required for entrypoint patch to be visible to workers,
-        # although they should ignore the entrypoint patch anyway
-        monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "fork")
+        # Register real dist-info package so spawned workers can
+        # discover the entrypoint via PYTHONPATH (spawn-compatible)
+        setup_fake_entrypoint(monkeypatch)
 
         llm = LLM(**llm_kwargs)
         # Require that no custom logitsprocs have been loaded
diff --git a/tests/v1/logits_processors/test_custom_online.py b/tests/v1/logits_processors/test_custom_online.py
index 3dc6b8979015..3b7add3b80f4 100644
--- a/tests/v1/logits_processors/test_custom_online.py
+++ b/tests/v1/logits_processors/test_custom_online.py
@@ -10,7 +10,7 @@
 import pytest
 import pytest_asyncio
 
-from tests.utils import RemoteOpenAIServerCustom, create_new_process_for_each_test
+from tests.utils import RemoteOpenAIServerCustom
 from tests.v1.logits_processors.utils import (
     DUMMY_LOGITPROC_ARG,
     DUMMY_LOGITPROC_FQCN,
@@ -18,8 +18,8 @@
     MODEL_NAME,
     TEMP_GREEDY,
     prompts,
+    setup_fake_entrypoint,
 )
-from tests.v1.logits_processors.utils import entry_points as fake_entry_points
 
 
 def _server_with_logitproc_entrypoint(
@@ -27,16 +27,9 @@ def _server_with_logitproc_entrypoint(
     model: str,
     vllm_serve_args: list[str],
 ) -> None:
-    """Start vLLM server, inject dummy logitproc entrypoint"""
-
-    # Patch `entry_points` to inject logitproc entrypoint
-    import importlib.metadata
-
-    importlib.metadata.entry_points = fake_entry_points  # type: ignore
+    """Start vLLM server with dummy logitproc entrypoint."""
     from vllm.entrypoints.cli import main
 
-    # fork is required for workers to see entrypoint patch
-    os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "fork"
     if env_dict is not None:
         os.environ.update(env_dict)
 
@@ -50,7 +43,7 @@ def _server_with_logitproc_fqcn(
     model: str,
     vllm_serve_args: list[str],
 ) -> None:
-    """Start vLLM server, inject module with dummy logitproc"""
+    """Start vLLM server with dummy logitproc specified by FQCN."""
     from vllm.entrypoints.cli import main
 
     if env_dict is not None:
@@ -80,8 +73,8 @@ def default_server_args():
 def server(default_server_args, request, monkeypatch):
     """Consider two server configurations:
     (1) --logits-processors cli arg specifies dummy logits processor via fully-
-    qualified class name (FQCN); patch in a dummy logits processor module
-    (2) No --logits-processors cli arg; patch in a dummy logits processor
+    qualified class name (FQCN)
+    (2) No --logits-processors cli arg; inject a dummy logits processor
     entrypoint
     """
 
@@ -94,6 +87,7 @@ def server(default_server_args, request, monkeypatch):
         _server_fxn = _server_with_logitproc_fqcn
     else:
         # Launch server, inject dummy logitproc entrypoint
+        setup_fake_entrypoint(monkeypatch)
         args = default_server_args
         _server_fxn = _server_with_logitproc_entrypoint
 
@@ -119,13 +113,11 @@ async def client(server):
 }
 
 
-@create_new_process_for_each_test()
-@pytest.mark.asyncio
 @pytest.mark.parametrize(
     "model_name",
     [MODEL_NAME],
 )
-async def test_custom_logitsprocs(client: openai.AsyncOpenAI, model_name: str):
+def test_custom_logitsprocs(server, model_name: str):
     """Test custom logitsprocs when starting OpenAI server from CLI
 
     Launch vLLM OpenAI-compatible server, configured to load a custom logitproc
@@ -139,36 +131,45 @@ async def test_custom_logitsprocs(client: openai.AsyncOpenAI, model_name: str):
     token
     """
 
-    use_dummy_logitproc = True
-    for prompt in prompts:
-        # Build request arguments
-        request_keyword_args: dict[str, Any] = {
-            **api_keyword_args,
-        }
-        if use_dummy_logitproc:
-            # 50% of requests pass target_token custom arg
-            target_token = random.choice([128, 67])
-            # For requests which activate the dummy logitproc, choose one of
-            # two `target_token` values which are known not to be EOS tokens
-            request_keyword_args["extra_body"] = {
-                "vllm_xargs": {DUMMY_LOGITPROC_ARG: target_token}
-            }
-        batch = await client.completions.create(
-            model=model_name,
-            prompt=prompt,
-            **request_keyword_args,
-        )
+    import asyncio
 
-        if use_dummy_logitproc:
-            # Only for requests which activate dummy logitproc - validate that
-            # output token is repeated
-            choices: openai.types.CompletionChoice = batch.choices
-            toks = choices[0].logprobs.tokens
-            if not all([x == toks[0] for x in toks]):
-                raise AssertionError(f"Generated {toks} should all be {toks[0]}")
+    async def _async_main(srv, mn):
+        async with srv.get_async_client() as client:
+            await _run(client)
 
-        # Alternate whether to activate dummy logitproc for each request
-        use_dummy_logitproc = not use_dummy_logitproc
+    async def _run(client):
+        use_dummy_logitproc = True
+        for prompt in prompts:
+            # Build request arguments
+            request_keyword_args: dict[str, Any] = {
+                **api_keyword_args,
+            }
+            if use_dummy_logitproc:
+                # 50% of requests pass target_token custom arg
+                target_token = random.choice([128, 67])
+                # For requests which activate the dummy logitproc, choose one of
+                # two `target_token` values which are known not to be EOS tokens
+                request_keyword_args["extra_body"] = {
+                    "vllm_xargs": {DUMMY_LOGITPROC_ARG: target_token}
+                }
+            batch = await client.completions.create(
+                model=model_name,
+                prompt=prompt,
+                **request_keyword_args,
+            )
+
+            if use_dummy_logitproc:
+                # Only for requests which activate dummy logitproc - validate that
+                # output token is repeated
+                choices: openai.types.CompletionChoice = batch.choices
+                toks = choices[0].logprobs.tokens
+                if not all([x == toks[0] for x in toks]):
+                    raise AssertionError(f"Generated {toks} should all be {toks[0]}")
+
+            # Alternate whether to activate dummy logitproc for each request
+            use_dummy_logitproc = not use_dummy_logitproc
+
+    asyncio.run(_async_main(server, model_name))
 
 
 @pytest.mark.asyncio
diff --git a/tests/v1/logits_processors/utils.py b/tests/v1/logits_processors/utils.py
index e54da72e5e2e..fc8ce50c05fa 100644
--- a/tests/v1/logits_processors/utils.py
+++ b/tests/v1/logits_processors/utils.py
@@ -1,12 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import types
+import os
+import tempfile
 from enum import Enum, auto
+from pathlib import Path
 from typing import Any
 
 import torch
 
+from tests.utils import requires_spawn_multiprocessing
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.sampling_params import SamplingParams
@@ -102,11 +105,6 @@ def apply(self, logits: torch.Tensor) -> torch.Tensor:
         return logits
 
 
-"""Dummy module with dummy logitproc class"""
-dummy_module = types.ModuleType(DUMMY_LOGITPROC_MODULE)
-dummy_module.DummyLogitsProcessor = DummyLogitsProcessor  # type: ignore
-
-
 class EntryPoint:
     """Dummy entrypoint class for logitsprocs testing"""
 
@@ -187,5 +185,59 @@ def new_req_logits_processor(
         return DummyPerReqLogitsProcessor(target_token)
 
 
-"""Fake version of importlib.metadata.entry_points"""
-entry_points = lambda group: EntryPoints(group)
+def register_fake_entrypoint(monkeypatch) -> str:
+    """Register the dummy logitsproc entrypoint in a way that is visible
+    to spawned subprocesses by creating a real dist-info directory on disk.
+
+    Unlike monkey-patching importlib.metadata.entry_points (which only works
+    with fork), this approach writes a real dist-info package that
+    importlib.metadata can discover in any subprocess via PYTHONPATH.
+
+    Returns the temp directory path.
+    """
+    tmpdir = Path(tempfile.mkdtemp(prefix="dummy-logitproc-"))
+    dist_info = tmpdir / "dummy_logitproc-0.1.dist-info"
+    dist_info.mkdir()
+
+    # Write METADATA file (required by importlib.metadata)
+    (dist_info / "METADATA").write_text(
+        "Metadata-Version: 2.1\nName: dummy-logitproc\nVersion: 0.1\n",
+        encoding="utf-8",
+    )
+
+    # Write entry_points.txt
+    (dist_info / "entry_points.txt").write_text(
+        f"[{LOGITSPROCS_GROUP}]\n"
+        f"{DUMMY_LOGITPROC_ENTRYPOINT} = {DUMMY_LOGITPROC_FQCN}\n",
+        encoding="utf-8",
+    )
+
+    # Add to PYTHONPATH so spawned subprocesses can discover it
+    existing = os.environ.get("PYTHONPATH", "")
+    monkeypatch.setenv(
+        "PYTHONPATH", str(tmpdir) + (os.pathsep + existing if existing else "")
+    )
+
+    # Also update sys.path for the current process so the driver can
+    # discover the entrypoint.
+    monkeypatch.syspath_prepend(str(tmpdir))
+
+    return str(tmpdir)
+
+
+def fake_entry_points(group: str) -> EntryPoints:
+    """Fake version of importlib.metadata.entry_points."""
+    return EntryPoints(group)
+
+
+def setup_fake_entrypoint(monkeypatch) -> None:
+    """Expose the dummy logitproc entrypoint for the current platform."""
+    if requires_spawn_multiprocessing():
+        register_fake_entrypoint(monkeypatch)
+        monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+        return
+
+    import importlib.metadata
+
+    monkeypatch.setattr(importlib.metadata, "entry_points", fake_entry_points)
+    monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "fork")
diff --git a/tests/v1/metrics/test_ray_metrics.py b/tests/v1/metrics/test_ray_metrics.py
index f08d9f684921..6bad1299b61e 100644
--- a/tests/v1/metrics/test_ray_metrics.py
+++ b/tests/v1/metrics/test_ray_metrics.py
@@ -1,13 +1,21 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from unittest.mock import MagicMock
+
 import pytest
 import ray
 
 from vllm.config.model import ModelDType
 from vllm.sampling_params import SamplingParams
 from vllm.v1.engine.async_llm import AsyncEngineArgs, AsyncLLM
-from vllm.v1.metrics.ray_wrappers import RayPrometheusMetric, RayPrometheusStatLogger
+from vllm.v1.metrics.ray_wrappers import (
+    RayCounterWrapper,
+    RayGaugeWrapper,
+    RayHistogramWrapper,
+    RayPrometheusMetric,
+    RayPrometheusStatLogger,
+)
 
 MODELS = [
     "distilbert/distilgpt2",
@@ -94,3 +102,148 @@ def test_sanitized_opentelemetry_name():
 
     # Test empty string
     assert RayPrometheusMetric._get_sanitized_opentelemetry_name("") == ""
+
+
+def _install_mock_metric(wrapper: RayPrometheusMetric) -> MagicMock:
+    """Swap the wrapper's underlying Ray metric for a MagicMock while
+    preserving the real metric's ``_tag_keys`` (labels() reads them to
+    validate arity)."""
+    real_metric = wrapper.metric
+    mock = MagicMock()
+    mock._tag_keys = real_metric._tag_keys
+    wrapper.metric = mock
+    return mock
+
+
+def test_ray_counter_labels_returns_independent_children():
+    """RayCounterWrapper.labels() must return distinct labeled children that
+    each carry their own tag set."""
+    base = RayCounterWrapper(
+        name="vllm_test_finish_reason",
+        documentation="",
+        labelnames=["reason"],
+    )
+
+    stop_child = base.labels("stop")
+    rep_child = base.labels("repetition")
+
+    assert stop_child is not rep_child
+    assert stop_child._tags["reason"] == "stop"
+    assert rep_child._tags["reason"] == "repetition"
+    # Mutating one child's tags must not leak into another.
+    stop_child._tags["reason"] = "mutated"
+    assert rep_child._tags["reason"] == "repetition"
+
+
+def test_ray_counter_inc_forwards_per_child_tags():
+    """.inc() on a labeled counter must forward that child's tags to the
+    underlying Ray metric (not rely on a shared set_default_tags)."""
+    wrapper = RayCounterWrapper(
+        name="vllm_test_counter_tag_forward",
+        documentation="",
+        labelnames=["reason"],
+    )
+    mock = _install_mock_metric(wrapper)
+
+    wrapper.labels("stop").inc()
+    wrapper.labels("repetition").inc(3)
+    wrapper.labels("stop").inc(0)  # zero increment must be a no-op.
+
+    # The zero-increment call should not reach the underlying metric.
+    assert mock.inc.call_count == 2
+    first, second = mock.inc.call_args_list
+    assert first.args == (1.0,)
+    assert first.kwargs["tags"]["reason"] == "stop"
+    assert second.args == (3,)
+    assert second.kwargs["tags"]["reason"] == "repetition"
+
+
+def test_ray_gauge_labels_returns_independent_children_and_forwards_tags():
+    wrapper = RayGaugeWrapper(
+        name="vllm_test_gauge_tag_forward",
+        documentation="",
+        labelnames=["kind"],
+    )
+    mock = _install_mock_metric(wrapper)
+
+    a = wrapper.labels("a")
+    b = wrapper.labels("b")
+    assert a is not b
+
+    a.set(1)
+    b.set(2)
+    assert mock.set.call_args_list[0].args == (1,)
+    assert mock.set.call_args_list[0].kwargs["tags"]["kind"] == "a"
+    assert mock.set.call_args_list[1].args == (2,)
+    assert mock.set.call_args_list[1].kwargs["tags"]["kind"] == "b"
+
+
+def test_ray_histogram_labels_returns_independent_children_and_forwards_tags():
+    wrapper = RayHistogramWrapper(
+        name="vllm_test_histogram_tag_forward",
+        documentation="",
+        labelnames=["bucket"],
+        buckets=[1.0, 2.0, 5.0],
+    )
+    mock = _install_mock_metric(wrapper)
+
+    x = wrapper.labels("x")
+    y = wrapper.labels("y")
+    assert x is not y
+
+    x.observe(0.5)
+    y.observe(4.0)
+    assert mock.observe.call_args_list[0].args == (0.5,)
+    assert mock.observe.call_args_list[0].kwargs["tags"]["bucket"] == "x"
+    assert mock.observe.call_args_list[1].args == (4.0,)
+    assert mock.observe.call_args_list[1].kwargs["tags"]["bucket"] == "y"
+
+
+def test_ray_counter_labels_accepts_non_string_label_values():
+    """RayPrometheusStatLogger passes ``str(idx)`` for engine indexes; this
+    covers the coercion path for any caller that passes a non-string label
+    value positionally."""
+    wrapper = RayCounterWrapper(
+        name="vllm_test_nonstr_label",
+        documentation="",
+        labelnames=["engine", "reason"],
+    )
+    child = wrapper.labels(0, "stop")
+    assert child._tags["engine"] == "0"
+    assert child._tags["reason"] == "stop"
+
+
+def test_ray_counter_labels_arity_validation():
+    wrapper = RayCounterWrapper(
+        name="vllm_test_arity",
+        documentation="",
+        labelnames=["a", "b"],
+    )
+    with pytest.raises(ValueError, match="Number of labels must match"):
+        wrapper.labels("only-one")
+
+
+def test_unlabeled_inc_carries_replica_id():
+    """Recording on an unlabeled metric must still pass ReplicaId — it's a
+    declared tag_key and Ray rejects updates that omit any declared key."""
+    wrapper = RayCounterWrapper(
+        name="vllm_test_unlabeled_replica_id",
+        documentation="",
+        labelnames=None,
+    )
+    mock = _install_mock_metric(wrapper)
+    wrapper.inc()
+    assert mock.inc.call_args.kwargs["tags"] == {"ReplicaId": ""}
+
+
+def test_double_labels_raises():
+    """labels() on an already-labeled child should raise, mirroring the
+    prometheus_client contract."""
+    wrapper = RayCounterWrapper(
+        name="vllm_test_double_labels",
+        documentation="",
+        labelnames=["reason"],
+    )
+    child = wrapper.labels("stop")
+    with pytest.raises(ValueError, match="already-labeled"):
+        child.labels("repetition")
diff --git a/tests/v1/metrics/test_stats.py b/tests/v1/metrics/test_stats.py
index 3d9315c1ae6c..21f496ea4aea 100644
--- a/tests/v1/metrics/test_stats.py
+++ b/tests/v1/metrics/test_stats.py
@@ -26,6 +26,7 @@ def test_prefill_kv_computed_with_cache():
     # Case 1: With prefix cache (1200 tokens cached)
     iteration_stats.update_from_finished_request(
         finish_reason=FinishReason.STOP,
+        request_id="test-req-001",
         num_prompt_tokens=10000,
         max_tokens_param=100,
         req_stats=req_stats,
@@ -35,6 +36,7 @@ def test_prefill_kv_computed_with_cache():
     finished_req = iteration_stats.finished_requests[0]
     assert finished_req.num_prompt_tokens == 10000
     assert finished_req.num_cached_tokens == 1200
+    assert finished_req.request_id == "test-req-001"
 
     # Verify calculation: prefill KV = prompt tokens - cached tokens
     prefill_kv_computed = finished_req.num_prompt_tokens - max(
@@ -55,6 +57,7 @@ def test_prefill_kv_computed_no_cache():
     # Case 2: No prefix cache
     iteration_stats.update_from_finished_request(
         finish_reason=FinishReason.STOP,
+        request_id="test-req-002",
         num_prompt_tokens=2000,
         max_tokens_param=100,
         req_stats=req_stats,
@@ -64,6 +67,7 @@ def test_prefill_kv_computed_no_cache():
     finished_req = iteration_stats.finished_requests[0]
     assert finished_req.num_prompt_tokens == 2000
     assert finished_req.num_cached_tokens == 0
+    assert finished_req.request_id == "test-req-002"
 
     # Verify calculation: prefill KV = full prompt when no cache
     prefill_kv_computed = finished_req.num_prompt_tokens - max(
@@ -84,6 +88,7 @@ def test_prefill_kv_computed_edge_cases():
     # Case 3: Negative num_cached_tokens (shouldn't happen, but handle gracefully)
     iteration_stats.update_from_finished_request(
         finish_reason=FinishReason.STOP,
+        request_id="test-req-003",
         num_prompt_tokens=100,
         max_tokens_param=10,
         req_stats=req_stats,
@@ -96,11 +101,13 @@ def test_prefill_kv_computed_edge_cases():
         finished_req.num_cached_tokens, 0
     )
     assert prefill_kv_computed == 100  # Should treat negative as 0
+    assert finished_req.request_id == "test-req-003"
 
     # Case 4: All tokens cached (shouldn't happen in practice)
     iteration_stats2 = IterationStats()
     iteration_stats2.update_from_finished_request(
         finish_reason=FinishReason.STOP,
+        request_id="test-req-004",
         num_prompt_tokens=100,
         max_tokens_param=10,
         req_stats=req_stats,
@@ -112,6 +119,7 @@ def test_prefill_kv_computed_edge_cases():
         finished_req2.num_cached_tokens, 0
     )
     assert prefill_kv_computed2 == 0  # All cached, nothing computed
+    assert finished_req2.request_id == "test-req-004"
 
 
 def test_prompt_token_stats_all_computed():
diff --git a/tests/v1/sample/test_logprobs.py b/tests/v1/sample/test_logprobs.py
index 28fb2931b229..460e0d685649 100644
--- a/tests/v1/sample/test_logprobs.py
+++ b/tests/v1/sample/test_logprobs.py
@@ -33,11 +33,10 @@
 SAMPLE_PROMPT = BatchLogprobsComposition.SAMPLE_PROMPT
 
 # On ROCm, floating-point reductions in attention and GEMM kernels are
-# non-associative and sensitive to batch geometry. The ref LLM (no spec
-# decode, default scheduling) and the spec-decode LLM (chunked prefill,
-# different effective batch sizes) follow different reduction orders,
-# producing numerically divergent logprobs that get misattributed to
-# spec-decode incorrectness.
+# non-associative and sensitive to batch geometry. If the ref LLM and
+# spec-decode LLM use different scheduling or batch geometry, they can
+# follow different reduction orders and produce numerically divergent
+# logprobs that get misattributed to spec-decode incorrectness.
 #
 # Force LLM instances into an identical, deterministic execution
 # mode so the test isolates spec-decode correctness only:
@@ -1086,18 +1085,25 @@ def test_spec_decode_logprobs(
     )
 
     max_model_len = 256
-
-    # Run base LLM.
-    ref_llm = LLM(
-        model=model_name,
+    llm_kwargs = dict(
         max_logprobs=5,
         max_model_len=max_model_len,
         seed=42,
         logprobs_mode=logprobs_mode,
         gpu_memory_utilization=0.4,
+        # Force the same prefill chunking for both the base model and
+        # spec decode model so the comparison isolates spec decode.
+        enable_chunked_prefill=True,
+        max_num_batched_tokens=32,
         enable_prefix_caching=False,
         **ROCM_DETERMINISM_KWARGS,
     )
+
+    # Run base LLM.
+    ref_llm = LLM(
+        model=model_name,
+        **llm_kwargs,
+    )
     ref_results = ref_llm.generate(
         [prompt, prompt], [sampling_params, penalty_sampling_params]
     )
@@ -1117,16 +1123,7 @@ def test_spec_decode_logprobs(
     spec_llm = LLM(
         model_name,
         speculative_config=spec_config_with_len,
-        max_logprobs=5,
-        max_model_len=max_model_len,
-        seed=42,
-        logprobs_mode=logprobs_mode,
-        gpu_memory_utilization=0.4,
-        # Force prefill chunking
-        enable_chunked_prefill=True,
-        max_num_batched_tokens=32,
-        enable_prefix_caching=False,
-        **ROCM_DETERMINISM_KWARGS,
+        **llm_kwargs,
     )
     spec_results = spec_llm.generate(
         [prompt, prompt], [sampling_params, penalty_sampling_params]
diff --git a/tests/v1/sample/test_rejection_sampler.py b/tests/v1/sample/test_rejection_sampler.py
index ecfcade2b61f..ae0cbeab53b2 100644
--- a/tests/v1/sample/test_rejection_sampler.py
+++ b/tests/v1/sample/test_rejection_sampler.py
@@ -933,3 +933,64 @@ def test_sample_recovered_tokens(
         device=DEVICE_TYPE,
     )
     assert torch.equal(recovered_token_ids, ref_recovered_token_ids)
+
+
+########################### Tests for Synthetic Rejection Sampling #########
+
+
+def _make_synthetic_sampler(rates: list[float]) -> RejectionSampler:
+    mock_sampler = Mock(spec=Sampler)
+    mock_sampler.logprobs_mode = "raw_logprobs"
+    spec_config = Mock()
+    spec_config.rejection_sample_method = "synthetic"
+    spec_config.synthetic_acceptance_rates = rates
+    return RejectionSampler(mock_sampler, spec_config, torch.device(DEVICE_TYPE))
+
+
+def _make_sampling_metadata(all_greedy: bool) -> SamplingMetadata:
+    temperature = None if all_greedy else torch.tensor([1.0, 1.0], device=DEVICE_TYPE)
+    return create_sampling_metadata(all_greedy=all_greedy, temperature=temperature)
+
+
+@pytest.mark.parametrize("all_greedy", [True, False])
+def test_synthetic_all_accepted(all_greedy: bool):
+    """With all rates=1.0, every draft token is accepted."""
+    sampler = _make_synthetic_sampler([1.0, 1.0])
+    spec_tokens = [[1, 2], [3]]
+    output_tokens = [[10, 20, 50], [30, 40]]
+
+    metadata = _make_sampling_metadata(all_greedy)
+    logits = create_logits_tensor(output_tokens)
+    bonus = torch.tensor([50, 40], device=DEVICE_TYPE)
+    spec_decode_metadata = create_spec_decode_metadata(spec_tokens, logits)
+
+    mock_sampler_output(sampler, bonus)
+    output = sampler(spec_decode_metadata, None, logits, metadata)
+    expected = torch.tensor(
+        [[1, 2, 50], [3, 40, PLACEHOLDER_TOKEN_ID]],
+        dtype=torch.int,
+        device=DEVICE_TYPE,
+    )
+    assert torch.equal(output.sampled_token_ids, expected)
+
+
+@pytest.mark.parametrize("all_greedy", [True, False])
+def test_synthetic_all_rejected(all_greedy: bool):
+    """With all rates=0.0, the first token is always rejected."""
+    sampler = _make_synthetic_sampler([0.0, 0.0])
+    spec_tokens = [[1, 2], [3]]
+    output_tokens = [[10, 20, 50], [30, 40]]
+
+    metadata = _make_sampling_metadata(all_greedy)
+    logits = create_logits_tensor(output_tokens)
+    bonus = torch.tensor([50, 40], device=DEVICE_TYPE)
+    spec_decode_metadata = create_spec_decode_metadata(spec_tokens, logits)
+
+    mock_sampler_output(sampler, bonus)
+    output = sampler(spec_decode_metadata, None, logits, metadata)
+    result = output.sampled_token_ids
+    # Exactly one token emitted per sequence (the rejection fallback),
+    # followed by placeholders.
+    for row in result:
+        assert row[0] != PLACEHOLDER_TOKEN_ID
+        assert (row[1:] == PLACEHOLDER_TOKEN_ID).all()
diff --git a/tests/v1/sample/test_topk_topp_sampler.py b/tests/v1/sample/test_topk_topp_sampler.py
index 511f2668075b..659577b754f6 100644
--- a/tests/v1/sample/test_topk_topp_sampler.py
+++ b/tests/v1/sample/test_topk_topp_sampler.py
@@ -13,6 +13,30 @@
 VOCAB_SIZE = 128 * 1024
 
 
+def _flashinfer_topk_topp_supported() -> bool:
+    """True iff the FlashInfer top-k/top-p sampler is usable on this host.
+
+    Mirrors the gate in `TopKTopPSampler.__init__`: CUDA + flashinfer
+    importable + GPU compute capability supported by the FlashInfer
+    backend.
+    """
+    if not current_platform.is_cuda():
+        return False
+    try:
+        import flashinfer  # noqa: F401
+
+        from vllm.v1.attention.backends.flashinfer import FlashInferBackend
+    except ImportError:
+        return False
+    capability = current_platform.get_device_capability()
+    if capability is None:
+        return False
+    return FlashInferBackend.supports_compute_capability(capability)
+
+
+FLASHINFER_TOPK_TOPP_SUPPORTED = _flashinfer_topk_topp_supported()
+
+
 @pytest.fixture(autouse=True)
 def reset_default_device():
     """
@@ -127,7 +151,7 @@ def test_flashinfer_sampler():
 # =============================================================================
 
 
-@pytest.mark.skipif("CPU" in DEVICE_TYPE, reason="CUDA/XPU not available")
+@pytest.mark.skipif("cpu" in DEVICE_TYPE, reason="CUDA/XPU not available")
 class TestTritonTopkTopp:
     """Tests for the Triton top-k/top-p kernel."""
 
@@ -568,3 +592,280 @@ def test_mixed_neginf_and_normal_rows(self):
             finite_in = (logits[i] > float("-inf")).sum().item()
             if finite_in > 0:
                 assert kept > 0, f"Row {i}: no tokens kept"
+
+
+# =============================================================================
+# FlashInfer top-k/top-p robustness tests
+# =============================================================================
+
+
+@pytest.mark.skipif(
+    not FLASHINFER_TOPK_TOPP_SUPPORTED,
+    reason="FlashInfer top-k/top-p sampler requires CUDA "
+    "and a GPU with FlashInfer support.",
+)
+class TestFlashInferTopkToppRobustness:
+    """Robustness of FlashInfer top-k / top-p sampling to NaN / Inf logits.
+
+    The FlashInfer sampler is enabled by default on supported GPUs. A
+    single poisoned request (NaN / +Inf / -Inf in row 0) must not:
+
+    1. crash or hang the process;
+    2. produce out-of-range token ids (anything outside ``[0, vocab)``);
+    3. corrupt other batch rows — neighbours of a poisoned row must
+       still receive valid token ids (regression for cross-row
+       corruption in a DP batch where one bad request would otherwise
+       poison its peers).
+
+    The reference is "no crash + valid token ids", not bit-exact equality
+    against the PyTorch-native path.
+    """
+
+    BATCH = 8
+    VOCAB = 32768
+    TOPK = 50
+    TOPP = 0.9
+
+    @pytest.fixture(autouse=True)
+    def setup(self):
+        torch.set_default_device(DEVICE_TYPE)
+        self.generator = Generator(device=DEVICE_TYPE).manual_seed(1234)
+
+    def _make_logits(self, pattern: str) -> torch.Tensor:
+        """Build (BATCH, VOCAB) logits with `pattern` applied to row 0
+        (rows 1..B-1 stay clean so we can detect cross-row corruption)."""
+        logits = (
+            torch.randn(
+                self.BATCH,
+                self.VOCAB,
+                generator=self.generator,
+                dtype=torch.float32,
+            )
+            * 5.0
+        )
+        if pattern == "clean":
+            return logits
+        if pattern == "nan_one_row":
+            logits[0, :] = float("nan")
+        elif pattern == "nan_few":
+            # Scatter 16 NaNs across row 0, keep the rest finite.
+            idx = torch.randperm(self.VOCAB, generator=self.generator)[:16]
+            logits[0, idx] = float("nan")
+        elif pattern == "nan_at_top":
+            # Poison the top-32 highest-scoring positions of row 0 — worst
+            # case for top-k since these are exactly the tokens that would
+            # otherwise be selected. Use argsort instead of topk to avoid
+            # a known compute-sanitizer false positive in mbtopk.
+            top_idx = logits[0].argsort(descending=True)[:32]
+            logits[0, top_idx] = float("nan")
+        elif pattern == "nan_all_rows":
+            logits[:, :] = float("nan")
+        elif pattern == "pos_inf_one_row":
+            logits[0, :] = float("inf")
+        elif pattern == "neg_inf_one_row":
+            logits[0, :] = float("-inf")
+        elif pattern == "mixed_inf_nan":
+            assert self.BATCH >= 3
+            logits[0, :] = float("nan")
+            logits[1, :] = float("inf")
+            logits[2, :] = float("-inf")
+        elif pattern == "degenerate_flat":
+            logits[:, :] = 1.0
+        else:
+            raise ValueError(f"unknown pattern: {pattern}")
+        return logits
+
+    def _check_tokens(self, tokens: torch.Tensor, ctx: str):
+        assert tokens.dim() == 1, f"{ctx}: expected 1-D output, got {tokens.shape}"
+        assert tokens.shape[0] == self.BATCH, (
+            f"{ctx}: expected batch size {self.BATCH}, got {tokens.shape[0]}"
+        )
+        ids = tokens.tolist()
+        min_id, max_id = min(ids), max(ids)
+        assert 0 <= min_id < self.VOCAB and 0 <= max_id < self.VOCAB, (
+            f"{ctx}: token id(s) outside [0, {self.VOCAB}): min={min_id}, max={max_id}"
+        )
+
+    @pytest.mark.parametrize(
+        "pattern",
+        [
+            "clean",
+            "nan_one_row",
+            "nan_few",
+            "nan_at_top",
+            "nan_all_rows",
+            "pos_inf_one_row",
+            "neg_inf_one_row",
+            "mixed_inf_nan",
+            "degenerate_flat",
+        ],
+    )
+    @pytest.mark.parametrize("path", ["topk_only", "topp_only", "topk_topp"])
+    def test_flashinfer_handles_pathological_logits(self, pattern: str, path: str):
+        """flashinfer_sample must return valid ids even on poisoned logits.
+
+        Direct call into ``flashinfer_sample`` — exactly the code path
+        ``TopKTopPSampler.forward_cuda`` takes when FI is enabled.
+        """
+        from vllm.v1.sample.ops.topk_topp_sampler import flashinfer_sample
+
+        logits = self._make_logits(pattern)
+        k = (
+            torch.full(
+                (self.BATCH,),
+                self.TOPK,
+                device=DEVICE_TYPE,
+                dtype=torch.int32,
+            )
+            if path in ("topk_only", "topk_topp")
+            else None
+        )
+        p = (
+            torch.full(
+                (self.BATCH,),
+                self.TOPP,
+                device=DEVICE_TYPE,
+                dtype=torch.float32,
+            )
+            if path in ("topp_only", "topk_topp")
+            else None
+        )
+
+        # flashinfer_sample may mutate its input in-place; pass a clone so
+        # the parametrize iterations stay independent.
+        tokens = flashinfer_sample(logits.clone().contiguous(), k, p, {})
+        # Surface any async CUDA error synchronously (e.g. illegal memory
+        # access from a malformed FlashInfer call) so it's attributed to
+        # this test rather than a later, unrelated GPU op.
+        torch.accelerator.synchronize()
+        self._check_tokens(tokens, ctx=f"pattern={pattern}, path={path}")
+
+
+# =============================================================================
+# FlashInfer top-k/top-p distribution-match tests
+# =============================================================================
+
+
+@pytest.mark.skipif(
+    not FLASHINFER_TOPK_TOPP_SUPPORTED,
+    reason="FlashInfer top-k/top-p sampler requires CUDA "
+    "and a GPU with FlashInfer support.",
+)
+class TestFlashInferDistributionMatch:
+    """Chi-square goodness-of-fit: FlashInfer and PyTorch-native samplers
+    both reproduce the expected token distribution after top-k / top-p.
+
+    Regression guard against historical FlashInfer distribution-shift.
+    Each impl is compared to the theoretical distribution (softmax of
+    filtered logits); if both pass they are statistically equivalent
+    to each other by transitivity.
+    """
+
+    VOCAB = 32
+    N_SAMPLES = 50_000
+    ALPHA = 1e-6
+    SEED = 0
+
+    @pytest.mark.parametrize(
+        "topk,topp",
+        [
+            (8, None),
+            (16, None),
+            (None, 0.5),
+            (None, 0.7),
+            (None, 0.99),
+            (8, 0.9),
+            (4, 0.5),
+        ],
+    )
+    def test_distribution_matches_theoretical(self, topk, topp):
+        from scipy.stats import chisquare
+
+        from vllm.v1.sample.ops.topk_topp_sampler import (
+            apply_top_k_top_p,
+            flashinfer_sample,
+            random_sample,
+        )
+
+        torch.set_default_device(DEVICE_TYPE)
+        torch.manual_seed(self.SEED)
+
+        # Same logits row used for both impls so the comparison is fair.
+        logits_one = (
+            torch.randn(
+                (1, self.VOCAB),
+                dtype=torch.float32,
+            )
+            * 2.0
+        )
+
+        # Theoretical expected distribution from PyTorch-native filter.
+        k_one = torch.tensor([topk], dtype=torch.int32) if topk is not None else None
+        p_one = torch.tensor([topp], dtype=torch.float32) if topp is not None else None
+        masked = apply_top_k_top_p_pytorch(logits_one.clone(), k_one, p_one)
+        expected_probs = masked.softmax(dim=-1).flatten().cpu().numpy()
+        expected_counts = expected_probs * self.N_SAMPLES
+
+        # Build a batch of N identical rows for both impls.
+        batch = logits_one.expand(self.N_SAMPLES, self.VOCAB).contiguous()
+        k_batch = (
+            torch.full((self.N_SAMPLES,), topk, dtype=torch.int32)
+            if topk is not None
+            else None
+        )
+        p_batch = (
+            torch.full((self.N_SAMPLES,), topp, dtype=torch.float32)
+            if topp is not None
+            else None
+        )
+
+        # FlashInfer dispatch path.
+        fi_tokens = flashinfer_sample(batch.contiguous(), k_batch, p_batch, {})
+        fi_counts = torch.bincount(fi_tokens, minlength=self.VOCAB).cpu().numpy()
+        self._chi2_check(
+            fi_counts,
+            expected_counts,
+            chisquare,
+            label=f"flashinfer top-k={topk} top-p={topp}",
+        )
+
+        # PyTorch-native dispatch path (Triton-routed filter + Gumbel sample).
+        processed = apply_top_k_top_p(batch.clone(), k_batch, p_batch)
+        probs = processed.softmax(dim=-1, dtype=torch.float32)
+        pt_tokens = random_sample(probs, {})
+        pt_counts = torch.bincount(pt_tokens, minlength=self.VOCAB).cpu().numpy()
+        self._chi2_check(
+            pt_counts,
+            expected_counts,
+            chisquare,
+            label=f"native top-k={topk} top-p={topp}",
+        )
+
+    def _chi2_check(self, empirical, expected, chisquare_fn, *, label):
+        import numpy as np
+
+        # Hard check: the sampler must never produce a token outside the
+        # expected support (zero theoretical probability).
+        outside = (expected == 0) & (empirical > 0)
+        assert not outside.any(), (
+            f"{label}: sampled out-of-support tokens "
+            f"(zero expected prob): indices={outside.nonzero()[0].tolist()}"
+        )
+        # Skip chi-square in the degenerate case where the support
+        # collapses to a single token (e.g. very restrictive joint
+        # top-k + top-p): all samples must land there and the hard
+        # check above already verified they do.
+        in_support = expected > 0
+        if int(in_support.sum()) <= 1:
+            return
+        # Soft check: chi-square goodness-of-fit on in-support tokens.
+        # Cast to float64 so the rescaling step below stays within
+        # scipy.chisquare's strict 1.5e-8 sum-equality tolerance.
+        emp = empirical[in_support].astype(np.float64)
+        exp = expected[in_support].astype(np.float64)
+        exp = exp * (emp.sum() / exp.sum())
+        chi2, p_value = chisquare_fn(emp, exp)
+        assert p_value > self.ALPHA, (
+            f"{label}: distribution differs from theoretical: "
+            f"chi2={chi2:.2f} p_value={p_value:.2e} alpha={self.ALPHA}"
+        )
diff --git a/tests/v1/sample/utils.py b/tests/v1/sample/utils.py
index a0abb3b4c6ce..907be3614b9c 100644
--- a/tests/v1/sample/utils.py
+++ b/tests/v1/sample/utils.py
@@ -198,22 +198,43 @@ def get_logitsprocs(self) -> Iterator[LogitsProcessor]:
 
 def fake_update_logitsprocs_state(
     test_fakes: LogitsprocsTestFakes,
-    batch_update: BatchUpdate,
+    batch_update: BatchUpdate | None,
 ) -> None:
     """Imitate logits processors persistent batch state update
     in engine core"""
     for logitproc in test_fakes.get_logitsprocs():
         logitproc.update_state(batch_update)
+    holder = test_fakes.sampling_metadata.thinking_budget_state_holder
+    if holder is not None:
+        holder.sync_batch(batch_update)
 
 
 def fake_apply_logitsprocs(
     test_fakes: LogitsprocsTestFakes,
     slice_indices: list[int],
+    slot_output_token_ids: list[list[int]] | None = None,
 ) -> torch.Tensor:
-    """Imitate application of logits processors in engine core"""
+    """Imitate application of logits processors in engine core.
+
+    When ``thinking_budget_state_holder`` has tracked requests, this mirrors
+    :meth:`Sampler.apply_logits_processors` by refreshing per-slot
+    ``output_token_ids`` (if ``slot_output_token_ids`` is provided), then
+    ``update_state`` + ``apply_to_logits`` on the holder after built-in logits
+    processors.
+    """
     logits = test_fakes.logits[torch.tensor(slice_indices, dtype=torch.long)].clone()
     for processor in test_fakes.get_logitsprocs():
         logits = processor.apply(logits)
+
+    md = test_fakes.sampling_metadata
+    holder = md.thinking_budget_state_holder
+    if holder is not None and holder.has_tracked_requests():
+        if slot_output_token_ids is not None:
+            for i, toks in enumerate(slot_output_token_ids):
+                if i < len(md.output_token_ids):
+                    md.output_token_ids[i] = list(toks)
+        holder.update_state(md.output_token_ids, md.spec_token_ids, None)
+        logits = holder.apply_to_logits(logits, False, md.spec_token_ids)
     return logits
 
 
diff --git a/tests/v1/simple_kv_offload/test_integration.py b/tests/v1/simple_kv_offload/test_integration.py
index 29399516be18..02f6360e08e8 100644
--- a/tests/v1/simple_kv_offload/test_integration.py
+++ b/tests/v1/simple_kv_offload/test_integration.py
@@ -10,8 +10,8 @@
 from vllm.config import KVTransferConfig
 from vllm.platforms import current_platform
 
-if not current_platform.is_cuda():
-    pytest.skip("Requires CUDA", allow_module_level=True)
+if not current_platform.is_cuda_alike():
+    pytest.skip("Requires CUDA or ROCm", allow_module_level=True)
 
 # Small models for default CI / local runs (accuracy only).
 SMALL_MODELS = [
diff --git a/tests/v1/simple_kv_offload/test_scheduler.py b/tests/v1/simple_kv_offload/test_scheduler.py
index 132f52fe3b36..4d685103df60 100644
--- a/tests/v1/simple_kv_offload/test_scheduler.py
+++ b/tests/v1/simple_kv_offload/test_scheduler.py
@@ -186,7 +186,13 @@ def make_request(
     if request_id is None:
         request_id = f"req-{_req_counter}"
 
-    num_tokens = num_blocks * BLOCK_SIZE
+    # Add one extra token beyond the last full block so that
+    # ``max_cache_hit_length = num_tokens - 1`` (see
+    # KVCacheManager.get_computed_blocks) does not truncate the final
+    # full block: ``find_longest_cache_hit`` uses
+    # ``max_length // block_size`` and would otherwise drop one block
+    # when the prompt is an exact multiple of block_size.
+    num_tokens = num_blocks * BLOCK_SIZE + 1
     start = _req_counter * 10000
     prompt_token_ids = list(range(start, start + num_tokens))
     sampling_params = SamplingParams(max_tokens=1)
diff --git a/tests/v1/spec_decode/test_acceptance_length.py b/tests/v1/spec_decode/test_acceptance_length.py
index aa8e40a2de5e..62ff100fdbf8 100644
--- a/tests/v1/spec_decode/test_acceptance_length.py
+++ b/tests/v1/spec_decode/test_acceptance_length.py
@@ -43,7 +43,8 @@ class Eagle3ModelConfig:
 
 # Model configurations for EAGLE3 acceptance length tests.
 # Expected acceptance lengths are determined by running baseline benchmarks
-# using examples/offline_inference/spec_decode.py with the MT-Bench dataset.
+# using examples/features/speculative_decoding/spec_decode_offline.py
+# with the MT-Bench dataset.
 EAGLE3_MODEL_CONFIGS = [
     Eagle3ModelConfig(
         verifier="meta-llama/Llama-3.1-8B-Instruct",
@@ -165,6 +166,7 @@ def get_mt_bench_prompts(
         no_stream=True,
         disable_shuffle=False,
         skip_chat_template=False,
+        trust_remote_code=False,
     )
     samples = get_samples(args, tokenizer)
     prompt_ids = [
@@ -210,8 +212,8 @@ def extract_acceptance_metrics(metrics, num_spec_tokens: int) -> dict:
 
 @large_gpu_mark(min_gb=40)
 @pytest.mark.skipif(
-    not current_platform.is_cuda(),
-    reason="This test is only supported on CUDA platform.",
+    not current_platform.is_cuda_alike(),
+    reason="This test is only supported on CUDA-alike platforms.",
 )
 @pytest.mark.parametrize(
     "model_config",
diff --git a/tests/v1/spec_decode/test_eagle.py b/tests/v1/spec_decode/test_eagle.py
index 188e84abca0b..32c152bf754c 100644
--- a/tests/v1/spec_decode/test_eagle.py
+++ b/tests/v1/spec_decode/test_eagle.py
@@ -49,7 +49,6 @@ def _create_proposer(
     method: str,
     num_speculative_tokens: int,
     attention_backend: str | None = None,
-    speculative_token_tree: list[tuple[int, ...]] | None = None,
     parallel_drafting: bool = False,
 ) -> EagleProposer:
     # Method-dependent setup
@@ -75,18 +74,12 @@ def _create_proposer(
         trust_remote_code=(method == "dflash"),
     )
 
-    spec_token_tree_str = None
-    if speculative_token_tree is not None:
-        assert num_speculative_tokens == len(speculative_token_tree)
-        spec_token_tree_str = str(speculative_token_tree)
-
     speculative_config = SpeculativeConfig(
         target_model_config=model_config,
         target_parallel_config=ParallelConfig(),
         model=draft_model_dir,
         method=method,
         num_speculative_tokens=num_speculative_tokens,
-        speculative_token_tree=spec_token_tree_str,
         parallel_drafting=parallel_drafting,
     )
     if parallel_drafting:
@@ -741,9 +734,9 @@ def test_set_inputs_first_pass_parallel_drafting():
 @pytest.mark.parametrize("pp_size", [1, 2])
 @pytest.mark.parametrize("use_distinct_embed_tokens", [True, False])
 @pytest.mark.parametrize("use_distinct_lm_head", [True, False])
-@mock.patch("vllm.v1.spec_decode.eagle.get_pp_group")
-@mock.patch("vllm.v1.spec_decode.eagle.get_layers_from_vllm_config")
-@mock.patch("vllm.v1.spec_decode.eagle.get_model")
+@mock.patch("vllm.v1.spec_decode.llm_base_proposer.get_pp_group")
+@mock.patch("vllm.v1.spec_decode.llm_base_proposer.get_layers_from_vllm_config")
+@mock.patch("vllm.v1.spec_decode.llm_base_proposer.get_model")
 def test_load_model(
     mock_get_model,
     mock_get_layers,
@@ -844,12 +837,6 @@ def test_propose(method, attn_backend, num_speculative_tokens, monkeypatch):
             "multi-token eagle spec decode on current platform"
         )
 
-    if attn_backend == "TREE_ATTN":
-        pytest.skip(
-            "TREE_ATTN is tested separately in test_propose_tree"
-            "because it requires special input mocking."
-        )
-
     if attn_backend == "ROCM_AITER_FA" and current_platform.is_rocm():
         monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")
 
@@ -955,10 +942,6 @@ def create_deterministic_logits(token_ids):
         attn_metadata_builder_cls, _ = try_get_attention_backend(
             AttentionBackendEnum.TRITON_ATTN
         )
-    elif attn_backend == "TREE_ATTN":
-        attn_metadata_builder_cls, _ = try_get_attention_backend(
-            AttentionBackendEnum.TREE_ATTN
-        )
     elif attn_backend == "ROCM_AITER_FA":
         attn_metadata_builder_cls, _ = try_get_attention_backend(
             AttentionBackendEnum.ROCM_AITER_FA
@@ -1014,149 +997,6 @@ def create_deterministic_logits(token_ids):
     assert torch.equal(result, expected_tokens)
 
 
-@pytest.mark.parametrize(
-    "spec_token_tree",
-    [
-        [(0,)],  # A single token
-        [(0,), (0, 0), (0, 0, 0)],  # Chain
-        [(0,), (1,), (2,)],  # Parallel
-        [(0,), (1,), (2,), (0, 0), (0, 1), (1, 0), (1, 1), (2, 0), (2, 1)],  # Tree
-    ],
-)
-def test_propose_tree(spec_token_tree):
-    # Get GPU device.
-    device = torch.device(DEVICE_TYPE)
-
-    # Setup test parameters.
-    batch_size = 2
-    seq_len_1 = 5
-    seq_len_2 = 3
-    total_tokens = seq_len_1 + seq_len_2
-    vocab_size = 100
-    seq_lens = [seq_len_1, seq_len_2]
-    num_speculative_tokens = len(spec_token_tree)
-
-    # Create proposer first so we can use its actual hidden_size.
-    proposer = _create_proposer(
-        "eagle",
-        num_speculative_tokens,
-        speculative_token_tree=spec_token_tree,
-    )
-    # Get the hidden_size from the proposer to ensure consistency.
-    hidden_size = proposer.hidden_size
-
-    # Helper to create deterministic logits that will produce specific tokens
-    def create_deterministic_logits(token_ids, k: int):
-        logits = torch.full((batch_size, vocab_size), -100.0, device=device)
-        for i, token_id in enumerate(token_ids):
-            # Assign decreasing values to the k, consecutive, tokens.
-            for j in range(k):
-                logits[i, token_id + j] = 100.0 - j
-        return logits
-
-    # Mock a model that returns deterministic logits.
-    base_token_ids = torch.tensor([42, 60], dtype=torch.int64, device=device)
-
-    # Skip loading the model and replace it with a mock that returns
-    # deterministic outputs.
-    model_mock = mock.MagicMock()
-
-    # Mock the model forward calls.
-    forward_returns = [
-        (
-            torch.zeros(total_tokens, hidden_size, device=device),
-            torch.zeros(total_tokens, hidden_size, device=device),
-        )
-    ]
-    for cu_num_drafts in proposer.cu_drafts_per_level:
-        h_logits = torch.zeros(batch_size * cu_num_drafts, hidden_size, device=device)
-        h_states = torch.zeros(batch_size * cu_num_drafts, hidden_size, device=device)
-        forward_returns.append((h_logits, h_states))
-    model_mock.side_effect = forward_returns
-
-    # Mock the compute_logits calls.
-    cu_num_drafts_tensor = torch.tensor(
-        [0] + proposer.cu_drafts_per_level, dtype=torch.int32, device=device
-    )
-    logits_returns = []
-    for level, num_children in enumerate(proposer.child_drafts_per_level):
-        token_ids = base_token_ids + cu_num_drafts_tensor[level]
-        level_num_drafts = cu_num_drafts_tensor[level + 1] - cu_num_drafts_tensor[level]
-        level_logits = []
-        for i in range(level_num_drafts // num_children):
-            level_logits.append(
-                create_deterministic_logits(token_ids + i * num_children, num_children)
-            )
-        logits_returns.append(torch.stack(level_logits, dim=1))
-    model_mock.compute_logits.side_effect = logits_returns
-
-    # Assign the mock to the proposer
-    proposer.model = model_mock
-
-    # Assign draft attn_layer_names since load_model is not invoked
-    proposer._draft_attn_layer_names = {"layer.0"}
-
-    # Get the tree attention metadata builder.
-    attn_metadata_builder_cls, _ = try_get_attention_backend(
-        AttentionBackendEnum.TREE_ATTN
-    )
-    attn_metadata_builder = attn_metadata_builder_cls(
-        kv_cache_spec=create_standard_kv_cache_spec(proposer.vllm_config),
-        layer_names=proposer._draft_attn_layer_names,
-        vllm_config=proposer.vllm_config,
-        device=device,
-    )
-
-    # Mock runner and draft_attn_groups for attention metadata building.
-    proposer.runner = mock.MagicMock()
-    mock_attn_group = mock.MagicMock()
-    mock_attn_group.get_metadata_builder.return_value = attn_metadata_builder
-    mock_attn_group.layer_names = list(proposer._draft_attn_layer_names)
-    mock_attn_group.kv_cache_spec = attn_metadata_builder.kv_cache_spec
-    proposer.draft_attn_groups = [mock_attn_group]
-
-    # Setup inputs for the proposer.
-    target_token_ids = torch.randint(0, vocab_size, (total_tokens,), device=device)
-    target_positions = torch.cat(
-        [torch.arange(seq_len_1, device=device), torch.arange(seq_len_2, device=device)]
-    )
-    target_hidden_states = torch.randn(total_tokens, hidden_size, device=device)
-    next_token_ids = torch.randint(
-        0, vocab_size, (batch_size,), dtype=torch.int32, device=device
-    )
-    batch_spec = BatchSpec(
-        seq_lens=seq_lens,
-        query_lens=seq_lens,
-    )
-    common_attn_metadata = create_common_attn_metadata(
-        batch_spec,
-        block_size=BLOCK_SIZE,
-        device=device,
-    )
-    sampling_metadata = mock.MagicMock()
-
-    # Propose draft tokens.
-    result = proposer.propose(
-        target_token_ids=target_token_ids,
-        target_positions=target_positions,
-        target_hidden_states=target_hidden_states,
-        next_token_ids=next_token_ids,
-        token_indices_to_sample=None,
-        common_attn_metadata=common_attn_metadata,
-        sampling_metadata=sampling_metadata,
-    )
-    assert result.shape == (batch_size, num_speculative_tokens)
-
-    # The tokens are expected to be consecutive integers starting
-    # from the base token IDs.
-    expected_tokens = base_token_ids[:, None] + torch.arange(
-        num_speculative_tokens, dtype=torch.int64, device=device
-    )
-
-    # Verify that the draft tokens match our expectations.
-    assert torch.equal(result, expected_tokens)
-
-
 def test_set_inputs_first_pass_dflash():
     """
     Test for DFlash set_inputs_first_pass.
diff --git a/tests/v1/spec_decode/test_max_len.py b/tests/v1/spec_decode/test_max_len.py
index 1e1c6745191d..77c041d84a94 100644
--- a/tests/v1/spec_decode/test_max_len.py
+++ b/tests/v1/spec_decode/test_max_len.py
@@ -6,6 +6,7 @@
 
 from tests.utils import get_attn_backend_list_based_on_platform
 from vllm import LLM, SamplingParams
+from vllm.config import ModelConfig, ParallelConfig, SpeculativeConfig
 from vllm.platforms import current_platform
 from vllm.sampling_params import StructuredOutputsParams
 
@@ -77,3 +78,23 @@ def test_eagle_max_len(
             "is longer than the eagle max length"
         )
         assert o.outputs[0].text == "a b c d e " * 15
+
+
+@pytest.mark.parametrize("spec_max_model_len", [80, 150])
+def test_mtp_speculative_config_max_model_len(spec_max_model_len: int):
+    """Regression test for #41456: max_model_len in speculative config
+    should be respected for the draft model."""
+    model_config = ModelConfig(
+        model="XiaomiMiMo/MiMo-7B-Base",
+        runner="generate",
+        max_model_len=200,
+        trust_remote_code=True,
+    )
+    spec_config = SpeculativeConfig(
+        target_model_config=model_config,
+        target_parallel_config=ParallelConfig(),
+        method="mtp",
+        num_speculative_tokens=1,
+        max_model_len=spec_max_model_len,
+    )
+    assert spec_config.draft_model_config.max_model_len == spec_max_model_len
diff --git a/tests/v1/spec_decode/test_mtp.py b/tests/v1/spec_decode/test_mtp.py
index 094611e05c1f..7c478f81d862 100644
--- a/tests/v1/spec_decode/test_mtp.py
+++ b/tests/v1/spec_decode/test_mtp.py
@@ -61,9 +61,9 @@ def _create_mtp_proposer(num_speculative_tokens: int) -> EagleProposer:
     return EagleProposer(vllm_config=vllm_config, device=DEVICE_TYPE)
 
 
-@mock.patch("vllm.v1.spec_decode.eagle.get_pp_group")
-@mock.patch("vllm.v1.spec_decode.eagle.get_layers_from_vllm_config")
-@mock.patch("vllm.v1.spec_decode.eagle.get_model")
+@mock.patch("vllm.v1.spec_decode.llm_base_proposer.get_pp_group")
+@mock.patch("vllm.v1.spec_decode.llm_base_proposer.get_layers_from_vllm_config")
+@mock.patch("vllm.v1.spec_decode.llm_base_proposer.get_model")
 def test_mtp_load_model_unified(mock_get_model, mock_get_layers, mock_get_pp_group):
     """Test MTP-specific model loading with unified model approach."""
 
diff --git a/tests/v1/spec_decode/test_speculators_dflash.py b/tests/v1/spec_decode/test_speculators_dflash.py
new file mode 100644
index 000000000000..2ba580695ddf
--- /dev/null
+++ b/tests/v1/spec_decode/test_speculators_dflash.py
@@ -0,0 +1,171 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+
+from tests.evals.gsm8k.gsm8k_eval import evaluate_gsm8k_offline
+from tests.utils import large_gpu_mark
+from vllm import LLM
+from vllm.config import SpeculativeConfig
+from vllm.distributed import cleanup_dist_env_and_memory
+
+MODEL_PATH = "nm-testing/dflash-qwen3-8b-speculators"
+
+EXPECTED_GSM8K_ACCURACY = 0.885
+ACCURACY_RTOL = 0.03
+EXPECTED_ACCEPTANCE_LEN = 3.45
+ACCEPTANCE_LEN_RTOL = 0.15
+
+# Expected per-position acceptance rates (accepted_at_pos / num_drafts)
+# Based on GSM8K evaluation with Qwen3-8B dflash speculators.
+EXPECTED_PER_POS_ACCEPTANCE_RATES = [0.795, 0.611, 0.429, 0.282]
+PER_POS_RTOL = 0.15
+
+
+def compute_spec_decode_stats(
+    metrics,
+) -> dict:
+    """Extract all spec-decode metrics and compute derived stats."""
+    name2metric = {m.name: m for m in metrics}
+
+    n_drafts = name2metric["vllm:spec_decode_num_drafts"].value
+    n_draft_tokens = name2metric["vllm:spec_decode_num_draft_tokens"].value
+    n_accepted = name2metric["vllm:spec_decode_num_accepted_tokens"].value
+
+    per_pos_vec = name2metric["vllm:spec_decode_num_accepted_tokens_per_pos"].values
+
+    acceptance_len = 1 + (n_accepted / n_drafts) if n_drafts > 0 else 1.0
+    draft_tokens_per_step = (n_draft_tokens / n_drafts) if n_drafts > 0 else 0
+    overall_acceptance_rate = (n_accepted / n_draft_tokens) if n_draft_tokens > 0 else 0
+    per_pos_rates = [v / n_drafts for v in per_pos_vec] if n_drafts > 0 else []
+
+    return {
+        "num_drafts": n_drafts,
+        "num_draft_tokens": n_draft_tokens,
+        "num_accepted_tokens": n_accepted,
+        "acceptance_len": acceptance_len,
+        "draft_tokens_per_step": draft_tokens_per_step,
+        "overall_acceptance_rate": overall_acceptance_rate,
+        "per_pos_accepted": list(per_pos_vec),
+        "per_pos_acceptance_rates": per_pos_rates,
+    }
+
+
+def print_spec_decode_stats(stats: dict) -> None:
+    """Print all spec-decode metrics and derived values."""
+    print("\n===== Spec Decode Metrics =====")
+    print(f"  num_drafts:              {stats['num_drafts']}")
+    print(f"  num_draft_tokens:        {stats['num_draft_tokens']}")
+    print(f"  num_accepted_tokens:     {stats['num_accepted_tokens']}")
+    print(f"  draft_tokens_per_step:   {stats['draft_tokens_per_step']:.2f}")
+    print(f"  overall_acceptance_rate: {stats['overall_acceptance_rate']:.4f}")
+    print(f"  acceptance_len (1+acc/drafts): {stats['acceptance_len']:.4f}")
+    print("  per-position accepted tokens:", stats["per_pos_accepted"])
+    print("  per-position acceptance rates:")
+    for i, rate in enumerate(stats["per_pos_acceptance_rates"]):
+        print(f"    pos {i}: {rate:.4f}")
+    print("===============================\n")
+
+
+def test_dflash_speculators_model(vllm_runner, example_prompts, monkeypatch):
+    """
+    Test DFlash speculators model properly initializes speculative decoding.
+
+    Verifies:
+    1. Speculative config is automatically initialized from speculators config
+    2. Method is detected as 'dflash'
+    3. The draft model path is correctly set
+    4. Speculative tokens count is valid (num_speculative_tokens=8)
+    5. Text generation works with speculative decoding enabled
+    """
+    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
+
+    with vllm_runner(
+        MODEL_PATH,
+        dtype=torch.bfloat16,
+        enforce_eager=True,
+        quantization="fp8",
+    ) as vllm_model:
+        vllm_config = vllm_model.llm.llm_engine.vllm_config
+
+        assert isinstance(vllm_config.speculative_config, SpeculativeConfig), (
+            "Speculative config should be initialized for speculators model"
+        )
+
+        spec_config = vllm_config.speculative_config
+        assert spec_config.method == "dflash", (
+            f"Expected method='dflash', got '{spec_config.method}'"
+        )
+        assert spec_config.num_speculative_tokens > 0, (
+            f"Expected positive speculative tokens, "
+            f"got {spec_config.num_speculative_tokens}"
+        )
+        assert spec_config.model == MODEL_PATH, (
+            f"Draft model should be {MODEL_PATH}, got {spec_config.model}"
+        )
+
+        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens=20)
+        assert vllm_outputs, f"No outputs generated for speculators model {MODEL_PATH}"
+
+
+@pytest.mark.slow_test
+@large_gpu_mark(min_gb=40)
+def test_dflash_speculators_correctness(monkeypatch):
+    """
+    E2E correctness test for DFlash via the speculators auto-detect path.
+
+    Evaluates GSM8k accuracy to ensure the speculators-format model produces
+    correct outputs, and checks that acceptance length does not collapse under
+    batched inference (lm-eval style).
+
+    Observed per-position acceptance rates on GSM8K (1319 prompts):
+        pos 0: 0.795, pos 1: 0.611, pos 2: 0.429, pos 3: 0.282,
+        pos 4: 0.169, pos 5: 0.093, pos 6: 0.048, pos 7: 0.023
+    Observed mean AL: 3.45 (GSM8K dataset, max_num_seqs=128)
+    """
+    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
+
+    spec_llm = LLM(
+        model=MODEL_PATH,
+        trust_remote_code=True,
+        max_model_len=4096,
+        max_num_seqs=128,
+        gpu_memory_utilization=0.85,
+        enforce_eager=False,
+        disable_log_stats=False,
+    )
+
+    results = evaluate_gsm8k_offline(spec_llm)
+    accuracy = results["accuracy"]
+    accuracy_threshold = EXPECTED_GSM8K_ACCURACY * (1 - ACCURACY_RTOL)
+    assert accuracy >= accuracy_threshold, (
+        f"Expected GSM8K accuracy >= {accuracy_threshold:.3f}, got {accuracy:.3f}"
+    )
+
+    current_metrics = spec_llm.get_metrics()
+    stats = compute_spec_decode_stats(current_metrics)
+    print_spec_decode_stats(stats)
+
+    acceptance_len = stats["acceptance_len"]
+    al_threshold = EXPECTED_ACCEPTANCE_LEN * (1 - ACCEPTANCE_LEN_RTOL)
+    assert acceptance_len >= al_threshold, (
+        f"DFlash speculators acceptance length too low: "
+        f"{acceptance_len:.2f} < {al_threshold:.2f}"
+    )
+
+    # Check per-position acceptance rates for the first few positions.
+    per_pos_rates = stats["per_pos_acceptance_rates"]
+    for i, expected_rate in enumerate(EXPECTED_PER_POS_ACCEPTANCE_RATES):
+        assert i < len(per_pos_rates), (
+            f"Missing per-position acceptance rate for position {i}"
+        )
+        threshold = expected_rate * (1 - PER_POS_RTOL)
+        assert per_pos_rates[i] >= threshold, (
+            f"Per-position acceptance rate at pos {i} too low: "
+            f"{per_pos_rates[i]:.4f} < {threshold:.4f} "
+            f"(expected ~{expected_rate:.4f})"
+        )
+
+    del spec_llm
+    torch.accelerator.empty_cache()
+    cleanup_dist_env_and_memory()
diff --git a/tests/v1/spec_decode/test_synthetic_rejection_sampler_utils.py b/tests/v1/spec_decode/test_synthetic_rejection_sampler_utils.py
index d817bc1b8fee..a5a23cf1b7e8 100644
--- a/tests/v1/spec_decode/test_synthetic_rejection_sampler_utils.py
+++ b/tests/v1/spec_decode/test_synthetic_rejection_sampler_utils.py
@@ -2,33 +2,48 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import pytest
 
-from vllm.v1.worker.gpu.spec_decode.synthetic_rejection_sampler_utils import (
-    compute_synthetic_rejection_sampler_params,
+from vllm.config.speculative import SpeculativeConfig
+from vllm.v1.spec_decode.utils import unconditional_to_conditional_rates
+
+
+def test_unconditional_to_conditional_rates_basic():
+    # c_0 = p_0; c_i = p_i / p_{i-1}
+    assert unconditional_to_conditional_rates([0.9, 0.5, 0.2]) == pytest.approx(
+        [0.9, 0.5 / 0.9, 0.2 / 0.5]
+    )
+
+
+def test_unconditional_to_conditional_rates_handles_zero():
+    # After a zero, subsequent conditional rates are clamped to 0 (the chain
+    # has already terminated in the kernel, so these values are unused).
+    assert unconditional_to_conditional_rates([1.0, 0.6, 0.0, 0.0]) == pytest.approx(
+        [1.0, 0.6, 0.0, 0.0]
+    )
+
+
+def test_unconditional_to_conditional_rates_all_ones():
+    assert unconditional_to_conditional_rates([1.0, 1.0, 1.0]) == pytest.approx(
+        [1.0, 1.0, 1.0]
+    )
+
+
+@pytest.mark.parametrize(
+    "length,n,expected",
+    [
+        (2.6, 3, [1.0, 0.6, 0.0]),
+        (1.0, 3, [0.0, 0.0, 0.0]),
+        (4.0, 3, [1.0, 1.0, 1.0]),
+        (2.0, 3, [1.0, 0.0, 0.0]),
+        (3.5, 4, [1.0, 1.0, 0.5, 0.0]),
+    ],
 )
+def test_acceptance_length_to_rates(length, n, expected):
+    assert SpeculativeConfig._acceptance_length_to_rates(length, n) == pytest.approx(
+        expected
+    )
+
 
-NUM_SPECULATIVE_STEPS = [1, 2, 3, 4, 5, 7, 10]
-ACCEPTANCE_RATES = [i / 100 for i in range(0, 100)]
-
-
-@pytest.mark.parametrize("num_speculative_steps", NUM_SPECULATIVE_STEPS)
-def test_compute_synthetic_rejection_sampler_params(num_speculative_steps: int):
-    """Test that the base acceptance rate and decay factor generated for
-    synthetic rejection sampling have a mean joint acceptance probability
-    that matches the desired acceptance rate."""
-    tol = 1e-9
-    for desired_acceptance_rate in ACCEPTANCE_RATES:
-        base_rate, decay_factor = compute_synthetic_rejection_sampler_params(
-            desired_acceptance_rate, num_speculative_steps, tol=tol
-        )
-
-        # Compute the mean of joint acceptance probabilities across
-        # all speculative positions.
-        joint_prob = 1.0
-        mean_joint = 0.0
-        for i in range(num_speculative_steps):
-            joint_prob *= base_rate * decay_factor**i
-            mean_joint += joint_prob
-        mean_joint /= num_speculative_steps
-
-        assert abs(desired_acceptance_rate - mean_joint) < 10 * tol
-        assert base_rate <= 1.0
+def test_resolve_length_produces_minvariance_schedule():
+    assert SpeculativeConfig._resolve_synthetic_acceptance_rates(
+        3, None, 2.6
+    ) == pytest.approx([1.0, 0.6, 0.0])
diff --git a/tests/v1/spec_decode/test_tree_attention.py b/tests/v1/spec_decode/test_tree_attention.py
deleted file mode 100644
index 1b6fa4f6f484..000000000000
--- a/tests/v1/spec_decode/test_tree_attention.py
+++ /dev/null
@@ -1,504 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import math
-
-import pytest
-import torch
-
-from tests.v1.attention.utils import (
-    create_standard_kv_cache_spec,
-    create_vllm_config,
-    try_backend_includes_kv_cache_update,
-    try_get_attention_backend,
-)
-from vllm.config import ParallelConfig, SpeculativeConfig
-from vllm.platforms import current_platform
-from vllm.utils.torch_utils import set_random_seed
-from vllm.v1.attention.backend import CommonAttentionMetadata
-from vllm.v1.attention.backends.fa_utils import is_flash_attn_varlen_func_available
-from vllm.v1.attention.backends.registry import AttentionBackendEnum
-
-DEVICE_TYPE = current_platform.device_type
-
-if not is_flash_attn_varlen_func_available():
-    pytest.skip(
-        "This test requires flash_attn_varlen_func, but it's not available.",
-        allow_module_level=True,
-    )
-
-# --------------------------------------------------------------------------- #
-#  KV cache layout adaptation
-# --------------------------------------------------------------------------- #
-# Two KV cache layouts exist across backends:
-#
-#   Flash layout: (2, num_blocks, block_size, num_kv_heads, head_size)
-#     - dim 0 separates key (index 0) and value (index 1)
-#     - Used by: FLASH_ATTN, TREE_ATTN, ROCM_AITER_FA, ROCM_ATTN
-#
-#   Block layout: (num_blocks, 2, block_size, num_kv_heads, head_size)
-#     - dim 1 separates key (index 0) and value (index 1)
-#     - Used by: TRITON_ATTN
-#
-# The test creates KV caches in flash layout (the canonical format used by
-# tree attention). When a reference backend needs block layout we transpose
-# dims 0 and 1.
-#
-# Note: ROCM_ATTN uses flash layout for storage but its forward path calls
-# PagedAttention.split_kv_cache which reinterprets the raw memory as paged
-# layout (num_blocks, num_kv_heads, head_size//x, block_size, x). This is
-# a view-level incompatibility, not a transpose - see the TODO in
-# _get_available_reference_backends for details.
-#
-# TODO: Replace this mapping with a `KV_CACHE_LAYOUT` class attribute on each
-# AttentionImpl so the layout is self-documented by the backend itself, e.g.:
-#     class TritonAttentionImpl(AttentionImpl):
-#         KV_CACHE_LAYOUT = "block"
-# --------------------------------------------------------------------------- #
-
-_BLOCK_KV_LAYOUT_BACKENDS = frozenset(
-    {
-        AttentionBackendEnum.TRITON_ATTN,
-    }
-)
-
-# Backends whose do_kv_cache_update requires engine-level state (e.g.
-# ForwardContext) that is not available in this test harness, but whose
-# KV cache is flash layout and can be written with reshape_and_cache_flash.
-# When a backend is listed here, forward_attention() bypasses
-# do_kv_cache_update and writes directly to the cache.
-_NEEDS_DIRECT_CACHE_UPDATE = frozenset(
-    {
-        AttentionBackendEnum.ROCM_AITER_FA,
-    }
-)
-
-# Backends with known test-harness incompatibilities - see the TODOs
-# inside _get_available_reference_backends for details.
-_INCOMPATIBLE_REFERENCE_BACKENDS = frozenset(
-    {
-        AttentionBackendEnum.ROCM_AITER_FA,
-        AttentionBackendEnum.ROCM_ATTN,
-    }
-)
-
-
-def _adapt_kv_cache_for_backend(
-    kv_cache: torch.Tensor,
-    backend: AttentionBackendEnum,
-) -> torch.Tensor:
-    """Convert kv_cache from flash layout ``(2, num_blocks, ...)`` to block
-    layout ``(num_blocks, 2, ...)`` if the backend requires it.  Returns the
-    original tensor unchanged when no conversion is needed."""
-    if backend in _BLOCK_KV_LAYOUT_BACKENDS:
-        return kv_cache.transpose(0, 1).contiguous()
-    return kv_cache
-
-
-def _get_platform_default_backend() -> AttentionBackendEnum:
-    """Ask the platform what backend it would auto-select at runtime."""
-    from vllm.v1.attention.selector import AttentionSelectorConfig
-
-    config = AttentionSelectorConfig(
-        block_size=32,
-        kv_cache_dtype="auto",
-        use_mla=False,
-        use_sparse=False,
-        head_size=128,
-        dtype=torch.bfloat16,
-    )
-    backend_path = current_platform.get_attn_backend_cls(
-        selected_backend=None,
-        attn_selector_config=config,
-    )
-    for backend in AttentionBackendEnum:
-        try:
-            if backend.get_path() == backend_path:
-                return backend
-        except ValueError:
-            continue
-    raise RuntimeError(
-        f"Platform returned backend path '{backend_path}' "
-        f"that doesn't match any AttentionBackendEnum member."
-    )
-
-
-def _get_available_reference_backends() -> list[AttentionBackendEnum]:
-    """Collect all reference backends the current platform can run.
-
-    On CUDA this is just FLASH_ATTN. On ROCm this includes the platform
-    default plus every backend the hardware supports, so the test validates
-    tree attention against all of them.
-    """
-    if current_platform.is_rocm():
-        backends: list[AttentionBackendEnum] = []
-
-        # 1. Whatever the platform would auto-select at runtime.
-        default_backend = _get_platform_default_backend()
-        if default_backend not in _INCOMPATIBLE_REFERENCE_BACKENDS:
-            backends.append(default_backend)
-
-        # 2. TRITON_ATTN - always available on ROCm.
-        if AttentionBackendEnum.TRITON_ATTN not in backends:
-            backends.append(AttentionBackendEnum.TRITON_ATTN)
-
-        # TODO: Enable ROCM_ATTN. Its forward path uses
-        # PagedAttention.split_kv_cache which reinterprets the raw
-        # cache memory as paged layout:
-        #   key:   (num_blocks, num_kv_heads, head_size//x, block_size, x)
-        #   value: (num_blocks, num_kv_heads, head_size, block_size)
-        # Tree attention writes prefix data in NHD flash layout, so the
-        # same bytes produce completely different values when read in
-        # paged format. Supporting ROCM_ATTN would require writing
-        # prefix data via PagedAttention.write_to_paged_cache into a
-        # separate paged-format KV cache.
-
-        # TODO: Enable ROCM_AITER_FA. Its metadata builder reads head
-        # counts from the model config at construction time and
-        # allocates extend_workspace with those dimensions. The test
-        # uses independent head count parameters (num_heads=2/4,
-        # num_kv_heads=2) that don't match the model config
-        # (Llama-3-8B: 32 q heads, 8 kv heads), causing a head count
-        # mismatch in flash_attn_varlen_func during extend_forward.
-        # Fixing this requires either matching test head counts to the
-        # model config or decoupling the builder from model config
-        # head geometry. The direct cache update path
-        # (_NEEDS_DIRECT_CACHE_UPDATE) is already in place for when
-        # this is resolved.
-
-        return backends
-
-    # CUDA: flash attention.
-    return [AttentionBackendEnum.FLASH_ATTN]
-
-
-class MockAttentionLayer(torch.nn.Module):
-    _q_scale = torch.tensor(1.0, dtype=torch.float32, device=DEVICE_TYPE)
-    _k_scale = torch.tensor(1.0, dtype=torch.float32, device=DEVICE_TYPE)
-    _v_scale = torch.tensor(1.0, dtype=torch.float32, device=DEVICE_TYPE)
-    layer_name = "mock_layer"
-
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x):
-        return x
-
-
-def forward_attention(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    kv_cache: torch.Tensor,
-    block_table: torch.Tensor,
-    slot_mapping: torch.Tensor,
-    seqlen_k: int,
-    backend: AttentionBackendEnum,
-    spec_token_tree: str | None = None,
-    num_spec_tokens: int = 0,
-) -> torch.Tensor:
-    """Run a single attention forward pass through the given backend.
-
-    ``kv_cache`` is expected in **flash layout**
-    ``(2, num_blocks, block_size, num_kv_heads, head_size)``.
-    It is automatically converted when the target backend needs a
-    different layout.
-    """
-    batch_size, q_len, num_heads, dim_per_head = q.shape
-    num_kv_heads = k.shape[-2]
-    # Initialize the query and KV sequence lengths.
-    query_start_loc = q_len * torch.arange(
-        batch_size + 1, device=q.device, dtype=torch.int32
-    )
-    query_lens = torch.diff(query_start_loc)
-    seq_lens = torch.full(
-        (batch_size,),
-        seqlen_k,
-        device=q.device,
-        dtype=torch.int32,
-    )
-    context_lens = seq_lens - query_lens
-    max_seq_len = int(seq_lens.max())
-    max_query_len = q_len
-    num_actual_tokens = query_start_loc[-1]
-
-    softmax_scale = q.shape[-1] ** (-0.5)
-    layer = MockAttentionLayer()
-
-    # Build common metadata.
-    model_name = "meta-llama/Meta-Llama-3-8B"
-    builder_cls, impl_cls = try_get_attention_backend(backend)
-    vllm_config = create_vllm_config(model_name=model_name, max_model_len=max(seq_lens))
-    if spec_token_tree is not None:
-        # Create speculative config if token tree is specified.
-        vllm_config.speculative_config = SpeculativeConfig(
-            target_model_config=vllm_config.model_config,
-            target_parallel_config=ParallelConfig(),
-            model=model_name,
-            method="eagle",
-            num_speculative_tokens=num_spec_tokens,
-            speculative_token_tree=spec_token_tree,
-        )
-    kv_cache_spec = create_standard_kv_cache_spec(vllm_config)
-    builder = builder_cls(kv_cache_spec, [], vllm_config, q.device)
-    common_attn_metadata = CommonAttentionMetadata(
-        query_start_loc=query_start_loc,
-        query_start_loc_cpu=query_start_loc.cpu(),
-        seq_lens=seq_lens,
-        _seq_lens_cpu=seq_lens.cpu(),
-        _num_computed_tokens_cpu=context_lens.cpu(),
-        num_reqs=batch_size,
-        num_actual_tokens=num_actual_tokens,
-        max_query_len=max_query_len,
-        max_seq_len=max_seq_len,
-        block_table_tensor=block_table,
-        slot_mapping=slot_mapping,
-    )
-
-    # Build attention metadata.
-    attn_metadata = builder.build(
-        common_prefix_len=0,
-        common_attn_metadata=common_attn_metadata,
-    )
-
-    # Initialize the backend implementation.
-    instance = impl_cls(
-        num_heads=num_heads,
-        head_size=dim_per_head,
-        scale=softmax_scale,
-        num_kv_heads=num_kv_heads,
-        alibi_slopes=None,
-        sliding_window=None,
-        kv_cache_dtype="auto",
-    )
-
-    # Adapt KV cache layout for this backend.
-    adapted_kv_cache = _adapt_kv_cache_for_backend(kv_cache, backend)
-
-    # Run forward pass and return output.
-    query = q.view(-1, num_heads, dim_per_head)
-    key = k.view(-1, num_kv_heads, dim_per_head)
-    value = v.view(-1, num_kv_heads, dim_per_head)
-    output = torch.empty_like(query)
-    if not try_backend_includes_kv_cache_update(backend):
-        if backend in _NEEDS_DIRECT_CACHE_UPDATE:
-            # This backend's do_kv_cache_update requires engine-level
-            # ForwardContext that isn't available in this test harness.
-            # Write directly using reshape_and_cache_flash since the
-            # KV cache layout is identical (flash layout, unbind on dim 0).
-            key_cache, value_cache = adapted_kv_cache.unbind(0)
-            torch.ops._C_cache_ops.reshape_and_cache_flash(
-                key,
-                value,
-                key_cache,
-                value_cache,
-                attn_metadata.slot_mapping,
-                "auto",
-                layer._k_scale,
-                layer._v_scale,
-            )
-        else:
-            instance.do_kv_cache_update(
-                layer=layer,
-                key=key,
-                value=value,
-                kv_cache=adapted_kv_cache,
-                slot_mapping=attn_metadata.slot_mapping,
-            )
-    return instance.forward(
-        layer=layer,
-        query=query,
-        key=key,
-        value=value,
-        kv_cache=adapted_kv_cache.clone(),
-        attn_metadata=attn_metadata,
-        output=output,
-    )
-
-
-@pytest.mark.parametrize(
-    "reference_backend",
-    _get_available_reference_backends(),
-    ids=lambda b: b.name,
-)
-def test_tree_attn_correctness(
-    reference_backend: AttentionBackendEnum,
-) -> None:
-    set_random_seed(42)
-
-    device = "cuda"
-    tree_attn_masks = {
-        # Chain.
-        "[(0,), (0, 0), (0, 0, 0)]": torch.tensor(
-            [
-                [1, 0, 0, 0],
-                [1, 1, 0, 0],
-                [1, 1, 1, 0],
-                [1, 1, 1, 1],
-            ],
-            device=device,
-            dtype=torch.int32,
-        ),
-        # Tree.
-        "[(0,), (1,), (0, 0), (0, 1), (1, 0), (1, 1)]": torch.tensor(
-            [
-                [1, 0, 0, 0, 0, 0, 0],
-                [1, 1, 0, 0, 0, 0, 0],
-                [1, 0, 1, 0, 0, 0, 0],
-                [1, 1, 0, 1, 0, 0, 0],
-                [1, 1, 0, 0, 1, 0, 0],
-                [1, 0, 1, 0, 0, 1, 0],
-                [1, 0, 1, 0, 0, 0, 1],
-            ],
-            device=device,
-            dtype=torch.int32,
-        ),
-    }
-
-    dim_per_head = 128
-    num_kv_heads = 2
-    block_size = 32
-    max_sequence_length = 8192
-    randomize_blocks = True
-    for batch_size in [1, 16, 32]:
-        for num_heads in [2, 4]:
-            for sequence_position in [16, 1024, 2048]:
-                for spec_token_tree, tree_attn_mask in tree_attn_masks.items():
-                    # Assert that the number of heads is divisible
-                    # by the number of KV heads.
-                    assert num_heads % num_kv_heads == 0
-
-                    # Initialize q, k, and v.
-                    tree_size_q = tree_attn_mask.shape[0]
-                    seqlen_k = sequence_position + tree_size_q
-                    q = torch.randn(
-                        (batch_size, tree_size_q, num_heads, dim_per_head),
-                        device=device,
-                        dtype=torch.bfloat16,
-                    )
-                    k = torch.randn(
-                        (batch_size, tree_size_q, num_kv_heads, dim_per_head),
-                        device=device,
-                        dtype=torch.bfloat16,
-                    )
-                    v = torch.randn(
-                        (batch_size, tree_size_q, num_kv_heads, dim_per_head),
-                        device=device,
-                        dtype=torch.bfloat16,
-                    )
-
-                    # KV cache in flash layout - the canonical format for
-                    # tree attention. forward_attention() handles conversion
-                    # when needed.
-                    assert max_sequence_length % block_size == 0
-                    max_blocks_per_batch = max_sequence_length // block_size
-                    kv_cache = torch.randn(
-                        (
-                            2,
-                            batch_size * max_blocks_per_batch,
-                            block_size,
-                            num_kv_heads,
-                            dim_per_head,
-                        ),
-                        device=q.device,
-                        dtype=torch.bfloat16,
-                    )
-                    num_alloc_blocks_per_batch = math.ceil(seqlen_k / block_size)
-                    block_table = torch.zeros(
-                        (batch_size, max_blocks_per_batch),
-                        device=q.device,
-                        dtype=torch.int32,
-                    )
-                    block_ids = torch.arange(
-                        0,
-                        batch_size * num_alloc_blocks_per_batch,
-                        device=q.device,
-                        dtype=torch.int32,
-                    )
-                    if randomize_blocks:
-                        # Randomize the block ids.
-                        block_ids = block_ids[torch.randperm(block_ids.numel())]
-                    block_table[:, :num_alloc_blocks_per_batch] = block_ids.view(
-                        -1, num_alloc_blocks_per_batch
-                    )
-
-                    # Set up the slot mapping for the input KVs.
-                    tree_positions = sequence_position + torch.arange(
-                        0,
-                        tree_size_q,
-                        device=q.device,
-                        dtype=torch.int64,
-                    ).repeat(batch_size, 1)
-                    tree_slot_mapping = _gen_slot_mapping(
-                        tree_positions, block_table, block_size
-                    )
-
-                    # Compute attention for the tree.
-                    tree_attn_output = forward_attention(
-                        q=q,
-                        k=k,
-                        v=v,
-                        kv_cache=kv_cache,
-                        block_table=block_table,
-                        slot_mapping=tree_slot_mapping,
-                        seqlen_k=seqlen_k,
-                        backend=AttentionBackendEnum.TREE_ATTN,
-                        spec_token_tree=spec_token_tree,
-                        num_spec_tokens=tree_size_q - 1,
-                    ).view(batch_size, -1, num_heads, dim_per_head)
-
-                    # Verify each branch against the reference backend.
-                    for q_index in range(tree_size_q):
-                        # Get the q, k, and v for the branch.
-                        branch_mask = tree_attn_mask[q_index, :]
-                        branch_indices = torch.nonzero(branch_mask, as_tuple=True)[0]
-                        q_len = branch_indices.shape[0]
-                        q_branch = q[:, branch_indices]
-                        k_branch = k[:, branch_indices]
-                        v_branch = v[:, branch_indices]
-
-                        # Setup slot mapping for the branch.
-                        branch_positions = sequence_position + torch.arange(
-                            0,
-                            q_len,
-                            device=q.device,
-                            dtype=torch.int64,
-                        ).repeat(batch_size, 1)
-                        branch_slot_mapping = _gen_slot_mapping(
-                            branch_positions, block_table, block_size
-                        )
-
-                        # Reference attention for this branch.
-                        ref_output = forward_attention(
-                            q=q_branch,
-                            k=k_branch,
-                            v=v_branch,
-                            kv_cache=kv_cache,
-                            block_table=block_table,
-                            slot_mapping=branch_slot_mapping,
-                            seqlen_k=sequence_position + q_len,
-                            backend=reference_backend,
-                        ).view(batch_size, -1, num_heads, dim_per_head)
-
-                        # Compare the outputs.
-                        assert torch.allclose(
-                            tree_attn_output[:, branch_indices],
-                            ref_output,
-                            atol=7.81e-3,
-                        ), (
-                            f"outputs are not close for "
-                            f"reference_backend: {reference_backend.name}, "
-                            f"batch_size: {batch_size}, "
-                            f"num_heads: {num_heads}, "
-                            f"sequence_position: {sequence_position}, "
-                            f"tree_attn_mask: {tree_attn_mask}, "
-                            f"q_index: {q_index}."
-                        )
-
-
-def _gen_slot_mapping(
-    positions: torch.Tensor, block_table: torch.Tensor, block_size: int
-):
-    block_indices = positions // block_size
-    blocks = block_table.gather(dim=1, index=block_indices)
-    return (blocks * block_size + positions % block_size).view(-1)
diff --git a/tests/v1/streaming_input/test_gpu_model_runner_streaming.py b/tests/v1/streaming_input/test_gpu_model_runner_streaming.py
index 0ed7b6cb3efc..946ca99507df 100644
--- a/tests/v1/streaming_input/test_gpu_model_runner_streaming.py
+++ b/tests/v1/streaming_input/test_gpu_model_runner_streaming.py
@@ -39,7 +39,6 @@ def mock_model_runner_with_input_batch():
         vocab_size=32000,
         block_sizes=[16],
         kernel_block_sizes=[16],
-        is_spec_decode=False,
         logitsprocs=None,
         is_pooling_model=False,
     )
diff --git a/tests/v1/streaming_input/test_scheduler_streaming.py b/tests/v1/streaming_input/test_scheduler_streaming.py
index fd9f6b17f9a9..7d680895b836 100644
--- a/tests/v1/streaming_input/test_scheduler_streaming.py
+++ b/tests/v1/streaming_input/test_scheduler_streaming.py
@@ -76,6 +76,7 @@ def create_scheduler() -> Scheduler:
         log_stats=True,
         structured_output_manager=StructuredOutputManager(vllm_config),
         block_size=16,
+        hash_block_size=16,
     )
 
 
diff --git a/tests/v1/structured_output/test_reasoning_structured_output.py b/tests/v1/structured_output/test_reasoning_structured_output.py
index 98a25e41dfe0..a12b36b8a44e 100644
--- a/tests/v1/structured_output/test_reasoning_structured_output.py
+++ b/tests/v1/structured_output/test_reasoning_structured_output.py
@@ -8,11 +8,16 @@
 import pytest
 
 from vllm.config import ModelConfig, SchedulerConfig, VllmConfig
-from vllm.reasoning import ReasoningParser
 from vllm.v1.request import Request
 from vllm.v1.structured_output import StructuredOutputManager
 
 
+class MockReasoner:
+    def __init__(self, tokenizer):
+        self.is_reasoning_end = Mock(return_value=False)
+        self.is_reasoning_end_streaming = Mock(return_value=False)
+
+
 class TestReasoningStructuredOutput:
     """Test reasoning-aware structured output functionality."""
 
@@ -50,13 +55,6 @@ def mock_vllm_config(self, mock_model_config, mock_scheduler_config):
         config.speculative_config = None
         return config
 
-    @pytest.fixture
-    def mock_reasoning_parser(self):
-        """Create a mock ReasoningParser."""
-        parser = Mock(spec=ReasoningParser)
-        parser.is_reasoning_end = Mock(return_value=False)
-        return parser
-
     @pytest.fixture
     def mock_request_with_structured_output(self):
         """Create a mock request with structured output."""
@@ -64,6 +62,8 @@ def mock_request_with_structured_output(self):
         request.structured_output_request = Mock()
         request.structured_output_request.reasoning_ended = None
         request.structured_output_request.grammar = Mock()
+        request.structured_output_request.reasoning_parser_kwargs = None
+        request.structured_output_request.reasoner = None
         request.structured_output_request.grammar.is_terminated = Mock(
             return_value=False
         )
@@ -74,6 +74,13 @@ def mock_request_with_structured_output(self):
         request.num_output_placeholders = 0
         return request
 
+    @pytest.fixture
+    def manager_with_reasoner(self, mock_vllm_config):
+        manager = StructuredOutputManager(mock_vllm_config)
+        manager.reasoner_cls = MockReasoner
+        manager.tokenizer = Mock()
+        return manager
+
     def test_should_fill_bitmask_with_enable_in_reasoning(
         self, mock_vllm_config, mock_request_with_structured_output
     ):
@@ -89,22 +96,17 @@ def test_should_fill_bitmask_with_enable_in_reasoning(
 
     def test_should_fill_bitmask_without_enable_in_reasoning(
         self,
-        mock_vllm_config,
+        manager_with_reasoner,
         mock_request_with_structured_output,
-        mock_reasoning_parser,
     ):
         """Test should_fill_bitmask when enable_in_reasoning is False."""
         # Keep enable_in_reasoning as False (default)
-        config = mock_vllm_config.structured_outputs_config
+        config = manager_with_reasoner.vllm_config.structured_outputs_config
         assert config.enable_in_reasoning is False
 
-        manager = StructuredOutputManager(mock_vllm_config)
-        manager.reasoner = mock_reasoning_parser
-
-        # Mock reasoning not ended
-        mock_reasoning_parser.is_reasoning_end.return_value = False
-
-        result = manager.should_fill_bitmask(mock_request_with_structured_output)
+        result = manager_with_reasoner.should_fill_bitmask(
+            mock_request_with_structured_output
+        )
 
         # Should set reasoning_ended and return its value
         assert (
@@ -118,68 +120,92 @@ def test_should_fill_bitmask_no_reasoner(
     ):
         """Test should_fill_bitmask when no reasoner is configured."""
         manager = StructuredOutputManager(mock_vllm_config)
-        manager.reasoner = None
 
         result = manager.should_fill_bitmask(mock_request_with_structured_output)
 
         # Should default to True when no reasoner
         assert result is True
 
+    def test_should_fill_bitmask_uses_request_reasoning_parser_kwargs(
+        self, mock_vllm_config, mock_request_with_structured_output
+    ):
+        """Test request-level parser kwargs override the default reasoner."""
+
+        class KwargReasoner:
+            def __init__(self, tokenizer, chat_template_kwargs=None):
+                self.chat_template_kwargs = chat_template_kwargs or {}
+
+            def is_reasoning_end(self, input_ids):
+                return not self.chat_template_kwargs.get("enable_thinking", False)
+
+        manager = StructuredOutputManager(mock_vllm_config)
+        manager.reasoner_cls = KwargReasoner
+        manager.tokenizer = Mock()
+
+        structured_req = mock_request_with_structured_output.structured_output_request
+        structured_req.reasoning_parser_kwargs = {
+            "chat_template_kwargs": {"enable_thinking": True}
+        }
+
+        result = manager.should_fill_bitmask(mock_request_with_structured_output)
+
+        assert result is False
+        assert (
+            mock_request_with_structured_output.structured_output_request.reasoner
+            is not None
+        )
+
     def test_should_advance_with_enable_in_reasoning(
         self,
-        mock_vllm_config,
+        manager_with_reasoner,
         mock_request_with_structured_output,
-        mock_reasoning_parser,
     ):
         """Test should_advance when enable_in_reasoning is True."""
         # Enable enable_in_reasoning
-        mock_vllm_config.structured_outputs_config.enable_in_reasoning = True
-
-        manager = StructuredOutputManager(mock_vllm_config)
-        manager.reasoner = mock_reasoning_parser
+        manager_with_reasoner.enable_in_reasoning = True
 
         # Should always return True when enable_in_reasoning is enabled
-        result = manager.should_advance(mock_request_with_structured_output)
+        result = manager_with_reasoner.should_advance(
+            mock_request_with_structured_output
+        )
         assert result is True
 
     def test_should_advance_reasoning_not_ended(
         self,
-        mock_vllm_config,
+        manager_with_reasoner,
         mock_request_with_structured_output,
-        mock_reasoning_parser,
     ):
         """Test should_advance when reasoning has not ended."""
-        manager = StructuredOutputManager(mock_vllm_config)
-        manager.reasoner = mock_reasoning_parser
-
         # Set reasoning as not ended
         (
             mock_request_with_structured_output.structured_output_request
         ).reasoning_ended = False
-        mock_reasoning_parser.is_reasoning_end.return_value = False
 
-        result = manager.should_advance(mock_request_with_structured_output)
+        result = manager_with_reasoner.should_advance(
+            mock_request_with_structured_output
+        )
 
         # Should return False since reasoning hasn't ended
         assert result is False
 
     def test_should_advance_reasoning_just_ended(
         self,
-        mock_vllm_config,
+        manager_with_reasoner,
         mock_request_with_structured_output,
-        mock_reasoning_parser,
     ):
         """Test should_advance when reasoning ends in current step."""
-        manager = StructuredOutputManager(mock_vllm_config)
-        manager.reasoner = mock_reasoning_parser
-
         # Set reasoning as not ended initially, but ends in this step
         (
             mock_request_with_structured_output.structured_output_request
         ).reasoning_ended = False
-        mock_reasoning_parser.is_reasoning_end.return_value = True
+        reasoner = MockReasoner(tokenizer=Mock())
+        reasoner.is_reasoning_end_streaming.return_value = True
+        structured_req = mock_request_with_structured_output.structured_output_request
+        structured_req.reasoner = reasoner
 
-        result = manager.should_advance(mock_request_with_structured_output)
+        result = manager_with_reasoner.should_advance(
+            mock_request_with_structured_output
+        )
 
         # Should set reasoning_ended to True but return False for this step
         assert (
@@ -190,20 +216,18 @@ def test_should_advance_reasoning_just_ended(
 
     def test_should_advance_reasoning_already_ended(
         self,
-        mock_vllm_config,
+        manager_with_reasoner,
         mock_request_with_structured_output,
-        mock_reasoning_parser,
     ):
         """Test should_advance when reasoning has already ended."""
-        manager = StructuredOutputManager(mock_vllm_config)
-        manager.reasoner = mock_reasoning_parser
-
         # Set reasoning as already ended
         (
             mock_request_with_structured_output.structured_output_request
         ).reasoning_ended = True
 
-        result = manager.should_advance(mock_request_with_structured_output)
+        result = manager_with_reasoner.should_advance(
+            mock_request_with_structured_output
+        )
 
         # Should return True since reasoning has ended
         assert result is True
diff --git a/tests/v1/test_tensor_ipc_queue.py b/tests/v1/test_tensor_ipc_queue.py
index a3fcb97ca171..a70f5d48cc54 100644
--- a/tests/v1/test_tensor_ipc_queue.py
+++ b/tests/v1/test_tensor_ipc_queue.py
@@ -14,6 +14,7 @@
 import torch
 import torch.multiprocessing as torch_mp
 
+from vllm.platforms import current_platform
 from vllm.v1.engine.tensor_ipc import (
     TensorIpcData,
     TensorIpcReceiver,
@@ -21,6 +22,8 @@
 )
 from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder
 
+DEVICE_TYPE = current_platform.device_type
+
 
 @pytest.fixture(scope="module", autouse=True)
 def setup_multiprocessing():
@@ -53,7 +56,7 @@ def encoder_process(
         encoder = MsgpackEncoder(oob_tensor_consumer=sender)
 
         if torch.cuda.is_available():
-            device = "cuda:0"
+            device = f"{DEVICE_TYPE}:0"
             tensor = torch.randn(
                 *tensor_data["shape"], dtype=tensor_data["dtype"], device=device
             )
@@ -384,7 +387,7 @@ def mixed_tensor_encoder_process(
 
         # Create only CUDA tensor for IPC (CPU will be serialized)
         # But actually, let's just send CUDA tensor directly
-        cuda_tensor = torch.randn(4, 5, device="cuda:0")
+        cuda_tensor = torch.randn(4, 5, device=f"{DEVICE_TYPE}:0")
 
         # Manually send via IPC to test the mechanism
         cuda_tensor_shared = cuda_tensor.share_memory_()
@@ -651,7 +654,7 @@ def test_ipc_disabled_mode():
 
     # If CUDA is available, test with CUDA tensor too
     if torch.cuda.is_available():
-        cuda_tensor = torch.randn(4, 5, device="cuda:0")
+        cuda_tensor = torch.randn(4, 5, device=f"{DEVICE_TYPE}:0")
         encoded_cuda = encoder.encode({"cuda_tensor": cuda_tensor})
         assert len(encoded_cuda) > 0
         assert tensor_queues[0].empty(), (
diff --git a/tests/v1/worker/test_gpu_model_runner.py b/tests/v1/worker/test_gpu_model_runner.py
index 0de443858c98..4942390cdd38 100644
--- a/tests/v1/worker/test_gpu_model_runner.py
+++ b/tests/v1/worker/test_gpu_model_runner.py
@@ -7,6 +7,7 @@
 import pytest
 import torch
 
+import vllm.v1.worker.gpu_model_runner as gpu_model_runner_module
 from vllm.config import (
     AttentionConfig,
     CacheConfig,
@@ -162,6 +163,34 @@ def _schedule_new_request(*req_ids: str) -> SchedulerOutput:
     )
 
 
+def _schedule_cached_requests(
+    req_ids: list[str],
+    num_scheduled_tokens: dict[str, int],
+    new_token_ids: list[list[int]],
+    num_computed_tokens: list[int],
+    num_output_tokens: list[int],
+) -> SchedulerOutput:
+    return SchedulerOutput(
+        scheduled_new_reqs=[],
+        scheduled_cached_reqs=CachedRequestData(
+            req_ids=req_ids,
+            resumed_req_ids=set(),
+            new_token_ids=new_token_ids,
+            all_token_ids={},
+            new_block_ids=[None] * len(req_ids),
+            num_computed_tokens=num_computed_tokens,
+            num_output_tokens=num_output_tokens,
+        ),
+        num_scheduled_tokens=num_scheduled_tokens,
+        total_num_scheduled_tokens=sum(num_scheduled_tokens.values()),
+        scheduled_spec_decode_tokens={},
+        scheduled_encoder_inputs={},
+        num_common_prefix_blocks=[],
+        finished_req_ids=set(),
+        free_encoder_mm_hashes=[],
+    )
+
+
 def _is_req_scheduled(model_runner, req_id: str) -> bool:
     return req_id in model_runner.input_batch.req_id_to_index
 
@@ -219,6 +248,58 @@ def test_select_common_block_size_uses_largest_shared_int():
     assert selected_size == 64
 
 
+@pytest.mark.skip_global_cleanup
+@pytest.mark.parametrize(
+    ("world_size", "is_last_rank", "expected_calls"),
+    [(1, True, 0), (2, True, 0), (2, False, 1)],
+)
+def test_sample_tokens_receives_pp_sampled_ids_only_on_non_last_rank(
+    monkeypatch: pytest.MonkeyPatch,
+    world_size: int,
+    is_last_rank: bool,
+    expected_calls: int,
+):
+    runner = GPUModelRunner.__new__(GPUModelRunner)
+    runner.execute_model_state = None
+    runner.kv_connector_output = None
+    runner.use_async_scheduling = True
+    receive_calls = 0
+
+    def receive_prev_sampled_token_ids():
+        nonlocal receive_calls
+        receive_calls += 1
+
+    runner._pp_receive_prev_sampled_token_ids_to_input_batch = (
+        receive_prev_sampled_token_ids
+    )
+    monkeypatch.setattr(
+        gpu_model_runner_module,
+        "get_pp_group",
+        lambda: SimpleNamespace(world_size=world_size, is_last_rank=is_last_rank),
+    )
+
+    assert GPUModelRunner.sample_tokens(runner, None) is None
+    assert receive_calls == expected_calls
+
+
+@pytest.mark.skip_global_cleanup
+def test_sample_tokens_skips_pp_group_lookup_without_async_scheduling(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    runner = GPUModelRunner.__new__(GPUModelRunner)
+    runner.execute_model_state = None
+    runner.kv_connector_output = None
+    runner.use_async_scheduling = False
+
+    monkeypatch.setattr(
+        gpu_model_runner_module,
+        "get_pp_group",
+        pytest.fail,
+    )
+
+    assert GPUModelRunner.sample_tokens(runner, None) is None
+
+
 def test_select_common_block_size_no_valid_option():
     backend_a = _make_mock_backend_for_kernel_block_size([64])
     backend_b = _make_mock_backend_for_kernel_block_size([MultipleOf(16)])
@@ -457,6 +538,135 @@ def test_update_states_request_unscheduled(model_runner, dist_init):
     assert not _is_req_scheduled(model_runner, req_ids[1])
 
 
+def test_update_states_pp_non_async_multi_request_keeps_token_buffers_consistent(
+    model_runner, model_runner_2, dist_init, monkeypatch
+):
+    req_ids = ["req_0", "req_1"]
+    non_last_runner = model_runner
+    last_runner = model_runner_2
+    non_last_runner.use_async_scheduling = False
+    last_runner.use_async_scheduling = False
+
+    # Both ranks start from the same request set.
+    monkeypatch.setattr(
+        "vllm.v1.worker.gpu_model_runner.get_pp_group",
+        lambda: SimpleNamespace(is_last_rank=False, world_size=2),
+    )
+    non_last_runner._update_states(_schedule_new_request(*req_ids))
+    last_runner._update_states(_schedule_new_request(*req_ids))
+
+    sampled_by_last_rank = {req_ids[0]: 101, req_ids[1]: 201}
+    # Emulate last-rank bookkeeping result from previous step:
+    # sampled tokens already cached in CPU token buffers.
+    for req_id, token_id in sampled_by_last_rank.items():
+        req_index = last_runner.input_batch.req_id_to_index[req_id]
+        start_idx = int(last_runner.input_batch.num_tokens_no_spec[req_index])
+        end_idx = start_idx + 1
+        last_runner.input_batch.token_ids_cpu[req_index, start_idx:end_idx] = [token_id]
+        last_runner.input_batch.is_token_ids[req_index, start_idx:end_idx] = True
+        last_runner.input_batch.num_tokens_no_spec[req_index] = end_idx
+        last_runner.requests[req_id].output_token_ids.append(token_id)
+
+    scheduler_output = _schedule_cached_requests(
+        req_ids=req_ids,
+        num_scheduled_tokens={req_ids[0]: 1, req_ids[1]: 1},
+        new_token_ids=[[101], [201]],
+        num_computed_tokens=[3, 3],  # prompt tokens only
+        num_output_tokens=[1, 1],
+    )
+    # non-last rank appends new_token_ids in _update_states.
+    monkeypatch.setattr(
+        "vllm.v1.worker.gpu_model_runner.get_pp_group",
+        lambda: SimpleNamespace(is_last_rank=False, world_size=2),
+    )
+    non_last_runner._update_states(scheduler_output)
+    # last rank should keep its already-bookkept CPU buffers unchanged.
+    monkeypatch.setattr(
+        "vllm.v1.worker.gpu_model_runner.get_pp_group",
+        lambda: SimpleNamespace(is_last_rank=True, world_size=2),
+    )
+    last_runner._update_states(scheduler_output)
+
+    # Verify consistency between PP ranks after _update_states.
+    for req_id in req_ids:
+        non_last_idx = non_last_runner.input_batch.req_id_to_index[req_id]
+        last_idx = last_runner.input_batch.req_id_to_index[req_id]
+        non_last_len = int(non_last_runner.input_batch.num_tokens_no_spec[non_last_idx])
+        last_len = int(last_runner.input_batch.num_tokens_no_spec[last_idx])
+        assert non_last_len == last_len
+        assert (
+            non_last_runner.input_batch.token_ids_cpu[
+                non_last_idx, :non_last_len
+            ].tolist()
+            == last_runner.input_batch.token_ids_cpu[last_idx, :last_len].tolist()
+        )
+
+
+def test_update_states_pp_async_multi_request_keeps_rank_state_consistent(
+    model_runner, model_runner_2, dist_init, monkeypatch
+):
+    req_ids = ["req_0", "req_1"]
+    non_last_runner = model_runner
+    last_runner = model_runner_2
+    non_last_runner.use_async_scheduling = True
+    last_runner.use_async_scheduling = True
+
+    # Both ranks start from the same request set.
+    monkeypatch.setattr(
+        "vllm.v1.worker.gpu_model_runner.get_pp_group",
+        lambda: SimpleNamespace(is_last_rank=False, world_size=2),
+    )
+    non_last_runner._update_states(_schedule_new_request(*req_ids))
+    last_runner._update_states(_schedule_new_request(*req_ids))
+
+    # Simulate async previous-step sampled tokens known on both ranks.
+    # non-last rank may receive them via PP communication; last rank has
+    # them from local sampling/bookkeeping.
+    sampled_by_last_rank = {req_ids[0]: 111, req_ids[1]: 222}
+    for runner in (non_last_runner, last_runner):
+        for req_id, token_id in sampled_by_last_rank.items():
+            req_index = runner.input_batch.req_id_to_index[req_id]
+            start_idx = int(runner.input_batch.num_tokens_no_spec[req_index])
+            end_idx = start_idx + 1
+            runner.input_batch.token_ids_cpu[req_index, start_idx:end_idx] = [token_id]
+            runner.input_batch.is_token_ids[req_index, start_idx:end_idx] = True
+            runner.input_batch.num_tokens_no_spec[req_index] = end_idx
+            runner.requests[req_id].output_token_ids.append(token_id)
+
+    scheduler_output = _schedule_cached_requests(
+        req_ids=req_ids,
+        num_scheduled_tokens={req_ids[0]: 1, req_ids[1]: 1},
+        new_token_ids=[],
+        num_computed_tokens=[4, 4],
+        num_output_tokens=[1, 1],
+    )
+    # non-last rank: async PP branch (new_token_ids empty).
+    monkeypatch.setattr(
+        "vllm.v1.worker.gpu_model_runner.get_pp_group",
+        lambda: SimpleNamespace(is_last_rank=False, world_size=2),
+    )
+    non_last_runner._update_states(scheduler_output)
+    # last rank: keep already-bookkept state aligned with scheduler view.
+    monkeypatch.setattr(
+        "vllm.v1.worker.gpu_model_runner.get_pp_group",
+        lambda: SimpleNamespace(is_last_rank=True, world_size=2),
+    )
+    last_runner._update_states(scheduler_output)
+
+    for req_id in req_ids:
+        non_last_idx = non_last_runner.input_batch.req_id_to_index[req_id]
+        last_idx = last_runner.input_batch.req_id_to_index[req_id]
+        non_last_len = int(non_last_runner.input_batch.num_tokens_no_spec[non_last_idx])
+        last_len = int(last_runner.input_batch.num_tokens_no_spec[last_idx])
+        assert non_last_len == last_len
+        assert (
+            non_last_runner.input_batch.token_ids_cpu[
+                non_last_idx, :non_last_len
+            ].tolist()
+            == last_runner.input_batch.token_ids_cpu[last_idx, :last_len].tolist()
+        )
+
+
 def test_kv_cache_stride_order(monkeypatch, model_runner):
     # This test checks if GPUModelRunner initializes correctly when an attention
     # backend enforces a non-default KV cache stride order.
diff --git a/tools/ep_kernels/install_python_libraries.sh b/tools/ep_kernels/install_python_libraries.sh
index c3deb7d6060c..f61aa868581e 100755
--- a/tools/ep_kernels/install_python_libraries.sh
+++ b/tools/ep_kernels/install_python_libraries.sh
@@ -101,7 +101,7 @@ NVSHMEM_URL="https://developer.download.nvidia.com/compute/nvshmem/redist/libnvs
 
 pushd "$WORKSPACE"
 echo "Downloading NVSHMEM ${NVSHMEM_VER} for ${NVSHMEM_SUBDIR} ..."
-curl -fSL "${NVSHMEM_URL}" -o "${NVSHMEM_FILE}"
+curl -fSL --retry 3 --retry-delay 2 "${NVSHMEM_URL}" -o "${NVSHMEM_FILE}"
 tar -xf "${NVSHMEM_FILE}"
 rm -rf nvshmem
 mv "${NVSHMEM_FILE%.tar.xz}" nvshmem
diff --git a/tools/flashinfer-build.sh b/tools/flashinfer-build.sh
index 8bb630070241..fb148f056f64 100755
--- a/tools/flashinfer-build.sh
+++ b/tools/flashinfer-build.sh
@@ -35,7 +35,7 @@ elif [[ "${CUDA_VERSION}" == 12.[8-9]* ]]; then
     FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a 10.0a 10.3a 12.0"
 else
     # CUDA 13.0+
-    FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a 10.0f 12.0"
+    FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a 10.0f 11.0 12.0f"
 fi
 
 echo "🏗️ Building FlashInfer AOT for arches: ${FI_TORCH_CUDA_ARCH_LIST}"
diff --git a/tools/install_deepgemm.sh b/tools/install_deepgemm.sh
index 9d1edee04720..a7bf17331073 100755
--- a/tools/install_deepgemm.sh
+++ b/tools/install_deepgemm.sh
@@ -7,7 +7,7 @@ set -e
 # Default values
 # Keep DEEPGEMM_GIT_REF in sync with cmake/external_projects/deepgemm.cmake
 DEEPGEMM_GIT_REPO="https://github.com/deepseek-ai/DeepGEMM.git"
-DEEPGEMM_GIT_REF="477618cd51baffca09c4b0b87e97c03fe827ef03"
+DEEPGEMM_GIT_REF="891d57b4db1071624b5c8fa0d1e51cb317fa709f"
 WHEEL_DIR=""
 
 # Parse command line arguments
diff --git a/tools/pre_commit/generate_attention_backend_docs.py b/tools/pre_commit/generate_attention_backend_docs.py
index 9e14f8739dcf..c0503fd69712 100644
--- a/tools/pre_commit/generate_attention_backend_docs.py
+++ b/tools/pre_commit/generate_attention_backend_docs.py
@@ -30,7 +30,6 @@
 RELEVANT_PATTERNS = [
     "vllm/v1/attention/backends/*.py",
     "vllm/v1/attention/backends/**/*.py",
-    "vllm/v1/attention/backends/fa_utils.py",
     "vllm/model_executor/layers/attention/mla_attention.py",
     "vllm/platforms/cuda.py",
     "tools/pre_commit/generate_attention_backend_docs.py",
@@ -68,6 +67,11 @@ def is_relevant_file(filepath: str) -> bool:
     return any(fnmatch.fnmatch(path_str, pattern) for pattern in RELEVANT_PATTERNS)
 
 
+MLA_PREFILL_DIR = BACKENDS_DIR / "mla" / "prefill"
+MLA_PREFILL_REGISTRY_FILE = MLA_PREFILL_DIR / "registry.py"
+MLA_PREFILL_SELECTOR_FILE = MLA_PREFILL_DIR / "selector.py"
+
+
 # ---------------------------------------------------------------------------
 # AST utility helpers
 # ---------------------------------------------------------------------------
@@ -235,11 +239,10 @@ def _resolve_import_to_file(
 
 
 def _find_cc_in_function(tree: ast.AST, func_name: str) -> str | None:
-    """Find a compute capability from is_device_capability*() calls in a function.
+    """Find a compute capability from is_device_capability_family() calls in a function.
 
-    Handles two patterns:
-    - is_device_capability_family(N): "M.x" (e.g. 100 -> "10.x")
-    - is_device_capability(N): "M.m" (e.g. 100 -> "10.0")
+    Looks for the pattern: current_platform.is_device_capability_family(N)
+    and converts N (e.g. 100) to a CC string (e.g. "10.x").
     """
     for node in ast.walk(tree):
         if not isinstance(node, ast.FunctionDef) or node.name != func_name:
@@ -248,15 +251,12 @@ def _find_cc_in_function(tree: ast.AST, func_name: str) -> str | None:
             if (
                 isinstance(n, ast.Call)
                 and isinstance(n.func, ast.Attribute)
+                and n.func.attr == "is_device_capability_family"
                 and n.args
                 and isinstance(n.args[0], ast.Constant)
                 and isinstance(n.args[0].value, int)
             ):
-                val = n.args[0].value
-                if n.func.attr == "is_device_capability_family":
-                    return f"{val // 10}.x"
-                elif n.func.attr == "is_device_capability":
-                    return f"{val // 10}.{val % 10}"
+                return f"{n.args[0].value // 10}.x"
     return None
 
 
@@ -297,6 +297,242 @@ def get_file_from_class_path(class_path: str) -> Path | None:
     return py_file if py_file.exists() else None
 
 
+def parse_mla_prefill_registry() -> dict[str, str]:
+    """Parse MLAPrefillBackendEnum from the prefill registry.
+
+    Returns:
+        A dict mapping backend names to their class paths.
+    """
+    if not MLA_PREFILL_REGISTRY_FILE.exists():
+        return {}
+
+    try:
+        tree = ast.parse(MLA_PREFILL_REGISTRY_FILE.read_text())
+    except Exception:
+        return {}
+
+    for node in ast.walk(tree):
+        if isinstance(node, ast.ClassDef) and node.name == "MLAPrefillBackendEnum":
+            return _extract_enum_values(node)
+    return {}
+
+
+def parse_mla_prefill_priorities() -> dict[str, list[str]]:
+    """Parse MLA prefill backend priorities from selector.py.
+
+    Returns:
+        A dict with keys like 'blackwell' and 'default' containing
+        lists of backend enum names in priority order.
+    """
+    if not MLA_PREFILL_SELECTOR_FILE.exists():
+        return {}
+
+    try:
+        tree = ast.parse(MLA_PREFILL_SELECTOR_FILE.read_text())
+    except Exception:
+        return {}
+
+    priorities: dict[str, list[str]] = {}
+
+    for node in ast.walk(tree):
+        if not isinstance(node, ast.FunctionDef):
+            continue
+        if node.name != "_get_mla_prefill_backend_priorities":
+            continue
+
+        # Look for if statements checking device_capability.major
+        for stmt in ast.walk(node):
+            if not isinstance(stmt, ast.If):
+                continue
+
+            # Check if it's a capability.major == 10 check (Blackwell)
+            is_blackwell = (
+                isinstance(stmt.test, ast.Compare)
+                and isinstance(stmt.test.left, ast.Attribute)
+                and stmt.test.left.attr == "major"
+                and stmt.test.comparators
+                and isinstance(stmt.test.comparators[0], ast.Constant)
+                and stmt.test.comparators[0].value == 10
+            )
+
+            # Extract backends from return statements
+            for body_stmt in stmt.body:
+                if isinstance(body_stmt, ast.Return) and isinstance(
+                    body_stmt.value, ast.List
+                ):
+                    backends = []
+                    for elt in body_stmt.value.elts:
+                        if isinstance(elt, ast.Attribute):
+                            backends.append(elt.attr)
+                    if is_blackwell:
+                        priorities["blackwell"] = backends
+                    else:
+                        priorities["default"] = backends
+
+            # Extract from else branch
+            for else_stmt in stmt.orelse:
+                if isinstance(else_stmt, ast.Return) and isinstance(
+                    else_stmt.value, ast.List
+                ):
+                    backends = []
+                    for elt in else_stmt.value.elts:
+                        if isinstance(elt, ast.Attribute):
+                            backends.append(elt.attr)
+                    priorities["default"] = backends
+
+    return priorities
+
+
+def parse_mla_prefill_backend_file(class_path: str) -> dict[str, Any] | None:
+    """Parse a single MLA prefill backend file to extract its properties.
+
+    Args:
+        class_path: The fully qualified class path.
+
+    Returns:
+        A dict with backend properties, or None if parsing fails.
+    """
+    file_path = get_file_from_class_path(class_path)
+    if file_path is None:
+        return None
+
+    try:
+        tree = ast.parse(file_path.read_text())
+    except Exception:
+        return None
+
+    class_name = class_path.rsplit(".", 1)[1]
+    class_node = find_class_in_ast(tree, class_name)
+    if class_node is None:
+        return None
+
+    info: dict[str, Any] = {
+        "compute_capability": "Any",
+        "requires_r1_dims": False,
+        "dtypes": "fp16, bf16",  # Default from base class
+    }
+
+    # Parse class variables
+    for item in class_node.body:
+        if isinstance(item, ast.Assign):
+            for target in item.targets:
+                if (
+                    isinstance(target, ast.Name)
+                    and target.id == "requires_r1_mla_dimensions"
+                    and isinstance(item.value, ast.Constant)
+                ):
+                    info["requires_r1_dims"] = item.value.value
+
+        # Parse supported_dtypes class variable
+        if (
+            isinstance(item, ast.AnnAssign)
+            and isinstance(item.target, ast.Name)
+            and item.target.id == "supported_dtypes"
+            and isinstance(item.value, ast.List)
+        ):
+            dtype_map = {"float16": "fp16", "bfloat16": "bf16", "float32": "fp32"}
+            dtypes = []
+            for elt in item.value.elts:
+                if isinstance(elt, ast.Attribute):
+                    dtypes.append(dtype_map.get(elt.attr, elt.attr))
+            if dtypes:
+                info["dtypes"] = ", ".join(dtypes)
+
+    # Parse get_name static method
+    get_name_method = find_method(class_node, "get_name")
+    if get_name_method:
+        for n in ast.walk(get_name_method):
+            if isinstance(n, ast.Return) and isinstance(n.value, ast.Constant):
+                info["name"] = n.value.value
+
+    # Parse supports_compute_capability classmethod
+    cc_method = find_method(class_node, "supports_compute_capability")
+    if cc_method:
+        for n in ast.walk(cc_method):
+            # Look for capability.major == 10 style checks
+            if (
+                isinstance(n, ast.Compare)
+                and isinstance(n.left, ast.Attribute)
+                and n.left.attr == "major"
+                and n.comparators
+                and isinstance(n.comparators[0], ast.Constant)
+            ):
+                major = n.comparators[0].value
+                info["compute_capability"] = f"{major}.x"
+
+    return info
+
+
+def parse_mla_prefill_backends() -> list[dict[str, Any]]:
+    """Parse MLA prefill backend options from the prefill registry.
+
+    MLA uses different backends for prefill vs decode. The decode backends are
+    registered in the main registry, but prefill backends have their own
+    registry at vllm/v1/attention/backends/mla/prefill/registry.py.
+
+    Returns a list of prefill backend info dicts with their requirements.
+    """
+    registry = parse_mla_prefill_registry()
+    priorities = parse_mla_prefill_priorities()
+
+    if not registry:
+        return []
+
+    # Get the priority order (Blackwell order shows all backends)
+    priority_order = priorities.get("blackwell", list(registry.keys()))
+
+    prefill_backends: list[dict[str, Any]] = []
+
+    # Backend-specific metadata that can't be easily parsed from code
+    backend_metadata = {
+        "TRTLLM_RAGGED": {
+            "description": "TensorRT-LLM ragged attention",
+        },
+        "FLASHINFER": {
+            "description": "FlashInfer CUTLASS backend",
+        },
+        "FLASH_ATTN": {
+            "description": "FlashAttention varlen (FA2/FA3/FA4)",
+        },
+    }
+
+    for backend_name in priority_order:
+        if backend_name not in registry:
+            continue
+
+        class_path = registry[backend_name]
+        backend_info = parse_mla_prefill_backend_file(class_path)
+        if backend_info is None:
+            continue
+
+        metadata = backend_metadata.get(backend_name, {})
+        display_name = backend_info.get("name", backend_name)
+
+        # Add marker for default Blackwell backend
+        marker = ""
+        if backend_name == priority_order[0] and priorities.get("blackwell"):
+            marker = "‡"
+
+        notes = ""
+        if backend_info.get("requires_r1_dims"):
+            notes = "DeepSeek R1 dims only"
+        elif backend_name == "FLASH_ATTN":
+            notes = "FA4 on SM100+, FA3 on SM90, FA2 otherwise"
+
+        prefill_backends.append(
+            {
+                "name": display_name,
+                "marker": marker,
+                "description": metadata.get("description", ""),
+                "dtypes": backend_info.get("dtypes", "fp16, bf16"),
+                "compute_capability": backend_info.get("compute_capability", "Any"),
+                "notes": notes,
+            }
+        )
+
+    return prefill_backends
+
+
 # ---------------------------------------------------------------------------
 # Backend feature extraction from AST
 # ---------------------------------------------------------------------------
@@ -574,6 +810,9 @@ def analyze_backend(backend_name: str, class_path: str) -> dict[str, Any] | None
         "compute_capability": compute_cap,
         "is_mla": is_mla_backend or check_method_overrides(class_node, "is_mla"),
         "supports_sink": check_method_overrides(class_node, "supports_sink"),
+        "supports_non_causal": check_method_overrides(
+            class_node, "supports_non_causal"
+        ),
         "is_sparse": check_method_overrides(class_node, "is_sparse"),
         "supports_mm_prefix": check_method_overrides(class_node, "supports_mm_prefix"),
         "supports_dcp": supports_dcp,
@@ -638,9 +877,10 @@ def parse_flash_attn_features() -> dict[str, dict[str, Any]]:
     except Exception:
         return {}
 
-    # Analyze the functions to determine FA3-specific features
+    # Analyze the functions to determine FA3/FA4-specific features
     fa3_supports_fp8 = False
     fa3_supports_sinks = False
+    fa4_supports_sinks = False
     fa3_compute_cap: str | None = None
     fa4_compute_cap: str | None = None
 
@@ -660,17 +900,49 @@ def parse_flash_attn_features() -> dict[str, dict[str, Any]]:
                     fa3_supports_fp8 = True
                     break
 
-        # Check flash_attn_supports_sinks - looks for `get_flash_attn_version() == 3`
+        # Check flash_attn_supports_sinks - looks for `fa_version == 3/4`
+        # or `get_flash_attn_version() == 3/4` (also accepts `in (3, 4)`)
         if node.name == "flash_attn_supports_sinks":
             for n in ast.walk(node):
                 if (
                     isinstance(n, ast.Compare)
-                    and isinstance(n.left, ast.Call)
-                    and isinstance(n.left.func, ast.Name)
-                    and n.left.func.id == "get_flash_attn_version"
+                    and len(n.ops) == 1
+                    and isinstance(n.ops[0], ast.Eq)
+                    and isinstance(n.comparators[0], ast.Constant)
                 ):
-                    fa3_supports_sinks = True
-                    break
+                    is_version_compare = (
+                        isinstance(n.left, ast.Name) and n.left.id == "fa_version"
+                    ) or (
+                        isinstance(n.left, ast.Call)
+                        and isinstance(n.left.func, ast.Name)
+                        and n.left.func.id == "get_flash_attn_version"
+                    )
+                    if is_version_compare:
+                        val = n.comparators[0].value
+                        if val == 3:
+                            fa3_supports_sinks = True
+                        elif val == 4:
+                            fa4_supports_sinks = True
+                elif (
+                    isinstance(n, ast.Compare)
+                    and len(n.ops) == 1
+                    and isinstance(n.ops[0], ast.In)
+                    and isinstance(n.comparators[0], (ast.Tuple, ast.List, ast.Set))
+                ):
+                    is_version_compare = (
+                        isinstance(n.left, ast.Name) and n.left.id == "fa_version"
+                    ) or (
+                        isinstance(n.left, ast.Call)
+                        and isinstance(n.left.func, ast.Name)
+                        and n.left.func.id == "get_flash_attn_version"
+                    )
+                    if is_version_compare:
+                        for elt in n.comparators[0].elts:
+                            if isinstance(elt, ast.Constant):
+                                if elt.value == 3:
+                                    fa3_supports_sinks = True
+                                elif elt.value == 4:
+                                    fa4_supports_sinks = True
 
         # Check get_flash_attn_version for FA3/FA4 compute capability
         if node.name == "get_flash_attn_version":
@@ -735,7 +1007,7 @@ def parse_flash_attn_features() -> dict[str, dict[str, Any]]:
         "fa4": {
             "compute_capability": fa4_compute_cap,
             "supports_fp8": False,
-            "supports_sink": False,
+            "supports_sink": fa4_supports_sinks,
         },
     }
 
@@ -759,6 +1031,11 @@ def parse_flashinfer_trtllm_features() -> dict[str, dict[str, Any]]:
     if not trtllm_compute_cap:
         return {}
 
+    # KV cache dtypes that only work with a dedicated kernel (e.g. nvfp4
+    # requires the SM100 NVFP4 MHA kernel) and should not appear in the
+    # generic attention-backend feature matrix.
+    kernel_only_kv_dtypes = ["nvfp4"]
+
     return {
         "native": {
             # Native FlashInfer: everything except SM100
@@ -769,89 +1046,10 @@ def parse_flashinfer_trtllm_features() -> dict[str, dict[str, Any]]:
             "compute_capability": trtllm_compute_cap,
             "supports_sink": True,
         },
+        "exclude_kv_dtypes": kernel_only_kv_dtypes,
     }
 
 
-def parse_mla_prefill_backends() -> list[dict[str, Any]]:
-    """Parse MLA prefill backend options from mla_attention.py.
-
-    MLA uses different backends for prefill vs decode. The decode backends are
-    registered in the registry, but prefill backends are selected at runtime
-    based on conditions in MLACommonImpl.__init__.
-
-    Returns a list of prefill backend info dicts with their requirements.
-    """
-    if not MLA_ATTENTION_FILE.exists():
-        return []
-
-    try:
-        tree = ast.parse(MLA_ATTENTION_FILE.read_text())
-    except Exception:
-        return []
-
-    # Find compute capability requirements by parsing use_* functions
-    trtllm_cc = _find_cc_in_function(tree, "use_trtllm_ragged_deepseek_prefill")
-    flashinfer_cc = _find_cc_in_function(tree, "use_flashinfer_prefill")
-    cudnn_cc = _find_cc_in_function(tree, "use_cudnn_prefill")
-
-    # Build prefill backend list based on what we found
-    # Order matches the priority in MLACommonImpl.__init__
-    prefill_backends: list[dict[str, Any]] = []
-
-    # TRT-LLM Ragged (highest priority if available)
-    if trtllm_cc:
-        prefill_backends.append(
-            {
-                "name": "TRT-LLM Ragged‡",
-                "description": "TensorRT-LLM ragged attention",
-                "compute_capability": trtllm_cc,
-                "enable": "Default on SM100",
-                "disable": "`-ac.use_trtllm_ragged_deepseek_prefill=0`",
-                "notes": "DeepSeek R1 dims only",
-            }
-        )
-
-    # FlashInfer prefill
-    if flashinfer_cc:
-        prefill_backends.append(
-            {
-                "name": "FlashInfer",
-                "description": "FlashInfer CUTLASS backend",
-                "compute_capability": flashinfer_cc,
-                "enable": "`-ac.disable_flashinfer_prefill=0`",
-                "disable": "`-ac.disable_flashinfer_prefill=1`",
-                "notes": "DeepSeek R1 dims only",
-            }
-        )
-
-    # cuDNN prefill
-    if cudnn_cc:
-        prefill_backends.append(
-            {
-                "name": "cuDNN",
-                "description": "cuDNN-based attention",
-                "compute_capability": cudnn_cc,
-                "enable": "`-ac.use_cudnn_prefill=1`",
-                "disable": "`-ac.use_cudnn_prefill=0`",
-                "notes": "",
-            }
-        )
-
-    # FlashAttention is always available as fallback
-    prefill_backends.append(
-        {
-            "name": "FlashAttention",
-            "description": "FlashAttention varlen (FA2/FA3)",
-            "compute_capability": "Any",
-            "enable": "Default fallback",
-            "disable": "Use other backends",
-            "notes": "FA3 on SM90, FA2 otherwise",
-        }
-    )
-
-    return prefill_backends
-
-
 # ---------------------------------------------------------------------------
 # Backend variant expansion (FA2/FA3/FA4, FlashInfer native/TRTLLM)
 # ---------------------------------------------------------------------------
@@ -934,6 +1132,15 @@ def _expand_flashinfer_variants(
         native["supports_sink"] = fi_features["native"]["supports_sink"]
         native["compute_capability"] = f"{min_cc}.x-9.x"
 
+        # Remove KV dtypes only supported by SM100 kernels (e.g. nvfp4)
+        exclude = fi_features.get("exclude_kv_dtypes", [])
+        if exclude:
+            native["kv_cache_dtypes"] = ", ".join(
+                d
+                for d in (d.strip() for d in native["kv_cache_dtypes"].split(","))
+                if d not in exclude
+            )
+
         # Create TRTLLM entry
         trtllm = backend.copy()
         trtllm["version"] = "TRTLLM†"
@@ -1107,6 +1314,10 @@ def _extract_priorities(body: list, priorities: dict[str, list[str]], prefix: st
 _COL_BLOCK_SIZES: TableColumn = ("Block Sizes", lambda b: b["block_sizes"])
 _COL_HEAD_SIZES: TableColumn = ("Head Sizes", lambda b: b["head_sizes"])
 _COL_SINK: TableColumn = ("Sink", lambda b: bool_to_emoji(b["supports_sink"]))
+_COL_NON_CAUSAL: TableColumn = (
+    "Non-Causal",
+    lambda b: bool_to_emoji(b["supports_non_causal"]),
+)
 _COL_SPARSE: TableColumn = ("Sparse", lambda b: bool_to_emoji(b["is_sparse"]))
 _COL_MM_PREFIX: TableColumn = (
     "MM Prefix",
@@ -1140,6 +1351,7 @@ def _build_columns(is_mla: bool, has_versions: bool) -> list[TableColumn]:
         cols.append(_COL_VERSION)
     cols.extend([_COL_DTYPES, _COL_KV_DTYPES, _COL_BLOCK_SIZES, _COL_HEAD_SIZES])
     cols.append(_COL_SINK)
+    cols.append(_COL_NON_CAUSAL)
     if is_mla:
         cols.append(_COL_SPARSE)
     cols.extend([_COL_MM_PREFIX, _COL_DCP, _COL_ATTN_TYPES, _COL_COMPUTE_CAP])
@@ -1350,6 +1562,7 @@ def generate_legend() -> str:
 | **Block Sizes** | Supported KV cache block sizes (%N means multiples of N) |
 | **Head Sizes** | Supported attention head sizes |
 | **Sink** | Attention sink support (for StreamingLLM) |
+| **Non-Causal** | Non-causal (bidirectional) attention support for decoder models |
 | **Sparse** | Sparse attention support (MLA only) |
 | **MM Prefix** | Multimodal prefix full attention support |
 | **DCP** | Decode Context Parallelism support (`--decode-context-parallel-size`) |
@@ -1371,20 +1584,22 @@ def generate_mla_section(
         "",
         "### Prefill Backends",
         "",
-        "The prefill backend is selected at runtime based on hardware and",
-        "configuration.",
+        "To explicitly select a prefill backend, use",
+        "`-ac.mla_prefill_backend=<BACKEND>` (e.g., `FLASH_ATTN`, `FLASHINFER`).",
+        "Otherwise, the prefill backend is selected automatically at runtime based on",
+        "hardware and configuration.",
         "",
-        "| Backend | Description | Compute Cap. | Enable | Disable | Notes |",
-        "| ------- | ----------- | ------------ | ------ | ------- | ----- |",
+        "| Backend | Description | Dtypes | Compute Cap. | Notes |",
+        "| ------- | ----------- | ------ | ------------ | ----- |",
     ]
 
     for backend in prefill_backends:
-        row = "| {} | {} | {} | {} | {} | {} |".format(
+        row = "| `{}`{} | {} | {} | {} | {} |".format(
             backend["name"],
+            backend.get("marker", ""),
             backend["description"],
+            backend.get("dtypes", "fp16, bf16"),
             backend["compute_capability"],
-            backend["enable"],
-            backend["disable"],
             backend.get("notes", ""),
         )
         lines.append(row.replace("  ", " "))
@@ -1397,6 +1612,9 @@ def generate_mla_section(
             "",
             "### Decode Backends",
             "",
+            "MLA decode backends are selected using the standard",
+            "`-ac.backend=<BACKEND>` argument (e.g., `FLASHMLA`, `TRITON_MLA`).",
+            "",
         ]
     )
 
diff --git a/tools/pre_commit/mypy.py b/tools/pre_commit/mypy.py
index 41c05efd201b..7c7b0ada60d6 100755
--- a/tools/pre_commit/mypy.py
+++ b/tools/pre_commit/mypy.py
@@ -29,7 +29,6 @@
     "tests",
     # v0 related
     "vllm/lora",
-    "vllm/model_executor/layers",
 ]
 
 # TODO(woosuk): Include the code from Megatron and HuggingFace.
diff --git a/vllm/_aiter_ops.py b/vllm/_aiter_ops.py
index 8c2659b9c7e6..e628c97835c6 100644
--- a/vllm/_aiter_ops.py
+++ b/vllm/_aiter_ops.py
@@ -2,18 +2,27 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import functools
 from collections.abc import Callable
+from contextlib import contextmanager
+from typing import Protocol
 
 import torch
 from torch._ops import OpOverload
+from torch.distributed import ProcessGroup
 
 import vllm.envs as envs
 from vllm.platforms import current_platform
+from vllm.utils.import_utils import PlaceholderModule
 from vllm.utils.torch_utils import direct_register_custom_op
 from vllm.v1.attention.ops.rocm_aiter_mla_sparse import (
     rocm_aiter_sparse_attn_indexer,
     rocm_aiter_sparse_attn_indexer_fake,
 )
 
+try:
+    import pandas as pd
+except ImportError:
+    pd = PlaceholderModule("pandas")
+
 # fp8_dtype is not cached.
 # on ROCm the fp8_dtype always calls is_fp8_fnuz
 # which is a host op, so we cache it once here.
@@ -33,6 +42,27 @@ def is_aiter_found() -> bool:
 IS_AITER_FOUND = is_aiter_found()
 
 
+class AiterCustomAllreduceProto(Protocol):
+    max_size: int
+    world_size: int
+    fully_connected: bool
+
+    @contextmanager
+    def capture(self): ...
+    def close(self) -> None: ...
+    def fused_ar_rms(
+        self,
+        inp: torch.Tensor,
+        res_inp: torch.Tensor,
+        *,
+        w: torch.Tensor,
+        eps: float,
+        registered: bool = False,
+        use_1stage: bool = False,
+    ) -> tuple[torch.Tensor, torch.Tensor]: ...
+    def should_custom_ar(self, inp: torch.Tensor) -> bool: ...
+
+
 def is_aiter_found_and_supported() -> bool:
     """Check if AITER library is available and platform supports it.
 
@@ -56,6 +86,29 @@ def is_aiter_found_and_supported() -> bool:
     return False
 
 
+@functools.cache
+def _load_gemm_tuned_configs(
+    q_dtype_w: torch.dtype, csv_path: str
+) -> set[tuple[int, int, int]]:
+    try:
+        df = pd.read_csv(csv_path).drop_duplicates()
+        df = df[df["q_dtype_w"] == str(q_dtype_w)]
+        return set(zip(df["N"].astype(int), df["K"].astype(int), df["M"].astype(int)))
+    except Exception:
+        return set()
+
+
+def _check_kernel_tuned(N: int, K: int, q_dtype_w: torch.dtype, csv_path: str) -> bool:
+    configs = _load_gemm_tuned_configs(q_dtype_w, csv_path)
+    l_m = (
+        [1, 2, 4]
+        + list(range(8, 513, 8))
+        + [1024, 1536]
+        + [2**i for i in range(11, 19)]
+    )
+    return any((N, K, M) in configs for M in l_m)
+
+
 def if_aiter_supported(func: Callable) -> Callable:
     """Decorator that only executes the function if
     ROCm AITER package is supported and enabled on gfx9 archs.
@@ -208,11 +261,19 @@ def _rocm_aiter_topk_softmax_impl(
     token_expert_indices: torch.Tensor,
     gating_output: torch.Tensor,
     renormalize: bool,
+    num_shared_experts: int = 0,
+    shared_expert_scoring_func: str = "",
 ) -> None:
     from aiter import topk_softmax
 
     topk_softmax(
-        topk_weights, topk_indices, token_expert_indices, gating_output, renormalize
+        topk_weights,
+        topk_indices,
+        token_expert_indices,
+        gating_output,
+        renormalize,
+        num_shared_experts,
+        shared_expert_scoring_func,
     )
 
 
@@ -222,6 +283,8 @@ def _rocm_aiter_topk_softmax_fake(
     token_expert_indices: torch.Tensor,
     gating_output: torch.Tensor,
     renormalize: bool,
+    num_shared_experts: int = 0,
+    shared_expert_scoring_func: str = "",
 ) -> None:
     pass
 
@@ -347,6 +410,22 @@ def _rocm_aiter_fused_topk_fake(
 
 # Cache whether aiter supports FP8 MLA parameters
 _AITER_MLA_SUPPORTS_FP8: bool | None = None
+_AITER_HAS_FUSED_QK_RMSNORM: bool | None = None
+
+
+def check_aiter_fused_qk_rmsnorm() -> bool:
+    """Check if aiter provides fused_qk_rmsnorm (requires AITer >= PR #2442)."""
+    global _AITER_HAS_FUSED_QK_RMSNORM
+    if _AITER_HAS_FUSED_QK_RMSNORM is None:
+        try:
+            from aiter.ops.fused_qk_norm_rope_cache_quant import (  # noqa: F401
+                fused_qk_rmsnorm,
+            )
+
+            _AITER_HAS_FUSED_QK_RMSNORM = True
+        except (ImportError, ModuleNotFoundError, AttributeError):
+            _AITER_HAS_FUSED_QK_RMSNORM = False
+    return _AITER_HAS_FUSED_QK_RMSNORM
 
 
 def _check_aiter_mla_fp8_support() -> bool:
@@ -468,7 +547,7 @@ def _rocm_aiter_mla_decode_fwd_fake(
     pass
 
 
-def _rocm_aiter_gemm_a8w8_impl(
+def _rocm_aiter_w8a8_gemm_impl(
     A: torch.Tensor,
     B: torch.Tensor,
     As: torch.Tensor,
@@ -485,7 +564,7 @@ def _rocm_aiter_gemm_a8w8_impl(
     return gemm_a8w8_CK(A, B, As, Bs, bias, output_dtype)
 
 
-def _rocm_aiter_gemm_a8w8_fake(
+def _rocm_aiter_w8a8_gemm_fake(
     A: torch.Tensor,
     B: torch.Tensor,
     As: torch.Tensor,
@@ -499,6 +578,35 @@ def _rocm_aiter_gemm_a8w8_fake(
     return Y
 
 
+def _rocm_aiter_preshuffled_per_token_w8a8_gemm_impl(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    As: torch.Tensor,
+    Bs: torch.Tensor,
+    bias: torch.Tensor | None = None,
+    output_dtype: torch.dtype = torch.float16,
+) -> torch.Tensor:
+    from aiter import gemm_a8w8_bpreshuffle
+
+    output = gemm_a8w8_bpreshuffle(A, B, As, Bs, None, output_dtype)
+    if bias is not None:
+        output.add_(bias)
+    return output
+
+
+def _rocm_aiter_preshuffled_per_token_w8a8_gemm_fake(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    As: torch.Tensor,
+    Bs: torch.Tensor,
+    bias: torch.Tensor | None = None,
+    output_dtype: torch.dtype = torch.float16,
+) -> torch.Tensor:
+    m = A.shape[0]
+    n = B.shape[0]
+    return torch.empty(m, n, dtype=output_dtype, device=A.device)
+
+
 def _rocm_aiter_triton_gemm_a8w8_blockscale_impl(
     A: torch.Tensor,
     B: torch.Tensor,
@@ -549,58 +657,6 @@ def _rocm_aiter_gemm_a8w8_blockscale_fake(
     return Y
 
 
-def _rocm_aiter_rms_norm_impl(
-    x: torch.Tensor, weight: torch.Tensor, variance_epsilon: float
-) -> torch.Tensor:
-    from aiter import rms_norm
-
-    if x.dim() > 2:
-        x_original_shape = x.shape
-        x = x.reshape(-1, x_original_shape[-1])
-        x = rms_norm(x, weight, variance_epsilon)
-        return x.reshape(x_original_shape)
-
-    return rms_norm(x, weight, variance_epsilon)
-
-
-def _rocm_aiter_rms_norm_fake(
-    x: torch.Tensor, weight: torch.Tensor, variance_epsilon: float
-) -> torch.Tensor:
-    return torch.empty_like(x)
-
-
-def _rocm_aiter_rmsnorm2d_fwd_with_add_impl(
-    x: torch.Tensor,
-    residual: torch.Tensor,
-    weight: torch.Tensor,
-    variance_epsilon: float,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    from aiter import rmsnorm2d_fwd_with_add
-
-    residual_out = torch.empty_like(residual)
-    out = torch.empty_like(x)
-    rmsnorm2d_fwd_with_add(
-        out,  # output
-        x,  # input
-        residual,  # residual input
-        residual_out,  # residual output
-        weight,
-        variance_epsilon,
-    )
-    return out, residual_out
-
-
-def _rocm_aiter_rmsnorm2d_fwd_with_add_fake(
-    x: torch.Tensor,
-    residual: torch.Tensor,
-    weight: torch.Tensor,
-    variance_epsilon: float,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    residual_out = torch.empty_like(residual)
-    out = torch.empty_like(x)
-    return out, residual_out
-
-
 def _rocm_aiter_rmsnorm_fused_add_dynamic_quant_impl(
     x: torch.Tensor,
     residual: torch.Tensor,
@@ -676,6 +732,55 @@ def _rocm_aiter_rmsnorm_fused_dynamic_quant_fake(
     return out, y_scale
 
 
+def _rocm_aiter_fused_allreduce_rmsnorm_impl(
+    input_: torch.Tensor,
+    residual: torch.Tensor,
+    weight: torch.Tensor,
+    epsilon: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    aiter_ar = rocm_aiter_ops.get_aiter_allreduce()
+    assert aiter_ar is not None, "aiter allreduce must be initialized"
+
+    total_bytes = input_.numel() * input_.element_size()
+    hidden_dim = input_.shape[-1]
+    token_num = input_.shape[0]
+    hidden_ok = hidden_dim in (512, 1024, 2048, 4096, 7168)
+    token_ok = token_num <= 80
+    world_size = aiter_ar.world_size
+    full_nvlink = aiter_ar.fully_connected
+
+    if world_size == 2:
+        size_ok = True
+    elif full_nvlink and world_size <= 4:
+        size_ok = total_bytes < 256 * 1024
+    elif full_nvlink and world_size <= 8:
+        size_ok = total_bytes < 128 * 1024
+    else:
+        size_ok = False
+
+    use_1stage = hidden_ok and token_ok and size_ok
+
+    result = aiter_ar.fused_ar_rms(
+        input_,
+        residual,
+        w=weight,
+        eps=epsilon,
+        registered=torch.cuda.is_current_stream_capturing(),
+        use_1stage=use_1stage,
+    )
+    assert result is not None
+    return result[0], result[1]
+
+
+def _rocm_aiter_fused_allreduce_rmsnorm_fake(
+    input_: torch.Tensor,
+    residual: torch.Tensor,
+    weight: torch.Tensor,
+    epsilon: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    return torch.empty_like(input_), torch.empty_like(residual)
+
+
 def _rocm_aiter_per_tensor_quant_impl(
     x: torch.Tensor,
     quant_dtype: torch.dtype,
@@ -704,7 +809,7 @@ def _rocm_aiter_per_token_quant_impl(
     assert quant_dtype in [torch.int8, FP8_DTYPE]
 
     out_shape = x.shape
-    out = torch.empty(x.shape, dtype=FP8_DTYPE, device=x.device)
+    out = torch.empty(x.shape, dtype=quant_dtype, device=x.device)
     if scale is None:
         scale = torch.empty((*out_shape[:-1], 1), dtype=torch.float32, device=x.device)
     dynamic_per_token_scaled_quant(
@@ -724,7 +829,7 @@ def _rocm_aiter_per_token_quant_fake(
 ) -> tuple[torch.Tensor, torch.Tensor]:
     out_shape = x.shape
     return (
-        torch.empty(x.shape, dtype=FP8_DTYPE, device=x.device),
+        torch.empty(x.shape, dtype=quant_dtype, device=x.device),
         torch.empty((*out_shape[:-1], 1), dtype=torch.float32, device=x.device),
     )
 
@@ -904,6 +1009,44 @@ def _rocm_aiter_triton_add_rmsnorm_pad_fake(
     return out, residual_out
 
 
+def _fused_mla_dual_rms_norm_impl(
+    x1: torch.Tensor,
+    x1_weight: torch.Tensor,
+    x2: torch.Tensor,
+    x2_weight: torch.Tensor,
+    x1_epsilon: float,
+    x2_epsilon: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    try:
+        from aiter.ops.fused_qk_norm_rope_cache_quant import fused_qk_rmsnorm
+    except (ImportError, ModuleNotFoundError) as exc:
+        raise ImportError(
+            "fused_qk_rmsnorm requires a newer AITer version "
+            "(>= PR #2442). Please upgrade aiter or disable the "
+            "fuse_mla_dual_rms_norm pass."
+        ) from exc
+
+    return fused_qk_rmsnorm(
+        q=x1,
+        q_weight=x1_weight,
+        q_eps=x1_epsilon,
+        k=x2,
+        k_weight=x2_weight,
+        k_eps=x2_epsilon,
+    )
+
+
+def _fused_mla_dual_rms_norm_fake(
+    x1: torch.Tensor,
+    x1_weight: torch.Tensor,
+    x2: torch.Tensor,
+    x2_weight: torch.Tensor,
+    x1_epsilon: float,
+    x2_epsilon: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    return (torch.empty_like(x1), torch.empty_like(x2))
+
+
 def _rocm_aiter_gemm_a8wfp4_impl(
     x: torch.Tensor,
     w: torch.Tensor,
@@ -1044,10 +1187,9 @@ class rocm_aiter_ops:
 
         # Check if aiter is enabled before using operations
         if rocm_aiter_ops.is_enabled():
-            result = rocm_aiter_ops.rms_norm(x, weight, epsilon)
+            result = rocm_aiter_ops.per_token_quant(x, FP8_DTYPE)
 
     Operations:
-        - RMS normalization: rms_norm, rms_norm2d_with_add
         - GEMM operations: gemm_a8w8, gemm_a8w8_blockscale
         - Fused MoE: fused_moe, asm_moe_tkw1
         - Routing: topk_softmax, biased_grouped_topk, grouped_topk
@@ -1059,7 +1201,6 @@ class rocm_aiter_ops:
     # Check if the env variable is set
     _AITER_ENABLED = envs.VLLM_ROCM_USE_AITER
     _LINEAR_ENABLED = envs.VLLM_ROCM_USE_AITER_LINEAR
-    _RMSNORM_ENABLED = envs.VLLM_ROCM_USE_AITER_RMSNORM
     _FMOE_ENABLED = envs.VLLM_ROCM_USE_AITER_MOE
     _MLA_ENABLED = envs.VLLM_ROCM_USE_AITER_MLA
     _MHA_ENABLED = envs.VLLM_ROCM_USE_AITER_MHA
@@ -1075,6 +1216,12 @@ class rocm_aiter_ops:
     _MOE_SHARED_EXPERTS_ENABLED = envs.VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS
     # TODO: Consolidate under _LINEAR_ENABLED
     _TRITON_UNQUANT_GEMM = envs.VLLM_ROCM_USE_AITER_TRITON_GEMM
+    # Lazily probed: whether aiter.topk_softmax supports the
+    # num_shared_experts / shared_expert_scoring_func args (7-arg form).
+    _TOPK_SOFTMAX_FUSED_SIGMOID: bool | None = None
+
+    _ALL_REDUCE_MAX_SIZE: int = 8192 * 1024 * 8 * 2
+    _CUSTOM_ALL_REDUCE: AiterCustomAllreduceProto | None = None
 
     @classmethod
     def refresh_env_variables(cls):
@@ -1087,7 +1234,6 @@ def refresh_env_variables(cls):
         """
         cls._AITER_ENABLED = envs.VLLM_ROCM_USE_AITER
         cls._LINEAR_ENABLED = envs.VLLM_ROCM_USE_AITER_LINEAR
-        cls._RMSNORM_ENABLED = envs.VLLM_ROCM_USE_AITER_RMSNORM
         cls._FMOE_ENABLED = envs.VLLM_ROCM_USE_AITER_MOE
         cls._MLA_ENABLED = envs.VLLM_ROCM_USE_AITER_MLA
         cls._MHA_ENABLED = envs.VLLM_ROCM_USE_AITER_MHA
@@ -1179,11 +1325,6 @@ def is_linear_enabled(cls) -> bool:
     def is_linear_fp8_enabled(cls) -> bool:
         return cls.is_linear_enabled()
 
-    @classmethod
-    @if_aiter_supported
-    def is_rmsnorm_enabled(cls) -> bool:
-        return cls._AITER_ENABLED and cls._RMSNORM_ENABLED
-
     @classmethod
     @if_aiter_supported
     def is_fused_moe_enabled(cls) -> bool:
@@ -1194,6 +1335,52 @@ def is_fused_moe_enabled(cls) -> bool:
     def is_fusion_moe_shared_experts_enabled(cls) -> bool:
         return cls.is_fused_moe_enabled() and cls._MOE_SHARED_EXPERTS_ENABLED
 
+    @classmethod
+    @if_aiter_supported
+    def topk_softmax_supports_fused_sigmoid(cls) -> bool:
+        """Check if topk_softmax supports fused shared expert activation."""
+        if cls._TOPK_SOFTMAX_FUSED_SIGMOID is None:
+            try:
+                import inspect
+
+                from aiter import topk_softmax
+
+                params = inspect.signature(topk_softmax).parameters
+                if "num_shared_experts" in params:
+                    cls._TOPK_SOFTMAX_FUSED_SIGMOID = True
+                else:
+                    # @compile_ops wrapper loses the original signature.
+                    # Fall back to the torch custom op schema.
+                    import torch
+
+                    schema = getattr(
+                        getattr(torch.ops.aiter, "topk_softmax", None), "default", None
+                    )
+                    schema_str = str(getattr(schema, "_schema", ""))
+                    cls._TOPK_SOFTMAX_FUSED_SIGMOID = "num_shared_experts" in schema_str
+            except (ImportError, ValueError):
+                cls._TOPK_SOFTMAX_FUSED_SIGMOID = False
+        return cls._TOPK_SOFTMAX_FUSED_SIGMOID
+
+    @classmethod
+    @if_aiter_supported
+    def fuse_sigmoid_in_kernel(cls, aiter_topK_meta_data: object) -> bool:
+        """Whether fused shared-expert sigmoid in the topk kernel is usable.
+
+        Combines the cached static capability checks (FSE enabled, fused-moe
+        enabled, topk_softmax supports fused sigmoid) with the runtime
+        readiness check (topK meta-data buffer initialized).
+
+        ``aiter_topK_meta_data`` is accepted as a parameter rather than
+        imported internally so callers cannot hit initialization-order
+        issues where the module-level global has not been set yet.
+        """
+        return (
+            cls.is_fusion_moe_shared_experts_enabled()
+            and cls.topk_softmax_supports_fused_sigmoid()
+            and aiter_topK_meta_data is not None
+        )
+
     @classmethod
     @if_aiter_supported
     def is_mla_enabled(cls) -> bool:
@@ -1243,6 +1430,61 @@ def is_triton_rotary_embed_enabled(cls) -> bool:
     def is_triton_gemm_enabled(cls) -> bool:
         return cls._AITER_ENABLED and cls._TRITON_UNQUANT_GEMM
 
+    @classmethod
+    @if_aiter_supported
+    def is_tgemm_enabled(cls) -> bool:
+        from vllm.platforms.rocm import on_gfx950
+
+        return cls.is_linear_enabled() and on_gfx950()
+
+    @classmethod
+    def initialize_aiter_allreduce(
+        cls, group: ProcessGroup, device: torch.device
+    ) -> None:
+        try:
+            from aiter.dist.device_communicators.custom_all_reduce import (
+                CustomAllreduce as AiterCustomAllreduce,
+            )
+
+            cls._CUSTOM_ALL_REDUCE = AiterCustomAllreduce(group, device)
+        except Exception:
+            cls._CUSTOM_ALL_REDUCE = None
+
+    @classmethod
+    def get_aiter_allreduce(cls) -> AiterCustomAllreduceProto | None:
+        return cls._CUSTOM_ALL_REDUCE
+
+    @classmethod
+    def destroy_aiter_allreduce(cls) -> None:
+        if cls._CUSTOM_ALL_REDUCE is not None:
+            cls._CUSTOM_ALL_REDUCE.close()
+            cls._CUSTOM_ALL_REDUCE = None
+
+    @classmethod
+    def get_aiter_allreduce_max_size(cls) -> int | None:
+        # effective max input size (based on upstream aiter version: v0.1.10.post3)
+        # https://github.com/ROCm/aiter/blob/6a0e7b26ccf33164785531212cc2ec2cde0b9243/aiter/dist/device_communicators/custom_all_reduce.py#L272-L273
+        return int(cls._ALL_REDUCE_MAX_SIZE / 2)
+
+    @classmethod
+    @if_aiter_supported
+    def are_gdn_triton_kernels_available(cls) -> bool:
+        """Check if AITER Triton kernels for GDN attention are importable.
+
+        These are optional Triton kernels (conv1d fast-path, gated delta net)
+        used by GatedDeltaNetAttention's decode fast-path.  They may be absent
+        in older aiter builds.
+        """
+        if not cls._AITER_ENABLED:
+            return False
+        try:
+            import aiter.ops.triton.causal_conv1d_update_single_token  # noqa: F401
+            import aiter.ops.triton.gated_delta_net  # noqa: F401
+
+            return True
+        except (ImportError, ModuleNotFoundError):
+            return False
+
     @staticmethod
     @if_aiter_supported
     def register_ops_once() -> None:
@@ -1313,11 +1555,15 @@ def register_ops_once() -> None:
             )
 
             direct_register_custom_op(
-                op_name="rocm_aiter_gemm_a8w8",
-                op_func=_rocm_aiter_gemm_a8w8_impl,
-                mutates_args=[],
-                fake_impl=_rocm_aiter_gemm_a8w8_fake,
-                dispatch_key=current_platform.dispatch_key,
+                op_name="rocm_aiter_w8a8_gemm",
+                op_func=_rocm_aiter_w8a8_gemm_impl,
+                fake_impl=_rocm_aiter_w8a8_gemm_fake,
+            )
+
+            direct_register_custom_op(
+                op_name="_rocm_aiter_preshuffled_per_token_w8a8_gemm",
+                op_func=_rocm_aiter_preshuffled_per_token_w8a8_gemm_impl,
+                fake_impl=_rocm_aiter_preshuffled_per_token_w8a8_gemm_fake,
             )
 
             direct_register_custom_op(
@@ -1332,19 +1578,6 @@ def register_ops_once() -> None:
                 fake_impl=_rocm_aiter_gemm_a8w8_blockscale_fake,
             )
 
-            direct_register_custom_op(
-                op_name="rocm_aiter_rms_norm",
-                op_func=_rocm_aiter_rms_norm_impl,
-                fake_impl=_rocm_aiter_rms_norm_fake,
-            )
-
-            direct_register_custom_op(
-                op_name="rocm_aiter_rmsnorm2d_fwd_with_add",
-                op_func=_rocm_aiter_rmsnorm2d_fwd_with_add_impl,
-                fake_impl=_rocm_aiter_rmsnorm2d_fwd_with_add_fake,
-                dispatch_key=current_platform.dispatch_key,
-            )
-
             direct_register_custom_op(
                 op_name="rocm_aiter_rmsnorm_fused_dynamic_quant",
                 op_func=_rocm_aiter_rmsnorm_fused_dynamic_quant_impl,
@@ -1429,15 +1662,20 @@ def register_ops_once() -> None:
                 fake_impl=_triton_rotary_embedding_fake,
             )
 
-            _OPS_REGISTERED = True
+            direct_register_custom_op(
+                op_name="rocm_aiter_fused_allreduce_rmsnorm",
+                op_func=_rocm_aiter_fused_allreduce_rmsnorm_impl,
+                fake_impl=_rocm_aiter_fused_allreduce_rmsnorm_fake,
+            )
 
-    @staticmethod
-    def get_rmsnorm_fused_add_op() -> OpOverload:
-        return torch.ops.vllm.rocm_aiter_rmsnorm2d_fwd_with_add.default
+            direct_register_custom_op(
+                op_name="fused_mla_dual_rms_norm",
+                op_func=_fused_mla_dual_rms_norm_impl,
+                mutates_args=[],
+                fake_impl=_fused_mla_dual_rms_norm_fake,
+            )
 
-    @staticmethod
-    def get_rmsnorm_op() -> OpOverload:
-        return torch.ops.vllm.rocm_aiter_rms_norm.default
+            _OPS_REGISTERED = True
 
     @staticmethod
     def get_rmsnorm_fused_add_dynamic_quant_op() -> OpOverload:
@@ -1476,24 +1714,15 @@ def get_triton_rotary_embedding_op() -> OpOverload:
         return torch.ops.vllm.rocm_aiter_triton_rotary_embedding.default
 
     @staticmethod
-    def rms_norm(
-        x: torch.Tensor, weight: torch.Tensor, variance_epsilon: float
-    ) -> torch.Tensor:
-        return torch.ops.vllm.rocm_aiter_rms_norm(x, weight, variance_epsilon)
+    def get_fused_allreduce_rmsnorm_op() -> OpOverload:
+        return torch.ops.vllm.rocm_aiter_fused_allreduce_rmsnorm.default
 
     @staticmethod
-    def rms_norm2d_with_add(
-        x: torch.Tensor,
-        residual: torch.Tensor,
-        weight: torch.Tensor,
-        variance_epsilon: float,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        return torch.ops.vllm.rocm_aiter_rmsnorm2d_fwd_with_add(
-            x, residual, weight, variance_epsilon
-        )
+    def get_fused_mla_dual_rms_norm_op() -> OpOverload:
+        return torch.ops.vllm.fused_mla_dual_rms_norm.default
 
     @staticmethod
-    def gemm_a8w8(
+    def w8a8_gemm(
         A: torch.Tensor,
         B: torch.Tensor,
         As: torch.Tensor,
@@ -1501,7 +1730,20 @@ def gemm_a8w8(
         bias: torch.Tensor | None = None,
         output_dtype: torch.dtype = torch.float16,
     ) -> torch.Tensor:
-        return torch.ops.vllm.rocm_aiter_gemm_a8w8(A, B, As, Bs, bias, output_dtype)
+        return torch.ops.vllm.rocm_aiter_w8a8_gemm(A, B, As, Bs, bias, output_dtype)
+
+    @staticmethod
+    def preshuffled_per_token_w8a8_gemm(
+        A: torch.Tensor,
+        B: torch.Tensor,
+        As: torch.Tensor,
+        Bs: torch.Tensor,
+        bias: torch.Tensor | None = None,
+        output_dtype: torch.dtype = torch.float16,
+    ) -> torch.Tensor:
+        return torch.ops.vllm._rocm_aiter_preshuffled_per_token_w8a8_gemm(
+            A, B, As, Bs, bias, output_dtype
+        )
 
     @staticmethod
     def triton_gemm_a8w8_blockscale(
@@ -1612,9 +1854,17 @@ def topk_softmax(
         token_expert_indices: torch.Tensor,
         gating_output: torch.Tensor,
         renormalize: bool,
+        num_shared_experts: int = 0,
+        shared_expert_scoring_func: str = "",
     ) -> tuple[torch.Tensor, ...]:
         torch.ops.vllm.rocm_aiter_topk_softmax(
-            topk_weights, topk_indices, token_expert_indices, gating_output, renormalize
+            topk_weights,
+            topk_indices,
+            token_expert_indices,
+            gating_output,
+            renormalize,
+            num_shared_experts,
+            shared_expert_scoring_func,
         )
         return topk_weights, topk_indices
 
@@ -1642,6 +1892,8 @@ def biased_grouped_topk(
         need_renorm: bool,
         routed_scaling_factor: float = 1.0,
     ) -> None:
+        if correction_bias.dtype != gating_output.dtype:
+            correction_bias = correction_bias.to(gating_output.dtype)
         torch.ops.vllm.rocm_aiter_biased_grouped_topk(
             gating_output,
             correction_bias,
@@ -1920,6 +2172,24 @@ def is_triton_gemm_afp4wfp4_presh_ws_tuned(n: int, k: int) -> bool:
             (8192, 3584),
         ]
 
+    @staticmethod
+    def is_shuffled_per_token_w8a8_gemm_tuned(
+        N: int, K: int, q_dtype_w: torch.dtype
+    ) -> bool:
+        import aiter.ops.gemm_op_a8w8 as aiter_gemm_a8w8_ops
+
+        csv_path = (
+            aiter_gemm_a8w8_ops.AITER_CONFIGS.AITER_CONFIG_GEMM_A8W8_BPRESHUFFLE_FILE
+        )
+        return _check_kernel_tuned(N, K, q_dtype_w, csv_path)
+
+    @staticmethod
+    def is_per_token_w8a8_gemm_tuned(N: int, K: int, q_dtype_w: torch.dtype) -> bool:
+        import aiter.ops.gemm_op_a8w8 as aiter_gemm_a8w8_ops
+
+        csv_path = aiter_gemm_a8w8_ops.AITER_CONFIGS.AITER_CONFIG_GEMM_A8W8_FILE
+        return _check_kernel_tuned(N, K, q_dtype_w, csv_path)
+
     @staticmethod
     def shuffle_weight(
         tensor: torch.Tensor, layout: tuple[int, int] = (16, 16)
diff --git a/vllm/_custom_ops.py b/vllm/_custom_ops.py
index d6780185be90..553513de6c04 100644
--- a/vllm/_custom_ops.py
+++ b/vllm/_custom_ops.py
@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from enum import IntEnum
 from typing import TYPE_CHECKING, Literal
 
 import torch
@@ -404,10 +405,24 @@ def rotary_embedding(
     head_size: int,
     cos_sin_cache: torch.Tensor,
     is_neox: bool,
+    rope_dim_offset: int = 0,
+    inverse: bool = False,
 ) -> None:
-    torch.ops._C.rotary_embedding(
-        positions, query, key, head_size, cos_sin_cache, is_neox
-    )
+    if rope_dim_offset == 0 and not inverse:
+        torch.ops._C.rotary_embedding(
+            positions, query, key, head_size, cos_sin_cache, is_neox
+        )
+    else:
+        torch.ops._C.rotary_embedding(
+            positions,
+            query,
+            key,
+            head_size,
+            cos_sin_cache,
+            is_neox,
+            rope_dim_offset,
+            inverse,
+        )
 
 
 # layer norm ops
@@ -420,6 +435,7 @@ def rms_norm(
 def fused_add_rms_norm(
     input: torch.Tensor, residual: torch.Tensor, weight: torch.Tensor, epsilon: float
 ) -> None:
+    # Note: this func is batch invariant
     torch.ops._C.fused_add_rms_norm(input, residual, weight, epsilon)
 
 
@@ -1150,6 +1166,38 @@ def cutlass_fp4_moe_mm(
     )
 
 
+def cutlass_mxfp4_moe_mm(
+    out_tensors: torch.Tensor,
+    a_tensors: torch.Tensor,
+    b_tensors: torch.Tensor,
+    a_scales: torch.Tensor,
+    b_scales: torch.Tensor,
+    problem_sizes: torch.Tensor,
+    expert_offsets: torch.Tensor,
+    sf_offsets: torch.Tensor,
+):
+    """
+    An MXFP4 Blockscaled Group Gemm for MoE (MXFP4 x MXFP4).
+
+    Uses mx_float4_t types with E8M0 scale factors and 32-element blocks.
+    - a/b_tensors: MXFP4 packed activations/weights (uint8, 2 E2M1 per byte)
+    - a_/b_scales: E8M0 blockscales (uint8, stored in swizzled layout)
+    - Epilogue uses scalar alpha=1, beta=0 inside the CUDA op (no global scales).
+    - expert_offsets/sf_offsets: expert boundary indices
+    - problem_sizes: (num_experts, 3) with (M, N, K) per expert
+    """
+    return torch.ops._C.cutlass_mxfp4_group_mm(
+        out_tensors,
+        a_tensors,
+        b_tensors,
+        a_scales,
+        b_scales,
+        problem_sizes,
+        expert_offsets,
+        sf_offsets,
+    )
+
+
 def mxfp8_experts_quant(
     input_tensor: torch.Tensor,
     problem_sizes: torch.Tensor,
@@ -1848,6 +1896,109 @@ def silu_and_mul_scaled_fp4_experts_quant(
     return output, output_scales
 
 
+def mxfp4_experts_quant(
+    input_tensor: torch.Tensor,
+    expert_offsets: torch.Tensor,
+    blockscale_offsets: torch.Tensor,
+    n_experts: int,
+    topk: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Quantize input tensor to MXFP4 for packed MoE inputs.
+    Uses 32-element blocks with E8M0 (power-of-two) scale factors.
+    MXFP4 has no global scale - only block-level E8M0 scale factors.
+
+    Args:
+        input_tensor: [m_topk, k] BF16/FP16 activations
+        expert_offsets: [n_experts+1] token boundaries per expert
+        blockscale_offsets: [n_experts+1] SF row boundaries per expert
+        n_experts: number of experts
+        topk: number of top-k experts
+    Returns:
+        output: [m_topk, k//2] packed E2M1 values (uint8)
+        output_scales: E8M0 blockscales in swizzled layout (uint8 view)
+    """
+    assert not current_platform.is_rocm()
+    assert input_tensor.ndim == 2
+
+    MAX_TOKENS_PER_EXPERT = envs.VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE
+    m_numtopk, k = input_tensor.shape
+
+    assert m_numtopk <= MAX_TOKENS_PER_EXPERT * topk, (
+        f"m_numtopk must be less than MAX_TOKENS_PER_EXPERT("
+        f"{MAX_TOKENS_PER_EXPERT})"
+        f" for cutlass_moe_mxfp4, observed m_numtopk = {m_numtopk}. Use"
+        f" VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE to set this value."
+    )
+    scales_k = k // 32
+    padded_k = (scales_k + (4 - 1)) // 4
+
+    output = torch.empty(
+        m_numtopk, k // 2, device=input_tensor.device, dtype=torch.uint8
+    )
+    output_scales = torch.empty(
+        MAX_TOKENS_PER_EXPERT * topk,
+        padded_k,
+        dtype=torch.int32,
+        device=input_tensor.device,
+    )
+    torch.ops._C.mxfp4_experts_quant(
+        output,
+        output_scales,
+        input_tensor,
+        expert_offsets,
+        blockscale_offsets,
+        n_experts,
+    )
+    # E8M0 SFs are stored as uint8
+    output_scales = output_scales.view(torch.uint8)
+    return output, output_scales
+
+
+def silu_and_mul_mxfp4_experts_quant(
+    input_tensor: torch.Tensor,
+    expert_offsets: torch.Tensor,
+    blockscale_offsets: torch.Tensor,
+    n_experts: int,
+    topk: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Fused SiLU+Mul+MXFP4 quantization for MoE intermediate activations.
+    MXFP4 has no global scale - only block-level E8M0 scale factors.
+    """
+    assert not current_platform.is_rocm()
+    assert input_tensor.ndim == 2
+
+    MAX_TOKENS_PER_EXPERT = envs.VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE
+    m_numtopk, k_times_2 = input_tensor.shape
+    assert k_times_2 % 2 == 0, "input width must be even (gate || up layout)"
+    k = k_times_2 // 2
+
+    assert m_numtopk <= MAX_TOKENS_PER_EXPERT * topk
+    scales_k = k // 32
+    padded_k = (scales_k + (4 - 1)) // 4
+
+    output = torch.empty(
+        m_numtopk, k // 2, device=input_tensor.device, dtype=torch.uint8
+    )
+    output_scales = torch.empty(
+        MAX_TOKENS_PER_EXPERT * topk,
+        padded_k,
+        dtype=torch.int32,
+        device=input_tensor.device,
+    )
+    torch.ops._C.silu_and_mul_mxfp4_experts_quant(
+        output,
+        output_scales,
+        input_tensor,
+        expert_offsets,
+        blockscale_offsets,
+        n_experts,
+    )
+    output_scales = output_scales.view(torch.uint8)
+    return output, output_scales
+
+
 # fp8
 def scaled_fp8_quant(
     input: torch.Tensor,
@@ -2299,23 +2450,6 @@ def moe_wna16_gemm(
     )
 
 
-def router_gemm_bf16_fp32(input: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
-    """bf16 x bf16 -> fp32 GEMM via cuBLAS. weight shape: (N, K)."""
-    return torch.ops._moe_C.router_gemm_bf16_fp32(input, weight)
-
-
-if hasattr(torch.ops, "_moe_C") and hasattr(torch.ops._moe_C, "router_gemm_bf16_fp32"):
-
-    @register_fake("_moe_C::router_gemm_bf16_fp32")
-    def router_gemm_bf16_fp32_fake(
-        input: torch.Tensor,
-        weight: torch.Tensor,
-    ) -> torch.Tensor:
-        return torch.empty(
-            input.shape[0], weight.shape[0], dtype=torch.float32, device=input.device
-        )
-
-
 def dsv3_router_gemm(
     hidden_states: torch.Tensor,
     router_weight: torch.Tensor,
@@ -2367,6 +2501,30 @@ def topk_sigmoid(
     )
 
 
+def topk_hash_softplus_sqrt(
+    topk_weights: torch.Tensor,
+    topk_indices: torch.Tensor,
+    token_expert_indices: torch.Tensor,
+    gating_output: torch.Tensor,
+    renormalize: bool = False,
+    routed_scaling_factor: float = 1.0,
+    e_score_correction_bias: torch.Tensor | None = None,
+    input_tokens: torch.Tensor | None = None,
+    hash_indices_table: torch.Tensor | None = None,
+) -> None:
+    torch.ops._moe_C.topk_softplus_sqrt(
+        topk_weights,
+        topk_indices,
+        token_expert_indices,
+        gating_output,
+        renormalize,
+        routed_scaling_factor,
+        e_score_correction_bias,
+        input_tokens,
+        hash_indices_table,
+    )
+
+
 def grouped_topk(
     scores: torch.Tensor,
     num_expert_group: int,
@@ -2647,6 +2805,7 @@ def swap_blocks_batch(
     src_ptrs: torch.Tensor,
     dst_ptrs: torch.Tensor,
     sizes: torch.Tensor,
+    is_src_access_order_any: bool = False,
 ) -> None:
     """
     Batch version of swap_blocks: submit all copies in a single driver call.
@@ -2655,8 +2814,16 @@ def swap_blocks_batch(
     of sizes[i] bytes. All three tensors must be int64 CPU tensors.
     On CUDA 12.8+ this uses cuMemcpyBatchAsync for minimal submission
     overhead; on older CUDA it falls back to a loop of cudaMemcpyAsync.
+
+    is_src_access_order_any: if True, pass CU_MEMCPY_SRC_ACCESS_ORDER_ANY to
+        cuMemcpyBatchAsync, letting the DMA engine prefetch source bytes
+        out of stream order. Only safe when no GPU stream is concurrently
+        writing to the source. Defaults to False (STREAM ordering), which
+        is always safe.
     """
-    torch.ops._C_cache_ops.swap_blocks_batch(src_ptrs, dst_ptrs, sizes)
+    torch.ops._C_cache_ops.swap_blocks_batch(
+        src_ptrs, dst_ptrs, sizes, is_src_access_order_any
+    )
 
 
 def convert_fp8(
@@ -2993,6 +3160,13 @@ def weight_packed_linear_fake(
         )
 
 
+class CPUQuantMethod(IntEnum):
+    UNQUANT = 0
+    INT8_W8A8 = 1
+    FP8_W8A16 = 2
+    INT4_W4A8 = 3
+
+
 if hasattr(torch.ops._C, "fused_experts_cpu"):
 
     @register_fake("_C::fused_experts_cpu")
@@ -3003,18 +3177,49 @@ def fused_experts_cpu_fake(
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
         inplace: bool,
-        use_int8_w8a8: bool,
-        use_fp8_w8a16: bool,
+        moe_comp_method: CPUQuantMethod,
         w1_scale: torch.Tensor | None,
         w2_scale: torch.Tensor | None,
+        w1_zero: torch.Tensor | None,
+        w2_zero: torch.Tensor | None,
         block_size: list[int] | None,
-        a1_scale: torch.Tensor | None,
-        a2_scale: torch.Tensor | None,
         is_vnni: bool,
     ) -> torch.Tensor:
         return torch.empty_like(hidden_states)
 
 
+def fused_experts_cpu(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    inplace: bool,
+    moe_comp_method: CPUQuantMethod,
+    w1_scale: torch.Tensor | None,
+    w2_scale: torch.Tensor | None,
+    w1_zero: torch.Tensor | None,
+    w2_zero: torch.Tensor | None,
+    block_size: list[int] | None,
+    is_vnni: bool,
+) -> torch.Tensor:
+    return torch.ops._C.fused_experts_cpu(
+        hidden_states,
+        w1,
+        w2,
+        topk_weights,
+        topk_ids,
+        inplace,
+        moe_comp_method,
+        w1_scale,
+        w2_scale,
+        w1_zero,
+        w2_zero,
+        block_size,
+        is_vnni,
+    )
+
+
 if hasattr(torch.ops._C, "int8_scaled_mm_with_quant"):
 
     @register_fake("_C::int8_scaled_mm_with_quant")
@@ -3031,6 +3236,11 @@ def int8_scaled_mm_with_quant_fake(
         return torch.empty((M, N), dtype=out_dtype)
 
 
+class CPUQuantAlgo(IntEnum):
+    AWQ = 0
+    GPTQ = 1
+
+
 if hasattr(torch.ops._C, "convert_weight_packed_scale_zp"):
 
     @register_fake("_C::convert_weight_packed_scale_zp")
@@ -3038,6 +3248,7 @@ def convert_weight_packed_scale_zp_fake(
         qweight: torch.Tensor,
         qzeros: torch.Tensor,
         scales: torch.Tensor,
+        quant_method_4bit: CPUQuantAlgo,
     ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         return (
             torch.empty_like(qweight),
@@ -3046,6 +3257,20 @@ def convert_weight_packed_scale_zp_fake(
         )
 
 
+def convert_weight_packed_scale_zp(
+    qweight: torch.Tensor,
+    qzeros: torch.Tensor,
+    scales: torch.Tensor,
+    quant_method_4bit: CPUQuantAlgo,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    return torch.ops._C.convert_weight_packed_scale_zp(
+        qweight,
+        qzeros,
+        scales,
+        quant_method_4bit,
+    )
+
+
 if hasattr(torch.ops._C, "int4_scaled_mm_cpu"):
 
     @register_fake("_C::int4_scaled_mm_cpu")
@@ -3060,7 +3285,59 @@ def int4_scaled_mm_cpu_fake(
         return torch.empty((x.size(0), N), dtype=x.dtype, device=x.device)
 
 
-_supports_cpu_w4a8_int8 = bool(hasattr(torch.ops._C, "convert_weight_packed_scale_zp"))
+def int4_scaled_mm_cpu(
+    x: torch.Tensor,
+    w: torch.Tensor,
+    w_zeros: torch.Tensor,
+    w_scales: torch.Tensor,
+    bias: torch.Tensor | None,
+) -> torch.Tensor:
+    x_shape = x.shape
+    x_2d = x.reshape(-1, x_shape[-1]) if len(x_shape) > 2 else x
+
+    out = torch.ops._C.int4_scaled_mm_cpu(
+        x_2d,
+        w,
+        w_zeros,
+        w_scales,
+        bias,
+    )
+    out = out.reshape(x_shape[:-1] + (out.size(-1),)) if len(x_shape) > 2 else out
+    return out
+
+
+if hasattr(torch.ops._C, "fp8_scaled_mm_cpu"):
+
+    @register_fake("_C::fp8_scaled_mm_cpu")
+    def fp8_scaled_mm_cpu_fake(
+        mat1: torch.Tensor,
+        mat2: torch.Tensor,
+        scales2: torch.Tensor,
+        block_size: list[int],
+        bias: torch.Tensor | None,
+        out_dtype: torch.dtype,
+        is_vnni: bool,
+    ) -> torch.Tensor:
+        M = mat1.size(0)
+        N = mat2.size(0)
+        return torch.empty((M, N), dtype=out_dtype, device=mat1.device)
+
+
+_supports_cpu_fp8_w8a16 = bool(hasattr(torch.ops._C, "fp8_scaled_mm_cpu"))
+
+
+def fp8_scaled_mm_cpu(
+    mat1: torch.Tensor,
+    mat2: torch.Tensor,
+    scales2: torch.Tensor,
+    block_size: list[int],
+    bias: torch.Tensor | None,
+    out_dtype: torch.dtype,
+    is_vnni: bool,
+) -> torch.Tensor:
+    return torch.ops._C.fp8_scaled_mm_cpu(
+        mat1, mat2, scales2, block_size, bias, out_dtype, is_vnni
+    )
 
 
 class CPUDNNLGEMMHandler:
@@ -3229,6 +3506,9 @@ def cpu_attn_reshape_and_cache(
     value_cache: torch.Tensor,
     slot_mapping: torch.Tensor,
     isa: str,
+    k_scale: float = 1.0,
+    v_scale: float = 1.0,
+    kv_cache_dtype: str = "auto",
 ) -> None:
     torch.ops._C.cpu_attn_reshape_and_cache(
         key,
@@ -3237,6 +3517,9 @@ def cpu_attn_reshape_and_cache(
         value_cache,
         slot_mapping,
         isa,
+        k_scale,
+        v_scale,
+        kv_cache_dtype,
     )
 
 
@@ -3255,6 +3538,9 @@ def cpu_attention_with_kv_cache(
     softcap: float,
     scheduler_metadata: torch.Tensor,
     s_aux: torch.Tensor | None,
+    k_scale: float = 1.0,
+    v_scale: float = 1.0,
+    kv_cache_dtype: str = "auto",
 ) -> None:
     torch.ops._C.cpu_attention_with_kv_cache(
         query,
@@ -3272,6 +3558,9 @@ def cpu_attention_with_kv_cache(
         softcap,
         scheduler_metadata,
         s_aux,
+        k_scale,
+        v_scale,
+        kv_cache_dtype,
     )
 
 
@@ -3300,6 +3589,12 @@ def cpu_gemm_wna16(
     return output
 
 
+def cpu_activation_lut_bf16(input: torch.Tensor, activation: str) -> torch.Tensor:
+    out = torch.empty_like(input)
+    torch.ops._C.activation_lut_bf16(out, input, activation)
+    return out
+
+
 def cpu_prepack_moe_weight(
     weight: torch.Tensor,
     isa: str,
diff --git a/vllm/_oink_ops.py b/vllm/_oink_ops.py
deleted file mode 100644
index c7a055410b71..000000000000
--- a/vllm/_oink_ops.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Small helper wrappers for external Oink Blackwell custom ops.
-
-vLLM does not depend on the external Oink repository/package. When an external
-plugin registers torch.library.custom_op entrypoints under the `oink::`
-namespace (e.g. via vLLM's general_plugins mechanism) and
-`VLLM_USE_OINK_OPS=1` is set, vLLM can route eligible calls to those ops.
-
-This module provides:
-- A single place to probe Oink op availability at module init time
-  (outside torch.compile tracing), and
-- Thin wrappers around the torch.ops entrypoints for use in CUDA fast paths,
-  without introducing graph breaks.
-
-Important:
-  Do not call the availability helpers in a compiled region. They may call
-  functions decorated with `torch._dynamo.disable` to safely check
-  conditions that should not be traced.
-"""
-
-from __future__ import annotations
-
-from collections.abc import Callable
-
-import torch
-
-try:
-    from torch._dynamo import disable as _dynamo_disable  # type: ignore[attr-defined]
-except Exception:  # pragma: no cover
-
-    def _dynamo_disable(fn: Callable):  # type: ignore[misc]
-        return fn
-
-
-def _has_oink_op(op_name: str) -> bool:
-    """Check if a specific oink op is registered."""
-    return hasattr(torch.ops, "oink") and hasattr(torch.ops.oink, op_name)
-
-
-@_dynamo_disable
-def is_oink_available_for_device(device_index: int) -> bool:
-    """Return True if Oink ops are registered and device is SM100+.
-
-    This function is intended to be called during module initialization
-    (e.g., in RMSNorm.__init__), not in the forward path.
-
-    External plugins are expected to gate registration on SM100+ and
-    VLLM_USE_OINK_OPS=1, so if the ops are present they should be usable.
-    """
-    if not torch.cuda.is_available():
-        return False
-
-    try:
-        major, minor = torch.cuda.get_device_capability(device_index)
-        sm = 10 * major + minor
-        if sm < 100:
-            return False
-    except Exception:
-        return False
-
-    return _has_oink_op("rmsnorm")
-
-
-def has_fused_add_rms_norm() -> bool:
-    """Return True if the in-place fused op is registered."""
-    return _has_oink_op("fused_add_rms_norm")
-
-
-def rmsnorm(x: torch.Tensor, weight: torch.Tensor, eps: float) -> torch.Tensor:
-    """Call `torch.ops.oink.rmsnorm`.
-
-    This wrapper is safe to call in torch.compile regions.
-    """
-    return torch.ops.oink.rmsnorm(x, weight, eps)
-
-
-def fused_add_rms_norm_(
-    x: torch.Tensor,
-    residual: torch.Tensor,
-    weight: torch.Tensor,
-    eps: float,
-) -> None:
-    """Call `torch.ops.oink.fused_add_rms_norm` (mutates x and residual)."""
-    torch.ops.oink.fused_add_rms_norm(x, residual, weight, eps)
-
-
-def fused_add_rms_norm(
-    x: torch.Tensor,
-    residual: torch.Tensor,
-    weight: torch.Tensor,
-    eps: float,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    """Convenience wrapper returning (x, residual) after in-place mutation."""
-    fused_add_rms_norm_(x, residual, weight, eps)
-    return x, residual
diff --git a/vllm/_xpu_ops.py b/vllm/_xpu_ops.py
index e4d2d8b68ed7..6a2e5e841ce1 100644
--- a/vllm/_xpu_ops.py
+++ b/vllm/_xpu_ops.py
@@ -22,6 +22,23 @@ def register_fake(fn):
     except ImportError:
         from torch.library import impl_abstract as register_fake
 
+if hasattr(torch.ops._xpu_C, "fp8_gemm"):
+
+    @register_fake("_xpu_C::fp8_gemm")
+    def _fp8_gemm_fake(
+        q_input: torch.Tensor,
+        q_weight: torch.Tensor,
+        out_dtype: torch.dtype,
+        input_scales: torch.Tensor,
+        weight_scale: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        input_2d = q_input.view(-1, q_input.shape[-1])
+        M = input_2d.size(0)
+        N = q_weight.size(1)
+        return torch.empty((M, N), dtype=out_dtype, device=q_input.device)
+
+
 if hasattr(torch.ops._xpu_C, "fp8_gemm_w8a16"):
 
     @register_fake("_xpu_C::fp8_gemm_w8a16")
@@ -75,6 +92,72 @@ def _int4_gemm_w4a16_fake(
         return torch.empty((M, N), dtype=input.dtype, device=input.device)
 
 
+def _gdn_attention_core_xpu_impl(
+    core_attn_out: torch.Tensor,
+    z: torch.Tensor,
+    projected_states_qkvz: torch.Tensor,
+    projected_states_ba: torch.Tensor,
+    layer_name: str,
+) -> None:
+    """Custom op wrapping the XPU SYCL GDN kernel for torch.compile."""
+    from vllm.forward_context import get_forward_context
+    from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
+
+    forward_context = get_forward_context()
+    self = forward_context.no_compile_layers[layer_name]
+    attn_metadata_raw = forward_context.attn_metadata
+
+    if attn_metadata_raw is None:
+        return
+
+    assert isinstance(attn_metadata_raw, dict)
+    attn_metadata = attn_metadata_raw[self.prefix]
+    assert isinstance(attn_metadata, GDNAttentionMetadata)
+
+    # TODO: xpu does not support speculative decoding yet
+    assert attn_metadata.spec_sequence_masks is None  # type: ignore[attr-defined]
+
+    conv_weights = self.conv1d.weight.view(
+        self.conv1d.weight.size(0), self.conv1d.weight.size(2)
+    )
+
+    torch.ops._xpu_C.gdn_attention(
+        core_attn_out,
+        z,
+        projected_states_qkvz,
+        projected_states_ba,
+        self.num_k_heads,
+        self.num_v_heads,
+        self.head_k_dim,
+        self.head_v_dim,
+        conv_state=self.kv_cache[0],
+        ssm_state=self.kv_cache[1],
+        conv_weights=conv_weights,
+        conv_bias=self.conv1d.bias,
+        activation=self.activation,
+        A_log=self.A_log,
+        dt_bias=self.dt_bias,
+        num_prefills=attn_metadata.num_prefills,  # type: ignore[attr-defined]
+        num_decodes=attn_metadata.num_decodes,  # type: ignore[attr-defined]
+        has_initial_state=attn_metadata.has_initial_state,  # type: ignore[attr-defined]
+        non_spec_query_start_loc=attn_metadata.non_spec_query_start_loc,  # type: ignore[attr-defined]
+        non_spec_state_indices_tensor=attn_metadata.non_spec_state_indices_tensor,  # type: ignore[attr-defined]
+        num_actual_tokens=attn_metadata.num_actual_tokens,  # type: ignore[attr-defined]
+        tp_size=self.tp_size,
+        reorder_input=not self.gqa_interleaved_layout,
+    )
+
+
+def _gdn_attention_core_xpu_fake(
+    core_attn_out: torch.Tensor,
+    z: torch.Tensor,
+    projected_states_qkvz: torch.Tensor,
+    projected_states_ba: torch.Tensor,
+    layer_name: str,
+) -> None:
+    return
+
+
 def _xpu_ops_deepseek_scaling_rope_impl(
     positions: torch.Tensor,
     query: torch.Tensor,
@@ -102,6 +185,35 @@ def _xpu_ops_deepseek_scaling_rope_fake(
     return query, key
 
 
+def _topk_topp_sample_impl(
+    random_sampled: torch.Tensor,
+    logits_to_return: torch.Tensor | None,
+    logits: torch.Tensor,
+    k: torch.Tensor | None,
+    p: torch.Tensor | None,
+    logprobs_mode: str,
+    seeds: torch.Tensor | None,
+    lambda_: float = 1.0,
+) -> None:
+    torch.ops._xpu_C.topk_topp_sampler(
+        random_sampled, logits_to_return, logits, k, p, logprobs_mode, seeds, lambda_
+    )
+    return
+
+
+def _topk_topp_sample_fake(
+    random_sampled: torch.Tensor,
+    logits_to_return: torch.Tensor | None,
+    logits: torch.Tensor,
+    k: torch.Tensor | None,
+    p: torch.Tensor | None,
+    logprobs_mode: str,
+    seeds: torch.Tensor | None,
+    lambda_: float = 1.0,
+) -> None:
+    return
+
+
 def _xpu_mxfp8_quantize_impl(
     x: torch.Tensor, dtype: torch.dtype | None = None
 ) -> tuple[torch.Tensor, torch.Tensor]:
@@ -144,6 +256,46 @@ def _xpu_mxfp8_quantize_fake(
     return x.to(dtype), x_s.to(torch.float8_e8m0fnu)
 
 
+def _xpu_mxfp4_quantize_impl(
+    x: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    MXFP4_BLOCK_SIZE = 32
+    eps = 1e-10
+    assert x.ndim == 2, "input must be 2-D"
+    assert x.shape[-1] % MXFP4_BLOCK_SIZE == 0, (
+        f"last dimension {x.shape[-1]} must be divisible by group_size "
+        f"{MXFP4_BLOCK_SIZE}"
+    )
+    assert x.is_contiguous(), "input groups must be contiguous"
+
+    M, N = x.shape
+
+    # Packed FP4 output: two nibbles per byte
+    x_q = torch.empty(M, N // 2, device=x.device, dtype=torch.uint8)
+    x_s = torch.empty(M, N // MXFP4_BLOCK_SIZE, device=x.device, dtype=torch.float32)
+
+    torch.ops._C.per_token_group_quant_mxfp4(x, x_q, x_s, MXFP4_BLOCK_SIZE, eps)
+
+    x_q = x_q.view(torch.float4_e2m1fn_x2)
+    x_s = x_s.to(dtype=torch.float8_e8m0fnu, memory_format=torch.preserve_format)
+    return x_q, x_s
+
+
+def _xpu_mxfp4_quantize_fake(
+    x: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    MXFP4_BLOCK_SIZE = 32
+    M, N = x.shape
+
+    # Packed FP4 output: two nibbles per byte
+    x_q = torch.empty(M, N // 2, device=x.device, dtype=torch.uint8)
+    x_s = torch.empty(M, N // MXFP4_BLOCK_SIZE, device=x.device, dtype=torch.float32)
+
+    x_q = x_q.view(torch.float4_e2m1fn_x2)
+    x_s = x_s.to(dtype=torch.float8_e8m0fnu, memory_format=torch.preserve_format)
+    return x_q, x_s
+
+
 # Global flag to ensure ops are registered only once
 _OPS_REGISTERED = False
 
@@ -235,14 +387,9 @@ def flash_attn_varlen_func(
             assert len(window_size) == 2
             real_window_size = (window_size[0], window_size[1])  # noqa: F841
 
-        # In encode attention, k and v maybe not contiguous and current
-        # kernel can't handle it
-        if block_table is None:
-            k = k.contiguous()
-            v = v.contiguous()
         return flash_attn_varlen_func(
             out=out,
-            q=q.contiguous(),
+            q=q,
             k=k,
             v=v,
             cu_seqlens_q=cu_seqlens_q,
@@ -555,6 +702,25 @@ def register_ops_once() -> None:
                 fake_impl=_xpu_mxfp8_quantize_fake,
             )
 
+            direct_register_custom_op(
+                op_name="xpu_mxfp4_quantize",
+                op_func=_xpu_mxfp4_quantize_impl,
+                fake_impl=_xpu_mxfp4_quantize_fake,
+            )
+
+            direct_register_custom_op(
+                op_name="gdn_attention_core_xpu",
+                op_func=_gdn_attention_core_xpu_impl,
+                mutates_args=["core_attn_out", "z"],
+                fake_impl=_gdn_attention_core_xpu_fake,
+            )
+
+            direct_register_custom_op(
+                op_name="xpu_topk_topp_sampler",
+                op_func=_topk_topp_sample_impl,
+                fake_impl=_topk_topp_sample_fake,
+            )
+
             _OPS_REGISTERED = True
 
 
diff --git a/vllm/assets/video.py b/vllm/assets/video.py
index f5e443db978f..9ec2e4d16770 100644
--- a/vllm/assets/video.py
+++ b/vllm/assets/video.py
@@ -139,6 +139,6 @@ def get_audio(self, sampling_rate: float | None = None) -> npt.NDArray:
         """
         Read audio data from the video asset, used in Qwen2.5-Omni examples.
 
-        See also: examples/offline_inference/qwen2_5_omni/only_thinker.py
+        See also: examples/generate/multimodal/qwen2_5_omni/only_thinker.py
         """
         return load_audio_pyav(self.video_path, sr=sampling_rate)[0]
diff --git a/vllm/benchmarks/datasets/datasets.py b/vllm/benchmarks/datasets/datasets.py
index d7ba9d8787ab..b032c0a0d613 100644
--- a/vllm/benchmarks/datasets/datasets.py
+++ b/vllm/benchmarks/datasets/datasets.py
@@ -25,6 +25,7 @@
 from dataclasses import dataclass, replace
 from functools import cache
 from io import BytesIO
+from pathlib import Path
 from tempfile import NamedTemporaryFile
 from typing import Any, cast
 
@@ -1372,26 +1373,6 @@ def sample(
         return samples
 
 
-class _ValidateDatasetArgs(argparse.Action):
-    """Argparse action to validate dataset name and path compatibility."""
-
-    def __call__(self, parser, namespace, values, option_string=None):
-        setattr(namespace, self.dest, values)
-
-        # Get current values of both dataset_name and dataset_path
-        dataset_name = getattr(namespace, "dataset_name", "random")
-        dataset_path = getattr(namespace, "dataset_path", None)
-
-        # Validate the combination
-        if dataset_name == "random" and dataset_path is not None:
-            parser.error(
-                "Cannot use 'random' dataset with --dataset-path. "
-                "Please specify the appropriate --dataset-name (e.g., "
-                "'sharegpt', 'custom', 'sonnet') for your dataset file: "
-                f"{dataset_path}"
-            )
-
-
 def add_dataset_parser(parser: FlexibleArgumentParser):
     parser.add_argument(
         "--trust-remote-code",
@@ -1409,7 +1390,6 @@ def add_dataset_parser(parser: FlexibleArgumentParser):
         "--dataset-name",
         type=str,
         default="random",
-        action=_ValidateDatasetArgs,
         choices=[
             "sharegpt",
             "burstgpt",
@@ -1422,6 +1402,7 @@ def add_dataset_parser(parser: FlexibleArgumentParser):
             "custom_mm",
             "prefix_repetition",
             "spec_bench",
+            "speed_bench",
         ],
         help="Name of the dataset to benchmark on.",
     )
@@ -1434,7 +1415,6 @@ def add_dataset_parser(parser: FlexibleArgumentParser):
         "--dataset-path",
         type=str,
         default=None,
-        action=_ValidateDatasetArgs,
         help="Path to the sharegpt/sonnet dataset or the HF dataset ID if "
         "using HF dataset.",
     )
@@ -1606,6 +1586,36 @@ def add_dataset_parser(parser: FlexibleArgumentParser):
         "repetition dataset.",
     )
 
+    speed_bench_group = parser.add_argument_group(
+        "speed bench dataset options", description=SpeedBench.__doc__
+    )
+    speed_bench_group.add_argument(
+        "--speed-bench-dataset-subset",
+        type=str,
+        default="qualitative",
+        choices={
+            "qualitative",
+            "throughput_1k",
+            "throughput_2k",
+            "throughput_8k",
+            "throughput_16k",
+            "throughput_32k",
+        },
+        help="Subset of the SPEED-Bench dataset.",
+    )
+    speed_bench_group.add_argument(
+        "--speed-bench-output-len",
+        type=int,
+        default=4096,
+        help="Num of output tokens per request, used only for speed bench dataset.",
+    )
+    speed_bench_group.add_argument(
+        "--speed-bench-category",
+        type=str,
+        default=None,
+        help="Category for speed bench dataset. If None, use all categories.",
+    )
+
 
 def add_random_dataset_base_args(
     parser_or_group: FlexibleArgumentParser | argparse._ArgumentGroup,
@@ -1793,7 +1803,9 @@ def get_samples(args, tokenizer: TokenizerLike) -> list[SampleRequest]:
 
     if args.dataset_name == "custom":
         dataset = CustomDataset(
-            dataset_path=args.dataset_path, disable_shuffle=args.disable_shuffle
+            dataset_path=args.dataset_path,
+            disable_shuffle=args.disable_shuffle,
+            random_seed=args.seed,
         )
         input_requests = dataset.sample(
             num_requests=args.num_prompts,
@@ -1806,7 +1818,9 @@ def get_samples(args, tokenizer: TokenizerLike) -> list[SampleRequest]:
 
     elif args.dataset_name == "custom_mm":
         dataset = CustomMMDataset(
-            dataset_path=args.dataset_path, disable_shuffle=args.disable_shuffle
+            dataset_path=args.dataset_path,
+            disable_shuffle=args.disable_shuffle,
+            random_seed=args.seed,
         )
         input_requests = dataset.sample(
             num_requests=args.num_prompts,
@@ -2074,6 +2088,19 @@ def get_samples(args, tokenizer: TokenizerLike) -> list[SampleRequest]:
                 request_id_prefix=args.request_id_prefix,
                 no_oversample=args.no_oversample,
             ),
+            "speed_bench": lambda: SpeedBench(
+                dataset_path=args.dataset_path,
+                dataset_subset=args.speed_bench_dataset_subset,
+                category=args.speed_bench_category,
+                disable_shuffle=args.disable_shuffle,
+            ).sample(
+                num_requests=args.num_prompts,
+                tokenizer=tokenizer,
+                output_len=args.speed_bench_output_len,
+                enable_multimodal_chat=args.enable_multimodal_chat,
+                request_id_prefix=args.request_id_prefix,
+                no_oversample=args.no_oversample,
+            ),
         }
 
         try:
@@ -2342,6 +2369,7 @@ def load_data(self) -> None:
             random.shuffle(self.data)
 
     def sample(
+        self,
         **kwargs,
     ) -> list[SampleRequest]:
         # leverage CustomDataset sample
@@ -3551,3 +3579,63 @@ def sample(
             sampled_requests, num_requests, request_id_prefix, no_oversample
         )
         return sampled_requests
+
+
+# -----------------------------------------------------------------------------
+# Speed Bench Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class SpeedBench(CustomDataset):
+    """
+    SPEED-Bench dataset: https://huggingface.co/datasets/nvidia/SPEED-Bench
+
+    Download the dataset using:
+
+    `curl -LsSf https://raw.githubusercontent.com/NVIDIA-NeMo/Skills/refs/heads/main/nemo_skills/dataset/speed-bench/prepare.py | python3 -`
+    """  # noqa: E501
+
+    DOWNLOAD_SCRIPT_URL = "https://raw.githubusercontent.com/NVIDIA-NeMo/Skills/refs/heads/main/nemo_skills/dataset/speed-bench/prepare.py"
+
+    def __init__(self, **kwargs) -> None:
+        self.dataset_subset = kwargs.pop("dataset_subset", "qualitative")
+        self.category = kwargs.pop("category", None)
+        super().__init__(**kwargs)
+        self.load_data()
+
+    def load_data(self) -> None:
+        if self.dataset_path is None:
+            raise ValueError("dataset_path must be provided for loading data.")
+
+        if not Path(self.dataset_path).is_dir():
+            raise ValueError(
+                f"dataset_path {self.dataset_path} is not a directory. "
+                f"Please make sure to download the dataset from HuggingFace using "
+                f"`curl -LsSf {self.DOWNLOAD_SCRIPT_URL} | python3 -`"
+            )
+
+        self.data = []
+
+        # Load the JSONL file
+        jsonl_data = pd.read_json(
+            path_or_buf=Path(self.dataset_path) / f"{self.dataset_subset}.jsonl",
+            lines=True,
+        )
+
+        # check if the JSONL file has a 'turns' column
+        if "messages" not in jsonl_data.columns:
+            raise ValueError(
+                "JSONL file must contain a 'messages' column. "
+                "Please make sure to download the dataset from HuggingFace using "
+                f"`curl -LsSf {self.DOWNLOAD_SCRIPT_URL} | python3 -`"
+            )
+
+        for _, row in jsonl_data.iterrows():
+            # sample only from a specific category if specified
+            if (not self.category) or (self.category == row["category"]):
+                prompt = row["messages"][0]["content"]
+                self.data.append({"prompt": prompt})
+
+        random.seed(self.random_seed)
+        if not getattr(self, "disable_shuffle", False):
+            random.shuffle(self.data)
diff --git a/vllm/benchmarks/lib/endpoint_request_func.py b/vllm/benchmarks/lib/endpoint_request_func.py
index 61af098f80d3..9c85ad686d3e 100644
--- a/vllm/benchmarks/lib/endpoint_request_func.py
+++ b/vllm/benchmarks/lib/endpoint_request_func.py
@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """The request function for API endpoints."""
 
+import codecs
 import io
 import json
 import os
@@ -25,11 +26,12 @@ class StreamedResponseHandler:
 
     def __init__(self):
         self.buffer = ""
+        self._decoder = codecs.getincrementaldecoder("utf-8")()
 
     def add_chunk(self, chunk_bytes: bytes) -> list[str]:
         """Add a chunk of bytes to the buffer and return any complete
         messages."""
-        chunk_str = chunk_bytes.decode("utf-8")
+        chunk_str = self._decoder.decode(chunk_bytes)
         self.buffer += chunk_str
 
         messages = []
diff --git a/vllm/benchmarks/serve.py b/vllm/benchmarks/serve.py
index 980ed1d412f4..2c39e7d1878f 100644
--- a/vllm/benchmarks/serve.py
+++ b/vllm/benchmarks/serve.py
@@ -128,26 +128,30 @@ async def fetch_spec_decode_metrics(
                     continue
 
                 if line.startswith("vllm:spec_decode"):
+                    # Extract metric name (before labels) to avoid matching
+                    # substrings inside label values.
+                    parts = line.split(None, 1)
+                    metric_name = parts[0].split("{")[0]
+                    if not metric_name.endswith("_total"):
+                        continue
                     found_spec_decode = True
-                    parts = line.split()
-                    if parts:
-                        with contextlib.suppress(ValueError):
-                            if "num_drafts" in line:
-                                num_drafts += int(float(parts[-1]))
-                            elif "num_draft_tokens" in line:
-                                num_draft_tokens += int(float(parts[-1]))
-                            elif "num_accepted_tokens_per_pos" in line:
-                                pos_label = 'position="'
-                                if pos_label in line:
-                                    start = line.index(pos_label) + len(pos_label)
-                                    end = line.index('"', start)
-                                    pos = int(line[start:end])
-                                    val = int(float(parts[-1]))
-                                    accepted_per_pos[pos] = (
-                                        accepted_per_pos.get(pos, 0) + val
-                                    )
-                            elif "num_accepted_tokens" in line:
-                                num_accepted_tokens += int(float(parts[-1]))
+                    with contextlib.suppress(ValueError):
+                        if "num_drafts" in metric_name:
+                            num_drafts += int(float(parts[-1]))
+                        elif "num_draft_tokens" in metric_name:
+                            num_draft_tokens += int(float(parts[-1]))
+                        elif "num_accepted_tokens_per_pos" in metric_name:
+                            pos_label = 'position="'
+                            if pos_label in line:
+                                start = line.index(pos_label) + len(pos_label)
+                                end = line.index('"', start)
+                                pos = int(line[start:end])
+                                val = int(float(parts[-1]))
+                                accepted_per_pos[pos] = (
+                                    accepted_per_pos.get(pos, 0) + val
+                                )
+                        elif "num_accepted_tokens" in metric_name:
+                            num_accepted_tokens += int(float(parts[-1]))
 
             if not found_spec_decode:
                 return None
@@ -1611,14 +1615,12 @@ def add_cli_args(parser: argparse.ArgumentParser):
     )
     parser.add_argument(
         "--timeline-itl-thresholds",
-        type=float,
-        nargs=2,
-        default=[25.0, 50.0],
-        metavar=("THRESHOLD1", "THRESHOLD2"),
+        type=str,
+        default="25,50",
         help="ITL thresholds in milliseconds for timeline plot coloring. "
-        "Specify two values to categorize inter-token latencies into three groups: "
-        "below first threshold (green), between thresholds (orange), "
-        "and above second threshold (red). Default: 25 50 (milliseconds).",
+        "Specify two comma-separated values to categorize inter-token "
+        "latencies into three groups: below first threshold (green), "
+        "between thresholds (orange), and above second threshold (red).",
     )
     parser.add_argument(
         "--plot-dataset-stats",
@@ -1637,6 +1639,19 @@ async def main_async(args: argparse.Namespace) -> dict[str, Any]:
     random.seed(args.seed)
     np.random.seed(args.seed)
 
+    # Validate timeline ITL thresholds
+    if args.plot_timeline:
+        try:
+            itl_thresholds = [
+                float(t.strip()) for t in args.timeline_itl_thresholds.split(",")
+            ]
+            if len(itl_thresholds) != 2:
+                raise ValueError(
+                    f"Expected 2 ITL threshold values, got {len(itl_thresholds)}"
+                )
+        except ValueError as e:
+            raise ValueError(f"Invalid --timeline-itl-thresholds format: {e}") from e
+
     # Validate ramp-up arguments
     if args.ramp_up_strategy is not None:
         if args.request_rate != float("inf"):
@@ -1711,12 +1726,25 @@ async def main_async(args: argparse.Namespace) -> dict[str, Any]:
             trust_remote_code=args.trust_remote_code,
         )
 
+    # Validate dataset name/path
     if args.dataset_name is None:
         raise ValueError(
             "Please specify '--dataset-name' and the corresponding "
             "'--dataset-path' if required."
         )
 
+    if (
+        args.dataset_name
+        in ["random", "random-mm", "random-rerank", "prefix_repetition"]
+        and args.dataset_path is not None
+    ):
+        raise ValueError(
+            f"Cannot use '{args.dataset_name}' dataset with --dataset-path. "
+            "Please specify the appropriate --dataset-name (e.g., "
+            "'sharegpt', 'custom', 'sonnet') for your dataset file: "
+            f"{args.dataset_path}"
+        )
+
     # Map general --input-len and --output-len to all dataset-specific arguments
     if args.input_len is not None:
         args.random_input_len = args.input_len
@@ -1893,7 +1921,9 @@ async def main_async(args: argparse.Namespace) -> dict[str, Any]:
 
                 timeline_path = Path(file_name).with_suffix(".timeline.html")
                 # Convert thresholds from milliseconds to seconds
-                itl_thresholds_sec = [t / 1000.0 for t in args.timeline_itl_thresholds]
+                itl_thresholds_sec = [
+                    float(t) / 1000.0 for t in args.timeline_itl_thresholds.split(",")
+                ]
                 generate_timeline_plot(
                     per_request_data, timeline_path, itl_thresholds=itl_thresholds_sec
                 )
diff --git a/vllm/benchmarks/sweep/plot.py b/vllm/benchmarks/sweep/plot.py
index 156e18f697f0..2d3692804446 100644
--- a/vllm/benchmarks/sweep/plot.py
+++ b/vllm/benchmarks/sweep/plot.py
@@ -8,7 +8,7 @@
 from functools import partial
 from pathlib import Path
 from types import TracebackType
-from typing import ClassVar
+from typing import TYPE_CHECKING, ClassVar
 
 from typing_extensions import Self, override
 
@@ -17,20 +17,8 @@
 
 from .utils import sanitize_filename
 
-try:
-    import matplotlib.pyplot as plt
-except ImportError:
-    plt = PlaceholderModule("matplotlib").placeholder_attr("pyplot")
-
-try:
+if TYPE_CHECKING:
     import pandas as pd
-except ImportError:
-    pd = PlaceholderModule("pandas")
-
-try:
-    import seaborn as sns
-except ImportError:
-    seaborn = PlaceholderModule("seaborn")
 
 
 @dataclass
@@ -265,6 +253,20 @@ def _plot_fig(
     fig_height: float,
     fig_dpi: int,
 ):
+    # Lazy-import matplotlib/pandas/seaborn
+    try:
+        import matplotlib.pyplot as plt
+    except ImportError:
+        plt = PlaceholderModule("matplotlib").placeholder_attr("pyplot")
+    try:
+        import pandas as pd
+    except ImportError:
+        pd = PlaceholderModule("pandas")
+    try:
+        import seaborn as sns
+    except ImportError:
+        sns = PlaceholderModule("seaborn")
+
     fig_group, fig_data = fig_group_data
 
     row_groups = full_groupby(
diff --git a/vllm/benchmarks/sweep/plot_pareto.py b/vllm/benchmarks/sweep/plot_pareto.py
index 365e87f757d1..8ec309a7a106 100644
--- a/vllm/benchmarks/sweep/plot_pareto.py
+++ b/vllm/benchmarks/sweep/plot_pareto.py
@@ -6,7 +6,7 @@
 from dataclasses import dataclass
 from functools import partial
 from pathlib import Path
-from typing import ClassVar
+from typing import TYPE_CHECKING, ClassVar
 
 from vllm.utils.collection_utils import full_groupby
 from vllm.utils.import_utils import PlaceholderModule
@@ -14,20 +14,8 @@
 from .plot import DummyExecutor, _json_load_bytes
 from .utils import sanitize_filename
 
-try:
-    import matplotlib.pyplot as plt
-except ImportError:
-    plt = PlaceholderModule("matplotlib").placeholder_attr("pyplot")
-
-try:
+if TYPE_CHECKING:
     import pandas as pd
-except ImportError:
-    pd = PlaceholderModule("pandas")
-
-try:
-    import seaborn as sns
-except ImportError:
-    seaborn = PlaceholderModule("seaborn")
 
 
 def _first_present(run_data: dict[str, object], keys: list[str]):
@@ -195,6 +183,20 @@ def _plot_fig(
         print("[END FIGURE]")
         return
 
+    # Lazy-import matplotlib/pandas/seaborn
+    try:
+        import matplotlib.pyplot as plt
+    except ImportError:
+        plt = PlaceholderModule("matplotlib").placeholder_attr("pyplot")
+    try:
+        import pandas as pd
+    except ImportError:
+        pd = PlaceholderModule("pandas")
+    try:
+        import seaborn as sns
+    except ImportError:
+        sns = PlaceholderModule("seaborn")
+
     df = pd.DataFrame.from_records(fig_data)
     df = df.dropna(subset=["tokens_per_user", "tokens_per_gpu"])
 
diff --git a/vllm/benchmarks/throughput.py b/vllm/benchmarks/throughput.py
index 42a8132ffe6e..1c3b728fdbef 100644
--- a/vllm/benchmarks/throughput.py
+++ b/vllm/benchmarks/throughput.py
@@ -393,29 +393,48 @@ def get_requests(args, tokenizer):
     elif args.dataset_name == "hf":
         if args.output_len is not None:
             sample_kwargs["output_len"] = args.output_len
-        if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
+        common_kwargs["hf_name"] = args.hf_name
+        if (
+            args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS
+            or args.hf_name in VisionArenaDataset.SUPPORTED_DATASET_PATHS
+        ):
             dataset_cls = VisionArenaDataset
             common_kwargs["dataset_subset"] = None
             common_kwargs["dataset_split"] = "train"
             sample_kwargs["enable_multimodal_chat"] = True
-        elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
+        elif (
+            args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS
+            or args.hf_name in InstructCoderDataset.SUPPORTED_DATASET_PATHS
+        ):
             dataset_cls = InstructCoderDataset
             common_kwargs["dataset_split"] = "train"
-        elif args.dataset_path in MultiModalConversationDataset.SUPPORTED_DATASET_PATHS:
+        elif (
+            args.dataset_path in MultiModalConversationDataset.SUPPORTED_DATASET_PATHS
+            or args.hf_name in MultiModalConversationDataset.SUPPORTED_DATASET_PATHS
+        ):
             dataset_cls = MultiModalConversationDataset
             common_kwargs["dataset_subset"] = args.hf_subset
             common_kwargs["dataset_split"] = args.hf_split
             sample_kwargs["enable_multimodal_chat"] = True
-        elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
+        elif (
+            args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS
+            or args.hf_name in ConversationDataset.SUPPORTED_DATASET_PATHS
+        ):
             dataset_cls = ConversationDataset
             common_kwargs["dataset_subset"] = args.hf_subset
             common_kwargs["dataset_split"] = args.hf_split
             sample_kwargs["enable_multimodal_chat"] = True
-        elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
+        elif (
+            args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS
+            or args.hf_name in AIMODataset.SUPPORTED_DATASET_PATHS
+        ):
             dataset_cls = AIMODataset
             common_kwargs["dataset_subset"] = None
             common_kwargs["dataset_split"] = "train"
-        elif args.dataset_path in ASRDataset.SUPPORTED_DATASET_PATHS:
+        elif (
+            args.dataset_path in ASRDataset.SUPPORTED_DATASET_PATHS
+            or args.hf_name in ASRDataset.SUPPORTED_DATASET_PATHS
+        ):
             dataset_cls = ASRDataset
             common_kwargs["dataset_subset"] = args.hf_subset
             common_kwargs["dataset_split"] = args.hf_split
@@ -557,6 +576,10 @@ def validate_args(args):
             VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
             | MultiModalConversationDataset.SUPPORTED_DATASET_PATHS
             | ConversationDataset.SUPPORTED_DATASET_PATHS
+        ) or args.hf_name in (
+            VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
+            | MultiModalConversationDataset.SUPPORTED_DATASET_PATHS
+            | ConversationDataset.SUPPORTED_DATASET_PATHS
         ):
             assert args.backend == "vllm-chat", (
                 f"{args.dataset_path} needs to use vllm-chat as the backend."
@@ -565,6 +588,10 @@ def validate_args(args):
             InstructCoderDataset.SUPPORTED_DATASET_PATHS
             | AIMODataset.SUPPORTED_DATASET_PATHS
             | ASRDataset.SUPPORTED_DATASET_PATHS
+        ) or args.hf_name in (
+            InstructCoderDataset.SUPPORTED_DATASET_PATHS
+            | AIMODataset.SUPPORTED_DATASET_PATHS
+            | ASRDataset.SUPPORTED_DATASET_PATHS
         ):
             assert args.backend == "vllm", (
                 f"{args.dataset_path} needs to use vllm as the backend."
@@ -795,7 +822,7 @@ def add_cli_args(parser: argparse.ArgumentParser):
         "context in a request (default: 0).",
     )
 
-    # hf dtaset
+    # hf dataset
     parser.add_argument(
         "--hf-subset",
         type=str,
@@ -808,6 +835,17 @@ def add_cli_args(parser: argparse.ArgumentParser):
         default=None,
         help="Split of the HF dataset.",
     )
+    parser.add_argument(
+        "--hf-name",
+        type=str,
+        default=None,
+        help=(
+            "Name of the dataset on HuggingFace "
+            "(e.g., 'lmms-lab/LLaVA-OneVision-Data'). "
+            "Specify this when --dataset-path is a local filesystem path "
+            "so the benchmark can identify the correct dataset class."
+        ),
+    )
     parser.add_argument(
         "--profile",
         action="store_true",
diff --git a/vllm/compilation/backends.py b/vllm/compilation/backends.py
index 7373ad751171..5a67415f1030 100644
--- a/vllm/compilation/backends.py
+++ b/vllm/compilation/backends.py
@@ -23,6 +23,10 @@
 from torch.fx._lazy_graph_module import _use_lazy_graph_module
 
 import vllm.envs as envs
+from vllm.compilation.codegen import (
+    compile_execution_fn,
+    generate_execution_code,
+)
 from vllm.config import CompilationConfig, CUDAGraphMode, VllmConfig
 from vllm.config.compilation import DynamicShapesType
 from vllm.config.utils import Range, hash_factors
@@ -46,6 +50,7 @@
     should_split,
 )
 from .passes.inductor_pass import InductorPass, pass_context
+from .passes.ir.inplace_functionalization import VllmIRInplaceFunctionalizationPass
 from .passes.pass_manager import PostGradPassManager
 
 logger = init_logger(__name__)
@@ -275,6 +280,7 @@ def compile(
         compilation_counter.num_backend_compilations += 1
 
         compiled_graph = None
+        handle = None
 
         # try to load from the cache
         compiled_graph = self.load(graph, example_inputs, graph_index, compile_range)
@@ -283,16 +289,11 @@ def compile(
                 # after loading the last graph for this shape, record the time.
                 # there can be multiple graphs due to piecewise compilation.
                 elapsed = time.perf_counter() - compilation_start_time
-                if is_encoder:
-                    compilation_config.encoder_compilation_time += elapsed
-                else:
-                    compilation_config.compilation_time += elapsed
                 logger.info_once(
                     "Directly load the compiled graph(s) for compile range %s "
                     "from the cache, took %.3f s",
                     str(compile_range),
                     elapsed,
-                    scope="local",
                 )
             return compiled_graph
 
@@ -358,7 +359,7 @@ def autograd_cache_key(*args, **kwargs):
                     )
                 except StopCompiling:
                     assert cache_key is not None
-                    return self.loaded_artifacts[cache_key]
+                    compiled_graph = self.loaded_artifacts[cache_key]
             if cache_key is not None and compiled_graph is not None:
                 self.loaded_artifacts[cache_key] = compiled_graph
 
@@ -377,7 +378,6 @@ def autograd_cache_key(*args, **kwargs):
                 logger.info_once(
                     "Cache the graph of compile range %s for later use",
                     str(compile_range),
-                    scope="local",
                 )
             logger.debug_once(
                 "Store the %s-th graph for compile range%s from %s via handle %s",
@@ -385,21 +385,15 @@ def autograd_cache_key(*args, **kwargs):
                 str(compile_range),
                 self.compiler.name,
                 handle,
-                scope="local",
             )
 
         # after compiling the last graph, record the end time
         if graph_index == num_graphs - 1:
             elapsed = time.perf_counter() - compilation_start_time
-            if is_encoder:
-                compilation_config.encoder_compilation_time += elapsed
-            else:
-                compilation_config.compilation_time += elapsed
             logger.info_once(
                 "Compiling a graph for compile range %s takes %.2f s",
                 str(compile_range),
                 elapsed,
-                scope="local",
             )
 
         return compiled_graph
@@ -933,6 +927,24 @@ def collect_standalone_compile_artifacts(
         return standalone_compile_artifacts, sym_shape_indices_map, returns_tuple_map
 
     def configure_post_pass(self) -> None:
+        # TODO proper PassManager?
+        pre_grad_pass_key = "pre_grad_custom_pass"
+        assert self.pass_key != pre_grad_pass_key
+        assert pre_grad_pass_key not in self.inductor_config
+        self.inductor_config[pre_grad_pass_key] = VllmIRInplaceFunctionalizationPass(
+            self.vllm_config
+        )
+
+        # Make sure pre_grad_custom_pass is not pickled
+        # as part of AOTAutograd built-in cache key
+        # TODO(luka) is there a cleaner way to do this
+        import torch._inductor.config as inductor_config
+
+        ignore = inductor_config._cache_config_ignore_prefix + [pre_grad_pass_key]
+        assert "_cache_config_ignore_prefix" not in self.inductor_config
+        self.inductor_config["_cache_config_ignore_prefix"] = ignore
+
+        # Configure the (nominally post-grad) pass manager
         self.pass_manager.configure(self.vllm_config)
 
         # Post-grad custom passes are run using the post_grad_custom_post_pass
@@ -1072,12 +1084,11 @@ def __call__(self, graph: fx.GraphModule, example_inputs: Sequence[Any]) -> Any:
         disable_cache = disable_cache or is_ngram_gpu_enabled
 
         if disable_cache:
-            logger.info_once("vLLM's torch.compile cache is disabled.", scope="local")
+            logger.info_once("vLLM's torch.compile cache is disabled.")
         else:
             logger.info_once(
                 "Using cache directory: %s for vLLM's torch.compile",
                 local_cache_dir,
-                scope="local",
             )
 
         self.compiler_manager.initialize_cache(
@@ -1135,12 +1146,9 @@ def __call__(self, graph: fx.GraphModule, example_inputs: Sequence[Any]) -> Any:
 
         dynamo_time = time.perf_counter() - torch_compile_start_time
         logger.info_once(
-            "Dynamo bytecode transform time: %.2f s", dynamo_time, scope="local"
+            "Dynamo bytecode transform time: %.2f s",
+            dynamo_time,
         )
-        if self.is_encoder:
-            self.compilation_config.encoder_compilation_time += dynamo_time
-        else:
-            self.compilation_config.compilation_time += dynamo_time
 
         # Record Dynamo time in tracing if available
         start_time = int(torch_compile_start_time * 1e9)
@@ -1215,7 +1223,6 @@ def __call__(self, graph: fx.GraphModule, example_inputs: Sequence[Any]) -> Any:
             logger.info_once(
                 "Saved compiler manager cache in %.2f seconds.",
                 elapsed,
-                scope="local",
             )
 
         from torch._guards import detect_fake_mode
@@ -1254,15 +1261,25 @@ def __call__(self, graph: fx.GraphModule, example_inputs: Sequence[Any]) -> Any:
             with open(graph_path, "w") as f:
                 f.write(src)
 
-            logger.debug_once(
-                "Computation graph saved to %s", graph_path, scope="local"
-            )
+            logger.debug_once("Computation graph saved to %s", graph_path)
 
         self._called = True
         graph_to_serialize = (
             original_split_gm if envs.VLLM_USE_MEGA_AOT_ARTIFACT else self.graph
         )
 
+        execution_code, submod_names, consts = generate_execution_code(self.split_gm)
+        # Use getattr to get correct callables: __dict__ has PiecewiseBackend
+        # instances (from PiecewiseCompileInterpreter), _modules has originals.
+        # getattr checks __dict__ first, then falls back to _modules.
+        submod_callables = {
+            name: getattr(self.split_gm, name)
+            for name, _ in self.split_gm.named_children()
+        }
+        runtime_callable = compile_execution_fn(
+            execution_code, submod_callables, submod_names, consts
+        )
+
         if (
             self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE
             or not self.compilation_config.cudagraph_copy_inputs
@@ -1271,9 +1288,12 @@ def __call__(self, graph: fx.GraphModule, example_inputs: Sequence[Any]) -> Any:
                 graph_to_serialize,
                 example_inputs,
                 self.prefix,
-                self.split_gm,
+                runtime_callable,
                 is_encoder=self.is_encoder,
                 vllm_backend=self,
+                execution_code=execution_code,
+                submod_names=submod_names,
+                consts=consts,
             )
 
         # index of tensors that have symbolic shapes (batch size)
@@ -1294,7 +1314,7 @@ def __call__(self, graph: fx.GraphModule, example_inputs: Sequence[Any]) -> Any:
         copy_and_call = make_copy_and_call(
             sym_tensor_indices,
             [example_inputs[x].clone() for x in sym_tensor_indices],
-            self.split_gm,
+            runtime_callable,
         )
 
         return VllmSerializableFunction(
@@ -1305,4 +1325,7 @@ def __call__(self, graph: fx.GraphModule, example_inputs: Sequence[Any]) -> Any:
             is_encoder=self.is_encoder,
             vllm_backend=self,
             sym_tensor_indices=sym_tensor_indices,
+            execution_code=execution_code,
+            submod_names=submod_names,
+            consts=consts,
         )
diff --git a/vllm/compilation/caching.py b/vllm/compilation/caching.py
index c089f02a37ff..62da2d9de35b 100644
--- a/vllm/compilation/caching.py
+++ b/vllm/compilation/caching.py
@@ -16,6 +16,7 @@
 from torch.utils import _pytree as pytree
 
 import vllm.envs as envs
+from vllm.compilation.codegen import compile_execution_fn
 from vllm.compilation.compiler_interface import get_inductor_factors
 from vllm.compilation.counter import compilation_counter
 from vllm.config import VllmConfig, get_current_vllm_config
@@ -176,7 +177,7 @@ class VllmSerializableFunction(SerializableCallable):  # type: ignore[misc]
 
     def __init__(
         self,
-        graph_module: torch.fx.GraphModule,
+        graph_module: torch.fx.GraphModule | bytes,
         example_inputs: Sequence[Any],
         prefix: str,
         optimized_call: Callable[..., Any],
@@ -184,8 +185,10 @@ def __init__(
         vllm_backend: Any | None = None,
         sym_tensor_indices: list[int] | None = None,
         aot_autograd_config: dict[str, Any] | None = None,
+        execution_code: str | None = None,
+        submod_names: list[str] | None = None,
+        consts: list[Any] | None = None,
     ) -> None:
-        assert isinstance(graph_module, torch.fx.GraphModule)
         self.graph_module = graph_module
         self.example_inputs = example_inputs
         self.prefix = prefix
@@ -194,6 +197,9 @@ def __init__(
         self.shape_env = None
         self.vllm_backend = vllm_backend
         self.sym_tensor_indices = sym_tensor_indices
+        self.execution_code = execution_code
+        self.submod_names = submod_names
+        self.consts = consts
         self._fake_mode: Any | None = None
 
         import torch._functorch.config as functorch_config
@@ -298,10 +304,6 @@ def deserialize_compile_artifacts(cls, data: bytes) -> "VllmSerializableFunction
         state = pickle.loads(data)
         fake_mode = FakeTensorMode(shape_env=ShapeEnv())
 
-        state["graph_module"] = cls.deserialize_graph_module(
-            state["graph_module"], fake_mode
-        )
-        state["graph_module"].recompile()
         state["example_inputs"] = GraphPickler.loads(state["example_inputs"], fake_mode)
 
         standalone_compile_artifacts = state.pop("standalone_compile_artifacts", None)
@@ -327,6 +329,7 @@ def deserialize_compile_artifacts(cls, data: bytes) -> "VllmSerializableFunction
                     vllm_config=get_current_vllm_config(),
                     sym_shape_indices_map=sym_shape_indices_map,
                     returns_tuple_map=returns_tuple_map,
+                    fake_mode=fake_mode,
                 )
 
             logger.info(
@@ -338,6 +341,11 @@ def deserialize_compile_artifacts(cls, data: bytes) -> "VllmSerializableFunction
 
             return fn
 
+        state["graph_module"] = cls.deserialize_graph_module(
+            state["graph_module"], fake_mode
+        )
+        state["graph_module"].recompile()
+
         # Fall back to standard VllmBackend.
         # Use a lazy closure: the backend needs traced_files for cache
         # dir computation, but those are only populated after
@@ -406,6 +414,7 @@ def reconstruct_serializable_fn_from_mega_artifact(
     vllm_config: VllmConfig,
     sym_shape_indices_map: dict[str, list[int]],
     returns_tuple_map: dict[str, bool],
+    fake_mode: FakeTensorMode,
 ) -> "VllmSerializableFunction":
     """Construct a VllmSerializableFunction from cached inductor artifacts.
 
@@ -448,12 +457,11 @@ def reconstruct_serializable_fn_from_mega_artifact(
 
     prefix = state["prefix"]
     is_encoder = state.get("is_encoder", False)
-    split_gm = state["graph_module"]
     compilation_config = vllm_config.compilation_config
 
     standalone_compile_artifacts.load_all()
 
-    submod_names = standalone_compile_artifacts.submodule_names()
+    piecewise_submod_names = standalone_compile_artifacts.submodule_names()
     compiled_callables: dict[str, dict[str, Callable[..., Any]]] = {}
 
     for cache_key in standalone_compile_artifacts.submodule_bytes:
@@ -472,14 +480,17 @@ def reconstruct_serializable_fn_from_mega_artifact(
     )
 
     # spot check that cached submodules exist in the graph structure
-    graph_children = {name for name, _ in split_gm.named_children()}
-    missing = set(submod_names) - graph_children
+    # if an old cache is used, this will fail but that's fine because
+    # we will just try this error and re-generate the new cache.
+    graph_children = set(state["submod_names"])
+    missing = set(piecewise_submod_names) - graph_children
     assert not missing, (
         f"artifacts reference submodules not in graph: {missing}. "
         f"graph has: {sorted(graph_children)}"
     )
 
-    for i, submod_name in enumerate(submod_names):
+    submod_callables = {}
+    for i, submod_name in enumerate(piecewise_submod_names):
         assert submod_name in sym_shape_indices_map and submod_name in returns_tuple_map
 
         sym_shape_indices = sym_shape_indices_map[submod_name]
@@ -490,7 +501,7 @@ def reconstruct_serializable_fn_from_mega_artifact(
             graph=None,  # not needed for cached artifacts
             vllm_config=vllm_config,
             piecewise_compile_index=i,
-            total_piecewise_compiles=len(submod_names),
+            total_piecewise_compiles=len(piecewise_submod_names),
             sym_shape_indices=sym_shape_indices,
             vllm_backend=vllm_backend,
             returns_tuple=returns_tuple,
@@ -498,7 +509,7 @@ def reconstruct_serializable_fn_from_mega_artifact(
         )
 
         is_first = i == 0
-        is_last = i == len(submod_names) - 1
+        is_last = i == len(piecewise_submod_names) - 1
         wrapped_backend = wrap_with_cudagraph_if_needed(
             piecewise_backend,
             vllm_config,
@@ -507,12 +518,28 @@ def reconstruct_serializable_fn_from_mega_artifact(
             is_last,
         )
 
-        split_gm.__dict__[submod_name] = wrapped_backend
+        submod_callables[submod_name] = wrapped_backend
         logger.debug(
             "Replaced submodule %s with piecewise backend from cache",
             submod_name,
         )
 
+    # Use codegen'd execution code if available, fall back to split_gm
+    execution_code = state.get("execution_code")
+    submod_names = state.get("submod_names")
+    if execution_code is not None and submod_names is not None:
+        consts = state.get("consts")
+        runtime_callable = compile_execution_fn(
+            execution_code, submod_callables, submod_names, consts
+        )
+    else:
+        logger.warning(
+            "No execution code found, falling back to graph module execution."
+        )
+        runtime_callable = GraphPickler.loads(
+            state["graph_module"], fake_mode=fake_mode
+        )
+
     if compilation_config.cudagraph_copy_inputs:
         sym_tensor_indices = state["sym_tensor_indices"]
         input_buffers = [
@@ -521,9 +548,11 @@ def reconstruct_serializable_fn_from_mega_artifact(
             )
             for idx in sym_tensor_indices
         ]
-        optimized_call = make_copy_and_call(sym_tensor_indices, input_buffers, split_gm)
+        optimized_call = make_copy_and_call(
+            sym_tensor_indices, input_buffers, runtime_callable
+        )
     else:
-        optimized_call = split_gm
+        optimized_call = runtime_callable
 
     fn = VllmSerializableFunction(
         **state,
diff --git a/vllm/compilation/codegen.py b/vllm/compilation/codegen.py
new file mode 100644
index 000000000000..67e9ac843a64
--- /dev/null
+++ b/vllm/compilation/codegen.py
@@ -0,0 +1,235 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Code generation for split_gm stitching graph execution.
+
+Generates a plain Python function that replaces the FX GraphModule's
+interpreter-based execution of the stitching graph, eliminating
+nn.Module.__call__ overhead and __getattr__ dispatch.
+"""
+
+import operator
+from collections.abc import Callable
+from functools import partial
+from typing import Any
+
+import torch.fx
+from torch._dynamo.utils import dynamo_timed
+from torch._logging import trace_structured
+from torch.fx.node import _get_qualified_name
+
+
+def generate_execution_code_with_name(
+    split_gm: torch.fx.GraphModule,
+    fn_name: str,
+    with_submod: bool,
+    consts: list[Any] | None = None,
+    const_index: dict[int, int] | None = None,
+) -> tuple[str, list[str], list[Any]]:
+    lines: list[str] = []
+    param_names: list[str] = []
+    submod_names: list[str] = []
+    submod_index: dict[str, int] = {}
+    if consts is None:
+        consts = []
+    if const_index is None:
+        const_index = {}
+
+    # Build node ordering for liveness analysis.
+    nodes = list(split_gm.graph.nodes)
+    node_order = {node: i for i, node in enumerate(nodes)}
+    inlined_submods: list[str] = []
+
+    # For each value-producing node, find the position of its last consumer.
+    # If the last consumer is the output node, skip (return handles cleanup).
+    # Otherwise, schedule a del after that consumer to free memory early.
+    del_after: dict[int, list[str]] = {}  # position -> names to delete
+    for node in nodes:
+        if node.op == "output":
+            continue
+        users = list(node.users.keys())
+        if not users:
+            continue
+        last_user = max(users, key=lambda u: node_order[u])
+        if last_user.op == "output":
+            continue
+        del_after.setdefault(node_order[last_user], []).append(node.name)
+
+    def ref(arg: Any) -> str:
+        return _node_ref(arg, consts, const_index)
+
+    for i, node in enumerate(nodes):
+        if node.op == "placeholder":
+            param_names.append(node.name)
+
+        elif node.op == "call_module":
+            target = node.target
+            if not with_submod:
+                raise RuntimeError(
+                    f"call_module is not allowed for codegen target {target}."
+                )
+            if target not in submod_index:
+                submod_index[target] = len(submod_names)
+                submod_names.append(target)
+            idx = submod_index[target]
+            args_str = ", ".join(ref(a) for a in node.args)
+            kwargs_str = ", ".join(f"{k}={ref(v)}" for k, v in node.kwargs.items())
+            all_args = ", ".join(filter(None, [args_str, kwargs_str]))
+            submod = getattr(split_gm, target)
+            if isinstance(submod, torch.fx.GraphModule):
+                callable_name = f"__vllm_inlined_submods__{idx}"
+                inlined_code, _, _ = generate_execution_code_with_name(
+                    submod,
+                    callable_name,
+                    with_submod=False,
+                    consts=consts,
+                    const_index=const_index,
+                )
+                inlined_submods.append(inlined_code)
+            else:
+                callable_name = f"__vllm_submods__[{idx}]"
+            lines.append(f"    {node.name} = {callable_name}({all_args})")
+
+        elif node.op == "call_function":
+            if node.target is operator.getitem:
+                source = ref(node.args[0])
+                index = node.args[1]
+                assert isinstance(index, int)
+                lines.append(f"    {node.name} = {source}[{index}]")
+            else:
+                args_str = ", ".join(ref(a) for a in node.args)
+                kwargs_str = ", ".join(f"{k}={ref(v)}" for k, v in node.kwargs.items())
+                all_args = ", ".join(filter(None, [args_str, kwargs_str]))
+                lines.append(
+                    f"    {node.name} = {_get_qualified_name(node.target)}({all_args})"
+                )
+
+        elif node.op == "output":
+            assert len(node.args) == 1
+            ret = ref(node.args[0])
+            lines.append(f"    return {ret}")
+
+        else:
+            raise RuntimeError(f"Unsupported node from codegen: {node.format_node()}")
+
+        # Emit del for variables whose last use was this node.
+        if i in del_after and i < len(nodes) - 2:
+            names = sorted(del_after[i])
+            lines.append(f"    del {', '.join(names)}")
+
+    assert len(param_names) > 0
+    params = ", ".join(param_names)
+    kw_params = ", *, __vllm_submods__" if with_submod else ""
+    header = f"\ndef {fn_name}({params}{kw_params}):"
+    return (
+        "".join(inlined_submods) + "\n".join([header] + lines) + "\n",
+        submod_names,
+        consts,
+    )
+
+
+@dynamo_timed("vllm.generate_execution_code")
+def generate_execution_code(
+    split_gm: torch.fx.GraphModule,
+) -> tuple[str, list[str], list[Any]]:
+    """Generate Python source code from a split_gm's stitching graph.
+
+    Walks split_gm.graph.nodes and produces a function that calls
+    submodules via a __vllm_submods__ list, avoiding FX GraphModule overhead
+    and dict lookup cost.
+
+    Non-primitive constant arguments (e.g. torch.device, DTensor placement
+    types) are collected into a constants list and referenced by index
+    in the generated code, avoiding reliance on repr() being eval-able.
+
+    If a submodule is a plain torch.fx.GraphModule, it is inlined directly
+    in the generated code and we do not need to serialize it in the artifact.
+
+    Args:
+        split_gm: The split graph module produced by split_graph().
+
+    Returns:
+        A tuple of (code, submod_names, consts) where code is the Python
+        source, submod_names is the ordered list of submodule target names
+        corresponding to list indices used in the generated code, and
+        consts is a list of non-primitive constant objects referenced
+        by the generated code via __vllm_consts__. These objects are
+        kept alive for the lifetime of the compiled function.
+    """
+    code, submod_names, consts = generate_execution_code_with_name(
+        split_gm, "execution_fn", with_submod=True
+    )
+    return "import torch\nimport operator\n" + code, submod_names, consts
+
+
+@dynamo_timed("vllm.compile_execution_fn")
+def compile_execution_fn(
+    code: str,
+    submod_callables: dict[str, Callable[..., Any]],
+    submod_names: list[str],
+    consts: list[Any] | None = None,
+) -> Callable[..., Any]:
+    """Compile execution code and bind submodule callables.
+
+    Args:
+        code: Python source from generate_execution_code().
+        submod_callables: Mapping of submodule names to their callables.
+        submod_names: Ordered list of submodule names matching the indices
+            used in the generated code.
+        consts: List of non-primitive constant objects referenced by the
+            generated code via __vllm_consts__. None for legacy cached
+            code that predates this feature.
+
+    Returns:
+        A callable that executes the stitching logic.
+    """
+    trace_structured(
+        "artifact",
+        metadata_fn=lambda: {
+            "name": "vllm_execution_code",
+            "encoding": "string",
+        },
+        payload_fn=lambda: code,
+    )
+    namespace: dict[str, Any] = {}
+    if consts is not None:
+        namespace["__vllm_consts__"] = consts
+    exec(code, namespace)  # noqa: S102
+    fn = namespace["execution_fn"]
+    # Using .get() is intentional here because only piecewise backend will
+    # be stored in submod_callables. The other submodules are inlined and
+    # we don't need to bind them to the execution function. Instead, we
+    # should use None as placeholder to ensure the list indices are preserved
+    # for better debuggability.
+    submods_list = [submod_callables.get(name) for name in submod_names]
+    return partial(fn, __vllm_submods__=submods_list)
+
+
+def _node_ref(arg: Any, consts: list[Any], const_index: dict[int, int]) -> str:
+    """Convert an FX node argument to a source code reference."""
+    if isinstance(arg, torch.fx.Node):
+        return arg.name
+    if isinstance(arg, list):
+        return f"[{', '.join(_node_ref(x, consts, const_index) for x in arg)}]"
+    if isinstance(arg, tuple):
+        items = ", ".join(_node_ref(x, consts, const_index) for x in arg)
+        return f"({items},)" if len(arg) == 1 else f"({items})"
+    if isinstance(arg, dict):
+        return (
+            "{"
+            + ", ".join(
+                f"{_node_ref(k, consts, const_index)}: "
+                f"{_node_ref(v, consts, const_index)}"
+                for k, v in arg.items()
+            )
+            + "}"
+        )
+    if isinstance(arg, (int, float, bool, str, bytes, type(None))):
+        return repr(arg)
+    # Dedup by identity, not equality: safe because FX graph args
+    # are live for the entire code-generation pass. Objects stored
+    # here must be picklable (for compile-artifact caching).
+    key = id(arg)
+    if key not in const_index:
+        const_index[key] = len(consts)
+        consts.append(arg)
+    return f"__vllm_consts__[{const_index[key]}]"
diff --git a/vllm/compilation/compiler_interface.py b/vllm/compilation/compiler_interface.py
index 79339ebaf8cc..2348ff3191b7 100644
--- a/vllm/compilation/compiler_interface.py
+++ b/vllm/compilation/compiler_interface.py
@@ -141,6 +141,10 @@ class AlwaysHitShapeEnv:
 
     def __init__(self) -> None:
         self.guards: list[Any] = []
+        # Read by torch._inductor.codecache.FxGraphHashDetails (torch>=2.11)
+        # to incorporate user-provided dynamic-shape hint overrides into the
+        # cache key. We never override hints, so an empty dict is correct.
+        self.var_to_hint_override: dict[Any, int] = {}
 
     def evaluate_guards_expression(self, *args: Any, **kwargs: Any) -> Literal[True]:
         return True
@@ -152,6 +156,17 @@ def produce_guards_expression(self, *args: Any, **kwargs: Any) -> Literal[""]:
         return ""
 
 
+def _get_vllm_functorch_config() -> dict[str, Any]:
+    """Return the functorch config overrides that vLLM applies at compile time.
+
+    Used by both set_functorch_config() and get_inductor_factors() to ensure
+    the compile-time config and cache key are always consistent."""
+    cfg: dict[str, Any] = {}
+    if not envs.VLLM_USE_MEGA_AOT_ARTIFACT:
+        cfg["bundled_autograd_cache"] = False
+    return cfg
+
+
 def get_inductor_factors() -> list[Any]:
     factors: list[Any] = []
     # summarize system state
@@ -165,6 +180,13 @@ def get_inductor_factors() -> list[Any]:
 
     torch_factors = torch_key()
     factors.append(torch_factors)
+
+    from torch._functorch import config as functorch_config
+    from torch._inductor import config as inductor_config
+
+    factors.append(inductor_config.save_config_portable())
+    with functorch_config.patch(_get_vllm_functorch_config()):
+        factors.append(functorch_config.save_config_portable())
     return factors
 
 
@@ -295,6 +317,9 @@ def compile(
             },
         }
 
+        if is_torch_equal_or_newer("2.13.0.dev"):
+            compile_kwargs["donate_graph_module"] = True  # type: ignore[assignment]
+
         use_aot: bool = supports_aot and envs.VLLM_USE_MEGA_AOT_ARTIFACT
         # only add 'aot' parameter if both supported and enabled...
         # this will set bundled_autograd_cache
@@ -736,8 +761,8 @@ def set_inductor_config(config: dict[str, Any], compile_range: Range) -> None:
 
 
 def set_functorch_config() -> None:
-    if not envs.VLLM_USE_MEGA_AOT_ARTIFACT:
-        torch._functorch.config.bundled_autograd_cache = False
+    for k, v in _get_vllm_functorch_config().items():
+        setattr(torch._functorch.config, k, v)
 
 
 class EagerAdaptor(CompilerInterface):
diff --git a/vllm/compilation/cuda_graph.py b/vllm/compilation/cuda_graph.py
index 00bf4bbc71f1..b63d86199720 100644
--- a/vllm/compilation/cuda_graph.py
+++ b/vllm/compilation/cuda_graph.py
@@ -290,9 +290,14 @@ def __call__(self, *args: Any, **kwargs: Any) -> Any | None:
                     # across layers will make the cudagraph capture very slow.
                     # therefore, we only run gc for the first graph,
                     # and disable gc for the rest of the graphs.
-                    stack.enter_context(patch("gc.collect", lambda: None))
                     stack.enter_context(
-                        patch("torch.accelerator.empty_cache", lambda: None)
+                        patch("gc.collect", lambda *args, **kwargs: None)
+                    )
+                    stack.enter_context(
+                        patch(
+                            "torch.accelerator.empty_cache",
+                            lambda *args, **kwargs: None,
+                        )
                     )
 
                 if self.graph_pool is not None:
diff --git a/vllm/compilation/decorators.py b/vllm/compilation/decorators.py
index 79daf00de66b..ad710a6fe3fc 100644
--- a/vllm/compilation/decorators.py
+++ b/vllm/compilation/decorators.py
@@ -32,6 +32,9 @@
 
 from .monitor import monitor_profiling_run, monitor_torch_compile
 
+# shape_id parameter was added to mark_unbacked in PyTorch 2.11.0
+_SUPPORTS_SHAPE_ID = is_torch_equal_or_newer("2.11.0")
+
 if TYPE_CHECKING:
     # Only added on nightly/2.10 so wrap
     try:
@@ -89,7 +92,7 @@ def support_torch_compile(
 @overload
 def support_torch_compile(
     *,
-    dynamic_arg_dims: dict[str, int | list[int]] | None,
+    dynamic_arg_dims: dict[str, int | list[int] | dict[int, str]] | None,
 ) -> Callable[[type[_T]], type[_T]]: ...
 
 
@@ -103,7 +106,7 @@ def support_torch_compile(
 @overload
 def support_torch_compile(
     *,
-    dynamic_arg_dims: dict[str, int | list[int]] | None,
+    dynamic_arg_dims: dict[str, int | list[int] | dict[int, str]] | None,
     mark_unbacked_dims: dict[str, int | list[int]] | None,
 ) -> Callable[[type[_T]], type[_T]]: ...
 
@@ -115,11 +118,10 @@ def support_torch_compile(cls: type[_T]) -> type[_T]: ...
 def support_torch_compile(
     cls: type[_T] | None = None,
     *,
-    dynamic_arg_dims: dict[str, int | list[int]] | None = None,
+    dynamic_arg_dims: dict[str, int | list[int] | dict[int, str]] | None = None,
     mark_unbacked_dims: dict[str, int | list[int]] | None = None,
     enable_if: Callable[[VllmConfig], bool] | None = None,
     is_encoder: bool = False,
-    shape_invariants: Callable[..., None] = lambda *args, **kwargs: None,
 ) -> Callable[[type[_T]], type[_T]] | type[_T]:
     """
     A decorator to add support for compiling the forward method of a class.
@@ -141,8 +143,12 @@ def forward(self, x: torch.Tensor, y: Optional[torch.Tensor]): ...
     ```
 
     `dynamic_arg_dims` is a dictionary that maps argument names to the dynamic
-    dimensions of the argument. The dynamic dimensions can be either a single
-    integer or a list of integers.
+    dimensions of the argument. The value can be:
+    - int: a single dimension index (e.g., 0)
+    - list[int]: multiple dimension indices (e.g., [0, 1])
+    - dict[int, str]: dimension to shape_id mapping for shape relations
+      (e.g., {0: "b"}). Dimensions with the same shape_id share the same
+      unbacked symbol.
 
     if `dynamic_arg_dims` is `None`, it is inferred from the type annotation
     of the `forward` method, based on the following default rules:
@@ -189,7 +195,7 @@ def forward(self, x: torch.Tensor, y: Optional[torch.Tensor]): ...
             torch._check(input_ids.size()[0] == inputs_embeds.size()[0])
     This enforces constraints on the symbolic shapes without hardcoding
     specific values. It is needed for some models to avoid data dependent
-    errors.
+    errors and maximize perf when unbacked shapes are used.
     """
 
     def cls_decorator_helper(cls: type[_T]) -> type[_T]:
@@ -229,13 +235,13 @@ def cls_decorator_helper(cls: type[_T]) -> type[_T]:
                 raise ValueError(
                     f"Argument {k} not found in the forward method of {cls}"
                 )
+
         return _support_torch_compile(
             cls,
             inferred_dynamic_arg_dims,
             mark_unbacked_dims,
             enable_if,
             is_encoder,
-            shape_invariants,
         )
 
     if cls is not None:
@@ -285,7 +291,7 @@ def _try_load_aot_compiled_fn(
     Re-raises on failure when ``VLLM_FORCE_AOT_LOAD`` is set.
     """
     try:
-        with monitor_torch_compile(model.vllm_config):
+        with monitor_torch_compile(model.vllm_config, is_encoder=model._is_encoder):
             with (
                 set_current_vllm_config(model.vllm_config),
                 open(aot_compilation_path, "rb") as f,
@@ -324,15 +330,13 @@ def _try_load_aot_compiled_fn(
 
 def _support_torch_compile(
     cls: type[_T],
-    dynamic_arg_dims: dict[str, int | list[int]],
+    dynamic_arg_dims: dict[str, int | list[int] | dict[int, str]],
     mark_unbacked_dims: dict[str, int | list[int]] | None = None,
     enable_if: Callable[[VllmConfig], bool] | None = None,
     is_encoder: bool = False,
-    shape_invariants: Callable[..., None] = lambda *args, **kwargs: None,
 ) -> type[_T]:
-    """
-    A decorator to add support for compiling the forward method of a class.
-    """
+    """Internal implementation of support_torch_compile decorator."""
+
     if TorchCompileWithNoGuardsWrapper in cls.__bases__:
         # support decorating multiple times
         return cls
@@ -392,7 +396,8 @@ def __init__(
         if self.do_not_compile:
             return
 
-        self._check_shape_invariants = shape_invariants
+        self._dynamic_arg_dims = dynamic_arg_dims
+
         self.was_aot_compile_fn_loaded_from_disk = False
         compilation_counter.num_models_seen += 1
         self.compiled = False
@@ -409,48 +414,83 @@ def __init__(
     def _mark_dynamic_inputs(
         mod: type[_T], ds_type: DynamicShapesType, *args: Any, **kwargs: Any
     ) -> None:
-        def mark_dynamic(arg: torch.Tensor, dims: list[int]) -> None:
+        def mark_dynamic(
+            arg: torch.Tensor, dim_shape_pairs: list[tuple[int, str | None]]
+        ) -> None:
             if ds_type == DynamicShapesType.UNBACKED:
                 if is_torch_equal_or_newer("2.10.0"):
-                    for dim in dims:
-                        torch._dynamo.decorators.mark_unbacked(
-                            arg, dim, hint_override=arg.size()[dim]
-                        )
+                    for dim, shape_id in dim_shape_pairs:
+                        if shape_id is not None:
+                            if not _SUPPORTS_SHAPE_ID:
+                                raise RuntimeError(
+                                    f"shape_id='{shape_id}' requires PyTorch >= 2.11.0"
+                                )
+                            torch._dynamo.decorators.mark_unbacked(
+                                arg,
+                                dim,
+                                hint_override=arg.size()[dim],
+                                shape_id=shape_id,
+                            )
+                        else:
+                            torch._dynamo.decorators.mark_unbacked(
+                                arg,
+                                dim,
+                                hint_override=arg.size()[dim],
+                            )
                 else:
+                    # For older versions, we can't use hint_override or shape_id
+                    dims = [dim for dim, _ in dim_shape_pairs]
                     torch._dynamo.decorators.mark_unbacked(arg, dims)
             else:
+                dims = [dim for dim, _ in dim_shape_pairs]
                 torch._dynamo.mark_dynamic(arg, dims)
 
         sig = inspect.signature(mod.__class__.forward)  # type: ignore[attr-defined]
         bound_args = sig.bind(mod, *args, **kwargs)
         bound_args.apply_defaults()
-        for k, dims in dynamic_arg_dims.items():
+
+        # Normalize dynamic_arg_dims to dict[str, dict[int, str | None]]
+        normalized_dims: dict[str, dict[int, str | None]] = {}
+        for k, v in dynamic_arg_dims.items():
+            if isinstance(v, dict):
+                normalized_dims[k] = {dim: shape_id for dim, shape_id in v.items()}
+            elif isinstance(v, int):
+                normalized_dims[k] = {v: None}
+            else:
+                normalized_dims[k] = {d: None for d in v}
+
+        for k, dim_to_shape_id in normalized_dims.items():
             arg = bound_args.arguments.get(k)
 
             if arg is not None:
-                dims = [dims] if isinstance(dims, int) else dims
+                dims = list(dim_to_shape_id.keys())
+
                 if isinstance(arg, torch.Tensor):
-                    # In case dims is specified with negative indexing
-                    dims = [arg.ndim + dim if dim < 0 else dim for dim in dims]
-                    mark_dynamic(arg, dims)
+                    dim_shape_pairs = [
+                        (arg.ndim + d if d < 0 else d, dim_to_shape_id.get(d))
+                        for d in dims
+                    ]
+                    mark_dynamic(arg, dim_shape_pairs)
                 elif isinstance(arg, IntermediateTensors):
                     for tensor in arg.tensors.values():
-                        # In case dims is specified with negative indexing
-                        dims = [tensor.ndim + dim if dim < 0 else dim for dim in dims]
-                        mark_dynamic(tensor, dims)
+                        dim_shape_pairs = [
+                            (tensor.ndim + d if d < 0 else d, dim_to_shape_id.get(d))
+                            for d in dims
+                        ]
+                        mark_dynamic(tensor, dim_shape_pairs)
                 else:
                     raise ValueError(
-                        "Unsupported dynamic dimensions"
-                        f" {dims} for argument {k} with type {type(arg)}."
+                        f"Unsupported dynamic dimensions {dims} "
+                        f"for argument {k} with type {type(arg)}."
                     )
+
         if mark_unbacked_dims:
-            for k, dims in mark_unbacked_dims.items():
+            for k, dims_val in mark_unbacked_dims.items():
                 arg = bound_args.arguments.get(k)
                 if arg is not None:
-                    dims = [dims] if isinstance(dims, int) else dims
+                    dims = [dims_val] if isinstance(dims_val, int) else list(dims_val)
                     if isinstance(arg, torch.Tensor):
-                        # In case dims is specified with negative indexing
-                        dims = [arg.ndim + dim if dim < 0 else dim for dim in dims]
+                        dims = [arg.ndim + d if d < 0 else d for d in dims]
                         if is_torch_equal_or_newer("2.10.0"):
                             for dim in dims:
                                 torch._dynamo.decorators.mark_unbacked(
@@ -617,7 +657,9 @@ def patched_inline_call(self_: Any) -> Any:
                 # store the path for saving after warmup
                 self._aot_compilation_path = aot_compilation_path
                 self._aot_cache_dir = cache_dir
-                with monitor_torch_compile(self.vllm_config):
+                with monitor_torch_compile(
+                    self.vllm_config, is_encoder=self._is_encoder
+                ):
                     self.aot_compiled_fn = self.aot_compile(*args, **kwargs)
                     compilation_counter.num_aot_compiles += 1
                     # All compilation is done at this point, save the
@@ -631,6 +673,7 @@ def patched_inline_call(self_: Any) -> Any:
                     self.vllm_config,
                     "torch.compile and initial profiling/warmup "
                     "run together took %.2f s in total",
+                    is_encoder=self._is_encoder,
                 ):
                     output = TorchCompileWithNoGuardsWrapper.__call__(
                         self,  # type: ignore[arg-type]
@@ -665,7 +708,6 @@ def save_aot_compiled_function(self: type[_T]) -> None:
             logger.info_once(
                 "saved AOT compiled function to %s",
                 self._aot_compilation_path,
-                scope="local",
             )
         except Exception as e:
             logger.warning(
diff --git a/vllm/compilation/monitor.py b/vllm/compilation/monitor.py
index f584f526f08f..c23a8f67228c 100644
--- a/vllm/compilation/monitor.py
+++ b/vllm/compilation/monitor.py
@@ -18,6 +18,7 @@
 def monitor_torch_compile(
     vllm_config: VllmConfig,
     message: str = "torch.compile took %.2f s in total",
+    is_encoder: bool = False,
 ) -> Generator[None, None, None]:
     """Context manager that times torch.compile and manages depyf debugging.
 
@@ -45,7 +46,11 @@ def monitor_torch_compile(
     else:
         total_compile_time = time.perf_counter() - torch_compile_start_time
         if compilation_config.mode == CompilationMode.VLLM_COMPILE:
-            logger.info_once(message, total_compile_time, scope="local")
+            if is_encoder:
+                compilation_config.encoder_compilation_time += total_compile_time
+            else:
+                compilation_config.compilation_time += total_compile_time
+            logger.info_once(message, total_compile_time)
     finally:
         if depyf_cm is not None:
             try:
@@ -76,7 +81,6 @@ def monitor_profiling_run() -> Generator[None, None, None]:
     logger.info_once(
         "Initial profiling/warmup run took %.2f s",
         elapsed,
-        scope="local",
     )
 
 
diff --git a/vllm/compilation/passes/fusion/act_quant_fusion.py b/vllm/compilation/passes/fusion/act_quant_fusion.py
index 3a961cf53488..e35fc5cd4084 100644
--- a/vllm/compilation/passes/fusion/act_quant_fusion.py
+++ b/vllm/compilation/passes/fusion/act_quant_fusion.py
@@ -1,16 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from abc import ABC, abstractmethod
+import itertools
 from typing import Any
 
 import torch
 from torch._higher_order_ops.auto_functionalize import auto_functionalized
-from torch._inductor.pattern_matcher import (
-    PatternMatcherPass,
-    fwd_only,
-    register_replacement,
-)
 from torch._ops import OpOverload
 
 from vllm.config import VllmConfig
@@ -24,8 +19,7 @@
 )
 from vllm.platforms import current_platform
 
-from ..inductor_pass import enable_fake_mode
-from ..vllm_inductor_pass import VllmInductorPass, VllmPatternMatcherPass
+from ..vllm_inductor_pass import VllmFusionPatternMatcherPass, VllmPatternReplacement
 from .matcher_utils import MatcherQuantFP8, MatcherSiluAndMul
 from .rms_quant_fusion import QUANT_OPS, empty_bf16, empty_fp32, empty_i32
 
@@ -50,9 +44,9 @@
     FUSED_OPS[kFp8Dynamic64Sym] = torch.ops._C.silu_and_mul_per_block_quant.default
 
 
-class ActivationQuantPattern(ABC):
+class ActivationQuantPattern(VllmPatternReplacement):
     """
-    The base class for Activation+Quant fusions.
+    Base class for Activation+Quant fusions.
     Should not be used directly.
     """
 
@@ -79,10 +73,6 @@ def empty_quant(self, *args: Any, **kwargs: Any) -> torch.Tensor:
         kwargs = {"dtype": self.quant_dtype, "device": "cuda", **kwargs}
         return torch.empty(*args, **kwargs)
 
-    @abstractmethod
-    def register(self, pm_pass: PatternMatcherPass) -> None:
-        raise NotImplementedError
-
 
 class SiluMulFp8StaticQuantPattern(ActivationQuantPattern):
     """
@@ -100,8 +90,9 @@ def get_inputs(self) -> list[torch.Tensor]:
             scale,
         ]
 
-    def register(self, pm_pass: PatternMatcherPass) -> None:
-        def pattern(
+    @property
+    def pattern(self):
+        def _pattern(
             input: torch.Tensor,
             scale: torch.Tensor,
         ) -> torch.Tensor:
@@ -109,7 +100,11 @@ def pattern(
             result_quant = self.quant_matcher(result_silu_mul, scale)
             return result_quant[0]
 
-        def replacement(
+        return _pattern
+
+    @property
+    def replacement(self):
+        def _replacement(
             input: torch.Tensor,
             scale: torch.Tensor,
         ) -> torch.Tensor:
@@ -123,10 +118,7 @@ def replacement(
             )
             return at[1]
 
-        inps = self.get_inputs()
-        pattern(*inps)
-
-        register_replacement(pattern, replacement, inps, fwd_only, pm_pass)
+        return _replacement
 
 
 class SiluMulNvfp4QuantPattern(ActivationQuantPattern):
@@ -144,8 +136,9 @@ def get_inputs(self) -> list[torch.Tensor]:
         scale = empty_fp32(1, 1)
         return [result, output_scale, input_, scale]
 
-    def register(self, pm_pass: PatternMatcherPass) -> None:
-        def pattern(
+    @property
+    def pattern(self):
+        def _pattern(
             result: torch.Tensor,
             output_scale: torch.Tensor,
             input: torch.Tensor,
@@ -162,7 +155,11 @@ def pattern(
             )
             return at[1], at[2]
 
-        def replacement(
+        return _pattern
+
+    @property
+    def replacement(self):
+        def _replacement(
             result: torch.Tensor,
             output_scale: torch.Tensor,
             input: torch.Tensor,
@@ -177,7 +174,7 @@ def replacement(
             )
             return at[1], at[2]
 
-        register_replacement(pattern, replacement, self.get_inputs(), fwd_only, pm_pass)
+        return _replacement
 
 
 class SiluMulBlockQuantPattern(ActivationQuantPattern):
@@ -193,6 +190,7 @@ def __init__(
         is_scale_transposed: bool = False,
         is_e8m0: bool = False,
         is_tma_aligned: bool = False,
+        match_aiter: bool = False,
     ) -> None:
         super().__init__(quant_key)
         self.quant_matcher = MatcherQuantFP8(
@@ -210,10 +208,9 @@ def get_inputs(self) -> list[torch.Tensor]:
         scale = self.quant_matcher.empty_f32(1, 1)
         return self.silu_and_mul_matcher.inputs() + [scale]
 
-    def register(self, pm_pass: PatternMatcherPass) -> None:
-        is_scale_transposed = self.is_scale_transposed
-
-        def pattern(
+    @property
+    def pattern(self):
+        def _pattern(
             input: torch.Tensor,
             scale: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
@@ -235,12 +232,16 @@ def pattern(
                 fp8_min=finfo.min,
                 fp8_max=finfo.max,
                 scale_ue8m0=self.is_e8m0,
-                dummy_is_scale_transposed=is_scale_transposed,
+                dummy_is_scale_transposed=self.is_scale_transposed,
                 dummy_is_tma_aligned=self.is_tma_aligned,
             )
             return result, scale
 
-        def replacement(
+        return _pattern
+
+    @property
+    def replacement(self):
+        def _replacement(
             input: torch.Tensor,
             scale: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
@@ -249,7 +250,7 @@ def replacement(
             result = torch.empty(
                 output_shape, device=input.device, dtype=self.quant_dtype
             )
-            if is_scale_transposed:
+            if self.is_scale_transposed:
                 scale = torch.empty(
                     (d // self.group_size, input.shape[0]),
                     device=input.device,
@@ -268,15 +269,14 @@ def replacement(
                 scales=scale,
                 group_size=self.group_size,
                 scale_ub=None,
-                is_scale_transposed=is_scale_transposed,
+                is_scale_transposed=self.is_scale_transposed,
             )
             return at[1], at[2]
 
-        inps = self.get_inputs()
-        register_replacement(pattern, replacement, inps, fwd_only, pm_pass)
+        return _replacement
 
 
-class ActivationQuantFusionPass(VllmPatternMatcherPass):
+class ActivationQuantFusionPass(VllmFusionPatternMatcherPass):
     """
     This pass fuses a pre-defined set of custom ops into fused ops.
     It uses the torch pattern matcher to find the patterns and replace them.
@@ -286,45 +286,33 @@ class ActivationQuantFusionPass(VllmPatternMatcherPass):
     https://github.com/pytorch/pytorch/pull/139321#issuecomment-2452354980
     """
 
-    @enable_fake_mode
     def __init__(self, config: VllmConfig) -> None:
-        super().__init__(config)
-
-        self.patterns: PatternMatcherPass = PatternMatcherPass(
-            pass_name="activation_quant_fusion_pass"
-        )
+        super().__init__(config, "activation_quant_fusion_pass")
 
-        pattern_silu_mul_fp8 = SiluMulFp8StaticQuantPattern()
-        pattern_silu_mul_fp8.register(self.patterns)
+        self.register(SiluMulFp8StaticQuantPattern())
 
         if silu_and_mul_nvfp4_quant_supported:
-            pattern_silu_mul_nvfp4 = SiluMulNvfp4QuantPattern()
-            pattern_silu_mul_nvfp4.register(self.patterns)
+            self.register(SiluMulNvfp4QuantPattern())
 
         if current_platform.is_cuda():
-            for quant_key in [kFp8Dynamic128Sym, kFp8Dynamic64Sym]:
-                for is_scale_transposed in [False, True]:
-                    for is_e8m0 in [True, False]:
-                        for is_tma_aligned in [False, True]:
-                            SiluMulBlockQuantPattern(
-                                quant_key,
-                                is_scale_transposed=is_scale_transposed,
-                                is_e8m0=is_e8m0,
-                                is_tma_aligned=is_tma_aligned,
-                            ).register(self.patterns)
-
-        self.dump_patterns(config, self.patterns)
-
-    @VllmInductorPass.time_and_log
-    def __call__(self, graph: torch.fx.Graph) -> None:
-        self.matched_count = self.patterns.apply(graph)
-        logger.debug("Replaced %s patterns", self.matched_count)
-
-    def uuid(self) -> str:
-        return VllmInductorPass.hash_source(
-            self,
-            ActivationQuantPattern,
-            SiluMulFp8StaticQuantPattern,
-            SiluMulNvfp4QuantPattern,
-            SiluMulBlockQuantPattern,
-        )
+            for (
+                quant_key,
+                is_scale_transposed,
+                is_e8m0,
+                is_tma_aligned,
+            ) in itertools.product(
+                [kFp8Dynamic128Sym, kFp8Dynamic64Sym],
+                [False, True],
+                [True, False],
+                [False, True],
+            ):
+                self.register(
+                    SiluMulBlockQuantPattern(
+                        quant_key,
+                        is_scale_transposed=is_scale_transposed,
+                        is_e8m0=is_e8m0,
+                        is_tma_aligned=is_tma_aligned,
+                    )
+                )
+
+        self.dump_patterns(config, self.pm_pass)
diff --git a/vllm/compilation/passes/fusion/allreduce_rms_fusion.py b/vllm/compilation/passes/fusion/allreduce_rms_fusion.py
index 09b9a557fe45..83423c02a6f1 100644
--- a/vllm/compilation/passes/fusion/allreduce_rms_fusion.py
+++ b/vllm/compilation/passes/fusion/allreduce_rms_fusion.py
@@ -12,12 +12,14 @@
 from torch._inductor.pattern_matcher import PatternMatcherPass
 
 import vllm.ir.ops
+from vllm._aiter_ops import rocm_aiter_ops
 from vllm.compilation.passes.fusion.rms_quant_fusion import (
     _rms_input_weight_dtype_match,
 )
 from vllm.config import VllmConfig
 from vllm.config.utils import Range
 from vllm.distributed import get_tp_group, tensor_model_parallel_all_reduce
+from vllm.distributed.device_communicators.custom_all_reduce import CustomAllreduce
 from vllm.distributed.parallel_state import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
@@ -32,8 +34,13 @@
 )
 
 from ..inductor_pass import enable_fake_mode
-from ..vllm_inductor_pass import VllmInductorPass, VllmPatternMatcherPass
-from .matcher_utils import MatcherFusedAddRMSNorm, MatcherQuantFP8
+from ..vllm_inductor_pass import (
+    VllmFusionPatternMatcherPass,
+    VllmInductorPass,
+    VllmPatternMatcherPass,
+    VllmPatternReplacement,
+)
+from .matcher_utils import MatcherQuantFP8
 
 FP8_DTYPE = current_platform.fp8_dtype()
 
@@ -349,10 +356,11 @@ def __init__(
         super().__init__(dtype, device)
         self.epsilon = epsilon
         self.allreduce_params = allreduce_params
-        self.rmsnorm_matcher = MatcherFusedAddRMSNorm(epsilon)
 
     def get_inputs(self) -> list[torch.Tensor]:
-        input, residual, weight = self.rmsnorm_matcher.inputs()
+        input = self.empty(5, 16)
+        residual = self.empty(5, 16)
+        weight = self.empty(16)
 
         # input goes through allreduce first, always 16-bit
         return [residual, input.to(self.dtype), weight]
@@ -362,7 +370,9 @@ def pattern(
             residual: torch.Tensor, input: torch.Tensor, weight: torch.Tensor
         ) -> tuple[torch.Tensor, torch.Tensor]:
             allreduce_output = tensor_model_parallel_all_reduce(input)
-            rms, residual = self.rmsnorm_matcher(allreduce_output, weight, residual)
+            rms, residual = vllm.ir.ops.fused_add_rms_norm(
+                allreduce_output, residual, weight, self.epsilon
+            )
             return rms, residual
 
         def replacement(
@@ -496,11 +506,12 @@ def __init__(
         self.allreduce_params = allreduce_params
         self.quant_dtype = torch.float8_e4m3fn
 
-        self.rmsnorm_matcher = MatcherFusedAddRMSNorm(epsilon)
         self.quant_matcher = MatcherQuantFP8(kFp8StaticTensorSym)
 
     def get_inputs(self) -> list[torch.Tensor]:
-        input, residual, weight = self.rmsnorm_matcher.inputs()
+        input = self.empty(5, 16)
+        residual = self.empty(5, 16)
+        weight = self.empty(16)
         _, scale = self.quant_matcher.inputs()
 
         # input goes through allreduce first, always 16-bit
@@ -514,7 +525,9 @@ def pattern(
             scale: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
             allreduce_output = tensor_model_parallel_all_reduce(input)
-            rms, res = self.rmsnorm_matcher(allreduce_output, weight, residual)
+            rms, res = vllm.ir.ops.fused_add_rms_norm(
+                allreduce_output, residual, weight, self.epsilon
+            )
             quant, _ = self.quant_matcher(rms, scale)
 
             return quant, res
@@ -661,7 +674,6 @@ def __init__(
         super().__init__(dtype, device)
         self.epsilon = epsilon
         self.allreduce_params = allreduce_params
-        self.rmsnorm_matcher = MatcherFusedAddRMSNorm(epsilon)
 
     def get_inputs(self) -> list[torch.Tensor]:
         input = torch.empty([16, 16], device=self.device, dtype=self.dtype)
@@ -693,7 +705,9 @@ def pattern(
             input_global_scale: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
             allreduce_output = tensor_model_parallel_all_reduce(input)
-            rms, residual = self.rmsnorm_matcher(allreduce_output, weight, residual)
+            rms, residual = vllm.ir.ops.fused_add_rms_norm(
+                allreduce_output, residual, weight, self.epsilon
+            )
             quant_out_tuple = auto_functionalized(
                 STATIC_FP4_QUANT_OP,
                 input=rms,
@@ -889,3 +903,201 @@ def __del__(self) -> None:
             return
         with contextlib.suppress(Exception):
             destroy_fi_ar_workspace()
+
+
+# TODO: make BasePattern to inherit from VllmPatternReplacement
+class AiterAllreduceFusedRMSNormPattern(BasePattern, VllmPatternReplacement):
+    def __init__(
+        self,
+        epsilon: float,
+        dtype: torch.dtype,
+        device: str | None,
+        use_aiter_rmsnorm: bool = True,
+    ) -> None:
+        super().__init__(dtype, device)
+        self.dtype = dtype
+        self.epsilon = epsilon
+        self.FUSED_AR_RMSNORM_OP = rocm_aiter_ops.get_fused_allreduce_rmsnorm_op()
+
+    def get_inputs(self) -> list[torch.Tensor]:
+        return [self.empty(5, 16), self.empty(16)]
+
+    @property
+    def pattern(self):
+        def _pattern(
+            input: torch.Tensor, weight: torch.Tensor
+        ) -> tuple[torch.Tensor, torch.Tensor]:
+            allreduce_output = tensor_model_parallel_all_reduce(input)
+            rms = vllm.ir.ops.rms_norm(allreduce_output, weight, self.epsilon)
+
+            return rms, allreduce_output
+
+        return _pattern
+
+    @property
+    def replacement(self):
+        def _replacement(
+            input: torch.Tensor, weight: torch.Tensor
+        ) -> tuple[torch.Tensor, torch.Tensor]:
+            residual = torch.zeros_like(input)
+            allreduce = self.FUSED_AR_RMSNORM_OP(
+                input_=input,
+                residual=residual,
+                weight=weight,
+                epsilon=self.epsilon,
+            )
+            return allreduce[0], allreduce[1]
+
+        return _replacement
+
+
+class AiterAllreduceFusedAddRMSNormPattern(BasePattern, VllmPatternReplacement):
+    def __init__(
+        self,
+        epsilon: float,
+        dtype: torch.dtype,
+        device: str | None,
+        use_aiter_rmsnorm: bool = True,
+    ) -> None:
+        super().__init__(dtype, device)
+        self.epsilon = epsilon
+        self.dtype = dtype
+        self.FUSED_AR_RMSNORM_OP = rocm_aiter_ops.get_fused_allreduce_rmsnorm_op()
+
+    def get_inputs(self) -> list[torch.Tensor]:
+        # input, residual, weight
+        return [self.empty(5, 16), self.empty(5, 16), self.empty(16)]
+
+    @property
+    def pattern(self):
+        def _pattern(
+            residual: torch.Tensor, input: torch.Tensor, weight: torch.Tensor
+        ) -> tuple[torch.Tensor, torch.Tensor]:
+            allreduce_output = tensor_model_parallel_all_reduce(input)
+            rms, residual = vllm.ir.ops.fused_add_rms_norm(
+                allreduce_output, residual, weight, self.epsilon
+            )
+            return rms, residual
+
+        return _pattern
+
+    @property
+    def replacement(self):
+        def _replacement(
+            residual: torch.Tensor, input: torch.Tensor, weight: torch.Tensor
+        ) -> tuple[torch.Tensor, torch.Tensor]:
+            allreduce = self.FUSED_AR_RMSNORM_OP(
+                input_=input,
+                residual=residual,
+                weight=weight,
+                epsilon=self.epsilon,
+            )
+            return allreduce[0], allreduce[1]
+
+        return _replacement
+
+
+class RocmAiterAllReduceFusionPass(VllmFusionPatternMatcherPass):
+    def __init__(self, config: VllmConfig) -> None:
+        super().__init__(config, "rocm_aiter_allreduce_fusion_pass")
+        self.disabled = True
+        self.tp_size = get_tensor_model_parallel_world_size()
+        if self.tp_size <= 1:
+            logger.warning_once("AllReduce fusion pass is disabled for tp_size <= 1.")
+            return
+
+        if config.model_config is None:
+            logger.warning_once(
+                "AllReduce fusion pass is disabled for missing model_config."
+            )
+            return
+
+        device_comm = get_tp_group().device_communicator
+        if device_comm is None:
+            logger.warning_once("Device communicator is required.")
+            return
+
+        ca_comm = getattr(device_comm, "ca_comm", None)
+        if ca_comm is None:
+            logger.warning_once("Custom Allreduce is required.")
+            return
+        self.ca_comm = ca_comm
+
+        assert isinstance(ca_comm, CustomAllreduce)
+
+        group = get_tp_group().cpu_group
+        rocm_aiter_ops.initialize_aiter_allreduce(group, self.device)
+        hidden_dim = config.model_config.get_hidden_size()
+        element_size = torch.tensor([], dtype=self.model_dtype).element_size()
+        max_size = rocm_aiter_ops.get_aiter_allreduce_max_size()
+        if max_size is None:
+            logger.warning("AITER allreduce fusion must be initialized")
+            return
+
+        # Aiter's fused_allreduce_rmsnorm kernel dispatches on hidden_dim.
+        # Before aiter v0.1.12 the launcher was template-specialized on HIDDEN_DIM
+        # and silently no-op'd for sizes outside {512, 1024, 2048, 4096}. From v0.1.12
+        # hidden_dim is a runtime argument. Detect the older API via the missing
+        # `_pool` attribute and skip fusion for unsupported sizes.
+        # Ref (old kernel): https://github.com/ROCm/aiter/blob/6a0e7b26ccf33164785531212cc2ec2cde0b9243/csrc/include/custom_all_reduce.cuh#L2590
+        aiter_ar = rocm_aiter_ops.get_aiter_allreduce()
+        _AITER_OLD_FUSED_AR_RMS_HIDDEN = (512, 1024, 2048, 4096)
+        if (
+            aiter_ar is not None
+            and not hasattr(aiter_ar, "_pool")
+            and hidden_dim not in _AITER_OLD_FUSED_AR_RMS_HIDDEN
+        ):
+            logger.warning_once(
+                "AITER allreduce-rmsnorm fusion disabled: aiter<0.1.12 "
+                "only supports hidden_dim in %s; got %d. Upgrade aiter to "
+                ">=0.1.12 to enable fusion for this model.",
+                _AITER_OLD_FUSED_AR_RMS_HIDDEN,
+                hidden_dim,
+            )
+            # Tear down aiter's custom-allreduce so its IPC handles don't
+            # race with vllm's ca_comm on the unfused fallback path.
+            with contextlib.suppress(Exception):
+                rocm_aiter_ops.destroy_aiter_allreduce()
+            return
+
+        max_token_num = max_size // (hidden_dim * element_size)
+        self.max_token_num = min(
+            max_token_num,
+            config.scheduler_config.max_num_batched_tokens,
+        )
+
+        for epsilon in [1e-5, 1e-6]:
+            self.register(
+                AiterAllreduceFusedRMSNormPattern(
+                    epsilon,
+                    self.model_dtype,
+                    self.device,
+                )
+            )
+            self.register(
+                AiterAllreduceFusedAddRMSNormPattern(
+                    epsilon,
+                    self.model_dtype,
+                    self.device,
+                )
+            )
+
+            # WARNING: This is a hack to clear the pattern matcher cache
+            # and allow multiple values of epsilon.
+            torch._inductor.pattern_matcher._seen_patterns.clear()
+
+        self.disabled = False
+
+        self.dump_patterns(config, self.pm_pass)
+
+    def is_applicable_for_range(self, compile_range: Range) -> bool:
+        if self.disabled:
+            logger.warning_once("AllReduce fusion pass is disabled.")
+            return False
+        return bool(compile_range.end <= self.max_token_num)
+
+    def __del__(self) -> None:
+        if getattr(self, "disabled", True):
+            return
+        with contextlib.suppress(Exception):
+            rocm_aiter_ops.destroy_aiter_allreduce()
diff --git a/vllm/compilation/passes/fusion/collective_fusion.py b/vllm/compilation/passes/fusion/collective_fusion.py
index a9b64adcb3f1..29d79c9b92ce 100644
--- a/vllm/compilation/passes/fusion/collective_fusion.py
+++ b/vllm/compilation/passes/fusion/collective_fusion.py
@@ -1,8 +1,12 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from collections.abc import Callable
+from contextlib import suppress
+
 import torch
 import torch._inductor.pattern_matcher as pm
+import torch.distributed.distributed_c10d as c10d
 import torch.fx as fx
 from torch._inductor.pattern_matcher import PatternMatcherPass
 from torch.distributed._symmetric_memory import enable_symm_mem_for_group
@@ -15,15 +19,317 @@
 )
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
+from vllm.utils.torch_utils import direct_register_custom_op
 
 from ..inductor_pass import enable_fake_mode
-from ..vllm_inductor_pass import VllmInductorPass, VllmPatternMatcherPass
+from ..vllm_inductor_pass import (
+    VllmFusionPatternMatcherPass,
+    VllmInductorPass,
+    VllmPatternMatcherPass,
+    VllmPatternReplacement,
+)
 
 FP8_DTYPE = current_platform.fp8_dtype()
 
 logger = init_logger(__name__)
 
 
+def _flashinfer_scaled_mm_out(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    *,
+    scale_a: torch.Tensor,
+    scale_b: torch.Tensor,
+    out: torch.Tensor,
+    bias: torch.Tensor | None = None,
+    scale_result: torch.Tensor | None = None,
+    out_dtype: torch.dtype | None = None,
+    use_fast_accum: bool = False,
+) -> None:
+    # Import lazily to avoid a circular import during module initialization
+    # when docs or other tooling import the pass without FlashInfer.
+    from vllm.utils.flashinfer import flashinfer_scaled_fp8_mm_out
+
+    assert bias is None, "FlashInfer symm_mem adapter does not support bias"
+    assert scale_result is None, (
+        "FlashInfer symm_mem adapter does not support result scaling"
+    )
+    assert not use_fast_accum, (
+        "FlashInfer symm_mem adapter does not support use_fast_accum"
+    )
+    assert A.ndim == 2 and B.ndim == 2 and out.ndim == 2, (
+        "FlashInfer symm_mem adapter expects 2D inputs and output"
+    )
+    assert scale_a.numel() == 1 and scale_b.numel() == 1, (
+        "FlashInfer symm_mem adapter only supports tensor-wise FP8 scales"
+    )
+
+    flashinfer_scaled_fp8_mm_out(
+        A,
+        B,
+        scale_a,
+        scale_b,
+        out=out,
+        out_dtype=out_dtype or out.dtype,
+    )
+
+
+def _flashinfer_fp4_mm_out(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    *,
+    scale_a: torch.Tensor,
+    scale_b: torch.Tensor,
+    out: torch.Tensor,
+    alpha: torch.Tensor,
+    out_dtype: torch.dtype | None = None,
+    use_8x4_sf_layout: bool = False,
+    backend: str = "cutlass",
+) -> None:
+    from vllm.utils.flashinfer import flashinfer_scaled_fp4_mm_out
+
+    assert A.ndim == 2 and B.ndim == 2 and out.ndim == 2, (
+        "FlashInfer FP4 symm_mem adapter expects 2D inputs and output"
+    )
+    flashinfer_scaled_fp4_mm_out(
+        A,
+        B,
+        scale_a,
+        scale_b,
+        alpha,
+        out=out,
+        out_dtype=out_dtype or out.dtype,
+        use_8x4_sf_layout=use_8x4_sf_layout,
+        backend=backend,
+    )
+
+
+def fused_flashinfer_scaled_matmul_reduce_scatter_fake(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    A_scale: torch.Tensor,
+    B_scale: torch.Tensor,
+    reduce_op: str,
+    orig_scatter_dim: int,
+    scatter_dim_after_maybe_reshape: int,
+    group_name: str,
+    output_shape: list[int],
+    out_dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    world_size = c10d._resolve_process_group(group_name).size()
+    result_shape = list(output_shape)
+    result_shape[orig_scatter_dim] //= world_size
+    return torch.empty(
+        result_shape,
+        dtype=out_dtype or torch.bfloat16,
+        device=A.device,
+    )
+
+
+def fused_flashinfer_scaled_matmul_reduce_scatter(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    A_scale: torch.Tensor,
+    B_scale: torch.Tensor,
+    reduce_op: str,
+    orig_scatter_dim: int,
+    scatter_dim_after_maybe_reshape: int,
+    group_name: str,
+    output_shape: list[int],
+    out_dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    assert orig_scatter_dim == 0 and scatter_dim_after_maybe_reshape == 0, (
+        "FlashInfer symm_mem adapter currently only supports scatter_dim=0"
+    )
+    world_size = c10d._resolve_process_group(group_name).size()
+    assert A.ndim == 2 and B.ndim == 2, "FlashInfer symm_mem adapter expects 2D inputs"
+    assert A.is_contiguous(), "FlashInfer symm_mem adapter expects contiguous A"
+    assert A_scale.numel() == 1 and B_scale.numel() == 1, (
+        "FlashInfer symm_mem adapter only supports tensor-wise FP8 scales"
+    )
+    assert A.shape[0] % world_size == 0, (
+        "FlashInfer symm_mem adapter expects M divisible by world size"
+    )
+
+    kwargs = {
+        "scale_b": B_scale,
+        "bias": None,
+        "scale_result": None,
+        "out_dtype": out_dtype,
+        "use_fast_accum": False,
+    }
+    return torch.distributed._symmetric_memory._fused_scaled_matmul_reduce_scatter_impl(
+        mm_out_op=_flashinfer_scaled_mm_out,
+        A=A,
+        B=B,
+        A_scale=A_scale,
+        kwargs=kwargs,
+        out_dtype=out_dtype,
+        reduce_op=reduce_op,
+        orig_scatter_dim=orig_scatter_dim,
+        scatter_dim_after_maybe_reshape=scatter_dim_after_maybe_reshape,
+        group_name=group_name,
+        output_shape=output_shape,
+    )
+
+
+def fused_all_gather_flashinfer_scaled_matmul_fake(
+    A_shard: torch.Tensor,
+    B: torch.Tensor,
+    A_scale: torch.Tensor,
+    B_scale: torch.Tensor,
+    gather_dim: int,
+    group_name: str,
+    out_dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    world_size = c10d._resolve_process_group(group_name).size()
+    output_shape = list(A_shard.shape)
+    output_shape[gather_dim] *= world_size
+    output_shape[-1] = B.shape[1]
+    return torch.empty(
+        output_shape,
+        dtype=out_dtype or torch.bfloat16,
+        device=A_shard.device,
+    )
+
+
+def fused_all_gather_flashinfer_scaled_matmul(
+    A_shard: torch.Tensor,
+    B: torch.Tensor,
+    A_scale: torch.Tensor,
+    B_scale: torch.Tensor,
+    gather_dim: int,
+    group_name: str,
+    out_dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    assert gather_dim == 0, (
+        "FlashInfer symm_mem adapter currently only supports gather_dim=0"
+    )
+    _, outputs = torch.distributed._symmetric_memory._fused_all_gather_matmul_impl(
+        mm_out_op=_flashinfer_scaled_mm_out,
+        A_shard=A_shard,
+        Bs=[B],
+        A_scale=A_scale,
+        kwargs_list=[
+            {
+                "scale_b": B_scale,
+                "bias": None,
+                "scale_result": None,
+                "out_dtype": out_dtype,
+                "use_fast_accum": False,
+            }
+        ],
+        out_dtypes=[out_dtype],
+        gather_dim=gather_dim,
+        group_name=group_name,
+        return_A=False,
+    )
+    return outputs[0]
+
+
+def fused_all_gather_flashinfer_fp4_matmul_fake(
+    A_shard: torch.Tensor,
+    B: torch.Tensor,
+    A_scale_shard: torch.Tensor,
+    B_scale: torch.Tensor,
+    alpha: torch.Tensor,
+    gather_dim: int,
+    group_name: str,
+    out_dtype: torch.dtype | None = None,
+    view_a_scale_as_fp8: bool = False,
+    use_8x4_sf_layout: bool = False,
+    backend: str = "cutlass",
+) -> torch.Tensor:
+    world_size = c10d._resolve_process_group(group_name).size()
+    output_shape = list(A_shard.shape)
+    output_shape[gather_dim] *= world_size
+    output_shape[-1] = B.shape[1]
+    return torch.empty(
+        output_shape,
+        dtype=out_dtype or torch.bfloat16,
+        device=A_shard.device,
+    )
+
+
+def fused_all_gather_flashinfer_fp4_matmul(
+    A_shard: torch.Tensor,
+    B: torch.Tensor,
+    A_scale_shard: torch.Tensor,
+    B_scale: torch.Tensor,
+    alpha: torch.Tensor,
+    gather_dim: int,
+    group_name: str,
+    out_dtype: torch.dtype | None = None,
+    view_a_scale_as_fp8: bool = False,
+    use_8x4_sf_layout: bool = False,
+    backend: str = "cutlass",
+) -> torch.Tensor:
+    assert gather_dim == 0, (
+        "FlashInfer FP4 symm_mem adapter currently only supports gather_dim=0"
+    )
+    assert A_shard.ndim == 2 and A_scale_shard.ndim == 2 and B.ndim == 2, (
+        "FlashInfer FP4 symm_mem adapter expects 2D inputs"
+    )
+    if view_a_scale_as_fp8:
+        A_scale_shard = A_scale_shard.view(torch.float8_e4m3fn)
+
+    group = c10d._resolve_process_group(group_name)
+    world_size = group.size()
+    output = A_shard.new_empty(
+        A_shard.shape[0] * world_size,
+        B.shape[1],
+        dtype=out_dtype or torch.bfloat16,
+    )
+    output_shards = output.chunk(world_size)
+
+    A = A_shard.new_empty(A_shard.shape[0] * world_size, A_shard.shape[1])
+    A_scale = A_scale_shard.new_empty(
+        A_scale_shard.shape[0] * world_size,
+        A_scale_shard.shape[1],
+    )
+
+    def fp4_shard_consumer(shards: list[torch.Tensor], rank: int) -> None:
+        _flashinfer_fp4_mm_out(
+            shards[0],
+            B,
+            scale_a=shards[1],
+            scale_b=B_scale,
+            alpha=alpha,
+            out=output_shards[rank],
+            out_dtype=out_dtype,
+            use_8x4_sf_layout=use_8x4_sf_layout,
+            backend=backend,
+        )
+
+    torch.distributed._symmetric_memory._pipelined_multi_all_gather_and_consume(
+        [A_shard, A_scale_shard],
+        fp4_shard_consumer,
+        [A, A_scale],
+        group_name,
+        False,
+    )
+    return output
+
+
+direct_register_custom_op(
+    op_name="fused_flashinfer_scaled_matmul_reduce_scatter",
+    op_func=fused_flashinfer_scaled_matmul_reduce_scatter,
+    fake_impl=fused_flashinfer_scaled_matmul_reduce_scatter_fake,
+)
+
+direct_register_custom_op(
+    op_name="fused_all_gather_flashinfer_scaled_matmul",
+    op_func=fused_all_gather_flashinfer_scaled_matmul,
+    fake_impl=fused_all_gather_flashinfer_scaled_matmul_fake,
+)
+
+direct_register_custom_op(
+    op_name="fused_all_gather_flashinfer_fp4_matmul",
+    op_func=fused_all_gather_flashinfer_fp4_matmul,
+    fake_impl=fused_all_gather_flashinfer_fp4_matmul_fake,
+)
+
+
 class BasePattern:
     def __init__(self, dtype: torch.dtype, device: str | None) -> None:
         self.dtype = dtype
@@ -371,53 +677,304 @@ def replacement(
         )
 
 
-class AsyncTPPass(VllmPatternMatcherPass):
+class FlashInferBMMFP8ReduceScatterPattern(
+    BasePattern, VllmPatternReplacement[..., torch.Tensor]
+):
+    def get_inputs(self) -> list[torch.Tensor]:
+        a_2d = torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE)
+        b_2d = (
+            torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE)
+            .contiguous()
+            .transpose(0, 1)
+        )
+        a_scale = torch.empty([1], device=self.device, dtype=torch.float32)
+        b_scale = torch.empty([1], device=self.device, dtype=torch.float32)
+        return [a_2d, b_2d, a_scale, b_scale]
+
+    @property
+    def pattern(self) -> Callable[..., torch.Tensor]:
+        def _pattern(
+            a_2d: torch.Tensor,
+            b_2d: torch.Tensor,
+            a_scale: torch.Tensor,
+            b_scale: torch.Tensor,
+        ) -> torch.Tensor:
+            bmm = torch.ops.vllm.bmm_fp8.default(
+                torch.ops.aten.unsqueeze.default(a_2d, 0),
+                torch.ops.aten.unsqueeze.default(b_2d, 0),
+                a_scale,
+                b_scale,
+                self.dtype,
+                "auto",
+            )
+            output = torch.ops.aten.reshape.default(bmm, list(bmm.shape[1:]))
+            return torch.ops.vllm.reduce_scatter.default(
+                output,
+                dim=0,
+                world_size=self.tp_size,
+                group_name=self.tp.unique_name,
+            )
+
+        return _pattern
+
+    @property
+    def replacement(self) -> Callable[..., torch.Tensor]:
+        def _replacement(
+            a_2d: torch.Tensor,
+            b_2d: torch.Tensor,
+            a_scale: torch.Tensor,
+            b_scale: torch.Tensor,
+        ) -> torch.Tensor:
+            return torch.ops.vllm.fused_flashinfer_scaled_matmul_reduce_scatter.default(
+                a_2d,
+                b_2d,
+                a_scale,
+                b_scale,
+                "sum",
+                0,
+                0,
+                self.tp.device_group.group_name,
+                [a_2d.shape[0], b_2d.shape[1]],
+                self.dtype,
+            )
+
+        return _replacement
+
+
+class FlashInferAllGatherBMMFP8Pattern(
+    BasePattern, VllmPatternReplacement[..., torch.Tensor]
+):
+    def get_inputs(self) -> list[torch.Tensor]:
+        a_shard_2d = torch.empty([8, 16], device=self.device, dtype=FP8_DTYPE)
+        b_2d = (
+            torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE)
+            .contiguous()
+            .transpose(0, 1)
+        )
+        a_scale = torch.empty([1], device=self.device, dtype=torch.float32)
+        b_scale = torch.empty([1], device=self.device, dtype=torch.float32)
+        return [a_shard_2d, b_2d, a_scale, b_scale]
+
+    @property
+    def pattern(self) -> Callable[..., torch.Tensor]:
+        def _pattern(
+            a_shard_2d: torch.Tensor,
+            b_2d: torch.Tensor,
+            a_scale: torch.Tensor,
+            b_scale: torch.Tensor,
+        ) -> torch.Tensor:
+            all_gather = torch.ops.vllm.all_gather.default(
+                a_shard_2d,
+                dim=0,
+                world_size=self.tp_size,
+                group_name=self.tp.unique_name,
+            )
+            return torch.ops.vllm.bmm_fp8.default(
+                torch.ops.aten.unsqueeze.default(all_gather, 0),
+                torch.ops.aten.unsqueeze.default(b_2d, 0),
+                a_scale,
+                b_scale,
+                self.dtype,
+                "auto",
+            )
+
+        return _pattern
+
+    @property
+    def replacement(self) -> Callable[..., torch.Tensor]:
+        def _replacement(
+            a_shard_2d: torch.Tensor,
+            b_2d: torch.Tensor,
+            a_scale: torch.Tensor,
+            b_scale: torch.Tensor,
+        ) -> torch.Tensor:
+            fused = torch.ops.vllm.fused_all_gather_flashinfer_scaled_matmul.default(
+                a_shard_2d,
+                b_2d,
+                a_scale,
+                b_scale,
+                0,
+                self.tp.device_group.group_name,
+                self.dtype,
+            )
+            return torch.ops.aten.unsqueeze.default(fused, 0)
+
+        return _replacement
+
+
+class FlashInferAllGatherFP4Pattern(
+    BasePattern, VllmPatternReplacement[..., torch.Tensor]
+):
+    def __init__(
+        self,
+        dtype: torch.dtype,
+        device: str | None,
+        backend: str,
+        use_8x4_sf_layout: bool,
+        a_scale_view: str,
+    ) -> None:
+        super().__init__(dtype, device)
+        self.backend = backend
+        self.use_8x4_sf_layout = use_8x4_sf_layout
+        self.a_scale_view = a_scale_view
+
+    def get_inputs(self) -> list[torch.Tensor]:
+        a_shard_2d = torch.empty([8, 8], device=self.device, dtype=torch.uint8)
+        b_2d = torch.empty([8, 16], device=self.device, dtype=torch.uint8)
+        a_scale_shard = torch.empty([128, 4], device=self.device, dtype=torch.int32)
+        b_scale = torch.empty([4, 128], device=self.device, dtype=torch.uint8)
+        alpha = torch.empty([], device=self.device, dtype=torch.float32)
+        return [
+            a_shard_2d,
+            b_2d,
+            a_scale_shard,
+            b_scale,
+            alpha,
+        ]
+
+    @property
+    def pattern(self) -> Callable[..., torch.Tensor]:
+        def _pattern(
+            a_shard_2d: torch.Tensor,
+            b_2d: torch.Tensor,
+            a_scale_shard: torch.Tensor,
+            b_scale: torch.Tensor,
+            alpha: torch.Tensor,
+        ) -> torch.Tensor:
+            all_gather_a = torch.ops.vllm.all_gather.default(
+                a_shard_2d,
+                dim=0,
+                world_size=self.tp_size,
+                group_name=self.tp.unique_name,
+            )
+            all_gather_a_scale = torch.ops.vllm.all_gather.default(
+                a_scale_shard,
+                dim=0,
+                world_size=self.tp_size,
+                group_name=self.tp.unique_name,
+            )
+            a_scale = all_gather_a_scale
+            if self.a_scale_view in ("float8", "float8_uint8"):
+                a_scale = torch.ops.aten.view.dtype(a_scale, torch.float8_e4m3fn)
+            if self.a_scale_view in ("uint8", "float8_uint8"):
+                a_scale = torch.ops.aten.view.dtype(a_scale, torch.uint8)
+            return torch.ops.vllm.flashinfer_mm_fp4.default(
+                all_gather_a,
+                b_2d,
+                a_scale,
+                b_scale,
+                alpha,
+                self.dtype,
+                self.use_8x4_sf_layout,
+                self.backend,
+            )
+
+        return _pattern
+
+    @property
+    def replacement(self) -> Callable[..., torch.Tensor]:
+        def _replacement(
+            a_shard_2d: torch.Tensor,
+            b_2d: torch.Tensor,
+            a_scale_shard: torch.Tensor,
+            b_scale: torch.Tensor,
+            alpha: torch.Tensor,
+        ) -> torch.Tensor:
+            return torch.ops.vllm.fused_all_gather_flashinfer_fp4_matmul.default(
+                a_shard_2d,
+                b_2d,
+                a_scale_shard,
+                b_scale,
+                alpha,
+                0,
+                self.tp.device_group.group_name,
+                self.dtype,
+                self.a_scale_view in ("float8", "float8_uint8"),
+                self.use_8x4_sf_layout,
+                self.backend,
+            )
+
+        return _replacement
+
+
+class AsyncTPPass(VllmFusionPatternMatcherPass):
     @enable_fake_mode
     def __init__(self, config: VllmConfig) -> None:
-        super().__init__(config)
+        super().__init__(config, pass_name="async_tp_pass")
 
-        # Enable symmetric memory for the TP process group
         enable_symm_mem_for_group(get_tp_group().device_group.group_name)
-        self.patterns: PatternMatcherPass = PatternMatcherPass(
-            pass_name="async_tp_pass"
-        )
-        GEMMReduceScatterPattern(self.model_dtype, self.device).register(self.patterns)
+        GEMMReduceScatterPattern(self.model_dtype, self.device).register(self.pm_pass)
 
-        AllGatherGEMMPattern(self.model_dtype, self.device).register(self.patterns)
+        AllGatherGEMMPattern(self.model_dtype, self.device).register(self.pm_pass)
 
         # These fusions are enabled only for bfloat16 models because
         # `scaled_mm` or `cutlass_scaled_mm` with per-token (row-wise) scaling
         # only supports bfloat16 as the output dtype.
         if self.model_dtype == torch.bfloat16:
             ScaledMMReduceScatterPattern(self.model_dtype, self.device).register(
-                self.patterns
+                self.pm_pass
             )
             AllGatherScaledMMPattern(self.model_dtype, self.device).register(
-                self.patterns
+                self.pm_pass
             )
 
             CutlassScaledMMReduceScatterPattern(self.model_dtype, self.device).register(
-                self.patterns
+                self.pm_pass
             )
             AllGatherCutlassScaledMMPattern(self.model_dtype, self.device).register(
-                self.patterns
+                self.pm_pass
             )
-
-        self.dump_patterns(config, self.patterns)
+            with suppress(ImportError):
+                import vllm.utils.flashinfer  # noqa: F401
+            if hasattr(torch.ops.vllm, "bmm_fp8"):
+                self.register(
+                    FlashInferAllGatherBMMFP8Pattern(self.model_dtype, self.device)
+                )
+                self.register(
+                    FlashInferBMMFP8ReduceScatterPattern(self.model_dtype, self.device)
+                )
+            if hasattr(torch.ops.vllm, "flashinfer_mm_fp4"):
+                for backend in ("cutlass", "cudnn"):
+                    for a_scale_view in ("float8_uint8", "uint8"):
+                        self.register(
+                            FlashInferAllGatherFP4Pattern(
+                                self.model_dtype,
+                                self.device,
+                                backend,
+                                use_8x4_sf_layout=False,
+                                a_scale_view=a_scale_view,
+                            )
+                        )
+                for use_8x4_sf_layout in (False, True):
+                    for a_scale_view in ("float8",):
+                        self.register(
+                            FlashInferAllGatherFP4Pattern(
+                                self.model_dtype,
+                                self.device,
+                                "trtllm",
+                                use_8x4_sf_layout=use_8x4_sf_layout,
+                                a_scale_view=a_scale_view,
+                            )
+                        )
+                # NVFP4 reduce-scatter does not need scale communication: FP4
+                # scales are consumed by the local GEMM and only BF16 partial
+                # outputs are reduced. Keep this PR scoped to the all-gather
+                # path; reduce-scatter needs a dedicated FP4 producer rather
+                # than the existing FP8-style helper.
+
+        self.dump_patterns(config, self.pm_pass)
 
     def is_applicable_for_range(self, compile_range: Range) -> bool:
-        # This pass is applied on top of the sequence parallelism pass.
-        # It inherits the same applicability condition as `SequenceParallelismPass`.
-        # See `SequenceParallelismPass.is_applicable` for more details.
-        if (
-            not self.compilation_config.splitting_ops
-            or self.compilation_config.use_inductor_graph_partition
-        ):
-            return True
-        tp_size = get_tensor_model_parallel_world_size()
-        return bool(compile_range.is_single_size() and compile_range.end % tp_size == 0)
+        # This pass is applied on top of the sequence parallelism pass,
+        # which is only supported in fullgraph compilation mode.
+        assert (
+            self.compilation_config.use_inductor_graph_partition
+            or not self.compilation_config.splitting_ops
+        ), "AsyncTPPass requires full-graph compilation"
+        return True
 
     @VllmInductorPass.time_and_log
     def __call__(self, graph: fx.Graph) -> None:
-        self.matched_count = self.patterns.apply(graph)
+        self.matched_count = self.pm_pass.apply(graph)
+        VllmPatternMatcherPass.match_table[self.pass_name] += self.matched_count
         logger.debug("Replaced %s patterns", self.matched_count)
diff --git a/vllm/compilation/passes/fusion/matcher_utils.py b/vllm/compilation/passes/fusion/matcher_utils.py
index c2490d8a21f5..e5130c19c392 100644
--- a/vllm/compilation/passes/fusion/matcher_utils.py
+++ b/vllm/compilation/passes/fusion/matcher_utils.py
@@ -10,7 +10,6 @@
 from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import get_current_vllm_config
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     GroupShape,
@@ -159,72 +158,6 @@ def forward_native(
         return result
 
 
-class MatcherFusedAddRMSNorm(MatcherCustomOp):
-    def __init__(
-        self,
-        epsilon: float,
-        enabled: bool | None = None,
-        match_rocm_aiter: bool = False,
-    ) -> None:
-        if enabled is None:
-            enabled = RMSNorm.enabled()
-
-        super().__init__(enabled)
-        self.epsilon = epsilon
-        self.match_rocm_aiter = match_rocm_aiter
-
-        self._rmsnorm_op = RMS_ADD_OP
-
-        if match_rocm_aiter:
-            self._rmsnorm_op = rocm_aiter_ops.get_rmsnorm_fused_add_op()
-
-    def inputs(self) -> list[torch.Tensor]:
-        input = self.empty(5, 16) if self.enabled else self.empty_f32(5, 16)
-        weight = self.empty(16)
-        residual = self.empty(5, 16)
-        return [input, weight, residual]
-
-    def forward_rocm_aiter(
-        self,
-        input: torch.Tensor,
-        weight: torch.Tensor,
-        residual: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        return self._rmsnorm_op(  # type: ignore[no-any-return]
-            x=input, residual=residual, weight=weight, variance_epsilon=self.epsilon
-        )
-
-    def forward_custom(
-        self,
-        input: torch.Tensor,
-        weight: torch.Tensor,
-        residual: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        if self.match_rocm_aiter:
-            return self.forward_rocm_aiter(input, weight, residual)
-
-        _, result, residual = auto_functionalized(
-            self._rmsnorm_op,
-            input=input,
-            residual=residual,
-            weight=weight,
-            epsilon=self.epsilon,
-        )
-
-        return result, residual
-
-    def forward_native(
-        self,
-        input: torch.Tensor,
-        weight: torch.Tensor,
-        residual: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        result: tuple[torch.Tensor, torch.Tensor] = RMSNorm.forward_static(
-            input, self.epsilon, input.size(-1), self.model_dtype, weight, residual
-        )
-        return result
-
-
 class MatcherQuantFP8(MatcherCustomOp):
     def __init__(
         self,
diff --git a/vllm/compilation/passes/fusion/mla_attn_quant_fusion.py b/vllm/compilation/passes/fusion/mla_attn_quant_fusion.py
index e36400ec8ecd..84c24bc60e53 100644
--- a/vllm/compilation/passes/fusion/mla_attn_quant_fusion.py
+++ b/vllm/compilation/passes/fusion/mla_attn_quant_fusion.py
@@ -6,15 +6,18 @@
 import torch
 from torch._higher_order_ops.auto_functionalize import auto_functionalized
 
-from vllm._custom_ops import create_fp4_output_tensors
 from vllm.config import VllmConfig, get_layers_from_vllm_config
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention.mla_attention import MLAAttention
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    QuantKey,
+    kFp8Dynamic64Sym,
+    kFp8Dynamic128Sym,
     kFp8StaticTensorSym,
     kNvfp4Dynamic,
 )
 from vllm.platforms import current_platform
+from vllm.utils.math_utils import round_up
 from vllm.utils.torch_utils import _USE_LAYERNAME, _encode_layer_name
 
 from ..vllm_inductor_pass import VllmFusionPatternMatcherPass, VllmPatternReplacement
@@ -203,6 +206,8 @@ def _pattern_with_ln(  # type: ignore[misc]
                 kv_c_normed,
                 k_pe,
                 output_attn,
+                output_quant,
+                output_scale,
                 input_scale,
                 kv_cache_dummy_dep,
                 layer_name,
@@ -218,9 +223,6 @@ def _pattern_with_ln(  # type: ignore[misc]
                     output_block_scale=None,
                     kv_cache_dummy_dep=kv_cache_dummy_dep,
                 )
-                output_quant, output_scale = create_fp4_output_tensors(
-                    at1[1].shape[0], at1[1].shape[1], at1[1].device, True
-                )
                 at2 = auto_functionalized(
                     self._QUANT_OP,
                     input=at1[1],
@@ -235,7 +237,14 @@ def _pattern_with_ln(  # type: ignore[misc]
             return _pattern_with_ln
 
         def _pattern(
-            q, kv_c_normed, k_pe, output_attn, input_scale, kv_cache_dummy_dep
+            q,
+            kv_c_normed,
+            k_pe,
+            output_attn,
+            output_quant,
+            output_scale,
+            input_scale,
+            kv_cache_dummy_dep,
         ):
             at1 = auto_functionalized(
                 MLA_ATTN_OP,
@@ -248,11 +257,6 @@ def _pattern(
                 output_block_scale=None,
                 kv_cache_dummy_dep=kv_cache_dummy_dep,
             )
-            # Replicate what scaled_fp4_quant() does: allocate output
-            # tensors inline then call the .out variant.
-            output_quant, output_scale = create_fp4_output_tensors(
-                at1[1].shape[0], at1[1].shape[1], at1[1].device, True
-            )
             at2 = auto_functionalized(
                 self._QUANT_OP,
                 input=at1[1],
@@ -279,6 +283,8 @@ def _replacement_with_ln(  # type: ignore[misc]
                 kv_c_normed,
                 k_pe,
                 output_attn,
+                _output_quant,
+                output_scale,
                 input_scale,
                 kv_cache_dummy_dep,
                 layer_name,
@@ -289,9 +295,6 @@ def _replacement_with_ln(  # type: ignore[misc]
                     dtype=FP4_DTYPE,
                     device=q.device,
                 )
-                output_scale = create_fp4_output_tensors(
-                    q.shape[0], self._output_dim, q.device, True
-                )[1]
                 output_scale_view = torch.ops.aten.view.dtype(output_scale, FP8_DTYPE)
                 at2 = auto_functionalized(
                     MLA_ATTN_OP,
@@ -309,7 +312,14 @@ def _replacement_with_ln(  # type: ignore[misc]
             return _replacement_with_ln
 
         def _replacement(
-            q, kv_c_normed, k_pe, output_attn, input_scale, kv_cache_dummy_dep
+            q,
+            kv_c_normed,
+            k_pe,
+            output_attn,
+            _output_quant,
+            output_scale,
+            input_scale,
+            kv_cache_dummy_dep,
         ):
             # MLA output in quant_dtype (FP4 packed as uint8)
             output_attn = torch.empty(
@@ -317,10 +327,6 @@ def _replacement(
                 dtype=FP4_DTYPE,
                 device=q.device,
             )
-            # attention output block scale
-            output_scale = create_fp4_output_tensors(
-                q.shape[0], self._output_dim, q.device, True
-            )[1]
             output_scale_view = torch.ops.aten.view.dtype(output_scale, FP8_DTYPE)
             at2 = auto_functionalized(
                 MLA_ATTN_OP,
@@ -343,6 +349,8 @@ def get_inputs(self) -> list[torch.Tensor]:
             self.empty(5, self._kv_lora_rank, dtype=self._dtype),
             self.empty(5, 1, self._qk_rope_head_dim, dtype=self._dtype),
             self.empty(5, self._output_dim, dtype=self._dtype),
+            self.empty(5, self._output_dim // 2, dtype=FP4_DTYPE),
+            self.empty_i32(128, round_up(self._output_dim // 16, 4)),
             self.empty_fp32(1, 1),
             self.empty(0, dtype=self._dtype),
         ]
@@ -351,6 +359,218 @@ def get_inputs(self) -> list[torch.Tensor]:
         return inputs
 
 
+class MLAAttnFp8GroupQuantPattern(
+    VllmPatternReplacement[..., tuple[torch.Tensor, torch.Tensor]]
+):
+    """
+    Fusion for MLA Attention+Fp8GroupQuant (per-group dynamic FP8).
+
+    Matches the pattern: MLA attention -> per_token_group_fp8_quant, and
+    replaces it with MLA attention(output_block_scale=group_scale_buffer).
+    Used by models with block FP8 quantization (e.g. DeepSeek V3).
+    """
+
+    def __init__(
+        self,
+        layer: MLAAttention,
+        dtype: torch.dtype,
+        quant_key: QuantKey,
+        has_col_major_scales: bool,
+        is_e8m0: bool,
+        is_tma_aligned: bool,
+    ) -> None:
+        self._layer_name = layer.layer_name
+        self._num_heads = layer.num_heads
+        self._v_head_dim = layer.v_head_dim
+        self._kv_lora_rank = layer.kv_lora_rank
+        self._qk_rope_head_dim = layer.qk_rope_head_dim
+        self._qk_head_dim = layer.qk_nope_head_dim + layer.qk_rope_head_dim
+        self._output_dim = layer.num_heads * layer.v_head_dim
+        self._dtype = dtype
+        self._layer = layer
+        self._group_size = quant_key.scale.group_shape[1]
+        self._has_col_major_scales = has_col_major_scales
+        self._is_e8m0 = is_e8m0
+        self._is_tma_aligned = is_tma_aligned
+
+        self._quant_matcher = MatcherQuantFP8(
+            quant_key,
+            has_col_major_scales=has_col_major_scales,
+            is_e8m0=is_e8m0,
+            is_tma_aligned=is_tma_aligned,
+        )
+
+    @property
+    def pattern(
+        self,
+    ) -> Callable[..., tuple[torch.Tensor, torch.Tensor]]:
+        _ln = _encode_layer_name(self._layer_name)
+
+        if _USE_LAYERNAME:
+
+            def _pattern_with_ln(  # type: ignore[misc]
+                q,
+                kv_c_normed,
+                k_pe,
+                output_attn,
+                kv_cache_dummy_dep,
+                scale,
+                layer_name,
+            ):
+                at1 = auto_functionalized(
+                    MLA_ATTN_OP,
+                    q=q,
+                    kv_c_normed=kv_c_normed,
+                    k_pe=k_pe,
+                    output=output_attn,
+                    layer_name=layer_name,
+                    output_scale=None,
+                    output_block_scale=None,
+                    kv_cache_dummy_dep=kv_cache_dummy_dep,
+                )
+                attn_out = at1[1]
+                result = torch.empty(
+                    attn_out.shape, device=attn_out.device, dtype=FP8_DTYPE
+                )
+                finfo = torch.finfo(FP8_DTYPE)
+                _, result, scale = auto_functionalized(
+                    self._quant_matcher.QUANT_OP,
+                    input=attn_out,
+                    output_q=result,
+                    output_s=scale,
+                    group_size=self._group_size,
+                    eps=1e-10,
+                    fp8_min=finfo.min,
+                    fp8_max=finfo.max,
+                    scale_ue8m0=self._is_e8m0,
+                    dummy_is_scale_transposed=self._has_col_major_scales,
+                    dummy_is_tma_aligned=self._is_tma_aligned,
+                )
+                return result, scale
+
+            return _pattern_with_ln
+
+        def _pattern(
+            q: torch.Tensor,
+            kv_c_normed: torch.Tensor,
+            k_pe: torch.Tensor,
+            output_attn: torch.Tensor,
+            kv_cache_dummy_dep: torch.Tensor,
+            scale: torch.Tensor,
+        ) -> tuple[torch.Tensor, torch.Tensor]:
+            at1 = auto_functionalized(
+                MLA_ATTN_OP,
+                q=q,
+                kv_c_normed=kv_c_normed,
+                k_pe=k_pe,
+                output=output_attn,
+                layer_name=_ln,
+                output_scale=None,
+                output_block_scale=None,
+                kv_cache_dummy_dep=kv_cache_dummy_dep,
+            )
+            attn_out = at1[1]
+            result = torch.empty(
+                attn_out.shape, device=attn_out.device, dtype=FP8_DTYPE
+            )
+            finfo = torch.finfo(FP8_DTYPE)
+            _, result, scale = auto_functionalized(
+                self._quant_matcher.QUANT_OP,
+                input=attn_out,
+                output_q=result,
+                output_s=scale,
+                group_size=self._group_size,
+                eps=1e-10,
+                fp8_min=finfo.min,
+                fp8_max=finfo.max,
+                scale_ue8m0=self._is_e8m0,
+                dummy_is_scale_transposed=self._has_col_major_scales,
+                dummy_is_tma_aligned=self._is_tma_aligned,
+            )
+            return result, scale
+
+        return _pattern
+
+    @property
+    def replacement(
+        self,
+    ) -> Callable[..., tuple[torch.Tensor, torch.Tensor]]:
+        _ln = _encode_layer_name(self._layer_name)
+
+        if _USE_LAYERNAME:
+
+            def _replacement_with_ln(  # type: ignore[misc]
+                q,
+                kv_c_normed,
+                k_pe,
+                output_attn,
+                kv_cache_dummy_dep,
+                scale,
+                layer_name,
+            ):
+                output_attn = torch.empty(
+                    [q.shape[0], self._output_dim],
+                    dtype=FP8_DTYPE,
+                    device=q.device,
+                )
+                at1 = auto_functionalized(
+                    MLA_ATTN_OP,
+                    q=q,
+                    kv_c_normed=kv_c_normed,
+                    k_pe=k_pe,
+                    output=output_attn,
+                    layer_name=layer_name,
+                    output_scale=None,
+                    output_block_scale=scale,
+                    kv_cache_dummy_dep=kv_cache_dummy_dep,
+                    quant_group_size=self._group_size,
+                    quant_scale_ue8m0=self._is_e8m0,
+                    quant_col_major=self._has_col_major_scales,
+                    quant_tma_aligned=self._is_tma_aligned,
+                )
+                return at1[1], at1[2]
+
+            return _replacement_with_ln
+
+        def _replacement(q, kv_c_normed, k_pe, output_attn, kv_cache_dummy_dep, scale):
+            output_attn = torch.empty(
+                [q.shape[0], self._output_dim],
+                dtype=FP8_DTYPE,
+                device=q.device,
+            )
+            at1 = auto_functionalized(
+                MLA_ATTN_OP,
+                q=q,
+                kv_c_normed=kv_c_normed,
+                k_pe=k_pe,
+                output=output_attn,
+                layer_name=_ln,
+                output_scale=None,
+                output_block_scale=scale,
+                kv_cache_dummy_dep=kv_cache_dummy_dep,
+                quant_group_size=self._group_size,
+                quant_scale_ue8m0=self._is_e8m0,
+                quant_col_major=self._has_col_major_scales,
+                quant_tma_aligned=self._is_tma_aligned,
+            )
+            return at1[1], at1[2]
+
+        return _replacement
+
+    def get_inputs(self) -> list[torch.Tensor]:
+        inputs: list = [
+            self.empty(5, self._num_heads, self._qk_head_dim, dtype=self._dtype),
+            self.empty(5, self._kv_lora_rank, dtype=self._dtype),
+            self.empty(5, 1, self._qk_rope_head_dim, dtype=self._dtype),
+            self.empty(5, self._output_dim, dtype=self._dtype),
+            self.empty(0, dtype=self._dtype),
+            self._quant_matcher.empty_f32(1, 1),
+        ]
+        if _USE_LAYERNAME:
+            inputs.append(_encode_layer_name(self._layer_name))
+        return inputs
+
+
 class MLAAttnQuantFusionPass(VllmFusionPatternMatcherPass):
     """
     This pass fuses post-attention quantization onto MLA attention if supported.
@@ -389,4 +609,25 @@ def __init__(self, config: VllmConfig) -> None:
                     if _USE_LAYERNAME:
                         break
 
+        # Per-group FP8 (block quant) — register all flag combinations.
+        if current_platform.is_cuda():
+            for quant_key in [kFp8Dynamic128Sym, kFp8Dynamic64Sym]:
+                for col_major in [True, False]:
+                    for is_e8m0 in [True, False]:
+                        for tma_aligned in [False, True]:
+                            for layer in layers:
+                                if layer.impl.fused_output_quant_supported(quant_key):
+                                    self.register(
+                                        MLAAttnFp8GroupQuantPattern(
+                                            layer,
+                                            dtype,
+                                            quant_key,
+                                            col_major,
+                                            is_e8m0,
+                                            tma_aligned,
+                                        )
+                                    )
+                                    if _USE_LAYERNAME:
+                                        break
+
         self.dump_patterns(config, self.pm_pass)
diff --git a/vllm/compilation/passes/fusion/rms_quant_fusion.py b/vllm/compilation/passes/fusion/rms_quant_fusion.py
index 850e434a3e73..cc986595d436 100644
--- a/vllm/compilation/passes/fusion/rms_quant_fusion.py
+++ b/vllm/compilation/passes/fusion/rms_quant_fusion.py
@@ -29,7 +29,6 @@
 from ..inductor_pass import enable_fake_mode
 from ..vllm_inductor_pass import VllmInductorPass, VllmPatternMatcherPass
 from .matcher_utils import (
-    MatcherFusedAddRMSNorm,
     MatcherQuantFP8,
 )
 
@@ -146,9 +145,6 @@ def __init__(
         assert key in FUSED_OPS, f"unsupported fused rmsnorm+quant op for {key}"
         self.FUSED_OP = FUSED_OPS[key]
 
-        if key.fused_add:
-            self.rmsnorm_matcher = MatcherFusedAddRMSNorm(epsilon)
-
         self.quant_matcher = MatcherQuantFP8(
             key.quant,
             has_col_major_scales=has_col_major_scales,
@@ -231,7 +227,9 @@ def pattern(
             residual: torch.Tensor,
             scale: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
-            result_rms, residual = self.rmsnorm_matcher(input, weight, residual)
+            result_rms, residual = vllm.ir.ops.fused_add_rms_norm(
+                input, residual, weight, self.epsilon
+            )
             result, _ = self.quant_matcher(result_rms, scale)
 
             return result, residual
@@ -261,8 +259,9 @@ def replacement(
             return at[1], at[2]
 
         inputs = [
-            # input, weight, residual
-            *self.rmsnorm_matcher.inputs(),
+            empty_bf16(5, 16),  # input
+            empty_bf16(16),  # weight
+            empty_bf16(5, 16),  # residual
             self.quant_matcher.inputs()[1],  # scale
         ]
 
@@ -311,7 +310,9 @@ def pattern(
             residual: torch.Tensor,
             scale: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-            result_rms, residual = self.rmsnorm_matcher(input, weight, residual)
+            result_rms, residual = vllm.ir.ops.fused_add_rms_norm(
+                input, residual, weight, self.epsilon
+            )
             result = torch.empty(
                 result_rms.shape,
                 device=result_rms.device,
@@ -366,12 +367,17 @@ def replacement(
             # result, residual, scale
             return at[1], at[3], at[2]
 
-        scale = self.quant_matcher.empty_f32(1, 1)
+        inputs = [
+            empty_bf16(5, 16),  # input
+            empty_bf16(16),  # weight
+            empty_bf16(5, 16),  # residual
+            self.quant_matcher.empty_f32(1, 1),  # scale
+        ]
 
         pm.register_replacement(
             pattern,
             replacement,
-            self.rmsnorm_matcher.inputs() + [scale],
+            inputs,
             pm.fwd_only,
             pm_pass,
             extra_check=_rms_input_weight_dtype_match,
@@ -552,7 +558,9 @@ def register(self, pm_pass: PatternMatcherPass) -> None:
         def pattern(
             input: torch.Tensor, weight: torch.Tensor, residual: torch.Tensor
         ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-            result_rms, residual = self.rmsnorm_matcher(input, weight, residual)
+            result_rms, residual = vllm.ir.ops.fused_add_rms_norm(
+                input, residual, weight, self.epsilon
+            )
             result, scale = self.quant_matcher(result_rms)
 
             return result, residual, scale
@@ -580,10 +588,16 @@ def replacement(
             # result, residual, scale
             return at[1], at[3], at[2]
 
+        inputs = [
+            empty_bf16(5, 16),  # input
+            empty_bf16(16),  # weight
+            empty_bf16(5, 16),  # residual
+        ]
+
         pm.register_replacement(
             pattern,
             replacement,
-            self.rmsnorm_matcher.inputs(),
+            inputs,
             pm.fwd_only,
             pm_pass,
             extra_check=_rms_input_weight_dtype_match,
diff --git a/vllm/compilation/passes/fusion/rocm_aiter_fusion.py b/vllm/compilation/passes/fusion/rocm_aiter_fusion.py
index 9a985472371e..83abcdeb5abd 100644
--- a/vllm/compilation/passes/fusion/rocm_aiter_fusion.py
+++ b/vllm/compilation/passes/fusion/rocm_aiter_fusion.py
@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Callable
 from typing import Any
 
 import torch
@@ -7,6 +8,7 @@
 from torch import fx
 from torch._inductor.pattern_matcher import PatternMatcherPass
 
+import vllm.ir.ops
 import vllm.model_executor.layers.quantization.utils.fp8_utils  # noqa: F401
 from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import VllmConfig
@@ -20,10 +22,13 @@
 from vllm.platforms import current_platform
 
 from ..inductor_pass import enable_fake_mode
-from ..vllm_inductor_pass import VllmInductorPass, VllmPatternMatcherPass
-from .act_quant_fusion import ActivationQuantPattern
+from ..vllm_inductor_pass import (
+    VllmFusionPatternMatcherPass,
+    VllmInductorPass,
+    VllmPatternMatcherPass,
+    VllmPatternReplacement,
+)
 from .matcher_utils import (
-    MatcherFusedAddRMSNorm,
     MatcherQuantFP8,
     MatcherSiluAndMul,
 )
@@ -43,10 +48,6 @@ def __init__(
         self.quant_dtype = key.quant.dtype
         self.device = torch.device("cuda")
 
-        if key.fused_add:
-            self.rmsnorm_matcher = MatcherFusedAddRMSNorm(
-                epsilon, match_rocm_aiter=True
-            )
         self.quant_matcher = MatcherQuantFP8(
             key.quant,
             match_rocm_aiter=match_aiter_quant,
@@ -139,7 +140,9 @@ def pattern(
             weight: torch.Tensor,
             residual: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-            result_rms, residual_out = self.rmsnorm_matcher(input, weight, residual)
+            result_rms, residual_out = torch.ops.vllm_ir.fused_add_rms_norm(
+                input, residual, weight, self.epsilon
+            )
             result, scale = self.quant_matcher(result_rms)
 
             return result, residual_out, scale
@@ -157,10 +160,16 @@ def replacement(
 
             return result[0], result[1], result[2]
 
+        inputs = [
+            self.empty(5, 16),  # input
+            self.empty(16),  # weight
+            self.empty(5, 16),  # residual
+        ]
+
         pm.register_replacement(
             pattern,
             replacement,
-            self.rmsnorm_matcher.inputs(),
+            inputs,
             pm.fwd_only,
             pm_pass,
         )
@@ -252,7 +261,9 @@ def pattern(
             weight: torch.Tensor,
             residual: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-            result_rms, residual_out = self.rmsnorm_matcher(input, weight, residual)
+            result_rms, residual_out = torch.ops.vllm_ir.fused_add_rms_norm(
+                input, residual, weight, self.epsilon
+            )
             result, scale = self.quant_matcher(result_rms)
 
             return result, residual_out, scale
@@ -273,9 +284,13 @@ def replacement(
             # result, scale, residual
             return at[0], at[1], at[2]
 
-        pm.register_replacement(
-            pattern, replacement, self.rmsnorm_matcher.inputs(), pm.fwd_only, pm_pass
-        )
+        inputs = [
+            self.empty(5, 16),  # input
+            self.empty(16),  # weight
+            self.empty(5, 16),  # residual
+        ]
+
+        pm.register_replacement(pattern, replacement, inputs, pm.fwd_only, pm_pass)
 
 
 class RocmAiterRMSNormQuantFusionPass(VllmPatternMatcherPass):
@@ -306,7 +321,18 @@ def __init__(self, config: VllmConfig) -> None:
                 epsilon, FP8_DTYPE, GroupShape(1, 128)
             ).register(self.patterns)
 
-            for match_aiter_quant in [True, False]:
+            # When quant_fp8 custom ops are disabled, both AITER and native
+            # quant matchers trace through QuantFP8's native implementation.
+            # Registering both variants would create duplicate Inductor
+            # patterns.
+            is_quant_fp8_enabled = config.compilation_config.is_custom_op_enabled(
+                "quant_fp8"
+            )
+            match_aiter_quant_options = (
+                [True, False] if is_quant_fp8_enabled else [False]
+            )
+
+            for match_aiter_quant in match_aiter_quant_options:
                 # Fuse aiter rms_norm + (aiter / vllm built-in)
                 # dynamic per-token fp8 quant
                 AiterRMSNormDynamicQuantPattern(
@@ -338,7 +364,7 @@ def uuid(self) -> str:
         return self.hash_source(self, *fusion_patterns)
 
 
-class AiterSiluMulFp8GroupQuantPattern(ActivationQuantPattern):
+class AiterSiluMulFp8GroupQuantPattern(VllmPatternReplacement):
     """
     This pattern fuses aiter silu_and_mul & group fp8 quant custom
     ops into an aiter silu_and_mul_group_fp8_quant op.
@@ -357,26 +383,29 @@ def get_inputs(self) -> list[torch.Tensor]:
             self.silu_and_mul_matcher.inputs()[0],
         ]
 
-    def register(self, pm_pass: PatternMatcherPass) -> None:
-        def pattern(
+    @property
+    def pattern(self):
+        def _pattern(
             input: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
             at1 = self.silu_and_mul_matcher(input)
             at2 = self.quant_matcher(at1)
             return at2[0], at2[1]
 
-        def replacement(
+        return _pattern
+
+    @property
+    def replacement(self):
+        def _replacement(
             input: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
             at = self.FUSED_SILU_MUL_QUANT_OP(x=input, group_size=128)
             return at[0], at[1]
 
-        pm.register_replacement(
-            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
-        )
+        return _replacement
 
 
-class RocmAiterSiluMulFp8GroupQuantFusionPass(VllmPatternMatcherPass):
+class RocmAiterSiluMulFp8GroupQuantFusionPass(VllmFusionPatternMatcherPass):
     """
     This pass fuses a pre-defined set of custom ops into fused ops.
     It uses the torch pattern matcher to find the patterns and replace them.
@@ -386,29 +415,12 @@ class RocmAiterSiluMulFp8GroupQuantFusionPass(VllmPatternMatcherPass):
     https://github.com/pytorch/pytorch/pull/139321#issuecomment-2452354980
     """
 
-    @enable_fake_mode
     def __init__(self, config: VllmConfig) -> None:
-        super().__init__(config)
-
-        self.patterns: PatternMatcherPass = PatternMatcherPass(
-            pass_name="rocm_aiter_silu_mul_fp8_group_quant_fusion_pass"
-        )
+        super().__init__(config, "rocm_aiter_silu_mul_fp8_group_quant_fusion_pass")
 
-        AiterSiluMulFp8GroupQuantPattern().register(self.patterns)
+        self.register(AiterSiluMulFp8GroupQuantPattern())
 
-        self.dump_patterns(config, self.patterns)
-
-    @VllmInductorPass.time_and_log
-    def __call__(self, graph: torch.fx.Graph) -> None:
-        self.matched_count = self.patterns.apply(graph)
-        logger.debug("Replaced %s patterns", self.matched_count)
-
-    def uuid(self) -> str:
-        fusion_patterns = [
-            ActivationQuantPattern,
-            AiterSiluMulFp8GroupQuantPattern,
-        ]
-        return VllmInductorPass.hash_source(self, *fusion_patterns)
+        self.dump_patterns(config, self.pm_pass)
 
 
 class AddAiterRMSNormPadPattern:
@@ -428,12 +440,15 @@ def __init__(
         self.epsilon = epsilon
         self.hidden_size = hidden_size
         self.x_pad_to_multiple = x_pad_to_multiple
-        self.rmsnorm_matcher = MatcherFusedAddRMSNorm(epsilon, match_rocm_aiter=True)
 
     def get_inputs(self) -> list[torch.Tensor]:
-        input, weight, residual = self.rmsnorm_matcher.inputs()
-        router_weight = torch.empty([8, 16], dtype=weight.dtype, device=weight.device)
-        router_bias = torch.empty([8], dtype=weight.dtype, device=weight.device)
+        device = torch.device("cuda")
+        dtype = torch.bfloat16
+        input = torch.empty(5, 16, dtype=dtype, device=device)
+        weight = torch.empty(16, dtype=dtype, device=device)
+        residual = torch.empty(5, 16, dtype=dtype, device=device)
+        router_weight = torch.empty([8, 16], dtype=dtype, device=device)
+        router_bias = torch.empty([8], dtype=dtype, device=device)
         return [input, weight, residual, router_weight, router_bias]
 
     def register(self, pm_pass: PatternMatcherPass) -> None:
@@ -447,7 +462,9 @@ def pattern(
             pad_size = self.x_pad_to_multiple - (
                 self.hidden_size % self.x_pad_to_multiple
             )
-            result_rms, residual_out = self.rmsnorm_matcher(input, weight, residual)
+            result_rms, residual_out = torch.ops.vllm_ir.fused_add_rms_norm(
+                input, residual, weight, self.epsilon
+            )
             router_logits = torch.ops.vllm.rocm_unquantized_gemm(
                 result_rms, router_weight, router_bias
             )
@@ -512,3 +529,101 @@ def __call__(self, graph: torch.fx.Graph) -> None:
 
     def uuid(self) -> str:
         return VllmInductorPass.hash_source(self, AddAiterRMSNormPadPattern)
+
+
+class MLADualRMSNormPattern(
+    VllmPatternReplacement[..., tuple[torch.Tensor, torch.Tensor, torch.Tensor]]
+):
+    """
+    Fuse paired q_a_layernorm + kv_a_layernorm in MLA attention into
+    AITER's ``fused_qk_rmsnorm`` HIP kernel.
+
+    Target FX-graph pattern (unfused, ``vllm_ir`` stage)::
+
+        gemm -> split_with_sizes([q_dim, kv_dim])
+            +-- q_c     -> vllm_ir.rms_norm(q_c, q_w, eps)
+            +-- kv_lora -> split_with_sizes([kv_c_dim, k_pe_dim])
+                            +-- kv_c -> vllm_ir.rms_norm(kv_c, kv_w, eps)
+                            +-- k_pe
+
+    The pattern covers the connected subgraph rooted at the first
+    ``split_with_sizes`` (which produces ``q_c`` and ``kv_lora``),
+    through the two ``rms_norm`` calls, and the ``k_pe`` passthrough.
+    """
+
+    def __init__(self, epsilon: float) -> None:
+        self._epsilon = epsilon
+
+    def get_inputs(self) -> list[torch.Tensor]:
+        q_dim, kv_c_dim, k_pe_dim = 8, 4, 2
+        return [
+            self.empty_bf16(5, q_dim + kv_c_dim + k_pe_dim),
+            self.empty_bf16(q_dim),
+            self.empty_bf16(kv_c_dim),
+        ]
+
+    @property
+    def pattern(
+        self,
+    ) -> Callable[..., tuple[torch.Tensor, torch.Tensor, torch.Tensor]]:
+        eps = self._epsilon
+
+        def _pattern(
+            projected: torch.Tensor,
+            q_weight: torch.Tensor,
+            kv_weight: torch.Tensor,
+        ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+            q_dim = q_weight.shape[0]
+            kv_dim = projected.shape[-1] - q_dim
+            kv_c_dim = kv_weight.shape[0]
+            k_pe_dim = kv_dim - kv_c_dim
+            q_c, kv_lora = projected.split([q_dim, kv_dim], dim=-1)
+            kv_c, k_pe = kv_lora.split([kv_c_dim, k_pe_dim], dim=-1)
+            q_normed = vllm.ir.ops.rms_norm(q_c, q_weight, eps)
+            kv_normed = vllm.ir.ops.rms_norm(kv_c, kv_weight, eps)
+            return q_normed, kv_normed, k_pe
+
+        return _pattern
+
+    @property
+    def replacement(
+        self,
+    ) -> Callable[..., tuple[torch.Tensor, torch.Tensor, torch.Tensor]]:
+        eps = self._epsilon
+
+        def _replacement(
+            projected: torch.Tensor,
+            q_weight: torch.Tensor,
+            kv_weight: torch.Tensor,
+        ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+            q_dim = q_weight.shape[0]
+            kv_dim = projected.shape[-1] - q_dim
+            kv_c_dim = kv_weight.shape[0]
+            k_pe_dim = kv_dim - kv_c_dim
+            q_c, kv_lora = projected.split([q_dim, kv_dim], dim=-1)
+            kv_c, k_pe = kv_lora.split([kv_c_dim, k_pe_dim], dim=-1)
+            q_normed, kv_normed = torch.ops.vllm.fused_mla_dual_rms_norm(
+                q_c,
+                q_weight,
+                kv_c,
+                kv_weight,
+                eps,
+                eps,
+            )
+            return q_normed, kv_normed, k_pe
+
+        return _replacement
+
+
+class MLADualRMSNormFusionPass(VllmFusionPatternMatcherPass):
+    """
+    Post-grad PatternMatcher pass that fuses paired q / kv RMS norms in
+    MLA attention into ``fused_mla_dual_rms_norm`` backed by aiter's
+    ``fused_qk_rmsnorm`` HIP kernel.
+    """
+
+    def __init__(self, config: VllmConfig) -> None:
+        super().__init__(config, "mla_dual_rms_norm_fusion_pass")
+
+        for epsilon in [1e-5, 1e-6]:
+            self.register(MLADualRMSNormPattern(epsilon))
diff --git a/vllm/compilation/passes/fusion/sequence_parallelism.py b/vllm/compilation/passes/fusion/sequence_parallelism.py
index e3cfb8a4896b..8d0f40e2c775 100644
--- a/vllm/compilation/passes/fusion/sequence_parallelism.py
+++ b/vllm/compilation/passes/fusion/sequence_parallelism.py
@@ -8,13 +8,17 @@
 import torch
 import torch._inductor.pattern_matcher as pm
 import torch.fx as fx
+from torch._higher_order_ops.auto_functionalize import auto_functionalized
 from torch._inductor.pattern_matcher import PatternMatcherPass
 
 import vllm.ir.ops
 from vllm.config import VllmConfig
 from vllm.config.utils import Range
 from vllm.distributed import get_tp_group, tensor_model_parallel_all_reduce
-from vllm.distributed.parallel_state import get_tensor_model_parallel_world_size
+from vllm.distributed.parallel_state import (
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     kFp8StaticTensorSym,
@@ -23,14 +27,19 @@
 from ..inductor_pass import enable_fake_mode
 from ..utility.noop_elimination import NoOpEliminationPass
 from ..vllm_inductor_pass import VllmInductorPass, VllmPatternMatcherPass
-from .matcher_utils import MatcherFusedAddRMSNorm, MatcherQuantFP8
+from .matcher_utils import MatcherQuantFP8
 
 logger = init_logger(__name__)
 
+if hasattr(torch.ops._C, "scaled_fp4_quant"):
+    SCALED_FP4_QUANT_OUT_OVERLOAD = torch.ops._C.scaled_fp4_quant.out
+    SCALED_FP4_QUANT_DEFAULT_OVERLOAD = torch.ops._C.scaled_fp4_quant.default
+
 # Min hidden size per device capability for sequence parallelism
 # Only apply sequence parallelism for models with hidden_size >= threshold
 SP_MIN_HIDDEN_SIZE: dict[int, int] = {
     90: 8192,  # H100: only for models with hidden_size >= 8192
+    100: 8192,  # Blackwell family: only for models with hidden_size >= 8192
 }
 
 # Min size per GPU per device capability for sequence parallelism
@@ -38,6 +47,8 @@
 # This ensures the threshold scales appropriately with tensor parallelism
 SP_MIN_PER_GPU_SIZE_MB: dict[int, float] = {
     90: 8,  # 8MB per GPU for H100
+    # Use a more conservative threshold on Blackwell so TP8 starts later.
+    100: 32,
 }
 
 
@@ -67,7 +78,12 @@ def get_sequence_parallelism_threshold(
     capability = current_platform.get_device_capability()
     if capability is None:
         return None
-    device_capability = capability.to_int()
+
+    # Collapse Blackwell variants (sm100/sm103/...) into one policy bucket.
+    if current_platform.is_device_capability_family(100):
+        device_capability = 100
+    else:
+        device_capability = capability.to_int()
 
     # Check if device has configured thresholds
     min_hidden_size = SP_MIN_HIDDEN_SIZE.get(device_capability)
@@ -109,6 +125,7 @@ def __init__(
         self.device = device
         self.tp_group = get_tp_group()
         self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
 
     def _all_reduce(self, x: torch.Tensor) -> torch.Tensor:
         return tensor_model_parallel_all_reduce(x)
@@ -166,7 +183,6 @@ def replacement(
 class MiddleAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
     def __init__(self, epsilon: float, dtype: torch.dtype, device: str | None) -> None:
         super().__init__(epsilon, dtype, device)
-        self.rmsnorm_matcher = MatcherFusedAddRMSNorm(epsilon)
 
     def get_inputs(self) -> list[torch.Tensor]:
         mm_1 = torch.empty([4, 4], device=self.device, dtype=self.dtype)
@@ -187,7 +203,9 @@ def pattern(
             rms_norm_weights: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
             all_reduce = self._all_reduce(mm_1)
-            rmsnorm = self.rmsnorm_matcher(all_reduce, rms_norm_weights, residual)
+            rmsnorm = vllm.ir.ops.fused_add_rms_norm(
+                all_reduce, residual, rms_norm_weights, self.epsilon
+            )
             return rmsnorm[0], rmsnorm[1]
 
         def replacement(
@@ -195,15 +213,35 @@ def replacement(
             mm_1: torch.Tensor,
             rms_norm_weights: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
-            # pattern matcher replaces from top-to-bottom,
-            # so residual is still the full size here.
-            # once the seqpar pattern with the previous rmsnorm is replaced
+            # The pattern matcher replaces from the end of the graph
+            # (last layer first). At the time each match is replaced,
+            # the preceding layer has NOT been replaced yet, so
+            # `residual` is still full-size and the slice below is
+            # correct. Once the preceding layer IS replaced, its
+            # residual output shrinks to [local_len, H], and this
+            # slice becomes semantically incorrect (e.g. for rank > 0,
+            # the indices would be out of bounds). However, since the
+            # symbolic output shape equals the input shape,
+            # NoOpEliminationPass (called at the end of
+            # SequenceParallelismPass.__call__) removes these slices
+            # before the graph is ever executed or compiled.
             reduce_scatter = self._reduce_scatter(mm_1)
-            residual = residual[0 : reduce_scatter.size(0), ...]
-            rmsnorm = self.rmsnorm_matcher(reduce_scatter, rms_norm_weights, residual)
+            local_len = reduce_scatter.size(0)
+            # when the preceding VocabParallelEmbedding is excluded
+            # from the FX graph (e.g., passing `inputs_embeds` directly in VLMs),
+            # the FirstAllReduceRMSNorm pattern is never matched. we must
+            # perform a proper TP-aware slice here. simply using `[0:local_len]`
+            # would incorrectly cause all ranks to process rank 0's chunk.
+            residual = residual[
+                self.tp_rank * local_len : self.tp_rank * local_len + local_len, ...
+            ]
+            rmsnorm = vllm.ir.ops.fused_add_rms_norm(
+                reduce_scatter, residual, rms_norm_weights, self.epsilon
+            )
             all_gather = self._all_gather(rmsnorm[0])
-            # shape of residual changes but that's fine,
-            # next node is already slicing it, now becomes a noop
+            # residual output is now [local_len, H]; the next layer's
+            # slice on it is semantically incorrect until
+            # NoOpEliminationPass removes it.
             return all_gather, rmsnorm[1]
 
         pm.register_replacement(
@@ -263,7 +301,6 @@ def replacement(
 class MiddleAllReduceRMSNormStaticFP8Pattern(_SequenceParallelPatternHelper):
     def __init__(self, epsilon: float, dtype: torch.dtype, device: str | None) -> None:
         super().__init__(epsilon, dtype, device)
-        self.rmsnorm_matcher = MatcherFusedAddRMSNorm(epsilon)
         self.quant_matcher = MatcherQuantFP8(kFp8StaticTensorSym)
 
     def get_inputs(self) -> list[torch.Tensor]:
@@ -282,8 +319,8 @@ def pattern(
             scale: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
             all_reduce = self._all_reduce(mm_1)
-            rms, residual_out = self.rmsnorm_matcher(
-                all_reduce, rms_norm_weights, residual
+            rms, residual_out = vllm.ir.ops.fused_add_rms_norm(
+                all_reduce, residual, rms_norm_weights, self.epsilon
             )
             quant, _ = self.quant_matcher(rms, scale)
             return quant, residual_out
@@ -294,19 +331,29 @@ def replacement(
             rms_norm_weights: torch.Tensor,
             scale: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
-            # pattern matcher replaces from top-to-bottom,
-            # so residual is still the full size here.
-            # add a temporary slice which will become a noop
-            # once the seqpar pattern with the previous rmsnorm is replaced
+            # See MiddleAllReduceRMSNormPattern.replacement for a
+            # detailed explanation of the temporary slice below:
+            # it is correct when first inserted, becomes semantically
+            # incorrect after the preceding layer is replaced, and is
+            # removed by NoOpEliminationPass before the graph is compiled.
             reduce_scatter = self._reduce_scatter(mm_1)
-            residual = residual[0 : reduce_scatter.size(0), ...]
-            rms, residual_out = self.rmsnorm_matcher(
-                reduce_scatter, rms_norm_weights, residual
+            local_len = reduce_scatter.size(0)
+            # when the preceding VocabParallelEmbedding is excluded
+            # from the FX graph (e.g., passing `inputs_embeds` directly in VLMs),
+            # the FirstAllReduceRMSNorm pattern is never matched. we must
+            # perform a proper TP-aware slice here. simply using `[0:local_len]`
+            # would incorrectly cause all ranks to process rank 0's chunk.
+            residual = residual[
+                self.tp_rank * local_len : self.tp_rank * local_len + local_len, ...
+            ]
+            rms, residual_out = vllm.ir.ops.fused_add_rms_norm(
+                reduce_scatter, residual, rms_norm_weights, self.epsilon
             )
             quant, _ = self.quant_matcher(rms, scale)
             all_gather = self._all_gather(quant)
-            # shape of residual changes but that's fine,
-            # next node is already slicing it, now becomes a noop
+            # residual output is now [local_len, H]; the next layer's
+            # slice on it is semantically incorrect until
+            # NoOpEliminationPass removes it.
             return all_gather, residual_out
 
         pm.register_replacement(
@@ -322,6 +369,129 @@ def replacement(
         )
 
 
+class FirstAllReduceRMSNormStaticNVFP4Pattern(_SequenceParallelPatternHelper):
+    def get_inputs(self) -> list[torch.Tensor]:
+        input = self.empty([8, 16])
+        weight = self.empty([16])
+        input_global_scale = self.empty_f32([1, 1])
+        quant_output = torch.empty([8, 8], device=self.device, dtype=torch.uint8)
+        output_scale = torch.empty([128, 4], device=self.device, dtype=torch.int32)
+        return [input, weight, input_global_scale, quant_output, output_scale]
+
+    def register(self, pm_pass: PatternMatcherPass) -> None:
+        def pattern(
+            input: torch.Tensor,
+            weight: torch.Tensor,
+            input_global_scale: torch.Tensor,
+            quant_output: torch.Tensor,
+            output_scale: torch.Tensor,
+        ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+            all_reduce = self._all_reduce(input)
+            rms = vllm.ir.ops.rms_norm(all_reduce, weight, self.epsilon)
+            quant = auto_functionalized(
+                SCALED_FP4_QUANT_OUT_OVERLOAD,
+                input=rms,
+                input_scale=input_global_scale,
+                is_sf_swizzled_layout=True,
+                output=quant_output,
+                output_scale=output_scale,
+            )
+            return quant[1], all_reduce, quant[2]
+
+        def replacement(
+            input: torch.Tensor,
+            weight: torch.Tensor,
+            input_global_scale: torch.Tensor,
+            quant_output: torch.Tensor,
+            output_scale: torch.Tensor,
+        ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+            reduce_scatter = self._reduce_scatter(input)
+            rms = vllm.ir.ops.rms_norm(reduce_scatter, weight, self.epsilon)
+            rms = torch.ops.aten.view.default(rms, [-1, rms.shape[-1]])
+            quant = SCALED_FP4_QUANT_DEFAULT_OVERLOAD(
+                rms,
+                input_global_scale,
+                True,
+            )
+            return (
+                self._all_gather(quant[0]),
+                reduce_scatter,
+                self._all_gather(quant[1]),
+            )
+
+        pm.register_replacement(
+            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
+        )
+
+
+class MiddleAllReduceRMSNormStaticNVFP4Pattern(_SequenceParallelPatternHelper):
+    def get_inputs(self) -> list[torch.Tensor]:
+        mm_1 = self.empty([8, 16])
+        residual = self.empty([8, 16])
+        rms_norm_weights = self.empty([16])
+        input_global_scale = self.empty_f32([1, 1])
+        quant_output = torch.empty([8, 8], device=self.device, dtype=torch.uint8)
+        output_scale = torch.empty([128, 4], device=self.device, dtype=torch.int32)
+        return [
+            residual,
+            mm_1,
+            rms_norm_weights,
+            input_global_scale,
+            quant_output,
+            output_scale,
+        ]
+
+    def register(self, pm_pass: PatternMatcherPass) -> None:
+        def pattern(
+            residual: torch.Tensor,
+            mm_1: torch.Tensor,
+            rms_norm_weights: torch.Tensor,
+            input_global_scale: torch.Tensor,
+            quant_output: torch.Tensor,
+            output_scale: torch.Tensor,
+        ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+            all_reduce = self._all_reduce(mm_1)
+            rms, residual_out = vllm.ir.ops.fused_add_rms_norm(
+                all_reduce, residual, rms_norm_weights, self.epsilon
+            )
+            quant = auto_functionalized(
+                SCALED_FP4_QUANT_OUT_OVERLOAD,
+                input=rms,
+                input_scale=input_global_scale,
+                is_sf_swizzled_layout=True,
+                output=quant_output,
+                output_scale=output_scale,
+            )
+            return quant[1], residual_out, quant[2]
+
+        def replacement(
+            residual: torch.Tensor,
+            mm_1: torch.Tensor,
+            rms_norm_weights: torch.Tensor,
+            input_global_scale: torch.Tensor,
+            quant_output: torch.Tensor,
+            output_scale: torch.Tensor,
+        ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+            # Keep this slice in sync with the non-quantized SP replacement:
+            # once the previous SP pattern fires, it becomes a no-op.
+            reduce_scatter = self._reduce_scatter(mm_1)
+            residual = residual[0 : reduce_scatter.size(0), ...]
+            rms, residual_out = vllm.ir.ops.fused_add_rms_norm(
+                reduce_scatter, residual, rms_norm_weights, self.epsilon
+            )
+            rms = torch.ops.aten.view.default(rms, [-1, rms.shape[-1]])
+            quant = SCALED_FP4_QUANT_DEFAULT_OVERLOAD(
+                rms,
+                input_global_scale,
+                True,
+            )
+            return self._all_gather(quant[0]), residual_out, self._all_gather(quant[1])
+
+        pm.register_replacement(
+            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
+        )
+
+
 class SequenceParallelismPass(VllmPatternMatcherPass):
     """
     This pass enables sequence parallelism for models.
@@ -341,22 +511,21 @@ class SequenceParallelismPass(VllmPatternMatcherPass):
     significantly reduce communication overhead and improve overall model
     performance.
 
-
-    This pass splits up the residual tensor across TP ranks and hence divides its size.
-    Because the pattern matcher starts at the end of the graph, the replacement
-    contains a slice that temporarily conforms the input residual to the correct size.
-    After all patterns have been matched, we use a NoOpEliminationPass to clean up
-    what have now become no-op slices.
-
-    Note that an older version of the pass did not need this as it operated only on
-    custom rms_norm and fused_rms_norm_add custom ops which did not complain about
-    mismatched shapes during replacement. So this approach has the same assumption that
-    correctness is only maintained if all rms_norm operations are split across ranks.
-
-    Correctness-wise, this is approach strictly better than before - before,
-    the graph was incorrect semantically and shape-wise during the pass.
-    With this approach there's only semantic incorrectness during the pass.
-    Both approaches restore a correct graph once all patterns are matched.
+    This pass is only supported when compiling the whole graph (fullgraph
+    mode, i.e. using Inductor graph partition or empty splitting_ops).
+    Piecewise compilation is not supported because the residual tensor
+    gets split across TP ranks, causing size mismatches at subgraph
+    boundaries.
+
+    This pass splits up the residual tensor across TP ranks and hence divides
+    its size. The pattern matcher starts at the end of the graph (last layer
+    first), so when each replacement inserts a residual slice, the preceding
+    layer has not been replaced yet and the slice is correct. Once the
+    preceding layer IS replaced, its residual output shrinks and the slice
+    becomes semantically incorrect (out-of-bounds indices for rank > 0).
+    The graph is never executed in this intermediate state —
+    NoOpEliminationPass removes these slices based on symbolic shape equality
+    (input shape == output shape) before the graph is compiled.
     """
 
     @enable_fake_mode
@@ -398,6 +567,14 @@ def __init__(self, config: VllmConfig) -> None:
                 epsilon, self.model_dtype, self.device
             ).register(self.patterns)
 
+            if "SCALED_FP4_QUANT_OUT_OVERLOAD" in globals():
+                FirstAllReduceRMSNormStaticNVFP4Pattern(
+                    epsilon, self.model_dtype, self.device
+                ).register(self.patterns)
+                MiddleAllReduceRMSNormStaticNVFP4Pattern(
+                    epsilon, self.model_dtype, self.device
+                ).register(self.patterns)
+
             # Normal RMSNorm patterns
             FirstAllReduceRMSNormPattern(
                 epsilon, self.model_dtype, self.device
@@ -419,19 +596,13 @@ def is_applicable_for_range(self, compile_range: Range) -> bool:
         and gathering tensors across TP ranks outweighs the benefits.
 
         Returns False (SP disabled) when:
-        - Using piecewise compilation with non-concrete or TP-indivisible sizes
         - min_token_num is None (SP disabled for this device/config)
         - The compile range starts below the minimum token threshold
         """
-        # For piecewise compilation (not using inductor graph partition),
-        # we need concrete sizes that are divisible by TP for correct splitting
-        if (
-            not self.compilation_config.use_inductor_graph_partition
-            and self.compilation_config.splitting_ops
-        ):
-            tp_size = get_tensor_model_parallel_world_size()
-            if not compile_range.is_single_size() or compile_range.end % tp_size != 0:
-                return False
+        assert (
+            self.compilation_config.use_inductor_graph_partition
+            or not self.compilation_config.splitting_ops
+        ), "SequenceParallelismPass requires full-graph compilation"
 
         # min_token_num is None when SP is disabled for this device/config
         # (e.g., non-CUDA platform, unsupported GPU, or small hidden_size)
diff --git a/vllm/compilation/passes/inductor_pass.py b/vllm/compilation/passes/inductor_pass.py
index 4610c62d1771..8a0d5326dd92 100644
--- a/vllm/compilation/passes/inductor_pass.py
+++ b/vllm/compilation/passes/inductor_pass.py
@@ -30,6 +30,9 @@ class PassContext:
     def __init__(self, compile_range: Range):
         self.compile_range: Range = compile_range
 
+        # set of arg indices
+        self.donated_input_ids: set[int] = set()
+
 
 def get_pass_context() -> PassContext:
     """Get the current pass context."""
@@ -51,6 +54,15 @@ def pass_context(compile_range: Range) -> Generator[None, None, None]:
         _pass_context = prev_context
 
 
+@functools.cache
+def _hash_source_cached(*srcs: str | type | types.FunctionType) -> str:
+    hasher = hashlib.sha256()
+    for src in srcs:
+        src_str = src if isinstance(src, str) else inspect.getsource(src)
+        hasher.update(src_str.encode("utf-8"))
+    return hasher.hexdigest()
+
+
 class InductorPass(CustomGraphPass):  # type: ignore[misc]
     """
     A custom graph pass that uses a hash of its source as the UUID.
@@ -72,19 +84,16 @@ def hash_source(*srcs: str | Any) -> str:
         Utility method to hash the sources of functions or objects.
         :param srcs: strings or objects to add to the hash.
         Objects and functions have their source inspected.
+        Results are cached by resolved types to avoid repeated
+        inspect.getsource() calls.
         :return:
         """
-        hasher = hashlib.sha256()
-        for src in srcs:
-            if isinstance(src, str):
-                src_str = src
-            elif isinstance(src, (types.FunctionType, type)):
-                src_str = inspect.getsource(src)
-            else:
-                # object instance
-                src_str = inspect.getsource(src.__class__)
-            hasher.update(src_str.encode("utf-8"))
-        return hasher.hexdigest()
+        # Resolve instances to their class for a hashable cache key.
+        cache_key = tuple(
+            src if isinstance(src, (str, type, types.FunctionType)) else src.__class__
+            for src in srcs
+        )
+        return _hash_source_cached(*cache_key)
 
     @staticmethod
     def hash_dict(dict_: dict[Any, Any]) -> str:
diff --git a/vllm/compilation/passes/ir/clone_elimination.py b/vllm/compilation/passes/ir/clone_elimination.py
new file mode 100644
index 000000000000..61ba750a6c4e
--- /dev/null
+++ b/vllm/compilation/passes/ir/clone_elimination.py
@@ -0,0 +1,126 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+from torch import fx
+from torch._higher_order_ops.auto_functionalize import auto_functionalized
+from torch._higher_order_ops.triton_kernel_wrap import TritonKernelWrapperFunctional
+from torch._ops import HigherOrderOperator, OpOverload
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+
+from ..fx_utils import is_func
+from ..inductor_pass import get_pass_context
+from ..vllm_inductor_pass import VllmInductorPass
+
+logger = init_logger(__name__)
+
+
+def user_writes_to_node(user: fx.Node, node: fx.Node) -> bool:
+    if user.op == "output":
+        return False
+
+    if is_func(user, auto_functionalized):
+        # While autofunc writes to the node,
+        # this is a follow-up use we're not interested in.
+        # It is also guaranteed to be the final use,
+        # as auto_functionalized returns the tensor back for follow-up use.
+        return False
+    elif user.op == "call_function" and isinstance(user.target, HigherOrderOperator):
+        # By default, be conservative, assume this could be a write
+        # (except functional HOPs)
+        return not isinstance(user.target, TritonKernelWrapperFunctional)
+
+    assert isinstance(user.target, OpOverload), (
+        f"{node=} {user=} {user.op=} {user.target=}"
+    )
+    schema = user.target._schema
+    assert len(user.args) <= len(schema.arguments)
+    for i, arg in enumerate(user.args):
+        # Only interested in writes to node
+        if arg is not node:
+            continue
+
+        # If not a write, next arg could be
+        if schema.arguments[i].is_write:
+            return True
+
+    # No writes found
+    return False
+
+
+class UnsafeCloneEliminationPass(VllmInductorPass):
+    """
+    This pass removes clone nodes that are no longer needed after vLLM IR lowering.
+    It uses donated_input_ids to eliminate clones of donated graph inputs, preserving
+    contents of non-donated graph inputs.
+
+    It is "unsafe" because it does not (yet) take aliasing into account. Solving
+    aliasing is an open problem, so this pass intends to support known vLLM cases
+    and not guarantee soundness on general graphs. In the future, this pass will likely
+    support basic forms of aliasing to handle simple views (e.g. qkv -> q,k,v).
+    """
+
+    def __init__(self, vllm_config: VllmConfig) -> None:
+        super().__init__(vllm_config)
+
+    @VllmInductorPass.time_and_log
+    def __call__(self, graph: fx.Graph) -> None:
+        count = 0
+        node_to_idx = {node: i for i, node in enumerate(graph.nodes)}
+        pass_context = get_pass_context()
+        donated_input_ids = pass_context.donated_input_ids
+        logger.debug("Donated input ids: %s", donated_input_ids)
+
+        for node in graph.nodes:
+            if not is_func(node, torch.ops.aten.clone.default):
+                continue
+
+            original_node = node.args[0]
+            assert isinstance(original_node, fx.Node)
+
+            # Clone needs to be preserved if node is getting written to and
+            # the old value is used again.
+            # This could only happen if an inplace implementation was lowered.
+            # Then node (the clone) will have one write.
+            # TODO(luka) hopefully this can be removed once we lower functional graphs.
+            write_idxs = [
+                node_to_idx[u] for u in node.users if user_writes_to_node(u, node)
+            ]
+            assert len(write_idxs) in (0, 1)
+            if write_idxs:
+                # Check if a user of original_node occurs after a write
+                write_idx = write_idxs[0]
+                if any(
+                    node_to_idx[orig_user] > write_idx
+                    for orig_user in original_node.users
+                ):
+                    logger.debug(
+                        "Clone removal not possible, "
+                        "original_node=%s used after mutation on node=%s",
+                        original_node,
+                        node,
+                    )
+                    continue
+
+                # Check if a node is a (non-donated) graph input
+                if (
+                    original_node.op == "placeholder"
+                    and node_to_idx[original_node] not in donated_input_ids
+                ):
+                    logger.debug(
+                        "Graph input %s not donated, cannot eliminate its clone",
+                        original_node,
+                    )
+                    continue
+
+            logger.debug(
+                "Node %s is a redundant clone node of %s, removing it",
+                node,
+                original_node,
+            )
+            node.replace_all_uses_with(original_node)
+            graph.erase_node(node)
+            count += 1
+
+        logger.debug("CloneCleanupPass removed %d clone nodes", count)
diff --git a/vllm/compilation/passes/ir/inplace_functionalization.py b/vllm/compilation/passes/ir/inplace_functionalization.py
new file mode 100644
index 000000000000..e69351075bca
--- /dev/null
+++ b/vllm/compilation/passes/ir/inplace_functionalization.py
@@ -0,0 +1,101 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections import defaultdict
+
+from torch import fx
+from torch._inductor.pattern_matcher import (
+    PatternMatcherPass,
+)
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+
+from ..inductor_pass import get_pass_context
+from ..vllm_inductor_pass import VllmInductorPass
+from .lowering_pass import get_ir_op
+from .utils import overload_or_default
+
+logger = init_logger(__name__)
+
+
+class VllmIRInplaceFunctionalizationPass(VllmInductorPass):
+    """
+    This pass functionalizes maybe_inplace vLLM IR ops to the default overload.
+    The maybe_inplace overloads have the same signature as the default overload
+    so the pass simply replaces the called overload.
+    That makes the graph properly functional.
+    The pass also validates that activations passed to maybe_inplace have no later
+    uses in the graph: they are donated to the maybe_inplace op call,
+    and their contents are not defined afterward.
+
+    This pass operates pre-AOTAutograd,
+    so it must handle non-normalized and non-functional IR.
+    """
+
+    def __init__(self, vllm_config: VllmConfig) -> None:
+        super().__init__(vllm_config)
+        self.patterns = PatternMatcherPass(self.pass_name)
+        self.functionalized_ops: dict[str, int] = defaultdict(lambda: 0)
+
+    @VllmInductorPass.time_and_log
+    def __call__(self, graph: fx.Graph) -> None:
+        # clear at the beginning instead of end, so that tests can inspect
+        self.functionalized_ops.clear()
+        assert graph.owning_module is not None
+        node_to_idx = {node: i for i, node in enumerate(graph.nodes)}
+
+        # Pass donated input via vLLM's pass context
+        pass_context = get_pass_context()
+        pass_context.donated_input_ids = set[int]()
+
+        for node in graph.nodes:
+            if (ir_op := get_ir_op(node)) is None:
+                continue
+
+            op_overload = overload_or_default(node.target)
+            overload_name = op_overload._overloadname
+            if overload_name != "maybe_inplace":
+                assert overload_name == "default", (
+                    f"Found overload {overload_name} for op {ir_op.name}, "
+                    f"expected maybe_inplace or default"
+                )
+                continue
+
+            # must have maybe_inplace overload and allow_inplace
+            assert ir_op.allow_inplace and hasattr(ir_op, "maybe_inplace")
+
+            # Check that activation inputs are not used after this op
+            for arg_idx in ir_op.activation_indices:
+                arg = node.args[arg_idx]
+                assert isinstance(arg, fx.Node), "Activation inputs must be fx.Node"
+                for user in arg.users:
+                    if node_to_idx[user] > node_to_idx[node]:
+                        raise ValueError(
+                            f"Input {arg} to maybe_inplace node {node} "
+                            f"is used again after the node. "
+                            f"This is not allowed; activation inputs to maybe_inplace "
+                            f"ops are donated to the op, meaning their memory may be "
+                            f"recycled for outputs.\n\n"
+                            f"To preserve the inputs, use the default overload or "
+                            f"clone them manually beforehand."
+                        )
+
+                if arg.op == "placeholder":
+                    # Graph input that maybe_inplace might modify.
+                    # Mark it so downstream passes know it's donated.
+                    # TODO(luka) store in placeholder node meta once supported
+                    pass_context.donated_input_ids.add(node_to_idx[arg])
+
+            # Same signature, just replace the overload that's called.
+            node.target = ir_op.torch_op
+            self.functionalized_ops[ir_op.name] += 1
+
+        count = sum(self.functionalized_ops.values())
+        ops = ",".join(self.functionalized_ops.keys())
+        logger.debug("Donated input IDs: %s", pass_context.donated_input_ids)
+        logger.debug(
+            "%s functionalized %d vLLM IR nodes for op(s) %s",
+            self.pass_name,
+            count,
+            ops,
+        )
diff --git a/vllm/compilation/passes/ir/lowering_pass.py b/vllm/compilation/passes/ir/lowering_pass.py
index 02acdd1a298b..f34f1c64b76e 100644
--- a/vllm/compilation/passes/ir/lowering_pass.py
+++ b/vllm/compilation/passes/ir/lowering_pass.py
@@ -10,7 +10,6 @@
     PatternMatcherPass,
     register_graph_pattern,
 )
-from torch._ops import OpOverload, OpOverloadPacket
 
 from vllm.config import VllmConfig
 from vllm.ir.op import IrOp
@@ -18,41 +17,11 @@
 from vllm.logging_utils import lazy
 
 from ..vllm_inductor_pass import VllmInductorPass
+from .utils import get_ir_op
 
 logger = init_logger(__name__)
 
 
-def get_default_overload(op: OpOverload | OpOverloadPacket) -> OpOverload:
-    if isinstance(op, OpOverloadPacket):
-        return op.default
-    assert isinstance(op, OpOverload), "Expected an OpOverload or OpOverloadPacket"
-    return op
-
-
-def get_ir_op(node: fx.Node) -> IrOp | None:
-    if node.op != "call_function":
-        return None
-
-    if not isinstance(node.target, (OpOverload, OpOverloadPacket)):
-        return None
-
-    op_overload = get_default_overload(node.target)
-    if op_overload.namespace != "vllm_ir":
-        return None
-
-    op_name = op_overload._opname
-    if op_name not in IrOp.registry:
-        logger.warning(
-            "Unknown vLLM IR op %s, there's likely an issue with torch registration, "
-            "or a torch custom op was registered in the vllm_ir namespace by mistake.",
-            op_name,
-        )
-        return None
-
-    ir_op = IrOp.registry[op_name]
-    return ir_op
-
-
 class VllmIRLoweringPass(VllmInductorPass):
     """
     This pass lowers vLLM IR ops to their implementations the priority list.
@@ -76,7 +45,7 @@ def lower_matched_op(self, match: Match, *args, **kwargs):
 
         assert len(match.nodes) == 1, "Expected single node match"
         node = match.nodes[0]
-        ir_op = get_ir_op(node)
+        ir_op = get_ir_op(node)  # TODO is node.target always an overload?
         assert ir_op is not None, "Expected vLLM IR op"
         assert not node.kwargs  # I think there should never be kwargs here
 
@@ -86,13 +55,18 @@ def lower_matched_op(self, match: Match, *args, **kwargs):
         self.selected_impls[ir_op.name][node.name] = ir_op_impl.provider
 
         # replace_by_example wants node args, not the fake tensors
+        # use func_impl_fn to properly handle in-place implementations
         # TODO(luka): Use aot_export_module to get functionalized graph
         # TODO(luka): Cache the fx_replacement to avoid re-tracing the same impl
 
         # Defaults not present on node.args but required for replacement tracing
         bound_args = ir_op._py_signature.bind(*node.args)
         bound_args.apply_defaults()
-        match.replace_by_example(ir_op_impl.impl_fn, bound_args.args)
+        # It is not safe to run functional passes (like DCE) on the replacements
+        # as they might not be functional.
+        match.replace_by_example(
+            ir_op_impl.func_impl_fn, bound_args.args, run_functional_passes=False
+        )
 
     @VllmInductorPass.time_and_log
     def __call__(self, graph: fx.Graph) -> None:
@@ -136,7 +110,7 @@ def print_count(counts: dict[str, int]) -> str:
 
         if failed_nodes or failed_ops:
             logger.warning("Failed to lower vLLM IR ops: %s", ",".join(failed_ops))
-            logger.warning("Full node list: %s", failed_nodes)
+            logger.warning("Full node list: %s", ",".join(str(n) for n in failed_nodes))
 
     def uuid(self) -> str:
         """
diff --git a/vllm/compilation/passes/ir/utils.py b/vllm/compilation/passes/ir/utils.py
new file mode 100644
index 000000000000..50b4773ce523
--- /dev/null
+++ b/vllm/compilation/passes/ir/utils.py
@@ -0,0 +1,40 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from torch import fx
+from torch._ops import OpOverload, OpOverloadPacket
+
+from vllm.ir.op import IrOp
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+def overload_or_default(op: OpOverload | OpOverloadPacket) -> OpOverload:
+    if isinstance(op, OpOverloadPacket):
+        return op.default
+    assert isinstance(op, OpOverload), "Expected an OpOverload or OpOverloadPacket"
+    return op
+
+
+def get_ir_op(node: fx.Node) -> IrOp | None:
+    if node.op != "call_function":
+        return None
+
+    if not isinstance(node.target, (OpOverload, OpOverloadPacket)):
+        return None
+
+    op_overload = overload_or_default(node.target)
+    if op_overload.namespace != "vllm_ir":
+        return None
+
+    op_name = op_overload._opname
+    if op_name not in IrOp.registry:
+        logger.warning(
+            "Unknown vLLM IR op %s, there's likely an issue with torch registration, "
+            "or a torch custom op was registered in the vllm_ir namespace by mistake.",
+            op_name,
+        )
+        return None
+
+    ir_op = IrOp.registry[op_name]
+    return ir_op
diff --git a/vllm/compilation/passes/pass_manager.py b/vllm/compilation/passes/pass_manager.py
index 91e101456074..5d4355a5b2b4 100644
--- a/vllm/compilation/passes/pass_manager.py
+++ b/vllm/compilation/passes/pass_manager.py
@@ -14,11 +14,16 @@
 from vllm.platforms import current_platform
 from vllm.utils.system_utils import set_env_var
 
+from .ir.clone_elimination import UnsafeCloneEliminationPass
 from .ir.lowering_pass import VllmIRLoweringPass
 from .vllm_inductor_pass import VllmInductorPass, VllmPatternMatcherPass
 
 if rocm_aiter_ops.is_enabled():
+    from .fusion.allreduce_rms_fusion import (
+        RocmAiterAllReduceFusionPass,
+    )
     from .fusion.rocm_aiter_fusion import (
+        MLADualRMSNormFusionPass,
         RocmAiterRMSNormQuantFusionPass,
         RocmAiterSiluMulFp8GroupQuantFusionPass,
         RocmAiterTritonAddRMSNormPadFusionPass,
@@ -111,6 +116,8 @@ def __call__(self, graph: fx.Graph) -> None:
         # DCE handles mutating ops correctly as well.
         self.ir_lowering(graph)
         VllmInductorPass.dump_prefix += 1
+        self.clone_elimination(graph)
+        VllmInductorPass.dump_prefix += 1
 
         # clean up after lowering again
         self.post_cleanup(graph)
@@ -136,17 +143,21 @@ def configure(self, config: VllmConfig) -> None:
                     self.passes += [AsyncTPPass(config)]
 
             if self.pass_config.fuse_allreduce_rms:
-                self.passes += [AllReduceFusionPass(config)]
+                if rocm_aiter_ops.is_enabled():
+                    self.passes += [RocmAiterAllReduceFusionPass(config)]
+                else:
+                    self.passes += [AllReduceFusionPass(config)]
 
             if self.pass_config.fuse_minimax_qk_norm:
                 self.passes += [MiniMaxQKNormPass(config)]
 
             if self.pass_config.fuse_norm_quant:
-                self.passes += [RMSNormQuantFusionPass(config)]
                 if rocm_aiter_ops.is_enabled():
                     self.passes += [
                         RocmAiterRMSNormQuantFusionPass(config),
                     ]
+                self.passes += [RMSNormQuantFusionPass(config)]
+
             if self.pass_config.fuse_act_quant:
                 self.passes += [ActivationQuantFusionPass(config)]
                 if rocm_aiter_ops.is_enabled():
@@ -155,6 +166,9 @@ def configure(self, config: VllmConfig) -> None:
             if self.pass_config.fuse_act_padding and rocm_aiter_ops.is_enabled():
                 self.passes += [RocmAiterTritonAddRMSNormPadFusionPass(config)]
 
+            if self.pass_config.fuse_mla_dual_rms_norm and rocm_aiter_ops.is_enabled():
+                self.passes += [MLADualRMSNormFusionPass(config)]
+
             if self.pass_config.fuse_rope_kvcache:
                 self.passes += [SplitCoalescingPass(config)]
                 self.passes += [ScatterSplitReplacementPass(config)]
@@ -169,6 +183,7 @@ def configure(self, config: VllmConfig) -> None:
                 self.passes += [QKNormRoPEFusionPass(config)]
 
             self.ir_lowering = VllmIRLoweringPass(config)
+            self.clone_elimination = UnsafeCloneEliminationPass(config)
             self.post_cleanup = PostCleanupPass(config)
             self.fix_functionalization = FixFunctionalizationPass(config)
 
@@ -190,6 +205,7 @@ def uuid(self) -> str:
 
         passes.append(self.post_cleanup.uuid())
         passes.append(self.ir_lowering.uuid())
+        passes.append(self.clone_elimination.uuid())
         passes.append(self.post_cleanup.uuid())
         passes.append(self.fix_functionalization.uuid())
 
diff --git a/vllm/compilation/piecewise_backend.py b/vllm/compilation/piecewise_backend.py
index e167a303de79..b647e0d8581a 100644
--- a/vllm/compilation/piecewise_backend.py
+++ b/vllm/compilation/piecewise_backend.py
@@ -11,6 +11,7 @@
 
 import torch._functorch.config
 import torch.fx as fx
+from torch._dynamo.utils import dynamo_timed
 from torch._inductor.runtime.triton_heuristics import CachingAutotuner
 from torch._logging._internal import trace_structured
 
@@ -275,6 +276,7 @@ def compile_all_ranges(self) -> None:
 
             range_entry.compiled = True
 
+    @dynamo_timed("vllm_log_compile_start_torch_trace_only")
     def _log_compile_start(self, compile_range: Range):
         """Log compilation event for TORCH_TRACE/tlparse."""
         is_cudagraph_size = (
@@ -354,12 +356,22 @@ def _find_range_for_shape(self, runtime_shape: int) -> RangeEntry | None:
         return None
 
     def __call__(self, *args: Any) -> Any:
-        runtime_shape = args[self.sym_shape_indices[0]]
-        range_entry = self._find_range_for_shape(runtime_shape)
+        if self.sym_shape_indices:
+            runtime_shape = args[self.sym_shape_indices[0]]
+            range_entry = self._find_range_for_shape(runtime_shape)
+            assert range_entry is not None, (
+                f"Shape: {runtime_shape} out of considered ranges: "
+                f"{self.compile_ranges}"
+            )
+        else:
+            # All inputs have static shapes; use the only compiled range_entry
+            compiled_entries = [re for re in self.range_entries.values() if re.compiled]
+            assert len(compiled_entries) == 1, (
+                f"Expected exactly one compiled range_entry for static shape "
+                f"compilation, but found {len(compiled_entries)}"
+            )
+            range_entry = compiled_entries[0]
 
-        assert range_entry is not None, (
-            f"Shape: {runtime_shape} out of considered ranges: {self.compile_ranges}"
-        )
         assert range_entry.compiled, (
             "All ranges should be compiled or loaded up front in "
             "PiecewiseBackend.__init__. "
diff --git a/vllm/compilation/wrapper.py b/vllm/compilation/wrapper.py
index dc48528e9fcf..5635fe03ae2f 100644
--- a/vllm/compilation/wrapper.py
+++ b/vllm/compilation/wrapper.py
@@ -53,12 +53,6 @@ class TorchCompileWithNoGuardsWrapper:
     since we drop all guards.
     """
 
-    def check_invariants_and_forward(self, *args: Any, **kwargs: Any) -> Any:
-        assert hasattr(self, "_check_shape_invariants")
-        self._check_shape_invariants(*args, **kwargs)
-
-        return self.forward(*args, **kwargs)
-
     def _call_with_optional_nvtx_range(
         self, callable_fn: Callable[P, R], *args: P.args, **kwargs: P.kwargs
     ) -> Any:
@@ -115,6 +109,9 @@ def __init__(
                     "compilation_config.dynamic_shapes_config.evaluate_guards "
                     "requires VLLM_USE_BYTECODE_HOOK=0. "
                 )
+                assert ds_type != DynamicShapesType.UNBACKED, (
+                    "UNBACKED dynamic shapes do not add guards"
+                )
 
                 options["guard_filter_fn"] = lambda x: [
                     entry.guard_type == "SHAPE_ENV" for entry in x
@@ -130,19 +127,6 @@ def __init__(
         compiled_ptr: Any = self.forward
         # Validate that unbacked dynamic shapes require VLLM_USE_BYTECODE_HOOK=False
 
-        if ds_type == DynamicShapesType.UNBACKED:
-            # reason is that bytecode does torch._dynamo.eval_frame.
-            # remove_from_cache(self.original_code_object()) to force a new
-            # re-compilation. And if we use
-            # compiled_ptr = self.check_invariants_and_forward
-            # it will reset all entries.
-            assert not envs.VLLM_USE_BYTECODE_HOOK, (
-                "UNBACKED dynamic shapes requires VLLM_USE_BYTECODE_HOOK=0. "
-            )
-            assert not self.evaluate_guards, "UNBACKED dynamic shapes do not add guards"
-
-            compiled_ptr = self.check_invariants_and_forward
-
         # Apply the constrain_to_fx_strides patch before first compilation.
         # This covers STOCK_TORCH_COMPILE and DYNAMO_ONCE paths. The VLLM
         # compile paths call this from their own compile() methods too.
diff --git a/vllm/config/__init__.py b/vllm/config/__init__.py
index 758605d25c60..b189c45c8d7a 100644
--- a/vllm/config/__init__.py
+++ b/vllm/config/__init__.py
@@ -37,7 +37,7 @@
 from vllm.config.reasoning import ReasoningConfig
 from vllm.config.scheduler import SchedulerConfig
 from vllm.config.speculative import SpeculativeConfig
-from vllm.config.speech_to_text import SpeechToTextConfig
+from vllm.config.speech_to_text import SpeechToTextConfig, SpeechToTextParams
 from vllm.config.structured_outputs import StructuredOutputsConfig
 from vllm.config.utils import (
     ConfigType,
@@ -113,6 +113,7 @@
     "SpeculativeConfig",
     # From vllm.config.speech_to_text
     "SpeechToTextConfig",
+    "SpeechToTextParams",
     # From vllm.config.structured_outputs
     "StructuredOutputsConfig",
     # From vllm.config.profiler
diff --git a/vllm/config/attention.py b/vllm/config/attention.py
index 1da647a6d6ff..b5dc7a5bf602 100644
--- a/vllm/config/attention.py
+++ b/vllm/config/attention.py
@@ -6,8 +6,12 @@
 from pydantic import field_validator
 
 from vllm.config.utils import config
+from vllm.logger import init_logger
+from vllm.v1.attention.backends.mla.prefill.registry import MLAPrefillBackendEnum
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
+logger = init_logger(__name__)
+
 
 @config
 class AttentionConfig:
@@ -27,8 +31,13 @@ class AttentionConfig:
     flash_attn_max_num_splits_for_cuda_graph: int = 32
     """Flash Attention max number splits for cuda graph decode."""
 
+    tq_max_kv_splits_for_cuda_graph: int = 32
+    """TurboQuant max NUM_KV_SPLITS for cuda graph decode.
+    Fixes the split count so grid dimensions are constant across captures,
+    and buffers can be pre-allocated to avoid inflating the memory estimate."""
+
     use_cudnn_prefill: bool = False
-    """Whether to use cudnn prefill."""
+    """Deprecated: cuDNN prefill backend has been removed."""
 
     use_trtllm_ragged_deepseek_prefill: bool = False
     """Whether to use TRTLLM ragged deepseek prefill."""
@@ -37,15 +46,27 @@ class AttentionConfig:
     """If set to True/False, use or don't use the TRTLLM attention backend
     in flashinfer. If None, auto-detect the attention backend in flashinfer."""
 
-    disable_flashinfer_prefill: bool = True
+    disable_flashinfer_prefill: bool | None = None
     """Whether to disable flashinfer prefill."""
 
     disable_flashinfer_q_quantization: bool = False
     """If set, when using fp8 kv, do not quantize Q to fp8."""
 
+    mla_prefill_backend: MLAPrefillBackendEnum | None = None
+    """MLA prefill backend to use. If None, will be selected automatically.
+    Valid options: FLASH_ATTN (FA3/FA4), FLASHINFER, TRTLLM_RAGGED.
+    This option supersedes use_trtllm_ragged_deepseek_prefill
+    and disable_flashinfer_prefill which are deprecated."""
+
     use_prefill_query_quantization: bool = False
     """If set, quantize query for attention in prefill."""
 
+    use_fp4_indexer_cache: bool = False
+    """If set, use fp4 indexer cache for dsv32 family model (not support yet)"""
+
+    use_non_causal: bool = False
+    """Whether to use non-causal (bidirectional) attention."""
+
     def compute_hash(self) -> str:
         """
         Provide a hash that uniquely identifies all the configs
@@ -73,3 +94,48 @@ def validate_backend_before(cls, value: Any) -> Any:
                 return None
             return AttentionBackendEnum[value.upper()]
         return value
+
+    @field_validator("mla_prefill_backend", mode="before")
+    @classmethod
+    def validate_mla_prefill_backend_before(cls, value: Any) -> Any:
+        """Enable parsing of the `mla_prefill_backend` enum type from string."""
+        if isinstance(value, str):
+            return MLAPrefillBackendEnum[value.upper()]
+        return value
+
+    def __post_init__(self) -> None:
+        self._migrate_deprecated_mla_prefill_flags()
+
+    def _migrate_deprecated_mla_prefill_flags(self) -> None:
+        """Migrate deprecated MLA prefill flags to mla_prefill_backend."""
+        # If the new option is already set, it takes precedence
+        if self.mla_prefill_backend is not None:
+            return
+
+        # Check for deprecated flags and migrate them.
+        # Only the first flag encountered sets the backend.
+        if self.use_cudnn_prefill:
+            raise ValueError(
+                "The cuDNN MLA prefill backend has been removed. "
+                "Use --attention-config.mla_prefill_backend=FLASH_ATTN or "
+                "FLASHINFER or TRTLLM_RAGGED instead."
+            )
+
+        if self.use_trtllm_ragged_deepseek_prefill:
+            if self.mla_prefill_backend is None:
+                self.mla_prefill_backend = MLAPrefillBackendEnum.TRTLLM_RAGGED
+            logger.warning_once(
+                "use_trtllm_ragged_deepseek_prefill is deprecated and "
+                "will be removed in v0.22. Use "
+                "--attention-config.mla_prefill_backend=TRTLLM_RAGGED "
+                "instead."
+            )
+
+        if self.disable_flashinfer_prefill:
+            if self.mla_prefill_backend is None:
+                self.mla_prefill_backend = MLAPrefillBackendEnum.FLASH_ATTN
+            logger.warning_once(
+                "disable_flashinfer_prefill is deprecated and will be removed "
+                "in v0.22. Use --attention-config.mla_prefill_backend="
+                "FLASH_ATTN instead."
+            )
diff --git a/vllm/config/cache.py b/vllm/config/cache.py
index 20721cc80923..ae5023f1e348 100644
--- a/vllm/config/cache.py
+++ b/vllm/config/cache.py
@@ -24,8 +24,13 @@
     "fp8_e5m2",
     "fp8_inc",
     "fp8_ds_mla",
+    "turboquant_k8v4",
+    "turboquant_4bit_nc",
+    "turboquant_k3v4_nc",
+    "turboquant_3bit_nc",
     "int8_per_token_head",
     "fp8_per_token_head",
+    "nvfp4",
 ]
 MambaDType = Literal["auto", "float32", "float16"]
 MambaCacheMode = Literal["all", "align", "none"]
@@ -46,10 +51,22 @@ class CacheConfig:
     """Whether block_size was explicitly provided. Derived automatically."""
     user_specified_mamba_block_size: bool = field(default=False, init=False)
     """Whether mamba_block_size was explicitly provided. Derived automatically."""
-    gpu_memory_utilization: float = Field(default=0.9, gt=0, le=1)
+    hash_block_size: SkipValidation[int] | None = None  # type: ignore
+    """Block size (in tokens) used for computing Request's block_hashes.
+
+    This can be set to a finer granularity than the physical KV cache block
+    sizes (e.g. 8) as long as every KV cache group's `block_size` is divisible
+    by it. This enables prefix-caching keys to be computed at the finest common
+    granularity and then merged for larger physical block sizes.
+
+    This config is not static default. If left unspecified, vLLM will choose a
+    default based on the resolved KV cache groups (typically the smallest KV
+    cache block size when there are multiple groups).
+    """
+    gpu_memory_utilization: float = Field(default=0.92, gt=0, le=1)
     """The fraction of GPU memory to be used for the model executor, which can
     range from 0 to 1. For example, a value of 0.5 would imply 50% GPU memory
-    utilization. If unspecified, will use the default value of 0.9. This is a
+    utilization. If unspecified, will use the default value of 0.92. This is a
     per-instance limit, and only applies to the current vLLM instance. It does
     not matter if you have another vLLM instance running on the same GPU. For
     example, if you have two vLLM instances running on the same GPU, you can
@@ -97,8 +114,6 @@ class CacheConfig:
     kv_cache_dtype_skip_layers: list[str] = field(default_factory=list)
     """Layer patterns to skip KV cache quantization. Accepts layer indices
     (e.g., '0', '2', '4') or attention type names (e.g., 'sliding_window')."""
-    cpu_kvcache_space_bytes: int | None = None
-    """(CPU backend only) CPU key-value cache space."""
     mamba_page_size_padded: int | None = None
     """ Optional override for mamba page size; used by hybrid mamba/attention
     models to ensure exact alignment with attention page size."""
@@ -179,7 +194,8 @@ def compute_hash(self) -> str:
             "num_gpu_blocks_override",
             "enable_prefix_caching",
             "prefix_caching_hash_algo",
-            "cpu_kvcache_space_bytes",
+            # Prefix-caching implementation detail (doesn't affect compiled graph).
+            "hash_block_size",
             "mamba_page_size_padded",
             "user_specified_block_size",
             "user_specified_mamba_block_size",
diff --git a/vllm/config/compilation.py b/vllm/config/compilation.py
index 6aca5c9825fb..0f02a92681c1 100644
--- a/vllm/config/compilation.py
+++ b/vllm/config/compilation.py
@@ -142,6 +142,8 @@ class PassConfig:
     # ROCm/AITER specific fusions
     fuse_act_padding: bool = None  # type: ignore[assignment]
     """Fuse the custom RMSNorm + padding ops."""
+    fuse_mla_dual_rms_norm: bool = None  # type: ignore[assignment]
+    """Fuse paired q/kv RMS norms in MLA attention."""
     fuse_rope_kvcache: bool = None  # type: ignore[assignment]
     """Fuse the QK rope + KV cache ops."""
 
@@ -224,6 +226,7 @@ def compute_hash(self) -> str:
         "fuse_gemm_comms",
         "fuse_allreduce_rms",
         "fuse_act_padding",
+        "fuse_mla_dual_rms_norm",
         "fuse_rope_kvcache",
         mode="wrap",
     )
@@ -270,6 +273,12 @@ def __post_init__(self) -> None:
                 "The fusion will be disabled."
             )
             self.fuse_act_padding = False
+        if self.fuse_mla_dual_rms_norm and not current_platform.is_rocm():
+            logger.warning_once(
+                "MLA dual RMS norm fusion requires ROCm/AITER. "
+                "The fusion will be disabled."
+            )
+            self.fuse_mla_dual_rms_norm = False
         if self.fuse_rope_kvcache and not current_platform.is_rocm():
             logger.warning_once(
                 "KV cache fusion currently only enabled on ROCm. "
@@ -526,13 +535,15 @@ class CompilationConfig:
     model's budget range. User-provided positive value overrides
     auto-inference."""
 
-    encoder_cudagraph_max_frames_per_batch: int = 0
+    encoder_cudagraph_max_frames_per_batch: int | None = None
     """Maximum total video frames per batch for encoder CUDA graph capture.
     Controls the cu_seqlens buffer size (one entry per attention sequence,
-    i.e. one per video frame). If 0 (default), auto-inferred per budget
-    level as token_budget (tight bound: packing guarantees
-    sum(T_i) <= token_budget). Positive value overrides auto-inference
-    and applies to all budget levels."""
+    i.e. one per video frame).
+    If None (default), auto-inferred as encoder_cudagraph_max_vision_items_per_batch
+    * max_frames_per_video (model-specific value according to processing_info).
+    Positive value overrides auto-inference and applies to all budget levels.
+    If we limit the video count per prompt to `0`, it will also be set to `0`
+    (i.e., fall back to image-only mode)."""
 
     # Inductor capture
     compile_sizes: list[int | str] | None = None
@@ -733,10 +744,12 @@ class CompilationConfig:
         "vllm::linear_attention",
         "vllm::plamo2_mamba_mixer",
         "vllm::gdn_attention_core",
+        "vllm::gdn_attention_core_xpu",
         "vllm::olmo_hybrid_gdn_full_forward",
         "vllm::kda_attention",
         "vllm::sparse_attn_indexer",
         "vllm::rocm_aiter_sparse_attn_indexer",
+        "vllm::deepseek_v4_attention",
     ]
 
     def compute_hash(self) -> str:
@@ -984,11 +997,12 @@ def __post_init__(self) -> None:
             )
         if (
             self.cudagraph_mm_encoder
+            and self.encoder_cudagraph_max_frames_per_batch is not None
             and self.encoder_cudagraph_max_frames_per_batch < 0
         ):
             raise ValueError(
                 "encoder_cudagraph_max_frames_per_batch must be "
-                "non-negative (0 = auto-infer)"
+                "non-negative (None = auto-infer)"
             )
 
         if self.backend == "":
@@ -1135,6 +1149,25 @@ def set_splitting_ops_for_v1(
                     self.cudagraph_mode = CUDAGraphMode.FULL
                 self.splitting_ops = []
 
+        if (
+            not self.use_inductor_graph_partition
+            and (self.pass_config.enable_sp or self.pass_config.fuse_gemm_comms)
+            and self.splitting_ops
+        ):
+            logger.warning_once(
+                "Sequence parallelism requires full-graph compilation when "
+                "use_inductor_graph_partition is off. Setting splitting_ops "
+                "to an empty list to preserve SP and async TP."
+            )
+            self.splitting_ops = []
+            if self.cudagraph_mode.has_piecewise_cudagraphs():
+                logger.warning_once(
+                    "Sequence parallelism is incompatible with piecewise "
+                    "cudagraph when use_inductor_graph_partition is off. "
+                    "Setting cudagraph_mode to FULL."
+                )
+                self.cudagraph_mode = CUDAGraphMode.FULL
+
         # Disable CUDA graphs for DeepEP high-throughput since its not CG compatible
         if (
             all2all_backend == "deepep_high_throughput"
diff --git a/vllm/config/kernel.py b/vllm/config/kernel.py
index f3ffbe4e8b19..ec9c87852513 100644
--- a/vllm/config/kernel.py
+++ b/vllm/config/kernel.py
@@ -31,6 +31,9 @@ class IrOpPriorityConfig:
     rms_norm: list[str] = Field(default_factory=list)
     """Priority list for vllm.ir.ops.rms_norm"""
 
+    fused_add_rms_norm: list[str] = Field(default_factory=list)
+    """Priority list for vllm.ir.ops.fused_add_rms_norm"""
+
     def compute_hash(self) -> str:
         """
         Produces a hash unique to the pass configuration.
@@ -50,7 +53,7 @@ def compute_hash(self) -> str:
             name: {
                 provider: IrOp.registry[name].impls[provider].uuid() for provider in p
             }
-            for name, p in asdict(self).items()
+            for name, p in asdict(self).items()  # type: ignore[call-overload]
         }
 
         return hash_factors(factors)
@@ -77,7 +80,7 @@ def set_priority(self):
         current_platform.import_ir_kernels()
 
         with contextlib.ExitStack() as stack:
-            for field in fields(self):
+            for field in fields(self):  # type: ignore[arg-type]
                 op_priority = getattr(self, field.name)
                 assert op_priority is not None, (
                     f"IR op priority for {field.name} must be set"
@@ -98,7 +101,7 @@ def with_default(
         A helper to create an IrOpPriorityConfig where fields not specified in kwargs
         use the given default list.
         """
-        for field in fields(cls):
+        for field in fields(cls):  # type: ignore[arg-type]
             if field.name not in kwargs:
                 kwargs[field.name] = list(default)
 
@@ -109,12 +112,16 @@ def with_default(
     "auto",
     "triton",
     "deep_gemm",
+    "deep_gemm_mega_moe",
     "cutlass",
     "flashinfer_trtllm",
     "flashinfer_cutlass",
     "flashinfer_cutedsl",
     "marlin",
+    "humming",
+    "triton_unfused",
     "aiter",
+    "emulation",
 ]
 
 
@@ -137,12 +144,18 @@ class KernelConfig:
     - "auto": Automatically select the best backend based on model and hardware
     - "triton": Use Triton-based fused MoE kernels
     - "deep_gemm": Use DeepGEMM kernels (FP8 block-quantized only)
+    - "deep_gemm_mega_moe": Use DeepGEMM mega MoE kernels
     - "cutlass": Use vLLM CUTLASS kernels
     - "flashinfer_trtllm": Use FlashInfer with TRTLLM-GEN kernels
     - "flashinfer_cutlass": Use FlashInfer with CUTLASS kernels
     - "flashinfer_cutedsl": Use FlashInfer with CuteDSL kernels (FP4 only)
     - "marlin": Use Marlin kernels (weight-only quantization)
-    - "aiter": Use AMD AITer kernels (ROCm only)"""
+    - "humming": Use Humming Mixed Precision kernels
+    - "triton_unfused": Use Triton unfused MoE kernels
+    - "aiter": Use AMD AITer kernels (ROCm only)
+    - "emulation": use BF16/FP16 GEMM, dequantizing weights and
+                   running QDQ on activations.
+    """
 
     @field_validator("moe_backend", mode="before")
     @classmethod
diff --git a/vllm/config/load.py b/vllm/config/load.py
index 93240ec5fc0f..07feaeb961ab 100644
--- a/vllm/config/load.py
+++ b/vllm/config/load.py
@@ -9,6 +9,9 @@
 from vllm.logger import init_logger
 from vllm.utils.hashing import safe_hash
 
+DEFAULT_SAFETENSORS_PREFETCH_NUM_THREADS = 8
+DEFAULT_SAFETENSORS_PREFETCH_BLOCK_SIZE = 16 * 1024 * 1024
+
 if TYPE_CHECKING:
     from vllm.model_executor.model_loader import LoadFormats
     from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
@@ -79,6 +82,15 @@ class LoadConfig:
       was quantized using torchao and saved using safetensors.
       Needs `torchao >= 0.14.0`.
     """
+    safetensors_prefetch_num_threads: int = Field(
+        default=DEFAULT_SAFETENSORS_PREFETCH_NUM_THREADS, ge=1
+    )
+    """Number of worker threads used to prefetch safetensors checkpoint files
+    into the OS page cache when safetensors prefetching is enabled."""
+    safetensors_prefetch_block_size: int = Field(
+        default=DEFAULT_SAFETENSORS_PREFETCH_BLOCK_SIZE, ge=1
+    )
+    """Read size in bytes for each safetensors checkpoint file prefetch."""
     model_loader_extra_config: dict | TensorizerConfig = Field(default_factory=dict)
     """Extra config for model loader. This will be passed to the model loader
     corresponding to the chosen load_format."""
diff --git a/vllm/config/model.py b/vllm/config/model.py
index 2b767b21a7c7..22a1f6a42981 100644
--- a/vllm/config/model.py
+++ b/vllm/config/model.py
@@ -83,7 +83,9 @@
 RunnerOption = Literal["auto", RunnerType]
 ConvertType = Literal["none", "embed", "classify"]
 ConvertOption = Literal["auto", ConvertType]
-TokenizerMode = Literal["auto", "hf", "slow", "mistral", "deepseek_v32"]
+TokenizerMode = Literal[
+    "auto", "hf", "slow", "mistral", "deepseek_v32", "deepseek_v4", "fastokens"
+]
 ModelDType = Literal["auto", "half", "float16", "bfloat16", "float", "float32"]
 LogprobsMode = Literal[
     "raw_logits", "raw_logprobs", "processed_logits", "processed_logprobs"
@@ -134,7 +136,11 @@ class ModelConfig:
     - "slow" will always use the slow tokenizer.
     - "mistral" will always use the tokenizer from `mistral_common`.
     - "deepseek_v32" will always use the tokenizer from `deepseek_v32`.
+    - "deepseek_v4" will always use the tokenizer from `deepseek_v4`.
     - "qwen_vl" will always use the tokenizer from `qwen_vl`.
+    - "fastokens" loads a Hugging Face fast tokenizer powered by the
+      [fastokens](https://github.com/crusoecloud/fastokens) Rust BPE backend
+      (requires the `fastokens` package to be installed).
     - Other custom values can be supported via plugins."""
     trust_remote_code: bool = False
     """Trust remote code (e.g., from HuggingFace) when downloading the model
@@ -325,6 +331,10 @@ class ModelConfig:
     mm_encoder_only: InitVar[bool | None] = None
     mm_encoder_tp_mode: InitVar[MMEncoderTPMode | None] = None
     mm_encoder_attn_backend: InitVar[AttentionBackendEnum | str | None] = None
+    mm_encoder_attn_dtype: InitVar[str | None] = None
+    mm_encoder_fp8_scale_path: InitVar[str | None] = None
+    mm_encoder_fp8_scale_save_path: InitVar[str | None] = None
+    mm_encoder_fp8_scale_save_margin: InitVar[float | None] = None
     interleave_mm_strings: InitVar[bool | None] = None
     skip_mm_profiling: InitVar[bool | None] = None
     video_pruning_rate: InitVar[float | None] = None
@@ -446,6 +456,10 @@ def __post_init__(
         mm_encoder_only: bool | None,
         mm_encoder_tp_mode: MMEncoderTPMode | None,
         mm_encoder_attn_backend: AttentionBackendEnum | str | None,
+        mm_encoder_attn_dtype: str | None,
+        mm_encoder_fp8_scale_path: str | None,
+        mm_encoder_fp8_scale_save_path: str | None,
+        mm_encoder_fp8_scale_save_margin: float | None,
         interleave_mm_strings: bool | None,
         skip_mm_profiling: bool | None,
         video_pruning_rate: float | None,
@@ -512,6 +526,7 @@ def __post_init__(
         if dict_overrides:
             self._apply_dict_overrides(hf_config, dict_overrides)
         self.hf_text_config = get_hf_text_config(self.hf_config)
+        self.model_arch_config = self.get_model_arch_config()
         self.attention_chunk_size = getattr(
             self.hf_text_config, "attention_chunk_size", None
         )
@@ -519,7 +534,6 @@ def __post_init__(
         self.hf_image_processor_config = get_hf_image_processor_config(
             self.model, hf_token=self.hf_token, revision=self.revision
         )
-        self.model_arch_config = self.get_model_arch_config()
 
         architectures = self.architectures
         registry = self.registry
@@ -565,6 +579,8 @@ def __post_init__(
                 self.tokenizer_mode = "qwen_vl"
             elif arch == "DeepseekV32ForCausalLM":
                 self.tokenizer_mode = "deepseek_v32"
+            elif arch == "DeepseekV4ForCausalLM":
+                self.tokenizer_mode = "deepseek_v4"
 
             if self.tokenizer_mode != "auto":
                 logger.info(
@@ -640,6 +656,10 @@ def __post_init__(
                 mm_encoder_only=mm_encoder_only,
                 mm_encoder_tp_mode=mm_encoder_tp_mode,
                 mm_encoder_attn_backend=mm_encoder_attn_backend,
+                mm_encoder_attn_dtype=mm_encoder_attn_dtype,
+                mm_encoder_fp8_scale_path=mm_encoder_fp8_scale_path,
+                mm_encoder_fp8_scale_save_path=mm_encoder_fp8_scale_save_path,
+                mm_encoder_fp8_scale_save_margin=mm_encoder_fp8_scale_save_margin,
                 interleave_mm_strings=interleave_mm_strings,
                 skip_mm_profiling=skip_mm_profiling,
                 video_pruning_rate=video_pruning_rate,
@@ -952,9 +972,14 @@ def _verify_quantization(self) -> None:
                 # imports during override detection (e.g., MXFP4 imports Triton)
                 "mxfp4",
                 "gpt_oss_mxfp4",
+                "deepseek_v4_fp8",
                 "cpu_awq",
+                "humming",
                 "gguf",
             ]
+            # if the user specifies humming, we should always use humming
+            if self.quantization == "humming":
+                overrides = ["humming"] + overrides
             quantization_methods = [
                 q for q in supported_quantization if q not in overrides
             ]
@@ -1197,22 +1222,9 @@ def get_inputs_embeds_size(self) -> int:
     def is_deepseek_mla(self) -> bool:
         return self.model_arch_config.is_deepseek_mla
 
-    @cached_property
+    @property
     def is_mm_prefix_lm(self) -> bool:
-        """Whether to use bidirectional attention for mm positions."""
-        if hasattr(self.hf_config, "is_mm_prefix_lm"):
-            return bool(self.hf_config.is_mm_prefix_lm)
-        # fallback to list of known models
-        MM_PREFIX_LM_MODELS = (
-            "bagel",
-            "gemma3",
-            "molmo2",
-            "paligemma",
-            "umm",
-        )
-        if not hasattr(self.hf_config, "model_type"):
-            return False
-        return self.hf_config.model_type in MM_PREFIX_LM_MODELS
+        return self.model_arch_config.is_mm_prefix_lm
 
     def get_head_size(self) -> int:
         return self.model_arch_config.head_size
diff --git a/vllm/config/model_arch.py b/vllm/config/model_arch.py
index 24d1baea0a9e..0b99df22b880 100644
--- a/vllm/config/model_arch.py
+++ b/vllm/config/model_arch.py
@@ -53,5 +53,8 @@ class ModelArchitectureConfig:
     is_deepseek_mla: bool
     """Whether the model is a DeepSeek MLA model."""
 
+    is_mm_prefix_lm: bool
+    """Whether the model uses image bidirectional attention."""
+
     derived_max_model_len_and_key: tuple[float, str | None]
     """Derived maximum model length and key from the hf config."""
diff --git a/vllm/config/multimodal.py b/vllm/config/multimodal.py
index e66511c92ab2..56333b1116c0 100644
--- a/vllm/config/multimodal.py
+++ b/vllm/config/multimodal.py
@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from collections.abc import Mapping
+from pathlib import Path
 from typing import Any, Literal, TypeAlias, TypedDict, final
 
 from pydantic import ConfigDict, Field, field_validator, model_validator
@@ -158,6 +159,24 @@ class MultiModalConfig:
     """Optional override for the multi-modal encoder attention backend when
     using vision transformers. Accepts any value from
     `vllm.v1.attention.backends.registry.AttentionBackendEnum` (e.g. `FLASH_ATTN`)."""
+    mm_encoder_attn_dtype: Literal["fp8"] | None = None
+    """Optional dtype override for ViT encoder attention. Set to `"fp8"` to
+    enable FP8 quantization via the FlashInfer cuDNN backend. When set to
+    `"fp8"` without a scale file, dynamic scaling is used automatically.
+    See docs/features/quantization/fp8_vit_attn.md for details."""
+    mm_encoder_fp8_scale_path: str | None = None
+    """Path to a JSON file containing per-layer FP8 Q/K/V scales for ViT
+    encoder attention. When provided (with `mm_encoder_attn_dtype="fp8"`),
+    static scaling is used. When omitted, dynamic scaling is used."""
+    mm_encoder_fp8_scale_save_path: str | None = None
+    """When set with dynamic FP8 scaling (`mm_encoder_attn_dtype="fp8"`
+    and no `mm_encoder_fp8_scale_path`), saves the calibrated scales to
+    this file after the amax history buffer is full. The saved file can
+    then be used as `mm_encoder_fp8_scale_path` in subsequent runs."""
+    mm_encoder_fp8_scale_save_margin: float = Field(default=1.5, gt=0.0)
+    """Safety margin multiplied onto scales when auto-saving. A value > 1
+    leaves headroom so that inputs with larger activations than the
+    calibration set do not overflow FP8 range. Default 1.5."""
     interleave_mm_strings: bool = False
     """Enable fully interleaved support for multimodal prompts, while using
     --chat-template-content-format=string."""
@@ -233,6 +252,36 @@ def _validate_multimodal_config(self):
                 "'mm_shm_cache_max_object_size_mb' should only be set when "
                 "'mm_processor_cache_type' is 'shm'."
             )
+        # Validate FP8 scale path combinations.
+        if self.mm_encoder_attn_dtype != "fp8" and (
+            self.mm_encoder_fp8_scale_path is not None
+            or self.mm_encoder_fp8_scale_save_path is not None
+        ):
+            raise ValueError(
+                "'mm_encoder_fp8_scale_path' and "
+                "'mm_encoder_fp8_scale_save_path' require "
+                "'mm_encoder_attn_dtype' to be 'fp8'."
+            )
+        if (
+            self.mm_encoder_fp8_scale_path is not None
+            and self.mm_encoder_fp8_scale_save_path is not None
+        ):
+            raise ValueError(
+                "'mm_encoder_fp8_scale_save_path' cannot be used with "
+                "'mm_encoder_fp8_scale_path' (saving requires dynamic scaling)."
+            )
+
+        # Validate file paths exist.
+        if self.mm_encoder_fp8_scale_path is not None:
+            scale_path = Path(self.mm_encoder_fp8_scale_path)
+            if not scale_path.is_file():
+                raise FileNotFoundError(f"FP8 scale file not found: {scale_path}")
+        if self.mm_encoder_fp8_scale_save_path is not None:
+            save_parent = Path(self.mm_encoder_fp8_scale_save_path).parent
+            if not save_parent.is_dir():
+                raise FileNotFoundError(
+                    f"Parent directory for FP8 scale save path not found: {save_parent}"
+                )
         return self
 
     def compute_hash(self) -> str:
@@ -252,6 +301,8 @@ def compute_hash(self) -> str:
             if self.mm_encoder_attn_backend is not None
             else None,
             self.mm_encoder_tp_mode,
+            self.mm_encoder_attn_dtype,
+            self.mm_encoder_fp8_scale_path,
         ]
         hash_str = safe_hash(str(factors).encode(), usedforsecurity=False).hexdigest()
         return hash_str
diff --git a/vllm/config/parallel.py b/vllm/config/parallel.py
index a42b8422ef3c..95fd8787afe5 100644
--- a/vllm/config/parallel.py
+++ b/vllm/config/parallel.py
@@ -36,7 +36,7 @@
 DataParallelBackend = Literal["ray", "mp"]
 EPLBPolicyOption = Literal["default"]
 DCPCommBackend = Literal["ag_rs", "a2a"]
-EPLBCommunicatorBackend = Literal["torch_nccl", "torch_gloo", "pynccl"]
+EPLBCommunicatorBackend = Literal["torch_nccl", "torch_gloo", "nixl", "pynccl"]
 All2AllBackend = Literal[
     "naive",
     "pplx",
@@ -90,6 +90,7 @@ class EPLBConfig:
     Backend for EPLB expert weight communication:
     - "torch_nccl": Use torch.distributed on the device process group
     - "torch_gloo": Use torch.distributed gloo with CPU staging
+    - "nixl": Use NIXL/ RIXL with staged send/recv buffers
     - "pynccl": Use PyNccl send/recv
     - None: Auto-select backend ("torch_gloo" for async, "torch_nccl" for sync)
     """
@@ -134,8 +135,10 @@ class ParallelConfig:
     data_parallel_external_lb: bool = False
     """Whether to use "external" DP LB mode. Applies only to online serving
     and when data_parallel_size > 0. This is useful for a "one-pod-per-rank"
-    wide-EP setup in Kubernetes. Set implicitly when --data-parallel-rank
-    is provided explicitly to vllm serve."""
+    wide-EP setup in Kubernetes. Supported only for MoE deployments; non-MoE
+    models should use independent vLLM instances without --data-parallel-*
+    arguments. Set implicitly when --data-parallel-rank is provided explicitly
+    to vllm serve."""
     data_parallel_hybrid_lb: bool = False
     """Whether to use "hybrid" DP LB mode. Applies only to online serving
     and when data_parallel_size > 0. Enables running an AsyncLLM
@@ -662,6 +665,33 @@ def has_unfinished_dp(dp_group: ProcessGroup, has_unfinished: bool) -> bool:
         aggregated_has_unfinished = bool(tensor.item())
         return aggregated_has_unfinished
 
+    @staticmethod
+    def sync_dp_state(
+        dp_group: ProcessGroup, has_unfinished: bool, pending_pause: bool
+    ) -> tuple[bool, bool]:
+        """Combined all-reduce for DP state synchronization.
+
+        Uses a single SUM all-reduce on a 2-element tensor:
+          [0] = 1 if this rank has unfinished work, else 0.
+                SUM > 0 ≡ logical OR across ranks → any rank has work.
+          [1] = 1 if this rank has a pending pause request, else 0.
+                SUM == dp_size ≡ all ranks reached pause consensus.
+
+        has_unfinished_global is true if any rank has unfinished work,
+        or if some ranks are waiting for a pause consensus.
+
+        Returns:
+            (has_unfinished_global, pause_consensus)
+        """
+        tensor = torch.tensor(
+            [int(has_unfinished), int(pending_pause)], dtype=torch.int32, device="cpu"
+        )
+        torch.distributed.all_reduce(tensor, op=ReduceOp.SUM, group=dp_group)
+        dp_size = dp_group.size()
+        pause_count = tensor[1].item()
+        has_unfinished_global = tensor[0].item() > 0 or pause_count % dp_size != 0
+        return has_unfinished_global, pause_count == dp_size
+
     @staticmethod
     def sync_kv_cache_memory_size(dp_group: ProcessGroup, kv_cache_memory: int) -> int:
         if kv_cache_memory == -1:
@@ -712,6 +742,14 @@ def compute_hash(self):
             "worker_extension_cls",
             "_api_process_count",
             "_api_process_rank",
+            # NUMA binding is per-rank host-side memory locality; it does
+            # not affect collective-communication semantics. When numa_bind
+            # is enabled with auto-detection, each DP rank stores its own
+            # NUMA node in numa_bind_nodes (see vllm/utils/numa_utils.py
+            # `_get_numa_node`), which would otherwise diverge the DP hash.
+            "numa_bind",
+            "numa_bind_nodes",
+            "numa_bind_cpus",
         }
 
         from vllm.config.utils import get_hash_factors, hash_factors
diff --git a/vllm/config/quantization.py b/vllm/config/quantization.py
index 1b7022380eef..05e91f940253 100644
--- a/vllm/config/quantization.py
+++ b/vllm/config/quantization.py
@@ -19,7 +19,12 @@ class OnlineQuantScheme(Enum):
     # blocks of 128x128 elements (popularized by DeepSeek)
     FP8_PER_BLOCK = "fp8_per_block"
 
-    # TODO(future PRs): add more online quant schemes here: mxfp8, etc
+    # int8, weight-only per-channel quantization for MoE expert weights.
+    # Linear layers remain unquantized.
+    INT8_PER_CHANNEL_WEIGHT_ONLY = "int8_per_channel_weight_only"
+
+    # mxfp8, weights scaled in blocks of 1x32 elements (microscaling FP8)
+    MXFP8 = "mxfp8"
 
 
 @config
diff --git a/vllm/config/scheduler.py b/vllm/config/scheduler.py
index b9a48144ded4..fb6951ea7dd1 100644
--- a/vllm/config/scheduler.py
+++ b/vllm/config/scheduler.py
@@ -239,7 +239,6 @@ def __post_init__(self, max_model_len: int, is_encoder_decoder: bool) -> None:
             logger.info_once(
                 "Chunked prefill is enabled with max_num_batched_tokens=%d.",
                 self.max_num_batched_tokens,
-                scope="local",
             )
 
         if self.max_num_partial_prefills > 1:
diff --git a/vllm/config/speculative.py b/vllm/config/speculative.py
index 61f311c8429a..4589820c9fdc 100644
--- a/vllm/config/speculative.py
+++ b/vllm/config/speculative.py
@@ -1,11 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import ast
 import copy
 from typing import TYPE_CHECKING, Any, Literal, get_args
 
-from pydantic import Field, SkipValidation, model_validator
+from pydantic import Field, SkipValidation, field_validator, model_validator
 from typing_extensions import Self
 
 from vllm.config import LoadConfig
@@ -17,6 +16,7 @@
 from vllm.transformers_utils.config import get_hf_text_config
 from vllm.utils.hashing import safe_hash
 from vllm.utils.import_utils import LazyLoader, has_arctic_inference
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 if TYPE_CHECKING:
     from transformers import PretrainedConfig
@@ -34,6 +34,7 @@
 MTPModelTypes = Literal[
     "deepseek_mtp",
     "mimo_mtp",
+    "mimo_v2_mtp",
     "glm4_moe_mtp",
     "glm4_moe_lite_mtp",
     "glm_ocr_mtp",
@@ -47,6 +48,8 @@
     "mtp",
     "pangu_ultra_moe_mtp",
     "step3p5_mtp",
+    "hy_v3_mtp",
+    "gemma4_mtp",
 ]
 NgramGPUTypes = Literal["ngram_gpu"]
 DFlashModelTypes = Literal["dflash"]
@@ -62,7 +65,8 @@
     EagleModelTypes,
     NgramGPUTypes,
 ]
-RejectionSampleMethod = Literal["strict", "probabilistic", "synthetic"]
+RejectionSampleMethod = Literal["standard", "synthetic"]
+DraftSampleMethod = Literal["greedy", "gumbel"]
 
 
 @config
@@ -103,6 +107,10 @@ class SpeculativeConfig:
     inherits the target model's `--moe-backend` setting. Useful when the
     drafter and generator require different MoE kernels (e.g. quantized
     generator with unquantized drafter)."""
+    attention_backend: AttentionBackendEnum | None = None
+    """Attention backend to use for the draft model. When `None`, the backend is
+    automatically selected. Useful when the drafter requires a different attention
+    backend (e.g. DFlash needs a non-causal-capable backend like FLASH_ATTN)."""
     max_model_len: int | None = Field(default=None, ge=1)
     """The maximum model length of the draft model. Used when testing the
     ability to skip speculation for some sequences."""
@@ -136,9 +144,6 @@ class SpeculativeConfig:
     provided. Defaults to 1."""
 
     # Alternative drafting strategies
-    speculative_token_tree: str | None = None
-    """Specifies the tree structure for speculative token generation.
-    """
     parallel_drafting: bool = False
     """Enable parallel drafting, where all speculative tokens are generated
     in parallel rather than sequentially. This can improve performance but
@@ -182,18 +187,78 @@ class SpeculativeConfig:
     """Load config for the draft model. If not specified, will use the load
     config from the target model."""
 
-    rejection_sample_method: RejectionSampleMethod = "strict"
-    """Whether to use strict (target and draft sampled tokens match exactly)
-    or probabilistic rejection sampling. Both respect the target model
-    distribution, but the latter yields a higher acceptance rate at the cost
-    of more memory to cache draft logits."""
+    rejection_sample_method: RejectionSampleMethod = "standard"
+    """The rejection sampling method to use. 'standard' uses probabilistic
+    rejection sampling (with or without cached draft logits, controlled by
+    draft_sample_method). 'synthetic' accepts draft tokens with a decaying
+    probability calibrated to synthetic_acceptance_rate."""
+
+    synthetic_acceptance_rates: list[float] | None = None
+    """Per-position *unconditional* acceptance rates for synthetic rejection
+    sampling. Position i's entry is the marginal probability that the first
+    i+1 draft tokens are all accepted; the list must have length
+    num_speculative_tokens, each entry in [0, 1], and be monotonically
+    non-increasing. Only valid when rejection_sample_method is 'synthetic'.
+    Mutually exclusive with synthetic_acceptance_length."""
+
+    synthetic_acceptance_length: float | None = None
+    """Target mean acceptance length for synthetic rejection sampling, in
+    [1, num_speculative_tokens + 1]. Resolved internally to
+    synthetic_acceptance_rates. Only valid when rejection_sample_method is 'synthetic'.
+    Mutually exclusive with synthetic_acceptance_rates."""
 
-    synthetic_acceptance_rate: float | None = None
-    """Average acceptance rate for synthetic rejection sampling. Draft
-    tokens are accepted with a position-dependent probability that decays
-    geometrically, calibrated so that the mean rate across all speculative
-    positions equals this value. Only used when rejection_sample_method
-    is 'synthetic'. Must be in [0, 1]."""
+    @staticmethod
+    def _acceptance_length_to_rates(length: float, n: int) -> list[float]:
+        """Mean acceptance length to unconditional per-position rates, using
+        the minimum-variance schedule."""
+        num_drafts = length - 1  # expected number of accepted draft tokens
+        num_full = int(num_drafts)
+        return (
+            [1.0] * num_full + [num_drafts - num_full] + [0.0] * (n - num_full - 1)
+        )[:n]
+
+    @staticmethod
+    def _resolve_synthetic_acceptance_rates(
+        n: int,
+        rates: list[float] | None,
+        length: float | None,
+    ) -> list[float]:
+        """Return per-position unconditional acceptance rates from exactly one
+        of `rates` or `length` (validates range, length, and monotonicity)."""
+        if (rates is None) == (length is None):
+            raise ValueError(
+                "rejection_sample_method='synthetic' requires exactly one of "
+                "synthetic_acceptance_rates or synthetic_acceptance_length."
+            )
+        if rates is not None:
+            if len(rates) != n:
+                raise ValueError(
+                    f"synthetic_acceptance_rates must have length {n}, got {rates}."
+                )
+            if not all(0.0 <= r <= 1.0 for r in rates):
+                raise ValueError(
+                    f"synthetic_acceptance_rates entries must be in [0, 1], "
+                    f"got {rates}."
+                )
+            if any(rates[i] > rates[i - 1] for i in range(1, n)):
+                raise ValueError(
+                    f"synthetic_acceptance_rates must be non-increasing, got {rates}."
+                )
+            return list(rates)
+        assert length is not None
+        if not 1.0 <= length <= float(n + 1):
+            raise ValueError(
+                f"synthetic_acceptance_length must be in [1, {n + 1}], got {length}."
+            )
+        return SpeculativeConfig._acceptance_length_to_rates(length, n)
+
+    draft_sample_method: DraftSampleMethod = "greedy"
+    """How the draft model samples tokens. 'greedy' always picks the argmax
+    token, and the draft probabilities are treated as one-hot during rejection
+    sampling. 'gumbel' adds Gumbel noise for stochastic sampling, and the full
+    draft logits are used for the probability ratio test during rejection
+    sampling. This comes at the cost of additional GPU memory usage. This
+    parameter currently only applies to Model Runner V2."""
 
     def compute_hash(self) -> str:
         """
@@ -234,13 +299,23 @@ def compute_hash(self) -> str:
     @staticmethod
     def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig:
         initial_architecture = hf_config.architectures[0]
-        if hf_config.model_type in ("deepseek_v3", "deepseek_v32", "glm_moe_dsa"):
+        if hf_config.model_type in (
+            "deepseek_v3",
+            "deepseek_v32",
+            "glm_moe_dsa",
+        ):
             hf_config.model_type = "deepseek_mtp"
         if hf_config.model_type == "deepseek_mtp":
             n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
             hf_config.update(
                 {"n_predict": n_predict, "architectures": ["DeepSeekMTPModel"]}
             )
+        if hf_config.model_type == "deepseek_v4":
+            hf_config.model_type = "deepseek_mtp"
+            n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
+            hf_config.update(
+                {"n_predict": n_predict, "architectures": ["DeepSeekV4MTPModel"]}
+            )
         if hf_config.model_type in ("pangu_ultra_moe"):
             hf_config.model_type = "pangu_ultra_moe_mtp"
         if hf_config.model_type == "pangu_ultra_moe_mtp":
@@ -260,6 +335,48 @@ def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig:
                 }
             )
 
+        if (arch := hf_config.architectures[0]) in (
+            "MiMoV2ForCausalLM",
+            "MiMoV2OmniForCausalLM",
+        ):
+            from vllm.model_executor.models.mimo_v2_mtp import (
+                _MIMO_V2_PRO_NUM_MTP_LAYERS,
+            )
+
+            mtp_arch_maps = {
+                "MiMoV2ForCausalLM": "MiMoV2MTPModel",
+                "MiMoV2OmniForCausalLM": "MiMoV2OmniMTPModel",
+            }
+
+            hf_config.model_type = "mimo_v2_mtp"
+            # vLLM currently supports only the first MiMo-V2 MTP layer.
+            n_predict = _MIMO_V2_PRO_NUM_MTP_LAYERS
+            hf_config.update(
+                {
+                    "num_hidden_layers": 0,
+                    "n_predict": n_predict,
+                    "num_nextn_predict_layers": n_predict,
+                    "architectures": [mtp_arch_maps[arch]],
+                }
+            )
+
+        if hf_config.architectures[0] == "MiMoV2FlashForCausalLM":
+            from vllm.model_executor.models.mimo_v2_mtp import (
+                _MIMO_V2_FLASH_NUM_MTP_LAYERS,
+            )
+
+            hf_config.model_type = "mimo_v2_mtp"
+            # vLLM currently supports only the first MiMo-V2 MTP layer.
+            n_predict = _MIMO_V2_FLASH_NUM_MTP_LAYERS
+            hf_config.update(
+                {
+                    "num_hidden_layers": 0,
+                    "n_predict": n_predict,
+                    "num_nextn_predict_layers": n_predict,
+                    "architectures": ["MiMoV2MTPModel"],
+                }
+            )
+
         if hf_config.architectures[0] == "Glm4MoeForCausalLM":
             hf_config.model_type = "glm4_moe_mtp"
             n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
@@ -300,6 +417,10 @@ def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig:
                 {"n_predict": n_predict, "architectures": ["ErnieMTPModel"]}
             )
 
+        if hf_config.architectures[0] == "NemotronH_Super_Omni_Reasoning_V3":
+            # Promote VLM's text_config so MTP detection below fires correctly
+            hf_config = hf_config.text_config
+
         if (
             hf_config.model_type in {"nemotron_h", "nemotron_h_puzzle"}
             and hasattr(hf_config, "num_nextn_predict_layers")
@@ -360,6 +481,24 @@ def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig:
         if initial_architecture == "MistralLarge3ForCausalLM":
             hf_config.update({"architectures": ["EagleMistralLarge3ForCausalLM"]})
 
+        if hf_config.model_type == "hy_v3":
+            hf_config.model_type = "hy_v3_mtp"
+            n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
+            hf_config.update(
+                {"n_predict": n_predict, "architectures": ["HYV3MTPModel"]}
+            )
+
+        if hf_config.model_type == "gemma4_assistant":
+            hf_config.model_type = "gemma4_mtp"
+            text_config = getattr(hf_config, "text_config", hf_config)
+            # The assistant runs all decoder layers in a single forward
+            # call to produce one draft token, so n_predict=1.
+            # num_kv_shared_layers must be 0: cross-model KV sharing is
+            # set up by the proposer after model construction.
+            if hasattr(text_config, "num_kv_shared_layers"):
+                text_config.num_kv_shared_layers = 0
+            hf_config.update({"n_predict": 1, "architectures": ["Gemma4MTPModel"]})
+
         return hf_config
 
     def __post_init__(self):
@@ -495,6 +634,7 @@ def __post_init__(self):
                     revision=self.revision,
                     code_revision=self.code_revision,
                     tokenizer_revision=self.target_model_config.tokenizer_revision,
+                    max_model_len=self.max_model_len,  # type: ignore[arg-type]
                     spec_target_max_model_len=self.target_model_config.max_model_len,
                     quantization=self.quantization,
                     enforce_eager=self.target_model_config.enforce_eager,
@@ -595,23 +735,10 @@ def __post_init__(self):
                             f" must be divisible by {n_predict=}"
                         )
 
-                if self.speculative_token_tree is None:
-                    if self.num_speculative_tokens is None:
-                        raise ValueError(
-                            "A speculative model was provided, but neither "
-                            "`speculative_token_tree` nor `num_speculative_tokens` "
-                            "was provided"
-                        )
-
-                    # Generate chain of tokens.
-                    self.speculative_token_tree = str(
-                        [(i + 1) * (0,) for i in range(self.num_speculative_tokens)]
-                    )
-                else:
-                    # Sort the token tree breadth-first.
-                    tree_choices = ast.literal_eval(self.speculative_token_tree)
-                    self.speculative_token_tree = str(
-                        sorted(tree_choices, key=lambda t: (len(t), t))
+                if self.num_speculative_tokens is None:
+                    raise ValueError(
+                        "A speculative model was provided, but "
+                        "`num_speculative_tokens` was not provided"
                     )
 
                 self.draft_tensor_parallel_size = (
@@ -706,10 +833,17 @@ def _maybe_override_draft_max_model_len(
 
             return speculative_max_model_len
 
-        return min(
+        result = min(
             draft_max_model_len,
             target_max_model_len,
         )
+        if result != draft_max_model_len:
+            logger.info(
+                "Overriding draft model max model len from %d to %d",
+                draft_max_model_len,
+                result,
+            )
+        return result
 
     @staticmethod
     def _verify_and_get_draft_tp(
@@ -785,6 +919,15 @@ def create_draft_parallel_config(
 
         return draft_parallel_config
 
+    @field_validator("attention_backend", mode="before")
+    @classmethod
+    def _parse_attention_backend(cls, value: Any) -> Any:
+        if isinstance(value, str):
+            if value.lower() == "auto":
+                return None
+            return AttentionBackendEnum[value.upper()]
+        return value
+
     @model_validator(mode="after")
     def _verify_args(self) -> Self:
         if self.tensor_parallel_size is not None:
@@ -806,6 +949,23 @@ def _verify_args(self) -> Self:
                 f"than zero ({self.num_speculative_tokens})."
             )
 
+        if self.rejection_sample_method == "synthetic":
+            # Consolidate to per-position rates
+            self.synthetic_acceptance_rates = self._resolve_synthetic_acceptance_rates(
+                self.num_speculative_tokens,
+                self.synthetic_acceptance_rates,
+                self.synthetic_acceptance_length,
+            )
+            self.synthetic_acceptance_length = None
+        elif (
+            self.synthetic_acceptance_rates is not None
+            or self.synthetic_acceptance_length is not None
+        ):
+            raise ValueError(
+                "synthetic_acceptance_rates / synthetic_acceptance_length "
+                "are only valid with rejection_sample_method='synthetic'."
+            )
+
         if self.draft_model_config:
             self.draft_model_config.verify_with_parallel_config(
                 self.draft_parallel_config
@@ -826,6 +986,7 @@ def _verify_args(self) -> Self:
             "kimi_k25",
             "minimax_m2",
             "gemma4",
+            "laguna",
         ]
         if (
             self.method in ("eagle3", "extract_hidden_states", "dflash")
@@ -875,6 +1036,14 @@ def max_num_new_slots_for_drafting(self) -> int:
             slots_per_req += 1
         return slots_per_req
 
+    def use_gemma4_mtp(self) -> bool:
+        return (
+            self.method == "mtp"
+            and self.draft_model_config is not None
+            and getattr(self.draft_model_config.hf_config, "model_type", None)
+            == "gemma4_mtp"
+        )
+
     def use_eagle(self) -> bool:
         return self.method in ("eagle", "eagle3", "mtp", "dflash")
 
diff --git a/vllm/config/speech_to_text.py b/vllm/config/speech_to_text.py
index e0d72eb203af..6d31713c8667 100644
--- a/vllm/config/speech_to_text.py
+++ b/vllm/config/speech_to_text.py
@@ -1,9 +1,55 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
 
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
 
 from vllm.config.utils import config
 
+if TYPE_CHECKING:
+    import numpy as np
+
+    from vllm.config.model import ModelConfig
+
+
+@dataclass
+class SpeechToTextParams:
+    """All parameters consumed by ``get_generation_prompt()``.
+
+    ``TranscriptionRequest.build_stt_params()`` constructs this object,
+    mapping API-level fields into typed attributes.  Models only receive
+    this object, so new parameters can be added here without changing the
+    ``get_generation_prompt`` signature.
+    """
+
+    audio: np.ndarray
+    """Resampled audio waveform for a single chunk."""
+
+    stt_config: SpeechToTextConfig
+    """Server-level speech-to-text configuration."""
+
+    model_config: ModelConfig
+    """Model configuration."""
+
+    language: str | None = None
+    """ISO 639-1 language code (validated / auto-detected)."""
+
+    hotwords: str | None = None
+    """
+    hotwords refers to a list of important words or phrases that the model
+    should pay extra attention to during transcription.
+    """
+
+    task_type: str = "transcribe"
+    """``"transcribe"`` or ``"translate"``."""
+
+    request_prompt: str = ""
+    """Optional text prompt to guide the model."""
+
+    to_language: str | None = None
+    """Target language for translation (model-dependent)."""
+
 
 @config
 class SpeechToTextConfig:
diff --git a/vllm/config/vllm.py b/vllm/config/vllm.py
index 47bc3547ce8d..53f91e1b650d 100644
--- a/vllm/config/vllm.py
+++ b/vllm/config/vllm.py
@@ -121,6 +121,13 @@ def enable_allreduce_rms_fusion(cfg: "VllmConfig") -> bool:
     from vllm.platforms import current_platform
     from vllm.utils.flashinfer import has_flashinfer
 
+    if current_platform.is_rocm():
+        from vllm._aiter_ops import rocm_aiter_ops
+
+        return (
+            rocm_aiter_ops.is_enabled() and cfg.parallel_config.tensor_parallel_size > 1
+        )
+
     return (
         cfg.parallel_config.tensor_parallel_size > 1
         and current_platform.is_cuda()
@@ -129,12 +136,6 @@ def enable_allreduce_rms_fusion(cfg: "VllmConfig") -> bool:
             current_platform.is_device_capability_family(100)
             or current_platform.is_device_capability(90)
         )
-        # tp-dp combination broken:
-        # https://github.com/vllm-project/vllm/issues/34458
-        and cfg.parallel_config.data_parallel_size == 1
-        # tp-pp combination broken:
-        # https://github.com/vllm-project/vllm/issues/35426
-        and cfg.parallel_config.pipeline_parallel_size == 1
     )
 
 
@@ -156,15 +157,21 @@ def enable_rope_kvcache_fusion(cfg: "VllmConfig") -> bool:
 
 def enable_norm_pad_fusion(cfg: "VllmConfig") -> bool:
     """Enable if using AITER RMSNorm and hidden size is 2880 i.e. gpt-oss."""
-    from vllm._aiter_ops import rocm_aiter_ops
 
     return (
-        rocm_aiter_ops.is_rmsnorm_enabled()
+        cfg.kernel_config.ir_op_priority.fused_add_rms_norm[0] == "aiter"
         and cfg.model_config is not None
         and cfg.model_config.get_hidden_size() == 2880
     )
 
 
+def enable_mla_dual_rms_norm_fusion(cfg: "VllmConfig") -> bool:
+    """Enable MLA dual RMS norm fusion when AITer has fused_qk_rmsnorm."""
+    from vllm._aiter_ops import check_aiter_fused_qk_rmsnorm, rocm_aiter_ops
+
+    return rocm_aiter_ops.is_enabled() and check_aiter_fused_qk_rmsnorm()
+
+
 OPTIMIZATION_LEVEL_00 = {
     "compilation_config": {
         "pass_config": {
@@ -175,6 +182,7 @@ def enable_norm_pad_fusion(cfg: "VllmConfig") -> bool:
             "enable_sp": False,
             "fuse_gemm_comms": False,
             "fuse_act_padding": False,
+            "fuse_mla_dual_rms_norm": False,
             "fuse_rope_kvcache": False,
         },
         "cudagraph_mode": CUDAGraphMode.NONE,
@@ -194,13 +202,16 @@ def enable_norm_pad_fusion(cfg: "VllmConfig") -> bool:
             "enable_sp": False,
             "fuse_gemm_comms": False,
             "fuse_act_padding": enable_norm_pad_fusion,
+            "fuse_mla_dual_rms_norm": enable_mla_dual_rms_norm_fusion,
             "fuse_rope_kvcache": False,
         },
         "cudagraph_mode": CUDAGraphMode.PIECEWISE,
         "use_inductor_graph_partition": False,
     },
     "kernel_config": {
-        "enable_flashinfer_autotune": True,
+        # Disabled for now due to correctness issues:
+        # https://github.com/flashinfer-ai/flashinfer/issues/3197
+        "enable_flashinfer_autotune": False,
     },
 }
 OPTIMIZATION_LEVEL_02 = {
@@ -213,13 +224,16 @@ def enable_norm_pad_fusion(cfg: "VllmConfig") -> bool:
             "enable_sp": IS_DENSE,
             "fuse_gemm_comms": IS_DENSE,
             "fuse_act_padding": enable_norm_pad_fusion,
+            "fuse_mla_dual_rms_norm": enable_mla_dual_rms_norm_fusion,
             "fuse_rope_kvcache": enable_rope_kvcache_fusion,
         },
         "cudagraph_mode": CUDAGraphMode.FULL_AND_PIECEWISE,
         "use_inductor_graph_partition": False,
     },
     "kernel_config": {
-        "enable_flashinfer_autotune": True,
+        # Disabled for now due to correctness issues:
+        # https://github.com/flashinfer-ai/flashinfer/issues/3197
+        "enable_flashinfer_autotune": False,
     },
 }
 OPTIMIZATION_LEVEL_03 = {
@@ -232,6 +246,7 @@ def enable_norm_pad_fusion(cfg: "VllmConfig") -> bool:
             "enable_sp": IS_DENSE,
             "fuse_gemm_comms": IS_DENSE,
             "fuse_act_padding": enable_norm_pad_fusion,
+            "fuse_mla_dual_rms_norm": enable_mla_dual_rms_norm_fusion,
             "fuse_rope_kvcache": enable_rope_kvcache_fusion,
         },
         "cudagraph_mode": CUDAGraphMode.FULL_AND_PIECEWISE,
@@ -698,6 +713,48 @@ def _post_init_kv_transfer_config(self) -> None:
         # This is the same for all backends
         self.kv_transfer_config.kv_role = "kv_both"
 
+    def _verify_kv_transfer_compat(self) -> None:
+        """Reject configurations that silently corrupt KV transfers."""
+        if (
+            self.kv_transfer_config is None
+            or self.kv_transfer_config.kv_connector is None
+        ):
+            return
+
+        # PyTorch's expandable_segments allocator uses CUDA VMM, which can
+        # remap a virtual address range to different physical pages over the
+        # engine's lifetime. KV connectors that pin KV cache memory (e.g.
+        # NixlConnector via ibv_reg_mr, MooncakeConnector) end up with their
+        # registrations pointing at stale physical pages after any remap,
+        # producing RDMA failures like IBV_WC_REM_ACCESS_ERR /
+        # NIXL_ERR_REMOTE_DISCONNECT at the first inter-node KV transfer.
+        # We can't enumerate every in-tree and out-of-tree connector that
+        # pins memory, so we conservatively reject the combination whenever
+        # any KV connector is configured.
+        #
+        # Sleep mode is exempt: CuMemAllocator.use_memory_pool toggles
+        # expandable_segments off around its pool (see #40812), so the KV
+        # cache allocated within that context lands on stable physical pages
+        # even when the env var is set.
+        if "expandable_segments:True" not in os.environ.get(
+            "PYTORCH_CUDA_ALLOC_CONF", ""
+        ):
+            return
+        if self.model_config is not None and self.model_config.enable_sleep_mode:
+            return
+
+        raise ValueError(
+            f"KV connector {self.kv_transfer_config.kv_connector} is "
+            "incompatible with PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True "
+            "unless enable_sleep_mode is also enabled. PyTorch's CUDA VMM "
+            "allocator can remap KV cache virtual addresses to different "
+            "physical pages, invalidating any pinned/registered KV memory "
+            "(e.g. IB memory regions registered by NIXL or Mooncake). Either "
+            "unset expandable_segments:True or enable sleep mode (which "
+            "routes KV allocations through CuMemAllocator's pool, where "
+            "expandable_segments is automatically disabled)."
+        )
+
     def __post_init__(self):
         """Verify configs are valid & consistent with each other."""
 
@@ -705,9 +762,7 @@ def __post_init__(self):
         self.instance_id = f"{time.time_ns()}"
 
         if self.performance_mode != "balanced":
-            logger.info_once(
-                "Performance mode set to '%s'.", self.performance_mode, scope="local"
-            )
+            logger.info_once("Performance mode set to '%s'.", self.performance_mode)
 
         self.try_verify_and_update_config()
 
@@ -807,7 +862,6 @@ def __post_init__(self):
                     "Async scheduling not supported with %s-based "
                     "speculative decoding and will be disabled.",
                     self.speculative_config.method,
-                    scope="local",
                 )
                 self.scheduler_config.async_scheduling = False
             elif (
@@ -817,7 +871,6 @@ def __post_init__(self):
                 logger.warning_once(
                     "Async scheduling is not compatible with "
                     "disable_padded_drafter_batch=True and will be disabled.",
-                    scope="local",
                 )
                 self.scheduler_config.async_scheduling = False
             elif not executor_supports_async_sched:
@@ -825,7 +878,6 @@ def __post_init__(self):
                     "Async scheduling will be disabled because it is not supported "
                     "with the `%s` distributed executor backend. ",
                     executor_backend,
-                    scope="local",
                 )
                 self.scheduler_config.async_scheduling = False
             else:
@@ -844,7 +896,6 @@ def __post_init__(self):
                     logger.info_once(
                         "Disabling NCCL for DP synchronization "
                         "when using async scheduling.",
-                        scope="local",
                     )
                 self.parallel_config.disable_nccl_for_dp_synchronization = True
             else:
@@ -859,7 +910,6 @@ def __post_init__(self):
             logger.warning_once(
                 "Disabling cascade attention (not yet compatible with "
                 "async speculative decoding).",
-                scope="local",
             )
             self.model_config.disable_cascade_attn = True
 
@@ -896,6 +946,13 @@ def __post_init__(self):
             self.compilation_config.mode = CompilationMode.NONE
             self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE
 
+        if os.environ.get("TORCH_COMPILE_DISABLE") == "1":
+            logger.warning(
+                "TORCH_COMPILE_DISABLE is set, disabling torch.compile. "
+                "This is equivalent to setting -cc.mode=none"
+            )
+            self.compilation_config.mode = CompilationMode.NONE
+
         if self.compilation_config.backend == "eager" or (
             self.compilation_config.mode is not None
             and self.compilation_config.mode != CompilationMode.VLLM_COMPILE
@@ -972,19 +1029,16 @@ def has_blocked_weights():
             )
             self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE
 
-        # async tp is built on top of sequence parallelism
-        # and requires it to be enabled.
-        if self.compilation_config.pass_config.fuse_gemm_comms:
-            self.compilation_config.pass_config.enable_sp = True
-        if self.compilation_config.pass_config.enable_sp:
+        # async tp is built on top of sequence parallelism and requires it.
+        pass_config = self.compilation_config.pass_config
+        if pass_config.fuse_gemm_comms:
+            pass_config.enable_sp = True
+        if pass_config.enable_sp:
             if self.parallel_config.tensor_parallel_size == 1:
                 logger.warning("Sequence Parallelism requires TP>1, disabling")
-                self.compilation_config.pass_config.enable_sp = False
-                self.compilation_config.pass_config.fuse_gemm_comms = False
+                pass_config.enable_sp = False
+                pass_config.fuse_gemm_comms = False
             else:
-                # Compute SP threshold early; disable if None (model too
-                # small for SP to be beneficial).
-                pass_config = self.compilation_config.pass_config
                 if pass_config.sp_min_token_num is None:
                     from vllm.compilation.passes.fusion.sequence_parallelism import (
                         get_sequence_parallelism_threshold,
@@ -1004,8 +1058,8 @@ def has_blocked_weights():
                         "threshold heuristic, disabling. To force SP, "
                         "set pass_config.sp_min_token_num manually."
                     )
-                    self.compilation_config.pass_config.enable_sp = False
-                    self.compilation_config.pass_config.fuse_gemm_comms = False
+                    pass_config.enable_sp = False
+                    pass_config.fuse_gemm_comms = False
 
         from vllm.utils.torch_utils import HAS_OPAQUE_TYPE
 
@@ -1087,6 +1141,7 @@ def has_blocked_weights():
                 self.compilation_config.cudagraph_num_of_warmups = 1
 
             self._set_cudagraph_sizes()
+
         else:
             self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE
 
@@ -1144,6 +1199,12 @@ def has_blocked_weights():
         if envs.VLLM_USE_V2_MODEL_RUNNER:
             self._validate_v2_model_runner()
 
+        if (
+            self.model_config is not None
+            and self.model_config.enable_return_routed_experts
+        ):
+            self._validate_return_routed_experts()
+
         # Re-compute compile ranges after platform-specific config updates
         # (e.g., XPU may lower max_num_batched_tokens when MLA is enabled)
         self._set_compile_ranges()
@@ -1160,8 +1221,8 @@ def has_blocked_weights():
         )
 
         if self.compilation_config.pass_config.enable_sp:
-            # With pipeline parallelism or dynamo partitioning,
-            # native rms norm tracing errors due to incorrect residual shape.
+            # With pipeline parallelism, native rms norm tracing errors due to
+            # incorrect residual shape.
             # Use custom rms norm to unblock. In the future,
             # the pass will operate on higher-level IR to avoid the issue.
             # TODO: https://github.com/vllm-project/vllm/issues/27894
@@ -1172,24 +1233,15 @@ def has_blocked_weights():
                     self.compilation_config.mode,
                 )
 
-            is_fullgraph = (
-                self.compilation_config.use_inductor_graph_partition
-                or len(self.compilation_config.splitting_ops or []) == 0
-            )
-            if self.parallel_config.pipeline_parallel_size > 1 or not is_fullgraph:
+            if self.parallel_config.pipeline_parallel_size > 1:
                 if "-rms_norm" not in self.compilation_config.custom_ops:
                     self.compilation_config.custom_ops.append("+rms_norm")
                 else:
-                    regime = (
-                        "Dynamo partition"
-                        if not is_fullgraph
-                        else "pipeline parallelism"
-                    )
                     logger.warning_once(
                         "Sequence parallelism not supported with "
                         "native rms_norm when using %s, "
                         "this will likely lead to an error.",
-                        regime,
+                        "pipeline parallelism",
                     )
 
         # final check of cudagraph mode after all possible updates
@@ -1201,9 +1253,9 @@ def has_blocked_weights():
                 and not self.compilation_config.cudagraph_mode.has_piecewise_cudagraphs()  # noqa: E501
             ):
                 logger.warning_once(
-                    "No piecewise cudagraph for executing cascade attention."
-                    " Will fall back to eager execution if a batch runs "
-                    "into cascade attentions."
+                    "No piecewise cudagraph for executing cascade attention. "
+                    "Will fall back to eager execution if a batch runs into "
+                    "cascade attentions."
                 )
 
             if self.compilation_config.cudagraph_mode.requires_piecewise_compilation():
@@ -1220,7 +1272,6 @@ def has_blocked_weights():
             self.model_config.disable_cascade_attn = True
             logger.warning_once(
                 "Disabling cascade attention when VLLM_BATCH_INVARIANT is enabled.",
-                scope="local",
             )
 
         if self.parallel_config.use_ubatching:
@@ -1344,6 +1395,7 @@ def has_blocked_weights():
 
         # Handle the KV connector configs
         self._post_init_kv_transfer_config()
+        self._verify_kv_transfer_compat()
 
         # Log the custom passes that are enabled
         self.compilation_config.pass_config.log_enabled_passes()
@@ -1407,7 +1459,6 @@ def _set_max_num_scheduled_tokens(self):
                     " performance. Consider increasing max_num_batched_tokens to"
                     " accommodate the additional draft token slots, or decrease"
                     " num_speculative_tokens or max_num_seqs.",
-                    scope="local",
                 )
 
             max_num_scheduled_tokens = self.scheduler_config.max_num_scheduled_tokens
@@ -1434,6 +1485,10 @@ def _set_cudagraph_sizes(self):
         cudagraph_capture_sizes = [1, 2, 4] + list(range(8, 256, 8)) + list(
             range(256, max_graph_size + 1, 16))
 
+        `max_num_batched_tokens` is also appended to the list if it fits
+        within `max_cudagraph_capture_size`, so the max batch size is captured
+        even when off-stride.
+
         In the end, `vllm_config.compilation_config.cudagraph_capture_sizes`
         will be the final sizes to capture cudagraph (in ascending order).
 
@@ -1522,6 +1577,12 @@ def _set_cudagraph_sizes(self):
                     cudagraph_capture_sizes += list(
                         range(256, max_cudagraph_capture_size + 1, 16)
                     )
+                # ensure max_num_tokens is captured if within max capture size
+                if (
+                    max_num_tokens <= max_cudagraph_capture_size
+                    and max_num_tokens not in cudagraph_capture_sizes
+                ):
+                    cudagraph_capture_sizes.append(max_num_tokens)
                 # de-duplicate and sort the sizes
                 cudagraph_capture_sizes = sorted(set(cudagraph_capture_sizes))
 
@@ -1596,11 +1657,16 @@ def _set_compile_ranges(self):
         if compile_range_end is not None:
             computed_compile_ranges_endpoints.append(compile_range_end)
 
-        # Add the compile ranges for flashinfer
+        # Add the compile ranges for flashinfer/aiter.
         if compilation_config.pass_config.fuse_allreduce_rms:
             tp_size = self.parallel_config.tensor_parallel_size
-            max_size = compilation_config.pass_config.flashinfer_max_size(tp_size)
-            if max_size is not None:
+            from vllm._aiter_ops import rocm_aiter_ops
+
+            if rocm_aiter_ops.is_enabled():
+                max_size = rocm_aiter_ops.get_aiter_allreduce_max_size()
+            else:
+                max_size = compilation_config.pass_config.flashinfer_max_size(tp_size)
+            if max_size is not None and self.model_config is not None:
                 assert isinstance(self.model_config.dtype, torch.dtype)
                 max_token_num = max_size // (
                     self.model_config.get_hidden_size()
@@ -1829,6 +1895,51 @@ def _validate_v2_model_runner(self) -> None:
                 + ", ".join(unsupported)
             )
 
+    def _validate_return_routed_experts(self) -> None:
+        """Reject parallelism configurations not yet validated with
+        --enable-return-routed-experts.
+
+        Validated scope (PR #39917): TP, EP, DP, single-node and multi-node,
+        prefix caching, and speculative decoding (MTP validated end-to-end;
+        Eagle/Eagle3/Ngram/Medusa supported by construction since the
+        routing buffer is bound only to the target model and verified-token
+        routing lands at the correct positions during the main forward).
+
+        Out-of-scope (block until validated): PP > 1, prefill context
+        parallelism (PCP) > 1, decode context parallelism (DCP) > 1,
+        async scheduling.
+        """
+        unsupported: list[str] = []
+
+        if self.parallel_config.pipeline_parallel_size > 1:
+            unsupported.append(
+                "pipeline parallelism "
+                f"(pipeline_parallel_size="
+                f"{self.parallel_config.pipeline_parallel_size})"
+            )
+        if self.parallel_config.prefill_context_parallel_size > 1:
+            unsupported.append(
+                "prefill context parallelism "
+                f"(prefill_context_parallel_size="
+                f"{self.parallel_config.prefill_context_parallel_size})"
+            )
+        if self.parallel_config.decode_context_parallel_size > 1:
+            unsupported.append(
+                "decode context parallelism "
+                f"(decode_context_parallel_size="
+                f"{self.parallel_config.decode_context_parallel_size})"
+            )
+        if self.scheduler_config.async_scheduling:
+            unsupported.append("async scheduling")
+
+        if unsupported:
+            raise ValueError(
+                "--enable-return-routed-experts is not yet validated with: "
+                + ", ".join(unsupported)
+                + ". Disable these features or omit "
+                "--enable-return-routed-experts."
+            )
+
     def validate_block_size(self) -> None:
         """Validate block_size against DCP and mamba constraints.
 
@@ -1875,6 +1986,22 @@ def validate_block_size(self) -> None:
                 "to schedule a multiple of block_size tokens even if they are "
                 "in the middle of a mm input"
             )
+            # TODO: support align mamba cache mode for model runner v2
+            assert not envs.VLLM_USE_V2_MODEL_RUNNER, (
+                "Model Runner V2 has not yet supported mamba_cache_mode='align'. "
+            )
+
+    @model_validator(mode="after")
+    def validate_nvfp4_kv_cache_with_mla(self) -> "VllmConfig":
+        if self.model_config is None:
+            return self
+        if self.cache_config.cache_dtype == "nvfp4" and self.model_config.use_mla:
+            raise ValueError(
+                "nvfp4 KV cache is not supported with MLA (Multi-head Latent "
+                "Attention) backends. Please use a different --kv-cache-dtype "
+                "(e.g., 'fp8' or 'auto') for MLA models such as DeepSeek."
+            )
+        return self
 
     @model_validator(mode="after")
     def validate_mamba_block_size(self) -> "VllmConfig":
diff --git a/vllm/device_allocator/cumem.py b/vllm/device_allocator/cumem.py
index 554a34b6a68e..b2202b7d08d4 100644
--- a/vllm/device_allocator/cumem.py
+++ b/vllm/device_allocator/cumem.py
@@ -128,13 +128,6 @@ def get_instance() -> "CuMemAllocator":
         return CuMemAllocator.instance
 
     def __init__(self):
-        conf = os.environ.get("PYTORCH_CUDA_ALLOC_CONF", "")
-        assert "expandable_segments:True" not in conf, (
-            "Expandable segments are not compatible with memory pool. "
-            "Please track https://github.com/pytorch/pytorch/issues/147851 "
-            "for the latest updates."
-        )
-
         self.pointer_to_data: dict[int, AllocationData] = {}
         self.current_tag: str = CuMemAllocator.default_tag
         self.allocator_and_pools: dict[str, Any] = {}
@@ -264,34 +257,49 @@ def use_memory_pool(self, tag: str | None = None):
 
         assert isinstance(tag, str)
 
+        # Expandable segments are incompatible with the memory pool used for
+        # sleep mode (see https://github.com/pytorch/pytorch/issues/147851).
+        # If the user has enabled expandable segments via
+        # PYTORCH_CUDA_ALLOC_CONF, temporarily disable them for the duration
+        # of the memory pool context and restore on exit.
+        conf = os.environ.get("PYTORCH_CUDA_ALLOC_CONF", "")
+        expandable_was_enabled = "expandable_segments:True" in conf
+        if expandable_was_enabled:
+            torch.cuda.memory._set_allocator_settings("expandable_segments:False")
+
         old_tag = self.current_tag
         self.current_tag = tag
-        with use_memory_pool_with_allocator(
-            self.python_malloc_callback, self.python_free_callback
-        ) as data:
-            # start to hit another PyTorch bug in PyTorch 2.6,
-            # possibly because of gc-related issue w.r.t. the allocator and
-            # the memory pool.
-            # to avoid the issue, we keep a reference of the data.
-            # see https://github.com/pytorch/pytorch/issues/146431 .
-            self.allocator_and_pools[tag] = data
-            yield
-            # PyTorch's bug, calling torch.cuda.empty_cache() will error
-            # when using pluggable allocator, see
-            # https://github.com/pytorch/pytorch/issues/145168 .
-            # if we have some memory allocated and then freed,
-            # the memory will not be released, e.g. in online quantization,
-            # where the model is created in higher precision, and then
-            # quantized in lower precision.
-            # Find all unused allocations and manually release them.
-            # TODO: we should expose `empty_cache` method in the memory pool.
-            # TODO: ask for help from PyTorch team to expose this method.
-            allocations = data[0].snapshot()
-            for allocation in allocations:
-                if allocation["allocated_size"] == 0:
-                    handle = self._python_free_callback(allocation["address"])
-                    unmap_and_release(handle)
+        try:
+            with use_memory_pool_with_allocator(
+                self.python_malloc_callback, self.python_free_callback
+            ) as data:
+                # start to hit another PyTorch bug in PyTorch 2.6,
+                # possibly because of gc-related issue w.r.t. the allocator
+                # and the memory pool.
+                # to avoid the issue, we keep a reference of the data.
+                # see https://github.com/pytorch/pytorch/issues/146431 .
+                self.allocator_and_pools[tag] = data
+                yield
+                # PyTorch's bug, calling torch.cuda.empty_cache() will error
+                # when using pluggable allocator, see
+                # https://github.com/pytorch/pytorch/issues/145168 .
+                # if we have some memory allocated and then freed,
+                # the memory will not be released, e.g. in online
+                # quantization, where the model is created in higher
+                # precision, and then quantized in lower precision.
+                # Find all unused allocations and manually release them.
+                # TODO: we should expose `empty_cache` method in the memory
+                # pool.
+                # TODO: ask for help from PyTorch team to expose this method.
+                allocations = data[0].snapshot()
+                for allocation in allocations:
+                    if allocation["allocated_size"] == 0:
+                        handle = self._python_free_callback(allocation["address"])
+                        unmap_and_release(handle)
+        finally:
             self.current_tag = old_tag
+            if expandable_was_enabled:
+                torch.cuda.memory._set_allocator_settings("expandable_segments:True")
 
     def get_current_usage(self) -> int:
         """
diff --git a/vllm/distributed/device_communicators/all2all.py b/vllm/distributed/device_communicators/all2all.py
index 075f4e0859e4..57ef6e9cf148 100644
--- a/vllm/distributed/device_communicators/all2all.py
+++ b/vllm/distributed/device_communicators/all2all.py
@@ -10,7 +10,6 @@
 from vllm.distributed import get_dp_group, get_ep_group
 from vllm.forward_context import get_forward_context
 from vllm.logger import init_logger
-from vllm.platforms import current_platform
 from vllm.utils.flashinfer import (
     has_flashinfer_nvlink_one_sided,
     has_flashinfer_nvlink_two_sided,
@@ -38,115 +37,6 @@
 logger = init_logger(__name__)
 
 
-class NaiveAll2AllManager(All2AllManagerBase):
-    """
-    A naive implementation of all2all communication.
-    It uses all-reduce under the hood, which is not
-    efficient at all. The main purpose is for testing and
-    debugging.
-    """
-
-    def __init__(self, cpu_group, tcp_store_group=None):
-        super().__init__(cpu_group, tcp_store_group)
-
-    def naive_multicast(
-        self,
-        x: torch.Tensor,
-        cu_tokens_across_sp_cpu: torch.Tensor,
-        is_sequence_parallel: bool,
-    ) -> torch.Tensor:
-        assert len(x.shape) == 2
-        buffer = torch.empty(
-            (cu_tokens_across_sp_cpu[-1], x.size(1)), device=x.device, dtype=x.dtype
-        )
-
-        rank = self.rank if is_sequence_parallel else self.dp_rank
-        world_size = self.world_size if is_sequence_parallel else self.dp_world_size
-
-        start = 0 if rank == 0 else cu_tokens_across_sp_cpu[rank - 1]
-        end = cu_tokens_across_sp_cpu[rank]
-        buffer[start:end, :].copy_(x)
-        for idx in range(world_size):
-            start = 0 if idx == 0 else cu_tokens_across_sp_cpu[idx - 1]
-            end = cu_tokens_across_sp_cpu[idx]
-            get_ep_group().broadcast(buffer[start:end, :], idx)
-
-        return buffer
-
-    def dispatch_router_logits(
-        self,
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-        is_sequence_parallel: bool = False,
-        extra_tensors: list[torch.Tensor] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        if extra_tensors is not None:
-            raise NotImplementedError(
-                "extra_tensors is not supported for NaiveAll2AllManager"
-            )
-        sp_size = self.tp_group.world_size if is_sequence_parallel else 1
-        dp_metadata = get_forward_context().dp_metadata
-        assert dp_metadata is not None
-        cu_tokens_across_sp_cpu = dp_metadata.cu_tokens_across_sp(sp_size)
-
-        hidden_states = self.naive_multicast(
-            hidden_states, cu_tokens_across_sp_cpu, is_sequence_parallel
-        )
-        router_logits = self.naive_multicast(
-            router_logits, cu_tokens_across_sp_cpu, is_sequence_parallel
-        )
-
-        return hidden_states, router_logits
-
-    def dispatch(
-        self,
-        hidden_states: torch.Tensor,
-        topk_weights: torch.Tensor,
-        topk_ids: torch.Tensor,
-        is_sequence_parallel: bool = False,
-        extra_tensors: list[torch.Tensor] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        if extra_tensors is not None:
-            raise NotImplementedError(
-                "extra_tensors is not supported for NaiveAll2AllManager"
-            )
-        sp_size = self.tp_group.world_size if is_sequence_parallel else 1
-        dp_metadata = get_forward_context().dp_metadata
-        assert dp_metadata is not None
-        cu_tokens_across_sp_cpu = dp_metadata.cu_tokens_across_sp(sp_size)
-
-        hidden_states = self.naive_multicast(
-            hidden_states, cu_tokens_across_sp_cpu, is_sequence_parallel
-        )
-        topk_weights = self.naive_multicast(
-            topk_weights, cu_tokens_across_sp_cpu, is_sequence_parallel
-        )
-        topk_ids = self.naive_multicast(
-            topk_ids, cu_tokens_across_sp_cpu, is_sequence_parallel
-        )
-        return hidden_states, topk_weights, topk_ids
-
-    def combine(
-        self, hidden_states: torch.Tensor, is_sequence_parallel: bool = False
-    ) -> torch.Tensor:
-        ep_rank = self.rank if is_sequence_parallel else self.dp_rank
-
-        dp_metadata = get_forward_context().dp_metadata
-        assert dp_metadata is not None
-        sp_size = self.tp_group.world_size if is_sequence_parallel else 1
-        cu_tokens_across_sp_cpu = dp_metadata.cu_tokens_across_sp(sp_size)
-
-        start = 0 if ep_rank == 0 else cu_tokens_across_sp_cpu[ep_rank - 1]
-        end = cu_tokens_across_sp_cpu[ep_rank]
-
-        all_hidden_states = get_ep_group().all_reduce(hidden_states)
-        hidden_states = all_hidden_states[start:end, :]
-        return hidden_states
-
-    def destroy(self):
-        pass
-
-
 class AgRsAll2AllManager(All2AllManagerBase):
     """
     An implementation of all2all communication based on
@@ -334,11 +224,8 @@ def _make_all2all_kwargs(self) -> dict[Any, Any]:
             num_rdma_bytes=num_rdma_bytes,
             low_latency_mode=False,
             num_qps_per_rank=num_qps_per_rank,
+            explicitly_destroy=True,
         )
-        if not current_platform.is_rocm():
-            kwargs.update(
-                explicitly_destroy=True,
-            )
         return kwargs
 
     def get_handle(self, kwargs):
@@ -412,13 +299,10 @@ def _make_all2all_kwargs(
             num_rdma_bytes=num_rdma_bytes,
             low_latency_mode=True,
             num_qps_per_rank=num_qps_per_rank,
+            allow_nvlink_for_low_latency_mode=True,
+            allow_mnnvl=envs.VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL,
+            explicitly_destroy=True,
         )
-        if not current_platform.is_rocm():
-            kwargs.update(
-                allow_nvlink_for_low_latency_mode=True,
-                allow_mnnvl=envs.VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL,
-                explicitly_destroy=True,
-            )
         return kwargs
 
     def get_handle(self, kwargs):
@@ -601,15 +485,18 @@ def initialize(
             CustomCommunicator,
         )
 
-        dp_config = MnnvlConfig(
-            comm_backend=CustomCommunicator(get_dp_group().cpu_group),
+        # MNNVL workspace is allocated per rank in the comm_backend's group; the
+        # flashinfer kernel asserts workspace.size(0) == moe_ep_size, so the backend
+        # must span the EP group (= DP*PCP*TP), not the DP group.
+        ep_config = MnnvlConfig(
+            comm_backend=CustomCommunicator(self.cpu_group),
             fabric_page_size=1 << 29,  # 512MB
             allocation_granularity=0,  # Auto-detect
         )
 
-        self.workspace_tensor = MnnvlMoe.get_moe_workspaces(self.mapping, dp_config)
+        self.workspace_tensor = MnnvlMoe.get_moe_workspaces(self.mapping, ep_config)
         self.prepare_workspace_tensor = MnnvlMoe.get_moe_prepare_workspace(
-            self.mapping, dp_config
+            self.mapping, ep_config
         )
 
         self.world_size = world_size
@@ -690,6 +577,8 @@ def initialize(
         top_k: int,
         num_experts: int,
         hidden_size: int,
+        dispatch_dtype_bytes_per_elem: int = 0,
+        dispatch_scale_bytes_per_token: int = 0,
     ):
         """Initialize the MoeAlltoAll workspace."""
         if self.initialized:
@@ -714,12 +603,19 @@ def initialize(
             CustomCommunicator,
         )
 
-        dp_config = MnnvlConfig(
-            comm_backend=CustomCommunicator(get_dp_group().cpu_group),
+        # MNNVL workspace is allocated per rank in the comm_backend's group; the
+        # flashinfer kernel asserts workspace.size(0) == moe_ep_size, so the backend
+        # must span the EP group (= DP*PCP*TP), not the DP group.
+        ep_config = MnnvlConfig(
+            comm_backend=CustomCommunicator(self.cpu_group),
         )
+        if dispatch_dtype_bytes_per_elem == 0:
+            hidden_bytes = hidden_size // 2
+        else:
+            hidden_bytes = hidden_size * dispatch_dtype_bytes_per_elem
         total_dispatch_payload_size_per_token = (
-            hidden_size // 2  # nvfp4 hidden states
-            + hidden_size // 16  # fp8 scaling factors
+            hidden_bytes
+            + dispatch_scale_bytes_per_token
             + top_k * 4  # int32 topks ids
             + top_k * 4  # float32 topk weights
         )
@@ -737,7 +633,7 @@ def initialize(
             top_k=top_k,
             num_experts=num_experts,
             workspace_size_per_rank=self.workspace_size,
-            mnnvl_config=dp_config,
+            mnnvl_config=ep_config,
         )
 
         self.gpus_per_node = gpus_per_node
diff --git a/vllm/distributed/device_communicators/base_device_communicator.py b/vllm/distributed/device_communicators/base_device_communicator.py
index 2125f7381fe2..0b4b81f93bb4 100644
--- a/vllm/distributed/device_communicators/base_device_communicator.py
+++ b/vllm/distributed/device_communicators/base_device_communicator.py
@@ -7,6 +7,8 @@
 import torch.distributed as dist
 from torch.distributed import ProcessGroup
 
+from vllm.utils import is_moe_layer
+
 
 class Cache:
     def __init__(self):
@@ -317,16 +319,7 @@ def prepare_communication_buffer_for_model(self, model: torch.nn.Module) -> None
         if not self.is_ep_communicator:
             return
 
-        moe_modules = [
-            module
-            for module in model.modules()
-            # TODO(bnell): Should use isinstance but can't.  Maybe search for
-            # presence of quant_method.maybe_init_modular_kernel?
-            if (
-                module.__class__.__name__ == "FusedMoE"
-                or module.__class__.__name__ == "SharedFusedMoE"
-            )
-        ]
+        moe_modules = [module for module in model.modules() if is_moe_layer(module)]
         for module in moe_modules:
             module.maybe_init_modular_kernel()
 
diff --git a/vllm/distributed/device_communicators/cpu_communicator.py b/vllm/distributed/device_communicators/cpu_communicator.py
index 2bce5faa8b66..067cdad7348a 100644
--- a/vllm/distributed/device_communicators/cpu_communicator.py
+++ b/vllm/distributed/device_communicators/cpu_communicator.py
@@ -45,19 +45,23 @@ def __init__(
                 unique_name,
             )
 
+        # send/recv tensor_dict is only supported through the SHM communicator backend
+        self.supports_tensor_dict = isinstance(self.dist_module, _CPUSHMDistributed)
+
         if self.use_all2all:
-            if self.all2all_backend != "naive":  # type: ignore[has-type]
+            if self.all2all_backend not in (
+                "naive",
+                "allgather_reducescatter",
+            ):  # type: ignore[has-type]
                 logger.warning(
                     "`%s` all2all manager is not supported on CPU. "
-                    "Falling back to `naive` all2all manager for CPU.",
+                    "Falling back to `allgather_reducescatter` manager.",
                     self.all2all_backend,  # type: ignore[has-type]
                 )
-                self.all2all_backend = "naive"
-            if self.all2all_backend == "naive":
-                from .all2all import NaiveAll2AllManager
+            from .all2all import AgRsAll2AllManager
 
-                self.all2all_manager = NaiveAll2AllManager(self.cpu_group)
-                logger.info("Using naive all2all manager.")
+            self.all2all_manager = AgRsAll2AllManager(self.cpu_group)
+            logger.info("Using allgather_reducescatter all2all manager.")
 
     def _all_group_ranks_share_shm_group_name(self) -> bool:
         """
@@ -143,12 +147,22 @@ def send_tensor_dict(
         tensor_dict: dict[str, torch.Tensor | Any],
         dst: int,
     ) -> None:
+        if not self.supports_tensor_dict:
+            raise NotImplementedError(
+                "CpuCommunicator does not support tensor dict fastpath with "
+                "torch.distributed backend."
+            )
         return self.dist_module.send_tensor_dict(tensor_dict, dst)
 
     def recv_tensor_dict(
         self,
         src: int,
     ) -> dict[str, torch.Tensor | Any]:
+        if not self.supports_tensor_dict:
+            raise NotImplementedError(
+                "CpuCommunicator does not support tensor dict fastpath with "
+                "torch.distributed backend."
+            )
         return self.dist_module.recv_tensor_dict(src)
 
     def dispatch_router_logits(
diff --git a/vllm/distributed/device_communicators/cuda_communicator.py b/vllm/distributed/device_communicators/cuda_communicator.py
index 4550bdb25629..74056134c095 100644
--- a/vllm/distributed/device_communicators/cuda_communicator.py
+++ b/vllm/distributed/device_communicators/cuda_communicator.py
@@ -115,13 +115,7 @@ def __init__(
                 self.qr_comm = QuickAllReduce(group=self.cpu_group, device=self.device)
 
         if self.use_all2all:
-            if self.all2all_backend == "naive":
-                from .all2all import NaiveAll2AllManager
-
-                self.all2all_manager = NaiveAll2AllManager(
-                    self.cpu_group, tcp_store_group
-                )
-            elif self.all2all_backend == "allgather_reducescatter":
+            if self.all2all_backend in ("naive", "allgather_reducescatter"):
                 from .all2all import AgRsAll2AllManager
 
                 self.all2all_manager = AgRsAll2AllManager(
diff --git a/vllm/distributed/device_communicators/pynccl.py b/vllm/distributed/device_communicators/pynccl.py
index 6ac3b9ea3c7c..990c808a9831 100644
--- a/vllm/distributed/device_communicators/pynccl.py
+++ b/vllm/distributed/device_communicators/pynccl.py
@@ -108,9 +108,7 @@ def __init__(
         if self.rank == 0:
             # get the unique id from NCCL
             self.unique_id = self.nccl.ncclGetUniqueId()
-            logger.info_once(
-                "vLLM is using nccl==%s", self.nccl.ncclGetVersion(), scope="local"
-            )
+            logger.info_once("vLLM is using nccl==%s", self.nccl.ncclGetVersion())
         else:
             # construct an empty unique id
             self.unique_id = ncclUniqueId()
diff --git a/vllm/distributed/device_communicators/xpu_communicator.py b/vllm/distributed/device_communicators/xpu_communicator.py
index d2e9e89e535d..209005c5b10a 100644
--- a/vllm/distributed/device_communicators/xpu_communicator.py
+++ b/vllm/distributed/device_communicators/xpu_communicator.py
@@ -23,13 +23,7 @@ def __init__(
     ):
         super().__init__(cpu_group, device, device_group, unique_name)
         if self.use_all2all:
-            if self.all2all_backend == "naive":
-                from .all2all import NaiveAll2AllManager
-
-                self.all2all_manager = NaiveAll2AllManager(self.cpu_group)
-                logger.info("Using naive all2all manager.")
-
-            elif self.all2all_backend == "allgather_reducescatter":
+            if self.all2all_backend in ("naive", "allgather_reducescatter"):
                 from .all2all import AgRsAll2AllManager
 
                 self.all2all_manager = AgRsAll2AllManager(self.cpu_group)
@@ -47,9 +41,10 @@ def __init__(
                 self.all2all_manager = AgRsAll2AllManager(self.cpu_group)
                 logger.info("Using AgRs manager on XPU device.")
 
-    def all_reduce(self, input_) -> torch.Tensor:
-        dist.all_reduce(input_, group=self.device_group)
-        return input_
+    def all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
+        output = input_.clone()
+        dist.all_reduce(output, group=self.device_group)
+        return output
 
     def reduce_scatter(self, input_: torch.Tensor, dim: int = -1):
         world_size = self.world_size
diff --git a/vllm/distributed/elastic_ep/elastic_execute.py b/vllm/distributed/elastic_ep/elastic_execute.py
index a316a54bd519..163cec47e4dd 100644
--- a/vllm/distributed/elastic_ep/elastic_execute.py
+++ b/vllm/distributed/elastic_ep/elastic_execute.py
@@ -38,6 +38,7 @@
 from vllm.distributed.stateless_coordinator import StatelessGroupCoordinator
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.layer import FusedMoEParallelConfig
+from vllm.utils import is_moe_layer
 from vllm.v1.engine import ReconfigureDistributedRequest, ReconfigureRankType
 from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
 from vllm.v1.worker.workspace import lock_workspace, unlock_workspace
@@ -319,10 +320,7 @@ def switch_and_prepare(self) -> None:
         moe_modules = [
             module
             for module in self.worker.model_runner.model.modules()
-            if (
-                module.__class__.__name__ == "FusedMoE"
-                or module.__class__.__name__ == "SharedFusedMoE"
-            )
+            if is_moe_layer(module)
         ]
         num_local_experts = moe_modules[0].moe_config.num_local_experts
         assert all(
@@ -568,3 +566,45 @@ def receive_expert_mapping(self) -> tuple[torch.Tensor, int, int]:
     def prepare_new_worker(self) -> None:
         with set_current_vllm_config(self.worker.vllm_config):
             prepare_communication_buffer_for_model(self.worker.model_runner.get_model())
+
+    def rewarm_workspace(self) -> None:
+        # Must run on every DP sibling in lockstep: _dummy_run calls
+        # coordinate_batch_across_dp whenever data_parallel_size > 1
+        # (gpu_model_runner.py:3663), which deadlocks if any rank skips it.
+
+        # Save and clear block tables so profile_run/compile_or_warm_up_model
+        # don't write dummy slot mappings into real KV-cache blocks (mirrors
+        # switch_and_prepare's pattern).
+        multi_block_table = self.worker.model_runner.input_batch.block_table
+        saved_block_tables: list[tuple[torch.Tensor, torch.Tensor]] = []
+        for bt in multi_block_table.block_tables:
+            saved_block_tables.append(
+                (bt.block_table.gpu.clone(), bt.block_table.cpu.clone())
+            )
+        multi_block_table.clear()
+
+        # _ensure_workspace_size allocates a fresh tensor on grow, leaving
+        # captured CUDA graphs with stale data pointers; drop graphs before
+        # re-warm so captures realign with the resized buffer.
+        self._release_cuda_graphs()
+        unlock_workspace()
+
+        # Grow the MoE workspace at max_num_tokens.
+        # compile_or_warm_up_model alone only exercises cudagraph-capture
+        # sizes (≤64 tokens for this test) and leaves the workspace at
+        # ~10-14 MB; the post-all-to-all per-rank token count under real
+        # post-reshuffle routing needs hundreds of MB. Use _dummy_run
+        # directly (rather than profile_run) with skip_eplb=True so dummy
+        # routing doesn't pollute the just-rebalanced EPLB stats — same
+        # convention compile_or_warm_up_model itself uses.
+        runner = self.worker.model_runner
+        runner._dummy_run(runner.max_num_tokens, is_profile=True, skip_eplb=True)
+        self.worker.compile_or_warm_up_model()
+
+        lock_workspace()
+
+        for bt, (saved_gpu, saved_cpu) in zip(
+            multi_block_table.block_tables, saved_block_tables
+        ):
+            bt.block_table.gpu.copy_(saved_gpu)
+            bt.block_table.cpu.copy_(saved_cpu)
diff --git a/vllm/distributed/elastic_ep/elastic_state.py b/vllm/distributed/elastic_ep/elastic_state.py
index bace771a2ab6..256efe46a4a4 100644
--- a/vllm/distributed/elastic_ep/elastic_state.py
+++ b/vllm/distributed/elastic_ep/elastic_state.py
@@ -538,6 +538,11 @@ def _eplb_reshuffle(self):
         self.model_executor.collective_rpc(
             "elastic_ep_execute", args=("perform_eplb_reshuffle",)
         )
+        # Reshuffle changes per-rank token routing; the locked MoE workspace
+        # may now be too small. Rewarm covers both new and existing engines.
+        self.model_executor.collective_rpc(
+            "elastic_ep_execute", args=("rewarm_workspace",)
+        )
         assert self.new_dp_group is not None
         if self.new_dp_group.rank() == 0:
             logger.info("[Elastic EP] EPLB reshuffle completed")
diff --git a/vllm/distributed/eplb/async_worker.py b/vllm/distributed/eplb/async_worker.py
index 41c52063ef58..542606fe7417 100644
--- a/vllm/distributed/eplb/async_worker.py
+++ b/vllm/distributed/eplb/async_worker.py
@@ -4,7 +4,6 @@
 The async worker that transfers experts in the background.
 """
 
-import asyncio
 import threading
 from typing import TYPE_CHECKING
 
@@ -14,7 +13,8 @@
 from vllm.distributed.parallel_state import get_eplb_group
 from vllm.logger import init_logger
 
-from .rebalance_execute import transfer_layer
+from .eplb_utils import CpuGpuEvent
+from .rebalance_execute import AsyncEplbLayerResult, transfer_layer
 
 if TYPE_CHECKING:
     from .eplb_state import EplbModelState, EplbState
@@ -35,21 +35,15 @@ def thread_target() -> None:
         assert device_index is not None
         torch.accelerator.set_device_index(device_index)
         cuda_stream = torch.cuda.Stream(device=device_index)
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
         try:
-            loop.run_until_complete(
-                transfer_run_periodically(
-                    state=state,
-                    eplb_group=eplb_group,
-                    cuda_stream=cuda_stream,
-                    is_profile=is_profile,
-                )
+            transfer_run_periodically(
+                state=state,
+                eplb_group=eplb_group,
+                cuda_stream=cuda_stream,
+                is_profile=is_profile,
             )
         except Exception as exc:  # pragma: no cover - diagnostic path
             logger.exception("async loop error (Rank %d): %s", rank, str(exc))
-        finally:
-            loop.close()
 
     thread = threading.Thread(target=thread_target, daemon=True)
     thread.start()
@@ -60,18 +54,14 @@ def run_rebalance_experts(
     model_state: "EplbModelState",
     eplb_state: "EplbState",
     physical_to_logical_map_cpu: torch.Tensor,
-) -> None:
+    cuda_stream: torch.cuda.Stream,
+) -> torch.Tensor:
     assert model_state.eplb_stats is not None
     eplb_stats = model_state.eplb_stats
 
-    # Wait for the main thread's all-reduce and clone to complete before
-    # accessing the global_expert_load_window tensor.
-    assert model_state.window_ready_event is not None
-    model_state.window_ready_event.wait()
-    model_state.window_ready_event = None
-
     # Move the global expert load window to CPU for computation.
-    global_expert_load_window = eplb_stats.global_expert_load_window.cpu()
+    with torch.cuda.stream(cuda_stream):
+        global_expert_load_window = eplb_stats.global_expert_load_window.cpu()
     # Compute new expert mappings for the model
     new_physical_to_logical_map = eplb_state.policy.rebalance_experts(
         global_expert_load_window,
@@ -83,95 +73,75 @@ def run_rebalance_experts(
     )
     assert new_physical_to_logical_map.device == torch.device("cpu")
 
-    model_state.new_physical_to_logical_map = new_physical_to_logical_map
+    return new_physical_to_logical_map
 
 
-async def transfer_run_periodically(
+def transfer_run_periodically(
     state: "EplbState",
     eplb_group: ProcessGroup,
     cuda_stream: torch.cuda.Stream,
     is_profile: bool = False,
 ) -> None:
     while True:
-        await asyncio.to_thread(state.rearrange_event.wait)
+        state.rearrange_event.wait(stream=cuda_stream)
         logger.info("async worker woke up for EPLB transfer")
 
         assert state.is_async
         for model_state in state.model_states.values():
+            layer_idx = 0
             # Set the async worker's CUDA stream on the communicator
             model_state.communicator.set_stream(cuda_stream)
-            rebalancing_algorithm_executed = False
-            physical_to_logical_map_cpu = None
-            current_num_layers = model_state.model.num_moe_layers
-            while (
-                model_state.rebalanced
-                and model_state.layer_to_transfer < current_num_layers
-            ):
-                if not model_state.ep_buffer_ready and model_state.rebalanced:
-                    # Polling the lock directly in the async thread avoids
-                    # the thread switch overhead of asyncio.to_thread.
-                    # This is typically faster than offloading to a worker thread.
-                    while not model_state.buffer_lock.acquire(blocking=False):
-                        await asyncio.sleep(0)
-                    try:
-                        if model_state.layer_to_transfer >= current_num_layers:
-                            break
-                        if (
-                            not rebalancing_algorithm_executed
-                            or model_state.new_physical_to_logical_map is None
-                        ):
-                            # Move the physical_to_logical_map to CPU
-                            # for rebalancing and transfer_layer.
-                            physical_to_logical_map_cpu = (
-                                model_state.physical_to_logical_map.cpu()
-                            )
-                            run_rebalance_experts(
-                                model_state, state, physical_to_logical_map_cpu
-                            )
-                            rebalancing_algorithm_executed = True
-                            logger.info(
-                                "Async worker computed new indices for model %s",
-                                model_state.model_name,
-                            )
-
-                        assert model_state.new_physical_to_logical_map is not None
-                        assert physical_to_logical_map_cpu is not None
-
-                        layer_idx = model_state.layer_to_transfer
-                        old_layer_indices = physical_to_logical_map_cpu[layer_idx]
-                        new_layer_indices = model_state.new_physical_to_logical_map[
-                            layer_idx
-                        ]
-
-                        # Wait for the main thread to finish consuming the buffer
-                        # before initiating an EPLB transfer on another layer.
-                        if model_state.buffer_consumed_event is not None:
-                            cuda_stream.wait_event(model_state.buffer_consumed_event)
-                            model_state.buffer_consumed_event = None
-
-                        (
-                            model_state.is_unchanged,
-                            model_state.is_received_locally,
-                            model_state.recv_metadata,
-                        ) = await transfer_layer(
-                            old_layer_indices=old_layer_indices,
-                            new_layer_indices=new_layer_indices,
-                            expert_weights=model_state.model.expert_weights[layer_idx],
-                            expert_weights_buffer=model_state.expert_buffer,
-                            ep_group=eplb_group,
-                            communicator=model_state.communicator,
-                            is_profile=is_profile,
-                            cuda_stream=cuda_stream,
-                        )
-                        # block the async thread until the transfer to
-                        # the intermediate buffer is complete.
-                        cuda_stream.synchronize()
-                        model_state.ep_buffer_ready = 1
-                    finally:
-                        model_state.buffer_lock.release()
-                else:
-                    if not model_state.rebalanced:
-                        break
-                    await asyncio.sleep(0.001)
-
-        state.rearrange_event.clear()
+            num_layers = model_state.model.num_moe_layers
+
+            # Snapshot the physical_to_logical_map (synchronized with
+            # rearrange_event) and copy it to CPU
+            with torch.cuda.stream(cuda_stream):
+                physical_to_logical_map_cpu = model_state.physical_to_logical_map.cpu()
+
+            new_physical_to_logical_map = run_rebalance_experts(
+                model_state, state, physical_to_logical_map_cpu, cuda_stream
+            )
+            logger.info(
+                "Async worker computed new indices for model %s",
+                model_state.model_name,
+            )
+
+            # Execute one EPLB layer transfer per model forward pass. Each iteration
+            # of this loop will copy the new set of expert weights into
+            # model_state.expert_buffer, which will be consumed by the main thread in
+            # move_to_workspace
+            while model_state.rebalanced and layer_idx < num_layers:
+                transfer_metadata = transfer_layer(
+                    old_layer_indices=physical_to_logical_map_cpu[layer_idx],
+                    new_layer_indices=new_physical_to_logical_map[layer_idx],
+                    expert_weights=model_state.model.expert_weights[layer_idx],
+                    expert_weights_buffer=model_state.expert_buffer,
+                    communicator=model_state.communicator,
+                    ep_group=eplb_group,
+                    is_profile=is_profile,
+                    cuda_stream=cuda_stream,
+                )
+
+                # Wait until all writes to expert_buffer have finished before making the
+                # AsyncEplbLayerResult visible to the main thread.
+                cuda_stream.synchronize()
+
+                # This event guarantees that expert_buffer will not be overwritten by
+                # subsequent iterations of this loop until the main thread has consumed
+                # it. Record is called by the main thread after move_from_buffer().
+                consumed_event = CpuGpuEvent()
+
+                model_state.pending_result = AsyncEplbLayerResult(
+                    layer_idx=layer_idx,
+                    new_physical_to_logical_map=new_physical_to_logical_map[layer_idx],
+                    transfer_metadata=transfer_metadata,
+                    consumed_event=consumed_event,
+                )
+
+                # Block this thread until the main thread and main stream
+                # finish copying model_state.expert_buffer into
+                # model_state.model.expert_weights[layer_idx]
+                consumed_event.wait(stream=cuda_stream)
+                logger.debug("Layer %d transfer complete", layer_idx)
+                assert model_state.pending_result is None
+                layer_idx += 1
diff --git a/vllm/distributed/eplb/eplb_communicator.py b/vllm/distributed/eplb/eplb_communicator.py
index 059d6b7cffb9..f8ee90b934fb 100644
--- a/vllm/distributed/eplb/eplb_communicator.py
+++ b/vllm/distributed/eplb/eplb_communicator.py
@@ -4,9 +4,14 @@
 EPLB communicator implementations and factory.
 """
 
+import contextlib
+import time
+import uuid
 from abc import ABC, abstractmethod
 from collections.abc import Sequence
+from datetime import timedelta
 
+import numpy as np
 import torch
 from torch.distributed import (
     P2POp,
@@ -14,32 +19,59 @@
     batch_isend_irecv,
 )
 
+import vllm.distributed.nixl_utils as nixl_utils
 from vllm.distributed.device_communicators.pynccl import PyNcclCommunicator
 from vllm.distributed.device_communicators.pynccl_wrapper import (
     ncclDataTypeEnum,
 )
-from vllm.distributed.parallel_state import GroupCoordinator, is_local_first_rank
+from vllm.distributed.parallel_state import (
+    GroupCoordinator,
+    get_pp_group,
+    is_local_first_rank,
+)
 from vllm.distributed.stateless_coordinator import StatelessGroupCoordinator
 from vllm.logger import init_logger
+from vllm.platforms import current_platform
 
 logger = init_logger(__name__)
 
 
+def has_nixl() -> bool:
+    """Whether the optional NIXL / RIXL package is available."""
+    return nixl_utils.NixlWrapper is not None
+
+
 class EplbCommunicator(ABC):
     """Abstract EPLB communicator for expert weight transfers."""
 
     @abstractmethod
-    def add_send(self, tensor: torch.Tensor, dst_rank: int) -> None:
+    def add_send(
+        self,
+        tensors: list[torch.Tensor],
+        dst_rank: int,
+        expert_id: int,
+    ) -> None:
         pass
 
     @abstractmethod
-    def add_recv(self, tensor: torch.Tensor, src_rank: int) -> None:
+    def add_recv(
+        self,
+        tensors: list[torch.Tensor],
+        src_rank: int,
+        expert_id: int,
+    ) -> None:
         pass
 
     @abstractmethod
-    def execute(self) -> None:
+    def execute(self, old_indices: np.ndarray | None = None) -> None:
         pass
 
+    @property
+    def needs_profile_buffer_reservation(self) -> bool:
+        """Whether the profile path must run a dummy collective operation to reserve
+        communication buffers."""
+        return True
+
     def set_stream(self, cuda_stream: torch.cuda.Stream | None) -> None:
         self._cuda_stream = cuda_stream
 
@@ -61,27 +93,39 @@ def __init__(
         self._p2p_ops: list[P2POp] = []
         self._log_initialized()
 
-    def add_send(self, tensor: torch.Tensor, dst_rank: int) -> None:
-        self._p2p_ops.append(
-            P2POp(
-                torch.distributed.isend,
-                tensor,
-                dst_rank,
-                self._ep_group,
+    def add_send(
+        self,
+        tensors: list[torch.Tensor],
+        dst_rank: int,
+        expert_id: int,  # unused by this backend
+    ) -> None:
+        for tensor in tensors:
+            self._p2p_ops.append(
+                P2POp(
+                    torch.distributed.isend,
+                    tensor,
+                    dst_rank,
+                    self._ep_group,
+                )
             )
-        )
 
-    def add_recv(self, tensor: torch.Tensor, src_rank: int) -> None:
-        self._p2p_ops.append(
-            P2POp(
-                torch.distributed.irecv,
-                tensor,
-                src_rank,
-                self._ep_group,
+    def add_recv(
+        self,
+        tensors: list[torch.Tensor],
+        src_rank: int,
+        expert_id: int,  # unused by this backend
+    ) -> None:
+        for tensor in tensors:
+            self._p2p_ops.append(
+                P2POp(
+                    torch.distributed.irecv,
+                    tensor,
+                    src_rank,
+                    self._ep_group,
+                )
             )
-        )
 
-    def execute(self) -> None:
+    def execute(self, old_indices: np.ndarray | None = None) -> None:
         if not self._p2p_ops:
             return
         try:
@@ -106,13 +150,25 @@ def __init__(
         self._ops: list[tuple[str, torch.Tensor, int]] = []
         self._log_initialized()
 
-    def add_send(self, tensor: torch.Tensor, dst_rank: int) -> None:
-        self._ops.append(("send", tensor, dst_rank))
+    def add_send(
+        self,
+        tensors: list[torch.Tensor],
+        dst_rank: int,
+        expert_id: int,  # unused by this backend
+    ) -> None:
+        for tensor in tensors:
+            self._ops.append(("send", tensor, dst_rank))
 
-    def add_recv(self, tensor: torch.Tensor, src_rank: int) -> None:
-        self._ops.append(("recv", tensor, src_rank))
+    def add_recv(
+        self,
+        tensors: list[torch.Tensor],
+        src_rank: int,
+        expert_id: int,  # unused by this backend
+    ) -> None:
+        for tensor in tensors:
+            self._ops.append(("recv", tensor, src_rank))
 
-    def execute(self) -> None:
+    def execute(self, old_indices: np.ndarray | None = None) -> None:
         if not self._ops:
             return
 
@@ -167,6 +223,345 @@ def build_ops() -> None:
                 dst_tensor.copy_(cpu_tensor, non_blocking=True)
 
 
+class NixlEplbCommunicator(EplbCommunicator):
+    """EPLB communicator backed by NIXL READ transfers."""
+
+    def __init__(
+        self,
+        cpu_group: ProcessGroup,
+        expert_weights: Sequence[torch.Tensor],
+        cuda_stream: torch.cuda.Stream | None = None,
+    ) -> None:
+        assert expert_weights, "NixlEplbCommunicator requires non-empty expert_weights."
+        nixl_wrapper_cls = nixl_utils.NixlWrapper
+        if nixl_wrapper_cls is None:
+            raise RuntimeError("NIXL/ RIXL is unavailable.")
+        self._cpu_group = cpu_group
+        self._cuda_stream = cuda_stream
+        self._world_size = cpu_group.size()
+        self._rank = cpu_group.rank()
+        # expert_id -> weight tensors to pack into the send buffer.
+        self._expert_send_map: dict[int, list[torch.Tensor]] = {}
+        # src_rank -> expert_id -> weight tensors to unpack after transfer.
+        self._recv_map: dict[int, dict[int, list[torch.Tensor]]] = {}
+        self._num_local_experts: int = expert_weights[0].shape[0]
+        self._device = expert_weights[0].device
+        for tensor in expert_weights:
+            assert tensor.device == self._device, (
+                "All local EPLB tensors are expected to be on the same device: "
+                f"expected={self._device}, got={tensor.device}"
+            )
+
+        nixl_agent_config = nixl_utils.nixl_agent_config
+        config = (
+            nixl_agent_config(capture_telemetry=False)
+            if nixl_agent_config is not None
+            else None
+        )
+        self._nixl_wrapper = nixl_wrapper_cls(self._make_agent_name(), config)
+        self._nixl_memory_type = "VRAM"
+        self._registered_desc: object | None = None
+        self._remote_agents: dict[int, str] = {}
+        self._remote_send_meta: dict[int, tuple[int, int]] = {}
+        self._send_buffer: torch.Tensor = torch.empty(0)
+        self._recv_buffer: torch.Tensor = torch.empty(0)
+        self._expert_bytes: int = 0
+
+        self._cuda_device_id = int(self._device.index or 0)
+        self._init_step("buffers", self._init_registered_buffers, expert_weights)
+        self._init_step("agents", self._init_remote_agents)
+        self._init_step("send meta", self._exchange_remote_send_meta)
+        self._log_initialized()
+
+    @property
+    def needs_profile_buffer_reservation(self) -> bool:
+        return False
+
+    @staticmethod
+    def _init_step(name: str, fn: object, *args: object, **kwargs: object) -> None:
+        try:
+            fn(*args, **kwargs)  # type: ignore[operator]
+        except Exception as exc:
+            raise RuntimeError(f"NIXL EPLB init failed: {name}") from exc
+
+    def _make_agent_name(self) -> str:
+        """Build a deployment-unique nixl agent name."""
+        pp_size = get_pp_group().world_size
+        pp_suffix = f"-pp{get_pp_group().rank_in_group}" if pp_size > 1 else ""
+        uid = uuid.uuid4().hex[:8]
+        return f"eplb-{self._rank}{pp_suffix}-{uid}"
+
+    def add_send(
+        self,
+        tensors: list[torch.Tensor],
+        dst_rank: int,
+        expert_id: int,
+    ) -> None:
+        assert dst_rank != self._rank, (
+            "EPLB communicator should not enqueue same-rank sends: "
+            f"rank={self._rank}, dst_rank={dst_rank}"
+        )
+        # An expert sent to multiple peers is packed only once; skip duplicates.
+        if expert_id not in self._expert_send_map:
+            self._expert_send_map[expert_id] = tensors
+
+    def add_recv(
+        self,
+        tensors: list[torch.Tensor],
+        src_rank: int,
+        expert_id: int,
+    ) -> None:
+        assert src_rank != self._rank, (
+            "EPLB communicator should not enqueue same-rank recvs: "
+            f"rank={self._rank}, src_rank={src_rank}"
+        )
+        recv_experts = self._recv_map.setdefault(src_rank, {})
+        if expert_id not in recv_experts:
+            recv_experts[expert_id] = tensors
+
+    def _init_remote_agents(self) -> None:
+        local_metadata = self._nixl_wrapper.get_agent_metadata()
+        gathered_metadata: list[bytes | None] = [None] * self._world_size
+        torch.distributed.all_gather_object(
+            gathered_metadata, local_metadata, group=self._cpu_group
+        )
+        for peer in range(self._world_size):
+            if peer == self._rank:
+                continue
+            peer_metadata = gathered_metadata[peer]
+            assert peer_metadata is not None
+            self._remote_agents[peer] = self._nixl_wrapper.add_remote_agent(
+                peer_metadata
+            )
+
+    def _init_registered_buffers(self, expert_weights: Sequence[torch.Tensor]) -> None:
+        total_bytes = max(sum(t.nbytes for t in expert_weights), 1)
+        assert total_bytes % self._num_local_experts == 0, (
+            f"Number of bytes in moe layer {total_bytes} is not divisible "
+            f"by number of local experts {self._num_local_experts}"
+        )
+        self._expert_bytes = total_bytes // self._num_local_experts
+
+        self._send_buffer = torch.empty(
+            total_bytes, device=self._device, dtype=torch.uint8
+        )
+        self._recv_buffer = torch.empty(
+            total_bytes, device=self._device, dtype=torch.uint8
+        )
+
+        descs = self._nixl_wrapper.get_reg_descs([self._send_buffer, self._recv_buffer])
+        self._nixl_wrapper.register_memory(descs)
+        self._registered_desc = descs
+
+    def _exchange_remote_send_meta(self) -> None:
+        """Exchange send-buffer metadata so each rank can build dynamic
+        descriptors at execute time."""
+        local_meta: tuple[int, int] = (
+            self._send_buffer.data_ptr(),
+            self._cuda_device_id,
+        )
+        gathered_meta: list[tuple[int, int] | None] = [None] * self._world_size
+        torch.distributed.all_gather_object(
+            gathered_meta, local_meta, group=self._cpu_group
+        )
+
+        for peer in self._remote_agents:
+            peer_meta = gathered_meta[peer]
+            assert peer_meta is not None
+            self._remote_send_meta[peer] = peer_meta
+
+    @staticmethod
+    def _pack_send_buffer(
+        in_tensors: list[torch.Tensor],
+        send_buffer: torch.Tensor,
+        byte_offset: int,
+    ) -> None:
+        for tensor in in_tensors:
+            raw = tensor.reshape(-1).view(torch.uint8)
+            if raw.numel() == 0:
+                continue
+            send_buffer[byte_offset : byte_offset + raw.numel()].copy_(
+                raw, non_blocking=True
+            )
+            byte_offset += raw.numel()
+
+    @staticmethod
+    def _unpack_recv_buffer(
+        recv_buffer: torch.Tensor,
+        out_tensors: list[torch.Tensor],
+        byte_offset: int,
+    ) -> None:
+        for tensor in out_tensors:
+            num_bytes = tensor.numel() * tensor.element_size()
+            if num_bytes == 0:
+                continue
+            tensor.reshape(-1).view(torch.uint8).copy_(
+                recv_buffer[byte_offset : byte_offset + num_bytes],
+                non_blocking=True,
+            )
+            byte_offset += num_bytes
+
+    def _wait_for_all_transfers(self, handles: list[int]) -> None:
+        pending = set(handles)
+        while pending:
+            completed: list[int] = []
+            for handle in pending:
+                state = self._nixl_wrapper.check_xfer_state(handle)
+                if state == "DONE":
+                    completed.append(handle)
+                    continue
+                if state != "PROC":
+                    raise RuntimeError(f"NIXL transfer failed with state={state}")
+            for handle in completed:
+                pending.remove(handle)
+            if pending:
+                time.sleep(0.0005)
+
+    def _create_peer_xfer(
+        self,
+        src: int,
+        local_descs: list[tuple[int, int, int]],
+        remote_descs: list[tuple[int, int, int]],
+    ) -> tuple[int, int, int]:
+        """Create a batched xfer for multiple descriptors from one peer.
+
+        Each element in *local_descs* / *remote_descs* is an
+        ``(address, size, device_id)`` tuple.
+
+        Returns ``(local_dlist, remote_dlist, xfer_handle)``.
+        """
+        local_desc = self._nixl_wrapper.get_xfer_descs(
+            local_descs, self._nixl_memory_type
+        )
+        local_handle = self._nixl_wrapper.prep_xfer_dlist(
+            "NIXL_INIT_AGENT",
+            local_desc,
+        )
+
+        remote_desc = self._nixl_wrapper.get_xfer_descs(
+            remote_descs, self._nixl_memory_type
+        )
+        remote_handle = self._nixl_wrapper.prep_xfer_dlist(
+            self._remote_agents[src],
+            remote_desc,
+        )
+
+        indices = list(range(len(local_descs)))
+        xfer_handle = self._nixl_wrapper.make_prepped_xfer(
+            "READ",
+            local_handle,
+            indices,
+            remote_handle,
+            indices,
+        )
+        return (local_handle, remote_handle, xfer_handle)
+
+    def execute(self, old_indices: np.ndarray | None = None) -> None:
+        assert old_indices is not None, (
+            "NixlEplbCommunicator.execute requires old_indices"
+        )
+
+        xfer_entries: list[tuple[int, int, int]] = []
+        try:
+            n = self._num_local_experts
+            rank_experts = old_indices[: self._world_size * n].reshape(
+                self._world_size, n
+            )
+            # Build expert_id -> send slot mapping per rank.
+            expert_to_send_slot: list[dict[int, int]] = [
+                {int(eid): i for i, eid in enumerate(row) if eid != -1}
+                for row in rank_experts
+            ]
+
+            # Phase 1: pack each expert at its slot offset in the send buffer.
+            with torch.cuda.stream(self._cuda_stream):
+                for expert_id, tensors in self._expert_send_map.items():
+                    slot = expert_to_send_slot[self._rank][expert_id]
+                    byte_offset = slot * self._expert_bytes
+                    self._pack_send_buffer(tensors, self._send_buffer, byte_offset)
+
+            # Ensure all packed data is visible in device memory before pulls.
+            if self._cuda_stream is not None:
+                self._cuda_stream.synchronize()
+            else:
+                torch.cuda.current_stream().synchronize()
+            # READ is receiver-initiated; synchronize all ranks before transfer.
+            # We use monitored_barrier so a rank that crashes or exits early
+            # produces a diagnostic timeout instead of a silent hang.
+            torch.distributed.monitored_barrier(
+                group=self._cpu_group,
+                timeout=timedelta(minutes=5),
+            )
+
+            # Phase 2: issue one batched READ per peer.
+            recv_offsets: dict[tuple[int, int], int] = {}
+            recv_offset = 0
+            recv_base = self._recv_buffer.data_ptr()
+            for src in range(self._world_size):
+                if src == self._rank:
+                    continue
+                recv_experts = self._recv_map.get(src)
+                if not recv_experts:
+                    continue
+                expert_ids = list(recv_experts.keys())
+                remote_base, remote_dev = self._remote_send_meta[src]
+                local_descs: list[tuple[int, int, int]] = []
+                remote_descs: list[tuple[int, int, int]] = []
+                for expert_id in expert_ids:
+                    slot = expert_to_send_slot[src][expert_id]
+                    remote_off = slot * self._expert_bytes
+                    recv_offsets[(src, expert_id)] = recv_offset
+                    local_descs.append(
+                        (
+                            recv_base + recv_offset,
+                            self._expert_bytes,
+                            self._cuda_device_id,
+                        )
+                    )
+                    remote_descs.append(
+                        (remote_base + remote_off, self._expert_bytes, remote_dev)
+                    )
+                    recv_offset += self._expert_bytes
+                    assert recv_offset <= self._recv_buffer.nbytes
+                local_h, remote_h, xfer_h = self._create_peer_xfer(
+                    src, local_descs, remote_descs
+                )
+                self._nixl_wrapper.transfer(xfer_h)
+                xfer_entries.append((local_h, remote_h, xfer_h))
+
+            # Phase 3: wait for all in-flight transfers, then unpack.
+            self._wait_for_all_transfers([x[2] for x in xfer_entries])
+
+            with torch.cuda.stream(self._cuda_stream):
+                for (src, expert_id), offset in recv_offsets.items():
+                    self._unpack_recv_buffer(
+                        self._recv_buffer,
+                        self._recv_map[src][expert_id],
+                        offset,
+                    )
+        finally:
+            for local_h, remote_h, xfer_h in xfer_entries:
+                with contextlib.suppress(Exception):
+                    self._nixl_wrapper.release_xfer_handle(xfer_h)
+                with contextlib.suppress(Exception):
+                    self._nixl_wrapper.release_dlist_handle(local_h)
+                with contextlib.suppress(Exception):
+                    self._nixl_wrapper.release_dlist_handle(remote_h)
+            self._expert_send_map.clear()
+            self._recv_map.clear()
+
+    def __del__(self) -> None:
+        try:
+            if self._registered_desc is not None:
+                self._nixl_wrapper.deregister_memory(self._registered_desc)
+                self._registered_desc = None
+            for agent_name in self._remote_agents.values():
+                self._nixl_wrapper.remove_remote_agent(agent_name)
+            self._remote_agents.clear()
+        except Exception as e:
+            logger.warning("Error during NixlEplbCommunicator cleanup: %s", e)
+
+
 class PyNcclEplbCommunicator(EplbCommunicator):
     """EPLB communicator backed by PyNcclCommunicator using ncclSend/ncclRecv."""
 
@@ -185,15 +580,27 @@ def _ensure_group_started(self) -> None:
             self._pynccl_comm.group_start()
             self._group_started = True
 
-    def add_send(self, tensor: torch.Tensor, dst_rank: int) -> None:
+    def add_send(
+        self,
+        tensors: list[torch.Tensor],
+        dst_rank: int,
+        expert_id: int,  # unused by this backend
+    ) -> None:
         self._ensure_group_started()
-        self._pynccl_comm.send(tensor, dst_rank, stream=self._cuda_stream)
+        for tensor in tensors:
+            self._pynccl_comm.send(tensor, dst_rank, stream=self._cuda_stream)
 
-    def add_recv(self, tensor: torch.Tensor, src_rank: int) -> None:
+    def add_recv(
+        self,
+        tensors: list[torch.Tensor],
+        src_rank: int,
+        expert_id: int,  # unused by this backend
+    ) -> None:
         self._ensure_group_started()
-        self._pynccl_comm.recv(tensor, src_rank, stream=self._cuda_stream)
+        for tensor in tensors:
+            self._pynccl_comm.recv(tensor, src_rank, stream=self._cuda_stream)
 
-    def execute(self) -> None:
+    def execute(self, old_indices: np.ndarray | None = None) -> None:
         if self._group_started:
             self._pynccl_comm.group_end()
             self._group_started = False
@@ -204,6 +611,24 @@ def create_eplb_communicator(
     backend: str | None,
     expert_weights: Sequence[torch.Tensor],
 ) -> EplbCommunicator:
+    """Create an EPLB communicator for the given backend.
+
+    Args:
+        group_coordinator: Process-group coordinator that provides the
+            device and CPU communication groups.
+        backend: Communicator backend name (``"torch_nccl"``,
+            ``"torch_gloo"``, ``"pynccl"``, or ``"nixl"``).
+            Falls back to ``"torch_nccl"`` when *None*.
+            Stateless (elastic EP) groups only support ``"torch_nccl"``
+            and ``"pynccl"``; ``"torch_nccl"`` is silently promoted to
+            ``"pynccl"`` in that case.  When tensors reside on CPU,
+            ``"torch_gloo"`` or ``"torch_nccl"`` are used via the CPU
+            process group.
+        expert_weights: Expert weight tensors from *one* MoE layer.
+            NixlEplbCommunicator pre-allocates send/recv buffers sized
+            to this layer, so all other MoE layers must have the same
+            tensor count, shapes, and dtypes.
+    """
     # Keep a safe default for callers that have not resolved communicator yet.
     if backend is None:
         backend = "torch_nccl"
@@ -256,7 +681,7 @@ def _create_pynccl() -> EplbCommunicator:
         if backend not in ("torch_nccl", "pynccl"):
             raise ValueError(
                 f"Elastic EP requires 'torch_nccl' or 'pynccl' EPLB communicator "
-                f"(got '{backend}'). torch_gloo is not supported with stateless groups."
+                f"(got '{backend}')."
             )
         if backend == "torch_nccl":
             logger.warning(
@@ -266,7 +691,26 @@ def _create_pynccl() -> EplbCommunicator:
             backend = "pynccl"
         return _create_pynccl()
 
-    if backend == "torch_gloo":
+    if backend == "nixl":
+        if not has_nixl():
+            raise RuntimeError(
+                "EPLB communicator 'nixl' requested but NIXL is unavailable."
+            )
+        if not (current_platform.is_cuda_alike() and tensor_device_type != "cpu"):
+            raise RuntimeError(
+                "EPLB communicator 'nixl' supports only cuda-like devices "
+                f"(got {tensor_device_type})."
+            )
+        try:
+            return NixlEplbCommunicator(
+                cpu_group=group_coordinator.cpu_group,
+                expert_weights=expert_weights,
+            )
+        except Exception as exc:
+            raise RuntimeError(
+                f"Failed to initialize NixlEplbCommunicator ({exc})."
+            ) from exc
+    elif backend == "torch_gloo":
         return TorchDistGlooStagedEplbCommunicator(
             cpu_group=group_coordinator.cpu_group,
         )
diff --git a/vllm/distributed/eplb/eplb_state.py b/vllm/distributed/eplb/eplb_state.py
index c56f8b0364aa..1da39caccd80 100644
--- a/vllm/distributed/eplb/eplb_state.py
+++ b/vllm/distributed/eplb/eplb_state.py
@@ -30,7 +30,6 @@
 from collections.abc import Sequence
 from dataclasses import dataclass
 
-import numpy as np
 import torch
 from torch.distributed import ProcessGroup, all_reduce
 
@@ -48,9 +47,10 @@
 
 from .async_worker import start_async_worker
 from .eplb_communicator import EplbCommunicator, create_eplb_communicator
+from .eplb_utils import CpuGpuEvent
 from .policy import EPLB_POLICIES, AbstractEplbPolicy, DefaultEplbPolicy
 from .rebalance_execute import (
-    RecvMetadata,
+    AsyncEplbLayerResult,
     move_from_buffer,
     rearrange_expert_weights_inplace,
 )
@@ -174,55 +174,20 @@ class EplbModelState:
     """
     The buffer to store the expert weights during transfer.
     """
-    buffer_lock: threading.Lock
-    """
-    The lock to protect the expert buffer.
-    """
-    buffer_consumed_event: torch.cuda.Event | None
-    """
-    CUDA event recorded after the main thread finishes consuming the buffer.
-    The async worker waits on this before writing to the buffer again.
-    """
-    window_ready_event: torch.cuda.Event | None
-    """
-    CUDA event recorded after all-reduce and clone on the main thread.
-    The async worker waits on this before accessing global_expert_load_window.
-    """
-    ep_buffer_ready: int
-    """
-    The flag indicates whether the expert buffer is ready for transfer.
-    0 or 1.
-    """
-    layer_to_transfer: int
-    """
-    The layer index to transfer in async mode.
-    """
     rebalanced: bool
     """
-    The flag indicates whether the experts rebalance have been computed.
-    """
-    pending_global_ready_check: bool
-    """
-    Whether the async EPLB needs to poll peers for buffer readiness.
+    This flag is only used when running Async EPLB. It is set to True by the main thread
+    after the new expert maps have been computed. This indicates that the async worker
+    should start transferring weights. move_to_workspace sets this flag to False when
+    all weights have been transferred and the new map has been successfully committed.
+
+    rebalanced relies on the GIL to synchronize access between the main thread and
+    the async worker.
     """
     eplb_stats: EplbStats | None
     """
     EPLB stats for the model.
     """
-    is_unchanged: np.ndarray
-    """
-    intermediate variable between `move_to_buffer` and `move_to_workspace`.
-    The size is same as the num of physical experts in the current layer.
-    """
-    is_received_locally: np.ndarray
-    """
-    intermediate variable between `move_to_buffer` and `move_to_workspace`.
-    The size is same as the num of physical experts in the current layer.
-    """
-    recv_metadata: RecvMetadata
-    """
-    intermediate variable between `move_to_buffer` and `move_to_workspace`.
-    """
     cuda_device_index: int | None
     """
     CUDA device index for the async EPLB worker thread.
@@ -231,10 +196,14 @@ class EplbModelState:
     """
     The communicator for expert weight transfers.
     """
-    new_physical_to_logical_map: torch.Tensor | None = None
+    pending_result: AsyncEplbLayerResult | None = None
     """
-    intermediate variable between `move_to_buffer` and `move_to_workspace`.
-    the size is same as physical_to_logical_map
+    Set by the async worker after all writes to expert_buffer are done. Consumed
+    and reset to None by the main thread in move_to_workspace() after the contents of
+    expert_buffer have been transferred out. At most one result is pending at a time.
+
+    pending_result relies on the GIL to synchronize access between the main thread and
+    the async worker.
     """
 
 
@@ -289,7 +258,7 @@ def __init__(self, parallel_config: ParallelConfig, device: torch.device):
         """
         The flag indicates whether the EPLB is running in async mode.
         """
-        self.rearrange_event = threading.Event()
+        self.rearrange_event: CpuGpuEvent = CpuGpuEvent()
         """
         Event to signal when a new rearrangement is needed for the async thread.
         """
@@ -493,25 +462,10 @@ def add_model(
             model_name=model_config.model,
             model=model,
             expert_buffer=expert_buffer,
-            buffer_lock=threading.Lock(),
-            buffer_consumed_event=None,
-            window_ready_event=None,
-            ep_buffer_ready=0,
-            layer_to_transfer=0,
             rebalanced=False,
-            pending_global_ready_check=False,
             eplb_stats=None,
-            is_unchanged=np.array([]),
-            is_received_locally=np.array([]),
-            recv_metadata=RecvMetadata(
-                recv_primary_mask=np.array([]),
-                recv_count=0,
-                recv_expert_ids=np.array([]),
-                recv_dst_rows=np.array([]),
-            ),
             cuda_device_index=self.cuda_device_index,
             communicator=communicator,
-            new_physical_to_logical_map=None,
         )
         self.model_states[model_config.compute_hash()] = model_state
         self.num_valid_physical_experts = model.num_physical_experts
@@ -622,17 +576,17 @@ def step(
         self.expert_rearrangement_step += 1
 
         if self.is_async:
+            # Run _move_to_workspace if all ranks have finished transferring the
+            # new weights to the intermediate buffer
             for eplb_model_state in self.model_states.values():
-                all_ranks_buffer_ready = False
-                if eplb_model_state.pending_global_ready_check:
-                    all_ranks_buffer_ready = self._all_ranks_buffer_ready(
-                        eplb_model_state
-                    )
-                if eplb_model_state.ep_buffer_ready and all_ranks_buffer_ready:
-                    self.move_to_workspace(
+                # rebalanced must remain consistent amongst all ranks otherwise the
+                # all_reduce in _all_ranks_result_ready will hang
+                if eplb_model_state.rebalanced and self._all_ranks_result_ready(
+                    eplb_model_state
+                ):
+                    _move_to_workspace(
                         model_state=eplb_model_state,
-                        ep_group=ep_group,
-                        is_profile=is_profile,
+                        ep_rank=ep_group.rank(),
                     )
 
         if self.expert_rearrangement_step >= self.expert_rearrangement_step_interval:
@@ -846,18 +800,10 @@ def rearrange(
                     num_nodes=num_nodes,
                     num_gpus=num_gpus,
                 )
-                # Record event after clone to signal async worker
-                # that load stats data is ready
-                sync_event = torch.cuda.Event()
-                sync_event.record()
-                eplb_model_state.window_ready_event = sync_event
-
                 eplb_model_state.rebalanced = True
-                eplb_model_state.layer_to_transfer = 0
-                eplb_model_state.pending_global_ready_check = True
         # Signal async thread to start transferring layers
         if self.is_async and (not is_profile):
-            self.rearrange_event.set()
+            self.rearrange_event.record()
         return None
 
     def start_async_loop(
@@ -873,121 +819,27 @@ def start_async_loop(
                 is_profile=is_profile,
             )
 
-    def _all_ranks_buffer_ready(self, model_state: EplbModelState) -> bool:
+    def _all_ranks_result_ready(self, model_state: EplbModelState) -> bool:
         parallel_state = get_ep_group()
+        has_result = int(model_state.pending_result is not None)
+
         cpu_group = getattr(parallel_state, "cpu_group", None)
         if cpu_group is not None and cpu_group.size() > 1:
-            flag = torch.tensor(
-                (int(model_state.ep_buffer_ready),), dtype=torch.int32, device="cpu"
-            )
+            flag = torch.tensor((has_result,), dtype=torch.int32, device="cpu")
             all_reduce(flag, group=cpu_group)
             return int(flag.item()) == cpu_group.size()
 
         device_group = parallel_state.device_group
         if device_group.size() <= 1:
-            return bool(model_state.ep_buffer_ready)
+            return bool(has_result)
 
         device = getattr(
             parallel_state, "device", model_state.physical_to_logical_map.device
         )
-        flag = torch.tensor(
-            (int(model_state.ep_buffer_ready),), dtype=torch.int32, device=device
-        )
+        flag = torch.tensor((has_result,), dtype=torch.int32, device=device)
         all_reduce(flag, group=device_group)
         return int(flag.item()) == device_group.size()
 
-    def move_to_workspace(
-        self,
-        model_state: EplbModelState,
-        ep_group: ProcessGroup,
-        is_profile: bool = False,
-    ):
-        # We call move_to_workspace only when ep_buffer_ready is 1.
-        # It means we only need to wait for the lock for a short time.
-        max_retries = 6  # 1 minute max
-        retries = 0
-        while not model_state.buffer_lock.acquire(blocking=True, timeout=10.0):
-            retries += 1
-            if retries >= max_retries:
-                raise RuntimeError(
-                    f"Rank {ep_group.rank()}: buffer_lock timeout after "
-                    "{max_retries * 10}s"
-                )
-            logger.warning(
-                "Rank %d: EPLB buffer_lock acquire failed, retrying (%d/%d)",
-                ep_group.rank(),
-                retries,
-                max_retries,
-            )
-        try:
-            assert model_state.new_physical_to_logical_map is not None
-            expert_weights = model_state.model.expert_weights[
-                model_state.layer_to_transfer
-            ]
-            expert_weights_buffer = model_state.expert_buffer
-            new_indices = model_state.new_physical_to_logical_map[
-                model_state.layer_to_transfer
-            ].numpy()
-            move_from_buffer(
-                expert_weights=expert_weights,
-                expert_weights_buffers=expert_weights_buffer,
-                is_unchanged=model_state.is_unchanged,
-                is_received_locally=model_state.is_received_locally,
-                recv_metadata=model_state.recv_metadata,
-                new_indices=new_indices,
-                ep_rank=ep_group.rank(),
-            )
-
-            transferred_layer = model_state.layer_to_transfer
-
-            transferred_layer = model_state.layer_to_transfer
-            assert model_state.new_physical_to_logical_map is not None
-            _commit_eplb_maps_for_layer(
-                model_state,
-                new_physical_to_logical_map=model_state.new_physical_to_logical_map,
-                layer=transferred_layer,
-            )
-
-            # Record event after consuming buffer to signal async thread
-            # that it's safe to overwrite the intermediate buffer
-            consumed_event = torch.cuda.Event()
-            consumed_event.record()
-            model_state.buffer_consumed_event = consumed_event
-
-            # After the main thread consumes, advance layer_to_transfer
-            model_state.layer_to_transfer += 1
-            model_state.ep_buffer_ready = 0
-            logger.debug(
-                "model %s successfully move_to_workspace layer %d",
-                model_state.model_name,
-                transferred_layer,
-            )
-            if model_state.layer_to_transfer >= model_state.model.num_moe_layers:
-                self.post_eplb(model_state)
-                model_state.rebalanced = False
-                model_state.layer_to_transfer = 0
-                model_state.pending_global_ready_check = False
-                logger.info(
-                    "finish async transfer for model %s rank %d layer %d",
-                    model_state.model_name,
-                    ep_group.rank(),
-                    model_state.model.num_moe_layers,
-                )
-
-        finally:
-            try:
-                model_state.buffer_lock.release()
-            except Exception as e:
-                logger.error(
-                    "Rank %d: buffer_lock release failed in move_to_workspace: %s",
-                    ep_group.rank(),
-                    str(e),
-                )
-
-    def post_eplb(self, model_state: EplbModelState) -> None:
-        assert model_state.new_physical_to_logical_map is not None
-        model_state.new_physical_to_logical_map = None
-
     def _allreduce_list(self, tensor_list: list[torch.Tensor]) -> list[torch.Tensor]:
         """
         All-reduce a list of tensors.
@@ -1225,7 +1077,7 @@ def _commit_eplb_maps_for_layer(
     """
 
     # Commit physical_to_logical_map
-    src = new_physical_to_logical_map[layer]
+    src = new_physical_to_logical_map
     dst = model_state.physical_to_logical_map[layer]
     assert src.shape == dst.shape, (
         "The number of physical experts must stay the same while running Async EPLB. "
@@ -1284,3 +1136,31 @@ def _commit_eplb_maps(
     src = new_replica_count
     dst = model_state.logical_replica_count
     dst.copy_(src, non_blocking=True)
+
+
+def _move_to_workspace(
+    model_state: EplbModelState,
+    ep_rank: int,
+) -> None:
+    result = model_state.pending_result
+    assert result is not None
+    move_from_buffer(
+        expert_weights=model_state.model.expert_weights[result.layer_idx],
+        expert_weights_buffers=model_state.expert_buffer,
+        transfer_metadata=result.transfer_metadata,
+        new_indices=result.new_physical_to_logical_map.numpy(),
+        ep_rank=ep_rank,
+    )
+
+    _commit_eplb_maps_for_layer(
+        model_state,
+        new_physical_to_logical_map=result.new_physical_to_logical_map,
+        layer=result.layer_idx,
+    )
+
+    if result.layer_idx == model_state.model.num_moe_layers - 1:
+        model_state.rebalanced = False
+
+    # Reset pending_result before unblocking the async worker
+    model_state.pending_result = None
+    result.consumed_event.record()
diff --git a/vllm/distributed/eplb/eplb_utils.py b/vllm/distributed/eplb/eplb_utils.py
index f499b3e518b8..92fffd229771 100644
--- a/vllm/distributed/eplb/eplb_utils.py
+++ b/vllm/distributed/eplb/eplb_utils.py
@@ -3,6 +3,9 @@
 """Utility functions for EPLB (Expert Parallel Load Balancing)."""
 
 import os
+import threading
+
+import torch
 
 from vllm.config import ParallelConfig
 from vllm.logger import init_logger
@@ -10,6 +13,54 @@
 logger = init_logger(__name__)
 
 
+class CpuGpuEvent:
+    """
+    Combines a CUDA event with a CPU threading event to enforce record->wait
+    ordering across two threads.
+
+    This class is designed for exactly two threads: one producer that calls
+    record() and one consumer that calls wait(). Using it with more than two
+    threads is not supported and will produce undefined behavior.
+
+    CUDA events alone are insufficient for cross-thread synchronization because
+    waiting on an unrecorded CUDA event is a no-op. The wait will return
+    immediately instead of blocking. This class adds a threading.Event so
+    that the waiting thread blocks on the CPU side until record() is called, at
+    which point the CUDA event is guaranteed to be in-flight and event.wait() will
+    correctly synchronize the GPU stream.
+    """
+
+    def __init__(self):
+        self._event = torch.cuda.Event()
+        self._recorded = threading.Event()
+
+    def wait(self, stream: torch.cuda.Stream | None = None):
+        """
+        Blocks the calling thread until record finishes. Used to guarantee that the
+        record kernel is called before wait.
+
+        Should only be called by the Async Eplb thread.
+        """
+        self._recorded.wait()
+        self._event.wait(stream)
+        self._recorded.clear()
+
+    def record(self, stream: torch.cuda.Stream | None = None):
+        """
+        Unblocks the waiting thread after calling event.record().
+
+        Should only be called by the main thread.
+        """
+        if self._recorded.is_set():
+            raise RuntimeError(
+                "CpuGpuEvent.record() called before the previous event was "
+                "consumed by wait()"
+            )
+        self._event = torch.cuda.Event()
+        self._event.record(stream)
+        self._recorded.set()
+
+
 def override_envs_for_eplb(parallel_config: ParallelConfig) -> None:
     """
     Override environment variables for EPLB when specific conditions are met.
diff --git a/vllm/distributed/eplb/rebalance_execute.py b/vllm/distributed/eplb/rebalance_execute.py
index 6670ccc6ee6c..cf8db9264890 100644
--- a/vllm/distributed/eplb/rebalance_execute.py
+++ b/vllm/distributed/eplb/rebalance_execute.py
@@ -13,13 +13,21 @@
 import torch
 from torch.distributed import ProcessGroup, all_gather
 
-from .eplb_communicator import EplbCommunicator
+from vllm.distributed.eplb.eplb_communicator import EplbCommunicator
+from vllm.distributed.eplb.eplb_utils import CpuGpuEvent
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
 
 
 @dataclass
-class RecvMetadata:
-    """Metadata describing remote receives during EPLB rebalancing."""
+class TransferMetadata:
+    """Metadata describing a completed EPLB buffer transfer."""
 
+    is_unchanged: np.ndarray
+    """Mask of (num_local_experts,) indicating experts unchanged after rebalance."""
+    is_received_locally: np.ndarray
+    """Mask of (num_local_experts,) indicating experts received from local data."""
     recv_primary_mask: np.ndarray
     """Mask of (num_local_experts,) indicating primary experts received."""
     recv_count: int
@@ -30,8 +38,28 @@ class RecvMetadata:
     """Target expert indices (num_local_experts,) in local tensors to send."""
 
 
-# Type alias for the result of move_to_buffer or transfer_layer
-MoveToBufferResult = tuple[np.ndarray, np.ndarray, RecvMetadata]
+@dataclass
+class AsyncEplbLayerResult:
+    """
+    The result of one completed async EPLB layer transfer.
+    """
+
+    layer_idx: int
+    """Index of the MoE layer that was transferred."""
+    new_physical_to_logical_map: torch.Tensor
+    """
+    New physical→logical mapping for layers_idx, on CPU.
+    Shape: (num_physical_experts)
+    """
+    transfer_metadata: TransferMetadata
+    """Metadata describing what was received during transfer_layer."""
+    consumed_event: CpuGpuEvent
+    """
+    Event used to synchronize access to the intermediate buffer. The async worker calls
+    wait() after it finishes transferring weights to the intermediate buffer. The main
+    thread calls record() after it finishes transferring weights out of the intermediate
+    buffer in _move_to_workspace()
+    """
 
 
 def get_ep_ranks_with_experts_batch(
@@ -150,7 +178,7 @@ def move_to_buffer(
     cuda_stream: torch.cuda.Stream | None,
     ep_rank: int,
     communicator: EplbCommunicator,
-) -> MoveToBufferResult:
+) -> TransferMetadata:
     """
     Rearranges expert weights during EPLB rebalancing.
 
@@ -167,11 +195,7 @@ def move_to_buffer(
         communicator: EplbCommunicator instance for P2P communication.
 
     Returns:
-        is_unchanged (np.ndarray): (num_local_experts,), True where an expert row
-            is unchanged after rebalance.
-        is_received_locally (np.ndarray): (num_local_experts,), True where a row
-            can be updated from local data.
-        RecvMetadata: Metadata needed for completing remote weight transfers.
+        TransferMetadata: Metadata needed for completing remote weight transfers.
     """
     assert old_indices.shape == new_indices.shape
     recv_primary_mask = np.zeros((num_local_experts,), dtype=np.bool_)
@@ -270,9 +294,9 @@ def move_to_buffer(
             recver_pos = remainder_start + sender_pos
             if recver_pos < len(ranks_to_recv):
                 recv_ranks.append(ranks_to_recv[recver_pos])
+            expert_tensors = [w[src] for w in expert_weights]
             for dst in recv_ranks:
-                for w in expert_weights:
-                    communicator.add_send(w[src], dst)
+                communicator.add_send(expert_tensors, dst, expert_id=int(expert))
 
     # 3. Post recvs
     if recv_count > 0:
@@ -301,30 +325,29 @@ def move_to_buffer(
                 src = ranks_to_send[recver_pos // num_dst_per_sender]
             else:
                 src = ranks_to_send[recver_pos - remainder_start]
-            for b in expert_weights_buffers:
-                communicator.add_recv(b[dst], src)
+            communicator.add_recv(
+                [b[dst] for b in expert_weights_buffers],
+                src,
+                expert_id=int(expert),
+            )
 
     # 4. Execute the P2P operations. The real communication happens here.
-    communicator.execute()
+    communicator.execute(old_indices=old_indices)
     # wait for the communication to finish
-    return (
-        is_unchanged,
-        is_received_locally,
-        RecvMetadata(
-            recv_primary_mask=recv_primary_mask,
-            recv_count=recv_count,
-            recv_expert_ids=recv_expert_ids,
-            recv_dst_rows=recv_dst_rows,
-        ),
+    return TransferMetadata(
+        is_unchanged=is_unchanged,
+        is_received_locally=is_received_locally,
+        recv_primary_mask=recv_primary_mask,
+        recv_count=recv_count,
+        recv_expert_ids=recv_expert_ids,
+        recv_dst_rows=recv_dst_rows,
     )
 
 
 def move_from_buffer(
     expert_weights: Sequence[torch.Tensor],
     expert_weights_buffers: list[torch.Tensor],
-    is_unchanged: np.ndarray,
-    is_received_locally: np.ndarray,
-    recv_metadata: RecvMetadata,
+    transfer_metadata: TransferMetadata,
     new_indices: np.ndarray,
     ep_rank: int,
 ) -> None:
@@ -336,17 +359,17 @@ def move_from_buffer(
         expert_weights: List of the actual MoE layer weights used in the execution.
         expert_weights_buffers: Intermediate buffers containing the experts weights
             after the transfer is completed.
-        is_unchanged: (num_local_experts,), True where an expert row is unchanged.
-        is_received_locally: (num_local_experts,), True where a row is updated locally.
-        recv_metadata: RecvMetadata containing remote receive metadata.
+        transfer_metadata: TransferMetadata containing transfer metadata.
         new_indices: (num_experts_total,) mapping from local rows to desired
             (possibly global) expert id, after rebalance.
         ep_rank: Rank of the process in the expert parallel group.
     """
-    recv_primary_mask = recv_metadata.recv_primary_mask
-    recv_count = recv_metadata.recv_count
-    recv_expert_ids = recv_metadata.recv_expert_ids
-    recv_dst_rows = recv_metadata.recv_dst_rows
+    is_unchanged = transfer_metadata.is_unchanged
+    is_received_locally = transfer_metadata.is_received_locally
+    recv_primary_mask = transfer_metadata.recv_primary_mask
+    recv_count = transfer_metadata.recv_count
+    recv_expert_ids = transfer_metadata.recv_expert_ids
+    recv_dst_rows = transfer_metadata.recv_dst_rows
     num_local_experts = is_unchanged.shape[0]
 
     # Mask for rows to copy back from buffers:
@@ -398,7 +421,7 @@ def move_from_buffer(
             w[dst].copy_(w[src], non_blocking=True)
 
 
-async def transfer_layer(
+def transfer_layer(
     old_layer_indices: torch.Tensor,
     new_layer_indices: torch.Tensor,
     expert_weights: Sequence[torch.Tensor],
@@ -408,7 +431,7 @@ async def transfer_layer(
     is_profile: bool = False,
     cuda_stream: torch.cuda.Stream | None = None,
     rank_mapping: dict[int, int] | None = None,
-) -> MoveToBufferResult:
+) -> TransferMetadata:
     """
     Rearranges the expert weights in place according to the new expert indices.
 
@@ -431,11 +454,8 @@ async def transfer_layer(
         rank_mapping: Optional rank mapping for elastic expert parallelism.
 
     Returns:
-        is_unchanged (np.ndarray): (num_local_experts,), True where expert
-            is left unchanged.
-        is_received_locally (np.ndarray): (num_local_experts,), True where expert
-            can be received locally.
-        RecvMetadata: Metadata needed for completing remote weight transfers.
+        TransferMetadata: Metadata needed for completing remote weight transfers,
+            including is_unchanged and is_received_locally masks.
     """
     ep_size = ep_group.size()
     if rank_mapping is not None:
@@ -470,7 +490,7 @@ async def transfer_layer(
     old_layer_indices_np = old_layer_indices.cpu().numpy()
     new_layer_indices_np = new_layer_indices.cpu().numpy()
 
-    is_unchanged, is_received_locally, recv_metadata = move_to_buffer(
+    return move_to_buffer(
         num_local_experts=num_local_physical_experts,
         old_indices=old_layer_indices_np,
         new_indices=new_layer_indices_np,
@@ -480,7 +500,6 @@ async def transfer_layer(
         ep_rank=ep_group.rank(),
         communicator=communicator,
     )
-    return is_unchanged, is_received_locally, recv_metadata
 
 
 def rearrange_expert_weights_inplace(
@@ -541,23 +560,29 @@ def rearrange_expert_weights_inplace(
     assert num_physical_experts == ep_size * num_local_physical_experts
 
     first_layer_weights = list(expert_weights[0])
+
+    if is_profile:
+        if communicator.needs_profile_buffer_reservation:
+            # Reserve NCCL communication buffers via a dummy all_gather.
+            # Backends that pre-allocate their own transfer buffers
+            # skip this to avoid the extra memory spike during profiling.
+            weights_buffer: list[torch.Tensor] = [
+                torch.empty_like(w) for w in first_layer_weights
+            ]
+            for weight, buffer in zip(expert_weights[0], weights_buffer):
+                dummy_recv_buffer = [buffer for _ in range(ep_size)]
+                torch.distributed.barrier()
+                all_gather(
+                    dummy_recv_buffer,
+                    weight,
+                    group=ep_group,
+                )
+        return
+
     # Buffers to hold the expert weights during the exchange.
     # NOTE: Currently we assume the same weights across different layers
     # have the same shape.
-    weights_buffer: list[torch.Tensor] = [
-        torch.empty_like(w) for w in first_layer_weights
-    ]
-    if is_profile:
-        # Reserve communication buffers via a minimal dummy all_gather on first layer
-        for weight, buffer in zip(expert_weights[0], weights_buffer):
-            dummy_recv_buffer = [buffer for _ in range(ep_size)]
-            torch.distributed.barrier()
-            all_gather(
-                dummy_recv_buffer,
-                weight,
-                group=ep_group,
-            )
-        return
+    weights_buffer = [torch.empty_like(w) for w in first_layer_weights]
 
     # NOTE(bowen): We need this synchronize to run, but I don't know why.
     # If you figure out the reason, please let me know -- thank you!
@@ -567,7 +592,7 @@ def rearrange_expert_weights_inplace(
     new_global_expert_indices_cpu = new_global_expert_indices.cpu().numpy()
 
     for layer_idx in range(num_moe_layers):
-        is_unchanged, is_received_locally, recv_metadata = move_to_buffer(
+        transfer_metadata = move_to_buffer(
             num_local_experts=num_local_physical_experts,
             old_indices=old_global_expert_indices_cpu[layer_idx],
             new_indices=new_global_expert_indices_cpu[layer_idx],
@@ -581,9 +606,7 @@ def rearrange_expert_weights_inplace(
         move_from_buffer(
             expert_weights=expert_weights[layer_idx],
             expert_weights_buffers=weights_buffer,
-            is_unchanged=is_unchanged,
-            is_received_locally=is_received_locally,
-            recv_metadata=recv_metadata,
+            transfer_metadata=transfer_metadata,
             new_indices=new_global_expert_indices_cpu[layer_idx],
             ep_rank=ep_rank,
         )
@@ -677,4 +700,4 @@ def _map_new_expert_indices_with_rank_mapping(
     return mapped_expert_indices
 
 
-__all__ = ["transfer_layer", "move_from_buffer", "RecvMetadata"]
+__all__ = ["transfer_layer", "move_from_buffer", "TransferMetadata"]
diff --git a/vllm/distributed/kv_transfer/README.md b/vllm/distributed/kv_transfer/README.md
index 39377aabcce3..64be074cf9f8 100644
--- a/vllm/distributed/kv_transfer/README.md
+++ b/vllm/distributed/kv_transfer/README.md
@@ -22,7 +22,7 @@ NOTE: If you want to not only transfer KV caches, but adjust the model execution
 
 ## Disaggregated prefilling
 
-The example usage is in [this file](../../../examples/online_serving/disaggregated_prefill.sh).
+The example usage is in [this file](../../../examples/disaggregated/disaggregated_prefill.sh).
 
 Here is the diagram of how we run disaggregated prefilling.
 
diff --git a/vllm/distributed/kv_transfer/kv_connector/factory.py b/vllm/distributed/kv_transfer/kv_connector/factory.py
index f691b9f18e92..b1ecf3c56ade 100644
--- a/vllm/distributed/kv_transfer/kv_connector/factory.py
+++ b/vllm/distributed/kv_transfer/kv_connector/factory.py
@@ -44,14 +44,12 @@ def create_connector(
         cls,
         config: "VllmConfig",
         role: KVConnectorRole,
-        kv_cache_config: "KVCacheConfig | None" = None,
+        kv_cache_config: "KVCacheConfig",
     ) -> KVConnectorBase:
         kv_transfer_config = config.kv_transfer_config
         if kv_transfer_config is None:
             raise ValueError("kv_transfer_config must be set to create a connector")
-        connector_cls, compat_sig = cls._get_connector_class_with_compat(
-            kv_transfer_config
-        )
+        connector_cls = cls.get_connector_class(kv_transfer_config)
 
         # check if the connector supports HMA
         hma_enabled = not config.scheduler_config.disable_hybrid_kv_cache_manager
@@ -74,12 +72,7 @@ def create_connector(
         # - Co-locate with worker process
         # - Should only be used inside the forward context & attention layer
         # We build separately to enforce strict separation
-        if compat_sig:
-            # Old signature: __init__(self, vllm_config, role)
-            return connector_cls(config, role)
-        else:
-            # New signature: __init__(self, vllm_config, role, kv_cache_config)
-            return connector_cls(config, role, kv_cache_config)
+        return connector_cls(config, role, kv_cache_config)
 
     @classmethod
     def get_connector_class_by_name(
@@ -100,13 +93,12 @@ def get_connector_class_by_name(
         return cls._registry[connector_name]()
 
     @classmethod
-    def _get_connector_class_with_compat(
+    def get_connector_class(
         cls, kv_transfer_config: "KVTransferConfig"
-    ) -> tuple[type[KVConnectorBaseType], bool]:
+    ) -> type[KVConnectorBaseType]:
         connector_name = kv_transfer_config.kv_connector
         if connector_name is None:
             raise ValueError("Connector name is not set in KVTransferConfig")
-        compat_sig = False
         connector_module_path = kv_transfer_config.kv_connector_module_path
         if connector_module_path is not None and not connector_module_path:
             raise ValueError("kv_connector_module_path cannot be an empty string.")
@@ -121,24 +113,18 @@ def _get_connector_class_with_compat(
                 ) from e
             connector_cls = cast(type[KVConnectorBaseType], connector_cls)
             if not supports_kw(connector_cls, "kv_cache_config"):
-                compat_sig = True
-                logger.warning(
-                    "Connector %s uses deprecated signature with 2 required arguments. "
-                    "Please update to include kv_cache_config as the second argument.",
-                    connector_cls.__name__,
+                msg = (
+                    f"Connector {connector_cls.__name__} uses deprecated "
+                    "2-argument constructor signature. External v1 KV "
+                    "connectors must accept kv_cache_config as the third "
+                    "constructor argument and pass it to super().__init__()."
                 )
+                logger.error(msg)
+                raise ValueError(msg)
         elif connector_name in cls._registry:
             connector_cls = cls._registry[connector_name]()
         else:
             raise ValueError(f"Unsupported connector type: {connector_name}")
-        return connector_cls, compat_sig
-
-    @classmethod
-    def get_connector_class(
-        cls, kv_transfer_config: "KVTransferConfig"
-    ) -> type[KVConnectorBaseType]:
-        """Get the connector class by name."""
-        connector_cls, _ = cls._get_connector_class_with_compat(kv_transfer_config)
         return connector_cls
 
 
diff --git a/vllm/distributed/kv_transfer/kv_connector/utils.py b/vllm/distributed/kv_transfer/kv_connector/utils.py
index e75c1c0a3a45..a86b25c75a4f 100644
--- a/vllm/distributed/kv_transfer/kv_connector/utils.py
+++ b/vllm/distributed/kv_transfer/kv_connector/utils.py
@@ -5,7 +5,7 @@
 """
 
 from collections.abc import Iterator
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Literal, cast
 
 import torch
@@ -319,31 +319,105 @@ def yield_req_data(
     )
 
 
-@dataclass
-class TpKVTopology:
+def get_current_attn_backends(
+    vllm_config: VllmConfig, layer_names: list[str] | None = None
+) -> list[type[AttentionBackend]]:
+    """Get all distinct attention backends for the given layers.
+
+    Args:
+        vllm_config: The current vLLM configuration.
+        layer_names: Optional list of layer names to scope the lookup.
+            When None, all attention layers are considered.
+
+    Returns:
+        Deduplicated list of attention backend classes.
     """
-    Helper class for tensor parallel and KV topology information for
-    mapping between local and remote TP workers.
+    layer_type = cast(type[Any], AttentionLayerBase)
+    layers = get_layers_from_vllm_config(vllm_config, layer_type, layer_names)
+    if layers:
+        seen: dict[str, type[AttentionBackend]] = {}
+        for layer in layers.values():
+            backend = layer.get_attn_backend()
+            seen[backend.full_cls_name()] = backend
+        return list(seen.values())
+
+    # Fallback for tests, when static_forward_context is empty.
+    logger.debug(
+        "No layers found in the vLLM config. Falling back to default attention backend."
+    )
+    from vllm.v1.attention.selector import get_attn_backend
+
+    return [
+        get_attn_backend(
+            head_size=vllm_config.model_config.get_head_size(),
+            dtype=vllm_config.model_config.dtype,
+            kv_cache_dtype=vllm_config.cache_config.cache_dtype,
+            use_mla=vllm_config.model_config.use_mla,
+        )
+    ]
+
+
+def get_current_attn_backend(
+    vllm_config: VllmConfig, layer_names: list[str] | None = None
+) -> type[AttentionBackend]:
+    """Get the first attention backend for the given layers."""
+    return get_current_attn_backends(vllm_config, layer_names)[0]
+
+
+# ---- Per-engine transfer info ----
+
+
+@dataclass(frozen=True)
+class EngineTransferInfo:
+    """Common per-remote-engine transfer state, computed at handshake.
+
+    Stored per ``engine_id`` inside ``TransferTopology._engines``.
     """
 
+    remote_tp_size: int
+
+    remote_block_len: int
+    """Block length (bytes)"""
+
+    remote_block_size: int
+    """Tokens per block."""
+
+    remote_physical_blocks_per_logical: int
+    """Physical blocks per logical block."""
+
+
+# ---- Transfer topology ----
+
+
+@dataclass
+class TransferTopology:
+    """Single source of truth for local TP identity and per-engine remote info."""
+
     tp_rank: int
-    remote_tp_size: dict[EngineId, int]
+    tp_size: int
+    block_size: int
+    engine_id: EngineId
     is_mla: bool
+    is_mamba: bool
     total_num_kv_heads: int
     attn_backends: list[type[AttentionBackend]]
-    engine_id: EngineId
-    remote_block_size: dict[EngineId, int]
     tensor_shape: torch.Size | None = None
-    is_mamba: bool = False
 
     def __post_init__(self):
+        self.local_physical_heads = max(1, self.total_num_kv_heads // self.tp_size)
+
+        self._engines: dict[EngineId, EngineTransferInfo] = {}
+
         # Figure out whether the first dimension of the cache is K/V
-        # or num_blocks. This is used to register the memory regions correctly.
+        # or num_blocks.
         attn_backend = self.attn_backends[0]
         if not self.is_mamba:
             _MOCK_BLOCK_SIZE = 16
             kv_cache_shape: tuple[int, ...] = attn_backend.get_kv_cache_shape(
-                num_blocks=1, block_size=_MOCK_BLOCK_SIZE, num_kv_heads=1, head_size=1
+                num_blocks=1,
+                block_size=_MOCK_BLOCK_SIZE,
+                num_kv_heads=1,
+                head_size=1,
             )
             logger.debug("Test kv_cache_shape: %s", kv_cache_shape)
         # Non-MLA backends caches have 5 dims [2, num_blocks, H,N,D],
@@ -358,11 +432,9 @@ def __post_init__(self):
             self._cross_layers_blocks = (
                 len(self.tensor_shape) == len(kv_cache_shape) + 1
             )
-            self.tensor_shape: torch.Size
 
         if self._cross_layers_blocks:
             logger.debug("Using cross-layer KV cache")
-            # prepend layers dimension
             _MOCK_NUM_LAYERS = 80
             kv_cache_shape = (_MOCK_NUM_LAYERS,) + kv_cache_shape
             try:
@@ -372,15 +444,46 @@ def __post_init__(self):
             except (AttributeError, NotImplementedError):
                 assert self.tensor_shape is not None
                 kv_cache_stride_order = tuple(range(len(self.tensor_shape)))
-
-            # In case of cross layers permute kv_cache_shape according to
-            # stride_order to retrieve physical position of block_size
             kv_cache_shape = tuple(kv_cache_shape[i] for i in kv_cache_stride_order)
 
+    # ============================================================
+    # Engine registration
+    # ============================================================
+
+    def register_remote_engine(
+        self,
+        remote_engine_id: EngineId,
+        info: EngineTransferInfo,
+    ) -> EngineTransferInfo:
+        """Register a remote engine, unifying worker dicts state.
+
+        The caller (worker) is responsible for computing the info via
+        the transfer policy.  This method only stores and deduplicates.
+        """
+        assert remote_engine_id != self.engine_id, (
+            f"Cannot register local engine {self.engine_id} as remote. "
+            f"Local identity is set via __init__ params."
+        )
+        if remote_engine_id in self._engines:
+            return self._engines[remote_engine_id]
+        self._engines[remote_engine_id] = info
+        return info
+
+    def get_engine_info(self, remote_engine_id: EngineId) -> EngineTransferInfo:
+        return self._engines[remote_engine_id]
+
+    # ============================================================
+    # Layout properties
+    # ============================================================
+
     @property
     def is_kv_layout_blocks_first(self) -> bool:
         return self._is_kv_layout_blocks_first
 
+    @property
+    def cross_layers_blocks(self) -> bool:
+        return self._cross_layers_blocks
+
     @property
     def split_k_and_v(self) -> bool:
         # Whether to register regions for K and V separately (when present).
@@ -388,29 +491,16 @@ def split_k_and_v(self) -> bool:
             self._cross_layers_blocks or self.is_mla or self.is_kv_layout_blocks_first
         )
 
-    @property
-    def tp_size(self) -> int:
-        return self.remote_tp_size[self.engine_id]
-
-    @property
-    def block_size(self) -> int:
-        return self.remote_block_size[self.engine_id]
+    # ============================================================
+    # Common methods
+    # ============================================================
 
-    @property
-    def cross_layers_blocks(self) -> bool:
-        return self._cross_layers_blocks
+    def tp_ratio(self, remote_tp_size: int) -> int:
+        """Calculate the tensor parallel ratio between local and remote TP.
 
-    def tp_ratio(
-        self,
-        remote_tp_size: int,
-    ) -> int:
-        """
-        Calculate the tensor parallel ratio between local and remote TP.
-        We can think of it as the number of local TP workers-per-remote TP
-        workers. Local workers will read from the same remote TP worker in
-        groups of size `tp_ratio`.If remote tp_size > local tp_size, the
-        ratio is flipped (remote_size/local_size) and the returned value is
-        negative.
+        Positive when local_tp >= remote_tp (local workers read from the
+        same remote worker in groups of size ``tp_ratio``).  Negative when
+        remote_tp > local_tp (ratio is flipped).
         """
         if self.tp_size >= remote_tp_size:
             assert self.tp_size % remote_tp_size == 0, (
@@ -418,78 +508,59 @@ def tp_ratio(
                 f"by remote tensor parallel size {remote_tp_size}."
             )
             return self.tp_size // remote_tp_size
-
         assert remote_tp_size % self.tp_size == 0, (
             f"Remote tensor parallel size {remote_tp_size} is not divisible "
             f"by local tensor parallel size {self.tp_size}."
         )
-        # P TP > D TP case, return the ratio as negative
-        return -remote_tp_size // self.tp_size
+        return -(remote_tp_size // self.tp_size)
 
-    def block_size_ratio(
-        self,
-        remote_block_size: int,
-    ) -> int:
-        """
-        Calculate the block size ratio between local and remote TP.
-        """
+    def block_size_ratio(self, remote_block_size: int) -> int:
+        """Calculate the block size ratio between local and remote."""
         assert self.block_size % remote_block_size == 0, (
             f"Local block size {self.block_size} is not divisible "
             f"by remote block size {remote_block_size} or vice versa."
         )
         return self.block_size // remote_block_size
 
-    def tp_ratio_from_engine_id(
-        self,
-        remote_engine_id: EngineId,
-    ) -> int:
-        remote_tp_size = self.remote_tp_size[remote_engine_id]
-        return self.tp_ratio(remote_tp_size)
-
-    def block_size_ratio_from_engine_id(
-        self,
-        remote_engine_id: EngineId,
-    ) -> int:
-        remote_block_size = self.remote_block_size[remote_engine_id]
-        return self.block_size_ratio(remote_block_size)
-
-    def is_kv_replicated(self, engine_id: EngineId) -> bool:
-        """
-        Whether the KV cache is replicated across TP workers due to the
+    def is_kv_replicated(self, remote_engine_id: EngineId) -> bool:
+        """Whether the KV cache is replicated across TP workers due to the
         number of TP workers being greater than the number of KV heads.
-        When they are equal, each TP rank still owns one distinct KV head,
-        so this is not considered replication.
         """
-        tp_size = self.remote_tp_size[engine_id]
-        return tp_size > self.total_num_kv_heads
+        return self._engines[remote_engine_id].remote_tp_size > self.total_num_kv_heads
 
     def replicates_kv_cache(self, remote_engine_id: EngineId) -> bool:
         # MLA is always replicated as the hidden dim can't be split.
         return self.is_mla or self.is_kv_replicated(remote_engine_id)
 
-    def get_target_remote_ranks(
-        self,
-        remote_tp_size: int,
-    ) -> list[int]:
-        """
-        Get the remote TP rank (on P) that the current local TP rank
-        (on D) will read from. When remote tp_size > local tp_size, we
-        read from multiple remote ranks.
+    @property
+    def local_replicates_kv_cache(self) -> bool:
+        """Whether the local engine's KV cache is replicated."""
+        return self.is_mla or self.tp_size > self.total_num_kv_heads
+
+    def handshake_target_ranks(self, remote_tp_size: int) -> list[int]:
+        """Pre-registration: compute which remote TP ranks to handshake with.
+
+        Pure math based on local/remote TP sizes — does not require
+        the remote engine to be registered yet.
         """
         tp_ratio = self.tp_ratio(remote_tp_size)
         if tp_ratio > 0:
             return [self.tp_rank // tp_ratio]
+        abs_ratio = -tp_ratio
+        return [self.tp_rank * abs_ratio + i for i in range(abs_ratio)]
 
-        # P TP > D TP case, D reads from |tp_ratio| remote workers.
-        tp_ratio = -tp_ratio
-        return [self.tp_rank * tp_ratio + i for i in range(tp_ratio)]
-
-    def get_target_remote_ranks_from_engine_id(
-        self,
-        remote_engine_id: EngineId,
-    ) -> list[int]:
-        remote_tp_size = self.remote_tp_size[remote_engine_id]
-        return self.get_target_remote_ranks(remote_tp_size)
+    def target_remote_ranks(self, remote_engine_id: EngineId) -> list[int]:
+        """Get the remote TP rank(s) that the current local TP rank will
+        read from.  When remote tp_size > local tp_size, reads from
+        multiple remote ranks.
+        """
+        info = self._engines[remote_engine_id]
+        tp_ratio = self.tp_ratio(info.remote_tp_size)
+        if tp_ratio > 0:
+            return [self.tp_rank // tp_ratio]
+        # remote TP > local TP: read from |tp_ratio| remote workers
+        abs_ratio = -tp_ratio
+        return [self.tp_rank * abs_ratio + i for i in range(abs_ratio)]
 
     def get_transfer_cache_regions(
         self, cache: torch.Tensor, layer_spec: "KVCacheSpec"
@@ -498,443 +569,33 @@ def get_transfer_cache_regions(
         also accounting for hybrid SSM models specificities.
         """
         if isinstance(layer_spec, MambaSpec):
-            # Register the whole kv cache shared tensor, including SSM/Conv. This is
-            # similar to FI with the difference that SSM/Conv have different sizes
+            # Register the whole kv cache shared tensor, including
+            # SSM/Conv.
             conv, ssm = cache
             return [conv]
 
-        # Check may be hacky but it's matching `_update_hybrid_attention_mamba_layout`.
+        # Check may be hacky but it's matching
+        # `_update_hybrid_attention_mamba_layout`.
         if self.is_mamba and cache.shape[0] == 2:
-            # When MAMBA is present, all backends are blocks first, so that blocks
-            # can be shared between attention layers and mamba layers. Runner
-            # `_update_hybrid_attention_mamba_layout` already adjusted strides
-            # for FlashAttn-like backends so its num_blocks first.
-            # Swap [2<>num_blocks] dims to get required layout for hybrid SSM.
+            # When MAMBA is present, all backends are blocks first, so
+            # that blocks can be shared between attention layers and mamba
+            # layers.  Runner already adjusted strides for FlashAttn-like
+            # backends so its num_blocks first.
+            # Swap [2<>num_blocks] dims for hybrid SSM layout.
             cache = cache.transpose(0, 1)
 
         # Regular case: backends like FA register K/V in separate regions
         return cache if self.split_k_and_v else [cache]
 
-
-# ---- Mamba-HMA hetero-TP transfer config ----
-#
-# Key insight: with hetero-TP (P_TP > D_TP), FA KV cache may be
-# replicated across P ranks (when P_TP > num_kv_heads), but Mamba
-# conv/SSM state is almost always uniquely sharded per P rank.  So the
-# number of P ranks D must read from can differ between FA and Mamba,
-# and they must be handled separately.
-
-
-def _physical_head_range(tp_size: int, num_heads: int, rank: int) -> range:
-    """Physical KV head range stored in a rank's KV cache tensor.
-
-    When ``tp_size <= num_heads``: sharded, K/TP contiguous heads per rank.
-    When ``tp_size > num_heads``: 1 physical head per rank.  Heads are
-    distributed **contiguously** (matching vLLM's GQA weight partitioning):
-    consecutive ranks share a head before moving to the next one.
-    """
-    if tp_size <= num_heads:
-        assert num_heads % tp_size == 0
-        per_rank = num_heads // tp_size
-        return range(rank * per_rank, (rank + 1) * per_rank)
-    else:
-        h = rank * num_heads // tp_size
-        return range(h, h + 1)
-
-
-def _range_overlap(a: range, b: range) -> range:
-    start = max(a.start, b.start)
-    stop = min(a.stop, b.stop)
-    return range(start, max(start, stop))
-
-
-@dataclass
-class HeteroTPTransferConfig:
-    """Precomputed transfer plan for one (D rank, P engine) pair.
-
-    Currently only instantiated for Mamba-HMA (hybrid SSM+Attention) models
-    where FA and mamba require different splitting factors.  Could be extended
-    to other model types that need non-uniform hetero-TP transfer sizing.
-
-    All descriptor sizes are computed here.  The guarantee is:
-        local_entry_size == remote_entry_size   (for NIXL)
-
-    Attributes that start with ``fa_`` concern FlashAttention KV cache.
-    Attributes that start with ``mamba_`` concern Mamba conv/SSM state.
-    """
-
-    # ---- Input parameters (from handshake) ----
-    tp_ratio: int
-    K: int  # total_num_kv_heads (before TP sharding)
-    d_tp: int  # D engine's tensor_parallel_size
-    p_tp: int  # P engine's tensor_parallel_size
-    d_rank: int  # this D worker's TP rank
-    use_mla: bool
-
-    # Per-layer block lengths (bytes, K+V combined for blocks_first).
-    # Uniform across layers for current models.
-    d_block_len: int  # D's block_len_per_layer (representative)
-    p_block_len: int  # P's block_len_per_layer (from handshake)
-    is_blocks_first: bool  # kv_topo.is_kv_layout_blocks_first
-
-    # ---- Derived: computed in __post_init__ ----
-    #
-    # Physical heads per rank (what the KV tensor actually stores)
-    d_physical_heads: int = field(init=False)
-    p_physical_heads: int = field(init=False)
-
-    # How many distinct P ranks D needs for FA data
-    physical_fa_num_reads: int = field(init=False)
-
-    # Which P ranks contribute unique FA heads (ordered by head index)
-    fa_read_targets: list[int] = field(init=False)
-
-    # All P ranks needed for mamba (always abs_tp for tp_ratio < 0)
-    mamba_num_reads: int = field(init=False)
-
-    # All P ranks this D rank communicates with (FA ∪ mamba)
-    transfer_targets: list[int] = field(init=False)
-
-    # FA descriptor entry size (K or V side, for blocks_first layout)
-    # Guaranteed: fa_entry_size is the SAME for local handle AND remote desc.
-    fa_entry_size: int = field(init=False)
-
-    # Replication flags
-    is_d_replicated: bool = field(init=False)
-    is_p_replicated: bool = field(init=False)
-
-    # Pre-built set for fast lookup
-    _fa_target_set: frozenset[int] = field(init=False, repr=False)
-    # Map: P rank → index in fa_read_targets (for head slot offset)
-    _fa_target_index: dict[int, int] = field(init=False, repr=False)
-
-    def __post_init__(self) -> None:
-        K = self.K
-        self.is_d_replicated = self.d_tp > K
-        self.is_p_replicated = self.p_tp > K
-
-        self.d_physical_heads = max(1, K // self.d_tp)
-        self.p_physical_heads = max(1, K // self.p_tp)
-
-        abs_tp = -self.tp_ratio if self.tp_ratio < 0 else 1
-
-        # ---- Mamba range (computed first so FA can prefer ranks in it) ----
-        mamba_range: range | None = None
-        if self.tp_ratio < 0:
-            mamba_range = range(self.d_rank * abs_tp, (self.d_rank + 1) * abs_tp)
-
-        # ---- FA read targets ----
-        if self.use_mla or self.tp_ratio >= 0:
-            self.physical_fa_num_reads = 1
-            self.fa_read_targets = (
-                [0]
-                if self.use_mla
-                # Must match kv_topo.get_target_remote_ranks (d_rank // tp_ratio).
-                else [
-                    self.d_rank // self.tp_ratio if self.tp_ratio > 0 else self.d_rank
-                ]
-            )
-        else:
-            d_needs = _physical_head_range(self.d_tp, K, self.d_rank)
-            # When mamba range exists, prefer P ranks within it so that
-            # FA targets are a subset of mamba transfer_targets (avoids
-            # orphaned FA targets outside the transfer loop).
-            search_range = mamba_range if mamba_range is not None else range(self.p_tp)
-            seen: set[tuple[int, int]] = set()
-            targets: list[int] = []
-            for p in search_range:
-                p_has = _physical_head_range(self.p_tp, K, p)
-                ov = _range_overlap(d_needs, p_has)
-                if len(ov) > 0:
-                    key = (ov.start, ov.stop)
-                    if key not in seen:
-                        seen.add(key)
-                        targets.append(p)
-            if not targets:
-                # Fallback: search globally (should not happen in practice)
-                for p in range(self.p_tp):
-                    p_has = _physical_head_range(self.p_tp, K, p)
-                    ov = _range_overlap(d_needs, p_has)
-                    if len(ov) > 0:
-                        key = (ov.start, ov.stop)
-                        if key not in seen:
-                            seen.add(key)
-                            targets.append(p)
-            self.fa_read_targets = targets
-            self.physical_fa_num_reads = len(targets)
-
-        self._fa_target_set = frozenset(self.fa_read_targets)
-        self._fa_target_index = {r: i for i, r in enumerate(self.fa_read_targets)}
-
-        # ---- Mamba targets ----
-        if mamba_range is not None and abs_tp > self.physical_fa_num_reads:
-            self.mamba_num_reads = abs_tp
-            self.transfer_targets = list(mamba_range)
-        else:
-            self.mamba_num_reads = self.physical_fa_num_reads
-            self.transfer_targets = list(self.fa_read_targets)
-
-        # ---- FA entry size ----
-        # For blocks_first: block_len_per_layer includes K+V; // 2 gives K (or V).
-        # Use min(D, P) because D indexes into P when tp_ratio > 0,
-        # and P is the natural unit when tp_ratio < 0.
-        effective_block_len = min(self.d_block_len, self.p_block_len)
-        if self.is_blocks_first:
-            self.fa_entry_size = effective_block_len // 2
-        else:
-            self.fa_entry_size = effective_block_len
-
-        self._validate()
-
-    def _validate(self) -> None:
-        """Cross-check internal consistency."""
-        if self.is_d_replicated and self.is_p_replicated and self.tp_ratio > 0:
-            logger.info(
-                "Both-replicated hetero-TP: D_TP=%d > P_TP=%d > K=%d. "
-                "Using d_rank // tp_ratio routing with relative head offset.",
-                self.d_tp,
-                self.p_tp,
-                self.K,
-            )
-
-        # FA targets must be a subset of transfer_targets
-        tt_set = set(self.transfer_targets)
-        for t in self.fa_read_targets:
-            if t not in tt_set:
-                logger.error(
-                    "FA target P rank %d is NOT in transfer_targets %s. "
-                    "This will cause missed FA reads!",
-                    t,
-                    self.transfer_targets,
-                )
-
-        # For tp_ratio < 0 with blocks_first: D_K_half / reads should == P_K_half
-        if (
-            self.is_blocks_first
-            and self.tp_ratio < 0
-            and self.physical_fa_num_reads > 0
-        ):
-            d_k_half = self.d_block_len // 2
-            p_k_half = self.p_block_len // 2
-            expected_local = d_k_half // self.physical_fa_num_reads
-            if expected_local != p_k_half:
-                logger.warning(
-                    "FA size mismatch: D_K_half=%d / reads=%d = %d, "
-                    "but P_K_half=%d.  This may indicate a head count or "
-                    "Mamba-HMA inflation inconsistency.",
-                    d_k_half,
-                    self.physical_fa_num_reads,
-                    expected_local,
-                    p_k_half,
-                )
-
-    # ---- Query methods ----
-
-    def should_skip_fa(self, p_rank: int) -> bool:
-        """Whether to skip FA groups for this P rank (mamba-only transfer)."""
-        return p_rank not in self._fa_target_set
-
-    def fa_head_slot(self, p_rank: int) -> int:
-        """Index into D's FA block for this P rank's head data.
-
-        For P ranks in fa_read_targets, returns 0, 1, ..., reads-1.
-        For P ranks NOT in fa_read_targets (replicated duplicates),
-        returns the slot of the matching FA target with the same head.
-        """
-        if p_rank in self._fa_target_index:
-            return self._fa_target_index[p_rank]
-        # Duplicate head: find which fa_target has the same physical head
-        p_head = _physical_head_range(self.p_tp, self.K, p_rank)
-        for target in self.fa_read_targets:
-            t_head = _physical_head_range(self.p_tp, self.K, target)
-            if _range_overlap(p_head, t_head):
-                return self._fa_target_index[target]
-        return 0  # fallback
-
-    def fa_rank_offset(self, remote_kv_block_len: int) -> int:
-        """Byte offset into P's FA block for this D rank.
-
-        When D is replicated (D_TP > K), multiple D ranks share a head.
-        Computes offset *relative to the target P rank's first head*
-        so it works regardless of how many heads P has.
-        When neither side replicates, falls back to tp_rank % tp_ratio.
-        Returns 0 when D does not index into P's block.
-        """
-        if self.use_mla or self.tp_ratio <= 0:
-            return 0
-        if self.is_d_replicated:
-            d_head = self.d_rank * self.K // self.d_tp
-            p_rank = self.fa_read_targets[0]
-            p_start = p_rank * self.K // self.p_tp
-            return (d_head - p_start) * remote_kv_block_len
-        return self.d_rank % self.tp_ratio * remote_kv_block_len
-
-    @property
-    def needs_split_handles(self) -> bool:
-        """Whether per-P-rank split handles are needed.
-
-        True when FA and mamba have different read counts, requiring
-        different splitting factors in the local handle.
-        """
-        return self.tp_ratio < 0 and not self.use_mla and len(self.transfer_targets) > 1
-
-    def compute_split_handle_data(
-        self,
-        src_blocks_data: list[tuple[int, int, int]],
-        num_fa_descs: int,
-        abs_tp: int,
-    ) -> list[list[tuple[int, int, int]]]:
-        """Compute per-P-rank (addr, len, tp) triples for Mamba-HMA split handles.
-
-        FA descriptors (indices < num_fa_descs) are sliced by
-        ``physical_fa_num_reads``; mamba descriptors are sliced uniformly
-        by ``abs_tp``.
-
-        Returns one list of triples per transfer target.
-        """
-        all_handle_data: list[list[tuple[int, int, int]]] = []
-        for p_idx, p_rank in enumerate(self.transfer_targets):
-            handle_data: list[tuple[int, int, int]] = []
-            skip_fa = self.should_skip_fa(p_rank)
-            fa_slot = self.fa_head_slot(p_rank) if not skip_fa else 0
-
-            for j, (addr, local_len, tp) in enumerate(src_blocks_data):
-                if j < num_fa_descs:
-                    assert self.physical_fa_num_reads >= 1
-                    fa_chunk = local_len // self.physical_fa_num_reads
-                    handle_data.append((addr + fa_slot * fa_chunk, fa_chunk, tp))
-                else:
-                    mamba_chunk = local_len // abs_tp
-                    handle_data.append((addr + p_idx * mamba_chunk, mamba_chunk, tp))
-            all_handle_data.append(handle_data)
-        return all_handle_data
-
-    def filter_block_ids_for_rank(
-        self,
-        remote_rank: int,
-        local_ids: BlockIds,
-        remote_ids: BlockIds,
-        is_mamba_group: list[bool],
-    ) -> tuple[BlockIds, BlockIds]:
-        """Zero out FA groups for P ranks outside fa_read_targets.
-
-        Returns (filtered_local_ids, filtered_remote_ids).  When the
-        remote rank carries FA data for this D rank, returns the inputs
-        unchanged.
-        """
-        if not self.should_skip_fa(remote_rank):
-            return local_ids, remote_ids
-        num_groups = len(local_ids)
-        filtered_local: list[list[int]] = [
-            [] if not is_mamba_group[g] else local_ids[g] for g in range(num_groups)
-        ]
-        filtered_remote: list[list[int]] = [
-            [] if not is_mamba_group[g] else remote_ids[g] for g in range(num_groups)
-        ]
-        return filtered_local, filtered_remote
-
-    def describe(self) -> str:
-        """One-line summary for logging."""
+    def describe(self, remote_engine_id: EngineId) -> str:
+        """One-line summary of transfer config for logging."""
+        info = self._engines[remote_engine_id]
         return (
-            f"HeteroTPTransferConfig("
-            f"tp_ratio={self.tp_ratio}, K={self.K}, "
-            f"d_tp={self.d_tp}, p_tp={self.p_tp}, d_rank={self.d_rank}, "
-            f"physical_fa_reads={self.physical_fa_num_reads}, "
-            f"mamba_reads={self.mamba_num_reads}, "
-            f"fa_targets={self.fa_read_targets}, "
-            f"transfer_targets={self.transfer_targets}, "
-            f"fa_entry_size={self.fa_entry_size}, "
-            f"d_block_len={self.d_block_len}, p_block_len={self.p_block_len})"
-        )
-
-
-def get_current_attn_backends(
-    vllm_config: VllmConfig, layer_names: list[str] | None = None
-) -> list[type[AttentionBackend]]:
-    """Get all distinct attention backends for the given layers.
-
-    Args:
-        vllm_config: The current vLLM configuration.
-        layer_names: Optional list of layer names to scope the lookup.
-            When None, all attention layers are considered.
-
-    Returns:
-        Deduplicated list of attention backend classes.
-    """
-    layer_type = cast(type[Any], AttentionLayerBase)
-    layers = get_layers_from_vllm_config(vllm_config, layer_type, layer_names)
-    if layers:
-        seen: dict[str, type[AttentionBackend]] = {}
-        for layer in layers.values():
-            backend = layer.get_attn_backend()
-            seen[backend.full_cls_name()] = backend
-        return list(seen.values())
-
-    # Fallback for tests, when static_forward_context is empty.
-    logger.debug(
-        "No layers found in the vLLM config. Falling back to default attention backend."
-    )
-    from vllm.v1.attention.selector import get_attn_backend
-
-    return [
-        get_attn_backend(
-            head_size=vllm_config.model_config.get_head_size(),
-            dtype=vllm_config.model_config.dtype,
-            kv_cache_dtype=vllm_config.cache_config.cache_dtype,
-            use_mla=vllm_config.model_config.use_mla,
+            f"TransferTopology("
+            f"tp_ratio={self.tp_ratio(info.remote_tp_size)}, "
+            f"num_kv_heads={self.total_num_kv_heads if not self.is_mla else 1}, "
+            f"local_tp={self.tp_size}, "
+            f"remote_tp={info.remote_tp_size}, "
+            f"local_rank={self.tp_rank}, "
+            f"remote_block_len={info.remote_block_len})"
         )
-    ]
-
-
-def get_current_attn_backend(
-    vllm_config: VllmConfig, layer_names: list[str] | None = None
-) -> type[AttentionBackend]:
-    """Get the first attention backend for the given layers."""
-    return get_current_attn_backends(vllm_config, layer_names)[0]
-
-
-# TODO (ZhanqiuHu): Consolidate TpKVTopology and HeteroTPTransferConfig
-# into a single engine-agnostic TransferTopology class.
-# 6 of 9 HeteroTPTransferConfig init fields duplicate TpKVTopology data.
-#
-# @dataclass
-# class EngineTransferInfo:
-#     """Per-remote-engine transfer state, computed at handshake."""
-#     p_tp: int
-#     tp_ratio: int
-#     p_block_len: int
-#     block_size: int
-#     # Mamba-specific (None for non-mamba models)
-#     fa_read_targets: list[int] | None = None
-#     transfer_targets: list[int] | None = None
-#     physical_fa_num_reads: int | None = None
-#     mamba_num_reads: int | None = None
-#     fa_entry_size: int | None = None
-#
-# class TransferTopology:
-#     """Single source of truth for TP topology + transfer sizing."""
-#     # Shared (set once at init, replaces duplicate fields)
-#     tp_rank: int          # == TpKVTopology.tp_rank == HeteroTP.d_rank
-#     tp_size: int          # == TpKVTopology.tp_size == HeteroTP.d_tp
-#     total_num_kv_heads: int  # == HeteroTP.K
-#     is_mla: bool          # == HeteroTP.use_mla
-#     is_mamba: bool
-#     is_blocks_first: bool # == HeteroTP.is_blocks_first
-#     d_block_len: int
-#
-#     # Per-engine (populated via register_engine() at handshake)
-#     _engines: dict[EngineId, EngineTransferInfo]
-#
-#     def register_engine(self, engine_id, p_tp, p_block_len, ...): ...
-#
-#     # General (from TpKVTopology)
-#     def tp_ratio(self, engine_id) -> int: ...
-#     def target_remote_ranks(self, engine_id) -> list[int]: ...
-#     def is_kv_replicated(self, engine_id) -> bool: ...
-#
-#     # Mamba-specific (from HeteroTPTransferConfig, gated by is_mamba)
-#     def fa_rank_offset(self, engine_id, block_len) -> int: ...
-#     def physical_fa_num_reads(self, engine_id) -> int: ...
-#     def transfer_targets(self, engine_id) -> list[int]: ...
-#     def should_skip_fa(self, engine_id, p_rank) -> bool: ...
-#     def filter_block_ids_for_rank(self, engine_id, ...) -> ...: ...
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/base.py b/vllm/distributed/kv_transfer/kv_connector/v1/base.py
index ef143cba7fb5..a2fa9fa6c646 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/base.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/base.py
@@ -184,7 +184,7 @@ def __init__(
         self,
         vllm_config: "VllmConfig",
         role: KVConnectorRole,
-        kv_cache_config: "KVCacheConfig | None" = None,
+        kv_cache_config: "KVCacheConfig",
     ):
         logger.warning(
             "Initializing KVConnectorBase_V1. This API is experimental and "
@@ -197,13 +197,6 @@ def __init__(
         else:
             raise ValueError("kv_transfer_config must be set for KVConnectorBase_V1")
         self._kv_cache_config = kv_cache_config
-        if self._kv_cache_config is None:
-            logger.warning(
-                "KVConnectorBase_V1 initialized without kv_cache_config. "
-                "This is deprecated - please update your connector to accept "
-                "kv_cache_config as the third constructor argument and pass it "
-                "to super().__init__()."
-            )
         self._role = role
 
     @property
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/decode_bench_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/decode_bench_connector.py
index 6e9e757ffbd5..0f835b1eebba 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/decode_bench_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/decode_bench_connector.py
@@ -40,7 +40,10 @@
     KVConnectorBase_V1,
     KVConnectorRole,
 )
-from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorMetadata
+from vllm.distributed.kv_transfer.kv_connector.v1.base import (
+    KVConnectorMetadata,
+    SupportsHMA,
+)
 from vllm.logger import init_logger
 from vllm.utils.math_utils import cdiv
 from vllm.v1.attention.backend import AttentionMetadata
@@ -71,7 +74,7 @@ class DecodeBenchConnectorMetadata(KVConnectorMetadata):
     reqs_to_fill: dict[str, tuple[tuple[list[int], ...], int]]
 
 
-class DecodeBenchConnector(KVConnectorBase_V1):
+class DecodeBenchConnector(KVConnectorBase_V1, SupportsHMA):
     """
     A KV Connector for decode instance performance testing.
 
@@ -84,7 +87,7 @@ def __init__(
         self,
         vllm_config: "VllmConfig",
         role: KVConnectorRole,
-        kv_cache_config: "KVCacheConfig | None" = None,
+        kv_cache_config: "KVCacheConfig",
     ):
         super().__init__(vllm_config, role, kv_cache_config)
 
@@ -164,6 +167,17 @@ def request_finished(
         self.connector_scheduler.request_finished(request)
         return False, None
 
+    def request_finished_all_groups(
+        self,
+        request: "Request",
+        block_ids: tuple[list[int], ...],
+    ) -> tuple[bool, dict[str, Any] | None]:
+        # HMA-enabled path: same cleanup as the single-group variant since
+        # this connector owns no external state per block.
+        assert self.connector_scheduler is not None
+        self.connector_scheduler.request_finished(request)
+        return False, None
+
 
 class DecodeBenchConnectorScheduler:
     """Scheduler-side implementation for DecodeBenchConnector."""
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/example_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/example_connector.py
index 24e156561dfb..a0279120ef03 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/example_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/example_connector.py
@@ -92,7 +92,7 @@ def __init__(
         self,
         vllm_config: "VllmConfig",
         role: KVConnectorRole,
-        kv_cache_config: "KVCacheConfig | None" = None,
+        kv_cache_config: "KVCacheConfig",
     ):
         super().__init__(
             vllm_config=vllm_config,
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/example_hidden_states_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/example_hidden_states_connector.py
index fcd1f365a715..bf56db32e4f8 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/example_hidden_states_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/example_hidden_states_connector.py
@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import os
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import safetensors
 import torch
@@ -120,7 +120,7 @@ def __init__(
         self,
         vllm_config: "VllmConfig",
         role: KVConnectorRole,
-        kv_cache_config: Optional["KVCacheConfig"] = None,
+        kv_cache_config: "KVCacheConfig",
     ):
         super().__init__(
             vllm_config=vllm_config,
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/lmcache_mp_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/lmcache_mp_connector.py
index 6686b60df4a3..d7d2a55376c7 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/lmcache_mp_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/lmcache_mp_connector.py
@@ -215,8 +215,11 @@ class LMCacheMPRequestTracker:
     # Main state
     state: LMCacheMPRequestState = LMCacheMPRequestState.PREFETCHING
 
+    cache_salt: str = ""
+
     def __init__(self, request: "Request"):
         self.request_id = request.request_id
+        self.cache_salt: str = request.cache_salt or ""
         self.all_token_ids = request.all_token_ids
         self.block_hashes = ConstantList(request.block_hashes)
         self.allocated_block_ids = []
@@ -289,6 +292,7 @@ class LMCacheMPRequestMetadata:
     request_id: str
     direction: Literal["STORE", "RETRIEVE"]
     op: LoadStoreOp
+    cache_salt: str = ""
 
     @staticmethod
     def GetStoreMetadata(
@@ -355,6 +359,7 @@ def GetStoreMetadata(
                 request_id=tracker.request_id,
                 direction="STORE",
                 op=op,
+                cache_salt=tracker.cache_salt,
             )
 
             # Update the request tracker
@@ -421,6 +426,7 @@ def GetRetrieveMetadata(
                 request_id=tracker.request_id,
                 direction="RETRIEVE",
                 op=op,
+                cache_salt=tracker.cache_salt,
             )
             return ret
 
@@ -470,7 +476,7 @@ def __init__(
         self,
         vllm_config: "VllmConfig",
         role: KVConnectorRole,
-        kv_cache_config: "KVCacheConfig | None" = None,
+        kv_cache_config: "KVCacheConfig",
     ):
         super().__init__(vllm_config, role, kv_cache_config)
 
@@ -569,12 +575,14 @@ def start_load_kv(self, forward_context: "ForwardContext", **kwargs: Any) -> Non
 
         request_ids = []
         ops = []
+        cache_salts = []
 
         for meta in metadata.requests:
             if meta.direction != "RETRIEVE":
                 continue
             request_ids.append(meta.request_id)
             ops.append(meta.op)
+            cache_salts.append(meta.cache_salt)
 
         if len(request_ids) == 0:
             return
@@ -583,7 +591,9 @@ def start_load_kv(self, forward_context: "ForwardContext", **kwargs: Any) -> Non
             event = torch.cuda.Event(interprocess=True)
             event.record()
 
-        self.worker_adapter.batched_submit_retrieve_requests(request_ids, ops, event)
+        self.worker_adapter.batched_submit_retrieve_requests(
+            request_ids, ops, event, cache_salts=cache_salts
+        )
 
     def wait_for_layer_load(self, layer_name: str) -> None:
         """
@@ -640,11 +650,13 @@ def wait_for_save(self):
 
         request_ids = []
         ops = []
+        cache_salts = []
         for meta in metadata.requests:
             if meta.direction != "STORE":
                 continue
             request_ids.append(meta.request_id)
             ops.append(meta.op)
+            cache_salts.append(meta.cache_salt)
 
         if len(request_ids) == 0:
             return
@@ -653,7 +665,9 @@ def wait_for_save(self):
             event = torch.cuda.Event(interprocess=True)
             event.record()
 
-        self.worker_adapter.batched_submit_store_requests(request_ids, ops, event)
+        self.worker_adapter.batched_submit_store_requests(
+            request_ids, ops, event, cache_salts=cache_salts
+        )
 
     def get_finished(
         self, finished_req_ids: set[str]
@@ -755,6 +769,7 @@ def get_num_new_matched_tokens(
         self.scheduler_adapter.maybe_submit_lookup_request(
             request.request_id,
             token_ids=list(request.all_token_ids),
+            cache_salt=tracker.cache_salt,
         )
 
         ret = self.scheduler_adapter.check_lookup_result(request.request_id)
@@ -915,12 +930,31 @@ def request_finished(
             Optional KVTransferParams to be included in the request outputs
             returned by the engine.
         """
+
+        params: dict[str, Any] | None = getattr(request, "kv_transfer_params", None)
+        return_params: dict[str, Any] | None = {} if params is not None else None
+
+        if (
+            params is not None
+            and return_params is not None
+            and "num_lmcache_extra_cached_tokens" in params
+        ):
+            request_tracker = self._get_request_tracker(request.request_id)
+            num_extra_cached_blocks = max(
+                0,
+                request_tracker.num_lmcache_hit_blocks
+                - request_tracker.num_vllm_hit_blocks,
+            )
+            return_params["num_lmcache_extra_cached_tokens"] = (
+                num_extra_cached_blocks * self.vllm_block_size
+            )
+
         # Clean up request tracker to prevent memory leak
         self._cleanup_request_tracker(request.request_id)
         # Notify LMCache to end the session for this request
         self.scheduler_adapter.end_session(request.request_id)
 
-        return True, None
+        return True, return_params
 
     def take_events(self) -> Iterable["KVCacheEvent"]:
         """
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/mooncake/mooncake_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/mooncake/mooncake_connector.py
index 6c17bf82ace6..c693902e6132 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/mooncake/mooncake_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/mooncake/mooncake_connector.py
@@ -21,18 +21,24 @@
 from vllm.config import VllmConfig
 from vllm.distributed.kv_transfer.kv_connector.utils import (
     EngineId,
-    TpKVTopology,
+    TransferTopology,
     get_current_attn_backend,
+    get_current_attn_backends,
 )
 from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorBase_V1,
     KVConnectorMetadata,
     KVConnectorRole,
+    SupportsHMA,
 )
+from vllm.distributed.kv_transfer.kv_connector.v1.metrics import KVConnectorStats
 from vllm.distributed.kv_transfer.kv_connector.v1.mooncake.mooncake_utils import (
     MooncakeBootstrapServer,
     RegisterWorkerPayload,
 )
+from vllm.distributed.kv_transfer.kv_connector.v1.mooncake.stats import (
+    MooncakeKVConnectorStats,
+)
 from vllm.distributed.parallel_state import (
     get_pp_group,
     get_tensor_model_parallel_rank,
@@ -42,11 +48,14 @@
 from vllm.forward_context import ForwardContext
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
+from vllm.utils.math_utils import cdiv
 from vllm.utils.network_utils import get_ip, make_zmq_path, make_zmq_socket
 from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.utils import get_kv_cache_layout
 from vllm.v1.core.sched.output import SchedulerOutput
+from vllm.v1.kv_cache_interface import FullAttentionSpec, SlidingWindowSpec
 from vllm.v1.request import RequestStatus
+from vllm.v1.worker.utils import select_common_block_size
 
 logger = init_logger(__name__)
 
@@ -250,7 +259,7 @@ class MooncakeXferMetadata(
     remote_port: int
     remote_tp_size: int
     remote_tp_rank: int
-    req_blocks: dict[ReqId, tuple[TransferId, list[int]]]
+    req_blocks: dict[ReqId, tuple[TransferId, list[list[int]]]]
     kv_caches_base_addr: list[int]
     block_lens: list[int]
 
@@ -278,7 +287,7 @@ class MooncakeXferResponse(
 class PullReqMeta:
     d_req_id: ReqId
     transfer_id: TransferId
-    local_block_ids: list[int]
+    local_block_ids: list[list[int]]
     remote_engine_id: EngineId
     remote_bootstrap_addr: str
     # Set expire time to avoid infinitely sending requests.
@@ -291,7 +300,7 @@ class PullReqMeta:
 class SendBlockMeta:
     p_req_id: ReqId
     transfer_id: TransferId
-    local_block_ids: list[int]
+    local_block_ids: list[list[int]]
     ready: asyncio.Event
     expire_time: float = float("inf")
     need_send: int = 0
@@ -304,13 +313,13 @@ def __init__(self):
         # Use (engine_id, dp_rank) to group reqs with same dp.
         # See comments in MooncakeBootstrapServer.
         self.reqs_to_recv: dict[EngineId, dict[ReqId, PullReqMeta]] = defaultdict(dict)
-        self.reqs_to_send: dict[ReqId, tuple[TransferId, list[int]]] = {}
+        self.reqs_to_send: dict[ReqId, tuple[TransferId, list[list[int]]]] = {}
         self.reqs_not_processed: set[TransferId] = set()
 
     def add_new_req(
         self,
         request_id: ReqId,
-        local_block_ids: list[int],
+        local_block_ids: list[list[int]],
         kv_transfer_params: dict[str, Any],
         load_remote_cache: bool = True,
     ):
@@ -328,12 +337,12 @@ def add_new_req(
             self.reqs_to_send[request_id] = (transfer_id, local_block_ids)
 
 
-class MooncakeConnector(KVConnectorBase_V1):
+class MooncakeConnector(KVConnectorBase_V1, SupportsHMA):
     def __init__(
         self,
         vllm_config: VllmConfig,
         role: KVConnectorRole,
-        kv_cache_config: "KVCacheConfig | None" = None,
+        kv_cache_config: "KVCacheConfig",
     ):
         super().__init__(vllm_config, role, kv_cache_config)
 
@@ -342,13 +351,18 @@ def __init__(
         self.engine_id: EngineId = vllm_config.kv_transfer_config.engine_id
 
         if role == KVConnectorRole.SCHEDULER:
+            assert kv_cache_config is not None, (
+                "kv_cache_config is required for SCHEDULER role"
+            )
             self.connector_scheduler: MooncakeConnectorScheduler | None = (
-                MooncakeConnectorScheduler(vllm_config, self.engine_id)
+                MooncakeConnectorScheduler(vllm_config, self.engine_id, kv_cache_config)
             )
             self.connector_worker: MooncakeConnectorWorker | None = None
         elif role == KVConnectorRole.WORKER:
             self.connector_scheduler = None
-            self.connector_worker = MooncakeConnectorWorker(vllm_config, self.engine_id)
+            self.connector_worker = MooncakeConnectorWorker(
+                vllm_config, self.engine_id, kv_cache_config
+            )
 
     @classmethod
     def get_required_kvcache_layout(cls, vllm_config: VllmConfig):
@@ -399,6 +413,14 @@ def request_finished(
         self,
         request: "Request",
         block_ids: list[int],
+    ) -> tuple[bool, dict[str, Any] | None]:
+        assert self.connector_scheduler is not None
+        return self.connector_scheduler.request_finished(request, (block_ids,))
+
+    def request_finished_all_groups(
+        self,
+        request: "Request",
+        block_ids: tuple[list[int], ...],
     ) -> tuple[bool, dict[str, Any] | None]:
         assert self.connector_scheduler is not None
         return self.connector_scheduler.request_finished(request, block_ids)
@@ -439,12 +461,37 @@ def save_kv_layer(
     def wait_for_save(self):
         pass
 
+    def get_kv_connector_stats(self) -> KVConnectorStats | None:
+        """Return worker-local transfer stats since the last call.
+
+        Note the P/D asymmetry: because Mooncake is P-push (P calls
+        batch_transfer_sync_write), P records successful transfer latency,
+        bytes, and descriptor counts, while D only records failures
+        (recv/ZMQ errors). Aggregated NIXL-style dashboards will find
+        successful-transfer metrics on the P worker, not D.
+        """
+        if self.connector_worker is None:
+            return None
+        return self.connector_worker.get_kv_connector_stats()
+
+    @classmethod
+    def build_kv_connector_stats(
+        cls, data: dict[str, Any] | None = None
+    ) -> KVConnectorStats | None:
+        return MooncakeKVConnectorStats(data=data or {})
+
 
 class MooncakeConnectorScheduler:
     """Implementation of Scheduler side methods"""
 
-    def __init__(self, vllm_config: VllmConfig, engine_id: str):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        engine_id: str,
+        kv_cache_config: "KVCacheConfig",
+    ):
         self.vllm_config = vllm_config
+        self.block_size = vllm_config.cache_config.block_size
 
         assert vllm_config.kv_transfer_config
         self.is_kv_producer: bool = (
@@ -455,15 +502,49 @@ def __init__(self, vllm_config: VllmConfig, engine_id: str):
         )
         logger.info("Initializing Mooncake Transfer Engine Scheduler %s", engine_id)
 
+        self._is_hma_required = (
+            not vllm_config.scheduler_config.disable_hybrid_kv_cache_manager
+            and any(
+                not isinstance(g.kv_cache_spec, FullAttentionSpec)
+                for g in kv_cache_config.kv_cache_groups
+            )
+        )
+
         # Requests that need to start recv/send.
         # New requests are added by update_state_after_alloc in
         # the scheduler. Used to make metadata passed to Worker.
-        self._reqs_need_recv: dict[ReqId, tuple[Request, list[int]]] = {}
-        self._reqs_need_send: dict[ReqId, tuple[Request, list[int]]] = {}
+        self._reqs_need_recv: dict[ReqId, tuple[Request, list[list[int]]]] = {}
+        self._reqs_need_send: dict[ReqId, tuple[Request, list[list[int]]]] = {}
         # Reqs to remove from processed set because they're not to send after
         # remote prefill or aborted.
         self._reqs_not_processed: set[TransferId] = set()
 
+        # Compute sliding window block counts per KV cache group.
+        sw_sizes_tokens: list[tuple[int, int]] = [
+            (g.kv_cache_spec.sliding_window, g.kv_cache_spec.block_size)
+            if isinstance(g.kv_cache_spec, SlidingWindowSpec)
+            else (0, self.block_size)
+            for g in kv_cache_config.kv_cache_groups
+        ]
+        # cdiv(n_tokens, block_size) gives blocks/window; add 1 to
+        # conservatively account for boundary overlap.
+        self.blocks_per_sw = [
+            cdiv(n_tokens, block_size) + 1 if n_tokens else 0
+            for n_tokens, block_size in sw_sizes_tokens
+        ]
+
+    def get_sw_clipped_blocks(
+        self,
+        block_ids: tuple[list[int], ...] | list[list[int]],
+    ) -> list[list[int]]:
+        """Clip per-group block IDs to sliding window size."""
+        if len(block_ids) == 0 or not self._is_hma_required:
+            return list(block_ids)
+        return [
+            blocks[-self.blocks_per_sw[i] :] if self.blocks_per_sw[i] > 0 else blocks
+            for i, blocks in enumerate(block_ids)
+        ]
+
     def get_num_new_matched_tokens(
         self, request: "Request", num_computed_tokens: int
     ) -> tuple[int, bool]:
@@ -528,9 +609,12 @@ def update_state_after_alloc(
                 # If remote_blocks and num_external_tokens = 0, we have
                 # a full prefix cache hit on the D worker. We need to call
                 # send_notif in _read_blocks to free the memory on the P.
-                local_block_ids = (
-                    blocks.get_unhashed_block_ids() if num_external_tokens > 0 else []
+                unhashed_block_ids = (
+                    blocks.get_unhashed_block_ids_all_groups()
+                    if num_external_tokens > 0
+                    else ()
                 )
+                local_block_ids = self.get_sw_clipped_blocks(unhashed_block_ids)
                 # Get unhashed blocks to pull from remote.
                 self._reqs_need_recv[request.request_id] = (request, local_block_ids)
             else:
@@ -585,7 +669,7 @@ def build_connector_meta(
     def request_finished(
         self,
         request: "Request",
-        block_ids: list[int],
+        block_ids: tuple[list[int], ...],
     ) -> tuple[bool, dict[str, Any] | None]:
         """
         Once a request is finished, determine whether request blocks
@@ -628,10 +712,13 @@ def request_finished(
 
         # TODO: check whether block_ids actually ever be 0. If not we could
         # remove the conditional below
-        delay_free_blocks = len(block_ids) > 0
+        delay_free_blocks = any(len(group) > 0 for group in block_ids)
 
         if delay_free_blocks:
-            self._reqs_need_send[request.request_id] = (request, block_ids)
+            self._reqs_need_send[request.request_id] = (
+                request,
+                self.get_sw_clipped_blocks(block_ids),
+            )
 
         return delay_free_blocks, None
 
@@ -639,7 +726,12 @@ def request_finished(
 class MooncakeConnectorWorker:
     """Implementation of Worker side methods"""
 
-    def __init__(self, vllm_config: VllmConfig, engine_id: str):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        engine_id: str,
+        kv_cache_config: "KVCacheConfig | None" = None,
+    ):
         if TransferEngine is None:
             logger.error("Mooncake is not available")
             raise RuntimeError("Mooncake is not available")
@@ -747,10 +839,14 @@ def __init__(self, vllm_config: VllmConfig, engine_id: str):
         self.finished_sending_reqs: set[ReqId] = set()
         self.finished_recving_reqs: set[ReqId] = set()
 
+        self.xfer_stats = MooncakeKVConnectorStats()
+
         self.block_size = vllm_config.cache_config.block_size
         self.model_config = vllm_config.model_config
         self.cache_config = vllm_config.cache_config
+        self.kv_cache_config = kv_cache_config
         self.use_mla = self.model_config.use_mla
+        self._sync_block_size_with_kernel()
 
         # Get the attention backend from the first layer
         # NOTE (NickLucche) models with multiple backends are not supported yet
@@ -761,13 +857,13 @@ def __init__(self, vllm_config: VllmConfig, engine_id: str):
         logger.debug("Detected kv cache layout %s", self.kv_cache_layout)
 
         self._tp_size: dict[EngineId, int] = {self.engine_id: self.tp_size}
-        self._block_size: dict[EngineId, int] = {self.engine_id: self.block_size}
-        self.kv_topo = TpKVTopology(
+        self.transfer_topo = TransferTopology(
             tp_rank=self.tp_rank,
+            tp_size=self.tp_size,
+            block_size=self.block_size,
             engine_id=self.engine_id,
-            remote_tp_size=self._tp_size,  # shared state
-            remote_block_size=self._block_size,  # shared state
             is_mla=self.use_mla,
+            is_mamba=False,
             total_num_kv_heads=self.model_config.get_total_num_kv_heads(),
             attn_backends=[backend],
         )
@@ -777,6 +873,23 @@ def __init__(self, vllm_config: VllmConfig, engine_id: str):
         self._xfer_meta_decoder = msgspec.msgpack.Decoder(MooncakeXferMetadata)
         self._xfer_resp_decoder = msgspec.msgpack.Decoder(MooncakeXferResponse)
 
+    def _sync_block_size_with_kernel(self) -> None:
+        # When speculative decoding (e.g. Eagle) is enabled, the main model
+        # and draft model may use different attention backends with different
+        # physical block sizes. Pick the common (smallest) block size so that
+        # KV-cache registration and transfer work correctly for both models.
+        backends = get_current_attn_backends(self.vllm_config)
+        kernel_block_size = select_common_block_size(self.block_size, backends)
+        if self.block_size != kernel_block_size:
+            logger.info_once(
+                "User-specified logical block size (%s) does not match"
+                " physical kernel block size (%s). Using the latter.",
+                self.block_size,
+                kernel_block_size,
+            )
+            assert self.block_size > kernel_block_size
+            self.block_size = kernel_block_size
+
     def __del__(self):
         self.shutdown()
 
@@ -891,7 +1004,7 @@ async def send_kv_to_decode(
         self, identity: bytes, sock: zmq.asyncio.Socket, meta: MooncakeXferMetadata
     ):
         pending_reqs: dict[ReqId, SendBlockMeta] = {}
-        remote_tp_ranks = self.kv_topo.get_target_remote_ranks(meta.remote_tp_size)
+        remote_tp_ranks = self.transfer_topo.handshake_target_ranks(meta.remote_tp_size)
         if meta.remote_tp_rank not in remote_tp_ranks:
             # This D worker does not pair with the P worker.
             msg = (
@@ -1083,27 +1196,61 @@ async def _build_transfer_params(
         remote_session = f"{agent_meta.remote_hostname}:{agent_meta.remote_port}"
 
         for d_req_id, send_meta in ready_reqs:
-            _, remote_block_ids = agent_meta.req_blocks[d_req_id]
-            num_remote_blocks = len(remote_block_ids)
-            if num_remote_blocks == 0:
+            _, remote_block_ids_per_group = agent_meta.req_blocks[d_req_id]
+
+            if not remote_block_ids_per_group or all(
+                len(g) == 0 for g in remote_block_ids_per_group
+            ):
                 continue
 
-            local_block_ids = send_meta.local_block_ids
-            # Partial prefix cache hit: just read uncomputed blocks.
-            num_local_blocks = len(local_block_ids)
-            if num_local_blocks < num_remote_blocks:
+            # Per-group partial hit trimming, then flatten.
+            # With HMA, groups share the same KV tensor but use different
+            # block ranges.  We trim and concatenate so the coalescer and
+            # address math see one flat block list — same as non-HMA, but
+            # now including blocks from every group.
+            local_block_ids: list[int] = []
+            remote_block_ids: list[int] = []
+            has_block_error = False
+            if len(send_meta.local_block_ids) != len(remote_block_ids_per_group):
                 logger.error(
-                    "req %s: local blocks(%d) less than remote blocks(%d)!",
+                    "req %s: KV group count mismatch: local=%d, remote=%d",
                     d_req_id,
-                    num_local_blocks,
-                    num_remote_blocks,
+                    len(send_meta.local_block_ids),
+                    len(remote_block_ids_per_group),
                 )
+                err_reqs.append(d_req_id)
+                if err_msg is None:
+                    err_msg = "KV group count mismatch"
+                continue
+            for local_group, remote_group in zip(
+                send_meta.local_block_ids, remote_block_ids_per_group
+            ):
+                n_local = len(local_group)
+                n_remote = len(remote_group)
+                if n_local < n_remote:
+                    logger.error(
+                        "req %s: local blocks(%d) < remote blocks(%d) "
+                        "in a KV cache group",
+                        d_req_id,
+                        n_local,
+                        n_remote,
+                    )
+                    has_block_error = True
+                    break
+                if n_local > n_remote:
+                    # Partial prefix cache hit: just read uncomputed blocks.
+                    local_group = local_group[-n_remote:]
+                local_block_ids.extend(local_group)
+                remote_block_ids.extend(remote_group)
+
+            if has_block_error:
                 err_reqs.append(d_req_id)
                 if err_msg is None:
                     err_msg = "P num blocks less than D"
                 continue
-            if num_local_blocks > num_remote_blocks:
-                local_block_ids = local_block_ids[-num_remote_blocks:]
+
+            if not local_block_ids:
+                continue
 
             # Group by indices
             group_local_block_ids, group_remote_block_ids = group_concurrent_contiguous(
@@ -1195,7 +1342,7 @@ async def _build_transfer_params(
             logger.debug(
                 "Sending kv_caches for request %s (%d blocks) to %s",
                 d_req_id,
-                num_remote_blocks,
+                len(local_block_ids),
                 remote_session,
             )
 
@@ -1218,11 +1365,23 @@ def _send_blocks(
         ret_value = self.engine.batch_transfer_sync_write(
             remote_session, src_ptrs, dst_ptrs, lengths
         )
+        duration = time.perf_counter() - start_time
         if ret_value == 0:
-            logger.debug(
-                "Sending to %s done, took %s",
+            self.xfer_stats.record_transfer(
+                duration_s=duration,
+                total_bytes=sum(lengths),
+                num_descs=len(src_ptrs),
+            )
+            logger.debug("Sending to %s done, took %s", remote_session, duration)
+        else:
+            self.xfer_stats.record_failed_transfer()
+            logger.warning(
+                "Sending to %s failed (ret=%s) after %s (%d descriptors, %d bytes)",
                 remote_session,
-                time.perf_counter() - start_time,
+                ret_value,
+                duration,
+                len(src_ptrs),
+                sum(lengths),
             )
         return ret_value
 
@@ -1236,7 +1395,7 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
         seen_base_addresses = []
         self.block_len_per_layer = []
 
-        split_k_and_v = self.kv_topo.split_k_and_v
+        split_k_and_v = self.transfer_topo.split_k_and_v
         tensor_size_bytes = None
         for layer_name, cache_or_caches in kv_caches.items():
             cache_list = cache_or_caches if split_k_and_v else [cache_or_caches]
@@ -1253,26 +1412,24 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
                     continue
 
                 seen_base_addresses.append(base_addr)
-                curr_tensor_size_bytes = cache.nbytes
 
                 if tensor_size_bytes is None:
-                    tensor_size_bytes = curr_tensor_size_bytes
+                    tensor_size_bytes = cache.nbytes
                     self.num_blocks = cache.shape[0]
                 assert cache.shape[0] == self.num_blocks, (
                     "All kv cache tensors must have the same number of blocks"
                 )
-                assert curr_tensor_size_bytes % self.num_blocks == 0, (
-                    "Mooncake expects each kv cache tensor size to be "
-                    "divisible by the number of blocks."
-                )
-                self.block_len_per_layer.append(
-                    curr_tensor_size_bytes // self.num_blocks
-                )
 
-                kernel_block_size = cache.shape[-2 if self.use_mla else -3]
-                assert self.block_size == kernel_block_size
+                # Use stride-based block length so RDMA reaches the last
+                # block's padding (e.g. DeepseekV4 MLA alignment). stride(0)
+                # reflects the actual byte distance between consecutive
+                # blocks in GPU memory, which matches or exceeds the
+                # shape-based size.
+                block_len = cache.stride(0) * cache.element_size()
+
+                self.block_len_per_layer.append(block_len)
                 kv_data_ptrs.append(base_addr)
-                kv_data_lens.append(curr_tensor_size_bytes)
+                kv_data_lens.append(self.num_blocks * block_len)
 
         self.kv_caches_base_addr = seen_base_addresses
         self.seen_base_addresses = seen_base_addresses
@@ -1325,6 +1482,7 @@ async def fetch_finished_sending_reqs(self) -> set[ReqId]:
                     send_meta.p_req_id,
                     envs.VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT,
                 )
+                self.xfer_stats.record_kv_expired_req()
                 finished_sending_reqs.add(send_meta.p_req_id)
                 expired_transfer_id.append(transfer_id)
 
@@ -1365,6 +1523,13 @@ def get_finished(self) -> tuple[set[str] | None, set[str] | None]:
 
         return finished_sending_reqs or None, finished_recving_reqs or None
 
+    def get_kv_connector_stats(self) -> KVConnectorStats | None:
+        """Return transfer stats collected since the last call, or None
+        if nothing has been recorded in this interval."""
+        if self.xfer_stats.is_empty():
+            return None
+        return self.xfer_stats.clone_and_reset()
+
     async def receive_kv_from_single_worker(
         self,
         worker_addr: str,
@@ -1411,6 +1576,7 @@ async def receive_kv_from_single_worker(
                             req_ids,
                             response.err_msg,
                         )
+                        self.xfer_stats.record_failed_recv()
                         return
                     self.process_pulling_result(response, pull_metas)
                     if response.status == MooncakeXferResponseStatus.FINISH:
@@ -1419,6 +1585,7 @@ async def receive_kv_from_single_worker(
             logger.debug("ZMQ context terminated, exiting Mooncake receiver thread.")
         except Exception as e:
             logger.error("MooncakeXferMetadata transfer failed for %s: %s", req_ids, e)
+            self.xfer_stats.record_failed_recv()
             return
 
     def process_pulling_result(
@@ -1478,8 +1645,8 @@ def receive_kv(
         remote_engine_id: EngineId,
         pull_metas: dict[ReqId, PullReqMeta],
     ):
-        remote_tp_ranks = self.kv_topo.get_target_remote_ranks_from_engine_id(
-            remote_engine_id
+        remote_tp_ranks = self.transfer_topo.handshake_target_ranks(
+            self._tp_size[remote_engine_id]
         )
         count = len(remote_tp_ranks)
         logger.debug(
@@ -1570,7 +1737,7 @@ def start_load_kv(self, metadata: MooncakeConnectorMetadata):
             )
 
     def _producer_cache_is_replicated(self) -> bool:
-        return self.kv_topo.replicates_kv_cache(self.engine_id)
+        return self.transfer_topo.local_replicates_kv_cache
 
     def _get_transfer_regions(
         self, base_addrs: list[int], block_lens: list[int]
@@ -1578,7 +1745,7 @@ def _get_transfer_regions(
         return _expand_transfer_regions(
             base_addrs=base_addrs,
             block_lens=block_lens,
-            is_kv_layout_blocks_first=self.kv_topo.is_kv_layout_blocks_first,
+            is_kv_layout_blocks_first=self.transfer_topo.is_kv_layout_blocks_first,
         )
 
     def _get_sender_transfer_plan(
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/mooncake/stats.py b/vllm/distributed/kv_transfer/kv_connector/v1/mooncake/stats.py
new file mode 100644
index 000000000000..d177f55cc720
--- /dev/null
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/mooncake/stats.py
@@ -0,0 +1,146 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Stats container for the Mooncake connector."""
+
+import threading
+from dataclasses import dataclass
+from typing import Any
+
+import numpy as np
+
+from vllm.distributed.kv_transfer.kv_connector.v1.metrics import (
+    KVConnectorStats,
+)
+
+# TODO(mooncake-stats): add MooncakePromMetrics (mirror NixlPromMetrics)
+# and wire it via MooncakeConnector.build_prom_metrics in a follow-up PR.
+
+
+@dataclass
+class MooncakeKVConnectorStats(KVConnectorStats):
+    """Container for Mooncake KV transfer performance metrics.
+
+    `_lock` serializes record_* against clone_and_reset so each row's
+    appends are atomic and column lengths stay aligned. Writers run on
+    the sender pool / receiver loop / sender loop; reader runs on the
+    main worker thread.
+    """
+
+    def __post_init__(self):
+        self._lock = threading.Lock()
+        if not self.data:
+            self.reset()
+
+    # threading.Lock is not picklable; strip it from the wire form and
+    # rebuild a fresh per-process lock on the receiver side.
+    def __getstate__(self) -> dict[str, Any]:
+        state = self.__dict__.copy()
+        state.pop("_lock", None)
+        return state
+
+    def __setstate__(self, state: dict[str, Any]) -> None:
+        self.__dict__.update(state)
+        self._lock = threading.Lock()
+
+    def reset(self):
+        self.data: dict[str, list[float | int]] = {
+            "transfer_duration": [],
+            "bytes_transferred": [],
+            "num_descriptors": [],
+            "num_failed_transfers": [],
+            "num_failed_recvs": [],
+            "num_kv_expired_reqs": [],
+        }
+
+    def record_transfer(self, duration_s: float, total_bytes: int, num_descs: int):
+        with self._lock:
+            self.data["transfer_duration"].append(duration_s)
+            self.data["bytes_transferred"].append(total_bytes)
+            self.data["num_descriptors"].append(num_descs)
+
+    # Failure counters store a list of 1s so a future Prom counter can iterate
+    # with .inc(list_item), mirroring NIXL's NixlPromMetrics.observe.
+    def record_failed_transfer(self):
+        with self._lock:
+            self.data["num_failed_transfers"].append(1)
+
+    def record_failed_recv(self):
+        with self._lock:
+            self.data["num_failed_recvs"].append(1)
+
+    def record_kv_expired_req(self):
+        with self._lock:
+            self.data["num_kv_expired_reqs"].append(1)
+
+    def clone_and_reset(self) -> "MooncakeKVConnectorStats":
+        # Copy lists under the lock for length alignment; return a fresh
+        # instance so the snapshot has its own _lock.
+        with self._lock:
+            snapshot_data: dict[str, list[float | int]] = {
+                k: list(v) for k, v in self.data.items()
+            }
+            self.reset()
+        return MooncakeKVConnectorStats(data=snapshot_data)
+
+    def is_empty(self) -> bool:
+        return (
+            self.num_successful_transfers == 0
+            and len(self.data["num_failed_transfers"]) == 0
+            and len(self.data["num_failed_recvs"]) == 0
+            and len(self.data["num_kv_expired_reqs"]) == 0
+        )
+
+    def aggregate(self, other: KVConnectorStats) -> KVConnectorStats:
+        if not other.is_empty():
+            for k, v in other.data.items():
+                accumulator = self.data[k]
+                assert isinstance(accumulator, list)
+                accumulator.extend(v)
+        return self
+
+    def reduce(self) -> dict[str, int | float]:
+        num_failed_transfers = len(self.data["num_failed_transfers"])
+        num_failed_recvs = len(self.data["num_failed_recvs"])
+        num_kv_expired_reqs = len(self.data["num_kv_expired_reqs"])
+
+        if self.num_successful_transfers == 0:
+            return {
+                "Num successful transfers": 0,
+                "Avg xfer time (ms)": 0,
+                "P90 xfer time (ms)": 0,
+                "Avg MB per transfer": 0,
+                "Throughput (MB/s)": 0,
+                "Avg number of descriptors": 0,
+                "Num failed transfers": num_failed_transfers,
+                "Num failed recvs": num_failed_recvs,
+                "Num KV expired reqs": num_kv_expired_reqs,
+            }
+
+        xfer_time = np.asarray(self.data["transfer_duration"])
+        mb = np.asarray(self.data["bytes_transferred"]) / 2**20
+        descs = np.asarray(self.data["num_descriptors"], dtype=np.uint32)
+        n = len(descs)
+        assert n == self.num_successful_transfers
+
+        total_mb = mb.sum()
+        avg_mb = total_mb / n
+        total_time_seconds = xfer_time.sum()
+        throughput_mb_s = (
+            total_mb / total_time_seconds if total_time_seconds > 0 else 0.0
+        )
+
+        return {
+            "Num successful transfers": n,
+            "Avg xfer time (ms)": round(xfer_time.mean() * 1e3, 3),
+            "P90 xfer time (ms)": round(np.percentile(xfer_time, 90).item() * 1e3, 3),
+            "Avg MB per transfer": round(avg_mb, 3),
+            "Throughput (MB/s)": round(throughput_mb_s, 3),
+            "Avg number of descriptors": round(descs.mean(), 1),
+            "Num failed transfers": num_failed_transfers,
+            "Num failed recvs": num_failed_recvs,
+            "Num KV expired reqs": num_kv_expired_reqs,
+        }
+
+    @property
+    def num_successful_transfers(self) -> int:
+        return len(self.data["transfer_duration"])
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/moriio/moriio_common.py b/vllm/distributed/kv_transfer/kv_connector/v1/moriio/moriio_common.py
index f3b2ce3b5bec..b843c5b59308 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/moriio/moriio_common.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/moriio/moriio_common.py
@@ -8,6 +8,7 @@
 from typing import TYPE_CHECKING, Any
 
 import msgspec
+import regex as re
 import torch
 import zmq
 
@@ -239,7 +240,7 @@ class MoRIIOConstants:
     COMPLETION_PREFIX = "cmpl"
     TRANSFER_PREFIX = "tx"
 
-    PING_INTERVAL = 5
+    PING_INTERVAL = 3
     MAX_PING_RETRIES = 100
     DEFAULT_HANDSHAKE_PORT = "6301"
     DEFAULT_NOTIFY_PORT = "61005"
@@ -247,6 +248,64 @@ class MoRIIOConstants:
     VLLM_MORI_READ_ABORT_REQUEST_TIMEOUT = 3600
 
 
+# The router embeds both zmq_addresses in the request_id (similar to P2pNcclConnector):
+#   "___prefill_addr_{zmq}___decode_addr_{zmq}_{32-hex-uuid}"
+# MoRIIO zmq_address format: "host:IP,handshake:PORT,notify:PORT"
+#
+# This lets each connector side parse the peer's connection info without
+# requiring the router to pass it explicitly in kv_transfer_params.
+_PREFILL_ZMQ_RE = re.compile(r"___prefill_addr_(.+?)___decode_addr_")
+# vLLM wraps the router's X-Request-Id as "cmpl-<id>-<seq>-<hex>" so there may
+# be a trailing "-<seq>-<hex>" suffix after the 32-char UUID.  Allow it.
+_DECODE_ZMQ_RE = re.compile(r"___decode_addr_(.+)_[0-9a-f]{32}(?:-.*)?$")
+
+
+def parse_moriio_zmq_address(
+    zmq_address: str,
+) -> tuple[str, int, int]:
+    """Parse the MoRI-IO zmq address into its components.
+
+    Parses ``"host:IP,handshake:PORT,notify:PORT"`` into
+        (host, handshake_port, notify_port).
+
+    Each key-value pair is split on the *first* colon so that IPv6 addresses
+    (e.g. ``host:::1``) are handled correctly.  Raises ``ValueError`` if any
+    of ``host``, ``handshake``, or ``notify`` keys are absent or if the port
+    values are non-numeric.
+    """
+    parts: dict[str, str] = {}
+    for segment in zmq_address.split(","):
+        key, _, val = segment.partition(":")
+        parts[key.strip()] = val.strip()
+    try:
+        host = parts["host"]
+        handshake_port = int(parts["handshake"])
+        notify_port = int(parts["notify"])
+    except (KeyError, ValueError) as e:
+        raise ValueError(
+            f"Malformed zmq_address {zmq_address!r}: expected "
+            f"'host:IP,handshake:PORT,notify:PORT' format"
+        ) from e
+    return host, handshake_port, notify_port
+
+
+def get_peer_zmq_from_request_id(request_id: str, is_producer: bool) -> str:
+    """Extract the *peer's* zmq_address from the vLLM router request_id.
+
+    The producer (prefill) needs the decode's address; the consumer (decode)
+    needs the prefill's address.
+    """
+    if is_producer:
+        m = _DECODE_ZMQ_RE.search(request_id)
+    else:
+        m = _PREFILL_ZMQ_RE.search(request_id)
+    if m is None:
+        raise ValueError(
+            f"Cannot parse peer zmq_address from request_id: {request_id!r}"
+        )
+    return m.group(1)
+
+
 @dataclass
 class ReqMeta:
     """Metadata for a single request."""
@@ -286,15 +345,23 @@ def add_new_req(
         write_mode=False,
     ):
         transfer_id = kv_transfer_params["transfer_id"]
+
+        # Parse host/ports from the request_id. The router embeds both zmq_addresses
+        # in the request_id
+        peer_zmq = get_peer_zmq_from_request_id(request_id, is_producer=write_mode)
+        remote_host, remote_handshake_port, remote_notify_port = (
+            parse_moriio_zmq_address(peer_zmq)
+        )
+
         _req = ReqMeta(
             transfer_id=transfer_id,
             local_block_ids=local_block_ids,
             remote_block_ids=kv_transfer_params["remote_block_ids"],
             remote_engine_id=kv_transfer_params["remote_engine_id"],
-            remote_host=kv_transfer_params["remote_host"],
-            remote_port=kv_transfer_params["remote_port"],
-            remote_handshake_port=kv_transfer_params["remote_handshake_port"],
-            remote_notify_port=kv_transfer_params["remote_notify_port"],
+            remote_host=remote_host,
+            remote_port=remote_handshake_port,
+            remote_handshake_port=remote_handshake_port,
+            remote_notify_port=remote_notify_port,
             tp_size=kv_transfer_params.get("tp_size", 1),
             remote_dp_size=kv_transfer_params.get("remote_dp_size", 1),
         )
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/moriio/moriio_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/moriio/moriio_connector.py
index dcde7665f344..bf0575dd2ac8 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/moriio/moriio_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/moriio/moriio_connector.py
@@ -35,8 +35,10 @@
     TransferId,
     WriteTask,
     get_moriio_mode,
+    get_peer_zmq_from_request_id,
     get_port_offset,
     get_role,
+    parse_moriio_zmq_address,
     set_role,
     zmq_ctx,
 )
@@ -91,9 +93,9 @@ def __init__(
         self,
         vllm_config: VllmConfig,
         role: KVConnectorRole,
-        kv_cache_config: "KVCacheConfig | None" = None,
+        kv_cache_config: "KVCacheConfig",
     ):
-        super().__init__(vllm_config, role)
+        super().__init__(vllm_config, role, kv_cache_config)
         assert vllm_config.kv_transfer_config is not None, (
             "kv_transfer_config must be set for MoRIIOConnector"
         )
@@ -379,13 +381,12 @@ def update_state_after_alloc(
         if params is not None and params.get("do_remote_prefill"):
             if self.mode == MoRIIOMode.READ:
                 if remote_block_ids := params.get("remote_block_ids"):
-                    if all(
-                        p in params
-                        for p in ("remote_engine_id", "remote_host", "remote_port")
-                    ):
-                        # If remote_blocks and num_external_tokens = 0, we
+                    # remote_engine_id is returned by the prefill's request_finished.
+                    # host/ports come from the request_id (parsed in add_new_req).
+                    if "remote_engine_id" in params:
+                        # If remote_blocks and num_external_tokens = 0, we have
                         # a full prefix cache hit on the D worker. We need to call
-                        # send_notif in _read_blocks to free the memory on the P.
+                        # send_notify in _read_blocks to free the memory on the P.
 
                         # Get unhashed blocks to pull from remote.
                         local_block_ids = blocks.get_block_ids()[0]
@@ -407,22 +408,30 @@ def update_state_after_alloc(
                         )
 
             else:
+                # WRITE mode: prefill scheduler notifies the decode side that
+                # blocks are ready.  Parse the decode's host/notify_port from
+                # the request_id
                 assert request.kv_transfer_params is not None, (
                     "kv_transfer_params should not be None"
                 )
 
                 remote_dp_rank = request.kv_transfer_params.get("remote_dp_rank", 0)
 
+                peer_zmq = get_peer_zmq_from_request_id(
+                    request.request_id, is_producer=True
+                )
+                remote_host, _, remote_notify_port = parse_moriio_zmq_address(peer_zmq)
+
                 for tp_index in range(self.tp_size):
-                    target_port = request.kv_transfer_params[
-                        "remote_notify_port"
-                    ] + get_port_offset(remote_dp_rank, tp_index)
+                    target_port = remote_notify_port + get_port_offset(
+                        remote_dp_rank, tp_index
+                    )
 
                     self.send_notify_block(
                         req_id=request.request_id,
                         transfer_id=request.kv_transfer_params["transfer_id"],
                         block_notify_list=blocks.get_block_ids()[0],
-                        host=params.get("remote_host"),
+                        host=remote_host,
                         port=target_port,
                     )
 
@@ -584,15 +593,15 @@ def request_finished(
                 + MoRIIOConstants.VLLM_MORI_READ_ABORT_REQUEST_TIMEOUT
             )
 
-        # If we execute in P-D serial mode, no notification port is needed.
+        # Return KV transfer params forwarded verbatim to the decode instance by
+        # the router.
         return delay_free_blocks, dict(
             do_remote_prefill=True,
             do_remote_decode=False,
             remote_block_ids=computed_block_ids,
             remote_engine_id=self.engine_id,
-            remote_host=self.host_ip,
-            remote_port=self.handshake_port,
             tp_size=self.vllm_config.parallel_config.tensor_parallel_size,
+            transfer_id=params["transfer_id"],
         )
 
 
@@ -846,7 +855,15 @@ def _get_built_session(self, remote_engine_id):
         ]
 
     def _ping(self, zmq_context):
-        http_request_address = f"http://{self.request_address}/v1/completions"
+        # Use host:port format for http_address (compatible with official router)
+        http_address = f"{self.request_address}"
+        # Include host so the router embeds it in the request_id; the connector
+        # on the other side parses host/ports from there.
+        zmq_address = (
+            f"host:{self.local_ip},"
+            f"handshake:{self.handshake_port},"
+            f"notify:{self.notify_port}"
+        )
         role = "P" if self.is_producer else "D"
 
         retry_count = 0
@@ -857,14 +874,17 @@ def _ping(self, zmq_context):
             while True:
                 try:
                     data = {
-                        "type": "register",
-                        "role": role,
-                        "index": str(index),
-                        "request_address": http_request_address,
-                        "handshake_port": self.handshake_port,
-                        "notify_port": self.notify_port,
+                        "type": role,  # "P" or "D"
+                        "http_address": http_address,
+                        "zmq_address": zmq_address,
+                        # dp_size/tp_size are not used by the official vLLM router
+                        # (routing operates at the http_address level); they are
+                        # consumed only by the toy proxy server.
                         "dp_size": self.moriio_config.dp_size,
                         "tp_size": self.moriio_config.tp_size,
+                        # transfer_mode is included so the router can distinguish
+                        # READ (prefill-then-decode, sequential) from WRITE (concurrent)
+                        # scheduling.
                         "transfer_mode": self.mode.name,
                     }
 
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
index 3888d2e0f44c..a340f313e0a2 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
@@ -1,9 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import copy
-from collections.abc import Iterable
+from collections.abc import Callable, Iterable
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, cast
 
 import torch
 
@@ -18,6 +18,8 @@
     KVConnectorMetadata,
     KVConnectorRole,
     KVConnectorWorkerMetadata,
+    SupportsHMA,
+    supports_hma,
 )
 from vllm.distributed.kv_transfer.kv_connector.v1.metrics import (
     KVConnectorPromMetrics,
@@ -123,7 +125,7 @@ def observe(self, transfer_stats_data: dict[str, Any], engine_idx: int = 0):
             self._prom_metrics[connector_id].observe(stats_data["data"], engine_idx)
 
 
-class MultiConnector(KVConnectorBase_V1):
+class MultiConnector(KVConnectorBase_V1, SupportsHMA):
     """
     A wrapper for using multiple KVConnectors at the same time.
 
@@ -166,6 +168,12 @@ def __init__(
             self._connectors.append(connector_cls(temp_config, role, kv_cache_config))
             self._ktc_kv_transfer_config.append(temp_config.kv_transfer_config)
 
+        self._all_support_hma = all(supports_hma(c) for c in self._connectors)
+        assert (
+            vllm_config.scheduler_config.disable_hybrid_kv_cache_manager
+            or self._all_support_hma
+        ), "HMA should not be enabled unless all sub-connectors support it"
+
         # A mapping from request id to the index of the connector chosen to
         # load the request from (if any).
         self._requests_to_connector: dict[str, int] = {}
@@ -436,15 +444,17 @@ def set_xfer_handshake_metadata(
         for c in self._connectors:
             c.set_xfer_handshake_metadata(metadata)
 
-    def request_finished(
+    def _aggregate_request_finished(
         self,
         request: "Request",
-        blocks: list[int],
+        per_connector_fn: Callable[
+            [KVConnectorBase_V1], tuple[bool, dict[str, Any] | None]
+        ],
     ) -> tuple[bool, dict[str, Any] | None]:
         async_saves = 0
         kv_txfer_params = None
         for c in self._connectors:
-            async_save, txfer_params = c.request_finished(request, blocks)
+            async_save, txfer_params = per_connector_fn(c)
             if async_save:
                 async_saves += 1
             if txfer_params is not None:
@@ -458,11 +468,39 @@ def request_finished(
         if async_saves > 1:
             self._extra_async_saves[request.request_id] = async_saves - 1
 
-        # Clean up other state for this request.
         self._requests_to_connector.pop(request.request_id, None)
 
         return async_saves > 0, kv_txfer_params
 
+    def request_finished(
+        self,
+        request: "Request",
+        blocks: list[int],
+    ) -> tuple[bool, dict[str, Any] | None]:
+        return self._aggregate_request_finished(
+            request,
+            lambda c: c.request_finished(request, blocks),
+        )
+
+    def request_finished_all_groups(
+        self,
+        request: "Request",
+        block_ids: tuple[list[int], ...],
+    ) -> tuple[bool, dict[str, Any] | None]:
+        if not self._all_support_hma:
+            assert len(block_ids) == 1, (
+                "HMA with multiple kv_cache_groups requires all "
+                "sub-connectors to support HMA"
+            )
+            return self.request_finished(request, block_ids[0])
+
+        return self._aggregate_request_finished(
+            request,
+            lambda c: cast(SupportsHMA, c).request_finished_all_groups(
+                request, block_ids
+            ),
+        )
+
     def take_events(self) -> Iterable["KVCacheEvent"]:
         for c in self._connectors:
             yield from c.take_events()
@@ -548,7 +586,13 @@ def get_kv_connector_stats(self) -> MultiKVConnectorStats | None:
             if stats_by_connector is None:
                 # Lazy init to allow optional return value.
                 stats_by_connector = MultiKVConnectorStats()
-            stats_by_connector[c.__class__.__name__] = stats
+            connector_id = c.__class__.__name__
+            if connector_id in stats_by_connector.data:
+                stats_by_connector[connector_id] = stats_by_connector[
+                    connector_id
+                ].aggregate(stats)
+            else:
+                stats_by_connector[connector_id] = stats
         return stats_by_connector
 
     @classmethod
@@ -560,9 +604,13 @@ def build_prom_metrics(
         per_engine_labelvalues: dict[int, list[object]],
     ) -> KVConnectorPromMetrics:
         prom_metrics: dict[str, KVConnectorPromMetrics] = {}
+        seen_classes: set[type] = set()
         for connector_cls, temp_config in cls._get_connector_classes_and_configs(
             vllm_config
         ):
+            if connector_cls in seen_classes:
+                continue
+            seen_classes.add(connector_cls)
             connector_prom = connector_cls.build_prom_metrics(
                 temp_config, metric_types, labelnames, per_engine_labelvalues
             )
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/nixl/metadata.py b/vllm/distributed/kv_transfer/kv_connector/v1/nixl/metadata.py
index 71ebbf1174fb..c56e373ba99d 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/nixl/metadata.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/nixl/metadata.py
@@ -32,8 +32,9 @@
 # Version History:
 #   1: Initial version with compatibility checking
 #   2: Add remote_request_id to kv_transfer_params
+#   3: Add physical_blocks_per_logical_kv_block to NixlAgentMetadata
 #
-NIXL_CONNECTOR_VERSION: int = 2
+NIXL_CONNECTOR_VERSION: int = 3
 
 
 @dataclass
@@ -48,6 +49,7 @@ class NixlAgentMetadata:
     block_size: int
     ssm_sizes: tuple[int, int]
     attn_backend_name: str
+    physical_blocks_per_logical_kv_block: int
 
 
 @dataclass
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/nixl/scheduler.py b/vllm/distributed/kv_transfer/kv_connector/v1/nixl/scheduler.py
index 9f67d0fc525d..a053f2c32611 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/nixl/scheduler.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/nixl/scheduler.py
@@ -119,6 +119,30 @@ def __init__(
             for n_tokens, block_size in sw_sizes_tokens
         ]
 
+        # Threshold to decide whether to compute kv cache locally
+        # or pull from a remote node: minimum number of remote
+        # tokens to amortize the xfer latencies
+        self.kv_recompute_threshold: int = int(
+            vllm_config.kv_transfer_config.get_from_extra_config(
+                "kv_recompute_threshold", 64
+            )
+        )
+
+        # Bi-directional KV transfer feature supports KV block
+        # transfers from D node to P node
+        self.is_bidirectional_kv_xfer_enabled = (
+            vllm_config.kv_transfer_config.get_from_extra_config(
+                "bidirectional_kv_xfer", False
+            )
+        )
+
+        if self.is_bidirectional_kv_xfer_enabled and self.kv_recompute_threshold > 0:
+            logger.info(
+                "Bidirectional KV transfer is enabled and the kv "
+                "recompute threshold is set to %d tokens",
+                self.kv_recompute_threshold,
+            )
+
     def shutdown(self):
         self._stop_event.set()
         if self._nixl_handshake_listener_t is not None:
@@ -298,6 +322,44 @@ def get_num_new_matched_tokens(
         if params is not None and params.get("do_remote_decode") and self._has_mamba:
             self._truncate_mamba_request_for_prefill(request)
 
+        if (
+            params is not None
+            and params.get("do_remote_decode")
+            and params.get("remote_block_ids")
+            and all(
+                p in params
+                for p in (
+                    "remote_engine_id",
+                    "remote_request_id",
+                    "remote_host",
+                    "remote_port",
+                )
+            )
+        ):
+            # Decode node has kv blocks for part of prefill request, so, provide them
+            # as an external token count to scheduler.
+            # The tokens will be loaded if not already present
+            # in the prefill node local cache
+            remote_num_tokens = params.get("remote_num_tokens") or 0
+            count = (
+                min(remote_num_tokens, request.num_prompt_tokens) - num_computed_tokens
+            )
+            if count > 0:
+                # Check kv_recompute_threshold: skip pull if
+                # remote tokens are below the threshold.
+                if (
+                    self.kv_recompute_threshold > 0
+                    and count < self.kv_recompute_threshold
+                ):
+                    logger.debug(
+                        "Skipping remote pull for %s: %d remote tokens < threshold %d",
+                        request.request_id,
+                        count,
+                        self.kv_recompute_threshold,
+                    )
+                    return 0, False
+                return count, True
+
         # No remote prefill for this request.
         return 0, False
 
@@ -315,13 +377,19 @@ def update_state_after_alloc(
         if not params:
             return
 
-        if params.get("do_remote_decode"):
+        if params.get("do_remote_decode") or (
+            params.get("do_remote_prefill") and self.is_bidirectional_kv_xfer_enabled
+        ):
             self._reqs_in_batch.add(request.request_id)
         if self.use_host_buffer and params.get("do_remote_decode"):
             # NOTE: when accelerator is not directly supported by Nixl,
             # prefilled blocks need to be saved to host memory before transfer.
             self._reqs_need_save[request.request_id] = request
-        elif params.get("do_remote_prefill"):
+        elif params.get("do_remote_prefill") or (
+            params.get("do_remote_decode")
+            and self.is_bidirectional_kv_xfer_enabled
+            and not params.get("_remote_blocks_processed")
+        ):
             if params.get("remote_block_ids"):
                 if all(
                     p in params
@@ -333,8 +401,8 @@ def update_state_after_alloc(
                     )
                 ):
                     # If remote_blocks and num_external_tokens = 0, we have
-                    # a full prefix cache hit on the D worker. We need to call
-                    # send_notif in _read_blocks to free the memory on the P.
+                    # a full prefix cache hit on the local node. We need to call
+                    # send_notif in _read_blocks to free the memory on the remote node.
 
                     unhashed_local_block_ids: BlockIds = (
                         blocks.get_unhashed_block_ids_all_groups()
@@ -362,6 +430,7 @@ def update_state_after_alloc(
                 assert num_external_tokens == 0
             # Only trigger 1 KV transfer per request.
             params["do_remote_prefill"] = False
+            params["_remote_blocks_processed"] = True
 
     def _build_save_meta(
         self,
@@ -450,10 +519,15 @@ def request_finished(
         if not params:
             return False, None
 
+        is_p_node = bool(params.get("do_remote_decode"))
+        is_d_node = not is_p_node
+
         if params.get("do_remote_prefill"):
             # If do_remote_prefill is still True when the request is finished,
             # update_state_after_alloc must not have been called (the request
-            # must have been aborted before it was scheduled).
+            # must have been aborted before it was scheduled, e.g. via the
+            # abort_immediately path used to clean up KV-transfer requests
+            # rejected at the D-side serving layer).
             # To avoid stranding the prefill blocks in the prefill instance,
             # we must add empty block_ids to _reqs_need_recv so that our
             # worker side will notify and free blocks in the prefill instance.
@@ -461,9 +535,13 @@ def request_finished(
             params["do_remote_prefill"] = False
             return False, None
 
-        if not params.get("do_remote_decode"):
+        if is_d_node and not self.is_bidirectional_kv_xfer_enabled:
             return False, None
-        if request.status != RequestStatus.FINISHED_LENGTH_CAPPED:
+
+        if request.status not in (
+            RequestStatus.FINISHED_LENGTH_CAPPED,
+            RequestStatus.FINISHED_STOPPED,
+        ):
             # Also include the case of a P/D Prefill request with immediate
             # block free (eg abort). Stop tracking this request.
             self._reqs_not_processed.add(request.request_id)
@@ -474,7 +552,7 @@ def request_finished(
         # TODO: check whether block_ids actually ever be 0. If not we could
         # remove the conditional below
         delay_free_blocks = any(len(group) > 0 for group in block_ids)
-
+        remote_num_tokens = 0
         if delay_free_blocks:
             # Prefill request on remote. It will be read from D upon completion
             logger.debug(
@@ -492,13 +570,16 @@ def request_finished(
             # Here we "unpad" blocks to send the actual remote blocks to be read.
             block_ids = self.get_sw_clipped_blocks(block_ids)
 
+            remote_num_tokens = request.num_computed_tokens
+
         return delay_free_blocks, dict(
-            do_remote_prefill=True,
-            do_remote_decode=False,
+            do_remote_prefill=is_p_node,
+            do_remote_decode=is_d_node,
             remote_block_ids=block_ids,
             remote_engine_id=self.engine_id,
             remote_request_id=request.request_id,
             remote_host=self.side_channel_host,
             remote_port=self.side_channel_port,
             tp_size=self.vllm_config.parallel_config.tensor_parallel_size,
+            remote_num_tokens=remote_num_tokens,
         )
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/nixl/stats.py b/vllm/distributed/kv_transfer/kv_connector/v1/nixl/stats.py
index fde99c1e2c20..1e4f5c48e0f7 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/nixl/stats.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/nixl/stats.py
@@ -4,7 +4,7 @@
 
 import copy
 from dataclasses import dataclass
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import numpy as np
 
@@ -15,11 +15,11 @@
     PromMetric,
     PromMetricT,
 )
-from vllm.distributed.kv_transfer.kv_connector.v1.nixl.utils import (
-    nixlXferTelemetry,
-)
 from vllm.v1.metrics.utils import create_metric_per_engine
 
+if TYPE_CHECKING:
+    from vllm.distributed.nixl_utils import nixlXferTelemetry
+
 
 @dataclass
 class NixlKVConnectorStats(KVConnectorStats):
@@ -42,7 +42,7 @@ def reset(self):
             "num_kv_expired_reqs": [],
         }
 
-    def record_transfer(self, res: nixlXferTelemetry):
+    def record_transfer(self, res: "nixlXferTelemetry"):
         # Keep metrics units consistent with rest of the code: time us->s
         self.data["transfer_duration"].append(res.xferDuration / 1e6)
         self.data["post_duration"].append(res.postDuration / 1e6)
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/nixl/tp_mapping.py b/vllm/distributed/kv_transfer/kv_connector/v1/nixl/tp_mapping.py
new file mode 100644
index 000000000000..b034b7605087
--- /dev/null
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/nixl/tp_mapping.py
@@ -0,0 +1,142 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""TP mapping computation for NIXL KV cache transfers."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+
+from vllm.distributed.kv_transfer.kv_connector.utils import (
+    BlockIds,
+    TransferTopology,
+)
+from vllm.v1.kv_cache_interface import AttentionSpec, KVCacheSpec, MambaSpec
+
+# ======================================================================
+# Data structures
+# ======================================================================
+
+
+@dataclass(frozen=True)
+class ReadSpec:
+    """Specification for a single remote block read operation."""
+
+    remote_rank: int
+    local_block_ids: BlockIds
+    remote_block_ids: BlockIds
+
+
+def _is_attention_spec(spec_type: type[KVCacheSpec]) -> bool:
+    return issubclass(spec_type, AttentionSpec)
+
+
+def _is_ssm_spec(spec_type: type[KVCacheSpec]) -> bool:
+    return issubclass(spec_type, MambaSpec)
+
+
+@dataclass(frozen=True)
+class TPMapping:
+    """Complete local-to-remote TP mapping for one remote engine.
+
+    Generated once per remote engine during handshake.
+    """
+
+    # Remote TP ranks that this local rank reads from, per group.
+    # Position = local piece index.
+    source_ranks_per_group: tuple[tuple[int, ...], ...]
+
+    # Superset of all source ranks (union of all groups).
+    all_source_ranks: tuple[int, ...]
+
+    # Maps each source rank to its FA head slot index.
+    rank_to_attention_slot: dict[int, int]
+
+    # FA head offset factor for hetero-TP (D_TP > P_TP).
+    rank_offset_factor: int
+
+
+# ======================================================================
+# TP mapping computation
+# ======================================================================
+
+
+def compute_tp_mapping(
+    transfer_topology: TransferTopology,
+    remote_tp_size: int,
+    group_spec_types: tuple[type[KVCacheSpec], ...],
+) -> TPMapping:
+    """Build the complete local-to-remote TP mapping.
+
+    Computes source ranks, head slot assignments, and the rank offset
+    factor in a single pass.
+    """
+    tp_rank = transfer_topology.tp_rank
+    tp_size = transfer_topology.tp_size
+    total_num_kv_heads = transfer_topology.total_num_kv_heads
+    # --- Attention source ranks ---
+    if transfer_topology.is_mla or tp_size >= remote_tp_size:
+        # D (local TP) > P (remote TP): multiple local ranks read different chunks from
+        # *one* remote rank, corresponding to different kv heads.
+        # For MLA, we only need one remote since cache is duplicated. When P TP=k*TP k,
+        # this will spread mla ranks to read from remote k*tp_rank.
+        attn_ranks = [tp_rank * remote_tp_size // tp_size]
+    else:
+        # P (remote TP) > D (local TP): one local rank
+        # reads from multiple remote ranks.
+        # GQA dedup: when K < remote_tp_size, several remote ranks
+        # hold the same KV head.  np.unique keeps only the first
+        # rank per unique head so we don't issue redundant reads.
+        abs_tp = remote_tp_size // tp_size
+        start = tp_rank * abs_tp
+        heads = np.arange(start, start + abs_tp) * total_num_kv_heads // remote_tp_size
+        _, unique_idx = np.unique(heads, return_index=True)
+        attn_ranks = (start + np.sort(unique_idx)).tolist()
+
+    # --- SSM source ranks ---
+    has_ssm = any(_is_ssm_spec(t) for t in group_spec_types)
+    if has_ssm:
+        if tp_size < remote_tp_size:
+            abs_tp = remote_tp_size // tp_size
+            ssm_ranks = list(range(tp_rank * abs_tp, (tp_rank + 1) * abs_tp))
+        else:
+            ssm_ranks = list(attn_ranks)
+    else:
+        ssm_ranks = []
+
+    all_ranks = sorted(set(attn_ranks) | set(ssm_ranks))
+
+    # --- Per-group ordered source ranks ---
+    source_ranks_per_group = tuple(
+        tuple(ssm_ranks) if _is_ssm_spec(t) else tuple(attn_ranks)
+        for t in group_spec_types
+    )
+
+    # --- Attention head slots ---
+    head_to_slot: dict[int, int] = {}
+    for i, r in enumerate(attn_ranks):
+        head_to_slot[r * total_num_kv_heads // remote_tp_size] = i
+    rank_to_attention_slot = {
+        r: head_to_slot.get(r * total_num_kv_heads // remote_tp_size, 0)
+        for r in all_ranks
+    }
+
+    # --- Rank offset factor ---
+    if transfer_topology.is_mla or tp_size <= remote_tp_size:
+        # We don't index into remote for reading, no offset needed.
+        rank_offset_factor = 0
+    elif tp_size > total_num_kv_heads:
+        local_head = tp_rank * total_num_kv_heads // tp_size
+        p_start = attn_ranks[0] * total_num_kv_heads // remote_tp_size
+        rank_offset_factor = local_head - p_start
+    else:
+        # D TP > P TP: we index into remote to read different heads depending on rank.
+        rank_offset_factor = tp_rank % (tp_size // remote_tp_size)
+
+    return TPMapping(
+        source_ranks_per_group=source_ranks_per_group,
+        all_source_ranks=tuple(all_ranks),
+        rank_to_attention_slot=rank_to_attention_slot,
+        rank_offset_factor=rank_offset_factor,
+    )
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/nixl/utils.py b/vllm/distributed/kv_transfer/kv_connector/v1/nixl/utils.py
index 514214347aed..2fa3829eaecb 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/nixl/utils.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/nixl/utils.py
@@ -3,59 +3,14 @@
 """Shared constants, lazy imports and helpers for the NIXL connector."""
 
 import contextlib
-import os
-import sys
 from collections.abc import Iterator
 from typing import Any
 
 import zmq
 
-from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.utils.network_utils import make_zmq_socket
-
-logger = init_logger(__name__)
-
-
-# Lazy import nixl_wrapper to avoid loading nixl_bindings if nixl is not used
-try:
-    if "UCX_RCACHE_MAX_UNRELEASED" not in os.environ:
-        # avoid a memory leak in UCX when using NIXL on some models
-        # see: https://github.com/vllm-project/vllm/issues/24264
-        if "nixl" in sys.modules or "rixl" in sys.modules:
-            logger.warning(
-                "NIXL was already imported, we can't reset UCX_RCACHE_MAX_UNRELEASED. "
-                "Please set it to '1024' manually."
-            )
-        else:
-            logger.info(
-                "Setting UCX_RCACHE_MAX_UNRELEASED to '1024' to avoid a rare "
-                "memory leak in UCX when using NIXL."
-            )
-            os.environ["UCX_RCACHE_MAX_UNRELEASED"] = "1024"
-
-    if not current_platform.is_rocm():
-        from nixl._api import nixl_agent as NixlWrapper
-        from nixl._bindings import nixlXferTelemetry
-    else:
-        from rixl._api import nixl_agent as NixlWrapper
-        from rixl._bindings import nixlXferTelemetry
-
-    logger.info("NIXL is available")
-except ImportError:
-    logger.warning("NIXL is not available")
-    NixlWrapper = None
-    nixlXferTelemetry = None
-
-
-try:
-    if not current_platform.is_rocm():
-        from nixl._api import nixl_agent_config
-    else:
-        from rixl._api import nixl_agent_config
-except ImportError:
-    nixl_agent_config = None
-    logger.warning("NIXL agent config is not available")
+from vllm.v1.kv_cache_interface import KVCacheSpec, UniformTypeKVCacheSpecs
 
 # Supported platforms and types of kv transfer buffer.
 # {device: tuple of supported kv buffer types}
@@ -92,3 +47,11 @@ def zmq_ctx(socket_type: Any, addr: str) -> Iterator[zmq.Socket]:
     finally:
         if ctx is not None:
             ctx.destroy(linger=0)
+
+
+def get_representative_spec_type(spec: KVCacheSpec) -> type[KVCacheSpec]:
+    if isinstance(spec, UniformTypeKVCacheSpecs):
+        # All inner specs are the same type; pick any.
+        inner = next(iter(spec.kv_cache_specs.values()))
+        return type(inner)
+    return type(spec)
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/nixl/worker.py b/vllm/distributed/kv_transfer/kv_connector/v1/nixl/worker.py
index 4f47a8bc91ab..18144f2ef5a5 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/nixl/worker.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/nixl/worker.py
@@ -9,6 +9,7 @@
 import time
 import uuid
 from collections import defaultdict
+from collections.abc import Iterator
 from concurrent.futures import Future, ThreadPoolExecutor
 from typing import TYPE_CHECKING, Any, cast
 
@@ -21,8 +22,8 @@
 from vllm.distributed.kv_transfer.kv_connector.utils import (
     BlockIds,
     EngineId,
-    HeteroTPTransferConfig,
-    TpKVTopology,
+    EngineTransferInfo,
+    TransferTopology,
     get_current_attn_backends,
     kv_postprocess_blksize_and_layout_on_receive,
     kv_postprocess_blksize_on_receive,
@@ -43,23 +44,28 @@
 from vllm.distributed.kv_transfer.kv_connector.v1.nixl.stats import (
     NixlKVConnectorStats,
 )
+from vllm.distributed.kv_transfer.kv_connector.v1.nixl.tp_mapping import (
+    ReadSpec,
+    TPMapping,
+    _is_attention_spec,
+    _is_ssm_spec,
+    compute_tp_mapping,
+)
 from vllm.distributed.kv_transfer.kv_connector.v1.nixl.utils import (
     _NIXL_SUPPORTED_DEVICE,
-    NixlWrapper,
-    nixl_agent_config,
+    get_representative_spec_type,
     zmq_ctx,
 )
 from vllm.distributed.kv_transfer.kv_connector.v1.ssm_conv_transfer_utils import (
     MambaConvSplitInfo,
-    compute_mamba_phys_ratio,
     derive_mamba_conv_split,
 )
+from vllm.distributed.nixl_utils import NixlWrapper, nixl_agent_config
 from vllm.distributed.parallel_state import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
 )
 from vllm.logger import init_logger
-from vllm.model_executor.layers.mamba.mamba_utils import is_conv_state_dim_first
 from vllm.platforms import current_platform
 from vllm.utils.network_utils import make_zmq_path
 from vllm.v1.attention.backends.utils import get_kv_cache_layout
@@ -81,13 +87,119 @@
 class NixlConnectorWorker:
     """Implementation of Worker side methods"""
 
+    def _compute_desc_ids(
+        self,
+        block_ids: BlockIds,
+        dst_num_blocks: int,
+        block_size_ratio: float | None,
+        physical_blocks_per_logical: int,
+    ) -> np.ndarray:
+        """Compute NIXL descriptor IDs for given block IDs."""
+        num_fa_regions = self.num_regions
+        num_ssm_regions = len(self.block_len_per_layer) * 4 if self._has_mamba else 0
+
+        num_blocks = dst_num_blocks
+        if block_size_ratio is not None:
+            num_blocks = int(num_blocks * block_size_ratio)
+        num_fa_descs = num_fa_regions * num_blocks
+
+        # All-attention fast path: single vectorized broadcast.
+        if num_ssm_regions == 0:
+            # NOTE (NickLucche) With HMA, every kv group has the same number of layers
+            # and layers from different groups share the same kv tensor.
+            # eg block_ids=[[1, 2], [3]]->blocks [1, 2] need to be
+            # read across all regions, same for [3], but group0-group1 blocks will
+            # always differ (different areas). Therefore we can just flatten the
+            # block_ids and compute the descs ids for all groups at once.
+            block_arr = np.concatenate(block_ids)[None, :]
+            region_ids = np.arange(num_fa_regions)[:, None]
+            return (region_ids * num_blocks + block_arr).flatten()
+
+        # Compute desc ids per group using the right stride: FA descs have
+        # num_blocks entries per region (kernel granularity), SSM descs have
+        # logical_blocks entries per region (no kernel splitting).
+        logical_blocks = num_blocks // physical_blocks_per_logical
+        all_descs: list[np.ndarray] = []
+        for i, group in enumerate(block_ids):
+            group_arr = np.asarray(group)
+            if _is_attention_spec(self._group_spec_types[i]):
+                fa_region_ids = np.arange(num_fa_regions)[:, None]
+                all_descs.append(
+                    (fa_region_ids * num_blocks + group_arr[None, :]).flatten()
+                )
+            elif _is_ssm_spec(self._group_spec_types[i]):
+                # NOTE (NickLucche) SSM and Attention block regions can
+                # be exchanged arbitrarily by manager.  Therefore, descs
+                # are laid out as:
+                #   [descs_fa (all regions) | descs_ssm (all regions)].
+                # num_fa_descs offset must be computed per-engine since
+                # P and D can have different num_blocks (and thus
+                # different FA desc counts).
+                ssm_region_ids = np.arange(num_ssm_regions)[:, None]
+                all_descs.append(
+                    (
+                        ssm_region_ids * logical_blocks
+                        + group_arr[None, :]
+                        + num_fa_descs
+                    ).flatten()
+                )
+            else:
+                raise ValueError(
+                    f"Unknown spec type {self._group_spec_types[i]} at index {i}"
+                )
+
+        return np.concatenate(all_descs)
+
+    def _build_local_splits_from_plan(
+        self,
+        plan: TPMapping,
+        src_blocks_data: list[tuple[int, int, int]],
+        num_fa_descs: int,
+    ) -> Iterator[list[tuple[int, int, int]]]:
+        """Build split handle data for P_TP > D_TP scenario.
+
+        num_fa_descs is the boundary between FA and SSM descriptors.
+        Split counts are derived from source_ranks_per_group lengths.
+        FA uses rank_to_attention_slot for the slot offset;
+        SSM uses the rank's positional index.
+        """
+        fa_idx = next(
+            i for i, t in enumerate(self._group_spec_types) if _is_attention_spec(t)
+        )
+        fa_num_splits = len(plan.source_ranks_per_group[fa_idx])
+
+        has_ssm_descs = num_fa_descs < len(src_blocks_data)
+        ssm_idx = next(
+            (i for i, t in enumerate(self._group_spec_types) if _is_ssm_spec(t)),
+            None,
+        )
+        ssm_num_splits = (
+            len(plan.source_ranks_per_group[ssm_idx])
+            if has_ssm_descs and ssm_idx is not None
+            else 0
+        )
+
+        for p_idx, p_rank in enumerate(plan.all_source_ranks):
+            fa_slot = plan.rank_to_attention_slot.get(p_rank, 0)
+
+            handle: list[tuple[int, int, int]] = []
+            for j, (addr, local_len, dev) in enumerate(src_blocks_data):
+                if j < num_fa_descs:
+                    chunk = local_len // fa_num_splits
+                    handle.append((addr + fa_slot * chunk, chunk, dev))
+                else:
+                    chunk = local_len // ssm_num_splits
+                    handle.append((addr + p_idx * chunk, chunk, dev))
+            yield handle
+
     def __init__(
         self,
         vllm_config: "VllmConfig",
         engine_id: str,
         kv_cache_config: "KVCacheConfig",
     ):
-        if NixlWrapper is None:
+        nixl_wrapper_cls = NixlWrapper
+        if nixl_wrapper_cls is None:
             logger.error("NIXL is not available")
             raise RuntimeError("NIXL is not available")
         logger.info("Initializing NIXL wrapper")
@@ -120,44 +232,37 @@ def __init__(
         }
         self.hma_group_size = len(kv_cache_config.kv_cache_tensors)
 
-        # ---- Mamba model state (derived from model config) ----
-        self._is_mamba_group = [
-            isinstance(group.kv_cache_spec, MambaSpec)
-            for group in kv_cache_config.kv_cache_groups
-        ]
+        # ---- Model state (derived from model config) ----
         mamba_ssm_size = (0, 0)
-        self._has_mamba = any(self._is_mamba_group)
-        if self._has_mamba:
-            assert self._is_hma_required
-            mamba_spec = next(
-                spec
-                for spec in self._layer_specs.values()
-                if isinstance(spec, MambaSpec)
-            )
-            conv_nbytes, ssm_nbytes = (
-                torch.tensor([], dtype=mamba_spec.dtypes[0]).element_size(),  # type: ignore[misc]
-                torch.tensor([], dtype=mamba_spec.dtypes[1]).element_size(),  # type: ignore[misc]
-            )
-            conv_shape, ssm_shape = (
-                torch.Size(mamba_spec.shapes[0]),
-                torch.Size(mamba_spec.shapes[1]),
-            )
-            mamba_ssm_size = (
-                conv_shape.numel() * conv_nbytes,
-                ssm_shape.numel() * ssm_nbytes,
-            )
-        self._mamba_ssm_size = mamba_ssm_size
         # Conv state sub-projection decomposition (None when no Mamba).
         # The 3-read transfer requires DS (dim, state_len) conv layout so
         # that x/B/C sub-projections are contiguous in memory.
         self._conv_decomp: MambaConvSplitInfo | None = None
+        self._has_mamba = any(
+            isinstance(g.kv_cache_spec, MambaSpec)
+            for g in kv_cache_config.kv_cache_groups
+        )
         if self._has_mamba:
+            assert self._is_hma_required
+            from vllm.model_executor.layers.mamba.mamba_utils import (
+                is_conv_state_dim_first,
+            )
+
             assert is_conv_state_dim_first(), (
                 "3-read Mamba conv transfer requires DS conv state layout. "
                 "Set VLLM_SSM_CONV_STATE_LAYOUT=DS"
             )
-            local_tp = vllm_config.parallel_config.tensor_parallel_size
-            self._conv_decomp = derive_mamba_conv_split(mamba_spec, local_tp)
+            mamba_spec = next(
+                spec
+                for spec in self._layer_specs.values()
+                if isinstance(spec, MambaSpec)
+            )
+            self._conv_decomp = derive_mamba_conv_split(
+                mamba_spec,
+                vllm_config.parallel_config.tensor_parallel_size,
+            )
+            mamba_ssm_size = self._conv_decomp.ssm_sizes
+        self._mamba_ssm_size = mamba_ssm_size
 
         # Agent.
         non_ucx_backends = [b for b in self.nixl_backends if b != "UCX"]
@@ -180,7 +285,7 @@ def __init__(
                 else nixl_agent_config(num_threads=num_threads, capture_telemetry=True)
             )
 
-        self.nixl_wrapper = NixlWrapper(str(uuid.uuid4()), config)
+        self.nixl_wrapper = nixl_wrapper_cls(str(uuid.uuid4()), config)
         # Map of engine_id -> {rank0: agent_name0, rank1: agent_name1..}.
         self._remote_agents: dict[EngineId, dict[int, str]] = defaultdict(dict)
 
@@ -269,16 +374,6 @@ def __init__(
         self.dst_num_blocks: dict[EngineId, int] = {}
         self._registered_descs: list[Any] = []
 
-        # ---- Mamba-HMA per-engine state (only used when self._has_mamba) ----
-        # Per-engine transfer config (source of truth for FA/mamba sizing).
-        self._transfer_configs: dict[str, HeteroTPTransferConfig] = {}
-        # NOTE (ZhanqiuHu): _mamba_phys_ratio MUST be per-engine.
-        # compute_mamba_phys_ratio = ceil((conv_bytes + ssm_bytes) / block_len)
-        # where conv/ssm bytes are per-TP-rank (dimension-sharded).  With
-        # heterogeneous TP the per-rank sizes differ, so the ratio differs:
-        #   e.g. Nemotron 30B: P(TP=4) → 131, D(TP=1) → 261.
-        self._mamba_phys_ratio: dict[EngineId, int] = {}
-
         # In progress transfers.
         # [req_id -> list[handle]]
         self._recving_metadata: dict[ReqId, ReqMeta] = {}
@@ -318,15 +413,16 @@ def __init__(
 
         self.kv_cache_layout = get_kv_cache_layout()
         self.host_buffer_kv_cache_layout = self.kv_cache_layout
-        logger.info("Detected attention backend %s", self.backend_name)
+        logger.info(
+            "Detected attention backend(s) %s",
+            [backend.get_name() for backend in self.attn_backends],
+        )
         logger.info("Detected kv cache layout %s", self.kv_cache_layout)
 
         # lazy initialized in register_kv_caches
         self.compat_hash: str | None = None
-        self.kv_topo: TpKVTopology | None = None
+        self.transfer_topo: TransferTopology | None = None
 
-        self._tp_size: dict[EngineId, int] = {self.engine_id: self.world_size}
-        self._block_size: dict[EngineId, int] = {self.engine_id: self.block_size}
         # With heterogeneous TP, P must wait for all assigned D TP workers to
         # finish reading before safely freeing the blocks.
         self.consumer_notification_counts_by_req = defaultdict[ReqId, int](int)
@@ -335,6 +431,15 @@ def __init__(
         self._physical_blocks_per_logical_kv_block = 1
         self._sync_block_size_with_kernel()
 
+        # Unwrap UniformTypeKVCacheSpecs to get the representative spec type
+        self._group_spec_types = tuple(
+            get_representative_spec_type(g.kv_cache_spec)
+            for g in self.kv_cache_config.kv_cache_groups
+        )
+
+        # Per-engine TP mappings. Generated during handshake.
+        self.tp_mappings: dict[EngineId, TPMapping] = {}
+
         self.enforce_compat_hash = self.kv_transfer_config.get_from_extra_config(
             "enforce_handshake_compat", True
         )
@@ -356,7 +461,6 @@ def _sync_block_size_with_kernel(self) -> None:
                 self.block_size // kernel_block_size
             )
             self.block_size = kernel_block_size
-            self._block_size[self.engine_id] = kernel_block_size
             self.num_blocks *= self._physical_blocks_per_logical_kv_block
 
     def _nixl_handshake(
@@ -385,8 +489,8 @@ def _nixl_handshake(
         # Regardless, only handshake with the remote TP rank(s) that current
         # local rank will read from. Note that With homogeneous TP,
         # this happens to be the same single rank_i.
-        assert self.kv_topo is not None
-        p_remote_ranks = self.kv_topo.get_target_remote_ranks(remote_tp_size)
+        assert self.transfer_topo is not None
+        p_remote_ranks = self.transfer_topo.handshake_target_ranks(remote_tp_size)
         remote_rank_to_agent_name = {}
         path = make_zmq_path("tcp", host, port)
 
@@ -650,11 +754,11 @@ def register_cross_layers_kv_caches(self, kv_cache: torch.Tensor) -> None:
 
     def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
         """Register the KV Cache data in nixl."""
-        self.kv_topo = TpKVTopology(
+        self.transfer_topo = TransferTopology(
             tp_rank=self.tp_rank,
+            tp_size=self.world_size,
+            block_size=self.block_size,
             engine_id=self.engine_id,
-            remote_tp_size=self._tp_size,  # shared state
-            remote_block_size=self._block_size,  # shared state
             is_mla=self.use_mla,
             total_num_kv_heads=self.model_config.get_total_num_kv_heads(),
             attn_backends=self.attn_backends,
@@ -665,7 +769,7 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
             is_mamba=self._has_mamba,
         )
         self.compat_hash = compute_nixl_compatibility_hash(
-            self.vllm_config, self.backend_name, self.kv_topo.cross_layers_blocks
+            self.vllm_config, self.backend_name, self.transfer_topo.cross_layers_blocks
         )
 
         if self.use_host_buffer:
@@ -717,7 +821,7 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
             if isinstance(layer_spec, UniformTypeKVCacheSpecs):
                 # MLA DSv32 Indexer case: UniformTypeKVCacheSpecs merges kv_cache_specs
                 layer_spec = layer_spec.kv_cache_specs[layer_name]
-            cache_list = self.kv_topo.get_transfer_cache_regions(
+            cache_list = self.transfer_topo.get_transfer_cache_regions(
                 cache_or_caches, layer_spec
             )
             # `layer_spec.page_size_bytes` only accounts for logical page_size, that is
@@ -730,7 +834,7 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
             )
             # For when registering multiple tensors eg K/V in separate regions.
             physical_page_size = physical_page_size // len(cache_list)
-            if self.kv_topo._cross_layers_blocks:
+            if self.transfer_topo._cross_layers_blocks:
                 # When cross-layers blocks are used, multiply by number of layers
                 physical_page_size = physical_page_size * len(
                     self.kv_cache_config.kv_cache_tensors
@@ -769,9 +873,21 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
                 else:
                     self.block_len_per_layer.append(physical_page_size)
 
-                assert cache.shape[0] == num_blocks, (
-                    "All kv cache tensors must have the same number of blocks"
-                )
+                if cache.shape[0] != num_blocks:
+                    raise AssertionError(
+                        "All kv cache tensors must have the same number of "
+                        f"blocks; layer={layer_name}, "
+                        f"expected_num_blocks={num_blocks}, "
+                        f"cache_shape={tuple(cache.shape)}, "
+                        f"cache_stride={tuple(cache.stride())}, "
+                        f"layer_spec={type(layer_spec).__name__}, "
+                        f"backend={self.backend_name}, "
+                        "all_backends="
+                        f"{[backend.get_name() for backend in self.attn_backends]}, "
+                        f"kv_cache_layout={self.kv_cache_layout}, "
+                        "blocks_first="
+                        f"{self.transfer_topo.is_kv_layout_blocks_first}"
+                    )
 
                 if not self.use_mla:
                     # Different kv cache shape is not supported by HeteroTP.
@@ -794,7 +910,7 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
         self.kv_caches_base_addr[self.engine_id][self.tp_rank] = seen_base_addresses
         self.num_regions = len(caches_data)
 
-        if self.kv_topo.is_kv_layout_blocks_first:
+        if self.transfer_topo.is_kv_layout_blocks_first:
             # NOTE (NickLucche) When FlashInfer is used, memory is registered
             # with joint KV for each block. This minimizes the overhead in
             # registerMem allowing faster descs queries. In order to be able to
@@ -818,9 +934,6 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
         self.dst_num_blocks[self.engine_id] = self.num_blocks
 
         if self._has_mamba:
-            self._mamba_phys_ratio[self.engine_id] = (
-                self._physical_blocks_per_logical_kv_block
-            )
             logger.info(
                 "Hybrid SSM registration: num_blocks=%s, "
                 "logical_num_blocks=%s, ratio=%s, num_regions=%s, "
@@ -853,6 +966,9 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
             block_size=self.block_size,
             ssm_sizes=self._mamba_ssm_size,
             attn_backend_name=self.backend_name,
+            physical_blocks_per_logical_kv_block=(
+                self._physical_blocks_per_logical_kv_block
+            ),
         )
         # Wrap metadata in payload with hash for defensive decoding
         assert self.compat_hash is not None
@@ -877,11 +993,15 @@ def _build_mamba_local(
         conv_offsets = self._conv_decomp.local_conv_offsets
         conv_size, ssm_size = self._mamba_ssm_size
         num_blocks = self._logical_num_blocks * block_size_ratio
-        phys_ratio = self._physical_blocks_per_logical_kv_block
+        physical_per_logical = self._physical_blocks_per_logical_kv_block
 
         result: list[tuple[int, int, int]] = []
         for i, base_addr in enumerate(base_addresses):
-            page_stride = self.block_len_per_layer[i] // block_size_ratio * phys_ratio
+            # Jump one page_size, but ssm page_size may be bigger when kernel
+            # locks block size to a specific value (physical_per_logical scale).
+            page_stride = (
+                self.block_len_per_layer[i] // block_size_ratio * physical_per_logical
+            )
             for off, sz in conv_offsets:
                 for blk in range(num_blocks):
                     result.append(
@@ -898,63 +1018,11 @@ def _build_mamba_local(
                 )
         return result
 
-    def _build_fa_remote_for_mamba(
-        self,
-        nixl_agent_meta: NixlAgentMetadata,
-        transfer_cfg: HeteroTPTransferConfig,
-        block_size_ratio: int,
-        kv_topo: TpKVTopology,
-    ) -> list[tuple[int, int, int]]:
-        """Build remote FA descriptors for mamba models.
-
-        Uses transfer_cfg for GQA-aware FA divisor and head-based rank offset
-        instead of the standard uniform tp_ratio split.
-        """
-        assert block_size_ratio == 1, (
-            "Mamba 3-read transfer with block_size_ratio != 1 is not tested. "
-            f"Got block_size_ratio={block_size_ratio}."
-        )
-        # TODO (ZhanqiuHu): unify with register_remote_blocks when Mamba-HMA
-        # hetero-TP logic stabilizes.
-        tp_ratio = transfer_cfg.tp_ratio
-        result: list[tuple[int, int, int]] = []
-        for i, base_addr in enumerate(nixl_agent_meta.kv_caches_base_addr):
-            local_block_len = self.get_backend_aware_kv_block_len(
-                layer_idx=i, first_split=True, mamba_view=False
-            )
-            remote_kv_block_len = local_block_len // block_size_ratio
-            if block_size_ratio > 1:
-                local_block_len = remote_kv_block_len
-
-            if tp_ratio < 0 and not self.use_mla:
-                local_block_len = local_block_len // transfer_cfg.physical_fa_num_reads
-
-            rank_offset = transfer_cfg.fa_rank_offset(remote_kv_block_len)
-
-            num_blocks = nixl_agent_meta.num_blocks
-            page_size = nixl_agent_meta.block_lens[i]
-            for block_id in range(num_blocks):
-                block_offset = block_id * page_size
-                addr = base_addr + block_offset + rank_offset
-                result.append((addr, local_block_len, nixl_agent_meta.device_id))
-
-            if kv_topo.is_kv_layout_blocks_first:
-                second_split = self.get_backend_aware_kv_block_len(
-                    layer_idx=i, first_split=False, mamba_view=False
-                )
-                if tp_ratio < 0 and not self.use_mla:
-                    second_split = second_split // transfer_cfg.physical_fa_num_reads
-                for block_id in range(num_blocks):
-                    block_offset = block_id * page_size
-                    addr = base_addr + block_offset + rank_offset
-                    v_addr = addr + nixl_agent_meta.block_lens[i] // 2
-                    result.append((v_addr, second_split, nixl_agent_meta.device_id))
-        return result
-
     def _build_mamba_remote(
         self,
         nixl_agent_meta: NixlAgentMetadata,
         tp_ratio: int,
+        transfer_info: EngineTransferInfo,
     ) -> list[tuple[int, int, int]]:
         """Build 4 remote desc regions (x, B, C, ssm) per layer for
         the 3-read transfer.  For hetero-TP, each D rank reads only its
@@ -974,23 +1042,23 @@ def _build_mamba_remote(
             ssm_read_size = self._mamba_ssm_size[1]
         else:
             # NOTE (ZhanqiuHu): tp_ratio < 0 means P_TP > D_TP, so P pages
-            # are smaller than D's.  self._conv_decomp has D-sized dimensions,
-            # but we need P-sized offsets.  Scale down by |tp_ratio|.
+            # are smaller than D's. self._conv_decomp has D-sized dimensions,
+            # but we need P-sized offsets. Scale down by |tp_ratio|.
             abs_ratio = -tp_ratio
             xb_p = self._conv_decomp.x_bytes // abs_ratio
             bb_p = self._conv_decomp.b_bytes // abs_ratio
             conv_offsets = [(0, xb_p), (xb_p, bb_p), (xb_p + bb_p, bb_p)]
             ssm_read_size = nixl_agent_meta.ssm_sizes[1]
 
-        remote_ratio = self._mamba_phys_ratio[nixl_agent_meta.engine_id]
-        num_blocks = nixl_agent_meta.num_blocks // remote_ratio
+        remote_physical_per_logical = transfer_info.remote_physical_blocks_per_logical
+        num_blocks = nixl_agent_meta.num_blocks // remote_physical_per_logical
         device_id = nixl_agent_meta.device_id
 
         result: list[tuple[int, int, int]] = []
         # NOTE (ZhanqiuHu): use per-layer block_lens[i], not [0], in case
         # block lengths vary across layers (e.g. MLA).
         for i, base_addr in enumerate(nixl_agent_meta.kv_caches_base_addr):
-            page_stride = nixl_agent_meta.block_lens[i] * remote_ratio
+            page_stride = nixl_agent_meta.block_lens[i] * remote_physical_per_logical
             for off, sz in conv_offsets:
                 for blk in range(num_blocks):
                     result.append((base_addr + blk * page_stride + off, sz, device_id))
@@ -1005,6 +1073,91 @@ def _build_mamba_remote(
                 result.append((ssm_addr, ssm_read_size, device_id))
         return result
 
+    def _build_fa_local(
+        self,
+        base_addresses: list[int],
+        block_size_ratio: int,
+    ) -> list[tuple[int, int, int]]:
+        """Build local FA descriptors for all layers."""
+        assert self.transfer_topo is not None
+        num_blocks = self.num_blocks * block_size_ratio
+        result: list[tuple[int, int, int]] = []
+        for i, base_addr in enumerate(base_addresses):
+            kv_block_len = (
+                self.get_backend_aware_kv_block_len(
+                    layer_idx=i, first_split=True, mamba_view=False
+                )
+                // block_size_ratio
+            )
+            page_stride = self.block_len_per_layer[i] // block_size_ratio
+            for block_id in range(num_blocks):
+                block_offset = block_id * page_stride
+                addr = base_addr + block_offset
+                result.append((addr, kv_block_len, self.device_id))
+
+            if self.transfer_topo.is_kv_layout_blocks_first:
+                # Separate and interleave K/V regions to maintain the same
+                # descs ordering. This is needed for selecting contiguous heads
+                # when split across TP ranks.
+                second_split = self.get_backend_aware_kv_block_len(
+                    layer_idx=i, first_split=False, mamba_view=False
+                )
+                for block_id in range(num_blocks):
+                    block_offset = block_id * page_stride
+                    addr = base_addr + block_offset
+                    v_addr = addr + kv_block_len
+                    result.append((v_addr, second_split, self.device_id))
+        return result
+
+    def _build_fa_remote(
+        self,
+        plan: TPMapping,
+        nixl_agent_meta: NixlAgentMetadata,
+        block_size_ratio: int,
+    ) -> list[tuple[int, int, int]]:
+        """Build remote FA descriptors for all layers."""
+        assert self.transfer_topo is not None
+        fa_group_idx = next(
+            i for i, t in enumerate(self._group_spec_types) if _is_attention_spec(t)
+        )
+        num_attn_reads = len(plan.source_ranks_per_group[fa_group_idx])
+        num_blocks = nixl_agent_meta.num_blocks
+        result: list[tuple[int, int, int]] = []
+        for i, base_addr in enumerate(nixl_agent_meta.kv_caches_base_addr):
+            # Read our whole local region size from remote..
+            local_block_len = self.get_backend_aware_kv_block_len(
+                layer_idx=i, first_split=True, mamba_view=False
+            )
+            remote_kv_block_len = local_block_len // block_size_ratio
+            if block_size_ratio > 1:
+                # ..using remote kv_block_len as transfer unit
+                local_block_len = remote_kv_block_len
+
+            local_block_len = local_block_len // num_attn_reads
+            rank_offset = plan.rank_offset_factor * remote_kv_block_len
+
+            page_size = nixl_agent_meta.block_lens[i]
+            for block_id in range(num_blocks):
+                block_offset = block_id * page_size
+                # For each block, grab the kv heads chunk belonging to current local
+                # tp rank of size local_block_len.
+                addr = base_addr + block_offset + rank_offset
+                result.append((addr, local_block_len, nixl_agent_meta.device_id))
+
+            if self.transfer_topo.is_kv_layout_blocks_first:
+                # With FlashInfer index V separately to allow head splitting.
+                second_split = self.get_backend_aware_kv_block_len(
+                    layer_idx=i, first_split=False, mamba_view=False
+                )
+                second_split = second_split // num_attn_reads
+                for block_id in range(num_blocks):
+                    block_offset = block_id * page_size
+                    addr = base_addr + block_offset + rank_offset
+                    # Hop over the first split of remote page, K, to read V.
+                    v_addr = addr + nixl_agent_meta.block_lens[i] // 2
+                    result.append((v_addr, second_split, nixl_agent_meta.device_id))
+        return result
+
     def register_local_xfer_handler(
         self,
         block_size: int,
@@ -1020,69 +1173,24 @@ def register_local_xfer_handler(
         register another local_xfer_handler using remote block len to ensure
         data copy correctness.
         """
-        assert self.kv_topo is not None
-        kv_topo = self.kv_topo
-
+        assert self.transfer_topo is not None
         block_size_ratio = self.block_size // block_size
-        blocks_data: list[tuple[int, int, int]] = []
         local_base_addresses = self.kv_caches_base_addr[self.engine_id][self.tp_rank]
 
-        def register_blocks(blocks_data: list[tuple[int, int, int]], mamba: bool):
-            for i, base_addr in enumerate(local_base_addresses):
-                # The new block_len is using prefill block_len;
-                # and num_blocks is multiple with N
-                kv_block_len = (
-                    self.get_backend_aware_kv_block_len(
-                        layer_idx=i, first_split=True, mamba_view=mamba
-                    )
-                    // block_size_ratio
-                )
-                # Jump one page_size, but ssm page_size may be bigger when kernel
-                # locks block size to a specific value.
-                block_len_per_layer = (
-                    self.block_len_per_layer[i]
-                    // block_size_ratio
-                    * (1 if not mamba else self._physical_blocks_per_logical_kv_block)
-                )
-                num_blocks = self._logical_num_blocks if mamba else self.num_blocks
-                num_blocks = num_blocks * block_size_ratio
-                for block_id in range(num_blocks):
-                    block_offset = block_id * block_len_per_layer
-                    addr = base_addr + block_offset
-                    # (addr, len, device id)
-                    blocks_data.append((addr, kv_block_len, self.device_id))
-
-                if kv_topo.is_kv_layout_blocks_first:
-                    second_split = self.get_backend_aware_kv_block_len(
-                        layer_idx=i, first_split=False, mamba_view=mamba
-                    )
-                    # Separate and interleave K/V regions to maintain the same
-                    # descs ordering. This is needed for selecting contiguous heads
-                    # when split across TP ranks.
-                    for block_id in range(num_blocks):
-                        block_offset = block_id * block_len_per_layer
-                        addr = base_addr + block_offset
-                        # Register addresses for V cache (K registered first).
-                        v_addr = addr + kv_block_len
-                        blocks_data.append((v_addr, second_split, self.device_id))
-            logger.debug(
-                "Created %s blocks for src engine %s and rank %s on device id %s",
-                len(blocks_data),
-                self.engine_id,
-                self.tp_rank,
-                self.device_id,
-            )
-
-        # NOTE (ZhanqiuHu): mamba=True path in register_blocks is not used
-        # right now — we use _build_mamba_local instead for the 3-read
-        # approach. However, we might still need this as a fallback for homogeneous TP.
-        register_blocks(blocks_data, mamba=False)
+        blocks_data = self._build_fa_local(local_base_addresses, block_size_ratio)
+        logger.debug(
+            "Created %s blocks for src engine %s and rank %s on device id %s",
+            len(blocks_data),
+            self.engine_id,
+            self.tp_rank,
+            self.device_id,
+        )
         if self._has_mamba:
             assert self.num_descs == len(blocks_data)
-            # TODO (ZhanqiuHu): For homogeneous TP (tp_ratio == 1), the 3-read split is
-            # unnecessary — a single conv desc per block suffices.  Consider
+            # TODO (ZhanqiuHu): For homogeneous TP (tp_ratio == 1), the 3-descs split
+            # is unnecessary — a single conv desc per block suffices.  Consider
             # adding a fast path that falls back to the standard 2-region
-            # registration (register_blocks mamba=True) when no hetero-TP
+            # registration (_build_fa_local mamba=True) when no hetero-TP
             # remote has been seen.  Currently we always register 4 regions
             # because local descs are created before knowing the remote TP.
             logger.debug("Registering local Mamba descriptors (4 regions/layer)")
@@ -1154,11 +1262,26 @@ def add_remote_agent(
             )
             return self._remote_agents[engine_id][remote_tp_rank]
 
-        ### Register remote agent metadata
-        if engine_id not in self._tp_size:
-            self._tp_size[engine_id] = remote_tp_size
-        if engine_id not in self._block_size:
-            self._block_size[engine_id] = nixl_agent_meta.block_size
+        ### Register remote engine in TransferTopology (idempotent).
+        assert self.transfer_topo is not None
+        transfer_topo = self.transfer_topo
+        physical_blocks_per_logical = (
+            nixl_agent_meta.physical_blocks_per_logical_kv_block
+        )
+        transfer_info = EngineTransferInfo(
+            remote_tp_size=remote_tp_size,
+            remote_block_size=nixl_agent_meta.block_size,
+            remote_block_len=nixl_agent_meta.block_lens[0],
+            remote_physical_blocks_per_logical=physical_blocks_per_logical,
+        )
+        transfer_topo.register_remote_engine(engine_id, transfer_info)
+        logger.info("Transfer plan: %s", transfer_topo.describe(engine_id))
+
+        self.tp_mappings[engine_id] = compute_tp_mapping(
+            transfer_topology=transfer_topo,
+            remote_tp_size=remote_tp_size,
+            group_spec_types=self._group_spec_types,
+        )
 
         remote_agent_name = self.nixl_wrapper.add_remote_agent(
             nixl_agent_meta.agent_metadata
@@ -1171,16 +1294,10 @@ def add_remote_agent(
         # remote:               | 0| 1| 2| 3| 4| 5| 6| 7| 8| 9|10|11|12|
         # local origin:|          0|          1|          8|         12|
         # local mapped:| 0| 1| 2| 3| 4| 5| 6| 7| 8| 9|10|11|12|13|14|15|
-        assert self.kv_topo is not None
-        kv_topo = self.kv_topo
-        block_size_ratio = kv_topo.block_size_ratio_from_engine_id(engine_id)
+        block_size_ratio = transfer_topo.block_size_ratio(nixl_agent_meta.block_size)
 
         if engine_id not in self.dst_num_blocks:
             self.dst_num_blocks[engine_id] = nixl_agent_meta.num_blocks
-        if self._has_mamba:
-            self._mamba_phys_ratio[engine_id] = compute_mamba_phys_ratio(
-                nixl_agent_meta.ssm_sizes, nixl_agent_meta.block_lens[0]
-            )
 
         # Keep track of remote agent kv caches base addresses.
         self.kv_caches_base_addr[engine_id][remote_tp_rank] = (
@@ -1190,27 +1307,7 @@ def add_remote_agent(
 
         # This is 1 when P and D `--tensor-parallel-size` match. Otherwise,
         # this is the ratio between the two sizes.
-        tp_ratio = self.kv_topo.tp_ratio_from_engine_id(engine_id)
-
-        # Handle tp_size>num_kv_heads: replicate KV cache.
-        indexes_into_remote = (
-            not self.kv_topo.replicates_kv_cache(engine_id) and tp_ratio > 0
-        )
-
-        # Create transfer config (single source of truth for descriptor sizes).
-        if self._has_mamba and engine_id not in self._transfer_configs:
-            self._transfer_configs[engine_id] = HeteroTPTransferConfig(
-                tp_ratio=tp_ratio,
-                K=kv_topo.total_num_kv_heads,
-                d_tp=self.world_size,
-                p_tp=remote_tp_size,
-                d_rank=self.tp_rank,
-                use_mla=self.use_mla,
-                d_block_len=self.block_len_per_layer[0],
-                p_block_len=nixl_agent_meta.block_lens[0],
-                is_blocks_first=kv_topo.is_kv_layout_blocks_first,
-            )
-            logger.info("Created %s", self._transfer_configs[engine_id].describe())
+        tp_ratio = transfer_topo.tp_ratio(remote_tp_size)
 
         logger.debug(
             "Registering remote agent (%s, rank %s) memory regions with tp_ratio %s",
@@ -1219,6 +1316,8 @@ def add_remote_agent(
             tp_ratio,
         )
 
+        plan = self.tp_mappings[engine_id]
+
         ### (Optional) Register local agent memory regions. MLA is not split.
         if (
             tp_ratio < 0
@@ -1228,157 +1327,51 @@ def add_remote_agent(
             # Remote tp_size > local tp_size: read from multiple remote ranks.
             # Logically "split" own regions into |tp_ratio| chunks. Mind that
             # we only do this once per remote tp_size (replica-friendly).
-            abs_tp = -tp_ratio
             self.src_xfer_handles_by_tp_ratio[tp_ratio] = []
 
-            if self._has_mamba:
-                transfer_cfg = self._transfer_configs.get(engine_id)
-                assert transfer_cfg is not None
-                if transfer_cfg.needs_split_handles:
-                    # Mamba-HMA: FA and Mamba use different split factors.
-                    for handle_data in transfer_cfg.compute_split_handle_data(
-                        self.src_blocks_data, self.num_descs, abs_tp
-                    ):
-                        descs = self.nixl_wrapper.get_xfer_descs(
-                            handle_data, self.nixl_memory_type
-                        )
-                        handle = self.nixl_wrapper.prep_xfer_dlist(
-                            "NIXL_INIT_AGENT", descs
-                        )
-                        self.src_xfer_handles_by_tp_ratio[tp_ratio].append(handle)
-
-                    logger.info(
-                        "Mamba-HMA split handles: targets=%s, fa_reads=%s, "
-                        "fa_entry=%s, mamba_reads=%s, num_descs=%s",
-                        transfer_cfg.transfer_targets,
-                        transfer_cfg.physical_fa_num_reads,
-                        transfer_cfg.fa_entry_size,
-                        transfer_cfg.mamba_num_reads,
-                        self.num_descs,
-                    )
-            else:
-                # Original path: uniform divide by abs_tp (non-Mamba-HMA).
-                for i in range(abs_tp):
-                    blocks_data = []
-                    for memory_region in self.src_blocks_data:
-                        addr, local_block_len, own_tp_rank = memory_region
-                        remote_block_len = local_block_len // abs_tp
-                        addr = addr + i * remote_block_len
-                        blocks_data.append((addr, remote_block_len, own_tp_rank))
-                    descs = self.nixl_wrapper.get_xfer_descs(
-                        blocks_data, self.nixl_memory_type
-                    )
-                    handle = self.nixl_wrapper.prep_xfer_dlist("NIXL_INIT_AGENT", descs)
-                    self.src_xfer_handles_by_tp_ratio[tp_ratio].append(handle)
+            for handle_data in self._build_local_splits_from_plan(
+                plan,
+                self.src_blocks_data,
+                self.num_descs,
+            ):
+                descs = self.nixl_wrapper.get_xfer_descs(
+                    handle_data, self.nixl_memory_type
+                )
+                handle = self.nixl_wrapper.prep_xfer_dlist("NIXL_INIT_AGENT", descs)
+                self.src_xfer_handles_by_tp_ratio[tp_ratio].append(handle)
 
         ### Register remote agent memory regions
-        blocks_data = []
-        # With homogeneous TP, D pulls the whole kv cache from corresponding
-        # rank. With heterogeneous TP, prepare the descriptors by splitting the
-        # P KV cache along kv_head dim, of D worker's kv_head size (D>P).
+        # With homogeneous TP, D pulls the whole kv cache from corresponding rank. With
+        # heterogeneous TP, prepare the descriptors by splitting the P KV cache along
+        # kv_head dim, of D worker's kv_head size (D>P).
         # Eg. PTP1 DTP2 => P0 KV:[block0-KV_0 | block0-KV_1..].
 
         # Register all remote blocks, but only the corresponding kv heads.
-        def register_remote_blocks(
-            blocks_data: list[tuple[int, int, int]], mamba: bool
-        ):
-            for i, base_addr in enumerate(nixl_agent_meta.kv_caches_base_addr):
-                # Read our whole local region size from remote.
-                local_block_len = self.get_backend_aware_kv_block_len(
-                    layer_idx=i, first_split=True, mamba_view=mamba
-                )
-                remote_kv_block_len = local_block_len // block_size_ratio
-                if block_size_ratio > 1:
-                    # using remote kv_block_len as transfer unit
-                    local_block_len = remote_kv_block_len
-
-                if tp_ratio < 0 and not self.use_mla:
-                    # Remote tp is bigger: read a chunk of local region from remote
-                    local_block_len = local_block_len // (-tp_ratio)
-                rank_offset = (
-                    self.tp_rank % tp_ratio * remote_kv_block_len
-                    if indexes_into_remote
-                    else 0
-                )
-
-                # Assume same num_blocks for mamba and fa
-                num_blocks = (
-                    nixl_agent_meta.num_blocks
-                    if not mamba
-                    else nixl_agent_meta.num_blocks
-                    // self._physical_blocks_per_logical_kv_block
-                )
-                page_size = nixl_agent_meta.block_lens[i] * (
-                    1 if not mamba else self._physical_blocks_per_logical_kv_block
-                )
-                for block_id in range(num_blocks):
-                    block_offset = block_id * page_size
-                    # For each block, grab the heads chunk belonging to rank_i
-                    # of size remote_nheads // tp_ratio, which correspond to
-                    # self.block_len == remote_block_len//tp_ratio bytes.
-                    addr = base_addr + block_offset + rank_offset
-                    # (addr, len, device id)
-                    blocks_data.append(
-                        (addr, local_block_len, nixl_agent_meta.device_id)
-                    )
-
-                if kv_topo.is_kv_layout_blocks_first:
-                    # With FlashInfer index V separately to allow head splitting.
-                    second_split = self.get_backend_aware_kv_block_len(
-                        layer_idx=i, first_split=False, mamba_view=mamba
-                    )
-                    # Apply the same scaling as local_block_len above for when we read
-                    # a chunk of local V from `tp_ratio` separate remote workers.
-                    if tp_ratio < 0 and not self.use_mla:
-                        second_split = second_split // (-tp_ratio)
-                    for block_id in range(num_blocks):
-                        block_offset = block_id * page_size
-                        addr = base_addr + block_offset + rank_offset
-                        # Hop over the first split of remote page: either K or Conv.
-                        if mamba:
-                            v_addr = addr + nixl_agent_meta.ssm_sizes[0]
-                        else:
-                            v_addr = addr + nixl_agent_meta.block_lens[i] // 2
-                        blocks_data.append(
-                            (v_addr, second_split, nixl_agent_meta.device_id)
-                        )
-
-            logger.debug(
-                "Created %s blocks for dst engine %s"
-                " with remote rank %s and local rank %s",
-                len(blocks_data),
-                engine_id,
-                remote_tp_rank,
-                self.tp_rank,
-            )
-
+        blocks_data = self._build_fa_remote(
+            plan,
+            nixl_agent_meta,
+            block_size_ratio,
+        )
+        logger.debug(
+            "Created %s blocks for dst engine %s with remote rank %s and local rank %s",
+            len(blocks_data),
+            engine_id,
+            remote_tp_rank,
+            self.tp_rank,
+        )
         if self._has_mamba:
-            # Mamba-HMA: separate FA registration with GQA-aware sizing,
-            # plus mamba 3-read registration for the Mamba "view" of the
-            # same KV cache tensors.
             logger.debug(
                 "Registering remote Mamba blocks for engine %s rank %s",
                 engine_id,
                 remote_tp_rank,
             )
-            transfer_cfg = self._transfer_configs.get(engine_id)
-            assert transfer_cfg is not None
-            blocks_data.extend(
-                self._build_fa_remote_for_mamba(
-                    nixl_agent_meta,
-                    transfer_cfg,
-                    block_size_ratio,
-                    kv_topo,
-                )
-            )
             blocks_data.extend(
                 self._build_mamba_remote(
                     nixl_agent_meta,
                     tp_ratio,
+                    transfer_info,
                 )
             )
-        else:
-            register_remote_blocks(blocks_data, mamba=False)
 
         # Register with NIXL.
         descs = self.nixl_wrapper.get_xfer_descs(blocks_data, self.nixl_memory_type)
@@ -1404,18 +1397,20 @@ def _validate_remote_agent_handshake(
         """
         remote_engine_id = nixl_agent_meta.engine_id
 
-        assert self._tp_size[remote_engine_id] == remote_tp_size
-        assert self.kv_topo is not None
+        assert self.transfer_topo is not None
+        remote_info = self.transfer_topo.get_engine_info(remote_engine_id)
+        assert remote_info.remote_tp_size == remote_tp_size
 
-        tp_ratio = self.kv_topo.tp_ratio_from_engine_id(remote_engine_id)
-        block_size_ratio = self.kv_topo.block_size_ratio_from_engine_id(
-            remote_engine_id
+        tp_ratio = self.transfer_topo.tp_ratio(remote_tp_size)
+        block_size_ratio = self.transfer_topo.block_size_ratio(
+            nixl_agent_meta.block_size
         )
         # num_kv_heads > tp_size with P_TP > D_TP not supported for non-mamba.
         # Mamba models can have replicated FA KV with tp_ratio < 0.
-        if not self._has_mamba:
+        # MLA models do not need to handle kv replication.
+        if not self.use_mla and not self._has_mamba:
             assert not (
-                tp_ratio < 0 and self.kv_topo.is_kv_replicated(remote_engine_id)
+                tp_ratio < 0 and self.transfer_topo.is_kv_replicated(remote_engine_id)
             )
 
         if self._is_hma_required:
@@ -1468,7 +1463,7 @@ def _validate_remote_agent_handshake(
         if (
             abs(tp_ratio) != 1
             and not self.use_mla
-            and not self.kv_topo.is_kv_replicated(remote_engine_id)
+            and not self.transfer_topo.is_kv_replicated(remote_engine_id)
             and kv_cache_layout != "HND"
             and not self.enable_permute_local_kv
         ):
@@ -1479,7 +1474,7 @@ def _validate_remote_agent_handshake(
 
         # Block len can only vary across layers when using MLA.
         remote_block_len = nixl_agent_meta.block_lens[0]
-        if self.use_mla or self.kv_topo.is_kv_replicated(remote_engine_id):
+        if self.use_mla or self.transfer_topo.is_kv_replicated(remote_engine_id):
             # With replicated KV cache, only the number of blocks can differ.
             # TODO (ZhanqiuHu): For mamba models, validate FA and mamba
             # block_lens separately.
@@ -1595,7 +1590,7 @@ def post_process_device_kv_on_receive(
         if len(self.device_kv_caches) == 0:
             return
         assert block_size_ratio >= 1, "Only nP < nD supported currently."
-        assert self.kv_topo is not None
+        assert self.transfer_topo is not None
         if self.enable_permute_local_kv and block_size_ratio > 1:
             logger.debug(
                 "Post-processing device kv cache on receive by converting "
@@ -1615,7 +1610,7 @@ def post_process_device_kv_on_receive(
                 block_size_ratio,
             )
 
-        split_k_and_v = self.kv_topo.split_k_and_v
+        split_k_and_v = self.transfer_topo.split_k_and_v
 
         for block_ids in block_ids_list:
             indices = torch.tensor(block_ids, device=self.device_type, dtype=torch.long)
@@ -1662,7 +1657,7 @@ def get_finished(self) -> tuple[set[str], set[str]]:
         The scheduler process (via the MultiprocExecutor) will use this output
         to track which workers are done.
         """
-        assert self.kv_topo is not None
+        assert self.transfer_topo is not None
         done_sending = self._get_new_notifs()
         done_recving = self._pop_done_transfers(self._recving_transfers)
 
@@ -1690,8 +1685,9 @@ def get_finished(self) -> tuple[set[str], set[str]]:
                 self.sync_recved_kv_to_device(req_id, meta)
 
             # post processing for heteroblocksize
-            block_size_ratio = self.kv_topo.block_size_ratio_from_engine_id(
-                meta.remote.engine_id
+            remote_info = self.transfer_topo.get_engine_info(meta.remote.engine_id)
+            block_size_ratio = self.transfer_topo.block_size_ratio(
+                remote_info.remote_block_size
             )
             if not self.use_mla and (
                 block_size_ratio > 1 or self.enable_permute_local_kv
@@ -1742,7 +1738,7 @@ def _get_new_notifs(self) -> set[str]:
         are reading from the same producer (heterogeneous TP scenario), wait
         for all consumers to be done pulling.
         """
-        assert self.kv_topo is not None
+        assert self.transfer_topo is not None
         notified_req_ids: set[str] = set()
         for notifs in self.nixl_wrapper.get_new_notifs().values():
             for notif in notifs:
@@ -1761,7 +1757,7 @@ def _get_new_notifs(self) -> set[str]:
 
                 # NOTE: `tp_ratio` is the opposite when swapping local<>remote
                 n_consumers = int(tp_size)
-                tp_ratio = self.kv_topo.tp_ratio(n_consumers)
+                tp_ratio = self.transfer_topo.tp_ratio(n_consumers)
 
                 # Number of reads *per producer* to wait for.
                 # When remote D TP > local P TP we expect `tp_ratio` reads.
@@ -1902,31 +1898,51 @@ def start_load_kv(self, metadata: NixlConnectorMetadata):
                 self._reqs_to_send[req_id] = expiration_time
 
     def _read_blocks_for_req(self, req_id: str, meta: ReqMeta):
-        assert meta.remote is not None and self.kv_topo is not None
-        remote_ranks = self.kv_topo.get_target_remote_ranks_from_engine_id(
-            meta.remote.engine_id
+        assert meta.remote is not None and self.transfer_topo is not None
+        engine_id = meta.remote.engine_id
+        plan = self.tp_mappings[engine_id]
+        remote_info = self.transfer_topo.get_engine_info(engine_id)
+        tp_ratio = self.transfer_topo.tp_ratio(remote_info.remote_tp_size)
+
+        meta.remote.block_ids = self._logical_to_remote_kernel_block_ids(
+            meta.remote.block_ids,
+            remote_info.remote_physical_blocks_per_logical,
         )
-        tp_ratio = self.kv_topo.tp_ratio_from_engine_id(meta.remote.engine_id)
-
-        if self._has_mamba:
-            # Expand remote logical → kernel block IDs.
-            meta.remote.block_ids = self._logical_to_remote_kernel_block_ids(
-                meta.remote.block_ids,
-                self._mamba_phys_ratio[meta.remote.engine_id],
+        remote_block_ids = meta.remote.block_ids
+        local_block_ids = meta.local_physical_block_ids
+        num_groups = len(local_block_ids)
+        read_specs = [
+            ReadSpec(
+                remote_rank=rank,
+                local_block_ids=[
+                    list(local_block_ids[g])
+                    if rank in plan.source_ranks_per_group[g]
+                    else []
+                    for g in range(num_groups)
+                ],
+                remote_block_ids=[
+                    list(remote_block_ids[g])
+                    if rank in plan.source_ranks_per_group[g]
+                    else []
+                    for g in range(num_groups)
+                ],
             )
+            for rank in plan.all_source_ranks
+        ]
+
         # D may have to perform multiple reads from different remote ranks.
-        for i, remote_rank in enumerate(remote_ranks):
-            if self.use_mla and tp_ratio < 0 and i > 0:
-                # MLA opt: when P TP > D TP, only a single read is executed for
-                # the first remote rank (cache is duplicated)..
-                break
+        # MLA opt: when P TP > D TP, only a single read is executed for
+        # the first remote rank (cache is duplicated)..
+        if self.use_mla and tp_ratio < 0:
+            assert len(read_specs) == 1
 
-            remote_block_size = self.kv_topo.remote_block_size[meta.remote.engine_id]
+        for i, spec in enumerate(read_specs):
+            remote_block_size = remote_info.remote_block_size
             logger.debug(
                 "Remote agent %s available, calling _read_blocks"
                 " on remote rank %s with remote block size %s for req %s",
                 meta.remote.engine_id,
-                remote_rank,
+                spec.remote_rank,
                 remote_block_size,
                 req_id,
             )
@@ -1945,50 +1961,33 @@ def _read_blocks_for_req(self, req_id: str, meta: ReqMeta):
 
             # Destination handle: remote_engine_id -> remote_rank -> handle.
             remote_xfer_side_handle = self.dst_xfer_side_handles[meta.remote.engine_id][
-                remote_rank
+                spec.remote_rank
             ]
 
-            local_ids: BlockIds = meta.local_physical_block_ids
-            remote_ids: BlockIds = meta.remote.block_ids
-            if self._has_mamba:
-                # Mamba-HMA: zero out FA groups for P ranks outside fa_read_targets.
-                transfer_cfg = self._transfer_configs.get(meta.remote.engine_id)
-                assert transfer_cfg is not None
-                local_ids, remote_ids = transfer_cfg.filter_block_ids_for_rank(
-                    remote_rank,
-                    local_ids,
-                    remote_ids,
-                    self._is_mamba_group,
-                )
-
             self._read_blocks(
+                read_spec=spec,
                 request_id=req_id,
                 dst_engine_id=meta.remote.engine_id,
                 remote_request_id=meta.remote.request_id,
-                local_block_ids=local_ids,
-                remote_block_ids=remote_ids,
-                remote_rank=remote_rank,
                 local_xfer_side_handle=local_xfer_side_handle,
                 remote_xfer_side_handle=remote_xfer_side_handle,
             )
 
-            if self.use_mla and tp_ratio < 0:
-                # ..but we still need to notify the other remote ranks that we
-                # have the blocks we need so they can update the request state.
-                notif_id = f"{req_id}:{self.world_size}".encode()
-                remote_agents = self._remote_agents[meta.remote.engine_id]
-                for rank_to_notify, agent in remote_agents.items():
-                    if rank_to_notify != remote_rank:
-                        self.nixl_wrapper.send_notif(agent, notif_msg=notif_id)
+        if self.use_mla and tp_ratio < 0 and read_specs:
+            # ..but we still need to notify the other remote ranks that we
+            # have the blocks we need so they can update the request state.
+            notif_id = f"{meta.remote.request_id}:{self.world_size}".encode()
+            remote_agents = self._remote_agents[meta.remote.engine_id]
+            for rank_to_notify, agent in remote_agents.items():
+                if rank_to_notify != read_specs[0].remote_rank:
+                    self.nixl_wrapper.send_notif(agent, notif_msg=notif_id)
 
     def _read_blocks(
         self,
-        local_block_ids: BlockIds,
-        remote_block_ids: BlockIds,
+        read_spec: ReadSpec,
         dst_engine_id: str,
         request_id: str,
         remote_request_id: str,
-        remote_rank: int,
         local_xfer_side_handle: int,
         remote_xfer_side_handle: int,
     ):
@@ -1996,8 +1995,15 @@ def _read_blocks(
         Post a READ point-to-point xfer request from a single local worker to
         a single remote worker.
         """
-        assert self.kv_topo is not None
-        block_size_ratio = self.kv_topo.block_size_ratio_from_engine_id(dst_engine_id)
+        assert self.transfer_topo is not None
+        remote_rank = read_spec.remote_rank
+        local_block_ids = read_spec.local_block_ids
+        remote_block_ids = read_spec.remote_block_ids
+
+        remote_info = self.transfer_topo.get_engine_info(dst_engine_id)
+        block_size_ratio = self.transfer_topo.block_size_ratio(
+            remote_info.remote_block_size
+        )
         if block_size_ratio > 1:
             # TODO (NickLucche) assume HMA is off. Change to handle multiple KV groups.
             assert not self._is_hma_required
@@ -2062,16 +2068,14 @@ def _read_blocks(
             == len(local_block_ids)
             == len(self.kv_cache_config.kv_cache_groups)
         )
+        # Partial prefix cache hit: just read uncomputed blocks.
+        # Skip mamba groups — their blocks represent full state (conv+ssm),
+        # not per-token data, so trimming would corrupt the transfer.
         remote_block_ids = list(remote_block_ids)
         for i, remote_group in enumerate(remote_block_ids):
-            num_remote_blocks = len(remote_group)
             num_local_blocks = len(local_block_ids[i])
-            if not self._is_mamba_group[i]:
-                assert num_local_blocks <= num_remote_blocks
-            # Partial prefix cache hit: just read uncomputed blocks.
-            # Skip mamba groups — their blocks represent full state (conv+ssm),
-            # not per-token data, so trimming would corrupt the transfer.
-            if num_local_blocks < num_remote_blocks and not self._is_mamba_group[i]:
+            assert num_local_blocks <= len(remote_group)
+            if num_local_blocks < len(remote_group):
                 remote_block_ids[i] = remote_group[-num_local_blocks:]
 
         # NOTE (nicolo) With homogeneous TP, each TP worker loads KV from
@@ -2079,14 +2083,17 @@ def _read_blocks(
         # workers will issue xfers to parts of the P worker remote kv caches.
 
         # Get descs ids.
-        remote_block_descs_ids = self._get_block_descs_ids(
-            dst_engine_id,
-            remote_block_ids,
+        remote_block_descs_ids = self._compute_desc_ids(
+            block_ids=remote_block_ids,
+            dst_num_blocks=self.dst_num_blocks[dst_engine_id],
+            block_size_ratio=None,
+            physical_blocks_per_logical=remote_info.remote_physical_blocks_per_logical,
         )
-        local_block_descs_ids = self._get_block_descs_ids(
-            self.engine_id,
-            local_block_ids,
+        local_block_descs_ids = self._compute_desc_ids(
+            block_ids=local_block_ids,
+            dst_num_blocks=self.dst_num_blocks[self.engine_id],
             block_size_ratio=block_size_ratio,
+            physical_blocks_per_logical=self._physical_blocks_per_logical_kv_block,
         )
 
         assert len(local_block_descs_ids) == len(remote_block_descs_ids)
@@ -2148,63 +2155,6 @@ def get_mapped_blocks(
 
         return mapped_2d.flatten().astype(np.int64)
 
-    def _get_block_descs_ids(
-        self,
-        engine_id: str,
-        block_ids: BlockIds,
-        block_size_ratio: float | None = None,
-    ) -> np.ndarray:
-        """
-        Get the descs ids for a set of block ids.
-        When HMA is enabled number of descriptors across kv cache groups might differ.
-        A single flattened array is returned for all groups anyway.
-        """
-        region_ids = np.arange(self.num_regions)
-
-        # NOTE (NickLucche) With HMA, every kv group has the same number of layers and
-        # layers from different groups share the same kv tensor.
-        # eg block_ids=[[1, 2], [3]]->blocks [1, 2] need to be read across all regions,
-        # same for [3], but group0-group1 blocks will always differ (different areas).
-        # Therefore we can just flatten the block_ids and compute the descs ids for all
-        # groups at once.
-        num_blocks = self.dst_num_blocks[engine_id]
-        if block_size_ratio is not None:
-            num_blocks = int(num_blocks * block_size_ratio)
-
-        # Compute desc ids per group using the right stride: FA descs have
-        # num_blocks entries per region (kernel granularity), SSM descs have
-        # logical_blocks entries per region (no kernel splitting).
-        region_ids = region_ids[:, None]
-        if not self._has_mamba:
-            block_ids = np.concatenate(block_ids)[None, :]
-            descs_ids = region_ids * num_blocks + block_ids
-            return descs_ids.flatten()
-        else:
-            # NOTE (NickLucche) SSM and Attention blocks regions can be exchanged
-            # arbitrarily by manager. Therefore, descs are duplicated for SSM and
-            # Attention like so:
-            # desc_handle->[descs_fa (all regions) | descs_ssm (all regions)].
-            # This is like having two "low-level views" of the same storage.
-            # `num_fa_descs` offset must be computed per-engine since P and D can
-            # have different num_blocks (and thus different FA descs counts).
-            ratio = self._mamba_phys_ratio[engine_id]
-            logical_blocks = num_blocks // ratio
-            num_fa_descs = self.num_regions * num_blocks
-            # 3-read mamba: 4 regions per unique cache tensor (x, B, C, ssm).
-            mamba_region_ids = np.arange(len(self.block_len_per_layer) * 4)[:, None]
-            all_descs = []
-            for i, group in enumerate(block_ids):
-                group_arr = np.asarray(group)[None, :]
-                if self._is_mamba_group[i]:
-                    all_descs.append(
-                        (
-                            mamba_region_ids * logical_blocks + group_arr + num_fa_descs
-                        ).flatten()
-                    )
-                else:
-                    all_descs.append((region_ids * num_blocks + group_arr).flatten())
-            return np.concatenate(all_descs)
-
     def _logical_to_kernel_block_ids(self, block_ids: BlockIds) -> BlockIds:
         """
         Convert logical block ids to kernel physical block ids.
@@ -2231,21 +2181,22 @@ def _logical_to_kernel_block_ids(self, block_ids: BlockIds) -> BlockIds:
         ]
 
     def _logical_to_remote_kernel_block_ids(
-        self, block_ids: BlockIds, remote_ratio: int
+        self, block_ids: BlockIds, remote_physical_per_logical: int
     ) -> BlockIds:
         """Map logical block IDs to physical kernel block IDs on the remote.
 
         Args:
             block_ids: per-group lists of logical block IDs.
-            remote_ratio: remote engine's physical blocks per logical block.
+            remote_physical_per_logical: remote engine's physical blocks
+                per logical block.
 
         Returns:
             Same structure with FA groups expanded (each logical block L
-            becomes kernel blocks [L*remote_ratio .. L*remote_ratio +
-            local_ratio - 1]).  Mamba groups are passed through unchanged.
+            becomes kernel blocks [L*ratio .. L*ratio + local_ratio - 1]).
+            Mamba groups are passed through unchanged.
         """
         local_ratio = self._physical_blocks_per_logical_kv_block
-        if remote_ratio == 1:
+        if remote_physical_per_logical == 1:
             return block_ids
         local_arange = np.arange(local_ratio).reshape(1, -1)
         group_specs = self.kv_cache_config.kv_cache_groups
@@ -2253,7 +2204,7 @@ def _logical_to_remote_kernel_block_ids(
         for i, group in enumerate(block_ids):
             if not isinstance(group_specs[i].kv_cache_spec, MambaSpec):
                 arr = np.array(group).reshape(-1, 1)
-                expanded = (arr * remote_ratio + local_arange).flatten()
+                expanded = (arr * remote_physical_per_logical + local_arange).flatten()
                 result.append(expanded.tolist())
             else:
                 # Mamba blocks are 1:1 logical-to-physical (no expansion).
@@ -2293,12 +2244,9 @@ def get_backend_aware_kv_block_len(
            +-------------------+         +--------------------+
            |1st_split-2nd_split|         |1st_split-2nd_split |
         """
-        assert self.kv_topo is not None
-        if self.kv_topo.is_kv_layout_blocks_first:
-            # For indexing only half (either just the K or V part).
+        assert self.transfer_topo is not None
+        if self.transfer_topo.is_kv_layout_blocks_first:
             if mamba_view:
-                # NOTE (NickLucche) Mamba Opt: this is already skipping the padding so
-                # we're only transferring the minimum required bytes.
                 block_len = self._mamba_ssm_size[not first_split]
             else:
                 block_len = self.block_len_per_layer[layer_idx] // 2
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/offloading/common.py b/vllm/distributed/kv_transfer/kv_connector/v1/offloading/common.py
index 06a727a27b55..c5a251a2a515 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/offloading/common.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/offloading/common.py
@@ -1,15 +1,60 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 
-from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorMetadata
+from vllm.distributed.kv_transfer.kv_connector.v1.base import (
+    KVConnectorMetadata,
+    KVConnectorWorkerMetadata,
+)
 from vllm.v1.kv_offload.worker.worker import TransferSpec
 
 ReqId = str
 
 
+@dataclass
+class TransferJob:
+    """A transfer job bundling request context with transfer spec.
+
+    Used for both loads and stores, keyed by scheduler-assigned job ID.
+    The worker reports the job ID back when the transfer finishes,
+    and the scheduler processes the completion.
+    """
+
+    req_id: ReqId
+    transfer_spec: TransferSpec
+
+
 @dataclass
 class OffloadingConnectorMetadata(KVConnectorMetadata):
-    reqs_to_load: dict[ReqId, TransferSpec]
-    reqs_to_store: dict[ReqId, TransferSpec]
-    reqs_to_flush: set[str] | None = None
+    # Keyed by scheduler-assigned job IDs.
+    load_jobs: dict[int, TransferJob]
+    store_jobs: dict[int, TransferJob]
+    jobs_to_flush: set[int] | None = None
+
+
+@dataclass
+class OffloadingWorkerMetadata(KVConnectorWorkerMetadata):
+    """Worker -> Scheduler metadata for completed transfer jobs.
+
+    Each worker reports {job_id: 1} for newly completed transfer jobs
+    (load or store). aggregate() sums counts across workers within a step.
+    The scheduler accumulates across steps and processes
+    a transfer completion only when count reaches num_workers.
+    """
+
+    completed_jobs: dict[int, int] = field(default_factory=dict)
+
+    def mark_completed(self, job_id: int) -> None:
+        """Record a transfer job completion from this worker."""
+        self.completed_jobs[job_id] = 1
+
+    def aggregate(
+        self, other: "KVConnectorWorkerMetadata"
+    ) -> "KVConnectorWorkerMetadata":
+        assert isinstance(other, OffloadingWorkerMetadata)
+
+        merged = dict(self.completed_jobs)
+        for job_id, v in other.completed_jobs.items():
+            merged[job_id] = merged.get(job_id, 0) + v
+
+        return OffloadingWorkerMetadata(completed_jobs=merged)
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/offloading/scheduler.py b/vllm/distributed/kv_transfer/kv_connector/v1/offloading/scheduler.py
index 52e3ce09eb88..137eaef9788c 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/offloading/scheduler.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/offloading/scheduler.py
@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from collections import defaultdict
-from collections.abc import Iterable
+from collections.abc import Iterable, Sequence
 from dataclasses import dataclass, field
 from itertools import islice
 from typing import Any, NamedTuple
@@ -11,40 +10,87 @@
 from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorMetadata
 from vllm.distributed.kv_transfer.kv_connector.v1.offloading.common import (
     OffloadingConnectorMetadata,
+    OffloadingWorkerMetadata,
     ReqId,
+    TransferJob,
 )
 from vllm.logger import init_logger
+from vllm.utils.math_utils import cdiv
 from vllm.v1.core.kv_cache_manager import KVCacheBlocks
 from vllm.v1.core.sched.output import SchedulerOutput
-from vllm.v1.kv_offload.abstract import (
+from vllm.v1.kv_cache_interface import (
+    FullAttentionSpec,
+    KVCacheSpec,
+    MambaSpec,
+    SlidingWindowSpec,
+)
+from vllm.v1.kv_offload.base import (
+    GPULoadStoreSpec,
     OffloadingManager,
+    OffloadingSpec,
     OffloadKey,
+    ReqContext,
     get_offload_block_hash,
     make_offload_key,
 )
-from vllm.v1.kv_offload.mediums import GPULoadStoreSpec
-from vllm.v1.kv_offload.spec import OffloadingSpec
-from vllm.v1.kv_offload.worker.worker import TransferSpec
 from vllm.v1.outputs import KVConnectorOutput
 from vllm.v1.request import Request
 
 logger = init_logger(__name__)
 
 
+@dataclass(slots=True)
+class TransferJobStatus:
+    """Tracks scheduler-side state for a single transfer job."""
+
+    req_id: ReqId
+    # Number of workers still pending. Starts at num_workers,
+    # decremented as each worker reports completion. Job is done at 0.
+    pending_count: int
+    # Offload keys this job covers; passed to manager.complete_*().
+    keys: set[OffloadKey]
+    is_store: bool
+    # Store src block IDs whose ref_cnt protects them while the request
+    # runs. Only registered in _block_id_to_pending_jobs on request_finished.
+    non_sliding_window_block_ids: list[int] | None = None
+    # Store src block IDs that may be freed before the request finishes.
+    # Registered in _block_id_to_pending_jobs at store creation time.
+    sliding_window_block_ids: list[int] | None = None
+
+
 class GroupOffloadConfig(NamedTuple):
     group_idx: int
     gpu_block_size: int
     offloaded_block_size: int
     hash_block_size_factor: int
+    # None below means full attention
+    sliding_window_size_in_blocks: int | None
+
+
+def get_sliding_window_size_in_blocks(
+    kv_cache_spec: KVCacheSpec, offloaded_block_size: int
+) -> int | None:
+    if isinstance(kv_cache_spec, SlidingWindowSpec):
+        assert kv_cache_spec.sliding_window > 0
+        return cdiv(kv_cache_spec.sliding_window, offloaded_block_size)
+
+    if isinstance(kv_cache_spec, MambaSpec):
+        # Mamba depends on a single state
+        return 1
+
+    assert isinstance(kv_cache_spec, FullAttentionSpec)
+    return None
 
 
 class SchedulerOffloadConfig(NamedTuple):
     kv_group_configs: tuple[GroupOffloadConfig, ...]
     block_size_factor: int
+    num_workers: int
 
     @classmethod
     def from_spec(cls, spec: OffloadingSpec) -> "SchedulerOffloadConfig":
         return cls(
+            num_workers=spec.vllm_config.parallel_config.world_size,
             kv_group_configs=tuple(
                 GroupOffloadConfig(
                     group_idx=idx,
@@ -54,6 +100,10 @@ def from_spec(cls, spec: OffloadingSpec) -> "SchedulerOffloadConfig":
                         (gpu_block_size * spec.block_size_factor)
                         // spec.hash_block_size
                     ),
+                    sliding_window_size_in_blocks=get_sliding_window_size_in_blocks(
+                        spec.kv_cache_config.kv_cache_groups[idx].kv_cache_spec,
+                        gpu_block_size * spec.block_size_factor,
+                    ),
                 )
                 for idx, gpu_block_size in enumerate(spec.gpu_block_size)
             ),
@@ -67,6 +117,9 @@ class RequestGroupState:
     block_ids: list[int] = field(default_factory=list)
     # index of next block (of size offloaded_block_size) to offload
     next_stored_block_idx: int = 0
+    # number of offloaded blocks hit (including GPU prefix cache)
+    # when the request first started
+    num_hit_blocks: int = 0
 
 
 @dataclass(slots=True)
@@ -74,13 +127,18 @@ class RequestOffloadState:
     config: SchedulerOffloadConfig
     req: Request
     group_states: tuple[RequestGroupState, ...] = field(init=False)
+    req_context: ReqContext = field(init=False)
     # number of hits in the GPU cache
     num_locally_computed_tokens: int = 0
+    # In-flight job IDs. Per the connector's invariant, at any given time
+    # this contains either a single load job, or one or more store jobs.
+    transfer_jobs: set[int] = field(default_factory=set)
 
     def __post_init__(self) -> None:
         self.group_states = tuple(
             RequestGroupState() for _ in self.config.kv_group_configs
         )
+        self.req_context = ReqContext(kv_transfer_params=self.req.kv_transfer_params)
 
     def update_offload_keys(self) -> None:
         for group_config, group_state in zip(
@@ -108,6 +166,21 @@ def update_block_id_groups(
         for group_state, new_blocks in zip(self.group_states, new_block_id_groups):
             group_state.block_ids.extend(new_blocks)
 
+    def advance_stored_idx(self, num_offloadable_tokens: int) -> None:
+        for group_config, group_state in zip(
+            self.config.kv_group_configs, self.group_states
+        ):
+            num_blocks = num_offloadable_tokens // group_config.offloaded_block_size
+            group_state.next_stored_block_idx = num_blocks
+
+    def update_num_hit_blocks(self, num_cached_tokens: int) -> None:
+        for group_config, group_state in zip(
+            self.config.kv_group_configs, self.group_states
+        ):
+            group_state.num_hit_blocks = (
+                num_cached_tokens // group_config.offloaded_block_size
+            )
+
 
 class OffloadingConnectorScheduler:
     """Implementation of Scheduler side methods"""
@@ -116,18 +189,259 @@ def __init__(self, spec: OffloadingSpec):
         self.config = SchedulerOffloadConfig.from_spec(spec)
         self.manager: OffloadingManager = spec.get_manager()
 
+        full_attention_groups: list[int] = []
+        sliding_window_groups: list[int] = []
+        for group_config in self.config.kv_group_configs:
+            if group_config.sliding_window_size_in_blocks is None:
+                full_attention_groups.append(group_config.group_idx)
+            else:
+                sliding_window_groups.append(group_config.group_idx)
+
+        # sort sliding window groups by window size in decreasing order
+        def _sliding_window_sort_key(i: int) -> int:
+            val = self.config.kv_group_configs[i].sliding_window_size_in_blocks
+            assert val is not None
+            return val
+
+        sliding_window_groups.sort(key=_sliding_window_sort_key, reverse=True)
+
+        # used by _lookup
+        self._sliding_window_groups: tuple[int, ...] = tuple(sliding_window_groups)
+        self._lookup_groups = tuple(full_attention_groups) + self._sliding_window_groups
+
         self._req_status: dict[ReqId, RequestOffloadState] = {}
-        # requests to load for the current scheduler step
-        self._reqs_to_load: dict[ReqId, TransferSpec] = {}
+        self._current_batch_load_jobs: dict[int, TransferJob] = {}
+        self._current_batch_jobs_to_flush: set[int] = set()
         # if GPU prefix caching is enabled,
         # track loaded blocks to avoid redundant loads
         self._blocks_being_loaded: set[OffloadKey] | None = (
             set() if spec.vllm_config.cache_config.enable_prefix_caching else None
         )
 
-        # request ID -> set(offload keys being stored/loaded)
-        self._reqs_being_stored = defaultdict[ReqId, set[OffloadKey]](set)
-        self._reqs_being_loaded = defaultdict[ReqId, set[OffloadKey]](set)
+        # Job ID counter shared by loads and stores.
+        self._job_counter: int = 0
+        self._jobs: dict[int, TransferJobStatus] = {}
+
+        # block_id -> pending store job_ids. Used to track jobs that needs
+        # flushing in case a block is re-allocated by the KV cache manager.
+        # Populated only for finished requests (running-request blocks are
+        # protected by their ref_cnt) and for sliding window blocks (which can
+        # be freed before a request finishes).
+        self._block_id_to_pending_jobs: dict[int, set[int]] = {}
+
+    def _generate_job_id(self) -> int:
+        job_id = self._job_counter
+        self._job_counter += 1
+        return job_id
+
+    def _remove_pending_job(self, job_id: int, block_ids: list[int] | None) -> None:
+        for bid in block_ids or ():
+            pending = self._block_id_to_pending_jobs[bid]
+            pending.remove(job_id)
+            if not pending:
+                del self._block_id_to_pending_jobs[bid]
+
+    def _maximal_prefix_lookup(
+        self, keys: Iterable[OffloadKey], req_context: ReqContext
+    ) -> int | None:
+        """Return the number of consecutive offloaded blocks from the start,
+        or None if the backend deferred a lookup."""
+        hit_count = 0
+        defer_lookup = False
+        for key in keys:
+            result = self.manager.lookup(key, req_context)
+            if result is None:
+                defer_lookup = True
+                # continue lookup to allow manager to kick-off async lookups
+                # for all blocks (until a miss is detected)
+                result = True
+            if not result:
+                break
+            hit_count += 1
+        return hit_count if not defer_lookup else None
+
+    def _sliding_window_lookup(
+        self,
+        keys: Sequence[OffloadKey],
+        sliding_window_size: int,
+        req_context: ReqContext,
+    ) -> int | None:
+        """Return the end index (in `keys`) of the last run of
+        `sliding_window_size` consecutive hits, scanning from the end.
+        Returns 0 on miss, None if the backend deferred a lookup."""
+        defer_lookup = False
+        consecutive_hits = 0
+        for idx in range(len(keys) - 1, -1, -1):
+            result = self.manager.lookup(keys[idx], req_context)
+            if result is None:
+                defer_lookup = True
+                # continue lookup to allow manager to kick-off async lookups
+                # for all blocks (until a hit is detected)
+                result = False
+            if not result:
+                consecutive_hits = 0
+            else:
+                consecutive_hits += 1
+                if consecutive_hits == sliding_window_size:
+                    return idx + sliding_window_size if not defer_lookup else None
+        return consecutive_hits if not defer_lookup else None
+
+    def _touch(self, req_status: RequestOffloadState):
+        for group_config, group_state in zip(
+            self.config.kv_group_configs, req_status.group_states
+        ):
+            if group_config.sliding_window_size_in_blocks is None:
+                self.manager.touch(group_state.offload_keys, req_status.req_context)
+            else:
+                # we aim to keep just blocks that are necessary to hit
+                # the original request (+ decoded blocks)
+                blocks_to_skip = max(
+                    0,
+                    group_state.num_hit_blocks
+                    - group_config.sliding_window_size_in_blocks,
+                )
+                self.manager.touch(
+                    group_state.offload_keys[blocks_to_skip:],
+                    req_status.req_context,
+                )
+
+    def _lookup(self, req_status: RequestOffloadState) -> int | None:
+        """
+        Find how many tokens beyond num_locally_computed_tokens can be loaded.
+
+        Iterates full-attention groups first (prefix lookup), then sliding-window
+        groups (suffix lookup). Each group may tighten max_hit_size_tokens, which
+        can invalidate an earlier group's result, so the loop re-runs when that
+        happens until num_hit_tokens converges.
+        """
+        num_computed_tokens = req_status.num_locally_computed_tokens
+        max_hit_size_tokens: int = req_status.req.num_tokens
+        if self._sliding_window_groups:
+            # the last prompt token has to be recomputed to get the logprobs
+            # for sliding window attention, we must reduce by 1 to make sure
+            # we still have a hit after reduction
+            max_hit_size_tokens -= 1
+        num_hit_tokens: int = 0
+        defer_lookup = False
+        lookup_groups = self._lookup_groups
+        while lookup_groups:
+            looked_up_sliding_window: bool = False
+            groups_iter = iter(lookup_groups)
+            lookup_groups = ()
+            for group_idx in groups_iter:
+                group_config: GroupOffloadConfig = self.config.kv_group_configs[
+                    group_idx
+                ]
+                group_state: RequestGroupState = req_status.group_states[group_idx]
+                offloaded_block_size = group_config.offloaded_block_size
+                offload_keys = group_state.offload_keys
+
+                assert (
+                    len(offload_keys)
+                    >= req_status.req.num_tokens // offloaded_block_size
+                )
+
+                # Constrain to block-aligned boundary for this group
+                max_hit_size_tokens = min(
+                    max_hit_size_tokens, len(offload_keys) * offloaded_block_size
+                )
+                if max_hit_size_tokens - num_computed_tokens < offloaded_block_size:
+                    # we can only load less than a block, better skip
+                    return 0
+
+                num_blocks = min(
+                    cdiv(max_hit_size_tokens, offloaded_block_size), len(offload_keys)
+                )
+                start_block_idx = num_computed_tokens // offloaded_block_size
+                offload_keys = offload_keys[start_block_idx:num_blocks]
+                sliding_window_size_in_blocks = (
+                    group_config.sliding_window_size_in_blocks
+                )
+
+                # end index (in the sliced offload_keys) up to which we
+                # have backend-confirmed hits
+                num_hit_blocks: int | None
+                if sliding_window_size_in_blocks is None:
+                    num_hit_blocks = self._maximal_prefix_lookup(
+                        offload_keys, req_status.req_context
+                    )
+                else:
+                    num_hit_blocks = self._sliding_window_lookup(
+                        offload_keys,
+                        sliding_window_size_in_blocks,
+                        req_status.req_context,
+                    )
+                if num_hit_blocks == 0:
+                    return 0
+
+                if num_hit_blocks is None:
+                    defer_lookup = True
+                else:
+                    max_hit_size_tokens = min(
+                        max_hit_size_tokens,
+                        offloaded_block_size * (start_block_idx + num_hit_blocks),
+                    )
+
+                new_num_hit_tokens = max_hit_size_tokens - num_computed_tokens
+                if new_num_hit_tokens < offloaded_block_size:
+                    # we can only load less than a block, better skip
+                    return 0
+
+                if new_num_hit_tokens < num_hit_tokens:
+                    if defer_lookup:
+                        # make another iteration on all groups to check
+                        # if we still need to defer lookup
+                        defer_lookup = False
+                        lookup_groups = self._lookup_groups
+                    elif looked_up_sliding_window and not lookup_groups:
+                        # we need another iteration to confirm previously looked up
+                        # sliding window works with the new_num_hit_tokens
+                        lookup_groups = self._sliding_window_groups
+
+                looked_up_sliding_window |= sliding_window_size_in_blocks is not None
+                num_hit_tokens = new_num_hit_tokens
+
+        if defer_lookup:
+            logger.debug(
+                "Offloading manager delayed request %s as backend requested",
+                req_status.req.request_id,
+            )
+            return None
+
+        # possibly delay request if any of the hit blocks is already being loaded
+        if self._blocks_being_loaded:
+            for group_config, group_state in zip(
+                self.config.kv_group_configs, req_status.group_states
+            ):
+                offloaded_block_size = group_config.offloaded_block_size
+                sliding_window_size_in_blocks = (
+                    group_config.sliding_window_size_in_blocks
+                )
+                offload_keys = group_state.offload_keys
+                num_blocks = cdiv(
+                    num_computed_tokens + num_hit_tokens, offloaded_block_size
+                )
+                start_block_idx = num_computed_tokens // offloaded_block_size
+                offload_keys = offload_keys[start_block_idx:num_blocks]
+                if sliding_window_size_in_blocks is not None:
+                    offload_keys = offload_keys[-sliding_window_size_in_blocks:]
+                if any(key in self._blocks_being_loaded for key in offload_keys):
+                    # hit blocks are being loaded, delay request
+                    logger.debug(
+                        "Delaying request %s since some of its"
+                        " blocks are already being loaded",
+                        req_status.req.request_id,
+                    )
+                    return None
+
+        logger.debug(
+            "Request %s hit %s offloaded tokens after %s GPU hit tokens",
+            req_status.req.request_id,
+            num_hit_tokens,
+            num_computed_tokens,
+        )
+
+        return num_hit_tokens
 
     def get_num_new_matched_tokens(
         self, request: Request, num_computed_tokens: int
@@ -151,68 +465,28 @@ def get_num_new_matched_tokens(
                 - `True` if tokens will be loaded asynchronously
                   (between scheduler steps).
         """
+        is_new_request = False
         if req_status := self._req_status.get(request.request_id):
             # make sure block IDs are cleared
             for group_state in req_status.group_states:
                 group_state.block_ids.clear()
         else:
+            is_new_request = True
             req_status = RequestOffloadState(config=self.config, req=request)
-            req_status.update_offload_keys()
             self._req_status[request.request_id] = req_status
 
+        req_status.update_offload_keys()
         req_status.num_locally_computed_tokens = num_computed_tokens
 
-        # Below assertions will be removed once this function supports HMA
-        assert len(self.config.kv_group_configs) == 1
-        assert len(req_status.group_states) == 1
-        group_config = self.config.kv_group_configs[0]
-        group_state = req_status.group_states[0]
-
-        num_blocks = request.num_tokens // group_config.offloaded_block_size
-
-        assert len(request.block_hashes) // self.config.block_size_factor == num_blocks
-        offload_keys = group_state.offload_keys
-
-        self.manager.touch(offload_keys)
-
-        full_block_tokens = group_config.offloaded_block_size * num_blocks
-        if full_block_tokens - num_computed_tokens < group_config.offloaded_block_size:
-            # we can load less than a block, skip
-            return 0, False
-
-        start_block_idx = num_computed_tokens // group_config.offloaded_block_size
-        hits = self.manager.lookup(offload_keys[start_block_idx:])
-        if hits is None:
-            # indicates a lookup that should be tried later
-            return None, False
-        if hits == 0:
-            return 0, False
-
-        num_hit_tokens = (
-            group_config.offloaded_block_size * (start_block_idx + hits)
-            - num_computed_tokens
-        )
-        logger.debug(
-            "Request %s hit %s offloaded tokens after %s GPU hit tokens",
-            request.request_id,
-            num_hit_tokens,
-            num_computed_tokens,
-        )
-        if num_hit_tokens < group_config.offloaded_block_size:
-            return 0, False
-
-        if self._blocks_being_loaded and any(
-            key in self._blocks_being_loaded
-            for key in offload_keys[start_block_idx : start_block_idx + hits]
-        ):
-            # hit blocks are being loaded, delay request
-            logger.debug(
-                "Delaying request %s since some of its blocks are already being loaded",
-                request.request_id,
+        num_hit_tokens = self._lookup(req_status)
+        if is_new_request:
+            req_status.update_num_hit_blocks(
+                num_computed_tokens + (num_hit_tokens or 0)
             )
-            return None, False
 
-        return num_hit_tokens, True
+        self._touch(req_status)
+
+        return num_hit_tokens, bool(num_hit_tokens)
 
     def update_state_after_alloc(
         self, request: Request, blocks: KVCacheBlocks, num_external_tokens: int
@@ -221,59 +495,117 @@ def update_state_after_alloc(
             return
 
         req_status = self._req_status[request.request_id]
-        block_groups = blocks.get_block_ids()
-
-        # Below assertions will be removed once this function supports HMA
-        assert len(self.config.kv_group_configs) == 1
-        assert len(req_status.group_states) == 1
-        assert len(block_groups) == 1
-        block_ids = block_groups[0]
-        group_config = self.config.kv_group_configs[0]
-        group_state = req_status.group_states[0]
-
-        num_computed_gpu_blocks = sum(
-            block.block_hash is not None for block in blocks.blocks[0]
-        )
-        num_computed_tokens = num_computed_gpu_blocks * group_config.gpu_block_size
-        full_block_tokens = num_computed_tokens + num_external_tokens
-        assert full_block_tokens % group_config.offloaded_block_size == 0
 
-        num_pending_gpu_blocks = len(block_ids) - num_computed_gpu_blocks
-        assert (
-            num_external_tokens == num_pending_gpu_blocks * group_config.gpu_block_size
-        )
+        num_locally_computed_tokens = req_status.num_locally_computed_tokens
+        num_cached_tokens = num_locally_computed_tokens + num_external_tokens
+
+        params = req_status.req_context.kv_transfer_params
+        do_remote_decode = params is not None and params.get("do_remote_decode")
+
+        keys_to_load: list[OffloadKey] = []
+        dst_block_ids: list[int] = []
+        # per group
+        group_sizes: list[int] = []
+        block_indices: list[int] = []
+        for group_config, group_state, group_blocks in zip(
+            self.config.kv_group_configs,
+            req_status.group_states,
+            blocks.blocks,
+        ):
+            gpu_block_size = group_config.gpu_block_size
+            offloaded_block_size = group_config.offloaded_block_size
+            offload_keys = group_state.offload_keys
+            num_gpu_blocks = cdiv(num_cached_tokens, gpu_block_size)
+
+            assert len(group_blocks) >= num_gpu_blocks
+            num_locally_computed_gpu_blocks = num_gpu_blocks
+            # Skip null placeholder blocks (used for sliding window or mamba padding).
+            for i, block in enumerate(group_blocks[:num_gpu_blocks]):
+                if not block.is_null and block.block_hash is None:
+                    num_locally_computed_gpu_blocks = i
+                    break
+
+            assert (
+                num_locally_computed_tokens
+                <= num_locally_computed_gpu_blocks * gpu_block_size
+            )
+            num_pending_gpu_blocks = num_gpu_blocks - num_locally_computed_gpu_blocks
 
-        start_block_idx = num_computed_tokens // group_config.offloaded_block_size
-        num_blocks = full_block_tokens // group_config.offloaded_block_size
+            if group_config.sliding_window_size_in_blocks is not None:
+                assert (
+                    num_pending_gpu_blocks
+                    <= group_config.sliding_window_size_in_blocks
+                    * self.config.block_size_factor
+                )
+
+            num_blocks = cdiv(num_cached_tokens, offloaded_block_size)
+            assert len(offload_keys) >= num_blocks
+            if num_pending_gpu_blocks:
+                start_block_idx = (
+                    num_locally_computed_gpu_blocks // self.config.block_size_factor
+                )
+                keys_to_load.extend(offload_keys[start_block_idx:num_blocks])
 
-        assert len(request.block_hashes) // self.config.block_size_factor >= num_blocks
-        offload_keys = group_state.offload_keys[start_block_idx:num_blocks]
+            dst_block_ids.extend(
+                block.block_id
+                for block in group_blocks[
+                    num_locally_computed_gpu_blocks:num_gpu_blocks
+                ]
+            )
+            group_sizes.append(num_pending_gpu_blocks)
+            block_indices.append(num_locally_computed_gpu_blocks)
+
+            if not do_remote_decode:
+                # For P/D prefill requests (do_remote_decode=True), we do
+                # NOT skip saving the hit prefix, as we need to stream the
+                # entire KV cache so a remote decode node can consume it.
+                group_state.next_stored_block_idx = num_blocks
+
+        # Fence dst blocks against finished-request pending stores.
+        if (
+            self._block_id_to_pending_jobs
+            and not self._block_id_to_pending_jobs.keys().isdisjoint(dst_block_ids)
+        ):
+            self._current_batch_jobs_to_flush.update(
+                jid
+                for bid in dst_block_ids
+                for jid in self._block_id_to_pending_jobs.get(bid, ())
+            )
 
-        src_spec = self.manager.prepare_load(offload_keys)
+        src_spec = self.manager.prepare_load(keys_to_load, req_status.req_context)
         dst_spec = GPULoadStoreSpec(
-            block_ids[num_computed_gpu_blocks:],
-            group_sizes=(num_pending_gpu_blocks,),
-            block_indices=(num_computed_gpu_blocks,),
+            dst_block_ids, group_sizes=group_sizes, block_indices=block_indices
         )
 
-        self._reqs_to_load[request.request_id] = (src_spec, dst_spec)
-        req_blocks_being_loaded = self._reqs_being_loaded[request.request_id]
-        req_blocks_being_loaded.update(offload_keys)
-        group_state.next_stored_block_idx = num_blocks
+        load_job_id = self._generate_job_id()
+        self._current_batch_load_jobs[load_job_id] = TransferJob(
+            req_id=request.request_id,
+            transfer_spec=(src_spec, dst_spec),
+        )
+        # a load can only be issued when no other jobs are pending.
+        assert not req_status.transfer_jobs
+        req_status.transfer_jobs.add(load_job_id)
+        self._jobs[load_job_id] = TransferJobStatus(
+            req_id=request.request_id,
+            pending_count=self.config.num_workers,
+            keys=set(keys_to_load),
+            is_store=False,
+        )
 
         if self._blocks_being_loaded is not None:
-            self._blocks_being_loaded.update(req_blocks_being_loaded)
+            self._blocks_being_loaded.update(keys_to_load)
 
-    def _get_reqs_to_store(self, scheduler_output: SchedulerOutput):
-        # Below assertion will be removed once this function supports HMA
-        assert len(self.config.kv_group_configs) == 1
-        group_config = self.config.kv_group_configs[0]
-
-        reqs_to_store: dict[ReqId, TransferSpec] = {}
+    def _build_store_jobs(
+        self,
+        scheduler_output: SchedulerOutput,
+    ) -> dict[int, TransferJob]:
+        block_size_factor = self.config.block_size_factor
+        store_jobs: dict[int, TransferJob] = {}
         # iterate over both new and cached requests
         for req_id, new_block_id_groups, preempted in yield_req_data(scheduler_output):
             req_status = self._req_status[req_id]
             req_status.update_offload_keys()
+            req = req_status.req
 
             if preempted:
                 for group_state in req_status.group_states:
@@ -281,87 +613,176 @@ def _get_reqs_to_store(self, scheduler_output: SchedulerOutput):
 
             if new_block_id_groups:
                 req_status.update_block_id_groups(new_block_id_groups)
-
-            # Below assertion will be removed once this function supports HMA
-            assert len(req_status.group_states) == 1
-            group_state = req_status.group_states[0]
-
-            block_ids = group_state.block_ids
-
-            req = req_status.req
-            new_tokens = scheduler_output.num_scheduled_tokens[req_id]
-            expected_tokens = req.num_computed_tokens + new_tokens
+                # Fence new blocks against in-flight stores.
+                if self._block_id_to_pending_jobs:
+                    new_blocks_flat = [
+                        bid for new_blocks in new_block_id_groups for bid in new_blocks
+                    ]
+                    if not self._block_id_to_pending_jobs.keys().isdisjoint(
+                        new_blocks_flat
+                    ):
+                        self._current_batch_jobs_to_flush.update(
+                            jid
+                            for bid in new_blocks_flat
+                            for jid in self._block_id_to_pending_jobs.get(bid, ())
+                        )
+
+            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
+            num_tokens_after_batch = req.num_computed_tokens + num_scheduled_tokens
             # with async scheduling, some tokens may be missing
-            total_tokens = min(expected_tokens, req.num_tokens)
-            num_blocks = total_tokens // group_config.offloaded_block_size
-            start_block_idx = group_state.next_stored_block_idx
-            num_new_blocks = num_blocks - start_block_idx
+            num_offloadable_tokens = min(num_tokens_after_batch, req.num_tokens)
 
-            if num_new_blocks <= 0:
+            # Filter out blocks skipped due to sliding window attention / SSM
+            new_offload_keys: list[OffloadKey] = []
+            for group_config, group_state in zip(
+                self.config.kv_group_configs, req_status.group_states
+            ):
+                num_blocks = num_offloadable_tokens // group_config.offloaded_block_size
+                start_block_idx = group_state.next_stored_block_idx
+                if num_blocks <= start_block_idx:
+                    continue
+                offload_keys = group_state.offload_keys[start_block_idx:num_blocks]
+                # For each block to offload, take the last corresponding GPU block.
+                # e.g. if block size factor is 3 and GPU block IDs are
+                # 1 5 6 7 2 4 9 3 8 then we'll take blocks 6 4 8.
+                # We will use these GPU blocks to determine if the block needs
+                # offloading, or (if the GPU block ID is 0) this block should
+                # be skipped due to sliding window attention / SSM.
+                # We know that if a block is skipped, then all the previous blocks
+                # are skipped as well. This is why we take the last of each block.
+                offload_block_ids = group_state.block_ids[
+                    start_block_idx * block_size_factor
+                    + block_size_factor
+                    - 1 : num_blocks * block_size_factor : block_size_factor
+                ]
+                assert len(offload_keys) == len(offload_block_ids)
+
+                for offload_key, block_id in zip(offload_keys, offload_block_ids):
+                    if block_id != 0:
+                        new_offload_keys.append(offload_key)
+
+            if not new_offload_keys:
+                req_status.advance_stored_idx(num_offloadable_tokens)
                 continue
 
-            num_gpu_blocks = num_blocks * self.config.block_size_factor
-            assert len(req.block_hashes) >= num_gpu_blocks
-
-            new_offload_keys = group_state.offload_keys[start_block_idx:num_blocks]
-            store_output = self.manager.prepare_store(new_offload_keys)
+            store_output = self.manager.prepare_store(
+                new_offload_keys, req_status.req_context
+            )
             if store_output is None:
-                logger.warning(
-                    "Request %s: cannot store %s blocks", req_id, num_new_blocks
-                )
+                logger.warning("Request %s: cannot store blocks", req_id)
                 continue
 
-            group_state.next_stored_block_idx = num_blocks
-
             if not store_output.keys_to_store:
+                req_status.advance_stored_idx(num_offloadable_tokens)
                 continue
-            keys_to_store = set(store_output.keys_to_store)
 
-            self.manager.touch(group_state.offload_keys[:num_blocks])
+            self._touch(req_status)
 
-            dst_spec = store_output.store_spec
+            keys_to_store = set(store_output.keys_to_store)
+
+            group_sizes: list[int] = []
+            block_indices: list[int] = []
             src_block_ids: list[int] = []
-            for idx, key in enumerate(new_offload_keys):
-                if key not in keys_to_store:
-                    continue
-                offloaded_block_idx = start_block_idx + idx
-                gpu_block_idx = offloaded_block_idx * self.config.block_size_factor
-                for i in range(self.config.block_size_factor):
-                    src_block_ids.append(block_ids[gpu_block_idx + i])
+            sliding_window_block_ids: list[int] = []
+            non_sliding_window_block_ids: list[int] = []
+            for group_config, group_state in zip(
+                self.config.kv_group_configs, req_status.group_states
+            ):
+                is_sliding_window = (
+                    group_config.sliding_window_size_in_blocks is not None
+                )
+                num_blocks = num_offloadable_tokens // group_config.offloaded_block_size
+                start_block_idx = group_state.next_stored_block_idx
+                block_ids = group_state.block_ids
+                num_group_blocks = 0
+                start_gpu_block_idx: int | None = None
+                for idx, offload_key in enumerate(
+                    group_state.offload_keys[start_block_idx:num_blocks]
+                ):
+                    if offload_key not in keys_to_store:
+                        continue
+
+                    offloaded_block_idx = start_block_idx + idx
+                    gpu_block_idx = offloaded_block_idx * block_size_factor
+                    num_group_blocks += block_size_factor
+                    for i in range(block_size_factor):
+                        block_id = block_ids[gpu_block_idx + i]
+                        if block_id == 0:
+                            # skipped blocks cannot appear after non-skipped blocks
+                            assert start_gpu_block_idx is None
+                            continue
+                        elif start_gpu_block_idx is None:
+                            start_gpu_block_idx = gpu_block_idx + i
+                        src_block_ids.append(block_id)
+                        if is_sliding_window:
+                            sliding_window_block_ids.append(block_id)
+                        else:
+                            non_sliding_window_block_ids.append(block_id)
+
+                group_sizes.append(num_group_blocks)
+                block_indices.append(start_gpu_block_idx or 0)
+                group_state.next_stored_block_idx = num_blocks
+
             src_spec = GPULoadStoreSpec(
-                src_block_ids, group_sizes=(len(src_block_ids),)
+                src_block_ids, group_sizes=group_sizes, block_indices=block_indices
+            )
+            dst_spec = store_output.store_spec
+
+            job_id = self._generate_job_id()
+            # a store can only be issued when no load is pending.
+            if req_status.transfer_jobs:
+                any_jid = next(iter(req_status.transfer_jobs))
+                assert self._jobs[any_jid].is_store
+            req_status.transfer_jobs.add(job_id)
+
+            # Watch sliding window blocks as they may get evicted
+            # before the request finishes
+            for bid in sliding_window_block_ids or ():
+                self._block_id_to_pending_jobs.setdefault(bid, set()).add(job_id)
+
+            # the non-sliding window blocks will be watched only
+            # when the request finishes
+            self._jobs[job_id] = TransferJobStatus(
+                req_id=req_id,
+                pending_count=self.config.num_workers,
+                keys=set(keys_to_store),
+                is_store=True,
+                non_sliding_window_block_ids=non_sliding_window_block_ids,
+                sliding_window_block_ids=sliding_window_block_ids or None,
             )
 
-            reqs_to_store[req_id] = (src_spec, dst_spec)
-            self._reqs_being_stored[req_id] |= keys_to_store
+            store_jobs[job_id] = TransferJob(
+                req_id=req_id, transfer_spec=(src_spec, dst_spec)
+            )
 
             logger.debug(
-                "Request %s offloading %s blocks starting from block #%d",
+                "Request %s offloading %s blocks upto %d tokens (job %d)",
                 req_id,
                 len(keys_to_store),
-                start_block_idx,
+                num_offloadable_tokens,
+                job_id,
             )
 
-        return reqs_to_store
+        return store_jobs
 
     def build_connector_meta(
         self, scheduler_output: SchedulerOutput
     ) -> KVConnectorMetadata:
-        meta = OffloadingConnectorMetadata(
-            reqs_to_load=self._reqs_to_load,
-            reqs_to_store=self._get_reqs_to_store(scheduler_output),
-            reqs_to_flush=scheduler_output.preempted_req_ids,
-        )
-        self._reqs_to_load = {}
-
-        # NOTE (orozery): we should move this logic to update_connector_output
-        # once KVConnectorOutput allows us to report completed transfers
         for req_id in scheduler_output.preempted_req_ids or ():
-            keys = self._reqs_being_stored.get(req_id)
-            if keys:
-                self.manager.complete_store(keys)
-                keys.clear()
+            req_status = self._req_status.get(req_id)
+            if req_status is None or not req_status.transfer_jobs:
+                continue
+            any_jid = next(iter(req_status.transfer_jobs))
+            assert self._jobs[any_jid].is_store
+            self._current_batch_jobs_to_flush.update(req_status.transfer_jobs)
 
+        meta = OffloadingConnectorMetadata(
+            load_jobs=self._current_batch_load_jobs,
+            store_jobs=self._build_store_jobs(scheduler_output),
+            jobs_to_flush=self._current_batch_jobs_to_flush,
+        )
+        self._current_batch_load_jobs = {}
+        self._current_batch_jobs_to_flush = set()
         return meta
 
     def update_connector_output(self, connector_output: KVConnectorOutput):
@@ -372,22 +793,44 @@ def update_connector_output(self, connector_output: KVConnectorOutput):
             connector_output (KVConnectorOutput): the worker-side
                 connectors output.
         """
-        for req_id in connector_output.finished_sending or []:
-            keys = self._reqs_being_stored.pop(req_id, None)
-            if keys:
-                self.manager.complete_store(keys)
-
-        for req_id in connector_output.finished_recving or []:
-            keys = self._reqs_being_loaded.pop(req_id, None)
-            if keys:
+        meta = connector_output.kv_connector_worker_meta
+        if not isinstance(meta, OffloadingWorkerMetadata):
+            assert meta is None
+            meta = OffloadingWorkerMetadata()
+        for job_id, count in meta.completed_jobs.items():
+            assert count > 0
+            job_status = self._jobs[job_id]
+            job_status.pending_count -= count
+            if job_status.pending_count > 0:
+                continue
+            assert job_status.pending_count == 0
+
+            req_status = self._req_status[job_status.req_id]
+            if job_status.is_store:
+                self.manager.complete_store(job_status.keys, req_status.req_context)
+            else:
+                self.manager.complete_load(job_status.keys, req_status.req_context)
                 if self._blocks_being_loaded:
-                    self._blocks_being_loaded.difference_update(keys)
-                self.manager.complete_load(keys)
+                    self._blocks_being_loaded.difference_update(job_status.keys)
+            if self._block_id_to_pending_jobs:
+                # Sliding window blocks are tracked from store creation
+                # and must be cleaned up unconditionally.
+                self._remove_pending_job(job_id, job_status.sliding_window_block_ids)
+                # Non-sliding-window blocks are only tracked after
+                # request_finished, so only clean up for finished requests.
+                if req_status.req.is_finished():
+                    self._remove_pending_job(
+                        job_id, job_status.non_sliding_window_block_ids
+                    )
+
+            del self._jobs[job_id]
+            req_status.transfer_jobs.remove(job_id)
+            if not req_status.transfer_jobs and req_status.req.is_finished():
+                del self._req_status[job_status.req_id]
 
     def request_finished(
         self,
         request: Request,
-        block_ids: list[int],
     ) -> tuple[bool, dict[str, Any] | None]:
         """
         Called when a request has finished, before its blocks are freed.
@@ -399,14 +842,21 @@ def request_finished(
             Optional KVTransferParams to be included in the request outputs
             returned by the engine.
         """
-        req_id = request.request_id
-
         # TODO(orozery): possibly kickoff offload for last block
         # which may have been deferred due to async scheduling
-        self._req_status.pop(req_id, None)
-
-        request_being_stored = req_id in self._reqs_being_stored
-        return request_being_stored, None
+        req_status = self._req_status.get(request.request_id)
+        if req_status is None:
+            return False, None
+        if not req_status.transfer_jobs:
+            del self._req_status[request.request_id]
+            return False, None
+        # Pending stores will outlive the request's block ownership.
+        # Register them so future block reuse triggers a flush.
+        for job_id in req_status.transfer_jobs:
+            job_status = self._jobs[job_id]
+            for bid in job_status.non_sliding_window_block_ids or ():
+                self._block_id_to_pending_jobs.setdefault(bid, set()).add(job_id)
+        return False, None
 
     def take_events(self) -> Iterable[KVCacheEvent]:
         """Take the KV cache events from the connector.
@@ -424,7 +874,7 @@ def take_events(self) -> Iterable[KVCacheEvent]:
                     parent_block_hash=None,
                     token_ids=[],
                     lora_id=None,
-                    block_size=event.block_size,
+                    block_size=0,
                     medium=event.medium,
                     lora_name=None,
                 )
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/offloading/worker.py b/vllm/distributed/kv_transfer/kv_connector/v1/offloading/worker.py
index cc6d8262c7e6..9acfd9c66ce4 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/offloading/worker.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/offloading/worker.py
@@ -11,6 +11,7 @@
 )
 from vllm.distributed.kv_transfer.kv_connector.v1.offloading.common import (
     OffloadingConnectorMetadata,
+    OffloadingWorkerMetadata,
     ReqId,
 )
 from vllm.distributed.kv_transfer.kv_connector.v1.offloading.metrics import (
@@ -24,7 +25,7 @@
     MambaSpec,
     UniformTypeKVCacheSpecs,
 )
-from vllm.v1.kv_offload.spec import (
+from vllm.v1.kv_offload.base import (
     CanonicalKVCacheRef,
     CanonicalKVCaches,
     CanonicalKVCacheTensor,
@@ -45,24 +46,11 @@ def __init__(self, spec: OffloadingSpec):
         self.spec = spec
         self.worker = OffloadingWorker()
 
-        self._job_counter = 0
-
         self.kv_connector_stats = OffloadingConnectorStats()
-        # req_id -> (job_id, store)
-        self._jobs: dict[int, tuple[ReqId, bool]] = {}
-        # req_id -> active job IDs
-        self._load_job: dict[ReqId, int] = {}
-        # req_id -> set(active job IDs)
-        self._store_jobs = defaultdict[ReqId, set[int]](set)
-        # list of store jobs pending submission (job_id, transfer_spec)
+        # job_id -> req_id for in-flight loads.
+        self._load_jobs: dict[int, ReqId] = {}
         self._unsubmitted_store_jobs: list[tuple[int, TransferSpec]] = []
-
-        self._finished_reqs_waiting_for_store: set[ReqId] = set()
-
-    def _generate_job_id(self) -> int:
-        job_id = self._job_counter
-        self._job_counter = job_id + 1
-        return job_id
+        self._connector_worker_meta = OffloadingWorkerMetadata()
 
     def _register_handlers(self, kv_caches: CanonicalKVCaches):
         for src_cls, dst_cls, handler in self.spec.get_handlers(kv_caches):
@@ -301,10 +289,8 @@ def handle_preemptions(self, kv_connector_metadata: OffloadingConnectorMetadata)
             assert success
         self._unsubmitted_store_jobs.clear()
 
-        for req_id in kv_connector_metadata.reqs_to_flush or ():
-            job_ids = self._store_jobs.get(req_id)
-            if job_ids:
-                self.worker.wait(job_ids)
+        if kv_connector_metadata.jobs_to_flush:
+            self.worker.wait(kv_connector_metadata.jobs_to_flush)
 
     def start_kv_transfers(self, metadata: OffloadingConnectorMetadata):
         for job_id, transfer_spec in self._unsubmitted_store_jobs:
@@ -312,41 +298,33 @@ def start_kv_transfers(self, metadata: OffloadingConnectorMetadata):
             assert success
         self._unsubmitted_store_jobs.clear()
 
-        for req_id, transfer_spec in metadata.reqs_to_load.items():
-            job_id = self._generate_job_id()
-            self._jobs[job_id] = (req_id, False)
-            assert req_id not in self._load_job
-            self._load_job[req_id] = job_id
-            success = self.worker.transfer_async(job_id, transfer_spec)
+        for job_id, entry in metadata.load_jobs.items():
+            self._load_jobs[job_id] = entry.req_id
+            success = self.worker.transfer_async(job_id, entry.transfer_spec)
             assert success
 
     def prepare_store_kv(self, metadata: OffloadingConnectorMetadata):
-        for req_id, transfer_spec in metadata.reqs_to_store.items():
-            job_id = self._generate_job_id()
-            self._jobs[job_id] = (req_id, True)
-            self._store_jobs[req_id].add(job_id)
-            # NOTE(orozery): defer the store to the beginning of the next engine step,
-            # so that offloading starts AFTER transfers related to token sampling,
-            # thereby avoiding delays to token generation due to offloading.
-            self._unsubmitted_store_jobs.append((job_id, transfer_spec))
+        for job_id, entry in metadata.store_jobs.items():
+            # NOTE(orozery): defer the store to the beginning of the next
+            # engine step, so that offloading starts AFTER transfers related
+            # to token sampling, thereby avoiding delays to token generation.
+            self._unsubmitted_store_jobs.append((job_id, entry.transfer_spec))
 
     def get_finished(self, finished_req_ids: set[str]) -> tuple[set[str], set[str]]:
         """
-        Notifies worker-side connector ids of requests that have
-        finished generating tokens.
-        Returns a list of request IDs that finished loading or storing.
-
         Returns:
-            ids of requests that have finished asynchronous transfer
-            tuple of (sending/saving ids, recving/loading ids).
+            tuple of (finished_sending, finished_recving). Stores never
+            emit finished_sending — the scheduler tracks store completion
+            via kv_connector_worker_meta.completed_jobs and fences any
+            block reuse via jobs_to_flush. Loads still emit
+            finished_recving so the base scheduler can resume requests
+            blocked on remote KV (and free aborted-during-load reqs).
         """
-        finished_sending = set()
-        finished_recving = set()
+        finished_recving: set[str] = set()
         for transfer_result in self.worker.get_finished():
             # we currently do not support job failures
             job_id = transfer_result.job_id
             assert transfer_result.success
-            req_id, store = self._jobs.pop(job_id)
             if (
                 transfer_result.transfer_time
                 and transfer_result.transfer_size is not None
@@ -357,31 +335,21 @@ def get_finished(self, finished_req_ids: set[str]) -> tuple[set[str], set[str]]:
                     time=transfer_result.transfer_time,
                     transfer_type=transfer_result.transfer_type,
                 )
-            if store:
-                req_jobs = self._store_jobs[req_id]
-                req_jobs.remove(job_id)
-                if req_jobs:
-                    continue
-
-                if req_id in self._finished_reqs_waiting_for_store:
-                    self._finished_reqs_waiting_for_store.remove(req_id)
-                    finished_sending.add(req_id)
-                    del self._store_jobs[req_id]
-            else:
-                req_job = self._load_job[req_id]
-                assert job_id == req_job
-                del self._load_job[req_id]
+
+            self._connector_worker_meta.mark_completed(job_id)
+            req_id = self._load_jobs.pop(job_id, None)
+            if req_id is not None:
                 finished_recving.add(req_id)
 
-        for req_id in finished_req_ids:
-            pending_req_jobs = self._store_jobs.get(req_id)
-            if pending_req_jobs:
-                self._finished_reqs_waiting_for_store.add(req_id)
-            elif pending_req_jobs is not None:
-                finished_sending.add(req_id)
-                del self._store_jobs[req_id]
+        return set(), finished_recving
 
-        return finished_sending, finished_recving
+    def build_connector_worker_meta(self) -> OffloadingWorkerMetadata | None:
+        """Return completed transfer job IDs since the last call."""
+        if not self._connector_worker_meta.completed_jobs:
+            return None
+        meta = self._connector_worker_meta
+        self._connector_worker_meta = OffloadingWorkerMetadata()
+        return meta
 
     def get_kv_connector_stats(self) -> KVConnectorStats | None:
         """
@@ -396,11 +364,7 @@ def get_kv_connector_stats(self) -> KVConnectorStats | None:
         return kv_connector_stats
 
     def shutdown(self) -> None:
-        # Drop deferred store jobs: there is no point in submitting
-        # them during shutdown.
         self._unsubmitted_store_jobs.clear()
-        self._jobs.clear()
-        self._load_job.clear()
-        self._store_jobs.clear()
-        self._finished_reqs_waiting_for_store.clear()
+        self._load_jobs.clear()
+        self._connector_worker_meta = OffloadingWorkerMetadata()
         self.worker.shutdown()
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
index f11281dcf14e..4ba59e2b1e21 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
@@ -10,6 +10,7 @@
 from vllm.distributed.kv_transfer.kv_connector.v1 import (
     KVConnectorBase_V1,
     KVConnectorRole,
+    SupportsHMA,
 )
 from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorMetadata
 from vllm.distributed.kv_transfer.kv_connector.v1.metrics import (
@@ -20,6 +21,7 @@
 )
 from vllm.distributed.kv_transfer.kv_connector.v1.offloading.common import (
     OffloadingConnectorMetadata,
+    OffloadingWorkerMetadata,
 )
 from vllm.distributed.kv_transfer.kv_connector.v1.offloading.metrics import (
     OffloadingConnectorStats,
@@ -41,7 +43,7 @@
 from vllm.v1.request import Request
 
 
-class OffloadingConnector(KVConnectorBase_V1):
+class OffloadingConnector(KVConnectorBase_V1, SupportsHMA):
     @property
     def prefer_cross_layer_blocks(self) -> bool:
         return True
@@ -50,11 +52,10 @@ def __init__(
         self,
         vllm_config: VllmConfig,
         role: KVConnectorRole,
-        kv_cache_config: KVCacheConfig | None = None,
+        kv_cache_config: KVCacheConfig,
     ):
         super().__init__(vllm_config, role, kv_cache_config)
 
-        assert kv_cache_config is not None
         spec = OffloadingSpecFactory.create_spec(vllm_config, kv_cache_config)
 
         self.connector_scheduler: OffloadingConnectorScheduler | None = None
@@ -111,6 +112,11 @@ def get_finished(self, finished_req_ids: set[str]) -> tuple[set[str], set[str]]:
         assert self.connector_worker is not None
         return self.connector_worker.get_finished(finished_req_ids)
 
+    def build_connector_worker_meta(self) -> OffloadingWorkerMetadata | None:
+        if self.connector_worker is not None:
+            return self.connector_worker.build_connector_worker_meta()
+        return None
+
     def get_num_new_matched_tokens(
         self, request: "Request", num_computed_tokens: int
     ) -> tuple[int | None, bool]:
@@ -143,7 +149,15 @@ def request_finished(
         block_ids: list[int],
     ) -> tuple[bool, dict[str, Any] | None]:
         assert self.connector_scheduler is not None
-        return self.connector_scheduler.request_finished(request, block_ids)
+        return self.connector_scheduler.request_finished(request)
+
+    def request_finished_all_groups(
+        self,
+        request: "Request",
+        block_ids: tuple[list[int], ...],
+    ) -> tuple[bool, dict[str, Any] | None]:
+        assert self.connector_scheduler is not None
+        return self.connector_scheduler.request_finished(request)
 
     def take_events(self) -> Iterable[KVCacheEvent]:
         assert self.connector_scheduler is not None
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_connector.py
index ce228b3c6f23..aa791921be1a 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_connector.py
@@ -76,7 +76,7 @@ def __init__(
         self,
         vllm_config: "VllmConfig",
         role: KVConnectorRole,
-        kv_cache_config: "KVCacheConfig | None" = None,
+        kv_cache_config: "KVCacheConfig",
     ):
         super().__init__(
             vllm_config=vllm_config,
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py
index 6475b941ba59..c7fca1664bc6 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py
@@ -49,7 +49,7 @@ def __init__(
         self,
         vllm_config: VllmConfig,
         role: KVConnectorRole,
-        kv_cache_config: "KVCacheConfig | None" = None,
+        kv_cache_config: "KVCacheConfig",
     ):
         super().__init__(vllm_config, role, kv_cache_config)
 
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/ssm_conv_transfer_utils.py b/vllm/distributed/kv_transfer/kv_connector/v1/ssm_conv_transfer_utils.py
index c8a5e10344bd..00b8e2bb7275 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/ssm_conv_transfer_utils.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/ssm_conv_transfer_utils.py
@@ -31,6 +31,7 @@ class MambaConvSplitInfo:
     x_local: int  # intermediate_size / TP  (columns for x)
     b_local: int  # groups_ss / TP  (columns for B; C is same size)
     conv_dtype_size: int  # bytes per element (e.g. 2 for float16)
+    ssm_sizes: tuple[int, int]  # (conv_state_bytes, ssm_state_bytes)
 
     @property
     def conv_dim_local(self) -> int:
@@ -99,8 +100,8 @@ def derive_mamba_conv_split(
         local_tp: this engine's tensor-parallel size.
 
     Returns:
-        MambaConvSplitInfo with per-rank x_local, b_local, conv_rows, and
-        conv_dtype_size.
+        MambaConvSplitInfo with per-rank x_local, b_local, conv_rows,
+        conv_dtype_size, and ssm_sizes (conv_state_bytes, ssm_state_bytes).
     """
     if mamba_spec.mamba_type != "mamba2":
         raise NotImplementedError(
@@ -142,16 +143,26 @@ def derive_mamba_conv_split(
         dtype=mamba_spec.dtypes[0],  # type: ignore[misc]
     ).element_size()
 
+    ssm_dtype_size = torch.tensor(
+        [],
+        dtype=mamba_spec.dtypes[1],  # type: ignore[misc]
+    ).element_size()
+    conv_state_bytes = torch.Size(mamba_spec.shapes[0]).numel() * conv_dtype_size
+    ssm_state_bytes = torch.Size(mamba_spec.shapes[1]).numel() * ssm_dtype_size
+
     # Divide by TP to get per-rank column counts.
     return MambaConvSplitInfo(
         conv_rows=conv_rows,
         x_local=intermediate_size // local_tp,
         b_local=groups_ss // local_tp,
         conv_dtype_size=conv_dtype_size,
+        ssm_sizes=(conv_state_bytes, ssm_state_bytes),
     )
 
 
-def compute_mamba_phys_ratio(ssm_sizes: tuple[int, ...], block_len: int) -> int:
+def compute_physical_blocks_per_logical(
+    ssm_sizes: tuple[int, ...], block_len: int
+) -> int:
     """Derive _physical_blocks_per_logical_kv_block from remote metadata.
 
     The remote engine's ratio is not sent directly in the handshake, so we
diff --git a/vllm/distributed/kv_transfer/kv_transfer_state.py b/vllm/distributed/kv_transfer/kv_transfer_state.py
index 2cc074bded6f..4392d6520779 100644
--- a/vllm/distributed/kv_transfer/kv_transfer_state.py
+++ b/vllm/distributed/kv_transfer/kv_transfer_state.py
@@ -49,7 +49,7 @@ def is_v1_kv_transfer_group(connector: KVConnectorBaseType | None = None) -> boo
 
 
 def ensure_kv_transfer_initialized(
-    vllm_config: "VllmConfig", kv_cache_config: "KVCacheConfig | None" = None
+    vllm_config: "VllmConfig", kv_cache_config: "KVCacheConfig"
 ) -> None:
     """
     Initialize KV cache transfer parallel group.
diff --git a/vllm/distributed/nixl_utils.py b/vllm/distributed/nixl_utils.py
new file mode 100644
index 000000000000..d7d262672d39
--- /dev/null
+++ b/vllm/distributed/nixl_utils.py
@@ -0,0 +1,82 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import importlib
+import os
+import sys
+from typing import Any
+
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+
+logger = init_logger(__name__)
+
+# declaration for static analyzers
+NixlWrapper: Any
+nixl_agent_config: Any
+nixlXferTelemetry: Any
+
+
+def _maybe_set_ucx_rcache_limit() -> None:
+    if "UCX_RCACHE_MAX_UNRELEASED" in os.environ:
+        return
+
+    if "nixl" in sys.modules or "rixl" in sys.modules:
+        logger.warning_once(
+            "NIXL was already imported, we can't reset "
+            "UCX_RCACHE_MAX_UNRELEASED. "
+            "Please set it to '1024' manually."
+        )
+        return
+
+    logger.info_once(
+        "Setting UCX_RCACHE_MAX_UNRELEASED to '1024' to avoid a rare "
+        "memory leak in UCX when using NIXL."
+    )
+    os.environ["UCX_RCACHE_MAX_UNRELEASED"] = "1024"
+
+
+def _get_nixl_module_name(name: str) -> str:
+    package_name = "rixl" if current_platform.is_rocm() else "nixl"
+    if name == "nixlXferTelemetry":
+        return f"{package_name}._bindings"
+    return f"{package_name}._api"
+
+
+def _load_nixl_attr(name: str) -> Any:
+    attr_name = {
+        "NixlWrapper": "nixl_agent",
+        "nixl_agent_config": "nixl_agent_config",
+        "nixlXferTelemetry": "nixlXferTelemetry",
+    }[name]
+
+    _maybe_set_ucx_rcache_limit()
+    try:
+        module = importlib.import_module(_get_nixl_module_name(name))
+    except ImportError:
+        if name == "NixlWrapper":
+            logger.warning_once("NIXL is not available")
+        elif name == "nixl_agent_config":
+            logger.warning_once("NIXL agent config is not available")
+        value = None
+    else:
+        value = getattr(module, attr_name, None)
+        if name == "NixlWrapper":
+            if value is None:
+                logger.warning_once("NIXL is not available")
+            else:
+                logger.info_once("NIXL is available")
+        elif name == "nixl_agent_config" and value is None:
+            logger.warning_once("NIXL agent config is not available")
+
+    globals()[name] = value
+    return value
+
+
+def __getattr__(name: str) -> Any:
+    if name in __all__:
+        return _load_nixl_attr(name)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+__all__ = ["NixlWrapper", "nixl_agent_config", "nixlXferTelemetry"]
diff --git a/vllm/distributed/parallel_state.py b/vllm/distributed/parallel_state.py
index cef902d9e4e5..58c49c09dc54 100644
--- a/vllm/distributed/parallel_state.py
+++ b/vllm/distributed/parallel_state.py
@@ -394,8 +394,10 @@ def __init__(
             current_platform.is_tpu() or current_platform.use_custom_op_collectives()
         )
 
-        self.use_cpu_custom_send_recv = current_platform.is_cpu() and hasattr(
-            torch.ops._C, "init_shm_manager"
+        self.use_cpu_custom_send_recv = (
+            current_platform.is_cpu()
+            and self.device_communicator
+            and getattr(self.device_communicator, "supports_tensor_dict", False)
         )
 
     def create_mq_broadcaster(
@@ -470,6 +472,7 @@ def graph_capture(self, graph_capture_context: GraphCaptureContext | None = None
         # only cuda uses this function,
         # so we don't abstract it into the base class
         maybe_ca_context = nullcontext()
+        maybe_aiter_context = nullcontext()
         from vllm.distributed.device_communicators.cuda_communicator import (
             CudaCommunicator,
         )
@@ -480,13 +483,20 @@ def graph_capture(self, graph_capture_context: GraphCaptureContext | None = None
             if ca_comm is not None:
                 maybe_ca_context = ca_comm.capture()  # type: ignore
 
+            from vllm._aiter_ops import rocm_aiter_ops
+
+            if rocm_aiter_ops.is_enabled():
+                aiter_ar = rocm_aiter_ops.get_aiter_allreduce()
+                if aiter_ar is not None:
+                    maybe_aiter_context = aiter_ar.capture()  # type: ignore
+
         # ensure all initialization operations complete before attempting to
         # capture the graph on another stream
         curr_stream = torch.cuda.current_stream()
         if curr_stream != stream:
             stream.wait_stream(curr_stream)
 
-        with torch.cuda.stream(stream), maybe_ca_context:
+        with torch.cuda.stream(stream), maybe_ca_context, maybe_aiter_context:
             yield graph_capture_context
 
     def all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
diff --git a/vllm/distributed/weight_transfer/base.py b/vllm/distributed/weight_transfer/base.py
index 788dcef128e5..743971156b93 100644
--- a/vllm/distributed/weight_transfer/base.py
+++ b/vllm/distributed/weight_transfer/base.py
@@ -4,7 +4,7 @@
 
 from abc import ABC, abstractmethod
 from collections.abc import Callable, Iterator
-from dataclasses import KW_ONLY, dataclass, field
+from dataclasses import dataclass, field
 from typing import Any, Generic, TypeVar
 
 import torch
@@ -28,11 +28,7 @@ class WeightTransferInitInfo(ABC):  # noqa: B024
 class WeightTransferUpdateInfo(ABC):  # noqa: B024
     """Base class for backend-specific weight update info."""
 
-    _: KW_ONLY
-    is_checkpoint_format: bool = True
-    """Set to True if weights are in checkpoint/original model format and need
-    layerwise processing. Set to False if weights have already been processed
-    into kernel format (repacking, renaming, etc.)."""
+    pass
 
 
 # API-level request classes (accept dicts for backend-agnostic serialization)
diff --git a/vllm/distributed/weight_transfer/ipc_engine.py b/vllm/distributed/weight_transfer/ipc_engine.py
index 43b23be544c1..1b108516f219 100644
--- a/vllm/distributed/weight_transfer/ipc_engine.py
+++ b/vllm/distributed/weight_transfer/ipc_engine.py
@@ -211,6 +211,11 @@ def trainer_send_weights(
         - 'ray': Sends weights via Ray RPC to a Ray-based LLM handle
         - 'http': Sends weights via HTTP POST to a vLLM HTTP server
 
+        .. note::
+            This method calls ``update_weights`` internally. The caller must
+            call ``start_weight_update`` before and ``finish_weight_update``
+            after this method.
+
         Args:
             iterator: Iterator of model parameters. Returns (name, tensor) tuples.
                      Tensors should be on the same GPU as the inference workers.
diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py
index 03a460fbe95a..e65ef6e425bb 100644
--- a/vllm/engine/arg_utils.py
+++ b/vllm/engine/arg_utils.py
@@ -105,7 +105,11 @@
 from vllm.transformers_utils.gguf_utils import is_gguf
 from vllm.transformers_utils.repo_utils import get_model_path
 from vllm.transformers_utils.utils import is_cloud_storage
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.argparse_utils import (
+    FlexibleArgumentParser,
+    human_readable_int,
+    human_readable_int_or_auto,
+)
 from vllm.utils.mem_constants import GiB_bytes
 from vllm.utils.network_utils import get_ip
 from vllm.utils.torch_utils import resolve_kv_cache_dtype_string
@@ -256,6 +260,28 @@ def _maybe_add_docs_url(cls: Any) -> str:
     return f"\n\nAPI docs: https://docs.vllm.ai/en/{version}/api/vllm/config/#vllm.config.{cls.__name__}"
 
 
+def _expand_json_human_readable_numbers(val: str) -> str:
+    """Expand human-readable number suffixes in a JSON string.
+
+    Based on :func:`human_readable_int` so that the ``k/m/g/t`` (decimal) and
+    ``K/M/G/T`` (binary) conventions work out the box.
+    Also works inside JSON config arguments such
+    as ``--kv-transfer-config '{"cpu_bytes_to_use": 80m}'``.
+
+    Only bare (unquoted) tokens are replaced so that JSON string values
+    like ``"model_name"`` are never modified.
+    """
+    # Split on quoted strings so we only touch non-string regions.
+    parts = re.split(r'("(?:[^"\\]|\\.)*")', val)
+    for i in range(0, len(parts), 2):  # even indices = outside strings
+        parts[i] = re.sub(
+            r"\b\d+(?:\.\d+)?[kKmMgGtT]\b",
+            lambda m: str(human_readable_int(m.group())),
+            parts[i],
+        )
+    return "".join(parts)
+
+
 @functools.lru_cache(maxsize=30)
 def _compute_kwargs(cls: ConfigType) -> dict[str, dict[str, Any]]:
     # Save time only getting attr docs if we're generating help text
@@ -301,6 +327,7 @@ def _compute_kwargs(cls: ConfigType) -> dict[str, dict[str, Any]]:
 
             def parse_dataclass(val: str, cls=dataclass_cls) -> Any:
                 try:
+                    val = _expand_json_human_readable_numbers(val)
                     return TypeAdapter(cls).validate_json(val)
                 except ValidationError as e:
                     raise argparse.ArgumentTypeError(repr(e)) from e
@@ -308,6 +335,11 @@ def parse_dataclass(val: str, cls=dataclass_cls) -> Any:
             kwargs[name]["type"] = parse_dataclass
             kwargs[name]["help"] += _maybe_add_docs_url(dataclass_cls)
             kwargs[name]["help"] += f"\n\n{json_tip}"
+        elif type_hints == {bool, str, type(None)}:
+            # Optional-valued flag: bare flag -> True, value -> str.
+            kwargs[name]["type"] = str
+            kwargs[name]["nargs"] = "?"
+            kwargs[name]["const"] = True
         elif contains_type(type_hints, bool):
             # Creates --no-<name> and --<name> flags
             kwargs[name]["action"] = argparse.BooleanOptionalAction
@@ -323,7 +355,11 @@ def parse_dataclass(val: str, cls=dataclass_cls) -> Any:
             if name == "max_model_len":
                 kwargs[name]["type"] = human_readable_int_or_auto
                 kwargs[name]["help"] += f"\n\n{human_readable_int_or_auto.__doc__}"
-            elif name in ("max_num_batched_tokens", "kv_cache_memory_bytes"):
+            elif name in (
+                "max_num_batched_tokens",
+                "kv_cache_memory_bytes",
+                "safetensors_prefetch_block_size",
+            ):
                 kwargs[name]["type"] = human_readable_int
                 kwargs[name]["help"] += f"\n\n{human_readable_int.__doc__}"
             else:
@@ -392,6 +428,8 @@ class EngineArgs:
     allowed_media_domains: list[str] | None = ModelConfig.allowed_media_domains
     download_dir: str | None = LoadConfig.download_dir
     safetensors_load_strategy: str | None = LoadConfig.safetensors_load_strategy
+    safetensors_prefetch_num_threads: int = LoadConfig.safetensors_prefetch_num_threads
+    safetensors_prefetch_block_size: int = LoadConfig.safetensors_prefetch_block_size
     load_format: str | LoadFormats = LoadConfig.load_format
     config_format: str = ModelConfig.config_format
     dtype: ModelDType = ModelConfig.dtype
@@ -515,6 +553,14 @@ class EngineArgs:
     mm_encoder_attn_backend: AttentionBackendEnum | str | None = (
         MultiModalConfig.mm_encoder_attn_backend
     )
+    mm_encoder_attn_dtype: str | None = MultiModalConfig.mm_encoder_attn_dtype
+    mm_encoder_fp8_scale_path: str | None = MultiModalConfig.mm_encoder_fp8_scale_path
+    mm_encoder_fp8_scale_save_path: str | None = (
+        MultiModalConfig.mm_encoder_fp8_scale_save_path
+    )
+    mm_encoder_fp8_scale_save_margin: float = (
+        MultiModalConfig.mm_encoder_fp8_scale_save_margin
+    )
     io_processor_plugin: str | None = None
     renderer_num_workers: int = 1
     skip_mm_profiling: bool = MultiModalConfig.skip_mm_profiling
@@ -766,16 +812,7 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             "--served-model-name", **model_kwargs["served_model_name"]
         )
         model_group.add_argument("--config-format", **model_kwargs["config_format"])
-        # This one is a special case because it can bool
-        # or str. TODO: Handle this in get_kwargs
-        model_group.add_argument(
-            "--hf-token",
-            type=str,
-            nargs="?",
-            const=True,
-            default=model_kwargs["hf_token"]["default"],
-            help=model_kwargs["hf_token"]["help"],
-        )
+        model_group.add_argument("--hf-token", **model_kwargs["hf_token"])
         model_group.add_argument("--hf-overrides", **model_kwargs["hf_overrides"])
         model_group.add_argument("--pooler-config", **model_kwargs["pooler_config"])
         model_group.add_argument(
@@ -813,6 +850,14 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
         load_group.add_argument(
             "--safetensors-load-strategy", **load_kwargs["safetensors_load_strategy"]
         )
+        load_group.add_argument(
+            "--safetensors-prefetch-num-threads",
+            **load_kwargs["safetensors_prefetch_num_threads"],
+        )
+        load_group.add_argument(
+            "--safetensors-prefetch-block-size",
+            **load_kwargs["safetensors_prefetch_block_size"],
+        )
         load_group.add_argument(
             "--model-loader-extra-config", **load_kwargs["model_loader_extra_config"]
         )
@@ -927,7 +972,9 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             "-dpn",
             type=int,
             help="Data parallel rank of this instance. "
-            "When set, enables external load balancer mode.",
+            "When set, enables external load balancer mode for MoE "
+            "data-parallel deployments. Unsupported for non-MoE models; "
+            "launch independent vLLM instances instead.",
         )
         parallel_group.add_argument(
             "--data-parallel-start-rank",
@@ -1152,6 +1199,22 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             "--mm-encoder-attn-backend",
             **multimodal_kwargs["mm_encoder_attn_backend"],
         )
+        multimodal_group.add_argument(
+            "--mm-encoder-attn-dtype",
+            **multimodal_kwargs["mm_encoder_attn_dtype"],
+        )
+        multimodal_group.add_argument(
+            "--mm-encoder-fp8-scale-path",
+            **multimodal_kwargs["mm_encoder_fp8_scale_path"],
+        )
+        multimodal_group.add_argument(
+            "--mm-encoder-fp8-scale-save-path",
+            **multimodal_kwargs["mm_encoder_fp8_scale_save_path"],
+        )
+        multimodal_group.add_argument(
+            "--mm-encoder-fp8-scale-save-margin",
+            **multimodal_kwargs["mm_encoder_fp8_scale_save_margin"],
+        )
         multimodal_group.add_argument(
             "--interleave-mm-strings", **multimodal_kwargs["interleave_mm_strings"]
         )
@@ -1490,6 +1553,10 @@ def create_model_config(self) -> ModelConfig:
             mm_encoder_only=self.mm_encoder_only,
             mm_encoder_tp_mode=self.mm_encoder_tp_mode,
             mm_encoder_attn_backend=self.mm_encoder_attn_backend,
+            mm_encoder_attn_dtype=self.mm_encoder_attn_dtype,
+            mm_encoder_fp8_scale_path=self.mm_encoder_fp8_scale_path,
+            mm_encoder_fp8_scale_save_path=self.mm_encoder_fp8_scale_save_path,
+            mm_encoder_fp8_scale_save_margin=self.mm_encoder_fp8_scale_save_margin,
             pooler_config=self.pooler_config,
             generation_config=self.generation_config,
             override_generation_config=self.override_generation_config,
@@ -1531,6 +1598,8 @@ def create_load_config(self) -> LoadConfig:
             load_format=self.load_format,
             download_dir=self.download_dir,
             safetensors_load_strategy=self.safetensors_load_strategy,
+            safetensors_prefetch_num_threads=self.safetensors_prefetch_num_threads,
+            safetensors_prefetch_block_size=self.safetensors_prefetch_block_size,
             model_loader_extra_config=self.model_loader_extra_config,
             ignore_patterns=self.ignore_patterns,
             use_tqdm_on_load=self.use_tqdm_on_load,
@@ -1642,6 +1711,17 @@ def create_engine_config(
             kv_offloading_backend=self.kv_offloading_backend,
         )
 
+        if resolved_cache_dtype.startswith("turboquant_"):
+            from vllm.model_executor.layers.quantization.turboquant.config import (
+                TurboQuantConfig,
+            )
+
+            boundary = TurboQuantConfig.get_boundary_skip_layers(model_config)
+            existing = set(cache_config.kv_cache_dtype_skip_layers)
+            cache_config.kv_cache_dtype_skip_layers = sorted(
+                existing | set(boundary), key=int
+            )
+
         ray_runtime_env = None
         if is_ray_initialized():
             # Ray Serve LLM calls `create_engine_config` in the context
@@ -1713,6 +1793,16 @@ def create_engine_config(
         data_parallel_external_lb = (
             self.data_parallel_external_lb or self.data_parallel_rank is not None
         )
+        if (
+            self.data_parallel_size > 1
+            and data_parallel_external_lb
+            and not model_config.is_moe
+        ):
+            raise ValueError(
+                "Non-MoE models do not support external data parallel mode. "
+                "For external load balancing, launch independent vLLM "
+                "instances without --data-parallel-* arguments."
+            )
         # Local DP rank = 1, use pure-external LB.
         if data_parallel_external_lb:
             assert self.data_parallel_rank is not None, (
@@ -1948,6 +2038,19 @@ def create_engine_config(
                 self.attention_backend
             )
 
+        # TurboQuant requires FlashAttention 2 — FA3 boundary layers assert
+        # FlashAttentionImpl which fails with TurboQuantAttentionImpl.
+        if resolved_cache_dtype.startswith("turboquant_") and (
+            attention_config.flash_attn_version is None
+            or attention_config.flash_attn_version >= 3
+        ):
+            logger.warning(
+                "TurboQuant is not yet compatible with FlashAttention >= 3. "
+                "Overriding flash_attn_version to 2. To silence this "
+                "warning, pass --attention-config.flash_attn_version=2"
+            )
+            attention_config.flash_attn_version = 2
+
         # Mamba config overrides
         mamba_config = copy.deepcopy(self.mamba_config)
         # Convert string to enum if needed (CLI parsing returns a string)
@@ -2216,7 +2319,6 @@ def _set_default_chunked_prefill_and_prefix_caching_args(
                 "This model does not officially support disabling chunked prefill. "
                 "Disabling this manually may cause the engine to crash "
                 "or produce incorrect outputs.",
-                scope="local",
             )
         elif (
             model_config.runner_type == "pooling"
@@ -2227,7 +2329,6 @@ def _set_default_chunked_prefill_and_prefix_caching_args(
                 "This model does not officially support chunked prefill. "
                 "Enabling this manually may cause the engine to crash "
                 "or produce incorrect outputs.",
-                scope="local",
             )
 
         if self.enable_prefix_caching is None:
@@ -2246,7 +2347,6 @@ def _set_default_chunked_prefill_and_prefix_caching_args(
                 "This model does not officially support prefix caching. "
                 "Enabling this manually may cause the engine to crash "
                 "or produce incorrect outputs.",
-                scope="local",
             )
 
         # Disable chunked prefill and prefix caching for:
@@ -2384,68 +2484,3 @@ def _raise_unsupported_error(feature_name: str):
         f"remove {feature_name} from your config."
     )
     raise NotImplementedError(msg)
-
-
-def human_readable_int(value: str) -> int:
-    """Parse human-readable integers like '1k', '2M', etc.
-    Including decimal values with decimal multipliers.
-
-    Examples:
-    - '1k' -> 1,000
-    - '1K' -> 1,024
-    - '25.6k' -> 25,600
-    """
-    value = value.strip()
-
-    match = re.fullmatch(r"(\d+(?:\.\d+)?)([kKmMgGtT])", value)
-    if match:
-        decimal_multiplier = {
-            "k": 10**3,
-            "m": 10**6,
-            "g": 10**9,
-            "t": 10**12,
-        }
-        binary_multiplier = {
-            "K": 2**10,
-            "M": 2**20,
-            "G": 2**30,
-            "T": 2**40,
-        }
-
-        number, suffix = match.groups()
-        if suffix in decimal_multiplier:
-            mult = decimal_multiplier[suffix]
-            return int(float(number) * mult)
-        elif suffix in binary_multiplier:
-            mult = binary_multiplier[suffix]
-            # Do not allow decimals with binary multipliers
-            try:
-                return int(number) * mult
-            except ValueError as e:
-                raise argparse.ArgumentTypeError(
-                    "Decimals are not allowed "
-                    f"with binary suffixes like {suffix}. Did you mean to use "
-                    f"{number}{suffix.lower()} instead?"
-                ) from e
-
-    # Regular plain number.
-    return int(value)
-
-
-def human_readable_int_or_auto(value: str) -> int:
-    """Parse human-readable integers like '1k', '2M', etc.
-    Including decimal values with decimal multipliers.
-    Also accepts -1 or 'auto' as a special value for auto-detection.
-
-    Examples:
-    - '1k' -> 1,000
-    - '1K' -> 1,024
-    - '25.6k' -> 25,600
-    - '-1' or 'auto' -> -1 (special value for auto-detection)
-    """
-    value = value.strip()
-
-    if value == "-1" or value.lower() == "auto":
-        return -1
-
-    return human_readable_int(value)
diff --git a/vllm/engine/protocol.py b/vllm/engine/protocol.py
index 50013a060a8f..3f83734a5b78 100644
--- a/vllm/engine/protocol.py
+++ b/vllm/engine/protocol.py
@@ -78,6 +78,7 @@ def generate(
         priority: int = 0,
         data_parallel_rank: int | None = None,
         reasoning_ended: bool | None = None,
+        reasoning_parser_kwargs: dict[str, Any] | None = None,
     ) -> AsyncGenerator[RequestOutput, None]:
         """Generate outputs for a request."""
         ...
@@ -107,6 +108,20 @@ async def abort(self, request_id: str | Iterable[str]) -> None:
         """
         ...
 
+    @abstractmethod
+    async def notify_kv_transfer_request_rejected(
+        self,
+        request_id: str,
+        kv_transfer_params: dict[str, Any],
+        *,
+        data_parallel_rank: int | None = None,
+    ) -> None:
+        """Notify the engine that a KV-transfer request was rejected before
+        engine admission, so connector-side cleanup can run (e.g. free
+        prefill blocks pinned on the P node).
+        """
+        ...
+
     @abstractmethod
     async def is_tracing_enabled(self) -> bool: ...
 
@@ -229,6 +244,14 @@ async def init_weight_transfer_engine(
         """Initialize weight transfer for RL training."""
         raise NotImplementedError
 
+    async def start_weight_update(self, is_checkpoint_format: bool = True) -> None:
+        """Start a new weight update."""
+        raise NotImplementedError
+
     async def update_weights(self, request: WeightTransferUpdateRequest) -> None:
         """Batched weight update for RL training."""
         raise NotImplementedError
+
+    async def finish_weight_update(self) -> None:
+        """Finish the current weight update."""
+        raise NotImplementedError
diff --git a/vllm/entrypoints/anthropic/protocol.py b/vllm/entrypoints/anthropic/protocol.py
index 3445f709109f..f3c4dd7f3e32 100644
--- a/vllm/entrypoints/anthropic/protocol.py
+++ b/vllm/entrypoints/anthropic/protocol.py
@@ -39,6 +39,7 @@ class AnthropicContentBlock(BaseModel):
         "image",
         "tool_use",
         "tool_result",
+        "tool_reference",
         "thinking",
         "redacted_thinking",
     ]
@@ -52,6 +53,8 @@ class AnthropicContentBlock(BaseModel):
     input: dict[str, Any] | None = None
     content: str | list[dict[str, Any]] | None = None
     is_error: bool | None = None
+    # For tool_reference content
+    tool_name: str | None = None
     # For thinking content
     thinking: str | None = None
     signature: str | None = None
@@ -72,6 +75,7 @@ class AnthropicTool(BaseModel):
     name: str
     description: str | None = None
     input_schema: dict[str, Any]
+    defer_loading: bool | None = None
 
     @field_validator("input_schema")
     @classmethod
@@ -117,6 +121,13 @@ class AnthropicMessagesRequest(BaseModel):
         default=None,
         description="KVTransfer parameters used for disaggregated serving.",
     )
+    chat_template_kwargs: dict[str, Any] | None = Field(
+        default=None,
+        description=(
+            "Additional keyword args to pass to the chat template renderer. "
+            "Will be accessible by the template."
+        ),
+    )
 
     @field_validator("model")
     @classmethod
@@ -212,6 +223,15 @@ class AnthropicCountTokensRequest(BaseModel):
     tool_choice: AnthropicToolChoice | None = None
     tools: list[AnthropicTool] | None = None
 
+    # vLLM-specific fields that are not in Anthropic spec
+    chat_template_kwargs: dict[str, Any] | None = Field(
+        default=None,
+        description=(
+            "Additional keyword args to pass to the chat template renderer. "
+            "Will be accessible by the template."
+        ),
+    )
+
     @field_validator("model")
     @classmethod
     def validate_model(cls, v):
diff --git a/vllm/entrypoints/anthropic/serving.py b/vllm/entrypoints/anthropic/serving.py
index 5635cfbd3596..867ee73948ff 100644
--- a/vllm/entrypoints/anthropic/serving.py
+++ b/vllm/entrypoints/anthropic/serving.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # Adapted from
-# https://github.com/vllm/vllm/entrypoints/openai/serving_chat.py
+# https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/chat_completion/serving.py
 
 """Anthropic Messages API serving handler"""
 
@@ -237,6 +237,10 @@ def _convert_block(
             cls._convert_tool_use_block(block, tool_calls)
         elif block.type == "tool_result":
             cls._convert_tool_result_block(block, role, openai_messages, content_parts)
+        elif block.type == "tool_reference":
+            # Tool references are expanded during tool_result processing
+            # when they appear inside tool_result content.
+            pass
 
     @classmethod
     def _convert_tool_use_block(cls, block, tool_calls: list[dict[str, Any]]) -> None:
@@ -275,6 +279,7 @@ def _convert_user_tool_result(
         """Convert user tool_result with text and image support"""
         tool_text = ""
         tool_image_urls: list[str] = []
+        tool_reference: list[dict[str, Any]] = []
 
         if isinstance(block.content, str):
             tool_text = block.content
@@ -291,6 +296,12 @@ def _convert_user_tool_result(
                     url = cls._convert_image_source_to_url(source)
                     if url:
                         tool_image_urls.append(url)
+                elif item_type == "tool_reference":
+                    ref_name = item.get("tool_name") or item.get("name")
+                    if ref_name:
+                        tool_reference.append(
+                            {"type": "tool_reference", "name": ref_name}
+                        )
             tool_text = "\n".join(text_parts)
 
         openai_messages.append(
@@ -312,6 +323,15 @@ def _convert_user_tool_result(
                 }
             )
 
+        if tool_reference:
+            openai_messages.append(
+                {
+                    "role": "tool",
+                    "tool_call_id": block.tool_use_id or "",
+                    "content": tool_reference,  # type: ignore[dict-item]
+                }
+            )
+
     @classmethod
     def _build_base_request(
         cls,
@@ -323,6 +343,7 @@ def _build_base_request(
             return ChatCompletionRequest(
                 model=anthropic_request.model,
                 messages=openai_messages,
+                chat_template_kwargs=anthropic_request.chat_template_kwargs,
             )
 
         return ChatCompletionRequest(
@@ -335,6 +356,7 @@ def _build_base_request(
             top_p=anthropic_request.top_p,
             top_k=anthropic_request.top_k,
             kv_transfer_params=anthropic_request.kv_transfer_params,
+            chat_template_kwargs=anthropic_request.chat_template_kwargs,
         )
 
     @classmethod
@@ -398,6 +420,7 @@ def _convert_tools(
                             "name": tool.name,
                             "description": tool.description,
                             "parameters": tool.input_schema,
+                            "defer_loading": tool.defer_loading,
                         },
                     }
                 )
diff --git a/vllm/entrypoints/chat_utils.py b/vllm/entrypoints/chat_utils.py
index c1324d2f039d..cfe0857b679e 100644
--- a/vllm/entrypoints/chat_utils.py
+++ b/vllm/entrypoints/chat_utils.py
@@ -11,7 +11,7 @@
 from functools import cached_property, lru_cache, partial
 from itertools import accumulate
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Generic, Literal, TypeAlias, TypeVar, cast
+from typing import TYPE_CHECKING, Any, Final, Generic, Literal, TypeAlias, TypeVar, cast
 
 from openai.types.chat import (
     ChatCompletionAssistantMessageParam,
@@ -36,10 +36,11 @@
 from pydantic import BaseModel, ConfigDict, TypeAdapter
 
 # pydantic needs the TypedDict from typing_extensions
-from typing_extensions import Required, TypedDict
+from typing_extensions import Required, TypedDict, override
 
 from vllm import envs
 from vllm.config import ModelConfig
+from vllm.exceptions import VLLMValidationError
 from vllm.inputs import MultiModalDataDict, MultiModalUUIDDict
 from vllm.logger import init_logger
 from vllm.model_executor.models import SupportsMultiModal
@@ -54,6 +55,10 @@
 )
 from vllm.multimodal.media import MEDIA_CONNECTOR_REGISTRY, MediaConnector
 from vllm.multimodal.processing import BaseMultiModalProcessor
+from vllm.renderers.embed_utils import (
+    safe_load_prompt_embeds,
+    safe_load_prompt_embeds_async,
+)
 from vllm.utils import random_uuid
 from vllm.utils.collection_utils import is_list_of
 from vllm.utils.import_utils import LazyLoader
@@ -97,9 +102,40 @@ class ChatTemplateResolutionError(ValueError):
     "image": "<##IMAGE##>",
     "audio": "<##AUDIO##>",
     "video": "<##VIDEO##>",
+    "prompt_embeds": "<##PROMPT_EMBEDS##>",
 }
 
 
+PROMPT_EMBEDS_PLACEHOLDER_TOKEN: Final[str] = "<prompt_embeds>"
+"""The special token used as a placeholder for each embedding
+position during chat template rendering.
+
+Registered as an additional special token when `--enable-prompt-embeds` is set.
+See `_ensure_prompt_embeds_placeholder_token` in `vllm/renderers/hf.py`.
+"""
+
+
+_REQUIRE_MM_PROCESSOR_ERROR: Final[str] = (
+    "Resolving modality {modality!r} requires a multimodal processor "
+    "but none is available."
+)
+
+_ENABLE_PROMPT_EMBEDS_ERROR: Final[str] = (
+    "You must set `--enable-prompt-embeds` to input `prompt_embeds`"
+)
+
+_PROMPT_EMBEDS_MISSING_DATA_ERROR: Final[str] = (
+    "prompt_embeds content part requires a non-empty `data` field "
+    "with base64-encoded tensor bytes."
+)
+
+_RESERVED_PLACEHOLDER_IN_TEXT_ERROR: Final[str] = (
+    "Text content may not contain the reserved placeholder {token!r}. "
+    "This placeholder is used internally to mark `prompt_embeds` splice "
+    "positions in the tokenized prompt."
+)
+
+
 class AudioURL(TypedDict, total=False):
     url: Required[str]
     """
@@ -146,6 +182,17 @@ class ChatCompletionContentPartAudioEmbedsParam(TypedDict, total=False):
     """
 
 
+class ChatCompletionContentPartPromptEmbedsParam(TypedDict, total=False):
+    data: Required[str]
+    """
+    Base64-encoded bytes of a serialized `torch.Tensor` of shape
+    `(num_tokens, hidden_size)`. The tensor's `dtype` and `hidden_size` must
+    match the model's input embedding layer.
+    """
+    type: Required[Literal["prompt_embeds"]]
+    """The type of the content part."""
+
+
 class VideoURL(TypedDict, total=False):
     url: Required[str]
     """
@@ -254,6 +301,23 @@ class CustomThinkCompletionContentParam(TypedDict, total=False):
     """The thinking type."""
 
 
+class CustomChatCompletionContentToolReferenceParam(TypedDict, total=False):
+    """A tool reference content param that only accepts a plain tool name.
+
+    Example:
+    {
+        "name": "get_weather",
+        "type": "tool_reference"
+    }
+    """
+
+    name: str
+    """The name of the tool being referenced."""
+
+    type: Literal["tool_reference"]
+    """The content type."""
+
+
 ChatCompletionContentPartParam: TypeAlias = (
     OpenAIChatCompletionContentPartParam
     | ChatCompletionContentPartAudioParam
@@ -264,8 +328,10 @@ class CustomThinkCompletionContentParam(TypedDict, total=False):
     | CustomChatCompletionContentSimpleImageParam
     | ChatCompletionContentPartImageEmbedsParam
     | ChatCompletionContentPartAudioEmbedsParam
+    | ChatCompletionContentPartPromptEmbedsParam
     | CustomChatCompletionContentSimpleAudioParam
     | CustomChatCompletionContentSimpleVideoParam
+    | CustomChatCompletionContentToolReferenceParam
     | str
     | CustomThinkCompletionContentParam
 )
@@ -290,7 +356,7 @@ class CustomChatCompletionMessageParam(TypedDict, total=False):
     tool_call_id: str | None
     """Tool call that this message is responding to."""
 
-    tool_calls: Iterable[ChatCompletionMessageToolCallParam] | None
+    tool_calls: list[ChatCompletionMessageToolCallParam] | None
     """The tool calls generated by the model, such as function calls."""
 
     reasoning: str | None
@@ -299,6 +365,9 @@ class CustomChatCompletionMessageParam(TypedDict, total=False):
     tools: list[ChatCompletionFunctionToolParam] | None
     """The tools for developer role."""
 
+    task: str | None
+    """Model-specific task marker. Currently passed through for DeepSeek V4."""
+
 
 ChatCompletionMessageParam: TypeAlias = (
     OpenAIChatCompletionMessageParam
@@ -321,7 +390,7 @@ class ConversationMessage(TypedDict, total=False):
     name: str | None
     """The name of the function to call"""
 
-    tool_calls: Iterable[ChatCompletionMessageToolCallParam] | None
+    tool_calls: list[ChatCompletionMessageToolCallParam] | None
     """The tool calls generated by the model, such as function calls."""
 
     reasoning: str | None
@@ -333,6 +402,9 @@ class ConversationMessage(TypedDict, total=False):
     tools: list[ChatCompletionFunctionToolParam] | None
     """The tools for developer role."""
 
+    task: str | None
+    """Model-specific task marker. Currently passed through for DeepSeek V4."""
+
 
 # Passed in by user
 ChatTemplateContentFormatOption = Literal["auto", "string", "openai"]
@@ -342,7 +414,13 @@ class ConversationMessage(TypedDict, total=False):
 
 
 ModalityStr = Literal[
-    "image", "audio", "video", "image_embeds", "audio_embeds", "vision_chunk"
+    "image",
+    "audio",
+    "video",
+    "image_embeds",
+    "audio_embeds",
+    "vision_chunk",
+    "prompt_embeds",
 ]
 _T = TypeVar("_T")
 
@@ -524,7 +602,17 @@ def add(self, modality: ModalityStr, item: _T) -> str | None:
 
         An optional uuid can be added which serves as a unique identifier of the
         media.
+
+        Note:
+            `prompt_embeds` bypass MM-processor validation because they are
+            pre-computed embeddings that do not go through any HF processor, encoder,
+            or model-specific placeholder logic. The corresponding placeholder string is
+            managed by the parser via `_add_placeholder`, so we return None here.
         """
+        if modality == "prompt_embeds":
+            self._items_by_modality["prompt_embeds"].append(item)
+            return None
+
         input_modality = modality.replace("_embeds", "")
         original_modality = modality
         use_vision_chunk = (
@@ -635,17 +723,32 @@ def _resolve_vision_chunk_items(
 
 def _resolve_items(
     items_by_modality: dict[str, list[tuple[object, str | None]]],
-    mm_processor: BaseMultiModalProcessor,
+    mm_processor: BaseMultiModalProcessor | None,
     modality_order: dict[str, list[str]],
 ) -> tuple[MultiModalDataDict, MultiModalUUIDDict]:
+    """
+    Materialize the tracker's per-modality items into `mm_data` / `mm_uuids`.
+
+    Note:
+        `mm_processor` is `None` for text-only models (no registered HF
+        processor) whose only modality is `prompt_embeds`. Every other
+        modality requires a processor, enforced by the guard below.
+    """
     if "image" in items_by_modality and "image_embeds" in items_by_modality:
         raise ValueError("Mixing raw image and embedding inputs is not allowed")
     if "audio" in items_by_modality and "audio_embeds" in items_by_modality:
         raise ValueError("Mixing raw audio and embedding inputs is not allowed")
+    # `prompt_embeds` bypasses HF MM processors. Every other modality requires one.
+    processor_modalities = items_by_modality.keys() - {"prompt_embeds"}
+    if processor_modalities and mm_processor is None:
+        raise RuntimeError(
+            _REQUIRE_MM_PROCESSOR_ERROR.format(modality=processor_modalities)
+        )
 
     mm_data = {}
     mm_uuids = {}
     if "image_embeds" in items_by_modality:
+        assert mm_processor is not None
         mm_data["image"] = _get_embeds_data(
             "image",
             [data for data, uuid in items_by_modality["image_embeds"]],
@@ -656,6 +759,7 @@ def _resolve_items(
         mm_data["image"] = [data for data, uuid in items_by_modality["image"]]
         mm_uuids["image"] = [uuid for data, uuid in items_by_modality["image"]]
     if "audio_embeds" in items_by_modality:
+        assert mm_processor is not None
         mm_data["audio"] = _get_embeds_data(
             "audio",
             [data for data, uuid in items_by_modality["audio_embeds"]],
@@ -669,6 +773,7 @@ def _resolve_items(
         mm_data["video"] = [data for data, uuid in items_by_modality["video"]]
         mm_uuids["video"] = [uuid for data, uuid in items_by_modality["video"]]
     if "vision_chunk" in items_by_modality:
+        assert mm_processor is not None
         # Process vision_chunk items - extract from (data, modality) tuples
         # and convert to VisionChunk types with proper UUID handling
         processed_chunks, vision_chunk_uuids = _resolve_vision_chunk_items(
@@ -678,6 +783,10 @@ def _resolve_items(
         )
         mm_data["vision_chunk"] = processed_chunks
         mm_uuids["vision_chunk"] = vision_chunk_uuids
+    if "prompt_embeds" in items_by_modality:
+        mm_data["prompt_embeds"] = [
+            data for data, _uuid in items_by_modality["prompt_embeds"]
+        ]
 
     return mm_data, mm_uuids
 
@@ -689,8 +798,16 @@ def resolve_items(
         if not self._items_by_modality:
             return None, None
 
+        # Text-only models (`is_multimodal_model=False`) with inputs of
+        # modality `prompt_embeds` have no MM processor since `prompt_embeds` are
+        # pre-computed and require no processing, so we pass `None`.
+        mm_processor = (
+            self.mm_processor if self._model_config.is_multimodal_model else None
+        )
         return _resolve_items(
-            dict(self._items_by_modality), self.mm_processor, self._modality_order
+            dict(self._items_by_modality),
+            mm_processor,
+            self._modality_order,
         )
 
     def create_parser(
@@ -713,8 +830,13 @@ async def resolve_items(
             for modality, coros in self._items_by_modality.items()
         }
 
+        mm_processor = (
+            self.mm_processor if self._model_config.is_multimodal_model else None
+        )
         return _resolve_items(
-            resolved_items_by_modality, self.mm_processor, self._modality_order
+            resolved_items_by_modality,
+            mm_processor,
+            self._modality_order,
         )
 
     def create_parser(
@@ -733,10 +855,16 @@ def __init__(self) -> None:
         # general MM placeholder:
         # {
         #   "<##IMAGE##>": ["<image>", "<image>", "<image>"],
-        #   "<##AUDIO##>": ["<audio>", "<audio>"]
+        #   "<##AUDIO##>": ["<audio>", "<audio>"],
+        #   "<##PROMPT_EMBEDS##>": ["<prompt_embeds>", "<prompt_embeds>"]
         # }
         self._placeholder_storage: dict[str, list] = defaultdict(list)
 
+    @property
+    @abstractmethod
+    def model_config(self) -> ModelConfig:
+        raise NotImplementedError
+
     def _add_placeholder(self, modality: ModalityStr, placeholder: str | None):
         mod_placeholder = MODALITY_PLACEHOLDERS_MAP[modality]
         if placeholder:
@@ -781,6 +909,10 @@ def parse_audio_embeds(
     ) -> None:
         raise NotImplementedError
 
+    @abstractmethod
+    def parse_prompt_embeds(self, data: str) -> None:
+        raise NotImplementedError
+
     @abstractmethod
     def parse_video(self, video_url: str | None, uuid: str | None = None) -> None:
         raise NotImplementedError
@@ -809,6 +941,21 @@ def __init__(
     def model_config(self) -> ModelConfig:
         return self._tracker.model_config
 
+    @override
+    def parse_prompt_embeds(self, data: str) -> None:
+        """Decode a base64 prompt embeds tensor and store it in the tracker.
+
+        Emits a single `PROMPT_EMBEDS_PLACEHOLDER_TOKEN` sentinel per
+        content part. The renderer later expands each sentinel to a span of
+        `tensor.shape[0]` placeholder tokens after tokenization.
+        """
+        if not self.model_config.enable_prompt_embeds:
+            raise ValueError(_ENABLE_PROMPT_EMBEDS_ERROR)
+
+        tensor = safe_load_prompt_embeds(self.model_config, data.encode())
+        self._tracker.add("prompt_embeds", (tensor, None))
+        self._add_placeholder("prompt_embeds", PROMPT_EMBEDS_PLACEHOLDER_TOKEN)
+
     def parse_image(self, image_url: str | None, uuid: str | None = None) -> None:
         image = self._connector.fetch_image(image_url) if image_url else None
 
@@ -933,6 +1080,29 @@ def __init__(
     def model_config(self) -> ModelConfig:
         return self._tracker.model_config
 
+    @override
+    def parse_prompt_embeds(self, data: str) -> None:
+        """Schedule async prompt embeds decode and store the coroutine in the tracker.
+
+        Like the sync variant, emits a single sentinel `PROMPT_EMBEDS_PLACEHOLDER_TOKEN`
+        per content part. Unlike the sync variant, the tensor decode is deferred to a
+        thread-pool executor via `safe_load_prompt_embeds_async`.
+        """
+        if not self.model_config.enable_prompt_embeds:
+            raise ValueError(_ENABLE_PROMPT_EMBEDS_ERROR)
+
+        coro = self._load_prompt_embeds_async(data.encode())
+        self._tracker.add("prompt_embeds", coro)
+        self._add_placeholder("prompt_embeds", PROMPT_EMBEDS_PLACEHOLDER_TOKEN)
+
+    async def _load_prompt_embeds_async(
+        self, data_bytes: bytes
+    ) -> tuple[torch.Tensor, None]:
+        # Second tuple slot fills the tracker's generic `(item, uuid | None)`
+        # contract. prompt_embeds has no UUID concept, so it's always `None`.
+        tensor = await safe_load_prompt_embeds_async(self.model_config, data_bytes)
+        return tensor, None
+
     async def _image_with_uuid_async(self, image_url: str | None, uuid: str | None):
         image = (
             await self._connector.fetch_image_async(image_url) if image_url else None
@@ -1244,6 +1414,7 @@ def _get_full_multimodal_text_prompt(
 _TextParser = partial(cast, ChatCompletionContentPartTextParam)
 _ImageEmbedsParser = partial(cast, ChatCompletionContentPartImageEmbedsParam)
 _AudioEmbedsParser = partial(cast, ChatCompletionContentPartAudioEmbedsParam)
+_PromptEmbedsParser = partial(cast, ChatCompletionContentPartPromptEmbedsParam)
 _InputAudioParser = partial(cast, ChatCompletionContentPartInputAudioParam)
 _RefusalParser = partial(cast, ChatCompletionContentPartRefusalParam)
 _PILImageParser = partial(cast, CustomChatCompletionContentPILImageParam)
@@ -1269,11 +1440,15 @@ def _get_full_multimodal_text_prompt(
     "image_url": lambda part: _ImageParser(part).get("image_url", {}).get("url", None),
     "image_embeds": lambda part: _ImageEmbedsParser(part).get("image_embeds", None),
     "audio_embeds": lambda part: _AudioEmbedsParser(part).get("audio_embeds", None),
+    "prompt_embeds": lambda part: _PromptEmbedsParser(part).get("data", None),
     "image_pil": lambda part: _PILImageParser(part).get("image_pil", None),
     "audio_url": lambda part: _AudioParser(part).get("audio_url", {}).get("url", None),
     "input_audio": lambda part: _InputAudioParser(part).get("input_audio", None),
     "refusal": lambda part: _RefusalParser(part).get("refusal", None),
     "video_url": lambda part: _VideoParser(part).get("video_url", {}).get("url", None),
+    "tool_reference": lambda part: cast(
+        CustomChatCompletionContentToolReferenceParam, part
+    ).get("name", None),
 }
 
 
@@ -1344,6 +1519,11 @@ def _parse_chat_message_content_mm_part(
             )
             audio_embeds = audio_params.get("audio_embeds", None)
             return "audio_embeds", audio_embeds
+        if "prompt_embeds" in part:
+            prompt_embeds_params = cast(  # type: ignore[assignment]
+                ChatCompletionContentPartPromptEmbedsParam, part
+            )
+            return "prompt_embeds", prompt_embeds_params.get("data", None)
         if "audio_url" in part:
             audio_params = cast(  # type: ignore[assignment]
                 CustomChatCompletionContentSimpleAudioParam, part
@@ -1365,6 +1545,12 @@ def _parse_chat_message_content_mm_part(
                 # with url as a dict of {"url": url}
                 video_url = video_url.get("url", None)
             return "video_url", video_url
+        if "tool_reference" in part:
+            tool_reference_params = cast(
+                CustomChatCompletionContentToolReferenceParam, part
+            )
+            tool_reference = tool_reference_params.get("name", None)
+            return "tool_reference", tool_reference
         # Raise an error if no 'type' or direct URL is found.
         raise ValueError("Missing 'type' field in multimodal part.")
 
@@ -1421,6 +1607,24 @@ def _parse_chat_message_content_parts(
     return [ConversationMessage(role=role, content=text_prompt)]
 
 
+def _reject_reserved_placeholder_in_text(text: str, model_config: ModelConfig) -> None:
+    """Reject user-supplied text parts that contains the reserved `prompt_embeds`
+    placeholder sentinel.
+
+    When the server accepts `prompt_embeds`, the placeholder token is
+    registered as a single unsplittable special token on the tokenizer. Any
+    user text that happens to contain the literal sequence would tokenize to
+    the same ID and be mistaken for a splice point by the renderer, letting a
+    caller move or inject splice positions via plain text content.
+    """
+    if model_config.enable_prompt_embeds and PROMPT_EMBEDS_PLACEHOLDER_TOKEN in text:
+        raise ValueError(
+            _RESERVED_PLACEHOLDER_IN_TEXT_ERROR.format(
+                token=PROMPT_EMBEDS_PLACEHOLDER_TOKEN
+            )
+        )
+
+
 def _parse_chat_message_content_part(
     part: ChatCompletionContentPartParam,
     mm_parser: BaseMultiModalContentParser,
@@ -1436,6 +1640,7 @@ def _parse_chat_message_content_part(
     with multimodal placeholders.
     """
     if isinstance(part, str):  # Handle plain text parts
+        _reject_reserved_placeholder_in_text(part, mm_parser.model_config)
         if wrap_dicts:
             return {"type": "text", "text": part}
         return part
@@ -1454,6 +1659,7 @@ def _parse_chat_message_content_part(
 
     if part_type in ("text", "input_text", "output_text", "refusal", "thinking"):
         str_content = cast(str, content)
+        _reject_reserved_placeholder_in_text(str_content, mm_parser.model_config)
         if wrap_dicts:
             return {"type": "text", "text": str_content}
         else:
@@ -1482,6 +1688,11 @@ def _parse_chat_message_content_part(
         content = cast(str | dict[str, str], content) if content is not None else None
         mm_parser.parse_audio_embeds(content, uuid)
         modality = "audio"
+    elif part_type == "prompt_embeds":
+        if not content:
+            raise ValueError(_PROMPT_EMBEDS_MISSING_DATA_ERROR)
+        mm_parser.parse_prompt_embeds(cast(str, content))
+        modality = "prompt_embeds"
     elif part_type == "audio_url":
         str_content = cast(str, content)
         mm_parser.parse_audio(str_content, uuid)
@@ -1494,11 +1705,34 @@ def _parse_chat_message_content_part(
         str_content = cast(str, content)
         mm_parser.parse_video(str_content, uuid)
         modality = "video"
+    elif part_type == "tool_reference":
+        # Tool references are not multimodal data — they reference deferred
+        # tools and are passed through as-is for the chat template to expand.
+        if wrap_dicts:
+            return {"type": "tool_reference", "name": cast(str, content)}
+        return cast(str, content)
     else:
-        raise NotImplementedError(f"Unknown part type: {part_type}")
+        supported = sorted(MM_PARSER_MAP.keys() | set(PART_TYPES_TO_SKIP_NONE_CONTENT))
+        raise VLLMValidationError(
+            f"Unsupported chat content part type: {part_type!r}. "
+            f"Supported types: {', '.join(supported)}.",
+            parameter="type",
+            value=part_type,
+        )
 
     if wrap_dicts:
+        if modality == "prompt_embeds":
+            # Chat templates don't know about the "prompt_embeds" modality,
+            # emit the single sentinel token as text so the template renders
+            # it inline. The renderer later expands it to N tokens post-tokenize.
+            return {"type": "text", "text": PROMPT_EMBEDS_PLACEHOLDER_TOKEN}
         return {"type": modality}
+    if modality == "prompt_embeds":
+        # Emit the renderer token inline regardless of `interleave_strings`,
+        # prompt_embeds are spliced at the token offset so position matters.
+        # Falling back to front-padding via `missing_placeholders` would
+        # reorder them relative to surrounding text.
+        return PROMPT_EMBEDS_PLACEHOLDER_TOKEN
     return MODALITY_PLACEHOLDERS_MAP[modality] if interleave_strings else None
 
 
@@ -1550,10 +1784,35 @@ def _parse_chat_message_content(
             parsed_msg = _ToolParser(message)
             if "tool_call_id" in parsed_msg:
                 result_msg["tool_call_id"] = parsed_msg["tool_call_id"]
+            # Normalize tool message content from OpenAI array format to plain
+            # string. Clients like Claude Code / Cursor send tool results as
+            # [{"type": "text", "text": "..."}], but most chat templates only
+            # handle string content for tool messages.
+            # However, tool_reference items must be preserved as structured
+            # dicts for the chat template to expand them.
+            msg_content = result_msg.get("content")
+            if isinstance(msg_content, list):
+                has_non_text = any(
+                    isinstance(item, dict) and item.get("type") != "text"
+                    for item in msg_content
+                )
+                if has_non_text:
+                    # Keep structured content (e.g., tool_reference)
+                    result_msg["content"] = msg_content
+                else:
+                    texts = [
+                        item.get("text", "")
+                        for item in msg_content
+                        if isinstance(item, dict) and item.get("type") == "text"
+                    ]
+                    result_msg["content"] = "\n".join(texts) if texts else ""
 
         if "name" in message and isinstance(message["name"], str):
             result_msg["name"] = message["name"]
 
+        if "task" in message and isinstance(message["task"], str):
+            result_msg["task"] = message["task"]
+
         if role == "developer":
             result_msg["tools"] = message.get("tools", None)
     return result
@@ -1597,7 +1856,10 @@ def parse_chat_messages(
     MultiModalUUIDDict | None,
 ]:
     conversation: list[ConversationMessage] = []
-    mm_tracker = MultiModalItemTracker(model_config, media_io_kwargs=media_io_kwargs)
+    mm_tracker = MultiModalItemTracker(
+        model_config,
+        media_io_kwargs=media_io_kwargs,
+    )
 
     for msg in messages:
         sub_messages = _parse_chat_message_content(
@@ -1634,7 +1896,8 @@ async def parse_chat_messages_async(
 ]:
     conversation: list[ConversationMessage] = []
     mm_tracker = AsyncMultiModalItemTracker(
-        model_config, media_io_kwargs=media_io_kwargs
+        model_config,
+        media_io_kwargs=media_io_kwargs,
     )
 
     for msg in messages:
diff --git a/vllm/entrypoints/cli/__init__.py b/vllm/entrypoints/cli/__init__.py
index 704d94d36f70..208f01a7cb5e 100644
--- a/vllm/entrypoints/cli/__init__.py
+++ b/vllm/entrypoints/cli/__init__.py
@@ -1,19 +1,2 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from vllm.entrypoints.cli.benchmark.latency import BenchmarkLatencySubcommand
-from vllm.entrypoints.cli.benchmark.mm_processor import (
-    BenchmarkMMProcessorSubcommand,
-)
-from vllm.entrypoints.cli.benchmark.serve import BenchmarkServingSubcommand
-from vllm.entrypoints.cli.benchmark.startup import BenchmarkStartupSubcommand
-from vllm.entrypoints.cli.benchmark.sweep import BenchmarkSweepSubcommand
-from vllm.entrypoints.cli.benchmark.throughput import BenchmarkThroughputSubcommand
-
-__all__: list[str] = [
-    "BenchmarkLatencySubcommand",
-    "BenchmarkMMProcessorSubcommand",
-    "BenchmarkServingSubcommand",
-    "BenchmarkStartupSubcommand",
-    "BenchmarkSweepSubcommand",
-    "BenchmarkThroughputSubcommand",
-]
diff --git a/vllm/entrypoints/cli/benchmark/main.py b/vllm/entrypoints/cli/benchmark/main.py
index 48f34fce1d44..f64de4cf6732 100644
--- a/vllm/entrypoints/cli/benchmark/main.py
+++ b/vllm/entrypoints/cli/benchmark/main.py
@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
+import sys
 import typing
 
 from vllm.entrypoints.cli.benchmark.base import BenchmarkSubcommandBase
@@ -14,6 +15,17 @@
     FlexibleArgumentParser = argparse.ArgumentParser
 
 
+def _import_bench_subcommand_modules() -> None:
+    # Imported lazily so `BenchmarkSubcommandBase` subclasses register only
+    # when `vllm bench` is actually invoked.
+    import vllm.entrypoints.cli.benchmark.latency  # noqa: F401
+    import vllm.entrypoints.cli.benchmark.mm_processor  # noqa: F401
+    import vllm.entrypoints.cli.benchmark.serve  # noqa: F401
+    import vllm.entrypoints.cli.benchmark.startup  # noqa: F401
+    import vllm.entrypoints.cli.benchmark.sweep  # noqa: F401
+    import vllm.entrypoints.cli.benchmark.throughput  # noqa: F401
+
+
 class BenchmarkSubcommand(CLISubcommand):
     """The `bench` subcommand for the vLLM CLI."""
 
@@ -38,18 +50,28 @@ def subparser_init(
         )
         bench_subparsers = bench_parser.add_subparsers(required=True, dest="bench_type")
 
-        for cmd_cls in BenchmarkSubcommandBase.__subclasses__():
-            cmd_subparser = bench_subparsers.add_parser(
-                cmd_cls.name,
-                help=cmd_cls.help,
-                description=cmd_cls.help,
-                usage=f"vllm {self.name} {cmd_cls.name} [options]",
-            )
-            cmd_subparser.set_defaults(dispatch_function=cmd_cls.cmd)
-            cmd_cls.add_cli_args(cmd_subparser)
-            cmd_subparser.epilog = VLLM_SUBCMD_PARSER_EPILOG.format(
-                subcmd=f"{self.name} {cmd_cls.name}"
-            )
+        # Only build the nested bench subparsers when the user is actually
+        # invoking `bench`; otherwise we'd drag in imports
+        # unnecessarily on every `vllm --help` and `vllm serve`.
+        # Scan for the first positional arg so global flags (e.g. `-v`)
+        # before the subcommand don't break detection.
+        first_positional = next(
+            (arg for arg in sys.argv[1:] if not arg.startswith("-")), None
+        )
+        if first_positional == self.name:
+            _import_bench_subcommand_modules()
+            for cmd_cls in BenchmarkSubcommandBase.__subclasses__():
+                cmd_subparser = bench_subparsers.add_parser(
+                    cmd_cls.name,
+                    help=cmd_cls.help,
+                    description=cmd_cls.help,
+                    usage=f"vllm {self.name} {cmd_cls.name} [options]",
+                )
+                cmd_subparser.set_defaults(dispatch_function=cmd_cls.cmd)
+                cmd_cls.add_cli_args(cmd_subparser)
+                cmd_subparser.epilog = VLLM_SUBCMD_PARSER_EPILOG.format(
+                    subcmd=f"{self.name} {cmd_cls.name}"
+                )
         return bench_parser
 
 
diff --git a/vllm/entrypoints/cli/main.py b/vllm/entrypoints/cli/main.py
index 2261ef233134..ac7f9e0a7e02 100644
--- a/vllm/entrypoints/cli/main.py
+++ b/vllm/entrypoints/cli/main.py
@@ -7,6 +7,7 @@
 
 import importlib.metadata
 import sys
+from importlib.util import find_spec
 
 from vllm.logger import init_logger
 
@@ -34,47 +35,63 @@ def main():
 
     cli_env_setup()
 
-    # For 'vllm bench *': use CPU instead of UnspecifiedPlatform by default
-    if len(sys.argv) > 1 and sys.argv[1] == "bench":
-        logger.debug(
-            "Bench command detected, must ensure current platform is not "
-            "UnspecifiedPlatform to avoid device type inference error"
-        )
-        from vllm import platforms
+    # If `--omni` arg is passed to the CLI, delegate to vLLM Omni's entrypoint handling
+    if "--omni" in sys.argv:
+        # NOTE: Check the spec instead of importing directly here, since things could
+        # fail with ImportError due to mismatched versions if things are moved around.
+        spec = find_spec("vllm_omni")
+        if spec is None:
+            logger.error(
+                "--omni flag requires a valid instance of vllm-omni to be installed."
+            )
+            sys.exit(1)
 
-        if platforms.current_platform.is_unspecified():
-            from vllm.platforms.cpu import CpuPlatform
+        from vllm_omni.entrypoints.cli.main import main as omni_main
 
-            platforms.current_platform = CpuPlatform()
-            logger.info(
-                "Unspecified platform detected, switching to CPU Platform instead."
+        logger.info("Delegating entrypoint handling to vllm-omni")
+        omni_main()
+    else:
+        # For 'vllm bench *': use CPU instead of UnspecifiedPlatform by default
+        if len(sys.argv) > 1 and sys.argv[1] == "bench":
+            logger.debug(
+                "Bench command detected, must ensure current platform is not "
+                "UnspecifiedPlatform to avoid device type inference error"
             )
+            from vllm import platforms
 
-    parser = FlexibleArgumentParser(
-        description="vLLM CLI",
-        epilog=VLLM_SUBCMD_PARSER_EPILOG.format(subcmd="[subcommand]"),
-    )
-    parser.add_argument(
-        "-v",
-        "--version",
-        action="version",
-        version=importlib.metadata.version("vllm"),
-    )
-    subparsers = parser.add_subparsers(required=False, dest="subparser")
-    cmds = {}
-    for cmd_module in CMD_MODULES:
-        new_cmds = cmd_module.cmd_init()
-        for cmd in new_cmds:
-            cmd.subparser_init(subparsers).set_defaults(dispatch_function=cmd.cmd)
-            cmds[cmd.name] = cmd
-    args = parser.parse_args()
-    if args.subparser in cmds:
-        cmds[args.subparser].validate(args)
-
-    if hasattr(args, "dispatch_function"):
-        args.dispatch_function(args)
-    else:
-        parser.print_help()
+            if platforms.current_platform.is_unspecified():
+                from vllm.platforms.cpu import CpuPlatform
+
+                platforms.current_platform = CpuPlatform()
+                logger.info(
+                    "Unspecified platform detected, switching to CPU Platform instead."
+                )
+
+        parser = FlexibleArgumentParser(
+            description="vLLM CLI",
+            epilog=VLLM_SUBCMD_PARSER_EPILOG.format(subcmd="[subcommand]"),
+        )
+        parser.add_argument(
+            "-v",
+            "--version",
+            action="version",
+            version=importlib.metadata.version("vllm"),
+        )
+        subparsers = parser.add_subparsers(required=False, dest="subparser")
+        cmds = {}
+        for cmd_module in CMD_MODULES:
+            new_cmds = cmd_module.cmd_init()
+            for cmd in new_cmds:
+                cmd.subparser_init(subparsers).set_defaults(dispatch_function=cmd.cmd)
+                cmds[cmd.name] = cmd
+        args = parser.parse_args()
+        if args.subparser in cmds:
+            cmds[args.subparser].validate(args)
+
+        if hasattr(args, "dispatch_function"):
+            args.dispatch_function(args)
+        else:
+            parser.print_help()
 
 
 if __name__ == "__main__":
diff --git a/vllm/entrypoints/grpc_server.py b/vllm/entrypoints/grpc_server.py
index ddd8a5c50e44..b9173b302ca3 100644
--- a/vllm/entrypoints/grpc_server.py
+++ b/vllm/entrypoints/grpc_server.py
@@ -26,8 +26,10 @@
 
 try:
     import grpc
+    from grpc_health.v1 import health_pb2_grpc
     from grpc_reflection.v1alpha import reflection
     from smg_grpc_proto import vllm_engine_pb2, vllm_engine_pb2_grpc
+    from smg_grpc_servicer.vllm.health_servicer import VllmHealthServicer
     from smg_grpc_servicer.vllm.servicer import VllmEngineServicer
 except ImportError as e:
     raise ImportError(
@@ -98,9 +100,14 @@ async def serve_grpc(args: argparse.Namespace):
     # Add servicer to server
     vllm_engine_pb2_grpc.add_VllmEngineServicer_to_server(servicer, server)
 
+    # Add standard gRPC health service for Kubernetes probes
+    health_servicer = VllmHealthServicer(async_llm)
+    health_pb2_grpc.add_HealthServicer_to_server(health_servicer, server)
+
     # Enable reflection for grpcurl and other tools
     service_names = (
         vllm_engine_pb2.DESCRIPTOR.services_by_name["VllmEngine"].full_name,
+        "grpc.health.v1.Health",
         reflection.SERVICE_NAME,
     )
     reflection.enable_server_reflection(service_names, server)
@@ -147,6 +154,10 @@ def signal_handler():
         logger.info("Shutting down vLLM gRPC server...")
         if stats_task is not None:
             stats_task.cancel()
+        try:
+            health_servicer.set_not_serving()
+        except Exception:  # broad: must not prevent server.stop() / shutdown()
+            logger.warning("Failed to set health status to NOT_SERVING", exc_info=True)
         await server.stop(grace=5.0)
         logger.info("gRPC server stopped")
         async_llm.shutdown()
diff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py
index d296e84d0411..4e8d45301b30 100644
--- a/vllm/entrypoints/llm.py
+++ b/vllm/entrypoints/llm.py
@@ -48,7 +48,7 @@
     ChatTemplateContentFormatOption,
     load_chat_template,
 )
-from vllm.entrypoints.pooling.io_processor_factories import init_pooling_io_processors
+from vllm.entrypoints.pooling.factories import init_pooling_io_processors
 from vllm.entrypoints.pooling.scoring.io_processor import ScoringIOProcessor
 from vllm.entrypoints.pooling.scoring.typing import ScoreInput
 from vllm.entrypoints.pooling.typing import OfflineInputsContext, OfflineOutputsContext
@@ -79,7 +79,7 @@
     prompt_to_seq,
 )
 from vllm.sampling_params import BeamSearchParams, RequestOutputKind, SamplingParams
-from vllm.tasks import PoolingTask
+from vllm.tasks import SCORE_TYPE_MAP, PoolingTask
 from vllm.tokenizers import TokenizerLike
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils.counter import Counter
@@ -228,7 +228,7 @@ def __init__(
         tokenizer_revision: str | None = None,
         chat_template: Path | str | None = None,
         seed: int = 0,
-        gpu_memory_utilization: float = 0.9,
+        gpu_memory_utilization: float = 0.92,
         cpu_offload_gb: float = 0,
         offload_group_size: int = 0,
         offload_num_in_group: int = 1,
@@ -334,7 +334,7 @@ def _make_config(value: Any, cls: type[_R]) -> _R:
                 f"LLM(data_parallel_size={_dp_size}) is not supported for single-"
                 "process usage and may hang. Please use "
                 "the explicit multi-process data-parallel example at "
-                "'examples/offline_inference/data_parallel.py'."
+                "'examples/features/data_parallel/data_parallel_offline.py'."
             )
 
         engine_args = EngineArgs(
@@ -452,6 +452,7 @@ def generate(
         lora_request: Sequence[LoRARequest] | LoRARequest | None = None,
         priority: list[int] | None = None,
         tokenization_kwargs: dict[str, Any] | None = None,
+        mm_processor_kwargs: dict[str, Any] | None = None,
     ) -> list[RequestOutput]:
         """Generates the completions for the input prompts.
 
@@ -479,6 +480,7 @@ def generate(
                 of `prompts`, where each priority value corresponds to the prompt
                 at the same index.
             tokenization_kwargs: Overrides for `tokenizer.encode`.
+            mm_processor_kwargs: Overrides for `processor.__call__`.
 
         Returns:
             A list of `RequestOutput` objects containing the
@@ -503,6 +505,7 @@ def generate(
             lora_request=lora_request,
             tokenization_kwargs=tokenization_kwargs,
             priority=priority,
+            mm_processor_kwargs=mm_processor_kwargs,
         )
 
     def enqueue(
@@ -513,6 +516,7 @@ def enqueue(
         priority: list[int] | None = None,
         use_tqdm: bool | Callable[..., tqdm] = True,
         tokenization_kwargs: dict[str, Any] | None = None,
+        mm_processor_kwargs: dict[str, Any] | None = None,
     ) -> list[str]:
         """Enqueue prompts for generation without waiting for completion.
 
@@ -527,6 +531,7 @@ def enqueue(
             priority: The priority of the requests, if any.
             use_tqdm: If True, shows a tqdm progress bar while adding requests.
             tokenization_kwargs: Overrides for `tokenizer.encode`.
+            mm_processor_kwargs: Overrides for `processor.__call__`.
 
         Returns:
             A list of request IDs for the enqueued requests.
@@ -545,6 +550,7 @@ def enqueue(
             lora_request=lora_request,
             priority=priority,
             tokenization_kwargs=tokenization_kwargs,
+            mm_processor_kwargs=mm_processor_kwargs,
         )
 
     @overload
@@ -843,6 +849,7 @@ def _preprocess_cmpl(
         self,
         prompts: Sequence[PromptType],
         tokenization_kwargs: dict[str, Any] | None = None,
+        mm_processor_kwargs: dict[str, Any] | None = None,
     ) -> Sequence[EngineInput]:
         """
         Convert prompt inputs from LLM APIs (other than [LLM.chat][]) into
@@ -862,15 +869,29 @@ def _preprocess_cmpl(
         tok_params = renderer.default_cmpl_tok_params.with_kwargs(
             **(tokenization_kwargs or {})
         )
+        prompt_extras = (
+            None
+            if mm_processor_kwargs is None
+            else {"mm_processor_kwargs": mm_processor_kwargs}
+        )
 
-        return renderer.render_cmpl(parsed_prompts, tok_params)
+        return renderer.render_cmpl(
+            parsed_prompts,
+            tok_params,
+            prompt_extras=prompt_extras,
+        )
 
     def _preprocess_cmpl_one(
         self,
         prompt: PromptType,
         tokenization_kwargs: dict[str, Any] | None = None,
+        mm_processor_kwargs: dict[str, Any] | None = None,
     ) -> EngineInput:
-        (engine_input,) = self._preprocess_cmpl([prompt], tokenization_kwargs)
+        (engine_input,) = self._preprocess_cmpl(
+            [prompt],
+            tokenization_kwargs,
+            mm_processor_kwargs=mm_processor_kwargs,
+        )
         return engine_input
 
     def _preprocess_chat(
@@ -905,19 +926,28 @@ def _preprocess_chat(
                     add_generation_prompt=add_generation_prompt,
                     continue_final_message=continue_final_message,
                     tools=tools,
-                    tokenize=is_mistral_tokenizer(renderer.tokenizer),
+                    tokenize=(
+                        is_mistral_tokenizer(renderer.tokenizer)
+                        or self.model_config.enable_prompt_embeds
+                    ),
                 ),
             ),
+            mm_processor_kwargs=mm_processor_kwargs,
         )
         tok_params = renderer.default_chat_tok_params.with_kwargs(
             **(tokenization_kwargs or {})
         )
+        prompt_extras = (
+            None
+            if mm_processor_kwargs is None
+            else {"mm_processor_kwargs": mm_processor_kwargs}
+        )
 
         _, engine_inputs = renderer.render_chat(
             conversations,
             chat_params,
             tok_params,
-            prompt_extras={"mm_processor_kwargs": mm_processor_kwargs},
+            prompt_extras=prompt_extras,
         )
 
         return engine_inputs
@@ -1139,19 +1169,16 @@ def _verify_pooling_task(self, pooling_task: PoolingTask | None):
 
         if pooling_task is None:
             raise ValueError(
-                "pooling_task required for `LLM.encode`\n"
-                "Please use one of the more specific methods or set the "
-                "pooling_task when using `LLM.encode`:\n"
-                "  - For embeddings, use `LLM.embed(...)` "
-                'or `pooling_task="embed"`.\n'
-                "  - For classification logits, use `LLM.classify(...)` "
-                'or `pooling_task="classify"`.\n'
-                "  - For similarity scores, use `LLM.score(...)`.\n"
-                "  - For rewards, use `LLM.reward(...)` "
-                'or `pooling_task="token_classify"`\n'
-                "  - For token classification, "
-                'use `pooling_task="token_classify"`\n'
-                '  - For multi-vector retrieval, use `pooling_task="token_embed"`'
+                """
+                pooling_task required for `LLM.encode`.
+                Please use one of the more specific methods or set the pooling_task when using `LLM.encode`:
+                  - For embeddings, use `LLM.embed(...)` or `pooling_task="embed"`.
+                  - For classification logits, use `LLM.classify(...)` or `pooling_task="classify"`.
+                  - For similarity scores, use `LLM.score(...)`.
+                  - For rewards, `pooling_task="classify"` or `pooling_task="token_classify"`.
+                  - For token classification, use `pooling_task="token_classify"`.
+                  - For multi-vector retrieval, use `pooling_task="token_embed"`.
+                """  # noqa: E501
             )
 
         if (
@@ -1180,12 +1207,9 @@ def _verify_pooling_task(self, pooling_task: PoolingTask | None):
                     f"Supported tasks: {self.supported_tasks}"
                 )
             else:
-                logger.warning_once(
-                    "Pooling multitask support is deprecated and will "
-                    "be removed in v0.20. When the default pooling task is "
-                    "not what you want, you need to manually specify it "
-                    'via PoolerConfig(task="%s"). ',
-                    pooling_task,
+                raise ValueError(
+                    f"Try switching the model's pooling_task "
+                    f'via `PoolerConfig(task="{pooling_task}")`'
                 )
 
         if pooling_task == "plugin" and "plugin" not in self.pooling_io_processors:
@@ -1316,6 +1340,11 @@ def reward(
             A list of `PoolingRequestOutput` objects containing the
             pooled hidden states in the same order as the input prompts.
         """
+        logger.warning_once(
+            "`llm.reward` api is deprecated and will be removed in v0.23. "
+            'Please use `LLM.encode` with `pooling_task="classify"` or '
+            '`pooling_task="token_classify"` instead.'
+        )
         return self.encode(
             prompts,
             use_tqdm=use_tqdm,
@@ -1383,7 +1412,7 @@ def score(
                 "pooling model."
             )
 
-        score_type = self.model_config.score_type
+        score_type: str | None = SCORE_TYPE_MAP.get(self.pooling_task, None)  # type: ignore[arg-type]
         if (
             score_type == "cross-encoder"
             and getattr(self.model_config.hf_config, "num_labels", 0) != 1
@@ -1571,15 +1600,20 @@ def _add_completion_requests(
         lora_request: Sequence[LoRARequest] | LoRARequest | None = None,
         priority: list[int] | None = None,
         tokenization_kwargs: dict[str, Any] | None = None,
+        mm_processor_kwargs: dict[str, Any] | None = None,
     ) -> list[str]:
         seq_prompts = prompt_to_seq(prompts)
         seq_params = self._params_to_seq(params, len(seq_prompts))
         seq_lora_requests = self._lora_request_to_seq(lora_request, len(seq_prompts))
-        seq_priority = self._priority_to_seq(priority, len(prompts))
+        seq_priority = self._priority_to_seq(priority, len(seq_prompts))
 
         return self._render_and_add_requests(
             prompts=(
-                self._preprocess_cmpl_one(prompt, tokenization_kwargs)
+                self._preprocess_cmpl_one(
+                    prompt,
+                    tokenization_kwargs,
+                    mm_processor_kwargs=mm_processor_kwargs,
+                )
                 for prompt in maybe_tqdm(
                     seq_prompts,
                     use_tqdm=use_tqdm,
@@ -1603,6 +1637,7 @@ def _run_completion(
         lora_request: Sequence[LoRARequest] | LoRARequest | None = None,
         priority: list[int] | None = None,
         tokenization_kwargs: dict[str, Any] | None = None,
+        mm_processor_kwargs: dict[str, Any] | None = None,
     ):
         self._add_completion_requests(
             prompts=prompts,
@@ -1611,6 +1646,7 @@ def _run_completion(
             lora_request=lora_request,
             priority=priority,
             tokenization_kwargs=tokenization_kwargs,
+            mm_processor_kwargs=mm_processor_kwargs,
         )
         return self._run_engine(use_tqdm=use_tqdm, output_type=output_type)
 
@@ -1638,6 +1674,17 @@ def _run_chat(
         seq_params = self._params_to_seq(params, len(seq_convs))
         seq_lora_requests = self._lora_request_to_seq(lora_request, len(seq_convs))
 
+        # When thinking is enabled or tools are provided, and the model
+        # uses special tokens for structured output (e.g. Gemma4's
+        # <|channel>, <|tool_call>, <|"|>), automatically set
+        # skip_special_tokens=False so these tokens are preserved in
+        # output.text for downstream parsing.
+        needs_parsing = (
+            chat_template_kwargs and chat_template_kwargs.get("enable_thinking")
+        ) or tools
+        if needs_parsing:
+            self._adjust_params_for_parsing(seq_params)
+
         return self._render_and_run_requests(
             prompts=(
                 self._preprocess_chat_one(
@@ -1663,6 +1710,53 @@ def _run_chat(
             use_tqdm=use_tqdm,
         )
 
+    def _adjust_params_for_parsing(
+        self, params: Sequence[SamplingParams | PoolingParams]
+    ) -> None:
+        """Set ``skip_special_tokens=False`` when the model encodes
+        structured output syntax as special tokens.
+
+        Models like Gemma4 register thinking delimiters
+        (``<|channel>``/``<channel|>``) and tool call tokens
+        (``<|tool_call>``/``<tool_call|>``/``<|"|>``) as special tokens.
+        The default ``skip_special_tokens=True`` strips them from
+        ``output.text``, breaking parsing of both reasoning blocks and
+        tool calls.
+
+        This is a no-op for models whose structured tokens are regular
+        text tokens (e.g. DeepSeek's ``<think>``/``</think>``).
+        """
+        # The offline API currently lacks a unified rendering pipeline.
+        # Until the planned Renderer refactor is complete, we hardcode
+        # this token preservation logic specifically for Gemma4 models
+        # to avoid regressions on other models.
+        hf_config = getattr(self.model_config, "hf_config", None)
+        architectures = getattr(hf_config, "architectures", [])
+
+        if any("Gemma4" in arch for arch in architectures):
+            tokenizer = self.renderer.get_tokenizer()
+            vocab = tokenizer.get_vocab()
+            special_ids = set(getattr(tokenizer, "all_special_ids", []))
+
+            # Tokens used for thinking delimiters and tool call syntax
+            # that some models (Gemma4) register as special tokens.
+            structured_tokens = (
+                "<|channel>",
+                "<channel|>",  # thinking delimiters
+                "<|tool_call>",
+                "<tool_call|>",  # tool call delimiters
+                '<|"|>',  # string quoting in tool args
+            )
+            needs_special = any(
+                vocab.get(tok) in special_ids
+                for tok in structured_tokens
+                if tok in vocab
+            )
+            if needs_special:
+                for sp in params:
+                    if isinstance(sp, SamplingParams) and sp.skip_special_tokens:
+                        sp.skip_special_tokens = False
+
     def _render_and_run_requests(
         self,
         prompts: Iterable[EngineInput],
@@ -1814,6 +1908,20 @@ def init_weight_transfer_engine(
             "init_weight_transfer_engine", kwargs={"init_info": init_info_dict}
         )
 
+    def start_weight_update(self, is_checkpoint_format: bool = True) -> None:
+        """
+        Start a new weight update.
+
+        Args:
+            is_checkpoint_format: Whether incoming weights are in checkpoint
+                format (need layerwise processing) or kernel format (direct
+                copy).
+        """
+        self.llm_engine.collective_rpc(
+            "start_weight_update",
+            kwargs={"is_checkpoint_format": is_checkpoint_format},
+        )
+
     def update_weights(self, request: WeightTransferUpdateRequest | dict) -> None:
         """
         Update the weights of the model.
@@ -1829,6 +1937,12 @@ def update_weights(self, request: WeightTransferUpdateRequest | dict) -> None:
             "update_weights", kwargs={"update_info": update_info_dict}
         )
 
+    def finish_weight_update(self) -> None:
+        """
+        Finish the current weight update.
+        """
+        self.llm_engine.collective_rpc("finish_weight_update")
+
     def __repr__(self) -> str:
         """Return a transformers-style hierarchical view of the model."""
         # Cache the result to avoid repeated collective_rpc calls
diff --git a/vllm/entrypoints/openai/api_server.py b/vllm/entrypoints/openai/api_server.py
index 85d2fe43d019..da2ec10284c5 100644
--- a/vllm/entrypoints/openai/api_server.py
+++ b/vllm/entrypoints/openai/api_server.py
@@ -220,6 +220,12 @@ def build_app(
 
         elastic_ep_attach_router(app)
 
+        from vllm.entrypoints.openai.generative_scoring.api_router import (
+            register_generative_scoring_api_router,
+        )
+
+        register_generative_scoring_api_router(app)
+
     if "generate" in supported_tasks or "render" in supported_tasks:
         from vllm.entrypoints.serve.render.api_router import (
             attach_router as attach_render_router,
@@ -242,17 +248,10 @@ def build_app(
         register_realtime_api_router(app)
 
     if any(task in POOLING_TASKS for task in supported_tasks):
-        from vllm.entrypoints.pooling import register_pooling_api_routers
+        from vllm.entrypoints.pooling.factories import register_pooling_api_routers
 
         register_pooling_api_routers(app, supported_tasks, model_config)
 
-    if "generate" in supported_tasks:
-        from vllm.entrypoints.openai.generative_scoring.api_router import (
-            register_generative_scoring_api_router,
-        )
-
-        register_generative_scoring_api_router(app)
-
     app.root_path = args.root_path
     app.add_middleware(
         CORSMiddleware,
@@ -322,6 +321,21 @@ async def init_app_state(
     supported_tasks: tuple["SupportedTask", ...] | None = None,
 ) -> None:
     vllm_config = engine_client.vllm_config
+
+    # Propagate enable_in_reasoning to the API-server process. The engine core
+    # runs in a separate process, so the contextvar that backs
+    # `get_current_vllm_config_or_none()` is None on this stack. Tool parsers
+    # call `get_enable_structured_outputs_in_reasoning()` during request
+    # handling and need to see the real flag, otherwise they silently fall
+    # back to False and mismatch the engine-side bitmask gating.
+    from vllm.tool_parsers.structural_tag_registry import (
+        set_enable_structured_outputs_in_reasoning,
+    )
+
+    set_enable_structured_outputs_in_reasoning(
+        vllm_config.structured_outputs_config.enable_in_reasoning
+    )
+
     if supported_tasks is None:
         warnings.warn(
             "The 'supported_tasks' parameter was not provided to "
@@ -401,6 +415,12 @@ async def init_app_state(
             engine_client, state, args, request_logger, supported_tasks
         )
 
+        from vllm.entrypoints.openai.generative_scoring.api_router import (
+            init_generative_scoring_state,
+        )
+
+        await init_generative_scoring_state(engine_client, state, args, request_logger)
+
     if "transcription" in supported_tasks:
         from vllm.entrypoints.openai.speech_to_text.api_router import (
             init_transcription_state,
@@ -416,17 +436,10 @@ async def init_app_state(
         init_realtime_state(engine_client, state, args, request_logger, supported_tasks)
 
     if any(task in POOLING_TASKS for task in supported_tasks):
-        from vllm.entrypoints.pooling import init_pooling_state
+        from vllm.entrypoints.pooling.factories import init_pooling_state
 
         init_pooling_state(engine_client, state, args, request_logger, supported_tasks)
 
-    if "generate" in supported_tasks:
-        from vllm.entrypoints.openai.generative_scoring.api_router import (
-            init_generative_scoring_state,
-        )
-
-        await init_generative_scoring_state(engine_client, state, args, request_logger)
-
     state.enable_server_load_tracking = args.enable_server_load_tracking
     state.server_load_metrics = 0
 
diff --git a/vllm/entrypoints/openai/chat_completion/batch_serving.py b/vllm/entrypoints/openai/chat_completion/batch_serving.py
index f97c93bb03c8..cc49909b8361 100644
--- a/vllm/entrypoints/openai/chat_completion/batch_serving.py
+++ b/vllm/entrypoints/openai/chat_completion/batch_serving.py
@@ -114,12 +114,15 @@ async def create_batch_chat_completion(
         """
         tokenizer = self.renderer.tokenizer
         assert tokenizer is not None
+        single_requests = [
+            request.to_chat_completion_request(messages)
+            for messages in request.messages
+        ]
 
         reasoning_parser: ReasoningParser | None = None
         if self.reasoning_parser_cls:
-            chat_template_kwargs = self._prepare_extra_chat_template_kwargs(
-                request.chat_template_kwargs,
-                self.default_chat_template_kwargs,
+            chat_template_kwargs = self._effective_chat_template_kwargs(
+                single_requests[0]
             )
             reasoning_parser = self.reasoning_parser_cls(
                 tokenizer,
@@ -155,7 +158,7 @@ async def create_batch_chat_completion(
                 self.default_sampling_params,
                 self.override_max_tokens,
             )
-            single_request = request.to_chat_completion_request(request.messages[i])
+            single_request = single_requests[i]
             sampling_params = single_request.to_sampling_params(
                 max_tokens, self.default_sampling_params
             )
@@ -314,4 +317,5 @@ async def chat_completion_full_generator_batch(
             model=model_name,
             choices=choices,
             usage=usage,
+            system_fingerprint=self.system_fingerprint,
         )
diff --git a/vllm/entrypoints/openai/chat_completion/protocol.py b/vllm/entrypoints/openai/chat_completion/protocol.py
index 533959df6094..742f9cced6f9 100644
--- a/vllm/entrypoints/openai/chat_completion/protocol.py
+++ b/vllm/entrypoints/openai/chat_completion/protocol.py
@@ -11,7 +11,7 @@
     ChatCompletionAudio as OpenAIChatCompletionAudio,
 )
 from openai.types.chat.chat_completion_message import Annotation as OpenAIAnnotation
-from pydantic import Field, model_validator
+from pydantic import Field, PrivateAttr, model_serializer, model_validator
 
 from vllm.config import ModelConfig
 from vllm.config.utils import replace
@@ -92,12 +92,16 @@ class ChatCompletionResponseChoice(OpenAIBaseModel):
     # not part of the OpenAI spec but is useful for tracing the tokens
     # in agent scenarios
     token_ids: list[int] | None = None
+    routed_experts: list[list[list[int]]] | None = None  # [gen_len, num_layers, top_k]
 
 
 class ChatCompletionResponse(OpenAIBaseModel):
     id: str = Field(default_factory=lambda: f"chatcmpl-{random_uuid()}")
     object: Literal["chat.completion"] = "chat.completion"
     created: int = Field(default_factory=lambda: int(time.time()))
+    prompt_routed_experts: list[list[list[int]]] | None = (
+        None  # [prompt_len, num_layers, top_k]
+    )
     model: str
     choices: list[ChatCompletionResponseChoice]
     service_tier: Literal["auto", "default", "flex", "scale", "priority"] | None = None
@@ -129,6 +133,9 @@ class ChatCompletionStreamResponse(OpenAIBaseModel):
     model: str
     choices: list[ChatCompletionResponseStreamChoice]
     usage: UsageInfo | None = Field(default=None)
+    # Set only on the final chunk of a stream to mirror non-streaming responses
+    # without the per-chunk serialization overhead.
+    system_fingerprint: str | None = None
     # not part of the OpenAI spec but for tracing the tokens
     prompt_token_ids: list[int] | None = None
 
@@ -136,6 +143,20 @@ class ChatCompletionStreamResponse(OpenAIBaseModel):
 class ChatCompletionToolsParam(OpenAIBaseModel):
     type: Literal["function"] = "function"
     function: FunctionDefinition
+    defer_loading: bool | None = None
+
+    @model_validator(mode="after")
+    def _propagate_defer_loading(self) -> "ChatCompletionToolsParam":
+        if self.defer_loading is not None and self.function.defer_loading is None:
+            self.function.defer_loading = self.defer_loading
+        return self
+
+    @model_serializer(mode="wrap")
+    def _serialize(self, handler):
+        data = handler(self)
+        if self.defer_loading is None:
+            data.pop("defer_loading", None)
+        return data
 
 
 class ChatCompletionNamedFunction(OpenAIBaseModel):
@@ -179,7 +200,19 @@ class ChatCompletionRequest(OpenAIBaseModel):
         | ChatCompletionNamedToolChoiceParam
         | None
     ) = "none"
-    reasoning_effort: Literal["none", "low", "medium", "high"] | None = None
+    reasoning_effort: (
+        Literal["none", "minimal", "low", "medium", "high", "xhigh", "max"] | None
+    ) = Field(
+        default=None,
+        description=(
+            "Constrains effort on reasoning for reasoning models. "
+            "Currently supported values are none, minimal, low, medium, "
+            "high, xhigh, and max. Reducing reasoning effort can result in "
+            "faster responses and fewer tokens used on reasoning in a response. "
+            "Note that 'max' is specific to the DeepSeek V4 series and is not "
+            "part of the standard OpenAI API specification."
+        ),
+    )
     thinking_token_budget: int | None = None
     include_reasoning: bool = True
     parallel_tool_calls: bool | None = True
@@ -357,6 +390,50 @@ class ChatCompletionRequest(OpenAIBaseModel):
 
     # --8<-- [end:chat-completion-extra-params]
 
+    @model_validator(mode="before")
+    @classmethod
+    def _materialize_tool_calls_before(cls, data: Any) -> Any:
+        """Eagerly convert tool_calls generators/iterators to lists.
+
+        Must run before Pydantic field validation so that one-shot
+        generators are not consumed during union type matching of
+        ChatCompletionAssistantMessageParam (which types tool_calls
+        as Iterable[...]).
+        """
+        if not isinstance(data, dict):
+            return data
+        messages = data.get("messages")
+        if not isinstance(messages, list):
+            return data
+        for msg in messages:
+            if not isinstance(msg, dict):
+                continue
+            tool_calls = msg.get("tool_calls")
+            if tool_calls is not None and not isinstance(tool_calls, list):
+                msg["tool_calls"] = list(tool_calls)
+        return data
+
+    @model_validator(mode="after")
+    def _materialize_tool_calls_after(self) -> "ChatCompletionRequest":
+        """Convert Pydantic ValidatorIterator wrappers back to lists.
+
+        Even after the "before" validator converts iterables to lists,
+        Pydantic re-wraps them in a ValidatorIterator when validating
+        against ChatCompletionAssistantMessageParam's Iterable[...] type.
+        This "after" pass materialises those wrappers so downstream code
+        (tokenizers, model_dump_json) always sees plain lists.
+        """
+        for msg in self.messages:
+            if not isinstance(msg, dict):
+                continue
+            tool_calls = msg.get("tool_calls")
+            if tool_calls is not None and not isinstance(tool_calls, list):
+                msg["tool_calls"] = list(tool_calls)
+        return self
+
+    _grammar_from_tool_parser: bool = PrivateAttr(default=False)
+    """CAUTION: Should only be set by ``ToolParser.adjust_request``."""
+
     def build_chat_params(
         self,
         default_template: str | None,
@@ -637,6 +714,18 @@ def check_structured_outputs_count(cls, data):
     @model_validator(mode="before")
     @classmethod
     def check_tool_usage(cls, data):
+        if isinstance(data, ValueError):
+            raise data
+        if not isinstance(data, dict):
+            return data
+
+        # Reject empty tools array, matching OpenAI API behavior
+        if data.get("tools") == []:
+            raise ValueError(
+                "`tools` must not be an empty array. "
+                "Either provide at least one tool or omit the field entirely."
+            )
+
         # if "tool_choice" is not specified but tools are provided,
         # default to "auto" tool_choice
         if "tool_choice" not in data and data.get("tools"):
@@ -663,18 +752,6 @@ def check_tool_usage(cls, data):
                     "are supported."
                 )
 
-            # if tool_choice is "required" but the "tools" list is empty,
-            # override the data to behave like "none" to align with
-            # OpenAI’s behavior.
-            if (
-                data["tool_choice"] == "required"
-                and isinstance(data["tools"], list)
-                and len(data["tools"]) == 0
-            ):
-                data["tool_choice"] = "none"
-                del data["tools"]
-                return data
-
             # ensure that if "tool_choice" is specified as an object,
             # it matches a valid tool
             correct_usage_message = (
@@ -781,13 +858,6 @@ def check_system_message_content_type(cls, data):
 
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def set_include_reasoning_for_none_effort(cls, data: Any) -> Any:
-        if data.get("reasoning_effort") == "none":
-            data["include_reasoning"] = False
-        return data
-
 
 class BatchChatCompletionRequest(OpenAIBaseModel):
     """Request model for the /v1/chat/completions/batch endpoint.
diff --git a/vllm/entrypoints/openai/chat_completion/serving.py b/vllm/entrypoints/openai/chat_completion/serving.py
index 0b8dd0aa28ef..375fe9794c1e 100644
--- a/vllm/entrypoints/openai/chat_completion/serving.py
+++ b/vllm/entrypoints/openai/chat_completion/serving.py
@@ -9,10 +9,7 @@
 from http import HTTPStatus
 from typing import TYPE_CHECKING, Any, Final
 
-import partial_json_parser
-import regex as re
 from fastapi import Request
-from partial_json_parser.core.options import Allow
 
 from vllm.engine.protocol import EngineClient
 from vllm.entrypoints.chat_utils import (
@@ -73,10 +70,8 @@
 from vllm.renderers import ChatParams
 from vllm.sampling_params import BeamSearchParams, SamplingParams
 from vllm.tokenizers import TokenizerLike
-from vllm.tool_parsers.mistral_tool_parser import MistralToolCall
-from vllm.tool_parsers.utils import partial_json_loads
 from vllm.utils.collection_utils import as_list
-from vllm.utils.mistral import is_mistral_tokenizer
+from vllm.utils.mistral import is_mistral_tokenizer, is_mistral_tool_parser
 
 if TYPE_CHECKING:
     from vllm.entrypoints.serve.render.serving import OpenAIServingRender
@@ -140,6 +135,14 @@ def __init__(
             enable_auto_tools=enable_auto_tools,
             model_name=self.model_config.model,
         )
+        if (
+            is_mistral_tool_parser(self.tool_parser)
+            and self.reasoning_parser_cls is not None
+        ):
+            from vllm.tool_parsers.mistral_tool_parser import MistralToolParser
+
+            MistralToolParser.model_can_reason = True
+
         self.exclude_tools_when_tool_choice_none = exclude_tools_when_tool_choice_none
 
         self.enable_prompt_tokens_details = enable_prompt_tokens_details
@@ -180,6 +183,18 @@ def warmup(self) -> None:
             )
         )
 
+    def _effective_chat_template_kwargs(
+        self, request: ChatCompletionRequest
+    ) -> dict[str, Any]:
+        return (
+            request.build_chat_params(
+                self.chat_template,
+                self.chat_template_content_format,
+            )
+            .with_defaults(self.default_chat_template_kwargs)
+            .chat_template_kwargs
+        )
+
     async def render_chat_request(
         self,
         request: ChatCompletionRequest,
@@ -219,13 +234,19 @@ async def create_chat_completion(
         for the API specification. This API mimics the OpenAI
         Chat Completion API.
         """
+        return await self._with_kv_transfer_rejection_cleanup(
+            self._create_chat_completion(request, raw_request), request, raw_request
+        )
+
+    async def _create_chat_completion(
+        self,
+        request: ChatCompletionRequest,
+        raw_request: Request | None = None,
+    ) -> AsyncGenerator[str, None] | ChatCompletionResponse | ErrorResponse:
         # Streaming response
         tokenizer = self.renderer.tokenizer
         assert tokenizer is not None
-        chat_template_kwargs = self._prepare_extra_chat_template_kwargs(
-            request.chat_template_kwargs,
-            self.default_chat_template_kwargs,
-        )
+        chat_template_kwargs = self._effective_chat_template_kwargs(request)
         reasoning_parser: ReasoningParser | None = None
         if self.reasoning_parser_cls:
             reasoning_parser = self.reasoning_parser_cls(
@@ -273,6 +294,7 @@ async def create_chat_completion(
                 self._extract_prompt_len(engine_input),
                 self.default_sampling_params,
                 self.override_max_tokens,
+                truncate_prompt_tokens=request.truncate_prompt_tokens,
             )
 
             sampling_params: SamplingParams | BeamSearchParams
@@ -310,6 +332,11 @@ async def create_chat_completion(
             else:
                 if not request.include_reasoning:
                     reasoning_ended = True
+                elif request._grammar_from_tool_parser:
+                    # The Mistral grammar already includes an optional
+                    # `think?` rule that handles both reasoning and
+                    # non-reasoning outputs.
+                    reasoning_ended = True
                 elif reasoning_parser:
                     reasoning_ended = reasoning_parser.is_reasoning_end(
                         prompt_token_ids or []
@@ -326,6 +353,11 @@ async def create_chat_completion(
                     priority=request.priority,
                     data_parallel_rank=data_parallel_rank,
                     reasoning_ended=reasoning_ended,
+                    reasoning_parser_kwargs={
+                        "chat_template_kwargs": chat_template_kwargs,
+                    }
+                    if reasoning_parser
+                    else None,
                 )
 
             generators.append(generator)
@@ -362,145 +394,6 @@ def get_chat_request_role(self, request: ChatCompletionRequest) -> str:
             return self.response_role
         return request.messages[-1]["role"]
 
-    @staticmethod
-    def _bracket_level(s: str, opening="{", closing="}") -> int:
-        """
-        Calculate the current level of nested brackets in a given string.
-        """
-        level = 0
-        for char in s:
-            if char == opening:
-                level += 1
-            elif char == closing:
-                level -= 1
-        return level
-
-    @staticmethod
-    def _filter_delta_text(delta_text: str, previous_text: str) -> tuple[str, bool]:
-        # remove last '},' of the tool definition stemming from the
-        # "name"/"parameters" outer object or closing ']' of the tool list
-        # count occurrences of opening and closing curly braces and
-        # once level 0 is reached stop outputting text
-        # if 0 is reached while parsing the delta_text we know the current
-        # tool will finish in this current iteration
-        bracket_level = OpenAIServingChat._bracket_level(previous_text)
-        updated_delta, passed_zero = "", False
-        for c in delta_text:
-            if c == "{":
-                bracket_level += 1
-                passed_zero = bracket_level == 0
-            elif c == "}":
-                bracket_level -= 1
-                passed_zero = bracket_level == 0
-
-            if bracket_level != 0:
-                updated_delta += c
-            else:
-                # if a comma is reached at level 0 we can stop
-                if c == ",":
-                    break
-        return updated_delta, passed_zero
-
-    def extract_tool_call_required_streaming(
-        self,
-        previous_text: str,
-        current_text: str | None,
-        delta_text: str,
-        function_name_returned: bool,
-        tool_call_idx: int | None = None,
-    ) -> tuple[DeltaMessage | None, bool]:
-        if current_text is None or current_text == "":
-            # if the current text is empty, we cannot parse it
-            return None, function_name_returned
-        try:
-            flags = Allow.ALL
-            obj, _ = partial_json_loads(current_text, flags)
-        except (
-            partial_json_parser.core.exceptions.MalformedJSON,
-            json.JSONDecodeError,
-        ):
-            logger.debug("not enough tokens to parse into JSON yet")
-            obj = None
-
-        # check if the current text is a valid array
-        # containing a partial tool calling object
-        # if not repeat
-        if obj is None or not isinstance(obj, list) or not len(obj) > 0:
-            function_name_returned = False
-            delta_message = None
-        else:
-            _, finishes_previous_tool = OpenAIServingChat._filter_delta_text(
-                delta_text, previous_text
-            )
-            # take the last tool call from the generated list
-            current_tool_call = obj[-1]
-
-            # once parameters have been generated the name is complete as well
-            if not finishes_previous_tool and (
-                "name" not in current_tool_call or "parameters" not in current_tool_call
-            ):
-                function_name_returned = False
-                delta_message = None
-            else:
-                if not function_name_returned:
-                    # get partly generated arguments from the latest tool call
-                    param_match = re.search(
-                        r'.*"parameters":\s*(.*)', current_text, re.DOTALL
-                    )
-                    arguments = param_match.group(1) if param_match else ""
-                    arguments, _ = OpenAIServingChat._filter_delta_text(
-                        arguments, previous_text
-                    )
-
-                    # if this iteration finishes a previous tool call but a
-                    # new incomplete tool is already generated, take the
-                    # previous from the list
-                    if finishes_previous_tool and "parameters" not in current_tool_call:
-                        current_tool_call = obj[-2]
-
-                    function_name_returned = True
-                    tool_call_id = make_tool_call_id(
-                        id_type=self.tool_call_id_type,
-                        func_name=current_tool_call["name"],
-                        idx=tool_call_idx,
-                    )
-                    delta_message = DeltaMessage(
-                        tool_calls=[
-                            DeltaToolCall(
-                                id=tool_call_id,
-                                function=DeltaFunctionCall(
-                                    name=current_tool_call["name"], arguments=arguments
-                                ),
-                                index=len(obj) - 1,
-                                type="function",
-                            )
-                        ]
-                    )
-
-                else:
-                    delta_text, _ = OpenAIServingChat._filter_delta_text(
-                        delta_text, previous_text
-                    )
-
-                    if delta_text != "":
-                        delta_message = DeltaMessage(
-                            tool_calls=[
-                                DeltaToolCall(
-                                    function=DeltaFunctionCall(
-                                        # OpenAI API returns None
-                                        # instead of name every time
-                                        name=None,
-                                        arguments=delta_text,
-                                    ),
-                                    index=len(obj) - 1,
-                                )
-                            ]
-                        )
-                    else:
-                        delta_message = None
-
-        return delta_message, function_name_returned
-
     async def chat_completion_stream_generator(
         self,
         request: ChatCompletionRequest,
@@ -530,6 +423,8 @@ async def chat_completion_stream_generator(
             harmony_tools_streamed = [False] * num_choices
         tools_streamed = [False] * num_choices
 
+        is_mistral_grammar_path = request._grammar_from_tool_parser
+
         if isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam):
             tool_choice_function_name = request.tool_choice.function.name
         else:
@@ -542,7 +437,6 @@ async def chat_completion_stream_generator(
         )
 
         all_previous_token_ids: list[list[int]] | None
-        function_name_returned = [False] * num_choices
         if self.tool_call_id_type == "kimi_k2":
             history_tool_call_cnt = get_history_tool_calls_cnt(conversation)
         else:
@@ -553,8 +447,13 @@ async def chat_completion_stream_generator(
 
         # Only one of these will be used, thus previous_texts and
         # all_previous_token_ids will not be used twice in the same iteration.
-        if tool_choice_auto or reasoning_parser:
-            # These are only required in "auto" tool choice case
+        if (
+            is_mistral_grammar_path
+            or tool_choice_auto
+            or tool_choice_function_name
+            or request.tool_choice == "required"
+            or reasoning_parser
+        ):
             all_previous_token_ids = [[] for _ in range(num_choices)]
             reasoning_end_arr = [False] * num_choices
             prompt_is_reasoning_end_arr: list[bool | None] = [None] * num_choices
@@ -575,6 +474,10 @@ async def chat_completion_stream_generator(
                     )
                     for _ in range(num_choices)
                 ]
+                for p in parsers:
+                    if p is not None:
+                        p._stream_state.tool_call_id_type = self.tool_call_id_type
+                        p._stream_state.history_tool_call_cnt = history_tool_call_cnt
             else:
                 parsers = [None] * num_choices
         except Exception as e:
@@ -748,7 +651,13 @@ async def chat_completion_stream_generator(
                     delta_message: DeltaMessage | None
 
                     # just update previous_texts and previous_token_ids
-                    if tool_choice_auto or reasoning_parser:
+                    if (
+                        is_mistral_grammar_path
+                        or tool_choice_auto
+                        or tool_choice_function_name
+                        or request.tool_choice == "required"
+                        or reasoning_parser
+                    ):
                         assert previous_texts is not None
                         assert all_previous_token_ids is not None
                         previous_text = previous_texts[i]
@@ -772,144 +681,34 @@ async def chat_completion_stream_generator(
                             )
                         )
                         harmony_tools_streamed[i] |= tools_streamed_flag
-                    # handle streaming deltas for tools with named tool_choice
-                    elif tool_choice_function_name:
-                        # When encountering think end id in prompt_token_ids
-                        # i.e {"enable_thinking": False},
-                        # check BEFORE calling the parser to avoid a spurious
-                        # reasoning delta on the first chunk.
-                        if (
-                            reasoning_parser
-                            and not reasoning_end_arr[i]
-                            and prompt_is_reasoning_end_arr[i]
-                        ):
-                            reasoning_end_arr[i] = True
-
-                        if (
-                            reasoning_parser
-                            and not reasoning_end_arr[i]
-                            and not reasoning_parser.is_reasoning_end(
-                                previous_token_ids
-                            )
-                        ):
-                            assert reasoning_parser is not None
-                            delta_message = (
-                                reasoning_parser.extract_reasoning_streaming(
-                                    previous_text,
-                                    current_text,
-                                    delta_text,
-                                    previous_token_ids,
-                                    current_token_ids,
-                                    output.token_ids,
-                                )
-                            )
-                            # When encountering think end id in delta_token_ids,
-                            # set reasoning status to end.
-                            # Only keep 'content', remove 'reasoning'.
-                            if reasoning_parser.is_reasoning_end(
-                                as_list(output.token_ids)
-                            ):
-                                reasoning_end_arr[i] = True
-                                if delta_message and delta_message.content:
-                                    # This need to be added to next `delta_text`
-                                    current_text = delta_message.content
-                                    delta_message.content = None
-                                else:
-                                    current_text = ""
-                        else:
-                            # Just to add remaining `content`
-                            if reasoning_parser:
-                                delta_text = previous_text + delta_text
-                                current_text = ""
-
-                            if function_name_returned[i]:
-                                delta_tool_call = DeltaToolCall(
-                                    function=DeltaFunctionCall(arguments=delta_text),
-                                    index=i,
-                                )
-                            else:
-                                # Generate ID based on tokenizer type
-                                if is_mistral_tokenizer(tokenizer):
-                                    tool_call_id = MistralToolCall.generate_random_id()
-                                else:
-                                    tool_call_id = make_tool_call_id(
-                                        id_type=self.tool_call_id_type,
-                                        func_name=tool_choice_function_name,
-                                        idx=history_tool_call_cnt,
-                                    )
-                                delta_tool_call = DeltaToolCall(
-                                    id=tool_call_id,
-                                    type="function",
-                                    function=DeltaFunctionCall(
-                                        name=tool_choice_function_name,
-                                        arguments=delta_text,
-                                    ),
-                                    index=i,
-                                )
-                                function_name_returned[i] = True
-                                history_tool_call_cnt += 1
-
-                            delta_message = DeltaMessage(
-                                tool_calls=[
-                                    delta_tool_call,
-                                ]
-                            )
-                            tools_streamed[i] = True
+                    # Mistral grammar path: combined reasoning + tool streaming
+                    elif is_mistral_grammar_path:
+                        from vllm.tool_parsers.mistral_tool_parser import (
+                            MistralToolParser,
+                        )
 
-                    elif request.tool_choice == "required":
-                        assert previous_texts is not None
-                        previous_text = previous_texts[i]
-                        current_text = previous_text + delta_text
-                        fn_name_returned = function_name_returned[i]
+                        assert tool_parser is not None
+                        assert isinstance(tool_parser, MistralToolParser)
+                        assert reasoning_end_arr is not None
                         output_token_ids = as_list(output.token_ids)
-
-                        if (
-                            reasoning_parser is not None
-                            and not reasoning_end_arr[i]
-                            and prompt_is_reasoning_end_arr[i]
-                        ):
-                            reasoning_end_arr[i] = True
-
-                        if reasoning_parser and not reasoning_end_arr[i]:
-                            delta_message = (
-                                reasoning_parser.extract_reasoning_streaming(
-                                    previous_text,
-                                    current_text,
-                                    delta_text,
-                                    previous_token_ids,
-                                    current_token_ids,
-                                    output_token_ids,
-                                )
-                            )
-                            if reasoning_parser.is_reasoning_end(output_token_ids):
-                                reasoning_end_arr[i] = True
-                                if delta_message and delta_message.content:
-                                    current_text = delta_message.content
-                                    delta_message.content = None
-                                else:
-                                    # reasoning ended
-                                    current_text = ""
-
-                        else:
-                            # either finished reasoning or no reasoning at all
-                            content = current_text
-
-                            delta_message, function_name_returned[i] = (
-                                self.extract_tool_call_required_streaming(
-                                    previous_text=previous_text,
-                                    current_text=content,
-                                    delta_text=delta_text,
-                                    function_name_returned=fn_name_returned,
-                                    tool_call_idx=history_tool_call_cnt,
-                                )
-                            )
-                            if (
-                                delta_message
-                                and delta_message.tool_calls
-                                and delta_message.tool_calls[0].id is not None
-                            ):
-                                history_tool_call_cnt += 1
-                                tools_streamed[i] = True
+                        result = tool_parser.extract_maybe_reasoning_and_tool_streaming(
+                            reasoning_parser=reasoning_parser,
+                            previous_text=previous_text,
+                            current_text=current_text,
+                            delta_text=delta_text,
+                            previous_token_ids=previous_token_ids,
+                            current_token_ids=current_token_ids,
+                            output_token_ids=output_token_ids,
+                            reasoning_ended=reasoning_end_arr[i],
+                            prompt_is_reasoning_end=(prompt_is_reasoning_end_arr[i]),
+                            request=request,
+                        )
+                        delta_message = result.delta_message
+                        reasoning_end_arr[i] = result.reasoning_ended
+                        current_text = result.current_text
+                        current_token_ids = result.current_token_ids
+                        if result.tools_called:
+                            tools_streamed[i] = True
 
                     elif parser is not None:
                         delta_message = parser.parse_delta(
@@ -925,7 +724,13 @@ async def chat_completion_stream_generator(
                         delta_message = DeltaMessage(content=delta_text)
 
                     # update the previous values for the next iteration
-                    if (tool_choice_auto or reasoning_parser) and not self.use_harmony:
+                    if (
+                        is_mistral_grammar_path
+                        or tool_choice_auto
+                        or tool_choice_function_name
+                        or request.tool_choice == "required"
+                        or reasoning_parser
+                    ) and not self.use_harmony:
                         assert previous_texts is not None
                         assert all_previous_token_ids is not None
                         previous_texts[i] = current_text
@@ -1105,6 +910,16 @@ async def chat_completion_stream_generator(
                         choices=[choice_data],
                         model=model_name,
                     )
+                    # Stamp the fingerprint on terminal chunks only (those with
+                    # finish_reason set). When ``include_usage`` is on, the
+                    # trailing usage chunk below overrides this as the true
+                    # final message.
+                    if (
+                        not include_usage
+                        and self.system_fingerprint is not None
+                        and choice_data.finish_reason is not None
+                    ):
+                        chunk.system_fingerprint = self.system_fingerprint
 
                     # handle usage stats if requested & if continuous
                     if include_continuous_usage:
@@ -1139,6 +954,7 @@ async def chat_completion_stream_generator(
                     choices=[],
                     model=model_name,
                     usage=final_usage,
+                    system_fingerprint=self.system_fingerprint,
                 )
                 final_usage_data = final_usage_chunk.model_dump_json(
                     exclude_unset=True, exclude_none=True
@@ -1191,8 +1007,6 @@ async def chat_completion_full_generator(
         request_metadata: RequestResponseMetadata,
         reasoning_parser: ReasoningParser | None = None,
     ) -> ErrorResponse | ChatCompletionResponse:
-        from vllm.tokenizers.mistral import MistralTokenizer
-
         created_time = int(time.time())
         final_res: RequestOutput | None = None
 
@@ -1283,6 +1097,11 @@ async def chat_completion_full_generator(
                     token_ids=(
                         as_list(output.token_ids) if request.return_token_ids else None
                     ),
+                    routed_experts=(
+                        output.routed_experts.tolist()
+                        if output.routed_experts is not None
+                        else None
+                    ),
                 )
                 choices.append(choice_data)
                 continue
@@ -1309,10 +1128,32 @@ async def chat_completion_full_generator(
                 enable_auto_tools=self.enable_auto_tools,
                 tool_parser_cls=self.tool_parser,
             )
-            tool_call_class = (
-                MistralToolCall if is_mistral_tokenizer(tokenizer) else ToolCall
-            )
-            if (not self.enable_auto_tools or not self.tool_parser) and (
+            if is_mistral_tokenizer(tokenizer):
+                from vllm.tool_parsers.mistral_tool_parser import MistralToolCall
+
+                tool_call_class: type[ToolCall] = MistralToolCall
+            else:
+                tool_call_class = ToolCall
+
+            use_mistral_tool_parser = request._grammar_from_tool_parser
+            if use_mistral_tool_parser:
+                from vllm.tool_parsers.mistral_tool_parser import MistralToolParser
+
+                tool_call_items = MistralToolParser.build_non_streaming_tool_calls(
+                    tool_calls
+                )
+                if tool_call_items:
+                    auto_tools_called = (
+                        request.tool_choice is None or request.tool_choice == "auto"
+                    )
+                message = ChatMessage(
+                    role=role,
+                    reasoning=reasoning,
+                    content=content,
+                    tool_calls=tool_call_items,
+                )
+
+            elif (not self.enable_auto_tools or not self.tool_parser) and (
                 not isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam)
                 and request.tool_choice != "required"
             ):
@@ -1322,8 +1163,8 @@ async def chat_completion_full_generator(
                 request.tool_choice
                 and type(request.tool_choice) is ChatCompletionNamedToolChoiceParam
             ):
-                assert tool_calls is not None and len(tool_calls) > 0
                 tool_call_class_items = []
+                tool_calls = tool_calls or []
                 for idx, tc in enumerate(tool_calls):
                     # Use native ID if available (e.g., Kimi K2),
                     # otherwise generate ID with correct id_type
@@ -1335,7 +1176,7 @@ async def chat_completion_full_generator(
                         # Generate ID using the correct format (kimi_k2 or random),
                         # but leave it to the class if it's Mistral to preserve
                         # 9-char IDs
-                        if isinstance(tokenizer, MistralTokenizer):
+                        if is_mistral_tokenizer(tokenizer):
                             tool_call_class_items.append(tool_call_class(function=tc))
                         else:
                             generated_id = make_tool_call_id(
@@ -1368,7 +1209,7 @@ async def chat_completion_full_generator(
                         # Generate ID using the correct format (kimi_k2 or random),
                         # but leave it to the class if it's Mistral to preserve
                         # 9-char IDs
-                        if isinstance(tokenizer, MistralTokenizer):
+                        if is_mistral_tokenizer(tokenizer):
                             tool_call_class_items.append(
                                 tool_call_class(function=tool_call)
                             )
@@ -1418,7 +1259,7 @@ async def chat_completion_full_generator(
                             # Generate ID using the correct format (kimi_k2 or random),
                             # but leave it to the class if it's Mistral to preserve
                             # 9-char IDs
-                            if isinstance(tokenizer, MistralTokenizer):
+                            if is_mistral_tokenizer(tokenizer):
                                 tool_call_items.append(tool_call_class(function=tc))
                             else:
                                 generated_id = make_tool_call_id(
@@ -1482,6 +1323,11 @@ async def chat_completion_full_generator(
                 token_ids=(
                     as_list(output.token_ids) if request.return_token_ids else None
                 ),
+                routed_experts=(
+                    output.routed_experts.tolist()
+                    if output.routed_experts is not None
+                    else None
+                ),
             )
             choice_data = maybe_filter_parallel_tool_calls(choice_data, request)
 
@@ -1521,17 +1367,23 @@ async def chat_completion_full_generator(
 
         request_metadata.final_usage_info = usage
 
+        prompt_routed_experts = None
+        if final_res.prompt_routed_experts is not None:
+            prompt_routed_experts = final_res.prompt_routed_experts.tolist()
+
         response = ChatCompletionResponse(
             id=request_id,
             created=created_time,
             model=model_name,
             choices=choices,
             usage=usage,
+            system_fingerprint=self.system_fingerprint,
             prompt_logprobs=clamp_prompt_logprobs(final_res.prompt_logprobs),
             prompt_token_ids=(
                 final_res.prompt_token_ids if request.return_token_ids else None
             ),
             kv_transfer_params=final_res.kv_transfer_params,
+            prompt_routed_experts=prompt_routed_experts,
         )
 
         # Log complete response if output logging is enabled
diff --git a/vllm/entrypoints/openai/cli_args.py b/vllm/entrypoints/openai/cli_args.py
index 898e62f7713d..3ebdec3c67fa 100644
--- a/vllm/entrypoints/openai/cli_args.py
+++ b/vllm/entrypoints/openai/cli_args.py
@@ -153,9 +153,21 @@ class BaseFrontendArgs:
     """If set to True, log the stack trace of error responses"""
     tokens_only: bool = False
     """
-    If set to True, only enable the Tokens In<>Out endpoint. 
+    If set to True, only enable the Tokens In<>Out endpoint.
     This is intended for use in a Disaggregated Everything setup.
     """
+    fingerprint_mode: Literal["full", "hash", "custom", "none"] = "full"
+    """Controls the ``system_fingerprint`` field on responses.
+
+    - ``full`` (default): ``vllm-<version>[-<parallelism>]-<hash8>``. Encodes
+      server version, non-trivial parallelism degrees (tp/pp/dp/ep), and an
+      8-char config hash.
+    - ``hash``: ``vllm-<version>-<hash8>``. Parallelism stripped.
+    - ``custom``: emits the literal string from ``--fingerprint-value``.
+    - ``none``: the field is omitted (serialized as ``null``).
+    """
+    fingerprint_value: str | None = None
+    """Literal fingerprint string used when ``--fingerprint-mode=custom``."""
 
     @classmethod
     def _customize_cli_kwargs(
diff --git a/vllm/entrypoints/openai/completion/protocol.py b/vllm/entrypoints/openai/completion/protocol.py
index c785d254084d..7bb3c8d8a0db 100644
--- a/vllm/entrypoints/openai/completion/protocol.py
+++ b/vllm/entrypoints/openai/completion/protocol.py
@@ -468,12 +468,16 @@ class CompletionResponseChoice(OpenAIBaseModel):
     token_ids: list[int] | None = None  # For response
     prompt_logprobs: list[dict[int, Logprob] | None] | None = None
     prompt_token_ids: list[int] | None = None  # For prompt
+    routed_experts: list[list[list[int]]] | None = None  # [gen_len, num_layers, top_k]
 
 
 class CompletionResponse(OpenAIBaseModel):
     id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
     object: Literal["text_completion"] = "text_completion"
     created: int = Field(default_factory=lambda: int(time.time()))
+    prompt_routed_experts: list[list[list[int]]] | None = (
+        None  # [prompt_len, num_layers, top_k]
+    )
     model: str
     choices: list[CompletionResponseChoice]
     service_tier: Literal["auto", "default", "flex", "scale", "priority"] | None = None
@@ -512,3 +516,6 @@ class CompletionStreamResponse(OpenAIBaseModel):
     model: str
     choices: list[CompletionResponseStreamChoice]
     usage: UsageInfo | None = Field(default=None)
+    # Set only on the final chunk of a stream to mirror non-streaming responses
+    # without the per-chunk serialization overhead.
+    system_fingerprint: str | None = None
diff --git a/vllm/entrypoints/openai/completion/serving.py b/vllm/entrypoints/openai/completion/serving.py
index fb7f253c7ea3..05efe86466d4 100644
--- a/vllm/entrypoints/openai/completion/serving.py
+++ b/vllm/entrypoints/openai/completion/serving.py
@@ -118,6 +118,15 @@ async def create_completion(
             - suffix (the language models we currently support do not support
             suffix)
         """
+        return await self._with_kv_transfer_rejection_cleanup(
+            self._create_completion(request, raw_request), request, raw_request
+        )
+
+    async def _create_completion(
+        self,
+        request: CompletionRequest,
+        raw_request: Request | None = None,
+    ) -> AsyncGenerator[str, None] | CompletionResponse | ErrorResponse:
         if request.stream and request.use_beam_search:
             return self.create_error_response(
                 "Streaming is not currently supported with beam search"
@@ -151,6 +160,7 @@ async def create_completion(
                 self._extract_prompt_len(engine_input),
                 self.default_sampling_params,
                 self.override_max_tokens,
+                truncate_prompt_tokens=request.truncate_prompt_tokens,
             )
 
             sampling_params: SamplingParams | BeamSearchParams
@@ -383,6 +393,7 @@ async def completion_stream_generator(
 
                     chunk = CompletionStreamResponse(
                         id=request_id,
+                        object="text_completion",
                         created=created_time,
                         model=model_name,
                         choices=[
@@ -401,6 +412,14 @@ async def completion_stream_generator(
                             )
                         ],
                     )
+                    # Stamp on terminal chunk only when no trailing usage chunk
+                    # will follow (that one is the true final message).
+                    if (
+                        not include_usage
+                        and self.system_fingerprint is not None
+                        and finish_reason is not None
+                    ):
+                        chunk.system_fingerprint = self.system_fingerprint
                     if include_continuous_usage:
                         prompt_tokens = num_prompt_tokens[prompt_idx]
                         completion_tokens = previous_num_tokens[i]
@@ -410,7 +429,7 @@ async def completion_stream_generator(
                             total_tokens=prompt_tokens + completion_tokens,
                         )
 
-                    response_json = chunk.model_dump_json(exclude_unset=False)
+                    response_json = chunk.model_dump_json(exclude_unset=True)
                     yield f"data: {response_json}\n\n"
 
             total_prompt_tokens = sum(num_prompt_tokens)
@@ -433,6 +452,7 @@ async def completion_stream_generator(
                     model=model_name,
                     choices=[],
                     usage=final_usage_info,
+                    system_fingerprint=self.system_fingerprint,
                 )
                 final_usage_data = final_usage_chunk.model_dump_json(
                     exclude_unset=False, exclude_none=True
@@ -531,6 +551,11 @@ def request_output_to_completion_response(
                     token_ids=(
                         as_list(output.token_ids) if request.return_token_ids else None
                     ),
+                    routed_experts=(
+                        output.routed_experts.tolist()
+                        if output.routed_experts is not None
+                        else None
+                    ),
                 )
                 choices.append(choice_data)
 
@@ -554,15 +579,22 @@ def request_output_to_completion_response(
             )
 
         request_metadata.final_usage_info = usage
+        prompt_routed_experts = None
         if final_res_batch:
             kv_transfer_params = final_res_batch[0].kv_transfer_params
+            pre = final_res_batch[0].prompt_routed_experts
+            if pre is not None:
+                prompt_routed_experts = pre.tolist()
+
         return CompletionResponse(
             id=request_id,
             created=created_time,
             model=model_name,
             choices=choices,
             usage=usage,
+            system_fingerprint=self.system_fingerprint,
             kv_transfer_params=kv_transfer_params,
+            prompt_routed_experts=prompt_routed_experts,
         )
 
     def _create_completion_logprobs(
diff --git a/vllm/entrypoints/openai/engine/protocol.py b/vllm/entrypoints/openai/engine/protocol.py
index 8f6cdb3e6241..890af0300efc 100644
--- a/vllm/entrypoints/openai/engine/protocol.py
+++ b/vllm/entrypoints/openai/engine/protocol.py
@@ -12,6 +12,7 @@
     BaseModel,
     ConfigDict,
     Field,
+    model_serializer,
     model_validator,
 )
 
@@ -166,6 +167,14 @@ class FunctionDefinition(OpenAIBaseModel):
     name: str
     description: str | None = None
     parameters: dict[str, Any] | None = None
+    defer_loading: bool | None = None
+
+    @model_serializer(mode="wrap")
+    def _serialize(self, handler):
+        data = handler(self)
+        if self.defer_loading is None:
+            data.pop("defer_loading", None)
+        return data
 
 
 # extra="forbid" is a workaround to have kwargs as a field,
diff --git a/vllm/entrypoints/openai/engine/serving.py b/vllm/entrypoints/openai/engine/serving.py
index 5bd415b4fbd2..2a51cc0bfac0 100644
--- a/vllm/entrypoints/openai/engine/serving.py
+++ b/vllm/entrypoints/openai/engine/serving.py
@@ -4,7 +4,7 @@
 import contextlib
 import json
 import time
-from collections.abc import AsyncGenerator, Mapping
+from collections.abc import AsyncGenerator, Awaitable, Mapping
 from dataclasses import dataclass, field
 from http import HTTPStatus
 from typing import Any, ClassVar, Generic, Protocol, TypeAlias, TypeVar
@@ -72,6 +72,7 @@
 )
 from vllm.utils import random_uuid
 from vllm.utils.async_utils import collect_from_async_generator
+from vllm.utils.mistral import is_mistral_tool_parser
 
 logger = init_logger(__name__)
 
@@ -117,6 +118,7 @@ def build_chat_params(
 )
 
 RequestT = TypeVar("RequestT", bound=AnyRequest)
+_T = TypeVar("_T")
 
 
 @dataclass(kw_only=True)
@@ -155,6 +157,22 @@ def __init__(
         self.model_config = engine_client.model_config
         self.renderer = engine_client.renderer
         self.input_processor = engine_client.input_processor
+        vllm_config = getattr(engine_client, "vllm_config", None)
+        kv_transfer_config = getattr(vllm_config, "kv_transfer_config", None)
+        self.has_kv_connector = kv_transfer_config is not None
+
+        # Computed once at startup (cached by ``vllm_config`` identity) and
+        # stamped on non-streaming responses. Streaming chunks deliberately
+        # omit it to avoid per-chunk overhead.
+        from vllm.entrypoints.openai.fingerprint import get_system_fingerprint
+
+        try:
+            self.system_fingerprint: str | None = get_system_fingerprint(
+                engine_client.vllm_config
+            )
+        except Exception:
+            # Never fail server startup over the fingerprint.
+            self.system_fingerprint = None
 
     async def beam_search(
         self,
@@ -602,6 +620,40 @@ def _get_data_parallel_rank(raw_request: Request | None) -> int | None:
         except ValueError:
             return None
 
+    async def _with_kv_transfer_rejection_cleanup(
+        self,
+        awaitable: Awaitable[_T],
+        request: ChatCompletionRequest | CompletionRequest | ResponsesRequest,
+        raw_request: Request | None,
+    ) -> _T:
+        """Wrap a `create_*` coroutine so that, if it raises or returns an
+        ErrorResponse (i.e. the request never reached the engine), the KV
+        connector is notified to free any pinned remote-prefill blocks."""
+        kv_transfer_params = self.has_kv_connector and request.kv_transfer_params
+        if not kv_transfer_params or not kv_transfer_params.get("do_remote_prefill"):
+            return await awaitable
+
+        notify = True
+        try:
+            result = await awaitable
+            if not isinstance(result, ErrorResponse):
+                notify = False
+            return result
+        finally:
+            if notify:
+                try:
+                    await self.engine_client.notify_kv_transfer_request_rejected(
+                        request.request_id,
+                        kv_transfer_params,
+                        data_parallel_rank=self._get_data_parallel_rank(raw_request),
+                    )
+                except Exception:
+                    logger.warning(
+                        "Failed to notify KV connector about rejected request %s",
+                        request.request_id,
+                        exc_info=True,
+                    )
+
     @staticmethod
     def _parse_tool_calls_from_content(
         request: ResponsesRequest | ChatCompletionRequest,
@@ -610,24 +662,46 @@ def _parse_tool_calls_from_content(
         tool_parser_cls: type[ToolParser] | None,
         content: str | None = None,
     ) -> tuple[list[FunctionCall] | None, str | None]:
+        # When the Mistral grammar factory injected structured outputs,
+        # let the parser handle the output.
+        use_mistral_tool_parser = (
+            isinstance(request, ChatCompletionRequest)
+            and is_mistral_tool_parser(tool_parser_cls)
+            and request._grammar_from_tool_parser
+        )
+
         function_calls = list[FunctionCall]()
-        if request.tool_choice and isinstance(request.tool_choice, ToolChoiceFunction):
-            assert content is not None
-            # Forced Function Call
+        if (
+            not use_mistral_tool_parser
+            and request.tool_choice
+            and isinstance(request.tool_choice, ToolChoiceFunction)
+        ):
+            # Forced Function Call (Responses API)
+            if content is None:
+                return [], None
             function_calls.append(
                 FunctionCall(name=request.tool_choice.name, arguments=content)
             )
             content = None  # Clear content since tool is called.
-        elif request.tool_choice and isinstance(
-            request.tool_choice, ChatCompletionNamedToolChoiceParam
+        elif (
+            not use_mistral_tool_parser
+            and request.tool_choice
+            and isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam)
+            and (tool_parser_cls is None or tool_parser_cls.supports_required_and_named)
         ):
-            assert content is not None
-            # Forced Function Call
+            # Named function with standard JSON-based parsing
+            if content is None:
+                return [], None
             function_calls.append(
                 FunctionCall(name=request.tool_choice.function.name, arguments=content)
             )
             content = None  # Clear content since tool is called.
-        elif request.tool_choice == "required":
+        elif (
+            not use_mistral_tool_parser
+            and request.tool_choice == "required"
+            and (tool_parser_cls is None or tool_parser_cls.supports_required_and_named)
+        ):
+            # "required" with standard JSON-based parsing
             tool_calls = []
             with contextlib.suppress(ValidationError):
                 content = content or ""
@@ -642,17 +716,34 @@ def _parse_tool_calls_from_content(
                     )
                 )
             content = None  # Clear content since tool is called.
-        elif (
-            tool_parser_cls
-            and enable_auto_tools
-            and (request.tool_choice == "auto" or request.tool_choice is None)
+        elif tool_parser_cls and (
+            use_mistral_tool_parser
+            or (
+                enable_auto_tools
+                and (
+                    request.tool_choice == "auto"
+                    or request.tool_choice is None
+                    or (
+                        not tool_parser_cls.supports_required_and_named
+                        and request.tools
+                        and (
+                            request.tool_choice == "required"
+                            or isinstance(
+                                request.tool_choice,
+                                ChatCompletionNamedToolChoiceParam,
+                            )
+                        )
+                    )
+                )
+            )
         ):
+            # Automatic Tool Call Parsing (also used as fallback for
+            # required/named when supports_required_and_named=False)
             if tokenizer is None:
                 raise ValueError(
                     "Tokenizer not available when `skip_tokenizer_init=True`"
                 )
 
-            # Automatic Tool Call Parsing
             try:
                 tool_parser = tool_parser_cls(tokenizer, request.tools)
             except RuntimeError as e:
@@ -704,6 +795,8 @@ def _get_decoded_token(
     def _is_model_supported(self, model_name: str | None) -> bool:
         if not model_name:
             return True
+        if envs.VLLM_SKIP_MODEL_NAME_VALIDATION:
+            return True
         return self.models.is_base_model(model_name)
 
 
diff --git a/vllm/entrypoints/openai/fingerprint.py b/vllm/entrypoints/openai/fingerprint.py
new file mode 100644
index 000000000000..e858e667d06c
--- /dev/null
+++ b/vllm/entrypoints/openai/fingerprint.py
@@ -0,0 +1,84 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Build the ``system_fingerprint`` string returned by the OpenAI-compatible
+server.
+
+Four modes, configured via ``--fingerprint-mode``:
+
+* ``full`` (default): ``vllm-<version>[-<parallelism>]-<hash8>`` — encodes
+  server version, any non-trivial parallelism degree (tp/pp/dp/ep), and an
+  8-char prefix of ``vllm_config.compute_hash()`` (covers model identity,
+  quant config, speculative, attention backend, etc.).
+* ``hash``: ``vllm-<version>-<hash8>`` — parallelism stripped.
+* ``custom``: user-provided literal via ``--fingerprint-value``.
+* ``none``: the field is omitted (serialized as ``null``).
+
+``get_system_fingerprint`` is only called at serving-class init (a handful
+of times per server); each subclass caches the returned string on
+``self.system_fingerprint``, so per-request cost is one attribute read.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Literal
+
+FingerprintMode = Literal["full", "hash", "custom", "none"]
+
+_DEFAULT_MODE: FingerprintMode = "full"
+_CUSTOM_VALUE: str | None = None
+
+
+def set_default_fingerprint_mode(
+    mode: FingerprintMode,
+    custom_value: str | None = None,
+) -> None:
+    """Configure the fingerprint mode for subsequent ``get_system_fingerprint``
+    calls. Called once at server startup."""
+    global _DEFAULT_MODE, _CUSTOM_VALUE
+    _DEFAULT_MODE = mode
+    _CUSTOM_VALUE = custom_value
+
+
+def get_system_fingerprint(vllm_config: Any) -> str | None:
+    """Return the fingerprint for ``vllm_config`` using the mode configured by
+    ``set_default_fingerprint_mode``."""
+    return build_system_fingerprint(vllm_config, _DEFAULT_MODE, _CUSTOM_VALUE)
+
+
+def build_system_fingerprint(
+    vllm_config: Any,
+    mode: FingerprintMode = "full",
+    custom_value: str | None = None,
+) -> str | None:
+    if mode == "none":
+        return None
+    if mode == "custom":
+        return custom_value
+
+    from vllm import __version__ as vllm_version
+
+    try:
+        hash8 = vllm_config.compute_hash()[:8]
+    except Exception:
+        hash8 = "nohash"
+
+    if mode == "hash":
+        return f"vllm-{vllm_version}-{hash8}"
+
+    # mode == "full"
+    parts: list[str] = [f"vllm-{vllm_version}"]
+    pc = getattr(vllm_config, "parallel_config", None)
+    if pc is not None:
+        tp = getattr(pc, "tensor_parallel_size", 1)
+        if tp > 1:
+            parts.append(f"tp{tp}")
+        pp = getattr(pc, "pipeline_parallel_size", 1)
+        if pp > 1:
+            parts.append(f"pp{pp}")
+        dp = getattr(pc, "data_parallel_size", 1)
+        if dp > 1:
+            parts.append(f"dp{dp}")
+        if getattr(pc, "enable_expert_parallel", False):
+            parts.append("ep")
+    parts.append(hash8)
+    return "-".join(parts)
diff --git a/vllm/entrypoints/openai/generate/api_router.py b/vllm/entrypoints/openai/generate/api_router.py
index 9a64db929e8f..4386baa14e10 100644
--- a/vllm/entrypoints/openai/generate/api_router.py
+++ b/vllm/entrypoints/openai/generate/api_router.py
@@ -61,9 +61,17 @@ async def init_generate_state(
     )
     from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
     from vllm.entrypoints.openai.completion.serving import OpenAIServingCompletion
+    from vllm.entrypoints.openai.fingerprint import set_default_fingerprint_mode
     from vllm.entrypoints.openai.responses.serving import OpenAIServingResponses
     from vllm.entrypoints.serve.disagg.serving import ServingTokens
 
+    # Applied before any serving class is constructed so that each one picks
+    # up the chosen mode on its first cache miss.
+    set_default_fingerprint_mode(
+        getattr(args, "fingerprint_mode", "full"),
+        getattr(args, "fingerprint_value", None),
+    )
+
     if args.tool_server == "demo":
         tool_server: ToolServer | None = DemoToolServer()
         assert isinstance(tool_server, DemoToolServer)
diff --git a/vllm/entrypoints/openai/generate/factories.py b/vllm/entrypoints/openai/generate/factories.py
new file mode 100644
index 000000000000..899601db3ca0
--- /dev/null
+++ b/vllm/entrypoints/openai/generate/factories.py
@@ -0,0 +1,42 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import TYPE_CHECKING
+
+from vllm.config import ModelConfig
+from vllm.tasks import SupportedTask
+
+if TYPE_CHECKING:
+    from vllm.entrypoints.sagemaker.api_router import (
+        EndpointFn,
+        GetHandlerFn,
+        RequestType,
+    )
+
+
+def get_generate_invocation_types(
+    supported_tasks: tuple["SupportedTask", ...],
+    model_config: ModelConfig | None = None,
+):
+    # NOTE: Items defined earlier take higher priority
+    invocation_types: list[tuple[RequestType, tuple[GetHandlerFn, EndpointFn]]] = []
+
+    if "generate" in supported_tasks:
+        from vllm.entrypoints.openai.chat_completion.api_router import (
+            chat,
+            create_chat_completion,
+        )
+        from vllm.entrypoints.openai.chat_completion.protocol import (
+            ChatCompletionRequest,
+        )
+        from vllm.entrypoints.openai.completion.api_router import (
+            completion,
+            create_completion,
+        )
+        from vllm.entrypoints.openai.completion.protocol import CompletionRequest
+
+        invocation_types += [
+            (ChatCompletionRequest, (chat, create_chat_completion)),
+            (CompletionRequest, (completion, create_completion)),
+        ]
+
+    return invocation_types
diff --git a/vllm/entrypoints/openai/parser/harmony_utils.py b/vllm/entrypoints/openai/parser/harmony_utils.py
index 7550a02867a6..33e68b4c40e8 100644
--- a/vllm/entrypoints/openai/parser/harmony_utils.py
+++ b/vllm/entrypoints/openai/parser/harmony_utils.py
@@ -3,7 +3,6 @@
 
 import datetime
 from collections.abc import Iterable, Sequence
-from typing import Literal
 
 from openai.types.responses.tool import Tool
 from openai_harmony import (
@@ -66,7 +65,7 @@ def get_encoding():
 
 def get_system_message(
     model_identity: str | None = None,
-    reasoning_effort: Literal["high", "medium", "low"] | None = None,
+    reasoning_effort: str | None = None,
     start_date: str | None = None,
     browser_description: str | None = None,
     python_description: str | None = None,
@@ -84,6 +83,12 @@ def get_system_message(
         )
         sys_msg_content = sys_msg_content.with_model_identity(new_identity)
     if reasoning_effort is not None:
+        if reasoning_effort not in REASONING_EFFORT:
+            supported_values = ", ".join(REASONING_EFFORT)
+            raise ValueError(
+                f"reasoning_effort={reasoning_effort!r} is not supported by "
+                f"Harmony. Supported values are: {supported_values}."
+            )
         sys_msg_content = sys_msg_content.with_reasoning_effort(
             REASONING_EFFORT[reasoning_effort]
         )
diff --git a/vllm/entrypoints/openai/parser/responses_parser.py b/vllm/entrypoints/openai/parser/responses_parser.py
index a31f20501e0b..1868a31ca285 100644
--- a/vllm/entrypoints/openai/parser/responses_parser.py
+++ b/vllm/entrypoints/openai/parser/responses_parser.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import logging
-from collections.abc import Callable
+from typing import Any
 
 from openai.types.responses import ResponseFunctionToolCall, ResponseOutputItem
 from openai.types.responses.response_function_tool_call_output_item import (
@@ -15,6 +15,7 @@
     ResponseReasoningItem,
 )
 
+from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption
 from vllm.entrypoints.constants import MCP_PREFIX
 from vllm.entrypoints.openai.responses.protocol import (
     ResponseInputOutputItem,
@@ -36,10 +37,12 @@ def __init__(
         self,
         *,
         tokenizer: TokenizerLike,
-        reasoning_parser_cls: Callable[[TokenizerLike], ReasoningParser],
+        reasoning_parser_cls: type[ReasoningParser],
         response_messages: list[ResponseInputOutputItem],
         request: ResponsesRequest,
         tool_parser_cls: type[ToolParser] | None,
+        chat_template: str | None,
+        chat_template_content_format: ChatTemplateContentFormatOption,
     ):
         self.response_messages: list[ResponseInputOutputItem] = (
             # TODO: initial messages may not be properly typed
@@ -49,7 +52,14 @@ def __init__(
         self.tokenizer = tokenizer
         self.request = request
 
-        self.reasoning_parser_instance = reasoning_parser_cls(tokenizer)
+        self.reasoning_parser_instance = reasoning_parser_cls(
+            tokenizer,
+            chat_template_kwargs=_effective_chat_template_kwargs(
+                request,
+                chat_template=chat_template,
+                chat_template_content_format=chat_template_content_format,
+            ),
+        )
         self.tool_parser_instance = None
         if tool_parser_cls is not None:
             self.tool_parser_instance = tool_parser_cls(tokenizer, request.tools)
@@ -159,10 +169,12 @@ def make_response_output_items_from_parsable_context(
 def get_responses_parser_for_simple_context(
     *,
     tokenizer: TokenizerLike,
-    reasoning_parser_cls: Callable[[TokenizerLike], ReasoningParser],
+    reasoning_parser_cls: type[ReasoningParser],
     response_messages: list[ResponseInputOutputItem],
     request: ResponsesRequest,
     tool_parser_cls,
+    chat_template: str | None,
+    chat_template_content_format: ChatTemplateContentFormatOption,
 ) -> ResponsesParser:
     """Factory function to create a ResponsesParser with
     optional reasoning parser.
@@ -176,4 +188,17 @@ def get_responses_parser_for_simple_context(
         response_messages=response_messages,
         request=request,
         tool_parser_cls=tool_parser_cls,
+        chat_template=chat_template,
+        chat_template_content_format=chat_template_content_format,
     )
+
+
+def _effective_chat_template_kwargs(
+    request: ResponsesRequest,
+    chat_template: str | None,
+    chat_template_content_format: ChatTemplateContentFormatOption,
+) -> dict[str, Any]:
+    return request.build_chat_params(
+        default_template=chat_template,
+        default_template_content_format=chat_template_content_format,
+    ).chat_template_kwargs
diff --git a/vllm/entrypoints/openai/responses/context.py b/vllm/entrypoints/openai/responses/context.py
index 48360173cf48..f0920ab09f4e 100644
--- a/vllm/entrypoints/openai/responses/context.py
+++ b/vllm/entrypoints/openai/responses/context.py
@@ -6,7 +6,6 @@
 import json
 import logging
 from abc import ABC, abstractmethod
-from collections.abc import Callable
 from contextlib import AsyncExitStack
 from dataclasses import replace
 from typing import TYPE_CHECKING, Any, Final, Union
@@ -273,7 +272,7 @@ def __init__(
         *,
         response_messages: list[ResponseInputOutputItem],
         tokenizer: TokenizerLike,
-        reasoning_parser_cls: Callable[[TokenizerLike], ReasoningParser] | None,
+        reasoning_parser_cls: type[ReasoningParser] | None,
         request: ResponsesRequest,
         available_tools: list[str] | None,
         tool_parser_cls: type[ToolParser] | None,
@@ -296,6 +295,8 @@ def __init__(
             response_messages=response_messages,
             request=request,
             tool_parser_cls=tool_parser_cls,
+            chat_template=chat_template,
+            chat_template_content_format=chat_template_content_format,
         )
         self.tool_parser_cls = tool_parser_cls
         self.request = request
diff --git a/vllm/entrypoints/openai/responses/protocol.py b/vllm/entrypoints/openai/responses/protocol.py
index 79f5894fb91d..b5d69ea1cccc 100644
--- a/vllm/entrypoints/openai/responses/protocol.py
+++ b/vllm/entrypoints/openai/responses/protocol.py
@@ -23,7 +23,9 @@
     ResponseOutputItem,
     ResponseOutputItemAddedEvent,
     ResponseOutputItemDoneEvent,
+    ResponseOutputMessage,
     ResponsePrompt,
+    ResponseReasoningItem,
     ResponseReasoningTextDeltaEvent,
     ResponseReasoningTextDoneEvent,
     ResponseStatus,
@@ -451,18 +453,21 @@ def check_cache_salt_support(cls, data):
 
     @model_validator(mode="before")
     @classmethod
-    def function_call_parsing(cls, data):
-        """Parse function_call dictionaries into ResponseFunctionToolCall objects.
-        This ensures Pydantic can properly resolve union types in the input field.
-        Function calls provided as dicts are converted to ResponseFunctionToolCall
-        objects before validation, while invalid structures are left for Pydantic
-        to reject with appropriate error messages.
-        """
+    def input_item_parsing(cls, data):
+        """Parse input items that are missing required fields or that Pydantic
+        cannot disambiguate in a Union of TypedDict / BaseModel types.
+
+        Specifically handles:
+        - function_call -> ResponseFunctionToolCall
+        - reasoning     -> ResponseReasoningItem (auto-generates id)
+        - message(role=assistant) -> ResponseOutputMessage (auto-generates
+          id/status and annotations)
 
+        Invalid structures are left for Pydantic to reject.
+        """
         input_data = data.get("input")
 
         # Early return for None, strings, or bytes
-        # (strings are iterable but shouldn't be processed)
         if input_data is None or isinstance(input_data, (str, bytes)):
             return data
 
@@ -476,22 +481,107 @@ def function_call_parsing(cls, data):
 
         processed_input = []
         for item in input_data:
-            if isinstance(item, dict) and item.get("type") == "function_call":
+            if not isinstance(item, dict):
+                processed_input.append(item)
+                continue
+
+            item_type = item.get("type")
+
+            if item_type == "function_call":
                 try:
                     processed_input.append(ResponseFunctionToolCall(**item))
                 except ValidationError:
-                    # Let Pydantic handle validation for malformed function calls
                     logger.debug(
                         "Failed to parse function_call to ResponseFunctionToolCall, "
                         "leaving for Pydantic validation"
                     )
                     processed_input.append(item)
+
+            elif item_type == "reasoning":
+                if "id" not in item:
+                    item = {**item, "id": f"rs_{random_uuid()}"}
+                try:
+                    processed_input.append(ResponseReasoningItem(**item))
+                except ValidationError:
+                    logger.debug(
+                        "Failed to parse reasoning to ResponseReasoningItem, "
+                        "leaving for Pydantic validation"
+                    )
+                    processed_input.append(item)
+
+            elif item_type == "message" and item.get("role") == "assistant":
+                item = dict(item)
+                if "id" not in item:
+                    item["id"] = f"msg_{random_uuid()}"
+                if "status" not in item:
+                    item["status"] = "completed"
+                # ResponseOutputText requires annotations
+                if isinstance(item.get("content"), list):
+                    new_content = []
+                    for c in item["content"]:
+                        if (
+                            isinstance(c, dict)
+                            and c.get("type") == "output_text"
+                            and "annotations" not in c
+                        ):
+                            c = {**c, "annotations": []}
+                        new_content.append(c)
+                    item["content"] = new_content
+                try:
+                    processed_input.append(ResponseOutputMessage(**item))
+                except ValidationError:
+                    logger.debug(
+                        "Failed to parse assistant message to ResponseOutputMessage, "
+                        "leaving for Pydantic validation"
+                    )
+                    processed_input.append(item)
+
             else:
                 processed_input.append(item)
 
         data["input"] = processed_input
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_tool_usage(cls, data):
+        if not isinstance(data, dict):
+            return data
+
+        tools = data.get("tools")
+        tool_choice = data.get("tool_choice", "auto")
+        has_tools = tools is not None and len(tools) > 0
+        is_named_tool_choice = (
+            isinstance(tool_choice, dict) and tool_choice.get("type") == "function"
+        )
+
+        if not has_tools:
+            if tool_choice in ("auto", "none"):
+                data["tool_choice"] = "none"
+            elif tool_choice == "required":
+                raise VLLMValidationError(
+                    "Tool choice 'required' must be specified with 'tools' parameter.",
+                    parameter="tool_choice",
+                )
+            elif is_named_tool_choice:
+                raise VLLMValidationError(
+                    "Tool choice 'function' not found in 'tools' parameter.",
+                    parameter="tool_choice",
+                )
+        elif is_named_tool_choice and tools is not None:
+            tool_name = tool_choice.get("name")
+            tool_names = {
+                t.get("name") if isinstance(t, dict) else getattr(t, "name", None)
+                for t in tools
+            }
+            if not tool_name or tool_name not in tool_names:
+                raise VLLMValidationError(
+                    "Tool choice 'function' not found in 'tools' parameter.",
+                    parameter="tool_choice",
+                )
+
+        return data
+
 
 class ResponsesResponse(OpenAIBaseModel):
     id: str = Field(default_factory=lambda: f"resp_{random_uuid()}")
diff --git a/vllm/entrypoints/openai/responses/serving.py b/vllm/entrypoints/openai/responses/serving.py
index 6a0c4c1e9b67..fb28a2256ad0 100644
--- a/vllm/entrypoints/openai/responses/serving.py
+++ b/vllm/entrypoints/openai/responses/serving.py
@@ -3,7 +3,6 @@
 
 import asyncio
 import time
-import uuid
 from collections import deque
 from collections.abc import AsyncGenerator, AsyncIterator, Callable, Mapping, Sequence
 from contextlib import AsyncExitStack
@@ -13,29 +12,14 @@
 
 from fastapi import Request
 from openai.types.responses import (
-    ResponseContentPartAddedEvent,
-    ResponseContentPartDoneEvent,
-    ResponseFunctionCallArgumentsDeltaEvent,
-    ResponseFunctionCallArgumentsDoneEvent,
     ResponseFunctionToolCall,
-    ResponseFunctionToolCallItem,
     ResponseOutputItem,
-    ResponseOutputItemAddedEvent,
-    ResponseOutputItemDoneEvent,
     ResponseOutputMessage,
     ResponseOutputText,
-    ResponseReasoningItem,
-    ResponseReasoningTextDeltaEvent,
-    ResponseReasoningTextDoneEvent,
     ResponseStatus,
-    ResponseTextDeltaEvent,
-    ResponseTextDoneEvent,
     response_text_delta_event,
 )
 from openai.types.responses.response_output_text import Logprob, LogprobTopLogprob
-from openai.types.responses.response_reasoning_item import (
-    Content as ResponseReasoningTextContent,
-)
 from openai.types.responses.tool import Mcp, Tool
 from openai_harmony import Message as OpenAIHarmonyMessage
 from pydantic import TypeAdapter
@@ -89,15 +73,15 @@
     ResponseInProgressEvent,
     ResponseInputOutputItem,
     ResponseInputOutputMessage,
-    ResponseReasoningPartAddedEvent,
-    ResponseReasoningPartDoneEvent,
     ResponsesRequest,
     ResponsesResponse,
     ResponseUsage,
     StreamingResponsesResponse,
 )
 from vllm.entrypoints.openai.responses.streaming_events import (
+    SimpleStreamingEventProcessor,
     StreamingState,
+    _StateType,
     emit_content_delta_events,
     emit_previous_item_done_events,
     emit_tool_action_events,
@@ -267,6 +251,14 @@ def __init__(
 
         self.tool_server = tool_server
 
+    def _effective_chat_template_kwargs(
+        self, request: ResponsesRequest
+    ) -> dict[str, Any]:
+        return request.build_chat_params(
+            self.chat_template,
+            self.chat_template_content_format,
+        ).chat_template_kwargs
+
     def _validate_generator_input(
         self,
         engine_input: EngineInput,
@@ -331,6 +323,17 @@ async def create_responses(
         AsyncGenerator[StreamingResponsesResponse, None]
         | ResponsesResponse
         | ErrorResponse
+    ):
+        return await self._with_kv_transfer_rejection_cleanup(
+            self._create_responses(request, raw_request), request, raw_request
+        )
+
+    async def _create_responses(
+        self, request: ResponsesRequest, raw_request: Request | None = None
+    ) -> (
+        AsyncGenerator[StreamingResponsesResponse, None]
+        | ResponsesResponse
+        | ErrorResponse
     ):
         error_check_ret = await self._check_model(request)
         if error_check_ret is not None:
@@ -424,6 +427,9 @@ async def create_responses(
                 self._extract_prompt_len(engine_input),
                 self.default_sampling_params,
                 self.override_max_tokens,
+                truncate_prompt_tokens=(
+                    -1 if request.truncation != "disabled" else None
+                ),
             )
 
             sampling_params = request.to_sampling_params(
@@ -464,7 +470,14 @@ async def create_responses(
                     context = SimpleContext()
 
             if self.parser and self.parser.reasoning_parser_cls is not None:
-                reasoning_parser = self.parser.reasoning_parser_cls(tokenizer)
+                chat_template_kwargs = self._effective_chat_template_kwargs(request)
+                reasoning_parser_kwargs = {
+                    "chat_template_kwargs": chat_template_kwargs,
+                }
+                reasoning_parser = self.parser.reasoning_parser_cls(
+                    tokenizer,
+                    chat_template_kwargs=chat_template_kwargs,
+                )
                 if (
                     isinstance(
                         struct_out := sampling_params.structured_outputs,
@@ -486,6 +499,9 @@ async def create_responses(
                 lora_request=lora_request,
                 priority=request.priority,
                 trace_headers=trace_headers,
+                reasoning_parser_kwargs=reasoning_parser_kwargs
+                if self.parser and self.parser.reasoning_parser_cls is not None
+                else None,
             )
             generators.append(generator)
 
@@ -632,6 +648,7 @@ async def _generate_with_builtin_tools(
         lora_request: LoRARequest | None = None,
         priority: int = 0,
         trace_headers: Mapping[str, str] | None = None,
+        reasoning_parser_kwargs: dict[str, Any] | None = None,
     ):
         max_model_len = self.model_config.max_model_len
 
@@ -655,6 +672,7 @@ async def _generate_with_builtin_tools(
                 lora_request=lora_request,
                 trace_headers=trace_headers,
                 priority=priority,
+                reasoning_parser_kwargs=reasoning_parser_kwargs,
             )
 
             async for res in generator:
@@ -696,6 +714,9 @@ async def _generate_with_builtin_tools(
                     self._extract_prompt_len(engine_input),
                     self.default_sampling_params,  # type: ignore
                     self.override_max_tokens,  # type: ignore
+                    truncate_prompt_tokens=(
+                        -1 if context.request.truncation != "disabled" else None
+                    ),
                 )
 
             # OPTIMIZATION
@@ -707,9 +728,10 @@ def _make_request_with_harmony(
         request: ResponsesRequest,
         prev_response: ResponsesResponse | None,
     ):
-        if request.tool_choice != "auto":
+        if request.tool_choice not in ("auto", "none"):
             raise NotImplementedError(
-                "Only 'auto' tool_choice is supported in response API with Harmony"
+                "Only 'auto' or 'none' tool_choice is supported "
+                "in response API with Harmony"
             )
 
         arrival_time = time.time()
@@ -835,7 +857,10 @@ async def responses_full_generator(
             and self.parser.reasoning_parser_cls is not None
             and isinstance(context, (SimpleContext, ParsableContext))
         ):
-            reasoning_parser = self.parser.reasoning_parser_cls(tokenizer)
+            reasoning_parser = self.parser.reasoning_parser_cls(
+                tokenizer,
+                chat_template_kwargs=self._effective_chat_template_kwargs(request),
+            )
             accumulated = getattr(context, "_accumulated_token_ids", []) or []
             num_reasoning_tokens = reasoning_parser.count_reasoning_tokens(accumulated)
 
@@ -1338,571 +1363,59 @@ async def _process_simple_streaming_events(
             [StreamingResponsesResponse], StreamingResponsesResponse
         ],
     ) -> AsyncGenerator[StreamingResponsesResponse, None]:
-        current_content_index = 0
-        current_output_index = 0
-        current_item_id = ""
-        current_tool_call_index: int | None = None
+        processor = SimpleStreamingEventProcessor()
         parser = self.parser(tokenizer, request.tools) if self.parser else None
-        first_delta_sent = False
-        previous_delta_messages: list[DeltaMessage] = []
+
+        def _get_logprobs(
+            output: CompletionOutput,
+        ) -> list[response_text_delta_event.Logprob]:
+            if not request.is_include_output_logprobs():
+                return []
+            return self._create_stream_response_logprobs(
+                token_ids=output.token_ids,
+                logprobs=output.logprobs,
+                tokenizer=tokenizer,
+                top_logprobs=request.top_logprobs,
+            )
+
         async for ctx in result_generator:
             assert isinstance(ctx, SimpleContext)
-            if ctx.last_output is None:
+            if ctx.last_output is None or not ctx.last_output.outputs:
                 continue
-            if ctx.last_output.outputs:
-                output = ctx.last_output.outputs[0]
-                # finish_reason='error' indicates a retryable error
-                self._raise_if_error(output.finish_reason, request.request_id)
-                delta_text = output.text
-                delta_token_ids = as_list(output.token_ids)
-
-                if parser:
-                    delta_message = parser.parse_delta(
-                        delta_text=delta_text,
-                        delta_token_ids=delta_token_ids,
-                        request=request,
-                        prompt_token_ids=ctx.last_output.prompt_token_ids,
-                    )
-                else:
-                    delta_message = DeltaMessage(
-                        content=output.text,
-                    )
-                if not delta_message:
-                    continue
-                tool_call_item_started = False
-                if not first_delta_sent:
-                    current_item_id = random_uuid()
-                    if delta_message.tool_calls:
-                        current_tool_call_id = f"call_{random_uuid()}"
-                        assert len(delta_message.tool_calls) == 1, (
-                            "Multiple tool calls in one delta is not supported"
-                        )
-                        assert delta_message.tool_calls[0].function is not None, (
-                            "Tool call without function is not supported"
-                        )
-                        assert delta_message.tool_calls[0].function.name is not None, (
-                            "Tool call without function name is not supported"
-                        )
-                        current_tool_call_name = delta_message.tool_calls[
-                            0
-                        ].function.name
-                        current_tool_call_index = delta_message.tool_calls[0].index
-                        yield _increment_sequence_number_and_return(
-                            ResponseOutputItemAddedEvent(
-                                type="response.output_item.added",
-                                sequence_number=-1,
-                                output_index=current_output_index,
-                                item=ResponseFunctionToolCallItem(
-                                    type="function_call",
-                                    id=current_item_id,
-                                    call_id=current_tool_call_id,
-                                    name=current_tool_call_name,
-                                    arguments="",
-                                    status="in_progress",
-                                ),
-                            )
-                        )
-                        tool_call_item_started = True
-                    elif delta_message.reasoning:
-                        yield _increment_sequence_number_and_return(
-                            ResponseOutputItemAddedEvent(
-                                type="response.output_item.added",
-                                sequence_number=-1,
-                                output_index=current_output_index,
-                                item=ResponseReasoningItem(
-                                    type="reasoning",
-                                    id=current_item_id,
-                                    summary=[],
-                                    status="in_progress",
-                                ),
-                            )
-                        )
-                        yield _increment_sequence_number_and_return(
-                            ResponseReasoningPartAddedEvent(
-                                type="response.reasoning_part.added",
-                                sequence_number=-1,
-                                output_index=current_output_index,
-                                item_id=current_item_id,
-                                content_index=current_content_index,
-                                part=ResponseReasoningTextContent(
-                                    text="",
-                                    type="reasoning_text",
-                                ),
-                            )
-                        )
-                    elif not delta_message.tool_calls:
-                        yield _increment_sequence_number_and_return(
-                            ResponseOutputItemAddedEvent(
-                                type="response.output_item.added",
-                                sequence_number=-1,
-                                output_index=current_output_index,
-                                item=ResponseOutputMessage(
-                                    id=current_item_id,
-                                    type="message",
-                                    role="assistant",
-                                    content=[],
-                                    status="in_progress",
-                                ),
-                            )
-                        )
-                        yield _increment_sequence_number_and_return(
-                            ResponseContentPartAddedEvent(
-                                type="response.content_part.added",
-                                sequence_number=-1,
-                                output_index=current_output_index,
-                                item_id=current_item_id,
-                                content_index=current_content_index,
-                                part=ResponseOutputText(
-                                    type="output_text",
-                                    text="",
-                                    annotations=[],
-                                    logprobs=[],
-                                ),
-                            )
-                        )
-                    first_delta_sent = True
 
-                # check delta message and previous delta message are
-                # same as content or reasoning content
-                if (
-                    previous_delta_messages
-                    and previous_delta_messages[-1].reasoning is not None
-                    and delta_message.content is not None
-                ):
-                    # from reasoning to normal content, send done
-                    # event for reasoning
-                    reason_content = "".join(
-                        pm.reasoning
-                        for pm in previous_delta_messages
-                        if pm.reasoning is not None
-                    )
+            output = ctx.last_output.outputs[0]
+            self._raise_if_error(output.finish_reason, request.request_id)
+            delta_text = output.text
+            delta_token_ids = as_list(output.token_ids)
+
+            if parser:
+                delta_message = parser.parse_delta(
+                    delta_text=delta_text,
+                    delta_token_ids=delta_token_ids,
+                    request=request,
+                    prompt_token_ids=ctx.last_output.prompt_token_ids,
+                )
+            else:
+                delta_message = DeltaMessage(content=output.text)
 
-                    # delta message could have both reasoning and
-                    # content. Include current delta's reasoning in the
-                    # finalization since it may carry the tail end of
-                    # reasoning text (e.g. when reasoning end and
-                    # content start arrive in the same delta).
-                    if delta_message.reasoning is not None:
-                        yield _increment_sequence_number_and_return(
-                            ResponseReasoningTextDeltaEvent(
-                                type="response.reasoning_text.delta",
-                                sequence_number=-1,
-                                content_index=current_content_index,
-                                output_index=current_output_index,
-                                item_id=current_item_id,
-                                delta=delta_message.reasoning,
-                            )
-                        )
-                        reason_content += delta_message.reasoning
-                        delta_message = DeltaMessage(content=delta_message.content)
-
-                    yield _increment_sequence_number_and_return(
-                        ResponseReasoningTextDoneEvent(
-                            type="response.reasoning_text.done",
-                            item_id=current_item_id,
-                            sequence_number=-1,
-                            output_index=current_output_index,
-                            content_index=current_content_index,
-                            text=reason_content,
-                        )
-                    )
-                    yield _increment_sequence_number_and_return(
-                        ResponseReasoningPartDoneEvent(
-                            type="response.reasoning_part.done",
-                            sequence_number=-1,
-                            item_id=current_item_id,
-                            output_index=current_output_index,
-                            content_index=current_content_index,
-                            part=ResponseReasoningTextContent(
-                                text=reason_content,
-                                type="reasoning_text",
-                            ),
-                        )
-                    )
-                    current_content_index = 0
-                    reasoning_item = ResponseReasoningItem(
-                        type="reasoning",
-                        content=[
-                            ResponseReasoningTextContent(
-                                text=reason_content,
-                                type="reasoning_text",
-                            ),
-                        ],
-                        status="completed",
-                        id=current_item_id,
-                        summary=[],
-                    )
-                    yield _increment_sequence_number_and_return(
-                        ResponseOutputItemDoneEvent(
-                            type="response.output_item.done",
-                            sequence_number=-1,
-                            output_index=current_output_index,
-                            item=reasoning_item,
-                        )
-                    )
-                    current_output_index += 1
-                    current_item_id = str(uuid.uuid4())
-                    yield _increment_sequence_number_and_return(
-                        ResponseOutputItemAddedEvent(
-                            type="response.output_item.added",
-                            sequence_number=-1,
-                            output_index=current_output_index,
-                            item=ResponseOutputMessage(
-                                id=current_item_id,
-                                type="message",
-                                role="assistant",
-                                content=[],
-                                status="in_progress",
-                            ),
-                        )
-                    )
-                    yield _increment_sequence_number_and_return(
-                        ResponseContentPartAddedEvent(
-                            type="response.content_part.added",
-                            sequence_number=-1,
-                            output_index=current_output_index,
-                            item_id=current_item_id,
-                            content_index=current_content_index,
-                            part=ResponseOutputText(
-                                type="output_text",
-                                text="",
-                                annotations=[],
-                                logprobs=[],
-                            ),
-                        )
-                    )
-                    # reset previous delta messages
-                    previous_delta_messages = []
-                if delta_message.tool_calls and delta_message.tool_calls[0].function:
-                    tool_call = delta_message.tool_calls[0]
-                    tool_call_function = tool_call.function
-                    if (
-                        current_tool_call_index is not None
-                        and tool_call.index is not None
-                        and tool_call.index != current_tool_call_index
-                        and tool_call_function is not None
-                        and tool_call_function.name is not None
-                    ):
-                        # From one tool call to another, finalize the previous
-                        # function-call item before opening the next one.
-                        parts = []
-                        for pm in previous_delta_messages:
-                            if pm.tool_calls:
-                                previous_tool_call = pm.tool_calls[0]
-                                if previous_tool_call.function is not None:
-                                    parts.append(
-                                        previous_tool_call.function.arguments or ""
-                                    )
-
-                        tool_call_arguments = "".join(parts)
-                        yield _increment_sequence_number_and_return(
-                            ResponseFunctionCallArgumentsDoneEvent(
-                                type="response.function_call_arguments.done",
-                                sequence_number=-1,
-                                output_index=current_output_index,
-                                item_id=current_item_id,
-                                arguments=tool_call_arguments,
-                                name=current_tool_call_name,
-                            )
-                        )
-                        function_call_item = ResponseFunctionToolCall(
-                            type="function_call",
-                            name=current_tool_call_name,
-                            arguments=tool_call_arguments,
-                            status="completed",
-                            id=current_item_id,
-                            call_id=current_tool_call_id,
-                        )
-                        yield _increment_sequence_number_and_return(
-                            ResponseOutputItemDoneEvent(
-                                type="response.output_item.done",
-                                sequence_number=-1,
-                                output_index=current_output_index,
-                                item=function_call_item,
-                            )
-                        )
-                        # Reset previous delta messages so the next tool call
-                        # does not reuse arguments from the completed item.
-                        previous_delta_messages = []
-                        current_output_index += 1
-                        current_item_id = random_uuid()
-                        current_tool_call_name = tool_call_function.name
-                        current_tool_call_id = f"call_{random_uuid()}"
-                        current_tool_call_index = tool_call.index
-                        yield _increment_sequence_number_and_return(
-                            ResponseOutputItemAddedEvent(
-                                type="response.output_item.added",
-                                sequence_number=-1,
-                                output_index=current_output_index,
-                                item=ResponseFunctionToolCallItem(
-                                    type="function_call",
-                                    id=current_item_id,
-                                    call_id=current_tool_call_id,
-                                    name=current_tool_call_name,
-                                    arguments="",
-                                    status="in_progress",
-                                ),
-                            )
-                        )
-                        current_content_index = 0
-                        tool_call_item_started = True
-
-                    if delta_message.tool_calls[0].function.arguments:
-                        yield _increment_sequence_number_and_return(
-                            ResponseFunctionCallArgumentsDeltaEvent(
-                                type="response.function_call_arguments.delta",
-                                sequence_number=-1,
-                                output_index=current_output_index,
-                                item_id=current_item_id,
-                                delta=delta_message.tool_calls[0].function.arguments,
-                            )
-                        )
-                    # tool call initiated with no arguments
-                    elif (
-                        delta_message.tool_calls[0].function.name
-                        and not tool_call_item_started
-                    ):
-                        # send done with current content part
-                        # and add new function call item
-                        yield _increment_sequence_number_and_return(
-                            ResponseTextDoneEvent(
-                                type="response.output_text.done",
-                                sequence_number=-1,
-                                output_index=current_output_index,
-                                content_index=current_content_index,
-                                text="",
-                                logprobs=[],
-                                item_id=current_item_id,
-                            )
-                        )
-                        yield _increment_sequence_number_and_return(
-                            ResponseContentPartDoneEvent(
-                                type="response.content_part.done",
-                                sequence_number=-1,
-                                item_id=current_item_id,
-                                output_index=current_output_index,
-                                content_index=current_content_index,
-                                part=ResponseOutputText(
-                                    type="output_text",
-                                    text="",
-                                    annotations=[],
-                                    logprobs=[],
-                                ),
-                            )
-                        )
-                        yield _increment_sequence_number_and_return(
-                            ResponseOutputItemDoneEvent(
-                                type="response.output_item.done",
-                                sequence_number=-1,
-                                output_index=current_output_index,
-                                item=ResponseOutputMessage(
-                                    id=current_item_id,
-                                    type="message",
-                                    role="assistant",
-                                    content=[],
-                                    status="completed",
-                                ),
-                            )
-                        )
-                        current_output_index += 1
-                        current_item_id = random_uuid()
-                        current_tool_call_name = delta_message.tool_calls[
-                            0
-                        ].function.name
-                        current_tool_call_id = f"call_{random_uuid()}"
-                        current_tool_call_index = delta_message.tool_calls[0].index
-                        yield _increment_sequence_number_and_return(
-                            ResponseOutputItemAddedEvent(
-                                type="response.output_item.added",
-                                sequence_number=-1,
-                                output_index=current_output_index,
-                                item=ResponseFunctionToolCallItem(
-                                    type="function_call",
-                                    id=current_item_id,
-                                    call_id=current_tool_call_id,
-                                    name=current_tool_call_name,
-                                    arguments="",
-                                    status="in_progress",
-                                ),
-                            )
-                        )
-                        # skip content part for tool call
-                        current_content_index = 1
-                        continue
-                elif delta_message.reasoning is not None:
-                    yield _increment_sequence_number_and_return(
-                        ResponseReasoningTextDeltaEvent(
-                            type="response.reasoning_text.delta",
-                            sequence_number=-1,
-                            content_index=current_content_index,
-                            output_index=current_output_index,
-                            item_id=current_item_id,
-                            delta=delta_message.reasoning,
-                        )
-                    )
-                elif delta_message.content:
-                    yield _increment_sequence_number_and_return(
-                        ResponseTextDeltaEvent(
-                            type="response.output_text.delta",
-                            sequence_number=-1,
-                            content_index=current_content_index,
-                            output_index=current_output_index,
-                            item_id=current_item_id,
-                            delta=delta_message.content,
-                            logprobs=(
-                                self._create_stream_response_logprobs(
-                                    token_ids=output.token_ids,
-                                    logprobs=output.logprobs,
-                                    tokenizer=tokenizer,
-                                    top_logprobs=request.top_logprobs,
-                                )
-                                if request.is_include_output_logprobs()
-                                else []
-                            ),
-                        )
-                    )
+            if not delta_message:
+                continue
 
-                previous_delta_messages.append(delta_message)
+            target_state, tool_call = processor.resolve_target_state(delta_message)
+            if target_state == _StateType.NONE:
+                continue
 
-        if previous_delta_messages:
-            parts = []
-            for pm in previous_delta_messages:
-                if pm.tool_calls:
-                    assert len(pm.tool_calls) == 1, (
-                        "Multiple tool calls in one delta is not supported"
-                    )
-                    assert pm.tool_calls[0].function is not None, (
-                        "Tool call without function is not supported"
-                    )
-                    parts.append(pm.tool_calls[0].function.arguments or "")
+            if processor.needs_transition(target_state, tool_call):
+                for event in processor.close_current():
+                    yield _increment_sequence_number_and_return(event)
+                for event in processor.open(target_state, tool_call):
+                    yield _increment_sequence_number_and_return(event)
 
-            tool_call_arguments = "".join(parts)
-            if tool_call_arguments:
-                yield _increment_sequence_number_and_return(
-                    ResponseFunctionCallArgumentsDoneEvent(
-                        type="response.function_call_arguments.done",
-                        sequence_number=-1,
-                        output_index=current_output_index,
-                        item_id=current_item_id,
-                        arguments=tool_call_arguments,
-                        name=current_tool_call_name,
-                    )
-                )
-                current_content_index = 0
-                function_call_item = ResponseFunctionToolCall(
-                    type="function_call",
-                    name=current_tool_call_name,
-                    arguments=tool_call_arguments,
-                    status="completed",
-                    id=current_item_id,
-                    call_id=current_tool_call_id,
-                )
-                yield _increment_sequence_number_and_return(
-                    ResponseOutputItemDoneEvent(
-                        type="response.output_item.done",
-                        sequence_number=-1,
-                        output_index=current_output_index,
-                        item=function_call_item,
-                    )
-                )
+            for event in processor.emit_delta(delta_message, output, _get_logprobs):
+                yield _increment_sequence_number_and_return(event)
 
-            elif previous_delta_messages[-1].reasoning is not None:
-                reason_content = "".join(
-                    pm.reasoning
-                    for pm in previous_delta_messages
-                    if pm.reasoning is not None
-                )
-                yield _increment_sequence_number_and_return(
-                    ResponseReasoningTextDoneEvent(
-                        type="response.reasoning_text.done",
-                        item_id=current_item_id,
-                        sequence_number=-1,
-                        output_index=current_output_index,
-                        content_index=current_content_index,
-                        text=reason_content,
-                    )
-                )
-                yield _increment_sequence_number_and_return(
-                    ResponseReasoningPartDoneEvent(
-                        type="response.reasoning_part.done",
-                        sequence_number=-1,
-                        item_id=current_item_id,
-                        output_index=current_output_index,
-                        content_index=current_content_index,
-                        part=ResponseReasoningTextContent(
-                            text=reason_content,
-                            type="reasoning_text",
-                        ),
-                    )
-                )
-                reasoning_item = ResponseReasoningItem(
-                    type="reasoning",
-                    content=[
-                        ResponseReasoningTextContent(
-                            text=reason_content,
-                            type="reasoning_text",
-                        ),
-                    ],
-                    status="completed",
-                    id=current_item_id,
-                    summary=[],
-                )
-                yield _increment_sequence_number_and_return(
-                    ResponseOutputItemDoneEvent(
-                        type="response.output_item.done",
-                        sequence_number=-1,
-                        output_index=current_output_index,
-                        item=reasoning_item,
-                    )
-                )
-            elif previous_delta_messages[-1].content:
-                final_content = "".join(
-                    pm.content for pm in previous_delta_messages if pm.content
-                )
-                yield _increment_sequence_number_and_return(
-                    ResponseTextDoneEvent(
-                        type="response.output_text.done",
-                        sequence_number=-1,
-                        output_index=current_output_index,
-                        content_index=current_content_index,
-                        text=final_content,
-                        logprobs=[],
-                        item_id=current_item_id,
-                    )
-                )
-                part = ResponseOutputText(
-                    text=final_content,
-                    type="output_text",
-                    annotations=[],
-                )
-                yield _increment_sequence_number_and_return(
-                    ResponseContentPartDoneEvent(
-                        type="response.content_part.done",
-                        sequence_number=-1,
-                        item_id=current_item_id,
-                        output_index=current_output_index,
-                        content_index=current_content_index,
-                        part=part,
-                    )
-                )
-                item = ResponseOutputMessage(
-                    type="message",
-                    role="assistant",
-                    content=[
-                        part,
-                    ],
-                    status="completed",
-                    id=current_item_id,
-                    summary=[],
-                )
-                yield _increment_sequence_number_and_return(
-                    ResponseOutputItemDoneEvent(
-                        type="response.output_item.done",
-                        sequence_number=-1,
-                        output_index=current_output_index,
-                        item=item,
-                    )
-                )
+        for event in processor.close_current():
+            yield _increment_sequence_number_and_return(event)
 
     async def _process_harmony_streaming_events(
         self,
diff --git a/vllm/entrypoints/openai/responses/streaming_events.py b/vllm/entrypoints/openai/responses/streaming_events.py
index cc242e7baa83..425ae379b638 100644
--- a/vllm/entrypoints/openai/responses/streaming_events.py
+++ b/vllm/entrypoints/openai/responses/streaming_events.py
@@ -16,8 +16,10 @@
 """
 
 import json
-from dataclasses import dataclass
-from typing import Final
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from enum import Enum, auto
+from typing import Any, ClassVar, Final, NamedTuple
 
 from openai.types.responses import (
     ResponseCodeInterpreterCallCodeDeltaEvent,
@@ -31,6 +33,7 @@
     ResponseFunctionCallArgumentsDeltaEvent,
     ResponseFunctionCallArgumentsDoneEvent,
     ResponseFunctionToolCall,
+    ResponseFunctionToolCallItem,
     ResponseFunctionWebSearch,
     ResponseMcpCallArgumentsDeltaEvent,
     ResponseMcpCallArgumentsDoneEvent,
@@ -49,6 +52,7 @@
     ResponseWebSearchCallInProgressEvent,
     ResponseWebSearchCallSearchingEvent,
     response_function_web_search,
+    response_text_delta_event,
 )
 from openai.types.responses.response_output_item import McpCall
 from openai.types.responses.response_reasoning_item import (
@@ -57,12 +61,14 @@
 from openai_harmony import Message as HarmonyMessage
 
 from vllm.entrypoints.mcp.tool_server import ToolServer
+from vllm.entrypoints.openai.engine.protocol import DeltaMessage
 from vllm.entrypoints.openai.responses.context import StreamingHarmonyContext
 from vllm.entrypoints.openai.responses.protocol import (
     ResponseReasoningPartAddedEvent,
     ResponseReasoningPartDoneEvent,
     StreamingResponsesResponse,
 )
+from vllm.outputs import CompletionOutput
 from vllm.utils import random_uuid
 
 TOOL_NAME_TO_MCP_SERVER_LABEL: Final[dict[str, str]] = {
@@ -796,3 +802,457 @@ def emit_tool_action_events(
             )
 
     return events
+
+
+# =====================================================================
+# Simple streaming helpers
+# =====================================================================
+
+
+class _StateType(Enum):
+    NONE = auto()
+    CONTENT = auto()
+    REASONING = auto()
+    TOOL_CALL = auto()
+
+
+@dataclass
+class SimpleStreamingState:
+    output_index: int = 0
+    current_item_id: str = ""
+    content_index: int = 0
+    accumulated_text: str = ""
+    tool_call_id: str = ""
+    tool_call_name: str = ""
+    tool_call_index: int | None = None
+    has_emitted_tool_call_delta: bool = False
+    current_state: _StateType = field(default_factory=lambda: _StateType.NONE)
+
+
+def emit_simple_content_open(
+    state: SimpleStreamingState,
+) -> list[StreamingResponsesResponse]:
+    state.current_state = _StateType.CONTENT
+    state.current_item_id = random_uuid()
+    state.content_index = 0
+    state.accumulated_text = ""
+    return [
+        ResponseOutputItemAddedEvent(
+            type="response.output_item.added",
+            sequence_number=-1,
+            output_index=state.output_index,
+            item=ResponseOutputMessage(
+                id=state.current_item_id,
+                type="message",
+                role="assistant",
+                content=[],
+                status="in_progress",
+            ),
+        ),
+        ResponseContentPartAddedEvent(
+            type="response.content_part.added",
+            sequence_number=-1,
+            output_index=state.output_index,
+            item_id=state.current_item_id,
+            content_index=state.content_index,
+            part=ResponseOutputText(
+                type="output_text",
+                text="",
+                annotations=[],
+                logprobs=[],
+            ),
+        ),
+    ]
+
+
+def emit_simple_content_delta(
+    state: SimpleStreamingState,
+    delta: str,
+    logprobs: list[response_text_delta_event.Logprob] | None = None,
+) -> list[StreamingResponsesResponse]:
+    state.accumulated_text += delta
+    return [
+        ResponseTextDeltaEvent(
+            type="response.output_text.delta",
+            sequence_number=-1,
+            content_index=state.content_index,
+            output_index=state.output_index,
+            item_id=state.current_item_id,
+            delta=delta,
+            logprobs=logprobs or [],
+        )
+    ]
+
+
+def emit_simple_content_done(
+    state: SimpleStreamingState,
+) -> list[StreamingResponsesResponse]:
+    part = ResponseOutputText(
+        type="output_text",
+        text=state.accumulated_text,
+        annotations=[],
+    )
+    events: list[StreamingResponsesResponse] = [
+        ResponseTextDoneEvent(
+            type="response.output_text.done",
+            sequence_number=-1,
+            output_index=state.output_index,
+            content_index=state.content_index,
+            text=state.accumulated_text,
+            logprobs=[],
+            item_id=state.current_item_id,
+        ),
+        ResponseContentPartDoneEvent(
+            type="response.content_part.done",
+            sequence_number=-1,
+            item_id=state.current_item_id,
+            output_index=state.output_index,
+            content_index=state.content_index,
+            part=part,
+        ),
+        ResponseOutputItemDoneEvent(
+            type="response.output_item.done",
+            sequence_number=-1,
+            output_index=state.output_index,
+            item=ResponseOutputMessage(
+                id=state.current_item_id,
+                type="message",
+                role="assistant",
+                content=[part] if state.accumulated_text else [],
+                status="completed",
+                summary=[],
+            ),
+        ),
+    ]
+    state.output_index += 1
+    state.current_state = _StateType.NONE
+    return events
+
+
+def emit_simple_reasoning_open(
+    state: SimpleStreamingState,
+) -> list[StreamingResponsesResponse]:
+    state.current_state = _StateType.REASONING
+    state.current_item_id = random_uuid()
+    state.content_index = 0
+    state.accumulated_text = ""
+    return [
+        ResponseOutputItemAddedEvent(
+            type="response.output_item.added",
+            sequence_number=-1,
+            output_index=state.output_index,
+            item=ResponseReasoningItem(
+                type="reasoning",
+                id=state.current_item_id,
+                summary=[],
+                status="in_progress",
+            ),
+        ),
+        ResponseReasoningPartAddedEvent(
+            type="response.reasoning_part.added",
+            sequence_number=-1,
+            output_index=state.output_index,
+            item_id=state.current_item_id,
+            content_index=state.content_index,
+            part=ResponseReasoningTextContent(
+                text="",
+                type="reasoning_text",
+            ),
+        ),
+    ]
+
+
+def emit_simple_reasoning_delta(
+    state: SimpleStreamingState,
+    delta: str,
+) -> list[StreamingResponsesResponse]:
+    state.accumulated_text += delta
+    return [
+        ResponseReasoningTextDeltaEvent(
+            type="response.reasoning_text.delta",
+            item_id=state.current_item_id,
+            sequence_number=-1,
+            output_index=state.output_index,
+            content_index=state.content_index,
+            delta=delta,
+        )
+    ]
+
+
+def emit_simple_reasoning_done(
+    state: SimpleStreamingState,
+) -> list[StreamingResponsesResponse]:
+    part = ResponseReasoningTextContent(
+        text=state.accumulated_text,
+        type="reasoning_text",
+    )
+    events: list[StreamingResponsesResponse] = [
+        ResponseReasoningTextDoneEvent(
+            type="response.reasoning_text.done",
+            item_id=state.current_item_id,
+            sequence_number=-1,
+            output_index=state.output_index,
+            content_index=state.content_index,
+            text=state.accumulated_text,
+        ),
+        ResponseReasoningPartDoneEvent(
+            type="response.reasoning_part.done",
+            sequence_number=-1,
+            item_id=state.current_item_id,
+            output_index=state.output_index,
+            content_index=state.content_index,
+            part=part,
+        ),
+        ResponseOutputItemDoneEvent(
+            type="response.output_item.done",
+            sequence_number=-1,
+            output_index=state.output_index,
+            item=ResponseReasoningItem(
+                type="reasoning",
+                content=[part],
+                status="completed",
+                id=state.current_item_id,
+                summary=[],
+            ),
+        ),
+    ]
+    state.output_index += 1
+    state.current_state = _StateType.NONE
+    return events
+
+
+def emit_simple_tool_call_open(
+    state: SimpleStreamingState,
+    name: str,
+    index: int | None,
+) -> list[StreamingResponsesResponse]:
+    state.current_state = _StateType.TOOL_CALL
+    state.current_item_id = random_uuid()
+    state.tool_call_id = f"call_{random_uuid()}"
+    state.tool_call_name = name
+    state.tool_call_index = index
+    state.accumulated_text = ""
+    state.has_emitted_tool_call_delta = False
+    return [
+        ResponseOutputItemAddedEvent(
+            type="response.output_item.added",
+            sequence_number=-1,
+            output_index=state.output_index,
+            item=ResponseFunctionToolCallItem(
+                type="function_call",
+                id=state.current_item_id,
+                call_id=state.tool_call_id,
+                name=name,
+                arguments="",
+                status="in_progress",
+            ),
+        ),
+    ]
+
+
+def emit_simple_tool_call_delta(
+    state: SimpleStreamingState,
+    delta: str,
+) -> list[StreamingResponsesResponse]:
+    state.accumulated_text += delta
+    state.has_emitted_tool_call_delta = True
+    return [
+        ResponseFunctionCallArgumentsDeltaEvent(
+            type="response.function_call_arguments.delta",
+            sequence_number=-1,
+            output_index=state.output_index,
+            item_id=state.current_item_id,
+            delta=delta,
+        )
+    ]
+
+
+def emit_simple_tool_call_done(
+    state: SimpleStreamingState,
+) -> list[StreamingResponsesResponse]:
+    events: list[StreamingResponsesResponse] = []
+    if state.has_emitted_tool_call_delta:
+        events.append(
+            ResponseFunctionCallArgumentsDoneEvent(
+                type="response.function_call_arguments.done",
+                sequence_number=-1,
+                output_index=state.output_index,
+                item_id=state.current_item_id,
+                arguments=state.accumulated_text,
+                name=state.tool_call_name,
+            )
+        )
+    events.append(
+        ResponseOutputItemDoneEvent(
+            type="response.output_item.done",
+            sequence_number=-1,
+            output_index=state.output_index,
+            item=ResponseFunctionToolCall(
+                type="function_call",
+                name=state.tool_call_name,
+                arguments=state.accumulated_text,
+                status="completed",
+                id=state.current_item_id,
+                call_id=state.tool_call_id,
+            ),
+        ),
+    )
+    state.output_index += 1
+    state.current_state = _StateType.NONE
+    return events
+
+
+class _StateHandlers(NamedTuple):
+    """Tuple for each state: open(start), delta(chunk), done(finish)."""
+
+    open_fn: Callable[..., list[StreamingResponsesResponse]]
+    delta_fn: Callable[..., list[StreamingResponsesResponse]]
+    done_fn: Callable[..., list[StreamingResponsesResponse]]
+
+
+class SimpleStreamingEventProcessor:
+    """
+    State-machine processor for the simple (non-Harmony) streaming path.
+
+    Core flow:
+      1. Resolve the target state from the delta_message
+         (CONTENT / REASONING / TOOL_CALL).
+      2. If the target state differs from the current one,
+         close_current() then open() the new state.
+      3. emit_delta() produces the incremental events for the state.
+
+    State lifecycle:
+      open()  ->  repeated emit_delta()  ->  close_current()
+    """
+
+    _STATE_HANDLERS: ClassVar[dict[_StateType, _StateHandlers]] = {
+        _StateType.CONTENT: _StateHandlers(
+            emit_simple_content_open,
+            emit_simple_content_delta,
+            emit_simple_content_done,
+        ),
+        _StateType.REASONING: _StateHandlers(
+            emit_simple_reasoning_open,
+            emit_simple_reasoning_delta,
+            emit_simple_reasoning_done,
+        ),
+        _StateType.TOOL_CALL: _StateHandlers(
+            emit_simple_tool_call_open,
+            emit_simple_tool_call_delta,
+            emit_simple_tool_call_done,
+        ),
+    }
+
+    def __init__(self, state: SimpleStreamingState | None = None) -> None:
+        self.state = state or SimpleStreamingState()
+
+    def resolve_target_state(
+        self, delta_message: DeltaMessage
+    ) -> tuple[_StateType, Any]:
+        """
+        Decide which state the next delta belongs to.
+
+        Priority: TOOL_CALL > REASONING > CONTENT, fallback to NONE.
+        For TOOL_CALL the first tool_call object is also returned so
+        callers can detect a switch between consecutive tools.
+        """
+        if (
+            delta_message.tool_calls
+            and delta_message.tool_calls[0].function is not None
+        ):
+            return _StateType.TOOL_CALL, delta_message.tool_calls[0]
+        if delta_message.reasoning is not None:
+            return _StateType.REASONING, None
+        if delta_message.content:
+            return _StateType.CONTENT, None
+        return _StateType.NONE, None
+
+    def needs_transition(self, target_state: _StateType, tool_call: Any) -> bool:
+        """
+        Return True when we must close the current state and open a new one.
+
+        Two cases trigger a transition:
+          1. The target state differs from the current state
+             (e.g. CONTENT -> TOOL_CALL).
+          2. We are already in TOOL_CALL but the next tool_call has a
+             different index (multiple consecutive tool calls).
+        """
+        if self.state.current_state != target_state:
+            return True
+        return (
+            target_state == _StateType.TOOL_CALL
+            and tool_call is not None
+            and self.state.tool_call_index is not None
+            and tool_call.index is not None
+            and self.state.tool_call_index != tool_call.index
+        )
+
+    def close_current(self) -> list[StreamingResponsesResponse]:
+        """Close the current state and emit its 'done' event sequence."""
+        handlers = self._STATE_HANDLERS.get(self.state.current_state)
+        if handlers is None:
+            return []
+        return handlers.done_fn(self.state)
+
+    def open(
+        self, target_state: _StateType, tool_call: Any = None
+    ) -> list[StreamingResponsesResponse]:
+        """Open a new state and emit its 'added' / 'open' event sequence."""
+        handlers = self._STATE_HANDLERS[target_state]
+        if target_state == _StateType.TOOL_CALL:
+            assert tool_call is not None
+            return handlers.open_fn(
+                self.state, tool_call.function.name, tool_call.index
+            )
+        return handlers.open_fn(self.state)
+
+    def emit_delta(
+        self,
+        delta_message: DeltaMessage,
+        output: CompletionOutput,
+        get_logprobs: Callable[
+            [CompletionOutput], list[response_text_delta_event.Logprob]
+        ]
+        | None = None,
+    ) -> list[StreamingResponsesResponse]:
+        """
+        Emit incremental events for the current state from the delta.
+
+        Special case: when already in REASONING and the same delta also
+        carries content, we emit the reasoning delta, close reasoning,
+        open content, and then emit the content delta.
+        """
+        handlers = self._STATE_HANDLERS[self.state.current_state]
+        events: list[StreamingResponsesResponse] = []
+
+        # Special case: reasoning -> content inside a single delta.
+        if (
+            self.state.current_state == _StateType.REASONING
+            and delta_message.reasoning is not None
+            and delta_message.content is not None
+        ):
+            events.extend(handlers.delta_fn(self.state, delta_message.reasoning))
+            events.extend(self.close_current())
+            events.extend(self.open(_StateType.CONTENT))
+            content_handlers = self._STATE_HANDLERS[_StateType.CONTENT]
+            logprobs = get_logprobs(output) if get_logprobs else []
+            events.extend(
+                content_handlers.delta_fn(self.state, delta_message.content, logprobs)
+            )
+            return events
+
+        if self.state.current_state == _StateType.TOOL_CALL:
+            assert delta_message.tool_calls is not None
+            tool_call_function = delta_message.tool_calls[0].function
+            assert tool_call_function is not None
+            if tool_call_function.arguments:
+                return handlers.delta_fn(self.state, tool_call_function.arguments)
+            return []
+        elif self.state.current_state == _StateType.REASONING:
+            assert delta_message.reasoning is not None
+            return handlers.delta_fn(self.state, delta_message.reasoning)
+        elif self.state.current_state == _StateType.CONTENT:
+            assert delta_message.content is not None
+            logprobs = get_logprobs(output) if get_logprobs else []
+            return handlers.delta_fn(self.state, delta_message.content, logprobs)
+        return []
diff --git a/vllm/entrypoints/openai/responses/utils.py b/vllm/entrypoints/openai/responses/utils.py
index 66239289f73a..ad94c8d8deca 100644
--- a/vllm/entrypoints/openai/responses/utils.py
+++ b/vllm/entrypoints/openai/responses/utils.py
@@ -264,7 +264,7 @@ def convert_tool_responses_to_completions_format(tool: dict) -> dict:
 def construct_tool_dicts(
     tools: list[Tool], tool_choice: ToolChoice
 ) -> list[dict[str, Any]] | None:
-    if tools is None or (tool_choice == "none"):
+    if not tools or (tool_choice == "none"):
         tool_dicts = None
     else:
         tool_dicts = [
diff --git a/vllm/entrypoints/openai/server_utils.py b/vllm/entrypoints/openai/server_utils.py
index 02b8c3352621..f8fe3366b060 100644
--- a/vllm/entrypoints/openai/server_utils.py
+++ b/vllm/entrypoints/openai/server_utils.py
@@ -69,7 +69,10 @@ def verify_token(self, headers: Headers) -> bool:
         return token_match
 
     def __call__(self, scope: Scope, receive: Receive, send: Send) -> Awaitable[None]:
-        if scope["type"] not in ("http", "websocket") or scope["method"] == "OPTIONS":
+        if (
+            scope["type"] not in ("http", "websocket")
+            or scope.get("method") == "OPTIONS"
+        ):
             # scope["type"] can be "lifespan" or "startup" for example,
             # in which case we don't need to do anything
             return self.app(scope, receive, send)
diff --git a/vllm/entrypoints/openai/speech_to_text/protocol.py b/vllm/entrypoints/openai/speech_to_text/protocol.py
index a8d978e33eb2..af1aaf086555 100644
--- a/vllm/entrypoints/openai/speech_to_text/protocol.py
+++ b/vllm/entrypoints/openai/speech_to_text/protocol.py
@@ -1,9 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import json
 import time
 from http import HTTPStatus
-from typing import Literal, TypeAlias
+from typing import TYPE_CHECKING, Literal, TypeAlias
 
 import torch
 from fastapi import HTTPException, UploadFile
@@ -12,6 +13,7 @@
     model_validator,
 )
 
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.entrypoints.openai.engine.protocol import (
     DeltaMessage,
     OpenAIBaseModel,
@@ -26,6 +28,11 @@
 )
 from vllm.utils import random_uuid
 
+if TYPE_CHECKING:
+    import numpy as np
+
+    from vllm.config import ModelConfig, SpeechToTextConfig
+
 logger = init_logger(__name__)
 _LONG_INFO = torch.iinfo(torch.long)
 
@@ -71,6 +78,12 @@ class TranscriptionRequest(OpenAIBaseModel):
     will improve accuracy and latency.
     """
 
+    hotwords: str | None = None
+    """
+    hotwords refers to a list of important words or phrases that the model
+    should pay extra attention to during transcription.
+    """
+
     prompt: str = Field(default="")
     """An optional text to guide the model's style or continue a previous audio
     segment.
@@ -183,6 +196,24 @@ class TranscriptionRequest(OpenAIBaseModel):
         "min_p": 0.0,
     }
 
+    def build_stt_params(
+        self,
+        audio: "np.ndarray",
+        stt_config: "SpeechToTextConfig",
+        model_config: "ModelConfig",
+        task_type: str,
+    ) -> SpeechToTextParams:
+        return SpeechToTextParams(
+            audio=audio,
+            stt_config=stt_config,
+            model_config=model_config,
+            language=self.language,
+            task_type=task_type,
+            request_prompt=self.prompt,
+            to_language=self.to_language,
+            hotwords=self.hotwords,
+        )
+
     def to_beam_search_params(
         self,
         default_max_tokens: int,
@@ -277,6 +308,17 @@ def validate_transcription_request(cls, data):
                 parameter=invalid_param,
             )
 
+        # Parse vllm_xargs from JSON string (form data sends it as a string)
+        xargs = data.get("vllm_xargs")
+        if isinstance(xargs, str):
+            try:
+                data["vllm_xargs"] = json.loads(xargs)
+            except json.JSONDecodeError as e:
+                raise VLLMValidationError(
+                    f"Failed to parse vllm_xargs. Must be valid JSON: {e}",
+                    parameter="vllm_xargs",
+                ) from e
+
         return data
 
 
@@ -446,6 +488,12 @@ class TranslationRequest(OpenAIBaseModel):
     will improve accuracy.
     """
 
+    hotwords: str | None = None
+    """
+    hotwords refers to a list of important words or phrases that the model
+    should pay extra attention to during transcription.
+    """
+
     to_language: str | None = None
     """The language of the input audio we translate to.
 
@@ -472,6 +520,24 @@ class TranslationRequest(OpenAIBaseModel):
         "temperature": 0,
     }
 
+    def build_stt_params(
+        self,
+        audio: "np.ndarray",
+        stt_config: "SpeechToTextConfig",
+        model_config: "ModelConfig",
+        task_type: str,
+    ) -> SpeechToTextParams:
+        return SpeechToTextParams(
+            audio=audio,
+            stt_config=stt_config,
+            model_config=model_config,
+            language=self.language,
+            task_type=task_type,
+            request_prompt=self.prompt,
+            to_language=self.to_language,
+            hotwords=self.hotwords,
+        )
+
     def to_beam_search_params(
         self,
         default_max_tokens: int,
diff --git a/vllm/entrypoints/openai/speech_to_text/speech_to_text.py b/vllm/entrypoints/openai/speech_to_text/speech_to_text.py
index 4ebc612a415e..8c3aed4531d4 100644
--- a/vllm/entrypoints/openai/speech_to_text/speech_to_text.py
+++ b/vllm/entrypoints/openai/speech_to_text/speech_to_text.py
@@ -49,6 +49,7 @@
 from vllm.renderers.inputs.preprocess import parse_enc_dec_prompt, parse_model_prompt
 from vllm.sampling_params import BeamSearchParams, SamplingParams
 from vllm.tokenizers import get_tokenizer
+from vllm.utils.async_utils import merge_async_iterators
 
 SpeechToTextResponse: TypeAlias = TranscriptionResponse | TranslationResponse
 SpeechToTextResponseVerbose: TypeAlias = (
@@ -163,10 +164,17 @@ async def _detect_language(
             request_id,
         )
 
-        final_output: RequestOutput
-        async for final_output in result_generator:
-            if final_output.finished:
-                break
+        try:
+            final_output: RequestOutput
+            async for final_output in result_generator:
+                if final_output.finished:
+                    break
+        except asyncio.CancelledError:
+            await asyncio.gather(
+                self.engine_client.abort(request_id),
+                return_exceptions=True,
+            )
+            raise
 
         token_ids = list(final_output.outputs[0].token_ids)
         lang = self.model_cls.parse_language_detection_output(
@@ -184,9 +192,8 @@ async def _preprocess_speech_to_text(
         request_id: str,
     ) -> tuple[list[EngineInput], float]:
         # Validate request
-        language = self.model_cls.validate_language(request.language)
-        # Skip to_language validation to avoid extra logging for Whisper.
-        to_language = (
+        request.language = self.model_cls.validate_language(request.language)
+        request.to_language = (
             self.model_cls.validate_language(request.to_language)
             if request.to_language
             else None
@@ -229,28 +236,23 @@ async def _preprocess_speech_to_text(
                 min_energy_window_size=self.asr_config.min_energy_split_window_size,
             )
 
-        if language is None and getattr(
+        if request.language is None and getattr(
             self.model_cls, "supports_explicit_language_detection", False
         ):
             # Auto-detect language from the first chunk.
-            language = await self._detect_language(
+            request.language = await self._detect_language(
                 chunks[0], f"{request_id}-lang_detect"
             )
-            request.language = language
 
         parsed_prompts: list[DictPrompt] = []
         for chunk in chunks:
-            # The model has control over the construction, as long as it
-            # returns a valid PromptType.
-            prompt = self.model_cls.get_generation_prompt(
+            stt_params = request.build_stt_params(
                 audio=chunk,
                 stt_config=self.asr_config,
                 model_config=self.model_config,
-                language=language,
                 task_type=self.task_type,
-                request_prompt=request.prompt,
-                to_language=to_language,
             )
+            prompt = self.model_cls.get_generation_prompt(stt_params)
 
             parsed_prompt: DictPrompt
             if request.response_format == "verbose_json":
@@ -464,41 +466,55 @@ async def _create_speech_to_text(
         if request.response_format == "verbose_json":
             sampling_params.logprobs = 1
 
+        engine_request_ids = [
+            request_id if len(engine_inputs) == 1 else f"{request_id}-{idx}"
+            for idx in range(len(engine_inputs))
+        ]
         list_result_generator = []
-        for i, engine_input in enumerate(engine_inputs):
-            request_id_item = f"{request_id}_{i}"
-
-            self._log_inputs(
-                request_id_item,
-                engine_input,
-                params=sampling_params,
-                lora_request=lora_request,
-            )
-
-            trace_headers = (
-                None
-                if raw_request is None
-                else await self._get_trace_headers(raw_request.headers)
-            )
-
-            if isinstance(sampling_params, BeamSearchParams):
-                generator = self.beam_search(
-                    prompt=engine_input,
+        try:
+            for request_id_item, engine_input in zip(engine_request_ids, engine_inputs):
+                self._log_inputs(
+                    request_id_item,
+                    engine_input,
                     params=sampling_params,
-                    request_id=request_id_item,
                     lora_request=lora_request,
-                    trace_headers=trace_headers,
                 )
-            else:
-                generator = self.engine_client.generate(
-                    engine_input,
-                    sampling_params,
-                    request_id_item,
-                    lora_request=lora_request,
-                    trace_headers=trace_headers,
+
+                trace_headers = (
+                    None
+                    if raw_request is None
+                    else await self._get_trace_headers(raw_request.headers)
                 )
 
-            list_result_generator.append(generator)
+                if isinstance(sampling_params, BeamSearchParams):
+                    generator = self.beam_search(
+                        prompt=engine_input,
+                        params=sampling_params,
+                        request_id=request_id_item,
+                        lora_request=lora_request,
+                        trace_headers=trace_headers,
+                    )
+                else:
+                    generator = self.engine_client.generate(
+                        engine_input,
+                        sampling_params,
+                        request_id_item,
+                        lora_request=lora_request,
+                        trace_headers=trace_headers,
+                    )
+
+                list_result_generator.append(generator)
+        except asyncio.CancelledError:
+            logger.info(
+                "Request %s cancelled; aborting %d transcription engine request(s).",
+                request_id,
+                len(engine_request_ids),
+            )
+            await asyncio.gather(
+                self.engine_client.abort(engine_request_ids),
+                return_exceptions=True,
+            )
+            raise
 
         separator = asr_inter_chunk_separator(
             request.language, self.model_cls.no_space_languages
@@ -514,10 +530,12 @@ async def _create_speech_to_text(
                 separator,
             )
         # Non-streaming response.
-        total_segments = []
-        text_parts = []
         try:
             assert list_result_generator is not None
+            chunk_segment_parts: list[list[SpeechToTextSegment]] = [
+                [] for _ in list_result_generator
+            ]
+            chunk_text_parts: list[list[str]] = [[] for _ in list_result_generator]
             segments_types: dict[str, type[SpeechToTextSegment]] = {
                 "transcribe": TranscriptionSegment,
                 "translate": TranslationSegment,
@@ -528,28 +546,34 @@ async def _create_speech_to_text(
                 assert len(list_result_generator) == 1, (
                     "`max_audio_clip_s` is set to None, audio cannot be chunked"
                 )
-            for idx, result_generator in enumerate(list_result_generator):
+            result_generator = merge_async_iterators(*list_result_generator)
+            async for idx, op in result_generator:
                 start_time = (
                     float(idx * chunk_size_in_s) if chunk_size_in_s is not None else 0.0
                 )
-                async for op in result_generator:
-                    if request.response_format == "verbose_json":
-                        assert op.outputs[0].logprobs
-                        segments: list[SpeechToTextSegment] = (
-                            self._get_verbose_segments(
-                                tokens=tuple(op.outputs[0].token_ids),
-                                segment_class=segment_class,
-                                request=request,
-                                start_time=start_time,
-                                log_probs=op.outputs[0].logprobs,
-                            )
-                        )
+                if request.response_format == "verbose_json":
+                    assert op.outputs[0].logprobs
+                    segments: list[SpeechToTextSegment] = self._get_verbose_segments(
+                        tokens=tuple(op.outputs[0].token_ids),
+                        segment_class=segment_class,
+                        request=request,
+                        start_time=start_time,
+                        log_probs=op.outputs[0].logprobs,
+                    )
 
-                        total_segments.extend(segments)
-                        text_parts.extend([seg.text for seg in segments])
-                    else:
-                        raw_text = op.outputs[0].text
-                        text_parts.append(self.model_cls.post_process_output(raw_text))
+                    chunk_segment_parts[idx].extend(segments)
+                    chunk_text_parts[idx].extend([seg.text for seg in segments])
+                else:
+                    raw_text = op.outputs[0].text
+                    chunk_text_parts[idx].append(
+                        self.model_cls.post_process_output(raw_text)
+                    )
+            total_segments = [
+                segment
+                for segment_parts in chunk_segment_parts
+                for segment in segment_parts
+            ]
+            text_parts = [text for text_part in chunk_text_parts for text in text_part]
             text = separator.join(text_parts)
             if self.task_type == "transcribe":
                 final_response: ResponseType
@@ -589,7 +613,16 @@ async def _create_speech_to_text(
                     )
             return final_response
         except asyncio.CancelledError:
-            return self.create_error_response("Client disconnected")
+            logger.info(
+                "Request %s cancelled; aborting %d transcription engine request(s).",
+                request_id,
+                len(engine_request_ids),
+            )
+            await asyncio.gather(
+                self.engine_client.abort(engine_request_ids),
+                return_exceptions=True,
+            )
+            raise
 
     async def _speech_to_text_stream_generator(
         self,
diff --git a/vllm/entrypoints/pooling/__init__.py b/vllm/entrypoints/pooling/__init__.py
index 1980750ec20f..e69de29bb2d1 100644
--- a/vllm/entrypoints/pooling/__init__.py
+++ b/vllm/entrypoints/pooling/__init__.py
@@ -1,130 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from typing import TYPE_CHECKING
-
-from fastapi import FastAPI
-
-from vllm.config import ModelConfig
-from vllm.entrypoints.pooling.utils import enable_scoring_api
-from vllm.logger import init_logger
-
-if TYPE_CHECKING:
-    from argparse import Namespace
-
-    from starlette.datastructures import State
-
-    from vllm.engine.protocol import EngineClient
-    from vllm.entrypoints.logger import RequestLogger
-    from vllm.tasks import SupportedTask
-else:
-    RequestLogger = object
-    SupportedTask = object
-
-logger = init_logger(__name__)
-
-
-def register_pooling_api_routers(
-    app: FastAPI,
-    supported_tasks: tuple["SupportedTask", ...],
-    model_config: ModelConfig | None = None,
-):
-    if model_config is None:
-        return
-
-    pooling_task = model_config.get_pooling_task(supported_tasks)
-
-    if pooling_task is not None:
-        from vllm.entrypoints.pooling.pooling.api_router import router as pooling_router
-
-        app.include_router(pooling_router)
-
-    if "classify" in supported_tasks:
-        from vllm.entrypoints.pooling.classify.api_router import (
-            router as classify_router,
-        )
-
-        app.include_router(classify_router)
-
-    if "embed" in supported_tasks:
-        from vllm.entrypoints.pooling.embed.api_router import router as embed_router
-
-        app.include_router(embed_router)
-
-    if enable_scoring_api(supported_tasks, model_config):
-        from vllm.entrypoints.pooling.scoring.api_router import router as score_router
-
-        app.include_router(score_router)
-
-
-def init_pooling_state(
-    engine_client: "EngineClient",
-    state: "State",
-    args: "Namespace",
-    request_logger: RequestLogger | None,
-    supported_tasks: tuple["SupportedTask", ...],
-):
-    from vllm.entrypoints.chat_utils import load_chat_template
-    from vllm.entrypoints.pooling.classify.serving import ServingClassification
-    from vllm.entrypoints.pooling.embed.serving import ServingEmbedding
-    from vllm.entrypoints.pooling.pooling.serving import ServingPooling
-    from vllm.entrypoints.pooling.scoring.serving import ServingScores
-    from vllm.tasks import POOLING_TASKS
-
-    model_config = engine_client.model_config
-    resolved_chat_template = load_chat_template(args.chat_template)
-
-    state.serving_pooling = (
-        (
-            ServingPooling(
-                engine_client,
-                state.openai_serving_models,
-                supported_tasks=supported_tasks,
-                request_logger=request_logger,
-                chat_template=resolved_chat_template,
-                chat_template_content_format=args.chat_template_content_format,
-                trust_request_chat_template=args.trust_request_chat_template,
-            )
-        )
-        if any(t in supported_tasks for t in POOLING_TASKS)
-        else None
-    )
-    state.serving_embedding = (
-        ServingEmbedding(
-            engine_client,
-            state.openai_serving_models,
-            request_logger=request_logger,
-            chat_template=resolved_chat_template,
-            chat_template_content_format=args.chat_template_content_format,
-            trust_request_chat_template=args.trust_request_chat_template,
-        )
-        if "embed" in supported_tasks
-        else None
-    )
-    state.serving_classification = (
-        ServingClassification(
-            engine_client,
-            state.openai_serving_models,
-            request_logger=request_logger,
-            chat_template=resolved_chat_template,
-            chat_template_content_format=args.chat_template_content_format,
-            trust_request_chat_template=args.trust_request_chat_template,
-        )
-        if "classify" in supported_tasks
-        else None
-    )
-    state.serving_scores = (
-        ServingScores(
-            engine_client,
-            state.openai_serving_models,
-            request_logger=request_logger,
-            chat_template=resolved_chat_template,
-            chat_template_content_format=args.chat_template_content_format,
-            trust_request_chat_template=args.trust_request_chat_template,
-            enable_flash_late_interaction=getattr(
-                args, "enable_flash_late_interaction", True
-            ),
-        )
-        if enable_scoring_api(supported_tasks, model_config)
-        else None
-    )
diff --git a/vllm/entrypoints/pooling/base/io_processor.py b/vllm/entrypoints/pooling/base/io_processor.py
index 83e82664ef1b..fc24bc657800 100644
--- a/vllm/entrypoints/pooling/base/io_processor.py
+++ b/vllm/entrypoints/pooling/base/io_processor.py
@@ -13,22 +13,29 @@
     ConversationMessage,
 )
 from vllm.entrypoints.openai.engine.serving import RendererChatRequest, RendererRequest
-from vllm.entrypoints.pooling.scoring.typing import ScoringData
-from vllm.entrypoints.pooling.typing import (
+from vllm.inputs import EngineInput, SingletonPrompt
+from vllm.renderers import BaseRenderer, TokenizeParams, merge_kwargs
+from vllm.renderers.inputs.preprocess import parse_model_prompt, prompt_to_seq
+from vllm.tool_parsers import ToolParser
+from vllm.utils.mistral import is_mistral_tokenizer
+
+from ..scoring.typing import ScoringData
+from ..typing import (
     OfflineInputsContext,
     OfflineOutputsContext,
     PoolingChatLikeRequest,
     PoolingCompletionLikeRequest,
     PoolingServeContext,
 )
-from vllm.inputs import EngineInput, SingletonPrompt
-from vllm.renderers import BaseRenderer, TokenizeParams, merge_kwargs
-from vllm.renderers.inputs.preprocess import parse_model_prompt, prompt_to_seq
-from vllm.tool_parsers import ToolParser
-from vllm.utils.mistral import is_mistral_tokenizer
 
 
 class PoolingIOProcessor:
+    """Processor for handling preprocessing & postprocessing ops for pooling requests.
+
+    This class manages both online (serving) and offline (batch) processing of pooling
+    requests, handling chat and completion formats.
+    """
+
     name: str
 
     def __init__(
@@ -58,7 +65,7 @@ def create_pooling_params(self, request):
     def pre_process_online(self, ctx: PoolingServeContext):
         request = ctx.request
 
-        if isinstance(ctx.request, PoolingChatLikeRequest):
+        if isinstance(request, PoolingChatLikeRequest):
             self._validate_chat_template(
                 request_chat_template=request.chat_template,
                 chat_template_kwargs=request.chat_template_kwargs,
diff --git a/vllm/entrypoints/pooling/base/serving.py b/vllm/entrypoints/pooling/base/serving.py
index c65bedc70f17..4a9ef4a06286 100644
--- a/vllm/entrypoints/pooling/base/serving.py
+++ b/vllm/entrypoints/pooling/base/serving.py
@@ -15,14 +15,10 @@
 from vllm import PoolingParams, PoolingRequestOutput, envs
 from vllm.config import VllmConfig
 from vllm.engine.protocol import EngineClient
-from vllm.entrypoints.chat_utils import (
-    ChatTemplateConfig,
-    ChatTemplateContentFormatOption,
-)
+from vllm.entrypoints.chat_utils import ChatTemplateConfig
 from vllm.entrypoints.logger import RequestLogger
 from vllm.entrypoints.openai.engine.protocol import ErrorResponse
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
-from vllm.entrypoints.pooling.typing import AnyPoolingRequest, PoolingServeContext
 from vllm.exceptions import VLLMNotFoundError
 from vllm.inputs import EngineInput
 from vllm.lora.request import LoRARequest
@@ -36,6 +32,7 @@
 from vllm.utils import random_uuid
 from vllm.utils.async_utils import make_async, merge_async_iterators
 
+from ..typing import AnyPoolingRequest, PoolingServeContext
 from .io_processor import PoolingIOProcessor
 
 
@@ -48,9 +45,7 @@ def __init__(
         models: OpenAIServingModels,
         *,
         request_logger: RequestLogger | None,
-        chat_template: str | None = None,
-        chat_template_content_format: ChatTemplateContentFormatOption = "auto",
-        trust_request_chat_template: bool = False,
+        chat_template_config: ChatTemplateConfig,
         return_tokens_as_token_ids: bool = False,
         log_error_stack: bool = False,
     ):
@@ -63,11 +58,7 @@ def __init__(
         self.request_logger = request_logger
         self.return_tokens_as_token_ids = return_tokens_as_token_ids
         self.log_error_stack = log_error_stack
-        self.chat_template_config = ChatTemplateConfig(
-            chat_template=chat_template,
-            chat_template_content_format=chat_template_content_format,
-            trust_request_chat_template=trust_request_chat_template,
-        )
+        self.chat_template_config = chat_template_config
 
         # Shared thread pool executor for preprocessing and postprocessing.
         self._executor: Executor = models.renderer._executor
diff --git a/vllm/entrypoints/pooling/classify/api_router.py b/vllm/entrypoints/pooling/classify/api_router.py
index f254a6c2b399..2d27628bc692 100644
--- a/vllm/entrypoints/pooling/classify/api_router.py
+++ b/vllm/entrypoints/pooling/classify/api_router.py
@@ -5,13 +5,14 @@
 from fastapi.responses import Response
 
 from vllm.entrypoints.openai.utils import validate_json_request
-from vllm.entrypoints.pooling.classify.protocol import ClassificationRequest
-from vllm.entrypoints.pooling.classify.serving import ServingClassification
 from vllm.entrypoints.utils import (
     load_aware_call,
     with_cancellation,
 )
 
+from .protocol import ClassificationRequest
+from .serving import ServingClassification
+
 router = APIRouter()
 
 
diff --git a/vllm/entrypoints/pooling/classify/io_processor.py b/vllm/entrypoints/pooling/classify/io_processor.py
index 9bb3774ab0b2..1b2cd0ffa9db 100644
--- a/vllm/entrypoints/pooling/classify/io_processor.py
+++ b/vllm/entrypoints/pooling/classify/io_processor.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from vllm.entrypoints.pooling.base.io_processor import PoolingIOProcessor
+from ..base.io_processor import PoolingIOProcessor
 
 
 class ClassifyIOProcessor(PoolingIOProcessor):
diff --git a/vllm/entrypoints/pooling/classify/protocol.py b/vllm/entrypoints/pooling/classify/protocol.py
index fe8c898e0945..b5948e406be4 100644
--- a/vllm/entrypoints/pooling/classify/protocol.py
+++ b/vllm/entrypoints/pooling/classify/protocol.py
@@ -9,15 +9,16 @@
 from vllm import PoolingParams
 from vllm.config import ModelConfig
 from vllm.entrypoints.openai.engine.protocol import OpenAIBaseModel, UsageInfo
-from vllm.entrypoints.pooling.base.protocol import (
+from vllm.logger import init_logger
+from vllm.renderers import TokenizeParams
+from vllm.utils import random_uuid
+
+from ..base.protocol import (
     ChatRequestMixin,
     ClassifyRequestMixin,
     CompletionRequestMixin,
     PoolingBasicRequestMixin,
 )
-from vllm.logger import init_logger
-from vllm.renderers import TokenizeParams
-from vllm.utils import random_uuid
 
 logger = init_logger(__name__)
 
diff --git a/vllm/entrypoints/pooling/classify/serving.py b/vllm/entrypoints/pooling/classify/serving.py
index a48ec819b7fd..232483ca3aec 100644
--- a/vllm/entrypoints/pooling/classify/serving.py
+++ b/vllm/entrypoints/pooling/classify/serving.py
@@ -7,11 +7,11 @@
 from fastapi.responses import JSONResponse
 
 from vllm.entrypoints.openai.engine.protocol import UsageInfo
-from vllm.entrypoints.pooling.base.serving import PoolingServing
-from vllm.entrypoints.pooling.typing import PoolingServeContext
 from vllm.logger import init_logger
 from vllm.outputs import ClassificationOutput
 
+from ..base.serving import PoolingServing
+from ..typing import PoolingServeContext
 from .io_processor import ClassifyIOProcessor
 from .protocol import (
     ClassificationData,
diff --git a/vllm/entrypoints/pooling/embed/api_router.py b/vllm/entrypoints/pooling/embed/api_router.py
index 390efc6a13ab..4eb86e4e2d29 100644
--- a/vllm/entrypoints/pooling/embed/api_router.py
+++ b/vllm/entrypoints/pooling/embed/api_router.py
@@ -7,13 +7,11 @@
 
 from vllm.entrypoints.openai.engine.protocol import ErrorResponse
 from vllm.entrypoints.openai.utils import validate_json_request
-from vllm.entrypoints.pooling.embed.protocol import (
-    CohereEmbedRequest,
-    EmbeddingRequest,
-)
-from vllm.entrypoints.pooling.embed.serving import ServingEmbedding
 from vllm.entrypoints.utils import load_aware_call, with_cancellation
 
+from .protocol import CohereEmbedRequest, EmbeddingRequest
+from .serving import ServingEmbedding
+
 router = APIRouter()
 
 
diff --git a/vllm/entrypoints/pooling/embed/io_processor.py b/vllm/entrypoints/pooling/embed/io_processor.py
index 09016253f09e..8c28f9f3d4e7 100644
--- a/vllm/entrypoints/pooling/embed/io_processor.py
+++ b/vllm/entrypoints/pooling/embed/io_processor.py
@@ -16,21 +16,6 @@
     ChatCompletionMessageParam,
     CustomChatCompletionMessageParam,
 )
-from vllm.entrypoints.pooling.base.io_processor import PoolingIOProcessor
-from vllm.entrypoints.pooling.embed.protocol import (
-    CohereEmbedContent,
-    CohereEmbedInput,
-    CohereEmbedRequest,
-    EmbeddingChatRequest,
-    EmbeddingCompletionRequest,
-)
-from vllm.entrypoints.pooling.scoring.io_processor import JinaRankingIOProcessorMixin
-from vllm.entrypoints.pooling.typing import (
-    OfflineInputsContext,
-    PoolingChatLikeRequest,
-    PoolingCompletionLikeRequest,
-    PoolingServeContext,
-)
 from vllm.inputs import EngineInput, tokens_input
 from vllm.logger import init_logger
 from vllm.outputs import PoolingOutput, PoolingRequestOutput
@@ -39,6 +24,22 @@
 from vllm.utils.collection_utils import chunk_list
 from vllm.utils.mistral import is_mistral_tokenizer
 
+from ..base.io_processor import PoolingIOProcessor
+from ..scoring.io_processor import JinaRankingIOProcessorMixin
+from ..typing import (
+    OfflineInputsContext,
+    PoolingChatLikeRequest,
+    PoolingCompletionLikeRequest,
+    PoolingServeContext,
+)
+from .protocol import (
+    CohereEmbedContent,
+    CohereEmbedInput,
+    CohereEmbedRequest,
+    EmbeddingChatRequest,
+    EmbeddingCompletionRequest,
+)
+
 logger = init_logger(__name__)
 
 
@@ -94,7 +95,7 @@ def _pre_process_chunked(self, ctx: PoolingServeContext) -> None:
         if ctx.engine_inputs is None:
             raise ValueError("Engine prompts not available")
 
-        ctx.intermediates = ctx.engine_inputs
+        ctx.original_engine_inputs = ctx.engine_inputs
         request_id = ctx.request_id
         max_model_len = self.model_config.max_model_len
         chunked_engine_inputs: list[EngineInput] = []
@@ -189,10 +190,10 @@ def _post_process_chunked(self, ctx: PoolingServeContext) -> None:
                 aggregator["total_weight"] += weight
                 aggregator["chunk_count"] += 1
 
-        if ctx.intermediates is None:
-            raise ValueError("Original prompts inputs not available")
+        if ctx.original_engine_inputs is None:
+            raise ValueError("Original engine inputs not available")
 
-        original_engine_inputs = cast(list[EngineInput], ctx.intermediates)
+        original_engine_inputs = ctx.original_engine_inputs
         num_prompts = len(original_engine_inputs)
 
         # Finalize aggregated results
diff --git a/vllm/entrypoints/pooling/embed/protocol.py b/vllm/entrypoints/pooling/embed/protocol.py
index 9b39b41df286..64087e2333e5 100644
--- a/vllm/entrypoints/pooling/embed/protocol.py
+++ b/vllm/entrypoints/pooling/embed/protocol.py
@@ -18,14 +18,15 @@
 from vllm import PoolingParams
 from vllm.config import ModelConfig
 from vllm.entrypoints.openai.engine.protocol import OpenAIBaseModel, UsageInfo
-from vllm.entrypoints.pooling.base.protocol import (
+from vllm.renderers import TokenizeParams
+from vllm.utils import random_uuid
+
+from ..base.protocol import (
     ChatRequestMixin,
     CompletionRequestMixin,
     EmbedRequestMixin,
     PoolingBasicRequestMixin,
 )
-from vllm.renderers import TokenizeParams
-from vllm.utils import random_uuid
 
 # ---------------------------------------------------------------------------
 # OpenAI /v1/embeddings — request models
diff --git a/vllm/entrypoints/pooling/embed/serving.py b/vllm/entrypoints/pooling/embed/serving.py
index 9389309efc77..d85d2372387f 100644
--- a/vllm/entrypoints/pooling/embed/serving.py
+++ b/vllm/entrypoints/pooling/embed/serving.py
@@ -9,9 +9,21 @@
 from typing_extensions import assert_never
 
 from vllm.entrypoints.openai.engine.protocol import UsageInfo
-from vllm.entrypoints.pooling.base.serving import PoolingServing
-from vllm.entrypoints.pooling.embed.io_processor import EmbedIOProcessor
-from vllm.entrypoints.pooling.embed.protocol import (
+from vllm.logger import init_logger
+from vllm.outputs import PoolingRequestOutput
+from vllm.utils.serial_utils import EmbedDType, Endianness
+
+from ..base.serving import PoolingServing
+from ..typing import PoolingServeContext
+from ..utils import (
+    encode_pooling_bytes,
+    encode_pooling_output_base64,
+    encode_pooling_output_float,
+    encode_pooling_output_float_or_ndarray,
+    get_json_response_cls,
+)
+from .io_processor import EmbedIOProcessor
+from .protocol import (
     CohereBilledUnits,
     CohereEmbedRequest,
     CohereEmbedResponse,
@@ -22,16 +34,6 @@
     EmbeddingResponseData,
     build_typed_embeddings,
 )
-from vllm.entrypoints.pooling.typing import PoolingServeContext
-from vllm.entrypoints.pooling.utils import (
-    encode_pooling_bytes,
-    encode_pooling_output_base64,
-    encode_pooling_output_float,
-    get_json_response_cls,
-)
-from vllm.logger import init_logger
-from vllm.outputs import PoolingRequestOutput
-from vllm.utils.serial_utils import EmbedDType, Endianness
 
 logger = init_logger(__name__)
 
@@ -103,6 +105,40 @@ def _openai_json_response(
         embed_dtype: EmbedDType,
         endianness: Endianness,
     ) -> JSONResponse:
+        use_ndarray_response = (
+            encoding_format == "float"
+            and self.json_response_cls.__name__ == "ORJSONResponse"
+        )
+        if use_ndarray_response:
+            ndarray_items: list[dict[str, object]] = []
+            ndarray_num_tokens = 0
+
+            for idx, final_res in enumerate(final_res_batch):
+                item_dict = EmbeddingResponseData(
+                    index=idx,
+                    embedding=[],
+                ).model_dump()
+                item_dict["embedding"] = encode_pooling_output_float_or_ndarray(
+                    final_res
+                )
+                ndarray_items.append(item_dict)
+                ndarray_num_tokens += len(final_res.prompt_token_ids)
+
+            ndarray_usage = UsageInfo(
+                prompt_tokens=ndarray_num_tokens,
+                total_tokens=ndarray_num_tokens,
+            )
+            ndarray_response = EmbeddingResponse(
+                id=request_id,
+                created=created_time,
+                model=model_name,
+                data=[],  # type: ignore[arg-type]
+                usage=ndarray_usage,
+            ).model_dump()
+            ndarray_response["data"] = ndarray_items
+
+            return self.json_response_cls(content=ndarray_response)
+
         encode_fn = cast(
             Callable[[PoolingRequestOutput], list[float] | str],
             (
diff --git a/vllm/entrypoints/pooling/factories.py b/vllm/entrypoints/pooling/factories.py
new file mode 100644
index 000000000000..62f76a7aa285
--- /dev/null
+++ b/vllm/entrypoints/pooling/factories.py
@@ -0,0 +1,265 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import TYPE_CHECKING
+
+from fastapi import FastAPI
+
+from vllm.config import ModelConfig, VllmConfig
+from vllm.entrypoints.chat_utils import ChatTemplateConfig
+from vllm.logger import init_logger
+from vllm.plugins.io_processors import has_io_processor
+from vllm.renderers import BaseRenderer
+from vllm.tasks import POOLING_TASKS, SCORE_TYPE_MAP, SupportedTask
+
+from .base.io_processor import PoolingIOProcessor
+from .utils import enable_scoring_api
+
+if TYPE_CHECKING:
+    from argparse import Namespace
+
+    from starlette.datastructures import State
+
+    from vllm.engine.protocol import EngineClient
+    from vllm.entrypoints.logger import RequestLogger
+    from vllm.entrypoints.sagemaker.api_router import (
+        EndpointFn,
+        GetHandlerFn,
+        RequestType,
+    )
+
+else:
+    RequestLogger = object
+
+
+logger = init_logger(__name__)
+
+
+def init_pooling_io_processors(
+    supported_tasks: tuple[SupportedTask, ...],
+    vllm_config: VllmConfig,
+    renderer: BaseRenderer,
+    chat_template_config: ChatTemplateConfig,
+) -> dict[str, PoolingIOProcessor]:
+    model_config = vllm_config.model_config
+    processors: dict[str, type[PoolingIOProcessor]] = {}
+    pooling_task = model_config.get_pooling_task(supported_tasks)
+
+    if pooling_task == "classify":
+        from .classify.io_processor import ClassifyIOProcessor
+
+        processors["classify"] = ClassifyIOProcessor
+
+    if pooling_task == "token_classify":
+        from .classify.io_processor import TokenClassifyIOProcessor
+
+        processors["token_classify"] = TokenClassifyIOProcessor
+
+    if pooling_task == "embed":
+        from .embed.io_processor import EmbedIOProcessor
+
+        processors["embed"] = EmbedIOProcessor
+
+    if pooling_task == "token_embed":
+        from .embed.io_processor import TokenEmbedIOProcessor
+
+        processors["token_embed"] = TokenEmbedIOProcessor
+
+    if has_io_processor(
+        vllm_config,
+        model_config.io_processor_plugin,
+    ):
+        from .pooling.io_processor import PluginWithIOProcessorPlugins
+
+        processors["plugin"] = PluginWithIOProcessorPlugins
+    elif pooling_task == "plugin":
+        from .pooling.io_processor import PluginWithoutIOProcessorPlugins
+
+        processors["plugin"] = PluginWithoutIOProcessorPlugins
+
+    if enable_scoring_api(supported_tasks, model_config):
+        from .scoring.io_processor import ScoringIOProcessors
+
+        score_type: str | None = SCORE_TYPE_MAP.get(pooling_task, None)  # type: ignore[arg-type]
+        if score_type is not None and score_type in ScoringIOProcessors:
+            processors[score_type] = ScoringIOProcessors[score_type]
+
+    if model_config.architecture == "JinaForRanking":
+        from .embed.io_processor import JinaRankingTokenEmbedIOProcessor
+        from .scoring.io_processor import ScoringIOProcessors
+
+        processors["token_embed"] = JinaRankingTokenEmbedIOProcessor
+        processors["late-interaction"] = ScoringIOProcessors["jina-reranking-scoring"]
+
+    return {
+        task: processor_cls(
+            vllm_config=vllm_config,
+            renderer=renderer,
+            chat_template_config=chat_template_config,
+        )
+        for task, processor_cls in processors.items()
+    }
+
+
+def register_pooling_api_routers(
+    app: FastAPI,
+    supported_tasks: tuple["SupportedTask", ...],
+    model_config: ModelConfig | None = None,
+):
+    if model_config is None:
+        return
+
+    pooling_task = model_config.get_pooling_task(supported_tasks)
+
+    if pooling_task is not None:
+        from .pooling.api_router import router as pooling_router
+
+        app.include_router(pooling_router)
+
+    if "classify" in supported_tasks:
+        from .classify.api_router import (
+            router as classify_router,
+        )
+
+        app.include_router(classify_router)
+
+    if "embed" in supported_tasks:
+        from .embed.api_router import router as embed_router
+
+        app.include_router(embed_router)
+
+    if enable_scoring_api(supported_tasks, model_config):
+        from .scoring.api_router import router as score_router
+
+        app.include_router(score_router)
+
+
+def init_pooling_state(
+    engine_client: "EngineClient",
+    state: "State",
+    args: "Namespace",
+    request_logger: RequestLogger | None,
+    supported_tasks: tuple["SupportedTask", ...],
+):
+    model_config = engine_client.model_config
+    if model_config is None:
+        return
+
+    from vllm.entrypoints.chat_utils import load_chat_template
+    from vllm.tasks import POOLING_TASKS
+
+    from .classify.serving import ServingClassification
+    from .embed.serving import ServingEmbedding
+    from .pooling.serving import ServingPooling
+    from .scoring.serving import ServingScores
+
+    resolved_chat_template = load_chat_template(args.chat_template)
+    pooling_task = model_config.get_pooling_task(supported_tasks)
+
+    chat_template_config = ChatTemplateConfig(
+        chat_template=resolved_chat_template,
+        chat_template_content_format=args.chat_template_content_format,
+        trust_request_chat_template=args.trust_request_chat_template,
+    )
+
+    state.serving_pooling = (
+        (
+            ServingPooling(
+                engine_client,
+                state.openai_serving_models,
+                supported_tasks=supported_tasks,
+                request_logger=request_logger,
+                chat_template_config=chat_template_config,
+            )
+        )
+        if any(t in supported_tasks for t in POOLING_TASKS)
+        else None
+    )
+    state.serving_embedding = (
+        ServingEmbedding(
+            engine_client,
+            state.openai_serving_models,
+            request_logger=request_logger,
+            chat_template_config=chat_template_config,
+        )
+        if pooling_task == "embed"
+        else None
+    )
+    state.serving_classification = (
+        ServingClassification(
+            engine_client,
+            state.openai_serving_models,
+            request_logger=request_logger,
+            chat_template_config=chat_template_config,
+        )
+        if pooling_task == "classify"
+        else None
+    )
+    state.serving_scores = (
+        ServingScores(
+            engine_client,
+            state.openai_serving_models,
+            supported_tasks=supported_tasks,
+            request_logger=request_logger,
+            chat_template_config=chat_template_config,
+            enable_flash_late_interaction=getattr(
+                args, "enable_flash_late_interaction", True
+            ),
+        )
+        if enable_scoring_api(supported_tasks, model_config)
+        else None
+    )
+
+
+def get_pooling_invocation_types(
+    supported_tasks: tuple["SupportedTask", ...],
+    model_config: ModelConfig | None = None,
+):
+    # NOTE: Items defined earlier take higher priority
+    invocation_types: list[tuple[RequestType, tuple[GetHandlerFn, EndpointFn]]] = []
+
+    if model_config is None:
+        return invocation_types
+
+    pooling_task = model_config.get_pooling_task(supported_tasks)
+
+    if pooling_task == "embed":
+        from .embed.api_router import create_embedding, embedding
+        from .embed.protocol import EmbeddingRequest
+
+        invocation_types += [
+            (EmbeddingRequest, (embedding, create_embedding)),
+        ]
+
+    if pooling_task == "classify":
+        from .classify.api_router import classify, create_classify
+        from .classify.protocol import ClassificationRequest
+
+        invocation_types += [
+            (ClassificationRequest, (classify, create_classify)),
+        ]
+
+    if enable_scoring_api(supported_tasks, model_config):
+        from .scoring.api_router import do_rerank, rerank
+        from .scoring.protocol import RerankRequest
+
+        invocation_types += [
+            (RerankRequest, (rerank, do_rerank)),
+        ]
+
+        from .scoring.api_router import create_score, score
+        from .scoring.protocol import ScoreRequest
+
+        invocation_types += [
+            (ScoreRequest, (score, create_score)),
+        ]
+
+    if any(task in POOLING_TASKS for task in supported_tasks):
+        from .pooling.api_router import create_pooling, pooling
+        from .pooling.protocol import PoolingRequest
+
+        invocation_types += [
+            (PoolingRequest, (pooling, create_pooling)),
+        ]
+
+    return invocation_types
diff --git a/vllm/entrypoints/pooling/io_processor_factories.py b/vllm/entrypoints/pooling/io_processor_factories.py
deleted file mode 100644
index 5e67d069da71..000000000000
--- a/vllm/entrypoints/pooling/io_processor_factories.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from vllm.config import VllmConfig
-from vllm.entrypoints.chat_utils import ChatTemplateConfig
-from vllm.plugins.io_processors import has_io_processor
-from vllm.renderers import BaseRenderer
-from vllm.tasks import SupportedTask
-
-from .base.io_processor import PoolingIOProcessor
-from .utils import enable_scoring_api
-
-
-def init_pooling_io_processors(
-    supported_tasks: tuple[SupportedTask, ...],
-    vllm_config: VllmConfig,
-    renderer: BaseRenderer,
-    chat_template_config: ChatTemplateConfig,
-) -> dict[str, PoolingIOProcessor]:
-    model_config = vllm_config.model_config
-    processors: dict[str, type[PoolingIOProcessor]] = {}
-
-    if "classify" in supported_tasks:
-        from .classify.io_processor import ClassifyIOProcessor
-
-        processors["classify"] = ClassifyIOProcessor
-
-    if "token_classify" in supported_tasks:
-        from .classify.io_processor import TokenClassifyIOProcessor
-
-        processors["token_classify"] = TokenClassifyIOProcessor
-
-    if "embed" in supported_tasks:
-        from .embed.io_processor import EmbedIOProcessor
-
-        processors["embed"] = EmbedIOProcessor
-
-    if "token_embed" in supported_tasks:
-        from .embed.io_processor import TokenEmbedIOProcessor
-
-        processors["token_embed"] = TokenEmbedIOProcessor
-
-    if has_io_processor(
-        vllm_config,
-        model_config.io_processor_plugin,
-    ):
-        from .pooling.io_processor import PluginWithIOProcessorPlugins
-
-        processors["plugin"] = PluginWithIOProcessorPlugins
-    elif "plugin" in supported_tasks:
-        from .pooling.io_processor import PluginWithoutIOProcessorPlugins
-
-        processors["plugin"] = PluginWithoutIOProcessorPlugins
-
-    if enable_scoring_api(supported_tasks, model_config):
-        score_type = model_config.score_type
-        from .scoring.io_processor import ScoringIOProcessors
-
-        if score_type is not None and score_type in ScoringIOProcessors:
-            processors[score_type] = ScoringIOProcessors[score_type]
-
-    if model_config.architecture == "JinaForRanking":
-        from .embed.io_processor import JinaRankingTokenEmbedIOProcessor
-        from .scoring.io_processor import ScoringIOProcessors
-
-        processors["token_embed"] = JinaRankingTokenEmbedIOProcessor
-        processors["late-interaction"] = ScoringIOProcessors["jina-reranking-scoring"]
-
-    return {
-        task: processor_cls(
-            vllm_config=vllm_config,
-            renderer=renderer,
-            chat_template_config=chat_template_config,
-        )
-        for task, processor_cls in processors.items()
-    }
diff --git a/vllm/entrypoints/pooling/pooling/api_router.py b/vllm/entrypoints/pooling/pooling/api_router.py
index a08570038c3a..0c77c050dc0d 100644
--- a/vllm/entrypoints/pooling/pooling/api_router.py
+++ b/vllm/entrypoints/pooling/pooling/api_router.py
@@ -6,10 +6,11 @@
 
 from vllm.entrypoints.openai.engine.protocol import ErrorResponse
 from vllm.entrypoints.openai.utils import validate_json_request
-from vllm.entrypoints.pooling.pooling.protocol import PoolingRequest
-from vllm.entrypoints.pooling.pooling.serving import ServingPooling
 from vllm.entrypoints.utils import load_aware_call, with_cancellation
 
+from .protocol import PoolingRequest
+from .serving import ServingPooling
+
 router = APIRouter()
 
 
diff --git a/vllm/entrypoints/pooling/pooling/io_processor.py b/vllm/entrypoints/pooling/pooling/io_processor.py
index 31f860144ea3..b07ceeede32b 100644
--- a/vllm/entrypoints/pooling/pooling/io_processor.py
+++ b/vllm/entrypoints/pooling/pooling/io_processor.py
@@ -4,12 +4,12 @@
 from typing import Any
 
 from vllm import PoolingParams, PoolingRequestOutput
-from vllm.entrypoints.pooling.base.io_processor import PoolingIOProcessor
 from vllm.inputs import EngineInput
 from vllm.logger import init_logger
 from vllm.plugins.io_processors import get_io_processor
 from vllm.renderers.inputs.preprocess import parse_model_prompt, prompt_to_seq
 
+from ..base.io_processor import PoolingIOProcessor
 from ..typing import OfflineInputsContext, OfflineOutputsContext, PoolingServeContext
 from .protocol import IOProcessorRequest, IOProcessorResponse
 
@@ -17,6 +17,8 @@
 
 
 class PluginWithoutIOProcessorPlugins(PoolingIOProcessor):
+    # Some models, such as Terratorch (tests/models/test_terratorch.py),
+    # use plugin tasks in the pooler but do not use IO Processor plugins.
     name = "plugin"
 
 
diff --git a/vllm/entrypoints/pooling/pooling/protocol.py b/vllm/entrypoints/pooling/pooling/protocol.py
index 098690db262d..ab9b132fee34 100644
--- a/vllm/entrypoints/pooling/pooling/protocol.py
+++ b/vllm/entrypoints/pooling/pooling/protocol.py
@@ -8,7 +8,11 @@
 from vllm import PoolingParams
 from vllm.config import ModelConfig
 from vllm.entrypoints.openai.engine.protocol import OpenAIBaseModel, UsageInfo
-from vllm.entrypoints.pooling.base.protocol import (
+from vllm.renderers import TokenizeParams
+from vllm.tasks import PoolingTask
+from vllm.utils import random_uuid
+
+from ..base.protocol import (
     ChatRequestMixin,
     ClassifyRequestMixin,
     CompletionRequestMixin,
@@ -16,9 +20,6 @@
     EncodingRequestMixin,
     PoolingBasicRequestMixin,
 )
-from vllm.renderers import TokenizeParams
-from vllm.tasks import PoolingTask
-from vllm.utils import random_uuid
 
 
 class PoolingCompletionRequest(
@@ -96,6 +97,11 @@ def build_tok_params(self, model_config: ModelConfig) -> TokenizeParams:
             max_total_tokens_param="max_model_len",
         )
 
+    def to_pooling_params(self):
+        return PoolingParams(
+            task=self.task,
+        )
+
 
 class IOProcessorResponse(OpenAIBaseModel, Generic[T]):
     request_id: str | None = None
diff --git a/vllm/entrypoints/pooling/pooling/serving.py b/vllm/entrypoints/pooling/pooling/serving.py
index ea8eff6eb30a..854fefc88a3d 100644
--- a/vllm/entrypoints/pooling/pooling/serving.py
+++ b/vllm/entrypoints/pooling/pooling/serving.py
@@ -11,27 +11,28 @@
 from typing_extensions import assert_never
 
 from vllm.entrypoints.openai.engine.protocol import UsageInfo
-from vllm.entrypoints.pooling.base.io_processor import PoolingIOProcessor
-from vllm.entrypoints.pooling.base.serving import PoolingServingBase
-from vllm.entrypoints.pooling.io_processor_factories import init_pooling_io_processors
-from vllm.entrypoints.pooling.pooling.protocol import (
+from vllm.logger import init_logger
+from vllm.outputs import PoolingRequestOutput
+from vllm.tasks import SupportedTask
+from vllm.utils.serial_utils import EmbedDType, Endianness
+
+from ..base.io_processor import PoolingIOProcessor
+from ..base.serving import PoolingServingBase
+from ..factories import init_pooling_io_processors
+from ..typing import AnyPoolingRequest, PoolingServeContext
+from ..utils import (
+    encode_pooling_bytes,
+    encode_pooling_output_base64,
+    encode_pooling_output_float,
+    get_json_response_cls,
+)
+from .protocol import (
     IOProcessorRequest,
     PoolingBytesResponse,
     PoolingRequest,
     PoolingResponse,
     PoolingResponseData,
 )
-from vllm.entrypoints.pooling.typing import AnyPoolingRequest, PoolingServeContext
-from vllm.entrypoints.pooling.utils import (
-    encode_pooling_bytes,
-    encode_pooling_output_base64,
-    encode_pooling_output_float,
-    get_json_response_cls,
-)
-from vllm.logger import init_logger
-from vllm.outputs import PoolingRequestOutput
-from vllm.tasks import SupportedTask
-from vllm.utils.serial_utils import EmbedDType, Endianness
 
 logger = init_logger(__name__)
 
@@ -77,17 +78,15 @@ def _verify_pooling_task(self, request: PoolingRequest) -> str:
 
         # plugin task uses io_processor.parse_request to verify inputs
         if pooling_task != "plugin" and pooling_task != self.pooling_task:
-            if pooling_task not in self.io_processors:
+            if pooling_task not in self.supported_tasks:
                 raise ValueError(
                     f"Unsupported task: {pooling_task!r} "
                     f"Supported tasks: {self.supported_tasks}"
                 )
             else:
-                logger.warning_once(
-                    "Pooling multitask support is deprecated and will be removed "
-                    "in v0.20. When the default pooling task is not what you want, you "
-                    "need to manually specify it via --pooler-config.task %s. ",
-                    pooling_task,
+                raise ValueError(
+                    "Try switching the model's pooling_task "
+                    f"via --pooler-config.task {request.task}."
                 )
 
         if pooling_task == "plugin" and "plugin" not in self.io_processors:
diff --git a/vllm/entrypoints/pooling/scoring/io_processor.py b/vllm/entrypoints/pooling/scoring/io_processor.py
index 549bae2775d5..e706601c568e 100644
--- a/vllm/entrypoints/pooling/scoring/io_processor.py
+++ b/vllm/entrypoints/pooling/scoring/io_processor.py
@@ -7,12 +7,6 @@
 import torch.nn.functional as F
 
 from vllm import PoolingParams, PoolingRequestOutput, TokensPrompt
-from vllm.entrypoints.pooling.base.io_processor import PoolingIOProcessor
-from vllm.entrypoints.pooling.typing import (
-    OfflineInputsContext,
-    OfflineOutputsContext,
-    PoolingServeContext,
-)
 from vllm.inputs import EngineInput
 from vllm.renderers import TokenizeParams
 from vllm.renderers.hf import safe_apply_chat_template
@@ -20,6 +14,12 @@
 from vllm.utils.mistral import is_mistral_tokenizer
 
 from ...chat_utils import ChatTemplateResolutionError
+from ..base.io_processor import PoolingIOProcessor
+from ..typing import (
+    OfflineInputsContext,
+    OfflineOutputsContext,
+    PoolingServeContext,
+)
 from .protocol import RerankRequest, ScoreRequest, ScoringRequest
 from .typing import ScoreData, ScoreInput, ScoringData
 from .utils import (
diff --git a/vllm/entrypoints/pooling/scoring/protocol.py b/vllm/entrypoints/pooling/scoring/protocol.py
index 83fdafb14587..d32a3660d029 100644
--- a/vllm/entrypoints/pooling/scoring/protocol.py
+++ b/vllm/entrypoints/pooling/scoring/protocol.py
@@ -8,18 +8,16 @@
 from vllm import PoolingParams
 from vllm.config import ModelConfig
 from vllm.entrypoints.openai.engine.protocol import OpenAIBaseModel, UsageInfo
-from vllm.entrypoints.pooling.base.protocol import (
-    ClassifyRequestMixin,
-    PoolingBasicRequestMixin,
-)
 from vllm.renderers import TokenizeParams
 from vllm.tasks import PoolingTask
 from vllm.utils import random_uuid
 
+from ..base.protocol import ClassifyRequestMixin, PoolingBasicRequestMixin
 from .typing import ScoreContentPartParam, ScoreInput
 
 
-class ScoreRequestMixin(PoolingBasicRequestMixin, ClassifyRequestMixin):
+class ScoringRequestMixin(PoolingBasicRequestMixin, ClassifyRequestMixin):
+    # --8<-- [start:scoring-common-params]
     max_tokens_per_query: int = Field(
         default=0,
         description=(
@@ -37,6 +35,7 @@ class ScoreRequestMixin(PoolingBasicRequestMixin, ClassifyRequestMixin):
             "applies to the combined query+document)."
         ),
     )
+    # --8<-- [end:scoring-common-params]
 
     def build_tok_params(self, model_config: ModelConfig) -> TokenizeParams:
         encoder_config = model_config.encoder_config or {}
@@ -57,14 +56,16 @@ def to_pooling_params(self, task: PoolingTask = "classify"):
         )
 
 
-class ScoreDataRequest(ScoreRequestMixin):
+class ScoreDataRequest(ScoringRequestMixin):
     data_1: ScoreInput | list[ScoreInput]
     data_2: ScoreInput | list[ScoreInput]
 
 
-class ScoreQueriesDocumentsRequest(ScoreRequestMixin):
+class ScoreQueriesDocumentsRequest(ScoringRequestMixin):
+    # --8<-- [start:score-request-params]
     queries: ScoreInput | list[ScoreInput]
     documents: ScoreInput | list[ScoreInput]
+    # --8<-- [end:score-request-params]
 
     @property
     def data_1(self):
@@ -75,7 +76,7 @@ def data_2(self):
         return self.documents
 
 
-class ScoreQueriesItemsRequest(ScoreRequestMixin):
+class ScoreQueriesItemsRequest(ScoringRequestMixin):
     queries: ScoreInput | list[ScoreInput]
     items: ScoreInput | list[ScoreInput]
 
@@ -88,7 +89,7 @@ def data_2(self):
         return self.items
 
 
-class ScoreTextRequest(ScoreRequestMixin):
+class ScoreTextRequest(ScoringRequestMixin):
     text_1: ScoreInput | list[ScoreInput]
     text_2: ScoreInput | list[ScoreInput]
 
@@ -109,10 +110,12 @@ def data_2(self):
 )
 
 
-class RerankRequest(ScoreRequestMixin):
+class RerankRequest(ScoringRequestMixin):
+    # --8<-- [start:rerank-request-params]
     query: ScoreInput
     documents: ScoreInput | list[ScoreInput]
     top_n: int = Field(default_factory=lambda: 0)
+    # --8<-- [end:rerank-request-params]
 
 
 ScoringRequest: TypeAlias = ScoreRequest | RerankRequest
diff --git a/vllm/entrypoints/pooling/scoring/serving.py b/vllm/entrypoints/pooling/scoring/serving.py
index df866efd56ea..5937664d5687 100644
--- a/vllm/entrypoints/pooling/scoring/serving.py
+++ b/vllm/entrypoints/pooling/scoring/serving.py
@@ -6,15 +6,16 @@
 from vllm import PoolingParams
 from vllm.engine.protocol import EngineClient
 from vllm.entrypoints.openai.engine.protocol import UsageInfo
-from vllm.entrypoints.pooling.base.io_processor import PoolingIOProcessor
-from vllm.entrypoints.pooling.base.serving import PoolingServing
 from vllm.logger import init_logger
 from vllm.outputs import PoolingRequestOutput, ScoringRequestOutput
+from vllm.tasks import SCORE_TYPE_MAP, SupportedTask
 from vllm.v1.pool.late_interaction import (
     build_late_interaction_doc_params,
     build_late_interaction_query_params,
 )
 
+from ..base.io_processor import PoolingIOProcessor
+from ..base.serving import PoolingServing
 from .io_processor import ScoringIOProcessors, ScoringServeContext
 from .protocol import (
     RerankDocument,
@@ -38,10 +39,15 @@ def __init__(
         self,
         engine_client: EngineClient,
         *args,
+        supported_tasks: tuple[SupportedTask, ...],
         enable_flash_late_interaction: bool = True,
         **kwargs,
     ):
-        self.io_processor_name: str = engine_client.model_config.score_type
+        pooling_task = engine_client.model_config.get_pooling_task(supported_tasks)
+        score_type = SCORE_TYPE_MAP.get(pooling_task, None)  # type: ignore[arg-type]
+        assert score_type is not None
+
+        self.io_processor_name: str = score_type
         self.enable_flash_late_interaction = (
             self.io_processor_name == "late-interaction"
             and enable_flash_late_interaction
@@ -90,7 +96,7 @@ def _build_response(
                 ctx.request.top_n if ctx.request.top_n > 0 else len(final_res_batch),
             )
         else:
-            raise NotImplementedError("")
+            raise ValueError(f"Invalid {self.request_id_prefix} request type")
 
     def _request_output_to_score_response(
         self,
diff --git a/vllm/entrypoints/pooling/typing.py b/vllm/entrypoints/pooling/typing.py
index 0ce9d5c8384c..ffcd3e7be434 100644
--- a/vllm/entrypoints/pooling/typing.py
+++ b/vllm/entrypoints/pooling/typing.py
@@ -9,29 +9,30 @@
 from pydantic import ConfigDict
 
 from vllm import PoolingParams, PoolingRequestOutput, PromptType
-from vllm.entrypoints.pooling.classify.protocol import (
+from vllm.inputs import DataPrompt, EngineInput
+from vllm.lora.request import LoRARequest
+
+from .classify.protocol import (
     ClassificationChatRequest,
     ClassificationCompletionRequest,
     ClassificationResponse,
 )
-from vllm.entrypoints.pooling.embed.protocol import (
+from .embed.protocol import (
     CohereEmbedRequest,
     EmbeddingBytesResponse,
     EmbeddingChatRequest,
     EmbeddingCompletionRequest,
     EmbeddingResponse,
 )
-from vllm.entrypoints.pooling.pooling.protocol import (
+from .pooling.protocol import (
     IOProcessorRequest,
     PoolingBytesResponse,
     PoolingChatRequest,
     PoolingCompletionRequest,
     PoolingResponse,
 )
-from vllm.entrypoints.pooling.scoring.protocol import ScoringRequest, ScoringResponse
-from vllm.entrypoints.pooling.scoring.typing import ScoringData
-from vllm.inputs import DataPrompt, EngineInput
-from vllm.lora.request import LoRARequest
+from .scoring.protocol import ScoringRequest, ScoringResponse
+from .scoring.typing import ScoringData
 
 PoolingCompletionLikeRequest: TypeAlias = (
     EmbeddingCompletionRequest
@@ -65,6 +66,8 @@
 
 @dataclass(kw_only=True)
 class PoolingServeContext(Generic[PoolingRequestT]):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
     request: PoolingRequestT
     raw_request: Request | None = None
     model_name: str
@@ -74,14 +77,14 @@ class PoolingServeContext(Generic[PoolingRequestT]):
     lora_request: LoRARequest | None = None
     engine_inputs: Sequence[EngineInput] | None = None
     prompt_request_ids: list[str] | None = None
-    intermediates: Any | None = None
 
     result_generator: AsyncGenerator[tuple[int, PoolingRequestOutput], None] | None = (
         None
     )
     final_res_batch: list[PoolingRequestOutput] = field(default_factory=list)
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    ## for Long Text Embedding with Chunked Processing
+    original_engine_inputs: Sequence[EngineInput] | None = None
 
     ## for bi-encoder & late-interaction
     n_queries: int | None = None
diff --git a/vllm/entrypoints/pooling/utils.py b/vllm/entrypoints/pooling/utils.py
index 480d2f20d80b..2036ed7aae8e 100644
--- a/vllm/entrypoints/pooling/utils.py
+++ b/vllm/entrypoints/pooling/utils.py
@@ -62,6 +62,17 @@ def encode_pooling_output_float(output: PoolingRequestOutput) -> list[float]:
     return output.outputs.data.tolist()
 
 
+def encode_pooling_output_float_or_ndarray(output: PoolingRequestOutput) -> Any:
+    """Return an ndarray when the response renderer can serialize NumPy."""
+    try:
+        data = output.outputs.data
+        if not data.is_contiguous():
+            data = data.contiguous()
+        return data.numpy()
+    except (RuntimeError, TypeError):
+        return output.outputs.data.tolist()
+
+
 def encode_pooling_output_base64(
     output: PoolingRequestOutput,
     embed_dtype: EmbedDType,
@@ -141,10 +152,14 @@ def enable_scoring_api(
     supported_tasks: tuple["SupportedTask", ...],
     model_config: ModelConfig | None = None,
 ) -> bool:
-    if any(t in supported_tasks for t in ("embed", "token_embed")):
+    if model_config is None:
+        return False
+
+    pooling_task = model_config.get_pooling_task(supported_tasks)
+    if pooling_task in ("embed", "token_embed"):
         return True
 
-    if model_config is not None and "classify" in supported_tasks:
+    if pooling_task == "classify":
         num_labels = getattr(model_config.hf_config, "num_labels", 0)
         if num_labels != 1:
             logger.debug_once("Scoring API is only enabled for num_labels == 1.")
diff --git a/vllm/entrypoints/sagemaker/api_router.py b/vllm/entrypoints/sagemaker/api_router.py
index 1d63793df398..b3b11cd07b4c 100644
--- a/vllm/entrypoints/sagemaker/api_router.py
+++ b/vllm/entrypoints/sagemaker/api_router.py
@@ -13,12 +13,13 @@
 from vllm.config import ModelConfig
 from vllm.entrypoints.openai.engine.protocol import ErrorResponse
 from vllm.entrypoints.openai.engine.serving import OpenAIServing
+from vllm.entrypoints.openai.generate.factories import get_generate_invocation_types
 from vllm.entrypoints.openai.utils import validate_json_request
 from vllm.entrypoints.pooling.base.serving import PoolingServingBase
-from vllm.entrypoints.pooling.utils import enable_scoring_api
+from vllm.entrypoints.pooling.factories import get_pooling_invocation_types
 from vllm.entrypoints.serve.instrumentator.basic import base
 from vllm.entrypoints.serve.instrumentator.health import health
-from vllm.tasks import POOLING_TASKS, SupportedTask
+from vllm.tasks import SupportedTask
 
 # TODO: RequestType = TypeForm[BaseModel] when recognized by type checkers
 # (requires typing_extensions >= 4.13)
@@ -27,80 +28,6 @@
 EndpointFn = Callable[[RequestType, Request], Awaitable[Any]]
 
 
-def get_invocation_types(
-    supported_tasks: tuple["SupportedTask", ...],
-    model_config: ModelConfig | None = None,
-):
-    # NOTE: Items defined earlier take higher priority
-    INVOCATION_TYPES: list[tuple[RequestType, tuple[GetHandlerFn, EndpointFn]]] = []
-
-    if "generate" in supported_tasks:
-        from vllm.entrypoints.openai.chat_completion.api_router import (
-            chat,
-            create_chat_completion,
-        )
-        from vllm.entrypoints.openai.chat_completion.protocol import (
-            ChatCompletionRequest,
-        )
-        from vllm.entrypoints.openai.completion.api_router import (
-            completion,
-            create_completion,
-        )
-        from vllm.entrypoints.openai.completion.protocol import CompletionRequest
-
-        INVOCATION_TYPES += [
-            (ChatCompletionRequest, (chat, create_chat_completion)),
-            (CompletionRequest, (completion, create_completion)),
-        ]
-
-    if "embed" in supported_tasks:
-        from vllm.entrypoints.pooling.embed.api_router import (
-            create_embedding,
-            embedding,
-        )
-        from vllm.entrypoints.pooling.embed.protocol import EmbeddingRequest
-
-        INVOCATION_TYPES += [
-            (EmbeddingRequest, (embedding, create_embedding)),
-        ]
-
-    if "classify" in supported_tasks:
-        from vllm.entrypoints.pooling.classify.api_router import (
-            classify,
-            create_classify,
-        )
-        from vllm.entrypoints.pooling.classify.protocol import ClassificationRequest
-
-        INVOCATION_TYPES += [
-            (ClassificationRequest, (classify, create_classify)),
-        ]
-
-    if enable_scoring_api(supported_tasks, model_config):
-        from vllm.entrypoints.pooling.scoring.api_router import do_rerank, rerank
-        from vllm.entrypoints.pooling.scoring.protocol import RerankRequest
-
-        INVOCATION_TYPES += [
-            (RerankRequest, (rerank, do_rerank)),
-        ]
-
-        from vllm.entrypoints.pooling.scoring.api_router import create_score, score
-        from vllm.entrypoints.pooling.scoring.protocol import ScoreRequest
-
-        INVOCATION_TYPES += [
-            (ScoreRequest, (score, create_score)),
-        ]
-
-    if any(task in POOLING_TASKS for task in supported_tasks):
-        from vllm.entrypoints.pooling.pooling.api_router import create_pooling, pooling
-        from vllm.entrypoints.pooling.pooling.protocol import PoolingRequest
-
-        INVOCATION_TYPES += [
-            (PoolingRequest, (pooling, create_pooling)),
-        ]
-
-    return INVOCATION_TYPES
-
-
 def attach_router(
     app: FastAPI,
     supported_tasks: tuple["SupportedTask", ...],
@@ -109,7 +36,10 @@ def attach_router(
     router = APIRouter()
 
     # NOTE: Construct the TypeAdapters only once
-    INVOCATION_TYPES = get_invocation_types(supported_tasks, model_config)
+    INVOCATION_TYPES = get_generate_invocation_types(
+        supported_tasks, model_config
+    ) + get_pooling_invocation_types(supported_tasks, model_config)
+
     INVOCATION_VALIDATORS = [
         (pydantic.TypeAdapter(request_type), (get_handler, endpoint))
         for request_type, (get_handler, endpoint) in INVOCATION_TYPES
diff --git a/vllm/entrypoints/serve/disagg/mm_serde.py b/vllm/entrypoints/serve/disagg/mm_serde.py
new file mode 100644
index 000000000000..60a3560ad737
--- /dev/null
+++ b/vllm/entrypoints/serve/disagg/mm_serde.py
@@ -0,0 +1,27 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Encode/decode utilities for multimodal tensors and field metadata
+over JSON/HTTP, used by the disaggregated generate endpoint."""
+
+from __future__ import annotations
+
+import pybase64
+
+from vllm.multimodal.inputs import MultiModalKwargsItem
+from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder
+
+_encoder = MsgpackEncoder(size_threshold=2**62)  # force all tensors inline
+_decoder = MsgpackDecoder(t=MultiModalKwargsItem)
+
+
+def encode_mm_kwargs_item(item: MultiModalKwargsItem) -> str:
+    """Serialize a MultiModalKwargsItem to a base64 string."""
+    bufs = _encoder.encode(item)
+    assert len(bufs) == 1, "All tensors should be inline"
+    return pybase64.b64encode(bufs[0]).decode("ascii")
+
+
+def decode_mm_kwargs_item(data: str) -> MultiModalKwargsItem:
+    """Deserialize a base64 string back to a MultiModalKwargsItem."""
+    raw = pybase64.b64decode(data)
+    return _decoder.decode(raw)
diff --git a/vllm/entrypoints/serve/disagg/protocol.py b/vllm/entrypoints/serve/disagg/protocol.py
index 345992d3b0b1..633696824663 100644
--- a/vllm/entrypoints/serve/disagg/protocol.py
+++ b/vllm/entrypoints/serve/disagg/protocol.py
@@ -35,14 +35,6 @@ class MultiModalFeatures(BaseModel):
     Carries hashes (for cache lookup / identification) and placeholder
     positions so the downstream `/generate` service knows *where* in
     the token sequence each multimodal item lives.
-
-    Note:
-        Phase 1 — metadata only.
-        Phase 2 should add `mm_kwargs` (processed tensor data) using a
-        binary transport so the ``/generate` side can skip re-processing.
-        The `/generate` endpoint must also be updated to inject these
-        features into `EngineInput` before passing to
-        `InputProcessor.process_inputs`.
     """
 
     mm_hashes: dict[str, list[str]]
@@ -51,6 +43,15 @@ class MultiModalFeatures(BaseModel):
     mm_placeholders: dict[str, list[PlaceholderRangeInfo]]
     """Per-modality placeholder ranges in the token sequence."""
 
+    kwargs_data: dict[str, list[str | None]] | None = None
+    """Per-modality serialized tensor data.
+
+    Each value is a list parallel to ``mm_hashes[modality]``.  A ``str``
+    entry is a base64-encoded ``MultiModalKwargsItem``; ``None`` means
+    the item should be resolved from cache.  The entire field is
+    ``None`` for metadata-only (cache-hit) responses.
+    """
+
 
 class GenerateRequest(BaseModel):
     request_id: str = Field(
diff --git a/vllm/entrypoints/serve/disagg/serving.py b/vllm/entrypoints/serve/disagg/serving.py
index 14ba85ecf8ca..d510125fd034 100644
--- a/vllm/entrypoints/serve/disagg/serving.py
+++ b/vllm/entrypoints/serve/disagg/serving.py
@@ -25,6 +25,7 @@
 )
 from vllm.entrypoints.openai.engine.serving import OpenAIServing, clamp_prompt_logprobs
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
+from vllm.entrypoints.serve.disagg.mm_serde import decode_mm_kwargs_item
 from vllm.entrypoints.serve.disagg.protocol import (
     GenerateRequest,
     GenerateResponse,
@@ -34,8 +35,14 @@
 )
 from vllm.entrypoints.serve.render.serving import OpenAIServingRender
 from vllm.entrypoints.utils import should_include_usage
+from vllm.inputs import EngineInput, mm_input
 from vllm.logger import init_logger
 from vllm.logprobs import Logprob
+from vllm.multimodal.inputs import (
+    MultiModalKwargsItem,
+    MultiModalKwargsItems,
+    PlaceholderRange,
+)
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import RequestOutputKind, SamplingParams
 from vllm.utils.collection_utils import as_list
@@ -103,11 +110,42 @@ async def serve_tokens(
         if raw_request:
             raw_request.state.request_metadata = request_metadata
 
-        (engine_input,) = await self.openai_serving_render.preprocess_completion(
-            request,
-            prompt_input=request.token_ids,
-            prompt_embeds=None,
-        )
+        engine_input: EngineInput
+        if features := request.features:
+            # Convert PlaceholderRangeInfo → PlaceholderRange per modality.
+            mm_placeholders: dict[str, list[PlaceholderRange]] = {
+                modality: [
+                    PlaceholderRange(offset=p.offset, length=p.length) for p in ranges
+                ]
+                for modality, ranges in features.mm_placeholders.items()
+            }
+
+            # Deserialize tensor data when present; None → cache hit.
+            mm_kwargs: dict[str, list[MultiModalKwargsItem | None]] = {}
+            if features.kwargs_data is not None:
+                for modality, items in features.kwargs_data.items():
+                    mm_kwargs[modality] = [
+                        decode_mm_kwargs_item(item) if item is not None else None
+                        for item in items
+                    ]
+            else:
+                for modality, hashes in features.mm_hashes.items():
+                    mm_kwargs[modality] = [None] * len(hashes)
+
+            engine_input = mm_input(
+                prompt_token_ids=request.token_ids,
+                mm_kwargs=MultiModalKwargsItems(mm_kwargs),
+                mm_hashes=features.mm_hashes,
+                mm_placeholders=mm_placeholders,
+                cache_salt=request.cache_salt,
+            )
+        else:
+            (engine_input,) = await self.openai_serving_render.preprocess_completion(
+                request,
+                prompt_input=request.token_ids,
+                prompt_embeds=None,
+                skip_mm_cache=True,
+            )
 
         # Schedule the request and get the result generator.
         result_generator: AsyncGenerator[RequestOutput, None] | None = None
diff --git a/vllm/entrypoints/serve/render/serving.py b/vllm/entrypoints/serve/render/serving.py
index 5aa2449797b3..782b2eaea24b 100644
--- a/vllm/entrypoints/serve/render/serving.py
+++ b/vllm/entrypoints/serve/render/serving.py
@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from collections.abc import Sequence
 from http import HTTPStatus
-from typing import Any
+from typing import Any, cast
 
 from openai_harmony import Message as OpenAIMessage
 
@@ -25,6 +25,7 @@
     render_for_completion,
 )
 from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
+from vllm.entrypoints.serve.disagg.mm_serde import encode_mm_kwargs_item
 from vllm.entrypoints.serve.disagg.protocol import (
     GenerateRequest,
     MultiModalFeatures,
@@ -37,6 +38,7 @@
 from vllm.inputs import (
     EngineInput,
     MultiModalHashes,
+    MultiModalInput,
     MultiModalPlaceholders,
     PromptType,
     SingletonPrompt,
@@ -54,7 +56,7 @@
 )
 from vllm.tool_parsers import ToolParser
 from vllm.utils import random_uuid
-from vllm.utils.mistral import is_mistral_tokenizer
+from vllm.utils.mistral import is_mistral_tokenizer, is_mistral_tool_parser
 from vllm.utils.mistral import mt as _mt
 
 logger = init_logger(__name__)
@@ -134,7 +136,7 @@ async def render_chat_request(
                 "Beam search is not supported by the render endpoint"
             )
 
-        result = await self.render_chat(request)
+        result = await self.render_chat(request, skip_mm_cache=True)
         if isinstance(result, ErrorResponse):
             return result
 
@@ -162,6 +164,7 @@ async def render_chat_request(
             input_length,
             self.default_sampling_params,
             self.override_max_tokens,
+            truncate_prompt_tokens=request.truncate_prompt_tokens,
         )
         params = request.to_sampling_params(max_tokens, self.default_sampling_params)
 
@@ -182,6 +185,8 @@ async def render_chat_request(
     async def render_chat(
         self,
         request: ChatCompletionRequest,
+        *,
+        skip_mm_cache: bool = False,
     ) -> tuple[list[ConversationMessage], list[EngineInput]] | ErrorResponse:
         """Core preprocessing logic for chat requests (no model/engine check).
 
@@ -250,6 +255,7 @@ async def render_chat(
                 default_template_kwargs=self.default_chat_template_kwargs,
                 tool_dicts=tool_dicts,
                 tool_parser=tool_parser,
+                skip_mm_cache=skip_mm_cache,
                 reasoning_parser=self.reasoning_parser,
             )
         else:
@@ -273,7 +279,7 @@ async def render_completion_request(
         error_check_ret = await self._check_model(request)
         if error_check_ret is not None:
             return error_check_ret
-        result = await self.render_completion(request)
+        result = await self.render_completion(request, skip_mm_cache=True)
         if isinstance(result, ErrorResponse):
             return result
         generate_requests: list[GenerateRequest] = []
@@ -293,6 +299,7 @@ async def render_completion_request(
                 input_length,
                 self.default_sampling_params,
                 self.override_max_tokens,
+                truncate_prompt_tokens=request.truncate_prompt_tokens,
             )
             params = request.to_sampling_params(
                 max_tokens, self.default_sampling_params
@@ -319,6 +326,8 @@ async def render_completion_request(
     async def render_completion(
         self,
         request: CompletionRequest,
+        *,
+        skip_mm_cache: bool = False,
     ) -> list[EngineInput] | ErrorResponse:
         """Core preprocessing logic for completion requests (no model/engine check).
 
@@ -341,6 +350,7 @@ async def render_completion(
             request,
             prompt_input=request.prompt,
             prompt_embeds=request.prompt_embeds,
+            skip_mm_cache=skip_mm_cache,
         )
 
         return engine_inputs
@@ -356,9 +366,10 @@ def _extract_mm_features(
         if engine_input.get("type") != "multimodal":
             return None
 
-        # At this point engine_input is a MultiModalInputs TypedDict.
-        mm_hashes: MultiModalHashes = engine_input["mm_hashes"]  # type: ignore[typeddict-item]
-        raw_placeholders: MultiModalPlaceholders = engine_input["mm_placeholders"]  # type: ignore[typeddict-item]
+        # At this point engine_input is a MultiModalInput TypedDict.
+        mm_engine_input = cast(MultiModalInput, engine_input)
+        mm_hashes: MultiModalHashes = mm_engine_input["mm_hashes"]
+        raw_placeholders: MultiModalPlaceholders = mm_engine_input["mm_placeholders"]
 
         mm_placeholders = {
             modality: [
@@ -367,9 +378,20 @@ def _extract_mm_features(
             for modality, ranges in raw_placeholders.items()
         }
 
+        # Serialize tensor data per modality.
+        kwargs_data: dict[str, list[str | None]] | None = None
+        if raw_mm_kwargs := mm_engine_input.get("mm_kwargs"):
+            kwargs_data = {}
+            for modality, items in raw_mm_kwargs.items():
+                kwargs_data[modality] = [
+                    encode_mm_kwargs_item(item) if item is not None else None
+                    for item in items
+                ]
+
         return MultiModalFeatures(
             mm_hashes=mm_hashes,
             mm_placeholders=mm_placeholders,
+            kwargs_data=kwargs_data,
         )
 
     def _make_request_with_harmony(
@@ -521,7 +543,10 @@ async def preprocess_chat(
             default_template_kwargs,
             dict(
                 tools=tool_dicts,
-                tokenize=is_mistral_tokenizer(renderer.tokenizer),
+                tokenize=(
+                    is_mistral_tokenizer(renderer.tokenizer)
+                    or self.model_config.enable_prompt_embeds
+                ),
             ),
         )
 
@@ -549,15 +574,27 @@ async def preprocess_chat(
         if reasoning_parser is not None:
             tokenizer = renderer.get_tokenizer()
             request = reasoning_parser(
-                tokenizer, model_config=self.model_config
+                tokenizer,
+                model_config=self.model_config,
+                chat_template_kwargs=chat_params.chat_template_kwargs,
             ).adjust_request(request=request)
 
         # tool parsing is done only if a tool_parser has been set and if
         # tool_choice is not "none" (if tool_choice is "none" but a tool_parser
         # is set, we want to prevent parsing a tool_call hallucinated by the LLM
+        #
+        # Exception: Mistral grammar-capable tokenizers always call
+        # adjust_request — even for tool_choice="none" — so that the grammar
+        # factory can prevent special-token leakage.
         if tool_parser is not None:
             tool_choice = getattr(request, "tool_choice", "none")
-            if tool_choice != "none":
+            tokenizer = renderer.get_tokenizer()
+            is_mistral_grammar_eligible = (
+                is_mistral_tool_parser(tool_parser)
+                and is_mistral_tokenizer(tokenizer)
+                and tokenizer.supports_grammar
+            )
+            if tool_choice != "none" or is_mistral_grammar_eligible:
                 if not isinstance(request, ChatCompletionRequest | ResponsesRequest):
                     msg = (
                         "Tool usage is only supported "
@@ -565,7 +602,6 @@ async def preprocess_chat(
                         f"but got {type(request).__name__}"
                     )
                     raise NotImplementedError(msg)
-                tokenizer = renderer.get_tokenizer()
                 request = tool_parser(tokenizer, request.tools).adjust_request(
                     request=request
                 )
diff --git a/vllm/entrypoints/serve/rlhf/api_router.py b/vllm/entrypoints/serve/rlhf/api_router.py
index 64a1dd20fdc7..dcae3889dc7c 100644
--- a/vllm/entrypoints/serve/rlhf/api_router.py
+++ b/vllm/entrypoints/serve/rlhf/api_router.py
@@ -128,6 +128,19 @@ async def init_weight_transfer_engine(raw_request: Request):
     return JSONResponse(content={"message": "Weight transfer initialized"})
 
 
+@router.post("/start_weight_update")
+async def start_weight_update(raw_request: Request):
+    try:
+        body = await raw_request.json()
+    except json.JSONDecodeError as e:
+        raise HTTPException(status_code=400, detail="Invalid JSON format") from e  # noqa: B904
+    is_checkpoint_format = body.get("is_checkpoint_format", True)
+    await engine_client(raw_request).start_weight_update(
+        is_checkpoint_format=is_checkpoint_format
+    )
+    return JSONResponse(content={"message": "Weight update started"})
+
+
 @router.post("/update_weights")
 async def update_weights(raw_request: Request):
     try:
@@ -146,6 +159,12 @@ async def update_weights(raw_request: Request):
     return JSONResponse(content={"message": "Weights updated"})
 
 
+@router.post("/finish_weight_update")
+async def finish_weight_update(raw_request: Request):
+    await engine_client(raw_request).finish_weight_update()
+    return JSONResponse(content={"message": "Weight update finished"})
+
+
 @router.get("/get_world_size")
 async def get_world_size(
     raw_request: Request,
diff --git a/vllm/entrypoints/utils.py b/vllm/entrypoints/utils.py
index e3682280ec50..cd1010457d98 100644
--- a/vllm/entrypoints/utils.py
+++ b/vllm/entrypoints/utils.py
@@ -177,7 +177,14 @@ def get_max_tokens(
     input_length: int,
     default_sampling_params: dict,
     override_max_tokens: int | None = None,
+    truncate_prompt_tokens: int | None = None,
 ) -> int:
+    if truncate_prompt_tokens is not None:
+        limit = truncate_prompt_tokens
+        input_length = min(
+            input_length,
+            max_model_len if limit == -1 else limit,
+        )
     if max_model_len < input_length:
         raise ValueError(
             f"Input length ({input_length}) exceeds model's maximum "
diff --git a/vllm/env_override.py b/vllm/env_override.py
index 2e3f02866abe..78270c2bee37 100644
--- a/vllm/env_override.py
+++ b/vllm/env_override.py
@@ -100,10 +100,9 @@ def _maybe_set_cuda_compatibility_path():
 # it avoids unintentional cuda initialization from torch.cuda.is_available()
 os.environ["PYTORCH_NVML_BASED_CUDA_CHECK"] = "1"
 
-# see https://github.com/vllm-project/vllm/issues/10480
+# see https://github.com/vllm-project/vllm/issues/10480 and
+# https://github.com/vllm-project/vllm/issues/10619.
 os.environ["TORCHINDUCTOR_COMPILE_THREADS"] = "1"
-# see https://github.com/vllm-project/vllm/issues/10619
-torch._inductor.config.compile_threads = 1
 
 # Enable Triton autotuning result caching to disk by default.
 # Without this, Triton re-runs autotuning on every process restart,
@@ -113,6 +112,13 @@ def _maybe_set_cuda_compatibility_path():
 # in the environment.
 os.environ.setdefault("TRITON_CACHE_AUTOTUNING", "1")
 
+# When unset, TileLang routes JIT temp dirs through a world-shared
+# /tmp/tvm-debug-mode-tempdirs/ whose ownership is pinned to whichever
+# user compiled first, breaking every other user on a shared host.
+# Opt into per-process tempdirs unless the user explicitly chose the
+# debug layout (see https://github.com/vllm-project/vllm/issues/41410).
+os.environ.setdefault("TILELANG_CLEANUP_TEMP_FILES", "1")
+
 # ===================================================
 # torch 2.9 Inductor PythonWrapperCodegen monkeypatch
 # ===================================================
@@ -554,7 +560,7 @@ def apply_constraint(arg, fx_arg):
     _lowering.constrain_to_fx_strides = _patched
 
 
-if is_torch_equal_or_newer("2.10.0") and not is_torch_equal_or_newer("2.12.0"):
+if is_torch_equal_or_newer("2.10.0") and not is_torch_equal_or_newer("2.12.0.dev"):
     import builtins as _builtins
     import pickle
 
@@ -635,3 +641,120 @@ def _patch_fxgraphcache_pickle_if_needed():
 
 
 _patch_fxgraphcache_pickle_if_needed()
+
+# ===================================================
+# torch 2.11 Inductor cpp codegen indirect_assert scalar-mask fix
+# ===================================================
+# CppVecKernel.indirect_assert wraps a scalar mask with
+# `VecMask<...>(scalar)`, which is not a valid constructor and triggers a
+# C++ compile error during torch.compile of any model that does indirect
+# indexing inside a tail-vectorized loop (e.g. Qwen3-VL).
+# Failure looks like:
+#   no matching function for call to 'VecMask<int64_t,2>::VecMask(int&)'
+# Upstream fix in PyTorch mainline replaces the call with
+# `VecMask<...>::from(scalar)`, see pytorch/pytorch#178148 (lands in 2.12).
+# This is a thin backport for torch >= 2.11 and < 2.12; remove once the
+# minimum supported torch is 2.12.
+
+
+def _apply_cpp_indirect_assert_patch():
+    """Replace CppVecKernel.indirect_assert with a fixed copy that uses
+    `VecMask<...>::from(scalar)` for scalar masks.
+
+    Idempotent: marks the class with `_vllm_indirect_assert_patched` after
+    the first apply.
+    """
+    from torch._inductor.codegen.cpp import CppVecKernel
+
+    if getattr(CppVecKernel, "_vllm_indirect_assert_patched", False):
+        return
+
+    from torch._inductor.codegen.cpp import CppCSEVariable, cexpr_index
+
+    def patched_indirect_assert(self, var, lower, upper, mask=None):
+        assert isinstance(var, CppCSEVariable)
+        assert var.dtype is not None
+        if not var.is_vec:
+            if isinstance(mask, CppCSEVariable) and mask.is_vec:
+                mask = f"({mask}).all_masked()"
+            return super(CppVecKernel, self).indirect_assert(var, lower, upper, mask)
+        lower_scalar = lower
+        upper_scalar = upper
+        if lower:
+            lower = f"{self._get_vec_type(var.dtype)}({lower})"
+        if upper:
+            upper = f"{self._get_vec_type(var.dtype)}({upper})"
+        if lower and upper:
+            cond = f"({lower} <= {var}) & ({var} < {upper})"
+            cond_print = f"{lower_scalar} <= {var} < {upper_scalar}"
+        elif lower:
+            cond = f"{lower} <= {var}"
+            cond_print = f"{lower_scalar} <= {var}"
+        else:
+            assert upper
+            cond = f"{var} < {upper}"
+            cond_print = f"{var} < {upper_scalar}"
+        cond = f"{self._get_mask_type(var.dtype)}({cond})"
+        if mask:
+            if not mask.is_vec:
+                # Backport of pytorch/pytorch#178148 -- use ::from for
+                # scalar masks so g++ picks the correct overload.
+                mask = f"{self._get_mask_type(var.dtype)}::from({mask})"
+            cond = f"({cond}) | ~({mask})"
+        if self.tail_size:
+            cond = (
+                f"{self._get_mask_type(var.dtype)}::set("
+                f"{self._get_mask_type(var.dtype)}::from(1)"
+                f", ({cond}), {cexpr_index(self.tail_size)})"
+            )
+        cond = f"({cond}).all_masked()"
+        return f'{self.assert_function}({cond}, "index out of bounds: {cond_print}")'
+
+    CppVecKernel.indirect_assert = patched_indirect_assert
+    CppVecKernel._vllm_indirect_assert_patched = True  # type: ignore[attr-defined]
+
+
+def _patch_cpp_indirect_assert_if_needed():
+    """Apply cpp codegen indirect_assert backport when on torch 2.11.x.
+
+    Defers application until torch._inductor.codegen.cpp is naturally
+    imported by Inductor. Importing it eagerly during vllm.__init__ pulls
+    in torch._inductor.scheduler, whose top-level
+    `import torch._inductor.async_compile` can fail with
+    `ModuleNotFoundError: import of torch._inductor.async_compile halted;
+    None in sys.modules` depending on the import order on the runner
+    (observed in vLLM CPU CI).
+    """
+    if not is_torch_equal_or_newer("2.11.0") or is_torch_equal_or_newer("2.12.0.dev"):
+        return
+
+    import sys
+
+    target_name = "torch._inductor.codegen.cpp"
+    if target_name in sys.modules:
+        _apply_cpp_indirect_assert_patch()
+        return
+
+    import importlib.abc
+
+    class _CppCodegenPatchFinder(importlib.abc.MetaPathFinder):
+        def find_spec(self, fullname, path, target=None):
+            if fullname != target_name:
+                return None
+            sys.meta_path.remove(self)
+            spec = importlib.util.find_spec(fullname)
+            if spec is None or spec.loader is None:
+                return None
+            original_exec = spec.loader.exec_module
+
+            def _exec_then_patch(module):
+                original_exec(module)
+                _apply_cpp_indirect_assert_patch()
+
+            spec.loader.exec_module = _exec_then_patch  # type: ignore[method-assign]
+            return spec
+
+    sys.meta_path.insert(0, _CppCodegenPatchFinder())
+
+
+_patch_cpp_indirect_assert_if_needed()
diff --git a/vllm/envs.py b/vllm/envs.py
index 8ed1d33434cb..ded474dc085a 100755
--- a/vllm/envs.py
+++ b/vllm/envs.py
@@ -45,7 +45,7 @@
     NO_COLOR: bool = False
     VLLM_LOG_STATS_INTERVAL: float = 10.0
     VLLM_TRACE_FUNCTION: int = 0
-    VLLM_USE_FLASHINFER_SAMPLER: bool | None = None
+    VLLM_USE_FLASHINFER_SAMPLER: bool = True
     VLLM_PP_LAYER_PARTITION: str | None = None
     VLLM_CPU_KVCACHE_SPACE: int | None = 0
     VLLM_CPU_OMP_THREADS_BIND: str = "auto"
@@ -79,7 +79,7 @@
     VLLM_MEDIA_CONNECTOR: str = "http"
     VLLM_MM_HASHER_ALGORITHM: str = "blake3"
     VLLM_TARGET_DEVICE: str = "cuda"
-    VLLM_MAIN_CUDA_VERSION: str = "12.9"
+    VLLM_MAIN_CUDA_VERSION: str = "13.0"
     VLLM_FLOAT32_MATMUL_PRECISION: Literal["highest", "high", "medium"] = "highest"
     VLLM_BATCH_INVARIANT: bool = False
     MAX_JOBS: str | None = None
@@ -143,7 +143,7 @@
     VLLM_DP_RANK_LOCAL: int = -1
     VLLM_DP_SIZE: int = 1
     VLLM_USE_STANDALONE_COMPILE: bool = True
-    VLLM_ENABLE_PREGRAD_PASSES: bool = False
+    VLLM_ENABLE_PREGRAD_PASSES: bool = True
     VLLM_DP_MASTER_IP: str = ""
     VLLM_DP_MASTER_PORT: int = 0
     VLLM_RANDOMIZE_DP_DUMMY_INPUTS: bool = False
@@ -152,6 +152,10 @@
     VLLM_RAY_EXTRA_ENV_VARS_TO_COPY: str = ""
     VLLM_MARLIN_USE_ATOMIC_ADD: bool = False
     VLLM_MARLIN_INPUT_DTYPE: Literal["int8", "fp8"] | None = None
+    VLLM_HUMMING_ONLINE_QUANT_CONFIG: dict[str, Any] | None = None
+    VLLM_HUMMING_INPUT_QUANT_CONFIG: dict[str, Any] | None = None
+    VLLM_HUMMING_USE_F16_ACCUM: bool = False
+    VLLM_HUMMING_MOE_GEMM_TYPE: Literal["indexed", "grouped", "auto"] | None = None
     VLLM_MXFP4_USE_MARLIN: bool | None = None
     VLLM_DEEPEPLL_NVFP4_DISPATCH: bool = False
     VLLM_V1_USE_OUTLINES_CACHE: bool = False
@@ -222,6 +226,7 @@
     VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS: bool = False
     VLLM_SYSTEM_START_DATE: str | None = None
     VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY: bool = False
+    VLLM_ENFORCE_STRICT_TOOL_CALLING: bool = False
     VLLM_CUSTOM_SCOPES_FOR_PROFILING: bool = False
     VLLM_NVTX_SCOPES_FOR_PROFILING: bool = False
     VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES: bool = True
@@ -241,6 +246,7 @@
     VLLM_DEBUG_WORKSPACE: bool = False
     VLLM_DISABLE_SHARED_EXPERTS_STREAM: bool = False
     VLLM_SHARED_EXPERTS_STREAM_TOKEN_THRESHOLD: int = 256
+    VLLM_MULTI_STREAM_GEMM_TOKEN_THRESHOLD: int = 1024
     VLLM_COMPILE_CACHE_SAVE_FORMAT: Literal["binary", "unpacked"] = "binary"
     VLLM_USE_V2_MODEL_RUNNER: bool = False
     VLLM_LOG_MODEL_INSPECTION: bool = False
@@ -251,11 +257,16 @@
     VLLM_LORA_DISABLE_PDL: bool = False
     VLLM_ENABLE_CUDA_COMPATIBILITY: bool = False
     VLLM_CUDA_COMPATIBILITY_PATH: str | None = None
+    VLLM_SKIP_MODEL_NAME_VALIDATION: bool = False
+    """If set, vLLM will skip model name validation in API requests.
+    This allows any model name to be accepted in the 'model' field of requests,
+    making the server model-name agnostic. Useful for proxy/gateway scenarios."""
     VLLM_ELASTIC_EP_SCALE_UP_LAUNCH: bool = False
     VLLM_ELASTIC_EP_DRAIN_REQUESTS: bool = False
-    VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS: bool = False
+    VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS: bool = True
     VLLM_NIXL_EP_MAX_NUM_RANKS: int = 32
     VLLM_XPU_ENABLE_XPU_GRAPH: bool = False
+    VLLM_XPU_USE_SAMPLER_KERNEL: bool = True
     VLLM_LORA_ENABLE_DUAL_STREAM: bool = False
 
 
@@ -285,6 +296,15 @@ def maybe_convert_bool(value: str | None) -> bool | None:
     return bool(int(value))
 
 
+def maybe_convert_json_str_or_file(value: str | None) -> dict[str, Any] | None:
+    if value is None:
+        return None
+    if os.path.exists(value):
+        with open(value) as f:
+            return json.load(f)
+    return json.loads(value)
+
+
 def disable_compile_cache() -> bool:
     return bool(int(os.getenv("VLLM_DISABLE_COMPILE_CACHE", "0")))
 
@@ -493,7 +513,7 @@ def _get_or_set_default() -> str:
     "VLLM_TARGET_DEVICE": lambda: os.getenv("VLLM_TARGET_DEVICE", "cuda").lower(),
     # Main CUDA version of vLLM. This follows PyTorch but can be overridden.
     "VLLM_MAIN_CUDA_VERSION": lambda: (
-        os.getenv("VLLM_MAIN_CUDA_VERSION", "").lower() or "12.9"
+        os.getenv("VLLM_MAIN_CUDA_VERSION", "").lower() or "13.0"
     ),
     # Controls PyTorch float32 matmul precision mode within vLLM workers.
     # Valid options mirror torch.set_float32_matmul_precision
@@ -602,9 +622,10 @@ def _get_or_set_default() -> str:
     # The pre-grad passes get run even on cache-hit and negatively impact
     # vllm cold compile times by O(1s)
     # Can remove this after the following issue gets fixed
+    # TODO(luka): maybe_inplace requires this
     # https://github.com/pytorch/pytorch/issues/174502
     "VLLM_ENABLE_PREGRAD_PASSES": lambda: (
-        os.environ.get("VLLM_ENABLE_PREGRAD_PASSES", "0") == "1"
+        os.environ.get("VLLM_ENABLE_PREGRAD_PASSES", "1") == "1"
     ),
     # Debug pattern matching inside custom passes.
     # Should be set to the fx.Node name (e.g. 'getitem_34' or 'scaled_mm_3').
@@ -699,11 +720,13 @@ def _get_or_set_default() -> str:
     # If set to 1, vllm will trace function calls
     # Useful for debugging
     "VLLM_TRACE_FUNCTION": lambda: int(os.getenv("VLLM_TRACE_FUNCTION", "0")),
-    # If set, vllm will use flashinfer sampler
+    # Whether to use the FlashInfer top-k / top-p sampler on CUDA. Enabled
+    # by default when the hardware supports it — set to 0 to opt out
+    # explicitly, which forces the PyTorch-native (Triton for bs>=8) path.
     "VLLM_USE_FLASHINFER_SAMPLER": lambda: (
         bool(int(os.environ["VLLM_USE_FLASHINFER_SAMPLER"]))
         if "VLLM_USE_FLASHINFER_SAMPLER" in os.environ
-        else None
+        else True
     ),
     # Pipeline stage partition strategy
     "VLLM_PP_LAYER_PARTITION": lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None),
@@ -760,9 +783,8 @@ def _get_or_set_default() -> str:
     ),
     # When True and distributed_executor_backend="ray", use RayExecutorV2
     # (MQ-based) instead of RayDistributedExecutor (compiled-graph backend).
-    # TODO (jeffreywang): Enabled by default in vLLM 0.20.0.
     "VLLM_USE_RAY_V2_EXECUTOR_BACKEND": lambda: bool(
-        int(os.getenv("VLLM_USE_RAY_V2_EXECUTOR_BACKEND", "0"))
+        int(os.getenv("VLLM_USE_RAY_V2_EXECUTOR_BACKEND", "1"))
     ),
     # Use dedicated multiprocess context for workers.
     # Both spawn and fork work
@@ -829,9 +851,10 @@ def _get_or_set_default() -> str:
     "VLLM_MAX_AUDIO_CLIP_FILESIZE_MB": lambda: int(
         os.getenv("VLLM_MAX_AUDIO_CLIP_FILESIZE_MB", "25")
     ),
-    # Backend for Video IO
-    # - "opencv": Default backend that uses OpenCV stream buffered backend.
-    # - "identity": Returns raw video bytes for model processor to handle.
+    # Backend for Video IO — selects the frame-sampling algorithm.
+    # - "opencv": uniform sampling.
+    # - "opencv_dynamic": duration-aware dynamic sampling.
+    # - "identity": returns raw video bytes for model processor to handle.
     #
     # Custom backend implementations can be registered
     # via `@VIDEO_LOADER_REGISTRY.register("my_custom_video_loader")` and
@@ -983,6 +1006,7 @@ def _get_or_set_default() -> str:
     # use aiter linear op if aiter ops are enabled
     # The following list of related ops
     # - scaled_mm (per-tensor / rowwise)
+    # - use aiter tuned gemms for unquantized gemms
     "VLLM_ROCM_USE_AITER_LINEAR": lambda: (
         os.getenv("VLLM_ROCM_USE_AITER_LINEAR", "True").lower() in ("true", "1")
     ),
@@ -1192,6 +1216,25 @@ def _get_or_set_default() -> str:
     "VLLM_MARLIN_INPUT_DTYPE": env_with_choices(
         "VLLM_MARLIN_INPUT_DTYPE", None, ["int8", "fp8"]
     ),
+    # The online quantization dtype for humming kernel
+    "VLLM_HUMMING_ONLINE_QUANT_CONFIG": lambda: maybe_convert_json_str_or_file(
+        os.environ.get("VLLM_HUMMING_ONLINE_QUANT_CONFIG", None)
+    ),
+    # The activation dtype config for humming kernel
+    "VLLM_HUMMING_INPUT_QUANT_CONFIG": lambda: maybe_convert_json_str_or_file(
+        os.environ.get("VLLM_HUMMING_INPUT_QUANT_CONFIG", None)
+    ),
+    # Whether to use fp16 accumulator mma
+    "VLLM_HUMMING_USE_F16_ACCUM": lambda: maybe_convert_bool(
+        os.environ.get("VLLM_HUMMING_USE_F16_ACCUM", "0")
+    ),
+    # Whether to use indexed gemm for humming moe
+    # if 1, force use indexed gemm
+    # if 0, force use grouped gemm
+    # if None, choose better gemm type automatically
+    "VLLM_HUMMING_MOE_GEMM_TYPE": lambda: os.environ.get(
+        "VLLM_HUMMING_MOE_GEMM_TYPE", None
+    ),
     # Whether to use DeepEPLL kernels for NVFP4 quantization and dispatch method
     # only supported on Blackwell GPUs and with
     # https://github.com/deepseek-ai/DeepEP/pull/341
@@ -1551,6 +1594,12 @@ def _get_or_set_default() -> str:
     "VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY": lambda: bool(
         int(os.getenv("VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY", "0"))
     ),
+    # When 1,the model structural tags will be used to enforce the model
+    # output conforming to the model's tool-calling format and schema.
+    # Default 0 (off).
+    "VLLM_ENFORCE_STRICT_TOOL_CALLING": lambda: bool(
+        int(os.getenv("VLLM_ENFORCE_STRICT_TOOL_CALLING", "0"))
+    ),
     # Add optional custom scopes for profiling, disable to avoid overheads
     "VLLM_CUSTOM_SCOPES_FOR_PROFILING": lambda: bool(
         int(os.getenv("VLLM_CUSTOM_SCOPES_FOR_PROFILING", "0"))
@@ -1587,9 +1636,18 @@ def _get_or_set_default() -> str:
     "VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL": lambda: bool(
         int(os.getenv("VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL", "0"))
     ),
-    # The number of SMs to allocate for communication kernels when running DBO
-    # the rest of the SMs on the device will be allocated to compute
-    "VLLM_DBO_COMM_SMS": lambda: int(os.getenv("VLLM_DBO_COMM_SMS", "20")),
+    # The number of SMs/CUs to allocate for communication kernels when
+    # running DBO; the rest will be allocated to compute.
+    # Default: 20 on CUDA (SMs), 64 on ROCm (CUs).
+    "VLLM_DBO_COMM_SMS": lambda: int(
+        os.getenv(
+            "VLLM_DBO_COMM_SMS",
+            "64"
+            if hasattr(__import__("torch").version, "hip")
+            and __import__("torch").version.hip is not None
+            else "20",
+        )
+    ),
     # Enable max_autotune & coordinate_descent_tuning in inductor_config
     # to compile static shapes passed from compile_sizes in compilation_config
     # If set to 1, enable max_autotune; By default, this is enabled (1)
@@ -1629,6 +1687,17 @@ def _get_or_set_default() -> str:
     "VLLM_SHARED_EXPERTS_STREAM_TOKEN_THRESHOLD": lambda: int(
         int(os.getenv("VLLM_SHARED_EXPERTS_STREAM_TOKEN_THRESHOLD", 256))
     ),
+    # Token-count cutoff for multi-stream overlap of the attention input
+    # GEMM with auxiliary GEMMs (e.g. fused_wqa_wkv overlapped with indexer
+    # weights / kv-score projections in DeepSeek-V4). At or below this many
+    # tokens the FP8 main GEMM has idle SMs to share with the bf16 aux GEMMs
+    # and overlap is a 5-45% win; above it the FP8 GEMM saturates the device
+    # and the cross-stream sync becomes pure overhead. Set to 0 to disable
+    # the multi-stream path entirely. See #PR 41526 for the empirical result
+    # for the default value of 1024 tokens.
+    "VLLM_MULTI_STREAM_GEMM_TOKEN_THRESHOLD": lambda: int(
+        os.getenv("VLLM_MULTI_STREAM_GEMM_TOKEN_THRESHOLD", "1024")
+    ),
     # Format for saving torch.compile cache artifacts
     # - "binary": saves as binary file
     #     Safe for multiple vllm serve processes accessing the same torch compile cache.
@@ -1675,6 +1744,14 @@ def _get_or_set_default() -> str:
     "VLLM_CUDA_COMPATIBILITY_PATH": lambda: os.environ.get(
         "VLLM_CUDA_COMPATIBILITY_PATH", None
     ),
+    # Skip model name validation in OpenAI API requests.
+    # When set to 1, any model name will be accepted in the 'model' field
+    # of API requests. This is useful for proxy/gateway scenarios where
+    # the actual model is served but different names may be used in requests.
+    "VLLM_SKIP_MODEL_NAME_VALIDATION": lambda: (
+        os.getenv("VLLM_SKIP_MODEL_NAME_VALIDATION", "0").strip().lower()
+        in ("1", "true")
+    ),
     # Whether it is a scale up launch engine for elastic EP,
     # Should only be set by EngineCoreClient.
     "VLLM_ELASTIC_EP_SCALE_UP_LAUNCH": lambda: bool(
@@ -1687,9 +1764,9 @@ def _get_or_set_default() -> str:
     ),
     # If set to 1, enable CUDA graph memory estimation during memory profiling.
     # This profiles CUDA graph memory usage to provide more accurate KV cache
-    # memory allocation. Disabled by default to preserve existing behavior.
+    # memory allocation. Enabled by default as of v0.21.0
     "VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS": lambda: bool(
-        int(os.getenv("VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS", "0"))
+        int(os.getenv("VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS", "1"))
     ),
     # NIXL EP environment variables
     "VLLM_NIXL_EP_MAX_NUM_RANKS": lambda: int(
@@ -1699,6 +1776,10 @@ def _get_or_set_default() -> str:
     "VLLM_XPU_ENABLE_XPU_GRAPH": lambda: bool(
         int(os.getenv("VLLM_XPU_ENABLE_XPU_GRAPH", "0"))
     ),
+    # whether use xpu specific sample kernel
+    "VLLM_XPU_USE_SAMPLER_KERNEL": lambda: bool(
+        int(os.getenv("VLLM_XPU_USE_SAMPLER_KERNEL", "1"))
+    ),
     # Enable simple KV offload.
     "VLLM_USE_SIMPLE_KV_OFFLOAD": lambda: bool(
         int(os.getenv("VLLM_USE_SIMPLE_KV_OFFLOAD", "0"))
@@ -1850,6 +1931,7 @@ def compile_factors() -> dict[str, object]:
         "VLLM_TEST_FORCE_LOAD_FORMAT",
         "VLLM_ENABLE_CUDA_COMPATIBILITY",
         "VLLM_CUDA_COMPATIBILITY_PATH",
+        "VLLM_SKIP_MODEL_NAME_VALIDATION",
         "LOCAL_RANK",
         "CUDA_VISIBLE_DEVICES",
         "NO_COLOR",
diff --git a/vllm/inputs/engine.py b/vllm/inputs/engine.py
index 2b426eba8f00..1c12fbc2c552 100644
--- a/vllm/inputs/engine.py
+++ b/vllm/inputs/engine.py
@@ -71,12 +71,27 @@ class EmbedsInput(_InputOptions):
     prompt: NotRequired[str]
     """The prompt text corresponding to the token IDs, if available."""
 
+    prompt_token_ids: NotRequired[list[int]]
+    """Token IDs of the rendered prompt. Only set for mixed-mode inputs
+    (chat completion with `prompt_embeds` content parts). When present,
+    `is_token_ids` MUST also be present and have the same length. 
+    For pure-embeds inputs this field is absent."""
+
+    is_token_ids: NotRequired[list[bool]]
+    """Per-position mask for mixed-mode inputs. `True` means the position
+    is a real token ID (use the model's embedding layer); `False` means
+    the position uses a pre-computed embedding row from `prompt_embeds`.
+    Length MUST equal `len(prompt_token_ids)`.
+    For pure-embeds inputs this field is absent."""
+
 
 def embeds_input(
     prompt_embeds: "torch.Tensor",
     *,
     prompt: str | None = None,
     cache_salt: str | None = None,
+    prompt_token_ids: list[int] | None = None,
+    is_token_ids: list[bool] | None = None,
 ) -> EmbedsInput:
     """
     Construct [`EmbedsInput`][vllm.inputs.engine.EmbedsInput]
@@ -88,6 +103,10 @@ def embeds_input(
         inputs["prompt"] = prompt
     if cache_salt is not None:
         inputs["cache_salt"] = cache_salt
+    if prompt_token_ids is not None:
+        inputs["prompt_token_ids"] = prompt_token_ids
+    if is_token_ids is not None:
+        inputs["is_token_ids"] = is_token_ids
 
     return inputs
 
diff --git a/vllm/inputs/llm.py b/vllm/inputs/llm.py
index ff22af819a28..918098b758ca 100644
--- a/vllm/inputs/llm.py
+++ b/vllm/inputs/llm.py
@@ -125,6 +125,17 @@ class EmbedsPrompt(_PromptOptions):
     prompt: NotRequired[str]
     """The prompt text corresponding to the token embeddings, if available."""
 
+    prompt_token_ids: NotRequired[list[int]]
+    """Token IDs for mixed-mode inputs (chat completion with
+    `prompt_embeds` content parts). The tokens at positions where 
+    `prompt_is_token_ids` is `False` are placeholder tokens that 
+    get replaced by entries from `prompt_embeds` in the forward pass."""
+
+    prompt_is_token_ids: NotRequired[list[bool]]
+    """Per-position mask, `True` uses the real token ID, `False` uses
+    the corresponding entry from `prompt_embeds`. 
+    Must be the same length as `prompt_token_ids` when both are set."""
+
 
 DecoderOnlyPrompt: TypeAlias = (
     str | TextPrompt | list[int] | TokensPrompt | EmbedsPrompt
diff --git a/vllm/ir/op.py b/vllm/ir/op.py
index 1cbd78e28f91..841df0f9adf4 100644
--- a/vllm/ir/op.py
+++ b/vllm/ir/op.py
@@ -4,15 +4,18 @@
 import inspect
 from collections.abc import Callable
 from pathlib import Path
-from typing import Any, ClassVar, overload
+from typing import Any, ClassVar, Literal, overload
 
 import torch
 from torch.library import Library, infer_schema
 
+from vllm.ir.tolerances import DEFAULT_TOLERANCES, ToleranceSpec
 from vllm.ir.util import hash_source, weak_cache
 from vllm.logger import init_logger
 from vllm.logging_utils import lazy, tensors_str_no_data
 
+InputGenerator = Callable[..., tuple[Any, ...]]
+
 vllm_ir_lib = Library("vllm_ir", "FRAGMENT")
 
 logger = init_logger(__name__)
@@ -43,35 +46,51 @@ def enable_torch_wrap(enable: bool = True):
         _ENABLE_TORCH_WRAP = old
 
 
-# 0-param decorator overload
+# 0-param decorator overload (no inplace)
 @overload
 def register_op(f: Callable[..., Any]) -> "IrOp": ...
 
 
-# parametrized decorator overload
+# parametrized decorator with allow_inplace=False (default)
 @overload
 def register_op(
     *,
     name: str | None = None,
+    activations: list[str] | None = None,
+    allow_inplace: Literal[False] = False,
 ) -> Callable[[Callable[..., Any]], "IrOp"]: ...
 
 
+# parametrized decorator with allow_inplace=True
+@overload
+def register_op(
+    *,
+    name: str | None = None,
+    activations: list[str] | None = None,
+    allow_inplace: Literal[True],
+) -> Callable[[Callable[..., Any]], "IrOpInplace"]: ...
+
+
 def register_op(
     f: Callable | None = None,
     *,
     name: str | None = None,
+    activations: list[str] | None = None,
+    allow_inplace: bool = False,
 ) -> "IrOp | Callable[[Callable], IrOp]":
     """
     Register a new vLLM IR op.
 
     :param f: the native implementation of the op
     :param name: the name of the op, defaults to the function name
+    :param activations: list of activation params, defaults to params starting with 'x'
+    :param allow_inplace: add a maybe_inplace overload that allows inplace impls
     :return: the IrOp object if f is provided, otherwise a decorator
 
     Example usage:
     ```python
     @vllm.ir.register_op
-    def my_op(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+    def my_add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         return x + y
 
 
@@ -82,7 +101,10 @@ def multiply(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
     def decorator(_f: Callable):
         op_name: str = _f.__name__ if name is None else name
         assert op_name not in IrOp.registry
-        op = IrOp(op_name, _f)
+        if allow_inplace:
+            op: IrOp = IrOpInplace(op_name, _f, activations)
+        else:
+            op = IrOp(op_name, _f, activations)
         IrOp.registry[op_name] = op
         return op
 
@@ -97,8 +119,14 @@ class IrOp:
 
     name: str
     impls: dict[str, "IrOpImpl"]
+    allow_inplace: bool = False
 
-    def __init__(self, name: str, native_impl: Callable):
+    def __init__(
+        self,
+        name: str,
+        native_impl: Callable,
+        activations: list[str] | None = None,
+    ):
         self._py_signature = inspect.signature(native_impl)
         if any(
             p.kind == inspect.Parameter.KEYWORD_ONLY
@@ -109,14 +137,35 @@ def __init__(self, name: str, native_impl: Callable):
                 f"supported. That's because kwargs are not allowed during lowering."
             )
 
+        # By convention, we consider parameters starting with 'x' as activations.
+        if activations is None:
+            activations = [
+                p.name
+                for p in self._py_signature.parameters.values()
+                if p.name.startswith("x")
+            ]
+
         self.name = name
         self.impls: dict[str, IrOpImpl] = {}
+        self.activations = activations
+        self.activation_indices = [
+            i
+            for i, p in enumerate(self._py_signature.parameters.values())
+            if p.name in activations
+        ]
         self._priority_impls: list[IrOpImpl] = []
         self._schema_str = infer_schema(native_impl, mutates_args=[])
+        self._input_generator: InputGenerator | None = None
+        self._tolerance_overrides: ToleranceSpec = {}
 
         # native implementation
         self.impls["native"] = IrOpImpl(
-            self, "native", native_impl, supported=True, supports_args=None
+            self,
+            "native",
+            native_impl,
+            # always supported
+            supported=True,
+            supports_args=None,
         )
 
         # By default, fake routes directly to native,
@@ -156,12 +205,14 @@ def register_impl(
         *,
         supported: bool = True,
         supports_args: Callable[..., bool] | None = None,
+        inplace: bool = False,
     ):
         """
         Register an implementation for this custom op.
         :param provider: The name of the provider, must be unique.
         :param supported: Static support check, use this to check platform support.
         :param supports_args: Dynamic arg support check, used for types and shapes.
+        :param inplace: Does this op reuse activation input memory for outputs
         :return: A decorator that registers the implementation.
 
         The decorated function must have the same semantics and signature as
@@ -188,7 +239,7 @@ def my_provider_impl(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: ...
         )
 
         def _register_impl(f: Callable):
-            impl = IrOpImpl(self, provider, f, supported, supports_args)
+            impl = IrOpImpl(self, provider, f, supported, supports_args, inplace)
             self.impls[provider] = impl
 
             if self.get_priority():
@@ -208,7 +259,10 @@ def _inner_call(self, *args, **kwargs) -> Any:
         __call__ routes straight here instead of going through torch op dispatching.
         """
         impl = self.dispatch(*args, **kwargs)
-        return impl.impl_fn(*args, **kwargs)
+
+        # Default overload must be functional,
+        # use func_impl_fn to correctly handle inplace impls.
+        return impl.func_impl_fn(*args, **kwargs)
 
     def apply_arg_defaults(self, args) -> tuple:
         """
@@ -309,6 +363,11 @@ def filter_priority_impls(p_list: list[str]) -> list[IrOpImpl]:
         old_priority_impls = self._priority_impls
         try:
             self._priority_impls = filter_priority_impls(priority)
+            logger.debug(
+                "Priority for vllm.ir.%s set to %s",
+                self.name,
+                lazy(lambda: [p.provider for p in self._priority_impls]),
+            )
             yield
         finally:
             self._priority_impls = old_priority_impls
@@ -316,6 +375,98 @@ def filter_priority_impls(p_list: list[str]) -> list[IrOpImpl]:
     def supported_providers(self) -> list[str]:
         return [p.provider for p in self.impls.values() if p.supported]
 
+    @property
+    def has_input_generator(self) -> bool:
+        return self._input_generator is not None
+
+    def register_input_generator(self, fn: InputGenerator) -> InputGenerator:
+        self._input_generator = fn
+        return fn
+
+    def generate_inputs(self, **kwargs: Any) -> tuple[Any, ...]:
+        if self._input_generator is None:
+            raise RuntimeError(
+                f"No input generator registered for op '{self.name}'. "
+                f"Use @ir.ops.{self.name}.register_input_generator"
+            )
+        return self._input_generator(**kwargs)
+
+    def override_tolerance(
+        self, dtype: torch.dtype, *, atol: float, rtol: float
+    ) -> None:
+        self._tolerance_overrides[dtype] = {"atol": atol, "rtol": rtol}
+
+    def get_tolerance(self, dtype: torch.dtype) -> dict[str, float]:
+        if dtype in self._tolerance_overrides:
+            return self._tolerance_overrides[dtype]
+        if dtype in DEFAULT_TOLERANCES:
+            return DEFAULT_TOLERANCES[dtype]
+        raise ValueError(
+            f"No tolerance defined for dtype {dtype} in op '{self.name}'. "
+            f"Use op.override_tolerance({dtype}, atol=..., rtol=...) "
+            f"or add {dtype} to DEFAULT_TOLERANCES."
+        )
+
+
+class IrOpInplace(IrOp):
+    """IR op with inplace support via maybe_inplace."""
+
+    maybe_inplace: "IrOpInplaceOverload"
+    allow_inplace: bool = True
+
+    def __init__(
+        self,
+        name: str,
+        native_impl: Callable,
+        activations: list[str] | None = None,
+    ):
+        super().__init__(name, native_impl, activations)
+
+        # Create the inplace overload
+        self.maybe_inplace = IrOpInplaceOverload(self)
+
+
+class IrOpInplaceOverload:
+    def __init__(self, op: IrOp):
+        params, returns = op._schema_str.split(" -> ")
+        n_outputs = returns.count("Tensor")
+
+        assert returns.count("Tensor") == len(op.activations), (
+            "Inplace overload requires the same number of outputs as activations."
+        )
+
+        assert returns.count(",") == n_outputs - 1, (
+            "Inplace overload only supports Tensor outputs for now."
+        )
+
+        self.op = op
+        self.name = f"{op.name}.maybe_inplace"
+        self._schema_str = infer_schema(
+            op.impls["native"].impl_fn, mutates_args=op.activations
+        )
+
+        # torch registration
+        vllm_ir_lib.define(self.name + self._schema_str)
+        vllm_ir_lib.impl(
+            self.name, self._inner_call, dispatch_key="CompositeExplicitAutograd"
+        )
+        # fake goes to default overload for now
+        vllm_ir_lib._register_fake(self.name, self.op._fake_call)
+
+        assert hasattr(getattr(torch.ops.vllm_ir, self.op.name), "maybe_inplace")
+        self.torch_op = getattr(torch.ops.vllm_ir, self.op.name).maybe_inplace
+
+    def __call__(self, *args, **kwargs) -> Any:
+        if not _ENABLE_TORCH_WRAP:
+            return self._inner_call(*args, **kwargs)
+
+        return self.torch_op(*args, **kwargs)
+
+    def _inner_call(self, *args, **kwargs) -> Any:
+        # Calling the maybe_inplace overload means we can use inplace impls directly.
+        impl = self.op.dispatch(*args, **kwargs)
+        return impl.impl_fn(*args, **kwargs)
+
 
 class IrOpImpl:
     def __init__(
@@ -325,6 +476,7 @@ def __init__(
         impl_fn: Callable,
         supported: bool,
         supports_args: Callable[..., bool] | None,
+        inplace: bool = False,
     ):
         assert provider not in op.impls, (
             f"Implementation for provider {provider} already registered."
@@ -383,11 +535,18 @@ def __init__(
                         f"native default {op_p.default}'"
                     )
 
+        if inplace:
+            assert op.allow_inplace, (
+                f"Inplace implementation cannot be registered for op {op.name}"
+                f" that does not allow inplace."
+            )
+
         self.op = op
         self.provider = provider
         self.impl_fn = impl_fn
         self.supported = supported
         self._supports_args = supports_args
+        self.inplace = inplace
 
     @property
     def supports_all_args(self) -> bool:
@@ -412,3 +571,19 @@ def uuid(self):
         """
         sources = [Path(inspect.getfile(self.impl_fn))]
         return hash_source(*sources)
+
+    def func_impl_fn(self, *args, **kwargs) -> Any:
+        """
+        Copy any inputs in activations if this is an inplace impl,
+        to ensure functional semantics.
+        """
+        if not self.inplace:
+            return self.impl_fn(*args, **kwargs)
+
+        # copy activations to ensure functional semantics
+        new_args = list(args)
+        for i in self.op.activation_indices:
+            assert isinstance(args[i], torch.Tensor)
+            new_args[i] = args[i].clone()
+
+        return self.impl_fn(*new_args, **kwargs)
diff --git a/vllm/ir/ops/__init__.py b/vllm/ir/ops/__init__.py
index 25ad27c8a078..d4d71afef723 100644
--- a/vllm/ir/ops/__init__.py
+++ b/vllm/ir/ops/__init__.py
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from .layernorm import rms_norm
+from .layernorm import fused_add_rms_norm, rms_norm
 
-__all__ = ["rms_norm"]
+__all__ = ["rms_norm", "fused_add_rms_norm"]
diff --git a/vllm/ir/ops/layernorm.py b/vllm/ir/ops/layernorm.py
index ac0c38a9e4d6..33a71b8f853f 100644
--- a/vllm/ir/ops/layernorm.py
+++ b/vllm/ir/ops/layernorm.py
@@ -19,3 +19,54 @@ def rms_norm(
     if weight is not None:
         x = x.to(weight.dtype) * weight
     return x.to(orig_dtype)
+
+
+@rms_norm.register_input_generator
+def _rms_norm_input_generator(
+    num_tokens: int, hidden_size: int, dtype: torch.dtype, epsilon: float = 1e-5
+) -> tuple:
+    x = torch.randn(num_tokens, hidden_size, dtype=dtype)
+    weight = torch.randn(hidden_size, dtype=dtype)
+    return x, weight, epsilon
+
+
+# Reductions in rms_norm accumulate rounding error at large shapes
+# (e.g. 32768x16384), causing a few elements out of millions to exceed
+# the default float16 tolerance.
+rms_norm.override_tolerance(torch.float16, atol=1e-2, rtol=2e-3)
+
+
+@register_op(allow_inplace=True)
+def fused_add_rms_norm(
+    x: Tensor,
+    x_residual: Tensor,
+    weight: Tensor | None,
+    epsilon: float,
+    variance_size: int | None = None,
+) -> tuple[Tensor, Tensor]:
+    """Fused add and weighted root-mean-square layer normalization"""
+    orig_dtype = x.dtype
+    x = x.to(torch.float32)
+    x = x + x_residual.to(torch.float32)
+    x_residual = x.to(orig_dtype)
+
+    x_var = x if variance_size is None else x[..., :variance_size]
+    variance = x_var.pow(2).mean(dim=-1, keepdim=True)
+    x = x * torch.rsqrt(variance + epsilon)
+    if weight is not None:
+        x = x.to(weight.dtype) * weight
+    return x.to(orig_dtype), x_residual
+
+
+# fused_add_rms_norm has similar rounding error accumulation as rms_norm
+fused_add_rms_norm.override_tolerance(torch.float16, atol=1e-2, rtol=2e-3)
+
+
+@fused_add_rms_norm.register_input_generator
+def _fused_add_rms_norm_input_generator(
+    num_tokens: int, hidden_size: int, dtype: torch.dtype, epsilon: float = 1e-5
+) -> tuple:
+    x = torch.randn(num_tokens, hidden_size, dtype=dtype)
+    x_residual = torch.randn(num_tokens, hidden_size, dtype=dtype)
+    weight = torch.randn(hidden_size, dtype=dtype)
+    return x, x_residual, weight, epsilon
diff --git a/vllm/ir/tolerances.py b/vllm/ir/tolerances.py
new file mode 100644
index 000000000000..a794a939765a
--- /dev/null
+++ b/vllm/ir/tolerances.py
@@ -0,0 +1,36 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+ToleranceSpec = dict[torch.dtype, dict[str, float]]
+
+# Default tolerances for comparing IR op implementations against native.
+# These are intentionally conservative (permissive) to avoid false failures
+# across different hardware and kernel implementations. Ops that need tighter
+# or looser bounds should use override_tolerance.
+DEFAULT_TOLERANCES: ToleranceSpec = {
+    # 52-bit mantissa; machine epsilon ~1.1e-16
+    torch.float64: {"atol": 1e-8, "rtol": 1e-8},
+    # 23-bit mantissa; machine epsilon ~1.2e-7.
+    # Values from PyTorch test_transformers.py reference defaults.
+    torch.float32: {"atol": 1e-5, "rtol": 1.3e-6},
+    # 10-bit mantissa; machine epsilon ~9.8e-4.
+    # Standard tolerance used across vLLM kernel tests.
+    torch.float16: {"atol": 1e-3, "rtol": 1e-3},
+    # 7-bit mantissa; machine epsilon ~7.8e-3.
+    # Wider rtol than float16 to account for the coarser mantissa.
+    torch.bfloat16: {"atol": 1e-3, "rtol": 1.6e-2},
+    # 3-bit mantissa; machine epsilon ~6.25e-2.
+    # Derived from vLLM fp8 kernel tests (merge_attn_states, silu_mul_fp8).
+    torch.float8_e4m3fn: {"atol": 1e-1, "rtol": 1e-1},
+    # 2-bit mantissa; machine epsilon ~1.25e-1.
+    # Wider than e4m3fn due to the smaller mantissa.
+    torch.float8_e5m2: {"atol": 2e-1, "rtol": 2e-1},
+    # 1-bit mantissa; machine epsilon ~2.5e-1. Packed pair format (x2).
+    # Derived from vLLM fp4 tests (test_silu_mul_nvfp4_quant: atol=3e-1).
+    torch.float4_e2m1fn_x2: {"atol": 3e-1, "rtol": 3e-1},
+    # Integer quantized; off-by-one from rounding is expected.
+    # rtol=0 because relative error is meaningless for small integers.
+    torch.int8: {"atol": 1, "rtol": 0},
+}
diff --git a/vllm/kernels/aiter_ops.py b/vllm/kernels/aiter_ops.py
index 14c2b87fbbdb..273bc58935b7 100644
--- a/vllm/kernels/aiter_ops.py
+++ b/vllm/kernels/aiter_ops.py
@@ -75,3 +75,72 @@ def _rms_norm_fake(x: Tensor, weight: Tensor, variance_epsilon: float) -> Tensor
 direct_register_aiter_op(
     op_name="rms_norm", op_func=_rms_norm_impl, fake_impl=_rms_norm_fake
 )
+
+rms_add_no_var_16bit_only = (
+    lambda x, x_residual, weight, epsilon, variance_size=None: variance_size is None
+    and x.dtype in (torch.float16, torch.bfloat16)
+    and (weight is None or weight.dtype == x.dtype)
+)
+"""
+AITER fused_add_rms_norm only supports 16-bit activations and no var_size override.
+Requires weight dtype to match x dtype.
+"""
+
+
+@ir.ops.fused_add_rms_norm.register_impl(
+    "aiter", supports_args=rms_add_no_var_16bit_only, supported=AITER_SUPPORTED
+)
+def fused_add_rms_norm(
+    x: Tensor,
+    x_residual: Tensor,
+    weight: Tensor | None,
+    epsilon: float,
+    variance_size: int | None = None,
+) -> tuple[Tensor, Tensor]:
+    assert variance_size is None
+    assert x.dtype in (torch.float16, torch.bfloat16)
+    if weight is None:
+        weight = torch.ones(x.shape[-1], device=x.device, dtype=x.dtype)
+    return torch.ops.vllm_aiter.fused_add_rms_norm(x, x_residual, weight, epsilon)
+
+
+def _rocm_aiter_rmsnorm2d_fwd_with_add_impl(
+    x: torch.Tensor,
+    residual: torch.Tensor,
+    weight: torch.Tensor,
+    variance_epsilon: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    from aiter import rmsnorm2d_fwd_with_add
+
+    # TODO can out = x and residual_out = residual to save memory?
+    #  Need to check if the kernel supports in-place residual output
+    #  (if yes set mutates_args and inplace)
+    residual_out = torch.empty_like(residual)
+    out = torch.empty_like(x)
+    rmsnorm2d_fwd_with_add(
+        out,  # output
+        x,  # input
+        residual,  # residual input
+        residual_out,  # residual output
+        weight,
+        variance_epsilon,
+    )
+    return out, residual_out
+
+
+def _rocm_aiter_rmsnorm2d_fwd_with_add_fake(
+    x: torch.Tensor,
+    residual: torch.Tensor,
+    weight: torch.Tensor,
+    variance_epsilon: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    residual_out = torch.empty_like(residual)
+    out = torch.empty_like(x)
+    return out, residual_out
+
+
+direct_register_aiter_op(
+    op_name="fused_add_rms_norm",
+    op_func=_rocm_aiter_rmsnorm2d_fwd_with_add_impl,
+    fake_impl=_rocm_aiter_rmsnorm2d_fwd_with_add_fake,
+)
diff --git a/vllm/kernels/helion/__init__.py b/vllm/kernels/helion/__init__.py
index 2568baa20dae..8c05c428bb07 100644
--- a/vllm/kernels/helion/__init__.py
+++ b/vllm/kernels/helion/__init__.py
@@ -3,11 +3,13 @@
 """Helion integration for vLLM."""
 
 import vllm.kernels.helion.ops  # noqa: F401  Auto-register all Helion ops
+from vllm.kernels.helion.case_key import CaseKey
 from vllm.kernels.helion.config_manager import (
     ConfigManager,
     ConfigSet,
 )
 from vllm.kernels.helion.register import (
+    ConfigPicker,
     ConfiguredHelionKernel,
     HelionKernelWrapper,
     get_kernel_by_name,
@@ -19,9 +21,11 @@
 
 __all__ = [
     # Config management
+    "CaseKey",
     "ConfigManager",
     "ConfigSet",
     # Kernel registration
+    "ConfigPicker",
     "ConfiguredHelionKernel",
     "HelionKernelWrapper",
     "get_kernel_by_name",
diff --git a/vllm/kernels/helion/case_key.py b/vllm/kernels/helion/case_key.py
new file mode 100644
index 000000000000..32b544de39cc
--- /dev/null
+++ b/vllm/kernels/helion/case_key.py
@@ -0,0 +1,72 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Structured key for identifying kernel config/autotune/benchmark cases.
+"""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+
+class CaseKey(dict[str, Any]):
+    """Immutable, hashable dict for identifying kernel cases.
+
+    Used as the key for config lookup, autotuning, benchmarking, and
+    input generation.  Behaves like a read-only dict and can be used
+    as a dict key or in sets.
+
+    The canonical string form (``__str__``) is stable JSON with sorted
+    keys.  Use ``CaseKey.default()`` for the default/fallback key.
+    The regular constructor requires at least one key-value pair::
+
+        CaseKey({"intermediate": 2048, "numtokens": 256})
+        CaseKey.default()  # default/fallback
+    """
+
+    def __init__(self, *args: Any, _allow_empty: bool = False, **kwargs: Any):
+        super().__init__(*args, **kwargs)
+        if not self and not _allow_empty:
+            raise TypeError(
+                "CaseKey requires at least one key-value pair. "
+                "Use CaseKey.default() for the default config key."
+            )
+        self._str: str | None = None
+        self._hash: int | None = None
+
+    @classmethod
+    def default(cls) -> CaseKey:
+        """Create a default case key (empty)."""
+        return cls(_allow_empty=True)
+
+    def __hash__(self) -> int:  # type: ignore[override]
+        if self._hash is None:
+            self._hash = hash(str(self))
+        return self._hash
+
+    def __str__(self) -> str:
+        if self._str is None:
+            self._str = json.dumps(dict(self), sort_keys=True, separators=(",", ":"))
+        return self._str
+
+    def __repr__(self) -> str:
+        if not self:
+            return "CaseKey.default()"
+        return f"CaseKey({dict(self)})"
+
+    def is_default(self) -> bool:
+        """Return True if this is the default case key (empty)."""
+        return not self
+
+    def _readonly(self, *args: Any, **kwargs: Any) -> Any:
+        raise TypeError("CaseKey is immutable")
+
+    __setitem__ = _readonly  # type: ignore[assignment]
+    __delitem__ = _readonly  # type: ignore[assignment]
+    __ior__ = _readonly  # type: ignore[assignment]
+    update = _readonly  # type: ignore[assignment]
+    pop = _readonly  # type: ignore[assignment]
+    popitem = _readonly  # type: ignore[assignment]
+    setdefault = _readonly  # type: ignore[assignment]
+    clear = _readonly  # type: ignore[assignment]
diff --git a/vllm/kernels/helion/config_manager.py b/vllm/kernels/helion/config_manager.py
index f34d936041f4..ca37a68e8101 100644
--- a/vllm/kernels/helion/config_manager.py
+++ b/vllm/kernels/helion/config_manager.py
@@ -11,25 +11,25 @@
 Each kernel has a directory: {kernel_name}/
 Inside, each GPU platform has its own JSON file: {kernel_name}/{platform}.json
 
-For example:
-    silu_mul_fp8/
-        nvidia_h100.json    # { "default": {...}, "batch_32_hidden_4096": {...} }
-        nvidia_h200.json    # { "batch_16_hidden_2048": {...} }
-
-Each platform file maps config keys to Helion config objects.
-Config keys should be structured strings that encode the relevant
-parameters (e.g., "batch_32_hidden_4096", "seq_512_heads_16", "fp8_batch_64", etc.).
-
-Classes
--------
-- ConfigSet: In-memory collection of configs for a kernel with lookup/query APIs.
-- ConfigManager: File-level operations for config persistence.
+Platform files store config entries as a JSON array::
+
+    [
+        {"key": {}, "config": {...}},
+        {"key": {"intermediate": 2048, "numtokens": 256}, "config": {...}},
+        ...,
+    ]
+
+Config keys are ``CaseKey`` instances mapping parameter names to
+values.  The default config uses ``CaseKey.default()``.
 """
 
+from __future__ import annotations
+
 import json
 from pathlib import Path
 from typing import Any
 
+from vllm.kernels.helion.case_key import CaseKey
 from vllm.logger import init_logger
 from vllm.utils.import_utils import has_helion
 
@@ -45,11 +45,13 @@
 
 
 class ConfigSet:
-    """In-memory collection of Helion configs with lookup/query capabilities."""
+    """In-memory collection of Helion configs with lookup/query capabilities.
 
-    # Type alias for nested config structure:
-    # platform -> config_key -> helion.Config
-    _ConfigDict = dict[str, dict[str, "helion.Config"]]
+    Configs are stored keyed by ``CaseKey``.  The default config
+    uses ``CaseKey.default()`` as its key.
+    """
+
+    _ConfigDict = dict[str, dict[CaseKey, "helion.Config"]]
 
     def __init__(self, kernel_name: str):
         self._kernel_name = kernel_name
@@ -59,7 +61,7 @@ def __init__(self, kernel_name: str):
     def kernel_name(self) -> str:
         return self._kernel_name
 
-    def get_config(self, platform: str, config_key: str) -> helion.Config:
+    def get_config(self, platform: str, config_key: CaseKey) -> helion.Config:
         platform_dict = self._configs.get(platform)
         if platform_dict is None:
             avail_platforms = self.get_platforms()
@@ -82,7 +84,8 @@ def get_config(self, platform: str, config_key: str) -> helion.Config:
             avail_keys = self.get_config_keys(platform)
             raise KeyError(
                 f"Config not found for kernel '{self._kernel_name}': "
-                f"config_key '{config_key}' not found for platform '{platform}'. "
+                f"config_key '{config_key}' not found for "
+                f"platform '{platform}'. "
                 f"Available config_keys: {avail_keys or '(none)'}"
             )
 
@@ -91,25 +94,34 @@ def get_config(self, platform: str, config_key: str) -> helion.Config:
     def get_platforms(self) -> list[str]:
         return sorted(self._configs.keys())
 
-    def get_config_keys(self, platform: str) -> list[str]:
+    def get_config_keys(self, platform: str) -> list[CaseKey]:
         platform_dict = self._configs.get(platform.lower())
         if platform_dict is None:
             return []
-        return sorted(platform_dict.keys())
-
-    def to_dict(self) -> dict[str, Any]:
-        result: dict[str, Any] = {}
-
-        for platform, config_keys_dict in self._configs.items():
-            result[platform] = {}
-
-            for config_key, config in config_keys_dict.items():
-                result[platform][config_key] = json.loads(config.to_json())
+        return sorted(platform_dict.keys(), key=str)
+
+    def to_config_entries(self) -> dict[str, list[dict[str, Any]]]:
+        """Serialize to config entries format for JSON output."""
+        result: dict[str, list[dict[str, Any]]] = {}
+        for platform, config_dict in self._configs.items():
+            pairs: list[dict[str, Any]] = []
+            for config_key, config in config_dict.items():
+                config_data = json.loads(config.to_json())
+                pairs.append({"key": dict(config_key), "config": config_data})
+            result[platform] = pairs
+        return result
 
+    def to_dict(self) -> dict[str, dict[CaseKey, Any]]:
+        """Return configs as a nested dict (platform -> key -> config)."""
+        result: dict[str, dict[CaseKey, Any]] = {}
+        for platform, config_dict in self._configs.items():
+            result[platform] = {
+                k: json.loads(v.to_json()) for k, v in config_dict.items()
+            }
         return result
 
     @classmethod
-    def from_dict(cls, kernel_name: str, data: dict[str, Any]) -> "ConfigSet":
+    def from_dict(cls, kernel_name: str, data: dict[str, Any]) -> ConfigSet:
         config_set = cls(kernel_name)
         count = 0
 
@@ -117,9 +129,11 @@ def from_dict(cls, kernel_name: str, data: dict[str, Any]) -> "ConfigSet":
             if platform not in config_set._configs:
                 config_set._configs[platform] = {}
 
-            for config_key, config_data in platform_data.items():
-                config = helion.Config(**config_data)
-                config_set._configs[platform][config_key] = config
+            for entry in platform_data:
+                raw_key = entry["key"]
+                key = CaseKey.default() if not raw_key else CaseKey(raw_key)
+                config = helion.Config(**entry["config"])
+                config_set._configs[platform][key] = config
                 count += 1
 
         if count > 0:
@@ -132,7 +146,10 @@ def from_dict(cls, kernel_name: str, data: dict[str, Any]) -> "ConfigSet":
         return config_set
 
     def set_config(
-        self, platform: str, config_key: str, config: "helion.Config"
+        self,
+        platform: str,
+        config_key: CaseKey,
+        config: helion.Config,
     ) -> None:
         platform = platform.lower()
         if platform not in self._configs:
@@ -145,7 +162,7 @@ def set_config(
             config_key,
         )
 
-    def has_config(self, platform: str, config_key: str) -> bool:
+    def has_config(self, platform: str, config_key: CaseKey) -> bool:
         platform = platform.lower()
         platform_dict = self._configs.get(platform)
         if platform_dict is None:
@@ -156,18 +173,18 @@ def has_config(self, platform: str, config_key: str) -> bool:
 class ConfigManager:
     """File-level configuration management for Helion kernels (global singleton)."""
 
-    _instance: "ConfigManager | None" = None
+    _instance: ConfigManager | None = None
     _instance_base_dir: Path | None = None
 
-    def __new__(cls, base_dir: str | Path | None = None) -> "ConfigManager":
+    def __new__(cls, base_dir: str | Path | None = None) -> ConfigManager:
         resolved_base_dir = cls._resolve_base_dir(base_dir)
 
         if cls._instance is not None:
             if cls._instance_base_dir != resolved_base_dir:
                 raise ValueError(
                     f"ConfigManager singleton already exists with base_dir "
-                    f"'{cls._instance_base_dir}', cannot create with different "
-                    f"base_dir '{resolved_base_dir}'"
+                    f"'{cls._instance_base_dir}', cannot create with "
+                    f"different base_dir '{resolved_base_dir}'"
                 )
             return cls._instance
 
@@ -190,7 +207,7 @@ def _resolve_base_dir(base_dir: str | Path | None) -> Path:
         return (Path(__file__).parent / "configs").resolve()
 
     @classmethod
-    def get_instance(cls) -> "ConfigManager":
+    def get_instance(cls) -> ConfigManager:
         if cls._instance is None:
             raise RuntimeError(
                 "ConfigManager instance has not been created. "
@@ -229,16 +246,16 @@ def ensure_base_dir_writable(self) -> None:
                 f"Config directory '{self._base_dir}' is not writable: {e}"
             ) from e
 
-    def _load_platform_file(self, kernel_name: str, platform: str) -> dict[str, Any]:
+    def _load_platform_file(self, kernel_name: str, platform: str) -> Any:
         config_path = self.get_config_file_path(kernel_name, platform)
         if not config_path.exists():
-            return {}
+            return []
         try:
             with open(config_path) as f:
                 return json.load(f)
         except (json.JSONDecodeError, OSError) as e:
             logger.error("Failed to load config file %s: %s", config_path, e)
-            return {}
+            return []
 
     def load_config_set(self, kernel_name: str) -> ConfigSet:
         kernel_dir = self.get_kernel_dir(kernel_name)
@@ -253,32 +270,36 @@ def load_config_set(self, kernel_name: str) -> ConfigSet:
                     platform_data = json.load(f)
                 data[platform] = platform_data
             except (json.JSONDecodeError, OSError) as e:
-                logger.error("Failed to load config file %s: %s", platform_file, e)
+                logger.error(
+                    "Failed to load config file %s: %s",
+                    platform_file,
+                    e,
+                )
 
         return ConfigSet.from_dict(kernel_name, data)
 
     def get_platform_configs(
         self, kernel_name: str, platform: str
-    ) -> dict[str, helion.Config]:
+    ) -> dict[CaseKey, helion.Config]:
         platform_data = self._load_platform_file(kernel_name, platform)
         if not platform_data:
             return {}
         config_set = ConfigSet.from_dict(kernel_name, {platform: platform_data})
-        config_keys = config_set.get_config_keys(platform)
         return {
-            config_key: config_set.get_config(platform, config_key)
-            for config_key in config_keys
+            k: config_set.get_config(platform, k)
+            for k in config_set.get_config_keys(platform)
         }
 
     def save_config_set(self, config_set: ConfigSet) -> Path:
         kernel_dir = self.get_kernel_dir(config_set.kernel_name)
         kernel_dir.mkdir(parents=True, exist_ok=True)
 
-        full_data = config_set.to_dict()
-        for platform, platform_data in full_data.items():
+        full_data = config_set.to_config_entries()
+        for platform, pairs in full_data.items():
             platform_path = kernel_dir / f"{platform}.json"
             with open(platform_path, "w") as f:
-                json.dump(platform_data, f, indent=2)
+                json.dump(pairs, f, indent=2)
+                f.write("\n")
             logger.info("Saved config to: %s", platform_path)
 
         return kernel_dir
@@ -287,21 +308,34 @@ def save_configs(
         self,
         kernel_name: str,
         platform: str,
-        configs: dict[str, "helion.Config"],
+        configs: dict[CaseKey, helion.Config],
     ) -> Path:
         """Save configs for a kernel/platform, merging with existing."""
-        platform_data = self._load_platform_file(kernel_name, platform)
-        for config_key, config in configs.items():
-            platform_data[config_key] = json.loads(config.to_json())
+        config_set = ConfigSet.from_dict(
+            kernel_name,
+            {platform: self._load_platform_file(kernel_name, platform)},
+        )
+        for key, config in configs.items():
+            config_set.set_config(platform, key, config)
 
+        pairs = config_set.to_config_entries().get(platform, [])
         platform_path = self.get_config_file_path(kernel_name, platform)
         platform_path.parent.mkdir(parents=True, exist_ok=True)
         with open(platform_path, "w") as f:
-            json.dump(platform_data, f, indent=2)
+            json.dump(pairs, f, indent=2)
+            f.write("\n")
 
         logger.info("Saved config to: %s", platform_path)
         return platform_path
 
-    def config_exists(self, kernel_name: str, platform: str, config_key: str) -> bool:
+    def config_exists(
+        self,
+        kernel_name: str,
+        platform: str,
+        config_key: CaseKey,
+    ) -> bool:
         platform_data = self._load_platform_file(kernel_name, platform)
-        return config_key in platform_data
+        if not platform_data:
+            return False
+        target = dict(config_key)
+        return any(entry["key"] == target for entry in platform_data)
diff --git a/vllm/kernels/helion/configs/silu_mul_fp8/nvidia_h100.json b/vllm/kernels/helion/configs/silu_mul_fp8/nvidia_h100.json
index c314eb2dab86..4dc5c2cab308 100644
--- a/vllm/kernels/helion/configs/silu_mul_fp8/nvidia_h100.json
+++ b/vllm/kernels/helion/configs/silu_mul_fp8/nvidia_h100.json
@@ -1,13866 +1,15711 @@
-{
-  "intermediate_2048_numtokens_256": {
-    "block_sizes": [
-      64,
-      32
-    ],
-    "loop_orders": [
-      [
-        0,
-        1
-      ]
-    ],
-    "flatten_loops": [
-      true
-    ],
-    "l2_groupings": [
-      1
-    ],
-    "range_unroll_factors": [
-      0
-    ],
-    "range_warp_specializes": [],
-    "range_num_stages": [
-      0
-    ],
-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
-      null
-    ],
-    "load_eviction_policies": [
-      "",
-      "",
-      ""
-    ],
-    "num_warps": 8,
-    "num_stages": 1,
-    "indexing": [
-      "pointer",
-      "pointer",
-      "pointer",
-      "pointer"
-    ],
-    "pid_type": "flat"
-  },
-  "intermediate_4096_numtokens_256": {
-    "block_sizes": [
-      256,
-      32
-    ],
-    "loop_orders": [
-      [
-        0,
-        1
-      ]
-    ],
-    "flatten_loops": [
-      true
-    ],
-    "l2_groupings": [
-      1
-    ],
-    "range_unroll_factors": [
-      0
-    ],
-    "range_warp_specializes": [],
-    "range_num_stages": [
-      0
-    ],
-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
-      null
-    ],
-    "load_eviction_policies": [
-      "",
-      "",
-      ""
-    ],
-    "num_warps": 8,
-    "num_stages": 1,
-    "indexing": [
-      "pointer",
-      "pointer",
-      "pointer",
-      "pointer"
-    ],
-    "pid_type": "flat"
-  },
-  "default": {
-    "block_sizes": [
-      1,
-      512
-    ],
-    "loop_orders": [
-      [
+[
+  {
+    "key": {
+      "intermediate": 2048,
+      "numtokens": 256
+    },
+    "config": {
+      "block_sizes": [
+        64,
+        32
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 8,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 4096,
+      "numtokens": 256
+    },
+    "config": {
+      "block_sizes": [
+        256,
+        32
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 8,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {},
+    "config": {
+      "block_sizes": [
+        1,
+        512
+      ],
+      "loop_orders": [
+        [
+          1,
+          0
+        ]
+      ],
+      "flatten_loops": [
+        false
+      ],
+      "l2_groupings": [
+        4
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "first",
+        ""
+      ],
+      "num_warps": 8,
+      "num_stages": 2,
+      "indexing": [
+        "tensor_descriptor",
+        "tensor_descriptor",
+        "tensor_descriptor",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 8192,
+      "numtokens": 256
+    },
+    "config": {
+      "block_sizes": [
+        256,
+        64
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 8,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 14336,
+      "numtokens": 256
+    },
+    "config": {
+      "block_sizes": [
+        8,
+        4096
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 8,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 11008,
+      "numtokens": 256
+    },
+    "config": {
+      "block_sizes": [
+        64,
+        32
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 2,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 7688,
+      "numtokens": 256
+    },
+    "config": {
+      "block_sizes": [
+        32,
+        512
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 8,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 2880,
+      "numtokens": 256
+    },
+    "config": {
+      "block_sizes": [
+        32,
+        64
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        2
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 8,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 2048,
+      "numtokens": 1
+    },
+    "config": {
+      "block_sizes": [
+        1,
+        256
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 2,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 2880,
+      "numtokens": 1
+    },
+    "config": {
+      "block_sizes": [
+        1,
+        128
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 4,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 4096,
+      "numtokens": 1
+    },
+    "config": {
+      "block_sizes": [
+        1,
+        256
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 2,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 8192,
+      "numtokens": 1
+    },
+    "config": {
+      "block_sizes": [
+        1,
+        256
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 2,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 11008,
+      "numtokens": 1
+    },
+    "config": {
+      "block_sizes": [
+        1,
+        256
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 2,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 14336,
+      "numtokens": 1
+    },
+    "config": {
+      "block_sizes": [
+        1,
+        256
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 2,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 2048,
+      "numtokens": 2
+    },
+    "config": {
+      "block_sizes": [
+        2,
+        128
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 2,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 2880,
+      "numtokens": 2
+    },
+    "config": {
+      "block_sizes": [
+        2,
+        256
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 4,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 4096,
+      "numtokens": 2
+    },
+    "config": {
+      "block_sizes": [
+        2,
+        128
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
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diff --git a/vllm/kernels/helion/configs/silu_mul_fp8/nvidia_h200.json b/vllm/kernels/helion/configs/silu_mul_fp8/nvidia_h200.json
index c314eb2dab86..4dc5c2cab308 100644
--- a/vllm/kernels/helion/configs/silu_mul_fp8/nvidia_h200.json
+++ b/vllm/kernels/helion/configs/silu_mul_fp8/nvidia_h200.json
@@ -1,13866 +1,15711 @@
-{
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-    "range_unroll_factors": [
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-    "range_flattens": [
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-    "load_eviction_policies": [
-      "",
-      "",
-      ""
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-      "pointer",
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-      32
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-    "loop_orders": [
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-        1
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+[
+  {
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+        1
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+      "range_unroll_factors": [
+        0
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+      "range_warp_specializes": [],
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+        null
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+      "load_eviction_policies": [
+        "",
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+        ""
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+    }
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+      "numtokens": 256
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+        32
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+        1
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+      "range_unroll_factors": [
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+      "load_eviction_policies": [
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+    }
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-    "l2_groupings": [
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-    "range_unroll_factors": [
-      0
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-    "range_num_stages": [
-      0
-    ],
-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
-      null
-    ],
-    "load_eviction_policies": [
-      "",
-      "",
-      ""
-    ],
-    "num_warps": 4,
-    "num_stages": 1,
-    "indexing": [
-      "pointer",
-      "pointer",
-      "pointer",
-      "pointer"
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-    "pid_type": "flat"
-  },
-  "intermediate_14336_numtokens_480": {
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-      16384
-    ],
-    "loop_orders": [
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-    ],
-    "flatten_loops": [
-      true
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-    "l2_groupings": [
-      1
-    ],
-    "range_unroll_factors": [
-      0
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-    "range_warp_specializes": [],
-    "range_num_stages": [
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-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
-      null
-    ],
-    "load_eviction_policies": [
-      "",
-      "last",
-      "first"
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-    "num_warps": 32,
-    "num_stages": 3,
-    "indexing": [
-      "tensor_descriptor",
-      "tensor_descriptor",
-      "tensor_descriptor",
-      "pointer"
-    ],
-    "pid_type": "flat"
-  },
-  "intermediate_2048_numtokens_496": {
-    "block_sizes": [
-      1,
-      2048
-    ],
-    "loop_orders": [
-      [
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 8,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 2880,
+      "numtokens": 512
+    },
+    "config": {
+      "block_sizes": [
+        8,
+        2048
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+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        ""
+      ],
+      "num_warps": 8,
+      "num_stages": 1,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
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+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 4096,
+      "numtokens": 512
+    },
+    "config": {
+      "block_sizes": [
+        8,
+        128
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        2
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "last",
+        "last",
+        "last"
+      ],
+      "num_warps": 16,
+      "num_stages": 2,
+      "indexing": [
+        "tensor_descriptor",
+        "tensor_descriptor",
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+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 8192,
+      "numtokens": 512
+    },
+    "config": {
+      "block_sizes": [
         1,
+        2048
+      ],
+      "loop_orders": [
+        [
+          1,
+          0
+        ]
+      ],
+      "flatten_loops": [
+        false
+      ],
+      "l2_groupings": [
+        64
+      ],
+      "range_unroll_factors": [
         0
-      ]
-    ],
-    "flatten_loops": [
-      false
-    ],
-    "l2_groupings": [
-      4
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-    "range_num_stages": [
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-      null
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-    "range_flattens": [
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-    "num_stages": 7,
-    "indexing": [
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-      "tensor_descriptor",
-      "pointer",
-      "pointer"
-    ],
-    "pid_type": "flat"
-  },
-  "intermediate_2880_numtokens_496": {
-    "block_sizes": [
-      8,
-      256
-    ],
-    "loop_orders": [
-      [
-        0,
-        1
-      ]
-    ],
-    "flatten_loops": [
-      true
-    ],
-    "l2_groupings": [
-      4
-    ],
-    "range_unroll_factors": [
-      0
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-    "range_warp_specializes": [],
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-      0
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-    "range_multi_buffers": [
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-    ],
-    "range_flattens": [
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-    "load_eviction_policies": [
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-      "last"
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-    "indexing": [
-      "pointer",
-      "pointer",
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-      "tensor_descriptor"
-    ],
-    "pid_type": "flat"
-  },
-  "intermediate_4096_numtokens_496": {
-    "block_sizes": [
-      256,
-      64
-    ],
-    "loop_orders": [
-      [
-        0,
-        1
-      ]
-    ],
-    "flatten_loops": [
-      true
-    ],
-    "l2_groupings": [
-      1
-    ],
-    "range_unroll_factors": [
-      0
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-    "range_warp_specializes": [],
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-      0
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-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
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-    "load_eviction_policies": [
-      "",
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-      ""
-    ],
-    "num_warps": 8,
-    "num_stages": 1,
-    "indexing": [
-      "pointer",
-      "pointer",
-      "pointer",
-      "pointer"
-    ],
-    "pid_type": "flat"
-  },
-  "intermediate_8192_numtokens_496": {
-    "block_sizes": [
-      256,
-      128
-    ],
-    "loop_orders": [
-      [
-        0,
-        1
-      ]
-    ],
-    "flatten_loops": [
-      true
-    ],
-    "l2_groupings": [
-      1
-    ],
-    "range_unroll_factors": [
-      0
-    ],
-    "range_warp_specializes": [],
-    "range_num_stages": [
-      0
-    ],
-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
-      null
-    ],
-    "load_eviction_policies": [
-      "",
-      "",
-      ""
-    ],
-    "num_warps": 8,
-    "num_stages": 1,
-    "indexing": [
-      "pointer",
-      "pointer",
-      "pointer",
-      "pointer"
-    ],
-    "pid_type": "flat"
-  },
-  "intermediate_11008_numtokens_496": {
-    "block_sizes": [
-      1,
-      2048
-    ],
-    "loop_orders": [
-      [
-        0,
-        1
-      ]
-    ],
-    "flatten_loops": [
-      false
-    ],
-    "l2_groupings": [
-      4
-    ],
-    "range_unroll_factors": [
-      0
-    ],
-    "range_warp_specializes": [],
-    "range_num_stages": [
-      0
-    ],
-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
-      null
-    ],
-    "load_eviction_policies": [
-      "first",
-      "last",
-      "last"
-    ],
-    "num_warps": 8,
-    "num_stages": 4,
-    "indexing": [
-      "tensor_descriptor",
-      "pointer",
-      "tensor_descriptor",
-      "pointer"
-    ],
-    "pid_type": "flat"
-  },
-  "intermediate_14336_numtokens_496": {
-    "block_sizes": [
-      4,
-      2048
-    ],
-    "loop_orders": [
-      [
-        0,
-        1
-      ]
-    ],
-    "flatten_loops": [
-      true
-    ],
-    "l2_groupings": [
-      64
-    ],
-    "range_unroll_factors": [
-      0
-    ],
-    "range_warp_specializes": [],
-    "range_num_stages": [
-      0
-    ],
-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
-      null
-    ],
-    "load_eviction_policies": [
-      "last",
-      "last",
-      "first"
-    ],
-    "num_warps": 4,
-    "num_stages": 4,
-    "indexing": [
-      "pointer",
-      "tensor_descriptor",
-      "tensor_descriptor",
-      "pointer"
-    ],
-    "pid_type": "flat"
-  },
-  "intermediate_2048_numtokens_512": {
-    "block_sizes": [
-      512,
-      16
-    ],
-    "loop_orders": [
-      [
-        0,
-        1
-      ]
-    ],
-    "flatten_loops": [
-      true
-    ],
-    "l2_groupings": [
-      1
-    ],
-    "range_unroll_factors": [
-      0
-    ],
-    "range_warp_specializes": [],
-    "range_num_stages": [
-      0
-    ],
-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
-      null
-    ],
-    "load_eviction_policies": [
-      "",
-      "",
-      ""
-    ],
-    "num_warps": 8,
-    "num_stages": 1,
-    "indexing": [
-      "pointer",
-      "pointer",
-      "pointer",
-      "pointer"
-    ],
-    "pid_type": "flat"
-  },
-  "intermediate_2880_numtokens_512": {
-    "block_sizes": [
-      8,
-      2048
-    ],
-    "loop_orders": [
-      [
-        0,
-        1
-      ]
-    ],
-    "flatten_loops": [
-      true
-    ],
-    "l2_groupings": [
-      1
-    ],
-    "range_unroll_factors": [
-      0
-    ],
-    "range_warp_specializes": [],
-    "range_num_stages": [
-      0
-    ],
-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
-      null
-    ],
-    "load_eviction_policies": [
-      "",
-      "",
-      ""
-    ],
-    "num_warps": 8,
-    "num_stages": 1,
-    "indexing": [
-      "pointer",
-      "pointer",
-      "pointer",
-      "pointer"
-    ],
-    "pid_type": "flat"
-  },
-  "intermediate_4096_numtokens_512": {
-    "block_sizes": [
-      8,
-      128
-    ],
-    "loop_orders": [
-      [
-        0,
-        1
-      ]
-    ],
-    "flatten_loops": [
-      true
-    ],
-    "l2_groupings": [
-      2
-    ],
-    "range_unroll_factors": [
-      0
-    ],
-    "range_warp_specializes": [],
-    "range_num_stages": [
-      0
-    ],
-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
-      null
-    ],
-    "load_eviction_policies": [
-      "last",
-      "last",
-      "last"
-    ],
-    "num_warps": 16,
-    "num_stages": 2,
-    "indexing": [
-      "tensor_descriptor",
-      "tensor_descriptor",
-      "pointer",
-      "pointer"
-    ],
-    "pid_type": "flat"
-  },
-  "intermediate_8192_numtokens_512": {
-    "block_sizes": [
-      1,
-      2048
-    ],
-    "loop_orders": [
-      [
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "",
+        "last"
+      ],
+      "num_warps": 4,
+      "num_stages": 4,
+      "indexing": [
+        "pointer",
+        "pointer",
+        "pointer",
+        "pointer"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 11008,
+      "numtokens": 512
+    },
+    "config": {
+      "block_sizes": [
         1,
+        4096
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        false
+      ],
+      "l2_groupings": [
+        1
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
+        0
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "first",
+        "",
+        "first"
+      ],
+      "num_warps": 16,
+      "num_stages": 7,
+      "indexing": [
+        "tensor_descriptor",
+        "tensor_descriptor",
+        "pointer",
+        "tensor_descriptor"
+      ],
+      "pid_type": "flat"
+    }
+  },
+  {
+    "key": {
+      "intermediate": 14336,
+      "numtokens": 512
+    },
+    "config": {
+      "block_sizes": [
+        128,
+        64
+      ],
+      "loop_orders": [
+        [
+          0,
+          1
+        ]
+      ],
+      "flatten_loops": [
+        true
+      ],
+      "l2_groupings": [
+        32
+      ],
+      "range_unroll_factors": [
+        0
+      ],
+      "range_warp_specializes": [],
+      "range_num_stages": [
         0
-      ]
-    ],
-    "flatten_loops": [
-      false
-    ],
-    "l2_groupings": [
-      64
-    ],
-    "range_unroll_factors": [
-      0
-    ],
-    "range_warp_specializes": [],
-    "range_num_stages": [
-      0
-    ],
-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
-      null
-    ],
-    "load_eviction_policies": [
-      "",
-      "",
-      "last"
-    ],
-    "num_warps": 4,
-    "num_stages": 4,
-    "indexing": [
-      "pointer",
-      "pointer",
-      "pointer",
-      "pointer"
-    ],
-    "pid_type": "flat"
-  },
-  "intermediate_11008_numtokens_512": {
-    "block_sizes": [
-      1,
-      4096
-    ],
-    "loop_orders": [
-      [
-        0,
-        1
-      ]
-    ],
-    "flatten_loops": [
-      false
-    ],
-    "l2_groupings": [
-      1
-    ],
-    "range_unroll_factors": [
-      0
-    ],
-    "range_warp_specializes": [],
-    "range_num_stages": [
-      0
-    ],
-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
-      null
-    ],
-    "load_eviction_policies": [
-      "first",
-      "",
-      "first"
-    ],
-    "num_warps": 16,
-    "num_stages": 7,
-    "indexing": [
-      "tensor_descriptor",
-      "tensor_descriptor",
-      "pointer",
-      "tensor_descriptor"
-    ],
-    "pid_type": "flat"
-  },
-  "intermediate_14336_numtokens_512": {
-    "block_sizes": [
-      128,
-      64
-    ],
-    "loop_orders": [
-      [
-        0,
-        1
-      ]
-    ],
-    "flatten_loops": [
-      true
-    ],
-    "l2_groupings": [
-      32
-    ],
-    "range_unroll_factors": [
-      0
-    ],
-    "range_warp_specializes": [],
-    "range_num_stages": [
-      0
-    ],
-    "range_multi_buffers": [
-      null
-    ],
-    "range_flattens": [
-      null
-    ],
-    "load_eviction_policies": [
-      "",
-      "first",
-      ""
-    ],
-    "num_warps": 2,
-    "num_stages": 7,
-    "indexing": [
-      "tensor_descriptor",
-      "tensor_descriptor",
-      "tensor_descriptor",
-      "tensor_descriptor"
-    ],
-    "pid_type": "flat"
+      ],
+      "range_multi_buffers": [
+        null
+      ],
+      "range_flattens": [
+        null
+      ],
+      "load_eviction_policies": [
+        "",
+        "first",
+        ""
+      ],
+      "num_warps": 2,
+      "num_stages": 7,
+      "indexing": [
+        "tensor_descriptor",
+        "tensor_descriptor",
+        "tensor_descriptor",
+        "tensor_descriptor"
+      ],
+      "pid_type": "flat"
+    }
   }
-}
\ No newline at end of file
+]
diff --git a/vllm/kernels/helion/ops/silu_mul_fp8.py b/vllm/kernels/helion/ops/silu_mul_fp8.py
index 1399b15d0092..e092efccc1ec 100644
--- a/vllm/kernels/helion/ops/silu_mul_fp8.py
+++ b/vllm/kernels/helion/ops/silu_mul_fp8.py
@@ -1,11 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from __future__ import annotations
+
 from typing import Any
 
-import regex as re
 import torch
 
+from vllm.kernels.helion.case_key import CaseKey
 from vllm.logger import init_logger
 from vllm.utils.import_utils import has_helion
 
@@ -22,14 +24,14 @@
 logger = init_logger(__name__)
 
 
-def generate_silu_mul_fp8_inputs() -> dict[str, tuple[Any, ...]]:
+def generate_silu_mul_fp8_inputs() -> dict[CaseKey, tuple[Any, ...]]:
     intermediate_sizes = [2048, 2880, 4096, 8192, 11008, 14336]
 
     # Use the same num_tokens values as vLLM's default cudagraph capture sizes.
     # See vllm/config/vllm.py _set_cudagraph_sizes() for the canonical formula.
     num_tokens_list = [1, 2, 4] + list(range(8, 256, 8)) + list(range(256, 513, 16))
 
-    inputs = {}
+    inputs: dict[CaseKey, tuple[Any, ...]] = {}
     for num_tokens in num_tokens_list:
         for intermediate_size in intermediate_sizes:
             input_tensor = torch.randn(
@@ -40,15 +42,18 @@ def generate_silu_mul_fp8_inputs() -> dict[str, tuple[Any, ...]]:
             )
             scale = torch.tensor([1.0], device="cuda", dtype=torch.float32)
 
-            config_key = f"intermediate_{intermediate_size}_numtokens_{num_tokens}"
-            inputs[config_key] = (input_tensor, scale)
+            key = CaseKey({"intermediate": intermediate_size, "numtokens": num_tokens})
+            inputs[key] = (input_tensor, scale)
 
     return inputs
 
 
+_pick_cache: dict[tuple[int, int], CaseKey | None] = {}
+
+
 def pick_silu_mul_fp8_config(
-    args: tuple[Any, ...], config_keys: list[str]
-) -> str | None:
+    args: tuple[Any, ...], config_keys: list[CaseKey]
+) -> CaseKey | None:
     """Pick the best pre-tuned config for the given input shape.
 
     Selection strategy:
@@ -57,39 +62,35 @@ def pick_silu_mul_fp8_config(
       2. Among the num_tokens values tuned for that intermediate_size, pick
          the smallest num_tokens >= the input's num_tokens. If the input is
          larger than all available num_tokens, fall back to the largest.
-
-    Config keys must be "default" or follow the format
-    "intermediate_{int}_numtokens_{int}".
     """
     if not config_keys:
         return None
 
     input_tensor, _scale = args
-    intermediate_size = input_tensor.shape[-1] // 2
-    num_tokens = input_tensor.view(-1, input_tensor.shape[-1]).shape[0]
-    configs: dict[int, list[int]] = {}
-    for key in config_keys:
-        if key == "default":
+    intermediate_size = int(input_tensor.shape[-1]) // 2
+    num_tokens = int(input_tensor.view(-1, input_tensor.shape[-1]).shape[0])
+
+    cache_key = (num_tokens, intermediate_size)
+    cached = _pick_cache.get(cache_key)
+    if cached is not None:
+        return cached
+
+    by_isize: dict[int, list[int]] = {}
+    for k in config_keys:
+        if k.is_default():
             continue
-        match = re.fullmatch(r"intermediate_(\d+)_numtokens_(\d+)", key)
-        if not match:
-            raise ValueError(
-                f"Malformed config key '{key}', "
-                f"expected format 'intermediate_{{int}}_numtokens_{{int}}'"
-            )
-        isize_str, ntokens_str = match.groups()
-        configs.setdefault(int(isize_str), []).append(int(ntokens_str))
+        by_isize.setdefault(k["intermediate"], []).append(k["numtokens"])
 
-    if not configs:
-        return "default" if "default" in config_keys else None
+    if not by_isize:
+        return None
 
-    best_isize = min(configs, key=lambda s: abs(s - intermediate_size))
-    available_ntokens = sorted(configs[best_isize])
-    best_ntokens = next(
-        (n for n in available_ntokens if n >= num_tokens), available_ntokens[-1]
-    )
+    best_isize = min(by_isize, key=lambda s: abs(s - intermediate_size))
+    available = sorted(by_isize[best_isize])
+    best_ntokens = next((n for n in available if n >= num_tokens), available[-1])
 
-    return f"intermediate_{best_isize}_numtokens_{best_ntokens}"
+    result = CaseKey({"intermediate": best_isize, "numtokens": best_ntokens})
+    _pick_cache[cache_key] = result
+    return result
 
 
 @register_kernel(
diff --git a/vllm/kernels/helion/register.py b/vllm/kernels/helion/register.py
index 1557a36b2754..f18120da45f9 100644
--- a/vllm/kernels/helion/register.py
+++ b/vllm/kernels/helion/register.py
@@ -36,12 +36,15 @@
 - PresetConfigSearch: Custom autotuner that returns pre-tuned configs
 """
 
+from __future__ import annotations
+
 from collections.abc import Callable
-from typing import Any, cast
+from typing import Any
 
 import torch
 from torch.library import Library
 
+from vllm.kernels.helion.case_key import CaseKey
 from vllm.logger import init_logger
 from vllm.utils.import_utils import has_helion
 from vllm.utils.torch_utils import direct_register_custom_op
@@ -53,18 +56,21 @@
     )
 
 import helion
-from helion._compat import requires_torch_version
 from helion.autotuner.base_search import BaseAutotuner
 from helion.runtime.config import Config
 from helion.runtime.settings import default_autotuner_fn
 
 # TODO(gmagogsfm): Remove CustomOp fallback path (_get_or_register_custom_op,
 # vllm_helion_lib, direct_register_custom_op) once vLLM requires PyTorch >= 2.11.
-_HOP_AVAILABLE = requires_torch_version("2.11")
+# FIXME(gmagogsfm): Re-enable HOP path once performance regression is fixed.
+# _HOP_AVAILABLE = requires_torch_version("2.11")
+_HOP_AVAILABLE = False
 
 if _HOP_AVAILABLE:
+    from helion._compat import supports_torch_compile_fusion
     from helion._compiler._dynamo.higher_order_ops import helion_kernel_side_table
     from helion._compiler._dynamo.variables import HelionKernelVariable
+    from helion.runtime.kernel import Kernel
     from torch._dynamo.guards import GuardBuilder
     from torch._dynamo.variables.builder import VariableBuilder
 
@@ -73,9 +79,11 @@
 
 vllm_helion_lib = Library("vllm_helion", "FRAGMENT")  # noqa
 
+ConfigPicker = Callable[[tuple[Any, ...], list[CaseKey]], CaseKey | None]
+
 
 def validate_helion_settings(
-    helion_settings: "helion.Settings | None", op_name: str
+    helion_settings: helion.Settings | None, op_name: str
 ) -> None:
     if helion_settings is None:
         return
@@ -104,7 +112,7 @@ def validate_helion_settings(
 
 def create_helion_decorated_kernel(
     raw_kernel_func: Callable,
-    helion_settings: "helion.Settings | None" = None,
+    helion_settings: helion.Settings | None = None,
     extra_kwargs: dict[str, Any] | None = None,
 ) -> Any:
     kernel_kwargs: dict[str, Any] = {}
@@ -141,9 +149,9 @@ class ConfiguredHelionKernel:
     def __init__(
         self,
         op_name: str,
-        config_picker: Callable[[tuple[Any, ...], list[str]], str | None] | None,
+        config_picker: ConfigPicker | None,
         raw_kernel_func: Callable,
-        helion_settings: "helion.Settings | None" = None,
+        helion_settings: helion.Settings | None = None,
     ):
         self.op_name = op_name
         self.config_picker = config_picker
@@ -167,41 +175,44 @@ def _create_key_computer(self):
                 f"A config_picker must be provided to register_kernel()."
             )
 
-        # After None check, config_picker is guaranteed to be non-None
-        assert self.config_picker is not None
+        picker = self.config_picker
+        all_keys = list(self.configs.keys())
+        default = CaseKey.default()
+        has_default = default in self.configs
 
         def key_computer(*args):
-            config_keys = list(self.configs.keys())
-            # Cast is safe because we checked for None above
-            config_picker = cast(
-                Callable[[tuple[Any, ...], list[str]], str | None], self.config_picker
-            )
-            selected_key = config_picker(args, config_keys)
-            if selected_key:
-                return selected_key
-            return "default" if "default" in self.configs else None
+            selected = picker(args, all_keys)
+            if selected is not None:
+                return str(selected)
+            if has_default:
+                return str(default)
+            return None
 
         return key_computer
 
     def _create_config_selector(self, key_computer):
+        str_to_key = {str(k): k for k in self.configs}
+
         def config_selector(args):
-            # args is a tuple; key_computer expects unpacked args
-            selected_config_key = key_computer(*args)
+            selected_str = key_computer(*args)
 
-            if selected_config_key is None:
+            if selected_str is None:
                 raise ValueError(
-                    f"Config picker returned None for kernel '{self.op_name}' "
-                    f"with available config keys: {list(self.configs.keys())}"
+                    f"Config picker returned None for kernel "
+                    f"'{self.op_name}' with available config keys: "
+                    f"{list(self.configs.keys())}"
                 )
 
-            if selected_config_key not in self.configs:
+            config_key = str_to_key.get(selected_str)
+            if config_key is None:
                 raise ValueError(
                     f"Config picker returned invalid config key "
-                    f"'{selected_config_key}' for kernel '{self.op_name}'. "
+                    f"'{selected_str}' for kernel "
+                    f"'{self.op_name}'. "
                     f"Available keys: {list(self.configs.keys())}"
                 )
 
-            return self.configs[selected_config_key]
+            return self.configs[config_key]
 
         return config_selector
 
@@ -248,9 +259,9 @@ def __init__(
         raw_kernel_func: Callable,
         op_name: str,
         fake_impl: Callable,
-        config_picker: Callable[[tuple[Any, ...], list[str]], str | None],
-        helion_settings: "helion.Settings | None" = None,
-        input_generator: Callable[[], dict[str, tuple[Any, ...]]] | None = None,
+        config_picker: ConfigPicker,
+        helion_settings: helion.Settings | None = None,
+        input_generator: (Callable[[], dict[CaseKey, tuple[Any, ...]]] | None) = None,
     ):
         # Validate helion_settings doesn't conflict with our custom autotuner
         validate_helion_settings(helion_settings, op_name)
@@ -299,7 +310,7 @@ def __call__(self, *args, **kwargs):
         # During eager execution, call the kernel directly.
         return self._configured_kernel(*args, **kwargs)
 
-    def get_inputs(self) -> dict[str, tuple[Any, ...]]:
+    def get_inputs(self) -> dict[CaseKey, tuple[Any, ...]]:
         if self._input_generator is None:
             raise NotImplementedError(
                 f"No input generator registered for kernel '{self.op_name}'. "
@@ -367,7 +378,7 @@ def get_kernel_by_name(kernel_name: str) -> HelionKernelWrapper | None:
 
 def infer_fake_impl(
     kernel_func: Callable,
-    helion_settings: "helion.Settings | None" = None,
+    helion_settings: helion.Settings | None = None,
 ) -> Callable:
     def helion_fake_kernel(*args, **kwargs):
         kernel_kwargs = {}
@@ -389,37 +400,29 @@ def helion_fake_kernel(*args, **kwargs):
 def register_kernel(
     op_name: str | None = None,
     *,
-    config_picker: Callable[[tuple[Any, ...], list[str]], str | None],
+    config_picker: ConfigPicker,
     fake_impl: Callable | None = None,
-    helion_settings: "helion.Settings | None" = None,
-    input_generator: Callable[[], dict[str, tuple[Any, ...]]] | None = None,
+    helion_settings: helion.Settings | None = None,
+    input_generator: (Callable[[], dict[CaseKey, tuple[Any, ...]]] | None) = None,
 ) -> Callable[[Callable], HelionKernelWrapper]:
     """Register a Helion kernel with pre-tuned config selection.
 
-    Wraps the kernel function in a HelionKernelWrapper that eagerly builds
-    the configured kernel and (on older PyTorch) registers a custom op.
-
     Args:
-        config_picker: Required. Function with signature
-            ``(args: tuple, config_keys: list[str]) -> str | None``
-            that picks the best config key from available options.
-            Return ``None`` to fall back to ``"default"``.
+        config_picker: Required. Receives ``(args, config_keys)``
+            where each config key is a ``dict[str, Any]`` mapping
+            parameter names to values.  Return the best-matching
+            dict, or ``None`` to fall back to the default config.
 
             Example::
 
                 def pick_config(args, config_keys):
                     x = args[0]
-                    hidden_size = x.shape[-1]
-                    batch_size = x.shape[0]
-                    for key in config_keys:
-                        if key == f"hiddensize_{hidden_size}_batchsize_{batch_size}":
-                            return key
-                    return "default" if "default" in config_keys else None
-
-        input_generator: Optional. Function that returns
-            ``dict[str, tuple]`` where each key is a configuration
-            identifier (e.g. ``"4096"``, ``"hidden_4096"``) and each
-            value is a tuple of arguments to pass to the kernel.
+                    best = min(config_keys, key=lambda k: abs(k["size"] - x.shape[0]))
+                    return best
+
+        input_generator: Optional. Returns ``dict[str, tuple]`` where
+            each key is a serialized config key and each value is a
+            tuple of arguments to pass to the kernel.
 
             Example::
 
@@ -475,19 +478,22 @@ def _register_vllm_helion_dynamo_variable():
         """Register HelionKernelWrapper with Dynamo's VariableBuilder.
 
         When Dynamo encounters a HelionKernelWrapper during tracing, this
-        extracts the underlying Helion Kernel, registers it in the side table,
-        and returns Helion's own HelionKernelVariable to handle HOP emission.
+        extracts the underlying Helion Kernel and delegates to Helion's own
+        registered Kernel handler, which handles HOP emission, side table
+        registration, and inductor lowering setup.
         """
 
         def wrap_helion_kernel_wrapper(
             builder: VariableBuilder, value: HelionKernelWrapper
         ):
             kernel = value.get_configured_op()._decorated_kernel
+            if supports_torch_compile_fusion():
+                helion_handler = VariableBuilder._type_dispatch()[Kernel]
+                return helion_handler(builder, kernel)
             kernel_idx = helion_kernel_side_table.add_kernel(kernel)
             builder.install_guards(GuardBuilder.ID_MATCH)
             return HelionKernelVariable(kernel, kernel_idx, source=builder.source)
 
-        # Register with Dynamo's type dispatch system
         dispatch = VariableBuilder._type_dispatch()
         dispatch[HelionKernelWrapper] = wrap_helion_kernel_wrapper
 
diff --git a/vllm/kernels/oink_ops.py b/vllm/kernels/oink_ops.py
index e8e3cb91f857..835cd062d037 100644
--- a/vllm/kernels/oink_ops.py
+++ b/vllm/kernels/oink_ops.py
@@ -1,6 +1,16 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""This file registers Oink implementations for vLLM IR ops.
+
+vLLM does not depend on the external Oink repository/package. When an external
+plugin registers torch.library.custom_op entrypoints under the `oink::`
+namespace (e.g. via vLLM's general_plugins mechanism), these ops will be marked
+ as supported. To dispatch to those ops, set kernel_config.ir_op_priority.<op> to oink.
+Alternatively, `VLLM_USE_OINK_OPS=1` will add this to priority by default.
+"""
+
 import torch
+from torch import Tensor
 
 from vllm import ir
 from vllm.platforms import current_platform
@@ -15,7 +25,7 @@ def has_oink_op(name: str) -> bool:
     return OINK_AVAILABLE and hasattr(torch.ops.oink, name)
 
 
-def _can_view_as_2d(x: torch.Tensor) -> bool:
+def _can_view_as_2d(x: Tensor) -> bool:
     """Return True if x.view(-1, x.shape[-1]) is viewable (no copy)."""
     if x.dim() < 2:
         return False
@@ -32,7 +42,7 @@ def _can_view_as_2d(x: torch.Tensor) -> bool:
     return True
 
 
-def _is_oink_stride_compatible_2d(x_2d: torch.Tensor) -> bool:
+def _is_oink_stride_compatible_2d(x_2d: Tensor) -> bool:
     """Return True if x_2d meets Oink's pointer-path stride constraints."""
     if x_2d.dim() != 2:
         return False
@@ -67,11 +77,51 @@ def _is_oink_stride_compatible_2d(x_2d: torch.Tensor) -> bool:
     "oink", supports_args=oink_rms_supported, supported=has_oink_op("rmsnorm")
 )
 def rms_norm(
-    x: torch.Tensor,
-    weight: torch.Tensor | None,
+    x: Tensor,
+    weight: Tensor | None,
     epsilon: float,
     variance_size: int | None = None,
-) -> torch.Tensor:
+) -> Tensor:
     assert variance_size is None
     x_2d = x.view(-1, x.shape[-1])
     return torch.ops.oink.rmsnorm(x_2d, weight, epsilon).view_as(x)
+
+
+oink_add_rms_supported = (
+    lambda x, x_residual, weight, epsilon, variance_size=None: variance_size is None
+    and weight is not None
+    and x.dim() >= 2
+    and x.dtype == weight.dtype
+    and weight.is_contiguous()
+    and _can_view_as_2d(x)
+    and _is_oink_stride_compatible_2d(x.view(-1, x.shape[-1]))
+    # residual must have 2d-compatible strides and match x shape/dtype
+    and x.dtype == x_residual.dtype
+    and x.shape == x_residual.shape
+    and _can_view_as_2d(x_residual)
+    and _is_oink_stride_compatible_2d(x_residual.view(-1, x_residual.shape[-1]))
+)
+"""
+Oink fused_add_rms_norm has the same constraints as rms_norm,
+and residual must be 2d-like with compatible strides.
+"""
+
+
+@ir.ops.fused_add_rms_norm.register_impl(
+    "oink",
+    supports_args=oink_add_rms_supported,
+    supported=has_oink_op("fused_add_rms_norm"),
+    inplace=True,
+)
+def fused_add_rms_norm(
+    x: Tensor,
+    x_residual: Tensor,
+    weight: Tensor | None,
+    epsilon: float,
+    variance_size: int | None = None,
+) -> tuple[Tensor, Tensor]:
+    assert variance_size is None
+    x_2d = x.view(-1, x.shape[-1])
+    residual_2d = x_residual.view(-1, x_residual.shape[-1])
+    torch.ops.oink.fused_add_rms_norm(x_2d, residual_2d, weight, epsilon)
+    return x, x_residual
diff --git a/vllm/kernels/triton/__init__.py b/vllm/kernels/triton/__init__.py
new file mode 100644
index 000000000000..6626213145ca
--- /dev/null
+++ b/vllm/kernels/triton/__init__.py
@@ -0,0 +1,3 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Triton kernel implementations."""
diff --git a/vllm/kernels/triton/qkv_padded_fp8_quant.py b/vllm/kernels/triton/qkv_padded_fp8_quant.py
new file mode 100644
index 000000000000..74dfe1043638
--- /dev/null
+++ b/vllm/kernels/triton/qkv_padded_fp8_quant.py
@@ -0,0 +1,180 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Stride-aware FP8 quantization with head_dim padding for ViT attention.
+
+Reads directly from non-contiguous QKV views using 3D strides and pads
+head_dim to a multiple of 16 for cuDNN compatibility.
+"""
+
+import torch
+
+from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    get_fp8_min_max,
+)
+from vllm.platforms import current_platform
+from vllm.triton_utils import HAS_TRITON, tl, triton
+from vllm.utils.math_utils import round_up
+
+_FP8_MIN, _FP8_MAX = get_fp8_min_max()
+
+
+@triton.jit
+def _quantize_pad_fp8_kernel(
+    x_ptr,
+    y_ptr,
+    scale_ptr,
+    stride_xs,
+    stride_xh,
+    stride_xd,
+    stride_ys,
+    stride_yh,
+    stride_yd,
+    num_heads,
+    n_rows,
+    n_cols,
+    n_cols_padded,
+    fp8_min,
+    fp8_max,
+    SKIP_SCALE: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    pid_m = tl.program_id(0)
+    pid_n = tl.program_id(1)
+
+    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
+    mask_m = offs_m < n_rows
+    mask_out = mask_m[:, None] & (offs_n[None, :] < n_cols_padded)
+    mask_in = mask_m[:, None] & (offs_n[None, :] < n_cols)
+
+    # Decompose flattened row into (token, head) for 3D stride indexing.
+    s = offs_m // num_heads
+    h = offs_m % num_heads
+
+    x_ptrs = (
+        x_ptr
+        + s[:, None] * stride_xs
+        + h[:, None] * stride_xh
+        + offs_n[None, :] * stride_xd
+    )
+    x = tl.load(x_ptrs, mask=mask_in, other=0.0).to(tl.float32)
+    if SKIP_SCALE:
+        x_q = x
+    else:
+        scale = tl.load(scale_ptr)
+        x_q = x / scale
+    x_q = tl.clamp(x_q, fp8_min, fp8_max).to(y_ptr.dtype.element_ty)
+
+    y_ptrs = (
+        y_ptr
+        + s[:, None] * stride_ys
+        + h[:, None] * stride_yh
+        + offs_n[None, :] * stride_yd
+    )
+    tl.store(y_ptrs, x_q, mask=mask_out)
+
+
+def _get_fp8_pad_quant_config(padded_head_dim: int) -> tuple[int, int, int]:
+    block_n = triton.next_power_of_2(padded_head_dim)
+    block_n = max(16, min(block_n, 128))
+    block_m = 16
+    num_warps = 4
+    return block_m, block_n, num_warps
+
+
+def quantize_fp8_pad_head_dim_triton(
+    tensor: torch.Tensor,
+    scale: torch.Tensor,
+    skip_scale: bool = False,
+    block_m: int | None = None,
+    block_n: int | None = None,
+    num_warps: int | None = None,
+) -> torch.Tensor:
+    """Quantize a 3D/4D tensor to FP8, padding head_dim to a multiple of 16.
+
+    Reads directly from the input using its 3D strides, so non-contiguous
+    views (e.g. Q/K/V slices from an interleaved QKV buffer) are handled
+    without an extra copy.  Output is always a fresh contiguous tensor
+    with shape (S, H, padded_D).
+    """
+    if not HAS_TRITON:
+        raise RuntimeError("Triton is required to quantize with head_dim padding.")
+
+    original_shape = tensor.shape
+    if tensor.dim() == 4:
+        tensor = tensor.view(-1, tensor.shape[-2], tensor.shape[-1])
+    assert tensor.dim() == 3, f"Expected 3D input (S, H, D), got {tensor.dim()}D"
+    S, H, D = tensor.shape
+    padded_head_dim = round_up(D, 16)
+    out_dtype = current_platform.fp8_dtype()
+    output = torch.empty(
+        (S, H, padded_head_dim),
+        device=tensor.device,
+        dtype=out_dtype,
+    )
+
+    scale_1d = scale.reshape(-1)
+    n_rows = S * H
+
+    if block_m is None or block_n is None or num_warps is None:
+        block_m, block_n, num_warps = _get_fp8_pad_quant_config(padded_head_dim)
+
+    grid = (
+        triton.cdiv(n_rows, block_m),
+        triton.cdiv(padded_head_dim, block_n),
+    )
+
+    _quantize_pad_fp8_kernel[grid](
+        tensor,
+        output,
+        scale_1d,
+        tensor.stride(0),
+        tensor.stride(1),
+        tensor.stride(2),
+        output.stride(0),
+        output.stride(1),
+        output.stride(2),
+        H,
+        n_rows,
+        D,
+        padded_head_dim,
+        _FP8_MIN,
+        _FP8_MAX,
+        SKIP_SCALE=skip_scale,
+        BLOCK_M=block_m,
+        BLOCK_N=block_n,
+        num_warps=num_warps,
+    )
+
+    return output.view((*original_shape[:-1], padded_head_dim))
+
+
+def quantize_fp8_maybe_pad_head_dim(
+    tensor: torch.Tensor,
+    scale: torch.Tensor,
+    fp8_quant: QuantFP8,
+    skip_scale: bool = False,
+) -> torch.Tensor:
+    """Quantize a 3D/4D tensor to FP8, padding head_dim to a multiple of 16
+    only when needed.
+
+    Accepts (S, H, D) or (B, S, H, D) input. Uses ``fp8_quant`` (a
+    :class:`QuantFP8` CustomOp) when head_dim is already aligned to 16
+    (no padding); otherwise falls back to a stride-aware Triton kernel
+    that pads head_dim to a multiple of 16.
+    """
+    head_dim = tensor.shape[-1]
+    if head_dim % 16 != 0:
+        return quantize_fp8_pad_head_dim_triton(tensor, scale, skip_scale=skip_scale)
+
+    if skip_scale:
+        return tensor.to(current_platform.fp8_dtype())
+
+    # QuantFP8 expects 2D: flatten all dims except (H, D).
+    orig_shape = tensor.shape
+    total_tokens = tensor.numel() // (orig_shape[-1] * orig_shape[-2])
+    tensor_2d = tensor.reshape(total_tokens, -1)
+    fp8_tensor, _ = fp8_quant(tensor_2d, scale=scale)
+    return fp8_tensor.reshape(orig_shape)
diff --git a/vllm/kernels/vllm_c.py b/vllm/kernels/vllm_c.py
index 124b02e4e27a..5c602c39843b 100644
--- a/vllm/kernels/vllm_c.py
+++ b/vllm/kernels/vllm_c.py
@@ -31,3 +31,33 @@ def rms_norm(
     output = torch.empty(x.shape, device=x.device, dtype=x.dtype)
     torch.ops._C.rms_norm(output, x, weight, epsilon)
     return output
+
+
+rms_add_no_var_size = (
+    lambda x, x_residual, weight, epsilon, variance_size=None: variance_size is None
+    and (weight is None or weight.dtype == x.dtype)
+)
+"""vLLM Kernel does not support variance_size parameter and requires
+matching input/weight dtype."""
+
+
+@ir.ops.fused_add_rms_norm.register_impl(
+    "vllm_c",
+    supports_args=rms_add_no_var_size,
+    supported=CUDA_ALIKE,
+    inplace=True,
+)
+def fused_add_rms_norm(
+    x: Tensor,
+    x_residual: Tensor,
+    weight: Tensor | None,
+    epsilon: float,
+    variance_size: int | None = None,
+) -> tuple[Tensor, Tensor]:
+    if weight is None:
+        # Kernel requires weight tensor, pass ones
+        weight = torch.ones(x.shape[-1], device=x.device, dtype=x.dtype)
+
+    assert variance_size is None
+    torch.ops._C.fused_add_rms_norm(x, x_residual, weight, epsilon)
+    return x, x_residual
diff --git a/vllm/kernels/xpu_ops.py b/vllm/kernels/xpu_ops.py
index c680c542c1df..5e7f90f70868 100644
--- a/vllm/kernels/xpu_ops.py
+++ b/vllm/kernels/xpu_ops.py
@@ -36,3 +36,31 @@ def rms_norm(
     output = torch.empty(x.shape, device=x.device, dtype=x.dtype)
     torch.ops._C.rms_norm(output, x, weight, epsilon)
     return output
+
+
+rms_add_no_var_size = (
+    lambda x, x_residual, weight, epsilon, variance_size=None: variance_size is None
+    and (weight is None or weight.dtype == x.dtype)
+)
+
+
+@ir.ops.fused_add_rms_norm.register_impl(
+    "xpu_kernels",
+    supports_args=rms_add_no_var_size,
+    supported=XPU_KERNELS_SUPPORTED,
+    inplace=True,
+)
+def fused_add_rms_norm(
+    x: Tensor,
+    x_residual: Tensor,
+    weight: Tensor | None,
+    epsilon: float,
+    variance_size: int | None = None,
+) -> tuple[Tensor, Tensor]:
+    if weight is None:
+        # Kernel requires weight tensor, pass ones
+        weight = torch.ones(x.shape[-1], device=x.device, dtype=x.dtype)
+
+    assert variance_size is None
+    torch.ops._C.fused_add_rms_norm(x, x_residual, weight, epsilon)
+    return x, x_residual
diff --git a/vllm/lora/layers/base_linear.py b/vllm/lora/layers/base_linear.py
index 4ea6b1ec8f05..68783ae50d4b 100644
--- a/vllm/lora/layers/base_linear.py
+++ b/vllm/lora/layers/base_linear.py
@@ -203,7 +203,16 @@ def _apply_sync(
         self, x: torch.Tensor, bias: torch.Tensor | None = None
     ) -> torch.Tensor:
         output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
+        return self._apply_lora_to_output(x, output)
 
+    def _apply_base_forward(self, x: torch.Tensor) -> torch.Tensor:
+        base_output = self.base_layer(x)
+        output = base_output[0] if isinstance(base_output, tuple) else base_output
+        return self._apply_lora_to_output(x, output)
+
+    def _apply_lora_to_output(
+        self, x: torch.Tensor, output: torch.Tensor
+    ) -> torch.Tensor:
         original_shape = output.shape if output.ndim == 3 else None
 
         # In transformers backend, x and output have extra batch dimension like
diff --git a/vllm/lora/layers/column_parallel_linear.py b/vllm/lora/layers/column_parallel_linear.py
index f49a3fcbb941..01bbc9366d8d 100644
--- a/vllm/lora/layers/column_parallel_linear.py
+++ b/vllm/lora/layers/column_parallel_linear.py
@@ -40,11 +40,19 @@ def _mcp_apply(x, bias, layer: "ColumnParallelLinearWithLoRA"):
 
     # Since communication is needed, the buffer is directly initialized as a
     # tensor rather than a tuple of tensor.
-    buffers = torch.zeros(
-        (layer.n_slices, x.shape[0], layer.lora_a_stacked[0].shape[2]),
+    local_lora_rank = layer.lora_a_stacked[0].shape[2]
+    buffer_shape = (layer.n_slices, x.shape[0], local_lora_rank)
+    # Under torch.compile, the local-rank-1 fully-sharded path can otherwise
+    # get lowered to a reinterpret view with a non-canonical layout. The
+    # Triton shrink op mutates this buffer in place and expects the standard
+    # contiguous [slice, token, rank] stride contract.
+    buffers = torch.empty_strided(
+        buffer_shape,
+        (x.shape[0] * local_lora_rank, local_lora_rank, 1),
         dtype=torch.float32,
         device=x.device,
     )
+    buffers.zero_()
 
     shrunk_buffers: torch.Tensor | None = layer.punica_wrapper.add_shrink(
         buffers, x, layer.lora_a_stacked, 1.0
@@ -86,7 +94,7 @@ def __init__(self, base_layer: ColumnParallelLinear) -> None:
         # The base_layer type is ColumnParallelLinear or
         # MergedColumnParallelLinear, their weight sharding logic is
         # inconsistent when TP is greater than 1.
-        self.is_merged_col_linear = type(base_layer) is MergedColumnParallelLinear
+        self.is_merged_col_linear = isinstance(base_layer, MergedColumnParallelLinear)
         self.output_size = self.base_layer.output_size_per_partition
         # There is only one LoRA layer
         self.n_slices = 1
@@ -158,7 +166,7 @@ def can_replace_layer(
     ) -> bool:
         if type(source_layer) is maybe_get_oot_by_class(ColumnParallelLinear):
             return True
-        if type(source_layer) is maybe_get_oot_by_class(MergedColumnParallelLinear):
+        if isinstance(source_layer, maybe_get_oot_by_class(MergedColumnParallelLinear)):
             if len(packed_modules_list) != 1:
                 return False
             # Exclude layers with 3+ output sizes - those are handled by
@@ -187,9 +195,9 @@ def __init__(
         # There are two LoRA layers
         # the output_sizes in MergedColumnParallelLinear is not sharded by tp
         # we need to divide it by the tp_size to get correct slices size
-        output_sizes = self.base_layer.output_sizes
+        self.output_sizes = self.base_layer.output_sizes
         self.output_slices = tuple(
-            divide(output_size, self.tp_size) for output_size in output_sizes
+            divide(output_size, self.tp_size) for output_size in self.output_sizes
         )
         self.n_slices = len(self.output_slices)
         self.output_ids = (self.tp_rank,) * self.n_slices
@@ -253,6 +261,42 @@ def slice_lora_b(
                 ]
         return sliced_lora_b
 
+    def expand_packed_lora(
+        self,
+        lora_a: list[torch.Tensor],
+        lora_b: list[torch.Tensor],
+    ) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
+        """
+        Expand packed adapter groups when they don't match n_slices.
+        E.g. in_proj_qkv (covers Q+K+V) + in_proj_z
+        """
+        expanded_a: list[torch.Tensor] = []
+        expanded_b: list[torch.Tensor] = []
+        start_idx = 0
+        for a_i, b_i in zip(lora_a, lora_b):
+            # Determine which output slices this b_i covers.
+            b_rows, cu_rows, covered = b_i.shape[0], 0, 0
+            for i in range(start_idx, self.n_slices):
+                cu_rows += self.output_sizes[i]
+                if cu_rows == b_rows:
+                    covered = i - start_idx + 1
+                    break
+            else:
+                raise ValueError(
+                    f"Cannot determine how to split lora_b with {b_rows} rows "
+                    f"into {self.n_slices} slices with output sizes "
+                    f"{self.output_sizes} starting from index {start_idx}."
+                )
+            # Split b_i into per-slice tensors and replicate a_i for each.
+            start = 0
+            for j in range(covered):
+                size = self.output_sizes[start_idx + j]
+                expanded_b.append(b_i[start : start + size, :])
+                expanded_a.append(a_i)
+                start += size
+            start_idx += covered
+        return expanded_a, expanded_b
+
     def set_lora(
         self,
         index: int,
@@ -261,6 +305,12 @@ def set_lora(
     ):
         self.reset_lora(index)
 
+        # Expand packed adapter groups when they don't match n_slices.
+        # E.g. in_proj_qkv (covers Q+K+V) + in_proj_z as 2 groups for a
+        # 4-slice layer: split b_qkv by output_sizes and replicate a_qkv.
+        if isinstance(lora_b, list) and len(lora_b) != self.n_slices:
+            lora_a, lora_b = self.expand_packed_lora(lora_a, lora_b)
+
         if self.tp_size > 1:
             lora_a = self.slice_lora_a(lora_a)
             lora_b = self.slice_lora_b(lora_b)
@@ -275,19 +325,41 @@ def set_lora(
                     index, 0, : lora_b_i.shape[0], : lora_b_i.shape[1]
                 ].copy_(lora_b_i, non_blocking=True)
 
+    def apply(self, x: torch.Tensor, bias: torch.Tensor | None = None) -> torch.Tensor:
+        merged_cls = maybe_get_oot_by_class(MergedColumnParallelLinear)
+        # Effectively unsharded subclasses can safely reuse their custom
+        # forward() implementation before applying the LoRA delta.
+        if (
+            self.tp_size == 1
+            and type(self.base_layer) is not merged_cls
+            and type(self.base_layer).forward is not merged_cls.forward
+        ):
+            return self._apply_base_forward(x)
+        return _mcp_apply(x, bias, self)
+
     @classmethod
-    @_not_fully_sharded_can_replace
     def can_replace_layer(
         cls,
         source_layer: nn.Module,
         lora_config: LoRAConfig,
         packed_modules_list: list,
         model_config: PretrainedConfig | None = None,
+        decorate: bool = True,
     ) -> bool:
-        return (
-            type(source_layer) is MergedColumnParallelLinear
-            and len(packed_modules_list) == 2
-        )
+        merged_cls = maybe_get_oot_by_class(MergedColumnParallelLinear)
+        if not isinstance(source_layer, merged_cls) or len(packed_modules_list) != 2:
+            return False
+
+        tp_size = getattr(source_layer, "tp_size", 1)
+        if type(source_layer) is merged_cls:
+            if not decorate:
+                return True
+            return not lora_config.fully_sharded_loras or tp_size == 1
+
+        # Only support effectively unsharded subclasses here. Sharded
+        # subclasses may have custom communication semantics that the generic
+        # merged-column LoRA path does not know how to preserve.
+        return tp_size == 1
 
 
 class QKVParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
@@ -467,18 +539,14 @@ class MergedColumnParallelLinearWithShardedLoRA(MergedColumnParallelLinearWithLo
     def slice_lora_a(
         self, lora_a: list[torch.Tensor | None]
     ) -> list[torch.Tensor | None]:
-        # NOTE: lora_a contains 2 subloras, and each sublora could be None.
         output_shard_size = self.lora_a_stacked[0].shape[2]
         output_start_idx = self.tp_rank * output_shard_size
-        lora_a = [
-            lora_a[0][output_start_idx : output_start_idx + output_shard_size, :]
-            if lora_a[0] is not None
-            else None,
-            lora_a[1][output_start_idx : output_start_idx + output_shard_size, :]
-            if lora_a[1] is not None
-            else None,
+        return [
+            lora_a_i[output_start_idx : output_start_idx + output_shard_size, :]
+            if (lora_a_i := lora_a[i]) is not None
+            else None
+            for i in range(len(lora_a))
         ]
-        return lora_a
 
     def apply(self, x: torch.Tensor, bias: torch.Tensor | None = None) -> torch.Tensor:
         return _mcp_apply(x, bias, self)
@@ -607,7 +675,9 @@ def can_replace_layer(
     ) -> bool:
         # Support MergedColumnParallelLinear with 3 or more slices
         # (2 slices are handled by MergedColumnParallelLinearWithLoRA)
-        if type(source_layer) is not maybe_get_oot_by_class(MergedColumnParallelLinear):
+        if not isinstance(
+            source_layer, maybe_get_oot_by_class(MergedColumnParallelLinear)
+        ):
             return False
 
         # If packed_modules_list has 3+ items, use this class
diff --git a/vllm/lora/layers/fused_moe.py b/vllm/lora/layers/fused_moe.py
index 835bffe58ca3..8cb32f07965b 100644
--- a/vllm/lora/layers/fused_moe.py
+++ b/vllm/lora/layers/fused_moe.py
@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import functools
 
 import torch
 import torch.nn as nn
@@ -8,349 +7,78 @@
 
 from vllm import envs
 from vllm.config.lora import LoRAConfig
-from vllm.distributed.parallel_state import (
-    get_tensor_model_parallel_rank,
-    get_tensor_model_parallel_world_size,
-)
 from vllm.distributed.utils import divide
 from vllm.lora.layers.base import BaseLayerWithLoRA
-from vllm.lora.ops.triton_ops.utils import get_lora_op_configs
 from vllm.model_executor.layers.fused_moe import FusedMoE
-from vllm.model_executor.layers.fused_moe.config import (
-    _get_config_dtype_str,
-)
-from vllm.model_executor.layers.fused_moe.experts.gpt_oss_triton_kernels_moe import (
-    UnfusedOAITritonExperts,
-)
-from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
-    MarlinExperts,
-)
-from vllm.model_executor.layers.fused_moe.fused_moe import (
-    TritonExperts,
-)
 from vllm.model_executor.layers.fused_moe.fused_moe_modular_method import (
     FusedMoEModularMethod,
 )
-from vllm.model_executor.layers.fused_moe.modular_kernel import (
-    FusedMoEKernel,
-)
+from vllm.model_executor.layers.fused_moe.lora_context import MoELoRAContext
+from vllm.model_executor.layers.fused_moe.modular_kernel import FusedMoEKernel
 from vllm.model_executor.layers.fused_moe.prepare_finalize import (
     MoEPrepareAndFinalizeNoDPEPModular,
 )
 
-from .utils import _get_lora_device, try_get_optimal_moe_lora_config
+from .utils import _get_lora_device
 
 
 class FusedMoEWithLoRA(BaseLayerWithLoRA):
     def __init__(self, base_layer: FusedMoE) -> None:
         super().__init__()
         self.base_layer = base_layer
-
-        assert not self.base_layer.use_ep, (
-            "EP support for Fused MoE LoRA is not implemented yet."
-        )
-        assert not self.base_layer.quant_method.is_monolithic, (
-            "Monolithic kernels are not supported for Fused MoE LoRA."
-        )
-        self.tp_size = get_tensor_model_parallel_world_size()
-        self.tp_rank = get_tensor_model_parallel_rank()
+        self._ep_check()
+        # Use the MoE-aware TP rank/size: when EP is active, FusedMoE collapses
+        # moe_parallel_config.tp_size to 1 (experts are sharded across the
+        # TP group instead).
+        self.tp_size = self.base_layer.tp_size
+        self.tp_rank = self.base_layer.tp_rank
         self.device = _get_lora_device(base_layer)
         # For non-gated MoE (is_act_and_mul=False), only 1 slice is needed
         # since there's only up_proj (w1), not gate_proj + up_proj (w1 + w3)
         self._w13_slices = 2 if base_layer.moe_config.is_act_and_mul else 1
-        self._inject_lora_into_fused_moe()
-
-    def _normalize_keys(self, config: dict[str, int | None]) -> dict[str, int | None]:
-        normalized_config = {}
-        for key, value in config.items():
-            if key.islower():
-                if key.startswith("block_"):
-                    normalized_key = "BLOCK_SIZE_" + key.split("_")[-1].upper()
-                else:
-                    normalized_key = key.upper()
-            else:
-                normalized_key = key
-            normalized_config[normalized_key] = value
-        return normalized_config
-
-    def _get_lora_moe_configs(
-        self,
-        op_prefix: str,
-        num_loras: int,
-        rank: int,
-        num_slices: int,
-        M: int,
-        layer: FusedMoE,
-        top_k: int,
-        config_dtype: str,
-    ):
-        if envs.VLLM_TUNED_CONFIG_FOLDER:
-            hidden_size = layer.hidden_size
-            intermediate_size = (
-                self.w2_lora_a_stacked[0].shape[-1]
-                if op_prefix == "w2"
-                else self.w13_lora_b_stacked[0].shape[-2]
-            )
-            shrink_config = get_lora_op_configs(
-                op_type=f"fused_moe_lora_{op_prefix}_shrink",
-                max_loras=num_loras,
-                batch=M,
-                hidden_size=hidden_size,
-                rank=rank,
-                num_slices=num_slices,
-                moe_intermediate_size=intermediate_size,
-            )
-            expand_config = get_lora_op_configs(
-                op_type=f"fused_moe_lora_{op_prefix}_expand",
-                max_loras=num_loras,
-                batch=M,
-                hidden_size=hidden_size,  # lora_a_stacked.shape[-1],
-                rank=rank,
-                num_slices=num_slices,
-                moe_intermediate_size=intermediate_size,  # lora_b_stacked.shape[-2],
-            )
-        else:  # fall back to the default config
-            get_config_func = functools.partial(
-                try_get_optimal_moe_lora_config,
-                w1_shape=layer.w13_weight.shape,
-                w2_shape=layer.w2_weight.shape,
-                rank=rank,
-                top_k=top_k,
-                dtype=config_dtype,
-                M=M,
-                block_shape=layer.quant_method.moe_quant_config.block_shape,
-            )
-            shrink_config = get_config_func(
-                op_type=f"fused_moe_lora_{op_prefix}_shrink"
-            )
-            expand_config = get_config_func(
-                op_type=f"fused_moe_lora_{op_prefix}_expand"
-            )
-        shrink_config = self._normalize_keys(shrink_config)
-        expand_config = self._normalize_keys(expand_config)
-        return shrink_config, expand_config
-
-    def _inject_lora_into_fused_moe(self):
-        moe_state_dict = {}
-        top_k = self.base_layer.top_k
 
         self.base_layer.ensure_moe_quant_config_init()
-        quant_config = self.base_layer.quant_method.moe_quant_config
-
         if getattr(self.base_layer.quant_method, "supports_internal_mk", False):
-            # Use the existing modular kernel from the quant method
-            m_fused_moe_fn = self.base_layer.quant_method.moe_kernel
+            moe_kernel = self.base_layer.quant_method.moe_kernel
             # Don't let the kernel own shared experts so the runner can
             # overlap them with routed experts via a separate CUDA stream.
-            m_fused_moe_fn.shared_experts = None
+            moe_kernel.shared_experts = None
         else:
-            # Create a new modular kernel via select_gemm_impl.
-            # Don't pass shared_experts to the kernel so the runner can
-            # overlap them with routed experts via a separate CUDA stream.
             prepare_finalize = MoEPrepareAndFinalizeNoDPEPModular()
-            m_fused_moe_fn = FusedMoEKernel(
+            moe_kernel = FusedMoEKernel(
                 prepare_finalize,
                 self.base_layer.quant_method.select_gemm_impl(
                     prepare_finalize, self.base_layer
                 ),
             )
-
-        if quant_config.use_mxfp4_w4a16:
-            assert isinstance(
-                m_fused_moe_fn.impl.fused_experts,
-                (MarlinExperts, UnfusedOAITritonExperts),
-            )
-        else:
-            assert isinstance(m_fused_moe_fn.impl.fused_experts, TritonExperts)
-
-        def fwd_decorator(layer, func):
-            def wrapper(*args, **kwargs):
-                moe_state_dict["hidden_states"] = kwargs["hidden_states"]
-                moe_state_dict["topk_ids"] = kwargs["topk_ids"]
-                moe_state_dict["topk_weights"] = kwargs["topk_weights"]
-                moe_state_dict["expert_map"] = kwargs["expert_map"]
-                moe_state_dict["apply_router_weight_on_input"] = kwargs[
-                    "apply_router_weight_on_input"
-                ]
-                result = func(*args, **kwargs)
-                return result
-
-            return wrapper
-
-        def act_decorator(layer, func):
-            def wrapper(*args, **kwargs):
-                _, output, input = args
-
-                hidden_states = moe_state_dict["hidden_states"]
-                topk_weights = moe_state_dict["topk_weights"]
-                curr_topk_ids = moe_state_dict["topk_ids"]
-
-                expert_map = moe_state_dict["expert_map"]
-
-                config_dtype = _get_config_dtype_str(
-                    dtype=hidden_states.dtype,
-                    use_fp8_w8a8=False,
-                    use_int8_w8a16=False,
-                    use_int4_w4a16=False,
-                )
-                num_tokens = hidden_states.size(0)
-                M = num_tokens
-                max_lora_rank = self.w13_lora_a_stacked[0].shape[-2]
-                shrink_config, expand_config = self._get_lora_moe_configs(
-                    op_prefix="w13",
-                    num_loras=self.max_loras,
-                    rank=max_lora_rank,
-                    num_slices=self._w13_slices,
-                    M=M,
-                    layer=layer,
-                    top_k=top_k,
-                    config_dtype=config_dtype,
-                )
-
-                # SPARSITY_FACTOR is a heuristic margin ensuring tokens * top_k
-                # activates only a small fraction of total experts * loras.
-                SPARSITY_FACTOR = 8
-                naive_block_assignment = (
-                    expert_map is None
-                    and num_tokens * top_k * SPARSITY_FACTOR
-                    <= self.base_layer.local_num_experts * self.max_loras
-                )
-
-                # get the block size of m from customized config or default config
-                (
-                    token_lora_mapping,
-                    sorted_token_ids_lora,
-                    expert_ids_lora,
-                    num_tokens_post_padded_lora,
-                ) = self.punica_wrapper.moe_lora_align_block_size(
-                    curr_topk_ids,
-                    num_tokens,
-                    shrink_config["BLOCK_SIZE_M"],
-                    self.base_layer.local_num_experts,
-                    self.max_loras,
-                    self.adapter_enabled,
-                    expert_map,
-                    naive_block_assignment=naive_block_assignment,
-                )
-
-                moe_state_dict["sorted_token_ids_lora"] = sorted_token_ids_lora
-                moe_state_dict["expert_ids_lora"] = expert_ids_lora
-                moe_state_dict["num_tokens_post_padded_lora"] = (
-                    num_tokens_post_padded_lora
-                )
-                moe_state_dict["token_lora_mapping"] = token_lora_mapping
-
-                if sorted_token_ids_lora is not None:
-                    expert_ids_lora = expert_ids_lora.view(self.max_loras, -1)
-                    sorted_token_ids_lora = sorted_token_ids_lora.view(
-                        self.max_loras, -1
-                    )
-                #
-
-                self.punica_wrapper.add_lora_fused_moe(
-                    input.view(-1, top_k, input.shape[-1]),
-                    hidden_states,
-                    self.w13_lora_a_stacked,
-                    self.w13_lora_b_stacked,
-                    topk_weights,
-                    sorted_token_ids_lora,
-                    expert_ids_lora,
-                    num_tokens_post_padded_lora,
-                    max_lora_rank,
-                    top_k,
-                    shrink_config,  ## pass the shrink config
-                    expand_config,  ## pass the expand config
-                    self.adapter_enabled,
-                    fully_sharded=self.fully_sharded,
-                    token_lora_mapping=token_lora_mapping,
-                )
-
-                result = func(*args, **kwargs)
-
-                moe_state_dict["intermediate_cache2"] = output
-                return result
-
-            return wrapper
-
-        def moe_sum_decorator(layer, func):
-            def wrapper(*args, **kwargs):
-                hidden_states = moe_state_dict["hidden_states"]
-                topk_weights = moe_state_dict["topk_weights"]
-
-                config_dtype = _get_config_dtype_str(
-                    dtype=hidden_states.dtype,
-                    use_fp8_w8a8=False,
-                    use_int8_w8a16=False,
-                    use_int4_w4a16=False,
-                )
-                num_tokens = hidden_states.size(0)
-                M = num_tokens
-                max_lora_rank = self.w2_lora_a_stacked[0].shape[-2]
-                shrink_config, expand_config = self._get_lora_moe_configs(
-                    op_prefix="w2",
-                    num_loras=self.max_loras,
-                    rank=max_lora_rank,
-                    num_slices=1,
-                    M=M,
-                    layer=layer,
-                    top_k=top_k,
-                    config_dtype=config_dtype,
-                )
-
-                sorted_token_ids_lora = moe_state_dict["sorted_token_ids_lora"]
-                expert_ids_lora = moe_state_dict["expert_ids_lora"]
-                num_tokens_post_padded_lora = moe_state_dict[
-                    "num_tokens_post_padded_lora"
-                ]
-                token_lora_mapping = moe_state_dict.get("token_lora_mapping")
-
-                if sorted_token_ids_lora is not None:
-                    expert_ids_lora = expert_ids_lora.view(self.max_loras, -1)
-                    sorted_token_ids_lora = sorted_token_ids_lora.view(
-                        self.max_loras, -1
-                    )
-                intermediate_cache2 = moe_state_dict["intermediate_cache2"]
-                intermediate_cache3 = args[0]
-
-                shard_size_w2 = divide(self.base_layer.hidden_size, self.tp_size)
-
-                self.punica_wrapper.add_lora_fused_moe(
-                    intermediate_cache3,
-                    intermediate_cache2,
-                    self.w2_lora_a_stacked,
-                    self.w2_lora_b_stacked,
-                    topk_weights,
-                    sorted_token_ids_lora,
-                    expert_ids_lora,
-                    num_tokens_post_padded_lora,
-                    max_lora_rank,
-                    top_k,
-                    shrink_config,  ## pass the shrink config
-                    expand_config,  ## pass the expand config
-                    self.adapter_enabled,
-                    True,
-                    fully_sharded=self.fully_sharded,
-                    offset=shard_size_w2 * self.tp_rank if self.fully_sharded else 0,
-                    token_lora_mapping=token_lora_mapping,
-                )
-
-                result = func(*args, **kwargs)
-                return result
-
-            return wrapper
-
-        fused_experts = m_fused_moe_fn.impl.fused_experts
-
-        m_fused_moe_fn.apply = fwd_decorator(self.base_layer, m_fused_moe_fn.apply)
-        fused_experts.activation = act_decorator(
-            self.base_layer, fused_experts.activation
-        )
-        fused_experts.moe_sum = moe_sum_decorator(
-            self.base_layer, fused_experts.moe_sum
+        assert moe_kernel.supports_lora(), (
+            f"{type(moe_kernel.fused_experts).__name__} does not support LoRA. "
+            "For unquantized MoE, set moe_backend='triton' or moe_backend='auto' "
+            "(auto selects Triton automatically when LoRA is enabled). "
+            "For quantized MoE, mix LoRAExpertsMixin into the experts class "
+            "and consume self._lora_context in apply()."
         )
-        # TODO(bnell): find a less intrusive way to handle this.
+        self._moe_kernel = moe_kernel
         self.base_layer._replace_quant_method(
-            FusedMoEModularMethod(self.base_layer.quant_method, m_fused_moe_fn)
+            FusedMoEModularMethod(self.base_layer.quant_method, moe_kernel)
+        )
+
+    def _build_lora_context(self):
+        return MoELoRAContext(
+            w13_lora_a_stacked=self.w13_lora_a_stacked,
+            w13_lora_b_stacked=self.w13_lora_b_stacked,
+            w2_lora_a_stacked=self.w2_lora_a_stacked,
+            w2_lora_b_stacked=self.w2_lora_b_stacked,
+            adapter_enabled=self.adapter_enabled,
+            max_loras=self.max_loras,
+            top_k=self.base_layer.top_k,
+            w13_num_slices=self._w13_slices,
+            fully_sharded=self.fully_sharded,
+            tp_rank=self.tp_rank,
+            tp_size=self.tp_size,
+            local_num_experts=self.base_layer.local_num_experts,
+            punica_wrapper=self.punica_wrapper,
+            use_tuned_config=bool(envs.VLLM_TUNED_CONFIG_FOLDER),
         )
 
     def _create_lora_a_weights(
@@ -415,6 +143,26 @@ def _create_lora_b_weights(self, max_loras: int, lora_config: LoRAConfig):
             ),
         )
 
+    def _ep_check(self):
+        if self.base_layer.use_ep:
+            moe_config = self.base_layer.moe_config
+            all2all_backend = moe_config.moe_parallel_config.all2all_backend
+            assert all2all_backend == "allgather_reducescatter", (
+                "Fused MoE LoRA with EP currently only supports "
+                f"all2all_backend='allgather_reducescatter', got '{all2all_backend}'."
+            )
+            assert not moe_config.moe_parallel_config.is_sequence_parallel
+
+    def _verify_ep_fs(self, lora_config: LoRAConfig):
+        # EP and fully_sharded LoRA both partition along the same TP group —
+        # EP on the expert dim, fully_sharded on the LoRA rank dim — with
+        # mutually contradictory assumptions about which rank holds which
+        # expert's rank-shard.
+        assert not (self.base_layer.use_ep and lora_config.fully_sharded_loras), (
+            "Fused MoE LoRA does not support enable_expert_parallel=True "
+            "together with fully_sharded_loras=True. Disable one of them."
+        )
+
     def create_lora_weights(
         self,
         max_loras: int,
@@ -422,6 +170,8 @@ def create_lora_weights(
         model_config: PretrainedConfig | None = None,
     ) -> None:
         """Initializes lora matrices."""
+
+        self._verify_ep_fs(lora_config)
         self.max_loras = lora_config.max_loras
         self.fully_sharded = lora_config.fully_sharded_loras
 
@@ -547,6 +297,10 @@ def set_lora(
 
         w1_lora_a, w2_lora_a, w3_lora_a = lora_a
         w1_lora_b, w2_lora_b, w3_lora_b = lora_b
+
+        # EP slicing is done once at add time in
+        # LoRAModelManager._slice_moe_lora_ep, so by here the cached
+        # tensors already match the local-expert dim of the stacked buffers.
         assert (
             num_experts
             == w1_lora_a.shape[0]
@@ -589,16 +343,25 @@ def set_lora(
             index, :, : sliced_w2_lora_b.shape[1], : sliced_w2_lora_b.shape[2]
         ].copy_(sliced_w2_lora_b, non_blocking=True)
 
+    def set_mapping(self, punica_wrapper):
+        super().set_mapping(punica_wrapper)
+        lora_context = self._build_lora_context()
+        self._moe_kernel.fused_experts.set_lora_context(lora_context)
+        prepare_finalize = self._moe_kernel.prepare_finalize
+        if hasattr(prepare_finalize, "set_lora_context"):
+            prepare_finalize.set_lora_context(lora_context)
+
     def forward(self, *args, **kwargs):
         return self.base_layer.forward(*args, **kwargs)
 
-    def maybe_all_reduce_tensor_model_parallel(self, *args, **kwargs):
-        return self.base_layer.maybe_all_reduce_tensor_model_parallel(*args, **kwargs)
-
     @property
     def quant_method(self):
         return self.base_layer.quant_method
 
+    @property
+    def runner(self):
+        return self.base_layer.runner
+
     @property
     def is_internal_router(self) -> bool:
         return self.base_layer.is_internal_router
@@ -613,7 +376,7 @@ def can_replace_layer(
     ) -> bool:
         """Returns True if the layer can be replaced by this LoRA layer."""
 
-        # source_layer is FusedMoE or SharedFusedMoE
+        # source_layer is FusedMoE
         return isinstance(source_layer, FusedMoE) and len(packed_modules_list) == 2
 
 
@@ -660,6 +423,7 @@ def create_lora_weights(
         """Initializes lora matrices."""
 
         assert isinstance(model_config, PretrainedConfig)
+        self._verify_ep_fs(lora_config)
         self._base_model = model_config.architectures[0]
         self.max_loras = lora_config.max_loras
         self.fully_sharded = lora_config.fully_sharded_loras
@@ -775,5 +539,5 @@ def can_replace_layer(
         model_config: PretrainedConfig | None = None,
     ) -> bool:
         """Returns True if the layer can be replaced by this LoRA layer."""
-        # source_layer is FusedMoE or SharedFusedMoE
+        # source_layer is FusedMoE
         return isinstance(source_layer, FusedMoE) and len(packed_modules_list) == 1
diff --git a/vllm/lora/layers/replicated_linear.py b/vllm/lora/layers/replicated_linear.py
index f1f499b841ba..53ae26be4c36 100644
--- a/vllm/lora/layers/replicated_linear.py
+++ b/vllm/lora/layers/replicated_linear.py
@@ -46,6 +46,12 @@ def forward(
 
         return output, output_bias
 
+    def apply(self, x: torch.Tensor, bias: torch.Tensor | None = None) -> torch.Tensor:
+        # ReplicatedLinear subclasses such as GateLinear override forward() to
+        # dispatch custom kernels and/or adjust the output dtype. Apply LoRA on
+        # top of the actual base-layer output instead of bypassing that path.
+        return self._apply_base_forward(x)
+
     # ReplicatedLinear should always be replaced, regardless of the fully
     # sharded LoRAs setting, because it is, by definition, copied per GPU.
     @classmethod
@@ -56,7 +62,7 @@ def can_replace_layer(
         packed_modules_list: list,
         model_config: PretrainedConfig | None = None,
     ) -> bool:
-        return type(source_layer) is maybe_get_oot_by_class(ReplicatedLinear)
+        return isinstance(source_layer, maybe_get_oot_by_class(ReplicatedLinear))
 
     def slice_lora_a(
         self, lora_a: torch.Tensor | list[torch.Tensor | None]
diff --git a/vllm/lora/layers/utils.py b/vllm/lora/layers/utils.py
index c19b097586f5..1b8083f5c4d1 100644
--- a/vllm/lora/layers/utils.py
+++ b/vllm/lora/layers/utils.py
@@ -90,11 +90,12 @@ def try_get_optimal_moe_lora_config(
     top_k: int,
     dtype: str | None,
     M: int,
-    block_shape: list[int] | None = None,
 ) -> dict[str, int | None]:
-    config = try_get_optimal_moe_config(
-        w1_shape, w2_shape, top_k, dtype, M, block_shape
-    ).copy()
+    # LoRA shrink/expand operates on bf16/fp16 adapters regardless of the
+    # base MoE weight's block-wise quantization, so block_shape is omitted
+    # from the config lookup — the non-quantized branch in get_default_config
+    # ignores it anyway.
+    config = try_get_optimal_moe_config(w1_shape, w2_shape, top_k, dtype, M).copy()
     if op_type in [
         "fused_moe_lora_w13_shrink",
         "fused_moe_lora_w2_shrink",
diff --git a/vllm/lora/lora_model.py b/vllm/lora/lora_model.py
index 7c1dd39bb5e3..48a2f6a1edd0 100644
--- a/vllm/lora/lora_model.py
+++ b/vllm/lora/lora_model.py
@@ -203,6 +203,7 @@ def check_unexpected_modules(modules: dict):
             tensors = TensorDeserializer(
                 lora_tensor_path,
                 dtype=tensorizer_config.dtype,
+                device=device,
                 **tensorizer_args.deserialization_kwargs,
             )
             check_unexpected_modules(tensors)
diff --git a/vllm/lora/model_manager.py b/vllm/lora/model_manager.py
index 9d3772560433..d68d0c4fbb74 100644
--- a/vllm/lora/model_manager.py
+++ b/vllm/lora/model_manager.py
@@ -14,6 +14,7 @@
 from vllm.lora.layers import (
     BaseLayerWithLoRA,
     FusedMoE3DWithLoRA,
+    FusedMoEWithLoRA,
     LoRAMapping,
     LoRAMappingType,
 )
@@ -387,7 +388,6 @@ def _parent_module(module_name: str) -> str:
                     "LoRA is not supported for non-gated MoE gate module."
                     " %s will be ignored.",
                     module_name,
-                    scope="local",
                 )
                 continue
 
@@ -437,12 +437,21 @@ def _parent_module(module_name: str) -> str:
                     ),
                 )
 
-            # In some models, especially multimodal ones, layers with the same
-            # name may have different types, such as nn.Linear and
-            # ReplicatedLinear. The nn.Linear layers cannot be replaced with
-            # LoRA layers, leading to assertion error. The following check
-            # aims to prevent this error
-            if self.supports_mm and not isinstance(new_module, BaseLayerWithLoRA):
+            # Some matched modules can be unsupported by LoRA wrappers
+            # (e.g. subclasses with specialized forward behavior).
+            if not isinstance(new_module, BaseLayerWithLoRA):
+                error_msg = (
+                    "LoRA target module "
+                    f"{module_name} ({type(module).__name__}) matched the "
+                    "deployment configuration but could not be wrapped by any "
+                    "LoRA layer implementation."
+                )
+                if self.lora_config.target_modules is not None:
+                    raise ValueError(
+                        f"{error_msg} target_modules="
+                        f"{sorted(self.lora_config.target_modules)}"
+                    )
+                logger.warning_once("%s It will be ignored.", error_msg)
                 continue
             self.register_module(module_name, new_module)
 
@@ -554,7 +563,16 @@ def create_dummy_lora(
             else:
                 parts = module_name.split(".")
                 replacements = self.packed_modules_mapping[parts[-1]]
+                n_slices = getattr(module, "n_slices", len(replacements))
+                if module.__class__.__name__ == "FusedMoEWithLoRA":
+                    replacements = replacements[
+                        : len(module.lora_a_stacked) // self.lora_slots
+                    ]
                 subloras: list[LoRALayerWeights | None] = []
+                # HACK: overrides replacements for qkvz = qkv + z case.
+                # Any better methods to handle this case?
+                if n_slices != len(replacements):
+                    replacements = [f"slice_{i}" for i in range(n_slices)]
                 for i, r in enumerate(replacements):
                     lora = LoRALayerWeights.create_dummy_lora_weights(
                         module_name + "." + r,
@@ -578,6 +596,38 @@ def create_dummy_lora(
                 model.loras[module_name] = lora
         return model
 
+    def get_dummy_lora_warmup_rank(self, default_rank: int) -> int:
+        """Return a dummy LoRA rank compatible with wrapped modules.
+
+        Dummy LoRAs keep warmup memory low by using a small rank. Fully
+        sharded MoE wrappers additionally require the dummy rank to be divisible
+        by tensor parallel size because they shard W13 along the rank axis.
+        """
+        if not self.lora_config.fully_sharded_loras:
+            return default_rank
+
+        required_multiple = 1
+        for module in self.modules.values():
+            if not getattr(module, "fully_sharded", False):
+                continue
+            required_multiple = math.lcm(required_multiple, module.tp_size)
+
+        if required_multiple == 1 or default_rank % required_multiple == 0:
+            return default_rank
+
+        adjusted_rank = (
+            (default_rank + required_multiple - 1) // required_multiple
+        ) * required_multiple
+        if adjusted_rank > self.lora_config.max_lora_rank:
+            raise ValueError(
+                "Unable to choose a dummy LoRA warmup rank compatible with "
+                "fully sharded MoE modules: "
+                f"default_rank={default_rank}, "
+                f"required_multiple={required_multiple}, "
+                f"max_lora_rank={self.lora_config.max_lora_rank}"
+            )
+        return adjusted_rank
+
     def _match_target_modules(self, module_name: str) -> bool:
         """Check if a module should have LoRA applied.
 
@@ -594,7 +644,11 @@ def _match_target_modules(self, module_name: str) -> bool:
         """
         if not is_supported_lora_module(module_name, self.supported_lora_modules):
             return False
-        return is_in_target_modules(module_name, self.lora_config.target_modules)
+        return is_in_target_modules(
+            module_name,
+            self.lora_config.target_modules,
+            self.packed_modules_mapping,
+        )
 
     def _get_punica_wrapper(self, module_name: str) -> PunicaWrapperBase | None:
         """
@@ -672,6 +726,8 @@ def _create_merged_loras_inplace(self, lora_model: LoRAModel) -> None:
         for module_name, module in self.modules.items():
             if isinstance(module, FusedMoE3DWithLoRA):
                 self._stack_moe_lora_weights(lora_model, module, module_name)
+            elif isinstance(module, FusedMoEWithLoRA):
+                self._slice_moe_lora_ep(lora_model, module, module_name)
 
         first_lora: LoRALayerWeights = next(iter(lora_model.loras.values()))
         assert first_lora.lora_a is not None
@@ -718,23 +774,33 @@ def _stack_moe_lora_weights(
             assert gate_up_proj_lora is not None
             assert down_proj_lora is not None
             if self._is_3d_moe_model:
-                num_experts = module.w13_lora_a_stacked[0].shape[1]
+                local_num_experts = module.w13_lora_a_stacked[0].shape[1]
+                # The checkpoint holds weights for all global experts, but
+                # each EP rank owns only local_num_experts. Reshape against
+                # the adapter's actual expert count, then slice this rank's
+                # owned expert range before it gets copied into the local
+                # stacked buffer. For non-EP (local == global) this is a
+                # no-op slice.
+                global_num_experts = module.base_layer.global_num_experts
+                ep_rank = module.base_layer.ep_rank
+                expert_start = ep_rank * local_num_experts
+                expert_end = expert_start + local_num_experts
 
                 # (num_experts,rank,input_size)
                 gate_up_proj_lora.lora_a = gate_up_proj_lora.lora_a.reshape(
-                    num_experts, -1, gate_up_proj_lora.lora_a.shape[-1]
-                )
+                    global_num_experts, -1, gate_up_proj_lora.lora_a.shape[-1]
+                )[expert_start:expert_end].contiguous()
                 down_proj_lora.lora_a = down_proj_lora.lora_a.reshape(
-                    num_experts, -1, down_proj_lora.lora_a.shape[-1]
-                )
+                    global_num_experts, -1, down_proj_lora.lora_a.shape[-1]
+                )[expert_start:expert_end].contiguous()
 
                 # (output_size,rank,num_experts)
                 gate_up_proj_lora.lora_b = gate_up_proj_lora.lora_b.reshape(
-                    gate_up_proj_lora.lora_b.shape[0], -1, num_experts
-                )
+                    gate_up_proj_lora.lora_b.shape[0], -1, global_num_experts
+                )[..., expert_start:expert_end]
                 down_proj_lora.lora_b = down_proj_lora.lora_b.reshape(
-                    down_proj_lora.lora_b.shape[0], -1, num_experts
-                )
+                    down_proj_lora.lora_b.shape[0], -1, global_num_experts
+                )[..., expert_start:expert_end]
 
                 # (num_experts,output_size,rank)
                 gate_up_proj_lora.lora_b = gate_up_proj_lora.lora_b.permute(
@@ -784,6 +850,43 @@ def _stack_moe_lora_weights(
                 module_lora.lora_a = lora_a
                 module_lora.lora_b = lora_b
 
+    def _slice_moe_lora_ep(
+        self,
+        lora_model: LoRAModel,
+        module: FusedMoEWithLoRA,
+        module_name: str,
+    ) -> None:
+        """Slice the cached LoRA tensors down to this rank's local experts.
+
+        The 2D MoE checkpoint enters as a list of per-(w1/w2/w3) tensors of
+        shape (num_experts, rank, in) / (num_experts, out, rank). When EP
+        is active each rank only owns local_num_experts; without this slice
+        the CPU LoRAModel keeps the full global weight and set_lora has to
+        re-slice on every activation.
+        """
+        if not module.base_layer.use_ep:
+            return
+        module_lora = self._get_lora_layer_weights(lora_model, module_name)
+        if module_lora is None or not isinstance(module_lora.lora_a, list):
+            return
+
+        local_num_experts = module.base_layer.local_num_experts
+        global_num_experts = module.base_layer.global_num_experts
+        ep_rank = module.base_layer.ep_rank
+        expert_start = ep_rank * local_num_experts
+        expert_end = expert_start + local_num_experts
+
+        new_lora_a: list[torch.Tensor | None] = []
+        new_lora_b: list[torch.Tensor | None] = []
+        for a, b in zip(module_lora.lora_a, module_lora.lora_b):
+            if a is not None and b is not None and a.shape[0] == global_num_experts:
+                a = a[expert_start:expert_end].contiguous()
+                b = b[expert_start:expert_end].contiguous()
+            new_lora_a.append(a)
+            new_lora_b.append(b)
+        module_lora.lora_a = new_lora_a
+        module_lora.lora_b = new_lora_b
+
     def _get_lora_layer_weights(
         self, lora_model: LoRAModel, module_name: str
     ) -> LoRALayerWeights | None:
diff --git a/vllm/lora/ops/triton_ops/utils.py b/vllm/lora/ops/triton_ops/utils.py
index 0ab52e698318..af0a3157f02a 100644
--- a/vllm/lora/ops/triton_ops/utils.py
+++ b/vllm/lora/ops/triton_ops/utils.py
@@ -321,3 +321,20 @@ def supports_pdl(device: torch.device | None = None) -> bool:
 def supports_tma(device: torch.device | None = None) -> bool:
     # TMA requires compute capability SM90 or above
     return current_platform.is_cuda() and current_platform.has_device_capability(90)
+
+
+def _normalize_lora_config_keys(
+    config: dict[str, int | None],
+) -> dict[str, int | None]:
+    """Normalize Triton config dict keys to uppercase BLOCK_SIZE_* format."""
+    out: dict[str, int | None] = {}
+    for key, val in config.items():
+        if key.islower():
+            if key.startswith("block_"):
+                nk = "BLOCK_SIZE_" + key.split("_")[-1].upper()
+            else:
+                nk = key.upper()
+        else:
+            nk = key
+        out[nk] = val
+    return out
diff --git a/vllm/lora/ops/xpu_ops/lora_ops.py b/vllm/lora/ops/xpu_ops/lora_ops.py
index 6d1751c3738e..070fd8645821 100644
--- a/vllm/lora/ops/xpu_ops/lora_ops.py
+++ b/vllm/lora/ops/xpu_ops/lora_ops.py
@@ -27,9 +27,42 @@ def bgmv_expand(
     lora_indices_tensor: torch.Tensor,
     add_inputs: bool = True,
 ) -> None:
-    torch.ops._xpu_C.bgmv_expand(
-        output_tensor, inputs, lora_b_weights, lora_indices_tensor, add_inputs
-    )
+    weight_out_dim = lora_b_weights.size(-2)
+    output_dim = output_tensor.size(1)
+
+    if weight_out_dim == output_dim:
+        torch.ops._xpu_C.bgmv_expand(
+            output_tensor,
+            inputs,
+            lora_b_weights,
+            lora_indices_tensor,
+            add_inputs,
+        )
+    elif weight_out_dim < output_dim:
+        # LoRA weight output dim can be smaller than the output tensor
+        # (e.g. vocab_size vs padded logits). Use expand_slice to write
+        # only the matching portion, mirroring torch_ops common_len logic.
+        torch.ops._xpu_C.bgmv_expand_slice(
+            output_tensor,
+            inputs,
+            lora_b_weights,
+            lora_indices_tensor,
+            0,
+            weight_out_dim,
+            add_inputs,
+        )
+    else:
+        # Weight output dim larger than output tensor: truncate weights.
+        lora_b_weights = lora_b_weights[..., :output_dim, :].contiguous()
+        torch.ops._xpu_C.bgmv_expand_slice(
+            output_tensor,
+            inputs,
+            lora_b_weights,
+            lora_indices_tensor,
+            0,
+            output_dim,
+            add_inputs,
+        )
 
 
 def bgmv_expand_slice(
diff --git a/vllm/lora/punica_wrapper/punica_base.py b/vllm/lora/punica_wrapper/punica_base.py
index facbd681a09a..0448a6d00cda 100644
--- a/vllm/lora/punica_wrapper/punica_base.py
+++ b/vllm/lora/punica_wrapper/punica_base.py
@@ -493,3 +493,70 @@ def add_lora_fused_moe(
         """
         # TODO: implement it based on torch ops
         raise NotImplementedError
+
+    def add_lora_w13(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        lora_a_stacked: tuple[torch.Tensor, ...],
+        lora_b_stacked: tuple[torch.Tensor, ...],
+        topk_ids: torch.Tensor,
+        topk_weights: torch.Tensor,
+        expert_map: torch.Tensor | None,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        num_tokens: int,
+        top_k_num: int,
+        max_loras: int,
+        adapter_enabled: torch.Tensor,
+        local_num_experts: int,
+        top_k: int,
+        num_slices: int,
+        fully_sharded: bool,
+        use_tuned_config: bool,
+        token_lora_mapping: torch.Tensor | None = None,
+    ) -> tuple[
+        torch.Tensor | None,
+        torch.Tensor | None,
+        torch.Tensor | None,
+        torch.Tensor | None,
+    ]:
+        """Apply w13 LoRA to y (intermediate_cache1) in-place before activation.
+
+        When `token_lora_mapping` is provided it overrides the punica_wrapper's
+        global mapping — used by EP+LoRA to pass the per-rank-local mapping
+        after all-to-all dispatch.
+
+        Returns (sorted_token_ids_lora, expert_ids_lora,
+                 num_tokens_post_padded_lora, token_lora_mapping)
+        for reuse by add_lora_w2.
+        """
+        raise NotImplementedError
+
+    def add_lora_w2(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        lora_a_stacked: tuple[torch.Tensor, ...],
+        lora_b_stacked: tuple[torch.Tensor, ...],
+        topk_weights: torch.Tensor,
+        sorted_token_ids_lora: torch.Tensor | None,
+        expert_ids_lora: torch.Tensor | None,
+        num_tokens_post_padded_lora: torch.Tensor | None,
+        token_lora_mapping: torch.Tensor | None,
+        num_tokens: int,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        top_k_num: int,
+        max_loras: int,
+        adapter_enabled: torch.Tensor,
+        top_k: int,
+        fully_sharded: bool,
+        tp_rank: int,
+        use_tuned_config: bool,
+    ) -> None:
+        """Apply w2 LoRA to y (intermediate_cache3) in-place before moe_sum.
+
+        Reuses routing tensors returned by add_lora_w13.
+        """
+        raise NotImplementedError
diff --git a/vllm/lora/punica_wrapper/punica_gpu.py b/vllm/lora/punica_wrapper/punica_gpu.py
index 321cbfcab7cd..bf951e074949 100644
--- a/vllm/lora/punica_wrapper/punica_gpu.py
+++ b/vllm/lora/punica_wrapper/punica_gpu.py
@@ -335,25 +335,49 @@ def moe_lora_align_block_size(
         expert_map: torch.Tensor | None = None,
         pad_sorted_ids: bool = False,
         naive_block_assignment: bool = False,
+        token_lora_mapping: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         Aligns tokens and experts into block-sized chunks for LoRA-based
         mixture-of-experts (MoE) execution.
+
+        When `token_lora_mapping` is provided, it overrides the global mapping
+        read from `self.token_mapping_meta`. This is how EP+LoRA injects the
+        per-rank-local token→LoRA map after all-to-all dispatch.
         """
-        (token_lora_mapping, _, _, _, lora_ids, _, _) = (
-            self.token_mapping_meta.meta_args(
-                num_tokens, self.lora_config.specialize_active_lora
-            )
+        (
+            token_lora_mapping_meta,
+            _,
+            _,
+            _,
+            lora_ids,
+            _,
+            _,
+        ) = self.token_mapping_meta.meta_args(
+            num_tokens, self.lora_config.specialize_active_lora
+        )
+        if token_lora_mapping is None:
+            token_lora_mapping = token_lora_mapping_meta
+        # Under EP the caller passes local_num_experts but topk_ids carries
+        # GLOBAL expert indices. The CUDA kernel uses num_experts to size
+        # its bucketing table; with EP we must size by global_num_experts
+        # so global topk_ids don't overflow. expert_map inside the kernel
+        # then translates global→local so the output expert_ids are local
+        # (mirrors the non-LoRA moe_align_block_size behavior).
+        kernel_num_experts = (
+            expert_map.numel() if expert_map is not None else num_experts
         )
         if naive_block_assignment:
             expert_ids = topk_ids.reshape(-1)
             sorted_ids = None
             num_tokens_post_pad = None
         else:
-            max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
+            max_num_tokens_padded = topk_ids.numel() + kernel_num_experts * (
+                block_size - 1
+            )
             if pad_sorted_ids:
                 max_num_tokens_padded = round_up(max_num_tokens_padded, block_size)
-            if topk_ids.numel() < num_experts:
+            if topk_ids.numel() < kernel_num_experts:
                 max_num_tokens_padded = topk_ids.numel() * block_size
             sorted_ids = torch.empty(
                 (max_loras * max_num_tokens_padded,),
@@ -361,9 +385,12 @@ def moe_lora_align_block_size(
                 device=topk_ids.device,
             )
             max_num_m_blocks = triton.cdiv(max_num_tokens_padded, block_size)
-            # Expert ids must be set default to -1 to prevent a blank block
-            expert_ids = torch.empty(
+            # Expert ids are initialized to -1 so unused (lora, expert)
+            # slots don't drive the LoRA Triton kernel into the wrong bucket.
+            # The kernel overwrites only active slots.
+            expert_ids = torch.full(
                 (max_loras * max_num_m_blocks,),
+                -1,
                 dtype=torch.int32,
                 device=topk_ids.device,
             )
@@ -374,7 +401,7 @@ def moe_lora_align_block_size(
             ops.moe_lora_align_block_size(
                 topk_ids,
                 token_lora_mapping,
-                num_experts,
+                kernel_num_experts,
                 block_size,
                 max_loras,
                 max_num_tokens_padded,
@@ -384,11 +411,10 @@ def moe_lora_align_block_size(
                 num_tokens_post_pad,
                 adapter_enabled,
                 lora_ids,
+                expert_map,
             )
-            if expert_map is not None:
-                expert_ids = expert_map[expert_ids]
 
-        return None, sorted_ids, expert_ids, num_tokens_post_pad
+        return token_lora_mapping, sorted_ids, expert_ids, num_tokens_post_pad
 
     def add_lora_fused_moe(
         self,
@@ -459,3 +485,241 @@ def add_lora_fused_moe(
             fully_sharded,
             offset,
         )
+
+    def add_lora_w13(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        lora_a_stacked: tuple[torch.Tensor, ...],
+        lora_b_stacked: tuple[torch.Tensor, ...],
+        topk_ids: torch.Tensor,
+        topk_weights: torch.Tensor,
+        expert_map: torch.Tensor | None,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        num_tokens: int,
+        top_k_num: int,
+        max_loras: int,
+        adapter_enabled: torch.Tensor,
+        local_num_experts: int,
+        top_k: int,
+        num_slices: int,
+        fully_sharded: bool,
+        use_tuned_config: bool,
+        token_lora_mapping: torch.Tensor | None = None,
+    ) -> tuple[
+        torch.Tensor | None,
+        torch.Tensor | None,
+        torch.Tensor | None,
+        torch.Tensor | None,
+    ]:
+        import functools
+
+        from vllm.lora.layers.utils import try_get_optimal_moe_lora_config
+        from vllm.lora.ops.triton_ops.utils import (
+            _normalize_lora_config_keys,
+            get_lora_op_configs,
+        )
+        from vllm.model_executor.layers.fused_moe.config import _get_config_dtype_str
+
+        config_dtype = _get_config_dtype_str(
+            dtype=x.dtype,
+            use_fp8_w8a8=False,
+            use_int8_w8a16=False,
+            use_int4_w4a16=False,
+        )
+        max_lora_rank = lora_a_stacked[0].shape[-2]
+
+        if use_tuned_config:
+            shrink_config = get_lora_op_configs(
+                op_type="fused_moe_lora_w13_shrink",
+                max_loras=max_loras,
+                batch=num_tokens,
+                hidden_size=x.shape[-1],
+                rank=max_lora_rank,
+                num_slices=num_slices,
+                moe_intermediate_size=lora_b_stacked[0].shape[-2],
+            )
+            expand_config = get_lora_op_configs(
+                op_type="fused_moe_lora_w13_expand",
+                max_loras=max_loras,
+                batch=num_tokens,
+                hidden_size=x.shape[-1],
+                rank=max_lora_rank,
+                num_slices=num_slices,
+                moe_intermediate_size=lora_b_stacked[0].shape[-2],
+            )
+        else:
+            get_config = functools.partial(
+                try_get_optimal_moe_lora_config,
+                w1_shape=w1.shape,
+                w2_shape=w2.shape,
+                rank=max_lora_rank,
+                top_k=top_k,
+                dtype=config_dtype,
+                M=num_tokens,
+            )
+            shrink_config = get_config(op_type="fused_moe_lora_w13_shrink")
+            expand_config = get_config(op_type="fused_moe_lora_w13_expand")
+
+        shrink_config = _normalize_lora_config_keys(shrink_config)
+        expand_config = _normalize_lora_config_keys(expand_config)
+
+        SPARSITY_FACTOR = 8
+        naive_block_assignment = (
+            expert_map is None
+            and num_tokens * top_k * SPARSITY_FACTOR <= local_num_experts * max_loras
+        )
+
+        (
+            token_lora_mapping,
+            sorted_token_ids_lora,
+            expert_ids_lora,
+            num_tokens_post_padded_lora,
+        ) = self.moe_lora_align_block_size(
+            topk_ids,
+            num_tokens,
+            int(shrink_config.get("BLOCK_SIZE_M") or 64),
+            local_num_experts,
+            max_loras,
+            adapter_enabled,
+            expert_map,
+            naive_block_assignment=naive_block_assignment,
+            token_lora_mapping=token_lora_mapping,
+        )
+
+        _sorted = sorted_token_ids_lora
+        _eids = expert_ids_lora
+        if _sorted is not None:
+            _eids = _eids.view(max_loras, -1)
+            _sorted = _sorted.view(max_loras, -1)
+
+        self.add_lora_fused_moe(
+            y.view(-1, top_k_num, y.shape[-1]),
+            x,
+            lora_a_stacked,
+            lora_b_stacked,
+            topk_weights,
+            _sorted,
+            _eids,
+            num_tokens_post_padded_lora,
+            max_lora_rank,
+            top_k,
+            shrink_config,
+            expand_config,
+            adapter_enabled,
+            fully_sharded=fully_sharded,
+            token_lora_mapping=token_lora_mapping,
+        )
+
+        return (
+            sorted_token_ids_lora,
+            expert_ids_lora,
+            num_tokens_post_padded_lora,
+            token_lora_mapping,
+        )
+
+    def add_lora_w2(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        lora_a_stacked: tuple[torch.Tensor, ...],
+        lora_b_stacked: tuple[torch.Tensor, ...],
+        topk_weights: torch.Tensor,
+        sorted_token_ids_lora: torch.Tensor | None,
+        expert_ids_lora: torch.Tensor | None,
+        num_tokens_post_padded_lora: torch.Tensor | None,
+        token_lora_mapping: torch.Tensor | None,
+        num_tokens: int,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        top_k_num: int,
+        max_loras: int,
+        adapter_enabled: torch.Tensor,
+        top_k: int,
+        fully_sharded: bool,
+        tp_rank: int,
+        use_tuned_config: bool,
+    ) -> None:
+        import functools
+
+        from vllm.lora.layers.utils import try_get_optimal_moe_lora_config
+        from vllm.lora.ops.triton_ops.utils import (
+            _normalize_lora_config_keys,
+            get_lora_op_configs,
+        )
+        from vllm.model_executor.layers.fused_moe.config import _get_config_dtype_str
+
+        config_dtype = _get_config_dtype_str(
+            dtype=x.dtype,
+            use_fp8_w8a8=False,
+            use_int8_w8a16=False,
+            use_int4_w4a16=False,
+        )
+        max_lora_rank = lora_a_stacked[0].shape[-2]
+
+        if use_tuned_config:
+            shrink_config = get_lora_op_configs(
+                op_type="fused_moe_lora_w2_shrink",
+                max_loras=max_loras,
+                batch=num_tokens,
+                hidden_size=y.shape[-1],
+                rank=max_lora_rank,
+                num_slices=1,
+                moe_intermediate_size=lora_a_stacked[0].shape[-1],
+            )
+            expand_config = get_lora_op_configs(
+                op_type="fused_moe_lora_w2_expand",
+                max_loras=max_loras,
+                batch=num_tokens,
+                hidden_size=y.shape[-1],
+                rank=max_lora_rank,
+                num_slices=1,
+                moe_intermediate_size=lora_a_stacked[0].shape[-1],
+            )
+        else:
+            get_config = functools.partial(
+                try_get_optimal_moe_lora_config,
+                w1_shape=w1.shape,
+                w2_shape=w2.shape,
+                rank=max_lora_rank,
+                top_k=top_k,
+                dtype=config_dtype,
+                M=num_tokens,
+            )
+            shrink_config = get_config(op_type="fused_moe_lora_w2_shrink")
+            expand_config = get_config(op_type="fused_moe_lora_w2_expand")
+
+        shrink_config = _normalize_lora_config_keys(shrink_config)
+        expand_config = _normalize_lora_config_keys(expand_config)
+
+        _sorted = sorted_token_ids_lora
+        _eids = expert_ids_lora
+        if _sorted is not None:
+            assert _eids is not None
+            _eids = _eids.view(max_loras, -1)
+            _sorted = _sorted.view(max_loras, -1)
+
+        # w2_lora_b shape[-2] is hidden_size // tp_size when fully_sharded
+        shard_size = lora_b_stacked[0].shape[-2]
+        offset = shard_size * tp_rank if fully_sharded else 0
+
+        self.add_lora_fused_moe(
+            y,
+            x,
+            lora_a_stacked,
+            lora_b_stacked,
+            topk_weights,
+            _sorted,
+            _eids,
+            num_tokens_post_padded_lora,
+            max_lora_rank,
+            top_k,
+            shrink_config,
+            expand_config,
+            adapter_enabled,
+            True,  # mul_routed_weight
+            fully_sharded=fully_sharded,
+            offset=offset,
+            token_lora_mapping=token_lora_mapping,
+        )
diff --git a/vllm/lora/punica_wrapper/punica_xpu.py b/vllm/lora/punica_wrapper/punica_xpu.py
old mode 100644
new mode 100755
index f031e1bfa341..58316cb75970
--- a/vllm/lora/punica_wrapper/punica_xpu.py
+++ b/vllm/lora/punica_wrapper/punica_xpu.py
@@ -14,6 +14,7 @@
 from vllm import _custom_ops as ops
 from vllm.lora.layers import LoRAMapping
 from vllm.lora.ops.xpu_ops import bgmv_expand, bgmv_expand_slice, bgmv_shrink
+from vllm.lora.utils import get_captured_lora_counts
 from vllm.triton_utils import HAS_TRITON, triton
 from vllm.utils.math_utils import round_up
 
@@ -48,8 +49,24 @@ def __init__(
 
         self.lora_config = kwargs["lora_config"]
         self.max_loras = self.lora_config.max_loras
+
+        # Compute captured LoRA counts for cudagraph specialization.
+        captured_lora_counts = get_captured_lora_counts(
+            self.max_loras, self.lora_config.specialize_active_lora
+        )
+
         self.token_mapping_meta = LoRAKernelMeta.make(
-            self.max_loras, max_num_batched_tokens, device=device
+            self.max_loras,
+            max_num_batched_tokens,
+            device=device,
+            captured_lora_counts=captured_lora_counts,
+        )
+
+        self.prompt_mapping_meta = LoRAKernelMeta.make(
+            self.max_loras,
+            max_num_batched_tokens,
+            device=device,
+            captured_lora_counts=captured_lora_counts,
         )
 
     def update_metadata(
@@ -63,6 +80,10 @@ def update_metadata(
         self.is_prefill = mapping.is_prefill
         self._update_base_metadata(mapping, lora_index_to_id, max_loras, vocab_size)
 
+        # Prepare kernel metadata tensors
+        self.token_mapping_meta.prepare_tensors(self.token_lora_indices)
+        self.prompt_mapping_meta.prepare_tensors(self.sampler_indices)
+
     def _get_token_lora_indices(self, x: torch.Tensor) -> torch.IntTensor:
         return torch.narrow(self._token_lora_indices, 0, 0, x.size(0))
 
@@ -419,3 +440,240 @@ def add_lora_fused_moe(
             fully_sharded,
             offset,
         )
+
+    def add_lora_w13(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        lora_a_stacked: tuple[torch.Tensor, ...],
+        lora_b_stacked: tuple[torch.Tensor, ...],
+        topk_ids: torch.Tensor,
+        topk_weights: torch.Tensor,
+        expert_map: torch.Tensor | None,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        num_tokens: int,
+        top_k_num: int,
+        max_loras: int,
+        adapter_enabled: torch.Tensor,
+        local_num_experts: int,
+        top_k: int,
+        num_slices: int,
+        fully_sharded: bool,
+        use_tuned_config: bool,
+        token_lora_mapping: torch.Tensor | None = None,
+    ) -> tuple[
+        torch.Tensor | None,
+        torch.Tensor | None,
+        torch.Tensor | None,
+        torch.Tensor | None,
+    ]:
+        import functools
+
+        from vllm.lora.layers.utils import try_get_optimal_moe_lora_config
+        from vllm.lora.ops.triton_ops.utils import (
+            _normalize_lora_config_keys,
+            get_lora_op_configs,
+        )
+        from vllm.model_executor.layers.fused_moe.config import _get_config_dtype_str
+
+        config_dtype = _get_config_dtype_str(
+            dtype=x.dtype,
+            use_fp8_w8a8=False,
+            use_int8_w8a16=False,
+            use_int4_w4a16=False,
+        )
+        max_lora_rank = lora_a_stacked[0].shape[-2]
+
+        if use_tuned_config:
+            shrink_config = get_lora_op_configs(
+                op_type="fused_moe_lora_w13_shrink",
+                max_loras=max_loras,
+                batch=num_tokens,
+                hidden_size=x.shape[-1],
+                rank=max_lora_rank,
+                num_slices=num_slices,
+                moe_intermediate_size=lora_b_stacked[0].shape[-2],
+            )
+            expand_config = get_lora_op_configs(
+                op_type="fused_moe_lora_w13_expand",
+                max_loras=max_loras,
+                batch=num_tokens,
+                hidden_size=x.shape[-1],
+                rank=max_lora_rank,
+                num_slices=num_slices,
+                moe_intermediate_size=lora_b_stacked[0].shape[-2],
+            )
+        else:
+            get_config = functools.partial(
+                try_get_optimal_moe_lora_config,
+                w1_shape=w1.shape,
+                w2_shape=w2.shape,
+                rank=max_lora_rank,
+                top_k=top_k,
+                dtype=config_dtype,
+                M=num_tokens,
+            )
+            shrink_config = get_config(op_type="fused_moe_lora_w13_shrink")
+            expand_config = get_config(op_type="fused_moe_lora_w13_expand")
+
+        shrink_config = _normalize_lora_config_keys(shrink_config)
+        expand_config = _normalize_lora_config_keys(expand_config)
+
+        SPARSITY_FACTOR = 8
+        naive_block_assignment = (
+            expert_map is None
+            and num_tokens * top_k * SPARSITY_FACTOR <= local_num_experts * max_loras
+        )
+
+        (
+            token_lora_mapping,
+            sorted_token_ids_lora,
+            expert_ids_lora,
+            num_tokens_post_padded_lora,
+        ) = self.moe_lora_align_block_size(
+            topk_ids,
+            num_tokens,
+            int(shrink_config.get("BLOCK_SIZE_M") or 64),
+            local_num_experts,
+            max_loras,
+            adapter_enabled,
+            expert_map,
+            naive_block_assignment=naive_block_assignment,
+        )
+
+        _sorted = sorted_token_ids_lora
+        _eids = expert_ids_lora
+        if _sorted is not None:
+            _eids = _eids.view(max_loras, -1)
+            _sorted = _sorted.view(max_loras, -1)
+
+        self.add_lora_fused_moe(
+            y.view(-1, top_k_num, y.shape[-1]),
+            x,
+            lora_a_stacked,
+            lora_b_stacked,
+            topk_weights,
+            _sorted,
+            _eids,
+            num_tokens_post_padded_lora,
+            max_lora_rank,
+            top_k,
+            shrink_config,
+            expand_config,
+            adapter_enabled,
+            fully_sharded=fully_sharded,
+            token_lora_mapping=token_lora_mapping,
+        )
+
+        return (
+            sorted_token_ids_lora,
+            expert_ids_lora,
+            num_tokens_post_padded_lora,
+            token_lora_mapping,
+        )
+
+    def add_lora_w2(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        lora_a_stacked: tuple[torch.Tensor, ...],
+        lora_b_stacked: tuple[torch.Tensor, ...],
+        topk_weights: torch.Tensor,
+        sorted_token_ids_lora: torch.Tensor | None,
+        expert_ids_lora: torch.Tensor | None,
+        num_tokens_post_padded_lora: torch.Tensor | None,
+        token_lora_mapping: torch.Tensor | None,
+        num_tokens: int,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        top_k_num: int,
+        max_loras: int,
+        adapter_enabled: torch.Tensor,
+        top_k: int,
+        fully_sharded: bool,
+        tp_rank: int,
+        use_tuned_config: bool,
+    ) -> None:
+        import functools
+
+        from vllm.lora.layers.utils import try_get_optimal_moe_lora_config
+        from vllm.lora.ops.triton_ops.utils import (
+            _normalize_lora_config_keys,
+            get_lora_op_configs,
+        )
+        from vllm.model_executor.layers.fused_moe.config import _get_config_dtype_str
+
+        config_dtype = _get_config_dtype_str(
+            dtype=x.dtype,
+            use_fp8_w8a8=False,
+            use_int8_w8a16=False,
+            use_int4_w4a16=False,
+        )
+        max_lora_rank = lora_a_stacked[0].shape[-2]
+
+        if use_tuned_config:
+            shrink_config = get_lora_op_configs(
+                op_type="fused_moe_lora_w2_shrink",
+                max_loras=max_loras,
+                batch=num_tokens,
+                hidden_size=y.shape[-1],
+                rank=max_lora_rank,
+                num_slices=1,
+                moe_intermediate_size=lora_a_stacked[0].shape[-1],
+            )
+            expand_config = get_lora_op_configs(
+                op_type="fused_moe_lora_w2_expand",
+                max_loras=max_loras,
+                batch=num_tokens,
+                hidden_size=y.shape[-1],
+                rank=max_lora_rank,
+                num_slices=1,
+                moe_intermediate_size=lora_a_stacked[0].shape[-1],
+            )
+        else:
+            get_config = functools.partial(
+                try_get_optimal_moe_lora_config,
+                w1_shape=w1.shape,
+                w2_shape=w2.shape,
+                rank=max_lora_rank,
+                top_k=top_k,
+                dtype=config_dtype,
+                M=num_tokens,
+            )
+            shrink_config = get_config(op_type="fused_moe_lora_w2_shrink")
+            expand_config = get_config(op_type="fused_moe_lora_w2_expand")
+
+        shrink_config = _normalize_lora_config_keys(shrink_config)
+        expand_config = _normalize_lora_config_keys(expand_config)
+
+        _sorted = sorted_token_ids_lora
+        _eids = expert_ids_lora
+        if _sorted is not None:
+            assert _eids is not None
+            _eids = _eids.view(max_loras, -1)
+            _sorted = _sorted.view(max_loras, -1)
+
+        # w2_lora_b shape[-2] is hidden_size // tp_size when fully_sharded
+        shard_size = lora_b_stacked[0].shape[-2]
+        offset = shard_size * tp_rank if fully_sharded else 0
+
+        self.add_lora_fused_moe(
+            y,
+            x,
+            lora_a_stacked,
+            lora_b_stacked,
+            topk_weights,
+            _sorted,
+            _eids,
+            num_tokens_post_padded_lora,
+            max_lora_rank,
+            top_k,
+            shrink_config,
+            expand_config,
+            adapter_enabled,
+            True,  # mul_routed_weight
+            fully_sharded=fully_sharded,
+            offset=offset,
+            token_lora_mapping=token_lora_mapping,
+        )
diff --git a/vllm/lora/utils.py b/vllm/lora/utils.py
index 2349ace70846..2991447a6ad4 100644
--- a/vllm/lora/utils.py
+++ b/vllm/lora/utils.py
@@ -73,7 +73,9 @@ def get_lora_id():
     return _GLOBAL_LORA_ID
 
 
-_all_lora_classes: set[type[BaseLayerWithLoRA]] = {
+# Order matters here: more specific wrappers must be checked before generic
+# merged/column-parallel wrappers in from_layer().
+_all_lora_classes: tuple[type[BaseLayerWithLoRA], ...] = (
     VocabParallelEmbeddingWithLoRA,
     ColumnParallelLinearWithLoRA,
     MergedColumnParallelLinearWithLoRA,
@@ -90,7 +92,7 @@ def get_lora_id():
     RowParallelLinearWithShardedLoRA,
     FusedMoEWithLoRA,
     FusedMoE3DWithLoRA,
-}
+)
 
 
 def is_moe_model(model: nn.Module) -> bool:
@@ -258,6 +260,7 @@ def is_supported_lora_module(
 def is_in_target_modules(
     module_name: str,
     target_modules: list[str] | None,
+    packed_modules_mapping: dict[str, list[str]] | None = None,
 ) -> bool:
     """Check if a module passes the deployment-time target_modules filter.
 
@@ -268,14 +271,33 @@ def is_in_target_modules(
         module_name: Full dot-separated module name.
         target_modules: Optional deployment-time restriction list from
             LoRAConfig.target_modules.
+        packed_modules_mapping: Optional model-defined mapping from packed
+            runtime module names to their adapter-visible submodule names
+            (e.g. ``{"gate_up_proj": ["gate_proj", "up_proj"]}``).
 
     Returns:
         True if the module passes the filter, False otherwise.
     """
     if target_modules is None:
         return True
+    target_module_set = set(target_modules)
     module_suffix = module_name.split(".")[-1]
-    return module_suffix in set(target_modules)
+    if module_suffix in target_module_set or module_name in target_module_set:
+        return True
+
+    if not packed_modules_mapping:
+        return False
+
+    # Runtime packed parent matched by deployment-time child targets.
+    packed_children = packed_modules_mapping.get(module_suffix)
+    if packed_children and any(child in target_module_set for child in packed_children):
+        return True
+
+    # Adapter-visible packed child matched by deployment-time parent target.
+    return any(
+        module_suffix in children and packed_parent in target_module_set
+        for packed_parent, children in packed_modules_mapping.items()
+    )
 
 
 def get_adapter_absolute_path(lora_path: str) -> str:
diff --git a/vllm/lora/worker_manager.py b/vllm/lora/worker_manager.py
index bea6d015e0a6..5b9bf2d76fbb 100644
--- a/vllm/lora/worker_manager.py
+++ b/vllm/lora/worker_manager.py
@@ -17,11 +17,7 @@
 )
 from vllm.lora.peft_helper import PEFTHelper
 from vllm.lora.request import LoRARequest
-from vllm.lora.utils import (
-    get_adapter_absolute_path,
-    is_in_target_modules,
-    is_supported_lora_module,
-)
+from vllm.lora.utils import get_adapter_absolute_path
 
 logger = init_logger(__name__)
 
@@ -146,30 +142,6 @@ def _load_adapter(self, lora_request: LoRARequest) -> LoRAModel:
                 skip_prefixes=lora_skip_prefixes,
             )
 
-            # Warn about adapter modules that will be ignored.
-            target_modules = self.lora_config.target_modules
-            expected_lora_modules_lst = list(expected_lora_modules)
-            for module_name in lora.loras:
-                if not is_supported_lora_module(module_name, expected_lora_modules_lst):
-                    logger.warning_once(
-                        "LoRA module '%s' in adapter '%s' is not in the "
-                        "model's supported LoRA target modules [%s]. "
-                        "These parameters will be ignored, which may "
-                        "cause abnormal model behavior.",
-                        module_name,
-                        lora_request.lora_path,
-                        ", ".join(sorted(expected_lora_modules_lst)),
-                    )
-                elif not is_in_target_modules(module_name, target_modules):
-                    logger.warning_once(
-                        "LoRA module '%s' in adapter '%s' is not in the "
-                        "deployment-time target_modules restriction [%s]."
-                        " These parameters will be ignored.",
-                        module_name,
-                        lora_request.lora_path,
-                        ", ".join(sorted(target_modules)),
-                    )
-
         except FileNotFoundError as e:
             # FileNotFoundError should be raised if both
             # - No adapter found to download from huggingface (or in
@@ -197,6 +169,9 @@ def add_dummy_lora(self, lora_request: LoRARequest, rank: int) -> bool:
                 self._cached_dummy_lora = dummy_lora
         return self._adapter_manager.add_adapter(dummy_lora)
 
+    def get_dummy_lora_warmup_rank(self, default_rank: int) -> int:
+        return self._adapter_manager.get_dummy_lora_warmup_rank(default_rank)
+
     def pin_adapter(self, adapter_id: int) -> bool:
         return self._adapter_manager.pin_adapter(adapter_id)
 
diff --git a/vllm/model_executor/kernels/linear/__init__.py b/vllm/model_executor/kernels/linear/__init__.py
index e0a9272c8902..b9a96a035309 100644
--- a/vllm/model_executor/kernels/linear/__init__.py
+++ b/vllm/model_executor/kernels/linear/__init__.py
@@ -106,8 +106,11 @@
 from vllm.model_executor.kernels.linear.scaled_mm.aiter import (
     AiterFp8BlockScaledMMKernel,
     AiterInt8ScaledMMLinearKernel,
+    AiterPerTokenFp8ScaledMMLinearKernel,
+    AiterPreshuffledPerTokenFp8ScaledMMLinearKernel,
 )
 from vllm.model_executor.kernels.linear.scaled_mm.cpu import (
+    CPUFp8BlockScaledMMKernel,
     CPUInt8ScaledMMLinearKernel,
 )
 from vllm.model_executor.kernels.linear.scaled_mm.cutlass import (
@@ -165,6 +168,8 @@
         ChannelWiseTorchFP8ScaledMMLinearKernel,
     ],
     PlatformEnum.ROCM: [
+        AiterPreshuffledPerTokenFp8ScaledMMLinearKernel,
+        AiterPerTokenFp8ScaledMMLinearKernel,
         ROCmFP8ScaledMMLinearKernel,
         PerTensorTorchFP8ScaledMMLinearKernel,
         RowWiseTorchFP8ScaledMMLinearKernel,
@@ -182,18 +187,22 @@
 
 # in priority/performance order (when available)
 _POSSIBLE_FP8_BLOCK_KERNELS: dict[
-    PlatformEnum, list[type[Fp8BlockScaledMMLinearKernel]]
+    PlatformEnum, list[type[Fp8BlockScaledMMLinearKernel | FP8ScaledMMLinearKernel]]
 ] = {
     PlatformEnum.CUDA: [
         FlashInferFp8DeepGEMMDynamicBlockScaledKernel,
         DeepGemmFp8BlockScaledMMKernel,
         CutlassFp8BlockScaledMMKernel,
+        MarlinFP8ScaledMMLinearKernel,
         TritonFp8BlockScaledMMKernel,
     ],
     PlatformEnum.ROCM: [
         AiterFp8BlockScaledMMKernel,
         TritonFp8BlockScaledMMKernel,
     ],
+    PlatformEnum.CPU: [
+        CPUFp8BlockScaledMMKernel,
+    ],
 }
 
 _POSSIBLE_WFP8A16_KERNELS: dict[PlatformEnum, list[type[FP8ScaledMMLinearKernel]]] = {
@@ -360,18 +369,18 @@ def choose_scaled_mm_linear_kernel(
 def init_fp8_linear_kernel(
     activation_quant_key: QuantKey,
     weight_quant_key: QuantKey,
-    weight_shape: tuple[int, int],
     input_dtype: torch.dtype,
     out_dtype: torch.dtype,
-    force_kernel: type[_KernelT] | None = None,
+    weight_shape: tuple[int, int],
+    force_kernel: type[FP8ScaledMMLinearKernel] | None = None,
     module_name: str | None = None,
 ) -> FP8ScaledMMLinearKernel | Fp8BlockScaledMMLinearKernel:
     scaled_mm_linear_kernel_config = FP8ScaledMMLinearLayerConfig(
         weight_quant_key=weight_quant_key,
         activation_quant_key=activation_quant_key,
-        weight_shape=weight_shape,
         input_dtype=input_dtype,
         out_dtype=out_dtype,
+        weight_shape=weight_shape,
     )
 
     if activation_quant_key.scale.group_shape.is_per_group():
@@ -388,6 +397,19 @@ def init_fp8_linear_kernel(
                 scope="global",
             )
 
+        # TODO make scaled_mm kernels inherit from MMLinearKernel
+        # only MarlinFP8ScaledMMLinearKernel is a type of FP8ScaledMMLinearKernel.
+        if issubclass(kernel_type, FP8ScaledMMLinearKernel):
+            return kernel_type(
+                scaled_mm_linear_kernel_config,
+                layer_param_names=[
+                    "weight",
+                    "weight_scale",
+                    "input_scale",
+                    "input_scale_ub",
+                ],
+            )
+
         return kernel_type(
             scaled_mm_linear_kernel_config,
         )
@@ -395,7 +417,7 @@ def init_fp8_linear_kernel(
     else:
         kernel_type = choose_scaled_mm_linear_kernel(
             config=scaled_mm_linear_kernel_config,
-            possible_kernels=_POSSIBLE_FP8_KERNELS,  # type: ignore[misc]
+            possible_kernels=_POSSIBLE_FP8_KERNELS,  # type: ignore[arg-type]
             force_kernel=force_kernel,
         )
         if module_name:
@@ -725,6 +747,8 @@ def register_linear_kernel(
     "FP8ScaledMMLinearLayerConfig",
     "Int8ScaledMMLinearLayerConfig",
     "ScaledMMLinearLayerConfig",
+    "AiterPreshuffledPerTokenFp8ScaledMMLinearKernel",
+    "AiterPerTokenFp8ScaledMMLinearKernel",
     "NvFp4LinearKernel",
     "NvFp4LinearLayerConfig",
     "AiterInt8ScaledMMLinearKernel",
diff --git a/vllm/model_executor/kernels/linear/scaled_mm/__init__.py b/vllm/model_executor/kernels/linear/scaled_mm/__init__.py
index e86684b2f8a1..f8f12f7b0cba 100644
--- a/vllm/model_executor/kernels/linear/scaled_mm/__init__.py
+++ b/vllm/model_executor/kernels/linear/scaled_mm/__init__.py
@@ -8,6 +8,7 @@
     Fp8BlockScaledMMLinearKernel,
 )
 from vllm.model_executor.kernels.linear.scaled_mm.cpu import (
+    CPUFp8BlockScaledMMKernel,
     CPUInt8ScaledMMLinearKernel,
 )
 from vllm.model_executor.kernels.linear.scaled_mm.cutlass import (
@@ -58,4 +59,5 @@
     "ROCmFP8ScaledMMLinearKernel",
     "TritonInt8ScaledMMLinearKernel",
     "Fp8BlockScaledMMLinearKernel",
+    "CPUFp8BlockScaledMMKernel",
 ]
diff --git a/vllm/model_executor/kernels/linear/scaled_mm/aiter.py b/vllm/model_executor/kernels/linear/scaled_mm/aiter.py
index 01d2298ed4a0..5ded5ca798ad 100644
--- a/vllm/model_executor/kernels/linear/scaled_mm/aiter.py
+++ b/vllm/model_executor/kernels/linear/scaled_mm/aiter.py
@@ -5,18 +5,27 @@
 import torch
 
 from vllm import _custom_ops as ops
-from vllm._aiter_ops import rocm_aiter_ops
+from vllm._aiter_ops import (
+    rocm_aiter_ops,
+)
+from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     GroupShape,
 )
+from vllm.model_executor.utils import replace_parameter
 from vllm.platforms import current_platform
 
 from .BlockScaledMMLinearKernel import (
     Fp8BlockScaledMMLinearKernel,
-    FP8ScaledMMLinearLayerConfig,
 )
 from .cutlass import CutlassInt8ScaledMMLinearKernel
-from .ScaledMMLinearKernel import Int8ScaledMMLinearLayerConfig
+from .ScaledMMLinearKernel import (
+    FP8ScaledMMLinearKernel,
+    FP8ScaledMMLinearLayerConfig,
+    Int8ScaledMMLinearLayerConfig,
+)
+
+logger = init_logger(__name__)
 
 
 class AiterInt8ScaledMMLinearKernel(CutlassInt8ScaledMMLinearKernel):
@@ -113,7 +122,154 @@ def apply_weights(
         # a to be [M, K]
         # b to be [N, K]
         # CutlassInt8ScaledMMLinearKernel prepare weight `w_q` in [K, N] format
-        return rocm_aiter_ops.gemm_a8w8(x_q, w_q.t(), x_s, w_s, bias, out_dtype)
+        return rocm_aiter_ops.w8a8_gemm(x_q, w_q.t(), x_s, w_s, bias, out_dtype)
+
+
+class AiterPreshuffledPerTokenFp8ScaledMMLinearKernel(FP8ScaledMMLinearKernel):
+    @classmethod
+    def is_supported(
+        cls, compute_capability: int | None = None
+    ) -> tuple[bool, str | None]:
+        if not current_platform.is_rocm():
+            return False, "requires ROCm."
+        if not rocm_aiter_ops.is_linear_fp8_enabled():
+            return (
+                False,
+                "requires setting `VLLM_ROCM_USE_AITER=1` "
+                "and `VLLM_ROCM_USE_AITER_LINEAR=1`. "
+                "`VLLM_ROCM_USE_AITER_LINEAR` default is True.",
+            )
+        try:
+            import aiter  # noqa: F401
+        except Exception:
+            return False, "requires aiter library to be installed."
+        return True, None
+
+    @classmethod
+    def can_implement(cls, c: FP8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+        is_ptpc = (
+            c.activation_quant_key.scale.group_shape.is_per_token()
+            and c.weight_quant_key.scale.group_shape.is_per_channel()
+        )
+        if c.weight_shape is None:
+            return False, "weight_shape is required for Aiter kernels"
+        N, K = c.weight_shape
+        fp8_dtype = current_platform.fp8_dtype()
+
+        if c.out_dtype is not torch.bfloat16:
+            return False, "requires bfloat16 output dtype."
+
+        if not is_ptpc:
+            return (
+                False,
+                "requires per token activation scales and per channel weight scales.",
+            )
+
+        if not (N % 16 == 0 and K % 16 == 0):
+            return (
+                False,
+                f"requires N and K dimensions divisible by 16, received "
+                f"N={N} and K={K}.",
+            )
+
+        # Aiter's shuffled per-token Gemm performs better than torch only when its
+        # tuned.
+        if not rocm_aiter_ops.is_shuffled_per_token_w8a8_gemm_tuned(N, K, fp8_dtype):
+            return (
+                False,
+                f"requires a tuned configuration for N: {N} and K: {K} "
+                f"and fp8 dtype {fp8_dtype}.",
+            )
+
+        return True, None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        w_name, *_ = self.layer_param_names
+        w, *_ = self._get_layer_params(layer)
+
+        replace_parameter(
+            layer,
+            w_name,
+            torch.nn.Parameter(
+                rocm_aiter_ops.shuffle_weight(w.t().contiguous()).data,
+                requires_grad=False,
+            ),
+        )
+
+    def apply_scaled_mm(
+        self,
+        *,
+        A: torch.Tensor,
+        B: torch.Tensor,
+        out_dtype: torch.dtype,
+        As: torch.Tensor,
+        Bs: torch.Tensor,
+        bias: torch.Tensor | None,
+        output_shape: list,
+    ) -> torch.Tensor:
+        return rocm_aiter_ops.preshuffled_per_token_w8a8_gemm(
+            A, B, As, Bs, bias, out_dtype
+        )
+
+
+class AiterPerTokenFp8ScaledMMLinearKernel(FP8ScaledMMLinearKernel):
+    @classmethod
+    def is_supported(
+        cls, compute_capability: int | None = None
+    ) -> tuple[bool, str | None]:
+        return AiterPreshuffledPerTokenFp8ScaledMMLinearKernel.is_supported(
+            compute_capability
+        )
+
+    @classmethod
+    def can_implement(cls, c: FP8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+        is_ptpc = (
+            c.activation_quant_key.scale.group_shape.is_per_token()
+            and c.weight_quant_key.scale.group_shape.is_per_channel()
+        )
+        if c.weight_shape is None:
+            return False, "weight_shape is required for Aiter kernels"
+        N, K = c.weight_shape
+        fp8_dtype = current_platform.fp8_dtype()
+
+        if not is_ptpc:
+            return (
+                False,
+                "requires per token activation scales and per channel weight scales.",
+            )
+
+        # Aiter's per-token Gemm performs better than torch oonly when its
+        # tuned.
+        if not rocm_aiter_ops.is_per_token_w8a8_gemm_tuned(N, K, fp8_dtype):
+            return (
+                False,
+                f"requires a tuned configuration for N: {N} and K: {K} "
+                f"and fp8 dtype {fp8_dtype}.",
+            )
+        return True, None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        w_name, *_ = self.layer_param_names
+        w, *_ = self._get_layer_params(layer)
+
+        replace_parameter(
+            layer,
+            w_name,
+            torch.nn.Parameter(w.t(), requires_grad=False),
+        )
+
+    def apply_scaled_mm(
+        self,
+        *,
+        A: torch.Tensor,
+        B: torch.Tensor,
+        out_dtype: torch.dtype,
+        As: torch.Tensor,
+        Bs: torch.Tensor,
+        bias: torch.Tensor | None,
+        output_shape: list,
+    ) -> torch.Tensor:
+        return rocm_aiter_ops.w8a8_gemm(A, B, As, Bs, bias, out_dtype)
 
 
 class AiterFp8BlockScaledMMKernel(Fp8BlockScaledMMLinearKernel):
@@ -156,6 +312,21 @@ def apply_block_scaled_mm(
         As: torch.Tensor,
         Bs: torch.Tensor,
     ) -> torch.Tensor:
+        if As.dtype != Bs.dtype:
+            from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+                _upcast_e8m0_to_fp32,
+            )
+
+            if As.dtype == torch.float8_e8m0fnu:
+                As = _upcast_e8m0_to_fp32(As).contiguous()
+            else:
+                As = As.to(torch.float32)
+
+            if Bs.dtype == torch.float8_e8m0fnu:
+                Bs = _upcast_e8m0_to_fp32(Bs).contiguous()
+            else:
+                Bs = Bs.to(torch.float32)
+
         out_dtype = self.config.out_dtype
         if self.use_triton:
             gemm_a8w8_blockscale_op = rocm_aiter_ops.triton_gemm_a8w8_blockscale
diff --git a/vllm/model_executor/kernels/linear/scaled_mm/cpu.py b/vllm/model_executor/kernels/linear/scaled_mm/cpu.py
index 3d67a73af433..083cb473aaca 100644
--- a/vllm/model_executor/kernels/linear/scaled_mm/cpu.py
+++ b/vllm/model_executor/kernels/linear/scaled_mm/cpu.py
@@ -14,6 +14,10 @@
 from vllm.platforms import current_platform
 from vllm.platforms.interface import CpuArchEnum
 
+from .BlockScaledMMLinearKernel import (
+    Fp8BlockScaledMMLinearKernel,
+    FP8ScaledMMLinearLayerConfig,
+)
 from .ScaledMMLinearKernel import (
     Int8ScaledMMLinearKernel,
     Int8ScaledMMLinearLayerConfig,
@@ -215,3 +219,109 @@ def _apply_weights_sgl(
             x.dtype,
             True,
         )
+
+
+class CPUFp8BlockScaledMMKernel(Fp8BlockScaledMMLinearKernel):
+    """FP8 W8A16 block-quantized GEMM via AMX BRGEMM on CPU."""
+
+    # Input stays BF16 — no FP8 activation quantization.
+    apply_input_quant = False
+
+    @classmethod
+    def is_supported(
+        cls, compute_capability: int | None = None
+    ) -> tuple[bool, str | None]:
+        if not current_platform.is_cpu():
+            return False, "requires CPU platform."
+        if not torch.cpu._is_amx_tile_supported():
+            return False, "requires AMX tile support (Sapphire Rapids or newer)."
+        if not ops._supports_cpu_fp8_w8a16:
+            return False, "fp8_scaled_mm_cpu op not available."
+        return True, None
+
+    @classmethod
+    def can_implement(
+        cls, config: FP8ScaledMMLinearLayerConfig
+    ) -> tuple[bool, str | None]:
+        # Validate weight block shape
+        weight_gs = config.weight_quant_key.scale.group_shape
+        if weight_gs.col <= 0 or weight_gs.col != 128:
+            return False, (
+                "CPU FP8 kernel requires K-dimension block size of 128, "
+                f"got {weight_gs.col}."
+            )
+        if weight_gs.row <= 0 or weight_gs.row % 32 != 0:
+            return False, (
+                "CPU FP8 kernel requires N-dimension block size to be "
+                f"a positive multiple of 32, got {weight_gs.row}."
+            )
+        if config.out_dtype not in (torch.bfloat16, torch.float32):
+            return False, "Only bfloat16/float32 output dtype supported."
+        return True, None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # Skip the base class process (FP8 padding / fnuz normalization)
+        # which is GPU-oriented.  Instead, VNNI-prepack weights for AMX.
+        params = self._get_layer_params(layer)
+        packed_weight = torch.ops._C.convert_weight_packed(params.weight)
+        replace_parameter(
+            layer,
+            params.WEIGHT,
+            torch.nn.Parameter(packed_weight, requires_grad=False),
+        )
+
+        # Re-wrap scale as a plain Parameter so the kernel can read it
+        # without weight-loader metadata interfering.
+        scale_attr = (
+            params.WEIGHT_SCALE_INV
+            if params.weight_scale_inv is not None
+            else params.WEIGHT_SCALE
+        )
+        weight_scale = (
+            params.weight_scale_inv
+            if params.weight_scale_inv is not None
+            else params.weight_scale
+        )
+        assert weight_scale is not None
+        replace_parameter(
+            layer,
+            scale_attr,
+            torch.nn.Parameter(weight_scale.data, requires_grad=False),
+        )
+
+    def apply_weights(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        params = self._get_layer_params(layer)
+        weight_scale = (
+            params.weight_scale_inv
+            if params.weight_scale_inv is not None
+            else params.weight_scale
+        )
+
+        x_2d = x.reshape(-1, x.shape[-1]) if x.dim() > 2 else x
+        out = torch.ops._C.fp8_scaled_mm_cpu(
+            x_2d,
+            params.weight,
+            weight_scale,
+            list(self.weight_group_shape),
+            bias,
+            x.dtype,
+            True,  # is_vnni (weight already prepacked)
+        )
+        return out.reshape(x.shape[:-1] + (out.size(-1),)) if x.dim() > 2 else out
+
+    def apply_block_scaled_mm(
+        self,
+        A: torch.Tensor,
+        B: torch.Tensor,
+        As: torch.Tensor,
+        Bs: torch.Tensor,
+    ) -> torch.Tensor:
+        raise NotImplementedError(
+            "CPUFp8BlockScaledMMKernel overrides apply_weights directly."
+        )
diff --git a/vllm/model_executor/kernels/linear/scaled_mm/cutlass.py b/vllm/model_executor/kernels/linear/scaled_mm/cutlass.py
index 618084029159..2a7b7dc1373c 100644
--- a/vllm/model_executor/kernels/linear/scaled_mm/cutlass.py
+++ b/vllm/model_executor/kernels/linear/scaled_mm/cutlass.py
@@ -162,6 +162,20 @@ def is_supported(
     def can_implement(cls, c: FP8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
         return True, None
 
+    @staticmethod
+    def _pad_to_alignment(
+        x: torch.Tensor, dim: int, alignment: int, value: float = 0.0
+    ) -> torch.Tensor:
+        """Pad tensor ``x`` along ``dim`` to the next multiple of
+        ``alignment``."""
+        remainder = x.shape[dim] % alignment
+        if remainder == 0:
+            return x
+        pad_size = alignment - remainder
+        pad_spec = [0] * (2 * x.dim())
+        pad_spec[-(2 * dim + 1)] = pad_size
+        return torch.nn.functional.pad(x, pad_spec, value=value)
+
     def apply_scaled_mm(
         self,
         *,
@@ -173,10 +187,38 @@ def apply_scaled_mm(
         bias: torch.Tensor | None,
         output_shape: list,
     ) -> torch.Tensor:
-        # Fused GEMM_DQ
+        # Per-tensor/Per-channel padding to use Cutlass instead of Triton.
+        K, N = B.shape
+        pad_k = (16 - K % 16) % 16
+        pad_n = (16 - N % 16) % 16
+
+        if pad_k > 0 or pad_n > 0:
+            # B is column-major [K, N].  Transpose to row-major [N, K],
+            # pad both dims in one call, then transpose back so the
+            # result keeps column-major layout with stride (1, K_padded).
+            B = torch.nn.functional.pad(B.t().contiguous(), (0, pad_k, 0, pad_n)).t()
+
+            if pad_k > 0:
+                A = self._pad_to_alignment(A, dim=1, alignment=16)
+            if pad_n > 0:
+                if bias is not None:
+                    bias = self._pad_to_alignment(bias, dim=0, alignment=16)
+                # Bs is per-tensor (numel==1) or per-channel (numel==N)
+                # in this kernel class — never 2D block-wise.
+                if Bs.numel() > 1:
+                    Bs = self._pad_to_alignment(
+                        Bs.view(-1), dim=0, alignment=16, value=1.0
+                    )
+                    if Bs.dim() == 1 and B.shape[1] > 1:
+                        Bs = Bs.view(-1, 1)
+
         output = ops.cutlass_scaled_mm(
             A, B, out_dtype=out_dtype, scale_a=As, scale_b=Bs, bias=bias
         )
+
+        if pad_n > 0:
+            output = output[..., :N].contiguous()
+
         return output.view(*output_shape)
 
 
diff --git a/vllm/model_executor/kernels/linear/scaled_mm/deep_gemm.py b/vllm/model_executor/kernels/linear/scaled_mm/deep_gemm.py
index a369623a3b17..70122f7b4ac6 100644
--- a/vllm/model_executor/kernels/linear/scaled_mm/deep_gemm.py
+++ b/vllm/model_executor/kernels/linear/scaled_mm/deep_gemm.py
@@ -100,6 +100,8 @@ def process_weights_after_loading(self, layer):
                 else params.weight_scale,
                 quant_block_shape=tuple(layer.weight_block_size),
                 use_e8m0=self.use_deep_gemm_e8m0,
+                is_bmm=getattr(layer, "is_bmm", False),
+                bmm_batch_size=getattr(layer, "bmm_batch_size", 0),
             )
             replace_parameter(layer, params.WEIGHT, dg_weight)
             replace_parameter(layer, scale_attr, dg_weight_scale)
diff --git a/vllm/model_executor/kernels/linear/scaled_mm/pytorch.py b/vllm/model_executor/kernels/linear/scaled_mm/pytorch.py
index 2fb6e87413aa..9182977e9577 100644
--- a/vllm/model_executor/kernels/linear/scaled_mm/pytorch.py
+++ b/vllm/model_executor/kernels/linear/scaled_mm/pytorch.py
@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import math
 
 import torch
 
@@ -13,6 +14,13 @@
 )
 
 
+def _get_num_tokens(output_shape: list) -> int:
+    # torch._scaled_mm works with 2D tensors, so input tensors are
+    # flattened if they are 3D. If output_shape is 3D, num_tokens is
+    # the product of all dims except the last (hidden dim).
+    return math.prod(output_shape[:-1])
+
+
 class TorchFP8ScaledMMLinearKernel(FP8ScaledMMLinearKernel):
     """
     Base class for FP8 linear kernels using Torch.
@@ -78,7 +86,8 @@ def apply_scaled_mm(
         if type(output) is tuple and len(output) == 2:
             output = output[0]
 
-        return torch.narrow(output, 0, 0, output_shape[0]).view(*output_shape)
+        num_tokens = _get_num_tokens(output_shape)
+        return torch.narrow(output, 0, 0, num_tokens).view(*output_shape)
 
 
 class RowWiseTorchFP8ScaledMMLinearKernel(TorchFP8ScaledMMLinearKernel):
@@ -135,17 +144,26 @@ def apply_scaled_mm(
         #  For CUDA platform please validate if the torch._scaled_mm supports
         #  rowwise scaled GEMM before using it
 
+        # torch._scaled_mm rowwise requires scale_a = (m, 1), scale_b = (1, n).
+        # CompressedTensors stores weight_scale as (n, 1), so `.t()` yields (1, n).
+        # ModelOpt FP8_PER_CHANNEL_PER_TOKEN stores it as 1-D (n,); reshape to
+        # (1, n) so both paths satisfy the rowwise contract.
+        scale_b = Bs.view(1, -1) if Bs.dim() == 1 else Bs.t()
+        if As.dim() == 1:
+            As = As.view(-1, 1)
+
         # Fused GEMM_DQ Rowwise GEMM
         output = torch._scaled_mm(
             A,
             B,
             out_dtype=out_dtype,
             scale_a=As,
-            scale_b=Bs.t(),
+            scale_b=scale_b,
             bias=bias,
         )
 
-        return torch.narrow(output, 0, 0, output_shape[0]).view(*output_shape)
+        num_tokens = _get_num_tokens(output_shape)
+        return torch.narrow(output, 0, 0, num_tokens).view(*output_shape)
 
 
 class ChannelWiseTorchFP8ScaledMMLinearKernel(TorchFP8ScaledMMLinearKernel):
@@ -206,8 +224,9 @@ def apply_scaled_mm(
             output = output[0]
 
         # Unpad (undo num_token_padding)
-        output = torch.narrow(output, 0, 0, output_shape[0])
-        x_scale = torch.narrow(As, 0, 0, output_shape[0])
+        num_tokens = _get_num_tokens(output_shape)
+        output = torch.narrow(output, 0, 0, num_tokens)
+        x_scale = torch.narrow(As, 0, 0, num_tokens)
 
         # DQ
         # C = sw * sx * (X * W) + bias
diff --git a/vllm/model_executor/kernels/linear/scaled_mm/rocm.py b/vllm/model_executor/kernels/linear/scaled_mm/rocm.py
index c8370dff512c..64bc5b6c8bbe 100644
--- a/vllm/model_executor/kernels/linear/scaled_mm/rocm.py
+++ b/vllm/model_executor/kernels/linear/scaled_mm/rocm.py
@@ -79,10 +79,10 @@ def is_supported(
         if not current_platform.is_rocm():
             return False, "requires ROCm."
 
-        from vllm.platforms.rocm import on_mi3xx
+        from vllm.platforms.rocm import on_gfx12x, on_mi3xx
 
-        if not on_mi3xx():
-            return False, "requires MI3xx."
+        if not (on_mi3xx() or on_gfx12x()):
+            return False, "requires MI3xx or gfx12x"
 
         if not envs.VLLM_ROCM_USE_SKINNY_GEMM:
             return False, "requires VLLM_ROCM_USE_SKINNY_GEMM to be enabled."
diff --git a/vllm/model_executor/layers/activation.py b/vllm/model_executor/layers/activation.py
index 3e00d21d5a1c..d5b67ef04e2d 100644
--- a/vllm/model_executor/layers/activation.py
+++ b/vllm/model_executor/layers/activation.py
@@ -16,7 +16,7 @@
 from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp
 from vllm.model_executor.utils import set_weight_attrs
-from vllm.platforms import current_platform
+from vllm.platforms import CpuArchEnum, current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils.collection_utils import LazyDict
 
@@ -151,6 +151,48 @@ def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
         return self.forward_cuda(x)
 
 
+@CustomOp.register("silu_and_mul_with_clamp")
+class SiluAndMulWithClamp(CustomOp):
+    """SwiGLU activation with input clamping (used by some MoE shared experts).
+
+    Computes:
+        gate = clamp(x[..., :d], max=swiglu_limit)
+        up   = clamp(x[..., d:], min=-swiglu_limit, max=swiglu_limit)
+        out  = silu(gate) * up
+    where d = x.shape[-1] // 2.
+
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+
+    def __init__(self, swiglu_limit: float, *, compile_native: bool = True):
+        super().__init__(compile_native=compile_native)
+        self.swiglu_limit = float(swiglu_limit)
+        if current_platform.is_rocm():
+            self._forward_method = self.forward_native
+        elif current_platform.is_cuda_alike() or current_platform.is_xpu():
+            self.op = torch.ops._C.silu_and_mul_with_clamp
+        elif current_platform.is_cpu():
+            self._forward_method = self.forward_native
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        gate = torch.clamp(x[..., :d], max=self.swiglu_limit)
+        up = torch.clamp(x[..., d:], min=-self.swiglu_limit, max=self.swiglu_limit)
+        return F.silu(gate) * up
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        self.op(out, x, self.swiglu_limit)
+        return out
+
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        return self.forward_cuda(x)
+
+
 # --8<-- [start:mul_and_silu]
 @CustomOp.register("mul_and_silu")
 class MulAndSilu(CustomOp):
@@ -247,6 +289,34 @@ def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
         return self.forward_native(x)
 
 
+# --8<-- [start:gelu]
+@CustomOp.register("gelu")
+class GELU(CustomOp):
+    # --8<-- [end:gelu]
+
+    def __init__(self):
+        super().__init__()
+        if current_platform.get_cpu_architecture() == CpuArchEnum.ARM and hasattr(
+            torch.ops._C, "activation_lut_bf16"
+        ):
+            self.op = torch.ops._C.activation_lut_bf16
+        else:
+            self.op = None
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        return F.gelu(x, approximate="none")
+
+    def forward_cpu(self, x: torch.Tensor) -> torch.Tensor:
+        if self.op and x.dtype == torch.bfloat16 and x.is_contiguous():
+            out = torch.empty_like(x)
+            self.op(out, x, "gelu")
+            return out
+        return self.forward_native(x)
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        return self.forward_native(x)
+
+
 # --8<-- [start:gelu_and_mul]
 @CustomOp.register("gelu_and_mul")
 class GeluAndMul(CustomOp):
@@ -635,22 +705,14 @@ def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
 
 _ACTIVATION_REGISTRY = LazyDict(
     {
-        "gelu": lambda: nn.GELU(),
+        "gelu": lambda: GELU(),
         "gelu_fast": lambda: FastGELU(),
         "gelu_new": lambda: NewGELU(),
-        "gelu_pytorch_tanh": lambda: (
-            # TODO:[ROCm] PyTorch native GELU with tanh is unstable with torch.compile
-            logger.warning_once(
-                "[ROCm] PyTorch's native GELU with tanh approximation is unstable. "
-                "Falling back to GELU(approximate='none')."
-            ),
-            nn.GELU(approximate="none"),
-        )[1]
-        if current_platform.is_rocm()
-        else nn.GELU(approximate="tanh"),
+        "gelu_pytorch_tanh": lambda: _get_gelu_pytorch_tanh(),
         "relu": lambda: nn.ReLU(),
         "relu2": lambda: ReLUSquaredActivation(),
         "silu": lambda: nn.SiLU(),
+        "swish": lambda: nn.SiLU(),
         "quick_gelu": lambda: QuickGELU(),
         "tanh": lambda: nn.Tanh(),
         "sigmoid": lambda: nn.Sigmoid(),
@@ -659,6 +721,18 @@ def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
 )
 
 
+def _get_gelu_pytorch_tanh() -> nn.Module:
+    """Get PyTorch GELU with tanh approximation, with ROCm fallback."""
+    if current_platform.is_rocm():
+        # TODO:[ROCm] PyTorch native GELU with tanh is unstable with torch.compile
+        logger.warning_once(
+            "[ROCm] PyTorch's native GELU with tanh approximation is unstable. "
+            "Falling back to GELU(approximate='none')."
+        )
+        return nn.GELU(approximate="none")
+    return nn.GELU(approximate="tanh")
+
+
 def get_act_fn(act_fn_name: str) -> nn.Module:
     """Get an activation function by name."""
     act_fn_name = act_fn_name.lower()
@@ -675,12 +749,14 @@ def get_act_fn(act_fn_name: str) -> nn.Module:
     return _ACTIVATION_REGISTRY[act_fn_name]
 
 
-_ACTIVATION_AND_MUL_REGISTRY = LazyDict(
+_ACTIVATION_AND_MUL_REGISTRY: LazyDict[nn.Module] = LazyDict(
     {
         "gelu": lambda: GeluAndMul(),
+        "gelu_pytorch_tanh": lambda: GeluAndMul(approximate="tanh"),
         "silu": lambda: SiluAndMul(),
+        "swish": lambda: SiluAndMul(),
         "geglu": lambda: GeluAndMul(),
-        "swigluoai": lambda *args, **kwargs: SwigluOAIAndMul(*args, **kwargs),
+        "swigluoai": lambda: SwigluOAIAndMul(),
     }
 )
 
diff --git a/vllm/model_executor/layers/attention/attention.py b/vllm/model_executor/layers/attention/attention.py
index 5e8825e2baf6..db9ae2bbda34 100644
--- a/vllm/model_executor/layers/attention/attention.py
+++ b/vllm/model_executor/layers/attention/attention.py
@@ -33,6 +33,7 @@
 )
 from vllm.v1.attention.backend import (
     AttentionBackend,
+    AttentionMetadata,
     AttentionType,
 )
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
@@ -209,6 +210,7 @@ def __init__(
         `self.kv_cache`.
         """
         super().__init__()
+        sliding_window: int | None
         if per_layer_sliding_window is not None:
             # per-layer sliding window
             sliding_window = per_layer_sliding_window
@@ -259,7 +261,7 @@ def __init__(
             if skip:
                 kv_cache_dtype = "auto"
                 calculate_kv_scales = False
-            logger.info(
+            logger.debug(
                 "Layer %s: kv_cache_dtype=%s, sliding_window=%s",
                 prefix,
                 kv_cache_dtype,
@@ -330,12 +332,17 @@ def __init__(
             logger.warning_once(
                 "Disabling prefix caching for FLASHINFER/TRITON_MLA "
                 "with batch invariance, as it is not yet supported.",
-                scope="local",
             )
             cache_config.enable_prefix_caching = False
 
+        if extra_impl_args.get("chunk_lookback", -1) > -1:
+            assert self.attn_backend.get_name() == "TRITON_ATTN", (
+                f"Chunked attention with lookback requires the Triton backend, "
+                f"but got {self.attn_backend.get_name()}."
+            )
+
         impl_cls = self.attn_backend.get_impl_cls()
-        self.impl = impl_cls(
+        self.impl = impl_cls(  # type: ignore[assignment]  # impl_cls always returns an AttentionImpl subclass
             num_heads,
             head_size,
             scale,
@@ -383,7 +390,9 @@ def __init__(
         self.query_quant = None
         if (
             self.impl.supports_quant_query_input
-            and self.kv_cache_dtype.startswith("fp8")
+            and (
+                self.kv_cache_dtype.startswith("fp8") or self.kv_cache_dtype == "nvfp4"
+            )
             and not self.kv_cache_dtype.endswith("per_token_head")
         ):
             is_per_head = (
@@ -427,7 +436,7 @@ def forward(
             # which reduces overheads during decoding.
             # Otherwise queries are quantized using custom ops
             # which causes decoding overheads
-            assert self.kv_cache_dtype in {"fp8", "fp8_e4m3"}
+            assert self.kv_cache_dtype in {"fp8", "fp8_e4m3", "nvfp4"}
 
             # check if query quantization is supported
             if self.impl.supports_quant_query_input:
@@ -526,7 +535,7 @@ def process_weights_after_loading(self, act_dtype: torch.dtype):
     def get_attn_backend(self) -> type[AttentionBackend]:
         return self.attn_backend
 
-    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec:
+    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec | None:
         # Block size may get updated after model loading, refresh it
         block_size = vllm_config.cache_config.block_size
         # Should not be called for enc-dec or encoder-only attention.
@@ -540,10 +549,28 @@ def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec:
                 block_size=block_size,
                 num_kv_heads=self.num_kv_heads,
                 head_size=self.head_size,
+                head_size_v=self.head_size_v,
                 dtype=self.kv_cache_torch_dtype,
                 kv_quant_mode=quant_mode,
                 sliding_window=self.sliding_window,
             )
+        elif self.kv_cache_dtype.startswith("turboquant_"):
+            from vllm.model_executor.layers.quantization.turboquant.config import (
+                TurboQuantConfig,
+            )
+            from vllm.v1.kv_cache_interface import TQFullAttentionSpec
+
+            tq_config = TurboQuantConfig.from_cache_dtype(
+                self.kv_cache_dtype, self.head_size
+            )
+            return TQFullAttentionSpec(
+                block_size=block_size,
+                num_kv_heads=self.num_kv_heads,
+                head_size=self.head_size,
+                head_size_v=self.head_size,
+                dtype=self.kv_cache_torch_dtype,
+                tq_slot_size=tq_config.slot_size_aligned,
+            )
         else:
             return FullAttentionSpec(
                 block_size=block_size,
@@ -613,9 +640,16 @@ def get_attention_context(
         extracted from the forward context.
     """
     forward_context: ForwardContext = get_forward_context()
-    attn_metadata = forward_context.attn_metadata
-    if isinstance(attn_metadata, dict):
-        attn_metadata = attn_metadata[layer_name]
+    attn_metadata_raw = forward_context.attn_metadata
+    attn_metadata: AttentionMetadata
+    if isinstance(attn_metadata_raw, dict):
+        attn_metadata = attn_metadata_raw[layer_name]
+    elif isinstance(attn_metadata_raw, list):
+        # list[dict[str, AttentionMetadata]]: used in speculative decoding
+        # where [0] is the base-model (non-speculative) metadata dict.
+        attn_metadata = attn_metadata_raw[0][layer_name]
+    else:
+        attn_metadata = attn_metadata_raw
     attn_layer: Attention | MLAAttention = forward_context.no_compile_layers[layer_name]
     kv_cache = attn_layer.kv_cache
     slot_mapping = forward_context.slot_mapping
@@ -641,7 +675,7 @@ def unified_kv_cache_update(
         assert hasattr(attn_layer.impl, "do_kv_cache_update"), (
             f"{attn_layer.impl.__class__.__name__} does not support kv cache update"
         )
-        attn_layer.impl.do_kv_cache_update(
+        attn_layer.impl.do_kv_cache_update(  # type: ignore[attr-defined]
             attn_layer,
             key,
             value,
diff --git a/vllm/model_executor/layers/attention/chunked_local_attention.py b/vllm/model_executor/layers/attention/chunked_local_attention.py
index 136574d97529..cb595438adef 100644
--- a/vllm/model_executor/layers/attention/chunked_local_attention.py
+++ b/vllm/model_executor/layers/attention/chunked_local_attention.py
@@ -29,7 +29,7 @@
 
 @functools.lru_cache
 def create_chunked_local_attention_backend(
-    underlying_attn_backend: AttentionBackend,
+    underlying_attn_backend: type[AttentionBackend],
     attention_chunk_size: int,
 ) -> type[AttentionBackend]:
     prefix = f"ChunkedLocalAttention_{attention_chunk_size}_"
diff --git a/vllm/model_executor/layers/attention/cross_attention.py b/vllm/model_executor/layers/attention/cross_attention.py
index 61699832a624..091f0a1856d4 100644
--- a/vllm/model_executor/layers/attention/cross_attention.py
+++ b/vllm/model_executor/layers/attention/cross_attention.py
@@ -72,7 +72,7 @@ def _get_cross_slot_mapping(
 
 @functools.lru_cache
 def create_cross_attention_backend(
-    underlying_attn_backend: AttentionBackend,
+    underlying_attn_backend: type[AttentionBackend],
 ) -> type[AttentionBackend]:
     prefix = "CrossAttention_"
     underlying_builder = underlying_attn_backend.get_builder_cls()
@@ -87,17 +87,26 @@ def build(
         ) -> AttentionMetadata:
             new_metadata = copy(common_attn_metadata)
             new_metadata.causal = False
+            assert new_metadata.encoder_seq_lens_cpu is not None
             max_encoder_len = int(new_metadata.encoder_seq_lens_cpu.max())
             new_metadata.max_seq_len = max_encoder_len
-            # Any computed tokens indicated decode step>1 (no chunked prefill)
-            num_cache_decodes = (
-                (common_attn_metadata.num_computed_tokens_cpu > 0).sum().item()
+            # Any computed tokens indicates decode step>1 (no chunked prefill).
+            # The upper bound is exact for this `> 0` test - prefill rows have
+            # num_computed == 0 and decode rows have num_computed > 0.
+            query_lens_cpu = (
+                common_attn_metadata.query_start_loc_cpu[1:]
+                - common_attn_metadata.query_start_loc_cpu[:-1]
             )
+            assert common_attn_metadata.seq_lens_cpu_upper_bound is not None
+            num_computed_tokens_cpu = (
+                common_attn_metadata.seq_lens_cpu_upper_bound - query_lens_cpu
+            )
+            num_cache_decodes = (num_computed_tokens_cpu > 0).sum().item()
             if num_cache_decodes > 0:
                 # CrossAttn KV cache has already been populated on first decoder step,
                 # skip slot_mapping calculation for requests that do not need
                 # reshape_and_cache.
-                num_tokens = common_attn_metadata.num_computed_tokens_cpu.numpy()
+                num_tokens = num_computed_tokens_cpu.numpy()
                 new_metadata.encoder_seq_lens_cpu = np.where(
                     num_tokens > 0, 0, new_metadata.encoder_seq_lens_cpu
                 )
@@ -118,7 +127,7 @@ def build(
                 self.device,
             )
             attn_metadata = super().build(common_prefix_len, new_metadata, fast_build)
-            attn_metadata.slot_mapping = slot_mapping
+            attn_metadata.slot_mapping = slot_mapping  # type: ignore[attr-defined]
             return attn_metadata
 
     # NOTE(Lucas): we need a custom impl so we can use the slot-mapping computed by
@@ -144,8 +153,12 @@ def forward(
                 and key is not None
                 and value is not None
             ):
-                self.do_kv_cache_update(
-                    layer, key, value, kv_cache, attn_metadata.slot_mapping
+                self.do_kv_cache_update(  # type: ignore[attr-defined]
+                    layer,
+                    key,
+                    value,
+                    kv_cache,
+                    attn_metadata.slot_mapping,  # type: ignore[attr-defined]
                 )
 
             return super().forward(
diff --git a/vllm/model_executor/layers/attention/encoder_only_attention.py b/vllm/model_executor/layers/attention/encoder_only_attention.py
index 0897ee45b84d..5805fe2ae1ca 100644
--- a/vllm/model_executor/layers/attention/encoder_only_attention.py
+++ b/vllm/model_executor/layers/attention/encoder_only_attention.py
@@ -21,7 +21,7 @@
 
 @functools.lru_cache
 def create_encoder_only_attention_backend(
-    underlying_attn_backend: AttentionBackend,
+    underlying_attn_backend: type[AttentionBackend],
 ) -> type[AttentionBackend]:
     prefix = "EncoderOnlyAttention_"
     underlying_builder = underlying_attn_backend.get_builder_cls()
@@ -93,6 +93,6 @@ def __init__(
             **kwargs,
         )
 
-    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec:
+    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec | None:
         # Does not need KV cache
         return None
diff --git a/vllm/model_executor/layers/attention/mla_attention.py b/vllm/model_executor/layers/attention/mla_attention.py
index cbbf5f3c3ca3..9cf7ebf5cdfc 100644
--- a/vllm/model_executor/layers/attention/mla_attention.py
+++ b/vllm/model_executor/layers/attention/mla_attention.py
@@ -189,12 +189,9 @@
 
 import functools
 from abc import abstractmethod
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from enum import Enum
-from typing import TYPE_CHECKING, ClassVar, Generic, TypeVar, cast
-
-if TYPE_CHECKING:
-    from flashinfer import BatchPrefillWithRaggedKVCacheWrapper
+from typing import ClassVar, Generic, TypeVar, cast
 
 import torch
 import torch.nn as nn
@@ -234,10 +231,15 @@
 from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     GroupShape,
+    QuantKey,
     get_and_maybe_dequant_weights,
+    kFp8Dynamic64Sym,
+    kFp8Dynamic128Sym,
+    kFp8StaticTensorSym,
+    kNvfp4Dynamic,
 )
 from vllm.platforms import current_platform
-from vllm.utils.flashinfer import has_flashinfer, has_nvidia_artifactory
+from vllm.utils.flashinfer import has_flashinfer
 from vllm.utils.math_utils import cdiv, round_down
 from vllm.utils.torch_utils import (
     LayerNameType,
@@ -257,11 +259,12 @@
     MLAAttentionImpl,
     SparseMLAAttentionImpl,
 )
-from vllm.v1.attention.backends.fa_utils import get_flash_attn_version
+from vllm.v1.attention.backends.mla.prefill import (
+    MLAPrefillBackend,
+    get_mla_prefill_backend,
+)
 from vllm.v1.attention.backends.utils import (
     get_dcp_local_seq_lens,
-    get_per_layer_parameters,
-    infer_global_hyperparameters,
     split_decodes_and_prefills,
 )
 from vllm.v1.attention.ops.common import cp_lse_ag_out_rs
@@ -276,6 +279,44 @@
 
 logger = init_logger(__name__)
 
+_FP8_DTYPE = current_platform.fp8_dtype()
+
+
+def _detect_output_quant_key(
+    output: torch.Tensor,
+    output_scale: torch.Tensor | None,
+    output_block_scale: torch.Tensor | None,
+    output_dim: int,
+) -> QuantKey | None:
+    """Detect the output quantization key from fusion pass parameters.
+
+    Returns the appropriate QuantKey, or None if no quantization is needed.
+    Detection is based on output dtype and which scale tensors are present.
+    """
+    if output_scale is None and output_block_scale is None:
+        return None
+    if output_block_scale is not None:
+        if output.dtype == _FP8_DTYPE:
+            # Per-group FP8 uses block scales only, not a separate output_scale
+            assert output_scale is None
+            # Infer group size from scale shape
+            num_groups = output_block_scale.shape[-1]
+            group_size = output_dim // num_groups
+            if group_size == 128:
+                return kFp8Dynamic128Sym
+            elif group_size == 64:
+                return kFp8Dynamic64Sym
+            else:
+                raise ValueError(
+                    f"Unsupported group FP8 group_size={group_size} "
+                    f"(output_dim={output_dim}, num_groups={num_groups}). "
+                    f"Only group_size 128 and 64 are supported."
+                )
+        # output_scale None implies MXFP4, not supported
+        assert output_scale is not None
+        return kNvfp4Dynamic
+    return kFp8StaticTensorSym
+
 
 class MLAAttention(nn.Module, AttentionLayerBase):
     """Multi-Head Latent Attention layer.
@@ -384,12 +425,11 @@ def __init__(
             logger.warning_once(
                 "Disabling prefix caching for TRITON_MLA / FLASHINFER "
                 "with batch invariance, as it is not yet supported.",
-                scope="local",
             )
             cache_config.enable_prefix_caching = False
 
         impl_cls = cast(type[MLAAttentionImpl], self.attn_backend.get_impl_cls())
-        self.impl = impl_cls(
+        self.impl = impl_cls(  # type: ignore[assignment]  # impl_cls always returns an MLAAttentionImpl subclass
             num_heads=self.num_heads,
             head_size=self.head_size,
             scale=self.scale,
@@ -414,20 +454,32 @@ def __init__(
         self.q_pad_num_heads = getattr(self.impl, "q_pad_num_heads", None)
         self.use_direct_call = not current_platform.opaque_attention_op()
 
-        compilation_config = get_current_vllm_config().compilation_config
+        vllm_config = get_current_vllm_config()
+        compilation_config = vllm_config.compilation_config
         if prefix in compilation_config.static_forward_context:
             raise ValueError(f"Duplicate layer name: {prefix}")
         compilation_config.static_forward_context[prefix] = self
 
+        prefill_backend_cls = get_mla_prefill_backend(vllm_config)
+        self.prefill_backend = prefill_backend_cls(
+            num_heads=self.num_heads,
+            scale=self.scale,
+            kv_lora_rank=self.kv_lora_rank,
+            qk_nope_head_dim=self.qk_nope_head_dim,
+            qk_rope_head_dim=self.qk_rope_head_dim,
+            v_head_dim=self.v_head_dim,
+            vllm_config=vllm_config,
+        )
+
         self.kv_cache = torch.tensor([])
 
         self.use_sparse = use_sparse
 
-        vllm_config = get_current_vllm_config_or_none()
+        _vllm_config = get_current_vllm_config_or_none()
         self.dcp_a2a = (
-            vllm_config is not None
-            and vllm_config.parallel_config.decode_context_parallel_size > 1
-            and vllm_config.parallel_config.dcp_comm_backend == "a2a"
+            _vllm_config is not None
+            and _vllm_config.parallel_config.decode_context_parallel_size > 1
+            and _vllm_config.parallel_config.dcp_comm_backend == "a2a"
         )
 
         # Initialize q/k/v range constants.
@@ -485,16 +537,23 @@ def forward(
 
         if self.use_direct_call:
             forward_context: ForwardContext = get_forward_context()
-            attn_metadata = forward_context.attn_metadata
-            if isinstance(attn_metadata, dict):
-                attn_metadata = attn_metadata[self.layer_name]
+            attn_metadata_raw = forward_context.attn_metadata
+            attn_metadata: MLACommonMetadata
+            if isinstance(attn_metadata_raw, dict):
+                attn_metadata = attn_metadata_raw[self.layer_name]  # type: ignore[assignment]
+            elif isinstance(attn_metadata_raw, list):
+                # list[dict[str, AttentionMetadata]]: used in speculative decoding
+                # where [0] is the base-model (non-speculative) metadata dict.
+                attn_metadata = attn_metadata_raw[0][self.layer_name]  # type: ignore[assignment]
+            else:
+                attn_metadata = attn_metadata_raw
             self_kv_cache = self.kv_cache
             slot_mapping = forward_context.slot_mapping
 
             assert isinstance(slot_mapping, dict), (
                 f"Expected slot_mapping to be a dict, got {type(slot_mapping)}. "
             )
-            self.impl.do_kv_cache_update(
+            self.impl.do_kv_cache_update(  # type: ignore[attr-defined]
                 kv_c_normed,
                 k_pe,
                 self_kv_cache,
@@ -542,9 +601,17 @@ def forward_impl(
         output: torch.Tensor,
         output_scale: torch.Tensor | None = None,
         output_block_scale: torch.Tensor | None = None,
+        quant_group_size: int | None = None,
+        quant_scale_ue8m0: bool | None = None,
+        quant_col_major: bool | None = None,
+        quant_tma_aligned: bool | None = None,
     ) -> torch.Tensor:
-        use_quant = output_scale is not None or output_block_scale is not None
-        if use_quant:
+        assert output is not None, "Output tensor must be provided."
+
+        quant_key = _detect_output_quant_key(
+            output, output_scale, output_block_scale, self.num_heads * self.v_head_dim
+        )
+        if quant_key is not None:
             # The fusion pass has allocated output with quantized dtype
             # (FP8 or uint8 for FP4). We can't write into it directly,
             # so we swap in a temp buffer for computation, then quantize
@@ -575,7 +642,7 @@ def forward_impl(
             # The zero fill is required when used with DP + EP
             # to ensure all ranks within a DP group compute the
             # same expert outputs.
-            if use_quant:
+            if quant_key is not None:
                 return quant_output.fill_(0)
             return output.fill_(0)
 
@@ -612,7 +679,7 @@ def forward_impl(
             num_mha_tokens = q.size(0) - num_mqa_tokens
 
         if num_mha_tokens > 0:
-            self.impl.forward_mha(
+            self.impl.forward_mha(  # type: ignore[attr-defined]
                 q[num_mqa_tokens:],
                 k_c_normed[num_mqa_tokens:],
                 k_pe[num_mqa_tokens:],
@@ -695,7 +762,7 @@ def forward_impl(
             # call decode attn
             if not is_sparse_impl:
                 assert attn_metadata.decode is not None
-            attn_out, lse = self.impl.forward_mqa(mqa_q, kv_cache, attn_metadata, self)
+            attn_out, lse = self.impl.forward_mqa(mqa_q, kv_cache, attn_metadata, self)  # type: ignore[attr-defined]
 
             # correct dcp attn_out with lse.
             if self.impl.dcp_world_size > 1:
@@ -717,18 +784,41 @@ def forward_impl(
             # v_up projection
             self._v_up_proj(attn_out, out=mqa_output_slice)
 
-        if use_quant:
+        if quant_key is not None:
             # Quantize the BF16 computation result into the quantized output
             actual = output[:num_actual_toks]
-            if output_block_scale is not None:
+            if quant_key == kNvfp4Dynamic:
                 # NVFP4: two FP4 values packed into one uint8
+                assert output_block_scale is not None
                 fp4_data, fp4_scales = ops.scaled_fp4_quant(actual, output_scale)
                 quant_output[:num_actual_toks].copy_(fp4_data)
-                output_block_scale.copy_(fp4_scales)
-            else:
+                output_block_scale[: fp4_scales.shape[0]].copy_(fp4_scales)
+            elif quant_key in (kFp8Dynamic128Sym, kFp8Dynamic64Sym):
+                # Per-group FP8
+                assert output_block_scale is not None
+                assert quant_group_size is not None, (
+                    "Group FP8 output quant requested but "
+                    "quant_group_size not passed through custom op"
+                )
+                finfo = torch.finfo(_FP8_DTYPE)
+                torch.ops._C.per_token_group_fp8_quant(
+                    actual,
+                    quant_output[:num_actual_toks],
+                    output_block_scale[:num_actual_toks],
+                    quant_group_size,
+                    1e-10,  # eps
+                    finfo.min,
+                    finfo.max,
+                    quant_scale_ue8m0,
+                    quant_col_major,
+                    quant_tma_aligned,
+                )
+            elif quant_key == kFp8StaticTensorSym:
                 # Static FP8 quantization
                 fp8_data, _ = self._quant_fp8_op(actual, output_scale)
                 quant_output[:num_actual_toks].copy_(fp8_data)
+            else:
+                raise ValueError(f"Unsupported quant_key: {quant_key}")
             return quant_output
 
         return output_padded
@@ -973,6 +1063,10 @@ def unified_mla_attention_with_output(
     output_scale: torch.Tensor | None = None,
     output_block_scale: torch.Tensor | None = None,
     kv_cache_dummy_dep: torch.Tensor | None = None,
+    quant_group_size: int | None = None,
+    quant_scale_ue8m0: bool | None = None,
+    quant_col_major: bool | None = None,
+    quant_tma_aligned: bool | None = None,
 ) -> None:
     # kv_cache_dummy_dep is not used but accepting it creates a data dependency
     # that ensures torch.compile preserves ordering between KV cache update and
@@ -989,6 +1083,10 @@ def unified_mla_attention_with_output(
         output=output,
         output_scale=output_scale,
         output_block_scale=output_block_scale,
+        quant_group_size=quant_group_size,
+        quant_scale_ue8m0=quant_scale_ue8m0,
+        quant_col_major=quant_col_major,
+        quant_tma_aligned=quant_tma_aligned,
     )
 
 
@@ -1001,6 +1099,10 @@ def unified_mla_attention_with_output_fake(
     output_scale: torch.Tensor | None = None,
     output_block_scale: torch.Tensor | None = None,
     kv_cache_dummy_dep: torch.Tensor | None = None,
+    quant_group_size: int | None = None,
+    quant_scale_ue8m0: bool | None = None,
+    quant_col_major: bool | None = None,
+    quant_tma_aligned: bool | None = None,
 ) -> None:
     return
 
@@ -1031,33 +1133,6 @@ class QueryLenSupport(Enum):
     VARLEN = "varlen"
 
 
-try:
-    from vllm.vllm_flash_attn import (  # type: ignore[attr-defined]
-        flash_attn_varlen_func,
-    )
-
-    is_vllm_fa = True
-except ImportError:
-    is_vllm_fa = False
-    flash_attn_varlen_func = None  # type: ignore[assignment]
-    # On ROCm, vllm_flash_attn is not available, try upstream flash_attn instead.
-    # On CUDA, vllm_flash_attn should always be available (built with vLLM),
-    # so we don't attempt the fallback there.
-    if current_platform.is_rocm():
-        try:
-            from flash_attn import flash_attn_varlen_func  # type: ignore[no-redef]
-        except ImportError:
-            logger.debug(
-                "flash_attn not available on ROCm; "
-                "MLA models using TRITON_MLA will require flash_attn. "
-                "AITER_MLA backends use aiter kernels instead."
-            )
-    elif current_platform.is_xpu():
-        from vllm._xpu_ops import xpu_ops as ops
-
-        flash_attn_varlen_func = ops.flash_attn_varlen_func  # type: ignore[no-redef]
-
-
 def dynamic_per_batched_tensor_quant(
     x: torch.Tensor, dtype: torch.dtype = torch.float8_e4m3fn
 ):
@@ -1069,9 +1144,6 @@ def dynamic_per_batched_tensor_quant(
     return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal()
 
 
-logger = init_logger(__name__)
-
-
 @CustomOp.register(
     "mla_decode_concat_quant_fp8",
     dynamic_arg_dims={"decode_ql_nope": 0, "decode_q_pe": 0},
@@ -1105,9 +1177,6 @@ def forward(
     forward_hip = _make_forward(QuantFP8.forward_hip)  # type: ignore[arg-type]
 
 
-CUDNN_WORKSPACE_SIZE = 12800
-
-
 class MLACommonBackend(AttentionBackend):
     @staticmethod
     def get_name() -> str:
@@ -1176,26 +1245,9 @@ class ChunkedContextMetadata:
     query_start_loc: torch.Tensor
     max_query_len: int
     chunked_context: ChunkedContextMetadata | None = None
-    query_seq_lens: torch.Tensor | None = None
-    workspace_buffer: torch.Tensor | None = None
     q_data_type: torch.dtype | None = None
     output_dtype: torch.dtype | None = None
-
-
-@dataclass
-class FlashInferPrefillMetadata(MLACommonPrefillMetadata):
-    prefill_main: "BatchPrefillWithRaggedKVCacheWrapper | None" = None
-    prefill_chunks: "list[BatchPrefillWithRaggedKVCacheWrapper]" = field(
-        default_factory=list
-    )
-
-
-@dataclass
-class CudnnPrefillMetadata(MLACommonPrefillMetadata):
-    class ChunkedContextMetadata(MLACommonPrefillMetadata.ChunkedContextMetadata):
-        seq_lens: torch.Tensor
-
-    cudnn_workspace: torch.Tensor | None = None
+    prefill_backend: MLAPrefillBackend | None = None
 
 
 @dataclass
@@ -1241,13 +1293,8 @@ class MLACommonMetadata(AttentionMetadata, Generic[D]):
     # The dimension of the attention heads
     head_dim: int | None = None
 
+    prefill: MLACommonPrefillMetadata | None = None
     decode: D | None = None
-    prefill: (
-        MLACommonPrefillMetadata
-        | FlashInferPrefillMetadata
-        | CudnnPrefillMetadata
-        | None
-    ) = None
 
     def __post_init__(self):
         if self.head_dim is not None and not MLACommonBackend.supports_head_size(
@@ -1260,64 +1307,6 @@ def __post_init__(self):
 A = TypeVar("A", bound=AttentionMetadata)
 
 
-def is_deepseek_r1_mla_compatible(vllm_config: VllmConfig) -> bool:
-    # Check if model has DeepSeek R1 compatible MLA dimensions:
-    # qk_nope_head_dim = 128, qk_rope_head_dim = 64, v_head_dim = 128
-    # which results in query/key head dim = 192.
-    if vllm_config.model_config is None:
-        return False
-    hf_text_config = vllm_config.model_config.hf_text_config
-    qk_nope_head_dim = getattr(hf_text_config, "qk_nope_head_dim", 1)
-    qk_rope_head_dim = getattr(hf_text_config, "qk_rope_head_dim", 1)
-    v_head_dim = getattr(hf_text_config, "v_head_dim", 1)
-    return qk_nope_head_dim == 128 and qk_rope_head_dim == 64 and v_head_dim == 128
-
-
-@functools.cache
-def use_flashinfer_prefill() -> bool:
-    from vllm.config import get_current_vllm_config
-
-    vllm_config = get_current_vllm_config()
-    if not (
-        not vllm_config.attention_config.disable_flashinfer_prefill
-        and has_flashinfer()
-        and not vllm_config.attention_config.use_cudnn_prefill
-        and current_platform.is_device_capability_family(100)
-    ):
-        return False
-
-    return is_deepseek_r1_mla_compatible(vllm_config)
-
-
-@functools.cache
-def use_cudnn_prefill() -> bool:
-    from vllm.config import get_current_vllm_config
-
-    vllm_config = get_current_vllm_config()
-    return (
-        has_flashinfer()
-        and vllm_config.attention_config.use_cudnn_prefill
-        and current_platform.is_device_capability_family(100)
-        and has_nvidia_artifactory()
-    )
-
-
-@functools.cache
-def use_trtllm_ragged_deepseek_prefill() -> bool:
-    """Check if TRT-LLM ragged DeepSeek prefill should be used."""
-    from vllm.config import get_current_vllm_config
-
-    vllm_config = get_current_vllm_config()
-    if not (
-        has_flashinfer()
-        and vllm_config.attention_config.use_trtllm_ragged_deepseek_prefill
-        and current_platform.is_device_capability_family(100)
-    ):
-        return False
-
-    return is_deepseek_r1_mla_compatible(vllm_config)
-
-
 @dataclass
 class MLADims:
     q_lora_rank: int | None
@@ -1330,6 +1319,20 @@ class MLADims:
 def get_mla_dims(model_config: ModelConfig) -> MLADims:
     hf_text_config = model_config.hf_text_config
 
+    # Check if this is a DeepseekV4 config (uses unified head_dim + rope_head_dim)
+    if hasattr(hf_text_config, "compress_ratios"):
+        # DeepseekV4 style config: unified head_dim with rope_head_dim
+        head_dim = hf_text_config.head_dim
+        rope_head_dim = hf_text_config.qk_rope_head_dim
+        return MLADims(
+            q_lora_rank=hf_text_config.q_lora_rank,
+            kv_lora_rank=head_dim,
+            qk_nope_head_dim=head_dim - rope_head_dim,
+            qk_rope_head_dim=rope_head_dim,
+            v_head_dim=head_dim,
+        )
+
+    # DeepseekV2/V3 style config
     return MLADims(
         q_lora_rank=getattr(hf_text_config, "q_lora_rank", None),
         kv_lora_rank=hf_text_config.kv_lora_rank,
@@ -1341,15 +1344,14 @@ def get_mla_dims(model_config: ModelConfig) -> MLADims:
 
 @functools.cache
 def backend_supports_prefill_query_quantization() -> bool:
-    """Check if the selected MLA backend supports prefill query quantization.
+    """Check if the selected MLA prefill backend supports query quantization.
 
     Currently supported backends:
-    - FlashInfer prefill
-    - TRT-LLM ragged DeepSeek prefill
+    - FlashInfer
+    - TRT-LLM Ragged
 
     Not supported:
-    - cuDNN Prefill
-    - FlashAttention
+    - FlashAttention (FA3/FA4)
     - Non-GB200 devices (FP8 prefill requires device capability 100)
     """
     # FP8 prefill query quantization requires GB200 (device capability 100)
@@ -1357,7 +1359,15 @@ def backend_supports_prefill_query_quantization() -> bool:
     if not current_platform.is_device_capability_family(100):
         return False
 
-    return use_flashinfer_prefill() or use_trtllm_ragged_deepseek_prefill()
+    from vllm.config import get_current_vllm_config
+    from vllm.v1.attention.backends.mla.prefill import get_mla_prefill_backend
+
+    vllm_config = get_current_vllm_config()
+    backend_cls = get_mla_prefill_backend(vllm_config)
+    return backend_cls.get_name() in (
+        "FLASHINFER",
+        "TRTLLM_RAGGED",
+    )
 
 
 class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
@@ -1430,9 +1440,7 @@ def determine_prefill_query_data_type(
 
         if use_fp8:
             fp8_dtype = current_platform.fp8_dtype()
-            logger.info_once(
-                "FP8 prefill attention enabled: query data type is FP8", scope="local"
-            )
+            logger.info_once("FP8 prefill attention enabled: query data type is FP8")
             return fp8_dtype
         elif vllm_config.attention_config.use_prefill_query_quantization:
             logger.info_once(
@@ -1440,7 +1448,6 @@ def determine_prefill_query_data_type(
                 " use_prefill_query_quantization is enabled. Please"
                 " ensure that --kv-cache-dtype is set to fp8 and your prefill"
                 " backend is compatible with FP8 attention.",
-                scope="local",
             )
             return model_dtype
         elif (
@@ -1454,7 +1461,6 @@ def determine_prefill_query_data_type(
                 "prefill latency. To enable, add: "
                 '--attention-config \'{"use_prefill_query_quantization"'
                 ": true}'",
-                scope="local",
             )
 
         return model_dtype
@@ -1472,7 +1478,6 @@ def __init__(
             metadata_cls if metadata_cls is not None else MLACommonMetadata
         )
         self.kv_cache_spec = kv_cache_spec
-        scheduler_config = vllm_config.scheduler_config
         self.model_config = vllm_config.model_config
         parallel_config = vllm_config.parallel_config
         self.compilation_config = vllm_config.compilation_config
@@ -1532,139 +1537,21 @@ def __init__(
                 device=device,
             )
 
-        self._use_cudnn_prefill = use_cudnn_prefill()
-        self._use_fi_prefill = use_flashinfer_prefill()
-        self._use_trtllm_ragged_prefill = use_trtllm_ragged_deepseek_prefill()
-        self.prefill_metadata_cls = (
-            FlashInferPrefillMetadata
-            if self._use_fi_prefill
-            else CudnnPrefillMetadata
-            if self._use_cudnn_prefill
-            else MLACommonPrefillMetadata
-        )
-
-        if self._use_fi_prefill:
-            self._workspace_buffer = torch.empty(
-                envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE,
-                dtype=torch.uint8,
-                device=device,
-            )
-
-            self._fi_prefill_main: BatchPrefillWithRaggedKVCacheWrapper | None = None
-            self._fi_prefill_chunks: list[BatchPrefillWithRaggedKVCacheWrapper] = []
-
-            self._global_hyperparameters = infer_global_hyperparameters(
-                get_per_layer_parameters(vllm_config, layer_names, MLACommonImpl)  # type: ignore[type-abstract]
-            )
-
-        if self._use_trtllm_ragged_prefill:
-            self._workspace_buffer = torch.empty(
-                envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE,
-                dtype=torch.uint8,
-                device=device,
-            )
-
-        if self._use_cudnn_prefill:
-            self.cudnn_workspace = torch.empty(
-                CUDNN_WORKSPACE_SIZE * scheduler_config.max_num_seqs,
-                dtype=torch.int8,
-                device=device,
-            )
+        self._prefill_backend = self.compilation_config.static_forward_context[
+            layer_names[0]
+        ].prefill_backend
 
         supports_spec_decode = self.query_len_support != QueryLenSupport.SINGLE_ONLY
         self._init_reorder_batch_threshold(
             self.reorder_batch_threshold, supports_spec_decode, supports_dcp_with_varlen
         )
 
-        # Validate consistency between query_len_support and reorder_batch_threshold
         if self.query_len_support == QueryLenSupport.SINGLE_ONLY:
             assert self.reorder_batch_threshold == 1, (
                 f"reorder_batch_threshold must be 1 when query_len_support is "
                 f"SINGLE_ONLY, got {self.reorder_batch_threshold}"
             )
 
-    def _build_fi_prefill_wrappers(self, prefill: FlashInferPrefillMetadata):
-        qo_indptr = prefill.query_start_loc
-
-        has_context = False
-        if prefill.chunked_context is not None:
-            chunked_context = prefill.chunked_context
-            has_context = True
-
-        if self._fi_prefill_main is None:
-            from flashinfer import BatchPrefillWithRaggedKVCacheWrapper
-
-            self._fi_prefill_main = BatchPrefillWithRaggedKVCacheWrapper(
-                self._workspace_buffer, "NHD", backend="cutlass"
-            )
-
-        if has_context:
-            num_chunks = chunked_context.cu_seq_lens.shape[0]
-            # Allocate more prefill chunk wrappers if needed
-            if len(self._fi_prefill_chunks) < num_chunks:
-                from flashinfer import BatchPrefillWithRaggedKVCacheWrapper
-
-                for _ in range(len(self._fi_prefill_chunks), num_chunks):
-                    self._fi_prefill_chunks.append(
-                        BatchPrefillWithRaggedKVCacheWrapper(
-                            self._workspace_buffer, "NHD", backend="cutlass"
-                        )
-                    )
-            assert num_chunks <= len(self._fi_prefill_chunks)
-
-        # In MLA, the non-latent num_qo_heads == num_kv_heads
-        num_qo_heads = self.num_heads
-        num_kv_heads = num_qo_heads
-
-        # Sanity: Verify that num_kv_heads == 1 since it is latent space
-        assert self.kv_cache_spec.num_kv_heads == 1
-
-        # Get non-latent head_dim_qk and head_dim_vo
-        head_dim_qk = self.mla_dims.qk_nope_head_dim + self.mla_dims.qk_rope_head_dim
-        head_dim_vo = self.mla_dims.v_head_dim
-
-        # For main run, qo_indptr == kv_indptr
-        kv_indptr = qo_indptr.clone()
-
-        # Prepare main prefill
-        self._fi_prefill_main.plan(
-            qo_indptr=qo_indptr,
-            kv_indptr=kv_indptr,
-            num_qo_heads=num_qo_heads,
-            num_kv_heads=num_kv_heads,
-            head_dim_qk=head_dim_qk,
-            head_dim_vo=head_dim_vo,
-            causal=True,  # This is main run
-            sm_scale=self._global_hyperparameters.sm_scale,
-            window_left=self._global_hyperparameters.window_left,
-            logits_soft_cap=self._global_hyperparameters.logits_soft_cap,
-            q_data_type=self.q_data_type,
-            o_data_type=prefill.output_dtype,
-        )
-
-        # Prepare context prefills
-        if has_context:
-            for i in range(num_chunks):
-                kv_indptr_chunk = chunked_context.cu_seq_lens[i]
-
-                self._fi_prefill_chunks[i].plan(
-                    qo_indptr=qo_indptr,
-                    kv_indptr=kv_indptr_chunk,
-                    num_qo_heads=num_qo_heads,
-                    num_kv_heads=num_kv_heads,
-                    head_dim_qk=head_dim_qk,
-                    head_dim_vo=head_dim_vo,
-                    causal=False,  # This is context run
-                    sm_scale=self._global_hyperparameters.sm_scale,
-                    window_left=self._global_hyperparameters.window_left,
-                    logits_soft_cap=self._global_hyperparameters.logits_soft_cap,
-                    q_data_type=self.q_data_type,
-                    o_data_type=prefill.output_dtype,
-                )
-
-        prefill.prefill_main = self._fi_prefill_main
-        prefill.prefill_chunks = self._fi_prefill_chunks
-
     def _build_decode(
         self,
         block_table_tensor: torch.Tensor,
@@ -1734,13 +1621,18 @@ def build(
 
         prefill_metadata = None
         if num_prefills > 0:
-            num_computed_tokens_cpu = (
-                common_attn_metadata.compute_num_computed_tokens().cpu()
-            )
-
             reqs_start = num_decodes  # prefill_start
 
-            context_lens_cpu = num_computed_tokens_cpu[reqs_start:num_reqs]
+            # Upper bound is exact for prefill rows (no D2H sync).
+            seq_lens_cpu = common_attn_metadata.seq_lens_cpu_upper_bound
+            assert seq_lens_cpu is not None
+            prefill_query_lens_cpu = (
+                query_start_loc_cpu[reqs_start + 1 : num_reqs + 1]
+                - query_start_loc_cpu[reqs_start:num_reqs]
+            )
+            context_lens_cpu = (
+                seq_lens_cpu[reqs_start:num_reqs] - prefill_query_lens_cpu
+            )
             max_context_len_cpu = context_lens_cpu.max().item()
             num_prefills_with_context_cpu = (context_lens_cpu > 0).sum().item()
             prefill_query_start_loc = (
@@ -1865,18 +1757,14 @@ def build(
                         dtype=torch.int32,
                     )
 
-                chunked_context_metadata_cls = (
-                    CudnnPrefillMetadata.ChunkedContextMetadata
-                    if self._use_cudnn_prefill
-                    else MLACommonPrefillMetadata.ChunkedContextMetadata
-                )
                 prefill_tokens_with_context = None
                 if num_prefills_with_context_cpu > 0:
                     prefill_tokens_with_context = prefill_query_start_loc_cpu[
                         num_prefills_with_context_cpu
                     ].item()
+                _ChunkedMetadata = MLACommonPrefillMetadata.ChunkedContextMetadata
                 if self.dcp_world_size > 1:
-                    chunked_context_metadata = chunked_context_metadata_cls(
+                    chunked_context_metadata = _ChunkedMetadata(
                         cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True),
                         starts=local_chunk_starts.to(device, non_blocking=True),
                         seq_tot=padded_local_chunk_seq_lens.sum(dim=1).tolist(),
@@ -1897,7 +1785,7 @@ def build(
                         prefill_tokens_with_context=prefill_tokens_with_context,
                     )
                 else:
-                    chunked_context_metadata = chunked_context_metadata_cls(
+                    chunked_context_metadata = _ChunkedMetadata(
                         cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True),
                         starts=chunk_starts.to(device, non_blocking=True),
                         seq_tot=chunk_seq_lens.sum(dim=1).tolist(),
@@ -1911,35 +1799,22 @@ def build(
                         prefill_tokens_with_context=prefill_tokens_with_context,
                     )
 
-                if self._use_cudnn_prefill:
-                    chunked_context_metadata.seq_lens = chunk_seq_lens
-
                 assert (
                     max(chunked_context_metadata.max_seq_lens)
                     <= self.chunked_prefill_workspace_size
                 )
 
-            prefill_metadata = self.prefill_metadata_cls(
+            prefill_metadata = MLACommonPrefillMetadata(
                 block_table=block_table_tensor[reqs_start:, ...],
                 query_start_loc=prefill_query_start_loc,
                 max_query_len=max_query_len,
                 chunked_context=chunked_context_metadata,
                 output_dtype=self.model_config.dtype,
                 q_data_type=self.q_data_type,
+                prefill_backend=self._prefill_backend,
             )
 
-            if self._use_cudnn_prefill:
-                assert isinstance(prefill_metadata, CudnnPrefillMetadata)
-                prefill_metadata.query_seq_lens = (
-                    prefill_query_start_loc[1:] - prefill_query_start_loc[:-1]
-                )
-                prefill_metadata.cudnn_workspace = self.cudnn_workspace
-
-            if self._use_trtllm_ragged_prefill:
-                prefill_metadata.query_seq_lens = (
-                    prefill_query_start_loc[1:] - prefill_query_start_loc[:-1]
-                )
-                prefill_metadata.workspace_buffer = self._workspace_buffer
+            self._prefill_backend.prepare_metadata(prefill_metadata)
 
         decode_metadata = None
         if num_decodes > 0:
@@ -1984,11 +1859,7 @@ def build(
             decode=decode_metadata,
         )
 
-        if self._use_fi_prefill and num_prefills > 0:
-            assert isinstance(attn_metadata.prefill, FlashInferPrefillMetadata)
-            self._build_fi_prefill_wrappers(attn_metadata.prefill)
-
-        return attn_metadata
+        return attn_metadata  # type: ignore[return-value]
 
 
 def reorg_kvcache(
@@ -2071,13 +1942,13 @@ class MLACommonImpl(MLAAttentionImpl[M], Generic[M]):
     """
 
     def fused_output_quant_supported(self, quant_key):
-        from vllm.model_executor.layers.quantization.utils.quant_utils import (
+        return quant_key in (
             kFp8StaticTensorSym,
             kNvfp4Dynamic,
+            kFp8Dynamic128Sym,
+            kFp8Dynamic64Sym,
         )
 
-        return quant_key in (kFp8StaticTensorSym, kNvfp4Dynamic)
-
     def __init__(
         self,
         num_heads: int,
@@ -2098,6 +1969,7 @@ def __init__(
         qk_head_dim: int,
         v_head_dim: int,
         kv_b_proj: ColumnParallelLinear,
+        # DSV3.2 MLA Specific Arguments
         indexer: object | None = None,
         q_pad_num_heads: int | None = None,
     ) -> None:
@@ -2120,6 +1992,7 @@ def __init__(
         self.indexer = indexer
         self.q_pad_num_heads = q_pad_num_heads
         self.supports_quant_query_input = True
+        self.is_aiter_triton_fp8_bmm_enabled = rocm_aiter_ops.is_fp8bmm_enabled()
 
         # Use flashinfer's optimized concat_mla_k kernel when available.
         # The kernel is optimized for DeepSeek V3 dimensions:
@@ -2131,310 +2004,12 @@ def __init__(
             and (self.qk_rope_head_dim == 64)
         )
 
-        if use_trtllm_ragged_deepseek_prefill():
-            logger.info_once(
-                "Using TRT-LLM ragged DeepSeek prefill for MLA", scope="local"
-            )
-            self._run_prefill_context_chunk = (
-                self._run_prefill_context_chunk_trtllm_ragged
-            )
-            self._run_prefill_new_tokens = self._run_prefill_new_tokens_trtllm_ragged
-            self._pad_v = False
-        elif use_flashinfer_prefill():
-            logger.info_once("Using FlashInfer prefill for MLA", scope="local")
-            self._run_prefill_context_chunk = self._run_prefill_context_chunk_fi
-            self._run_prefill_new_tokens = self._run_prefill_new_tokens_fi
-            self._pad_v = False
-        elif use_cudnn_prefill():
-            logger.info_once("Using CUDNN prefill for MLA", scope="local")
-            self._run_prefill_context_chunk = self._run_prefill_context_chunk_cudnn
-            self._run_prefill_new_tokens = self._run_prefill_new_tokens_cudnn
-            self._pad_v = False
-        else:  # Use FlashAttention
-            if flash_attn_varlen_func is None:
-                raise RuntimeError(
-                    "MLA attention requires FlashAttention but it is not "
-                    "available. Please install flash_attn or use "
-                    "--attention-backend ROCM_AITER_MLA."
-                )
-            logger.info_once("Using FlashAttention prefill for MLA", scope="local")
-            self._run_prefill_context_chunk = self._run_prefill_context_chunk_fa
-            self._run_prefill_new_tokens = self._run_prefill_new_tokens_fa
-
-            # Handle the differences between the flash_attn_varlen from
-            # flash_attn and the one from vllm_flash_attn. The former is used on
-            # RoCM and the latter has an additional parameter to control
-            # FA2 vs FA3
-            self.flash_attn_varlen_func = flash_attn_varlen_func
-            self.vllm_flash_attn_version = get_flash_attn_version(
-                head_size=self.qk_head_dim
-            )
-            if self.vllm_flash_attn_version is not None:
-                self.flash_attn_varlen_func = functools.partial(
-                    flash_attn_varlen_func, fa_version=self.vllm_flash_attn_version
-                )
-
-            # For MLA the v head dim is smaller than qk head dim so we pad out
-            # v with 0s to match the qk head dim for attention backends that do
-            # not support different headdims.
-            # FA3 on Hopper (SM90) and FA4 natively handle diff headdims.
-            device_capability = current_platform.get_device_capability()
-            self._pad_v = self.vllm_flash_attn_version is None or not (
-                (
-                    self.vllm_flash_attn_version == 3
-                    and device_capability is not None
-                    and device_capability[0] == 9
-                )
-                or self.vllm_flash_attn_version == 4
-            )
-
         self.dcp_world_size: int = -1
 
         self.cp_kv_cache_interleave_size: int = (
             get_current_vllm_config().parallel_config.cp_kv_cache_interleave_size
         )
 
-    def _flash_attn_varlen_diff_headdims(
-        self, q, k, v, return_softmax_lse=False, softmax_scale=None, **kwargs
-    ):
-        maybe_padded_v = v
-        if self._pad_v:
-            maybe_padded_v = torch.nn.functional.pad(
-                v, [0, q.shape[-1] - v.shape[-1]], value=0
-            )
-
-        if is_vllm_fa:
-            kwargs["return_softmax_lse"] = return_softmax_lse
-        else:
-            # ROCm leverages the upstream flash_attn, which takes a parameter
-            # called "return_attn_probs" instead of return_softmax_lse
-            kwargs["return_attn_probs"] = return_softmax_lse
-        if envs.VLLM_BATCH_INVARIANT:
-            kwargs["num_splits"] = 1
-
-        attn_out = self.flash_attn_varlen_func(
-            q=q,
-            k=k,
-            v=maybe_padded_v,
-            softmax_scale=softmax_scale,
-            **kwargs,
-        )
-
-        # Unpack the output if there is multiple results
-        lse = None
-        if isinstance(attn_out, tuple):
-            attn_out, lse = attn_out[0], attn_out[1]
-
-        # Remain consistent with old `flash_attn_varlen_func` where there
-        # is only one output tensor if `return_softmax_lse` is False.
-        if return_softmax_lse:
-            return attn_out, lse
-        return attn_out
-
-    def _run_prefill_new_tokens_fa(
-        self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
-    ):
-        return self._flash_attn_varlen_diff_headdims(
-            q=q,
-            k=k,
-            v=v,
-            cu_seqlens_q=prefill.query_start_loc,
-            cu_seqlens_k=prefill.query_start_loc,
-            max_seqlen_q=prefill.max_query_len,
-            max_seqlen_k=prefill.max_query_len,
-            softmax_scale=self.scale,
-            causal=True,
-            return_softmax_lse=return_softmax_lse,
-        )
-
-    def _run_prefill_new_tokens_fi(
-        self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
-    ):
-        assert isinstance(prefill, FlashInferPrefillMetadata)
-        assert prefill.prefill_main is not None
-
-        ret = prefill.prefill_main.run(
-            q=q,
-            k=k,
-            v=v,
-            return_lse=return_softmax_lse,
-        )
-
-        if isinstance(ret, tuple):
-            return ret[0], ret[1].transpose(0, 1).contiguous()
-        return ret
-
-    def _run_prefill_new_tokens_cudnn(
-        self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
-    ):
-        assert isinstance(prefill, CudnnPrefillMetadata)
-        assert prefill.query_seq_lens is not None
-        from flashinfer.prefill import cudnn_batch_prefill_with_kv_cache
-
-        output, lse = cudnn_batch_prefill_with_kv_cache(
-            q=q,
-            k_cache=k,
-            v_cache=v,
-            scale=self.scale,
-            workspace_buffer=prefill.cudnn_workspace,
-            max_token_per_sequence=prefill.max_query_len,
-            max_sequence_kv=prefill.max_query_len,
-            actual_seq_lens_q=prefill.query_seq_lens.view(-1, 1, 1, 1),
-            actual_seq_lens_kv=prefill.query_seq_lens.view(-1, 1, 1, 1),
-            causal=True,
-            # Do not support False for now
-            return_lse=True,
-            # Indicates actual_seq_lens are on GPU or CPU.
-            is_cuda_graph_compatible=True,
-        )
-        if return_softmax_lse:
-            return output, lse
-        return output
-
-    def _run_prefill_context_chunk_fa(
-        self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
-    ):
-        assert prefill.chunked_context is not None
-        return self._flash_attn_varlen_diff_headdims(
-            q=q,
-            k=k,
-            v=v,
-            cu_seqlens_q=prefill.query_start_loc,
-            cu_seqlens_k=prefill.chunked_context.cu_seq_lens[chunk_idx],
-            max_seqlen_q=prefill.max_query_len,
-            max_seqlen_k=prefill.chunked_context.max_seq_lens[chunk_idx],
-            softmax_scale=self.scale,
-            causal=False,  # Context is unmasked
-            return_softmax_lse=True,
-        )
-
-    def _run_prefill_context_chunk_fi(
-        self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
-    ):
-        assert isinstance(prefill, FlashInferPrefillMetadata)
-
-        attn_out, lse = prefill.prefill_chunks[chunk_idx].run(
-            q=q,
-            k=k,
-            v=v,
-            return_lse=True,
-        )
-
-        # Convert from (q_len, num_heads) to (num_heads, q_len)
-        return attn_out, lse.transpose(0, 1).contiguous()
-
-    def _run_prefill_context_chunk_cudnn(
-        self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
-    ):
-        assert isinstance(prefill, CudnnPrefillMetadata)
-        assert prefill.chunked_context is not None
-        assert prefill.chunked_context.seq_lens[chunk_idx] is not None
-        assert prefill.query_seq_lens is not None
-        from flashinfer.prefill import cudnn_batch_prefill_with_kv_cache
-
-        return cudnn_batch_prefill_with_kv_cache(
-            q=q,
-            k_cache=k,
-            v_cache=v,
-            scale=self.scale,
-            workspace_buffer=prefill.cudnn_workspace,
-            max_token_per_sequence=prefill.max_query_len,
-            max_sequence_kv=prefill.chunked_context.max_seq_lens[chunk_idx],
-            actual_seq_lens_q=prefill.query_seq_lens.view(-1, 1, 1, 1),
-            actual_seq_lens_kv=prefill.chunked_context.seq_lens[chunk_idx].view(
-                -1, 1, 1, 1
-            ),
-            causal=False,
-            return_lse=True,
-            # Indicates actual_seq_lens are on GPU or CPU.
-            is_cuda_graph_compatible=True,
-        )
-
-    def _run_prefill_new_tokens_trtllm_ragged(
-        self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
-    ):
-        """TRT-LLM ragged attention for new tokens (causal)."""
-        from flashinfer.prefill import trtllm_ragged_attention_deepseek
-
-        assert prefill.query_seq_lens is not None
-        assert prefill.workspace_buffer is not None
-        # allocate BF16 / FP16 output tensor for TRT-LLM ragged attention
-        out = torch.empty(
-            q.shape[0],
-            q.shape[1],
-            v.shape[2],
-            device=q.device,
-            dtype=prefill.output_dtype,
-        )
-
-        ret = trtllm_ragged_attention_deepseek(
-            query=q,
-            key=k,
-            value=v,
-            workspace_buffer=prefill.workspace_buffer,
-            seq_lens=prefill.query_seq_lens,
-            max_q_len=prefill.max_query_len,
-            max_kv_len=prefill.max_query_len,
-            bmm1_scale=self.scale,
-            bmm2_scale=1.0,
-            o_sf_scale=1.0,
-            batch_size=prefill.query_seq_lens.shape[0],
-            window_left=-1,
-            cum_seq_lens_q=prefill.query_start_loc,
-            cum_seq_lens_kv=prefill.query_start_loc,
-            enable_pdl=False,
-            is_causal=True,
-            return_lse=return_softmax_lse,
-            out=out,
-        )
-
-        if isinstance(ret, tuple):
-            # Convert from (q_len, num_heads) to (num_heads, q_len)
-            return ret[0], ret[1].transpose(0, 1).contiguous()
-        return ret
-
-    def _run_prefill_context_chunk_trtllm_ragged(
-        self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
-    ):
-        """TRT-LLM ragged attention for context chunks (non-causal)."""
-        from flashinfer.prefill import trtllm_ragged_attention_deepseek
-
-        assert prefill.chunked_context is not None
-        assert prefill.chunked_context.seq_lens[chunk_idx] is not None
-        assert prefill.workspace_buffer is not None
-
-        out = torch.empty(
-            q.shape[0],
-            q.shape[1],
-            v.shape[2],
-            device=q.device,
-            dtype=prefill.output_dtype,
-        )
-
-        attn_out, lse = trtllm_ragged_attention_deepseek(
-            query=q,
-            key=k,
-            value=v,
-            workspace_buffer=prefill.workspace_buffer,
-            seq_lens=prefill.chunked_context.seq_lens[chunk_idx],
-            max_q_len=prefill.max_query_len,
-            max_kv_len=prefill.chunked_context.max_seq_lens[chunk_idx],
-            bmm1_scale=self.scale,
-            bmm2_scale=1.0,
-            o_sf_scale=1.0,
-            batch_size=prefill.chunked_context.seq_lens[chunk_idx].shape[0],
-            window_left=-1,
-            cum_seq_lens_q=prefill.query_start_loc,
-            cum_seq_lens_kv=prefill.chunked_context.cu_seq_lens[chunk_idx],
-            enable_pdl=False,
-            is_causal=False,
-            return_lse=True,
-            out=out,
-        )
-
-        # Convert from (q_len, num_heads) to (num_heads, q_len)
-        return attn_out, lse.transpose(0, 1).contiguous()
-
     def _concat_k_nope_k_pe(
         self, k_nope: torch.Tensor, k_pe: torch.Tensor
     ) -> torch.Tensor:
@@ -2475,6 +2050,7 @@ def _compute_prefill_context(
     ):
         assert attn_metadata.prefill is not None
         prefill_metadata = attn_metadata.prefill
+        assert prefill_metadata.prefill_backend is not None
         assert prefill_metadata.chunked_context is not None
 
         use_fp8_prefill = prefill_metadata.q_data_type == current_platform.fp8_dtype()
@@ -2542,12 +2118,13 @@ def _compute_prefill_context(
 
             k = self._concat_k_nope_k_pe(k_nope, k_pe)
 
-            attn_output, attn_softmax_lse = self._run_prefill_context_chunk(
-                prefill=prefill_metadata,
-                chunk_idx=i,
-                q=q,
-                k=k,
-                v=v,
+            attn_output, attn_softmax_lse = (
+                prefill_metadata.prefill_backend.run_prefill_context_chunk(
+                    chunk_idx=i,
+                    q=q,
+                    k=k,
+                    v=v,
+                )
             )
 
             if output is None:
@@ -2580,6 +2157,7 @@ def _context_parallel_compute_prefill_context(
         assert k_scale is None, "DCP not support scaled kvcache now."
         assert attn_metadata.prefill is not None
         prefill_metadata = attn_metadata.prefill
+        assert prefill_metadata.prefill_backend is not None
         assert prefill_metadata.chunked_context is not None
         assert prefill_metadata.chunked_context.padded_local_chunk_seq_lens is not None
         assert prefill_metadata.chunked_context.local_context_lens_allranks is not None
@@ -2646,12 +2224,13 @@ def _context_parallel_compute_prefill_context(
             k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
             k = self._concat_k_nope_k_pe(k_nope, k_pe)
 
-            attn_output, attn_softmax_lse = self._run_prefill_context_chunk(
-                prefill=prefill_metadata,
-                chunk_idx=i,
-                q=q,
-                k=k,
-                v=v,
+            attn_output, attn_softmax_lse = (
+                prefill_metadata.prefill_backend.run_prefill_context_chunk(
+                    chunk_idx=i,
+                    q=q,
+                    k=k,
+                    v=v,
+                )
             )
 
             if output is None:
@@ -2683,11 +2262,11 @@ def forward_mha(
         k_scale: torch.Tensor,
         output: torch.Tensor,
     ) -> None:
-        # TODO (zyongye): Prefill function here
         assert attn_metadata.prefill is not None
         assert self.dcp_world_size != -1
 
         prefill_metadata = attn_metadata.prefill
+        assert prefill_metadata.prefill_backend is not None
         use_fp8_prefill = prefill_metadata.q_data_type == current_platform.fp8_dtype()
 
         # Convert q to FP8 if FP8 prefill attention is enabled
@@ -2706,8 +2285,7 @@ def forward_mha(
             k = k.to(prefill_metadata.q_data_type)
             v = v.to(prefill_metadata.q_data_type)
 
-        output_prefill = self._run_prefill_new_tokens(
-            prefill=prefill_metadata,
+        output_prefill = prefill_metadata.prefill_backend.run_prefill_new_tokens(
             q=q,
             k=k,
             v=v,
@@ -2732,11 +2310,6 @@ def forward_mha(
                     q, kv_c_and_k_pe_cache, attn_metadata, k_scale
                 )
 
-            # unpad if necessary
-            if self._pad_v:
-                context_output = context_output[..., : v.shape[-1]]
-                suffix_output = suffix_output[..., : v.shape[-1]]
-
             output = output.view(-1, self.num_heads, self.v_head_dim)
             merge_attn_states(
                 output=output,
@@ -2747,7 +2320,8 @@ def forward_mha(
                 prefill_tokens_with_context=prefill_metadata.chunked_context.prefill_tokens_with_context,
             )
         else:
-            output_prefill = output_prefill[..., : v.shape[-1]].flatten(start_dim=-2)
+            assert isinstance(output_prefill, torch.Tensor)
+            output_prefill = output_prefill.flatten(start_dim=-2)
             output.copy_(output_prefill)
 
     @abstractmethod
diff --git a/vllm/model_executor/layers/attention/mm_encoder_attention.py b/vllm/model_executor/layers/attention/mm_encoder_attention.py
index 6755e9af9e65..1731cc26bc36 100644
--- a/vllm/model_executor/layers/attention/mm_encoder_attention.py
+++ b/vllm/model_executor/layers/attention/mm_encoder_attention.py
@@ -1,13 +1,32 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import functools
+import json
 
 import numpy as np
 import torch
 
+from vllm.config import MultiModalConfig
+from vllm.kernels.triton.qkv_padded_fp8_quant import (
+    quantize_fp8_maybe_pad_head_dim,
+)
 from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp, maybe_get_oot_by_class
-from vllm.model_executor.models.vision import get_vit_attn_backend
+from vllm.model_executor.layers.quantization.input_quant_fp8 import (
+    QuantFP8,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape,
+    get_fp8_min_max,
+)
+from vllm.model_executor.models.vision import (
+    get_multimodal_config,
+    get_vit_attn_backend,
+)
+from vllm.utils.flashinfer import (
+    is_flashinfer_cudnn_fp8_prefill_attn_supported,
+)
 from vllm.utils.math_utils import round_up
 from vllm.v1.attention.backends.fa_utils import get_flash_attn_version
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
@@ -20,6 +39,108 @@
 
 logger = init_logger(__name__)
 
+_, _FP8_MAX = get_fp8_min_max()
+_FP8_AMAX_HISTORY_LEN = 16
+
+# Module-level state for auto-saving dynamic scales. The save is a one-shot
+# triggered by the first layer whose amax buffer wraps. Path and margin are
+# captured during layer init (set_current_vllm_config context only lives
+# across model init, not forward passes).
+_fp8_scale_save_path: str | None = None
+_fp8_scale_save_margin: float = MultiModalConfig.mm_encoder_fp8_scale_save_margin
+_fp8_saved_scale_refs: dict[str, tuple[torch.Tensor, torch.Tensor, torch.Tensor]] = {}
+
+
+@functools.cache
+def _load_fp8_scales_file(path: str | None) -> dict[str, dict[str, float]]:
+    """Load per-layer FP8 Q/K/V scales from a JSON file. Results are cached.
+
+    Expected format (keys ``q_scale`` / ``k_scale`` / ``v_scale`` also accepted)::
+
+        {
+            "visual.blocks.0.attn.attn": {"q": 224.0, "k": 198.0, "v": 210.0},
+            "visual.blocks.1.attn.attn": {"q": 218.0, "k": 195.0, "v": 207.0},
+        }
+
+    To produce such a file, run with ``mm_encoder_fp8_scale_save_path`` set.
+    """
+    if path is None:
+        return {}
+
+    with open(path, encoding="utf-8") as f:
+        data = json.load(f)
+
+    # Handle nested "layers" format
+    if "layers" in data and isinstance(data["layers"], dict):
+        data = data["layers"]
+
+    scales: dict[str, dict[str, float]] = {}
+    for layer_name, layer_scales in data.items():
+        if not isinstance(layer_scales, dict):
+            continue
+        q = layer_scales.get("q", layer_scales.get("q_scale"))
+        k = layer_scales.get("k", layer_scales.get("k_scale"))
+        v = layer_scales.get("v", layer_scales.get("v_scale"))
+        if q is not None and k is not None and v is not None:
+            q_f, k_f, v_f = float(q), float(k), float(v)
+            if q_f <= 0 or k_f <= 0 or v_f <= 0:
+                raise ValueError(
+                    f"FP8 scales must be positive, got q={q_f}, "
+                    f"k={k_f}, v={v_f} for layer '{layer_name}'"
+                )
+            scales[layer_name] = {"q": q_f, "k": k_f, "v": v_f}
+
+    logger.info_once(
+        "Loaded FP8 attention scales from %s (%d layers)", path, len(scales)
+    )
+    return scales
+
+
+def _maybe_save_fp8_scales(
+    layer_name: str,
+    q_scale: torch.Tensor,
+    k_scale: torch.Tensor,
+    v_scale: torch.Tensor,
+    buffer_wrapped: bool,
+) -> None:
+    """Accumulate a layer's scale tensors; on the first amax buffer wrap,
+    dump all accumulated scales to ``mm_encoder_fp8_scale_save_path``.
+
+    No-op unless auto-save is configured. Tensor references are stored on
+    every call (no GPU->CPU sync); ``.item()`` is only called at the single
+    save point to avoid stalling the forward path.
+    """
+    global _fp8_scale_save_path
+    # Fast path: auto-save either disabled or already finished. Path is
+    # captured at layer init and cleared once the save fires.
+    if _fp8_scale_save_path is None:
+        return
+
+    # Stash scale tensor refs (no GPU->CPU sync yet); wait until the amax
+    # history has seen a full cycle before committing scales to disk.
+    _fp8_saved_scale_refs[layer_name] = (q_scale, k_scale, v_scale)
+    if not buffer_wrapped:
+        return
+
+    # Buffer just wrapped for the first time: materialize scales (with
+    # safety margin) and dump to disk. Clearing _fp8_scale_save_path
+    # makes this a one-shot across all layers.
+    path, margin = _fp8_scale_save_path, _fp8_scale_save_margin
+    scales = {
+        name: {
+            "q": q.item() * margin,
+            "k": k.item() * margin,
+            "v": v.item() * margin,
+        }
+        for name, (q, k, v) in _fp8_saved_scale_refs.items()
+    }
+    _fp8_scale_save_path = None
+    _fp8_saved_scale_refs.clear()
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(scales, f, indent=2)
+    logger.info("Saved FP8 scales (%d layers) to %s", len(scales), path)
+
+
 # Batch buckets for cuDNN graph caching.
 # Graphs use batch size and max sequence length as cache key.
 # This avoids creating a new graph for each unique set of
@@ -148,27 +269,47 @@ def maybe_recompute_cu_seqlens(
         hidden_size: int,
         tp_size: int,
         device: torch.device,
+        fp8_padded_hidden_size: int | None = None,
     ) -> torch.Tensor:
         if (oot_class := maybe_get_oot_by_class(cls)) is not cls:
             return oot_class.maybe_recompute_cu_seqlens(  # type: ignore[attr-defined]
-                attn_backend, cu_seqlens, hidden_size, tp_size, device
+                attn_backend,
+                cu_seqlens,
+                hidden_size,
+                tp_size,
+                device,
+                fp8_padded_hidden_size=fp8_padded_hidden_size,
             )
 
         if attn_backend == AttentionBackendEnum.FLASHINFER:
             batch_size = len(cu_seqlens) - 1
-            scale = hidden_size // tp_size
-            cu_seqlens = cu_seqlens * scale
 
-            cu_seqlens_qko = cu_seqlens
-            cu_seqlens_v = cu_seqlens * 3
-
-            cu_seqlens_qko = add_padding_to_seqlens(
-                cu_seqlens_qko, batch_size, cu_seqlens_qko[-1]
-            )
-            cu_seqlens_v = add_padding_to_seqlens(
-                cu_seqlens_v, batch_size, cu_seqlens_v[-1]
-            )
-            cu_seqlens = np.concatenate([cu_seqlens_qko, cu_seqlens_v])
+            if fp8_padded_hidden_size is not None:
+                # FP8 path: after quantization Q/K/V are each independent
+                # contiguous tensors with stride H * padded_D per token.
+                # All sections use the same element stride.
+                scale = fp8_padded_hidden_size // tp_size
+                cu_seqlens = cu_seqlens * scale
+                cu_seqlens_padded = add_padding_to_seqlens(
+                    cu_seqlens, batch_size, cu_seqlens[-1]
+                )
+                cu_seqlens = np.concatenate([cu_seqlens_padded, cu_seqlens_padded])
+            else:
+                # BF16 path: Q/K/V are non-contiguous views into shared
+                # buffers. V section has 3x stride from interleaved QKV.
+                scale = hidden_size // tp_size
+                cu_seqlens = cu_seqlens * scale
+
+                cu_seqlens_qko = cu_seqlens
+                cu_seqlens_v = cu_seqlens * 3
+
+                cu_seqlens_qko = add_padding_to_seqlens(
+                    cu_seqlens_qko, batch_size, cu_seqlens_qko[-1]
+                )
+                cu_seqlens_v = add_padding_to_seqlens(
+                    cu_seqlens_v, batch_size, cu_seqlens_v[-1]
+                )
+                cu_seqlens = np.concatenate([cu_seqlens_qko, cu_seqlens_v])
 
         cu_seqlens = torch.from_numpy(cu_seqlens).to(device, non_blocking=True)
         return cu_seqlens
@@ -206,6 +347,7 @@ def __init__(
         # During model initialization, the default dtype is set as the model
         # weight and activation dtype.
         dtype = torch.get_default_dtype()
+        self.dtype = dtype
 
         # Get device-specific vision attention backend.
         self.attn_backend = get_vit_attn_backend(
@@ -227,8 +369,113 @@ def __init__(
         if self.attn_backend == AttentionBackendEnum.FLASHINFER:
             _get_flashinfer_workspace_buffer()
 
-        logger.info_once(
-            f"Using {self.attn_backend} for MMEncoderAttention.", scope="local"
+        logger.info_once(f"Using {self.attn_backend} for MMEncoderAttention.")
+
+        self._init_fp8_state()
+
+    def _init_fp8_state(self) -> None:
+        """Initialize FP8 attention state from multimodal config.
+
+        No-op if FP8 is not requested. Raises ``ValueError`` if FP8 is
+        requested but the platform does not support it.
+        """
+        # Populate defaults so ``_forward_flashinfer`` can
+        # check ``self.fp8_enabled`` and others without AttributeError.
+        self.fp8_enabled = False
+        self._fp8_dynamic_scale = False
+        self.fp8_quant: QuantFP8 | None = None
+        self.skip_scale_q = False
+        self.skip_scale_k = False
+        self.skip_scale_v = False
+
+        mm_cfg = get_multimodal_config()
+        if mm_cfg is None or mm_cfg.mm_encoder_attn_dtype != "fp8":
+            return
+
+        # FP8 path
+        if not is_flashinfer_cudnn_fp8_prefill_attn_supported():
+            raise ValueError(
+                "mm_encoder_attn_dtype='fp8' requires the FlashInfer "
+                "cuDNN backend with cuDNN >= 9.17.1 on a GPU with native "
+                "FP8 support."
+            )
+
+        self.fp8_enabled = True
+        self._fp8_dynamic_scale = mm_cfg.mm_encoder_fp8_scale_path is None
+        self.fp8_quant = QuantFP8(static=True, group_shape=GroupShape.PER_TENSOR)
+
+        # Register buffers pre-device-move; values populated in
+        # process_weights_after_loading. Shape (1, 1, 1, 1) is required by cuDNN.
+        for attr in ("_fp8_q_scale", "_fp8_k_scale", "_fp8_v_scale"):
+            self.register_buffer(
+                attr, torch.ones(1, dtype=torch.float32).view(1, 1, 1, 1)
+            )
+        if self._fp8_dynamic_scale:
+            for attr in ("_fp8_q_amax", "_fp8_k_amax", "_fp8_v_amax"):
+                self.register_buffer(
+                    attr,
+                    torch.zeros(_FP8_AMAX_HISTORY_LEN, dtype=torch.float32),
+                    persistent=False,
+                )
+            self._fp8_amax_pos = 0
+
+        # Capture auto-save config now: the VllmConfig context only lives
+        # across model init, not forward passes, so ``_maybe_save_fp8_scales``
+        # reads these globals instead of re-querying ``get_multimodal_config``.
+        if (
+            mm_cfg.mm_encoder_fp8_scale_save_path is not None
+            and self._fp8_dynamic_scale
+        ):
+            global _fp8_scale_save_path, _fp8_scale_save_margin
+            _fp8_scale_save_path = mm_cfg.mm_encoder_fp8_scale_save_path
+            _fp8_scale_save_margin = mm_cfg.mm_encoder_fp8_scale_save_margin
+
+    def process_weights_after_loading(self, act_dtype: torch.dtype) -> None:
+        """Populate FP8 scale buffers after weights are loaded.
+
+        ``act_dtype`` matches the signature used by :class:`Attention` and
+        :class:`MLAAttention` for the loader auto-scan but is unused:
+        FP8 scales are always float32.
+        """
+        if not self.fp8_enabled:
+            return
+
+        mm_cfg = get_multimodal_config()
+        scale_path = mm_cfg.mm_encoder_fp8_scale_path if mm_cfg is not None else None
+        if scale_path is None:
+            logger.info_once(
+                "FP8 attention enabled with dynamic scaling "
+                "(no scale file provided). Scales will adapt from "
+                "observed Q/K/V amax values (history_len=%d).",
+                _FP8_AMAX_HISTORY_LEN,
+            )
+            return
+
+        all_scales = _load_fp8_scales_file(scale_path)
+        layer_scales = all_scales.get(self.layer_name)
+        if layer_scales is None:
+            raise ValueError(
+                "FP8 attention enabled but scales not found for layer "
+                f"'{self.layer_name}' in {scale_path}. "
+                f"Available layers: {list(all_scales.keys())}"
+            )
+
+        for attr, key in (
+            ("_fp8_q_scale", "q"),
+            ("_fp8_k_scale", "k"),
+            ("_fp8_v_scale", "v"),
+        ):
+            getattr(self, attr).fill_(layer_scales[key])
+        self.skip_scale_q = layer_scales["q"] == 1.0
+        self.skip_scale_k = layer_scales["k"] == 1.0
+        self.skip_scale_v = layer_scales["v"] == 1.0
+
+        logger.debug(
+            "FP8 attention enabled for %s: q=%.4f, k=%.4f, v=%.4f",
+            self.layer_name if self.layer_name else "MMEncoderAttention",
+            layer_scales["q"],
+            layer_scales["k"],
+            layer_scales["v"],
         )
 
     @classmethod
@@ -355,6 +602,44 @@ def _forward_triton(
             output = output.reshape(bsz, q_len, -1)
         return output
 
+    @torch.no_grad()
+    def _record_amax_and_update_scales(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+    ) -> None:
+        """Record Q/K/V amax into circular history and recompute scales.
+
+        All work stays on GPU with no device-to-host sync. The Python-side
+        history position counter is mutated, so this method must NOT be
+        called inside CUDA graph capture/replay. When CUDA graphs are
+        used for the encoder, dynamic scaling should be disabled by
+        providing a static scale file via --mm-encoder-fp8-scale-path.
+        """
+        pos = self._fp8_amax_pos
+        self._fp8_amax_pos = (pos + 1) % _FP8_AMAX_HISTORY_LEN
+
+        for tensor, amax_buf, scale_buf in (
+            (query, self._fp8_q_amax, self._fp8_q_scale),
+            (key, self._fp8_k_amax, self._fp8_k_scale),
+            (value, self._fp8_v_amax, self._fp8_v_scale),
+        ):
+            amax_buf[pos] = tensor.amax()
+            max_amax = amax_buf.max()
+            scale_buf.fill_(
+                torch.clamp(max_amax, min=torch.finfo(torch.float32).tiny) / _FP8_MAX
+            )
+
+        buffer_wrapped = self._fp8_amax_pos == 0 and pos == _FP8_AMAX_HISTORY_LEN - 1
+        _maybe_save_fp8_scales(
+            self.layer_name,
+            self._fp8_q_scale,
+            self._fp8_k_scale,
+            self._fp8_v_scale,
+            buffer_wrapped,
+        )
+
     def _forward_flashinfer(
         self,
         query: torch.Tensor,
@@ -365,7 +650,32 @@ def _forward_flashinfer(
         sequence_lengths: torch.Tensor
         | None = None,  # Only used for FlashInfer CuDNN backend
     ) -> torch.Tensor:
-        return vit_flashinfer_wrapper(
+        if self.fp8_enabled:
+            assert self.fp8_quant is not None
+
+            if self._fp8_dynamic_scale:
+                self._record_amax_and_update_scales(query, key, value)
+
+            query = quantize_fp8_maybe_pad_head_dim(
+                query,
+                self._fp8_q_scale,
+                skip_scale=self.skip_scale_q,
+                fp8_quant=self.fp8_quant,
+            )
+            key = quantize_fp8_maybe_pad_head_dim(
+                key,
+                self._fp8_k_scale,
+                skip_scale=self.skip_scale_k,
+                fp8_quant=self.fp8_quant,
+            )
+            value = quantize_fp8_maybe_pad_head_dim(
+                value,
+                self._fp8_v_scale,
+                skip_scale=self.skip_scale_v,
+                fp8_quant=self.fp8_quant,
+            )
+
+        output = vit_flashinfer_wrapper(
             q=query,
             k=key,
             v=value,
@@ -374,8 +684,17 @@ def _forward_flashinfer(
             cu_seqlens=cu_seqlens,
             max_seqlen=max_seqlen,
             sequence_lengths=sequence_lengths,
+            q_scale=self._fp8_q_scale if self.fp8_enabled else None,
+            k_scale=self._fp8_k_scale if self.fp8_enabled else None,
+            v_scale=self._fp8_v_scale if self.fp8_enabled else None,
+            o_data_type=self.dtype if self.fp8_enabled else None,
         )
 
+        if self.fp8_enabled and output.shape[-1] != self.head_size:
+            output = output[..., : self.head_size].contiguous()
+
+        return output
+
     def forward_native(
         self,
         query: torch.Tensor,
diff --git a/vllm/model_executor/layers/batch_invariant.py b/vllm/model_executor/layers/batch_invariant.py
index 98fd6be8f6a6..bcdd30500329 100644
--- a/vllm/model_executor/layers/batch_invariant.py
+++ b/vllm/model_executor/layers/batch_invariant.py
@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import math
 import os
 from collections.abc import Callable
 from typing import Any
@@ -13,7 +14,6 @@
 from vllm.utils.mem_utils import get_max_shared_memory_bytes
 from vllm.utils.platform_utils import num_compute_units
 from vllm.utils.torch_utils import is_torch_equal_or_newer
-from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 logger = init_logger(__name__)
 
@@ -39,7 +39,7 @@ def _matmul_launch_metadata(
 
 
 @triton.jit
-def _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS):
+def _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M):
     group_id = tile_id // num_pid_in_group
     first_pid_m = group_id * GROUP_SIZE_M
     group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
@@ -85,9 +85,7 @@ def matmul_kernel_persistent(
     num_pid_in_group = GROUP_SIZE_M * num_pid_n
 
     for tile_id in tl.range(start_pid, num_tiles, NUM_SMS, flatten=True):
-        pid_m, pid_n = _compute_pid(
-            tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS
-        )
+        pid_m, pid_n = _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M)
         start_m = pid_m * BLOCK_SIZE_M
         start_n = pid_n * BLOCK_SIZE_N
         offs_am = start_m + tl.arange(0, BLOCK_SIZE_M)
@@ -124,7 +122,7 @@ def matmul_kernel_persistent(
 
         tile_id_c += NUM_SMS
         pid_m, pid_n = _compute_pid(
-            tile_id_c, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS
+            tile_id_c, num_pid_in_group, num_pid_m, GROUP_SIZE_M
         )
         offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
         offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
@@ -611,51 +609,43 @@ def matmul_batch_invariant(a, b, *, out=None):
             out.copy_(result)
             return out
         return result
-    elif a.ndim == 3 and b.ndim == 3:
-        # Handle batched case like bmm
-        return bmm_batch_invariant(a, b, out=out)
-    elif a.ndim == 3 and b.ndim == 2:
-        # Handle 3D x 2D: common for linear layers
-        # (batch, seq, hidden) @ (hidden, out) -> (batch, seq, out)
-        # Reshape to 2D, do mm, reshape back
-        batch, seq, hidden = a.shape
+    elif b.ndim == 2:
+        # Handle ND x 2D: Common for linear layers
+        # (..., batch, seq, hidden) @ (hidden, out) -> (..., batch, seq, out)
+        batch_dims = a.shape[:-1]
+        hidden = a.shape[-1]
+        out_dim = b.shape[-1]
         a_2d = a.reshape(-1, hidden)
         result_2d = matmul_persistent(a_2d, b)
-        result = result_2d.reshape(batch, seq, -1)
+        result = result_2d.reshape(batch_dims + (out_dim,))
         if out is not None:
             out.copy_(result)
             return out
         return result
-    elif a.ndim == 2 and b.ndim == 3:
-        # Handle 2D x 3D: (M, K) @ (B, K, N) -> (B, M, N)
-        # By broadcasting `a` to 3D, we can reuse the batched matrix
-        # multiplication logic.
-        a_expanded = a.unsqueeze(0).expand(b.shape[0], -1, -1)
-        return bmm_batch_invariant(a_expanded, b, out=out)
-    elif a.ndim == 4 and b.ndim == 4:
-        # Handle 4D attention tensors: [batch, heads, seq, dim]
-        # Reshape to 3D, process, reshape back
-        batch, heads, seq_a, dim_a = a.shape
-        _, _, dim_b, seq_b = b.shape
-
-        # Reshape to [batch*heads, seq_a, dim_a]
-        a_3d = a.reshape(batch * heads, seq_a, dim_a)
-        b_3d = b.reshape(batch * heads, dim_b, seq_b)
-
+    elif a.ndim >= 2 and b.ndim >= 3:
+        # Generic handler for 2D x ND and ND x ND (except 1D)
+        # Broadcast dims to ensure both matrices have the same shape
+        # If 2D x ND, then unsqueeze to add a dim to a
+        if a.ndim == 2:
+            a = a.unsqueeze(0)
+        broadcast_shape = torch.broadcast_shapes(a.shape[:-2], b.shape[:-2])
+        a = a.expand(broadcast_shape + a.shape[-2:])
+        b = b.expand(broadcast_shape + b.shape[-2:])
+        batch_dim = math.prod(broadcast_shape)
+        # Reuse broadcast shape to get all dims except mm dims
+        a_3d = a.reshape(batch_dim, a.shape[-2], a.shape[-1])
+        b_3d = b.reshape(batch_dim, b.shape[-2], b.shape[-1])
         # Do batched matmul
         result_3d = bmm_batch_invariant(a_3d, b_3d)
-
-        # Reshape back to [batch, heads, seq_a, seq_b]
-        result = result_3d.reshape(batch, heads, seq_a, seq_b)
-
+        # Reshape back to [broadcast_shape, seq_a, seq_b]
+        result = result_3d.reshape(broadcast_shape + (a.shape[-2], b.shape[-1]))
         if out is not None:
             out.copy_(result)
             return out
         return result
     else:
         raise ValueError(
-            f"matmul_batch_invariant currently only supports 2D x 2D, 3D x 3D, "
-            f"3D x 2D, 2D x 3D, and 4D x 4D, "
+            f"matmul_batch_invariant requires both inputs be at least 2D "
             f"got shapes {a.shape} and {b.shape}"
         )
 
@@ -940,22 +930,17 @@ def enable_batch_invariant_mode():
     _batch_invariant_MODE = True
     _batch_invariant_LIB = torch.library.Library("aten", "IMPL")
 
-    if current_platform.is_device_capability_family(
-        100
-    ) or current_platform.is_device_capability_family(80):
-        # For PyTorch 2.9, B200 uses GEMV for bs=1
-        # Requires https://github.com/pytorch/pytorch/pull/166735
+    if current_platform.is_device_capability_family(80):
+        # SM80 (Ampere) cannot rely on cuBLASLt-only determinism; install the
+        # triton persistent matmul overrides for mm/addmm/matmul/linear.
         _batch_invariant_LIB.impl("aten::mm", mm_batch_invariant, "CUDA")
         _batch_invariant_LIB.impl("aten::addmm", addmm_batch_invariant, "CUDA")
         _batch_invariant_LIB.impl("aten::matmul", matmul_batch_invariant, "CUDA")
         _batch_invariant_LIB.impl("aten::linear", linear_batch_invariant, "CUDA")
-
-        # Query the shared memory size and set block size
-        # accordingly to avoid triton OutOfResources
-        _fp16_block_size_n = 256 if get_max_shared_memory_bytes() > 106496 else 128
     else:
-        # Only source of batch invariance for Hopper is split-k, can disable through
-        # cuBLAS workspace config
+        # Hopper (SM90) and Blackwell (SM100): the only source of batch
+        # variance is split-k, which we disable via the cuBLAS workspace
+        # config.
         _original_cublas_workspace_cfg = os.environ.get("CUBLAS_WORKSPACE_CONFIG", None)
         _original_cublaslt_workspace_size = os.environ.get(
             "CUBLASLT_WORKSPACE_SIZE", None
@@ -963,6 +948,11 @@ def enable_batch_invariant_mode():
         os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
         os.environ["CUBLASLT_WORKSPACE_SIZE"] = "1"
 
+    # Triton bmm/persistent-matmul kernels read this for the FP16 N-tile size;
+    # set unconditionally because bmm is overridden on all CUDA platforms.
+    if current_platform.is_cuda():
+        _fp16_block_size_n = 256 if get_max_shared_memory_bytes() > 106496 else 128
+
     _batch_invariant_LIB.impl(
         "aten::_log_softmax", _log_softmax_batch_invariant, "CUDA"
     )
@@ -970,8 +960,12 @@ def enable_batch_invariant_mode():
     _batch_invariant_LIB.impl("aten::_softmax", softmax_batch_invariant, "CUDA")
     _batch_invariant_LIB.impl("aten::mean.dim", mean_batch_invariant, "CUDA")
 
-    # Also monkeypatch torch.bmm directly as a fallback
-    _batch_invariant_LIB.impl("aten::bmm", bmm_batch_invariant, "CUDA")
+    # torch 2.12+ registers a built-in Triton bmm kernel for CUDA
+    # (torch._native.ops.bmm_outer_product), so we need allow_override
+    # to replace it at the dispatcher level.
+    _batch_invariant_LIB.impl(
+        "aten::bmm", bmm_batch_invariant, "CUDA", allow_override=True
+    )
     _original_torch_bmm = torch.bmm
     torch.bmm = bmm_batch_invariant
 
@@ -994,40 +988,7 @@ def enable_batch_invariant_mode():
     torch.backends.cuda.preferred_blas_library(backend="cublaslt")
 
 
-def override_envs_for_invariance(
-    attention_backend: AttentionBackendEnum | None,
-):
-    decode_invariant_backends = [
-        AttentionBackendEnum.FLASH_ATTN,  # best supported backend
-        AttentionBackendEnum.TRITON_ATTN,
-    ]
-    supported_backends = decode_invariant_backends + [
-        # FlashInfer temporarily disabled due to invariant CTA sizes.
-        # See FlashInfer issue #2424
-        # AttentionBackendEnum.FLASHINFER,
-        AttentionBackendEnum.FLASH_ATTN_MLA,
-        AttentionBackendEnum.TRITON_MLA,
-        # Not yet supported MLA backends
-        # AttentionBackendEnum.FLASHMLA,
-        # AttentionBackendEnum.FLEX_ATTENTION,  # IMA issue
-        # AttentionBackendEnum.FLASHINFER_MLA,  # PR #28967
-    ]
-    if attention_backend not in supported_backends:
-        supported_names = [b.name for b in supported_backends]
-        backend_name = attention_backend.name if attention_backend else None
-        error = (
-            "VLLM batch_invariant mode requires an attention backend in "
-            f"{supported_names}, but got '{backend_name}'. "
-            "Please use --attention-backend or attention_config to set "
-            "one of the supported backends before enabling batch_invariant."
-        )
-        raise RuntimeError(error)
-    if attention_backend not in decode_invariant_backends:
-        warning = (
-            "You are using a non-decode-invariant form of batch invariance. "
-            "This will not be invariant between prefill and decode."
-        )
-        logger.warning_once(warning, scope="local")
+def override_envs_for_invariance():
     os.environ["VLLM_ALLREDUCE_USE_SYMM_MEM"] = "0"
 
     os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
@@ -1048,12 +1009,10 @@ def override_envs_for_invariance(
     os.environ["VLLM_USE_AOT_COMPILE"] = "0"
 
 
-def init_batch_invariance(
-    attention_backend: AttentionBackendEnum | None,
-):
+def init_batch_invariance():
     # this will hit all the csrc overrides as well
     if envs.VLLM_BATCH_INVARIANT:
-        override_envs_for_invariance(attention_backend)
+        override_envs_for_invariance()
         enable_batch_invariant_mode()
 
         # Disable TF32 for batch invariance - it causes non-deterministic rounding
diff --git a/vllm/model_executor/layers/deepseek_compressor.py b/vllm/model_executor/layers/deepseek_compressor.py
new file mode 100644
index 000000000000..48628fec46e0
--- /dev/null
+++ b/vllm/model_executor/layers/deepseek_compressor.py
@@ -0,0 +1,434 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass
+from typing import Any, ClassVar, cast
+
+import torch
+from torch import nn
+
+from vllm.config import VllmConfig, get_current_vllm_config
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+)
+from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
+from vllm.v1.attention.backend import (
+    AttentionBackend,
+    AttentionCGSupport,
+    AttentionMetadataBuilder,
+    CommonAttentionMetadata,
+    MultipleOf,
+)
+from vllm.v1.attention.ops.deepseek_v4_ops.fused_compress_quant_cache import (
+    _fused_kv_compress_norm_rope_insert_indexer_attn,
+    _fused_kv_compress_norm_rope_insert_indexer_mxfp4_attn,
+    _fused_kv_compress_norm_rope_insert_sparse_attn,
+)
+from vllm.v1.attention.ops.deepseek_v4_ops.fused_indexer_q import (
+    MXFP4_BLOCK_SIZE,
+)
+from vllm.v1.kv_cache_interface import (
+    KVCacheSpec,
+    MLAAttentionSpec,
+    SlidingWindowMLASpec,
+)
+
+
+class CompressorBackend(AttentionBackend):
+    def __init__(self):
+        super().__init__()
+
+    @staticmethod
+    def get_name() -> str:
+        return "CompressorBackend"
+
+    @staticmethod
+    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
+        return [MultipleOf(1)]
+
+    @classmethod
+    def get_supported_head_sizes(cls) -> list[int]:
+        return [512, 1024]
+
+    @staticmethod
+    def get_builder_cls() -> type["CompressorMetadataBuilder"]:
+        return CompressorMetadataBuilder
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+        cache_dtype_str: str = "auto",
+    ) -> tuple[int, ...]:
+        assert num_kv_heads == 1
+        return (num_blocks, block_size, head_size)
+
+    @staticmethod
+    def get_kv_cache_stride_order(
+        include_num_layers_dimension: bool = False,
+    ) -> tuple[int, ...]:
+        if include_num_layers_dimension:
+            return (0, 1, 2, 3)
+        return (0, 1, 2)
+
+
+@dataclass
+class CompressorMetadata:
+    block_table: torch.Tensor
+    slot_mapping: torch.Tensor
+    block_size: int
+
+    token_to_req_indices: torch.Tensor | None = None  # [num_tokens]
+
+
+class CompressorMetadataBuilder(AttentionMetadataBuilder):
+    _cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.ALWAYS
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert isinstance(self.kv_cache_spec, SlidingWindowMLASpec | MLAAttentionSpec)
+        mla_spec = cast(SlidingWindowMLASpec | MLAAttentionSpec, self.kv_cache_spec)
+        self.block_size = mla_spec.block_size
+
+        self.token_to_req_indices = torch.zeros(
+            self.vllm_config.scheduler_config.max_num_batched_tokens,
+            dtype=torch.int32,
+            device=self.device,
+        )
+
+    def build(
+        self,
+        common_prefix_len: int,
+        common_attn_metadata: CommonAttentionMetadata,
+        fast_build: bool = False,
+    ) -> CompressorMetadata:
+        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
+        num_reqs = common_attn_metadata.num_reqs
+        query_lens = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
+        x = torch.repeat_interleave(torch.arange(num_reqs), query_lens).pin_memory()
+        token_to_req_indices = self.token_to_req_indices[: x.shape[0]]
+        token_to_req_indices.copy_(x, non_blocking=True)
+        return CompressorMetadata(
+            block_table=common_attn_metadata.block_table_tensor.clamp_(min=0),
+            slot_mapping=common_attn_metadata.slot_mapping,
+            block_size=self.block_size,
+            token_to_req_indices=token_to_req_indices,
+        )
+
+
+class CompressorStateCache(torch.nn.Module, AttentionLayerBase):
+    def __init__(
+        self,
+        state_dim: int,
+        dtype: torch.dtype,
+        compress_ratio: int,
+        prefix: str,
+    ):
+        super().__init__()
+        self.state_dim = state_dim
+        self.dtype = dtype
+        self.prefix = prefix
+        self.kv_cache = torch.tensor([])
+        compilation_config = get_current_vllm_config().compilation_config
+        if prefix in compilation_config.static_forward_context:
+            raise ValueError(f"Duplicate layer name: {prefix}")
+        compilation_config.static_forward_context[prefix] = self
+
+        assert self.dtype == torch.float32
+        assert compress_ratio in [4, 128]
+        coff = 1 + (compress_ratio == 4)
+        self.sliding_window = coff * compress_ratio
+        # Block size is constrained by tensor sharing between compressor states
+        # and KV blocks. Since compressor states share the same physical tensor
+        # as KV blocks, they must use the same page size.
+        # The KV block shape [256//4, head_dim] = [64, 584] determines:
+        # - C4 compressor block shape [4, 2*512*2*4] -> block_size = 4
+        # - C128 compressor block shape [8, 512*2*4] -> block_size = 8
+        # TODO(yifan): make block size automatically determined and configurable.
+        if compress_ratio == 4:
+            self.block_size = 4
+        elif compress_ratio == 128:
+            self.block_size = 8
+        else:
+            raise ValueError(f"Invalid compress ratio: {compress_ratio}")
+
+    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec:
+        return SlidingWindowMLASpec(  # only has one vector instead of K + V
+            block_size=self.block_size,
+            num_kv_heads=1,
+            head_size=self.state_dim,
+            dtype=self.dtype,
+            sliding_window=self.sliding_window,
+            alignment=576,  # NOTE: FlashMLA requires 576B alignment
+        )
+
+    def forward(self): ...
+
+    def get_attn_backend(self) -> type[AttentionBackend]:
+        return CompressorBackend
+
+
+class DeepseekCompressor(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        compress_ratio: int,
+        hidden_size: int,
+        head_dim: int,
+        rotate: bool = False,
+        prefix: str = "",
+        k_cache_prefix="",
+        use_fp4_cache: bool = False,
+    ):
+        super().__init__()
+        self.compress_ratio = compress_ratio
+        self.hidden_size = hidden_size
+        self.head_dim = head_dim
+        self.rotate = rotate
+        self.prefix = prefix
+        self.k_cache_prefix = k_cache_prefix
+        self.use_fp4_cache = use_fp4_cache
+
+        config = vllm_config.model_config.hf_config
+        self.rope_head_dim = config.qk_rope_head_dim
+        self.nope_head_dim = self.head_dim - self.rope_head_dim
+        self.rms_norm_eps = config.rms_norm_eps
+        self.device = current_platform.device_type
+        self.max_num_reqs = vllm_config.scheduler_config.max_num_seqs
+        self.max_model_len = vllm_config.model_config.max_model_len
+
+        self.overlap = compress_ratio == 4
+        self.coff = 1 + self.overlap
+
+        state_dtype = torch.float32
+        self.ape = nn.Parameter(
+            torch.empty(
+                (compress_ratio, self.coff * self.head_dim),
+                dtype=state_dtype,
+                device=self.device,
+            ),
+            requires_grad=False,
+        )
+
+        self.fused_wkv_wgate = MergedColumnParallelLinear(
+            self.hidden_size,
+            [self.coff * self.head_dim, self.coff * self.head_dim],
+            bias=False,
+            return_bias=False,
+            quant_config=None,
+            disable_tp=True,
+            prefix=f"{prefix}.fused_wkv_wgate",
+        )
+        self.norm = RMSNorm(self.head_dim, self.rms_norm_eps)
+
+        self.state_cache = CompressorStateCache(
+            state_dim=2 * self.coff * self.head_dim,  # kv_state + score_state
+            dtype=state_dtype,
+            compress_ratio=compress_ratio,
+            prefix=f"{prefix}.state_cache",
+        )
+
+        # Save reference to static_forward_context for forward-time KV cache lookup.
+        # get_current_vllm_config() is only available during __init__, not forward.
+        self._static_forward_context = (
+            vllm_config.compilation_config.static_forward_context
+        )
+
+        if self.head_dim == 512:
+            assert not use_fp4_cache, (
+                "MXFP4 cache is only supported for indexer (head=128)"
+            )
+            self._fused_kernel = _fused_kv_compress_norm_rope_insert_sparse_attn
+            self._quant_block = 64
+            self._token_stride = self.nope_head_dim + self.rope_head_dim * 2
+            self._scale_dim = self.nope_head_dim // 64 + 1  # 7 real + 1 pad
+            self._num_warps = 4
+        elif self.head_dim == 128:
+            if use_fp4_cache:
+                self._fused_kernel = (
+                    _fused_kv_compress_norm_rope_insert_indexer_mxfp4_attn
+                )
+                self._quant_block = MXFP4_BLOCK_SIZE
+                self._token_stride = self.head_dim // 2
+                self._scale_dim = self.head_dim // MXFP4_BLOCK_SIZE
+            else:
+                self._fused_kernel = _fused_kv_compress_norm_rope_insert_indexer_attn
+                self._quant_block = 128
+                self._token_stride = self.head_dim
+                self._scale_dim = 4  # single float32 scale
+            self._num_warps = 1
+        else:
+            raise ValueError(
+                f"Unsupported head_dim for fused quant+cache: {self.head_dim}"
+            )
+
+    def forward(
+        self,
+        # [num_tokens, 2 * self.coff * self.head_dim]
+        kv_score: torch.Tensor,
+        # [num_tokens]
+        positions: torch.Tensor,
+        rotary_emb,
+    ) -> None:
+        # Each of shape [num_tokens, coff * self.head_dim]
+        # input bf16, output are fp32
+        kv, score = kv_score.split(
+            [self.coff * self.head_dim, self.coff * self.head_dim], dim=-1
+        )
+
+        # Get the metadata and handle dummy profiling run.
+        attn_metadata = get_forward_context().attn_metadata
+        if not isinstance(attn_metadata, dict):
+            return
+
+        state_metadata = cast(
+            CompressorMetadata, attn_metadata[self.state_cache.prefix]
+        )
+        token_to_req_indices = state_metadata.token_to_req_indices
+        slot_mapping = state_metadata.slot_mapping
+        num_actual = slot_mapping.shape[0]
+        block_table = state_metadata.block_table
+        block_size = state_metadata.block_size
+
+        # [num_blocks, block_size, kv_dim+score_dim], where kv_dim == score_dim
+        state_cache = self.state_cache.kv_cache
+        # kv_state stored in first half, score_state stored in second half
+        state_width = state_cache.shape[-1] // 2
+        pdl_kwargs = {} if current_platform.is_rocm() else {"launch_pdl": False}
+
+        # Store the KV and score (with fused APE addition) in the state.
+        # NOTE: PDL is disabled — both this kernel and _fused_kernel below
+        # depend on preceding kernel outputs (kv/score from the cublas GEMM;
+        # state_cache from this kernel) but neither emits/waits on PDL grid
+        # dependency primitives, so launch_pdl=True caused a read-after-write
+        # race and non-deterministic output.
+        _save_partial_states_kernel[(num_actual,)](
+            kv,
+            kv.stride(0),
+            score,
+            score.stride(0),
+            self.ape,
+            self.ape.stride(0),
+            positions,
+            state_cache,
+            state_cache.stride(0),
+            state_cache.stride(1),
+            slot_mapping,
+            block_size,
+            HEAD_SIZE=kv.shape[-1],
+            TRITON_BLOCK_SIZE=triton.next_power_of_2(kv.shape[-1]),
+            STATE_WIDTH=state_width,
+            COMPRESS_RATIO=self.compress_ratio,
+            **pdl_kwargs,
+        )
+
+        # Fused: compress → RMSNorm → RoPE → FP8 quant → KV cache write.
+        # RoPE requirements (kernel applies forward GPT-J style rotation):
+        # - is_neox_style=False (interleaved pairs, NOT split-half)
+        # - cos_sin_cache layout: [max_pos, rope_head_dim] with first half cos,
+        #   second half sin (per-pair, length rope_head_dim // 2 each)
+        # - applied to LAST rope_head_dim elements of head_dim
+        # - position used: (positions // compress_ratio) * compress_ratio
+        cos_sin_cache = rotary_emb.cos_sin_cache
+        k_cache_metadata = cast(Any, attn_metadata[self.k_cache_prefix])
+        kv_cache = self._static_forward_context[self.k_cache_prefix].kv_cache
+
+        self._fused_kernel[(num_actual,)](
+            # state cache
+            state_cache,
+            state_cache.stride(0),
+            state_cache.stride(1),
+            # metadata
+            token_to_req_indices,
+            positions,
+            slot_mapping,
+            block_table,
+            block_table.stride(0),
+            block_size,
+            # RMSNorm
+            self.norm.weight,
+            self.rms_norm_eps,
+            # RoPE
+            cos_sin_cache,
+            cos_sin_cache.stride(0),
+            # KV cache
+            kv_cache,
+            k_cache_metadata.slot_mapping,
+            kv_cache.shape[1],  # paged KV cache block size (tokens per block)
+            # constexprs
+            HEAD_SIZE=self.head_dim,
+            TRITON_BLOCK_SIZE=triton.next_power_of_2(self.head_dim),
+            STATE_WIDTH=state_width,
+            COMPRESS_RATIO=self.compress_ratio,
+            OVERLAP=self.overlap,
+            ROPE_HEAD_DIM=self.rope_head_dim,
+            FP8_MAX=448.0,
+            QUANT_BLOCK=self._quant_block,
+            TOKEN_STRIDE=self._token_stride,
+            SCALE_DIM=self._scale_dim,
+            KV_BLOCK_STRIDE=kv_cache.stride(0),
+            num_warps=self._num_warps,
+            **pdl_kwargs,
+        )
+
+
+@triton.jit
+def _save_partial_states_kernel(
+    kv_ptr,
+    kv_stride,
+    score_ptr,
+    score_stride,
+    ape_ptr,
+    ape_stride,
+    positions_ptr,
+    state_cache_ptr,
+    state_cache_stride0,
+    state_cache_stride1,
+    slot_mapping_ptr,
+    block_size,
+    HEAD_SIZE: tl.constexpr,
+    TRITON_BLOCK_SIZE: tl.constexpr,
+    # state_cache last dim packs [kv_state, score_state], each STATE_WIDTH wide.
+    STATE_WIDTH: tl.constexpr,
+    COMPRESS_RATIO: tl.constexpr,
+):
+    token_idx = tl.program_id(0)
+    slot_id = tl.load(slot_mapping_ptr + token_idx)
+
+    # Skip padded / invalid tokens (slot_id == -1 is the PAD sentinel used
+    # by vLLM).  During CUDA graph replay the batch may contain padding
+    # tokens whose slot_mapping is -1; writing to kv_state[-1] would be an
+    # illegal memory access.
+    if slot_id < 0:
+        return
+
+    block_idx = slot_id // block_size
+    pos_in_block = slot_id % block_size
+    base_ptr = (
+        state_cache_ptr
+        + block_idx * state_cache_stride0
+        + pos_in_block * state_cache_stride1
+    )
+
+    block = tl.arange(0, TRITON_BLOCK_SIZE)
+    mask = block < HEAD_SIZE
+
+    kv = tl.load(kv_ptr + token_idx * kv_stride + block, mask=mask)
+    tl.store(base_ptr + block, kv, mask=mask)
+
+    # Fused: score += ape[position % compress_ratio]
+    position = tl.load(positions_ptr + token_idx)
+    ape_row = position % COMPRESS_RATIO
+    ape = tl.load(ape_ptr + ape_row * ape_stride + block, mask=mask)
+    score = tl.load(score_ptr + token_idx * score_stride + block, mask=mask)
+    tl.store(
+        base_ptr + STATE_WIDTH + block,
+        score + ape,
+        mask=mask,
+    )
diff --git a/vllm/model_executor/layers/deepseek_v4_attention.py b/vllm/model_executor/layers/deepseek_v4_attention.py
new file mode 100644
index 000000000000..494d61338084
--- /dev/null
+++ b/vllm/model_executor/layers/deepseek_v4_attention.py
@@ -0,0 +1,1209 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+DeepseekV4 MLA Attention Layer
+"""
+
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, cast
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import DeepseekV2Config, DeepseekV3Config
+
+import vllm.envs as envs
+from vllm.model_executor.layers.linear import (
+    ReplicatedLinear,
+)
+from vllm.model_executor.layers.sparse_attn_indexer import SparseAttnIndexer
+from vllm.utils.deep_gemm import fp8_einsum
+from vllm.utils.torch_utils import direct_register_custom_op
+from vllm.v1.attention.ops.deepseek_v4_ops import (
+    combine_topk_swa_indices,
+    compute_global_topk_indices_and_lens,
+    dequantize_and_gather_k_cache,
+    fused_indexer_q_rope_quant,
+    fused_inv_rope_fp8_quant,
+    fused_q_kv_rmsnorm,
+)
+from vllm.v1.attention.ops.rocm_aiter_mla_sparse import (
+    rocm_forward_decode_fallback,
+    rocm_inv_rope_einsum,
+    rocm_sparse_attn_prefill,
+)
+
+if TYPE_CHECKING:
+    from vllm.v1.attention.backends.mla.sparse_swa import (
+        DeepseekSparseSWAMetadata,
+    )
+
+from vllm.config import (
+    CacheConfig,
+    VllmConfig,
+    get_current_vllm_config,
+)
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.forward_context import ForwardContext, get_forward_context
+from vllm.logger import init_logger
+from vllm.model_executor.custom_op import PluggableLayer
+from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
+from vllm.model_executor.layers.deepseek_compressor import DeepseekCompressor
+from vllm.model_executor.layers.layernorm import LayerNorm, RMSNorm
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.input_quant_fp8 import (
+    QuantFP8,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape,
+)
+from vllm.platforms import current_platform
+from vllm.utils.multi_stream_utils import (
+    execute_in_parallel,
+    maybe_execute_in_parallel,
+)
+from vllm.v1.attention.backend import AttentionBackend, AttentionMetadata
+from vllm.v1.attention.backends.mla.flashmla_sparse import (
+    DeepseekV4FlashMLASparseBackend,
+    FlashMLASparseBackend,
+    FlashMLASparseMetadata,
+)
+from vllm.v1.attention.backends.mla.indexer import (
+    DeepseekV4IndexerBackend,
+    get_max_prefill_buffer_size,
+)
+from vllm.v1.attention.backends.mla.sparse_swa import DeepseekV4SWACache
+from vllm.v1.attention.ops.flashmla import (
+    flash_mla_sparse_fwd,
+    flash_mla_with_kvcache,
+)
+from vllm.v1.kv_cache_interface import KVCacheSpec, MLAAttentionSpec
+from vllm.v1.worker.workspace import current_workspace_manager
+
+logger = init_logger(__name__)
+
+# Prefill is processed in fixed-size chunks; this bounds the bf16 kv-gather
+# workspace allocated at _forward_prefill (and the matching profile-time
+# reservation in attention_impl's dummy-run branch).
+PREFILL_CHUNK_SIZE = 4
+
+
+@dataclass
+class DeepseekV4MLAModules:
+    """Modules used in DeepseekV4 MLA."""
+
+    vllm_config: VllmConfig
+    fused_wqa_wkv: torch.nn.Module
+    q_norm: torch.nn.Module
+    wq_b: torch.nn.Module
+    kv_norm: torch.nn.Module
+    wo_a: torch.nn.Module
+    wo_b: torch.nn.Module
+    attn_sink: torch.nn.Module
+    rotary_emb: torch.nn.Module
+    indexer: torch.nn.Module | None
+    indexer_rotary_emb: torch.nn.Module
+    topk_indices_buffer: torch.Tensor | None
+    aux_stream_list: list[torch.cuda.Stream] | None = None
+
+
+# --8<-- [start:multi_head_latent_attention]
+@PluggableLayer.register("deepseek_v4_multi_head_latent_attention")
+class DeepseekV4MultiHeadLatentAttentionWrapper(PluggableLayer):
+    """Pluggable MLA layer which allows OOT backends to add
+    custom implementations of the outer MLA layer (including rope & o_proj).
+    Note that currently oot platforms can still use CustomOp.register_oot to
+    replace MLA layer entirely, although we use PluggableLayer to register
+    this layer now.
+
+    This class takes positions and hidden_states as input.
+    The input tensors can either contain prefill tokens or decode tokens.
+    The class does the following:
+
+    1. MLA Preprocess.
+    2. Perform multi-head attention to prefill tokens and
+       multi-query attention to decode tokens separately.
+    3. Return the output tensor.
+    """
+
+    # --8<-- [end:multi_head_latent_attention]
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        head_dim: int,
+        scale: float,
+        qk_nope_head_dim: int,
+        qk_rope_head_dim: int,
+        v_head_dim: int,
+        q_lora_rank: int | None,
+        kv_lora_rank: int,
+        o_lora_rank: int | None,
+        mla_modules: DeepseekV4MLAModules,
+        window_size: int,
+        compress_ratio: int | None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.n_local_heads = num_heads
+        self.head_dim = head_dim
+        self.scale = scale
+
+        # FlashMLA sparse kernel only supports 64 or 128 heads; pad up to the
+        # next supported size. Must match DeepseekV4MLAAttention.padded_heads.
+        if num_heads <= 64:
+            self.padded_heads = 64
+        elif num_heads <= 128:
+            self.padded_heads = 128
+        else:
+            raise ValueError(
+                f"DeepseekV4 attention does not support {num_heads} heads "
+                "(must be <= 128)."
+            )
+
+        self.q_lora_rank = q_lora_rank
+        self.kv_lora_rank = kv_lora_rank
+        self.window_size = window_size
+        self.compress_ratio = compress_ratio if compress_ratio is not None else 1
+        self.prefix = prefix
+
+        # Extract config from vllm_config
+        config = mla_modules.vllm_config.model_config.hf_config
+        tp_size = get_tensor_model_parallel_world_size()
+
+        # DeepseekV4-specific attributes (num_heads is already TP-adjusted)
+        self.eps = config.rms_norm_eps
+        self.rope_head_dim = config.qk_rope_head_dim
+        self.nope_head_dim = head_dim - self.rope_head_dim
+        self.n_local_groups = config.o_groups // tp_size
+        self.o_lora_rank = config.o_lora_rank
+
+        # Store projection modules
+        self.fused_wqa_wkv = mla_modules.fused_wqa_wkv
+        self.q_norm = mla_modules.q_norm
+        self.wq_b = mla_modules.wq_b
+
+        self.kv_norm = mla_modules.kv_norm
+        self.wo_a = mla_modules.wo_a
+
+        self._wo_a_act_quant = QuantFP8(
+            static=False,
+            group_shape=GroupShape(1, 128),
+            use_ue8m0=True,
+        )
+        # Bypass packed-for-deepgemm path — we need FP32 scales (not packed
+        # INT32) so fp8_einsum can handle layout transform internally.
+        self._wo_a_act_quant.use_deep_gemm_supported = False
+        self.wo_b = mla_modules.wo_b
+
+        # Pick fp8_einsum recipe based on GPU arch:
+        # SM90: FP32 block scales stay [g, r/128, d/128] → sfb_gran_mn=128
+        # SM100: INT32 packed scales become [g, r, ...] → sfb_gran_mn=1
+        cap = current_platform.get_device_capability()
+        assert cap is not None, "DeepseekV4 attention requires a CUDA device"
+        self._einsum_recipe = (1, 128, 128) if cap.major <= 9 else (1, 1, 128)
+        self._tma_aligned_scales = cap.major >= 10
+
+        self.rotary_emb = mla_modules.rotary_emb
+        self.indexer_rotary_emb = mla_modules.indexer_rotary_emb
+        self.topk_indices_buffer = mla_modules.topk_indices_buffer
+
+        self.indexer = mla_modules.indexer
+
+        # Per-head RMS normalization for Q (no learnable weights)
+        self.q_head_norm = RMSNorm(head_dim, eps=self.eps, has_weight=False)
+
+        # TODO(yifan): currently hardcoded for FP8 sparse, make it more generic
+        head_bytes = (
+            self.nope_head_dim  # 448 fp8 NoPE
+            + self.rope_head_dim * 2  # 64 bf16 RoPE
+            + self.nope_head_dim // 64  # 7B scale factors
+            + 1  # 1B pad
+        )
+
+        # Will be None on ROCm for now.
+        self.aux_stream_list = mla_modules.aux_stream_list
+        # [0]: GEMM start / post-GEMM event0. [1..3]: GEMM done events;
+        # [1] doubles as post-GEMM event1. Reuse is safe: GEMM fully joins
+        # before post-GEMM starts.
+        self.ln_events = [torch.cuda.Event() for _ in range(4)]
+
+        assert cache_config is not None, "DeepseekV4 attention requires cache_config"
+        self.swa_cache_layer = DeepseekV4SWACache(
+            head_dim=self.head_dim,
+            window_size=self.window_size,
+            dtype=torch.uint8,
+            prefix=f"{prefix}.swa_cache",
+            cache_config=cache_config,
+        )
+
+        self.mla_attn = DeepseekV4MLAAttention(
+            num_heads=self.n_local_heads,
+            head_dim=self.head_dim,
+            scale=self.scale,
+            qk_nope_head_dim=self.nope_head_dim,
+            qk_rope_head_dim=self.rope_head_dim,
+            q_lora_rank=self.q_lora_rank,
+            kv_lora_rank=self.kv_lora_rank,
+            compress_ratio=self.compress_ratio,
+            window_size=self.window_size,
+            head_bytes=head_bytes,
+            swa_cache_layer=self.swa_cache_layer,
+            attn_sink=mla_modules.attn_sink,  # already padded with -inf
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=prefix,
+            indexer=self.indexer,
+            topk_indices_buffer=self.topk_indices_buffer,
+        )
+        # Register this layer in the compilation config's static forward context
+        # This allows the custom op to retrieve the layer during execution
+        compilation_config = mla_modules.vllm_config.compilation_config
+        # HACK
+        self.layer_name = prefix + ".deepseek_v4_multi_head_latent_attention"
+        if self.layer_name in compilation_config.static_forward_context:
+            raise ValueError(f"Duplicate layer name: {self.layer_name}")
+        compilation_config.static_forward_context[self.layer_name] = self
+
+        # Create the compressor for layers with compress_ratio > 1; after
+        # creating the DeepseekV4MLAAttention layer to get its cache.
+        self.compressor = None
+        if self.compress_ratio > 1:
+            self.compressor = DeepseekCompressor(
+                vllm_config=mla_modules.vllm_config,
+                compress_ratio=self.compress_ratio,
+                hidden_size=self.hidden_size,
+                head_dim=self.head_dim,
+                rotate=True,
+                prefix=f"{prefix}.compressor",
+                k_cache_prefix=self.mla_attn.prefix,
+            )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        llama_4_scaling: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        # Pre-allocate attention output with FlashMLA-padded head count.
+        # The op writes into `o_padded`; we slice to n_local_heads after.
+        num_tokens = hidden_states.shape[0]
+        o_padded = torch.empty(
+            (num_tokens, self.padded_heads, self.head_dim),
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
+        )
+
+        # Attention (inside custom op for torch.compile boundary)
+        torch.ops.vllm.deepseek_v4_attention(
+            hidden_states,
+            positions,
+            o_padded,
+            self.layer_name,
+        )
+        o = o_padded[:, : self.n_local_heads, :]
+
+        # Keep ROCm on the BF16 reference wo_a path util kernel ready.
+        if current_platform.is_rocm():
+            z = rocm_inv_rope_einsum(
+                self.rotary_emb,
+                o,
+                positions,
+                self.rope_head_dim,
+                self.n_local_groups,
+                self.o_lora_rank,
+                self.wo_a,
+            )
+            return self.wo_b(z.flatten(1))
+
+        # O projection: inverse RoPE + FP8 quant + einsum + wo_b
+        o_fp8, o_scale = fused_inv_rope_fp8_quant(
+            o,
+            positions,
+            self.rotary_emb.cos_sin_cache,
+            n_groups=self.n_local_groups,
+            heads_per_group=self.n_local_heads // self.n_local_groups,
+            nope_dim=self.nope_head_dim,
+            rope_dim=self.rope_head_dim,
+            tma_aligned_scales=self._tma_aligned_scales,
+        )
+
+        wo_a_fp8 = self.wo_a.weight
+        wo_a_scale = self.wo_a.weight_scale_inv
+
+        z = torch.empty(
+            (num_tokens, self.n_local_groups, self.o_lora_rank),
+            device=o.device,
+            dtype=torch.bfloat16,
+        )
+        torch.ops.vllm.deepseek_v4_fp8_einsum(
+            o_fp8,
+            o_scale,
+            wo_a_fp8,
+            wo_a_scale,
+            z,
+            "bhr,hdr->bhd",
+            list(self._einsum_recipe),
+        )
+
+        return self.wo_b(z.flatten(1))
+
+    def attn_gemm_parallel_execute(self, hidden_states) -> tuple[Any, ...]:
+        aux_streams = self.aux_stream_list
+        if aux_streams is not None:
+            assert len(aux_streams) >= 3
+            aux_streams = aux_streams[:3]
+
+        # fused_wqa_wkv (heaviest) on default; the three lighter input GEMMs
+        # on aux streams 0..2 when their owning module exists. ln_events[0]
+        # is the fan-out start event; ln_events[1..3] are per-aux done events.
+        # On ROCm, aux_streams is None and execute_in_parallel runs serially.
+        aux_fns: list[Callable[[], Any] | None] = [None, None, None]
+
+        if self.compressor is not None:
+            # Local ref so the closure keeps a non-None type for mypy.
+            compressor = self.compressor
+
+            def compressor_kv_score() -> torch.Tensor:
+                return torch.mm(
+                    hidden_states,
+                    compressor.fused_wkv_wgate.weight.T,
+                    out_dtype=torch.float32,
+                )
+
+            aux_fns[0] = compressor_kv_score
+
+        if self.indexer is not None:
+            indexer = self.indexer
+
+            def indexer_weights_proj() -> torch.Tensor:
+                # ReplicatedLinear returns (output, bias); bias is None.
+                weights, _ = indexer.weights_proj(hidden_states)
+                return weights
+
+            def indexer_compressor_kv_score() -> torch.Tensor:
+                return torch.mm(
+                    hidden_states,
+                    indexer.compressor.fused_wkv_wgate.weight.T,
+                    out_dtype=torch.float32,
+                )
+
+            aux_fns[1] = indexer_weights_proj
+            aux_fns[2] = indexer_compressor_kv_score
+
+        def fused_wqa_wkv() -> torch.Tensor:
+            # MergedColumnParallelLinear returns (output, bias); bias is None.
+            qr_kv, _ = self.fused_wqa_wkv(hidden_states)
+            return qr_kv
+
+        qr_kv, (kv_score, indexer_weights, indexer_kv_score) = execute_in_parallel(
+            fused_wqa_wkv,
+            aux_fns,
+            self.ln_events[0],
+            self.ln_events[1:4],
+            aux_streams,
+            enable=hidden_states.shape[0]
+            <= envs.VLLM_MULTI_STREAM_GEMM_TOKEN_THRESHOLD,
+        )
+
+        return qr_kv, kv_score, indexer_kv_score, indexer_weights
+
+    def attention_impl(
+        self,
+        hidden_states: torch.Tensor,
+        positions: torch.Tensor,
+        out: torch.Tensor,  # [num_tokens, padded_heads, head_dim], written in place
+    ) -> None:
+        forward_context = get_forward_context()
+        attn_metadata = forward_context.attn_metadata
+
+        qr_kv, kv_score, indexer_kv_score, indexer_weights = (
+            self.attn_gemm_parallel_execute(hidden_states)
+        )
+
+        qr, kv = qr_kv.split([self.q_lora_rank, self.head_dim], dim=-1)
+        qr, kv = fused_q_kv_rmsnorm(
+            qr,
+            kv,
+            self.q_norm.weight.data,
+            self.kv_norm.weight.data,
+            self.eps,
+        )
+
+        # wq_b + kv_insert (+ MLA compressor when an indexer is present) ride
+        # on the default stream so q stays on its consumer stream (mla_attn
+        # downstream reads q on default). Indexer/compressor go on aux for
+        # overlap with default's GEMM + cache write.
+        if self.indexer is not None:
+            aux_stream = (
+                self.aux_stream_list[0] if self.aux_stream_list is not None else None
+            )
+            indexer = self.indexer
+            # Local ref so the closure keeps a non-None type for mypy.
+            assert self.compressor is not None
+            compressor = self.compressor
+
+            def wq_b_kv_insert_and_compress() -> torch.Tensor:
+                q = self.wq_b(qr).view(-1, self.n_local_heads, self.head_dim)
+                self._fused_qnorm_rope_kv_insert(q, kv, positions, attn_metadata)
+                compressor(kv_score, positions, self.rotary_emb)
+                return q
+
+            q, _ = maybe_execute_in_parallel(
+                wq_b_kv_insert_and_compress,
+                lambda: indexer(
+                    hidden_states,
+                    qr,
+                    indexer_kv_score,
+                    indexer_weights,
+                    positions,
+                    self.indexer_rotary_emb,
+                ),
+                self.ln_events[0],
+                self.ln_events[1],
+                aux_stream,
+            )
+        elif self.compressor is not None:
+            # wq_b + kv_insert on default, compressor on aux.
+            aux_stream = (
+                self.aux_stream_list[0] if self.aux_stream_list is not None else None
+            )
+            compressor = self.compressor
+
+            def wq_b_kv_insert() -> torch.Tensor:
+                q = self.wq_b(qr).view(-1, self.n_local_heads, self.head_dim)
+                self._fused_qnorm_rope_kv_insert(q, kv, positions, attn_metadata)
+                return q
+
+            q, _ = maybe_execute_in_parallel(
+                wq_b_kv_insert,
+                lambda: compressor(kv_score, positions, self.rotary_emb),
+                self.ln_events[0],
+                self.ln_events[1],
+                aux_stream,
+            )
+        else:
+            # SWA-only layer: no compressor, no overlap.
+            q = self.wq_b(qr).view(-1, self.n_local_heads, self.head_dim)
+            self._fused_qnorm_rope_kv_insert(q, kv, positions, attn_metadata)
+
+        # Handle dummy run (no metadata).
+        if not isinstance(attn_metadata, dict):
+            # Reserve _forward_prefill's bf16-gather workspace; the dummy
+            # run returns before mla_attn runs, so without this the shared
+            # workspace locks below the real prefill size.
+            sub = self.mla_attn
+            swa_only = sub.compress_ratio <= 1
+            N = (
+                0
+                if swa_only
+                else (sub.max_model_len + sub.compress_ratio - 1) // sub.compress_ratio
+            )
+            M = N + sub.window_size + sub.max_num_batched_tokens
+            current_workspace_manager().get_simultaneous(
+                ((PREFILL_CHUNK_SIZE, M, q.shape[-1]), torch.bfloat16),
+            )
+            out.zero_()
+            return
+
+        # Pad q to FlashMLA-required head count (64 or 128)
+        if self.n_local_heads < self.padded_heads:
+            pad_size = self.padded_heads - self.n_local_heads
+            q = F.pad(q, (0, 0, 0, pad_size), value=0.0)
+
+        # MLA attention writes into the pre-allocated `out` buffer
+        # ([num_tokens, padded_heads, head_dim]).
+        self.mla_attn(q, kv, positions, output=out)
+
+    def _fused_qnorm_rope_kv_insert(
+        self,
+        q: torch.Tensor,
+        kv: torch.Tensor,
+        positions: torch.Tensor,
+        attn_metadata: (
+            dict[str, AttentionMetadata] | list[dict[str, AttentionMetadata]] | None
+        ),
+    ) -> None:
+        if not isinstance(attn_metadata, dict):
+            return
+
+        swa_metadata = cast(
+            "DeepseekSparseSWAMetadata | None",
+            attn_metadata.get(self.swa_cache_layer.prefix),
+        )
+        assert swa_metadata is not None
+
+        swa_kv_cache = self.swa_cache_layer.kv_cache
+        swa_kv_cache_2d = swa_kv_cache.view(swa_kv_cache.shape[0], -1)
+
+        # Horizontally fused:
+        #   Q side:  q_head_norm (per-head RMSNorm, no weight) + GPT-J RoPE
+        #   KV side: GPT-J RoPE + UE8M0 FP8 quant + paged cache insert
+        # kv is unchanged; mla_attn reads kv solely via swa_kv_cache.
+        torch.ops._C.fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert(
+            q,
+            kv,
+            swa_kv_cache_2d,
+            swa_metadata.slot_mapping,
+            positions.to(torch.int64),
+            self.rotary_emb.cos_sin_cache,
+            self.eps,
+            swa_metadata.block_size,
+        )
+
+
+def deepseek_v4_attention(
+    hidden_states: torch.Tensor,
+    positions: torch.Tensor,
+    out: torch.Tensor,
+    layer_name: str,
+) -> None:
+    forward_context: ForwardContext = get_forward_context()
+    self = forward_context.no_compile_layers[layer_name]
+    self.attention_impl(hidden_states, positions, out)
+
+
+def deepseek_v4_attention_fake(
+    hidden_states: torch.Tensor,
+    positions: torch.Tensor,
+    out: torch.Tensor,
+    layer_name: str,
+) -> None:
+    return None
+
+
+direct_register_custom_op(
+    op_name="deepseek_v4_attention",
+    op_func=deepseek_v4_attention,
+    mutates_args=["out"],
+    fake_impl=deepseek_v4_attention_fake,
+)
+
+
+def deepseek_v4_fp8_einsum(
+    a: torch.Tensor,
+    a_scale: torch.Tensor,
+    b: torch.Tensor,
+    b_scale: torch.Tensor,
+    out: torch.Tensor,
+    equation: str,
+    recipe: list[int],
+) -> None:
+    fp8_einsum(equation, (a, a_scale), (b, b_scale), out, recipe=tuple(recipe))
+
+
+def deepseek_v4_fp8_einsum_fake(
+    a: torch.Tensor,
+    a_scale: torch.Tensor,
+    b: torch.Tensor,
+    b_scale: torch.Tensor,
+    out: torch.Tensor,
+    equation: str,
+    recipe: list[int],
+) -> None:
+    return None
+
+
+direct_register_custom_op(
+    op_name="deepseek_v4_fp8_einsum",
+    op_func=deepseek_v4_fp8_einsum,
+    mutates_args=["out"],
+    fake_impl=deepseek_v4_fp8_einsum_fake,
+)
+
+
+class DeepseekV4MLAAttention(nn.Module, AttentionLayerBase):
+    # FlashMLA FP8 sparse only supports 64 or 128 heads
+    SUPPORTED_HEAD_COUNTS = (64, 128)
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_dim: int,
+        scale: float,
+        qk_nope_head_dim: int,
+        qk_rope_head_dim: int,
+        q_lora_rank: int | None,
+        kv_lora_rank: int,
+        compress_ratio: int,
+        window_size: int,
+        head_bytes: int,
+        swa_cache_layer: DeepseekV4SWACache,
+        attn_sink: torch.Tensor,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        # Sparse MLA Args
+        indexer: object | None = None,
+        topk_indices_buffer: torch.Tensor | None = None,
+        aux_stream: torch.cuda.Stream | None = None,
+        **extra_impl_args,
+    ) -> None:
+        super().__init__()
+        self.num_heads = num_heads
+        self.num_kv_heads = 1
+        self.head_dim = head_dim
+        self.scale = scale
+        self.window_size = window_size
+        self.head_bytes = head_bytes
+        self.compress_ratio = compress_ratio
+        self.q_lora_rank = q_lora_rank
+        self.kv_lora_rank = kv_lora_rank
+        self.nope_head_dim = qk_nope_head_dim
+        self.rope_head_dim = qk_rope_head_dim
+        self.indexer = indexer
+        self.topk_indices_buffer = topk_indices_buffer
+
+        self.prefix = prefix  # Alias for compatibility with compressor
+
+        self.aux_stream = aux_stream
+        self.ln_events = [torch.cuda.Event(), torch.cuda.Event()]
+
+        # Determine padded head count for FlashMLA
+        if num_heads not in self.SUPPORTED_HEAD_COUNTS:
+            if num_heads < 64:
+                self.padded_heads = 64
+            elif num_heads < 128:
+                self.padded_heads = 128
+            else:
+                raise ValueError(
+                    f"DeepseekV4MLAAttention does not support {num_heads} heads. "
+                    f"Supported: <= 128 (will be padded to 64 or 128)"
+                )
+        else:
+            self.padded_heads = num_heads
+
+        # Store attention sink
+        assert attn_sink is not None
+        self.attn_sink: torch.Tensor = attn_sink
+        # Store SWA cache
+        assert swa_cache_layer is not None
+        self.swa_cache_layer: DeepseekV4SWACache = swa_cache_layer
+
+        # Get vllm config for cache setup
+        vllm_config = get_current_vllm_config()
+        self.max_num_batched_tokens = (
+            vllm_config.scheduler_config.max_num_batched_tokens
+        )
+        self.max_model_len = vllm_config.model_config.max_model_len
+        # DeepseekV4 only supports fp8 kv-cache format for now.
+        kv_cache_dtype = cache_config.cache_dtype if cache_config is not None else "fp8"
+
+        assert kv_cache_dtype.startswith("fp8"), (
+            f"DeepseekV4 only supports fp8 kv-cache format for now, "
+            f"got {kv_cache_dtype}"
+        )
+        assert issubclass(self.get_attn_backend(), FlashMLASparseBackend), (
+            "Only FlashMLA Sparse Attention backend is supported for DeepseekV4 for now"
+        )
+        # FlashMLA Sparse Attention fp8 backend uses "fp8_ds_mla" kv-cache format
+        # Automatically convert fp8 kv-cache format to "fp8_ds_mla"
+        if (
+            issubclass(self.get_attn_backend(), FlashMLASparseBackend)
+            and kv_cache_dtype.startswith("fp8")
+            and kv_cache_dtype != "fp8_ds_mla"
+        ):
+            assert cache_config is not None
+            cache_config.cache_dtype = "fp8_ds_mla"
+            kv_cache_dtype = "fp8_ds_mla"
+            logger.info_once("Using DeepSeek's fp8_ds_mla KV cache format.")
+
+        self.kv_cache_dtype = kv_cache_dtype
+
+        # Register with compilation context for metadata lookup
+        compilation_config = vllm_config.compilation_config
+        if prefix and prefix in compilation_config.static_forward_context:
+            raise ValueError(f"Duplicate layer name: {prefix}")
+        if prefix:
+            compilation_config.static_forward_context[prefix] = self
+
+        self.kv_cache = torch.tensor([])
+
+    def get_attn_backend(self) -> type[AttentionBackend]:
+        return DeepseekV4FlashMLASparseBackend
+
+    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec | None:
+        if (
+            self.compress_ratio <= 1
+        ):  # SWA part. Allocated separately as DeepseekV4SWACache.
+            return None
+        return MLAAttentionSpec(
+            block_size=vllm_config.cache_config.block_size,
+            num_kv_heads=1,
+            head_size=self.head_dim,
+            dtype=torch.uint8,
+            compress_ratio=self.compress_ratio,
+            cache_dtype_str=self.kv_cache_dtype,
+            alignment=576,  # NOTE: FlashMLA requires 576B alignment
+            model_version="deepseek_v4",
+        )
+
+    def forward(
+        self,
+        q: torch.Tensor,
+        kv: torch.Tensor,
+        positions: torch.Tensor,
+        output: torch.Tensor,
+    ) -> None:
+        assert output.shape == q.shape, (
+            f"output buffer shape {output.shape} must match q shape {q.shape}"
+        )
+        assert output.dtype == q.dtype, (
+            f"output buffer dtype {output.dtype} must match q dtype {q.dtype}"
+        )
+
+        # Get SWA and indexer metadata from forward context
+        forward_context = get_forward_context()
+        attn_metadata = forward_context.attn_metadata
+        assert isinstance(attn_metadata, dict)
+        flashmla_metadata = cast(
+            FlashMLASparseMetadata | None, attn_metadata.get(self.prefix)
+        )
+        swa_metadata = cast(
+            "DeepseekSparseSWAMetadata | None",
+            attn_metadata.get(self.swa_cache_layer.prefix),
+        )
+        assert swa_metadata is not None
+
+        swa_only = self.compress_ratio <= 1
+        # SWA-only layers (compress_ratio <= 1) don't have their own KV cache
+        # allocation, so self.kv_cache may be empty after profiling cleanup.
+        self_kv_cache = self.kv_cache if not swa_only else None
+        swa_kv_cache = self.swa_cache_layer.kv_cache
+
+        # Split prefill and decode
+        num_decodes = swa_metadata.num_decodes
+        num_prefills = swa_metadata.num_prefills
+        num_decode_tokens = swa_metadata.num_decode_tokens
+
+        if num_prefills > 0:
+            self._forward_prefill(
+                q=q[num_decode_tokens:],
+                positions=positions[num_decode_tokens:],
+                compressed_k_cache=self_kv_cache,
+                swa_k_cache=swa_kv_cache,
+                output=output[num_decode_tokens:],
+                attn_metadata=flashmla_metadata,
+                swa_metadata=swa_metadata,
+            )
+        if num_decodes > 0:
+            self._forward_decode(
+                q=q[:num_decode_tokens],
+                kv_cache=self_kv_cache,
+                swa_metadata=swa_metadata,
+                attn_metadata=flashmla_metadata,
+                swa_only=swa_only,
+                output=output[:num_decode_tokens],
+            )
+
+    def _forward_decode(
+        self,
+        q: torch.Tensor,
+        kv_cache: torch.Tensor | None,  # Only used when compress_ratio > 1
+        swa_metadata: "DeepseekSparseSWAMetadata",
+        attn_metadata: FlashMLASparseMetadata | None,
+        swa_only: bool,
+        output: torch.Tensor,
+    ) -> None:
+        num_decodes = swa_metadata.num_decodes
+        num_decode_tokens = swa_metadata.num_decode_tokens
+
+        topk_indices = None
+        topk_lens = None
+        if not swa_only:
+            assert attn_metadata is not None
+            assert swa_metadata.is_valid_token is not None
+            block_size = attn_metadata.block_size // self.compress_ratio
+            is_valid = swa_metadata.is_valid_token[:num_decode_tokens]
+            if self.compress_ratio == 4:
+                # C4A: local indices differ per layer (filled by Indexer).
+                assert self.topk_indices_buffer is not None
+                global_indices, topk_lens = compute_global_topk_indices_and_lens(
+                    self.topk_indices_buffer[:num_decode_tokens],
+                    swa_metadata.token_to_req_indices,
+                    attn_metadata.block_table[:num_decodes],
+                    block_size,
+                    is_valid,
+                )
+                topk_indices = global_indices.view(num_decode_tokens, 1, -1)
+            else:
+                # C128A: pre-computed during metadata build.
+                topk_indices = attn_metadata.c128a_global_decode_topk_indices
+                topk_lens = attn_metadata.c128a_decode_topk_lens
+
+        swa_indices = swa_metadata.decode_swa_indices
+        swa_lens = swa_metadata.decode_swa_lens
+
+        if current_platform.is_rocm():
+            rocm_forward_decode_fallback(
+                q=q,
+                kv_cache=kv_cache,
+                swa_k_cache=self.swa_cache_layer.kv_cache,
+                swa_only=swa_only,
+                topk_indices=topk_indices,
+                topk_lens=topk_lens,
+                swa_indices=swa_indices,
+                swa_lens=swa_lens,
+                attn_sink=self.attn_sink,
+                scale=self.scale,
+                head_dim=self.head_dim,
+                nope_head_dim=self.nope_head_dim,
+                rope_head_dim=self.rope_head_dim,
+                output=output,
+            )
+            return
+
+        # We treat queries in the same seq as different queries
+        # and later we only attend by generated indices.
+        # q arrives pre-padded to self.padded_heads by the outer wrapper.
+        q = q.unsqueeze(1)
+
+        # Prepare SWA cache (num_blocks, swa_block_size, 1, head_bytes)
+        # Use unsqueeze to preserve strides (handles padded blocks correctly)
+        swa_cache = self.swa_cache_layer.kv_cache.unsqueeze(-2)
+        # Reshape KV cache to (num_blocks, block_size, 1, head_bytes)
+        if kv_cache is not None:
+            kv_cache = kv_cache.unsqueeze(-2)
+
+        # One FlashMLASchedMeta per layer type, shared across all same-type
+        # layers within this decode step. The first forward call per type
+        # triggers the in-kernel planner (allocating tile_scheduler_metadata
+        # and num_splits via PyTorch's graph-aware allocator so CUDA graph
+        # capture reuses the same addresses on replay); subsequent same-type
+        # layers see have_initialized=True and skip the planner.
+        if self.compress_ratio <= 1:
+            tile_metadata = swa_metadata.tile_sched_swaonly
+        elif self.compress_ratio == 4:
+            tile_metadata = swa_metadata.tile_sched_c4a
+        elif self.compress_ratio == 128:
+            tile_metadata = swa_metadata.tile_sched_c128a
+        else:
+            raise ValueError(
+                f"Unsupported compress_ratio={self.compress_ratio}; "
+                "expected 1, 4, or 128."
+            )
+        assert tile_metadata is not None, (
+            "swa_metadata missing tile_sched entry for "
+            f"compress_ratio={self.compress_ratio}; "
+            "DeepseekSparseSWAMetadataBuilder.build_tile_scheduler did not "
+            "allocate one for this layer type."
+        )
+
+        out, _ = flash_mla_with_kvcache(
+            q=q,
+            k_cache=swa_cache,
+            block_table=None,
+            head_dim_v=512,
+            tile_scheduler_metadata=tile_metadata,
+            cache_seqlens=None,
+            is_fp8_kvcache=True,
+            indices=swa_indices,
+            topk_length=swa_lens,
+            softmax_scale=self.scale,
+            attn_sink=self.attn_sink,
+            extra_k_cache=kv_cache if not swa_only else None,
+            extra_indices_in_kvcache=topk_indices,
+            extra_topk_length=topk_lens,
+            out=output.unsqueeze(1),
+        )
+
+    def _forward_prefill(
+        self,
+        q: torch.Tensor,
+        positions: torch.Tensor,
+        compressed_k_cache: torch.Tensor | None,  # Only used when compress_ratio > 1
+        swa_k_cache: torch.Tensor,
+        output: torch.Tensor,
+        attn_metadata: FlashMLASparseMetadata | None,
+        swa_metadata: "DeepseekSparseSWAMetadata",
+    ) -> None:
+        swa_only = attn_metadata is None
+
+        num_prefills = swa_metadata.num_prefills
+        num_prefill_tokens = swa_metadata.num_prefill_tokens
+        num_decodes = swa_metadata.num_decodes
+        num_decode_tokens = swa_metadata.num_decode_tokens
+
+        # Use pre-computed prefill metadata.
+        seq_lens = swa_metadata.prefill_seq_lens
+        gather_lens = swa_metadata.prefill_gather_lens
+        assert seq_lens is not None
+        assert gather_lens is not None
+
+        # Derive prefill-local token offsets from the full query_start_loc_cpu.
+        query_start_loc_cpu = swa_metadata.query_start_loc_cpu
+        query_start_loc = swa_metadata.query_start_loc
+        assert query_start_loc_cpu is not None
+        assert query_start_loc is not None
+        prefill_token_base = query_start_loc_cpu[num_decodes]
+
+        if not swa_only:
+            if self.compress_ratio == 4:
+                assert self.topk_indices_buffer is not None
+                topk_indices = self.topk_indices_buffer[num_decode_tokens:]
+                topk_indices = topk_indices[:num_prefill_tokens]
+            else:
+                # C128A: pre-computed during metadata build.
+                assert attn_metadata is not None
+                topk_indices = attn_metadata.c128a_prefill_topk_indices
+            top_k = topk_indices.shape[-1]
+            # Compressed region must fit the full compressed pool (seq_len //
+            # compress_ratio), not just top_k. top_k bounds how many indices
+            # the indexer selects, not the pool size it indexes into.
+            N = (self.max_model_len + self.compress_ratio - 1) // self.compress_ratio
+        else:
+            # NOTE(woosuk): topk_indices will not be used for SWA-only layers.
+            assert self.topk_indices_buffer is not None
+            topk_indices = self.topk_indices_buffer[num_decode_tokens:]
+            top_k = 0
+            N = 0
+
+        M = N + self.window_size + self.max_num_batched_tokens
+        num_chunks = (num_prefills + PREFILL_CHUNK_SIZE - 1) // PREFILL_CHUNK_SIZE
+
+        workspace_manager = current_workspace_manager()
+        kv = workspace_manager.get_simultaneous(
+            ((PREFILL_CHUNK_SIZE, M, q.shape[-1]), torch.bfloat16),
+        )[0]
+        for chunk_idx in range(num_chunks):
+            chunk_start = chunk_idx * PREFILL_CHUNK_SIZE
+            chunk_end = min(chunk_start + PREFILL_CHUNK_SIZE, num_prefills)
+            chunk_size = chunk_end - chunk_start
+            if not swa_only:
+                # Gather compressed KV
+                assert attn_metadata is not None
+                block_table = attn_metadata.block_table[num_decodes:]
+                dequantize_and_gather_k_cache(
+                    kv[:chunk_size],
+                    compressed_k_cache,
+                    seq_lens=seq_lens[chunk_start:chunk_end] // self.compress_ratio,
+                    gather_lens=None,
+                    block_table=block_table[chunk_start:chunk_end],
+                    block_size=attn_metadata.block_size // self.compress_ratio,
+                    offset=0,
+                )
+
+            # Gather SWA KV
+            swa_block_table = swa_metadata.block_table[num_decodes:]
+            dequantize_and_gather_k_cache(
+                kv[:chunk_size],
+                swa_k_cache,
+                seq_lens=seq_lens[chunk_start:chunk_end],
+                gather_lens=gather_lens[chunk_start:chunk_end],
+                block_table=swa_block_table[chunk_start:chunk_end],
+                block_size=swa_metadata.block_size,
+                offset=N,
+            )
+
+            # Combine the topk indices and SWA indices for gathered KV cache
+            query_start = (
+                query_start_loc_cpu[num_decodes + chunk_start] - prefill_token_base
+            )
+            query_end = (
+                query_start_loc_cpu[num_decodes + chunk_end] - prefill_token_base
+            )
+
+            combined_indices, combined_lens = combine_topk_swa_indices(
+                topk_indices[query_start:query_end],
+                query_start_loc[
+                    num_decodes + chunk_start : num_decodes + chunk_end + 1
+                ],
+                seq_lens[chunk_start:chunk_end],
+                gather_lens[chunk_start:chunk_end],
+                self.window_size,
+                self.compress_ratio,
+                top_k,
+                M,
+                N,
+            )
+
+            if current_platform.is_rocm():
+                rocm_sparse_attn_prefill(
+                    q=q[query_start:query_end],
+                    kv=kv.view(-1, 1, q.shape[-1]),
+                    indices=combined_indices.unsqueeze(1),
+                    topk_length=combined_lens,
+                    scale=self.scale,
+                    head_dim=self.head_dim,
+                    attn_sink=self.attn_sink,
+                    output=output[query_start:query_end],
+                )
+            else:
+                output_chunk, _, _ = flash_mla_sparse_fwd(
+                    q=q[query_start:query_end],
+                    kv=kv.view(-1, 1, q.shape[-1]),
+                    indices=combined_indices.unsqueeze(1),
+                    sm_scale=self.scale,
+                    attn_sink=self.attn_sink,
+                    topk_length=combined_lens,
+                    out=output[query_start:query_end],
+                )
+
+
+class DeepseekV4IndexerCache(torch.nn.Module, AttentionLayerBase):
+    def __init__(
+        self,
+        head_dim: int,
+        dtype: torch.dtype,
+        prefix: str,
+        cache_config: CacheConfig,
+        compress_ratio: int = 1,
+    ):
+        super().__init__()
+        self.kv_cache = torch.tensor([])
+        self.head_dim = head_dim
+        self.prefix = prefix
+        self.cache_config = cache_config
+        self.dtype = dtype
+        self.compress_ratio = compress_ratio
+        compilation_config = get_current_vllm_config().compilation_config
+        if prefix in compilation_config.static_forward_context:
+            raise ValueError(f"Duplicate layer name: {prefix}")
+        compilation_config.static_forward_context[prefix] = self
+
+    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec:
+        # head_dim already carries the fp8 scale padding
+        # compress_ratio=1 for V3.2, >1 for DeepseekV4; both use the same cache layout.
+        return MLAAttentionSpec(
+            block_size=self.cache_config.block_size,
+            num_kv_heads=1,
+            head_size=self.head_dim,
+            dtype=self.dtype,
+            compress_ratio=self.compress_ratio,
+            # DeepseekV4 aligns indexer pages to FlashMLA's 576B so they can pack with
+            # the indexer's compressor state cache. V3.2 keeps the legacy layout.
+            alignment=576,
+        )
+
+    def forward(self): ...
+
+    def get_attn_backend(self) -> type[AttentionBackend]:
+        return DeepseekV4IndexerBackend
+
+
+class DeepseekV4Indexer(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        config: DeepseekV2Config | DeepseekV3Config,
+        hidden_size: int,
+        q_lora_rank: int,
+        quant_config: QuantizationConfig | None,
+        cache_config: CacheConfig | None,
+        topk_indices_buffer: torch.Tensor | None,
+        compress_ratio: int = 1,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.vllm_config = vllm_config
+        self.config = config
+        self.quant_config = quant_config
+        # self.indexer_cfg = config.attn_module_list_cfg[0]["attn_index"]
+        self.topk_tokens = config.index_topk
+        self.n_head = config.index_n_heads  # 64
+        self.head_dim = config.index_head_dim  # 128
+        self.rope_dim = config.qk_rope_head_dim  # 64
+        self.q_lora_rank = q_lora_rank  # 1536
+        self.compress_ratio = compress_ratio
+        self.use_fp4_kv = self.vllm_config.attention_config.use_fp4_indexer_cache
+        logger.info_once(
+            "Using %s indexer cache for Lightning Indexer.",
+            "MXFP4" if self.use_fp4_kv else "FP8",
+        )
+
+        # no tensor parallel, just replicated
+        self.wq_b = ReplicatedLinear(
+            self.q_lora_rank,
+            self.head_dim * self.n_head,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.wq_b",
+        )
+        self.weights_proj = ReplicatedLinear(
+            hidden_size,
+            self.n_head,
+            bias=False,
+            quant_config=None,
+            prefix=f"{prefix}.weights_proj",
+        )
+        self.k_norm = LayerNorm(self.head_dim, eps=1e-6)
+        self.softmax_scale = self.head_dim**-0.5
+
+        self.scale_fmt = "ue8m0"
+        self.quant_block_size = 128  # TODO: get from config
+        self.topk_indices_buffer = topk_indices_buffer
+
+        self.max_model_len = (
+            vllm_config.model_config.max_model_len // self.compress_ratio
+        )
+        self.prefix = prefix
+
+        self.max_total_seq_len = (
+            get_max_prefill_buffer_size(vllm_config) // self.compress_ratio
+        )
+
+        assert cache_config is not None, "Deepseek V4 indexer requires cache_config"
+        # NOTE(yifan): FP8 indxer cache use the same layout as V3.2:
+        # head_dim bytes = 128 fp8 + 4 fp32 scale = 132.
+        # For FP4 indexer cache, we still allocate the same amount of memory as FP8,
+        # but only use the first half of the memory.
+        k_cache_head_dim = self.head_dim + self.head_dim // self.quant_block_size * 4
+        self.k_cache = DeepseekV4IndexerCache(
+            head_dim=k_cache_head_dim,
+            dtype=torch.uint8,
+            prefix=f"{prefix}.k_cache",
+            cache_config=cache_config,
+            compress_ratio=self.compress_ratio,
+        )
+        self.compressor = DeepseekCompressor(
+            vllm_config=vllm_config,
+            compress_ratio=self.compress_ratio,
+            hidden_size=hidden_size,
+            head_dim=self.head_dim,
+            rotate=True,
+            prefix=f"{prefix}.compressor",
+            k_cache_prefix=self.k_cache.prefix,
+            use_fp4_cache=self.use_fp4_kv,
+        )
+
+        self.indexer_op = SparseAttnIndexer(
+            self.k_cache,
+            self.quant_block_size,
+            self.scale_fmt,
+            self.topk_tokens,
+            self.head_dim,
+            self.max_model_len,
+            self.max_total_seq_len,
+            self.topk_indices_buffer,
+            skip_k_cache_insert=True,
+            use_fp4_cache=self.use_fp4_kv,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        qr: torch.Tensor,
+        compressed_kv_score: torch.Tensor,
+        indexer_weights: torch.Tensor,
+        positions: torch.Tensor,
+        rotary_emb: nn.Module,
+    ) -> torch.Tensor:
+        # ReplicatedLinear returns (output, bias); bias is None.
+        q, _ = self.wq_b(qr)
+        q = q.view(-1, self.n_head, self.head_dim)
+        k = self.compressor(compressed_kv_score, positions, rotary_emb)
+        q_quant, weights = fused_indexer_q_rope_quant(
+            positions,
+            q,
+            rotary_emb.cos_sin_cache,
+            indexer_weights,
+            self.softmax_scale,
+            self.n_head**-0.5,
+            use_fp4=self.use_fp4_kv,
+        )
+        return self.indexer_op(hidden_states, q_quant, k, weights)
diff --git a/vllm/model_executor/layers/fla/ops/chunk.py b/vllm/model_executor/layers/fla/ops/chunk.py
index 02e48921d419..caf8b0c97654 100644
--- a/vllm/model_executor/layers/fla/ops/chunk.py
+++ b/vllm/model_executor/layers/fla/ops/chunk.py
@@ -32,6 +32,7 @@ def chunk_gated_delta_rule_fwd(
     cu_seqlens: torch.Tensor | None = None,
     chunk_indices: torch.Tensor | None = None,
     chunk_offsets: torch.Tensor | None = None,
+    core_attn_out: torch.Tensor | None = None,
 ):
     g = chunk_local_cumsum(
         g, chunk_size=FLA_CHUNK_SIZE, cu_seqlens=cu_seqlens, chunk_indices=chunk_indices
@@ -77,6 +78,7 @@ def chunk_gated_delta_rule_fwd(
         scale=scale,
         cu_seqlens=cu_seqlens,
         chunk_indices=chunk_indices,
+        core_attn_out=core_attn_out,
     )
     if SUPPRESS_LEVEL < 3:
         return g, o, A, final_state, None, None, None
@@ -102,6 +104,7 @@ def forward(
         chunk_indices: torch.Tensor | None = None,
         chunk_offsets: torch.Tensor | None = None,
         use_qk_l2norm_in_kernel: bool = False,
+        core_attn_out: torch.Tensor | None = None,
     ):
         if use_qk_l2norm_in_kernel:
             q = l2norm_fwd(q)
@@ -119,9 +122,15 @@ def forward(
             cu_seqlens=cu_seqlens,
             chunk_indices=chunk_indices,
             chunk_offsets=chunk_offsets,
+            core_attn_out=core_attn_out,
         )
         ctx.scale = scale
         ctx.use_qk_l2norm_in_kernel = use_qk_l2norm_in_kernel
+        if core_attn_out is not None:
+            assert not torch.is_grad_enabled(), (
+                "core_attn_out buffer reuse is only supported for inference"
+            )
+            assert q.dtype == o.dtype, "Incompatible dtype for inplace computation"
         return o.to(q.dtype), final_state
 
 
@@ -139,6 +148,7 @@ def chunk_gated_delta_rule(
     chunk_indices: torch.Tensor | None = None,
     chunk_offsets: torch.Tensor | None = None,
     use_qk_l2norm_in_kernel: bool = False,
+    core_attn_out: torch.Tensor | None = None,
 ):
     r"""
     Args:
@@ -230,5 +240,6 @@ def chunk_gated_delta_rule(
         chunk_indices,
         chunk_offsets,
         use_qk_l2norm_in_kernel,
+        core_attn_out,
     )
     return o, final_state
diff --git a/vllm/model_executor/layers/fla/ops/chunk_o.py b/vllm/model_executor/layers/fla/ops/chunk_o.py
index d812ec433720..0c323b8ce215 100644
--- a/vllm/model_executor/layers/fla/ops/chunk_o.py
+++ b/vllm/model_executor/layers/fla/ops/chunk_o.py
@@ -148,6 +148,7 @@ def chunk_fwd_o(
     cu_seqlens: torch.Tensor | None = None,
     chunk_indices: torch.Tensor | None = None,
     chunk_size: int = FLA_CHUNK_SIZE,
+    core_attn_out: torch.Tensor | None = None,
 ) -> torch.Tensor:
     B, T, Hg, K, V = *q.shape, v.shape[-1]
     H = v.shape[-2]
@@ -158,7 +159,13 @@ def chunk_fwd_o(
     if scale is None:
         scale = k.shape[-1] ** -0.5
 
-    o = torch.empty_like(v)
+    if core_attn_out is not None:
+        assert core_attn_out.numel() >= v.numel(), (
+            f"core_attn_out too small: {core_attn_out.numel()} < {v.numel()}"
+        )
+        o = core_attn_out[: v.numel()].view(*v.shape)
+    else:
+        o = torch.empty_like(v)
 
     def grid(meta):
         return (triton.cdiv(V, meta["BV"]), NT, B * H)
diff --git a/vllm/model_executor/layers/fla/ops/chunk_scaled_dot_kkt.py b/vllm/model_executor/layers/fla/ops/chunk_scaled_dot_kkt.py
index 3f7628487d69..a715504da6b7 100644
--- a/vllm/model_executor/layers/fla/ops/chunk_scaled_dot_kkt.py
+++ b/vllm/model_executor/layers/fla/ops/chunk_scaled_dot_kkt.py
@@ -83,7 +83,7 @@ def chunk_scaled_dot_kkt_fwd_kernel(
         )
         b_k = tl.load(p_k, boundary_check=(0, 1))
         b_kb = b_k * b_beta[:, None]
-        b_A += tl.dot(b_kb.to(b_k.dtype), tl.trans(b_k))
+        b_A += tl.dot(b_kb, tl.trans(b_k).to(b_kb.dtype))
 
     if USE_G:
         p_g = tl.make_block_ptr(g + bos * H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
diff --git a/vllm/model_executor/layers/fla/ops/kda.py b/vllm/model_executor/layers/fla/ops/kda.py
index 67cd0231d6e9..1a6f7cc0f5ae 100644
--- a/vllm/model_executor/layers/fla/ops/kda.py
+++ b/vllm/model_executor/layers/fla/ops/kda.py
@@ -1076,10 +1076,10 @@ def chunk_gla_fwd_kernel_o(
         )
         p_h = tl.make_block_ptr(
             h + (i_tg * H + i_h) * K * V,
-            (K, V),
-            (V, 1),
-            (i_k * BK, i_v * BV),
-            (BK, BV),
+            (V, K),
+            (K, 1),
+            (i_v * BV, i_k * BK),
+            (BV, BK),
             (1, 0),
         )
 
@@ -1090,12 +1090,11 @@ def chunk_gla_fwd_kernel_o(
         b_g = tl.load(p_g, boundary_check=(0, 1))
         # [BT, BK]
         b_qg = (b_q * exp(b_g)).to(b_q.dtype)
-        # [BK, BV]
+        # [BV, BK]
         b_h = tl.load(p_h, boundary_check=(0, 1))
-        # works but dkw, owing to divine benevolence
         # [BT, BV]
         if i_k >= 0:
-            b_o += tl.dot(b_qg, b_h.to(b_qg.dtype))
+            b_o += tl.dot(b_qg, tl.trans(b_h).to(b_qg.dtype))
     p_v = tl.make_block_ptr(
         v + (bos * H + i_h) * V,
         (T, V),
diff --git a/vllm/model_executor/layers/fused_moe/__init__.py b/vllm/model_executor/layers/fused_moe/__init__.py
index 926f0d1d0154..aeb704e18d86 100644
--- a/vllm/model_executor/layers/fused_moe/__init__.py
+++ b/vllm/model_executor/layers/fused_moe/__init__.py
@@ -19,6 +19,7 @@
 from vllm.model_executor.layers.fused_moe.layer import (
     FusedMoE,
     FusedMoeWeightScaleSupported,
+    fused_moe_make_expert_params_mapping,
 )
 from vllm.model_executor.layers.fused_moe.modular_kernel import (
     FusedMoEActivationFormat,
@@ -29,7 +30,6 @@
     FusedMoERouter,
 )
 from vllm.model_executor.layers.fused_moe.router.gate_linear import GateLinear
-from vllm.model_executor.layers.fused_moe.shared_fused_moe import SharedFusedMoE
 from vllm.model_executor.layers.fused_moe.unquantized_fused_moe_method import (
     UnquantizedFusedMoEMethod,
 )
@@ -64,39 +64,46 @@ def get_config() -> dict[str, Any] | None:
     "FusedMoEPrepareAndFinalizeModular",
     "GateLinear",
     "RoutingMethodType",
-    "SharedFusedMoE",
     "activation_without_mul",
     "apply_moe_activation",
+    "fused_moe_make_expert_params_mapping",
     "override_config",
     "get_config",
 ]
 
 if HAS_TRITON:
     # import to register the custom ops
-    from vllm.model_executor.layers.fused_moe.cutlass_moe import (
+    from vllm.model_executor.layers.fused_moe.experts.batched_deep_gemm_moe import (
+        BatchedDeepGemmExperts,
+    )
+    from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import (
         CutlassBatchedExpertsFp8,
         CutlassExpertsFp8,
         CutlassExpertsW4A8Fp8,
         cutlass_moe_w4a8_fp8,
     )
-    from vllm.model_executor.layers.fused_moe.experts.batched_deep_gemm_moe import (
-        BatchedDeepGemmExperts,
-    )
     from vllm.model_executor.layers.fused_moe.experts.deep_gemm_moe import (
         DeepGemmExperts,
     )
+    from vllm.model_executor.layers.fused_moe.experts.rocm_aiter_moe import (
+        AiterExperts,
+    )
+    from vllm.model_executor.layers.fused_moe.experts.triton_moe import (
+        TritonExperts,
+        TritonWNA16Experts,
+    )
+    from vllm.model_executor.layers.fused_moe.experts.xpu_moe import (
+        XPUExperts,
+        XPUExpertsFp8,
+        XPUExpertsMXFp4,
+    )
     from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
         BatchedTritonExperts,
     )
     from vllm.model_executor.layers.fused_moe.fused_moe import (
-        TritonExperts,
-        TritonWNA16Experts,
         fused_experts,
         get_config_file_name,
     )
-    from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
-        AiterExperts,
-    )
     from vllm.model_executor.layers.fused_moe.router.fused_topk_router import (
         fused_topk,
     )
@@ -106,10 +113,6 @@ def get_config() -> dict[str, Any] | None:
     from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
         TritonOrDeepGemmExperts,
     )
-    from vllm.model_executor.layers.fused_moe.xpu_fused_moe import (
-        XPUExperts,
-        XPUExpertsFp8,
-    )
 
     __all__ += [
         "AiterExperts",
@@ -129,6 +132,7 @@ def get_config() -> dict[str, Any] | None:
         "TritonOrDeepGemmExperts",
         "XPUExperts",
         "XPUExpertsFp8",
+        "XPUExpertsMXFp4",
     ]
 else:
     # Some model classes directly use the custom ops. Add placeholders
diff --git a/vllm/model_executor/layers/fused_moe/activation.py b/vllm/model_executor/layers/fused_moe/activation.py
index 3112b3054fcd..b2e67e6220a9 100644
--- a/vllm/model_executor/layers/fused_moe/activation.py
+++ b/vllm/model_executor/layers/fused_moe/activation.py
@@ -15,6 +15,7 @@ class MoEActivation(Enum):
     # and produce output of shape [..., d]
     SILU = "silu"
     GELU = "gelu"
+    GELU_TANH = "gelu_tanh"
     RELU2 = "relu2"
     SWIGLUOAI = "swigluoai"
     SWIGLUSTEP = "swiglustep"
@@ -24,6 +25,7 @@ class MoEActivation(Enum):
     # NOTE: Non-gated activations require the "_no_mul" suffix to be present.
     SILU_NO_MUL = "silu_no_mul"
     GELU_NO_MUL = "gelu_no_mul"
+    GELU_TANH_NO_MUL = "gelu_tanh_no_mul"
     RELU2_NO_MUL = "relu2_no_mul"
 
     @property
@@ -53,6 +55,7 @@ def without_mul(self) -> "MoEActivation":
     @classmethod
     def from_str(cls, s: str) -> "MoEActivation":
         """Parse from string for backward compatibility."""
+        s = _STR_ALIASES.get(s, s)
         for member in cls:
             if member.value == s:
                 return member
@@ -61,20 +64,27 @@ def from_str(cls, s: str) -> "MoEActivation":
 
 
 # Module-level lookup tables used by MoEActivation functions.
+_STR_ALIASES: dict[str, str] = {
+    "gelu_pytorch_tanh": "gelu_tanh",
+}
+
 _CUSTOM_OP_NAMES: dict[MoEActivation, str] = {
     MoEActivation.SILU: "silu_and_mul",
     MoEActivation.GELU: "gelu_and_mul",
+    MoEActivation.GELU_TANH: "gelu_tanh_and_mul",
     MoEActivation.SWIGLUOAI: "swigluoai_and_mul",
     MoEActivation.SWIGLUSTEP: "swiglustep_and_mul",
     MoEActivation.RELU2: "relu2",
     MoEActivation.SILU_NO_MUL: "silu_and_mul",
     MoEActivation.GELU_NO_MUL: "gelu_and_mul",
+    MoEActivation.GELU_TANH_NO_MUL: "gelu_tanh_and_mul",
     MoEActivation.RELU2_NO_MUL: "relu2",
 }
 
 _WITHOUT_MUL: dict[MoEActivation, MoEActivation] = {
     MoEActivation.SILU: MoEActivation.SILU_NO_MUL,
     MoEActivation.GELU: MoEActivation.GELU_NO_MUL,
+    MoEActivation.GELU_TANH: MoEActivation.GELU_TANH_NO_MUL,
     MoEActivation.RELU2: MoEActivation.RELU2_NO_MUL,
 }
 
@@ -115,6 +125,8 @@ def apply_moe_activation(
         torch.ops._C.silu_and_mul(output, input)
     elif activation == MoEActivation.GELU:
         torch.ops._C.gelu_and_mul(output, input)
+    elif activation == MoEActivation.GELU_TANH:
+        torch.ops._C.gelu_tanh_and_mul(output, input)
     elif activation == MoEActivation.SWIGLUOAI:
         torch.ops._C.swigluoai_and_mul(output, input)
     elif activation == MoEActivation.SWIGLUSTEP:
@@ -127,6 +139,8 @@ def apply_moe_activation(
         output.copy_(F.silu(input))
     elif activation == MoEActivation.GELU_NO_MUL:
         output.copy_(F.gelu(input))
+    elif activation == MoEActivation.GELU_TANH_NO_MUL:
+        output.copy_(F.gelu(input, approximate="tanh"))
     elif activation == MoEActivation.RELU2_NO_MUL:
         F.relu(input, inplace=True)
         torch.square(input, out=output)
diff --git a/vllm/model_executor/layers/fused_moe/all2all_utils.py b/vllm/model_executor/layers/fused_moe/all2all_utils.py
index 62b2602928f1..2a6f0c71d936 100644
--- a/vllm/model_executor/layers/fused_moe/all2all_utils.py
+++ b/vllm/model_executor/layers/fused_moe/all2all_utils.py
@@ -41,9 +41,9 @@
             DeepEPLLPrepareAndFinalize,
         )
     if has_mori():
-        from .mori_prepare_finalize import MoriPrepareAndFinalize
+        from .prepare_finalize.mori import MoriPrepareAndFinalize
     if has_nixl_ep():
-        from .nixl_ep_prepare_finalize import (
+        from .prepare_finalize.nixl_ep import (
             NIXL_EP_QUANT_BLOCK_SHAPE,
             NixlEPPrepareAndFinalize,
         )
@@ -117,17 +117,20 @@ def maybe_make_prepare_finalize(
                 "Detected DP deployment with no --enable-expert-parallel. "
                 "Falling back to AllGather+ReduceScatter dispatch/combine."
             )
+            device_communicator = get_ep_group().device_communicator
+            assert device_communicator is not None
+            assert device_communicator.all2all_manager is not None
             return make_moe_prepare_and_finalize_naive_dp_ep(
                 is_sequence_parallel=moe.moe_parallel_config.is_sequence_parallel,
-                num_dispatchers=(
-                    get_ep_group().device_communicator.all2all_manager.world_size
-                ),
+                num_dispatchers=(device_communicator.all2all_manager.world_size),
                 use_monolithic=use_monolithic,
             )
         else:
             return make_moe_prepare_and_finalize_no_dp_ep(use_monolithic)
 
-    all2all_manager = get_ep_group().device_communicator.all2all_manager
+    device_communicator = get_ep_group().device_communicator
+    assert device_communicator is not None
+    all2all_manager = device_communicator.all2all_manager
     assert all2all_manager is not None
 
     prepare_finalize: FusedMoEPrepareAndFinalize | None = None
@@ -225,23 +228,37 @@ def maybe_make_prepare_finalize(
 
     elif moe.use_fi_nvl_one_sided_kernels:
         assert quant_config is not None
-        if quant_config.quant_dtype != "nvfp4":
-            raise ValueError(
-                "The 'flashinfer_nvlink_one_sided' all2all backend only "
-                "supports nvfp4 activation quantization, but got "
-                f"quant_dtype={quant_config.quant_dtype!r}. Use a different "
-                "all2all backend (e.g. 'flashinfer_nvlink_two_sided' or "
-                "'allgather_reducescatter') for non-nvfp4 models."
-            )
         max_num_tokens = (
             get_current_vllm_config().scheduler_config.max_num_batched_tokens
         )
+        if quant_config.quant_dtype is None:
+            dispatch_dtype_bytes_per_elem = 2
+            dispatch_scale_bytes_per_token = 0
+        elif quant_config.quant_dtype == "nvfp4":
+            dispatch_dtype_bytes_per_elem = 0
+            dispatch_scale_bytes_per_token = moe.hidden_dim // 16
+        elif quant_config.quant_dtype == "mxfp8":
+            dispatch_dtype_bytes_per_elem = 1
+            align = quant_config.mx_alignment
+            if align > 0:
+                padded_k = ((moe.hidden_dim + align - 1) // align) * align
+            else:
+                padded_k = moe.hidden_dim
+            dispatch_scale_bytes_per_token = padded_k // 32
+        else:
+            raise NotImplementedError(
+                "flashinfer_nvlink_one_sided dispatch supports nvfp4, mxfp8, "
+                "and bf16 (quant_dtype=None) today; got "
+                f"quant_dtype={quant_config.quant_dtype!r}"
+            )
         prepare_finalize = FlashInferNVLinkOneSidedPrepareAndFinalize(
             max_num_tokens=max_num_tokens,
             top_k=moe.experts_per_token,
             num_experts=moe.num_experts,
             hidden_size=moe.hidden_dim,
             num_dispatchers=all2all_manager.world_size,
+            dispatch_dtype_bytes_per_elem=dispatch_dtype_bytes_per_elem,
+            dispatch_scale_bytes_per_token=dispatch_scale_bytes_per_token,
         )
 
     elif moe.use_ag_rs_all2all_kernels and allow_new_interface:
diff --git a/vllm/model_executor/layers/fused_moe/config.py b/vllm/model_executor/layers/fused_moe/config.py
index a3b941dfa451..d5435261327f 100644
--- a/vllm/model_executor/layers/fused_moe/config.py
+++ b/vllm/model_executor/layers/fused_moe/config.py
@@ -7,6 +7,7 @@
 import torch
 
 from vllm.config import ParallelConfig, SchedulerConfig
+from vllm.config.kernel import MoEBackend
 from vllm.distributed import get_dp_group, get_pcp_group, get_tensor_model_parallel_rank
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
@@ -112,12 +113,17 @@ class RoutingMethodType(IntEnum):
     RenormalizeNaive = (4,)
     # TopK: TopK (no softmax)
     TopK = (5,)
-    # Custom
-    Custom = (6,)
-    # Simulated
-    Simulated = (7,)
+    # SigmoidRenorm: Sigmoid -> TopK -> Renormalize (divide by sum of top-K)
+    SigmoidRenorm = (6,)
+    # MiniMax2: Sigmoid + Bias -> TopK -> ScaledSumNormalize
+    MiniMax2 = (7,)
     # Unspecified
-    Unspecified = 8.0
+    Unspecified = (8,)
+    # other routing types (not passed to FlashInfer kernels)
+    # Deepseek V4 -> sqrtsoftplus + Bias + Normalize
+    DeepseekV4 = (100,)
+    Custom = (101,)
+    Simulated = (102,)
 
 
 def get_routing_method_type(
@@ -127,15 +133,27 @@ def get_routing_method_type(
     num_expert_group: int | None,
     has_e_score_bias: bool,
 ) -> RoutingMethodType:
+    if scoring_func == "sqrtsoftplus":
+        # DeepSeek V4 uses sqrtsoftplus routing with optional routing bias
+        # and top-k renormalization.
+        if renormalize:
+            return RoutingMethodType.DeepseekV4
+        else:
+            return RoutingMethodType.Unspecified
+
     if has_e_score_bias:
         if (num_expert_group or 0) > 0 and scoring_func == "sigmoid":
             return RoutingMethodType.DeepSeekV3
+        elif scoring_func == "sigmoid":
+            return RoutingMethodType.MiniMax2
         else:
             return RoutingMethodType.Unspecified
 
     if scoring_func == "sigmoid":
         if top_k == 1:
             return RoutingMethodType.Llama4
+        elif renormalize:
+            return RoutingMethodType.SigmoidRenorm
         else:
             return RoutingMethodType.Unspecified
 
@@ -227,7 +245,16 @@ class FusedMoEQuantConfig:
     _a2: FusedMoEQuantDesc
     _w1: FusedMoEQuantDesc
     _w2: FusedMoEQuantDesc
-    is_nvfp4_scale_swizzled: bool = True
+    is_scale_swizzled: bool = True
+
+    # MXFP4-specific TRTLLM parameters for SwiGLU activation clamping.
+    # These correspond to gemm1_alpha, gemm1_beta, gemm1_clamp_limit
+    # in TrtLlmMxfp4ExpertsBase.
+    gemm1_alpha: float | None = None
+    gemm1_beta: float | None = None
+    gemm1_clamp_limit: float | None = None
+
+    mx_alignment: int = 0
 
     def __post_init__(self):
         assert not self.per_act_token_quant or self.block_shape is None, (
@@ -475,7 +502,10 @@ def make(
         w1_zp: torch.Tensor | None = None,
         w2_zp: torch.Tensor | None = None,
         weight_dtype: torch.dtype | str | None = None,
-        is_nvfp4_scale_swizzled: bool = True,
+        is_scale_swizzled: bool = True,
+        gemm1_alpha: float | None = None,
+        gemm1_beta: float | None = None,
+        gemm1_clamp_limit: float | None = None,
     ) -> "FusedMoEQuantConfig":
         """
         General builder function for a FusedMoEQuantConfig.
@@ -505,7 +535,12 @@ def make(
         - w2_bias: Optional biases for w1 (GPT OSS Triton).
         - w1_zp: Optional w1 zero points for int4/int8 quantization.
         - w2_zp: Optional w2 zero points for int4/int8 quantization.
-        - is_nvfp4_scale_swizzled: Whether to swizzle the nvfp4 scale swizzling.
+        - is_scale_swizzled: Whether the activation scale-factor layout is
+          swizzled. Pass through to the underlying quantization kernel for
+          dtypes that distinguish layouts (nvfp4, mxfp8). Defaults to True.
+        - gemm1_alpha: Optional MXFP4 TRTLLM SwiGLU alpha parameter.
+        - gemm1_beta: Optional MXFP4 TRTLLM SwiGLU beta parameter.
+        - gemm1_clamp_limit: Optional MXFP4 TRTLLM SwiGLU clamp limit.
         """
         assert not isinstance(quant_dtype, str) or quant_dtype in {
             "nvfp4",
@@ -538,7 +573,10 @@ def make(
             _w2=FusedMoEQuantDesc(
                 weight_dtype, w_shape, w2_scale, g2_alphas, w2_zp, w2_bias
             ),
-            is_nvfp4_scale_swizzled=is_nvfp4_scale_swizzled,
+            is_scale_swizzled=is_scale_swizzled,
+            gemm1_alpha=gemm1_alpha,
+            gemm1_beta=gemm1_beta,
+            gemm1_clamp_limit=gemm1_clamp_limit,
         )
         assert quant_config.per_act_token_quant == per_act_token_quant
         assert quant_config.per_out_ch_quant == per_out_ch_quant
@@ -649,6 +687,9 @@ def mxfp4_w4a16_moe_quant_config(
     w2_scale: Union[torch.Tensor, "PrecisionConfig"],
     w1_bias: torch.Tensor | None = None,
     w2_bias: torch.Tensor | None = None,
+    gemm1_alpha: float | None = None,
+    gemm1_beta: float | None = None,
+    gemm1_clamp_limit: float | None = None,
 ) -> FusedMoEQuantConfig:
     """
     Construct a quant config for unquantized activations and mxfp4 weights.
@@ -658,6 +699,9 @@ def mxfp4_w4a16_moe_quant_config(
         _a2=FusedMoEQuantDesc(),
         _w1=FusedMoEQuantDesc("mxfp4", None, w1_scale, None, None, w1_bias),
         _w2=FusedMoEQuantDesc("mxfp4", None, w2_scale, None, None, w2_bias),
+        gemm1_alpha=gemm1_alpha,
+        gemm1_beta=gemm1_beta,
+        gemm1_clamp_limit=gemm1_clamp_limit,
     )
 
 
@@ -669,6 +713,11 @@ def mxfp4_mxfp8_moe_quant_config(
     w1_bias: torch.Tensor | None = None,
     w2_bias: torch.Tensor | None = None,
     block_shape: list[int] | None = None,
+    gemm1_alpha: float | None = None,
+    gemm1_beta: float | None = None,
+    gemm1_clamp_limit: float | None = None,
+    mx_alignment: int = 0,
+    is_scale_swizzled: bool = True,
 ) -> FusedMoEQuantConfig:
     """
     Construct a quant config for mxfp4 activations and mxfp4 weights.
@@ -678,6 +727,11 @@ def mxfp4_mxfp8_moe_quant_config(
         _a2=FusedMoEQuantDesc("mxfp8"),
         _w1=FusedMoEQuantDesc("mxfp4", None, w1_scale, None, None, w1_bias),
         _w2=FusedMoEQuantDesc("mxfp4", None, w2_scale, None, None, w2_bias),
+        gemm1_alpha=gemm1_alpha,
+        gemm1_beta=gemm1_beta,
+        gemm1_clamp_limit=gemm1_clamp_limit,
+        mx_alignment=mx_alignment,
+        is_scale_swizzled=is_scale_swizzled,
     )
 
 
@@ -711,6 +765,9 @@ def ocp_mx_moe_quant_config(
     w1_bias: torch.Tensor | None = None,
     w2_bias: torch.Tensor | None = None,
     block_shape: list[int] | None = None,
+    gemm1_alpha: float | None = None,
+    gemm1_beta: float | None = None,
+    gemm1_clamp_limit: float | None = None,
 ) -> FusedMoEQuantConfig:
     """
     Construct a quant config for mxfp4 activations and mxfp4 weights.
@@ -728,6 +785,9 @@ def ocp_mx_moe_quant_config(
         per_act_token_quant=False,
         per_out_ch_quant=False,
         block_shape=block_shape,
+        gemm1_alpha=gemm1_alpha,
+        gemm1_beta=gemm1_beta,
+        gemm1_clamp_limit=gemm1_clamp_limit,
     )
 
 
@@ -740,7 +800,7 @@ def nvfp4_moe_quant_config(
     w2_scale: torch.Tensor,
     w1_bias: torch.Tensor | None = None,
     w2_bias: torch.Tensor | None = None,
-    is_nvfp4_scale_swizzled: bool = True,
+    is_scale_swizzled: bool = True,
 ) -> FusedMoEQuantConfig:
     """
     Construct a quant config for mxfp4 activations and nvp4 weights.
@@ -758,7 +818,26 @@ def nvfp4_moe_quant_config(
         per_act_token_quant=False,
         per_out_ch_quant=False,
         block_shape=None,
-        is_nvfp4_scale_swizzled=is_nvfp4_scale_swizzled,
+        is_scale_swizzled=is_scale_swizzled,
+    )
+
+
+def mxfp4_moe_quant_config(
+    w1_scale: torch.Tensor,
+    w2_scale: torch.Tensor,
+) -> FusedMoEQuantConfig:
+    """
+    Construct a quant config for MXFP4 x MXFP4 MoE.
+    MXFP4 uses block scaling only (E8M0 scales, 32-element groups), with no
+    separate alphas / global activation scales in this config.
+    """
+    return FusedMoEQuantConfig.make(
+        "mxfp4",
+        w1_scale=w1_scale,
+        w2_scale=w2_scale,
+        per_act_token_quant=False,
+        per_out_ch_quant=False,
+        block_shape=None,
     )
 
 
@@ -971,7 +1050,11 @@ def use_fi_nvl_one_sided_kernels(self):
 
     @property
     def use_batched_activation_format(self):
-        return self.use_deepep_ll_kernels
+        return self.use_deepep_ll_kernels or self.use_nixl_ep_kernels
+
+    @property
+    def needs_round_robin_routing_tables(self):
+        return self.use_deepep_ll_kernels or self.use_nixl_ep_kernels
 
     @property
     def use_ag_rs_all2all_kernels(self):
@@ -1173,7 +1256,7 @@ class FusedMoEConfig:
     # Defaults to intermediate_size_per_partition if not specified.
     intermediate_size_per_partition_unpadded: int | None = None
 
-    moe_backend: str = "auto"
+    moe_backend: MoEBackend = "auto"
     max_num_tokens: int = SchedulerConfig.DEFAULT_MAX_NUM_BATCHED_TOKENS_FOR_BATCHED_DP
     has_bias: bool = False
     is_act_and_mul: bool = True
@@ -1274,3 +1357,7 @@ def use_ag_rs_all2all_kernels(self):
     @property
     def use_nixl_ep_kernels(self):
         return self.moe_parallel_config.use_nixl_ep_kernels
+
+    @property
+    def needs_round_robin_routing_tables(self):
+        return self.moe_parallel_config.needs_round_robin_routing_tables
diff --git a/vllm/model_executor/layers/fused_moe/configs/E=128,N=2880,device_name=NVIDIA_H100_80GB_HBM3.json b/vllm/model_executor/layers/fused_moe/configs/E=128,N=2880,device_name=NVIDIA_H100_80GB_HBM3.json
new file mode 100644
index 000000000000..2d53aedbed48
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/configs/E=128,N=2880,device_name=NVIDIA_H100_80GB_HBM3.json
@@ -0,0 +1,147 @@
+{
+    "triton_version": "3.6.0",
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "512": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 5
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 5
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    }
+}
diff --git a/vllm/model_executor/layers/fused_moe/cpu_fused_moe.py b/vllm/model_executor/layers/fused_moe/cpu_fused_moe.py
index e1bedd6f45be..c26090a56b2d 100644
--- a/vllm/model_executor/layers/fused_moe/cpu_fused_moe.py
+++ b/vllm/model_executor/layers/fused_moe/cpu_fused_moe.py
@@ -7,7 +7,12 @@
 from torch.nn import functional as F
 
 from vllm import _custom_ops as ops
-from vllm._custom_ops import cpu_fused_moe, cpu_prepack_moe_weight
+from vllm._custom_ops import (
+    CPUQuantMethod,
+    cpu_fused_moe,
+    cpu_prepack_moe_weight,
+    fused_experts_cpu,
+)
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.quantization.utils.layer_utils import replace_parameter
@@ -45,7 +50,7 @@ def _gelu_and_mul(
 # Uses static methods or standalone functions to avoid instantiating CustomOp
 # classes, which would call get_current_vllm_config() before config is set.
 _CPU_MOE_ACT_FN: dict[MoEActivation, Callable[[torch.Tensor], torch.Tensor]] = {
-    MoEActivation.SILU: SiluAndMul.forward_native,
+    MoEActivation.SILU: lambda x: SiluAndMul(compile_native=False).forward_native(x),
     MoEActivation.SWIGLUOAI: _swigluoai_forward_native,
     MoEActivation.GELU: _gelu_and_mul,
 }
@@ -195,23 +200,21 @@ def __call__(
             e_score_correction_bias=e_score_correction_bias,
         )
 
-        torch.ops._C.fused_experts_cpu(
+        return fused_experts_cpu(
             x,
             layer.w13_weight,
             layer.w2_weight,
             topk_weights,
             topk_ids,
-            True,
-            False,
-            False,
-            None,
-            None,
-            None,
-            None,
-            None,
-            True,
+            False,  # inplace
+            CPUQuantMethod.UNQUANT,  # moe_comp_method
+            None,  # w1_scale
+            None,  # w2_scale
+            None,  # w1_zero
+            None,  # w2_zero
+            None,  # block_size
+            True,  # is_vnni
         )
-        return x
 
 
 class CPUFusedMOE:
diff --git a/vllm/model_executor/layers/fused_moe/deep_gemm_utils.py b/vllm/model_executor/layers/fused_moe/deep_gemm_utils.py
index a2d267bd7490..df69fa328ca7 100644
--- a/vllm/model_executor/layers/fused_moe/deep_gemm_utils.py
+++ b/vllm/model_executor/layers/fused_moe/deep_gemm_utils.py
@@ -140,6 +140,8 @@ def _fwd_kernel_ep_scatter_2(
     offset_in_s = tl.arange(0, SCALE_HIDDEN_SIZE_PAD)
     mask_s = offset_in_s < SCALE_HIDDEN_SIZE
 
+    output_tensor_stride0 = output_tensor_stride0.to(tl.int64)
+
     for token_id in range(start_token_id, total_token_num, grid_num):
         to_copy = tl.load(recv_x + token_id * recv_x_stride0 + offset_in, mask=mask)
         to_copy_s = tl.load(
@@ -154,12 +156,13 @@ def _fwd_kernel_ep_scatter_2(
 
             if expert_id >= 0:
                 dest_token_index = tl.atomic_add(expert_start_loc + expert_id, 1)
+                dest_token_index_i64 = dest_token_index.to(tl.int64)
                 tl.store(
                     output_index + token_id * output_index_stride0 + topk_index,
                     dest_token_index,
                 )
                 output_tensor_ptr = (
-                    output_tensor + dest_token_index * output_tensor_stride0
+                    output_tensor + dest_token_index_i64 * output_tensor_stride0
                 )
                 output_tensor_scale_ptr = (
                     output_tensor_scale + dest_token_index * output_tensor_scale_stride0
diff --git a/vllm/model_executor/layers/fused_moe/experts/aiter_mxfp4_w4a8_moe.py b/vllm/model_executor/layers/fused_moe/experts/aiter_mxfp4_w4a8_moe.py
new file mode 100644
index 000000000000..3906a7e057ca
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/experts/aiter_mxfp4_w4a8_moe.py
@@ -0,0 +1,292 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm._aiter_ops import rocm_aiter_ops
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEParallelConfig,
+    FusedMoEQuantConfig,
+    RoutingMethodType,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    QuantKey,
+    kFp8StaticTensorSym,
+    kMxfp4Static,
+)
+
+__all__ = [
+    "AiterW4A8ExpertsMonolithic",
+    "aiter_triton_kernel_w4a8_moe_forward",
+]
+
+
+def aiter_triton_kernel_w4a8_moe_forward(
+    hidden_states: torch.Tensor,
+    w1,  # Tensor or triton_kernels.Tensor
+    w2,  # Tensor or triton_kernels.Tensor
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    activation: MoEActivation = MoEActivation.SWIGLUOAI,
+    quant_config: FusedMoEQuantConfig | None = None,
+    apply_router_weight_on_input: bool = False,
+    global_num_experts: int = -1,
+    expert_map: torch.Tensor | None = None,
+    unpadded_N_w1=None,
+    unpadded_K_w1=None,
+    unpadded_N_w2=None,
+    unpadded_K_w2=None,
+):
+    assert (
+        quant_config is not None
+        and quant_config.use_mxfp4_w4a8
+        and rocm_aiter_ops.is_enabled()
+    )
+    from aiter.ops.triton.moe_routing.routing import routing as aiter_routing
+
+    routing_data, gather_idx, scatter_idx = aiter_routing(
+        gating_output, topk, sm_first=not renormalize
+    )
+    return triton_kernel_fused_mxfp4_w4a8_experts(
+        None,
+        hidden_states,
+        w1,
+        w2,
+        routing_data,
+        gather_idx,
+        scatter_idx,
+        activation=activation.value,
+        quant_config=quant_config,
+        apply_router_weight_on_input=apply_router_weight_on_input,
+        global_num_experts=global_num_experts,
+        expert_map=expert_map,
+        unpadded_N_w1=unpadded_N_w1,
+        unpadded_K_w1=unpadded_K_w1,
+        unpadded_N_w2=unpadded_N_w2,
+        unpadded_K_w2=unpadded_K_w2,
+    )
+
+
+def triton_kernel_fused_mxfp4_w4a8_experts(
+    output_tensor: torch.Tensor,
+    hidden_states: torch.Tensor,
+    w1,  # Tensor or triton_kernels.Tensor
+    w2,  # Tensor or triton_kernels.Tensor
+    routing_data,  # RoutingData
+    gather_indx,  # GatherIndx
+    scatter_indx,  # ScatterIndx
+    activation: str = "silu",
+    quant_config: FusedMoEQuantConfig | None = None,
+    swiglu_alpha: float = 1.702,
+    swiglu_limit: float = 7.0,
+    apply_router_weight_on_input: bool = False,
+    global_num_experts: int = -1,
+    expert_map: torch.Tensor | None = None,
+    a1q_scale: torch.Tensor | None = None,
+    unpadded_N_w1=None,
+    unpadded_K_w1=None,
+    unpadded_N_w2=None,
+    unpadded_K_w2=None,
+) -> torch.Tensor:
+    assert quant_config is not None
+    # type check, uint8 means mxfp4
+    assert hidden_states.dtype == torch.bfloat16
+    assert quant_config.w1_bias is None or quant_config.w1_bias.dtype == torch.float32
+    assert quant_config.w2_bias is None or quant_config.w2_bias.dtype == torch.float32
+
+    # Shape check: weights are padded (e.g. hidden_size padded for
+    # GFX950 swizzle).
+    assert hidden_states.shape[-1] == w1.shape[-2]
+    assert w2.shape[-1] == w1.shape[1]
+
+    E, _, N = w1.shape
+
+    if global_num_experts == -1:
+        global_num_experts = E
+
+    gammas = routing_data.gate_scal if routing_data else None
+
+    from aiter.ops.triton.moe_op_gemm_a8w4 import moe_gemm_a8w4
+    from aiter.ops.triton.quant_moe import downcast_to_static_fp8
+
+    assert quant_config.w1_precision is not None, (
+        "w1_precision in quant config can't be None"
+    )
+    assert quant_config.w2_precision is not None, (
+        "w2_precision in quant config can't be None"
+    )
+
+    hidden_states = downcast_to_static_fp8(
+        hidden_states, quant_config.w1_precision.flex_ctx.lhs_data.scale
+    )
+
+    intermediate_cache1 = moe_gemm_a8w4(
+        hidden_states,
+        w1.storage.data,
+        None,
+        quant_config.w1_precision.weight_scale.storage.data,
+        quant_config.w1_precision.flex_ctx.lhs_data.scale,
+        quant_config.w2_precision.flex_ctx.lhs_data.scale,
+        quant_config.w1_bias,
+        routing_data,
+        gather_indx=gather_indx,
+        gammas=gammas if apply_router_weight_on_input else None,
+        swizzle_mx_scale="CDNA4_SCALE",
+        out_dtype=torch.float8_e4m3fn,
+        apply_swiglu=True,
+        alpha=swiglu_alpha,
+        limit=swiglu_limit,
+        unpadded_N=unpadded_N_w1,
+        unpadded_K=unpadded_K_w1,
+    )
+
+    intermediate_cache3 = moe_gemm_a8w4(
+        intermediate_cache1,
+        w2.storage.data,
+        None,
+        quant_config.w2_precision.weight_scale.storage.data,
+        quant_config.w2_precision.flex_ctx.lhs_data.scale,
+        None,
+        quant_config.w2_bias,
+        routing_data,
+        scatter_indx=scatter_indx,
+        gammas=None if apply_router_weight_on_input else gammas,
+        swizzle_mx_scale="CDNA4_SCALE",
+        unpadded_N=unpadded_N_w2,
+        unpadded_K=unpadded_K_w2,
+    )
+
+    return intermediate_cache3
+
+
+class AiterW4A8ExpertsMonolithic(mk.FusedMoEExpertsMonolithic):
+    """
+    Monolithic MXFP4 W4A8 expert using AITER triton kernels.
+
+    This backend uses:
+    - aiter.ops.triton.moe_routing.routing for routing
+    - aiter.ops.triton.moe_op_gemm_a8w4.moe_gemm_a8w4 for computation
+
+    Weight format: MXFP4 weights with GFX950 swizzle
+    Activation: Static FP8 quantization
+    """
+
+    def __init__(
+        self,
+        moe_config: FusedMoEConfig,
+        quant_config: FusedMoEQuantConfig,
+    ):
+        super().__init__(moe_config, quant_config)
+        self.topk = moe_config.experts_per_token
+        self.renormalize = moe_config.routing_method in (
+            RoutingMethodType.Renormalize,
+            RoutingMethodType.RenormalizeNaive,
+        )
+
+    @staticmethod
+    def activation_format() -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.Standard
+
+    @staticmethod
+    def _supports_current_device() -> bool:
+        # Requires AITER and GFX950
+        if not rocm_aiter_ops.is_enabled():
+            return False
+        from vllm.platforms.rocm import on_gfx950
+
+        return on_gfx950()
+
+    @staticmethod
+    def _supports_no_act_and_mul() -> bool:
+        return False
+
+    @staticmethod
+    def _supports_quant_scheme(
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        # W4A8: MXFP4 weights with static FP8 activations
+        SUPPORTED_W_A = [
+            (kMxfp4Static, kFp8StaticTensorSym),
+        ]
+        return (weight_key, activation_key) in SUPPORTED_W_A
+
+    @staticmethod
+    def _supports_activation(activation: MoEActivation) -> bool:
+        # Only SILU activation (swiglu) is supported
+        return activation == MoEActivation.SWIGLUOAI
+
+    @staticmethod
+    def _supports_parallel_config(
+        moe_parallel_config: FusedMoEParallelConfig,
+    ) -> bool:
+        return (
+            not moe_parallel_config.use_all2all_kernels
+            and not moe_parallel_config.enable_eplb
+            and moe_parallel_config.dp_size <= 1
+        )
+
+    @staticmethod
+    def _supports_routing_method(
+        routing_method: RoutingMethodType,
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        return routing_method in [
+            RoutingMethodType.Renormalize,
+            RoutingMethodType.RenormalizeNaive,
+        ]
+
+    @staticmethod
+    def _supports_router_logits_dtype(
+        router_logits_dtype: torch.dtype | None,
+        routing_method: RoutingMethodType,
+    ) -> bool:
+        return True
+
+    def supports_expert_map(self) -> bool:
+        return False  # Expert parallelism not yet supported
+
+    @property
+    def expects_unquantized_inputs(self) -> bool:
+        return True
+
+    def apply(
+        self,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        router_logits: torch.Tensor,
+        activation: MoEActivation,
+        global_num_experts: int,
+        expert_map: torch.Tensor | None,
+        a1q_scale: torch.Tensor | None,
+        apply_router_weight_on_input: bool,
+        # grouped topk + fused topk bias parameters
+        num_expert_group: int | None = None,
+        e_score_correction_bias: torch.Tensor | None = None,
+        routed_scaling_factor: float | None = None,
+        topk_group: int | None = None,
+    ) -> torch.Tensor:
+        assert self.moe_config.intermediate_size_per_partition_unpadded is not None
+        assert self.moe_config.hidden_dim_unpadded is not None
+        return aiter_triton_kernel_w4a8_moe_forward(
+            hidden_states=hidden_states,
+            w1=w1,
+            w2=w2,
+            gating_output=router_logits,
+            topk=self.topk,
+            renormalize=self.renormalize,
+            global_num_experts=global_num_experts,
+            expert_map=expert_map,
+            quant_config=self.quant_config,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            unpadded_N_w1=self.moe_config.intermediate_size_per_partition_unpadded * 2,
+            unpadded_K_w1=self.moe_config.hidden_dim_unpadded,
+            unpadded_N_w2=self.moe_config.hidden_dim_unpadded,
+            unpadded_K_w2=self.moe_config.intermediate_size_per_partition_unpadded,
+        )
diff --git a/vllm/model_executor/layers/fused_moe/experts/batched_deep_gemm_moe.py b/vllm/model_executor/layers/fused_moe/experts/batched_deep_gemm_moe.py
index 2cb0bd7649f5..7bd383b9cdac 100644
--- a/vllm/model_executor/layers/fused_moe/experts/batched_deep_gemm_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/batched_deep_gemm_moe.py
@@ -210,9 +210,9 @@ def persistent_masked_m_silu_mul_quant(
         DeepGemmQuantScaleFMT.UE8M0,
     ]
 
-    cuda_arch = current_platform.get_device_capability(
-        device_id=y.device.index
-    ).to_int()
+    device_capability = current_platform.get_device_capability(device_id=y.device.index)
+    assert device_capability is not None
+    cuda_arch = device_capability.to_int()
 
     if current_platform.is_cuda() and cuda_arch >= 80:
         torch.ops._C.persistent_masked_m_silu_mul_quant(
@@ -369,7 +369,6 @@ def estimate_expected_m(
             logger.warning_once(
                 "DPMetadata unavailable. Defaulting expected_m to "
                 f"{max_tokens_per_expert}.",
-                scope="local",
             )
             return max_tokens_per_expert
 
diff --git a/vllm/model_executor/layers/fused_moe/experts/cpu_moe.py b/vllm/model_executor/layers/fused_moe/experts/cpu_moe.py
new file mode 100644
index 000000000000..3b3d5cdfae06
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/experts/cpu_moe.py
@@ -0,0 +1,175 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""CPU FP8 W8A16 block-quantized fused MoE experts."""
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm._custom_ops import CPUQuantMethod, fused_experts_cpu
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEParallelConfig,
+    FusedMoEQuantConfig,
+    RoutingMethodType,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    QuantKey,
+    kFp8Dynamic128Sym,
+    kFp8Static128BlockSym,
+)
+from vllm.platforms import current_platform
+
+
+def prepare_fp8_moe_layer_for_cpu(
+    w13: torch.Tensor,
+    w2: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """VNNI-prepack FP8 MoE weights for CPU kernel."""
+    num_experts = w13.size(0)
+    packed_w13_list = []
+    packed_w2_list = []
+    for i in range(num_experts):
+        packed_w13_list.append(torch.ops._C.convert_weight_packed(w13[i]))
+        packed_w2_list.append(torch.ops._C.convert_weight_packed(w2[i]))
+    packed_w13 = torch.stack(packed_w13_list)
+    packed_w2 = torch.stack(packed_w2_list)
+    return packed_w13, packed_w2
+
+
+class CPUExpertsFp8(mk.FusedMoEExpertsMonolithic):
+    """CPU FP8 W8A16 block-quantized monolithic MoE experts."""
+
+    def __init__(
+        self,
+        moe_config: FusedMoEConfig,
+        quant_config: FusedMoEQuantConfig,
+    ):
+        super().__init__(
+            moe_config,
+            quant_config,
+        )
+
+    @property
+    def expects_unquantized_inputs(self) -> bool:
+        return True
+
+    @staticmethod
+    def activation_format() -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.Standard
+
+    @staticmethod
+    def _supports_current_device() -> bool:
+        return current_platform.is_cpu()
+
+    @staticmethod
+    def _supports_no_act_and_mul() -> bool:
+        return False
+
+    @staticmethod
+    def _supports_activation(activation: MoEActivation) -> bool:
+        return activation == MoEActivation.SILU
+
+    @staticmethod
+    def _supports_parallel_config(
+        moe_parallel_config: FusedMoEParallelConfig,
+    ) -> bool:
+        return True
+
+    @staticmethod
+    def _supports_quant_scheme(
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        SUPPORTED_W_A = [
+            (kFp8Static128BlockSym, kFp8Dynamic128Sym),
+        ]
+        return (weight_key, activation_key) in SUPPORTED_W_A
+
+    @staticmethod
+    def _supports_routing_method(
+        routing_method: RoutingMethodType,
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        return routing_method in [
+            RoutingMethodType.Default,
+            RoutingMethodType.Renormalize,
+            RoutingMethodType.RenormalizeNaive,
+        ]
+
+    @staticmethod
+    def _supports_router_logits_dtype(
+        router_logits_dtype: torch.dtype | None,
+        routing_method: RoutingMethodType,
+    ) -> bool:
+        return True
+
+    def supports_expert_map(self) -> bool:
+        return False
+
+    def apply(
+        self,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        router_logits: torch.Tensor,
+        activation: MoEActivation,
+        global_num_experts: int,
+        expert_map: torch.Tensor | None,
+        a1q_scale: torch.Tensor | None,
+        apply_router_weight_on_input: bool,
+        # grouped topk + fused topk bias parameters
+        num_expert_group: int | None = None,
+        e_score_correction_bias: torch.Tensor | None = None,
+        routed_scaling_factor: float | None = None,
+        topk_group: int | None = None,
+    ) -> torch.Tensor:
+        from vllm.model_executor.layers.fused_moe.cpu_fused_moe import (
+            select_experts,
+        )
+
+        topk_weights, topk_ids = select_experts(
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+            use_grouped_topk=num_expert_group is not None,
+            top_k=self.moe_config.experts_per_token,
+            renormalize=self.moe_config.routing_method
+            in (
+                RoutingMethodType.Renormalize,
+                RoutingMethodType.RenormalizeNaive,
+            ),
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            scoring_func="softmax",
+            routed_scaling_factor=(
+                routed_scaling_factor if routed_scaling_factor is not None else 1.0
+            ),
+            e_score_correction_bias=e_score_correction_bias,
+        )
+
+        block_shape = (
+            list(self.quant_config.block_shape)
+            if self.quant_config.block_shape
+            else (
+                [self.quant_config._w1.shape.row, self.quant_config._w1.shape.col]
+                if self.quant_config._w1.shape is not None
+                else None
+            )
+        )
+
+        return fused_experts_cpu(
+            hidden_states,
+            w1,
+            w2,
+            topk_weights,
+            topk_ids,
+            False,  # inplace
+            CPUQuantMethod.FP8_W8A16,  # moe_comp_method
+            self.w1_scale,  # w1_scale
+            self.w2_scale,  # w2_scale
+            None,  # w1_zero
+            None,  # w2_zero
+            block_shape,  # block_size
+            True,  # is_vnni
+        )
diff --git a/vllm/model_executor/layers/fused_moe/cutlass_moe.py b/vllm/model_executor/layers/fused_moe/experts/cutlass_moe.py
similarity index 82%
rename from vllm/model_executor/layers/fused_moe/cutlass_moe.py
rename to vllm/model_executor/layers/fused_moe/experts/cutlass_moe.py
index 43082b3675a6..fdd802e7da3a 100644
--- a/vllm/model_executor/layers/fused_moe/cutlass_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/cutlass_moe.py
@@ -36,6 +36,8 @@
     kFp8DynamicTokenSym,
     kFp8StaticChannelSym,
     kFp8StaticTensorSym,
+    kMxfp4Dynamic,
+    kMxfp4Static,
     kNvfp4Dynamic,
     kNvfp4Static,
 )
@@ -795,6 +797,299 @@ def apply(
         )
 
 
+def run_cutlass_moe_mxfp4(
+    output: torch.Tensor,
+    a: torch.Tensor,
+    w1_fp4: torch.Tensor,
+    w1_blockscale: torch.Tensor,
+    w2_fp4: torch.Tensor,
+    w2_blockscale: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    activation: MoEActivation,
+    workspace13: torch.Tensor,
+    workspace2: torch.Tensor,
+    m: int,
+    n: int,
+    k: int,
+    e: int,
+    device: torch.device,
+    apply_router_weight_on_input: bool = False,
+) -> None:
+    """MXFP4 x MXFP4 MoE implementation using CUTLASS grouped GEMM."""
+    is_gated = activation.is_gated
+    w1_n = n * 2 if is_gated else n
+
+    assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
+    assert w1_fp4.dtype == torch.uint8, "weight 1 must be uint8"
+    assert w2_fp4.dtype == torch.uint8, "weight 2 must be uint8"
+    assert (
+        w1_fp4.ndim == 3
+        and w2_fp4.ndim == 3
+        and w1_blockscale.ndim == 3
+        and w2_blockscale.ndim == 3
+    ), "All Weights must be of rank 3 for cutlass_moe_mxfp4"
+    m_a, k_a = a.shape
+    e_w1, w1_n_actual, half_k_w1 = w1_fp4.shape
+    e_w2, k_w2, half_n_w2 = w2_fp4.shape
+
+    assert e_w1 == e_w2 and e_w1 == e
+    assert k_a == half_k_w1 * 2 and k == k_w2
+    assert w1_n_actual == w1_n and half_n_w2 * 2 == n
+    assert m == m_a
+    assert 2 * half_k_w1 == k_w2
+    assert a.dtype in [torch.half, torch.bfloat16], "Invalid input dtype"
+    assert topk_weights.size(0) == m and topk_ids.size(0) == m
+
+    topk = topk_ids.size(1)
+    out_dtype = a.dtype
+    num_topk = topk_ids.size(1)
+
+    expert_offsets = torch.empty((e + 1), dtype=torch.int32, device=device)
+    blockscale_offsets = torch.empty((e + 1), dtype=torch.int32, device=device)
+    problem_sizes1 = torch.empty((e, 3), dtype=torch.int32, device=device)
+    problem_sizes2 = torch.empty((e, 3), dtype=torch.int32, device=device)
+
+    a_map = torch.empty((topk_ids.numel()), dtype=torch.int32, device=device)
+    c_map = torch.empty((topk_ids.numel()), dtype=torch.int32, device=device)
+
+    if apply_router_weight_on_input:
+        assert num_topk == 1, (
+            "apply_router_weight_on_input is only implemented for topk=1"
+        )
+        a.mul_(topk_weights.to(out_dtype))
+
+    ops.get_cutlass_moe_mm_data(
+        topk_ids,
+        expert_offsets,
+        problem_sizes1,
+        problem_sizes2,
+        a_map,
+        c_map,
+        e,
+        n,
+        k,
+        blockscale_offsets,
+        is_gated=is_gated,
+    )
+
+    a = ops.shuffle_rows(a, a_map)
+    rep_a_fp4, rep_a_blockscale = ops.mxfp4_experts_quant(
+        a,
+        expert_offsets,
+        blockscale_offsets,
+        e,
+        num_topk,
+    )
+    c1 = _resize_cache(workspace13, (m * topk, w1_n))
+    c2 = _resize_cache(workspace2, (m * topk, n))
+    c3 = _resize_cache(workspace13, (m * topk, k))
+
+    ops.cutlass_mxfp4_moe_mm(
+        c1,
+        rep_a_fp4,
+        w1_fp4,
+        rep_a_blockscale,
+        w1_blockscale,
+        problem_sizes1,
+        expert_offsets[:-1],
+        blockscale_offsets[:-1],
+    )
+    del rep_a_fp4, rep_a_blockscale
+    if activation == MoEActivation.SILU:
+        int_fp4, int_blockscale = ops.silu_and_mul_mxfp4_experts_quant(
+            c1, expert_offsets, blockscale_offsets, e, num_topk
+        )
+    else:
+        apply_moe_activation(activation, c2, c1)
+        int_fp4, int_blockscale = ops.mxfp4_experts_quant(
+            c2, expert_offsets, blockscale_offsets, e, num_topk
+        )
+
+    ops.cutlass_mxfp4_moe_mm(
+        c3,
+        int_fp4,
+        w2_fp4,
+        int_blockscale,
+        w2_blockscale,
+        problem_sizes2,
+        expert_offsets[:-1],
+        blockscale_offsets[:-1],
+    )
+    del int_fp4, int_blockscale
+
+    c3 = ops.shuffle_rows(c3, c_map)
+
+    assert output.dtype == out_dtype
+    if not apply_router_weight_on_input:
+        output.copy_(
+            (
+                c3.view(m, num_topk, k)
+                * topk_weights.view(m, num_topk, 1).to(out_dtype)
+            ).sum(dim=1),
+            non_blocking=True,
+        )
+    else:
+        output.copy_(c3.view(m, num_topk, k).sum(dim=1), non_blocking=True)
+    return
+
+
+def swizzle_mxfp4_scales(
+    scales: torch.Tensor,
+    N: int,
+    K: int,
+) -> torch.Tensor:
+    """Swizzle flat [N, K//32] E8M0 scales to CUTLASS tiled layout.
+
+    CUTLASS expects MX scale factors in a tiled layout:
+        [numMTiles, numKTiles, 32, 4, 4]
+    where numMTiles = ceil(N/128), numKTiles = ceil(K/128),
+    and the inner dimensions correspond to the swizzle pattern:
+        mTileIdx = mIdx / 128
+        outerMIdx = mIdx % 32
+        innerMIdx = (mIdx / 32) % 4
+        kTileIdx = kIdx / 4
+        innerKIdx = kIdx % 4
+    with kIdx = col_in_scale_space (i.e., index into K//32).
+    """
+    assert scales.dtype == torch.uint8
+    num_scale_cols = K // 32  # number of E8M0 scale values per row
+
+    num_m_tiles = (N + 127) // 128
+    num_k_tiles = (num_scale_cols + 3) // 4
+
+    # Pad N to multiple of 128 and scale_cols to multiple of 4
+    padded_N = num_m_tiles * 128
+    padded_scale_cols = num_k_tiles * 4
+
+    # Start with flat scales, pad if needed
+    padded = torch.zeros(
+        padded_N, padded_scale_cols, dtype=torch.uint8, device=scales.device
+    )
+    padded[:N, :num_scale_cols] = scales
+
+    # Reshape to tile structure:
+    # [numMTiles, 4, 32, numKTiles, 4]
+    #  mTileIdx, innerMIdx, outerMIdx, kTileIdx, innerKIdx
+    tiled = padded.reshape(num_m_tiles, 4, 32, num_k_tiles, 4)
+    # Permute to [numMTiles, numKTiles, 32, 4, 4]
+    #            (outerMIdx, innerMIdx, innerKIdx)
+    tiled = tiled.permute(0, 3, 2, 1, 4).contiguous()
+    return tiled.reshape(-1)
+
+
+class CutlassExpertsMxfp4(mk.FusedMoEExpertsModular):
+    """CUTLASS MXFP4 x MXFP4 fused MoE expert implementation."""
+
+    @property
+    def expects_unquantized_inputs(self) -> bool:
+        return True
+
+    @staticmethod
+    def _supports_current_device() -> bool:
+        p = current_platform
+        return p.is_cuda() and p.is_device_capability_family(100)
+
+    @staticmethod
+    def _supports_no_act_and_mul() -> bool:
+        return True
+
+    @staticmethod
+    def _supports_quant_scheme(
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        return (weight_key, activation_key) == (kMxfp4Static, kMxfp4Dynamic)
+
+    @staticmethod
+    def _supports_activation(activation: MoEActivation) -> bool:
+        return activation in [
+            MoEActivation.SILU,
+            MoEActivation.GELU,
+            MoEActivation.SWIGLUOAI,
+            MoEActivation.SWIGLUSTEP,
+            MoEActivation.SILU_NO_MUL,
+            MoEActivation.GELU_NO_MUL,
+            MoEActivation.RELU2_NO_MUL,
+        ]
+
+    @staticmethod
+    def _supports_parallel_config(
+        moe_parallel_config: FusedMoEParallelConfig,
+    ) -> bool:
+        return moe_parallel_config.ep_size == 1
+
+    @staticmethod
+    def activation_format() -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.Standard
+
+    def supports_expert_map(self) -> bool:
+        return False
+
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        return TopKWeightAndReduceNoOP()
+
+    def workspace_dtype(self, act_dtype: torch.dtype) -> torch.dtype:
+        return act_dtype
+
+    def workspace_shapes(
+        self,
+        M: int,
+        N: int,
+        K: int,
+        topk: int,
+        global_num_experts: int,
+        local_num_experts: int,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+        activation: MoEActivation,
+    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
+        workspace1 = (M * topk, max(2 * N, K))
+        workspace2 = (M * topk, N)
+        output = (M, K)
+        return (workspace1, workspace2, output)
+
+    def apply(
+        self,
+        output: torch.Tensor,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        activation: MoEActivation,
+        global_num_experts: int,
+        expert_map: torch.Tensor | None,
+        a1q_scale: torch.Tensor | None,
+        a2_scale: torch.Tensor | None,
+        workspace13: torch.Tensor | None,
+        workspace2: torch.Tensor | None,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+        apply_router_weight_on_input: bool,
+    ):
+        e, m, n, k, _ = self.moe_problem_size(hidden_states, w1, w2, topk_ids)
+        n = w2.shape[2] * 2
+
+        run_cutlass_moe_mxfp4(
+            output=output,
+            a=hidden_states,
+            w1_fp4=w1,
+            w1_blockscale=self.w1_scale,
+            w2_fp4=w2,
+            w2_blockscale=self.w2_scale,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            activation=activation,
+            workspace13=workspace13,
+            workspace2=workspace2,
+            m=m,
+            n=n,
+            k=k,
+            e=e,
+            device=hidden_states.device,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+        )
+
+
 # W4A8
 def run_cutlass_moe_w4a8_fp8(
     output: torch.Tensor,
diff --git a/vllm/model_executor/layers/fused_moe/experts/deep_gemm_moe.py b/vllm/model_executor/layers/fused_moe/experts/deep_gemm_moe.py
index 03341378a13c..b4394b5fd382 100644
--- a/vllm/model_executor/layers/fused_moe/experts/deep_gemm_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/deep_gemm_moe.py
@@ -25,15 +25,20 @@
     per_token_group_quant_fp8_packed_for_deepgemm,
     silu_mul_per_token_group_quant_fp8_colmajor,
 )
+from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+    silu_mul_quant_fp8_packed_triton as fused_silu_mul_fp8_quant_packed,
+)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
     kFp8Dynamic128Sym,
     kFp8Static128BlockSym,
+    kMxfp4Static,
 )
 from vllm.utils.deep_gemm import (
     DeepGemmQuantScaleFMT,
     get_mk_alignment_for_contiguous_layout,
     is_deep_gemm_supported,
+    m_grouped_fp8_fp4_gemm_nt_contiguous,
     m_grouped_fp8_gemm_nt_contiguous,
 )
 from vllm.utils.import_utils import has_deep_gemm
@@ -197,8 +202,14 @@ def _act_mul_quant(
         M_sum, N = input.size()
         activation_out_dim = self.adjust_N_for_activation(N, activation)
 
-        # 1. DeepGemm UE8M0: use packed per-token-group quant
+        # 1. DeepGemm UE8M0: fused SiLU+mul+clamp+quant+pack
         if scale_fmt == DeepGemmQuantScaleFMT.UE8M0:
+            if activation == MoEActivation.SILU:
+                return fused_silu_mul_fp8_quant_packed(
+                    input=input,
+                    output_q=output,
+                    group_size=block_k,
+                )
             act_out = torch.empty(
                 (M_sum, activation_out_dim), dtype=input.dtype, device=input.device
             )
@@ -312,3 +323,225 @@ def apply(
             expert_map=expert_map,
             output=output,
         )
+
+
+class DeepGemmFP4Experts(mk.FusedMoEExpertsModular):
+    """DeepGemm-based fused MoE expert implementation for FP4 weights.
+
+    Uses m_grouped_fp8_fp4_gemm_nt_contiguous with FP8 activations and
+    MXFP4 (FP4 E2M1 packed as uint8) weights. Requires SM100+ (Blackwell).
+    """
+
+    # FP8 activation block size (hardcoded since mxfp4_w4a8 quant config
+    # does not set a block_shape on the activation descriptor).
+    _ACT_BLOCK_K = 128
+    # FP4 weight block size
+    _WEIGHT_BLOCK_K = 32
+
+    def __init__(self, moe_config: FusedMoEConfig, quant_config: FusedMoEQuantConfig):
+        super().__init__(moe_config=moe_config, quant_config=quant_config)
+        assert quant_config.weight_quant_dtype == "mxfp4"
+        assert not quant_config.per_act_token_quant
+        assert not quant_config.per_out_ch_quant
+
+        self.gemm1_clamp_limit = quant_config.gemm1_clamp_limit
+
+    @staticmethod
+    def activation_format() -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.Standard
+
+    @staticmethod
+    def _supports_current_device() -> bool:
+        from vllm.platforms import current_platform
+
+        return (
+            is_deep_gemm_supported()
+            and current_platform.is_device_capability_family(100)
+        )
+
+    @staticmethod
+    def _supports_no_act_and_mul() -> bool:
+        return False
+
+    @staticmethod
+    def _supports_quant_scheme(
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        SUPPORTED_W_A = [
+            (kMxfp4Static, kFp8Dynamic128Sym),
+        ]
+        return (weight_key, activation_key) in SUPPORTED_W_A
+
+    @staticmethod
+    def _supports_activation(activation: MoEActivation) -> bool:
+        return activation in [MoEActivation.SILU, MoEActivation.SWIGLUSTEP]
+
+    @staticmethod
+    def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:
+        return not (
+            moe_parallel_config.use_fi_nvl_two_sided_kernels
+            or moe_parallel_config.use_fi_nvl_one_sided_kernels
+        )
+
+    def supports_expert_map(self) -> bool:
+        return True
+
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        return TopKWeightAndReduceNoOP()
+
+    def workspace_shapes(
+        self,
+        M: int,
+        N: int,
+        K: int,
+        topk: int,
+        global_num_experts: int,
+        local_num_experts: int,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+        activation: MoEActivation,
+    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
+        block_m = get_mk_alignment_for_contiguous_layout()[0]
+        M_sum = compute_aligned_M(
+            M, topk, local_num_experts, block_m, expert_tokens_meta
+        )
+        assert M_sum % block_m == 0
+
+        activation_out_dim = self.adjust_N_for_activation(N, activation)
+        workspace1 = (M_sum, max(activation_out_dim, K))
+        workspace2 = (M_sum, max(N, K))
+        output = (M, K)
+        return (workspace1, workspace2, output)
+
+    def _act_mul_quant(
+        self, input: torch.Tensor, output: torch.Tensor, activation: MoEActivation
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        block_k = self._ACT_BLOCK_K
+        scale_fmt = DeepGemmQuantScaleFMT.from_oracle()
+
+        M_sum, N = input.size()
+        activation_out_dim = self.adjust_N_for_activation(N, activation)
+
+        if scale_fmt == DeepGemmQuantScaleFMT.UE8M0:
+            assert activation == MoEActivation.SILU
+            return fused_silu_mul_fp8_quant_packed(
+                input=input,
+                output_q=output,
+                group_size=block_k,
+                clamp_limit=self.gemm1_clamp_limit,
+            )
+
+        if activation == MoEActivation.SILU:
+            use_ue8m0 = scale_fmt == DeepGemmQuantScaleFMT.FLOAT32_CEIL_UE8M0
+            return silu_mul_per_token_group_quant_fp8_colmajor(
+                input=input,
+                output=output,
+                use_ue8m0=use_ue8m0,
+            )
+
+        act_out = torch.empty(
+            (M_sum, activation_out_dim), dtype=input.dtype, device=input.device
+        )
+        self.activation(activation, act_out, input)
+        return per_token_group_quant_fp8(
+            act_out, block_k, column_major_scales=True, out_q=output
+        )
+
+    def apply(
+        self,
+        output: torch.Tensor,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        activation: MoEActivation,
+        global_num_experts: int,
+        expert_map: torch.Tensor | None,
+        a1q_scale: torch.Tensor | None,
+        a2_scale: torch.Tensor | None,
+        workspace13: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+        apply_router_weight_on_input: bool,
+    ):
+        assert a1q_scale is not None
+        assert a2_scale is None
+        assert self.w1_scale is not None
+        assert self.w2_scale is not None
+
+        a1q = hidden_states
+        _, N, _ = w1.size()
+        # K comes from activations (full hidden dim), not from w1 which is
+        # packed FP4 (E, N, K//2).
+        K = a1q.size(1)
+
+        local_num_experts = w1.size(0)
+        if global_num_experts == -1:
+            global_num_experts = local_num_experts
+
+        M_sum = compute_aligned_M(
+            M=topk_ids.size(0),
+            num_topk=topk_ids.size(1),
+            local_num_experts=local_num_experts,
+            alignment=get_mk_alignment_for_contiguous_layout()[0],
+            expert_tokens_meta=expert_tokens_meta,
+        )
+
+        a1q_perm = _resize_cache(
+            workspace13.view(dtype=torch.float8_e4m3fn), (M_sum, K)
+        )
+        a1q, a1q_scale, expert_ids, inv_perm = deepgemm_moe_permute(
+            aq=a1q,
+            aq_scale=a1q_scale,
+            topk_ids=topk_ids,
+            local_num_experts=local_num_experts,
+            expert_map=expert_map,
+            expert_tokens_meta=expert_tokens_meta,
+            aq_out=a1q_perm,
+        )
+        assert a1q.size(0) == M_sum
+
+        # FC1: FP8 activations x FP4 weights
+        # DeepGEMM 2.4.2 requires FP4-packed weights as int8 (kPackedFP4).
+        mm1_out = _resize_cache(workspace2, (M_sum, N))
+        m_grouped_fp8_fp4_gemm_nt_contiguous(
+            (a1q, a1q_scale),
+            (w1.view(torch.int8), self.w1_scale),
+            mm1_out,
+            expert_ids,
+            recipe_a=(1, self._ACT_BLOCK_K),
+            recipe_b=(1, self._WEIGHT_BLOCK_K),
+        )
+
+        # SwiGLU activation + FP8 requant
+        activation_out_dim = self.adjust_N_for_activation(N, activation)
+        quant_out = _resize_cache(
+            workspace13.view(dtype=torch.float8_e4m3fn), (M_sum, activation_out_dim)
+        )
+        a2q, a2q_scale = self._act_mul_quant(
+            input=mm1_out.view(-1, N), output=quant_out, activation=activation
+        )
+
+        # FC2: FP8 activations x FP4 weights
+        mm2_out = _resize_cache(workspace2, (M_sum, K))
+        m_grouped_fp8_fp4_gemm_nt_contiguous(
+            (a2q, a2q_scale),
+            (w2.view(torch.int8), self.w2_scale),
+            mm2_out,
+            expert_ids,
+            recipe_a=(1, self._ACT_BLOCK_K),
+            recipe_b=(1, self._WEIGHT_BLOCK_K),
+        )
+
+        if apply_router_weight_on_input:
+            topk_weights = torch.ones_like(topk_weights)
+
+        deepgemm_unpermute_and_reduce(
+            a=mm2_out,
+            topk_ids=topk_ids,
+            topk_weights=topk_weights,
+            inv_perm=inv_perm,
+            expert_map=expert_map,
+            output=output,
+        )
diff --git a/vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py b/vllm/model_executor/layers/fused_moe/experts/flashinfer_cutlass_moe.py
similarity index 96%
rename from vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py
rename to vllm/model_executor/layers/fused_moe/experts/flashinfer_cutlass_moe.py
index 0d47b0f31748..ea1b48b4b25e 100644
--- a/vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/flashinfer_cutlass_moe.py
@@ -129,15 +129,8 @@ def _supports_current_device() -> bool:
             and (
                 p.is_device_capability(90)
                 or p.is_device_capability_family(100)
-                or p.is_device_capability_family(110)
-                or p.is_device_capability(120)
-                # NOTE: SM121 (DGX Spark) is excluded because the bf16
-                # unquantized CUTLASS MoE GEMM in flashinfer <= 0.6.7 has no
-                # Relu2 template instantiation and throws "Invalid activation
-                # type" on Nemotron-H. Fixed upstream by
-                # https://github.com/flashinfer-ai/flashinfer/pull/2926
-                # (merged 2026-04-01, not yet in a stable release); lift this
-                # restriction once flashinfer >= 0.6.8 is the minimum.
+                # SM110 excluded: flashinfer-ai/flashinfer#3134
+                or p.is_device_capability_family(120)
             )
             and has_flashinfer_cutlass_fused_moe()
         )
diff --git a/vllm/model_executor/layers/fused_moe/experts/gpt_oss_triton_kernels_moe.py b/vllm/model_executor/layers/fused_moe/experts/gpt_oss_triton_kernels_moe.py
index a21ddaba0755..e10514debd08 100644
--- a/vllm/model_executor/layers/fused_moe/experts/gpt_oss_triton_kernels_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/gpt_oss_triton_kernels_moe.py
@@ -1,12 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-
 import torch
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import _custom_ops as ops
-from vllm._aiter_ops import rocm_aiter_ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import (
@@ -16,6 +14,7 @@
     FusedMoEQuantConfig,
     RoutingMethodType,
 )
+from vllm.model_executor.layers.fused_moe.lora_experts_mixin import LoRAExpertsMixin
 from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
     TopKWeightAndReduceNoOP,
 )
@@ -28,8 +27,162 @@
 from vllm.triton_utils import tl, triton
 from vllm.utils.import_utils import has_triton_kernels
 
+from ..utils import swiglu_limit_func
+
 logger = init_logger(__name__)
 
+
+def _patch_make_bitmatrix_metadata() -> None:
+    """Monkey-patch make_bitmatrix_metadata to support non-power-of-2 top_k.
+
+    triton's tl.arange requires a power-of-2 range.  The original kernel
+    computes BLOCK_SIZE = BLOCK_PER_TOK * TOKS_PER_ROW (= 32 * top_k).  For
+    DeepSeek-V4 with top_k=6 this gives 192, which is not a power of 2 and
+    causes a compile error at the first forward pass.
+
+    Fix: define a drop-in replacement kernel that accepts an extra constexpr
+    BLOCK_SIZE_PADDED (next power of 2 >= BLOCK_SIZE) and uses it for the
+    tl.arange call while keeping the actual BLOCK_SIZE as the stride between
+    thread-blocks so that all flat indices into NonzeroIndx stay correct.
+    Elements beyond BLOCK_SIZE are masked out (col_indx = 0xffff) and ignored.
+
+    This function is called once at module load time and patches the function
+    inside the triton_kernels tensor module so that SparseMatrix.__post_init__
+    picks up the fixed version transparently.
+    """
+    import torch
+    import triton
+    import triton.language as tl
+
+    try:
+        from vllm.third_party.triton_kernels.tensor_details import (
+            bitmatrix as _bm,
+        )
+        from vllm.third_party.triton_kernels.tensor_details.bitmatrix import (
+            BitmatrixMetadata,
+            _keyed_add,
+            cdiv,
+        )
+        from vllm.third_party.triton_kernels.tensor_details.bitmatrix_details.sum_bitmatrix_rows import (  # noqa: E501
+            sum_bitmatrix_rows,
+        )
+    except ImportError:
+        return
+
+    @triton.jit
+    def _stage2_pow2(
+        ColSortedIndx,
+        RowSortedIndx,
+        NonzeroIndx,
+        n_tokens,
+        ColPartialSum,
+        stride_pm,
+        stride_pn,
+        ColOffs,
+        TOKS_PER_ROW: tl.constexpr,
+        BLOCK_PER_TOK: tl.constexpr,
+        BLOCK_SIZE_PADDED: tl.constexpr,
+    ):
+        # Actual number of elements per block (may not be a power of 2).
+        BLOCK_SIZE: tl.constexpr = BLOCK_PER_TOK * TOKS_PER_ROW
+        tl.static_assert(BLOCK_SIZE_PADDED <= 32768)
+        if isinstance(n_tokens, tl.tensor) and n_tokens.dtype.is_ptr():
+            n_tokens = tl.load(n_tokens)
+        nonzero_indx_size = n_tokens * TOKS_PER_ROW
+        pid_m = tl.program_id(0)
+        # Use BLOCK_SIZE_PADDED (a power of 2) for tl.arange, but stride by
+        # the actual BLOCK_SIZE so flat positions in NonzeroIndx are correct.
+        # Elements with offs_local >= BLOCK_SIZE have offs_global beyond the
+        # valid range, get col_indx = 0xffff, and are filtered by the mask
+        # below without producing any output.
+        offs_local = tl.arange(0, BLOCK_SIZE_PADDED)
+        offs_global = pid_m * BLOCK_SIZE + offs_local
+        mask = offs_global < nonzero_indx_size
+        col_indx = tl.load(NonzeroIndx + offs_global, mask=mask, other=-1).to(tl.uint32)
+        kv_pairs = ((col_indx << 16) | offs_local).to(tl.uint32)
+        kv_pairs = tl.sort(kv_pairs, 0)
+        col_indx = kv_pairs >> 16
+        offs_global = pid_m * BLOCK_SIZE + (kv_pairs & 0xFFFF)
+        mask = col_indx != 0xFFFF
+        x = kv_pairs & 0xFFFF0000 | 0x00000001
+        cols_and_inclusive_run_lengths = tl.associative_scan(x, 0, _keyed_add)
+        exclusive_run_lengths = (cols_and_inclusive_run_lengths - 1) & 0xFFFF
+        row_sorted_indx = tl.load(
+            ColPartialSum + pid_m * stride_pm + col_indx * stride_pn, mask=mask
+        )
+        row_sorted_indx += tl.load(ColOffs + col_indx, mask=mask)
+        row_sorted_indx += exclusive_run_lengths
+        tl.store(RowSortedIndx + offs_global, row_sorted_indx, mask=mask)
+        tl.store(ColSortedIndx + row_sorted_indx, offs_global, mask=mask)
+
+    def _make_bitmatrix_metadata_pow2_safe(nonzero_indx, bitmatrix):
+        assert nonzero_indx.ndim == 2
+        PARTIAL_BLOCK_M = 32
+        col_sum, col_partial_sum = sum_bitmatrix_rows(
+            bitmatrix, partials_block_size=PARTIAL_BLOCK_M
+        )
+        device = bitmatrix.device
+        n_indx = nonzero_indx.numel()
+        n_cols = bitmatrix.shape[1]
+        col_offs = torch.empty(n_cols, dtype=torch.int32, device=device)
+        combined_indx = torch.empty(n_indx * 2, dtype=torch.int32, device=device)
+        col_sorted_indx = combined_indx[:n_indx]
+        row_sorted_indx = combined_indx[n_indx:]
+        MEMSET_BLOCK = 1024
+        memset_grid = (cdiv(n_indx * 2, MEMSET_BLOCK) + n_cols + 1,)
+        _bm._bitmatrix_metadata_compute_stage1[memset_grid](
+            combined_indx,
+            n_indx * 2,
+            -1,
+            MEMSET_BLOCK,
+            col_sum,
+            col_offs,
+            col_sum.shape[0],
+            col_partial_sum,
+            col_partial_sum.shape[0],
+            col_partial_sum.stride(0),
+            col_partial_sum.stride(1),
+            BLOCK_M=512,
+            BLOCK_N=512,
+        )
+        toks_per_row = nonzero_indx.shape[-1]
+        block_size = PARTIAL_BLOCK_M * toks_per_row
+        # Next power of 2 >= block_size (required by tl.arange).
+        block_size_padded = 1 << (max(block_size, 1) - 1).bit_length()
+        compute_grid = (cdiv(bitmatrix.shape_max[0], PARTIAL_BLOCK_M),)
+        _stage2_pow2[compute_grid](
+            col_sorted_indx,
+            row_sorted_indx,
+            nonzero_indx,
+            bitmatrix.shape[0],
+            col_partial_sum,
+            col_partial_sum.stride(0),
+            col_partial_sum.stride(1),
+            col_offs,
+            TOKS_PER_ROW=toks_per_row,
+            BLOCK_PER_TOK=PARTIAL_BLOCK_M,
+            BLOCK_SIZE_PADDED=block_size_padded,
+        )
+        return BitmatrixMetadata(
+            col_sum=col_sum,
+            col_sorted_indx=col_sorted_indx,
+            row_sorted_indx=row_sorted_indx,
+        )
+
+    # The most reliable patch point: SparseMatrix.__post_init__ looks up
+    # make_bitmatrix_metadata via its own __globals__ dict (the tensor.py
+    # module dict).  Patching through __globals__ works regardless of how
+    # sys.modules maps "triton_kernels.tensor" vs
+    # "vllm.third_party.triton_kernels.tensor".
+    from triton_kernels.tensor import SparseMatrix as _SparseMatrix
+
+    _SparseMatrix.__post_init__.__globals__["make_bitmatrix_metadata"] = (
+        _make_bitmatrix_metadata_pow2_safe
+    )
+    # Also patch the bitmatrix module itself in case it is imported directly.
+    _bm.make_bitmatrix_metadata = _make_bitmatrix_metadata_pow2_safe
+
+
 use_legacy_triton_kernels = False
 
 if has_triton_kernels():
@@ -59,6 +212,8 @@
                 use_legacy_triton_kernels = True
             else:
                 raise
+        if not use_legacy_triton_kernels:
+            _patch_make_bitmatrix_metadata()
     except (AttributeError, ImportError) as e:
         logger.error(
             "Failed to import Triton kernels. Please make sure your triton "
@@ -130,35 +285,6 @@ def triton_kernel_moe_forward(
     unpadded_N_w2=None,
     unpadded_K_w2=None,
 ) -> torch.Tensor:
-    if (
-        quant_config is not None
-        and quant_config.use_mxfp4_w4a8
-        and rocm_aiter_ops.is_enabled()
-    ):
-        from aiter.ops.triton.moe_routing.routing import routing as aiter_routing
-
-        routing_data, gather_idx, scatter_idx = aiter_routing(
-            gating_output, topk, sm_first=not renormalize
-        )
-        return triton_kernel_fused_mxfp4_w4a8_experts(
-            None,
-            hidden_states,
-            w1,
-            w2,
-            routing_data,
-            gather_idx,
-            scatter_idx,
-            activation=activation.value,
-            quant_config=quant_config,
-            apply_router_weight_on_input=apply_router_weight_on_input,
-            global_num_experts=global_num_experts,
-            expert_map=expert_map,
-            unpadded_N_w1=unpadded_N_w1,
-            unpadded_K_w1=unpadded_K_w1,
-            unpadded_N_w2=unpadded_N_w2,
-            unpadded_K_w2=unpadded_K_w2,
-        )
-
     from triton_kernels.topk import topk as topk_fn
 
     sm_first = not renormalize
@@ -315,99 +441,6 @@ def triton_kernel_fused_experts(
     return output_tensor
 
 
-# This is a triton implementation of the fused_experts function
-def triton_kernel_fused_mxfp4_w4a8_experts(
-    output_tensor: torch.Tensor,
-    hidden_states: torch.Tensor,
-    w1,  # Tensor or triton_kernels.Tensor
-    w2,  # Tensor or triton_kernels.Tensor
-    routing_data,  # RoutingData
-    gather_indx,  # GatherIndx
-    scatter_indx,  # ScatterIndx
-    activation: str = "silu",
-    quant_config: FusedMoEQuantConfig | None = None,
-    swiglu_alpha: float = 1.702,
-    swiglu_limit: float = 7.0,
-    apply_router_weight_on_input: bool = False,
-    global_num_experts: int = -1,
-    expert_map: torch.Tensor | None = None,
-    a1q_scale: torch.Tensor | None = None,
-    unpadded_N_w1=None,
-    unpadded_K_w1=None,
-    unpadded_N_w2=None,
-    unpadded_K_w2=None,
-) -> torch.Tensor:
-    assert quant_config is not None
-    # type check, uint8 means mxfp4
-    assert hidden_states.dtype == torch.bfloat16
-    assert quant_config.w1_bias is None or quant_config.w1_bias.dtype == torch.float32
-    assert quant_config.w2_bias is None or quant_config.w2_bias.dtype == torch.float32
-
-    # Shape check: weights are padded (e.g. hidden_size padded for
-    # GFX950 swizzle).
-    assert hidden_states.shape[-1] == w1.shape[-2]
-    assert w2.shape[-1] == w1.shape[1]
-
-    E, _, N = w1.shape
-
-    if global_num_experts == -1:
-        global_num_experts = E
-
-    gammas = routing_data.gate_scal if routing_data else None
-
-    from aiter.ops.triton.moe_op_gemm_a8w4 import moe_gemm_a8w4
-    from aiter.ops.triton.quant_moe import downcast_to_static_fp8
-
-    assert quant_config.w1_precision is not None, (
-        "w1_precision in quant config can't be None"
-    )
-    assert quant_config.w2_precision is not None, (
-        "w2_precision in quant config can't be None"
-    )
-
-    hidden_states = downcast_to_static_fp8(
-        hidden_states, quant_config.w1_precision.flex_ctx.lhs_data.scale
-    )
-
-    intermediate_cache1 = moe_gemm_a8w4(
-        hidden_states,
-        w1.storage.data,
-        None,
-        quant_config.w1_precision.weight_scale.storage.data,
-        quant_config.w1_precision.flex_ctx.lhs_data.scale,
-        quant_config.w2_precision.flex_ctx.lhs_data.scale,
-        quant_config.w1_bias,
-        routing_data,
-        gather_indx=gather_indx,
-        gammas=gammas if apply_router_weight_on_input else None,
-        swizzle_mx_scale="CDNA4_SCALE",
-        out_dtype=torch.float8_e4m3fn,
-        apply_swiglu=True,
-        alpha=swiglu_alpha,
-        limit=swiglu_limit,
-        unpadded_N=unpadded_N_w1,
-        unpadded_K=unpadded_K_w1,
-    )
-
-    intermediate_cache3 = moe_gemm_a8w4(
-        intermediate_cache1,
-        w2.storage.data,
-        None,
-        quant_config.w2_precision.weight_scale.storage.data,
-        quant_config.w2_precision.flex_ctx.lhs_data.scale,
-        None,
-        quant_config.w2_bias,
-        routing_data,
-        scatter_indx=scatter_indx,
-        gammas=None if apply_router_weight_on_input else gammas,
-        swizzle_mx_scale="CDNA4_SCALE",
-        unpadded_N=unpadded_N_w2,
-        unpadded_K=unpadded_K_w2,
-    )
-
-    return intermediate_cache3
-
-
 def make_routing_data(
     topk_ids: torch.Tensor,
     topk_weights: torch.Tensor,
@@ -497,6 +530,8 @@ def _supports_current_device() -> bool:
             return False
         # (9,0) <= cap < (11,0) covers CUDA SM90 (Hopper), SM100+ (Blackwell)
         # and ROCm gfx942/gfx950 (which map to 9.4/9.5).
+        if not has_triton_kernels():
+            return False
         return (9, 0) <= (cap.major, cap.minor) < (11, 0)
 
     @staticmethod
@@ -654,7 +689,7 @@ def apply(
         )
 
 
-class UnfusedOAITritonExperts(BaseOAITritonExperts):
+class UnfusedOAITritonExperts(LoRAExpertsMixin, BaseOAITritonExperts):
     """
     A Triton based MoE expert class that operates on expert standard
     format and explicitly keeps the activation and reduction (moe_sum) steps
@@ -698,6 +733,37 @@ def workspace_shapes(
     def moe_sum(self, input: torch.Tensor, output: torch.Tensor):
         ops.moe_sum(input, output)
 
+    def activation(
+        self,
+        activation: MoEActivation,
+        output: torch.Tensor,
+        input: torch.Tensor,
+    ) -> None:
+        quant_config = self.quant_config or FUSED_MOE_UNQUANTIZED_CONFIG
+        if activation == MoEActivation.SWIGLUOAI:
+            alpha = (
+                quant_config.gemm1_alpha
+                if quant_config.gemm1_alpha is not None
+                else 1.702
+            )
+            limit = (
+                quant_config.gemm1_clamp_limit
+                if quant_config.gemm1_clamp_limit is not None
+                else 7.0
+            )
+            torch.ops._C.swigluoai_and_mul(output, input, alpha, limit)
+        elif (
+            activation == MoEActivation.SILU
+            and quant_config.gemm1_clamp_limit is not None
+        ):
+            swiglu_limit_func(
+                output,
+                input,
+                quant_config.gemm1_clamp_limit,
+            )
+        else:
+            super().activation(activation, output, input)
+
     def apply(
         self,
         output: torch.Tensor,
@@ -721,6 +787,7 @@ def apply(
         if quant_config is None:
             quant_config = FUSED_MOE_UNQUANTIZED_CONFIG
 
+        global_topk_ids = topk_ids
         if expert_map is not None:
             topk_ids = expert_map[topk_ids]
 
@@ -775,15 +842,45 @@ def apply(
             y=intermediate_cache1,
         )
 
+        # w13 LoRA: gather the activation input from expert-sorted
+        # intermediate_cache1, then add the LoRA delta in-place on that copy
+        # before passing it to activation — exactly mirroring the old
+        # decorator approach which modified the gathered tensor in-place.
+        act_input = intermediate_cache1.view(-1, N)[gather_indx.dst_indx]
+
+        sorted_token_ids_lora = None
+        expert_ids_lora = None
+        num_tokens_post_padded_lora = None
+        token_lora_mapping = None
+        lora_context = self._lora_context
+        if lora_context is not None:
+            (
+                sorted_token_ids_lora,
+                expert_ids_lora,
+                num_tokens_post_padded_lora,
+                token_lora_mapping,
+            ) = self.apply_w13_lora(
+                lora_context,
+                y=act_input,
+                x=hidden_states,
+                topk_ids=global_topk_ids,
+                topk_weights=topk_weights,
+                expert_map=expert_map,
+                w1=w1,
+                w2=w2,
+                num_tokens=M,
+                top_k_num=topk,
+            )
+
         self.activation(
             activation,
             intermediate_cache2,
-            intermediate_cache1.view(-1, N)[gather_indx.dst_indx],
+            act_input,
         )
 
-        # matmul_ogs grouped reduction fuse sum across multiple experts:
+        # matmul_ogs grouped reduction fuses sum across multiple experts:
         # y[dst_indx // n_expts_act, :] += x
-        # Need to set n_expts_act to 1 to unfuse moe_sum
+        # Set n_expts_act to 1 to unfuse the sum so we can do it manually via moe_sum.
         routing_data.n_expts_act = 1
 
         matmul_ogs(
@@ -797,6 +894,24 @@ def apply(
             y=intermediate_cache3,
         )
 
+        # w2 LoRA: after matmul_ogs with scatter_indx, intermediate_cache3 is
+        # in token-topk order, matching the (M, topk, K) layout add_lora_w2 expects.
+        if lora_context is not None:
+            self.apply_w2_lora(
+                lora_context,
+                y=intermediate_cache3.view(-1, topk, K),
+                x=intermediate_cache2,
+                topk_weights=topk_weights,
+                sorted_token_ids_lora=sorted_token_ids_lora,
+                expert_ids_lora=expert_ids_lora,
+                num_tokens_post_padded_lora=num_tokens_post_padded_lora,
+                token_lora_mapping=token_lora_mapping,
+                num_tokens=M,
+                w1=w1,
+                w2=w2,
+                top_k_num=topk,
+            )
+
         self.moe_sum(intermediate_cache3.view(-1, topk, K), output)
 
 
@@ -829,6 +944,8 @@ def _supports_current_device() -> bool:
             return False
         # (9,0) <= cap < (11,0) covers CUDA SM90 (Hopper), SM100+ (Blackwell)
         # and ROCm gfx942/gfx950 (which map to 9.4/9.5).
+        if not has_triton_kernels():
+            return False
         return (9, 0) <= (cap.major, cap.minor) < (11, 0)
 
     @staticmethod
diff --git a/vllm/model_executor/layers/fused_moe/fused_marlin_moe.py b/vllm/model_executor/layers/fused_moe/experts/marlin_moe.py
similarity index 85%
rename from vllm/model_executor/layers/fused_moe/fused_marlin_moe.py
rename to vllm/model_executor/layers/fused_moe/experts/marlin_moe.py
index 6c916cf3cb66..621b33dd8f30 100644
--- a/vllm/model_executor/layers/fused_moe/fused_marlin_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/marlin_moe.py
@@ -17,6 +17,7 @@
     FusedMoEParallelConfig,
     FusedMoEQuantConfig,
 )
+from vllm.model_executor.layers.fused_moe.lora_experts_mixin import LoRAExpertsMixin
 from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
     batched_moe_align_block_size,
     moe_align_block_size,
@@ -28,6 +29,7 @@
 from vllm.model_executor.layers.fused_moe.utils import (
     _resize_cache,
     disable_inplace,
+    swiglu_limit_func,
 )
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     get_marlin_input_dtype,
@@ -40,6 +42,8 @@
     kFp8Static128BlockSym,
     kFp8StaticChannelSym,
     kFp8StaticTensorSym,
+    kInt4Static,
+    kInt8Static,
     kMxfp4Static,
     kMxfp8Static,
     kNvfp4Static,
@@ -85,6 +89,7 @@ def _fused_marlin_moe(
     output: torch.Tensor | None = None,
     input_dtype: torch.dtype | None = None,
     is_k_full: bool = True,
+    clamp_limit: float | None = None,
 ) -> torch.Tensor:
     assert hidden_states.ndim == 2
     M, K = hidden_states.size()
@@ -152,11 +157,18 @@ def _fused_marlin_moe(
         use_fp32_reduce=True,
         is_zp_float=False,
     )
-    activation_func(
-        activation,
-        intermediate_cache2,
-        intermediate_cache1.view(-1, w13_num_shards * N),
-    )
+    if clamp_limit is not None and activation == MoEActivation.SILU:
+        swiglu_limit_func(
+            intermediate_cache2,
+            intermediate_cache1.view(-1, w13_num_shards * N),
+            clamp_limit,
+        )
+    else:
+        activation_func(
+            activation,
+            intermediate_cache2,
+            intermediate_cache1.view(-1, w13_num_shards * N),
+        )
 
     if output is None:
         output = intermediate_cache3
@@ -244,6 +256,7 @@ def fused_marlin_moe(
     output: torch.Tensor | None = None,
     input_dtype: torch.dtype | None = None,
     inplace: bool = False,
+    clamp_limit: float | None = None,
 ) -> torch.Tensor:
     """
     This function computes a Mixture of Experts (MoE) layer using two sets of
@@ -360,6 +373,7 @@ def fused_marlin_moe(
         output=None,
         input_dtype=input_dtype,
         is_k_full=is_k_full,
+        clamp_limit=clamp_limit,
     ).view(-1, topk, K)
 
     if output is None:
@@ -554,6 +568,7 @@ def __init__(
         self.w2_g_idx_sort_indices = w2_g_idx_sort_indices
         self.is_k_full = is_k_full
         self.input_dtype = get_marlin_input_dtype()
+        self.gemm1_clamp_limit = quant_config.gemm1_clamp_limit
 
         super().__init__(
             moe_config=moe_config,
@@ -585,6 +600,8 @@ def _supports_quant_scheme(
             kMxfp4Static,
             kMxfp8Static,
             kNvfp4Static,
+            kInt4Static,
+            kInt8Static,
         ]
         return weight_key in SUPPORTED_W
 
@@ -595,10 +612,12 @@ def _supports_activation(activation: MoEActivation) -> bool:
         return activation in [
             MoEActivation.SILU,
             MoEActivation.GELU,
+            MoEActivation.GELU_TANH,
             MoEActivation.SWIGLUOAI,
             MoEActivation.SWIGLUSTEP,
             MoEActivation.SILU_NO_MUL,
             MoEActivation.GELU_NO_MUL,
+            MoEActivation.GELU_TANH_NO_MUL,
             MoEActivation.RELU2_NO_MUL,
         ]
 
@@ -651,7 +670,7 @@ def moe_problem_size(
         return E, M, N, K, topk
 
 
-class MarlinExperts(MarlinExpertsBase):
+class MarlinExperts(LoRAExpertsMixin, MarlinExpertsBase):
     """Marlin-based fused MoE expert implementation."""
 
     def supports_expert_map(self) -> bool:
@@ -716,6 +735,107 @@ def apply(
     ):
         assert self.w1_scale is not None
         assert self.w2_scale is not None
+
+        ctx = self._lora_context
+        if ctx is None:
+            fused_marlin_moe(
+                hidden_states=hidden_states,
+                w1=w1,
+                w2=w2,
+                bias1=self.w1_bias,
+                bias2=self.w2_bias,
+                w1_scale=self.w1_scale,
+                w2_scale=self.w2_scale,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                global_scale1=self.g1_alphas,
+                global_scale2=self.g2_alphas,
+                quant_type_id=self.quant_type_id,
+                apply_router_weight_on_input=apply_router_weight_on_input,
+                global_num_experts=global_num_experts,
+                activation=activation,
+                activation_func=self.activation,
+                moe_sum=self.moe_sum,
+                expert_map=expert_map,
+                output=output,
+                # Workspaces are swapped in workspace_shapes() to account for proper
+                # output buffer allocation. Please refer to workspace_shapes().
+                intermediate_cache13=workspace2,
+                intermediate_cache2=workspace13,
+                g_idx1=self.w13_g_idx,
+                g_idx2=self.w2_g_idx,
+                sort_indices1=self.w13_g_idx_sort_indices,
+                sort_indices2=self.w2_g_idx_sort_indices,
+                is_k_full=self.is_k_full,
+                input_dtype=self.input_dtype,
+            )
+            return
+
+        # LoRA path: wrap activation_func and moe_sum to inject LoRA at the
+        # two natural injection points.
+        #
+        # Marlin uses moe_align_block_size (same as TritonExperts) so
+        # intermediate_cache1 is indexed by flat (token, expert) pair index,
+        # which is compatible with add_lora_fused_moe's scatter mechanism.
+
+        M = hidden_states.size(0)
+        top_k_num = topk_ids.size(1)
+        lora_state: dict = {}
+
+        def activation_with_lora(
+            act_enum: MoEActivation,
+            act_output: torch.Tensor,
+            act_input: torch.Tensor,
+        ) -> None:
+            # act_input  = intermediate_cache1 (M*topk, 2N for gated)
+            # act_output = intermediate_cache2 (M*topk, N)
+
+            (
+                sorted_token_ids_lora,
+                expert_ids_lora,
+                num_tokens_post_padded_lora,
+                token_lora_mapping,
+            ) = self.apply_w13_lora(
+                ctx,
+                y=act_input,
+                x=hidden_states,
+                topk_ids=topk_ids,
+                topk_weights=topk_weights,
+                expert_map=expert_map,
+                w1=w1,
+                w2=w2,
+                num_tokens=M,
+                top_k_num=top_k_num,
+            )
+            lora_state.update(
+                {
+                    "sorted": sorted_token_ids_lora,
+                    "eids": expert_ids_lora,
+                    "npad": num_tokens_post_padded_lora,
+                    "tlm": token_lora_mapping,
+                }
+            )
+            self.activation(act_enum, act_output, act_input)
+            lora_state["cache2"] = act_output
+
+        def moe_sum_with_lora(moe_out: torch.Tensor, out: torch.Tensor) -> None:
+            # moe_out shape: (M, topk, K)
+            self.apply_w2_lora(
+                ctx,
+                y=moe_out,
+                x=lora_state["cache2"],
+                topk_weights=topk_weights,
+                sorted_token_ids_lora=lora_state["sorted"],
+                expert_ids_lora=lora_state["eids"],
+                num_tokens_post_padded_lora=lora_state["npad"],
+                token_lora_mapping=lora_state["tlm"],
+                num_tokens=M,
+                w1=w1,
+                w2=w2,
+                top_k_num=top_k_num,
+            )
+            self.moe_sum(moe_out, out)
+
         return fused_marlin_moe(
             hidden_states=hidden_states,
             w1=w1,
@@ -732,12 +852,10 @@ def apply(
             apply_router_weight_on_input=apply_router_weight_on_input,
             global_num_experts=global_num_experts,
             activation=activation,
-            activation_func=self.activation,
-            moe_sum=self.moe_sum,
+            activation_func=activation_with_lora,
+            moe_sum=moe_sum_with_lora,
             expert_map=expert_map,
             output=output,
-            # Workspaces are swapped in workspace_shapes() to account for proper
-            # output buffer allocation. Please refer to workspace_shapes().
             intermediate_cache13=workspace2,
             intermediate_cache2=workspace13,
             g_idx1=self.w13_g_idx,
@@ -746,6 +864,7 @@ def apply(
             sort_indices2=self.w2_g_idx_sort_indices,
             is_k_full=self.is_k_full,
             input_dtype=self.input_dtype,
+            clamp_limit=self.gemm1_clamp_limit,
         )
 
     def moe_sum(self, input: torch.Tensor, output: torch.Tensor) -> None:
diff --git a/vllm/model_executor/layers/fused_moe/experts/nvfp4_emulation_moe.py b/vllm/model_executor/layers/fused_moe/experts/nvfp4_emulation_moe.py
new file mode 100644
index 000000000000..de5b45ccb87b
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/experts/nvfp4_emulation_moe.py
@@ -0,0 +1,164 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+NVFP4 quantization emulation for MoE.
+
+This file implements NVFP4 emulation for NVFP4 MOE in case the hardware used does not
+natively support NVFP4 MOE.
+
+Weights are dequantized on the fly during each forward, we fall back to calling
+`TritonExperts` using BF16, and fake NVFP4 quantize-dequantize
+is applied on `a13`, `a2`.
+"""
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEQuantConfig,
+)
+from vllm.model_executor.layers.fused_moe.experts.triton_moe import TritonExperts
+from vllm.model_executor.layers.fused_moe.utils import moe_kernel_quantize_input
+from vllm.model_executor.layers.quantization.utils.nvfp4_emulation_utils import (
+    dequantize_to_dtype,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    QuantKey,
+    kNvfp4Dynamic,
+    kNvfp4Static,
+)
+
+logger = init_logger(__name__)
+
+
+class Nvfp4QuantizationEmulationTritonExperts(TritonExperts):
+    """
+    Extension of TritonExperts to support emulated NVFP4 MoE experts.
+
+    It may be used for NVFP4 models when the device does not have
+    native support for this dtype.
+    """
+
+    def __init__(
+        self,
+        moe_config: FusedMoEConfig,
+        quant_config: FusedMoEQuantConfig,
+    ):
+        super().__init__(moe_config, quant_config)
+        logger.warning_once(
+            "Using Nvfp4QuantizationEmulationTritonExperts MOE backend. This will"
+            " dequantize weights on the fly and may be slower than native"
+            " quantized MOE. Consider using a device with native quantization"
+            " support (e.g. Nvidia Blackwell) for better performance."
+        )
+
+        # `TritonExperts.apply` expects pre-dequantized weights,
+        # which we handle in `apply` below.
+        self.w1_scale_val = self.quant_config.w1_scale
+        self.w2_scale_val = self.quant_config.w2_scale
+
+        self.quant_config._w1.scale = None
+        self.quant_config._w2.scale = None
+
+        self.quantization_emulation = True
+
+    @property
+    def quant_dtype(self) -> torch.dtype | str | None:
+        return "nvfp4"
+
+    @property
+    def expects_unquantized_inputs(self) -> bool:
+        return True
+
+    @staticmethod
+    def _supports_quant_scheme(
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        return (weight_key, activation_key) == (kNvfp4Static, kNvfp4Dynamic)
+
+    def apply(
+        self,
+        output: torch.Tensor,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        activation: MoEActivation,
+        global_num_experts: int,
+        expert_map: torch.Tensor | None,
+        a1q_scale: torch.Tensor | None,
+        a2_scale: torch.Tensor | None,
+        workspace13: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+        apply_router_weight_on_input: bool,
+    ):
+        """
+        Apply emulated quantized MoE computation.
+
+        This dequantizes the weights on the fly and calls fused_experts_impl
+        with activation quantization support.
+        """
+        # Dequantize weights if they are quantized
+        # For NVFP4, weights are packed in uint8 format
+        # w1 shape: [num_experts, 2*intermediate_size, hidden_size//2]
+        # w2 shape: [num_experts, hidden_size, intermediate_size//2]
+        assert w1.dtype == torch.uint8
+        assert w2.dtype == torch.uint8
+
+        # Dequantize w1 from packed NVFP4 to fp16/bf16
+        w13_global_scale = self.quant_config.g1_alphas
+
+        w1_dequant = dequantize_to_dtype(
+            tensor_fp4=w1,
+            tensor_sf=self.w1_scale_val,
+            global_scale=w13_global_scale,
+            dtype=hidden_states.dtype,
+            block_size=16,
+            swizzle=False,
+        )
+
+        # Dequantize w2 from packed NVFP4 to fp16/bf16
+        w2_global_scale = self.quant_config.g2_alphas
+
+        w2_dequant = dequantize_to_dtype(
+            tensor_fp4=w2,
+            tensor_sf=self.w2_scale_val,
+            global_scale=w2_global_scale,
+            dtype=hidden_states.dtype,
+            block_size=16,
+            swizzle=False,
+        )
+
+        hidden_states, _ = moe_kernel_quantize_input(
+            A=hidden_states,
+            A_scale=self.quant_config.a1_gscale,
+            quant_dtype="nvfp4",
+            per_act_token_quant=False,
+            quantization_emulation=True,
+        )
+
+        # Activation quantization/dequantization is deferred to
+        # `moe_kernel_quantize_input` in TritonExperts.apply.
+        super().apply(
+            output=output,
+            hidden_states=hidden_states,
+            w1=w1_dequant,
+            w2=w2_dequant,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            activation=activation,
+            global_num_experts=global_num_experts,
+            expert_map=expert_map,
+            a1q_scale=None,
+            a2_scale=self.quant_config.a2_gscale,
+            workspace13=workspace13,
+            workspace2=workspace2,
+            expert_tokens_meta=expert_tokens_meta,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+        )
diff --git a/vllm/model_executor/layers/fused_moe/experts/ocp_mx_emulation_moe.py b/vllm/model_executor/layers/fused_moe/experts/ocp_mx_emulation_moe.py
new file mode 100644
index 000000000000..feb8c2ea769b
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/experts/ocp_mx_emulation_moe.py
@@ -0,0 +1,186 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+OCP MX quantization emulation for MoE.
+
+This file implements OCP MX (MXFP4/MXFP6) emulation for MoE in case the
+hardware used does not natively support OCP MX MoE.
+
+Weights are dequantized on the fly during each forward, we fall back to calling
+`TritonExperts` using BF16, and fake OCP MX quantize-dequantize
+is applied on activations via `moe_kernel_quantize_input`.
+"""
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEQuantConfig,
+)
+from vllm.model_executor.layers.fused_moe.experts.triton_moe import TritonExperts
+from vllm.model_executor.layers.fused_moe.utils import moe_kernel_quantize_input
+from vllm.model_executor.layers.quantization.utils.mxfp4_utils import dequant_mxfp4
+from vllm.model_executor.layers.quantization.utils.mxfp6_utils import dequant_mxfp6
+from vllm.model_executor.layers.quantization.utils.ocp_mx_utils import (
+    OCP_MX_Scheme,
+)
+
+logger = init_logger(__name__)
+
+
+class OCP_MXQuantizationEmulationTritonExperts(TritonExperts):
+    """
+    Extension of TritonExperts to support emulated OCP MX MoE experts.
+
+    It may be used for OCP MX (MXFP4/MXFP6) models when the device does not
+    have native support for these dtypes.
+    """
+
+    def __init__(
+        self,
+        moe_config: FusedMoEConfig,
+        quant_config: FusedMoEQuantConfig,
+    ):
+        super().__init__(moe_config, quant_config)
+        logger.warning_once(
+            "Using OCP_MXQuantizationEmulationTritonExperts MOE backend. This"
+            " will dequantize weights on the fly and may be slower than native"
+            " quantized MOE. Consider using a device with native OCP MX"
+            " quantization support for better performance."
+        )
+
+        self.ocp_mx_scheme = quant_config.ocp_mx_scheme
+        assert self.ocp_mx_scheme is not None, (
+            "ocp_mx_scheme must be set in quant_config for"
+            " OCP_MXQuantizationEmulationTritonExperts"
+        )
+
+        # `TritonExperts.apply` expects pre-dequantized weights,
+        # which we handle in `apply` below.
+        self.w1_scale_val = self.quant_config.w1_scale
+        self.w2_scale_val = self.quant_config.w2_scale
+
+        self.quant_config._w1.scale = None
+        self.quant_config._w2.scale = None
+
+        self.quantization_emulation = True
+
+        if self.ocp_mx_scheme in {
+            OCP_MX_Scheme.w_mxfp4_a_mxfp4,
+        }:
+            # Weight has to be dequantized for mxfp4 emulation.
+            self._quant_dtype = "mxfp4"
+        elif self.ocp_mx_scheme in [
+            OCP_MX_Scheme.w_mxfp4_a_mxfp6_e3m2,
+            OCP_MX_Scheme.w_mxfp4_a_mxfp6_e2m3,
+            OCP_MX_Scheme.w_mxfp6_e3m2_a_mxfp6_e3m2,
+            OCP_MX_Scheme.w_mxfp6_e2m3_a_mxfp6_e2m3,
+        ]:
+            self._quant_dtype = "mxfp6"
+        elif self.ocp_mx_scheme in [
+            OCP_MX_Scheme.w_mxfp4_a_fp8,
+            OCP_MX_Scheme.w_mxfp6_e3m2_a_fp8,
+        ]:
+            # TODO: double check this one
+            self._quant_dtype = "mxfp8"
+
+    @property
+    def quant_dtype(self) -> torch.dtype | str | None:
+        return self._quant_dtype
+
+    @property
+    def expects_unquantized_inputs(self) -> bool:
+        return True
+
+    @staticmethod
+    def _supports_quant_scheme(
+        weight_key,
+        activation_key,
+    ) -> bool:
+        # This class is used for emulation only - the oracle selects it
+        # directly rather than via quant scheme matching.
+        return True
+
+    def _dequantize_weights(
+        self,
+        w: torch.Tensor,
+        w_scale: torch.Tensor,
+        dtype: torch.dtype,
+    ) -> torch.Tensor:
+        """Dequantize weights based on the OCP MX scheme."""
+        if self.ocp_mx_scheme.startswith("w_mxfp4"):  # type: ignore[union-attr]
+            return dequant_mxfp4(w, w_scale, dtype)
+        elif self.ocp_mx_scheme.startswith("w_mxfp6_e3m2"):  # type: ignore[union-attr]
+            return dequant_mxfp6(w, w_scale, quant_dtype="fp6_e3m2", float_dtype=dtype)
+        elif self.ocp_mx_scheme.startswith("w_mxfp6_e2m3"):  # type: ignore[union-attr]
+            return dequant_mxfp6(w, w_scale, quant_dtype="fp6_e2m3", float_dtype=dtype)
+        else:
+            raise NotImplementedError(f"Unsupported ocp_mx_scheme={self.ocp_mx_scheme}")
+
+    def apply(
+        self,
+        output: torch.Tensor,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        activation: MoEActivation,
+        global_num_experts: int,
+        expert_map: torch.Tensor | None,
+        a1q_scale: torch.Tensor | None,
+        a2_scale: torch.Tensor | None,
+        workspace13: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+        apply_router_weight_on_input: bool,
+    ):
+        """
+        Apply emulated quantized MoE computation.
+
+        This dequantizes the weights on the fly and calls TritonExperts.apply
+        with activation quantization support.
+        """
+        assert w1.dtype == torch.uint8
+        assert w2.dtype == torch.uint8
+
+        # Dequantize w1 and w2 from packed OCP MX format to bf16/fp16
+        w1_dequant = self._dequantize_weights(
+            w1, self.w1_scale_val, hidden_states.dtype
+        )
+        w2_dequant = self._dequantize_weights(
+            w2, self.w2_scale_val, hidden_states.dtype
+        )
+
+        # Apply activation QDQ if needed by the OCP MX scheme
+        hidden_states, _ = moe_kernel_quantize_input(
+            A=hidden_states,
+            A_scale=None,
+            quant_dtype=self.quant_config.quant_dtype,
+            per_act_token_quant=False,
+            ocp_mx_scheme=self.ocp_mx_scheme,
+            quantization_emulation=True,
+        )
+
+        # Activation quantization/dequantization is deferred to
+        # `moe_kernel_quantize_input` in TritonExperts.apply.
+        super().apply(
+            output=output,
+            hidden_states=hidden_states,
+            w1=w1_dequant,
+            w2=w2_dequant,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            activation=activation,
+            global_num_experts=global_num_experts,
+            expert_map=expert_map,
+            a1q_scale=None,
+            a2_scale=None,
+            workspace13=workspace13,
+            workspace2=workspace2,
+            expert_tokens_meta=expert_tokens_meta,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+        )
diff --git a/vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py b/vllm/model_executor/layers/fused_moe/experts/rocm_aiter_moe.py
similarity index 86%
rename from vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py
rename to vllm/model_executor/layers/fused_moe/experts/rocm_aiter_moe.py
index d24bda101ffa..dd16f6bc24d4 100644
--- a/vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/rocm_aiter_moe.py
@@ -17,6 +17,7 @@
 from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
     TopKWeightAndReduceNoOP,
 )
+from vllm.model_executor.layers.fused_moe.utils import disable_inplace
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
     kFp8Dynamic128Sym,
@@ -108,6 +109,55 @@ def init_aiter_topK_meta_data(
     aiter_topK_meta_data = (total_topk_weights, total_topk_ids)
 
 
+def inject_shared_expert_weights(
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    topk: int,
+    num_fused_shared_experts: int,
+    shared_expert_weights: torch.Tensor | None = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Merge routed topk results with the shared expert buffer and inject
+    dynamic per-token shared expert gate values for AITER fusion.
+
+    For routers that already return the combined buffer (e.g. GroupedTopKRouter
+    via rocm_aiter_grouped_topk), only the dynamic weight injection is needed.
+    For routers that return only routed slots (e.g. FusedTopKRouter), this also
+    copies the routed results into the pre-allocated combined buffer.
+    """
+    if num_fused_shared_experts == 0:
+        return topk_weights, topk_ids
+
+    assert aiter_topK_meta_data is not None, (
+        "aiter_topK_meta_data is not initialized but "
+        "num_fused_shared_experts > 0. Ensure init_aiter_topK_meta_data "
+        "is called before routing."
+    )
+
+    total_topk_weights, total_topk_ids = aiter_topK_meta_data
+    token = topk_weights.shape[0]
+
+    assert total_topk_weights.shape[0] >= token, (
+        f"AITER topK meta data supports {total_topk_weights.shape[0]} "
+        f"tokens, but got {token} tokens."
+    )
+
+    total_topk_weights_slice = total_topk_weights[:token]
+    total_topk_ids_slice = total_topk_ids[:token]
+
+    if topk_weights.shape[1] == topk:
+        total_topk_weights_slice[:, :topk] = topk_weights
+        total_topk_ids_slice[:, :topk] = topk_ids
+        topk_weights = total_topk_weights_slice
+        topk_ids = total_topk_ids_slice
+
+    if shared_expert_weights is not None:
+        topk_weights[:, topk : topk + num_fused_shared_experts] = shared_expert_weights[
+            :token
+        ]
+
+    return topk_weights, topk_ids
+
+
 def rocm_aiter_grouped_topk(
     hidden_states: torch.Tensor,
     gating_output: torch.Tensor,
@@ -152,7 +202,7 @@ def rocm_aiter_grouped_topk(
     if e_score_correction_bias is not None:
         rocm_aiter_ops.biased_grouped_topk(
             gating_output,
-            e_score_correction_bias.to(gating_output.dtype),
+            e_score_correction_bias,
             topk_weights,
             topk_ids,
             num_expert_group,
@@ -252,7 +302,8 @@ def rocm_aiter_fused_experts(
 
     else:
         quant_method = QuantMethod.NO.value
-        # mxfp4: both w4a4 (quark) and w4a16 (oracle CK) use BLOCK_1X32
+        # mxfp4 i.e. w4a4, w4a16 uses BLOCK_1X32
+        # mxfp6 and mxfp8 are unsupported in AITER currently and use emulation instead
         if quant_config.use_mxfp4_w4a4 or quant_config.use_mxfp4_w4a16:
             quant_method = QuantMethod.BLOCK_1X32.value
         # w8a8 block-scaled
@@ -437,4 +488,16 @@ def apply(
             num_local_tokens=num_local_tokens,
             output_dtype=output.dtype,
         )
-        output.copy_(result)
+        # avoid redundant copy when output is a view of the result
+        if (
+            output.shape == result.shape
+            and output.dtype == result.dtype
+            and output.device == result.device
+            and output.is_contiguous()
+            and result.is_contiguous()
+            and output._base is None
+            and disable_inplace()
+        ):
+            output.set_(result)
+        else:
+            output.copy_(result)
diff --git a/vllm/model_executor/layers/fused_moe/experts/triton_moe.py b/vllm/model_executor/layers/fused_moe/experts/triton_moe.py
new file mode 100644
index 000000000000..e99f307ec0ec
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/experts/triton_moe.py
@@ -0,0 +1,522 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Triton-based MoE expert implementations."""
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEParallelConfig,
+    FusedMoEQuantConfig,
+)
+from vllm.model_executor.layers.fused_moe.fused_moe import (
+    _prepare_expert_assignment,
+    invoke_fused_moe_triton_kernel,
+    invoke_fused_moe_wna16_triton_kernel,
+    try_get_optimal_moe_config,
+)
+from vllm.model_executor.layers.fused_moe.lora_experts_mixin import (
+    LoRAExpertsMixin,
+)
+from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
+    moe_align_block_size,
+)
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceNoOP,
+)
+from vllm.model_executor.layers.fused_moe.utils import (
+    _resize_cache,
+    moe_kernel_quantize_input,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    QuantKey,
+    kFp8Dynamic128Sym,
+    kFp8DynamicTensorSym,
+    kFp8DynamicTokenSym,
+    kFp8Static128BlockSym,
+    kFp8StaticChannelSym,
+    kFp8StaticTensorSym,
+    kInt8DynamicTokenSym,
+    kInt8StaticChannelSym,
+)
+from vllm.platforms import current_platform
+from vllm.triton_utils import tl
+
+
+class TritonExperts(LoRAExpertsMixin, mk.FusedMoEExpertsModular):
+    """Triton-based fused MoE expert implementation."""
+
+    def __init__(
+        self,
+        moe_config: FusedMoEConfig,
+        quant_config: FusedMoEQuantConfig,
+    ):
+        # Whether quantized MOE runs natively, or through
+        # higher-precision + activation QDQ.
+        self.quantization_emulation = False
+        super().__init__(moe_config, quant_config)
+
+    @staticmethod
+    def activation_format() -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.Standard
+
+    @staticmethod
+    def _supports_current_device() -> bool:
+        return current_platform.is_cuda_alike() or current_platform.is_xpu()
+
+    @staticmethod
+    def _supports_no_act_and_mul() -> bool:
+        return True
+
+    @staticmethod
+    def _supports_quant_scheme(
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        # INT8 requires at least 7.5 (Turing).
+        device_supports_int8 = (
+            current_platform.is_cuda()
+            and current_platform.has_device_capability((7, 5))
+        )
+
+        supported: list[tuple[QuantKey | None, QuantKey | None]] = [(None, None)]
+        if device_supports_int8:
+            supported.append((kInt8StaticChannelSym, kInt8DynamicTokenSym))
+        if current_platform.supports_fp8():
+            supported += [
+                (kFp8Static128BlockSym, kFp8Dynamic128Sym),
+                (kFp8StaticChannelSym, kFp8DynamicTokenSym),
+                (kFp8StaticTensorSym, kFp8DynamicTokenSym),
+                (kFp8StaticTensorSym, kFp8StaticTensorSym),
+                (kFp8StaticTensorSym, kFp8DynamicTensorSym),
+            ]
+        return (weight_key, activation_key) in supported
+
+    @staticmethod
+    def _supports_activation(activation: MoEActivation) -> bool:
+        return activation in [
+            MoEActivation.SILU,
+            MoEActivation.GELU,
+            MoEActivation.GELU_TANH,
+            MoEActivation.SWIGLUOAI,
+            MoEActivation.SWIGLUSTEP,
+            MoEActivation.SILU_NO_MUL,
+            MoEActivation.GELU_NO_MUL,
+            MoEActivation.GELU_TANH_NO_MUL,
+            MoEActivation.RELU2_NO_MUL,
+        ]
+
+    @staticmethod
+    def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:
+        return not (
+            moe_parallel_config.use_fi_nvl_two_sided_kernels
+            or moe_parallel_config.use_fi_nvl_one_sided_kernels
+        )
+
+    @staticmethod
+    def _supports_batch_invariance():
+        return True
+
+    def supports_expert_map(self) -> bool:
+        return True
+
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        return TopKWeightAndReduceNoOP()
+
+    def workspace_shapes(
+        self,
+        M: int,
+        N: int,
+        K: int,
+        topk: int,
+        global_num_experts: int,
+        local_num_experts: int,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+        activation: MoEActivation,
+    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
+        activation_out_dim = self.adjust_N_for_activation(N, activation)
+        workspace1 = (M, topk, max(activation_out_dim, K))
+        workspace2 = (M, topk, max(N, K))
+        output = (M, K)
+        return (workspace1, workspace2, output)
+
+    def apply(
+        self,
+        output: torch.Tensor,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        activation: MoEActivation,
+        global_num_experts: int,
+        expert_map: torch.Tensor | None,
+        a1q_scale: torch.Tensor | None,
+        a2_scale: torch.Tensor | None,
+        workspace13: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+        apply_router_weight_on_input: bool,
+    ):
+        # Check constraints.
+        if self.quant_config.use_int4_w4a16:
+            assert hidden_states.size(-1) // 2 == w1.size(2), "Hidden size mismatch"
+        else:
+            assert hidden_states.size(-1) == w1.size(2), (
+                f"Hidden size mismatch {hidden_states.size(-1)} != {w1.size(2)}"
+            )
+
+        assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
+        assert hidden_states.dim() == 2
+        assert w1.stride(-1) == 1, "Stride of last dimension must be 1"
+        assert w2.stride(-1) == 1, "Stride of last dimension must be 1"
+        assert hidden_states.dtype in [
+            torch.float32,
+            torch.float16,
+            torch.bfloat16,
+            torch.float8_e4m3fn,
+            torch.float8_e4m3fnuz,
+        ]
+
+        E, num_tokens, N, K, top_k_num = self.moe_problem_size(
+            hidden_states, w1, w2, topk_ids
+        )
+
+        if global_num_experts == -1:
+            global_num_experts = E
+
+        config = try_get_optimal_moe_config(
+            w1.size(),
+            w2.size(),
+            top_k_num,
+            self.quant_config.config_name(hidden_states.dtype),
+            num_tokens,
+            block_shape=self.block_shape,
+        )
+
+        if hidden_states.dtype == torch.bfloat16:
+            compute_type = tl.bfloat16
+        elif hidden_states.dtype == torch.float16:
+            compute_type = tl.float16
+        elif hidden_states.dtype == torch.float32:
+            compute_type = tl.float32
+        elif (
+            hidden_states.dtype == torch.float8_e4m3fn
+            or hidden_states.dtype == torch.float8_e4m3fnuz
+        ):
+            compute_type = tl.bfloat16
+        else:
+            raise ValueError(f"Unsupported compute_type: {hidden_states.dtype}")
+
+        # Note that the output tensor might be in workspace1
+        intermediate_cache1 = _resize_cache(workspace2, (num_tokens, top_k_num, N))
+        cache2_dim = self.adjust_N_for_activation(N, activation)
+        intermediate_cache2 = _resize_cache(
+            workspace13, (num_tokens * top_k_num, cache2_dim)
+        )
+        intermediate_cache3 = _resize_cache(workspace2, (num_tokens, top_k_num, K))
+
+        sorted_token_ids, expert_ids, num_tokens_post_padded = (
+            _prepare_expert_assignment(
+                topk_ids,
+                config,
+                num_tokens,
+                top_k_num,
+                global_num_experts,
+                expert_map,
+                use_int8_w8a16=self.quant_config.use_int8_w8a16,
+                use_int4_w4a16=self.quant_config.use_int4_w4a16,
+                block_shape=self.block_shape,
+            )
+        )
+
+        invoke_fused_moe_triton_kernel(
+            hidden_states,
+            w1,
+            intermediate_cache1,
+            a1q_scale,
+            self.w1_scale,
+            None,  # topk_weights
+            sorted_token_ids,
+            expert_ids,
+            num_tokens_post_padded,
+            False,  # mul_routed_weights
+            top_k_num,
+            config,
+            compute_type=compute_type,
+            use_fp8_w8a8=self.quant_config.use_fp8_w8a8,
+            use_int8_w8a8=self.quant_config.use_int8_w8a8,
+            use_int8_w8a16=self.quant_config.use_int8_w8a16,
+            use_int4_w4a16=self.quant_config.use_int4_w4a16,
+            per_channel_quant=self.per_act_token_quant,
+            block_shape=self.block_shape,
+            B_bias=self.w1_bias,
+        )
+
+        # LoRA w13: applied to intermediate_cache1 before activation, using
+        # hidden_states as the lora_a input.  moe_lora_align_block_size is
+        # called once here and results reused for the w2 LoRA below.
+        sorted_token_ids_lora = None
+        expert_ids_lora = None
+        num_tokens_post_padded_lora = None
+        token_lora_mapping = None
+        lora_context = self._lora_context
+        if lora_context is not None:
+            (
+                sorted_token_ids_lora,
+                expert_ids_lora,
+                num_tokens_post_padded_lora,
+                token_lora_mapping,
+            ) = self.apply_w13_lora(
+                lora_context,
+                y=intermediate_cache1,
+                x=hidden_states,
+                topk_ids=topk_ids,
+                topk_weights=topk_weights,
+                expert_map=expert_map,
+                w1=w1,
+                w2=w2,
+                num_tokens=num_tokens,
+                top_k_num=top_k_num,
+            )
+
+        self.activation(
+            activation, intermediate_cache2, intermediate_cache1.view(-1, N)
+        )
+
+        a2q_scale: torch.Tensor | None = None
+
+        qintermediate_cache2, a2q_scale = moe_kernel_quantize_input(
+            intermediate_cache2,
+            a2_scale,
+            self.quant_dtype,
+            self.per_act_token_quant,
+            self.block_shape,
+            quantization_emulation=self.quantization_emulation,
+        )
+
+        invoke_fused_moe_triton_kernel(
+            qintermediate_cache2,
+            w2,
+            intermediate_cache3,
+            a2q_scale,
+            self.w2_scale,
+            topk_weights,
+            sorted_token_ids,
+            expert_ids,
+            num_tokens_post_padded,
+            not apply_router_weight_on_input,
+            1,
+            config,
+            compute_type=compute_type,
+            use_fp8_w8a8=self.quant_config.use_fp8_w8a8,
+            use_int8_w8a8=self.quant_config.use_int8_w8a8,
+            use_int8_w8a16=self.quant_config.use_int8_w8a16,
+            use_int4_w4a16=self.quant_config.use_int4_w4a16,
+            per_channel_quant=self.per_act_token_quant,
+            block_shape=self.block_shape,
+            B_bias=self.w2_bias,
+        )
+
+        # LoRA w2: applied to intermediate_cache3 before moe_sum, using the
+        # unquantized intermediate_cache2 as the lora_a input.  Reuses the
+        # sorted_token_ids_lora computed above.
+        if lora_context is not None:
+            self.apply_w2_lora(
+                lora_context,
+                y=intermediate_cache3,
+                x=intermediate_cache2,
+                topk_weights=topk_weights,
+                sorted_token_ids_lora=sorted_token_ids_lora,
+                expert_ids_lora=expert_ids_lora,
+                num_tokens_post_padded_lora=num_tokens_post_padded_lora,
+                token_lora_mapping=token_lora_mapping,
+                num_tokens=num_tokens,
+                w1=w1,
+                w2=w2,
+                top_k_num=top_k_num,
+            )
+
+        # separate function is required for MoE + LoRA
+        self.moe_sum(intermediate_cache3, output)
+
+    def moe_sum(self, input: torch.Tensor, output: torch.Tensor) -> None:
+        ops.moe_sum(input, output)
+
+
+class TritonWNA16Experts(TritonExperts):
+    @staticmethod
+    def _supports_current_device() -> bool:
+        raise NotImplementedError(
+            "TritonWNA16Experts is not yet used by an Oracle. "
+            "This method should not be called."
+        )
+
+    @staticmethod
+    def _supports_no_act_and_mul() -> bool:
+        raise NotImplementedError(
+            "TritonWNA16Experts is not yet used by an Oracle. "
+            "This method should not be called."
+        )
+
+    @staticmethod
+    def _supports_quant_scheme(
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        raise NotImplementedError(
+            "TritonWNA16Experts is not yet used by an Oracle. "
+            "This method should not be called."
+        )
+
+    @staticmethod
+    def _supports_activation(activation: MoEActivation) -> bool:
+        raise NotImplementedError(
+            "TritonWNA16Experts is not yet used by an Oracle. "
+            "This method should not be called."
+        )
+
+    @staticmethod
+    def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:
+        raise NotImplementedError(
+            "TritonWNA16Experts is not yet used by an Oracle. "
+            "This method should not be called."
+        )
+
+    def apply(
+        self,
+        output: torch.Tensor,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        activation: MoEActivation,
+        global_num_experts: int,
+        expert_map: torch.Tensor | None,
+        a1q_scale: torch.Tensor | None,
+        a2_scale: torch.Tensor | None,
+        workspace13: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+        apply_router_weight_on_input: bool,
+    ):
+        # Check constraints.
+        if self.quant_config.use_int4_w4a16:
+            assert hidden_states.size(-1) // 2 == w1.size(2), "Hidden size mismatch"
+        else:
+            assert hidden_states.size(-1) == w1.size(2), (
+                f"Hidden size mismatch {hidden_states.size(-1)} != {w1.size(2)}"
+            )
+
+        assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
+        assert hidden_states.dim() == 2
+        assert w1.stride(-1) == 1, "Stride of last dimension must be 1"
+        assert w2.stride(-1) == 1, "Stride of last dimension must be 1"
+        assert hidden_states.dtype in [
+            torch.float32,
+            torch.float16,
+            torch.bfloat16,
+            torch.float8_e4m3fn,
+            torch.float8_e4m3fnuz,
+        ]
+
+        E, num_tokens, N, K, top_k_num = self.moe_problem_size(
+            hidden_states, w1, w2, topk_ids
+        )
+
+        if global_num_experts == -1:
+            global_num_experts = E
+
+        config = try_get_optimal_moe_config(
+            w1.size(),
+            w2.size(),
+            top_k_num,
+            self.quant_config.config_name(hidden_states.dtype),
+            num_tokens,
+            block_shape=self.block_shape,
+        )
+
+        if hidden_states.dtype == torch.bfloat16:
+            compute_type = tl.bfloat16
+        elif hidden_states.dtype == torch.float16:
+            compute_type = tl.float16
+        elif hidden_states.dtype == torch.float32:
+            compute_type = tl.float32
+        elif (
+            hidden_states.dtype == torch.float8_e4m3fn
+            or hidden_states.dtype == torch.float8_e4m3fnuz
+        ):
+            compute_type = tl.bfloat16
+        else:
+            raise ValueError(f"Unsupported compute_type: {hidden_states.dtype}")
+
+        # Note that the output tensor might be in workspace1
+        intermediate_cache1 = _resize_cache(workspace2, (num_tokens, top_k_num, N))
+        activation_out_dim = self.adjust_N_for_activation(N, activation)
+        intermediate_cache2 = _resize_cache(
+            workspace13, (num_tokens * top_k_num, activation_out_dim)
+        )
+        intermediate_cache3 = _resize_cache(workspace2, (num_tokens, top_k_num, K))
+
+        sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size(
+            topk_ids, config["BLOCK_SIZE_M"], global_num_experts, expert_map
+        )
+
+        invoke_fused_moe_wna16_triton_kernel(
+            hidden_states,
+            w1,
+            intermediate_cache1,
+            self.w1_scale,
+            self.quant_config.w1_zp,
+            None,  # topk_weights
+            sorted_token_ids,
+            expert_ids,
+            num_tokens_post_padded,
+            False,  # mul_routed_weights
+            top_k_num,
+            config,
+            compute_type=compute_type,
+            use_int8_w8a16=self.quant_config.use_int8_w8a16,
+            use_int4_w4a16=self.quant_config.use_int4_w4a16,
+            block_shape=self.block_shape,
+        )
+
+        self.activation(
+            activation, intermediate_cache2, intermediate_cache1.view(-1, N)
+        )
+
+        a2q_scale: torch.Tensor | None = None
+
+        qintermediate_cache2, a2q_scale = moe_kernel_quantize_input(
+            intermediate_cache2,
+            a2_scale,
+            self.quant_dtype,
+            self.per_act_token_quant,
+            self.block_shape,
+        )
+
+        invoke_fused_moe_wna16_triton_kernel(
+            qintermediate_cache2,
+            w2,
+            intermediate_cache3,
+            self.w2_scale,
+            self.quant_config.w2_zp,
+            topk_weights,
+            sorted_token_ids,
+            expert_ids,
+            num_tokens_post_padded,
+            not apply_router_weight_on_input,
+            1,
+            config,
+            compute_type=compute_type,
+            use_int8_w8a16=self.quant_config.use_int8_w8a16,
+            use_int4_w4a16=self.quant_config.use_int4_w4a16,
+            block_shape=self.block_shape,
+        )
+
+        # separate function is required for MoE + LoRA
+        self.moe_sum(intermediate_cache3, output)
diff --git a/vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py b/vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py
index 1f0258fb657f..31af4a32bae2 100644
--- a/vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py
@@ -175,13 +175,6 @@ def apply(
         # Pack topk ids and weights into format expected by the kernel.
         packed_topk_ids = trtllm_moe_pack_topk_ids_weights(topk_ids, topk_weights)
 
-        # trtllm_fp8_block_scale_routed_moe does not support autotuning
-        # so skip this kernel during dummy run for autotuning.
-        import vllm.utils.flashinfer as fi_utils
-
-        if fi_utils._is_fi_autotuning:
-            return
-
         assert a1q_scale is not None
 
         is_mxfp8 = self.quant_config.block_shape == [1, 32]
@@ -196,11 +189,7 @@ def apply(
             weight_layout = WeightLayout.BlockMajorK
             hidden_states_scale = a1q_scale.t().contiguous()
 
-        # `trtllm_fp8_block_scale_routed_moe` has a bug and does not write to the
-        # output tensor in-place so we need to manually copy the result to the
-        # output tensor
-        # https://github.com/flashinfer-ai/flashinfer/issues/2703
-        result = flashinfer.fused_moe.trtllm_fp8_block_scale_routed_moe(
+        flashinfer.fused_moe.trtllm_fp8_block_scale_routed_moe(
             topk_ids=packed_topk_ids,
             routing_bias=None,
             hidden_states=hidden_states,
@@ -217,13 +206,12 @@ def apply(
             local_expert_offset=self.ep_rank * self.local_num_experts,
             local_num_experts=self.local_num_experts,
             routed_scaling_factor=None,
-            routing_method_type=1,
+            routing_method_type=1,  # not used
             use_shuffled_weight=use_shuffled_weight,
             weight_layout=weight_layout,
             fp8_quantization_type=fp8_quant_type,
-            # output=output,
+            output=output,
         )
-        output.copy_(result)
 
 
 class TrtLlmFp8ExpertsMonolithic(TrtLlmFp8ExpertsBase, mk.FusedMoEExpertsMonolithic):
@@ -275,20 +263,6 @@ def _supports_router_logits_dtype(
         router_logits_dtype: torch.dtype | None,
         routing_method: RoutingMethodType,
     ) -> bool:
-        """
-        The FlashInfer TRTLLM FP8 kernel expects bfloat16 router_logits by default.
-        DeepSeekV3 routing supports float32 router_logits (converted internally).
-        Simulated routing generates synthetic decisions and is agnostic to dtype.
-        """
-        if router_logits_dtype == torch.float32:
-            # DeepSeekV3 routing handles float32 logits internally.
-            # Simulated routing generates synthetic decisions, so the
-            # kernel doesn't care about the actual logits dtype.
-            # https://github.com/flashinfer-ai/flashinfer/issues/2469
-            return routing_method in (
-                RoutingMethodType.DeepSeekV3,
-                RoutingMethodType.Simulated,
-            )
         return True
 
     @staticmethod
@@ -308,18 +282,22 @@ def _supports_routing_method(
             # NOTE(rob): potentially allow others here. This is a conservative list.
             return routing_method in [
                 RoutingMethodType.DeepSeekV3,
-                RoutingMethodType.Simulated,
                 RoutingMethodType.Renormalize,
                 RoutingMethodType.RenormalizeNaive,
+                RoutingMethodType.SigmoidRenorm,
+                RoutingMethodType.MiniMax2,
+                RoutingMethodType.Simulated,
             ]
         elif (weight_key, activation_key) == (kFp8StaticTensorSym, kFp8StaticTensorSym):
             # NOTE(dbari): as above, potentially allow others here.
             return routing_method in [
                 RoutingMethodType.DeepSeekV3,
                 RoutingMethodType.Llama4,
-                RoutingMethodType.Simulated,
                 RoutingMethodType.Renormalize,
                 RoutingMethodType.RenormalizeNaive,
+                RoutingMethodType.SigmoidRenorm,
+                RoutingMethodType.MiniMax2,
+                RoutingMethodType.Simulated,
             ]
         else:
             raise ValueError("Unsupported quantization scheme.")
@@ -355,14 +333,6 @@ def _apply_block_scale(
         # TODO: fuse into the quant kernel.
         assert a1q_scale is not None
 
-        if self.routing_method_type == RoutingMethodType.DeepSeekV3:
-            router_logits = router_logits.to(torch.float32)
-
-        # Currently FI requires bfloat16 routing bias.
-        # https://github.com/flashinfer-ai/flashinfer/issues/2909
-        if e_score_correction_bias is not None:
-            e_score_correction_bias = e_score_correction_bias.to(torch.bfloat16)
-
         is_mxfp8 = self.quant_config.block_shape == [1, 32]
         if is_mxfp8:
             fp8_quant_type = Fp8QuantizationType.MxFp8
@@ -429,10 +399,6 @@ def _apply_per_tensor(
         else:
             assert not apply_router_weight_on_input
 
-        # The DeepSeekV3 routing method requires float32 router logits.
-        if self.routing_method_type == RoutingMethodType.DeepSeekV3:
-            router_logits = router_logits.to(torch.float32)
-
         # Currently FI requires bfloat16 routing bias.
         # https://github.com/flashinfer-ai/flashinfer/issues/2909
         if e_score_correction_bias is not None:
diff --git a/vllm/model_executor/layers/fused_moe/experts/trtllm_mxfp4_moe.py b/vllm/model_executor/layers/fused_moe/experts/trtllm_mxfp4_moe.py
index d084283360c4..69e5b7fe4f0e 100644
--- a/vllm/model_executor/layers/fused_moe/experts/trtllm_mxfp4_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/trtllm_mxfp4_moe.py
@@ -14,6 +14,7 @@
 from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
     TopKWeightAndReduceNoOP,
 )
+from vllm.model_executor.layers.fused_moe.utils import trtllm_moe_pack_topk_ids_weights
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
     kMxfp4Static,
@@ -32,10 +33,8 @@ def __init__(
         self,
         moe_config: FusedMoEConfig,
         quant_config: FusedMoEQuantConfig,
+        **kwargs,
     ):
-        # NOTE: FusedMoEExperts.__init__ is called by the concrete subclass
-        # (Monolithic/Modular) via MRO, not here, to avoid mypy issues with
-        # multiple inheritance. This matches the NvFP4 expert pattern.
         self.moe_config = moe_config
         self.quant_config = quant_config
 
@@ -45,26 +44,40 @@ def __init__(
             moe_config.intermediate_size_per_partition
         )
         self.hidden_dim = moe_config.hidden_dim
+        self.hidden_dim_unpadded = (
+            moe_config.hidden_dim_unpadded or moe_config.hidden_dim
+        )
         self.local_num_experts = moe_config.num_local_experts
         self.ep_rank = moe_config.moe_parallel_config.ep_rank
 
-        # MXFP4-specific TRTLLM parameters
+        # MXFP4-specific TRTLLM parameters from quant_config
         device = torch.accelerator.current_device_index()
-        self.gemm1_alpha = torch.tensor(
-            [1.702] * self.local_num_experts,
-            dtype=torch.float32,
-            device=device,
-        )
-        self.gemm1_beta = torch.tensor(
-            [1.0] * self.local_num_experts,
-            dtype=torch.float32,
-            device=device,
-        )
-        self.gemm1_clamp_limit = torch.tensor(
-            [7.0] * self.local_num_experts,
-            dtype=torch.float32,
-            device=device,
-        )
+        if quant_config.gemm1_alpha is not None:
+            self.gemm1_alpha = torch.tensor(
+                [quant_config.gemm1_alpha] * self.local_num_experts,
+                dtype=torch.float32,
+                device=device,
+            )
+        else:
+            self.gemm1_alpha = None
+
+        if quant_config.gemm1_beta is not None:
+            self.gemm1_beta = torch.tensor(
+                [quant_config.gemm1_beta] * self.local_num_experts,
+                dtype=torch.float32,
+                device=device,
+            )
+        else:
+            self.gemm1_beta = None
+
+        if quant_config.gemm1_clamp_limit is not None:
+            self.gemm1_clamp_limit = torch.tensor(
+                [quant_config.gemm1_clamp_limit] * self.local_num_experts,
+                dtype=torch.float32,
+                device=device,
+            )
+        else:
+            self.gemm1_clamp_limit = None
 
         from vllm.config import get_current_vllm_config
 
@@ -72,9 +85,6 @@ def __init__(
             get_current_vllm_config().compilation_config.max_cudagraph_capture_size
         )
 
-        # P1-5 fix: use public quant_dtype property instead of private _a1
-        self.use_mxfp8_input = quant_config.quant_dtype == "mxfp8"
-
     @staticmethod
     def _supports_current_device() -> bool:
         p = current_platform
@@ -97,7 +107,7 @@ def _supports_quant_scheme(
 
     @staticmethod
     def _supports_activation(activation: MoEActivation) -> bool:
-        return activation == MoEActivation.SWIGLUOAI
+        return activation in (MoEActivation.SWIGLUOAI, MoEActivation.SILU)
 
     @staticmethod
     def activation_format() -> mk.FusedMoEActivationFormat:
@@ -111,8 +121,7 @@ def supports_expert_map(self) -> bool:
 
     @property
     def expects_unquantized_inputs(self) -> bool:
-        # Expert handles MXFP8 quantization internally if needed
-        return True
+        return False
 
 
 class TrtLlmMxfp4ExpertsMonolithic(
@@ -171,55 +180,55 @@ def apply(
     ) -> torch.Tensor:
         from flashinfer import trtllm_fp4_block_scale_moe
 
-        # Handle input quantization
-        if self.use_mxfp8_input:
-            from flashinfer import mxfp8_quantize
-
-            x_quant, x_scale = mxfp8_quantize(
-                hidden_states,
-                is_sf_swizzled_layout=False,
-                alignment=256,
-            )
-            x_scale = x_scale.view(torch.float8_e4m3fn).reshape(
-                *hidden_states.shape[:-1], -1
-            )
+        if a1q_scale is not None:
+            x_quant = hidden_states
+            x_scale = a1q_scale.view(torch.float8_e4m3fn)
         else:
             assert hidden_states.dtype == torch.bfloat16
             x_quant = hidden_states
             x_scale = None
+        output = torch.empty(
+            *hidden_states.shape[:-1],
+            self.hidden_dim_unpadded,
+            dtype=torch.bfloat16,
+            device=hidden_states.device,
+        )
 
-        output = torch.empty_like(hidden_states)
-
-        return trtllm_fp4_block_scale_moe(
-            routing_logits=router_logits.to(torch.bfloat16),
-            routing_bias=None,
-            hidden_states=x_quant,
-            hidden_states_scale=x_scale,
-            gemm1_weights=w1,
-            gemm1_weights_scale=self.w1_scale,
-            gemm1_bias=self.w1_bias,
-            gemm1_alpha=self.gemm1_alpha,
-            gemm1_beta=self.gemm1_beta,
-            gemm1_clamp_limit=self.gemm1_clamp_limit,
-            gemm2_weights=w2,
-            gemm2_weights_scale=self.w2_scale,
-            gemm2_bias=self.w2_bias,
-            output1_scale_scalar=None,
-            output1_scale_gate_scalar=None,
-            output2_scale_scalar=None,
-            num_experts=global_num_experts,
-            top_k=self.topk,
-            n_group=None,
-            topk_group=None,
-            intermediate_size=self.intermediate_size_per_partition,
-            local_expert_offset=self.ep_rank * self.local_num_experts,
-            local_num_experts=self.local_num_experts,
-            routed_scaling_factor=None,
-            routing_method_type=self.routing_method_type,
-            do_finalize=True,
-            tune_max_num_tokens=max(self.max_capture_size, 1),
-            output=output,
-        )[0]
+        from vllm.utils.flashinfer import _is_fi_autotuning, autotune
+
+        with autotune(_is_fi_autotuning):
+            trtllm_fp4_block_scale_moe(
+                routing_logits=router_logits.to(torch.bfloat16),
+                routing_bias=None,
+                hidden_states=x_quant,
+                hidden_states_scale=x_scale,
+                gemm1_weights=w1,
+                gemm1_weights_scale=self.w1_scale,
+                gemm1_bias=self.w1_bias,
+                gemm1_alpha=self.gemm1_alpha,
+                gemm1_beta=self.gemm1_beta,
+                gemm1_clamp_limit=self.gemm1_clamp_limit,
+                gemm2_weights=w2,
+                gemm2_weights_scale=self.w2_scale,
+                gemm2_bias=self.w2_bias,
+                output1_scale_scalar=None,
+                output1_scale_gate_scalar=None,
+                output2_scale_scalar=None,
+                num_experts=global_num_experts,
+                top_k=self.topk,
+                n_group=None,
+                topk_group=None,
+                intermediate_size=self.intermediate_size_per_partition,
+                local_expert_offset=self.ep_rank * self.local_num_experts,
+                local_num_experts=self.local_num_experts,
+                routed_scaling_factor=None,
+                routing_method_type=self.routing_method_type,
+                do_finalize=True,
+                tune_max_num_tokens=max(self.max_capture_size, 1),
+                output=output,
+            )
+
+        return output
 
 
 class TrtLlmMxfp4ExpertsModular(TrtLlmMxfp4ExpertsBase, mk.FusedMoEExpertsModular):
@@ -229,16 +238,22 @@ class TrtLlmMxfp4ExpertsModular(TrtLlmMxfp4ExpertsBase, mk.FusedMoEExpertsModula
     Moved from trtllm_moe.py.
     """
 
-    @property
-    def expects_unquantized_inputs(self) -> bool:
-        return True
-
     @staticmethod
     def _supports_parallel_config(
         moe_parallel_config: FusedMoEParallelConfig,
     ) -> bool:
         return True
 
+    @staticmethod
+    def _supports_routing_method(
+        routing_method: RoutingMethodType,
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        # Modular kernel handles only the expert computation;
+        # routing is done externally, so accept any routing method.
+        return True
+
     def supports_expert_map(self) -> bool:
         return True
 
@@ -259,7 +274,7 @@ def workspace_shapes(
         # The workspaces for this implementation are managed by flashinfer.
         workspace1 = (0,)
         workspace2 = (0,)
-        output = (M, K)
+        output = (M, self.hidden_dim_unpadded)
         return (workspace1, workspace2, output)
 
     def apply(
@@ -282,29 +297,19 @@ def apply(
     ):
         topk = topk_ids.size(-1)
         local_num_experts = w1.size(0)
-        intermediate_size = w2.size(1)
+        intermediate_size = self.intermediate_size_per_partition
         local_expert_offset = self.moe_config.ep_rank * local_num_experts
 
-        # Handle input quantization
-        if self.use_mxfp8_input:
-            from flashinfer import mxfp8_quantize
-
-            x_quant, x_scale = mxfp8_quantize(
-                hidden_states,
-                is_sf_swizzled_layout=False,
-                alignment=256,
-            )
-            x_scale = x_scale.view(torch.float8_e4m3fn).reshape(
-                *hidden_states.shape[:-1], -1
-            )
+        if a1q_scale is not None:
+            x_quant = hidden_states
+            x_scale = a1q_scale.view(torch.float8_e4m3fn)
         else:
             assert hidden_states.dtype == torch.bfloat16
             x_quant = hidden_states
             x_scale = None
 
-        packed_tensor = (topk_ids.to(torch.int32) << 16) | topk_weights.to(
-            torch.bfloat16
-        ).view(torch.int16)
+        # Pack topk ids and weights into format expected by the kernel.
+        packed_tensor = trtllm_moe_pack_topk_ids_weights(topk_ids, topk_weights)
 
         assert self.w1_scale is not None
         assert self.w2_scale is not None
@@ -333,7 +338,10 @@ def apply(
             "local_expert_offset": local_expert_offset,
             "local_num_experts": local_num_experts,
             "routed_scaling_factor": None,
-            "routing_method_type": self.routing_method_type,
+            # Modular kernel receives pre-routed tokens, so routing
+            # is already done. Use Renormalize as a safe default that
+            # the TRTLLM C++ kernel supports.
+            "routing_method_type": RoutingMethodType.Renormalize,
             "do_finalize": True,
             "output": output,
             "tune_max_num_tokens": max(self.max_capture_size, 1),
@@ -341,12 +349,9 @@ def apply(
 
         from flashinfer import trtllm_fp4_block_scale_routed_moe
 
-        from vllm.utils.flashinfer import autotune
+        from vllm.utils.flashinfer import _is_fi_autotuning, autotune
 
-        with autotune(False):
-            # Enable autotune when,
-            # https://github.com/flashinfer-ai/flashinfer/issues/2023 is
-            # resolved.
+        with autotune(_is_fi_autotuning):
             trtllm_fp4_block_scale_routed_moe(**kwargs)
 
         return output
diff --git a/vllm/model_executor/layers/fused_moe/experts/trtllm_nvfp4_moe.py b/vllm/model_executor/layers/fused_moe/experts/trtllm_nvfp4_moe.py
index fc30815f7191..96202cad622e 100644
--- a/vllm/model_executor/layers/fused_moe/experts/trtllm_nvfp4_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/trtllm_nvfp4_moe.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import flashinfer
+
 import torch
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
@@ -112,8 +112,12 @@ def _supports_quant_scheme(
 
     @staticmethod
     def _supports_activation(activation: MoEActivation) -> bool:
-        """Supports only SiLU and RELU^2 non-gated activation."""
-        return activation in [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL]
+        """Supports only SiLU, RELU^2 non-gated and GELU activation."""
+        return activation in [
+            MoEActivation.SILU,
+            MoEActivation.RELU2_NO_MUL,
+            MoEActivation.GELU,
+        ]
 
     @staticmethod
     def _supports_shape(hidden_dim: int) -> bool:
@@ -188,7 +192,9 @@ def apply(
         expert_tokens_meta: mk.ExpertTokensMetadata | None,
         apply_router_weight_on_input: bool,
     ):
-        assert activation in [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL]
+        import flashinfer
+
+        assert self._supports_activation(activation)
         assert a1q_scale is not None
         assert self.quant_config.w1_scale is not None
         assert self.quant_config.w2_scale is not None
@@ -196,13 +202,6 @@ def apply(
         # Pack topk ids and weights into format expected by the kernel.
         packed_tensor = trtllm_moe_pack_topk_ids_weights(topk_ids, topk_weights)
 
-        # trtllm_fp4_block_scale_routed_moe does not support autotuning
-        # so skip this kernel during dummy run for autotuning.
-        import vllm.utils.flashinfer as fi_utils
-
-        if fi_utils._is_fi_autotuning:
-            return
-
         # Invoke kernel.
         flashinfer.fused_moe.trtllm_fp4_block_scale_routed_moe(
             topk_ids=packed_tensor,
@@ -231,7 +230,7 @@ def apply(
             local_expert_offset=self.ep_rank * self.local_num_experts,
             local_num_experts=self.local_num_experts,
             routed_scaling_factor=None,
-            routing_method_type=1,
+            routing_method_type=1,  # not used
             do_finalize=True,
             activation_type=activation_to_flashinfer_int(activation),
             output=output,
@@ -265,7 +264,10 @@ def _supports_routing_method(
             RoutingMethodType.Renormalize,
             RoutingMethodType.RenormalizeNaive,
             RoutingMethodType.Llama4,
+            RoutingMethodType.SigmoidRenorm,
+            RoutingMethodType.MiniMax2,
             RoutingMethodType.Simulated,
+            RoutingMethodType.SigmoidRenorm,
         ]
 
     @staticmethod
@@ -273,20 +275,6 @@ def _supports_router_logits_dtype(
         router_logits_dtype: torch.dtype | None,
         routing_method: RoutingMethodType,
     ) -> bool:
-        """
-        The FlashInfer TRTLLM NvFp4 kernel expects bfloat16 router_logits by default.
-        DeepSeekV3 routing supports float32 router_logits (converted internally).
-        Simulated routing generates synthetic decisions and is agnostic to dtype.
-        """
-        if router_logits_dtype == torch.float32:
-            # DeepSeekV3 routing handles float32 logits internally.
-            # Simulated routing generates synthetic decisions, so the
-            # kernel doesn't care about the actual logits dtype.
-            # https://github.com/flashinfer-ai/flashinfer/issues/2469
-            return routing_method in (
-                RoutingMethodType.DeepSeekV3,
-                RoutingMethodType.Simulated,
-            )
         return True
 
     def apply(
@@ -306,7 +294,9 @@ def apply(
         routed_scaling_factor: float | None = None,
         topk_group: int | None = None,
     ) -> torch.Tensor:
-        assert activation in [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL]
+        import flashinfer
+
+        assert self._supports_activation(activation)
         assert a1q_scale is not None
         assert self.quant_config.w1_scale is not None
         assert self.quant_config.w2_scale is not None
@@ -318,13 +308,6 @@ def apply(
             and self.routing_method_type != RoutingMethodType.Llama4
         )
 
-        # Prepare router logits for kernel format.
-        router_logits = (
-            router_logits.to(torch.float32)
-            if self.routing_method_type == RoutingMethodType.DeepSeekV3
-            else router_logits
-        )
-
         # Currently FI requires bfloat16 routing bias.
         # https://github.com/flashinfer-ai/flashinfer/issues/2909
         if e_score_correction_bias is not None:
diff --git a/vllm/model_executor/layers/fused_moe/xpu_fused_moe.py b/vllm/model_executor/layers/fused_moe/experts/xpu_moe.py
similarity index 95%
rename from vllm/model_executor/layers/fused_moe/xpu_fused_moe.py
rename to vllm/model_executor/layers/fused_moe/experts/xpu_moe.py
index 9cc0ade288c7..d6bd2b140087 100644
--- a/vllm/model_executor/layers/fused_moe/xpu_fused_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/xpu_moe.py
@@ -24,6 +24,13 @@
     from vllm_xpu_kernels.fused_moe_interface import xpu_fused_moe
 
 
+def prepare_fp8_moe_layer_for_xpu(
+    w13: torch.Tensor,
+    w2: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    return w13.transpose(-1, -2).contiguous(), w2.transpose(-1, -2).contiguous()
+
+
 class XPUExperts(mk.FusedMoEExpertsModular):
     def __init__(
         self,
@@ -55,7 +62,7 @@ def _supports_current_device() -> bool:
 
     @staticmethod
     def _supports_no_act_and_mul() -> bool:
-        return False
+        return True
 
     @staticmethod
     def _supports_activation(activation: MoEActivation) -> bool:
@@ -63,6 +70,7 @@ def _supports_activation(activation: MoEActivation) -> bool:
             MoEActivation.SILU,
             MoEActivation.GELU,
             MoEActivation.SWIGLUOAI,
+            MoEActivation.RELU2_NO_MUL,
         ]
 
     @staticmethod
diff --git a/vllm/model_executor/layers/fused_moe/fused_batched_moe.py b/vllm/model_executor/layers/fused_moe/fused_batched_moe.py
index e2b5a8f6764e..8638a11466c0 100644
--- a/vllm/model_executor/layers/fused_moe/fused_batched_moe.py
+++ b/vllm/model_executor/layers/fused_moe/fused_batched_moe.py
@@ -14,13 +14,11 @@
 from vllm.model_executor.layers.fused_moe.fused_moe import try_get_optimal_moe_config
 from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
     TopKWeightAndReduceDelegate,
-    TopKWeightAndReduceNaiveBatched,
 )
 from vllm.model_executor.layers.fused_moe.utils import (
     _resize_cache,
     moe_kernel_quantize_input,
     normalize_batched_scales_shape,
-    normalize_scales_shape,
 )
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
@@ -489,162 +487,6 @@ def invoke_moe_batched_triton_kernel(
     )
 
 
-class BatchedPrepareAndFinalize(mk.FusedMoEPrepareAndFinalizeModular):
-    """
-    A reference prepare/finalize class that reorganizes the tokens into
-    expert batched format, i.e. E x max_num_tokens x K.  This is the format
-    that the batched dispatch/combine kernels use.
-    """
-
-    def __init__(
-        self,
-        max_num_tokens: int,
-        num_local_experts: int,
-        num_dispatchers: int,
-        rank: int,
-    ):
-        super().__init__()
-        self.max_num_tokens = max_num_tokens
-        self.num_local_experts = num_local_experts
-        self.rank = rank
-        self.num_dispatchers_ = num_dispatchers
-
-    @property
-    def activation_format(self) -> mk.FusedMoEActivationFormat:
-        return mk.FusedMoEActivationFormat.BatchedExperts
-
-    def max_num_tokens_per_rank(self) -> int | None:
-        return self.max_num_tokens
-
-    def topk_indices_dtype(self) -> torch.dtype | None:
-        return None
-
-    def num_dispatchers(self) -> int:
-        return self.num_dispatchers_
-
-    def output_is_reduced(self) -> bool:
-        return False
-
-    def prepare(
-        self,
-        a1: torch.Tensor,
-        topk_weights: torch.Tensor,
-        topk_ids: torch.Tensor,
-        num_experts: int,
-        expert_map: torch.Tensor | None,
-        apply_router_weight_on_input: bool,
-        quant_config: FusedMoEQuantConfig,
-        defer_input_quant: bool = False,
-    ) -> mk.PrepareResultType:
-        if defer_input_quant:
-            raise NotImplementedError(
-                f"{self.__class__.__name__} does not support defer_input_quant=True. "
-                "Please select an MoE kernel that accepts quantized inputs."
-            )
-        assert a1.dim() == 2
-        assert topk_ids.dim() == 2
-        assert topk_ids.size(0) == a1.size(0)
-
-        if apply_router_weight_on_input:
-            topk = topk_ids.size(1)
-            # TODO: this only works for topK=1, will need to update for topK>1
-            assert topk == 1, (
-                "apply_router_weight_on_input is only implemented for topk=1"
-            )
-            a1.mul_(topk_weights.to(a1.dtype))
-
-        num_tokens, hidden_dim = a1.size()
-        topk = topk_ids.size(1)
-
-        tokens_per_expert = torch.zeros(num_experts, dtype=torch.int, device=a1.device)
-
-        num_local_experts = self.num_local_experts
-
-        if quant_config.quant_dtype is None:
-            b_type = a1.dtype
-        else:
-            b_type = quant_config.quant_dtype
-
-        b_a1 = torch.zeros(
-            (num_local_experts, self.max_num_tokens, hidden_dim),
-            dtype=b_type,
-            device=a1.device,
-        )
-
-        if quant_config.is_quantized:
-            scale_shape = quant_config.batched_scale_shape(
-                num_local_experts, self.max_num_tokens, hidden_dim
-            )
-
-            b_a1_scale = torch.empty(scale_shape, dtype=torch.float32, device=a1.device)
-        else:
-            assert quant_config.a1_scale is None
-            b_a1_scale = None
-
-        first_expert = num_local_experts * self.rank
-        last_expert = first_expert + num_local_experts
-
-        a1_scale = normalize_scales_shape(quant_config.a1_scale)
-
-        for expert_id in range(first_expert, last_expert):
-            topks = torch.any(topk_ids == expert_id, dim=1).flatten()
-            rows = torch.count_nonzero(topks.flatten())
-            if rows == 0:
-                continue
-            idx = expert_id - first_expert
-            tokens_per_expert[idx] = rows
-            rhs = a1[: topks.numel()][topks]
-            if quant_config.quant_dtype is not None:
-                if a1_scale is not None:
-                    if quant_config.is_per_act_token:
-                        rhs_a1_scale = a1_scale[: topks.numel()][topks]
-                    else:
-                        rhs_a1_scale = a1_scale
-                else:
-                    rhs_a1_scale = None
-                b_a1[idx, :rows, :], b_s = moe_kernel_quantize_input(
-                    rhs,
-                    rhs_a1_scale,
-                    quant_config.quant_dtype,
-                    quant_config.per_act_token_quant,
-                    quant_config.block_shape,
-                )
-                assert b_s is not None
-                if quant_config.is_per_act_token:
-                    b_a1_scale[idx, :rows] = b_s[:rows]
-                else:
-                    b_a1_scale[idx, : b_s.shape[0]] = b_s
-            else:
-                b_a1[idx, :rows, :] = rhs
-
-        assert b_a1_scale is None or b_a1_scale.ndim == 3
-
-        expert_tokens_meta = mk.ExpertTokensMetadata(
-            expert_num_tokens=tokens_per_expert, expert_num_tokens_cpu=None
-        )
-
-        return b_a1, b_a1_scale, expert_tokens_meta, None, None
-
-    def finalize(
-        self,
-        output: torch.Tensor,
-        fused_expert_output: torch.Tensor,
-        topk_weights: torch.Tensor,
-        topk_ids: torch.Tensor,
-        apply_router_weight_on_input: bool,
-        weight_and_reduce_impl: mk.TopKWeightAndReduce,
-    ) -> None:
-        if isinstance(weight_and_reduce_impl, TopKWeightAndReduceDelegate):
-            weight_and_reduce_impl = TopKWeightAndReduceNaiveBatched(self.rank)
-        weight_and_reduce_impl.apply(
-            output=output,
-            fused_expert_output=fused_expert_output,
-            topk_weights=topk_weights,
-            topk_ids=topk_ids,
-            apply_router_weight_on_input=apply_router_weight_on_input,
-        )
-
-
 class NaiveBatchedExperts(mk.FusedMoEExpertsModular):
     """
     A reference MoE expert class that operates on expert batched format,
@@ -928,25 +770,27 @@ def _supports_quant_scheme(
             p.is_cuda() and p.has_device_capability((8, 9))
         )
 
-        SUPPORTED_W_A_FP8 = [
-            (kFp8Static128BlockSym, kFp8Dynamic128Sym),
-            (kFp8StaticChannelSym, kFp8DynamicTokenSym),
-            (kFp8StaticTensorSym, kFp8DynamicTokenSym),
-            (kFp8StaticTensorSym, kFp8StaticTensorSym),
-            (kFp8StaticTensorSym, kFp8DynamicTensorSym),
-        ]
-        return (weight_key, activation_key) == (None, None) or (
-            device_supports_fp8 and (weight_key, activation_key) in SUPPORTED_W_A_FP8
-        )
+        supported: list[tuple[QuantKey | None, QuantKey | None]] = [(None, None)]
+        if device_supports_fp8:
+            supported += [
+                (kFp8Static128BlockSym, kFp8Dynamic128Sym),
+                (kFp8StaticChannelSym, kFp8DynamicTokenSym),
+                (kFp8StaticTensorSym, kFp8DynamicTokenSym),
+                (kFp8StaticTensorSym, kFp8StaticTensorSym),
+                (kFp8StaticTensorSym, kFp8DynamicTensorSym),
+            ]
+        return (weight_key, activation_key) in supported
 
     @staticmethod
     def _supports_activation(activation: MoEActivation) -> bool:
         return activation in [
             MoEActivation.SILU,
             MoEActivation.GELU,
+            MoEActivation.GELU_TANH,
             MoEActivation.SWIGLUOAI,
             MoEActivation.SILU_NO_MUL,
             MoEActivation.GELU_NO_MUL,
+            MoEActivation.GELU_TANH_NO_MUL,
             MoEActivation.RELU2_NO_MUL,
         ]
 
diff --git a/vllm/model_executor/layers/fused_moe/fused_humming_moe.py b/vllm/model_executor/layers/fused_moe/fused_humming_moe.py
new file mode 100644
index 000000000000..5876f1c87ed6
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/fused_humming_moe.py
@@ -0,0 +1,713 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Fused MoE utilities for Humming."""
+
+import json
+import math
+from typing import Any
+
+import torch
+from humming import dtypes
+from humming.config import GemmType as HummingGemmType
+from humming.layer import HummingLayerMeta, HummingMethod
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm import envs
+from vllm.forward_context import get_forward_context
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEParallelConfig,
+    FusedMoEQuantConfig,
+)
+from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
+    moe_align_block_size,
+)
+from vllm.model_executor.layers.fused_moe.moe_fused_mul_sum import moe_fused_mul_sum
+from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
+    moe_permute,
+    moe_unpermute,
+)
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceDelegate,
+    TopKWeightAndReduceNoOP,
+)
+from vllm.model_executor.layers.fused_moe.utils import _resize_cache
+from vllm.model_executor.layers.quantization.utils.quant_utils import QuantKey
+from vllm.platforms import current_platform
+from vllm.v1.worker.workspace import current_workspace_manager
+
+logger = init_logger(__name__)
+
+
+def get_humming_moe_gemm_type() -> str:
+    env_gemm_type: str = envs.VLLM_HUMMING_MOE_GEMM_TYPE or ""
+    env_gemm_type = env_gemm_type.lower()
+    if env_gemm_type == "indexed":
+        gemm_type = env_gemm_type
+    elif env_gemm_type in ["grouped_contiguous", "grouped"]:
+        gemm_type = "grouped_contiguous"
+    else:
+        gemm_type = "indexed"
+
+    logger.info_once(f"Using {gemm_type} gemm for humming moe")  # noqa
+    return gemm_type
+
+
+class HummingExpertsBase(mk.FusedMoEExpertsModular):
+    def __init__(
+        self,
+        layer: torch.nn.Module,
+        moe_config: FusedMoEConfig,
+        quant_config: FusedMoEQuantConfig,
+        max_num_tokens: int | None = None,
+        num_dispatchers: int | None = None,
+    ):
+        self.layer = layer
+        self.num_experts = self.layer.num_experts
+        self.global_num_experts = self.layer.global_num_experts
+        self.init_humming_moe()
+
+        if self.is_batched():
+            assert max_num_tokens is not None and num_dispatchers is not None
+
+        super().__init__(
+            moe_config=moe_config,
+            quant_config=quant_config,
+            max_num_tokens=max_num_tokens,
+            num_dispatchers=num_dispatchers,
+        )
+
+    def init_humming_moe(self):
+        self.compute_config = {
+            "use_batch_invariant": envs.VLLM_BATCH_INVARIANT,
+            "use_f16_accum": envs.VLLM_HUMMING_USE_F16_ACCUM,
+            "gemm_type": self.humming_gemm_type().value,
+        }
+        self.w13_tuning_config = HummingMethod.get_default_tuning_configs(
+            layer=self.layer,
+            use_f16_accum=envs.VLLM_HUMMING_USE_F16_ACCUM,
+            use_batch_invariant=envs.VLLM_BATCH_INVARIANT,
+            gemm_type=self.humming_gemm_type(),
+            sublayer_name="w13",
+        )
+        self.w2_tuning_config = HummingMethod.get_default_tuning_configs(
+            layer=self.layer,
+            use_f16_accum=envs.VLLM_HUMMING_USE_F16_ACCUM,
+            use_batch_invariant=envs.VLLM_BATCH_INVARIANT,
+            gemm_type=self.humming_gemm_type(),
+            sublayer_name="w2",
+        )
+        self.compute_config_str = json.dumps(self.compute_config)
+        self.w13_tuning_config_str = json.dumps(self.w13_tuning_config)
+        self.w2_tuning_config_str = json.dumps(self.w2_tuning_config)
+
+    def get_global_valid_shape_m(self, topk_ids: torch.Tensor):
+        num_tokens = topk_ids.size(0)
+        ctx = get_forward_context()
+        if ctx.dp_metadata is not None:
+            num_tokens = ctx.dp_metadata.num_tokens_across_dp_cpu.sum().item()
+
+        return num_tokens * topk_ids.size(1)
+
+    def estimate_local_valid_shape_m(self, topk_ids: torch.Tensor):
+        # estimate shape_m for kernel tuning
+        global_valid_shape_m = self.get_global_valid_shape_m(topk_ids)
+        num_experts = self.num_experts
+        global_num_experts = self.global_num_experts
+        return math.ceil(global_valid_shape_m * num_experts / global_num_experts)
+
+    @staticmethod
+    def humming_gemm_type() -> HummingGemmType:
+        raise NotImplementedError
+
+    @classmethod
+    def is_batched(cls) -> bool:
+        return cls.activation_format() == mk.FusedMoEActivationFormat.BatchedExperts
+
+    @staticmethod
+    def _supports_quant_scheme(
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+    ) -> bool:
+        return True
+
+    def supports_expert_map(self) -> bool:
+        return True
+
+    @staticmethod
+    def _supports_current_device() -> bool:
+        platform = current_platform
+        return platform.is_cuda() and platform.has_device_capability((7, 5))
+
+    @staticmethod
+    def _supports_no_act_and_mul() -> bool:
+        return True
+
+    @staticmethod
+    def _supports_activation(activation: MoEActivation) -> bool:
+        # Humming uses apply_moe_activation() callback for activation,
+        # so any activation supported there can be used here.
+        return activation in [
+            MoEActivation.SILU,
+            MoEActivation.GELU,
+            MoEActivation.GELU_TANH,
+            MoEActivation.SWIGLUOAI,
+            MoEActivation.SWIGLUSTEP,
+            MoEActivation.SILU_NO_MUL,
+            MoEActivation.GELU_NO_MUL,
+            MoEActivation.GELU_TANH_NO_MUL,
+            MoEActivation.RELU2_NO_MUL,
+        ]
+
+    @staticmethod
+    def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:
+        return not (
+            moe_parallel_config.use_fi_nvl_two_sided_kernels
+            or moe_parallel_config.use_fi_nvl_one_sided_kernels
+        )
+
+    def moe_problem_size(
+        self,
+        a1: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_ids: torch.Tensor,
+    ) -> tuple[int, int, int, int, int]:
+        meta1: HummingLayerMeta = self.layer.humming_metas["w13"]
+        meta2: HummingLayerMeta = self.layer.humming_metas["w2"]
+
+        assert meta1.num_experts == meta2.num_experts
+
+        num_experts = meta1.num_experts
+        top_k = topk_ids.size(1)
+        assert w1.size(0) == num_experts
+        assert w2.size(0) == num_experts
+
+        if not self.is_batched():
+            num_tokens = a1.size(0)
+            assert topk_ids.size(0) == num_tokens
+        else:
+            assert a1.dim() == 3
+            assert a1.size(0) == num_experts
+            num_tokens = a1.size(1)
+
+        return meta1.num_experts, num_tokens, meta1.shape_n // 2, meta1.shape_k, top_k
+
+    def get_buffer_metas(self, M: int, topk: int, activation: MoEActivation):
+        num_experts = self.num_experts
+        N = self.layer.intermediate_size_per_partition
+        K = self.layer.hidden_size
+        assert isinstance(num_experts, int)
+        assert isinstance(N, int)
+        assert isinstance(K, int)
+
+        # hidden_states
+        # (-> quanted_gate_up_input) (if not BF16/FP16 activation)
+        # -> gate_up_output
+        # -> activation_output
+        # (-> quanted_down_input) (if not BF16/FP16 activation)
+        # -> down_output
+        # (-> output) (if not is_batched)
+        # Neighboring nodes are required to utilize distinct workspaces.
+        # The output must be derived from workspace1.
+
+        output_shape: tuple[int, ...]
+        if self.is_batched():
+            max_num_tokens = self.max_num_tokens
+            num_dispatchers = self.num_dispatchers
+            assert max_num_tokens is not None and num_dispatchers is not None
+            input_shape_m = num_experts * max_num_tokens
+            real_shape_m = num_experts * max_num_tokens * num_dispatchers
+            output_shape = (num_experts, max_num_tokens * num_dispatchers, K)
+        else:
+            input_shape_m = M
+            if self.humming_gemm_type() != HummingGemmType.INDEXED:
+                input_shape_m = M * topk
+            real_shape_m = M * topk
+            output_shape = (M, K)
+
+        down_input_size = N if activation.is_gated else (N * 2)
+        a_dtype = self.layer.humming_metas["w13"].a_dtype
+        c_dtype = self.layer.humming_metas["w13"].c_dtype
+        num_bits = a_dtype.num_bits
+        torch_dtype_map = {
+            dtypes.float16: torch.float16,
+            dtypes.bfloat16: torch.bfloat16,
+            dtypes.float8e4m3: torch.float8_e4m3fn,
+            dtypes.int8: torch.int8,
+            dtypes.int4: torch.uint8,
+        }
+
+        buffer_metas = {
+            "quanted_gate_up_input": {
+                "shape": (input_shape_m, K),
+                "dtype": torch_dtype_map[a_dtype],
+            },
+            "gate_up_output": {
+                "shape": (real_shape_m, N * 2),
+                "dtype": torch_dtype_map[c_dtype],
+            },
+            "activation_output": {
+                "shape": (real_shape_m, down_input_size),
+                "dtype": torch_dtype_map[c_dtype],
+            },
+            "quanted_down_input": {
+                "shape": (real_shape_m, down_input_size),
+                "dtype": torch_dtype_map[a_dtype],
+            },
+            "down_output": {
+                "shape": output_shape if self.is_batched() else (real_shape_m, K),
+                "dtype": torch_dtype_map[c_dtype],
+            },
+            "output": {
+                "shape": output_shape,
+                "dtype": torch_dtype_map[c_dtype],
+            },
+        }
+
+        for key in buffer_metas:
+            meta = buffer_metas[key]
+            if "quanted" in key and a_dtype.num_bits == 4:
+                meta["shape"] = meta["shape"][:-1] + (meta["shape"][-1] // 2,)
+
+        if num_bits == 16:
+            required_buffers = ["gate_up_output", "activation_output", "down_output"]
+        else:
+            required_buffers = [
+                "quanted_gate_up_input",
+                "gate_up_output",
+                "activation_output",
+                "quanted_down_input",
+                "down_output",
+            ]
+
+        # batched moe use down_output as output
+        if not self.is_batched():
+            required_buffers.append("output")
+
+        return buffer_metas, required_buffers
+
+    def _workspace_shapes(self, M: int, topk: int, activation: MoEActivation):
+        buffer_metas, required_buffers = self.get_buffer_metas(M, topk, activation)
+
+        workspace1_nbytes = 0
+        workspace2_nbytes = 0
+
+        for index, name in enumerate(required_buffers[::-1]):
+            buffer_meta = buffer_metas[name]
+            nelement = math.prod(buffer_meta["shape"])
+            nbytes = nelement * buffer_meta["dtype"].itemsize
+            if index % 2 == 0:
+                workspace1_nbytes = max(workspace1_nbytes, nbytes)
+            else:
+                workspace2_nbytes = max(workspace2_nbytes, nbytes)
+
+        output_key = "down_output" if self.is_batched() else "output"
+        output_shape = buffer_metas[output_key]["shape"]
+
+        return (workspace1_nbytes // 2,), (workspace2_nbytes // 2,), output_shape
+
+    def workspace_shapes(
+        self,
+        M: int,
+        N: int,
+        K: int,
+        topk: int,
+        global_num_experts: int,
+        local_num_experts: int,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+        activation: MoEActivation,
+    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
+        return self._workspace_shapes(M, topk, activation)
+
+    def make_workspaces(self, M: int, topk: int, activation: MoEActivation):
+        shapes = self._workspace_shapes(M, topk, activation)
+        workspace1_shape, workspace2_shape, output_shape = shapes
+        torch_dtype = self.layer.param_dtype
+        workspace1, workspace2 = current_workspace_manager().get_simultaneous(
+            (workspace1_shape, torch_dtype),
+            (workspace2_shape, torch_dtype),
+        )
+        output = _resize_cache(workspace1, output_shape)
+        return workspace1, workspace2, output
+
+    def prepare_buffers(
+        self,
+        workspace1: torch.Tensor,
+        workspace2: torch.Tensor,
+        M: int,
+        topk: int,
+        activation: MoEActivation,
+    ) -> dict[str, torch.Tensor]:
+        buffer_metas, required_buffers = self.get_buffer_metas(M, topk, activation)
+        buffers = {}
+        for index, name in enumerate(required_buffers[::-1]):
+            buffer_meta = buffer_metas[name]
+            workspace = workspace1 if index % 2 == 0 else workspace2
+            workspace = workspace.view(buffer_meta["dtype"])
+            buffers[name] = _resize_cache(workspace, buffer_meta["shape"])
+
+        return buffers
+
+    def apply(
+        self,
+        output: torch.Tensor,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        activation: MoEActivation,
+        global_num_experts: int,
+        expert_map: torch.Tensor | None,
+        a1q_scale: torch.Tensor | None,
+        a2_scale: torch.Tensor | None,
+        workspace13: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+        apply_router_weight_on_input: bool,
+    ):
+        assert not apply_router_weight_on_input
+
+        self.main_apply(
+            hidden_states=hidden_states,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            workspace1=workspace13,
+            workspace2=workspace2,
+            expert_tokens_meta=expert_tokens_meta,
+        )
+
+    def main_apply(
+        self,
+        hidden_states: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        workspace1: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+    ):
+        raise NotImplementedError
+
+    @staticmethod
+    def is_supported_config(
+        cls: type[mk.FusedMoEExperts],
+        moe_config: FusedMoEConfig,
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+        activation_format: mk.FusedMoEActivationFormat,
+    ) -> tuple[bool, str | None]:
+        if activation_format == mk.FusedMoEActivationFormat.BatchedExperts:
+            supported = cls.activation_format() == activation_format
+            reason = "activation_format mismatched"
+        elif activation_format == mk.FusedMoEActivationFormat.Standard:
+            if cls.activation_format() != mk.FusedMoEActivationFormat.Standard:
+                supported = False
+                reason = "activation_format mismatched"
+            else:
+                assert hasattr(cls, "humming_gemm_type")
+                gemm_type = cls.humming_gemm_type().value.lower()
+                preferred_gemm_type = get_humming_moe_gemm_type().lower()
+                supported = preferred_gemm_type == gemm_type
+                reason = "preferred gemm type mismatched"
+        else:
+            supported = False
+            reason = "unsupported activation_format"
+
+        return supported, None if supported else reason
+
+
+class HummingIndexedExperts(HummingExpertsBase):
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        return TopKWeightAndReduceNoOP()
+
+    @staticmethod
+    def activation_format() -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.Standard
+
+    @staticmethod
+    def humming_gemm_type() -> HummingGemmType:
+        return HummingGemmType.INDEXED
+
+    def prepare_humming_moe_kwargs(
+        self,
+        topk_ids: torch.Tensor,
+        expert_map: torch.Tensor | None,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+    ) -> tuple[dict[str, Any], dict[str, Any]]:
+        valid_shape_m = self.estimate_local_valid_shape_m(topk_ids)
+
+        for min_shape_m, max_shape_m, config in self.w13_tuning_config:
+            if valid_shape_m > min_shape_m and valid_shape_m <= max_shape_m:
+                moe_block_size = config["block_shape"][0]
+                break
+        else:
+            raise ValueError(f"cannot found moe_block_size for shape {valid_shape_m}")
+
+        sorted_ids, expert_ids, num_tokens_padded = moe_align_block_size(
+            topk_ids=topk_ids,
+            block_size=moe_block_size,
+            num_experts=self.global_num_experts,
+            expert_map=expert_map,
+            ignore_invalid_experts=True,
+        )
+
+        moe_common_kwargs = {
+            "sorted_ids": sorted_ids,
+            "expert_ids": expert_ids,
+            "num_tokens_padded": num_tokens_padded,
+            "compute_config": self.compute_config_str,
+            "valid_shape_m": valid_shape_m,
+        }
+
+        top_k = topk_ids.size(1)
+        moe_kwargs1 = {"top_k": top_k, "tuning_config": self.w13_tuning_config_str}
+        moe_kwargs2 = {"top_k": 1, "tuning_config": self.w2_tuning_config_str}
+        moe_kwargs1.update(moe_common_kwargs)
+        moe_kwargs2.update(moe_common_kwargs)
+
+        return moe_kwargs1, moe_kwargs2
+
+    def main_apply(
+        self,
+        hidden_states: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        workspace1: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+    ):
+        hidden_states = hidden_states.view(-1, hidden_states.size(-1))
+        buffers = self.prepare_buffers(
+            workspace1,
+            workspace2,
+            topk_ids.size(0),
+            topk_ids.size(1),
+            self.layer.activation,
+        )
+
+        moe_kwargs1, moe_kwargs2 = self.prepare_humming_moe_kwargs(
+            topk_ids=topk_ids,
+            expert_map=self.layer.expert_map,
+            expert_tokens_meta=expert_tokens_meta,
+        )
+
+        inputs, input_scale = HummingMethod.may_quant_input(
+            layer=self.layer,
+            inputs=hidden_states,
+            quanted_input=buffers.get("quanted_gate_up_input", None),
+            sublayer_name="w13",
+        )
+
+        HummingMethod.forward_layer(
+            layer=self.layer,
+            inputs=inputs,
+            input_scale=input_scale,
+            outputs=buffers["gate_up_output"],
+            sublayer_name="w13",
+            **moe_kwargs1,
+        )
+
+        self.activation(
+            activation=self.layer.activation,
+            input=buffers["gate_up_output"],
+            output=buffers["activation_output"],
+        )
+
+        inputs, input_scale = HummingMethod.may_quant_input(
+            layer=self.layer,
+            inputs=buffers["activation_output"],
+            quanted_input=buffers.get("quanted_down_input", None),
+            sublayer_name="w2",
+        )
+
+        HummingMethod.forward_layer(
+            layer=self.layer,
+            inputs=inputs,
+            input_scale=input_scale,
+            outputs=buffers["down_output"].view(-1, hidden_states.size(-1)),
+            sublayer_name="w2",
+            **moe_kwargs2,
+        )
+
+        moe_fused_mul_sum(
+            inputs=buffers["down_output"].view(*topk_ids.shape, -1),
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            expert_map=self.layer.expert_map,
+            outputs=buffers["output"],
+        )
+
+
+class HummingGroupedExperts(HummingExpertsBase):
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        return TopKWeightAndReduceNoOP()
+
+    @staticmethod
+    def activation_format() -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.Standard
+
+    @staticmethod
+    def humming_gemm_type() -> HummingGemmType:
+        return HummingGemmType.GROUPED_CONTIGUOUS
+
+    def main_apply(
+        self,
+        hidden_states: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        workspace1: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+    ):
+        valid_shape_m = self.estimate_local_valid_shape_m(topk_ids)
+
+        buffers = self.prepare_buffers(
+            workspace1,
+            workspace2,
+            topk_ids.size(0),
+            topk_ids.size(1),
+            self.layer.activation,
+        )
+
+        hidden_states, _, expert_first_token_offset, inv_perm, _ = moe_permute(
+            hidden_states=hidden_states,
+            a1q_scale=None,
+            topk_ids=topk_ids,
+            n_expert=self.global_num_experts,
+            n_local_expert=self.num_experts,
+            expert_map=self.layer.expert_map,
+        )
+
+        inputs, input_scale = HummingMethod.may_quant_input(
+            layer=self.layer,
+            inputs=hidden_states,
+            quanted_input=buffers.get("quanted_gate_up_input", None),
+            sublayer_name="w13",
+        )
+
+        HummingMethod.forward_layer(
+            layer=self.layer,
+            inputs=inputs,
+            input_scale=input_scale,
+            outputs=buffers["gate_up_output"],
+            valid_shape_m=valid_shape_m,
+            expert_layout=expert_first_token_offset,
+            compute_config=self.compute_config_str,
+            tuning_config=self.w13_tuning_config_str,
+            sublayer_name="w13",
+        )
+
+        self.activation(
+            activation=self.layer.activation,
+            input=buffers["gate_up_output"],
+            output=buffers["activation_output"],
+        )
+
+        inputs, input_scale = HummingMethod.may_quant_input(
+            layer=self.layer,
+            inputs=buffers["activation_output"],
+            quanted_input=buffers.get("quanted_down_input", None),
+            sublayer_name="w2",
+        )
+
+        HummingMethod.forward_layer(
+            layer=self.layer,
+            inputs=inputs,
+            input_scale=input_scale,
+            outputs=buffers["down_output"],
+            valid_shape_m=valid_shape_m,
+            expert_layout=expert_first_token_offset,
+            compute_config=self.compute_config_str,
+            tuning_config=self.w2_tuning_config_str,
+            sublayer_name="w2",
+        )
+
+        moe_unpermute(
+            out=buffers["output"],
+            permuted_hidden_states=buffers["down_output"].view(*topk_ids.shape, -1),
+            topk_weights=topk_weights,
+            inv_permuted_idx=inv_perm,
+            expert_first_token_offset=expert_first_token_offset,
+        )
+
+
+class BatchedHummingGroupedExperts(HummingExpertsBase):
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        return TopKWeightAndReduceDelegate()
+
+    @staticmethod
+    def activation_format() -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.BatchedExperts
+
+    @staticmethod
+    def humming_gemm_type() -> HummingGemmType:
+        return HummingGemmType.GROUPED_MASKED
+
+    def main_apply(
+        self,
+        hidden_states: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        workspace1: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_tokens_meta: mk.ExpertTokensMetadata | None,
+    ):
+        assert expert_tokens_meta is not None
+        hidden_states = hidden_states.view(-1, hidden_states.size(-1))
+        valid_shape_m = self.estimate_local_valid_shape_m(topk_ids)
+        expert_num_tokens = expert_tokens_meta.expert_num_tokens
+
+        buffers = self.prepare_buffers(
+            workspace1,
+            workspace2,
+            topk_ids.size(0),
+            topk_ids.size(1),
+            self.layer.activation,
+        )
+
+        inputs, input_scale = HummingMethod.may_quant_input(
+            layer=self.layer,
+            inputs=hidden_states,
+            quanted_input=buffers.get("quanted_gate_up_input", None),
+            sublayer_name="w13",
+        )
+
+        HummingMethod.forward_layer(
+            layer=self.layer,
+            inputs=inputs,
+            input_scale=input_scale,
+            outputs=buffers["gate_up_output"],
+            valid_shape_m=valid_shape_m,
+            expert_layout=expert_num_tokens,
+            compute_config=self.compute_config_str,
+            tuning_config=self.w13_tuning_config_str,
+            sublayer_name="w13",
+        )
+
+        self.activation(
+            activation=self.layer.activation,
+            input=buffers["gate_up_output"],
+            output=buffers["activation_output"],
+        )
+
+        inputs, input_scale = HummingMethod.may_quant_input(
+            layer=self.layer,
+            inputs=buffers["activation_output"],
+            quanted_input=buffers.get("quanted_down_input", None),
+            sublayer_name="w2",
+        )
+
+        HummingMethod.forward_layer(
+            layer=self.layer,
+            inputs=inputs,
+            input_scale=input_scale,
+            outputs=buffers["down_output"].view(-1, hidden_states.size(-1)),
+            valid_shape_m=valid_shape_m,
+            expert_layout=expert_num_tokens,
+            compute_config=self.compute_config_str,
+            tuning_config=self.w2_tuning_config_str,
+            sublayer_name="w2",
+        )
diff --git a/vllm/model_executor/layers/fused_moe/fused_moe.py b/vllm/model_executor/layers/fused_moe/fused_moe.py
index 6de25da051ad..8d974ea5671d 100644
--- a/vllm/model_executor/layers/fused_moe/fused_moe.py
+++ b/vllm/model_executor/layers/fused_moe/fused_moe.py
@@ -20,33 +20,16 @@
 )
 from vllm.model_executor.layers.fused_moe.config import (
     FUSED_MOE_UNQUANTIZED_CONFIG,
-    FusedMoEConfig,
-    FusedMoEParallelConfig,
     FusedMoEQuantConfig,
     _get_config_dtype_str,
 )
 from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
     moe_align_block_size,
 )
-from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
-    TopKWeightAndReduceNoOP,
-)
 from vllm.model_executor.layers.fused_moe.utils import (
-    _resize_cache,
     disable_inplace,
     moe_kernel_quantize_input,
 )
-from vllm.model_executor.layers.quantization.utils.mxfp4_utils import dequant_mxfp4
-from vllm.model_executor.layers.quantization.utils.mxfp6_utils import dequant_mxfp6
-from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    QuantKey,
-    kFp8Dynamic128Sym,
-    kFp8DynamicTensorSym,
-    kFp8DynamicTokenSym,
-    kFp8Static128BlockSym,
-    kFp8StaticChannelSym,
-    kFp8StaticTensorSym,
-)
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils.torch_utils import direct_register_custom_op
@@ -1091,7 +1074,6 @@ def get_moe_configs(
         "Using default MoE config. Performance might be sub-optimal! "
         "Config file not found at %s",
         ", ".join(config_file_paths),
-        scope="local",
     )
     return None
 
@@ -1551,6 +1533,55 @@ def dispatch_fused_experts_func(inplace: bool) -> Callable[..., torch.Tensor]:
     return torch_vllm_outplace_fused_experts
 
 
+def _prepare_expert_assignment(
+    topk_ids: torch.Tensor,
+    config: dict[str, Any],
+    num_tokens: int,
+    top_k_num: int,
+    global_num_experts: int,
+    expert_map: torch.Tensor | None,
+    *,
+    use_int8_w8a16: bool = False,
+    use_int4_w4a16: bool = False,
+    block_shape: list[int] | None = None,
+    ignore_invalid_experts: bool = False,
+) -> tuple[torch.Tensor | None, torch.Tensor, torch.Tensor]:
+    """Prepare expert assignments for the aligned and low-latency Triton paths."""
+    # SPARSITY_FACTOR is a heuristic margin ensuring tokens_in_chunk * top_k
+    # activates only a small fraction of total experts
+    # Skips moe_align_block_size and activates the `sorted_token_ids is None`
+    # path of the fused_moe_kernel kernel
+    naive_block_assignment = (
+        expert_map is None
+        and num_tokens * top_k_num * 4 <= global_num_experts
+        and not (
+            (use_int8_w8a16 or use_int4_w4a16)
+            and block_shape is not None
+            and block_shape[1] > 0
+        )
+    )
+
+    if naive_block_assignment:
+        return (
+            None,
+            topk_ids.view(-1),
+            torch.full(
+                (1,),
+                topk_ids.numel() * config["BLOCK_SIZE_M"],
+                dtype=torch.int32,
+                device=topk_ids.device,
+            ),
+        )
+
+    return moe_align_block_size(
+        topk_ids,
+        config["BLOCK_SIZE_M"],
+        global_num_experts,
+        expert_map,
+        ignore_invalid_experts=ignore_invalid_experts,
+    )
+
+
 # TODO (bnell): replace this with modular op.  Can get rid of inplace/outplace
 # torch ops.
 def fused_experts(
@@ -1657,22 +1688,18 @@ def fused_experts_impl(
     w1_bias: torch.Tensor | None = None,
     w2_bias: torch.Tensor | None = None,
 ) -> torch.Tensor:
+    if ocp_mx_scheme is not None:
+        raise NotImplementedError(
+            f"Using ocp_mx_scheme={ocp_mx_scheme} in functional fused_experts call is "
+            "deprecated. Please use OCP_MXQuantizationEmulationTritonExperts."
+        )
+
     # Convert string activation to enum for internal use
     activation_enum = MoEActivation.from_str(activation)
 
     # Check constraints.
     if use_int4_w4a16:
         assert hidden_states.size(1) // 2 == w1.size(2), "Hidden size mismatch"
-    elif ocp_mx_scheme is not None:
-        if ocp_mx_scheme.startswith("w_mxfp4"):
-            # 16bit activation and fp4x2 packed weight
-            assert hidden_states.size(1) == w1.size(2) * 2, "hidden size mismatch"
-        elif ocp_mx_scheme.startswith("w_mxfp6"):
-            assert hidden_states.size(1) == (w1.size(2) * 4) // 3, (
-                "hidden size mismatch"
-            )
-        else:
-            raise NotImplementedError(f"Unsupported ocp_mx_scheme={ocp_mx_scheme}")
     else:
         assert hidden_states.size(1) == w1.size(2), (
             f"Hidden size mismatch {hidden_states.size(1)} != {w1.size(2)}"
@@ -1697,7 +1724,6 @@ def fused_experts_impl(
         use_fp8_w8a8=use_fp8_w8a8,
         use_int8_w8a16=use_int8_w8a16,
         use_int4_w4a16=use_int4_w4a16,
-        ocp_mx_scheme=ocp_mx_scheme,
         dtype=hidden_states.dtype,
     )
 
@@ -1706,7 +1732,7 @@ def fused_experts_impl(
     quant_dtype = _get_config_quant_dtype(
         use_fp8_w8a8=use_fp8_w8a8,
         use_int8_w8a8=use_int8_w8a8,
-        ocp_mx_scheme=ocp_mx_scheme,
+        ocp_mx_scheme=None,
     )
 
     get_config_func = functools.partial(
@@ -1751,77 +1777,27 @@ def fused_experts_impl(
 
     out_hidden_states = hidden_states if inplace else torch.empty_like(hidden_states)
 
-    if ocp_mx_scheme is not None:
-        # TODO: On platforms for which `current_platform.supports_mx()` is True
-        # and for which we have a native OCP mx fused MOE kernel,
-        # this dequantization step should not be done.
-        if ocp_mx_scheme.startswith("w_mxfp4"):
-            # Weight has to be dequantized for mxfp4 emulation.
-            w1 = dequant_mxfp4(w1, w1_scale, hidden_states.dtype)
-            w1_scale = None
-            w2 = dequant_mxfp4(w2, w2_scale, hidden_states.dtype)
-            w2_scale = None
-        elif ocp_mx_scheme.startswith("w_mxfp6_e3m2"):
-            w1 = dequant_mxfp6(
-                w1, w1_scale, quant_dtype="fp6_e3m2", float_dtype=hidden_states.dtype
-            )
-            w1_scale = None
-            w2 = dequant_mxfp6(
-                w2, w2_scale, quant_dtype="fp6_e3m2", float_dtype=hidden_states.dtype
-            )
-            w2_scale = None
-        elif ocp_mx_scheme.startswith("w_mxfp6_e2m3"):
-            w1 = dequant_mxfp6(
-                w1, w1_scale, quant_dtype="fp6_e2m3", float_dtype=hidden_states.dtype
-            )
-            w1_scale = None
-            w2 = dequant_mxfp6(
-                w2, w2_scale, quant_dtype="fp6_e2m3", float_dtype=hidden_states.dtype
-            )
-            w2_scale = None
-        else:
-            raise NotImplementedError(f"Unsupported ocp_mx_scheme={ocp_mx_scheme}")
-
     qhidden_states, a1q_scale = moe_kernel_quantize_input(
         A=hidden_states,
         A_scale=a1_scale,
         quant_dtype=quant_dtype,
         per_act_token_quant=per_channel_quant,
         block_shape=block_shape,
-        ocp_mx_scheme=ocp_mx_scheme,
     )
 
-    # SPARSITY_FACTOR is a heuristic margin ensuring num_tokens * top_k
-    # activates only a small fraction of total experts
-    SPARSITY_FACTOR = 4
-    # block quantized code path is not implemented yet.
-    naive_block_assignment = (
-        expert_map is None
-        and num_tokens * top_k_num * SPARSITY_FACTOR <= global_num_experts
-        and not (
-            (use_int8_w8a16 or use_int4_w4a16)
-            and block_shape is not None
-            and block_shape[1] > 0
-        )
+    sorted_token_ids, expert_ids, num_tokens_post_padded = _prepare_expert_assignment(
+        topk_ids,
+        config,
+        num_tokens,
+        top_k_num,
+        global_num_experts,
+        expert_map,
+        use_int8_w8a16=use_int8_w8a16,
+        use_int4_w4a16=use_int4_w4a16,
+        block_shape=block_shape,
+        ignore_invalid_experts=True,
     )
 
-    if not naive_block_assignment:
-        sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size(
-            topk_ids,
-            config["BLOCK_SIZE_M"],
-            global_num_experts,
-            expert_map,
-            ignore_invalid_experts=True,
-        )
-    else:
-        max_num_tokens_padded = topk_ids.numel() * config["BLOCK_SIZE_M"]
-        expert_ids = topk_ids.view(-1)
-        num_tokens_post_padded = torch.empty(
-            (1), dtype=torch.int32, device=topk_ids.device
-        )
-        num_tokens_post_padded.fill_(max_num_tokens_padded)
-        sorted_token_ids = None
-
     dispatch_fused_moe_kernel(
         qhidden_states,
         w1,
@@ -1856,7 +1832,6 @@ def fused_experts_impl(
         quant_dtype=quant_dtype,
         per_act_token_quant=per_channel_quant,
         block_shape=block_shape,
-        ocp_mx_scheme=ocp_mx_scheme,
     )
 
     if expert_map is not None:
@@ -1892,428 +1867,3 @@ def fused_experts_impl(
     )
 
     return out_hidden_states
-
-
-class TritonExperts(mk.FusedMoEExpertsModular):
-    """Triton-based fused MoE expert implementation."""
-
-    def __init__(
-        self,
-        moe_config: FusedMoEConfig,
-        quant_config: FusedMoEQuantConfig,
-    ):
-        super().__init__(moe_config, quant_config)
-
-    @staticmethod
-    def activation_format() -> mk.FusedMoEActivationFormat:
-        return mk.FusedMoEActivationFormat.Standard
-
-    @staticmethod
-    def _supports_current_device() -> bool:
-        return current_platform.is_cuda_alike() or current_platform.is_xpu()
-
-    @staticmethod
-    def _supports_no_act_and_mul() -> bool:
-        return True
-
-    @staticmethod
-    def _supports_quant_scheme(
-        weight_key: QuantKey | None,
-        activation_key: QuantKey | None,
-    ) -> bool:
-        p = current_platform
-        if p.is_rocm():
-            from vllm.platforms.rocm import on_gfx9, on_gfx12x
-
-            is_rocm_on_gfx9 = on_gfx9()
-            is_rocm_on_gfx12x = on_gfx12x()
-        else:
-            is_rocm_on_gfx9 = False
-            is_rocm_on_gfx12x = False
-
-        device_supports_fp8 = (
-            is_rocm_on_gfx9
-            or is_rocm_on_gfx12x
-            or (p.is_cuda() and p.has_device_capability((8, 9)))
-            or p.is_xpu()
-        )
-
-        if not device_supports_fp8:
-            return (weight_key, activation_key) == (None, None)
-
-        SUPPORTED_W_A = [
-            (None, None),
-            (kFp8Static128BlockSym, kFp8Dynamic128Sym),
-            (kFp8StaticChannelSym, kFp8DynamicTokenSym),
-            (kFp8StaticTensorSym, kFp8DynamicTokenSym),
-            (kFp8StaticTensorSym, kFp8StaticTensorSym),
-            (kFp8StaticTensorSym, kFp8DynamicTensorSym),
-        ]
-        return (weight_key, activation_key) in SUPPORTED_W_A
-
-    @staticmethod
-    def _supports_activation(activation: MoEActivation) -> bool:
-        return activation in [
-            MoEActivation.SILU,
-            MoEActivation.GELU,
-            MoEActivation.SWIGLUOAI,
-            MoEActivation.SWIGLUSTEP,
-            MoEActivation.SILU_NO_MUL,
-            MoEActivation.GELU_NO_MUL,
-            MoEActivation.RELU2_NO_MUL,
-        ]
-
-    @staticmethod
-    def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:
-        return not (
-            moe_parallel_config.use_fi_nvl_two_sided_kernels
-            or moe_parallel_config.use_fi_nvl_one_sided_kernels
-        )
-
-    @staticmethod
-    def _supports_batch_invariance():
-        return True
-
-    def supports_expert_map(self) -> bool:
-        return True
-
-    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
-        return TopKWeightAndReduceNoOP()
-
-    def workspace_shapes(
-        self,
-        M: int,
-        N: int,
-        K: int,
-        topk: int,
-        global_num_experts: int,
-        local_num_experts: int,
-        expert_tokens_meta: mk.ExpertTokensMetadata | None,
-        activation: MoEActivation,
-    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
-        activation_out_dim = self.adjust_N_for_activation(N, activation)
-        workspace1 = (M, topk, max(activation_out_dim, K))
-        workspace2 = (M, topk, max(N, K))
-        output = (M, K)
-        return (workspace1, workspace2, output)
-
-    def apply(
-        self,
-        output: torch.Tensor,
-        hidden_states: torch.Tensor,
-        w1: torch.Tensor,
-        w2: torch.Tensor,
-        topk_weights: torch.Tensor,
-        topk_ids: torch.Tensor,
-        activation: MoEActivation,
-        global_num_experts: int,
-        expert_map: torch.Tensor | None,
-        a1q_scale: torch.Tensor | None,
-        a2_scale: torch.Tensor | None,
-        workspace13: torch.Tensor,
-        workspace2: torch.Tensor,
-        expert_tokens_meta: mk.ExpertTokensMetadata | None,
-        apply_router_weight_on_input: bool,
-    ):
-        # Check constraints.
-        if self.quant_config.use_int4_w4a16:
-            assert hidden_states.size(-1) // 2 == w1.size(2), "Hidden size mismatch"
-        else:
-            assert hidden_states.size(-1) == w1.size(2), (
-                f"Hidden size mismatch {hidden_states.size(-1)} != {w1.size(2)}"
-            )
-
-        assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
-        assert hidden_states.dim() == 2
-        assert w1.stride(-1) == 1, "Stride of last dimension must be 1"
-        assert w2.stride(-1) == 1, "Stride of last dimension must be 1"
-        assert hidden_states.dtype in [
-            torch.float32,
-            torch.float16,
-            torch.bfloat16,
-            torch.float8_e4m3fn,
-            torch.float8_e4m3fnuz,
-        ]
-
-        E, num_tokens, N, K, top_k_num = self.moe_problem_size(
-            hidden_states, w1, w2, topk_ids
-        )
-
-        if global_num_experts == -1:
-            global_num_experts = E
-
-        config = try_get_optimal_moe_config(
-            w1.size(),
-            w2.size(),
-            top_k_num,
-            self.quant_config.config_name(hidden_states.dtype),
-            num_tokens,
-            block_shape=self.block_shape,
-        )
-
-        if hidden_states.dtype == torch.bfloat16:
-            compute_type = tl.bfloat16
-        elif hidden_states.dtype == torch.float16:
-            compute_type = tl.float16
-        elif hidden_states.dtype == torch.float32:
-            compute_type = tl.float32
-        elif (
-            hidden_states.dtype == torch.float8_e4m3fn
-            or hidden_states.dtype == torch.float8_e4m3fnuz
-        ):
-            compute_type = tl.bfloat16
-        else:
-            raise ValueError(f"Unsupported compute_type: {hidden_states.dtype}")
-
-        # Note that the output tensor might be in workspace1
-        intermediate_cache1 = _resize_cache(workspace2, (num_tokens, top_k_num, N))
-        cache2_dim = self.adjust_N_for_activation(N, activation)
-        intermediate_cache2 = _resize_cache(
-            workspace13, (num_tokens * top_k_num, cache2_dim)
-        )
-        intermediate_cache3 = _resize_cache(workspace2, (num_tokens, top_k_num, K))
-
-        sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size(
-            topk_ids, config["BLOCK_SIZE_M"], global_num_experts, expert_map
-        )
-
-        invoke_fused_moe_triton_kernel(
-            hidden_states,
-            w1,
-            intermediate_cache1,
-            a1q_scale,
-            self.w1_scale,
-            None,  # topk_weights
-            sorted_token_ids,
-            expert_ids,
-            num_tokens_post_padded,
-            False,  # mul_routed_weights
-            top_k_num,
-            config,
-            compute_type=compute_type,
-            use_fp8_w8a8=self.quant_config.use_fp8_w8a8,
-            use_int8_w8a8=self.quant_config.use_int8_w8a8,
-            use_int8_w8a16=self.quant_config.use_int8_w8a16,
-            use_int4_w4a16=self.quant_config.use_int4_w4a16,
-            per_channel_quant=self.per_act_token_quant,
-            block_shape=self.block_shape,
-            B_bias=self.w1_bias,
-        )
-
-        self.activation(
-            activation, intermediate_cache2, intermediate_cache1.view(-1, N)
-        )
-
-        a2q_scale: torch.Tensor | None = None
-
-        qintermediate_cache2, a2q_scale = moe_kernel_quantize_input(
-            intermediate_cache2,
-            a2_scale,
-            self.quant_dtype,
-            self.per_act_token_quant,
-            self.block_shape,
-        )
-
-        invoke_fused_moe_triton_kernel(
-            qintermediate_cache2,
-            w2,
-            intermediate_cache3,
-            a2q_scale,
-            self.w2_scale,
-            topk_weights,
-            sorted_token_ids,
-            expert_ids,
-            num_tokens_post_padded,
-            not apply_router_weight_on_input,
-            1,
-            config,
-            compute_type=compute_type,
-            use_fp8_w8a8=self.quant_config.use_fp8_w8a8,
-            use_int8_w8a8=self.quant_config.use_int8_w8a8,
-            use_int8_w8a16=self.quant_config.use_int8_w8a16,
-            use_int4_w4a16=self.quant_config.use_int4_w4a16,
-            per_channel_quant=self.per_act_token_quant,
-            block_shape=self.block_shape,
-            B_bias=self.w2_bias,
-        )
-
-        # separate function is required for MoE + LoRA
-        self.moe_sum(intermediate_cache3, output)
-
-    def moe_sum(self, input: torch.Tensor, output: torch.Tensor) -> None:
-        ops.moe_sum(input, output)
-
-
-class TritonWNA16Experts(TritonExperts):
-    @staticmethod
-    def _supports_current_device() -> bool:
-        raise NotImplementedError(
-            "TritonWNA16Experts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
-
-    @staticmethod
-    def _supports_no_act_and_mul() -> bool:
-        raise NotImplementedError(
-            "TritonWNA16Experts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
-
-    @staticmethod
-    def _supports_quant_scheme(
-        weight_key: QuantKey | None,
-        activation_key: QuantKey | None,
-    ) -> bool:
-        raise NotImplementedError(
-            "TritonWNA16Experts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
-
-    @staticmethod
-    def _supports_activation(activation: MoEActivation) -> bool:
-        raise NotImplementedError(
-            "TritonWNA16Experts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
-
-    @staticmethod
-    def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:
-        raise NotImplementedError(
-            "TritonWNA16Experts is not yet used by an Oracle. "
-            "This method should not be called."
-        )
-
-    def apply(
-        self,
-        output: torch.Tensor,
-        hidden_states: torch.Tensor,
-        w1: torch.Tensor,
-        w2: torch.Tensor,
-        topk_weights: torch.Tensor,
-        topk_ids: torch.Tensor,
-        activation: MoEActivation,
-        global_num_experts: int,
-        expert_map: torch.Tensor | None,
-        a1q_scale: torch.Tensor | None,
-        a2_scale: torch.Tensor | None,
-        workspace13: torch.Tensor,
-        workspace2: torch.Tensor,
-        expert_tokens_meta: mk.ExpertTokensMetadata | None,
-        apply_router_weight_on_input: bool,
-    ):
-        # Check constraints.
-        if self.quant_config.use_int4_w4a16:
-            assert hidden_states.size(-1) // 2 == w1.size(2), "Hidden size mismatch"
-        else:
-            assert hidden_states.size(-1) == w1.size(2), (
-                f"Hidden size mismatch {hidden_states.size(-1)} != {w1.size(2)}"
-            )
-
-        assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
-        assert hidden_states.dim() == 2
-        assert w1.stride(-1) == 1, "Stride of last dimension must be 1"
-        assert w2.stride(-1) == 1, "Stride of last dimension must be 1"
-        assert hidden_states.dtype in [
-            torch.float32,
-            torch.float16,
-            torch.bfloat16,
-            torch.float8_e4m3fn,
-            torch.float8_e4m3fnuz,
-        ]
-
-        E, num_tokens, N, K, top_k_num = self.moe_problem_size(
-            hidden_states, w1, w2, topk_ids
-        )
-
-        if global_num_experts == -1:
-            global_num_experts = E
-
-        config = try_get_optimal_moe_config(
-            w1.size(),
-            w2.size(),
-            top_k_num,
-            self.quant_config.config_name(hidden_states.dtype),
-            num_tokens,
-            block_shape=self.block_shape,
-        )
-
-        if hidden_states.dtype == torch.bfloat16:
-            compute_type = tl.bfloat16
-        elif hidden_states.dtype == torch.float16:
-            compute_type = tl.float16
-        elif hidden_states.dtype == torch.float32:
-            compute_type = tl.float32
-        elif (
-            hidden_states.dtype == torch.float8_e4m3fn
-            or hidden_states.dtype == torch.float8_e4m3fnuz
-        ):
-            compute_type = tl.bfloat16
-        else:
-            raise ValueError(f"Unsupported compute_type: {hidden_states.dtype}")
-
-        # Note that the output tensor might be in workspace1
-        intermediate_cache1 = _resize_cache(workspace2, (num_tokens, top_k_num, N))
-        activation_out_dim = self.adjust_N_for_activation(N, activation)
-        intermediate_cache2 = _resize_cache(
-            workspace13, (num_tokens * top_k_num, activation_out_dim)
-        )
-        intermediate_cache3 = _resize_cache(workspace2, (num_tokens, top_k_num, K))
-
-        sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size(
-            topk_ids, config["BLOCK_SIZE_M"], global_num_experts, expert_map
-        )
-
-        invoke_fused_moe_wna16_triton_kernel(
-            hidden_states,
-            w1,
-            intermediate_cache1,
-            self.w1_scale,
-            self.quant_config.w1_zp,
-            None,  # topk_weights
-            sorted_token_ids,
-            expert_ids,
-            num_tokens_post_padded,
-            False,  # mul_routed_weights
-            top_k_num,
-            config,
-            compute_type=compute_type,
-            use_int8_w8a16=self.quant_config.use_int8_w8a16,
-            use_int4_w4a16=self.quant_config.use_int4_w4a16,
-            block_shape=self.block_shape,
-        )
-
-        self.activation(
-            activation, intermediate_cache2, intermediate_cache1.view(-1, N)
-        )
-
-        a2q_scale: torch.Tensor | None = None
-
-        qintermediate_cache2, a2q_scale = moe_kernel_quantize_input(
-            intermediate_cache2,
-            a2_scale,
-            self.quant_dtype,
-            self.per_act_token_quant,
-            self.block_shape,
-        )
-
-        invoke_fused_moe_wna16_triton_kernel(
-            qintermediate_cache2,
-            w2,
-            intermediate_cache3,
-            self.w2_scale,
-            self.quant_config.w2_zp,
-            topk_weights,
-            sorted_token_ids,
-            expert_ids,
-            num_tokens_post_padded,
-            not apply_router_weight_on_input,
-            1,
-            config,
-            compute_type=compute_type,
-            use_int8_w8a16=self.quant_config.use_int8_w8a16,
-            use_int4_w4a16=self.quant_config.use_int4_w4a16,
-            block_shape=self.block_shape,
-        )
-
-        # separate function is required for MoE + LoRA
-        self.moe_sum(intermediate_cache3, output)
diff --git a/vllm/model_executor/layers/fused_moe/fused_moe_method_base.py b/vllm/model_executor/layers/fused_moe/fused_moe_method_base.py
index a239dfea92e4..7d54e2b717d6 100644
--- a/vllm/model_executor/layers/fused_moe/fused_moe_method_base.py
+++ b/vllm/model_executor/layers/fused_moe/fused_moe_method_base.py
@@ -169,5 +169,6 @@ def apply_monolithic(
         layer: "FusedMoE",  # type: ignore[name-defined] # noqa: F821
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
     ) -> torch.Tensor:
         raise NotImplementedError
diff --git a/vllm/model_executor/layers/fused_moe/layer.py b/vllm/model_executor/layers/fused_moe/layer.py
index 190a9cc3b5d7..2eef89793a6e 100644
--- a/vllm/model_executor/layers/fused_moe/layer.py
+++ b/vllm/model_executor/layers/fused_moe/layer.py
@@ -26,20 +26,23 @@
     FusedMoEQuantConfig,
     RoutingMethodType,
 )
+from vllm.model_executor.layers.fused_moe.experts.rocm_aiter_moe import (
+    init_aiter_topK_meta_data,
+)
 from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
     FusedMoEMethodBase,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe_modular_method import (
     FusedMoEModularMethod,
 )
-from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
-    init_aiter_topK_meta_data,
-)
 from vllm.model_executor.layers.fused_moe.router.router_factory import (
     create_fused_moe_router,
 )
-from vllm.model_executor.layers.fused_moe.runner.moe_runner_factory import (
-    create_moe_runner,
+from vllm.model_executor.layers.fused_moe.runner.moe_runner import (
+    MoERunner,
+)
+from vllm.model_executor.layers.fused_moe.runner.moe_runner_interface import (
+    MoERunnerInterface,
 )
 from vllm.model_executor.layers.fused_moe.runner.shared_experts import (
     SharedExperts,
@@ -177,8 +180,7 @@ def determine_expert_placement_strategy(
             return "linear"
         if (
             moe_parallel_config.use_all2all_kernels
-            and not moe_parallel_config.use_deepep_ll_kernels
-            and not moe_parallel_config.use_nixl_ep_kernels
+            and not moe_parallel_config.needs_round_robin_routing_tables
         ):
             logger.warning(
                 "Round-robin expert placement currently only supports "
@@ -230,13 +232,23 @@ class FusedMoE(PluggableLayer):
         hidden_size: Input hidden state size of the transformer
         intermediate_size: Intermediate size of the experts
         params_dtype: Data type for the parameters.
-        reduce_results: Whether to all_reduce on the output of the layer
         renormalize: Whether to renormalize the logits in the fused_moe kernel
         quant_config: Quantization configure.
         enable_eplb: Whether to enable expert parallelism load balancer.
         router_logits_dtype: Data type for router logits buffers.
+        routed_scaling_factor: A scaling factor that is applied to the topk_weights
+                               by the router or the output of the layer depending
+                               on the value of `apply_routed_scale_to_output`
+        apply_routed_scale_to_output: Determine whether or not `routed_scaling_factor`
+                                      is applied to the topk_weights or to the experts
+                                      output. It is applied to the experts output
+                                      instead of the topk_weights when this feature is
+                                      not supported by the router (or the experts).
     """
 
+    # Auto-incrementing layer ID for routing replay buffer binding.
+    _next_moe_layer_id: int = 0
+
     # --8<-- [end:fused_moe]
 
     def __init__(
@@ -246,7 +258,6 @@ def __init__(
         hidden_size: int,
         intermediate_size: int,
         params_dtype: torch.dtype | None = None,
-        reduce_results: bool = False,
         renormalize: bool = True,
         use_grouped_topk: bool = False,
         num_expert_group: int | None = None,
@@ -260,6 +271,7 @@ def __init__(
         custom_routing_function: Callable | None = None,
         scoring_func: str = "softmax",
         routed_scaling_factor: float = 1.0,
+        swiglu_limit: float | None = None,
         e_score_correction_bias: torch.Tensor | None = None,
         apply_router_weight_on_input: bool = False,
         activation: str = "silu",
@@ -273,12 +285,18 @@ def __init__(
         router_logits_dtype: torch.dtype | None = None,
         gate: torch.nn.Module | None = None,
         shared_experts: torch.nn.Module | None = None,
+        shared_expert_gate: torch.nn.Module | None = None,
         routed_input_transform: torch.nn.Module | None = None,
+        routed_output_transform: torch.nn.Module | None = None,
+        apply_routed_scale_to_output: bool = False,
         zero_expert_type: str | None = None,
+        hash_indices_table: torch.Tensor | None = None,
     ):
         super().__init__()
 
-        self._routed_input_transform = routed_input_transform
+        # Assign unique layer ID for routing replay buffer binding.
+        self.moe_layer_id = FusedMoE._next_moe_layer_id
+        FusedMoE._next_moe_layer_id += 1
 
         if params_dtype is None:
             params_dtype = torch.get_default_dtype()
@@ -286,6 +304,7 @@ def __init__(
 
         vllm_config = get_current_vllm_config()
         self.vllm_config = vllm_config
+        self.swiglu_limit = swiglu_limit
 
         # FIXME (varun): We should have a better way of inferring the activation
         # datatype. This works for now as the tensor datatype entering the MoE
@@ -352,6 +371,8 @@ def __init__(
             if n_shared_experts is not None and self.aiter_fmoe_shared_expert_enabled
             else 0
         )
+        self.shared_expert_gate = shared_expert_gate
+
         if (
             not self.aiter_fmoe_shared_expert_enabled
             and self.num_fused_shared_experts != 0
@@ -425,7 +446,6 @@ def __init__(
 
         assert intermediate_size % self.tp_size == 0
         intermediate_size_per_partition = intermediate_size // self.tp_size
-        self.reduce_results = reduce_results
         self.renormalize = renormalize
 
         # TODO(bnell): these attributes are only used by monolithic kernels.
@@ -437,10 +457,18 @@ def __init__(
         self.topk_group = topk_group
         self.custom_routing_function = custom_routing_function
         self.scoring_func = scoring_func
-        self.routed_scaling_factor = routed_scaling_factor
+        # When apply_routed_scale_to_output is True, we set the scaling factor
+        # to 1.0 so it ends up being a nop. Applying the scale will be handled
+        # by the runner in this case.
+        # The member variable must be set in the same way as the router since
+        # some quantization methods can access it.
+        self.routed_scaling_factor = (
+            routed_scaling_factor if not apply_routed_scale_to_output else 1.0
+        )
         self.e_score_correction_bias = e_score_correction_bias
         # TODO(bnell): end attributes
 
+        self.hash_indices_table = hash_indices_table
         self.apply_router_weight_on_input = apply_router_weight_on_input
         self.activation = MoEActivation.from_str(activation)
 
@@ -456,7 +484,7 @@ def __init__(
             topk_group=topk_group,
             custom_routing_function=custom_routing_function,
             scoring_func=scoring_func,
-            routed_scaling_factor=routed_scaling_factor,
+            routed_scaling_factor=self.routed_scaling_factor,
             e_score_correction_bias=e_score_correction_bias,
             num_fused_shared_experts=self.num_fused_shared_experts,
             enable_eplb=enable_eplb,
@@ -465,6 +493,7 @@ def __init__(
             indices_type_getter=lambda: self.quant_method.topk_indices_dtype,
             zero_expert_type=zero_expert_type,
             num_logical_experts=self.logical_num_experts,
+            hash_indices_table=self.hash_indices_table,
         )
         self.routing_method_type: RoutingMethodType = self.router.routing_method_type
 
@@ -519,9 +548,11 @@ def _get_quant_method() -> FusedMoEMethodBase:
         # for heuristic purposes, so it must be initialized first.
         self.quant_method: FusedMoEMethodBase = _get_quant_method()
 
-        if not self.moe_config.is_act_and_mul and not current_platform.is_cuda_alike():
+        if not self.moe_config.is_act_and_mul and not (
+            current_platform.is_cuda_alike() or current_platform.is_xpu()
+        ):
             raise NotImplementedError(
-                "is_act_and_mul=False is supported only for CUDA and ROCm for now"
+                "is_act_and_mul=False is supported only for CUDA and XPU for now"
             )
 
         if self.enable_eplb and not self.quant_method.supports_eplb:
@@ -574,16 +605,23 @@ def _get_quant_method() -> FusedMoEMethodBase:
         # Storing the runner in the FusedMoE is an intermediate state, eventually
         # the runner will own the FusedMoE layer and provide the execution interface
         # for MoE ops.
-        self.runner = create_moe_runner(
+        self.runner: MoERunnerInterface = MoERunner(
             layer_name=self.layer_name,
             moe_config=self.moe_config,
             router=self.router,
-            routed_input_transform=self._routed_input_transform,
             gate=gate,
             shared_experts=shared_experts,
+            shared_expert_gate=self.shared_expert_gate,
             quant_method=self.quant_method,
-            reduce_results=self.reduce_results,
             enable_dbo=self.vllm_config.parallel_config.enable_dbo,
+            routed_input_transform=routed_input_transform,
+            routed_output_transform=routed_output_transform,
+            # When apply_routed_scale_to_output is True, we allow
+            # the scaling factor to be passed to the runner, otherwise
+            # we pass 1.0 so it ends up being a nop.
+            routed_scaling_factor=routed_scaling_factor
+            if apply_routed_scale_to_output
+            else 1.0,
         )
 
     # TODO(bnell): This method is provided as a hook so vllm/lora/layers/fused_moe.py
@@ -666,8 +704,7 @@ def _maybe_init_expert_routing_tables(
         # Currently routing_tables only needed for round-robin expert placement
         # with DeepEP-ll or NIXL EP all2all backends.
         if self.expert_placement_strategy != "round_robin" or (
-            not self.moe_parallel_config.use_deepep_ll_kernels
-            and not self.moe_parallel_config.use_nixl_ep_kernels
+            not self.moe_parallel_config.needs_round_robin_routing_tables
         ):
             return None
 
@@ -1078,7 +1115,8 @@ def weight_loader(
         expert_id: int,
         return_success: bool = False,
     ) -> bool | None:
-        if self.quant_config and self.quant_config.get_name() == "gpt_oss_mxfp4":
+        quant_config_name = self.quant_config and self.quant_config.get_name()
+        if quant_config_name == "gpt_oss_mxfp4":
             # (FIXME) for gpt-oss all experts are combined
             if "bias" in weight_name:
                 dim1 = loaded_weight.shape[1]
@@ -1459,15 +1497,19 @@ def _maybe_make_contiguous(
             "w2_input_scale",
         }
 
+        # Parameters of non-expert submodules that live inside runner (MoERunner).
+        # These must be excluded from EPLB weight rearrangement.
+        NON_EXPERT_PREFIXES = (
+            "runner._shared_experts.",
+            "runner.gate.",
+            "runner.routed_input_transform.",
+            "runner.routed_output_transform.",
+        )
+
         assert all(
             weight.is_contiguous()
             for name, weight in weights
-            if not (
-                name.startswith("_shared_experts.")
-                or name.startswith("_gate.")
-                or name.startswith("_routed_input_transform.")
-                or name.startswith("_routed_output_transform.")
-            )
+            if not name.startswith(NON_EXPERT_PREFIXES)
             and name not in NON_EXPERT_WEIGHTS
         )
 
@@ -1476,12 +1518,7 @@ def _maybe_make_contiguous(
             for name, weight in weights
             if name not in NON_EXPERT_WEIGHTS
             and weight.shape != torch.Size([])
-            and not name.startswith("_shared_experts.")
-            # exclude parameters from non-expert submodules,
-            # e.g. gate/shared/transforms.
-            and not name.startswith("_gate.")
-            and not name.startswith("_routed_input_transform.")
-            and not name.startswith("_routed_output_transform.")
+            and not name.startswith(NON_EXPERT_PREFIXES)
         ]
 
     def set_eplb_state(
@@ -1514,35 +1551,16 @@ def moe_quant_config(self) -> FusedMoEQuantConfig | None:
         self.ensure_moe_quant_config_init()
         return self.quant_method.moe_quant_config
 
-    def must_reduce_shared_expert_outputs(self) -> bool:
-        """
-        The shared_experts are typically computed using the RowParallelLinear
-        layer. The result of this function is typically used as
-        the reduce_results argument to the module.
-        When just tensor-parallel is used, it is not required to reduce
-        the shared_experts results immediately. Instead we reduce at the
-        once at the end of the MoE op. (Refer to DeepSeekV2MoE module)
-        With EP and all2all kernels - this is no longer viable as all
-        GPU ranks in DP, produce the complete set of hidden_states.
-        Therefore it is required that we reduce the shared_experts output
-        early.
-        """
-        return self.runner.must_reduce_shared_expert_outputs()
-
-    def maybe_all_reduce_tensor_model_parallel(self, final_hidden_states: torch.Tensor):
-        """
-        Some combine kernels reduce across GPU ranks by default.
-        """
-        return self.runner.maybe_all_reduce_tensor_model_parallel(final_hidden_states)
-
     def forward(
         self,
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        input_ids: torch.Tensor | None = None,
+    ) -> torch.Tensor:
         return self.runner.forward(
             hidden_states,
             router_logits,
+            input_ids,
         )
 
     @property
@@ -1613,12 +1631,30 @@ def extra_repr(self) -> str:
             f"intermediate_size_per_partition={self.intermediate_size_per_partition}, "  # noqa: E501
             f"tp_size={self.tp_size},\n"
             f"ep_size={self.ep_size}, "
-            f"reduce_results={self.reduce_results}, "
         )
 
         return s
 
 
+# This is a temporary forwarding method which will be removed/modified layer.
+def fused_moe_make_expert_params_mapping(
+    model: torch.nn.Module,
+    ckpt_gate_proj_name: str,
+    ckpt_down_proj_name: str,
+    ckpt_up_proj_name: str,
+    num_experts: int,
+    num_redundant_experts: int = 0,
+) -> list[tuple[str, str, int, str]]:
+    return FusedMoE.make_expert_params_mapping(
+        model,
+        ckpt_gate_proj_name,
+        ckpt_down_proj_name,
+        ckpt_up_proj_name,
+        num_experts,
+        num_redundant_experts,
+    )
+
+
 # Mark the FusedMoE weight_loader as supporting MoE-specific parameters
 # to avoid expensive runtime reflection in model loading code
 FusedMoE.weight_loader.supports_moe_loading = True  # type: ignore[attr-defined]
diff --git a/vllm/model_executor/layers/fused_moe/lora_context.py b/vllm/model_executor/layers/fused_moe/lora_context.py
new file mode 100644
index 000000000000..ab1f0bfc1476
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/lora_context.py
@@ -0,0 +1,51 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass
+
+import torch
+
+from vllm.lora.punica_wrapper.punica_base import PunicaWrapperBase
+
+
+@dataclass
+class MoELoRAContext:
+    """
+    Carries all LoRA state for one MoE forward pass.
+
+    Built by FusedMoEWithLoRA.forward() and propagated explicitly through the
+    modular kernel path (FusedMoEKernel -> FusedMoEExpertsModular.apply) so
+    that TritonExperts.apply() can compute the LoRA contribution inline,
+    replacing the decorator-based monkey-patch approach.
+    """
+
+    # LoRA weight tensors (same shapes as FusedMoEWithLoRA attributes)
+    w13_lora_a_stacked: tuple[torch.Tensor, ...]
+    w13_lora_b_stacked: tuple[torch.Tensor, ...]
+    w2_lora_a_stacked: tuple[torch.Tensor, ...]
+    w2_lora_b_stacked: tuple[torch.Tensor, ...]
+
+    # (max_loras + 1,) int32; slot 0 is the "no-adapter" sentinel
+    adapter_enabled: torch.Tensor
+
+    # Metadata
+    max_loras: int
+    top_k: int
+    w13_num_slices: int  # 2 = gated (gate + up), 1 = non-gated or 3D-fused
+    fully_sharded: bool
+    tp_rank: int
+    tp_size: int
+    local_num_experts: int
+
+    punica_wrapper: PunicaWrapperBase
+
+    # Whether VLLM_TUNED_CONFIG_FOLDER is set; selects get_lora_op_configs vs
+    # try_get_optimal_moe_lora_config for Triton kernel tile configs.
+    use_tuned_config: bool
+
+    # Per-rank token→LoRA mapping after EP dispatch. Set by
+    # FusedMoEPrepareAndFinalizeModular.prepare() when EP+LoRA is active, read
+    # by LoRAExpertsMixin helpers in place of punica_wrapper's global mapping.
+    # None means no dispatch happened (non-EP path), in which case callers
+    # fall back to punica_wrapper.token_mapping_meta.
+    local_token_lora_mapping: torch.Tensor | None = None
diff --git a/vllm/model_executor/layers/fused_moe/lora_experts_mixin.py b/vllm/model_executor/layers/fused_moe/lora_experts_mixin.py
new file mode 100644
index 000000000000..10707b91b70e
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/lora_experts_mixin.py
@@ -0,0 +1,112 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+from vllm.model_executor.layers.fused_moe.lora_context import MoELoRAContext
+
+
+class LoRAExpertsMixin:
+    """
+    Mixin for FusedMoEExpertsModular subclasses that natively handle
+    MoELoRAContext inside their apply() implementation.
+
+    Mixing this class in:
+    - Flips supports_lora() to True so _can_fused_experts_support lets
+      LoRA through the gate check.
+    - Stashes a MoELoRAContext on the experts instance via
+      set_lora_context(), which apply() consumes from self._lora_context.
+    - Provides apply_w13_lora / apply_w2_lora helpers that dispatch to
+      the PunicaWrapper kernels.
+
+    The helper methods are pure functions of their inputs; all required
+    state is on lora_context or passed as arguments.
+    """
+
+    _lora_context: MoELoRAContext | None = None
+
+    def set_lora_context(self, ctx: MoELoRAContext) -> None:
+        self._lora_context = ctx
+
+    @staticmethod
+    def supports_lora() -> bool:
+        return True
+
+    def apply_w13_lora(
+        self,
+        lora_context: MoELoRAContext,
+        *,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        topk_ids: torch.Tensor,
+        topk_weights: torch.Tensor,
+        expert_map: torch.Tensor | None,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        num_tokens: int,
+        top_k_num: int,
+    ) -> tuple[
+        torch.Tensor | None,
+        torch.Tensor | None,
+        torch.Tensor | None,
+        torch.Tensor | None,
+    ]:
+        return lora_context.punica_wrapper.add_lora_w13(
+            y,
+            x,
+            lora_context.w13_lora_a_stacked,
+            lora_context.w13_lora_b_stacked,
+            topk_ids,
+            topk_weights,
+            expert_map,
+            w1,
+            w2,
+            num_tokens,
+            top_k_num,
+            lora_context.max_loras,
+            lora_context.adapter_enabled,
+            lora_context.local_num_experts,
+            lora_context.top_k,
+            lora_context.w13_num_slices,
+            lora_context.fully_sharded,
+            lora_context.use_tuned_config,
+            token_lora_mapping=lora_context.local_token_lora_mapping,
+        )
+
+    def apply_w2_lora(
+        self,
+        lora_context: MoELoRAContext,
+        *,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        topk_weights: torch.Tensor,
+        sorted_token_ids_lora: torch.Tensor | None,
+        expert_ids_lora: torch.Tensor | None,
+        num_tokens_post_padded_lora: torch.Tensor | None,
+        token_lora_mapping: torch.Tensor | None,
+        num_tokens: int,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        top_k_num: int,
+    ) -> None:
+        lora_context.punica_wrapper.add_lora_w2(
+            y,
+            x,
+            lora_context.w2_lora_a_stacked,
+            lora_context.w2_lora_b_stacked,
+            topk_weights,
+            sorted_token_ids_lora,
+            expert_ids_lora,
+            num_tokens_post_padded_lora,
+            token_lora_mapping,
+            num_tokens,
+            w1,
+            w2,
+            top_k_num,
+            lora_context.max_loras,
+            lora_context.adapter_enabled,
+            lora_context.top_k,
+            lora_context.fully_sharded,
+            lora_context.tp_rank,
+            lora_context.use_tuned_config,
+        )
diff --git a/vllm/model_executor/layers/fused_moe/modular_kernel.py b/vllm/model_executor/layers/fused_moe/modular_kernel.py
index f2e6e2560e70..135a677ffde2 100644
--- a/vllm/model_executor/layers/fused_moe/modular_kernel.py
+++ b/vllm/model_executor/layers/fused_moe/modular_kernel.py
@@ -570,6 +570,8 @@ def _make_reason(reason: str) -> str:
             return False, _make_reason(f"{activation_format.value} activation format")
         elif envs.VLLM_BATCH_INVARIANT and not cls._supports_batch_invariance():
             return False, _make_reason("batch invariance")
+        elif moe_config.is_lora_enabled and not cls.supports_lora():
+            return False, _make_reason("LoRA")
         return True, None
 
     @staticmethod
@@ -734,6 +736,15 @@ def g1_alphas(self) -> torch.Tensor | None:
     def g2_alphas(self) -> torch.Tensor | None:
         return self.quant_config.g2_alphas
 
+    @staticmethod
+    def supports_lora() -> bool:
+        """Return True if this expert impl natively handles LoRA.
+
+        LoRA-aware experts should mix in LoRAExpertsMixin, which flips this
+        to True and provides the per-forward LoRA state plumbing.
+        """
+        return False
+
     @abstractmethod
     def supports_expert_map(self) -> bool:
         """
@@ -1204,6 +1215,7 @@ def _fused_experts(
         expert_map: torch.Tensor | None,
         apply_router_weight_on_input: bool,
         expert_tokens_meta: ExpertTokensMetadata | None,
+        output_alias: torch.Tensor | None = None,
     ) -> torch.Tensor:
         _, M_full, N, K, top_k = self.fused_experts.moe_problem_size(
             a1q, w1, w2, topk_ids
@@ -1232,6 +1244,23 @@ def _fused_experts(
             activation,
         )
 
+        # If caller's output buffer already matches fused_out shape/dtype, alias
+        # to skip the redundant copy in TopKWeightAndReduceNoOP.apply downstream.
+        # This eliminates ~94% of __amd_rocclr_copyBuffer events (Copy 2 of the
+        # double-copy MoE write-back path).
+        if current_platform.is_rocm():
+            from vllm._aiter_ops import rocm_aiter_ops
+
+            if (
+                rocm_aiter_ops.is_fused_moe_enabled()
+                and output_alias is not None
+                and output_alias.shape == fused_out.shape
+                and output_alias.dtype == fused_out.dtype
+                and output_alias.device == fused_out.device
+                and output_alias.is_contiguous()
+            ):
+                fused_out = output_alias
+
         self.fused_experts.apply(
             output=fused_out,
             hidden_states=a1q,
@@ -1261,7 +1290,7 @@ def _finalize(
         topk_ids: torch.Tensor,
         apply_router_weight_on_input: bool,
         shared_experts_input: torch.Tensor | None,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+    ) -> torch.Tensor:
         """
         The _finalize method is a wrapper around self.prepare_finalize.finalize
         that handles DBO, async and shared expert overlap.
@@ -1392,6 +1421,7 @@ def apply(
             expert_map=expert_map,
             apply_router_weight_on_input=apply_router_weight_on_input,
             expert_tokens_meta=expert_tokens_meta,
+            output_alias=output,
         )
 
         return self._finalize(
@@ -1527,6 +1557,9 @@ def prepare_finalize(self) -> FusedMoEPrepareAndFinalize:
     def fused_experts(self) -> FusedMoEExperts:
         return self.impl.fused_experts
 
+    def supports_lora(self) -> bool:
+        return self.fused_experts.supports_lora()
+
     def _post_init_setup(self):
         """
         Resolve any leftover setup dependencies between self.prepare_finalize
diff --git a/vllm/model_executor/layers/fused_moe/moe_fused_mul_sum.py b/vllm/model_executor/layers/fused_moe/moe_fused_mul_sum.py
new file mode 100644
index 000000000000..768f41db854e
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/moe_fused_mul_sum.py
@@ -0,0 +1,202 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+from torch._subclasses.fake_tensor import FakeTensor
+
+from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
+
+
+@triton.jit
+def moe_fused_mul_sum_kernel(
+    inputs_ptr,
+    topk_weights_ptr,
+    outputs_ptr,
+    top_ids_ptr,
+    expert_map_ptr,
+    num_tokens,
+    stride_m,
+    has_expert_map: tl.constexpr,
+    top_k: tl.constexpr,
+    size: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+):
+    pid_k = tl.program_id(0)
+    pid_m = tl.program_id(1)
+
+    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_k = pid_k * BLOCK_K + tl.arange(0, BLOCK_K)
+
+    m_mask = offs_m < num_tokens
+    k_mask = offs_k < size
+    mask = m_mask[:, None] & k_mask[None, :]
+
+    a_base = inputs_ptr + (offs_m * stride_m)[:, None] + offs_k[None, :]
+    b_base = topk_weights_ptr + offs_m * top_k
+
+    acc = tl.zeros((BLOCK_M, BLOCK_K), dtype=tl.float32)
+
+    for n in tl.static_range(top_k):
+        b_val = tl.load(b_base + n, mask=m_mask, other=0.0).to(tl.float32)
+        if has_expert_map:
+            id_val = tl.load(top_ids_ptr + offs_m * top_k + n, mask=m_mask, other=0)
+            expert_mask = tl.load(expert_map_ptr + id_val) >= 0
+            a_vec = tl.load(
+                a_base + n * size,
+                mask=mask & expert_mask[:, None],
+                other=0.0,
+            ).to(tl.float32)
+        else:
+            a_vec = tl.load(
+                a_base + n * size,
+                mask=mask,
+                other=0.0,
+            ).to(tl.float32)
+        acc += a_vec * b_val[:, None]
+
+    out_ptrs = outputs_ptr + (offs_m * size)[:, None] + offs_k[None, :]
+    tl.store(
+        out_ptrs,
+        acc.to(outputs_ptr.dtype.element_ty),
+        mask=mask,
+    )
+
+
+def _heuristic_config(
+    num_tokens: int,
+    top_k: int,
+    size: int,
+    element_size: int,
+):
+    is_fp32 = element_size > 2
+    is_sm90_plus = current_platform.has_device_capability(90)
+    is_sm80_before = not current_platform.has_device_capability(80)
+
+    if current_platform.has_device_capability(90):
+        # SM90/SM100+: prefer small tiles + many CTAs.
+        if is_fp32:
+            BLOCK_M = 1 if num_tokens <= 4 else 2
+        else:
+            if num_tokens <= 4:
+                BLOCK_M = 1
+            elif num_tokens <= 128:
+                BLOCK_M = 2
+            else:
+                BLOCK_M = 4
+    elif is_fp32:
+        if num_tokens <= 4:
+            BLOCK_M = 1
+        elif num_tokens <= 32:
+            BLOCK_M = 2
+        elif num_tokens <= 128:
+            BLOCK_M = 4
+        else:
+            BLOCK_M = 4
+    else:
+        if num_tokens <= 4:
+            BLOCK_M = 1
+        elif num_tokens <= 32:
+            BLOCK_M = 2
+        elif num_tokens <= 128:
+            BLOCK_M = 4
+        elif num_tokens <= 1024:
+            BLOCK_M = 16
+        else:
+            BLOCK_M = 8
+
+    if is_fp32:
+        max_block_k = 256
+    elif is_sm80_before or is_sm90_plus:
+        max_block_k = 512
+    else:
+        max_block_k = 1024
+    BLOCK_K = min(triton.next_power_of_2(size), max_block_k)
+    BLOCK_K = max(BLOCK_K, 256)
+
+    total = BLOCK_M * BLOCK_K
+    if is_fp32:
+        num_warps = max(8, min(16, total // 64))
+    else:
+        num_warps = max(4, min(16, total // 256))
+
+    if is_sm80_before:
+        num_warps = min(num_warps, 8)
+        num_stages = 2
+    elif is_sm90_plus:
+        num_warps = min(num_warps, 8)
+        num_stages = 4 if total <= 2048 else 2
+    else:
+        num_stages = 4 if total <= 2048 else 2
+
+    return BLOCK_M, BLOCK_K, num_warps, num_stages
+
+
+def moe_fused_mul_sum(
+    inputs: torch.Tensor,
+    topk_weights: torch.Tensor,
+    outputs: torch.Tensor | None = None,
+    topk_ids: torch.Tensor | None = None,
+    expert_map: torch.Tensor | None = None,
+) -> torch.Tensor:
+    """
+    Fused kernel for MoE (Mixture of Experts) to perform weighted summation
+    of expert outputs.
+
+    Args:
+        inputs: The output from experts.
+            Shape: (num_tokens, top_k, hidden_size).
+        topk_weights: The weights assigned to each expert for each token.
+            Shape: (num_tokens, top_k).
+        outputs: Optional pre-allocated output tensor.
+            Shape: (num_tokens, hidden_size).
+        topk_ids: Optional indices of the top-k experts. Used when
+            `expert_map` is provided. Shape: (num_tokens, top_k).
+        expert_map: Optional mapping for Expert Parallelism. A value < 0
+            indicates an invalid token/expert pair that will be skipped.
+
+    Returns:
+        The fused weighted sum of expert outputs.
+        Shape: (num_tokens, hidden_size).
+    """
+    assert inputs.ndim == 3
+    assert topk_weights.ndim == 2
+    assert inputs.is_contiguous()
+    assert topk_weights.is_contiguous()
+    assert inputs.dtype in (torch.float32, torch.float16, torch.bfloat16)
+    assert topk_weights.dtype in (torch.float32, torch.float16, torch.bfloat16)
+
+    num_tokens, top_k, size = inputs.shape
+    output_shape = (num_tokens, size)
+    if outputs is None:
+        outputs = torch.empty(output_shape, dtype=inputs.dtype, device=inputs.device)
+
+    assert outputs.shape == output_shape
+    assert topk_weights.shape == (num_tokens, top_k)
+
+    if not isinstance(inputs, FakeTensor):
+        BLOCK_M, BLOCK_K, num_warps, num_stages = _heuristic_config(
+            num_tokens,
+            top_k,
+            size,
+            inputs.element_size(),
+        )
+        grid = (triton.cdiv(size, BLOCK_K), triton.cdiv(num_tokens, BLOCK_M))
+        moe_fused_mul_sum_kernel[grid](
+            inputs,
+            topk_weights,
+            outputs,
+            topk_ids,
+            expert_map,
+            num_tokens,
+            top_k * size,
+            expert_map is not None,
+            top_k,
+            size,
+            BLOCK_M,
+            BLOCK_K,
+            num_warps=num_warps,
+            num_stages=num_stages,
+        )
+
+    return outputs
diff --git a/vllm/model_executor/layers/fused_moe/oracle/fp8.py b/vllm/model_executor/layers/fused_moe/oracle/fp8.py
index d3c70d2d0a5f..1ce5a1e3213b 100644
--- a/vllm/model_executor/layers/fused_moe/oracle/fp8.py
+++ b/vllm/model_executor/layers/fused_moe/oracle/fp8.py
@@ -55,6 +55,7 @@ class Fp8MoeBackend(Enum):
     VLLM_CUTLASS = "VLLM_CUTLASS"
     BATCHED_VLLM_CUTLASS = "BATCHED_VLLM_CUTLASS"
     XPU = "XPU"
+    CPU = "CPU"
 
 
 def _get_priority_backends(
@@ -80,6 +81,7 @@ def _get_priority_backends(
         Fp8MoeBackend.BATCHED_VLLM_CUTLASS,
         Fp8MoeBackend.BATCHED_TRITON,
         Fp8MoeBackend.XPU,
+        Fp8MoeBackend.CPU,
     ]
 
     def _move_to_front(backends: list[Fp8MoeBackend], backend: Fp8MoeBackend) -> None:
@@ -102,6 +104,10 @@ def _move_to_front(backends: list[Fp8MoeBackend], backend: Fp8MoeBackend) -> Non
         # move XPU backend to the front.
         _move_to_front(_AVAILABLE_BACKENDS, Fp8MoeBackend.XPU)
 
+    if current_platform.is_cpu():
+        # CPU platform uses FP8 W8A16 fused MoE kernel.
+        _move_to_front(_AVAILABLE_BACKENDS, Fp8MoeBackend.CPU)
+
     return _AVAILABLE_BACKENDS
 
 
@@ -117,7 +123,7 @@ def backend_to_kernel_cls(
         return [TrtLlmFp8ExpertsMonolithic, TrtLlmFp8ExpertsModular]
 
     elif backend == Fp8MoeBackend.FLASHINFER_CUTLASS:
-        from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.flashinfer_cutlass_moe import (  # noqa: E501
             FlashInferExperts,
         )
 
@@ -138,14 +144,14 @@ def backend_to_kernel_cls(
         return [BatchedDeepGemmExperts]
 
     elif backend == Fp8MoeBackend.MARLIN:
-        from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.marlin_moe import (
             MarlinExperts,
         )
 
         return [MarlinExperts]
 
     elif backend == Fp8MoeBackend.TRITON:
-        from vllm.model_executor.layers.fused_moe.fused_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.triton_moe import (
             TritonExperts,
         )
 
@@ -159,7 +165,7 @@ def backend_to_kernel_cls(
         return [BatchedTritonExperts]
 
     elif backend == Fp8MoeBackend.AITER:
-        from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.rocm_aiter_moe import (
             AiterExperts,
         )
 
@@ -173,19 +179,26 @@ def backend_to_kernel_cls(
         return [TritonOrCutlassExperts]
 
     elif backend == Fp8MoeBackend.BATCHED_VLLM_CUTLASS:
-        from vllm.model_executor.layers.fused_moe.cutlass_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import (
             CutlassBatchedExpertsFp8,
         )
 
         return [CutlassBatchedExpertsFp8]
 
     elif backend == Fp8MoeBackend.XPU:
-        from vllm.model_executor.layers.fused_moe.xpu_fused_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.xpu_moe import (
             XPUExpertsFp8,
         )
 
         return [XPUExpertsFp8]
 
+    elif backend == Fp8MoeBackend.CPU:
+        from vllm.model_executor.layers.fused_moe.experts.cpu_moe import (
+            CPUExpertsFp8,
+        )
+
+        return [CPUExpertsFp8]
+
     else:
         raise ValueError(f"Unknown FP8 MoE backend: {backend.value}")
 
@@ -220,9 +233,6 @@ def select_fp8_moe_backend(
     Note: Shape-specific fallbacks may still occur at runtime.
     """
 
-    if config.is_lora_enabled:
-        return Fp8MoeBackend.TRITON, backend_to_kernel_cls(Fp8MoeBackend.TRITON)[0]
-
     # NOTE: the kernels are selected in the following order.
     AVAILABLE_BACKENDS = _get_priority_backends(config, weight_key, activation_key)
 
@@ -266,7 +276,7 @@ def _return_or_raise(
                 k_cls, config, weight_key, activation_key, activation_format
             )
             if supported:
-                logger.info_once(_make_log_backend(backend), scope="local")
+                logger.info_once(_make_log_backend(backend))
                 return backend, k_cls
         raise ValueError(_make_log_unsupported(backend, reason))
 
@@ -337,12 +347,10 @@ def _return_or_raise(
                     )
 
                     if supported:
-                        logger.info_once(_make_log_backend(backend), scope="local")
+                        logger.info_once(_make_log_backend(backend))
                         return backend, k_cls
                     else:
-                        logger.debug_once(
-                            _make_log_unsupported(backend, reason), scope="local"
-                        )
+                        logger.debug_once(_make_log_unsupported(backend, reason))
 
             raise NotImplementedError(
                 "Found VLLM_USE_FLASHINFER_MOE_FP8=1, but no "
@@ -396,10 +404,10 @@ def _return_or_raise(
                 activation_format,
             )
             if supported:
-                logger.info_once(_make_log_backend(backend), scope="local")
+                logger.info_once(_make_log_backend(backend))
                 return backend, k_cls
             else:
-                logger.debug_once(_make_log_unsupported(backend, reason), scope="local")
+                logger.debug_once(_make_log_unsupported(backend, reason))
 
     # TODO(rob): per discussion with TPU team, we need a way to register
     # MoE backends by OOT plugins, rather than having an explicit list
@@ -471,6 +479,18 @@ def convert_to_fp8_moe_kernel_format(
             w2_input_scale=w2_input_scale,
             is_trtllm=(fp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM),
         )
+    elif fp8_backend == Fp8MoeBackend.XPU:
+        from vllm.model_executor.layers.fused_moe.experts.xpu_moe import (
+            prepare_fp8_moe_layer_for_xpu,
+        )
+
+        w13, w2 = prepare_fp8_moe_layer_for_xpu(w13, w2)
+    elif fp8_backend == Fp8MoeBackend.CPU:
+        from vllm.model_executor.layers.fused_moe.experts.cpu_moe import (
+            prepare_fp8_moe_layer_for_cpu,
+        )
+
+        w13, w2 = prepare_fp8_moe_layer_for_cpu(w13, w2)
     else:
         if fp8_backend not in [
             Fp8MoeBackend.TRITON,
@@ -515,6 +535,14 @@ def make_fp8_moe_quant_config(
             block_shape=block_shape,
         )
 
+    # CPU is mixed precision W8A16 config.
+    if fp8_backend == Fp8MoeBackend.CPU:
+        return fp8_w8a16_moe_quant_config(
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            block_shape=block_shape,
+        )
+
     # Flashinfer CUTLASS per-tensor uses single dq scale
     # (alpha = w_scale * a_scale) and inverse a2 scale.
     if fp8_backend == Fp8MoeBackend.FLASHINFER_CUTLASS and block_shape is None:
@@ -541,7 +569,7 @@ def make_fp8_moe_quant_config(
             a1_scale=a1_scale,
             a2_scale=a2_scale,
             block_shape=block_shape,
-            is_nvfp4_scale_swizzled=False,
+            is_scale_swizzled=False,
         )
 
     # All other backends use normal config.
@@ -574,7 +602,7 @@ def make_fp8_moe_kernel(
     )
     assert prepare_finalize is not None
 
-    logger.info_once("Using %s", prepare_finalize.__class__.__name__, scope="local")
+    logger.info_once("Using %s", prepare_finalize.__class__.__name__)
 
     # Create Experts.
     if prepare_finalize.activation_format == mk.FusedMoEActivationFormat.BatchedExperts:
diff --git a/vllm/model_executor/layers/fused_moe/oracle/int8.py b/vllm/model_executor/layers/fused_moe/oracle/int8.py
new file mode 100644
index 000000000000..0c1d641a8e62
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/oracle/int8.py
@@ -0,0 +1,218 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from enum import Enum
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.config.kernel import MoEBackend
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.all2all_utils import (
+    maybe_make_prepare_finalize,
+)
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEQuantConfig,
+    int8_w8a8_moe_quant_config,
+    int8_w8a16_moe_quant_config,
+)
+from vllm.model_executor.layers.fused_moe.runner.shared_experts import (
+    SharedExperts,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    QuantKey,
+    kInt8DynamicTokenSym,
+    kInt8StaticChannelSym,
+)
+
+logger = init_logger(__name__)
+
+
+class Int8MoeBackend(Enum):
+    TRITON = "TRITON"
+
+
+def _get_priority_backends(
+    moe_config: FusedMoEConfig,
+) -> list[Int8MoeBackend]:
+    """
+    Get available backends in priority order based on platform and config.
+    """
+    return [Int8MoeBackend.TRITON]
+
+
+def backend_to_kernel_cls(
+    backend: Int8MoeBackend,
+) -> list[type[mk.FusedMoEExperts]]:
+    if backend == Int8MoeBackend.TRITON:
+        from vllm.model_executor.layers.fused_moe.experts.triton_moe import (
+            TritonExperts,
+        )
+
+        return [TritonExperts]
+
+    else:
+        raise ValueError(f"Unknown Int8 MoE backend: {backend.value}")
+
+
+def map_int8_backend(runner_backend: MoEBackend) -> Int8MoeBackend:
+    """Map user's MoEBackend to Int8MoeBackend."""
+    mapping = {
+        "triton": Int8MoeBackend.TRITON,
+    }
+    if backend := mapping.get(runner_backend):
+        return backend
+    raise ValueError(
+        f"moe_backend='{runner_backend}' is not supported for Int8 MoE. "
+        f"Expected one of {list(mapping.keys())}."
+    )
+
+
+def select_int8_moe_backend(
+    config: FusedMoEConfig,
+    weight_key: QuantKey | None = kInt8StaticChannelSym,
+    activation_key: QuantKey | None = kInt8DynamicTokenSym,
+) -> tuple[Int8MoeBackend, type[mk.FusedMoEExperts]]:
+    """
+    Select the primary Int8 MoE backend.
+    Note: Shape-specific fallbacks may still occur at runtime.
+    """
+
+    AVAILABLE_BACKENDS = _get_priority_backends(config)
+
+    activation_format = (
+        mk.FusedMoEActivationFormat.BatchedExperts
+        if config.moe_parallel_config.use_batched_activation_format
+        else mk.FusedMoEActivationFormat.Standard
+    )
+
+    def _make_log_backend(backend: Int8MoeBackend) -> str:
+        available_backend_strs = [b.value for b in AVAILABLE_BACKENDS]
+        return (
+            f"Using {backend.value} Int8 MoE backend out "
+            f"of potential backends: {available_backend_strs}."
+        )
+
+    def _make_log_unsupported(backend: Int8MoeBackend, reason: str | None) -> str:
+        if reason:
+            return (
+                f"Int8 MoE backend {backend.value} does not support the "
+                f"deployment configuration since {reason}."
+            )
+        else:
+            return (
+                f"Int8 MoE backend '{backend.value}' does not support the "
+                "deployment configuration."
+            )
+
+    def _return_or_raise(
+        backend: Int8MoeBackend,
+    ) -> tuple[Int8MoeBackend, type[mk.FusedMoEExperts]]:
+        for k_cls in backend_to_kernel_cls(backend):
+            supported, reason = k_cls.is_supported_config(
+                k_cls, config, weight_key, activation_key, activation_format
+            )
+            if supported:
+                logger.info_once(_make_log_backend(backend))
+                return backend, k_cls
+        raise ValueError(_make_log_unsupported(backend, reason))
+
+    # Handle explicit moe_backend from user.
+    runner_backend = config.moe_backend
+    if runner_backend != "auto":
+        requested_backend = map_int8_backend(runner_backend)
+        return _return_or_raise(requested_backend)
+
+    # Select kernels in order of backend.
+    for backend in AVAILABLE_BACKENDS:
+        for k_cls in backend_to_kernel_cls(backend):
+            supported, reason = k_cls.is_supported_config(
+                k_cls,
+                config,
+                weight_key,
+                activation_key,
+                activation_format,
+            )
+            if supported:
+                logger.info_once(_make_log_backend(backend))
+                return backend, k_cls
+            else:
+                logger.debug_once(_make_log_unsupported(backend, reason))
+
+    raise NotImplementedError(
+        "No Int8 MoE backend supports the deployment configuration."
+    )
+
+
+def make_int8_moe_quant_config(
+    w1_scale: torch.Tensor,
+    w2_scale: torch.Tensor,
+    a1_scale: torch.Tensor | None = None,
+    a2_scale: torch.Tensor | None = None,
+    per_act_token_quant: bool = False,
+) -> FusedMoEQuantConfig:
+    assert (a1_scale is None and a2_scale is None) or (
+        a1_scale is not None and a2_scale is not None
+    ), "a1_scale and a2_scale must both be provided or both be None"
+
+    if a1_scale is None or a2_scale is None:
+        return int8_w8a16_moe_quant_config(
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            w1_zp=None,
+            w2_zp=None,
+        )
+
+    return int8_w8a8_moe_quant_config(
+        w1_scale=w1_scale,
+        w2_scale=w2_scale,
+        a1_scale=a1_scale,
+        a2_scale=a2_scale,
+        per_act_token_quant=per_act_token_quant,
+    )
+
+
+def make_int8_moe_kernel(
+    moe_quant_config: FusedMoEQuantConfig,
+    moe_config: FusedMoEConfig,
+    experts_cls: type[mk.FusedMoEExperts],
+    routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
+    shared_experts: SharedExperts | None = None,
+) -> mk.FusedMoEKernel:
+    # Create Prepare/Finalize.
+    prepare_finalize = maybe_make_prepare_finalize(
+        moe=moe_config,
+        quant_config=moe_quant_config,
+        routing_tables=routing_tables,
+        allow_new_interface=True,
+        use_monolithic=issubclass(experts_cls, mk.FusedMoEExpertsMonolithic),
+    )
+    assert prepare_finalize is not None
+
+    logger.info_once("Using %s", prepare_finalize.__class__.__name__)
+
+    # Create Experts.
+    if prepare_finalize.activation_format == mk.FusedMoEActivationFormat.BatchedExperts:
+        max_num_tokens = prepare_finalize.max_num_tokens_per_rank()
+        assert max_num_tokens is not None
+        experts = experts_cls(
+            moe_config=moe_config,
+            quant_config=moe_quant_config,
+            max_num_tokens=max_num_tokens,
+            num_dispatchers=prepare_finalize.num_dispatchers(),
+        )
+    else:
+        experts = experts_cls(
+            moe_config=moe_config,
+            quant_config=moe_quant_config,
+        )
+
+    kernel = mk.FusedMoEKernel(
+        prepare_finalize,
+        experts,
+        shared_experts=shared_experts,
+        inplace=not moe_config.disable_inplace,
+    )
+
+    return kernel
diff --git a/vllm/model_executor/layers/fused_moe/oracle/int_wna16.py b/vllm/model_executor/layers/fused_moe/oracle/int_wna16.py
new file mode 100644
index 000000000000..a0dde3cb7f77
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/oracle/int_wna16.py
@@ -0,0 +1,445 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from enum import Enum
+from typing import TYPE_CHECKING
+
+import torch
+
+import vllm._custom_ops as ops
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEQuantConfig,
+)
+from vllm.model_executor.layers.fused_moe.experts.marlin_moe import (
+    BatchedMarlinExperts,
+    MarlinExperts,
+)
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.quantization.utils.marlin_utils import (
+    marlin_act_int8_process_scales,
+    marlin_moe_permute_scales,
+    marlin_permute_bias,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    QuantKey,
+)
+
+if TYPE_CHECKING:
+    from vllm.model_executor.layers.quantization.gptq_marlin import GPTQMarlinConfig
+
+logger = init_logger(__name__)
+
+
+class WNA16MoEBackend(Enum):
+    MARLIN = "MARLIN"
+    BATCHED_MARLIN = "BATCHED_MARLIN"
+
+
+def backend_to_kernel_cls(
+    backend: WNA16MoEBackend,
+) -> list[type[mk.FusedMoEExperts]]:
+    """Return the experts class for the given backend, or None for NONE."""
+    if backend == WNA16MoEBackend.MARLIN:
+        from vllm.model_executor.layers.fused_moe.experts.marlin_moe import (
+            MarlinExperts,
+        )
+
+        return [MarlinExperts]
+
+    elif backend == WNA16MoEBackend.BATCHED_MARLIN:
+        from vllm.model_executor.layers.fused_moe.experts.marlin_moe import (
+            BatchedMarlinExperts,
+        )
+
+        return [BatchedMarlinExperts]
+
+    else:
+        raise ValueError(f"Unknown WNA16 MoE backend: {backend.value}")
+
+
+def _get_priority_backends() -> list[WNA16MoEBackend]:
+    """
+    Get available backends in priority order based on platform and config.
+    """
+    _AVAILABLE_BACKENDS = [
+        WNA16MoEBackend.MARLIN,
+        WNA16MoEBackend.BATCHED_MARLIN,
+    ]
+    return _AVAILABLE_BACKENDS
+
+
+def select_wna16_moe_backend(
+    config: FusedMoEConfig,
+    weight_key: QuantKey,
+    weight_bits: int,
+) -> tuple[WNA16MoEBackend, type[mk.FusedMoEExperts]]:
+    """Select the WNA16 MoE backend.
+
+    Args:
+        config: the shared ``FusedMoEConfig`` for this layer.
+        weight_bits: quantization bit-width (4 or 8). 8-bit weights are not
+            supported by the modular Marlin kernel, so ``NONE`` is returned.
+
+    Returns:
+        A tuple of (``WNA16MoEBackend``, experts class or ``None``).
+    """
+
+    activation_format = (
+        mk.FusedMoEActivationFormat.BatchedExperts
+        if config.moe_parallel_config.use_batched_activation_format
+        else mk.FusedMoEActivationFormat.Standard
+    )
+
+    def _make_log_backend(backend: WNA16MoEBackend):
+        return f"Using '{backend.value}' WNA16 MoE backend."
+
+    def _make_log_unsupported(backend: WNA16MoEBackend, reason: str | None) -> str:
+        if reason:
+            return (
+                f"WNA16 MoE backend '{backend.value}' does not support the "
+                f"deployment configuration since {reason}."
+            )
+        return (
+            f"WNA16 MoE backend '{backend.value}' does not support the "
+            "deployment configuration."
+        )
+
+    def _return_or_raise(
+        backend: WNA16MoEBackend,
+        config: FusedMoEConfig,
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+        activation_format: mk.FusedMoEActivationFormat,
+    ) -> tuple[WNA16MoEBackend, type[mk.FusedMoEExperts]]:
+        reason: str | None = None
+        for k_cls in backend_to_kernel_cls(backend):
+            supported, reason = k_cls.is_supported_config(
+                k_cls, config, weight_key, activation_key, activation_format
+            )
+            if supported:
+                logger.info_once(_make_log_backend(backend), scope="local")
+                return backend, k_cls
+        raise ValueError(_make_log_unsupported(backend, reason))
+
+    # Select kernels in order of backend.
+    AVAILABLE_BACKENDS = _get_priority_backends()
+
+    for backend in AVAILABLE_BACKENDS:
+        activation_key = None  # always BF16 activation for WNA16 MoE
+        for k_cls in backend_to_kernel_cls(backend):
+            supported, reason = k_cls.is_supported_config(
+                k_cls, config, weight_key, activation_key, activation_format
+            )
+            if supported:
+                logger.info_once(_make_log_backend(backend), scope="local")
+                return backend, k_cls
+            else:
+                logger.debug_once(_make_log_unsupported(backend, reason), scope="local")
+
+    raise NotImplementedError(
+        "No WNA16 MoE backend supports the deployment configuration."
+    )
+
+
+def make_wna16_moe_kernel(
+    moe_quant_config: FusedMoEQuantConfig,
+    moe_config: FusedMoEConfig,
+    experts_cls: type[mk.FusedMoEExperts] | None,
+    layer: torch.nn.Module,
+    is_k_full: bool,
+    w13_g_idx: torch.Tensor | None,
+    w2_g_idx: torch.Tensor | None,
+    w13_g_idx_sort_indices: torch.Tensor | None,
+    w2_g_idx_sort_indices: torch.Tensor | None,
+    routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
+    shared_experts: torch.nn.Module | None = None,
+) -> mk.FusedMoEKernel:
+    # Currently, we only support MarlinExperts and BatchedMarlinExperts
+    assert experts_cls in (MarlinExperts, BatchedMarlinExperts)
+
+    from vllm.model_executor.layers.fused_moe.all2all_utils import (
+        maybe_make_prepare_finalize,
+    )
+
+    prepare_finalize = maybe_make_prepare_finalize(
+        moe=moe_config,
+        quant_config=moe_quant_config,
+        routing_tables=routing_tables,
+        allow_new_interface=True,
+    )
+    assert prepare_finalize is not None
+    assert isinstance(prepare_finalize, mk.FusedMoEPrepareAndFinalizeModular)
+
+    if prepare_finalize.activation_format == mk.FusedMoEActivationFormat.BatchedExperts:
+        assert experts_cls == BatchedMarlinExperts
+        max_num_tokens = prepare_finalize.max_num_tokens_per_rank()
+        assert max_num_tokens is not None
+        experts: mk.FusedMoEExperts = BatchedMarlinExperts(
+            max_num_tokens=max_num_tokens,
+            num_dispatchers=prepare_finalize.num_dispatchers(),
+            moe_config=moe_config,
+            quant_config=moe_quant_config,
+            w13_g_idx=w13_g_idx,
+            w2_g_idx=w2_g_idx,
+            w13_g_idx_sort_indices=w13_g_idx_sort_indices,
+            w2_g_idx_sort_indices=w2_g_idx_sort_indices,
+            is_k_full=is_k_full,
+        )
+    else:
+        assert experts_cls == MarlinExperts
+        experts = MarlinExperts(
+            moe_config=moe_config,
+            quant_config=moe_quant_config,
+            w13_g_idx=w13_g_idx,
+            w2_g_idx=w2_g_idx,
+            w13_g_idx_sort_indices=w13_g_idx_sort_indices,
+            w2_g_idx_sort_indices=w2_g_idx_sort_indices,
+            is_k_full=is_k_full,
+        )
+
+    return mk.FusedMoEKernel(
+        prepare_finalize,
+        experts,
+        shared_experts=shared_experts,
+        inplace=not moe_config.disable_inplace,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Per-backend weight post-processing
+# ---------------------------------------------------------------------------
+
+
+def _process_weights_marlin(
+    layer: torch.nn.Module,
+    quant_config: "GPTQMarlinConfig",
+    input_dtype: torch.dtype | None,
+    w13_qweight: torch.Tensor,
+    w2_qweight: torch.Tensor,
+    w13_scales: torch.Tensor,
+    w2_scales: torch.Tensor,
+    w13_g_idx: torch.Tensor,
+    w2_g_idx: torch.Tensor,
+    w13_bias: torch.Tensor | None = None,
+    w2_bias: torch.Tensor | None = None,
+) -> tuple[
+    torch.Tensor,  # w13_qweight
+    torch.Tensor,  # w2_qweight
+    torch.Tensor,  # w13_scales
+    torch.Tensor,  # w2_scales
+    torch.Tensor,  # w13_g_idx
+    torch.Tensor,  # w2_g_idx
+    torch.Tensor,  # w13_g_idx_sort_indices
+    torch.Tensor,  # w2_g_idx_sort_indices
+    torch.Tensor | None,  # w13_input_global_scale
+    torch.Tensor | None,  # w2_input_global_scale
+    torch.Tensor | None,  # w13_bias
+    torch.Tensor | None,  # w2_bias
+]:
+    """Standard Marlin weight post-processing shared by MARLIN and
+    BATCHED_MARLIN backends.
+
+    Steps
+    -----
+    1. Optional FP8 preprocessing of packed weights / scales.
+    2. Sort / reset g_idx tensors for act-order handling.
+    3. Repack weights via ``gptq_marlin_moe_repack``.
+    4. Permute scales (and optionally extract INT8 global scales).
+    5. Permute bias tensors.
+    """
+    is_a_8bit = input_dtype is not None and input_dtype.itemsize == 1
+
+    marlin_w13_qweight: torch.Tensor
+    marlin_w2_qweight: torch.Tensor
+    marlin_w13_scales: torch.Tensor
+    marlin_w2_scales: torch.Tensor
+    w13_g_idx_sort_indices: torch.Tensor | None = None
+    w2_g_idx_sort_indices: torch.Tensor | None = None
+    w13_input_global_scale: torch.Tensor | None = None
+    w2_input_global_scale: torch.Tensor | None = None
+    w13_bias_out: torch.Tensor | None = None
+    w2_bias_out: torch.Tensor | None = None
+
+    # --- FP8 weight / scale adjustment ---
+    if input_dtype == torch.float8_e4m3fn:
+        marlin_w13_qweight = ops.marlin_int4_fp8_preprocess(w13_qweight, inplace=False)
+        marlin_w2_qweight = ops.marlin_int4_fp8_preprocess(w2_qweight, inplace=False)
+        marlin_w13_scales = w13_scales.data * 512
+        marlin_w2_scales = w2_scales.data * 512
+    else:
+        marlin_w13_qweight = w13_qweight
+        marlin_w2_qweight = w2_qweight
+        marlin_w13_scales = w13_scales
+        marlin_w2_scales = w2_scales
+
+    # --- Process act_order (g_idx) ---
+    if quant_config.desc_act:
+        num_experts = w13_g_idx.shape[0]
+        w13_g_idx_sort_indices = torch.empty_like(w13_g_idx)
+        w2_g_idx_sort_indices = torch.empty_like(w2_g_idx)
+        w13_sorted_g_idx = torch.empty_like(w13_g_idx)
+        w2_sorted_g_idx = torch.empty_like(w2_g_idx)
+        for e in range(num_experts):
+            w13_g_idx_sort_indices[e] = torch.argsort(w13_g_idx[e]).to(torch.int32)
+            w2_g_idx_sort_indices[e] = torch.argsort(w2_g_idx[e]).to(torch.int32)
+            w13_sorted_g_idx[e] = w13_g_idx[e][w13_g_idx_sort_indices[e]]
+            w2_sorted_g_idx[e] = w2_g_idx[e][w2_g_idx_sort_indices[e]]
+    else:
+        num_experts = w13_g_idx.shape[0]
+        device = w13_g_idx.device
+        w13_g_idx = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+        w2_g_idx = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+        w13_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+        w2_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+
+    # --- Repack weights ---
+    marlin_w13_qweight = ops.gptq_marlin_moe_repack(
+        marlin_w13_qweight,
+        w13_g_idx_sort_indices,
+        marlin_w13_qweight.shape[1] * quant_config.pack_factor,
+        marlin_w13_qweight.shape[2],
+        quant_config.quant_type.size_bits,
+        is_a_8bit=is_a_8bit,
+    )
+    marlin_w2_qweight = ops.gptq_marlin_moe_repack(
+        marlin_w2_qweight,
+        w2_g_idx_sort_indices,
+        marlin_w2_qweight.shape[1] * quant_config.pack_factor,
+        marlin_w2_qweight.shape[2],
+        quant_config.quant_type.size_bits,
+        is_a_8bit=is_a_8bit,
+    )
+
+    # --- Permute scales ---
+    marlin_w13_scales = marlin_moe_permute_scales(
+        s=marlin_w13_scales,
+        size_k=layer.intermediate_size_per_partition,
+        size_n=marlin_w13_scales.shape[2],
+        group_size=quant_config.group_size,
+        is_a_8bit=is_a_8bit,
+    )
+    marlin_w2_scales = marlin_moe_permute_scales(
+        s=marlin_w2_scales,
+        size_k=marlin_w2_scales.shape[1]
+        * (
+            quant_config.group_size
+            if quant_config.group_size != -1
+            else quant_config.pack_factor
+        ),
+        size_n=marlin_w2_scales.shape[2],
+        group_size=quant_config.group_size,
+        is_a_8bit=is_a_8bit,
+    )
+
+    if input_dtype == torch.int8:
+        if layer.num_groups_w13 > 1:
+            marlin_w13_scales, w13_input_global_scale = marlin_act_int8_process_scales(
+                marlin_w13_scales
+            )
+        if layer.num_groups_w2 > 1:
+            marlin_w2_scales, w2_input_global_scale = marlin_act_int8_process_scales(
+                marlin_w2_scales
+            )
+
+    # --- Permute bias ---
+    if w13_bias is not None:
+        w13_bias_out = marlin_permute_bias(w13_bias)
+    if w2_bias is not None:
+        w2_bias_out = marlin_permute_bias(w2_bias)
+
+    return (
+        marlin_w13_qweight,
+        marlin_w2_qweight,
+        marlin_w13_scales,
+        marlin_w2_scales,
+        w13_g_idx,
+        w2_g_idx,
+        w13_g_idx_sort_indices,
+        w2_g_idx_sort_indices,
+        w13_input_global_scale,
+        w2_input_global_scale,
+        w13_bias_out,
+        w2_bias_out,
+    )
+
+
+def convert_to_wna16_moe_kernel_format(
+    backend: WNA16MoEBackend,
+    layer: torch.nn.Module,
+    quant_config: QuantizationConfig,
+    input_dtype: torch.dtype | None,
+    w13: torch.Tensor,
+    w2: torch.Tensor,
+    w13_scale: torch.Tensor,
+    w2_scale: torch.Tensor,
+    w13_g_idx: torch.Tensor,
+    w2_g_idx: torch.Tensor,
+    w13_bias: torch.Tensor | None = None,
+    w2_bias: torch.Tensor | None = None,
+) -> tuple[
+    torch.Tensor,  # w13_qweight
+    torch.Tensor,  # w2_qweight
+    torch.Tensor,  # w13_scales
+    torch.Tensor,  # w2_scales
+    torch.Tensor | None,  # w13_g_idx
+    torch.Tensor | None,  # w2_g_idx
+    torch.Tensor | None,  # w13_g_idx_sort_indices
+    torch.Tensor | None,  # w2_g_idx_sort_indices
+    torch.Tensor | None,  # w13_input_global_scale
+    torch.Tensor | None,  # w2_input_global_scale
+    torch.Tensor | None,  # w13_bias
+    torch.Tensor | None,  # w2_bias
+]:
+    """Dispatch weight post-processing to the appropriate per-backend handler.
+
+    To add a new backend, implement a ``_process_weights_<name>`` helper and
+    add a branch here.
+
+    Args:
+        backend: the selected ``WNA16MoEBackend``.
+        layer: the ``FusedMoE`` layer whose parameters are being prepared.
+        quant_config: the ``QuantizationConfig`` for this layer.
+        input_dtype: optional activation dtype, usually should be 16 bit.
+    """
+    if backend in (
+        WNA16MoEBackend.MARLIN,
+        WNA16MoEBackend.BATCHED_MARLIN,
+    ):
+        from vllm.model_executor.layers.quantization.gptq_marlin import (
+            GPTQMarlinConfig,
+        )
+
+        if not isinstance(quant_config, GPTQMarlinConfig):
+            raise TypeError(
+                "Marlin WNA16 MoE backend requires GPTQMarlinConfig, got "
+                f"{type(quant_config).__name__}."
+            )
+        return _process_weights_marlin(
+            layer,
+            quant_config,
+            input_dtype,
+            w13,
+            w2,
+            w13_scale,
+            w2_scale,
+            w13_g_idx,
+            w2_g_idx,
+            w13_bias,
+            w2_bias,
+        )
+    else:
+        raise ValueError(f"Unsupported wna16 MoE backend: {backend.value}")
diff --git a/vllm/model_executor/layers/fused_moe/oracle/mxfp4.py b/vllm/model_executor/layers/fused_moe/oracle/mxfp4.py
index 917d474fc9bd..4de0d722d50b 100644
--- a/vllm/model_executor/layers/fused_moe/oracle/mxfp4.py
+++ b/vllm/model_executor/layers/fused_moe/oracle/mxfp4.py
@@ -7,6 +7,7 @@
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import envs
+from vllm.config.kernel import MoEBackend
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import (
     FusedMoEConfig,
@@ -16,16 +17,22 @@
 )
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEQuantConfig,
+    FusedMoEQuantDesc,
+    RoutingMethodType,
     mxfp4_mxfp8_moe_quant_config,
+    mxfp4_w4a8_moe_quant_config,
     mxfp4_w4a16_moe_quant_config,
     ocp_mx_moe_quant_config,
 )
 from vllm.model_executor.layers.quantization.utils.mxfp4_utils import _swizzle_mxfp4
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
+    kFp8Dynamic128Sym,
+    kFp8StaticTensorSym,
     kMxfp4Static,
     kMxfp8Dynamic,
 )
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import all_close_1d
 from vllm.platforms import current_platform
 from vllm.utils.import_utils import has_triton_kernels
 from vllm.utils.math_utils import round_up
@@ -45,6 +52,8 @@
 
 class Mxfp4MoeBackend(Enum):
     NONE = "None"
+    # DeepGEMM FP8xFP4 backend (SM100+)
+    DEEPGEMM_MXFP4 = "DEEPGEMM_MXFP4"
     # FlashInfer TRTLLM backends
     FLASHINFER_TRTLLM_MXFP4_MXFP8 = "FLASHINFER_TRTLLM_MXFP4_MXFP8"
     FLASHINFER_TRTLLM_MXFP4_BF16 = "FLASHINFER_TRTLLM_MXFP4_BF16"
@@ -54,13 +63,27 @@ class Mxfp4MoeBackend(Enum):
     # Marlin
     BATCHED_MARLIN = "BATCHED_MARLIN"
     MARLIN = "MARLIN"
-    # ROCm AITER
-    AITER = "AITER"
+    # ROCm AITER backends
+    AITER_MXFP4_BF16 = "AITER_MXFP4_BF16"  # W4A16: CK kernel
+    # Keep the legacy name as an alias while the ROCm split backend rename settles.
+    AITER = "AITER_MXFP4_BF16"
+    AITER_MXFP4_FP8 = "AITER_MXFP4_FP8"  # W4A8: triton kernel
     # Triton
     TRITON = "TRITON"
     TRITON_UNFUSED = "TRITON_UNFUSED"
     # XPU
     XPU = "XPU"
+    # Emulation
+    EMULATION = "EMULATION"
+    # Humming
+    HUMMING = "HUMMING"
+
+
+# AITER backends group
+AITER_BACKENDS = (
+    Mxfp4MoeBackend.AITER_MXFP4_BF16,
+    Mxfp4MoeBackend.AITER_MXFP4_FP8,
+)
 
 
 # Backends that share the same TRTLLM weight format
@@ -78,7 +101,14 @@ class Mxfp4MoeBackend(Enum):
 def backend_to_kernel_cls(
     backend: Mxfp4MoeBackend,
 ) -> list[type[mk.FusedMoEExperts]]:
-    if backend in (
+    if backend == Mxfp4MoeBackend.DEEPGEMM_MXFP4:
+        from vllm.model_executor.layers.fused_moe.experts.deep_gemm_moe import (
+            DeepGemmFP4Experts,
+        )
+
+        return [DeepGemmFP4Experts]
+
+    elif backend in (
         Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_BF16,
         Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_MXFP8,
     ):
@@ -94,7 +124,7 @@ def backend_to_kernel_cls(
         Mxfp4MoeBackend.FLASHINFER_CUTLASS_MXFP4_BF16,
         Mxfp4MoeBackend.FLASHINFER_CUTLASS_MXFP4_MXFP8,
     ):
-        from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.flashinfer_cutlass_moe import (  # noqa: E501
             FlashInferExperts,
         )
 
@@ -116,47 +146,79 @@ def backend_to_kernel_cls(
 
         return [UnfusedOAITritonExperts]
 
+    elif backend == Mxfp4MoeBackend.HUMMING:
+        from vllm.model_executor.layers.fused_moe.fused_humming_moe import (
+            BatchedHummingGroupedExperts,
+            HummingGroupedExperts,
+            HummingIndexedExperts,
+        )
+
+        return [
+            BatchedHummingGroupedExperts,
+            HummingGroupedExperts,
+            HummingIndexedExperts,
+        ]
+
     elif backend == Mxfp4MoeBackend.MARLIN:
-        from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.marlin_moe import (
             MarlinExperts,
         )
 
         return [MarlinExperts]
 
     elif backend == Mxfp4MoeBackend.BATCHED_MARLIN:
-        from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.marlin_moe import (
             BatchedMarlinExperts,
         )
 
         return [BatchedMarlinExperts]
 
-    elif backend == Mxfp4MoeBackend.AITER:
-        from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+    elif backend == Mxfp4MoeBackend.AITER_MXFP4_BF16:
+        from vllm.model_executor.layers.fused_moe.experts.rocm_aiter_moe import (
             AiterExperts,
         )
 
         return [AiterExperts]
 
+    elif backend == Mxfp4MoeBackend.AITER_MXFP4_FP8:
+        from vllm.model_executor.layers.fused_moe.experts.aiter_mxfp4_w4a8_moe import (
+            AiterW4A8ExpertsMonolithic,
+        )
+
+        return [AiterW4A8ExpertsMonolithic]
+
     elif backend == Mxfp4MoeBackend.XPU:
-        from vllm.model_executor.layers.fused_moe.xpu_fused_moe import XPUExpertsMXFp4
+        from vllm.model_executor.layers.fused_moe.experts.xpu_moe import XPUExpertsMXFp4
 
         return [XPUExpertsMXFp4]
 
+    elif backend == Mxfp4MoeBackend.EMULATION:
+        from vllm.model_executor.layers.fused_moe.experts.ocp_mx_emulation_moe import (
+            OCP_MXQuantizationEmulationTritonExperts,
+        )
+
+        return [OCP_MXQuantizationEmulationTritonExperts]
+
     else:
         raise ValueError(f"Unknown MXFP4 MoE backend: {backend.value}")
 
 
-def map_mxfp4_backend(runner_backend: str) -> Mxfp4MoeBackend:
+def map_mxfp4_backend(runner_backend: MoEBackend) -> Mxfp4MoeBackend:
     """Map user's moe_backend string to Mxfp4MoeBackend."""
     mapping: dict[str, Mxfp4MoeBackend] = {
+        "deep_gemm": Mxfp4MoeBackend.DEEPGEMM_MXFP4,
         "flashinfer_trtllm": Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_BF16,
         "flashinfer_trtllm_afp8": Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_MXFP8,
         "flashinfer_cutlass": Mxfp4MoeBackend.FLASHINFER_CUTLASS_MXFP4_BF16,
         "flashinfer_cutlass_afp8": Mxfp4MoeBackend.FLASHINFER_CUTLASS_MXFP4_MXFP8,
         "triton": Mxfp4MoeBackend.TRITON,
+        "triton_unfused": Mxfp4MoeBackend.TRITON_UNFUSED,
+        "humming": Mxfp4MoeBackend.HUMMING,
         "marlin": Mxfp4MoeBackend.MARLIN,
-        "aiter": Mxfp4MoeBackend.AITER,
+        "aiter": Mxfp4MoeBackend.AITER_MXFP4_BF16,
+        "aiter_mxfp4_fp8": Mxfp4MoeBackend.AITER_MXFP4_FP8,
         "xpu": Mxfp4MoeBackend.XPU,
+        "emulation": Mxfp4MoeBackend.EMULATION,
     }
     if backend := mapping.get(runner_backend):
         return backend
@@ -166,61 +228,79 @@ def map_mxfp4_backend(runner_backend: str) -> Mxfp4MoeBackend:
     )
 
 
-def _get_priority_backends() -> list[Mxfp4MoeBackend]:
+def _get_priority_backends_for_gpt_oss() -> list[Mxfp4MoeBackend]:
     """
     Get available backends in priority order based on platform and config.
     Only includes BF16 backends. MXFP8 backends are selected via env vars.
     """
     _AVAILABLE_BACKENDS = [
         Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_BF16,
-        Mxfp4MoeBackend.AITER,
+        Mxfp4MoeBackend.AITER_MXFP4_BF16,
+        Mxfp4MoeBackend.AITER_MXFP4_FP8,
         Mxfp4MoeBackend.TRITON,
         Mxfp4MoeBackend.FLASHINFER_CUTLASS_MXFP4_BF16,
-        Mxfp4MoeBackend.TRITON_UNFUSED,
+        # TRITON_UNFUSED has bug with MTP support
+        # TODO re-enable after kernel is fixed
+        # TRITON_UNFUSED
         Mxfp4MoeBackend.MARLIN,
         Mxfp4MoeBackend.BATCHED_MARLIN,
         Mxfp4MoeBackend.XPU,
+        Mxfp4MoeBackend.EMULATION,
+    ]
+    return _AVAILABLE_BACKENDS
+
+
+def _get_priority_backends() -> list[Mxfp4MoeBackend]:
+    """
+    Get available backends in priority order. SM100+ prefers DeepGEMM FP4 /
+    TRTLLM MXFP8; SM90 falls through to Triton_unfused or Marlin (the
+    backend-level ``is_supported_config`` check filters by device capability).
+    """
+    if current_platform.is_rocm():
+        return [Mxfp4MoeBackend.AITER_MXFP4_BF16]
+    _AVAILABLE_BACKENDS = [
+        Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_MXFP8,
+        Mxfp4MoeBackend.DEEPGEMM_MXFP4,
+        # TRITON_UNFUSED has bug with MTP support
+        # TODO re-enable after kernel is fixed
+        # TRITON_UNFUSED
+        Mxfp4MoeBackend.MARLIN,
+        Mxfp4MoeBackend.BATCHED_MARLIN,
     ]
     return _AVAILABLE_BACKENDS
 
 
 def _backend_activation_key(backend: Mxfp4MoeBackend) -> QuantKey | None:
-    """Map backend to its activation key (MXFP8 or None for BF16)."""
+    """Map backend to its activation key (FP8, MXFP8, or None for BF16)."""
+    if backend == Mxfp4MoeBackend.DEEPGEMM_MXFP4:
+        return kFp8Dynamic128Sym
     if backend in (
         Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_MXFP8,
         Mxfp4MoeBackend.FLASHINFER_CUTLASS_MXFP4_MXFP8,
     ):
         return kMxfp8Dynamic
-    return None
+    if backend == Mxfp4MoeBackend.AITER_MXFP4_FP8:
+        return kFp8StaticTensorSym
+    return None  # BF16 activation
 
 
-def select_gpt_oss_mxfp4_moe_backend(
+def select_mxfp4_moe_backend(
     config: FusedMoEConfig,
+    activation_key: QuantKey | None = None,
 ) -> tuple[Mxfp4MoeBackend, type[mk.FusedMoEExperts] | None]:
     """
     Select the primary MXFP4 MoE backend.
+
+    Args:
+        config: MoE configuration
+        activation_key: Optional activation quantization key. If provided,
+            overrides the default activation key for backend selection.
+            Use kFp8StaticTensorSym for W4A8 scheme.
+
     Note: Shape-specific fallbacks may still occur at runtime.
     """
-    triton_kernels_supported = has_triton_kernels() and (
-        9,
-        0,
-    ) <= current_platform.get_device_capability() < (11, 0)
-
-    # LoRA: separate experts backend path
-    if config.is_lora_enabled:
-        if not current_platform.is_cuda():
-            # ROCm: Triton mxfp4 LoRA hits GPU memory faults due to
-            # triton_kernels.tensor.Tensor / HIP read-only page issues
-            # during weight swizzle and LoRA forward. Needs work from
-            # the triton_kernels/aiter side.
-            raise NotImplementedError("Mxfp4 LoRA is currently only supported on CUDA.")
-        if envs.VLLM_MXFP4_USE_MARLIN is False and triton_kernels_supported:
-            logger.info_once("Using Triton backend for mxfp4 lora")
-            return Mxfp4MoeBackend.TRITON_UNFUSED, backend_to_kernel_cls(
-                Mxfp4MoeBackend.TRITON_UNFUSED
-            )[0]
-        logger.info_once("Using Marlin backend for mxfp4 lora")
-        return Mxfp4MoeBackend.MARLIN, backend_to_kernel_cls(Mxfp4MoeBackend.MARLIN)[0]
+    # If activation_key is explicitly provided (e.g., W4A8), use it
+    requested_activation_key = activation_key
 
     activation_format = (
         mk.FusedMoEActivationFormat.BatchedExperts
@@ -255,7 +335,7 @@ def _return_or_raise(
                 k_cls, config, weight_key, activation_key, activation_format
             )
             if supported:
-                logger.info_once(_make_log_backend(backend), scope="local")
+                logger.info_once(_make_log_backend(backend))
                 return backend, k_cls
         raise ValueError(_make_log_unsupported(backend, reason))
 
@@ -267,16 +347,22 @@ def _return_or_raise(
             and requested_backend == Mxfp4MoeBackend.MARLIN
         ):
             requested_backend = Mxfp4MoeBackend.BATCHED_MARLIN
+        # Use requested_activation_key if provided, otherwise use backend default
+        act_key = (
+            requested_activation_key
+            if requested_activation_key is not None
+            else _backend_activation_key(requested_backend)
+        )
         return _return_or_raise(
             requested_backend,
             config,
             kMxfp4Static,
-            _backend_activation_key(requested_backend),
+            act_key,
             activation_format,
         )
 
     # Select kernels in order of backend.
-    AVAILABLE_BACKENDS = _get_priority_backends()
+    AVAILABLE_BACKENDS = _get_priority_backends_for_gpt_oss()
 
     # Handle explicit FlashInfer MXFP4 BF16 configuration.
     if envs.is_set("VLLM_USE_FLASHINFER_MOE_MXFP4_BF16"):
@@ -343,16 +429,21 @@ def _return_or_raise(
         )
 
     for backend in AVAILABLE_BACKENDS:
-        activation_key = _backend_activation_key(backend)
+        # Use requested_activation_key if provided, otherwise use backend default
+        act_key = (
+            requested_activation_key
+            if requested_activation_key is not None
+            else _backend_activation_key(backend)
+        )
         for k_cls in backend_to_kernel_cls(backend):
             supported, reason = k_cls.is_supported_config(
-                k_cls, config, kMxfp4Static, activation_key, activation_format
+                k_cls, config, kMxfp4Static, act_key, activation_format
             )
             if supported:
-                logger.info_once(_make_log_backend(backend), scope="local")
+                logger.info_once(_make_log_backend(backend))
                 return backend, k_cls
             else:
-                logger.debug_once(_make_log_unsupported(backend, reason), scope="local")
+                logger.debug_once(_make_log_unsupported(backend, reason))
 
     if current_platform.is_xpu():
         backend = Mxfp4MoeBackend.XPU
@@ -373,11 +464,109 @@ def _return_or_raise(
     return Mxfp4MoeBackend.NONE, None
 
 
+def select_deepseek_v4_mxfp4_moe_backend(
+    config: FusedMoEConfig,
+) -> tuple[Mxfp4MoeBackend, type[mk.FusedMoEExperts] | None]:
+    """
+    Select the MXFP4 MoE backend with MXFP8 activation as top priority.
+    Falls back through BF16 and other backends.
+    """
+    activation_format = (
+        mk.FusedMoEActivationFormat.BatchedExperts
+        if config.moe_parallel_config.use_batched_activation_format
+        else mk.FusedMoEActivationFormat.Standard
+    )
+
+    def _make_log_backend(backend: Mxfp4MoeBackend):
+        return f"Using '{backend.value}' Mxfp4 MoE backend."
+
+    def _make_log_unsupported(backend: Mxfp4MoeBackend, reason: str | None) -> str:
+        if reason:
+            return (
+                f"Mxfp4 MoE backend '{backend.value}' does not support the "
+                f"deployment configuration since {reason}."
+            )
+        return (
+            f"Mxfp4 MoE backend '{backend.value}' does not support the "
+            "deployment configuration."
+        )
+
+    def _return_or_raise(
+        backend: Mxfp4MoeBackend,
+        config: FusedMoEConfig,
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+        activation_format: mk.FusedMoEActivationFormat,
+    ) -> tuple[Mxfp4MoeBackend, type[mk.FusedMoEExperts]]:
+        reason: str | None = None
+        for k_cls in backend_to_kernel_cls(backend):
+            supported, reason = k_cls.is_supported_config(
+                k_cls, config, weight_key, activation_key, activation_format
+            )
+            if supported:
+                logger.info_once(_make_log_backend(backend), scope="local")
+                return backend, k_cls
+        raise ValueError(_make_log_unsupported(backend, reason))
+
+    # Honor explicit moe_backend (e.g. "marlin", "triton_unfused") before
+    # falling back to the auto priority list.
+    runner_backend = config.moe_backend
+    if runner_backend != "auto":
+        requested_backend = map_mxfp4_backend(runner_backend)
+        if (
+            activation_format == mk.FusedMoEActivationFormat.BatchedExperts
+            and requested_backend == Mxfp4MoeBackend.MARLIN
+        ):
+            requested_backend = Mxfp4MoeBackend.BATCHED_MARLIN
+        return _return_or_raise(
+            requested_backend,
+            config,
+            kMxfp4Static,
+            _backend_activation_key(requested_backend),
+            activation_format,
+        )
+
+    # DeepSeek-V4 on ROCm is more accurate with the unfused Triton MXFP4 path
+    # than the default AITER path. Prefer Triton-unfused for this routing mode,
+    # while keeping AITER as a fallback if Triton-unfused rejects the config.
+    if (
+        current_platform.is_rocm()
+        and config.routing_method == RoutingMethodType.DeepseekV4
+    ):
+        priority_backends = [
+            Mxfp4MoeBackend.TRITON_UNFUSED,
+            Mxfp4MoeBackend.AITER_MXFP4_BF16,
+        ]
+    else:
+        priority_backends = _get_priority_backends()
+
+    # Iterate priority backends: TRTLLM MXFP8, then Triton.
+    for backend in priority_backends:
+        activation_key = _backend_activation_key(backend)
+        for k_cls in backend_to_kernel_cls(backend):
+            supported, reason = k_cls.is_supported_config(
+                k_cls, config, kMxfp4Static, activation_key, activation_format
+            )
+            if supported:
+                logger.info_once(_make_log_backend(backend), scope="local")
+                return backend, k_cls
+            else:
+                logger.debug_once(_make_log_unsupported(backend, reason), scope="local")
+
+    raise NotImplementedError(
+        "No MXFP4 MoE backend supports the deployment configuration."
+    )
+
+
 def mxfp4_round_up_hidden_size_and_intermediate_size(
     backend: Mxfp4MoeBackend, hidden_size: int, intermediate_size: int
 ) -> tuple[int, int]:
     """Round up hidden_size and intermediate_size based on backend requirements."""
-    if backend in (Mxfp4MoeBackend.MARLIN, Mxfp4MoeBackend.BATCHED_MARLIN):
+    if backend == Mxfp4MoeBackend.DEEPGEMM_MXFP4:
+        # DeepGEMM requires M/N/K alignment
+        intermediate_size = round_up(intermediate_size, 128)
+        hidden_size = round_up(hidden_size, 128)
+    elif backend in (Mxfp4MoeBackend.MARLIN, Mxfp4MoeBackend.BATCHED_MARLIN):
         intermediate_size = round_up(intermediate_size, 128)
         if current_platform.is_xpu():
             hidden_size = round_up(hidden_size, 128)
@@ -420,13 +609,41 @@ def convert_gpt_oss_weight_to_mxfp4_moe_kernel_format(
 ]:
     """Convert loaded weights into backend-specific kernel format."""
 
+    if mxfp4_backend == Mxfp4MoeBackend.DEEPGEMM_MXFP4:
+        from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+            _upcast_e8m0_to_fp32,
+        )
+
+        return (
+            w13_weight.data,
+            w2_weight.data,
+            _upcast_e8m0_to_fp32(w13_weight_scale.data),
+            _upcast_e8m0_to_fp32(w2_weight_scale.data),
+            w13_bias,
+            w2_bias,
+        )
+
     num_experts = w13_weight.shape[0]
     intermediate_size = w13_weight.shape[1] // 2
     hidden_size = w13_weight.shape[2] * 2
 
     sf_block_size = 32  # mxfp4 block size
 
-    if mxfp4_backend in (
+    if mxfp4_backend == Mxfp4MoeBackend.HUMMING:
+        from vllm.model_executor.layers.quantization.utils.humming_utils import (
+            prepare_humming_moe_layer,
+        )
+
+        prepare_humming_moe_layer(layer, {"quant_method": "gpt_oss_mxfp4"})
+        return (
+            layer.w13_weight,
+            layer.w2_weight,
+            layer.w13_weight_scale,
+            layer.w2_weight_scale,
+            getattr(layer, "w13_bias", None),
+            getattr(layer, "w2_bias", None),
+        )
+    elif mxfp4_backend in (
         Mxfp4MoeBackend.MARLIN,
         Mxfp4MoeBackend.BATCHED_MARLIN,
     ):
@@ -659,7 +876,7 @@ def _interleave_mxfp4_cutlass_sm90(w):
                 w2_bias,
             )
 
-    elif mxfp4_backend == Mxfp4MoeBackend.AITER:
+    elif mxfp4_backend == Mxfp4MoeBackend.AITER_MXFP4_BF16:
         from vllm._aiter_ops import rocm_aiter_ops
 
         if w13_bias is not None:
@@ -721,12 +938,70 @@ def _interleave_mxfp4_cutlass_sm90(w):
             w2_bias,
         )
 
+    elif mxfp4_backend == Mxfp4MoeBackend.AITER_MXFP4_FP8:
+        # W4A8: MXFP4 weights + static FP8 activations (triton kernel)
+        from triton_kernels.matmul_ogs import FlexCtx, PrecisionConfig
+        from triton_kernels.numerics import InFlexData
+
+        if w13_bias is not None:
+            w13_bias = w13_bias.to(torch.float32)
+        if w2_bias is not None:
+            w2_bias = w2_bias.to(torch.float32)
+
+        # Process static FP8 input scales (reduce to scalar, warn if not uniform)
+        w13_input_scale = layer.w13_input_scale
+        w2_input_scale = layer.w2_input_scale
+        if w13_input_scale is None or w2_input_scale is None:
+            raise ValueError(
+                "W4A8 (AITER_MXFP4_FP8) requires static input scales, but found "
+                "w13_input_scale or w2_input_scale is None."
+            )
+        if not all_close_1d(w13_input_scale) or not all_close_1d(w2_input_scale):
+            logger.warning_once(
+                "Found input_scales that are not equal for "
+                "fp8 MoE layer. Using the maximum across experts "
+                "for each layer."
+            )
+        w13_input_scale = w13_input_scale.max().to(torch.float32)
+        w2_input_scale = w2_input_scale.max().to(torch.float32)
+
+        # Swizzle weights for GFX950
+        w13_weight, w13_flex, w13_scale = _swizzle_mxfp4(w13_weight, w13_weight_scale)
+        w2_weight, w2_flex, w2_scale = _swizzle_mxfp4(w2_weight, w2_weight_scale)
+
+        # Create InFlexData for activation scales
+        lhs_data13 = InFlexData(scale=w13_input_scale)
+        lhs_data2 = InFlexData(scale=w2_input_scale)
+
+        # Create PrecisionConfig with both weight and activation info
+        w13_precision_config = PrecisionConfig(
+            weight_scale=w13_scale,
+            flex_ctx=FlexCtx(rhs_data=w13_flex, lhs_data=lhs_data13),
+        )
+        w2_precision_config = PrecisionConfig(
+            weight_scale=w2_scale,
+            flex_ctx=FlexCtx(rhs_data=w2_flex, lhs_data=lhs_data2),
+        )
+
+        del layer.w13_weight
+        del layer.w2_weight
+
+        return (
+            w13_weight,
+            w2_weight,
+            w13_precision_config,
+            w2_precision_config,
+            w13_bias,
+            w2_bias,
+        )
+
     elif mxfp4_backend in TRITON_BACKENDS:
         from triton_kernels.matmul_ogs import FlexCtx, PrecisionConfig
 
-        assert w13_bias is not None and w2_bias is not None
-        w13_bias = w13_bias.to(torch.float32)
-        w2_bias = w2_bias.to(torch.float32)
+        if w13_bias is not None:
+            w13_bias = w13_bias.to(torch.float32)
+        if w2_bias is not None:
+            w2_bias = w2_bias.to(torch.float32)
 
         w13_weight, w13_flex, w13_scale = _swizzle_mxfp4(
             w13_weight,
@@ -765,6 +1040,17 @@ def _interleave_mxfp4_cutlass_sm90(w):
             w13_bias,
             w2_bias,
         )
+    elif mxfp4_backend == Mxfp4MoeBackend.EMULATION:
+        # No additional transformation needed for emulation backend,
+        # weights are dequantized on the fly in the experts class.
+        return (
+            w13_weight,
+            w2_weight,
+            w13_weight_scale,
+            w2_weight_scale,
+            w13_bias,
+            w2_bias,
+        )
     else:
         raise ValueError(
             f"Unsupported mxfp4_backend: {mxfp4_backend}: "
@@ -772,23 +1058,381 @@ def _interleave_mxfp4_cutlass_sm90(w):
         )
 
 
+def convert_weight_to_mxfp4_moe_kernel_format(
+    mxfp4_backend: Mxfp4MoeBackend,
+    layer: torch.nn.Module,
+    w13_weight: torch.Tensor,
+    w2_weight: torch.Tensor,
+    w13_weight_scale: torch.Tensor,
+    w2_weight_scale: torch.Tensor,
+    w13_bias: torch.Tensor | None = None,
+    w2_bias: torch.Tensor | None = None,
+    _cache_permute_indices: dict[torch.Size, torch.Tensor] | None = None,
+) -> tuple[
+    torch.Tensor,
+    torch.Tensor,
+    Union[torch.Tensor, "PrecisionConfig"],
+    Union[torch.Tensor, "PrecisionConfig"],
+    torch.Tensor | None,
+    torch.Tensor | None,
+]:
+    """Convert loaded weights into backend-specific kernel format.
+
+    Supports DeepGEMM, TRTLLM MXFP8, Triton and Marlin backends.
+    """
+
+    if mxfp4_backend == Mxfp4MoeBackend.DEEPGEMM_MXFP4:
+        from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+            _upcast_e8m0_to_fp32,
+        )
+
+        # Weights stay as uint8 packed FP4 — no layout change needed.
+        # Convert E8M0 uint8 scales to float32.
+        return (
+            w13_weight.data,
+            w2_weight.data,
+            _upcast_e8m0_to_fp32(w13_weight_scale.data),
+            _upcast_e8m0_to_fp32(w2_weight_scale.data),
+            w13_bias,
+            w2_bias,
+        )
+
+    if mxfp4_backend == Mxfp4MoeBackend.HUMMING:
+        from vllm.model_executor.layers.quantization.utils.humming_utils import (
+            prepare_humming_moe_layer,
+        )
+
+        prepare_humming_moe_layer(layer, {"quant_method": "mxfp4"})
+        return (
+            layer.w13_weight,
+            layer.w2_weight,
+            layer.w13_weight_scale,
+            layer.w2_weight_scale,
+            getattr(layer, "w13_bias", None),
+            getattr(layer, "w2_bias", None),
+        )
+
+    if mxfp4_backend in (Mxfp4MoeBackend.MARLIN, Mxfp4MoeBackend.BATCHED_MARLIN):
+        from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
+            prepare_moe_mxfp4_layer_for_marlin,
+        )
+
+        return prepare_moe_mxfp4_layer_for_marlin(
+            layer,
+            w13_weight,
+            w2_weight,
+            w13_weight_scale,
+            w2_weight_scale,
+            w13_bias,
+            w2_bias,
+        )
+
+    num_experts = w13_weight.shape[0]
+    intermediate_size = w13_weight.shape[1] // 2
+    hidden_size = w13_weight.shape[2] * 2
+
+    sf_block_size = 32  # mxfp4 block size
+
+    if mxfp4_backend in TRTLLM_BACKENDS:
+        assert _cache_permute_indices is not None
+        from flashinfer.fp4_quantization import nvfp4_block_scale_interleave
+        from flashinfer.fused_moe.core import get_w2_permute_indices_with_cache
+
+        w13_weight = w13_weight.data
+        w2_weight = w2_weight.data
+        w13_weight_scale = w13_weight_scale.data
+        w2_weight_scale = w2_weight_scale.data
+        if w13_bias is not None:
+            w13_bias = w13_bias.data.to(torch.float32)
+        if w2_bias is not None:
+            w2_bias = w2_bias.data.to(torch.float32)
+
+        # Swap w1/w3 and interleave to match TRTLLM SwiGLU convention.
+        # Standard loading gives contiguous [w1/gate, w3/up].
+        # TRTLLM kernel expects interleaved [w3_0, w1_0, w3_1, w1_1, ...].
+        w1_weight = w13_weight[:, :intermediate_size, :]
+        w3_weight = w13_weight[:, intermediate_size:, :]
+        w13_weight = torch.stack([w3_weight, w1_weight], dim=2).reshape(
+            w13_weight.shape
+        )
+
+        w1_scale = w13_weight_scale[:, :intermediate_size, :]
+        w3_scale = w13_weight_scale[:, intermediate_size:, :]
+        w13_weight_scale = torch.stack([w3_scale, w1_scale], dim=2).reshape(
+            w13_weight_scale.shape
+        )
+
+        if w13_bias is not None:
+            b1 = w13_bias[:, :intermediate_size]
+            b3 = w13_bias[:, intermediate_size:]
+            w13_bias = torch.stack([b3, b1], dim=2).reshape(w13_bias.shape)
+
+        # Shuffle weights and scaling factors for transposed mma output.
+        # Permute indices depend only on shape (cached by torch.Size),
+        # so compute once and apply to all experts via batched indexing.
+        epilogue_tile_m = 128
+
+        # w13 weight permute
+        w13_perm = get_w2_permute_indices_with_cache(
+            _cache_permute_indices,
+            w13_weight[0].view(torch.uint8),
+            epilogue_tile_m,
+        ).to(w13_weight.device)
+        w13_weight = w13_weight.view(torch.uint8)[:, w13_perm].contiguous()
+
+        # w13 scale permute + interleave
+        w13_sf_perm = get_w2_permute_indices_with_cache(
+            _cache_permute_indices,
+            w13_weight_scale[0].view(torch.uint8),
+            epilogue_tile_m,
+            num_elts_per_sf=16,
+        ).to(w13_weight_scale.device)
+        w13_s = w13_weight_scale.view(torch.uint8)[:, w13_sf_perm].contiguous()
+        E, N_s, K_s = w13_s.shape
+        w13_weight_scale = (
+            nvfp4_block_scale_interleave(w13_s.reshape(E * N_s, K_s))
+            .reshape(num_experts, 2 * intermediate_size, hidden_size // sf_block_size)
+            .view(torch.float8_e4m3fn)
+        )
+
+        # w2 weight permute
+        w2_perm = get_w2_permute_indices_with_cache(
+            _cache_permute_indices,
+            w2_weight[0].view(torch.uint8),
+            epilogue_tile_m,
+        ).to(w2_weight.device)
+        w2_weight = w2_weight.view(torch.uint8)[:, w2_perm].contiguous()
+
+        # w2 scale permute + interleave
+        w2_sf_perm = get_w2_permute_indices_with_cache(
+            _cache_permute_indices,
+            w2_weight_scale[0].view(torch.uint8),
+            epilogue_tile_m,
+            num_elts_per_sf=16,
+        ).to(w2_weight_scale.device)
+        w2_s = w2_weight_scale.view(torch.uint8)[:, w2_sf_perm].contiguous()
+        E2, N2_s, K2_s = w2_s.shape
+        w2_weight_scale = (
+            nvfp4_block_scale_interleave(w2_s.reshape(E2 * N2_s, K2_s))
+            .reshape(num_experts, hidden_size, intermediate_size // sf_block_size)
+            .view(torch.float8_e4m3fn)
+        )
+
+        # w13 bias permute
+        if w13_bias is not None:
+            w13_b_perm = get_w2_permute_indices_with_cache(
+                _cache_permute_indices,
+                w13_bias[0].reshape(-1, 1),
+                epilogue_tile_m,
+            ).to(w13_bias.device)
+            w13_bias = w13_bias.reshape(num_experts, -1, 1)[:, w13_b_perm].reshape(
+                num_experts, -1
+            )
+
+        # w2 bias permute
+        if w2_bias is not None:
+            w2_b_perm = get_w2_permute_indices_with_cache(
+                _cache_permute_indices,
+                w2_bias[0].reshape(-1, 1),
+                epilogue_tile_m,
+            ).to(w2_bias.device)
+            w2_bias = w2_bias.reshape(num_experts, -1, 1)[:, w2_b_perm].reshape(
+                num_experts, -1
+            )
+
+        return (
+            w13_weight,
+            w2_weight,
+            w13_weight_scale,
+            w2_weight_scale,
+            w13_bias,
+            w2_bias,
+        )
+
+    elif mxfp4_backend == Mxfp4MoeBackend.AITER_MXFP4_BF16:
+        from vllm._aiter_ops import rocm_aiter_ops
+
+        if w13_bias is not None:
+            w13_bias = w13_bias.data.to(torch.float32)
+        if w2_bias is not None:
+            w2_bias = w2_bias.data.to(torch.float32)
+
+        e, n, k = w13_weight.shape
+
+        w13_weight.view(torch.uint8).copy_(
+            w13_weight.data.view(torch.uint8)
+            .view(e, n // 2, 2, k)
+            .permute(0, 2, 1, 3)
+            .contiguous()
+            .view(e, n, k)
+        )
+        w13_weight_scale.data = (
+            w13_weight_scale.data.view(e, n // 2, 2, -1)
+            .permute(0, 2, 1, 3)
+            .contiguous()
+            .view(e, n, -1)
+        )
+
+        w13_weight.data = w13_weight.data.view(torch.float4_e2m1fn_x2)
+        w2_weight.data = w2_weight.data.view(torch.float4_e2m1fn_x2)
+
+        w13_weight.data = rocm_aiter_ops.shuffle_weight_a16w4(w13_weight, 16, True)
+        shuffled_w13_scale = rocm_aiter_ops.shuffle_scale_a16w4(
+            w13_weight_scale.view(-1, w13_weight_scale.shape[-1]),
+            num_experts,
+            True,
+        )
+
+        w2_weight.data = rocm_aiter_ops.shuffle_weight_a16w4(w2_weight, 16, False)
+        shuffled_w2_scale = rocm_aiter_ops.shuffle_scale_a16w4(
+            w2_weight_scale.view(-1, w2_weight_scale.shape[-1]),
+            num_experts,
+            False,
+        )
+
+        if w13_bias is not None:
+            w13_bias = (
+                w13_bias.data.view(-1, n // 2, 2)
+                .permute(0, 2, 1)
+                .contiguous()
+                .view(-1, n)
+            )
+
+        return (
+            w13_weight,
+            w2_weight,
+            shuffled_w13_scale,
+            shuffled_w2_scale,
+            w13_bias,
+            w2_bias,
+        )
+
+    elif mxfp4_backend in TRITON_BACKENDS:
+        from triton_kernels.matmul_ogs import FlexCtx, PrecisionConfig
+
+        if mxfp4_backend == Mxfp4MoeBackend.TRITON:
+
+            def shuffle_weight(w: torch.Tensor) -> torch.Tensor:
+                shape = w.shape
+                n = shape[-1]
+                first = w[..., : n // 2]
+                second = w[..., n // 2 :]
+                stacked = torch.stack((first, second), dim=-1)
+                return stacked.reshape(shape)
+
+            w13_weight = shuffle_weight(w13_weight)
+            w13_weight_scale = shuffle_weight(w13_weight_scale)
+
+            if w13_bias is not None:
+                w13_bias = shuffle_weight(w13_bias.to(torch.float32))
+        else:
+            if w13_bias is not None:
+                w13_bias = w13_bias.to(torch.float32)
+
+        if w2_bias is not None:
+            w2_bias = w2_bias.to(torch.float32)
+
+        w13_weight, w13_flex, w13_scale = _swizzle_mxfp4(
+            w13_weight,
+            w13_weight_scale,
+        )
+        w2_weight, w2_flex, w2_scale = _swizzle_mxfp4(
+            w2_weight,
+            w2_weight_scale,
+        )
+
+        w13_precision_config = PrecisionConfig(
+            weight_scale=w13_scale, flex_ctx=FlexCtx(rhs_data=w13_flex)
+        )
+        w2_precision_config = PrecisionConfig(
+            weight_scale=w2_scale, flex_ctx=FlexCtx(rhs_data=w2_flex)
+        )
+
+        del layer.w13_weight
+        del layer.w2_weight
+
+        return (
+            w13_weight,
+            w2_weight,
+            w13_precision_config,
+            w2_precision_config,
+            w13_bias,
+            w2_bias,
+        )
+    else:
+        raise ValueError(
+            f"Unsupported mxfp4_backend for Mxfp4MoEMethod: {mxfp4_backend}. "
+            f"Expected TRTLLM, Triton, or AITER backend."
+        )
+
+
 def make_mxfp4_moe_quant_config(
     mxfp4_backend: Mxfp4MoeBackend,
     w1_scale: Union[torch.Tensor, "PrecisionConfig"],
     w2_scale: Union[torch.Tensor, "PrecisionConfig"],
+    gemm1_alpha: float | None = None,
+    gemm1_beta: float | None = None,
+    swiglu_limit: float | None = None,
     w1_bias: torch.Tensor | None = None,
     w2_bias: torch.Tensor | None = None,
+    a1_scale: torch.Tensor | None = None,
+    a2_scale: torch.Tensor | None = None,
+    layer: torch.nn.Module | None = None,
 ) -> FusedMoEQuantConfig | None:
     """Create a FusedMoEQuantConfig for the given MXFP4 backend."""
-    if mxfp4_backend in (
-        Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_MXFP8,
-        Mxfp4MoeBackend.FLASHINFER_CUTLASS_MXFP4_MXFP8,
-    ):
+    if mxfp4_backend == Mxfp4MoeBackend.DEEPGEMM_MXFP4:
+        from vllm.model_executor.layers.quantization.utils.quant_utils import (
+            GroupShape,
+        )
+
+        # DeepGEMM FP4 uses FP8 per-token-group activation quantization
+        # with block 128, matching the FP8 DeepGEMM path.
+        _fp8_dtype = current_platform.fp8_dtype()
+        _block_shape = GroupShape(128, 128)
+        return FusedMoEQuantConfig(
+            _a1=FusedMoEQuantDesc(_fp8_dtype, _block_shape, None, None, None, None),
+            _a2=FusedMoEQuantDesc(_fp8_dtype, _block_shape, None, None, None, None),
+            _w1=FusedMoEQuantDesc("mxfp4", None, w1_scale, None, None, w1_bias),
+            _w2=FusedMoEQuantDesc("mxfp4", None, w2_scale, None, None, w2_bias),
+            gemm1_alpha=gemm1_alpha,
+            gemm1_beta=gemm1_beta,
+            gemm1_clamp_limit=swiglu_limit,
+        )
+    elif mxfp4_backend == Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_MXFP8:
+        # TRTLLM kernel expects non-swizzled mxfp8 activation scales.
+        return mxfp4_mxfp8_moe_quant_config(
+            w1_bias=w1_bias,
+            w2_bias=w2_bias,
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            gemm1_alpha=gemm1_alpha,
+            gemm1_beta=gemm1_beta,
+            gemm1_clamp_limit=swiglu_limit,
+            mx_alignment=256,
+            is_scale_swizzled=False,
+        )
+    elif mxfp4_backend == Mxfp4MoeBackend.FLASHINFER_CUTLASS_MXFP4_MXFP8:
+        # CUTLASS kernel expects swizzled mxfp8 activation scales.
         return mxfp4_mxfp8_moe_quant_config(
             w1_bias=w1_bias,
             w2_bias=w2_bias,
             w1_scale=w1_scale,
             w2_scale=w2_scale,
+            gemm1_alpha=gemm1_alpha,
+            gemm1_beta=gemm1_beta,
+            gemm1_clamp_limit=swiglu_limit,
+            is_scale_swizzled=True,
+        )
+    elif mxfp4_backend == Mxfp4MoeBackend.AITER_MXFP4_FP8:
+        # W4A8: MXFP4 weights + static FP8 activations
+        return mxfp4_w4a8_moe_quant_config(
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+            w1_bias=w1_bias,
+            w2_bias=w2_bias,
+            block_shape=None,
         )
     elif mxfp4_backend in (
         Mxfp4MoeBackend.MARLIN,
@@ -797,14 +1441,25 @@ def make_mxfp4_moe_quant_config(
         Mxfp4MoeBackend.TRITON_UNFUSED,
         Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_BF16,
         Mxfp4MoeBackend.FLASHINFER_CUTLASS_MXFP4_BF16,
-        Mxfp4MoeBackend.AITER,
+        Mxfp4MoeBackend.AITER_MXFP4_BF16,
     ):
         return mxfp4_w4a16_moe_quant_config(
             w1_bias=w1_bias,
             w2_bias=w2_bias,
             w1_scale=w1_scale,
             w2_scale=w2_scale,
+            gemm1_alpha=gemm1_alpha,
+            gemm1_beta=gemm1_beta,
+            gemm1_clamp_limit=swiglu_limit,
         )
+    elif mxfp4_backend == Mxfp4MoeBackend.HUMMING:
+        from vllm.model_executor.layers.fused_moe.layer import FusedMoE
+        from vllm.model_executor.layers.quantization.utils.humming_utils import (
+            get_humming_moe_quant_config,
+        )
+
+        assert isinstance(layer, FusedMoE)
+        return get_humming_moe_quant_config(layer)
     else:
         return ocp_mx_moe_quant_config(
             quant_dtype="mxfp4",
@@ -812,6 +1467,9 @@ def make_mxfp4_moe_quant_config(
             w2_bias=w2_bias,
             w1_scale=w1_scale,
             w2_scale=w2_scale,
+            gemm1_alpha=gemm1_alpha,
+            gemm1_beta=gemm1_beta,
+            gemm1_clamp_limit=swiglu_limit,
         )
 
 
@@ -822,11 +1480,11 @@ def make_mxfp4_moe_kernel(
     mxfp4_backend: Mxfp4MoeBackend,
     routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
     shared_experts: torch.nn.Module | None = None,
+    layer: torch.nn.Module | None = None,
 ) -> mk.FusedMoEKernel:
     """Create a FusedMoEKernel for the given MXFP4 backend."""
     is_monolithic = issubclass(experts_cls, mk.FusedMoEExpertsMonolithic)
 
-    # Create Prepare/Finalize.
     prepare_finalize = maybe_make_prepare_finalize(
         moe=moe_config,
         quant_config=moe_quant_config,
@@ -836,7 +1494,12 @@ def make_mxfp4_moe_kernel(
     )
     assert prepare_finalize is not None
 
-    logger.info_once("Using %s", prepare_finalize.__class__.__name__, scope="local")
+    logger.info_once("Using %s", prepare_finalize.__class__.__name__)
+
+    extra_kwargs = {}
+    if mxfp4_backend == Mxfp4MoeBackend.HUMMING:
+        assert layer is not None
+        extra_kwargs["layer"] = layer
 
     # Create Experts.
     if prepare_finalize.activation_format == mk.FusedMoEActivationFormat.BatchedExperts:
@@ -847,11 +1510,13 @@ def make_mxfp4_moe_kernel(
             quant_config=moe_quant_config,
             max_num_tokens=max_num_tokens,
             num_dispatchers=prepare_finalize.num_dispatchers(),
+            **extra_kwargs,
         )
     else:
         experts = experts_cls(
             moe_config=moe_config,
             quant_config=moe_quant_config,
+            **extra_kwargs,
         )
 
     kernel = mk.FusedMoEKernel(
@@ -859,7 +1524,7 @@ def make_mxfp4_moe_kernel(
         experts,
         shared_experts=(
             shared_experts
-            if moe_config.moe_parallel_config.use_deepep_ll_kernels
+            if moe_config.moe_parallel_config.use_batched_activation_format
             else None
         ),
         inplace=(
diff --git a/vllm/model_executor/layers/fused_moe/oracle/mxfp8.py b/vllm/model_executor/layers/fused_moe/oracle/mxfp8.py
index c67def149b9d..8133902d519b 100644
--- a/vllm/model_executor/layers/fused_moe/oracle/mxfp8.py
+++ b/vllm/model_executor/layers/fused_moe/oracle/mxfp8.py
@@ -61,8 +61,6 @@ def select_mxfp8_moe_backend(
     Returns:
         A tuple of (fp8_backend, experts_cls).
     """
-    if config.is_lora_enabled:
-        raise NotImplementedError("LoRA is not supported for MXFP8 MoE.")
 
     runner_backend = config.moe_backend
     if runner_backend != "auto":
diff --git a/vllm/model_executor/layers/fused_moe/oracle/nvfp4.py b/vllm/model_executor/layers/fused_moe/oracle/nvfp4.py
index 597d784d3b63..d22fe95f1e51 100644
--- a/vllm/model_executor/layers/fused_moe/oracle/nvfp4.py
+++ b/vllm/model_executor/layers/fused_moe/oracle/nvfp4.py
@@ -31,6 +31,9 @@
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
     prepare_nvfp4_moe_layer_for_marlin,
 )
+from vllm.model_executor.layers.quantization.utils.nvfp4_emulation_utils import (
+    kE2M1ToFloat_handle,
+)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
 )
@@ -45,6 +48,7 @@ class NvFp4MoeBackend(Enum):
     FLASHINFER_CUTEDSL_BATCHED = "FLASHINFER_CUTEDSL_BATCHED"
     VLLM_CUTLASS = "VLLM_CUTLASS"
     MARLIN = "MARLIN"
+    EMULATION = "EMULATION"
 
 
 FLASHINFER_NVFP4_MOE_BACKENDS = [
@@ -85,7 +89,7 @@ def backend_to_kernel_cls(
         ]
 
     elif backend == NvFp4MoeBackend.FLASHINFER_CUTLASS:
-        from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.flashinfer_cutlass_moe import (  # noqa: E501
             FlashInferExperts,
         )
 
@@ -106,18 +110,24 @@ def backend_to_kernel_cls(
         return [FlashInferCuteDSLBatchedExperts]
 
     elif backend == NvFp4MoeBackend.VLLM_CUTLASS:
-        from vllm.model_executor.layers.fused_moe.cutlass_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import (
             CutlassExpertsFp4,
         )
 
         return [CutlassExpertsFp4]
 
     elif backend == NvFp4MoeBackend.MARLIN:
-        from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.marlin_moe import (
             MarlinExperts,
         )
 
         return [MarlinExperts]
+    elif backend == NvFp4MoeBackend.EMULATION:
+        from vllm.model_executor.layers.fused_moe.experts.nvfp4_emulation_moe import (
+            Nvfp4QuantizationEmulationTritonExperts,
+        )
+
+        return [Nvfp4QuantizationEmulationTritonExperts]
     else:
         raise ValueError(f"Unknown NvFP4 MoE backend: {backend.value}")
 
@@ -130,6 +140,7 @@ def map_nvfp4_backend(runner_backend: MoEBackend) -> NvFp4MoeBackend:
         "flashinfer_cutlass": NvFp4MoeBackend.FLASHINFER_CUTLASS,
         "flashinfer_cutedsl": NvFp4MoeBackend.FLASHINFER_CUTEDSL,
         "marlin": NvFp4MoeBackend.MARLIN,
+        "emulation": NvFp4MoeBackend.EMULATION,
     }
     if backend := mapping.get(runner_backend):
         return backend
@@ -157,12 +168,10 @@ def select_nvfp4_moe_backend(
         NvFp4MoeBackend.FLASHINFER_CUTLASS,
         NvFp4MoeBackend.VLLM_CUTLASS,
         NvFp4MoeBackend.MARLIN,
+        NvFp4MoeBackend.EMULATION,
     ]
 
-    # NOTE(rob): this is kind of a hack. We need to peak into
-    # the prepare-finalize selection to determine if we are using
-    # the batched or standard expert format.
-    use_batched = config.moe_parallel_config.use_deepep_ll_kernels
+    use_batched = config.moe_parallel_config.use_batched_activation_format
     activation_format = (
         mk.FusedMoEActivationFormat.BatchedExperts
         if use_batched
@@ -243,12 +252,10 @@ def _return_or_raise(
                         activation_format,
                     )
                     if supported:
-                        logger.info_once(_make_log_backend(backend), scope="local")
+                        logger.info_once(_make_log_backend(backend))
                         return backend, k_cls
                     else:
-                        logger.debug_once(
-                            _make_log_unsupported(backend, reason), scope="local"
-                        )
+                        logger.debug_once(_make_log_unsupported(backend, reason))
 
             raise NotImplementedError(
                 "Found VLLM_USE_FLASHINFER_MOE_FP4=1, but no "
@@ -271,12 +278,11 @@ def _return_or_raise(
                 activation_key,
                 activation_format,
             )
-
             if supported:
-                logger.info_once(_make_log_backend(backend), scope="local")
+                logger.info_once(_make_log_backend(backend))
                 return backend, k_cls
             else:
-                logger.debug_once(_make_log_unsupported(backend, reason), scope="local")
+                logger.debug_once(_make_log_unsupported(backend, reason))
 
     raise NotImplementedError(
         "No NvFp4 MoE backend supports the deployment configuration."
@@ -372,6 +378,34 @@ def convert_to_nvfp4_moe_kernel_format(
             w2_scale_2=w2_scale_2,
             is_act_and_mul=is_act_and_mul,
         )
+    elif nvfp4_backend == NvFp4MoeBackend.EMULATION:
+        # Move the E2M1 lookup table to the device now, because
+        # `.to(device)` is not allowed during CUDA graph capture.
+        kE2M1ToFloat_handle.val = kE2M1ToFloat_handle.val.to(w13.device)
+
+        if a13_scale is None or a2_scale is None:
+            raise ValueError(
+                "Activation global scales should not be None, got"
+                f" a13_scale={a13_scale}, a2_scale={a2_scale}"
+            )
+
+        if torch.unique(a13_scale).numel() != 1 or torch.unique(a2_scale).numel() != 1:
+            logger.warning_once(
+                "In NVFP4 linear, the activation global scale for inputs are different"
+                " for MOE w13 (gate_up_proj) layer or MOE w2 (down_proj). Using"
+                " a13_scale = a13_scale.max() and a2_scale = a2_scale.max()."
+            )
+
+        # 1. We take the max following e.g. quantization/utils/flashinfer_fp4_moe.py.
+        # 2. moe_kernel_quantize_input -> ref_nvfp4_quant_dequant
+        # use the inverse scale directly (large global scale).
+        # NOTE: Before this point, `a13_scale` and `a2_scale` are such that:
+        # `FP8_MAX = activation[expert_id].abs().max() * global_scale[expert_id]`,
+        # and `global_scale[expert_id]` are small (~1e-4).
+        # Taking the largest global scale likely results in overflowing the FP8 range
+        # for other experts - other selection strategies may be used.
+        a13_scale = 1.0 / a13_scale.max().to(torch.float32)
+        a2_scale = 1.0 / a2_scale.max().to(torch.float32)
     else:
         raise ValueError(f"Unknown NvFp4 backend for MoE: {nvfp4_backend}")
 
@@ -403,6 +437,15 @@ def make_nvfp4_moe_quant_config(
             w1_scale=w13_scale,
             w2_scale=w2_scale,
         )
+    elif backend == NvFp4MoeBackend.EMULATION:
+        return nvfp4_moe_quant_config(
+            g1_alphas=w13_scale_2,
+            g2_alphas=w2_scale_2,
+            a1_gscale=a13_scale,
+            a2_gscale=a2_scale,
+            w1_scale=w13_scale,
+            w2_scale=w2_scale,
+        )
 
     # Pass w13_scale_2 / w2_scale_2 directly as g1/g2_alphas.
     # The expert's process_weights_after_loading will fuse activation
@@ -418,7 +461,7 @@ def make_nvfp4_moe_quant_config(
         # NOTE(rob): this is a hack until the MoE kernels
         # create their own quant configs. TRTLLM kernel
         # does not accept swizzled input quant scales.
-        is_nvfp4_scale_swizzled=(
+        is_scale_swizzled=(
             backend
             not in (
                 NvFp4MoeBackend.FLASHINFER_TRTLLM,
diff --git a/vllm/model_executor/layers/fused_moe/oracle/unquantized.py b/vllm/model_executor/layers/fused_moe/oracle/unquantized.py
index 33bf7a0c75f6..f1fd196c09c1 100644
--- a/vllm/model_executor/layers/fused_moe/oracle/unquantized.py
+++ b/vllm/model_executor/layers/fused_moe/oracle/unquantized.py
@@ -95,21 +95,23 @@ def backend_to_kernel_cls(
         return TrtLlmBf16Experts
 
     elif backend == UnquantizedMoeBackend.FLASHINFER_CUTLASS:
-        from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.flashinfer_cutlass_moe import (  # noqa: E501
             FlashInferExperts,
         )
 
         return FlashInferExperts
 
     elif backend == UnquantizedMoeBackend.AITER:
-        from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.rocm_aiter_moe import (
             AiterExperts,
         )
 
         return AiterExperts
 
     elif backend == UnquantizedMoeBackend.TRITON:
-        from vllm.model_executor.layers.fused_moe.fused_moe import TritonExperts
+        from vllm.model_executor.layers.fused_moe.experts.triton_moe import (
+            TritonExperts,
+        )
 
         return TritonExperts
 
@@ -121,7 +123,7 @@ def backend_to_kernel_cls(
         return BatchedTritonExperts
 
     elif backend == UnquantizedMoeBackend.XPU:
-        from vllm.model_executor.layers.fused_moe.xpu_fused_moe import XPUExperts
+        from vllm.model_executor.layers.fused_moe.experts.xpu_moe import XPUExperts
 
         return XPUExperts
 
@@ -163,6 +165,11 @@ def select_unquantized_moe_backend(
     if current_platform.is_out_of_tree():
         return UnquantizedMoeBackend.OOT, None
 
+    if moe_config.is_lora_enabled:
+        return UnquantizedMoeBackend.TRITON, backend_to_kernel_cls(
+            UnquantizedMoeBackend.TRITON
+        )
+
     # NOTE: the kernels are selected in the following order.
     AVAILABLE_BACKENDS = _get_priority_backends(moe_config)
 
@@ -205,7 +212,7 @@ def _return_or_raise(
             k_cls, config, None, None, activation_format
         )
         if supported:
-            logger.info_once(_make_log_backend(backend), scope="local")
+            logger.info_once(_make_log_backend(backend))
             return backend, k_cls
         raise ValueError(_make_log_unsupported(backend, reason))
 
@@ -253,12 +260,10 @@ def _return_or_raise(
                     k_cls, moe_config, None, None, activation_format
                 )
                 if supported:
-                    logger.info_once(_make_log_backend(backend), scope="local")
+                    logger.info_once(_make_log_backend(backend))
                     return backend, k_cls
                 else:
-                    logger.debug_once(
-                        _make_log_unsupported(backend, reason), scope="local"
-                    )
+                    logger.debug_once(_make_log_unsupported(backend, reason))
 
             raise NotImplementedError(
                 "Found VLLM_USE_FLASHINFER_MOE_FP16=1, but no "
@@ -280,10 +285,10 @@ def _return_or_raise(
             k_cls, moe_config, None, None, activation_format
         )
         if supported:
-            logger.info_once(_make_log_backend(backend), scope="local")
+            logger.info_once(_make_log_backend(backend))
             return backend, k_cls
 
-        logger.debug_once(_make_log_unsupported(backend, reason), scope="local")
+        logger.debug_once(_make_log_unsupported(backend, reason))
 
     raise NotImplementedError(
         "No Unquantized MoE backend supports the deployment configuration."
@@ -337,7 +342,7 @@ def make_unquantized_moe_kernel(
     )
     assert prepare_finalize is not None
 
-    logger.info_once("Using %s", prepare_finalize.__class__.__name__, scope="local")
+    logger.info_once("Using %s", prepare_finalize.__class__.__name__)
 
     # Create Experts
     if prepare_finalize.activation_format == mk.FusedMoEActivationFormat.BatchedExperts:
diff --git a/vllm/model_executor/layers/fused_moe/prepare_finalize/__init__.py b/vllm/model_executor/layers/fused_moe/prepare_finalize/__init__.py
index d388ee411407..b3529c99565f 100644
--- a/vllm/model_executor/layers/fused_moe/prepare_finalize/__init__.py
+++ b/vllm/model_executor/layers/fused_moe/prepare_finalize/__init__.py
@@ -1,6 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from vllm.model_executor.layers.fused_moe.prepare_finalize.batched import (
+    BatchedPrepareAndFinalize,
+)
 from vllm.model_executor.layers.fused_moe.prepare_finalize.naive_dp_ep import (
     MoEPrepareAndFinalizeNaiveDPEPModular,
     MoEPrepareAndFinalizeNaiveDPEPMonolithic,
@@ -13,6 +16,7 @@
 )
 
 __all__ = [
+    "BatchedPrepareAndFinalize",
     "MoEPrepareAndFinalizeNaiveDPEPMonolithic",
     "MoEPrepareAndFinalizeNaiveDPEPModular",
     "make_moe_prepare_and_finalize_naive_dp_ep",
diff --git a/vllm/model_executor/layers/fused_moe/prepare_finalize/batched.py b/vllm/model_executor/layers/fused_moe/prepare_finalize/batched.py
new file mode 100644
index 000000000000..943027717bbe
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/prepare_finalize/batched.py
@@ -0,0 +1,171 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceDelegate,
+    TopKWeightAndReduceNaiveBatched,
+)
+from vllm.model_executor.layers.fused_moe.utils import (
+    moe_kernel_quantize_input,
+    normalize_scales_shape,
+)
+
+
+class BatchedPrepareAndFinalize(mk.FusedMoEPrepareAndFinalizeModular):
+    """
+    A reference prepare/finalize class that reorganizes the tokens into
+    expert batched format, i.e. E x max_num_tokens x K.  This is the format
+    that the batched dispatch/combine kernels use.
+    """
+
+    def __init__(
+        self,
+        max_num_tokens: int,
+        num_local_experts: int,
+        num_dispatchers: int,
+        rank: int,
+    ):
+        super().__init__()
+        self.max_num_tokens = max_num_tokens
+        self.num_local_experts = num_local_experts
+        self.rank = rank
+        self.num_dispatchers_ = num_dispatchers
+
+    @property
+    def activation_format(self) -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.BatchedExperts
+
+    def max_num_tokens_per_rank(self) -> int | None:
+        return self.max_num_tokens
+
+    def topk_indices_dtype(self) -> torch.dtype | None:
+        return None
+
+    def num_dispatchers(self) -> int:
+        return self.num_dispatchers_
+
+    def output_is_reduced(self) -> bool:
+        return False
+
+    def prepare(
+        self,
+        a1: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        num_experts: int,
+        expert_map: torch.Tensor | None,
+        apply_router_weight_on_input: bool,
+        quant_config: FusedMoEQuantConfig,
+        defer_input_quant: bool = False,
+    ) -> mk.PrepareResultType:
+        if defer_input_quant:
+            raise NotImplementedError(
+                f"{self.__class__.__name__} does not support defer_input_quant=True. "
+                "Please select an MoE kernel that accepts quantized inputs."
+            )
+        assert a1.dim() == 2
+        assert topk_ids.dim() == 2
+        assert topk_ids.size(0) == a1.size(0)
+
+        if apply_router_weight_on_input:
+            topk = topk_ids.size(1)
+            # TODO: this only works for topK=1, will need to update for topK>1
+            assert topk == 1, (
+                "apply_router_weight_on_input is only implemented for topk=1"
+            )
+            a1.mul_(topk_weights.to(a1.dtype))
+
+        num_tokens, hidden_dim = a1.size()
+        topk = topk_ids.size(1)
+
+        tokens_per_expert = torch.zeros(num_experts, dtype=torch.int, device=a1.device)
+
+        num_local_experts = self.num_local_experts
+
+        if quant_config.quant_dtype is None:
+            b_type = a1.dtype
+        else:
+            b_type = quant_config.quant_dtype
+
+        b_a1 = torch.zeros(
+            (num_local_experts, self.max_num_tokens, hidden_dim),
+            dtype=b_type,
+            device=a1.device,
+        )
+
+        if quant_config.is_quantized:
+            scale_shape = quant_config.batched_scale_shape(
+                num_local_experts, self.max_num_tokens, hidden_dim
+            )
+
+            b_a1_scale = torch.empty(scale_shape, dtype=torch.float32, device=a1.device)
+        else:
+            assert quant_config.a1_scale is None
+            b_a1_scale = None
+
+        first_expert = num_local_experts * self.rank
+        last_expert = first_expert + num_local_experts
+
+        a1_scale = normalize_scales_shape(quant_config.a1_scale)
+
+        for expert_id in range(first_expert, last_expert):
+            topks = torch.any(topk_ids == expert_id, dim=1).flatten()
+            rows = torch.count_nonzero(topks.flatten())
+            if rows == 0:
+                continue
+            idx = expert_id - first_expert
+            tokens_per_expert[idx] = rows
+            rhs = a1[: topks.numel()][topks]
+            if quant_config.quant_dtype is not None:
+                if a1_scale is not None:
+                    if quant_config.is_per_act_token:
+                        rhs_a1_scale = a1_scale[: topks.numel()][topks]
+                    else:
+                        rhs_a1_scale = a1_scale
+                else:
+                    rhs_a1_scale = None
+                b_a1[idx, :rows, :], b_s = moe_kernel_quantize_input(
+                    rhs,
+                    rhs_a1_scale,
+                    quant_config.quant_dtype,
+                    quant_config.per_act_token_quant,
+                    quant_config.block_shape,
+                )
+                assert b_s is not None
+                if quant_config.is_per_act_token:
+                    b_a1_scale[idx, :rows] = b_s[:rows]
+                else:
+                    b_a1_scale[idx, : b_s.shape[0]] = b_s
+            else:
+                b_a1[idx, :rows, :] = rhs
+
+        assert b_a1_scale is None or b_a1_scale.ndim == 3
+
+        expert_tokens_meta = mk.ExpertTokensMetadata(
+            expert_num_tokens=tokens_per_expert, expert_num_tokens_cpu=None
+        )
+
+        return b_a1, b_a1_scale, expert_tokens_meta, None, None
+
+    def finalize(
+        self,
+        output: torch.Tensor,
+        fused_expert_output: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        apply_router_weight_on_input: bool,
+        weight_and_reduce_impl: mk.TopKWeightAndReduce,
+    ) -> None:
+        if isinstance(weight_and_reduce_impl, TopKWeightAndReduceDelegate):
+            weight_and_reduce_impl = TopKWeightAndReduceNaiveBatched(self.rank)
+        weight_and_reduce_impl.apply(
+            output=output,
+            fused_expert_output=fused_expert_output,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+        )
diff --git a/vllm/model_executor/layers/fused_moe/prepare_finalize/deepep_ht.py b/vllm/model_executor/layers/fused_moe/prepare_finalize/deepep_ht.py
index 61777fd99031..9ca94521363e 100644
--- a/vllm/model_executor/layers/fused_moe/prepare_finalize/deepep_ht.py
+++ b/vllm/model_executor/layers/fused_moe/prepare_finalize/deepep_ht.py
@@ -241,7 +241,7 @@ def _receiver(
                     quant_dtype=quant_config.quant_dtype,
                     per_act_token_quant=False,
                     block_shape=quant_config.block_shape,
-                    is_fp4_scale_swizzled=quant_config.is_nvfp4_scale_swizzled,
+                    is_scale_swizzled=quant_config.is_scale_swizzled,
                 )
 
         return (
diff --git a/vllm/model_executor/layers/fused_moe/prepare_finalize/deepep_ll.py b/vllm/model_executor/layers/fused_moe/prepare_finalize/deepep_ll.py
index 0c6e32ae4a53..e5d2b601a768 100644
--- a/vllm/model_executor/layers/fused_moe/prepare_finalize/deepep_ll.py
+++ b/vllm/model_executor/layers/fused_moe/prepare_finalize/deepep_ll.py
@@ -16,7 +16,6 @@
     moe_kernel_quantize_input,
     normalize_batched_scales_shape,
 )
-from vllm.platforms import current_platform
 from vllm.v1.worker.ubatching import (
     dbo_current_ubatch_id,
     dbo_enabled,
@@ -135,7 +134,6 @@ def post_init_setup(self, fused_experts: mk.FusedMoEExperts):
                 "DeepEPLLPrepareAndFinalize is setup to dispatch raw/unquantized "
                 f"activations despite ({fused_experts.__class__.__name__}) being able "
                 "to support quantized activations.",
-                scope="local",
             )
 
     def num_dispatchers(self) -> int:
@@ -291,46 +289,29 @@ def prepare_async(
 
         # Dispatch
         dispatch_topk_ids = self._map_global_to_physical_ids(topk_ids)
-        if current_platform.is_rocm():
-            (
-                expert_x,
-                expert_num_tokens,
-                handle,
-                _,
-                hook,
-            ) = self.buffer.low_latency_dispatch(
-                a1,
-                dispatch_topk_ids,
-                self.max_tokens_per_rank,
-                num_experts,
-                use_fp8=self.use_fp8_dispatch,
-                async_finish=False,
-                return_recv_hook=True,
-            )
-        else:
-            (
-                expert_x,
-                expert_num_tokens,
-                handle,
-                _,
-                hook,
-            ) = self.buffer.low_latency_dispatch(
-                a1,
-                dispatch_topk_ids,
-                self.max_tokens_per_rank,
-                num_experts,
-                use_fp8=self.use_fp8_dispatch,
-                round_scale=self.use_ue8m0_dispatch,
-                use_ue8m0=self.use_ue8m0_dispatch,
-                **(dict(use_nvfp4=True) if use_nvfp4 else dict()),
-                **(
-                    dict(x_global_scale=qc_a1_gscale_or_scale)
-                    if qc_a1_gscale_or_scale is not None and nvfp4_dispatch
-                    else dict()
-                ),
-                async_finish=False,
-                return_recv_hook=True,
-            )
+        (
+            expert_x,
+            expert_num_tokens,
+            handle,
+            _,
+            hook,
+        ) = self.buffer.low_latency_dispatch(
+            a1,
+            dispatch_topk_ids,
+            self.max_tokens_per_rank,
+            num_experts,
+            use_fp8=self.use_fp8_dispatch,
+            round_scale=self.use_ue8m0_dispatch,
+            use_ue8m0=self.use_ue8m0_dispatch,
+            **(dict(use_nvfp4=True) if use_nvfp4 else dict()),
+            **(
+                dict(x_global_scale=qc_a1_gscale_or_scale)
+                if qc_a1_gscale_or_scale is not None and nvfp4_dispatch
+                else dict()
+            ),
+            async_finish=False,
+            return_recv_hook=True,
+        )
         self.handles[a2a_idx] = handle
 
         return (
diff --git a/vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_one_sided.py b/vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_one_sided.py
index bdde3da6b3a3..254153a27c48 100644
--- a/vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_one_sided.py
+++ b/vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_one_sided.py
@@ -4,6 +4,9 @@
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm.distributed import get_ep_group
+from vllm.distributed.device_communicators.base_device_communicator import (
+    All2AllManagerBase,
+)
 from vllm.forward_context import get_forward_context
 from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
 from vllm.model_executor.layers.fused_moe.utils import moe_kernel_quantize_input
@@ -11,12 +14,16 @@
 
 
 def get_local_sizes():
-    return get_forward_context().dp_metadata.get_chunk_sizes_across_dp_rank()
+    dp_metadata = get_forward_context().dp_metadata
+    assert dp_metadata is not None
+    return dp_metadata.get_chunk_sizes_across_dp_rank()
 
 
 class FlashInferNVLinkOneSidedPrepareAndFinalize(mk.FusedMoEPrepareAndFinalizeModular):
     """FlashInfer implementation using the Moe AlltoAll kernel."""
 
+    all2all_manager: All2AllManagerBase
+
     def __init__(
         self,
         max_num_tokens: int,
@@ -24,6 +31,8 @@ def __init__(
         num_experts: int,
         hidden_size: int,
         num_dispatchers: int = 1,
+        dispatch_dtype_bytes_per_elem: int = 0,
+        dispatch_scale_bytes_per_token: int = 0,
     ):
         super().__init__()
         self.max_num_tokens = max_num_tokens
@@ -31,13 +40,20 @@ def __init__(
         self.num_experts = num_experts
         self.hidden_size = hidden_size
         self.num_dispatchers_ = num_dispatchers
-
-        self.all2all_manager = get_ep_group().device_communicator.all2all_manager
-        self.all2all_manager.initialize(
+        self.scale_elems_per_token = dispatch_scale_bytes_per_token
+
+        device_communicator = get_ep_group().device_communicator
+        assert device_communicator is not None
+        all2all_manager = device_communicator.all2all_manager
+        assert all2all_manager is not None
+        self.all2all_manager = all2all_manager
+        self.all2all_manager.initialize(  # type: ignore[attr-defined]
             max_num_tokens=self.max_num_tokens,
             top_k=self.top_k,
             num_experts=self.num_experts,
             hidden_size=self.hidden_size,
+            dispatch_dtype_bytes_per_elem=dispatch_dtype_bytes_per_elem,
+            dispatch_scale_bytes_per_token=dispatch_scale_bytes_per_token,
         )
 
     @property
@@ -81,38 +97,44 @@ def prepare(
             else a1.shape[0]
         )
 
-        a1q, a1q_scale = moe_kernel_quantize_input(
-            a1,
-            quant_config.a1_gscale,
-            quant_config.quant_dtype,
-            quant_config.per_act_token_quant,
-            quant_config.block_shape,
-            is_fp4_scale_swizzled=False,  # delay swizzle to after comm
-        )
+        if defer_input_quant:
+            a1q, a1q_scale = a1, None
+        else:
+            a1q, a1q_scale = moe_kernel_quantize_input(
+                a1,
+                quant_config.a1_gscale,
+                quant_config.quant_dtype,
+                quant_config.per_act_token_quant,
+                quant_config.block_shape,
+                is_scale_swizzled=False,  # delay swizzle to after comm
+                mx_alignment=quant_config.mx_alignment,
+            )
 
         payloads = []
         payloads.append(a1q)
         if a1q_scale is not None:
             payloads.append(a1q_scale)
+        topk_ids_payload_index = len(payloads)
         payloads.append(topk_ids)
         payloads.append(topk_weights)
 
-        recv_payloads = self.all2all_manager.moe_alltoall.dispatch(
+        assert self.all2all_manager.moe_alltoall is not None  # type: ignore[attr-defined]
+        recv_payloads = self.all2all_manager.moe_alltoall.dispatch(  # type: ignore[attr-defined]
             token_selected_experts=topk_ids,
             input_payloads=payloads,
             runtime_max_tokens_per_rank=self.runtime_max_tokens_per_rank,
+            invalid_token_expert_id=-1,  # Follow TRTLLM Pattern
+            expert_id_payload_index=topk_ids_payload_index,
         )
         if a1q_scale is not None:
             a1q_recv, a1q_scale_recv, topk_ids_recv, topk_weights_recv = recv_payloads
             # Apply scale interleaving only for CUTLASS (not TRT-LLM)
-            if (
-                quant_config.quant_dtype == "nvfp4"
-                and quant_config.is_nvfp4_scale_swizzled
-            ):
+            if quant_config.quant_dtype == "nvfp4" and quant_config.is_scale_swizzled:
                 a1q_scale_recv = a1q_scale_recv.view(-1, a1q_scale_recv.shape[-1])
                 a1q_scale_recv = a1q_scale_recv.view(torch.uint8)
                 a1q_scale_recv = nvfp4_block_scale_interleave(a1q_scale_recv)
-            a1q_scale_recv = a1q_scale_recv.view(-1, self.hidden_size // 16)
+            assert self.scale_elems_per_token > 0
+            a1q_scale_recv = a1q_scale_recv.view(-1, self.scale_elems_per_token)
         else:
             a1q_recv, topk_ids_recv, topk_weights_recv = recv_payloads
             a1q_scale_recv = None
@@ -131,7 +153,7 @@ def finalize(
         apply_router_weight_on_input: bool,
         weight_and_reduce_impl: mk.TopKWeightAndReduce,
     ) -> None:
-        assert self.all2all_manager.moe_alltoall is not None
+        assert self.all2all_manager.moe_alltoall is not None  # type: ignore[attr-defined]
 
         ep_size = self.all2all_manager.world_size
         hidden_size = fused_expert_output.shape[-1]
@@ -139,7 +161,7 @@ def finalize(
             ep_size, self.runtime_max_tokens_per_rank, hidden_size
         )
 
-        combined_output = self.all2all_manager.moe_alltoall.combine(
+        combined_output = self.all2all_manager.moe_alltoall.combine(  # type: ignore[attr-defined]
             payload=fused_expert_output,
             runtime_max_tokens_per_rank=self.runtime_max_tokens_per_rank,
         )
diff --git a/vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_two_sided.py b/vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_two_sided.py
index be63bd4e3f61..b975e6cc5e15 100644
--- a/vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_two_sided.py
+++ b/vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_two_sided.py
@@ -15,19 +15,26 @@
 
 
 def get_local_sizes():
-    return get_forward_context().dp_metadata.get_chunk_sizes_across_dp_rank()
+    dp_metadata = get_forward_context().dp_metadata
+    assert dp_metadata is not None
+    return dp_metadata.get_chunk_sizes_across_dp_rank()
 
 
 class FlashInferNVLinkTwoSidedPrepareAndFinalize(mk.FusedMoEPrepareAndFinalizeModular):
     """Base class for FlashInfer MoE prepare and finalize operations."""
 
+    all2all_manager: All2AllManagerBase
+
     def __init__(
         self,
         num_dispatchers: int = 1,
     ):
         super().__init__()
         self.num_dispatchers_ = num_dispatchers
-        self.all2all_manager = get_ep_group().device_communicator.all2all_manager
+        device_communicator = get_ep_group().device_communicator
+        assert device_communicator is not None
+        assert device_communicator.all2all_manager is not None
+        self.all2all_manager = device_communicator.all2all_manager
 
     @property
     def activation_format(self) -> mk.FusedMoEActivationFormat:
@@ -129,7 +136,7 @@ def flashinfer_alltoall_dispatch(
 ):
     from flashinfer.comm.trtllm_alltoall import MnnvlMoe
 
-    assert all2all_manager.ensure_alltoall_workspace_initialized(), (
+    assert all2all_manager.ensure_alltoall_workspace_initialized(), (  # type: ignore[attr-defined]
         "FlashInfer AllToAll workspace not available"
     )
 
@@ -144,7 +151,7 @@ def flashinfer_alltoall_dispatch(
             topk_ids,
             topk_weights,
             None,
-            all2all_manager.prepare_workspace_tensor,
+            all2all_manager.prepare_workspace_tensor,  # type: ignore[attr-defined]
             max_num_token,
             ep_rank,
             ep_size,
@@ -166,13 +173,14 @@ def flashinfer_alltoall_dispatch(
             # which makes the scales tensor different shape than
             # the hidden states, breaking the A2A kernel. So, we
             # delay the swizzling until after the A2A.
-            is_fp4_scale_swizzled=False,
+            is_scale_swizzled=False,
+            mx_alignment=quant_config.mx_alignment,
         )
 
         x = MnnvlMoe.mnnvl_moe_alltoallv(
             x,
             alltoall_info,
-            all2all_manager.workspace_tensor,
+            all2all_manager.workspace_tensor,  # type: ignore[attr-defined]
             ep_rank,
             ep_size,
         )
@@ -180,13 +188,13 @@ def flashinfer_alltoall_dispatch(
         x_sf = MnnvlMoe.mnnvl_moe_alltoallv(
             x_sf,
             alltoall_info,
-            all2all_manager.workspace_tensor,
+            all2all_manager.workspace_tensor,  # type: ignore[attr-defined]
             ep_rank,
             ep_size,
         )
 
         # Swizzle after the A2A if MoE kernel expects swizzled scales.
-        if quant_config.quant_dtype == "nvfp4" and quant_config.is_nvfp4_scale_swizzled:
+        if quant_config.quant_dtype == "nvfp4" and quant_config.is_scale_swizzled:
             if x_sf.element_size() == 1:
                 x_sf = x_sf.view(torch.uint8)
             x_sf = nvfp4_block_scale_interleave(x_sf)
@@ -196,7 +204,7 @@ def flashinfer_alltoall_dispatch(
         x = MnnvlMoe.mnnvl_moe_alltoallv(
             x,
             alltoall_info,
-            all2all_manager.workspace_tensor,
+            all2all_manager.workspace_tensor,  # type: ignore[attr-defined]
             ep_rank,
             ep_size,
         )
@@ -212,13 +220,13 @@ def flashinfer_alltoall_combine(
 ):
     from flashinfer.comm.trtllm_alltoall import MnnvlMoe
 
-    assert all2all_manager.ensure_alltoall_workspace_initialized(), (
+    assert all2all_manager.ensure_alltoall_workspace_initialized(), (  # type: ignore[attr-defined]
         "FlashInfer AllToAll workspace not available"
     )
     return MnnvlMoe.mnnvl_moe_alltoallv_combine(
         output,
         alltoall_info,
-        all2all_manager.workspace_tensor,
+        all2all_manager.workspace_tensor,  # type: ignore[attr-defined]
         ep_rank=all2all_manager.rank,
         ep_size=all2all_manager.world_size,
         top_k=top_k,
diff --git a/vllm/model_executor/layers/fused_moe/mori_prepare_finalize.py b/vllm/model_executor/layers/fused_moe/prepare_finalize/mori.py
similarity index 100%
rename from vllm/model_executor/layers/fused_moe/mori_prepare_finalize.py
rename to vllm/model_executor/layers/fused_moe/prepare_finalize/mori.py
diff --git a/vllm/model_executor/layers/fused_moe/prepare_finalize/naive_dp_ep.py b/vllm/model_executor/layers/fused_moe/prepare_finalize/naive_dp_ep.py
index 6dc9f6958048..b8633726c72b 100644
--- a/vllm/model_executor/layers/fused_moe/prepare_finalize/naive_dp_ep.py
+++ b/vllm/model_executor/layers/fused_moe/prepare_finalize/naive_dp_ep.py
@@ -39,7 +39,8 @@ def _quantize_and_setup_dispatch(
             quant_dtype=quant_config.quant_dtype,
             per_act_token_quant=quant_config.per_act_token_quant,
             block_shape=quant_config.block_shape,
-            is_fp4_scale_swizzled=False,
+            is_scale_swizzled=False,
+            mx_alignment=quant_config.mx_alignment,
         )
 
     # Skip gathering scales if we have static quantization
@@ -58,7 +59,7 @@ def _unwrap_scale_and_prepare_for_moe(
     assert scales is not None and len(scales) == 1
     a1q_scale = scales[0]
     # Apply swizzling after a2a if the MoE kernel needs it.
-    if quant_config.quant_dtype == "nvfp4" and quant_config.is_nvfp4_scale_swizzled:
+    if quant_config.quant_dtype == "nvfp4" and quant_config.is_scale_swizzled:
         assert a1q_scale is not None
         if a1q_scale.element_size() == 1:
             a1q_scale = a1q_scale.view(torch.uint8)
@@ -83,6 +84,14 @@ def __init__(
         super().__init__()
         self.is_sequence_parallel = is_sequence_parallel
         self._num_dispatchers = num_dispatchers
+        # Set by FusedMoEWithLoRA.set_mapping() when LoRA is active. When
+        # present, prepare() dispatches the per-token LoRA mapping alongside
+        # hidden_states and writes the gathered result back to the context so
+        # experts can use the per-rank-local mapping.
+        self._lora_context = None
+
+    def set_lora_context(self, ctx) -> None:
+        self._lora_context = ctx
 
     @property
     def activation_format(self) -> mk.FusedMoEActivationFormat:
@@ -123,20 +132,54 @@ def prepare(
 
         a1q, scales = _quantize_and_setup_dispatch(a1, quant_config, defer_input_quant)
 
+        # When LoRA is active, dispatch the per-token LoRA id along with
+        # hidden_states so every rank receives the correct mapping for the
+        # tokens it ends up processing. The punica_wrapper stores indices as
+        # int64 but the moe_lora_align_block_size kernel expects int32, so
+        # pull the pre-cast view from token_mapping_meta.
+        lora_ctx = self._lora_context
+        local_token_lora_mapping = None
+        if lora_ctx is not None:
+            local_token_lora_mapping = (
+                lora_ctx.punica_wrapper.token_mapping_meta.token_lora_mapping[
+                    : a1.shape[0]
+                ]
+            )
+
+        extra_tensors: list[torch.Tensor] | None = None
+        if scales is not None:
+            extra_tensors = list(scales)
+        if local_token_lora_mapping is not None:
+            if extra_tensors is None:
+                extra_tensors = []
+            extra_tensors.append(local_token_lora_mapping)
+
         res = get_ep_group().dispatch(
             a1q,
             topk_weights,
             topk_ids,
             is_sequence_parallel=self.is_sequence_parallel,
-            extra_tensors=scales,
+            extra_tensors=extra_tensors,
         )
 
-        if scales is None:
+        if extra_tensors is None:
+            assert len(res) == 3
             a1q, topk_weights, topk_ids = res
             a1q_scale = None
         else:
-            a1q, topk_weights, topk_ids, scales = res
-            a1q_scale = _unwrap_scale_and_prepare_for_moe(scales, quant_config)
+            assert len(res) == 4
+            a1q, topk_weights, topk_ids, gathered_extras = res
+            gathered_extras = list(gathered_extras)
+            if local_token_lora_mapping is not None:
+                dispatched_lora_mapping = gathered_extras.pop()
+                assert lora_ctx is not None
+                lora_ctx.local_token_lora_mapping = dispatched_lora_mapping
+            if scales is not None:
+                a1q_scale = _unwrap_scale_and_prepare_for_moe(
+                    gathered_extras, quant_config
+                )
+            else:
+                a1q_scale = None
 
         return a1q, a1q_scale, None, topk_ids, topk_weights
 
@@ -217,9 +260,11 @@ def prepare(
         )
 
         if scales is None:
+            assert len(res) == 2
             a1q, router_logits = res
             a1q_scale = None
         else:
+            assert len(res) == 3
             a1q, router_logits, scales = res
             a1q_scale = _unwrap_scale_and_prepare_for_moe(scales, quant_config)
 
diff --git a/vllm/model_executor/layers/fused_moe/nixl_ep_prepare_finalize.py b/vllm/model_executor/layers/fused_moe/prepare_finalize/nixl_ep.py
similarity index 99%
rename from vllm/model_executor/layers/fused_moe/nixl_ep_prepare_finalize.py
rename to vllm/model_executor/layers/fused_moe/prepare_finalize/nixl_ep.py
index dbc54e2c9def..a1068a752427 100644
--- a/vllm/model_executor/layers/fused_moe/nixl_ep_prepare_finalize.py
+++ b/vllm/model_executor/layers/fused_moe/prepare_finalize/nixl_ep.py
@@ -123,7 +123,6 @@ def post_init_setup(self, fused_experts: mk.FusedMoEExperts):
                 "NixlEPPrepareAndFinalize is setup to dispatch raw/unquantized "
                 f"activations despite ({fused_experts.__class__.__name__}) being able "
                 "to support quantized activations.",
-                scope="local",
             )
 
     def num_dispatchers(self) -> int:
diff --git a/vllm/model_executor/layers/fused_moe/prepare_finalize/no_dp_ep.py b/vllm/model_executor/layers/fused_moe/prepare_finalize/no_dp_ep.py
index b9d57da08326..82079127251c 100644
--- a/vllm/model_executor/layers/fused_moe/prepare_finalize/no_dp_ep.py
+++ b/vllm/model_executor/layers/fused_moe/prepare_finalize/no_dp_ep.py
@@ -30,7 +30,8 @@ def _quantize_input(
         quant_dtype=quant_config.quant_dtype,
         per_act_token_quant=quant_config.per_act_token_quant,
         block_shape=quant_config.block_shape,
-        is_fp4_scale_swizzled=quant_config.is_nvfp4_scale_swizzled,
+        is_scale_swizzled=quant_config.is_scale_swizzled,
+        mx_alignment=quant_config.mx_alignment,
     )
 
     return a1q, a1q_scale
diff --git a/vllm/model_executor/layers/fused_moe/routed_experts_capturer.py b/vllm/model_executor/layers/fused_moe/routed_experts_capturer.py
index 5b93b3d5c6ea..e9d1a8f22008 100644
--- a/vllm/model_executor/layers/fused_moe/routed_experts_capturer.py
+++ b/vllm/model_executor/layers/fused_moe/routed_experts_capturer.py
@@ -1,353 +1,833 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-# Adapted from
-# https://github.com/sgl-project/sglang/blob/bed301a5acaa9577c9aa706468bdf242f6a43051/python/sglang/srt/layers/moe/routed_experts_capturer.py
-
 from __future__ import annotations
 
-import fcntl
+import contextlib
 import logging
-import os
-import tempfile
-from collections.abc import Generator
-from contextlib import contextmanager
-from multiprocessing import shared_memory
-from unittest.mock import patch
+from abc import ABC, abstractmethod
 
 import numpy as np
 import torch
+import torch.distributed
 
-from vllm.config import VllmConfig
-from vllm.distributed import get_tensor_model_parallel_rank
-from vllm.forward_context import get_forward_context
-from vllm.platforms import current_platform
+from vllm.config.model import ModelConfig
 
 logger = logging.getLogger(__name__)
 
-# Constants
-_TMP_DIR = tempfile.gettempdir()
-_LOCK_FILE_PREFIX = os.path.join(_TMP_DIR, "vllm_routed_experts")
-_BUFFER_PREFIX = "vllm_routed_experts_buffer"
-
-# Global singleton instances
-_global_experts_capturer: RoutedExpertsCapturer | None = None
-_global_experts_reader: RoutedExpertsReader | None = None
-
-
-@contextmanager
-def _file_lock(lock_file: str, mode: str = "wb+") -> Generator[None, None, None]:
-    """Context manager for file-based locking."""
-    with open(lock_file, mode) as fp:
-        fcntl.flock(fp, fcntl.LOCK_EX)
-        try:
-            yield
-        finally:
-            fcntl.flock(fp, fcntl.LOCK_UN)
-
-
-def _create_or_attach_shared_memory(
-    name: str, size: int, lock_file: str
-) -> shared_memory.SharedMemory:
-    """Create or attach to shared memory with proper locking."""
-    # Ensure lock file exists before acquiring lock
-    with open(lock_file, "wb"):
-        pass
-
-    with _file_lock(lock_file):
-        try:
-            shm = shared_memory.SharedMemory(name=name, create=True, size=size)
-        except FileExistsError:
-            shm = shared_memory.SharedMemory(name=name, create=False, size=size)
 
-        if shm.size != size:
-            logger.warning(
-                "Shared memory %s size mismatch; recreating",
-                name,
-            )
-            shm.close()
-            shm.unlink()
-            try:
-                shm = shared_memory.SharedMemory(name=name, create=True, size=size)
-                logger.info("Created shared memory %s", name)
-            except FileExistsError:
-                shm = shared_memory.SharedMemory(name=name, create=False, size=size)
-                logger.info("Linked to existing shared memory %s", name)
+# ---------------------------------------------------------------------------
+# Custom op for routing capture -- traceable by torch.compile / Dynamo.
+#
+# Registered as a formal custom op so that torch.compile traces through it
+# cleanly without graph breaks.  ALL TP ranks call this op with a real
+# device buffer to ensure identical CUDA graph structure (symmetry).
+# Non-rank-0 buffers are written but never read for D2H.
+# ---------------------------------------------------------------------------
 
-    return shm
 
+@torch.library.custom_op("vllm::capture_routing", mutates_args={"buffer"})
+def capture_routing_op(
+    buffer: torch.Tensor,
+    topk_ids: torch.Tensor,
+    layer_id: int,
+    batch_size: int,
+) -> None:
+    buffer[layer_id, :batch_size, :].copy_(
+        topk_ids[:batch_size].to(buffer.dtype), non_blocking=True
+    )
 
-class RoutedExpertsCapturer:
-    """
-    Capturer for routed experts with device and optional shared memory buffer.
 
-    This class captures expert routing decisions during model forward passes
-    and optionally stores them in shared memory for cross-process access.
-    """
+@capture_routing_op.register_fake
+def _capture_routing_op_fake(
+    buffer: torch.Tensor,
+    topk_ids: torch.Tensor,
+    layer_id: int,
+    batch_size: int,
+) -> None:
+    pass
 
-    _instance: RoutedExpertsCapturer | None = None
 
-    def __init__(self) -> None:
-        self._device_buffer: torch.Tensor | None = None
-        self._shm: shared_memory.SharedMemory | None = None
-        self._host_buffer_view: np.ndarray | None = None
-        self._lock_file: str | None = None
+_MB = 1024 * 1024
 
-    @classmethod
-    def create(cls) -> RoutedExpertsCapturer:
-        """Create a global singleton instance."""
-        global _global_experts_capturer
-        if _global_experts_capturer is not None:
-            raise RuntimeError("Experts capturer already created.")
 
-        _global_experts_capturer = cls()
-        return _global_experts_capturer
+class _RoutedExpertsDeviceCache:
+    """Per-device (GPU) cache for capturing routed expert IDs during forward
+    pass.  Always writes at row 0 so that CUDA graph replay sees the same
+    addresses that were recorded at capture time.
+    """
 
-    @staticmethod
-    def get_instance() -> RoutedExpertsCapturer | None:
-        """Get the global singleton instance."""
-        return _global_experts_capturer
+    DTYPE = torch.int16
 
-    def init_buffer(
+    def __init__(
         self,
         max_num_batched_tokens: int,
-        max_num_kv_tokens: int,
-        vllm_config: VllmConfig,
+        num_hidden_layers: int,
+        num_experts_per_tok: int,
+        device: str,
     ) -> None:
-        """
-        Initialize the device buffer and optionally shared memory buffer.
+        # Layout: (L, N, K) so that buffer[layer_id] is a contiguous (N, K)
+        # view — required by the FlashInfer routing-replay kernel which
+        # writes expert IDs assuming contiguous row-major memory.
+        self.num_hidden_layers = num_hidden_layers
+        self.buffer = torch.zeros(
+            (num_hidden_layers, max_num_batched_tokens, num_experts_per_tok),
+            dtype=self.DTYPE,
+            device=device,
+        )
+        self._finalize_allocation_log()
+
+    def get_buffer_size_bytes(self):
+        return self.buffer.nbytes
+
+    def capture_fwd_routed_experts(self, layer_id: int, topk_ids: torch.Tensor):
+        assert layer_id is not None, "capturing routing experts but get layer_id None"
+        batch, _ = topk_ids.shape
+        self.buffer[layer_id, :batch, :].copy_(topk_ids, non_blocking=True)
+
+    def _finalize_allocation_log(self):
+        buf_mb = self.get_buffer_size_bytes() / _MB
+        logger.info(
+            "Routing experts device buffer allocated. shape=%s, size=%.2f MB",
+            tuple(self.buffer.shape),
+            buf_mb,
+        )
 
-        Args:
-            max_num_batched_tokens: Maximum number of tokens in a batch.
-            max_num_kv_tokens: Maximum number of KV tokens for shared memory.
-            vllm_config: vllm configuration containing layer and expert info.
-        """
 
-        if self._device_buffer is not None:
-            raise RuntimeError("Device buffer has already been initialized")
+class _RoutedExpertsHostCache:
+    """Host (CPU) cache using numpy arrays for per-request routing data.
 
-        hf_config = vllm_config.model_config.hf_text_config
-        num_layers = hf_config.num_hidden_layers
-        num_experts_per_tok = hf_config.num_experts_per_tok
+    Numpy arrays avoid torch dispatcher overhead for scatter operations.
+    Lazy per-request allocation avoids a massive up-front buffer.
+    """
 
-        # Initialize device buffer
-        self._device_buffer = torch.zeros(
-            (max_num_batched_tokens, num_layers, num_experts_per_tok),
-            dtype=torch.int32,
-            device=current_platform.device_type,
-        )
-        self.dp_rank = vllm_config.parallel_config.data_parallel_rank
+    DTYPE = np.int16
 
-        if get_tensor_model_parallel_rank() != 0:
-            return
+    def __init__(
+        self,
+        num_hidden_layers: int,
+        num_experts_per_tok: int,
+        max_model_len: int,
+    ) -> None:
+        self.max_model_len = max_model_len
+        self.num_hidden_layers = num_hidden_layers
+        self.num_experts_per_tok = num_experts_per_tok
 
-        # Initialize shared memory
-        shape = (max_num_kv_tokens, num_layers, num_experts_per_tok)
-        buffer_size = int(np.prod(shape)) * np.dtype(np.int32).itemsize
-        instance_id = vllm_config.instance_id
-        self._lock_file = f"{_LOCK_FILE_PREFIX}_{instance_id}_{self.dp_rank}.lock"
-        shm_name = f"{_BUFFER_PREFIX}_{instance_id}_{self.dp_rank}"
+        self._req_buffers: dict[str, np.ndarray] = {}
+        self._filled_len: dict[str, int] = {}
+        self._total_allocated_bytes = 0
 
-        self._shm = _create_or_attach_shared_memory(
-            shm_name, buffer_size, self._lock_file
-        )
-        self._host_buffer_view = np.ndarray(shape, dtype=np.int32, buffer=self._shm.buf)
-        self._host_buffer_view.fill(0)
+        self._finalize_allocation_log()
 
-        logger.debug(
-            "Created shared memory buffer '%s' with shape %s",
-            shm_name,
-            shape,
-        )
+    def get_buffer_size_bytes(self) -> int:
+        return self._total_allocated_bytes
 
-    def capture(self, layer_id: int, topk_ids: torch.Tensor) -> None:
-        """
-        Capture expert routing decisions for a specific layer.
+    def get_or_grow_buffer(self, req_id: str, max_pos: int) -> np.ndarray:
+        required_len = max_pos + 1
 
-        Args:
-            layer_id: The layer index.
-            topk_ids: Tensor of shape (batch_size, num_routed_experts).
-        """
-        if self._device_buffer is None:
-            raise RuntimeError("Buffer not initialized. Call init_buffer() first.")
-
-        ctx = get_forward_context()
-        if ctx.dp_metadata is None:  # single dp
-            start_loc = 0
-            end_loc = topk_ids.shape[0]
-            token_num_per_dp = topk_ids.shape[0]
-        else:  # multi dp
-            num_tokens_dp = ctx.dp_metadata.num_tokens_across_dp_cpu
-            token_num_per_dp = int(num_tokens_dp[self.dp_rank].item())
-            total = int(num_tokens_dp.sum().item())
-            n = topk_ids.shape[0]
-
-            if n == total:
-                # Naive dispatch: all DP ranks' tokens concatenated before routing.
-                cumsum = torch.cumsum(num_tokens_dp, dim=0)
-                end_loc = int(cumsum[self.dp_rank].item())
-                start_loc = end_loc - token_num_per_dp
-            elif n == token_num_per_dp:
-                # Modular-kernel path: DP combine happens inside quant_method.apply;
-                # select_experts only sees this rank's tokens.
-                start_loc = 0
-                end_loc = token_num_per_dp
-            else:
-                raise AssertionError(
-                    "RoutedExpertsCapturer: unexpected topk_ids batch dim "
-                    f"{n} (expected {total} or {token_num_per_dp} "
-                    f"for dp_rank={self.dp_rank})"
-                )
+        if req_id not in self._req_buffers:
+            buf = np.zeros(
+                (required_len, self.num_hidden_layers, self.num_experts_per_tok),
+                dtype=self.DTYPE,
+            )
+            self._req_buffers[req_id] = buf
+            self._total_allocated_bytes += buf.nbytes
+            return buf
+
+        buf = self._req_buffers[req_id]
+        if buf.shape[0] >= required_len:
+            return buf
+
+        new_len = min(max(required_len, buf.shape[0] * 2), self.max_model_len)
+        new_buf = np.zeros(
+            (new_len, self.num_hidden_layers, self.num_experts_per_tok),
+            dtype=self.DTYPE,
+        )
+        new_buf[: buf.shape[0]] = buf
+        self._total_allocated_bytes += new_buf.nbytes - buf.nbytes
+        self._req_buffers[req_id] = new_buf
+        return new_buf
+
+    def get_buffer(self, req_id: str) -> np.ndarray | None:
+        return self._req_buffers.get(req_id)
+
+    def update_filled_len(self, req_id: str, max_pos: int) -> None:
+        new_len = max_pos + 1
+        self._filled_len[req_id] = max(self._filled_len.get(req_id, 0), new_len)
+
+    def get_filled_len(self, req_id: str) -> int:
+        return self._filled_len.get(req_id, 0)
+
+    def free_request(self, req_id: str) -> None:
+        if req_id in self._req_buffers:
+            self._total_allocated_bytes -= self._req_buffers.pop(req_id).nbytes
+        self._filled_len.pop(req_id, None)
+
+    def _finalize_allocation_log(self):
+        logger.info(
+            "Routing experts host cache initialized (lazy allocation). "
+            "max_model_len=%s, layers=%s, experts_per_tok=%s",
+            self.max_model_len,
+            self.num_hidden_layers,
+            self.num_experts_per_tok,
+        )
 
-        if layer_id >= self._device_buffer.shape[1]:
-            return
 
-        self._device_buffer[:token_num_per_dp, layer_id, :] = topk_ids[
-            start_loc:end_loc, :
-        ]
+class RoutedExpertsCapturer(ABC):
+    @staticmethod
+    def create(
+        enable: bool,
+        model_config: ModelConfig,
+        num_fused_shared_experts: int,
+        max_num_batched_tokens: int,
+        max_model_len: int,
+        device: str,
+        shared_host_cache: _RoutedExpertsHostCache | None = None,
+        skip_host_cache: bool = False,
+    ):
+        if enable:
+            return _RoutedExpertsCapturerReal(
+                model_config,
+                max_num_batched_tokens=max_num_batched_tokens,
+                num_fused_shared_experts=num_fused_shared_experts,
+                max_model_len=max_model_len,
+                device=device,
+                shared_host_cache=shared_host_cache,
+                skip_host_cache=skip_host_cache,
+            )
+        return _RoutedExpertsCapturerNoop()
 
-    def clear_buffer(self) -> None:
-        """Clear the device buffer."""
-        if self._device_buffer is not None:
-            self._device_buffer.zero_()
+    @abstractmethod
+    def capture(self, layer_id: int, topk_ids: torch.Tensor):
+        raise NotImplementedError
 
-    def save_captured_experts(self, indices: np.ndarray) -> None:
-        """
-        Save captured experts from device buffer to shared memory.
+    def get_routed_experts(
+        self, req_id: str, seqlen: int | None = None, free_slot: bool = True
+    ):
+        raise NotImplementedError
 
-        Args:
-            indices: Array of indices indicating where to store the data.
-        """
-        if get_tensor_model_parallel_rank() != 0:
-            return
-        if self._lock_file is None:
-            raise RuntimeError("Shared memory not initialized.")
-        if self._host_buffer_view is None:
-            return
-        if self._device_buffer is None:
-            raise RuntimeError("Device buffer not initialized.")
+    def sync_fwd_experts_buffer_DtoH(
+        self,
+        positions: torch.Tensor,
+        num_scheduled_tokens: dict[str, int],
+    ):
+        raise NotImplementedError
 
-        num_tokens = len(indices)
-        data = self._device_buffer[:num_tokens, :, :].cpu().numpy()
+    def finalize_pending_copy(self):
+        raise NotImplementedError
 
-        with _file_lock(self._lock_file):
-            self._host_buffer_view[indices, :, :] = data
+    def get_host_cache(self):
+        raise NotImplementedError
 
-    def cleanup(self) -> None:
-        """Explicitly clean up shared memory resources."""
-        if self._shm is not None:
-            try:
-                self._shm.close()
-                self._shm.unlink()
-            except Exception:
-                logger.debug("Exception during cleanup for capturer", exc_info=True)
-            finally:
-                self._shm = None
+    def get_device_cache(self):
+        raise NotImplementedError
 
-    def __del__(self) -> None:
-        """Clean up shared memory on destruction."""
-        self.cleanup()
 
+def _count_moe_layers(hf_config) -> int:
+    """Count the number of MoE layers in a model.
 
-class RoutedExpertsReader:
+    Resolves three known config shapes:
+    - Nemotron-style: an explicit ``layers_block_type`` list with "moe" entries.
+    - Qwen3MoE / DeepSeek-style sparse: ``decoder_sparse_step > 1`` with optional
+      ``mlp_only_layers`` exclusions.
+    - Default: every layer is MoE except those listed in ``mlp_only_layers``.
     """
-    Reader for routed experts from shared memory.
-
-    This class attaches to shared memory created by RoutedExpertsCapturer
-    and reads expert routing decisions.
+    layers_block_type = getattr(hf_config, "layers_block_type", None)
+    if layers_block_type is not None:
+        return layers_block_type.count("moe")
+    n = hf_config.num_hidden_layers
+    mlp_only = getattr(hf_config, "mlp_only_layers", None) or []
+    step = getattr(hf_config, "decoder_sparse_step", 1) or 1
+    if step > 1:
+        return sum(1 for i in range(n) if (i + 1) % step == 0 and i not in mlp_only)
+    return n - sum(1 for i in mlp_only if 0 <= i < n)
+
+
+class _RoutedExpertsCapturerReal(RoutedExpertsCapturer):
+    """Capturer with GPU device cache and CPU host cache.
+
+    Performance strategy -- async D2H with optimized host-cache scatter:
+
+    Every decode step we issue a non-blocking D2H copy on a dedicated
+    CUDA stream.  The scatter into per-request host-cache buffers is
+    deferred to the start of the NEXT step (by which time the copy has
+    finished).  The scatter loop is optimized with direct scalar access
+    to avoid numpy slice views, int() conversions, and .max() calls.
+
+    At extraction time (when a request finishes), data is already in a
+    contiguous host buffer -- just a numpy slice, no concatenation.
     """
 
-    _instance: RoutedExpertsReader | None = None
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        max_num_batched_tokens: int,
+        num_fused_shared_experts: int,
+        max_model_len: int,
+        device: str,
+        shared_host_cache: _RoutedExpertsHostCache | None = None,
+        skip_host_cache: bool = False,
+    ):
+        self.num_fused_shared_experts = num_fused_shared_experts
+        self.num_hidden_layers = _count_moe_layers(model_config.hf_text_config)
+        self.num_experts_per_tok = model_config.hf_text_config.num_experts_per_tok
+        self.max_num_batched_tokens = max_num_batched_tokens
+        self.max_model_len = max_model_len
+        self._skip_host_cache = skip_host_cache
+
+        if skip_host_cache:
+            self.host_cache = None
+            logger.info("Skipping host cache for device %s (non-rank-0)", device)
+        elif shared_host_cache is not None:
+            self.host_cache = shared_host_cache
+        else:
+            self.host_cache = _RoutedExpertsHostCache(
+                num_hidden_layers=self.num_hidden_layers,
+                num_experts_per_tok=self.num_experts_per_tok,
+                max_model_len=self.max_model_len,
+            )
 
-    def __init__(self) -> None:
-        self._shm: shared_memory.SharedMemory | None = None
-        self._host_buffer_view: np.ndarray | None = None
-        self._lock_file: str | None = None
+        self.device_cache = _RoutedExpertsDeviceCache(
+            max_num_batched_tokens=self.max_num_batched_tokens,
+            num_hidden_layers=self.num_hidden_layers,
+            num_experts_per_tok=self.num_experts_per_tok,
+            device=device,
+        )
 
-    @classmethod
-    def create(cls) -> RoutedExpertsReader:
-        """Create a global singleton instance."""
-        global _global_experts_reader
-        if _global_experts_reader is not None:
-            raise RuntimeError("Experts reader already created.")
+        # ---- Async D2H pipeline (rank-0 only) ----
+        # Non-rank-0 workers only need the device buffer for symmetric
+        # CUDA graph capture; they skip the D2H pipeline entirely.
+        self._has_pending_copy = False
+        self._pending_positions: np.ndarray | None = None
+        self._pending_num_scheduled: dict[str, int] | None = None
+        self._pending_total_tokens: int = 0
+
+        if not skip_host_cache:
+            # Same (L, N, K) layout as device_cache.buffer.
+            self._pinned_staging = torch.zeros(
+                (
+                    self.num_hidden_layers,
+                    max_num_batched_tokens,
+                    self.num_experts_per_tok,
+                ),
+                dtype=_RoutedExpertsDeviceCache.DTYPE,
+                pin_memory=True,
+            )
+            # Private device snapshot: source for the async D2H. Decouples
+            # the in-flight copy from device_cache.buffer, which the next
+            # step's MoE writes overwrite in place on main_stream.
+            self._device_staging = torch.empty_like(self.device_cache.buffer)
+            self._copy_stream = torch.cuda.Stream(device=device)
+            self._copy_event = torch.cuda.Event()
+
+            pinned_mb = self._pinned_staging.nbytes / _MB
+            logger.info(
+                "Routing experts pinned staging buffer allocated. "
+                "shape=%s, size=%.2f MB",
+                tuple(self._pinned_staging.shape),
+                pinned_mb,
+            )
+        else:
+            self._pinned_staging = None
+            self._device_staging = None
+            self._copy_stream = None
+            self._copy_event = None
+            logger.info(
+                "Routing experts device-only capturer (rank != 0). "
+                "Device buffer shape=%s",
+                tuple(self.device_cache.buffer.shape),
+            )
 
-        _global_experts_reader = cls()
-        return _global_experts_reader
+    def capture(self, layer_id: int, topk_ids: torch.Tensor):
+        self.device_cache.capture_fwd_routed_experts(layer_id, topk_ids)
 
-    @staticmethod
-    def get_instance() -> RoutedExpertsReader | None:
-        """Get the global singleton instance."""
-        if _global_experts_reader is None:
-            logger.info("Experts reader not initialized.")
-        return _global_experts_reader
+    # ------------------------------------------------------------------
+    # sync_fwd_experts_buffer_DtoH -- called AFTER the forward pass
+    # ------------------------------------------------------------------
 
-    def attach_buffer(
+    def sync_fwd_experts_buffer_DtoH(
         self,
-        max_num_kv_tokens: int,
-        vllm_config: VllmConfig,
-    ) -> None:
-        """
-        Attach to an existing shared memory buffer.
+        positions: torch.Tensor,
+        num_scheduled_tokens: dict[str, int],
+    ):
+        if self.host_cache is None:
+            return
 
-        Args:
-            max_num_kv_tokens: Maximum number of KV tokens.
-            vllm_config: vllm configuration.
-        """
-        if self._shm is not None:
-            logger.warning("Already attached to shared memory buffer.")
-            return  # Already attached
-
-        hf_config = vllm_config.model_config.hf_text_config
-        shape = (
-            max_num_kv_tokens,
-            hf_config.num_hidden_layers,
-            hf_config.num_experts_per_tok,
+        # 1. Finalize previous async copy -- the copy had an entire
+        #    forward pass to complete so event.synchronize() is ~free.
+        if self._has_pending_copy:
+            self._copy_event.synchronize()
+            self._scatter_to_host()
+            self._has_pending_copy = False
+
+        total_tokens = sum(num_scheduled_tokens.values())
+        if total_tokens == 0:
+            return
+
+        # 2. Snapshot the device buffer on main_stream into a private
+        #    staging buffer, then issue the D2H from the staging buffer
+        #    on a dedicated copy stream. The snapshot serializes after
+        #    the current step's MoE writes (same stream) and is private
+        #    from the next step's MoE writes, so the in-flight D2H is
+        #    not aliased by step N+1's forward under async scheduling.
+        main_stream = torch.cuda.current_stream(self._copy_stream.device)
+        self._device_staging[:, :total_tokens, :].copy_(
+            self.device_cache.buffer[:, :total_tokens, :], non_blocking=True
         )
+        with torch.cuda.stream(self._copy_stream):
+            self._copy_stream.wait_stream(main_stream)
+            self._pinned_staging[:, :total_tokens, :].copy_(
+                self._device_staging[:, :total_tokens, :], non_blocking=True
+            )
+            self._copy_event.record()
 
-        self.dp_rank = vllm_config.parallel_config.data_parallel_rank
-        instance_id = vllm_config.instance_id
-        self._lock_file = f"{_LOCK_FILE_PREFIX}_{instance_id}_{self.dp_rank}.lock"
-        shm_name = f"{_BUFFER_PREFIX}_{instance_id}_{self.dp_rank}"
+        # 3. Save metadata for deferred scatter.
+        self._pending_positions = positions.numpy().copy()
+        self._pending_num_scheduled = num_scheduled_tokens
+        self._pending_total_tokens = total_tokens
+        self._has_pending_copy = True
 
-        with _file_lock(self._lock_file, mode="rb+"):
-            # Avoid resource_tracker registering the shared memory
-            with patch(
-                "multiprocessing.resource_tracker.register",
-                lambda *args, **kwargs: None,
-            ):
-                self._shm = shared_memory.SharedMemory(name=shm_name)
+    # ------------------------------------------------------------------
+    # Optimized scatter into pre-allocated host-cache buffers
+    # ------------------------------------------------------------------
 
-            self._host_buffer_view = np.ndarray(
-                shape, dtype=np.int32, buffer=self._shm.buf
-            )
+    def _scatter_to_host(self):
+        """Scatter D2H data into per-request host cache buffers.
 
-    def get_routed_experts(self, indices: np.ndarray) -> np.ndarray:
+        Staging layout is (L, N, K).  Host cache layout is (seq_len, L, K).
+        We transpose the staging slice to (N, L, K) before scattering so
+        that indexing by token position naturally yields (L, K) rows.
         """
-        Read routed expert data from shared memory.
+        # Transpose (L, N, K) -> (N, L, K) for the active token range.
+        host_values = (
+            self._pinned_staging[:, : self._pending_total_tokens, :]
+            .numpy()
+            .transpose(1, 0, 2)
+        )
+        positions_np = self._pending_positions
+        host_cache = self.host_cache
+        assert self._pending_num_scheduled is not None
+        assert positions_np is not None
+        assert host_cache is not None
+
+        offset = 0
+        for req_id, n_tokens in self._pending_num_scheduled.items():
+            if n_tokens == 0:
+                continue
+
+            if n_tokens == 1:
+                pos_val = int(positions_np[offset])
+                buf = host_cache.get_or_grow_buffer(req_id, pos_val)
+                buf[pos_val] = host_values[offset]
+                host_cache.update_filled_len(req_id, pos_val)
+            else:
+                pos = positions_np[offset : offset + n_tokens]
+                max_pos = int(pos[-1]) if n_tokens > 0 else 0
+                if n_tokens > 1:
+                    max_pos = int(pos.max())
+                buf = host_cache.get_or_grow_buffer(req_id, max_pos)
+                buf[pos] = host_values[offset : offset + n_tokens]
+                host_cache.update_filled_len(req_id, max_pos)
+
+            offset += n_tokens
+
+        self._pending_positions = None
+        self._pending_num_scheduled = None
+        self._pending_total_tokens = 0
+
+    # ------------------------------------------------------------------
+    # finalize_pending_copy -- call before reading host cache
+    # ------------------------------------------------------------------
+
+    def finalize_pending_copy(self):
+        """Ensure the most recent async D2H copy has been scattered into
+        host cache buffers.  Call before get_routed_experts."""
+        if self._has_pending_copy:
+            self._copy_event.synchronize()
+            self._scatter_to_host()
+            self._has_pending_copy = False
+
+    # ------------------------------------------------------------------
+    # Extraction -- O(1), just a numpy slice
+    # ------------------------------------------------------------------
+
+    def get_routed_experts(
+        self,
+        req_id: str,
+        seqlen: int | None = None,
+        free_slot: bool = True,
+    ):
+        if self.host_cache is None:
+            return None
+        buf = self.host_cache.get_buffer(req_id)
+        if buf is None:
+            return None
+        filled = self.host_cache.get_filled_len(req_id)
+        if filled <= 0:
+            return None
+        effective_len = min(filled, seqlen) if seqlen is not None else filled
+        result = buf[:effective_len].copy()
+        if free_slot:
+            self.host_cache.free_request(req_id)
+        return result
+
+    def get_host_cache(self):
+        return self.host_cache
+
+    def get_device_cache(self):
+        return self.device_cache
+
+
+class _RoutedExpertsCapturerNoop(RoutedExpertsCapturer):
+    def __init__(self):
+        pass
 
-        Args:
-            indices: Array of indices to read.
+    def capture(self, layer_id: int, topk_ids: torch.Tensor):
+        pass
 
-        Returns:
-            Copy of the expert routing data for the given indices.
-        """
-        if self._host_buffer_view is None:
-            raise RuntimeError("Buffer not attached. Call attach_buffer() first.")
-        if self._lock_file is None:
-            raise RuntimeError("Lock file not initialized.")
-
-        with _file_lock(self._lock_file, mode="rb+"):
-            return self._host_buffer_view[indices, :, :].copy()
-
-    def cleanup(self) -> None:
-        """Explicitly clean up resources (close without unlink)."""
-        if self._shm is not None:
-            try:
-                self._shm.close()
-            except Exception:
-                logger.debug("Exception during cleanup for reader", exc_info=True)
-            finally:
-                self._shm = None
-
-    def __del__(self) -> None:
-        """Close shared memory on destruction (do not unlink)."""
-        self.cleanup()
+    def get_routed_experts(self, req_id: str, seqlen=None, free_slot=True):
+        return None
+
+    def sync_fwd_experts_buffer_DtoH(self, positions, num_scheduled_tokens):
+        pass
+
+    def finalize_pending_copy(self):
+        pass
+
+    def get_host_cache(self):
+        return None
+
+    def get_device_cache(self):
+        pass
+
+
+# Global capturer instance (per-process)
+_global_expert_capturer: RoutedExpertsCapturer | None = _RoutedExpertsCapturerNoop()
+_shared_host_cache: _RoutedExpertsHostCache | None = None
+
+
+def get_global_experts_capturer():
+    return _global_expert_capturer
+
+
+def set_global_experts_capturer(capturer: RoutedExpertsCapturer):
+    global _global_expert_capturer
+    _global_expert_capturer = capturer
+
+
+def extract_routed_experts_for_current_batch(
+    req_ids: list[str],
+    requests: dict,
+    req_id_to_index: dict[str, int],
+    num_tokens_no_spec: np.ndarray,
+    max_model_len: int,
+) -> dict[str, np.ndarray] | None:
+    """Extract routed experts for requests predicted to finish this step.
+
+    Checks all stop conditions the scheduler will check (max_tokens,
+    EOS token, stop tokens, max_model_len) so that every finished
+    request gets its routing data attached to the ModelRunnerOutput.
+
+    Args:
+        req_ids: Ordered request IDs for the current batch.
+        requests: Map of req_id to CachedRequestState (read-only).
+        req_id_to_index: Map of req_id to input batch index.
+        num_tokens_no_spec: Array of total token counts per request index.
+        max_model_len: Maximum model sequence length.
+    """
+    capturer = get_global_experts_capturer()
+    if capturer is None:
+        return None
+    host_cache = capturer.get_host_cache()
+    if host_cache is None:
+        return None
+
+    finishing_req_ids: list[str] = []
+    for req_id in req_ids:
+        req_state = requests.get(req_id)
+        if req_state is None:
+            continue
+        sp = req_state.sampling_params
+        if sp is None:
+            continue
+        output_ids = req_state.output_token_ids
+        if not output_ids:
+            continue
+        if len(output_ids) < sp.min_tokens:
+            continue
+
+        finishing = False
+        last_token = output_ids[-1]
+
+        # EOS token (mirrors check_stop: eos_token_id is None
+        # when ignore_eos=True, so this naturally respects that)
+        if last_token == sp.eos_token_id:
+            finishing = True
+
+        # Explicit stop token IDs
+        if not finishing and sp.stop_token_ids and last_token in sp.stop_token_ids:
+            finishing = True
+
+        # max_tokens / max_model_len length cap
+        if not finishing:
+            if sp.max_tokens is not None and len(output_ids) >= sp.max_tokens:
+                finishing = True
+            else:
+                req_idx = req_id_to_index.get(req_id)
+                if req_idx is not None:
+                    total = num_tokens_no_spec[req_idx]
+                    if total >= max_model_len:
+                        finishing = True
+
+        if finishing:
+            finishing_req_ids.append(req_id)
+
+    if not finishing_req_ids:
+        return None
+
+    # At least one request is finishing: ensure the latest async D2H
+    # copy has been scattered into the host cache.
+    capturer.finalize_pending_copy()
+
+    result: dict[str, np.ndarray] = {}
+    for req_id in finishing_req_ids:
+        seqlen = host_cache.get_filled_len(req_id)
+        if seqlen <= 0:
+            continue
+        experts = capturer.get_routed_experts(req_id, seqlen=seqlen, free_slot=False)
+        if experts is not None:
+            result[req_id] = experts
+
+    return result if result else None
+
+
+def free_routing_buffers(
+    finished_req_ids: set[str],
+    preempted_req_ids: set[str] | None = None,
+) -> None:
+    """Free host cache buffers for finished and preempted requests.
+
+    Finished requests had their routing data extracted in the previous
+    step.
+
+    Preempted requests are re-prefilled from scratch when they resume,
+    so their host-cache buffer is freed here. This means any routing
+    already accumulated in the host cache for the preempted request is
+    dropped without being emitted on a ``ModelRunnerOutput`` --
+    consumers see ``routed_experts=None`` for those requests with no
+    other signal. Partial-rollout / async-RL pipelines that depend on
+    receiving routing for preempted requests should treat preemption
+    as a routing-data loss event and either keep preemption disabled
+    or reconstruct routing on the resumed prefill.
+    """
+    capturer = get_global_experts_capturer()
+    if capturer is None:
+        return
+    host_cache = capturer.get_host_cache()
+    if host_cache is None:
+        return
+
+    for req_id in finished_req_ids:
+        host_cache.free_request(req_id)
+    if preempted_req_ids:
+        for req_id in preempted_req_ids:
+            host_cache.free_request(req_id)
+
+
+def issue_routing_d2h_copy(
+    input_batch_req_ids: list[str],
+    num_scheduled_tokens: dict[str, int],
+    positions: torch.Tensor,
+    positions_cpu: torch.Tensor,
+) -> None:
+    """Issue async D2H copy of routed experts after the forward pass.
+
+    Called EARLY in the execute_model epilogue so the copy overlaps with
+    eplb, kv_connector finalization, and draft work.
+    finalize_pending_copy() + get_routed_experts() happen later in
+    extract_routed_experts_for_current_batch().
+    """
+    capturer = get_global_experts_capturer()
+    if capturer is None:
+        return
+
+    ordered = {
+        req_id: num_scheduled_tokens[req_id]
+        for req_id in input_batch_req_ids
+        if req_id in num_scheduled_tokens
+    }
+    n = sum(ordered.values())
+    positions_cpu[:n].copy_(positions[:n])
+    capturer.sync_fwd_experts_buffer_DtoH(
+        positions=positions_cpu[:n],
+        num_scheduled_tokens=ordered,
+    )
+
+
+def split_routed_experts(
+    routed_experts: np.ndarray,
+    prompt_len: int,
+    num_output_tokens: int | None = None,
+) -> tuple[np.ndarray | None, np.ndarray | None]:
+    """Split routing data into prompt and generation portions.
+
+    Args:
+        routed_experts: Full routing array of shape (seq_len, L, K).
+        prompt_len: Number of prompt tokens for the request.
+        num_output_tokens: Actual number of generated tokens (from
+            detokenizer).  When provided, the generation portion is
+            clipped to this length — necessary with MTP where the model
+            runner may capture routing for more tokens than the final
+            output contains.
+
+    Returns:
+        (prompt_routed_experts, gen_routed_experts) numpy arrays, either
+        of which may be None if the corresponding portion is empty.
+    """
+    prompt_routed_experts = routed_experts[:prompt_len]
+    gen_routed_experts = routed_experts[prompt_len:]
+
+    # Clip generation routing to match actual output tokens.
+    if (
+        num_output_tokens is not None
+        and gen_routed_experts.shape[0] > num_output_tokens
+        and num_output_tokens > 0
+    ):
+        gen_routed_experts = gen_routed_experts[:num_output_tokens]
+
+    if prompt_routed_experts.size == 0:
+        prompt_routed_experts = None
+    if gen_routed_experts.size == 0:
+        gen_routed_experts = None
+
+    return prompt_routed_experts, gen_routed_experts
+
+
+def get_shared_host_cache() -> _RoutedExpertsHostCache | None:
+    return _shared_host_cache
+
+
+def create_shared_host_cache(
+    model_config: ModelConfig,
+    max_model_len: int,
+) -> _RoutedExpertsHostCache:
+    global _shared_host_cache
+    num_hidden_layers = _count_moe_layers(model_config.hf_text_config)
+    num_experts_per_tok = model_config.hf_text_config.num_experts_per_tok
+    _shared_host_cache = _RoutedExpertsHostCache(
+        num_hidden_layers=num_hidden_layers,
+        num_experts_per_tok=num_experts_per_tok,
+        max_model_len=max_model_len,
+    )
+    return _shared_host_cache
+
+
+def init_routed_experts_capturer_with_shared_cache(
+    enable: bool,
+    model_config: ModelConfig,
+    num_fused_shared_experts: int,
+    max_num_batched_tokens: int,
+    max_model_len: int,
+    device: str,
+    rank: int = 0,
+    world_size: int = 1,
+) -> RoutedExpertsCapturer:
+    """Initialize capturer with rank-aware handling (only rank 0 captures)."""
+    if not enable:
+        capturer = _RoutedExpertsCapturerNoop()
+        set_global_experts_capturer(capturer)
+        return capturer
+
+    if world_size > 1 and rank != 0:
+        # Non-rank-0 workers get a device-only capturer (no host cache,
+        # no D2H pipeline) so that ALL ranks have a real device buffer.
+        # This ensures the custom op call in every MoE layer produces
+        # identical CUDA graph structure across TP ranks.
+        logger.info("Creating device-only routed experts capturer for rank %s", rank)
+        capturer = RoutedExpertsCapturer.create(
+            enable=True,
+            model_config=model_config,
+            num_fused_shared_experts=num_fused_shared_experts,
+            max_num_batched_tokens=max_num_batched_tokens,
+            max_model_len=max_model_len,
+            device=device,
+            skip_host_cache=True,
+        )
+        set_global_experts_capturer(capturer)
+        return capturer
+
+    capturer = RoutedExpertsCapturer.create(
+        enable=True,
+        model_config=model_config,
+        num_fused_shared_experts=num_fused_shared_experts,
+        max_num_batched_tokens=max_num_batched_tokens,
+        max_model_len=max_model_len,
+        device=device,
+        skip_host_cache=False,
+    )
+    set_global_experts_capturer(capturer)
+    return capturer
+
+
+def bind_routing_capture_to_model(model) -> None:
+    """Bind routing capture buffers to all FusedMoE layers in the model.
+
+    Must be called AFTER init_routed_experts_capturer_with_shared_cache()
+    and BEFORE CUDA graph capture.  All TP ranks get a real buffer so
+    that the custom op call produces identical graph structure.
+    """
+    from vllm.model_executor.layers.fused_moe.layer import FusedMoE
+
+    capturer = get_global_experts_capturer()
+    device_cache = capturer.get_device_cache()
+    if device_cache is None:
+        return  # routing capture not enabled
+
+    buffer = device_cache.buffer
+
+    # Mark the buffer so CUDA graphs do NOT snapshot/restore its contents.
+    if hasattr(torch.compiler, "cudagraph_mark_tensor_static"):
+        torch.compiler.cudagraph_mark_tensor_static(buffer)
+    elif hasattr(torch._C, "_set_static_address_tag"):
+        torch._C._set_static_address_tag(buffer, True)
+    with contextlib.suppress(Exception):
+        torch._dynamo.mark_static_address(buffer)
+
+    bound = 0
+    for module in model.modules():
+        if isinstance(module, FusedMoE) and hasattr(module, "moe_layer_id"):
+            # Per-FusedMoE configurations not yet validated for routing
+            # capture. These signals are only set after model init, so a
+            # config-level guard cannot see them.
+            if module.moe_config.is_sequence_parallel:
+                raise NotImplementedError(
+                    "routed-experts capture is not yet validated with "
+                    "sequence parallelism on the FusedMoE layer "
+                    "(moe_config.is_sequence_parallel=True)."
+                )
+            if (
+                module.moe_config.dp_size > 1
+                and not module.quant_method.supports_internal_mk
+            ):
+                raise NotImplementedError(
+                    "routed-experts capture is not yet validated with "
+                    "naive DP dispatch (non-modular quant method "
+                    f"{type(module.quant_method).__name__}, "
+                    f"dp_size={module.moe_config.dp_size})."
+                )
+
+            layer_id = module.moe_layer_id
+            layer_buf = buffer[layer_id]  # (N_max, K)
+            module._routing_replay_out = layer_buf
+            # Mark each per-layer view as static so CUDA graphs don't
+            # snapshot/restore or relocate the buffer during replay.
+            if hasattr(torch.compiler, "cudagraph_mark_tensor_static"):
+                torch.compiler.cudagraph_mark_tensor_static(layer_buf)
+            with contextlib.suppress(Exception):
+                torch._dynamo.mark_static_address(layer_buf)
+            bound += 1
+
+    logger.info(
+        "Bound routing capture buffer to %s FusedMoE layers. Buffer shape=%s",
+        bound,
+        tuple(buffer.shape),
+    )
diff --git a/vllm/model_executor/layers/fused_moe/router/aiter_shared_routed_fused_moe_router.py b/vllm/model_executor/layers/fused_moe/router/aiter_shared_routed_fused_moe_router.py
new file mode 100644
index 000000000000..9d92f570df5a
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/router/aiter_shared_routed_fused_moe_router.py
@@ -0,0 +1,143 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Callable
+
+import torch
+
+from vllm._aiter_ops import rocm_aiter_ops
+from vllm.distributed.eplb.eplb_state import EplbLayerState
+from vllm.model_executor.layers.fused_moe.config import (
+    RoutingMethodType,
+    get_routing_method_type,
+)
+from vllm.model_executor.layers.fused_moe.router.base_router import BaseRouter
+from vllm.model_executor.layers.fused_moe.router.fused_topk_router import (
+    dispatch_topk_softmax_func,
+)
+
+
+class AiterSharedRoutedFusedMoERouter(BaseRouter):
+    """
+    ROCm AITER router for models with fused shared experts (e.g. Qwen3-MoE).
+
+    When the AITER topk_softmax kernel supports sigmoid fusion, the routing
+    softmax and shared-expert sigmoid are computed in a single kernel launch.
+    Otherwise the shared-expert weights are injected into the pre-allocated
+    AITER buffer via a fallback path.
+
+    Only instantiated when rocm_aiter fused-MoE is active and
+    num_fused_shared_experts > 0.
+    """
+
+    def __init__(
+        self,
+        top_k: int,
+        global_num_experts: int,
+        eplb_state: EplbLayerState,
+        num_fused_shared_experts: int,
+        scoring_func: str = "softmax",
+        renormalize: bool = True,
+        enable_eplb: bool = False,
+        indices_type_getter: Callable[[], torch.dtype | None] | None = None,
+    ):
+        super().__init__(
+            top_k=top_k,
+            global_num_experts=global_num_experts,
+            eplb_state=eplb_state,
+            enable_eplb=enable_eplb,
+            indices_type_getter=indices_type_getter,
+        )
+        self.renormalize = renormalize
+        self.scoring_func = scoring_func
+        self.num_fused_shared_experts = num_fused_shared_experts
+
+    @property
+    def routing_method_type(self) -> RoutingMethodType:
+        return get_routing_method_type(
+            scoring_func=self.scoring_func,
+            top_k=self.top_k,
+            renormalize=self.renormalize,
+            num_expert_group=None,
+            has_e_score_bias=False,
+        )
+
+    def _compute_routing(
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+        indices_type: torch.dtype | None,
+        *,
+        input_ids: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        assert hidden_states.size(0) == router_logits.size(0), (
+            "Number of tokens mismatch"
+        )
+
+        from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+            aiter_topK_meta_data,
+        )
+
+        M = hidden_states.size(0)
+        topk = self.top_k
+        num_fse = self.num_fused_shared_experts
+
+        token_expert_indices = torch.empty(
+            M, topk, dtype=torch.int32, device=hidden_states.device
+        )
+
+        if rocm_aiter_ops.fuse_sigmoid_in_kernel(aiter_topK_meta_data):
+            total_topk_weights, total_topk_ids = aiter_topK_meta_data  # type: ignore[misc]
+            total_topk_weights_slice = total_topk_weights[:M]
+            topk_ids_slice = total_topk_ids[:M, :topk]
+
+            topk_func = dispatch_topk_softmax_func(use_rocm_aiter=True)
+            topk_func(
+                total_topk_weights_slice,
+                topk_ids_slice,
+                token_expert_indices,
+                router_logits,
+                self.renormalize,
+                num_fse,
+                "sigmoid",
+            )
+            return total_topk_weights_slice, total_topk_ids[:M]
+
+        routing_logits = router_logits[:, :-num_fse]
+        shared_logits = router_logits[:, -num_fse:]
+
+        topk_weights = torch.empty(
+            M, topk, dtype=torch.float32, device=hidden_states.device
+        )
+        topk_ids = torch.empty(
+            M,
+            topk,
+            dtype=torch.int32 if indices_type is None else indices_type,
+            device=hidden_states.device,
+        )
+
+        topk_func = dispatch_topk_softmax_func(
+            use_rocm_aiter=rocm_aiter_ops.is_fused_moe_enabled()
+        )
+        topk_weights, topk_ids = topk_func(
+            topk_weights,
+            topk_ids,
+            token_expert_indices,
+            routing_logits,
+            self.renormalize,
+        )
+
+        if aiter_topK_meta_data is not None:
+            from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+                inject_shared_expert_weights,
+            )
+
+            shared_weights = torch.sigmoid(shared_logits)
+            topk_weights, topk_ids = inject_shared_expert_weights(
+                topk_weights,
+                topk_ids,
+                topk=topk,
+                num_fused_shared_experts=num_fse,
+                shared_expert_weights=shared_weights,
+            )
+
+        return topk_weights, topk_ids
diff --git a/vllm/model_executor/layers/fused_moe/router/base_router.py b/vllm/model_executor/layers/fused_moe/router/base_router.py
index bcc66887f8e8..0138eb59c91c 100644
--- a/vllm/model_executor/layers/fused_moe/router/base_router.py
+++ b/vllm/model_executor/layers/fused_moe/router/base_router.py
@@ -26,6 +26,7 @@ def _eplb_map_and_record_i32_kernel(
         map_slots,
         out_size,
         numel,
+        num_active_experts,
         BLOCK_SIZE: tl.constexpr,
     ):
         pid = tl.program_id(0)
@@ -37,7 +38,6 @@ def _eplb_map_and_record_i32_kernel(
         safe_expert_id = tl.where(valid_expert, expert_id, 0)
 
         # 1. Convert the logical expert ids to physical expert ids
-        # Directly select a random replica for each logical expert
         replica_count = tl.load(
             logical_replica_count_ptr + safe_expert_id,
             mask=mask & valid_expert,
@@ -45,8 +45,11 @@ def _eplb_map_and_record_i32_kernel(
         )
         # Avoid invalid modulo/div by forcing at least 1.
         replica_count = tl.maximum(replica_count, 1)
-        # Match torch.compile path: use flattened token position.
-        replica_idx = offs % replica_count
+        # floor(2^32 / phi), classic Knuth multiplicative hash multiplier.
+        KNUTH_MULTIPLIER = 2654435769
+        token_idx = (offs // num_active_experts).to(tl.int64)
+        hashed = (token_idx * KNUTH_MULTIPLIER) & 0xFFFFFFFF
+        replica_idx = hashed % replica_count
 
         # 2. Record expert load metrics.
 
@@ -85,6 +88,7 @@ def _eplb_map_and_record_triton(
         numel = topk_ids_in.numel()
         if numel == 0:
             return topk_ids
+        num_active_experts = topk_ids_in.shape[-1]
         out_flat = torch.empty((numel,), device=topk_ids.device, dtype=topk_ids.dtype)
         grid = lambda meta: (triton.cdiv(numel, meta["BLOCK_SIZE"]),)
         assert expert_load_view.is_contiguous()
@@ -99,6 +103,7 @@ def _eplb_map_and_record_triton(
             logical_to_physical_map.shape[1],
             expert_load_view.shape[0],
             numel,
+            num_active_experts,
             BLOCK_SIZE=256,
         )
         return out_flat.reshape(topk_ids.shape)
@@ -223,6 +228,8 @@ def _compute_routing(
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
         indices_type: torch.dtype | None,
+        *,
+        input_ids: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Compute the actual routing logic.
@@ -244,6 +251,8 @@ def select_experts(
         self,
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
+        *,
+        input_ids: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Route the input hidden states to the top-k experts based on the
@@ -273,7 +282,7 @@ def select_experts(
 
         # Step 3: Compute routing (delegated to subclass)
         topk_weights, topk_ids = self._compute_routing(
-            hidden_states, router_logits, indices_type
+            hidden_states, router_logits, indices_type, input_ids=input_ids
         )
 
         # Capture logical ids before EPLB mapping.
diff --git a/vllm/model_executor/layers/fused_moe/router/custom_routing_router.py b/vllm/model_executor/layers/fused_moe/router/custom_routing_router.py
index 0367189ca1ab..a3e0075f2b7d 100644
--- a/vllm/model_executor/layers/fused_moe/router/custom_routing_router.py
+++ b/vllm/model_executor/layers/fused_moe/router/custom_routing_router.py
@@ -34,11 +34,15 @@ def __init__(
 
     @property
     def routing_method_type(self) -> RoutingMethodType:
+        from vllm.model_executor.models.cohere2_moe import token_choice_with_bias
         from vllm.model_executor.models.llama4 import Llama4MoE
 
         # NOTE: FLASHINFER_TRTLLM support the Llama4 router.
         if self.custom_routing_function == Llama4MoE.custom_routing_function:
             return RoutingMethodType.Llama4
+        # Cohere MoE uses a sigmoid -> top-k -> renormalize routing function.
+        if self.custom_routing_function == token_choice_with_bias:
+            return RoutingMethodType.SigmoidRenorm
         return RoutingMethodType.Custom
 
     def _compute_routing(
@@ -46,6 +50,8 @@ def _compute_routing(
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
         indices_type: torch.dtype | None,
+        *,
+        input_ids: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """Compute routing using the custom routing function."""
         topk_weights, topk_ids = self.custom_routing_function(
diff --git a/vllm/model_executor/layers/fused_moe/router/fused_moe_router.py b/vllm/model_executor/layers/fused_moe/router/fused_moe_router.py
index d7aed4fdeb2b..d82085254f9b 100644
--- a/vllm/model_executor/layers/fused_moe/router/fused_moe_router.py
+++ b/vllm/model_executor/layers/fused_moe/router/fused_moe_router.py
@@ -31,6 +31,8 @@ def select_experts(
         self,
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
+        *,
+        input_ids: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Route the input hidden states to the top-k experts based on the
diff --git a/vllm/model_executor/layers/fused_moe/router/fused_topk_bias_router.py b/vllm/model_executor/layers/fused_moe/router/fused_topk_bias_router.py
index bcabb1f3672b..84eaad7f65e6 100644
--- a/vllm/model_executor/layers/fused_moe/router/fused_topk_bias_router.py
+++ b/vllm/model_executor/layers/fused_moe/router/fused_topk_bias_router.py
@@ -4,6 +4,7 @@
 from collections.abc import Callable
 
 import torch
+import torch.nn.functional as F
 
 import vllm._custom_ops as ops
 import vllm.envs as envs
@@ -56,6 +57,32 @@ def vllm_topk_sigmoid(
     return topk_weights, topk_indices
 
 
+def vllm_topk_softplus_sqrt(
+    topk_weights: torch.Tensor,
+    topk_indices: torch.Tensor,
+    token_expert_indices: torch.Tensor,
+    gating_output: torch.Tensor,
+    renormalize: bool = False,
+    e_score_correction_bias: torch.Tensor | None = None,
+    input_tokens: torch.Tensor | None = None,
+    hash_indices_table: torch.Tensor | None = None,
+    routed_scaling_factor: float = 1.0,
+) -> tuple[torch.Tensor, ...]:
+    ops.topk_hash_softplus_sqrt(
+        topk_weights,
+        topk_indices,
+        token_expert_indices,
+        gating_output,
+        renormalize,
+        routed_scaling_factor,
+        e_score_correction_bias,
+        input_tokens,
+        hash_indices_table,
+    )
+
+    return topk_weights, topk_indices
+
+
 @functools.lru_cache(maxsize=8)
 def _aiter_get_num_expert_group(num_experts: int) -> int:
     _AITER_MAX_EXPERTS_PER_GROUP = 32
@@ -72,11 +99,14 @@ def _aiter_get_num_expert_group(num_experts: int) -> int:
 def fused_topk_bias(
     hidden_states: torch.Tensor,
     gating_output: torch.Tensor,
+    scoring_func: str,
     e_score_correction_bias: torch.Tensor,
     topk: int,
     renormalize: bool,
-    scoring_func: str = "softmax",
     indices_type: torch.dtype | None = None,
+    input_tokens: torch.Tensor | None = None,
+    hash_indices_table: torch.Tensor | None = None,
+    routed_scaling_factor: float = 1.0,
 ):
     if not rocm_aiter_ops.is_fused_moe_enabled():
         assert hidden_states.size(0) == gating_output.size(0), (
@@ -107,6 +137,8 @@ def fused_topk_bias(
                 renormalize,
                 e_score_correction_bias,
             )
+            if routed_scaling_factor != 1.0:
+                topk_weights *= routed_scaling_factor
             return topk_weights, topk_ids
         elif scoring_func == "sigmoid":
             topk_weights, topk_ids = vllm_topk_sigmoid(
@@ -117,9 +149,24 @@ def fused_topk_bias(
                 renormalize,
                 e_score_correction_bias,
             )
+            if routed_scaling_factor != 1.0:
+                topk_weights *= routed_scaling_factor
             return topk_weights, topk_ids
+        elif scoring_func == "sqrtsoftplus":
+            return vllm_topk_softplus_sqrt(
+                topk_weights,
+                topk_ids,
+                token_expert_indices,
+                gating_output,
+                renormalize,
+                e_score_correction_bias,
+                input_tokens,
+                hash_indices_table,
+                routed_scaling_factor,
+            )
         else:
             raise ValueError(f"Unsupported scoring function: {scoring_func}")
+
     elif rocm_aiter_ops.is_fused_moe_enabled() and scoring_func == "sigmoid":
         M = hidden_states.size(0)
         num_experts = gating_output.shape[-1]
@@ -136,13 +183,15 @@ def fused_topk_bias(
             )
             rocm_aiter_ops.biased_grouped_topk(
                 gating_output,
-                e_score_correction_bias.to(gating_output.dtype),
+                e_score_correction_bias,
                 topk_weights,
                 topk_ids,
                 num_expert_group=num_expert_group,
                 topk_group=num_expert_group,
                 need_renorm=renormalize,
             )
+            if routed_scaling_factor != 1.0:
+                topk_weights *= routed_scaling_factor
             return topk_weights, topk_ids
 
     n_routed_experts = gating_output.shape[-1]
@@ -150,20 +199,31 @@ def fused_topk_bias(
         scores = gating_output.softmax(dim=-1)
     elif scoring_func == "sigmoid":
         scores = gating_output.sigmoid()
+    elif scoring_func == "sqrtsoftplus":
+        scores = F.softplus(gating_output).sqrt()
     else:
         raise ValueError(f"Unsupported scoring function: {scoring_func}")
-
-    scores_for_choice = scores.view(
-        -1, n_routed_experts
-    ) + e_score_correction_bias.unsqueeze(0)
-
+    if e_score_correction_bias is not None:
+        scores_for_choice = scores.view(
+            -1, n_routed_experts
+        ) + e_score_correction_bias.unsqueeze(0)
+    else:
+        scores_for_choice = scores.view(-1, n_routed_experts)
     # For batch invariance, use sorted=True to ensure deterministic expert selection
-    use_sorted = envs.VLLM_BATCH_INVARIANT
-    topk_indices = torch.topk(scores_for_choice, k=topk, dim=-1, sorted=use_sorted)[1]
+    if hash_indices_table is not None:
+        topk_indices = hash_indices_table[input_tokens]
+    else:
+        use_sorted = envs.VLLM_BATCH_INVARIANT
+        topk_indices = torch.topk(scores_for_choice, k=topk, dim=-1, sorted=use_sorted)[
+            1
+        ]
     topk_weights = scores.gather(1, topk_indices)
     if renormalize:
         topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
-    return topk_weights.to(torch.float32), topk_indices.to(
+    topk_weights = topk_weights.to(torch.float32)
+    if routed_scaling_factor != 1.0:
+        topk_weights *= routed_scaling_factor
+    return topk_weights, topk_indices.to(
         torch.int32 if indices_type is None else indices_type
     )
 
@@ -176,12 +236,14 @@ def __init__(
         top_k: int,
         global_num_experts: int,
         eplb_state: EplbLayerState,
-        e_score_correction_bias: torch.Tensor,
-        scoring_func: str,
+        e_score_correction_bias: torch.Tensor | None = None,
         renormalize: bool = True,
         routed_scaling_factor: float = 1.0,
         enable_eplb: bool = False,
         indices_type_getter: Callable[[], torch.dtype | None] | None = None,
+        *,
+        scoring_func: str = "sigmoid",
+        hash_indices_table: torch.Tensor | None = None,
     ):
         super().__init__(
             top_k=top_k,
@@ -194,6 +256,8 @@ def __init__(
         self.renormalize = renormalize
         self.scoring_func = scoring_func
         self.routed_scaling_factor = routed_scaling_factor
+        self.scoring_func = scoring_func
+        self._hash_indices_table = hash_indices_table
 
     @property
     def routing_method_type(self) -> RoutingMethodType:
@@ -210,19 +274,23 @@ def _compute_routing(
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
         indices_type: torch.dtype | None,
+        *,
+        input_ids: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """Compute routing using fused top-k with bias."""
         topk_weights, topk_ids = fused_topk_bias(
             hidden_states=hidden_states,
             gating_output=router_logits,
-            e_score_correction_bias=self.e_score_correction_bias.data,
+            scoring_func=self.scoring_func,
+            e_score_correction_bias=self.e_score_correction_bias.data
+            if self.e_score_correction_bias is not None
+            else None,
             topk=self.top_k,
             renormalize=self.renormalize,
-            scoring_func=self.scoring_func,
             indices_type=indices_type,
+            input_tokens=input_ids,
+            hash_indices_table=self._hash_indices_table,
+            routed_scaling_factor=self.routed_scaling_factor,
         )
 
-        if self.routed_scaling_factor != 1.0:
-            topk_weights *= self.routed_scaling_factor
-
         return topk_weights, topk_ids
diff --git a/vllm/model_executor/layers/fused_moe/router/fused_topk_router.py b/vllm/model_executor/layers/fused_moe/router/fused_topk_router.py
index 01376e6b16b5..45311dba08e3 100644
--- a/vllm/model_executor/layers/fused_moe/router/fused_topk_router.py
+++ b/vllm/model_executor/layers/fused_moe/router/fused_topk_router.py
@@ -151,6 +151,8 @@ def _compute_routing(
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
         indices_type: torch.dtype | None,
+        *,
+        input_ids: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """Compute routing using standard fused top-k."""
         topk_weights, topk_ids, token_expert_indices = fused_topk(
diff --git a/vllm/model_executor/layers/fused_moe/router/gate_linear.py b/vllm/model_executor/layers/fused_moe/router/gate_linear.py
index 77d8e756026d..a868c9c8487e 100644
--- a/vllm/model_executor/layers/fused_moe/router/gate_linear.py
+++ b/vllm/model_executor/layers/fused_moe/router/gate_linear.py
@@ -105,7 +105,7 @@ def forward(
 
         # Tier 2: cuBLAS bf16→fp32
         if self.allow_cublas_router_gemm and x.dtype == torch.bfloat16:
-            output = ops.router_gemm_bf16_fp32(x, self.weight)
+            output = torch.mm(x, self.weight.T, out_dtype=torch.float32)
             return output, None
 
         # Tier 3: F.linear (ReplicatedLinear)
diff --git a/vllm/model_executor/layers/fused_moe/router/grouped_topk_router.py b/vllm/model_executor/layers/fused_moe/router/grouped_topk_router.py
index 1bf141d81e4b..37d812a24bbe 100644
--- a/vllm/model_executor/layers/fused_moe/router/grouped_topk_router.py
+++ b/vllm/model_executor/layers/fused_moe/router/grouped_topk_router.py
@@ -14,7 +14,7 @@
     RoutingMethodType,
     get_routing_method_type,
 )
-from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+from vllm.model_executor.layers.fused_moe.experts.rocm_aiter_moe import (
     rocm_aiter_grouped_topk,
 )
 from vllm.model_executor.layers.fused_moe.router.base_router import BaseRouter
@@ -292,6 +292,8 @@ def _compute_routing(
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
         indices_type: torch.dtype | None,
+        *,
+        input_ids: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """Compute routing using grouped top-k."""
 
@@ -308,6 +310,7 @@ def valid_grouping() -> bool:
                 topk_weights, topk_ids = fused_topk_bias(
                     hidden_states=hidden_states,
                     gating_output=router_logits,
+                    scoring_func=self.scoring_func,
                     e_score_correction_bias=self.e_score_correction_bias.data,
                     topk=self.top_k,
                     renormalize=self.renormalize,
diff --git a/vllm/model_executor/layers/fused_moe/router/router_factory.py b/vllm/model_executor/layers/fused_moe/router/router_factory.py
index 42d418d7e537..2f2c4d39c460 100644
--- a/vllm/model_executor/layers/fused_moe/router/router_factory.py
+++ b/vllm/model_executor/layers/fused_moe/router/router_factory.py
@@ -5,8 +5,14 @@
 import torch
 
 import vllm.envs as envs
+from vllm._aiter_ops import rocm_aiter_ops
 from vllm.distributed.eplb.eplb_state import EplbLayerState
-from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
+from vllm.model_executor.layers.fused_moe.config import (
+    RoutingMethodType,
+)
+from vllm.model_executor.layers.fused_moe.router.aiter_shared_routed_fused_moe_router import (  # noqa: E501
+    AiterSharedRoutedFusedMoERouter,
+)
 from vllm.model_executor.layers.fused_moe.router.custom_routing_router import (
     CustomRoutingRouter,
 )
@@ -55,6 +61,7 @@ def create_fused_moe_router(
     # zero expert parameters
     zero_expert_type: str | None = None,
     num_logical_experts: int | None = None,
+    hash_indices_table: torch.Tensor | None = None,
 ) -> FusedMoERouter:
     """
     Factory function to create the appropriate FusedMoERouter subclass based on
@@ -66,7 +73,8 @@ def create_fused_moe_router(
     3. GroupedTopKRouter - if use_grouped_topk is True
     4. CustomRoutingRouter - if custom_routing_function is not None
     5. FusedTopKBiasRouter - if e_score_correction_bias is not None
-    6. FusedTopKRouter - default fallback
+    6. AiterSharedRoutedFusedMoERouter - if num_fused_shared_experts > 0
+    7. FusedTopKRouter - default fallback
 
     Common arguments:
         top_k: Number of experts to select per token
@@ -99,6 +107,9 @@ def create_fused_moe_router(
         num_logical_experts: Number of real (non-zero) experts. Required when
             zero_expert_type is not None.
 
+    Hash Indices Table:
+        Used to map input_ids to experts, need for Deepseek V4
+
     Returns:
         An instance of the appropriate FusedMoERouter subclass
     """
@@ -179,17 +190,36 @@ def create_fused_moe_router(
             indices_type_getter=indices_type_getter,
         )
 
-    if e_score_correction_bias is not None:
+    assert scoring_func in ["sigmoid", "softmax", "sqrtsoftplus"]
+
+    if e_score_correction_bias is not None or hash_indices_table is not None:
         return FusedTopKBiasRouter(
             top_k=top_k,
             global_num_experts=global_num_experts,
             eplb_state=eplb_state,
             e_score_correction_bias=e_score_correction_bias,
-            scoring_func=scoring_func,
             renormalize=renormalize,
             routed_scaling_factor=routed_scaling_factor,
             enable_eplb=enable_eplb,
             indices_type_getter=indices_type_getter,
+            scoring_func=scoring_func,
+            hash_indices_table=hash_indices_table,
+        )
+
+    if (
+        num_fused_shared_experts > 0
+        and scoring_func == "softmax"
+        and rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
+    ):
+        return AiterSharedRoutedFusedMoERouter(
+            top_k=top_k,
+            global_num_experts=global_num_experts,
+            eplb_state=eplb_state,
+            num_fused_shared_experts=num_fused_shared_experts,
+            renormalize=renormalize,
+            scoring_func=scoring_func,
+            enable_eplb=enable_eplb,
+            indices_type_getter=indices_type_getter,
         )
 
     return FusedTopKRouter(
diff --git a/vllm/model_executor/layers/fused_moe/router/routing_simulator_router.py b/vllm/model_executor/layers/fused_moe/router/routing_simulator_router.py
index f8e46371841a..8fb36b72cb70 100644
--- a/vllm/model_executor/layers/fused_moe/router/routing_simulator_router.py
+++ b/vllm/model_executor/layers/fused_moe/router/routing_simulator_router.py
@@ -334,6 +334,8 @@ def _compute_routing(
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
         indices_type: torch.dtype | None,
+        *,
+        input_ids: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """Use routing simulator to compute routing."""
         routing_strategy = envs.VLLM_MOE_ROUTING_SIMULATION_STRATEGY
diff --git a/vllm/model_executor/layers/fused_moe/router/zero_expert_router.py b/vllm/model_executor/layers/fused_moe/router/zero_expert_router.py
index c87070bc5acf..65760727770a 100644
--- a/vllm/model_executor/layers/fused_moe/router/zero_expert_router.py
+++ b/vllm/model_executor/layers/fused_moe/router/zero_expert_router.py
@@ -72,6 +72,8 @@ def _compute_routing(
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
         indices_type: torch.dtype | None,
+        *,
+        input_ids: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """Compute routing with full bias, compute zero expert output,
         mask zero expert IDs."""
diff --git a/vllm/model_executor/layers/fused_moe/runner/default_moe_runner.py b/vllm/model_executor/layers/fused_moe/runner/default_moe_runner.py
deleted file mode 100644
index 85c6563c0849..000000000000
--- a/vllm/model_executor/layers/fused_moe/runner/default_moe_runner.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import torch
-
-from vllm.distributed import (
-    get_ep_group,
-    get_pcp_group,
-)
-from vllm.model_executor.layers.fused_moe.runner.moe_runner_base import MoERunnerBase
-
-
-class DefaultMoERunner(MoERunnerBase):
-    """
-    Standard MoE runner implementation for executing Mixture of Experts layers.
-
-    This is the primary concrete implementation of MoE execution logic, providing
-    comprehensive support for standard MoE operations. It handles:
-    - Expert routing and token dispatching using various routing strategies
-    - Shared experts computation with optional parallel execution using CUDA streams
-    - Tensor model parallel and expert parallel operations
-    - Multiple quantization methods and optimized kernel selection
-    - Both monolithic and decomposed expert execution paths
-    - Integration with various parallel execution modes (TP, EP, DP)
-
-    The runner orchestrates the complete MoE forward pass including routing tokens
-    to experts, executing expert computations in parallel, and combining results.
-    It supports advanced features like overlapped execution of shared experts,
-    optimized kernels for different parallel configurations, and seamless
-    integration with vLLM's distributed execution framework.
-
-    This implementation is suitable for most standard MoE use cases. For specialized
-    scenarios like large batch chunking, alternative runners like ChunkingMoERunner
-    may be more appropriate.
-
-    Eventually, this class may be split into more specialized implementations
-    for different configurations (e.g., with/without shared experts, gates, etc.).
-    """
-
-    @property
-    def reduce_results(self) -> bool:
-        return self._reduce_results
-
-    @property
-    def do_naive_dispatch_combine(self) -> bool:
-        return (
-            self.moe_config.dp_size > 1 and not self.quant_method.supports_internal_mk
-        )
-
-    def _maybe_dispatch(
-        self,
-        layer: torch.nn.Module,
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        # For naive dispatch/combine Dp/Ep, dispatch the hidden states and
-        # router logits to all experts.
-        # NOTE: this will be removed once all kernels are migrated into the
-        # MoEKernel framework.
-        if self.do_naive_dispatch_combine:
-            hidden_states, router_logits = get_ep_group().dispatch_router_logits(
-                hidden_states,
-                router_logits,
-                self.moe_config.is_sequence_parallel,
-            )
-
-        # NOTE: Similar with DP, PCP also needs dispatch and combine. For
-        # simplicity, AgRsAll2All was added separately for PCP here. Maybe
-        # we should modify All2AllManager abstraction to better support PCP.
-        if self.moe_config.pcp_size > 1:
-            hidden_states = get_pcp_group().all_gather(
-                hidden_states,
-                dim=0,
-            )
-            router_logits = get_pcp_group().all_gather(
-                router_logits,
-                dim=0,
-            )
-
-        return hidden_states, router_logits
-
-    def _maybe_combine(
-        self,
-        shared_output: torch.Tensor | None,
-        hidden_states: torch.Tensor,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor | None]:
-        if self.do_naive_dispatch_combine:
-            hidden_states = get_ep_group().combine(
-                hidden_states, self.moe_config.is_sequence_parallel
-            )
-
-        if self.moe_config.pcp_size > 1:
-            hidden_states = get_pcp_group().reduce_scatter(
-                hidden_states,
-                dim=0,
-            )
-
-        if self.shared_experts is not None:
-            assert shared_output is not None
-            return shared_output, hidden_states
-        else:
-            return hidden_states
-
-    def _forward_impl(
-        self,
-        layer: torch.nn.Module,
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-        shared_experts_input: torch.Tensor | None,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        # TODO(bnell): parts of the dispatch/combine steps will go away once
-        # #32567 lands and the remaining kernels are made MKs.  The PCP
-        # code will probably remain
-        hidden_states, router_logits = self._maybe_dispatch(
-            layer,
-            hidden_states,
-            router_logits,
-        )
-
-        shared_output, hidden_states = self._apply_quant_method(
-            layer=layer,
-            hidden_states=hidden_states,
-            router_logits=router_logits,
-            shared_experts_input=shared_experts_input,
-        )
-
-        return self._maybe_combine(
-            shared_output,
-            hidden_states,
-        )
diff --git a/vllm/model_executor/layers/fused_moe/runner/moe_runner.py b/vllm/model_executor/layers/fused_moe/runner/moe_runner.py
index 9ffbf3108f8e..37b15ed40637 100644
--- a/vllm/model_executor/layers/fused_moe/runner/moe_runner.py
+++ b/vllm/model_executor/layers/fused_moe/runner/moe_runner.py
@@ -1,55 +1,806 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from abc import ABC, abstractmethod
+from collections.abc import Callable
+from contextlib import nullcontext
+from typing import TYPE_CHECKING
 
 import torch
+import torch.nn.functional as F
 
+from vllm.distributed import (
+    get_ep_group,
+    get_pcp_group,
+    tensor_model_parallel_all_reduce,
+)
+from vllm.forward_context import (
+    ForwardContext,
+    get_forward_context,
+    is_forward_context_available,
+)
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+)
 from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
     FusedMoEMethodBase,
 )
+from vllm.model_executor.layers.fused_moe.router.fused_moe_router import (
+    FusedMoERouter,
+)
+from vllm.model_executor.layers.fused_moe.router.zero_expert_router import (
+    ZeroExpertRouter,
+)
+from vllm.model_executor.layers.fused_moe.runner.moe_runner_interface import (
+    MoERunnerInterface,
+)
 from vllm.model_executor.layers.fused_moe.runner.shared_experts import (
     SharedExperts,
+    SharedExpertsOrder,
+)
+from vllm.platforms import current_platform
+from vllm.utils.torch_utils import (
+    _USE_LAYERNAME,
+    LayerName,
+    direct_register_custom_op,
+)
+
+
+def get_layer_from_name(layer_name: str) -> torch.nn.Module:
+    forward_context: ForwardContext = get_forward_context()
+    if not _USE_LAYERNAME and layer_name == "from_forward_context":
+        all_moe_layers = forward_context.all_moe_layers
+        assert all_moe_layers is not None
+        moe_layer_index = forward_context.moe_layer_index
+        if moe_layer_index >= len(all_moe_layers):
+            raise AssertionError(
+                "We expected the number of MOE layers in `all_moe_layers` "
+                "to be equal to the number of "
+                "{vllm.moe_forward, vllm.moe_forward_shared} calls."
+            )
+        layer_name = all_moe_layers[moe_layer_index]
+        forward_context.moe_layer_index += 1
+    return forward_context.no_compile_layers[layer_name]
+
+
+# On torch >= 2.11, layer_name is a hoisted LayerName opaque object;
+# on older versions it remains a plain str.
+if TYPE_CHECKING:
+    from typing import TypeAlias
+
+    _layer_name_type: TypeAlias = str | LayerName
+else:
+    _layer_name_type = LayerName if _USE_LAYERNAME else str
+
+
+@torch.compiler.assume_constant_result
+def _resolve_layer_name(layer_name: str | LayerName) -> str:
+    from torch._library.fake_class_registry import FakeScriptObject
+
+    if isinstance(layer_name, LayerName):
+        return layer_name.value
+    elif isinstance(layer_name, FakeScriptObject):
+        return layer_name.real_obj.value
+    return layer_name
+
+
+# Note: _moe_forward and _moe_forward_shared should not contain any
+# implementation details, They should merely pass along control to
+# the runner's '_forward_impl' method.
+# These functions should never be called directly since they do not
+# include all the functionality of the MoE layer.
+def _moe_forward(
+    hidden_states: torch.Tensor,
+    router_logits: torch.Tensor,
+    shared_experts_input: torch.Tensor | None,
+    input_ids: torch.Tensor | None,
+    layer_name: _layer_name_type,
+    hidden_dim_unpadded: int,
+) -> torch.Tensor:
+    layer = get_layer_from_name(_resolve_layer_name(layer_name))
+    return layer.runner._forward_impl(
+        layer,
+        hidden_states,
+        router_logits,
+        shared_experts_input,
+        input_ids,
+    )
+
+
+def _moe_forward_fake(
+    hidden_states: torch.Tensor,
+    router_logits: torch.Tensor,
+    shared_experts_input: torch.Tensor | None,
+    input_ids: torch.Tensor | None,
+    layer_name: _layer_name_type,
+    hidden_dim_unpadded: int,
+) -> torch.Tensor:
+    # `hidden_dim_unpadded > 0` only on the TRT-LLM MXFP4 path, where the
+    # real kernel writes narrower than `hidden_states.shape[-1]`. Plumbed
+    # as an op arg (not peeked from the layer registry) to keep the fake
+    # a pure shape function of its inputs and preserve subgraph dedup.
+    if hidden_dim_unpadded > 0:
+        return hidden_states.new_empty((*hidden_states.shape[:-1], hidden_dim_unpadded))
+    return torch.empty_like(hidden_states)
+
+
+def _moe_forward_shared(
+    hidden_states: torch.Tensor,
+    router_logits: torch.Tensor,
+    shared_experts_input: torch.Tensor | None,
+    input_ids: torch.Tensor | None,
+    layer_name: _layer_name_type,
+    hidden_dim_unpadded: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    layer = get_layer_from_name(_resolve_layer_name(layer_name))
+    return layer.runner._forward_impl(
+        layer,
+        hidden_states,
+        router_logits,
+        shared_experts_input,
+        input_ids,
+    )
+
+
+def _moe_forward_shared_fake(
+    hidden_states: torch.Tensor,
+    router_logits: torch.Tensor,
+    shared_experts_input: torch.Tensor | None,
+    input_ids: torch.Tensor | None,
+    layer_name: _layer_name_type,
+    hidden_dim_unpadded: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    # `fused_out`: see `_moe_forward_fake` for hidden_dim_unpadded semantics.
+    # `shared_out`: matches `shared_experts_input` if provided (latent MoE),
+    # else `hidden_states`.
+    if hidden_dim_unpadded > 0:
+        fused_out = hidden_states.new_empty(
+            (*hidden_states.shape[:-1], hidden_dim_unpadded)
+        )
+    else:
+        fused_out = torch.empty_like(hidden_states)
+    if shared_experts_input is not None:
+        shared_out = torch.empty_like(shared_experts_input)
+    else:
+        shared_out = torch.empty_like(hidden_states)
+    return shared_out, fused_out
+
+
+direct_register_custom_op(
+    op_name="moe_forward",
+    op_func=_moe_forward,
+    mutates_args=["hidden_states"],
+    fake_impl=_moe_forward_fake,
+    tags=(torch.Tag.needs_fixed_stride_order,),
+)
+
+
+direct_register_custom_op(
+    op_name="moe_forward_shared",
+    op_func=_moe_forward_shared,
+    fake_impl=_moe_forward_shared_fake,
+    tags=(torch.Tag.needs_fixed_stride_order,),
 )
 
 
-class MoERunner(ABC):
+def _unpack(
+    result: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
+) -> tuple[torch.Tensor | None, torch.Tensor]:
+    if isinstance(result, tuple):
+        return result
+    else:
+        return (None, result)
+
+
+class MoERunner(MoERunnerInterface):
     """
-    Abstract base class for Mixture of Experts (MoE) runners.
+    Standard MoE runner implementation for executing Mixture of Experts layers.
+
+    This is the primary concrete implementation of MoE execution logic, providing
+    comprehensive support for standard MoE operations. It handles:
+    - Expert routing and token dispatching using various routing strategies
+    - Shared experts computation with optional parallel execution using CUDA streams
+    - Tensor model parallel and expert parallel operations
+    - Multiple quantization methods and optimized kernel selection
+    - Both monolithic and decomposed expert execution paths
+    - Integration with various parallel execution modes (TP, EP, DP)
 
-    This class defines the interface that all MoE runner implementations must follow.
-    MoE runners are responsible for executing the forward pass of MoE layers, handling
-    expert routing, and managing tensor parallel operations.
+    The runner orchestrates the complete MoE forward pass including routing tokens
+    to experts, executing expert computations in parallel, and combining results.
+    It supports advanced features like overlapped execution of shared experts,
+    optimized kernels for different parallel configurations, and seamless
+    integration with vLLM's distributed execution framework.
+
+    Eventually, this class may be split into more specialized implementations
+    for different configurations (e.g., with/without shared experts, gates, etc.).
     """
 
-    @abstractmethod
-    def forward(
+    def __init__(
+        self,
+        layer_name: str,
+        moe_config: FusedMoEConfig,
+        router: FusedMoERouter,
+        routed_input_transform: torch.nn.Module | None,
+        gate: torch.nn.Module | None,
+        shared_experts: torch.nn.Module | None,
+        quant_method: FusedMoEMethodBase,
+        enable_dbo: bool,
+        shared_expert_gate: torch.nn.Module | None = None,
+        routed_output_transform: torch.nn.Module | None = None,
+        routed_scaling_factor: float = 1.0,
+    ):
+        super().__init__()
+        self.moe_config = moe_config
+        self.router = router
+        self.routed_input_transform = routed_input_transform
+        self.routed_output_transform = routed_output_transform
+        self.routed_scaling_factor = routed_scaling_factor
+        self.gate = gate
+        self.shared_expert_gate = shared_expert_gate
+        self._quant_method = quant_method
+        self.enable_dbo = enable_dbo
+
+        # When both gates are present and FSE is enabled, fuse their
+        # weight matrices into [num_experts + num_shared, hidden] so one
+        # F.linear produces combined logits. The topk kernel can then
+        # apply routing softmax and shared expert activation (sigmoid)
+        # in a single launch.
+        self._fse_fuse_gate = gate is not None and shared_expert_gate is not None
+        self._combined_gate_weight: torch.Tensor | None = None
+
+        self._shared_experts: SharedExperts | None = None
+        if shared_experts is not None:
+            self._shared_experts = SharedExperts(
+                shared_experts,
+                moe_config=moe_config,
+                # Note: For now we must pass quant_method along to SharedExperts so it
+                # can property determine where the shared experts are supposed to be
+                # called, i.e. by a MK or by the MoERunner.
+                # Once the MK can be created upfront, we can just pass in the proper
+                # flags derived from the quant_method's MK.
+                quant_method=quant_method,
+                enable_dbo=enable_dbo,
+            )
+
+        # Needed for string -> FusedMoE layer lookup in custom ops.
+        self.layer_name = layer_name
+
+        self._forward_entry = self._select_forward()
+
+    def _select_forward(self) -> Callable:
+        if current_platform.is_tpu() or current_platform.is_cpu():
+            # TODO: Once the OOM issue for the TPU backend is resolved, we
+            # will switch to using the moe_forward custom op.
+            # Note: CPU doesn't require wrapped _forward_impl.
+            return _moe_forward if self._shared_experts is None else _moe_forward_shared
+
+        return (
+            torch.ops.vllm.moe_forward
+            if self._shared_experts is None
+            else torch.ops.vllm.moe_forward_shared
+        )
+
+    @property
+    def shared_experts(self) -> SharedExperts | None:
+        return self._shared_experts
+
+    # TODO(bnell): temporary hack, do not call this method.
+    def _replace_quant_method(self, quant_method: FusedMoEMethodBase):
+        if self._shared_experts is not None:
+            self._shared_experts._quant_method = quant_method
+        self._quant_method = quant_method
+
+    def _maybe_fuse_gate_weights(self):
+        """Fuse router and shared expert gate weights on first call.
+
+        Cannot be done at __init__ because gate weights are loaded after
+        module construction (via weight_loader). Called once from
+        _forward_impl before the first forward pass.
+        """
+        if self._combined_gate_weight is None:
+            assert self.gate is not None and self.shared_expert_gate is not None
+            self._combined_gate_weight = torch.cat(
+                [self.gate.weight, self.shared_expert_gate.weight],
+                dim=0,
+            )
+
+    def is_internal_router(self) -> bool:
+        return self.gate is not None
+
+    def apply_routed_input_transform(
+        self, hidden_states: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """Apply transform for routed experts (e.g., latent projection).
+
+        This is called by FusedMoE.forward_native. The original hidden_states
+        is saved separately so shared experts get [S, hidden_size] while
+        routed experts get the transformed [S, moe_latent_size].
+
+        Returns (possibly transformed) hidden states and the input for shared
+        experts (or None if there are no shared experts).
+        """
+        if self.routed_input_transform is not None:
+            result = self.routed_input_transform(hidden_states)
+            # ReplicatedLinear returns (output, extra_bias) tuple.
+            # We only need the output tensor; extra_bias is not used here.
+            if isinstance(result, tuple):
+                return result[0], hidden_states
+            return result, hidden_states
+
+        return (
+            hidden_states,
+            hidden_states if self._shared_experts is not None else None,
+        )
+
+    def apply_routed_output_transform(
+        self,
+        fused_output: torch.Tensor,
+    ) -> torch.Tensor:
+        """Apply transform to routed expert output (e.g., latent to full dim).
+
+        Used by latent MoE models (e.g., NemotronH) where routed experts
+        operate in a compressed latent space and need projection back to
+        the full hidden dimension before combining with shared expert output.
+        """
+        if self.routed_output_transform is not None:
+            r = self.routed_output_transform(fused_output)
+            fused_output = r[0] if isinstance(r, tuple) else r
+        return fused_output
+
+    def _maybe_apply_routed_scale_to_output(
+        self,
+        shared_output: torch.Tensor | None,
+        fused_output: torch.Tensor,
+    ) -> tuple[torch.Tensor | None, torch.Tensor]:
+        """Apply routed_scaling_factor to the output with FP16 overflow
+        protection.
+
+        Scale the fused expert output by routed_scaling_factor. For FP16,
+        avoid overflow by dividing shared_output by the scale instead
+        (the decoder layer compensates with matching divisions).
+        """
+        if self.routed_scaling_factor != 1.0:
+            if fused_output.dtype != torch.float16 or shared_output is None:
+                fused_output *= self.routed_scaling_factor
+            elif shared_output is not None:
+                shared_output *= 1.0 / self.routed_scaling_factor
+        return shared_output, fused_output
+
+    @property
+    def _fused_output_is_reduced(self) -> bool:
+        return (
+            self._quant_method.moe_kernel is not None
+            and self._quant_method.moe_kernel.output_is_reduced()
+        )
+
+    def _maybe_reduce_shared_expert_output(
+        self,
+        shared_output: torch.Tensor | None,
+    ) -> torch.Tensor | None:
+        """All-reduce shared expert output when the combine kernel already
+        reduced fused output.
+
+        * If the combine kernel does the reduction for fused_output, reduce
+          shared_output separately. O.w, reduce fused_output+shared_output later.
+        * If we have SP (TP=N, DP=M, EP), there is a separate AG step handled
+          in the model.
+        """
+        if (
+            shared_output is not None
+            and not self.moe_config.is_sequence_parallel
+            and self._fused_output_is_reduced
+        ):
+            shared_output = tensor_model_parallel_all_reduce(shared_output)
+        return shared_output
+
+    def _maybe_reduce_final_output(
+        self,
+        states: torch.Tensor,
+        trunc_size: int,
+    ) -> torch.Tensor:
+        """Truncate padded dimensions and all-reduce the combined output.
+
+        This is the "late" all-reduce path. When neither fused nor shared
+        output was individually reduced, the combined sum is all-reduced
+        here. Skipped when sequence-parallel is active (SP handles its
+        own reduction) or when the early path already reduced both outputs.
+        """
+        # We don't need to reduce the final output if:
+        # - We are not running with TP or DP
+        # - The MK already reduced the fused output itself.
+        if (
+            not self.moe_config.is_sequence_parallel
+            and (self.moe_config.tp_size > 1 or self.moe_config.ep_size > 1)
+            and not self._fused_output_is_reduced
+        ):
+            states = tensor_model_parallel_all_reduce(states)
+
+        return states[..., :trunc_size]
+
+    def _encode_layer_name(self) -> str | LayerName:
+        if _USE_LAYERNAME:
+            return LayerName(self.layer_name)
+        # Can be unavailable or None in unittests
+        if (
+            is_forward_context_available()
+            and get_forward_context().all_moe_layers is not None
+        ):
+            return "from_forward_context"
+        return self.layer_name
+
+    def _trtllm_mxfp4_unpadded_dim(self) -> int:
+        """Return ``hidden_dim_unpadded`` when the active backend is TRT-LLM
+        MXFP4 (whose kernel writes narrower than the padded
+        ``hidden_states.shape[-1]``), else 0. Other MXFP4 backends (notably
+        Cutlass MXFP4 MXFP8) write the full padded width, so
+        ``moe_config.hidden_dim_unpadded`` alone is insufficient: it encodes
+        the model's logical hidden, not whether the kernel narrows. Computed
+        caller-side and passed as an op arg; doing the isinstance check
+        inside the fake would specialize per ``layer_name`` and break
+        subgraph dedup for identical-architecture models (e.g. Phi-MoE).
+        """
+        from vllm.model_executor.layers.fused_moe.experts.trtllm_mxfp4_moe import (
+            TrtLlmMxfp4ExpertsBase,
+        )
+
+        moe_kernel = getattr(self._quant_method, "moe_kernel", None)
+        fused_experts = getattr(
+            getattr(moe_kernel, "impl", None), "fused_experts", None
+        )
+        if isinstance(fused_experts, TrtLlmMxfp4ExpertsBase):
+            return self.moe_config.hidden_dim_unpadded or self.moe_config.hidden_dim
+        return 0
+
+    def _maybe_pad_hidden_states(
+        self,
+        shared_experts_input: torch.Tensor | None,
+        hidden_states: torch.Tensor,
+    ) -> tuple[torch.Tensor, int]:
+        """Pad hidden_states to moe_config.hidden_dim and compute the
+        original dimension for later truncation.
+
+        For latent MoE, the routed hidden_states may be smaller than
+        hidden_dim. Padding ensures uniform tensor sizes through the
+        fused MoE kernel. The returned trunc_size is used by
+        _maybe_reduce_final_output to strip the padding from the result.
+        """
+        shared_experts_hidden_dim = (
+            shared_experts_input.shape[-1] if shared_experts_input is not None else 0
+        )
+        transformed_hidden_dim = hidden_states.shape[-1]
+        if (
+            not self._quant_method.skip_forward_padding
+            and self.moe_config.hidden_dim != transformed_hidden_dim
+        ):
+            hidden_states = F.pad(
+                hidden_states,
+                (0, self.moe_config.hidden_dim - transformed_hidden_dim),
+                mode="constant",
+                value=0.0,
+            )
+
+        if self.routed_output_transform is not None and shared_experts_hidden_dim > 0:
+            orig_hidden_dims = shared_experts_hidden_dim
+        else:
+            orig_hidden_dims = transformed_hidden_dim
+
+        return hidden_states, orig_hidden_dims
+
+    def _maybe_apply_shared_experts(
         self,
+        shared_experts_input: torch.Tensor | None,
+        order: SharedExpertsOrder,
+    ):
+        if self._shared_experts is not None:
+            assert shared_experts_input is not None
+            self._shared_experts.apply(shared_experts_input, order)
+
+    def _apply_quant_method(
+        self,
+        layer: torch.nn.Module,
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        raise NotImplementedError
+        shared_experts_input: torch.Tensor | None,
+        input_ids: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor | None, torch.Tensor]:
+        """Run expert routing and the fused MoE kernel via the quant method.
+
+        Orchestrates shared expert execution (before/after), expert selection
+        via the router, and the actual fused MoE computation. Returns
+        (shared_expert_output, fused_expert_output).
+        """
+        self._maybe_apply_shared_experts(
+            shared_experts_input, SharedExpertsOrder.NO_OVERLAP
+        )
+
+        # Get routing replay buffer from persistent layer attribute
+        # (set by bind_routing_capture_to_model during capturer init)
+        routing_replay_out = getattr(layer, "_routing_replay_out", None)
+
+        if self._quant_method.is_monolithic:
+            fused_out = self._quant_method.apply_monolithic(
+                layer=layer,
+                x=hidden_states,
+                router_logits=router_logits,
+                input_ids=input_ids,
+            )
+        else:
+            topk_weights, topk_ids = self.router.select_experts(
+                hidden_states=hidden_states,
+                router_logits=router_logits,
+                input_ids=input_ids,
+            )
+
+            # Write routing data for non-monolithic path (Triton, etc.)
+            if routing_replay_out is not None:
+                routing_replay_out[: topk_ids.shape[0]].copy_(topk_ids.to(torch.int16))
 
-    @abstractmethod
-    def must_reduce_shared_expert_outputs(self) -> bool:
-        raise NotImplementedError
+            # Passing shared_experts_input in case SharedExpertsOrder is
+            # MK_INTERNAL_OVERLAPPED.
+            fused_out = self._quant_method.apply(
+                layer=layer,
+                x=hidden_states,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                shared_experts_input=shared_experts_input,
+            )
 
-    @abstractmethod
-    def maybe_all_reduce_tensor_model_parallel(
+        self._maybe_apply_shared_experts(
+            shared_experts_input,
+            SharedExpertsOrder.MULTI_STREAM_OVERLAPPED,
+        )
+
+        return (
+            self._shared_experts.output if self._shared_experts is not None else None,
+            fused_out,
+        )
+
+    def _sequence_parallel_context(self):
+        """Return a context manager for sequence-parallel token
+        redistribution.
+
+        When sequence parallelism is active, returns a context that handles
+        local size tracking for proper token scatter/gather. Otherwise
+        returns a no-op context.
+        """
+        ctx = get_forward_context()
+        return (
+            ctx.dp_metadata.sp_local_sizes(self.moe_config.sp_size)
+            if ctx.dp_metadata
+            else nullcontext()
+        )
+
+    def _maybe_sync_shared_experts_stream(
         self,
-        final_hidden_states: torch.Tensor,
+        shared_experts_input: torch.Tensor | None,
     ):
-        raise NotImplementedError
+        # If router/gate provided, then apply it here.
+        # (Note: This code runs only when "overlapped mode" is on to allow
+        #        parallel execution of shared experts with the FusedMoE via
+        #        separate cuda stream)
+        if self._shared_experts is not None:
+            assert shared_experts_input is not None
+            self._shared_experts.maybe_sync_shared_experts_stream(shared_experts_input)
 
-    @abstractmethod
-    def is_internal_router(self) -> bool:
-        raise NotImplementedError
+    def _maybe_add_zero_expert_output(
+        self,
+        result: torch.Tensor,
+    ) -> torch.Tensor:
+        """Add the zero expert's contribution to the final result.
+
+        When a ZeroExpertRouter is used, it computes a bias-like output
+        from the "zero expert" that is added to the combined routed+shared
+        expert output.
+        """
+        if isinstance(self.router, ZeroExpertRouter):
+            zero_expert_output = self.router.zero_expert_output
+            assert zero_expert_output is not None
+            result = result + zero_expert_output
+        return result
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """Invoke the fused moe layer.
+
+        Input:
+        - hidden_states
+        - router_logits
+
+        Output:
+        - The new hidden_states.
+
+        Calling sequence
+        - forward
+          - self._forward_entry (_moe_forward or _moe_forward_shared custom op)
+            - _forward_impl
+
+        Note: The existence of _moe_forward and _moe_forward_shared custom ops are due
+        to the following reason:
+        1. pytorch cannot handle union types in custom op signatures so
+           _moe_forward and _moe_forward_shared must be split.
+        """
+
+        # Apply transform for routed experts (e.g., latent projection
+        # for latent MoE)
+        hidden_states, shared_experts_input = self.apply_routed_input_transform(
+            hidden_states
+        )
+
+        # Record before `_maybe_pad_hidden_states` pads activations to match
+        # `moe_config.hidden_dim`, e.g. after `align_trtllm_fp4_moe_hidden_dim_for_fi`
+        # so routed output can be trimmed before
+        # shared+routed add / latent up proj if needed.
+        routed_hidden_dim = hidden_states.shape[-1]
+        hidden_states, og_hidden_dim = self._maybe_pad_hidden_states(
+            shared_experts_input,
+            hidden_states,
+        )
+        hidden_dim_was_padded = hidden_states.shape[-1] > routed_hidden_dim
+
+        result = self._forward_entry(
+            hidden_states,
+            router_logits,
+            shared_experts_input,
+            input_ids,
+            self._encode_layer_name(),
+            self._trtllm_mxfp4_unpadded_dim(),
+        )
+
+        #
+        # Note: there are two all-reduce points below. They are mutually
+        # exclusive, controlled by _fused_output_is_reduced
+        #  - When True: the combine kernel already reduced fused_output,
+        #    so we reduce shared_output here to match, then skip the
+        #    all-reduce in _maybe_reduce_final_output.
+        #  - When False: neither output is reduced yet, so we combine
+        #    them first and all-reduce the sum in _maybe_reduce_final_output.
+
+        # Extract outputs from result
+        shared_output, fused_output = _unpack(result)
+        if (
+            shared_output is not None or self.routed_output_transform is not None
+        ) and hidden_dim_was_padded:
+            fused_output = fused_output[..., :routed_hidden_dim]
+
+        # If combine kernel already reduced fused, reduce shared to match.
+        # See note above re: the two all-reduce points.
+        shared_output = self._maybe_reduce_shared_expert_output(shared_output)
+
+        shared_output, fused_output = self._maybe_apply_routed_scale_to_output(
+            shared_output, fused_output
+        )
+
+        # Apply output transform (e.g. latent -> full dim)
+        fused_output = self.apply_routed_output_transform(fused_output)
+
+        if shared_output is not None:
+            result = shared_output + fused_output
+        else:
+            result = fused_output
+
+        result = self._maybe_reduce_final_output(result, og_hidden_dim)
+
+        return self._maybe_add_zero_expert_output(result)
 
     @property
-    @abstractmethod
-    def shared_experts(self) -> SharedExperts | None:
-        raise NotImplementedError
+    def do_naive_dispatch_combine(self) -> bool:
+        return (
+            self.moe_config.dp_size > 1 and not self._quant_method.supports_internal_mk
+        )
 
-    # TODO(bnell): temporary hack, do not call this method.
-    @abstractmethod
-    def _replace_quant_method(self, quant_method: FusedMoEMethodBase):
-        raise NotImplementedError
+    def _maybe_dispatch(
+        self,
+        layer: torch.nn.Module,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # For naive dispatch/combine Dp/Ep, dispatch the hidden states and
+        # router logits to all experts.
+        # NOTE: this will be removed once all kernels are migrated into the
+        # MoEKernel framework.
+        if self.do_naive_dispatch_combine:
+            result = get_ep_group().dispatch_router_logits(
+                hidden_states,
+                router_logits,
+                self.moe_config.is_sequence_parallel,
+            )
+            assert len(result) == 2
+            hidden_states, router_logits = result
+
+        # NOTE: Similar with DP, PCP also needs dispatch and combine. For
+        # simplicity, AgRsAll2All was added separately for PCP here. Maybe
+        # we should modify All2AllManager abstraction to better support PCP.
+        if self.moe_config.pcp_size > 1:
+            hidden_states = get_pcp_group().all_gather(
+                hidden_states,
+                dim=0,
+            )
+            router_logits = get_pcp_group().all_gather(
+                router_logits,
+                dim=0,
+            )
+
+        return hidden_states, router_logits
+
+    def _maybe_combine(
+        self,
+        shared_output: torch.Tensor | None,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor | None]:
+        if self.do_naive_dispatch_combine:
+            hidden_states = get_ep_group().combine(
+                hidden_states, self.moe_config.is_sequence_parallel
+            )
+
+        if self.moe_config.pcp_size > 1:
+            hidden_states = get_pcp_group().reduce_scatter(
+                hidden_states,
+                dim=0,
+            )
+
+        if self.shared_experts is not None:
+            assert shared_output is not None
+            return shared_output, hidden_states
+        else:
+            return hidden_states
+
+    def _forward_impl(
+        self,
+        layer: torch.nn.Module,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+        shared_experts_input: torch.Tensor | None,
+        input_ids: torch.Tensor | None = None,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        """Entry point called by the custom op to run the MoE computation.
+
+        Handles pre-dispatch setup (gate application, external shared expert
+        triggering, quant config init) then performs the following steps
+        within the sequence-parallel context.
+
+        - Performs expert routing
+        - fused MoE kernel execution
+        - shared expert computation.
+
+        Returns a single tensor of combined fused and shared output (if present).
+        """
+        # TODO(bnell): this can be removed after MK migration is complete.
+        layer.ensure_moe_quant_config_init()
+
+        # Sync aux and main stream for shared expert multi-stream overlap.
+        self._maybe_sync_shared_experts_stream(shared_experts_input)
+
+        # If the Runner holds the gate, apply it after the stream sync,
+        # so it can run overlapped with the
+        # NOTE: in future PR, MoE runner will always hold the gate.
+        if self.gate is not None:
+            if self._fse_fuse_gate:
+                self._maybe_fuse_gate_weights()
+                router_logits = F.linear(hidden_states, self._combined_gate_weight)
+            else:
+                router_logits, _ = self.gate(hidden_states)
+
+        with self._sequence_parallel_context():
+            # TODO(bnell): parts of the dispatch/combine steps will go away once
+            # #32567 lands and the remaining kernels are made MKs.  The PCP
+            # code will probably remain
+            hidden_states, router_logits = self._maybe_dispatch(
+                layer,
+                hidden_states,
+                router_logits,
+            )
+
+            shared_output, hidden_states = self._apply_quant_method(
+                layer=layer,
+                hidden_states=hidden_states,
+                router_logits=router_logits,
+                shared_experts_input=shared_experts_input,
+                input_ids=input_ids,
+            )
+
+            return self._maybe_combine(
+                shared_output,
+                hidden_states,
+            )
diff --git a/vllm/model_executor/layers/fused_moe/runner/moe_runner_base.py b/vllm/model_executor/layers/fused_moe/runner/moe_runner_base.py
deleted file mode 100644
index 692d45d34607..000000000000
--- a/vllm/model_executor/layers/fused_moe/runner/moe_runner_base.py
+++ /dev/null
@@ -1,552 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from abc import abstractmethod
-from collections.abc import Callable
-from contextlib import nullcontext
-from typing import TYPE_CHECKING
-
-import torch
-import torch.nn.functional as F
-
-from vllm.distributed import (
-    tensor_model_parallel_all_reduce,
-)
-from vllm.forward_context import (
-    ForwardContext,
-    get_forward_context,
-    is_forward_context_available,
-)
-from vllm.model_executor.layers.fused_moe.config import (
-    FusedMoEConfig,
-)
-from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
-    FusedMoEMethodBase,
-)
-from vllm.model_executor.layers.fused_moe.router.fused_moe_router import (
-    FusedMoERouter,
-)
-from vllm.model_executor.layers.fused_moe.router.zero_expert_router import (
-    ZeroExpertRouter,
-)
-from vllm.model_executor.layers.fused_moe.runner.moe_runner import MoERunner
-from vllm.model_executor.layers.fused_moe.runner.shared_experts import (
-    SharedExperts,
-    SharedExpertsOrder,
-)
-from vllm.platforms import current_platform
-from vllm.utils.torch_utils import (
-    _USE_LAYERNAME,
-    LayerName,
-    direct_register_custom_op,
-)
-
-
-def get_layer_from_name(layer_name: str) -> torch.nn.Module:
-    forward_context: ForwardContext = get_forward_context()
-    if not _USE_LAYERNAME and layer_name == "from_forward_context":
-        all_moe_layers = forward_context.all_moe_layers
-        assert all_moe_layers is not None
-        moe_layer_index = forward_context.moe_layer_index
-        if moe_layer_index >= len(all_moe_layers):
-            raise AssertionError(
-                "We expected the number of MOE layers in `all_moe_layers` "
-                "to be equal to the number of "
-                "{vllm.moe_forward, vllm.moe_forward_shared} calls."
-            )
-        layer_name = all_moe_layers[moe_layer_index]
-        forward_context.moe_layer_index += 1
-    return forward_context.no_compile_layers[layer_name]
-
-
-# On torch >= 2.11, layer_name is a hoisted LayerName opaque object;
-# on older versions it remains a plain str.
-if TYPE_CHECKING:
-    from typing import TypeAlias
-
-    _layer_name_type: TypeAlias = str | LayerName
-else:
-    _layer_name_type = LayerName if _USE_LAYERNAME else str
-
-
-@torch.compiler.assume_constant_result
-def _resolve_layer_name(layer_name: str | LayerName) -> str:
-    from torch._library.fake_class_registry import FakeScriptObject
-
-    if isinstance(layer_name, LayerName):
-        return layer_name.value
-    elif isinstance(layer_name, FakeScriptObject):
-        return layer_name.real_obj.value
-    return layer_name
-
-
-# Note: _moe_forward and _moe_forward_shared should not contain any
-# implementation details, They should merely pass along control to
-# the runner's 'forward_dispatch' method.
-def _moe_forward(
-    hidden_states: torch.Tensor,
-    router_logits: torch.Tensor,
-    shared_experts_input: torch.Tensor | None,
-    layer_name: _layer_name_type,
-) -> torch.Tensor:
-    layer = get_layer_from_name(_resolve_layer_name(layer_name))
-    return layer.runner.forward_dispatch(
-        layer,
-        hidden_states,
-        router_logits,
-        shared_experts_input,
-    )
-
-
-def _moe_forward_fake(
-    hidden_states: torch.Tensor,
-    router_logits: torch.Tensor,
-    shared_experts_input: torch.Tensor | None,
-    layer_name: _layer_name_type,
-) -> torch.Tensor:
-    return torch.empty_like(hidden_states)
-
-
-def _moe_forward_shared(
-    hidden_states: torch.Tensor,
-    router_logits: torch.Tensor,
-    shared_experts_input: torch.Tensor | None,
-    layer_name: _layer_name_type,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    layer = get_layer_from_name(_resolve_layer_name(layer_name))
-    return layer.runner.forward_dispatch(
-        layer,
-        hidden_states,
-        router_logits,
-        shared_experts_input,
-    )
-
-
-def _moe_forward_shared_fake(
-    hidden_states: torch.Tensor,
-    router_logits: torch.Tensor,
-    shared_experts_input: torch.Tensor | None,
-    layer_name: _layer_name_type,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    # Output shapes:
-    # - fused_out: same as hidden_states (routed experts use transformed size)
-    # - shared_out: same as shared_experts_input if provided, else same as
-    #               hidden_states
-    # (For latent MoE: shared experts use original hidden_size, not latent size)
-    fused_out = torch.empty_like(hidden_states)
-    if shared_experts_input is not None:
-        shared_out = torch.empty_like(shared_experts_input)
-    else:
-        shared_out = torch.empty_like(hidden_states)
-    return shared_out, fused_out
-
-
-direct_register_custom_op(
-    op_name="moe_forward",
-    op_func=_moe_forward,
-    mutates_args=["hidden_states"],  # is this still true?
-    fake_impl=_moe_forward_fake,
-    tags=(torch.Tag.needs_fixed_stride_order,),
-)
-
-
-direct_register_custom_op(
-    op_name="moe_forward_shared",
-    op_func=_moe_forward_shared,
-    fake_impl=_moe_forward_shared_fake,
-    tags=(torch.Tag.needs_fixed_stride_order,),
-)
-
-
-class MoERunnerBase(MoERunner):
-    """
-    Abstract base class providing common functionality for MoE runner implementations.
-
-    This class serves as the foundation for concrete MoE runner implementations by
-    providing shared state management and common utilities. It handles:
-    - Common initialization and configuration management
-    - Shared expert output reduction logic for tensor parallel scenarios
-    - Base methods for tensor model parallel reductions
-    - Common properties and utility functions used across different runner types
-
-    Concrete subclasses must implement the abstract methods to define their specific
-    execution strategies, such as standard execution, chunked processing, or other
-    specialized approaches. The base class provides the infrastructure while
-    allowing flexibility in the actual MoE computation implementation.
-
-    Key abstract methods that subclasses must implement:
-    - reduce_results: Determines whether results should be reduced across ranks
-    - _forward_impl: The core MoE computation logic specific to each runner type
-    """
-
-    def __init__(
-        self,
-        layer_name: str,
-        moe_config: FusedMoEConfig,
-        router: FusedMoERouter,
-        routed_input_transform: torch.nn.Module | None,
-        gate: torch.nn.Module | None,
-        shared_experts: torch.nn.Module | None,
-        quant_method: FusedMoEMethodBase,
-        reduce_results: bool,
-        enable_dbo: bool,
-    ):
-        super().__init__()
-        self.moe_config = moe_config
-        self.router = router
-        self.routed_input_transform = routed_input_transform
-        self.gate = gate
-        self.quant_method = quant_method
-        self._reduce_results = reduce_results
-        self.enable_dbo = enable_dbo
-
-        self._shared_experts: SharedExperts | None = None
-        if shared_experts is not None:
-            self._shared_experts = SharedExperts(
-                shared_experts,
-                moe_config=moe_config,
-                # Note: For now we must pass quant_method along to SharedExperts so it
-                # can property determine where the shared experts are supposed to be
-                # called, i.e. by a MK or by the MoERunner.
-                # Once the MK can be created upfront, we can just pass in the proper
-                # flags derived from the quant_method's MK.
-                reduce_results=reduce_results,
-                quant_method=quant_method,
-                enable_dbo=enable_dbo,
-            )
-
-        # Needed for string -> FusedMoE layer lookup in custom ops.
-        self.layer_name = layer_name
-
-        self.forward_entry = self._select_forward()
-
-    def _select_forward(self) -> Callable:
-        if current_platform.is_tpu() or current_platform.is_cpu():
-            # TODO: Once the OOM issue for the TPU backend is resolved, we
-            # will switch to using the moe_forward custom op.
-            # Note: CPU doesn't require wrapped _forward_impl.
-            return _moe_forward if self._shared_experts is None else _moe_forward_shared
-
-        return (
-            torch.ops.vllm.moe_forward
-            if self._shared_experts is None
-            else torch.ops.vllm.moe_forward_shared
-        )
-
-    @property
-    def shared_experts(self) -> SharedExperts | None:
-        return self._shared_experts
-
-    # TODO(bnell): temporary hack, do not call this method.
-    def _replace_quant_method(self, quant_method: FusedMoEMethodBase):
-        if self._shared_experts is not None:
-            self._shared_experts._quant_method = quant_method
-        self.quant_method = quant_method
-
-    def is_internal_router(self) -> bool:
-        return self.gate is not None
-
-    @property
-    @abstractmethod
-    def reduce_results(self) -> bool:
-        raise NotImplementedError
-
-    def must_reduce_shared_expert_outputs(self) -> bool:
-        """
-        The shared_experts are typically computed using the RowParallelLinear
-        layer. The result of this function is typically used as
-        the reduce_results argument to the module.
-        When just tensor-parallel is used, it is not required to reduce
-        the shared_experts results immediately. Instead we reduce at the
-        once at the end of the MoE op. (Refer to DeepSeekV2MoE module)
-        With EP and all2all kernels - this is no longer viable as all
-        GPU ranks in DP, produce the complete set of hidden_states.
-        Therefore it is required that we reduce the shared_experts output
-        early.
-        """
-        return (
-            self.quant_method.moe_kernel is not None
-            and self.quant_method.moe_kernel.output_is_reduced()
-        )
-
-    def maybe_all_reduce_tensor_model_parallel(self, final_hidden_states: torch.Tensor):
-        """
-        Some combine kernels reduce across GPU ranks by default.
-        """
-        if self.must_reduce_shared_expert_outputs():
-            return final_hidden_states
-        else:
-            return tensor_model_parallel_all_reduce(final_hidden_states)
-
-    def apply_routed_input_transform(
-        self, hidden_states: torch.Tensor
-    ) -> tuple[torch.Tensor, torch.Tensor | None]:
-        """Apply transform for routed experts (e.g., latent projection).
-
-        This is called by FusedMoE.forward_native. The original hidden_states
-        is saved separately so shared experts get [S, hidden_size] while
-        routed experts get the transformed [S, moe_latent_size].
-
-        TODO: For latent MoE bandwidth optimization, fc2_latent_proj could be
-        moved inside SharedFusedMoE to all-reduce on the smaller latent
-        dimension.
-
-        Returns (possibly transformed) hidden states and the input for shared
-        experts (or None if there are no shared experts).
-        """
-        if self.routed_input_transform is not None:
-            result = self.routed_input_transform(hidden_states)
-            # ReplicatedLinear returns (output, extra_bias) tuple.
-            # We only need the output tensor; extra_bias is not used here.
-            if isinstance(result, tuple):
-                return result[0], hidden_states
-            return result, hidden_states
-
-        return (
-            hidden_states,
-            hidden_states if self._shared_experts is not None else None,
-        )
-
-    def _maybe_reduce_output(
-        self,
-        states: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
-        trunc_sizes: list[int],
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        def trunc(x: torch.Tensor, trunc_size: int) -> torch.Tensor:
-            return x[..., :trunc_size]
-
-        def reduce_and_trunc(x: torch.Tensor, trunc_size: int) -> torch.Tensor:
-            return trunc(self.maybe_all_reduce_tensor_model_parallel(x), trunc_size)
-
-        if (
-            not self.moe_config.is_sequence_parallel
-            and self.reduce_results
-            and (self.moe_config.tp_size > 1 or self.moe_config.ep_size > 1)
-        ):
-            func = reduce_and_trunc
-        else:
-            func = trunc
-
-        if isinstance(states, tuple):
-            return tuple(
-                [func(s, trunc_size) for s, trunc_size in zip(states, trunc_sizes)]
-            )
-        else:
-            assert len(trunc_sizes) == 1
-            return func(states, trunc_sizes[0])
-
-    def _encode_layer_name(self) -> str | LayerName:
-        if _USE_LAYERNAME:
-            return LayerName(self.layer_name)
-        # Can be unavailable or None in unittests
-        if (
-            is_forward_context_available()
-            and get_forward_context().all_moe_layers is not None
-        ):
-            return "from_forward_context"
-        return self.layer_name
-
-    def _maybe_pad_hidden_states(
-        self,
-        shared_experts_input: torch.Tensor | None,
-        hidden_states: torch.Tensor,
-    ) -> tuple[torch.Tensor, list[int]]:
-        shared_experts_hidden_dim = (
-            shared_experts_input.shape[-1] if shared_experts_input is not None else 0
-        )
-        transformed_hidden_dim = hidden_states.shape[-1]
-        if (
-            not self.quant_method.skip_forward_padding
-            and self.moe_config.hidden_dim != transformed_hidden_dim
-        ):
-            hidden_states = F.pad(
-                hidden_states,
-                (0, self.moe_config.hidden_dim - transformed_hidden_dim),
-                mode="constant",
-                value=0.0,
-            )
-
-        if self._shared_experts is not None:
-            orig_hidden_dims = [shared_experts_hidden_dim, transformed_hidden_dim]
-        else:
-            orig_hidden_dims = [transformed_hidden_dim]
-
-        return hidden_states, orig_hidden_dims
-
-    def _maybe_apply_shared_experts(
-        self,
-        shared_experts_input: torch.Tensor | None,
-        order: SharedExpertsOrder,
-    ):
-        if self._shared_experts is not None:
-            assert shared_experts_input is not None
-            self._shared_experts.apply(shared_experts_input, order)
-
-    def _apply_quant_method(
-        self,
-        layer: torch.nn.Module,
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-        shared_experts_input: torch.Tensor | None,
-    ) -> tuple[torch.Tensor | None, torch.Tensor]:
-        # Run this before quant_method to avoid inplace issues.
-        # TODO(bnell): probably not needed anymore since inplace is
-        # disabled when shared experts are present.
-        self._maybe_apply_shared_experts(
-            shared_experts_input, SharedExpertsOrder.NO_OVERLAP
-        )
-
-        if self.quant_method.is_monolithic:
-            fused_out = self.quant_method.apply_monolithic(
-                layer=layer,
-                x=hidden_states,
-                router_logits=router_logits,
-            )
-        else:
-            topk_weights, topk_ids = self.router.select_experts(
-                hidden_states=hidden_states,
-                router_logits=router_logits,
-            )
-
-            # Passing shared_experts_input in case SharedExpertsOrder is
-            # NO_OVERLAP or MK_INTERNAL_OVERLAPPED.
-            fused_out = self.quant_method.apply(
-                layer=layer,
-                x=hidden_states,
-                topk_weights=topk_weights,
-                topk_ids=topk_ids,
-                shared_experts_input=shared_experts_input,
-            )
-
-        self._maybe_apply_shared_experts(
-            shared_experts_input,
-            SharedExpertsOrder.MULTI_STREAM_OVERLAPPED,
-        )
-
-        return (
-            self._shared_experts.output if self._shared_experts is not None else None,
-            fused_out,
-        )
-
-    def _sequence_parallel_context(self):
-        ctx = get_forward_context()
-        return (
-            ctx.dp_metadata.sp_local_sizes(self.moe_config.sp_size)
-            if ctx.dp_metadata
-            else nullcontext()
-        )
-
-    def _maybe_sync_shared_experts_stream(
-        self,
-        shared_experts_input: torch.Tensor | None,
-    ):
-        # If router/gate provided, then apply it here.
-        # (Note: This code runs only when "overlapped mode" is on to allow
-        #        parallel execution of shared experts with the FusedMoE via
-        #        separate cuda stream)
-        if self._shared_experts is not None:
-            self._shared_experts.maybe_sync_shared_experts_stream(shared_experts_input)
-
-    def _maybe_add_zero_expert_output(
-        self,
-        result: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        if isinstance(self.router, ZeroExpertRouter):
-            zero_expert_output = self.router.zero_expert_output
-            assert zero_expert_output is not None
-            if isinstance(result, tuple):
-                result = (result[0], result[1] + zero_expert_output)
-            else:
-                result = result + zero_expert_output
-        return result
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        """Invoke the fused moe layer.
-
-        Input:
-        - hidden_states
-        - router_logits
-
-        Output:
-        - The new hidden_states.
-        or
-        - A tuple of (shared experts output, new hidden_states).
-
-        Calling sequence
-        - forward
-          - self.forward_entry (_moe_forward or _moe_forward_shared custom op)
-            - forward_dispatch
-              - _forward_impl
-
-        Note: The existence of _moe_forward and _moe_forward_shared custom ops are due
-        to the following reasons:
-        1. the chunking loop in ChunkingMoERunner._forward_impl cannot be compiled by
-           torch.compile
-        2. pytorch cannot handle union types in custom op signatures so _moe_forward
-           and _moe_forward_shared must be split.
-
-        If ChunkingMoERunner._forward_impl can be implemented via torch.scan we can
-        potentially get rid of _moe_forward and _moe_forward_shared and collapse the
-        whole sequence into the 'forward' method.
-        """
-
-        # Apply transform for routed experts (e.g., latent projection for latent MoE)
-        hidden_states, shared_experts_input = self.apply_routed_input_transform(
-            hidden_states
-        )
-
-        hidden_states, og_hidden_dims = self._maybe_pad_hidden_states(
-            shared_experts_input,
-            hidden_states,
-        )
-
-        fused_output = self.forward_entry(
-            hidden_states,
-            router_logits,
-            shared_experts_input,
-            self._encode_layer_name(),
-        )
-
-        result = self._maybe_reduce_output(fused_output, og_hidden_dims)
-
-        return self._maybe_add_zero_expert_output(result)
-
-    def forward_dispatch(
-        self,
-        layer: torch.nn.Module,
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-        shared_experts_input: torch.Tensor | None,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        # TODO(bnell): this can be removed after MK migration is complete.
-        layer.ensure_moe_quant_config_init()
-
-        # Sync aux and main stream for shared expert multi-stream overlap.
-        self._maybe_sync_shared_experts_stream(shared_experts_input)
-
-        # If the Runner holds the gate, apply it after the stream sync,
-        # so it can run overlapped with the
-        # NOTE: in future PR, MoE runner will always hold the gate.
-        if self.gate is not None:
-            router_logits, _ = self.gate(hidden_states)
-
-        with self._sequence_parallel_context():
-            return self._forward_impl(
-                layer,
-                hidden_states,
-                router_logits,
-                shared_experts_input,
-            )
-
-    @abstractmethod
-    def _forward_impl(
-        self,
-        layer: torch.nn.Module,
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-        shared_experts_input: torch.Tensor | None,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        raise NotImplementedError
diff --git a/vllm/model_executor/layers/fused_moe/runner/moe_runner_factory.py b/vllm/model_executor/layers/fused_moe/runner/moe_runner_factory.py
deleted file mode 100644
index 2143fa3ce08a..000000000000
--- a/vllm/model_executor/layers/fused_moe/runner/moe_runner_factory.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import torch
-
-from vllm.model_executor.layers.fused_moe.config import (
-    FusedMoEConfig,
-)
-from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
-    FusedMoEMethodBase,
-)
-from vllm.model_executor.layers.fused_moe.router.fused_moe_router import (
-    FusedMoERouter,
-)
-from vllm.model_executor.layers.fused_moe.runner.default_moe_runner import (
-    DefaultMoERunner,
-)
-from vllm.model_executor.layers.fused_moe.runner.moe_runner import MoERunner
-from vllm.model_executor.layers.fused_moe.runner.shared_experts import (
-    SharedExperts,
-)
-
-
-def create_moe_runner(
-    layer_name: str,
-    moe_config: FusedMoEConfig,
-    router: FusedMoERouter,
-    routed_input_transform: torch.nn.Module | None,
-    gate: torch.nn.Module | None,
-    shared_experts: SharedExperts | None,
-    quant_method: FusedMoEMethodBase,
-    reduce_results: bool,
-    enable_dbo: bool,
-) -> MoERunner:
-    return DefaultMoERunner(
-        layer_name,
-        moe_config,
-        router,
-        routed_input_transform,
-        gate,
-        shared_experts,
-        quant_method,
-        reduce_results,
-        enable_dbo,
-    )
diff --git a/vllm/model_executor/layers/fused_moe/runner/moe_runner_interface.py b/vllm/model_executor/layers/fused_moe/runner/moe_runner_interface.py
new file mode 100644
index 000000000000..e3b239ca60fa
--- /dev/null
+++ b/vllm/model_executor/layers/fused_moe/runner/moe_runner_interface.py
@@ -0,0 +1,46 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from abc import ABC, abstractmethod
+
+import torch
+
+from vllm.model_executor.custom_op import PluggableLayer
+from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
+    FusedMoEMethodBase,
+)
+from vllm.model_executor.layers.fused_moe.runner.shared_experts import (
+    SharedExperts,
+)
+
+
+class MoERunnerInterface(PluggableLayer, ABC):
+    """
+    Abstract base class for Mixture of Experts (MoE) runners.
+
+    This class defines the interface that all MoE runner implementations must follow.
+    MoE runners are responsible for executing the forward pass of MoE layers, handling
+    expert routing, and managing tensor parallel operations.
+    """
+
+    @abstractmethod
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        raise NotImplementedError
+
+    @abstractmethod
+    def is_internal_router(self) -> bool:
+        raise NotImplementedError
+
+    @property
+    @abstractmethod
+    def shared_experts(self) -> SharedExperts | None:
+        raise NotImplementedError
+
+    # TODO(bnell): temporary hack, do not call this method.
+    @abstractmethod
+    def _replace_quant_method(self, quant_method: FusedMoEMethodBase):
+        raise NotImplementedError
diff --git a/vllm/model_executor/layers/fused_moe/runner/shared_experts.py b/vllm/model_executor/layers/fused_moe/runner/shared_experts.py
index 827a6e6bd3ea..227014e23973 100644
--- a/vllm/model_executor/layers/fused_moe/runner/shared_experts.py
+++ b/vllm/model_executor/layers/fused_moe/runner/shared_experts.py
@@ -5,10 +5,6 @@
 import torch
 
 import vllm.envs as envs
-from vllm.distributed import (
-    get_tensor_model_parallel_world_size,
-    tensor_model_parallel_all_reduce,
-)
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
@@ -48,7 +44,6 @@ def __init__(
         layer: torch.nn.Module,
         moe_config: FusedMoEConfig,
         quant_method: QuantizeMethodBase,
-        reduce_results: bool,
         enable_dbo: bool,
     ):
         from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
@@ -68,28 +63,39 @@ def __init__(
         self._layer = layer
         self._moe_config = moe_config
         self._quant_method = quant_method
-        self._reduce_results = reduce_results
 
         # Allow disabling of the separate shared experts stream for
         # debug purposes.
         # TODO: Remove this after more extensive testings with TP/DP
         # and other execution modes
         if envs.VLLM_DISABLE_SHARED_EXPERTS_STREAM:
-            logger.debug_once("Disabling MoE shared_experts cuda stream", scope="local")
+            logger.debug_once("Disabling MoE shared_experts cuda stream")
             self._stream = None
         else:
             # TODO(rob): enable shared expert overlap with non-cuda-alike.
             # aux_stream() returns None on non-cuda-alike platforms.
             self._stream = aux_stream()
             if self._stream is not None:
-                logger.debug_once(
-                    "Enabled separate cuda stream for MoE shared_experts", scope="local"
-                )
+                logger.debug_once("Enabled separate cuda stream for MoE shared_experts")
+
+    @property
+    def _disable_shared_experts_overlap(self) -> bool:
+        # Disable shared expert overlap if:
+        #   - we are using eplb with non-default backend, because of correctness issues
+        #   - we are using flashinfer with DP, since there nothing to gain
+        parallel_config = self._moe_config.moe_parallel_config
+        return (
+            parallel_config.enable_eplb
+            and parallel_config.all2all_backend != "allgather_reducescatter"
+        ) or parallel_config.use_fi_nvl_two_sided_kernels
 
     def _determine_shared_experts_order(
         self,
         hidden_states: torch.Tensor,
     ) -> SharedExpertsOrder:
+        if self._disable_shared_experts_overlap:
+            return SharedExpertsOrder.NO_OVERLAP
+
         if self._quant_method.mk_owns_shared_expert:
             return SharedExpertsOrder.MK_INTERNAL_OVERLAPPED
 
@@ -139,18 +145,6 @@ def _run_in_aux_stream(
 
         return output
 
-    def _maybe_reduce_shared_out(self, shared_out: torch.Tensor) -> torch.Tensor:
-        # Reduce shared expert outputs if necessary, since the MLP
-        # should have been created with reduce_results=False.
-        if (
-            self._reduce_results
-            and self._quant_method.moe_kernel is not None
-            and self._quant_method.moe_kernel.output_is_reduced()
-            and get_tensor_model_parallel_world_size() > 1
-        ):
-            shared_out = tensor_model_parallel_all_reduce(shared_out)
-        return shared_out
-
     @property
     def _output_idx(self) -> int:
         return dbo_current_ubatch_id() if self.enable_dbo else 0
diff --git a/vllm/model_executor/layers/fused_moe/shared_fused_moe.py b/vllm/model_executor/layers/fused_moe/shared_fused_moe.py
deleted file mode 100644
index ed243e992636..000000000000
--- a/vllm/model_executor/layers/fused_moe/shared_fused_moe.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import torch
-
-from vllm.model_executor.layers.fused_moe.layer import FusedMoE
-
-
-# TODO(bnell): Remove this entirely
-class SharedFusedMoE(FusedMoE):
-    """
-    A FusedMoE operation that also computes the results of shared experts.
-    If an all2all communicator is being used the shared expert computation
-    can be interleaved with the fused all2all dispatch communication step.
-    """
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        result = super().forward(
-            hidden_states=hidden_states,
-            router_logits=router_logits,
-        )
-        if self.shared_experts is None:
-            return None, result
-        else:
-            return result
diff --git a/vllm/model_executor/layers/fused_moe/topk_weight_and_reduce.py b/vllm/model_executor/layers/fused_moe/topk_weight_and_reduce.py
index 4cebe608a6b4..837c1498622f 100644
--- a/vllm/model_executor/layers/fused_moe/topk_weight_and_reduce.py
+++ b/vllm/model_executor/layers/fused_moe/topk_weight_and_reduce.py
@@ -62,6 +62,10 @@ def apply(
         if output is None:
             return fused_expert_output
 
+        # Skip self-copy when caller aliased fused_out to output upstream.
+        if output is fused_expert_output:
+            return output
+
         # MoEPrepareAndFinalizeNoDPEPModular needs the output to be in the `output`
         # tensor.
         assert output.size() == fused_expert_output.size(), (
diff --git a/vllm/model_executor/layers/fused_moe/triton_cutlass_moe.py b/vllm/model_executor/layers/fused_moe/triton_cutlass_moe.py
index 4aa396d24b0c..ee3622b4ebe3 100644
--- a/vllm/model_executor/layers/fused_moe/triton_cutlass_moe.py
+++ b/vllm/model_executor/layers/fused_moe/triton_cutlass_moe.py
@@ -10,9 +10,9 @@
     FusedMoEConfig,
     FusedMoEQuantConfig,
 )
-from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
+from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import CutlassExpertsFp8
+from vllm.model_executor.layers.fused_moe.experts.triton_moe import TritonExperts
 from vllm.model_executor.layers.fused_moe.fallback import FallbackExperts
-from vllm.model_executor.layers.fused_moe.fused_moe import TritonExperts
 from vllm.platforms import current_platform
 
 
diff --git a/vllm/model_executor/layers/fused_moe/triton_deep_gemm_moe.py b/vllm/model_executor/layers/fused_moe/triton_deep_gemm_moe.py
index d58dbc20e244..fbcf030a9256 100644
--- a/vllm/model_executor/layers/fused_moe/triton_deep_gemm_moe.py
+++ b/vllm/model_executor/layers/fused_moe/triton_deep_gemm_moe.py
@@ -14,8 +14,8 @@
     _valid_deep_gemm,
     _valid_deep_gemm_shape,
 )
+from vllm.model_executor.layers.fused_moe.experts.triton_moe import TritonExperts
 from vllm.model_executor.layers.fused_moe.fallback import FallbackExperts
-from vllm.model_executor.layers.fused_moe.fused_moe import TritonExperts
 from vllm.utils.deep_gemm import (
     is_deep_gemm_e8m0_used,
 )
diff --git a/vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py b/vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py
index 190821562130..d9cfce2c2141 100644
--- a/vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py
+++ b/vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py
@@ -154,32 +154,57 @@ def _setup_kernel(
         w2: torch.Tensor,
     ) -> None:
         # Shuffle weights to runtime format.
-        w13, w2 = convert_to_unquantized_kernel_format(
+        w13_new, w2_new = convert_to_unquantized_kernel_format(
             self.unquantized_backend,
             layer=layer,
             w13_weight=w13,
             w2_weight=w2,
         )
-        replace_parameter(layer, "w13_weight", w13)
-        replace_parameter(layer, "w2_weight", w2)
+        # `moe_kernel` is initialized to None in FusedMoEMethodBase.__init__;
+        # On the first call we replace the parameter normally. On subsequent
+        # calls (e.g. RL weight updates that re-trigger
+        # process_weights_after_loading) the moe kernel has already been set
+        # up and CUDA graphs may have captured the parameter addresses, so
+        # we copy the shuffled data into the existing storage instead of
+        # re-registering a new Parameter.
+        is_weight_update = self.moe_kernel is not None  # type: ignore[has-type]
+        replace_parameter(layer, "w13_weight", w13_new, prefer_copy=is_weight_update)
+        replace_parameter(layer, "w2_weight", w2_new, prefer_copy=is_weight_update)
 
-        # Setup moe kernel.
-        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
-        assert self.moe_quant_config is not None
-        assert self.experts_cls is not None
-        self.moe_kernel = make_unquantized_moe_kernel(
-            quant_config=self.moe_quant_config,
-            moe_config=self.moe,
-            backend=self.unquantized_backend,
-            experts_cls=self.experts_cls,
-            routing_tables=layer._maybe_init_expert_routing_tables(),
-            shared_experts=layer.shared_experts,
-        )
+        # AITER backend requires weights to be marked as shuffled.
+        if self.unquantized_backend == UnquantizedMoeBackend.AITER:
+            layer.w13_weight.is_shuffled = True
+            layer.w2_weight.is_shuffled = True
+
+        if not is_weight_update:
+            # Setup moe kernel only on the first call. For the unquantized
+            # method, moe_quant_config is either the constant
+            # FUSED_MOE_UNQUANTIZED_CONFIG or biased_moe_quant_config(...)
+            # which references layer.w{13,2}_bias; since weight updates
+            # mutate those bias tensors in place, the kernel does not need
+            # to be re-built.
+            self.moe_quant_config = self.get_fused_moe_quant_config(layer)
+            assert self.moe_quant_config is not None
+            assert self.experts_cls is not None
+            self.moe_kernel = make_unquantized_moe_kernel(
+                quant_config=self.moe_quant_config,
+                moe_config=self.moe,
+                backend=self.unquantized_backend,
+                experts_cls=self.experts_cls,
+                routing_tables=layer._maybe_init_expert_routing_tables(),
+                shared_experts=layer.shared_experts,
+            )
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         super().process_weights_after_loading(layer)
 
-        # Padding the weight for better performance on ROCm
+        # Padding the weight for better performance on ROCm.
+        # _maybe_pad_weight is idempotent: on the first call it allocates a
+        # padded storage and returns a strided view; on subsequent calls
+        # (weight updates) the stride condition no longer matches so it
+        # returns the input unchanged. The reassignment to .data is therefore
+        # a no-op on updates and preserves the storage address (data_ptr)
+        # used by captured CUDA graphs.
         layer.w13_weight.data = self._maybe_pad_weight(layer.w13_weight.data)
         layer.w2_weight.data = self._maybe_pad_weight(layer.w2_weight.data)
 
@@ -297,7 +322,11 @@ def forward_cuda(
         shared_experts_input: torch.Tensor | None,
     ) -> torch.Tensor:
         return self.forward_native(
-            layer, x, topk_weights, topk_ids, shared_experts_input
+            layer,
+            x,
+            topk_weights,
+            topk_ids,
+            shared_experts_input,
         )
 
     def apply_monolithic(
@@ -305,6 +334,7 @@ def apply_monolithic(
         layer: "FusedMoE",  # type: ignore[name-defined] # noqa: F821
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
     ) -> torch.Tensor:
         assert self.is_monolithic
         if self.unquantized_backend == UnquantizedMoeBackend.CPU:
diff --git a/vllm/model_executor/layers/fused_moe/utils.py b/vllm/model_executor/layers/fused_moe/utils.py
index ce1e49bc4b0b..b37d17bdac3a 100644
--- a/vllm/model_executor/layers/fused_moe/utils.py
+++ b/vllm/model_executor/layers/fused_moe/utils.py
@@ -4,6 +4,7 @@
 from math import prod
 
 import torch
+import torch.nn.functional as F
 
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
@@ -22,6 +23,9 @@
 from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
     mxfp8_e4m3_quantize,
 )
+from vllm.model_executor.layers.quantization.utils.nvfp4_emulation_utils import (
+    ref_nvfp4_quant_dequant,
+)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     per_tensor_dequantize,
 )
@@ -204,11 +208,12 @@ def _mxfp8_e4m3_quantize(
     per_act_token_quant: bool,
     block_shape: list[int] | None = None,
     is_sf_swizzled_layout: bool = False,
+    mx_alignment: int = 0,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     assert A_scale is None
     assert not per_act_token_quant
     assert block_shape is None or block_shape == [1, 32]
-    return mxfp8_e4m3_quantize(A, is_sf_swizzled_layout)
+    return mxfp8_e4m3_quantize(A, is_sf_swizzled_layout, mx_alignment)
 
 
 def _mxfp6_e3m2_quantize(
@@ -251,8 +256,10 @@ def moe_kernel_quantize_input(
     quant_dtype: None | torch.dtype | str,
     per_act_token_quant: bool,
     block_shape: list[int] | None = None,
-    is_fp4_scale_swizzled: bool = True,
+    is_scale_swizzled: bool = True,
     ocp_mx_scheme: str | None = None,
+    quantization_emulation: bool = False,
+    mx_alignment: int = 0,
 ) -> tuple[torch.Tensor, torch.Tensor | None]:
     # Handle OCP MX scheme that requires QDQ (quantize-dequantize) for emulation
     if ocp_mx_scheme is not None:
@@ -274,26 +281,63 @@ def moe_kernel_quantize_input(
         # activation quantization below.
 
     if quant_dtype == current_platform.fp8_dtype():
+        if quantization_emulation:
+            raise NotImplementedError(
+                f"moe_kernel_quantize_input does not support quant_dtype={quant_dtype}"
+                " MOE quantization emulation. Please open an issue."
+            )
         return _fp8_quantize(A, A_scale, per_act_token_quant, block_shape)
     elif quant_dtype == torch.int8:
+        if quantization_emulation:
+            raise NotImplementedError(
+                "moe_kernel_quantize_input does not support quant_dtype=torch.int8"
+                " MOE quantization emulation. Please open an issue."
+            )
         return _int8_quantize(A, A_scale, per_act_token_quant, block_shape)
     elif quant_dtype == "nvfp4":
-        return _nvfp4_quantize(A, A_scale, is_sf_swizzled_layout=is_fp4_scale_swizzled)
+        if not quantization_emulation:
+            return _nvfp4_quantize(A, A_scale, is_sf_swizzled_layout=is_scale_swizzled)
+        else:
+            A = ref_nvfp4_quant_dequant(A, A_scale, block_size=16)
+            return A, None
     elif quant_dtype == "mxfp4":
+        if not quantization_emulation:
+            raise NotImplementedError(
+                "moe_kernel_quantize_input should not be used for native"
+                " quant_dtype='mxfp4' MOE. Please open an issue."
+            )
         return _mxfp4_quantize(A, A_scale, per_act_token_quant, block_shape)
     elif quant_dtype == "mxfp8":
         # TODO: `quant_dtype == "mxfp8"` is ambiguous,
         # should be fp8_e4m3. OCP MX also defines `fp8_e5m2`.
+        if quantization_emulation:
+            raise NotImplementedError(
+                "moe_kernel_quantize_input does not support quant_dtype='mxfp8' MOE "
+                "quantization emulation. Please open an issue."
+            )
         return _mxfp8_e4m3_quantize(
             A,
             A_scale,
             per_act_token_quant,
             block_shape,
-            is_sf_swizzled_layout=is_fp4_scale_swizzled,
+            is_sf_swizzled_layout=is_scale_swizzled,
+            mx_alignment=mx_alignment,
         )
     elif quant_dtype == "mxfp6_e3m2":
+        if not quantization_emulation:
+            raise NotImplementedError(
+                "moe_kernel_quantize_input should not be used for native "
+                " quant_dtype='mxfp6_e3m2'MOE. Please open an issue."
+            )
+
         return _mxfp6_e3m2_quantize(A, A_scale, per_act_token_quant, block_shape)
     elif quant_dtype == "mxfp6_e2m3":
+        if not quantization_emulation:
+            raise NotImplementedError(
+                "moe_kernel_quantize_input should not be used for native"
+                " quant_dtype='mxfp6_e2m3' MOE. Please open an issue."
+            )
+
         return _mxfp6_e2m3_quantize(A, A_scale, per_act_token_quant, block_shape)
     else:
         return A, A_scale
@@ -343,3 +387,20 @@ def trtllm_moe_pack_topk_ids_weights(
     return (topk_ids.to(torch.int32) << 16) | topk_weights.to(torch.bfloat16).view(
         torch.int16
     )
+
+
+@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
+def swiglu_limit_func(
+    output: torch.Tensor,
+    input: torch.Tensor,  # first half is gate, second half is up
+    swiglu_limit: float = 0.0,
+) -> None:
+    d = input.shape[1] // 2
+    gate = input[:, :d]
+    up = input[:, d:]
+
+    if swiglu_limit > 0:
+        gate = torch.clamp(gate, max=swiglu_limit)
+        up = torch.clamp(up, min=-swiglu_limit, max=swiglu_limit)
+
+    output.copy_(F.silu(gate) * up)
diff --git a/vllm/model_executor/layers/kda.py b/vllm/model_executor/layers/kda.py
index b09f980c7e60..70c67f33f0ac 100644
--- a/vllm/model_executor/layers/kda.py
+++ b/vllm/model_executor/layers/kda.py
@@ -16,7 +16,6 @@
 from vllm.model_executor.model_loader.weight_utils import sharded_weight_loader
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.utils.torch_utils import direct_register_custom_op
-from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
 
 from .fla.ops.kda import (
@@ -123,7 +122,7 @@ def __init__(
         self.cache_config = cache_config
         if model_config is None:
             raise ValueError("model_config must be provided")
-        kda_config = model_config.linear_attn_config
+        kda_config = model_config.linear_attn_config  # type: ignore[attr-defined]
         self.head_dim = kda_config["head_dim"]
         self.num_heads = kda_config["num_heads"]
         self.layer_idx = layer_idx
@@ -297,19 +296,21 @@ def _forward(
         core_attn_out: torch.Tensor,
     ) -> None:
         forward_context = get_forward_context()
-        attn_metadata: AttentionMetadata = forward_context.attn_metadata
+        attn_metadata_raw = forward_context.attn_metadata
 
-        if attn_metadata is None:
+        if attn_metadata_raw is None:
             #     # V1 profile run
             return
 
-        assert isinstance(attn_metadata, dict)
-        attn_metadata = attn_metadata[self.prefix]
-        assert isinstance(attn_metadata, GDNAttentionMetadata)
-        has_initial_state = attn_metadata.has_initial_state
-        non_spec_query_start_loc = attn_metadata.non_spec_query_start_loc
-        non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor  # noqa: E501
-        num_actual_tokens = attn_metadata.num_actual_tokens
+        assert isinstance(attn_metadata_raw, dict)
+        attn_metadata_narrowed = attn_metadata_raw[self.prefix]
+        assert isinstance(attn_metadata_narrowed, GDNAttentionMetadata)
+        has_initial_state = attn_metadata_narrowed.has_initial_state
+        non_spec_query_start_loc = attn_metadata_narrowed.non_spec_query_start_loc
+        non_spec_state_indices_tensor = (
+            attn_metadata_narrowed.non_spec_state_indices_tensor
+        )  # noqa: E501
+        num_actual_tokens = attn_metadata_narrowed.num_actual_tokens
         constant_caches = self.kv_cache
 
         q_proj_states = q_proj_states[:num_actual_tokens]
@@ -335,7 +336,7 @@ def _forward(
         v_conv_weights = self.v_conv1d.weight.view(
             self.v_conv1d.weight.size(0), self.v_conv1d.weight.size(2)
         )
-        if attn_metadata.num_prefills > 0:
+        if attn_metadata_narrowed.num_prefills > 0:
             q_proj_states = q_proj_states.transpose(0, 1)
             k_proj_states = k_proj_states.transpose(0, 1)
             v_proj_states = v_proj_states.transpose(0, 1)
@@ -348,7 +349,7 @@ def _forward(
                 has_initial_state=has_initial_state,
                 cache_indices=non_spec_state_indices_tensor,
                 query_start_loc=non_spec_query_start_loc,
-                metadata=attn_metadata,
+                metadata=attn_metadata_narrowed,
             ).transpose(0, 1)
             k = causal_conv1d_fn(
                 k_proj_states,
@@ -359,7 +360,7 @@ def _forward(
                 has_initial_state=has_initial_state,
                 cache_indices=non_spec_state_indices_tensor,
                 query_start_loc=non_spec_query_start_loc,
-                metadata=attn_metadata,
+                metadata=attn_metadata_narrowed,
             ).transpose(0, 1)
             v = causal_conv1d_fn(
                 v_proj_states,
@@ -370,11 +371,12 @@ def _forward(
                 has_initial_state=has_initial_state,
                 cache_indices=non_spec_state_indices_tensor,
                 query_start_loc=non_spec_query_start_loc,
-                metadata=attn_metadata,
+                metadata=attn_metadata_narrowed,
             ).transpose(0, 1)
         else:
+            assert non_spec_state_indices_tensor is not None
             decode_conv_indices = non_spec_state_indices_tensor[
-                : attn_metadata.num_actual_tokens
+                : attn_metadata_narrowed.num_actual_tokens
             ]
             q = causal_conv1d_update(
                 q_proj_states,
@@ -408,7 +410,9 @@ def _forward(
             lambda x: rearrange(x, "n (h d) -> 1 n h d", d=self.head_dim), (q, k, v)
         )
 
-        if attn_metadata.num_prefills > 0:
+        if attn_metadata_narrowed.num_prefills > 0:
+            assert non_spec_state_indices_tensor is not None
+            assert has_initial_state is not None
             zero_idx = non_spec_state_indices_tensor[~has_initial_state]
             recurrent_state[zero_idx] = 0
             initial_state = recurrent_state[non_spec_state_indices_tensor].contiguous()
@@ -429,6 +433,7 @@ def _forward(
             # Init cache
             recurrent_state[non_spec_state_indices_tensor] = last_recurrent_state
         else:
+            assert non_spec_query_start_loc is not None
             (
                 core_attn_out_non_spec,
                 last_recurrent_state,
@@ -440,7 +445,9 @@ def _forward(
                 beta=beta,
                 initial_state=recurrent_state,
                 use_qk_l2norm_in_kernel=True,
-                cu_seqlens=non_spec_query_start_loc[: attn_metadata.num_decodes + 1],
+                cu_seqlens=non_spec_query_start_loc[
+                    : attn_metadata_narrowed.num_decodes + 1
+                ],
                 ssm_state_indices=non_spec_state_indices_tensor,
             )
         core_attn_out[0, :num_actual_tokens] = core_attn_out_non_spec[
diff --git a/vllm/model_executor/layers/layernorm.py b/vllm/model_executor/layers/layernorm.py
index 9afc4c9c08d6..a5d4e4db79fe 100644
--- a/vllm/model_executor/layers/layernorm.py
+++ b/vllm/model_executor/layers/layernorm.py
@@ -8,79 +8,22 @@
 
 # Import kernels
 import vllm.kernels  # noqa: F401
-from vllm import _oink_ops, envs, ir
-from vllm._aiter_ops import rocm_aiter_ops
+from vllm import envs, ir
+from vllm.config import get_current_vllm_config
 from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp
-from vllm.model_executor.layers.batch_invariant import (
-    rms_norm_batch_invariant,
-)
-from vllm.platforms import current_platform
+from vllm.model_executor.layers.batch_invariant import rms_norm_batch_invariant
 
 logger = init_logger(__name__)
 
 
-def _can_view_as_2d(x: torch.Tensor) -> bool:
-    """Return True if x.view(-1, x.shape[-1]) is viewable (no copy)."""
-    if x.dim() < 2:
-        return False
-    if x.dim() == 2:
-        return True
-    # For a view(-1, N) to be valid, all leading dims must be contiguous with
-    # respect to each other (size-1 dims are ignored).
-    for dim in range(x.dim() - 1):
-        # Strides for size-1 dims are irrelevant and can be arbitrary.
-        if x.size(dim + 1) != 1 and x.stride(dim) != x.stride(dim + 1) * x.size(
-            dim + 1
-        ):
-            return False
-    return True
-
-
-def _is_oink_stride_compatible_2d(x_2d: torch.Tensor) -> bool:
-    """Return True if x_2d meets Oink's pointer-path stride constraints."""
-    if x_2d.dim() != 2:
-        return False
-    if x_2d.stride(1) != 1:
-        return False
-    # Match Oink's vectorization constraint: stride(0) divisible by 256b.
-    if x_2d.dtype in (torch.float16, torch.bfloat16):
-        divby = 16
-    elif x_2d.dtype == torch.float32:
-        divby = 8
-    else:
-        return False
-    return (x_2d.stride(0) % divby) == 0
-
-
-def fused_add_rms_norm(
-    x: torch.Tensor,
-    residual: torch.Tensor,
-    weight: torch.Tensor,
-    variance_epsilon: float,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    from vllm import _custom_ops as ops
-
-    if envs.VLLM_BATCH_INVARIANT:
-        return rms_norm_batch_invariant(
-            x + residual, weight, variance_epsilon
-        ), x + residual
-    ops.fused_add_rms_norm(
-        x,
-        residual,
-        weight,
-        variance_epsilon,
-    )
-    return x, residual
-
-
 def poly_norm(
     x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, variance_epsilon: float
 ) -> torch.Tensor:
     from vllm import _custom_ops as ops
 
     out = torch.empty_like(x)
-    ops.poly_norm(
+    ops.poly_norm(  # type: ignore[attr-defined]
         out,
         x,
         weight,
@@ -90,18 +33,6 @@ def poly_norm(
     return out
 
 
-def dispatch_rocm_rmsnorm_func(dtype: torch.dtype, use_aiter: bool = False):
-    use_aiter = use_aiter and dtype in [
-        torch.float16,
-        torch.bfloat16,
-    ]
-
-    if use_aiter:
-        return rocm_aiter_ops.rms_norm2d_with_add
-    else:
-        return fused_add_rms_norm
-
-
 # --8<-- [start:rms_norm]
 @CustomOp.register("rms_norm")
 class RMSNorm(CustomOp):
@@ -134,105 +65,19 @@ def __init__(
         if self.has_weight:
             self.weight = nn.Parameter(self.weight)
 
-        if current_platform.is_rocm():
-            aiter_rmsnorm_enabled = rocm_aiter_ops.is_rmsnorm_enabled()
-            self.rocm_norm_func_with_add = dispatch_rocm_rmsnorm_func(
-                dtype=weight_dtype, use_aiter=aiter_rmsnorm_enabled
-            )
-
-        # Optional: enable Oink Blackwell RMSNorm custom-op fast path on
-        # compatible CUDA devices (e.g., SM100) when the external Oink
-        # package is available. This is detected once at construction time
-        # to avoid per-call device queries in the hot path.
-        self._use_oink_fused_add_rmsnorm = False
-        if (
-            not current_platform.is_rocm()
-            and torch.cuda.is_available()
-            and bool(getattr(envs, "VLLM_USE_OINK_OPS", False))
-        ):
-            # NOTE: vLLM disables custom ops by default when using Inductor.
-            # If this op is disabled, CustomOp will dispatch to forward_native,
-            # and the Oink path in forward_cuda will never run.
-            if getattr(self._forward_method, "__func__", None) is getattr(
-                self.forward_native, "__func__", None
-            ):
-                try:
-                    from vllm.config import get_cached_compilation_config
-
-                    custom_ops = get_cached_compilation_config().custom_ops
-                except Exception:
-                    custom_ops = ["<unknown>"]
-                logger.warning_once(
-                    "VLLM_USE_OINK_OPS=1 but the `rms_norm` custom op is "
-                    "disabled (CompilationConfig.custom_ops=%s). Enable it via "
-                    "`compilation_config={'custom_ops': ['none', '+rms_norm']}` "
-                    "(or `['all']`) to let vLLM call into torch.ops.oink.*.",
-                    custom_ops,
-                )
-                # Custom op disabled => forward_cuda won't run. Avoid doing any
-                # external Oink initialization work in this case.
-            else:
-                try:
-                    device_index = torch.accelerator.current_device_index()
-                    if _oink_ops.is_oink_available_for_device(device_index):
-                        self._use_oink_fused_add_rmsnorm = (
-                            _oink_ops.has_fused_add_rms_norm()
-                        )
-                except Exception as e:
-                    # If anything goes wrong (no Oink install, CPU-only env, etc.),
-                    # silently fall back to the built-in RMSNorm path.
-                    logger.warning_once(
-                        "VLLM_USE_OINK_OPS=1 but failed to initialize Oink "
-                        "RMSNorm; falling back to vLLM RMSNorm. Error: %s",
-                        e,
-                    )
-                    self._use_oink_fused_add_rmsnorm = False
-
-    @staticmethod
-    def forward_static(
-        x: torch.Tensor,
-        variance_epsilon: float,
-        hidden_size: int,
-        orig_dtype: torch.dtype,
-        weight: torch.Tensor | None = None,
-        residual: torch.Tensor | None = None,
-        variance_size_override: int | None = None,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        """PyTorch-native implementation equivalent to forward()."""
-        x = x.to(torch.float32)
-        if residual is not None:
-            # residual promoted f16->f32 automatically,
-            # otherwise Inductor eliminates the casts to and from f16,
-            # increasing memory usage (and complicating pattern matching)
-            x = x + residual
-            residual = x.to(orig_dtype)
-
-        if x.shape[-1] != hidden_size:
-            raise ValueError(
-                f"Expected hidden_size to be {hidden_size}, but found: {x.shape[-1]}"
-            )
-
-        if variance_size_override is None:
-            x_var = x
-        else:
-            if hidden_size < variance_size_override:
-                raise ValueError(
-                    "Expected hidden_size to be at least "
-                    f"{variance_size_override}, but found: {hidden_size}"
-                )
-
-            x_var = x[:, :, :variance_size_override]
-
-        variance = x_var.pow(2).mean(dim=-1, keepdim=True)
-
-        x = x * torch.rsqrt(variance + variance_epsilon)
-        x = x.to(orig_dtype)
-        if weight is not None:
-            x = x * weight
-        if residual is None:
-            return x
-        else:
-            return x, residual
+        # Do not pass identity weight to native implementation (causes issue on TPU).
+        # Other implementations require weight to be passed even if all ones.
+        # Cheat and predict if native will be dispatched to:
+        #  1) if native is first in priority list
+        #  2) if variance_size_override is given (only supported by native impl)
+        # TODO(luka): address weight passing inconsistency:
+        # https://github.com/vllm-project/vllm/issues/39370
+        priority = get_current_vllm_config().kernel_config.ir_op_priority
+        var_override = self.variance_size_override is not None
+        native_rms_norm = priority.rms_norm[0] == "native" or var_override
+        native_add_rms_norm = priority.fused_add_rms_norm[0] == "native" or var_override
+        self.pass_weight = self.has_weight or not native_rms_norm
+        self.pass_weight_add = self.has_weight or not native_add_rms_norm
 
     def forward_native(
         self,
@@ -241,106 +86,34 @@ def forward_native(
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         """PyTorch-native implementation equivalent to forward()."""
         if residual is None:
-            # TODO(luka): address the weight=None passing issue more generally
             return ir.ops.rms_norm(
                 x,
-                self.weight.data if self.has_weight else None,
+                self.weight.data if self.pass_weight else None,
+                self.variance_epsilon,
+                self.variance_size_override,
+            )
+        else:
+            return ir.ops.fused_add_rms_norm.maybe_inplace(
+                x,
+                residual,
+                self.weight.data if self.pass_weight_add else None,
                 self.variance_epsilon,
                 self.variance_size_override,
             )
-
-        return self.forward_static(
-            x,
-            self.variance_epsilon,
-            self.hidden_size,
-            x.dtype,
-            self.weight.data if self.has_weight else None,
-            residual,
-            self.variance_size_override,
-        )
 
     def forward_cuda(
         self,
         x: torch.Tensor,
         residual: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        if residual is None and not envs.VLLM_BATCH_INVARIANT:
-            return ir.ops.rms_norm(
-                x, self.weight.data, self.variance_epsilon, self.variance_size_override
-            )
-
-        if self.variance_size_override is not None:
-            return self.forward_native(x, residual)
-
-        # Optional Oink SM100 fast path (fused residual-add + RMSNorm, in-place).
-        # This mirrors vLLM's fused_add_rms_norm semantics by mutating both
-        # `x` (normalized output) and `residual` (residual-out buffer).
         if (
-            residual is not None
-            and getattr(self, "_use_oink_fused_add_rmsnorm", False)
-            and x.is_cuda
-            and residual.is_cuda
-            and x.shape == residual.shape
-            and x.dtype == residual.dtype
-            and x.dim() >= 2
-            and self.has_weight
-            and not envs.VLLM_BATCH_INVARIANT
-            and self.weight.data.dtype == x.dtype
-            and self.weight.data.is_contiguous()
+            envs.VLLM_BATCH_INVARIANT
+            and residual is None
+            and self.variance_size_override is None
         ):
-            orig_shape = x.shape
-            hidden_size = orig_shape[-1]
-            if _can_view_as_2d(x) and _can_view_as_2d(residual):
-                x_2d = x.view(-1, hidden_size)
-                res_2d = residual.view(-1, hidden_size)
-
-                # The Oink in-place pointer path supports the common vLLM
-                # layout where:
-                # - `x` may be strided/padded row-major (stride(1) == 1), and
-                # - `residual` is contiguous row-major ([M, N] with stride(0) == N).
-                # If these conditions are not met, fall back to vLLM's built-in
-                # fused kernel.
-                if (
-                    _is_oink_stride_compatible_2d(x_2d)
-                    and _is_oink_stride_compatible_2d(res_2d)
-                    and res_2d.is_contiguous()
-                ):
-                    _oink_ops.fused_add_rms_norm_(
-                        x_2d,
-                        res_2d,
-                        self.weight.data,
-                        self.variance_epsilon,
-                    )
-                    return x, residual
-
-        if residual is not None:
-            return fused_add_rms_norm(
-                x, residual, self.weight.data, self.variance_epsilon
-            )
-        else:
-            assert envs.VLLM_BATCH_INVARIANT
             return rms_norm_batch_invariant(x, self.weight.data, self.variance_epsilon)
 
-    def forward_hip(
-        self,
-        x: torch.Tensor,
-        residual: torch.Tensor | None = None,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        if residual is None and not envs.VLLM_BATCH_INVARIANT:
-            return ir.ops.rms_norm(
-                x, self.weight.data, self.variance_epsilon, self.variance_size_override
-            )
-
-        if self.variance_size_override is not None:
-            return self.forward_native(x, residual)
-
-        if residual is not None:
-            return self.rocm_norm_func_with_add(
-                x, residual, self.weight.data, self.variance_epsilon
-            )
-        else:
-            assert envs.VLLM_BATCH_INVARIANT
-            return rms_norm_batch_invariant(x, self.weight.data, self.variance_epsilon)
+        return self.forward_native(x, residual)
 
     def forward_xpu(
         self,
@@ -478,9 +251,12 @@ def forward_native(
         weight = self.weight.float()
         z = z.float() if z is not None else None
 
+        assert self.activation in ["silu", "sigmoid", "swish"]
+        act_fn = F.sigmoid if self.activation == "sigmoid" else F.silu
+
         # Apply gating before normalization if needed
         if z is not None and not self.norm_before_gate:
-            x = x * F.silu(z)
+            x = x * act_fn(z)
 
         # RMS Normalization
         if self.group_size is None:
@@ -499,7 +275,7 @@ def forward_native(
 
         # Apply gating after normalization if needed
         if z is not None and self.norm_before_gate:
-            out = out * F.silu(z)
+            out = out * act_fn(z)
 
         return out.to(orig_dtype)
 
diff --git a/vllm/model_executor/layers/linear.py b/vllm/model_executor/layers/linear.py
index 975fedabd675..323cad9427d3 100644
--- a/vllm/model_executor/layers/linear.py
+++ b/vllm/model_executor/layers/linear.py
@@ -60,6 +60,8 @@
     "ModelOptFp8PbWoLinearMethod",
     "QuarkLinearMethod",
     "ModelOptNvFp4LinearMethod",
+    "ModelOptNvFp4W4A16LinearMethod",
+    "HummingLinearMethod",
 ]
 
 
@@ -212,6 +214,9 @@ def create_weights(
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         if current_platform.is_cpu():
+            if layer.weight.ndim != 2:
+                # this is not a linear layer
+                return
             from vllm.model_executor.layers.utils import dispatch_cpu_unquantized_gemm
 
             dispatch_cpu_unquantized_gemm(layer, remove_weight=True)
@@ -245,6 +250,7 @@ def __init__(
         self,
         input_size: int,
         output_size: int,
+        bias: bool = False,
         skip_bias_add: bool = False,
         params_dtype: torch.dtype | None = None,
         quant_config: QuantizationConfig | None = None,
@@ -258,6 +264,7 @@ def __init__(
         # Keep input parameters
         self.input_size = input_size
         self.output_size = output_size
+        self.has_bias = bias
         self.skip_bias_add = skip_bias_add
         if params_dtype is None:
             params_dtype = torch.get_default_dtype()
@@ -265,10 +272,13 @@ def __init__(
         self.quant_config = quant_config
         self.prefix = prefix
         self.allow_fp8_block_shape_mismatch = False
+        self.quant_method: QuantizeMethodBase
         if quant_config is None:
-            self.quant_method: QuantizeMethodBase | None = UnquantizedLinearMethod()
+            self.quant_method = UnquantizedLinearMethod()
+        elif quant_method := quant_config.get_quant_method(self, prefix=prefix):
+            self.quant_method = quant_method
         else:
-            self.quant_method = quant_config.get_quant_method(self, prefix=prefix)
+            raise ValueError("All linear layers should support quant method.")
         self.return_bias = return_bias
         self.disable_tp = disable_tp
         self.tp_rank = get_tensor_model_parallel_rank() if not disable_tp else 0
@@ -323,6 +333,7 @@ def __init__(
         super().__init__(
             input_size,
             output_size,
+            bias,
             skip_bias_add,
             params_dtype,
             quant_config,
@@ -331,8 +342,6 @@ def __init__(
             disable_tp=disable_tp,
         )
 
-        # All the linear layer supports quant method.
-        assert self.quant_method is not None
         self.quant_method.create_weights(
             self,
             self.input_size,
@@ -385,7 +394,6 @@ def forward(
         x: torch.Tensor,
     ) -> torch.Tensor | tuple[torch.Tensor, Parameter | None]:
         bias = self.bias if not self.skip_bias_add else None
-        assert self.quant_method is not None
 
         output = self.quant_method.apply(self, x, bias)
 
@@ -458,6 +466,7 @@ def __init__(
         super().__init__(
             input_size,
             output_size,
+            bias,
             skip_bias_add,
             params_dtype,
             quant_config,
@@ -469,7 +478,6 @@ def __init__(
         self._maybe_allow_fp8_block_shape_mismatch()
         self.gather_output = gather_output
 
-        assert self.quant_method is not None
         self.quant_method.create_weights(
             layer=self,
             input_size_per_partition=self.input_size_per_partition,
@@ -483,6 +491,7 @@ def __init__(
                 else self.weight_loader
             ),
         )
+
         if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size_per_partition, dtype=params_dtype)
@@ -577,7 +586,6 @@ def forward(
         bias = self.bias if not self.skip_bias_add else None
 
         # Matrix multiply.
-        assert self.quant_method is not None
         output_parallel = self.quant_method.apply(self, input_, bias)
 
         if self.gather_output and self.tp_size > 1:
@@ -817,8 +825,8 @@ def weight_loader(
             # for the packing.
             packed_dim = getattr(param, "packed_dim", None)
             if packed_dim == output_dim:
-                shard_size = shard_size // param.packed_factor
-                shard_offset = shard_offset // param.packed_factor
+                shard_size = round(shard_size // param.packed_factor)
+                shard_offset = round(shard_offset // param.packed_factor)
                 # Special case for Marlin.
                 shard_size, shard_offset = adjust_marlin_shard(
                     param, shard_size, shard_offset
@@ -916,9 +924,15 @@ def weight_loader_v2(
                             loaded_weight=loaded_weight, shard_id=idx
                         )
                 else:
-                    param.load_merged_column_weight(
-                        loaded_weight=loaded_weight, shard_id=0
-                    )
+                    # When weights are already fused on disk (e.g. Phi-3's
+                    # gate_up_proj), there is only a single scale for the
+                    # entire fused matrix. Fill all slots with this scale
+                    # to ensure that any subsequent reduction (like .max())
+                    # works correctly while preserving the parameter shape.
+                    for idx in range(param.data.shape[0]):
+                        param.load_merged_column_weight(
+                            loaded_weight=loaded_weight, shard_id=idx
+                        )
                 return
             elif type(param) in (RowvLLMParameter, BasevLLMParameter):
                 param.load_merged_column_weight(loaded_weight=loaded_weight)
@@ -1108,7 +1122,12 @@ def _load_fused_module_from_checkpoint(
             # Special case for Quantization.
             # If quantized, we need to adjust the offset and size to account
             # for the packing.
-            if (
+            if isinstance(param, BlockQuantScaleParameter):
+                weight_block_size = getattr(self, "weight_block_size", None)
+                shard_size, shard_offset = adjust_block_scale_shard(
+                    weight_block_size, shard_size, shard_offset
+                )
+            elif (
                 isinstance(param, (PackedColumnParameter, PackedvLLMParameter))
                 and param.packed_dim == param.output_dim
             ):
@@ -1130,9 +1149,15 @@ def weight_loader_v2(
         self.validate_shard_id(loaded_shard_id)
         if loaded_shard_id is None:  # special case for certain models
             if isinstance(param, PerTensorScaleParameter):
-                param.load_qkv_weight(
-                    loaded_weight=loaded_weight, shard_id=0, tp_rank=self.tp_rank
-                )
+                # When weights are already fused on disk (e.g. Phi-3's
+                # qkv_proj), there is only a single scale for the entire
+                # fused matrix. Fill all slots (q, k, v) with this scale
+                # to ensure that any subsequent reduction (like .max())
+                # works correctly while preserving the parameter shape.
+                for idx in range(param.data.shape[0]):
+                    param.load_qkv_weight(
+                        loaded_weight=loaded_weight, shard_id=idx, tp_rank=self.tp_rank
+                    )
                 return
             elif type(param) in (RowvLLMParameter, BasevLLMParameter):
                 param.load_qkv_weight(loaded_weight=loaded_weight, tp_rank=self.tp_rank)
@@ -1240,8 +1265,8 @@ def weight_loader(
                     )
 
                 if packed_dim == output_dim:
-                    shard_size = shard_size // param.packed_factor
-                    shard_offset = shard_offset // param.packed_factor
+                    shard_size = round(shard_size // param.packed_factor)
+                    shard_offset = round(shard_offset // param.packed_factor)
 
                     # Special case for Marlin.
                     shard_size, shard_offset = adjust_marlin_shard(
@@ -1303,8 +1328,8 @@ def weight_loader(
             # for the packing.
             packed_dim = getattr(param, "packed_dim", None)
             if packed_dim == output_dim:
-                shard_size = shard_size // param.packed_factor
-                shard_offset = shard_offset // param.packed_factor
+                shard_size = round(shard_size // param.packed_factor)
+                shard_offset = round(shard_offset // param.packed_factor)
 
                 # Special case for Marlin.
                 shard_size, shard_offset = adjust_marlin_shard(
@@ -1428,6 +1453,7 @@ def __init__(
         super().__init__(
             input_size,
             output_size,
+            bias,
             skip_bias_add,
             params_dtype,
             quant_config,
@@ -1439,7 +1465,6 @@ def __init__(
         self.input_is_parallel = input_is_parallel
         self.reduce_results = reduce_results
 
-        assert self.quant_method is not None
         self.quant_method.create_weights(
             layer=self,
             input_size_per_partition=self.input_size_per_partition,
@@ -1529,7 +1554,6 @@ def forward(
             input_parallel = split_input[self.tp_rank].contiguous()
 
         # Matrix multiply.
-        assert self.quant_method is not None
         # Only fuse bias add into GEMM for rank 0 (this ensures that
         # bias will not get added more than once in TP>1 case)
         bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
diff --git a/vllm/model_executor/layers/mamba/abstract.py b/vllm/model_executor/layers/mamba/abstract.py
index 3c6b0139424d..2c05880c0fe1 100644
--- a/vllm/model_executor/layers/mamba/abstract.py
+++ b/vllm/model_executor/layers/mamba/abstract.py
@@ -42,9 +42,10 @@ def get_state_dtype(self) -> tuple[torch.dtype, ...]:
 
     def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec | None:
         mamba_block_size = vllm_config.cache_config.mamba_block_size
+        assert mamba_block_size is not None
         page_size_padded = vllm_config.cache_config.mamba_page_size_padded
         return MambaSpec(
-            shapes=self.get_state_shape(),
+            shapes=tuple(self.get_state_shape()),
             dtypes=self.get_state_dtype(),
             block_size=mamba_block_size,
             page_size_padded=page_size_padded,
diff --git a/vllm/model_executor/layers/mamba/gdn_linear_attn.py b/vllm/model_executor/layers/mamba/gdn_linear_attn.py
index 3d875683d26a..3a2e99b30606 100644
--- a/vllm/model_executor/layers/mamba/gdn_linear_attn.py
+++ b/vllm/model_executor/layers/mamba/gdn_linear_attn.py
@@ -8,6 +8,7 @@
 from transformers.activations import ACT2FN
 
 from vllm import envs
+from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import (
     VllmConfig,
     get_current_vllm_config,
@@ -62,9 +63,22 @@
     _resolve_layer_name,
     direct_register_custom_op,
 )
-from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
 
+# Optional ROCm AITER Triton kernels for the GDN decode fast-path.
+# Availability is checked centrally via rocm_aiter_ops; the actual function
+# references are imported here so that they can be called without per-call
+# import overhead.
+GDN_AITER_TRITON_AVAILABLE = rocm_aiter_ops.are_gdn_triton_kernels_available()
+
+if GDN_AITER_TRITON_AVAILABLE:
+    from aiter.ops.triton.causal_conv1d_update_single_token import (
+        fused_reshape_causal_conv1d_update_single_token as gdn_aiter_fused_reshape_causal_conv1d_update_single_token,  # noqa: E501
+    )
+    from aiter.ops.triton.gated_delta_net.fused_rearrange_sigmoid_gdr import (
+        fused_rearrange_sigmoid_gated_delta_rule as gdn_aiter_fused_rearrange_sigmoid_gated_delta_rule,  # noqa: E501
+    )
+
 logger = init_logger(__name__)
 
 
@@ -121,9 +135,9 @@ def fi_chunk_gated_delta_rule(
 class ChunkGatedDeltaRule(CustomOp):
     def __init__(self) -> None:
         super().__init__()
-        backend_cfg = get_current_vllm_config().additional_config.get(
-            "gdn_prefill_backend", "auto"
-        )
+        additional_config = get_current_vllm_config().additional_config
+        assert isinstance(additional_config, dict)
+        backend_cfg = additional_config.get("gdn_prefill_backend", "auto")
         backend = str(backend_cfg).strip().lower()
 
         supports_flashinfer = (
@@ -144,15 +158,14 @@ def __init__(self) -> None:
             use_flashinfer = supports_flashinfer
 
         if use_flashinfer:
-            logger.info_once("Using FlashInfer GDN prefill kernel", scope="local")
+            logger.info_once("Using FlashInfer GDN prefill kernel")
             logger.info_once(
                 "FlashInfer GDN prefill kernel is JIT-compiled; first run may "
                 "take a while to compile. Set `--gdn-prefill-backend triton` to "
                 "avoid JIT compile time.",
-                scope="local",
             )
         else:
-            logger.info_once("Using Triton/FLA GDN prefill kernel", scope="local")
+            logger.info_once("Using Triton/FLA GDN prefill kernel")
 
         self._forward_method = (
             self.forward_cuda if use_flashinfer else self.forward_native
@@ -171,8 +184,9 @@ def forward_cuda(
         chunk_indices: torch.Tensor | None = None,
         chunk_offsets: torch.Tensor | None = None,
         use_qk_l2norm_in_kernel: bool = True,
+        core_attn_out: torch.Tensor | None = None,
     ):
-        return fi_chunk_gated_delta_rule(
+        o, final_state = fi_chunk_gated_delta_rule(
             q=q,
             k=k,
             v=v,
@@ -183,6 +197,11 @@ def forward_cuda(
             cu_seqlens=cu_seqlens,
             use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel,
         )
+        if core_attn_out is not None:
+            o_flat = o.squeeze(0).reshape(-1)
+            co_flat = core_attn_out.reshape(-1)
+            co_flat[: o_flat.numel()].copy_(o_flat)
+        return o, final_state
 
     def forward_native(
         self,
@@ -197,6 +216,7 @@ def forward_native(
         chunk_indices: torch.Tensor | None = None,
         chunk_offsets: torch.Tensor | None = None,
         use_qk_l2norm_in_kernel: bool = True,
+        core_attn_out: torch.Tensor | None = None,
     ):
         return fla_chunk_gated_delta_rule(
             q=q,
@@ -210,6 +230,7 @@ def forward_native(
             chunk_indices=chunk_indices,
             chunk_offsets=chunk_offsets,
             use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel,
+            core_attn_out=core_attn_out,
         )
 
 
@@ -242,7 +263,6 @@ def __init__(
         config: Qwen3NextConfig,
         vllm_config: VllmConfig,
         prefix: str = "",
-        create_in_proj_qkvz: bool = True,
         gqa_interleaved_layout=False,
     ) -> None:
         super().__init__()
@@ -273,9 +293,19 @@ def __init__(
             else 0
         )
         self.gqa_interleaved_layout = gqa_interleaved_layout
-        self._forward_method = (
-            self.forward_xpu if current_platform.is_xpu() else self.forward_cuda
-        )
+        if current_platform.is_xpu():
+            self._forward_method = self.forward_xpu
+        elif current_platform.is_cpu():
+            from vllm.model_executor.layers.mamba.ops.cpu.gdn_attention import (
+                register_cpu_gdn_attention_ops,
+            )
+
+            register_cpu_gdn_attention_ops()
+            self._forward_method = self.forward_cpu
+        elif current_platform.is_rocm():
+            self._forward_method = self.forward_hip
+        else:
+            self._forward_method = self.forward_cuda
 
         # QKV
         self.conv_dim = self.key_dim * 2 + self.value_dim
@@ -292,31 +322,14 @@ def __init__(
         # we need to create qkvz_proj adaptively here.
         # When create_in_proj_qkvz is False (e.g. LoRA enabled in Qwen3.5),
         # in_proj_qkv and in_proj_z are created separately instead.
-        if create_in_proj_qkvz:
-            self.in_proj_qkvz = self.create_qkvz_proj(
-                hidden_size=self.hidden_size,
-                key_dim=self.key_dim,
-                value_dim=self.value_dim,
-                quant_config=quant_config,
-                prefix=f"{prefix}.in_proj_qkvz",
-            )
-        else:
-            # LoRA case (Qwen3.5 only): keep q/k/v and z as separate modules
-            # so that LoRA adapters can be applied independently.
-            self.in_proj_qkv = MergedColumnParallelLinear(
-                input_size=self.hidden_size,
-                output_sizes=[self.key_dim, self.key_dim, self.value_dim],
-                bias=False,
-                quant_config=quant_config,
-                prefix=f"{prefix}.in_proj_qkv",
-            )
-            self.in_proj_z = ColumnParallelLinear(
-                input_size=self.hidden_size,
-                output_size=self.value_dim,
-                bias=False,
-                quant_config=quant_config,
-                prefix=f"{prefix}.in_proj_z",
-            )
+        self.in_proj_qkvz = self.create_qkvz_proj(
+            hidden_size=self.hidden_size,
+            key_dim=self.key_dim,
+            value_dim=self.value_dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.in_proj_qkvz",
+        )
+
         # ba_proj doesn't support blockwise fp8 quantization.
         # Qwen3-Next and Qwen3.5 have different in_proj_ba checkpoint
         # layouts, so we use a factory method to create the projection.
@@ -357,11 +370,19 @@ def __init__(
         set_weight_attrs(self.A_log, {"weight_loader": sharded_weight_loader(0)})
         set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)})
 
+        output_gate_type = getattr(config, "output_gate_type", "silu")
+        if output_gate_type == "swish":
+            output_gate_type = "silu"
+        assert output_gate_type in ["silu", "swish", "sigmoid"], (
+            f"unsupported {output_gate_type=}"
+        )
+
         self.norm = RMSNormGated(
             self.head_v_dim,
             eps=self.layer_norm_epsilon,
             group_size=None,
             norm_before_gate=True,
+            activation=output_gate_type,
             device=current_platform.current_device(),
         )
 
@@ -484,24 +505,154 @@ def fix_query_key_value_ordering(
 
         return query, key, value, z, b, a
 
-    def rearrange_mixed_qkv(self, mixed_qkv):
-        if mixed_qkv is None:
-            return None, None, None
-        query, key, value = torch.split(
-            mixed_qkv,
+    @torch.compile(fullgraph=True)
+    def prepare_gdn_attention_core_inputs(
+        self,
+        mixed_qkvz: torch.Tensor,
+        mixed_ba: torch.Tensor,
+        num_tokens: int,
+    ):
+        """
+        Derives mixed_qkv, z, b, a from projected qkvz/ba for the GDN custom op.
+
+        For gqa_interleaved_layout (Qwen3-Next): unpack the interleaved
+        [ng, (hk + hk + np/ng*hv + np/ng*hv)] layout into contiguous qkv.
+        For non-interleaved layout (Qwen3.5): simple split along last dim.
+        """
+        if not self.gqa_interleaved_layout:
+            # Qwen3.5: weights are in [q, k, v, z] order
+            assert num_tokens == mixed_qkvz.shape[0]
+            qkv_size = (self.key_dim * 2 + self.value_dim) // self.tp_size
+            z_size = self.value_dim // self.tp_size
+            mixed_qkv, z_flat = mixed_qkvz.split([qkv_size, z_size], dim=-1)
+            n = mixed_qkvz.shape[0]
+            z_out = z_flat.reshape(n, -1, self.head_v_dim)
+            b, a = mixed_ba.chunk(2, dim=-1)
+            return mixed_qkv, z_out, b, a
+
+        # Qwen3-Next: interleaved GQA layout
+        base_shape_qkvz = mixed_qkvz.size()[:-1]
+        base_shape_ba = mixed_ba.size()[:-1]
+        ng = self.num_k_heads // self.tp_size
+
+        new_tensor_shape_qkvz = base_shape_qkvz + (
+            ng,
+            (
+                self.head_k_dim
+                + self.head_k_dim
+                + (self.head_v_dim + self.head_v_dim)
+                * self.num_v_heads
+                // self.num_k_heads
+            ),
+        )
+        new_tensor_shape_ba = base_shape_ba + (
+            ng,
+            2 * self.num_v_heads // self.num_k_heads,
+        )
+
+        mixed_qkvz = mixed_qkvz.view(*new_tensor_shape_qkvz)
+        mixed_ba = mixed_ba.view(*new_tensor_shape_ba)
+
+        split_arg_list_qkvz = [
+            self.head_k_dim,
+            self.head_k_dim,
+            (self.num_v_heads // self.num_k_heads * self.head_v_dim),
+            (self.num_v_heads // self.num_k_heads * self.head_v_dim),
+        ]
+        split_arg_list_ba = [
+            self.num_v_heads // self.num_k_heads,
+            self.num_v_heads // self.num_k_heads,
+        ]
+
+        (query, key, value, z) = torch.split(mixed_qkvz, split_arg_list_qkvz, dim=-1)
+        (b, a) = torch.split(mixed_ba, split_arg_list_ba, dim=-1)
+
+        mixed_qkv_logical = torch.cat(
             [
-                self.key_dim // self.tp_size,
-                self.key_dim // self.tp_size,
-                self.value_dim // self.tp_size,
+                query.reshape(num_tokens, -1),
+                key.reshape(num_tokens, -1),
+                value.reshape(num_tokens, -1),
             ],
             dim=-1,
         )
-        query, key = map(
-            lambda x: rearrange(x, "l (h d) -> 1 l h d", d=self.head_k_dim),
-            (query, key),
+
+        # The split above produces non-contiguous views into the interleaved
+        # buffer.  Concatenating everything into a single flat tensor forces a
+        # contiguous copy, then slicing back out gives contiguous q/k/v/z/b/a
+        # tensors that downstream kernels require.  Doing this in one cat+slice
+        # keeps torch.compile in a single Triton graph instead of emitting
+        # separate copy kernels per tensor.  The original code used
+        # rearrange(...).contiguous() on each tensor individually.
+        fused = torch.cat(
+            [
+                mixed_qkv_logical.reshape(-1),
+                z.reshape(-1),
+                b.reshape(-1),
+                a.reshape(-1),
+            ],
+            dim=0,
+        )
+
+        curr = 0
+        qkv_numel = mixed_qkv_logical.numel()
+        z_numel = z.numel()
+        b_numel = b.numel()
+        a_numel = a.numel()
+
+        mixed_qkv_out = fused[curr : curr + qkv_numel].view(num_tokens, -1)
+        curr += qkv_numel
+
+        z_out = fused[curr : curr + z_numel].view(
+            num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim
+        )
+        curr += z_numel
+
+        b_out = fused[curr : curr + b_numel].view(
+            num_tokens, self.num_v_heads // self.tp_size
         )
-        value = rearrange(value, "l (h d) -> 1 l h d", d=self.head_v_dim)
-        return query.contiguous(), key.contiguous(), value.contiguous()
+        curr += b_numel
+
+        a_out = fused[curr : curr + a_numel].view(
+            num_tokens, self.num_v_heads // self.tp_size
+        )
+
+        return mixed_qkv_out, z_out, b_out, a_out
+
+    def rearrange_mixed_qkv(self, mixed_qkv):
+        """Split packed qkv into contiguous (1, seq, heads, dim) tensors.
+
+        The original code used ``rearrange(x, "l (h d) -> 1 l h d", d=...)``
+        followed by ``.contiguous()`` on each tensor.  This version flattens
+        all three splits into a single buffer via ``torch.cat`` so that
+        torch.compile emits one Triton copy kernel instead of three separate
+        contiguous() calls.
+        """
+        if mixed_qkv is None:
+            return None, None, None
+
+        seq_len = mixed_qkv.shape[0]
+        q_dim = self.key_dim // self.tp_size
+        k_dim = self.key_dim // self.tp_size
+        v_dim = self.value_dim // self.tp_size
+
+        query, key, value = torch.split(mixed_qkv, [q_dim, k_dim, v_dim], dim=-1)
+
+        fused = torch.cat(
+            [query.reshape(-1), key.reshape(-1), value.reshape(-1)], dim=0
+        )
+
+        q_size = seq_len * q_dim
+        k_size = seq_len * k_dim
+
+        q_contig = fused[0:q_size]
+        k_contig = fused[q_size : q_size + k_size]
+        v_contig = fused[q_size + k_size :]
+
+        query = q_contig.view(1, seq_len, -1, self.head_k_dim)
+        key = k_contig.view(1, seq_len, -1, self.head_k_dim)
+        value = v_contig.view(1, seq_len, -1, self.head_v_dim)
+
+        return query, key, value
 
     def forward(
         self,
@@ -510,6 +661,63 @@ def forward(
     ):
         self._forward_method(hidden_states, output)
 
+    def _output_projection(
+        self,
+        core_attn_out: torch.Tensor,
+        z: torch.Tensor,
+        output: torch.Tensor,
+        num_tokens: int,
+    ):
+        """Part 3: RMSNormGated + output linear projection.
+
+        The RMSNormGated + quant sequence is eligible for fusion
+        by the compilation pass when fuse_norm_quant is enabled.
+        """
+        z_shape_og = z.shape
+        core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
+        z = z.reshape(-1, z.shape[-1])
+        core_attn_out = self.norm(core_attn_out, z)
+        core_attn_out = core_attn_out.reshape(z_shape_og)
+        core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
+        output[:num_tokens], _ = self.out_proj(core_attn_out)
+
+    def forward_hip(
+        self,
+        hidden_states: torch.Tensor,
+        output: torch.Tensor,
+    ):
+        """ROCm forward using AITER Triton fused projection+attention when
+        available, otherwise falling back to the generic CUDA path."""
+        if GDN_AITER_TRITON_AVAILABLE:
+            num_tokens = hidden_states.size(0)
+            projected_states_qkvz, _ = self.in_proj_qkvz(hidden_states)
+            projected_states_ba, _ = self.in_proj_ba(hidden_states)
+            projected_states_qkvz = projected_states_qkvz.view(num_tokens, -1)
+            projected_states_ba = projected_states_ba.view(num_tokens, -1)
+            core_attn_out = torch.empty(
+                (num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim),
+                dtype=hidden_states.dtype,
+                device=hidden_states.device,
+            )
+            z = torch.empty(
+                (num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim),
+                dtype=projected_states_qkvz.dtype,
+                device=projected_states_qkvz.device,
+            )
+
+            torch.ops.vllm.gdn_attention_core(
+                projected_states_qkvz,
+                projected_states_ba,
+                z,
+                core_attn_out,
+                fast_kernel=True,
+                layer_name=_encode_layer_name(self.prefix),
+            )
+
+            self._output_projection(core_attn_out, z, output, num_tokens)
+        else:
+            self.forward_cuda(hidden_states, output)
+
     def forward_cuda(
         self,
         hidden_states: torch.Tensor,
@@ -525,37 +733,27 @@ def forward_cuda(
         # ============================================================
         # Part 1: Input Projection
         # ============================================================
-        if hasattr(self, "in_proj_qkv"):
-            # LoRA path (Qwen3.5 only): separate in_proj_qkv and in_proj_z
-            mixed_qkv, _ = self.in_proj_qkv(hidden_states)
-            ba, _ = self.in_proj_ba(hidden_states)
-            z, _ = self.in_proj_z(hidden_states)
+        mixed_qkvz, _ = self.in_proj_qkvz(hidden_states)
+        ba, _ = self.in_proj_ba(hidden_states)
+
+        if self.gqa_interleaved_layout:
+            # Qwen3-Next: unpack the interleaved GQA layout
+            query, key, value, z, b, a = self.fix_query_key_value_ordering(
+                mixed_qkvz, ba
+            )
+            query, key, value = map(
+                lambda x: rearrange(x, "l p d -> l (p d)"), (query, key, value)
+            )
+            mixed_qkv = torch.cat((query, key, value), dim=-1)
+        else:
+            # Qwen3.5: weights are already in [q, k, v, z] and [b, a] order
+            qkv_size = (self.key_dim * 2 + self.value_dim) // self.tp_size
+            z_size = self.value_dim // self.tp_size
+            mixed_qkv, z = mixed_qkvz.split([qkv_size, z_size], dim=-1)
             z = z.reshape(z.size(0), -1, self.head_v_dim)
             b, a = ba.chunk(2, dim=-1)
             b = b.contiguous()
             a = a.contiguous()
-        else:
-            mixed_qkvz, _ = self.in_proj_qkvz(hidden_states)
-            ba, _ = self.in_proj_ba(hidden_states)
-
-            if self.gqa_interleaved_layout:
-                # Qwen3-Next: unpack the interleaved GQA layout
-                query, key, value, z, b, a = self.fix_query_key_value_ordering(
-                    mixed_qkvz, ba
-                )
-                query, key, value = map(
-                    lambda x: rearrange(x, "l p d -> l (p d)"), (query, key, value)
-                )
-                mixed_qkv = torch.cat((query, key, value), dim=-1)
-            else:
-                # Qwen3.5: weights are already in [q, k, v, z] and [b, a] order
-                qkv_size = (self.key_dim * 2 + self.value_dim) // self.tp_size
-                z_size = self.value_dim // self.tp_size
-                mixed_qkv, z = mixed_qkvz.split([qkv_size, z_size], dim=-1)
-                z = z.reshape(z.size(0), -1, self.head_v_dim)
-                b, a = ba.chunk(2, dim=-1)
-                b = b.contiguous()
-                a = a.contiguous()
 
         # ============================================================
         # Part 2: Core Attention (Custom Op)
@@ -573,20 +771,14 @@ def forward_cuda(
             b,
             a,
             core_attn_out,
-            _encode_layer_name(self.prefix),
+            fast_kernel=False,
+            layer_name=_encode_layer_name(self.prefix),
         )
 
         # ============================================================
         # Part 3: Output Projection
         # ============================================================
-        z_shape_og = z.shape
-        # Reshape input data into 2D tensor
-        core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
-        z = z.reshape(-1, z.shape[-1])
-        core_attn_out = self.norm(core_attn_out, z)
-        core_attn_out = core_attn_out.reshape(z_shape_og)
-        core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
-        output[:num_tokens], _ = self.out_proj(core_attn_out)
+        self._output_projection(core_attn_out, z, output, num_tokens)
 
     def forward_xpu(
         self,
@@ -601,8 +793,6 @@ def forward_xpu(
         """
         num_tokens = hidden_states.size(0)
 
-        assert not hasattr(self, "in_proj_qkv"), "lora isn't supported on XPU."
-
         # ============================================================
         # Part 1: Input Projection
         # ============================================================
@@ -612,53 +802,20 @@ def forward_xpu(
         # ============================================================
         # Part 2: Core Attention
         # ============================================================
-        forward_context = get_forward_context()
-        attn_metadata: AttentionMetadata = forward_context.attn_metadata
         core_attn_out = torch.zeros(
             (num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim),
             dtype=hidden_states.dtype,
             device=hidden_states.device,
         )
         z = torch.empty_like(core_attn_out)
-        if attn_metadata is not None:
-            attn_metadata = attn_metadata[self.prefix]
-
-            # TODO: xpu does not support this param yet
-            spec_sequence_masks = attn_metadata.spec_sequence_masks
-            assert spec_sequence_masks is None
 
-            conv_weights = self.conv1d.weight.view(
-                self.conv1d.weight.size(0), self.conv1d.weight.size(2)
-            )
-
-            conv_state = self.kv_cache[0]
-            ssm_state = self.kv_cache[1]
-
-            torch.ops._xpu_C.gdn_attention(
-                core_attn_out,
-                z,
-                projected_states_qkvz,
-                projected_states_ba,
-                self.num_k_heads,
-                self.num_v_heads,
-                self.head_k_dim,
-                self.head_v_dim,
-                conv_state=conv_state,
-                ssm_state=ssm_state,
-                conv_weights=conv_weights,
-                conv_bias=self.conv1d.bias,
-                activation=self.activation,
-                A_log=self.A_log,
-                dt_bias=self.dt_bias,
-                num_prefills=attn_metadata.num_prefills,
-                num_decodes=attn_metadata.num_decodes,
-                has_initial_state=attn_metadata.has_initial_state,
-                non_spec_query_start_loc=attn_metadata.non_spec_query_start_loc,
-                non_spec_state_indices_tensor=attn_metadata.non_spec_state_indices_tensor,
-                num_actual_tokens=attn_metadata.num_actual_tokens,
-                tp_size=self.tp_size,
-                reorder_input=not self.gqa_interleaved_layout,
-            )
+        torch.ops.vllm.gdn_attention_core_xpu(
+            core_attn_out,
+            z,
+            projected_states_qkvz,
+            projected_states_ba,
+            self.prefix,
+        )
 
         # ============================================================
         # Part 3: Output Projection
@@ -672,7 +829,57 @@ def forward_xpu(
         core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
         output[:num_tokens], _ = self.out_proj(core_attn_out)
 
-    def _warmup_prefill_kernels(self, mixed_qkv: torch.Tensor) -> None:
+    def forward_cpu(
+        self,
+        hidden_states: torch.Tensor,
+        output: torch.Tensor,
+    ):
+        assert not hasattr(self, "in_proj_qkv"), "lora isn't supported on CPU."
+
+        mixed_qkvz, _ = self.in_proj_qkvz(hidden_states)
+        ba, _ = self.in_proj_ba(hidden_states)
+
+        if self.gqa_interleaved_layout:
+            # Qwen3-Next: unpack the interleaved GQA layout
+            query, key, value, z, b, a = self.fix_query_key_value_ordering(
+                mixed_qkvz, ba
+            )
+            query, key, value = map(
+                lambda x: rearrange(x, "l p d -> l (p d)"), (query, key, value)
+            )
+            mixed_qkv = torch.cat((query, key, value), dim=-1)
+        else:
+            # Qwen3.5: weights are already in [q, k, v, z] and [b, a] order
+            qkv_size = (self.key_dim * 2 + self.value_dim) // self.tp_size
+            z_size = self.value_dim // self.tp_size
+            mixed_qkv, z = mixed_qkvz.split([qkv_size, z_size], dim=-1)
+            z = z.reshape(z.size(0), -1, self.head_v_dim)
+            b, a = ba.chunk(2, dim=-1)
+
+        num_tokens = hidden_states.size(0)
+        core_attn_out = torch.zeros(
+            (num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim),
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
+        )
+
+        torch.ops.vllm.cpu_gdn_attention_core(
+            mixed_qkv,
+            b,
+            a,
+            core_attn_out,
+            _encode_layer_name(self.prefix),
+        )
+
+        z_shape_og = z.shape
+        core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
+        z = z.reshape(-1, z.shape[-1])
+        core_attn_out = self.norm(core_attn_out, z)
+        core_attn_out = core_attn_out.reshape(z_shape_og)
+        core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
+        output[:num_tokens], _ = self.out_proj(core_attn_out)
+
+    def _warmup_prefill_kernels(self, qkv_or_qkvz: torch.Tensor, v_dim: int) -> None:
         """Warm up GDN prefill kernels during V1 profiling.
 
         During V1 profile runs, ``_forward_core`` returns early because
@@ -693,7 +900,7 @@ def _warmup_prefill_kernels(self, mixed_qkv: torch.Tensor) -> None:
         ``BT = chunk_size`` (64).  A single warmup pass with T = 64
         is sufficient to populate the autotuner cache.
 
-        The decode path uses ``fused_sigmoid_gating_delta_rule_update``
+        The decode path uses ``gdn_aiter_fused_rearrange_sigmoid_gated_delta_rule``
         which has fixed kernel parameters (no autotuning), so only the
         prefill (chunked) path needs warming up.
         """
@@ -701,8 +908,8 @@ def _warmup_prefill_kernels(self, mixed_qkv: torch.Tensor) -> None:
             return
         self._prefill_kernels_warmed_up = True
 
-        device = mixed_qkv.device
-        dtype = mixed_qkv.dtype
+        device = qkv_or_qkvz.device
+        dtype = qkv_or_qkvz.dtype
         num_k_heads = self.num_k_heads // self.tp_size
         num_v_heads = self.num_v_heads // self.tp_size
         _, state_dtype = self.get_state_dtype()
@@ -713,7 +920,7 @@ def _warmup_prefill_kernels(self, mixed_qkv: torch.Tensor) -> None:
         # then run chunk_gated_delta_rule with in-kernel L2 norm disabled.
         T = FLA_CHUNK_SIZE
         dummy_mixed_qkv = torch.randn(
-            T, mixed_qkv.shape[-1], device=device, dtype=dtype
+            T, qkv_or_qkvz.shape[-1] - v_dim, device=device, dtype=dtype
         )
         dummy_a = torch.randn(T, num_v_heads, device=device, dtype=dtype)
         dummy_b = torch.randn(T, num_v_heads, device=device, dtype=dtype)
@@ -776,6 +983,66 @@ def _warmup_prefill_kernels(self, mixed_qkv: torch.Tensor) -> None:
 
         torch.accelerator.empty_cache()
 
+    def _forward_core_rocm(
+        self,
+        qkvz: torch.Tensor,
+        ba: torch.Tensor,
+        z_out: torch.Tensor,
+        core_attn_out: torch.Tensor,
+    ):
+        """ROCm AITER fast path: conv1d + recurrent attention from packed
+        qkvz/ba layout.
+
+        For decode-only (no spec, no prefill), dispatches directly to
+        ``_forward_core_decode_fast``.  Otherwise unpacks the packed
+        layout and falls through to ``_forward_core``.
+
+        Args:
+            qkvz: packed [q, k, v, z] projection (num_tokens, qkvz_dim)
+            ba:   packed [b, a] gating vectors    (num_tokens, 2*num_heads)
+            z_out: **output** buffer for z        (num_tokens, num_heads,
+                   head_dim); mutated in-place.
+            core_attn_out: Pre-allocated output buffer for attention results.
+        """
+        forward_context = get_forward_context()
+        attn_metadata_raw = forward_context.attn_metadata
+
+        if attn_metadata_raw is None:
+            v_dim = core_attn_out.shape[-1] * core_attn_out.shape[-2]
+            self._warmup_prefill_kernels(qkvz, v_dim)
+            return
+
+        assert isinstance(attn_metadata_raw, dict)
+        attn_metadata = attn_metadata_raw[self.prefix]  # type: ignore[index]
+        assert isinstance(attn_metadata, GDNAttentionMetadata)
+
+        if (
+            attn_metadata.spec_sequence_masks is None
+            and attn_metadata.num_prefills == 0
+            and attn_metadata.num_decodes > 0
+        ):
+            return self._forward_core_decode_fast(
+                qkvz=qkvz,
+                ba=ba,
+                z_out=z_out,
+                core_attn_out=core_attn_out,
+                attn_metadata=attn_metadata,
+            )
+
+        core_attn_out.zero_()
+        z_out.zero_()
+        num_tokens_all = qkvz.shape[0]
+        mixed_qkv, z, b, a = self.prepare_gdn_attention_core_inputs(
+            qkvz, ba, num_tokens_all
+        )
+        z_out[:] = z
+        self._forward_core(
+            mixed_qkv=mixed_qkv,
+            b=b,
+            a=a,
+            core_attn_out=core_attn_out,
+        )
+
     def _forward_core(
         self,
         mixed_qkv: torch.Tensor,
@@ -783,17 +1050,23 @@ def _forward_core(
         a: torch.Tensor,
         core_attn_out: torch.Tensor,
     ):
+        """Core conv1d + recurrent attention (standard path).
+
+        Args:
+            mixed_qkv: packed [q, k, v] projection (num_tokens, qkv_dim)
+            b: beta gating vector                   (num_tokens, num_heads)
+            a: alpha gating vector                  (num_tokens, num_heads)
+            core_attn_out: Pre-allocated output buffer for attention results.
+        """
         forward_context = get_forward_context()
-        attn_metadata: AttentionMetadata = forward_context.attn_metadata
+        attn_metadata_raw = forward_context.attn_metadata
 
-        if attn_metadata is None:
-            # V1 profile run — warm up prefill kernels so that
-            # autotuning completes before KV cache allocation.
-            self._warmup_prefill_kernels(mixed_qkv)
+        if attn_metadata_raw is None:
+            self._warmup_prefill_kernels(mixed_qkv, 0)
             return
 
-        assert isinstance(attn_metadata, dict)
-        attn_metadata = attn_metadata[self.prefix]
+        assert isinstance(attn_metadata_raw, dict)
+        attn_metadata = attn_metadata_raw[self.prefix]  # type: ignore[index]
         assert isinstance(attn_metadata, GDNAttentionMetadata)
 
         if (
@@ -852,14 +1125,16 @@ def _forward_core(
 
         # 1.1: Process the multi-query part
         if spec_sequence_masks is not None:
+            # spec_state_indices_tensor is always set when spec_sequence_masks is set
+            assert spec_state_indices_tensor is not None
             mixed_qkv_spec = causal_conv1d_update(
                 mixed_qkv_spec,
                 conv_state,
                 conv_weights,
                 self.conv1d.bias,
                 self.activation,
-                conv_state_indices=spec_state_indices_tensor[:, 0][
-                    : attn_metadata.num_spec_decodes
+                conv_state_indices=spec_state_indices_tensor[:, 0][  # type: ignore[index]
+                    : attn_metadata.num_spec_decodes  # type: ignore[attr-defined]
                 ],
                 num_accepted_tokens=num_accepted_tokens,
                 query_start_loc=spec_query_start_loc,
@@ -892,8 +1167,8 @@ def _forward_core(
                 conv_weights,
                 self.conv1d.bias,
                 self.activation,
-                conv_state_indices=non_spec_state_indices_tensor[
-                    : attn_metadata.num_actual_tokens
+                conv_state_indices=non_spec_state_indices_tensor[  # type: ignore[index]
+                    : attn_metadata.num_actual_tokens  # type: ignore[attr-defined]
                 ],
                 validate_data=True,
             )
@@ -957,8 +1232,9 @@ def _forward_core(
                     v=value_spec,
                     initial_state=ssm_state,
                     inplace_final_state=True,
-                    cu_seqlens=spec_query_start_loc[
-                        : attn_metadata.num_spec_decodes + 1
+                    cu_seqlens=spec_query_start_loc[  # type: ignore[index]
+                        : attn_metadata.num_spec_decodes
+                        + 1  # type: ignore[attr-defined]
                     ],
                     ssm_state_indices=spec_state_indices_tensor,
                     num_accepted_tokens=num_accepted_tokens,
@@ -970,8 +1246,10 @@ def _forward_core(
 
         # 2.2: Process the remaining part
         if attn_metadata.num_prefills > 0:
-            initial_state = ssm_state[non_spec_state_indices_tensor].contiguous()
-            initial_state[~has_initial_state, ...] = 0
+            assert non_spec_state_indices_tensor is not None
+            initial_state = ssm_state[non_spec_state_indices_tensor].contiguous()  # type: ignore[index]
+            assert has_initial_state is not None
+            initial_state[~has_initial_state, ...] = 0  # type: ignore[operator]
             (
                 core_attn_out_non_spec,
                 last_recurrent_state,
@@ -1004,8 +1282,9 @@ def _forward_core(
                     v=value_non_spec,
                     initial_state=ssm_state,
                     inplace_final_state=True,
-                    cu_seqlens=non_spec_query_start_loc[
-                        : attn_metadata.num_decodes + 1
+                    cu_seqlens=non_spec_query_start_loc[  # type: ignore[index]
+                        : attn_metadata.num_decodes
+                        + 1  # type: ignore[attr-defined]
                     ],
                     ssm_state_indices=non_spec_state_indices_tensor,
                     use_qk_l2norm_in_kernel=True,
@@ -1029,6 +1308,72 @@ def _forward_core(
         else:
             core_attn_out[:num_actual_tokens] = core_attn_out_non_spec.squeeze(0)
 
+    def _forward_core_decode_fast(
+        self,
+        qkvz: torch.Tensor,
+        ba: torch.Tensor,
+        z_out: torch.Tensor,
+        core_attn_out: torch.Tensor,
+        attn_metadata: GDNAttentionMetadata,
+    ):
+        non_spec_query_start_loc = attn_metadata.non_spec_query_start_loc
+        non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor  # noqa: E501
+        self_kv_cache = self.kv_cache
+        # conv_state must be (..., dim, width-1) for the conv kernels.
+        # DS layout stores it that way directly; SD layout needs a transpose.
+        conv_state = (
+            self_kv_cache[0]
+            if is_conv_state_dim_first()
+            else self_kv_cache[0].transpose(-1, -2)
+        )
+        ssm_state = self_kv_cache[1]
+
+        # 1. Convolution sequence transformation
+        conv_weights = self.conv1d.weight.view(
+            self.conv1d.weight.size(0), self.conv1d.weight.size(2)
+        )
+
+        mixed_qkv_non_spec, b, a = (
+            gdn_aiter_fused_reshape_causal_conv1d_update_single_token(
+                qkvz,
+                attn_metadata.num_actual_tokens,
+                self.num_k_heads // self.tp_size,
+                self.num_v_heads // self.tp_size,
+                self.head_k_dim,
+                self.head_v_dim,
+                ba,
+                z_out,
+                core_attn_out,
+                conv_state,
+                conv_weights,
+                self.conv1d.bias,
+                self.activation,
+                conv_state_indices=non_spec_state_indices_tensor[  # type: ignore[index]
+                    : attn_metadata.num_actual_tokens
+                ],
+                validate_data=True,
+            )
+        )
+
+        # 2. Recurrent attention
+        gdn_aiter_fused_rearrange_sigmoid_gated_delta_rule(
+            A_log=self.A_log,
+            a=a,
+            b=b,
+            dt_bias=self.dt_bias,
+            qkv=mixed_qkv_non_spec,
+            key_dim=self.key_dim // self.tp_size,
+            value_dim=self.value_dim // self.tp_size,
+            head_k_dim=self.head_k_dim,
+            head_v_dim=self.head_v_dim,
+            initial_state=ssm_state,
+            inplace_final_state=True,
+            cu_seqlens=non_spec_query_start_loc[: attn_metadata.num_decodes + 1],  # type: ignore[index]
+            ssm_state_indices=non_spec_state_indices_tensor,
+            use_qk_l2norm_in_kernel=True,
+            core_attn_out=core_attn_out.reshape(-1),
+        )
+
     def _forward_core_decode_non_spec(
         self,
         mixed_qkv: torch.Tensor,
@@ -1065,7 +1410,7 @@ def _forward_core_decode_non_spec(
             conv_weights,
             self.conv1d.bias,
             self.activation,
-            conv_state_indices=non_spec_state_indices_tensor[:num_actual_tokens],
+            conv_state_indices=non_spec_state_indices_tensor[:num_actual_tokens],  # type: ignore[index]
             validate_data=False,
         )
         out_buf = core_attn_out[:num_actual_tokens].unsqueeze(1)
@@ -1078,40 +1423,58 @@ def _forward_core_decode_non_spec(
             scale=self.head_k_dim**-0.5,
             initial_state=ssm_state,
             out=out_buf,
-            ssm_state_indices=non_spec_state_indices_tensor[:num_actual_tokens],
+            ssm_state_indices=non_spec_state_indices_tensor[:num_actual_tokens],  # type: ignore[index]
             use_qk_l2norm_in_kernel=True,
         )
         return
 
 
 def gdn_attention_core(
-    mixed_qkv: torch.Tensor,
-    b: torch.Tensor,
-    a: torch.Tensor,
+    qkv_or_qkvz: torch.Tensor,
+    b_or_ba: torch.Tensor,
+    a_or_z_out: torch.Tensor,
     core_attn_out: torch.Tensor,
+    fast_kernel: bool,
     layer_name: LayerNameType,
 ) -> None:
-    """
-    Custom op for the core attention computation.
-    Only handles the convolution + recurrent attention part.
-    Input/output projections are handled outside this op.
+    """Custom op dispatching to _forward_core or _forward_core_rocm.
+
+    Handles conv1d + recurrent attention only; input/output projections
+    are performed by the caller.
+
+    When ``fast_kernel=False`` (standard path):
+        qkv_or_qkvz is [q, k, v], b_or_ba is b, a_or_z_out is a (read-only).
+    When ``fast_kernel=True`` (AITER Triton fast path, ROCm only):
+        qkv_or_qkvz is [q, k, v, z], b_or_ba is [b, a], a_or_z_out is the
+        z output buffer (mutated in-place).
+
+    ``core_attn_out`` is always mutated in-place.
     """
     layer_name = _resolve_layer_name(layer_name)
     forward_context: ForwardContext = get_forward_context()
     self = forward_context.no_compile_layers[layer_name]
-    self._forward_core(
-        mixed_qkv=mixed_qkv,
-        b=b,
-        a=a,
-        core_attn_out=core_attn_out,
-    )
+    if fast_kernel:
+        self._forward_core_rocm(
+            qkvz=qkv_or_qkvz,
+            ba=b_or_ba,
+            z_out=a_or_z_out,
+            core_attn_out=core_attn_out,
+        )
+    else:
+        self._forward_core(
+            mixed_qkv=qkv_or_qkvz,
+            b=b_or_ba,
+            a=a_or_z_out,
+            core_attn_out=core_attn_out,
+        )
 
 
 def gdn_attention_core_fake(
-    mixed_qkv: torch.Tensor,
-    b: torch.Tensor,
-    a: torch.Tensor,
+    qkv_or_qkvz: torch.Tensor,
+    b_or_ba: torch.Tensor,
+    a_or_z_out: torch.Tensor,
     core_attn_out: torch.Tensor,
+    fast_kernel: bool,
     layer_name: LayerNameType,
 ) -> None:
     """Fake implementation for torch.compile."""
@@ -1121,7 +1484,7 @@ def gdn_attention_core_fake(
 direct_register_custom_op(
     op_name="gdn_attention_core",
     op_func=gdn_attention_core,
-    mutates_args=["core_attn_out"],
+    mutates_args=["a_or_z_out", "core_attn_out"],
     fake_impl=gdn_attention_core_fake,
 )
 
diff --git a/vllm/model_executor/layers/mamba/linear_attn.py b/vllm/model_executor/layers/mamba/linear_attn.py
index 18fcc1426cc5..b277c9ed6b74 100644
--- a/vllm/model_executor/layers/mamba/linear_attn.py
+++ b/vllm/model_executor/layers/mamba/linear_attn.py
@@ -3,6 +3,7 @@
 
 import math
 from collections.abc import Callable
+from functools import partial
 
 import torch
 import torch.nn.functional as F
@@ -33,28 +34,46 @@
 from vllm.v1.attention.backends.linear_attn import LinearAttentionMetadata
 
 
+@CustomOp.register("minimax_text01_rmsnorm_tp")
 class MiniMaxText01RMSNormTP(CustomOp):
-    name = "MiniMaxText01RMSNormTP"
-
-    def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
+    def __init__(
+        self,
+        hidden_size: int,
+        eps: float = 1e-6,
+        *,
+        weight_shard_world_size: int | None = None,
+        weight_shard_rank: int | None = None,
+    ) -> None:
         super().__init__()
         self.tp_world = get_tensor_model_parallel_world_size()
         self.tp_rank = get_tensor_model_parallel_rank()
-        self.weight = nn.Parameter(torch.ones(int(hidden_size / self.tp_world)))
+        self.weight_shard_world = weight_shard_world_size or self.tp_world
+        self.weight_shard_rank = (
+            self.tp_rank if weight_shard_rank is None else weight_shard_rank
+        )
 
-        self.weight.weight_loader = self.weight_loader
+        self.weight = nn.Parameter(torch.ones(hidden_size // self.weight_shard_world))
+        self.weight.weight_loader = partial(
+            self.weight_loader,
+            shard_world_size=self.weight_shard_world,
+            shard_rank=self.weight_shard_rank,
+        )
         self.variance_epsilon = eps
 
     @staticmethod
     def weight_loader(
         param: nn.Parameter,
         loaded_weight: torch.Tensor,
+        shard_world_size: int | None = None,
+        shard_rank: int | None = None,
     ) -> None:
-        tp_world = get_tensor_model_parallel_world_size()
-        tp_rank = get_tensor_model_parallel_rank()
+        if shard_world_size is None:
+            shard_world_size = get_tensor_model_parallel_world_size()
+        if shard_rank is None:
+            shard_rank = get_tensor_model_parallel_rank()
 
-        shard_size = loaded_weight.shape[0] // tp_world
-        shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
+        shard_size = loaded_weight.shape[0] // shard_world_size
+        shard = slice(shard_rank * shard_size, (shard_rank + 1) * shard_size)
         param.data.copy_(loaded_weight[shard])
 
     def _forward(
@@ -396,10 +415,11 @@ def _forward(
         self, hidden_states: torch.Tensor, output: torch.Tensor, positions: torch.Tensor
     ) -> None:
         forward_context = get_forward_context()
-        attn_metadata: AttentionMetadata = forward_context.attn_metadata
-        if attn_metadata is not None:
-            assert isinstance(attn_metadata, dict)
-            attn_metadata = attn_metadata[self.prefix]
+        attn_metadata_raw = forward_context.attn_metadata
+        attn_metadata: AttentionMetadata | None = None
+        if attn_metadata_raw is not None:
+            assert isinstance(attn_metadata_raw, dict)
+            attn_metadata = attn_metadata_raw[self.prefix]
             assert isinstance(attn_metadata, LinearAttentionMetadata)
             num_actual_tokens = (
                 attn_metadata.num_prefill_tokens + attn_metadata.num_decode_tokens
diff --git a/vllm/model_executor/layers/mamba/mamba_mixer.py b/vllm/model_executor/layers/mamba/mamba_mixer.py
index 4509a0956280..0e476755201e 100644
--- a/vllm/model_executor/layers/mamba/mamba_mixer.py
+++ b/vllm/model_executor/layers/mamba/mamba_mixer.py
@@ -40,6 +40,7 @@
     _resolve_layer_name,
     direct_register_custom_op,
 )
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.mamba1_attn import Mamba1AttentionMetadata
 
 
@@ -258,15 +259,16 @@ def forward_impl(self, hidden_states: torch.Tensor, output: torch.Tensor):
         """
 
         forward_context: ForwardContext = get_forward_context()
-        attn_metadata = forward_context.attn_metadata
+        attn_metadata_raw = forward_context.attn_metadata
 
         assert self.cache_config is not None
         mamba_block_size = self.cache_config.mamba_block_size
         is_mamba_cache_all = self.cache_config.mamba_cache_mode == "all"
 
-        if attn_metadata is not None:
-            assert isinstance(attn_metadata, dict)
-            attn_metadata = attn_metadata[self.prefix]
+        attn_metadata: AttentionMetadata | None = None
+        if attn_metadata_raw is not None:
+            assert isinstance(attn_metadata_raw, dict)
+            attn_metadata = attn_metadata_raw[self.prefix]
             assert isinstance(attn_metadata, Mamba1AttentionMetadata)
             query_start_loc_p = attn_metadata.query_start_loc_p
             state_indices_tensor_p = attn_metadata.state_indices_tensor_p
@@ -391,6 +393,9 @@ def forward_impl(self, hidden_states: torch.Tensor, output: torch.Tensor):
             ssm_outputs.append(scan_out_p)
 
         if has_decode:
+            # state_indices_tensor_d is assigned when attn_metadata is not None,
+            # and has_decode is only True when attn_metadata is not None
+            assert state_indices_tensor_d is not None
             if is_mamba_cache_all:
                 state_indices_tensor_d_input = state_indices_tensor_d.gather(
                     1, block_idx_last_computed_token_d.unsqueeze(1)
diff --git a/vllm/model_executor/layers/mamba/mamba_mixer2.py b/vllm/model_executor/layers/mamba/mamba_mixer2.py
index 0518bde2f427..2b4b1934f9b3 100644
--- a/vllm/model_executor/layers/mamba/mamba_mixer2.py
+++ b/vllm/model_executor/layers/mamba/mamba_mixer2.py
@@ -572,14 +572,16 @@ def conv_ssm_forward(
         # kernels to operate in continuous batching and in chunked prefill
         # modes; they are computed at top-level model forward since they
         # stay the same and reused for all mamba layers in the same iteration
-        attn_metadata: AttentionMetadata = forward_context.attn_metadata
+        attn_metadata_raw = forward_context.attn_metadata
 
         assert self.cache_config is not None
         mamba_block_size = self.cache_config.mamba_block_size
         is_mamba_cache_all = self.cache_config.mamba_cache_mode == "all"
-        if attn_metadata is not None:
-            assert isinstance(attn_metadata, dict)
-            attn_metadata = attn_metadata[self.prefix]
+
+        attn_metadata: AttentionMetadata | None = None
+        if attn_metadata_raw is not None:
+            assert isinstance(attn_metadata_raw, dict)
+            attn_metadata = attn_metadata_raw[self.prefix]
             assert isinstance(attn_metadata, Mamba2AttentionMetadata)
             # conv_state must be (..., dim, width-1) for the conv kernels.
             # DS layout stores it that way directly; SD layout needs a
@@ -708,6 +710,7 @@ def conv_ssm_forward(
             # 3. State Space Model sequence transformation
             initial_states = None
             if has_initial_states_p is not None and prep_initial_states:
+                assert state_indices_tensor_p is not None
                 kernel_ssm_indices = state_indices_tensor_p
                 if is_mamba_cache_all:
                     kernel_ssm_indices = state_indices_tensor_p.gather(
@@ -746,6 +749,13 @@ def conv_ssm_forward(
             )
 
             if is_mamba_cache_all:
+                assert mamba_block_size is not None
+                assert state_indices_tensor_p is not None
+                assert block_idx_first_scheduled_token_p is not None
+                assert block_idx_last_scheduled_token_p is not None
+                assert last_chunk_indices_p is not None
+                assert num_computed_tokens_p is not None
+
                 # The chunk_stride is the number of chunks per mamba block
                 # e.g., if mamba_block_size = 512 and chunk_size = 256,
                 # then chunk_stride = 2
@@ -810,6 +820,7 @@ def conv_ssm_forward(
                     ssm_state[cache_blocks_to_fill] = from_where
 
                 # For all seqs, store the last state (note: might be partial):
+                assert state_indices_tensor_p is not None
                 ssm_state[
                     state_indices_tensor_p.gather(
                         1, block_idx_last_scheduled_token_p.unsqueeze(1)
@@ -820,10 +831,12 @@ def conv_ssm_forward(
                 # update ssm states
                 # - varlen state is a (num_prefills, nheads, headdim, dstate)
                 #   tensor
+                assert state_indices_tensor_p is not None
                 ssm_state[state_indices_tensor_p] = varlen_states
 
         # Process decode requests
         if has_decode:
+            assert state_indices_tensor_d is not None
             if is_mamba_cache_all:
                 state_indices_tensor_d_input = state_indices_tensor_d.gather(
                     1, block_idx_last_computed_token_d.unsqueeze(1)
diff --git a/vllm/model_executor/layers/mamba/mamba_utils.py b/vllm/model_executor/layers/mamba/mamba_utils.py
index a5a30502b218..c1fd81e40e34 100644
--- a/vllm/model_executor/layers/mamba/mamba_utils.py
+++ b/vllm/model_executor/layers/mamba/mamba_utils.py
@@ -55,9 +55,6 @@ def linear_attention_state_dtype(
         model_dtype: ModelDType | torch.dtype,
         mamba_cache_dtype: MambaDType,
     ) -> tuple[torch.dtype, ...]:
-        # TODO (tdoublep) requires testing
-        if mamba_cache_dtype == "float32":
-            raise ValueError("fp32 state for minimax is not yet supported")
         state_dtype = get_kv_cache_torch_dtype(mamba_cache_dtype, model_dtype)
         return (state_dtype,)
 
diff --git a/vllm/model_executor/layers/mamba/ops/causal_conv1d.py b/vllm/model_executor/layers/mamba/ops/causal_conv1d.py
index 1160105ad101..d87a7638533e 100644
--- a/vllm/model_executor/layers/mamba/ops/causal_conv1d.py
+++ b/vllm/model_executor/layers/mamba/ops/causal_conv1d.py
@@ -37,7 +37,7 @@ def _causal_conv1d_fwd_kernel(  # continuous batching
     num_cache_lines: tl.constexpr,  # added to support vLLM larger cache lines
     # Strides
     stride_x_dim: tl.constexpr,  # stride to get to next feature-value,
-    stride_x_token: tl.constexpr,  # stride to get to next token (same feature-index, same sequence-index)
+    stride_x_token: tl.int64,  # stride to get to next token (same feature-index, same sequence-index)
     stride_w_dim: tl.constexpr,  # stride to get to next dim-axis value
     stride_w_width: tl.constexpr,  # stride to get to next width-axis value
     stride_istate_seq: tl.constexpr,
@@ -45,7 +45,7 @@ def _causal_conv1d_fwd_kernel(  # continuous batching
     stride_istate_token: tl.constexpr,
     stride_cache_indices: tl.constexpr,
     stride_o_dim: tl.constexpr,
-    stride_o_token: tl.constexpr,
+    stride_o_token: tl.int64,
     stride_block_m: tl.constexpr,  # Stride block to align divided by BLOCK_M
     # others
     pad_slot_id: tl.constexpr,
@@ -769,7 +769,7 @@ def _causal_conv1d_update_kernel(
     # Strides
     stride_x_seq: tl.constexpr,
     stride_x_dim: tl.constexpr,
-    stride_x_token: tl.constexpr,
+    stride_x_token: tl.int64,
     stride_w_dim: tl.constexpr,
     stride_w_width: tl.constexpr,
     stride_conv_state_seq: tl.constexpr,
@@ -778,7 +778,7 @@ def _causal_conv1d_update_kernel(
     stride_state_indices: tl.constexpr,
     stride_o_seq: tl.constexpr,
     stride_o_dim: tl.constexpr,
-    stride_o_token: tl.constexpr,
+    stride_o_token: tl.int64,
     # others
     null_block_id: tl.constexpr,
     # Meta-parameters
diff --git a/vllm/model_executor/layers/mamba/ops/cpu/__init__.py b/vllm/model_executor/layers/mamba/ops/cpu/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/vllm/model_executor/layers/mamba/ops/cpu/causal_conv1d.py b/vllm/model_executor/layers/mamba/ops/cpu/causal_conv1d.py
new file mode 100644
index 000000000000..b047ca6d6169
--- /dev/null
+++ b/vllm/model_executor/layers/mamba/ops/cpu/causal_conv1d.py
@@ -0,0 +1,88 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as F
+
+
+# for prefill
+def causal_conv1d_torch(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor | None,
+    conv_states: torch.Tensor,
+    query_start_loc: torch.Tensor,
+    cache_indices: torch.Tensor,
+    has_initial_state: torch.Tensor,
+    activation: str | None = "silu",
+) -> torch.Tensor:
+    out = torch.empty_like(x)
+    state_len = weight.shape[1] - 1
+    assert activation in {None, "silu", "swish"}
+
+    seq_begin_end_idx = [
+        (int(query_start_loc[idx].item()), int(query_start_loc[idx + 1].item()))
+        for idx in range(query_start_loc.shape[0] - 1)
+    ]
+    weight = weight.unsqueeze(1)
+    for seq_idx, (bos, eos) in enumerate(seq_begin_end_idx):
+        slot = int(cache_indices[seq_idx].item())
+
+        seq_x = x[:, bos:eos].unsqueeze(0)
+        if bool(has_initial_state[seq_idx].item()):
+            initial_state = conv_states[slot, :, :state_len].unsqueeze(0)
+        else:
+            initial_state = torch.zeros(
+                1,
+                weight.shape[0],
+                state_len,
+                device=seq_x.device,
+                dtype=seq_x.dtype,
+            )
+
+        conv_input = torch.cat([initial_state, seq_x], dim=-1).to(weight.dtype)
+        seq_out = F.conv1d(
+            conv_input,
+            weight,
+            bias,
+            padding=0,
+            groups=weight.shape[0],
+        )
+        seq_out = seq_out[..., -seq_x.shape[-1] :].to(dtype=x.dtype)
+        if activation in ("silu", "swish"):
+            seq_out = F.silu(seq_out)
+
+        out[:, bos:eos] = seq_out.squeeze(0)
+        conv_states[slot, :, :state_len].copy_(conv_input[..., -state_len:].squeeze(0))
+
+    return out
+
+
+# for decode
+def causal_conv1d_update_torch(
+    x: torch.Tensor,
+    conv_state: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor | None = None,
+    activation: str | None = None,
+) -> torch.Tensor:
+    assert activation in {None, "silu", "swish"}
+
+    _, dim, seq_len = x.shape
+    state_len = conv_state.shape[-1]
+
+    x_new = torch.cat([conv_state, x], dim=-1).to(weight.dtype)
+    conv_state.copy_(x_new[:, :, -state_len:])
+
+    out = F.conv1d(
+        x_new,
+        weight.unsqueeze(1),
+        bias,
+        padding=0,
+        groups=dim,
+    )[:, :, -seq_len:]
+    if activation in ("silu", "swish"):
+        out = F.silu(out)
+    return out
diff --git a/vllm/model_executor/layers/mamba/ops/cpu/gdn_attention.py b/vllm/model_executor/layers/mamba/ops/cpu/gdn_attention.py
new file mode 100644
index 000000000000..8b4122cc487b
--- /dev/null
+++ b/vllm/model_executor/layers/mamba/ops/cpu/gdn_attention.py
@@ -0,0 +1,208 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import torch
+
+from vllm.forward_context import ForwardContext, get_forward_context
+from vllm.model_executor.layers.mamba.mamba_utils import is_conv_state_dim_first
+from vllm.model_executor.layers.mamba.ops.cpu.causal_conv1d import (
+    causal_conv1d_torch,
+    causal_conv1d_update_torch,
+)
+from vllm.model_executor.layers.mamba.ops.cpu.recurrent_gated_delta_rule import (
+    chunk_gated_delta_rule,
+    gdn_gating,
+    recurrent_gated_delta_rule,
+)
+from vllm.utils.torch_utils import (
+    LayerNameType,
+    _resolve_layer_name,
+    direct_register_custom_op,
+)
+from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
+
+_CPU_GDN_ATTENTION_OPS_REGISTERED = False
+
+
+def cpu_gdn_attention_core(
+    mixed_qkv: torch.Tensor,
+    b: torch.Tensor,
+    a: torch.Tensor,
+    core_attn_out: torch.Tensor,
+    layer_name: LayerNameType,
+) -> None:
+    """CPU custom op for the core GDN attention computation."""
+    layer_name = _resolve_layer_name(layer_name)
+    forward_context: ForwardContext = get_forward_context()
+    layer = forward_context.no_compile_layers[layer_name]
+
+    attn_metadata = forward_context.attn_metadata
+
+    if attn_metadata is None:
+        return
+
+    assert isinstance(attn_metadata, dict)
+    attn_metadata_i = attn_metadata[layer.prefix]
+    assert isinstance(attn_metadata_i, GDNAttentionMetadata)
+
+    if attn_metadata_i.num_actual_tokens == 0:
+        return
+
+    assert (
+        attn_metadata_i.spec_sequence_masks is None
+        and attn_metadata_i.num_accepted_tokens is None
+    ), "speculative decode not supported in CPU GDN attention."
+
+    state_indices_tensor = attn_metadata_i.non_spec_state_indices_tensor
+    query_start_loc = attn_metadata_i.non_spec_query_start_loc
+    assert state_indices_tensor is not None
+    assert query_start_loc is not None
+
+    # [num_allocated_slots, conv_dim, kernel - 1]
+    conv_state = layer.kv_cache[0]
+    if not is_conv_state_dim_first():
+        conv_state = conv_state.transpose(-1, -2)
+
+    # [num_allocated_slots, num_v_heads / tp_size, v_dim, k_dim]
+    ssm_state = layer.kv_cache[1]
+
+    num_decodes = attn_metadata_i.num_decodes
+    num_decode_tokens = attn_metadata_i.num_decode_tokens
+    num_prefills = attn_metadata_i.num_prefills
+    num_prefill_tokens = attn_metadata_i.num_prefill_tokens
+
+    conv_weights = layer.conv1d.weight.view(
+        layer.conv1d.weight.size(0), layer.conv1d.weight.size(2)
+    )
+
+    # all decode requests (batched)
+    if num_decodes > 0:
+        decode_mixed_qkv = mixed_qkv[:num_decode_tokens]
+        decode_b = b[:num_decode_tokens]
+        decode_a = a[:num_decode_tokens]
+        decode_state_indices = state_indices_tensor[:num_decodes]
+        decode_conv_state = conv_state[decode_state_indices].contiguous()
+
+        decode_mixed_qkv = causal_conv1d_update_torch(
+            # [B, dim] -> [B, dim, 1]
+            x=decode_mixed_qkv.unsqueeze(-1),
+            conv_state=decode_conv_state,
+            weight=conv_weights,
+            bias=layer.conv1d.bias,
+            activation=layer.activation,
+        ).squeeze(-1)
+        conv_state[decode_state_indices] = decode_conv_state
+
+        query, key, value = layer.rearrange_mixed_qkv(decode_mixed_qkv)
+
+        # [1, L, H, D] -> [B, 1, H, D] for batched decode
+        query = query.transpose(0, 1).contiguous()
+        key = key.transpose(0, 1).contiguous()
+        value = value.transpose(0, 1).contiguous()
+
+        g, beta_output = gdn_gating(
+            A_log=layer.A_log,
+            a=decode_a,
+            b=decode_b,
+            dt_bias=layer.dt_bias,
+        )
+        if g.ndim == 2:
+            g = g.unsqueeze(1)
+            beta_output = beta_output.unsqueeze(1)
+
+        initial_state = ssm_state[decode_state_indices].contiguous()
+        attn_out, last_recurrent_state = recurrent_gated_delta_rule(
+            query=query,
+            key=key,
+            value=value,
+            g=g,
+            beta=beta_output,
+            initial_state=initial_state,
+            scale=None,
+            use_qk_l2norm_in_kernel=True,
+        )
+        ssm_state[decode_state_indices] = last_recurrent_state.to(
+            ssm_state.dtype
+        ).contiguous()
+        core_attn_out[:num_decode_tokens] = attn_out.squeeze(1)
+
+    # all prefill requests: (varlen) currently naively loops over sequences
+    if num_prefills > 0:
+        has_initial_state = attn_metadata_i.has_initial_state
+        assert has_initial_state is not None
+
+        prefill_token_start = num_decode_tokens
+        prefill_token_end = prefill_token_start + num_prefill_tokens
+        prefill_mixed_qkv = mixed_qkv[prefill_token_start:prefill_token_end]
+        prefill_b = b[prefill_token_start:prefill_token_end]
+        prefill_a = a[prefill_token_start:prefill_token_end]
+        prefill_state_indices = state_indices_tensor[
+            num_decodes : num_decodes + num_prefills
+        ]
+        prefill_query_start_loc = (
+            query_start_loc[num_decodes : num_decodes + num_prefills + 1]
+            - num_decode_tokens
+        )
+        prefill_has_initial_state = has_initial_state[
+            num_decodes : num_decodes + num_prefills
+        ]
+
+        prefill_mixed_qkv = causal_conv1d_torch(
+            x=prefill_mixed_qkv.transpose(0, 1),
+            weight=conv_weights,
+            bias=layer.conv1d.bias,
+            conv_states=conv_state,
+            query_start_loc=prefill_query_start_loc,
+            cache_indices=prefill_state_indices,
+            has_initial_state=prefill_has_initial_state,
+            activation=layer.activation,
+        ).transpose(0, 1)
+
+        query, key, value = layer.rearrange_mixed_qkv(prefill_mixed_qkv)
+        g, beta = gdn_gating(layer.A_log, prefill_a, prefill_b, layer.dt_bias)
+        if g.ndim == 2:
+            g = g.unsqueeze(0)
+            beta = beta.unsqueeze(0)
+
+        initial_state = ssm_state[prefill_state_indices].contiguous()
+        initial_state[~prefill_has_initial_state, ...] = 0
+        attn_out, last_recurrent_state = chunk_gated_delta_rule(
+            q=query,
+            k=key,
+            v=value,
+            g=g,
+            beta=beta,
+            scale=None,
+            initial_state=initial_state,
+            cu_seqlens=prefill_query_start_loc,
+            use_qk_l2norm_in_kernel=True,
+        )
+        ssm_state[prefill_state_indices] = last_recurrent_state.to(ssm_state.dtype)
+        core_attn_out[prefill_token_start:prefill_token_end] = attn_out.squeeze(0)
+
+
+def cpu_gdn_attention_core_fake(
+    mixed_qkv: torch.Tensor,
+    b: torch.Tensor,
+    a: torch.Tensor,
+    core_attn_out: torch.Tensor,
+    layer_name: LayerNameType,
+) -> None:
+    """Fake implementation for torch.compile."""
+    return
+
+
+def register_cpu_gdn_attention_ops() -> None:
+    global _CPU_GDN_ATTENTION_OPS_REGISTERED
+    if _CPU_GDN_ATTENTION_OPS_REGISTERED:
+        return
+
+    direct_register_custom_op(
+        op_name="cpu_gdn_attention_core",
+        op_func=cpu_gdn_attention_core,
+        mutates_args=["core_attn_out"],
+        fake_impl=cpu_gdn_attention_core_fake,
+    )
+    _CPU_GDN_ATTENTION_OPS_REGISTERED = True
diff --git a/vllm/model_executor/layers/mamba/ops/cpu/recurrent_gated_delta_rule.py b/vllm/model_executor/layers/mamba/ops/cpu/recurrent_gated_delta_rule.py
new file mode 100644
index 000000000000..30fca3423a38
--- /dev/null
+++ b/vllm/model_executor/layers/mamba/ops/cpu/recurrent_gated_delta_rule.py
@@ -0,0 +1,223 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+
+import torch
+import torch.nn.functional as F
+
+
+def l2norm(
+    x: torch.Tensor,
+    dim: int = -1,
+    eps: float = 1e-6,
+) -> torch.Tensor:
+    inv_norm = torch.rsqrt((x * x).sum(dim=dim, keepdim=True) + eps)
+    return x * inv_norm
+
+
+def recurrent_gated_delta_rule(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    g: torch.Tensor,
+    beta: torch.Tensor,
+    initial_state: torch.Tensor,
+    scale: float | None = None,
+    use_qk_l2norm_in_kernel: bool = False,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    initial_dtype = query.dtype
+    if use_qk_l2norm_in_kernel:
+        query = l2norm(query, dim=-1, eps=1e-6)
+        key = l2norm(key, dim=-1, eps=1e-6)
+
+    if query.shape[2] != value.shape[2]:
+        repeat_factor = value.shape[2] // query.shape[2]
+        query = query.repeat_interleave(repeat_factor, dim=2)
+        key = key.repeat_interleave(repeat_factor, dim=2)
+
+    query, key, value, beta, g = [
+        x.transpose(1, 2).contiguous().to(torch.float32)
+        for x in (query, key, value, beta, g)
+    ]
+
+    batch_size, num_heads, sequence_length, _ = key.shape
+    v_head_dim = value.shape[-1]
+    if scale is None:
+        scale = 1 / (query.shape[-1] ** 0.5)
+    query = query * scale
+
+    core_attn_out = torch.empty(
+        batch_size,
+        num_heads,
+        sequence_length,
+        v_head_dim,
+        dtype=value.dtype,
+    )
+    last_recurrent_state = initial_state.to(value)
+
+    for token_idx in range(sequence_length):
+        q_t = query[:, :, token_idx]
+        k_t = key[:, :, token_idx]
+        v_t = value[:, :, token_idx]
+        g_t = g[:, :, token_idx].exp().unsqueeze(-1).unsqueeze(-1)
+        beta_t = beta[:, :, token_idx].unsqueeze(-1)
+
+        last_recurrent_state = last_recurrent_state * g_t
+        kv_mem = (last_recurrent_state * k_t.unsqueeze(-2)).sum(dim=-1)
+        delta = (v_t - kv_mem) * beta_t
+        last_recurrent_state = last_recurrent_state + delta.unsqueeze(
+            -1
+        ) * k_t.unsqueeze(-2)
+        core_attn_out[:, :, token_idx] = (last_recurrent_state * q_t.unsqueeze(-2)).sum(
+            dim=-1
+        )
+
+    core_attn_out = core_attn_out.transpose(1, 2).contiguous().to(initial_dtype)
+    return core_attn_out, last_recurrent_state
+
+
+def gdn_gating(
+    A_log: torch.Tensor,
+    a: torch.Tensor,
+    b: torch.Tensor,
+    dt_bias: torch.Tensor,
+    beta: float = 1.0,
+    threshold: float = 20.0,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    softplus_x = F.softplus(a.float() + dt_bias.float(), beta=beta, threshold=threshold)
+    g = -torch.exp(A_log.float()) * softplus_x
+    beta_output = torch.sigmoid(b.float()).to(dtype=b.dtype)
+    return g, beta_output
+
+
+def chunk_gated_delta_rule(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    g: torch.Tensor,
+    beta: torch.Tensor,
+    *,
+    initial_state: torch.Tensor,
+    scale: float | None = None,
+    cu_seqlens: torch.Tensor,
+    use_qk_l2norm_in_kernel: bool = False,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    output = torch.empty_like(v)
+    state_dtype = initial_state.dtype
+    chunk_size = 128
+    sequence_bounds = [
+        (
+            seq_idx,
+            int(cu_seqlens[seq_idx].item()),
+            int(cu_seqlens[seq_idx + 1].item()),
+        )
+        for seq_idx in range(len(cu_seqlens) - 1)
+    ]
+    chunk_eye = torch.eye(chunk_size, dtype=torch.float32)
+    num_sequences = len(sequence_bounds)
+    num_value_heads = v.shape[2]
+    value_head_dim = v.shape[3]
+    key_head_dim = k.shape[3]
+    final_state = torch.empty(
+        (num_sequences, num_value_heads, value_head_dim, key_head_dim),
+        dtype=state_dtype,
+    )
+
+    for seq_idx, begin, end in sequence_bounds:
+        q_seq = q[:, begin:end]
+        k_seq = k[:, begin:end]
+        v_seq = v[:, begin:end]
+        g_seq = g[:, begin:end]
+        beta_seq = beta[:, begin:end]
+
+        initial_dtype = q_seq.dtype
+        if use_qk_l2norm_in_kernel:
+            q_seq = l2norm(q_seq, dim=-1, eps=1e-6)
+            k_seq = l2norm(k_seq, dim=-1, eps=1e-6)
+
+        num_qk_heads = q_seq.shape[2]
+        num_value_heads = v_seq.shape[2]
+        if num_qk_heads != num_value_heads:
+            repeat_factor = num_value_heads // num_qk_heads
+            q_seq = q_seq.repeat_interleave(repeat_factor, dim=2)
+            k_seq = k_seq.repeat_interleave(repeat_factor, dim=2)
+
+        q_seq, k_seq, v_seq, beta_seq, g_seq = [
+            x.transpose(1, 2).contiguous().to(torch.float32)
+            for x in (q_seq, k_seq, v_seq, beta_seq, g_seq)
+        ]
+        seq_batch_size, num_heads, seq_len, qk_head_dim = q_seq.shape
+        value_head_dim = v_seq.shape[-1]
+
+        if scale is None:
+            scale = 1 / (qk_head_dim**0.5)
+
+        q_seq = q_seq * scale
+
+        seq_state = initial_state[seq_idx : seq_idx + 1].to(v_seq)
+        seq_output = torch.empty(
+            seq_batch_size,
+            num_heads,
+            seq_len,
+            value_head_dim,
+            dtype=v_seq.dtype,
+        )
+
+        for chunk_start in range(0, seq_len, chunk_size):
+            chunk_end = min(chunk_start + chunk_size, seq_len)
+            q_chunk = q_seq[:, :, chunk_start:chunk_end]
+            k_chunk = k_seq[:, :, chunk_start:chunk_end]
+            v_chunk = v_seq[:, :, chunk_start:chunk_end]
+            beta_chunk = beta_seq[:, :, chunk_start:chunk_end]
+            g_chunk = g_seq[:, :, chunk_start:chunk_end]
+            chunk_len = chunk_end - chunk_start
+
+            cum_g = g_chunk.cumsum(dim=-1)
+            exp_cum_g = cum_g.exp()
+            decay = (cum_g.unsqueeze(-1) - cum_g.unsqueeze(-2)).exp()
+
+            interaction = (k_chunk * beta_chunk.unsqueeze(-1)) @ k_chunk.transpose(
+                -1, -2
+            )
+            interaction = torch.tril(interaction * decay, diagonal=-1)
+            system = interaction + chunk_eye[:chunk_len, :chunk_len]
+
+            solved_values = torch.linalg.solve_triangular(
+                system,
+                v_chunk * beta_chunk.unsqueeze(-1),
+                upper=False,
+            )
+            solved_keys = torch.linalg.solve_triangular(
+                system,
+                (k_chunk * beta_chunk.unsqueeze(-1)) * exp_cum_g.unsqueeze(-1),
+                upper=False,
+            )
+
+            incoming_memory = torch.einsum("bhvk,bhck->bhcv", seq_state, solved_keys)
+            transformed_values = solved_values - incoming_memory
+
+            # Each chunk contributes both from the incoming recurrent state and
+            # from its own in-chunk interactions.
+            inter_chunk = torch.einsum(
+                "bhvk,bhck->bhcv",
+                seq_state,
+                q_chunk * exp_cum_g.unsqueeze(-1),
+            )
+            intra_chunk = torch.tril((q_chunk @ k_chunk.transpose(-1, -2)) * decay)
+            seq_output[:, :, chunk_start:chunk_end] = (
+                inter_chunk + intra_chunk @ transformed_values
+            )
+
+            # Carry the recurrent state forward to the next chunk boundary.
+            end_decay = (cum_g[:, :, -1:] - cum_g).exp().unsqueeze(-1)
+            decayed_keys = k_chunk * end_decay
+            seq_state = seq_state * exp_cum_g[:, :, -1, None, None] + torch.einsum(
+                "bhcv,bhck->bhvk", transformed_values, decayed_keys
+            )
+
+        output[0, begin:end].copy_(
+            seq_output.transpose(1, 2).contiguous().to(initial_dtype).squeeze(0)
+        )
+        final_state[seq_idx].copy_(seq_state.squeeze(0).to(state_dtype).contiguous())
+
+    return output, final_state
diff --git a/vllm/model_executor/layers/mamba/ops/ssu_dispatch.py b/vllm/model_executor/layers/mamba/ops/ssu_dispatch.py
index 8a86b1a068bb..33a08feb9cfb 100644
--- a/vllm/model_executor/layers/mamba/ops/ssu_dispatch.py
+++ b/vllm/model_executor/layers/mamba/ops/ssu_dispatch.py
@@ -15,6 +15,7 @@
 from vllm.config.mamba import MambaBackendEnum, MambaConfig
 from vllm.logger import init_logger
 from vllm.v1.attention.backends.utils import NULL_BLOCK_ID
+from vllm.v1.kv_cache_interface import KVCacheConfig, MambaSpec
 
 logger = init_logger(__name__)
 
@@ -188,12 +189,22 @@ def __call__(
 _mamba_ssu_backend: MambaSSUBackend | None = None
 
 
-def initialize_mamba_ssu_backend(mamba_config: MambaConfig) -> None:
+def initialize_mamba_ssu_backend(
+    mamba_config: MambaConfig,
+    kv_cache_config: KVCacheConfig,
+) -> None:
     """Initialize the global Mamba SSU backend.
 
-    Args:
-        mamba_config: Mamba configuration.
+    No-op if `kv_cache_config` contains no specs that call
+    selective_state_update.
     """
+    if not any(
+        isinstance(g.kv_cache_spec, MambaSpec)
+        and g.kv_cache_spec.mamba_type in ("mamba1", "mamba2")
+        for g in kv_cache_config.kv_cache_groups
+    ):
+        return
+
     global _mamba_ssu_backend
 
     backend = mamba_config.backend
@@ -203,7 +214,11 @@ def initialize_mamba_ssu_backend(mamba_config: MambaConfig) -> None:
             f"Valid options: {list(_BACKEND_REGISTRY.keys())}"
         )
 
-    _mamba_ssu_backend = _BACKEND_REGISTRY[backend](mamba_config)
+    backend_cls = _BACKEND_REGISTRY[backend]
+    if isinstance(_mamba_ssu_backend, backend_cls):
+        return
+
+    _mamba_ssu_backend = backend_cls(mamba_config)
     logger.info("Using %s Mamba SSU backend.", _mamba_ssu_backend.name)
 
 
diff --git a/vllm/model_executor/layers/mamba/short_conv.py b/vllm/model_executor/layers/mamba/short_conv.py
index 11e9b590f86a..629167acfe52 100644
--- a/vllm/model_executor/layers/mamba/short_conv.py
+++ b/vllm/model_executor/layers/mamba/short_conv.py
@@ -113,10 +113,11 @@ def forward_cuda(
         # chunked prefill modes; they are computed at top-level model forward
         # since they stay the same and reused for all mamba layers in the same
         # iteration.
-        attn_metadata: AttentionMetadata = forward_context.attn_metadata
-        if attn_metadata is not None:
-            assert isinstance(attn_metadata, dict)
-            attn_metadata = attn_metadata[self.prefix]
+        attn_metadata_raw = forward_context.attn_metadata
+        attn_metadata: AttentionMetadata | None = None
+        if attn_metadata_raw is not None:
+            assert isinstance(attn_metadata_raw, dict)
+            attn_metadata = attn_metadata_raw[self.prefix]
             assert isinstance(attn_metadata, ShortConvAttentionMetadata)
             conv_state = (
                 self.kv_cache[0]
diff --git a/vllm/model_executor/layers/mhc.py b/vllm/model_executor/layers/mhc.py
new file mode 100644
index 000000000000..cbc5ec2962ec
--- /dev/null
+++ b/vllm/model_executor/layers/mhc.py
@@ -0,0 +1,687 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import math
+from functools import cache
+from typing import TYPE_CHECKING
+
+import torch
+
+from vllm.platforms import current_platform
+from vllm.utils.import_utils import has_tilelang
+from vllm.utils.math_utils import cdiv
+from vllm.utils.torch_utils import direct_register_custom_op
+
+# tilelang is only available on CUDA platforms
+if TYPE_CHECKING or current_platform.is_cuda_alike():
+    if not has_tilelang():
+        raise ImportError(
+            "tilelang is required for mhc but is not installed. Install it with "
+            "`pip install tilelang`."
+        )
+    import tilelang
+    import tilelang.language as T
+else:
+    tilelang = None  # type: ignore[assignment]
+    T = None  # type: ignore[assignment]
+
+
+@cache
+def compute_num_split(block_k: int, k: int | None, grid_size: int) -> int:
+    device_props = torch.cuda.get_device_properties(0)
+    n_sms = device_props.multi_processor_count
+    split_k = n_sms // grid_size
+    if k is not None:
+        # avoid split_k for small k
+        num_block_k = cdiv(k, block_k)
+        split_k = min(split_k, num_block_k // 4)
+    split_k = max(split_k, 1)
+    return split_k
+
+
+@tilelang.jit(
+    pass_configs={
+        tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
+        tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
+        tilelang.PassConfigKey.TL_PTXAS_REGISTER_USAGE_LEVEL: 10,
+    },
+)
+def mhc_pre_big_fuse_tilelang(
+    gemm_out_mul,
+    gemm_out_sqrsum,
+    hc_scale,
+    hc_base,
+    residual,
+    post_mix,
+    comb_mix,
+    layer_input,
+    hidden_size: int,
+    rms_eps: float,
+    hc_pre_eps: float,
+    hc_sinkhorn_eps: float,
+    hc_post_mult_value: float,
+    sinkhorn_repeat: int,
+    n_splits: int = 16,
+    hc_mult: int = 4,
+):
+    """Deeply fused kernels, everything other than gemm & sqrsum in mHC pre block."""
+    num_tokens = T.dynamic("num_tokens")
+    hc_mult3 = hc_mult * (2 + hc_mult)
+    hidden_block = math.gcd(512, hidden_size)
+
+    gemm_out_mul: T.Tensor[[n_splits, num_tokens, hc_mult3], T.float32]  # type: ignore[no-redef, valid-type]
+    gemm_out_sqrsum: T.Tensor[[n_splits, num_tokens], T.float32]  # type: ignore[no-redef, valid-type]
+    hc_scale: T.Tensor[[3], T.float32]  # type: ignore[no-redef, valid-type]
+    hc_base: T.Tensor[[hc_mult3], T.float32]  # type: ignore[no-redef, valid-type]
+    residual: T.Tensor[[num_tokens, hc_mult, hidden_size], T.bfloat16]  # type: ignore[no-redef, valid-type]
+    # outputs
+    post_mix: T.Tensor[[num_tokens, hc_mult], T.float32]  # type: ignore[no-redef, valid-type]
+    comb_mix: T.Tensor[[num_tokens, hc_mult * hc_mult], T.float32]  # type: ignore[no-redef, valid-type]
+    layer_input: T.Tensor[[num_tokens, hidden_size], T.bfloat16]  # type: ignore[no-redef, valid-type]
+
+    with T.Kernel(num_tokens, threads=96) as i:
+        T.pdl_sync()
+        ##################################################################
+        # _pre_norm_fn_fwd_norm
+        rms = T.alloc_fragment(1, T.float32)
+        mixes = T.alloc_fragment(hc_mult3, T.float32)
+        T.clear(mixes)
+        rms[0] = 0
+        for i_split in T.serial(n_splits):
+            rms[0] += gemm_out_sqrsum[i_split, i]
+        rms[0] = T.rsqrt(rms[0] / (hc_mult * hidden_size) + rms_eps)
+        for j in T.Parallel(hc_mult3):
+            mixes[j] = 0
+            for i_split in T.serial(n_splits):
+                mixes[j] += gemm_out_mul[i_split, i, j]
+            mixes[j] *= rms[0]
+        mixes_shared = T.alloc_shared(hc_mult3, T.float32)
+        T.copy(mixes, mixes_shared)
+
+        if T.get_thread_binding() < 32:
+            ##################################################################
+            # _pre_split_mixes_fwd (post & comb)
+            cm = T.alloc_fragment((hc_mult, hc_mult), T.float32)
+            for j in T.Parallel(hc_mult):
+                post_mix[i, j] = (
+                    T.sigmoid(
+                        mixes_shared[j + hc_mult] * hc_scale[1] + hc_base[j + hc_mult]
+                    )
+                    * hc_post_mult_value
+                )
+            for j, k in T.Parallel(hc_mult, hc_mult):
+                cm[j, k] = (
+                    mixes_shared[j * hc_mult + k + hc_mult * 2] * hc_scale[2]
+                    + hc_base[j * hc_mult + k + hc_mult * 2]
+                )
+
+            ##################################################################
+            # _sinkhorn_fwd
+            row_sum = T.alloc_fragment(hc_mult, T.float32)
+            col_sum = T.alloc_fragment(hc_mult, T.float32)
+
+            # comb = comb.softmax(-1) + eps
+            row_max = T.alloc_fragment(hc_mult, T.float32)
+            T.reduce_max(cm, row_max, dim=1)
+            for j, k in T.Parallel(hc_mult, hc_mult):
+                cm[j, k] = T.exp(cm[j, k] - row_max[j])
+            T.reduce_sum(cm, row_sum, dim=1)
+            for j, k in T.Parallel(hc_mult, hc_mult):
+                cm[j, k] = cm[j, k] / row_sum[j] + hc_sinkhorn_eps
+
+            # comb = comb / (comb.sum(-2) + eps)
+            T.reduce_sum(cm, col_sum, dim=0)
+            for j, k in T.Parallel(hc_mult, hc_mult):
+                cm[j, k] = cm[j, k] / (col_sum[k] + hc_sinkhorn_eps)
+
+            for _ in T.serial(sinkhorn_repeat - 1):
+                # comb = comb / (comb.sum(-1) + eps)
+                T.reduce_sum(cm, row_sum, dim=1)
+                for j, k in T.Parallel(hc_mult, hc_mult):
+                    cm[j, k] = cm[j, k] / (row_sum[j] + hc_sinkhorn_eps)
+
+                # comb = comb / (comb.sum(-2) + eps)
+                T.reduce_sum(cm, col_sum, dim=0)
+                for j, k in T.Parallel(hc_mult, hc_mult):
+                    cm[j, k] = cm[j, k] / (col_sum[k] + hc_sinkhorn_eps)
+
+            # save comb_mix to global memory
+            for j, k in T.Parallel(hc_mult, hc_mult):
+                comb_mix[i, j * hc_mult + k] = cm[j, k]
+        else:
+            ##################################################################
+            # _pre_split_mixes_fwd (pre)
+            pre_mix_shared = T.alloc_shared(hc_mult, T.float32)
+            for j in T.Parallel(hc_mult):
+                pre_mix_shared[j] = (
+                    T.sigmoid(
+                        mixes_shared[j] * hc_scale[0] + hc_base[j],
+                    )
+                    + hc_pre_eps
+                )
+            ###################################################################
+            # _pre_apply_mix_fwd
+            for i0_h in T.Pipelined(hidden_size // hidden_block, num_stages=2):
+                xs = T.alloc_shared((hc_mult, hidden_block), T.float32)
+                xl = T.alloc_fragment((hc_mult, hidden_block), T.float32)
+                T.copy(residual[i, 0, i0_h * hidden_block], xs)
+                T.copy(xs, xl)
+
+                ol = T.alloc_fragment(hidden_block, T.float32)
+                T.clear(ol)
+
+                for i_hc in T.serial(hc_mult):
+                    pre = pre_mix_shared[i_hc]
+                    for i1_h in T.Parallel(hidden_block):
+                        ol[i1_h] += pre * xl[i_hc, i1_h]
+
+                T.copy(ol, layer_input[i, i0_h * hidden_block])
+        T.pdl_trigger()
+
+
+def mhc_pre(
+    residual: torch.Tensor,
+    fn: torch.Tensor,
+    hc_scale: torch.Tensor,
+    hc_base: torch.Tensor,
+    rms_eps: float,
+    hc_pre_eps: float,
+    hc_sinkhorn_eps: float,
+    hc_post_mult_value: float,
+    sinkhorn_repeat: int,
+    n_splits: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    Forward pass for mHC pre block.
+
+    Args:
+        residual: shape (..., hc_mult, hidden_size), dtype torch.bfloat16
+        fn: shape (hc_mult3, hc_mult * hidden_size), dtype torch.float32
+        hc_scale: shape (3,), dtype torch.float32
+        hc_base: shape (hc_mult3,), dtype torch.float32
+        rms_eps: RMS normalization epsilon
+        hc_pre_eps: pre-mix epsilon
+        hc_sinkhorn_eps: sinkhorn epsilon
+        hc_post_mult_value: post-mix multiplier value
+        sinkhorn_repeat: number of sinkhorn iterations
+        n_splits: split-k factor;
+
+    Returns:
+        post_mix: shape (..., hc_mult), dtype torch.float32
+        comb_mix: shape (..., hc_mult, hc_mult), dtype torch.float32
+        layer_input: shape (..., hidden_size), dtype torch.bfloat16
+    """
+
+    # Validate shapes
+    assert residual.dtype == torch.bfloat16
+    assert fn.dtype == torch.float32
+    assert hc_scale.dtype == torch.float32
+    assert hc_base.dtype == torch.float32
+
+    hc_mult = residual.shape[-2]
+    hidden_size = residual.shape[-1]
+    hc_mult2 = hc_mult * hc_mult
+    hc_mult3 = hc_mult * 2 + hc_mult2
+
+    hc_hidden_size = hc_mult * hidden_size
+    assert fn.shape[0] == hc_mult3
+    assert fn.shape[1] == hc_hidden_size
+    assert hc_scale.shape == (3,)
+    assert hc_base.shape == (hc_mult3,)
+
+    outer_shape = residual.shape[:-2]
+
+    residual_flat = residual.view(-1, hc_mult, hidden_size)
+    num_tokens = residual_flat.shape[0]
+    fn_flat = fn
+
+    if current_platform.is_rocm():
+        x = residual_flat.view(num_tokens, hc_mult * hidden_size).to(torch.float32)
+        mixes = torch.matmul(x, fn_flat.t())
+        sqrsum = x.square().sum(dim=-1, keepdim=True)
+        mixes = mixes * torch.rsqrt(sqrsum / (hc_mult * hidden_size) + rms_eps)
+
+        pre_logits = mixes[:, :hc_mult] * hc_scale[0] + hc_base[:hc_mult]
+        pre_mix = torch.sigmoid(pre_logits) + hc_pre_eps
+
+        post_logits = (
+            mixes[:, hc_mult : 2 * hc_mult] * hc_scale[1]
+            + hc_base[hc_mult : 2 * hc_mult]
+        )
+        post_mix = torch.sigmoid(post_logits) * hc_post_mult_value
+
+        comb_logits = mixes[:, 2 * hc_mult :].view(
+            num_tokens, hc_mult, hc_mult
+        ) * hc_scale[2] + hc_base[2 * hc_mult :].view(1, hc_mult, hc_mult)
+        comb_mix = torch.softmax(comb_logits, dim=-1) + hc_sinkhorn_eps
+        comb_mix = comb_mix / (comb_mix.sum(dim=-2, keepdim=True) + hc_sinkhorn_eps)
+        for _ in range(sinkhorn_repeat - 1):
+            comb_mix = comb_mix / (comb_mix.sum(dim=-1, keepdim=True) + hc_sinkhorn_eps)
+            comb_mix = comb_mix / (comb_mix.sum(dim=-2, keepdim=True) + hc_sinkhorn_eps)
+
+        layer_input = torch.sum(
+            pre_mix.unsqueeze(-1) * residual_flat.to(torch.float32), dim=1
+        ).to(torch.bfloat16)
+        return (
+            post_mix.view(*outer_shape, hc_mult, 1),
+            comb_mix.view(*outer_shape, hc_mult, hc_mult),
+            layer_input.view(*outer_shape, hidden_size),
+        )
+
+    # these number are from deepgemm kernel impl
+    block_k = 64
+    block_m = 64
+    n_splits = compute_num_split(block_k, hc_hidden_size, cdiv(num_tokens, block_m))
+
+    post_mix = torch.empty(
+        num_tokens,
+        hc_mult,
+        dtype=torch.float32,
+        device=residual.device,
+    )
+    comb_mix = torch.empty(
+        num_tokens,
+        hc_mult2,
+        dtype=torch.float32,
+        device=residual.device,
+    )
+    layer_input = torch.empty(
+        num_tokens,
+        hidden_size,
+        dtype=torch.bfloat16,
+        device=residual.device,
+    )
+
+    gemm_out_mul = torch.empty(
+        n_splits,
+        num_tokens,
+        hc_mult3,
+        dtype=torch.float32,
+        device=residual.device,
+    )
+    gemm_out_sqrsum = torch.empty(
+        n_splits,
+        num_tokens,
+        dtype=torch.float32,
+        device=residual.device,
+    )
+
+    from vllm.utils.deep_gemm import tf32_hc_prenorm_gemm
+
+    tf32_hc_prenorm_gemm(
+        residual_flat.view(num_tokens, hc_mult * hidden_size),
+        fn_flat,
+        gemm_out_mul,
+        gemm_out_sqrsum,
+        n_splits,
+    )
+
+    mhc_pre_big_fuse_tilelang(
+        gemm_out_mul,
+        gemm_out_sqrsum,
+        hc_scale,
+        hc_base,
+        residual_flat,
+        post_mix,
+        comb_mix,
+        layer_input,
+        hidden_size,
+        rms_eps,
+        hc_pre_eps,
+        hc_sinkhorn_eps,
+        hc_post_mult_value,
+        sinkhorn_repeat,
+        n_splits,
+        hc_mult,
+    )
+
+    post_mix = post_mix.view(*outer_shape, hc_mult, 1)
+    comb_mix = comb_mix.view(*outer_shape, hc_mult, hc_mult)
+    layer_input = layer_input.view(*outer_shape, hidden_size)
+
+    return post_mix, comb_mix, layer_input
+
+
+def _mhc_pre_fake(
+    residual: torch.Tensor,
+    fn: torch.Tensor,
+    hc_scale: torch.Tensor,
+    hc_base: torch.Tensor,
+    rms_eps: float,
+    hc_pre_eps: float,
+    hc_sinkhorn_eps: float,
+    hc_post_mult_value: float,
+    sinkhorn_repeat: int,
+    n_splits: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    hc_mult = residual.shape[-2]
+    hidden_size = residual.shape[-1]
+    outer_shape = residual.shape[:-2]
+
+    # Create empty tensors with correct shapes for meta device / shape inference
+    post_mix = torch.empty(
+        *outer_shape,
+        hc_mult,
+        1,
+        dtype=torch.float32,
+        device=residual.device,
+    )
+    comb_mix = torch.empty(
+        *outer_shape,
+        hc_mult,
+        hc_mult,
+        dtype=torch.float32,
+        device=residual.device,
+    )
+    layer_input = torch.empty(
+        *outer_shape,
+        hidden_size,
+        dtype=torch.bfloat16,
+        device=residual.device,
+    )
+
+    return post_mix, comb_mix, layer_input
+
+
+@tilelang.jit(
+    pass_configs={
+        tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
+        tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
+        tilelang.PassConfigKey.TL_PTXAS_REGISTER_USAGE_LEVEL: 10,
+    },
+)
+def mhc_post_tilelang(
+    a,
+    b,
+    c,
+    d,
+    x,
+    hc: int,
+    hidden: int,
+    n_thr: int = 128,
+    h_blk: int = 1024,
+) -> tilelang.JITKernel:
+    # rename for shorter code
+    n = T.dynamic("num_tokens")
+    h = hidden
+
+    h_blk = math.gcd(hidden, h_blk)
+    a: T.Tensor((n, hc, hc), T.float32)  # type: ignore[no-redef, valid-type]
+    b: T.Tensor((n, hc, h), T.bfloat16)  # type: ignore[no-redef, valid-type]
+    c: T.Tensor((n, hc), T.float32)  # type: ignore[no-redef, valid-type]
+    d: T.Tensor((n, h), T.bfloat16)  # type: ignore[no-redef, valid-type]
+    x: T.Tensor((n, hc, h), T.bfloat16)  # type: ignore[no-redef, valid-type]
+    with T.Kernel(n, threads=n_thr) as i_n:
+        x_shared = T.alloc_shared((hc, h_blk), T.bfloat16)
+        b_shared = T.alloc_shared((hc, h_blk), T.bfloat16)
+        d_shared = T.alloc_shared(h_blk, T.bfloat16)
+
+        x_local = T.alloc_fragment((hc, h_blk), T.float32)
+        b_local = T.alloc_fragment((hc, h_blk), T.float32)
+        d_local = T.alloc_fragment(h_blk, T.float32)
+
+        a_local = T.alloc_fragment((hc, hc), T.float32)
+        c_local = T.alloc_fragment(hc, T.float32)
+        T.pdl_sync()
+        T.copy(a[i_n, 0, 0], a_local)
+        T.copy(c[i_n, 0], c_local)
+
+        for i0_h in T.Pipelined(T.ceildiv(h, h_blk), num_stages=2):
+            T.copy(b[i_n, 0, i0_h * h_blk], b_shared)
+            T.copy(d[i_n, i0_h * h_blk], d_shared)
+
+            T.copy(b_shared, b_local)
+            T.copy(d_shared, d_local)
+            for i_hco, i1_h in T.Parallel(hc, h_blk):
+                x_local[i_hco, i1_h] = c_local[i_hco] * d_local[i1_h]
+                for i_hci in T.serial(hc):
+                    x_local[i_hco, i1_h] += a_local[i_hci, i_hco] * b_local[i_hci, i1_h]
+            T.copy(x_local, x_shared)
+
+            T.copy(x_shared, x[i_n, 0, i0_h * h_blk])
+        T.pdl_trigger()
+
+
+def mhc_post(
+    x: torch.Tensor,
+    residual: torch.Tensor,
+    post_layer_mix: torch.Tensor,
+    comb_res_mix: torch.Tensor,
+) -> torch.Tensor:
+    if current_platform.is_rocm():
+        mixed_residual = torch.einsum(
+            "...ij,...ih->...jh",
+            comb_res_mix.to(torch.float32),
+            residual.to(torch.float32),
+        )
+        post_term = post_layer_mix.to(torch.float32) * x.unsqueeze(-2).to(torch.float32)
+        return (mixed_residual + post_term).to(residual.dtype)
+    out = torch.empty_like(residual)
+    mhc_post_tilelang(
+        comb_res_mix,
+        residual,
+        post_layer_mix.squeeze(-1),
+        x,
+        out,
+        residual.shape[-2],
+        residual.shape[-1],
+    )
+    return out
+
+
+def _mhc_post_fake(
+    x: torch.Tensor,
+    residual: torch.Tensor,
+    post_layer_mix: torch.Tensor,
+    comb_res_mix: torch.Tensor,
+) -> torch.Tensor:
+    return torch.empty_like(residual)
+
+
+direct_register_custom_op(
+    op_name="mhc_pre",
+    op_func=mhc_pre,
+    mutates_args=[],
+    fake_impl=_mhc_pre_fake,
+)
+direct_register_custom_op(
+    op_name="mhc_post",
+    op_func=mhc_post,
+    mutates_args=[],
+    fake_impl=_mhc_post_fake,
+)
+
+
+@tilelang.jit(
+    pass_configs={
+        tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
+        tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
+        tilelang.PassConfigKey.TL_PTXAS_REGISTER_USAGE_LEVEL: 10,
+    },
+)
+def hc_head_fuse_tilelang(
+    residual,
+    fn,
+    hc_scale,
+    hc_base,
+    out,
+    hidden_size: int,
+    rms_eps: float,
+    hc_eps: float,
+    hc_mult: int = 4,
+    n_thr: int = 128,
+    h_blk: int = 1024,
+):
+    """Two-pass fused kernel for hc_head.
+
+    Pass 1: accumulate per-token squared sum and hc_mult dot-products
+            (projections onto fn rows) using cross-thread reducers.
+    Pass 2: apply sigmoid-gated weighted sum of residual channels to output.
+
+    Avoids materialising mixes / rsqrt / pre tensors to global memory.
+    """
+    num_tokens = T.dynamic("num_tokens")
+    hc_dim = hc_mult * hidden_size
+    h_block = math.gcd(h_blk, hidden_size)
+    n_h = hidden_size // h_block
+
+    residual: T.Tensor[[num_tokens, hc_mult, hidden_size], T.bfloat16]  # type: ignore[no-redef,valid-type]
+    fn: T.Tensor[[hc_mult, hc_dim], T.float32]  # type: ignore[no-redef,valid-type]
+    hc_scale: T.Tensor[[1], T.float32]  # type: ignore[no-redef,valid-type]
+    hc_base: T.Tensor[[hc_mult], T.float32]  # type: ignore[no-redef,valid-type]
+    out: T.Tensor[[num_tokens, hidden_size], T.bfloat16]  # type: ignore[no-redef,valid-type]
+
+    with T.Kernel(num_tokens, threads=n_thr) as i:
+        T.pdl_sync()
+
+        # ------------------------------------------------------------------
+        # Pass 1 – for each residual channel m_c and h_block:
+        #   • accumulate squared sum (for RMS norm denominator)
+        #   • accumulate hc_mult dot-products with fn rows
+        # ------------------------------------------------------------------
+        sqrsum_r = T.alloc_reducer((1,), T.float32, replication="all")
+        mixes_r = T.alloc_reducer((hc_mult,), T.float32, replication="all")
+        T.fill(sqrsum_r, 0.0)
+        T.fill(mixes_r, 0.0)
+
+        for m_c in T.serial(hc_mult):
+            for i_h in T.serial(n_h):
+                x_local = T.alloc_fragment(h_block, T.float32)
+                T.copy(residual[i, m_c, i_h * h_block], x_local)
+
+                for k in T.Parallel(h_block):
+                    sqrsum_r[0] += x_local[k] * x_local[k]
+
+                for m_m in T.unroll(hc_mult):
+                    fn_local = T.alloc_fragment(h_block, T.float32)
+                    T.copy(fn[m_m, m_c * hidden_size + i_h * h_block], fn_local)
+                    for k in T.Parallel(h_block):
+                        mixes_r[m_m] += x_local[k] * fn_local[k]
+
+        T.finalize_reducer(sqrsum_r)
+        T.finalize_reducer(mixes_r)
+
+        # ------------------------------------------------------------------
+        # Compute pre_mix = sigmoid(mix * rsqrt * scale + base) + eps
+        # ------------------------------------------------------------------
+        pre_mix_shared = T.alloc_shared(hc_mult, T.float32)
+        rsqrt_val = T.alloc_fragment(1, T.float32)
+        rsqrt_val[0] = T.rsqrt(sqrsum_r[0] / hc_dim + rms_eps)
+        for m in T.Parallel(hc_mult):
+            pre_mix_shared[m] = (
+                T.sigmoid(mixes_r[m] * rsqrt_val[0] * hc_scale[0] + hc_base[m]) + hc_eps
+            )
+
+        # ------------------------------------------------------------------
+        # Pass 2 – apply_mix: pipelined weighted sum over residual channels
+        # ------------------------------------------------------------------
+        for i0_h in T.Pipelined(n_h, num_stages=2):
+            xs = T.alloc_shared((hc_mult, h_block), T.bfloat16)
+            xl = T.alloc_fragment((hc_mult, h_block), T.float32)
+            T.copy(residual[i, 0, i0_h * h_block], xs, disable_tma=True)
+            T.copy(xs, xl)
+
+            ol = T.alloc_fragment(h_block, T.float32)
+            T.clear(ol)
+            for i_hc in T.serial(hc_mult):
+                pre = pre_mix_shared[i_hc]
+                for i1_h in T.Parallel(h_block):
+                    ol[i1_h] += pre * xl[i_hc, i1_h]
+
+            T.copy(ol, out[i, i0_h * h_block], disable_tma=True)
+
+        T.pdl_trigger()
+
+
+def _hc_head_fused_reference(
+    hs_flat: torch.Tensor,
+    fn: torch.Tensor,
+    hc_scale: torch.Tensor,
+    hc_base: torch.Tensor,
+    out: torch.Tensor,
+    hidden_size: int,
+    rms_eps: float,
+    hc_eps: float,
+    hc_mult: int,
+) -> None:
+    """Pure-PyTorch reference for `hc_head_fuse_tilelang`.
+
+    Used on platforms where the tilelang HIP/CUDA backend is not available
+    (e.g. ROCm builds shipping a tilelang wheel without `target.build.tilelang_hip`).
+    Mirrors the math of the tilelang kernel exactly:
+
+        x      = hs_flat.flatten(-2, -1)                # (T, hc_mult * H), fp32
+        mixes  = x @ fn.T                               # (T, hc_mult)
+        rsqrt  = 1 / sqrt(||x||^2 / (hc_mult * H) + rms_eps)
+        pre[m] = sigmoid(mixes[m] * rsqrt * hc_scale[0] + hc_base[m]) + hc_eps
+        out    = sum_m pre[m] * hs_flat[:, m, :]        # cast back to bf16
+
+    `out` is mutated in place to keep the same op contract
+    (`mutates_args=["out"]`).
+    """
+    num_tokens = hs_flat.shape[0]
+    if num_tokens == 0:
+        return
+    x = hs_flat.reshape(num_tokens, hc_mult * hidden_size).to(torch.float32)
+    # fn: (hc_mult, hc_mult * hidden_size) → mixes: (T, hc_mult)
+    mixes = torch.matmul(x, fn.t())
+    sqrsum = x.square().sum(dim=-1, keepdim=True)
+    rsqrt = torch.rsqrt(sqrsum / (hc_mult * hidden_size) + rms_eps)
+    # hc_scale has shape (1,); hc_base has shape (hc_mult,)
+    pre_mix = torch.sigmoid(mixes * rsqrt * hc_scale[0] + hc_base) + hc_eps
+    # weighted sum over the hc_mult channel dim
+    result = torch.sum(pre_mix.unsqueeze(-1) * hs_flat.to(torch.float32), dim=1).to(
+        out.dtype
+    )
+    out.copy_(result)
+
+
+def _hc_head_fused_kernel(
+    hs_flat: torch.Tensor,
+    fn: torch.Tensor,
+    hc_scale: torch.Tensor,
+    hc_base: torch.Tensor,
+    out: torch.Tensor,
+    hidden_size: int,
+    rms_eps: float,
+    hc_eps: float,
+    hc_mult: int,
+) -> None:
+    """Fill pre-allocated `out` (T, H) in-place with the hc_head result."""
+    if hs_flat.shape[0] == 0:
+        return
+    if current_platform.is_rocm():
+        # tilelang ships only the CUDA codegen in upstream wheels, so the HIP
+        # FFI target (`target.build.tilelang_hip`) is missing and the JIT call
+        # would raise `ValueError: Cannot find global function ...`. Use a
+        # numerically equivalent torch fallback instead. `mhc_pre` and
+        # `mhc_post` already follow this same pattern above.
+        _hc_head_fused_reference(
+            hs_flat,
+            fn,
+            hc_scale,
+            hc_base,
+            out,
+            hidden_size,
+            rms_eps,
+            hc_eps,
+            hc_mult,
+        )
+        return
+    hc_head_fuse_tilelang(
+        hs_flat,
+        fn,
+        hc_scale,
+        hc_base,
+        out,
+        hidden_size,
+        rms_eps,
+        hc_eps,
+        hc_mult,
+    )
+
+
+direct_register_custom_op(
+    op_name="hc_head_fused_kernel",
+    op_func=_hc_head_fused_kernel,
+    mutates_args=["out"],
+)
diff --git a/vllm/model_executor/layers/mla.py b/vllm/model_executor/layers/mla.py
index 1d3e987b7e17..856f6bb8a3cf 100644
--- a/vllm/model_executor/layers/mla.py
+++ b/vllm/model_executor/layers/mla.py
@@ -64,6 +64,7 @@ def __init__(
         cache_config: CacheConfig | None = None,
         quant_config: QuantizationConfig | None = None,
         prefix: str = "",
+        skip_topk: bool = False,
     ) -> None:
         super().__init__()
         self.hidden_size = hidden_size
@@ -87,6 +88,11 @@ def __init__(
         self.indexer_rope_emb = mla_modules.indexer_rotary_emb
         self.is_sparse = mla_modules.is_sparse
 
+        # Whether to skip top-k token selection computation in this layer.
+        # When True, the indexer will not be called, and the layer will reuse
+        # the topk_tokens buffer written by a previous layer in the same pass.
+        # Refer: https://arxiv.org/abs/2603.12201 for more details.
+        self.skip_topk = skip_topk
         if self.indexer is not None:
             assert hasattr(self.indexer, "topk_tokens")
             self.topk_tokens = self.indexer.topk_tokens
@@ -159,10 +165,8 @@ def forward(
                 positions, q[..., self.qk_nope_head_dim :], k_pe
             )
 
-        if self.indexer and self.is_sparse:
-            _topk_indices = self.indexer(
-                hidden_states, q_c, positions, self.indexer_rope_emb
-            )
+        if self.indexer and self.is_sparse and not self.skip_topk:
+            self.indexer(hidden_states, q_c, positions, self.indexer_rope_emb)
 
         if llama_4_scaling is not None:
             q *= llama_4_scaling
diff --git a/vllm/model_executor/layers/pooler/seqwise/methods.py b/vllm/model_executor/layers/pooler/seqwise/methods.py
index b967ff4ede7b..82170b5fbdc4 100644
--- a/vllm/model_executor/layers/pooler/seqwise/methods.py
+++ b/vllm/model_executor/layers/pooler/seqwise/methods.py
@@ -68,21 +68,23 @@ def forward(
             "partial prefill not supported with MEAN pooling"
         )
 
-        prompt_lens = pooling_cursor.prompt_lens_cpu.to(
-            hidden_states.device, dtype=torch.int64, non_blocking=True
-        )
-
-        num_seqs = prompt_lens.numel()
+        prompt_lens_cpu = pooling_cursor.prompt_lens_cpu
+        num_seqs = prompt_lens_cpu.numel()
         hidden_size = hidden_states.shape[-1]
 
         if num_seqs == 0:
             # early return for empty batch
             return hidden_states.new_empty((0, hidden_size), dtype=torch.float32)
 
-        # eg. [2, 1, 3] -> [0, 0, 1, 2, 2, 2]
+        # Build segment_ids on CPU so repeat_interleave doesn't need to sync
+        # GPU->CPU to learn its data-dependent output length, then upload
+        # non-blocking. eg. [2, 1, 3] -> [0, 0, 1, 2, 2, 2]
         segment_ids = torch.repeat_interleave(
-            torch.arange(num_seqs, device=hidden_states.device, dtype=torch.long),
-            prompt_lens,
+            torch.arange(num_seqs, dtype=torch.long),
+            prompt_lens_cpu,
+        ).to(hidden_states.device, non_blocking=True)
+        prompt_lens = prompt_lens_cpu.to(
+            hidden_states.device, dtype=torch.int64, non_blocking=True
         )
         segment_sums = torch.zeros(
             (num_seqs, hidden_size),
diff --git a/vllm/model_executor/layers/pooler/seqwise/poolers.py b/vllm/model_executor/layers/pooler/seqwise/poolers.py
index 74fa4cdbbe43..762869c67823 100644
--- a/vllm/model_executor/layers/pooler/seqwise/poolers.py
+++ b/vllm/model_executor/layers/pooler/seqwise/poolers.py
@@ -115,6 +115,7 @@ def pooler_for_classify(
 
     vllm_config = get_current_vllm_config()
     model_config = vllm_config.model_config
+    assert model_config.pooler_config is not None
     head = ClassifierPoolerHead(
         head_dtype=model_config.head_dtype,
         classifier=classifier,
diff --git a/vllm/model_executor/layers/pooler/special.py b/vllm/model_executor/layers/pooler/special.py
index d06663b5b947..ae5926cd62ff 100644
--- a/vllm/model_executor/layers/pooler/special.py
+++ b/vllm/model_executor/layers/pooler/special.py
@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import dataclasses
 from collections.abc import Mapping, Set
 from itertools import groupby
 
@@ -80,9 +81,11 @@ def forward(
         pooling_metadata: PoolingMetadata,
     ) -> PoolerOutput:
         poolers_by_task = self.poolers_by_task
+        cursor = pooling_metadata.pooling_cursor
 
         outputs = list[torch.Tensor | None]()
         offset = 0
+        token_offset = 0
         for task, group in groupby(pooling_metadata.tasks):
             if not (pooler := poolers_by_task.get(task)):
                 raise ValueError(
@@ -91,10 +94,37 @@ def forward(
                 )
 
             num_items = len(list(group))
-            group_output: PoolerOutput = pooler(
-                hidden_states,
-                pooling_metadata[offset : offset + num_items],
-            )
+            group_metadata = pooling_metadata[offset : offset + num_items]
+            if cursor is None:
+                group_hidden_states = hidden_states
+            else:
+                # Slice out this group's tokens so sub-poolers see only their
+                # portion of the batch. Token offset is computed from the CPU
+                # `num_scheduled_tokens_cpu` to avoid a GPU->CPU sync.
+                group_cursor = group_metadata.pooling_cursor
+                num_group_tokens = int(group_cursor.num_scheduled_tokens_cpu.sum())
+                group_hidden_states = hidden_states[
+                    token_offset : token_offset + num_group_tokens
+                ]
+                if token_offset:
+                    # Shift first/last indices to be relative to the slice
+                    # so seqwise poolers (which index `hidden_states` directly)
+                    # remain correct.
+                    pooling_cursor = dataclasses.replace(
+                        group_cursor,
+                        first_token_indices_gpu=(
+                            group_cursor.first_token_indices_gpu - token_offset
+                        ),
+                        last_token_indices_gpu=(
+                            group_cursor.last_token_indices_gpu - token_offset
+                        ),
+                    )
+                    group_metadata = dataclasses.replace(
+                        group_metadata, pooling_cursor=pooling_cursor
+                    )
+                token_offset += num_group_tokens
+
+            group_output: PoolerOutput = pooler(group_hidden_states, group_metadata)
 
             outputs.extend(group_output)
             offset += num_items
diff --git a/vllm/model_executor/layers/pooler/tokwise/methods.py b/vllm/model_executor/layers/pooler/tokwise/methods.py
index 9ee6e8527c9a..59b7234661b5 100644
--- a/vllm/model_executor/layers/pooler/tokwise/methods.py
+++ b/vllm/model_executor/layers/pooler/tokwise/methods.py
@@ -47,13 +47,12 @@ def forward(
         pooling_metadata: PoolingMetadata,
     ) -> list[TokenPoolingMethodOutputItem]:
         pooling_cursor = pooling_metadata.get_pooling_cursor()
-        hidden_states_lst = [
-            hidden_states[first : last + 1]
-            for first, last in zip(
-                pooling_cursor.first_token_indices_gpu.tolist(),
-                pooling_cursor.last_token_indices_gpu.tolist(),
-            )
-        ]
+        # Use the already-CPU num_scheduled_tokens tensor so `.tolist()`
+        # doesn't trigger a GPU->CPU sync. torch.split produces the same
+        # consecutive slices as indexing with first/last per-sequence indices.
+        hidden_states_lst = list(
+            torch.split(hidden_states, pooling_cursor.num_scheduled_tokens_cpu.tolist())
+        )
 
         if not self.enable_chunked_prefill:
             return hidden_states_lst
@@ -91,12 +90,14 @@ def forward(
         pooling_metadata: PoolingMetadata,
     ) -> list[TokenPoolingMethodOutputItem]:
         pooled_data_lst = super().forward(hidden_states, pooling_metadata)
-        prompt_token_ids = pooling_metadata.get_prompt_token_ids()
+        # Use the CPU copy of prompt_token_ids so the step_tag_id mask can be
+        # resolved to indices without a d2h sync from boolean indexing.
+        prompt_token_ids_cpu = pooling_metadata.get_prompt_token_ids_cpu()
         pooling_params = pooling_metadata.pooling_params
 
         pooled_data = list[torch.Tensor | None]()
-        for data, token_id, pooling_param in zip(
-            pooled_data_lst, prompt_token_ids, pooling_params
+        for data, token_id_cpu, pooling_param in zip(
+            pooled_data_lst, prompt_token_ids_cpu, pooling_params
         ):
             # for unfinished chunked prefill
             if data is None:
@@ -109,9 +110,11 @@ def forward(
                     data = data[:, returned_token_ids]
 
                 if step_tag_id is not None:
-                    data = data[token_id == step_tag_id]
+                    idx_cpu = (token_id_cpu == step_tag_id).nonzero(as_tuple=True)[0]
+                    idx = idx_cpu.to(data.device, non_blocking=True)
+                    data = data[idx]
 
-            pooled_data.append(data)
+                pooled_data.append(data)
 
         return pooled_data
 
diff --git a/vllm/model_executor/layers/pooler/tokwise/poolers.py b/vllm/model_executor/layers/pooler/tokwise/poolers.py
index 6462a5056c55..131074a5556a 100644
--- a/vllm/model_executor/layers/pooler/tokwise/poolers.py
+++ b/vllm/model_executor/layers/pooler/tokwise/poolers.py
@@ -124,6 +124,7 @@ def pooler_for_token_classify(
 
     vllm_config = get_current_vllm_config()
     model_config = vllm_config.model_config
+    assert model_config.pooler_config is not None
     head = TokenClassifierPoolerHead(
         head_dtype=model_config.head_dtype,
         classifier=classifier,
diff --git a/vllm/model_executor/layers/quantization/__init__.py b/vllm/model_executor/layers/quantization/__init__.py
index 6313db78a82b..9f2b4e70206c 100644
--- a/vllm/model_executor/layers/quantization/__init__.py
+++ b/vllm/model_executor/layers/quantization/__init__.py
@@ -22,6 +22,7 @@
     "gptq_marlin",
     "awq_marlin",
     "gptq",
+    "humming",
     "compressed-tensors",
     "bitsandbytes",
     "experts_int8",
@@ -31,7 +32,7 @@
     "inc",
     "mxfp4",
     "gpt_oss_mxfp4",
-    "mxfp8",
+    "deepseek_v4_fp8",
     "cpu_awq",
     "online",
     # Below are values of the OnlineQuantScheme enum, specified as strings to
@@ -40,6 +41,8 @@
     # shorthand for creating a more complicated online quant config object
     "fp8_per_tensor",
     "fp8_per_block",
+    "int8_per_channel_weight_only",
+    "mxfp8",
 ]
 QUANTIZATION_METHODS: list[str] = list(get_args(QuantizationMethods))
 
@@ -47,7 +50,6 @@
     "tpu_int8",
     "fbgemm_fp8",
     "fp_quant",
-    "experts_int8",
 ]
 
 # The customized quantization methods which will be added to this dict.
@@ -111,6 +113,7 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
     # lazy import to avoid triggering `torch.compile` too early
     from vllm.config.quantization import OnlineQuantScheme
     from vllm.model_executor.layers.quantization.quark.quark import QuarkConfig
+    from vllm.model_executor.models.deepseek_v4 import DeepseekV4FP8Config
 
     from .awq import AWQConfig
     from .awq_marlin import AWQMarlinConfig
@@ -126,6 +129,7 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
     from .gguf import GGUFConfig
     from .gptq import GPTQConfig
     from .gptq_marlin import GPTQMarlinConfig
+    from .humming import HummingConfig
     from .inc import INCConfig
     from .modelopt import (
         ModelOptFp8Config,
@@ -135,7 +139,6 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
     )
     from .moe_wna16 import MoeWNA16Config
     from .mxfp4 import GptOssMxfp4Config, Mxfp4Config
-    from .mxfp8 import Mxfp8Config
     from .online.base import OnlineQuantizationConfig
     from .torchao import TorchAOConfig
 
@@ -162,8 +165,9 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
         "inc": INCConfig,
         "mxfp4": Mxfp4Config,
         "gpt_oss_mxfp4": GptOssMxfp4Config,
-        "mxfp8": Mxfp8Config,
+        "deepseek_v4_fp8": DeepseekV4FP8Config,
         "cpu_awq": CPUAWQConfig,
+        "humming": HummingConfig,
         "online": OnlineQuantizationConfig,
     }
 
diff --git a/vllm/model_executor/layers/quantization/awq_marlin.py b/vllm/model_executor/layers/quantization/awq_marlin.py
index cfad1f86faa2..8f455220fe3b 100644
--- a/vllm/model_executor/layers/quantization/awq_marlin.py
+++ b/vllm/model_executor/layers/quantization/awq_marlin.py
@@ -20,7 +20,7 @@
     FusedMoEConfig,
     FusedMoEQuantConfig,
 )
-from vllm.model_executor.layers.fused_moe.fused_marlin_moe import fused_marlin_moe
+from vllm.model_executor.layers.fused_moe.experts.marlin_moe import fused_marlin_moe
 from vllm.model_executor.layers.fused_moe.layer import (
     FusedMoE,
     FusedMoEMethodBase,
@@ -764,7 +764,7 @@ def select_gemm_impl(
             )
 
         from vllm.model_executor.layers.fused_moe import modular_kernel as mk
-        from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.marlin_moe import (
             BatchedMarlinExperts,
             MarlinExperts,
         )
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
index 8d16a143b10a..2910e63678fe 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from contextlib import suppress
 from functools import partial
 from typing import TYPE_CHECKING, Any, Literal, cast
 
@@ -747,13 +746,13 @@ def get_scheme(
             self.sparsity_ignore_list
         )
         sparsity_scheme: SparsityCompressionConfig | None = None
-        with suppress(ValueError):
-            matched_target = find_matched_target(
-                layer_name=layer_name,
-                module=layer,
-                targets=sparsity_targets,
-                fused_mapping=self.packed_modules_mapping,
-            )
+        matched_target = find_matched_target(
+            layer_name=layer_name,
+            module=layer,
+            targets=sparsity_targets,
+            fused_mapping=self.packed_modules_mapping,
+        )
+        if matched_target is not None:
             sparsity_scheme = self.sparsity_scheme_map[matched_target]
 
         if self.supports_cutlass_24(
@@ -821,10 +820,11 @@ def get_scheme_dict(
                 targets=self.target_scheme_map.keys(),
                 fused_mapping=self.packed_modules_mapping,
             )
-            scheme_dict = self.target_scheme_map[matched_target]
-            if scheme_dict.get("format") is None:
-                scheme_dict["format"] = self.quant_format
-            return scheme_dict
+            if matched_target is not None:
+                scheme_dict = self.target_scheme_map[matched_target]
+                if scheme_dict.get("format") is None:
+                    scheme_dict["format"] = self.quant_format
+                return scheme_dict
 
         return None
 
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a4_mxfp4.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a4_mxfp4.py
index 8cc6d17bcc18..2ac2e28f20b5 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a4_mxfp4.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a4_mxfp4.py
@@ -4,6 +4,7 @@
 
 import torch
 
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import (
     FusedMoE,
@@ -11,8 +12,12 @@
 )
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEQuantConfig,
+    mxfp4_moe_quant_config,
 )
-from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import (
+    CutlassExpertsMxfp4,
+)
+from vllm.model_executor.layers.fused_moe.experts.marlin_moe import (
     MarlinExperts,
 )
 from vllm.model_executor.layers.fused_moe.oracle.mxfp4 import (
@@ -36,7 +41,14 @@ def __init__(self, moe):
         super().__init__(moe)
         self.group_size = 32
         self.mxfp4_backend = Mxfp4MoeBackend.MARLIN
-        self.experts_cls = MarlinExperts
+        self.use_cutlass_mxfp4 = CutlassExpertsMxfp4._supports_current_device()
+        self.experts_cls: type[mk.FusedMoEExperts]
+        if self.use_cutlass_mxfp4:
+            logger.info_once("Using CutlassExpertsMxfp4 for MXFP4 MoE")
+            self.experts_cls = CutlassExpertsMxfp4
+        else:
+            logger.info_once("Using MarlinExperts for MXFP4 MoE")
+            self.experts_cls = MarlinExperts
 
     def create_weights(
         self,
@@ -109,11 +121,19 @@ def create_weights(
     def get_fused_moe_quant_config(
         self, layer: torch.nn.Module
     ) -> FusedMoEQuantConfig | None:
-        return make_mxfp4_moe_quant_config(
-            mxfp4_backend=self.mxfp4_backend,
-            w1_scale=layer.w13_weight_scale,
-            w2_scale=layer.w2_weight_scale,
-        )
+        if self.use_cutlass_mxfp4:
+            # W4A4: both weights and activations quantized to MXFP4
+            return mxfp4_moe_quant_config(
+                w1_scale=layer.w13_weight_scale,
+                w2_scale=layer.w2_weight_scale,
+            )
+        else:
+            # W4A16: weight-only via Marlin
+            return make_mxfp4_moe_quant_config(
+                mxfp4_backend=self.mxfp4_backend,
+                w1_scale=layer.w13_weight_scale,
+                w2_scale=layer.w2_weight_scale,
+            )
 
     def process_weights_after_loading(self, layer: FusedMoE) -> None:
         layer.w13_weight = torch.nn.Parameter(
@@ -126,13 +146,45 @@ def process_weights_after_loading(self, layer: FusedMoE) -> None:
         )
         delattr(layer, "w2_weight_packed")
 
-        logger.warning_once(
-            "Your GPU does not have native support for FP4 computation but "
-            "FP4 quantization is being used. Weight-only FP4 compression "
-            "will be used leveraging the Marlin kernel. This may degrade "
-            "performance for compute-heavy workloads."
-        )
-        prepare_moe_fp4_layer_for_marlin(layer)
+        if self.use_cutlass_mxfp4:
+            # Swizzle weight scales from flat checkpoint layout [E, N, K//32]
+            # to CUTLASS tiled layout [E, numMTiles*numKTiles*512].
+            from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import (
+                swizzle_mxfp4_scales,
+            )
+
+            E = layer.w13_weight_scale.shape[0]
+            w13_N = layer.w13_weight_scale.shape[1]
+            w13_scale_K = layer.w13_weight_scale.shape[2]
+            w13_K = w13_scale_K * 32
+
+            w2_M = layer.w2_weight_scale.shape[1]
+            w2_scale_N = layer.w2_weight_scale.shape[2]
+            w2_N = w2_scale_N * 32
+
+            swizzled_w13 = []
+            swizzled_w2 = []
+            for e_idx in range(E):
+                s13 = layer.w13_weight_scale[e_idx]
+                sw13 = swizzle_mxfp4_scales(s13, w13_N, w13_K)
+                swizzled_w13.append(sw13.reshape(w13_N, w13_scale_K))
+                s2 = layer.w2_weight_scale[e_idx]
+                sw2 = swizzle_mxfp4_scales(s2, w2_M, w2_N)
+                swizzled_w2.append(sw2.reshape(w2_M, w2_scale_N))
+            layer.w13_weight_scale = torch.nn.Parameter(
+                torch.stack(swizzled_w13), requires_grad=False
+            )
+            layer.w2_weight_scale = torch.nn.Parameter(
+                torch.stack(swizzled_w2), requires_grad=False
+            )
+        else:
+            logger.warning_once(
+                "Your GPU does not have native support for FP4 computation "
+                "but FP4 quantization is being used. Weight-only FP4 "
+                "compression will be used leveraging the Marlin kernel. "
+                "This may degrade performance for compute-heavy workloads."
+            )
+            prepare_moe_fp4_layer_for_marlin(layer)
 
         self.moe_quant_config = self.get_fused_moe_quant_config(layer)
         if self.moe_quant_config is not None:
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a4_nvfp4.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a4_nvfp4.py
index 09a216fd2cb1..29c673d0f6e3 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a4_nvfp4.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a4_nvfp4.py
@@ -265,6 +265,7 @@ def apply_monolithic(
         layer: FusedMoE,
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
     ) -> torch.Tensor:
         assert self.is_monolithic
         assert self.moe_kernel is not None
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a8_fp8.py
index 74cb0b4f6e1f..b14571fe5013 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a8_fp8.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a8_fp8.py
@@ -198,11 +198,15 @@ def process_weights_after_loading(self, layer):
         # encode and reorder weight tensors, and get the layout to pass to
         # the grouped gemm kernel. `b_strides1/2` specifies the entire layout
         convert_packed_uint4b8_to_signed_int4_inplace(layer.w13_weight_packed)
+        # mirror the sync in CutlassW4A8LinearKernel; required for tp>1 correctness
+        torch.accelerator.synchronize()
         w13_weight_shuffled, self.b_strides1 = (
             ops.cutlass_encode_and_reorder_int4b_grouped(layer.w13_weight_packed)
         )
         replace_parameter(layer, "w13_weight_packed", w13_weight_shuffled)
         convert_packed_uint4b8_to_signed_int4_inplace(layer.w2_weight_packed)
+        # mirror the sync in CutlassW4A8LinearKernel; required for tp>1 correctness
+        torch.accelerator.synchronize()
         w2_weight_shuffled, self.b_strides2 = (
             ops.cutlass_encode_and_reorder_int4b_grouped(layer.w2_weight_packed)
         )
@@ -311,7 +315,7 @@ def apply(
             )
         assert self.moe_quant_config is not None
 
-        from vllm.model_executor.layers.fused_moe.cutlass_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import (
             cutlass_moe_w4a8_fp8,
         )
 
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a8_int8.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a8_int8.py
index 2e8a935cad6f..88cdbadd3f83 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a8_int8.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w4a8_int8.py
@@ -305,6 +305,7 @@ def apply_monolithic(
         layer: FusedMoE,
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
     ) -> torch.Tensor:
         assert not layer.enable_eplb, "EPLB not supported for W4A8-int MoE yet."
         assert layer.activation in (
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w8a8_fp8.py
index ed8ed79c50c6..bba7e0e7abce 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w8a8_fp8.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w8a8_fp8.py
@@ -367,6 +367,7 @@ def apply_monolithic(
         layer: FusedMoE,
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
     ) -> torch.Tensor:
         assert self.moe_kernel is not None
         return self.moe_kernel.apply_monolithic(
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w8a8_int8.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w8a8_int8.py
index de155f9e179f..bad5b3895b8f 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w8a8_int8.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w8a8_int8.py
@@ -8,6 +8,7 @@
     QuantizationStrategy,
 )
 
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import (
     FusedMoE,
@@ -16,17 +17,27 @@
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEQuantConfig,
-    int8_w8a8_moe_quant_config,
+)
+from vllm.model_executor.layers.fused_moe.oracle.int8 import (
+    make_int8_moe_kernel,
+    make_int8_moe_quant_config,
+    select_int8_moe_backend,
 )
 from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe import (  # noqa E501
     CompressedTensorsMoEMethod,
 )
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    kInt8DynamicTokenSym,
+    kInt8StaticChannelSym,
+)
 from vllm.model_executor.utils import set_weight_attrs
 
 logger = init_logger(__name__)
 
 
 class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
+    """W8A8 Int8 MoE quantization using compressed tensors."""
+
     def __init__(
         self,
         weight_quant: QuantizationArgs,
@@ -56,6 +67,13 @@ def __init__(
                 "dynamic per token quantization. Found static input scales."
             )
 
+        # Select Int8 MoE backend.
+        self.int8_backend, self.experts_cls = select_int8_moe_backend(
+            config=self.moe,
+            weight_key=kInt8StaticChannelSym,
+            activation_key=kInt8DynamicTokenSym,
+        )
+
     def create_weights(
         self,
         layer: torch.nn.Module,
@@ -122,13 +140,28 @@ def create_weights(
         layer.w13_input_scale = None
         layer.w2_input_scale = None
 
-    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
-        pass
+    def process_weights_after_loading(self, layer: FusedMoE) -> None:
+        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
+        assert self.experts_cls is not None
+        self.moe_kernel = make_int8_moe_kernel(
+            moe_quant_config=self.moe_quant_config,
+            moe_config=self.moe,
+            experts_cls=self.experts_cls,
+            routing_tables=layer._maybe_init_expert_routing_tables(),
+            shared_experts=layer.shared_experts,
+        )
+
+    def maybe_make_prepare_finalize(
+        self,
+        routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
+    ) -> mk.FusedMoEPrepareAndFinalizeModular | None:
+        raise ValueError(
+            f"{self.__class__.__name__} uses the new modular kernel initialization "
+            "logic. This function should not be called."
+        )
 
-    def get_fused_moe_quant_config(
-        self, layer: torch.nn.Module
-    ) -> FusedMoEQuantConfig | None:
-        return int8_w8a8_moe_quant_config(
+    def get_fused_moe_quant_config(self, layer: torch.nn.Module) -> FusedMoEQuantConfig:
+        return make_int8_moe_quant_config(
             w1_scale=layer.w13_weight_scale,
             w2_scale=layer.w2_weight_scale,
             a1_scale=layer.w13_input_scale,
@@ -144,18 +177,17 @@ def apply(
         topk_ids: torch.Tensor,
         shared_experts_input: torch.Tensor | None,
     ) -> torch.Tensor:
-        from vllm.model_executor.layers.fused_moe import fused_experts
-
-        return fused_experts(
-            hidden_states=x,
-            w1=layer.w13_weight,
-            w2=layer.w2_weight,
+        assert not self.is_monolithic
+        assert self.moe_kernel is not None
+        return self.moe_kernel.apply(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
             topk_weights=topk_weights,
             topk_ids=topk_ids,
-            inplace=not self.moe.disable_inplace,
             activation=layer.activation,
-            apply_router_weight_on_input=layer.apply_router_weight_on_input,
             global_num_experts=layer.global_num_experts,
             expert_map=layer.expert_map,
-            quant_config=self.moe_quant_config,
+            apply_router_weight_on_input=layer.apply_router_weight_on_input,
+            shared_experts_input=shared_experts_input,
         )
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w8a8_mxfp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w8a8_mxfp8.py
index 02e946b1b61e..ecd0b54890d1 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w8a8_mxfp8.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w8a8_mxfp8.py
@@ -168,6 +168,7 @@ def apply_monolithic(
         layer: FusedMoE,
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
     ) -> torch.Tensor:
         assert self.moe_kernel is not None
         return self.moe_kernel.apply_monolithic(
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_wna16_marlin.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_wna16_marlin.py
index 216eed6372a9..10a7302dc1d0 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_wna16_marlin.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_wna16_marlin.py
@@ -20,7 +20,7 @@
     FusedMoEQuantConfig,
     int4_w4a16_moe_quant_config,
 )
-from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+from vllm.model_executor.layers.fused_moe.experts.marlin_moe import (
     BatchedMarlinExperts,
     MarlinExperts,
     fused_marlin_moe,
@@ -87,7 +87,6 @@ def __init__(
         logger.info_once(
             f"Using {self.kernel_backend} backend for WNA16 MoE "
             f"(group_size={self.group_size}, num_bits={self.num_bits})",
-            scope="local",
         )
 
     def get_weight_shape(
@@ -518,6 +517,7 @@ def apply_monolithic(
         layer: FusedMoE,
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
     ) -> torch.Tensor:
         assert self.kernel_backend == "Flashinfer"
         return flashinfer_trtllm_mxint4_moe(
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py
index 3bf606ddb332..7445634a8253 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py
@@ -31,8 +31,8 @@
     GroupShape,
     create_fp8_quant_key,
     kFp8DynamicTokenSym,
+    kFp8StaticChannelSym,
     kFp8StaticTensorSym,
-    kFp8StaticTokenSym,
 )
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     cutlass_block_fp8_supported,
@@ -47,7 +47,7 @@
     DYNAMIC_QUANT: kFp8DynamicTokenSym,
 }
 weight_quant_key_mapping = {
-    QuantizationStrategy.CHANNEL: kFp8StaticTokenSym,
+    QuantizationStrategy.CHANNEL: kFp8StaticChannelSym,
     QuantizationStrategy.TENSOR: kFp8StaticTensorSym,
 }
 logger = init_logger(__name__)
@@ -67,7 +67,6 @@ def __init__(self, weight_quant: QuantizationArgs, is_static_input_scheme: bool)
             self.use_aiter_and_is_supported = rocm_aiter_ops.is_linear_fp8_enabled()
             assert not self.is_static_input_scheme
             self.act_q_group_shape = GroupShape(1, self.weight_block_size[0])
-
             self.weight_quant_key = create_fp8_quant_key(
                 static=True, group_shape=GroupShape(*self.weight_block_size)
             )
@@ -76,7 +75,7 @@ def __init__(self, weight_quant: QuantizationArgs, is_static_input_scheme: bool)
             )
         else:
             self.activation_quant_key = activation_quant_key_mapping[
-                is_static_input_scheme
+                self.is_static_input_scheme
             ]
             self.weight_quant_key = weight_quant_key_mapping[self.strategy]
 
@@ -138,9 +137,9 @@ def create_weights(
         self.fp8_linear = init_fp8_linear_kernel(
             activation_quant_key=self.activation_quant_key,
             weight_quant_key=self.weight_quant_key,
-            weight_shape=layer.weight.shape,
             input_dtype=self.input_dtype,
             out_dtype=self.out_dtype,
+            weight_shape=(output_size_per_partition, input_size_per_partition),
             module_name=self.__class__.__name__,
         )
 
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/utils.py b/vllm/model_executor/layers/quantization/compressed_tensors/utils.py
index 04c64d9bd56f..def4797b1396 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/utils.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/utils.py
@@ -115,7 +115,7 @@ def find_matched_target(
     module: Module,
     targets: Iterable[str],
     fused_mapping: Mapping[str, list[str]] = MappingProxyType({}),
-) -> str:
+) -> str | None:
     """
     Helper function to look up which "target" in the compressed-tensors
     config that a layer corresponds to.
@@ -150,12 +150,6 @@ def find_matched_target(
         or _match_fused_layer(layer_name, targets, fused_mapping)
     )
 
-    if matched_target is None:
-        raise ValueError(
-            f"Unable to find matching target for {layer_name} in the "
-            "compressed-tensors config."
-        )
-
     return matched_target
 
 
diff --git a/vllm/model_executor/layers/quantization/cpu_wna16.py b/vllm/model_executor/layers/quantization/cpu_wna16.py
index aea1067ff262..6853013af88d 100644
--- a/vllm/model_executor/layers/quantization/cpu_wna16.py
+++ b/vllm/model_executor/layers/quantization/cpu_wna16.py
@@ -287,8 +287,11 @@ def _process_weights_sglang_int4(self, layer: torch.nn.Module) -> None:
         packed_weight = layer.qweight.data
         packed_zeros = layer.qzeros.data
         scales = layer.scales.data
-        blocked_w, blocked_zp, blocked_s = torch.ops._C.convert_weight_packed_scale_zp(
-            packed_weight, packed_zeros, scales
+        blocked_w, blocked_zp, blocked_s = ops.convert_weight_packed_scale_zp(
+            packed_weight,
+            packed_zeros,
+            scales,
+            ops.CPUQuantAlgo.AWQ,
         )
 
         layer.packed_weight = blocked_w
@@ -334,18 +337,13 @@ def _apply_sglang_int4(
         bias: torch.Tensor | None = None,
     ) -> torch.Tensor:
         """SGLang INT4 W4A8 GEMM path."""
-        x_shape = x.shape
-        x_2d = x.reshape(-1, x_shape[-1]) if len(x_shape) > 2 else x
-
-        out = torch.ops._C.int4_scaled_mm_cpu(
-            x_2d,
+        return ops.int4_scaled_mm_cpu(
+            x,
             layer.packed_weight,
             layer.packed_qzeros,
             layer.packed_scales,
             bias,
         )
-        out = out.reshape(x_shape[:-1] + (out.size(-1),)) if len(x_shape) > 2 else out
-        return out
 
 
 def _get_isa_hint(dtype: torch.dtype) -> str:
diff --git a/vllm/model_executor/layers/quantization/experts_int8.py b/vllm/model_executor/layers/quantization/experts_int8.py
index 301441ff019d..007c3c214eed 100644
--- a/vllm/model_executor/layers/quantization/experts_int8.py
+++ b/vllm/model_executor/layers/quantization/experts_int8.py
@@ -5,27 +5,25 @@
 
 import torch
 
-from vllm.distributed import get_tensor_model_parallel_rank, get_tp_group
-from vllm.model_executor.layers.fused_moe import (
-    FusedMoE,
-    FusedMoEConfig,
-    FusedMoEMethodBase,
-)
-from vllm.model_executor.layers.fused_moe.config import (
-    FusedMoEQuantConfig,
-    int8_w8a16_moe_quant_config,
-)
+from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.linear import LinearBase, UnquantizedLinearMethod
 from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
 )
-from vllm.model_executor.utils import set_weight_attrs
+from vllm.model_executor.layers.quantization.online.int8 import (
+    Int8OnlineMoEMethod,
+)
 
 
 class ExpertsInt8Config(QuantizationConfig):
-    """Config class for Int8 experts quantization."""
+    """Online int8 quantization for MoE expert weights.
+    Linear layers are left unquantized.
+
+    Backward-compatible config for ``--quantization experts_int8``.
+    Prefer ``--quantization int8_per_channel``
+    """
 
     def __init__(self) -> None:
         super().__init__()
@@ -56,149 +54,5 @@ def get_quant_method(
         if isinstance(layer, LinearBase):
             return UnquantizedLinearMethod()
         elif isinstance(layer, FusedMoE):
-            return ExpertsInt8MoEMethod(self, layer.moe_config)
+            return Int8OnlineMoEMethod(layer=layer)
         return None
-
-
-class ExpertsInt8MoEMethod(FusedMoEMethodBase):
-    def __init__(
-        self,
-        quant_config: ExpertsInt8Config,
-        moe: FusedMoEConfig,
-    ):
-        super().__init__(moe)
-        self.quant_config = quant_config
-
-    def create_weights(
-        self,
-        layer: torch.nn.Module,
-        num_experts: int,
-        hidden_size: int,
-        intermediate_size_per_partition: int,
-        params_dtype: torch.dtype,
-        **extra_weight_attrs,
-    ):
-        int8_dtype = torch.int8
-
-        assert "weight_loader" in extra_weight_attrs
-        weight_loader = extra_weight_attrs["weight_loader"]
-        wrapped_weight_loader = ExpertsInt8MoEMethod.quantizing_weight_loader(
-            layer, weight_loader
-        )
-        extra_weight_attrs["weight_loader"] = wrapped_weight_loader
-
-        # Fused gate_up_proj (column parallel)
-        w13_weight = torch.nn.Parameter(
-            torch.empty(
-                num_experts,
-                2 * intermediate_size_per_partition,
-                hidden_size,
-                dtype=int8_dtype,
-            ),
-            requires_grad=False,
-        )
-        layer.register_parameter("w13_weight", w13_weight)
-        set_weight_attrs(w13_weight, extra_weight_attrs)
-
-        # down_proj (row parallel)
-        w2_weight = torch.nn.Parameter(
-            torch.empty(
-                num_experts,
-                hidden_size,
-                intermediate_size_per_partition,
-                dtype=int8_dtype,
-            ),
-            requires_grad=False,
-        )
-        layer.register_parameter("w2_weight", w2_weight)
-        set_weight_attrs(w2_weight, extra_weight_attrs)
-
-        w13_scale = torch.nn.Parameter(
-            torch.zeros(
-                num_experts, 2 * intermediate_size_per_partition, dtype=torch.float32
-            ),
-            requires_grad=False,
-        )
-        layer.register_parameter("w13_scale", w13_scale)
-
-        w2_scale = torch.nn.Parameter(
-            torch.zeros(num_experts, hidden_size, dtype=torch.float32),
-            requires_grad=False,
-        )
-        layer.register_parameter("w2_scale", w2_scale)
-
-    def get_fused_moe_quant_config(
-        self, layer: torch.nn.Module
-    ) -> FusedMoEQuantConfig | None:
-        return int8_w8a16_moe_quant_config(
-            w1_scale=layer.w13_scale, w2_scale=layer.w2_scale, w1_zp=None, w2_zp=None
-        )
-
-    def apply(
-        self,
-        layer: FusedMoE,
-        x: torch.Tensor,
-        topk_weights: torch.Tensor,
-        topk_ids: torch.Tensor,
-        shared_experts_input: torch.Tensor | None,
-    ) -> torch.Tensor:
-        from vllm.model_executor.layers.fused_moe import fused_experts
-
-        return fused_experts(
-            x,
-            layer.w13_weight,
-            layer.w2_weight,
-            topk_weights=topk_weights,
-            topk_ids=topk_ids,
-            inplace=not self.moe.disable_inplace,
-            activation=layer.activation,
-            apply_router_weight_on_input=layer.apply_router_weight_on_input,
-            global_num_experts=layer.global_num_experts,
-            expert_map=layer.expert_map,
-            quant_config=self.moe_quant_config,
-        )
-
-    @staticmethod
-    def quantizing_weight_loader(layer, weight_loader):
-        def quantize_and_call_weight_loader(
-            param: torch.nn.Parameter,
-            loaded_weight: torch.Tensor,
-            weight_name: str,
-            shard_id: int,
-            expert_id: int,
-        ):
-            tp_rank = get_tensor_model_parallel_rank()
-            shard_size = layer.intermediate_size_per_partition
-            shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
-            device = get_tp_group().device
-            loaded_weight = loaded_weight.to(device)
-            # w1, gate_proj case: Load into first shard of w13.
-            if shard_id == "w1":
-                scales = quantize_in_place_and_get_scales(loaded_weight[shard, :])
-                layer.w13_scale.data[expert_id, 0:shard_size].copy_(scales[:, 0])
-            # w3, up_proj case: Load into second shard of w13.
-            elif shard_id == "w3":
-                scales = quantize_in_place_and_get_scales(loaded_weight[shard, :])
-                layer.w13_scale.data[expert_id, shard_size : 2 * shard_size].copy_(
-                    scales[:, 0]
-                )
-            # w2, down_proj case: Load into only shard of w2.
-            elif shard_id == "w2":
-                scales = quantize_in_place_and_get_scales(loaded_weight[:, shard])
-                layer.w2_scale.data[expert_id, :].copy_(scales[:, 0])
-            else:
-                raise ValueError(f"Shard id must be in [0,1,2] but got {shard_id}")
-            weight_loader(param, loaded_weight, weight_name, shard_id, expert_id)
-
-        return quantize_and_call_weight_loader
-
-
-def quantize_in_place_and_get_scales(weight: torch.Tensor) -> torch.Tensor:
-    vmax = torch.iinfo(torch.int8).max
-    scales = torch.max(torch.abs(weight), dim=1, keepdim=True)[0] / vmax
-
-    weight.div_(scales)
-    weight.round_()
-    weight.clamp_(-vmax, vmax)
-
-    return scales
diff --git a/vllm/model_executor/layers/quantization/fbgemm_fp8.py b/vllm/model_executor/layers/quantization/fbgemm_fp8.py
index 377cec364bfc..d95c51be0102 100644
--- a/vllm/model_executor/layers/quantization/fbgemm_fp8.py
+++ b/vllm/model_executor/layers/quantization/fbgemm_fp8.py
@@ -175,6 +175,8 @@ def process_weights_after_loading(self, layer: Module) -> None:
             # Activations not quantized for marlin.
             del layer.input_scale_ub
 
+        self.fp8_linear.process_weights_after_loading(layer)
+
     def apply(
         self,
         layer: torch.nn.Module,
diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py
index d7920462e613..30e9a6344bc7 100644
--- a/vllm/model_executor/layers/quantization/fp8.py
+++ b/vllm/model_executor/layers/quantization/fp8.py
@@ -28,6 +28,7 @@
 )
 from vllm.model_executor.layers.fused_moe.layer import UnquantizedFusedMoEMethod
 from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
+    Fp8MoeBackend,
     convert_to_fp8_moe_kernel_format,
     make_fp8_moe_kernel,
     make_fp8_moe_quant_config,
@@ -269,6 +270,7 @@ class Fp8LinearMethod(LinearMethodBase):
 
     def __init__(self, quant_config: Fp8Config):
         self.quant_config = quant_config
+        self.is_scale_e8m0 = getattr(quant_config, "is_scale_e8m0", False)
         self.cutlass_block_fp8_supported = cutlass_block_fp8_supported()
         self.out_dtype = torch.get_default_dtype()
         self.input_dtype = get_current_vllm_config().model_config.dtype
@@ -362,6 +364,7 @@ def create_weights(
                 input_size_per_partition,
                 self.weight_block_size,
                 weight_loader,
+                scale_dtype=(torch.float8_e8m0fnu if self.is_scale_e8m0 else None),
             )
             # The weight_scale_inv name is intentional for deepseekv3
             layer.register_parameter("weight_scale_inv", scale)
@@ -397,8 +400,6 @@ def process_weights_after_loading(self, layer: Module) -> None:
         if self.block_quant:
             assert not self.act_q_static
 
-            self.fp8_linear.process_weights_after_loading(layer)
-
         # If checkpoint not serialized fp8, quantize the weights.
         else:
             # If checkpoint is fp8 per-tensor, handle that there are N scales for N
@@ -428,6 +429,8 @@ def process_weights_after_loading(self, layer: Module) -> None:
         else:
             layer.input_scale = None
 
+        self.fp8_linear.process_weights_after_loading(layer)
+
     def apply(
         self,
         layer: torch.nn.Module,
@@ -515,14 +518,6 @@ def create_weights(
 
         initialize_online_processing(layer)
 
-        # TODO: remove this check once the following RFC is resolved.
-        # https://github.com/vllm-project/vllm/issues/33314
-        # Subclasses (e.g. Mxfp8OnlineLinearMethod) only need the weight
-        # registration above and manage their own kernel, so skip fp8_linear
-        # kernel creation for them.
-        if type(self) is not Fp8OnlineLinearMethod:
-            return
-
         self.fp8_linear = init_fp8_linear_kernel(
             activation_quant_key=self.activation_quant_key,
             weight_quant_key=self.weight_quant_key,
@@ -772,6 +767,11 @@ def _setup_kernel(
         replace_parameter(layer, f"w13_{self.weight_scale_name}", w13_scale)
         replace_parameter(layer, f"w2_{self.weight_scale_name}", w2_scale)
 
+        # AITER backend requires weights to be marked as shuffled.
+        if self.fp8_backend == Fp8MoeBackend.AITER:
+            layer.w13_weight.is_shuffled = True
+            layer.w2_weight.is_shuffled = True
+
         self.moe_quant_config = self.get_fused_moe_quant_config(layer)
         if self.moe_quant_config:
             assert self.experts_cls is not None
@@ -874,6 +874,7 @@ def apply_monolithic(
         layer: FusedMoE,
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
     ) -> torch.Tensor:
         assert self.is_monolithic
         assert self.moe_kernel is not None
@@ -1027,10 +1028,6 @@ def process_weights_after_loading(self, layer: Module) -> None:
                 layer.w2_weight[expert, :, :]
             )
 
-        if current_platform.is_xpu():
-            w13.data = w13.transpose(-1, -2).contiguous()
-            w2.data = w2.transpose(-1, -2).contiguous()
-
         # Shuffle weights to runtime format and setup kernel.
         self._setup_kernel(
             layer,
diff --git a/vllm/model_executor/layers/quantization/fp_quant.py b/vllm/model_executor/layers/quantization/fp_quant.py
index 4ed8d57dd430..7d0b6a974d7c 100644
--- a/vllm/model_executor/layers/quantization/fp_quant.py
+++ b/vllm/model_executor/layers/quantization/fp_quant.py
@@ -3,7 +3,7 @@
 
 # Supports FP-Quant compression, see https://arxiv.org/abs/2509.23202
 
-from typing import Any
+from typing import Any, Literal, cast
 
 import torch
 from torch.nn.parameter import Parameter
@@ -251,7 +251,11 @@ def apply(
 def fused_quantize_mx(
     x_flat: torch.Tensor, hadamard_matrix: torch.Tensor, forward_method: str
 ) -> tuple[torch.Tensor, torch.Tensor]:
-    return fusedQuantizeMx(x_flat, hadamard_matrix, method=forward_method)
+    return fusedQuantizeMx(
+        x_flat,
+        hadamard_matrix,
+        method=cast(Literal["quest", "abs_max"], forward_method),
+    )
 
 
 def fused_quantize_mx_fake(x_flat, hadamard_matrix, forward_method):
diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py
index 1ca551d6351b..7b6f1f9cf6cd 100644
--- a/vllm/model_executor/layers/quantization/gptq_marlin.py
+++ b/vllm/model_executor/layers/quantization/gptq_marlin.py
@@ -9,7 +9,6 @@
 from transformers import PretrainedConfig
 
 import vllm.model_executor.layers.fused_moe  # noqa
-from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.kernels.linear import (
     MPLinearLayerConfig,
@@ -19,13 +18,17 @@
     FusedMoEConfig,
     FusedMoEQuantConfig,
 )
-from vllm.model_executor.layers.fused_moe.fused_marlin_moe import fused_marlin_moe
 from vllm.model_executor.layers.fused_moe.layer import (
     FusedMoE,
     FusedMoEMethodBase,
     FusedMoeWeightScaleSupported,
     UnquantizedFusedMoEMethod,
 )
+from vllm.model_executor.layers.fused_moe.oracle.int_wna16 import (
+    convert_to_wna16_moe_kernel_format,
+    make_wna16_moe_kernel,
+    select_wna16_moe_backend,
+)
 from vllm.model_executor.layers.linear import LinearMethodBase, set_weight_attrs
 from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
@@ -42,13 +45,15 @@
     check_marlin_supported,
     check_moe_marlin_supports_layer,
     get_marlin_input_dtype,
-    marlin_act_int8_process_scales,
     marlin_make_workspace_new,
-    marlin_moe_permute_scales,
-    marlin_permute_bias,
     marlin_repeat_scales_on_all_ranks,
     verify_marlin_supported,
 )
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    QuantKey,
+    kInt4StaticGroupScale,
+    kInt8StaticGroupScale,
+)
 from vllm.model_executor.parameter import (
     ChannelQuantScaleParameter,
     GroupQuantScaleParameter,
@@ -500,13 +505,20 @@ def __init__(
         super().__init__(moe)
         self.quant_config = quant_config
         if self.quant_config.quant_type.size_bits == 4:
-            self.quant_type = scalar_types.uint4b8
+            quant_type = scalar_types.uint4b8
+            scale = kInt4StaticGroupScale
         elif self.quant_config.quant_type.size_bits == 8:
-            self.quant_type = scalar_types.uint8b128
+            quant_type = scalar_types.uint8b128
+            scale = kInt8StaticGroupScale
         else:
             raise ValueError("GPTQMarlinMoEMethod only supports int4 and int8 now.")
         self.input_dtype = None
         self.use_marlin = True
+        weight_key = QuantKey(quant_type, scale)
+
+        self.wna16_moe_backend, self.experts_cls = select_wna16_moe_backend(
+            moe, weight_key, quant_config.weight_bits
+        )
 
     def create_weights(
         self,
@@ -521,7 +533,7 @@ def create_weights(
         is_a_8bit = self.input_dtype is not None and self.input_dtype.itemsize == 1
 
         if is_a_8bit:
-            assert self.quant_type == scalar_types.uint4b8, (
+            assert self.quant_config.quant_type.size_bits == 8, (
                 "W8A8-INT8 is not supported by marlin kernel."
             )
 
@@ -668,134 +680,100 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         is_a_8bit = self.input_dtype is not None and self.input_dtype.itemsize == 1
 
         if is_a_8bit:
-            assert self.quant_type == scalar_types.uint4b8, (
+            assert self.quant_config.quant_type.size_bits == 8, (
                 "W8A8-INT8 is not supported by marlin kernel."
             )
 
-        if self.input_dtype == torch.float8_e4m3fn:
-            ops.marlin_int4_fp8_preprocess(layer.w13_qweight, inplace=True)
-            ops.marlin_int4_fp8_preprocess(layer.w2_qweight, inplace=True)
-            layer.w13_scales.data = layer.w13_scales.data * 512
-            layer.w2_scales.data = layer.w2_scales.data * 512
-
-        # Process act_order
-        if self.quant_config.desc_act:
-            # Get sorting based on g_idx
-            num_experts = layer.w13_g_idx.shape[0]
-            w13_g_idx_sort_indices = torch.empty_like(layer.w13_g_idx)
-            w2_g_idx_sort_indices = torch.empty_like(layer.w2_g_idx)
-            w13_sorted_g_idx = torch.empty_like(layer.w13_g_idx)
-            w2_sorted_g_idx = torch.empty_like(layer.w2_g_idx)
-            for e in range(num_experts):
-                w13_g_idx_sort_indices[e] = torch.argsort(layer.w13_g_idx[e]).to(
-                    torch.int32
-                )
-                w2_g_idx_sort_indices[e] = torch.argsort(layer.w2_g_idx[e]).to(
-                    torch.int32
-                )
-                w13_sorted_g_idx[e] = layer.w13_g_idx[e][w13_g_idx_sort_indices[e]]
-                w2_sorted_g_idx[e] = layer.w2_g_idx[e][w2_g_idx_sort_indices[e]]
-            replace_parameter(layer, "w13_g_idx", w13_sorted_g_idx)
-            replace_parameter(layer, "w2_g_idx", w2_sorted_g_idx)
-            replace_parameter(layer, "w13_g_idx_sort_indices", w13_g_idx_sort_indices)
-            replace_parameter(layer, "w2_g_idx_sort_indices", w2_g_idx_sort_indices)
-        else:
-            # Reset g_idx related tensors
-            num_experts = layer.w13_g_idx.shape[0]
-            device = layer.w13_g_idx.device
-            layer.w13_g_idx = torch.nn.Parameter(
-                torch.empty((num_experts, 0), dtype=torch.int32, device=device),
-                requires_grad=False,
-            )
-            layer.w2_g_idx = torch.nn.Parameter(
-                torch.empty((num_experts, 0), dtype=torch.int32, device=device),
-                requires_grad=False,
-            )
-            layer.w13_g_idx_sort_indices = torch.nn.Parameter(
-                torch.empty((num_experts, 0), dtype=torch.int32, device=device),
-                requires_grad=False,
-            )
-            layer.w2_g_idx_sort_indices = torch.nn.Parameter(
-                torch.empty((num_experts, 0), dtype=torch.int32, device=device),
-                requires_grad=False,
-            )
-        # Repack weights
-        marlin_w13_qweight = ops.gptq_marlin_moe_repack(
-            layer.w13_qweight,
-            layer.w13_g_idx_sort_indices,
-            layer.w13_qweight.shape[1] * self.quant_config.pack_factor,
-            layer.w13_qweight.shape[2],
-            self.quant_config.quant_type.size_bits,
-            is_a_8bit=is_a_8bit,
-        )
-        replace_parameter(layer, "w13_qweight", marlin_w13_qweight)
-        marlin_w2_qweight = ops.gptq_marlin_moe_repack(
-            layer.w2_qweight,
-            layer.w2_g_idx_sort_indices,
-            layer.w2_qweight.shape[1] * self.quant_config.pack_factor,
-            layer.w2_qweight.shape[2],
-            self.quant_config.quant_type.size_bits,
-            is_a_8bit=is_a_8bit,
-        )
-        replace_parameter(layer, "w2_qweight", marlin_w2_qweight)
-
-        # The modular kernel expects w13_weight and w2_weight,
-        # but GPTQ uses w13_qweight and w2_qweight
-        # Alias for modular kernel
-        layer.w13_weight = layer.w13_qweight
-        # Alias for modular kernel
-        layer.w2_weight = layer.w2_qweight
-
-        # Repack scales
-        marlin_w13_scales = marlin_moe_permute_scales(
-            s=layer.w13_scales,
-            size_k=layer.intermediate_size_per_partition,
-            size_n=layer.w13_scales.shape[2],
-            group_size=self.quant_config.group_size,
-            is_a_8bit=is_a_8bit,
+        (
+            w13,
+            w2,
+            w13_scale,
+            w2_scale,
+            w13_g_idx,
+            w2_g_idx,
+            w13_g_idx_sort_indices,
+            w2_g_idx_sort_indices,
+            w13_input_global_scale,
+            w2_input_global_scale,
+            w13_bias,
+            w2_bias,
+        ) = convert_to_wna16_moe_kernel_format(
+            backend=self.wna16_moe_backend,
+            layer=layer,
+            quant_config=self.quant_config,
+            input_dtype=self.input_dtype,
+            w13=layer.w13_qweight,
+            w2=layer.w2_qweight,
+            w13_scale=layer.w13_scales,
+            w2_scale=layer.w2_scales,
+            w13_g_idx=layer.w13_g_idx,
+            w2_g_idx=layer.w2_g_idx,
+            w13_bias=getattr(layer, "w13_bias", None),
+            w2_bias=getattr(layer, "w2_bias", None),
         )
-        if self.input_dtype == torch.int8 and layer.num_groups_w13 > 1:
-            marlin_w13_scales, w13_input_global_scale = marlin_act_int8_process_scales(
-                marlin_w13_scales
-            )
-            layer.register_parameter(
-                "w13_input_global_scale",
-                torch.nn.Parameter(w13_input_global_scale, requires_grad=False),
-            )
 
-        replace_parameter(layer, "w13_scales", marlin_w13_scales)
-        marlin_w2_scales = marlin_moe_permute_scales(
-            s=layer.w2_scales,
-            size_k=layer.w2_scales.shape[1]
-            * (
-                self.quant_config.group_size
-                if self.quant_config.group_size != -1
-                else self.quant_config.pack_factor
-            ),
-            size_n=layer.w2_scales.shape[2],
-            group_size=self.quant_config.group_size,
-            is_a_8bit=is_a_8bit,
-        )
-        if self.input_dtype == torch.int8 and layer.num_groups_w2 > 1:
-            marlin_w2_scales, w2_input_global_scale = marlin_act_int8_process_scales(
-                marlin_w2_scales
-            )
-            layer.register_parameter(
-                "w2_input_global_scale",
-                torch.nn.Parameter(w2_input_global_scale, requires_grad=False),
-            )
+        replace_parameter(layer, "w13_qweight", w13)
+        replace_parameter(layer, "w2_qweight", w2)
+        replace_parameter(layer, "w13_scales", w13_scale)
+        replace_parameter(layer, "w2_scales", w2_scale)
+        replace_parameter(layer, "w13_g_idx", w13_g_idx)
+        replace_parameter(layer, "w2_g_idx", w2_g_idx)
+        replace_parameter(layer, "w13_g_idx_sort_indices", w13_g_idx_sort_indices)
+        replace_parameter(layer, "w2_g_idx_sort_indices", w2_g_idx_sort_indices)
+        if w13_input_global_scale is not None:
+            if hasattr(layer, "w13_input_global_scale"):
+                replace_parameter(
+                    layer, "w13_input_global_scale", w13_input_global_scale
+                )
+            else:
+                layer.register_parameter(
+                    "w13_input_global_scale",
+                    torch.nn.Parameter(w13_input_global_scale, requires_grad=False),
+                )
+        if w2_input_global_scale is not None:
+            if hasattr(layer, "w2_input_global_scale"):
+                replace_parameter(layer, "w2_input_global_scale", w2_input_global_scale)
+            else:
+                layer.register_parameter(
+                    "w2_input_global_scale",
+                    torch.nn.Parameter(w2_input_global_scale, requires_grad=False),
+                )
+        if w13_bias is not None:
+            if hasattr(layer, "w13_bias"):
+                replace_parameter(layer, "w13_bias", w13_bias)
+            else:
+                layer.register_parameter(
+                    "w13_bias", torch.nn.Parameter(w13_bias, requires_grad=False)
+                )
+        if w2_bias is not None:
+            if hasattr(layer, "w2_bias"):
+                replace_parameter(layer, "w2_bias", w2_bias)
+            else:
+                layer.register_parameter(
+                    "w2_bias", torch.nn.Parameter(w2_bias, requires_grad=False)
+                )
 
-        replace_parameter(layer, "w2_scales", marlin_w2_scales)
+        self._setup_kernel(layer)
 
-        if hasattr(layer, "w13_bias") and layer.w13_bias is not None:
-            layer.w13_bias.data = marlin_permute_bias(layer.w13_bias)
+    def _setup_kernel(self, layer: FusedMoE) -> None:
+        """Build the FusedMoEKernel for this layer."""
 
-        if hasattr(layer, "w2_bias") and layer.w2_bias is not None:
-            layer.w2_bias.data = marlin_permute_bias(layer.w2_bias)
+        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
+        self.moe_kernel = make_wna16_moe_kernel(
+            moe_quant_config=self.moe_quant_config,
+            moe_config=self.moe,
+            experts_cls=self.experts_cls,
+            layer=layer,
+            is_k_full=self.is_k_full,
+            w13_g_idx=layer.w13_g_idx,
+            w2_g_idx=layer.w2_g_idx,
+            w13_g_idx_sort_indices=layer.w13_g_idx_sort_indices,
+            w2_g_idx_sort_indices=layer.w2_g_idx_sort_indices,
+            routing_tables=layer._maybe_init_expert_routing_tables(),
+            shared_experts=layer.shared_experts,
+        )
 
-    def get_fused_moe_quant_config(
-        self, layer: torch.nn.Module
-    ) -> FusedMoEQuantConfig | None:
+    def get_fused_moe_quant_config(self, layer: torch.nn.Module) -> FusedMoEQuantConfig:
         from vllm.model_executor.layers.fused_moe.config import (
             gptq_marlin_moe_quant_config,
         )
@@ -820,86 +798,11 @@ def select_gemm_impl(
         prepare_finalize,
         layer: torch.nn.Module,
     ):
-        """
-        Select the GEMM implementation for GPTQ-Marlin MoE.
-
-        Returns MarlinExperts configured for GPTQ quantization.
-        This is ONLY used when LoRA is enabled.
-        Without LoRA, GPTQ uses its own apply() method.
-        """
-        # Only use modular kernels when LoRA is enabled
-        # Without LoRA, GPTQ's own apply() method works fine and is more efficient
-        if not self.moe.is_lora_enabled:
-            raise NotImplementedError(
-                "GPTQ-Marlin uses its own apply() method when LoRA is not enabled. "
-                "Modular kernels are only used for LoRA support."
-            )
-
-        # The modular marlin kernels do not support 8-bit weights.
-        if self.quant_config.weight_bits == 8:
-            raise NotImplementedError(
-                "GPTQ-Marlin kernel does not support 8-bit weights."
-            )
-
-        from vllm.model_executor.layers.fused_moe import modular_kernel as mk
-        from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
-            BatchedMarlinExperts,
-            MarlinExperts,
-        )
-
-        # Ensure quant config is initialized
-        assert self.moe_quant_config is not None, (
-            "moe_quant_config must be initialized before select_gemm_impl"
-        )
-
-        w13_g_idx = (
-            getattr(layer, "w13_g_idx", None) if self.quant_config.desc_act else None
-        )
-        w2_g_idx = (
-            getattr(layer, "w2_g_idx", None) if self.quant_config.desc_act else None
-        )
-        w13_g_idx_sort_indices = (
-            getattr(layer, "w13_g_idx_sort_indices", None)
-            if self.quant_config.desc_act
-            else None
-        )
-        w2_g_idx_sort_indices = (
-            getattr(layer, "w2_g_idx_sort_indices", None)
-            if self.quant_config.desc_act
-            else None
+        raise ValueError(
+            f"{self.__class__.__name__} uses the new modular kernel "
+            "initialization logic. This function should not be called."
         )
 
-        # Check if using batched expert format (for Expert Parallelism)
-        if (
-            prepare_finalize.activation_format
-            == mk.FusedMoEActivationFormat.BatchedExperts
-        ):
-            # For batched format, use BatchedMarlinExperts
-            max_num_tokens_per_rank = prepare_finalize.max_num_tokens_per_rank()
-            assert max_num_tokens_per_rank is not None
-            return BatchedMarlinExperts(
-                max_num_tokens=max_num_tokens_per_rank,
-                num_dispatchers=prepare_finalize.num_dispatchers(),
-                moe_config=self.moe,
-                quant_config=self.moe_quant_config,
-                w13_g_idx=w13_g_idx,
-                w2_g_idx=w2_g_idx,
-                w13_g_idx_sort_indices=w13_g_idx_sort_indices,
-                w2_g_idx_sort_indices=w2_g_idx_sort_indices,
-                is_k_full=self.is_k_full,
-            )
-        else:
-            # Standard Marlin experts for GPTQ
-            return MarlinExperts(
-                moe_config=self.moe,
-                quant_config=self.moe_quant_config,
-                w13_g_idx=w13_g_idx,
-                w2_g_idx=w2_g_idx,
-                w13_g_idx_sort_indices=w13_g_idx_sort_indices,
-                w2_g_idx_sort_indices=w2_g_idx_sort_indices,
-                is_k_full=self.is_k_full,
-            )
-
     def apply(
         self,
         layer: FusedMoE,
@@ -908,28 +811,17 @@ def apply(
         topk_ids: torch.Tensor,
         shared_experts_input: torch.Tensor | None,
     ) -> torch.Tensor:
-        return fused_marlin_moe(
-            x,
-            layer.w13_qweight,
-            layer.w2_qweight,
-            getattr(layer, "w13_bias", None),
-            getattr(layer, "w2_bias", None),
-            layer.w13_scales,
-            layer.w2_scales,
-            topk_weights,
-            topk_ids,
-            input_global_scale1=getattr(layer, "w13_input_global_scale", None),
-            input_global_scale2=getattr(layer, "w2_input_global_scale", None),
-            quant_type_id=self.quant_type.id,
-            apply_router_weight_on_input=layer.apply_router_weight_on_input,
+        assert not self.is_monolithic
+        assert self.moe_kernel is not None
+        return self.moe_kernel.apply(
+            hidden_states=x,
+            w1=layer.w13_qweight,
+            w2=layer.w2_qweight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            activation=layer.activation,
             global_num_experts=layer.global_num_experts,
+            apply_router_weight_on_input=layer.apply_router_weight_on_input,
             expert_map=layer.expert_map,
-            g_idx1=layer.w13_g_idx,
-            g_idx2=layer.w2_g_idx,
-            sort_indices1=layer.w13_g_idx_sort_indices,
-            sort_indices2=layer.w2_g_idx_sort_indices,
-            workspace=layer.workspace,
-            is_k_full=self.is_k_full,
-            input_dtype=self.input_dtype,
-            inplace=not self.moe.disable_inplace,
+            shared_experts_input=shared_experts_input,
         )
diff --git a/vllm/model_executor/layers/quantization/humming.py b/vllm/model_executor/layers/quantization/humming.py
new file mode 100644
index 000000000000..79a1057c6003
--- /dev/null
+++ b/vllm/model_executor/layers/quantization/humming.py
@@ -0,0 +1,924 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import json
+import math
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any
+
+import regex as re
+import torch
+
+from vllm import envs
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEQuantConfig,
+)
+from vllm.model_executor.layers.fused_moe.layer import (
+    FusedMoE,
+    FusedMoEMethodBase,
+)
+from vllm.model_executor.layers.fused_moe.unquantized_fused_moe_method import (
+    UnquantizedFusedMoEMethod,
+)
+from vllm.model_executor.layers.linear import (
+    LinearBase,
+    LinearMethodBase,
+    UnquantizedLinearMethod,
+)
+from vllm.model_executor.layers.quantization import QuantizationMethods
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig,
+    QuantizeMethodBase,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.parameter import (
+    BasevLLMParameter,
+    BlockQuantScaleParameter,
+    ChannelQuantScaleParameter,
+    GroupQuantScaleParameter,
+    ModelWeightParameter,
+    PackedvLLMParameter,
+    PerTensorScaleParameter,
+    RowvLLMParameter,
+)
+from vllm.model_executor.utils import set_weight_attrs
+
+if TYPE_CHECKING:
+    from vllm.model_executor.models.utils import WeightsMapper
+
+
+try:
+    from humming.dtypes import DataType
+    from humming.layer import HummingMethod
+    from humming.schema import (
+        BaseInputSchema,
+        BaseWeightSchema,
+        HummingInputSchema,
+        HummingWeightSchema,
+    )
+    from humming.utils.weight import quantize_weight
+
+    from vllm.model_executor.layers.fused_moe.fused_humming_moe import (
+        BatchedHummingGroupedExperts,
+        HummingGroupedExperts,
+        HummingIndexedExperts,
+        get_humming_moe_gemm_type,
+    )
+except ModuleNotFoundError:
+    HummingMethod = None
+
+
+def assert_humming_available():
+    assert HummingMethod is not None, (
+        "humming is not available, please run "
+        "'pip install git+https://github.com/inclusionAI/humming' to install it."
+    )
+
+
+def prepare_padded_shape(shape, x):
+    padded_shape = math.ceil(shape / x) * x
+    return padded_shape, padded_shape - shape
+
+
+def prepare_param(tensor, name, extra_attrs):
+    extra_attrs = extra_attrs.copy()
+    scale_type = extra_attrs.pop("scale_type", None)
+    param_cls_name_map = {
+        "block": BlockQuantScaleParameter,
+        "tensor": PerTensorScaleParameter,
+        "group": GroupQuantScaleParameter,
+        "channel": ChannelQuantScaleParameter,
+        "input_scale": PerTensorScaleParameter,
+    }
+
+    param_cls: type[BasevLLMParameter]
+    if "packed_dim" in extra_attrs:
+        param_cls = PackedvLLMParameter
+    elif scale_type in param_cls_name_map:
+        param_cls = param_cls_name_map[scale_type]
+    elif "output_dim" in extra_attrs and "input_dim" in extra_attrs:
+        param_cls = ModelWeightParameter
+    elif "input_dim" in extra_attrs:
+        param_cls = RowvLLMParameter
+    elif "output_dim" in extra_attrs:
+        param_cls = ChannelQuantScaleParameter
+    else:
+        param_cls = BasevLLMParameter
+
+    kwargs_keys = [
+        "input_dim",
+        "output_dim",
+        "packed_dim",
+        "packed_factor",
+        "weight_loader",
+    ]
+    cls_kwargs = {}
+    for key in extra_attrs.copy():
+        if key in kwargs_keys:
+            cls_kwargs[key] = extra_attrs.pop(key)
+
+    param = param_cls(data=tensor, **cls_kwargs)
+    set_weight_attrs(param, extra_attrs)
+
+    param.param_name = name
+    param.ignore_warning = True
+    if scale_type in ["tensor", "input_scale"]:
+        param.needs_scalar_to_array = True
+
+    return param
+
+
+def prepare_moe_param(tensor, name, extra_attrs):
+    param = torch.nn.Parameter(tensor, requires_grad=False)
+    if "scale_type" in extra_attrs:
+        extra_attrs["quant_method"] = extra_attrs["scale_type"]
+
+    if "input_dim" in extra_attrs and "output_dim" in extra_attrs:
+        input_dim = extra_attrs["input_dim"]
+        output_dim = extra_attrs["output_dim"]
+        extra_attrs["is_transposed"] = input_dim < output_dim
+
+    set_weight_attrs(param, extra_attrs)
+    param.param_name = name
+    return param
+
+
+def may_pad_loaded_weight(param, loaded_weight):
+    pad_shape = getattr(param, "pad_shape", None)
+    if pad_shape is None:
+        return loaded_weight
+    value = 1 if loaded_weight.dtype == torch.float8_e8m0fnu else 0
+    padding = []
+    for x in pad_shape[::-1][: loaded_weight.ndim]:
+        padding += [0, x]
+    loaded_weight = torch.nn.functional.pad(
+        input=loaded_weight,
+        pad=padding,
+        value=value,
+    )
+    return loaded_weight
+
+
+def compressed_tensors_get_config(config: dict[str, Any], key: str):
+    assert key in ["weights", "input_activations"]
+    target_group_config = None
+    for group_config in config["config_groups"].values():
+        if "Linear" in group_config["targets"]:
+            if "weights" not in group_config:
+                return None
+            if key not in group_config or group_config[key] is None:
+                return None
+            target_group_config = group_config[key].copy()
+            break
+
+    if target_group_config is None:
+        return None
+    target_group_config["quant_method"] = config["quant_method"]
+    if config["quant_method"] == "compressed-tensors":
+        target_group_config["format"] = config["format"]
+    elif config["quant_method"] == "modelopt":
+        target_group_config["quant_algo"] = config["quant_algo"]
+    return target_group_config
+
+
+class HummingConfig(QuantizationConfig):
+    packed_modules_mapping = {}
+
+    def __init__(self, full_config: dict[str, Any] | None = None):
+        assert_humming_available()
+        self.full_config: dict[str, Any] = full_config or {}
+
+    @classmethod
+    def get_name(cls) -> QuantizationMethods:
+        return "humming"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 75
+
+    @classmethod
+    def get_config_filenames(cls) -> list[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> "HummingConfig":
+        return cls(full_config=config)
+
+    @classmethod
+    def override_quantization_method(
+        cls, hf_quant_cfg, user_quant, hf_config=None
+    ) -> QuantizationMethods | None:
+        if user_quant == "humming" and hf_config is not None:
+            model_type = hf_config.model_type
+            quant_method = hf_quant_cfg.get("quant_method", None)
+            if model_type == "gpt_oss" and quant_method == "mxfp4":
+                msg = (
+                    "For gpt-oss model, use '--moe-backend humming' "
+                    "instead of '--quantization humming'."
+                )
+                raise ValueError(msg)
+        return "humming" if user_quant == "humming" else None
+
+    def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
+        self.hf_to_vllm_mapper = hf_to_vllm_mapper
+
+    def is_layer_skipped(self, config: dict[str, Any], prefix: str):
+        keys = ["ignored_layers", "ignore", "modules_to_not_convert"]
+        ignored_layers = self.get_from_keys_or(config, keys, []) or []
+        if hasattr(self, "hf_to_vllm_mapper"):
+            ignored_layers = self.hf_to_vllm_mapper.apply_list(ignored_layers)
+
+        if any(module_name in prefix for module_name in ignored_layers):
+            return True
+        if "lm_head" in prefix:
+            return True
+
+        for regex in config.get("dynamic", {}):
+            if regex[:1] != "-":
+                continue
+            if re.match(regex[2:], prefix):
+                return True
+
+        return False
+
+    def get_layer_weight_schema(self, config: dict[str, Any], prefix: str):
+        if self.is_layer_skipped(config, prefix):
+            return None
+
+        if config["quant_method"] in ["compressed-tensors", "modelopt"]:
+            group_config = compressed_tensors_get_config(config, "weights")
+            if group_config is None:
+                return None
+            config = group_config
+
+        layer_config = config
+        layer_dynamic = config.get("dynamic", {})
+        if not isinstance(layer_dynamic, dict):
+            layer_dynamic = {}
+        for regex, override_config in layer_dynamic.items():
+            if regex[:1] != "+":
+                continue
+            if re.match(regex[2:], prefix):
+                layer_config = config.copy()
+                layer_config.update(override_config)
+                break
+
+        if "quant_method" in layer_config:
+            return BaseWeightSchema.from_config(layer_config)
+        return None
+
+    def get_layer_input_schema(self, config: dict[str, Any], prefix: str):
+        if self.is_layer_skipped(config, prefix):
+            return None
+        if config["quant_method"] in ["compressed-tensors", "modelopt"]:
+            group_config = compressed_tensors_get_config(config, "input_activations")
+            if group_config is None:
+                return None
+            config = group_config
+
+        if config.get("quant_method", None) in BaseInputSchema.INPUT_SCHEMA_MAP:
+            return BaseInputSchema.from_config(config)
+        return None
+
+    def get_quant_config_for_layer(
+        self, prefix: str, layer_type: str
+    ) -> "HummingLayerQuantizationConfig | None":
+        weight_schema: BaseWeightSchema | None = None
+        force_weight_schema: HummingWeightSchema | None = None
+
+        if self.full_config:
+            weight_schema = self.get_layer_weight_schema(self.full_config, prefix)
+
+        is_online_quant = False
+        online_quant_config = envs.VLLM_HUMMING_ONLINE_QUANT_CONFIG or {}
+        if not self.full_config or online_quant_config.get("force_requant", False):
+            online_quant_config["quant_method"] = "humming"
+            schema = self.get_layer_weight_schema(online_quant_config, prefix)
+            if not self.full_config:
+                weight_schema = schema
+                is_online_quant = True
+            else:
+                force_weight_schema = schema
+
+        if weight_schema is not None:
+            input_schema = None
+            force_input_schema = None
+
+            if self.full_config:
+                input_schema = self.get_layer_input_schema(self.full_config, prefix)
+
+            if envs.VLLM_HUMMING_INPUT_QUANT_CONFIG:
+                quant_config = envs.VLLM_HUMMING_INPUT_QUANT_CONFIG.copy()
+                quant_config["quant_method"] = "humming"
+                force_input_schema = self.get_layer_input_schema(quant_config, prefix)
+                if input_schema is None:
+                    input_schema = force_input_schema
+
+            if force_weight_schema is not None and force_input_schema is None:
+                force_input_schema = HummingInputSchema()
+
+            return HummingLayerQuantizationConfig(
+                weight_schema=weight_schema,
+                input_schema=input_schema,
+                force_weight_schema=force_weight_schema,
+                force_input_schema=force_input_schema,
+                is_online_quant=is_online_quant,
+            )
+        return None
+
+    def get_quant_method(
+        self, layer: torch.nn.Module, prefix: str
+    ) -> "QuantizeMethodBase | None":
+        layer_type = "other"
+        if isinstance(layer, FusedMoE):
+            layer_type = "moe"
+        elif isinstance(layer, LinearBase):
+            layer_type = "linear"
+
+        quant_config = self.get_quant_config_for_layer(prefix, layer_type)
+        if quant_config is None:
+            if isinstance(layer, FusedMoE):
+                return UnquantizedFusedMoEMethod(layer.moe_config)
+            elif isinstance(layer, LinearBase):
+                return UnquantizedLinearMethod()
+        elif isinstance(layer, LinearBase):
+            return HummingLinearMethod(quant_config)
+        elif isinstance(layer, FusedMoE):
+            return HummingMoEMethod(quant_config, layer.moe_config)
+        return None
+
+
+class HummingLayerQuantizationConfig(HummingConfig):
+    def __init__(
+        self,
+        weight_schema: "BaseWeightSchema",
+        input_schema: "BaseInputSchema | None" = None,
+        force_weight_schema: "HummingWeightSchema | None" = None,
+        force_input_schema: "HummingInputSchema | None" = None,
+        is_online_quant: bool = False,
+    ):
+        self.weight_schema = weight_schema
+        if input_schema is None:
+            input_schema = HummingInputSchema()
+        self.input_schema = input_schema
+        self.force_weight_schema = force_weight_schema
+        self.force_input_schema = force_input_schema
+        self.is_online_quant = is_online_quant
+
+    @classmethod
+    def from_config(cls, config):
+        weight_schema = BaseWeightSchema.from_config(config)
+        return cls(weight_schema)
+
+    def get_quant_method(
+        self, layer: torch.nn.Module, prefix: str
+    ) -> QuantizeMethodBase | None:
+        raise NotImplementedError
+
+
+class HummingLinearMethod(LinearMethodBase):
+    def __init__(self, quant_config: HummingLayerQuantizationConfig):
+        self.quant_config = quant_config
+        self.weight_schema = quant_config.weight_schema
+        self.input_schema = quant_config.input_schema
+        self.force_weight_schema = quant_config.force_weight_schema
+        self.force_input_schema = quant_config.force_input_schema
+        self.is_online_quant = self.quant_config.is_online_quant
+
+    def prepare_weight_loader(self, layer: torch.nn.Module, weight_loader: Callable):
+        def new_weight_loader(
+            param: torch.nn.Parameter,
+            loaded_weight: torch.Tensor,
+            shard_id: str | int | None = None,
+        ):
+            name = param.param_name
+            float_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+            is_unquantized = name == "weight" and loaded_weight.dtype in float_dtypes
+            if is_unquantized and self.is_online_quant:
+                # online quant (fp16/bf16 -> quant_type)
+                assert isinstance(self.weight_schema, HummingWeightSchema)
+                f16_dtype = DataType.from_torch_dtype(layer.param_dtype)
+                has_global_scale = "TENSOR" in str(self.weight_schema.weight_scale_type)
+                tensor_list = quantize_weight(
+                    weight=loaded_weight,
+                    dtype=self.weight_schema.b_dtype,
+                    scale_dtype=self.weight_schema.bs_dtype or f16_dtype,
+                    group_size=self.weight_schema.weight_scale_group_size,
+                    has_zero_point=self.weight_schema.has_zero_point,
+                    has_global_scale=has_global_scale,
+                    is_fp_zero_point=self.weight_schema.is_fp_zero_point,
+                    pack=True,
+                )
+
+                key_list = ["weight", "weight_scale", "zero_point", "global_scale"]
+                for key, tensor in zip(key_list, tensor_list):
+                    if tensor is None or tensor.nelement() == 0:
+                        continue
+                    param = getattr(layer, key)
+                    param.weight_loader(param, tensor, shard_id)
+
+                return None
+            elif is_unquantized and not self.is_online_quant:
+                # fallback to unquantized linear
+                # some model skip some layer when quantizing model, but
+                # don't mark the layer as unquantized.
+                if not layer.is_fallback:
+                    layer.is_fallback = True
+                    for name, _ in list(layer.named_parameters()):
+                        if name != "bias":
+                            delattr(layer, name)
+                    delattr(layer, "locks")
+                    self.__class__ = UnquantizedLinearMethod  # type: ignore
+                    tensor = torch.empty(
+                        (
+                            layer.output_partition_sizes_sum,
+                            layer.input_size_per_partition,
+                        ),
+                        dtype=layer.param_dtype,
+                        device=param.device,
+                    )
+                    extra_weight_attrs = layer.extra_weight_attrs.copy()
+                    orig_weight_loader = extra_weight_attrs.pop("weight_loader")
+                    layer.weight = ModelWeightParameter(
+                        data=tensor,
+                        input_dim=1,
+                        output_dim=0,
+                        weight_loader=orig_weight_loader,
+                    )
+                    layer.weight.tp_size = layer.tp_size
+                    layer.weight.tp_rank = layer.tp_rank
+                    set_weight_attrs(layer.weight, extra_weight_attrs)
+
+                param = layer.weight
+                if shard_id is not None:
+                    return layer.weight.weight_loader(param, loaded_weight, shard_id)
+                return layer.weight.weight_loader(param, loaded_weight)
+
+            # weight processing logic for specific quantization schema
+            loaded_weight = self.weight_schema.process_loaded_weight(
+                tensor=loaded_weight,
+                name=name,
+            )
+            if shard_id is not None:
+                return weight_loader(param, loaded_weight, shard_id)
+            return weight_loader(param, loaded_weight)
+
+        return new_weight_loader
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: list[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        layer.is_fallback = False
+        layer.param_dtype = params_dtype
+        layer.input_size = input_size
+        layer.output_size = output_size
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_partition_sizes_sum = sum(output_partition_sizes)
+        layer.output_partition_sizes = output_partition_sizes
+        layer.extra_weight_attrs = extra_weight_attrs.copy()
+
+        weight_loader = extra_weight_attrs.get("weight_loader", default_weight_loader)
+        new_weight_loader = self.prepare_weight_loader(layer, weight_loader)
+        extra_weight_attrs["weight_loader"] = new_weight_loader
+
+        for key in ["weight_block_size", "block_structure"]:
+            block_size = getattr(self.weight_schema, key, None)
+            if block_size is not None:
+                layer.weight_block_size = block_size
+
+        weight_tensor_attrs = self.weight_schema.get_tensors_attrs(
+            shape_n=layer.output_partition_sizes_sum,
+            shape_k=layer.input_size_per_partition,
+            param_dtype=params_dtype,
+            stack_size=len(layer.output_partition_sizes),
+        )
+
+        input_tensor_attrs = self.input_schema.get_tensors_attrs(
+            shape_k=layer.input_size_per_partition,
+            param_dtype=params_dtype,
+            stack_size=len(layer.output_partition_sizes),
+        )
+
+        tensors_attrs = weight_tensor_attrs | input_tensor_attrs
+
+        for name, attrs in tensors_attrs.items():
+            tensor = torch.empty(attrs["shape"], dtype=attrs["dtype"])
+            extra_attrs = attrs.get("extra_attrs", {}).copy()
+            extra_attrs.update(extra_weight_attrs)
+            param = prepare_param(tensor, name, extra_attrs)
+            setattr(layer, name, param)
+
+        locks = torch.zeros(1024, dtype=torch.int32)
+        layer.register_buffer("locks", locks)
+
+        if self.force_input_schema is not None:
+            self.input_schema = self.force_input_schema
+
+        if not hasattr(layer, "weight"):
+            param = prepare_param(torch.tensor(0), "weight", extra_weight_attrs)
+            layer.weight = param
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        if layer.is_fallback:
+            return None
+
+        # convert from checkpoint format to humming format
+        if not isinstance(self.weight_schema, HummingWeightSchema):
+            self.weight_schema, tensors = self.weight_schema.convert_humming(
+                tensors=layer.state_dict(),
+                shape_n_stacks=layer.output_partition_sizes,
+                shape_k_stacks=[layer.input_size_per_partition],
+                param_dtype=layer.param_dtype,
+            )
+
+            self.input_schema, _ = self.input_schema.convert_humming(
+                tensors=layer.state_dict(),
+                shape_n_stacks=layer.output_partition_sizes,
+                shape_k_stacks=[layer.input_size_per_partition],
+                param_dtype=layer.param_dtype,
+            )
+
+            for name, _ in list(layer.named_parameters()):
+                delattr(layer, name)
+
+            for name, tensor in tensors.items():
+                param = torch.nn.Parameter(tensor, requires_grad=False)
+                setattr(layer, name, param)
+
+            del tensors
+
+        # force requant (origin quant setting -> fp16/bf16 -> new_quant setting)
+        assert isinstance(self.weight_schema, HummingWeightSchema)
+        force_requant = self.force_weight_schema is not None
+        if force_requant and self.weight_schema != self.force_weight_schema:
+            tensors = self.weight_schema.requant_tensors(
+                tensors=layer.state_dict(),
+                target_weight_schema=self.force_weight_schema,
+                param_dtype=layer.param_dtype,
+            )
+
+            self.weight_schema = self.force_weight_schema
+
+            for name, _ in list(layer.named_parameters()):
+                if name != "bias":
+                    delattr(layer, name)
+
+            for name, tensor in tensors.items():
+                param = torch.nn.Parameter(tensor, requires_grad=False)
+                setattr(layer, name, param)
+
+            del tensors
+
+        # prepare layer config from humming kernel
+        HummingMethod.prepare_layer_meta(
+            layer=layer,
+            shape_n=layer.output_partition_sizes_sum,
+            shape_k=layer.input_size_per_partition,
+            weight_schema=self.weight_schema,
+            input_schema=self.input_schema,
+            pad_n_to_multiple=256,
+            pad_k_to_multiple=128,
+            has_bias=layer.has_bias,
+            torch_dtype=layer.param_dtype,
+        )
+
+        # preprocess weight for inference
+        HummingMethod.transform_humming_layer(layer)
+
+        # compute_config: kernel configs that do not directly affect weights
+        # but significantly impact kernel behavior or computation precision.
+        # see https://github.com/inclusionAI/humming/blob/main/docs/config.md
+        compute_config = {
+            "use_batch_invariant": envs.VLLM_BATCH_INVARIANT,
+            "use_f16_accum": envs.VLLM_HUMMING_USE_F16_ACCUM,
+            "gemm_type": "dense",
+        }
+        self.compute_config = json.dumps(compute_config)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        flatten_inputs = x.view(-1, x.size(-1))
+        output = HummingMethod.forward_layer(
+            layer=layer,
+            inputs=flatten_inputs,
+            compute_config=self.compute_config,
+        )
+        output = output.view(*x.shape[:-1], output.size(-1))
+        return output
+
+
+class HummingMoEMethod(FusedMoEMethodBase):
+    def __init__(
+        self, quant_config: HummingLayerQuantizationConfig, moe: "FusedMoEConfig"
+    ) -> None:
+        super().__init__(moe)
+        self.quant_config = quant_config
+        self.moe = moe
+        self.weight_schema = quant_config.weight_schema
+        self.input_schema = quant_config.input_schema
+        self.force_weight_schema = quant_config.force_weight_schema
+        self.force_input_schema = quant_config.force_input_schema
+
+    def prepare_weight_loader(self, layer, weight_loader):
+        def new_weight_loader(
+            param: torch.nn.Parameter,
+            loaded_weight: torch.Tensor,
+            weight_name: str,
+            shard_id: str,
+            expert_id: int | None = None,
+            return_success: bool = False,
+        ):
+            name = param.param_name
+            float_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+            is_unquantized = name == "weight" and loaded_weight.dtype in float_dtypes
+            # online quant (fp16/bf16 -> quant_type)
+            if is_unquantized:
+                assert isinstance(self.weight_schema, HummingWeightSchema)
+                f16_dtype = DataType.from_torch_dtype(layer.param_dtype)
+                has_global_scale = "TENSOR" in str(self.weight_schema.weight_scale_type)
+                tensor_list = quantize_weight(
+                    weight=loaded_weight,
+                    dtype=self.weight_schema.b_dtype,
+                    scale_dtype=self.weight_schema.bs_dtype or f16_dtype,
+                    group_size=self.weight_schema.weight_scale_group_size,
+                    has_zero_point=self.weight_schema.has_zero_point,
+                    has_global_scale=has_global_scale,
+                    is_fp_zero_point=self.weight_schema.is_fp_zero_point,
+                    pack=True,
+                )
+
+                key_list = ["weight", "weight_scale", "zero_point", "global_scale"]
+                success = True
+                for key, tensor in zip(key_list, tensor_list):
+                    if tensor is None or tensor.nelement() == 0:
+                        continue
+                    sublayer_name = "w2" if shard_id == "w2" else "w13"
+
+                    param = getattr(layer, sublayer_name + "_" + key)
+                    part_subccess = param.weight_loader(
+                        param=param,
+                        loaded_weight=tensor.cpu(),
+                        weight_name=shard_id + "_" + key,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                        return_success=return_success,
+                    )
+                    success = success and part_subccess
+
+                return success if return_success else None
+
+            # weight processing logic for specific quantization schema
+            loaded_weight = self.weight_schema.process_loaded_weight(
+                tensor=loaded_weight,
+                name=name,
+            )
+            return weight_loader(
+                param,
+                loaded_weight,
+                weight_name,
+                shard_id=shard_id,
+                expert_id=expert_id,
+                return_success=return_success,
+            )
+
+        return new_weight_loader
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size_per_partition: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        layer.num_experts = num_experts
+        layer.param_dtype = params_dtype
+        layer.intermediate_size = intermediate_size_per_partition
+        weight_loader = extra_weight_attrs.get("weight_loader", default_weight_loader)
+        weight_loader = self.prepare_weight_loader(layer, weight_loader)
+        extra_weight_attrs["weight_loader"] = weight_loader
+
+        # sublayer: a layer contains multiple sets of weights for quantized GEMM
+        # (e.g., weight, weight_scale, etc.).
+        # The weight names of sublayer start with the prefix "{sublayer_name}_"
+        layer.sublayer_configs = {
+            "w13": {
+                "shape_n": intermediate_size_per_partition * 2,
+                "shape_k": hidden_size,
+                "tensors_attrs": self.weight_schema.get_padded_tensors_attrs(
+                    shape_n=intermediate_size_per_partition * 2,
+                    shape_k=hidden_size,
+                    num_experts=num_experts,
+                    param_dtype=params_dtype,
+                    has_bias=self.moe.has_bias,
+                ),
+            },
+            "w2": {
+                "shape_n": hidden_size,
+                "shape_k": intermediate_size_per_partition,
+                "tensors_attrs": self.weight_schema.get_padded_tensors_attrs(
+                    shape_n=hidden_size,
+                    shape_k=intermediate_size_per_partition,
+                    num_experts=num_experts,
+                    param_dtype=params_dtype,
+                    has_bias=self.moe.has_bias,
+                ),
+            },
+        }
+
+        for sublayer_name, configs in layer.sublayer_configs.items():
+            for name, attrs in configs["tensors_attrs"].items():
+                tensor = torch.empty(attrs["shape"], dtype=attrs["dtype"])
+                param = torch.nn.Parameter(tensor, requires_grad=False)
+                extra_attrs = attrs.get("extra_attrs", {}).copy()
+                extra_attrs.update(extra_weight_attrs)
+                param = prepare_moe_param(tensor, name, extra_attrs)
+                setattr(layer, f"{sublayer_name}_{name}", param)
+
+        if self.force_input_schema is not None:
+            self.input_schema = self.force_input_schema
+
+        locks = torch.zeros(1024, dtype=torch.int32)
+        layer.register_buffer("locks", locks)
+
+    def get_fused_moe_quant_config(self, layer: torch.nn.Module) -> FusedMoEQuantConfig:
+        from vllm.model_executor.layers.quantization.utils.humming_utils import (
+            get_humming_moe_quant_config,
+        )
+
+        return get_humming_moe_quant_config(layer)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        if getattr(self, "processed", False):
+            return
+        self.processed = True
+        layer.weight_schemas = {}
+        layer.input_schemas = {}
+        for sublayer_name, configs in layer.sublayer_configs.items():
+            input_schema = self.input_schema
+            weight_schema = self.weight_schema
+            # convert from checkpoint format to humming format
+            if not isinstance(weight_schema, HummingWeightSchema):
+                tensors: dict[str, torch.Tensor] = dict(
+                    (key.removeprefix(sublayer_name + "_"), value)
+                    for key, value in layer.state_dict().items()
+                    if key.startswith(sublayer_name + "_")
+                )
+
+                shape_k_stacks = [configs["shape_k"]]
+                shape_n_stacks = [configs["shape_n"]]
+                if sublayer_name == "w13":
+                    shape_n_stacks = [configs["shape_n"] // 2] * 2
+
+                weight_schema, tensors = weight_schema.convert_humming(
+                    tensors=tensors,
+                    shape_n_stacks=shape_n_stacks,
+                    shape_k_stacks=shape_k_stacks,
+                    param_dtype=layer.param_dtype,
+                    num_experts=layer.num_experts,
+                )
+
+                input_schema, _ = input_schema.convert_humming(
+                    tensors=tensors,
+                    shape_n_stacks=shape_n_stacks,
+                    shape_k_stacks=shape_k_stacks,
+                    param_dtype=layer.param_dtype,
+                    num_experts=layer.num_experts,
+                )
+
+                for name, _ in list(layer.named_parameters()):
+                    if not name.startswith(sublayer_name + "_"):
+                        continue
+                    delattr(layer, name)
+
+                for name, tensor in tensors.items():
+                    name = f"{sublayer_name}_{name}"
+                    param = torch.nn.Parameter(tensor, requires_grad=False)
+                    setattr(layer, name, param)
+
+                layer.weight_schemas[sublayer_name] = weight_schema
+                layer.input_schemas[sublayer_name] = input_schema
+
+            # force requant (origin quant setting -> fp16/bf16 -> new_quant setting)
+            assert isinstance(weight_schema, HummingWeightSchema)
+            force_requant = self.force_weight_schema is not None
+            if force_requant and weight_schema != self.force_weight_schema:
+                tensors = dict(
+                    (key.removeprefix(sublayer_name + "_"), value)
+                    for key, value in layer.state_dict().items()
+                    if key.startswith(sublayer_name + "_")
+                )
+
+                tensors = weight_schema.requant_tensors(
+                    tensors=tensors,
+                    target_weight_schema=self.force_weight_schema,
+                    param_dtype=layer.param_dtype,
+                )
+
+                weight_schema = self.force_weight_schema
+
+                for name, _ in list(layer.named_parameters()):
+                    if not name.startswith(sublayer_name + "_"):
+                        continue
+                    if name == sublayer_name + "_bias":
+                        continue
+                    delattr(layer, name)
+
+                for name, tensor in tensors.items():
+                    name = f"{sublayer_name}_{name}"
+                    param = torch.nn.Parameter(tensor, requires_grad=False)
+                    setattr(layer, name, param)
+
+                del tensors
+
+            # prepare layer config from humming kernel
+            HummingMethod.prepare_layer_meta(
+                layer=layer,
+                shape_n=configs["shape_n"],
+                shape_k=configs["shape_k"],
+                pad_n_to_multiple=256,
+                pad_k_to_multiple=128,
+                input_schema=input_schema,
+                weight_schema=weight_schema,
+                has_bias=self.moe.has_bias,
+                num_experts=layer.num_experts,
+                torch_dtype=layer.param_dtype,
+                sublayer_name=sublayer_name,
+            )
+
+            # preprocess weight for inference
+            HummingMethod.transform_humming_layer(layer, sublayer_name=sublayer_name)
+
+        # use moe modular
+        experts: HummingIndexedExperts | HummingGroupedExperts
+        assert self.moe_quant_config is not None
+        if get_humming_moe_gemm_type() == "indexed":
+            experts = HummingIndexedExperts(layer, self.moe, self.moe_quant_config)
+        else:
+            experts = HummingGroupedExperts(layer, self.moe, self.moe_quant_config)
+        self.experts = experts
+
+    def select_gemm_impl(
+        self,
+        prepare_finalize,
+        layer: torch.nn.Module,
+    ):
+        from vllm.model_executor.layers.fused_moe import modular_kernel as mk
+
+        activation_format = prepare_finalize.activation_format
+        assert self.moe_quant_config is not None
+        if activation_format == mk.FusedMoEActivationFormat.BatchedExperts:
+            return BatchedHummingGroupedExperts(
+                layer=layer,
+                moe_config=self.moe,
+                quant_config=self.moe_quant_config,
+                max_num_tokens=prepare_finalize.max_num_tokens_per_rank(),
+                num_dispatchers=prepare_finalize.num_dispatchers(),
+            )
+        elif get_humming_moe_gemm_type() == "indexed":
+            return HummingIndexedExperts(layer, self.moe, self.moe_quant_config)
+        else:
+            return HummingGroupedExperts(layer, self.moe, self.moe_quant_config)
+
+    def apply(
+        self,
+        layer: FusedMoE,
+        x: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        shared_experts_input: torch.Tensor | None,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        workspace1, workspace2, output = self.experts.make_workspaces(
+            M=topk_ids.size(0),
+            topk=topk_ids.size(1),
+            activation=layer.activation,
+        )
+
+        assert workspace1.data_ptr() == output.data_ptr()
+
+        self.experts.main_apply(
+            hidden_states=x,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            workspace1=workspace1,
+            workspace2=workspace2,
+            expert_tokens_meta=None,
+        )
+
+        return output
diff --git a/vllm/model_executor/layers/quantization/inc.py b/vllm/model_executor/layers/quantization/inc.py
index 29d73928478d..4457555c0764 100644
--- a/vllm/model_executor/layers/quantization/inc.py
+++ b/vllm/model_executor/layers/quantization/inc.py
@@ -414,6 +414,7 @@ def apply_gptq_quant_layer(self, layer, prefix: str, backend: str = "auto"):
 
     def apply_xpu_w4a16_quant_layer(self, layer, prefix: str):
         weight_bits, group_size, sym = self.get_layer_config(layer, prefix)
+
         if not self.check_quantized(weight_bits):
             if isinstance(layer, (LinearBase, ParallelLMHead)):
                 return UnquantizedLinearMethod()
@@ -437,6 +438,27 @@ def apply_xpu_w4a16_quant_layer(self, layer, prefix: str):
             )
         return None
 
+    def apply_cpu_w4a16_quant_layer(self, layer, prefix: str):
+        weight_bits, group_size, sym = self.get_layer_config(layer, prefix)
+        if not self.check_quantized(weight_bits):
+            if isinstance(layer, (LinearBase, ParallelLMHead)):
+                return UnquantizedLinearMethod()
+            else:
+                return None
+
+        if weight_bits != 4:
+            raise NotImplementedError(
+                f"INC on CPU only supports 4-bit quantization, "
+                f"got weight_bits={weight_bits}."
+            )
+        if not sym:
+            raise NotImplementedError(
+                "INC W4A16 on CPU only supports symmetric quantization for now."
+            )
+        if isinstance(layer, (LinearBase, ParallelLMHead)):
+            return self.apply_gptq_quant_layer(layer, prefix)
+        return None
+
     def get_quant_method(self, layer: torch.nn.Module, prefix: str):
         if prefix and self.extra_config:
             for layer_name in self.extra_config:
@@ -446,11 +468,21 @@ def get_quant_method(self, layer: torch.nn.Module, prefix: str):
                     return UnquantizedLinearMethod()
         if current_platform.is_xpu():
             return self.apply_xpu_w4a16_quant_layer(layer, prefix)
-        if "gptq" in self.packing_format or "gptq" in self.backend:
+        is_gptq = "gptq" in self.packing_format or "gptq" in self.backend
+        if current_platform.is_cpu() and is_gptq:
+            return self.apply_cpu_w4a16_quant_layer(layer, prefix)
+        if is_gptq:
             return self.apply_gptq_quant_layer(layer, prefix)
         if "awq" in self.packing_format or "awq" in self.backend:
             return self.apply_awq_quant_layer(layer, prefix)
 
+        raise NotImplementedError(
+            f"Unsupported quantization configuration for layer '{prefix}'. "
+            f"Platform: CPU={current_platform.is_cpu()}. "
+            f"Platform: XPU={current_platform.is_xpu()}. "
+            f"Format: {self.packing_format}, Backend: {self.backend}."
+        )
+
     @classmethod
     def override_quantization_method(
         cls, hf_quant_cfg, user_quant, hf_config=None
diff --git a/vllm/model_executor/layers/quantization/modelopt.py b/vllm/model_executor/layers/quantization/modelopt.py
index 0b8ad0cbc1ed..72178a6e6dd3 100644
--- a/vllm/model_executor/layers/quantization/modelopt.py
+++ b/vllm/model_executor/layers/quantization/modelopt.py
@@ -11,6 +11,8 @@
 from vllm.config import get_current_vllm_config
 from vllm.logger import init_logger
 from vllm.model_executor.kernels.linear import (
+    MarlinNvFp4LinearKernel,
+    NvFp4LinearLayerConfig,
     init_fp8_linear_kernel,
     init_mxfp8_linear_kernel,
     init_nvfp4_linear_kernel,
@@ -89,6 +91,7 @@
 from vllm.model_executor.parameter import (
     BlockQuantScaleParameter,
     ChannelQuantScaleParameter,
+    GroupQuantScaleParameter,
     ModelWeightParameter,
     PerTensorScaleParameter,
 )
@@ -107,19 +110,21 @@
     "FP8_PER_CHANNEL_PER_TOKEN",
     # FP8 per-block weight-only (ModelOpt may emit this as lowercase).
     "FP8_PB_WO",
-    # FP4
+    # NVFP4 W4A4 (4-bit float weights AND 4-bit float activations).
     "NVFP4",
+    # W4A16 NVFP4 (4-bit float weights, fp16/bf16 activations).
+    "W4A16_NVFP4",
     # MXFP8
     "MXFP8",
     # MIXED_PRECISION,
     "MIXED_PRECISION",
 ]
-KV_CACHE_QUANT_ALGOS = ["FP8"]
+KV_CACHE_QUANT_ALGOS = ["FP8", "NVFP4"]
 
 
-class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
+class ModelOptKVCacheMethod(BaseKVCacheMethod):
     """
-    Supports loading kv-cache scaling factors from FP8 checkpoints.
+    Supports loading kv-cache scaling factors from FP8 or NVFP4 checkpoints.
     """
 
     def __init__(self, quant_config: "ModelOptQuantConfigBase"):
@@ -517,6 +522,7 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         layer.weight = Parameter(weight.t(), requires_grad=False)
         layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
         layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False)
+        self.fp8_linear.process_weights_after_loading(layer)
 
     def apply(
         self,
@@ -597,6 +603,7 @@ def create_weights(
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         layer.weight = Parameter(layer.weight.t(), requires_grad=False)
         layer.weight_scale = Parameter(layer.weight_scale.data, requires_grad=False)
+        self.fp8_linear.process_weights_after_loading(layer)
 
     def apply(
         self,
@@ -948,6 +955,7 @@ def apply_monolithic(
         layer: FusedMoE,
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
     ) -> torch.Tensor:
         assert self.is_monolithic
         assert self.moe_kernel is not None
@@ -992,7 +1000,7 @@ def apply(
 
 ModelOptFp8Config.LinearMethodCls = ModelOptFp8LinearMethod
 ModelOptFp8Config.FusedMoEMethodCls = ModelOptFp8MoEMethod
-ModelOptFp8Config.KVCacheMethodCls = ModelOptFp8KVCacheMethod
+ModelOptFp8Config.KVCacheMethodCls = ModelOptKVCacheMethod
 
 
 class ModelOptNvFp4Config(ModelOptQuantConfigBase):
@@ -1000,22 +1008,41 @@ class ModelOptNvFp4Config(ModelOptQuantConfigBase):
 
     def __init__(
         self,
-        is_checkpoint_nvfp4_serialized: bool,
-        kv_cache_quant_algo: str | None,
-        exclude_modules: list[str],
+        quant_method: str = "NVFP4",
+        is_checkpoint_nvfp4_serialized: bool = False,
+        kv_cache_quant_algo: str | None = None,
+        exclude_modules: list[str] | None = None,
         group_size: int = 16,
     ) -> None:
+        if exclude_modules is None:
+            exclude_modules = []
         super().__init__(exclude_modules)
+        self.quant_method = quant_method
         self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
         if is_checkpoint_nvfp4_serialized:
             logger.warning(
-                "Detected ModelOpt NVFP4 checkpoint. Please note that"
-                " the format is experimental and could change in future."
+                "Detected ModelOpt NVFP4 checkpoint (quant_algo=%s). Please "
+                "note that the format is experimental and could change in "
+                "future.",
+                quant_method,
             )
 
             self.group_size = group_size
             self.kv_cache_quant_algo = kv_cache_quant_algo
 
+        # Select LinearMethod implementation based on quant_algo (FP8 pattern).
+        # NVFP4         -> W4A4: cutlass NVFP4 GEMM with input quantization
+        # W4A16_NVFP4   -> W4A16: FP4 Marlin GEMM with bf16/fp16 activations
+        if quant_method == "NVFP4":
+            self.LinearMethodCls = ModelOptNvFp4LinearMethod
+        elif quant_method == "W4A16_NVFP4":
+            self.LinearMethodCls = ModelOptNvFp4W4A16LinearMethod
+        else:
+            raise ValueError(
+                f"Unsupported ModelOpt NVFP4 quant_algo: {quant_method}. "
+                "Supported: NVFP4 / W4A16_NVFP4."
+            )
+
     def get_name(self) -> QuantizationMethods:
         return "modelopt_fp4"
 
@@ -1066,6 +1093,7 @@ def _from_config(
                 )
 
         return cls(
+            quant_method,
             is_checkpoint_nvfp4_serialized,
             kv_cache_quant_method,
             exclude_modules,
@@ -1205,6 +1233,152 @@ def apply(
         return self.kernel.apply_weights(layer=layer, x=x, bias=bias)
 
 
+class ModelOptNvFp4W4A16LinearMethod(LinearMethodBase):
+    """Linear method for ModelOpt NVFP4 W4A16.
+
+    4-bit NVFP4 weights, fp16/bf16 activations. Loads ModelOpt-style names
+    directly (no on-disk conversion) and dispatches to the FP4 Marlin GEMM:
+
+        weight          uint8     packed NVFP4 (2 nibbles/byte along input dim)
+        weight_scale    fp8-e4m3  per 16-elem group along input dim
+        weight_scale_2  fp32      per-tensor global scale = amax / (6.0 * 448.0)
+
+    No activation quantization. Marlin expects the global scale in the same
+    form ModelOpt stores (amax/2688), so we rename weight_scale_2 ->
+    weight_global_scale **without reciprocation** -- the CT W4A16 path
+    reciprocates only because CT stores the inverse on disk.
+
+    We also register a placeholder input_scale parameter so that W4A4-shaped
+    checkpoints (which contain *_proj.input_scale tensors) can be loaded
+    under this method without the per-shard loader hitting a KeyError on
+    the merged-name lookup. The placeholder is discarded in
+    process_weights_after_loading -- its value is never used.
+    """
+
+    def __init__(self, quant_config: ModelOptNvFp4Config) -> None:
+        self.quant_config = quant_config
+        # Vestigial slot mirrored from ModelOptNvFp4LinearMethod: the parent
+        # config's get_quant_method only fills marlin_input_dtype when
+        # backend == "marlin"; we don't set that since we pin the kernel
+        # below, but we keep the attribute for shape parity.
+        self.marlin_input_dtype = None
+        # Direct-instantiate the Marlin NVFP4 adapter rather than going through
+        # init_nvfp4_linear_kernel(): the latter's priority list returns a
+        # cutlass W4A4 kernel as first-pick on this hardware, which would
+        # silently try to quantize activations (we have no input_scale). For
+        # W4A16 there is exactly one valid kernel, so we pin it.
+        self.kernel = MarlinNvFp4LinearKernel(NvFp4LinearLayerConfig())
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: list[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        del input_size, output_size
+        if not self.quant_config.is_checkpoint_nvfp4_serialized:
+            raise ValueError(
+                "W4A16_NVFP4 quantization was selected; "
+                "dynamic quantization is not supported."
+            )
+        output_size_per_partition = sum(output_partition_sizes)
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        layer.logical_widths = output_partition_sizes
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+
+        if input_size_per_partition % 16 != 0:
+            raise ValueError(
+                "Unsupported model: input feature size is not a multiple of 16."
+            )
+
+        # Packed NVFP4 weights: uint8, 2 nibbles per byte along the input dim.
+        weight = ModelWeightParameter(
+            data=torch.empty(
+                output_size_per_partition,
+                input_size_per_partition // 2,
+                dtype=torch.uint8,
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight", weight)
+
+        # Per-tensor global weight scale (fp32). ModelOpt stores
+        # amax / (NVFP4_max * fp8_e4m3_max) = amax / 2688. PerTensorScaleParameter
+        # holds one entry per fused output partition (e.g. q/k/v in a fused QKV).
+        weight_scale_2 = PerTensorScaleParameter(
+            data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight_scale_2", weight_scale_2)
+
+        # Per-group fp8 weight scale.
+        weight_scale = GroupQuantScaleParameter(
+            data=torch.empty(
+                output_size_per_partition,
+                input_size_per_partition // self.quant_config.group_size,
+                dtype=torch.float8_e4m3fn,
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight_scale", weight_scale)
+
+        # Placeholder input_scale param so W4A4-shaped checkpoints can be
+        # loaded under this method without KeyError on the merged-name
+        # lookup (qwen2-style stacked-loader path renames *_proj.input_scale
+        # to e.g. qkv_proj.input_scale and looks it up unconditionally).
+        # Discarded in process_weights_after_loading; never read by the kernel.
+        # For native W4A16 checkpoints (no input_scale on disk) the param
+        # stays uninitialized and is simply deleted.
+        input_scale = PerTensorScaleParameter(
+            data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("input_scale", input_scale)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # Discard the input_scale placeholder. Whether it carries values
+        # (W4A4 ckpt loaded as W4A16) or is uninitialized (native W4A16
+        # ckpt), W4A16 mode does not quantize activations, so this is unused.
+        if hasattr(layer, "input_scale"):
+            del layer.input_scale
+
+        if torch.unique(layer.weight_scale_2).numel() != 1:
+            logger.warning_once(
+                "In W4A16_NVFP4 linear, the global weight scale "
+                "(weight_scale_2) differs across fused parallel layers "
+                "(e.g. q/k/v_proj). This will likely reduce accuracy. "
+                "Consider a checkpoint with a shared global scale."
+            )
+
+        # Rename weight_scale_2 -> weight_global_scale. NO reciprocation:
+        # ModelOpt already stores amax/2688, which is exactly what Marlin
+        # consumes via nvfp4_marlin_process_global_scale (called inside the
+        # Marlin adapter's process_weights_after_loading).
+        layer.weight_global_scale = Parameter(
+            layer.weight_scale_2.max().to(torch.float32), requires_grad=False
+        )
+        del layer.weight_scale_2
+
+        self.kernel.process_weights_after_loading(layer)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        return self.kernel.apply_weights(layer=layer, x=x, bias=bias)
+
+
 class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
     """
     MoE Method for FP4 Quantization.
@@ -1440,6 +1614,7 @@ def apply_monolithic(
         layer: FusedMoE,
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
     ) -> torch.Tensor:
         assert self.is_monolithic
         assert self.moe_kernel is not None
@@ -1484,7 +1659,7 @@ def apply(
 
 ModelOptNvFp4Config.LinearMethodCls = ModelOptNvFp4LinearMethod
 ModelOptNvFp4Config.FusedMoEMethodCls = ModelOptNvFp4FusedMoE
-ModelOptNvFp4Config.KVCacheMethodCls = ModelOptFp8KVCacheMethod
+ModelOptNvFp4Config.KVCacheMethodCls = ModelOptKVCacheMethod
 
 
 class ModelOptMxFp8Config(ModelOptQuantConfigBase):
@@ -1918,6 +2093,7 @@ def apply_monolithic(
         layer: FusedMoE,
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         from flashinfer.fused_moe.core import (
             ActivationType,
@@ -2013,7 +2189,7 @@ def apply(
 # Register the method classes for ModelOptMxFp8Config
 ModelOptMxFp8Config.LinearMethodCls = ModelOptMxFp8LinearMethod
 ModelOptMxFp8Config.FusedMoEMethodCls = ModelOptMxFp8FusedMoE
-ModelOptMxFp8Config.KVCacheMethodCls = ModelOptFp8KVCacheMethod
+ModelOptMxFp8Config.KVCacheMethodCls = ModelOptKVCacheMethod
 
 
 class ModelOptMixedPrecisionConfig(ModelOptQuantConfigBase):
@@ -2161,7 +2337,7 @@ def get_quant_method(
         # KV-cache quantization
         if isinstance(layer, Attention):
             if self.kv_cache_quant_method:
-                return ModelOptFp8KVCacheMethod(self)
+                return ModelOptKVCacheMethod(self)
             return None
 
         # Excluded layers
diff --git a/vllm/model_executor/layers/quantization/mxfp4.py b/vllm/model_executor/layers/quantization/mxfp4.py
index 019bb45d65dc..d3f3033be7b3 100644
--- a/vllm/model_executor/layers/quantization/mxfp4.py
+++ b/vllm/model_executor/layers/quantization/mxfp4.py
@@ -20,10 +20,12 @@
     TRITON_BACKENDS,
     Mxfp4MoeBackend,
     convert_gpt_oss_weight_to_mxfp4_moe_kernel_format,
+    convert_weight_to_mxfp4_moe_kernel_format,
     make_mxfp4_moe_kernel,
     make_mxfp4_moe_quant_config,
     mxfp4_round_up_hidden_size_and_intermediate_size,
-    select_gpt_oss_mxfp4_moe_backend,
+    select_deepseek_v4_mxfp4_moe_backend,
+    select_mxfp4_moe_backend,
 )
 from vllm.model_executor.layers.linear import LinearBase, UnquantizedLinearMethod
 from vllm.model_executor.layers.quantization import QuantizationMethods
@@ -83,7 +85,6 @@ def get_quant_method(
             logger.debug_once(
                 "MXFP4 linear layer is not implemented - falling back to "
                 "UnquantizedLinearMethod.",
-                scope="local",
             )
             return UnquantizedLinearMethod()
         elif isinstance(layer, FusedMoE):
@@ -92,7 +93,6 @@ def get_quant_method(
             logger.debug_once(
                 "MXFP4 attention layer is not implemented. "
                 "Skipping quantization for this layer.",
-                scope="local",
             )
         return None
 
@@ -140,7 +140,7 @@ class GptOssMxfp4MoEMethod(FusedMoEMethodBase):
     def __init__(self, moe: FusedMoEConfig):
         super().__init__(moe)
         self.weight_dtype = "gpt_oss_mxfp4"
-        self.mxfp4_backend, self.experts_cls = select_gpt_oss_mxfp4_moe_backend(moe)
+        self.mxfp4_backend, self.experts_cls = select_mxfp4_moe_backend(moe)
 
         self.max_capture_size = (
             get_current_vllm_config().compilation_config.max_cudagraph_capture_size
@@ -219,6 +219,7 @@ def create_weights(
         )
         layer.register_parameter("w13_weight_scale", w13_weight_scale)
         set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+        w13_weight_scale.quant_method = "block"
 
         # down_proj (row parallel)
         w2_weight = torch.nn.Parameter(
@@ -244,6 +245,7 @@ def create_weights(
         )
         layer.register_parameter("w2_weight_scale", w2_weight_scale)
         set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+        w2_weight_scale.quant_method = "block"
 
         if self.moe.has_bias:
             w13_bias = torch.nn.Parameter(
@@ -348,6 +350,344 @@ def _setup_kernel(
             self.w13_precision_config = w13_scale
             self.w2_precision_config = w2_scale
 
+        # AITER backend requires weights to be marked as shuffled.
+        if self.mxfp4_backend == Mxfp4MoeBackend.AITER_MXFP4_BF16:
+            layer.w13_weight.is_shuffled = True
+            layer.w2_weight.is_shuffled = True
+
+        if w13_bias is not None and w2_bias is not None:
+            replace_parameter(layer, "w13_bias", w13_bias)
+            replace_parameter(layer, "w2_bias", w2_bias)
+
+        # Build quant config
+        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
+
+        # Build kernel (modular or monolithic)
+        if self.moe_quant_config is not None and self.experts_cls is not None:
+            self.moe_kernel = make_mxfp4_moe_kernel(
+                moe_quant_config=self.moe_quant_config,
+                moe_config=self.moe,
+                mxfp4_backend=self.mxfp4_backend,
+                experts_cls=self.experts_cls,
+                routing_tables=layer._maybe_init_expert_routing_tables(),
+                shared_experts=layer.shared_experts,
+                layer=layer,
+            )
+
+    def process_weights_after_loading(self, layer):
+        w13 = layer.w13_weight
+        w2 = layer.w2_weight
+        w13_scale = layer.w13_weight_scale
+        w2_scale = layer.w2_weight_scale
+        w13_bias = getattr(layer, "w13_bias", None)
+        w2_bias = getattr(layer, "w2_bias", None)
+
+        if self.mxfp4_backend == Mxfp4MoeBackend.NONE:
+            return
+
+        self._setup_kernel(layer, w13, w2, w13_scale, w2_scale, w13_bias, w2_bias)
+
+    def get_fused_moe_quant_config(
+        self, layer: torch.nn.Module
+    ) -> FusedMoEQuantConfig | None:
+        w1_scale = layer.w13_weight_scale
+        w2_scale = layer.w2_weight_scale
+        w1_bias = getattr(layer, "w13_bias", None)
+        w2_bias = getattr(layer, "w2_bias", None)
+
+        if self.mxfp4_backend in TRITON_BACKENDS:
+            assert self.w13_precision_config is not None
+            assert self.w2_precision_config is not None
+            w1_scale = self.w13_precision_config
+            w2_scale = self.w2_precision_config
+
+        return make_mxfp4_moe_quant_config(
+            mxfp4_backend=self.mxfp4_backend,
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            w1_bias=w1_bias,
+            w2_bias=w2_bias,
+            gemm1_alpha=1.702,
+            gemm1_beta=1.0,
+            swiglu_limit=7.0,
+            layer=layer,
+        )
+
+    def select_gemm_impl(
+        self,
+        prepare_finalize: mk.FusedMoEPrepareAndFinalize,
+        layer: torch.nn.Module,
+    ) -> mk.FusedMoEExpertsModular:
+        raise ValueError(
+            f"{self.__class__.__name__} uses the new modular kernel "
+            "initialization logic. This function should not be called."
+        )
+
+    def apply(
+        self,
+        layer: FusedMoE,
+        x: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        shared_experts_input: torch.Tensor | None,
+    ) -> torch.Tensor:
+        assert not self.is_monolithic
+        assert self.moe_kernel is not None
+        return self.moe_kernel.apply(
+            hidden_states=x,
+            w1=layer.w13_weight,
+            w2=layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            activation=layer.activation,
+            global_num_experts=layer.global_num_experts,
+            apply_router_weight_on_input=layer.apply_router_weight_on_input,
+            expert_map=layer.expert_map,
+            shared_experts_input=shared_experts_input,
+        )
+
+    def apply_monolithic(
+        self,
+        layer: FusedMoE,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        assert self.is_monolithic
+        assert self.moe_kernel is not None
+        return self.moe_kernel.apply_monolithic(
+            hidden_states=x,
+            w1=layer.w13_weight,
+            w2=layer.w2_weight,
+            router_logits=router_logits,
+            activation=layer.activation,
+            global_num_experts=layer.global_num_experts,
+            expert_map=layer.expert_map,
+            apply_router_weight_on_input=layer.apply_router_weight_on_input,
+        )
+
+
+class Mxfp4MoEMethod(FusedMoEMethodBase):
+    """MXFP4 MoE quantization method."""
+
+    def __init__(self, moe: FusedMoEConfig):
+        super().__init__(moe)
+        self.weight_dtype = "mxfp4"
+        self.mxfp4_backend, self.experts_cls = select_deepseek_v4_mxfp4_moe_backend(moe)
+
+        self.max_capture_size = (
+            get_current_vllm_config().compilation_config.max_cudagraph_capture_size
+        )
+
+        self._cache_permute_indices: dict[torch.Size, torch.Tensor] = {}
+        self.moe_kernel: mk.FusedMoEKernel | None = None
+
+        # Used for triton kernel precision configs
+        self.w13_precision_config = None
+        self.w2_precision_config = None
+
+    @property
+    def skip_forward_padding(self) -> bool:
+        # SM100_FI_MXFP4_MXFP8_TRTLLM supports padding with mxfp8 quant
+        # so can skip the padding in the forward before applying the moe method
+        return self.mxfp4_backend == Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_MXFP8
+
+    def maybe_roundup_sizes(
+        self,
+        hidden_size: int,
+        intermediate_size_per_partition: int,
+        act_dtype: torch.dtype,
+        moe_parallel_config: FusedMoEParallelConfig,
+    ) -> tuple[int, int]:
+        hidden_size, intermediate_size_per_partition = super().maybe_roundup_sizes(
+            hidden_size=hidden_size,
+            intermediate_size_per_partition=intermediate_size_per_partition,
+            act_dtype=act_dtype,
+            moe_parallel_config=moe_parallel_config,
+        )
+        return mxfp4_round_up_hidden_size_and_intermediate_size(
+            self.mxfp4_backend, hidden_size, intermediate_size_per_partition
+        )
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size_per_partition: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        self.num_experts = num_experts
+        weight_dtype = torch.uint8
+        scale_dtype = torch.uint8
+        mxfp4_block = 32
+
+        layer.params_dtype = params_dtype
+        layer.num_experts = num_experts
+        self.intermediate_size = intermediate_size_per_partition
+        self.hidden_size = hidden_size
+
+        # Fused gate_up_proj (column parallel)
+        w13_weight = torch.nn.Parameter(
+            torch.zeros(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                hidden_size // 2,
+                dtype=weight_dtype,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w13_weight_scale = torch.nn.Parameter(
+            torch.zeros(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                hidden_size // mxfp4_block,
+                dtype=scale_dtype,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+        w13_weight_scale.quant_method = "block"
+
+        # down_proj (row parallel)
+        w2_weight = torch.nn.Parameter(
+            torch.zeros(
+                num_experts,
+                hidden_size,
+                intermediate_size_per_partition // 2,
+                dtype=weight_dtype,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        w2_weight_scale = torch.nn.Parameter(
+            torch.zeros(
+                num_experts,
+                hidden_size,
+                intermediate_size_per_partition // mxfp4_block,
+                dtype=scale_dtype,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+        w2_weight_scale.quant_method = "block"
+
+        if self.moe.has_bias:
+            w13_bias = torch.nn.Parameter(
+                torch.zeros(
+                    num_experts,
+                    2 * intermediate_size_per_partition,
+                    dtype=torch.bfloat16,
+                ),
+                requires_grad=False,
+            )
+            layer.register_parameter("w13_bias", w13_bias)
+            set_weight_attrs(w13_bias, extra_weight_attrs)
+
+            w2_bias = torch.nn.Parameter(
+                torch.zeros(
+                    num_experts,
+                    hidden_size,
+                    dtype=torch.bfloat16,
+                ),
+                requires_grad=False,
+            )
+            layer.register_parameter("w2_bias", w2_bias)
+            set_weight_attrs(w2_bias, extra_weight_attrs)
+
+    def _setup_kernel(
+        self,
+        layer: FusedMoE,
+        w13: torch.Tensor,
+        w2: torch.Tensor,
+        w13_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        w13_bias: torch.Tensor | None = None,
+        w2_bias: torch.Tensor | None = None,
+    ) -> None:
+        num_experts = self.num_experts
+        intermediate_size = self.intermediate_size
+        hidden_size = self.hidden_size
+        sf_block_size = 32
+
+        # Shape assertions
+        assert (
+            w13.dim() == 3
+            and w13.shape[0] == num_experts
+            and w13.shape[1] == intermediate_size * 2
+            and w13.shape[2] == hidden_size // 2
+        )
+        assert (
+            w13_scale.dim() == 3
+            and w13_scale.shape[0] == num_experts
+            and w13_scale.shape[1] == intermediate_size * 2
+            and w13_scale.shape[2] == hidden_size // sf_block_size
+        )
+        assert (
+            w2.dim() == 3
+            and w2.shape[0] == num_experts
+            and w2.shape[1] == hidden_size
+            and w2.shape[2] == intermediate_size // 2
+        )
+        assert (
+            w2_scale.dim() == 3
+            and w2_scale.shape[1] == hidden_size
+            and w2_scale.shape[2] == intermediate_size // sf_block_size
+        )
+        if w13_bias is not None:
+            assert (
+                w13_bias.dim() == 2
+                and w13_bias.shape[0] == num_experts
+                and w13_bias.shape[1] == intermediate_size * 2
+            )
+        if w2_bias is not None:
+            assert (
+                w2_bias.dim() == 2
+                and w2_bias.shape[0] == num_experts
+                and w2_bias.shape[1] == hidden_size
+            )
+
+        # Convert weights to kernel format
+        w13, w2, w13_scale, w2_scale, w13_bias, w2_bias = (
+            convert_weight_to_mxfp4_moe_kernel_format(
+                mxfp4_backend=self.mxfp4_backend,
+                layer=layer,
+                w13_weight=w13,
+                w2_weight=w2,
+                w13_weight_scale=w13_scale,
+                w2_weight_scale=w2_scale,
+                w13_bias=w13_bias,
+                w2_bias=w2_bias,
+                _cache_permute_indices=self._cache_permute_indices,
+            )
+        )
+
+        # For TRITON backends, weights are wrapped tensors from triton_kernels
+        # that don't support .detach(). Manually assign parameters.
+        if self.mxfp4_backend not in TRITON_BACKENDS:
+            replace_parameter(layer, "w13_weight", w13)
+            replace_parameter(layer, "w2_weight", w2)
+            replace_parameter(layer, "w13_weight_scale", w13_scale)
+            replace_parameter(layer, "w2_weight_scale", w2_scale)
+        else:
+            layer.w13_weight = w13
+            layer.w2_weight = w2
+            self.w13_precision_config = w13_scale
+            self.w2_precision_config = w2_scale
+
+        # AITER backend requires weights to be marked as shuffled.
+        if self.mxfp4_backend == Mxfp4MoeBackend.AITER_MXFP4_BF16:
+            layer.w13_weight.is_shuffled = True
+            layer.w2_weight.is_shuffled = True
+
         if w13_bias is not None and w2_bias is not None:
             replace_parameter(layer, "w13_bias", w13_bias)
             replace_parameter(layer, "w2_bias", w2_bias)
@@ -364,6 +704,7 @@ def _setup_kernel(
                 experts_cls=self.experts_cls,
                 routing_tables=layer._maybe_init_expert_routing_tables(),
                 shared_experts=layer.shared_experts,
+                layer=layer,
             )
 
     def process_weights_after_loading(self, layer):
@@ -386,6 +727,7 @@ def get_fused_moe_quant_config(
         w2_scale = layer.w2_weight_scale
         w1_bias = getattr(layer, "w13_bias", None)
         w2_bias = getattr(layer, "w2_bias", None)
+        swiglu_limit = getattr(layer, "swiglu_limit", None)
 
         if self.mxfp4_backend in TRITON_BACKENDS:
             assert self.w13_precision_config is not None
@@ -399,6 +741,8 @@ def get_fused_moe_quant_config(
             w2_scale=w2_scale,
             w1_bias=w1_bias,
             w2_bias=w2_bias,
+            swiglu_limit=swiglu_limit,
+            layer=layer,
         )
 
     def select_gemm_impl(
@@ -439,6 +783,7 @@ def apply_monolithic(
         layer: FusedMoE,
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
     ) -> torch.Tensor:
         assert self.is_monolithic
         assert self.moe_kernel is not None
diff --git a/vllm/model_executor/layers/quantization/online/base.py b/vllm/model_executor/layers/quantization/online/base.py
index 87997f8ef68f..315dcfacffcd 100644
--- a/vllm/model_executor/layers/quantization/online/base.py
+++ b/vllm/model_executor/layers/quantization/online/base.py
@@ -9,6 +9,7 @@
     OnlineQuantizationConfigArgs,
     OnlineQuantScheme,
 )
+from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import (
     FusedMoE,
 )
@@ -33,6 +34,15 @@
     Fp8PerTensorOnlineLinearMethod,
     Fp8PerTensorOnlineMoEMethod,
 )
+from vllm.model_executor.layers.quantization.online.int8 import (
+    Int8OnlineMoEMethod,
+)
+from vllm.model_executor.layers.quantization.online.mxfp8 import (
+    Mxfp8OnlineLinearMethod,
+    Mxfp8OnlineMoEMethod,
+)
+
+logger = init_logger(__name__)
 
 
 class OnlineQuantizationConfig(QuantizationConfig):
@@ -96,8 +106,16 @@ def get_quant_method(
                 return UnquantizedLinearMethod()
 
             linear_scheme = self.args.linear_scheme_override or self.args.global_scheme
-            if linear_scheme == OnlineQuantScheme.FP8_PER_BLOCK:
+            if linear_scheme == OnlineQuantScheme.INT8_PER_CHANNEL_WEIGHT_ONLY:
+                logger.warning_once(
+                    "INT8 online quantization only quantizes MoE expert "
+                    "weights. linear layers remain in full precision."
+                )
+                return UnquantizedLinearMethod()
+            elif linear_scheme == OnlineQuantScheme.FP8_PER_BLOCK:
                 return Fp8PerBlockOnlineLinearMethod()
+            elif linear_scheme == OnlineQuantScheme.MXFP8:
+                return Mxfp8OnlineLinearMethod()
             else:
                 return Fp8PerTensorOnlineLinearMethod()
         elif isinstance(layer, FusedMoE):
@@ -109,8 +127,12 @@ def get_quant_method(
                 return UnquantizedFusedMoEMethod(layer.moe_config)
 
             moe_scheme = self.args.moe_scheme_override or self.args.global_scheme
-            if moe_scheme == OnlineQuantScheme.FP8_PER_BLOCK:
+            if moe_scheme == OnlineQuantScheme.INT8_PER_CHANNEL_WEIGHT_ONLY:
+                return Int8OnlineMoEMethod(layer=layer)
+            elif moe_scheme == OnlineQuantScheme.FP8_PER_BLOCK:
                 return Fp8PerBlockOnlineMoEMethod(layer=layer)
+            elif moe_scheme == OnlineQuantScheme.MXFP8:
+                return Mxfp8OnlineMoEMethod(layer=layer)
             else:
                 return Fp8PerTensorOnlineMoEMethod(layer=layer)
         return None
diff --git a/vllm/model_executor/layers/quantization/online/fp8.py b/vllm/model_executor/layers/quantization/online/fp8.py
index fa8cf240627b..9cb697289d7e 100644
--- a/vllm/model_executor/layers/quantization/online/fp8.py
+++ b/vllm/model_executor/layers/quantization/online/fp8.py
@@ -10,7 +10,6 @@
     import vllm.model_executor.layers.fused_moe.modular_kernel as mk
     from vllm.model_executor.layers.fused_moe import FusedMoE
     from vllm.model_executor.layers.fused_moe.config import (
-        FusedMoEConfig,
         FusedMoEQuantConfig,
     )
     from vllm.model_executor.layers.fused_moe.oracle.fp8 import Fp8MoeBackend
@@ -19,15 +18,15 @@
 from vllm import _custom_ops as ops
 from vllm.config import get_current_vllm_config
 from vllm.model_executor.kernels.linear import init_fp8_linear_kernel
-from vllm.model_executor.layers.fused_moe import (
-    FusedMoEMethodBase,
-)
 from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
     select_fp8_moe_backend,
 )
 from vllm.model_executor.layers.linear import (
     LinearMethodBase,
 )
+from vllm.model_executor.layers.quantization.online.moe_base import (
+    OnlineMoEMethodBase,
+)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     GroupShape,
     create_fp8_quant_key,
@@ -44,7 +43,7 @@
     initialize_online_processing,
 )
 from vllm.model_executor.parameter import ModelWeightParameter
-from vllm.model_executor.utils import replace_parameter, set_weight_attrs
+from vllm.model_executor.utils import replace_parameter
 from vllm.platforms import current_platform
 from vllm.utils.deep_gemm import per_block_cast_to_fp8
 
@@ -268,21 +267,15 @@ def apply(
 # ---------------------------------------------------------------------------
 
 
-class _Fp8OnlineMoEBase(FusedMoEMethodBase):
+class _Fp8OnlineMoEBase(OnlineMoEMethodBase):
     """Shared base for online FP8 MoE methods. Loads fp16/bf16 checkpoint
     weights onto meta device and materializes them just-in-time."""
 
-    uses_meta_device: bool = True
-
     # Declared here for mypy; actual values are set in __init__.
     fp8_backend: "Fp8MoeBackend"
     experts_cls: "type[mk.FusedMoEExperts] | None"
     weight_scale_name: str
     weight_block_size: list[int] | None
-    moe: "FusedMoEConfig"
-    is_monolithic: bool
-    moe_quant_config: "FusedMoEQuantConfig | None"
-    moe_kernel: "mk.FusedMoEKernel | None"
 
     def __init__(
         self,
@@ -313,77 +306,6 @@ def __init__(
             allow_vllm_cutlass=False,
         )
 
-    def create_weights(
-        self,
-        layer: Module,
-        num_experts: int,
-        hidden_size: int,
-        intermediate_size_per_partition: int,
-        params_dtype: torch.dtype,
-        **extra_weight_attrs,
-    ):
-        layer.num_experts = num_experts
-        layer.orig_dtype = params_dtype
-        layer.weight_block_size = None
-
-        # WEIGHTS
-        w13_weight = torch.nn.Parameter(
-            torch.empty(
-                num_experts,
-                2 * intermediate_size_per_partition,
-                hidden_size,
-                device="meta",
-                dtype=params_dtype,
-            ),
-            requires_grad=False,
-        )
-        layer.register_parameter("w13_weight", w13_weight)
-        set_weight_attrs(w13_weight, extra_weight_attrs)
-
-        w2_weight = torch.nn.Parameter(
-            torch.empty(
-                num_experts,
-                hidden_size,
-                intermediate_size_per_partition,
-                device="meta",  # materialized and processed during loading
-                dtype=params_dtype,
-            ),
-            requires_grad=False,
-        )
-        layer.register_parameter("w2_weight", w2_weight)
-        set_weight_attrs(w2_weight, extra_weight_attrs)
-
-        # BIASES (for models like GPT-OSS that have biased MoE)
-        if self.moe.has_bias:
-            w13_bias = torch.nn.Parameter(
-                torch.zeros(
-                    num_experts,
-                    2 * intermediate_size_per_partition,
-                    device="meta",  # materialized and processed during loading
-                    dtype=layer.orig_dtype,
-                ),
-                requires_grad=False,
-            )
-            layer.register_parameter("w13_bias", w13_bias)
-            set_weight_attrs(w13_bias, extra_weight_attrs)
-
-            w2_bias = torch.nn.Parameter(
-                torch.zeros(
-                    num_experts,
-                    hidden_size,
-                    device="meta",  # materialized and processed during loading
-                    dtype=layer.orig_dtype,
-                ),
-                requires_grad=False,
-            )
-            layer.register_parameter("w2_bias", w2_bias)
-            set_weight_attrs(w2_bias, extra_weight_attrs)
-
-        layer.w13_input_scale = None
-        layer.w2_input_scale = None
-
-        initialize_online_processing(layer)
-
     def _setup_kernel(
         self,
         layer: "FusedMoE",
@@ -430,15 +352,6 @@ def _setup_kernel(
                 shared_experts=layer.shared_experts,
             )
 
-    def maybe_make_prepare_finalize(
-        self,
-        routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
-    ) -> "mk.FusedMoEPrepareAndFinalizeModular | None":
-        raise ValueError(
-            f"{self.__class__.__name__} uses the new modular kernel "
-            "initialization logic. This function should not be called."
-        )
-
     def get_fused_moe_quant_config(
         self, layer: torch.nn.Module
     ) -> "FusedMoEQuantConfig":
@@ -460,68 +373,9 @@ def get_fused_moe_quant_config(
             block_shape=self.weight_block_size,
         )
 
-        # Inject biases into the quant config if the model has them
-        # (e.g. GPT-OSS biased MoE)
-        if quant_config is not None and self.moe.has_bias:
-            w13_bias = getattr(layer, "w13_bias", None)
-            w2_bias = getattr(layer, "w2_bias", None)
-            if w13_bias is not None:
-                quant_config._w1.bias = w13_bias
-            if w2_bias is not None:
-                quant_config._w2.bias = w2_bias
-
+        self._maybe_inject_biases(quant_config, layer)
         return quant_config
 
-    @property
-    def supports_eplb(self) -> bool:
-        return True
-
-    def apply_monolithic(
-        self,
-        layer: "FusedMoE",
-        x: torch.Tensor,
-        router_logits: torch.Tensor,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        assert self.is_monolithic
-        assert self.moe_kernel is not None
-        return self.moe_kernel.apply_monolithic(
-            x,
-            layer.w13_weight,
-            layer.w2_weight,
-            router_logits,
-            activation=layer.activation,
-            global_num_experts=layer.global_num_experts,
-            expert_map=layer.expert_map,
-            apply_router_weight_on_input=layer.apply_router_weight_on_input,
-            num_expert_group=layer.num_expert_group,
-            topk_group=layer.topk_group,
-            e_score_correction_bias=layer.e_score_correction_bias,
-            routed_scaling_factor=layer.routed_scaling_factor,
-        )
-
-    def apply(
-        self,
-        layer: "FusedMoE",
-        x: torch.Tensor,
-        topk_weights: torch.Tensor,
-        topk_ids: torch.Tensor,
-        shared_experts_input: torch.Tensor | None,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        assert not self.is_monolithic
-        assert self.moe_kernel is not None
-        return self.moe_kernel.apply(
-            x,
-            layer.w13_weight,
-            layer.w2_weight,
-            topk_weights,
-            topk_ids,
-            activation=layer.activation,
-            global_num_experts=layer.global_num_experts,
-            expert_map=layer.expert_map,
-            apply_router_weight_on_input=layer.apply_router_weight_on_input,
-            shared_experts_input=shared_experts_input,
-        )
-
 
 class Fp8PerTensorOnlineMoEMethod(_Fp8OnlineMoEBase):
     """Online tensorwise FP8 MoE quantization.
diff --git a/vllm/model_executor/layers/quantization/online/int8.py b/vllm/model_executor/layers/quantization/online/int8.py
new file mode 100644
index 000000000000..4b4c87fbce96
--- /dev/null
+++ b/vllm/model_executor/layers/quantization/online/int8.py
@@ -0,0 +1,114 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import TYPE_CHECKING
+
+import torch
+from torch.nn import Module
+
+if TYPE_CHECKING:
+    from vllm.model_executor.layers.fused_moe import FusedMoE
+    from vllm.model_executor.layers.fused_moe.config import (
+        FusedMoEQuantConfig,
+    )
+
+from vllm.model_executor.layers.fused_moe.oracle.int8 import (
+    make_int8_moe_kernel,
+    make_int8_moe_quant_config,
+    select_int8_moe_backend,
+)
+from vllm.model_executor.layers.quantization.online.moe_base import (
+    OnlineMoEMethodBase,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    kInt8DynamicTokenSym,
+    kInt8StaticChannelSym,
+)
+from vllm.model_executor.utils import replace_parameter
+
+
+class Int8OnlineMoEMethod(OnlineMoEMethodBase):
+    """Online per-channel INT8 MoE quantization.
+    Loads fp16/bf16 weights and quantizes them per-row to int8 during loading.
+    """
+
+    def __init__(
+        self,
+        *,
+        layer: torch.nn.Module,
+    ):
+        super().__init__(layer.moe_config)
+        self.int8_backend, self.experts_cls = select_int8_moe_backend(
+            config=self.moe,
+            weight_key=kInt8StaticChannelSym,
+            activation_key=kInt8DynamicTokenSym,
+        )
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+        if getattr(layer, "_already_called_process_weights_after_loading", False):
+            return
+
+        self._quantize_weights(layer)
+        self._setup_kernel(layer)
+
+        layer._already_called_process_weights_after_loading = True
+
+    def _quantize_weights(self, layer: Module) -> None:
+        vmax = torch.iinfo(torch.int8).max
+
+        w13 = torch.empty_like(layer.w13_weight, dtype=torch.int8)
+        w2 = torch.empty_like(layer.w2_weight, dtype=torch.int8)
+        w13_scale = torch.zeros(
+            layer.num_experts,
+            layer.w13_weight.shape[1],
+            device=w13.device,
+            dtype=torch.float32,
+        )
+        w2_scale = torch.zeros(
+            layer.num_experts,
+            layer.w2_weight.shape[1],
+            device=w2.device,
+            dtype=torch.float32,
+        )
+
+        for expert in range(layer.local_num_experts):
+            # w13: per-row quantization over hidden_size dim
+            w = layer.w13_weight[expert, :, :]
+            scales = w.abs().amax(dim=1) / vmax
+            q = w.div(scales.unsqueeze(1)).round().clamp(-vmax, vmax)
+            w13[expert, :, :] = q.to(torch.int8)
+            w13_scale[expert, :] = scales
+
+            # w2: per-row quantization over intermediate_size dim
+            w = layer.w2_weight[expert, :, :]
+            scales = w.abs().amax(dim=1) / vmax
+            q = w.div(scales.unsqueeze(1)).round().clamp(-vmax, vmax)
+            w2[expert, :, :] = q.to(torch.int8)
+            w2_scale[expert, :] = scales
+
+        replace_parameter(layer, "w13_weight", w13)
+        replace_parameter(layer, "w2_weight", w2)
+        replace_parameter(layer, "w13_scale", w13_scale)
+        replace_parameter(layer, "w2_scale", w2_scale)
+
+    def _setup_kernel(self, layer: "FusedMoE") -> None:
+        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
+        assert self.moe_quant_config is not None
+        assert self.experts_cls is not None
+        self.moe_kernel = make_int8_moe_kernel(
+            moe_quant_config=self.moe_quant_config,
+            moe_config=self.moe,
+            experts_cls=self.experts_cls,
+            routing_tables=layer._maybe_init_expert_routing_tables(),
+            shared_experts=layer.shared_experts,
+        )
+
+    def get_fused_moe_quant_config(
+        self, layer: torch.nn.Module
+    ) -> "FusedMoEQuantConfig | None":
+        quant_config = make_int8_moe_quant_config(
+            w1_scale=layer.w13_scale,
+            w2_scale=layer.w2_scale,
+        )
+        self._maybe_inject_biases(quant_config, layer)
+        return quant_config
diff --git a/vllm/model_executor/layers/quantization/online/moe_base.py b/vllm/model_executor/layers/quantization/online/moe_base.py
new file mode 100644
index 000000000000..417ce1770f9e
--- /dev/null
+++ b/vllm/model_executor/layers/quantization/online/moe_base.py
@@ -0,0 +1,173 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from abc import abstractmethod
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.model_executor.layers.fused_moe import FusedMoEMethodBase
+from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
+from vllm.model_executor.model_loader.reload.layerwise import (
+    initialize_online_processing,
+)
+from vllm.model_executor.utils import set_weight_attrs
+
+
+class OnlineMoEMethodBase(FusedMoEMethodBase):
+    """Base for MoE methods that load full-precision weights on meta device
+    and quantize them after loading via the QeRL layerwise processing system.
+    """
+
+    uses_meta_device: bool = True
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size_per_partition: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        layer.num_experts = num_experts
+        layer.orig_dtype = params_dtype
+        layer.weight_block_size = None
+
+        # Fused gate_up_proj (column parallel) — full precision on meta device
+        w13_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                hidden_size,
+                device="meta",
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        # down_proj (row parallel) — full precision on meta device
+        w2_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                intermediate_size_per_partition,
+                device="meta",
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # BIASES (for models like GPT-OSS that have biased MoE)
+        if self.moe.has_bias:
+            w13_bias = torch.nn.Parameter(
+                torch.zeros(
+                    num_experts,
+                    2 * intermediate_size_per_partition,
+                    device="meta",
+                    dtype=layer.orig_dtype,
+                ),
+                requires_grad=False,
+            )
+            layer.register_parameter("w13_bias", w13_bias)
+            set_weight_attrs(w13_bias, extra_weight_attrs)
+
+            w2_bias = torch.nn.Parameter(
+                torch.zeros(
+                    num_experts,
+                    hidden_size,
+                    device="meta",
+                    dtype=layer.orig_dtype,
+                ),
+                requires_grad=False,
+            )
+            layer.register_parameter("w2_bias", w2_bias)
+            set_weight_attrs(w2_bias, extra_weight_attrs)
+
+        layer.w13_input_scale = None
+        layer.w2_input_scale = None
+
+        initialize_online_processing(layer)
+
+    @abstractmethod
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        pass
+
+    def _maybe_inject_biases(
+        self,
+        quant_config: FusedMoEQuantConfig,
+        layer: torch.nn.Module,
+    ) -> None:
+        """Inject biases into the quant config if the model has them
+        (e.g. GPT-OSS biased MoE)."""
+        if self.moe.has_bias:
+            w13_bias = getattr(layer, "w13_bias", None)
+            w2_bias = getattr(layer, "w2_bias", None)
+            if w13_bias is not None:
+                quant_config._w1.bias = w13_bias
+            if w2_bias is not None:
+                quant_config._w2.bias = w2_bias
+
+    def maybe_make_prepare_finalize(
+        self,
+        routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
+    ) -> mk.FusedMoEPrepareAndFinalizeModular | None:
+        raise ValueError(
+            f"{self.__class__.__name__} uses the new modular kernel "
+            "initialization logic. This function should not be called."
+        )
+
+    @property
+    def supports_eplb(self) -> bool:
+        return True
+
+    def apply_monolithic(
+        self,
+        layer: "FusedMoE",  # type: ignore[name-defined] # noqa: F821
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        assert self.is_monolithic
+        assert self.moe_kernel is not None
+        return self.moe_kernel.apply_monolithic(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            router_logits,
+            activation=layer.activation,
+            global_num_experts=layer.global_num_experts,
+            expert_map=layer.expert_map,
+            apply_router_weight_on_input=layer.apply_router_weight_on_input,
+            num_expert_group=layer.num_expert_group,
+            topk_group=layer.topk_group,
+            e_score_correction_bias=layer.e_score_correction_bias,
+            routed_scaling_factor=layer.routed_scaling_factor,
+        )
+
+    def apply(
+        self,
+        layer: "FusedMoE",  # type: ignore[name-defined] # noqa: F821
+        x: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        shared_experts_input: torch.Tensor | None,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        assert not self.is_monolithic
+        assert self.moe_kernel is not None
+        return self.moe_kernel.apply(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights,
+            topk_ids,
+            activation=layer.activation,
+            global_num_experts=layer.global_num_experts,
+            expert_map=layer.expert_map,
+            apply_router_weight_on_input=layer.apply_router_weight_on_input,
+            shared_experts_input=shared_experts_input,
+        )
diff --git a/vllm/model_executor/layers/quantization/mxfp8.py b/vllm/model_executor/layers/quantization/online/mxfp8.py
similarity index 57%
rename from vllm/model_executor/layers/quantization/mxfp8.py
rename to vllm/model_executor/layers/quantization/online/mxfp8.py
index 5acf843f1088..39a32604442c 100644
--- a/vllm/model_executor/layers/quantization/mxfp8.py
+++ b/vllm/model_executor/layers/quantization/online/mxfp8.py
@@ -1,131 +1,47 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-"""Online MXFP8 (microscaling FP8, block-32) quantization config and methods."""
+"""Online MXFP8 (microscaling FP8, block-32) quantization methods."""
 
-from typing import Any
+from typing import TYPE_CHECKING
 
 import torch
 from torch.nn import Module
 
-from vllm.logger import init_logger
+if TYPE_CHECKING:
+    import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+    from vllm.model_executor.layers.fused_moe import FusedMoE
+    from vllm.model_executor.layers.fused_moe.config import (
+        FusedMoEQuantConfig,
+    )
+    from vllm.model_executor.layers.fused_moe.oracle.fp8 import Fp8MoeBackend
+
 from vllm.model_executor.kernels.linear import init_mxfp8_linear_kernel
-from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import (
-    FusedMoE,
-    FusedMoEMethodBase,
-)
-from vllm.model_executor.layers.fused_moe.layer import UnquantizedFusedMoEMethod
 from vllm.model_executor.layers.fused_moe.oracle.mxfp8 import (
     select_mxfp8_moe_backend,
 )
-from vllm.model_executor.layers.linear import (
-    LinearBase,
-    UnquantizedLinearMethod,
-)
-from vllm.model_executor.layers.quantization import QuantizationMethods
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizeMethodBase,
+from vllm.model_executor.layers.quantization.online.fp8 import (
+    _Fp8OnlineLinearBase,
 )
-from vllm.model_executor.layers.quantization.fp8 import (
-    Fp8Config,
-    Fp8KVCacheMethod,
-    Fp8OnlineLinearMethod,
-    Fp8OnlineMoEMethod,
+from vllm.model_executor.layers.quantization.online.moe_base import (
+    OnlineMoEMethodBase,
 )
 from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
     MXFP8_BLOCK_SIZE,
     mxfp8_e4m3_quantize,
 )
-from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    is_layer_skipped,
-)
 from vllm.model_executor.utils import replace_parameter
 from vllm.platforms import current_platform
 
-logger = init_logger(__name__)
-
 
-class Mxfp8Config(Fp8Config):
-    """Config class for online MXFP8 MoE quantization."""
-
-    def __init__(
-        self,
-        activation_scheme: str = "dynamic",
-        ignored_layers: list[str] | None = None,
-    ) -> None:
-        if activation_scheme != "dynamic":
-            raise ValueError("mxfp8 only supports dynamic activation scheme.")
-        super().__init__(
-            is_checkpoint_fp8_serialized=False,
-            activation_scheme=activation_scheme,
-            ignored_layers=ignored_layers,
-            weight_block_size=None,
-        )
-
-    @classmethod
-    def get_name(cls) -> QuantizationMethods:
-        return "mxfp8"
-
-    @classmethod
-    def get_min_capability(cls) -> int:
-        # Marlin kernel supports MXFP8 on SM80+
-        return 80
-
-    @classmethod
-    def from_config(cls, config: dict[str, Any]) -> "Mxfp8Config":
-        activation_scheme = cls.get_from_keys_or(
-            config, ["activation_scheme"], "dynamic"
-        )
-        ignored_layers = cls.get_from_keys_or(config, ["ignored_layers"], None)
-        if not ignored_layers:
-            ignored_layers = cls.get_from_keys_or(
-                config, ["modules_to_not_convert"], None
-            )
-        return cls(
-            activation_scheme=activation_scheme,
-            ignored_layers=ignored_layers,
-        )
-
-    def get_quant_method(
-        self, layer: torch.nn.Module, prefix: str
-    ) -> "QuantizeMethodBase | None":
-        if isinstance(layer, LinearBase):
-            if is_layer_skipped(
-                prefix=prefix,
-                ignored_layers=self.ignored_layers,
-                fused_mapping=self.packed_modules_mapping,
-                skip_with_substr=True,
-            ):
-                return UnquantizedLinearMethod()
-            return Mxfp8OnlineLinearMethod(self)
-        elif isinstance(layer, FusedMoE):
-            if is_layer_skipped(
-                prefix=prefix,
-                ignored_layers=self.ignored_layers,
-                fused_mapping=self.packed_modules_mapping,
-                skip_with_substr=True,
-            ):
-                return UnquantizedFusedMoEMethod(layer.moe_config)
-            return Mxfp8OnlineMoEMethod(self, layer)
-        elif isinstance(layer, Attention):
-            return Fp8KVCacheMethod(self)
-        return None
-
-
-class Mxfp8OnlineLinearMethod(Fp8OnlineLinearMethod):
+class Mxfp8OnlineLinearMethod(_Fp8OnlineLinearBase):
     """Online MXFP8 linear method.
     Loads bf16/fp16 checkpoints and quantizes weights to MXFP8 (microscaling
     FP8 with block-32 scales) during weight loading.
-
-    Args:
-        quant_config: The MXFP8 quantization config.
     """
 
-    uses_meta_device: bool = True
-
-    def __init__(self, quant_config: "Mxfp8Config"):
-        self.quant_config = quant_config
+    def __init__(self):
+        super().__init__()
         self.kernel = init_mxfp8_linear_kernel()
 
     def create_weights(
@@ -178,19 +94,15 @@ def apply(
         return self.kernel.apply_weights(layer, x, bias)
 
 
-class Mxfp8OnlineMoEMethod(Fp8OnlineMoEMethod):
+class Mxfp8OnlineMoEMethod(OnlineMoEMethodBase):
     """MoE method for online MXFP8 (block) quantization."""
 
-    uses_meta_device: bool = True
-
-    def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module):
-        FusedMoEMethodBase.__init__(self, layer.moe_config)
-        self.quant_config = quant_config
-        assert not quant_config.is_checkpoint_fp8_serialized
-        assert quant_config.activation_scheme == "dynamic"
+    fp8_backend: "Fp8MoeBackend"
+    experts_cls: "type[mk.FusedMoEExperts] | None"
 
-        self.weight_block_size = [1, MXFP8_BLOCK_SIZE]
-        self.block_quant = True
+    def __init__(self, *, layer: torch.nn.Module):
+        super().__init__(layer.moe_config)
+        self.weight_block_size: list[int] = [1, MXFP8_BLOCK_SIZE]
         self.weight_scale_name = "weight_scale"
 
         self.fp8_backend, self.experts_cls = select_mxfp8_moe_backend(config=self.moe)
@@ -247,6 +159,74 @@ def _quantize_mxfp8_moe_weight(
 
         return w_quant, w_scales
 
+    def _setup_kernel(
+        self,
+        layer: "FusedMoE",
+        w13: torch.Tensor,
+        w2: torch.Tensor,
+        w13_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        w13_input_scale: torch.Tensor | None,
+        w2_input_scale: torch.Tensor | None,
+    ) -> None:
+        from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
+            convert_to_fp8_moe_kernel_format,
+            make_fp8_moe_kernel,
+        )
+
+        # Shuffle weights to runtime format.
+        w13, w2, w13_scale, w2_scale = convert_to_fp8_moe_kernel_format(
+            fp8_backend=self.fp8_backend,
+            layer=layer,
+            w13=w13,
+            w2=w2,
+            w13_scale=w13_scale,
+            w2_scale=w2_scale,
+            w13_input_scale=w13_input_scale,
+            w2_input_scale=w2_input_scale,
+        )
+
+        replace_parameter(layer, "w13_weight", w13)
+        replace_parameter(layer, "w2_weight", w2)
+        replace_parameter(layer, f"w13_{self.weight_scale_name}", w13_scale)
+        replace_parameter(layer, f"w2_{self.weight_scale_name}", w2_scale)
+
+        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
+        if self.moe_quant_config:
+            assert self.experts_cls is not None
+            self.moe_kernel = make_fp8_moe_kernel(
+                moe_quant_config=self.moe_quant_config,
+                moe_config=self.moe,
+                fp8_backend=self.fp8_backend,
+                experts_cls=self.experts_cls,
+                routing_tables=layer._maybe_init_expert_routing_tables(),
+                shared_experts=layer.shared_experts,
+            )
+
+    def get_fused_moe_quant_config(
+        self, layer: torch.nn.Module
+    ) -> "FusedMoEQuantConfig":
+        from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
+            make_fp8_moe_quant_config,
+        )
+
+        w1_scale = getattr(layer, f"w13_{self.weight_scale_name}")
+        w2_scale = getattr(layer, f"w2_{self.weight_scale_name}")
+        a1_scale = layer.w13_input_scale
+        a2_scale = layer.w2_input_scale
+
+        quant_config = make_fp8_moe_quant_config(
+            fp8_backend=self.fp8_backend,
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+            block_shape=self.weight_block_size,
+        )
+
+        self._maybe_inject_biases(quant_config, layer)
+        return quant_config
+
     def process_weights_after_loading(self, layer: Module) -> None:
         if getattr(layer, "_already_called_process_weights_after_loading", False):
             return
diff --git a/vllm/model_executor/layers/quantization/quark/quark.py b/vllm/model_executor/layers/quantization/quark/quark.py
index 33bd0cfc22e8..dd7f33e2619c 100644
--- a/vllm/model_executor/layers/quantization/quark/quark.py
+++ b/vllm/model_executor/layers/quantization/quark/quark.py
@@ -5,7 +5,6 @@
 from typing import TYPE_CHECKING, Any, cast
 
 import torch
-from transformers import PretrainedConfig
 
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention import Attention
@@ -45,10 +44,6 @@
 
 logger = init_logger(__name__)
 
-# model_type values that use dynamic MXFP4 re-quantization for
-# OCP MX fp4 Quark checkpoints
-_DEEPSEEK_V3_FAMILY_MODEL_TYPES = frozenset({"deepseek_v3"})
-
 
 class QuarkConfig(QuantizationConfig):
     def __init__(
@@ -65,32 +60,12 @@ def __init__(
         self.kv_cache_group = kv_cache_group
         self.kv_cache_config = kv_cache_config
         self.pack_method = pack_method
+        # Note : this flag is kept disabled because the overhead of
+        # dynamic mxfp4 quantization negates the performance gains
+        # that come from shifting to mxfp4. It is left here in case
+        # we want to re-enable it in the future.
         self.dynamic_mxfp4_quant = False
 
-    def maybe_update_config(
-        self,
-        model_name: str,
-        hf_config: PretrainedConfig | None = None,
-        revision: str | None = None,
-    ):
-        """Enable dynamic MXFP4 only for DeepSeek-V3-family + fp4 Quark checkpoints."""
-
-        if (
-            getattr(hf_config, "model_type", None)
-            not in _DEEPSEEK_V3_FAMILY_MODEL_TYPES
-        ):
-            return
-
-        quant_config = getattr(hf_config, "quantization_config", None)
-        if quant_config is not None:
-            quant_dtype = (
-                quant_config.get("global_quant_config", {})
-                .get("weight", {})
-                .get("dtype")
-            )
-            if quant_dtype == "fp4":
-                self.dynamic_mxfp4_quant = True
-
     def get_linear_method(self) -> "QuarkLinearMethod":
         return QuarkLinearMethod(self)
 
@@ -114,7 +89,7 @@ def apply_vllm_mapper(  # noqa: B027
         :param hf_to_vllm_mapper: maps from hf model structure (the assumed
             structure of the qconfig) to vllm model structure
         """
-        quant_config_with_hf_to_vllm_mapper = {}
+        quant_config_with_hf_to_vllm_mapper: dict[str, Any] = {}
 
         for k, v in self.quant_config.items():
             if isinstance(v, list):
diff --git a/vllm/model_executor/layers/quantization/quark/quark_moe.py b/vllm/model_executor/layers/quantization/quark/quark_moe.py
index 2bab66709ddd..3889e376b560 100644
--- a/vllm/model_executor/layers/quantization/quark/quark_moe.py
+++ b/vllm/model_executor/layers/quantization/quark/quark_moe.py
@@ -26,27 +26,28 @@
     mxfp4_w4a16_moe_quant_config,
     ocp_mx_moe_quant_config,
 )
-from vllm.model_executor.layers.fused_moe.fused_marlin_moe import fused_marlin_moe
+from vllm.model_executor.layers.fused_moe.experts.marlin_moe import fused_marlin_moe
 from vllm.model_executor.layers.fused_moe.oracle.mxfp4 import (
     TRITON_BACKENDS,
     Mxfp4MoeBackend,
+    backend_to_kernel_cls,
     convert_gpt_oss_weight_to_mxfp4_moe_kernel_format,
     make_mxfp4_moe_kernel,
     make_mxfp4_moe_quant_config,
     mxfp4_round_up_hidden_size_and_intermediate_size,
-    select_gpt_oss_mxfp4_moe_backend,
+    select_mxfp4_moe_backend,
 )
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     prepare_fp8_moe_layer_for_marlin,
 )
-from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
-    _swizzle_mxfp4,
-)
 from vllm.model_executor.layers.quantization.utils.ocp_mx_utils import (
     OCP_MX_BLOCK_SIZE,
     OCP_MX_Scheme,
 )
-from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape,
+    kFp8StaticTensorSym,
+)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     all_close_1d,
     normalize_e4m3fn_to_e4m3fnuz,
@@ -61,7 +62,6 @@
 __all__ = [
     "QuarkMoEMethod",
     "QuarkOCP_MX_MoEMethod",
-    "QuarkOCP_MX_MoEMethod_OSS",
 ]
 
 
@@ -93,22 +93,9 @@ def get_moe_method(
         elif quant_config._is_fp8_w8a8(weight_config, input_config):
             return QuarkW8A8Fp8MoEMethod(weight_config, input_config, module.moe_config)
         elif quant_config._is_w_ocp_mx_a_x(weight_config, input_config):
-            emulate = not current_platform.supports_mx() or not (
-                rocm_aiter_ops.is_fused_moe_enabled()
-            )
-            if (
-                input_config is not None
-                and input_config.get("dtype") == "fp8_e4m3"
-                and not input_config.get("is_dynamic")
-                and not emulate
-            ):
-                return QuarkOCP_MX_MoEMethod_OSS(
-                    weight_config, input_config, module.moe_config
-                )
-            else:
-                return QuarkOCP_MX_MoEMethod(
-                    weight_config, input_config, module.moe_config
-                )
+            # All OCP MX schemes (W4A16, W4A8, etc.) handled by QuarkOCP_MX_MoEMethod
+            # Backend selection happens inside via oracle
+            return QuarkOCP_MX_MoEMethod(weight_config, input_config, module.moe_config)
         elif quant_config._is_static_tensor_w8a8(
             weight_config, input_config
         ) or quant_config._is_dynamic_per_token_w8a8(weight_config, input_config):
@@ -457,7 +444,7 @@ def apply(
         shared_experts_input: torch.Tensor | None,
     ) -> torch.Tensor:
         if self.rocm_aiter_moe_enabled:
-            from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+            from vllm.model_executor.layers.fused_moe.experts.rocm_aiter_moe import (
                 rocm_aiter_fused_experts,
             )
 
@@ -922,7 +909,7 @@ def apply(
         topk_ids: torch.Tensor,
         shared_experts_input: torch.Tensor | None,
     ) -> torch.Tensor:
-        from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+        from vllm.model_executor.layers.fused_moe.experts.rocm_aiter_moe import (
             rocm_aiter_fused_experts,
         )
 
@@ -986,25 +973,32 @@ def __init__(
                 f"Please check that the combination is supported in OCP_MX_Scheme."
             )
 
+        # TODO(bowenbao): refactor and introduce backends for other OCP MX schemes,
+        # use kernel abstraction for all OCP MX MOE implementations.
         self.mxfp4_backend: Mxfp4MoeBackend = Mxfp4MoeBackend.NONE
         self.experts_cls: type[mk.FusedMoEExperts] | None = None
         self.moe_kernel: mk.FusedMoEKernel | None = None
 
-        # Used for triton kernel precision configs
+        # Used for triton kernel precision configs (W4A8, TRITON backends)
         self.w13_precision_config = None
         self.w2_precision_config = None
 
-        if self.ocp_mx_scheme == "w_mxfp4":
-            self.mxfp4_backend, self.experts_cls = select_gpt_oss_mxfp4_moe_backend(moe)
-        elif self.ocp_mx_scheme.startswith("w_mxfp4"):
-            # TODO(bowenbao): refactor and introduce backends for other OCP MX schemes.
-            self.mxfp4_backend = Mxfp4MoeBackend.NONE
-
         if self.input_quant is not None:
             self.static_input_scales = not self.input_quant.get("is_dynamic")
         else:
             self.static_input_scales = False
 
+        # Select backend based on OCP MX scheme
+        if self.ocp_mx_scheme == "w_mxfp4":
+            # W4A16: weight-only MXFP4
+            self.mxfp4_backend, self.experts_cls = select_mxfp4_moe_backend(moe)
+        elif self.ocp_mx_scheme == "w_mxfp4_a_fp8" and self.static_input_scales:
+            # W4A8: MXFP4 weights + static FP8 activations
+            self.mxfp4_backend, self.experts_cls = select_mxfp4_moe_backend(
+                moe, activation_key=kFp8StaticTensorSym
+            )
+
+        # Validation for unsupported schemes
         if any(
             self.ocp_mx_scheme.endswith(a_scheme)
             for a_scheme in ["a_mxfp4", "a_mxfp6_e3m2", "a_mxfp6_e2m3"]
@@ -1028,14 +1022,30 @@ def __init__(
             get_current_vllm_config().model_config.hf_config, "model_type", None
         )
 
+        # TODO: Remove once all OCP MX schemes use the kernel abstraction
+        _AITER_NATIVE_OCP_MX_SCHEMES = ("w_mxfp4", "w_mxfp4_a_mxfp4", "w_mxfp4_a_fp8")
         self.emulate = (
             not current_platform.supports_mx()
-            or not self.ocp_mx_scheme.startswith("w_mxfp4")
+            or self.ocp_mx_scheme not in _AITER_NATIVE_OCP_MX_SCHEMES
         ) and (
             self.mxfp4_backend is Mxfp4MoeBackend.NONE or not self.use_rocm_aiter_moe
         )
 
         if self.emulate:
+            # We use the same code path between MXFP4/MXFP6 emulation.
+            self.mxfp4_backend = Mxfp4MoeBackend.EMULATION
+
+        # TODO: Remove `self.mxfp4_backend != Mxfp4MoeBackend.NONE` and make it so that
+        # all MXFP4 backends use the kernel abstraction.
+        if self.mxfp4_backend != Mxfp4MoeBackend.NONE:
+            self.experts_cls = backend_to_kernel_cls(self.mxfp4_backend)[0]
+
+        # Log backend selection
+        if self.mxfp4_backend != Mxfp4MoeBackend.NONE:
+            logger.info_once(
+                f"Using {self.mxfp4_backend.value} backend for {self.ocp_mx_scheme}"
+            )
+        elif self.emulate:
             logger.warning_once(
                 f"The current mode (supports_mx={current_platform.supports_mx()}, "
                 f"use_rocm_aiter_moe={self.use_rocm_aiter_moe}, "
@@ -1045,10 +1055,6 @@ def __init__(
                 "QDQ (quantize and dequantize) will be used, with the linear "
                 "layers computed in high precision."
             )
-        else:
-            logger.warning_once(
-                "The current mode supports native MoE MXFP4 computation"
-            )
 
     def maybe_roundup_sizes(
         self,
@@ -1063,7 +1069,12 @@ def maybe_roundup_sizes(
             act_dtype=act_dtype,
             moe_parallel_config=moe_parallel_config,
         )
-        if self.mxfp4_backend is not None:
+        # In case quantization emulation backend is used, there is no need to apply
+        # MXFP4-specific padding logic as the compute happens in higher precision.
+        if (
+            self.mxfp4_backend is not None
+            and self.mxfp4_backend != Mxfp4MoeBackend.EMULATION
+        ):
             hidden_size, intermediate_size_per_partition = (
                 mxfp4_round_up_hidden_size_and_intermediate_size(
                     self.mxfp4_backend, hidden_size, intermediate_size_per_partition
@@ -1188,6 +1199,11 @@ def create_weights(
             layer.w2_input_scale = None
 
     def process_weights_after_loading(self, layer):
+        # For MXFP4 schemes with native backend, use oracle
+        if self.mxfp4_backend != Mxfp4MoeBackend.NONE:
+            self._setup_kernel(layer)
+            return
+
         if self.static_input_scales and self.input_dtype == "fp8":
             # firstly, process activations if fp8 static input
             if layer.w13_input_scale is None or layer.w2_input_scale is None:
@@ -1236,22 +1252,7 @@ def process_weights_after_loading(self, layer):
                         w2_input_scale, requires_grad=False
                     )
 
-        # For w_mxfp4, use oracle functions
-        if (
-            self.ocp_mx_scheme == "w_mxfp4"
-            and self.mxfp4_backend != Mxfp4MoeBackend.NONE
-        ):
-            self._setup_kernel_via_oracle(layer)
-            return
-
         # TODO(bowenbao): gradually migrate to oracles.
-        # secondly, process mxfp weights for other schemes
-        if self.emulate:
-            # Build quant config for emulation path
-            self.moe_quant_config = self.get_fused_moe_quant_config(layer)
-            torch.accelerator.empty_cache()
-            return
-
         # Existing AITER path for w_mxfp4_a_mxfp4 and other schemes
         from aiter.utility.fp4_utils import e8m0_shuffle
 
@@ -1289,46 +1290,48 @@ def process_weights_after_loading(self, layer):
         self.moe_quant_config = self.get_fused_moe_quant_config(layer)
         torch.accelerator.empty_cache()
 
-    def _setup_kernel_via_oracle(self, layer: FusedMoE):
-        """Setup kernel using oracle functions for w_mxfp4 scheme."""
-        w13 = layer.w13_weight
-        w2 = layer.w2_weight
-        w13_scale = layer.w13_weight_scale
-        w2_scale = layer.w2_weight_scale
+    def _setup_kernel(self, layer: FusedMoE):
+        """Setup kernel using oracle functions for MXFP4 schemes (W4A16, W4A8)."""
         w13_bias = getattr(layer, "w13_bias", None)
         w2_bias = getattr(layer, "w2_bias", None)
 
-        # Convert weights to kernel format
+        # Convert weights to kernel format (handles all backend-specific logic)
         w13, w2, w13_scale, w2_scale, w13_bias, w2_bias = (
             convert_gpt_oss_weight_to_mxfp4_moe_kernel_format(
                 mxfp4_backend=self.mxfp4_backend,
                 layer=layer,
-                w13_weight=w13,
-                w2_weight=w2,
-                w13_weight_scale=w13_scale,
-                w2_weight_scale=w2_scale,
+                w13_weight=layer.w13_weight,
+                w2_weight=layer.w2_weight,
+                w13_weight_scale=layer.w13_weight_scale,
+                w2_weight_scale=layer.w2_weight_scale,
                 w13_bias=w13_bias,
                 w2_bias=w2_bias,
             )
         )
 
-        # For TRITON backends, weights are wrapped tensors from triton_kernels
-        # that don't support .detach(). Manually assign parameters.
-        if self.mxfp4_backend not in TRITON_BACKENDS:
-            replace_parameter(layer, "w13_weight", w13)
-            replace_parameter(layer, "w2_weight", w2)
-            replace_parameter(layer, "w13_weight_scale", w13_scale)
-            replace_parameter(layer, "w2_weight_scale", w2_scale)
-        else:
+        # Handle weight/scale assignment based on backend type
+        if self.mxfp4_backend in TRITON_BACKENDS or self.mxfp4_backend in (
+            Mxfp4MoeBackend.AITER_MXFP4_FP8,
+        ):
+            # Triton-based backends: w13/w2 are triton_kernels.tensor.Tensor
+            # Store on layer for apply(), scales are PrecisionConfig
             layer.w13_weight = w13
             layer.w2_weight = w2
             self.w13_precision_config = w13_scale
             self.w2_precision_config = w2_scale
+        else:
+            # Standard backends: replace parameters
+            replace_parameter(layer, "w13_weight", w13)
+            replace_parameter(layer, "w2_weight", w2)
+            replace_parameter(layer, "w13_weight_scale", w13_scale)
+            replace_parameter(layer, "w2_weight_scale", w2_scale)
 
         if w13_bias is not None and w2_bias is not None:
             replace_parameter(layer, "w13_bias", w13_bias)
             replace_parameter(layer, "w2_bias", w2_bias)
 
+        torch.accelerator.empty_cache()
+
         # Build quant config and kernel
         self.moe_quant_config = self.get_fused_moe_quant_config(layer)
         if self.moe_quant_config is not None and self.experts_cls is not None:
@@ -1344,27 +1347,29 @@ def _setup_kernel_via_oracle(self, layer: FusedMoE):
     def get_fused_moe_quant_config(
         self, layer: torch.nn.Module
     ) -> FusedMoEQuantConfig | None:
-        # For w_mxfp4 with oracle backend, use oracle function
-        if (
-            self.ocp_mx_scheme == "w_mxfp4"
-            and self.mxfp4_backend != Mxfp4MoeBackend.NONE
-        ):
-            w1_scale = layer.w13_weight_scale
-            w2_scale = layer.w2_weight_scale
-            if self.mxfp4_backend in TRITON_BACKENDS:
+        # For oracle-based backends (W4A16, W4A8), use make_mxfp4_moe_quant_config
+        if self.mxfp4_backend not in (Mxfp4MoeBackend.NONE, Mxfp4MoeBackend.EMULATION):
+            # Determine scale source based on backend type
+            if self.mxfp4_backend in TRITON_BACKENDS or self.mxfp4_backend in (
+                Mxfp4MoeBackend.AITER_MXFP4_FP8,
+            ):
                 w1_scale = self.w13_precision_config
                 w2_scale = self.w2_precision_config
+            else:
+                w1_scale = layer.w13_weight_scale
+                w2_scale = layer.w2_weight_scale
+
             return make_mxfp4_moe_quant_config(
                 mxfp4_backend=self.mxfp4_backend,
                 w1_scale=w1_scale,
                 w2_scale=w2_scale,
                 w1_bias=getattr(layer, "w13_bias", None),
                 w2_bias=getattr(layer, "w2_bias", None),
+                a1_scale=getattr(layer, "w13_input_scale", None),
+                a2_scale=getattr(layer, "w2_input_scale", None),
             )
 
-        # Existing code for other schemes
-        # TODO(bowenbao): kept for emulation fallback, to be refactored into
-        # dedicated emulation backend.
+        # Emulation and other schemes
         if self.ocp_mx_scheme == "w_mxfp4":
             return mxfp4_w4a16_moe_quant_config(
                 w1_scale=layer.w13_weight_scale,
@@ -1414,7 +1419,7 @@ def apply(
         topk_ids: torch.Tensor,
         shared_experts_input: torch.Tensor | None,
     ) -> torch.Tensor:
-        # For w_mxfp4 with oracle kernel
+        # For oracle-based kernels (W4A16, W4A8) or emulation kernel
         if self.moe_kernel is not None:
             return self.moe_kernel.apply(
                 hidden_states=x,
@@ -1429,45 +1434,30 @@ def apply(
                 shared_experts_input=shared_experts_input,
             )
 
-        # Existing code for emulation/AITER paths
-        if not self.emulate:
-            from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
-                rocm_aiter_fused_experts,
-            )
-
-            return rocm_aiter_fused_experts(
-                x,
-                layer.w13_weight,
-                layer.w2_weight,
-                topk_weights=topk_weights,
-                topk_ids=topk_ids,
-                activation=layer.activation,
-                quant_config=self.moe_quant_config,
-                moe_config=layer.moe_config,
-                expert_map=layer.expert_map,
-            )
-        else:
-            from vllm.model_executor.layers.fused_moe import fused_experts
+        # AITER path
+        # TODO: Refactor this to use modular MOE kernel as well.
+        from vllm.model_executor.layers.fused_moe.experts.rocm_aiter_moe import (
+            rocm_aiter_fused_experts,
+        )
 
-            return fused_experts(
-                x,
-                layer.w13_weight,
-                layer.w2_weight,
-                topk_weights=topk_weights,
-                topk_ids=topk_ids,
-                inplace=not self.moe.disable_inplace,
-                activation=layer.activation,
-                global_num_experts=layer.global_num_experts,
-                apply_router_weight_on_input=layer.apply_router_weight_on_input,
-                expert_map=layer.expert_map,
-                quant_config=self.moe_quant_config,
-            )
+        return rocm_aiter_fused_experts(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            activation=layer.activation,
+            quant_config=self.moe_quant_config,
+            moe_config=layer.moe_config,
+            expert_map=layer.expert_map,
+        )
 
     def apply_monolithic(
         self,
         layer: FusedMoE,
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        input_ids: torch.Tensor | None = None,
     ) -> torch.Tensor:
         assert self.is_monolithic
         assert self.moe_kernel is not None
@@ -1481,135 +1471,3 @@ def apply_monolithic(
             expert_map=layer.expert_map,
             apply_router_weight_on_input=layer.apply_router_weight_on_input,
         )
-
-
-class QuarkOCP_MX_MoEMethod_OSS(QuarkOCP_MX_MoEMethod):
-    def __init__(
-        self,
-        weight_config: dict[str, Any],
-        input_config: dict[str, Any],
-        moe: FusedMoEConfig,
-    ):
-        super().__init__(weight_config, input_config, moe)
-
-    def process_weights_after_loading(self, layer):
-        from triton_kernels.matmul_ogs import FlexCtx, PrecisionConfig
-
-        w13_bias = layer.w13_bias.to(torch.float32)
-        w2_bias = layer.w2_bias.to(torch.float32)
-
-        layer.w13_bias = torch.nn.Parameter(w13_bias, requires_grad=False)
-        layer.w2_bias = torch.nn.Parameter(w2_bias, requires_grad=False)
-
-        # FIXME warp need to be adjusted based on batch size
-        # only apply to batched mode
-        if self.moe.use_ep:
-            num_warps = 4 if self.moe.max_num_tokens <= 512 else 8
-        else:
-            num_warps = 8
-
-        w13_weight, w13_flex, w13_scale = _swizzle_mxfp4(
-            layer.w13_weight, layer.w13_weight_scale, num_warps
-        )
-        w2_weight, w2_flex, w2_scale = _swizzle_mxfp4(
-            layer.w2_weight, layer.w2_weight_scale, num_warps
-        )
-
-        self.w13_weight_triton_tensor = w13_weight
-        self.w2_weight_triton_tensor = w2_weight
-
-        # need to delete the original weights to save memory on single GPU
-        del layer.w13_weight
-        del layer.w2_weight
-        layer.w13_weight = None
-        layer.w2_weight = None
-        torch.accelerator.empty_cache()
-
-        if self.static_input_scales:
-            if layer.w13_input_scale is None or layer.w2_input_scale is None:
-                raise ValueError(
-                    "QuantConfig has static quantization, but found "
-                    "activation scales are None."
-                )
-            if not all_close_1d(layer.w13_input_scale) or not all_close_1d(
-                layer.w2_input_scale
-            ):
-                logger.warning_once(
-                    "Found input_scales that are not equal for "
-                    "fp8 MoE layer. Using the maximum across experts "
-                    "for each layer."
-                )
-
-            layer.w13_input_scale = torch.nn.Parameter(
-                layer.w13_input_scale.max().to(torch.float32), requires_grad=False
-            )
-            layer.w2_input_scale = torch.nn.Parameter(
-                layer.w2_input_scale.max().to(torch.float32), requires_grad=False
-            )
-
-            from triton_kernels.numerics import InFlexData
-
-            lhs_data13 = InFlexData(scale=layer.w13_input_scale)
-            lhs_data2 = InFlexData(scale=layer.w2_input_scale)
-
-            self.w13_precision_config = PrecisionConfig(
-                weight_scale=w13_scale,
-                flex_ctx=FlexCtx(rhs_data=w13_flex, lhs_data=lhs_data13),
-            )
-
-            self.w2_precision_config = PrecisionConfig(
-                weight_scale=w2_scale,
-                flex_ctx=FlexCtx(rhs_data=w2_flex, lhs_data=lhs_data2),
-            )
-
-    def get_fused_moe_quant_config(
-        self, layer: torch.nn.Module
-    ) -> FusedMoEQuantConfig | None:
-        return mxfp4_w4a8_moe_quant_config(
-            w1_scale=self.w13_precision_config,
-            w2_scale=self.w2_precision_config,
-            a1_scale=layer.w13_input_scale,
-            a2_scale=layer.w2_input_scale,
-            w1_bias=layer.w13_bias,
-            w2_bias=layer.w2_bias,
-            block_shape=None,
-        )
-
-    @property
-    def is_monolithic(self) -> bool:
-        return True
-
-    def apply_monolithic(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        router_logits: torch.Tensor,
-        expert_map: torch.Tensor | None = None,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        if layer.enable_eplb:
-            raise NotImplementedError(
-                "EPLB not supported for `QuarkW4MXFp4MoEMethod_OSS` yet."
-            )
-
-        from vllm.model_executor.layers.fused_moe.experts.gpt_oss_triton_kernels_moe import (  # noqa: E501
-            triton_kernel_moe_forward,
-        )
-
-        assert self.moe.hidden_dim_unpadded is not None
-        assert self.moe.intermediate_size_per_partition_unpadded is not None
-        return triton_kernel_moe_forward(
-            hidden_states=x,
-            w1=self.w13_weight_triton_tensor,
-            w2=self.w2_weight_triton_tensor,
-            gating_output=router_logits,
-            topk=layer.top_k,
-            renormalize=layer.renormalize,
-            global_num_experts=layer.global_num_experts,
-            expert_map=expert_map,
-            quant_config=self.moe_quant_config,
-            apply_router_weight_on_input=layer.apply_router_weight_on_input,
-            unpadded_N_w1=self.moe.intermediate_size_per_partition_unpadded * 2,
-            unpadded_K_w1=self.moe.hidden_dim_unpadded,
-            unpadded_N_w2=self.moe.hidden_dim_unpadded,
-            unpadded_K_w2=self.moe.intermediate_size_per_partition_unpadded,
-        )
diff --git a/vllm/model_executor/layers/quantization/quark/schemes/quark_ocp_mx.py b/vllm/model_executor/layers/quantization/quark/schemes/quark_ocp_mx.py
index e96bc7c58d74..70a7e81cc455 100644
--- a/vllm/model_executor/layers/quantization/quark/schemes/quark_ocp_mx.py
+++ b/vllm/model_executor/layers/quantization/quark/schemes/quark_ocp_mx.py
@@ -96,21 +96,12 @@ def gemm_with_dynamic_quant(
                     x_q = x
                     x_s = x_scales
 
-                # 32 alignment is enough for dim0 padding of output for
-                # gemm_a4w4 kernel
-                y = torch.empty(
-                    (M + 31) // 32 * 32,
-                    weight.shape[0],
-                    device=x_q.device,
-                    dtype=out_dtype,
-                )
-
-                gemm_a4w4(
+                y = gemm_a4w4(
                     x_q,
                     weight.view(x_q.dtype),
                     x_s,
                     weight_scale.view(x_s.dtype),
-                    y,
+                    dtype=out_dtype,
                     bpreshuffle=True,
                 )
             return y[:M]
@@ -376,11 +367,15 @@ def apply_weights(
             dq_w = self.dequant_func(layer.weight, layer.weight_scale, x.dtype)
             qdq_x = self.quant_dequant_func(x)
             return F.linear(qdq_x, dq_w, bias)
-        else:
-            return torch.ops.vllm.gemm_with_dynamic_quant(
-                x,
-                layer.weight,
-                layer.weight_scale,
-                self.rocm_use_aiter_fp4_asm_gemm,
-                self.out_dtype,
-            )
+        y = torch.ops.vllm.gemm_with_dynamic_quant(
+            x,
+            layer.weight,
+            layer.weight_scale,
+            self.rocm_use_aiter_fp4_asm_gemm,
+            self.out_dtype,
+        )
+        # gemm_with_dynamic_quant has no bias argument; add it here so the
+        # native path matches F.linear (e.g. qkv_proj with qkv_bias=True).
+        if bias is not None:
+            y = y + bias
+        return y
diff --git a/vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_fp8.py b/vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_fp8.py
index 3312e6901d6d..6d94e26f960c 100644
--- a/vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_fp8.py
+++ b/vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_fp8.py
@@ -120,6 +120,8 @@ def process_weights_after_loading(self, layer) -> None:
         if self.is_static_input_scheme:
             layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False)
 
+        self.fp8_linear.process_weights_after_loading(layer)
+
     def create_weights(
         self,
         layer: torch.nn.Module,
diff --git a/vllm/model_executor/layers/quantization/turboquant/__init__.py b/vllm/model_executor/layers/quantization/turboquant/__init__.py
new file mode 100644
index 000000000000..333aef5f8256
--- /dev/null
+++ b/vllm/model_executor/layers/quantization/turboquant/__init__.py
@@ -0,0 +1,21 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""TurboQuant: KV-cache quantization for vLLM.
+
+Hadamard rotation + per-coordinate Lloyd-Max scalar quantization for
+keys, uniform quantization for values.
+
+The technique implemented here consists of the scalar case of the HIGGS
+quantization method (Malinovskii et al., "Pushing the Limits of Large
+Language Model Quantization via the Linearity Theorem", NAACL 2025;
+preprint arXiv:2411.17525): rotation + optimized grid + optional
+re-normalization, applied to KV cache compression. A first application
+of this approach to KV-cache compression is in "Cache Me If You Must:
+Adaptive Key-Value Quantization for Large Language Models" (Shutova
+et al., ICML 2025; preprint arXiv:2501.19392). Both these references
+pre-date the TurboQuant paper (Zandieh et al., ICLR 2026).
+"""
+
+from vllm.model_executor.layers.quantization.turboquant.config import TurboQuantConfig
+
+__all__ = ["TurboQuantConfig"]
diff --git a/vllm/model_executor/layers/quantization/turboquant/centroids.py b/vllm/model_executor/layers/quantization/turboquant/centroids.py
new file mode 100644
index 000000000000..490265747c5b
--- /dev/null
+++ b/vllm/model_executor/layers/quantization/turboquant/centroids.py
@@ -0,0 +1,86 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Lloyd-Max optimal scalar quantizer for TurboQuant.
+
+After rotating a d-dimensional unit vector by a random orthogonal matrix,
+each coordinate approximately follows N(0, 1/d) for d >= 64.
+We solve the Lloyd-Max conditions to find optimal centroids.
+
+Based on: turboquant-pytorch/lloyd_max.py (Zandieh et al.)
+"""
+
+import math
+from functools import lru_cache
+
+import torch
+
+
+def _gaussian_pdf(x: float, sigma2: float) -> float:
+    return (1.0 / math.sqrt(2 * math.pi * sigma2)) * math.exp(-x * x / (2 * sigma2))
+
+
+def _trapz(f, a: float, b: float, n: int = 200) -> float:
+    """Trapezoidal numerical integration (replaces scipy.integrate.quad)."""
+    h = (b - a) / n
+    result = 0.5 * (f(a) + f(b))
+    for i in range(1, n):
+        result += f(a + i * h)
+    return result * h
+
+
+def solve_lloyd_max(
+    d: int,
+    bits: int,
+    max_iter: int = 200,
+    tol: float = 1e-10,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Solve Lloyd-Max optimal quantizer for N(0, 1/d) distribution.
+
+    Args:
+        d: Vector dimension (determines variance = 1/d).
+        bits: Number of quantization bits.
+        max_iter: Maximum Lloyd-Max iterations.
+        tol: Convergence tolerance.
+
+    Returns:
+        centroids: Sorted tensor of 2^bits optimal centroids.
+        boundaries: Sorted tensor of 2^bits - 1 decision boundaries.
+    """
+    n_levels = 2**bits
+    sigma2 = 1.0 / d
+    sigma = math.sqrt(sigma2)
+
+    def pdf(x):
+        return _gaussian_pdf(x, sigma2)
+
+    lo, hi = -3.5 * sigma, 3.5 * sigma
+    centroids = [lo + (hi - lo) * (i + 0.5) / n_levels for i in range(n_levels)]
+
+    for _ in range(max_iter):
+        boundaries = [
+            (centroids[i] + centroids[i + 1]) / 2.0 for i in range(n_levels - 1)
+        ]
+        edges = [lo * 3] + boundaries + [hi * 3]
+        new_centroids = []
+        for i in range(n_levels):
+            a, b = edges[i], edges[i + 1]
+            num = _trapz(lambda x: x * pdf(x), a, b)
+            den = _trapz(pdf, a, b)
+            new_centroids.append(num / den if den > 1e-15 else centroids[i])
+
+        if max(abs(new_centroids[i] - centroids[i]) for i in range(n_levels)) < tol:
+            break
+        centroids = new_centroids
+
+    boundaries = [(centroids[i] + centroids[i + 1]) / 2.0 for i in range(n_levels - 1)]
+    return (
+        torch.tensor(centroids, dtype=torch.float32),
+        torch.tensor(boundaries, dtype=torch.float32),
+    )
+
+
+@lru_cache(maxsize=32)
+def get_centroids(d: int, bits: int) -> torch.Tensor:
+    """Get precomputed Lloyd-Max centroids (cached)."""
+    centroids, _ = solve_lloyd_max(d, bits)
+    return centroids
diff --git a/vllm/model_executor/layers/quantization/turboquant/config.py b/vllm/model_executor/layers/quantization/turboquant/config.py
new file mode 100644
index 000000000000..50beb8d1d9bf
--- /dev/null
+++ b/vllm/model_executor/layers/quantization/turboquant/config.py
@@ -0,0 +1,254 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""TurboQuant configuration."""
+
+from __future__ import annotations
+
+import logging
+import math
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from vllm.config import ModelConfig
+
+logger = logging.getLogger(__name__)
+
+# Named TQ presets: each maps to frozen config parameters.
+# key_quant_bits: 8 = FP8 keys, 3-4 = MSE (Lloyd-Max) quantized keys.
+# value_quant_bits: 3-4 = uniform quantized values.
+TQ_PRESETS: dict[str, dict] = {
+    "turboquant_k8v4": {
+        "key_quant_bits": 8,
+        "value_quant_bits": 4,
+        "norm_correction": False,
+    },
+    "turboquant_4bit_nc": {
+        "key_quant_bits": 4,
+        "value_quant_bits": 4,
+        "norm_correction": True,
+    },
+    "turboquant_k3v4_nc": {
+        "key_quant_bits": 3,
+        "value_quant_bits": 4,
+        "norm_correction": True,
+    },
+    "turboquant_3bit_nc": {
+        "key_quant_bits": 3,
+        "value_quant_bits": 3,
+        "norm_correction": True,
+    },
+}
+
+
+@dataclass
+class TurboQuantConfig:
+    """Configuration for TurboQuant KV-cache quantization.
+
+    Applies Hadamard rotation followed by per-coordinate Lloyd-Max scalar
+    quantization for keys, and uniform quantization for values.
+
+    Historical note: this is the scalar case of the HIGGS quantization
+    method (Malinovskii et al., "Pushing the Limits of Large Language Model
+    Quantization via the Linearity Theorem", NAACL 2025; preprint
+    arXiv:2411.17525): rotation + optimized grid + optional re-normalization,
+    applied to KV cache compression. A first application of this approach to
+    KV-cache compression is in "Cache Me If You Must: Adaptive Key-Value
+    Quantization for Large Language Models" (Shutova et al., ICML 2025;
+    preprint arXiv:2501.19392). Both these references pre-date the
+    TurboQuant paper.
+
+    QJL is intentionally omitted — community consensus (5+ independent
+    groups) found it hurts attention quality by amplifying variance through
+    softmax.
+
+    Named presets (use via --kv-cache-dtype):
+        turboquant_k8v4:   FP8 keys + 4-bit values, 2.6x, +1.17% PPL
+        turboquant_4bit_nc: 4-bit MSE keys + 4-bit values + NC, 3.8x, +2.71%
+        turboquant_k3v4_nc: 3-bit MSE keys + 4-bit values + NC, ~3.5x, +10.63%
+        turboquant_3bit_nc: 3-bit MSE keys + 3-bit values + NC, 4.9x, +20.59%
+
+    Args:
+        head_dim: Attention head dimension (e.g. 64, 96, 128).
+        key_quant_bits: Bits for key quantization. 8 = FP8 keys (no
+            rotation/MSE). 3-4 = Lloyd-Max MSE quantized keys.
+        value_quant_bits: Bits per value dimension for uniform quantization.
+            3 = 8 levels, 4 = 16 levels (default).
+        norm_correction: Re-normalize centroid vectors to unit norm before
+            inverse rotation during dequant. Fixes quantization-induced norm
+            distortion, improving PPL by ~0.8% at 4-bit.
+    """
+
+    head_dim: int = 128
+    key_quant_bits: int = 3  # 3-4 = MSE keys, 8 = FP8 keys
+    value_quant_bits: int = 4  # 3-4 = uniform quantized values
+    seed: int = 42  # kept for backward compatibility; no longer used internally
+    norm_correction: bool = False
+
+    @property
+    def key_fp8(self) -> bool:
+        """Whether keys are stored as FP8 — no rotation/quantization needed."""
+        return self.key_quant_bits == 8
+
+    @property
+    def mse_bits(self) -> int:
+        """MSE quantizer bit-width (determines centroid count: 2^mse_bits).
+
+        For MSE key modes, equals key_quant_bits.
+        For FP8 key mode, falls back to value_quant_bits (centroids are still
+        needed for continuation-prefill dequant and decode kernel params).
+        """
+        if self.key_fp8:
+            return self.value_quant_bits
+        return self.key_quant_bits
+
+    @property
+    def key_mse_bits(self) -> int:
+        """MSE bits actually used for key quantization (0 if FP8 keys)."""
+        if self.key_fp8:
+            return 0
+        return self.key_quant_bits
+
+    @property
+    def centroid_bits(self) -> int:
+        """Bits for centroid generation — always non-zero."""
+        return self.mse_bits
+
+    @property
+    def n_centroids(self) -> int:
+        return 2**self.mse_bits
+
+    @property
+    def key_packed_size(self) -> int:
+        """Packed bytes for a single KEY vector.
+
+        FP8 mode (key_quant_bits=8):
+          head_dim bytes (1 byte per element, no overhead).
+
+        TQ mode:
+          - MSE indices: ceil(head_dim * key_mse_bits / 8) bytes
+          - vec_norm:     2 bytes (float16)
+        """
+        if self.key_fp8:
+            return self.head_dim  # 1 byte per element
+        mse_bytes = math.ceil(self.head_dim * self.key_mse_bits / 8)
+        norm_bytes = 2  # vec_norm fp16
+        return mse_bytes + norm_bytes
+
+    @property
+    def effective_value_quant_bits(self) -> int:
+        """Actual bits used for value storage."""
+        return self.value_quant_bits
+
+    @property
+    def value_packed_size(self) -> int:
+        """Packed bytes for a single VALUE vector.
+
+        Uniform quantization: ceil(head_dim * bits / 8) + 4 bytes (scale + zero fp16).
+        """
+        data_bytes = math.ceil(self.head_dim * self.value_quant_bits / 8)
+        return data_bytes + 4  # +2 scale(fp16) +2 zero(fp16)
+
+    @property
+    def slot_size(self) -> int:
+        """Total packed bytes per head per position (key + value combined).
+
+        Layout: [key_packed | value_packed]
+        """
+        return self.key_packed_size + self.value_packed_size
+
+    @property
+    def slot_size_aligned(self) -> int:
+        """Slot size rounded up to next even number.
+
+        Even-number is required so effective_head_size = slot_size_aligned // 2
+        is integral.
+        """
+        s = self.slot_size
+        return s + (s % 2)  # round up to even
+
+    @staticmethod
+    def get_boundary_skip_layers(
+        model_config: ModelConfig,
+        n: int = 2,
+    ) -> list[str]:
+        """Layer indices to skip TQ compression (boundary protection).
+
+        For hybrid models (attention + Mamba/linear-attention), boundary
+        protection is disabled — hybrids typically have only 8-12
+        full-attention layers and a hard n=2 on each side would cover
+        ~40 % of them.  The dense GSM8K baselines that motivate n=2
+        don't apply to hybrids.
+
+        For dense models, skips first N and last N attention layers.
+        Empirically required for aggressive presets (k3v4_nc, 3bit_nc)
+        — without it GSM8K drops ~30 points on Qwen3-4B.
+        """
+        if model_config.is_hybrid:
+            attn_indices = _get_full_attention_layer_indices(model_config)
+            if not attn_indices:
+                raise NotImplementedError(
+                    "TurboQuant KV cache requires identifiable "
+                    "full-attention layers, but none were found in "
+                    "the hybrid model config."
+                )
+            logger.info("TQ hybrid: full-attention layers %s", attn_indices)
+            return []
+
+        num_layers = model_config.hf_text_config.num_hidden_layers
+        if n <= 0 or num_layers <= 0:
+            return []
+        n = min(n, num_layers // 2)  # don't skip more than half
+        first = list(range(n))
+        last = list(range(num_layers - n, num_layers))
+        # Deduplicate (if num_layers <= 2*n)
+        indices = sorted(set(first + last))
+        return [str(i) for i in indices]
+
+    @staticmethod
+    def from_cache_dtype(cache_dtype: str, head_dim: int) -> TurboQuantConfig:
+        """Create config from a named preset.
+
+        Valid presets: turboquant_k8v4, turboquant_4bit_nc, etc.
+        """
+        if cache_dtype not in TQ_PRESETS:
+            valid = ", ".join(TQ_PRESETS.keys())
+            raise ValueError(
+                f"Unknown TurboQuant cache dtype: {cache_dtype!r}. "
+                f"Valid presets: {valid}"
+            )
+        preset = TQ_PRESETS[cache_dtype]
+        return TurboQuantConfig(
+            head_dim=head_dim,
+            key_quant_bits=preset["key_quant_bits"],
+            value_quant_bits=preset["value_quant_bits"],
+            norm_correction=preset["norm_correction"],
+        )
+
+
+def _get_full_attention_layer_indices(model_config: ModelConfig) -> list[int]:
+    """Global indices of full-attention layers in a hybrid model.
+
+    Covers the conventions used across vLLM: ``layer_types`` (Qwen3.5/Next),
+    ``layers_block_type`` (Jamba/Zamba2), ``attn_type_list`` (Minimax).
+    """
+    text_cfg = model_config.hf_text_config
+    hf_cfg = model_config.hf_config
+
+    layer_types = getattr(text_cfg, "layer_types", None)
+    if layer_types is not None:
+        return [
+            i for i, t in enumerate(layer_types) if t in ("full_attention", "attention")
+        ]
+
+    layers_block_type = getattr(text_cfg, "layers_block_type", None)
+    if layers_block_type is not None:
+        return [
+            i for i, t in enumerate(layers_block_type) if t in ("attention", "hybrid")
+        ]
+
+    attn_type_list = getattr(hf_cfg, "attn_type_list", None)
+    if attn_type_list is not None:
+        return [i for i, t in enumerate(attn_type_list) if t == 1]
+
+    return []
diff --git a/vllm/model_executor/layers/quantization/turboquant/quantizer.py b/vllm/model_executor/layers/quantization/turboquant/quantizer.py
new file mode 100644
index 000000000000..82a0c3391ce8
--- /dev/null
+++ b/vllm/model_executor/layers/quantization/turboquant/quantizer.py
@@ -0,0 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""TurboQuant quantizer utilities.
+
+Triton kernels handle all quantization, packing, and dequantization on GPU.
+"""
diff --git a/vllm/model_executor/layers/quantization/utils/flashinfer_utils.py b/vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
index 32c7a772f3fd..973f759698f0 100644
--- a/vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
+++ b/vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
@@ -111,7 +111,6 @@ def get_flashinfer_moe_backend() -> FlashinferMoeBackend:
             logger.info_once(
                 "Flashinfer TRTLLM MOE backend is only supported on "
                 "SM100 and later, using CUTLASS backend instead",
-                scope="local",
             )
             return FlashinferMoeBackend.CUTLASS
         return backend_map[flashinfer_moe_backend]
@@ -239,7 +238,6 @@ def align_fp4_moe_weights_for_fi(
         "Padding intermediate size from %d to %d for up/down projection weights.",
         intermediate,
         padded_intermediate,
-        scope="local",
     )
 
     up_mult = 2 if is_act_and_mul else 1
@@ -285,7 +283,6 @@ def align_trtllm_fp4_moe_hidden_dim_for_fi(
         "performance degradation.",
         hidden_size,
         padded_hidden_size,
-        scope="local",
     )
 
     padded_w13 = w13.new_zeros((num_experts, gate_up_dim, padded_hidden_size // 2))
@@ -331,7 +328,6 @@ def align_fp8_moe_weights_for_fi(
         "Padding intermediate size from %d to %d for up/down projection weights.",
         intermediate,
         padded_intermediate,
-        scope="local",
     )
 
     up_mult = 2 if is_act_and_mul else 1
diff --git a/vllm/model_executor/layers/quantization/utils/fp8_utils.py b/vllm/model_executor/layers/quantization/utils/fp8_utils.py
index 19fdb1ec884d..d9aab35c25f4 100644
--- a/vllm/model_executor/layers/quantization/utils/fp8_utils.py
+++ b/vllm/model_executor/layers/quantization/utils/fp8_utils.py
@@ -149,6 +149,148 @@ def _per_token_group_quant_fp8(
     tl.store(y_s_ptr, y_s)
 
 
+@triton.jit
+def _silu_mul_quant_fp8_packed_kernel(
+    input_ptr,
+    output_q_ptr,
+    output_scale_ptr,
+    M,
+    input_stride_m,
+    output_q_stride_m,
+    output_scale_stride_k,
+    clamp_limit,
+    N: tl.constexpr,
+    NUM_GROUPS: tl.constexpr,
+    fp8_min: tl.constexpr,
+    fp8_max: tl.constexpr,
+    GROUP_SIZE: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    HAS_CLAMP: tl.constexpr,
+):
+    N_2: tl.constexpr = N // 2
+
+    pid_pack = tl.program_id(0)
+    pid_m = tl.program_id(1)
+    m_offset = pid_m * BLOCK_M
+
+    if m_offset >= M:
+        return
+
+    offs_m = tl.arange(0, BLOCK_M)
+    offs_n = tl.arange(0, GROUP_SIZE)
+    row_mask = (m_offset + offs_m) < M
+
+    base_row_offset = (m_offset + offs_m[:, None]) * input_stride_m
+    base_out_offset = (m_offset + offs_m[:, None]) * output_q_stride_m
+
+    packed_scale = tl.zeros((BLOCK_M,), dtype=tl.int32)
+
+    for pack_idx in tl.static_range(4):
+        group_id = pid_pack * 4 + pack_idx
+
+        if group_id < NUM_GROUPS:
+            n_offset = group_id * GROUP_SIZE
+
+            act_ptrs = input_ptr + base_row_offset + n_offset + offs_n[None, :]
+            act_in = tl.load(act_ptrs, mask=row_mask[:, None], other=0.0)
+
+            mul_ptrs = act_ptrs + N_2
+            mul_in = tl.load(mul_ptrs, mask=row_mask[:, None], other=0.0)
+
+            act_f32 = act_in.to(tl.float32)
+            mul_f32 = mul_in.to(tl.float32)
+
+            if HAS_CLAMP:
+                act_f32 = tl.minimum(act_f32, clamp_limit)
+                mul_f32 = tl.clamp(mul_f32, -clamp_limit, clamp_limit)
+
+            y = (act_f32 / (1.0 + tl.exp(-act_f32))) * mul_f32
+            # Round through bf16 to match unfused precision path
+            y = y.to(tl.bfloat16).to(tl.float32)
+
+            absmax = tl.max(tl.abs(y), axis=1)
+
+            scale_raw = tl.maximum(absmax / fp8_max, 1e-10)
+            exponent = tl.ceil(tl.log2(scale_raw))
+            scale = tl.math.exp2(exponent)
+
+            y_q = tl.clamp(y / scale[:, None], fp8_min, fp8_max)
+
+            out_q_ptrs = output_q_ptr + base_out_offset + n_offset + offs_n[None, :]
+            tl.store(
+                out_q_ptrs,
+                y_q.to(output_q_ptr.dtype.element_ty),
+                mask=row_mask[:, None],
+            )
+
+            exponent_biased = tl.clamp(exponent + 127.0, 0.0, 255.0).to(tl.int32)
+            packed_scale = packed_scale | (exponent_biased << (pack_idx * 8))
+
+    scale_ptrs = output_scale_ptr + pid_pack * output_scale_stride_k + m_offset + offs_m
+    tl.store(scale_ptrs, packed_scale, mask=row_mask)
+
+
+def silu_mul_quant_fp8_packed_triton(
+    input: torch.Tensor,
+    group_size: int = 128,
+    output_q: torch.Tensor | None = None,
+    clamp_limit: float | None = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    assert input.dim() == 2
+    assert input.is_contiguous()
+
+    M, N = input.shape
+    N_2 = N // 2
+
+    assert N_2 % group_size == 0
+
+    fp8_dtype = torch.float8_e4m3fn
+    finfo = torch.finfo(fp8_dtype)
+    fp8_min, fp8_max = finfo.min, finfo.max
+
+    num_groups_per_row = N_2 // group_size
+    num_packed_groups = (num_groups_per_row + 3) // 4
+    tma_aligned_M = ((M + 3) // 4) * 4
+
+    if output_q is None:
+        output_q = torch.empty((M, N_2), dtype=fp8_dtype, device=input.device)
+
+    output_scale_packed = torch.zeros(
+        (num_packed_groups, tma_aligned_M),
+        dtype=torch.int32,
+        device=input.device,
+    ).T[:M, :]
+
+    BLOCK_M = 8
+    grid = (num_packed_groups, (M + BLOCK_M - 1) // BLOCK_M)
+
+    num_warps = max(4, group_size // 32)
+    num_stages = 2
+
+    has_clamp = clamp_limit is not None
+    _silu_mul_quant_fp8_packed_kernel[grid](
+        input,
+        output_q,
+        output_scale_packed,
+        M,
+        input.stride(0),
+        output_q.stride(0),
+        output_scale_packed.stride(1),
+        clamp_limit if has_clamp else 0.0,
+        N=N,
+        NUM_GROUPS=num_groups_per_row,
+        fp8_min=fp8_min,
+        fp8_max=fp8_max,
+        GROUP_SIZE=group_size,
+        BLOCK_M=BLOCK_M,
+        HAS_CLAMP=has_clamp,
+        num_warps=num_warps,
+        num_stages=num_stages,
+    )
+
+    return output_q, output_scale_packed
+
+
 @triton.jit
 def _silu_mul_per_token_group_quant_fp8_colmajor(
     y_ptr,  # [M, N]
@@ -701,6 +843,15 @@ def w8a8_triton_block_scaled_mm(
     assert len(block_size) == 2
     block_n, block_k = block_size[0], block_size[1]
 
+    # Triton cannot currently bind E8M0 scale tensors directly. On ROCm,
+    # DeepSeek-V4 checkpoints store block scales in exponent-only E8M0 format,
+    # so decode them to fp32 before launching the kernel.
+    if current_platform.is_rocm():
+        if As.dtype == torch.float8_e8m0fnu:
+            As = _upcast_e8m0_to_fp32(As).contiguous()
+        if Bs.dtype == torch.float8_e8m0fnu:
+            Bs = _upcast_e8m0_to_fp32(Bs).contiguous()
+
     assert A.shape[-1] == B.shape[-1]
     assert A.shape[:-1] == As.shape[:-1] and A.is_contiguous()
     assert triton.cdiv(A.shape[-1], block_k) == As.shape[-1]
@@ -823,19 +974,65 @@ def requant_weight_ue8m0_inplace(
         s_old.copy_(s_requant)
 
 
+def _upcast_e8m0_to_fp32(scale: torch.Tensor) -> torch.Tensor:
+    """Upcast E8M0 (exponent-only) scale to float32.
+
+    E8M0 stores only the 8-bit biased exponent (bias=127). To convert
+    to float32 we place those 8 bits into the exponent field of an
+    IEEE-754 float32 (bits 23-30) with sign=0 and mantissa=0.
+    """
+    exp_bits = scale.view(torch.uint8).to(torch.int32)
+    fp32_bits = exp_bits << 23
+    return fp32_bits.view(torch.float32)
+
+
 def deepgemm_post_process_fp8_weight_block(
-    wq: torch.Tensor, ws: torch.Tensor, quant_block_shape: tuple[int], use_e8m0: bool
+    wq: torch.Tensor,
+    ws: torch.Tensor,
+    quant_block_shape: tuple[int, ...],
+    use_e8m0: bool,
+    is_bmm: bool = False,
+    bmm_batch_size: int = 0,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     assert wq.dtype == torch.float8_e4m3fn, (
         "Expected quantized tensor dtype "
         f"to be torch.float8_e4m3fn, got {wq.dtype} instead."
     )
-    assert ws.dtype == torch.float32, (
-        f"Expected tensor scales dtype to be torch.float32, got {ws.dtype} instead"
-    )
 
-    if use_e8m0:
-        requant_weight_ue8m0_inplace(wq, ws, block_size=quant_block_shape)
+    if ws.dtype == torch.float8_e8m0fnu:
+        # Scales already in E8M0 from checkpoint — upcast to fp32
+        # and skip requantization (weights already have power-of-two scales).
+        ws = _upcast_e8m0_to_fp32(ws)
+    else:
+        assert ws.dtype == torch.float32, (
+            f"Expected tensor scales dtype to be torch.float32 or "
+            f"torch.float8_e8m0fnu, got {ws.dtype} instead"
+        )
+        if use_e8m0:
+            requant_weight_ue8m0_inplace(wq, ws, block_size=quant_block_shape)
+
+    if is_bmm:
+        # Reshape 2D weight/scale to 3D for grouped BMM (einsum):
+        # wq: (g*r, d) -> (g, r, d)
+        # ws: (g*r/128, d/128) -> (g, r/128, d/128)
+        g = bmm_batch_size
+        assert wq.ndim == 2 and ws.ndim == 2
+        d = wq.size(1)
+        r = wq.size(0) // g
+        wq = wq.view(g, r, d)
+        ws = ws.view(g, r // quant_block_shape[0], d // quant_block_shape[1])
+        # Pre-transform scale with recipe=(1, 128, 128) to broadcast + pack
+        # into TMA-aligned UE8M0 (INT32) layout. At runtime fp8_einsum uses
+        # recipe=(1, 1, 128) which sees INT dtype and skips re-transform.
+        dg_ws = transform_sf_into_required_layout(
+            sf=ws,
+            mn=r,
+            k=d,
+            recipe=(1, quant_block_shape[0], quant_block_shape[1]),
+            num_groups=g,
+            is_sfa=False,
+        )
+        return wq, dg_ws
 
     original_ndim = wq.ndim
     if wq.ndim == 2:
@@ -984,11 +1181,13 @@ def create_fp8_scale_parameter(
     input_size_per_partition: int,
     block_size: list[int] | None,
     weight_loader: Callable | None,
+    scale_dtype: torch.dtype | None = None,
 ) -> torch.nn.Parameter:
     """Create scale parameter based on quantization strategy."""
+    dtype = scale_dtype if scale_dtype is not None else torch.float32
     if parameter_type == ChannelQuantScaleParameter:
         scale = parameter_type(
-            data=torch.empty((sum(output_partition_sizes), 1), dtype=torch.float32),
+            data=torch.empty((sum(output_partition_sizes), 1), dtype=dtype),
             output_dim=0,
             weight_loader=weight_loader,
         )
@@ -1000,7 +1199,7 @@ def create_fp8_scale_parameter(
             data=torch.empty(
                 (output_size_per_partition + block_n - 1) // block_n,
                 (input_size_per_partition + block_k - 1) // block_k,
-                dtype=torch.float32,
+                dtype=dtype,
             ),
             input_dim=1,
             output_dim=0,
@@ -1008,13 +1207,14 @@ def create_fp8_scale_parameter(
         )
     elif parameter_type == PerTensorScaleParameter:
         scale = parameter_type(
-            data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
+            data=torch.empty(len(output_partition_sizes), dtype=dtype),
             weight_loader=weight_loader,
         )
     else:
         raise ValueError(f"Unknown parameter type: {parameter_type}")
 
-    scale[:] = torch.finfo(torch.float32).min
+    if dtype == torch.float32:
+        scale[:] = torch.finfo(torch.float32).min
     set_weight_attrs(scale, {"scale_type": "weight_scale"})
     return scale
 
diff --git a/vllm/model_executor/layers/quantization/utils/humming_utils.py b/vllm/model_executor/layers/quantization/utils/humming_utils.py
new file mode 100644
index 000000000000..f8c10bdcae16
--- /dev/null
+++ b/vllm/model_executor/layers/quantization/utils/humming_utils.py
@@ -0,0 +1,214 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+import regex as re
+import torch
+from humming.layer import HummingInputSchema, HummingMethod
+from humming.schema import BaseWeightSchema
+
+from vllm import envs
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEQuantConfig,
+    FusedMoEQuantDesc,
+)
+from vllm.model_executor.layers.fused_moe.layer import FusedMoE
+from vllm.model_executor.layers.linear import LinearBase
+from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
+
+
+def humming_is_layer_skipped(config: dict[str, Any], prefix: str):
+    if not config:
+        return True
+
+    keys = ["ignored_layers", "ignore", "modules_to_not_convert"]
+    ignored_layers: list[str] = []
+    for key in keys:
+        ignored_layers = config.get(key, []) or []
+        if not ignored_layers:
+            break
+
+    if any(module_name in prefix for module_name in ignored_layers):
+        return True
+    if "lm_head" in prefix:
+        return True
+
+    for regex in config.get("dynamic", {}):
+        if regex[:1] != "-":
+            continue
+        if re.match(regex[2:], prefix):
+            return True
+
+    return False
+
+
+def prepare_humming_layer(layer: LinearBase, quant_config: dict):
+    weight_schema = BaseWeightSchema.from_config(quant_config)
+    input_schema = HummingInputSchema()
+
+    shape_k_stacks = [layer.input_size_per_partition]
+    shape_n_stacks = layer.output_partition_sizes
+
+    # Step 1: convert weight to humming standard format
+    weight_schema, tensors = weight_schema.convert_humming(
+        tensors=layer.named_parameters(),
+        shape_n_stacks=shape_n_stacks,
+        shape_k_stacks=shape_k_stacks,
+        param_dtype=layer.params_dtype,
+    )
+
+    layer.weight_schema = weight_schema
+
+    for name, _ in list(layer.named_parameters()):
+        delattr(layer, name)
+
+    for name, tensor in tensors.items():
+        param = torch.nn.Parameter(tensor, requires_grad=False)
+        setattr(layer, name, param)
+
+    # Step 2: transform weight (humming standard format) for forwarding
+    HummingMethod.prepare_layer_meta(
+        layer=layer,
+        shape_n=layer.output_partition_sizes_sum,
+        shape_k=layer.input_size_per_partition,
+        weight_schema=weight_schema,
+        input_schema=input_schema,
+        pad_n_to_multiple=256,
+        pad_k_to_multiple=128,
+        has_bias=layer.has_bias,
+        torch_dtype=layer.param_dtype,
+    )
+
+    HummingMethod.transform_humming_layer(layer)
+
+
+def prepare_humming_moe_layer(layer: FusedMoE, quant_config: dict):
+    weight_schema = BaseWeightSchema.from_config(quant_config)
+    input_quant_config = envs.VLLM_HUMMING_INPUT_QUANT_CONFIG or {}
+    if humming_is_layer_skipped(input_quant_config, layer.layer_name):
+        input_schema = HummingInputSchema()
+    else:
+        # TODO: read input_quant_config from quant_config
+        input_schema = HummingInputSchema.from_config(input_quant_config)
+
+    is_gated = layer.activation.is_gated
+    shape_config = {
+        "w13": (
+            layer.moe_config.intermediate_size_per_partition * 2,
+            layer.moe_config.hidden_dim,
+        ),
+        "w2": (
+            layer.moe_config.hidden_dim,
+            layer.moe_config.intermediate_size_per_partition * (1 if is_gated else 2),
+        ),
+    }
+
+    layer.weight_schemas = {}
+    layer.input_schemas = {}
+
+    for sublayer_name in shape_config:
+        # Step 1: convert weight to humming standard format
+        tensors: dict[str, torch.Tensor] = dict(
+            (key.removeprefix(sublayer_name + "_"), value)
+            for key, value in layer.state_dict().items()
+            if key.startswith(sublayer_name + "_")
+        )
+
+        shape_n, shape_k = shape_config[sublayer_name]
+        shape_n_stacks = [shape_n]
+        shape_k_stacks = [shape_k]
+        if sublayer_name == "w13":
+            shape_n_stacks = [shape_n // 2] * 2
+
+        weight_schema_new, tensors = weight_schema.convert_humming(
+            tensors=tensors,
+            shape_n_stacks=shape_n_stacks,
+            shape_k_stacks=shape_k_stacks,
+            num_experts=layer.local_num_experts,
+            param_dtype=layer.params_dtype,
+        )
+
+        layer.weight_schemas[sublayer_name] = weight_schema_new
+        layer.input_schemas[sublayer_name] = input_schema
+
+        for name, _ in list(layer.named_parameters()):
+            if not name.startswith(sublayer_name + "_"):
+                continue
+            delattr(layer, name)
+
+        for name, tensor in tensors.items():
+            name = f"{sublayer_name}_{name}"
+            param = torch.nn.Parameter(tensor, requires_grad=False)
+            setattr(layer, name, param)
+
+        # Step 2: transform weight (humming standard format) for forwarding
+        HummingMethod.prepare_layer_meta(
+            layer=layer,
+            shape_n=shape_n,
+            shape_k=shape_k,
+            pad_n_to_multiple=256,
+            pad_k_to_multiple=128,
+            input_schema=input_schema,
+            weight_schema=weight_schema_new,
+            has_bias=layer.moe_config.has_bias,
+            num_experts=layer.num_experts,
+            torch_dtype=layer.params_dtype,
+            sublayer_name=sublayer_name,
+        )
+
+        HummingMethod.transform_humming_layer(layer, sublayer_name=sublayer_name)
+
+    if not hasattr(layer, "locks"):
+        device = layer.w13_weight.device
+        locks = torch.zeros(1024, dtype=torch.int32, device=device)
+        layer.register_buffer("locks", locks)
+
+
+def get_humming_moe_quant_config(layer: FusedMoE):
+    input_schema = layer.input_schemas["w13"]
+    weight_schema = layer.weight_schemas["w13"]
+
+    a_dtype = input_schema.a_dtype
+    if a_dtype is None or a_dtype.num_bits == 16:
+        a_quant_desc = FusedMoEQuantDesc(dtype=None)
+    else:
+        shape = GroupShape(row=1, col=-1)
+        a_quant_desc = FusedMoEQuantDesc(dtype=str(a_dtype), shape=shape)
+
+    weight_scale_group_size = weight_schema.weight_scale_group_size
+    weight_scale_group_size_n = weight_schema.weight_scale_group_size_n
+    weight_group_shape: tuple[int, ...] = ()
+    if weight_scale_group_size_n > 1:
+        weight_group_shape = GroupShape(
+            row=weight_scale_group_size,
+            col=weight_scale_group_size_n,
+        )
+    elif weight_scale_group_size == 0:
+        weight_group_shape = GroupShape(row=-1, col=1)
+    else:
+        weight_group_shape = GroupShape(row=weight_scale_group_size, col=1)
+
+    w1_quant_desc = FusedMoEQuantDesc(
+        dtype=str(weight_schema.b_dtype),
+        shape=weight_group_shape,
+        scale=getattr(layer, "w13_weight_scale", None),
+        alpha_or_gscale=getattr(layer, "w13_global_scale", None),
+        zp=getattr(layer, "w13_zero_point", None),
+        bias=getattr(layer, "w13_bias", None),
+    )
+
+    w2_quant_desc = FusedMoEQuantDesc(
+        dtype=str(weight_schema.b_dtype),
+        shape=weight_group_shape,
+        scale=getattr(layer, "w2_weight_scale", None),
+        alpha_or_gscale=getattr(layer, "w2_global_scale", None),
+        zp=getattr(layer, "w2_zero_point", None),
+        bias=getattr(layer, "w2_bias", None),
+    )
+
+    return FusedMoEQuantConfig(
+        _a1=a_quant_desc,
+        _a2=a_quant_desc,
+        _w1=w1_quant_desc,
+        _w2=w2_quant_desc,
+    )
diff --git a/vllm/model_executor/layers/quantization/utils/mxfp4_utils.py b/vllm/model_executor/layers/quantization/utils/mxfp4_utils.py
index 21c8aba1d56c..51b7b29551d0 100644
--- a/vllm/model_executor/layers/quantization/utils/mxfp4_utils.py
+++ b/vllm/model_executor/layers/quantization/utils/mxfp4_utils.py
@@ -162,3 +162,7 @@ def _quant_dequant_mxfp4_fake(
     quant_dequant_mxfp4 = torch.ops.vllm.quant_dequant_mxfp4
 except AttributeError as error:
     raise error
+
+
+def xpu_mxfp4_quantize(x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    return torch.ops.vllm.xpu_mxfp4_quantize(x)
diff --git a/vllm/model_executor/layers/quantization/utils/mxfp8_utils.py b/vllm/model_executor/layers/quantization/utils/mxfp8_utils.py
index b9b7bd542738..a12918225348 100644
--- a/vllm/model_executor/layers/quantization/utils/mxfp8_utils.py
+++ b/vllm/model_executor/layers/quantization/utils/mxfp8_utils.py
@@ -85,7 +85,9 @@ def _mxfp8_e4m3_quantize_torch(
 
 
 def _mxfp8_e4m3_quantize_impl(
-    x: torch.Tensor, is_sf_swizzled_layout: bool = False
+    x: torch.Tensor,
+    is_sf_swizzled_layout: bool = False,
+    alignment: int = 0,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     from vllm.platforms import current_platform
 
@@ -93,7 +95,9 @@ def _mxfp8_e4m3_quantize_impl(
         from flashinfer import mxfp8_quantize as flashinfer_mxfp8_quantize
 
         x_q, x_scales = flashinfer_mxfp8_quantize(
-            x, is_sf_swizzled_layout=is_sf_swizzled_layout
+            x,
+            is_sf_swizzled_layout=is_sf_swizzled_layout,
+            alignment=alignment if alignment > 0 else 32,
         )
         if x_scales.ndim == 1 and x.ndim == 2 and not is_sf_swizzled_layout:
             x_scales = x_scales.view(x.size(0), -1)
@@ -103,9 +107,11 @@ def _mxfp8_e4m3_quantize_impl(
 
 
 def mxfp8_e4m3_quantize(
-    x: torch.Tensor, is_sf_swizzled_layout: bool = False
+    x: torch.Tensor,
+    is_sf_swizzled_layout: bool = False,
+    alignment: int = 0,
 ) -> tuple[torch.Tensor, torch.Tensor]:
-    return torch.ops.vllm.mxfp8_quantize(x, is_sf_swizzled_layout)
+    return torch.ops.vllm.mxfp8_quantize(x, is_sf_swizzled_layout, alignment)
 
 
 def dequant_mxfp8_to_bf16(x: torch.Tensor, scales: torch.Tensor) -> torch.Tensor:
@@ -125,7 +131,9 @@ def dequant_mxfp8_to_bf16(x: torch.Tensor, scales: torch.Tensor) -> torch.Tensor
 
 
 def mxfp8_e4m3_quantize_fake(
-    x: torch.Tensor, is_sf_swizzled_layout: bool = False
+    x: torch.Tensor,
+    is_sf_swizzled_layout: bool = False,
+    alignment: int = 0,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     """Fake implementation for torch.compile tracing."""
     fp_data = torch.empty_like(x, dtype=MXFP8_VALUE_DTYPE)
diff --git a/vllm/model_executor/layers/quantization/utils/nvfp4_emulation_utils.py b/vllm/model_executor/layers/quantization/utils/nvfp4_emulation_utils.py
index 9a0c52b62c10..39c78a9062be 100644
--- a/vllm/model_executor/layers/quantization/utils/nvfp4_emulation_utils.py
+++ b/vllm/model_executor/layers/quantization/utils/nvfp4_emulation_utils.py
@@ -4,7 +4,9 @@
 
 import torch
 
+from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
+from vllm.triton_utils import tl, triton
 
 __all__ = [
     "break_fp4_bytes",
@@ -20,6 +22,309 @@
 )
 
 
+@triton.jit
+def _e2m1_inline(magnitude):
+    """Inline E2M1 lookup using binary tree - 3 levels instead of 7 sequential.
+
+    Maps 3-bit magnitude to float: [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0]
+    Uses bit decomposition for fewer comparisons.
+    """
+    # Bit 2 (MSB): separates 0-3 from 4-7
+    # Bit 1: separates within groups
+    # Bit 0 (LSB): separates within pairs
+    b2 = (magnitude >> 2) & 1  # 0 for mag 0-3, 1 for mag 4-7
+    b1 = (magnitude >> 1) & 1  # middle bit
+    b0 = magnitude & 1  # LSB
+
+    # For mag 0-3: [0.0, 0.5, 1.0, 1.5]
+    low_group = tl.where(
+        b1 == 1, tl.where(b0 == 1, 1.5, 1.0), tl.where(b0 == 1, 0.5, 0.0)
+    )
+    # For mag 4-7: [2.0, 3.0, 4.0, 6.0]
+    high_group = tl.where(
+        b1 == 1, tl.where(b0 == 1, 6.0, 4.0), tl.where(b0 == 1, 3.0, 2.0)
+    )
+    return tl.where(b2 == 1, high_group, low_group)
+
+
+@triton.jit
+def _dequantize_nvfp4_kernel(
+    fp4_ptr,
+    scale_ptr,
+    global_scale_ptr,
+    output_ptr,
+    rows_per_batch: tl.constexpr,
+    num_blocks: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+    has_batch_global_scale: tl.constexpr,
+    TILE_BLOCKS: tl.constexpr,
+):
+    """Triton kernel for NVFP4 dequantization (swizzle=False).
+
+    Optimized with 2D tile processing + interleave for coalesced stores.
+    """
+    BLOCK_PACKED: tl.constexpr = BLOCK_SIZE // 2
+
+    row_idx = tl.program_id(0)
+    tile_idx = tl.program_id(1)
+
+    if has_batch_global_scale:
+        batch_idx = row_idx // rows_per_batch
+        global_scale = tl.load(global_scale_ptr + batch_idx).to(tl.float32)
+    else:
+        global_scale = tl.load(global_scale_ptr).to(tl.float32)
+
+    fp4_row_offset = row_idx * num_blocks * BLOCK_PACKED
+    scale_row_offset = row_idx * num_blocks
+    output_row_offset = row_idx * num_blocks * BLOCK_SIZE
+
+    start_block = tile_idx * TILE_BLOCKS
+
+    # Load scales for this tile: [TILE_BLOCKS]
+    block_offsets = tl.arange(0, TILE_BLOCKS)
+    block_mask = (start_block + block_offsets) < num_blocks
+
+    raw_scales = tl.load(
+        scale_ptr + scale_row_offset + start_block + block_offsets,
+        mask=block_mask,
+        other=0,
+    )
+    scale_f32 = tl.cast(raw_scales, tl.float8e4nv, bitcast=True).to(tl.float32)
+    scale_values = (scale_f32 * global_scale)[:, None]
+
+    # Load [TILE_BLOCKS, BLOCK_PACKED] packed bytes
+    packed_offsets = tl.arange(0, BLOCK_PACKED)[None, :]
+    byte_indices = (
+        fp4_row_offset
+        + (start_block + block_offsets[:, None]) * BLOCK_PACKED
+        + packed_offsets
+    )
+    elem_mask = block_mask[:, None]
+    raw_bytes = tl.load(fp4_ptr + byte_indices, mask=elem_mask, other=0)
+
+    low_nibble = raw_bytes & 0x0F
+    high_nibble = (raw_bytes >> 4) & 0x0F
+
+    # Binary tree E2M1 decode
+    low_mag = low_nibble & 0x07
+    low_val = _e2m1_inline(low_mag)
+    low_sign = (low_nibble >> 3) & 1
+    low_result = tl.where(low_sign == 1, -low_val, low_val) * scale_values
+
+    high_mag = high_nibble & 0x07
+    high_val = _e2m1_inline(high_mag)
+    high_sign = (high_nibble >> 3) & 1
+    high_result = tl.where(high_sign == 1, -high_val, high_val) * scale_values
+
+    # Interleave for coalesced contiguous store
+    result = tl.interleave(low_result, high_result)
+
+    elem_offsets = tl.arange(0, BLOCK_SIZE)[None, :]
+    out_indices = (
+        output_row_offset
+        + (start_block + block_offsets[:, None]) * BLOCK_SIZE
+        + elem_offsets
+    )
+    tl.store(output_ptr + out_indices, result, mask=block_mask[:, None])
+
+
+@triton.jit
+def _e2m1_lookup(magnitude):
+    """Lookup E2M1 float value from 3-bit magnitude."""
+    result = tl.where(magnitude == 1, 0.5, 0.0)
+    result = tl.where(magnitude == 2, 1.0, result)
+    result = tl.where(magnitude == 3, 1.5, result)
+    result = tl.where(magnitude == 4, 2.0, result)
+    result = tl.where(magnitude == 5, 3.0, result)
+    result = tl.where(magnitude == 6, 4.0, result)
+    result = tl.where(magnitude == 7, 6.0, result)
+    return result
+
+
+@triton.jit
+def _round_to_fp4(x):
+    """Round float values to the nearest E2M1 representable value.
+
+    Matches the thresholds in the Python ``cast_to_fp4`` exactly.
+    """
+    sign = tl.where(x < 0.0, -1.0, 1.0)
+    abs_x = tl.abs(x)
+    result = tl.where(abs_x > 5.0, 6.0, 0.0)
+    result = tl.where((abs_x >= 3.5) & (abs_x <= 5.0), 4.0, result)
+    result = tl.where((abs_x > 2.5) & (abs_x < 3.5), 3.0, result)
+    result = tl.where((abs_x >= 1.75) & (abs_x <= 2.5), 2.0, result)
+    result = tl.where((abs_x > 1.25) & (abs_x < 1.75), 1.5, result)
+    result = tl.where((abs_x >= 0.75) & (abs_x <= 1.25), 1.0, result)
+    result = tl.where((abs_x > 0.25) & (abs_x < 0.75), 0.5, result)
+    return result * sign
+
+
+@triton.jit
+def _nvfp4_quant_dequant_kernel(
+    input_ptr,
+    output_ptr,
+    global_scale_ptr,
+    k: tl.constexpr,
+    num_blocks: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+    FP4_MAX_RECIPROCAL: tl.constexpr,
+    TILE_BLOCKS: tl.constexpr,
+):
+    """Fused NVFP4 quantize-dequantize kernel.
+
+    Uses a 2D grid (rows x tiles) to parallelize across both rows
+    and quantization groups within a row. Each program handles
+    TILE_BLOCKS groups at once using vectorized 2D operations.
+    """
+    row_idx = tl.program_id(0)
+    tile_idx = tl.program_id(1)
+    global_scale = tl.load(global_scale_ptr).to(tl.float32)
+    row_offset = row_idx * k
+
+    start_block = tile_idx * TILE_BLOCKS
+    block_offsets = tl.arange(0, TILE_BLOCKS)
+    block_mask = (start_block + block_offsets) < num_blocks
+
+    # Load [TILE_BLOCKS, BLOCK_SIZE] elements
+    indices = (
+        row_offset
+        + (start_block + block_offsets[:, None]) * BLOCK_SIZE
+        + tl.arange(0, BLOCK_SIZE)[None, :]
+    )
+    mask_2d = block_mask[:, None]
+    x = tl.load(input_ptr + indices, mask=mask_2d, other=0.0).to(tl.float32)
+
+    # Per-group scale: [TILE_BLOCKS]
+    vec_max = tl.max(tl.abs(x), axis=1)
+    scale = global_scale * (vec_max * FP4_MAX_RECIPROCAL)
+    scale = tl.clamp(scale, -448.0, 448.0)
+    scale = scale.to(tl.float8e4nv).to(tl.float32)
+
+    # Safe reciprocal, broadcast to [TILE_BLOCKS, 1]
+    output_scale = tl.where(scale == 0.0, 0.0, global_scale / scale)[:, None]
+
+    # Quantize: scale, clamp, round to FP4
+    scaled_x = tl.clamp(x * output_scale, -6.0, 6.0)
+    fp4_val = _round_to_fp4(scaled_x)
+
+    # Dequantize: fp4_val * (scale / global_scale)
+    dequant_scale = (scale / global_scale)[:, None]
+    result = fp4_val * dequant_scale
+
+    tl.store(output_ptr + indices, result, mask=mask_2d)
+
+
+def _triton_nvfp4_quant_dequant(
+    x: torch.Tensor,
+    global_scale: torch.Tensor,
+    block_size: int,
+) -> torch.Tensor:
+    """Triton-accelerated NVFP4 quantize-dequantize."""
+    x_m, x_k = x.shape
+
+    if not torch.compiler.is_compiling():
+        assert x_k % block_size == 0, (
+            f"Weight shape K={x_k} is not divisible by block_size={block_size}"
+        )
+
+    output_dtype = x.dtype
+    num_blocks = x_k // block_size
+
+    output = torch.empty(x_m, x_k, dtype=output_dtype, device=x.device)
+
+    tile_blocks = min(64, triton.next_power_of_2(num_blocks))
+    num_tiles = (num_blocks + tile_blocks - 1) // tile_blocks
+    grid = (x_m, num_tiles)
+    _nvfp4_quant_dequant_kernel[grid](
+        x,
+        output,
+        global_scale,
+        x_k,
+        num_blocks,
+        block_size,
+        FLOAT4_E2M1_MAX_RECIPROCAL,
+        tile_blocks,
+    )
+
+    return output
+
+
+def _triton_dequantize_nvfp4(
+    tensor_fp4: torch.Tensor,
+    tensor_sf: torch.Tensor,
+    global_scale: torch.Tensor,
+    dtype: torch.dtype,
+    block_size: int = 16,
+) -> torch.Tensor:
+    """Dequantize NVFP4 using Triton (swizzle=False only).
+
+    Supports both 2D and 3D inputs:
+    - 2D: [m, packed_k] -> [m, k]
+    - 3D: [dim0, m, packed_k] -> [dim0, m, k]
+    """
+    assert tensor_fp4.dtype == torch.uint8
+
+    is_3d = tensor_fp4.ndim == 3
+    if is_3d:
+        dim0, m_per_batch, packed_k = tensor_fp4.shape
+        tensor_fp4_2d = tensor_fp4.reshape(-1, packed_k)
+        tensor_sf_2d = tensor_sf.reshape(-1, tensor_sf.shape[-1])
+        total_rows_flat = dim0 * m_per_batch
+    else:
+        m_per_batch, packed_k = tensor_fp4.shape
+        tensor_fp4_2d = tensor_fp4
+        tensor_sf_2d = tensor_sf
+        total_rows_flat = m_per_batch
+
+    k = packed_k * 2
+    num_blocks = k // block_size
+
+    output = torch.empty(total_rows_flat, k, dtype=dtype, device=tensor_fp4.device)
+
+    # View as uint8 so Triton can load raw bytes and bitcast to float8_e4m3fn
+    scale_raw = tensor_sf_2d.contiguous().view(torch.uint8)
+
+    # Shape-adaptive tile sizing: for large row counts (3D), process
+    # entire row in one tile. For small row counts (2D), use smaller
+    # tiles to increase parallelism across CUs.
+    np2 = triton.next_power_of_2(num_blocks)
+    if total_rows_flat >= 4096:
+        # Many rows: maximize work per CTA, one tile per row
+        tile_blocks = np2
+        nw = 1
+        ns = 2
+    elif total_rows_flat >= 2048:
+        # Medium-many rows: full row, 2 warps
+        tile_blocks = np2
+        nw = 2
+        ns = 2
+    else:
+        # Few rows: use moderate tiles for CU utilization
+        tile_blocks = min(64, np2)
+        nw = 4
+        ns = 2
+    num_tiles = (num_blocks + tile_blocks - 1) // tile_blocks
+    grid = (total_rows_flat, num_tiles)
+    _dequantize_nvfp4_kernel[grid](
+        tensor_fp4_2d,
+        scale_raw,
+        global_scale,
+        output,
+        m_per_batch,
+        num_blocks,
+        block_size,
+        is_3d,
+        tile_blocks,
+        num_warps=nw,
+        num_stages=ns,
+    )
+
+    if is_3d:
+        output = output.reshape(dim0, m_per_batch, k)
+
+    return output
+
+
 def break_fp4_bytes(a, dtype):
     assert a.dtype == torch.uint8
     m, n = a.shape
@@ -53,26 +358,57 @@ def convert_swizzled_to_linear(a_sf_swizzled: torch.Tensor, m, k, block_size):
 def dequantize_to_dtype(
     tensor_fp4: torch.Tensor,
     tensor_sf: torch.Tensor,
-    global_scale: torch.Tensor | float,
+    global_scale: torch.Tensor,
     dtype: torch.dtype,
     block_size: int = 16,
     swizzle: bool | None = True,
 ):
-    """Dequantize the fp4 tensor back to high precision."""
+    """Dequantize the fp4 tensor back to high precision.
+
+    Supports both 2D and 3D inputs:
+    - 2D: [m, packed_k] -> [m, k]
+    - 3D: [dim0, m, packed_k] -> [dim0, m, k]
+    """
     # Two fp4 values are packed into one uint8.
     assert tensor_fp4.dtype == torch.uint8
-    m, packed_k = tensor_fp4.shape
+
+    if not swizzle and current_platform.is_cuda_alike():
+        return _triton_dequantize_nvfp4(
+            tensor_fp4, tensor_sf, global_scale, dtype, block_size
+        )
+
+    # We handle 3D tensors reshaping them to 2D.
+    is_3d = tensor_fp4.ndim == 3
+
+    if is_3d:
+        dim0, m, packed_k = tensor_fp4.shape
+        tensor_fp4 = tensor_fp4.reshape(-1, packed_k)
+        tensor_sf = tensor_sf.reshape(-1, tensor_sf.shape[-1])
+        global_scale = global_scale[:, None, None]
+    else:
+        m, packed_k = tensor_fp4.shape
+
     k = packed_k * 2
     tensor_f32 = break_fp4_bytes(tensor_fp4, torch.float32)
-    tensor_f32 = tensor_f32.reshape(m, k // block_size, block_size)
+    tensor_f32 = tensor_f32.reshape(-1, k // block_size, block_size)
     tensor_sf = tensor_sf.view(torch.float8_e4m3fn)
 
     if swizzle:
-        tensor_sf = convert_swizzled_to_linear(tensor_sf, m, k, block_size)
+        tensor_sf = convert_swizzled_to_linear(  # noqa: E501
+            tensor_sf, tensor_f32.size(0), k, block_size
+        )
+
+    if is_3d:
+        tensor_sf = tensor_sf.reshape(dim0, m, k // block_size)
     tensor_sf_dtype = tensor_sf.to(torch.float32) * global_scale
 
+    if is_3d:
+        tensor_f32 = tensor_f32.reshape(dim0, m, -1, block_size)
+
     # scale the tensor
-    out = (tensor_f32 * tensor_sf_dtype.unsqueeze(-1)).reshape(m, k)
+    out = tensor_f32 * tensor_sf_dtype.unsqueeze(-1)
+    out = out.reshape(*out.shape[:-2], -1)
+
     return out.to(dtype)
 
 
@@ -117,6 +453,31 @@ def ref_nvfp4_quant(x, global_scale, block_size):
     return cast_to_fp4(clipped_x), scale.squeeze(-1)
 
 
+def ref_nvfp4_quant_dequant(
+    x: torch.Tensor, global_scale: torch.Tensor, block_size: int
+) -> torch.Tensor:
+    """
+    NVFP4 quantize-dequantize operation.
+
+    `global_scale` is expected to have a single element.
+    """
+    if current_platform.is_cuda_alike():
+        return _triton_nvfp4_quant_dequant(x, global_scale, block_size)
+
+    x_m, x_k = x.shape
+    output_dtype = x.dtype
+
+    # quantize input to (FP4 and interleaved block scale)
+    x_fp4, x_blockscale = ref_nvfp4_quant(x, global_scale, block_size)
+
+    # dequantize input
+    x_fp4 = x_fp4.reshape(x_m, x_k // block_size, block_size)
+    x_blockscale = x_blockscale.unsqueeze(-1) / global_scale
+    x_dq = (x_fp4 * x_blockscale).reshape(x_m, x_k).to(output_dtype)
+
+    return x_dq
+
+
 def run_nvfp4_emulations(
     x: torch.Tensor,
     input_global_scale: torch.Tensor,
@@ -125,18 +486,10 @@ def run_nvfp4_emulations(
     weight_global_scale: torch.Tensor,
     swizzle: bool | None = True,
 ):
-    group_size = 16
-    x_m, x_k = x.shape
     output_dtype = x.dtype
+    group_size = 16
 
-    # quantize input to (FP4 and interleaved block scale)
-    x_fp4, x_blockscale = ref_nvfp4_quant(x, input_global_scale, group_size)
-
-    # dequantize input
-    x_fp4 = x_fp4.reshape(x_m, x_k // group_size, group_size)
-    x_blockscale = x_blockscale.unsqueeze(-1) / input_global_scale
-    x_dq = (x_fp4 * x_blockscale).reshape(x_m, x_k).to(output_dtype)
-    del x_fp4, x_blockscale
+    x_dq = ref_nvfp4_quant_dequant(x, input_global_scale, block_size=group_size)
 
     # dequantize weight
     w_fp4 = weight.data.view(torch.uint8)
@@ -151,5 +504,4 @@ def run_nvfp4_emulations(
 
     # matmul
     out = torch.matmul(x_dq, w_dq.t())
-    del w_dq, x_dq
     return out
diff --git a/vllm/model_executor/layers/quantization/utils/quant_utils.py b/vllm/model_executor/layers/quantization/utils/quant_utils.py
index d1b1b77988c7..0b1802522415 100644
--- a/vllm/model_executor/layers/quantization/utils/quant_utils.py
+++ b/vllm/model_executor/layers/quantization/utils/quant_utils.py
@@ -20,6 +20,8 @@
 FP8_DTYPE = current_platform.fp8_dtype()
 FP4_DTYPE = torch.uint8
 MXFP_SCALE_DTYPE = torch.uint8
+INT4_DTYPE = scalar_types.uint4b8
+INT8_DTYPE = scalar_types.uint8b128
 
 
 def get_fp8_min_max() -> tuple[float, float]:
@@ -170,6 +172,15 @@ def __str__(self):
 kMxfp4StaticGroupScale = ScaleDesc(MXFP_SCALE_DTYPE, True, GroupShape(1, 32))
 kMxfp4Static = QuantKey(FP4_DTYPE, scale=kMxfp4StaticGroupScale, symmetric=True)
 
+# TODO: convert this to use SCALAR_TYPE. This is not right.
+kInt4StaticGroupScale = ScaleDesc(torch.float16, True, GroupShape(1, -1))
+kInt4Static = QuantKey(INT4_DTYPE, scale=kInt4StaticGroupScale, symmetric=True)
+kInt8StaticGroupScale = ScaleDesc(torch.float16, True, GroupShape(1, -1))
+kInt8Static = QuantKey(INT8_DTYPE, scale=kInt8StaticGroupScale, symmetric=True)
+
+kInt8StaticChannelSym = QuantKey(torch.int8, kStaticChannelScale, symmetric=True)
+kInt8DynamicTokenSym = QuantKey(torch.int8, kDynamicTokenScale, symmetric=True)
+
 
 def create_fp8_quant_key(
     static: bool,
@@ -356,6 +367,12 @@ def get_and_maybe_dequant_weights(
     from vllm.model_executor.layers.linear import UnquantizedLinearMethod
     from vllm.model_executor.layers.quantization.fp8 import Fp8LinearMethod
 
+    # LoRA linear wrappers store quantization metadata on `base_layer`.
+    # Unwrap here so callers can pass either a raw linear layer or its LoRA
+    # wrapper without special-casing.
+    while hasattr(layer, "base_layer") and hasattr(layer.base_layer, "quant_method"):
+        layer = layer.base_layer
+
     weight = get_attribute_fallback(layer, ["weight", "qweight", "weight_packed"])
 
     # Unquantized layer: just return base weights
@@ -818,7 +835,7 @@ def convert_bf16_scales_to_fp8(
 
     # restore original shape
     fp8_scales = fp8_scales.view(orig_shape)
-    chan_scales = chan_scales.view(orig_shape[:-1], -1)
+    chan_scales = chan_scales.view(*orig_shape[:-1], -1)
 
     return fp8_scales, chan_scales
 
diff --git a/vllm/model_executor/layers/rotary_embedding/__init__.py b/vllm/model_executor/layers/rotary_embedding/__init__.py
index 9a541877575d..b955b37be603 100644
--- a/vllm/model_executor/layers/rotary_embedding/__init__.py
+++ b/vllm/model_executor/layers/rotary_embedding/__init__.py
@@ -7,7 +7,10 @@
 import torch
 
 from .base import RotaryEmbedding
-from .deepseek_scaling_rope import DeepseekScalingRotaryEmbedding
+from .deepseek_scaling_rope import (
+    DeepseekScalingRotaryEmbedding,
+    DeepseekV4ScalingRotaryEmbedding,
+)
 from .dual_chunk_rope import DualChunkRotaryEmbedding
 from .dynamic_ntk_alpha_rope import DynamicNTKAlphaRotaryEmbedding
 from .dynamic_ntk_scaling_rope import DynamicNTKScalingRotaryEmbedding
@@ -60,11 +63,13 @@ def get_rope(
     rope_parameters = rope_parameters or {}
     base = rope_parameters.get("rope_theta", 10000)
     scaling_type = rope_parameters.get("rope_type", "default")
-    partial_rotary_factor = rope_parameters.get("partial_rotary_factor", 1.0)
-
-    if partial_rotary_factor <= 0.0 or partial_rotary_factor > 1.0:
-        raise ValueError(f"{partial_rotary_factor=} must be between 0.0 and 1.0")
-    rotary_dim = int(head_size * partial_rotary_factor)
+    if rotary_dim := rope_parameters.get("rope_dim", None):
+        pass
+    else:
+        partial_rotary_factor = rope_parameters.get("partial_rotary_factor", 1.0)
+        if partial_rotary_factor <= 0.0 or partial_rotary_factor > 1.0:
+            raise ValueError(f"{partial_rotary_factor=} must be between 0.0 and 1.0")
+        rotary_dim = int(head_size * partial_rotary_factor)
 
     key = (
         head_size,
@@ -289,7 +294,11 @@ def get_rope(
                 "mscale_all_dim",
             )
         }
-        rotary_emb = DeepseekScalingRotaryEmbedding(
+        if rope_parameters.get("is_deepseek_v4", False):
+            cls = DeepseekV4ScalingRotaryEmbedding
+        else:
+            cls = DeepseekScalingRotaryEmbedding
+        rotary_emb = cls(
             head_size,
             rotary_dim,
             original_max_position,
diff --git a/vllm/model_executor/layers/rotary_embedding/deepseek_scaling_rope.py b/vllm/model_executor/layers/rotary_embedding/deepseek_scaling_rope.py
index 69c1101664d0..9a06eedd0f7d 100644
--- a/vllm/model_executor/layers/rotary_embedding/deepseek_scaling_rope.py
+++ b/vllm/model_executor/layers/rotary_embedding/deepseek_scaling_rope.py
@@ -45,6 +45,7 @@ def __init__(
         beta_slow: int = 1,
         mscale: float = 1,
         mscale_all_dim: float = 0,
+        init_cache: bool = True,
     ) -> None:
         self.scaling_factor = scaling_factor
         self.extrapolation_factor = extrapolation_factor
@@ -65,7 +66,13 @@ def __init__(
             and head_size in [64, 128, 256, 512]
         )
         super().__init__(
-            head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
+            head_size,
+            rotary_dim,
+            max_position_embeddings,
+            base,
+            is_neox_style,
+            dtype,
+            init_cache=init_cache,
         )
 
     def _compute_inv_freq(self, scaling_factor: float) -> torch.Tensor:
@@ -132,10 +139,8 @@ def forward_native(
         ]
         cos, sin = cos_sin.chunk(2, dim=-1)
         if self.is_neox_style:
-            # NOTE(woosuk): Here we assume that the positions tensor has the
-            # shape [batch_size, seq_len].
-            cos = cos.repeat(1, 1, 2).unsqueeze(-2)
-            sin = sin.repeat(1, 1, 2).unsqueeze(-2)
+            cos = torch.cat((cos, cos), dim=-1).unsqueeze(-2)
+            sin = torch.cat((sin, sin), dim=-1).unsqueeze(-2)
         else:
             cos = cos.repeat_interleave(2, dim=-1).unsqueeze(-2)
             sin = sin.repeat_interleave(2, dim=-1).unsqueeze(-2)
@@ -197,3 +202,120 @@ def forward_cuda(
             return query, key
         else:
             return self.forward_native(positions, query, key, offsets)
+
+
+class DeepseekV4ScalingRotaryEmbedding(DeepseekScalingRotaryEmbedding):
+    """RotaryEmbedding extended with YaRN method.
+
+    Credits to Peng et al. github.com/jquesnelle/yarn
+
+    Compared to DeepseekScalingRotaryEmbedding:
+    - Applies RoPE to the last rotary_dim
+    - The forward method requires an inverse parameter to indicate
+      whether to negate the sin
+    - Supports applying RoPE to query only (without key)
+    - cos_sin_cache stored as fp32 for higher precision RoPE
+    """
+
+    def __init__(self, *args, **kwargs):
+        # Avoid compute cache repeatedly
+        kwargs.pop("init_cache", None)
+        super().__init__(*args, **kwargs, init_cache=False)
+        cache_fp32 = self._compute_cos_sin_cache()
+        self.register_buffer("cos_sin_cache", cache_fp32, persistent=False)
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        inv_freq = self._compute_inv_freq(self.scaling_factor)
+        t = torch.arange(
+            self.max_position_embeddings * self.scaling_factor,
+            device=current_platform.device_type,
+            dtype=torch.float32,
+        )
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)
+        cos = freqs.cos() * self.mscale
+        sin = freqs.sin() * self.mscale
+        cache = torch.cat((cos, sin), dim=-1)
+        return cache
+
+    def forward_native(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor | None = None,
+        offsets: torch.Tensor | None = None,
+        inverse: bool = False,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """PyTorch-native implementation equivalent to forward()."""
+
+        head_size = query.size(-1)
+        query_rot = query[..., -self.rotary_dim :]
+        key_rot = key[..., -self.rotary_dim :] if key is not None else None
+
+        if self.rotary_dim < head_size:
+            query_pass = query[..., : -self.rotary_dim]
+            key_pass = key[..., : -self.rotary_dim] if key is not None else None
+
+        cos_sin = self.cos_sin_cache[
+            torch.add(positions, offsets) if offsets is not None else positions
+        ]
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        if self.is_neox_style:
+            cos = torch.cat((cos, cos), dim=-1).unsqueeze(-2)
+            sin = torch.cat((sin, sin), dim=-1).unsqueeze(-2)
+        else:
+            cos = cos.repeat_interleave(2, dim=-1).unsqueeze(-2)
+            sin = sin.repeat_interleave(2, dim=-1).unsqueeze(-2)
+        if inverse:
+            sin = -sin
+        rotate_fn = rotate_neox if self.is_neox_style else rotate_gptj
+        orig_dtype = query.dtype
+        query_rot = (query_rot * cos + rotate_fn(query_rot) * sin).to(orig_dtype)
+        if key_rot is not None:
+            key_rot = (key_rot * cos + rotate_fn(key_rot) * sin).to(orig_dtype)
+
+        if self.rotary_dim < head_size:
+            query = torch.cat((query_pass, query_rot), dim=-1)
+            key = torch.cat((key_pass, key_rot), dim=-1) if key is not None else None
+        else:
+            query = query_rot
+            key = key_rot
+
+        return query, key
+
+    def forward_hip(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor | None = None,
+        offsets: torch.Tensor | None = None,
+        inverse: bool = False,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        return self.forward_native(positions, query, key, offsets)
+
+    def forward_cuda(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor | None = None,
+        offsets: torch.Tensor | None = None,
+        inverse: bool = False,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        from vllm import _custom_ops as ops
+
+        # The indexer and attention have different head_dim,
+        # we obtain the corresponding head_dim via the query.
+        head_size = query.size(-1)
+        rope_dim_offset = head_size - self.rotary_dim
+        # ops.rotary_embedding() is an in-place operation
+        # that updates the query and key tensors.
+        ops.rotary_embedding(
+            torch.add(positions, offsets) if offsets is not None else positions,
+            query,
+            key,
+            head_size,
+            self.cos_sin_cache,
+            self.is_neox_style,
+            rope_dim_offset=rope_dim_offset,
+            inverse=inverse,
+        )
+        return query, key
diff --git a/vllm/model_executor/layers/sparse_attn_indexer.py b/vllm/model_executor/layers/sparse_attn_indexer.py
index bdaa6af09456..4bf52a49c43f 100644
--- a/vllm/model_executor/layers/sparse_attn_indexer.py
+++ b/vllm/model_executor/layers/sparse_attn_indexer.py
@@ -10,7 +10,11 @@
 from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp
 from vllm.platforms import current_platform
-from vllm.utils.deep_gemm import fp8_mqa_logits, fp8_paged_mqa_logits, has_deep_gemm
+from vllm.utils.deep_gemm import (
+    fp8_fp4_mqa_logits,
+    fp8_fp4_paged_mqa_logits,
+    has_deep_gemm,
+)
 from vllm.utils.torch_utils import (
     LayerNameType,
     _encode_layer_name,
@@ -26,18 +30,63 @@
 if current_platform.is_cuda_alike():
     from vllm import _custom_ops as ops
 elif current_platform.is_xpu():
-    from vllm._xpu_ops import xpu_ops as ops
+    from vllm._xpu_ops import xpu_ops
 
 logger = init_logger(__name__)
 
 RADIX_TOPK_WORKSPACE_SIZE = 1024 * 1024
 
+# MXFP4 layout: 2 values packed per byte, ue8m0 (1-byte) scale per block of 32.
+MXFP4_BLOCK_SIZE = 32
+
+
+def _gather_workspace_shapes(
+    total_seq_lens: int,
+    head_dim: int,
+    fp8_dtype: torch.dtype,
+    use_fp4_cache: bool,
+) -> tuple[tuple[tuple[int, int], torch.dtype], tuple[tuple[int, int], torch.dtype]]:
+    """Return ((values_shape, values_dtype), (scales_shape, scales_dtype)) for
+    the K-gather workspace. FP8 path: (T, head_dim) fp8 + (T, 4) uint8 fp32
+    scales. MXFP4 path: (T, head_dim // 2) uint8 packed mxfp4 +
+    (T, head_dim // MXFP4_BLOCK_SIZE) uint8 ue8m0 scales."""
+    if use_fp4_cache:
+        return (
+            ((total_seq_lens, head_dim // 2), torch.uint8),
+            ((total_seq_lens, head_dim // MXFP4_BLOCK_SIZE), torch.uint8),
+        )
+    return (
+        ((total_seq_lens, head_dim), fp8_dtype),
+        ((total_seq_lens, 4), torch.uint8),
+    )
+
+
+def kv_cache_as_quant_view(
+    kv_cache: torch.Tensor,
+    head_dim: int,
+    use_fp4_cache: bool,
+) -> torch.Tensor:
+    """4D ``[num_blocks, block_size, 1, head_width]`` view expected by
+    DeepGEMM, from the 3D indexer kv-cache allocation."""
+    if use_fp4_cache:
+        assert kv_cache.ndim == 3 and kv_cache.dtype == torch.uint8
+        num_blocks, block_size, _ = kv_cache.shape
+        page_bytes = int(kv_cache.stride(0))
+        fp4_bytes = head_dim // 2 + head_dim // MXFP4_BLOCK_SIZE
+        return torch.as_strided(
+            kv_cache,
+            size=(num_blocks, block_size, 1, fp4_bytes),
+            stride=(page_bytes, fp4_bytes, fp4_bytes, 1),
+        )
+    return kv_cache.unsqueeze(-2)
+
 
 def sparse_attn_indexer(
     hidden_states: torch.Tensor,
     k_cache_prefix: LayerNameType,
     kv_cache: torch.Tensor,
-    q_fp8: torch.Tensor,
+    q_quant: torch.Tensor,
+    q_scale: torch.Tensor | None,
     k: torch.Tensor,
     weights: torch.Tensor,
     quant_block_size: int,
@@ -47,6 +96,8 @@ def sparse_attn_indexer(
     max_model_len: int,
     total_seq_lens: int,
     topk_indices_buffer: torch.Tensor,
+    skip_k_cache_insert: bool,
+    use_fp4_cache: bool = False,
 ) -> torch.Tensor:
     # careful! this will be None in dummy run
     attn_metadata = get_forward_context().attn_metadata
@@ -56,9 +107,12 @@ def sparse_attn_indexer(
     # assert isinstance(attn_metadata, dict)
     if not isinstance(attn_metadata, dict):
         # Reserve workspace for indexer during profiling run
+        values_spec, scales_spec = _gather_workspace_shapes(
+            total_seq_lens, head_dim, fp8_dtype, use_fp4_cache
+        )
         current_workspace_manager().get_simultaneous(
-            ((total_seq_lens, head_dim), torch.float8_e4m3fn),
-            ((total_seq_lens, 4), torch.uint8),
+            values_spec,
+            scales_spec,
             ((RADIX_TOPK_WORKSPACE_SIZE,), torch.uint8),
         )
 
@@ -73,7 +127,8 @@ def sparse_attn_indexer(
             hidden_states,
             k_cache_prefix,
             kv_cache,
-            q_fp8,
+            q_quant,
+            q_scale,
             k,
             weights,
             quant_block_size,
@@ -83,55 +138,91 @@ def sparse_attn_indexer(
             max_model_len,
             total_seq_lens,
             topk_indices_buffer,
+            skip_k_cache_insert,
+            use_fp4_cache,
         )
-    attn_metadata = attn_metadata[k_cache_prefix]
-    assert isinstance(attn_metadata, DeepseekV32IndexerMetadata)
-    slot_mapping = attn_metadata.slot_mapping
-    has_decode = attn_metadata.num_decodes > 0
-    has_prefill = attn_metadata.num_prefills > 0
-    num_decode_tokens = attn_metadata.num_decode_tokens
+    attn_metadata_narrowed = attn_metadata[k_cache_prefix]
+    assert isinstance(attn_metadata_narrowed, DeepseekV32IndexerMetadata)
+    slot_mapping = attn_metadata_narrowed.slot_mapping
+    has_decode = attn_metadata_narrowed.num_decodes > 0
+    has_prefill = attn_metadata_narrowed.num_prefills > 0
+    num_decode_tokens = attn_metadata_narrowed.num_decode_tokens
+
+    # q_scale is required iff the FP4 cache path is enabled; the FP8 path
+    # folds the Q scale into `weights` inside fused_indexer_q_rope_quant.
+    if use_fp4_cache:
+        assert q_scale is not None, "use_fp4_cache=True requires q_scale"
+    else:
+        assert q_scale is None, "q_scale must be None when use_fp4_cache=False"
 
     # During speculative decoding, k may be padded to the CUDA graph batch
     # size while slot_mapping only covers actual tokens. Truncate k to avoid
     # out-of-bounds reads in the kernel.
     num_tokens = slot_mapping.shape[0]
-    k = k[:num_tokens]
-
-    ops.indexer_k_quant_and_cache(
-        k,
-        kv_cache,
-        slot_mapping,
-        quant_block_size,
-        scale_fmt,
-    )
+    if k is not None:
+        k = k[:num_tokens]
+
+    if not skip_k_cache_insert:
+        # scale_fmt can be None, but the function expects str
+        assert scale_fmt is not None
+        assert not use_fp4_cache, "Unfused FP4 Insert is not supported yet"
+        ops.indexer_k_quant_and_cache(
+            k,
+            kv_cache,
+            slot_mapping,
+            quant_block_size,
+            scale_fmt,
+        )
 
     topk_indices_buffer[: hidden_states.shape[0]] = -1
     if has_prefill:
-        prefill_metadata = attn_metadata.prefill
+        prefill_metadata = attn_metadata_narrowed.prefill
         assert prefill_metadata is not None
 
-        # Get the full shared workspace buffers once (will allocate on first use)
+        # Get the full shared workspace buffers once (will allocate on first use).
+        # Layout switches between FP8 (head_dim bytes + 4-byte fp32 scale) and
+        # MXFP4 (head_dim/2 bytes packed + head_dim/MXFP4_BLOCK_SIZE ue8m0
+        # scales) based on use_fp4_cache.
         workspace_manager = current_workspace_manager()
-        k_fp8_full, k_scale_full = workspace_manager.get_simultaneous(
-            ((total_seq_lens, head_dim), fp8_dtype),
-            ((total_seq_lens, 4), torch.uint8),
+        values_spec, scales_spec = _gather_workspace_shapes(
+            total_seq_lens, head_dim, fp8_dtype, use_fp4_cache
+        )
+        k_quant_full, k_scale_full = workspace_manager.get_simultaneous(
+            values_spec,
+            scales_spec,
         )
         for chunk in prefill_metadata.chunks:
-            k_fp8 = k_fp8_full[: chunk.total_seq_lens]
+            k_quant = k_quant_full[: chunk.total_seq_lens]
             k_scale = k_scale_full[: chunk.total_seq_lens]
 
             if not chunk.skip_kv_gather:
                 ops.cp_gather_indexer_k_quant_cache(
                     kv_cache,
-                    k_fp8,
+                    k_quant,
                     k_scale,
                     chunk.block_table,
                     chunk.cu_seq_lens,
                 )
 
-            logits = fp8_mqa_logits(
-                q_fp8[chunk.token_start : chunk.token_end],
-                (k_fp8, k_scale.view(torch.float32).flatten()),
+            q_slice = q_quant[chunk.token_start : chunk.token_end]
+            q_scale_slice = (
+                q_scale[chunk.token_start : chunk.token_end]
+                if q_scale is not None
+                else None
+            )
+            # DeepGEMM scalar-type tags (zero-copy): MXFP4 values → int8
+            # (kPackedFP4), scales → int32 squeezed to 1-D kv_sf / 2-D q_sf.
+            if use_fp4_cache:
+                q_slice_cast = q_slice.view(torch.int8)
+                k_quant_cast = k_quant.view(torch.int8)
+                k_scale_cast = k_scale.view(torch.int32).squeeze(-1)
+            else:
+                q_slice_cast = q_slice
+                k_quant_cast = k_quant
+                k_scale_cast = k_scale.view(torch.float32).squeeze(-1)
+            logits = fp8_fp4_mqa_logits(
+                (q_slice_cast, q_scale_slice),
+                (k_quant_cast, k_scale_cast),
                 weights[chunk.token_start : chunk.token_end],
                 chunk.cu_seqlen_ks,
                 chunk.cu_seqlen_ke,
@@ -144,7 +235,7 @@ def sparse_attn_indexer(
             ]
 
             if current_platform.is_xpu():
-                ops.top_k_per_row_prefill(
+                xpu_ops.top_k_per_row_prefill(  # type: ignore[attr-defined]
                     logits,
                     chunk.cu_seqlen_ks,
                     chunk.cu_seqlen_ke,
@@ -167,34 +258,57 @@ def sparse_attn_indexer(
                 )
 
     if has_decode:
-        decode_metadata = attn_metadata.decode
+        decode_metadata = attn_metadata_narrowed.decode
         assert decode_metadata is not None
-        # kv_cache shape [
-        # kv_cache size requirement [num_block, block_size, n_head, head_dim],
-        # we only have [num_block, block_size, head_dim],
-        kv_cache = kv_cache.unsqueeze(-2)
+        kv_cache = kv_cache_as_quant_view(kv_cache, head_dim, use_fp4_cache)
         decode_lens = decode_metadata.decode_lens
         if decode_metadata.requires_padding:
             # pad in edge case where we have short chunked prefill length <
             # decode_threshold since we unstrictly split
             # prefill and decode by decode_threshold
-            # (currently set to 1 + speculative tokens)
-            padded_q_fp8_decode_tokens = pack_seq_triton(
-                q_fp8[:num_decode_tokens], decode_lens
-            )
+            # (currently set to 1 + speculative tokens).
+            # FP8 Q is float8_e4m3fn (pack_seq_triton's fp32 pad path is OK —
+            # downstream context_lens masks stale slots). MXFP4 Q is two
+            # uint8 tensors (values + ue8m0 scales) — use the dedicated uint8
+            # packer with pad_byte=0 so padded slots dequantize to 0 and
+            # can't produce NaN/Inf in the logits kernel.
+            if q_scale is not None:
+                padded_q_quant_decode_tokens = pack_seq_triton(
+                    q_quant[:num_decode_tokens], decode_lens, pad_value=0
+                )
+                padded_q_scale = pack_seq_triton(
+                    q_scale[:num_decode_tokens], decode_lens, pad_value=0
+                )
+            else:
+                padded_q_quant_decode_tokens = pack_seq_triton(
+                    q_quant[:num_decode_tokens], decode_lens
+                )
+                padded_q_scale = None
         else:
-            padded_q_fp8_decode_tokens = q_fp8[:num_decode_tokens].reshape(
-                decode_lens.shape[0], -1, *q_fp8.shape[1:]
+            padded_q_quant_decode_tokens = q_quant[:num_decode_tokens].reshape(
+                decode_lens.shape[0], -1, *q_quant.shape[1:]
             )
+            if q_scale is not None:
+                padded_q_scale = q_scale[:num_decode_tokens].reshape(
+                    decode_lens.shape[0], -1, *q_scale.shape[1:]
+                )
+            else:
+                padded_q_scale = None
         # TODO: move and optimize below logic with triton kernels
-        batch_size = padded_q_fp8_decode_tokens.shape[0]
-        next_n = padded_q_fp8_decode_tokens.shape[1]
+        batch_size = padded_q_quant_decode_tokens.shape[0]
+        next_n = padded_q_quant_decode_tokens.shape[1]
         num_padded_tokens = batch_size * next_n
         seq_lens = decode_metadata.seq_lens[:batch_size]
-        # seq_lens is (B, next_n) for native spec decode, (B,) otherwise.
-        # fp8_paged_mqa_logits and all topk kernels accept both shapes.
-        logits = fp8_paged_mqa_logits(
-            padded_q_fp8_decode_tokens,
+        # seq_lens is always 2D: (B, next_n) for native spec decode, (B, 1)
+        # otherwise. deep_gemm fp8_fp4_paged_mqa_logits requires 2D context_lens;
+        # the downstream topk kernels accept both 1D and 2D.
+        padded_q_quant_cast = (
+            padded_q_quant_decode_tokens.view(torch.int8)
+            if use_fp4_cache
+            else padded_q_quant_decode_tokens
+        )
+        logits = fp8_fp4_paged_mqa_logits(
+            (padded_q_quant_cast, padded_q_scale),
             kv_cache,
             weights[:num_padded_tokens],
             seq_lens,
@@ -206,7 +320,7 @@ def sparse_attn_indexer(
         num_rows = logits.shape[0]
         topk_indices = topk_indices_buffer[:num_padded_tokens, :topk_tokens]
 
-        if current_platform.is_cuda():
+        if current_platform.is_cuda() and topk_tokens in (512, 1024, 2048):
             workspace_manager = current_workspace_manager()
             (topk_workspace,) = workspace_manager.get_simultaneous(
                 ((RADIX_TOPK_WORKSPACE_SIZE,), torch.uint8),
@@ -217,11 +331,11 @@ def sparse_attn_indexer(
                 topk_indices,
                 topk_workspace,
                 topk_tokens,
-                attn_metadata.max_seq_len,
+                attn_metadata_narrowed.max_seq_len,
             )
         else:
             if current_platform.is_xpu():
-                ops.top_k_per_row_decode(
+                xpu_ops.top_k_per_row_decode(  # type: ignore[attr-defined]
                     logits,
                     next_n,
                     seq_lens,
@@ -261,7 +375,8 @@ def sparse_attn_indexer_fake(
     hidden_states: torch.Tensor,
     k_cache_prefix: LayerNameType,
     kv_cache: torch.Tensor,
-    q_fp8: torch.Tensor,
+    q_quant: torch.Tensor,
+    q_scale: torch.Tensor | None,
     k: torch.Tensor,
     weights: torch.Tensor,
     quant_block_size: int,
@@ -271,6 +386,8 @@ def sparse_attn_indexer_fake(
     max_model_len: int,
     total_seq_lens: int,
     topk_indices_buffer: torch.Tensor | None,
+    skip_k_cache_insert: bool,
+    use_fp4_cache: bool = False,
 ) -> torch.Tensor:
     return topk_indices_buffer
 
@@ -307,6 +424,8 @@ def __init__(
         max_model_len: int,
         max_total_seq_len: int,
         topk_indices_buffer: torch.Tensor,
+        skip_k_cache_insert: bool = False,
+        use_fp4_cache: bool = False,
     ):
         super().__init__()
         self.k_cache = k_cache
@@ -317,6 +436,8 @@ def __init__(
         self.max_model_len = max_model_len
         self.max_total_seq_len = max_total_seq_len
         self.topk_indices_buffer = topk_indices_buffer
+        self.skip_k_cache_insert = skip_k_cache_insert
+        self.use_fp4_cache = use_fp4_cache
         if current_platform.is_cuda() and not has_deep_gemm():
             raise RuntimeError(
                 "Sparse Attention Indexer CUDA op requires DeepGEMM to be installed."
@@ -325,14 +446,14 @@ def __init__(
     def forward_native(
         self,
         hidden_states: torch.Tensor,
-        q_fp8: torch.Tensor,
+        q_quant: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
         k: torch.Tensor,
         weights: torch.Tensor,
     ):
         if current_platform.is_cuda() or current_platform.is_xpu():
-            return self.forward_cuda(hidden_states, q_fp8, k, weights)
+            return self.forward_cuda(hidden_states, q_quant, k, weights)
         elif current_platform.is_rocm():
-            return self.forward_hip(hidden_states, q_fp8, k, weights)
+            return self.forward_hip(hidden_states, q_quant, k, weights)
         else:
             raise NotImplementedError(
                 "SparseAttnIndexer native forward is only implemented for "
@@ -342,15 +463,22 @@ def forward_native(
     def forward_cuda(
         self,
         hidden_states: torch.Tensor,
-        q_fp8: torch.Tensor,
+        q_quant: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
         k: torch.Tensor,
         weights: torch.Tensor,
     ):
+        # FP8 path: single tensor (per-token scale is folded into `weights`).
+        # FP4 path: (values, scales) tuple with scales required by the kernel.
+        if isinstance(q_quant, tuple):
+            q_values, q_scale = q_quant
+        else:
+            q_values, q_scale = q_quant, None
         return torch.ops.vllm.sparse_attn_indexer(
             hidden_states,
             _encode_layer_name(self.k_cache.prefix),
             self.k_cache.kv_cache,
-            q_fp8,
+            q_values,
+            q_scale,
             k,
             weights,
             self.quant_block_size,
@@ -360,21 +488,48 @@ def forward_cuda(
             self.max_model_len,
             self.max_total_seq_len,
             self.topk_indices_buffer,
+            self.skip_k_cache_insert,
+            self.use_fp4_cache,
         )
 
     def forward_hip(
         self,
         hidden_states: torch.Tensor,
-        q_fp8: torch.Tensor,
+        q_quant: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
         k: torch.Tensor,
         weights: torch.Tensor,
     ):
+        assert not self.use_fp4_cache, "AMD platform doesn't support fp4 cache yet"
+        assert isinstance(q_quant, torch.Tensor), (
+            "AMD sparse_attn_indexer expects a single FP8 q_quant tensor"
+        )
+        if self.skip_k_cache_insert or not rocm_aiter_ops.is_enabled():
+            from vllm.v1.attention.ops.rocm_aiter_mla_sparse import (
+                rocm_aiter_sparse_attn_indexer_native,
+            )
+
+            return rocm_aiter_sparse_attn_indexer_native(
+                hidden_states,
+                _encode_layer_name(self.k_cache.prefix),
+                self.k_cache.kv_cache,
+                q_quant,
+                k,
+                weights,
+                self.quant_block_size,
+                self.scale_fmt,
+                self.topk_tokens,
+                self.head_dim,
+                self.max_model_len,
+                self.max_total_seq_len,
+                self.topk_indices_buffer,
+                skip_k_cache_insert=self.skip_k_cache_insert,
+            )
         if rocm_aiter_ops.is_enabled():
             return torch.ops.vllm.rocm_aiter_sparse_attn_indexer(
                 hidden_states,
                 _encode_layer_name(self.k_cache.prefix),
                 self.k_cache.kv_cache,
-                q_fp8,
+                q_quant,
                 k,
                 weights,
                 self.quant_block_size,
@@ -385,8 +540,4 @@ def forward_hip(
                 self.max_total_seq_len,
                 self.topk_indices_buffer,
             )
-        else:
-            raise RuntimeError(
-                "Sparse attention indexer ROCm custom op requires ROCm "
-                "Aiter ops to be enabled."
-            )
+        raise RuntimeError("Sparse attention indexer ROCm path could not be selected.")
diff --git a/vllm/model_executor/layers/utils.py b/vllm/model_executor/layers/utils.py
index 4918c83bdc39..63a79f668edf 100644
--- a/vllm/model_executor/layers/utils.py
+++ b/vllm/model_executor/layers/utils.py
@@ -159,6 +159,7 @@ def rocm_unquantized_gemm_impl(
             and weight.is_contiguous()
         )
     )
+
     if use_skinny_reduce_counting:
         return ops.wvSplitKrc(x, weight, cu_count, bias)
 
@@ -174,17 +175,21 @@ def rocm_unquantized_gemm_impl(
         and k % 8 == 0
     )
 
-    if not use_skinny:
-        return torch.nn.functional.linear(x, weight, bias)
-
-    x_view = x.reshape(-1, x.size(-1))
-    if m > 8 and 0 < n <= 4:
-        cu_count = num_compute_units()
-        out = ops.wvSplitK(weight, x_view, cu_count, bias)
-        return out.reshape(*x.shape[:-1], weight.shape[0])
-    elif m % 4 == 0 and n == 1 and k <= 8192 and bias is None:
-        out = ops.LLMM1(weight, x_view, 4)
-        return out.reshape(*x.shape[:-1], weight.shape[0])
+    if use_skinny:
+        x_view = x.reshape(-1, x.size(-1))
+        if m > 8 and 0 < n <= 4:
+            cu_count = num_compute_units()
+            out = ops.wvSplitK(weight, x_view, cu_count, bias)
+            return out.reshape(*x.shape[:-1], weight.shape[0])
+        elif m % 4 == 0 and n == 1 and k <= 8192 and bias is None:
+            out = ops.LLMM1(weight, x_view, 4)
+            return out.reshape(*x.shape[:-1], weight.shape[0])
+
+    if rocm_aiter_ops.is_tgemm_enabled():
+        from aiter.tuned_gemm import tgemm
+
+        return tgemm.mm(x, weight, bias)
+
     return torch.nn.functional.linear(x, weight, bias)
 
 
diff --git a/vllm/model_executor/model_loader/base_loader.py b/vllm/model_executor/model_loader/base_loader.py
index d6c38664fde6..55a4dd4c28fc 100644
--- a/vllm/model_executor/model_loader/base_loader.py
+++ b/vllm/model_executor/model_loader/base_loader.py
@@ -65,12 +65,11 @@ def load_model(
 
             # Log peak GPU memory after loading weights. This is needed
             # to have test coverage on peak memory for online quantization.
-            if current_platform.is_cuda():
+            if current_platform.is_cuda_alike():
                 peak_memory = torch.accelerator.max_memory_allocated()
                 logger.debug_once(
                     "Peak GPU memory after loading weights: %s GiB",
                     format_gib(peak_memory),
-                    scope="local",
                 )
 
             # Process weights into kernel format. Note that when using online
diff --git a/vllm/model_executor/model_loader/default_loader.py b/vllm/model_executor/model_loader/default_loader.py
index 5c9c97f4b64a..5b5632e319c9 100644
--- a/vllm/model_executor/model_loader/default_loader.py
+++ b/vllm/model_executor/model_loader/default_loader.py
@@ -76,7 +76,11 @@ def __init__(self, load_config: LoadConfig):
         self.local_expert_ids: set[int] | None = None
 
         extra_config = load_config.model_loader_extra_config
-        allowed_keys = {"enable_multithread_load", "num_threads"}
+        allowed_keys = {
+            "enable_multithread_load",
+            "num_threads",
+            "enable_weights_track",
+        }
         unexpected_keys = set(extra_config.keys()) - allowed_keys
 
         if unexpected_keys:
@@ -86,6 +90,10 @@ def __init__(self, load_config: LoadConfig):
                 f"{unexpected_keys}"
             )
 
+        self.enable_weights_track: bool | None = extra_config.get(
+            "enable_weights_track", None
+        )
+
     def _prepare_weights(
         self,
         model_name_or_path: str,
@@ -248,6 +256,12 @@ def _get_weights_iterator(
                         self.load_config.use_tqdm_on_load,
                         self.load_config.safetensors_load_strategy,
                         local_expert_ids=self.local_expert_ids,
+                        safetensors_prefetch_num_threads=(
+                            self.load_config.safetensors_prefetch_num_threads
+                        ),
+                        safetensors_prefetch_block_size=(
+                            self.load_config.safetensors_prefetch_block_size
+                        ),
                     )
         else:
             if extra_config.get("enable_multithread_load"):
@@ -377,18 +391,44 @@ def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
 
         self._init_ep_weight_filter(model_config)
 
-        weights_to_load = {name for name, _ in model.named_parameters()}
         loaded_weights = model.load_weights(self.get_all_weights(model_config, model))
 
         self.counter_after_loading_weights = time.perf_counter()
         logger.info_once(
             "Loading weights took %.2f seconds",
             self.counter_after_loading_weights - self.counter_before_loading_weights,
-            scope="local",
         )
         # We only enable strict check for non-quantized models
-        # that have loaded weights tracking currently.
-        if model_config.quantization is None and loaded_weights is not None:
+        # that have loaded weights tracking by default.
+        default_enable_weights_track = (
+            model_config.quantization is None and loaded_weights is not None
+        )
+        enable_weights_track = (
+            self.enable_weights_track
+            if self.enable_weights_track is not None
+            else default_enable_weights_track
+        )
+        if enable_weights_track:
+            self.track_weights_loading(model, loaded_weights)
+
+    def track_weights_loading(
+        self, model: nn.Module, loaded_weights: set[str] | None
+    ) -> None:
+        weights_to_load = {name for name, _ in model.named_parameters()}
+        if loaded_weights is not None:
+            # ignore online quantization scales
+            for name, module in model.named_modules():
+                quant_method = getattr(module, "quant_method", None)
+                has_online_quant = getattr(quant_method, "uses_meta_device", False)
+                has_postprocess_quant = getattr(
+                    quant_method, "process_weights_after_loading", None
+                )
+                # ignore kv_cache scale and online quant scale,
+                # which can be missing in checkpoints
+                if has_online_quant or has_postprocess_quant:
+                    for param_name, _ in module.named_parameters():
+                        full_name = f"{name}.{param_name}" if name else param_name
+                        loaded_weights.add(full_name)
             weights_not_loaded = weights_to_load - loaded_weights
             if weights_not_loaded:
                 raise ValueError(
diff --git a/vllm/model_executor/model_loader/gguf_loader.py b/vllm/model_executor/model_loader/gguf_loader.py
index ce6a813b8da5..fc6f88b49ee1 100644
--- a/vllm/model_executor/model_loader/gguf_loader.py
+++ b/vllm/model_executor/model_loader/gguf_loader.py
@@ -265,12 +265,24 @@ def find_hf_name_in_tensor_map(hf_name: str) -> str | None:
                 GGUF tensor name with suffix (e.g., 'mm.soft_emb_norm.weight')
                 or None if no mapping found
             """
+            # In transformers v5, multimodal models (e.g. Gemma3) wrap
+            # all sub-models under an outer 'model.' attribute, producing
+            # state_dict keys like 'model.language_model.layers.0...' and
+            # 'model.vision_tower.vision_model...'.  Strip this outer
+            # prefix so the keys match what gguf-py expects.
+            if is_multimodal and hf_name.startswith("model."):
+                hf_name = hf_name[6:]  # Remove outer 'model.'
+
             # Strip 'language_model.' prefix for multimodal models - gguf-py
             # tensor mappings expect parameter names without this prefix.
             # Note: 'model.' prefix should be KEPT for text-only models as
             # gguf-py expects it.
             if hf_name.startswith("language_model."):
                 hf_name = hf_name[15:]  # Remove 'language_model.'
+                # Re-add 'model.' prefix because gguf-py text tensor maps
+                # expect 'model.layers...' format.
+                if is_multimodal:
+                    hf_name = "model." + hf_name
 
             # Parse parameter name and suffix
             if hf_name.endswith((".weight", ".bias")):
diff --git a/vllm/model_executor/model_loader/reload/__init__.py b/vllm/model_executor/model_loader/reload/__init__.py
index 56a9d88ac4e4..61dc1b66e6f3 100644
--- a/vllm/model_executor/model_loader/reload/__init__.py
+++ b/vllm/model_executor/model_loader/reload/__init__.py
@@ -8,10 +8,9 @@
 
 Limitations:
 1. Composition with CPU offloading has not been implemented
-2. Reloading Attention/MLA weights (q_scale, k_scale, v_scale) has not been implemented
-3. Tied parameters will only reflect processing from one of the parent layers (for
+2. Tied parameters will only reflect processing from one of the parent layers (for
    example, only processing from embed_tokens will have an effect)
-4. This design assumes that the number of weights loaded from disk is the same as the
+3. This design assumes that the number of weights loaded from disk is the same as the
    number of weights created at model init time. This is not true for quant methods
    which (1) pad weights or (2) load qkv weights into the same parameter. Both of these
    cases are non-issues for today's quant methods, but future quantizations may cause
diff --git a/vllm/model_executor/model_loader/reload/layerwise.py b/vllm/model_executor/model_loader/reload/layerwise.py
index 2934b8b5ad52..f8d8304199ea 100644
--- a/vllm/model_executor/model_loader/reload/layerwise.py
+++ b/vllm/model_executor/model_loader/reload/layerwise.py
@@ -3,7 +3,7 @@
 import inspect
 from collections.abc import Callable
 from functools import wraps
-from weakref import WeakKeyDictionary
+from weakref import WeakKeyDictionary, WeakSet
 
 import torch
 
@@ -21,7 +21,13 @@
     restore_layer_on_meta,
 )
 from .types import LayerReloadingInfo
-from .utils import get_layer_params_buffers, get_layer_size, get_layer_tensors
+from .utils import (
+    get_info_size,
+    get_layer_params_buffers,
+    get_layer_size,
+    get_layer_tensors,
+    has_device_tensors,
+)
 
 logger = init_logger(__name__)
 
@@ -43,6 +49,9 @@
     WeakKeyDictionary()
 )
 
+# Global set used to track loading for logging purposes only
+LOADING_LAYERS: WeakSet[torch.nn.Module] = WeakSet()
+
 
 def get_layerwise_info(layer: torch.nn.Module) -> LayerReloadingInfo:
     """
@@ -174,11 +183,30 @@ def online_process_loader(*args, **kwargs):
             info.load_numel_total,
         )
 
+        # Do not online process attention layers, must wait until finalize
+        if isinstance(layer, (Attention, MLAAttention)):
+            return ret
+
+        # Log warnings allocating excessive buffers on device
+        if has_device_tensors(bound_args):
+            LOADING_LAYERS.add(layer)
+            if len(LOADING_LAYERS) >= 2:
+                names = sorted([layer.__class__.__name__ for layer in LOADING_LAYERS])
+                mem_used = sum(
+                    get_info_size(LAYERWISE_INFO[layer]) for layer in LOADING_LAYERS
+                )
+                logger.warning_once(
+                    "Allocating %.1f MB of device memory to buffers to load %s layers. "
+                    "This extra memory usage can be avoided by ordering weights "
+                    "by their parent layer when reloading.",
+                    mem_used / 1e6,
+                    str(list(names)),
+                )
+
         # Process and copy when all weights are loaded
-        if info.load_numel >= info.load_numel_total and not isinstance(  # type: ignore[operator]
-            layer, (Attention, MLAAttention)
-        ):
+        if info.load_numel >= info.load_numel_total:  # type: ignore[operator]
             _layerwise_process(layer, info)
+            LOADING_LAYERS.discard(layer)
 
         return ret
 
@@ -200,6 +228,8 @@ def finalize_layerwise_processing(model: torch.nn.Module, model_config: ModelCon
     if hasattr(model, "_original_do_torchao_reload"):
         model._do_torchao_reload = model._original_do_torchao_reload
 
+    deferred_attn: list[tuple[torch.nn.Module, LayerReloadingInfo]] = []
+
     for layer in model.modules():
         info = get_layerwise_info(layer)
         if not info.can_load():
@@ -208,22 +238,11 @@ def finalize_layerwise_processing(model: torch.nn.Module, model_config: ModelCon
 
         # Attention/MLA layers are processed after all other layers
         if isinstance(layer, (Attention, MLAAttention)):
-            if info.load_numel > 0:
-                raise NotImplementedError(
-                    "Layerwise reloading of Q/K/V scale weights is not implemented yet"
-                )
-
-            elif info.kernel_tensors is None:
-                raise NotImplementedError(
-                    "Layerwise loading of Q/K/V scale weights is not implemented yet"
-                )
-
-            else:
-                _place_kernel_tensors(layer, info)
-                layer.process_weights_after_loading(model_config.dtype)
+            deferred_attn.append((layer, info))
+            continue
 
         # No weights were loaded
-        elif info.load_numel <= 0:
+        if info.load_numel <= 0:
             # first load: checkpoint did not contain weights for this layer
             if info.kernel_tensors is None:
                 _layerwise_process(layer, info)
@@ -244,11 +263,60 @@ def finalize_layerwise_processing(model: torch.nn.Module, model_config: ModelCon
 
         info.reset()
 
+    # Process attention layers after all other layers are done
+    for layer, info in deferred_attn:
+        _finalize_attention_layer(layer, info, model_config)
+        info.reset()
+
+    LOADING_LAYERS.clear()
+
 
 def finalize_layerwise_reload(*args, **kwargs):
     finalize_layerwise_processing(*args, **kwargs)
 
 
+def _finalize_attention_layer(
+    layer: torch.nn.Module, info: LayerReloadingInfo, model_config: ModelConfig
+) -> None:
+    if info.load_numel > 0 and info.kernel_tensors is not None:
+        # Reload with new scale weights from checkpoint
+        _place_kernel_tensors(layer, info)
+        _reload_attention_scales(layer, info)
+    elif info.load_numel > 0 or info.kernel_tensors is None:
+        raise ValueError(
+            "Layerwise loading of attention layers is not supported. "
+            "Attention must always process after linears."
+        )
+    else:
+        _place_kernel_tensors(layer, info)
+    layer.process_weights_after_loading(model_config.dtype)
+
+
+def _reload_attention_scales(layer: torch.nn.Module, info: LayerReloadingInfo) -> None:
+    """Load and process attention scale weights (k_scale, v_scale, etc.)
+    during reload.
+
+    Assumes dtype/shapes of attention tensors do not change during
+    processing, since we use .data.copy_() to preserve kernel tensor
+    references."""
+    quant_method = getattr(layer, "quant_method", None)
+    if quant_method is None:
+        return
+
+    # Re-create scale Parameters with sentinel values so unloaded scales
+    # are correctly detected by process_weights_after_loading
+    quant_method.create_weights(layer)
+
+    for name, args in info.loaded_weights:
+        param = getattr(layer, name)
+        args.arguments["param"] = param
+        _get_weight_loader(param)(*args.args, **args.kwargs)
+
+    quant_method.process_weights_after_loading(layer)
+
+    _copy_and_restore_kernel_tensors(layer, info)
+
+
 def _layerwise_process(layer: torch.nn.Module, info: LayerReloadingInfo):
     """
     Finalize layer loading after all weights have been buffered.
@@ -278,7 +346,6 @@ def _layerwise_process(layer: torch.nn.Module, info: LayerReloadingInfo):
         param.weight_loader(*args.args, **args.kwargs)
 
     # Process weights (quantization, repacking, etc.)
-    # Attention/MLA are processed in `finalize_layerwise_reload`
     quant_method = getattr(layer, "quant_method", None)
     if isinstance(quant_method, QuantizeMethodBase):
         quant_method.process_weights_after_loading(layer)
@@ -286,13 +353,7 @@ def _layerwise_process(layer: torch.nn.Module, info: LayerReloadingInfo):
     # Copy processed values into original tensor storage (preserves cudagraph refs)
     # this code is a no-op if not reloading (because kernel tensors is empty)
     if info.kernel_tensors is not None:
-        parameters, buffers = info.kernel_tensors
-        for name, param in parameters.items():
-            param.data.copy_(getattr(layer, name))
-        for name, buffer in buffers.items():
-            buffer.data.copy_(getattr(layer, name))
-
-        _place_kernel_tensors(layer, info)
+        _copy_and_restore_kernel_tensors(layer, info)
 
     info.reset()
     logger.debug("%s: Processed", layer.__class__.__name__)
@@ -311,6 +372,19 @@ def _get_weight_loader(tensor: torch.Tensor):
     return getattr(tensor, "weight_loader", default_weight_loader)
 
 
+def _copy_and_restore_kernel_tensors(layer: torch.nn.Module, info: LayerReloadingInfo):
+    """Copy processed values into original kernel tensor storage and restore
+    kernel tensor references on the layer. Preserves cudagraph references."""
+    assert info.kernel_tensors is not None
+    parameters, buffers = info.kernel_tensors
+    for name, param in parameters.items():
+        param.data.copy_(getattr(layer, name))
+    for name, buffer in buffers.items():
+        buffer.data.copy_(getattr(layer, name))
+
+    _place_kernel_tensors(layer, info)
+
+
 def _place_kernel_tensors(layer: torch.nn.Module, info: LayerReloadingInfo):
     for name in get_layer_tensors(layer):
         delattr(layer, name)
diff --git a/vllm/model_executor/model_loader/reload/meta.py b/vllm/model_executor/model_loader/reload/meta.py
index 91fce6f57b3e..baa2081d58b2 100644
--- a/vllm/model_executor/model_loader/reload/meta.py
+++ b/vllm/model_executor/model_loader/reload/meta.py
@@ -102,7 +102,7 @@ def materialize_layer(layer: torch.nn.Module, info: LayerReloadingInfo):
 
     with info.restore_device:
         for name, tensor in get_layer_tensors(layer).items():
-            if name not in SKIP_TENSORS:
+            if name not in SKIP_TENSORS and tensor.is_meta:
                 setattr(layer, name, materialize_meta_tensor(tensor))
 
 
diff --git a/vllm/model_executor/model_loader/reload/utils.py b/vllm/model_executor/model_loader/reload/utils.py
index 463ff6422213..7a3d6873e101 100644
--- a/vllm/model_executor/model_loader/reload/utils.py
+++ b/vllm/model_executor/model_loader/reload/utils.py
@@ -1,14 +1,18 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from inspect import BoundArguments
+
 import torch
 
-from .types import LayerTensors
+from .types import LayerReloadingInfo, LayerTensors
 
 __all__ = [
     "get_layer_tensors",
     "get_layer_params_buffers",
     "get_layer_size",
+    "has_device_tensors",
+    "get_info_size",
 ]
 
 
@@ -39,3 +43,31 @@ def get_layer_size(layer: torch.nn.Module) -> int:
         for name, tensor in get_layer_tensors(layer).items()
         if name not in SKIP_TENSORS
     )
+
+
+def has_device_tensors(bound_args: BoundArguments) -> bool:
+    """
+    Return True if the loaded weights exist on an accelerator device
+
+    :param bound_args: args to load weights
+    :return: True if weights are on accelerator device
+    """
+    return any(
+        isinstance(value, torch.Tensor) and value.device.type not in ("meta", "cpu")
+        for value in bound_args.arguments.values()
+    )
+
+
+def get_info_size(info: LayerReloadingInfo) -> int:
+    """
+    Calculate the number of bytes used by loaded weights for a given layer
+
+    :param info: layerwise info to get size of
+    :return: number of bytes used by loaded weights
+    """
+    return sum(
+        value.nbytes
+        for _, args in info.loaded_weights
+        for value in args.arguments.values()
+        if isinstance(value, torch.Tensor) and value.device.type not in ("meta", "cpu")
+    )
diff --git a/vllm/model_executor/model_loader/sharded_state_loader.py b/vllm/model_executor/model_loader/sharded_state_loader.py
index a87731e8bc0b..3f57fe7e0265 100644
--- a/vllm/model_executor/model_loader/sharded_state_loader.py
+++ b/vllm/model_executor/model_loader/sharded_state_loader.py
@@ -31,8 +31,8 @@ class ShardedStateLoader(BaseModelLoader):
     Model loader that directly loads each worker's model state dict, which
     enables a fast load path for large tensor-parallel models where each worker
     only needs to read its own shard rather than the entire checkpoint. See
-    `examples/offline_inference/save_sharded_state.py` for creating a sharded
-    checkpoint.
+    `examples/features/sharded_state/save_sharded_state_offline.py` for creating
+    a sharded checkpoint.
     """
 
     DEFAULT_PATTERN = "model-rank-{rank}-part-{part}.safetensors"
@@ -157,7 +157,6 @@ def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
         logger.info_once(
             "Loading weights took %.2f seconds",
             counter_after_loading_weights - counter_before_loading_weights,
-            scope="local",
         )
         if state_dict:
             raise ValueError(f"Missing keys {tuple(state_dict)} in loaded state!")
diff --git a/vllm/model_executor/model_loader/tensorizer.py b/vllm/model_executor/model_loader/tensorizer.py
index 3e6ed248ff3a..37d37d55f543 100644
--- a/vllm/model_executor/model_loader/tensorizer.py
+++ b/vllm/model_executor/model_loader/tensorizer.py
@@ -211,7 +211,7 @@ class TensorizerConfig(MutableMapping):
         encryption_keyfile: File path to a binary file containing a  
             binary key to use for decryption. `None` (the default) means 
             no decryption. See the example script in 
-            examples/others/tensorize_vllm_model.py. 
+            examples/features/tensorize_vllm_model.py. 
         s3_access_key_id: The access key for the S3 bucket. Can also be set via
             the S3_ACCESS_KEY_ID environment variable.
         s3_secret_access_key: The secret access key for the S3 bucket. Can also
@@ -579,7 +579,7 @@ def tensorizer_weights_iterator(
         "loading on vLLM, as tensorizer is forced to load to CPU. "
         "Consider deserializing a vLLM model instead for faster "
         "load times. See the "
-        "examples/others/tensorize_vllm_model.py example script "
+        "examples/features/tensorize_vllm_model.py example script "
         "for serializing vLLM models."
     )
 
diff --git a/vllm/model_executor/model_loader/tensorizer_loader.py b/vllm/model_executor/model_loader/tensorizer_loader.py
index c5bff1312932..338f9eac072a 100644
--- a/vllm/model_executor/model_loader/tensorizer_loader.py
+++ b/vllm/model_executor/model_loader/tensorizer_loader.py
@@ -73,7 +73,7 @@ def _load_model_serialized_cpu(
         """Load a serialized model with tensorizer to the CPU.
 
         This is only necessary when the model isn't vLLM-tensorized (see
-        examples/others/tensorize_vllm_model.py) This should still
+        examples/features/tensorize_vllm_model.py) This should still
         be faster than default HuggingFace loading, but will be slower than
         loading a vLLM-tensorized model.
         """
@@ -104,7 +104,7 @@ def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
         """Load serialized model weights with tensorizer.
 
         Expects a vLLM-tensorized model. See the
-        examples/others/tensorize_vllm_model.py example script
+        examples/features/tensorize_vllm_model.py example script
         for serializing vLLM models."""
         if is_vllm_tensorized(self.tensorizer_config):
             tensorizer_config = self._patch_tensorizer_config(model_config)
diff --git a/vllm/model_executor/model_loader/utils.py b/vllm/model_executor/model_loader/utils.py
index 4ee9e90d7410..2a5f746d783e 100644
--- a/vllm/model_executor/model_loader/utils.py
+++ b/vllm/model_executor/model_loader/utils.py
@@ -15,7 +15,11 @@
 import vllm.envs as envs
 from vllm.config import ModelConfig, VllmConfig, set_current_vllm_config
 from vllm.logger import init_logger
-from vllm.model_executor.layers.attention import Attention, MLAAttention
+from vllm.model_executor.layers.attention import (
+    Attention,
+    MLAAttention,
+    MMEncoderAttention,
+)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
@@ -106,12 +110,12 @@ def process_weights_after_loading(
             with device_loading_context(module, target_device):
                 quant_method.process_weights_after_loading(module)
 
-    # Initialize post-load attention weights for both Attention and MLA.
+    # Initialize post-load attention weights for Attention, MLA, and MM encoder.
     # NOTE: Happens after other modules so we can easily decompress weights.
     for _, module in model.named_modules():
-        if isinstance(module, (Attention, MLAAttention)) and hasattr(
-            module, "process_weights_after_loading"
-        ):
+        if isinstance(
+            module, (Attention, MLAAttention, MMEncoderAttention)
+        ) and hasattr(module, "process_weights_after_loading"):
             # TODO(lucas): see if there is a way to unify the signatures
             # of process_weights_after_loading
             with device_loading_context(module, target_device):
diff --git a/vllm/model_executor/model_loader/weight_utils.py b/vllm/model_executor/model_loader/weight_utils.py
index 3b961e8e143d..c418c29a8c72 100644
--- a/vllm/model_executor/model_loader/weight_utils.py
+++ b/vllm/model_executor/model_loader/weight_utils.py
@@ -30,7 +30,11 @@
 
 from vllm import envs
 from vllm.config import ModelConfig
-from vllm.config.load import LoadConfig
+from vllm.config.load import (
+    DEFAULT_SAFETENSORS_PREFETCH_BLOCK_SIZE,
+    DEFAULT_SAFETENSORS_PREFETCH_NUM_THREADS,
+    LoadConfig,
+)
 from vllm.distributed import get_tensor_model_parallel_rank, get_world_group
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization import (
@@ -810,40 +814,57 @@ def _get_fs_type(files: list[str]) -> str:
         return ""
 
 
-def _prefetch_checkpoint(file_path: str) -> None:
+def _prefetch_checkpoint(
+    file_path: str,
+    block_size: int = DEFAULT_SAFETENSORS_PREFETCH_BLOCK_SIZE,
+) -> None:
     """Prefetch a checkpoint file into the OS page cache.
 
-    Reads the file in 16MB blocks so the kernel caches its pages before
-    workers load the same file.
+    Reads the file in blocks so the kernel caches its pages before workers load
+    the same file.
     """
-    block_size = 16 * 1024 * 1024  # 16MB
+    if block_size < 1:
+        raise ValueError("safetensors prefetch block size must be >= 1")
+
     with open(file_path, "rb") as f:
         while f.read(block_size):
             pass
 
 
-def _prefetch_all_checkpoints(sorted_files: list[str]) -> None:
+def _prefetch_all_checkpoints(
+    sorted_files: list[str],
+    num_prefetch_threads: int = DEFAULT_SAFETENSORS_PREFETCH_NUM_THREADS,
+    block_size: int = DEFAULT_SAFETENSORS_PREFETCH_BLOCK_SIZE,
+) -> None:
     """Start prefetching checkpoint files into page cache in a background thread."""
+    if num_prefetch_threads < 1:
+        raise ValueError("safetensors prefetch num threads must be >= 1")
+    if block_size < 1:
+        raise ValueError("safetensors prefetch block size must be >= 1")
+
     if torch.distributed.is_initialized():
         rank = torch.distributed.get_rank()
         world_size = torch.distributed.get_world_size()
     else:
         rank = 0
         world_size = 1
-    num_prefetch_threads = 8
     paths_to_prefetch = sorted_files[rank::world_size]
     total_for_rank = len(paths_to_prefetch)
 
     async def _prefetch_all() -> None:
-        semaphore = asyncio.Semaphore(num_prefetch_threads)
+        loop = asyncio.get_running_loop()
         completed = 0
         next_log_pct = 10
 
-        async def prefetch_one(path: str) -> None:
+        async def prefetch_one(
+            path: str,
+            executor: concurrent.futures.ThreadPoolExecutor,
+        ) -> None:
             nonlocal completed, next_log_pct
             try:
-                async with semaphore:
-                    await asyncio.to_thread(_prefetch_checkpoint, path)
+                await loop.run_in_executor(
+                    executor, _prefetch_checkpoint, path, block_size
+                )
                 completed += 1
                 if total_for_rank > 0 and next_log_pct <= 100:
                     pct = 100 * completed / total_for_rank
@@ -860,7 +881,12 @@ async def prefetch_one(path: str) -> None:
                     "Failed to prefetch checkpoint file %r.", path, exc_info=True
                 )
 
-        await asyncio.gather(*(prefetch_one(p) for p in paths_to_prefetch))
+        with concurrent.futures.ThreadPoolExecutor(
+            max_workers=num_prefetch_threads
+        ) as executor:
+            await asyncio.gather(
+                *(prefetch_one(p, executor) for p in paths_to_prefetch)
+            )
 
     def _run_prefetch() -> None:
         start = time.perf_counter()
@@ -871,7 +897,12 @@ def _run_prefetch() -> None:
             elapsed,
         )
 
-    logger.info("Prefetching checkpoint files into page cache started (in background)")
+    logger.info(
+        "Prefetching checkpoint files into page cache started "
+        "(in background, num_threads=%d, block_size=%d bytes)",
+        num_prefetch_threads,
+        block_size,
+    )
     threading.Thread(target=_run_prefetch, daemon=True).start()
 
 
@@ -880,6 +911,9 @@ def safetensors_weights_iterator(
     use_tqdm_on_load: bool,
     safetensors_load_strategy: str | None = None,
     local_expert_ids: set[int] | None = None,
+    *,
+    safetensors_prefetch_num_threads: int = DEFAULT_SAFETENSORS_PREFETCH_NUM_THREADS,
+    safetensors_prefetch_block_size: int = DEFAULT_SAFETENSORS_PREFETCH_BLOCK_SIZE,
 ) -> Generator[tuple[str, torch.Tensor], None, None]:
     """Iterate over the weights in the model safetensor files.
 
@@ -951,7 +985,11 @@ def safetensors_weights_iterator(
         )
 
     if should_prefetch:
-        _prefetch_all_checkpoints(sorted_files)
+        _prefetch_all_checkpoints(
+            sorted_files,
+            num_prefetch_threads=safetensors_prefetch_num_threads,
+            block_size=safetensors_prefetch_block_size,
+        )
 
     leftover_state_dict: dict[str, torch.Tensor] = {}
     for st_file in tqdm(
@@ -1364,8 +1402,8 @@ def default_weight_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> N
         if param.numel() == 1 and loaded_weight.numel() == 1:
             # Sometimes scalar values aren't considered tensors with shapes
             # so if both param and loaded_weight are a scalar,
-            # "broadcast" instead of copy
-            param.data.fill_(loaded_weight.item())
+            # reshape to match before copying
+            param.data.copy_(loaded_weight.view(param.shape))
         else:
             assert param.size() == loaded_weight.size(), (
                 f"Attempted to load weight ({loaded_weight.size()}) "
@@ -1562,6 +1600,11 @@ def maybe_remap_kv_scale_name(name: str, params_dict: dict) -> str | None:
         # NemotronH format: .mixer.{k,v}_proj.{k,v}_scale ->
         # .mixer.attn.{k,v}_scale
         (r"\.mixer\.[kv]_proj\.([kv])_scale$", r".mixer.attn.\1_scale"),
+        # HYV3 format: .self_attn.q.scale -> .self_attn.attn.q_scale
+        (r"\.self_attn\.q\.scale$", r".self_attn.attn.q_scale"),
+        # HYV3 format: .self_attn.{k,v}_cache.scale ->
+        # .self_attn.attn.{k,v}_scale
+        (r"\.self_attn\.([kv])_cache\.scale$", r".self_attn.attn.\1_scale"),
         # Default format: .{k,v}_scale -> .attn.{k,v}_scale
         (r"\.([qkv])_scale$", r".attn.\1_scale"),
         (r"\.([qkv])_zero_point$", r".attn.\1_zero_point"),
@@ -1576,6 +1619,9 @@ def maybe_remap_kv_scale_name(name: str, params_dict: dict) -> str | None:
             ".k_zero_point",
             ".v_zero_point",
             ".q_zero_point",
+            ".q.scale",
+            ".k_cache.scale",
+            ".v_cache.scale",
         )
     ):
         import regex as re
diff --git a/vllm/model_executor/models/AXK1.py b/vllm/model_executor/models/AXK1.py
index f5ed4400fb65..701ec67c855c 100644
--- a/vllm/model_executor/models/AXK1.py
+++ b/vllm/model_executor/models/AXK1.py
@@ -42,7 +42,10 @@
 )
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -74,6 +77,7 @@
 
 from .interfaces import MixtureOfExperts, SupportsEagle, SupportsLoRA, SupportsPP
 from .utils import (
+    AutoWeightsLoader,
     PPMissingLayer,
     is_pp_missing_parameter,
     make_empty_intermediate_tensors_factory,
@@ -100,7 +104,7 @@ def __init__(
         self.tp_size = get_tensor_model_parallel_world_size()
         self.tp_rank = get_tensor_model_parallel_rank()
 
-        self.routed_scaling_factor = config.routed_scaling_factor
+        self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
 
         self.ep_group = get_ep_group().device_group
         self.ep_rank = get_ep_group().rank_in_group
@@ -163,14 +167,13 @@ def __init__(
                 prefix=f"{prefix}.shared_experts",
             )
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_experts,
             gate=self.gate,
             num_experts=config.n_routed_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             use_grouped_topk=True,
@@ -180,9 +183,8 @@ def __init__(
             scoring_func=config.scoring_func,
             # we do scaling outside, set factor to 1.0 to avoid double mul
             # aiter applies routed_scaling_factor internally
-            routed_scaling_factor=1.0
-            if not self.is_rocm_aiter_moe_enabled
-            else self.routed_scaling_factor,
+            routed_scaling_factor=self.routed_scaling_factor,
+            apply_routed_scale_to_output=not self.is_rocm_aiter_moe_enabled,
             e_score_correction_bias=self.gate.e_score_correction_bias,
             enable_eplb=self.enable_eplb,
             num_redundant_experts=self.n_redundant_experts,
@@ -204,43 +206,20 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             hidden_states = sequence_parallel_chunk(hidden_states)
 
         if self.experts.is_internal_router:
-            # In this case, the gate/router runs inside the FusedMoE class
-            fused_moe_out = self.experts(
+            final_hidden_states = self.experts(
                 hidden_states=hidden_states, router_logits=hidden_states
             )
         else:
-            # router_logits: (num_tokens, n_experts)
             router_logits, _ = self.gate(hidden_states)
-            fused_moe_out = self.experts(
+            final_hidden_states = self.experts(
                 hidden_states=hidden_states, router_logits=router_logits
             )
 
-        shared_output, final_hidden_states = fused_moe_out
-        if self.shared_experts is None:
-            assert shared_output is None
-
-        # Fix FP16 overflow
-        # See AXK1DecoderLayer for more details.
-        if hidden_states.dtype != torch.float16:
-            if not self.is_rocm_aiter_moe_enabled:
-                final_hidden_states *= self.routed_scaling_factor
-        elif self.shared_experts is not None:
-            assert shared_output is not None
-            shared_output *= 1.0 / self.routed_scaling_factor
-
-        if self.shared_experts is not None:
-            assert shared_output is not None
-            final_hidden_states += shared_output
-
         if self.is_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
                 final_hidden_states, 0
             )
             final_hidden_states = final_hidden_states[:num_tokens]
-        elif self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
 
         return final_hidden_states.view(num_tokens, hidden_dim)
 
@@ -749,6 +728,13 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
             ["hidden_states", "residual"], config.hidden_size
         )
+        self.use_mha = all(
+            dim == 0 for dim in (config.qk_nope_head_dim, config.qk_rope_head_dim)
+        )
+        self.fuse_qkv_a_proj = config.q_lora_rank is not None
+        self.num_redundant_experts = (
+            vllm_config.parallel_config.eplb_config.num_redundant_experts
+        )
 
     def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
@@ -798,158 +784,6 @@ def forward(
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
-
-class AXK1MixtureOfExperts(MixtureOfExperts):
-    moe_mlp_layers: list[AXK1MoE]
-    """
-    List of MoE MLP layers in the model.
-    """
-
-    def extract_moe_parameters(self, example_moe: AXK1MoE | None):
-        if example_moe is None:
-            self.num_moe_layers = 0
-            self.num_expert_groups = 0
-            self.num_logical_experts = 0
-            self.num_physical_experts = 0
-            self.num_local_physical_experts = 0
-            self.num_routed_experts = 0
-            self.num_shared_experts = 0
-            self.num_redundant_experts = 0
-            logger.warning("AXK1: No AXK1MoE layer found in model.layers.")
-        else:
-            self.num_logical_experts = example_moe.n_logical_experts
-            self.num_physical_experts = example_moe.n_physical_experts
-            self.num_local_physical_experts = example_moe.n_local_physical_experts
-            self.num_routed_experts = example_moe.n_routed_experts
-            self.num_shared_experts = example_moe.n_shared_experts
-            self.num_redundant_experts = example_moe.n_redundant_experts
-
-    def update_physical_experts_metadata(
-        self,
-        num_physical_experts: int,
-        num_local_physical_experts: int,
-    ) -> None:
-        assert self.num_local_physical_experts == num_local_physical_experts
-        self.num_physical_experts = num_physical_experts
-        self.num_local_physical_experts = num_local_physical_experts
-        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
-        for moe in self.moe_mlp_layers:
-            moe.n_local_physical_experts = num_local_physical_experts
-            moe.n_physical_experts = num_physical_experts
-            moe.n_redundant_experts = self.num_redundant_experts
-            moe.experts.update_expert_map()
-
-
-class AXK1ForCausalLM(
-    nn.Module, SupportsPP, AXK1MixtureOfExperts, SupportsLoRA, SupportsEagle
-):
-    packed_modules_mapping = {
-        "gate_up_proj": ["gate_proj", "up_proj"],
-    }
-    model_cls = AXK1Model
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-        config: AXK1Config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
-        self.config = config
-        self.quant_config = quant_config
-
-        qk_nope_head_dim = config.qk_nope_head_dim
-        qk_rope_head_dim = config.qk_rope_head_dim
-        self.use_mha = all(dim == 0 for dim in (qk_nope_head_dim, qk_rope_head_dim))
-
-        if self.use_mha:
-            self.packed_modules_mapping["qkv_proj"] = ["q_proj", "k_proj", "v_proj"]
-
-        # `packed_modules_mapping` needs to be modified before
-        # initializing AXK1Model, as it is passed inplace to
-        # quantization config init and may be used to select the
-        # quant_method for relevant layers during initialization.
-        self.fuse_qkv_a_proj = config.q_lora_rank is not None
-        if self.fuse_qkv_a_proj:
-            self.packed_modules_mapping["fused_qkv_a_proj"] = [
-                "q_a_proj",
-                "kv_a_proj_with_mqa",
-            ]
-
-        self.model = self.model_cls(
-            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
-        )
-        if get_pp_group().is_last_rank:
-            self.lm_head = ParallelLMHead(
-                config.vocab_size,
-                config.hidden_size,
-                quant_config=quant_config,
-                prefix=maybe_prefix(prefix, "lm_head"),
-            )
-        else:
-            self.lm_head = PPMissingLayer()
-        self.logits_processor = LogitsProcessor(config.vocab_size)
-        self.make_empty_intermediate_tensors = (
-            self.model.make_empty_intermediate_tensors
-        )
-        # Set MoE hyperparameters
-        self.num_moe_layers = (
-            self.config.num_hidden_layers - self.config.first_k_dense_replace
-        )
-        self.set_moe_parameters()
-
-    def set_moe_parameters(self):
-        self.expert_weights = []
-
-        self.num_expert_groups = getattr(self.config, "n_group", 1)
-
-        self.moe_layers = []
-        self.moe_mlp_layers = []
-        example_moe = None
-        for layer in self.model.layers:
-            if isinstance(layer, PPMissingLayer):
-                continue
-
-            assert isinstance(layer, AXK1DecoderLayer)
-            if isinstance(layer.mlp, AXK1MoE):
-                # Pick last one layer since the first ones may be dense layers.
-                example_moe = layer.mlp
-                self.moe_mlp_layers.append(layer.mlp)
-                self.moe_layers.append(layer.mlp.experts)
-
-        self.extract_moe_parameters(example_moe)
-
-    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.embed_input_ids(input_ids)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor | None,
-        positions: torch.Tensor,
-        intermediate_tensors: IntermediateTensors | None = None,
-        inputs_embeds: torch.Tensor | None = None,
-    ) -> torch.Tensor | IntermediateTensors:
-        hidden_states = self.model(
-            input_ids, positions, intermediate_tensors, inputs_embeds
-        )
-        return hidden_states
-
-    def compute_logits(
-        self,
-        hidden_states: torch.Tensor,
-    ) -> torch.Tensor | None:
-        logits = self.logits_processor(self.lm_head, hidden_states)
-        return logits
-
-    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        # Params for weights, fp8 weight scales, fp8 activation scales
-        # (param_name, weight_name, expert_id, shard_id)
-        return SharedFusedMoE.make_expert_params_mapping(
-            self,
-            ckpt_gate_proj_name="gate_proj",
-            ckpt_down_proj_name="down_proj",
-            ckpt_up_proj_name="up_proj",
-            num_experts=self.config.n_routed_experts,
-            num_redundant_experts=0,
-        )
-
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         rocm_aiter_moe_shared_expert_enabled = (
             rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
@@ -975,7 +809,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -1157,12 +991,170 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         return loaded_params
 
 
+class AXK1MixtureOfExperts(MixtureOfExperts):
+    moe_mlp_layers: list[AXK1MoE]
+    """
+    List of MoE MLP layers in the model.
+    """
+
+    def extract_moe_parameters(self, example_moe: AXK1MoE | None):
+        if example_moe is None:
+            self.num_moe_layers = 0
+            self.num_expert_groups = 0
+            self.num_logical_experts = 0
+            self.num_physical_experts = 0
+            self.num_local_physical_experts = 0
+            self.num_routed_experts = 0
+            self.num_shared_experts = 0
+            self.num_redundant_experts = 0
+            logger.warning("AXK1: No AXK1MoE layer found in model.layers.")
+        else:
+            self.num_logical_experts = example_moe.n_logical_experts
+            self.num_physical_experts = example_moe.n_physical_experts
+            self.num_local_physical_experts = example_moe.n_local_physical_experts
+            self.num_routed_experts = example_moe.n_routed_experts
+            self.num_shared_experts = example_moe.n_shared_experts
+            self.num_redundant_experts = example_moe.n_redundant_experts
+
+    def update_physical_experts_metadata(
+        self,
+        num_physical_experts: int,
+        num_local_physical_experts: int,
+    ) -> None:
+        assert self.num_local_physical_experts == num_local_physical_experts
+        self.num_physical_experts = num_physical_experts
+        self.num_local_physical_experts = num_local_physical_experts
+        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
+        for moe in self.moe_mlp_layers:
+            moe.n_local_physical_experts = num_local_physical_experts
+            moe.n_physical_experts = num_physical_experts
+            moe.n_redundant_experts = self.num_redundant_experts
+            moe.experts.update_expert_map()
+
+
+class AXK1ForCausalLM(
+    nn.Module, SupportsPP, AXK1MixtureOfExperts, SupportsLoRA, SupportsEagle
+):
+    packed_modules_mapping = {
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+    model_cls = AXK1Model
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config: AXK1Config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+
+        qk_nope_head_dim = config.qk_nope_head_dim
+        qk_rope_head_dim = config.qk_rope_head_dim
+        self.use_mha = all(dim == 0 for dim in (qk_nope_head_dim, qk_rope_head_dim))
+
+        if self.use_mha:
+            self.packed_modules_mapping["qkv_proj"] = ["q_proj", "k_proj", "v_proj"]
+
+        # `packed_modules_mapping` needs to be modified before
+        # initializing AXK1Model, as it is passed inplace to
+        # quantization config init and may be used to select the
+        # quant_method for relevant layers during initialization.
+        self.fuse_qkv_a_proj = config.q_lora_rank is not None
+        if self.fuse_qkv_a_proj:
+            self.packed_modules_mapping["fused_qkv_a_proj"] = [
+                "q_a_proj",
+                "kv_a_proj_with_mqa",
+            ]
+
+        self.model = self.model_cls(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+        # Set MoE hyperparameters
+        self.num_moe_layers = (
+            self.config.num_hidden_layers - self.config.first_k_dense_replace
+        )
+        self.set_moe_parameters()
+
+    def set_moe_parameters(self):
+        self.expert_weights = []
+
+        self.num_expert_groups = getattr(self.config, "n_group", 1)
+
+        self.moe_layers = []
+        self.moe_mlp_layers = []
+        example_moe = None
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+
+            assert isinstance(layer, AXK1DecoderLayer)
+            if isinstance(layer.mlp, AXK1MoE):
+                # Pick last one layer since the first ones may be dense layers.
+                example_moe = layer.mlp
+                self.moe_mlp_layers.append(layer.mlp)
+                self.moe_layers.append(layer.mlp.experts)
+
+        self.extract_moe_parameters(example_moe)
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return fused_moe_make_expert_params_mapping(
+            self,
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.n_routed_experts,
+            num_redundant_experts=0,
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+
 def get_spec_layer_idx_from_weight_name(
     config: AXK1Config, weight_name: str
 ) -> int | None:
     if config.num_nextn_predict_layers and config.num_nextn_predict_layers > 0:
         layer_idx = config.num_hidden_layers
         for i in range(config.num_nextn_predict_layers):
-            if weight_name.startswith(f"model.layers.{layer_idx + i}."):
+            if weight_name.startswith(
+                f"model.layers.{layer_idx + i}."
+            ) or weight_name.startswith(f"layers.{layer_idx + i}."):
                 return layer_idx + i
     return None
diff --git a/vllm/model_executor/models/afmoe.py b/vllm/model_executor/models/afmoe.py
index 22037336411a..2216e4948bd9 100644
--- a/vllm/model_executor/models/afmoe.py
+++ b/vllm/model_executor/models/afmoe.py
@@ -18,7 +18,10 @@
 )
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe.shared_fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -124,14 +127,13 @@ def __init__(
                 prefix=f"{prefix}.shared_experts",
             )
 
-        # Routed experts using SharedFusedMoE
-        self.experts = SharedFusedMoE(
+        # Routed experts using FusedMoE
+        self.experts = FusedMoE(
             shared_experts=self.shared_experts,
             num_experts=config.num_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=self.route_norm if self.score_func == "sigmoid" else False,
             quant_config=quant_config,
             use_grouped_topk=True,
@@ -152,20 +154,10 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
 
         router_logits = self.gate(hidden_states.to(dtype=torch.float32))
 
-        fused_moe_out = self.experts(
+        final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
 
-        if self.shared_experts is not None:
-            shared_output, final_hidden_states = fused_moe_out
-            final_hidden_states = final_hidden_states + shared_output
-        else:
-            final_hidden_states = fused_moe_out
-        if self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
-
         return final_hidden_states.view(num_tokens, hidden_dim)
 
 
@@ -490,7 +482,7 @@ def make_empty_intermediate_tensors(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return SharedFusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -648,7 +640,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.num_moe_layers = config.num_hidden_layers - config.num_dense_layers
         self.num_expert_groups = config.n_group
 
-        self.moe_layers: list[SharedFusedMoE] = []
+        self.moe_layers: list[FusedMoE] = []
         example_moe = None
         for layer in self.model.layers:
             if isinstance(layer, PPMissingLayer):
diff --git a/vllm/model_executor/models/arctic.py b/vllm/model_executor/models/arctic.py
index 0c9267994b0a..6ab55a4b1bf3 100644
--- a/vllm/model_executor/models/arctic.py
+++ b/vllm/model_executor/models/arctic.py
@@ -18,7 +18,10 @@
 )
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import fused_experts, fused_topk
+from vllm.model_executor.layers.fused_moe import (
+    fused_experts,
+    fused_topk,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
diff --git a/vllm/model_executor/models/aria.py b/vllm/model_executor/models/aria.py
index 7b891f8ee429..48c8d9a441e5 100644
--- a/vllm/model_executor/models/aria.py
+++ b/vllm/model_executor/models/aria.py
@@ -14,7 +14,9 @@
 from vllm.distributed import get_tensor_model_parallel_rank
 from vllm.inputs import MultiModalDataDict
 from vllm.model_executor.layers.activation import get_act_fn
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+)
 from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -214,7 +216,7 @@ def forward(
         return out
 
 
-class AriaFusedMoE(SharedFusedMoE):
+class AriaFusedMoE(FusedMoE):
     def weight_loader(
         self, param: nn.Parameter, loaded_weight: torch.Tensor, shard_id: str
     ) -> None:
@@ -283,7 +285,6 @@ def __init__(
             hidden_size=config.hidden_size,
             intermediate_size=config.intermediate_size,
             quant_config=quant_config,
-            reduce_results=True,
             prefix=f"{prefix}.experts",
         )
 
@@ -301,12 +302,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
 
         router_output = torch.nn.functional.linear(hidden_states, self.router_weight)
 
-        sparse_expert_output = self.experts(hidden_states, router_output)
-
-        if self.shared_experts is not None:
-            return sparse_expert_output[0] + sparse_expert_output[1]
-        else:
-            return sparse_expert_output
+        return self.experts(hidden_states, router_output)
 
 
 class AriaTextDecoderLayer(LlamaDecoderLayer):
diff --git a/vllm/model_executor/models/bailing_moe.py b/vllm/model_executor/models/bailing_moe.py
index 7725dfa2a887..56e119207dae 100644
--- a/vllm/model_executor/models/bailing_moe.py
+++ b/vllm/model_executor/models/bailing_moe.py
@@ -41,7 +41,10 @@
 )
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -285,13 +288,12 @@ def __init__(
         else:
             self.shared_experts = None
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_experts,
             num_experts=self.num_experts,
             top_k=self.top_k,
             hidden_size=self.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=self.norm_expert_prob,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -301,6 +303,7 @@ def __init__(
             topk_group=self.topk_group,
             use_grouped_topk=self.use_grouped_topk,
             router_logits_dtype=self.router_dtype,
+            routed_scaling_factor=self.routed_scaling_factor,
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -314,21 +317,6 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
-
-        if self.shared_experts is not None:
-            shared_output, final_hidden_states = final_hidden_states
-        else:
-            shared_output = None
-
-        final_hidden_states *= self.routed_scaling_factor
-
-        if shared_output is not None:
-            final_hidden_states = final_hidden_states + shared_output
-
-        if self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
         return final_hidden_states.view(num_tokens, hidden_size)
 
 
@@ -476,7 +464,7 @@ def forward(
         return hidden_states
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return SharedFusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/bailing_moe_linear.py b/vllm/model_executor/models/bailing_moe_linear.py
index ecc5d63ced75..bcf1200bd1b4 100644
--- a/vllm/model_executor/models/bailing_moe_linear.py
+++ b/vllm/model_executor/models/bailing_moe_linear.py
@@ -17,11 +17,15 @@
 )
 from vllm.forward_context import get_forward_context
 from vllm.logger import init_logger
+from vllm.model_executor.custom_op import PluggableLayer
 from vllm.model_executor.layers.fla.ops.layernorm_guard import (
     RMSNormGated,
     layernorm_fn,
 )
-from vllm.model_executor.layers.fused_moe import FusedMoE, SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -201,14 +205,19 @@ def __init__(
             self.q_a_layernorm = None
             self.q_b_proj = None
 
-        rope_parameters = _build_rope_parameters(config)
+        rope_parameters = _build_rope_parameters(config) or {}
+        # MLA rotates the full qk_rope_head_dim,
+        # partial_rotary_factor is for the linear-attn head only.
+        rope_parameters = {
+            k: v for k, v in rope_parameters.items() if k != "partial_rotary_factor"
+        }
+        rope_parameters["rope_dim"] = self.qk_rope_head_dim
         max_position = getattr(config, "max_position_embeddings", 8192)
         self.rotary_emb = get_rope(
             head_size=self.qk_rope_head_dim,
             max_position=max_position,
             is_neox_style=False,
-            rope_parameters=rope_parameters or None,
-            dtype=torch.float32,
+            rope_parameters=rope_parameters,
         )
 
         # Build MLAModules for MultiHeadLatentAttentionWrapper
@@ -351,14 +360,13 @@ def __init__(
         else:
             self.shared_experts = None
 
-        # Routed experts using SharedFusedMoE
-        self.experts = SharedFusedMoE(
+        # Routed experts using FusedMoE
+        self.experts = FusedMoE(
             shared_experts=self.shared_experts,
             num_experts=self.num_experts,
             top_k=self.top_k,
             hidden_size=self.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=self.norm_expert_prob,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -368,6 +376,8 @@ def __init__(
             topk_group=self.topk_group,
             use_grouped_topk=self.use_grouped_topk,
             router_logits_dtype=self.router_dtype,
+            routed_scaling_factor=self.routed_scaling_factor,
+            apply_routed_scale_to_output=True,
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -383,22 +393,6 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             hidden_states=hidden_states, router_logits=router_logits
         )
 
-        # Handle tuple return from SharedFusedMoE
-        if self.shared_experts is not None:
-            shared_output, final_hidden_states = final_hidden_states
-        else:
-            shared_output = None
-
-        final_hidden_states *= self.routed_scaling_factor
-
-        if shared_output is not None:
-            final_hidden_states = final_hidden_states + shared_output
-
-        if self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
-
         return final_hidden_states.view(num_tokens, hidden_size)
 
 
@@ -437,14 +431,18 @@ def _weight_loader(param: torch.nn.Parameter, loaded_weight: torch.Tensor) -> No
         param.data.copy_(loaded_weight[shard].contiguous())
 
 
-class BailingMoELinearAttention(nn.Module, MambaBase):
-    """
-    Bailing MoE Linear Attention implementation using minimax backend.
+# --8<-- [start:bailing_moe_linear_attention]
+@PluggableLayer.register("bailing_moe_linear_attention")
+class BailingMoELinearAttention(PluggableLayer, MambaBase):
+    """Pluggable Bailing MoE Linear Attention layer which allows OOT backends
+    to add custom implementations.
 
-    This implements the linear attention mechanism from sglang, adapted for vLLM's
-    v1 engine with MambaBase interface support.
+    This implements the linear attention mechanism from sglang, adapted for
+    vLLM's v1 engine with MambaBase interface support.
     """
 
+    # --8<-- [end:bailing_moe_linear_attention]
+
     @property
     def mamba_type(self) -> str:
         return "linear_attention"
@@ -581,7 +579,6 @@ def __init__(
             self.head_dim,
             max_position=self.max_position_embeddings,
             is_neox_style=True,
-            dtype=torch.float32,
             rope_parameters=rope_parameters or None,
         )
 
@@ -766,8 +763,6 @@ def _prefill_and_mix_infer(
 
     def _decode_infer(self, q, k, v, kv_cache, state_indices_tensor, attn_metadata):
         """Handle decode (single token per sequence)."""
-        num_prefill_tokens = attn_metadata.num_prefill_tokens
-        num_prefills = attn_metadata.num_prefills
         hidden = linear_attention_decode(
             q,
             k,
@@ -775,10 +770,10 @@ def _decode_infer(self, q, k, v, kv_cache, state_indices_tensor, attn_metadata):
             kv_cache,
             self.tp_slope,
             state_indices_tensor,
-            q_start=num_prefill_tokens,
-            q_end=None,
-            slot_start=num_prefills,
-            slot_end=None,
+            q_start=0,
+            q_end=attn_metadata.num_decode_tokens,
+            slot_start=0,
+            slot_end=attn_metadata.num_decodes,
             block_size=32,
         )
         return hidden
@@ -1005,7 +1000,7 @@ def forward(
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         """Get expert parameter mapping for MoE layers."""
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -1161,6 +1156,7 @@ def __init__(
                 config.vocab_size,
                 config.hidden_size,
                 quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
             self.logits_processor = LogitsProcessor(config.vocab_size)
         else:
diff --git a/vllm/model_executor/models/cohere2_moe.py b/vllm/model_executor/models/cohere2_moe.py
new file mode 100644
index 000000000000..aa8adff188f7
--- /dev/null
+++ b/vllm/model_executor/models/cohere2_moe.py
@@ -0,0 +1,593 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable
+from itertools import islice
+
+import torch
+from torch import nn
+from transformers import CohereConfig
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (
+    get_pp_group,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import Attention
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+    row_parallel_weight_loader,
+)
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.platforms import current_platform
+from vllm.sequence import IntermediateTensors
+
+from .commandr import LayerNorm
+from .interfaces import SupportsPP, SupportsQuant
+from .utils import (
+    AutoWeightsLoader,
+    extract_layer_index,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+
+@torch.compile(backend=current_platform.simple_compile_backend)
+def token_choice_with_bias(
+    hidden_states: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+):
+    """Sigmoid -> top-k (-> renormalize) custom routing for Cohere2Moe."""
+    assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
+
+    scores = gating_output.float().sigmoid()
+    topk_weights, topk_ids = torch.topk(scores, k=topk, dim=-1, sorted=False)
+
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+    return topk_weights.to(torch.float32), topk_ids.to(torch.int32)
+
+
+@torch.compile(backend=current_platform.simple_compile_backend)
+def rms_norm_func(hidden_states, weight, variance_epsilon):
+    input_dtype = hidden_states.dtype
+    hidden_states = hidden_states.to(torch.float32)
+    variance = hidden_states.pow(2).mean(-1, keepdim=True)
+    hidden_states = hidden_states * torch.rsqrt(variance + variance_epsilon)
+    hidden_states = weight.to(torch.float32) * hidden_states
+    return hidden_states.to(input_dtype)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, param_shape=None, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(param_shape))
+        self.variance_epsilon = eps
+        set_weight_attrs(self.weight, {"weight_loader": row_parallel_weight_loader})
+
+    def forward(self, hidden_states, residuals=None):
+        hidden_states = rms_norm_func(hidden_states, self.weight, self.variance_epsilon)
+        return hidden_states, residuals
+
+
+def select_norm_impl(config: CohereConfig) -> tuple[type[nn.Module], float]:
+    """Returns (norm_class, eps). Uses RMSNorm when config.rms_norm_eps is set,
+    otherwise falls back to LayerNorm with config.layer_norm_eps."""
+    rms_eps = getattr(config, "rms_norm_eps", None)
+    if rms_eps is not None:
+        return RMSNorm, rms_eps
+    return LayerNorm, config.layer_norm_eps
+
+
+class Cohere2MoeMLP(nn.Module):
+    """Cohere MLP used as shared experts in the MoE block."""
+
+    def __init__(
+        self,
+        config: CohereConfig,
+        intermediate_size: int | None = None,
+        quant_config: QuantizationConfig | None = None,
+        reduce_results: bool = False,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = (
+            intermediate_size
+            if intermediate_size is not None
+            else config.intermediate_size
+        )
+        self.gate_up_proj = MergedColumnParallelLinear(
+            self.hidden_size,
+            [self.intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            self.intermediate_size,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=f"{prefix}.down_proj",
+        )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Cohere2MoeAttention(nn.Module):
+    """Cohere MoE attention with sliding-window interleave."""
+
+    def __init__(
+        self,
+        config: CohereConfig,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        tp_size = get_tensor_model_parallel_world_size()
+        self.config = config
+        self.layer_idx = extract_layer_index(prefix)
+        self.hidden_size = config.hidden_size
+        self.total_num_heads = config.num_attention_heads
+        self.num_heads = self.total_num_heads // tp_size
+        self.head_dim = getattr(
+            config, "head_dim", self.hidden_size // self.total_num_heads
+        )
+        self.total_num_kv_heads = config.num_key_value_heads
+        if self.total_num_kv_heads >= tp_size:
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.max_position_embeddings = getattr(
+            config, "model_max_length", None
+        ) or getattr(config, "max_position_embeddings", 8192)
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            max_position=self.max_position_embeddings,
+            rope_parameters=config.rope_parameters,
+            is_neox_style=False,
+        )
+
+        self.sliding_window = None
+        layer_types = getattr(config, "layer_types", None)
+        if (
+            layer_types is not None
+            and layer_types[self.layer_idx] == "sliding_attention"
+        ):
+            self.sliding_window = config.sliding_window
+
+        # Prefix-dense layers (layer_idx < first_k_dense_replace) have full
+        # attention (no sliding window). When prefix_dense_sliding_window_pattern
+        # == 1, they keep RoPE even though they are not sliding-window layers.
+        first_k_dense_replace = getattr(config, "first_k_dense_replace", 0)
+        prefix_dense_sliding_window_pattern = getattr(
+            config, "prefix_dense_sliding_window_pattern", 1
+        )
+        self.force_rope = bool(
+            first_k_dense_replace
+            and prefix_dense_sliding_window_pattern == 1
+            and self.layer_idx < first_k_dense_replace
+        )
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            per_layer_sliding_window=self.sliding_window,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        if self.sliding_window or self.force_rope:
+            q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Cohere2Moe(nn.Module):
+    """Tensor-parallel MoE block for Cohere2Moe with shared experts."""
+
+    def __init__(
+        self,
+        config: CohereConfig,
+        params_dtype: torch.dtype | None = None,
+        quant_config: QuantizationConfig | None = None,
+        tp_size: int | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.tp_size = get_tensor_model_parallel_world_size()
+
+        if self.tp_size > config.num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.num_experts}."
+            )
+
+        if (
+            hasattr(config, "expert_selection_fn")
+            and config.expert_selection_fn == "sigmoid"
+        ):
+            self.custom_routing_function = token_choice_with_bias
+        else:
+            self.custom_routing_function = None
+
+        self.gate = ReplicatedLinear(
+            config.hidden_size,
+            config.num_experts,
+            bias=False,
+            params_dtype=params_dtype,
+            quant_config=None,
+            prefix=f"{prefix}.gate",
+        )
+
+        if hasattr(config, "num_shared_experts") and config.num_shared_experts > 0:
+            self.shared_experts = Cohere2MoeMLP(
+                config=config,
+                intermediate_size=config.intermediate_size * config.num_shared_experts,
+                quant_config=quant_config,
+                prefix=f"{prefix}.shared_experts",
+            )
+            self.shared_expert_combination_strategy = getattr(
+                config, "shared_expert_combination_strategy", "sum"
+            )
+            assert self.shared_expert_combination_strategy in ("average", "sum"), (
+                "shared_expert_combination_strategy must be one of ['average', 'sum']"
+            )
+        else:
+            self.shared_experts = None
+            self.shared_expert_combination_strategy = None
+
+        self.experts = FusedMoE(
+            num_experts=config.num_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            params_dtype=params_dtype,
+            renormalize=getattr(config, "norm_topk_prob", True),
+            quant_config=quant_config,
+            tp_size=tp_size,
+            prefix=f"{prefix}.experts",
+            custom_routing_function=self.custom_routing_function,
+            shared_experts=self.shared_experts,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        orig_shape = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_size)
+        router_logits, _ = self.gate(hidden_states)
+        # FusedMoE handles shared expert overlap internally and returns
+        # shared_output + routed_output when shared_experts is set.
+        final_hidden_states = self.experts(hidden_states, router_logits)
+        if self.shared_expert_combination_strategy == "average":
+            final_hidden_states = final_hidden_states / 2
+        return final_hidden_states.view(orig_shape)
+
+
+class Cohere2MoeDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: CohereConfig,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.layer_idx = extract_layer_index(prefix)
+
+        self.self_attn = Cohere2MoeAttention(
+            config,
+            cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+
+        # Layers before first_k_dense_replace use a dense MLP instead of MoE.
+        first_k_dense_replace = getattr(config, "first_k_dense_replace", 0)
+        if self.layer_idx < first_k_dense_replace:
+            self.mlp = Cohere2MoeMLP(
+                config=config,
+                intermediate_size=getattr(
+                    config, "prefix_dense_intermediate_size", config.intermediate_size
+                ),
+                quant_config=quant_config,
+                reduce_results=True,
+                prefix=f"{prefix}.mlp",
+            )
+        else:
+            self.mlp = Cohere2Moe(
+                config=config, quant_config=quant_config, prefix=f"{prefix}.mlp"
+            )
+
+        norm_cls, norm_eps = select_norm_impl(config)
+        self.input_layernorm = norm_cls(param_shape=(config.hidden_size,), eps=norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        residual = hidden_states
+        hidden_states, residual = self.input_layernorm(hidden_states, residual)
+        hidden_states_attention = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+        hidden_states_mlp = self.mlp(hidden_states)
+
+        hidden_states = residual + hidden_states_attention + hidden_states_mlp
+        return hidden_states, residual
+
+
+@support_torch_compile
+class Cohere2MoeModel(nn.Module):
+    """Transformer decoder for Cohere2Moe."""
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        self.quant_config = quant_config
+        self.vocab_size = config.vocab_size
+        self.org_vocab_size = config.vocab_size
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size, config.hidden_size
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Cohere2MoeDecoderLayer(
+                config, cache_config, quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers",
+        )
+        norm_cls, norm_eps = select_norm_impl(config)
+        self.norm = norm_cls(param_shape=(config.hidden_size,), eps=norm_eps)
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
+            hidden_states, residual = layer(positions, hidden_states, residual)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+            self,
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_experts,
+        )
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            if self.quant_config is not None and (
+                scale_name := self.quant_config.get_cache_scale(name)
+            ):
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                loaded_weight = (
+                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
+                )
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
+            for param_name, shard_name, shard_id in stacked_params_mapping:
+                if shard_name not in name:
+                    continue
+                if "mlp.experts" in name:
+                    continue
+                name = name.replace(shard_name, param_name)
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    if (
+                        name.endswith(".bias") or name.endswith("_bias")
+                    ) and name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(
+                        param,
+                        loaded_weight,
+                        name,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                    )
+                    break
+                else:
+                    if (
+                        name.endswith(".bias") or name.endswith("_bias")
+                    ) and name not in params_dict:
+                        continue
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        return loaded_params
+
+
+class Cohere2MoeForCausalLM(nn.Module, SupportsPP, SupportsQuant):
+    is_text_generation_model = True
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        assert getattr(config, "tie_word_embeddings", True)
+        self.unpadded_vocab_size = config.vocab_size
+        self.quant_config = quant_config
+        self.logits_scale = config.logit_scale
+        self.logits_processor = LogitsProcessor(
+            self.unpadded_vocab_size, config.vocab_size, scale=self.logits_scale
+        )
+        self.model = Cohere2MoeModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        return self.model(input_ids, positions, intermediate_tensors, inputs_embeds)
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        return self.logits_processor(self.model.embed_tokens, hidden_states)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self, skip_prefixes=["lm_head."])
+        return loader.load_weights(weights)
diff --git a/vllm/model_executor/models/cohere2_vision.py b/vllm/model_executor/models/cohere2_vision.py
index c3118ee7778d..cdb3bcbedb25 100644
--- a/vllm/model_executor/models/cohere2_vision.py
+++ b/vllm/model_executor/models/cohere2_vision.py
@@ -44,7 +44,12 @@
 from vllm.sequence import IntermediateTensors
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
-from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .interfaces import (
+    MultiModalEmbeddings,
+    SupportsMultiModal,
+    SupportsPP,
+    SupportsQuant,
+)
 from .siglip import SiglipVisionModel
 from .utils import (
     AutoWeightsLoader,
@@ -309,16 +314,22 @@ def get_replacement(item_idx: int):
     info=Cohere2VisionProcessingInfo,
     dummy_inputs=Cohere2VisionDummyInputsBuilder,
 )
-class Cohere2VisionForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
+class Cohere2VisionForConditionalGeneration(
+    nn.Module, SupportsMultiModal, SupportsPP, SupportsQuant
+):
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             "model.vision_tower.": "vision_tower.",
             "model.multi_modal_projector.": "multi_modal_projector.",
             "model.language_model.": "language_model.model.",
-            "lm_head.": "language_model.lm_head.",
         }
     )
 
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config: Cohere2VisionConfig = vllm_config.model_config.hf_config
diff --git a/vllm/model_executor/models/cohere_asr.py b/vllm/model_executor/models/cohere_asr.py
index 42206c11cb97..584793832dc7 100644
--- a/vllm/model_executor/models/cohere_asr.py
+++ b/vllm/model_executor/models/cohere_asr.py
@@ -3,9 +3,8 @@
 
 import math
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Literal
+from typing import Any, ClassVar
 
-import numpy as np
 import torch
 import torch.nn.functional as F
 from torch import nn
@@ -13,8 +12,10 @@
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, ModelConfig, SpeechToTextConfig, VllmConfig
+from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.distributed import get_tensor_model_parallel_world_size
-from vllm.inputs import MultiModalDataDict, PromptType, TextPrompt
+from vllm.inputs import MultiModalDataDict, PromptType, TokensPrompt
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.attention import (
@@ -48,6 +49,7 @@
     PromptUpdate,
 )
 from vllm.renderers import TokenizeParams
+from vllm.tokenizers import cached_tokenizer_from_config
 from vllm.transformers_utils.processors.cohere_asr import (
     INF_VAL,
     CohereASRFeatureExtractor,
@@ -1900,7 +1902,7 @@ def get_dummy_mm_data(
         self,
         seq_len: int,
         mm_counts: Mapping[str, int],
-        mm_options=None,
+        mm_options: Mapping[str, BaseDummyOptions],
         mm_processor_kwargs=None,
     ) -> MultiModalDataDict:
         feature_extractor = self.info.get_feature_extractor()
@@ -2008,6 +2010,9 @@ class CohereAsrForConditionalGeneration(
     supported_languages = ISO639_1_SUPPORTED_LANGS
     skip_warmup_audio_preprocessing = True
     no_space_languages = {"ja", "zh"}
+    _default_prompt_token_ids_cache: ClassVar[
+        dict[tuple[str | None, str | None, str], tuple[int, ...]]
+    ] = {}
 
     @classmethod
     def validate_language(cls, language: str | None) -> str | None:
@@ -2021,38 +2026,85 @@ def validate_language(cls, language: str | None) -> str | None:
         return super().validate_language(language)
 
     @classmethod
-    def get_generation_prompt(
-        cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,  # not needed here
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
-    ) -> PromptType:
+    def get_generation_prompt(cls, stt_params: SpeechToTextParams) -> PromptType:
+        audio = stt_params.audio
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+        model_config = stt_params.model_config
+
         if language is None:
             raise ValueError(
                 "Language must be specified when creating the CohereASR prompt"
             )
 
-        # NOTE: this function is used only by online inference and not offline inference
-        # CohereASR doesnt have encoder prompt
-        language_tag = f"<|{language}|><|{language}|>"
-        pnc = True  # TODO(ekagra): make this configurable later
-        pnc_tag = "<|pnc|>" if pnc else "<|nopnc|>"
-        default_prompt = (
-            f"<|startofcontext|><|startoftranscript|>"
-            f"<|emo:undefined|>{language_tag}{pnc_tag}"
-            f"<|noitn|><|notimestamp|><|nodiarize|>"
+        tokenizer = cached_tokenizer_from_config(model_config)
+
+        # prompt_text is None because CoherASR uses fast implementation of
+        # sentencepiece tokenizer which needs "▁" as the first token
+        # (which is different from "_") and encode("▁ABC") ignores the first token
+        # so the prompt_text is unreliable. However, prompt_token_ids can be used
+        # to get prompt_text but it wont have the first token "▁".
+        prompt_text = None
+        prompt_token_ids = cls._get_default_prompt_token_ids(
+            tokenizer,
+            model_config,
+            language,
         )
-        prompt_text = request_prompt if request_prompt else default_prompt
 
-        return TextPrompt(
+        return TokensPrompt(
             prompt=prompt_text,
+            prompt_token_ids=prompt_token_ids,
             multi_modal_data={"audio": (audio, stt_config.sample_rate)},
         )
 
+    @classmethod
+    def _get_default_prompt_tokens(cls, language: str) -> tuple[str, ...]:
+        # Use token-level control tags so fast tokenizers do not have to parse
+        # the raw string form of the decoder prefix.
+        return (
+            "▁",
+            "<|startofcontext|>",
+            "<|startoftranscript|>",
+            "<|emo:undefined|>",
+            f"<|{language}|>",
+            f"<|{language}|>",
+            "<|pnc|>",
+            "<|noitn|>",
+            "<|notimestamp|>",
+            "<|nodiarize|>",
+        )
+
+    @classmethod
+    def _get_default_prompt_token_ids(
+        cls,
+        tokenizer: Any,
+        model_config: ModelConfig,
+        language: str,
+    ) -> list[int]:
+        cache_key = (
+            getattr(model_config, "tokenizer", None),
+            getattr(model_config, "tokenizer_revision", None),
+            language,
+        )
+        prompt_token_ids = cls._default_prompt_token_ids_cache.get(cache_key)
+        if prompt_token_ids is None:
+            prompt_tokens = list(cls._get_default_prompt_tokens(language))
+            token_ids = tokenizer.convert_tokens_to_ids(prompt_tokens)
+            if not isinstance(token_ids, list):
+                token_ids = [token_ids]
+            unk_token_id = getattr(tokenizer, "unk_token_id", None)
+            if unk_token_id is not None and any(
+                token_id == unk_token_id for token_id in token_ids
+            ):
+                raise ValueError(
+                    "Failed to resolve the CohereASR decoder control tokens "
+                    "with the configured tokenizer."
+                )
+            prompt_token_ids = tuple(int(token_id) for token_id in token_ids)
+            cls._default_prompt_token_ids_cache[cache_key] = prompt_token_ids
+
+        return list(prompt_token_ids)
+
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> str | None:
         # Required as part of SupportsMultiModal interface.
diff --git a/vllm/model_executor/models/cohere_eagle.py b/vllm/model_executor/models/cohere_eagle.py
new file mode 100644
index 000000000000..5c22d6e34dd5
--- /dev/null
+++ b/vllm/model_executor/models/cohere_eagle.py
@@ -0,0 +1,247 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable
+
+import torch
+import torch.nn as nn
+from transformers import CohereConfig
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.commandr import (
+    CohereDecoderLayer,
+    CohereForCausalLM,
+    LayerNorm,
+)
+
+from .utils import (
+    AutoWeightsLoader,
+    get_draft_quant_config,
+    maybe_prefix,
+    process_eagle_weight,
+)
+
+logger = init_logger(__name__)
+
+
+class CohereEagleDecoderLayer(CohereDecoderLayer):
+    """Eagle draft variant of CohereDecoderLayer."""
+
+    def __init__(
+        self,
+        config: CohereConfig,
+        cache_config=None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(
+            config,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=prefix,
+        )
+
+
+@support_torch_compile
+class CohereEagleModel(nn.Module):
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        start_layer_id: int = 0,
+    ) -> None:
+        super().__init__()
+        self.config = vllm_config.speculative_config.draft_model_config.hf_config
+        self.quant_config = get_draft_quant_config(vllm_config)
+
+        # Cohere2-targeted EAGLE drafts inherit the target's sliding-window
+        # attention pattern. ``CohereAttention`` resolves per-layer behavior
+        # via ``config.layer_types[layer_idx]`` and the eagle layers use
+        # absolute indices (target_layer_num + i), so prepend the target's
+        # ``layer_types`` to the draft's so the lookup succeeds.
+        target_text_config = vllm_config.model_config.hf_text_config
+        if hasattr(target_text_config, "layer_types") and hasattr(
+            self.config, "layer_types"
+        ):
+            self.config.layer_types = list(target_text_config.layer_types) + list(
+                self.config.layer_types
+            )
+
+        self.vocab_size = self.config.vocab_size
+        self.embed_tokens = VocabParallelEmbedding(
+            self.config.vocab_size,
+            self.config.hidden_size,
+            prefix=maybe_prefix(prefix, "embed_tokens"),
+        )
+
+        self.layers = nn.ModuleList(
+            [
+                CohereEagleDecoderLayer(
+                    self.config,
+                    cache_config=vllm_config.cache_config,
+                    quant_config=self.quant_config,
+                    prefix=maybe_prefix(prefix, f"layers.{i + start_layer_id}"),
+                )
+                for i in range(self.config.num_hidden_layers)
+            ]
+        )
+
+        # Cohere EAGLE checkpoints include a bias term on the input fusion
+        # projection (unlike LLaMA EAGLE which uses bias=False).
+        self.fc = ReplicatedLinear(
+            input_size=self.config.hidden_size * 2,
+            output_size=self.config.hidden_size,
+            bias=True,
+            params_dtype=vllm_config.model_config.dtype,
+            quant_config=self.quant_config,
+            prefix=maybe_prefix(prefix, "fc"),
+            return_bias=False,
+        )
+
+        # Cohere EAGLE applies an explicit final LayerNorm to the draft
+        # hidden states before they are consumed by the logits processor.
+        self.norm = LayerNorm(
+            param_shape=(self.config.hidden_size),
+            eps=self.config.layer_norm_eps,
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        input_embeds = self.embed_tokens(input_ids)
+        hidden_states = self.fc(torch.cat((input_embeds, hidden_states), dim=-1))
+        residual = None
+        for layer in self.layers:
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states, hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            if self.quant_config is not None and (
+                scale_name := self.quant_config.get_cache_scale(name)
+            ):
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                loaded_weight = (
+                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
+                )
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class EagleCohereForCausalLM(CohereForCausalLM):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        nn.Module.__init__(self)
+        self.config = vllm_config.speculative_config.draft_model_config.hf_config
+        # Flags checked by the speculative proposer to decide whether to share
+        # embed_tokens / lm_head with the target model. Cohere EAGLE checkpoints
+        # use tied embeddings so these weights are absent from the draft file.
+        self.has_own_embed_tokens = False
+        self.has_own_lm_head = False
+        target_layer_num = vllm_config.model_config.get_num_layers(
+            vllm_config.parallel_config
+        )
+        self.model = CohereEagleModel(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "model"),
+            start_layer_id=target_layer_num,
+        )
+
+        logit_scale = getattr(self.config, "logit_scale", 1.0)
+        self.logits_processor = LogitsProcessor(
+            self.config.vocab_size, scale=logit_scale
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if inputs_embeds is not None:
+            raise NotImplementedError(
+                f"{type(self).__name__} does not support multimodal inputs yet."
+            )
+        return self.model(input_ids, positions, hidden_states)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        def _track_and_forward(inputs):
+            name, weight = inputs
+            process_eagle_weight(self, name)
+            return name, weight
+
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(
+                ["lm_head.", "model.embed_tokens."]
+                if self.config.tie_word_embeddings
+                else None
+            ),
+        )
+
+        loaded_weight_names = loader.load_weights(map(_track_and_forward, weights))
+
+        # Embed tokens are tied with the target model and therefore not
+        # present in the EAGLE checkpoint; mark them as loaded explicitly to
+        # avoid a spurious "weight not found" warning from the default
+        # weight loader.
+        loaded_weight_names.add("model.embed_tokens.weight")
+        return loaded_weight_names
diff --git a/vllm/model_executor/models/config.py b/vllm/model_executor/models/config.py
index 521184e4c686..459c16f8ec97 100644
--- a/vllm/model_executor/models/config.py
+++ b/vllm/model_executor/models/config.py
@@ -107,6 +107,31 @@ def verify_and_update_config(vllm_config: "VllmConfig") -> None:
             )
 
 
+class DeepseekV4ForCausalLMConfig(VerifyAndUpdateConfig):
+    @staticmethod
+    def verify_and_update_model_config(model_config: "ModelConfig") -> None:
+        quant_config = getattr(model_config.hf_config, "quantization_config", None)
+        if quant_config is not None and quant_config.get("quant_method") == "fp8":
+            model_type = getattr(model_config.hf_config, "model_type", None)
+            if model_type == "deepseek_v4":
+                model_config.hf_config.quantization_config["quant_method"] = (
+                    "deepseek_v4_fp8"
+                )
+
+        hf_text_quant_config = getattr(
+            model_config.hf_text_config, "quantization_config", None
+        )
+        if (
+            hf_text_quant_config is not None
+            and hf_text_quant_config.get("quant_method") == "fp8"
+        ):
+            model_type = getattr(model_config.hf_text_config, "model_type", None)
+            if model_type == "deepseek_v4":
+                model_config.hf_text_config.quantization_config["quant_method"] = (
+                    "deepseek_v4_fp8"
+                )
+
+
 class GptOssForCausalLMConfig(VerifyAndUpdateConfig):
     @staticmethod
     def verify_and_update_model_config(model_config: "ModelConfig") -> None:
@@ -325,15 +350,26 @@ def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None:
 
         if cache_config.enable_prefix_caching:
             if cache_config.mamba_cache_mode == "none":
-                cache_config.mamba_cache_mode = (
-                    "all" if model_config.supports_mamba_prefix_caching else "align"
-                )
-                logger.warning(
-                    "Mamba cache mode is set to '%s' for %s by default "
-                    "when prefix caching is enabled",
-                    cache_config.mamba_cache_mode,
-                    model_config.architecture,
-                )
+                if (
+                    model_config.supports_mamba_prefix_caching
+                    and vllm_config.speculative_config is not None
+                ):
+                    cache_config.mamba_cache_mode = "align"
+                    logger.warning(
+                        "Mamba cache mode is set to 'align' for %s by default "
+                        "when prefix caching and speculative decoding are enabled",
+                        model_config.architecture,
+                    )
+                else:
+                    cache_config.mamba_cache_mode = (
+                        "all" if model_config.supports_mamba_prefix_caching else "align"
+                    )
+                    logger.warning(
+                        "Mamba cache mode is set to '%s' for %s by default "
+                        "when prefix caching is enabled",
+                        cache_config.mamba_cache_mode,
+                        model_config.architecture,
+                    )
             if (
                 cache_config.mamba_cache_mode == "all"
                 and not model_config.supports_mamba_prefix_caching
@@ -481,7 +517,7 @@ def verify_and_update_model_config(model_config: "ModelConfig") -> None:
                     "Nomic context extension is disabled. "
                     "Changing max_model_len from %s to %s. "
                     "To enable context extension, see: "
-                    "https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/context_extension.py",
+                    "https://github.com/vllm-project/vllm/tree/main/examples/features/context_extension/context_extension_offline.py",
                     max_model_len_before,
                     model_config.max_model_len,
                 )
@@ -624,6 +660,7 @@ def verify_and_update_model_config(model_config: "ModelConfig") -> None:
 MODELS_CONFIG_MAP: dict[str, type[VerifyAndUpdateConfig]] = {
     "ColBERTJinaRobertaModel": JinaRobertaModelConfig,
     "ColQwen3_5": Qwen3_5ForConditionalGenerationConfig,
+    "DeepseekV4ForCausalLM": DeepseekV4ForCausalLMConfig,
     "DeepseekV32ForCausalLM": DeepseekV32ForCausalLM,
     "Ernie4_5_VLMoeForConditionalGeneration": Ernie4_5_VLMoeForConditionalGenerationConfig,  # noqa: E501
     "FalconMambaForCausalLM": MambaModelConfig,
diff --git a/vllm/model_executor/models/dbrx.py b/vllm/model_executor/models/dbrx.py
index ca6e6a49a98a..6c798bf2f36b 100644
--- a/vllm/model_executor/models/dbrx.py
+++ b/vllm/model_executor/models/dbrx.py
@@ -15,7 +15,9 @@
     get_tensor_model_parallel_world_size,
 )
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+)
 from vllm.model_executor.layers.linear import (
     QKVParallelLinear,
     ReplicatedLinear,
@@ -85,7 +87,6 @@ def __init__(
             hidden_size=config.d_model,
             intermediate_size=config.ffn_config.ffn_hidden_size,
             params_dtype=params_dtype,
-            reduce_results=True,
             renormalize=True,
             quant_config=quant_config,
             tp_size=get_tensor_model_parallel_world_size(),
diff --git a/vllm/model_executor/models/deepseek_eagle.py b/vllm/model_executor/models/deepseek_eagle.py
index 5c439cdf486d..f975b32adc19 100644
--- a/vllm/model_executor/models/deepseek_eagle.py
+++ b/vllm/model_executor/models/deepseek_eagle.py
@@ -8,7 +8,9 @@
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -105,7 +107,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/deepseek_mtp.py b/vllm/model_executor/models/deepseek_mtp.py
index 126efb6f88e6..37f94c687a21 100644
--- a/vllm/model_executor/models/deepseek_mtp.py
+++ b/vllm/model_executor/models/deepseek_mtp.py
@@ -11,7 +11,9 @@
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -30,6 +32,7 @@
     DeepseekV2DecoderLayer,
     DeepseekV2MixtureOfExperts,
     DeepseekV2MoE,
+    _try_load_fp8_indexer_wk,
     get_spec_layer_idx_from_weight_name,
 )
 from .utils import maybe_prefix
@@ -190,10 +193,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         )
         # Set MoE hyperparameters
         self.set_moe_parameters()
-        self.is_fp4_ckpt = (
-            self.quant_config is not None
-            and self.quant_config.get_name() == "modelopt_fp4"
-        )
 
     def set_moe_parameters(self):
         self.expert_weights = []
@@ -248,15 +247,14 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             ("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
         ]
 
-        if self.is_fp4_ckpt:
-            # Fused indexer wk + weights_proj (shard 0 = wk, shard 1 = weights_proj)
-            indexer_fused_mapping = [
-                ("wk_weights_proj", "wk", 0),
-                ("wk_weights_proj", "weights_proj", 1),
-            ]
-            stacked_params_mapping.extend(indexer_fused_mapping)
+        # Fused indexer wk + weights_proj (shard 0 = wk, shard 1 = weights_proj)
+        indexer_fused_mapping = [
+            ("wk_weights_proj", "wk", 0),
+            ("wk_weights_proj", "weights_proj", 1),
+        ]
+        stacked_params_mapping.extend(indexer_fused_mapping)
 
-        expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -271,6 +269,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
+        _pending_wk_fp8: dict = {}  # FP8 indexer wk dequant buffer
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
                 continue
@@ -281,6 +280,12 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                 rocm_aiter_moe_shared_expert_enabled and ("mlp.shared_experts" in name)
             )
             name = self._rewrite_spec_layer_name(spec_layer, name)
+
+            if _try_load_fp8_indexer_wk(
+                name, loaded_weight, _pending_wk_fp8, params_dict, loaded_params
+            ):
+                continue
+
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 # Skip non-stacked layers and experts (experts handled below).
                 if weight_name not in name:
diff --git a/vllm/model_executor/models/deepseek_v2.py b/vllm/model_executor/models/deepseek_v2.py
index 17ddd5edeced..44797874a4c5 100644
--- a/vllm/model_executor/models/deepseek_v2.py
+++ b/vllm/model_executor/models/deepseek_v2.py
@@ -48,9 +48,9 @@
 from vllm.model_executor.layers.attention import Attention
 from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
 from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
     GateLinear,
-    RoutingMethodType,
-    SharedFusedMoE,
+    fused_moe_make_expert_params_mapping,
 )
 from vllm.model_executor.layers.layernorm import LayerNorm, RMSNorm
 from vllm.model_executor.layers.linear import (
@@ -66,6 +66,10 @@
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     per_token_group_quant_fp8,
 )
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape,
+    scaled_dequantize,
+)
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sparse_attn_indexer import (
     SparseAttnIndexer,
@@ -78,7 +82,11 @@
     default_weight_loader,
     maybe_remap_kv_scale_name,
 )
-from vllm.model_executor.models.utils import sequence_parallel_chunk
+from vllm.model_executor.models.utils import (
+    AutoWeightsLoader,
+    extract_layer_index,
+    sequence_parallel_chunk,
+)
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 from vllm.utils.torch_utils import direct_register_custom_op
@@ -292,6 +300,15 @@ def __init__(
         self.is_fusion_moe_shared_experts_enabled = (
             rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
         )
+        if (
+            self.is_rocm_aiter_moe_enabled
+            and self.gate.e_score_correction_bias is not None
+        ):
+            # AITER biased_grouped_topk requires the correction bias dtype to
+            # match the router logits. Keep DeepSeek's correction bias in fp32
+            # by requesting fp32 router logits for this routing path.
+            self.gate.set_out_dtype(torch.float32)
+
         if config.n_shared_experts is None or self.is_fusion_moe_shared_experts_enabled:
             self.shared_experts = None
         else:
@@ -307,14 +324,13 @@ def __init__(
                 prefix=f"{prefix}.shared_experts",
             )
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_experts,
             gate=self.gate,
             num_experts=config.n_routed_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             use_grouped_topk=True,
@@ -322,11 +338,9 @@ def __init__(
             topk_group=getattr(config, "topk_group", 1),
             prefix=f"{prefix}.experts",
             scoring_func=getattr(config, "scoring_func", "softmax"),
-            # we do scaling outside, set factor to 1.0 to avoid double mul
             # aiter applies routed_scaling_factor internally
-            routed_scaling_factor=1.0
-            if not self.is_rocm_aiter_moe_enabled
-            else self.routed_scaling_factor,
+            routed_scaling_factor=self.routed_scaling_factor,
+            apply_routed_scale_to_output=not self.is_rocm_aiter_moe_enabled,
             e_score_correction_bias=self.gate.e_score_correction_bias,
             enable_eplb=self.enable_eplb,
             num_redundant_experts=self.n_redundant_experts,
@@ -334,18 +348,16 @@ def __init__(
             n_shared_experts=config.n_shared_experts
             if self.is_fusion_moe_shared_experts_enabled
             else None,
+            router_logits_dtype=self.gate.out_dtype,
         )
 
-        # NOTE(rob): this is a hack until we finish off the PR for
-        # merging TRTLLM kernels into the MK framework. Then we can
-        # query the MonolithicMK for the expected router logits.
-        # NOTE(dbari): Use BF16 if routing is not Deepseek, e.g. Mistral Large 3
-        self.gate.set_out_dtype(
-            torch.float32
-            if self.experts.quant_method.is_monolithic
-            and self.experts.routing_method_type == RoutingMethodType.DeepSeekV3
-            else torch.bfloat16
-        )
+        if (
+            self.is_rocm_aiter_moe_enabled
+            and self.gate.e_score_correction_bias is not None
+        ):
+            self.gate.e_score_correction_bias.data = (
+                self.gate.e_score_correction_bias.data.to(self.gate.out_dtype)
+            )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         num_tokens, hidden_dim = hidden_states.shape
@@ -359,43 +371,20 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             hidden_states = sequence_parallel_chunk(hidden_states)
 
         if self.experts.is_internal_router:
-            # In this case, the gate/router runs inside the FusedMoE class
-            fused_moe_out = self.experts(
+            final_hidden_states = self.experts(
                 hidden_states=hidden_states, router_logits=hidden_states
             )
         else:
-            # router_logits: (num_tokens, n_experts)
             router_logits, _ = self.gate(hidden_states)
-            fused_moe_out = self.experts(
+            final_hidden_states = self.experts(
                 hidden_states=hidden_states, router_logits=router_logits
             )
 
-        shared_output, final_hidden_states = fused_moe_out
-        if self.shared_experts is None:
-            assert shared_output is None
-
-        # Fix FP16 overflow
-        # See DeepseekV2DecoderLayer for more details.
-        if hidden_states.dtype != torch.float16:
-            if not self.is_rocm_aiter_moe_enabled:
-                final_hidden_states *= self.routed_scaling_factor
-        elif self.shared_experts is not None:
-            assert shared_output is not None
-            shared_output *= 1.0 / self.routed_scaling_factor
-
-        if self.shared_experts is not None:
-            assert shared_output is not None
-            final_hidden_states += shared_output
-
         if self.is_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
                 final_hidden_states, 0
             )
             final_hidden_states = final_hidden_states[:num_tokens]
-        elif self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
 
         return final_hidden_states.view(num_tokens, hidden_dim)
 
@@ -628,10 +617,6 @@ def __init__(
         self.vllm_config = vllm_config
         self.config = config
         self.quant_config = quant_config
-        self.is_fp4_ckpt = (
-            self.quant_config is not None
-            and self.quant_config.get_name() == "modelopt_fp4"
-        )
         # self.indexer_cfg = config.attn_module_list_cfg[0]["attn_index"]
         self.topk_tokens = config.index_topk
         self.n_head = config.index_n_heads  # 64
@@ -646,36 +631,16 @@ def __init__(
             quant_config=quant_config,
             prefix=f"{prefix}.wq_b",
         )
-        if self.is_fp4_ckpt:
-            # Fused wk + weights_proj: single GEMM producing [head_dim + n_head].
-            # weights_proj does not get quantized,
-            # so we run both with quant_config=None
-            # wk may be upcasted from the default quant;
-            # experiments show fusion is always faster unless WK proj is in FP4,
-            # which is not the case for all known quants.
-            self.wk_weights_proj = MergedColumnParallelLinear(
-                hidden_size,
-                [self.head_dim, self.n_head],
-                bias=False,
-                quant_config=None,
-                disable_tp=True,
-                prefix=f"{prefix}.wk_weights_proj",
-            )
-        else:
-            self.wk = ReplicatedLinear(
-                hidden_size,
-                self.head_dim,
-                bias=False,
-                quant_config=quant_config,
-                prefix=f"{prefix}.wk",
-            )
-            self.weights_proj = ReplicatedLinear(
-                hidden_size,
-                self.n_head,
-                bias=False,
-                quant_config=None,
-                prefix=f"{prefix}.weights_proj",
-            )
+        # Fused wk + weights_proj: single GEMM producing [head_dim + n_head].
+        # FP8 wk weights are upcasted to BF16 during loading to maintain fusion.
+        self.wk_weights_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [self.head_dim, self.n_head],
+            bias=False,
+            quant_config=None,
+            disable_tp=True,
+            prefix=f"{prefix}.wk_weights_proj",
+        )
         self.k_norm = LayerNorm(self.head_dim, eps=1e-6)
         self.softmax_scale = self.head_dim**-0.5
 
@@ -713,34 +678,45 @@ def forward(
     ) -> torch.Tensor:
         q, _ = self.wq_b(qr)
         q = q.view(-1, self.n_head, self.head_dim)
-        q_pe, q_nope = torch.split(
-            q, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1
-        )
-        if self.is_fp4_ckpt:
+
+        if current_platform.is_rocm():
+            # This path should works on all platform, will remove extra
+            # branches in the future
             # Fused wk + weights_proj: one GEMM, then split
             kw, _ = self.wk_weights_proj(hidden_states)
             k = kw[:, : self.head_dim]
             weights = kw[:, self.head_dim :]
+
+            k = self.k_norm(k)
+
+            rotary_emb(
+                positions, q[..., : self.rope_dim], k[..., : self.rope_dim].unsqueeze(1)
+            )
         else:
-            k, _ = self.wk(hidden_states)
-            weights, _ = self.weights_proj(hidden_states)
+            q_pe, q_nope = torch.split(
+                q, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1
+            )
+            # Fused wk + weights_proj: one GEMM, then split
+            kw, _ = self.wk_weights_proj(hidden_states)
+            k = kw[:, : self.head_dim]
+            weights = kw[:, self.head_dim :]
 
-        k = self.k_norm(k)
-        k_pe, k_nope = torch.split(
-            k, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1
-        )
+            k = self.k_norm(k)
+            k_pe, k_nope = torch.split(
+                k, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1
+            )
 
-        q_pe, k_pe = rotary_emb(positions, q_pe, k_pe.unsqueeze(1))
-        # Note: RoPE (NeoX) can introduce extra leading dimensions during compilation
-        # so we need to reshape back to token-flattened shapes
-        q_pe = q_pe.reshape(-1, self.n_head, self.rope_dim)
-        k_pe = k_pe.reshape(-1, 1, self.rope_dim)
+            q_pe, k_pe = rotary_emb(positions, q_pe, k_pe.unsqueeze(1))
+            # Note: RoPE (NeoX) can introduce extra leading dimensions during
+            # compilation so we need to reshape back to token-flattened shapes
+            q_pe = q_pe.reshape(-1, self.n_head, self.rope_dim)
+            k_pe = k_pe.reshape(-1, 1, self.rope_dim)
 
-        # `rotary_emb` is shape-preserving; `q_pe` is already
-        # [num_tokens, n_head, rope_dim].
-        q = torch.cat([q_pe, q_nope], dim=-1)
-        # `k_pe` is [num_tokens, 1, rope_dim] (MQA).
-        k = torch.cat([k_pe.squeeze(-2), k_nope], dim=-1)
+            # `rotary_emb` is shape-preserving; `q_pe` is already
+            # [num_tokens, n_head, rope_dim].
+            q = torch.cat([q_pe, q_nope], dim=-1)
+            # `k_pe` is [num_tokens, 1, rope_dim] (MQA).
+            k = torch.cat([k_pe.squeeze(-2), k_nope], dim=-1)
 
         # we only quant q here since k quant is fused with cache insertion
         q = q.view(-1, self.head_dim)
@@ -761,6 +737,46 @@ def forward(
         return self.indexer_op(hidden_states, q_fp8, k, weights)
 
 
+def _try_load_fp8_indexer_wk(name, tensor, buf, params_dict, loaded_params):
+    """
+    We fuse the WK and weights_proj projections, but in some checkpoints WK is stored
+    in FP8 with a separate weight_scale_inv, while weights_proj is stored in BF16.
+    Upcasting to BF16 during loading enables the fusion. This function loads the FP8 WK
+    weights and scale, and when both are available, dequantizes to BF16 and stores into
+    the fused wk_weights_proj.weight parameter.
+    """
+    if "indexer.wk." not in name or "wk_weights" in name:
+        return False  # Weight is not an isolated WK weight for the indexer, ignore.
+    is_weight = name.endswith(".weight") and tensor.dtype == torch.float8_e4m3fn
+    is_scale = "weight_scale_inv" in name
+    if not is_weight and not is_scale:
+        return False  # WK is not in FP8 format, ignore.
+    # Buffer this tensor (weight or scale) until both have arrived.
+    layer_prefix = name.rsplit(".wk.", 1)[0]  # e.g. "model.layers.0.self_attn.indexer"
+    entry = buf.setdefault(layer_prefix, {})
+    entry["weight" if is_weight else "scale"] = tensor
+    if "weight" not in entry or "scale" not in entry:
+        return True  # still waiting for the other param
+
+    # We have both weight and scale: dequantize FP8 to BF16.
+    weight_fp8, scale_inv = entry["weight"], entry["scale"]
+    del buf[layer_prefix]
+    block_size = weight_fp8.shape[1] // scale_inv.shape[1]
+    weight_bf16 = scaled_dequantize(
+        weight_fp8,
+        scale_inv,
+        group_shape=GroupShape(block_size, block_size),
+        out_dtype=torch.bfloat16,
+    )
+
+    # Load the dequantized weight into shard 0 of the fused buffer.
+    fused_name = f"{layer_prefix}.wk_weights_proj.weight"
+    param = params_dict[fused_name]
+    param.weight_loader(param, weight_bf16, 0)
+    loaded_params.add(fused_name)
+    return True
+
+
 def _min_latency_fused_qkv_a_proj_impl(
     input_: torch.Tensor,
     weight: torch.Tensor,
@@ -969,6 +985,7 @@ def __init__(
 
         self.is_v32 = hasattr(config, "index_topk")
 
+        _skip_topk = False
         if self.is_v32:
             self.indexer_rope_emb = get_rope(
                 qk_rope_head_dim,
@@ -986,6 +1003,21 @@ def __init__(
                 topk_indices_buffer,
                 f"{prefix}.indexer",
             )
+
+            # Enable IndexCache for DeepSeek models to reduce redundant top-k
+            # token selection computations in sparse attention.
+            use_index_cache = getattr(config, "use_index_cache", False)
+            if use_index_cache:
+                # IndexCache config
+                # Refer: https://arxiv.org/abs/2603.12201 for more details.
+                _index_topk_freq = getattr(config, "index_topk_freq", 1)
+                _index_topk_pattern = getattr(config, "index_topk_pattern", None)
+                layer_id = extract_layer_index(prefix)
+                if _index_topk_pattern is None:
+                    _skip_topk = max(layer_id - 1, 0) % _index_topk_freq != 0
+                elif 0 <= layer_id < len(_index_topk_pattern):
+                    _skip_topk = _index_topk_pattern[layer_id] == "S"
+
         else:
             self.indexer_rope_emb = None
             self.indexer = None
@@ -1023,6 +1055,7 @@ def __init__(
             cache_config,
             quant_config,
             prefix,
+            skip_topk=_skip_topk,
         )
 
     def forward(
@@ -1222,6 +1255,16 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
 
         self.aux_hidden_state_layers = tuple[int, ...]()
 
+        # Needed by load_weights
+        qk_nope_head_dim = getattr(config, "qk_nope_head_dim", 0)
+        qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 0)
+        self.use_mha = config.model_type == "deepseek" or all(
+            dim == 0 for dim in (qk_nope_head_dim, qk_rope_head_dim)
+        )
+        self.num_redundant_experts = (
+            vllm_config.parallel_config.eplb_config.num_redundant_experts
+        )
+
     def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
@@ -1283,178 +1326,6 @@ def forward(
             return hidden_states, aux_hidden_states
         return hidden_states
 
-
-class DeepseekV2MixtureOfExperts(MixtureOfExperts):
-    moe_mlp_layers: list[DeepseekV2MoE]
-    """
-    List of MoE MLP layers in the model.
-    """
-
-    def extract_moe_parameters(self, example_moe: DeepseekV2MoE | None):
-        if example_moe is None:
-            self.num_moe_layers = 0
-            self.num_expert_groups = 0
-            self.num_logical_experts = 0
-            self.num_physical_experts = 0
-            self.num_local_physical_experts = 0
-            self.num_routed_experts = 0
-            self.num_shared_experts = 0
-            self.num_redundant_experts = 0
-            logger.warning("DeepSeekV2: No DeepseekV2MoE layer found in model.layers.")
-        else:
-            self.num_logical_experts = example_moe.n_logical_experts
-            self.num_physical_experts = example_moe.n_physical_experts
-            self.num_local_physical_experts = example_moe.n_local_physical_experts
-            self.num_routed_experts = example_moe.n_routed_experts
-            self.num_shared_experts = example_moe.n_shared_experts
-            self.num_redundant_experts = example_moe.n_redundant_experts
-
-    def update_physical_experts_metadata(
-        self,
-        num_physical_experts: int,
-        num_local_physical_experts: int,
-    ) -> None:
-        assert self.num_local_physical_experts == num_local_physical_experts
-        self.num_physical_experts = num_physical_experts
-        self.num_local_physical_experts = num_local_physical_experts
-        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
-        for moe in self.moe_mlp_layers:
-            moe.n_local_physical_experts = num_local_physical_experts
-            moe.n_physical_experts = num_physical_experts
-            moe.n_redundant_experts = self.num_redundant_experts
-            moe.experts.update_expert_map()
-
-
-class DeepseekV2ForCausalLM(
-    nn.Module,
-    SupportsPP,
-    DeepseekV2MixtureOfExperts,
-    SupportsLoRA,
-    SupportsEagle,
-    SupportsEagle3,
-):
-    packed_modules_mapping = {
-        "gate_up_proj": ["gate_proj", "up_proj"],
-    }
-    model_cls = DeepseekV2Model
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-        config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
-        self.config = config
-        self.quant_config = quant_config
-        self.is_fp4_ckpt = (
-            self.quant_config is not None
-            and self.quant_config.get_name() == "modelopt_fp4"
-        )
-
-        qk_nope_head_dim = getattr(config, "qk_nope_head_dim", 0)
-        qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 0)
-        self.use_mha = config.model_type == "deepseek" or all(
-            dim == 0 for dim in (qk_nope_head_dim, qk_rope_head_dim)
-        )
-
-        if self.use_mha:
-            self.packed_modules_mapping["qkv_proj"] = ["q_proj", "k_proj", "v_proj"]
-
-        # `packed_modules_mapping` needs to be modified before
-        # initializing DeepseekV2Model, as it is passed inplace to
-        # quantization config init and may be used to select the
-        # quant_method for relevant layers during initialization.
-        self.fuse_qkv_a_proj = (
-            hasattr(config, "q_lora_rank") and config.q_lora_rank is not None
-        )
-        if self.fuse_qkv_a_proj:
-            self.packed_modules_mapping["fused_qkv_a_proj"] = [
-                "q_a_proj",
-                "kv_a_proj_with_mqa",
-            ]
-
-        self.model = self.model_cls(
-            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
-        )
-        if get_pp_group().is_last_rank:
-            self.lm_head = ParallelLMHead(
-                config.vocab_size,
-                config.hidden_size,
-                quant_config=quant_config,
-                prefix=maybe_prefix(prefix, "lm_head"),
-            )
-        else:
-            self.lm_head = PPMissingLayer()
-        self.logits_processor = LogitsProcessor(config.vocab_size)
-        self.make_empty_intermediate_tensors = (
-            self.model.make_empty_intermediate_tensors
-        )
-        # Set MoE hyperparameters
-        self.num_moe_layers = (
-            self.config.num_hidden_layers - self.config.first_k_dense_replace
-        )
-        self.set_moe_parameters()
-
-    def set_moe_parameters(self):
-        self.expert_weights = []
-
-        self.num_expert_groups = getattr(self.config, "n_group", 1)
-
-        self.moe_layers = []
-        self.moe_mlp_layers = []
-        example_moe = None
-        for layer in self.model.layers:
-            if isinstance(layer, PPMissingLayer):
-                continue
-
-            assert isinstance(layer, DeepseekV2DecoderLayer)
-            if isinstance(layer.mlp, DeepseekV2MoE):
-                # Pick last one layer since the first ones may be dense layers.
-                example_moe = layer.mlp
-                self.moe_mlp_layers.append(layer.mlp)
-                self.moe_layers.append(layer.mlp.experts)
-
-        self.extract_moe_parameters(example_moe)
-
-    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
-        self.model.aux_hidden_state_layers = layers
-
-    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
-        num_layers = len(self.model.layers)
-        return (2, num_layers // 2, num_layers - 3)
-
-    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.embed_input_ids(input_ids)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor | None,
-        positions: torch.Tensor,
-        intermediate_tensors: IntermediateTensors | None = None,
-        inputs_embeds: torch.Tensor | None = None,
-    ) -> torch.Tensor | IntermediateTensors:
-        hidden_states = self.model(
-            input_ids, positions, intermediate_tensors, inputs_embeds
-        )
-        return hidden_states
-
-    def compute_logits(
-        self,
-        hidden_states: torch.Tensor,
-    ) -> torch.Tensor | None:
-        logits = self.logits_processor(self.lm_head, hidden_states)
-        return logits
-
-    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        # Params for weights, fp8 weight scales, fp8 activation scales
-        # (param_name, weight_name, expert_id, shard_id)
-        return SharedFusedMoE.make_expert_params_mapping(
-            self,
-            ckpt_gate_proj_name="gate_proj",
-            ckpt_down_proj_name="down_proj",
-            ckpt_up_proj_name="up_proj",
-            num_experts=self.config.n_routed_experts,
-            num_redundant_experts=0,
-        )
-
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         rocm_aiter_moe_shared_expert_enabled = (
             rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
@@ -1473,13 +1344,13 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             ("qkv_proj", "k_proj", "k"),
             ("qkv_proj", "v_proj", "v"),
         ]
-        if self.is_fp4_ckpt:
-            # Fused indexer wk + weights_proj (shard 0 = wk, shard 1 = weights_proj)
-            indexer_fused_mapping = [
-                ("wk_weights_proj", "wk", 0),
-                ("wk_weights_proj", "weights_proj", 1),
-            ]
-            stacked_params_mapping.extend(indexer_fused_mapping)
+        # Fused indexer wk + weights_proj (shard 0 = wk, shard 1 = weights_proj)
+        _pending_wk_fp8: dict = {}  # When WK is in FP8, we dequant to BF16 for fusion
+        indexer_fused_mapping = [
+            ("wk_weights_proj", "wk", 0),
+            ("wk_weights_proj", "weights_proj", 1),
+        ]
+        stacked_params_mapping.extend(indexer_fused_mapping)
 
         if self.use_mha:
             stacked_params_mapping.extend(mha_params_mapping)
@@ -1488,7 +1359,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -1516,6 +1387,11 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                 rocm_aiter_moe_shared_expert_enabled and ("mlp.shared_experts" in name)
             )
 
+            if _try_load_fp8_indexer_wk(
+                name, loaded_weight, _pending_wk_fp8, params_dict, loaded_params
+            ):
+                continue
+
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 # Skip non-stacked layers and experts (experts handled below).
                 if weight_name not in name:
@@ -1670,6 +1546,178 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         return loaded_params
 
 
+class DeepseekV2MixtureOfExperts(MixtureOfExperts):
+    moe_mlp_layers: list[DeepseekV2MoE]
+    """
+    List of MoE MLP layers in the model.
+    """
+
+    def extract_moe_parameters(self, example_moe: DeepseekV2MoE | None):
+        if example_moe is None:
+            self.num_moe_layers = 0
+            self.num_expert_groups = 0
+            self.num_logical_experts = 0
+            self.num_physical_experts = 0
+            self.num_local_physical_experts = 0
+            self.num_routed_experts = 0
+            self.num_shared_experts = 0
+            self.num_redundant_experts = 0
+            logger.warning("DeepSeekV2: No DeepseekV2MoE layer found in model.layers.")
+        else:
+            self.num_logical_experts = example_moe.n_logical_experts
+            self.num_physical_experts = example_moe.n_physical_experts
+            self.num_local_physical_experts = example_moe.n_local_physical_experts
+            self.num_routed_experts = example_moe.n_routed_experts
+            self.num_shared_experts = example_moe.n_shared_experts
+            self.num_redundant_experts = example_moe.n_redundant_experts
+
+    def update_physical_experts_metadata(
+        self,
+        num_physical_experts: int,
+        num_local_physical_experts: int,
+    ) -> None:
+        assert self.num_local_physical_experts == num_local_physical_experts
+        self.num_physical_experts = num_physical_experts
+        self.num_local_physical_experts = num_local_physical_experts
+        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
+        for moe in self.moe_mlp_layers:
+            moe.n_local_physical_experts = num_local_physical_experts
+            moe.n_physical_experts = num_physical_experts
+            moe.n_redundant_experts = self.num_redundant_experts
+            moe.experts.update_expert_map()
+
+
+class DeepseekV2ForCausalLM(
+    nn.Module,
+    SupportsPP,
+    DeepseekV2MixtureOfExperts,
+    SupportsLoRA,
+    SupportsEagle,
+    SupportsEagle3,
+):
+    packed_modules_mapping = {
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+    model_cls = DeepseekV2Model
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+
+        qk_nope_head_dim = getattr(config, "qk_nope_head_dim", 0)
+        qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 0)
+        self.use_mha = config.model_type == "deepseek" or all(
+            dim == 0 for dim in (qk_nope_head_dim, qk_rope_head_dim)
+        )
+
+        if self.use_mha:
+            self.packed_modules_mapping["qkv_proj"] = ["q_proj", "k_proj", "v_proj"]
+
+        # `packed_modules_mapping` needs to be modified before
+        # initializing DeepseekV2Model, as it is passed inplace to
+        # quantization config init and may be used to select the
+        # quant_method for relevant layers during initialization.
+        self.fuse_qkv_a_proj = (
+            hasattr(config, "q_lora_rank") and config.q_lora_rank is not None
+        )
+        if self.fuse_qkv_a_proj:
+            self.packed_modules_mapping["fused_qkv_a_proj"] = [
+                "q_a_proj",
+                "kv_a_proj_with_mqa",
+            ]
+
+        self.model = self.model_cls(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+        # Set MoE hyperparameters
+        self.num_moe_layers = (
+            self.config.num_hidden_layers - self.config.first_k_dense_replace
+        )
+        self.set_moe_parameters()
+
+    def set_moe_parameters(self):
+        self.expert_weights = []
+
+        self.num_expert_groups = getattr(self.config, "n_group", 1)
+
+        self.moe_layers = []
+        self.moe_mlp_layers = []
+        example_moe = None
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+
+            assert isinstance(layer, DeepseekV2DecoderLayer)
+            if isinstance(layer.mlp, DeepseekV2MoE):
+                # Pick last one layer since the first ones may be dense layers.
+                example_moe = layer.mlp
+                self.moe_mlp_layers.append(layer.mlp)
+                self.moe_layers.append(layer.mlp.experts)
+
+        self.extract_moe_parameters(example_moe)
+
+    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
+        self.model.aux_hidden_state_layers = layers
+
+    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
+        num_layers = len(self.model.layers)
+        return (2, num_layers // 2, num_layers - 3)
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return fused_moe_make_expert_params_mapping(
+            self,
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.n_routed_experts,
+            num_redundant_experts=0,
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+
 class DeepseekForCausalLM(DeepseekV2ForCausalLM):
     pass
 
@@ -1693,6 +1741,8 @@ def get_spec_layer_idx_from_weight_name(
     ):
         layer_idx = config.num_hidden_layers
         for i in range(config.num_nextn_predict_layers):
-            if weight_name.startswith(f"model.layers.{layer_idx + i}."):
+            if weight_name.startswith(
+                f"model.layers.{layer_idx + i}."
+            ) or weight_name.startswith(f"layers.{layer_idx + i}."):
                 return layer_idx + i
     return None
diff --git a/vllm/model_executor/models/deepseek_v4.py b/vllm/model_executor/models/deepseek_v4.py
new file mode 100644
index 000000000000..8c1fc56c0eb0
--- /dev/null
+++ b/vllm/model_executor/models/deepseek_v4.py
@@ -0,0 +1,1650 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import typing
+from collections.abc import Callable, Iterable
+from itertools import islice
+
+import regex as re
+import torch
+import torch.nn as nn
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import VllmConfig, get_current_vllm_config
+from vllm.distributed import (
+    get_ep_group,
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.activation import SiluAndMul, SiluAndMulWithClamp
+from vllm.model_executor.layers.deepseek_v4_attention import (
+    DeepseekV4Indexer,
+    DeepseekV4MLAModules,
+    DeepseekV4MultiHeadLatentAttentionWrapper,
+)
+from vllm.model_executor.layers.fused_moe import FusedMoE, GateLinear
+from vllm.model_executor.layers.fused_moe.layer import UnquantizedFusedMoEMethod
+from vllm.model_executor.layers.fused_moe.router.fused_topk_bias_router import (
+    fused_topk_bias,
+)
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import (
+    QuantizationConfig,
+    QuantizationMethods,
+)
+from vllm.model_executor.layers.quantization.fp8 import Fp8Config
+from vllm.model_executor.layers.quantization.mxfp4 import Mxfp4MoEMethod
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    is_layer_skipped,
+)
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.interfaces import SupportsPP
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.platforms import current_platform
+from vllm.sequence import IntermediateTensors
+from vllm.triton_utils import tl, triton
+from vllm.utils.torch_utils import direct_register_custom_op
+
+from .utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    WeightsMapper,
+    extract_layer_index,
+    is_pp_missing_parameter,
+    make_layers,
+    maybe_prefix,
+)
+
+_DEEPSEEK_V4_EXPERT_DTYPES = ("fp4", "fp8")
+
+
+class DeepseekV4MLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        swiglu_limit: float | None = None,
+        quant_config: QuantizationConfig | None = None,
+        reduce_results: bool = True,
+        is_sequence_parallel: bool = False,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        # If is_sequence_parallel, the input and output tensors are sharded
+        # across the ranks within the tp_group. In this case the weights are
+        # replicated and no collective ops are needed.
+        # Otherwise we use standard TP with an allreduce at the end.
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            disable_tp=is_sequence_parallel,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            disable_tp=is_sequence_parallel,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
+            )
+        if swiglu_limit is not None:
+            self.act_fn = SiluAndMulWithClamp(swiglu_limit)
+        else:
+            self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class DeepseekV4FP8Config(Fp8Config):
+    """FP8 config for DeepSeek V4 with expert-dtype-aware MoE dispatch.
+
+    DeepSeek V4 checkpoints always use FP8 block quantization for
+    linear/attention layers. The MoE expert weights vary by checkpoint:
+    - ``expert_dtype="fp4"`` (e.g. DeepSeek-V4-Flash): MXFP4 experts
+      with ue8m0 (e8m0fnu) FP8 linear scales.
+    - ``expert_dtype="fp8"`` (e.g. DeepSeek-V4-Flash-Base): FP8 block
+      experts with float32 FP8 linear scales.
+
+    The dispatch and the linear scale dtype are both keyed off
+    ``expert_dtype`` from the model's hf_config; missing values default
+    to ``"fp4"`` so existing FP4 checkpoints stay unchanged.
+
+    NOTE: ``expert_dtype`` is resolved lazily because this config is
+    constructed during VllmConfig setup, before ``set_current_vllm_config``
+    is active. Reading hf_config eagerly in ``__init__`` would always see
+    the default ``"fp4"`` and silently misroute Flash-Base checkpoints.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._resolved_expert_dtype: str | None = None
+        # ``is_scale_e8m0`` is a property that resolves on first read,
+        # by which time the current vllm_config has been set.
+
+    @property
+    def expert_dtype(self) -> str:
+        if self._resolved_expert_dtype is None:
+            try:
+                hf_config = get_current_vllm_config().model_config.hf_config
+            except Exception:
+                # vllm_config not yet set; defer the decision until a
+                # later call lands inside set_current_vllm_config.
+                return "fp4"
+            expert_dtype = getattr(hf_config, "expert_dtype", "fp4")
+            if expert_dtype not in _DEEPSEEK_V4_EXPERT_DTYPES:
+                raise ValueError(
+                    f"Unsupported DeepSeek V4 expert_dtype={expert_dtype!r}; "
+                    f"expected one of {_DEEPSEEK_V4_EXPERT_DTYPES}."
+                )
+            self._resolved_expert_dtype = expert_dtype
+            from vllm.logger import init_logger
+
+            init_logger(__name__).info_once(
+                "DeepSeek V4 expert_dtype resolved to %r", expert_dtype
+            )
+        return self._resolved_expert_dtype
+
+    @property
+    def is_scale_e8m0(self) -> bool:
+        # FP4 checkpoints store FP8 linear scales as e8m0fnu; FP8 expert
+        # checkpoints (Flash-Base) store them as float32.
+        return self.expert_dtype == "fp4"
+
+    @classmethod
+    def get_name(cls) -> QuantizationMethods:
+        return "deepseek_v4_fp8"
+
+    @classmethod
+    def override_quantization_method(
+        cls, hf_quant_cfg, user_quant, hf_config=None
+    ) -> QuantizationMethods | None:
+        if not (
+            isinstance(hf_quant_cfg, dict)
+            and hf_quant_cfg.get("quant_method") in ("fp8", "deepseek_v4_fp8")
+        ):
+            return None
+        model_type = getattr(hf_config, "model_type", None)
+        if model_type == "deepseek_v4" or user_quant == "deepseek_v4_fp8":
+            return "deepseek_v4_fp8"
+        return None
+
+    def get_quant_method(self, layer, prefix):
+        if isinstance(layer, FusedMoE):
+            if is_layer_skipped(
+                prefix=prefix,
+                ignored_layers=self.ignored_layers,
+                fused_mapping=self.packed_modules_mapping,
+            ):
+                return UnquantizedFusedMoEMethod(layer.moe_config)
+            if self.expert_dtype == "fp4":
+                return Mxfp4MoEMethod(layer.moe_config)
+            # expert_dtype == "fp8": fall through to Fp8Config which
+            # returns Fp8MoEMethod with block-wise float32 scales.
+        return super().get_quant_method(layer, prefix)
+
+    def is_mxfp4_quant(self, prefix, layer):
+        return isinstance(layer, FusedMoE) and self.expert_dtype == "fp4"
+
+
+@triton.jit
+def _deepseek_v4_stage_mega_moe_inputs_kernel(
+    hidden_states,
+    x_fp8,
+    x_sf,
+    topk_ids,
+    topk_weights,
+    topk_idx_out,
+    topk_weights_out,
+    hidden_stride_m: tl.constexpr,
+    hidden_stride_k: tl.constexpr,
+    x_stride_m: tl.constexpr,
+    x_stride_k: tl.constexpr,
+    x_sf_stride_m: tl.constexpr,
+    x_sf_stride_k: tl.constexpr,
+    topk_ids_stride_m: tl.constexpr,
+    topk_ids_stride_k: tl.constexpr,
+    topk_weights_stride_m: tl.constexpr,
+    topk_weights_stride_k: tl.constexpr,
+    topk_idx_stride_m: tl.constexpr,
+    topk_idx_stride_k: tl.constexpr,
+    topk_weights_out_stride_m: tl.constexpr,
+    topk_weights_out_stride_k: tl.constexpr,
+    hidden_size: tl.constexpr,
+    top_k: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+    GROUP_K: tl.constexpr,
+    BLOCK_TOPK: tl.constexpr,
+) -> None:
+    token_id = tl.program_id(0)
+    k_block_id = tl.program_id(1)
+
+    k_offsets = k_block_id * BLOCK_K + tl.arange(0, BLOCK_K)
+    k_mask = k_offsets < hidden_size
+    hidden = tl.load(
+        hidden_states + token_id * hidden_stride_m + k_offsets * hidden_stride_k,
+        mask=k_mask,
+        other=0.0,
+    ).to(tl.float32)
+
+    num_groups: tl.constexpr = BLOCK_K // GROUP_K
+    hidden_groups = tl.reshape(tl.abs(hidden), [num_groups, GROUP_K])
+    amax = tl.max(hidden_groups, axis=1)
+    amax = tl.maximum(amax, 1.0e-4)
+
+    scale = amax / 448.0
+    scale_bits = scale.to(tl.uint32, bitcast=True)
+    scale_exp = ((scale_bits >> 23) & 0xFF) + ((scale_bits & 0x7FFFFF) != 0).to(
+        tl.uint32
+    )
+    scale_exp = tl.minimum(tl.maximum(scale_exp, 1), 254)
+    rounded_scale = (scale_exp << 23).to(tl.float32, bitcast=True)
+
+    hidden_groups = tl.reshape(hidden, [num_groups, GROUP_K])
+    scaled = hidden_groups * (1.0 / rounded_scale)[:, None]
+    scaled = tl.reshape(scaled, [BLOCK_K])
+    fp8 = scaled.to(tl.float8e4nv)
+    tl.store(
+        x_fp8 + token_id * x_stride_m + k_offsets * x_stride_k,
+        fp8,
+        mask=k_mask,
+    )
+
+    scale_offsets = tl.arange(0, num_groups)
+    packed_scale = tl.sum(scale_exp << (scale_offsets * 8), axis=0).to(tl.int32)
+    tl.store(
+        x_sf + token_id * x_sf_stride_m + k_block_id * x_sf_stride_k,
+        packed_scale,
+    )
+
+    if k_block_id == 0:
+        topk_offsets = tl.arange(0, BLOCK_TOPK)
+        topk_mask = topk_offsets < top_k
+
+        ids = tl.load(
+            topk_ids + token_id * topk_ids_stride_m + topk_offsets * topk_ids_stride_k,
+            mask=topk_mask,
+            other=0,
+        ).to(tl.int64)
+        tl.store(
+            topk_idx_out
+            + token_id * topk_idx_stride_m
+            + topk_offsets * topk_idx_stride_k,
+            ids,
+            mask=topk_mask,
+        )
+
+        weights = tl.load(
+            topk_weights
+            + token_id * topk_weights_stride_m
+            + topk_offsets * topk_weights_stride_k,
+            mask=topk_mask,
+            other=0.0,
+        )
+        tl.store(
+            topk_weights_out
+            + token_id * topk_weights_out_stride_m
+            + topk_offsets * topk_weights_out_stride_k,
+            weights,
+            mask=topk_mask,
+        )
+
+
+def _stage_deepseek_v4_mega_moe_inputs(
+    hidden_states: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    x_fp8: torch.Tensor,
+    x_sf: torch.Tensor,
+    topk_idx_out: torch.Tensor,
+    topk_weights_out: torch.Tensor,
+) -> None:
+    num_tokens, hidden_size = hidden_states.shape
+    if num_tokens == 0:
+        return
+    if hidden_size % 128 != 0:
+        raise ValueError(
+            "DeepSeek V4 MegaMoE input staging requires hidden_size to be "
+            "a multiple of 128."
+        )
+    top_k = topk_ids.shape[1]
+    if topk_weights.shape != topk_ids.shape:
+        raise ValueError(
+            "DeepSeek V4 MegaMoE input staging requires topk_weights and "
+            "topk_ids to have the same shape."
+        )
+
+    block_k = 128
+    grid = (num_tokens, triton.cdiv(hidden_size, block_k))
+    block_topk = triton.next_power_of_2(top_k)
+    _deepseek_v4_stage_mega_moe_inputs_kernel[grid](
+        hidden_states,
+        x_fp8,
+        x_sf,
+        topk_ids,
+        topk_weights,
+        topk_idx_out,
+        topk_weights_out,
+        hidden_states.stride(0),
+        hidden_states.stride(1),
+        x_fp8.stride(0),
+        x_fp8.stride(1),
+        x_sf.stride(0),
+        x_sf.stride(1),
+        topk_ids.stride(0),
+        topk_ids.stride(1),
+        topk_weights.stride(0),
+        topk_weights.stride(1),
+        topk_idx_out.stride(0),
+        topk_idx_out.stride(1),
+        topk_weights_out.stride(0),
+        topk_weights_out.stride(1),
+        hidden_size,
+        top_k,
+        BLOCK_K=block_k,
+        GROUP_K=32,
+        BLOCK_TOPK=block_topk,
+        num_warps=4,
+    )
+
+
+def make_deepseek_v4_expert_params_mapping(
+    num_experts: int,
+) -> list[tuple[str, str, int, str]]:
+    return [
+        (
+            "experts.w13_" if shard_id in ("w1", "w3") else "experts.w2_",
+            f"experts.{expert_id}.{weight_name}.",
+            expert_id,
+            shard_id,
+        )
+        for expert_id in range(num_experts)
+        for shard_id, weight_name in [
+            ("w1", "w1"),
+            ("w2", "w2"),
+            ("w3", "w3"),
+        ]
+    ]
+
+
+class DeepseekV4MegaMoEExperts(nn.Module):
+    _symm_buffer_cache: dict[tuple[int, int, int, int, int, int, int], object] = {}
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        *,
+        num_experts: int,
+        num_local_experts: int,
+        experts_start_idx: int,
+        top_k: int,
+        hidden_size: int,
+        intermediate_size: int,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.prefix = prefix
+        self.num_experts = num_experts
+        self.num_local_experts = num_local_experts
+        self.experts_start_idx = experts_start_idx
+        self.experts_end_idx = experts_start_idx + num_local_experts
+        self.top_k = top_k
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.max_num_tokens = vllm_config.scheduler_config.max_num_batched_tokens
+
+        weight_attrs = {"weight_loader": self.weight_loader}
+        self.w13_weight = nn.Parameter(
+            torch.zeros(
+                num_local_experts,
+                2 * intermediate_size,
+                hidden_size // 2,
+                dtype=torch.uint8,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(self.w13_weight, weight_attrs)
+
+        self.w13_weight_scale = nn.Parameter(
+            torch.zeros(
+                num_local_experts,
+                2 * intermediate_size,
+                hidden_size // 32,
+                dtype=torch.uint8,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(self.w13_weight_scale, weight_attrs)
+        self.w13_weight_scale.quant_method = "block"
+
+        self.w2_weight = nn.Parameter(
+            torch.zeros(
+                num_local_experts,
+                hidden_size,
+                intermediate_size // 2,
+                dtype=torch.uint8,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(self.w2_weight, weight_attrs)
+
+        self.w2_weight_scale = nn.Parameter(
+            torch.zeros(
+                num_local_experts,
+                hidden_size,
+                intermediate_size // 32,
+                dtype=torch.uint8,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(self.w2_weight_scale, weight_attrs)
+        self.w2_weight_scale.quant_method = "block"
+
+        self._transformed_l1_weights: tuple[torch.Tensor, torch.Tensor] | None = None
+        self._transformed_l2_weights: tuple[torch.Tensor, torch.Tensor] | None = None
+
+        # Register in the static forward context so the custom-op wrapper
+        # can look up this module by name from within a torch.compile graph.
+        compilation_config = vllm_config.compilation_config
+        if prefix in compilation_config.static_forward_context:
+            raise ValueError(f"Duplicate layer name: {prefix}")
+        compilation_config.static_forward_context[prefix] = self
+
+    def _map_global_expert_id(self, expert_id: int) -> int:
+        if expert_id < self.experts_start_idx or expert_id >= self.experts_end_idx:
+            return -1
+        return expert_id - self.experts_start_idx
+
+    def weight_loader(
+        self,
+        param: nn.Parameter,
+        loaded_weight: torch.Tensor,
+        weight_name: str,
+        shard_id: str,
+        expert_id: int,
+        return_success: bool = False,
+    ) -> bool | None:
+        local_expert_id = self._map_global_expert_id(expert_id)
+        if local_expert_id == -1:
+            return False if return_success else None
+
+        expert_data = param.data[local_expert_id]
+        if shard_id in ("w1", "w3"):
+            if "w13_" not in weight_name:
+                return False if return_success else None
+            shard_offset = 0 if shard_id == "w1" else self.intermediate_size
+            expert_data = expert_data.narrow(0, shard_offset, self.intermediate_size)
+        elif shard_id == "w2":
+            if "w2_" not in weight_name:
+                return False if return_success else None
+        else:
+            raise ValueError(f"Unsupported expert shard id: {shard_id}")
+
+        if expert_data.shape != loaded_weight.shape:
+            raise ValueError(
+                f"DeepSeek V4 MegaMoE expert weight shape mismatch for "
+                f"{weight_name}: parameter shard {tuple(expert_data.shape)} "
+                f"vs checkpoint {tuple(loaded_weight.shape)}"
+            )
+        expert_data.copy_(loaded_weight)
+        return True if return_success else None
+
+    @staticmethod
+    def _ue8m0_uint8_to_float(sf: torch.Tensor) -> torch.Tensor:
+        return (sf.to(torch.int32) << 23).view(torch.float32)
+
+    def _check_runtime_supported(self) -> None:
+        if not torch.cuda.is_available():
+            raise NotImplementedError("DeepSeek V4 MegaMoE requires CUDA.")
+        device = self.w13_weight.device
+        if device.type != "cuda":
+            raise NotImplementedError(
+                "DeepSeek V4 MegaMoE expert weights must be loaded on CUDA."
+            )
+        if torch.cuda.get_device_capability(device)[0] != 10:
+            raise NotImplementedError("DeepGEMM MegaMoE requires SM100 GPUs.")
+        if self.hidden_size % 128 != 0 or self.intermediate_size % 128 != 0:
+            raise ValueError(
+                "DeepGEMM MegaMoE requires hidden and intermediate sizes "
+                "to be multiples of 128."
+            )
+
+    def finalize_weights(self) -> None:
+        if self._transformed_l1_weights is not None:
+            return
+
+        self._check_runtime_supported()
+        import vllm.third_party.deep_gemm as deep_gemm
+
+        w13_scale = deep_gemm.transform_sf_into_required_layout(
+            self._ue8m0_uint8_to_float(self.w13_weight_scale.data).contiguous(),
+            2 * self.intermediate_size,
+            self.hidden_size,
+            (1, 32),
+            self.num_local_experts,
+        )
+        w2_scale = deep_gemm.transform_sf_into_required_layout(
+            self._ue8m0_uint8_to_float(self.w2_weight_scale.data).contiguous(),
+            self.hidden_size,
+            self.intermediate_size,
+            (1, 32),
+            self.num_local_experts,
+        )
+        self._transformed_l1_weights, self._transformed_l2_weights = (
+            deep_gemm.transform_weights_for_mega_moe(
+                (self.w13_weight.data.view(torch.int8).contiguous(), w13_scale),
+                (self.w2_weight.data.view(torch.int8).contiguous(), w2_scale),
+            )
+        )
+        # Drop the original loader-side parameters: the MegaMoE kernels only
+        # consume the transformed views above. transform_weights_for_mega_moe
+        # allocates a fresh tensor for the L1 weight (see _interleave_l1_weights)
+        # and fresh SF tensors for L1/L2; the L2 weight is the only tensor that
+        # aliases the original storage, and _transformed_l2_weights still holds
+        # it, so the storage stays live after we drop the Parameter.
+        self.w13_weight = None
+        self.w13_weight_scale = None
+        self.w2_weight = None
+        self.w2_weight_scale = None
+
+    def get_symm_buffer(self):
+        import vllm.third_party.deep_gemm as deep_gemm
+
+        group = get_ep_group().device_group
+        device = torch.accelerator.current_device_index()
+        key = (
+            id(group),
+            device,
+            self.num_experts,
+            self.max_num_tokens,
+            self.top_k,
+            self.hidden_size,
+            self.intermediate_size,
+        )
+        symm_buffer = self._symm_buffer_cache.get(key)
+        if symm_buffer is None:
+            symm_buffer = deep_gemm.get_symm_buffer_for_mega_moe(
+                group,
+                self.num_experts,
+                self.max_num_tokens,
+                self.top_k,
+                self.hidden_size,
+                self.intermediate_size,
+            )
+            self._symm_buffer_cache[key] = symm_buffer
+        return symm_buffer
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        *,
+        activation_clamp: float | None,
+        fast_math: bool = True,
+    ) -> torch.Tensor:
+        if hidden_states.shape[0] > self.max_num_tokens:
+            raise ValueError(
+                f"DeepSeek V4 MegaMoE got {hidden_states.shape[0]} tokens, "
+                f"but the symmetric buffer was sized for {self.max_num_tokens}."
+            )
+        y = torch.empty_like(hidden_states, dtype=torch.bfloat16)
+        torch.ops.vllm.deepseek_v4_mega_moe_experts(
+            hidden_states,
+            topk_weights,
+            topk_ids,
+            y,
+            self.prefix,
+            activation_clamp,
+            fast_math,
+        )
+        return y
+
+    def _run_mega_moe(
+        self,
+        hidden_states: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        y: torch.Tensor,
+        activation_clamp: float | None,
+        fast_math: bool,
+    ) -> None:
+        import vllm.third_party.deep_gemm as deep_gemm
+
+        symm_buffer = self.get_symm_buffer()
+        num_tokens = hidden_states.shape[0]
+        _stage_deepseek_v4_mega_moe_inputs(
+            hidden_states,
+            topk_weights,
+            topk_ids,
+            symm_buffer.x[:num_tokens],
+            symm_buffer.x_sf[:num_tokens],
+            symm_buffer.topk_idx[:num_tokens],
+            symm_buffer.topk_weights[:num_tokens],
+        )
+
+        # This method must have been already called during the weight loading phase.
+        # We call it again here to cover the dummy weight loading case.
+        self.finalize_weights()
+
+        assert self._transformed_l1_weights is not None
+        assert self._transformed_l2_weights is not None
+        deep_gemm.fp8_fp4_mega_moe(
+            y,
+            self._transformed_l1_weights,
+            self._transformed_l2_weights,
+            symm_buffer,
+            activation_clamp=activation_clamp,
+            fast_math=fast_math,
+        )
+
+
+DeepseekV4MegaMoEExperts.weight_loader.supports_moe_loading = True  # type: ignore[attr-defined]
+
+
+def _deepseek_v4_mega_moe_experts_op(
+    hidden_states: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    out: torch.Tensor,
+    layer_name: str,
+    activation_clamp: float | None,
+    fast_math: bool,
+) -> None:
+    self = get_forward_context().no_compile_layers[layer_name]
+    self._run_mega_moe(
+        hidden_states,
+        topk_weights,
+        topk_ids,
+        out,
+        activation_clamp,
+        fast_math,
+    )
+
+
+def _deepseek_v4_mega_moe_experts_op_fake(
+    hidden_states: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    out: torch.Tensor,
+    layer_name: str,
+    activation_clamp: float | None,
+    fast_math: bool,
+) -> None:
+    return None
+
+
+direct_register_custom_op(
+    op_name="deepseek_v4_mega_moe_experts",
+    op_func=_deepseek_v4_mega_moe_experts_op,
+    mutates_args=["out"],
+    fake_impl=_deepseek_v4_mega_moe_experts_op_fake,
+)
+
+
+class DeepseekV4MoE(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.tp_size = get_tensor_model_parallel_world_size()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.prefix = prefix
+        self.use_mega_moe = (
+            vllm_config.kernel_config.moe_backend == "deep_gemm_mega_moe"
+        )
+        if self.use_mega_moe and not vllm_config.parallel_config.enable_expert_parallel:
+            raise NotImplementedError(
+                "DeepSeek V4 MegaMoE currently requires expert parallel. "
+                "Enable it with --enable-expert-parallel, or pick a different "
+                "moe backend."
+            )
+
+        self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
+        self.hidden_size = config.hidden_size
+
+        self.n_routed_experts = config.n_routed_experts
+        self.n_activated_experts = config.num_experts_per_tok
+        self.moe_intermediate_size = config.moe_intermediate_size
+        self.swiglu_limit = config.swiglu_limit
+        self.renormalize = config.norm_topk_prob
+        self.scoring_func = getattr(config, "scoring_func", "sqrtsoftplus")
+        if self.use_mega_moe and self.scoring_func != "sqrtsoftplus":
+            raise NotImplementedError(
+                "DeepSeek V4 MegaMoE currently supports sqrtsoftplus routing only."
+            )
+        if self.use_mega_moe and getattr(config, "expert_dtype", "fp4") != "fp4":
+            raise NotImplementedError(
+                "DeepSeek V4 MegaMoE only supports fp4 experts; got expert_dtype="
+                f"{config.expert_dtype!r}. Drop --kernel-config moe_backend="
+                "deep_gemm_mega_moe for this checkpoint."
+            )
+
+        self.gate = GateLinear(
+            config.hidden_size,
+            config.n_routed_experts,
+            out_dtype=torch.float32,
+            bias=False,
+            prefix=f"{prefix}.gate",
+        )
+        self.gate.e_score_correction_bias = None
+        self.gate.tid2eid = None
+        is_hash_moe = extract_layer_index(prefix) < config.num_hash_layers
+        self.hash_indices_dtype = torch.int64 if self.use_mega_moe else torch.int32
+
+        if is_hash_moe:
+            # hash MoE doesn't use e_score_correction_bias
+            # Use randint instead of empty to avoid garbage values causing
+            # invalid memory access in dummy mode (--load-format="dummy")
+            self.gate.tid2eid = nn.Parameter(
+                torch.randint(
+                    0,
+                    config.n_routed_experts,
+                    (config.vocab_size, config.num_experts_per_tok),
+                    dtype=self.hash_indices_dtype,
+                ),
+                requires_grad=False,
+            )
+        elif getattr(config, "topk_method", None) == "noaux_tc":
+            self.gate.e_score_correction_bias = nn.Parameter(
+                torch.empty(config.n_routed_experts, dtype=torch.float32),
+                requires_grad=False,
+            )
+
+        if config.n_shared_experts is None:
+            self.shared_experts = None
+        else:
+            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
+
+            self.shared_experts = DeepseekV4MLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=intermediate_size,
+                hidden_act=config.hidden_act,
+                swiglu_limit=self.swiglu_limit,
+                quant_config=quant_config,
+                reduce_results=self.use_mega_moe,
+                prefix=f"{prefix}.shared_experts",
+            )
+
+        if self.use_mega_moe:
+            self._init_mega_moe_experts(vllm_config, config, prefix)
+        else:
+            self._init_fused_moe_experts(config, quant_config, prefix)
+
+    def _init_mega_moe_experts(
+        self,
+        vllm_config: VllmConfig,
+        config,
+        prefix: str,
+    ) -> None:
+        self.ep_group = get_ep_group()
+        self.ep_size = self.ep_group.world_size
+        self.ep_rank = self.ep_group.rank_in_group
+        assert config.n_routed_experts % self.ep_size == 0
+
+        self.n_local_experts = config.n_routed_experts // self.ep_size
+        self.experts_start_idx = self.ep_rank * self.n_local_experts
+        self.experts_end_idx = self.experts_start_idx + self.n_local_experts
+
+        self.experts = DeepseekV4MegaMoEExperts(
+            vllm_config,
+            num_experts=config.n_routed_experts,
+            num_local_experts=self.n_local_experts,
+            experts_start_idx=self.experts_start_idx,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            prefix=f"{prefix}.experts",
+        )
+
+    def _init_fused_moe_experts(
+        self,
+        config,
+        quant_config,
+        prefix: str,
+    ) -> None:
+        self.tp_rank = get_tensor_model_parallel_rank()
+        assert config.n_routed_experts % self.tp_size == 0
+
+        self.n_local_experts = config.n_routed_experts // self.tp_size
+        self.experts_start_idx = self.tp_rank * self.n_local_experts
+        self.experts_end_idx = self.experts_start_idx + self.n_local_experts
+
+        self.experts = FusedMoE(
+            shared_experts=self.shared_experts,
+            gate=self.gate,
+            num_experts=config.n_routed_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            renormalize=config.norm_topk_prob,
+            quant_config=quant_config,
+            prefix=f"{prefix}.experts",
+            scoring_func=self.scoring_func,
+            routed_scaling_factor=self.routed_scaling_factor,
+            e_score_correction_bias=self.gate.e_score_correction_bias,
+            hash_indices_table=self.gate.tid2eid,
+            swiglu_limit=self.swiglu_limit,
+            router_logits_dtype=torch.float32,
+        )
+
+    def forward(
+        self, hidden_states: torch.Tensor, input_ids: torch.Tensor | None = None
+    ) -> torch.Tensor:
+        if self.gate.tid2eid is not None and input_ids is None:
+            raise ValueError("DeepSeek V4 hash MoE routing requires input_ids.")
+
+        if not self.use_mega_moe:
+            return self._forward_fused_moe(hidden_states, input_ids)
+
+        org_shape = hidden_states.shape
+        router_logits, _ = self.gate(hidden_states)
+        topk_weights, topk_ids = fused_topk_bias(
+            hidden_states=hidden_states,
+            gating_output=router_logits,
+            scoring_func=self.scoring_func,
+            e_score_correction_bias=self.gate.e_score_correction_bias.data
+            if self.gate.e_score_correction_bias is not None
+            else None,
+            topk=self.n_activated_experts,
+            renormalize=self.renormalize,
+            indices_type=self.hash_indices_dtype,
+            input_tokens=input_ids,
+            hash_indices_table=self.gate.tid2eid,
+            routed_scaling_factor=self.routed_scaling_factor,
+        )
+        activation_clamp = (
+            float(self.swiglu_limit) if self.swiglu_limit is not None else None
+        )
+        final_hidden_states = self.experts(
+            hidden_states,
+            topk_weights,
+            topk_ids,
+            activation_clamp=activation_clamp,
+        )
+
+        if self.shared_experts is not None:
+            shared_output = self.shared_experts(hidden_states)
+            final_hidden_states += shared_output
+
+        return final_hidden_states.view(org_shape)
+
+    def _forward_fused_moe(
+        self, hidden_states: torch.Tensor, input_ids: torch.Tensor | None = None
+    ) -> torch.Tensor:
+        org_shape = hidden_states.shape
+        if self.experts.is_internal_router:
+            # In this case, the gate/router runs inside the FusedMoE class
+            final_hidden_states = self.experts(
+                hidden_states=hidden_states,
+                router_logits=hidden_states,
+                input_ids=input_ids,
+            )
+        else:
+            router_logits, _ = self.gate(hidden_states)
+            final_hidden_states = self.experts(
+                hidden_states=hidden_states,
+                router_logits=router_logits,
+                input_ids=input_ids,
+            )
+
+        return final_hidden_states.view(org_shape)
+
+    def finalize_mega_moe_weights(self) -> None:
+        if self.use_mega_moe:
+            self.experts.finalize_weights()
+
+
+class DeepseekV4Attention(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str,
+        topk_indices_buffer: torch.Tensor | None = None,
+        aux_stream_list: list[torch.cuda.Stream] | None = None,
+    ):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        layer_id = extract_layer_index(prefix)
+
+        self.layer_id = layer_id
+        self.hidden_size = config.hidden_size
+        self.n_heads = config.num_attention_heads
+        tp_size = get_tensor_model_parallel_world_size()
+        assert self.n_heads % tp_size == 0
+
+        self.n_local_heads = self.n_heads // tp_size
+        self.q_lora_rank = config.q_lora_rank
+        self.o_lora_rank = config.o_lora_rank
+        self.head_dim = config.head_dim
+        self.rope_head_dim = config.qk_rope_head_dim
+        self.nope_head_dim = self.head_dim - self.rope_head_dim
+        self.n_groups = config.o_groups
+        self.n_local_groups = self.n_groups // tp_size
+        self.window_size = config.sliding_window
+        # NOTE(zyongye) Compress ratio can't be 0
+        # we do this for because MTP layer is not included
+        # in the compress ratio list
+        if layer_id < config.num_hidden_layers:
+            self.compress_ratio = max(1, config.compress_ratios[layer_id])
+        else:
+            self.compress_ratio = 1
+        self.eps = config.rms_norm_eps
+        self.max_position_embeddings = config.max_position_embeddings
+
+        # Padded to min 64 heads for FlashMLA, initialized to -inf
+        # (no sink effect). Weight loading fills the first n_local_heads slots.
+        padded_heads = max(self.n_local_heads, 64)
+        self.attn_sink = nn.Parameter(
+            torch.full((padded_heads,), -float("inf"), dtype=torch.float32),
+            requires_grad=False,
+        )
+
+        self.fused_wqa_wkv = MergedColumnParallelLinear(
+            self.hidden_size,
+            [self.q_lora_rank, self.head_dim],
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fused_wqa_wkv",
+            disable_tp=True,  # fused ReplicatedLinear
+        )
+        self.q_norm = RMSNorm(self.q_lora_rank, self.eps)
+        self.wq_b = ColumnParallelLinear(
+            self.q_lora_rank,
+            self.n_heads * self.head_dim,
+            bias=False,
+            quant_config=quant_config,
+            return_bias=False,
+            prefix=f"{prefix}.wq_b",
+        )
+
+        self.kv_norm = RMSNorm(self.head_dim, self.eps)
+        self.wo_a = ColumnParallelLinear(
+            self.n_heads * self.head_dim // self.n_groups,
+            self.n_groups * self.o_lora_rank,
+            bias=False,
+            quant_config=quant_config,
+            return_bias=False,
+            prefix=f"{prefix}.wo_a",
+        )
+        self.wo_a.is_bmm = True
+        self.wo_a.bmm_batch_size = self.n_local_groups
+        self.wo_b = RowParallelLinear(
+            self.n_groups * self.o_lora_rank,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            return_bias=False,
+            prefix=f"{prefix}.wo_b",
+        )
+        self.softmax_scale = self.head_dim**-0.5
+        self.scale_fmt = config.quantization_config["scale_fmt"]
+
+        self.rope_parameters = config.rope_scaling
+
+        # Initialize rotary embedding BEFORE DeepseekV4MLAModules (which needs it)
+        rope_parameters = config.rope_parameters
+        rope_parameters["rope_theta"] = (
+            config.compress_rope_theta if self.compress_ratio > 1 else config.rope_theta
+        )
+        if config.rope_parameters["rope_type"] != "default":
+            config.rope_parameters["rope_type"] = (
+                "deepseek_yarn"
+                if config.rope_parameters.get("apply_yarn_scaling", True)
+                else "deepseek_llama_scaling"
+            )
+        rope_parameters["mscale"] = 0  # Disable mscale
+        rope_parameters["mscale_all_dim"] = 0  # Disable mscale
+        rope_parameters["is_deepseek_v4"] = True
+        rope_parameters["rope_dim"] = self.rope_head_dim
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            max_position=self.max_position_embeddings,
+            rope_parameters=rope_parameters,
+            is_neox_style=False,
+        )
+
+        self.indexer = None
+        if self.compress_ratio == 4:
+            # Only C4A uses sparse attention and hence has indexer.
+            self.indexer = DeepseekV4Indexer(
+                vllm_config,
+                config=config,
+                hidden_size=self.hidden_size,
+                q_lora_rank=self.q_lora_rank,
+                quant_config=quant_config,
+                cache_config=vllm_config.cache_config,
+                topk_indices_buffer=topk_indices_buffer,
+                compress_ratio=self.compress_ratio,
+                prefix=f"{prefix}.indexer",
+            )
+
+        mla_modules = DeepseekV4MLAModules(
+            vllm_config=vllm_config,
+            fused_wqa_wkv=self.fused_wqa_wkv,
+            q_norm=self.q_norm,
+            wq_b=self.wq_b,
+            kv_norm=self.kv_norm,
+            wo_a=self.wo_a,
+            wo_b=self.wo_b,
+            attn_sink=self.attn_sink,
+            rotary_emb=self.rotary_emb,
+            indexer=self.indexer,
+            indexer_rotary_emb=self.rotary_emb,
+            topk_indices_buffer=topk_indices_buffer,
+            aux_stream_list=aux_stream_list,
+        )
+        self.mla_attn = DeepseekV4MultiHeadLatentAttentionWrapper(
+            hidden_size=self.hidden_size,
+            num_heads=self.n_local_heads,
+            head_dim=self.head_dim,
+            scale=self.softmax_scale,
+            qk_nope_head_dim=self.nope_head_dim,
+            qk_rope_head_dim=self.rope_head_dim,
+            v_head_dim=self.head_dim,
+            q_lora_rank=self.q_lora_rank,
+            kv_lora_rank=self.head_dim,
+            o_lora_rank=self.o_lora_rank,
+            mla_modules=mla_modules,
+            window_size=self.window_size,
+            compress_ratio=self.compress_ratio,
+            cache_config=vllm_config.cache_config,
+            quant_config=quant_config,
+            prefix=prefix,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        llama_4_scaling: torch.Tensor | None,
+    ):
+        return self.mla_attn(positions, hidden_states, llama_4_scaling)
+
+
+class DeepseekV4DecoderLayer(nn.Module):
+    def __init__(
+        self,
+        vllm_config,
+        prefix,
+        topk_indices_buffer: torch.Tensor | None = None,
+        aux_stream_list: list[torch.cuda.Stream] | None = None,
+    ):
+        super().__init__()
+
+        # Lazy import to avoid top-level tilelang dependency.
+        # Registers both torch.ops.vllm.mhc_pre and mhc_post
+        import vllm.model_executor.layers.mhc  # noqa: F401
+
+        config = vllm_config.model_config.hf_config
+        self.hidden_size = config.hidden_size
+
+        self.rms_norm_eps = config.rms_norm_eps
+        self.attn = DeepseekV4Attention(
+            vllm_config,
+            prefix=f"{prefix}.attn",
+            topk_indices_buffer=topk_indices_buffer,
+            aux_stream_list=aux_stream_list,
+        )
+        self.ffn = DeepseekV4MoE(vllm_config, prefix=f"{prefix}.ffn")
+
+        self.attn_norm = RMSNorm(self.hidden_size, self.rms_norm_eps)
+        self.ffn_norm = RMSNorm(self.hidden_size, self.rms_norm_eps)
+        self.hc_mult = config.hc_mult
+        self.hc_sinkhorn_iters = config.hc_sinkhorn_iters
+        self.hc_eps = config.hc_eps
+        self.hc_post_alpha = 2.0
+        mix_hc = (2 + self.hc_mult) * self.hc_mult
+        hc_dim = self.hc_mult * self.hidden_size
+        self.hc_attn_fn = nn.Parameter(
+            torch.empty(
+                (mix_hc, hc_dim),
+                dtype=torch.float32,
+            ),
+            requires_grad=False,
+        )
+        self.hc_ffn_fn = nn.Parameter(
+            torch.empty(
+                (mix_hc, hc_dim),
+                dtype=torch.float32,
+            ),
+            requires_grad=False,
+        )
+        self.hc_attn_base = nn.Parameter(
+            torch.empty(
+                mix_hc,
+                dtype=torch.float32,
+            ),
+            requires_grad=False,
+        )
+        self.hc_ffn_base = nn.Parameter(
+            torch.empty(
+                mix_hc,
+                dtype=torch.float32,
+            ),
+            requires_grad=False,
+        )
+        self.hc_attn_scale = nn.Parameter(
+            torch.empty(
+                3,
+                dtype=torch.float32,
+            ),
+            requires_grad=False,
+        )
+        self.hc_ffn_scale = nn.Parameter(
+            torch.empty(
+                3,
+                dtype=torch.float32,
+            ),
+            requires_grad=False,
+        )
+
+    def hc_pre(
+        self,
+        x: torch.Tensor,
+        hc_fn: torch.Tensor,
+        hc_scale: torch.Tensor,
+        hc_base: torch.Tensor,
+    ):
+        post_mix, res_mix, layer_input = torch.ops.vllm.mhc_pre(
+            residual=x,
+            fn=hc_fn,
+            hc_scale=hc_scale,
+            hc_base=hc_base,
+            rms_eps=self.rms_norm_eps,
+            hc_pre_eps=self.hc_eps,
+            hc_sinkhorn_eps=self.hc_eps,
+            hc_post_mult_value=self.hc_post_alpha,
+            sinkhorn_repeat=self.hc_sinkhorn_iters,
+        )
+        return layer_input, post_mix, res_mix
+
+    def hc_post(
+        self,
+        x: torch.Tensor,
+        residual: torch.Tensor,
+        post: torch.Tensor,
+        comb: torch.Tensor,
+    ):
+        return torch.ops.vllm.mhc_post(x, residual, post, comb)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        positions: torch.Tensor,
+        input_ids: torch.Tensor | None,
+    ) -> torch.Tensor:
+        residual = x
+        x, post, comb = self.hc_pre(
+            x, self.hc_attn_fn, self.hc_attn_scale, self.hc_attn_base
+        )
+        x = self.attn_norm(x)
+        x = self.attn(positions, x, None)
+        x = self.hc_post(x, residual, post, comb)
+
+        residual = x
+        x, post, comb = self.hc_pre(
+            x, self.hc_ffn_fn, self.hc_ffn_scale, self.hc_ffn_base
+        )
+        x = self.ffn_norm(x)
+        x = self.ffn(x, input_ids)
+        x = self.hc_post(x, residual, post, comb)
+        return x
+
+
+@support_torch_compile
+class DeepseekV4Model(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.use_mega_moe = (
+            vllm_config.kernel_config.moe_backend == "deep_gemm_mega_moe"
+        )
+        if self.use_mega_moe and not vllm_config.parallel_config.enable_expert_parallel:
+            raise NotImplementedError(
+                "DeepSeek V4 MegaMoE currently requires expert parallel. "
+                "Enable it with --enable-expert-parallel, or pick a different "
+                "moe backend."
+            )
+        self.vocab_size = config.vocab_size
+        self.hc_eps = config.hc_eps
+        self.hc_mult = config.hc_mult
+        self.hc_dim = self.hc_mult * config.hidden_size
+        self.rms_norm_eps = config.rms_norm_eps
+
+        # Three aux streams: one per non-default input GEMM in
+        # DeepseekV4MultiHeadLatentAttentionWrapper.attn_gemm_parallel_execute
+        # (compressor kv_score, indexer.weights_proj, indexer.compressor
+        # kv_score). fused_wqa_wkv stays on the default stream.
+        # Disable them on ROCm because of hang issues.
+        aux_stream_list = (
+            None
+            if current_platform.is_rocm()
+            else [torch.cuda.Stream() for _ in range(3)]
+        )
+
+        self.device = current_platform.device_type
+        # Reserved topk indices buffer for all Indexer layers to reuse.
+        self.topk_indices_buffer = torch.empty(
+            vllm_config.scheduler_config.max_num_batched_tokens,
+            config.index_topk,
+            dtype=torch.int32,
+            device=self.device,
+        )
+
+        if get_pp_group().is_first_rank:
+            self.embed_tokens = VocabParallelEmbedding(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=f"{prefix}.embed_tokens",
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: DeepseekV4DecoderLayer(
+                vllm_config,
+                prefix=prefix,
+                topk_indices_buffer=self.topk_indices_buffer,
+                aux_stream_list=aux_stream_list,
+            ),
+            prefix=f"{prefix}.layers",
+        )
+
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, self.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        self.hc_head_fn = nn.Parameter(
+            torch.empty(
+                self.hc_mult,
+                self.hc_dim,
+                dtype=torch.float32,
+            ),
+            requires_grad=False,
+        )
+        self.hc_head_base = nn.Parameter(
+            torch.empty(
+                self.hc_mult,
+                dtype=torch.float32,
+            ),
+            requires_grad=False,
+        )
+        self.hc_head_scale = nn.Parameter(
+            torch.empty(1, dtype=torch.float32),
+            requires_grad=False,
+        )
+
+        # Pre-hc_head residual stream buffer for the MTP draft. Stable
+        # address (outside the cudagraph pool) so the copy_ in forward()
+        # refreshes it correctly across captured shapes.
+        # refreshes it correctly across captured shapes. Only allocated on
+        # the last PP rank — that's where MTP target hidden states are
+        # produced.
+        if get_pp_group().is_last_rank:
+            self._mtp_hidden_buffer = torch.empty(
+                vllm_config.scheduler_config.max_num_batched_tokens,
+                self.hc_dim,
+                dtype=vllm_config.model_config.dtype,
+                device=self.device,
+            )
+        else:
+            self._mtp_hidden_buffer = None
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def make_empty_intermediate_tensors(
+        self,
+        batch_size: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> IntermediateTensors:
+        # PP intermediate tensors carry the multi-stream hidden_states
+        # of shape (num_tokens, hc_mult, hidden_size) — V4 expands the
+        # token embedding to hc_mult streams before the first decoder
+        # layer and keeps that shape until hc_head() collapses it.
+        return IntermediateTensors(
+            {
+                "hidden_states": torch.zeros(
+                    (batch_size, self.hc_mult, self.config.hidden_size),
+                    dtype=dtype,
+                    device=device,
+                ),
+            }
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_input_ids(input_ids)
+            hidden_states = hidden_states.unsqueeze(-2).repeat(1, self.hc_mult, 1)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+
+        if self.use_mega_moe:
+            input_ids = input_ids.to(torch.int64)
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
+            hidden_states = layer(
+                hidden_states,
+                positions,
+                input_ids,
+            )
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+
+        # Stash pre-hc_head residual for the MTP draft (captured copy_).
+        num_tokens = hidden_states.shape[0]
+        self._mtp_hidden_buffer[:num_tokens].copy_(hidden_states.flatten(1))
+
+        hidden_states = hc_head(
+            hidden_states,
+            self.hc_head_fn,
+            self.hc_head_scale,
+            self.hc_head_base,
+            self.rms_norm_eps,
+            self.hc_eps,
+        )
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("gate_up_proj", "w1", 0),
+            ("gate_up_proj", "w3", 1),
+            ("attn.fused_wqa_wkv", "attn.wq_a", 0),
+            ("attn.fused_wqa_wkv", "attn.wkv", 1),
+            ("compressor.fused_wkv_wgate", "compressor.wkv", 0),
+            ("compressor.fused_wkv_wgate", "compressor.wgate", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        # TP for attention
+        tp_size = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+        n_head = self.config.num_attention_heads
+        n_local_head = n_head // tp_size
+        head_rank_start = n_local_head * tp_rank
+        head_rank_end = n_local_head * (tp_rank + 1)
+
+        # Pre-compute expert mapping ONCE.
+        expert_mapping = self.get_expert_mapping()
+
+        for name, loaded_weight in weights:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                # Skip non-stacked layers and experts (experts handled below).
+                if ".experts." in name:
+                    continue
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                if is_pp_missing_parameter(name, self):
+                    break
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(name)
+                break
+            else:
+                if ".experts." in name:
+                    # E8M0 scales are stored as float8_e8m0fnu in
+                    # checkpoints but the MoE param is uint8. copy_()
+                    # would do a numeric conversion (e.g. 2^-7 → 0),
+                    # destroying the raw exponent bytes.
+                    if (
+                        "weight_scale" in name
+                        and loaded_weight.dtype == torch.float8_e8m0fnu
+                    ):
+                        loaded_weight = loaded_weight.view(torch.uint8)
+                    for mapping in expert_mapping:
+                        param_name, weight_name, expert_id, shard_id = mapping
+                        if weight_name not in name:
+                            continue
+                        name_mapped = name.replace(weight_name, param_name)
+                        if is_pp_missing_parameter(name_mapped, self):
+                            continue
+                        param = params_dict[name_mapped]
+                        # We should ask the weight loader to return success or not
+                        # here since otherwise we may skip experts with other
+                        # available replicas.
+                        weight_loader = typing.cast(
+                            Callable[..., bool], param.weight_loader
+                        )
+                        success = weight_loader(
+                            param,
+                            loaded_weight,
+                            name_mapped,
+                            shard_id=shard_id,
+                            expert_id=expert_id,
+                            return_success=True,
+                        )
+                        if success:
+                            name = name_mapped
+                            break
+                    loaded_params.add(name_mapped)
+                    continue
+                elif "attn_sink" in name:
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    narrow_weight = loaded_weight[head_rank_start:head_rank_end]
+                    n = narrow_weight.shape[0]
+                    params_dict[name][:n].copy_(narrow_weight)
+                    loaded_params.add(name)
+                    continue
+                else:
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+                    loaded_params.add(name)
+                    continue
+
+        return loaded_params
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        first_layer = next(iter(islice(self.layers, self.start_layer, self.end_layer)))
+        if first_layer.ffn.use_mega_moe:
+            return make_deepseek_v4_expert_params_mapping(self.config.n_routed_experts)
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return FusedMoE.make_expert_params_mapping(
+            self,
+            ckpt_gate_proj_name="w1",
+            ckpt_down_proj_name="w2",
+            ckpt_up_proj_name="w3",
+            num_experts=self.config.n_routed_experts,
+        )
+
+    def finalize_mega_moe_weights(self) -> None:
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
+            layer.ffn.finalize_mega_moe_weights()
+
+
+@torch.compile(backend=current_platform.simple_compile_backend)
+def hc_head(
+    hidden_states: torch.Tensor,
+    hc_fn: torch.Tensor,
+    hc_scale: torch.Tensor,
+    hc_base: torch.Tensor,
+    rms_norm_eps: float,
+    hc_eps: float,
+) -> torch.Tensor:
+    hc_mult, hidden_size = hidden_states.shape[-2:]
+    outer_shape = hidden_states.shape[:-2]
+    hs_flat = hidden_states.view(-1, hc_mult, hidden_size)
+    num_tokens = hs_flat.shape[0]
+    out = torch.empty(
+        num_tokens, hidden_size, dtype=torch.bfloat16, device=hidden_states.device
+    )
+    torch.ops.vllm.hc_head_fused_kernel(
+        hs_flat,
+        hc_fn,
+        hc_scale,
+        hc_base,
+        out,
+        hidden_size,
+        rms_norm_eps,
+        hc_eps,
+        hc_mult,
+    )
+    return out.view(*outer_shape, hidden_size)
+
+
+def _make_deepseek_v4_weights_mapper(expert_dtype: str) -> WeightsMapper:
+    if expert_dtype == "fp4":
+        # MXFP4 experts use Mxfp4MoEMethod, which registers scales as
+        # ``w{1,2,3}_weight_scale`` (no _inv suffix). FP8 linear and
+        # shared experts use Fp8LinearMethod's block scales, which
+        # register as ``weight_scale_inv``.
+        scale_regex = {
+            re.compile(r"(\.experts\.\d+\.w[123])\.scale$"): r"\1.weight_scale",
+            re.compile(r"\.scale$"): ".weight_scale_inv",
+        }
+    else:
+        # FP8 experts use Fp8MoEMethod (block_quant=True), which registers
+        # scales as ``w{13,2}_weight_scale_inv``. Map all ``.scale`` keys
+        # there.
+        scale_regex = {
+            re.compile(r"\.scale$"): ".weight_scale_inv",
+        }
+    return WeightsMapper(
+        orig_to_new_prefix={
+            "layers.": "model.layers.",
+            "embed.": "model.embed.",
+            "norm.": "model.norm.",
+            "hc_head": "model.hc_head",
+            "mtp.": "model.mtp.",
+        },
+        orig_to_new_regex=scale_regex,
+        orig_to_new_suffix={
+            "head.weight": "lm_head.weight",
+            "embed.weight": "embed_tokens.weight",
+            ".ffn.gate.bias": ".ffn.gate.e_score_correction_bias",
+        },
+        orig_to_new_substr={
+            ".attn.compressor.": ".attn.mla_attn.compressor.",
+            ".shared_experts.w2": ".shared_experts.down_proj",
+        },
+    )
+
+
+class DeepseekV4ForCausalLM(nn.Module, SupportsPP):
+    model_cls = DeepseekV4Model
+
+    # Default mapper assumes the original FP4-expert checkpoint layout.
+    # Overridden per-instance in __init__ when expert_dtype != "fp4".
+    hf_to_vllm_mapper = _make_deepseek_v4_weights_mapper("fp4")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        self.config = config
+        expert_dtype = getattr(config, "expert_dtype", "fp4")
+        if expert_dtype != "fp4":
+            self.hf_to_vllm_mapper = _make_deepseek_v4_weights_mapper(expert_dtype)
+
+        self.model = self.model_cls(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def get_mtp_target_hidden_states(self) -> torch.Tensor | None:
+        """Pre-hc_head residual stream buffer (max_num_batched_tokens,
+        hc_mult * hidden_size) for the MTP draft model. Populated by
+        forward(); valid after each target step."""
+        return getattr(self.model, "_mtp_hidden_buffer", None)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self, skip_substrs=["mtp."])
+        loaded_params = loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+        self.model.finalize_mega_moe_weights()
+        return loaded_params
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
diff --git a/vllm/model_executor/models/deepseek_v4_mtp.py b/vllm/model_executor/models/deepseek_v4_mtp.py
new file mode 100644
index 000000000000..195709c9dacf
--- /dev/null
+++ b/vllm/model_executor/models/deepseek_v4_mtp.py
@@ -0,0 +1,503 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""MTP draft model for DeepSeek V4 (internal codename: DeepseekV4).
+
+Split from ``deepseek_mtp.py`` because the V4 architecture introduces several
+pieces that have no analogue in V3/V32:
+  * separate ``e_proj`` / ``h_proj`` with fp8 linear quantization (instead of
+    the fused ``eh_proj``);
+  * ``hc_head`` hypercompressed vocab projection applied in ``compute_logits``;
+  * ``DeepseekV4DecoderLayer`` with its own aux-stream management;
+  * V4-specific checkpoint weight-name remapping in ``load_weights``.
+"""
+
+import typing
+from collections.abc import Callable, Iterable
+
+import regex as re
+import torch
+import torch.nn as nn
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import VllmConfig
+from vllm.distributed import (
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.platforms import current_platform
+from vllm.sequence import IntermediateTensors
+
+from .deepseek_mtp import SharedHead
+from .deepseek_v2 import get_spec_layer_idx_from_weight_name
+from .deepseek_v4 import (
+    DeepseekV4DecoderLayer,
+    hc_head,
+    make_deepseek_v4_expert_params_mapping,
+)
+from .utils import maybe_prefix
+
+logger = init_logger(__name__)
+
+# MoE expert scales are fused into per-layer w13/w2 tensors. The exact
+# parameter suffix depends on which FusedMoE method handles the experts:
+# - fp4 experts (Mxfp4MoEMethod) register ``w{1,2,3}_weight_scale``;
+# - fp8 experts (Fp8MoEMethod with block_quant=True) register
+#   ``w{1,2,3}_weight_scale_inv``.
+# Other FP8 linear scales (including shared experts) always use
+# ``.weight_scale_inv``. Mirrors the per-instance mapper built by
+# ``_make_deepseek_v4_weights_mapper`` in deepseek_v4.py.
+_EXPERT_SCALE_RE = re.compile(r"\.experts\.\d+\.w[123]\.scale$")
+
+
+class DeepSeekV4MultiTokenPredictorLayer(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        topk_indices_buffer: torch.Tensor,
+        prefix: str,
+        aux_stream_list: list[torch.cuda.Stream] | None = None,
+    ) -> None:
+        super().__init__()
+
+        config = vllm_config.speculative_config.draft_model_config.hf_config
+        self.config = config
+        quant_config = vllm_config.quant_config
+        self.rms_norm_eps = config.rms_norm_eps
+
+        self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        # V4 keeps e_ and h_ proj separate (with fp8 linear quant) rather than
+        # fusing them the way V3 does with eh_proj.
+        self.e_proj = ReplicatedLinear(
+            config.hidden_size,
+            config.hidden_size,
+            bias=False,
+            return_bias=False,
+            quant_config=quant_config,
+        )
+        self.h_proj = ReplicatedLinear(
+            config.hidden_size,
+            config.hidden_size,
+            bias=False,
+            return_bias=False,
+            quant_config=quant_config,
+        )
+
+        self.hc_eps = config.hc_eps
+        self.hc_mult = config.hc_mult
+        self.hc_dim = self.hc_mult * config.hidden_size
+        self.hc_head_fn = nn.Parameter(
+            torch.empty(self.hc_mult, self.hc_dim, dtype=torch.float32),
+            requires_grad=False,
+        )
+        self.hc_head_base = nn.Parameter(
+            torch.empty(self.hc_mult, dtype=torch.float32),
+            requires_grad=False,
+        )
+        self.hc_head_scale = nn.Parameter(
+            torch.empty(1, dtype=torch.float32),
+            requires_grad=False,
+        )
+
+        self.shared_head = SharedHead(
+            config=config, prefix=prefix, quant_config=quant_config
+        )
+        self.mtp_block = DeepseekV4DecoderLayer(
+            vllm_config,
+            prefix,
+            topk_indices_buffer=topk_indices_buffer,
+            aux_stream_list=aux_stream_list,
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_index: int = 0,
+    ) -> torch.Tensor:
+        assert inputs_embeds is not None
+        # masking inputs at position 0, as not needed by MTP
+        inputs_embeds = torch.where(positions.unsqueeze(-1) == 0, 0, inputs_embeds)
+        inputs_embeds = self.enorm(inputs_embeds)
+
+        # Target stashes pre-hc_head residual as flat (T, hc_mult * D);
+        # reshape to (T, hc_mult, D) — the training-time layout.
+        previous_hidden_states = previous_hidden_states.view(
+            -1, self.hc_mult, self.config.hidden_size
+        )
+        previous_hidden_states = self.hnorm(previous_hidden_states)
+        hidden_states = self.h_proj(previous_hidden_states) + self.e_proj(
+            inputs_embeds
+        ).unsqueeze(-2)
+        hidden_states = self.mtp_block(
+            positions=positions, x=hidden_states, input_ids=None
+        )
+        # Return the flat pre-hc_head residual so it can be re-fed as the
+        # next spec step's `previous_hidden_states` when
+        # num_speculative_tokens > 1. hc_head is deferred to compute_logits.
+        return hidden_states.flatten(1)
+
+
+class DeepSeekV4MultiTokenPredictor(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.mtp_start_layer_idx = config.num_hidden_layers
+        self.num_mtp_layers = config.num_nextn_predict_layers
+        self.device = current_platform.device_type
+
+        topk_tokens = config.index_topk
+        self.topk_indices_buffer = torch.empty(
+            vllm_config.scheduler_config.max_num_batched_tokens,
+            topk_tokens,
+            dtype=torch.int32,
+            device=self.device,
+        )
+
+        # Three aux streams shared across all MTP layers, mirroring
+        # DeepseekV4Model. ROCm runs the same work serially for now.
+        aux_stream_list = (
+            None
+            if current_platform.is_rocm()
+            else [torch.cuda.Stream() for _ in range(3)]
+        )
+
+        # to map the exact layer index from weights
+        self.layers = torch.nn.ModuleDict(
+            {
+                str(idx): DeepSeekV4MultiTokenPredictorLayer(
+                    vllm_config,
+                    self.topk_indices_buffer,
+                    f"{prefix}.layers.{idx}",
+                    aux_stream_list=aux_stream_list,
+                )
+                for idx in range(
+                    self.mtp_start_layer_idx,
+                    self.mtp_start_layer_idx + self.num_mtp_layers,
+                )
+            }
+        )
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            prefix=maybe_prefix(prefix, "embed_tokens"),
+        )
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        current_step_idx = spec_step_idx % self.num_mtp_layers
+        return self.layers[str(self.mtp_start_layer_idx + current_step_idx)](
+            input_ids,
+            positions,
+            previous_hidden_states,
+            inputs_embeds,
+            current_step_idx,
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        current_step_idx = spec_step_idx % self.num_mtp_layers
+        mtp_layer = self.layers[str(self.mtp_start_layer_idx + current_step_idx)]
+        # MTP forward returns the pre-hc_head residual (T, hc_mult * D); apply
+        # hc_head here so logits are computed from the dense hidden state.
+        hidden_states = hidden_states.view(
+            -1, mtp_layer.hc_mult, mtp_layer.config.hidden_size
+        )
+        hidden_states = hc_head(
+            hidden_states,
+            mtp_layer.hc_head_fn,
+            mtp_layer.hc_head_scale,
+            mtp_layer.hc_head_base,
+            mtp_layer.rms_norm_eps,
+            mtp_layer.hc_eps,
+        )
+        logits = self.logits_processor(
+            mtp_layer.shared_head.head, mtp_layer.shared_head(hidden_states)
+        )
+        return logits
+
+
+@support_torch_compile
+class DeepSeekV4MTP(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        self.config = vllm_config.model_config.hf_config
+        self.quant_config = vllm_config.quant_config
+        self.model = DeepSeekV4MultiTokenPredictor(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        hidden_states = self.model(
+            input_ids, positions, hidden_states, inputs_embeds, spec_step_idx
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor | None:
+        return self.model.compute_logits(hidden_states, spec_step_idx)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        # Weight name remapping for checkpoint compatibility.
+        # Maps checkpoint weight paths to model parameter paths.
+        WEIGHT_NAME_REMAPPING: dict[str, str] = {
+            ".emb.tok_emb.weight": ".embed_tokens.weight",
+            ".head.weight": ".shared_head.head.weight",
+            ".norm.weight": ".shared_head.norm.weight",
+        }
+
+        def _remap_weight_name(name: str) -> str:
+            """Remap checkpoint weight names to model parameter names."""
+            for old_pattern, new_pattern in WEIGHT_NAME_REMAPPING.items():
+                if old_pattern in name:
+                    name = name.replace(old_pattern, new_pattern)
+            return name
+
+        def _find_mtp_layer_idx(name: str) -> int:
+            subnames = name.split(".")
+            for subname in subnames:
+                try:
+                    # we return the first encountered integer
+                    return int(subname)
+                except ValueError:
+                    continue
+            return 0
+
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("gate_up_proj", "w1", 0),
+            ("gate_up_proj", "w3", 1),
+            ("attn.fused_wqa_wkv", "attn.wq_a", 0),
+            ("attn.fused_wqa_wkv", "attn.wkv", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        # TP for attention
+        tp_size = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+        n_head = self.config.num_attention_heads
+        n_local_head = n_head // tp_size
+        head_rank_start = n_local_head * tp_rank
+        head_rank_end = n_local_head * (tp_rank + 1)
+
+        # Pre-compute expert mapping ONCE.
+        first_layer = next(iter(self.model.layers.values()))
+        if first_layer.mtp_block.ffn.use_mega_moe:
+            expert_mapping = make_deepseek_v4_expert_params_mapping(
+                self.config.n_routed_experts
+            )
+        else:
+            expert_mapping = FusedMoE.make_expert_params_mapping(
+                self,
+                ckpt_gate_proj_name="w1",
+                ckpt_down_proj_name="w2",
+                ckpt_up_proj_name="w3",
+                num_experts=self.config.n_routed_experts,
+            )
+
+        # FP8 experts register ``..._weight_scale_inv`` (block_quant) while
+        # FP4/MXFP4 experts register ``..._weight_scale``. Choose the suffix
+        # for the rename below based on the model's expert dtype.
+        expert_scale_suffix = (
+            ".weight_scale"
+            if getattr(self.config, "expert_dtype", "fp4") == "fp4"
+            else ".weight_scale_inv"
+        )
+
+        for name, loaded_weight in weights:
+            mtp_layer_idx = _find_mtp_layer_idx(name)
+            # V4 checkpoints store MTP weights as `mtp.{i}.*`; remap to
+            # `model.layers.{num_hidden_layers + i}.*` so that
+            # get_spec_layer_idx_from_weight_name can identify them.
+            name = name.replace(
+                f"mtp.{mtp_layer_idx}.",
+                f"model.layers.{self.config.num_hidden_layers + mtp_layer_idx}.",
+            )
+
+            spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
+            if spec_layer is None:
+                continue
+
+            name = _remap_weight_name(name)
+            name = self._rewrite_spec_layer_name(spec_layer, name)
+
+            if spec_layer != self.model.mtp_start_layer_idx and ".layers" not in name:
+                continue
+            if name.endswith(".scale"):
+                suffix = (
+                    expert_scale_suffix
+                    if _EXPERT_SCALE_RE.search(name)
+                    else ".weight_scale_inv"
+                )
+                name = name.removesuffix(".scale") + suffix
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                # Skip non-stacked layers and experts (experts handled below).
+                if ".experts." in name:
+                    continue
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(name)
+                break
+            else:
+                if ".experts." in name:
+                    # Reinterpret E8M0 scales as uint8 to preserve raw
+                    # exponent bytes; numeric copy_() would zero them.
+                    # Mirrors the main DeepseekV4 loader.
+                    if (
+                        "weight_scale" in name
+                        and loaded_weight.dtype == torch.float8_e8m0fnu
+                    ):
+                        loaded_weight = loaded_weight.view(torch.uint8)
+                    for mapping in expert_mapping:
+                        param_name, weight_name, expert_id, shard_id = mapping
+                        if weight_name not in name:
+                            continue
+                        name_mapped = name.replace(weight_name, param_name)
+                        param = params_dict[name_mapped]
+                        # We should ask the weight loader to return success or not
+                        # here since otherwise we may skip experts with other
+                        # available replicas.
+                        weight_loader = typing.cast(
+                            Callable[..., bool], param.weight_loader
+                        )
+                        success = weight_loader(
+                            param,
+                            loaded_weight,
+                            name_mapped,
+                            shard_id=shard_id,
+                            expert_id=expert_id,
+                            return_success=True,
+                        )
+                        if success:
+                            name = name_mapped
+                            loaded_params.add(name_mapped)
+                            break
+                    continue
+                elif "attn_sink" in name:
+                    narrow_weight = loaded_weight[head_rank_start:head_rank_end]
+                    n = narrow_weight.shape[0]
+                    params_dict[name][:n].copy_(narrow_weight)
+                    loaded_params.add(name)
+                    continue
+                else:
+                    if ".shared_experts.w2" in name:
+                        name = name.replace(
+                            ".shared_experts.w2", ".shared_experts.down_proj"
+                        )
+                    if name.endswith(".ffn.gate.bias"):
+                        name = name.replace(".bias", ".e_score_correction_bias")
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+                    loaded_params.add(name)
+                    continue
+
+        loaded_layers: set[int] = set()
+        for param_name in loaded_params:
+            spec_layer = get_spec_layer_idx_from_weight_name(self.config, param_name)
+            if spec_layer is not None:
+                loaded_layers.add(spec_layer)
+        for layer_idx in range(
+            self.model.mtp_start_layer_idx,
+            self.model.mtp_start_layer_idx + self.model.num_mtp_layers,
+        ):
+            if layer_idx not in loaded_layers:
+                raise ValueError(
+                    f"MTP speculative decoding layer {layer_idx} weights "
+                    f"missing from checkpoint. The checkpoint may have "
+                    f"been quantized without including the MTP layers. "
+                    f"Use a checkpoint that includes MTP layer weights, "
+                    f"or disable speculative decoding."
+                )
+        self.finalize_mega_moe_weights()
+        logger.info_once("MTP draft model loaded: %d params", len(loaded_params))
+        return loaded_params
+
+    def finalize_mega_moe_weights(self) -> None:
+        for layer in self.model.layers.values():
+            layer.mtp_block.ffn.finalize_mega_moe_weights()
+
+    def _rewrite_spec_layer_name(self, spec_layer: int, name: str) -> str:
+        """
+        Rewrite the weight name to match the format of the original model.
+        Add .mtp_block for modules in transformer layer block for spec layer
+        and rename shared layer weights to be top level.
+        """
+        spec_layer_weight_names = [
+            "embed_tokens",
+            "enorm",
+            "hnorm",
+            "h_proj",
+            "e_proj",
+            "shared_head",
+            "hc_head_fn",
+            "hc_head_base",
+            "hc_head_scale",
+        ]
+        shared_weight_names = ["embed_tokens"]
+        spec_layer_weight = False
+        shared_weight = False
+        for weight_name in spec_layer_weight_names:
+            if weight_name in name:
+                spec_layer_weight = True
+                if weight_name in shared_weight_names:
+                    shared_weight = True
+                break
+        if not spec_layer_weight:
+            # treat rest weights as weights for transformer layer block
+            name = name.replace(
+                f"model.layers.{spec_layer}.", f"model.layers.{spec_layer}.mtp_block."
+            )
+        elif shared_weight:
+            # treat shared weights as top level weights
+            name = name.replace(f"model.layers.{spec_layer}.", "model.")
+        return name
diff --git a/vllm/model_executor/models/dots1.py b/vllm/model_executor/models/dots1.py
index 4e393145462a..f58fc4da92b2 100644
--- a/vllm/model_executor/models/dots1.py
+++ b/vllm/model_executor/models/dots1.py
@@ -37,11 +37,13 @@
 from vllm.distributed import (
     get_pp_group,
     get_tensor_model_parallel_world_size,
-    tensor_model_parallel_all_reduce,
 )
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -120,7 +122,6 @@ def __init__(
         prefix: str = "",
     ):
         super().__init__()
-        self.tp_size = get_tensor_model_parallel_world_size()
         self.routed_scaling_factor = config.routed_scaling_factor
         self.n_shared_experts = config.n_shared_experts
 
@@ -157,13 +158,12 @@ def __init__(
         else:
             self.shared_experts = None
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_experts,
             num_experts=config.n_routed_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             use_grouped_topk=True,
@@ -171,9 +171,9 @@ def __init__(
             topk_group=config.topk_group,
             prefix=f"{prefix}.experts",
             scoring_func=config.scoring_func,
-            # we do scaling outside, set factor to 1.0 to avoid double mul
-            routed_scaling_factor=1.0,
             e_score_correction_bias=self.gate.e_score_correction_bias,
+            routed_scaling_factor=self.routed_scaling_factor,
+            apply_routed_scale_to_output=True,
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -182,16 +182,9 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
 
         router_logits, _ = self.gate(hidden_states)
 
-        shared_out, routed_out = self.experts(
+        final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
-        if self.shared_experts is not None:
-            final_hidden_states = (routed_out + shared_out) * self.routed_scaling_factor
-        else:
-            final_hidden_states = routed_out * self.routed_scaling_factor
-
-        if self.tp_size > 1:
-            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
         return final_hidden_states.view(num_tokens, hidden_dim)
 
 
@@ -423,7 +416,7 @@ def forward(
         return hidden_states
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return SharedFusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/ernie45_moe.py b/vllm/model_executor/models/ernie45_moe.py
index f038cfb21f28..a2b0eccde65f 100644
--- a/vllm/model_executor/models/ernie45_moe.py
+++ b/vllm/model_executor/models/ernie45_moe.py
@@ -42,7 +42,10 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -188,13 +191,12 @@ def __init__(
         else:
             self.shared_experts = None
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_experts,
             num_experts=config.moe_num_experts,
             top_k=config.moe_k,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=True,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -215,16 +217,6 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             hidden_states=hidden_states, router_logits=router_logits
         )
 
-        if self.has_shared_experts:
-            final_hidden_states = final_hidden_states[0] + final_hidden_states[1]
-        else:
-            final_hidden_states = final_hidden_states[1]
-
-        if self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
-
         return final_hidden_states.view(orig_shape)
 
 
@@ -496,7 +488,7 @@ def forward(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return SharedFusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -678,7 +670,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.num_moe_layers = len(moe_layers_indices)
         self.num_expert_groups = 1
 
-        self.moe_layers: list[SharedFusedMoE] = []
+        self.moe_layers: list[FusedMoE] = []
         example_moe = None
         for layer in self.model.layers:
             if isinstance(layer, PPMissingLayer):
diff --git a/vllm/model_executor/models/ernie45_vl_moe.py b/vllm/model_executor/models/ernie45_vl_moe.py
index 418fdcfa072b..38ed756ba415 100644
--- a/vllm/model_executor/models/ernie45_vl_moe.py
+++ b/vllm/model_executor/models/ernie45_vl_moe.py
@@ -36,7 +36,10 @@
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     QKVParallelLinear,
@@ -257,13 +260,12 @@ def __init__(
                 prefix=f"{prefix}.text_experts_gate",
             )
 
-            self.text_experts = SharedFusedMoE(
+            self.text_experts = FusedMoE(
                 shared_experts=self.shared_experts,
                 num_experts=config.moe_num_experts[0],
                 top_k=config.moe_k,
                 hidden_size=config.hidden_size,
                 intermediate_size=config.moe_intermediate_size[0],
-                reduce_results=False,
                 renormalize=True,
                 quant_config=quant_config,
                 e_score_correction_bias=self.e_score_correction_bias[0],
@@ -295,13 +297,12 @@ def __init__(
                 prefix=f"{prefix}.vision_experts_gate",
             )
 
-            self.vision_experts = SharedFusedMoE(
+            self.vision_experts = FusedMoE(
                 shared_experts=self.shared_experts,
                 num_experts=config.moe_num_experts[1],
                 top_k=config.moe_k,
                 hidden_size=config.hidden_size,
                 intermediate_size=config.moe_intermediate_size[1],
-                reduce_results=False,
                 renormalize=True,
                 quant_config=quant_config,
                 e_score_correction_bias=self.e_score_correction_bias[1],
@@ -342,9 +343,6 @@ def forward(
             visual_token_mask = visual_token_mask.repeat(1, self.hidden_size).bool()
             text_token_mask = ~visual_token_mask
             final_experts_hidden_states = torch.zeros_like(hidden_states)
-            final_shared_output = (
-                torch.zeros_like(hidden_states) if self.has_shared_experts else None
-            )
 
             text_hidden_states = hidden_states[text_token_mask].reshape(
                 -1, self.hidden_size
@@ -356,26 +354,20 @@ def forward(
             text_router_logits, _ = self.text_experts_gate(
                 text_hidden_states.to(dtype=torch.float32)
             )
-            text_shared_output, text_experts_output = self.text_experts(
+            text_output = self.text_experts(
                 hidden_states=text_hidden_states, router_logits=text_router_logits
             )
-            final_experts_hidden_states[text_token_mask] = text_experts_output.flatten()
-            if self.has_shared_experts:
-                final_shared_output[text_token_mask] = text_shared_output.flatten()
+            final_experts_hidden_states[text_token_mask] = text_output.flatten()
 
             vision_router_logits, _ = self.vision_experts_gate(
                 vision_hidden_states.to(dtype=torch.float32)
             )
-            vision_shared_output, vision_experts_output = self.vision_experts(
+            vision_output = self.vision_experts(
                 hidden_states=vision_hidden_states, router_logits=vision_router_logits
             )
-            final_experts_hidden_states[visual_token_mask] = (
-                vision_experts_output.flatten()
-            )
-            if self.has_shared_experts:
-                final_shared_output[visual_token_mask] = vision_shared_output.flatten()
+            final_experts_hidden_states[visual_token_mask] = vision_output.flatten()
 
-            final_hidden_states = (final_shared_output, final_experts_hidden_states)
+            final_hidden_states = final_experts_hidden_states
         else:
             # only text modal input
             text_router_logits, _ = self.text_experts_gate(
@@ -386,20 +378,6 @@ def forward(
                 hidden_states=hidden_states, router_logits=text_router_logits
             )
 
-        if self.has_shared_experts:
-            # for shared_experts model
-            final_hidden_states = final_hidden_states[0] + final_hidden_states[1]
-        else:
-            # for not shared_experts model
-            final_hidden_states = final_hidden_states[1]
-
-        if self.tp_size > 1:
-            final_hidden_states = (
-                self.text_experts.maybe_all_reduce_tensor_model_parallel(
-                    final_hidden_states
-                )
-            )
-
         return final_hidden_states.view(orig_shape)
 
 
@@ -674,7 +652,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/exaone_moe.py b/vllm/model_executor/models/exaone_moe.py
index d7282edcf4f6..80b7e0957e82 100644
--- a/vllm/model_executor/models/exaone_moe.py
+++ b/vllm/model_executor/models/exaone_moe.py
@@ -30,7 +30,10 @@
     get_pp_group,
     get_tensor_model_parallel_world_size,
 )
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import ReplicatedLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -116,12 +119,26 @@ def __init__(
             self.physical_expert_start + self.n_local_physical_experts
         )
 
+        if getattr(config, "num_shared_experts", 0) > 0:
+            intermediate_size = config.moe_intermediate_size * config.num_shared_experts
+            self.shared_experts = ExaoneMoeGatedMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=False,
+                prefix=f"{prefix}.shared_experts",
+            )
+        else:
+            self.shared_experts = None
+
         self.experts = FusedMoE(
+            shared_experts=self.shared_experts,
+            gate=self.gate,
             num_experts=self.n_routed_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             use_grouped_topk=True,
@@ -135,41 +152,16 @@ def __init__(
             num_redundant_experts=self.n_redundant_experts,
         )
 
-        if getattr(config, "num_shared_experts", 0) > 0:
-            intermediate_size = config.moe_intermediate_size * config.num_shared_experts
-            self.shared_experts = ExaoneMoeGatedMLP(
-                hidden_size=config.hidden_size,
-                intermediate_size=intermediate_size,
-                hidden_act=config.hidden_act,
-                quant_config=quant_config,
-                reduce_results=self.experts.must_reduce_shared_expert_outputs(),
-                prefix=f"{prefix}.shared_experts",
-            )
-        else:
-            self.shared_experts = None
-
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         # NOTE: hidden_states can have either 1D or 2D shape.
         orig_shape = hidden_states.shape
         hidden_dim = hidden_states.shape[-1]
         hidden_states = hidden_states.view(-1, hidden_dim)
 
-        # router_logits: (num_tokens, n_experts)
-        router_logits, _ = self.gate(hidden_states)
-
         final_hidden_states = self.experts(
-            hidden_states=hidden_states, router_logits=router_logits
+            hidden_states=hidden_states, router_logits=hidden_states
         )
 
-        if self.shared_experts is not None:
-            shared_output = self.shared_experts(hidden_states)
-            final_hidden_states = final_hidden_states + shared_output
-
-        if self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(  # noqa E501
-                final_hidden_states
-            )
-
         return final_hidden_states.view(orig_shape)
 
 
@@ -337,7 +329,7 @@ def forward(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/fireredasr2.py b/vllm/model_executor/models/fireredasr2.py
index 41b4318504fd..eea0c7d8897e 100644
--- a/vllm/model_executor/models/fireredasr2.py
+++ b/vllm/model_executor/models/fireredasr2.py
@@ -2,9 +2,8 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import math
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Annotated, Literal, cast
+from typing import Annotated, cast
 
-import numpy as np
 import torch
 from torch import nn
 from transformers import (
@@ -14,6 +13,7 @@
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.inputs import MultiModalDataDict, PromptType
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import _ACTIVATION_REGISTRY
@@ -356,14 +356,12 @@ def validate_language(cls, language: str | None) -> str | None:
     @classmethod
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,  # not needed here
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
+        audio = stt_params.audio
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+
         if language is None:
             raise ValueError(
                 "Language must be specified when creating the fireredasr2 prompt"
diff --git a/vllm/model_executor/models/flex_olmo.py b/vllm/model_executor/models/flex_olmo.py
index 67be99a879ff..2ff9d860567d 100644
--- a/vllm/model_executor/models/flex_olmo.py
+++ b/vllm/model_executor/models/flex_olmo.py
@@ -20,7 +20,9 @@
 from vllm.config import VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import ReplicatedLinear
 from vllm.model_executor.models.olmoe import OlmoeAttention, OlmoeForCausalLM
@@ -76,7 +78,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             top_k=hf_config.num_experts_per_tok,
             hidden_size=hf_config.hidden_size,
             intermediate_size=hf_config.intermediate_size,
-            reduce_results=True,
             renormalize=False,
             quant_config=None,
             tp_size=tp_size,
diff --git a/vllm/model_executor/models/funasr.py b/vllm/model_executor/models/funasr.py
index 98313db79806..4b5a1c025937 100644
--- a/vllm/model_executor/models/funasr.py
+++ b/vllm/model_executor/models/funasr.py
@@ -3,9 +3,8 @@
 
 import math
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Annotated, Literal, cast
+from typing import Annotated, cast
 
-import numpy as np
 import torch
 import torch.nn.functional as F
 from torch import nn
@@ -16,6 +15,7 @@
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.inputs import MultiModalDataDict, PromptType
 from vllm.logger import init_logger
@@ -876,20 +876,25 @@ def validate_language(cls, language: str | None) -> str | None:
     @classmethod
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,  # not needed here
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
+        audio = stt_params.audio
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+        hotwords = stt_params.hotwords
+
         if language is None:
             raise ValueError(
                 "Language must be specified when creating the funasr prompt"
             )
 
-        funasr_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n语音转写：<|AUDIO|><|im_end|>\n<|im_start|>assistant\n"  # noqa: E501
+        if hotwords is not None:
+            funasr_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n请结合上下文信息，更加准确地完成语音转写任务。如果没有相关信息，我们会留空。\n\n\n**上下文信息：**\n\n\n热词列表：[{}]\n语音转写：<|AUDIO|><|im_end|>\n<|im_start|>assistant\n".format(  # noqa: E501
+                hotwords
+            )
+        else:
+            funasr_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n语音转写：<|AUDIO|><|im_end|>\n<|im_start|>assistant\n"  # noqa: E501
+
         prompt = {
             "prompt": funasr_prompt,
             "multi_modal_data": {
diff --git a/vllm/model_executor/models/gemma3.py b/vllm/model_executor/models/gemma3.py
index b2352a3c9268..f61f7c6f780b 100644
--- a/vllm/model_executor/models/gemma3.py
+++ b/vllm/model_executor/models/gemma3.py
@@ -26,7 +26,7 @@
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
-from vllm.model_executor.layers.activation import GeluAndMul
+from vllm.model_executor.layers.activation import get_act_and_mul_fn
 from vllm.model_executor.layers.attention import (
     Attention,
     EncoderOnlyAttention,
@@ -88,13 +88,7 @@ def __init__(
             quant_config=quant_config,
             prefix=f"{prefix}.down_proj",
         )
-        if hidden_activation != "gelu_pytorch_tanh":
-            raise ValueError(
-                "Gemma3 uses `gelu_pytorch_tanh` as the hidden activation "
-                "function. Please set `hidden_act` and `hidden_activation` to "
-                "`gelu_pytorch_tanh`."
-            )
-        self.act_fn = GeluAndMul(approximate="tanh")
+        self.act_fn = get_act_and_mul_fn(hidden_activation)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         gate_up, _ = self.gate_up_proj(x)
diff --git a/vllm/model_executor/models/gemma3n_mm.py b/vllm/model_executor/models/gemma3n_mm.py
index 342d6c476dfa..4e9838805e58 100644
--- a/vllm/model_executor/models/gemma3n_mm.py
+++ b/vllm/model_executor/models/gemma3n_mm.py
@@ -3,7 +3,6 @@
 from collections.abc import Iterable, Mapping, Sequence
 from typing import Annotated, Any, Literal
 
-import numpy as np
 import torch
 from torch import nn
 from transformers import AutoModel, BatchFeature
@@ -19,6 +18,7 @@
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.inputs import MultiModalDataDict, PromptType, TextPrompt
 from vllm.logger import init_logger
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -769,21 +769,17 @@ def get_placeholder_str(cls, modality: str, i: int) -> str | None:
             raise ValueError(f"Unsupported modality: {modality}")
 
     @classmethod
-    def get_generation_prompt(
-        cls,
-        audio: np.ndarray,
-        stt_config: SpeechToTextConfig,
-        model_config: ModelConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
-    ) -> PromptType:
+    def get_generation_prompt(cls, stt_params: SpeechToTextParams) -> PromptType:
         """
         Gemma3n supports "free-form" transcription.
         We fix its prompt here to standardize transcriptions/translations
         requests.
         """
+        audio = stt_params.audio
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+        task_type = stt_params.task_type
+        to_language = stt_params.to_language
         # Transcribe this audio [into <>] | for transcription
         # Translate this audio [from <> into <>] | for translation
         prompt = "<start_of_turn>user\n"
diff --git a/vllm/model_executor/models/gemma4.py b/vllm/model_executor/models/gemma4.py
index 06189540090d..1b50db61c288 100644
--- a/vllm/model_executor/models/gemma4.py
+++ b/vllm/model_executor/models/gemma4.py
@@ -35,9 +35,13 @@
 )
 from vllm.forward_context import get_forward_context
 from vllm.logger import init_logger
-from vllm.model_executor.layers.activation import GeluAndMul
+from vllm.model_executor.layers.activation import get_act_and_mul_fn
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE, GateLinear
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    GateLinear,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -57,7 +61,9 @@
     default_weight_loader,
     maybe_remap_kv_scale_name,
 )
+from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
+from vllm.triton_utils import tl, triton
 from vllm.v1.attention.backends.utils import KVSharingFastPrefillMetadata
 
 from .interfaces import (
@@ -79,6 +85,124 @@
 logger = init_logger(__name__)
 
 
+def _remap_gemma4_expert_weight_name(name: str) -> str:
+    return re.sub(r"(?<!\.moe)\.experts\.(\d+)\.", r".moe.experts.\1.", name)
+
+
+@triton.jit
+def _gemma4_routing_kernel(
+    gating_ptr,
+    per_expert_scale_ptr,
+    topk_weights_ptr,
+    topk_ids_ptr,
+    E: tl.constexpr,
+    K: tl.constexpr,
+    BLOCK_E: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    offs_e = tl.arange(0, BLOCK_E)
+    valid = offs_e < E
+
+    logits = tl.load(
+        gating_ptr + pid * E + offs_e,
+        mask=valid,
+        other=-float("inf"),
+    ).to(tl.float32)
+
+    max_l = tl.max(logits, axis=0)
+
+    # Float32 → ascending-sortable bijection
+    MIN32 = -2147483648
+    logit_bits = logits.to(tl.int32, bitcast=True)
+    sign_b = logit_bits >> 31
+    key = tl.where(sign_b == 0, logit_bits ^ -1, logit_bits ^ MIN32)
+    key = tl.where(valid, key, 0x7FFFFFFF)
+    sk64 = key.to(tl.int64) & 0x00000000FFFFFFFF
+    packed = (sk64 << 32) | offs_e.to(tl.int64)
+    sorted_p = tl.sort(packed, descending=False)
+
+    # Vectorized extraction of ALL sorted elements — no K-loop, no cross-lane reductions
+    all_keys = ((sorted_p >> 32) & 0x00000000FFFFFFFF).to(tl.int32)
+    all_ids = (sorted_p & 0x00000000FFFFFFFF).to(tl.int32)
+
+    # Inverse bijection: recover original logit bits
+    sign_k = all_keys >> 31
+    all_bits = tl.where(sign_k < 0, all_keys ^ -1, all_keys ^ MIN32)
+    all_logits = all_bits.to(tl.float32, bitcast=True)
+
+    # Compute raw_exp for ALL BLOCK_E elements — vectorized, ~2 VALU clocks
+    all_raw_exp = tl.math.exp2((all_logits - max_l) * 1.4426950408889634)
+
+    # Sum only top-K for renorm — ONE masked reduction
+    top_mask = offs_e < K
+    renorm_raw = tl.sum(tl.where(top_mask, all_raw_exp, 0.0), axis=0)
+    renorm_raw = tl.where(renorm_raw > 0.0, renorm_raw, 1.0)
+    inv_renorm = 1.0 / renorm_raw
+
+    # Load scales for top-K only (masked gather; scale array is tiny → L1 cached)
+    all_scales = tl.load(
+        per_expert_scale_ptr + all_ids.to(tl.int64),
+        mask=top_mask,
+        other=1.0,
+    ).to(tl.float32)
+
+    # Final weights: vectorized multiply (only top-K will be stored)
+    all_weights = (all_raw_exp * inv_renorm * all_scales).to(tl.float32)
+
+    # Write results with TWO masked stores — replaces K × 2 serial scalar stores
+    base_off = pid * K + offs_e
+    tl.store(topk_ids_ptr + base_off, all_ids, mask=top_mask)
+    tl.store(topk_weights_ptr + base_off, all_weights, mask=top_mask)
+
+
+def gemma4_fused_routing_kernel_triton(
+    gating_output: torch.Tensor,
+    topk: int,
+    per_expert_scale: torch.Tensor,
+    num_warps: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    gating_output = gating_output.contiguous()
+    per_expert_scale = per_expert_scale.contiguous()
+    T, E = gating_output.shape
+    weights = torch.empty(T, topk, dtype=torch.float32, device=gating_output.device)
+    ids = torch.empty(T, topk, dtype=torch.int32, device=gating_output.device)
+    BLOCK_E = triton.next_power_of_2(E)
+    _gemma4_routing_kernel[(T,)](
+        gating_output,
+        per_expert_scale,
+        weights,
+        ids,
+        E,
+        topk,
+        BLOCK_E,
+        num_warps=num_warps,
+    )
+    return weights, ids
+
+
+def gemma4_routing_function_torch(
+    gating_output: torch.Tensor,
+    topk: int,
+    per_expert_scale: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    _, topk_ids = torch.topk(gating_output, k=topk, dim=-1)
+    router_probabilities = torch.nn.functional.softmax(gating_output, dim=-1)
+    indicator = torch.nn.functional.one_hot(
+        topk_ids, num_classes=gating_output.size(-1)
+    ).sum(dim=-2)
+    gate_weights = indicator * router_probabilities
+    renorm_factor = torch.sum(gate_weights, dim=-1, keepdim=True)
+    renorm_factor = torch.where(renorm_factor > 0.0, renorm_factor, 1.0)
+    dispatch_weights = gate_weights / renorm_factor
+
+    topk_weights = dispatch_weights.gather(1, topk_ids)
+
+    # Fold per_expert_scale into routing weights
+    expert_scales = per_expert_scale[topk_ids].to(topk_weights.dtype)
+    topk_weights = topk_weights * expert_scales
+    return topk_weights.to(torch.float32), topk_ids.to(torch.int32)
+
+
 def _get_text_config(config):
     """Dereference text_config if config is a nested Gemma4Config.
 
@@ -115,13 +239,7 @@ def __init__(
             quant_config=quant_config,
             prefix=f"{prefix}.down_proj",
         )
-        if hidden_activation != "gelu_pytorch_tanh":
-            raise ValueError(
-                "Gemma4 uses `gelu_pytorch_tanh` as the hidden activation "
-                "function. Please set `hidden_act` and `hidden_activation` to "
-                "`gelu_pytorch_tanh`."
-            )
-        self.act_fn = GeluAndMul(approximate="tanh")
+        self.act_fn = get_act_and_mul_fn(hidden_activation)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         gate_up, _ = self.gate_up_proj(x)
@@ -216,22 +334,12 @@ def routing_function(
             topk: int,
             renormalize: bool,
         ) -> tuple[torch.Tensor, torch.Tensor]:
-            _, topk_ids = torch.topk(gating_output, k=topk, dim=-1)
-            router_probabilities = torch.nn.functional.softmax(gating_output, dim=-1)
-            indicator = torch.nn.functional.one_hot(
-                topk_ids, num_classes=gating_output.size(-1)
-            ).sum(dim=-2)
-            gate_weights = indicator * router_probabilities
-            renorm_factor = torch.sum(gate_weights, dim=-1, keepdim=True)
-            renorm_factor = torch.where(renorm_factor > 0.0, renorm_factor, 1.0)
-            dispatch_weights = gate_weights / renorm_factor
-
-            topk_weights = dispatch_weights.gather(1, topk_ids)
-
-            # Fold per_expert_scale into routing weights
-            expert_scales = per_expert_scale[topk_ids].to(topk_weights.dtype)
-            topk_weights = topk_weights * expert_scales
-            return topk_weights.to(torch.float32), topk_ids.to(torch.int32)
+            if current_platform.is_cuda_alike() or current_platform.is_xpu():
+                return gemma4_fused_routing_kernel_triton(
+                    gating_output, topk, per_expert_scale
+                )
+
+            return gemma4_routing_function_torch(gating_output, topk, per_expert_scale)
 
         # FusedMoE experts with custom Gemma4 routing
         self.experts = FusedMoE(
@@ -243,12 +351,11 @@ def routing_function(
                 "moe_intermediate_size",
                 getattr(config, "expert_intermediate_size", None),
             ),
-            reduce_results=True,
             renormalize=True,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
             custom_routing_function=routing_function,
-            activation="gelu",
+            activation="gelu_tanh",
         )
 
     def forward(self, x: torch.Tensor, router_logits: torch.Tensor) -> torch.Tensor:
@@ -1036,11 +1143,6 @@ def _make_empty_intermediate_tensors(
                     dtype=dtype,
                     device=device,
                 ),
-                "residual": torch.zeros(
-                    (batch_size, hidden_size),
-                    dtype=dtype,
-                    device=device,
-                ),
             }
             if ple_dim and ple_dim > 0:
                 tensors["per_layer_inputs"] = torch.zeros(
@@ -1204,13 +1306,12 @@ def forward(
                 per_layer_inputs = self.project_per_layer_inputs(
                     hidden_states, per_layer_embeds
                 )
-            residual = None
         else:
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
-            residual = intermediate_tensors["residual"]
-            per_layer_inputs = intermediate_tensors.get("per_layer_inputs")
-
+            if per_layer_inputs is not None:
+                per_layer_inputs = intermediate_tensors["per_layer_inputs"]
+        residual = None
         aux_hidden_states = self._maybe_add_hidden_state([], 0, hidden_states, residual)
         for layer_idx, layer in enumerate(
             islice(self.layers, self.start_layer, self.end_layer)
@@ -1234,13 +1335,12 @@ def forward(
                 aux_hidden_states, layer_idx + 1, hidden_states, residual
             )
         if not get_pp_group().is_last_rank:
-            return IntermediateTensors(
-                {
-                    "hidden_states": hidden_states,
-                    "residual": residual,
-                    "per_layer_inputs": per_layer_inputs,
-                }
-            )
+            tensors: dict[str, torch.Tensor] = {
+                "hidden_states": hidden_states,
+            }
+            if per_layer_inputs is not None:
+                tensors["per_layer_inputs"] = per_layer_inputs
+            return IntermediateTensors(tensors)
         # Gemma4 incorporates residual into hidden_states directly
         # Apply norm without residual fusion when possible.
         if residual is None:
@@ -1269,30 +1369,35 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         #   moe.experts.{id}.gate_proj → FusedMoE w1 (shard of w13)
         #   moe.experts.{id}.up_proj   → FusedMoE w3 (shard of w13)
         #   moe.experts.{id}.down_proj → FusedMoE w2
-        #
-        # Use prefix matching to handle both weights and
-        # quantization scale parameters. The param_name is a prefix ending
-        # in underscore, and weight_name ends with a dot, so that:
-        #   "experts.0.gate_proj.weight_scale" -> "experts.w13_weight_scale"
-        #   "experts.0.gate_proj.weight" -> "experts.w13_weight"
         num_experts = getattr(self.config, "num_experts", None) or 0
-        expert_params_mapping = [
-            # (param_name, weight_name, expert_id, shard_id)
+        # Strategy A: dot-separated suffix
+        # (standard AWQ/GPTQ e.g. .qweight, .scales, .weight)
+        dot_suffix_expert_params_mapping = fused_moe_make_expert_params_mapping(
+            self,
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=num_experts,
+        )
+        # Strategy B: underscore-separated suffix
+        # (CompressedTensors-format AWQ/W4A16 _packed, _scale)
+        underscore_suffix_expert_params_mapping = [
             (
-                "experts.w13_"
-                if proj_name in ["gate_proj", "up_proj"]
-                else "experts.w2_",
-                f"experts.{expert_id}.{proj_name}.",
+                f"{param_name}weight_",
+                f"{weight_name.rstrip('.')}_",
                 expert_id,
                 shard_id,
             )
-            for expert_id in range(num_experts)
-            for shard_id, proj_name in [
-                ("w1", "gate_proj"),
-                ("w2", "down_proj"),
-                ("w3", "up_proj"),
-            ]
+            for (
+                param_name,
+                weight_name,
+                expert_id,
+                shard_id,
+            ) in dot_suffix_expert_params_mapping
         ]
+        expert_params_mapping = (
+            dot_suffix_expert_params_mapping + underscore_suffix_expert_params_mapping
+        )
         params_dict = dict(self.named_parameters())
         # Include buffers (e.g. layer_scalar) so they can be loaded too
         params_dict.update(dict(self.named_buffers()))
@@ -1549,7 +1654,7 @@ def _weight_iterator():
                 # Remap individual 2D expert weights:
                 # .experts.{id}.{proj} → .moe.experts.{id}.{proj}
                 # (This handles per-expert 2D quantized weights)
-                name = re.sub(r"\.experts\.(\d+)\.", r".moe.experts.\1.", name)
+                name = _remap_gemma4_expert_weight_name(name)
 
                 # MoE expert weights: checkpoint stores as 3D packed
                 # tensors.  Explode into per-expert 2D weights for
diff --git a/vllm/model_executor/models/gemma4_mm.py b/vllm/model_executor/models/gemma4_mm.py
index e22f23c5c8bc..73a5e701e7af 100644
--- a/vllm/model_executor/models/gemma4_mm.py
+++ b/vllm/model_executor/models/gemma4_mm.py
@@ -67,6 +67,7 @@
 from .interfaces import (
     MultiModalEmbeddings,
     SupportsEagle3,
+    SupportsLoRA,
     SupportsMultiModal,
     SupportsPP,
 )
@@ -80,10 +81,26 @@
 logger = init_logger(__name__)
 
 # Video constants — match transformers Gemma4VideoProcessor defaults.
+_SUPPORTED_SOFT_TOKENS = (70, 140, 280, 560, 1120)
 _VIDEO_MAX_SOFT_TOKENS = 70  # soft tokens per video frame (vs 280 for images)
 _VIDEO_MAX_FRAMES = 32  # max sampled frames per video
 
 
+def _get_max_soft_tokens(
+    merged_kwargs: Mapping[str, object],
+) -> tuple[object | None, bool]:
+    """Return configured image max_soft_tokens and whether it is top-level."""
+    val = merged_kwargs.get("max_soft_tokens")
+    if val is not None:
+        return val, True
+
+    images_kwargs = merged_kwargs.get("images_kwargs")
+    if isinstance(images_kwargs, Mapping):
+        return images_kwargs.get("max_soft_tokens"), False
+
+    return None, False
+
+
 # ---------------------------------------------------------------------------
 # Input schema
 # ---------------------------------------------------------------------------
@@ -125,8 +142,12 @@ class Gemma4AudioInputs(TensorSchema):
     """
 
     type: Literal["audio"] = "audio"
-    input_features_padded: Annotated[torch.Tensor, TensorShape("bn", "s", "f")]
-    input_features_mask: Annotated[torch.Tensor, TensorShape("bn", "s")]
+    input_features_padded: Annotated[
+        torch.Tensor, TensorShape("bn", "s", "f", dynamic_dims={"s"})
+    ]
+    input_features_mask: Annotated[
+        torch.Tensor, TensorShape("bn", "s", dynamic_dims={"s"})
+    ]
 
 
 Gemma4ImageInputs = Gemma4ImagePixelInputs
@@ -167,10 +188,15 @@ def get_default_tok_params(self):
 
         Setting ``add_special_tokens=False`` here prevents the duplicate and
         ensures both ``llm.generate()`` and the chat/completions API behave
-        correctly.
+        correctly for IT models. For PT models (without chat template), we
+        keep the default (True) to ensure BOS is added for raw prompts.
         """
+        tokenizer = self.ctx.get_tokenizer()
+        has_chat_template = getattr(tokenizer, "chat_template", None) is not None
+
         params = super().get_default_tok_params()
-        params = params.with_kwargs(add_special_tokens=False)
+        if has_chat_template:
+            params = params.with_kwargs(add_special_tokens=False)
         return params
 
     def get_hf_processor(self, **kwargs: object) -> Gemma4Processor:
@@ -206,17 +232,29 @@ def get_mm_max_tokens_per_item(
         self, seq_len: int, mm_counts: Mapping[str, int]
     ) -> Mapping[str, int] | None:
         config = self.get_hf_config()
-        # Upper bound: the pooler outputs default_output_length slots
-        # per image (280).  After padding is stripped the actual count
-        # is ≤ this value, but vLLM needs the max for memory planning.
+        # Upper bound: the pooler outputs max_soft_tokens slots per image.
+        # After padding is stripped the actual count is ≤ this value, but
+        # vLLM needs the max for memory planning.
         tokens_per_image = config.vision_config.default_output_length
+        merged_kwargs = self.ctx.get_merged_mm_kwargs({})
+        val, _ = _get_max_soft_tokens(merged_kwargs)
+        if isinstance(val, int) and val in _SUPPORTED_SOFT_TOKENS:
+            tokens_per_image = val
         tokens: dict[str, int] = {"image": tokens_per_image}
         if config.audio_config is not None:
             # Audio max tokens from the processor's audio_seq_length.
             processor = self.get_hf_processor()
             tokens["audio"] = processor.audio_seq_length
         # Video: each frame ≤ 70 soft tokens + boi + eoi + ~6 ts tokens.
-        tokens["video"] = _VIDEO_MAX_FRAMES * (_VIDEO_MAX_SOFT_TOKENS + 2 + 6)
+        num_frames = _VIDEO_MAX_FRAMES
+        mm_config = self.ctx.model_config.get_multimodal_config()
+        video_opts = mm_config.limit_per_prompt.get("video")
+        if (
+            isinstance(video_opts, VideoDummyOptions)
+            and video_opts.num_frames is not None
+        ):
+            num_frames = min(num_frames, video_opts.num_frames)
+        tokens["video"] = num_frames * (_VIDEO_MAX_SOFT_TOKENS + 2 + 6)
         return tokens
 
     def get_data_parser(self) -> MultiModalDataParser:
@@ -255,7 +293,14 @@ def _compute_num_soft_tokens(
         target_h = max(unit, int(math.floor(image_height * scale / unit)) * unit)
         target_w = max(unit, int(math.floor(image_width * scale / unit)) * unit)
         num_patches = (target_h // patch_size) * (target_w // patch_size)
-        return num_patches // (pooling_kernel_size**2)
+        # Clamp to ``max_soft_tokens``: extreme aspect ratios (e.g. 3x900)
+        # cause the floor() above to round one dim up to ``unit`` while the
+        # other scales freely, which over-shoots ``max_patches``. The HF
+        # Gemma 4 image processor caps its vision-tower output at
+        # ``max_soft_tokens``, so without this clamp the prompt-side
+        # placeholder count exceeds the encoder output and
+        # ``_merge_multimodal_embeddings`` crashes.
+        return min(num_patches // (pooling_kernel_size**2), max_soft_tokens)
 
     def get_image_repl(
         self,
@@ -475,13 +520,8 @@ def _call_hf_processor(
         mm_kwargs: Mapping[str, object],
         tok_kwargs: Mapping[str, object],
     ) -> BatchFeature:
-        # Validate max_soft_tokens early and exit cleanly on bad values.
-        _SUPPORTED_SOFT_TOKENS = (70, 140, 280, 560, 1120)
-
         merged_kwargs = self.info.ctx.get_merged_mm_kwargs(mm_kwargs)
-        val = merged_kwargs.get("max_soft_tokens")
-        if val is None:
-            val = merged_kwargs.get("images_kwargs", {}).get("max_soft_tokens")
+        val, is_top_level_max_soft_tokens = _get_max_soft_tokens(merged_kwargs)
 
         if val is not None and val not in _SUPPORTED_SOFT_TOKENS:
             raise ValueError(
@@ -505,6 +545,8 @@ def _call_hf_processor(
             video_timestamps_per_video: list[list[float]] = []
             video_frame_counts: list[int] = []
 
+            video_replacements: list[str] = []
+
             for item in videos:
                 video_array, metadata = item
 
@@ -557,10 +599,7 @@ def _call_hf_processor(
                 video_timestamps_per_video.append(timestamps)
                 video_frame_counts.append(len(frames))
 
-                # Build expanded replacement text and replace the
-                # <|video|> placeholder in the prompt.
-                # Use split(token, 1) to avoid collision — the
-                # replacement text itself contains <|video|> tokens.
+                # Build expanded replacement text for this video.
                 ts_strs = [f"{int(s // 60):02d}:{int(s % 60):02d}" for s in timestamps]
                 replacement = " ".join(
                     f"{t} {processor.boi_token}"
@@ -568,9 +607,23 @@ def _call_hf_processor(
                     f"{processor.eoi_token}"
                     for t, n in zip(ts_strs, num_soft_per_frame)
                 )
-                parts = prompt.split(processor.video_token, 1)
-                if len(parts) == 2:
-                    prompt = parts[0] + replacement + parts[1]
+                video_replacements.append(replacement)
+
+            # Replace all <|video|> placeholders at once. We split on
+            # video_token to get N+1 parts, then interleave with the
+            # N replacement strings. This avoids the iterative
+            # split-replace bug where replacement text (which itself
+            # contains <|video|> tokens) collides with later splits.
+            vt = processor.video_token
+            parts = prompt.split(vt, len(video_replacements))
+
+            # NOTE: len(parts) <= len(video_replacements) + 1
+            parts_with_repl: list[str] = []
+            for part, repl in zip(parts, video_replacements):
+                parts_with_repl.extend([part, repl])
+            parts_with_repl.extend(parts[len(video_replacements) :])
+
+            prompt = "".join(parts_with_repl)
 
             video_outputs = {
                 "pixel_values_videos": torch.cat(all_video_pixel_values, dim=0),
@@ -615,7 +668,7 @@ def _call_hf_processor(
         # HF side (Gemma4ProcessorKwargs.images_kwargs) so that
         # _merge_kwargs routes max_soft_tokens into images_kwargs.
         patched_mm_kwargs = dict(mm_kwargs)
-        if val is not None:
+        if val is not None and is_top_level_max_soft_tokens:
             patched_mm_kwargs["max_soft_tokens"] = val
 
         processed_outputs = super()._call_hf_processor(
@@ -633,19 +686,23 @@ def _call_hf_processor(
             )
 
         if "input_features" in processed_outputs:
-            # Keep padded features for batched audio tower execution.
-            processed_outputs["input_features_padded"] = processed_outputs[
-                "input_features"
-            ]
-            # Unpad per-item so each item's cache entry is self-contained.
+            # Unpad per-item so each item's cache entry is
+            # self-contained. The batched() field config in
+            # _get_mm_fields_config will re-pad all fields to the
+            # batch's max length at batch time, ensuring consistent
+            # padding regardless of cache history.
+            masks = processed_outputs["input_features_mask"]
             unpadded_features = [
                 f[mask]
                 for f, mask in zip(
                     processed_outputs["input_features"],
-                    processed_outputs["input_features_mask"],
+                    masks,
                 )
             ]
+            unpadded_masks = [mask[mask] for mask in masks]
             processed_outputs["input_features"] = unpadded_features
+            processed_outputs["input_features_padded"] = unpadded_features
+            processed_outputs["input_features_mask"] = unpadded_masks
 
         # Merge video outputs into the final result
         combined_outputs = dict(processed_outputs, **video_outputs)
@@ -721,7 +778,12 @@ def get_replacement_image(item_idx: int):
                 merged_kwargs = self.info.ctx.get_merged_mm_kwargs(
                     hf_processor_mm_kwargs,
                 )
-                max_soft_tokens = merged_kwargs.get("max_soft_tokens")
+                val, _ = _get_max_soft_tokens(merged_kwargs)
+                max_soft_tokens = (
+                    val
+                    if isinstance(val, int) and val in _SUPPORTED_SOFT_TOKENS
+                    else None
+                )
                 return self.info.get_image_repl(
                     image_width=image_size.width,
                     image_height=image_size.height,
@@ -822,22 +884,23 @@ def __init__(
             or multimodal_config.hidden_size
         )
 
-        self.embedding_projection = ReplicatedLinear(
+        self.embedding_pre_projection_norm = RMSNorm(
             embedding_dim,
-            self.text_hidden_size,
-            bias=False,
+            eps=self.eps,
+            has_weight=False,
         )
 
-        self.embedding_post_projection_norm = RMSNorm(
+        self.embedding_projection = ReplicatedLinear(
+            embedding_dim,
             self.text_hidden_size,
-            eps=self.eps,
-            has_weight=False,
+            bias=False,
         )
 
     def forward(self, inputs_embeds: torch.Tensor) -> torch.Tensor:
         """Project soft tokens from a multimodal tower into LM space."""
-        embs_proj, _ = self.embedding_projection(inputs_embeds)
-        return self.embedding_post_projection_norm(embs_proj)
+        embs_normed = self.embedding_pre_projection_norm(inputs_embeds)
+        embs_proj, _ = self.embedding_projection(embs_normed)
+        return embs_proj
 
 
 # ---------------------------------------------------------------------------
@@ -854,6 +917,7 @@ class Gemma4ForConditionalGeneration(
     nn.Module,
     SupportsMultiModal,
     SupportsPP,
+    SupportsLoRA,
     SupportsEagle3,
 ):
     packed_modules_mapping = {
@@ -940,6 +1004,16 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             self.language_model.make_empty_intermediate_tensors
         )
 
+        # --- Precompute full-attention layer indices for bidi clearing ---
+        self._full_attn_layer_idxs: frozenset[int] = frozenset()
+        text_config = config.text_config
+        if getattr(text_config, "use_bidirectional_attention", None) == "vision":
+            layer_types = getattr(text_config, "layer_types", None)
+            if layer_types:
+                self._full_attn_layer_idxs = frozenset(
+                    i for i, lt in enumerate(layer_types) if lt != "sliding_attention"
+                )
+
         # --- MixtureOfExperts delegation to language_model ---
         self.expert_weights = self.language_model.expert_weights
         self.moe_layers = self.language_model.moe_layers
@@ -1228,9 +1302,10 @@ def embed_input_ids(
             # computation (using token_type_ids == 0 as text_mask).
             # Replicate this: map image token positions to token 0.
             if is_multimodal is not None:
-                is_multimodal = is_multimodal.to(input_ids.device)
                 ple_input_ids = torch.where(
-                    is_multimodal, torch.zeros_like(input_ids), input_ids
+                    is_multimodal.to(input_ids.device, non_blocking=True),
+                    torch.zeros_like(input_ids),
+                    input_ids,
                 )
             else:
                 ple_input_ids = input_ids
@@ -1280,6 +1355,12 @@ def forward(
             else None
         )
 
+        # Gemma4 bidi: clear mm_prefix_range for full_attention layers.
+        # Must run here (outside @support_torch_compile boundary) because
+        # _run_decoder_layers is inside a compiled graph where Python
+        # side effects are eliminated.
+        self._clear_mm_prefix_for_full_attn_layers()
+
         hidden_states = self.language_model.model(
             input_ids,
             positions,
@@ -1297,6 +1378,49 @@ def compute_logits(
     ) -> torch.Tensor | None:
         return self.language_model.compute_logits(hidden_states)
 
+    # ------------------------------------------------------------------ #
+    # Bidirectional attention helpers
+    # ------------------------------------------------------------------ #
+
+    def _clear_mm_prefix_for_full_attn_layers(self) -> None:
+        """Clear mm_prefix_range for non-sliding layers.
+
+        Gemma4 with use_bidirectional_attention='vision' applies
+        bidirectional attention only to sliding_attention layers.
+        Full attention layers use plain causal masking.
+
+        Uses _full_attn_layer_idxs (precomputed in __init__) for O(1)
+        lookup instead of per-call regex parsing.
+        """
+        if not self._full_attn_layer_idxs:
+            return
+
+        from vllm.forward_context import get_forward_context
+
+        attn_metadata = get_forward_context().attn_metadata
+        if attn_metadata is None:
+            return
+
+        def _process(metadata_dict: dict) -> None:
+            for layer_name, metadata in metadata_dict.items():
+                if ".layers." not in layer_name:
+                    continue
+                try:
+                    layer_idx = int(layer_name.split(".layers.")[1].split(".")[0])
+                except (ValueError, IndexError):
+                    continue
+                if layer_idx in self._full_attn_layer_idxs:
+                    if hasattr(metadata, "mm_prefix_range"):
+                        metadata.mm_prefix_range = None
+                    if hasattr(metadata, "mm_prefix_range_tensor"):
+                        metadata.mm_prefix_range_tensor = None
+
+        if isinstance(attn_metadata, list):
+            for ub_metadata in attn_metadata:
+                _process(ub_metadata)
+        elif isinstance(attn_metadata, dict):
+            _process(attn_metadata)
+
     # ------------------------------------------------------------------ #
     # Weight loading
     # ------------------------------------------------------------------ #
@@ -1331,10 +1455,16 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
     def get_mm_mapping(self) -> MultiModelKeys:
         """Get the module prefix mapping for multimodal models."""
+        connectors = ["embed_vision"]
+        tower_models = ["vision_tower"]
+        if self.audio_tower is not None:
+            connectors.append("embed_audio")
+            tower_models.append("audio_tower")
+
         return MultiModelKeys.from_string_field(
             language_model="language_model",
-            connector=["embed_vision", "embed_audio"],
-            tower_model=["vision_tower", "audio_tower"],
+            connector=connectors,
+            tower_model=tower_models,
         )
 
     @classmethod
diff --git a/vllm/model_executor/models/gemma4_mtp.py b/vllm/model_executor/models/gemma4_mtp.py
new file mode 100644
index 000000000000..c294ffc6f9a7
--- /dev/null
+++ b/vllm/model_executor/models/gemma4_mtp.py
@@ -0,0 +1,603 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only Gemma4 MTP (Multi-Token Prediction) model.
+
+The Gemma4 assistant model is a lightweight decoder that shares KV cache
+with the target (backbone) model.  All assistant decoder layers are
+KV-shared: they only have Q projections (no K/V projections or norms),
+and read K/V from the target model's cache at runtime.
+
+Checkpoint layout (``gemma4_assistant``)::
+
+    model.embed_tokens.*          -- token embeddings
+    model.layers.{i}.*            -- decoder layers (Q-only attention + MLP)
+    model.norm.*                  -- final RMSNorm
+    pre_projection.*              -- Linear(2 * backbone_hidden_size, hidden_size)
+    post_projection.*             -- Linear(hidden_size, backbone_hidden_size)
+    lm_head.*                     -- language model head (tied to embed_tokens)
+    masked_embedding.centroids.*  -- centroid projection (when use_ordered_embeddings)
+    masked_embedding.token_ordering -- token-to-centroid mapping buffer
+"""
+
+from collections.abc import Iterable
+
+import torch
+from torch import nn
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_gather,
+)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.attention import Attention
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.sequence import IntermediateTensors
+
+from .gemma4 import Gemma4MLP, _get_text_config
+from .utils import (
+    AutoWeightsLoader,
+    WeightsMapper,
+    extract_layer_index,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+class Gemma4MTPMaskedEmbedder(nn.Module):
+    """Sparse logit computation via centroid-based vocabulary masking.
+
+    Instead of computing logits against the full vocabulary, projects
+    hidden states to centroid scores, selects top-K centroids, and
+    computes logits only for the ~top_k * (vocab_size / num_centroids)
+    tokens belonging to those centroids.
+    """
+
+    token_ordering: torch.Tensor
+
+    def __init__(
+        self,
+        hidden_size: int,
+        vocab_size: int,
+        num_centroids: int,
+        centroid_intermediate_top_k: int,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.vocab_size = vocab_size
+        self.num_centroids = num_centroids
+        self.centroid_intermediate_top_k = centroid_intermediate_top_k
+        self.vocab_size_per_centroid = vocab_size // num_centroids
+        self.num_selected = centroid_intermediate_top_k * self.vocab_size_per_centroid
+
+        self.centroids = nn.Linear(hidden_size, num_centroids, bias=False)
+        self.register_buffer(
+            "token_ordering",
+            torch.empty(vocab_size, dtype=torch.long),
+        )
+
+    def _select_and_score(
+        self,
+        hidden_states: torch.Tensor,
+        lm_head_weight: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Centroid selection + sparse dot product.
+
+        Returns:
+            logits: (num_tokens, num_selected) sparse logits.
+            indices: (num_tokens, num_selected) corresponding vocab indices.
+        """
+        num_tokens = hidden_states.shape[0]
+        _, top_k_indices = torch.topk(
+            self.centroids(hidden_states),
+            k=self.centroid_intermediate_top_k,
+            dim=-1,
+        )
+        clusters = self.token_ordering.view(
+            self.num_centroids,
+            self.vocab_size_per_centroid,
+        )
+        selected = clusters[top_k_indices]
+        embeddings = lm_head_weight[selected.reshape(-1)].view(
+            num_tokens,
+            self.num_selected,
+            self.hidden_size,
+        )
+        logits = torch.einsum("td,tsd->ts", hidden_states, embeddings)
+        return logits, selected.view(num_tokens, -1)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        lm_head_weight: torch.Tensor,
+    ) -> torch.Tensor:
+        """Full-vocab logits with non-selected positions masked to -inf."""
+        logits, indices = self._select_and_score(hidden_states, lm_head_weight)
+        output = torch.full(
+            (hidden_states.shape[0], self.vocab_size),
+            fill_value=torch.finfo(hidden_states.dtype).min,
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
+        )
+        return output.scatter_(-1, indices, logits)
+
+    def get_top_tokens(
+        self,
+        hidden_states: torch.Tensor,
+        lm_head_weight: torch.Tensor,
+    ) -> torch.Tensor:
+        """Sparse argmax — returns vocab token IDs without full-vocab tensor."""
+        logits, indices = self._select_and_score(hidden_states, lm_head_weight)
+        return indices.gather(-1, logits.argmax(-1, keepdim=True)).squeeze(-1)
+
+
+class Gemma4MTPAttention(nn.Module):
+    """Q-only attention for Gemma4 MTP layers.
+
+    K/V come from the target model's KV cache via
+    ``kv_sharing_target_layer_name`` (set by the proposer after
+    model construction).
+    """
+
+    def __init__(
+        self,
+        config,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        head_dim: int,
+        max_position_embeddings: int,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        attn_logits_soft_cap: float | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = hidden_size
+
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim
+        self.q_size = self.num_heads * self.head_dim
+        self.scaling = 1.0
+
+        self.q_proj = ColumnParallelLinear(
+            hidden_size,
+            self.total_num_heads * self.head_dim,
+            bias=config.attention_bias,
+            quant_config=None,
+            prefix=f"{prefix}.q_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=config.attention_bias,
+            quant_config=None,
+            prefix=f"{prefix}.o_proj",
+        )
+        self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+        layer_idx = extract_layer_index(prefix)
+        layer_type = config.layer_types[layer_idx]
+        self.is_sliding = layer_type == "sliding_attention"
+        sliding_window = config.sliding_window if self.is_sliding else None
+
+        if layer_type in config.rope_parameters:
+            rope_parameters = dict(config.rope_parameters[layer_type])
+        else:
+            rope_parameters = dict(config.rope_parameters.copy())
+            if self.is_sliding:
+                rope_parameters["rope_theta"] = getattr(
+                    config, "rope_local_base_freq", 10000.0
+                )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            max_position=max_position_embeddings,
+            rope_parameters=rope_parameters,
+            is_neox_style=True,
+        )
+
+        # kv_sharing_target_layer_name is set after model construction
+        # by Gemma4Proposer._setup_gemma4_kv_sharing().
+        self.is_kv_shared_layer = True
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            logits_soft_cap=attn_logits_soft_cap,
+            per_layer_sliding_window=sliding_window,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        q, _ = self.q_proj(hidden_states)
+
+        q = q.unflatten(-1, (self.num_heads, self.head_dim))
+        q = self.q_norm(q)
+        q = q.flatten(-2, -1)
+
+        q, _ = self.rotary_emb(positions, q, None)
+
+        # Attention reads K/V from the target's cache via KV sharing;
+        # these dummy tensors are never consumed but required by the API.
+        num_tokens = q.shape[0]
+        kv_dummy = torch.empty(
+            num_tokens,
+            self.num_kv_heads * self.head_dim,
+            dtype=q.dtype,
+            device=q.device,
+        )
+        attn_output = self.attn(q, kv_dummy, kv_dummy)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Gemma4MTPDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        layer_idx = extract_layer_index(prefix)
+        layer_type = config.layer_types[layer_idx]
+        is_full_attention = layer_type == "full_attention"
+        head_dim = (
+            getattr(config, "global_head_dim", config.head_dim)
+            if is_full_attention
+            else config.head_dim
+        )
+
+        self.self_attn = Gemma4MTPAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            head_dim=head_dim,
+            max_position_embeddings=config.max_position_embeddings,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            attn_logits_soft_cap=getattr(config, "attn_logit_softcapping", None),
+            prefix=f"{prefix}.self_attn",
+        )
+
+        text_config = _get_text_config(config)
+        self.mlp = Gemma4MLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=text_config.intermediate_size,
+            hidden_activation=text_config.hidden_activation,
+            quant_config=None,
+            prefix=f"{prefix}.mlp",
+        )
+
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.pre_feedforward_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.post_feedforward_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+        self.register_buffer("layer_scalar", torch.ones(1))
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor | None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(residual)
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            **kwargs,
+        )
+
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = hidden_states + residual
+        residual = hidden_states
+
+        hidden_states = self.pre_feedforward_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+
+        hidden_states = self.post_feedforward_layernorm(hidden_states)
+        hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states * self.layer_scalar
+        return hidden_states, None
+
+
+class Gemma4MultiTokenPredictor(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.speculative_config.draft_model_config.hf_config
+        text_config = _get_text_config(config)
+        self.config = text_config
+
+        self.hidden_size = text_config.hidden_size
+        self.backbone_hidden_size = getattr(
+            config, "backbone_hidden_size", self.hidden_size
+        )
+        self.vocab_size = text_config.vocab_size
+        self.num_mtp_layers = text_config.num_hidden_layers
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            self.hidden_size,
+        )
+
+        self.pre_projection = ColumnParallelLinear(
+            2 * self.backbone_hidden_size,
+            self.hidden_size,
+            bias=False,
+            gather_output=True,
+            prefix=f"{prefix}.pre_projection",
+        )
+
+        self.post_projection = RowParallelLinear(
+            self.hidden_size,
+            self.backbone_hidden_size,
+            bias=False,
+            input_is_parallel=False,
+            prefix=f"{prefix}.post_projection",
+        )
+
+        self.layers = nn.ModuleList(
+            Gemma4MTPDecoderLayer(
+                text_config,
+                cache_config=vllm_config.cache_config,
+                quant_config=vllm_config.quant_config,
+                prefix=f"{prefix}.layers.{idx}",
+            )
+            for idx in range(self.num_mtp_layers)
+        )
+
+        self.norm = RMSNorm(self.hidden_size, eps=text_config.rms_norm_eps)
+
+        # After embedding sharing, embed_tokens is replaced with the
+        # target model's backbone-dim embedding.  Scale by
+        # sqrt(backbone_hidden_size) to match the target's convention.
+        self.register_buffer(
+            "normalizer",
+            torch.tensor(self.backbone_hidden_size**0.5),
+            persistent=False,
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids) * self.normalizer
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        params_dict.update(dict(self.named_buffers()))
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        return loaded_params
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Returns (draft_hidden_states, backbone_hidden_states).
+
+        draft_hidden_states: draft-dim, used by compute_logits via lm_head.
+        backbone_hidden_states: backbone-dim, stored in the proposer's
+            hidden-state buffer and fed back as input to the next step.
+        """
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_input_ids(input_ids)
+
+        combined = torch.cat([inputs_embeds, hidden_states], dim=-1)
+        hidden_states, _ = self.pre_projection(combined)
+
+        residual = None
+        for layer in self.layers:
+            hidden_states, residual = layer(
+                positions=positions,
+                hidden_states=hidden_states,
+                residual=residual,
+            )
+
+        draft_hidden_states = self.norm(hidden_states)
+
+        backbone_hidden_states, _ = self.post_projection(draft_hidden_states)
+        return draft_hidden_states, backbone_hidden_states
+
+
+@support_torch_compile
+class Gemma4MTP(nn.Module):
+    """Gemma4 Multi-Token Prediction model for speculative decoding.
+
+    forward() returns (draft_hidden_states, backbone_hidden_states).
+    The proposer uses draft_hidden_states for compute_logits (via
+    the draft-dim lm_head) and backbone_hidden_states for the
+    hidden-state feedback buffer.
+    """
+
+    has_own_lm_head = True
+
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "pre_projection.": "model.pre_projection.",
+            "post_projection.": "model.post_projection.",
+        },
+    )
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.speculative_config.draft_model_config.hf_config
+        text_config = _get_text_config(config)
+        self.config = config
+
+        self.model = Gemma4MultiTokenPredictor(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "draft_model"),
+        )
+
+        # lm_head operates in draft-dim.  Tied to embed_tokens at init
+        # so load_weights populates both from a single checkpoint entry.
+        # After embedding sharing, lm_head.weight still references the
+        # original draft-dim tensor.
+        self.lm_head = ParallelLMHead(
+            text_config.vocab_size,
+            text_config.hidden_size,
+            prefix=maybe_prefix(prefix, "lm_head"),
+        )
+        if getattr(config, "tie_word_embeddings", True):
+            self.lm_head.weight = self.model.embed_tokens.weight
+
+        self.logits_processor = LogitsProcessor(
+            text_config.vocab_size,
+            soft_cap=getattr(text_config, "final_logit_softcapping", None),
+        )
+
+        if getattr(config, "use_ordered_embeddings", False):
+            num_centroids = getattr(config, "num_centroids", 2048)
+            top_k = getattr(config, "centroid_intermediate_top_k", 32)
+            self.masked_embedding = Gemma4MTPMaskedEmbedder(
+                hidden_size=text_config.hidden_size,
+                vocab_size=text_config.vocab_size,
+                num_centroids=num_centroids,
+                centroid_intermediate_top_k=top_k,
+            )
+            logger.info(
+                "Gemma4 MTP: centroids masking enabled "
+                "(num_centroids=%d, top_k=%d, active_tokens=%d/%d).",
+                num_centroids,
+                top_k,
+                top_k * (text_config.vocab_size // num_centroids),
+                text_config.vocab_size,
+            )
+        else:
+            self.masked_embedding = None
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+        **kwargs: object,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        return self.model(
+            input_ids,
+            positions,
+            hidden_states,
+            intermediate_tensors,
+            inputs_embeds,
+            spec_step_idx,
+        )
+
+    def _get_full_lm_head_weight(self) -> torch.Tensor:
+        lm_head_weight = self.lm_head.weight
+        tp_size = get_tensor_model_parallel_world_size()
+        if tp_size > 1:
+            lm_head_weight = tensor_model_parallel_all_gather(
+                lm_head_weight,
+                dim=0,
+            )
+        return lm_head_weight[: self.masked_embedding.vocab_size]
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor | None:
+        if self.masked_embedding is not None:
+            return self.masked_embedding(
+                hidden_states,
+                self._get_full_lm_head_weight(),
+            )
+        return self.logits_processor(self.lm_head, hidden_states)
+
+    def get_top_tokens(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        """Sparse argmax via centroids masking. Returns token IDs directly."""
+        return self.masked_embedding.get_top_tokens(
+            hidden_states,
+            self._get_full_lm_head_weight(),
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
diff --git a/vllm/model_executor/models/glm4_moe.py b/vllm/model_executor/models/glm4_moe.py
index d0e6cb6ada8b..98cc9a50adcc 100644
--- a/vllm/model_executor/models/glm4_moe.py
+++ b/vllm/model_executor/models/glm4_moe.py
@@ -42,7 +42,10 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -178,13 +181,12 @@ def __init__(
         else:
             self.shared_experts = None
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_experts,
             num_experts=config.n_routed_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             use_grouped_topk=True,
@@ -192,8 +194,8 @@ def __init__(
             topk_group=config.topk_group,
             prefix=f"{prefix}.experts",
             scoring_func="sigmoid",
-            # we do scaling outside, set factor to 1.0 to avoid double mul
-            routed_scaling_factor=1.0,
+            routed_scaling_factor=self.routed_scaling_factor,
+            apply_routed_scale_to_output=True,
             e_score_correction_bias=self.gate.e_score_correction_bias,
             enable_eplb=self.enable_eplb,
             num_redundant_experts=self.n_redundant_experts,
@@ -207,23 +209,9 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         # router_logits: (num_tokens, n_experts)
         router_logits = self.gate(hidden_states.to(dtype=torch.float32))
 
-        fused_moe_out = self.experts(
+        final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
-
-        if self.shared_experts is not None:
-            shared_output, final_hidden_states = fused_moe_out
-            assert shared_output is not None
-            final_hidden_states = (
-                final_hidden_states * self.routed_scaling_factor + shared_output
-            )
-        else:
-            final_hidden_states = fused_moe_out * self.routed_scaling_factor
-
-        if self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
         return final_hidden_states.view(num_tokens, hidden_dim)
 
 
@@ -481,7 +469,7 @@ def forward(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return SharedFusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -518,16 +506,24 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                 # for mlp.experts[0].gate_gate_up_proj, which breaks load.
                 if ("mlp.experts." in name) and name not in params_dict:
                     continue
+
                 name = name.replace(weight_name, param_name)
                 # Skip loading extra bias for GPTQ models.
                 if name.endswith(".bias") and name not in params_dict:
                     continue
+
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
                 if is_pp_missing_parameter(name, self):
                     continue
 
                 param = params_dict[name]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                if weight_loader == default_weight_loader:
+                    weight_loader(param, loaded_weight)
+                else:
+                    weight_loader(param, loaded_weight, shard_id)
                 break
             else:
                 is_expert_weight = False
diff --git a/vllm/model_executor/models/glm4_moe_lite.py b/vllm/model_executor/models/glm4_moe_lite.py
index 6d96f748e3ea..77aaa179aa52 100644
--- a/vllm/model_executor/models/glm4_moe_lite.py
+++ b/vllm/model_executor/models/glm4_moe_lite.py
@@ -41,7 +41,9 @@
     get_pp_group,
 )
 from vllm.logger import init_logger
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -308,7 +310,7 @@ def make_empty_intermediate_tensors(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return SharedFusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -334,7 +336,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -616,7 +618,7 @@ def compute_logits(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return SharedFusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/glm4_moe_lite_mtp.py b/vllm/model_executor/models/glm4_moe_lite_mtp.py
index efa96c40d042..596cb48face0 100644
--- a/vllm/model_executor/models/glm4_moe_lite_mtp.py
+++ b/vllm/model_executor/models/glm4_moe_lite_mtp.py
@@ -32,7 +32,10 @@
 
 from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import VllmConfig
-from vllm.model_executor.layers.fused_moe import FusedMoE, SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -260,7 +263,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             ("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
         ]
 
-        expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/glm4_moe_mtp.py b/vllm/model_executor/models/glm4_moe_mtp.py
index cde94673e53a..791ecabebebc 100644
--- a/vllm/model_executor/models/glm4_moe_mtp.py
+++ b/vllm/model_executor/models/glm4_moe_mtp.py
@@ -31,7 +31,10 @@
 from transformers import PretrainedConfig
 
 from vllm.config import CacheConfig, ParallelConfig, VllmConfig
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -247,7 +250,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/glmasr.py b/vllm/model_executor/models/glmasr.py
index 0d54588cc2b0..cd168b6b4617 100644
--- a/vllm/model_executor/models/glmasr.py
+++ b/vllm/model_executor/models/glmasr.py
@@ -4,7 +4,6 @@
 from collections.abc import Iterable, Mapping, Sequence
 from typing import Annotated, Any, Literal, TypeAlias
 
-import numpy as np
 import torch
 import torch.nn as nn
 from transformers import BatchFeature
@@ -13,6 +12,7 @@
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.distributed.parallel_state import get_tensor_model_parallel_world_size
 from vllm.inputs import ModalityData, MultiModalDataDict, PromptType, TokensPrompt
 from vllm.model_executor.layers.activation import get_act_fn
@@ -66,7 +66,7 @@
     SupportsTranscription,
 )
 from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix
-from .whisper import ISO639_1_SUPPORTED_LANGS
+from .whisper import ISO639_1_SUPPORTED_LANGS, _create_fake_bias_for_k_proj
 
 
 class GlmAsrEncoderRotaryEmbedding(nn.Module):
@@ -499,6 +499,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         """Custom weight loading to handle q_proj/k_proj/v_proj -> qkv_proj mapping."""
         from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
+        weights = _create_fake_bias_for_k_proj(weights, ".k_proj.weight")
+
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id)
             ("qkv_proj", "q_proj", "q"),
@@ -1129,17 +1131,12 @@ def get_speech_to_text_config(
         )
 
     @classmethod
-    def get_generation_prompt(
-        cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
-    ) -> PromptType:
+    def get_generation_prompt(cls, stt_params: SpeechToTextParams) -> PromptType:
         """Get the generation prompt to be used for transcription requests."""
+        audio = stt_params.audio
+        model_config = stt_params.model_config
+        task_type = stt_params.task_type
+        to_language = stt_params.to_language
         tokenizer = cached_tokenizer_from_config(model_config)
         audio_token = cls._get_audio_token(model_config)
 
diff --git a/vllm/model_executor/models/gpt_oss.py b/vllm/model_executor/models/gpt_oss.py
index 4e4eb581842d..d12db96c5d46 100644
--- a/vllm/model_executor/models/gpt_oss.py
+++ b/vllm/model_executor/models/gpt_oss.py
@@ -20,7 +20,10 @@
     tensor_model_parallel_all_gather,
 )
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.fused_moe.config import FusedMoEParallelConfig
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
@@ -189,7 +192,6 @@ def __init__(
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.intermediate_size,
-            reduce_results=True,
             renormalize=True,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -332,7 +334,7 @@ def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, weight scales, activation scales
         # (param_name, weight_name, expert_id, shard_id)
         # NOTE: this is only used for quark.
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="w1",
             ckpt_down_proj_name="w2",
diff --git a/vllm/model_executor/models/granite4_vision.py b/vllm/model_executor/models/granite4_vision.py
new file mode 100644
index 000000000000..710fc94ee5f8
--- /dev/null
+++ b/vllm/model_executor/models/granite4_vision.py
@@ -0,0 +1,931 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""vLLM implementation of Granite 4 Vision.
+
+Uses GraniteForCausalLM as the language backbone with SigLIP vision encoder
+and deepstack feature injection via WindowQFormer projectors.
+
+LoRA support: use --enable-lora --default-mm-loras for LM-only LoRA adapters.
+"""
+
+import math
+from collections.abc import Iterable, Mapping
+from fractions import Fraction
+from itertools import islice
+
+import torch
+import torch.nn as nn
+from transformers import BatchFeature
+from transformers.models.blip_2.configuration_blip_2 import Blip2QFormerConfig
+from transformers.models.llava_next.modeling_llava_next import (
+    get_anyres_image_grid_shape,
+    image_size_to_num_patches,
+    unpad_image,
+)
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed.parallel_state import get_pp_group
+from vllm.logger import init_logger
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.models.granite import GraniteForCausalLM, GraniteModel
+from vllm.model_executor.models.interfaces import (
+    MultiModalEmbeddings,
+    SupportsLoRA,
+    SupportsMultiModal,
+    SupportsPP,
+)
+from vllm.model_executor.models.llava import LlavaDummyInputsBuilder
+from vllm.model_executor.models.llava_next import (
+    BaseLlavaNextMultiModalProcessor,
+    LlavaNextImageEmbeddingInputs,
+    LlavaNextImageInputs,
+    LlavaNextImagePixelInputs,
+    LlavaNextProcessingInfo,
+)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.model_executor.models.siglip import SiglipVisionModel
+from vllm.model_executor.models.utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    WeightsMapper,
+    maybe_prefix,
+)
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalFieldConfig
+from vllm.sequence import IntermediateTensors
+
+from .blip2 import Blip2QFormerModel
+
+logger = init_logger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Downsampler modules (translated from HF downsampling.py)
+# ---------------------------------------------------------------------------
+
+
+class InterpolateDownsampler:
+    """Spatial downsampling via area interpolation."""
+
+    def __init__(self, config, mode="area"):
+        self.orig_image_side = (
+            config.vision_config.image_size // config.vision_config.patch_size
+        )
+        self.new_image_side = int(
+            self.orig_image_side * Fraction(config.downsample_rate)
+        )
+        self.mode = mode
+
+    def __call__(self, image_features: torch.Tensor) -> torch.Tensor:
+        batch_size, _, dim = image_features.size()
+        up_shape = [batch_size, self.orig_image_side, self.orig_image_side, dim]
+        large = image_features.view(up_shape).permute(0, 3, 1, 2)
+        small = torch.nn.functional.interpolate(
+            large,
+            size=(self.new_image_side, self.new_image_side),
+            mode=self.mode,
+        )
+        return small.permute(0, 2, 3, 1).flatten(1, 2)
+
+
+class SpatialOffsetDownsampler:
+    """Sample one position from each 2x2 block (offset 0-3 = TL/TR/BL/BR)."""
+
+    def __init__(self, config, offset: int = 0):
+        self.orig_image_side = (
+            config.vision_config.image_size // config.vision_config.patch_size
+        )
+        self.new_image_side = self.orig_image_side // 2
+        offsets = [(0, 0), (0, 1), (1, 0), (1, 1)]
+        self.offset_h, self.offset_w = offsets[offset]
+
+    def __call__(self, image_features: torch.Tensor) -> torch.Tensor:
+        B, _, C = image_features.shape
+        features_2d = image_features.reshape(
+            B, self.orig_image_side, self.orig_image_side, C
+        )
+        n = self.new_image_side
+        blocks = features_2d.reshape(B, n, 2, n, 2, C)
+        sampled = blocks[:, :, self.offset_h, :, self.offset_w, :]
+        return sampled.reshape(B, -1, C)
+
+
+class WindowQFormerDownsampler(nn.Module):
+    """Window-based QFormer downsampler (matches HF downsampling.py exactly)."""
+
+    def __init__(
+        self,
+        config,
+        quant_config: QuantizationConfig | None = None,
+        cache_config: CacheConfig | None = None,
+        spatial_offset: int | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        llm_hidden_size = config.text_config.hidden_size
+        vision_hidden_size = config.vision_config.hidden_size
+
+        self.dropout = nn.Dropout(config.projector_dropout)
+
+        if spatial_offset is not None:
+            self.downsampler = SpatialOffsetDownsampler(config, offset=spatial_offset)
+        else:
+            self.downsampler = InterpolateDownsampler(config)
+
+        qformer_config = Blip2QFormerConfig(
+            hidden_size=vision_hidden_size,
+            num_attention_heads=vision_hidden_size // 64,
+            intermediate_size=3072,
+            num_hidden_layers=1,
+            encoder_hidden_size=vision_hidden_size,
+            cross_attention_frequency=1,
+            max_position_embeddings=2048,
+            use_qformer_text_input=False,
+        )
+        self.qformer = Blip2QFormerModel(
+            qformer_config,
+            quant_config=quant_config,
+            cache_config=cache_config,
+            prefix=f"{prefix}.qformer",
+        )
+
+        self.image_side = (
+            config.vision_config.image_size // config.vision_config.patch_size
+        )
+        q, w = config.downsample_rate.split("/")
+        self.query_side, self.window_side = int(q), int(w)
+        self.query_length = self.query_side**2
+
+        embed_std = 1 / math.sqrt(vision_hidden_size)
+        self.norm = nn.LayerNorm(vision_hidden_size, eps=1e-6)
+        self.query = nn.Parameter(
+            torch.randn(1, self.query_length, vision_hidden_size) * embed_std
+        )
+        self.image_positions = nn.Parameter(
+            torch.randn(1, self.window_side**2, vision_hidden_size) * embed_std
+        )
+        self.out_linear = nn.Linear(vision_hidden_size, llm_hidden_size, bias=True)
+
+    def _win(self, x: torch.Tensor, side: int, win: int) -> torch.Tensor:
+        """(B, side*side, C) → (B*n*n, win*win, C) where n=side//win."""
+        B, _, C = x.shape
+        n = side // win
+        return (
+            x.view(B, side, side, C)
+            .view(B, n, win, n, win, C)
+            .transpose(2, 3)
+            .flatten(0, 2)
+            .flatten(1, 2)
+        )
+
+    def _unwin(self, xw: torch.Tensor, n: int, win: int) -> torch.Tensor:
+        """(B*n*n, win*win, C) → (B, (n*win)^2, C)."""
+        Bnn, _, C = xw.shape
+        B = Bnn // (n * n)
+        side = n * win
+        return (
+            xw.view(B, n, n, win, win, C)
+            .transpose(2, 3)
+            .contiguous()
+            .view(B, side, side, C)
+            .flatten(1, 2)
+        )
+
+    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
+        B, HW, C = image_features.shape
+        assert self.image_side * self.image_side == HW
+        n = self.image_side // self.window_side
+
+        image_features = self.norm(image_features)
+        enc = self._win(image_features, self.image_side, self.window_side)
+
+        downsampled = self.downsampler(image_features)
+        new_side = n * self.query_side
+        downsampled_w = self._win(downsampled, new_side, self.query_side)
+
+        query_embeds = self.query + downsampled_w
+        encoder_embeds = self.dropout(enc + self.image_positions)
+        out_w = self.qformer(
+            query_embeds=query_embeds,
+            encoder_hidden_states=encoder_embeds,
+        )
+
+        out = self._unwin(out_w, n=n, win=self.query_side)
+        out = self.dropout(out)
+        return self.out_linear(out)
+
+
+# ---------------------------------------------------------------------------
+# LLM subclasses with deepstack injection in the layer loop
+# ---------------------------------------------------------------------------
+
+
+@support_torch_compile(
+    dynamic_arg_dims={
+        "input_ids": 0,
+        "positions": 0,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+        "deepstack_input_embeds": 0,
+    }
+)
+class Granite4VisionLLMModel(GraniteModel):
+    """GraniteModel with deepstack feature injection in the layer loop."""
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        deepstack_input_embeds: IntermediateTensors | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_input_ids(input_ids)
+                hidden_states = hidden_states * self.config.embedding_multiplier
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            # Recover deepstack features forwarded from the previous PP rank.
+            if deepstack_input_embeds is None:
+                ds_keys = [
+                    k for k in intermediate_tensors.tensors if k.startswith("ds_")
+                ]
+                if ds_keys:
+                    deepstack_input_embeds = IntermediateTensors(
+                        {k: intermediate_tensors[k] for k in ds_keys}
+                    )
+
+        for layer_idx, layer in islice(
+            enumerate(self.layers), self.start_layer, self.end_layer
+        ):
+            if deepstack_input_embeds is not None:
+                key = f"ds_{layer_idx}"
+                if key in deepstack_input_embeds.tensors:
+                    feat = deepstack_input_embeds[key]
+                    # Resize to match hidden_states in case of CUDA graph padding
+                    num_tokens = hidden_states.size(0)
+                    buf_len = feat.shape[0]
+                    if buf_len != num_tokens:
+                        feat = torch.nn.functional.pad(
+                            feat[:num_tokens],
+                            (0, 0, 0, max(0, num_tokens - buf_len)),
+                        )
+                    hidden_states = hidden_states + feat
+            hidden_states = layer(positions, hidden_states)
+
+        if not get_pp_group().is_last_rank:
+            # Forward hidden_states and any deepstack features for later ranks.
+            it = {"hidden_states": hidden_states}
+            if deepstack_input_embeds is not None:
+                remaining = {
+                    k: v
+                    for k, v in deepstack_input_embeds.tensors.items()
+                    if int(k.split("_")[1]) >= self.end_layer
+                }
+                it.update(remaining)
+            return IntermediateTensors(it)
+
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class Granite4VisionLLMForCausalLM(GraniteForCausalLM):
+    """GraniteForCausalLM backed by Granite4VisionLLMModel."""
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        nn.Module.__init__(self)
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Granite4VisionLLMModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+            if config.tie_word_embeddings:
+                self.lm_head.weight = self.model.embed_tokens.weight
+            logit_scale = getattr(config, "logit_scale", 1.0)
+            if hasattr(config, "logits_scaling"):
+                logit_scale /= config.logits_scaling
+            self.logits_processor = LogitsProcessor(
+                config.vocab_size, scale=logit_scale
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+
+    def make_empty_intermediate_tensors(
+        self, batch_size: int, dtype: torch.dtype, device: torch.device
+    ) -> IntermediateTensors:
+        tensors = super().make_empty_intermediate_tensors(batch_size, dtype, device)
+        # Include deepstack buffers so non-first PP ranks receive them.
+        # _ds_layer_indices is set directly on this instance by the outer model.
+        for llm_layer in getattr(self, "_ds_layer_indices", []):
+            tensors.tensors[f"ds_{llm_layer}"] = torch.zeros(
+                (batch_size, self.config.hidden_size), dtype=dtype, device=device
+            )
+        return tensors
+
+
+# ---------------------------------------------------------------------------
+# Processing info / processor (reuses LlavaNext patterns)
+# ---------------------------------------------------------------------------
+
+
+class Granite4VisionProcessingInfo(LlavaNextProcessingInfo):
+    def get_hf_config(self):
+        return self.ctx.get_hf_config()
+
+    def get_hf_processor(self, **kwargs):
+        return self.ctx.get_hf_processor(**kwargs)
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        hf_config = self.get_hf_config()
+        vision_encoder_info = self.get_vision_encoder_info()
+
+        # After QFormer downsampling, patch grid is scaled by downsample_rate
+        ds_rate = Fraction(hf_config.downsample_rate)
+        patch_grid = vision_encoder_info.get_patch_grid_length()  # 24 for 384/16
+        downsampled_grid = int(patch_grid * ds_rate)  # 12 for rate 4/8
+
+        # Base feature: downsampled_grid^2
+        base_feature_size = downsampled_grid * downsampled_grid
+
+        num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+            image_size=(image_height, image_width),
+            grid_pinpoints=hf_config.image_grid_pinpoints,
+            patch_size=vision_encoder_info.get_image_size(),
+        )
+
+        (
+            unpadded_feature_size,
+            newline_feature_size,
+        ) = self._get_num_unpadded_features(
+            original_height=image_height,
+            original_width=image_width,
+            npatches=downsampled_grid,
+            num_patch_height=num_patch_height,
+            num_patch_width=num_patch_width,
+        )
+
+        return unpadded_feature_size + newline_feature_size + base_feature_size
+
+
+class Granite4VisionMultiModalProcessor(
+    BaseLlavaNextMultiModalProcessor[Granite4VisionProcessingInfo]
+):
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(
+            pixel_values=MultiModalFieldConfig.batched("image"),
+            image_sizes=MultiModalFieldConfig.batched("image"),
+        )
+
+
+# ---------------------------------------------------------------------------
+# Top-level model
+# ---------------------------------------------------------------------------
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Granite4VisionMultiModalProcessor,
+    info=Granite4VisionProcessingInfo,
+    dummy_inputs=LlavaDummyInputsBuilder,
+)
+class Granite4VisionForConditionalGeneration(
+    nn.Module, SupportsLoRA, SupportsMultiModal, SupportsPP
+):
+    """vLLM implementation of Granite 4 Vision.
+
+    Architecture:
+    - SigLIP vision tower -> WindowQFormerDownsampler projectors
+    - Deepstack: 4 vision layers projected and injected at 4 LLM layers
+    - Spatial: 4 offset groups from last vision layer injected at 4 more LLM layers
+    - Granite language backbone with embedding_multiplier
+    - logits_scaling via LogitsProcessor
+
+    The outer model runs the LLM layer loop directly (like HF does) to inject
+    deepstack features. This avoids wrapping the inner model and keeps weight
+    loading simple.
+
+    LoRA support:
+    - Full merge: --hf-overrides '{"adapter_path": "path/to/lora"}' merges
+      LM-only LoRA deltas at load time (W += scaling * B @ A).
+    - Native LoRA: --enable-lora --default-mm-loras '{"image": "path/to/lora"}'
+      lets vLLM runtime serve LM LoRA per-request.
+    Both modes expect a LM-only adapter (no modules_to_save).
+    """
+
+    # LoRA class attributes (matches GraniteForCausalLM)
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+    embedding_modules = {}
+
+    # Weight mapping: HF checkpoint -> vLLM parameter names
+    # HF: model.language_model.layers.0...
+    # vLLM: language_model.model.layers.0...
+    # (because GraniteForCausalLM.model = GraniteModel)
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "model.language_model.": "language_model.model.",
+            "model.layerwise_projectors.": "layerwise_projectors.",
+            "model.spatial_projectors.": "spatial_projectors.",
+            "model.image_newline": "image_newline",
+            "model.vision_tower.": "vision_tower.",
+            "lm_head.": "language_model.lm_head.",
+        }
+    )
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
+        if modality.startswith("image"):
+            return "<image>"
+        raise ValueError(f"Only image modality is supported, got {modality}")
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector=["layerwise_projectors", "spatial_projectors"],
+            tower_model="vision_tower",
+        )
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.vllm_config = vllm_config
+
+        # ----- Vision tower + projectors (marked as tower) -----
+        with self._mark_tower_model(vllm_config, "image"):
+            # Do NOT use init_vision_tower_for_llava here — it truncates the
+            # encoder to vision_feature_layer depth. Deepstack needs ALL hidden
+            # states (deepstack_layer_map uses negative indices into the full
+            # encoder output list).
+            self.vision_tower = SiglipVisionModel(
+                config.vision_config,
+                quant_config=quant_config,
+                require_post_norm=False,
+                prefix=maybe_prefix(prefix, "vision_tower"),
+            )
+
+            # image_newline parameter
+            if config.use_image_newline_parameter:
+                self.image_newline = nn.Parameter(
+                    torch.empty(config.text_config.hidden_size)
+                )
+            else:
+                self.image_newline = None
+
+            cache_config = vllm_config.cache_config
+
+            # Deepstack projectors: one per (vision_layer, llm_layer) pair
+            self.layerwise_projectors = nn.ModuleList(
+                [
+                    WindowQFormerDownsampler(
+                        config,
+                        quant_config=quant_config,
+                        cache_config=cache_config,
+                        prefix=maybe_prefix(prefix, f"layerwise_projectors.{i}"),
+                    )
+                    for i in range(len(config.deepstack_layer_map))
+                ]
+            )
+
+            # Spatial projectors: 4 offset groups
+            self.spatial_projectors = None
+            if config.use_spatial_sampling:
+                self.spatial_projectors = nn.ModuleList(
+                    [
+                        WindowQFormerDownsampler(
+                            config,
+                            quant_config=quant_config,
+                            cache_config=cache_config,
+                            spatial_offset=i,
+                            prefix=maybe_prefix(prefix, f"spatial_projectors.{i}"),
+                        )
+                        for i in range(4)
+                    ]
+                )
+
+        # ----- Language model (marked as LM) -----
+        with self._mark_language_model(vllm_config):
+            self.language_model = Granite4VisionLLMForCausalLM(
+                vllm_config=vllm_config.with_hf_config(config.text_config),
+                prefix=maybe_prefix(prefix, "language_model"),
+            )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors
+        )
+
+        # Store config values we need
+        self._deepstack_layer_map = config.deepstack_layer_map  # [[-19, 9], ...]
+        self._use_spatial_sampling = getattr(config, "use_spatial_sampling", False)
+        self._spatial_vision_layer = getattr(config, "spatial_vision_layer", -1)
+        self._spatial_target_layers = getattr(config, "spatial_target_layers", [])
+        self._vision_feature_select_strategy = getattr(
+            config, "vision_feature_select_strategy", "full"
+        )
+        self._downsample_rate = Fraction(config.downsample_rate)
+
+        # Ordered list of LLM layer indices for each deepstack level.
+        # Pre-populated from config so it's available during CUDA graph capture
+        # (before any embed_multimodal call).
+        self._ds_layer_indices: list[int] = [
+            llm_layer for _, llm_layer in config.deepstack_layer_map
+        ] + list(getattr(config, "spatial_target_layers", []))
+
+        # Share ds_layer_indices with the LLM causal model so
+        # make_empty_intermediate_tensors includes the correct keys
+        # (its self.config is text_config, no deepstack_layer_map).
+        self.language_model._ds_layer_indices = self._ds_layer_indices
+
+        # Pre-allocated persistent GPU buffers for deepstack features.
+        # Written via .copy_() in embed_input_ids(), read by forward() via a
+        # slice. Because the buffer address is fixed, CUDA graph replay sees
+        # the updated values written just before each prefill.
+        # Shape: (max_num_batched_tokens, lm_hidden_size) per level.
+        n_layerwise = len(config.deepstack_layer_map)
+        n_spatial = len(getattr(config, "spatial_target_layers", []))
+        num_ds_levels = n_layerwise + n_spatial
+        lm_hidden = config.text_config.hidden_size
+        max_tokens = vllm_config.scheduler_config.max_num_batched_tokens
+        # Allocated on CPU first; moved to GPU in embed_input_ids on first use.
+        self._ds_buffers: list[torch.Tensor] = [
+            torch.zeros(max_tokens, lm_hidden) for _ in range(num_ds_levels)
+        ]
+        self._ds_num_tokens: int = 0  # tokens written in last embed_input_ids call
+
+    # ----- Vision feature extraction -----
+
+    def _get_vision_hidden_states(
+        self, pixel_values: torch.Tensor
+    ) -> list[torch.Tensor]:
+        """Run vision tower and return all hidden states (including input embeddings).
+
+        Uses SiglipEncoder's built-in return_all_hidden_states support.
+        Returns list[Tensor] where index 0 = embeddings, index i = after layer i-1.
+        """
+        vt = self.vision_tower
+        vm = vt.vision_model if hasattr(vt, "vision_model") else vt
+
+        hidden_states = vm.embeddings(pixel_values)
+        all_hidden_states = vm.encoder(
+            inputs_embeds=hidden_states,
+            return_all_hidden_states=True,
+        )
+        return all_hidden_states
+
+    def _pack_and_unpad_image_features(
+        self,
+        image_features: list[torch.Tensor] | tuple[torch.Tensor, ...],
+        image_sizes: torch.Tensor,
+    ) -> list[torch.Tensor]:
+        """Reshape, unpad, and pack image features.
+
+        Matches HF Granite4VisionModel.pack_and_unpad_image_features exactly.
+        """
+        config = self.config
+        ds_rate = self._downsample_rate
+        new_image_features = []
+
+        for image_idx, image_feature in enumerate(image_features):
+            if image_feature.shape[0] > 1:
+                # Multi-patch: first is base, rest are high-res
+                base_image_feature = image_feature[0]
+                image_feature = image_feature[1:]
+
+                height = width = (
+                    config.vision_config.image_size // config.vision_config.patch_size
+                )
+                # After QFormer downsampling
+                height = int(height * ds_rate)
+                width = int(width * ds_rate)
+
+                num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+                    image_sizes[image_idx],
+                    config.image_grid_pinpoints,
+                    config.vision_config.image_size,
+                )
+
+                image_feature = image_feature.view(
+                    num_patch_height, num_patch_width, height, width, -1
+                )
+                image_feature = (
+                    image_feature.permute(4, 0, 2, 1, 3)
+                    .contiguous()
+                    .flatten(1, 2)
+                    .flatten(2, 3)
+                )
+                image_feature = unpad_image(image_feature, image_sizes[image_idx])
+
+                if self.image_newline is not None:
+                    image_feature = torch.cat(
+                        (
+                            image_feature,
+                            self.image_newline[:, None, None]
+                            .expand(*image_feature.shape[:-1], 1)
+                            .to(image_feature.device, image_feature.dtype),
+                        ),
+                        dim=-1,
+                    )
+
+                image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+                image_feature = torch.cat((base_image_feature, image_feature), dim=0)
+            else:
+                image_feature = image_feature[0]
+                if self.image_newline is not None:
+                    image_feature = torch.cat(
+                        (image_feature, self.image_newline[None].to(image_feature)),
+                        dim=0,
+                    )
+
+            new_image_features.append(image_feature)
+
+        return new_image_features
+
+    def _get_all_layer_features(
+        self,
+        pixel_values: torch.Tensor,
+        image_sizes: torch.Tensor,
+    ) -> tuple[list[int], list[torch.Tensor]]:
+        """Extract deepstack + spatial features for all levels.
+
+        Returns:
+          llm_layer_indices: ordered list of target LLM layer indices
+          per_image_packed:  one tensor per image, shape
+                             (num_tokens_i, lm_hidden_size * num_levels),
+                             all levels packed on dim=-1.
+
+        Packing on dim=-1 means the framework's token-level slicing for
+        chunked prefill preserves all levels intact.
+        """
+        select_strategy = self._vision_feature_select_strategy
+
+        image_num_patches = [
+            image_size_to_num_patches(
+                image_size=imsize,
+                grid_pinpoints=self.config.image_grid_pinpoints,
+                patch_size=self.config.vision_config.image_size,
+            )
+            for imsize in image_sizes
+        ]
+
+        if pixel_values.dim() == 5:
+            pixel_values = torch.cat(
+                [pv[:np_] for pv, np_ in zip(pixel_values, image_num_patches)],
+                dim=0,
+            )
+
+        all_hidden_states = self._get_vision_hidden_states(pixel_values)
+
+        # Collect per-level: (llm_layer, [per_image_tensor, ...])
+        levels: list[tuple[int, list[torch.Tensor]]] = []
+
+        for proj_idx, (vision_layer, llm_layer) in enumerate(self._deepstack_layer_map):
+            selected = all_hidden_states[vision_layer]
+            if select_strategy == "default":
+                selected = selected[:, 1:]
+            projected = self.layerwise_projectors[proj_idx](selected)
+            per_image = self._pack_and_unpad_image_features(
+                torch.split(projected, image_num_patches, dim=0), image_sizes
+            )
+            levels.append((llm_layer, per_image))
+
+        if self._use_spatial_sampling and self.spatial_projectors is not None:
+            spatial_hidden = all_hidden_states[self._spatial_vision_layer]
+            if select_strategy == "default":
+                spatial_hidden = spatial_hidden[:, 1:]
+            for group_idx, llm_layer in enumerate(self._spatial_target_layers):
+                projected = self.spatial_projectors[group_idx](spatial_hidden)
+                per_image = self._pack_and_unpad_image_features(
+                    torch.split(projected, image_num_patches, dim=0), image_sizes
+                )
+                levels.append((llm_layer, per_image))
+
+        llm_layer_indices = [llm_layer for llm_layer, _ in levels]
+        num_images = len(image_sizes)
+        per_image_packed = [
+            torch.cat([levels[lvl][1][img] for lvl in range(len(levels))], dim=-1)
+            for img in range(num_images)
+        ]
+
+        return llm_layer_indices, per_image_packed
+
+    # ----- Multimodal interface -----
+
+    def _parse_and_validate_image_input(
+        self, **kwargs: object
+    ) -> LlavaNextImageInputs | None:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_sizes = kwargs.pop("image_sizes", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None:
+            expected_h = expected_w = self.config.vision_config.image_size
+            return LlavaNextImagePixelInputs(
+                type="pixel_values",
+                pixel_values=pixel_values,
+                image_sizes=image_sizes,
+                resolve_bindings={"h": expected_h, "w": expected_w},
+            )
+
+        if image_embeds is not None:
+            return LlavaNextImageEmbeddingInputs(
+                type="image_embeds",
+                data=image_embeds,
+            )
+
+        raise AssertionError("Unreachable")
+
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
+        """Run vision tower and return per-image packed feature tensors.
+
+        Each returned tensor has shape (num_tokens_i, lm_hidden_size * num_levels)
+        with all deepstack levels packed on dim=-1. The framework caches these
+        tensors and slices along dim=0 for chunked prefill — all levels survive
+        intact because slicing is token-wise, not feature-wise.
+
+        embed_input_ids() splits the packed tensor back into per-level buffers.
+        """
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return []
+
+        if image_input["type"] == "image_embeds":
+            return [image_input["data"]]
+
+        pixel_values = image_input["pixel_values"]
+        image_sizes = image_input.get("image_sizes")
+
+        if isinstance(pixel_values, list):
+            pixel_values = torch.cat(pixel_values, dim=0)
+
+        llm_layer_indices, per_image_packed = self._get_all_layer_features(
+            pixel_values, image_sizes
+        )
+        self._ds_layer_indices = llm_layer_indices
+        return per_image_packed
+
+    def embed_input_ids(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: MultiModalEmbeddings | None = None,
+        *,
+        is_multimodal: torch.Tensor | None = None,
+        handle_oov_mm_token: bool = True,
+    ) -> torch.Tensor:
+        """Merge text and vision embeddings, apply embedding_multiplier.
+
+        HF flow:
+        1. inputs_embeds = embed_tokens(input_ids)
+        2. inputs_embeds.masked_fill(vision_mask, 0.0)
+        3. hidden_states = inputs_embeds * embedding_multiplier
+        4. layer loop injects deepstack features at target layers
+
+        multimodal_embeddings contains packed tensors from embed_multimodal():
+        shape (num_tokens_i, lm_hidden_size * num_levels). We split on dim=-1
+        to get per-level features, build batch-sized buffers (zero at text
+        positions), and store in self._ds_features for forward().
+        """
+        lm_inner = self.language_model.model
+
+        has_vision = (
+            multimodal_embeddings is not None
+            and is_multimodal is not None
+            and len(multimodal_embeddings) > 0
+            and is_multimodal.any()
+        )
+
+        if not has_vision:
+            self._ds_num_tokens = 0
+            embeds = lm_inner.embed_input_ids(input_ids)
+            return embeds * lm_inner.config.embedding_multiplier
+
+        # 1. Text embeddings
+        text_embeds = lm_inner.embed_input_ids(input_ids)
+
+        # 2. Zero image positions (matches HF masked_fill(vision_mask, 0.0))
+        text_embeds[is_multimodal] = 0.0
+
+        # 3. Apply embedding_multiplier
+        inputs_embeds = text_embeds * lm_inner.config.embedding_multiplier
+
+        # 4. Split packed tensors into per-level features and build buffers.
+        #    multimodal_embeddings is a list of per-image packed tensors
+        #    (possibly a chunk slice from the framework's encoder cache).
+        #    Concatenate along token dim → (total_mm_tokens, lm_h * num_levels).
+        N, lm_h = inputs_embeds.shape
+        all_packed = torch.cat(
+            [t.to(dtype=inputs_embeds.dtype) for t in multimodal_embeddings],
+            dim=0,
+        )
+        level_features = all_packed.split(lm_h, dim=-1)  # num_levels tensors
+
+        # Ensure persistent buffers are on the right device/dtype (first call).
+        buf0 = self._ds_buffers[0]
+        if buf0.device != inputs_embeds.device or buf0.dtype != inputs_embeds.dtype:
+            self._ds_buffers = [
+                b.to(device=inputs_embeds.device, dtype=inputs_embeds.dtype)
+                for b in self._ds_buffers
+            ]
+
+        for level_idx in range(len(self._ds_layer_indices)):
+            target = self._ds_buffers[level_idx][:N]
+            target.zero_()
+            target[is_multimodal] = level_features[level_idx]
+
+        self._ds_num_tokens = N
+        return inputs_embeds
+
+    # ----- Forward -----
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs: object,
+    ) -> torch.Tensor | IntermediateTensors:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # Build IntermediateTensors from pre-allocated persistent buffers.
+        # Always pass deepstack when inputs_embeds is non-None (prefill path),
+        # including during CUDA graph capture (buffers are zero → no-op injection).
+        # This ensures the graph captures the injection code path.
+        if (
+            inputs_embeds is not None
+            and get_pp_group().is_first_rank
+            and self._ds_layer_indices
+        ):
+            n = inputs_embeds.size(0)
+            ds: IntermediateTensors | None = IntermediateTensors(
+                {
+                    f"ds_{llm_layer}": self._ds_buffers[lvl][:n]
+                    for lvl, llm_layer in enumerate(self._ds_layer_indices)
+                }
+            )
+        else:
+            ds = None
+
+        hidden_states = self.language_model.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+            deepstack_input_embeds=ds,
+        )
+
+        # Clear buffers after use so stale features don't leak into the next request.
+        if (
+            inputs_embeds is not None
+            and get_pp_group().is_first_rank
+            and self._ds_num_tokens > 0
+        ):
+            n = self._ds_num_tokens
+            for buf in self._ds_buffers:
+                buf[:n].zero_()
+            self._ds_num_tokens = 0
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        # GraniteForCausalLM.compute_logits uses
+        # LogitsProcessor(scale=1/logits_scaling)
+        return self.language_model.compute_logits(hidden_states)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
diff --git a/vllm/model_executor/models/granite_speech.py b/vllm/model_executor/models/granite_speech.py
index dca54425c706..036b92ed8808 100644
--- a/vllm/model_executor/models/granite_speech.py
+++ b/vllm/model_executor/models/granite_speech.py
@@ -26,9 +26,8 @@
 
 import math
 from collections.abc import Iterable, Mapping
-from typing import Annotated, Literal
+from typing import Annotated
 
-import numpy as np
 import torch
 import torch.nn.functional as F
 from torch import nn
@@ -36,6 +35,7 @@
 
 from vllm.config import CacheConfig, ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.inputs import MultiModalDataDict, PromptType, TokensPrompt
 from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
 from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -852,15 +852,14 @@ def get_mm_mapping(self) -> MultiModelKeys:
     @classmethod
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
         """Get the generation prompt to be used for transcription requests."""
+        audio = stt_params.audio
+        model_config = stt_params.model_config
+        task_type = stt_params.task_type
+        to_language = stt_params.to_language
+
         # Audio placeholders don't use an index, so value doesn't matter
         audio_tok = cls.get_placeholder_str("audio", 0)
 
diff --git a/vllm/model_executor/models/granitemoe.py b/vllm/model_executor/models/granitemoe.py
index 171b2e0ec5a0..e3585a6dd746 100644
--- a/vllm/model_executor/models/granitemoe.py
+++ b/vllm/model_executor/models/granitemoe.py
@@ -39,7 +39,10 @@
     tensor_model_parallel_all_gather,
 )
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     QKVParallelLinear,
@@ -104,7 +107,6 @@ def __init__(
             hidden_size=hidden_size,
             intermediate_size=intermediate_size,
             params_dtype=params_dtype,
-            reduce_results=True,
             renormalize=True,
             quant_config=quant_config,
             tp_size=tp_size,
@@ -352,7 +354,7 @@ def _load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]
 
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="w1",
             ckpt_down_proj_name="w2",
diff --git a/vllm/model_executor/models/grok1.py b/vllm/model_executor/models/grok1.py
index 0bd6a8f3d606..f06122a7fd19 100644
--- a/vllm/model_executor/models/grok1.py
+++ b/vllm/model_executor/models/grok1.py
@@ -38,7 +38,10 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import GeluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -209,7 +212,6 @@ def __init__(
             hidden_size=hidden_size,
             intermediate_size=intermediate_size,
             params_dtype=params_dtype,
-            reduce_results=True,
             renormalize=renormalize,
             quant_config=quant_config,
             tp_size=tp_size,
@@ -520,7 +522,7 @@ def forward(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Map expert parameter names to standard names
         num_experts = _get_num_experts(self.config)
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name=self.ckpt_gate_proj_name,
             ckpt_down_proj_name=self.ckpt_down_proj_name,
diff --git a/vllm/model_executor/models/hunyuan_v1.py b/vllm/model_executor/models/hunyuan_v1.py
index a0130402c66f..fca801b74823 100644
--- a/vllm/model_executor/models/hunyuan_v1.py
+++ b/vllm/model_executor/models/hunyuan_v1.py
@@ -39,11 +39,13 @@
     get_ep_group,
     get_pp_group,
     get_tensor_model_parallel_world_size,
-    tensor_model_parallel_all_reduce,
 )
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -439,13 +441,12 @@ def __init__(
         else:
             self.shared_mlp = None
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_mlp,
             num_experts=self.n_routed_experts,
             top_k=top_k,
             hidden_size=config.hidden_size,
             intermediate_size=intermediate_size,
-            reduce_results=False,
             renormalize=top_k > 1,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -464,11 +465,6 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
-        if self.shared_mlp is not None:
-            final_hidden_states = final_hidden_states[0] + final_hidden_states[1]
-
-        if self.tp_size > 1:
-            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
 
         return final_hidden_states.view(orig_shape)
 
@@ -719,7 +715,7 @@ def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         if _is_moe(self.config):
             # Params for weights, fp8 weight scales, fp8 activation scales
             # (param_name, weight_name, expert_id, shard_id)
-            return SharedFusedMoE.make_expert_params_mapping(
+            return fused_moe_make_expert_params_mapping(
                 self,
                 ckpt_gate_proj_name="gate_proj",
                 ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/hy_v3.py b/vllm/model_executor/models/hy_v3.py
new file mode 100644
index 000000000000..bfff84b80490
--- /dev/null
+++ b/vllm/model_executor/models/hy_v3.py
@@ -0,0 +1,707 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# coding=utf-8
+# Copyright 2026 The HY team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only HY model compatible with HuggingFace weights."""
+
+import typing
+from collections.abc import Callable, Iterable
+from itertools import islice
+from typing import Any
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
+from vllm.distributed import (
+    get_ep_group,
+    get_pp_group,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import Attention
+from vllm.model_executor.layers.fused_moe import FusedMoE, GateLinear
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs.hy_v3 import HYV3Config
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+class HYV3FeedForward(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: QuantizationConfig | None = None,
+        reduce_results: bool = True,
+        expert_gate: torch.nn.Linear | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
+            )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        out = self.act_fn(gate_up)
+        out, _ = self.down_proj(out)
+        return out
+
+
+class HYV3MoEFused(nn.Module):
+    def __init__(
+        self,
+        config: HYV3Config,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        enable_eplb: bool = False,
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.ep_group = get_ep_group().device_group
+        self.ep_rank = get_ep_group().rank_in_group
+        self.ep_size = self.ep_group.size()
+        self.n_routed_experts = config.num_experts
+        if self.tp_size > config.num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.num_experts}."
+            )
+        top_k = config.num_experts_per_tok
+        intermediate_size = config.expert_hidden_dim
+        router_scaling_factor = getattr(config, "router_scaling_factor", 1.0)
+        vllm_config = get_current_vllm_config()
+        eplb_config = vllm_config.parallel_config.eplb_config
+        self.enable_eplb = enable_eplb
+
+        self.n_logical_experts = self.n_routed_experts
+        self.n_redundant_experts = eplb_config.num_redundant_experts
+        self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
+        self.n_local_physical_experts = self.n_physical_experts // self.ep_size
+        self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
+        self.physical_expert_end = (
+            self.physical_expert_start + self.n_local_physical_experts
+        )
+        self.gate = GateLinear(
+            config.hidden_size,
+            config.num_experts,
+            bias=False,
+            out_dtype=torch.float32,
+            params_dtype=torch.float32,
+            prefix=f"{prefix}.gate",
+        )
+
+        if config.num_shared_experts > 0:
+            self.shared_mlp = HYV3FeedForward(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.expert_hidden_dim * config.num_shared_experts,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                prefix=f"{prefix}",
+                reduce_results=False,
+            )
+        else:
+            self.shared_mlp = None
+
+        self.expert_bias = nn.Parameter(torch.empty(config.num_experts))
+        scoring_func = "sigmoid"
+        e_score_correction_bias = self.expert_bias
+
+        self.experts = FusedMoE(
+            num_experts=self.n_routed_experts,
+            top_k=top_k,
+            hidden_size=config.hidden_size,
+            intermediate_size=intermediate_size,
+            renormalize=config.route_norm,
+            quant_config=quant_config,
+            prefix=f"{prefix}.experts",
+            enable_eplb=self.enable_eplb,
+            num_redundant_experts=self.n_redundant_experts,
+            scoring_func=scoring_func,
+            use_grouped_topk=True,
+            num_expert_group=1,
+            topk_group=1,
+            routed_scaling_factor=router_scaling_factor,
+            e_score_correction_bias=e_score_correction_bias,
+            n_shared_experts=config.num_shared_experts,
+            shared_experts=self.shared_mlp,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        orig_shape = hidden_states.shape
+        hidden_dim = hidden_states.shape[-1]
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states, router_logits=router_logits
+        )
+        return final_hidden_states.view(orig_shape)
+
+
+class HYV3Attention(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_parameters: dict[str, Any],
+        max_position_embeddings: int = 8192,
+        head_dim: int | None = None,
+        rms_norm_eps: float = 1e-5,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        dual_chunk_attention_config: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+
+        if hasattr(config, "head_dim") and config.head_dim:
+            self.head_dim = config.head_dim
+        else:
+            self.head_dim = head_dim or (hidden_size // self.total_num_heads)
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.use_qk_norm = getattr(config, "qk_norm", False)
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            quant_config=quant_config,
+            bias=None,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            max_position=max_position_embeddings,
+            rope_parameters=rope_parameters,
+            is_neox_style=True,
+        )
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+        if self.use_qk_norm:
+            self.q_norm = RMSNorm(self.head_dim, rms_norm_eps)
+            self.k_norm = RMSNorm(self.head_dim, rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        output_shape = None
+        if self.use_qk_norm:
+            q_by_head = q.view(
+                *q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim
+            )
+            q_by_head = self.q_norm(q_by_head)
+            q = q_by_head.view(q.shape)
+
+            k_by_head = k.view(
+                *k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim
+            )
+            k_by_head = self.k_norm(k_by_head)
+            k = k_by_head.view(k.shape)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v, output_shape)
+        attn_output = attn_output.view(q.shape[0], -1)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class HYV3DecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        layer_idx = int(prefix.split(".")[-1])
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
+        self.self_attn = HYV3Attention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_parameters=config.rope_parameters,
+            max_position_embeddings=max_position_embeddings,
+            head_dim=config.head_dim,
+            rms_norm_eps=config.rms_norm_eps,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        if not hasattr(config, "first_k_dense_replace"):
+            raise ValueError("first_k_dense_replace not exist,please check config")
+        if layer_idx < config.first_k_dense_replace:
+            self.mlp = HYV3FeedForward(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+            )
+            self.block_type = "feedforward"
+        else:
+            self.mlp = HYV3MoEFused(
+                config=config, quant_config=quant_config, prefix=f"{prefix}.mlp"
+            )
+            self.block_type = "moe"
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor | None,
+        idx: int = -1,
+    ) -> torch.Tensor:
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+
+        hidden_states = self.mlp(hidden_states)
+
+        return hidden_states, residual
+
+
+@support_torch_compile
+class HYV3Model(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        parallel_config = vllm_config.parallel_config
+        eplb_config = parallel_config.eplb_config
+        self.num_redundant_experts = eplb_config.num_redundant_experts
+
+        self.vocab_size = config.vocab_size
+        self.config = config
+        self.quant_config = quant_config
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: HYV3DecoderLayer(
+                config=config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=prefix,
+            ),
+            prefix=f"{prefix}.layers",
+        )
+        self.norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+
+        # Set MoE hyperparameters
+        self.expert_weights = []
+        self.num_expert_groups = 1
+        self.moe_layers = []
+        example_layer = None
+        for layer in self.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+
+            assert isinstance(layer, HYV3DecoderLayer)
+            if layer.block_type == "moe":
+                example_layer = layer.mlp
+                self.moe_layers.append(layer.mlp.experts)
+
+        if example_layer is None:
+            self.num_moe_layers = 0
+            raise RuntimeError("No MoE layer found in model.layers.")
+
+        self.num_moe_layers = len(self.moe_layers)
+        self.num_logical_experts = getattr(example_layer, "n_logical_experts", None)
+        self.num_physical_experts = getattr(example_layer, "n_physical_experts", None)
+        self.num_local_physical_experts = getattr(
+            example_layer, "n_local_physical_experts", None
+        )
+        self.num_routed_experts = getattr(example_layer, "n_routed_experts", None)
+        self.num_redundant_experts = getattr(example_layer, "n_redundant_experts", None)
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def update_physical_experts_metadata(
+        self,
+        num_physical_experts: int,
+        num_local_physical_experts: int,
+    ) -> None:
+        assert self.num_local_physical_experts == num_local_physical_experts
+        self.num_physical_experts = num_physical_experts
+        self.num_local_physical_experts = num_local_physical_experts
+        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
+        for layer in self.layers:
+            if isinstance(layer.mlp, HYV3MoEFused):
+                moe = layer.mlp
+                moe.n_local_physical_experts = num_local_physical_experts
+                moe.n_physical_experts = num_physical_experts
+                moe.n_redundant_experts = self.num_redundant_experts
+                moe.experts.update_expert_map()
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return FusedMoE.make_expert_params_mapping(
+            self,
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_experts,
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_input_ids(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for idx, layer in enumerate(
+            islice(self.layers, self.start_layer, self.end_layer)
+        ):
+            hidden_states, residual = layer(positions, hidden_states, residual, idx=idx)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+
+        hidden_states = hidden_states + residual
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        expert_params_mapping = self.get_expert_mapping()
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if self.config.tie_word_embeddings and "lm_head.weight" in name:
+                continue
+            if self.quant_config is not None and (
+                scale_name := self.quant_config.get_cache_scale(name)
+            ):
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                loaded_weight = (
+                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
+                )
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+            if "scale" in name:
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+            is_found = False
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if "mlp.experts" in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(name)
+                is_found = True
+                break
+            if is_found:
+                continue
+
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            is_expert_weight = False
+            for mapping in expert_params_mapping:
+                param_name, weight_name, expert_id, shard_id = mapping
+                if weight_name not in name:
+                    continue
+                is_expert_weight = True
+                name_mapped = name.replace(weight_name, param_name)
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name_mapped, self):
+                    continue
+
+                param = params_dict[name_mapped]
+                weight_loader = typing.cast(Callable[..., bool], param.weight_loader)
+                success = weight_loader(
+                    param,
+                    loaded_weight,
+                    name_mapped,
+                    shard_id=shard_id,
+                    expert_id=expert_id,
+                    return_success=True,
+                )
+                if success:
+                    name = name_mapped
+                    break
+            else:
+                if is_expert_weight:
+                    # We've checked that this is an expert weight
+                    # However it's not mapped locally to this rank
+                    # So we simply skip it
+                    continue
+                if name is None:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                if "router.gate." in name:
+                    name = name.replace("router.", "")
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        return loaded_params
+
+
+def get_spec_layer_idx_from_weight_name(
+    config: PretrainedConfig, weight_name: str
+) -> int | None:
+    # HYV3MTP is enabled only when num_nextn_predict_layers is greater than 1
+    if (
+        hasattr(config, "num_nextn_predict_layers")
+        and config.num_nextn_predict_layers > 0
+    ):
+        layer_idx = config.num_hidden_layers
+        for i in range(config.num_nextn_predict_layers):
+            if weight_name.startswith(f"model.layers.{layer_idx + i}."):
+                return layer_idx + i
+    return None
+
+
+class HYV3ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+
+        parallel_config = vllm_config.parallel_config
+        eplb_config = parallel_config.eplb_config
+        self.num_redundant_experts = eplb_config.num_redundant_experts
+
+        self.model = HYV3Model(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
+        )
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        def _filter_weights(weights):
+            for name, weight in weights:
+                spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
+                if spec_layer is not None:
+                    continue
+                yield name, weight
+
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(_filter_weights(weights))
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
diff --git a/vllm/model_executor/models/hy_v3_mtp.py b/vllm/model_executor/models/hy_v3_mtp.py
new file mode 100644
index 000000000000..8594a38c3ab9
--- /dev/null
+++ b/vllm/model_executor/models/hy_v3_mtp.py
@@ -0,0 +1,470 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# coding=utf-8
+# Copyright 2026 The HY team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only HY V3 MTP model compatible with HuggingFace weights."""
+
+from collections.abc import Iterable
+
+import regex as re
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from vllm.sequence import IntermediateTensors
+from vllm.v1.outputs import SamplerOutput
+from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.sample.sampler import Sampler
+
+from .hy_v3 import HYV3DecoderLayer, get_spec_layer_idx_from_weight_name
+from .utils import is_pp_missing_parameter, maybe_prefix
+
+
+def _is_moe(config: PretrainedConfig) -> bool:
+    return bool(
+        getattr(config, "num_experts", None)
+        and (
+            (isinstance(config.num_experts, int) and config.num_experts > 1)
+            or (isinstance(config.num_experts, list) and max(config.num_experts) > 1)
+        )
+    )
+
+
+def _get_cla_factor(config: PretrainedConfig) -> int:
+    if not getattr(config, "use_cla", False):
+        return 1
+    return getattr(config, "cla_share_factor", 1)
+
+
+class HYV3SharedHead(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: QuantizationConfig | None = None,
+    ) -> None:
+        super().__init__()
+        self.head = ParallelLMHead(
+            config.vocab_size, config.hidden_size, quant_config=quant_config
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return hidden_states
+
+
+class HYV3MultiTokenPredictorLayer(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        prefix: str,
+        model_config: ModelConfig,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+    ) -> None:
+        super().__init__()
+
+        self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False)
+        self.shared_head = HYV3SharedHead(config=config, quant_config=quant_config)
+        self.mtp_block = HYV3DecoderLayer(
+            config=config,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=prefix,
+        )
+        # Final layernorm applied after transformer block, before logits
+        # projection (matches HF HYV3MTPDecoderLayer.final_layernorm)
+        self.final_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_index: int = 0,
+    ) -> torch.Tensor:
+        assert inputs_embeds is not None
+        # masking inputs at position 0, as not needed by MTP
+        inputs_embeds[positions == 0] = 0
+        inputs_embeds = self.enorm(inputs_embeds)
+        previous_hidden_states = self.hnorm(previous_hidden_states)
+
+        hidden_states = self.eh_proj(
+            torch.cat([inputs_embeds, previous_hidden_states], dim=-1)
+        )
+
+        # HYV3DecoderLayer returns (hidden_states, residual)
+        hidden_states, residual = self.mtp_block(
+            positions=positions, hidden_states=hidden_states, residual=None
+        )
+        hidden_states = residual + hidden_states
+        hidden_states = self.final_layernorm(hidden_states)
+        return hidden_states
+
+
+class HYV3MultiTokenPredictor(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.mtp_start_layer_idx = config.num_hidden_layers
+        self.num_mtp_layers = config.num_nextn_predict_layers
+
+        # to map the exact layer index from weights
+        self.layers = torch.nn.ModuleDict(
+            {
+                str(idx): HYV3MultiTokenPredictorLayer(
+                    config,
+                    f"{prefix}.layers.{idx}",
+                    model_config=vllm_config.model_config,
+                    cache_config=vllm_config.cache_config,
+                    quant_config=vllm_config.quant_config,
+                )
+                for idx in range(
+                    self.mtp_start_layer_idx,
+                    self.mtp_start_layer_idx + self.num_mtp_layers,
+                )
+            }
+        )
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        current_step_idx = spec_step_idx % self.num_mtp_layers
+        return self.layers[str(self.mtp_start_layer_idx + current_step_idx)](
+            input_ids,
+            positions,
+            previous_hidden_states,
+            inputs_embeds,
+            current_step_idx,
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        current_step_idx = spec_step_idx % self.num_mtp_layers
+        mtp_layer = self.layers[str(self.mtp_start_layer_idx + current_step_idx)]
+        logits = self.logits_processor(
+            mtp_layer.shared_head.head, mtp_layer.shared_head(hidden_states)
+        )
+        return logits
+
+
+class HYV3MTP(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        self.config = vllm_config.model_config.hf_config
+        self.quant_config = vllm_config.quant_config
+        self.model = HYV3MultiTokenPredictor(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+
+        self.sampler = Sampler()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        hidden_states = self.model(
+            input_ids, positions, hidden_states, inputs_embeds, spec_step_idx
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor | None:
+        return self.model.compute_logits(hidden_states, spec_step_idx)
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> SamplerOutput | None:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    def _split_qkv_weight(self, qkv: torch.Tensor):
+        num_attention_heads = self.config.num_attention_heads
+        num_kv_heads = getattr(
+            self.config, "num_key_value_heads", self.config.num_attention_heads
+        )
+        num_key_value_groups = num_attention_heads // num_kv_heads
+        hidden_size = self.config.hidden_size
+
+        if hasattr(self.config, "head_dim"):
+            attention_head_dim = self.config.head_dim
+        elif hasattr(self.config, "attention_head_dim"):
+            attention_head_dim = self.config.attention_head_dim
+        else:
+            attention_head_dim = self.config.hidden_size // num_attention_heads
+
+        qkv = qkv.reshape(
+            num_kv_heads, num_key_value_groups + 2, attention_head_dim, hidden_size
+        )
+        q, k, v = torch.split(qkv, (num_key_value_groups, 1, 1), dim=1)
+        q = q.reshape(-1, hidden_size)
+        k = k.reshape(-1, hidden_size)
+        v = v.reshape(-1, hidden_size)
+        return torch.concat((q, k, v))
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        cla_factor = _get_cla_factor(self.config)
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+
+        num_attention_heads = self.config.num_attention_heads
+        num_kv_heads = getattr(
+            self.config, "num_key_value_heads", self.config.num_attention_heads
+        )
+        split_params_mapping = [
+            (".gate_up_proj", ".gate_and_up_proj", 2, [(1, 1), (0, 1)], None),
+            (
+                ".qkv_proj",
+                ".qkv_proj",
+                num_attention_heads + num_kv_heads * 2,
+                [("q", num_attention_heads), ("k", num_kv_heads), ("v", num_kv_heads)],
+                self._split_qkv_weight,
+            ),
+        ]
+
+        if _is_moe(self.config):
+            expert_params_mapping = FusedMoE.make_expert_params_mapping(
+                self,
+                ckpt_gate_proj_name="gate_proj",
+                ckpt_down_proj_name="down_proj",
+                ckpt_up_proj_name="up_proj",
+                num_experts=self.config.num_experts,
+            )
+        else:
+            expert_params_mapping = {}
+
+        params_dict = dict(self.named_parameters())
+
+        # V3 shared weights mapping:
+        # - embed_tokens: from main model's model.embed_tokens.weight
+        # - lm_head: from main model's lm_head.weight → MTP shared_head.head
+        #   (HF infer_mtp uses head_weight=self.lm_head.weight, not the
+        #    checkpoint's model.layers.<N>.shared_head.weight)
+        # - No norm mapping (V3 MTP has no intermediate norm before lm_head)
+        mtp_start = self.config.num_hidden_layers
+        v3_shared_weights = {
+            "model.embed_tokens.weight": "model.embed_tokens.weight",
+            "lm_head.weight": f"model.layers.{mtp_start}.shared_head.head.weight",
+        }
+
+        for name, loaded_weight in weights:
+            # Intercept shared weights before any other processing
+            if name in v3_shared_weights:
+                target_name = v3_shared_weights[name]
+                if target_name in params_dict:
+                    param = params_dict[target_name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+                continue
+
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if "gate_proj_bias" in name:
+                name = name.replace("gate_proj_bias", "gate_proj.bias")
+            if "up_proj_bias" in name:
+                name = name.replace("up_proj_bias", "up_proj.bias")
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                continue
+            if self.config.tie_word_embeddings and "lm_head.weight" in name:
+                continue
+            if self.quant_config is not None and (
+                scale_name := self.quant_config.get_cache_scale(name)
+            ):
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                loaded_weight = loaded_weight[0]
+                weight_loader(param, loaded_weight)
+                continue
+            spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
+            if spec_layer is None:
+                continue
+            name = self._rewrite_spec_layer_name(spec_layer, name)
+            # Skip weights that _rewrite_spec_layer_name marked for skipping
+            if name == "__skip__":
+                continue
+            if "scale" in name:
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+            is_found = False
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if "mlp.experts" in name:
+                    continue
+                if weight_name == ".q_proj":
+                    match = re.search(r"layers\.\d+", name)
+                    if match:
+                        layer_id = int(match.group(0).split(".")[-1])
+                        if cla_factor > 1 and layer_id % cla_factor != 0:
+                            continue
+                name = name.replace(weight_name, param_name)
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+
+                is_found = True
+                break
+            if is_found:
+                continue
+
+            for param_name, weight_name, den, split_param, func in split_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                assert loaded_weight.shape[0] % den == 0
+                units = loaded_weight.shape[0] // den
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                offset = 0
+                for shard_id, num in split_param:
+                    new_offset = offset + num * units
+                    if func:
+                        weight_loader(
+                            param, func(loaded_weight)[offset:new_offset], shard_id
+                        )
+                    else:
+                        weight_loader(param, loaded_weight[offset:new_offset], shard_id)
+                    offset = new_offset
+
+                break
+            else:
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(
+                        param,
+                        loaded_weight,
+                        name,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                    )
+                    break
+                else:
+                    if is_pp_missing_parameter(name, self):
+                        continue
+
+                    if "mlp.gate.wg." in name:
+                        name = name.replace("wg.", "")
+                    # V3 checkpoint: mlp.router.gate -> mlp.gate
+                    if "mlp.router.gate." in name:
+                        name = name.replace("router.gate.", "gate.")
+
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+
+    def _rewrite_spec_layer_name(self, spec_layer: int, name: str) -> str:
+        """Rewrite spec layer weight names to match vLLM module structure."""
+        # Skip embed_tokens (doesn't exist in V3 MTP checkpoint under spec
+        # layer) and shared_head (we use main model's lm_head instead)
+        if f"model.layers.{spec_layer}.embed_tokens" in name:
+            return "__skip__"
+        if f"model.layers.{spec_layer}.shared_head" in name:
+            return "__skip__"
+
+        spec_layer_weight_names = ["enorm", "hnorm", "eh_proj", "final_layernorm"]
+        spec_layer_weight = False
+        for weight_name in spec_layer_weight_names:
+            if weight_name in name:
+                spec_layer_weight = True
+                break
+        if not spec_layer_weight:
+            # Transformer block weights go under .mtp_block
+            name = name.replace(
+                f"model.layers.{spec_layer}.", f"model.layers.{spec_layer}.mtp_block."
+            )
+        return name
diff --git a/vllm/model_executor/models/interfaces.py b/vllm/model_executor/models/interfaces.py
index c24798e08402..7caf4c220751 100644
--- a/vllm/model_executor/models/interfaces.py
+++ b/vllm/model_executor/models/interfaces.py
@@ -29,7 +29,7 @@
 from transformers.models.whisper.tokenization_whisper import LANGUAGES
 from typing_extensions import Self, TypeIs
 
-from vllm.config import ModelConfig, SpeechToTextConfig
+from vllm.config import ModelConfig, SpeechToTextConfig, SpeechToTextParams
 from vllm.inputs import PromptType, TokensPrompt
 from vllm.logger import init_logger
 from vllm.model_executor.layers.mamba.mamba_utils import MambaStateCopyFunc
@@ -1119,13 +1119,7 @@ def __init_subclass__(cls, **kwargs):
     @classmethod
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        stt_config: SpeechToTextConfig,
-        model_config: ModelConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
         """Get the prompt for the ASR model.
         The model has control over the construction, as long as it
@@ -1531,6 +1525,12 @@ def get_input_modality(
         """Return the modality of the inputs."""
         ...
 
+    def get_max_frames_per_video(
+        self,
+    ) -> int:
+        """Return model-specific max frames per video."""
+        ...
+
     def get_encoder_cudagraph_budget_range(
         self,
         vllm_config: "VllmConfig",
diff --git a/vllm/model_executor/models/interns1_pro.py b/vllm/model_executor/models/interns1_pro.py
index 28331b8ef3e8..36f669179c53 100644
--- a/vllm/model_executor/models/interns1_pro.py
+++ b/vllm/model_executor/models/interns1_pro.py
@@ -41,7 +41,9 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -176,7 +178,6 @@ def __init__(
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=True,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
diff --git a/vllm/model_executor/models/jamba.py b/vllm/model_executor/models/jamba.py
index 980bcffb5f9b..84e96def6c1f 100644
--- a/vllm/model_executor/models/jamba.py
+++ b/vllm/model_executor/models/jamba.py
@@ -14,7 +14,10 @@
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     QKVParallelLinear,
@@ -90,7 +93,6 @@ def __init__(
             self.intermediate_size,
             tp_size=tp_size,
             params_dtype=params_dtype,
-            reduce_results=True,
             renormalize=False,
             use_grouped_topk=False,
             quant_config=quant_config,
@@ -379,7 +381,7 @@ def forward(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/jina.py b/vllm/model_executor/models/jina.py
index 980502191ddf..2b07937df08e 100644
--- a/vllm/model_executor/models/jina.py
+++ b/vllm/model_executor/models/jina.py
@@ -1,14 +1,19 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # Adapted from https://huggingface.co/jinaai/jina-reranker-v3/blob/main/modeling.py
+import json
+import logging
+from collections import defaultdict
 from collections.abc import Iterable
 
 import torch
+from safetensors.torch import load as safetensors_load
 from torch import nn
 
 from vllm.config import VllmConfig
 from vllm.sequence import IntermediateTensors
 from vllm.tasks import PoolingTask
+from vllm.transformers_utils.repo_utils import get_hf_file_bytes
 from vllm.v1.pool.metadata import PoolingMetadata
 
 from ..layers.pooler import DispatchPooler
@@ -18,9 +23,12 @@
     TokenPoolingMethodOutputItem,
 )
 from .interfaces import SupportsLateInteraction
-from .qwen3 import Qwen3Model
+from .interfaces_base import VllmModelForPooling
+from .qwen3 import Qwen3ForCausalLM, Qwen3Model
 from .utils import AutoWeightsLoader, maybe_prefix
 
+logger = logging.getLogger(__name__)
+
 
 class JinaForRanking(nn.Module, SupportsLateInteraction):
     is_pooling_model = True
@@ -108,3 +116,143 @@ def forward(
                 embeds_list.append(embeds)
 
         return embeds_list
+
+
+# jina-embeddings-v5-text-small wraps Qwen3-0.6B-Base with four task-specific
+# LoRA adapters. This implementation merges the selected adapter into the base
+# weights at load time to avoid any runtime dependency on peft.
+#
+# Task selection:
+#     Pass --hf-overrides '{"jina_task": "retrieval"}' to select one of:
+#     retrieval (default), text-matching, classification, clustering.
+
+_DEFAULT_TASK = "retrieval"
+_SUPPORTED_TASKS = {"retrieval", "text-matching", "classification", "clustering"}
+
+
+def _load_adapter(
+    model: str,
+    task: str,
+    revision: str | None,
+) -> tuple[dict, dict[str, torch.Tensor]] | None:
+    """Load adapter config and weights from a local path or HF repo.
+
+    Returns (adapter_config, adapter_weights) or None if not found.
+    """
+    config_bytes = get_hf_file_bytes(
+        f"adapters/{task}/adapter_config.json",
+        model,
+        revision,
+    )
+    if config_bytes is None:
+        return None
+
+    adapter_config = json.loads(config_bytes)
+
+    weights_bytes = get_hf_file_bytes(
+        f"adapters/{task}/adapter_model.safetensors",
+        model,
+        revision,
+    )
+    if weights_bytes is None:
+        return None
+
+    adapter_weights = safetensors_load(weights_bytes)
+    return adapter_config, adapter_weights
+
+
+def _build_lora_pairs(adapter_weights: dict) -> dict:
+    """Group raw adapter tensors into {base_key: {"A": tensor, "B": tensor}} pairs.
+
+    Transforms adapter keys like:
+        base_model.model.layers.0.self_attn.q_proj.lora_A.weight
+    Into base keys like:
+        layers.0.self_attn.q_proj.weight
+    """
+    lora_pairs = defaultdict(dict)
+    for key, tensor in adapter_weights.items():
+        clean_key = key
+        if clean_key.startswith("base_model.model."):
+            clean_key = clean_key[len("base_model.model.") :]
+
+        if ".lora_A." in clean_key:
+            base_key = clean_key.split(".lora_A.")[0] + ".weight"
+            lora_pairs[base_key]["A"] = tensor
+        elif ".lora_B." in clean_key:
+            base_key = clean_key.split(".lora_B.")[0] + ".weight"
+            lora_pairs[base_key]["B"] = tensor
+
+    return dict(lora_pairs)
+
+
+class JinaEmbeddingsV5Model(Qwen3ForCausalLM, VllmModelForPooling):
+    """Jina Embeddings V5 with task-specific LoRA adapters merged at load time.
+
+    Extends Qwen3ForCausalLM (the underlying architecture) and declares itself
+    as a pooling model so that as_embedding_model() does not wrap it.
+    """
+
+    is_pooling_model = True
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+        self._model_name = vllm_config.model_config.model
+        self._revision = vllm_config.model_config.revision
+
+        self._task = getattr(
+            vllm_config.model_config.hf_config, "jina_task", _DEFAULT_TASK
+        )
+        if self._task not in _SUPPORTED_TASKS:
+            logger.warning(
+                "Unknown jina_task=%r. Falling back to %r.",
+                self._task,
+                _DEFAULT_TASK,
+            )
+            self._task = _DEFAULT_TASK
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+        self.pooler = DispatchPooler.for_embedding(pooler_config)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        lora_pairs: dict = {}
+        scaling = 1.0
+
+        result = _load_adapter(self._model_name, self._task, self._revision)
+        if result is None:
+            logger.warning(
+                "No adapter found for task %r in %r. Loading raw base weights.",
+                self._task,
+                self._model_name,
+            )
+        else:
+            adapter_config, adapter_weights = result
+            scaling = adapter_config["lora_alpha"] / adapter_config["r"]
+            lora_pairs = _build_lora_pairs(adapter_weights)
+            logger.info(
+                "Loaded %d adapter tensors for task %r (scaling=%.4f, %d LoRA pairs)",
+                len(adapter_weights),
+                self._task,
+                scaling,
+                len(lora_pairs),
+            )
+
+        def _merge_weights(
+            weights: Iterable[tuple[str, torch.Tensor]],
+        ) -> Iterable[tuple[str, torch.Tensor]]:
+            for name, tensor in weights:
+                clean_name = name
+                if clean_name.startswith("model."):
+                    clean_name = clean_name[len("model.") :]
+
+                if clean_name in lora_pairs:
+                    pair = lora_pairs[clean_name]
+                    if "A" in pair and "B" in pair:
+                        lora_A = pair["A"].to(device=tensor.device, dtype=tensor.dtype)
+                        lora_B = pair["B"].to(device=tensor.device, dtype=tensor.dtype)
+                        tensor = tensor + (lora_B @ lora_A) * scaling
+                yield name, tensor
+
+        loaded = self.model.load_weights(_merge_weights(weights))
+        return {f"model.{name}" for name in loaded}
diff --git a/vllm/model_executor/models/keye.py b/vllm/model_executor/models/keye.py
index a987c89ae094..7166f725774f 100644
--- a/vllm/model_executor/models/keye.py
+++ b/vllm/model_executor/models/keye.py
@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import math
 from abc import abstractmethod
-from collections.abc import Iterable, Mapping, Sequence
+from collections.abc import Iterable, Iterator, Mapping, Sequence
 from functools import partial
 from typing import Annotated, Any, Literal, TypeAlias, TypeVar
 
@@ -1595,91 +1595,92 @@ def _process_video_input(
             self._process_video_embeds(video_type, video_grid_thw, pixel_values_videos)
         )
 
+    @staticmethod
+    def _split_video_grid_thw(
+        grid_thw: torch.Tensor | list[list[int]] | list[int],
+    ) -> list[list[int]]:
+        """
+        Split video grid_thw along the t dimension into per-frame rows.
+
+        This preserves Keye's current M-RoPE behavior, where a video is emitted
+        as consecutive frame-level multimodal blocks rather than a single block
+        spanning the whole video.
+        """
+        if isinstance(grid_thw, list):
+            if len(grid_thw) == 0:
+                return []
+            if isinstance(grid_thw[0], int):
+                grid_thw = torch.tensor([grid_thw], dtype=torch.long)
+            else:
+                grid_thw = torch.tensor(grid_thw, dtype=torch.long)
+        elif grid_thw.ndim == 1:
+            grid_thw = grid_thw.unsqueeze(0)
+
+        if grid_thw.numel() == 0:
+            return []
+
+        t, hw = grid_thw[:, 0], grid_thw[:, 1:]
+        ones = torch.ones_like(hw[:, :1])
+        out = torch.cat([ones, hw], dim=1).repeat_interleave(t, dim=0)
+        return out.tolist()
+
+    def iter_mm_grid_thw(
+        self, mm_features: list[MultiModalFeatureSpec]
+    ) -> Iterator[tuple[int, int, int, int]]:
+        spatial_merge_size = self.config.vision_config.spatial_merge_size
+
+        for mm_feature in sorted(mm_features, key=lambda f: f.mm_position.offset):
+            if mm_feature.data is None:
+                raise ValueError("M-RoPE calculation requires multimodal feature data")
+
+            if mm_feature.modality == "image":
+                grid_thw = mm_feature.data["image_grid_thw"].data
+                if isinstance(grid_thw, torch.Tensor):
+                    if grid_thw.ndim == 2:
+                        assert grid_thw.shape[0] == 1
+                        t, h, w = grid_thw[0].tolist()
+                    else:
+                        t, h, w = grid_thw.tolist()
+                else:
+                    if isinstance(grid_thw[0], list):
+                        assert len(grid_thw) == 1
+                        t, h, w = grid_thw[0]
+                    else:
+                        t, h, w = grid_thw
+
+                yield (
+                    mm_feature.mm_position.offset,
+                    t,
+                    h // spatial_merge_size,
+                    w // spatial_merge_size,
+                )
+            elif mm_feature.modality == "video":
+                current_offset = mm_feature.mm_position.offset
+                for t, h, w in self._split_video_grid_thw(
+                    mm_feature.data["video_grid_thw"].data
+                ):
+                    llm_grid_h = h // spatial_merge_size
+                    llm_grid_w = w // spatial_merge_size
+                    yield (current_offset, t, llm_grid_h, llm_grid_w)
+                    current_offset += t * llm_grid_h * llm_grid_w
+            else:
+                raise ValueError(f"Unsupported modality: {mm_feature.modality}")
+
     def get_mrope_input_positions(
         self,
         input_tokens: list[int],
         mm_features: list[MultiModalFeatureSpec],
     ) -> tuple[torch.Tensor, int]:
-        kwargs = MultiModalFeatureSpec.gather_kwargs(
-            mm_features,
-            {"image_grid_thw", "video_grid_thw"},
-        )
-        image_grid_thw = [item.tolist() for item in kwargs.get("image_grid_thw", [])]
-        video_grid_thw = [item.tolist() for item in kwargs.get("video_grid_thw", [])]
-
-        if isinstance(video_grid_thw, list) and len(video_grid_thw) > 0:
-            video_grid_thw = video_grid_thw[0]
-
-        def split_thw(grid_thw: torch.Tensor | list[int]) -> list[list[int]]:
-            """
-            Split grid_thw along the t dimension.
-
-            Args:
-                grid_thw: shape [N, 3] tensor or nested list of [t, h, w].
-
-            Returns:
-                List of [1, h, w] rows, repeated t times for each original row.
-            """
-
-            if isinstance(grid_thw, list):
-                grid_thw = torch.tensor(grid_thw, dtype=torch.long)
-
-            if grid_thw.numel() == 0:
-                return []
-
-            t, hw = grid_thw[:, 0], grid_thw[:, 1:]
-            ones = torch.ones_like(hw[:, :1])  # [N,1]
-            out = torch.cat([ones, hw], dim=1).repeat_interleave(t, dim=0)
-            return out.tolist()
-
-        video_grid_thw = split_thw(video_grid_thw)
-
-        hf_config = self.config
-        image_token_id = hf_config.image_token_id
-        video_token_id = hf_config.video_token_id
-        spatial_merge_size = hf_config.vision_config.spatial_merge_size
-
-        image_nums = len(image_grid_thw)
-        frame_nums = len(video_grid_thw)
         llm_pos_ids_list: list = []
-
         st = 0
-        remain_images, remain_frames = image_nums, frame_nums
-
-        image_index, video_index = 0, 0
-        for _ in range(image_nums + frame_nums):
-            if remain_images > 0:
-                try:
-                    ed_image = input_tokens.index(image_token_id, st)
-                except ValueError:
-                    ed_image = len(input_tokens) + 1
-            else:
-                ed_image = len(input_tokens) + 1
-            if remain_frames > 0:
-                try:
-                    ed_video = input_tokens.index(video_token_id, st)
-                except ValueError:
-                    ed_video = len(input_tokens) + 1
-            else:
-                ed_video = len(input_tokens) + 1
 
-            if ed_image < ed_video:
-                t, h, w = image_grid_thw[image_index]
-                image_index += 1
-                remain_images -= 1
-                ed = ed_image
-            else:
-                t, h, w = video_grid_thw[video_index]
-                video_index += 1
-                remain_frames -= 1
-                ed = ed_video
-
-            llm_grid_t, llm_grid_h, llm_grid_w = (
-                t,
-                h // spatial_merge_size,
-                w // spatial_merge_size,
-            )
-            text_len = ed - st
+        for (
+            offset,
+            llm_grid_t,
+            llm_grid_h,
+            llm_grid_w,
+        ) in self.iter_mm_grid_thw(mm_features):
+            text_len = offset - st
 
             st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
             llm_pos_ids_list.append(
@@ -1711,7 +1712,7 @@ def split_thw(grid_thw: torch.Tensor | list[int]) -> list[list[int]]:
             llm_pos_ids_list.append(
                 torch.stack([t_index, h_index, w_index]) + text_len + st_idx
             )
-            st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+            st = offset + llm_grid_t * llm_grid_h * llm_grid_w
 
         if st < len(input_tokens):
             st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
diff --git a/vllm/model_executor/models/keye_vl1_5.py b/vllm/model_executor/models/keye_vl1_5.py
index bc33f5d7d723..a8400b8d17ee 100644
--- a/vllm/model_executor/models/keye_vl1_5.py
+++ b/vllm/model_executor/models/keye_vl1_5.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import itertools
-from collections.abc import Mapping, Sequence
+from collections.abc import Iterator, Mapping, Sequence
 from functools import partial
 from typing import Annotated, Any, Literal, TypeAlias
 
@@ -608,91 +608,67 @@ def _process_video_input(
             new_video_embeds.append(video_embeds[start:end])
         return tuple(new_video_embeds)
 
+    def iter_mm_grid_thw(
+        self, mm_features: list[MultiModalFeatureSpec]
+    ) -> Iterator[tuple[int, int, int, int]]:
+        spatial_merge_size = self.config.vision_config.spatial_merge_size
+
+        for mm_feature in sorted(mm_features, key=lambda f: f.mm_position.offset):
+            if mm_feature.data is None:
+                raise ValueError("M-RoPE calculation requires multimodal feature data")
+
+            embed_ranges = mm_feature.mm_position.extract_embeds_range()
+            if mm_feature.modality == "image":
+                assert len(embed_ranges) == 1
+                grid_thw = mm_feature.data["image_grid_thw"].data
+                if isinstance(grid_thw, torch.Tensor):
+                    if grid_thw.ndim == 2:
+                        assert grid_thw.shape[0] == 1
+                        t, h, w = grid_thw[0].tolist()
+                    else:
+                        t, h, w = grid_thw.tolist()
+                else:
+                    if isinstance(grid_thw[0], list):
+                        assert len(grid_thw) == 1
+                        t, h, w = grid_thw[0]
+                    else:
+                        t, h, w = grid_thw
+
+                yield (
+                    embed_ranges[0][0],
+                    t,
+                    h // spatial_merge_size,
+                    w // spatial_merge_size,
+                )
+            elif mm_feature.modality == "video":
+                split_video_grids = split_thw(mm_feature.data["video_grid_thw"].data)
+                assert len(embed_ranges) == split_video_grids.shape[0]
+                for (start_idx, end_idx), (t, h, w) in zip(
+                    embed_ranges, split_video_grids.tolist()
+                ):
+                    llm_grid_h = h // spatial_merge_size
+                    llm_grid_w = w // spatial_merge_size
+                    num_mm_tokens = t * llm_grid_h * llm_grid_w
+                    assert end_idx - start_idx + 1 == num_mm_tokens
+                    yield (start_idx, t, llm_grid_h, llm_grid_w)
+            else:
+                raise ValueError(f"Unsupported modality: {mm_feature.modality}")
+
     def get_mrope_input_positions(
         self,
         input_tokens: list[int],
         mm_features: list[MultiModalFeatureSpec],
     ) -> tuple[torch.Tensor, int]:
-        kwargs = MultiModalFeatureSpec.gather_kwargs(
-            mm_features,
-            {"image_grid_thw", "video_grid_thw"},
-        )
-        image_grid_thw = [item.tolist() for item in kwargs.get("image_grid_thw", [])]
-        video_grid_thw = [item.tolist() for item in kwargs.get("video_grid_thw", [])]
-
-        if isinstance(video_grid_thw, list) and len(video_grid_thw) > 0:
-            video_grid_thw = video_grid_thw[0]
-
-        def split_thw(grid_thw: torch.Tensor | list[int]) -> list[list[int]]:
-            """
-            Split grid_thw along the t dimension.
-
-            Args:
-                grid_thw: shape [N, 3] tensor or nested list of [t, h, w].
-
-            Returns:
-                List of [1, h, w] rows, repeated t times for each original row.
-            """
-
-            if isinstance(grid_thw, list):
-                grid_thw = torch.tensor(grid_thw, dtype=torch.long)
-
-            if grid_thw.numel() == 0:
-                return []
-
-            t, hw = grid_thw[:, 0], grid_thw[:, 1:]
-            ones = torch.ones_like(hw[:, :1])  # [N,1]
-            out = torch.cat([ones, hw], dim=1).repeat_interleave(t, dim=0)
-            return out.tolist()
-
-        video_grid_thw = split_thw(video_grid_thw)
-
-        hf_config = self.config
-        image_token_id = hf_config.image_token_id
-        video_token_id = hf_config.video_token_id
-        spatial_merge_size = hf_config.vision_config.spatial_merge_size
-
-        image_nums = len(image_grid_thw)
-        frame_nums = len(video_grid_thw)
         llm_pos_ids_list: list = []
-
         st = 0
-        remain_images, remain_frames = image_nums, frame_nums
-
-        image_index, video_index = 0, 0
-        for _ in range(image_nums + frame_nums):
-            if remain_images > 0:
-                try:
-                    ed_image = input_tokens.index(image_token_id, st)
-                except ValueError:
-                    ed_image = len(input_tokens) + 1
-            else:
-                ed_image = len(input_tokens) + 1
-            if remain_frames > 0:
-                try:
-                    ed_video = input_tokens.index(video_token_id, st)
-                except ValueError:
-                    ed_video = len(input_tokens) + 1
-            else:
-                ed_video = len(input_tokens) + 1
 
-            if ed_image < ed_video:
-                t, h, w = image_grid_thw[image_index]
-                image_index += 1
-                remain_images -= 1
-                ed = ed_image
-            else:
-                t, h, w = video_grid_thw[video_index]
-                video_index += 1
-                remain_frames -= 1
-                ed = ed_video
-
-            llm_grid_t, llm_grid_h, llm_grid_w = (
-                t,
-                h // spatial_merge_size,
-                w // spatial_merge_size,
-            )
-            text_len = ed - st
+        for (
+            offset,
+            llm_grid_t,
+            llm_grid_h,
+            llm_grid_w,
+        ) in self.iter_mm_grid_thw(mm_features):
+            text_len = offset - st
 
             st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
             llm_pos_ids_list.append(
@@ -724,7 +700,7 @@ def split_thw(grid_thw: torch.Tensor | list[int]) -> list[list[int]]:
             llm_pos_ids_list.append(
                 torch.stack([t_index, h_index, w_index]) + text_len + st_idx
             )
-            st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+            st = offset + llm_grid_t * llm_grid_h * llm_grid_w
 
         if st < len(input_tokens):
             st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
diff --git a/vllm/model_executor/models/kimi_audio.py b/vllm/model_executor/models/kimi_audio.py
index fc5065065e96..475661e0ebad 100644
--- a/vllm/model_executor/models/kimi_audio.py
+++ b/vllm/model_executor/models/kimi_audio.py
@@ -14,6 +14,7 @@
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.inputs import PromptType, TokensPrompt
 from vllm.model_executor.model_loader import DefaultModelLoader
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@@ -626,16 +627,12 @@ def get_speech_to_text_config(
         )
 
     @classmethod
-    def get_generation_prompt(
-        cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
-    ) -> PromptType:
+    def get_generation_prompt(cls, stt_params: SpeechToTextParams) -> PromptType:
+        audio = stt_params.audio
+        model_config = stt_params.model_config
+        task_type = stt_params.task_type
+        request_prompt = stt_params.request_prompt
+
         tokenizer = cached_get_tokenizer(
             model_config.tokenizer,
             tokenizer_cls=KimiAudioTokenizer,
diff --git a/vllm/model_executor/models/kimi_linear.py b/vllm/model_executor/models/kimi_linear.py
index 4cd7b63c1472..29d827d196f1 100644
--- a/vllm/model_executor/models/kimi_linear.py
+++ b/vllm/model_executor/models/kimi_linear.py
@@ -11,11 +11,13 @@
 from vllm.distributed import (
     get_pp_group,
     get_tensor_model_parallel_world_size,
-    tensor_model_parallel_all_reduce,
 )
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.kda import KimiDeltaAttention
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
@@ -132,12 +134,25 @@ def __init__(
 
         self.gate.e_score_correction_bias = nn.Parameter(torch.empty(num_experts))
 
+        if self.num_shared_experts is not None:
+            intermediate_size = moe_intermediate_size * self.num_shared_experts
+            self.shared_experts = KimiMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=False,
+                prefix=f"{prefix}.shared_experts",
+            )
+        else:
+            self.shared_experts = None
+
         self.experts = FusedMoE(
+            shared_experts=self.shared_experts,
             num_experts=num_experts,
             top_k=config.num_experts_per_token,
             hidden_size=hidden_size,
             intermediate_size=moe_intermediate_size,
-            reduce_results=False,
             renormalize=moe_renormalize,
             quant_config=quant_config,
             use_grouped_topk=config.use_grouped_topk,
@@ -146,34 +161,16 @@ def __init__(
             prefix=f"{prefix}.experts",
             scoring_func=config.moe_router_activation_func,
             e_score_correction_bias=self.gate.e_score_correction_bias,
+            routed_scaling_factor=self.routed_scaling_factor,
         )
 
-        if self.num_shared_experts is not None:
-            intermediate_size = moe_intermediate_size * self.num_shared_experts
-            self.shared_experts = KimiMLP(
-                hidden_size=config.hidden_size,
-                intermediate_size=intermediate_size,
-                hidden_act=config.hidden_act,
-                quant_config=quant_config,
-                reduce_results=False,
-                prefix=f"{prefix}.shared_experts",
-            )
-
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         num_tokens, hidden_size = hidden_states.shape
         hidden_states = hidden_states.view(-1, hidden_size)
-        if self.num_shared_experts is not None:
-            shared_output = self.shared_experts(hidden_states)
         router_logits, _ = self.gate(hidden_states)
-        final_hidden_states = (
-            self.experts(hidden_states=hidden_states, router_logits=router_logits)
-            * self.routed_scaling_factor
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states, router_logits=router_logits
         )
-        if shared_output is not None:
-            final_hidden_states = final_hidden_states + shared_output
-
-        if self.tp_size > 1:
-            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
         return final_hidden_states.view(num_tokens, hidden_size)
 
 
@@ -482,7 +479,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         if self.config.is_moe:
             # Params for weights, fp8 weight scales, fp8 activation scales
             # (param_name, weight_name, expert_id, shard_id)
-            expert_params_mapping = FusedMoE.make_expert_params_mapping(
+            expert_params_mapping = fused_moe_make_expert_params_mapping(
                 self,
                 ckpt_gate_proj_name="w1",
                 ckpt_down_proj_name="w2",
diff --git a/vllm/model_executor/models/laguna.py b/vllm/model_executor/models/laguna.py
new file mode 100644
index 000000000000..5bf5b7cb021e
--- /dev/null
+++ b/vllm/model_executor/models/laguna.py
@@ -0,0 +1,902 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only Laguna model compatible with HuggingFace weights."""
+
+import typing
+from collections.abc import Callable, Iterable
+from itertools import islice
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
+from vllm.distributed import (
+    get_ep_group,
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.attention import Attention
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from vllm.model_executor.models.interfaces import (
+    EagleModelMixin,
+    SupportsEagle3,
+    SupportsLoRA,
+    SupportsPP,
+)
+from vllm.model_executor.models.utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    extract_layer_index,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+from vllm.sequence import IntermediateTensors
+
+logger = init_logger(__name__)
+
+
+class LagunaMLP(nn.Module):
+    """Dense MLP for Laguna (used in mlp_only_layers)."""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: QuantizationConfig | None = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        # gate_proj and up_proj are kept as separate ColumnParallelLinear
+        # rather than merged via MergedColumnParallelLinear. The merged form
+        # requires per-partition NVFP4 global scales (weight_global_scale,
+        # input_global_scale) to be packed into a length-2 PerTensorScaleParameter
+        # and then collapsed via .max() in process_weights_after_loading; this
+        # doesn't round-trip cleanly through Marlin's NVFP4 stacked-layer code
+        # path. Splitting yields one global scale per Linear, exactly matching
+        # the standard compressed-tensors per-Linear schema on disk.
+        self.gate_proj = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_proj",
+        )
+        self.up_proj = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. Only silu is supported."
+            )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate, _ = self.gate_proj(x)
+        up, _ = self.up_proj(x)
+        x, _ = self.down_proj(F.silu(gate) * up)
+        return x
+
+
+class LagunaMoE(nn.Module):
+    """Sparse MoE block for Laguna with optional shared expert and sigmoid routing.
+
+    Key differences from other MoE implementations:
+    - Uses SIGMOID routing activation (not softmax)
+    - Shared expert runs in parallel with routed experts (when enabled)
+    - Matches HF reference: modular_laguna.py LagunaSparseMoeBlock
+    """
+
+    def __init__(
+        self,
+        config,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        enable_eplb: bool = False,
+    ):
+        super().__init__()
+        self.config = config
+        self.num_experts = config.num_experts
+        self.top_k = config.num_experts_per_tok
+
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.ep_group = get_ep_group().device_group
+        self.ep_rank = self.ep_group.rank()
+        self.ep_size = self.ep_group.size()
+
+        self.n_routed_experts = config.num_experts
+        self.n_shared_experts = 1 if config.shared_expert_intermediate_size > 0 else 0
+        self.routed_scaling_factor = float(
+            getattr(config, "moe_routed_scaling_factor", 1.0)
+        )
+
+        if self.tp_size > config.num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.num_experts}."
+            )
+
+        # Load balancing settings.
+        vllm_config = get_current_vllm_config()
+        eplb_config = vllm_config.parallel_config.eplb_config
+        self.enable_eplb = enable_eplb
+        eplb_config.num_redundant_experts = (
+            eplb_config.num_redundant_experts
+            if eplb_config.num_redundant_experts is not None
+            else 0
+        )
+        self.n_redundant_experts = eplb_config.num_redundant_experts
+        self.n_logical_experts = self.n_routed_experts
+        self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
+        self.n_local_physical_experts = self.n_physical_experts // self.ep_size
+        self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
+        self.physical_expert_end = (
+            self.physical_expert_start + self.n_local_physical_experts
+        )
+
+        # Router gate
+        self.gate = ReplicatedLinear(
+            config.hidden_size,
+            config.num_experts,
+            bias=False,
+            quant_config=None,
+            prefix=f"{prefix}.gate",
+        )
+
+        # Shared expert (optional) - passed to FusedMoE for overlap optimization
+        self.shared_expert: LagunaMLP | None
+        if config.shared_expert_intermediate_size > 0:
+            self.shared_expert = LagunaMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.shared_expert_intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=False,  # Reduce after shared+routed combine
+                prefix=f"{prefix}.shared_expert",
+            )
+        else:
+            self.shared_expert = None
+
+        # Auxiliary-loss-free load-balancing bias (arXiv:2408.15664). The
+        # checkpoint stores one [num_experts] tensor per MoE layer at
+        # `mlp.experts.e_score_correction_bias`; registering it as a Parameter
+        # on the FusedMoE lets the weight loader pick it up and the router
+        # add it during top-k selection. The fused top-k bias router requires
+        # float32 regardless of model dtype.
+        e_score_correction_bias = torch.nn.Parameter(
+            torch.zeros(config.num_experts, dtype=torch.float32),
+            requires_grad=False,
+        )
+
+        # FusedMoE with SIGMOID routing. Passing `shared_experts=` lets the
+        # layer overlap the shared-expert compute with the all2all dispatch.
+        # `apply_routed_scale_to_output=True` makes FusedMoE handle the
+        # routed_scaling_factor, shared+routed combine, and TP all-reduce
+        # internally, so forward() just returns the final hidden states.
+        self.experts = FusedMoE(
+            shared_experts=self.shared_expert,
+            num_experts=config.num_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            renormalize=config.norm_topk_prob,
+            quant_config=quant_config,
+            prefix=f"{prefix}.experts",
+            scoring_func="sigmoid",
+            use_grouped_topk=False,
+            apply_router_weight_on_input=bool(config.moe_apply_router_weight_on_input),
+            e_score_correction_bias=e_score_correction_bias,
+            enable_eplb=self.enable_eplb,
+            num_redundant_experts=self.n_redundant_experts,
+            routed_scaling_factor=self.routed_scaling_factor,
+            apply_routed_scale_to_output=True,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        orig_shape = hidden_states.shape
+        hidden_dim = hidden_states.shape[-1]
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        router_logits, _ = self.gate(hidden_states)
+        router_logits = router_logits.float()
+        softcap = getattr(self.config, "moe_router_logit_softcapping", 0.0) or 0.0
+        if softcap > 0.0:
+            router_logits = torch.tanh(router_logits / softcap) * softcap
+
+        final_hidden_states = self.experts(hidden_states, router_logits)
+        return final_hidden_states.view(orig_shape)
+
+
+class LagunaAttention(nn.Module):
+    """Laguna attention with optional softplus output gating.
+
+    Supports per-layer sliding window attention when ``config.layer_types``
+    is present.  Layers whose type is ``"sliding_attention"`` use
+    ``config.sliding_window``; all other layers (typically labelled
+    ``"full_attention"``) use full attention.  When ``layer_types`` is
+    absent every layer defaults to full attention for backwards
+    compatibility.
+    """
+
+    def __init__(
+        self,
+        config,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        max_position_embeddings: int = 131072,
+        head_dim: int | None = None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        attention_sink: bool = False,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim or (hidden_size // self.total_num_heads)
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.max_position_embeddings = max_position_embeddings
+
+        # Gating flag
+        self.gating = config.gating
+
+        # Per-layer sliding window (follows Gemma2/Cohere2 convention)
+        layer_types = getattr(config, "layer_types", None)
+        if layer_types is not None:
+            layer_idx = extract_layer_index(prefix)
+            is_sliding = layer_types[layer_idx] == "sliding_attention"
+            self.sliding_window = config.sliding_window if is_sliding else None
+        else:
+            self.sliding_window = None
+
+        # QKV projection (no bias for Laguna)
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=config.qkv_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        # Output projection
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        # Gating projection (Laguna-specific, optional)
+        # config.gating may be:
+        #   - True / "per-element": one gate per (head, head_dim) channel
+        #   - "per-head":           one gate per head, broadcast across head_dim
+        if self.gating:
+            # v5 LagunaConfig uses ``gating=True`` for per-head; older configs
+            # used ``"per-head"``. Accept both. ``"per-element"`` (or legacy
+            # ``True``) means per-element gating with output size num_heads ×
+            # head_dim.
+            gate_per_head = self.gating is True or self.gating == "per-head"
+            g_out = (
+                self.total_num_heads
+                if gate_per_head
+                else self.total_num_heads * self.head_dim
+            )
+            self.g_proj = ColumnParallelLinear(
+                hidden_size,
+                g_out,
+                bias=False,
+                quant_config=quant_config,
+                prefix=f"{prefix}.g_proj",
+            )
+            self.gate_per_head = gate_per_head
+        else:
+            self.g_proj = None
+            self.gate_per_head = False
+
+        # Attention sinks (learnable per-head bias for SWA layers)
+        sinks = None
+        if attention_sink:
+            self.sink = torch.nn.Parameter(
+                torch.empty(self.total_num_heads // tp_size, requires_grad=False)
+            )
+            sinks = self.sink
+
+        # Resolve rope params per-layer-type. ``config.rope_parameters`` is
+        # either a flat dict (legacy) or a nested ``{layer_type: rope_dict}``
+        # (v5 Laguna-XS schema). The v5 form is unhashable as-is and would
+        # crash `get_rope`'s cache lookup, so always pull out the layer's
+        # sub-dict before forwarding.
+        layer_type = (
+            layer_types[extract_layer_index(prefix)]
+            if layer_types is not None
+            else "full_attention"
+        )
+        is_sliding = layer_type == "sliding_attention"
+
+        top_rope = getattr(config, "rope_parameters", None) or {}
+        if any(isinstance(v, dict) for v in top_rope.values()):
+            # Nested per-layer-type form.
+            base_rope = top_rope.get(layer_type) or top_rope.get("full_attention") or {}
+        else:
+            base_rope = top_rope
+
+        # Older flat-rope ckpts can carry a separate `swa_rope_parameters`
+        # for SWA layers. Prefer it when present; otherwise the nested
+        # rope dict above already supplies the correct sub-config.
+        swa_rope = getattr(config, "swa_rope_parameters", None)
+        if (
+            is_sliding
+            and swa_rope is None
+            and not any(isinstance(v, dict) for v in top_rope.values())
+        ):
+            logger.warning_once(
+                "Laguna config has sliding_attention layers but neither "
+                "`swa_rope_parameters` nor a nested per-layer-type "
+                "`rope_parameters` — SWA layers will reuse the global rope. "
+                "If the checkpoint was trained with distinct SWA rope "
+                "(theta / partial_rotary_factor), regenerate its HF config "
+                "to include either form."
+            )
+        rope_params = swa_rope if (is_sliding and swa_rope is not None) else base_rope
+        # `partial_rotary_factor` may live on the top-level config (main attention)
+        # or on the per-layer rope dict itself (e.g. SWA can differ). Inject the
+        # top-level value into `rope_params` if the dict doesn't already set it.
+        top_partial = getattr(config, "partial_rotary_factor", None)
+        if top_partial is not None and "partial_rotary_factor" not in rope_params:
+            rope_params = {**rope_params, "partial_rotary_factor": top_partial}
+
+        # Rotary embeddings (YaRN)
+        self.rotary_emb = get_rope(
+            head_size=self.head_dim,
+            max_position=max_position_embeddings,
+            is_neox_style=True,
+            rope_parameters=rope_params,
+        )
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            per_layer_sliding_window=self.sliding_window,
+            prefix=f"{prefix}.attn",
+            sinks=sinks,
+        )
+
+        # QK normalization (like Qwen3)
+        self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+        q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim)
+        q_by_head = self.q_norm(q_by_head)
+        q = q_by_head.view(q.shape)
+
+        k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim)
+        k_by_head = self.k_norm(k_by_head)
+        k = k_by_head.view(k.shape)
+
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+
+        # Apply gating if enabled (compute softplus in float32 for precision)
+        if self.gating and self.g_proj is not None:
+            gate, _ = self.g_proj(hidden_states)
+            gate = F.softplus(gate.float()).type_as(attn_output)
+            if self.gate_per_head:
+                # gate: [..., num_heads]; broadcast across head_dim
+                attn_shape = attn_output.shape
+                attn_output = (
+                    attn_output.view(*attn_shape[:-1], self.num_heads, self.head_dim)
+                    * gate.unsqueeze(-1)
+                ).view(attn_shape)
+            else:
+                attn_output = attn_output * gate
+
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class LagunaDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        enable_eplb: bool = False,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        layer_idx = extract_layer_index(prefix)
+
+        # Determine if this layer uses sliding window attention
+        layer_types = getattr(config, "layer_types", None)
+        is_sliding = (
+            layer_types is not None and layer_types[layer_idx] == "sliding_attention"
+        )
+
+        # Enable attention sinks on SWA layers when configured
+        attention_sink = is_sliding and getattr(
+            config, "swa_attention_sink_enabled", False
+        )
+
+        # Optional per-layer override of head count (Laguna-XS).
+        per_layer_heads = getattr(config, "num_attention_heads_per_layer", None)
+        layer_num_heads = (
+            per_layer_heads[layer_idx]
+            if per_layer_heads is not None
+            else config.num_attention_heads
+        )
+
+        self.self_attn = LagunaAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=layer_num_heads,
+            num_kv_heads=config.num_key_value_heads,
+            max_position_embeddings=config.max_position_embeddings,
+            head_dim=getattr(config, "head_dim", None),
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+            attention_sink=attention_sink,
+        )
+
+        # Check if this layer uses MoE or dense MLP (matches Qwen2/Qwen3 convention)
+        mlp_only_layers = (
+            [] if not hasattr(config, "mlp_only_layers") else config.mlp_only_layers
+        )
+        self.is_moe_layer = (
+            (layer_idx not in mlp_only_layers)
+            and (config.num_experts > 0)
+            and ((layer_idx + 1) % config.decoder_sparse_step == 0)
+        )
+
+        if self.is_moe_layer:
+            self.mlp = LagunaMoE(
+                config=config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+                enable_eplb=enable_eplb,
+            )
+        else:
+            self.mlp = LagunaMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+            )
+
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+
+        return hidden_states, residual
+
+
+@support_torch_compile
+class LagunaModel(nn.Module, EagleModelMixin):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        enable_eplb = vllm_config.parallel_config.enable_eplb
+        eplb_config = vllm_config.parallel_config.eplb_config
+        self.num_redundant_experts = eplb_config.num_redundant_experts
+        self.config = config
+        self.quant_config = quant_config
+
+        # Disable the model-level sliding-window fallback in Attention.__init__.
+        # Laguna drives SWA per-layer via `layer_types`, passing
+        # `per_layer_sliding_window=self.sliding_window` (None for global
+        # layers). Without this, global layers whose `per_layer_sliding_window`
+        # is None would pick up `cache_config.sliding_window`
+        # (populated from `config.sliding_window`) as a fallback, silently
+        # applying a 512-token window to full-attention layers.
+        if cache_config is not None:
+            cache_config.sliding_window = None
+
+        self.vocab_size = config.vocab_size
+
+        if get_pp_group().is_first_rank or (
+            config.tie_word_embeddings and get_pp_group().is_last_rank
+        ):
+            self.embed_tokens = VocabParallelEmbedding(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=f"{prefix}.embed_tokens",
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: LagunaDecoderLayer(
+                config=config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=prefix,
+                enable_eplb=enable_eplb,
+            ),
+            prefix=f"{prefix}.layers",
+        )
+
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_tokens(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        aux_hidden_states = self._maybe_add_hidden_state(
+            [], self.start_layer, hidden_states, residual
+        )
+        for layer_idx, layer in enumerate(
+            islice(self.layers, self.start_layer, self.end_layer),
+            start=self.start_layer,
+        ):
+            hidden_states, residual = layer(positions, hidden_states, residual)
+            self._maybe_add_hidden_state(
+                aux_hidden_states, layer_idx + 1, hidden_states, residual
+            )
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        if len(aux_hidden_states) > 0:
+            return hidden_states, aux_hidden_states
+        return hidden_states
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        """Get expert parameter mapping for weight loading.
+
+        Returns mapping tuples of (param_name, weight_name, expert_id, shard_id)
+        that handle both weights and quantization scales.
+        """
+        return FusedMoE.make_expert_params_mapping(
+            self,
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_experts,
+            num_redundant_experts=self.num_redundant_experts,
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            # gate_proj and up_proj are loaded as separate Linears (see
+            # LagunaMLP) so no merge entry is needed here.
+        ]
+
+        # Suffixes to skip for GPTQ/modelopt models if param doesn't exist
+        ignore_suffixes = (
+            ".bias",
+            "_bias",
+            ".k_scale",
+            "_k_scale",
+            ".v_scale",
+            "_v_scale",
+            ".weight_scale",
+            "_weight_scale",
+            ".input_scale",
+            "_input_scale",
+        )
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
+
+        tp_rank = get_tensor_model_parallel_rank()
+
+        for name, loaded_weight in weights:
+            # Handle attention sinks (distributed across ranks). Derive the
+            # per-rank slice from the parameter's own shape so per-layer
+            # variations in head count are handled correctly.
+            if "sink" in name:
+                param = params_dict.get(name)
+                if param is not None:
+                    layer_heads_per_rank = param.shape[0]
+                    layer_head_start = tp_rank * layer_heads_per_rank
+                    narrow_weight = loaded_weight.narrow(
+                        0, layer_head_start, layer_heads_per_rank
+                    )
+                    param.data.copy_(narrow_weight)
+                    loaded_params.add(name)
+                continue
+
+            # Handle KV cache quantization scales
+            if self.quant_config is not None and (
+                scale_name := self.quant_config.get_cache_scale(name)
+            ):
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                assert loaded_weight.numel() == 1, (
+                    f"KV scale numel {loaded_weight.numel()} != 1"
+                )
+                loaded_weight = loaded_weight.squeeze()
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
+            # Handle stacked params (QKV, gate_up for
+            # non-expert layers and shared_expert)
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                # Skip expert weights - handled below via expert_params_mapping
+                if "mlp.experts" in name and "shared_expert" not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                if name.endswith(ignore_suffixes) and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                # Remap FP8 kv_scale names for backwards compatibility
+                if name.endswith("scale"):
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+                if name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                if weight_loader == default_weight_loader:
+                    weight_loader(param, loaded_weight)
+                else:
+                    weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(name)
+                break
+            else:
+                # Try expert params mapping (handles weights + quantization scales)
+                is_expert_weight = False
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+
+                    # Mark as expert weight so we skip regular loading below
+                    is_expert_weight = True
+
+                    # Create mapped name without modifying original
+                    name_mapped = name.replace(weight_name, param_name)
+
+                    if is_pp_missing_parameter(name_mapped, self):
+                        continue
+                    if (
+                        name_mapped.endswith(ignore_suffixes)
+                        and name_mapped not in params_dict
+                    ):
+                        continue
+                    if name_mapped not in params_dict:
+                        continue
+
+                    param = params_dict[name_mapped]
+                    # Use return_success to handle expert parallelism correctly
+                    weight_loader = typing.cast(
+                        Callable[..., bool], param.weight_loader
+                    )
+                    success = weight_loader(
+                        param,
+                        loaded_weight,
+                        name_mapped,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                        return_success=True,
+                    )
+                    if success:
+                        loaded_params.add(name_mapped)
+                        break
+                else:
+                    # Expert weight not mapped to this rank - skip
+                    if is_expert_weight:
+                        continue
+
+                    # Remap kv_scale names before the ignore_suffixes filter:
+                    # the suffix list includes .k_scale/.v_scale, so filtering
+                    # first drops the checkpoint key before remap can rewrite
+                    # it to the .attn.* name that exists in params_dict.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+
+                    if name.endswith(ignore_suffixes) and name not in params_dict:
+                        continue
+
+                    if is_pp_missing_parameter(name, self):
+                        continue
+
+                    if name not in params_dict:
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+                    loaded_params.add(name)
+
+        return loaded_params
+
+
+class LagunaForCausalLM(nn.Module, SupportsPP, SupportsLoRA, SupportsEagle3):
+    fall_back_to_pt_during_load = False
+
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+
+        self.model = LagunaModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+            if self.config.tie_word_embeddings:
+                self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
diff --git a/vllm/model_executor/models/lfm2_moe.py b/vllm/model_executor/models/lfm2_moe.py
index d955b7127adc..55b00d2b9ea2 100644
--- a/vllm/model_executor/models/lfm2_moe.py
+++ b/vllm/model_executor/models/lfm2_moe.py
@@ -15,7 +15,10 @@
 )
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -150,7 +153,6 @@ def __init__(
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             use_grouped_topk=True,  # needed for softmax score func
@@ -161,6 +163,7 @@ def __init__(
             num_redundant_experts=self.n_redundant_experts,
             scoring_func="sigmoid",
             e_score_correction_bias=self.gate.e_score_correction_bias,
+            routed_scaling_factor=self.routed_scaling_factor,
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -170,16 +173,10 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
 
         # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
-        final_hidden_states = (
-            self.experts(hidden_states=hidden_states, router_logits=router_logits)
-            * self.routed_scaling_factor
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states, router_logits=router_logits
         )
 
-        if self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(  # noqa E501
-                final_hidden_states
-            )
-
         return final_hidden_states.view(orig_shape)
 
 
@@ -488,7 +485,7 @@ def forward(
         return hidden_states
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="w1",
             ckpt_down_proj_name="w2",
diff --git a/vllm/model_executor/models/llama.py b/vllm/model_executor/models/llama.py
index 2ecced3df8ba..3c797d05e932 100644
--- a/vllm/model_executor/models/llama.py
+++ b/vllm/model_executor/models/llama.py
@@ -337,20 +337,15 @@ def get_quant_config(self, vllm_config: VllmConfig) -> QuantizationConfig | None
         return vllm_config.quant_config
 
 
-def llama_model_invariants(
-    input_ids, positions, intermediate_tensors=None, inputs_embeds=None
-):
-    """Shape invariants for Llama model compilation, those are translated to
-    runtime assertions for unbacked dynamic shapes and are compiled away for
-    backed"""
-    if input_ids is not None:
-        torch._check(positions.size()[0] == input_ids.size()[0])
-
-
 @support_torch_compile(
     # TODO[#32068]: Investigate recompilation
     # mark_unbacked_dims={"input_ids": 0},
-    shape_invariants=llama_model_invariants
+    dynamic_arg_dims={
+        "input_ids": {0: "b"},
+        "positions": {0: "b"},
+        "intermediate_tensors": {0: "b"},
+        "inputs_embeds": {0: "b"},
+    },
 )
 class LlamaModel(nn.Module, EagleModelMixin):
     def __init__(
diff --git a/vllm/model_executor/models/llama4.py b/vllm/model_executor/models/llama4.py
index b84b4e2ae512..bfcb72a6a744 100644
--- a/vllm/model_executor/models/llama4.py
+++ b/vllm/model_executor/models/llama4.py
@@ -36,7 +36,10 @@
     Attention,
     ChunkedLocalAttention,
 )
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     QKVParallelLinear,
@@ -127,7 +130,7 @@ def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
         self.n_physical_experts = self.n_local_experts + self.n_redundant_experts
         self.n_local_physical_experts = self.n_physical_experts // self.ep_size
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_expert,
             num_experts=config.num_local_experts,
             top_k=config.num_experts_per_tok,
@@ -135,7 +138,6 @@ def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
             custom_routing_function=Llama4MoE.custom_routing_function,
             intermediate_size=intermediate_size_moe,
             apply_router_weight_on_input=True,
-            reduce_results=False,
             renormalize=False,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -151,19 +153,14 @@ def forward(self, hidden_states):
 
         router_logits, _ = self.router(hidden_states)
 
-        shared_out, routed_out = self.experts(
+        experts_out = self.experts(
             hidden_states=hidden_states,
             router_logits=router_logits,
         )
-        experts_out = routed_out + shared_out
 
         if self.is_sequence_parallel:
             experts_out = tensor_model_parallel_all_gather(experts_out, 0)
             experts_out = experts_out[:num_tokens]
-        elif self.tp_size > 1:
-            experts_out = self.experts.maybe_all_reduce_tensor_model_parallel(
-                experts_out
-            )
 
         return experts_out
 
@@ -420,7 +417,7 @@ def load_moe_expert_weights(
             params_dict: The dictionary of module parameters.
             loaded_params: The set of already loaded parameters.
             expert_params_mapping: The mapping of expert parameters. Must be
-                generated by SharedFusedMoE.make_expert_params_mapping().
+                generated by fused_moe_make_expert_params_mapping().
             fused: Whether the expert weights are fused into a single weight
                 tensor or are separate weight tensors for each expert.
                 When fused is True, loaded_weight should have shape of:
@@ -560,7 +557,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         fused_experts_params = False
         # Expert parameter mapping for the case where the expert weights are
         # not fused into a single weight tensor.
-        expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -570,7 +567,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         )
         # Expert parameter mapping for the case where the expert weights are
         # fused into a single weight tensor.
-        expert_params_mapping_fused = SharedFusedMoE.make_expert_params_mapping(
+        expert_params_mapping_fused = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_up_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/longcat_flash.py b/vllm/model_executor/models/longcat_flash.py
index 375b0b69b1f9..3dd1118aa8a4 100644
--- a/vllm/model_executor/models/longcat_flash.py
+++ b/vllm/model_executor/models/longcat_flash.py
@@ -46,7 +46,10 @@
 from vllm.distributed import get_pp_group
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -66,6 +69,7 @@
 
 from .interfaces import SupportsLoRA, SupportsPP
 from .utils import (
+    AutoWeightsLoader,
     PPMissingLayer,
     is_pp_missing_parameter,
     make_empty_intermediate_tensors_factory,
@@ -300,7 +304,6 @@ def __init__(
             top_k=top_k,
             hidden_size=hidden_size,
             intermediate_size=intermediate_size,
-            reduce_results=True,
             params_dtype=params_dtype,
             renormalize=False,
             quant_config=quant_config,
@@ -483,6 +486,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         self.config = config
+        self.quant_config = quant_config
 
         self.vocab_size = config.vocab_size
 
@@ -549,81 +553,10 @@ def forward(
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
-
-class LongcatFlashForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
-    """Flash model for causal language modeling."""
-
-    packed_modules_mapping = {
-        "qkv_proj": [
-            "q_proj",
-            "k_proj",
-            "v_proj",
-        ],
-        "gate_up_proj": [
-            "gate_proj",
-            "up_proj",
-        ],
-    }
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-        config = FlashConfig(**vllm_config.model_config.hf_config.__dict__)
-        quant_config = vllm_config.quant_config
-
-        self.config = config
-        config.intermediate_size = (
-            config.ffn_hidden_size
-            if hasattr(config, "ffn_hidden_size")
-            else config.intermediate_size
-        )
-
-        self.quant_config = quant_config
-
-        self.model = FlashModel(
-            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
-        )
-
-        if get_pp_group().is_last_rank:
-            self.lm_head = ParallelLMHead(
-                config.vocab_size,
-                config.hidden_size,
-                quant_config=quant_config,
-                prefix=maybe_prefix(prefix, "lm_head"),
-            )
-        else:
-            self.lm_head = PPMissingLayer()
-
-        self.logits_processor = LogitsProcessor(config.vocab_size)
-        self.make_empty_intermediate_tensors = (
-            self.model.make_empty_intermediate_tensors
-        )
-
-    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.embed_input_ids(input_ids)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor | None,
-        positions: torch.Tensor,
-        intermediate_tensors: IntermediateTensors | None = None,
-        inputs_embeds: torch.Tensor | None = None,
-    ) -> torch.Tensor | IntermediateTensors:
-        hidden_states = self.model(
-            input_ids, positions, intermediate_tensors, inputs_embeds
-        )
-        return hidden_states
-
-    def compute_logits(
-        self,
-        hidden_states: torch.Tensor,
-    ) -> torch.Tensor | None:
-        logits = self.logits_processor(self.lm_head, hidden_states)
-        return logits
-
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -728,9 +661,9 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             loaded_params.add(name)
         for layer_id in range(self.config.num_hidden_layers):
             for i in range(2):
-                if isinstance(self.model.layers[layer_id], PPMissingLayer):
+                if isinstance(self.layers[layer_id], PPMissingLayer):
                     continue
-                self_attn = self.model.layers[layer_id].self_attn[i]
+                self_attn = self.layers[layer_id].self_attn[i]
                 if hasattr(
                     self.quant_config, "weight_block_size"
                 ) and self_attn.kv_b_proj.weight.dtype in (
@@ -763,3 +696,81 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                         self.config.hidden_size / self.config.kv_lora_rank
                     ) ** 0.5
         return loaded_params
+
+
+class LongcatFlashForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    """Flash model for causal language modeling."""
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = FlashConfig(**vllm_config.model_config.hf_config.__dict__)
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        config.intermediate_size = (
+            config.ffn_hidden_size
+            if hasattr(config, "ffn_hidden_size")
+            else config.intermediate_size
+        )
+
+        self.quant_config = quant_config
+
+        self.model = FlashModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
diff --git a/vllm/model_executor/models/mimo_audio.py b/vllm/model_executor/models/mimo_audio.py
new file mode 100644
index 000000000000..91d46b1ceacd
--- /dev/null
+++ b/vllm/model_executor/models/mimo_audio.py
@@ -0,0 +1,1389 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""MiMo audio: tokenizer, encoding utilities, and audio encoder.
+
+Ported from SGLang's mimo_audio.py.
+Audio tokenizer adapted from https://github.com/XiaomiMiMo/MiMo-Audio-Tokenizer.git
+"""
+
+import dataclasses
+import json
+import logging
+import math
+import os
+import typing as tp
+from dataclasses import dataclass
+from functools import wraps
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from transformers.activations import ACT2FN
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_utils import PreTrainedModel
+from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
+from transformers.models.qwen2.modeling_qwen2 import Qwen2Model
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Vector quantization (from MiMo-Audio-Tokenizer)
+# ---------------------------------------------------------------------------
+
+
+def _vq_default(val: tp.Any, d: tp.Any) -> tp.Any:
+    return val if val is not None else d
+
+
+def _ema_inplace(moving_avg, new, decay: float):
+    if dist.is_initialized():
+        dist.all_reduce(new, op=dist.ReduceOp.SUM)
+    moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
+
+
+def _laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5):
+    return (x + epsilon) / (x.sum() + n_categories * epsilon)
+
+
+def _uniform_init(*shape: int):
+    t = torch.empty(shape)
+    nn.init.kaiming_uniform_(t)
+    return t
+
+
+def _sample_vectors(samples, num: int):
+    num_samples, device = samples.shape[0], samples.device
+
+    if num_samples >= num:
+        indices = torch.randperm(num_samples, device=device)[:num]
+    else:
+        indices = torch.randint(0, num_samples, (num,), device=device)
+
+    selected_samples = samples[indices]
+
+    if dist.is_initialized():
+        dist.broadcast(selected_samples, src=0)
+
+    return selected_samples
+
+
+def _kmeans(samples, num_clusters: int, num_iters: int = 10):
+    dim, dtype = samples.shape[-1], samples.dtype
+
+    means = _sample_vectors(samples, num_clusters)
+
+    for _ in range(num_iters):
+        dists = -(
+            samples.pow(2).sum(1, keepdim=True)
+            - 2 * samples @ means.t()
+            + means.t().pow(2).sum(0, keepdim=True)
+        )
+
+        buckets = dists.max(dim=-1).indices
+        bins = torch.bincount(buckets, minlength=num_clusters)
+
+        new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype)
+        new_means = new_means.scatter_add_(
+            0, repeat(buckets, "n -> n d", d=dim), samples
+        )
+
+        if dist.is_initialized():
+            dist.all_reduce(bins, op=dist.ReduceOp.SUM)
+            dist.all_reduce(new_means, op=dist.ReduceOp.SUM)
+
+        zero_mask = bins == 0
+        bins_min_clamped = bins.masked_fill(zero_mask, 1)
+
+        new_means = new_means / bins_min_clamped[..., None]
+
+        means = torch.where(zero_mask[..., None], means, new_means)
+
+    return means, bins
+
+
+def _rotate_half(x):
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (_rotate_half(q) * sin)
+    k_embed = (k * cos) + (_rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def _compute_default_rope_parameters(
+    config=None, device=None, seq_len=None, **rope_kwargs
+):
+    if config is not None and len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive"
+        )
+    if len(rope_kwargs) > 0:
+        base = rope_kwargs["base"]
+        dim = rope_kwargs["dim"]
+    elif config is not None:
+        base = config.rope_theta
+        partial_rotary_factor = (
+            config.partial_rotary_factor
+            if hasattr(config, "partial_rotary_factor")
+            else 1.0
+        )
+        head_dim = (
+            getattr(config, "head_dim", None)
+            or config.hidden_size // config.num_attention_heads
+        )
+        dim = int(head_dim * partial_rotary_factor)
+    attention_factor = 1.0
+    inv_freq = 1.0 / (
+        base
+        ** (
+            torch.arange(0, dim, 2, dtype=torch.int64).to(
+                device=device, dtype=torch.float
+            )
+            / dim
+        )
+    )
+    return inv_freq, attention_factor
+
+
+_ROPE_INIT_FUNCTIONS = {
+    "default": _compute_default_rope_parameters,
+}
+
+
+def _dynamic_rope_update(rope_forward):
+    def dynamic_frequency_update(self, position_ids, device):
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_seq_len_cached:
+            inv_freq, self.attention_scaling = self.rope_init_fn(
+                self.config, device, seq_len=seq_len
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+            self.max_seq_len_cached = seq_len
+
+        if (
+            seq_len < self.original_max_seq_len
+            and self.max_seq_len_cached > self.original_max_seq_len
+        ):
+            self.original_inv_freq = self.original_inv_freq.to(device)
+            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
+            self.max_seq_len_cached = self.original_max_seq_len
+
+    @wraps(rope_forward)
+    def wrapper(self, x, position_ids):
+        if "dynamic" in self.rope_type:
+            dynamic_frequency_update(self, position_ids, device=x.device)
+        return rope_forward(self, x, position_ids)
+
+    return wrapper
+
+
+class AudioRotaryEmbedding(nn.Module):
+    def __init__(self, base, dim, max_seq_len, rope_type="default", device=None):
+        super().__init__()
+        self.max_seq_len = max_seq_len
+        self.rope_type = rope_type
+        self.rope_init_fn = _ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = self.rope_init_fn(
+            device=device, base=base, dim=dim
+        )
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @torch.no_grad()
+    @_dynamic_rope_update
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[:, None].float().expand(-1, 1).to(x.device)
+        position_ids_expanded = position_ids[None, :].float()
+        device_type = (
+            x.device.type
+            if isinstance(x.device.type, str) and x.device.type != "mps"
+            else "cpu"
+        )
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (
+                inv_freq_expanded.float() @ position_ids_expanded.float()
+            ).transpose(0, 1)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class EuclideanCodebook(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        codebook_size: int,
+        kmeans_init: int = False,
+        kmeans_iters: int = 10,
+        decay: float = 0.99,
+        epsilon: float = 1e-5,
+        threshold_ema_dead_code: int = 2,
+    ):
+        super().__init__()
+        self.decay = decay
+        init_fn: tp.Callable[..., torch.Tensor] | tp.Any = (
+            _uniform_init if not kmeans_init else torch.zeros
+        )
+        embed = init_fn(codebook_size, dim)
+
+        self.codebook_size = codebook_size
+        self.kmeans_iters = kmeans_iters
+        self.epsilon = epsilon
+        self.threshold_ema_dead_code = threshold_ema_dead_code
+
+        self.register_buffer("inited", torch.Tensor([not kmeans_init]))
+        self.register_buffer("cluster_size", torch.zeros(codebook_size))
+        self.register_buffer("embed", embed)
+        self.register_buffer("embed_avg", embed.clone())
+
+    @torch.jit.ignore
+    def init_embed_(self, data):
+        if self.inited:
+            return
+
+        embed, cluster_size = _kmeans(data, self.codebook_size, self.kmeans_iters)
+        self.embed.data.copy_(embed)
+        self.embed_avg.data.copy_(embed.clone())
+        self.cluster_size.data.copy_(cluster_size)
+        self.inited.data.copy_(torch.Tensor([True]))
+
+    def replace_(self, samples, mask):
+        replace_num = mask.sum()
+        modified_codebook = self.embed.clone()
+        modified_codebook[mask] = _sample_vectors(samples, replace_num)
+        self.embed.data.copy_(modified_codebook)
+
+    def expire_codes_(self, batch_samples):
+        if self.threshold_ema_dead_code == 0:
+            return
+
+        expired_codes = self.cluster_size < self.threshold_ema_dead_code
+        if not torch.any(expired_codes):
+            return
+
+        batch_samples = rearrange(batch_samples, "... d -> (...) d")
+        self.replace_(batch_samples, mask=expired_codes)
+
+    def preprocess(self, x):
+        x = rearrange(x, "... d -> (...) d")
+        return x
+
+    def quantize(self, x):
+        embed = self.embed.t()
+        dist_val = -(
+            x.pow(2).sum(1, keepdim=True)
+            - 2 * x @ embed
+            + embed.pow(2).sum(0, keepdim=True)
+        )
+        embed_ind = dist_val.max(dim=-1).indices
+        return embed_ind
+
+    def postprocess_emb(self, embed_ind, shape):
+        return embed_ind.view(*shape[:-1])
+
+    def dequantize(self, embed_ind):
+        quantize = F.embedding(embed_ind, self.embed)
+        return quantize
+
+    def encode(self, x):
+        shape = x.shape
+        x = self.preprocess(x)
+        embed_ind = self.quantize(x)
+        embed_ind = self.postprocess_emb(embed_ind, shape)
+        return embed_ind
+
+    def decode(self, embed_ind):
+        quantize = self.dequantize(embed_ind)
+        return quantize
+
+    def forward(self, x):
+        shape, dtype = x.shape, x.dtype
+        x = self.preprocess(x)
+
+        self.init_embed_(x)
+
+        embed_ind = self.quantize(x)
+        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
+        embed_ind = self.postprocess_emb(embed_ind, shape)
+        quantize = self.dequantize(embed_ind)
+
+        if self.training:
+            self.expire_codes_(x)
+            _ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
+            embed_sum = x.t() @ embed_onehot
+            _ema_inplace(self.embed_avg, embed_sum.t().contiguous(), self.decay)
+            cluster_size = (
+                _laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon)
+                * self.cluster_size.sum()
+            )
+            embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
+            self.embed.data.copy_(embed_normalized)
+
+        return quantize, embed_ind
+
+
+class VectorQuantization(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        codebook_size: int,
+        codebook_dim: int | None = None,
+        decay: float = 0.99,
+        epsilon: float = 1e-5,
+        kmeans_init: bool = True,
+        kmeans_iters: int = 50,
+        threshold_ema_dead_code: int = 2,
+        commitment_weight: float = 1.0,
+    ):
+        super().__init__()
+        _codebook_dim: int = _vq_default(codebook_dim, dim)
+
+        requires_projection = _codebook_dim != dim
+        self.project_in = (
+            nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity()
+        )
+        self.project_out = (
+            nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity()
+        )
+
+        self.epsilon = epsilon
+        self.commitment_weight = commitment_weight
+
+        self._codebook = EuclideanCodebook(
+            dim=_codebook_dim,
+            codebook_size=codebook_size,
+            kmeans_init=kmeans_init,
+            kmeans_iters=kmeans_iters,
+            decay=decay,
+            epsilon=epsilon,
+            threshold_ema_dead_code=threshold_ema_dead_code,
+        )
+        self.codebook_size = codebook_size
+
+    @property
+    def codebook(self):
+        return self._codebook.embed
+
+    def encode(self, x):
+        x = self.project_in(x)
+        embed_in = self._codebook.encode(x)
+        return embed_in
+
+    def decode(self, embed_ind):
+        quantize = self._codebook.decode(embed_ind)
+        quantize = self.project_out(quantize)
+        return quantize
+
+    def forward(self, x):
+        device = x.device
+        x = self.project_in(x)
+
+        quantize, embed_ind = self._codebook(x)
+
+        if self.training:
+            quantize = x + (quantize - x).detach()
+
+        loss = torch.tensor([0.0], device=device, requires_grad=self.training)
+
+        quantize = self.project_out(quantize)
+        return quantize, embed_ind, loss
+
+
+class ResidualVectorQuantization(nn.Module):
+    def __init__(self, *, num_quantizers, codebook_size, **kwargs):
+        super().__init__()
+        if isinstance(codebook_size, int):
+            codebook_size = [codebook_size] * num_quantizers
+        elif len(codebook_size) < num_quantizers:
+            codebook_size += [codebook_size[-1]] * (num_quantizers - len(codebook_size))
+        self.layers = nn.ModuleList(
+            [
+                VectorQuantization(codebook_size=codebook_size[i], **kwargs)
+                for i in range(num_quantizers)
+            ]
+        )
+
+    def forward(self, x, n_q: int | None = None, layers: list | None = None):
+        quantized_out = 0.0
+        residual = x
+
+        all_losses = []
+        all_indices = []
+        out_quantized = []
+
+        n_q = n_q or len(self.layers)
+
+        for i, layer in enumerate(self.layers[:n_q]):
+            quantized, indices, loss = layer(residual)
+            residual = residual - quantized
+            quantized_out = quantized_out + quantized
+
+            all_indices.append(indices)
+            all_losses.append(loss)
+            if layers and i in layers:
+                out_quantized.append(quantized_out)
+
+        out_losses, out_indices = map(torch.stack, (all_losses, all_indices))
+        return quantized_out, out_indices, out_losses, out_quantized
+
+    def encode(
+        self, x: torch.Tensor, n_q: int | None = None, st: int | None = None
+    ) -> torch.Tensor:
+        residual = x
+        all_indices = []
+        n_q = len(self.layers) if n_q is None else n_q
+        st = 0 if st is None else st
+        for layer in self.layers[st:n_q]:
+            indices = layer.encode(residual)
+            quantized = layer.decode(indices)
+            residual = residual - quantized
+            all_indices.append(indices)
+        out_indices = torch.stack(all_indices)
+        return out_indices
+
+    def decode(self, q_indices: torch.Tensor, st: int = 0) -> torch.Tensor:
+        quantized_out = self.layers[st].decode(q_indices[0])
+        for i in range(1, len(q_indices)):
+            layer = self.layers[st + i]
+            quantized = layer.decode(q_indices[i])
+            quantized_out = quantized_out + quantized
+        return quantized_out
+
+
+class ResidualVectorQuantizer(nn.Module):
+    def __init__(
+        self,
+        dimension: int = 256,
+        n_q: int = 8,
+        bins: int | list = 1024,
+        decay: float = 0.99,
+        kmeans_init: bool = True,
+        kmeans_iters: int = 50,
+        threshold_ema_dead_code: int = 2,
+    ):
+        super().__init__()
+        self.n_q = n_q
+        self.dimension = dimension
+        self.bins = bins
+        self.decay = decay
+        self.kmeans_init = kmeans_init
+        self.kmeans_iters = kmeans_iters
+        self.threshold_ema_dead_code = threshold_ema_dead_code
+        self.vq = ResidualVectorQuantization(
+            dim=self.dimension,
+            codebook_size=self.bins,
+            num_quantizers=self.n_q,
+            decay=self.decay,
+            kmeans_init=self.kmeans_init,
+            kmeans_iters=self.kmeans_iters,
+            threshold_ema_dead_code=self.threshold_ema_dead_code,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        n_q: int | None = None,
+        layers: list | None = None,
+    ):
+        n_q = n_q if n_q else self.n_q
+        quantized, codes, commit_loss, quantized_list = self.vq(
+            x, n_q=n_q, layers=layers
+        )
+        return quantized, codes, torch.mean(commit_loss), quantized_list
+
+    def encode(
+        self, x: torch.Tensor, n_q: int | None = None, st: int | None = None
+    ) -> torch.Tensor:
+        n_q = n_q if n_q else self.n_q
+        st = st or 0
+        codes = self.vq.encode(x, n_q=n_q, st=st)
+        return codes
+
+    def decode(self, codes: torch.Tensor, st: int = 0) -> torch.Tensor:
+        quantized = self.vq.decode(codes, st=st)
+        return quantized
+
+
+# ---------------------------------------------------------------------------
+# Audio tokenizer
+# ---------------------------------------------------------------------------
+
+
+class MiMoAudioTokenizerConfig(PretrainedConfig):
+    model_type = "mimo_audio_tokenizer"
+
+    def __init__(
+        self,
+        max_audio_seconds: int = 1800,
+        stride_size: int = 2,
+        avg_pooler: int = 1,
+        d_model: int = 768,
+        scale_embedding: bool = True,
+        kernel_size: int = 3,
+        activation_function: str = "gelu",
+        encoder_layers: int = 8,
+        encoder_skip_layer_id: int = None,
+        encoder_attention_heads: int = 12,
+        encoder_ffn_dim: int = 3072,
+        encoder_causal: bool = False,
+        encoder_attn_window_size: list = None,
+        decoder_layers: int = 8,
+        decoder_attention_heads: int = 12,
+        decoder_ffn_dim: int = 3072,
+        decoder_kernel_size: int = 3,
+        decoder_stride_size: int = 2,
+        decoder_causal: bool = True,
+        decoder_attn_window_size: list = None,
+        nfft: int = 1024,
+        vocoder_dim: int = 512,
+        vocoder_intermediate_dim: int = 4096,
+        vocoder_num_layers: int = 30,
+        n_mels: int = 80,
+        sampling_rate: int = 24000,
+        hop_length: int = 240,
+        window_size: int = 1024,
+        vocoder_padding: str = "same",
+        fmin: int = 0,
+        fmax: int = None,
+        num_quantizers: int = 12,
+        codebook_size: list = None,
+        threshold_ema_dead_code: int = 10,
+        position_embedding_type: str = "rope",
+        rope_theta: int = 10000,
+        rope_type: str = "default",
+        ln_type: str = "LayerNorm",
+        vocoder_attention_heads: int = 4,
+        vocoder_attn_window_size: list = None,
+        use_istft_only: bool = False,
+        hybrid_attention: bool = False,
+        hybrid_block_size: int = 8,
+        swa_per_block: int = 2,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.max_audio_seconds = max_audio_seconds
+        self.stride_size = stride_size
+        self.avg_pooler = avg_pooler
+        self.d_model = d_model
+        self.scale_embedding = scale_embedding
+        self.kernel_size = kernel_size
+        self.activation_function = activation_function
+        self.encoder_layers = encoder_layers
+        self.encoder_skip_layer_id = encoder_skip_layer_id
+        self.encoder_attention_heads = encoder_attention_heads
+        self.encoder_ffn_dim = encoder_ffn_dim
+        self.encoder_causal = encoder_causal
+        self.encoder_attn_window_size = (
+            encoder_attn_window_size
+            if encoder_attn_window_size is not None
+            else [-1, -1]
+        )
+        self.decoder_layers = decoder_layers
+        self.decoder_attention_heads = decoder_attention_heads
+        self.decoder_ffn_dim = decoder_ffn_dim
+        self.decoder_kernel_size = decoder_kernel_size
+        self.decoder_stride_size = decoder_stride_size
+        self.decoder_causal = decoder_causal
+        self.decoder_attn_window_size = (
+            decoder_attn_window_size
+            if decoder_attn_window_size is not None
+            else [-1, -1]
+        )
+        self.nfft = nfft
+        self.vocoder_dim = vocoder_dim
+        self.vocoder_intermediate_dim = vocoder_intermediate_dim
+        self.vocoder_num_layers = vocoder_num_layers
+        self.n_mels = n_mels
+        self.sampling_rate = sampling_rate
+        self.hop_length = hop_length
+        self.window_size = window_size
+        self.vocoder_padding = vocoder_padding
+        self.fmin = fmin
+        self.fmax = fmax
+        self.num_quantizers = num_quantizers
+        self.codebook_size = codebook_size if codebook_size is not None else [1024]
+        self.threshold_ema_dead_code = threshold_ema_dead_code
+        self.position_embedding_type = position_embedding_type
+        self.rope_theta = rope_theta
+        self.rope_type = rope_type
+        self.ln_type = ln_type
+        self.vocoder_attention_heads = vocoder_attention_heads
+        self.vocoder_attn_window_size = (
+            vocoder_attn_window_size
+            if vocoder_attn_window_size is not None
+            else [40, 10]
+        )
+        self.use_istft_only = use_istft_only
+        self.hybrid_attention = hybrid_attention
+        self.hybrid_block_size = hybrid_block_size
+        self.swa_per_block = swa_per_block
+
+
+def get_sequence_mask(inputs, inputs_length):
+    if inputs.dim() == 3:
+        bsz, tgt_len, _ = inputs.size()
+    else:
+        bsz, tgt_len = inputs_length.shape[0], torch.max(inputs_length)
+    sequence_mask = torch.arange(0, tgt_len).to(inputs.device)
+    sequence_mask = torch.lt(sequence_mask, inputs_length.reshape(bsz, 1)).view(
+        bsz, tgt_len, 1
+    )
+    unpacking_index = torch.cumsum(sequence_mask.to(torch.int64).view(-1), dim=0) - 1
+    return sequence_mask, unpacking_index
+
+
+def unpack_hidden_states(
+    hidden_states, lengths, sequence_mask=None, unpacking_index=None
+):
+    bsz = lengths.shape[0]
+    if sequence_mask is None or unpacking_index is None:
+        sequence_mask, unpacking_index = get_sequence_mask(hidden_states, lengths)
+    hidden_states = torch.index_select(hidden_states, 0, unpacking_index).view(
+        bsz, torch.max(lengths), hidden_states.shape[-1]
+    )
+    return torch.where(sequence_mask, hidden_states, 0)
+
+
+def get_position_ids(lengths):
+    total_len = lengths.sum()
+    offset = torch.cat([torch.zeros(1).to(lengths), lengths[:-1].cumsum(dim=0)])
+    offset = torch.repeat_interleave(offset, lengths)
+    return torch.arange(0, total_len).to(offset) - offset
+
+
+LAYER_NORM = {"LayerNorm": nn.LayerNorm}
+
+
+class AudioEncoderAttention(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        window_size: tuple[int, int] = (-1, -1),
+        causal: bool = False,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.head_dim = embed_dim // num_heads
+        self.window_size = window_size
+        self.causal = causal
+
+        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False)
+        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=True)
+        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=True)
+        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        max_seqlen: int,
+        rope_position_embeddings=None,
+    ):
+        from vllm.vllm_flash_attn import flash_attn_varlen_func
+
+        bsz, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states).view(
+            bsz, self.num_heads, self.head_dim
+        )
+        key_states = self.k_proj(hidden_states).view(bsz, self.num_heads, self.head_dim)
+        value_states = self.v_proj(hidden_states).view(
+            bsz, self.num_heads, self.head_dim
+        )
+
+        if rope_position_embeddings is not None:
+            cos, sin = rope_position_embeddings
+            query_states, key_states = apply_rotary_pos_emb(
+                query_states, key_states, cos, sin
+            )
+
+        attn_output = flash_attn_varlen_func(
+            query_states,
+            key_states,
+            value_states,
+            cu_seqlens_q=cu_seqlens,
+            cu_seqlens_k=cu_seqlens,
+            max_seqlen_q=max_seqlen,
+            max_seqlen_k=max_seqlen,
+            causal=self.causal,
+            window_size=list(self.window_size),
+        )
+
+        attn_output = attn_output.reshape(bsz, self.embed_dim)
+        attn_output = self.out_proj(attn_output)
+        return attn_output
+
+
+class AudioEncoderTransformerLayer(nn.Module):
+    def __init__(
+        self,
+        config: MiMoAudioTokenizerConfig,
+        causal: bool,
+        attn_window_size: tuple[int, int] = (-1, -1),
+    ):
+        super().__init__()
+        self.embed_dim = config.d_model
+
+        self.self_attn = AudioEncoderAttention(
+            embed_dim=self.embed_dim,
+            num_heads=config.encoder_attention_heads,
+            window_size=attn_window_size,
+            causal=causal,
+        )
+        self.self_attn_layer_norm = LAYER_NORM[config.ln_type](self.embed_dim)
+
+        self.activation_fn = ACT2FN[config.activation_function]
+        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
+        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
+        self.final_layer_norm = LAYER_NORM[config.ln_type](self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        max_seqlen: int,
+        rope_position_embeddings: tuple[torch.Tensor, torch.Tensor],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.self_attn_layer_norm(hidden_states)
+        hidden_states = self.self_attn(
+            hidden_states,
+            cu_seqlens,
+            max_seqlen,
+            rope_position_embeddings=rope_position_embeddings,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.final_layer_norm(hidden_states)
+        hidden_states = self.activation_fn(self.fc1(hidden_states))
+        hidden_states = self.fc2(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class AudioEncoder(nn.Module):
+    def __init__(
+        self,
+        config: MiMoAudioTokenizerConfig,
+    ):
+        super().__init__()
+        self.config = config
+        self.max_source_positions = (
+            config.max_audio_seconds * config.sampling_rate // config.hop_length
+        ) // config.stride_size
+        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+        self.skip_layer_idx = config.encoder_skip_layer_id
+
+        self.conv1 = nn.Conv1d(
+            config.n_mels,
+            config.d_model,
+            kernel_size=config.kernel_size,
+            padding=1,
+        )
+        self.conv2 = nn.Conv1d(
+            config.d_model,
+            config.d_model,
+            kernel_size=config.kernel_size,
+            stride=config.stride_size,
+            padding=1,
+        )
+
+        self.position_embedding = AudioRotaryEmbedding(
+            config.rope_theta,
+            config.d_model // config.encoder_attention_heads,
+            self.max_source_positions,
+            config.rope_type,
+        )
+
+        attn_window_sizes = []
+        if config.hybrid_attention:
+            for i in range(config.encoder_layers):
+                if i % config.swa_per_block < config.swa_per_block - 1:
+                    attn_window_sizes.append(tuple(config.encoder_attn_window_size))
+                else:
+                    attn_window_sizes.append((-1, -1))
+        else:
+            attn_window_sizes = [
+                tuple(config.encoder_attn_window_size)
+            ] * config.encoder_layers
+
+        self.layers = nn.ModuleList(
+            [
+                AudioEncoderTransformerLayer(
+                    config=config,
+                    causal=config.encoder_causal,
+                    attn_window_size=attn_window_sizes[i],
+                )
+                for i in range(config.encoder_layers)
+            ]
+        )
+
+        self.layer_norm = LAYER_NORM[config.ln_type](config.d_model)
+
+        if config.avg_pooler != 1:
+            self.down_sample_layer = nn.Sequential(
+                nn.Conv1d(
+                    config.d_model,
+                    config.d_model,
+                    config.avg_pooler,
+                    config.avg_pooler,
+                    bias=False,
+                ),
+                nn.GELU(),
+            )
+            self.down_sample_norm = LAYER_NORM[config.ln_type](config.d_model)
+        else:
+            self.down_sample_layer = None
+
+        if config.num_quantizers != 0:
+            self.quantizer = ResidualVectorQuantizer(
+                dimension=config.d_model,
+                n_q=config.num_quantizers,
+                bins=config.codebook_size,
+                threshold_ema_dead_code=config.threshold_ema_dead_code,
+            )
+        else:
+            self.quantizer = None
+
+    def get_features(self, input_features, output_length):
+        input_features = input_features.to(self.conv1.weight)
+        inputs_embeds = nn.functional.gelu(self.conv1(input_features))
+        inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
+        inputs_embeds = inputs_embeds.permute(0, 2, 1)
+        bsz, tgt_len, _ = inputs_embeds.size()
+        hidden_states = inputs_embeds
+
+        position_ids = get_position_ids(output_length).long().to(input_features.device)
+        rope_position_embeddings = self.position_embedding(input_features, position_ids)
+
+        attention_mask, unpacking_index = get_sequence_mask(
+            hidden_states, output_length
+        )
+        hidden_states = torch.masked_select(hidden_states, attention_mask).view(
+            torch.sum(output_length), self.config.d_model
+        )
+
+        cu_seqlens = F.pad(
+            torch.cumsum(output_length, dim=0), (1, 0), "constant", 0
+        ).to(device=hidden_states.device, dtype=torch.int32)
+        max_seqlen = torch.max(output_length).to(torch.int32).item()
+
+        skip_connect_hidden_states = 0.0
+        for idx, encoder_layer in enumerate(self.layers):
+            hidden_states = encoder_layer(
+                hidden_states,
+                cu_seqlens,
+                max_seqlen,
+                rope_position_embeddings=rope_position_embeddings,
+            )
+            if (self.skip_layer_idx is not None) and idx == self.skip_layer_idx - 1:
+                skip_connect_hidden_states = hidden_states.clone()
+
+        hidden_states += skip_connect_hidden_states
+        hidden_states = self.layer_norm(hidden_states)
+
+        if self.down_sample_layer is not None:
+            hidden_states = torch.index_select(hidden_states, 0, unpacking_index).view(
+                bsz, tgt_len, self.config.d_model
+            )
+            if hidden_states.size(1) % self.config.avg_pooler:
+                pad_len = (
+                    self.config.avg_pooler
+                    - hidden_states.size(1) % self.config.avg_pooler
+                )
+                hidden_states = torch.nn.functional.pad(
+                    hidden_states, (0, 0, 0, pad_len), mode="constant", value=0.0
+                )
+                tgt_len += pad_len
+            tgt_len = tgt_len // self.config.avg_pooler
+            hidden_states = self.down_sample_layer(hidden_states.transpose(1, 2))
+            output_length = (
+                output_length // self.config.avg_pooler
+                + (output_length % self.config.avg_pooler != 0).int()
+            )
+            hidden_states = hidden_states.transpose(1, 2)
+            attention_mask, unpacking_index = get_sequence_mask(
+                hidden_states, output_length
+            )
+            hidden_states = torch.masked_select(hidden_states, attention_mask).view(
+                torch.sum(output_length), self.config.d_model
+            )
+            hidden_states = self.down_sample_norm(hidden_states)
+
+        return (
+            hidden_states,
+            output_length,
+            attention_mask,
+            unpacking_index,
+            tgt_len,
+            bsz,
+        )
+
+    def get_output_length(self, mel_len):
+        tgt_len = mel_len + 3 - self.config.kernel_size
+        return (tgt_len + 2 - self.config.kernel_size) // self.config.stride_size + 1
+
+    @torch.no_grad()
+    def encode(
+        self,
+        input_features,
+        input_lens=None,
+        output_length=None,
+        return_codes_only=False,
+        n_q=None,
+        use_quantizer=True,
+    ):
+        if output_length is None:
+            output_length = self.get_output_length(input_lens)
+        input_features = unpack_hidden_states(input_features, input_lens)
+        hidden_states, output_length, attention_mask, unpacking_index, tgt_len, bsz = (
+            self.get_features(
+                input_features=input_features.transpose(1, 2),
+                output_length=output_length,
+            )
+        )
+
+        dtype = hidden_states.dtype
+        if use_quantizer and self.quantizer is not None:
+            self.quantizer.float()
+            codes = self.quantizer.encode(hidden_states.float(), n_q=n_q)
+            if return_codes_only:
+                return codes, output_length
+            hidden_states = self.quantizer.decode(codes)
+            hidden_states = hidden_states.to(dtype)
+        else:
+            codes = None
+
+        hidden_states_packed = hidden_states.clone()
+        hidden_states = torch.index_select(hidden_states, 0, unpacking_index).view(
+            bsz, tgt_len, self.config.d_model
+        )
+        hidden_states = torch.where(attention_mask, hidden_states, 0)
+        return hidden_states, hidden_states_packed, output_length, codes
+
+    @torch.no_grad()
+    def decode_vq(self, codes):
+        self.quantizer.float()
+        return self.quantizer.decode(codes)
+
+
+class MiMoAudioTokenizer(PreTrainedModel):
+    config_class = MiMoAudioTokenizerConfig
+
+    def __init__(self, config: MiMoAudioTokenizerConfig):
+        super().__init__(config)
+        self.config = config
+        self.sampling_rate = config.sampling_rate
+        self.encoder = AudioEncoder(config=config)
+        self.downsample_rate = int(config.hop_length * 2 * config.avg_pooler)
+
+    def get_output_length(self, mel_len):
+        tgt_len = mel_len + 3 - self.config.kernel_size
+        return (tgt_len + 2 - self.config.kernel_size) // self.config.stride_size + 1
+
+    @torch.no_grad()
+    def encode(self, mels, input_lens, use_quantizer=True):
+        input_features = mels
+        encoder_output_length = self.get_output_length(input_lens)
+        hidden_states, hidden_states_packed, encoder_output_length, codes = (
+            self.encoder.encode(
+                input_features, input_lens=input_lens, use_quantizer=use_quantizer
+            )
+        )
+        return hidden_states, hidden_states_packed, encoder_output_length, codes
+
+
+# ---------------------------------------------------------------------------
+# Audio encoding utilities
+# ---------------------------------------------------------------------------
+
+
+def group_by_length(features: torch.Tensor, lengths: torch.Tensor, max_length: int):
+    if features.size(0) != lengths.sum().item():
+        raise ValueError(
+            f"Feature size mismatch: {features.size(0)} vs {lengths.sum().item()}"
+        )
+
+    split_points = []
+    current_sum = 0
+
+    for i, seq_len in enumerate(lengths):
+        if current_sum + seq_len > max_length and current_sum > 0:
+            split_points.append(i)
+            current_sum = seq_len.item()
+        else:
+            current_sum += seq_len.item()
+
+    group_sizes = []
+    prev = 0
+    for point in split_points:
+        group_sizes.append(point - prev)
+        prev = point
+    if prev < len(lengths):
+        group_sizes.append(len(lengths) - prev)
+
+    len_groups = torch.split(lengths, group_sizes)
+    feature_sizes = [group.sum().item() for group in len_groups]
+    feature_groups = torch.split(features, feature_sizes)
+
+    return feature_groups, len_groups
+
+
+@torch.no_grad()
+def encode_batch(
+    audio_tokenizer_encoder,
+    input_features: torch.Tensor,
+    input_lens: torch.Tensor,
+    max_length: int = 256000,
+):
+    feature_groups, len_groups = group_by_length(input_features, input_lens, max_length)
+
+    encoded_parts = []
+    for features, lengths in zip(feature_groups, len_groups):
+        codes, _ = audio_tokenizer_encoder.encode(
+            input_features=features, input_lens=lengths, return_codes_only=True
+        )
+        encoded_parts.append(codes)
+
+    return torch.cat(encoded_parts, dim=-1)
+
+
+def _segment_lengths_for_mel(mel: torch.Tensor, segment_size: int):
+    """Split mel into segments of segment_size with a possible shorter remainder."""
+    input_len = mel.size(0)
+    segs = [segment_size] * (input_len // segment_size)
+    if input_len % segment_size > 0:
+        segs.append(input_len % segment_size)
+    return segs
+
+
+@torch.no_grad()
+def tokenize_audio_batch(mels, audio_tokenizer_encoder, segment_size=6000, device=None):
+    """Tokenize multiple mels in one encode_batch call.
+
+    Returns list of code tensors, each [T_i, C] for that mel.
+    """
+    if not mels:
+        return []
+    if device is None:
+        device = next(audio_tokenizer_encoder.parameters()).device
+    input_len_seg_per_mel = [_segment_lengths_for_mel(m, segment_size) for m in mels]
+    input_lens_flat = [s for segs in input_len_seg_per_mel for s in segs]
+    input_features = torch.cat([m.to(device) for m in mels], dim=0)
+    input_lens_t = torch.tensor(input_lens_flat, dtype=torch.long, device=device)
+    codes_packed = encode_batch(
+        audio_tokenizer_encoder,
+        input_features=input_features,
+        input_lens=input_lens_t,
+    )
+    codes = codes_packed.transpose(0, 1).detach()  # [total_code_T, C]
+    code_lengths = []
+    for segs in input_len_seg_per_mel:
+        out_len = audio_tokenizer_encoder.get_output_length(
+            torch.tensor(segs, dtype=torch.long, device=device)
+        )
+        if getattr(audio_tokenizer_encoder, "down_sample_layer", None) is not None:
+            avg = audio_tokenizer_encoder.config.avg_pooler
+            out_len = out_len // avg + (out_len % avg != 0).long()
+        code_lengths.append(out_len.sum().item())
+    code_list = torch.split(codes, code_lengths)
+    return list(code_list)
+
+
+# ---------------------------------------------------------------------------
+# MimoAudioEncoderConfig
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class MimoAudioEncoderConfig:
+    """Config for MimoAudioEncoder.
+
+    Field names match the audio_config dict in the model checkpoint.
+    """
+
+    speech_vocab_size: str = "1025-1025-129-129-129-129-129-129"
+    speech_zeroemb_idx: str = "1024-1024-128-128-128-128-128-128"
+    group_size: int = 4
+    audio_channels: int = 8
+    input_local_layers: int = 6
+    input_local_dim: int = 1024
+    input_full_attention: bool = True
+    input_local_attn_heads: int = 64
+    input_local_head_dim: int = 16
+    input_local_intermediate_size: int = 4096
+    input_local_hidden_dropout: float = 0.0
+    out_hidden_size: int = 4096
+    rope_theta: float = 640000.0
+    partial_rotary_factor: float = 0.334
+    projection_layers: int = 1
+    add_post_norm: bool = False
+    audio_segment_size: int = 6000
+
+    @classmethod
+    def from_dict(cls, d: dict) -> "MimoAudioEncoderConfig":
+        known = {f.name for f in dataclasses.fields(cls)}
+        return cls(**{k: v for k, v in d.items() if k in known})
+
+
+# ---------------------------------------------------------------------------
+# AudioProjection
+# ---------------------------------------------------------------------------
+
+
+class AudioProjection(nn.Module):
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        output_size: int,
+    ) -> None:
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(input_size, hidden_size, bias=False),
+            nn.GELU(),
+            nn.Linear(hidden_size, output_size, bias=False),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.mlp(x)
+
+
+# ---------------------------------------------------------------------------
+# MimoAudioEncoder
+# ---------------------------------------------------------------------------
+
+
+class MimoAudioEncoder(nn.Module):
+    """Audio encoder for MiMo-V2-Omni.
+
+    Encodes mel spectrograms into LLM-compatible embeddings via:
+      1. Audio tokenizer (VQ codes)
+      2. Speech embeddings lookup
+      3. Local Qwen2 transformer
+      4. Linear projection
+    """
+
+    def __init__(self, config, model_path: str = "") -> None:
+        super().__init__()
+        if isinstance(config, dict):
+            config = MimoAudioEncoderConfig.from_dict(config)
+        self.config = config
+        self.audio_channels = config.audio_channels
+        self.audio_group_size = config.group_size
+        self.audio_segment_size = config.audio_segment_size
+
+        speech_vocab_sizes = self._parse_maybe_list(
+            config.speech_vocab_size, config.audio_channels
+        )
+        speech_empty_ids = self._parse_maybe_list(
+            config.speech_zeroemb_idx, config.audio_channels
+        )
+
+        input_local_config = Qwen2Config(
+            hidden_size=config.input_local_dim,
+            num_hidden_layers=config.input_local_layers,
+            num_attention_heads=config.input_local_attn_heads,
+            num_key_value_heads=config.input_local_attn_heads,
+            intermediate_size=config.input_local_intermediate_size,
+            attention_dropout=config.input_local_hidden_dropout,
+            rope_theta=config.rope_theta,
+            partial_rotary_factor=config.partial_rotary_factor,
+        )
+
+        self.input_local_transformer = Qwen2Model(input_local_config)
+
+        if not config.add_post_norm:
+            self.input_local_transformer.norm = nn.Identity()
+
+        self.speech_embeddings = nn.ModuleList(
+            [
+                nn.Embedding(
+                    speech_vocab_sizes[i],
+                    config.input_local_dim,
+                    padding_idx=speech_empty_ids[i],
+                )
+                for i in range(config.audio_channels)
+            ]
+        )
+
+        if config.projection_layers == 1:
+            self.projection = nn.Linear(
+                config.input_local_dim * config.group_size,
+                config.out_hidden_size,
+                bias=False,
+            )
+        elif config.projection_layers == 2:
+            self.projection = AudioProjection(
+                config.input_local_dim * config.group_size,
+                config.input_local_dim * config.group_size * 4,
+                config.out_hidden_size,
+            )
+        else:
+            raise ValueError(f"Invalid projection_layers: {config.projection_layers}")
+
+        self.audio_tokenizer: MiMoAudioTokenizer | None = None
+        if model_path:
+            audio_tokenizer_path = os.path.join(model_path, "audio_tokenizer")
+            if os.path.exists(audio_tokenizer_path):
+                dev = torch.get_default_device()
+                self.audio_tokenizer = self._load_audio_tokenizer(
+                    audio_tokenizer_path, dev
+                )
+            else:
+                logger.warning(
+                    "Audio tokenizer not found at %s, audio encoding disabled",
+                    audio_tokenizer_path,
+                )
+
+    @staticmethod
+    def _load_audio_tokenizer(path: str, device: torch.device) -> MiMoAudioTokenizer:
+        """Load MiMoAudioTokenizer from directory."""
+        from safetensors.torch import load_file
+
+        config_path = os.path.join(path, "config.json")
+        with open(config_path) as f:
+            config_dict = json.load(f)
+        config = MiMoAudioTokenizer.config_class(**config_dict)
+        model = MiMoAudioTokenizer(config)
+        safetensors_path = os.path.join(path, "model.safetensors")
+        bin_path = os.path.join(path, "pytorch_model.bin")
+        if os.path.exists(safetensors_path):
+            state_dict = load_file(safetensors_path, device="cpu")
+        elif os.path.exists(bin_path):
+            state_dict = torch.load(bin_path, map_location="cpu", weights_only=True)
+        else:
+            raise FileNotFoundError(
+                f"No model weights found in {path} "
+                "(expected model.safetensors or pytorch_model.bin)"
+            )
+        model.load_state_dict(state_dict, strict=False)
+        model = model.to(device=device, dtype=torch.bfloat16)
+        model.eval()
+        model.requires_grad_(False)
+        return model
+
+    def _parse_maybe_list(self, value, length: int) -> list[int]:
+        if isinstance(value, str) and "-" in value:
+            return [int(s) for s in value.split("-")]
+        return [int(value)] * length
+
+    def apply_input_local_transformer(self, speech_embeddings: torch.Tensor):
+        output = self.input_local_transformer(
+            inputs_embeds=speech_embeddings,
+            return_dict=True,
+            is_causal=not self.config.input_full_attention,
+        )
+        return output.last_hidden_state
+
+    def apply_speech_embeddings(self, audio_codes: torch.Tensor) -> torch.Tensor:
+        num_segments = audio_codes.shape[0]
+        _audio_embeddings = torch.zeros(
+            (num_segments, self.config.group_size, self.config.input_local_dim),
+            dtype=next(self.speech_embeddings[0].parameters()).dtype,
+            device=audio_codes.device,
+        )
+        for i in range(self.config.audio_channels):
+            _audio_embeddings.add_(self.speech_embeddings[i](audio_codes[:, :, i]))
+        return _audio_embeddings
+
+    def process_audio(self, audio: torch.Tensor) -> torch.Tensor:
+        """Pad audio codes to group_size boundary.
+
+        Args:
+            audio: [T, audio_channels] code tensor
+
+        Returns:
+            [T//group_size, group_size, audio_channels]
+        """
+        T = audio.shape[0]
+        audio = audio[:, : self.audio_channels]
+        padded_T = (
+            (T + self.audio_group_size - 1)
+            // self.audio_group_size
+            * self.audio_group_size
+        )
+        padded_audio = torch.cat(
+            [
+                audio,
+                torch.zeros(
+                    padded_T - T,
+                    self.audio_channels,
+                    dtype=torch.int32,
+                    device=audio.device,
+                )
+                + audio[-1, :],
+            ],
+            dim=0,
+        )
+        padded_audio = padded_audio.reshape(
+            padded_T // self.audio_group_size,
+            self.audio_group_size,
+            self.audio_channels,
+        )
+        return padded_audio
+
+    def get_audio_feature(
+        self, mel_specs: list[torch.Tensor]
+    ) -> tuple[torch.Tensor, list[int]]:
+        """Encode mel spectrograms into LLM embedding space.
+
+        Args:
+            mel_specs: list of mel spectrogram tensors, each [T, n_mels]
+
+        Returns:
+            Tuple of:
+            - audio_embeds: [total_tokens, out_hidden_size] concatenated embeddings
+            - item_token_lens: list of int, number of tokens per input item
+        """
+        if self.audio_tokenizer is None:
+            raise RuntimeError(
+                "audio_tokenizer is not loaded. "
+                "Ensure model_path points to a directory containing audio_tokenizer/."
+            )
+
+        if not mel_specs:
+            device = next(self.projection.parameters()).device
+            dtype = next(self.projection.parameters()).dtype
+            return (
+                torch.empty(0, self.config.out_hidden_size, device=device, dtype=dtype),
+                [],
+            )
+
+        device = next(self.audio_tokenizer.encoder.parameters()).device
+        code_list = tokenize_audio_batch(
+            mel_specs,
+            self.audio_tokenizer.encoder,
+            segment_size=self.audio_segment_size,
+            device=device,
+        )
+
+        item_token_lens: list[int] = []
+        codecs_to_concat = []
+        for codecs in code_list:
+            padded_codes = self.process_audio(codecs)
+            codecs_to_concat.append(padded_codes)
+            item_token_lens.append(padded_codes.shape[0])
+
+        audio_codes = torch.cat(
+            codecs_to_concat, dim=0
+        )  # [total_T//group_size, group_size, audio_channels]
+
+        _audio_embeddings = self.apply_speech_embeddings(audio_codes)
+        audio_embeds = self.apply_input_local_transformer(_audio_embeddings)
+        B = audio_embeds.shape[0]
+        audio_embeds = self.projection(audio_embeds.reshape(B, -1))
+        return audio_embeds, item_token_lens
diff --git a/vllm/model_executor/models/mimo_v2_flash.py b/vllm/model_executor/models/mimo_v2.py
similarity index 94%
rename from vllm/model_executor/models/mimo_v2_flash.py
rename to vllm/model_executor/models/mimo_v2.py
index 43475ed690c9..3f466162649c 100644
--- a/vllm/model_executor/models/mimo_v2_flash.py
+++ b/vllm/model_executor/models/mimo_v2.py
@@ -6,6 +6,7 @@
 import torch
 from torch import nn
 
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import (
     CacheConfig,
     VllmConfig,
@@ -22,7 +23,10 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -43,6 +47,9 @@
 from vllm.model_executor.models.utils import sequence_parallel_chunk
 from vllm.sequence import IntermediateTensors
 from vllm.v1.attention.backend import AttentionType
+from vllm.v1.attention.backends.flash_attn_diffkv import (
+    FlashAttentionDiffKVBackend,
+)
 
 from .interfaces import MixtureOfExperts, SupportsPP
 from .utils import (
@@ -162,7 +169,6 @@ def __init__(
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=True,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -263,7 +269,7 @@ def __init__(
             self.total_num_heads * self.v_head_dim,
             hidden_size,
             bias=False,
-            quant_config=quant_config,
+            quant_config=quant_config if "mtp.layers" not in prefix else None,
             reduce_results=True,
             prefix=f"{prefix}.o_proj",
         )
@@ -285,6 +291,15 @@ def __init__(
         )
 
         sliding_window = sliding_window_size if sliding_window_size > -1 else None
+
+        # Use DiffKV backend when V has a different head dim than K
+        if self.v_head_dim != self.head_dim:
+            FlashAttentionDiffKVBackend.set_head_size_v(self.v_head_dim)
+            attn_backend = FlashAttentionDiffKVBackend
+            logger.info_once("Using FlashAttentionDiffKVBackend for attention.")
+        else:
+            attn_backend = None
+
         self.attn = Attention(
             self.num_heads,
             self.head_dim,
@@ -296,6 +311,8 @@ def __init__(
             attn_type=AttentionType.DECODER,
             prefix=f"{prefix}.attn",
             sinks=self.attention_sink_bias,
+            attn_backend=attn_backend,
+            head_size_v=self.v_head_dim,
         )
 
     def forward(
@@ -311,16 +328,8 @@ def forward(
         if self.v_scale is not None:
             v = v * self.v_scale
 
-        v = v.view(-1, self.num_kv_heads, self.v_head_dim)
-        v = torch.nn.functional.pad(v, [0, self.head_dim - self.v_head_dim], value=0)
-        v = v.view(-1, self.num_kv_heads * self.head_dim)
-
         attn_output = self.attn(q, k, v)
 
-        attn_output = attn_output.view(-1, self.num_heads, self.head_dim)[
-            ..., : self.v_head_dim
-        ].reshape(-1, self.num_heads * self.v_head_dim)
-
         output, _ = self.o_proj(attn_output)
         return output
 
@@ -432,6 +441,7 @@ def is_compressed_softmax_layer(self) -> bool:
         return self.config.hybrid_layer_pattern[self.layer_id] == 1
 
 
+@support_torch_compile
 class MiMoV2Model(nn.Module):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
@@ -512,7 +522,7 @@ def forward(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -595,7 +605,13 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
             if expert_matched:
                 continue
-
+            # Support fused qkv_proj checkpoint (Pro format)
+            if "qkv_proj" in name:
+                if name in params_dict:
+                    param = params_dict[name]
+                    loaded_weight = loaded_weight.chunk(tp_size, dim=0)[tp_rank]
+                    default_weight_loader(param, loaded_weight)
+                continue
             stacked_matched = False
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 if weight_name not in name:
@@ -654,6 +670,11 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
 
 class MiMoV2FlashForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
@@ -710,3 +731,10 @@ def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(self)
         return loader.load_weights(weights)
+
+
+class MiMoV2ForCausalLM(MiMoV2FlashForCausalLM):
+    packed_modules_mapping = {
+        "qkv_proj": ["qkv_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
diff --git a/vllm/model_executor/models/mimo_v2_mtp.py b/vllm/model_executor/models/mimo_v2_mtp.py
new file mode 100644
index 000000000000..c863cedaeb88
--- /dev/null
+++ b/vllm/model_executor/models/mimo_v2_mtp.py
@@ -0,0 +1,366 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Inference-only MiMo-V2 MTP (Multi-Token Prediction) draft model.
+
+Supports both MiMo-V2-Pro and MiMo-V2-Flash checkpoints.
+
+Checkpoint weight layout (model.mtp.layers.{idx}.*):
+  enorm            - RMSNorm for token embeddings
+  hnorm            - RMSNorm for previous hidden states
+  eh_proj          - ReplicatedLinear(hidden*2 -> hidden)
+  input_layernorm  - pre-attention RMSNorm
+  self_attn.*      - attention weights; format differs by variant:
+                       Pro:   fused qkv_proj  [Q;K;V] concatenated
+                       Flash: separate q_proj, k_proj, v_proj
+  pre_mlp_layernorm - post-attention / pre-MLP RMSNorm
+  mlp.*            - dense MLP (gate_proj / up_proj / down_proj)
+  final_layernorm  - norm applied before logit computation
+"""
+
+from collections.abc import Iterable
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig
+
+from vllm.config import VllmConfig
+from vllm.distributed import (
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import (
+    MultiModalEmbeddings,
+    SupportsMultiModal,
+    _require_is_multimodal,
+)
+from .mimo_v2 import MiMoV2Attention, MiMoV2MLP
+from .utils import _merge_multimodal_embeddings, maybe_prefix
+
+# MiMo-V2 checkpoints contain multiple MTP layers, but vLLM currently supports
+# only the first layer
+_MIMO_V2_PRO_NUM_MTP_LAYERS = 1
+_MIMO_V2_FLASH_NUM_MTP_LAYERS = 1
+
+
+class MiMoV2MTPLayer(nn.Module):
+    """Single MTP predictor layer for MiMo-V2 (Pro and Flash).
+
+    Mirrors the single-layer MiMo-V2 nextn reference implementation.
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        prefix: str,
+        quant_config: QuantizationConfig | None = None,
+    ) -> None:
+        super().__init__()
+
+        # Predictor head components
+        self.enorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
+        self.hnorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
+        self.eh_proj = ReplicatedLinear(
+            config.hidden_size * 2, config.hidden_size, bias=False
+        )
+
+        # MTP uses the SWA attention configuration
+        # implementation.
+        swa_rope_theta = getattr(
+            config,
+            "swa_rope_theta",
+            getattr(config, "rope_theta", 1000000),
+        )
+        sliding_window_size = getattr(config, "sliding_window_size", -1)
+
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
+        self.self_attn = MiMoV2Attention(
+            hidden_size=config.hidden_size,
+            num_heads=config.swa_num_attention_heads,
+            num_kv_heads=config.swa_num_key_value_heads,
+            head_dim=config.swa_head_dim,
+            v_head_dim=getattr(config, "swa_v_head_dim", None),
+            v_scale=getattr(config, "attention_value_scale", None),
+            sliding_window_size=sliding_window_size,
+            attention_bias=config.attention_bias,
+            add_swa_attention_sink_bias=getattr(
+                config, "add_swa_attention_sink_bias", False
+            ),
+            layer_id=0,
+            rope_theta=swa_rope_theta,
+            max_position_embeddings=getattr(config, "max_position_embeddings", 32768),
+            quant_config=quant_config,
+            partial_rotary_factor=getattr(config, "partial_rotary_factor", 1.0),
+            prefix=f"{prefix}.self_attn",
+        )
+        self.pre_mlp_layernorm = RMSNorm(
+            config.hidden_size, eps=config.layernorm_epsilon
+        )
+        self.mlp = MiMoV2MLP(
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+        self.final_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        # Combine token embedding and previous hidden state
+        h, _ = self.eh_proj(
+            torch.cat(
+                [self.enorm(inputs_embeds), self.hnorm(previous_hidden_states)], dim=-1
+            )
+        )
+
+        # Transformer block with fused residual norms
+        residual = h
+        h = self.input_layernorm(h)
+        h = self.self_attn(positions=positions, hidden_states=h)
+        h, residual = self.pre_mlp_layernorm(h, residual)
+        h = self.mlp(h)
+        h = h + residual
+
+        return self.final_layernorm(h)
+
+
+class _MiMoV2MTPLayers(nn.Module):
+    """Thin wrapper so parameter paths match checkpoint: model.mtp.layers.*"""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        num_mtp_layers: int,
+        quant_config: QuantizationConfig | None,
+        prefix: str,
+    ) -> None:
+        super().__init__()
+        self.layers = nn.ModuleDict(
+            {
+                str(i): MiMoV2MTPLayer(
+                    config=config,
+                    prefix=f"{prefix}.{i}",
+                    quant_config=quant_config,
+                )
+                for i in range(num_mtp_layers)
+            }
+        )
+
+
+class MiMoV2MultiTokenPredictor(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        spec_cfg = vllm_config.speculative_config
+        assert spec_cfg is not None
+        num_mtp_layers = 1
+
+        self.num_mtp_layers = num_mtp_layers
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+
+        self.mtp = _MiMoV2MTPLayers(
+            config=config,
+            num_mtp_layers=num_mtp_layers,
+            quant_config=vllm_config.quant_config,
+            prefix=maybe_prefix(prefix, "mtp.layers"),
+        )
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_input_ids(input_ids)
+        current_step_idx = spec_step_idx % self.num_mtp_layers
+        return self.mtp.layers[str(current_step_idx)](
+            inputs_embeds, positions, previous_hidden_states
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        lm_head: ParallelLMHead,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        return self.logits_processor(lm_head, hidden_states)
+
+
+class MiMoV2MTP(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+        self.config = vllm_config.model_config.hf_config
+        self.model = MiMoV2MultiTokenPredictor(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+        self.lm_head = ParallelLMHead(
+            self.config.vocab_size,
+            self.config.hidden_size,
+            prefix=maybe_prefix(prefix, "lm_head"),
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        return self.model(
+            input_ids, positions, hidden_states, inputs_embeds, spec_step_idx
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor | None:
+        return self.model.compute_logits(hidden_states, self.lm_head, spec_step_idx)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        tp_rank = get_tensor_model_parallel_rank()
+        tp_size = get_tensor_model_parallel_world_size()
+
+        stacked_params_mapping = [
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+            # Flash format: separate projections → fused qkv_proj
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            # Only load MTP-related weights, shared embeddings, and lm_head
+            if (
+                "model.mtp" not in name
+                and "model.embed_tokens" not in name
+                and not name.startswith("lm_head")
+            ):
+                continue
+
+            # Support fused qkv_proj checkpoint (Pro format).
+            # The checkpoint is stored pre-sharded for TP=8 as
+            # [Q_rank0, K_rank0, V_rank0, Q_rank1, ...], so splitting along
+            # dim 0 with chunk(tp_size) gives each rank its Q+K+V slice for
+            # both the FP8 weight and the block weight_scale_inv. This matches
+            # how the main model loads the same layout.
+            if "qkv_proj" in name:
+                if name in params_dict:
+                    param = params_dict[name]
+                    loaded_weight = loaded_weight.chunk(tp_size, dim=0)[tp_rank]
+                    default_weight_loader(param, loaded_weight)
+                    loaded_params.add(name)
+                continue
+
+            # gate_proj/up_proj → gate_up_proj stacking (both formats);
+            # Flash: q_proj/k_proj/v_proj → qkv_proj merging.
+            stacked_matched = False
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name_rewritten = name.replace(weight_name, param_name)
+                if (
+                    name_rewritten.endswith(".bias")
+                    and name_rewritten not in params_dict
+                ):
+                    continue
+                if name_rewritten not in params_dict:
+                    continue
+                param = params_dict[name_rewritten]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(name_rewritten)
+                stacked_matched = True
+                break
+
+            if stacked_matched:
+                continue
+
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            if name not in params_dict:
+                continue
+
+            param = params_dict[name]
+            # attention_sink_bias is head-parallel; slice by tp
+            if "attention_sink_bias" in name:
+                total_heads = loaded_weight.shape[0]
+                heads_per_rank = total_heads // tp_size
+                loaded_weight = loaded_weight.narrow(
+                    0, tp_rank * heads_per_rank, heads_per_rank
+                )
+
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        return loaded_params
+
+
+class MiMoV2OmniMTP(MiMoV2MTP, SupportsMultiModal):
+    def embed_input_ids(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: MultiModalEmbeddings | None = None,
+        *,
+        is_multimodal: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self._embed_text_input_ids(
+            input_ids,
+            self.model.embed_input_ids,
+            is_multimodal=is_multimodal,
+        )
+
+        if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
+            return inputs_embeds
+
+        is_multimodal = _require_is_multimodal(is_multimodal)
+
+        inputs_embeds = _merge_multimodal_embeddings(
+            inputs_embeds=inputs_embeds,
+            multimodal_embeddings=multimodal_embeddings,
+            is_multimodal=is_multimodal,
+        )
+
+        return inputs_embeds
diff --git a/vllm/model_executor/models/mimo_v2_omni.py b/vllm/model_executor/models/mimo_v2_omni.py
new file mode 100644
index 000000000000..1cd2c6919a34
--- /dev/null
+++ b/vllm/model_executor/models/mimo_v2_omni.py
@@ -0,0 +1,1488 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import math
+from collections.abc import Callable, Iterable, Mapping, Sequence
+from functools import partial
+from typing import Any
+
+import einops
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import BatchFeature, PretrainedConfig
+from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
+
+from vllm.config import VllmConfig
+from vllm.config.multimodal import BaseDummyOptions
+from vllm.distributed import parallel_state
+from vllm.distributed import utils as dist_utils
+from vllm.inputs import MultiModalDataDict
+from vllm.model_executor.layers.activation import get_act_and_mul_fn
+from vllm.model_executor.layers.attention import MMEncoderAttention
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.rotary_embedding.common import ApplyRotaryEmb
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.vision import is_vit_use_data_parallel
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalFieldConfig, MultiModalKwargsItems
+from vllm.multimodal.parse import ImageSize, MultiModalDataItems
+from vllm.multimodal.processing import (
+    BaseDummyInputsBuilder,
+    BaseMultiModalProcessor,
+    BaseProcessingInfo,
+    PromptReplacement,
+    PromptUpdate,
+    PromptUpdateDetails,
+)
+from vllm.transformers_utils.configs.mimo_v2_omni import Mimo_VLVisionConfig
+from vllm.transformers_utils.processors.mimo_v2_omni import (
+    MiMoOmniProcessor,
+    VideoAudioInput,
+    _format_timestamp,
+)
+
+from .interfaces import (
+    MultiModalEmbeddings,
+    SupportsMultiModal,
+    SupportsPP,
+    SupportsQuant,
+)
+from .mimo_audio import MimoAudioEncoder
+from .mimo_v2 import MiMoV2FlashForCausalLM
+from .qwen2_5_vl import (
+    Qwen2_5_VisionMLP,
+    Qwen2_5_VisionPatchEmbed,
+    Qwen2_5_VLImageEmbeddingInputs,
+    Qwen2_5_VLImageInputs,
+    Qwen2_5_VLImagePixelInputs,
+    Qwen2_5_VLVideoEmbeddingInputs,
+    Qwen2_5_VLVideoInputs,
+    Qwen2_5_VLVideoPixelInputs,
+)
+from .qwen2_vl import _create_qwen2vl_field_factory
+from .utils import AutoWeightsLoader, IntermediateTensors, WeightsMapper, maybe_prefix
+
+
+class MiMoVisionMLP(Qwen2_5_VisionMLP):
+    pass
+
+
+class MiMoVisionPatchEmbed(Qwen2_5_VisionPatchEmbed):
+    pass
+
+
+class MiMoVisionPatchMerger(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        context_dim: int,
+        norm_layer: Callable[[int], nn.Module] | None = None,
+        spatial_merge_size: int = 2,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        use_data_parallel = is_vit_use_data_parallel()
+        self.hidden_size = context_dim * (spatial_merge_size**2)
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.ln_q = norm_layer(context_dim)
+
+        self.mlp = nn.Sequential(
+            ColumnParallelLinear(
+                self.hidden_size,
+                self.hidden_size,
+                bias=False,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp.0",
+                return_bias=False,
+                disable_tp=use_data_parallel,
+            ),
+            nn.GELU(),
+            RowParallelLinear(
+                self.hidden_size,
+                d_model,
+                bias=False,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp.2",
+                return_bias=False,
+                disable_tp=use_data_parallel,
+            ),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.ln_q(x)
+        x = x.view(-1, self.hidden_size)
+        out = self.mlp(x)
+        return out
+
+
+class MiMoVisionAttention(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        num_kv_heads: int,
+        qk_channels: int,
+        kv_channels: int,
+        use_sink: bool = False,
+        visual_token_window_size: int = 64,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        use_data_parallel = is_vit_use_data_parallel()
+        self.tp_size = (
+            1
+            if use_data_parallel
+            else parallel_state.get_tensor_model_parallel_world_size()
+        )
+        self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
+
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.qk_channels = qk_channels
+        self.kv_channels = kv_channels
+        self.embed_dim = embed_dim
+
+        self.num_heads_per_partition = dist_utils.divide(num_heads, self.tp_size)
+        self.num_kv_heads_per_partition = dist_utils.divide(num_kv_heads, self.tp_size)
+
+        # Attention scale uses the Q/K head dimension (qk_channels)
+        self.scale = qk_channels**-0.5
+
+        # QKV: Q is (num_heads * qk_channels), KV are (num_kv_heads * kv_channels)
+        self.qkv = QKVParallelLinear(
+            hidden_size=embed_dim,
+            head_size=qk_channels,
+            total_num_heads=num_heads,
+            total_num_kv_heads=num_kv_heads,
+            v_head_size=kv_channels,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv",
+            disable_tp=use_data_parallel,
+        )
+
+        # Output projection: input is (num_heads * kv_channels) after attention
+        self.proj = RowParallelLinear(
+            input_size=num_heads * kv_channels,
+            output_size=embed_dim,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.proj",
+            disable_tp=use_data_parallel,
+        )
+
+        # For full attention (non-window blocks)
+        self.attn = MMEncoderAttention(
+            num_heads=self.num_heads_per_partition,
+            head_size=kv_channels,
+            scale=self.scale,
+            num_kv_heads=self.num_kv_heads_per_partition,
+            prefix=f"{prefix}.attn",
+        )
+
+        # Rotary embeddings applied separately to Q and K
+        self.apply_rotary_emb = ApplyRotaryEmb(enforce_enable=True)
+
+        # Sink attention weights (loaded but not used in vLLM flash_attn)
+        # The checkpoint stores these only for non-full-attention blocks
+        self.use_sink = use_sink
+        if use_sink:
+            self.sinks = nn.Parameter(
+                torch.empty(num_heads),
+                requires_grad=False,
+            )
+        else:
+            self.sinks = None
+
+        self.visual_token_window_size = visual_token_window_size
+
+    def _forward_window_attn(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        max_seqlen: torch.Tensor,
+    ) -> torch.Tensor:
+        """Window attention via flash_attn_varlen_func with window_size."""
+        from vllm.vllm_flash_attn import flash_attn_varlen_func
+
+        w = self.visual_token_window_size
+        output = flash_attn_varlen_func(
+            q,
+            k,
+            v,
+            cu_seqlens_q=cu_seqlens,
+            cu_seqlens_k=cu_seqlens,
+            max_seqlen_q=max_seqlen,
+            max_seqlen_k=max_seqlen,
+            softmax_scale=self.scale,
+            causal=False,
+            window_size=[w, w],
+        )
+        return output
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
+        max_seqlen: torch.Tensor,
+        full_attn: bool = True,
+    ) -> torch.Tensor:
+        """
+        Args:
+            x: [seq_len, batch=1, embed_dim]  (seq-first convention)
+            cu_seqlens: cumulative sequence lengths [num_seqs+1], int32
+            rotary_pos_emb_cos: [seq_len, qk_channels // 2]
+            rotary_pos_emb_sin: [seq_len, qk_channels // 2]
+            max_seqlen: maximum sequence length
+            full_attn: if True, full attention; if False, window attention
+        """
+        # [seq_len, 1, embed_dim] -> QKV projection
+        qkv, _ = self.qkv(x)  # [seq_len, 1, q_size + kv_size + kv_size]
+        seq_len, batch_size, _ = qkv.shape
+
+        q_size = self.num_heads_per_partition * self.qk_channels
+        kv_size = self.num_kv_heads_per_partition * self.kv_channels
+        q, k, v = qkv.split([q_size, kv_size, kv_size], dim=-1)
+
+        # Rearrange to [batch, seq, head, head_dim] for rotary application
+        q = einops.rearrange(q, "s b (h d) -> b s h d", h=self.num_heads_per_partition)
+        k = einops.rearrange(
+            k, "s b (h d) -> b s h d", h=self.num_kv_heads_per_partition
+        )
+        v = einops.rearrange(
+            v, "s b (h d) -> b s h d", h=self.num_kv_heads_per_partition
+        )
+
+        # Apply rotary embeddings to Q and K independently (handles GQA)
+        if rotary_pos_emb_cos is not None and rotary_pos_emb_sin is not None:
+            q = self.apply_rotary_emb(q, rotary_pos_emb_cos, rotary_pos_emb_sin)
+            k = self.apply_rotary_emb(k, rotary_pos_emb_cos, rotary_pos_emb_sin)
+
+        if full_attn:
+            # Full attention via MMEncoderAttention
+            # Flatten to [batch, seq, heads * head_dim]
+            q_flat = q.reshape(batch_size, seq_len, -1)
+            k_flat = k.reshape(batch_size, seq_len, -1)
+            v_flat = v.reshape(batch_size, seq_len, -1)
+            context_layer = self.attn(
+                query=q_flat,
+                key=k_flat,
+                value=v_flat,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+            )
+            # context_layer: [batch, seq, num_heads, head_dim] or [batch, seq, hidden]
+            # Ensure shape is [seq, batch, num_heads * kv_channels]
+            if context_layer.dim() == 4:
+                context_layer = einops.rearrange(
+                    context_layer, "b s h d -> s b (h d)"
+                ).contiguous()
+            else:
+                context_layer = einops.rearrange(
+                    context_layer, "b s d -> s b d"
+                ).contiguous()
+        else:
+            # Window attention via flash_attn_varlen_func with window_size
+            # Flatten batch dimension: [seq, head, head_dim]
+            q_varlen = einops.rearrange(q, "b s h d -> (b s) h d")
+            k_varlen = einops.rearrange(k, "b s h d -> (b s) h d")
+            v_varlen = einops.rearrange(v, "b s h d -> (b s) h d")
+            output = self._forward_window_attn(
+                q_varlen, k_varlen, v_varlen, cu_seqlens, max_seqlen
+            )
+            # output: [total_tokens, num_heads, kv_channels]
+            context_layer = einops.rearrange(
+                output, "(b s) h d -> s b (h d)", b=batch_size
+            ).contiguous()
+
+        output, _ = self.proj(context_layer)
+        return output
+
+
+class MiMoVisionBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        num_kv_heads: int,
+        qk_channels: int,
+        kv_channels: int,
+        mlp_hidden_dim: int,
+        act_fn: Callable[[torch.Tensor], torch.Tensor] = F.silu,
+        norm_eps: float = 1e-6,
+        use_sink: bool = False,
+        visual_token_window_size: int = 64,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.norm1 = RMSNorm(dim, eps=norm_eps)
+        self.norm2 = RMSNorm(dim, eps=norm_eps)
+        self.attn = MiMoVisionAttention(
+            embed_dim=dim,
+            num_heads=num_heads,
+            num_kv_heads=num_kv_heads,
+            qk_channels=qk_channels,
+            kv_channels=kv_channels,
+            use_sink=use_sink,
+            visual_token_window_size=visual_token_window_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+        self.mlp = MiMoVisionMLP(
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_fn=act_fn,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
+        max_seqlen: torch.Tensor,
+        full_attn: bool = True,
+    ) -> torch.Tensor:
+        # x: [seq_len, batch=1, dim]
+        x_attn = self.attn(
+            self.norm1(x),
+            cu_seqlens=cu_seqlens,
+            rotary_pos_emb_cos=rotary_pos_emb_cos,
+            rotary_pos_emb_sin=rotary_pos_emb_sin,
+            max_seqlen=max_seqlen,
+            full_attn=full_attn,
+        )
+        # Fused residual add + norm2
+        x_norm, residual = self.norm2(x, residual=x_attn)
+        x = residual + self.mlp(x_norm)
+        return x
+
+
+class MiMoVisionTransformer(nn.Module):
+    def __init__(
+        self,
+        vision_cfg: PretrainedConfig,
+        *,
+        norm_eps: float = 1e-6,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.spatial_merge_size = vision_cfg.spatial_merge_size
+        self.spatial_merge_unit = self.spatial_merge_size**2
+        self.fullatt_block_indexes = vision_cfg.fullatt_block_indexes
+        self.vit_window_attn_types = vision_cfg.vit_window_attn_types
+        self.visual_token_window_size = vision_cfg.visual_token_window_size
+        self.hidden_size = vision_cfg.hidden_size
+        self.num_heads = vision_cfg.num_heads
+        self.num_kv_heads = vision_cfg.num_key_value_heads
+        self.qk_channels = vision_cfg.qk_channels
+        self.kv_channels = vision_cfg.kv_channels
+
+        self.patch_embed = MiMoVisionPatchEmbed(
+            patch_size=vision_cfg.patch_size,
+            temporal_patch_size=vision_cfg.temporal_patch_size,
+            in_channels=vision_cfg.in_channels,
+            hidden_size=vision_cfg.hidden_size,
+        )
+
+        norm_layer = partial(RMSNorm, eps=norm_eps)
+
+        # Rotary embedding for 2D positions.
+        # With partial_rotary_factor=0.5 and head_size=qk_channels:
+        #   rotary_dim = qk_channels // 2
+        #   get_cos_sin returns cos, sin each of shape [pos, rotary_dim // 2]
+        # After indexing with 2D pos_ids and flattening:
+        #   result shape = [tokens, rotary_dim] = [tokens, qk_channels // 2]
+        # which is what ApplyRotaryEmb expects as cos/sin input.
+        self.rotary_pos_emb = get_rope(
+            head_size=vision_cfg.qk_channels,
+            max_position=8192,
+            is_neox_style=True,
+            rope_parameters={"partial_rotary_factor": 0.5},
+        )
+
+        self.blocks = nn.ModuleList(
+            [
+                MiMoVisionBlock(
+                    dim=vision_cfg.hidden_size,
+                    num_heads=vision_cfg.num_heads,
+                    num_kv_heads=vision_cfg.num_key_value_heads,
+                    qk_channels=vision_cfg.qk_channels,
+                    kv_channels=vision_cfg.kv_channels,
+                    mlp_hidden_dim=vision_cfg.intermediate_size,
+                    act_fn=get_act_and_mul_fn(vision_cfg.hidden_act),
+                    norm_eps=norm_eps,
+                    use_sink=(
+                        vision_cfg.use_sink
+                        and i not in vision_cfg.fullatt_block_indexes
+                    ),
+                    visual_token_window_size=vision_cfg.visual_token_window_size,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.blocks.{i}",
+                )
+                for i in range(vision_cfg.depth)
+            ]
+        )
+
+        self.merger = MiMoVisionPatchMerger(
+            d_model=vision_cfg.out_hidden_size,
+            context_dim=vision_cfg.hidden_size,
+            norm_layer=norm_layer,
+            spatial_merge_size=vision_cfg.spatial_merge_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.merger",
+        )
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.patch_embed.proj.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.patch_embed.proj.weight.device
+
+    def apply_index(self, tensor: torch.Tensor, index: torch.Tensor) -> torch.Tensor:
+        """Reindex tensor at the spatial_merge_unit granularity."""
+        tensor = tensor.unflatten(0, (-1, self.spatial_merge_unit))
+        tensor = tensor[index]
+        tensor = tensor.flatten(0, 1)
+        return tensor
+
+    def get_window_index_1d(
+        self, grid_thw: torch.Tensor, col: bool = True
+    ) -> torch.Tensor:
+        """Compute 1D window indices for col-based or row-based SWA reordering."""
+        window_index: list[torch.Tensor] = []
+        window_index_id = 0
+        for grid_t, grid_h, grid_w in grid_thw:
+            llm_grid_h = grid_h // self.spatial_merge_size
+            llm_grid_w = grid_w // self.spatial_merge_size
+            index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(
+                grid_t, llm_grid_h, llm_grid_w
+            )
+            index_new = index.transpose(1, 2).reshape(-1) if col else index.reshape(-1)
+            window_index.append(index_new + window_index_id)
+            window_index_id += int((grid_t * llm_grid_h * llm_grid_w).item())
+        return torch.cat(window_index, dim=0)
+
+    def rot_pos_emb(self, grid_thw: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        """Compute 2D rotary position embedding cos/sin for given grid sizes.
+
+        Returns:
+            cos: [total_tokens, qk_channels // 2]
+            sin: [total_tokens, qk_channels // 2]
+        """
+        cos_list, sin_list = [], []
+        for i in range(grid_thw.size(0)):
+            t, h, w = int(grid_thw[i, 0]), int(grid_thw[i, 1]), int(grid_thw[i, 2])
+
+            # Build 2D position IDs with spatial_merge_size interleaving
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+            hpos_ids = (
+                hpos_ids.reshape(
+                    h // self.spatial_merge_size,
+                    self.spatial_merge_size,
+                    w // self.spatial_merge_size,
+                    self.spatial_merge_size,
+                )
+                .permute(0, 2, 1, 3)
+                .flatten()
+            )
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+            wpos_ids = (
+                wpos_ids.reshape(
+                    h // self.spatial_merge_size,
+                    self.spatial_merge_size,
+                    w // self.spatial_merge_size,
+                    self.spatial_merge_size,
+                )
+                .permute(0, 2, 1, 3)
+                .flatten()
+            )
+            pos_ids = torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)
+            # pos_ids: [t*h*w, 2]
+
+            max_grid_size = max(h, w)
+            # get_cos_sin returns cos, sin each of shape [max_grid_size, rotary_dim//2]
+            # where rotary_dim = qk_channels // 2 (from partial_rotary_factor=0.5)
+            cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size)
+
+            # [t*h*w, 2, rotary_dim//2] -> [t*h*w, rotary_dim] (= qk_channels // 2)
+            cos_img = cos[pos_ids].flatten(1)
+            sin_img = sin[pos_ids].flatten(1)
+            cos_list.append(cos_img)
+            sin_list.append(sin_img)
+
+        return torch.cat(cos_list, dim=0), torch.cat(sin_list, dim=0)
+
+    def forward(self, x: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x: [total_tokens, C] pre-flattened patches
+            grid_thw: [num_images, 3] tensor of (t, h, w) for each image/video
+        Returns:
+            [merged_tokens, out_hidden_size]
+        """
+        # Ensure grid_thw is a tensor
+        if not isinstance(grid_thw, torch.Tensor):
+            grid_thw = torch.tensor(grid_thw, dtype=torch.long)
+
+        # Move to visual model device/dtype
+        x = x.to(device=self.device, dtype=self.dtype)
+
+        # Patch embedding: [total_tokens, hidden_size]
+        x = self.patch_embed(x)
+
+        # Compute 2D rotary positional embeddings
+        # cos, sin: [total_tokens, qk_channels // 2]
+        rotary_cos, rotary_sin = self.rot_pos_emb(grid_thw)
+        rotary_cos = rotary_cos.to(device=x.device)
+        rotary_sin = rotary_sin.to(device=x.device)
+
+        # Compute cu_seqlens for flash_attn (per-image/video sequence lengths)
+        seqlens = torch.repeat_interleave(
+            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
+        )
+        cu_seqlens = torch.cat(
+            [
+                torch.tensor([0], device=x.device, dtype=torch.int32),
+                seqlens.cumsum(dim=0).to(device=x.device, dtype=torch.int32),
+            ]
+        )
+        max_seqlen = seqlens.max()
+
+        # Precompute col-based window index for type=1 (col SWA) layers
+        window_index_1d_col = self.get_window_index_1d(grid_thw, col=True).to(
+            device=x.device
+        )
+        reverse_window_index_1d_col = torch.argsort(window_index_1d_col)
+
+        # Col-based rotary embeddings (reordered at spatial_merge_unit granularity).
+        # apply_index reorders groups of spatial_merge_unit tokens, just like x.
+        col_cos = self.apply_index(rotary_cos, window_index_1d_col)
+        col_sin = self.apply_index(rotary_sin, window_index_1d_col)
+
+        # Add batch dimension: [total_tokens, 1, hidden_size]
+        x = x.unsqueeze(1)
+
+        for i, blk in enumerate(self.blocks):
+            window_attn_type = self.vit_window_attn_types[i]
+
+            # Reorder tokens to col-based layout when entering col-SWA region
+            if window_attn_type == 1 and (
+                i == 0 or self.vit_window_attn_types[i - 1] != 1
+            ):
+                x = self.apply_index(x, window_index_1d_col)
+
+            # Restore row-based order when leaving col-SWA region
+            if (
+                i > 0
+                and window_attn_type != 1
+                and self.vit_window_attn_types[i - 1] == 1
+            ):
+                x = self.apply_index(x, reverse_window_index_1d_col)
+
+            # Use col-based embeddings for col-SWA layers
+            cos_now = col_cos if window_attn_type == 1 else rotary_cos
+            sin_now = col_sin if window_attn_type == 1 else rotary_sin
+
+            full_attn = i in self.fullatt_block_indexes
+            x = blk(
+                x,
+                cu_seqlens=cu_seqlens,
+                rotary_pos_emb_cos=cos_now,
+                rotary_pos_emb_sin=sin_now,
+                max_seqlen=max_seqlen,
+                full_attn=full_attn,
+            )
+
+        # Restore row-based order if last block was col-SWA
+        if self.vit_window_attn_types[-1] == 1:
+            x = self.apply_index(x, reverse_window_index_1d_col)
+
+        # Remove batch dim and merge spatial tokens
+        # x: [total_tokens, 1, hidden_size] -> [total_tokens, hidden_size]
+        x = x.squeeze(1)
+        x = self.merger(x)
+        return x
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            ("mlp.gate_up_proj", "mlp.gate_proj", 0),
+            ("mlp.gate_up_proj", "mlp.up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class MiMoV2OmniProcessingInfo(BaseProcessingInfo):
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
+        return {"audio": None, "image": None, "video": None}
+
+    def get_hf_config(self):
+        config = self.ctx.get_hf_config()
+        if isinstance(config.vision_config, dict):
+            config.vision_config = Mimo_VLVisionConfig.from_dict(config.vision_config)
+        return config
+
+    def get_hf_processor(self, **kwargs: object) -> MiMoOmniProcessor:
+        hf_config = self.get_hf_config()
+        tokenizer = self.get_tokenizer()
+        return MiMoOmniProcessor.from_hf_config(tokenizer, hf_config)
+
+    def get_image_processor(self, **kwargs: object):
+        return self.get_hf_processor(**kwargs).image_processor
+
+    def get_data_parser(self):
+        from vllm.multimodal.parse import MultiModalDataParser
+
+        return MultiModalDataParser(target_sr=24000.0)
+
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Mapping[str, int]:
+        return {
+            "image": self.get_max_image_tokens(),
+            "video": self.get_max_video_tokens(seq_len, mm_counts),
+        }
+
+    def _get_vision_info(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        num_frames: int = 1,
+        do_resize: bool = True,
+        image_processor,
+        mm_kwargs: Mapping[str, object],
+    ) -> tuple[ImageSize, int]:
+        hf_config = self.get_hf_config()
+        vision_config = hf_config.vision_config
+        patch_size = vision_config.patch_size
+        merge_size = vision_config.spatial_merge_size
+        temporal_patch_size = vision_config.temporal_patch_size
+        tokens_per_second = vision_config.tokens_per_second
+
+        mm_kwargs = self.ctx.get_merged_mm_kwargs(mm_kwargs)
+        size = image_processor.size
+        if override_size := mm_kwargs.get("size"):
+            size = size | override_size
+        if (override_min_pixels := mm_kwargs.get("min_pixels")) is not None:
+            size = size | {"shortest_edge": override_min_pixels}
+        if (override_max_pixels := mm_kwargs.get("max_pixels")) is not None:
+            size = size | {"longest_edge": override_max_pixels}
+
+        if do_resize:
+            resized_height, resized_width = smart_resize(
+                height=image_height,
+                width=image_width,
+                factor=patch_size * merge_size,
+                min_pixels=size["shortest_edge"],
+                max_pixels=size["longest_edge"],
+            )
+            preprocessed_size = ImageSize(width=resized_width, height=resized_height)
+        else:
+            preprocessed_size = ImageSize(width=image_width, height=image_height)
+
+        # For video, MiMo resamples to tokens_per_second fps before temporal patching,
+        # effective tokens = num_frames * tokens_per_second / temporal_patch_size.
+        # For images (num_frames == 1) no resampling is applied.
+        if num_frames > 1:
+            effective_frames = num_frames * tokens_per_second
+        else:
+            effective_frames = num_frames
+        padded_num_frames = effective_frames + effective_frames % temporal_patch_size
+        grid_t = max(padded_num_frames // temporal_patch_size, 1)
+        grid_h = preprocessed_size.height // patch_size
+        grid_w = preprocessed_size.width // patch_size
+        num_patches = grid_t * grid_h * grid_w
+        num_vision_tokens = num_patches // (merge_size**2)
+        return preprocessed_size, num_vision_tokens
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        image_processor,
+        mm_kwargs: Mapping[str, object],
+    ) -> int:
+        _, num_image_tokens = self._get_vision_info(
+            image_width=image_width,
+            image_height=image_height,
+            num_frames=1,
+            image_processor=image_processor,
+            mm_kwargs=mm_kwargs,
+        )
+        return num_image_tokens
+
+    def get_num_video_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        num_frames: int,
+        image_processor,
+        mm_kwargs: Mapping[str, object],
+    ) -> int:
+        _, num_video_tokens = self._get_vision_info(
+            image_width=image_width,
+            image_height=image_height,
+            num_frames=num_frames,
+            image_processor=image_processor,
+            mm_kwargs=mm_kwargs,
+        )
+        return num_video_tokens
+
+    def get_image_size_with_most_features(
+        self, max_pixels: int | None = None
+    ) -> ImageSize:
+        hf_config = self.get_hf_config()
+        vision_config = hf_config.vision_config
+        patch_size = vision_config.patch_size
+        merge_size = vision_config.spatial_merge_size
+
+        if max_pixels is None:
+            image_processor = self.get_image_processor()
+            mm_kwargs = self.ctx.get_merged_mm_kwargs({})
+            size = image_processor.size
+            if override_size := mm_kwargs.get("size"):
+                size = size | override_size
+            if (override_min_pixels := mm_kwargs.get("min_pixels")) is not None:
+                size = size | {"shortest_edge": override_min_pixels}
+            if (override_max_pixels := mm_kwargs.get("max_pixels")) is not None:
+                size = size | {"longest_edge": override_max_pixels}
+            max_pixels = size["longest_edge"]
+
+        unit = patch_size * merge_size
+        max_seq_len = max_pixels // (unit * unit)
+
+        def closest_factor_pair(n: int) -> tuple[int, int]:
+            for d in range(math.isqrt(n), 0, -1):
+                if n % d == 0:
+                    return d, n // d
+            return 1, n
+
+        height_factor, width_factor = 1, max_seq_len
+        for seq_len in range(max_seq_len, 0, -1):
+            height_factor, width_factor = closest_factor_pair(seq_len)
+            if width_factor / height_factor <= 200:
+                break
+
+        return ImageSize(width=unit * width_factor, height=unit * height_factor)
+
+    def get_max_image_tokens(self) -> int:
+        image_processor = self.get_image_processor()
+        target_width, target_height = self.get_image_size_with_most_features()
+        return self.get_num_image_tokens(
+            image_width=target_width,
+            image_height=target_height,
+            image_processor=image_processor,
+            mm_kwargs={},
+        )
+
+    def _get_max_video_frames(self, max_tokens: int, start_num_frames: int = 1) -> int:
+        image_processor = self.get_image_processor()
+        target_width, target_height = self.get_image_size_with_most_features()
+        num_frames = start_num_frames
+        while True:
+            next_num_frames = num_frames + 1
+            next_max_tokens = self.get_num_video_tokens(
+                image_width=target_width,
+                image_height=target_height,
+                num_frames=next_num_frames,
+                image_processor=image_processor,
+                mm_kwargs={},
+            )
+            if next_max_tokens > max_tokens:
+                break
+            num_frames = next_num_frames
+        return num_frames
+
+    def get_num_frames_with_most_features(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+        max_frames_per_video: int = 14,
+    ) -> int:
+        max_videos = mm_counts.get("video", 0)
+        max_total_frames = self._get_max_video_frames(seq_len)
+        max_frames_per_video = min(
+            max_total_frames // max(max_videos, 1), max_frames_per_video
+        )
+        return max(max_frames_per_video, 1)
+
+    def get_max_video_tokens(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        image_processor = self.get_image_processor()
+        target_width, target_height = self.get_image_size_with_most_features()
+        return self.get_num_video_tokens(
+            image_width=target_width,
+            image_height=target_height,
+            num_frames=self.get_num_frames_with_most_features(seq_len, mm_counts),
+            image_processor=image_processor,
+            mm_kwargs={},
+        )
+
+
+class MiMoV2OmniMultiModalProcessor(BaseMultiModalProcessor[MiMoV2OmniProcessingInfo]):
+    """vLLM multimodal processor for MiMo-Omni (image + video).
+
+    Key differences from Qwen2.5-VL:
+    - Videos use timestamp tokens between temporal grid positions.
+    - The HF processor expects ``(TCHW_tensor, timestamps_T_tensor)`` video
+      tuples rather than plain numpy arrays.
+    - ``video_start_times`` is tracked so prompt-update reconstruction can
+      regenerate the exact same timestamp token IDs.
+    """
+
+    # fps assumed for vllm-decoded video (numpy T,H,W,C arrays).
+    # The video loader samples ~32 frames; treat each frame as 1 s apart so
+    # MiMoVLProcessor sees 1 fps input and resamples internally.
+    _INPUT_FPS: float = 1.0
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        merge_size = self.info.get_hf_config().vision_config.spatial_merge_size
+        fields: dict[str, MultiModalFieldConfig] = dict(
+            **_create_qwen2vl_field_factory(merge_size)(hf_inputs),
+            second_per_grid_ts=MultiModalFieldConfig.batched("video"),
+            video_start_times=MultiModalFieldConfig.batched("video"),
+            audio_features=MultiModalFieldConfig.batched("audio"),
+            audio_token_lens=MultiModalFieldConfig.batched("audio"),
+        )
+        # video_audio fields: only present when video_audio content was processed
+        if "video_audio_n_segs" in hf_inputs:
+            fields["video_audio_n_segs"] = MultiModalFieldConfig.batched("video")
+        # video_audio_seg_lens: list of per-video 1D tensors, batched("video")
+        if "video_audio_seg_lens" in hf_inputs:
+            fields["video_audio_seg_lens"] = MultiModalFieldConfig.batched("video")
+        if "va_audio_features" in hf_inputs:
+            fields["va_audio_features"] = MultiModalFieldConfig.batched("va_audio")
+        return fields
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        """Convert numpy video arrays to (TCHW, timestamps) tuples for MiMo.
+        Also remap 'audios' → 'audio' since MiMoOmniProcessor.__call__ uses
+        the singular form.
+        """
+        # Remap audios → audio (MiMoOmniProcessor uses singular param name)
+        if "audios" in mm_data:
+            mm_data = {**mm_data, "audio": mm_data["audios"]}
+            mm_data = {k: v for k, v in mm_data.items() if k != "audios"}
+
+        # Handle video_audio items: convert video part to (TCHW, timestamps) tuple
+        if "video_audio" in mm_data:
+            va_converted: list[VideoAudioInput] = []
+            for va_item in mm_data["video_audio"]:
+                if isinstance(va_item, VideoAudioInput):
+                    vid = va_item.video
+                else:
+                    # Expect (video_frames, audio_source) tuple
+                    vid, audio_src = va_item
+                    va_item = VideoAudioInput(video=vid, audio=audio_src)
+                    vid = vid
+                # Convert video frames to (TCHW, timestamps) if needed
+                if (
+                    isinstance(vid, tuple)
+                    and len(vid) == 2
+                    and isinstance(vid[0], torch.Tensor)
+                    and isinstance(vid[1], torch.Tensor)
+                ):
+                    va_converted.append(va_item)
+                else:
+                    if isinstance(vid, np.ndarray):
+                        frames = torch.from_numpy(vid)
+                    elif isinstance(vid, torch.Tensor):
+                        frames = vid
+                    else:
+                        frames = torch.tensor(np.array(vid))
+                    if frames.ndim == 4 and frames.shape[-1] in (1, 3, 4):
+                        frames = frames.permute(0, 3, 1, 2).float()
+                    else:
+                        frames = frames.float()
+                    T = frames.shape[0]
+                    timestamps = torch.arange(T, dtype=torch.float32) / self._INPUT_FPS
+                    va_converted.append(
+                        VideoAudioInput(
+                            video=(frames, timestamps),
+                            audio=va_item.audio,
+                        )
+                    )
+            mm_data = {**mm_data, "video_audio": va_converted}
+
+        if "videos" in mm_data:
+            converted: list[tuple[torch.Tensor, torch.Tensor]] = []
+            for video in mm_data["videos"]:
+                if (
+                    isinstance(video, tuple)
+                    and len(video) == 2
+                    and isinstance(video[0], torch.Tensor)
+                    and isinstance(video[1], torch.Tensor)
+                ):
+                    # already in MiMo format
+                    converted.append(video)
+                else:
+                    # numpy (T, H, W, C) or torch (T, H, W, C) / (T, C, H, W)
+                    if isinstance(video, np.ndarray):
+                        frames = torch.from_numpy(video)
+                    elif isinstance(video, torch.Tensor):
+                        frames = video
+                    else:
+                        frames = torch.tensor(np.array(video))
+
+                    if frames.ndim == 4 and frames.shape[-1] in (1, 3, 4):
+                        # THWC → TCHW
+                        frames = frames.permute(0, 3, 1, 2).float()
+                    else:
+                        frames = frames.float()
+
+                    T = frames.shape[0]
+                    timestamps = torch.arange(T, dtype=torch.float32) / self._INPUT_FPS
+                    converted.append((frames, timestamps))
+
+            mm_data = {**mm_data, "videos": converted}
+
+        return super()._call_hf_processor(prompt, mm_data, mm_kwargs, tok_kwargs)
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        hf_config = self.info.get_hf_config()
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+
+        merge_size = hf_config.vision_config.spatial_merge_size
+        p = hf_processor.mimo_processor
+
+        image_pad_id = vocab[hf_processor.image_token]
+        video_pad_id = vocab[hf_processor.video_token]
+        audio_pad_id = vocab.get("<|audio_pad|>")
+        vision_start_id = p.vision_start_token_id
+        vision_end_id = p.vision_end_token_id
+        video_start_id = p.video_start_token_id
+        video_end_id = p.video_end_token_id
+        audio_start_id = p.audio_start_token_id
+        audio_end_id = p.audio_end_token_id
+
+        def get_image_replacement(item_idx: int) -> PromptUpdateDetails:
+            out_item = out_mm_kwargs["image"][item_idx]
+            grid_thw = out_item["image_grid_thw"].data
+            n_tokens = int(grid_thw.prod()) // merge_size**2
+            return [image_pad_id] * n_tokens
+
+        def get_video_replacement(item_idx: int) -> PromptUpdateDetails:
+            out_item = out_mm_kwargs["video"][item_idx]
+            grid_thw = out_item["video_grid_thw"].data
+            spt = float(out_item["second_per_grid_ts"].data)
+            start = float(out_item["video_start_times"].data)
+
+            T, H, W = map(int, grid_thw)
+            n_per_grid = H * W // (merge_size * merge_size)
+
+            # Check if this is a video_audio item
+            n_segs_field = out_item.get("video_audio_n_segs")
+            n_segs_val = int(n_segs_field.data) if n_segs_field is not None else 0
+            va_seg_lens: list[int] | None = None
+            if n_segs_val > 0:
+                seg_lens_field = out_item.get("video_audio_seg_lens")
+                if seg_lens_field is not None:
+                    va_seg_lens = seg_lens_field.data[:n_segs_val].tolist()
+
+            full: list[int] = [video_start_id]
+            is_embed_mask: list[bool] = [False]
+
+            if va_seg_lens is None:
+                # Regular video: timestamp + vision tokens per grid
+                for j in range(T):
+                    ts_text = _format_timestamp(start + j * spt)
+                    ts_ids = tokenizer.encode(ts_text, add_special_tokens=False)
+                    full.extend(ts_ids)
+                    is_embed_mask.extend([False] * len(ts_ids))
+                    full.append(vision_start_id)
+                    is_embed_mask.append(False)
+                    full.extend([video_pad_id] * n_per_grid)
+                    is_embed_mask.extend([True] * n_per_grid)
+                    full.append(vision_end_id)
+                    is_embed_mask.append(False)
+            else:
+                # video_audio: interleaved vision+audio per group
+                n_groups = len(va_seg_lens)
+                frames_per_group = T // n_groups  # 1 for il=0, T for il=-1
+                for g in range(n_groups):
+                    # Timestamp for first frame of this group
+                    frame0 = g * frames_per_group
+                    ts_text = _format_timestamp(start + frame0 * spt)
+                    ts_ids = tokenizer.encode(ts_text, add_special_tokens=False)
+                    full.extend(ts_ids)
+                    is_embed_mask.extend([False] * len(ts_ids))
+                    # Vision tokens for all frames in this group
+                    for f in range(frames_per_group):
+                        full.append(vision_start_id)
+                        is_embed_mask.append(False)
+                        full.extend([video_pad_id] * n_per_grid)
+                        is_embed_mask.extend([True] * n_per_grid)
+                        full.append(vision_end_id)
+                        is_embed_mask.append(False)
+                    # Audio tokens for this group
+                    seg_len = va_seg_lens[g]
+                    full.append(audio_start_id)
+                    is_embed_mask.append(False)
+                    full.extend([audio_pad_id] * seg_len)
+                    is_embed_mask.extend([True] * seg_len)
+                    full.append(audio_end_id)
+                    is_embed_mask.append(False)
+
+            full.append(video_end_id)
+            is_embed_mask.append(False)
+
+            embed_t = torch.tensor(is_embed_mask)
+            return PromptUpdateDetails(
+                full=full,
+                is_embed=lambda _tok, _seq: embed_t,
+            )
+
+        def get_audio_replacement(item_idx: int) -> PromptUpdateDetails:
+            out_item = out_mm_kwargs["audio"][item_idx]
+            tok_len = int(out_item["audio_token_lens"].data)
+            return [audio_pad_id] * tok_len
+
+        updates: list[PromptUpdate] = [
+            PromptReplacement(
+                modality="image",
+                target=[image_pad_id],
+                replacement=get_image_replacement,
+            ),
+            PromptReplacement(
+                modality="video",
+                target=[video_pad_id],
+                replacement=get_video_replacement,
+            ),
+        ]
+        if audio_pad_id is not None and audio_start_id is not None:
+            updates.append(
+                PromptReplacement(
+                    modality="audio",
+                    target=[audio_pad_id],
+                    replacement=get_audio_replacement,
+                )
+            )
+        return updates
+
+
+class MiMoV2OmniDummyInputsBuilder(BaseDummyInputsBuilder[MiMoV2OmniProcessingInfo]):
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+        num_audios = mm_counts.get("audio", 0)
+        image_ph = "<|vision_start|><|image_pad|><|vision_end|>"
+        video_ph = "<|vision_start|><|video_pad|><|vision_end|>"
+        audio_ph = "<|mimo_audio_start|><|audio_pad|><|mimo_audio_end|>"
+        return image_ph * num_images + video_ph * num_videos + audio_ph * num_audios
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+        mm_options: Mapping[str, BaseDummyOptions],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        target_width, target_height = self.info.get_image_size_with_most_features()
+        target_num_frames = self.info.get_num_frames_with_most_features(
+            seq_len, mm_counts
+        )
+
+        return {
+            "image": self._get_dummy_images(
+                width=target_width,
+                height=target_height,
+                num_images=num_images,
+                overrides=mm_options.get("image"),
+            ),
+            "video": self._get_dummy_videos(
+                width=target_width,
+                height=target_height,
+                num_frames=target_num_frames,
+                num_videos=num_videos,
+                overrides=mm_options.get("video"),
+            ),
+        }
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    MiMoV2OmniMultiModalProcessor,
+    info=MiMoV2OmniProcessingInfo,
+    dummy_inputs=MiMoV2OmniDummyInputsBuilder,
+)
+class MiMoV2OmniForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsQuant):
+    # To ensure correct weight loading and mapping.
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            # audio encoder
+            "speech_embeddings.": "audio_encoder.speech_embeddings.",
+            # mapping for new names in checkpoint saved after transformers v4.52
+            "model.language_model.": "language_model.model.",
+            "model.visual.": "visual.",
+            # mapping for original checkpoint
+            "lm_head.": "language_model.lm_head.",
+            "model.": "language_model.model.",
+        }
+    )
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
+        if modality.startswith("image"):
+            return "<|vision_start|><|image_pad|><|vision_end|>"
+        if modality.startswith("video"):
+            return "<|vision_start|><|video_pad|><|vision_end|>"
+        if modality.startswith("audio"):
+            return "<|mimo_audio_start|><|audio_pad|><|mimo_audio_end|>"
+
+        raise ValueError(f"Unsupported modality: {modality}")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.config = config
+        # Omni ViT/Audio Encoder BF16
+        vision_config = (
+            Mimo_VLVisionConfig.from_dict(config.vision_config)
+            if isinstance(config.vision_config, dict)
+            else config.vision_config
+        )
+        with self._mark_tower_model(vllm_config, {"image", "video"}):
+            self.visual = MiMoVisionTransformer(
+                vision_config,
+                norm_eps=getattr(vllm_config, "rms_norm_eps", 1e-6),
+                quant_config=None,
+                prefix=maybe_prefix(prefix, "visual"),
+            )
+        audio_config = getattr(config, "audio_config", None)
+        model_path = vllm_config.model_config.model
+        if audio_config is not None:
+            with self._mark_tower_model(vllm_config, "audio"):
+                self.audio_encoder = MimoAudioEncoder(
+                    audio_config, model_path=model_path
+                )
+        else:
+            self.audio_encoder = None
+        with self._mark_language_model(vllm_config):
+            self.language_model = MiMoV2FlashForCausalLM(
+                vllm_config=vllm_config,
+                prefix=maybe_prefix(prefix, "language_model"),
+            )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors
+        )
+
+    def _parse_and_validate_image_input(
+        self, **kwargs: object
+    ) -> Qwen2_5_VLImageInputs | None:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        image_grid_thw = kwargs.pop("image_grid_thw", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None:
+            return Qwen2_5_VLImagePixelInputs(
+                type="pixel_values",
+                pixel_values=pixel_values,
+                image_grid_thw=image_grid_thw,
+            )
+
+        if image_embeds is not None:
+            return Qwen2_5_VLImageEmbeddingInputs(
+                type="image_embeds",
+                image_embeds=image_embeds,
+                image_grid_thw=image_grid_thw,
+            )
+
+    def _parse_and_validate_video_input(
+        self, **kwargs: object
+    ) -> Qwen2_5_VLVideoInputs | None:
+        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
+        video_embeds = kwargs.pop("video_embeds", None)
+        video_grid_thw = kwargs.pop("video_grid_thw", None)
+        second_per_grid_ts = kwargs.pop("second_per_grid_ts", None)
+
+        if pixel_values_videos is None and video_embeds is None:
+            return None
+
+        if pixel_values_videos is not None:
+            return Qwen2_5_VLVideoPixelInputs(
+                type="pixel_values_videos",
+                pixel_values_videos=pixel_values_videos,
+                video_grid_thw=video_grid_thw,
+                second_per_grid_ts=second_per_grid_ts,
+            )
+
+        if video_embeds is not None:
+            return Qwen2_5_VLVideoEmbeddingInputs(
+                type="video_embeds",
+                video_embeds=video_embeds,
+                video_grid_thw=video_grid_thw,
+                second_per_grid_ts=second_per_grid_ts,
+            )
+
+    def _process_image_input(
+        self, image_input: Qwen2_5_VLImageInputs
+    ) -> tuple[torch.Tensor, ...]:
+        grid_thw = image_input["image_grid_thw"]
+        assert grid_thw.ndim == 2
+        grid_thw_list = grid_thw.tolist()
+
+        if image_input["type"] == "image_embeds":
+            image_embeds = image_input["image_embeds"].type(self.visual.dtype)
+        else:
+            pixel_values = image_input["pixel_values"]
+            image_embeds = self.visual(pixel_values, grid_thw=grid_thw_list)
+
+        # Split concatenated embeddings for each image item.
+        merge_size = self.visual.spatial_merge_size
+        sizes = (grid_thw.prod(-1) // merge_size // merge_size).tolist()
+        return image_embeds.split(sizes)
+
+    def _process_video_input(
+        self, video_input: Qwen2_5_VLVideoInputs
+    ) -> tuple[torch.Tensor, ...]:
+        grid_thw = video_input["video_grid_thw"]
+        assert grid_thw.ndim == 2
+        grid_thw_list = grid_thw.tolist()
+
+        if video_input["type"] == "video_embeds":
+            video_embeds = video_input["video_embeds"].type(self.visual.dtype)
+        else:
+            pixel_values_videos = video_input["pixel_values_videos"]
+            video_embeds = self.visual(pixel_values_videos, grid_thw=grid_thw_list)
+
+        # Split concatenated embeddings for each video item.
+        merge_size = self.visual.spatial_merge_size
+        sizes = (grid_thw.prod(-1) // merge_size // merge_size).tolist()
+        return video_embeds.split(sizes)
+
+    def _parse_and_validate_audio_input(self, **kwargs: object) -> dict | None:
+        audio_features = kwargs.pop("audio_features", None)
+        audio_token_lens = kwargs.pop("audio_token_lens", None)
+        if audio_features is None:
+            return None
+        return {
+            "type": "audio",
+            "audio_features": audio_features,
+            "audio_token_lens": audio_token_lens,
+        }
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        mm_input_by_modality = {}
+
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if (
+                input_key in ("pixel_values", "image_embeds")
+                and "image" not in mm_input_by_modality
+            ):
+                mm_input_by_modality["image"] = self._parse_and_validate_image_input(
+                    **kwargs
+                )
+            if (
+                input_key in ("pixel_values_videos", "video_embeds")
+                and "video" not in mm_input_by_modality
+            ):
+                mm_input_by_modality["video"] = self._parse_and_validate_video_input(
+                    **kwargs
+                )
+            if input_key == "audio_features" and "audio" not in mm_input_by_modality:
+                mm_input_by_modality["audio"] = self._parse_and_validate_audio_input(
+                    **kwargs
+                )
+        return mm_input_by_modality
+
+    def _process_audio_input(self, audio_input: dict) -> tuple[torch.Tensor, ...]:
+        mel_specs = audio_input["audio_features"]
+        if self.audio_encoder is None:
+            return ()
+        # Normalize to List[2D-Tensor].
+        # MultiModalBatchedField._reduce_data either wraps a single [T, 128]
+        # into [1, T, 128] via unsqueeze(0) or stacks N same-T items into
+        # [N, T, 128]. Indexing along dim-0 extracts the per-item [T, 128].
+        if isinstance(mel_specs, torch.Tensor):
+            mel_specs = list(mel_specs)  # [1,T,128] or [N,T,128] → [[T,128],...]
+        if not mel_specs:
+            return ()
+        audio_embeds, item_token_lens = self.audio_encoder.get_audio_feature(mel_specs)
+        return tuple(audio_embeds.split(item_token_lens))
+
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
+        # Pop video_audio-specific fields before main mm parsing
+        video_audio_n_segs = kwargs.pop("video_audio_n_segs", None)
+        video_audio_seg_lens = kwargs.pop("video_audio_seg_lens", None)
+        va_audio_features = kwargs.pop("va_audio_features", None)
+
+        mm_input_by_modality = self._parse_and_validate_multimodal_inputs(**kwargs)
+        if not mm_input_by_modality and va_audio_features is None:
+            return []
+
+        # The result multimodal_embeddings is tuple of tensors, with each
+        # tensor corresponding to a multimodal data item (image, video, or audio).
+        multimodal_embeddings: list[torch.Tensor] = []
+
+        # Pre-process va audio: one mel spec per va video → per-video audio embeddings
+        # keyed by va video index (0-based among va videos only)
+        va_audio_embs_list: list[tuple[torch.Tensor, ...]] = []
+        if va_audio_features is not None and self.audio_encoder is not None:
+            mel_list = (
+                list(va_audio_features)
+                if isinstance(va_audio_features, torch.Tensor)
+                else list(va_audio_features)
+            )
+            for mel_spec in mel_list:
+                embs, tok_lens = self.audio_encoder.get_audio_feature([mel_spec])
+                # tok_lens is a list/tensor with one entry (total tokens for this mel)
+                va_audio_embs_list.append(embs)  # shape (total_tok, hidden)
+
+        va_cursor = 0  # index into va_audio_embs_list
+
+        # NOTE: Iterate in dict insertion order to preserve token sequence order.
+        for modality in mm_input_by_modality:
+            multimodal_input = mm_input_by_modality[modality]
+            if modality == "image":
+                multimodal_embeddings.extend(
+                    self._process_image_input(multimodal_input)
+                )
+            elif modality == "video":
+                video_embs_tuple = self._process_video_input(multimodal_input)
+                if video_audio_n_segs is None:
+                    multimodal_embeddings.extend(video_embs_tuple)
+                else:
+                    grid_thw = multimodal_input["video_grid_thw"]
+                    for i, vid_embs in enumerate(video_embs_tuple):
+                        n_segs = int(video_audio_n_segs[i])
+                        if n_segs == 0 or not va_audio_embs_list:
+                            multimodal_embeddings.append(vid_embs)
+                        else:
+                            T = int(grid_thw[i][0])
+                            n_per_grid = vid_embs.shape[0] // T
+                            frames = list(vid_embs.split(n_per_grid, dim=0))
+                            frames_per_group = T // n_segs
+                            # Per-group audio token lengths for this va video
+                            # video_audio_seg_lens is (num_videos, max_T); row i
+                            # has valid values in [:n_segs], rest are zeros.
+                            seg_lens = video_audio_seg_lens[i][:n_segs].tolist()
+                            # Split full audio embs for this va video by group lengths
+                            full_va_embs = va_audio_embs_list[va_cursor]
+                            va_cursor += 1
+                            group_audio_embs = full_va_embs.split(seg_lens)
+                            # Interleave: all vid frames in group, then audio for group
+                            for g in range(n_segs):
+                                for f in range(frames_per_group):
+                                    multimodal_embeddings.append(
+                                        frames[g * frames_per_group + f]
+                                    )
+                                multimodal_embeddings.append(group_audio_embs[g])
+            elif modality == "audio":
+                multimodal_embeddings.extend(
+                    self._process_audio_input(multimodal_input)
+                )
+        return tuple(multimodal_embeddings)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs: object,
+    ) -> torch.Tensor | IntermediateTensors:
+        """Run forward pass for Qwen2.5-VL.
+
+        Args:
+            input_ids: Flattened (concatenated) input_ids corresponding to a
+                batch.
+            positions: Flattened (concatenated) position ids corresponding to a
+                batch. **NOTE**: If mrope is enabled (default setting for
+                Qwen2.5-VL opensource models), the shape will be `(3, seq_len)`,
+                otherwise it will be `(seq_len,).
+        """
+
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        hidden_states = self.language_model.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        return self.language_model.compute_logits(hidden_states)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        audio_loaded: set[str] = set()
+
+        loader = AutoWeightsLoader(self, skip_prefixes=["audio_tokenizer."])
+        auto_loaded = loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+        return audio_loaded | auto_loaded
diff --git a/vllm/model_executor/models/minicpmv.py b/vllm/model_executor/models/minicpmv.py
index 79162eef3f64..cda07ea291ea 100644
--- a/vllm/model_executor/models/minicpmv.py
+++ b/vllm/model_executor/models/minicpmv.py
@@ -1050,9 +1050,17 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
                 quant_config=quant_config,
                 prefix=maybe_prefix(prefix, "resampler"),
             )
+            self._resampler_moved = False
 
         self.make_empty_intermediate_tensors = self.llm.make_empty_intermediate_tensors
 
+    def _ensure_resampler_device(self) -> None:
+        if self._resampler_moved:
+            return
+        # Only move device, DO NOT touch dtype (fp8 quant needs its own dtype)
+        self.resampler.to(current_platform.device_type)
+        self._resampler_moved = True
+
     def _parse_and_validate_vision_input(
         self,
         modality: str,
@@ -1171,7 +1179,9 @@ def compute_logits(
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(self)
-        return loader.load_weights(weights)
+        loaded = loader.load_weights(weights)
+        self._ensure_resampler_device()
+        return loaded
 
     def get_mm_mapping(self) -> MultiModelKeys:
         """
@@ -1276,9 +1286,7 @@ def init_resampler(
                 prefix=prefix,
             )
 
-        return resampler.to(
-            device=current_platform.device_type, dtype=torch.get_default_dtype()
-        )
+        return resampler.to(dtype=torch.get_default_dtype())
 
     def get_vision_hidden_states(self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
         pixel_values = data["pixel_values"]
@@ -1359,9 +1367,7 @@ def init_resampler(
                 prefix=prefix,
             )
 
-        return resampler.to(
-            device=current_platform.device_type, dtype=torch.get_default_dtype()
-        )
+        return resampler.to(dtype=torch.get_default_dtype())
 
     def get_vision_hidden_states(self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
         pixel_values = data["pixel_values"]
@@ -1452,11 +1458,8 @@ def init_resampler(
                 quant_config=quant_config,
                 prefix=prefix,
             )
-        target_device = current_platform.device_type
-        target_dtype = torch.get_default_dtype()
-        if any(p.is_meta for p in resampler.parameters()):
-            return resampler.to_empty(device=target_device).to(dtype=target_dtype)
-        return resampler.to(device=target_device, dtype=target_dtype)
+
+        return resampler.to(dtype=torch.get_default_dtype())
 
     def get_vision_hidden_states(self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
         pixel_values = data["pixel_values"]
@@ -1491,7 +1494,9 @@ def get_vision_hidden_states(self, data: MiniCPMVImagePixelInputs) -> torch.Tens
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(self, skip_prefixes=["apm.", "audio", "tts"])
-        return loader.load_weights(weights)
+        loaded = loader.load_weights(weights)
+        self._ensure_resampler_device()
+        return loaded
 
 
 class MiniCPMV4_0(MiniCPMVBaseModel, SupportsLoRA):
@@ -1551,10 +1556,7 @@ def init_resampler(
                 quant_config=quant_config,
                 prefix=prefix,
             )
-
-        return resampler.to(
-            device=current_platform.device_type, dtype=torch.get_default_dtype()
-        )
+        return resampler.to(dtype=torch.get_default_dtype())
 
     def get_vision_hidden_states(self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
         pixel_values = data["pixel_values"]
@@ -1589,7 +1591,9 @@ def get_vision_hidden_states(self, data: MiniCPMVImagePixelInputs) -> torch.Tens
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(self, skip_prefixes=["apm.", "audio", "tts"])
-        return loader.load_weights(weights)
+        loaded = loader.load_weights(weights)
+        self._ensure_resampler_device()
+        return loaded
 
 
 class MiniCPMV4_5(MiniCPMVBaseModel, SupportsLoRA):
@@ -1649,11 +1653,8 @@ def init_resampler(
                 quant_config=quant_config,
                 prefix=prefix,
             )
-        target_device = current_platform.device_type
-        target_dtype = torch.get_default_dtype()
-        if any(p.is_meta for p in resampler.parameters()):
-            return resampler.to_empty(device=target_device).to(dtype=target_dtype)
-        return resampler.to(device=target_device, dtype=target_dtype)
+
+        return resampler.to(dtype=torch.get_default_dtype())
 
     def get_vision_hidden_states(self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
         pixel_values = data["pixel_values"]
@@ -1692,7 +1693,9 @@ def get_vision_hidden_states(self, data: MiniCPMVImagePixelInputs) -> torch.Tens
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(self, skip_prefixes=["apm.", "audio", "tts"])
-        return loader.load_weights(weights)
+        loaded = loader.load_weights(weights)
+        self._ensure_resampler_device()
+        return loaded
 
 
 _SUPPORT_VERSION = {
diff --git a/vllm/model_executor/models/minimax_m2.py b/vllm/model_executor/models/minimax_m2.py
index 2a9e5f4e08ac..818b7e6811c9 100644
--- a/vllm/model_executor/models/minimax_m2.py
+++ b/vllm/model_executor/models/minimax_m2.py
@@ -35,11 +35,14 @@
 from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed import (
     get_pp_group,
+    get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
-    tensor_model_parallel_all_reduce,
 )
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     QKVParallelLinear,
@@ -104,7 +107,6 @@ def __init__(
             e_score_correction_bias=self.e_score_correction_bias,
             hidden_size=config.hidden_size,
             intermediate_size=config.intermediate_size,
-            reduce_results=False,
             renormalize=True,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -134,9 +136,6 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
-        final_hidden_states = final_hidden_states
-        if self.tp_size > 1:
-            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
 
         return final_hidden_states.view(num_tokens, hidden_dim)
 
@@ -222,9 +221,21 @@ def __init__(
         self.q_norm = MiniMaxText01RMSNormTP(
             self.head_dim * self.total_num_heads, eps=rms_norm_eps
         )
-        self.k_norm = MiniMaxText01RMSNormTP(
-            self.head_dim * self.total_num_kv_heads, eps=rms_norm_eps
-        )
+        if self.total_num_kv_heads >= tp_size:
+            self.k_norm = MiniMaxText01RMSNormTP(
+                self.head_dim * self.total_num_kv_heads, eps=rms_norm_eps
+            )
+        else:
+            # KV heads are replicated across TP ranks; shard k_norm weight by
+            # total_num_kv_heads rather than tp_size to avoid incorrect sharding.
+            num_kv_head_replicas = tp_size // self.total_num_kv_heads
+            self.k_norm = MiniMaxText01RMSNormTP(
+                self.head_dim * self.total_num_kv_heads,
+                eps=rms_norm_eps,
+                weight_shard_world_size=self.total_num_kv_heads,
+                weight_shard_rank=get_tensor_model_parallel_rank()
+                // num_kv_head_replicas,
+            )
 
     def forward(
         self,
@@ -398,7 +409,7 @@ def forward(
         return hidden_states
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="w1",
             ckpt_down_proj_name="w2",
diff --git a/vllm/model_executor/models/minimax_text_01.py b/vllm/model_executor/models/minimax_text_01.py
index 21d74d8b0580..c73fbf7009d6 100644
--- a/vllm/model_executor/models/minimax_text_01.py
+++ b/vllm/model_executor/models/minimax_text_01.py
@@ -24,7 +24,9 @@
 from vllm.forward_context import get_forward_context
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -162,7 +164,6 @@ def __init__(
             hidden_size=self.hidden_size,
             intermediate_size=self.intermediate_size * self.tp_size,
             params_dtype=self.params_dtype,
-            reduce_results=True,
             renormalize=True,
             quant_config=self.quant_config,
             tp_size=self.tp_size,
diff --git a/vllm/model_executor/models/mistral_eagle.py b/vllm/model_executor/models/mistral_eagle.py
new file mode 100644
index 000000000000..908b50f7ca00
--- /dev/null
+++ b/vllm/model_executor/models/mistral_eagle.py
@@ -0,0 +1,166 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable
+
+import torch
+import torch.nn as nn
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import RowParallelLinear
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from vllm.model_executor.models.interfaces import MultiModalEmbeddings
+from vllm.model_executor.models.llama import LlamaConfig
+from vllm.model_executor.models.mistral import (
+    MistralDecoderLayer,
+    MistralForCausalLM,
+    MistralModel,
+)
+from vllm.model_executor.models.utils import (
+    _merge_multimodal_embeddings,
+    get_draft_quant_config,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+class EagleMistralDecoderLayer(MistralDecoderLayer):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        config: LlamaConfig | None = None,
+    ) -> None:
+        super().__init__(vllm_config, prefix=prefix, config=config)
+
+    def get_quant_config(self, vllm_config: VllmConfig) -> QuantizationConfig | None:
+        return get_draft_quant_config(vllm_config)
+
+
+@support_torch_compile
+class EagleMistralModel(MistralModel):
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        start_layer_id: int = 0,
+    ) -> None:
+        # Bypass MistralModel.__init__ to avoid creating duplicate attention
+        # layer entries in the global context.
+        nn.Module.__init__(self)
+        self.config = vllm_config.speculative_config.draft_model_config.hf_config
+        self.vocab_size = self.config.vocab_size
+        # Get drafter's quantization config
+        self.quant_config = get_draft_quant_config(vllm_config)
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.config.vocab_size,
+            self.config.hidden_size,
+            prefix=maybe_prefix(prefix, "embed_tokens"),
+            quant_config=self.quant_config,
+        )
+
+        self.layers = nn.ModuleList(
+            [
+                EagleMistralDecoderLayer(
+                    vllm_config,
+                    prefix=maybe_prefix(prefix, f"layers.{i + start_layer_id}"),
+                    config=self.config,
+                )
+                for i in range(self.config.num_hidden_layers)
+            ]
+        )
+        self.fc = RowParallelLinear(
+            self.config.hidden_size * 2,
+            self.config.hidden_size,
+            bias=False,
+            input_is_parallel=False,
+            quant_config=self.quant_config,
+            prefix=maybe_prefix(prefix, "fc"),
+            return_bias=False,
+        )
+        self.norm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_input_ids(input_ids)
+        hidden_states = self.fc(torch.cat((inputs_embeds, hidden_states), dim=-1))
+        residual = None
+        for layer in self.layers:
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states, hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        # Pretend embed_tokens is loaded; the actual weight is shared
+        # from the target model at runtime by `load_eagle_model`.
+        return super().load_weights(weights) | {"embed_tokens.weight"}
+
+
+class EagleMistralForCausalLM(MistralForCausalLM):
+    mistral_mapping = MistralForCausalLM.mistral_mapping | {
+        "eagle_linear": "model.fc",
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        # Bypass MistralForCausalLM.__init__ to use the draft model config
+        # and to avoid creating an lm_head.
+        nn.Module.__init__(self)
+        self.config = vllm_config.speculative_config.draft_model_config.hf_config
+        target_layer_num = vllm_config.model_config.get_num_layers(
+            vllm_config.parallel_config
+        )
+        self.model = EagleMistralModel(
+            vllm_config=vllm_config, prefix="model", start_layer_id=target_layer_num
+        )
+
+        logit_scale = getattr(self.config, "logit_scale", 1.0)
+        self.logits_processor = LogitsProcessor(
+            self.config.vocab_size, scale=logit_scale
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        return self.model(input_ids, positions, hidden_states, inputs_embeds)
+
+    def embed_input_ids(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: MultiModalEmbeddings | None = None,
+        *,
+        is_multimodal: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        inputs_embeds = super().embed_input_ids(input_ids)
+
+        if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
+            return inputs_embeds
+
+        assert is_multimodal is not None
+
+        return _merge_multimodal_embeddings(
+            inputs_embeds=inputs_embeds,
+            multimodal_embeddings=multimodal_embeddings,
+            is_multimodal=is_multimodal,
+        )
diff --git a/vllm/model_executor/models/mixtral.py b/vllm/model_executor/models/mixtral.py
index 376fd7a1709d..cbfc254dda36 100644
--- a/vllm/model_executor/models/mixtral.py
+++ b/vllm/model_executor/models/mixtral.py
@@ -40,7 +40,10 @@
     get_tensor_model_parallel_world_size,
 )
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     QKVParallelLinear,
@@ -132,7 +135,6 @@ def __init__(
             hidden_size=hidden_size,
             intermediate_size=intermediate_size,
             params_dtype=params_dtype,
-            reduce_results=True,
             renormalize=True,
             quant_config=quant_config,
             tp_size=tp_size,
@@ -365,7 +367,7 @@ def forward(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="w1",
             ckpt_down_proj_name="w2",
diff --git a/vllm/model_executor/models/mllama4.py b/vllm/model_executor/models/mllama4.py
index 227ef2fa669a..8fe1be721c79 100644
--- a/vllm/model_executor/models/mllama4.py
+++ b/vllm/model_executor/models/mllama4.py
@@ -40,7 +40,9 @@
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.inputs import MultiModalDataDict
 from vllm.model_executor.layers.attention import MMEncoderAttention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
     QKVParallelLinear,
@@ -1072,7 +1074,7 @@ def _load_other_weights(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/molmo2.py b/vllm/model_executor/models/molmo2.py
index aa58fa6d1583..ae28326a32f9 100644
--- a/vllm/model_executor/models/molmo2.py
+++ b/vllm/model_executor/models/molmo2.py
@@ -1338,6 +1338,9 @@ def exif_transpose(
 def build_flat_image_bool_length(
     image_grids: torch.LongTensor,
     hf_config: PretrainedConfig,
+    image_use_col_tokens: bool = True,
+    use_single_crop_col_tokens: bool | None = None,
+    use_single_crop_start_token: bool = True,
 ) -> tuple[torch.LongTensor, torch.LongTensor]:
     image_patch_id = hf_config.image_patch_id
     low_res_image_start_id = hf_config.low_res_image_start_token_id
@@ -1353,7 +1356,17 @@ def build_flat_image_bool_length(
     h = image_grids[:, 2]
     w = image_grids[:, 3]
 
-    lengths = resized_h * resized_w + h * (w + 1) + 4  # [B]
+    low_res_use_col_tokens = (
+        image_use_col_tokens
+        if use_single_crop_col_tokens is None
+        else use_single_crop_col_tokens
+    )
+    low_res_extra = int(low_res_use_col_tokens)
+    high_res_extra = int(image_use_col_tokens)
+
+    lengths = (
+        resized_h * (resized_w + low_res_extra) + h * (w + high_res_extra) + 4
+    )  # [B]
     total_len = int(lengths.sum().item())
 
     flat = torch.empty(total_len, dtype=torch.long, device=device)
@@ -1363,16 +1376,24 @@ def build_flat_image_bool_length(
         resized_h_i, resized_w_i, h_i, w_i = image_grids[i].tolist()
         L_i = int(lengths[i].item())
 
-        num_low_res_patches = resized_h_i * resized_w_i
-
         idx = offset
 
-        flat[idx] = low_res_image_start_id
+        flat[idx] = (
+            low_res_image_start_id if use_single_crop_start_token else image_start_id
+        )
         idx += 1
 
-        if num_low_res_patches > 0:
-            flat[idx : idx + num_low_res_patches] = image_patch_id
-            idx += num_low_res_patches
+        low_res_block_len = resized_w_i + low_res_extra
+        if low_res_block_len > 0 and resized_h_i > 0:
+            line = torch.empty(low_res_block_len, dtype=torch.long, device=device)
+            if resized_w_i > 0:
+                line[:resized_w_i] = image_patch_id
+            if low_res_use_col_tokens:
+                line[resized_w_i] = image_col_id
+
+            block = line.repeat(resized_h_i)
+            flat[idx : idx + resized_h_i * low_res_block_len] = block
+            idx += resized_h_i * low_res_block_len
 
         flat[idx] = image_end_id
         idx += 1
@@ -1380,12 +1401,13 @@ def build_flat_image_bool_length(
         flat[idx] = image_start_id
         idx += 1
 
-        block_len = w_i + 1
+        block_len = w_i + high_res_extra
         if block_len > 0 and h_i > 0:
             line = torch.empty(block_len, dtype=torch.long, device=device)
             if w_i > 0:
                 line[:w_i] = image_patch_id
-            line[w_i] = image_col_id
+            if image_use_col_tokens:
+                line[w_i] = image_col_id
 
             block = line.repeat(h_i)
             flat[idx : idx + h_i * block_len] = block
@@ -2108,7 +2130,13 @@ def patched_call(text=None, images=None, videos=None, **kwargs) -> BatchFeature:
             (
                 processed_outputs["image_tokens"],
                 processed_outputs["num_image_tokens"],
-            ) = build_flat_image_bool_length(image_grids, hf_config)
+            ) = build_flat_image_bool_length(
+                image_grids,
+                hf_config,
+                image_use_col_tokens=hf_processor.image_use_col_tokens,
+                use_single_crop_col_tokens=hf_processor.use_single_crop_col_tokens,
+                use_single_crop_start_token=hf_processor.use_single_crop_start_token,
+            )
 
         return BatchFeature({**processed_outputs, **all_video_outputs})
 
diff --git a/vllm/model_executor/models/moondream3.py b/vllm/model_executor/models/moondream3.py
new file mode 100644
index 000000000000..d5f3e6b195fb
--- /dev/null
+++ b/vllm/model_executor/models/moondream3.py
@@ -0,0 +1,1423 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only Moondream3 model implementation."""
+
+from collections.abc import Iterable, Mapping
+from dataclasses import dataclass
+from functools import cached_property
+from itertools import islice
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import BatchFeature
+
+from vllm.config import VllmConfig
+from vllm.config.multimodal import BaseDummyOptions
+from vllm.distributed import (
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_gather,
+    tensor_model_parallel_all_reduce,
+)
+from vllm.inputs import MultiModalDataDict
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import Attention
+from vllm.model_executor.layers.attention.mm_encoder_attention import (
+    MMEncoderAttention,
+)
+from vllm.model_executor.layers.fused_moe import MoEActivation, fused_experts
+from vllm.model_executor.layers.fused_moe.config import biased_moe_quant_config
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (
+    MultiModalFieldConfig,
+    MultiModalKwargsItems,
+)
+from vllm.multimodal.parse import ImageSize, MultiModalDataItems
+from vllm.multimodal.processing import (
+    BaseDummyInputsBuilder,
+    BaseMultiModalProcessor,
+    BaseProcessingInfo,
+    PromptReplacement,
+    PromptUpdate,
+    PromptUpdateDetails,
+)
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs.moondream3 import (
+    Moondream3Config,
+    Moondream3TextConfig,
+    Moondream3VisionConfig,
+)
+from vllm.transformers_utils.processors.moondream3 import Moondream3Processor
+
+from .interfaces import (
+    MultiModalEmbeddings,
+    SupportsMultiModal,
+    SupportsPP,
+)
+from .utils import (
+    extract_layer_index,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+# ============================================================================
+# Image Processing Utilities
+# ============================================================================
+
+
+def reconstruct_from_crops(
+    crops: torch.Tensor,
+    tiling: tuple[int, int],
+    overlap_margin: int,
+    patch_size: int = 14,
+) -> torch.Tensor:
+    """Reconstruct features from overlapping crops."""
+    tiling_h, tiling_w = tiling
+    crop_height, crop_width = crops[0].shape[:2]
+    margin_pixels = overlap_margin * patch_size
+
+    output_h = (crop_height - 2 * margin_pixels) * tiling_h + 2 * margin_pixels
+    output_w = (crop_width - 2 * margin_pixels) * tiling_w + 2 * margin_pixels
+
+    reconstructed = torch.zeros(
+        (output_h, output_w, crops[0].shape[2]),
+        device=crops[0].device,
+        dtype=crops[0].dtype,
+    )
+
+    for i, crop in enumerate(crops):
+        tile_y = i // tiling_w
+        tile_x = i % tiling_w
+
+        x_start = 0 if tile_x == 0 else margin_pixels
+        x_end = crop_width if tile_x == tiling_w - 1 else crop_width - margin_pixels
+        y_start = 0 if tile_y == 0 else margin_pixels
+        y_end = crop_height if tile_y == tiling_h - 1 else crop_height - margin_pixels
+
+        out_x = tile_x * (crop_width - 2 * margin_pixels)
+        out_y = tile_y * (crop_height - 2 * margin_pixels)
+
+        reconstructed[
+            out_y + y_start : out_y + y_end, out_x + x_start : out_x + x_end
+        ] = crop[y_start:y_end, x_start:x_end]
+
+    return reconstructed
+
+
+# ============================================================================
+# Vision Encoder Components
+# ============================================================================
+
+
+class Moondream3VisionMLP(nn.Module):
+    """MLP for vision encoder blocks."""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.fc1 = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc1",
+        )
+        self.act = get_act_fn("gelu_pytorch_tanh")
+        self.fc2 = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc2",
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.fc1(x)
+        x = self.act(x)
+        x, _ = self.fc2(x)
+        return x
+
+
+class Moondream3VisionAttention(nn.Module):
+    """Self-attention for vision encoder (bidirectional)."""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.num_heads = num_heads
+        self.head_dim = hidden_size // num_heads
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=num_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.out_proj = RowParallelLinear(
+            input_size=hidden_size,
+            output_size=hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+
+        tp_size = get_tensor_model_parallel_world_size()
+        self.num_heads_per_partition = num_heads // tp_size
+
+        self.attn = MMEncoderAttention(
+            num_heads=self.num_heads_per_partition,
+            head_size=self.head_dim,
+            scale=self.head_dim**-0.5,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.chunk(3, dim=-1)
+        out = self.attn(q, k, v)
+        out, _ = self.out_proj(out)
+        return out
+
+
+class Moondream3VisionBlock(nn.Module):
+    """Transformer block for vision encoder."""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        num_heads: int,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(hidden_size, eps=1e-5)
+        self.attn = Moondream3VisionAttention(
+            hidden_size=hidden_size,
+            num_heads=num_heads,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+        self.ln2 = nn.LayerNorm(hidden_size, eps=1e-5)
+        self.mlp = Moondream3VisionMLP(
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.attn(self.ln1(x))
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+
+class Moondream3VisionEncoder(nn.Module):
+    """Vision encoder (SigLIP-style ViT)."""
+
+    def __init__(
+        self,
+        config: Moondream3VisionConfig,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+
+        # Patch embedding
+        self.patch_emb = nn.Linear(
+            config.enc_patch_size * config.enc_patch_size * 3,
+            config.enc_dim,
+            bias=True,
+        )
+
+        # Position embeddings (27x27 = 729 patches for 378x378 / 14)
+        num_patches = (config.crop_size // config.enc_patch_size) ** 2
+        self.pos_emb = nn.Parameter(torch.zeros(1, num_patches, config.enc_dim))
+
+        # Transformer blocks
+        self.blocks = nn.ModuleList(
+            [
+                Moondream3VisionBlock(
+                    hidden_size=config.enc_dim,
+                    intermediate_size=config.enc_ff_dim,
+                    num_heads=config.enc_n_heads,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.blocks.{i}",
+                )
+                for i in range(config.enc_n_layers)
+            ]
+        )
+
+        self.post_ln = nn.LayerNorm(config.enc_dim, eps=1e-5)
+
+    def create_patches(self, images: torch.Tensor) -> torch.Tensor:
+        """Convert images to patch embeddings.
+
+        Args:
+            images: (batch, channels, height, width)
+
+        Returns:
+            patches: (batch, num_patches, patch_dim)
+        """
+        patch_size = self.config.enc_patch_size
+        batch, channels, height, width = images.shape
+        patches_h = height // patch_size
+        patches_w = width // patch_size
+
+        # Unfold into patches
+        patches = images.unfold(2, patch_size, patch_size).unfold(
+            3, patch_size, patch_size
+        )
+        # (batch, channels, patches_h, patches_w, patch_size, patch_size)
+        patches = patches.permute(0, 2, 3, 1, 4, 5).contiguous()
+        # (batch, patches_h, patches_w, channels, patch_size, patch_size)
+        patches = patches.view(batch, patches_h * patches_w, -1)
+        # (batch, num_patches, channels * patch_size * patch_size)
+
+        return patches
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        """Encode images.
+
+        Args:
+            pixel_values: (batch, channels, height, width)
+
+        Returns:
+            features: (batch, num_patches, hidden_size)
+        """
+        # Create patches and embed
+        patches = self.create_patches(pixel_values)
+        x = self.patch_emb(patches)
+
+        # Add position embeddings
+        x = x + self.pos_emb
+
+        # Apply transformer blocks
+        for block in self.blocks:
+            x = block(x)
+
+        # Final layer norm
+        x = self.post_ln(x)
+
+        return x
+
+
+class Moondream3VisionProjection(nn.Module):
+    """Projects vision features to text embedding dimension."""
+
+    def __init__(
+        self,
+        input_dim: int,
+        inner_dim: int,
+        output_dim: int,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        # Input is concatenated global and local features (2 * input_dim)
+        self.fc1 = ColumnParallelLinear(
+            input_dim * 2,
+            inner_dim,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc1",
+        )
+        self.act = get_act_fn("gelu_pytorch_tanh")
+        self.fc2 = RowParallelLinear(
+            inner_dim,
+            output_dim,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc2",
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.fc1(x)
+        x = self.act(x)
+        x, _ = self.fc2(x)
+        return x
+
+
+# ============================================================================
+# Text Decoder Components
+# ============================================================================
+
+
+class Moondream3TextMLP(nn.Module):
+    """Standard MLP for non-MoE layers (layers 0-3)."""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.fc1 = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc1",
+        )
+        self.act = get_act_fn("gelu_pytorch_tanh")
+        self.fc2 = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc2",
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.fc1(x)
+        x = self.act(x)
+        x, _ = self.fc2(x)
+        return x
+
+
+class Moondream3TextMoE(nn.Module):
+    """Mixture of Experts layer for layers 4+ with expert parallelism.
+
+    Moondream3 uses a custom GeGLU activation: gelu(h) * (g + 1)
+    where fc1 outputs [gate, up] and the activation is gelu(gate) * (up + 1).
+
+    Uses expert parallelism where each GPU stores num_experts/tp_size experts.
+    Routing and communication handled via all-to-all or replicated computation.
+
+    Checkpoint format:
+    - fc1.weight: [num_experts, expert_inner_dim * 2, hidden_size] (gate+up)
+    - fc2.weight: [num_experts, hidden_size, expert_inner_dim] (down)
+    - router.weight: [num_experts, hidden_size]
+    - router.bias: [num_experts]
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        expert_inner_dim: int,
+        num_experts: int,
+        experts_per_token: int,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.expert_inner_dim = expert_inner_dim
+        self.num_experts = num_experts
+        self.experts_per_token = experts_per_token
+
+        # Expert parallelism: each GPU stores a subset of experts
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.experts_per_rank = num_experts // self.tp_size
+        self.num_local_experts = self.experts_per_rank
+
+        # Router (gate) - use ReplicatedLinear for compatibility
+        self.gate = ReplicatedLinear(
+            hidden_size,
+            num_experts,
+            bias=True,
+            quant_config=None,
+            prefix=f"{prefix}.gate",
+        )
+
+        # Local expert weights (only store experts_per_rank experts)
+        # fc1: [experts_per_rank, expert_inner_dim * 2, hidden_size]
+        # fc2: [experts_per_rank, hidden_size, expert_inner_dim]
+        self.fc1_weight = nn.Parameter(
+            torch.empty(self.num_local_experts, expert_inner_dim * 2, hidden_size)
+        )
+        self.fc2_weight = nn.Parameter(
+            torch.empty(self.num_local_experts, hidden_size, expert_inner_dim)
+        )
+        self._use_fused_moe = True
+
+        local_expert_start = get_tensor_model_parallel_rank() * self.experts_per_rank
+        expert_map = torch.full((num_experts,), -1, dtype=torch.int32)
+        expert_map[local_expert_start : local_expert_start + self.num_local_experts] = (
+            torch.arange(self.num_local_experts, dtype=torch.int32)
+        )
+        self.register_buffer("_expert_map", expert_map, persistent=False)
+
+        # Preserve Moondream3's exact GeGLU variant (gelu(h) * (g + 1)) by
+        # adding +1 bias to the second half of the fused fc1 activations.
+        fused_w1_bias = torch.zeros(
+            self.num_local_experts,
+            expert_inner_dim * 2,
+            dtype=torch.float32,
+        )
+        fused_w1_bias[:, expert_inner_dim:] = 1.0
+        self.register_buffer("_fused_w1_bias", fused_w1_bias, persistent=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Forward pass with expert parallelism and custom GeGLU activation."""
+
+        # Get router logits and compute top-k
+        router_logits, _ = self.gate(x)  # [num_tokens, num_experts]
+        topk_logits, topk_ids = torch.topk(
+            router_logits, self.experts_per_token, dim=-1
+        )
+        # Softmax over selected experts
+        topk_weights = F.softmax(topk_logits, dim=-1, dtype=torch.float32).to(x.dtype)
+
+        if self._use_fused_moe and x.is_cuda:
+            try:
+                out = fused_experts(
+                    hidden_states=x.contiguous(),
+                    w1=self.fc1_weight,
+                    w2=self.fc2_weight,
+                    topk_weights=topk_weights.contiguous(),
+                    topk_ids=topk_ids.contiguous(),
+                    activation=MoEActivation.GELU,
+                    global_num_experts=self.num_experts,
+                    expert_map=self._expert_map,
+                    quant_config=biased_moe_quant_config(self._fused_w1_bias, None),
+                )
+                out = tensor_model_parallel_all_reduce(out)
+                return out
+            except (NotImplementedError, RuntimeError) as exc:
+                self._use_fused_moe = False
+                logger.warning_once(
+                    "Disabling fused Moondream3 MoE path and falling back to "
+                    "the Python expert loop: %s",
+                    str(exc),
+                )
+
+        tp_rank = get_tensor_model_parallel_rank()
+        # Compute local expert range
+        local_expert_start = tp_rank * self.experts_per_rank
+
+        # Fallback path for environments where fused kernels are unavailable.
+        out = x.new_zeros(x.shape)
+
+        for local_expert_idx in range(self.num_local_experts):
+            global_expert_id = local_expert_start + local_expert_idx
+
+            # Find tokens assigned to this expert
+            token_pos, which_k = (topk_ids == global_expert_id).nonzero(as_tuple=True)
+            if token_pos.numel() == 0:
+                continue
+
+            # Get tokens and their routing weights
+            x_tok = x.index_select(0, token_pos)  # [n_tokens, hidden_size]
+            gate_tok = topk_weights[token_pos, which_k]  # [n_tokens]
+
+            # fc1: [expert_inner_dim * 2, hidden_size]
+            # h_full: [n_tokens, expert_inner_dim * 2]
+            h_full = F.linear(x_tok, self.fc1_weight[local_expert_idx])
+
+            # GeGLU with (g + 1): h, g = split; output = gelu(h) * (g + 1)
+            # HF MoE uses exact GELU (not tanh approximation).
+            h, g = h_full.chunk(2, dim=-1)  # Each [n_tokens, expert_inner_dim]
+            h = F.gelu(h) * (g + 1.0)
+
+            # fc2: [hidden_size, expert_inner_dim]
+            # y: [n_tokens, hidden_size]
+            y = F.linear(h, self.fc2_weight[local_expert_idx])
+
+            # Apply routing weight
+            y = y * gate_tok.unsqueeze(-1)
+
+            # Accumulate output
+            out.index_add_(0, token_pos, y)
+
+        # All-reduce to combine results from all experts across GPUs
+        out = tensor_model_parallel_all_reduce(out)
+
+        return out
+
+
+class Moondream3Attention(nn.Module):
+    """Decoder attention with RoPE and tau scaling.
+
+    Moondream3 uses a tau attention mechanism that scales Q and V
+    based on both token content and position.
+    """
+
+    def __init__(
+        self,
+        config: Moondream3TextConfig,
+        layer_idx: int,
+        cache_config=None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = config.dim
+        self.num_heads = config.n_heads
+        self.num_kv_heads = config.n_kv_heads
+        self.head_dim = config.dim // config.n_heads
+
+        tp_size = get_tensor_model_parallel_world_size()
+        self.num_heads_per_partition = self.num_heads // tp_size
+        self.num_kv_heads_per_partition = max(1, self.num_kv_heads // tp_size)
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=self.hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.num_heads,
+            total_num_kv_heads=self.num_kv_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.out_proj = RowParallelLinear(
+            input_size=self.hidden_size,
+            output_size=self.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+
+        # Moondream uses 32-dim rotation out of 64-dim head (partial_rotary_factor=0.5)
+        # HF Moondream uses non-interleaved RoPE (split by half)
+        # In vLLM, is_neox_style=True means split by half (GPT-NeoX style)
+        rope_parameters = {
+            "rope_theta": config.rope_theta,
+            "partial_rotary_factor": 32 / self.head_dim,  # 32/64 = 0.5
+        }
+        self.rotary_emb = get_rope(
+            head_size=self.head_dim,
+            max_position=config.max_context,
+            rope_parameters=rope_parameters,
+            is_neox_style=True,  # Moondream uses split-by-half (GPT-NeoX) style
+        )
+
+        self.scaling = self.head_dim**-0.5
+        self.attn = Attention(
+            num_heads=self.num_heads_per_partition,
+            head_size=self.head_dim,
+            scale=self.scaling,
+            num_kv_heads=self.num_kv_heads_per_partition,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+
+        # Tau scaling parameters for position-dependent attention
+        # These are learned during training to modulate attention based on position
+        # tau_wq and tau_wv need full qkv_dim for correct computation
+        # Only heads are partitioned, qkv dimension is kept full for all-gather
+        qkv_dim = self.hidden_size * 3  # Q + K + V dimension (full)
+        self.tau_alpha = nn.Parameter(torch.zeros(self.num_heads_per_partition))
+        self.tau_wq = nn.Parameter(torch.zeros(self.num_heads_per_partition, qkv_dim))
+        self.tau_wv = nn.Parameter(torch.zeros(self.num_heads_per_partition, qkv_dim))
+        self.tp_size = tp_size
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+
+        q, k, v = qkv.split(
+            [
+                self.num_heads_per_partition * self.head_dim,
+                self.num_kv_heads_per_partition * self.head_dim,
+                self.num_kv_heads_per_partition * self.head_dim,
+            ],
+            dim=-1,
+        )
+
+        # Apply tau scaling to Q and V
+        # Tau scaling has two components:
+        # 1. Token-based: tok_q = tanh(gelu(qkv) @ tau_wq.T)
+        # 2. Position-based: tau_pos = 1 + (sigmoid(alpha * log(pos+1)) - 0.5)
+        # Final: tau = tok + tau_pos
+        #
+        # For TP, tau weights are sharded by head, but qkv_dim is kept full
+
+        # Get full qkv for tau computation
+        # With TP, reconstruct qkv in correct layout [q_full, k_full, v_full]
+        # (all-gather would produce [q_0, k_0, v_0, q_1, k_1, v_1] - wrong)
+        if self.tp_size > 1:
+            # All-gather once, then reconstruct [q_full, k_full, v_full].
+            qkv_full_sharded = tensor_model_parallel_all_gather(qkv.contiguous())
+            q_local_dim = q.shape[-1]
+            kv_local_dim = k.shape[-1]
+            qkv_full_sharded = qkv_full_sharded.view(
+                qkv.shape[0],
+                self.tp_size,
+                q_local_dim + 2 * kv_local_dim,
+            )
+            q_full = qkv_full_sharded[:, :, :q_local_dim].reshape(qkv.shape[0], -1)
+            k_full = qkv_full_sharded[
+                :, :, q_local_dim : q_local_dim + kv_local_dim
+            ].reshape(qkv.shape[0], -1)
+            v_full = qkv_full_sharded[:, :, q_local_dim + kv_local_dim :].reshape(
+                qkv.shape[0], -1
+            )
+            qkv_full = torch.cat([q_full, k_full, v_full], dim=-1).contiguous()
+        else:
+            qkv_full = qkv
+
+        # Compute tau scaling factors matching HF implementation exactly:
+        # tok_feat = gelu(qkv)
+        # tok_q = tanh(tok_feat @ tau_wq.T)  # [num_tokens, num_heads]
+        # tau_pos = 1 + (sigmoid(alpha * log(pos+1)) - 0.5)  # [num_heads, num_tokens]
+        # tau = (tok_q.T + tau_pos).T  # [num_tokens, num_heads]
+        num_tokens = qkv_full.shape[0]
+        orig_dtype = q.dtype
+
+        # Token-based component
+        tok_feat = F.gelu(qkv_full)  # Apply GELU activation
+        tok_q = torch.tanh(tok_feat @ self.tau_wq.t())  # [N, H_per_partition]
+        tok_v = torch.tanh(tok_feat @ self.tau_wv.t())  # [N, H_per_partition]
+
+        # Position-based component
+        # tau_pos = 1 + (sigmoid(alpha * log(pos+1)) - 0.5)
+        # positions is [num_tokens], need to compute for each head
+        # tau_alpha: [num_heads_per_partition]
+        pos_float = (positions.to(orig_dtype) + 1.0).clamp(min=1e-6)
+        pos_log = pos_float.log()  # [num_tokens]
+        # alpha[:, None] * pos_log[None, :] -> [num_heads, num_tokens]
+        tau_pos = 1.0 + (
+            torch.sigmoid(self.tau_alpha[:, None] * pos_log[None, :]) - 0.5
+        )  # [H_per_partition, N]
+
+        # Combine token and position components
+        tau_q = (tok_q + tau_pos.t()).to(orig_dtype)  # [N, H_per_partition]
+        tau_v = (tok_v + tau_pos.t()).to(orig_dtype)  # [N, H_per_partition]
+
+        # Reshape q and v to apply per-head tau scaling
+        q = q.view(num_tokens, self.num_heads_per_partition, self.head_dim)
+        v = v.view(num_tokens, self.num_kv_heads_per_partition, self.head_dim)
+
+        # Apply tau scaling
+        q = q * tau_q.unsqueeze(-1)
+        v = v * tau_v[:, : self.num_kv_heads_per_partition].unsqueeze(-1)
+
+        # Reshape back
+        q = q.view(num_tokens, -1)
+        v = v.view(num_tokens, -1)
+
+        q, k = self.rotary_emb(positions, q, k)
+
+        attn_output = self.attn(q, k, v)
+
+        output, _ = self.out_proj(attn_output)
+        return output
+
+
+class Moondream3DecoderLayer(nn.Module):
+    """Decoder layer with attention + MLP/MoE."""
+
+    def __init__(
+        self,
+        config: Moondream3TextConfig,
+        cache_config=None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        layer_idx = extract_layer_index(prefix)
+        self.layer_idx = layer_idx
+
+        self.ln = nn.LayerNorm(config.dim, eps=1e-5, bias=True)
+
+        self.attn = Moondream3Attention(
+            config=config,
+            layer_idx=layer_idx,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+
+        # Use MoE for layers >= moe_start_layer, standard MLP otherwise
+        if layer_idx >= config.moe_start_layer:
+            self.mlp = Moondream3TextMoE(
+                hidden_size=config.dim,
+                expert_inner_dim=config.moe_expert_inner_dim,
+                num_experts=config.moe_num_experts,
+                experts_per_token=config.moe_experts_per_token,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+            )
+        else:
+            self.mlp = Moondream3TextMLP(
+                hidden_size=config.dim,
+                intermediate_size=config.ff_dim,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+            )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        # Pre-norm architecture
+        normed = self.ln(hidden_states)
+        attn_out = self.attn(positions, normed)
+        mlp_out = self.mlp(normed)
+        hidden_states = hidden_states + attn_out + mlp_out
+        return hidden_states
+
+
+class Moondream3TextModel(nn.Module):
+    """Text decoder model."""
+
+    def __init__(
+        self,
+        config: Moondream3TextConfig,
+        cache_config=None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+
+        self.wte = VocabParallelEmbedding(
+            config.vocab_size,
+            config.dim,
+            prefix=f"{prefix}.wte",
+        )
+
+        blocks_prefix = maybe_prefix(prefix, "blocks")
+        self.start_layer, self.end_layer, self.blocks = make_layers(
+            config.n_layers,
+            lambda prefix: Moondream3DecoderLayer(
+                config=config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=prefix,
+            ),
+            prefix=blocks_prefix,
+        )
+
+        self.post_ln = nn.LayerNorm(config.dim, eps=1e-5, bias=True)
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states"], config.dim
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.wte(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        pp_group = get_pp_group()
+        if pp_group.is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                assert input_ids is not None
+                hidden_states = self.embed_input_ids(input_ids)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+
+        for i, layer in enumerate(
+            islice(self.blocks, self.start_layer, self.end_layer)
+        ):
+            hidden_states = layer(positions, hidden_states)
+
+        if not pp_group.is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+
+        hidden_states = self.post_ln(hidden_states)
+        return hidden_states
+
+
+@dataclass(frozen=True)
+class Moondream3ImageInput:
+    """Container holding per-image inputs for embedding."""
+
+    pixel_values: torch.Tensor
+    tiling: tuple[int, int] | None
+
+
+# ============================================================================
+# Multimodal Processing
+# ============================================================================
+
+
+class Moondream3ProcessingInfo(BaseProcessingInfo):
+    """Processing info for Moondream3."""
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config()
+
+    def get_hf_processor(self, **kwargs: object):
+        return self.ctx.get_hf_processor(Moondream3Processor, **kwargs)
+
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
+        return {"image": 1}
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        # HF pre-fills BOS together with the fixed 27x27 vision grid under
+        # the same bidirectional prefix mask: 1 BOS + 729 image embeddings.
+        return 730
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        return ImageSize(width=378, height=378)
+
+    def get_max_image_tokens(self) -> int:
+        return 730
+
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Mapping[str, int]:
+        return {"image": self.get_max_image_tokens()}
+
+
+class Moondream3DummyInputsBuilder(BaseDummyInputsBuilder[Moondream3ProcessingInfo]):
+    """Dummy inputs builder for profiling."""
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        return (
+            "<|endoftext|><image><|md_reserved_0|>query<|md_reserved_1|>"
+            "What is this image?<|md_reserved_2|>"
+        )
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+        mm_options: Mapping[str, BaseDummyOptions] | None = None,
+        mm_processor_kwargs: Mapping[str, object] | None = None,
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+        return {
+            "image": self._get_dummy_images(
+                width=378,
+                height=378,
+                num_images=num_images,
+            )
+        }
+
+
+class Moondream3MultiModalProcessor(BaseMultiModalProcessor[Moondream3ProcessingInfo]):
+    """Multimodal processor for Moondream3."""
+
+    image_placeholder: str = "<image>"
+    bos_image_placeholder: str = "<|endoftext|><image>"
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        # Moondream3's processor handles images directly rather than exposing a
+        # separate `image_processor`, so keep the cache path on text+MM calls.
+        return super()._call_hf_processor(prompt, mm_data, mm_kwargs, tok_kwargs)
+
+    @cached_property
+    def bos_image_placeholder_tokens(self) -> list[int]:
+        tokenizer = self.info.get_tokenizer()
+        token_ids = tokenizer.encode(
+            self.bos_image_placeholder,
+            add_special_tokens=False,
+        )
+        if len(token_ids) < 2:
+            raise ValueError(
+                "Tokenizer could not encode Moondream3 BOS/image placeholder "
+                f"{self.bos_image_placeholder!r}."
+            )
+        return token_ids
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return {
+            "pixel_values": MultiModalFieldConfig.batched("image"),
+            "tilings": MultiModalFieldConfig.batched("image", keep_on_cpu=True),
+        }
+
+    def _hf_processor_applies_updates(
+        self,
+        prompt_text: str,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        tokenization_kwargs: Mapping[str, object],
+    ) -> bool:
+        # Moondream3 HF processor does NOT expand placeholder tokens.
+        # vLLM expands BOS + <image> so the whole HF image prefix is marked
+        # bidirectional by the multimodal prefix-LM mask.
+        return False
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> list[PromptUpdate]:
+        image_size = self.info.get_image_size_with_most_features()
+        num_image_tokens = self.info.get_num_image_tokens(
+            image_width=image_size.width,
+            image_height=image_size.height,
+        )
+        placeholder_tokens = self.bos_image_placeholder_tokens
+        bos_token = placeholder_tokens[0]
+        image_token = placeholder_tokens[-1]
+        return [
+            PromptReplacement(
+                modality="image",
+                target=placeholder_tokens,
+                replacement=PromptUpdateDetails(
+                    full=[bos_token] + [image_token] * (num_image_tokens - 1),
+                ),
+            ),
+        ]
+
+
+# ============================================================================
+# Main Model
+# ============================================================================
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Moondream3MultiModalProcessor,
+    info=Moondream3ProcessingInfo,
+    dummy_inputs=Moondream3DummyInputsBuilder,
+)
+class Moondream3ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
+    """Moondream3 multimodal model for causal language modeling.
+
+    vLLM supports the standard autoregressive Moondream3 query and caption
+    prompt formats. The region-module point/detect skills require custom
+    coordinate decoding and are intentionally not exposed here.
+    """
+
+    supports_multimodal = True
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+    }
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        hf_config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        cache_config = vllm_config.cache_config
+
+        # Reuse the transformers_utils config implementation.
+        if isinstance(hf_config, Moondream3Config):
+            self.config = hf_config
+        else:
+            config_dict = hf_config.config if hasattr(hf_config, "config") else {}
+            self.config = Moondream3Config(config=config_dict)
+
+        with self._mark_tower_model(vllm_config, "image"):
+            # Vision encoder
+            self.vision = Moondream3VisionEncoder(
+                config=self.config.vision_config,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "vision"),
+            )
+
+            # Vision projection
+            self.vision_proj = Moondream3VisionProjection(
+                input_dim=self.config.vision_config.enc_dim,
+                inner_dim=self.config.vision_config.proj_inner_dim,
+                output_dim=self.config.text_config.dim,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "vision_proj"),
+            )
+
+        with self._mark_language_model(vllm_config):
+            # Text decoder
+            self.text = Moondream3TextModel(
+                config=self.config.text_config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "text"),
+            )
+
+            # LM head (with bias - Moondream3 has lm_head bias)
+            self.lm_head = ParallelLMHead(
+                self.config.text_config.vocab_size,
+                self.config.text_config.dim,
+                bias=True,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+
+        self.logits_processor = LogitsProcessor(self.config.text_config.vocab_size)
+        self.make_empty_intermediate_tensors = self.text.make_empty_intermediate_tensors
+        self._answer_id = getattr(
+            self.config,
+            "answer_token_id",
+            getattr(hf_config, "answer_token_id", 3),
+        )
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
+        if modality == "image":
+            return "<image>"
+        return None
+
+    def get_language_model(self) -> nn.Module:
+        return self.text
+
+    def get_num_mm_encoder_tokens(self, num_image_tokens: int) -> int:
+        return num_image_tokens
+
+    def get_num_mm_connector_tokens(self, num_vision_tokens: int) -> int:
+        return num_vision_tokens
+
+    def _split_pixel_values(
+        self,
+        pixel_values: object,
+    ) -> list[torch.Tensor]:
+        # The processor should standardize image inputs into:
+        # - torch.Tensor [num_images, num_crops, C, H, W], or
+        # - list[torch.Tensor[num_crops, C, H, W]] for ragged crops.
+        if isinstance(pixel_values, torch.Tensor):
+            if pixel_values.dim() != 5:
+                raise ValueError(
+                    "Expected `pixel_values` tensor with shape "
+                    "[num_images, num_crops, C, H, W], got "
+                    f"{tuple(pixel_values.shape)}."
+                )
+            return [pv.contiguous() for pv in pixel_values]
+
+        if isinstance(pixel_values, (list, tuple)):
+            tensors: list[torch.Tensor] = []
+            for value in pixel_values:
+                if not isinstance(value, torch.Tensor):
+                    raise TypeError(
+                        "Expected each `pixel_values` element to be a tensor, "
+                        f"got {type(value)!r}."
+                    )
+                if value.dim() != 4:
+                    raise ValueError(
+                        f"Unsupported pixel_values element shape {tuple(value.shape)}."
+                    )
+                tensors.append(value.contiguous())
+            return tensors
+
+        raise TypeError(
+            "pixel_values must be a tensor or a sequence of tensors, "
+            f"got {type(pixel_values)!r}."
+        )
+
+    def _split_tilings(
+        self,
+        tilings: object,
+        expected: int,
+    ) -> list[tuple[int, int] | None]:
+        if tilings is None:
+            return [None] * expected
+
+        if isinstance(tilings, torch.Tensor):
+            if tilings.dim() != 2 or tilings.shape[1] != 2:
+                raise ValueError(
+                    "Expected `tilings` tensor with shape [num_images, 2], got "
+                    f"{tuple(tilings.shape)}."
+                )
+            tiling_items = tilings.tolist()
+        elif isinstance(tilings, (list, tuple)):
+            tiling_items = list(tilings)
+        else:
+            raise TypeError(
+                "tilings must be None, a tensor or a sequence of tuples, "
+                f"got {type(tilings)!r}."
+            )
+
+        if len(tiling_items) != expected:
+            raise ValueError(
+                "Mismatch between the number of pixel_values entries "
+                f"({expected}) and tilings ({len(tiling_items)})."
+            )
+
+        normalized: list[tuple[int, int] | None] = []
+        for tiling in tiling_items:
+            if tiling is None:
+                normalized.append(None)
+                continue
+            if isinstance(tiling, torch.Tensor):
+                tiling = tiling.tolist()
+            if isinstance(tiling, (list, tuple)) and len(tiling) == 2:
+                normalized.append((int(tiling[0]), int(tiling[1])))
+            else:
+                raise ValueError(
+                    f"Each tiling entry must be a pair of integers, got {tiling!r}."
+                )
+        return normalized
+
+    def _parse_image_inputs(self, **kwargs: object) -> list[Moondream3ImageInput]:
+        pixel_values = kwargs.get("pixel_values")
+        if pixel_values is None:
+            return []
+
+        pixel_values_list = self._split_pixel_values(pixel_values)
+        tilings_list = self._split_tilings(
+            kwargs.get("tilings"), len(pixel_values_list)
+        )
+
+        image_inputs: list[Moondream3ImageInput] = []
+        for value, tiling in zip(pixel_values_list, tilings_list):
+            if value.dim() != 4:
+                raise ValueError(
+                    f"Expected 4D tensor for crops, got {tuple(value.shape)}."
+                )
+            image_inputs.append(Moondream3ImageInput(pixel_values=value, tiling=tiling))
+        return image_inputs
+
+    def _encode_image_input(self, image_input: Moondream3ImageInput) -> torch.Tensor:
+        pixel_values = image_input.pixel_values
+        if pixel_values.dim() != 4:
+            raise ValueError(
+                f"Expected 4D tensor for crops, got {tuple(pixel_values.shape)}."
+            )
+
+        device = self.vision.patch_emb.weight.device
+        dtype = self.vision.patch_emb.weight.dtype
+        pixel_values = pixel_values.to(device=device, dtype=dtype)
+
+        features = self.vision(pixel_values)
+
+        # Grid size = crop_size / patch_size (e.g., 378 / 14 = 27)
+        grid_size = (
+            self.config.vision_config.crop_size
+            // self.config.vision_config.enc_patch_size
+        )
+        enc_dim = self.config.vision_config.enc_dim
+        global_features = features[0]
+
+        if features.shape[0] > 1:
+            if image_input.tiling is None:
+                raise ValueError(
+                    "Missing tiling metadata for multi-crop Moondream image."
+                )
+            local = features[1:].contiguous().view(-1, grid_size, grid_size, enc_dim)
+            reconstructed = reconstruct_from_crops(
+                local,
+                image_input.tiling,
+                overlap_margin=self.config.vision_config.overlap_margin,
+                patch_size=1,
+            )
+        else:
+            reconstructed = global_features.view(grid_size, grid_size, enc_dim)
+
+        recon = reconstructed.permute(2, 0, 1).contiguous()
+        # Mirror HF reference behavior: reconstructed local features are pooled
+        # to enc_n_layers x enc_n_layers. For moondream3-preview this is 27x27.
+        pooled_size = self.config.vision_config.enc_n_layers
+        if pooled_size != grid_size:
+            logger.warning_once(
+                "Moondream3 pooled_size (%d) differs from crop grid (%d). "
+                "Using enc_n_layers to match HF reference behavior.",
+                pooled_size,
+                grid_size,
+            )
+        recon = F.adaptive_avg_pool2d(recon, output_size=(pooled_size, pooled_size))
+        recon = recon.permute(1, 2, 0).contiguous().view(-1, enc_dim)
+
+        combined = torch.cat([global_features, recon], dim=-1).unsqueeze(0)
+        projected = self.vision_proj(combined).squeeze(0)
+
+        # Note: Vision embeddings are already synchronized across TP ranks
+        # because the vision projection uses RowParallelLinear which performs
+        # all-reduce internally, ensuring identical outputs on all ranks.
+
+        return projected
+
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
+        """Generate the HF image prefix: BOS embedding + 729 image embeddings."""
+        image_inputs = self._parse_image_inputs(**kwargs)
+        if not image_inputs:
+            return []
+
+        device = self.vision.patch_emb.weight.device
+        bos_ids = torch.tensor([self.config.bos_token_id], device=device)
+        bos_embedding = self.text.embed_input_ids(bos_ids)
+
+        embeddings: list[torch.Tensor] = []
+        for image_input in image_inputs:
+            image_embeddings = self._encode_image_input(image_input)
+            embeddings.append(
+                torch.cat([bos_embedding.to(image_embeddings.dtype), image_embeddings])
+            )
+        return embeddings
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.text(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        if logits is not None:
+            logits[:, self._answer_id] = float("-inf")
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        """Load weights with remapping from HuggingFace format."""
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        # Get expert intermediate size for fc1 splitting
+
+        for name, loaded_weight in weights:
+            # Map from HF naming to vLLM naming
+            # model.vision.* -> vision.*
+            # model.text.* -> text.*
+            if name.startswith("model."):
+                name = name[6:]  # Remove "model." prefix
+
+            # Specific name mappings
+            # Vision projection: vision.proj_mlp.fc1 -> vision_proj.fc1
+            name = name.replace("vision.proj_mlp.", "vision_proj.")
+
+            # Text embedding: text.wte (no suffix) -> text.wte.weight
+            if name == "text.wte":
+                name = "text.wte.weight"
+
+            # LM head: text.lm_head -> lm_head
+            name = name.replace("text.lm_head.", "lm_head.")
+
+            # Attention mapping
+            name = name.replace(".attn.qkv.", ".attn.qkv_proj.")
+            name = name.replace(".attn.proj.", ".attn.out_proj.")
+
+            # Tau attention scaling weights
+            # HF format: .attn.tau.alpha -> .attn.tau_alpha
+            name = name.replace(".attn.tau.alpha", ".attn.tau_alpha")
+            name = name.replace(".attn.tau.wq", ".attn.tau_wq")
+            name = name.replace(".attn.tau.wv", ".attn.tau_wv")
+
+            # MoE router mapping: mlp.router -> mlp.gate
+            name = name.replace(".mlp.router.", ".mlp.gate.")
+
+            # Handle MoE expert weights for layers 4+ with expert parallelism
+            # fc1.weight: [n_experts, expert_inner_dim * 2, hidden_size] (gate+up)
+            # fc2.weight: [n_experts, hidden_size, expert_inner_dim] (down)
+            # Each GPU stores n_experts/tp_size experts
+            # Note: Only 3D weights are MoE, 2D weights are standard MLP
+            if ".mlp.fc1.weight" in name and loaded_weight.dim() == 3:
+                from vllm.distributed import get_tensor_model_parallel_rank
+
+                tp_size = get_tensor_model_parallel_world_size()
+                tp_rank = get_tensor_model_parallel_rank()
+                num_experts = loaded_weight.shape[0]
+                experts_per_rank = num_experts // tp_size
+                expert_start = tp_rank * experts_per_rank
+                expert_end = expert_start + experts_per_rank
+                # Shard by expert dimension
+                loaded_weight = loaded_weight[expert_start:expert_end].contiguous()
+                # Map to our custom MoE format: mlp.fc1_weight
+                name = name.replace(".mlp.fc1.weight", ".mlp.fc1_weight")
+
+            if ".mlp.fc2.weight" in name and loaded_weight.dim() == 3:
+                from vllm.distributed import get_tensor_model_parallel_rank
+
+                tp_size = get_tensor_model_parallel_world_size()
+                tp_rank = get_tensor_model_parallel_rank()
+                num_experts = loaded_weight.shape[0]
+                experts_per_rank = num_experts // tp_size
+                expert_start = tp_rank * experts_per_rank
+                expert_end = expert_start + experts_per_rank
+                # Shard by expert dimension
+                loaded_weight = loaded_weight[expert_start:expert_end].contiguous()
+                # Map to our custom MoE format: mlp.fc2_weight
+                name = name.replace(".mlp.fc2.weight", ".mlp.fc2_weight")
+
+            # Handle tau weights with tensor parallelism
+            # tau_alpha: [num_heads] -> [num_heads/tp]
+            # tau_wq: [num_heads, qkv_dim] -> [num_heads/tp, qkv_dim/tp]
+            # tau_wv: [num_heads, qkv_dim] -> [num_heads/tp, qkv_dim/tp]
+            if ".tau_alpha" in name:
+                from vllm.distributed import get_tensor_model_parallel_rank
+
+                tp_size = get_tensor_model_parallel_world_size()
+                tp_rank = get_tensor_model_parallel_rank()
+                num_heads = loaded_weight.shape[0]
+                heads_per_partition = num_heads // tp_size
+                start = tp_rank * heads_per_partition
+                end = start + heads_per_partition
+                loaded_weight = loaded_weight[start:end].contiguous()
+
+            if ".tau_wq" in name or ".tau_wv" in name:
+                from vllm.distributed import get_tensor_model_parallel_rank
+
+                tp_size = get_tensor_model_parallel_world_size()
+                tp_rank = get_tensor_model_parallel_rank()
+                num_heads, qkv_dim = loaded_weight.shape
+                heads_per_partition = num_heads // tp_size
+                # Only shard by head dimension, keep full qkv_dim for all-gather
+                head_start = tp_rank * heads_per_partition
+                head_end = head_start + heads_per_partition
+                loaded_weight = loaded_weight[head_start:head_end, :].contiguous()
+
+            if name in params_dict:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+                loaded_params.add(name)
+
+        return loaded_params
diff --git a/vllm/model_executor/models/musicflamingo.py b/vllm/model_executor/models/musicflamingo.py
index f4e3bbe379a3..497b2e63a7e9 100644
--- a/vllm/model_executor/models/musicflamingo.py
+++ b/vllm/model_executor/models/musicflamingo.py
@@ -32,9 +32,9 @@
 
 from vllm.config import VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.inputs import MultiModalDataDict
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (
-    MultiModalDataDict,
     MultiModalFieldConfig,
     MultiModalKwargsItems,
 )
diff --git a/vllm/model_executor/models/nano_nemotron_vl.py b/vllm/model_executor/models/nano_nemotron_vl.py
index 81b3ac26026e..994b52606b18 100644
--- a/vllm/model_executor/models/nano_nemotron_vl.py
+++ b/vllm/model_executor/models/nano_nemotron_vl.py
@@ -37,6 +37,7 @@
 from vllm.model_executor.models.parakeet import ParakeetExtractor, ProjectedParakeet
 from vllm.model_executor.models.radio import RadioModel, calc_seq_lens
 from vllm.model_executor.models.utils import (
+    WeightsMapper,
     init_vllm_registered_model,
     maybe_prefix,
 )
@@ -903,6 +904,12 @@ class NemotronH_Nano_VL_V2(
     requires_sequential_video_encoding = True
     """Temporarily needed for dynamic res video w/ conv3d, doesn't support bs>1 yet"""
 
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "language_model.backbone": "language_model.model",
+        },
+    )
+
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> str | None:
         if modality.startswith("image"):
@@ -1117,7 +1124,9 @@ def _parse_and_validate_image_input(
             )
         else:
             return NanoNemotronVLImagePixelInputs(
-                num_patches=kwargs.pop("image_num_patches"), **kwargs
+                pixel_values_flat=pixel_values_flat,
+                num_patches=kwargs.pop("image_num_patches"),
+                **kwargs,
             )
 
     def _process_image_input_dynamic(
@@ -1490,6 +1499,11 @@ def compute_logits(
         return self.language_model.compute_logits(hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        mm_config = self.model_config.multimodal_config
+        load_multimodal_weights = not all(
+            mm_config.get_limit_per_prompt(modality) == 0
+            for modality in ("image", "video", "audio")
+        )
         adapter_dict = dict(self.mlp1.named_parameters())
 
         def is_llm(name: str) -> bool:
@@ -1514,23 +1528,30 @@ def is_sound_weights(name: str) -> bool:
                 # Strip 'language_model.' prefix for LLM weights
                 llm_weights.append((".".join(name.split(".")[1:]), w))
             elif is_adapter_weights((name, w)):
+                if not load_multimodal_weights:
+                    continue
                 # Load vision-language adapter weights directly
                 trimmed_name = ".".join(name.split(".")[1:])
                 param = adapter_dict[trimmed_name]
                 with torch.no_grad():
                     default_weight_loader(param, w)
             elif is_vision_weights(name):
+                if not load_multimodal_weights:
+                    continue
                 # Convert: vision_model.radio_model.* → radio_model.*
                 hf_key = name[len("vision_model.") :]  # Remove "vision_model." prefix
                 vision_weights.append((hf_key, w))
             elif is_sound_weights(name):
+                if not load_multimodal_weights:
+                    continue
                 assert self.sound_encoder is not None
                 sound_weights.append((name, w))
 
         self.language_model.load_weights(llm_weights)
-        self.vision_model.load_weights(vision_weights)
-        if self.sound_encoder is not None and len(sound_weights) > 0:
-            self.sound_encoder.load_weights(sound_weights)
+        if load_multimodal_weights:
+            self.vision_model.load_weights(vision_weights)
+            if self.sound_encoder is not None and len(sound_weights) > 0:
+                self.sound_encoder.load_weights(sound_weights)
 
     def get_vit_model_from_radio_config(self, hf_config):
         hf_config_vision = hf_config.vision_config
diff --git a/vllm/model_executor/models/nemotron_h.py b/vllm/model_executor/models/nemotron_h.py
index 8abbc808cce1..537e19afbcaf 100644
--- a/vllm/model_executor/models/nemotron_h.py
+++ b/vllm/model_executor/models/nemotron_h.py
@@ -34,9 +34,10 @@
 from vllm.model_executor.layers.activation import ReLUSquaredActivation
 from vllm.model_executor.layers.attention import Attention
 from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
     GateLinear,
-    SharedFusedMoE,
     activation_without_mul,
+    fused_moe_make_expert_params_mapping,
 )
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
@@ -210,13 +211,12 @@ def __init__(
             self.fc1_latent_proj = None
             self.fc2_latent_proj = None
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_experts,
             num_experts=config.n_routed_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=self.moe_hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             use_grouped_topk=True,
@@ -231,6 +231,9 @@ def __init__(
             num_redundant_experts=self.n_redundant_experts,
             is_sequence_parallel=self.is_sequence_parallel,
             routed_input_transform=self.fc1_latent_proj,
+            routed_output_transform=self.fc2_latent_proj,
+            routed_scaling_factor=self.routed_scaling_factor,
+            apply_routed_scale_to_output=True,
             router_logits_dtype=self.gate.out_dtype,
         )
 
@@ -244,38 +247,15 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
 
-        # SharedFusedMoE handles:
-        #   - shared experts (with original hidden_states)
-        #   - routed_input_transform (fc1_latent_proj) for latent MoE
-        #   - multistream parallelism between shared and routed experts
-        shared_output, final_hidden_states = self.experts(
+        final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
 
-        # Fix FP16 overflow
-        # See DeepseekV2DecoderLayer for more details.
-        if hidden_states.dtype != torch.float16:
-            final_hidden_states *= self.routed_scaling_factor
-        elif self.shared_experts is not None:
-            shared_output *= 1.0 / self.routed_scaling_factor
-
-        # TODO: See SharedFusedMoE.apply_routed_input_transform
-        # for bandwidth optimization
-        if self.use_latent_moe:
-            final_hidden_states, _ = self.fc2_latent_proj(final_hidden_states)
-
-        if self.shared_experts is not None:
-            final_hidden_states += shared_output
-
         if self.is_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
                 final_hidden_states, 0
             )
             final_hidden_states = final_hidden_states[:num_tokens]
-        elif self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
 
         return final_hidden_states.view(num_tokens, hidden_dim)
 
@@ -673,7 +653,7 @@ def _get_max_n_routed_experts(self) -> int:
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         if self.has_moe:
             # (param_name, weight_name, expert_id, shard_id)
-            expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
+            expert_params_mapping = fused_moe_make_expert_params_mapping(
                 # - FusedMoe.w1 (aka gate_proj) should be up_proj since that's
                 #   what the activation is applied to
                 # - FusedMoe.w3 (aka up_proj) should be ignored since we're
diff --git a/vllm/model_executor/models/nemotron_h_mtp.py b/vllm/model_executor/models/nemotron_h_mtp.py
index 12551d4254ed..fe737438c30f 100644
--- a/vllm/model_executor/models/nemotron_h_mtp.py
+++ b/vllm/model_executor/models/nemotron_h_mtp.py
@@ -11,7 +11,9 @@
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.config.parallel import ParallelConfig
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import ColumnParallelLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -399,7 +401,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         if getattr(self.config, "model_type", None) == "nemotron_h_puzzle":
             num_experts = self.config.mtp_n_routed_experts
         if num_experts is not None:
-            expert_params_mapping = FusedMoE.make_expert_params_mapping(
+            expert_params_mapping = fused_moe_make_expert_params_mapping(
                 self,
                 ckpt_gate_proj_name="up_proj",
                 ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/olmoe.py b/vllm/model_executor/models/olmoe.py
index f0afe0e997cc..1f342ad1733d 100644
--- a/vllm/model_executor/models/olmoe.py
+++ b/vllm/model_executor/models/olmoe.py
@@ -32,7 +32,10 @@
 from vllm.distributed.utils import split_tensor_along_last_dim
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     QKVParallelLinear,
@@ -98,7 +101,6 @@ def __init__(
             top_k=top_k,
             hidden_size=hidden_size,
             intermediate_size=intermediate_size,
-            reduce_results=True,
             renormalize=False,
             quant_config=quant_config,
             tp_size=tp_size,
@@ -337,7 +339,7 @@ def forward(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/openpangu.py b/vllm/model_executor/models/openpangu.py
index 994ae82529ab..68ab4a9ae4cb 100644
--- a/vllm/model_executor/models/openpangu.py
+++ b/vllm/model_executor/models/openpangu.py
@@ -44,7 +44,10 @@
     Attention,
     StaticSinkAttention,
 )
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -200,13 +203,12 @@ def __init__(
         else:
             self.shared_experts = None
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_experts,
             num_experts=config.n_routed_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             use_grouped_topk=True,
@@ -214,8 +216,8 @@ def __init__(
             topk_group=1,
             prefix=f"{prefix}.experts",
             scoring_func="sigmoid",
-            # we do scaling outside, set factor to 1.0 to avoid double mul
-            routed_scaling_factor=1.0,
+            routed_scaling_factor=self.routed_scaling_factor,
+            apply_routed_scale_to_output=True,
             e_score_correction_bias=self.gate.e_score_correction_bias,
             enable_eplb=self.enable_eplb,
             num_redundant_experts=self.n_redundant_experts,
@@ -234,33 +236,15 @@ def forward(
 
         router_logits, _ = self.gate(hidden_states)
 
-        fused_moe_out = self.experts(
+        final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
 
-        shared_output, final_hidden_states = fused_moe_out
-        if self.shared_experts is None:
-            assert shared_output is None
-
-        if hidden_states.dtype != torch.float16:
-            final_hidden_states *= self.routed_scaling_factor
-        elif self.shared_experts is not None:
-            assert shared_output is not None
-            shared_output *= 1.0 / self.routed_scaling_factor
-
-        if self.shared_experts is not None:
-            assert shared_output is not None
-            final_hidden_states += shared_output
-
         if self.is_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
                 final_hidden_states, 0
             )
             final_hidden_states = final_hidden_states[:num_tokens]
-        elif self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
 
         return final_hidden_states.view(num_tokens, hidden_dim)
 
@@ -1168,7 +1152,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         ]
         has_experts = hasattr(self.config, "n_routed_experts")
         if has_experts:
-            expert_merge_mapping = SharedFusedMoE.make_expert_params_mapping(
+            expert_merge_mapping = fused_moe_make_expert_params_mapping(
                 self,
                 ckpt_gate_proj_name="gate_proj",
                 ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/openpangu_mtp.py b/vllm/model_executor/models/openpangu_mtp.py
index 91b454a4bc38..3a04ccdff5be 100644
--- a/vllm/model_executor/models/openpangu_mtp.py
+++ b/vllm/model_executor/models/openpangu_mtp.py
@@ -28,7 +28,9 @@
 
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -147,7 +149,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             ("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
         ]
 
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/paddleocr_vl.py b/vllm/model_executor/models/paddleocr_vl.py
index 48a285bc0f00..8eaf94620c11 100644
--- a/vllm/model_executor/models/paddleocr_vl.py
+++ b/vllm/model_executor/models/paddleocr_vl.py
@@ -15,7 +15,7 @@
 # limitations under the License.
 
 import math
-from collections.abc import Iterable, Mapping, Sequence
+from collections.abc import Iterable, Iterator, Mapping, Sequence
 from functools import partial
 from typing import Annotated, Literal
 
@@ -1056,121 +1056,83 @@ def compute_logits(
     ) -> torch.Tensor | None:
         return self.language_model.compute_logits(hidden_states)
 
+    def iter_mm_grid_thw(
+        self, mm_features: list[MultiModalFeatureSpec]
+    ) -> Iterator[tuple[int, int, int, int, float]]:
+        """
+        Iterate over multimodal features and yield grid information.
+
+        Args:
+            mm_features: List of multimodal feature specifications
+
+        Yields:
+            Tuple of (offset, grid_t, grid_h, grid_w, t_factor) for each frame/image
+        """
+        spatial_merge_size = self.config.vision_config.spatial_merge_size
+        tokens_per_second = getattr(self.config.vision_config, "tokens_per_second", 1.0)
+        for mm_feature in sorted(mm_features, key=lambda f: f.mm_position.offset):
+            offset = mm_feature.mm_position.offset
+            if mm_feature.modality == "image":
+                t, h, w = mm_feature.data["image_grid_thw"].data.tolist()
+                assert t == 1, f"Image must have 1 frame, got {t}"
+                yield offset, 1, h // spatial_merge_size, w // spatial_merge_size, 1.0
+            elif mm_feature.modality == "video":
+                t, h, w = mm_feature.data["video_grid_thw"].data.tolist()
+                second_per_grid_ts = 1.0
+                if mm_feature.data.get("second_per_grid_ts", None):
+                    second_per_grid_ts = mm_feature.data[
+                        "second_per_grid_ts"
+                    ].data.item()
+                t_factor = second_per_grid_ts * tokens_per_second
+                yield (
+                    offset,
+                    t,
+                    h // spatial_merge_size,
+                    w // spatial_merge_size,
+                    t_factor,
+                )
+            else:
+                raise ValueError(f"Unsupported modality: {mm_feature.modality}")
+
     def get_mrope_input_positions(
         self,
         input_tokens: list[int],
         mm_features: list[MultiModalFeatureSpec],
     ) -> tuple[torch.Tensor, int]:
-        kwargs = MultiModalFeatureSpec.gather_kwargs(
-            mm_features,
-            {"image_grid_thw", "video_grid_thw", "second_per_grid_ts"},
-        )
-        image_grid_thw = [item.tolist() for item in kwargs.get("image_grid_thw", [])]
-        video_grid_thw = [item.tolist() for item in kwargs.get("video_grid_thw", [])]
-        second_per_grid_ts = kwargs.get("second_per_grid_ts", [])
-
-        hf_config = self.config
-        image_token_id = hf_config.image_token_id
-        video_token_id = hf_config.video_token_id
-        vision_start_token_id = hf_config.vision_start_token_id
-        spatial_merge_size = hf_config.vision_config.spatial_merge_size
-        tokens_per_second = getattr(hf_config.vision_config, "tokens_per_second", 1.0)
-
-        input_tokens_tensor = torch.tensor(input_tokens)
-        vision_start_indices = torch.argwhere(
-            input_tokens_tensor == vision_start_token_id
-        ).squeeze(1)
-        vision_tokens = input_tokens_tensor[vision_start_indices + 1]
-        image_nums = (vision_tokens == image_token_id).sum()
-        video_nums = (vision_tokens == video_token_id).sum()
         llm_pos_ids_list: list = []
-
         st = 0
-        remain_images, remain_videos = image_nums, video_nums
-
-        image_index, video_index = 0, 0
-        for _ in range(image_nums + video_nums):
-            video_second_per_grid_t = 0.0
-            if remain_images > 0:
-                try:
-                    ed_image = input_tokens.index(image_token_id, st)
-                except ValueError:
-                    ed_image = len(input_tokens) + 1
-            else:
-                ed_image = len(input_tokens) + 1
-            if remain_videos > 0:
-                try:
-                    ed_video = input_tokens.index(video_token_id, st)
-                except ValueError:
-                    ed_video = len(input_tokens) + 1
-            else:
-                ed_video = len(input_tokens) + 1
-            if ed_image < ed_video:
-                t, h, w = image_grid_thw[image_index]
-                image_index += 1
-                remain_images -= 1
-                ed = ed_image
-            else:
-                t, h, w = video_grid_thw[video_index]
-                video_second_per_grid_t = 1.0
-                if second_per_grid_ts:
-                    video_second_per_grid_t = second_per_grid_ts[video_index]
-                video_index += 1
-                remain_videos -= 1
-                ed = ed_video
-
-            llm_grid_t, llm_grid_h, llm_grid_w = (
-                t,
-                h // spatial_merge_size,
-                w // spatial_merge_size,
-            )
-            text_len = ed - st
 
+        for (
+            offset,
+            llm_grid_t,
+            llm_grid_h,
+            llm_grid_w,
+            t_factor,
+        ) in self.iter_mm_grid_thw(mm_features):
+            text_len = offset - st
             st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
             llm_pos_ids_list.append(
-                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
+                np.broadcast_to(np.arange(text_len), (3, text_len)) + st_idx
             )
 
-            t_index = (
-                (
-                    torch.arange(llm_grid_t)
-                    .view(-1, 1)
-                    .expand(-1, llm_grid_h * llm_grid_w)
-                    * video_second_per_grid_t
-                    * tokens_per_second
-                )
-                .long()
-                .flatten()
-            )
+            grid_indices = np.indices((llm_grid_t, llm_grid_h, llm_grid_w))
+            if t_factor != 1.0:
+                grid_indices[0] = (grid_indices[0] * t_factor).astype(np.int64)
 
-            h_index = (
-                torch.arange(llm_grid_h)
-                .view(1, -1, 1)
-                .expand(llm_grid_t, -1, llm_grid_w)
-                .flatten()
-            )
-            w_index = (
-                torch.arange(llm_grid_w)
-                .view(1, 1, -1)
-                .expand(llm_grid_t, llm_grid_h, -1)
-                .flatten()
-            )
-            llm_pos_ids_list.append(
-                torch.stack([t_index, h_index, w_index]) + text_len + st_idx
-            )
-            st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+            llm_pos_ids_list.append(grid_indices.reshape(3, -1) + text_len + st_idx)
+            st = offset + llm_grid_t * llm_grid_h * llm_grid_w
 
         if st < len(input_tokens):
             st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
             text_len = len(input_tokens) - st
             llm_pos_ids_list.append(
-                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
+                np.broadcast_to(np.arange(text_len), (3, text_len)) + st_idx
             )
 
-        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+        llm_positions = np.concatenate(llm_pos_ids_list, axis=1).reshape(3, -1)
         mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
 
-        return llm_positions, mrope_position_delta
+        return torch.from_numpy(llm_positions), mrope_position_delta
 
     def _parse_and_validate_image_input(
         self, **kwargs: object
diff --git a/vllm/model_executor/models/parakeet.py b/vllm/model_executor/models/parakeet.py
index 40539cafb314..5671ba05c56d 100644
--- a/vllm/model_executor/models/parakeet.py
+++ b/vllm/model_executor/models/parakeet.py
@@ -99,6 +99,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             if target is None:
                 target = buffers_dict.get(target_name)
             if target is None:
+                if self._can_skip_missing_named_param(target_name):
+                    continue
                 raise ValueError(f"Unknown weight: {name}")
             weight_loader = getattr(target, "weight_loader", default_weight_loader)
             with torch.no_grad():
@@ -107,6 +109,27 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         return loaded_params
 
+    def _can_skip_missing_named_param(self, target_name: str) -> bool:
+        if self.config.convolution_bias:
+            return False
+
+        # In transformers v5 (not v4), `convolution_bias=False` is
+        # propagated from parakeet config. If `False`, torch.conv1d will
+        # *skip registering the param*, thus it will be missing in the
+        # module's named params. *If* you happen to also have the bias
+        # tensors in the weights, it will cause a mismatch between the
+        # weights and the params.
+        # This allows us to have `convolution_bias=False` in the sound config,
+        # but still allow for the weights to exist.
+
+        return target_name.endswith(
+            (
+                ".conv.pointwise_conv1.bias",
+                ".conv.depthwise_conv.bias",
+                ".conv.pointwise_conv2.bias",
+            )
+        )
+
 
 EPSILON = 1e-5
 LOG_ZERO_GUARD_VALUE = 2**-24
diff --git a/vllm/model_executor/models/param2moe.py b/vllm/model_executor/models/param2moe.py
index 925f94e06005..e8ea2dbc0e60 100644
--- a/vllm/model_executor/models/param2moe.py
+++ b/vllm/model_executor/models/param2moe.py
@@ -32,7 +32,10 @@
 )
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -353,13 +356,12 @@ def __init__(
         else:
             self.shared_experts = None  # type: ignore[assignment]
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_experts,
             num_experts=self.num_experts,
             top_k=self.top_k,
             hidden_size=self.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=self.norm_expert_prob,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -371,7 +373,7 @@ def __init__(
             routed_scaling_factor=self.routed_scaling_factor,
         )
 
-    def maybe_get_fused_moe(self) -> SharedFusedMoE:
+    def maybe_get_fused_moe(self) -> FusedMoE:
         return self.experts
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -388,24 +390,11 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             self.gate.weight.float(),
         ).to(hidden_states.dtype)
 
-        final_hidden = self.experts(
+        expert_output = self.experts(
             hidden_states=hidden_states,
             router_logits=router_logits,
         )
 
-        if self.shared_experts is not None:
-            shared_output, expert_output = final_hidden
-        else:
-            shared_output, expert_output = None, final_hidden
-
-        if shared_output is not None:
-            expert_output = expert_output + shared_output
-
-        if self.tp_size > 1:
-            expert_output = self.experts.maybe_all_reduce_tensor_model_parallel(
-                expert_output
-            )
-
         return expert_output.view(num_tokens, hidden_dim)
 
 
@@ -704,7 +693,7 @@ def load_weights(
         return loaded_params
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return SharedFusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/phimoe.py b/vllm/model_executor/models/phimoe.py
index 0b55b7ec8392..5770420ce565 100644
--- a/vllm/model_executor/models/phimoe.py
+++ b/vllm/model_executor/models/phimoe.py
@@ -35,7 +35,10 @@
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.linear import (
     QKVParallelLinear,
     ReplicatedLinear,
@@ -281,7 +284,6 @@ def __init__(
             hidden_size=hidden_size,
             intermediate_size=intermediate_size,
             params_dtype=params_dtype,
-            reduce_results=True,
             renormalize=False,
             quant_config=quant_config,
             tp_size=tp_size,
@@ -515,7 +517,7 @@ def forward(
         return hidden_states
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="w1",
             ckpt_down_proj_name="w2",
diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py
index e179638a869b..447d6edd9864 100644
--- a/vllm/model_executor/models/pixtral.py
+++ b/vllm/model_executor/models/pixtral.py
@@ -458,13 +458,27 @@ def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
         _vision_encoder_stacked_params = [
             # (param_name, shard_name, shard_id)
+            # HF format
             (".qkv_proj", ".q_proj", "q"),
             (".qkv_proj", ".k_proj", "k"),
             (".qkv_proj", ".v_proj", "v"),
             (".gate_up_proj", ".gate_proj", 0),
             (".gate_up_proj", ".up_proj", 1),
+            # Mistral native (consolidated) format
+            (".qkv_proj", ".wq", "q"),
+            (".qkv_proj", ".wk", "k"),
+            (".qkv_proj", ".wv", "v"),
+            (".gate_up_proj", ".w1", 0),
+            (".gate_up_proj", ".w3", 1),
         ]
 
+        # Remap Mistral native names to HF-style names
+        # used by the vLLM vision encoder modules.
+        _vision_encoder_name_remap = {
+            ".wo.": ".o_proj.",
+            ".w2.": ".down_proj.",
+        }
+
         def is_vision_encoder_weights(weight: tuple[str, torch.Tensor]):
             return weight[0].startswith(("vision_encoder", "vision_tower"))
 
@@ -518,6 +532,11 @@ def llm_weights_generator():
                             weight_loader(param, w, shard_id)
                             break
                     else:
+                        for old, new in _vision_encoder_name_remap.items():
+                            if old in trimmed_name:
+                                trimmed_name = trimmed_name.replace(old, new)
+                                break
+
                         param = vision_encoder_dict.get(trimmed_name)
                         if param is not None:
                             weight_loader = getattr(
diff --git a/vllm/model_executor/models/plamo2.py b/vllm/model_executor/models/plamo2.py
index e81541b29aec..c6d369fffa1d 100644
--- a/vllm/model_executor/models/plamo2.py
+++ b/vllm/model_executor/models/plamo2.py
@@ -797,6 +797,83 @@ def forward(
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            # Update the weight names to be compatible with the vllm version
+            # of the model.
+            # Do not change the order of the replacements.
+            replacements = {
+                # Rename incompatible weight names.
+                ".A_log": ".A",
+                ".B_norm_weight": ".B_norm.weight",
+                ".C_norm_weight": ".C_norm.weight",
+                ".dt_norm_weight": ".dt_norm.weight",
+                ".q_weight": ".q_norm.weight",
+                ".k_weight": ".k_norm.weight",
+            }
+            # Apply replacements based on the defined mappings
+            for old, new in replacements.items():
+                if old in name:
+                    name = name.replace(old, new)
+
+            # Reshape the in_proj weights to match the shape expected
+            # by MergedColumnParallelLinear.
+            # This works both for unquantized weights and
+            # for quantized weights.
+            # In the quantized case, the weights are already transposed.
+            # Also, in addition to the quantized weights,
+            # the zero points and scales have to be reshaped as well.
+            # Packing should not be affected by this.
+            if (
+                ".mixer.in_proj.weight" in name
+                or "mixer.in_proj.qweight" in name
+                or "mixer.in_proj.scales" in name
+                or "mixer.in_proj.qzeros" in name
+            ):
+                if "mixer.in_proj.weight" in name:
+                    loaded_weight = loaded_weight.transpose(0, 1)
+                # for weight:
+                # loaded_weight.shape[0] == self.config.hidden_size
+                # for qweight:
+                # loaded_weight.shape[0] == self.config.hidden_size // param.pack_factor  # noqa
+                # for scales and qzeros:
+                # loaded_weight.shape[0] == self.config.hidden_size // self.vllm_config.quant_config.group_size  # noqa
+                loaded_weight = loaded_weight.reshape(
+                    loaded_weight.shape[0], self.config.mamba_num_heads, -1
+                )
+                gate_weight, hidden_states_weight = loaded_weight.chunk(2, dim=-1)
+                gate_weight = gate_weight.reshape(loaded_weight.shape[0], -1)
+                hidden_states_weight = hidden_states_weight.reshape(
+                    loaded_weight.shape[0], -1
+                )
+                loaded_weight = torch.cat([gate_weight, hidden_states_weight], dim=-1)
+                if "mixer.in_proj.weight" in name:
+                    loaded_weight = loaded_weight.transpose(0, 1)
+
+            # Offset parameter with vllm's RMSNorm haven't been supported yet.
+            if ".pre_mixer_norm" in name:
+                loaded_weight += 1.0
+            elif ".post_mixer_norm" in name:
+                loaded_weight += 1.0 / 5
+            elif ".pre_mlp_norm" in name:
+                loaded_weight += 1.0
+            elif ".post_mlp_norm" in name:
+                loaded_weight += 1.0 / (5**1.5)
+            elif name == "norm.weight":
+                loaded_weight += 1.0
+
+            # Skip layers on other devices.
+            if is_pp_missing_parameter(name, self):
+                continue
+
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
 
 class Plamo2ForCausalLM(
     torch.nn.Module, HasInnerState, SupportsLoRA, SupportsPP, IsHybrid
@@ -906,88 +983,9 @@ def compute_logits(
         logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
-    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
-        params_dict = dict(self.named_parameters())
-        for name, loaded_weight in weights:
-            # Both tie_word_embeddings=True and lm_head.weight in the safetensor
-            # at the same time causes dict key access error.
-            if name == "lm_head.weight" and self.config.tie_word_embeddings:
-                assert "lm_head.weight" not in params_dict
-                continue
-            # Same workaround as AutoWeightsLoader for GPTQModel
-            if any(
-                substr in name
-                for substr in AutoWeightsLoader.ROTARY_EMBEDS_UNUSED_WEIGHTS
-            ):
-                continue
-
-            # Update the weight names to be compatible with the vllm version
-            # of the model.
-            # Do not change the order of the replacements.
-            replacements = {
-                # Rename incompatible weight names.
-                ".A_log": ".A",
-                ".B_norm_weight": ".B_norm.weight",
-                ".C_norm_weight": ".C_norm.weight",
-                ".dt_norm_weight": ".dt_norm.weight",
-                ".q_weight": ".q_norm.weight",
-                ".k_weight": ".k_norm.weight",
-            }
-            # Apply replacements based on the defined mappings
-            for old, new in replacements.items():
-                if old in name:
-                    name = name.replace(old, new)
-
-            # Reshape the in_proj weights to match the shape expected
-            # by MergedColumnParallelLinear.
-            # This works both for unquantized weights and
-            # for quantized weights.
-            # In the quantized case, the weights are already transposed.
-            # Also, in addition to the quantized weights,
-            # the zero points and scales have to be reshaped as well.
-            # Packing should not be affected by this.
-            if (
-                ".mixer.in_proj.weight" in name
-                or "mixer.in_proj.qweight" in name
-                or "mixer.in_proj.scales" in name
-                or "mixer.in_proj.qzeros" in name
-            ):
-                if "mixer.in_proj.weight" in name:
-                    loaded_weight = loaded_weight.transpose(0, 1)
-                # for weight:
-                # loaded_weight.shape[0] == self.config.hidden_size
-                # for qweight:
-                # loaded_weight.shape[0] == self.config.hidden_size // param.pack_factor  # noqa
-                # for scales and qzeros:
-                # loaded_weight.shape[0] == self.config.hidden_size // self.vllm_config.quant_config.group_size  # noqa
-                loaded_weight = loaded_weight.reshape(
-                    loaded_weight.shape[0], self.config.mamba_num_heads, -1
-                )
-                gate_weight, hidden_states_weight = loaded_weight.chunk(2, dim=-1)
-                gate_weight = gate_weight.reshape(loaded_weight.shape[0], -1)
-                hidden_states_weight = hidden_states_weight.reshape(
-                    loaded_weight.shape[0], -1
-                )
-                loaded_weight = torch.cat([gate_weight, hidden_states_weight], dim=-1)
-                if "mixer.in_proj.weight" in name:
-                    loaded_weight = loaded_weight.transpose(0, 1)
-
-            # Offset parameter with vllm's RMSNorm haven't been supported yet.
-            if ".pre_mixer_norm" in name:
-                loaded_weight += 1.0
-            elif ".post_mixer_norm" in name:
-                loaded_weight += 1.0 / 5
-            elif ".pre_mlp_norm" in name:
-                loaded_weight += 1.0
-            elif ".post_mlp_norm" in name:
-                loaded_weight += 1.0 / (5**1.5)
-            elif "model.norm.weight" in name:
-                loaded_weight += 1.0
-
-            # Skip layers on other devices.
-            if is_pp_missing_parameter(name, self):
-                continue
-
-            param = params_dict[name]
-            weight_loader = getattr(param, "weight_loader", default_weight_loader)
-            weight_loader(param, loaded_weight)
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
diff --git a/vllm/model_executor/models/qianfan_ocr.py b/vllm/model_executor/models/qianfan_ocr.py
new file mode 100644
index 000000000000..ef2bec1e2900
--- /dev/null
+++ b/vllm/model_executor/models/qianfan_ocr.py
@@ -0,0 +1,92 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# QianfanOCR is built on InternVL with a Qwen3 language backbone.
+# The model architecture and weights are fully compatible with InternVLChatModel,
+# only the config model_type / architectures strings differ.
+
+from transformers import PretrainedConfig
+
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.fp8 import Fp8Config
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.transformers_utils.processors.internvl import (
+    InternVLImageProcessor,
+    InternVLProcessor,
+)
+
+from .internvl import (
+    BaseInternVLDummyInputsBuilder,
+    BaseInternVLMultiModalProcessor,
+    BaseInternVLProcessingInfo,
+    InternVLChatModel,
+)
+
+
+class QianfanOCRProcessingInfo(BaseInternVLProcessingInfo):
+    """Image-only ProcessingInfo for QianfanOCR (no video support)."""
+
+    def get_hf_processor(self, **kwargs: object) -> InternVLProcessor:
+        config = self.get_hf_config()
+        vision_config = config.vision_config
+
+        kwargs = self.ctx.get_merged_mm_kwargs(kwargs)
+        kwargs.setdefault("image_size", vision_config.image_size)
+        kwargs.setdefault("min_dynamic_patch", config.min_dynamic_patch)
+        kwargs.setdefault("max_dynamic_patch", config.max_dynamic_patch)
+        kwargs.setdefault("dynamic_image_size", config.dynamic_image_size)
+        kwargs.setdefault("use_thumbnail", config.use_thumbnail)
+
+        image_processor = InternVLImageProcessor(**kwargs)
+        image_size = image_processor.image_size
+        patch_size = vision_config.patch_size
+        downsample_ratio = config.downsample_ratio
+        image_seq_length = int((image_size // patch_size) ** 2 * (downsample_ratio**2))
+
+        return InternVLProcessor(
+            tokenizer=self.get_tokenizer(),
+            image_processor=image_processor,
+            video_processor=None,
+            image_seq_length=image_seq_length,
+            ctx_video_token=None,
+        )
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    BaseInternVLMultiModalProcessor,
+    info=QianfanOCRProcessingInfo,
+    dummy_inputs=BaseInternVLDummyInputsBuilder,
+)
+class QianfanOCRForConditionalGeneration(InternVLChatModel):
+    """QianfanOCR multimodal model.
+
+    Identical in structure to InternVLChatModel (InternViT vision encoder +
+    pixel-shuffle MLP connector + Qwen3 language model).  This class exists
+    solely to register the ``QianfanOCRForConditionalGeneration`` architecture
+    name that appears in the model's config.json.
+    """
+
+    def _patch_quant_config(
+        self, config: PretrainedConfig, quant_config: QuantizationConfig
+    ) -> None:
+        super()._patch_quant_config(config, quant_config)
+        # ignore vit layers to preserve model performance
+        if isinstance(quant_config, Fp8Config):
+            _FP8_IGNORED_LAYERS = [
+                *(
+                    layer
+                    for i in range(config.vision_config.num_hidden_layers)
+                    for layer in [
+                        f"vision_model.encoder.layers.{i}.attn.qkv",
+                        f"vision_model.encoder.layers.{i}.attn.proj",
+                        f"vision_model.encoder.layers.{i}.mlp.fc1",
+                        f"vision_model.encoder.layers.{i}.mlp.fc2",
+                    ]
+                ),
+                "language_model.lm_head",
+                "mlp1.1",
+                "mlp1.3",
+            ]
+            for layer in _FP8_IGNORED_LAYERS:
+                if layer not in quant_config.ignored_layers:
+                    quant_config.ignored_layers.append(layer)
diff --git a/vllm/model_executor/models/qwen2.py b/vllm/model_executor/models/qwen2.py
index 27aa6175b9bc..b83fedc70db0 100644
--- a/vllm/model_executor/models/qwen2.py
+++ b/vllm/model_executor/models/qwen2.py
@@ -312,48 +312,15 @@ def forward(
         return hidden_states, residual
 
 
-def qwen_2_model_invariants(
-    input_ids: torch.Tensor,
-    positions: torch.Tensor,
-    intermediate_tensors: IntermediateTensors | None = None,
-    inputs_embeds: torch.Tensor | None = None,
-):
-    """Shape invariants for Qwen2Model Model, those are translated to
-    runtime assertions for unbacked dynamic shapes and are compiled away for
-    backed"""
-    # All these should be equal.
-    # input_ids.size()[0]
-    # positions.size()[-1]
-    # intermediate_tensors["hidden_states"].size()[0]
-    # inputs_embeds.size()[0]
-    torch._check(input_ids.size()[0] == positions.size()[-1])
-    if intermediate_tensors is not None:
-        torch._check(
-            input_ids.size()[0] == intermediate_tensors["hidden_states"].size()[0]
-        )
-
-    if inputs_embeds is not None:
-        torch._check(input_ids.size()[0] == inputs_embeds.size()[0])
-
-    # Hidden dimensions should match (hidden_size)
-    # intermediate_tensors["hidden_states"].size()[1]
-    # inputs_embeds.size()[1]
-    if inputs_embeds is not None and intermediate_tensors is not None:
-        torch._check(
-            inputs_embeds.size()[1] == intermediate_tensors["hidden_states"].size()[1]
-        )
-
-
 @support_torch_compile(
     dynamic_arg_dims={
-        "input_ids": 0,
+        "input_ids": {0: "b"},
         # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
         # otherwise (seq_len, ).
-        "positions": -1,
-        "intermediate_tensors": 0,
-        "inputs_embeds": 0,
-    },
-    shape_invariants=qwen_2_model_invariants,
+        "positions": {-1: "b"},
+        "intermediate_tensors": {0: "b"},
+        "inputs_embeds": {0: "b"},
+    }
 )
 class Qwen2Model(nn.Module, EagleModelMixin):
     def __init__(
diff --git a/vllm/model_executor/models/qwen2_5_omni_thinker.py b/vllm/model_executor/models/qwen2_5_omni_thinker.py
index 7ce7dc8319c9..14f4c424bb31 100644
--- a/vllm/model_executor/models/qwen2_5_omni_thinker.py
+++ b/vllm/model_executor/models/qwen2_5_omni_thinker.py
@@ -952,8 +952,6 @@ def _parse_and_validate_video_input(
     def _process_audio_input(
         self,
         audio_input: Qwen2_5OmniAudioFeatureInputs,
-        audio_hashes: list[str] | None = None,
-        cached_audio_features: torch.Tensor | None = None,
     ) -> torch.Tensor:
         input_features = audio_input["input_features"]
         audio_feature_lengths = audio_input["audio_feature_lengths"]
@@ -990,8 +988,6 @@ def _process_image_input(
     def _process_video_input(
         self,
         video_input: Qwen2_5_VLVideoInputs,
-        video_hashes: list[str] = None,
-        cached_video_embeds: torch.Tensor = None,
     ) -> torch.Tensor:
         if video_input["type"] == "video_embeds":
             return video_input["video_embeds"].type(self.visual.dtype)
diff --git a/vllm/model_executor/models/qwen2_5_vl.py b/vllm/model_executor/models/qwen2_5_vl.py
index c11684b4b89b..54334c91bfa6 100644
--- a/vllm/model_executor/models/qwen2_5_vl.py
+++ b/vllm/model_executor/models/qwen2_5_vl.py
@@ -85,11 +85,13 @@
 from vllm.utils.platform_utils import is_pin_memory_available
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
+from vllm.v1.worker.encoder_cudagraph_defs import EncoderCudaGraphReplayBuffers
 
 from .interfaces import (
     MultiModalEmbeddings,
     SupportsEagle,
     SupportsEagle3,
+    SupportsEncoderCudaGraph,
     SupportsLoRA,
     SupportsMRoPE,
     SupportsMultiModal,
@@ -771,22 +773,54 @@ def invert_permutation(perm: torch.Tensor) -> torch.Tensor:
         inv[perm] = torch.arange(perm.numel(), device=perm.device, dtype=perm.dtype)
         return inv
 
-    def forward(
+    def prepare_encoder_metadata(
         self,
-        x: torch.Tensor,
         grid_thw: list[list[int]],
-    ) -> torch.Tensor:
+        *,
+        max_batch_size: int | None = None,
+        max_frames_per_batch: int | None = None,
+        max_window_seqs_per_batch: int | None = None,
+        max_seqlen_override: int | None = None,
+        max_seqlen_window_override: int | None = None,
+        device: torch.device | None = None,
+    ) -> dict[str, torch.Tensor]:
+        """Compute encoder metadata from grid_thw.
+
+        Shared by the eager forward path, CUDA graph capture, and
+        CUDA graph replay to avoid duplicated implementation.
+
+        Args:
+            grid_thw: Grid configurations as list of [t, h, w].
+            max_batch_size: If set, pad cu_seqlens to this size
+                (needed for CUDA graph capture/replay).
+            max_frames_per_batch: If set, overrides max_batch_size for
+                cu_seqlens padding. For video inputs each item contributes
+                T attention sequences (frames); this sizes the buffer to
+                the total frame budget so video replays never overflow.
+            max_window_seqs_per_batch: If set, pad cu_window_seqlens to this
+                number of window sequences. This keeps cu_window_seqlens shape
+                stable across capture/replay for CUDA graph safety.
+            max_seqlen_override: If set, use this value for max_seqlen
+                instead of computing from cu_seqlens (needed for CUDA
+                graph capture to cover worst-case replay scenarios).
+            max_seqlen_window_override: If set, use this value for
+                window-attention max_seqlen instead of computing from
+                cu_window_seqlens (needed for CUDA graph capture to
+                cover worst-case replay scenarios).
+            device: Device to place tensors on. Defaults to self.device.
+        """
+
+        if device is None:
+            device = self.device
+        metadata: dict[str, torch.Tensor] = {}
+
         # patchify
-        seq_len, _ = x.size()
         rotary_pos_emb_cos = []
         rotary_pos_emb_sin = []
         window_index: list = []
         cu_window_seqlens: list = [torch.tensor([0], dtype=torch.int32)]
         cu_seqlens: list = []
 
-        hidden_states = x.to(device=self.device, dtype=self.dtype)
-        hidden_states = self.patch_embed(hidden_states)
-
         window_index_id = 0
         cu_window_seqlens_last = 0
         for t, h, w in grid_thw:
@@ -825,23 +859,99 @@ def forward(
         cu_seqlens = torch.cumsum(cu_seqlens, dim=0, dtype=torch.int32)
         cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
 
+        # Pad cu_seqlens to the required number of sequences.
+        # For videos each item contributes T frames = T attention sequences,
+        # so the total can exceed max_batch_size. max_frames_per_batch
+        # overrides the pad target when set.
+        pad_to = (
+            max_frames_per_batch if max_frames_per_batch is not None else max_batch_size
+        )
+        if pad_to is not None:
+            num_seqs = len(cu_seqlens) - 1
+            if num_seqs < pad_to:
+                cu_seqlens = torch.cat(
+                    (
+                        cu_seqlens,
+                        torch.full(
+                            (pad_to - num_seqs,),
+                            cu_seqlens[-1],
+                            dtype=cu_seqlens.dtype,
+                            device=cu_seqlens.device,
+                        ),
+                    )
+                )
+
+        # Pad cu_window_seqlens to a stable number of window sequences.
+        # Like cu_seqlens, we repeat the last cumulative offset so padded
+        # entries represent empty sequences.
+        if max_window_seqs_per_batch is not None:
+            num_window_seqs = len(cu_window_seqlens) - 1
+            if num_window_seqs < max_window_seqs_per_batch:
+                cu_window_seqlens = torch.cat(
+                    (
+                        cu_window_seqlens,
+                        torch.full(
+                            (max_window_seqs_per_batch - num_window_seqs,),
+                            cu_window_seqlens[-1],
+                            dtype=cu_window_seqlens.dtype,
+                            device=cu_window_seqlens.device,
+                        ),
+                    )
+                )
+
         # transformers
         # pre-compute seqlens for window/full attn to reduce cuMemcpy operations
-        max_seqlen_full = self.compute_attn_mask_seqlen(cu_seqlens)
-        max_seqlen_window = self.compute_attn_mask_seqlen(cu_window_seqlens)
+        if max_seqlen_override is None:
+            max_seqlen_full = self.compute_attn_mask_seqlen(cu_seqlens)
+        else:
+            max_seqlen_full = torch.tensor(max_seqlen_override, dtype=torch.int32)
+        if max_seqlen_window_override is None:
+            max_seqlen_window = self.compute_attn_mask_seqlen(cu_window_seqlens)
+        else:
+            max_seqlen_window = torch.tensor(
+                max_seqlen_window_override, dtype=torch.int32
+            )
 
-        cu_seqlens = cu_seqlens.to(device=self.device, non_blocking=True)
-        cu_window_seqlens = cu_window_seqlens.to(device=self.device, non_blocking=True)
-        rotary_pos_emb_cos = rotary_pos_emb_cos.to(
-            device=self.device, non_blocking=True
-        )
-        rotary_pos_emb_sin = rotary_pos_emb_sin.to(
-            device=self.device, non_blocking=True
-        )
-        window_index = window_index.to(device=hidden_states.device, non_blocking=True)
-        reverse_indices = reverse_indices.to(
-            device=hidden_states.device, non_blocking=True
-        )
+        cu_seqlens = cu_seqlens.to(device=device, non_blocking=True)
+        cu_window_seqlens = cu_window_seqlens.to(device=device, non_blocking=True)
+        rotary_pos_emb_cos = rotary_pos_emb_cos.to(device=device, non_blocking=True)
+        rotary_pos_emb_sin = rotary_pos_emb_sin.to(device=device, non_blocking=True)
+        window_index = window_index.to(device=device, non_blocking=True)
+        reverse_indices = reverse_indices.to(device=device, non_blocking=True)
+
+        metadata["rotary_pos_emb_cos"] = rotary_pos_emb_cos
+        metadata["rotary_pos_emb_sin"] = rotary_pos_emb_sin
+        metadata["window_index"] = window_index
+        metadata["reverse_indices"] = reverse_indices
+        metadata["cu_seqlens"] = cu_seqlens
+        metadata["cu_window_seqlens"] = cu_window_seqlens
+        metadata["max_seqlen_full"] = max_seqlen_full
+        metadata["max_seqlen_window"] = max_seqlen_window
+
+        return metadata
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        grid_thw: list[list[int]],
+        *,
+        encoder_metadata: dict[str, torch.Tensor] | None = None,
+    ) -> torch.Tensor:
+        hidden_states = x.to(device=self.device, dtype=self.dtype)
+        hidden_states = self.patch_embed(hidden_states)
+
+        seq_len = hidden_states.shape[0]
+        if encoder_metadata is None:
+            encoder_metadata = self.prepare_encoder_metadata(grid_thw)
+
+        rotary_pos_emb_cos = encoder_metadata["rotary_pos_emb_cos"]
+        rotary_pos_emb_sin = encoder_metadata["rotary_pos_emb_sin"]
+        window_index = encoder_metadata["window_index"]
+        reverse_indices = encoder_metadata["reverse_indices"]
+        cu_seqlens = encoder_metadata["cu_seqlens"]
+        cu_window_seqlens = encoder_metadata["cu_window_seqlens"]
+        max_seqlen_full = encoder_metadata["max_seqlen_full"]
+        max_seqlen_window = encoder_metadata["max_seqlen_window"]
 
         hidden_states = hidden_states.reshape(
             seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1
@@ -1003,6 +1113,7 @@ def get_replacement_qwen2vl(item_idx: int, modality: str):
 class Qwen2_5_VLForConditionalGeneration(
     nn.Module,
     SupportsMultiModal,
+    SupportsEncoderCudaGraph,
     SupportsLoRA,
     SupportsPP,
     SupportsQuant,
@@ -1124,6 +1235,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
 
         self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
         self.config = config
+        self.model_config = vllm_config.model_config
         self.vllm_config = vllm_config
         self.multimodal_config = multimodal_config
         self.video_pruning_rate = multimodal_config.video_pruning_rate
@@ -1447,6 +1559,302 @@ def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
                 multimodal_embeddings += tuple(video_embeddings)
         return multimodal_embeddings
 
+    # -- SupportsEncoderCudaGraph protocol methods --
+
+    def get_encoder_cudagraph_config(self):
+        from vllm.v1.worker.encoder_cudagraph_defs import (
+            EncoderCudaGraphConfig,
+        )
+
+        # NOTE: With EVS pruning enabled, multimodal embeddings are post-processed
+        # (append positions for image and prune+append positions for video) in
+        # embed_multimodal(). The encoder CUDA graph path bypasses that postprocess
+        # hook, so disable CUDA graph for all modalities to avoid inconsistent
+        # embedding formats between eager and cudagraph paths.
+        modalities = [] if self.is_multimodal_pruning_enabled else ["image", "video"]
+
+        return EncoderCudaGraphConfig(
+            modalities=modalities,
+            input_key_by_modality={
+                "image": "pixel_values",
+                "video": "pixel_values_videos",
+            },
+            buffer_keys=[
+                "rotary_pos_emb_cos",
+                "rotary_pos_emb_sin",
+                "window_index",
+                "reverse_indices",
+                "cu_seqlens",
+                "cu_window_seqlens",
+                "max_seqlen_full",
+                "max_seqlen_window",
+            ],
+            out_hidden_size=self.visual.out_hidden_size,
+        )
+
+    def get_input_modality(
+        self,
+        mm_kwargs: dict[str, Any],
+    ) -> str:
+        if "image_grid_thw" in mm_kwargs:
+            return "image"
+        return "video"
+
+    def get_max_frames_per_video(self) -> int:
+        mm_registry = MULTIMODAL_REGISTRY
+        info = mm_registry.get_processing_info(self.model_config)
+        max_frames_per_video = info.get_num_frames_with_most_features(
+            seq_len=self.model_config.max_model_len,
+            mm_counts={"video": self.multimodal_config.get_limit_per_prompt("video")},
+        )
+        return max_frames_per_video
+
+    def get_encoder_cudagraph_budget_range(
+        self,
+        vllm_config: VllmConfig,
+    ) -> tuple[int, int]:
+        # Min: estimated smallest possible encoder input.
+        # 224x224 image → 16x16 patches (patch_size=14)
+        #                 spatial_merge_size=2 → 8x8 = 64 tokens
+        min_budget = 64
+        # Max: capped by max_num_batched_tokens
+        max_budget = min(
+            vllm_config.scheduler_config.max_num_batched_tokens,
+            self.model_config.max_model_len,
+        )
+        return (min_budget, max_budget)
+
+    def _get_pixel_values_by_modality(
+        self,
+        mm_kwargs: dict[str, Any],
+    ) -> torch.Tensor:
+        if self.get_input_modality(mm_kwargs) == "image":
+            pixel_values = mm_kwargs["pixel_values"]
+        else:
+            pixel_values = mm_kwargs["pixel_values_videos"]
+        return pixel_values
+
+    def _get_grid_thw_by_modality(
+        self,
+        mm_kwargs: dict[str, Any],
+    ) -> list[tuple[int, int, int]]:
+        grid_thw_key = f"{self.get_input_modality(mm_kwargs)}_grid_thw"
+        grid_thw = mm_kwargs[grid_thw_key]
+        if not isinstance(grid_thw, list):
+            grid_thw = grid_thw.tolist()
+        return grid_thw
+
+    def get_encoder_cudagraph_num_items(
+        self,
+        mm_kwargs: dict[str, Any],
+    ) -> int:
+        return len(self._get_grid_thw_by_modality(mm_kwargs))
+
+    def get_encoder_cudagraph_per_item_output_tokens(
+        self,
+        mm_kwargs: dict[str, Any],
+    ) -> list[int]:
+        m = self.visual.spatial_merge_size
+        grid_thw = self._get_grid_thw_by_modality(mm_kwargs)
+        return [t * (h // m) * (w // m) for t, h, w in grid_thw]
+
+    def get_encoder_cudagraph_per_item_input_sizes(
+        self,
+        mm_kwargs: dict[str, Any],
+    ) -> list[int]:
+        grid_thw = self._get_grid_thw_by_modality(mm_kwargs)
+        return [t * h * w for t, h, w in grid_thw]
+
+    def select_encoder_cudagraph_items(
+        self,
+        mm_kwargs: dict[str, Any],
+        indices: list[int],
+    ) -> dict[str, Any]:
+        grid_thw = self._get_grid_thw_by_modality(mm_kwargs)
+        pixel_values = self._get_pixel_values_by_modality(mm_kwargs)
+
+        if len(indices) == 0:
+            if self.get_input_modality(mm_kwargs) == "image":
+                return {
+                    "pixel_values": pixel_values[:0],
+                    "image_grid_thw": [],
+                }
+            else:
+                return {
+                    "pixel_values_videos": pixel_values[:0],
+                    "video_grid_thw": [],
+                }
+
+        # Compute cumulative patch offsets for slicing pixel_values
+        patches_per_item = [t * h * w for t, h, w in grid_thw]
+        cum_patches = [0]
+        for p in patches_per_item:
+            cum_patches.append(cum_patches[-1] + p)
+
+        selected_pv = torch.cat(
+            [pixel_values[cum_patches[i] : cum_patches[i + 1]] for i in indices]
+        )
+        selected_grid = [grid_thw[i] for i in indices]
+
+        if self.get_input_modality(mm_kwargs) == "image":
+            return {
+                "pixel_values": selected_pv,
+                "image_grid_thw": selected_grid,
+            }
+        else:
+            return {
+                "pixel_values_videos": selected_pv,
+                "video_grid_thw": selected_grid,
+            }
+
+    def prepare_encoder_cudagraph_capture_inputs(
+        self,
+        token_budget: int,
+        max_batch_size: int,
+        max_frames_per_batch: int,
+        device: torch.device,
+        dtype: torch.dtype,
+    ):
+        from vllm.v1.worker.encoder_cudagraph_defs import (
+            EncoderCudaGraphCaptureInputs,
+        )
+
+        spatial_merge_size = self.visual.spatial_merge_size
+        max_window_seqs_per_batch = min(
+            self.vllm_config.scheduler_config.max_num_batched_tokens,
+            self.model_config.max_model_len,
+        )
+        # Use ceil here (not floor) so total captured capacity is never smaller
+        # than token_budget when token_budget is not divisible by max_batch_size
+        # (e.g., 324 budget with max_batch_size=8). Floor under-allocates
+        # input_buffer and can fail replay copy for valid single-item batches.
+        per_mm_item_output = (token_budget + max_batch_size - 1) // max_batch_size
+
+        frames_per_item = max_frames_per_batch // max_batch_size
+        if frames_per_item > 1:
+            # Build the capture grid using a video-format layout so that
+            # cu_seqlens is sized for video replays from the start.
+            # cu_seqlens has one entry per attention sequence (one per frame),
+            # so using T > 1 per item makes the buffer large enough without
+            # relying solely on padding.
+            # Ceiling ensures frames_per_item * tokens_per_frame >= per_mm_item_output
+            # so the pixel_values buffer covers any valid single-item replay.
+            tokens_per_frame = (
+                per_mm_item_output + frames_per_item - 1
+            ) // frames_per_item
+            # Video-format grid_config (T=frames_per_item).
+            grid_config = [
+                [
+                    frames_per_item,
+                    spatial_merge_size,
+                    tokens_per_frame * spatial_merge_size,
+                ]
+                for _ in range(max_batch_size)
+            ]
+        else:
+            # Image-format grid_config (T=1).
+            grid_config = [
+                [1, spatial_merge_size, per_mm_item_output * spatial_merge_size]
+                for _ in range(max_batch_size)
+            ]
+
+        # Create dummy pixel_values
+        patch_embed = self.visual.patch_embed
+        in_channels = patch_embed.proj.in_channels
+        patch_size = patch_embed.patch_size
+        temporal_patch_size = patch_embed.temporal_patch_size
+        total_patches = sum(t * h * w for t, h, w in grid_config)
+        flattened_patch_size = (
+            in_channels * temporal_patch_size * patch_size * patch_size
+        )
+        dummy_pixel_values = torch.randn(
+            total_patches, flattened_patch_size, device=device, dtype=dtype
+        )
+
+        # Override max_seqlen with a safe upper bound for capture.
+        # max_seqlen.item() gets baked into the CUDA graph (not replayed),
+        # so the capture value must cover any replay scenario.
+        # Worst case: 1 item consuming the full budget ->
+        # seq_len = token_budget * spatial_merge_size^2.
+        # For window-attention, each local window is bounded by fixed geometry:
+        # (window_size / patch_size / spatial_merge_size)^2 windows in merged
+        # token space, multiplied by spatial_merge_size^2 to map back to the
+        # unmerged sequence length used by attention kernels.
+        vit_merger_window_size = (
+            self.visual.window_size
+            // self.visual.spatial_merge_size
+            // self.visual.patch_size
+        )
+        max_seqlen_window_override = vit_merger_window_size**2 * (spatial_merge_size**2)
+        buffers = self.visual.prepare_encoder_metadata(
+            grid_config,
+            max_batch_size=max_batch_size,
+            max_frames_per_batch=max_frames_per_batch,
+            max_window_seqs_per_batch=max_window_seqs_per_batch,
+            max_seqlen_override=token_budget * (spatial_merge_size**2),
+            max_seqlen_window_override=max_seqlen_window_override,
+            device=device,
+        )
+
+        # Just use image-modality dummy input_buffer for capturing, since it's also
+        # compatible for video inputs (has the same shape: [num_patches, C*T*P*P]).
+        mm_kwargs = {
+            "pixel_values": dummy_pixel_values,
+            "image_grid_thw": grid_config,
+        }
+
+        return EncoderCudaGraphCaptureInputs(
+            mm_kwargs=mm_kwargs,
+            buffers=buffers,
+        )
+
+    def prepare_encoder_cudagraph_replay_buffers(
+        self,
+        mm_kwargs: dict[str, Any],
+        max_batch_size: int,
+        max_frames_per_batch: int,
+    ):
+        modality = self.get_input_modality(mm_kwargs)
+        grid_thw_list = self._get_grid_thw_by_modality(mm_kwargs)
+
+        if modality == "image":
+            buffers = self.visual.prepare_encoder_metadata(
+                grid_thw_list,
+                max_batch_size=max_batch_size,
+                max_window_seqs_per_batch=min(
+                    self.vllm_config.scheduler_config.max_num_batched_tokens,
+                    self.model_config.max_model_len,
+                ),
+            )
+        else:
+            buffers = self.visual.prepare_encoder_metadata(
+                grid_thw_list,
+                max_frames_per_batch=max_frames_per_batch,
+                max_window_seqs_per_batch=min(
+                    self.vllm_config.scheduler_config.max_num_batched_tokens,
+                    self.model_config.max_model_len,
+                ),
+            )
+
+        return EncoderCudaGraphReplayBuffers(buffers=buffers)
+
+    def encoder_cudagraph_forward(
+        self,
+        mm_kwargs: dict[str, Any],
+        buffers: dict[str, torch.Tensor],
+    ) -> torch.Tensor:
+        pixel_values = self._get_pixel_values_by_modality(mm_kwargs)
+        grid_thw = self._get_grid_thw_by_modality(mm_kwargs)
+        return self.visual(pixel_values, grid_thw, encoder_metadata=buffers)
+
+    def encoder_eager_forward(
+        self,
+        mm_kwargs: dict[str, Any],
+    ) -> torch.Tensor:
+        pixel_values = self._get_pixel_values_by_modality(mm_kwargs)
+        grid_thw = self._get_grid_thw_by_modality(mm_kwargs)
+        return self.visual(pixel_values, grid_thw)
+
     def forward(
         self,
         input_ids: torch.Tensor | None,
diff --git a/vllm/model_executor/models/qwen2_moe.py b/vllm/model_executor/models/qwen2_moe.py
index 750835cccd92..77eea390eda9 100644
--- a/vllm/model_executor/models/qwen2_moe.py
+++ b/vllm/model_executor/models/qwen2_moe.py
@@ -40,7 +40,10 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -164,13 +167,12 @@ def __init__(
         else:
             self.shared_expert = None
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_expert,
             num_experts=config.num_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -187,12 +189,6 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
-        if self.shared_expert is not None:
-            final_hidden_states = final_hidden_states[0] + final_hidden_states[1]
-        if self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(  # noqa E501
-                final_hidden_states
-            )
 
         return final_hidden_states.view(orig_shape)
 
@@ -425,7 +421,7 @@ def forward(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return SharedFusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/qwen3_5.py b/vllm/model_executor/models/qwen3_5.py
index 03ea538127a2..dc761e69b487 100644
--- a/vllm/model_executor/models/qwen3_5.py
+++ b/vllm/model_executor/models/qwen3_5.py
@@ -138,7 +138,6 @@ def __init__(
                 vllm_config=vllm_config,
                 prefix=f"{prefix}.linear_attn",
                 gqa_interleaved_layout=False,
-                create_in_proj_qkvz=vllm_config.lora_config is None,
             )
         elif self.layer_type == "full_attention":
             self.self_attn = Qwen3NextAttention(
@@ -217,7 +216,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.num_redundant_experts = eplb_config.num_redundant_experts
 
         self.config = config
-        self.enable_lora = vllm_config.lora_config is not None
 
         self.vocab_size = config.vocab_size
 
@@ -276,6 +274,9 @@ def load_fused_expert_weights(
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id)
+            # GDN
+            ("in_proj_qkvz", "in_proj_qkv", (0, 1, 2)),
+            ("in_proj_qkvz", "in_proj_z", 3),
             # self attention
             ("qkv_proj", "q_proj", "q"),
             ("qkv_proj", "k_proj", "k"),
@@ -287,28 +288,16 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             ("in_proj_ba", "in_proj_a", 1),
         ]
 
-        if self.enable_lora:
-            stacked_params_mapping.extend(
-                [
-                    ("in_proj_qkv", "in_proj_qkv", (0, 1, 2)),
-                    ("in_proj_z", "in_proj_z", 0),
-                ]
-            )
-        else:
-            stacked_params_mapping.extend(
-                [
-                    ("in_proj_qkvz", "in_proj_qkv", (0, 1, 2)),
-                    ("in_proj_qkvz", "in_proj_z", 3),
-                ]
-            )
-
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
         expert_params_mapping = self.get_expert_mapping()
         is_fused_expert = False
+        base_layer = (
+            "base_layer." if any(".base_layer." in name for name in params_dict) else ""
+        )
         fused_expert_params_mapping = [
-            ("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
-            ("experts.w2_weight", "experts.down_proj", 0, "w2"),
+            (f"experts.{base_layer}w13_weight", "experts.gate_up_proj", 0, "w1"),
+            (f"experts.{base_layer}w2_weight", "experts.down_proj", 0, "w2"),
         ]
         num_experts = (
             self.config.num_experts if hasattr(self.config, "num_experts") else 0
@@ -349,10 +338,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     continue
                 param = params_dict[name]
                 weight_loader = param.weight_loader
-                if param_name == "in_proj_z" and self.enable_lora:
-                    weight_loader(param, loaded_weight)
-                else:
-                    weight_loader(param, loaded_weight, shard_id)
+                weight_loader(param, loaded_weight, shard_id)
                 break
             else:
                 is_expert_weight = False
@@ -482,15 +468,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
         )
 
-        # When LoRA is enabled, GDN uses separate in_proj_qkv and in_proj_z
-        # instead of merged in_proj_qkvz; pack mapping must match.
-        if vllm_config.lora_config:
-            base = getattr(Qwen3_5ForCausalLMBase, "packed_modules_mapping", {})
-            self.packed_modules_mapping = {k: list(v) for k, v in base.items()}
-            self.packed_modules_mapping.pop("in_proj_qkvz", None)
-            self.packed_modules_mapping["in_proj_qkv"] = ["in_proj_qkv"]
-            self.packed_modules_mapping["in_proj_z"] = ["in_proj_z"]
-
         if get_pp_group().is_last_rank:
             if config.tie_word_embeddings:
                 self.lm_head = self.model.embed_tokens
@@ -583,7 +560,6 @@ class Qwen3_5ForConditionalGeneration(Qwen3VLForConditionalGeneration, IsHybrid)
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
         # protocols have not __init__ method, so we need to use nn.Module.__init__
         nn.Module.__init__(self)
-        self.update_packed_mapping(enable_lora=vllm_config.lora_config is not None)
         config: Qwen3_5Config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
         multimodal_config = vllm_config.model_config.multimodal_config
@@ -611,17 +587,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
             self.language_model.make_empty_intermediate_tensors
         )
 
-    def update_packed_mapping(self, enable_lora: bool):
-        # When LoRA is enabled, GDN uses separate in_proj_qkv and in_proj_z
-        if enable_lora:
-            base = getattr(
-                Qwen3_5ForConditionalGeneration, "packed_modules_mapping", {}
-            )
-            self.packed_modules_mapping = {k: list(v) for k, v in base.items()}
-            self.packed_modules_mapping.pop("in_proj_qkvz", None)
-            self.packed_modules_mapping["in_proj_qkv"] = ["in_proj_qkv"]
-            self.packed_modules_mapping["in_proj_z"] = ["in_proj_z"]
-
     def embed_input_ids(
         self,
         input_ids: torch.Tensor,
@@ -808,7 +773,6 @@ class Qwen3_5MoeForConditionalGeneration(
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
         # protocols have not __init__ method, so we need to use nn.Module.__init__
         nn.Module.__init__(self)
-        self.update_packed_mapping(enable_lora=vllm_config.lora_config is not None)
         config: Qwen3_5MoeConfig = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
         multimodal_config = vllm_config.model_config.multimodal_config
diff --git a/vllm/model_executor/models/qwen3_5_mtp.py b/vllm/model_executor/models/qwen3_5_mtp.py
index e49806365e3f..e86b205b9f31 100644
--- a/vllm/model_executor/models/qwen3_5_mtp.py
+++ b/vllm/model_executor/models/qwen3_5_mtp.py
@@ -12,7 +12,9 @@
 from vllm.config import VllmConfig
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.logger import init_logger
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.linear import ColumnParallelLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -194,7 +196,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -207,9 +209,12 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
         is_fused_expert = False
+        base_layer = (
+            "base_layer." if any(".base_layer." in name for name in params_dict) else ""
+        )
         fused_expert_params_mapping = [
-            ("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
-            ("experts.w2_weight", "experts.down_proj", 0, "w2"),
+            (f"experts.{base_layer}w13_weight", "experts.gate_up_proj", 0, "w1"),
+            (f"experts.{base_layer}w2_weight", "experts.down_proj", 0, "w2"),
         ]
         num_experts = (
             self.config.num_experts if hasattr(self.config, "num_experts") else 0
diff --git a/vllm/model_executor/models/qwen3_asr.py b/vllm/model_executor/models/qwen3_asr.py
index 1d8e1bbaa4c4..950beba77541 100644
--- a/vllm/model_executor/models/qwen3_asr.py
+++ b/vllm/model_executor/models/qwen3_asr.py
@@ -23,9 +23,8 @@
 """Inference-only Qwen3-ASR model."""
 
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Any, Literal
+from typing import Any
 
-import numpy as np
 import torch
 import torch.nn as nn
 from transformers.feature_extraction_utils import BatchFeature
@@ -33,6 +32,7 @@
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.inputs import ModalityData, MultiModalDataDict, PromptType, TokensPrompt
 from vllm.logger import init_logger
 from vllm.model_executor.models.interfaces import (
@@ -363,8 +363,6 @@ def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
     def _process_audio_input(
         self,
         audio_input: Qwen2_5OmniAudioFeatureInputs,
-        audio_hashes: list[str] | None = None,
-        cached_audio_features: torch.Tensor | None = None,
     ) -> torch.Tensor:
         input_features = audio_input["input_features"]
         audio_feature_lengths = audio_input["audio_feature_lengths"]
@@ -551,17 +549,12 @@ def get_speech_to_text_config(
         )
 
     @classmethod
-    def get_generation_prompt(
-        cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
-    ) -> PromptType:
+    def get_generation_prompt(cls, stt_params: SpeechToTextParams) -> PromptType:
         """Get the generation prompt to be used for transcription requests."""
+        audio = stt_params.audio
+        model_config = stt_params.model_config
+        task_type = stt_params.task_type
+        to_language = stt_params.to_language
         tokenizer = cached_tokenizer_from_config(model_config)
         audio_placeholder = cls.get_placeholder_str("audio", 0)
 
diff --git a/vllm/model_executor/models/qwen3_dflash.py b/vllm/model_executor/models/qwen3_dflash.py
index ce45136d7c0b..cffe6267a4b3 100644
--- a/vllm/model_executor/models/qwen3_dflash.py
+++ b/vllm/model_executor/models/qwen3_dflash.py
@@ -523,7 +523,14 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.logits_processor = LogitsProcessor(
             self.config.draft_vocab_size, scale=logit_scale
         )
-        self.draft_id_to_target_id = None
+        target_vocab_size = vllm_config.model_config.get_vocab_size()
+        if self.config.draft_vocab_size != target_vocab_size:
+            self.draft_id_to_target_id = nn.Parameter(
+                torch.zeros(self.config.draft_vocab_size, dtype=torch.long),
+                requires_grad=False,
+            )
+        else:
+            self.draft_id_to_target_id = None
 
     def embed_input_ids(
         self,
diff --git a/vllm/model_executor/models/qwen3_moe.py b/vllm/model_executor/models/qwen3_moe.py
index f2ce070be8b4..4ec1be3367d8 100644
--- a/vllm/model_executor/models/qwen3_moe.py
+++ b/vllm/model_executor/models/qwen3_moe.py
@@ -43,7 +43,10 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -205,14 +208,13 @@ def __init__(
             self.shared_expert_gate = None
             self.shared_expert = None
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_expert,
             gate=self.gate,
             num_experts=self.n_routed_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -232,24 +234,25 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         if self.is_sequence_parallel:
             hidden_states = sequence_parallel_chunk(hidden_states)
 
-        # router_logits: (num_tokens, n_experts)
-        router_logits, _ = self.gate(hidden_states)
-        shared_out, fused_out = self.experts(
-            hidden_states=hidden_states, router_logits=router_logits
-        )
-        final_hidden_states = (
-            shared_out + fused_out if shared_out is not None else fused_out
-        )
+        if self.experts.is_internal_router:
+            # In this case, the gate/router runs inside the FusedMoE class
+            final_hidden_states = self.experts(
+                hidden_states=hidden_states, router_logits=hidden_states
+            )
+        else:
+            # Actually this will be dead code, since we always pass gate into
+            # FusedMoE in the current implementation. But we keep this code
+            # here for clarity and future flexibility.
+            router_logits, _ = self.gate(hidden_states)
+            final_hidden_states = self.experts(
+                hidden_states=hidden_states, router_logits=router_logits
+            )
 
         if self.is_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
                 final_hidden_states, 0
             )
             final_hidden_states = final_hidden_states[:num_tokens]
-        elif self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(  # noqa E501
-                final_hidden_states
-            )
 
         # return to 1d if input is 1d
         return final_hidden_states.squeeze(0) if is_input_1d else final_hidden_states
@@ -516,7 +519,7 @@ def forward(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return SharedFusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/qwen3_next.py b/vllm/model_executor/models/qwen3_next.py
index 3bd026d9dc9f..f4c7167890bb 100644
--- a/vllm/model_executor/models/qwen3_next.py
+++ b/vllm/model_executor/models/qwen3_next.py
@@ -8,6 +8,7 @@
 import torch
 from torch import nn
 
+from vllm._aiter_ops import rocm_aiter_ops
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import (
     CacheConfig,
@@ -23,7 +24,10 @@
 )
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import (
     GemmaRMSNorm as Qwen3NextRMSNorm,
 )
@@ -132,7 +136,12 @@ def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
             prefix=f"{prefix}.shared_expert_gate",
         )
 
-        if config.shared_expert_intermediate_size > 0:
+        if (
+            rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
+            or config.shared_expert_intermediate_size <= 0
+        ):
+            self.shared_expert = None
+        else:
             self.shared_expert = Qwen3NextMLP(
                 hidden_size=config.hidden_size,
                 intermediate_size=config.shared_expert_intermediate_size,
@@ -143,23 +152,24 @@ def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
                 is_sequence_parallel=self.is_sequence_parallel,
                 prefix=f"{prefix}.shared_expert",
             )
-        else:
-            self.shared_expert = None
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_expert,
             gate=self.gate,
             num_experts=self.n_routed_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=getattr(config, "norm_topk_prob", True),
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
             enable_eplb=self.enable_eplb,
             num_redundant_experts=self.n_redundant_experts,
             is_sequence_parallel=self.is_sequence_parallel,
+            n_shared_experts=1 if self.shared_expert is None else None,
+            shared_expert_gate=self.shared_expert_gate
+            if self.shared_expert is None
+            else None,
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -183,18 +193,11 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
                 hidden_states=hidden_states, router_logits=router_logits
             )
 
-        if self.shared_expert is not None:
-            final_hidden_states = final_hidden_states[0] + final_hidden_states[1]
-
         if self.is_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
                 final_hidden_states, 0
             )
             final_hidden_states = final_hidden_states[:num_tokens]
-        elif self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(  # noqa E501
-                final_hidden_states
-            )
 
         return final_hidden_states.view(orig_shape)
 
@@ -541,12 +544,15 @@ def forward(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return SharedFusedMoE.make_expert_params_mapping(
+        num_experts = getattr(self.config, "num_experts", 0)
+        if rocm_aiter_ops.is_fusion_moe_shared_experts_enabled():
+            num_experts += 1
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
             ckpt_up_proj_name="up_proj",
-            num_experts=getattr(self.config, "num_experts", 0),
+            num_experts=num_experts,
             num_redundant_experts=self.num_redundant_experts,
         )
 
@@ -563,6 +569,10 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
         expert_params_mapping = self.get_expert_mapping()
+
+        is_fse = rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
+        num_routed = getattr(self.config, "num_experts", 0)
+
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
                 continue
@@ -576,6 +586,13 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                 if name is None:
                     continue
 
+            # FSE: remap shared_expert weights to the fused expert slot
+            if is_fse and "mlp.shared_expert." in name:
+                name = name.replace(
+                    "mlp.shared_expert.",
+                    f"mlp.experts.{num_routed}.",
+                )
+
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 if weight_name not in name:
                     continue
diff --git a/vllm/model_executor/models/qwen3_next_mtp.py b/vllm/model_executor/models/qwen3_next_mtp.py
index 751d7c23eb97..4d8ff951c098 100644
--- a/vllm/model_executor/models/qwen3_next_mtp.py
+++ b/vllm/model_executor/models/qwen3_next_mtp.py
@@ -7,11 +7,14 @@
 import torch
 from torch import nn
 
+from vllm._aiter_ops import rocm_aiter_ops
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.logger import init_logger
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.linear import ColumnParallelLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -145,13 +148,18 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+        is_fse = rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
+        num_experts = self.config.num_experts
+        if is_fse:
+            num_experts += 1
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
             ckpt_up_proj_name="up_proj",
-            num_experts=self.config.num_experts,
+            num_experts=num_experts,
         )
+        num_routed = self.config.num_experts
 
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
@@ -159,6 +167,13 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             if "rotary_emb.inv_freq" in name:
                 continue
 
+            # FSE: remap shared_expert weights to the fused expert slot
+            if is_fse and "mlp.shared_expert." in name:
+                name = name.replace(
+                    "mlp.shared_expert.",
+                    f"mlp.experts.{num_routed}.",
+                )
+
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 if weight_name not in name:
                     continue
diff --git a/vllm/model_executor/models/qwen3_omni_moe_thinker.py b/vllm/model_executor/models/qwen3_omni_moe_thinker.py
index 8cee51a12699..44e656ab820b 100755
--- a/vllm/model_executor/models/qwen3_omni_moe_thinker.py
+++ b/vllm/model_executor/models/qwen3_omni_moe_thinker.py
@@ -24,7 +24,7 @@
 
 from collections.abc import Callable, Iterable, Iterator, Mapping, Sequence
 from functools import partial
-from typing import Any, Literal, cast
+from typing import Any, cast
 
 import numpy as np
 import torch
@@ -46,6 +46,7 @@
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.inputs import PromptType
 from vllm.logger import init_logger
@@ -1646,8 +1647,6 @@ class Qwen3OmniMoeConditionalGenerationMixin(Qwen2_5OmniConditionalGenerationMix
     def _process_audio_input(
         self,
         audio_input: Qwen2_5OmniAudioFeatureInputs,
-        audio_hashes: list[str] | None = None,
-        cached_audio_features: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, ...]:
         input_features = audio_input["input_features"]
         audio_feature_lengths = audio_input["audio_feature_lengths"]
@@ -1754,6 +1753,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
                     )
                     for _ in range(self.deepstack_num_level)
                 ]
+                # Tracks the valid token span currently stored in the buffer.
+                # Zero means there is no active deepstack payload to consume.
+                self.deepstack_input_embeds_num_tokens = 0
 
         with self._mark_language_model(vllm_config):
             self.language_model = Qwen3MoeLLMForCausalLM(
@@ -1774,6 +1776,8 @@ def _get_deepstack_input_embeds(
     ) -> IntermediateTensors | None:
         if not getattr(self, "deepstack_input_embeds", None):
             return None  # If vision tower is skipped
+        if getattr(self, "deepstack_input_embeds_num_tokens", 0) == 0:
+            return None
 
         # get deepstack_input_embeds from buffer, and clear the buffer
         return IntermediateTensors(
@@ -1805,15 +1809,19 @@ def _set_deepstack_input_embeds(self, deepstack_input_embeds: torch.Tensor) -> N
             self.deepstack_input_embeds[idx][:num_tokens].copy_(
                 deepstack_input_embeds[idx]
             )
+        self.deepstack_input_embeds_num_tokens = num_tokens
 
     def _clear_deepstack_input_embeds(self, num_tokens: int) -> None:
         if not getattr(self, "deepstack_input_embeds", None):
             return
+        if getattr(self, "deepstack_input_embeds_num_tokens", 0) == 0:
+            return
 
         # clear deepstack_input_embeds in buffer
         if num_tokens > 0:
             for idx in range(self.deepstack_num_level):
                 self.deepstack_input_embeds[idx][:num_tokens].zero_()
+            self.deepstack_input_embeds_num_tokens = 0
 
     def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
         mm_input_by_modality = {}
@@ -2203,19 +2211,17 @@ def get_speech_to_text_config(
         )
 
     @classmethod
-    def get_generation_prompt(
-        cls,
-        audio: np.ndarray,
-        stt_config: SpeechToTextConfig,
-        model_config: ModelConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
-    ) -> PromptType:
+    def get_generation_prompt(cls, stt_params: SpeechToTextParams) -> PromptType:
         """
         Construct a transcription/translation prompt for Qwen3-Omni.
         """
+        audio = stt_params.audio
+        stt_config = stt_params.stt_config
+        model_config = stt_params.model_config
+        language = stt_params.language
+        task_type = stt_params.task_type
+        to_language = stt_params.to_language
+        request_prompt = stt_params.request_prompt
         # Transcribe this audio [into <language>] | for transcription
         # Translate this audio [from <language> into <to_language>] | for translation
         instruction = "Transcribe" if task_type == "transcribe" else "Translate"
diff --git a/vllm/model_executor/models/qwen3_vl.py b/vllm/model_executor/models/qwen3_vl.py
index fee0b937b7da..2575f634be1c 100644
--- a/vllm/model_executor/models/qwen3_vl.py
+++ b/vllm/model_executor/models/qwen3_vl.py
@@ -136,6 +136,7 @@
     maybe_prefix,
 )
 from .vision import (
+    get_fp8_padded_hidden_size,
     get_vit_attn_backend,
     is_vit_use_data_parallel,
     run_dp_sharded_mrope_vision_model,
@@ -562,6 +563,13 @@ def __init__(
 
         norm_layer = partial(nn.LayerNorm, eps=norm_eps)
         head_dim = self.hidden_size // self.num_heads
+
+        # FP8 attention: Q/K/V become independent contiguous tensors
+        # after quantization, so cu_seqlens uses uniform stride (no 3x V).
+        self.fp8_padded_hidden_size = get_fp8_padded_hidden_size(
+            self.num_heads, head_dim
+        )
+
         self.rotary_pos_emb = get_rope(
             head_size=head_dim,
             max_position=8192,
@@ -776,6 +784,7 @@ def prepare_encoder_metadata(
             self.hidden_size,
             self.tp_size,
             device,
+            fp8_padded_hidden_size=self.fp8_padded_hidden_size,
         )
 
         return metadata
@@ -1640,6 +1649,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
         multimodal_config = vllm_config.model_config.multimodal_config
 
         self.config = config
+        self.model_config = vllm_config.model_config
         self._tokenizer = cached_tokenizer_from_config(vllm_config.model_config)
         self.multimodal_config = multimodal_config
         self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
@@ -1674,6 +1684,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
                     )
                     for _ in range(self.deepstack_num_level)
                 ]
+                # Tracks the valid token span currently stored in the buffer.
+                # Zero means there is no active deepstack payload to consume.
+                self.deepstack_input_embeds_num_tokens = 0
 
         with self._mark_language_model(vllm_config):
             self.language_model = Qwen3LLMForCausalLM(
@@ -1701,6 +1714,8 @@ def _get_deepstack_input_embeds(
     ) -> IntermediateTensors | None:
         if not getattr(self, "deepstack_input_embeds", None):
             return None  # If vision tower is skipped
+        if getattr(self, "deepstack_input_embeds_num_tokens", 0) == 0:
+            return None
 
         # get deepstack_input_embeds from buffer, and clear the buffer
         return IntermediateTensors(
@@ -1732,15 +1747,19 @@ def _set_deepstack_input_embeds(self, deepstack_input_embeds: torch.Tensor) -> N
             self.deepstack_input_embeds[idx][:num_tokens].copy_(
                 deepstack_input_embeds[idx]
             )
+        self.deepstack_input_embeds_num_tokens = num_tokens
 
     def _clear_deepstack_input_embeds(self, num_tokens: int) -> None:
         if not getattr(self, "deepstack_input_embeds", None):
             return
+        if getattr(self, "deepstack_input_embeds_num_tokens", 0) == 0:
+            return
 
         # clear deepstack_input_embeds in buffer
         if num_tokens > 0:
             for idx in range(self.deepstack_num_level):
                 self.deepstack_input_embeds[idx][:num_tokens].zero_()
+            self.deepstack_input_embeds_num_tokens = 0
 
     # -- SupportsEncoderCudaGraph protocol methods --
 
@@ -1783,6 +1802,15 @@ def get_input_modality(
             return "image"
         return "video"
 
+    def get_max_frames_per_video(self) -> int:
+        mm_registry = MULTIMODAL_REGISTRY
+        info = mm_registry.get_processing_info(self.model_config)
+        max_frames_per_video = info.get_num_frames_with_most_features(
+            seq_len=self.model_config.max_model_len,
+            mm_counts={"video": self.multimodal_config.get_limit_per_prompt("video")},
+        )
+        return max_frames_per_video
+
     def get_encoder_cudagraph_budget_range(
         self,
         vllm_config,
@@ -1792,7 +1820,11 @@ def get_encoder_cudagraph_budget_range(
         #                 spatial_merge_size=2 → 8x8 = 64 tokens
         min_budget = 64
         # Max: capped by max_num_batched_tokens
-        max_budget = vllm_config.scheduler_config.max_num_batched_tokens
+        # TODO(shen-shanshan): the max_budget auto-infer needs to be optimized later.
+        max_budget = min(
+            vllm_config.scheduler_config.max_num_batched_tokens,
+            self.model_config.max_model_len,
+        )
         return (min_budget, max_budget)
 
     def _get_pixel_values_by_modality(
diff --git a/vllm/model_executor/models/qwen3_vl_moe.py b/vllm/model_executor/models/qwen3_vl_moe.py
index a9c01ccf5959..7a3cda1ccbf1 100644
--- a/vllm/model_executor/models/qwen3_vl_moe.py
+++ b/vllm/model_executor/models/qwen3_vl_moe.py
@@ -183,9 +183,12 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loaded_params: set[str] = set()
         expert_params_mapping = self.get_expert_mapping()
         is_fused_expert = False
+        base_layer = (
+            "base_layer." if any(".base_layer." in name for name in params_dict) else ""
+        )
         fused_expert_params_mapping = [
-            ("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
-            ("experts.w2_weight", "experts.down_proj", 0, "w2"),
+            (f"experts.{base_layer}w13_weight", "experts.gate_up_proj", 0, "w1"),
+            (f"experts.{base_layer}w2_weight", "experts.down_proj", 0, "w2"),
         ]
         num_experts = self.config.num_experts
         for name, loaded_weight in weights:
diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py
index ef89e159feb6..5b77da7aab11 100644
--- a/vllm/model_executor/models/registry.py
+++ b/vllm/model_executor/models/registry.py
@@ -89,6 +89,7 @@
     "ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"),
     "CohereForCausalLM": ("commandr", "CohereForCausalLM"),
     "Cohere2ForCausalLM": ("commandr", "CohereForCausalLM"),
+    "Cohere2MoeForCausalLM": ("cohere2_moe", "Cohere2MoeForCausalLM"),
     "CwmForCausalLM": ("llama", "LlamaForCausalLM"),
     "DbrxForCausalLM": ("dbrx", "DbrxForCausalLM"),
     "DeciLMForCausalLM": ("nemotron_nas", "DeciLMForCausalLM"),
@@ -96,6 +97,7 @@
     "DeepseekV2ForCausalLM": ("deepseek_v2", "DeepseekV2ForCausalLM"),
     "DeepseekV3ForCausalLM": ("deepseek_v2", "DeepseekV3ForCausalLM"),
     "DeepseekV32ForCausalLM": ("deepseek_v2", "DeepseekV3ForCausalLM"),
+    "DeepseekV4ForCausalLM": ("deepseek_v4", "DeepseekV4ForCausalLM"),
     "Dots1ForCausalLM": ("dots1", "Dots1ForCausalLM"),
     "Ernie4_5ForCausalLM": ("ernie45", "Ernie4_5ForCausalLM"),
     "Ernie4_5_MoeForCausalLM": ("ernie45_moe", "Ernie4_5_MoeForCausalLM"),
@@ -110,6 +112,7 @@
     "GemmaForCausalLM": ("gemma", "GemmaForCausalLM"),
     "Gemma2ForCausalLM": ("gemma2", "Gemma2ForCausalLM"),
     "Gemma3ForCausalLM": ("gemma3", "Gemma3ForCausalLM"),
+    "Rnj1ForCausalLM": ("rnj1", "Rnj1ForCausalLM"),
     "Gemma3nForCausalLM": ("gemma3n", "Gemma3nForCausalLM"),
     "Gemma4ForCausalLM": ("gemma4", "Gemma4ForCausalLM"),
     "Qwen3NextForCausalLM": ("qwen3_next", "Qwen3NextForCausalLM"),
@@ -132,6 +135,7 @@
     "Grok1ForCausalLM": ("grok1", "GrokForCausalLM"),
     "HunYuanMoEV1ForCausalLM": ("hunyuan_v1", "HunYuanMoEV1ForCausalLM"),
     "HunYuanDenseV1ForCausalLM": ("hunyuan_v1", "HunYuanDenseV1ForCausalLM"),
+    "HYV3ForCausalLM": ("hy_v3", "HYV3ForCausalLM"),
     "HCXVisionForCausalLM": ("hyperclovax_vision", "HCXVisionForCausalLM"),
     "HCXVisionV2ForCausalLM": ("hyperclovax_vision_v2", "HCXVisionV2ForCausalLM"),
     "HyperCLOVAXForCausalLM": ("hyperclovax", "HyperCLOVAXForCausalLM"),
@@ -147,6 +151,7 @@
     "KimiLinearForCausalLM": ("kimi_linear", "KimiLinearForCausalLM"),
     "Lfm2ForCausalLM": ("lfm2", "Lfm2ForCausalLM"),
     "Lfm2MoeForCausalLM": ("lfm2_moe", "Lfm2MoeForCausalLM"),
+    "LagunaForCausalLM": ("laguna", "LagunaForCausalLM"),
     "LlamaForCausalLM": ("llama", "LlamaForCausalLM"),
     "Llama4ForCausalLM": ("llama4", "Llama4ForCausalLM"),
     # For decapoda-research/llama-*
@@ -168,7 +173,8 @@
     "MptForCausalLM": ("mpt", "MPTForCausalLM"),
     "MPTForCausalLM": ("mpt", "MPTForCausalLM"),
     "MiMoForCausalLM": ("mimo", "MiMoForCausalLM"),
-    "MiMoV2FlashForCausalLM": ("mimo_v2_flash", "MiMoV2FlashForCausalLM"),
+    "MiMoV2FlashForCausalLM": ("mimo_v2", "MiMoV2FlashForCausalLM"),
+    "MiMoV2ForCausalLM": ("mimo_v2", "MiMoV2ForCausalLM"),
     "NemotronForCausalLM": ("nemotron", "NemotronForCausalLM"),
     "NemotronHForCausalLM": ("nemotron_h", "NemotronHForCausalLM"),
     "NemotronHPuzzleForCausalLM": ("nemotron_h", "NemotronHForCausalLM"),
@@ -227,6 +233,7 @@
     "GritLM": ("gritlm", "GritLM"),
     "GteModel": ("bert_with_rope", "SnowflakeGteNewModel"),
     "GteNewModel": ("bert_with_rope", "GteNewModel"),
+    "JinaEmbeddingsV5Model": ("jina", "JinaEmbeddingsV5Model"),
     "LlamaBidirectionalModel": ("llama", "LlamaBidirectionalModel"),
     "LlamaModel": ("llama", "LlamaForCausalLM"),
     **{
@@ -406,6 +413,10 @@
         "granite_speech",
         "GraniteSpeechForConditionalGeneration",
     ),
+    "Granite4VisionForConditionalGeneration": (
+        "granite4_vision",
+        "Granite4VisionForConditionalGeneration",
+    ),
     "H2OVLChatModel": ("h2ovl", "H2OVLChatModel"),
     "HunYuanVLForConditionalGeneration": (
         "hunyuan_vision",
@@ -460,6 +471,7 @@
     ),
     "MantisForConditionalGeneration": ("llava", "MantisForConditionalGeneration"),
     "MiDashengLMModel": ("midashenglm", "MiDashengLMModel"),
+    "MiMoV2OmniForCausalLM": ("mimo_v2_omni", "MiMoV2OmniForCausalLM"),
     "MiniMaxVL01ForConditionalGeneration": (
         "minimax_vl_01",
         "MiniMaxVL01ForConditionalGeneration",
@@ -472,7 +484,11 @@
     ),
     "MolmoForCausalLM": ("molmo", "MolmoForCausalLM"),
     "Molmo2ForConditionalGeneration": ("molmo2", "Molmo2ForConditionalGeneration"),
+    "Moondream3ForCausalLM": ("moondream3", "Moondream3ForCausalLM"),
+    "HfMoondream": ("moondream3", "Moondream3ForCausalLM"),
     "NemotronH_Nano_VL_V2": ("nano_nemotron_vl", "NemotronH_Nano_VL_V2"),
+    "NemotronH_Nano_Omni_Reasoning_V3": ("nano_nemotron_vl", "NemotronH_Nano_VL_V2"),
+    "NemotronH_Super_Omni_Reasoning_V3": ("nano_nemotron_vl", "NemotronH_Nano_VL_V2"),
     "NVLM_D": ("nvlm_d", "NVLM_D_Model"),
     "OpenCUAForConditionalGeneration": ("opencua", "OpenCUAForConditionalGeneration"),
     "OpenPanguVLForConditionalGeneration": (
@@ -495,6 +511,10 @@
     "Phi4ForCausalLMV": ("phi4siglip", "Phi4ForCausalLMV"),
     "Phi4MMForCausalLM": ("phi4mm", "Phi4MMForCausalLM"),
     "PixtralForConditionalGeneration": ("pixtral", "PixtralForConditionalGeneration"),
+    "QianfanOCRForConditionalGeneration": (
+        "qianfan_ocr",
+        "QianfanOCRForConditionalGeneration",
+    ),
     "QwenVLForConditionalGeneration": ("qwen_vl", "QwenVLForConditionalGeneration"),
     "Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration"),
     "Qwen2_5_VLForConditionalGeneration": (
@@ -560,6 +580,9 @@
 _SPECULATIVE_DECODING_MODELS = {
     "ExtractHiddenStatesModel": ("extract_hidden_states", "ExtractHiddenStatesModel"),
     "MiMoMTPModel": ("mimo_mtp", "MiMoMTP"),
+    "MiMoV2MTPModel": ("mimo_v2_mtp", "MiMoV2MTP"),
+    "MiMoV2OmniMTPModel": ("mimo_v2_mtp", "MiMoV2OmniMTP"),
+    "EagleCohereForCausalLM": ("cohere_eagle", "EagleCohereForCausalLM"),
     "EagleLlamaForCausalLM": ("llama_eagle", "EagleLlamaForCausalLM"),
     "EagleLlama4ForCausalLM": ("llama4_eagle", "EagleLlama4ForCausalLM"),
     "EagleMiniCPMForCausalLM": ("minicpm_eagle", "EagleMiniCPMForCausalLM"),
@@ -569,6 +592,7 @@
     "LlamaForCausalLMEagle3": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
     "Eagle3Qwen2_5vlForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
     "Eagle3Qwen3vlForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
+    "EagleMistralForCausalLM": ("mistral_eagle", "EagleMistralForCausalLM"),
     "EagleMistralLarge3ForCausalLM": (
         "mistral_large_3_eagle",
         "EagleMistralLarge3ForCausalLM",
@@ -577,6 +601,8 @@
     "Eagle3DeepseekV3ForCausalLM": ("deepseek_eagle3", "Eagle3DeepseekV2ForCausalLM"),
     "EagleDeepSeekMTPModel": ("deepseek_eagle", "EagleDeepseekV3ForCausalLM"),
     "DeepSeekMTPModel": ("deepseek_mtp", "DeepSeekMTP"),
+    "DeepSeekV4MTPModel": ("deepseek_v4_mtp", "DeepSeekV4MTP"),
+    "Gemma4MTPModel": ("gemma4_mtp", "Gemma4MTP"),
     "ErnieMTPModel": ("ernie_mtp", "ErnieMTP"),
     "ExaoneMoeMTP": ("exaone_moe_mtp", "ExaoneMoeMTP"),
     "Exaone4_5_MTP": ("exaone4_5_mtp", "Exaone4_5_MTP"),
@@ -591,6 +617,7 @@
     "Step3p5MTP": ("step3p5_mtp", "Step3p5MTP"),
     "Qwen3_5MTP": ("qwen3_5_mtp", "Qwen3_5MTP"),
     "Qwen3_5MoeMTP": ("qwen3_5_mtp", "Qwen3_5MoeMTP"),
+    "HYV3MTPModel": ("hy_v3_mtp", "HYV3MTP"),
     # Temporarily disabled.
     # # TODO(woosuk): Re-enable this once the MLP Speculator is supported in V1.
     # "MLPSpeculatorPreTrainedModel": ("mlp_speculator", "MLPSpeculator"),
diff --git a/vllm/model_executor/models/rnj1.py b/vllm/model_executor/models/rnj1.py
new file mode 100644
index 000000000000..f83577b7a397
--- /dev/null
+++ b/vllm/model_executor/models/rnj1.py
@@ -0,0 +1,470 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# RNJ-1 model: Gemma3-based architecture with chunked (block-local) attention.
+# Chunked attention restricts local layers to attend within aligned blocks,
+# with lookback to one previous block.
+from collections.abc import Iterable
+from itertools import islice
+
+import torch
+from torch import nn
+from transformers import Gemma3TextConfig
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import GeluAndMul
+from vllm.model_executor.layers.attention import Attention
+from vllm.model_executor.layers.layernorm import GemmaRMSNorm
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from vllm.sequence import IntermediateTensors
+from vllm.v1.attention.backend import AttentionType
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (
+    AutoWeightsLoader,
+    extract_layer_index,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+class Rnj1MLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_activation: str,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_activation != "gelu_pytorch_tanh":
+            raise ValueError(
+                "RNJ-1 uses `gelu_pytorch_tanh` as the hidden activation "
+                "function. Please set `hidden_act` and `hidden_activation` to "
+                "`gelu_pytorch_tanh`."
+            )
+        self.act_fn = GeluAndMul(approximate="tanh")
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Rnj1Attention(nn.Module):
+    def __init__(
+        self,
+        config: Gemma3TextConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        head_dim: int,
+        max_position_embeddings: int,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        attn_logits_soft_cap: float | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = config.query_pre_attn_scalar**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        self.q_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.k_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+        layer_idx = extract_layer_index(prefix)
+        layer_type = config.layer_types[layer_idx]
+        self.is_chunked = layer_type == "chunked_attention"
+        self.chunk_lookback = 1 if self.is_chunked else -1
+        sliding_window = config.sliding_window if self.is_chunked else None
+
+        # Initialize the rotary embedding.
+        # Expects v5-style rope_parameters keyed by layer type.
+        if layer_type in config.rope_parameters:
+            rope_parameters = config.rope_parameters[layer_type]
+        else:
+            rope_parameters = config.rope_parameters
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            max_position=max_position_embeddings,
+            rope_parameters=rope_parameters,
+            is_neox_style=True,
+        )
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            attn_type=AttentionType.DECODER,
+            logits_soft_cap=attn_logits_soft_cap,
+            per_layer_sliding_window=sliding_window,
+            chunk_lookback=self.chunk_lookback,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+        q = q.unflatten(-1, (self.num_heads, self.head_dim))
+        q = self.q_norm(q)
+        q = q.flatten(-2, -1)
+        k = k.unflatten(-1, (self.num_kv_heads, self.head_dim))
+        k = self.k_norm(k)
+        k = k.flatten(-2, -1)
+
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Rnj1DecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: Gemma3TextConfig,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Rnj1Attention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            head_dim=config.head_dim,
+            max_position_embeddings=config.max_position_embeddings,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            attn_logits_soft_cap=None,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.hidden_size = config.hidden_size
+        self.mlp = Rnj1MLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_activation=config.hidden_activation,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+        self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = GemmaRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.pre_feedforward_layernorm = GemmaRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.post_feedforward_layernorm = GemmaRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor | None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            **kwargs,
+        )
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        hidden_states, residual = self.pre_feedforward_layernorm(
+            hidden_states, residual
+        )
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.post_feedforward_layernorm(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile
+class Rnj1Model(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.embed_tokens",
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Rnj1DecoderLayer(
+                config, cache_config, quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers",
+        )
+        self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        normalizer = self.config.hidden_size**0.5
+        self.register_buffer("normalizer", torch.tensor(normalizer), persistent=False)
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids) * self.normalizer
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_input_ids(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+                **kwargs,
+            )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if (
+                self.quant_config
+                and self.quant_config.get_name() == "gguf"
+                and name.endswith("norm.weight")
+            ):
+                loaded_weight -= 1
+
+            if self.quant_config is not None and (
+                scale_name := self.quant_config.get_cache_scale(name)
+            ):
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                loaded_weight = loaded_weight[0]
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
+            if name.endswith((".k_scale", ".v_scale", ".q_scale", ".prob_scale")):
+                remapped_name = maybe_remap_kv_scale_name(name, params_dict)
+                if remapped_name is not None and remapped_name in params_dict:
+                    param = params_dict[remapped_name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+                    loaded_params.add(remapped_name)
+                    continue
+
+            for param_name, shard_name, shard_id in stacked_params_mapping:
+                if shard_name not in name:
+                    continue
+                name = name.replace(shard_name, param_name)
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        return loaded_params
+
+
+class Rnj1ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Rnj1Model(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
+        )
+        if config.tie_word_embeddings:
+            self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
+
+        self.logits_processor = LogitsProcessor(
+            config.vocab_size, soft_cap=config.final_logit_softcapping
+        )
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds, **kwargs
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
diff --git a/vllm/model_executor/models/sarvam.py b/vllm/model_executor/models/sarvam.py
index fa5ec44d7e72..a0ab6c0ce260 100644
--- a/vllm/model_executor/models/sarvam.py
+++ b/vllm/model_executor/models/sarvam.py
@@ -35,7 +35,10 @@
     get_tensor_model_parallel_world_size,
 )
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -335,13 +338,12 @@ def __init__(
         else:
             self.shared_experts = None
 
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.shared_experts,
             num_experts=self.num_experts,
             top_k=self.top_k,
             hidden_size=self.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=self.norm_expert_prob,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -353,7 +355,7 @@ def __init__(
             routed_scaling_factor=self.routed_scaling_factor,
         )
 
-    def maybe_get_fused_moe(self) -> SharedFusedMoE:
+    def maybe_get_fused_moe(self) -> FusedMoE:
         return self.experts
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -370,20 +372,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             router_logits=router_logits,
         )
 
-        if self.shared_experts is not None:
-            shared_output, expert_output = final_hidden
-        else:
-            shared_output, expert_output = None, final_hidden
-
-        if shared_output is not None:
-            expert_output = expert_output + shared_output
-
-        if self.tp_size > 1:
-            expert_output = self.experts.maybe_all_reduce_tensor_model_parallel(
-                expert_output
-            )
-
-        return expert_output.view(num_tokens, hidden_dim)
+        return final_hidden.view(num_tokens, hidden_dim)
 
 
 class SarvamMLABlock(nn.Module):
@@ -543,7 +532,7 @@ def forward(
         return hidden_states
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return SharedFusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/step3_text.py b/vllm/model_executor/models/step3_text.py
index 18b689166a5f..a0e7e16a9bbf 100644
--- a/vllm/model_executor/models/step3_text.py
+++ b/vllm/model_executor/models/step3_text.py
@@ -14,12 +14,13 @@
 from vllm.distributed import (
     get_pp_group,
     get_tensor_model_parallel_world_size,
-    tensor_model_parallel_all_reduce,
 )
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -40,6 +41,7 @@
 
 from .interfaces import SupportsPP
 from .utils import (
+    AutoWeightsLoader,
     PPMissingLayer,
     is_pp_missing_parameter,
     make_empty_intermediate_tensors_factory,
@@ -71,7 +73,6 @@ def __init__(
             top_k=config.moe_top_k,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_expert_weight,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -94,8 +95,6 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
-        if self.tp_size > 1:
-            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
 
         return final_hidden_states.view(orig_shape)
 
@@ -384,55 +383,6 @@ def forward(
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
-
-class Step3TextForCausalLM(nn.Module, SupportsPP):
-    def __init__(
-        self,
-        *,
-        vllm_config: VllmConfig,
-        prefix: str = "",
-    ):
-        super().__init__()
-        config = vllm_config.model_config.hf_config
-
-        self.config = config
-        self.vllm_config = vllm_config
-
-        self.model = Step3TextModel(vllm_config=vllm_config, prefix=prefix)
-
-        if get_pp_group().is_last_rank:
-            self.lm_head = ParallelLMHead(
-                config.vocab_size,
-                config.hidden_size,
-                prefix=maybe_prefix(prefix, "lm_head"),
-            )
-            self.logits_processor = LogitsProcessor(config.vocab_size)
-        else:
-            self.lm_head = PPMissingLayer()
-
-        self.make_empty_intermediate_tensors = (
-            self.model.make_empty_intermediate_tensors
-        )
-
-    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.embed_input_ids(input_ids)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor | None,
-        positions: torch.Tensor,
-        intermediate_tensors: IntermediateTensors | None = None,
-        inputs_embeds: torch.Tensor | None = None,
-    ):
-        hidden_states = self.model(
-            input_ids, positions, intermediate_tensors, inputs_embeds
-        )
-        return hidden_states
-
-    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head, hidden_states)
-        return logits
-
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         qkv_params_mapping = [
             # (param_name, shard_name, relative_start_idx, relative_end_idx)
@@ -467,11 +417,14 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         ]
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
+        base_layer = (
+            "base_layer." if any(".base_layer." in name for name in params_dict) else ""
+        )
 
         expert_params_mapping = [
-            (".moe.experts.w13_weight", ".moe.gate_proj.weight", "w1"),
-            (".moe.experts.w13_weight", ".moe.up_proj.weight", "w3"),
-            (".moe.experts.w2_weight", ".moe.down_proj.weight", "w2"),
+            (f".moe.experts.{base_layer}w13_weight", ".moe.gate_proj.weight", "w1"),
+            (f".moe.experts.{base_layer}w13_weight", ".moe.up_proj.weight", "w3"),
+            (f".moe.experts.{base_layer}w2_weight", ".moe.down_proj.weight", "w2"),
         ]
 
         disable_moe_stacked_params = [data[1] for data in expert_params_mapping]
@@ -552,3 +505,56 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                         weight_loader(param, loaded_weight)
                         loaded_params.add(name)
         return loaded_params
+
+
+class Step3TextForCausalLM(nn.Module, SupportsPP):
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+
+        self.config = config
+        self.vllm_config = vllm_config
+
+        self.model = Step3TextModel(vllm_config=vllm_config, prefix=prefix)
+
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+            self.logits_processor = LogitsProcessor(config.vocab_size)
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ):
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
diff --git a/vllm/model_executor/models/step3p5.py b/vllm/model_executor/models/step3p5.py
index bb4bf14a9632..df051fb87354 100644
--- a/vllm/model_executor/models/step3p5.py
+++ b/vllm/model_executor/models/step3p5.py
@@ -23,8 +23,10 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul, SwigluStepAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
-from vllm.model_executor.layers.fused_moe.shared_fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import GemmaRMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -372,14 +374,13 @@ def __init__(
             quant_config=quant_config,
             prefix=f"{prefix}.share_expert",
         )
-        self.experts = SharedFusedMoE(
+        self.experts = FusedMoE(
             shared_experts=self.share_expert,
             gate=self.gate,
             num_experts=config.moe_num_experts,
             top_k=config.moe_top_k,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_expert_weight,
             quant_config=quant_config,
             activation=activation,
@@ -397,30 +398,16 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         hidden_states = hidden_states.view(-1, hidden_dim)
 
         if self.experts.is_internal_router:
-            # In this case, the gate/router runs inside the FusedMoE class
-            fused_moe_out = self.experts(
+            final_hidden_states = self.experts(
                 hidden_states=hidden_states, router_logits=hidden_states
             )
         else:
-            # router_logits: (num_tokens, n_experts)
+            # TODO(bnell): this gate could be moved into the FusedMoE?
             router_logits, _ = self.gate(hidden_states)
-            fused_moe_out = self.experts(
+            final_hidden_states = self.experts(
                 hidden_states=hidden_states, router_logits=router_logits
             )
 
-        shared_output, final_hidden_states = fused_moe_out
-        if self.share_expert is None:
-            assert shared_output is None
-
-        if self.share_expert is not None:
-            assert shared_output is not None
-            final_hidden_states += shared_output
-
-        if self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
-
         return final_hidden_states.view(num_tokens, hidden_dim)
 
 
@@ -641,16 +628,19 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
+        base_layer = (
+            "base_layer." if any(".base_layer." in name for name in params_dict) else ""
+        )
 
         # Old packed 3D format: .moe.gate_proj.weight [num_experts, out, in]
         expert_params_mapping = [
-            (".moe.experts.w13_weight", ".moe.gate_proj.weight", "w1"),
-            (".moe.experts.w13_weight", ".moe.up_proj.weight", "w3"),
-            (".moe.experts.w2_weight", ".moe.down_proj.weight", "w2"),
+            (f".moe.experts.{base_layer}w13_weight", ".moe.gate_proj.weight", "w1"),
+            (f".moe.experts.{base_layer}w13_weight", ".moe.up_proj.weight", "w3"),
+            (f".moe.experts.{base_layer}w2_weight", ".moe.down_proj.weight", "w2"),
         ]
 
         # New per-expert format: .moe.experts.E.gate_proj.weight_packed [out, in]
-        per_expert_mapping = FusedMoE.make_expert_params_mapping(
+        per_expert_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
diff --git a/vllm/model_executor/models/step3p5_mtp.py b/vllm/model_executor/models/step3p5_mtp.py
index 83e43dce5114..092f7a31aa7d 100644
--- a/vllm/model_executor/models/step3p5_mtp.py
+++ b/vllm/model_executor/models/step3p5_mtp.py
@@ -181,14 +181,17 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             ("gate_up_proj", "gate_proj", 0),
             ("gate_up_proj", "up_proj", 1),
         ]
+        params_dict = dict(self.named_parameters())
+        base_layer = (
+            "base_layer." if any(".base_layer." in name for name in params_dict) else ""
+        )
 
         expert_params_mapping = [
-            (".moe.experts.w13_weight", ".moe.gate_proj.weight", "w1"),
-            (".moe.experts.w13_weight", ".moe.up_proj.weight", "w3"),
-            (".moe.experts.w2_weight", ".moe.down_proj.weight", "w2"),
+            (f".moe.experts.{base_layer}w13_weight", ".moe.gate_proj.weight", "w1"),
+            (f".moe.experts.{base_layer}w13_weight", ".moe.up_proj.weight", "w3"),
+            (f".moe.experts.{base_layer}w2_weight", ".moe.down_proj.weight", "w2"),
         ]
 
-        params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
diff --git a/vllm/model_executor/models/transformers/base.py b/vllm/model_executor/models/transformers/base.py
index b7967985f222..a3e4b844b805 100644
--- a/vllm/model_executor/models/transformers/base.py
+++ b/vllm/model_executor/models/transformers/base.py
@@ -275,6 +275,11 @@ def _decorate_cls_for_torch_compile(
         )
         class SupportTorchCompileWrapper(cls): ...
 
+        # Preserve __module__ so transformers v5's source-file checks
+        # (e.g. _can_set_experts_implementation) read the original
+        # model's module instead of this file.
+        SupportTorchCompileWrapper.__module__ = cls.__module__
+
         # Patch the class in its module
         module = sys.modules[cls.__module__]
         setattr(module, cls.__name__, SupportTorchCompileWrapper)
diff --git a/vllm/model_executor/models/transformers/moe.py b/vllm/model_executor/models/transformers/moe.py
index 81d21abbd069..51a51799ffc0 100644
--- a/vllm/model_executor/models/transformers/moe.py
+++ b/vllm/model_executor/models/transformers/moe.py
@@ -25,7 +25,10 @@
 from vllm.distributed import get_dp_group, get_ep_group
 from vllm.forward_context import ForwardContext, get_forward_context
 from vllm.model_executor.custom_op import PluggableLayer
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.models.interfaces import MixtureOfExperts
 from vllm.model_executor.models.utils import maybe_prefix
 from vllm.platforms import current_platform
@@ -179,7 +182,7 @@ def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         num_redundant_experts = self.parallel_config.eplb_config.num_redundant_experts
         for gate_proj, down_proj, up_proj in ckpt_names:
             expert_mapping.extend(
-                FusedMoE.make_expert_params_mapping(
+                fused_moe_make_expert_params_mapping(
                     self,
                     ckpt_gate_proj_name=gate_proj,
                     ckpt_down_proj_name=down_proj,
@@ -204,8 +207,6 @@ def recursive_replace(self):
         )
         assert intermediate_size is not None
 
-        # If there are shared experts, the results are
-        # reduced after mlp.forward() not inside FusedMoE
         num_shared_experts = getattr_iter(
             text_config,
             [
@@ -214,17 +215,6 @@ def recursive_replace(self):
             ],
             0,
         )
-        reduce_results = num_shared_experts == 0
-
-        def add_all_reduce(mlp: nn.Module):
-            """Adds an all-reduce to the output of `mlp.forward()`."""
-
-            class MLPWithAllReduce(mlp.__class__):
-                def forward(self, *args, **kwargs):
-                    output = super().forward(*args, **kwargs)
-                    return self.experts.maybe_all_reduce_tensor_model_parallel(output)
-
-            mlp.__class__ = MLPWithAllReduce
 
         # Unused kwargs since we use custom_routing_function:
         # - `scoring_func` and `e_score_correction_bias` only used for grouped
@@ -289,14 +279,11 @@ def _recursive_replace(module: nn.Module, prefix: str):
                         if "bias" in experts_param_name:
                             has_bias = True
                             break
-                    # Double check there are no shared experts
-                    nonlocal reduce_results
-                    if reduce_results:
+                    # If the config does not specify num_shared_experts, but
+                    # the model has shared experts, we assume there is one.
+                    if self.num_shared_experts == 0:
                         for mlp_param_name, _ in mlp.named_parameters():
                             if "shared_expert" in mlp_param_name:
-                                reduce_results = False
-                                # If the config does not specify num_shared_experts, but
-                                # the model has shared experts, we assume there is one.
                                 self.num_shared_experts = 1
                                 break
                     # Replace experts module with FusedMoE
@@ -305,7 +292,6 @@ def _recursive_replace(module: nn.Module, prefix: str):
                         top_k=top_k,
                         hidden_size=hidden_size,
                         intermediate_size=intermediate_size,
-                        reduce_results=reduce_results,
                         renormalize=renormalize,
                         # Hard coded because topk happens in Transformers
                         use_grouped_topk=False,
@@ -326,13 +312,6 @@ def _recursive_replace(module: nn.Module, prefix: str):
                     self.moe_layers.append(fused_experts)
                     self.expert_weights.append(fused_experts.get_expert_weights())
                     self.num_moe_layers += 1
-                    # If results are not all-reduced in FusedMoE, ensure they
-                    # are all-reduced at the end of mlp.forward() if tensor
-                    # parallel or expert parallel is enabled
-                    if not reduce_results and (
-                        fused_experts.tp_size > 1 or fused_experts.ep_size > 1
-                    ):
-                        add_all_reduce(mlp)
                 else:
                     _recursive_replace(child_module, prefix=qual_name)
 
diff --git a/vllm/model_executor/models/transformers/utils.py b/vllm/model_executor/models/transformers/utils.py
index e47f3bba5cfb..04d6de28efd0 100644
--- a/vllm/model_executor/models/transformers/utils.py
+++ b/vllm/model_executor/models/transformers/utils.py
@@ -94,7 +94,15 @@ def wrapper(*args, **kwargs):
             setattr(torch, torch_function_name, old_torch_function)
 
 
-Style = Literal["colwise", "colwise_rep", "rowwise", "rowwise_rep", "replicate"]
+Style = Literal[
+    "colwise",
+    "rowwise",
+    "replicate",
+    "colwise_gather_output",
+    "rowwise_split_input",
+    "colwise_rep",
+    "rowwise_rep",
+]
 
 
 def replace_linear_class(
@@ -120,10 +128,14 @@ def replace_linear_class(
 
     vllm_linear_cls, vllm_linear_kwargs = {
         "colwise": (ColumnParallelLinear, {}),
-        "colwise_rep": (ColumnParallelLinear, {"gather_output": True}),
         "rowwise": (RowParallelLinear, {}),
-        "rowwise_rep": (RowParallelLinear, {"input_is_parallel": False}),
         "replicate": (ReplicatedLinear, {}),
+        # Transformers v5
+        "colwise_gather_output": (ColumnParallelLinear, {"gather_output": True}),
+        "rowwise_split_input": (RowParallelLinear, {"input_is_parallel": False}),
+        # Transformers v4
+        "colwise_rep": (ColumnParallelLinear, {"gather_output": True}),
+        "rowwise_rep": (RowParallelLinear, {"input_is_parallel": False}),
     }.get(style, (ReplicatedLinear, {}))
 
     return vllm_linear_cls(
diff --git a/vllm/model_executor/models/vision.py b/vllm/model_executor/models/vision.py
index e6a243006759..0582c125c66a 100644
--- a/vllm/model_executor/models/vision.py
+++ b/vllm/model_executor/models/vision.py
@@ -10,7 +10,7 @@
 import torch
 from transformers import PretrainedConfig
 
-from vllm.config import MultiModalConfig, VllmConfig, get_current_vllm_config
+from vllm.config import MultiModalConfig, get_current_vllm_config_or_none
 from vllm.distributed import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
@@ -18,6 +18,7 @@
 )
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
+from vllm.utils.math_utils import round_up
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 logger = init_logger(__name__)
@@ -102,45 +103,48 @@ def get_vit_attn_backend(
     """
     Get the attention backend for Vision Transformer.
     """
-    try:
-        vllm_config: VllmConfig = get_current_vllm_config()
-        model_config = vllm_config.model_config
-        multimodal_config: MultiModalConfig | None = (
-            model_config.multimodal_config if model_config is not None else None
-        )
-    except (AssertionError, AttributeError):
-        multimodal_config = None
-
+    mm_cfg = get_multimodal_config()
     attn_backend_override = (
-        multimodal_config.mm_encoder_attn_backend
-        if multimodal_config is not None
-        else None
+        mm_cfg.mm_encoder_attn_backend if mm_cfg is not None else None
     )
-    attn_backend = _get_vit_attn_backend(
+    return _get_vit_attn_backend(
         head_size,
         dtype,
         attn_backend_override=attn_backend_override,
     )
-    return attn_backend
+
+
+def get_multimodal_config() -> MultiModalConfig | None:
+    """Return the current ``MultiModalConfig``, or ``None`` when no engine
+    config context is active (e.g., during unit tests) or when the current
+    ``model_config`` does not carry a ``multimodal_config`` (e.g., minimal
+    stubs used in tests)."""
+    vllm_config = get_current_vllm_config_or_none()
+    if vllm_config is None or vllm_config.model_config is None:
+        return None
+    return getattr(vllm_config.model_config, "multimodal_config", None)
+
+
+def get_fp8_padded_hidden_size(num_heads: int, head_dim: int) -> int | None:
+    """Return the padded hidden size for FP8 ViT encoder attention, or
+    ``None`` when FP8 is not enabled.
+
+    cuDNN FP8 prefill attention requires ``head_dim`` to be a multiple of
+    16. For non-aligned ``head_dim`` (e.g. 72), Q/K/V are padded to the
+    nearest multiple of 16.
+    """
+    mm_cfg = get_multimodal_config()
+    if mm_cfg is None or mm_cfg.mm_encoder_attn_dtype != "fp8":
+        return None
+    return num_heads * round_up(head_dim, 16)
 
 
 def is_vit_use_data_parallel():
     """
     Get the tensor parallel type for Vision Transformer.
     """
-    try:
-        vllm_config: VllmConfig = get_current_vllm_config()
-        model_config = vllm_config.model_config
-        multimodal_config: MultiModalConfig | None = (
-            model_config.multimodal_config if model_config is not None else None
-        )
-    except (AssertionError, AttributeError):
-        multimodal_config = None
-
-    mm_encoder_tp_mode = (
-        multimodal_config.mm_encoder_tp_mode if multimodal_config is not None else None
-    )
-    return mm_encoder_tp_mode == "data"
+    mm_cfg = get_multimodal_config()
+    return mm_cfg is not None and mm_cfg.mm_encoder_tp_mode == "data"
 
 
 VisionFeatureSelectStrategyStr = Literal["class", "default", "full"]
diff --git a/vllm/model_executor/models/voxtral.py b/vllm/model_executor/models/voxtral.py
index d44960ca8117..42e811edaeca 100644
--- a/vllm/model_executor/models/voxtral.py
+++ b/vllm/model_executor/models/voxtral.py
@@ -4,7 +4,7 @@
 import math
 from collections.abc import Iterable, Mapping, Sequence
 from functools import partial
-from typing import Literal, cast
+from typing import cast
 
 import numpy as np
 import regex as re
@@ -19,6 +19,7 @@
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.inputs import MultiModalDataDict, PromptType, TokensPrompt
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -446,14 +447,13 @@ def get_speech_to_text_config(
     # for speech-to-text transcription
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
+        audio = stt_params.audio
+        model_config = stt_params.model_config
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+
         tokenizer = cached_tokenizer_from_config(model_config)
         audio = Audio(audio, int(stt_config.sample_rate), format="wav")  # lossless
         req = TranscriptionRequest(
diff --git a/vllm/model_executor/models/voxtral_realtime.py b/vllm/model_executor/models/voxtral_realtime.py
index b70714a0d83a..2628e1443e2d 100644
--- a/vllm/model_executor/models/voxtral_realtime.py
+++ b/vllm/model_executor/models/voxtral_realtime.py
@@ -4,7 +4,6 @@
 import asyncio
 import math
 from collections.abc import AsyncGenerator, Iterable, Iterator, Mapping
-from typing import Literal
 
 import numpy as np
 import torch
@@ -18,6 +17,7 @@
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.engine.protocol import StreamingInput
 from vllm.envs import VLLM_ENGINE_ITERATION_TIMEOUT_S
 from vllm.inputs import PromptType, TokensPrompt
@@ -465,14 +465,13 @@ def get_speech_to_text_config(
     # for speech-to-text transcription
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
+        audio = stt_params.audio
+        model_config = stt_params.model_config
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+
         tokenizer = cached_tokenizer_from_config(model_config)
         audio = Audio(audio, int(stt_config.sample_rate), format="wav")  # lossless
 
diff --git a/vllm/model_executor/models/whisper.py b/vllm/model_executor/models/whisper.py
index f0f6f619b022..628186e7598b 100644
--- a/vllm/model_executor/models/whisper.py
+++ b/vllm/model_executor/models/whisper.py
@@ -5,7 +5,7 @@
 import math
 from collections.abc import Iterable, Mapping, Sequence
 from contextlib import nullcontext
-from typing import Annotated, Literal
+from typing import Annotated
 
 import numpy as np
 import torch
@@ -20,6 +20,7 @@
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.inputs import (
     ExplicitEncoderDecoderPrompt,
@@ -830,14 +831,14 @@ def validate_language(cls, language: str | None) -> str | None:
     @classmethod
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,  # not needed here
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
+        audio = stt_params.audio
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+        task_type = stt_params.task_type
+        request_prompt = stt_params.request_prompt
+
         if language is None:
             raise ValueError(
                 "Language must be specified when creating the Whisper prompt"
diff --git a/vllm/model_executor/offloader/__init__.py b/vllm/model_executor/offloader/__init__.py
index a6522ff7c0a3..f1b49c69ef93 100644
--- a/vllm/model_executor/offloader/__init__.py
+++ b/vllm/model_executor/offloader/__init__.py
@@ -8,6 +8,7 @@
     create_offloader,
     get_offloader,
     set_offloader,
+    should_pin_memory,
 )
 from vllm.model_executor.offloader.prefetch import PrefetchOffloader
 from vllm.model_executor.offloader.uva import UVAOffloader
@@ -20,4 +21,5 @@
     "create_offloader",
     "get_offloader",
     "set_offloader",
+    "should_pin_memory",
 ]
diff --git a/vllm/model_executor/offloader/base.py b/vllm/model_executor/offloader/base.py
index 7cb0ddfd1848..ceff60cd4cde 100644
--- a/vllm/model_executor/offloader/base.py
+++ b/vllm/model_executor/offloader/base.py
@@ -10,7 +10,9 @@
 
 import torch.nn as nn
 
+import vllm.envs as envs
 from vllm.logger import init_logger
+from vllm.utils.platform_utils import is_pin_memory_available
 
 if TYPE_CHECKING:
     from vllm.config import OffloadConfig
@@ -18,6 +20,18 @@
 logger = init_logger(__name__)
 
 
+def should_pin_memory() -> bool:
+    """Check if pinned memory should be used for weight offloading.
+
+    Combines the platform capability check with the user override env var.
+    On unified-memory systems (e.g. GH200) pinned memory eats into GPU
+    memory, so users can disable it via VLLM_WEIGHT_OFFLOADING_DISABLE_PIN_MEMORY.
+    """
+    return (
+        is_pin_memory_available() and not envs.VLLM_WEIGHT_OFFLOADING_DISABLE_PIN_MEMORY
+    )
+
+
 """
 class relation:
 
@@ -104,11 +118,9 @@ def set_offloader(instance: BaseOffloader) -> None:
     global _instance
     _instance = instance
     if isinstance(instance, NoopOffloader):
-        logger.debug_once(
-            "Offloader set to NoopOffloader (no offloading).", scope="local"
-        )
+        logger.debug_once("Offloader set to NoopOffloader (no offloading).")
     else:
-        logger.info_once("Offloader set to %s", type(instance).__name__, scope="local")
+        logger.info_once("Offloader set to %s", type(instance).__name__)
 
 
 def create_offloader(offload_config: "OffloadConfig") -> BaseOffloader:
diff --git a/vllm/model_executor/offloader/prefetch.py b/vllm/model_executor/offloader/prefetch.py
index 5bdde8c3a18a..466d8c13ce76 100644
--- a/vllm/model_executor/offloader/prefetch.py
+++ b/vllm/model_executor/offloader/prefetch.py
@@ -20,8 +20,8 @@
 # Import prefetch_ops to register custom ops at module load time
 import vllm.model_executor.offloader.prefetch_ops  # noqa: F401
 from vllm.logger import init_logger
-from vllm.model_executor.offloader.base import BaseOffloader
-from vllm.utils.platform_utils import is_pin_memory_available
+from vllm.model_executor.offloader.base import BaseOffloader, should_pin_memory
+from vllm.utils.torch_utils import get_dtype_size
 
 logger = init_logger(__name__)
 
@@ -54,7 +54,7 @@ def num_bytes(self) -> int:
         numel = 1
         for dim in self.shape:
             numel *= dim
-        return numel * torch.finfo(self.dtype).bits // 8
+        return numel * get_dtype_size(self.dtype)
 
 
 class StaticBufferPool:
@@ -528,7 +528,7 @@ def start_onload_to_static(self):
                 gpu_buffer = offloader._gpu_buffer
                 assert cpu_storage is not None, "CPU storage not initialized"
                 assert gpu_buffer is not None, "GPU buffer not assigned"
-                assert not is_pin_memory_available() or cpu_storage.is_pinned(), (
+                assert not should_pin_memory() or cpu_storage.is_pinned(), (
                     f"CPU storage for {name} is not pinned! "
                     "non_blocking=True H2D copy from non-pinned memory "
                     "causes stream synchronization that breaks "
@@ -629,7 +629,7 @@ def _offload_to_cpu_internal(self):
         original GPU tensor is garbage collected.
         """
         param = self._param
-        pin_memory = is_pin_memory_available()
+        pin_memory = should_pin_memory()
 
         # Create pinned CPU storage and copy current GPU data
         self._cpu_storage = torch.empty_strided(
@@ -666,7 +666,7 @@ def _update_cpu_storage_from_param(self) -> None:
         param = self._param
 
         if param.data.device.type == "cpu":
-            if is_pin_memory_available() and not param.data.is_pinned():
+            if should_pin_memory() and not param.data.is_pinned():
                 pinned = torch.empty_strided(
                     size=param.data.size(),
                     stride=param.data.stride(),
diff --git a/vllm/model_executor/offloader/uva.py b/vllm/model_executor/offloader/uva.py
index c524e43cddae..51eb1a14fcb0 100644
--- a/vllm/model_executor/offloader/uva.py
+++ b/vllm/model_executor/offloader/uva.py
@@ -10,9 +10,9 @@
 
 import vllm.envs as envs
 from vllm.logger import init_logger
-from vllm.model_executor.offloader.base import BaseOffloader
+from vllm.model_executor.offloader.base import BaseOffloader, should_pin_memory
 from vllm.utils.mem_utils import format_gib
-from vllm.utils.platform_utils import is_pin_memory_available, is_uva_available
+from vllm.utils.platform_utils import is_uva_available
 from vllm.utils.torch_utils import get_accelerator_view_from_cpu_tensor
 
 logger = init_logger(__name__)
@@ -43,10 +43,7 @@ def __init__(
         self.cpu_offload_bytes = 0
         self.cpu_offload_params = cpu_offload_params or set()
 
-        self.pin_memory = (
-            is_pin_memory_available()
-            and not envs.VLLM_WEIGHT_OFFLOADING_DISABLE_PIN_MEMORY
-        )
+        self.pin_memory = should_pin_memory()
         self.uva_offloading = (
             is_uva_available() and not envs.VLLM_WEIGHT_OFFLOADING_DISABLE_UVA
         )
diff --git a/vllm/model_executor/parameter.py b/vllm/model_executor/parameter.py
index 410e277493b0..4106672d5011 100644
--- a/vllm/model_executor/parameter.py
+++ b/vllm/model_executor/parameter.py
@@ -605,8 +605,8 @@ def _adjust_shard_indexes_for_marlin(shard_size, shard_offset, marlin_tile_size)
 def _adjust_shard_indexes_for_packing(
     shard_size, shard_offset, packed_factor, marlin_tile_size
 ):
-    shard_size = shard_size // packed_factor
-    shard_offset = shard_offset // packed_factor
+    shard_size = round(shard_size // packed_factor)
+    shard_offset = round(shard_offset // packed_factor)
     if marlin_tile_size is not None:
         return _adjust_shard_indexes_for_marlin(
             shard_size=shard_size,
diff --git a/vllm/model_executor/utils.py b/vllm/model_executor/utils.py
index 0b844d1493d9..a0269be855a9 100644
--- a/vllm/model_executor/utils.py
+++ b/vllm/model_executor/utils.py
@@ -45,7 +45,10 @@ def set_weight_attrs(
 
 
 def replace_parameter(
-    layer: torch.nn.Module, param_name: str, new_data: torch.Tensor | None
+    layer: torch.nn.Module,
+    param_name: str,
+    new_data: torch.Tensor | None,
+    prefer_copy: bool = False,
 ):
     """
     Replace a parameter of a layer while maintaining the ability to reload the weight.
@@ -57,6 +60,12 @@ def replace_parameter(
         layer: Layer containing parameter to replace
         param_name: Name of parameter to replace
         new_data: New data of the new parameter, or None to set the parameter to None
+        prefer_copy: If True and the existing parameter is compatible with
+            ``new_data`` (same shape, dtype, and device), copy ``new_data``
+            into the existing parameter in place rather than re-registering
+            a new parameter. This preserves the parameter's storage address
+            (``data_ptr``), which is required for captured CUDA graphs to
+            remain valid across weight updates (e.g. in RL training loops).
     """
     # should not be used on a tied/shared param
 
@@ -67,9 +76,21 @@ def replace_parameter(
 
     if isinstance(new_data, torch.nn.Parameter):
         new_data = new_data.data
-    new_param = torch.nn.Parameter(new_data, requires_grad=False)
 
     old_param: torch.nn.Parameter | None = getattr(layer, param_name, None)
+
+    if (
+        prefer_copy
+        and old_param is not None
+        and old_param.shape == new_data.shape
+        and old_param.dtype == new_data.dtype
+        and old_param.device == new_data.device
+    ):
+        old_param.copy_(new_data)
+        return
+
+    new_param = torch.nn.Parameter(new_data, requires_grad=False)
+
     if old_param is not None and hasattr(old_param, "weight_loader"):
         weight_loader = old_param.weight_loader
         set_weight_attrs(new_param, {"weight_loader": weight_loader})
diff --git a/vllm/model_executor/warmup/deep_gemm_warmup.py b/vllm/model_executor/warmup/deep_gemm_warmup.py
index e41b2c5e1005..a352cc116f57 100644
--- a/vllm/model_executor/warmup/deep_gemm_warmup.py
+++ b/vllm/model_executor/warmup/deep_gemm_warmup.py
@@ -19,7 +19,7 @@
 )
 from vllm.model_executor.layers.linear import LinearBase
 from vllm.model_executor.layers.quantization.fp8 import Fp8LinearMethod
-from vllm.model_executor.layers.quantization.mxfp8 import Mxfp8OnlineLinearMethod
+from vllm.model_executor.layers.quantization.online.mxfp8 import Mxfp8OnlineLinearMethod
 from vllm.tracing import instrument
 from vllm.utils.deep_gemm import (
     fp8_gemm_nt,
diff --git a/vllm/multimodal/audio.py b/vllm/multimodal/audio.py
index 0a748a6d15c6..c232bb9ea6a5 100644
--- a/vllm/multimodal/audio.py
+++ b/vllm/multimodal/audio.py
@@ -16,11 +16,6 @@
 except ImportError:
     av = PlaceholderModule("av")  # type: ignore[assignment]
 
-try:
-    import resampy
-except ImportError:
-    resampy = PlaceholderModule("resampy")  # type: ignore[assignment]
-
 try:
     import scipy.signal as scipy_signal
 except ImportError:
@@ -229,15 +224,6 @@ def resample_audio_pyav(
     return result[:expected_len]
 
 
-def resample_audio_resampy(
-    audio: npt.NDArray[np.floating],
-    *,
-    orig_sr: float,
-    target_sr: float,
-) -> npt.NDArray[np.floating]:
-    return resampy.resample(audio, sr_orig=orig_sr, sr_new=target_sr)
-
-
 def resample_audio_scipy(
     audio: npt.NDArray[np.floating],
     *,
@@ -257,7 +243,7 @@ class AudioResampler:
     def __init__(
         self,
         target_sr: float | None = None,
-        method: Literal["pyav", "resampy", "scipy"] = "resampy",
+        method: Literal["pyav", "scipy"] = "pyav",
     ):
         self.target_sr = target_sr
         self.method = method
@@ -281,10 +267,6 @@ def resample(
             return audio
         if self.method == "pyav":
             return resample_audio_pyav(audio, orig_sr=orig_sr, target_sr=self.target_sr)
-        if self.method == "resampy":
-            return resample_audio_resampy(
-                audio, orig_sr=orig_sr, target_sr=self.target_sr
-            )
         elif self.method == "scipy":
             return resample_audio_scipy(
                 audio, orig_sr=orig_sr, target_sr=self.target_sr
diff --git a/vllm/multimodal/cache.py b/vllm/multimodal/cache.py
index c0df19d4f483..833b405ff651 100644
--- a/vllm/multimodal/cache.py
+++ b/vllm/multimodal/cache.py
@@ -511,11 +511,27 @@ def get_and_update_item(
 
             self._p0_cache[mm_hash] = prompt_updates
             return self.address_as_item(address, monotonic_id), prompt_updates
-        except (ValueError, MemoryError) as e:
-            # put may fail if the object is too large or
-            # the cache is full.
-            # In this case we log the error and keep the original mm_input.
-            logger.debug("Failed to cache mm_input with hash %s: %s", mm_hash, e)
+        except ValueError as e:
+            # `put` raises ValueError either for an oversize item or for a
+            # duplicate key (concurrent insert); the latter is benign so we
+            # only warn on the oversize case. Subsequent UUID-only requests
+            # for an oversize item will fail with a cache miss.
+            if "already exists" not in str(e):
+                logger.warning_once(
+                    "mm_input %s too large to cache; "
+                    "raise --mm-shm-cache-max-object-size-mb. (%s)",
+                    mm_hash,
+                    str(e),
+                )
+            return mm_item
+        except MemoryError as e:
+            # Cache full and protected items prevent eviction.
+            logger.debug(
+                "mm_input %s not cached; shm cache full, "
+                "consider raising --mm-processor-cache-gb. (%s)",
+                mm_hash,
+                str(e),
+            )
             return mm_item
 
     @override
diff --git a/vllm/multimodal/inputs.py b/vllm/multimodal/inputs.py
index 12356b8727cb..d98a1624ac3b 100644
--- a/vllm/multimodal/inputs.py
+++ b/vllm/multimodal/inputs.py
@@ -145,14 +145,15 @@ class PlaceholderRange:
     """
 
     @cached_property
-    def embeds_cumsum(self) -> torch.Tensor | None:
-        return None if self.is_embed is None else self.is_embed.cumsum(dim=0)
+    def embeds_cumsum(self) -> list[int] | None:
+        # python list so python indexing avoids torch C++ overhead/conversions/deallocs
+        return None if self.is_embed is None else self.is_embed.cumsum(dim=0).tolist()
 
     def get_num_embeds(self) -> int:
         if self.embeds_cumsum is None:
             return self.length
 
-        return int(self.embeds_cumsum[-1])
+        return self.embeds_cumsum[-1] if self.embeds_cumsum else 0
 
     def get_embeds_indices_in_range(
         self, start_idx: int, end_idx: int
@@ -170,10 +171,8 @@ def get_embeds_indices_in_range(
         if self.embeds_cumsum is None:
             return start_idx, end_idx
 
-        embeds_start_idx = (
-            int(self.embeds_cumsum[start_idx - 1]) if start_idx > 0 else 0
-        )
-        embeds_end_idx = int(self.embeds_cumsum[end_idx - 1])
+        embeds_start_idx = self.embeds_cumsum[start_idx - 1] if start_idx > 0 else 0
+        embeds_end_idx = self.embeds_cumsum[end_idx - 1] if end_idx > 0 else 0
 
         return embeds_start_idx, embeds_end_idx
 
diff --git a/vllm/multimodal/media/audio.py b/vllm/multimodal/media/audio.py
index 4d41248f1475..37b8662a76b6 100644
--- a/vllm/multimodal/media/audio.py
+++ b/vllm/multimodal/media/audio.py
@@ -10,6 +10,7 @@
 import torch
 
 from vllm.logger import init_logger
+from vllm.multimodal.audio import resample_audio_pyav
 from vllm.utils.import_utils import PlaceholderModule
 from vllm.utils.serial_utils import tensor2base64
 
@@ -28,15 +29,9 @@
     soundfile = PlaceholderModule("soundfile")  # type: ignore[assignment]
 
 
-try:
-    import resampy
-except ImportError:
-    resampy = PlaceholderModule("resampy")  # type: ignore[assignment]
-
-
-# Public libsndfile error codes exposed via `soundfile.LibsndfileError.code`, soundfile
-# being librosa's main backend. Used to validate if an audio loading error is due to a
-# server error vs a client error (invalid audio file).
+# Public libsndfile error codes exposed via `soundfile.LibsndfileError.code`,
+# soundfile being the main audio loading backend. Used to validate if an audio
+# loading error is due to a server error vs a client error (invalid audio file).
 # 0 = sf_error(NULL) race condition: when multiple threads fail sf_open_virtual
 #     concurrently, one thread may clear the global error before another reads it,
 #     producing code=0 ("Garbled error message from libsndfile" in soundfile).
@@ -129,7 +124,7 @@ def load_audio_soundfile(
         y = np.mean(y, axis=tuple(range(y.ndim - 1)))
 
     if sr is not None and sr != native_sr:
-        y = resampy.resample(y, sr_orig=native_sr, sr_new=sr)
+        y = resample_audio_pyav(y, orig_sr=native_sr, target_sr=sr)
         return y, int(sr)
     return y, native_sr
 
diff --git a/vllm/multimodal/media/video.py b/vllm/multimodal/media/video.py
index 691d9444b81b..404f5a0e7cfe 100644
--- a/vllm/multimodal/media/video.py
+++ b/vllm/multimodal/media/video.py
@@ -92,14 +92,48 @@ def load_base64(
             )
             total = int(frames.shape[0])
             fps = float(self.kwargs.get("fps", 1))
-            duration = total / fps if fps > 0 else 0.0
+
+            # validate and extract frames_indices
+            frames_indices = self.kwargs.get("frames_indices")
+            if frames_indices is not None:
+                if not (
+                    isinstance(frames_indices, list)
+                    and all(isinstance(i, int) for i in frames_indices)
+                ):
+                    raise ValueError("frames_indices must be a list of integers")
+                if len(frames_indices) != total:
+                    raise ValueError(
+                        f"frames_indices length ({len(frames_indices)}) must "
+                        f"match number of frames sent ({total})"
+                    )
+            else:
+                frames_indices = list(range(total))
+
+            # validate and extract total_num_frames
+            total_num_frames = self.kwargs.get("total_num_frames", total)
+            if not isinstance(total_num_frames, int) or total_num_frames < 1:
+                raise ValueError("total_num_frames must be a positive integer")
+            if total_num_frames < total:
+                raise ValueError(
+                    f"total_num_frames ({total_num_frames}) must be >= "
+                    f"number of frames sent ({total})"
+                )
+
+            # validate and extract duration
+            duration = self.kwargs.get("duration")
+            if duration is not None:
+                if not isinstance(duration, (int, float)) or duration < 0:
+                    raise ValueError("duration must be a non-negative number")
+            else:
+                duration = total_num_frames / fps if fps > 0 else 0.0
+
             metadata = {
-                "total_num_frames": total,
+                "total_num_frames": total_num_frames,
                 "fps": fps,
                 "duration": duration,
                 "video_backend": "jpeg_sequence",
-                "frames_indices": list(range(total)),
-                "do_sample_frames": False,
+                "frames_indices": frames_indices,
+                "do_sample_frames": self.kwargs.get("do_sample_frames", False),
             }
             return frames, metadata
 
diff --git a/vllm/multimodal/processing/context.py b/vllm/multimodal/processing/context.py
index ef9710374d81..bed66d0a4e9d 100644
--- a/vllm/multimodal/processing/context.py
+++ b/vllm/multimodal/processing/context.py
@@ -268,28 +268,6 @@ def call_hf_processor(
         try:
             output = hf_processor(**data, **allowed_kwargs)
         except Exception as exc:
-            # See https://github.com/huggingface/tokenizers/issues/537
-            if (
-                isinstance(exc, RuntimeError)
-                and exc
-                and exc.args[0] == "Already borrowed"
-                and num_tries < max_tries
-            ):
-                logger.warning(
-                    "Failed to acquire tokenizer in current thread. "
-                    "Retrying (%d/%d)...",
-                    num_tries,
-                    max_tries,
-                )
-                time.sleep(0.5)
-                return self.call_hf_processor(
-                    hf_processor,
-                    data,
-                    kwargs,
-                    num_tries=num_tries + 1,
-                    max_tries=max_tries,
-                )
-
             msg = (
                 f"Failed to apply {type(hf_processor).__name__} "
                 f"on data={data} with kwargs={allowed_kwargs}"
diff --git a/vllm/multimodal/registry.py b/vllm/multimodal/registry.py
index fa414a5928d6..6fdae470839f 100644
--- a/vllm/multimodal/registry.py
+++ b/vllm/multimodal/registry.py
@@ -111,7 +111,15 @@ def supports_multimodal_inputs(self, model_config: "ModelConfig") -> bool:
             return False
 
         mm_config = model_config.get_multimodal_config()
-        info = self._create_processing_info(model_config, tokenizer=None)
+        try:
+            info = self._create_processing_info(model_config, tokenizer=None)
+        except ValueError:
+            logger.warning_once(
+                "Model %s is treated as multimodal but has no registered "
+                "multimodal processor; running in text-only mode.",
+                model_config.model,
+            )
+            return False
 
         # Check if all supported modalities have limit == 0
         if all(
@@ -170,7 +178,11 @@ def _get_model_cls(self, model_config: "ModelConfig") -> "SupportsMultiModal":
         from vllm.model_executor.model_loader import get_model_architecture
 
         model_cls, _ = get_model_architecture(model_config)
-        assert hasattr(model_cls, "_processor_factory")
+        if not hasattr(model_cls, "_processor_factory"):
+            raise ValueError(
+                f"Model class {model_cls.__name__} has no registered "
+                "multimodal processor"
+            )
         return cast("SupportsMultiModal", model_cls)
 
     def _create_processing_ctx(
@@ -193,6 +205,9 @@ def _create_processing_info(
         ctx = self._create_processing_ctx(model_config, tokenizer)
         return factories.info(ctx)
 
+    def get_processing_info(self, model_config: "ModelConfig") -> BaseProcessingInfo:
+        return self._create_processing_info(model_config, tokenizer=None)
+
     def create_processor(
         self,
         model_config: "ModelConfig",
@@ -204,7 +219,8 @@ def create_processor(
         Create a multi-modal processor for a specific model and tokenizer.
         """
         if not model_config.is_multimodal_model:
-            raise ValueError(f"{model_config.model} is not a multimodal model")
+            model_name = model_config.served_model_name or model_config.model
+            raise ValueError(f"{model_name} is not a multimodal model")
 
         model_cls = self._get_model_cls(model_config)
         factories = model_cls._processor_factory
diff --git a/vllm/multimodal/video.py b/vllm/multimodal/video.py
index 90102151423f..5b118af8fc53 100644
--- a/vllm/multimodal/video.py
+++ b/vllm/multimodal/video.py
@@ -3,7 +3,7 @@
 import math
 from abc import abstractmethod
 from io import BytesIO
-from typing import Any, NamedTuple, cast
+from typing import Any, ClassVar, Literal, NamedTuple, cast
 
 import numpy as np
 import numpy.typing as npt
@@ -19,6 +19,11 @@
     cv2 = PlaceholderModule("cv2")
     vr = PlaceholderModule("cv2").placeholder_attr("videoio_registry")
 
+try:
+    import av
+except ImportError:
+    av = PlaceholderModule("av")  # type: ignore[assignment]
+
 
 logger = init_logger(__name__)
 
@@ -355,8 +360,75 @@ def read_frames(
         return frames, valid_frame_indices
 
 
+class PyAVVideoBackendMixin:
+    """PyAV (in-process FFmpeg bindings) codec utilities.
+
+    Reads stream metadata and decodes target frames via per-frame
+    ``container.seek()``. The seek releases the GIL between frames and
+    scales with the number of sampled frames rather than the video
+    length, enabling concurrent decoding under serving load.
+    """
+
+    @staticmethod
+    def get_metadata(
+        container: "av.container.InputContainer",
+    ) -> VideoSourceMetadata:
+        if not container.streams.video:
+            raise ValueError("No video streams found in container")
+        stream = container.streams.video[0]
+        total_frames = stream.frames or 0
+        fps = float(stream.average_rate) if stream.average_rate else 0.0
+        duration = float(stream.duration * stream.time_base) if stream.duration else 0.0
+        if total_frames == 0 and duration > 0 and fps > 0:
+            total_frames = int(duration * fps)
+        return VideoSourceMetadata(total_frames, fps, duration)
+
+    @staticmethod
+    def decode_frames(
+        container: "av.container.InputContainer",
+        frame_indices: list[int],
+        fps: float,
+        duration: float,
+    ) -> tuple[npt.NDArray, list[int]]:
+        """Decode target frames via per-frame seek + keyframe decode."""
+        stream = container.streams.video[0]
+        # SLICE parallelizes within a single frame without the
+        # one-frame-per-thread latency penalty of FRAME threading.
+        stream.thread_type = "SLICE"
+        time_base = stream.time_base
+
+        frames_list: list[npt.NDArray] = []
+        valid_indices: list[int] = []
+        frame_interval = 1.0 / fps if fps > 0 else 0.1
+        max_ts = max(0.0, duration - frame_interval) if duration > 0 else float("inf")
+
+        for idx in frame_indices:
+            ts = min(idx / fps, max_ts) if fps > 0 else 0.0
+            pts = int(ts / time_base)
+            container.seek(pts, stream=stream)
+            frame = next(container.decode(video=0), None)
+            if frame is not None:
+                frames_list.append(frame.to_ndarray(format="rgb24"))
+                valid_indices.append(idx)
+
+        if not frames_list:
+            return np.empty((0,), dtype=np.uint8), valid_indices
+        return np.stack(frames_list), valid_indices
+
+
 @VIDEO_LOADER_REGISTRY.register("opencv")
-class OpenCVVideoBackend(VideoLoader, OpenCVVideoBackendMixin):
+class VideoBackend(VideoLoader, OpenCVVideoBackendMixin, PyAVVideoBackendMixin):
+    """Uniform-sampling video backend.
+
+    Samples ``num_frames`` uniformly across the video (or one frame every
+    ``1/fps`` seconds, whichever produces fewer frames). The decoding codec
+    is selected via the ``backend`` kwarg (``"opencv"`` or ``"pyav"``),
+    which can be passed through ``--media-io-kwargs``. Defaults to
+    ``"pyav"`` for concurrent decoding.
+    """
+
+    _sampling_suffix: ClassVar[str] = ""
+
     @classmethod
     def compute_frames_index_to_sample(
         cls,
@@ -366,7 +438,6 @@ def compute_frames_index_to_sample(
     ) -> list[int]:
         total_frames_num = source.total_frames_num
         duration = source.duration
-
         num_frames = target.num_frames
         fps = target.fps
         # resample video to target num_frames and fps
@@ -376,16 +447,18 @@ def compute_frames_index_to_sample(
             num_frames_to_sample = min(num_frames, total_frames_num)
         if fps > 0:
             num_frames_to_sample = min(num_frames_to_sample, math.floor(duration * fps))
-        num_frames_to_sample = max(1, num_frames_to_sample)  # at least one sample
+        num_frames_to_sample = max(1, num_frames_to_sample)
 
         if num_frames_to_sample == total_frames_num:
-            frame_idx = list(range(0, num_frames_to_sample))
-        else:
-            uniform_sampled_frames = np.linspace(
-                0, total_frames_num - 1, num_frames_to_sample, dtype=int
-            )
-            frame_idx = uniform_sampled_frames.tolist()
-        return frame_idx
+            return list(range(num_frames_to_sample))
+        return np.linspace(
+            0, total_frames_num - 1, num_frames_to_sample, dtype=int
+        ).tolist()
+
+    @classmethod
+    def _prepare_source(cls, source: VideoSourceMetadata) -> VideoSourceMetadata:
+        """Sampling-algorithm-specific metadata adjustment hook."""
+        return source
 
     @classmethod
     def load_bytes(
@@ -395,55 +468,101 @@ def load_bytes(
         fps: int = -1,
         max_duration: int = 300,
         frame_recovery: bool = False,
+        *,
+        backend: Literal["opencv", "pyav"] = "opencv",
         **kwargs,
     ) -> tuple[npt.NDArray, dict[str, Any]]:
-        """
-        Load video frames from bytes.
+        """Load sampled frames from raw video bytes.
 
         Args:
-            data: Raw video bytes
-            num_frames: Target number of frames to sample (-1 for all)
-            fps: Target FPS for sampling (-1 for original)
-            max_duration: Maximum duration (unused in base backend)
-            frame_recovery: Enable forward-scan recovery for failed frames
+            data: Raw video bytes.
+            num_frames: Target number of frames to sample (``-1`` for all).
+            fps: Target FPS for sampling (``-1`` for original).
+            max_duration: Maximum duration in seconds — only used by the
+                dynamic subclass; ignored here.
+            frame_recovery: Enable forward-scan recovery for failed frames.
+                Only honored by the OpenCV codec.
+            backend: Decoding codec — ``"opencv"`` or ``"pyav"`` .
 
         Returns:
-            Tuple of (frames_array, metadata_dict)
+            Tuple of ``(frames_array, metadata_dict)``.
         """
-        cap = cls.open_video_capture(data)
-
-        source = OpenCVVideoBackendMixin.get_video_metadata(cap)
         target = VideoTargetMetadata(
-            num_frames=num_frames,
-            fps=fps,
-            max_duration=max_duration,
+            num_frames=num_frames, fps=fps, max_duration=max_duration
         )
 
-        # resample video to target num_frames and fps
-        # - the minimum of the two will be used
-        frame_idx = cls.compute_frames_index_to_sample(
-            source=source,
-            target=target,
-        )
+        if backend == "opencv":
+            cap = cls.open_video_capture(data)
+            source = cls._prepare_source(cls.get_video_metadata(cap))
+            frame_idx = cls.compute_frames_index_to_sample(
+                source=source, target=target, **kwargs
+            )
+            frames, valid = cls.read_frames(
+                cap,
+                frame_idx,
+                total_frames_num=source.total_frames_num,
+                frame_recovery=frame_recovery,
+            )
+        elif backend == "pyav":
+            assert not frame_recovery, (
+                "frame_recovery is only available for `opencv` backend"
+            )
+            with av.open(BytesIO(data)) as container:
+                source = cls._prepare_source(cls.get_metadata(container))
+                frame_idx = cls.compute_frames_index_to_sample(
+                    source=source, target=target, **kwargs
+                )
+                frames, valid = cls.decode_frames(
+                    container, frame_idx, source.original_fps, source.duration
+                )
+        else:
+            raise ValueError(
+                f"Unknown video codec backend {backend!r}; "
+                "valid options: 'opencv', 'pyav'."
+            )
 
-        frames, valid_frame_indices = cls.read_frames(
-            cap,
-            frame_idx,
-            total_frames_num=source.total_frames_num,
-            frame_recovery=frame_recovery,
-        )
+        if len(valid) < len(frame_idx):
+            logger.warning(
+                "%s video loading: expected %d frames but got %d.",
+                backend,
+                len(frame_idx),
+                len(valid),
+            )
 
-        metadata = cls.create_hf_metadata(
+        return frames, cls.create_hf_metadata(
             source=source,
-            video_backend="opencv",
-            valid_frame_indices=valid_frame_indices,
+            video_backend=f"{backend}{cls._sampling_suffix}",
+            valid_frame_indices=valid,
         )
 
-        return frames, metadata
-
 
 @VIDEO_LOADER_REGISTRY.register("opencv_dynamic")
-class OpenCVDynamicVideoBackend(VideoLoader, OpenCVVideoBackendMixin):
+class DynamicVideoBackend(VideoBackend):
+    """Duration-aware dynamic-sampling video backend.
+
+    Samples at ``fps`` up to ``max_duration`` seconds, falling back to
+    uniform sampling across the full duration when the video is longer
+    than ``max_duration``. Codec is selectable the same way as
+    :class:`VideoBackend`.
+    """
+
+    _sampling_suffix: ClassVar[str] = "_dynamic"
+
+    @classmethod
+    def _prepare_source(cls, source: VideoSourceMetadata) -> VideoSourceMetadata:
+        # Estimate duration from frame count and fps when the container
+        # does not report it (common for WebM/streaming inputs).
+        if source.duration:
+            return source
+        if source.original_fps > 0:
+            max_frame_idx = source.total_frames_num - 1
+            duration = round(max_frame_idx / source.original_fps) + 1
+        else:
+            duration = 0
+        return VideoSourceMetadata(
+            source.total_frames_num, source.original_fps, duration
+        )
+
     @classmethod
     def compute_frames_index_to_sample(
         cls,
@@ -456,8 +575,8 @@ def compute_frames_index_to_sample(
         original_fps = source.original_fps
         max_duration = target.max_duration
         fps = target.fps
-
         max_frame_idx = source.total_frames_num - 1
+
         # Refer to:
         # https://github.com/huggingface/transformers/blob/v4.55.4/src/transformers/models/glm4v/video_processing_glm4v.py#L103-L140
         frame_indices_list: list[int]
@@ -491,62 +610,20 @@ def load_bytes(
         fps: int = 2,
         max_duration: int = 300,
         frame_recovery: bool = False,
+        *,
+        backend: Literal["opencv", "pyav"] = "opencv",
         **kwargs,
     ) -> tuple[npt.NDArray, dict[str, Any]]:
-        """
-        Load video frames with dynamic sampling based on duration.
-
-        Args:
-            data: Raw video bytes
-            num_frames: Not used in dynamic backend
-            fps: Target FPS for sampling (default: 2)
-            max_duration: Maximum video duration to process (default: 300s)
-            frame_recovery: Enable forward-scan recovery for failed frames
-
-        Returns:
-            Tuple of (frames_array, metadata_dict)
-        """
-        cap = cls.open_video_capture(data)
-
-        orig_source = OpenCVVideoBackendMixin.get_video_metadata(cap)
-        max_frame_idx = orig_source.total_frames_num - 1
-        duration = (
-            orig_source.duration or round(max_frame_idx / orig_source.original_fps) + 1
-        )
-
-        # recompute source metadata with adjusted duration to ensure correct
-        # sampling indices computation
-        source = VideoSourceMetadata(
-            total_frames_num=orig_source.total_frames_num,
-            original_fps=orig_source.original_fps,
-            duration=duration,
-        )
-        target = VideoTargetMetadata(
+        return super().load_bytes(
+            data,
             num_frames=num_frames,
             fps=fps,
             max_duration=max_duration,
-        )
-
-        frame_indices_list = cls.compute_frames_index_to_sample(
-            source=source,
-            target=target,
-        )
-
-        frames, valid_frame_indices = cls.read_frames(
-            cap,
-            frame_indices_list,
-            total_frames_num=source.total_frames_num,
             frame_recovery=frame_recovery,
+            backend=backend,
+            **kwargs,
         )
 
-        metadata = cls.create_hf_metadata(
-            source=source,
-            video_backend="opencv_dynamic",
-            valid_frame_indices=valid_frame_indices,
-        )
-
-        return frames, metadata
-
 
 @VIDEO_LOADER_REGISTRY.register("molmo2")
 class Molmo2VideoBackend(VideoLoader, OpenCVVideoBackendMixin):
@@ -835,7 +912,7 @@ def load_bytes(
 
 
 @VIDEO_LOADER_REGISTRY.register("nemotron_vl")
-class NemotronVLVideoBackend(OpenCVVideoBackend):
+class NemotronVLVideoBackend(VideoBackend):
     @classmethod
     def load_bytes(
         cls,
@@ -844,14 +921,17 @@ def load_bytes(
         fps: int = -1,
         max_duration: int = 300,
         frame_recovery: bool = False,
+        *,
+        backend: Literal["opencv", "pyav"] = "opencv",
         **kwargs,
     ) -> tuple[npt.NDArray, dict[str, Any]]:
-        frames, metadata = OpenCVVideoBackend.load_bytes(
+        frames, metadata = super().load_bytes(
             data,
             num_frames=num_frames,
             fps=fps,
             max_duration=max_duration,
             frame_recovery=frame_recovery,
+            backend=backend,
             **kwargs,
         )
 
diff --git a/vllm/outputs.py b/vllm/outputs.py
index 2c71d2afb1b5..aa6d12768ccc 100644
--- a/vllm/outputs.py
+++ b/vllm/outputs.py
@@ -121,6 +121,7 @@ def __init__(
         num_cached_tokens: int | None = None,
         *,
         kv_transfer_params: dict[str, Any] | None = None,
+        prompt_routed_experts: np.ndarray | None = None,
         # Forward compatibility, code that uses args added in new release can
         # still run with older versions of vLLM without breaking.
         **kwargs: Any,
@@ -141,12 +142,15 @@ def __init__(
         self.encoder_prompt_token_ids = encoder_prompt_token_ids
         self.num_cached_tokens = num_cached_tokens
         self.kv_transfer_params = kv_transfer_params
+        self.prompt_routed_experts = prompt_routed_experts
 
     def add(self, next_output: "RequestOutput", aggregate: bool) -> None:
         """Merge subsequent RequestOutput into this one"""
 
         self.finished |= next_output.finished
         self.kv_transfer_params = next_output.kv_transfer_params
+        if next_output.prompt_routed_experts is not None:
+            self.prompt_routed_experts = next_output.prompt_routed_experts
 
         for next_completion in next_output.outputs:
             for i, completion in enumerate(self.outputs):
diff --git a/vllm/parser/abstract_parser.py b/vllm/parser/abstract_parser.py
index b87455ab6f71..46acc7fb155e 100644
--- a/vllm/parser/abstract_parser.py
+++ b/vllm/parser/abstract_parser.py
@@ -38,6 +38,10 @@
 from vllm.reasoning.abs_reasoning_parsers import ReasoningParser
 from vllm.tokenizers import TokenizerLike
 from vllm.tool_parsers.abstract_tool_parser import ToolParser
+from vllm.tool_parsers.streaming import (
+    extract_named_tool_call_streaming,
+    extract_required_tool_call_streaming,
+)
 from vllm.tool_parsers.utils import Tool
 from vllm.utils import random_uuid
 
@@ -53,6 +57,11 @@ class StreamState:
     prompt_reasoning_checked: bool = False
     previous_text: str = ""
     previous_token_ids: list[int] = field(default_factory=list)
+    history_tool_call_cnt: int = 0
+    tool_call_id_type: str = "random"
+    # only used for "required" and "named tool" choices,
+    # tracks whether function name has been fully returned in the stream yet
+    function_name_returned: bool = False
 
 
 class Parser:
@@ -417,6 +426,17 @@ def extract_response_outputs(
 
         return outputs
 
+    def _get_function_name(
+        self, request: ChatCompletionRequest | ResponsesRequest
+    ) -> str:
+        if request.tool_choice and isinstance(request.tool_choice, ToolChoiceFunction):
+            return request.tool_choice.name
+        if request.tool_choice and isinstance(
+            request.tool_choice, ChatCompletionNamedToolChoiceParam
+        ):
+            return request.tool_choice.function.name
+        raise ValueError("Invalid tool_choice for function name extraction.")
+
     def _parse_tool_calls(
         self,
         request: ResponsesRequest,
@@ -434,21 +454,15 @@ def _parse_tool_calls(
         """
         function_calls: list[FunctionCall] = []
 
-        if request.tool_choice and isinstance(request.tool_choice, ToolChoiceFunction):
-            # Forced Function Call (Responses API style)
-            assert content is not None
-            function_calls.append(
-                FunctionCall(name=request.tool_choice.name, arguments=content)
-            )
-            return function_calls, None  # Clear content since tool is called.
-
         if request.tool_choice and isinstance(
-            request.tool_choice, ChatCompletionNamedToolChoiceParam
+            request.tool_choice,
+            (ToolChoiceFunction, ChatCompletionNamedToolChoiceParam),
         ):
-            # Forced Function Call (Chat Completion API style)
-            assert content is not None
+            # Forced Function Call
+            if content is None:
+                return [], None
             function_calls.append(
-                FunctionCall(name=request.tool_choice.function.name, arguments=content)
+                FunctionCall(name=self._get_function_name(request), arguments=content)
             )
             return function_calls, None  # Clear content since tool is called.
 
@@ -558,6 +572,63 @@ def extract_tool_calls_streaming(
             request,
         )
 
+    def _extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest | ResponsesRequest,
+        # The following parameters are used for "required" tool choice parsing and are
+        # tracked in StreamState for streaming parsing.
+        tool_call_idx: int | None = None,
+        tool_call_id_type: str = "random",
+        function_name_returned: bool = False,
+    ) -> tuple[DeltaMessage | None, bool]:
+        assert self._tool_parser is not None
+        supports_required_and_named = self._tool_parser.supports_required_and_named
+        if (
+            supports_required_and_named
+            and request.tool_choice
+            and isinstance(
+                request.tool_choice,
+                (ToolChoiceFunction, ChatCompletionNamedToolChoiceParam),
+            )
+        ):
+            delta_message, function_name_returned = extract_named_tool_call_streaming(
+                delta_text=delta_text,
+                function_name=self._get_function_name(request),
+                function_name_returned=function_name_returned,
+                tool_call_idx=tool_call_idx,
+                tool_call_id_type=tool_call_id_type,
+                tokenizer=self.model_tokenizer,
+            )
+            return delta_message, function_name_returned
+
+        if supports_required_and_named and request.tool_choice == "required":
+            delta_message, function_name_returned = (
+                extract_required_tool_call_streaming(
+                    previous_text=previous_text,
+                    current_text=current_text,
+                    delta_text=delta_text,
+                    function_name_returned=function_name_returned,
+                    tool_call_idx=tool_call_idx,
+                    tool_call_id_type=tool_call_id_type,
+                )
+            )
+            return delta_message, function_name_returned
+        return self.extract_tool_calls_streaming(
+            previous_text,
+            current_text,
+            delta_text,
+            previous_token_ids,
+            current_token_ids,
+            delta_token_ids,
+            request,  # type: ignore[arg-type]
+        ), False
+
     def is_reasoning_end(self, input_ids: list[int]) -> bool:
         if self._reasoning_parser is None:
             return False
@@ -571,15 +642,11 @@ def extract_content_ids(self, input_ids: list[int]) -> list[int]:
     def _in_reasoning_phase(self, state: StreamState) -> bool:
         if self._reasoning_parser is None:
             return False
-        if self._tool_parser is None:
-            return True
         return not state.reasoning_ended
 
     def _in_tool_call_phase(self, state: StreamState) -> bool:
         if self._tool_parser is None:
             return False
-        if self._reasoning_parser is None:
-            return True
         return state.reasoning_ended
 
     def parse_delta(
@@ -593,7 +660,9 @@ def parse_delta(
 
         if not state.prompt_reasoning_checked and prompt_token_ids is not None:
             state.prompt_reasoning_checked = True
-            if self.is_reasoning_end(prompt_token_ids):
+            if self._reasoning_parser is None or self.is_reasoning_end(
+                prompt_token_ids
+            ):
                 state.reasoning_ended = True
 
         current_text = state.previous_text + delta_text
@@ -628,18 +697,34 @@ def parse_delta(
                 state.previous_token_ids = []
                 delta_text = current_text
                 delta_token_ids = current_token_ids
-            delta_message = self.extract_tool_calls_streaming(
-                previous_text=state.previous_text,
-                current_text=current_text,
-                delta_text=delta_text,
-                previous_token_ids=state.previous_token_ids,
-                current_token_ids=current_token_ids,
-                delta_token_ids=delta_token_ids,
-                request=request,  # type: ignore[arg-type]
-            )
 
-        # No parsers: pass through as content
-        if self._reasoning_parser is None and self._tool_parser is None:
+            delta_message, state.function_name_returned = (
+                self._extract_tool_calls_streaming(
+                    previous_text=state.previous_text,
+                    current_text=current_text,
+                    delta_text=delta_text,
+                    previous_token_ids=state.previous_token_ids,
+                    current_token_ids=current_token_ids,
+                    delta_token_ids=delta_token_ids,
+                    request=request,  # type: ignore[arg-type]
+                    tool_call_idx=state.history_tool_call_cnt,
+                    tool_call_id_type=state.tool_call_id_type,
+                    function_name_returned=state.function_name_returned,
+                )
+            )
+            if (
+                delta_message
+                and delta_message.tool_calls
+                and delta_message.tool_calls[0].id is not None
+            ):
+                state.history_tool_call_cnt += 1
+
+        # No phase active: pass through as content
+        if (
+            delta_message is None
+            and not self._in_reasoning_phase(state)
+            and not self._in_tool_call_phase(state)
+        ):
             delta_message = DeltaMessage(content=delta_text)
 
         state.previous_text = current_text
diff --git a/vllm/parser/minimax_m2_parser.py b/vllm/parser/minimax_m2_parser.py
index c32114294644..34aaa7268446 100644
--- a/vllm/parser/minimax_m2_parser.py
+++ b/vllm/parser/minimax_m2_parser.py
@@ -43,11 +43,17 @@ class MiniMaxM2Parser(DelegatingParser):
     reasoning_parser_cls = MiniMaxM2ReasoningParser
     tool_parser_cls = MinimaxM2ToolParser
 
-    def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
-        super().__init__(tokenizer)
+    def __init__(
+        self,
+        tokenizer: TokenizerLike,
+        tools: list[Tool] | None = None,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(tokenizer, *args, **kwargs)
 
         # Initialize the underlying parsers
-        self._reasoning_parser = MiniMaxM2ReasoningParser(tokenizer)
+        self._reasoning_parser = MiniMaxM2ReasoningParser(tokenizer, *args, **kwargs)
         self._tool_parser = MinimaxM2ToolParser(tokenizer, tools)
 
         logger.debug(
diff --git a/vllm/platforms/__init__.py b/vllm/platforms/__init__.py
index af344acfcbc7..645da0a1fe97 100644
--- a/vllm/platforms/__init__.py
+++ b/vllm/platforms/__init__.py
@@ -177,7 +177,6 @@ def cpu_platform_plugin() -> str | None:
                 logger.debug(
                     "Confirmed CPU platform is available because the machine is MacOS."
                 )
-
     except Exception as e:
         logger.debug("CPU platform is not available because: %s", str(e))
 
diff --git a/vllm/platforms/cpu.py b/vllm/platforms/cpu.py
index eb4a5e14827f..750b7f2f4b9a 100644
--- a/vllm/platforms/cpu.py
+++ b/vllm/platforms/cpu.py
@@ -6,16 +6,16 @@
 import platform
 import subprocess
 import sys
-from dataclasses import dataclass
 from typing import TYPE_CHECKING
 
-import psutil
 import torch
 
-from vllm import envs
 from vllm.logger import init_logger
-from vllm.utils.ompmultiprocessing import OMPProcessManager
-from vllm.utils.torch_utils import is_quantized_kv_cache
+from vllm.utils.cpu_resource_utils import (
+    DEVICE_CONTROL_ENV_VAR,
+    get_memory_node_info,
+)
+from vllm.utils.mem_constants import GiB_bytes
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 from .interface import CpuArchEnum, Platform, PlatformEnum
@@ -38,49 +38,13 @@ def get_max_threads(pid=0):
         raise NotImplementedError("Unsupported OS")
 
 
-@dataclass
-class LogicalCPUInfo:
-    id: int = -1
-    physical_core: int = -1
-    numa_node: int = -1
-
-    @classmethod
-    def _int(cls, value: str) -> int:
-        try:
-            int_value = int(value)
-        except Exception:
-            int_value = -1
-        return int_value
-
-    @staticmethod
-    def json_decoder(obj_dict: dict):
-        id = obj_dict.get("cpu")
-        physical_core = obj_dict.get("core")
-        numa_node = obj_dict.get("node")
-
-        if not (id is None or physical_core is None or numa_node is None):
-            return LogicalCPUInfo(
-                id=LogicalCPUInfo._int(id),
-                physical_core=LogicalCPUInfo._int(physical_core),
-                numa_node=LogicalCPUInfo._int(numa_node),
-            )
-        else:
-            return obj_dict
-
-
 class CpuPlatform(Platform):
     _enum = PlatformEnum.CPU
     device_name: str = "cpu"
     device_type: str = "cpu"
     dispatch_key: str = "CPU"
     dist_backend: str = "gloo"
-    device_control_env_var = "CPU_VISIBLE_MEMORY_NODES"
-    omp_process_manager = None
-    # Simultaneous Multithreading (SMT) level for OpenMP:
-    # 4 on PowerPC, 1 on non-PowerPC architectures
-    smt = 1
-    global_cpu_mask = None
-    simulate_numa = int(os.environ.get("_SIM_MULTI_NUMA", 0))
+    device_control_env_var = DEVICE_CONTROL_ENV_VAR
 
     @property
     def supported_dtypes(self) -> list[torch.dtype]:
@@ -123,29 +87,9 @@ def get_attn_backend_cls(
 
     @classmethod
     def get_device_total_memory(cls, device_id: int = 0) -> int:
-        from vllm.utils.mem_constants import GiB_bytes
-        from vllm.utils.mem_utils import format_gib
-
-        kv_cache_space = envs.VLLM_CPU_KVCACHE_SPACE
-        node_dir = "/sys/devices/system/node"
-        if kv_cache_space is None:
-            nodes = (
-                [d for d in os.listdir(node_dir) if d.startswith("node")]
-                if os.path.exists(node_dir)
-                else []
-            )
-            num_numa_nodes = len(nodes) or 1
-            free_cpu_memory = psutil.virtual_memory().total // num_numa_nodes
-            DEFAULT_CPU_MEM_UTILIZATION = 0.5
-            kv_cache_space = int(free_cpu_memory * DEFAULT_CPU_MEM_UTILIZATION)
-            logger.warning_once(
-                "VLLM_CPU_KVCACHE_SPACE not set. Using %s GiB for KV cache.",
-                format_gib(kv_cache_space),
-            )
-        else:
-            kv_cache_space *= GiB_bytes
+        meminfo = get_memory_node_info(device_id)
 
-        return kv_cache_space
+        return meminfo.total_memory
 
     @classmethod
     def set_device(cls, device: torch.device) -> None:
@@ -180,25 +124,15 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
                 "otherwise the performance is not optimized."
             )
 
+        # Lagecy setting
+        env_key = "VLLM_CPU_KVCACHE_SPACE"
+        if env_key in os.environ and os.environ[env_key] != "":
+            kv_cache_space = int(os.environ[env_key])
+            cache_config.kv_cache_memory_bytes = kv_cache_space * GiB_bytes
+
         scheduler_config = vllm_config.scheduler_config
         # async scheduling is not required on CPU
         scheduler_config.async_scheduling = False
-        if (
-            scheduler_config.enable_chunked_prefill
-            or cache_config.enable_prefix_caching
-        ) and is_quantized_kv_cache(cache_config.cache_dtype):
-            raise RuntimeError(
-                "Chunked-prefill and prefix-cache on the CPU "
-                "backend is not compatible with FP8 KV cache."
-            )
-
-        if is_quantized_kv_cache(cache_config.cache_dtype):
-            logger.warning(
-                "CPU backend doesn't support KV cache quantization fallback to auto."
-            )
-            cache_config.cache_dtype = "auto"
-
-        cache_config.cpu_kvcache_space_bytes = CpuPlatform.get_device_total_memory()
 
         parallel_config = vllm_config.parallel_config
         # OMP requires the MP executor to function correctly, UniProc is not
@@ -242,10 +176,18 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
                     "cpp.dynamic_threads": True,
                 }
             )
+            compilation_config.ir_enable_torch_wrap = False
 
         if vllm_config.lora_config is not None:
             compilation_config.mode = CompilationMode.NONE
 
+        if (
+            cls.get_cpu_architecture() == CpuArchEnum.ARM
+            and "+gelu" not in compilation_config.custom_ops
+            and "-gelu" not in compilation_config.custom_ops
+        ):
+            compilation_config.custom_ops.append("+gelu")
+
         vllm_config.profiler_config.torch_profiler_dump_cuda_time_total = False
 
         assert vllm_config.device_config.device_type == "cpu"
@@ -270,21 +212,45 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
         os.environ["TORCHINDUCTOR_CPP_DYNAMIC_THREADS"] = "1"
 
         ld_preload_str = os.getenv("LD_PRELOAD", "")
-
-        # Intel and CLANG OpenMP setting
-        if "libiomp5.so" in ld_preload_str or "libomp5" in ld_preload_str:
-            # The time(milliseconds) that a thread should wait after
-            # completing the execution of a parallel region, before sleeping.
-            os.environ["KMP_BLOCKTIME"] = "1"
-            # Prevents the CPU to run into low performance state
-            os.environ["KMP_TPAUSE"] = "0"
-            # Provides fine granularity parallelism
-            os.environ["KMP_FORKJOIN_BARRIER_PATTERN"] = "dist,dist"
-            os.environ["KMP_PLAIN_BARRIER_PATTERN"] = "dist,dist"
-            os.environ["KMP_REDUCTION_BARRIER_PATTERN"] = "dist,dist"
-
         cpu_architecture = Platform.get_cpu_architecture()
 
+        if (
+            platform.system() == "Linux"
+            and cpu_architecture
+            in (CpuArchEnum.ARM, CpuArchEnum.POWERPC, CpuArchEnum.X86)
+            and not (
+                "libomp" in ld_preload_str
+                or "libgomp" in ld_preload_str
+                or "libiomp" in ld_preload_str
+            )
+        ):
+            # We need to LD_PRELOAD PyTorch's libgomp, otherwise only
+            # one core will be properly utilized when we thread-bind
+            # See: https://github.com/vllm-project/vllm/issues/27369
+            # TODO: Remove once:
+            # https://github.com/pytorch/pytorch/issues/166087 is fixed
+
+            # We need to find the location of PyTorch's libgomp
+            torch_pkg = os.path.dirname(torch.__file__)
+            site_root = os.path.dirname(torch_pkg)
+            # Search both torch.libs and torch/lib - See:
+            # https://github.com/vllm-project/vllm/issues/30470
+            torch_libs_paths = [
+                os.path.join(site_root, "torch.libs"),
+                os.path.join(torch_pkg, "lib"),
+            ]
+            pytorch_libgomp_so_candidates = []
+            for torch_libs in torch_libs_paths:
+                pytorch_libgomp_so_candidates.extend(
+                    glob.glob(os.path.join(torch_libs, "libgomp*.so*"))
+                )
+            if pytorch_libgomp_so_candidates:
+                pytorch_libgomp_so = pytorch_libgomp_so_candidates[0]
+                if ld_preload_str:
+                    ld_preload_str += ":"
+                ld_preload_str += pytorch_libgomp_so
+                os.environ["LD_PRELOAD"] = ld_preload_str
+
         # LD_PRELOAD libtcmalloc, bundled under vllm/libs to reduce
         # memory allocation overhead
         if (
@@ -323,91 +289,19 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
                 vllm_config.model_config.max_model_len,
                 vllm_config.scheduler_config.DEFAULT_MAX_NUM_BATCHED_TOKENS,
             )
-        # CI specific "quick" NUMA simulation - split all available CPUs
-        # into a fake NUMA topology
-        if os.environ.get("VLLM_CPU_SIM_MULTI_NUMA", None) is not None:
-            os.environ["_SIM_MULTI_NUMA"] = str(
-                vllm_config.parallel_config.world_size
-                * vllm_config.parallel_config._api_process_count
-            )
 
     @classmethod
     def update_block_size_for_backend(cls, vllm_config: "VllmConfig") -> None:
-        # TODO: CPU still sets block_size in check_and_update_config.
-        # Move that logic here so block_size is chosen by the backend.
-        pass
-
-    @classmethod
-    def get_omp_manager(cls) -> OMPProcessManager:
-        # initialise the OMP resource management if need be and return the manager
-        if cls.omp_process_manager is None:
-            if cls.get_cpu_architecture() == CpuArchEnum.POWERPC:
-                cls.smt = 4
-            cls.omp_process_manager = OMPProcessManager(
-                affinity=cls.get_global_cpu_mask(), smt=cls.smt
-            )
-            # we need to fix up the topology returned by the OMP Manager for
-            # simulated NUMA environments in CI
-            if cls.simulate_numa > 0:
-                logger.info(
-                    "Adjusting numa topology to resemble at least %d nodes",
-                    int(cls.simulate_numa),
-                )
-                om = cls.omp_process_manager
-                while len(om.omp_places) < cls.simulate_numa:
-                    new_omp_places = []
-                    touched = False
-                    for omp_place in om.omp_places:
-                        if len(omp_place["mask"]) > 1:
-                            touched = True
-                            cpu_list = sorted(list(omp_place["mask"]))
-                            new_omp_places.append(
-                                {
-                                    "mask": set(cpu_list[0 : int(len(cpu_list) / 2)]),
-                                    "available": True,
-                                }
-                            )
-                            new_omp_places.append(
-                                {
-                                    "mask": set(cpu_list[int(len(cpu_list) / 2) :]),
-                                    "available": True,
-                                }
-                            )
-                    if touched:
-                        om.omp_places = new_omp_places
-                    else:
-                        raise ValueError(
-                            "Cannot split the existing NUMA topology to match "
-                            "simulation requirements"
-                        )
+        model_config = vllm_config.model_config
+        if model_config is None or not model_config.is_hybrid:
+            return
 
-        return cls.omp_process_manager
+        # reconcile attention and mamba page sizes
+        backend_cls = cls._find_non_ssm_backend(vllm_config)
+        if backend_cls is None:
+            return
 
-    @classmethod
-    def get_global_cpu_mask(cls) -> set[int]:
-        # get global cpu mask
-        if cls.global_cpu_mask is None:
-            if hasattr(os, "sched_getaffinity"):
-                cls.global_cpu_mask = os.sched_getaffinity(0)
-            else:
-                # macOS does not support sched_getaffinity
-                cpu_count = os.cpu_count() or 1
-                cls.global_cpu_mask = set(range(cpu_count))
-        return cls.global_cpu_mask
-
-    @classmethod
-    def reserve_cpus(cls, reserve: set[int]) -> bool:
-        # remove CPUs from global mask, for now there is no "release" mechanism
-        if cls.omp_process_manager is not None:
-            for place in cls.omp_process_manager.omp_places:
-                if not place["available"]:
-                    return False
-        cls.global_cpu_mask = cls.get_global_cpu_mask() - reserve
-        # reinitialize OMP resource management
-        cls.omp_process_manager = OMPProcessManager(
-            affinity=cls.global_cpu_mask, smt=cls.smt
-        )
-        return True
+        cls._align_hybrid_block_size(vllm_config, backend_cls)
 
     @classmethod
     def discover_numa_topology(cls) -> list[list[int]]:
@@ -556,6 +450,10 @@ def pack_kv_cache(
             block_offsets.reshape(1, block_size)
             + indices.reshape(num_blocks, 1) * block_size
         ).flatten()
+        if key_cache.dtype == torch.uint8:
+            raise NotImplementedError(
+                "FP8 KV cache is not yet supported with KV transfer on CPU"
+            )
         cpu_attn_reshape_and_cache(
             key,
             value,
diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py
index 045298dbb36a..9f04bf11660a 100644
--- a/vllm/platforms/cuda.py
+++ b/vllm/platforms/cuda.py
@@ -131,6 +131,7 @@ def _get_backend_priorities(
                 AttentionBackendEnum.FLASH_ATTN,
                 AttentionBackendEnum.TRITON_ATTN,
                 AttentionBackendEnum.FLEX_ATTENTION,
+                AttentionBackendEnum.TURBOQUANT,
             ]
         else:
             return [
@@ -138,6 +139,7 @@ def _get_backend_priorities(
                 AttentionBackendEnum.FLASHINFER,
                 AttentionBackendEnum.TRITON_ATTN,
                 AttentionBackendEnum.FLEX_ATTENTION,
+                AttentionBackendEnum.TURBOQUANT,
             ]
 
 
@@ -367,7 +369,6 @@ def get_attn_backend_cls(
             "Using %s attention backend out of potential backends: %s.",
             selected_backend.name,
             "[" + ", ".join(f"'{b[0].name}'" for b in valid_backends_priorities) + "]",
-            scope="local",
         )
 
         return selected_backend.get_path()
@@ -421,7 +422,6 @@ def get_vit_attn_backend(
                 if is_backend_supported:
                     logger.info_once(
                         f"Using backend {vit_attn_backend} for vit attention",
-                        scope="local",
                     )
                     return vit_attn_backend
             except ImportError:
@@ -578,7 +578,9 @@ def get_default_ir_op_priority(cls, vllm_config: VllmConfig) -> IrOpPriorityConf
         if envs.VLLM_USE_OINK_OPS:
             rms_norm = ["oink"] + default
 
-        return IrOpPriorityConfig.with_default(default, rms_norm=rms_norm)
+        return IrOpPriorityConfig.with_default(
+            default, rms_norm=rms_norm, fused_add_rms_norm=rms_norm
+        )
 
 
 # NVML utils
diff --git a/vllm/platforms/interface.py b/vllm/platforms/interface.py
index c0d52620c086..80952ced73d1 100644
--- a/vllm/platforms/interface.py
+++ b/vllm/platforms/interface.py
@@ -86,6 +86,9 @@ def __gt__(self, other: Any) -> bool:
             return NotImplemented
         return (self.major, self.minor) > (other.major, other.minor)
 
+    def __hash__(self) -> int:
+        return hash((self.major, self.minor))
+
     def as_version_str(self) -> str:
         return f"{self.major}.{self.minor}"
 
@@ -542,6 +545,42 @@ def _align_hybrid_block_size(
                 dtype=kv_cache_dtype,
                 kv_quant_mode=kv_quant_mode,
             ).page_size_bytes
+        elif cache_config.cache_dtype.startswith("turboquant_"):
+            # TQ has a packed K|V layout; the standard FullAttentionSpec
+            # formula over-sizes it and trips unify_kv_cache_spec_page_size
+            # when all attention layers are TQ. With mixed skip+TQ the skip
+            # layers still use the standard layout — take max so mamba
+            # padding covers the largest actual page.
+            from vllm.model_executor.layers.quantization.turboquant.config import (
+                TurboQuantConfig,
+            )
+            from vllm.v1.kv_cache_interface import TQFullAttentionSpec
+
+            tq_cfg = TurboQuantConfig.from_cache_dtype(
+                cache_config.cache_dtype, model_config.get_head_size()
+            )
+            tq_page = TQFullAttentionSpec(
+                block_size=1,
+                num_kv_heads=model_config.get_num_kv_heads(parallel_config),
+                head_size=model_config.get_head_size(),
+                head_size_v=model_config.get_head_size(),
+                dtype=kv_cache_dtype,
+                kv_quant_mode=kv_quant_mode,
+                tq_slot_size=tq_cfg.slot_size_aligned,
+            ).page_size_bytes
+            if cache_config.kv_cache_dtype_skip_layers:
+                skip_page = FullAttentionSpec(
+                    block_size=1,
+                    num_kv_heads=model_config.get_num_kv_heads(parallel_config),
+                    head_size=model_config.get_head_size(),
+                    dtype=model_config.dtype,
+                ).page_size_bytes
+                # lcm, not max: skip_page is often not a multiple of
+                # tq_page, so max would leave per-layer page sizes
+                # un-unifiable downstream.
+                attn_page_size_1_token = lcm(tq_page, skip_page)
+            else:
+                attn_page_size_1_token = tq_page
         else:
             attn_page_size_1_token = FullAttentionSpec(
                 block_size=1,
diff --git a/vllm/platforms/rocm.py b/vllm/platforms/rocm.py
index 11a85e924b8f..984b706e7041 100644
--- a/vllm/platforms/rocm.py
+++ b/vllm/platforms/rocm.py
@@ -33,6 +33,7 @@
         amdsmi_init,
         amdsmi_shut_down,
         amdsmi_topo_get_link_type,
+        amdsmi_topo_get_numa_node_number,
     )
 except ImportError as e:
     logger.warning("Failed to import from amdsmi with %r", e)
@@ -382,6 +383,7 @@ def _get_backend_priorities(
     if is_aiter_found_and_supported():
         backends.append(AttentionBackendEnum.ROCM_AITER_UNIFIED_ATTN)
     backends.append(AttentionBackendEnum.TRITON_ATTN)
+    backends.append(AttentionBackendEnum.TURBOQUANT)
 
     return backends
 
@@ -407,15 +409,23 @@ class RocmPlatform(Platform):
         "gptq",
         "gptq_marlin",  # will be overwritten with gptq
         "fp8",
+        "deepseek_v4_fp8",
         "compressed-tensors",
         "fbgemm_fp8",
         "gguf",
         "quark",
         "mxfp4",
-        "gpt_oss_mxfp4",
+        "mxfp8",
         "torchao",
         "bitsandbytes",
+        "modelopt",
         "modelopt_fp4",
+        "modelopt_mxfp8",
+        "modelopt_mixed",
+        "fp8_per_tensor",
+        "fp8_per_block",
+        "online",
+        "gpt_oss_mxfp4",
     ]
 
     @classmethod
@@ -684,21 +694,11 @@ def get_device_total_memory(cls, device_id: int = 0) -> int:
     @classmethod
     def apply_config_platform_defaults(cls, vllm_config: "VllmConfig") -> None:
         from vllm._aiter_ops import rocm_aiter_ops
-        from vllm.config.compilation import CUDAGraphMode
 
         compilation_config = vllm_config.compilation_config
-        is_eager_execution = compilation_config.cudagraph_mode == CUDAGraphMode.NONE
         use_aiter_fused_moe = rocm_aiter_ops.is_fused_moe_enabled()
-        use_aiter_rms_norm = rocm_aiter_ops.is_rmsnorm_enabled()
         use_aiter_fp8_linear = rocm_aiter_ops.is_linear_fp8_enabled()
         use_aiter_fused_se = rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
-        #  Aiter rms norm perform best when CUDA Graph capture is enabled.
-        if (
-            use_aiter_rms_norm
-            and not is_eager_execution
-            and "-rms_norm" not in compilation_config.custom_ops
-        ):
-            compilation_config.custom_ops.append("+rms_norm")
 
         if use_aiter_fp8_linear and "-quant_fp8" not in compilation_config.custom_ops:
             compilation_config.custom_ops.append("+quant_fp8")
@@ -783,9 +783,9 @@ def get_punica_wrapper(cls) -> str:
     def get_current_memory_usage(
         cls, device: torch.types.Device | None = None
     ) -> float:
+        torch.cuda.empty_cache()
         torch.cuda.reset_peak_memory_stats(device)
-        free_mem, total_mem = torch.cuda.mem_get_info(device)
-        return total_mem - free_mem
+        return torch.cuda.max_memory_allocated(device)
 
     @classmethod
     def get_device_communicator_cls(cls) -> str:
@@ -799,7 +799,7 @@ def supports_mx(cls) -> bool:
 
     @classmethod
     def supports_fp8(cls) -> bool:
-        return any(gfx in _GCN_ARCH for gfx in ["gfx94", "gfx95", "gfx12"])
+        return on_gfx9() or on_gfx12x()
 
     @classmethod
     def is_fp8_fnuz(cls) -> bool:
@@ -930,7 +930,7 @@ def use_custom_op_collectives(cls) -> bool:
     def get_default_ir_op_priority(
         cls, vllm_config: "VllmConfig"
     ) -> "IrOpPriorityConfig":
-        from vllm.config.compilation import CompilationMode
+        from vllm.config.compilation import CompilationMode, CUDAGraphMode
         from vllm.config.kernel import IrOpPriorityConfig
 
         # Native used by default when compiling,
@@ -940,12 +940,10 @@ def get_default_ir_op_priority(
         using_inductor = cc.backend == "inductor" and cc.mode != CompilationMode.NONE
         default = ["native"] if using_inductor else ["vllm_c", "native"]
 
-        # This (mostly) preserves previous CustomOp behavior
-        # Necessary on ROCm because it's common that users
-        # enable rms_norm to use the aiter kernel.
+        #  Aiter rms norm perform best when CUDA Graph capture is enabled.
         # TODO(luka/TJ) remove env vars completely
         if (
-            cc.is_custom_op_enabled("rms_norm")
+            cc.cudagraph_mode != CUDAGraphMode.NONE
             and envs.VLLM_ROCM_USE_AITER
             and envs.VLLM_ROCM_USE_AITER_RMSNORM
         ):
@@ -953,4 +951,33 @@ def get_default_ir_op_priority(
         else:
             rms_norm = default
 
-        return IrOpPriorityConfig.with_default(default, rms_norm=rms_norm)
+        return IrOpPriorityConfig.with_default(
+            default, rms_norm=rms_norm, fused_add_rms_norm=rms_norm
+        )
+
+    @classmethod
+    @with_amdsmi_context
+    def get_all_device_numa_nodes(cls) -> list[int] | None:
+        """Get NUMA nodes for all visible GPU devices."""
+        try:
+            handles = amdsmi_get_processor_handles()
+            numa_nodes = []
+            for device_id in range(cls.device_count()):
+                physical_device_id = cls.device_id_to_physical_device_id(device_id)
+                try:
+                    numa_node = amdsmi_topo_get_numa_node_number(
+                        handles[physical_device_id]
+                    )
+                except AmdSmiException as e:
+                    logger.warning(
+                        "Could not detect NUMA node for GPU %d, "
+                        "disabling automatic NUMA binding: %s",
+                        device_id,
+                        e,
+                    )
+                    return None
+                numa_nodes.append(numa_node)
+            return numa_nodes
+        except Exception as e:
+            logger.warning("Failed to get NUMA nodes for GPUs: %s", e)
+            return None
diff --git a/vllm/platforms/xpu.py b/vllm/platforms/xpu.py
index 892fc0c950be..bd9006f3f8fc 100644
--- a/vllm/platforms/xpu.py
+++ b/vllm/platforms/xpu.py
@@ -61,6 +61,12 @@ def get_attn_backend_cls(
             "only NHD layout is supported by XPU attention kernels."
         )
 
+        # TurboQuant KV cache: route directly to TQ backend
+        kv_cache_dtype = attn_selector_config.kv_cache_dtype
+        if kv_cache_dtype is not None and kv_cache_dtype.startswith("turboquant_"):
+            logger.info_once("Using TurboQuant attention backend.")
+            return AttentionBackendEnum.TURBOQUANT.get_path()
+
         dtype = attn_selector_config.dtype
         if attn_selector_config.use_sparse:
             logger.info_once("Using XPU MLA Sparse backend.")
@@ -207,6 +213,26 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
                     "falling back to PIECEWISE graph mode on XPU platform."
                 )
 
+        # Disable fusion passes not yet supported on XPU.
+        pass_config = compilation_config.pass_config
+        fusion_passes_to_disable = {
+            "enable_sp": "Sequence parallelism",
+            "fuse_gemm_comms": "Async TP",
+            "fuse_allreduce_rms": "AllReduce + RMSNorm fusion",
+            "fuse_norm_quant": "RMSNorm + quant fusion",
+            "fuse_act_quant": "Activation + quant fusion",
+            "fuse_attn_quant": "Attention + quant fusion",
+            "fuse_act_padding": "Activation + padding fusion",
+            "fuse_rope_kvcache": "RoPE + KV cache fusion",
+        }
+        for flag, feature_name in fusion_passes_to_disable.items():
+            if getattr(pass_config, flag):
+                logger.warning(
+                    "Feature %r is not yet supported on XPU and will be disabled.",
+                    feature_name,
+                )
+                setattr(pass_config, flag, False)
+
         # check and update parallel config
         parallel_config = vllm_config.parallel_config
         # Only override worker_cls if it's still the default "auto"
@@ -222,6 +248,10 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
         # ref. https://openucx.readthedocs.io/en/master/faq.html
         os.environ["UCX_MEMTYPE_CACHE"] = "n"
 
+        # spawn is the only supported multiprocessing method on XPU
+        if "VLLM_WORKER_MULTIPROC_METHOD" not in os.environ:
+            os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
     @classmethod
     def update_block_size_for_backend(cls, vllm_config: "VllmConfig") -> None:
         super().update_block_size_for_backend(vllm_config)
@@ -313,6 +343,10 @@ def get_device_communicator_cls(cls) -> str:
             )
         return "vllm.distributed.device_communicators.xpu_communicator.XpuCommunicator"  # noqa
 
+    @classmethod
+    def supports_fp8(cls) -> bool:
+        return True
+
     @classmethod
     def get_default_ir_op_priority(
         cls, vllm_config: "VllmConfig"
diff --git a/vllm/platforms/zen_cpu.py b/vllm/platforms/zen_cpu.py
index 481eec1cb4e2..2af64e5e9f53 100644
--- a/vllm/platforms/zen_cpu.py
+++ b/vllm/platforms/zen_cpu.py
@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import torch
+
 from vllm.logger import init_logger
 from vllm.platforms.cpu import CpuPlatform
 
@@ -22,3 +24,9 @@ class ZenCpuPlatform(CpuPlatform):
     def is_zen_cpu(self) -> bool:
         # is_cpu() also returns True for this platform (inherited from CpuPlatform).
         return True
+
+    # Currently, AMD CPUs do not support float16 compute.
+    # Hence explicitly return bfloat16 and float32.
+    @property
+    def supported_dtypes(self) -> list[torch.dtype]:
+        return [torch.bfloat16, torch.float32]
diff --git a/vllm/pooling_params.py b/vllm/pooling_params.py
index b347ec831abc..b4cf23abac59 100644
--- a/vllm/pooling_params.py
+++ b/vllm/pooling_params.py
@@ -87,13 +87,6 @@ def clone(self) -> "PoolingParams":
         return deepcopy(self)
 
     def verify(self, model_config: ModelConfig) -> None:
-        if self.task == "score":
-            logger.warning_once(
-                "`score` task is deprecated and will be removed in v0.20. "
-                "Please use `classify` instead."
-            )
-            self.task = "classify"
-
         # plugin task uses io_processor.parse_request to verify inputs,
         # skipping PoolingParams verify
         if self.task == "plugin":
diff --git a/vllm/profiler/wrapper.py b/vllm/profiler/wrapper.py
index 7cd4d8874df2..201b45078492 100644
--- a/vllm/profiler/wrapper.py
+++ b/vllm/profiler/wrapper.py
@@ -63,7 +63,7 @@ def _call_stop(self) -> None:
         """Call _stop with error handling but no safeguards."""
         try:
             self._stop()
-            logger.info_once("Profiler stopped successfully.", scope="local")
+            logger.info_once("Profiler stopped successfully.")
         except Exception as e:
             logger.warning("Failed to stop profiler: %s", e)
         self._running = False  # Always mark as not running, assume stop worked
@@ -93,7 +93,7 @@ def step(self) -> None:
             and self._delay_iters > 0
             and self._active_iteration_count == self._delay_iters
         ):
-            logger.info_once("Starting profiler after delay...", scope="local")
+            logger.info_once("Starting profiler after delay...")
             self._call_start()
 
         # Call profiler step for schedule-based profiling
@@ -109,9 +109,7 @@ def step(self) -> None:
             # Automatically stop the profiler after max iters
             # will be marked as not running, but leave as active so that stop
             # can clean up properly
-            logger.info_once(
-                "Max profiling iterations reached. Stopping profiler...", scope="local"
-            )
+            logger.info_once("Max profiling iterations reached. Stopping profiler...")
             self._call_stop()
             return
 
@@ -141,7 +139,7 @@ def stop(self) -> None:
 
     def shutdown(self) -> None:
         """Ensure profiler is stopped when shutting down."""
-        logger.info_once("Shutting down profiler", scope="local")
+        logger.info_once("Shutting down profiler")
         if self._running:
             self.stop()
 
@@ -176,7 +174,6 @@ def __init__(
             logger.info_once(
                 "Torch profiling enabled. Traces will be saved to: %s",
                 torch_profiler_trace_dir,
-                scope="local",
             )
             logger.debug(
                 "Profiler config: record_shapes=%s,"
@@ -216,7 +213,6 @@ def __init__(
                     profiler_config.wait_iterations,
                     profiler_config.warmup_iterations,
                     profiler_config.active_iterations,
-                    scope="local",
                 )
 
         self.profiler = torch.profiler.profile(
diff --git a/vllm/reasoning/__init__.py b/vllm/reasoning/__init__.py
index 2d57b93369d7..cd51f106503a 100644
--- a/vllm/reasoning/__init__.py
+++ b/vllm/reasoning/__init__.py
@@ -28,6 +28,22 @@
         "deepseek_v3_reasoning_parser",
         "DeepSeekV3ReasoningParser",
     ),
+    "deepseek_v4": (
+        "deepseek_v3_reasoning_parser",
+        "DeepSeekV3ReasoningParser",
+    ),
+    "poolside_v1": (
+        "poolside_v1_reasoning_parser",
+        "PoolsideV1ReasoningParser",
+    ),
+    "cohere_command3": (
+        "cohere_command_reasoning_parser",
+        "CohereCommand3ReasoningParser",
+    ),
+    "cohere_command4": (
+        "cohere_command_reasoning_parser",
+        "CohereCommand4ReasoningParser",
+    ),
     "ernie45": (
         "ernie45_reasoning_parser",
         "Ernie45ReasoningParser",
@@ -56,10 +72,18 @@
         "hunyuan_a13b_reasoning_parser",
         "HunyuanA13BReasoningParser",
     ),
+    "hy_v3": (
+        "hy_v3_reasoning_parser",
+        "HYV3ReasoningParser",
+    ),
     "kimi_k2": (
         "kimi_k2_reasoning_parser",
         "KimiK2ReasoningParser",
     ),
+    "mimo": (
+        "qwen3_reasoning_parser",
+        "Qwen3ReasoningParser",
+    ),
     "minimax_m2": (
         "minimax_m2_reasoning_parser",
         "MiniMaxM2ReasoningParser",
diff --git a/vllm/reasoning/cohere_command_reasoning_parser.py b/vllm/reasoning/cohere_command_reasoning_parser.py
new file mode 100644
index 000000000000..b28a59089e73
--- /dev/null
+++ b/vllm/reasoning/cohere_command_reasoning_parser.py
@@ -0,0 +1,565 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import json
+from collections.abc import Mapping, Sequence
+from typing import Any, NamedTuple, TypedDict, TypeGuard
+
+import regex as re
+import xgrammar as xgr
+
+try:
+    from cohere_melody import PyFilter, PyFilterOptions
+except ImportError as e:
+    raise ImportError(
+        "The Cohere reasoning parser requires the `cohere_melody` "
+        "package, which is not installed. Install it with:\n"
+        "    pip install cohere_melody"
+    ) from e
+
+
+from vllm.entrypoints.mcp.tool_server import ToolServer
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+)
+from vllm.entrypoints.openai.engine.protocol import (
+    AnyResponseFormat,
+    DeltaFunctionCall,
+    DeltaMessage,
+    DeltaToolCall,
+)
+from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
+from vllm.reasoning import ReasoningParser
+from vllm.sampling_params import StructuredOutputsParams
+from vllm.tokenizers import TokenizerLike
+
+REPLACEMENT_CHAR = "\ufffd"
+
+
+class CohereTagRegistry(NamedTuple):
+    """A single ``structural_tag`` trigger / end pair (``begin`` uses ``trigger``)."""
+
+    trigger: str
+    end: str
+
+
+class CohereTagStyle(NamedTuple):
+    """The structural tags style for a given model architecture.
+
+    ``json_tags`` lists every JSON-schema wrapper the model may emit (MOE uses
+    both response and text delimiters). ``tools`` is the tool-call wrapper.
+    """
+
+    json_tags: tuple[CohereTagRegistry, ...]
+    tools: CohereTagRegistry
+
+
+class CohereNormalizedTool(TypedDict):
+    """A tool definition normalized to the shape ``collect_tool_schema`` expects.
+
+    ``parameters`` is a JSON Schema object (possibly empty) describing the tool's
+    call signature.
+    """
+
+    name: str
+    parameters: dict[str, Any]
+
+
+COMMAND_A_TOOLS_TAG = CohereTagRegistry(
+    trigger="<|START_ACTION|>",
+    end="<|END_ACTION|>",
+)
+COMMAND_A_JSON_TAG = CohereTagRegistry(
+    trigger="<|START_RESPONSE|>",
+    end="<|END_RESPONSE|>",
+)
+COMMAND_A_PLUS_JSON_TAG = CohereTagRegistry(
+    trigger="<|START_TEXT|>",
+    end="<|END_TEXT|>",
+)
+
+MODEL_TO_TAG_STYLE: dict[str, CohereTagStyle] = {
+    "Cohere2ForCausalLM": CohereTagStyle(
+        json_tags=(COMMAND_A_JSON_TAG,),
+        tools=COMMAND_A_TOOLS_TAG,
+    ),
+    "Cohere2VisionForConditionalGeneration": CohereTagStyle(
+        json_tags=(COMMAND_A_JSON_TAG, COMMAND_A_PLUS_JSON_TAG),
+        tools=COMMAND_A_TOOLS_TAG,
+    ),
+    "Cohere2MoeForCausalLM": CohereTagStyle(
+        json_tags=(COMMAND_A_JSON_TAG,),
+        tools=COMMAND_A_TOOLS_TAG,
+    ),
+}
+
+
+def collect_tool_schema(tool_schema: list[CohereNormalizedTool]) -> str:
+    """Build an xgrammar EBNF grammar that matches a JSON array of tool calls.
+
+    The grammar shape is architecture-independent; callers are responsible for
+    wrapping it in the correct structural tag (see ``CohereTagStyle.tools``).
+    """
+    tool_dictionary: dict[str, str] = {}
+    for tool in tool_schema:
+        tool_name = tool["name"]
+        tool_parameters = json.dumps(tool["parameters"])
+        json_schema = f"""{{
+                        "type": "object",
+                        "properties": {{
+                            "tool_call_id": {{
+                                "type": "string",
+                                "pattern": "^[0-9]+$"
+                            }},
+                            "tool_name": {{
+                                "type": "string",
+                                "const": "{tool_name}"
+                            }},
+                            "parameters": {tool_parameters}
+                            }}
+                            }}"""
+        tool_grammar = str(xgr.Grammar.from_json_schema(json_schema))
+        for match in re.findall(r"\b(\w+)\s*::=", tool_grammar):
+            tool_grammar = re.sub(
+                rf"\b{re.escape(match)}\b", tool_name + match, tool_grammar
+            )
+        tool_dictionary[tool_name] = f"{tool_name} ::= {tool_name}root\n{tool_grammar}"
+    # Emitted grammar shape:
+    #   root  ::= tools
+    #   tools ::= ws "[" ws tool ws ("," ws tool)* ws "]" ws
+    #   ws    ::= (" " | "\t" | "\n")*
+    #   tool  ::= <tool_a> | <tool_b> | ...         (one alternative per input)
+    #   <tool_x>     ::= <tool_x>root               (per-tool xgrammar rules)
+    #   <tool_x>root ::= ...                        (from xgr.Grammar.from_json_schema)
+    tool_alternatives = "tool ::= " + " | ".join(tool_dictionary.keys())
+    tool_rules = "\n    ".join(tool_dictionary.values())
+    grammar = f"""root ::= tools
+    tools ::= ws "[" ws tool ws ("," ws tool)*  ws "]" ws
+    ws    ::= (" " | "\\t" | "\\n")*
+    {tool_alternatives}
+    {tool_rules}
+    """
+    return grammar
+
+
+def _tool_definitions_to_schema_list(
+    tools: str | list[Any],
+) -> list[CohereNormalizedTool]:
+    """
+    Build the list of ``CohereNormalizedTool`` dicts expected by
+    ``collect_tool_schema``.
+
+    Accepts:
+    - JSON string
+    - list of dicts with top-level ``name`` / ``parameters``
+    - list of Chat Completions-style ``{"type": "function", "function": {...}}``
+    - list of Pydantic models with ``model_dump()``
+    """
+    if isinstance(tools, str):
+        try:
+            parsed = json.loads(tools)
+        except json.JSONDecodeError:
+            return []
+        if not isinstance(parsed, list):
+            return []
+    else:
+        parsed = list(tools)
+
+    out: list[CohereNormalizedTool] = []
+    for raw in parsed:
+        t = raw.model_dump() if hasattr(raw, "model_dump") else raw
+        if not isinstance(t, dict):
+            continue
+        # Unwrap Chat Completions' ``{"type": "function", "function": {...}}``
+        # shape; otherwise take the dict as-is.
+        if t.get("type") == "function" and isinstance(t.get("function"), dict):
+            t = t["function"]
+        name = t.get("name")
+        if not isinstance(name, str):
+            continue
+        params = t.get("parameters")
+        out.append(
+            CohereNormalizedTool(
+                name=name,
+                parameters=params if isinstance(params, dict) else {},
+            )
+        )
+    return out
+
+
+def _has_effective_tools(
+    tools: str | list[Any] | None,
+) -> TypeGuard[str | list[Any]]:
+    """
+    True when ``tools`` contains at least one tool definition to convert.
+
+    ``ResponsesRequest`` defaults ``tools`` to ``[]``; ``ChatCompletionRequest``
+    uses ``None``. Both mean "no tools" here. Strings (e.g. a JSON blob) are
+    treated as effective only when non-blank.
+    """
+    if tools is None:
+        return False
+    if isinstance(tools, str):
+        return bool(tools.strip())
+    return len(tools) > 0
+
+
+# Builder: produces vLLM response_format in xgrammar's canonical format.
+# See xgrammar docs: type "structural_tag" with "format" = triggered_tags
+# and tag content type = json_schema | grammar.
+def convert_schema_to_structural_tags(
+    schema: dict | None = None,
+    tools: str | list[Any] | None = None,
+    model_architecture: str | None = None,
+) -> str | None:
+    """
+    Returns a response_format string accepted by xgrammar's structural tag format.
+    Uses the canonical shape: {"type": "structural_tag", "format": {...}} with
+    format.type "triggered_tags" and tag content type "json_schema" or "grammar".
+
+    Callers that are not on an engine path (e.g. the reasoning parser) must pass
+    ``model_architecture`` explicitly.
+    """
+    if model_architecture is None or model_architecture not in MODEL_TO_TAG_STYLE:
+        return None
+    style = MODEL_TO_TAG_STYLE[model_architecture]
+
+    tags: list[dict] = []
+    triggers: list[str] = []
+
+    def _add_tag(tag: CohereTagRegistry, content: dict) -> None:
+        tags.append({"begin": tag.trigger, "content": content, "end": tag.end})
+        triggers.append(tag.trigger)
+
+    if schema is not None:
+        # One structural tag per JSON wrapper (e.g. MOE: response + text).
+        # Same for schema-only and "tools plus JSON mode" (North: schema when
+        # the model does not call tools).
+        for jt in style.json_tags:
+            _add_tag(jt, {"type": "json_schema", "json_schema": schema})
+
+    if _has_effective_tools(tools):
+        # ``tools`` may be a JSON string (poseidon / RESPONSE_FORMAT_TOOL_DEFINITIONS)
+        # or a list (Chat Completions ``request.tools`` as Pydantic models or dicts).
+        tool_schema_list = _tool_definitions_to_schema_list(tools)
+        if not tool_schema_list:
+            raise ValueError(
+                "No valid tool definitions could be parsed from the request for "
+                "structural tag conversion."
+            )
+        tool_grammar = collect_tool_schema(tool_schema_list)
+        _add_tag(style.tools, {"type": "grammar", "grammar": tool_grammar})
+
+    if not tags:
+        return None
+    return json.dumps(
+        {
+            "type": "structural_tag",
+            "format": {
+                "type": "triggered_tags",
+                "triggers": triggers,
+                "tags": tags,
+            },
+        }
+    )
+
+
+def _response_format_type(
+    response_format: AnyResponseFormat | dict | None,
+) -> str | None:
+    if response_format is None:
+        return None
+    if isinstance(response_format, dict):
+        t = response_format.get("type")
+        return t if isinstance(t, str) else None
+    return response_format.type
+
+
+def _maybe_parse_json_dict(value: Any) -> dict | None:
+    """If value is a JSON string, parse to dict; otherwise require dict."""
+    if isinstance(value, dict):
+        return value
+    if isinstance(value, str):
+        try:
+            parsed = json.loads(value)
+        except (TypeError, json.JSONDecodeError):
+            return None
+        return parsed if isinstance(parsed, dict) else None
+    return None
+
+
+def _unwrap_nested_schema(candidate: Any) -> dict | None:
+    """Return ``candidate`` as a dict, unwrapping a nested ``schema`` if present.
+
+    Returns ``None`` if ``candidate`` is not (and cannot be parsed into) a dict.
+    """
+    cand = _maybe_parse_json_dict(candidate)
+    if not isinstance(cand, dict):
+        return None
+    nested = cand.get("schema")
+    return nested if isinstance(nested, dict) else cand
+
+
+def _schema_from_json_schema_field(js_wr: Any) -> dict | None:
+    """
+    Extract the JSON Schema object from Chat Completions ``json_schema`` payload.
+
+    Accepts:
+    - ``JsonSchemaResponseFormat`` (Pydantic) with ``schema`` / ``json_schema`` field
+    - dict in OpenAI shape ``{"name": ..., "schema": {...}}``
+    - dict with ``json_schema`` key holding either the schema or a nested wrapper
+    - dict that is already a JSON Schema document (some clients omit the wrapper)
+    - JSON strings for any of the above
+    """
+    if js_wr is None:
+        return None
+
+    parsed_wr = _maybe_parse_json_dict(js_wr)
+    if parsed_wr is not None:
+        js_wr = parsed_wr
+
+    if hasattr(js_wr, "model_dump"):
+        for by_alias in (True, False):
+            try:
+                data = js_wr.model_dump(by_alias=by_alias, exclude_none=False)
+            except TypeError:
+                data = js_wr.model_dump(by_alias=by_alias)
+            out = _unwrap_nested_schema(data.get("schema") or data.get("json_schema"))
+            if out is not None:
+                return out
+        inner_attr = getattr(js_wr, "json_schema", None)
+        return inner_attr if isinstance(inner_attr, dict) else None
+
+    if isinstance(js_wr, dict):
+        for key in ("schema", "json_schema"):
+            out = _unwrap_nested_schema(js_wr.get(key))
+            if out is not None:
+                return out
+        return js_wr
+
+    return None
+
+
+def _schema_dict_from_chat_response_format(
+    rf: AnyResponseFormat | dict | None,
+) -> dict | None:
+    """JSON schema dict from Chat Completions ``request.response_format`` only."""
+    if rf is None:
+        return None
+    rf_type = _response_format_type(rf)
+    if rf_type == "json_object":
+        return {"type": "object"}
+    if rf_type != "json_schema":
+        return None
+    js_wr = (
+        rf.get("json_schema")
+        if isinstance(rf, dict)
+        else getattr(rf, "json_schema", None)
+    )
+    return _schema_from_json_schema_field(js_wr)
+
+
+def _schema_dict_from_structured_outputs(
+    so: StructuredOutputsParams | None,
+) -> dict | None:
+    """Schema dict from ``structured_outputs`` (``json`` / ``json_object``).
+
+    Same unwrapping as ``json_schema``. ``json`` is expected to be ``str`` or
+    ``dict`` (enforced by ``StructuredOutputsParams`` / request models); other
+    types raise ``ValueError`` only if a caller bypasses that validation.
+    """
+    if so is None:
+        return None
+    if so.json_object:
+        return {"type": "object"}
+    raw: Any = so.json
+    if raw is None:
+        return None
+
+    if hasattr(raw, "model_dump"):
+        out = _schema_from_json_schema_field(raw)
+        if out is None:
+            raise ValueError(
+                "structured_outputs.json model has no extractable JSON Schema."
+            )
+        return out
+
+    if isinstance(raw, str):
+        if not raw.strip():
+            raise ValueError("structured_outputs.json cannot be empty.")
+        try:
+            raw = json.loads(raw)
+        except json.JSONDecodeError as e:
+            raise ValueError("structured_outputs.json must be valid JSON.") from e
+        if not isinstance(raw, dict):
+            raise ValueError("structured_outputs.json must decode to a JSON object.")
+
+    if isinstance(raw, Mapping):
+        body = raw if isinstance(raw, dict) else dict(raw)
+        return _schema_from_json_schema_field(body) or body
+
+    raise ValueError(
+        f"structured_outputs.json has unsupported type {type(raw).__name__}."
+    )
+
+
+class BaseCohereCommandReasoningParser(ReasoningParser):
+    def __init__(
+        self,
+        tokenizer: TokenizerLike,
+        *args,
+        streaming_opts: PyFilterOptions,
+        unary_opts: PyFilterOptions,
+        **kwargs,
+    ):
+        super().__init__(tokenizer, *args, **kwargs)
+        self.end_token_id = tokenizer.convert_tokens_to_ids("<|END_THINKING|>")
+        self.unary_opts = unary_opts
+        self.melody_unary = PyFilter(unary_opts)
+        self.melody_streaming = PyFilter(streaming_opts)
+
+    @property
+    def reasoning_start_str(self) -> str | None:
+        return "<|START_THINKING|>"
+
+    @property
+    def reasoning_end_str(self) -> str | None:
+        return "<|END_THINKING|>"
+
+    def extract_reasoning_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+    ) -> DeltaMessage | None:
+        r = self.melody_streaming.write_decoded(delta_text)
+        if r.content is None and r.reasoning is None and not r.tool_calls:
+            return None
+        msg = DeltaMessage()
+        if r.content is not None:
+            msg.content = r.content
+        if r.reasoning is not None:
+            msg.reasoning = r.reasoning
+        if r.tool_calls:
+            msg.tool_calls = [
+                DeltaToolCall(
+                    id=tc.id,
+                    index=tc.index,
+                    type="function",
+                    function=DeltaFunctionCall(name=tc.name, arguments=tc.arguments),
+                )
+                for tc in r.tool_calls
+            ]
+        return msg
+
+    def extract_reasoning(
+        self, model_output: str, request: ChatCompletionRequest | ResponsesRequest
+    ) -> tuple[str | None, str | None]:
+        result = self.melody_unary.process_full_text(model_output)
+        return result.reasoning, result.content
+
+    def extract_content_ids(self, input_ids: list[int]) -> list[int]:
+        token_buf: list[int] = []
+        content_ids: list[int] = []
+        content_filter = PyFilter(self.unary_opts)
+        for t in input_ids:
+            token_buf.append(t)
+            s = self.model_tokenizer.decode(token_buf, skip_special_tokens=False)
+            if s.endswith(REPLACEMENT_CHAR):
+                continue
+            r = content_filter.write_decoded(s)
+            if r.content is not None:
+                content_ids.extend(token_buf)
+            token_buf = []
+        return content_ids
+
+    def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
+        return any(tid == self.end_token_id for tid in reversed(input_ids))
+
+    def prepare_structured_tag(
+        self, original_tag: str | None, tool_server: ToolServer | None
+    ) -> str | None:
+        # Responses API replaces ``structural_tag`` via the reasoning parser.
+        # Default ``ReasoningParser.prepare_structured_tag`` returns None, which
+        # would clear a Cohere tag produced in ``adjust_request`` and break
+        # ``StructuredOutputsParams`` validation. Preserve the existing tag.
+        return original_tag
+
+    def adjust_request(
+        self, request: ChatCompletionRequest | ResponsesRequest
+    ) -> ChatCompletionRequest | ResponsesRequest:
+        so = request.structured_outputs
+        if so is not None and so.structural_tag:
+            return request
+        # Schema: prefer ``response_format`` (OpenAI Chat Completions), then
+        # ``structured_outputs.json`` / ``json_object`` (vLLM direct). Tools stay
+        # on ``request.tools``.
+        rf = (
+            request.response_format
+            if isinstance(request, ChatCompletionRequest)
+            else None
+        )
+        if rf is not None and _response_format_type(rf) == "structural_tag":
+            return request
+        model_architecture = (
+            self._model_config.architecture if self._model_config is not None else None
+        )
+        tools = request.tools
+        # ``response_format`` wins if both it and ``structured_outputs`` supply JSON.
+        schema = _schema_dict_from_chat_response_format(rf)
+        if schema is None:
+            schema = _schema_dict_from_structured_outputs(so)
+        if schema is None and not _has_effective_tools(tools):
+            return request
+        if model_architecture is None:
+            return request
+        result = convert_schema_to_structural_tags(
+            schema=schema,
+            tools=tools,
+            model_architecture=model_architecture,
+        )
+        if result is None:
+            # Unsupported architectures are not in ``MODEL_TO_TAG_STYLE``.
+            raise ValueError(
+                "Failed to build structural_tag guided decoding constraints from "
+                "this request's JSON schema and/or tools. The configured model "
+                f"architecture ({model_architecture!r}) does not support Cohere "
+                "command structural tags, or the schema cannot be expressed in "
+                "that format."
+            )
+        request.structured_outputs = StructuredOutputsParams(structural_tag=result)
+        # Folded JSON constraints into ``structural_tag``; drop ``response_format``
+        # when it was the source so ``to_sampling_params`` does not also set ``json`` /
+        # ``json_object`` (mutually exclusive in ``StructuredOutputsParams``).
+        if isinstance(request, ChatCompletionRequest) and rf is not None:
+            rf_type = _response_format_type(rf)
+            if rf_type in ("json_schema", "json_object"):
+                request.response_format = None
+        return request
+
+
+class CohereCommand3ReasoningParser(BaseCohereCommandReasoningParser):
+    def __init__(self, tokenizer: TokenizerLike, *args, **kwargs):
+        super().__init__(
+            tokenizer,
+            *args,
+            streaming_opts=PyFilterOptions().cmd3(),
+            unary_opts=PyFilterOptions().cmd3().no_tools(),
+            **kwargs,
+        )
+
+
+class CohereCommand4ReasoningParser(BaseCohereCommandReasoningParser):
+    def __init__(self, tokenizer: TokenizerLike, *args, **kwargs):
+        super().__init__(
+            tokenizer,
+            *args,
+            streaming_opts=PyFilterOptions().cmd4(),
+            unary_opts=PyFilterOptions().cmd4().no_tools(),
+            **kwargs,
+        )
diff --git a/vllm/reasoning/deepseek_v3_reasoning_parser.py b/vllm/reasoning/deepseek_v3_reasoning_parser.py
index d2f7f50a3284..bb79afd8dede 100644
--- a/vllm/reasoning/deepseek_v3_reasoning_parser.py
+++ b/vllm/reasoning/deepseek_v3_reasoning_parser.py
@@ -40,6 +40,14 @@ def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs):
         else:
             self._parser = IdentityReasoningParser(tokenizer, *args, **kwargs)
 
+    @property
+    def reasoning_start_str(self) -> str | None:
+        return self._parser.reasoning_start_str
+
+    @property
+    def reasoning_end_str(self) -> str | None:
+        return self._parser.reasoning_end_str
+
     def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
         return self._parser.is_reasoning_end(input_ids)
 
diff --git a/vllm/reasoning/gptoss_reasoning_parser.py b/vllm/reasoning/gptoss_reasoning_parser.py
index 89299d4b12b8..1ba933cca31e 100644
--- a/vllm/reasoning/gptoss_reasoning_parser.py
+++ b/vllm/reasoning/gptoss_reasoning_parser.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import json
-from collections.abc import Sequence
+from collections.abc import Iterable, Sequence
 from typing import TYPE_CHECKING
 
 from transformers import PreTrainedTokenizerBase
@@ -112,6 +112,25 @@ def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
                         return True
         return False
 
+    def is_reasoning_end_streaming(
+        self, input_ids: Sequence[int], delta_ids: Iterable[int]
+    ) -> bool:
+        # The pattern window covers the end-of-reasoning marker itself.
+        # We add len(delta_ids) so that under speculative decoding (where
+        # a single step can accept many tokens) the entire accepted chunk
+        # is always inside the scan region.
+        delta_ids = tuple(delta_ids)
+        pattern_len = (
+            len(self.reasoning_end_token_ids_prefix)
+            + self.reasoning_max_num_between_tokens
+            + len(self.reasoning_end_token_ids_suffix)
+        )
+        window = pattern_len + len(delta_ids)
+        n = len(input_ids)
+        if n <= window:
+            return self.is_reasoning_end(input_ids)
+        return self.is_reasoning_end(input_ids[n - window :])
+
     def extract_content_ids(self, input_ids: list[int]) -> list[int]:
         _, content, _ = parse_chat_output(input_ids)
         if content is None:
diff --git a/vllm/reasoning/hy_v3_reasoning_parser.py b/vllm/reasoning/hy_v3_reasoning_parser.py
new file mode 100644
index 000000000000..5beac22996dd
--- /dev/null
+++ b/vllm/reasoning/hy_v3_reasoning_parser.py
@@ -0,0 +1,141 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable, Sequence
+
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+)
+from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
+from vllm.logger import init_logger
+from vllm.reasoning.basic_parsers import BaseThinkingReasoningParser
+from vllm.reasoning.identity_reasoning_parser import IdentityReasoningParser
+from vllm.tokenizers import TokenizerLike
+
+logger = init_logger(__name__)
+
+
+class HYV3ReasoningParser(BaseThinkingReasoningParser):
+    """
+    HYV3 parser that delegates to either HYV3ReasoningParser or
+    IdentityReasoningParser based on `reasoning_effort`.
+
+    The HYV3 model uses <think>...</think> tokens to denote reasoning text.
+    This parser extracts the reasoning content from the model output.
+    """
+
+    def __init__(self, tokenizer: TokenizerLike, *args, **kwargs):
+        super().__init__(tokenizer, *args, **kwargs)
+
+        # First, If there is reasoning_effort in chat_kwargs,
+        # prioritize using chat_kwargs.reasoning_effort.
+        # If it's not present, use the "reasoning_effort" field
+        # at the outer level of the chat message.
+        # Otherwise, If both are empty, assign "no_think".
+
+        chat_kwargs = kwargs.get("chat_template_kwargs", {}) or {}
+        reasoning_effort = (
+            chat_kwargs.get("reasoning_effort")
+            or kwargs.get("reasoning_effort")
+            or "no_think"
+        )
+
+        logger.debug("reasoning_effort for choosing parser: %s", reasoning_effort)
+
+        self._identity_parser: IdentityReasoningParser | None
+        if reasoning_effort == "no_think":
+            self._identity_parser = IdentityReasoningParser(tokenizer, *args, **kwargs)
+        else:
+            self._identity_parser = None
+
+    @property
+    def start_token(self) -> str:
+        """The token that starts reasoning content."""
+        return "<think>"
+
+    @property
+    def end_token(self) -> str:
+        """The token that ends reasoning content."""
+        return "</think>"
+
+    def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
+        if self._identity_parser is not None:
+            return self._identity_parser.is_reasoning_end(input_ids)
+
+        return super().is_reasoning_end(input_ids)
+
+    def is_reasoning_end_streaming(
+        self, input_ids: Sequence[int], delta_ids: Iterable[int]
+    ) -> bool:
+        if self._identity_parser is not None:
+            return self._identity_parser.is_reasoning_end_streaming(
+                input_ids, delta_ids
+            )
+
+        return super().is_reasoning_end_streaming(input_ids, delta_ids)
+
+    def extract_content_ids(self, input_ids: list[int]) -> list[int]:
+        if self._identity_parser is not None:
+            return self._identity_parser.extract_content_ids(input_ids)
+
+        return super().extract_content_ids(input_ids)
+
+    def extract_reasoning(
+        self, model_output: str, request: "ChatCompletionRequest | ResponsesRequest"
+    ) -> tuple[str | None, str | None]:
+        if self._identity_parser is not None:
+            return self._identity_parser.extract_reasoning(model_output, request)
+
+        return super().extract_reasoning(model_output, request)
+
+    def extract_reasoning_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+    ) -> DeltaMessage | None:
+        if self._identity_parser is not None:
+            return self._identity_parser.extract_reasoning_streaming(
+                previous_text,
+                current_text,
+                delta_text,
+                previous_token_ids,
+                current_token_ids,
+                delta_token_ids,
+            )
+
+        ret = super().extract_reasoning_streaming(
+            previous_text,
+            current_text,
+            delta_text,
+            previous_token_ids,
+            current_token_ids,
+            delta_token_ids,
+        )
+        if (
+            ret is not None
+            and self.start_token_id not in previous_token_ids
+            and self.start_token_id not in delta_token_ids
+        ):
+            if self.end_token_id in delta_token_ids:
+                # end token in delta with more tokens,
+                # extract reasoning content and content
+                end_index = delta_text.find(self.end_token)
+                reasoning = delta_text[:end_index]
+                content = delta_text[end_index + len(self.end_token) :]
+                return DeltaMessage(
+                    reasoning=reasoning,
+                    content=content if content else None,
+                )
+            elif self.end_token_id in previous_token_ids:
+                # end token in previous, thinking content ends
+                return DeltaMessage(content=delta_text)
+            else:
+                # no end token in previous or delta, reasoning content continues
+                return DeltaMessage(reasoning=delta_text)
+
+        return ret
diff --git a/vllm/reasoning/identity_reasoning_parser.py b/vllm/reasoning/identity_reasoning_parser.py
index b02a9d3184ae..c6f117e2f983 100644
--- a/vllm/reasoning/identity_reasoning_parser.py
+++ b/vllm/reasoning/identity_reasoning_parser.py
@@ -33,6 +33,14 @@ def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs):
                 "constructor during construction."
             )
 
+    @property
+    def reasoning_start_str(self) -> str | None:
+        return None
+
+    @property
+    def reasoning_end_str(self) -> str | None:
+        return None
+
     def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
         # Always return True, since we never treat reasoning specially
         return True
diff --git a/vllm/reasoning/kimi_k2_reasoning_parser.py b/vllm/reasoning/kimi_k2_reasoning_parser.py
index 8ee05ffd23a0..0b64c5c62ea1 100644
--- a/vllm/reasoning/kimi_k2_reasoning_parser.py
+++ b/vllm/reasoning/kimi_k2_reasoning_parser.py
@@ -65,6 +65,14 @@ def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs):
                 "tokens in the tokenizer!"
             )
 
+    @property
+    def reasoning_start_str(self) -> str | None:
+        return self._start_token
+
+    @property
+    def reasoning_end_str(self) -> str | None:
+        return self._end_token
+
     def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
         """
         Check if the reasoning content ends in the input_ids.
@@ -213,6 +221,10 @@ def extract_reasoning_streaming(
             return None
 
         if self._end_token_id in delta_token_ids:
+            if self._end_token not in delta_text:
+                # Token ID arrived before text was flushed (stop-sequence buffering).
+                # Wait for the next delta when the text becomes visible.
+                return None
             end_index = delta_text.find(self._end_token)
             reasoning = delta_text[:end_index]
             content = delta_text[end_index + len(self._end_token) :]
@@ -221,6 +233,9 @@ def extract_reasoning_streaming(
             )
 
         if self._tool_section_start_token_id in delta_token_ids:
+            if self._tool_section_start_token not in delta_text:
+                # Token ID arrived before text was flushed (stop-sequence buffering).
+                return None
             tool_index = delta_text.find(self._tool_section_start_token)
             reasoning = delta_text[:tool_index]
             content = delta_text[tool_index:]
diff --git a/vllm/reasoning/nemotron_v3_reasoning_parser.py b/vllm/reasoning/nemotron_v3_reasoning_parser.py
index 52a57ccc8e93..7256f0f1283d 100644
--- a/vllm/reasoning/nemotron_v3_reasoning_parser.py
+++ b/vllm/reasoning/nemotron_v3_reasoning_parser.py
@@ -26,7 +26,7 @@ def extract_reasoning(
                 chat_template_kwargs.get("enable_thinking") is False
                 or chat_template_kwargs.get("force_nonempty_content") is True
             )
-            and final_content is None
+            and (final_content is None or not final_content.strip())
         ):
             reasoning, final_content = final_content, reasoning
 
diff --git a/vllm/reasoning/olmo3_reasoning_parser.py b/vllm/reasoning/olmo3_reasoning_parser.py
index 9697b500447f..102508b9ac18 100644
--- a/vllm/reasoning/olmo3_reasoning_parser.py
+++ b/vllm/reasoning/olmo3_reasoning_parser.py
@@ -218,28 +218,51 @@ class Olmo3ReasoningParser(ReasoningParser):
           token is missing from generation.
     """
 
+    think_start: str = r"<think>"
+    think_end: str = r"</think>"
+    # </think> is split in 3 by the pre-tokenizer, first split can be tokenized
+    # with an optional leading space, so there are 2 possible tokenizations
+    think_end_first_split: list[str] = [r"Ġ</", r"</"]
+    think_end_rest_split: list[str] = [r"think", r">"]
+    # notice that the first think is optional; this allows template to
+    # work in cases when we hardcode a <think> at the beginning of the
+    # reasoning template.
+    reasoning_regex: re.Pattern = re.compile(
+        rf"^(?:{think_start})?(?P<reasoning>.*?)"
+        rf"{think_end}(?P<content>.*)$",
+        re.DOTALL,
+    )
+
     def __init__(self, tokenizer: "TokenizerLike", *args, **kwargs):
         super().__init__(tokenizer, *args, **kwargs)
-
-        self.think_start = r"<think>"
-        self.think_end = r"</think>"
-
-        # notice that the first think is optional; this allows template to
-        # work in cases when we hardcode a <think> at the beginning of the
-        # reasoning template.
-        reasoning_expr = (
-            rf"^(?:{self.think_start})?(?P<reasoning>.*?)"
-            rf"{self.think_end}(?P<content>.*)$"
-        )
-        self.reasoning_regex = re.compile(reasoning_expr, re.DOTALL)
-
         self.buffer = Olmo3ReasoningBuffer(
             think_start=self.think_start, think_end=self.think_end
         )
+        self.think_end_first_token_ids: list[int] = [
+            self.vocab[token] for token in self.think_end_first_split
+        ]
+        self.think_end_rest_token_ids: list[int] = [
+            self.vocab[token] for token in self.think_end_rest_split
+        ]
+
+    @property
+    def reasoning_start_str(self) -> str:
+        return self.think_start
+
+    @property
+    def reasoning_end_str(self) -> str:
+        return self.think_end
 
     def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
-        text = self.model_tokenizer.decode(input_ids)
-        return self.think_end in text
+        rest_ids = self.think_end_rest_token_ids
+        rest_len = len(rest_ids)
+        for i in range(len(input_ids) - rest_len, -1, -1):
+            if (
+                list(input_ids[i + 1 : i + 1 + rest_len]) == rest_ids
+                and input_ids[i] in self.think_end_first_token_ids
+            ):
+                return True
+        return False
 
     def extract_content_ids(self, input_ids: list[int]) -> list[int]:
         # for Olmo 3 streaming reason parsing, the stream parse
diff --git a/vllm/reasoning/poolside_v1_reasoning_parser.py b/vllm/reasoning/poolside_v1_reasoning_parser.py
new file mode 100644
index 000000000000..30031d8513a9
--- /dev/null
+++ b/vllm/reasoning/poolside_v1_reasoning_parser.py
@@ -0,0 +1,72 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Laguna reasoning parser.
+
+``DeepSeekV3ReasoningParser.is_reasoning_end`` walks the entire
+token sequence backwards and returns ``True`` on the first ``</think>`` it
+sees. When called on ``prompt_token_ids`` that mistakes any stray
+``</think>`` in conversation history, few-shot examples or tool descriptions
+for a template-injected "thinking already ended" marker. In the streaming
+path (see ``vllm/entrypoints/openai/chat_completion/serving.py``,
+``prompt_is_reasoning_end_arr``) that false positive short-circuits the
+reasoning parser for the whole response, so any ``<think>...</think>`` the
+model emits itself ends up in the content field instead of the reasoning
+field.
+
+As we have more flexible templates, we instead scope
+the backward search to the current assistant turn: the
+walk terminates as soon as we hit the ``<assistant>`` start-of-message
+token. A ``</think>`` in a prior user turn or few-shot example is no longer
+visible.
+"""
+
+from collections.abc import Sequence
+
+from transformers import PreTrainedTokenizerBase
+
+from vllm.reasoning.deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser
+from vllm.reasoning.deepseek_v3_reasoning_parser import DeepSeekV3ReasoningParser
+from vllm.reasoning.identity_reasoning_parser import IdentityReasoningParser
+
+
+class PoolsideV1ReasoningParser(DeepSeekV3ReasoningParser):
+    """Drop-in replacement for ``deepseek_v3`` that tolerates ``</think>``
+    tokens appearing anywhere in the prompt other than the generation prefix.
+    """
+
+    _start_of_assistant_message = "<assistant>"
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs):
+        super().__init__(tokenizer, *args, **kwargs)
+
+        if self._start_of_assistant_message not in self.vocab:
+            raise ValueError(
+                f"Tokenizer must contain {self._start_of_assistant_message!r} token"
+            )
+        self._start_of_assistant_message_token_id = self.vocab[
+            self._start_of_assistant_message
+        ]
+
+    def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
+        # IdentityReasoningParser always returns True: no reasoning to parse.
+        if isinstance(self._parser, IdentityReasoningParser):
+            return True
+
+        assert isinstance(self._parser, DeepSeekR1ReasoningParser)
+        for tok_id in reversed(input_ids):
+            # <think>: reasoning is not yet ended.
+            if tok_id == self._parser.start_token_id:
+                return False
+            # </think>: reasoning has ended.
+            if tok_id == self._parser.end_token_id:
+                return True
+            # <assistant>: reached the start of the current assistant turn
+            # without seeing either marker. Anything further back belongs to
+            # the prior conversation and should be ignored.
+            if tok_id == self._start_of_assistant_message_token_id:
+                return False
+        return False
+
+
+__all__ = ["PoolsideV1ReasoningParser"]
diff --git a/vllm/reasoning/qwen3_reasoning_parser.py b/vllm/reasoning/qwen3_reasoning_parser.py
index 9a54aa759518..e38b0de3d822 100644
--- a/vllm/reasoning/qwen3_reasoning_parser.py
+++ b/vllm/reasoning/qwen3_reasoning_parser.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from collections.abc import Sequence
+from collections.abc import Iterable, Sequence
 from typing import TYPE_CHECKING
 
 from vllm.entrypoints.openai.engine.protocol import DeltaMessage
@@ -31,6 +31,10 @@ class Qwen3ReasoningParser(BaseThinkingReasoningParser):
     use an older chat template where the model generates <think> itself.
     This parser handles both styles: if <think> appears in the generated output
     it is stripped before extraction (non-streaming) or skipped (streaming).
+
+    NOTE: Qwen3.5 models may emit <tool_call> inside the thinking block
+    without closing </think> first. <tool_call> is treated as an implicit
+    end of reasoning, matching the approach in KimiK2ReasoningParser.
     """
 
     def __init__(self, tokenizer: "TokenizerLike", *args, **kwargs):
@@ -41,6 +45,11 @@ def __init__(self, tokenizer: "TokenizerLike", *args, **kwargs):
         # pure content when the user explicitly disables it.
         self.thinking_enabled = chat_kwargs.get("enable_thinking", True)
 
+        self._tool_call_tag = "<tool_call>"
+        self._tool_call_token_id = self.vocab.get(self._tool_call_tag)
+        self._tool_call_end_tag = "</tool_call>"
+        self._tool_call_end_token_id = self.vocab.get(self._tool_call_end_tag)
+
     @property
     def start_token(self) -> str:
         """The token that starts reasoning content."""
@@ -51,6 +60,58 @@ def end_token(self) -> str:
         """The token that ends reasoning content."""
         return "</think>"
 
+    def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
+        start_token_id = self.start_token_id
+        end_token_id = self.end_token_id
+        tool_call_token_id = self._tool_call_token_id
+        tool_call_end_token_id = self._tool_call_end_token_id
+
+        for i in range(len(input_ids) - 1, -1, -1):
+            token_id = input_ids[i]
+            if token_id == start_token_id:
+                # Found <think> before </think> or <tool_call>
+                return False
+            if token_id == end_token_id:
+                return True
+            if tool_call_token_id is not None and token_id == tool_call_token_id:
+                # Only treat as implicit reasoning end if this <tool_call>
+                # is NOT followed by </tool_call>.  Paired occurrences are
+                # template examples in the prompt, not model output.
+                if tool_call_end_token_id is not None and any(
+                    input_ids[j] == tool_call_end_token_id
+                    for j in range(i + 1, len(input_ids))
+                ):
+                    continue
+                return True
+        return False
+
+    def is_reasoning_end_streaming(
+        self, input_ids: Sequence[int], delta_ids: Iterable[int]
+    ) -> bool:
+        if super().is_reasoning_end_streaming(input_ids, delta_ids):
+            return True
+        if self._tool_call_token_id is not None:
+            return self._tool_call_token_id in delta_ids
+        return False
+
+    def extract_content_ids(self, input_ids: list[int]) -> list[int]:
+        """
+        Extract content token ids from the input_ids.
+        """
+        result = super().extract_content_ids(input_ids)
+        if result:
+            return result
+        # Fall back: content starts at <tool_call> (implicit reasoning end).
+        if (
+            self._tool_call_token_id is not None
+            and self._tool_call_token_id in input_ids
+        ):
+            tool_call_index = (
+                len(input_ids) - 1 - input_ids[::-1].index(self._tool_call_token_id)
+            )
+            return input_ids[tool_call_index:]
+        return []
+
     def extract_reasoning(
         self, model_output: str, request: "ChatCompletionRequest | ResponsesRequest"
     ) -> tuple[str | None, str | None]:
@@ -78,19 +139,23 @@ def extract_reasoning(
             model_output_parts[2] if model_output_parts[1] else model_output_parts[0]
         )
 
-        if self.end_token not in model_output:
-            if not self.thinking_enabled:
-                # Thinking explicitly disabled — treat everything as content.
-                return None, model_output
-            # Thinking enabled but no </think>: output was truncated.
-            # Everything generated so far is reasoning.
-            return model_output, None
+        if self.end_token in model_output:
+            reasoning, _, content = model_output.partition(self.end_token)
+            return reasoning, content or None
 
-        # Extract reasoning content from the model output.
-        reasoning, _, content = model_output.partition(self.end_token)
+        if not self.thinking_enabled:
+            # Thinking explicitly disabled — treat everything as content.
+            return None, model_output
 
-        final_content = content or None
-        return reasoning, final_content
+        # No </think> — check for implicit reasoning end via <tool_call>.
+        tool_call_index = model_output.find(self._tool_call_tag)
+        if tool_call_index != -1:
+            reasoning = model_output[:tool_call_index]
+            content = model_output[tool_call_index:]
+            return reasoning or None, content or None
+        # Thinking enabled but no </think>: output was truncated.
+        # Everything generated so far is reasoning.
+        return model_output, None
 
     def extract_reasoning_streaming(
         self,
@@ -135,6 +200,20 @@ def extract_reasoning_streaming(
             # end_token_id in IDs but not in text (already stripped)
             return None
 
+        # Implicit reasoning end via <tool_call>.
+        if (
+            self._tool_call_token_id is not None
+            and self._tool_call_token_id in delta_token_ids
+        ):
+            tool_index = delta_text.find(self._tool_call_tag)
+            if tool_index >= 0:
+                reasoning = delta_text[:tool_index]
+                content = delta_text[tool_index:]
+                return DeltaMessage(
+                    reasoning=reasoning if reasoning else None,
+                    content=content if content else None,
+                )
+
         # No end token in this delta.
         if not delta_text:
             # Nothing left after stripping start token.
@@ -142,6 +221,11 @@ def extract_reasoning_streaming(
         elif self.end_token_id in previous_token_ids:
             # End token already passed: everything is content now.
             return DeltaMessage(content=delta_text)
+        elif (
+            self._tool_call_token_id is not None
+            and self._tool_call_token_id in previous_token_ids
+        ):
+            return DeltaMessage(content=delta_text)
         else:
             # No end token yet: still in reasoning phase.
             return DeltaMessage(reasoning=delta_text)
diff --git a/vllm/reasoning/step3_reasoning_parser.py b/vllm/reasoning/step3_reasoning_parser.py
index 5837f0673b7e..a50fcf02db48 100644
--- a/vllm/reasoning/step3_reasoning_parser.py
+++ b/vllm/reasoning/step3_reasoning_parser.py
@@ -29,6 +29,7 @@ class Step3ReasoningParser(ReasoningParser):
 
     def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs):
         super().__init__(tokenizer, *args, **kwargs)
+        self.think_start_token = "<think>"
         self.think_end_token = "</think>"
 
         self.reasoning_regex = re.compile(rf"(.*?){self.think_end_token}", re.DOTALL)
@@ -47,6 +48,14 @@ def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs):
             )
         self.think_end_token_id: int = think_end_token_id
 
+    @property
+    def reasoning_start_str(self) -> str:
+        return self.think_start_token
+
+    @property
+    def reasoning_end_str(self) -> str:
+        return self.think_end_token
+
     def extract_reasoning_streaming(
         self,
         previous_text: str,
diff --git a/vllm/renderers/base.py b/vllm/renderers/base.py
index 9947f8c9187e..41d8c0075fb1 100644
--- a/vllm/renderers/base.py
+++ b/vllm/renderers/base.py
@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import asyncio
-import copy
 import time
 from abc import ABC, abstractmethod
 from collections.abc import Mapping, Sequence
@@ -105,20 +104,13 @@ def __init__(self, config: "VllmConfig", tokenizer: _T | None) -> None:
         self._process_multimodal_async = make_async(
             self._process_multimodal, executor=self._mm_executor
         )
-        if config.model_config.is_multimodal_model:
+        if mm_registry.supports_multimodal_inputs(config.model_config):
             mm_processor_cache = mm_registry.processor_cache_from_config(config)
 
-            # Deep-copy the tokenizer so the multimodal processor gets its
-            # own Rust tokenizer backend.  Without this, concurrent access
-            # from AsyncMicrobatchTokenizer and call_hf_processor causes
-            # "RuntimeError: Already borrowed" from the Rust RefCell.
-            # See: https://github.com/huggingface/tokenizers/issues/537
-            mm_tokenizer = copy.deepcopy(tokenizer)
-
             with set_default_torch_num_threads():
                 self.mm_processor = mm_registry.create_processor(
                     config.model_config,
-                    tokenizer=mm_tokenizer,
+                    tokenizer=self.tokenizer,
                     cache=mm_processor_cache,
                 )
 
@@ -130,11 +122,10 @@ def __init__(self, config: "VllmConfig", tokenizer: _T | None) -> None:
             # requests don't pollute the sender cache.
             ro_cache = mm_registry.processor_only_cache_from_config(config)
             if ro_cache is not None:
-                ro_tokenizer = copy.deepcopy(tokenizer)
                 with set_default_torch_num_threads():
                     self._readonly_mm_processor = mm_registry.create_processor(
                         config.model_config,
-                        tokenizer=ro_tokenizer,
+                        tokenizer=self.tokenizer,
                         cache=ro_cache,
                     )
 
@@ -198,6 +189,40 @@ def clear_mm_cache(self) -> None:
         if self._mm_cache_stats is not None:
             self._mm_cache_stats.reset = True
 
+    @staticmethod
+    def _clear_processor_cache(
+        processor: "BaseMultiModalProcessor | None",
+    ) -> None:
+        if processor is None:
+            return
+
+        processor_cache = processor.cache
+        if processor_cache is not None:
+            processor_cache.clear_cache()
+
+    def _warmup_mm_processor(
+        self,
+        processor: "BaseMultiModalProcessor",
+        *,
+        log_prefix: str,
+    ) -> None:
+        from vllm.multimodal.processing import TimingContext
+
+        model_config = self.model_config
+        mm_config = model_config.get_multimodal_config()
+        mm_limits = {k: v for k, v in processor.info.allowed_mm_limits.items() if v > 0}
+
+        start_time = time.perf_counter()
+        processor_inputs = processor.dummy_inputs.get_dummy_processor_inputs(
+            seq_len=model_config.max_model_len,
+            mm_counts=dict.fromkeys(mm_limits, 1),
+            mm_options=mm_config.limit_per_prompt,
+        )
+        _ = processor.apply(processor_inputs, timing_ctx=TimingContext(enabled=False))
+
+        elapsed = time.perf_counter() - start_time
+        logger.info("%s warmup completed in %.3fs", log_prefix, elapsed)
+
     def warmup(self, chat_params: ChatParams) -> None:
         """
         Warm up this renderer to avoid first-request latency.
@@ -221,33 +246,29 @@ def warmup(self, chat_params: ChatParams) -> None:
             logger.warning("Chat template warmup failed", exc_info=True)
 
         if self.mm_processor:
-            from vllm.multimodal.processing import TimingContext
-
-            model_config = self.model_config
-            mm_config = model_config.get_multimodal_config()
-            processor = self.mm_processor
-            mm_limits = processor.info.allowed_mm_limits
-
             try:
                 logger.debug("Warming up multi-modal processing...")
-                start_time = time.perf_counter()
-
-                processor_inputs = processor.dummy_inputs.get_dummy_processor_inputs(
-                    seq_len=model_config.max_model_len,
-                    mm_counts=dict.fromkeys(mm_limits, 1),
-                    mm_options=mm_config.limit_per_prompt,
+                self._warmup_mm_processor(
+                    self.mm_processor,
+                    log_prefix="Multi-modal",
                 )
-                _ = processor.apply(
-                    processor_inputs, timing_ctx=TimingContext(enabled=False)
-                )
-
-                elapsed = time.perf_counter() - start_time
-                logger.info("Multi-modal warmup completed in %.3fs", elapsed)
             except Exception:
                 logger.warning("Multi-modal warmup failed")
             finally:
                 self.clear_mm_cache()
 
+        if self._readonly_mm_processor is not None:
+            try:
+                logger.debug("Warming up readonly multi-modal processing...")
+                self._warmup_mm_processor(
+                    self._readonly_mm_processor,
+                    log_prefix="Readonly multi-modal",
+                )
+            except Exception:
+                logger.warning("Readonly multi-modal warmup failed")
+            finally:
+                self._clear_processor_cache(self._readonly_mm_processor)
+
     async def clear_mm_cache_async(self) -> None:
         """Serialize clear_mm_cache through the shared executor to avoid
         races with concurrent process_inputs on the mm_processor_cache."""
@@ -435,6 +456,11 @@ def _tokenize_singleton_prompt(
         params: TokenizeParams,
     ) -> SingletonTokPrompt:
         if "prompt_token_ids" not in prompt and "prompt_embeds" not in prompt:
+            if not isinstance(prompt.get("prompt"), str):
+                raise TypeError(
+                    "Expected prompt['prompt'] to be a string before tokenization; "
+                    "use 'prompt_token_ids' for token ID inputs"
+                )
             prompt = params.apply_pre_tokenization(self.tokenizer, prompt)  # type: ignore[arg-type]
             prompt = self._tokenize_prompt(prompt, params)
 
@@ -466,6 +492,11 @@ async def _tokenize_singleton_prompt_async(
         params: TokenizeParams,
     ) -> SingletonTokPrompt:
         if "prompt_token_ids" not in prompt and "prompt_embeds" not in prompt:
+            if not isinstance(prompt.get("prompt"), str):
+                raise TypeError(
+                    "Expected prompt['prompt'] to be a string before tokenization; "
+                    "use 'prompt_token_ids' for token ID inputs"
+                )
             prompt = params.apply_pre_tokenization(self.tokenizer, prompt)  # type: ignore[arg-type]
             prompt = await self._tokenize_prompt_async(prompt, params)
 
@@ -729,6 +760,8 @@ def _process_embeds(self, prompt: EmbedsPrompt) -> EmbedsInput:
         return embeds_input(
             prompt_embeds=prompt_embeds,
             cache_salt=prompt.get("cache_salt"),
+            prompt_token_ids=prompt.get("prompt_token_ids"),
+            is_token_ids=prompt.get("prompt_is_token_ids"),
         )
 
     async def _process_tokens_async(
diff --git a/vllm/renderers/deepseek_v4.py b/vllm/renderers/deepseek_v4.py
new file mode 100644
index 000000000000..3dc82b9622e5
--- /dev/null
+++ b/vllm/renderers/deepseek_v4.py
@@ -0,0 +1,90 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from vllm.config import VllmConfig
+from vllm.entrypoints.chat_utils import (
+    ChatCompletionMessageParam,
+    ConversationMessage,
+    parse_chat_messages,
+    parse_chat_messages_async,
+)
+from vllm.logger import init_logger
+from vllm.tokenizers.deepseek_v4 import DeepseekV4Tokenizer
+from vllm.utils.async_utils import make_async
+
+from .base import BaseRenderer
+from .inputs import DictPrompt
+from .inputs.preprocess import parse_dec_only_prompt
+from .params import ChatParams
+
+logger = init_logger(__name__)
+
+
+class DeepseekV4Renderer(BaseRenderer[DeepseekV4Tokenizer]):
+    def __init__(
+        self,
+        config: VllmConfig,
+        tokenizer: DeepseekV4Tokenizer | None,
+    ) -> None:
+        super().__init__(config, tokenizer)
+
+        self._apply_chat_template_async = make_async(
+            self._apply_chat_template, executor=self._executor
+        )
+
+    def _apply_chat_template(self, *args, **kwargs):
+        return self.get_tokenizer().apply_chat_template(*args, **kwargs)
+
+    def render_messages(
+        self,
+        messages: list[ChatCompletionMessageParam],
+        params: ChatParams,
+    ) -> tuple[list[ConversationMessage], DictPrompt]:
+        conversation, mm_data, mm_uuids = parse_chat_messages(
+            messages,
+            self.model_config,
+            content_format="string",
+            media_io_kwargs=params.media_io_kwargs,
+            mm_processor_kwargs=params.mm_processor_kwargs,
+        )
+
+        prompt_raw = self._apply_chat_template(
+            conversation=conversation,
+            messages=messages,
+            **params.get_apply_chat_template_kwargs(),
+        )
+
+        prompt = parse_dec_only_prompt(prompt_raw)
+        if mm_data is not None:
+            prompt["multi_modal_data"] = mm_data
+        if mm_uuids is not None:
+            prompt["multi_modal_uuids"] = mm_uuids
+
+        return conversation, prompt
+
+    async def render_messages_async(
+        self,
+        messages: list[ChatCompletionMessageParam],
+        params: ChatParams,
+    ) -> tuple[list[ConversationMessage], DictPrompt]:
+        conversation, mm_data, mm_uuids = await parse_chat_messages_async(
+            messages,
+            self.model_config,
+            content_format="string",
+            media_io_kwargs=params.media_io_kwargs,
+            mm_processor_kwargs=params.mm_processor_kwargs,
+        )
+
+        prompt_raw = await self._apply_chat_template_async(
+            conversation=conversation,
+            messages=messages,
+            **params.get_apply_chat_template_kwargs(),
+        )
+
+        prompt = parse_dec_only_prompt(prompt_raw)
+        if mm_data is not None:
+            prompt["multi_modal_data"] = mm_data
+        if mm_uuids is not None:
+            prompt["multi_modal_uuids"] = mm_uuids
+
+        return conversation, prompt
diff --git a/vllm/renderers/embed_utils.py b/vllm/renderers/embed_utils.py
index a51fc53a24ad..84c28dcf7e0f 100644
--- a/vllm/renderers/embed_utils.py
+++ b/vllm/renderers/embed_utils.py
@@ -7,6 +7,7 @@
 import torch
 
 from vllm.exceptions import VLLMValidationError
+from vllm.utils.async_utils import make_async
 
 if TYPE_CHECKING:
     from vllm.config import ModelConfig
@@ -30,15 +31,53 @@ def safe_load_prompt_embeds(
             weights_only=True,
             map_location=torch.device("cpu"),
         )
-        assert isinstance(tensor, torch.Tensor) and tensor.dtype in (
-            torch.float32,
-            torch.bfloat16,
-            torch.float16,
-        )
+        if not isinstance(tensor, torch.Tensor):
+            raise VLLMValidationError(
+                "`prompt_embeds` payload did not deserialize to a torch.Tensor.",
+                parameter="prompt_embeds",
+            )
         tensor = tensor.to_dense()
 
     if tensor.dim() > 2:
         tensor = tensor.squeeze(0)
-        assert tensor.dim() == 2
+    if tensor.dim() != 2:
+        raise VLLMValidationError(
+            "`prompt_embeds` must be a 2D tensor of shape "
+            f"(num_tokens, hidden_size); got shape {tuple(tensor.shape)}.",
+            parameter="prompt_embeds",
+        )
+
+    # Pin each tensor to the model's hidden_size. Validating here
+    # also transitively guarantees cross-tensor consistency for requests that
+    # include multiple `prompt_embeds` parts, which is required by downstream
+    # concatenation in `_build_mixed_prompt_embeds`.
+    expected_hidden_size = model_config.get_hidden_size()
+    if tensor.shape[1] != expected_hidden_size:
+        raise VLLMValidationError(
+            f"`prompt_embeds` hidden_size {tensor.shape[1]} does not match "
+            f"the model's hidden_size {expected_hidden_size}.",
+            parameter="prompt_embeds",
+        )
+
+    # Cast to the model's dtype so API clients don't need to know the server's
+    # `--dtype` setting ahead of time. Only floating-point source dtypes are
+    # allowed. integer / bool / complex inputs almost certainly indicate caller
+    # error (e.g. quantized payloads, wrong tensor), and a silent `.to()`
+    # could hide a real mistake.
+    expected_dtype = model_config.dtype
+    if tensor.dtype != expected_dtype:
+        if not tensor.is_floating_point():
+            raise VLLMValidationError(
+                f"`prompt_embeds` dtype {tensor.dtype} is not a floating-point "
+                f"type, cannot safely cast to the model's dtype {expected_dtype}.",
+                parameter="prompt_embeds",
+            )
+        tensor = tensor.to(expected_dtype)
 
     return tensor
+
+
+safe_load_prompt_embeds_async = make_async(safe_load_prompt_embeds)
+"""Async variant of `safe_load_prompt_embeds` that defers the decode to a
+thread-pool executor, so the asyncio event loop is not blocked by the base64
+decode + `torch.load` work."""
diff --git a/vllm/renderers/hf.py b/vllm/renderers/hf.py
index 690ffb2a8954..6425bc647a1c 100644
--- a/vllm/renderers/hf.py
+++ b/vllm/renderers/hf.py
@@ -1,11 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
+
+import copy
 import inspect
 import itertools
+import weakref
 from collections import defaultdict, deque
-from collections.abc import Set
+from collections.abc import Sequence
 from functools import lru_cache
-from typing import Any, Literal, cast, overload
+from typing import TYPE_CHECKING, Any, Final, Literal, cast, overload
 
 import jinja2
 import jinja2.ext
@@ -13,34 +17,199 @@
 import jinja2.nodes
 import jinja2.parser
 import jinja2.sandbox
+import torch
+from typing_extensions import override
 
-from vllm.config import ModelConfig, VllmConfig
 from vllm.entrypoints.chat_utils import (
-    ChatCompletionMessageParam,
-    ChatTemplateContentFormat,
-    ChatTemplateContentFormatOption,
+    PROMPT_EMBEDS_PLACEHOLDER_TOKEN,
     ChatTemplateResolutionError,
-    ConversationMessage,
     load_chat_template,
     parse_chat_messages,
     parse_chat_messages_async,
 )
-from vllm.inputs import MultiModalDataDict, MultiModalUUIDDict
+from vllm.inputs import EmbedsPrompt
+from vllm.inputs.engine import MultiModalInput
 from vllm.logger import init_logger
-from vllm.tokenizers.hf import HfTokenizer
+from vllm.multimodal.hasher import MultiModalHasher
+from vllm.multimodal.inputs import (
+    MultiModalFieldElem,
+    MultiModalKwargsItem,
+    MultiModalKwargsItems,
+    MultiModalSharedField,
+    PlaceholderRange,
+)
+from vllm.multimodal.processing.processor import (
+    PromptReplacement,
+    apply_token_matches,
+    find_mm_placeholders,
+)
+from vllm.tokenizers.hf import HfTokenizer, maybe_make_thread_pool
 from vllm.transformers_utils.chat_templates import get_chat_template_fallback_path
 from vllm.transformers_utils.processor import cached_get_processor
 from vllm.utils.async_utils import make_async
 from vllm.utils.func_utils import supports_kw
 
 from .base import BaseRenderer
-from .inputs import DictPrompt
 from .inputs.preprocess import parse_dec_only_prompt
-from .params import ChatParams
+
+if TYPE_CHECKING:
+    from collections.abc import Set
+
+    from vllm.config import ModelConfig, VllmConfig
+    from vllm.entrypoints.chat_utils import (
+        ChatCompletionMessageParam,
+        ChatTemplateContentFormat,
+        ChatTemplateContentFormatOption,
+        ConversationMessage,
+    )
+    from vllm.inputs import MultiModalDataDict, MultiModalUUIDDict, TokensPrompt
+    from vllm.inputs.engine import TokensInput
+    from vllm.multimodal.processing.processor import (
+        MultiModalPromptUpdates,
+        ResolvedPromptUpdate,
+    )
+
+    from .inputs import DictPrompt
+    from .params import ChatParams
 
 logger = init_logger(__name__)
 
 
+# Cache of `tokenizer -> prompt_embeds placeholder token ID`. Keyed by the
+# tokenizer object (not `id(tokenizer)`) so a fresh tokenizer landing at a
+# recycled memory address can't pick up a stale tid. Entries evict atomically
+# with the tokenizer's garbage-collection.
+_PROMPT_EMBEDS_PLACEHOLDER_TOKEN_ID_CACHE: Final[
+    weakref.WeakKeyDictionary[HfTokenizer, int]
+] = weakref.WeakKeyDictionary()
+_PROMPT_EMBEDS_PLACEHOLDER_TOKEN_ID_ERROR: Final[str] = (
+    "Expected {token!r} to tokenize to exactly 1 token, got {num_ids} ({ids!r})."
+)
+_PROMPT_EMBEDS_PLACEHOLDER_SPAN_MISMATCH_ERROR: Final[str] = (
+    "Expected {expected} prompt_embeds placeholder spans in the "
+    "tokenized prompt, found {actual}."
+)
+_MISSING_PROMPT_TOKEN_IDS_ERROR: Final[str] = (
+    "Expected prompt_token_ids in rendered prompt when prompt_embeds "
+    "are present. This indicates the chat template was invoked with "
+    "tokenize=False."
+)
+_TOKENIZE_OVERRIDE_WARNING: Final[str] = (
+    "Overriding `tokenize=False` to `True` because `prompt_embeds` "
+    "post-processing requires tokenized IDs."
+)
+
+
+def _ensure_prompt_embeds_placeholder_token(tokenizer: HfTokenizer) -> int:
+    """Register `PROMPT_EMBEDS_PLACEHOLDER_TOKEN` as a special token and return
+    its token ID."""
+    cached = _PROMPT_EMBEDS_PLACEHOLDER_TOKEN_ID_CACHE.get(tokenizer)
+    if cached is not None:
+        return cached
+
+    tokenizer.add_special_tokens(
+        {"additional_special_tokens": [PROMPT_EMBEDS_PLACEHOLDER_TOKEN]}
+    )
+
+    ids = tokenizer.encode(PROMPT_EMBEDS_PLACEHOLDER_TOKEN, add_special_tokens=False)
+    if len(ids) != 1:
+        raise RuntimeError(
+            _PROMPT_EMBEDS_PLACEHOLDER_TOKEN_ID_ERROR.format(
+                token=PROMPT_EMBEDS_PLACEHOLDER_TOKEN,
+                num_ids=len(ids),
+                ids=ids,
+            )
+        )
+
+    token_id = ids[0]
+    _PROMPT_EMBEDS_PLACEHOLDER_TOKEN_ID_CACHE[tokenizer] = token_id
+    return token_id
+
+
+def _build_prompt_embeds_updates(
+    prompt_embeds_tensors: Sequence[torch.Tensor],
+    placeholder_token_id: int,
+) -> MultiModalPromptUpdates:
+    """Build `MultiModalPromptUpdates` for `prompt_embeds` expansion.
+
+    Each tensor produces a `PromptReplacement` that maps
+    `[placeholder_token_id]` -> `[placeholder_token_id] x N`
+    (where `N = tensor.shape[0]`).
+    """
+    updates: list[Sequence[ResolvedPromptUpdate]] = []
+    for i, tensor in enumerate(prompt_embeds_tensors):
+        update = PromptReplacement(
+            modality="prompt_embeds",
+            target=[placeholder_token_id],
+            replacement=[placeholder_token_id] * tensor.shape[0],
+        )
+        updates.append([update.resolve(item_idx=i)])
+    return {"prompt_embeds": updates}
+
+
+def _expand_prompt_embeds_placeholders(
+    token_ids: list[int],
+    mm_prompt_updates: MultiModalPromptUpdates,
+) -> list[int]:
+    """Expand each 1-token `prompt_embeds` sentinel into an N-token span.
+
+    Uses `apply_token_matches`.  Each single placeholder token in
+    `token_ids` is replaced with a consecutive span of
+    `tensor.shape[0]` copies, following tensors in order.
+    """
+    expanded, _ = apply_token_matches(token_ids, mm_prompt_updates, tokenizer=None)
+    return expanded
+
+
+def _build_prompt_embeds_positions(
+    token_ids: list[int],
+    num_tensors: int,
+    mm_prompt_updates: MultiModalPromptUpdates,
+) -> list[tuple[int, int]]:
+    """Locate each prompt_embeds placeholder span in `token_ids`.
+
+    Expects `token_ids` to already contain expanded N-token spans.
+    Returns `[(start_idx, length), ...]` aligned with the tensors.
+    """
+    placeholders = find_mm_placeholders(
+        prompt=token_ids,
+        mm_prompt_updates=mm_prompt_updates,
+        tokenizer=None,
+    )
+    features = placeholders.get("prompt_embeds", [])
+
+    if len(features) != num_tensors:
+        raise ValueError(
+            _PROMPT_EMBEDS_PLACEHOLDER_SPAN_MISMATCH_ERROR.format(
+                expected=num_tensors,
+                actual=len(features),
+            )
+        )
+
+    return [(f.start_idx, f.length) for f in features]
+
+
+def _build_mixed_prompt_embeds(
+    token_ids: list[int],
+    prompt_embeds_tensors: Sequence[torch.Tensor],
+    positions: list[tuple[int, int]],
+) -> tuple[torch.Tensor, list[bool]]:
+    """Build the full-length `prompt_embeds` tensor and the `is_token_ids`
+    mask aligned to `token_ids`."""
+    total_len = len(token_ids)
+    hidden_size = prompt_embeds_tensors[0].shape[1]
+    dtype = prompt_embeds_tensors[0].dtype
+
+    full_embeds = torch.zeros(total_len, hidden_size, dtype=dtype)
+    is_token_ids = torch.ones(total_len, dtype=torch.bool)
+
+    for (start, length), tensor in zip(positions, prompt_embeds_tensors, strict=True):
+        full_embeds[start : start + length] = tensor
+        is_token_ids[start : start + length] = False
+
+    return full_embeds, is_token_ids.tolist()
+
+
 _PROCESSOR_CHAT_TEMPLATES = dict[tuple[str, bool], str | None]()
 """
 Used in `_try_get_processor_chat_template` to avoid calling
@@ -98,7 +267,7 @@ def resolve_chat_template(
     chat_template: str | None,
     tools: list[dict[str, Any]] | None,
     *,
-    model_config: "ModelConfig",
+    model_config: ModelConfig,
 ) -> str | None:
     # 1st priority: The given chat template
     if chat_template is not None:
@@ -281,7 +450,7 @@ def _resolve_chat_template_content_format(
     tools: list[dict[str, Any]] | None,
     tokenizer: HfTokenizer,
     *,
-    model_config: "ModelConfig",
+    model_config: ModelConfig,
 ) -> ChatTemplateContentFormat:
     resolved_chat_template = resolve_chat_template(
         tokenizer,
@@ -335,7 +504,7 @@ def resolve_chat_template_content_format(
     given_format: ChatTemplateContentFormatOption,
     tokenizer: HfTokenizer,
     *,
-    model_config: "ModelConfig",
+    model_config: ModelConfig,
 ) -> ChatTemplateContentFormat:
     if given_format != "auto":
         return given_format
@@ -437,7 +606,7 @@ def resolve_chat_template_kwargs(
 
 @overload
 def safe_apply_chat_template(
-    model_config: "ModelConfig",
+    model_config: ModelConfig,
     tokenizer: HfTokenizer,
     conversation: list[ConversationMessage],
     *,
@@ -448,7 +617,7 @@ def safe_apply_chat_template(
 ) -> list[int]: ...
 @overload
 def safe_apply_chat_template(
-    model_config: "ModelConfig",
+    model_config: ModelConfig,
     tokenizer: HfTokenizer,
     conversation: list[ConversationMessage],
     *,
@@ -458,7 +627,7 @@ def safe_apply_chat_template(
     **kwargs,
 ) -> str: ...
 def safe_apply_chat_template(
-    model_config: "ModelConfig",
+    model_config: ModelConfig,
     tokenizer: HfTokenizer,
     conversation: list[ConversationMessage],
     *,
@@ -486,6 +655,14 @@ def safe_apply_chat_template(
         chat_template_kwargs=kwargs,
     )
 
+    # transformers v5 changed the default of `return_dict` to True, which
+    # makes `apply_chat_template(tokenize=True)` return a `BatchEncoding`
+    # instead of `list[int]`. Force `return_dict=False` so downstream code
+    # that expects a flat token list (e.g. `parse_dec_only_prompt`) works
+    # consistently across v4 and v5.
+    if tokenize and "return_dict" not in resolved_kwargs:
+        resolved_kwargs["return_dict"] = False
+
     try:
         return tokenizer.apply_chat_template(
             conversation=conversation,  # type: ignore[arg-type]
@@ -609,6 +786,14 @@ def __init__(
         config: VllmConfig,
         tokenizer: HfTokenizer | None,
     ) -> None:
+        # Ensure the og tokenizer is never modified by maybe_make_thread_pool
+        tokenizer = copy.copy(tokenizer)
+        if (
+            # Skip for mock configs and tokenizers
+            getattr(config.model_config, "enable_prompt_embeds", False)
+            and isinstance(tokenizer, HfTokenizer)
+        ):
+            _ensure_prompt_embeds_placeholder_token(tokenizer)
         super().__init__(config, tokenizer)
 
         self.use_unified_vision_chunk = getattr(
@@ -619,6 +804,11 @@ def __init__(
             safe_apply_chat_template, executor=self._executor
         )
 
+        if self.tokenizer is not None:
+            maybe_make_thread_pool(
+                self.tokenizer, config.model_config.renderer_num_workers + 1
+            )
+
     def render_messages(
         self,
         messages: list[ChatCompletionMessageParam],
@@ -627,6 +817,12 @@ def render_messages(
         model_config = self.model_config
         tokenizer = self.get_tokenizer()
 
+        prompt_embeds_placeholder_token_id: int | None = None
+        if model_config.enable_prompt_embeds:
+            prompt_embeds_placeholder_token_id = (
+                _ensure_prompt_embeds_placeholder_token(tokenizer)
+            )
+
         conversation, mm_data, mm_uuids = parse_chat_messages(
             messages,
             model_config,
@@ -641,11 +837,30 @@ def render_messages(
             mm_processor_kwargs=params.mm_processor_kwargs,
         )
 
+        # prompt_embeds tensors are carried by the tracker through mm_data,
+        # but they must NOT be fed to the MM processor (which would reject
+        # the unknown key). Extract them here.
+        prompt_embeds_tensors: list[torch.Tensor] | None = None
+        if mm_data is not None and "prompt_embeds" in mm_data:
+            prompt_embeds_tensors = list(
+                cast(Sequence[torch.Tensor], mm_data["prompt_embeds"])
+            )
+            mm_data = {k: v for k, v in mm_data.items() if k != "prompt_embeds"}
+            if not mm_data:
+                mm_data = None
+
+        chat_template_kwargs = params.get_apply_chat_template_kwargs()
+        if prompt_embeds_tensors:
+            # prompt_embeds post-processing requires prompt_token_ids.
+            if chat_template_kwargs.get("tokenize") is False:
+                logger.warning_once(_TOKENIZE_OVERRIDE_WARNING)
+            chat_template_kwargs["tokenize"] = True
+
         prompt_raw = safe_apply_chat_template(
             model_config,
             tokenizer,
             conversation,
-            **params.get_apply_chat_template_kwargs(),
+            **chat_template_kwargs,
         )
 
         # NOTE: use_unified_vision_chunk is currently specific to Kimi-K2.5
@@ -671,6 +886,29 @@ def render_messages(
             )
 
         prompt = parse_dec_only_prompt(prompt_raw)
+
+        # When `prompt_embeds` is mixed with other modality data,
+        # `_process_tokens` runs `_process_multimodal` first (expanding
+        # `<|AUDIO|>` / `<|IMAGE|>` placeholders) and then
+        # `_apply_prompt_embeds_to_engine_input` augments the result.
+        # Stash the tensors and placeholder ID for that override to consume.
+        if prompt_embeds_tensors and mm_data:
+            assert prompt_embeds_placeholder_token_id is not None
+            cast(dict, prompt)["_prompt_embeds"] = (
+                prompt_embeds_tensors,
+                prompt_embeds_placeholder_token_id,
+            )
+            if params.mm_processor_kwargs:
+                cast(dict, prompt)["mm_processor_kwargs"] = params.mm_processor_kwargs
+        elif prompt_embeds_tensors:
+            # Pure mode: no other MM data, mutate prompt to EmbedsPrompt shape.
+            assert prompt_embeds_placeholder_token_id is not None
+            self._apply_prompt_embeds_to_prompt(
+                prompt,
+                prompt_embeds_tensors,
+                prompt_embeds_placeholder_token_id,
+            )
+
         if mm_data is not None:
             prompt["multi_modal_data"] = mm_data
         if mm_uuids is not None:
@@ -686,6 +924,12 @@ async def render_messages_async(
         model_config = self.model_config
         tokenizer = self.get_tokenizer()
 
+        prompt_embeds_placeholder_token_id: int | None = None
+        if model_config.enable_prompt_embeds:
+            prompt_embeds_placeholder_token_id = (
+                _ensure_prompt_embeds_placeholder_token(tokenizer)
+            )
+
         conversation, mm_data, mm_uuids = await parse_chat_messages_async(
             messages,
             model_config,
@@ -700,11 +944,27 @@ async def render_messages_async(
             mm_processor_kwargs=params.mm_processor_kwargs,
         )
 
+        prompt_embeds_tensors: list[torch.Tensor] | None = None
+        if mm_data is not None and "prompt_embeds" in mm_data:
+            prompt_embeds_tensors = list(
+                cast(Sequence[torch.Tensor], mm_data["prompt_embeds"])
+            )
+            mm_data = {k: v for k, v in mm_data.items() if k != "prompt_embeds"}
+            if not mm_data:
+                mm_data = None
+
+        chat_template_kwargs = params.get_apply_chat_template_kwargs()
+        if prompt_embeds_tensors:
+            # prompt_embeds post-processing requires prompt_token_ids.
+            if chat_template_kwargs.get("tokenize") is False:
+                logger.warning_once(_TOKENIZE_OVERRIDE_WARNING)
+            chat_template_kwargs["tokenize"] = True
+
         prompt_raw = await self._apply_chat_template_async(
             model_config,
             tokenizer,
             conversation,
-            **params.get_apply_chat_template_kwargs(),
+            **chat_template_kwargs,
         )
 
         # NOTE: use_unified_vision_chunk is currently specific to Kimi-K2.5
@@ -728,9 +988,185 @@ async def render_messages_async(
             )
 
         prompt = parse_dec_only_prompt(prompt_raw)
+
+        # See `render_messages` for the rationale.
+        if prompt_embeds_tensors and mm_data:
+            assert prompt_embeds_placeholder_token_id is not None
+            cast(dict, prompt)["_prompt_embeds"] = (
+                prompt_embeds_tensors,
+                prompt_embeds_placeholder_token_id,
+            )
+            if params.mm_processor_kwargs:
+                cast(dict, prompt)["mm_processor_kwargs"] = params.mm_processor_kwargs
+        elif prompt_embeds_tensors:
+            assert prompt_embeds_placeholder_token_id is not None
+            self._apply_prompt_embeds_to_prompt(
+                prompt,
+                prompt_embeds_tensors,
+                prompt_embeds_placeholder_token_id,
+            )
+
         if mm_data is not None:
             prompt["multi_modal_data"] = mm_data
         if mm_uuids is not None:
             prompt["multi_modal_uuids"] = mm_uuids
 
         return conversation, prompt
+
+    @override
+    def _process_tokens(
+        self,
+        prompt: TokensPrompt,
+        *,
+        skip_mm_cache: bool = False,
+    ) -> TokensInput | MultiModalInput:
+        """Pre-expand `prompt_embeds` sentinels before delegating to the MM
+        processor, then attach `prompt_embeds` modality data to the result.
+
+        Mixed mode only: the `_prompt_embeds` stash is set by
+        `render_messages` when `prompt_embeds` co-exist with other MM data
+        (images, audio, …).  We expand each 1-token sentinel to an N-token
+        span *before* calling `super()._process_tokens()` so the MM
+        processor records all placeholder offsets in the final (post-expansion)
+        coordinate space, no offset shifting needed afterwards.
+        """
+        prompt_embeds_info = cast(dict, prompt).pop("_prompt_embeds", None)
+        if prompt_embeds_info is not None:
+            tensors, placeholder_token_id = prompt_embeds_info
+            mm_updates = _build_prompt_embeds_updates(tensors, placeholder_token_id)
+            cast(dict, prompt)["prompt_token_ids"] = _expand_prompt_embeds_placeholders(
+                list(prompt["prompt_token_ids"]), mm_updates
+            )
+        engine_input = super()._process_tokens(prompt, skip_mm_cache=skip_mm_cache)
+        if prompt_embeds_info is not None:
+            tensors, _ = prompt_embeds_info
+            self._apply_prompt_embeds_to_engine_input(
+                cast(MultiModalInput, engine_input),
+                tensors,
+                mm_updates,
+            )
+        return engine_input
+
+    @override
+    async def _process_tokens_async(
+        self,
+        prompt: TokensPrompt,
+        *,
+        skip_mm_cache: bool = False,
+    ) -> TokensInput | MultiModalInput:
+        """Async equivalent of `_process_tokens`."""
+        prompt_embeds_info = cast(dict, prompt).pop("_prompt_embeds", None)
+        if prompt_embeds_info is not None:
+            tensors, placeholder_token_id = prompt_embeds_info
+            mm_updates = _build_prompt_embeds_updates(tensors, placeholder_token_id)
+            cast(dict, prompt)["prompt_token_ids"] = _expand_prompt_embeds_placeholders(
+                list(prompt["prompt_token_ids"]), mm_updates
+            )
+        engine_input = await super()._process_tokens_async(
+            prompt, skip_mm_cache=skip_mm_cache
+        )
+        if prompt_embeds_info is not None:
+            tensors, _ = prompt_embeds_info
+            self._apply_prompt_embeds_to_engine_input(
+                cast(MultiModalInput, engine_input),
+                tensors,
+                mm_updates,
+            )
+        return engine_input
+
+    @staticmethod
+    def _apply_prompt_embeds_to_prompt(
+        prompt: DictPrompt,
+        prompt_embeds_tensors: list[torch.Tensor],
+        placeholder_token_id: int,
+    ) -> None:
+        """Mutate `prompt` from `TokensPrompt` to `EmbedsPrompt` shape.
+
+        Pure `prompt_embeds` path only (no other MM modalities).  Expands
+        each `<prompt_embeds>` sentinel token into an N-token span and builds
+        the full-length `prompt_embeds` tensor + `prompt_is_token_ids` mask
+        that the engine's `enable_prompt_embeds` worker branch consumes.
+        """
+        token_ids = cast(list[int] | None, prompt.get("prompt_token_ids"))
+        if token_ids is None:
+            raise RuntimeError(_MISSING_PROMPT_TOKEN_IDS_ERROR)
+
+        embeds_orig_positions: list[int] = [
+            i for i, tok in enumerate(token_ids) if tok == placeholder_token_id
+        ]
+        if len(embeds_orig_positions) != len(prompt_embeds_tensors):
+            raise ValueError(
+                f"Expected {len(prompt_embeds_tensors)} prompt_embeds "
+                f"placeholder tokens in the rendered prompt, found "
+                f"{len(embeds_orig_positions)}."
+            )
+
+        mm_updates = _build_prompt_embeds_updates(
+            prompt_embeds_tensors, placeholder_token_id
+        )
+        expanded = _expand_prompt_embeds_placeholders(token_ids, mm_updates)
+        positions = _build_prompt_embeds_positions(
+            expanded, len(prompt_embeds_tensors), mm_updates
+        )
+
+        embeds_prompt = cast(EmbedsPrompt, prompt)
+        embeds_prompt["prompt_token_ids"] = expanded
+        full_embeds, is_token_ids_mask = _build_mixed_prompt_embeds(
+            expanded, prompt_embeds_tensors, positions
+        )
+        embeds_prompt["prompt_embeds"] = full_embeds
+        embeds_prompt["prompt_is_token_ids"] = is_token_ids_mask
+
+    @staticmethod
+    def _apply_prompt_embeds_to_engine_input(
+        engine_input: MultiModalInput,
+        prompt_embeds_tensors: list[torch.Tensor],
+        mm_updates: MultiModalPromptUpdates,
+    ) -> None:
+        """Augment `engine_input` in-place with a `prompt_embeds` modality.
+
+        Mixed mode: called after `_process_multimodal` has already run on the
+        pre-expanded token IDs (expansion was done in `_process_tokens` before
+        calling `super()`).  Locates the already-expanded `prompt_embeds` spans
+        and adds `prompt_embeds` entries to `mm_kwargs`, `mm_hashes`, and
+        `mm_placeholders`.
+        """
+        # token_ids already contain the pre-expanded N-token spans.
+        token_ids = list(engine_input["prompt_token_ids"])
+
+        positions = _build_prompt_embeds_positions(
+            token_ids, len(prompt_embeds_tensors), mm_updates
+        )
+
+        pe_kwargs_items: list[MultiModalKwargsItem] = []
+        pe_hashes: list[str] = []
+        pe_placeholders: list[PlaceholderRange] = []
+        for tensor, (start, length) in zip(
+            prompt_embeds_tensors, positions, strict=True
+        ):
+            pe_kwargs_items.append(
+                MultiModalKwargsItem(
+                    {
+                        "embedding": MultiModalFieldElem(
+                            data=tensor,
+                            field=MultiModalSharedField(batch_size=1),
+                        )
+                    }
+                )
+            )
+            pe_hashes.append(MultiModalHasher.hash_kwargs(prompt_embeds=tensor))
+            # `is_embed=None` matches the existing image_embeds-style
+            # "no encoder, just splice the tensor directly" semantics.
+            pe_placeholders.append(
+                PlaceholderRange(offset=start, length=length, is_embed=None)
+            )
+
+        cast(
+            MultiModalKwargsItems[MultiModalKwargsItem | None],
+            engine_input["mm_kwargs"],
+        )["prompt_embeds"] = pe_kwargs_items
+        engine_input["mm_hashes"] = {
+            **engine_input["mm_hashes"],
+            "prompt_embeds": pe_hashes,
+        }
+        cast(dict, engine_input["mm_placeholders"])["prompt_embeds"] = pe_placeholders
diff --git a/vllm/renderers/inputs/preprocess.py b/vllm/renderers/inputs/preprocess.py
index 1828c4ff58d9..dcf80ac73899 100644
--- a/vllm/renderers/inputs/preprocess.py
+++ b/vllm/renderers/inputs/preprocess.py
@@ -4,7 +4,7 @@
 
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from collections.abc import Sequence
+from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, NamedTuple, TypeAlias, TypedDict, overload
 
 from vllm.inputs import (
@@ -116,6 +116,19 @@ class EncoderDecoderDictPrompt(TypedDict):
 """
 
 
+def _validate_prompt_dict(prompt: Mapping[str, object]) -> None:
+    """Reject malformed dict prompts before renderer tokenization."""
+    if (
+        "prompt" not in prompt
+        or "prompt_token_ids" in prompt
+        or "prompt_embeds" in prompt
+    ):
+        return
+
+    if not isinstance(prompt["prompt"], str):
+        raise TypeError("Prompt text should be a string")
+
+
 def parse_dec_only_prompt(prompt: PromptType | object) -> DecoderOnlyDictPrompt:
     """
     Parse a prompt for a decoder-only model and normalize it to a dictionary.
@@ -133,6 +146,8 @@ def parse_dec_only_prompt(prompt: PromptType | object) -> DecoderOnlyDictPrompt:
         if "encoder_prompt" in prompt:
             raise TypeError("Cannot pass encoder-decoder prompt to decoder-only models")
 
+        _validate_prompt_dict(prompt)
+
         if (
             "prompt" in prompt
             or "prompt_token_ids" in prompt
@@ -156,6 +171,8 @@ def _parse_enc_prompt(prompt: PromptType | object) -> EncoderDictPrompt:
         return TokensPrompt(prompt_token_ids=prompt)
 
     if isinstance(prompt, dict):
+        _validate_prompt_dict(prompt)
+
         if "prompt_embeds" in prompt:
             raise TypeError("Cannot pass embeddings prompt to encoder-decoder models")
 
@@ -178,6 +195,8 @@ def _parse_dec_prompt(prompt: PromptType | object) -> DecoderDictPrompt:
         return TokensPrompt(prompt_token_ids=prompt)
 
     if isinstance(prompt, dict):
+        _validate_prompt_dict(prompt)
+
         if "prompt_embeds" in prompt:
             raise TypeError("Cannot pass embeddings prompt to encoder-decoder models")
 
diff --git a/vllm/renderers/params.py b/vllm/renderers/params.py
index a2c95690c792..10bc01043f71 100644
--- a/vllm/renderers/params.py
+++ b/vllm/renderers/params.py
@@ -204,7 +204,7 @@ def __post_init__(self) -> None:
             and max_output_tokens > max_total_tokens
         ):
             raise VLLMValidationError(
-                f"{self.max_output_tokens_param}={max_output_tokens}"
+                f"{self.max_output_tokens_param}={max_output_tokens} "
                 f"cannot be greater than "
                 f"{self.max_total_tokens_param}={max_total_tokens=}. "
                 f"Please request fewer output tokens.",
diff --git a/vllm/renderers/registry.py b/vllm/renderers/registry.py
index 85a34a986720..fff580bd6268 100644
--- a/vllm/renderers/registry.py
+++ b/vllm/renderers/registry.py
@@ -21,8 +21,10 @@
 
 _VLLM_RENDERERS = {
     "deepseek_v32": ("deepseek_v32", "DeepseekV32Renderer"),
-    "hf": ("hf", "HfRenderer"),
+    "deepseek_v4": ("deepseek_v4", "DeepseekV4Renderer"),
+    "fastokens": ("hf", "HfRenderer"),
     "grok2": ("grok2", "Grok2Renderer"),
+    "hf": ("hf", "HfRenderer"),
     "kimi_audio": ("hf", "HfRenderer"),
     "mistral": ("mistral", "MistralRenderer"),
     "qwen_vl": ("hf", "HfRenderer"),
diff --git a/vllm/sampling_params.py b/vllm/sampling_params.py
index 9bcc669591eb..88b1b0b8e8e9 100644
--- a/vllm/sampling_params.py
+++ b/vllm/sampling_params.py
@@ -25,6 +25,10 @@
 _SAMPLING_EPS = 1e-5
 _MAX_TEMP = 1e-2
 
+MAX_LOGPROB_TOKEN_IDS = 128
+"""Upper bound on `SamplingParams.logprob_token_ids` list length. Must match
+the per-request row width allocated by the sampler's `LogprobTokenIdsState`."""
+
 
 class SamplingType(IntEnum):
     GREEDY = 0
@@ -157,6 +161,10 @@ def _is_non_tekken_mistral(tokenizer: TokenizerLike) -> bool:
     return is_mistral_tokenizer(tokenizer) and not tokenizer.is_tekken
 
 
+def _get_llg_tokenizer(tokenizer: TokenizerLike) -> Any:
+    return tokenizer.llg_tokenizer if is_mistral_tokenizer(tokenizer) else None
+
+
 class SamplingParams(
     PydanticMsgspecMixin,
     msgspec.Struct,
@@ -624,6 +632,16 @@ def bad_words_token_ids(self) -> list[list[int]] | None:
         # For internal use only. Backward compatibility not guaranteed
         return self._bad_words_token_ids
 
+    @property
+    def num_logprobs(self) -> int | None:
+        """Number of sample logprobs to return per output token, or `None` if
+        no sample logprobs were requested. Takes `logprob_token_ids` into
+        account: when `logprobs` is unset but `logprob_token_ids` is set,
+        returns `len(logprob_token_ids)`."""
+        if self.logprobs is not None:
+            return self.logprobs
+        return len(self.logprob_token_ids) if self.logprob_token_ids else None
+
     def clone(self) -> "SamplingParams":
         """If skip_clone is True, uses shallow copy instead of deep copy."""
         if self.skip_clone:
@@ -662,6 +680,17 @@ def _validate_logprobs(self, model_config: ModelConfig) -> None:
                     value=num_logprobs,
                 )
 
+        # Validate logprob_token_ids.
+        if self.logprob_token_ids is not None:
+            n = len(self.logprob_token_ids)
+            if n > MAX_LOGPROB_TOKEN_IDS:
+                raise VLLMValidationError(
+                    f"Requested logprob_token_ids of length {n}, "
+                    f"which is greater than max allowed: {MAX_LOGPROB_TOKEN_IDS}",
+                    parameter="logprob_token_ids",
+                    value=n,
+                )
+
         # Validate prompt logprobs.
         if num_prompt_logprobs := self.prompt_logprobs:
             if num_prompt_logprobs == -1:
@@ -816,7 +845,10 @@ def _validate_structured_outputs(
             # allows <|special_token|> and similar, see
             # https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#special-tokens
             # Without tokenizer these are disallowed in grammars.
-            validate_guidance_grammar(self, tokenizer=None)
+            validate_guidance_grammar(
+                self,
+                tokenizer=_get_llg_tokenizer(tokenizer),
+            )
         elif backend == "outlines":
             # outlines backend
             validate_structured_output_request_outlines(self)
@@ -862,7 +894,10 @@ def _validate_structured_outputs(
                     self.structured_outputs._backend = "outlines"
                 else:
                     # Fall back to guidance by default.
-                    validate_guidance_grammar(self, tokenizer=None)
+                    validate_guidance_grammar(
+                        self,
+                        tokenizer=_get_llg_tokenizer(tokenizer),
+                    )
                     self.structured_outputs._backend = "guidance"
             # Remember that this backend was set automatically
             self.structured_outputs._backend_was_auto = True
diff --git a/vllm/tasks.py b/vllm/tasks.py
index 4e324c188519..017bc31197b6 100644
--- a/vllm/tasks.py
+++ b/vllm/tasks.py
@@ -16,6 +16,11 @@
 POOLING_TASKS: tuple[PoolingTask, ...] = get_args(PoolingTask)
 
 ScoreType = Literal["bi-encoder", "cross-encoder", "late-interaction"]
+SCORE_TYPE_MAP: dict[PoolingTask, ScoreType] = {
+    "embed": "bi-encoder",
+    "classify": "cross-encoder",
+    "token_embed": "late-interaction",
+}
 
 FrontendTask = Literal["render"]
 FRONTEND_TASKS: tuple[FrontendTask, ...] = get_args(FrontendTask)
diff --git a/vllm/tokenizers/__init__.py b/vllm/tokenizers/__init__.py
index 2daba409881f..6531989a9f35 100644
--- a/vllm/tokenizers/__init__.py
+++ b/vllm/tokenizers/__init__.py
@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from .hf import maybe_make_thread_pool
 from .protocol import TokenizerLike
 from .registry import (
     TokenizerRegistry,
@@ -15,4 +16,5 @@
     "cached_get_tokenizer",
     "get_tokenizer",
     "cached_tokenizer_from_config",
+    "maybe_make_thread_pool",
 ]
diff --git a/vllm/tokenizers/deepseek_v4.py b/vllm/tokenizers/deepseek_v4.py
new file mode 100644
index 000000000000..2a6aaaf73975
--- /dev/null
+++ b/vllm/tokenizers/deepseek_v4.py
@@ -0,0 +1,96 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import copy
+from typing import Any
+
+from transformers import PreTrainedTokenizerFast
+
+from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
+
+from .deepseek_v4_encoding import encode_messages
+from .hf import HfTokenizer, get_cached_tokenizer
+from .protocol import TokenizerLike
+
+
+def get_deepseek_v4_tokenizer(tokenizer: HfTokenizer) -> HfTokenizer:
+    """
+    Wraps a tokenizer to use the custom DeepSeek V4 chat template encoding.
+    """
+    dsv4_tokenizer = copy.copy(tokenizer)
+
+    added_vocab = tokenizer.get_added_vocab()
+    added_vocab_size = len(added_vocab)
+    tokenizer_vocab_size = tokenizer.vocab_size
+
+    class _DeepseekV4Tokenizer(tokenizer.__class__):  # type: ignore
+        def apply_chat_template(
+            self,
+            messages: list["ChatCompletionMessageParam"],
+            tools: list[dict[str, Any]] | None = None,
+            **kwargs,
+        ) -> str | list[int]:
+            thinking = kwargs.get("thinking", False)
+            enable_thinking = kwargs.get("enable_thinking", False)
+            thinking = thinking or enable_thinking
+            thinking_mode = "thinking" if thinking else "chat"
+
+            conversation = kwargs.get("conversation", messages)
+            messages = conversation.copy()
+            if tools is not None and len(tools) > 0:
+                messages.insert(0, {"role": "system"})
+                messages[0]["tools"] = tools  # type: ignore[typeddict-unknown-key]
+
+            reasoning_effort = kwargs.get("reasoning_effort")
+            if not isinstance(reasoning_effort, str):
+                reasoning_effort = None
+            elif reasoning_effort == "none":
+                thinking_mode = "chat"
+                reasoning_effort = None
+            elif reasoning_effort in ("max", "xhigh"):
+                reasoning_effort = "max"
+            else:
+                reasoning_effort = "high"
+
+            encode_config = dict(
+                thinking_mode=thinking_mode,
+                drop_thinking=kwargs.get("drop_thinking", True),
+                reasoning_effort=reasoning_effort,
+            )
+
+            prompt_str = encode_messages(messages, **encode_config)  # type: ignore
+
+            if kwargs.get("tokenize", True):
+                tokenizer_kwargs = {
+                    k: kwargs[k] for k in ("truncation", "max_length") if k in kwargs
+                }
+                return self.encode(
+                    prompt_str,
+                    add_special_tokens=False,
+                    **tokenizer_kwargs,
+                )
+
+            return prompt_str
+
+        def num_special_tokens_to_add(self) -> int:
+            return len(self.encode(""))
+
+        def __len__(self) -> int:
+            return tokenizer_vocab_size + added_vocab_size
+
+        def get_added_vocab(self) -> dict[str, int]:
+            return added_vocab.copy()
+
+        def __reduce__(self):
+            return get_deepseek_v4_tokenizer, (tokenizer,)
+
+    _DeepseekV4Tokenizer.__name__ = f"DSV4{tokenizer.__class__.__name__}"
+
+    dsv4_tokenizer.__class__ = _DeepseekV4Tokenizer
+    return dsv4_tokenizer
+
+
+class DeepseekV4Tokenizer(TokenizerLike):
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs) -> HfTokenizer:
+        tokenizer = PreTrainedTokenizerFast.from_pretrained(*args, **kwargs)
+        return get_cached_tokenizer(get_deepseek_v4_tokenizer(tokenizer))
diff --git a/vllm/tokenizers/deepseek_v4_encoding.py b/vllm/tokenizers/deepseek_v4_encoding.py
new file mode 100644
index 000000000000..6895771e2f59
--- /dev/null
+++ b/vllm/tokenizers/deepseek_v4_encoding.py
@@ -0,0 +1,757 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# ruff: noqa
+# fmt: off
+
+"""
+DeepSeek-V4 Encoding
+
+A self-contained implementation for encoding/decoding DeepSeek-V4 chat messages
+with tool calling, thinking mode, and quick instruction task support.
+"""
+
+from typing import Any, Dict, List, Union, Optional, Tuple
+import copy
+import json
+
+import regex as re
+
+# ============================================================
+# Special Tokens
+# ============================================================
+
+bos_token: str = "<｜begin▁of▁sentence｜>"
+eos_token: str = "<｜end▁of▁sentence｜>"
+thinking_start_token: str = "<think>"
+thinking_end_token: str = "</think>"
+dsml_token: str = "｜DSML｜"
+
+USER_SP_TOKEN = "<｜User｜>"
+ASSISTANT_SP_TOKEN = "<｜Assistant｜>"
+LATEST_REMINDER_SP_TOKEN = "<｜latest_reminder｜>"
+
+# Task special tokens for internal classification tasks
+DS_TASK_SP_TOKENS = {
+    "action": "<｜action｜>",
+    "query": "<｜query｜>",
+    "authority": "<｜authority｜>",
+    "domain": "<｜domain｜>",
+    "title": "<｜title｜>",
+    "read_url": "<｜read_url｜>",
+}
+VALID_TASKS = set(DS_TASK_SP_TOKENS.keys())
+
+# ============================================================
+# Templates
+# ============================================================
+
+system_msg_template: str = "{content}"
+user_msg_template: str = "{content}"
+latest_reminder_msg_template: str = "{content}"
+assistant_msg_template: str = "{reasoning}{content}{tool_calls}" + eos_token
+assistant_msg_wo_eos_template: str = "{reasoning}{content}{tool_calls}"
+thinking_template: str = "{reasoning}"
+
+response_format_template: str = (
+    "## Response Format:\n\nYou MUST strictly adhere to the following schema to reply:\n{schema}"
+)
+tool_call_template: str = (
+    "<{dsml_token}invoke name=\"{name}\">\n{arguments}\n</{dsml_token}invoke>"
+)
+tool_calls_template = (
+    "<{dsml_token}{tc_block_name}>\n{tool_calls}\n</{dsml_token}{tc_block_name}>"
+)
+tool_calls_block_name: str = "tool_calls"
+
+tool_output_template: str = (
+    "<tool_result>{content}</tool_result>"
+)
+
+REASONING_EFFORT_MAX = (
+    "Reasoning Effort: Absolute maximum with no shortcuts permitted.\n"
+    "You MUST be very thorough in your thinking and comprehensively decompose the problem to resolve the root cause, rigorously stress-testing your logic against all potential paths, edge cases, and adversarial scenarios.\n"
+    "Explicitly write out your entire deliberation process, documenting every intermediate step, considered alternative, and rejected hypothesis to ensure absolutely no assumption is left unchecked.\n\n"
+)
+
+TOOLS_TEMPLATE = """## Tools
+
+You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<{dsml_token}tool_calls>" block like the following:
+
+<{dsml_token}tool_calls>
+<{dsml_token}invoke name="$TOOL_NAME">
+<{dsml_token}parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</{dsml_token}parameter>
+...
+</{dsml_token}invoke>
+<{dsml_token}invoke name="$TOOL_NAME2">
+...
+</{dsml_token}invoke>
+</{dsml_token}tool_calls>
+
+String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.
+
+If thinking_mode is enabled (triggered by {thinking_start_token}), you MUST output your complete reasoning inside {thinking_start_token}...{thinking_end_token} BEFORE any tool calls or final response.
+
+Otherwise, output directly after {thinking_end_token} with tool calls or final response.
+
+### Available Tool Schemas
+
+{tool_schemas}
+
+You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.
+"""
+
+# ============================================================
+# Utility Functions
+# ============================================================
+
+def to_json(value: Any) -> str:
+    """Serialize a value to JSON string."""
+    try:
+        return json.dumps(value, ensure_ascii=False)
+    except Exception:
+        return json.dumps(value, ensure_ascii=True)
+
+
+def tools_from_openai_format(tools):
+    """Extract function definitions from OpenAI-format tool list."""
+    return [tool["function"] for tool in tools]
+
+
+def tool_calls_from_openai_format(tool_calls):
+    """Convert OpenAI-format tool calls to internal format."""
+    return [
+        {
+            "name": tool_call["function"]["name"],
+            "arguments": tool_call["function"]["arguments"],
+        }
+        for tool_call in tool_calls
+    ]
+
+
+def tool_calls_to_openai_format(tool_calls):
+    """Convert internal tool calls to OpenAI format."""
+    return [
+        {
+            "type": "function",
+            "function": {
+                "name": tool_call["name"],
+                "arguments": tool_call["arguments"],
+            }
+        }
+        for tool_call in tool_calls
+    ]
+
+
+def encode_arguments_to_dsml(tool_call: Dict[str, Any]) -> str:
+    """
+    Encode tool call arguments into DSML parameter format.
+
+    Args:
+        tool_call: Dict with "name" and "arguments" keys.
+
+    Returns:
+        DSML-formatted parameter string.
+    """
+    p_dsml_template = '<{dsml_token}parameter name="{key}" string="{is_str}">{value}</{dsml_token}parameter>'
+    P_dsml_strs = []
+
+    if isinstance(tool_call["arguments"], str):
+        arguments = json.loads(tool_call["arguments"])
+    else:
+        arguments = tool_call["arguments"]
+
+    for k, v in arguments.items():
+        p_dsml_str = p_dsml_template.format(
+            dsml_token=dsml_token,
+            key=k,
+            is_str="true" if isinstance(v, str) else "false",
+            value=v if isinstance(v, str) else to_json(v),
+        )
+        P_dsml_strs.append(p_dsml_str)
+
+    return "\n".join(P_dsml_strs)
+
+
+def decode_dsml_to_arguments(tool_name: str, tool_args: Dict[str, Tuple[str, str]]) -> Dict[str, str]:
+    """
+    Decode DSML parameters back to a tool call dict.
+
+    Args:
+        tool_name: Name of the tool.
+        tool_args: Dict mapping param_name -> (value, is_string_flag).
+
+    Returns:
+        Dict with "name" and "arguments" (JSON string) keys.
+    """
+    def _decode_value(key: str, value: str, string: str):
+        if string == "true":
+            value = to_json(value)
+        return f"{to_json(key)}: {value}"
+
+    tool_args_json = "{" + ", ".join([_decode_value(k, v, string=is_str) for k, (v, is_str) in tool_args.items()]) + "}"
+    return dict(name=tool_name, arguments=tool_args_json)
+
+
+def render_tools(tools: List[Dict[str, Union[str, Dict[str, Any]]]]) -> str:
+    """
+    Render tool schemas into the system prompt format.
+
+    Args:
+        tools: List of tool schema dicts (each with name, description, parameters).
+
+    Returns:
+        Formatted tools section string.
+    """
+    tools_json = [to_json(t) for t in tools]
+
+    return TOOLS_TEMPLATE.format(
+        tool_schemas="\n".join(tools_json),
+        dsml_token=dsml_token,
+        thinking_start_token=thinking_start_token,
+        thinking_end_token=thinking_end_token,
+    )
+
+
+def find_last_user_index(messages: List[Dict[str, Any]]) -> int:
+    """Find the index of the last user/developer message."""
+    last_user_index = -1
+    for idx in range(len(messages) - 1, -1, -1):
+        if messages[idx].get("role") in ["user", "developer"]:
+            last_user_index = idx
+            break
+    return last_user_index
+
+
+# ============================================================
+# Message Rendering
+# ============================================================
+
+def render_message(index: int, messages: List[Dict[str, Any]], thinking_mode: str, drop_thinking: bool = True, reasoning_effort: Optional[str] = None) -> str:
+    """
+    Render a single message at the given index into its encoded string form.
+
+    This is the core function that converts each message in the conversation
+    into the DeepSeek-V4 format.
+
+    Args:
+        index: Index of the message to render.
+        messages: Full list of messages in the conversation.
+        thinking_mode: Either "chat" or "thinking".
+        drop_thinking: Whether to drop reasoning content from earlier turns.
+        reasoning_effort: Optional reasoning effort level ("max", "high", or None).
+
+    Returns:
+        Encoded string for this message.
+    """
+    assert 0 <= index < len(messages)
+    assert thinking_mode in ["chat", "thinking"], f"Invalid thinking_mode `{thinking_mode}`"
+
+    prompt = ""
+    msg = messages[index]
+    last_user_idx = find_last_user_index(messages)
+
+    role = msg.get("role")
+    content = msg.get("content")
+    tools = msg.get("tools")
+    response_format = msg.get("response_format")
+    tool_calls = msg.get("tool_calls")
+    reasoning = msg.get("reasoning")
+    wo_eos = msg.get("wo_eos", False)
+
+    if tools:
+        tools = tools_from_openai_format(tools)
+    if tool_calls:
+        tool_calls = tool_calls_from_openai_format(tool_calls)
+
+    # Reasoning effort prefix (only at index 0 in thinking mode with max effort)
+    assert reasoning_effort in ['max', None, 'high'], f"Invalid reasoning effort: {reasoning_effort}"
+    if index == 0 and thinking_mode == "thinking" and reasoning_effort == 'max':
+        prompt += REASONING_EFFORT_MAX
+
+    if role == "system":
+        prompt += system_msg_template.format(content=content or "")
+        if tools:
+            prompt += "\n\n" + render_tools(tools)
+        if response_format:
+            prompt += "\n\n" + response_format_template.format(schema=to_json(response_format))
+
+    elif role == "developer":
+        assert content, f"Invalid message for role `{role}`: {msg}"
+
+        content_developer = USER_SP_TOKEN
+        content_developer += content
+
+        if tools:
+            content_developer += "\n\n" + render_tools(tools)
+        if response_format:
+            content_developer += "\n\n" + response_format_template.format(schema=to_json(response_format))
+
+        prompt += user_msg_template.format(content=content_developer)
+
+    elif role == "user":
+        prompt += USER_SP_TOKEN
+
+        # Handle content blocks (tool results mixed with text)
+        content_blocks = msg.get("content_blocks")
+        if content_blocks:
+            parts = []
+            for block in content_blocks:
+                block_type = block.get("type")
+                if block_type == "text":
+                    parts.append(block.get("text", ""))
+                elif block_type == "tool_result":
+                    tool_content = block.get("content", "")
+                    if isinstance(tool_content, list):
+                        text_parts = []
+                        for b in tool_content:
+                            if b.get("type") == "text":
+                                text_parts.append(b.get("text", ""))
+                            else:
+                                text_parts.append(f"[Unsupported {b.get('type')}]")
+                        tool_content = "\n\n".join(text_parts)
+                    parts.append(tool_output_template.format(content=tool_content))
+                else:
+                    parts.append(f"[Unsupported {block_type}]")
+            prompt += "\n\n".join(parts)
+        else:
+            prompt += content or ""
+
+    elif role == "latest_reminder":
+        prompt += LATEST_REMINDER_SP_TOKEN + latest_reminder_msg_template.format(content=content)
+
+    elif role == "tool":
+        raise NotImplementedError("deepseek_v4 merges tool messages into user; please preprocess with merge_tool_messages()")
+
+    elif role == "assistant":
+        thinking_part = ""
+        tc_content = ""
+
+        if tool_calls:
+            tc_list = [
+                tool_call_template.format(
+                    dsml_token=dsml_token,
+                    name=tc.get("name"),
+                    arguments=encode_arguments_to_dsml(tc)
+                )
+                for tc in tool_calls
+            ]
+            tc_content += '\n\n' + tool_calls_template.format(
+                dsml_token=dsml_token,
+                tool_calls="\n".join(tc_list),
+                tc_block_name=tool_calls_block_name,
+            )
+
+        summary_content = content or ""
+        reasoning = reasoning or ""
+
+        # Check if previous message has a task - if so, this is a task output (no thinking)
+        prev_has_task = index - 1 >= 0 and messages[index - 1].get("task") is not None
+
+        if thinking_mode == "thinking" and not prev_has_task:
+            if not drop_thinking or index > last_user_idx:
+                thinking_part = thinking_template.format(reasoning=reasoning) + thinking_end_token
+            else:
+                thinking_part = ""
+
+        if wo_eos:
+            prompt += assistant_msg_wo_eos_template.format(
+                reasoning=thinking_part,
+                content=summary_content,
+                tool_calls=tc_content,
+            )
+        else:
+            prompt += assistant_msg_template.format(
+                reasoning=thinking_part,
+                content=summary_content,
+                tool_calls=tc_content,
+            )
+    else:
+        raise NotImplementedError(f"Unknown role: {role}")
+
+    # Append transition tokens based on what follows
+    if index + 1 < len(messages) and messages[index + 1].get("role") not in ["assistant", "latest_reminder"]:
+        return prompt
+
+    task = messages[index].get("task")
+    if task is not None:
+        # Task special token for internal classification tasks
+        assert task in VALID_TASKS, f"Invalid task: '{task}'. Valid tasks are: {list(VALID_TASKS)}"
+        task_sp_token = DS_TASK_SP_TOKENS[task]
+
+        if task != "action":
+            # Non-action tasks: append task sp token directly after the message
+            prompt += task_sp_token
+        else:
+            # Action task: append Assistant + thinking token + action sp token
+            prompt += ASSISTANT_SP_TOKEN
+            prompt += thinking_end_token if thinking_mode != "thinking" else thinking_start_token
+            prompt += task_sp_token
+
+    elif messages[index].get("role") in ["user", "developer"]:
+        # Normal generation: append Assistant + thinking token
+        prompt += ASSISTANT_SP_TOKEN
+        if not drop_thinking and thinking_mode == "thinking":
+            prompt += thinking_start_token
+        elif drop_thinking and thinking_mode == "thinking" and index >= last_user_idx:
+            prompt += thinking_start_token
+        else:
+            prompt += thinking_end_token
+
+    return prompt
+
+
+# ============================================================
+# Preprocessing
+# ============================================================
+
+def merge_tool_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """
+    Merge tool messages into the preceding user message using content_blocks format.
+
+    DeepSeek-V4 does not have a standalone "tool" role; instead, tool results
+    are encoded as <tool_result> blocks within user messages.
+
+    This function converts a standard OpenAI-format conversation (with separate
+    "tool" role messages) into V4 format where tool results are merged into
+    user messages.
+
+    Args:
+        messages: List of message dicts in OpenAI format.
+
+    Returns:
+        Processed message list with tool messages merged into user messages.
+    """
+    merged: List[Dict[str, Any]] = []
+
+    for msg in messages:
+        msg = copy.deepcopy(msg)
+        role = msg.get("role")
+
+        if role == "tool":
+            # Convert tool message to a user message with tool_result block
+            tool_block = {
+                "type": "tool_result",
+                "tool_use_id": msg.get("tool_call_id", ""),
+                "content": msg.get("content", ""),
+            }
+            # Merge into previous message if it's already a user (merged tool)
+            if merged and merged[-1].get("role") == "user" and "content_blocks" in merged[-1]:
+                merged[-1]["content_blocks"].append(tool_block)
+            else:
+                merged.append({
+                    "role": "user",
+                    "content_blocks": [tool_block],
+                })
+        elif role == "user":
+            text_block = {"type": "text", "text": msg.get("content", "")}
+            if merged and merged[-1].get("role") == "user" and "content_blocks" in merged[-1] and merged[-1].get("task") is None:
+                merged[-1]["content_blocks"].append(text_block)
+            else:
+                new_msg = {
+                    "role": "user",
+                    "content": msg.get("content", ""),
+                    "content_blocks": [text_block],
+                }
+                # Preserve extra fields (task, wo_eos, mask, etc.)
+                for key in ("task", "wo_eos", "mask"):
+                    if key in msg:
+                        new_msg[key] = msg[key]
+                merged.append(new_msg)
+        else:
+            merged.append(msg)
+
+    return merged
+
+
+def sort_tool_results_by_call_order(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """
+    Sort tool_result blocks within user messages by the order of tool_calls
+    in the preceding assistant message.
+
+    Args:
+        messages: Preprocessed message list (after merge_tool_messages).
+
+    Returns:
+        Message list with sorted tool result blocks.
+    """
+    last_tool_call_order: Dict[str, int] = {}
+
+    for msg in messages:
+        role = msg.get("role")
+        if role == "assistant" and msg.get("tool_calls"):
+            last_tool_call_order = {}
+            for idx, tc in enumerate(msg["tool_calls"]):
+                tc_id = tc.get("id") or tc.get("function", {}).get("id", "")
+                if tc_id:
+                    last_tool_call_order[tc_id] = idx
+
+        elif role == "user" and msg.get("content_blocks"):
+            tool_blocks = [b for b in msg["content_blocks"] if b.get("type") == "tool_result"]
+            if len(tool_blocks) > 1 and last_tool_call_order:
+                sorted_blocks = sorted(
+                    tool_blocks,
+                    key=lambda b: last_tool_call_order.get(b.get("tool_use_id", ""), 0)
+                )
+                sorted_idx = 0
+                new_blocks = []
+                for block in msg["content_blocks"]:
+                    if block.get("type") == "tool_result":
+                        new_blocks.append(sorted_blocks[sorted_idx])
+                        sorted_idx += 1
+                    else:
+                        new_blocks.append(block)
+                msg["content_blocks"] = new_blocks
+
+    return messages
+
+
+# ============================================================
+# Main Encoding Function
+# ============================================================
+
+def encode_messages(
+    messages: List[Dict[str, Any]],
+    thinking_mode: str,
+    context: Optional[List[Dict[str, Any]]] = None,
+    drop_thinking: bool = True,
+    add_default_bos_token: bool = True,
+    reasoning_effort: Optional[str] = None,
+) -> str:
+    """
+    Encode a list of messages into the DeepSeek-V4 prompt format.
+
+    This is the main entry point for encoding conversations. It handles:
+    - BOS token insertion
+    - Thinking mode with optional reasoning content dropping
+    - Tool message merging into user messages
+    - Multi-turn conversation context
+
+    Args:
+        messages: List of message dicts to encode.
+        thinking_mode: Either "chat" or "thinking".
+        context: Optional preceding context messages (already encoded prefix).
+        drop_thinking: If True, drop reasoning from earlier assistant turns
+                      (only keep reasoning for messages after the last user message).
+        add_default_bos_token: Whether to prepend BOS token at conversation start.
+        reasoning_effort: Optional reasoning effort level ("max", "high", or None).
+
+    Returns:
+        The encoded prompt string.
+    """
+    context = context if context else []
+
+    # Preprocess: merge tool messages and sort tool results
+    messages = merge_tool_messages(messages)
+    messages = sort_tool_results_by_call_order(context + messages)[len(context):]
+    if context:
+        context = merge_tool_messages(context)
+        context = sort_tool_results_by_call_order(context)
+
+    full_messages = context + messages
+
+    prompt = bos_token if add_default_bos_token and len(context) == 0 else ""
+
+    # Resolve drop_thinking: if any message has tools defined, don't drop thinking
+    effective_drop_thinking = drop_thinking
+    if any(m.get("tools") for m in full_messages):
+        effective_drop_thinking = False
+
+    if thinking_mode == "thinking" and effective_drop_thinking:
+        full_messages = _drop_thinking_messages(full_messages)
+        # After dropping, recalculate how many messages to render
+        # (context may have shrunk too)
+        num_to_render = len(full_messages) - len(_drop_thinking_messages(context))
+        context_len = len(full_messages) - num_to_render
+    else:
+        num_to_render = len(messages)
+        context_len = len(context)
+
+    for idx in range(num_to_render):
+        prompt += render_message(
+            idx + context_len,
+            full_messages,
+            thinking_mode=thinking_mode,
+            drop_thinking=effective_drop_thinking,
+            reasoning_effort=reasoning_effort,
+        )
+
+    return prompt
+
+
+def _drop_thinking_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """
+    Drop reasoning and non-essential messages before the last user message.
+
+    Behavior:
+    - Messages with role in ["user", "system", "tool", "latest_reminder"] are always kept.
+    - Messages at or after the last user index are always kept.
+    - Assistant messages before the last user get reasoning removed.
+    - Developer messages before the last user are dropped entirely.
+    """
+    last_user_idx = find_last_user_index(messages)
+    result = []
+    keep_roles = {"user", "system", "tool", "latest_reminder", "direct_search_results"}
+
+    for idx, msg in enumerate(messages):
+        role = msg.get("role")
+        if role in keep_roles or idx >= last_user_idx:
+            result.append(msg)
+        elif role == "assistant":
+            msg = copy.copy(msg)
+            msg.pop("reasoning", None)
+            result.append(msg)
+        # developer and other roles before last_user_idx are dropped
+
+    return result
+
+
+# ============================================================
+# Parsing (Decoding model output)
+# ============================================================
+
+def _read_until_stop(index: int, text: str, stop: List[str]) -> Tuple[int, str, Optional[str]]:
+    """
+    Read text from index until one of the stop strings is found.
+
+    Returns:
+        Tuple of (new_index, content_before_stop, matched_stop_string_or_None).
+    """
+    min_pos = len(text)
+    matched_stop = None
+
+    for s in stop:
+        pos = text.find(s, index)
+        if pos != -1 and pos < min_pos:
+            min_pos = pos
+            matched_stop = s
+
+    if matched_stop:
+        content = text[index:min_pos]
+        return min_pos + len(matched_stop), content, matched_stop
+    else:
+        content = text[index:]
+        return len(text), content, None
+
+
+def parse_tool_calls(index: int, text: str) -> Tuple[int, Optional[str], List[Dict[str, str]]]:
+    """
+    Parse DSML tool calls from text starting at the given index.
+
+    Args:
+        index: Starting position in text.
+        text: The full text to parse.
+
+    Returns:
+        Tuple of (new_index, last_stop_token, list_of_tool_call_dicts).
+        Each tool call dict has "name" and "arguments" keys.
+    """
+    tool_calls: List[Dict[str, Any]] = []
+    stop_token = None
+    tool_calls_end_token = f"</{dsml_token}{tool_calls_block_name}>"
+
+    while index < len(text):
+        index, content_before, stop_token = _read_until_stop(index, text, [f"<{dsml_token}invoke", tool_calls_end_token])
+        if content_before != ">\n":
+            raise ValueError(f"Tool call format error: expected '>\\n' but got '{content_before}'")
+
+        if stop_token == tool_calls_end_token:
+            break
+
+        if stop_token is None:
+            raise ValueError("Missing special token in tool calls")
+
+        index, tool_name_content, stop_token = _read_until_stop(index, text, [f"<{dsml_token}parameter", f"</{dsml_token}invoke"])
+
+        p_tool_name = re.findall(r'^\s*name="(.*?)">\n$', tool_name_content, flags=re.DOTALL)
+        if len(p_tool_name) != 1:
+            raise ValueError(f"Tool name format error: '{tool_name_content}'")
+        tool_name = p_tool_name[0]
+
+        tool_args: Dict[str, Tuple[str, str]] = {}
+        while stop_token == f"<{dsml_token}parameter":
+            index, param_content, stop_token = _read_until_stop(index, text, [f"/{dsml_token}parameter"])
+
+            param_kv = re.findall(r'^ name="(.*?)" string="(true|false)">(.*?)<$', param_content, flags=re.DOTALL)
+            if len(param_kv) != 1:
+                raise ValueError(f"Parameter format error: '{param_content}'")
+            param_name, string, param_value = param_kv[0]
+
+            if param_name in tool_args:
+                raise ValueError(f"Duplicate parameter name: '{param_name}'")
+            tool_args[param_name] = (param_value, string)
+
+            index, content, stop_token = _read_until_stop(index, text, [f"<{dsml_token}parameter", f"</{dsml_token}invoke"])
+            if content != ">\n":
+                raise ValueError(f"Parameter format error: expected '>\\n' but got '{content}'")
+
+        tool_call = decode_dsml_to_arguments(tool_name=tool_name, tool_args=tool_args)
+        tool_calls.append(tool_call)
+
+    return index, stop_token, tool_calls
+
+
+def parse_message_from_completion_text(text: str, thinking_mode: str) -> Dict[str, Any]:
+    """
+    Parse a model completion text into a structured assistant message.
+
+    This function takes the raw text output from the model (a single assistant turn)
+    and extracts:
+    - reasoning (thinking block)
+    - content (summary/response)
+    - tool_calls (if any)
+
+    NOTE: This function is designed to parse only correctly formatted strings and
+    will raise ValueError for malformed output.
+
+    Args:
+        text: The raw completion text (including EOS token).
+        thinking_mode: Either "chat" or "thinking".
+
+    Returns:
+        Dict with keys: "role", "content", "reasoning", "tool_calls".
+        tool_calls are in OpenAI format.
+    """
+    summary_content, reasoning = "", ""
+    tool_calls: List[Dict[str, str]] = []
+    index, stop_token = 0, None
+    tool_calls_start_token = f"\n\n<{dsml_token}{tool_calls_block_name}"
+
+    is_thinking = thinking_mode == "thinking"
+    is_tool_calling = False
+
+    if is_thinking:
+        index, content_delta, stop_token = _read_until_stop(index, text, [thinking_end_token, tool_calls_start_token])
+        reasoning = content_delta
+        if stop_token != thinking_end_token:
+            raise ValueError("Invalid thinking format: missing </think>")
+
+    index, content_delta, stop_token = _read_until_stop(index, text, [eos_token, tool_calls_start_token])
+    summary_content = content_delta
+    if stop_token == tool_calls_start_token:
+        is_tool_calling = True
+    else:
+        if stop_token != eos_token:
+            raise ValueError("Invalid format: missing EOS token")
+
+    if is_tool_calling:
+        index, stop_token, tool_calls = parse_tool_calls(index, text)
+
+        index, tool_ends_text, stop_token = _read_until_stop(index, text, [eos_token])
+        if tool_ends_text:
+            raise ValueError("Unexpected content after tool calls")
+
+    if len(text) != index or stop_token not in [eos_token, None]:
+        raise ValueError("Unexpected content at end")
+
+    for sp_token in [bos_token, eos_token, thinking_start_token, thinking_end_token, dsml_token]:
+        if sp_token in summary_content or sp_token in reasoning:
+            raise ValueError(f"Unexpected special token '{sp_token}' in content")
+
+    return {
+        "role": "assistant",
+        "content": summary_content,
+        "reasoning": reasoning,
+        "tool_calls": tool_calls_to_openai_format(tool_calls)
+    }
+
+# fmt: on
diff --git a/vllm/tokenizers/fastokens.py b/vllm/tokenizers/fastokens.py
new file mode 100644
index 000000000000..f708aa3a70e8
--- /dev/null
+++ b/vllm/tokenizers/fastokens.py
@@ -0,0 +1,47 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""``fastokens`` tokenizer mode.
+
+Loads a Hugging Face fast tokenizer whose internal Rust tokenizer is replaced
+by the fastokens shim. fastokens also rebinds
+``tokenizers.decoders.DecodeStream`` so the streaming detokenizer accepts the
+shim. Both patches are installed for the lifetime of the process —
+``patch_transformers()`` is idempotent.
+"""
+
+from pathlib import Path
+
+from .hf import CachedHfTokenizer, HfTokenizer
+from .protocol import TokenizerLike
+
+
+def _apply_fastokens_patch() -> None:
+    try:
+        import fastokens
+    except ImportError as e:
+        raise ImportError(
+            "The 'fastokens' package is required for tokenizer_mode='fastokens'."
+        ) from e
+    fastokens.patch_transformers()
+
+
+class FastokensTokenizer(TokenizerLike):
+    @classmethod
+    def from_pretrained(
+        cls,
+        path_or_repo_id: str | Path,
+        *args,
+        trust_remote_code: bool = False,
+        revision: str | None = None,
+        download_dir: str | None = None,
+        **kwargs,
+    ) -> HfTokenizer:
+        _apply_fastokens_patch()
+        return CachedHfTokenizer.from_pretrained(
+            path_or_repo_id,
+            *args,
+            trust_remote_code=trust_remote_code,
+            revision=revision,
+            download_dir=download_dir,
+            **kwargs,
+        )
diff --git a/vllm/tokenizers/hf.py b/vllm/tokenizers/hf.py
index 85c812398529..b4248e229a68 100644
--- a/vllm/tokenizers/hf.py
+++ b/vllm/tokenizers/hf.py
@@ -2,8 +2,9 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import contextlib
 import copy
+import queue
 from pathlib import Path
-from typing import TypeAlias
+from typing import TypeAlias, TypeVar
 
 from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
 
@@ -12,6 +13,92 @@
 from .protocol import TokenizerLike
 
 HfTokenizer: TypeAlias = PreTrainedTokenizer | PreTrainedTokenizerFast
+_T = TypeVar("_T", bound=TokenizerLike)
+
+
+class ThreadSafeHFTokenizerMixin:
+    """Mixin class for thread-safe HF fast tokenizers."""
+
+    pass
+
+
+def maybe_make_thread_pool(tokenizer: _T, copies: int = 1):
+    """
+    If `tokenizer` is a `PreTrainedTokenizerFast`, modify the tokenizer
+    in-place to make the public interface thread-safe by routing calls
+    through a deep-copied tokenizer pool.
+
+    Note that:
+    - Only ``TokenizerLike``'s public interface is thread-safe.
+      This doesn't include ``_tokenizer`` property nor any mutation
+      methods like ``add_special_tokens`` or ``add_tokens``.
+    - Adjacent method calls could happen on different deep copies.
+    """
+    if not isinstance(tokenizer, PreTrainedTokenizerFast) or isinstance(
+        tokenizer, ThreadSafeHFTokenizerMixin
+    ):
+        return tokenizer
+
+    og_tokenizer = copy.copy(tokenizer)
+
+    tokenizer_pool: queue.Queue[PreTrainedTokenizerFast] = queue.Queue()
+    for _ in range(copies):
+        tokenizer_pool.put(copy.deepcopy(og_tokenizer))
+
+    @contextlib.contextmanager
+    def _borrow_from_pool():
+        try:
+            tok = tokenizer_pool.get_nowait()
+            yield tok
+        except queue.Empty:
+            tok = copy.deepcopy(og_tokenizer)
+            yield tok
+        finally:
+            tokenizer_pool.put(tok)
+
+    class TokenizerPool(tokenizer.__class__, ThreadSafeHFTokenizerMixin):  # type: ignore
+        def apply_chat_template(self, *args, **kwargs):
+            with _borrow_from_pool() as tok:
+                return tok.apply_chat_template(*args, **kwargs)
+
+        def batch_decode(self, *args, **kwargs):
+            with _borrow_from_pool() as tok:
+                return tok.batch_decode(*args, **kwargs)
+
+        def batch_encode(self, *args, **kwargs):
+            with _borrow_from_pool() as tok:
+                return tok.batch_encode(*args, **kwargs)
+
+        def convert_tokens_to_ids(self, *args, **kwargs):
+            with _borrow_from_pool() as tok:
+                return tok.convert_tokens_to_ids(*args, **kwargs)
+
+        def convert_ids_to_tokens(self, *args, **kwargs):
+            with _borrow_from_pool() as tok:
+                return tok.convert_ids_to_tokens(*args, **kwargs)
+
+        def convert_tokens_to_string(self, *args, **kwargs):
+            with _borrow_from_pool() as tok:
+                return tok.convert_tokens_to_string(*args, **kwargs)
+
+        def decode(self, *args, **kwargs):
+            with _borrow_from_pool() as tok:
+                return tok.decode(*args, **kwargs)
+
+        def encode(self, *args, **kwargs):
+            with _borrow_from_pool() as tok:
+                return tok.encode(*args, **kwargs)
+
+        def __call__(self, *args, **kwargs):
+            with _borrow_from_pool() as tok:
+                return tok(*args, **kwargs)
+
+        def __reduce__(self):
+            return maybe_make_thread_pool, (og_tokenizer, copies)
+
+    TokenizerPool.__name__ = f"TokenizerPool{og_tokenizer.__class__.__name__}"
+
+    tokenizer.__class__ = TokenizerPool
 
 
 def get_cached_tokenizer(tokenizer: HfTokenizer) -> HfTokenizer:
@@ -103,7 +190,10 @@ def from_pretrained(
                     "is a custom tokenizer not yet available in the "
                     "HuggingFace transformers library, consider "
                     "setting `trust_remote_code=True` in LLM or using "
-                    "the `--trust-remote-code` flag in the CLI."
+                    "the `--trust-remote-code` flag in the CLI. If the "
+                    "model was created with a newer version of "
+                    "transformers, consider upgrading: "
+                    "`uv pip install --upgrade transformers`"
                 )
                 raise RuntimeError(err_msg) from e
             else:
diff --git a/vllm/tokenizers/mistral.py b/vllm/tokenizers/mistral.py
index 147dca88877b..8fce690433ef 100644
--- a/vllm/tokenizers/mistral.py
+++ b/vllm/tokenizers/mistral.py
@@ -13,7 +13,6 @@
 from mistral_common.protocol.instruct.request import (
     ReasoningEffort,
 )
-from mistral_common.protocol.instruct.tool_calls import Function, Tool
 from mistral_common.protocol.instruct.validator import ValidationMode
 from mistral_common.tokens.tokenizers.base import (
     SpecialTokenPolicy,
@@ -54,6 +53,20 @@
 logger = init_logger(__name__)
 
 
+def _pop_unallowed_keys_and_warn(
+    dictionary: dict[str, Any], allowed_keys: set[str], err_dict_name: str
+):
+    keys = list(dictionary.keys())
+    for key in keys:
+        if key not in allowed_keys:
+            dictionary.pop(key)
+            logger.warning_once(
+                f"'{key=}' is not supported by mistral-common "
+                f"for {err_dict_name}. It has been popped from the "
+                "object."
+            )
+
+
 def maybe_serialize_tool_calls(request: "MistralChatCompletionRequest"):
     # SEE: https://github.com/vllm-project/vllm/pull/9951
     # Credits go to: @gcalmettes
@@ -123,12 +136,11 @@ def truncate_tool_call_ids(request: "MistralChatCompletionRequest"):
                 request.messages[i]["tool_call_id"] = tool_call_id
 
 
-def _prepare_apply_chat_template_tools_and_messages(
+def _validate_apply_chat_template_args(
     messages: list["ChatCompletionMessageParam"],
-    tools: list[dict[str, Any]] | None = None,
     continue_final_message: bool = False,
     add_generation_prompt: bool = False,
-) -> tuple[list["ChatCompletionMessageParam"], list[dict[str, Any]] | None]:
+) -> None:
     if add_generation_prompt and continue_final_message:
         raise ValueError(
             "Cannot set both `add_generation_prompt` and "
@@ -152,54 +164,6 @@ def _prepare_apply_chat_template_tools_and_messages(
             "the last message is not from the assistant."
         )
 
-    # mistral-common requires AssistantMessage content to be string [1].
-    #
-    # [1]: https://github.com/mistralai/mistral-common/blob/f4a06998b75ed78bbf5aaf569590b772ea26c9f6/src/mistral_common/protocol/instruct/messages.py#L80
-    for message in messages:
-        # Remove reasoning as unsupported by Mistral
-        _ = message.pop("reasoning", None)  # type: ignore
-
-    # The Mistral client, in comparison to the OpenAI client, requires the
-    # "parameters" dict and the "description" string to be present
-    # even if they are empty.
-    if tools:
-        for function in [
-            tool["function"] for tool in tools if tool["type"] == "function"
-        ]:
-            if function.get("parameters") is None:
-                function["parameters"] = {}
-            if function.get("description") is None:
-                function["description"] = ""
-
-        # We filter not supported arguments to avoid throwing an error.
-        # TODO(juliendenize): remove this once OpenAI API is better supported by
-        # `mistral-common`.
-        tools_fields = set(Tool.model_fields.keys())
-        function_fields = set(Function.model_fields.keys())
-        for tool in tools:
-            tool_keys = list(tool.keys())
-            for tool_key in tool_keys:
-                if tool_key not in tools_fields:
-                    tool.pop(tool_key)
-                    logger.warning_once(
-                        f"'{tool_key}' is not supported by mistral-common for tools. "
-                        "It has been popped from the tool definition."
-                    )
-                if tool["type"] == "function":
-                    function_keys = list(tool["function"].keys())
-                    for function_key in function_keys:
-                        if function_key not in function_fields:
-                            tool["function"].pop(function_key)
-                            logger.warning_once(
-                                f"'{function_key}' is not supported by mistral-common "
-                                "for function tools. It has been popped from the "
-                                "function definition."
-                            )
-                else:
-                    raise ValueError("mistral-common only supports function tools.")
-
-    return messages, tools
-
 
 def validate_request_params(request: "ChatCompletionRequest"):
     if request.chat_template is not None or request.chat_template_kwargs is not None:
@@ -438,8 +402,8 @@ def apply_chat_template(
         if self.version >= 15:
             version_kwargs["reasoning_effort"] = kwargs.get("reasoning_effort")
 
-        messages, tools = _prepare_apply_chat_template_tools_and_messages(
-            messages, tools, continue_final_message, add_generation_prompt
+        _validate_apply_chat_template_args(
+            messages, continue_final_message, add_generation_prompt
         )
 
         return self.transformers_tokenizer.apply_chat_template(
diff --git a/vllm/tokenizers/registry.py b/vllm/tokenizers/registry.py
index 7d48e3c6ff91..88ad15efeda4 100644
--- a/vllm/tokenizers/registry.py
+++ b/vllm/tokenizers/registry.py
@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import contextlib
 from dataclasses import dataclass, field
 from functools import lru_cache
 from pathlib import Path
@@ -10,6 +11,7 @@
 
 import vllm.envs as envs
 from vllm.logger import init_logger
+from vllm.transformers_utils.config import get_config
 from vllm.transformers_utils.gguf_utils import (
     check_gguf_file,
     get_gguf_file_path_from_hf,
@@ -31,8 +33,17 @@
 logger = init_logger(__name__)
 
 
+# Model types whose hub tokenizer_class is incorrect and should be overridden with
+# TokenizersBackend (the generic fast tokenizer). Adding a model type here is always a
+# temporary workaround and better long term solutions are:
+# - Add model type to MODELS_WITH_INCORRECT_HUB_TOKENIZER_CLASS in transformers (better)
+# - Fix tokenizer_class on the hub for the affected models (best)
+_MODEL_TYPES_WITH_INCORRECT_TOKENIZER_CLASS: set[str] = {"step3_vl"}
+
 _VLLM_TOKENIZERS = {
     "deepseek_v32": ("deepseek_v32", "DeepseekV32Tokenizer"),
+    "deepseek_v4": ("deepseek_v4", "DeepseekV4Tokenizer"),
+    "fastokens": ("fastokens", "FastokensTokenizer"),
     "grok2": ("grok2", "Grok2Tokenizer"),
     "hf": ("hf", "CachedHfTokenizer"),
     "kimi_audio": ("kimi_audio", "KimiAudioTokenizer"),
@@ -202,7 +213,31 @@ def get_tokenizer(
         **kwargs,
     )
 
-    if tokenizer_cls == TokenizerLike:
+    # Ensure that, if the config were to come from vllm.transformers_utils.config, it is
+    # registered with AutoConfig before the tokenizer is loaded. This is necessary since
+    # tokenizer_cls_.from_pretrained will call AutoConfig.from_pretrained internally.
+    # This may fail for paths that don't have a model config (e.g. LoRA adapters),
+    # which is fine — those don't need custom config registration.
+    config = None
+    with contextlib.suppress(ValueError, OSError):
+        config = get_config(
+            tokenizer_name,
+            trust_remote_code=trust_remote_code,
+            revision=revision,
+        )
+
+    # Some models have an incorrect tokenizer_class on the hub.
+    # For these model types, bypass AutoTokenizer and use TokenizersBackend directly.
+    model_type = getattr(config, "model_type", None) if config else None
+    if model_type in _MODEL_TYPES_WITH_INCORRECT_TOKENIZER_CLASS:
+        from transformers.tokenization_utils_tokenizers import TokenizersBackend
+
+        logger.debug(
+            "Overriding tokenizer_class to TokenizersBackend for model_type=%r",
+            model_type,
+        )
+        tokenizer_cls_ = TokenizersBackend
+    elif tokenizer_cls == TokenizerLike:
         tokenizer_cls_ = TokenizerRegistry.load_tokenizer_cls(tokenizer_mode)
     else:
         tokenizer_cls_ = tokenizer_cls
diff --git a/vllm/tool_parsers/__init__.py b/vllm/tool_parsers/__init__.py
index bffa00c4ef31..7c5f45d2022e 100644
--- a/vllm/tool_parsers/__init__.py
+++ b/vllm/tool_parsers/__init__.py
@@ -34,6 +34,18 @@
         "deepseekv32_tool_parser",
         "DeepSeekV32ToolParser",
     ),
+    "deepseek_v4": (
+        "deepseekv4_tool_parser",
+        "DeepSeekV4ToolParser",
+    ),
+    "cohere_command3": (
+        "cohere_command_tool_parser",
+        "CohereCommand3ToolParser",
+    ),
+    "cohere_command4": (
+        "cohere_command_tool_parser",
+        "CohereCommand4ToolParser",
+    ),
     "ernie45": (
         "ernie45_tool_parser",
         "Ernie45ToolParser",
@@ -62,10 +74,18 @@
         "hermes_tool_parser",
         "Hermes2ProToolParser",
     ),
+    "poolside_v1": (
+        "poolside_v1_tool_parser",
+        "PoolsideV1ToolParser",
+    ),
     "hunyuan_a13b": (
         "hunyuan_a13b_tool_parser",
         "HunyuanA13BToolParser",
     ),
+    "hy_v3": (
+        "hy_v3_tool_parser",
+        "HYV3ToolParser",
+    ),
     "internlm": (
         "internlm2_tool_parser",
         "Internlm2ToolParser",
@@ -74,6 +94,10 @@
         "jamba_tool_parser",
         "JambaToolParser",
     ),
+    "lfm2": (
+        "lfm2_tool_parser",
+        "Lfm2ToolParser",
+    ),
     "kimi_k2": (
         "kimi_k2_tool_parser",
         "KimiK2ToolParser",
@@ -94,6 +118,10 @@
         "longcat_tool_parser",
         "LongcatFlashToolParser",
     ),
+    "mimo": (
+        "qwen3xml_tool_parser",
+        "Qwen3XMLToolParser",
+    ),
     "minimax_m2": (
         "minimax_m2_tool_parser",
         "MinimaxM2ToolParser",
diff --git a/vllm/tool_parsers/abstract_tool_parser.py b/vllm/tool_parsers/abstract_tool_parser.py
index c127bd9dd689..c3438082a72d 100644
--- a/vllm/tool_parsers/abstract_tool_parser.py
+++ b/vllm/tool_parsers/abstract_tool_parser.py
@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import importlib
+import json
 import os
 from collections.abc import Callable, Sequence
 from functools import cached_property
@@ -13,6 +14,7 @@
 from openai.types.responses.function_tool import FunctionTool
 
 from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionNamedToolChoiceParam,
     ChatCompletionRequest,
     ChatCompletionToolsParam,
 )
@@ -23,6 +25,7 @@
 from vllm.entrypoints.openai.responses.protocol import (
     ResponsesRequest,
 )
+from vllm.envs import VLLM_ENFORCE_STRICT_TOOL_CALLING
 from vllm.logger import init_logger
 from vllm.sampling_params import (
     StructuredOutputsParams,
@@ -44,6 +47,17 @@ class ToolParser:
     derived classes.
     """
 
+    # When True (default), the serving layer uses the standard JSON-based
+    # parsing for tool_choice="required" and named function tool_choice,
+    # which works for models where guided decoding produces well-formed
+    # JSON output (e.g. Hermes).
+    # Subclasses set False when the standard parsing does not work for
+    # their model's output format (e.g. GLM models that use XML).  When
+    # False, the serving layer falls back to the tool_parser's
+    # extract_tool_calls / extract_tool_calls_streaming methods for
+    # required/named tool_choice, treating them the same as "auto".
+    supports_required_and_named: bool = True
+
     def __init__(
         self,
         tokenizer: TokenizerLike,
@@ -72,13 +86,39 @@ def vocab(self) -> dict[str, int]:
         return self.model_tokenizer.get_vocab()
 
     def adjust_request(
-        self, request: ChatCompletionRequest | ResponsesRequest
+        self,
+        request: ChatCompletionRequest | ResponsesRequest,
     ) -> ChatCompletionRequest | ResponsesRequest:
-        """
-        Static method that used to adjust the request parameters.
-        """
+        # If there are no tools, return the request as is.
         if not request.tools:
             return request
+
+        # Step 1 (highest priority for ChatCompletionRequest): apply
+        # vLLM-owned structural tag support for model-specific tool formats.
+        if (
+            isinstance(request, ChatCompletionRequest)
+            and VLLM_ENFORCE_STRICT_TOOL_CALLING
+        ):
+            need_tool_calling = (
+                request.tool_choice == "auto"
+                or request.tool_choice == "required"
+                or isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam)
+            )
+            if need_tool_calling:
+                structure_tag = self.get_structural_tag(request)
+                if structure_tag is not None:
+                    if request.structured_outputs is None:
+                        request.structured_outputs = StructuredOutputsParams(
+                            structural_tag=json.dumps(structure_tag.model_dump()),
+                        )
+                    else:
+                        request.structured_outputs.structural_tag = json.dumps(
+                            structure_tag.model_dump()
+                        )
+                    return request
+
+        # Step 2: set structured output params when tool constraints are
+        # derived from the tool schema.
         json_schema_from_tool = get_json_schema_from_tools(
             tool_choice=request.tool_choice, tools=request.tools
         )
@@ -92,17 +132,27 @@ def adjust_request(
                 )
                 request.response_format = None
             if isinstance(request, ResponsesRequest):
-                request.text = ResponseTextConfig()
-                request.text.format = ResponseFormatTextJSONSchemaConfig(
-                    name="tool_calling_response",
-                    schema=json_schema_from_tool,
-                    type="json_schema",
-                    description="Response format for tool calling",
-                    strict=True,
+                # Single-shot construction so Pydantic v2 tracks `format`
+                # in __fields_set__ — assigning to `.format` after the bare
+                # `ResponseTextConfig()` constructor does not, which can
+                # drop the nested config from `model_dump`. Also drop the
+                # `description` kwarg: it is not a field on
+                # ResponseFormatTextJSONSchemaConfig and was being silently
+                # passed through as extra.
+                request.text = ResponseTextConfig(
+                    format=ResponseFormatTextJSONSchemaConfig(
+                        type="json_schema",
+                        name="tool_calling_response",
+                        schema=json_schema_from_tool,
+                        strict=True,
+                    )
                 )
 
         return request
 
+    def get_structural_tag(self, request: ChatCompletionRequest):
+        return None
+
     def extract_tool_calls(
         self, model_output: str, request: ChatCompletionRequest
     ) -> ExtractedToolCallInformation:
diff --git a/vllm/tool_parsers/cohere_command_tool_parser.py b/vllm/tool_parsers/cohere_command_tool_parser.py
new file mode 100644
index 000000000000..0b252ce3177a
--- /dev/null
+++ b/vllm/tool_parsers/cohere_command_tool_parser.py
@@ -0,0 +1,127 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Sequence
+
+try:
+    from cohere_melody import PyFilter, PyFilterOptions
+except ImportError as e:
+    raise ImportError(
+        "The Cohere tool parser requires the `cohere_melody` "
+        "package, which is not installed. Install it with:\n"
+        "    pip install cohere_melody"
+    ) from e
+
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+)
+from vllm.entrypoints.openai.engine.protocol import (
+    DeltaFunctionCall,
+    DeltaMessage,
+    DeltaToolCall,
+    ExtractedToolCallInformation,
+    FunctionCall,
+    ToolCall,
+)
+from vllm.entrypoints.openai.responses.protocol import (
+    ResponsesRequest,
+)
+from vllm.tokenizers import TokenizerLike
+from vllm.tool_parsers import ToolParser
+from vllm.tool_parsers.utils import Tool
+
+
+class BaseCohereCommandToolParser(ToolParser):
+    def __init__(
+        self,
+        tokenizer: TokenizerLike,
+        streaming_opts: PyFilterOptions,
+        unary_opts: PyFilterOptions,
+    ):
+        super().__init__(tokenizer)
+        self.melody_streaming = PyFilter(streaming_opts)
+        self.melody_unary = PyFilter(unary_opts)
+
+    def adjust_request(
+        self, request: ChatCompletionRequest | ResponsesRequest
+    ) -> ChatCompletionRequest | ResponsesRequest:
+        request = super().adjust_request(request)
+        request.skip_special_tokens = False
+        return request
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> DeltaMessage | None:
+        r = self.melody_streaming.write_decoded(delta_text)
+        if r.content is not None:
+            return DeltaMessage(content=r.content)
+        if r.reasoning is not None:
+            return DeltaMessage(reasoning=r.reasoning)
+        if r.tool_calls:
+            return DeltaMessage(
+                tool_calls=[
+                    DeltaToolCall(
+                        id=tc.id,
+                        index=tc.index,
+                        type="function",
+                        function=DeltaFunctionCall(
+                            name=tc.name, arguments=tc.arguments
+                        ),
+                    )
+                    for tc in r.tool_calls
+                ]
+            )
+        return None
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        result = self.melody_unary.process_full_text(model_output)
+        tool_calls = [
+            ToolCall(
+                id=tc.id,
+                type="function",
+                function=FunctionCall(name=tc.name, arguments=tc.arguments),
+            )
+            for tc in result.tool_calls
+        ]
+        return ExtractedToolCallInformation(
+            tools_called=len(tool_calls) > 0,
+            tool_calls=tool_calls,
+            content=result.content,
+        )
+
+
+class CohereCommand3ToolParser(BaseCohereCommandToolParser):
+    def __init__(
+        self,
+        tokenizer: TokenizerLike,
+        tools: list[Tool] | None = None,
+    ):
+        super().__init__(
+            tokenizer,
+            streaming_opts=PyFilterOptions().cmd3(),
+            unary_opts=PyFilterOptions().cmd3(),
+        )
+
+
+class CohereCommand4ToolParser(BaseCohereCommandToolParser):
+    def __init__(
+        self,
+        tokenizer: TokenizerLike,
+        tools: list[Tool] | None = None,
+    ):
+        super().__init__(
+            tokenizer,
+            streaming_opts=PyFilterOptions().cmd4(),
+            unary_opts=PyFilterOptions().cmd4(),
+        )
diff --git a/vllm/tool_parsers/deepseekv32_tool_parser.py b/vllm/tool_parsers/deepseekv32_tool_parser.py
index c4c62390c81b..f01f7f929426 100644
--- a/vllm/tool_parsers/deepseekv32_tool_parser.py
+++ b/vllm/tool_parsers/deepseekv32_tool_parser.py
@@ -26,6 +26,7 @@
     Tool,
     ToolParser,
 )
+from vllm.tool_parsers.utils import partial_tag_overlap
 
 logger = init_logger(__name__)
 
@@ -45,27 +46,30 @@ class DeepSeekV32ToolParser(ToolParser):
     </｜DSML｜function_calls>
     """
 
+    tool_call_start_token: str = "<｜DSML｜function_calls>"
+    tool_call_end_token: str = "</｜DSML｜function_calls>"
+
     def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
         super().__init__(tokenizer, tools)
 
         self.prev_tool_call_arr: list[dict] = []
 
-        # Sentinel token
-        self.tool_call_start_token: str = "<｜DSML｜function_calls>"
-
         # Streaming state
-        self.is_tool_call_started: bool = False
         self.current_tool_index: int = 0
+        self._sent_content_idx: int = 0
 
         # Regex patterns for complete parsing
         self.tool_call_complete_regex = re.compile(
-            r"<｜DSML｜function_calls>(.*?)</｜DSML｜function_calls>", re.DOTALL
+            re.escape(self.tool_call_start_token)
+            + r"(.*?)"
+            + re.escape(self.tool_call_end_token),
+            re.DOTALL,
         )
         self.invoke_complete_regex = re.compile(
             r'<｜DSML｜invoke\s+name="([^"]+)"\s*>(.*?)</｜DSML｜invoke>', re.DOTALL
         )
         self.parameter_complete_regex = re.compile(
-            r'<｜DSML｜parameter\s+name="([^"]+)"\s+string="(?:true|false)"\s*>(.*?)</｜DSML｜parameter>',
+            r'<｜DSML｜parameter\s+name="([^"]+)"\s+string="(true|false)"\s*>(.*?)</｜DSML｜parameter>',
             re.DOTALL,
         )
 
@@ -85,7 +89,7 @@ def adjust_request(
         request = super().adjust_request(request)
         if request.tools and request.tool_choice != "none":
             # Ensure tool call tokens
-            # (<｜DSML｜function_calls>, </｜DSML｜function_calls>)
+            # (e.g. <｜DSML｜function_calls>, </｜DSML｜function_calls>)
             # are not skippedduring decoding.
             # Even though they are not marked as special tokens,
             # setting skip_special_tokens=False ensures proper handling in
@@ -97,10 +101,12 @@ def _generate_tool_call_id(self) -> str:
         """Generate a unique tool call ID."""
         return f"call_{uuid.uuid4().hex[:24]}"
 
-    def _parse_invoke_params(self, invoke_str: str) -> dict:
-        param_dict = dict()
-        for param_name, param_val in self.parameter_complete_regex.findall(invoke_str):
-            param_dict[param_name] = param_val
+    def _parse_invoke_params(self, invoke_str: str) -> dict[str, tuple[str, str]]:
+        param_dict: dict[str, tuple[str, str]] = {}
+        for param_name, string_attr, param_val in self.parameter_complete_regex.findall(
+            invoke_str
+        ):
+            param_dict[param_name] = (param_val, string_attr)
         return param_dict
 
     def _convert_param_value_checked(self, value: str, param_type: str) -> Any:
@@ -138,10 +144,32 @@ def _convert_param_value(self, value: str, param_type: str | list[str]) -> Any:
         # return value as fallback
         return value
 
+    @staticmethod
+    def _repair_param_dict(
+        param_dict: dict[str, Any],
+        param_config: dict[str, dict],
+    ) -> dict[str, Any]:
+        """Unwrap single 'arguments' / 'input' wrappers when the wrapper
+        is not part of the requested tool schema and the wrapped object
+        matches the schema fields."""
+        allowed = set(param_config.keys())
+        for wrapper in ("arguments", "input"):
+            if set(param_dict.keys()) != {wrapper} or wrapper in allowed:
+                continue
+            inner = param_dict[wrapper]
+            if isinstance(inner, str):
+                try:
+                    inner = json.loads(inner)
+                except json.JSONDecodeError:
+                    return param_dict
+            if isinstance(inner, dict) and set(inner.keys()).issubset(allowed):
+                return inner
+        return param_dict
+
     def _convert_params_with_schema(
         self,
         function_name: str,
-        param_dict: dict[str, str],
+        param_dict: dict[str, tuple[str, str]],
     ) -> dict[str, Any]:
         """Convert raw string param values using the tool schema types."""
         param_config: dict = {}
@@ -158,12 +186,16 @@ def _convert_params_with_schema(
                     break
 
         converted: dict[str, Any] = {}
-        for name, value in param_dict.items():
+        for name, (value, string_attr) in param_dict.items():
+            if string_attr == "true":
+                converted[name] = value
+                continue
+
             param_type = "string"
             if name in param_config and isinstance(param_config[name], dict):
                 param_type = param_config[name].get("type", "string")
             converted[name] = self._convert_param_value(value, param_type)
-        return converted
+        return self._repair_param_dict(converted, param_config)
 
     def extract_tool_calls(
         self,
@@ -187,12 +219,13 @@ def extract_tool_calls(
                     tool_call_match
                 ):
                     param_dict = self._parse_invoke_params(invoke_content)
+                    params = self._convert_params_with_schema(invoke_name, param_dict)
                     tool_calls.append(
                         ToolCall(
                             type="function",
                             function=FunctionCall(
                                 name=invoke_name,
-                                arguments=json.dumps(param_dict, ensure_ascii=False),
+                                arguments=json.dumps(params, ensure_ascii=False),
                             ),
                         )
                     )
@@ -219,7 +252,7 @@ def extract_tool_calls(
     def _reset_streaming_state(self):
         """Reset all streaming state."""
         self.current_tool_index = 0
-        self.is_tool_call_started = False
+        self._sent_content_idx = 0
         self.prev_tool_call_arr.clear()
         self.streamed_args_for_tool.clear()
 
@@ -264,6 +297,24 @@ def _extract_delta_tool_calls(
 
         return delta_tool_calls
 
+    def _extract_content(self, current_text: str) -> str | None:
+        """Return unsent non-tool-call text, or None.
+
+        Holds back any suffix that could be a partial start marker
+        so that split markers are never leaked as content.
+        """
+        if self.tool_call_start_token not in current_text:
+            overlap = partial_tag_overlap(current_text, self.tool_call_start_token)
+            sendable_idx = len(current_text) - overlap
+        else:
+            sendable_idx = current_text.index(self.tool_call_start_token)
+
+        if sendable_idx > self._sent_content_idx:
+            content = current_text[self._sent_content_idx : sendable_idx]
+            self._sent_content_idx = sendable_idx
+            return content
+        return None
+
     def extract_tool_calls_streaming(
         self,
         previous_text: str,
@@ -285,29 +336,11 @@ def extract_tool_calls_streaming(
         if not previous_text:
             self._reset_streaming_state()
 
-        # Detect whether we've entered the tool-call region.
-        # Use current_text (not delta_text) since the start token may
-        # be split across chunks.
-        content_before = None
-        if self.is_tool_call_started:
-            pass
-        elif self.tool_call_start_token in current_text:
-            # Tool-call region found, capture any plain text before it.
-            self.is_tool_call_started = True
-            start_idx = current_text.index(self.tool_call_start_token)
-            content_before = current_text[len(previous_text) : start_idx] or None
-        else:
-            # Still in plain-text region, forward as content.
-            return DeltaMessage(content=delta_text) if delta_text else None
-
-        # Inside tool-call region: emit any newly completed invokes.
+        content = self._extract_content(current_text)
         delta_tool_calls = self._extract_delta_tool_calls(current_text, request)
 
-        if delta_tool_calls or content_before:
-            return DeltaMessage(
-                content=content_before,
-                tool_calls=delta_tool_calls,
-            )
+        if delta_tool_calls or content:
+            return DeltaMessage(content=content, tool_calls=delta_tool_calls)
 
         # Empty delta with token ids means EOS or closing tag; return
         # non-None so the serving framework can finalize finish_reason.
diff --git a/vllm/tool_parsers/deepseekv4_tool_parser.py b/vllm/tool_parsers/deepseekv4_tool_parser.py
new file mode 100644
index 000000000000..e32451cd8bbd
--- /dev/null
+++ b/vllm/tool_parsers/deepseekv4_tool_parser.py
@@ -0,0 +1,31 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+)
+from vllm.tool_parsers.deepseekv32_tool_parser import DeepSeekV32ToolParser
+from vllm.tool_parsers.structural_tag_registry import (
+    get_enable_structured_outputs_in_reasoning,
+    get_model_structural_tag,
+)
+
+
+class DeepSeekV4ToolParser(DeepSeekV32ToolParser):
+    """
+    DeepSeek V4 DSML tool parser.
+
+    V4 keeps the V3.2 DSML invoke/parameter grammar, but wraps tool calls in
+    ``<｜DSML｜tool_calls>`` instead of ``<｜DSML｜function_calls>``.
+    """
+
+    tool_call_start_token: str = "<｜DSML｜tool_calls>"
+    tool_call_end_token: str = "</｜DSML｜tool_calls>"
+
+    def get_structural_tag(self, request: ChatCompletionRequest):
+        return get_model_structural_tag(
+            model="deepseek_v4",
+            tools=request.tools,
+            tool_choice=request.tool_choice,
+            reasoning=get_enable_structured_outputs_in_reasoning(),
+        )
diff --git a/vllm/tool_parsers/functiongemma_tool_parser.py b/vllm/tool_parsers/functiongemma_tool_parser.py
index 35c4c6b84fe4..776792ea1d6d 100644
--- a/vllm/tool_parsers/functiongemma_tool_parser.py
+++ b/vllm/tool_parsers/functiongemma_tool_parser.py
@@ -34,6 +34,21 @@ class FunctionGemmaToolParser(ToolParser):
     <start_function_call>call:func_name{param:<escape>value<escape>}<end_function_call>
     """
 
+    # FunctionGemma tokens
+    tool_call_start_token: str = "<start_function_call>"
+    tool_call_end_token: str = "<end_function_call>"
+
+    # Regex patterns
+    tool_call_regex: re.Pattern = re.compile(
+        r"<start_function_call>call:(\w+)\{(.*?)\}<end_function_call>"
+        r"|<start_function_call>call:(\w+)\{(.*)",
+        re.DOTALL,
+    )
+    arg_regex: re.Pattern = re.compile(
+        r"(\w+):<escape>(.*?)<escape>",
+        re.DOTALL,
+    )
+
     def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
         super().__init__(tokenizer, tools)
 
@@ -42,33 +57,6 @@ def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
         self.prev_tool_call_arr: list[dict] = []
         self.current_tool_id: int = -1
         self.streamed_args_for_tool: list[str] = []
-
-        # FunctionGemma tokens
-        self.tool_call_start_token: str = "<start_function_call>"
-        self.tool_call_end_token: str = "<end_function_call>"
-
-        # Regex patterns
-        self.tool_call_regex = re.compile(
-            r"<start_function_call>call:(\w+)\{(.*?)\}<end_function_call>"
-            r"|<start_function_call>call:(\w+)\{(.*)",
-            re.DOTALL,
-        )
-        self.arg_regex = re.compile(
-            r"(\w+):<escape>(.*?)<escape>",
-            re.DOTALL,
-        )
-
-        if self.model_tokenizer:
-            self.tool_call_start_token_ids = self.model_tokenizer.encode(
-                self.tool_call_start_token, add_special_tokens=False
-            )
-            self.tool_call_end_token_ids = self.model_tokenizer.encode(
-                self.tool_call_end_token, add_special_tokens=False
-            )
-        else:
-            self.tool_call_start_token_ids = []
-            self.tool_call_end_token_ids = []
-
         self.buffered_delta_text = ""
 
     def _parse_arguments(self, args_str: str) -> dict:
diff --git a/vllm/tool_parsers/gemma4_tool_parser.py b/vllm/tool_parsers/gemma4_tool_parser.py
index ac48ef26cc19..95ec7edcd8a2 100644
--- a/vllm/tool_parsers/gemma4_tool_parser.py
+++ b/vllm/tool_parsers/gemma4_tool_parser.py
@@ -204,6 +204,13 @@ def _parse_gemma4_args(args_str: str, *, partial: bool = False) -> dict:
                 # Value may be incomplete (e.g. partial boolean) —
                 # withhold to avoid type instability during streaming.
                 break
+            if i == val_start:
+                logger.warning(
+                    "Gemma4 args parser made no progress at position %d; "
+                    "aborting on malformed input.",
+                    i,
+                )
+                break
             result[key] = _parse_gemma4_value(args_str[val_start:i])
 
     return result
@@ -258,6 +265,11 @@ def _parse_gemma4_array(arr_str: str, *, partial: bool = False) -> list:
             sub_start = i + 1
             i += 1
             while i < n and depth > 0:
+                if arr_str[i:].startswith(STRING_DELIM):
+                    i += len(STRING_DELIM)
+                    nd = arr_str.find(STRING_DELIM, i)
+                    i = nd + len(STRING_DELIM) if nd != -1 else n
+                    continue
                 if arr_str[i] == "[":
                     depth += 1
                 elif arr_str[i] == "]":
@@ -275,6 +287,13 @@ def _parse_gemma4_array(arr_str: str, *, partial: bool = False) -> list:
                 i += 1
             if partial and i >= n:
                 break
+            if i == val_start:
+                logger.warning(
+                    "Gemma4 array parser made no progress at position %d; "
+                    "aborting on malformed input.",
+                    i,
+                )
+                break
             items.append(_parse_gemma4_value(arr_str[val_start:i]))
 
     return items
@@ -360,12 +379,13 @@ def adjust_request(
         self, request: ChatCompletionRequest | ResponsesRequest
     ) -> ChatCompletionRequest | ResponsesRequest:
         request = super().adjust_request(request)
-        if (
-            isinstance(request, ChatCompletionRequest)
-            and request.tools
-            and request.tool_choice != "none"
-        ):
-            # Don't skip special tokens — <|tool_call> etc. are needed
+        if request.tools and request.tool_choice != "none":
+            # Don't skip special tokens — <|tool_call> etc. are needed for
+            # the parser to detect tool calls. Apply to BOTH
+            # ChatCompletionRequest and ResponsesRequest (the previous
+            # isinstance(ChatCompletionRequest) guard caused tool-call
+            # delimiters to be stripped on /v1/responses, leaking raw
+            # `call:fn{...}` text via output_text.delta).
             request.skip_special_tokens = False
         return request
 
diff --git a/vllm/tool_parsers/glm47_moe_tool_parser.py b/vllm/tool_parsers/glm47_moe_tool_parser.py
index 765d6d37de11..47b6ad2f5afe 100644
--- a/vllm/tool_parsers/glm47_moe_tool_parser.py
+++ b/vllm/tool_parsers/glm47_moe_tool_parser.py
@@ -23,6 +23,8 @@
 
 
 class Glm47MoeModelToolParser(Glm4MoeModelToolParser):
+    supports_required_and_named = False
+
     def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
         super().__init__(tokenizer, tools)
         # GLM-4.7 format: <tool_call>func_name[<arg_key>...]*</tool_call>
diff --git a/vllm/tool_parsers/glm4_moe_tool_parser.py b/vllm/tool_parsers/glm4_moe_tool_parser.py
index 491c9599b618..64c4beb14357 100644
--- a/vllm/tool_parsers/glm4_moe_tool_parser.py
+++ b/vllm/tool_parsers/glm4_moe_tool_parser.py
@@ -20,6 +20,7 @@
 
 from vllm.entrypoints.chat_utils import make_tool_call_id
 from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionNamedToolChoiceParam,
     ChatCompletionRequest,
 )
 from vllm.entrypoints.openai.engine.protocol import (
@@ -50,6 +51,8 @@ class Glm4MoeModelToolParser(ToolParser):
     call, and diffs against what was previously sent to emit only new content.
     """
 
+    supports_required_and_named = False
+
     def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
         super().__init__(tokenizer, tools)
         # Stateful streaming fields
@@ -156,7 +159,25 @@ def _tools_enabled(request: ChatCompletionRequest) -> bool:
     def adjust_request(
         self, request: ChatCompletionRequest | ResponsesRequest
     ) -> ChatCompletionRequest | ResponsesRequest:
-        """Adjust request parameters for tool call token handling."""
+        """Adjust request parameters for tool call token handling.
+
+        For required/named tool_choice, skip setting structured_outputs
+        because GLM models output tool calls in XML format (per chat
+        template).  Guided decoding would force JSON output, conflicting
+        with the XML format and causing parsing failures.
+        """
+        if request.tools:
+            tc = request.tool_choice
+            if tc == "required" or isinstance(tc, ChatCompletionNamedToolChoiceParam):
+                # Do NOT call super().adjust_request() for required/named,
+                # because it would set structured_outputs and force JSON
+                # output via guided decoding.  GLM models use XML tool-call
+                # syntax (defined in the chat template), so guided decoding
+                # must be skipped to let the model output XML freely.
+                # The tool_parser handles extraction from XML output.
+                if request.tool_choice != "none":
+                    request.skip_special_tokens = False
+                return request
         request = super().adjust_request(request)
         if request.tools and request.tool_choice != "none":
             # Ensure tool call tokens (<tool_call>, </tool_call>) are not skipped
@@ -189,9 +210,10 @@ def extract_tool_calls(
                 arg_dct: dict[str, Any] = {}
                 for key, value in pairs:
                     arg_key = key.strip()
-                    arg_val = value.strip()
-                    if not self._is_string_type(tc_name, arg_key, self.tools):
-                        arg_val = self._deserialize(arg_val)
+                    if self._is_string_type(tc_name, arg_key, self.tools):
+                        arg_val = value
+                    else:
+                        arg_val = self._deserialize(value.strip())
                     logger.debug("arg_key = %s, arg_val = %s", arg_key, arg_val)
                     arg_dct[arg_key] = arg_val
                 tool_calls.append(
diff --git a/vllm/tool_parsers/hy_v3_tool_parser.py b/vllm/tool_parsers/hy_v3_tool_parser.py
new file mode 100644
index 000000000000..809a85ce4171
--- /dev/null
+++ b/vllm/tool_parsers/hy_v3_tool_parser.py
@@ -0,0 +1,645 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import ast
+import json
+from collections.abc import Sequence
+from typing import Any
+
+import regex as re
+
+from vllm.entrypoints.chat_utils import make_tool_call_id
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+    ChatCompletionToolsParam,
+)
+from vllm.entrypoints.openai.engine.protocol import (
+    DeltaFunctionCall,
+    DeltaMessage,
+    DeltaToolCall,
+    ExtractedToolCallInformation,
+    FunctionCall,
+    ToolCall,
+)
+from vllm.logger import init_logger
+from vllm.tokenizers import TokenizerLike
+from vllm.tool_parsers.abstract_tool_parser import (
+    Tool,
+    ToolParser,
+)
+
+logger = init_logger(__name__)
+
+
+class HYV3ToolParser(ToolParser):
+    _TYPE_ALIASES: dict[str, str] = {
+        "str": "string",
+        "text": "string",
+        "varchar": "string",
+        "char": "string",
+        "enum": "string",
+        "bool": "boolean",
+        "binary": "boolean",
+        "int": "integer",
+        "float": "number",
+        "double": "number",
+        "list": "array",
+        "dict": "object",
+        "map": "object",
+    }
+
+    # Prefix-based wildcard matching for non-standard type names.
+    # Following the same approach as
+    # qwen3coder_tool_parser._convert_param_value which uses
+    # param_type.startswith("int"), startswith("uint"), etc.
+    _INTEGER_PREFIXES: tuple[str, ...] = (
+        "int",
+        "uint",
+        "long",
+        "short",
+        "unsigned",
+    )
+    _NUMBER_PREFIXES: tuple[str, ...] = ("num", "float")
+
+    @staticmethod
+    def _normalize_type(raw_type: str) -> str:
+        """Map non-standard type aliases to JSON Schema standard names.
+
+        First performs exact lookup in _TYPE_ALIASES. On miss, falls back
+        to prefix-based matching using startswith()
+          - int*/uint*/long*/short*/unsigned* → "integer"
+          - num*/float* → "number"
+        """
+        exact = HYV3ToolParser._TYPE_ALIASES.get(raw_type)
+        if exact is not None:
+            return exact
+        lower = raw_type.lower()
+        if any(lower.startswith(p) for p in HYV3ToolParser._INTEGER_PREFIXES):
+            return "integer"
+        if any(lower.startswith(p) for p in HYV3ToolParser._NUMBER_PREFIXES):
+            return "number"
+        return raw_type
+
+    @staticmethod
+    def _get_arg_schema(
+        function_name: str,
+        arg_key: str,
+        tools: list[ChatCompletionToolsParam] | None,
+    ) -> dict:
+        """Look up a specific argument's property schema from the tools list."""
+        if tools is None:
+            return {}
+        for tool in tools:
+            if tool.function.name == function_name:
+                if tool.function.parameters is None:
+                    return {}
+                return tool.function.parameters.get("properties", {}).get(arg_key, {})
+        logger.warning("No tool named '%s'.", function_name)
+        return {}
+
+    @staticmethod
+    def _get_schema_options(arg_schema: dict) -> list[dict]:
+        """Normalize any property schema into a list of sub-schemas.
+        - has type (single type) → return [arg_schema]
+        - anyOf  → return the anyOf list
+        - oneOf  → return the oneOf list
+        - fallback → [{"type": "string"}]
+
+        Note: single ``type`` has the highest priority.
+        """
+        if "type" in arg_schema:
+            return [arg_schema]
+        if "anyOf" in arg_schema:
+            return arg_schema["anyOf"]
+        if "oneOf" in arg_schema:
+            return arg_schema["oneOf"]
+
+        return [{"type": "string"}]
+
+    @staticmethod
+    def _get_types(arg_schema: dict) -> set[str]:
+        """Extract normalized, non-null type set from a property schema."""
+        schemas = HYV3ToolParser._get_schema_options(arg_schema)
+        return {
+            HYV3ToolParser._normalize_type(s.get("type", "string")) for s in schemas
+        } - {"null"}
+
+    @staticmethod
+    def _is_only_string_type(
+        function_name: str,
+        arg_key: str,
+        tools: list[ChatCompletionToolsParam] | None,
+    ) -> bool:
+        """Return True if the parameter's type set is exactly {"string"}.
+
+        Only pure string types get partial value streaming; compound types
+        like anyOf(string | array) do not, since the partial value might
+        end up being a JSON array or object.
+        """
+        arg_schema = HYV3ToolParser._get_arg_schema(function_name, arg_key, tools)
+        types = HYV3ToolParser._get_types(arg_schema)
+        return types == {"string"}
+
+    @staticmethod
+    def _try_parse_bool(value: str) -> bool | None:
+        """Try to parse a string as bool; return None on failure."""
+        lower = value.lower()
+        if lower == "true":
+            return True
+        elif lower == "false":
+            return False
+        return None
+
+    @staticmethod
+    def _try_parse_int(value: str) -> int | None:
+        """Try to parse a string as int; return None on failure."""
+        try:
+            return int(value)
+        except (ValueError, TypeError):
+            return None
+
+    @staticmethod
+    def _try_parse_wildcard_number(value: str) -> int | float | None:
+        """Try to parse a string as a number (int or float).
+
+        Decision rule: if the string contains '.' or 'e'/'E' (scientific
+        notation), parse as float; otherwise parse as int.
+
+        Examples:
+            "5"    → int(5)
+            "5.0"  → float(5.0)
+            "5.3"  → float(5.3)
+            "1e3"  → float(1000.0)
+            "-3"   → int(-3)
+
+        Return None on failure.
+        """
+        try:
+            if "." in value or "e" in value or "E" in value:
+                return float(value)
+            return int(value)
+        except (ValueError, TypeError):
+            return None
+
+    @staticmethod
+    def _deserialize(value: str) -> Any:
+        """Deserialize a string value using json.loads then ast.literal_eval."""
+        try:
+            return json.loads(value)
+        except Exception:
+            pass
+        try:
+            return ast.literal_eval(value)
+        except Exception:
+            pass
+        return value
+
+    @staticmethod
+    def _parse_value(
+        value: str,
+        function_name: str,
+        arg_key: str,
+        tools: list[ChatCompletionToolsParam] | None,
+    ) -> Any:
+        """Unified argument value parser with anyOf/oneOf support.
+
+        Fallthrough chain:
+            bool → int → number(wildcard_number)
+            → json.loads for array/object
+            → string → _deserialize
+        """
+        arg_schema = HYV3ToolParser._get_arg_schema(function_name, arg_key, tools)
+        types = HYV3ToolParser._get_types(arg_schema)
+
+        # 1. Try bool
+        if "boolean" in types:
+            result_bool = HYV3ToolParser._try_parse_bool(value)
+            if result_bool is not None:
+                return result_bool
+
+        # 2. Try int
+        if "integer" in types:
+            result_int = HYV3ToolParser._try_parse_int(value)
+            if result_int is not None:
+                return result_int
+
+        # 3. Try number (wildcard_number: int if no '.'/e/E, float otherwise)
+        if "number" in types:
+            result_number = HYV3ToolParser._try_parse_wildcard_number(value)
+            if result_number is not None:
+                return result_number
+
+        # 4. Try json.loads (covers array/object and other unlisted types)
+        if types - {"string", "boolean", "integer", "number"}:
+            try:
+                return json.loads(value)
+            except (json.JSONDecodeError, ValueError):
+                pass
+
+        # 5. String fallback
+        if "string" in types:
+            return value
+
+        # 6. Final fallback
+        return HYV3ToolParser._deserialize(value)
+
+    def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
+        super().__init__(tokenizer, tools)
+
+        self.current_tool_name_sent: bool = False
+        self.prev_tool_call_arr: list[dict] = []
+        self.current_tool_id: int = -1
+        self.streamed_args_for_tool: list[
+            str
+        ] = []  # map what has been streamed for each tool so far to a list
+
+        # Streaming state: send tool name first, then return arguments at once
+        self._streaming_tool_name: str | None = None  # tool name being streamed
+
+        # State fields for incremental argument streaming
+        self._completed_args: dict = {}  # closed {key: parsed_value}
+        self._current_arg_key: str | None = None  # key being collected
+        self._current_arg_is_string: bool = False  # is current arg pure string?
+        self._streamed_json_len: int = 0  # bytes of JSON already sent
+
+        self.tool_calls_start_token: str = "<tool_calls>"
+        self.tool_calls_end_token: str = "</tool_calls>"
+
+        self.tool_call_start_token: str = "<tool_call>"
+        self.tool_call_end_token: str = "</tool_call>"
+
+        self.tool_sep_token: str = "<tool_sep>"
+
+        self.arg_key_start_token: str = "<arg_key>"
+        self.arg_key_end_token: str = "</arg_key>"
+
+        self.arg_value_start_token: str = "<arg_value>"
+        self.arg_value_end_token: str = "</arg_value>"
+
+        self.tool_call_regex = re.compile(
+            rf"{self.tool_call_start_token}(.*?){self.tool_sep_token}"
+            rf"(.*?){self.tool_call_end_token}",
+            re.DOTALL,
+        )
+
+        self.tool_call_portion_regex = re.compile(
+            rf"{self.tool_call_start_token}(.*?){self.tool_sep_token}(.*)", re.DOTALL
+        )
+
+        self.func_args_regex = re.compile(
+            rf"{self.arg_key_start_token}(.*?){self.arg_key_end_token}\s*"
+            rf"{self.arg_value_start_token}(.*?){self.arg_value_end_token}",
+            re.DOTALL,
+        )
+
+        if not self.model_tokenizer:
+            raise ValueError(
+                "The model tokenizer must be passed to the ToolParser "
+                "constructor during construction."
+            )
+        self.tool_calls_start_token_id = self.vocab.get(self.tool_calls_start_token)
+        self.tool_calls_end_token_id = self.vocab.get(self.tool_calls_end_token)
+
+        self.tool_call_start_token_id = self.vocab.get(self.tool_call_start_token)
+        self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
+        self._buffer = ""
+
+        if (
+            self.tool_calls_start_token_id is None
+            or self.tool_calls_end_token_id is None
+        ):
+            raise RuntimeError(
+                "HYV3 Tool parser could not locate tool call "
+                "start/end tokens in the tokenizer!"
+            )
+
+    def _extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> list[ToolCall]:
+        try:
+            function_call_tuples = []
+            # start_token{name}sep_token{args}end_token...
+            function_calls = self.tool_call_regex.findall(model_output)
+            if function_calls:
+                function_call_tuples.extend(function_calls)
+                remaining = model_output.split(self.tool_call_end_token)[-1]
+                function_calls = self.tool_call_portion_regex.findall(remaining)
+                function_call_tuples += function_calls
+            else:
+                function_calls = self.tool_call_portion_regex.findall(model_output)
+                if function_calls:
+                    function_call_tuples.extend(function_calls)
+            tool_calls = []
+            for match in function_call_tuples:
+                function_name, function_args = match
+                function_name = function_name.strip()
+                function_args = function_args.strip()
+
+                arg_pairs = self.func_args_regex.findall(function_args)
+                arg_dict = {}
+                for key, value in arg_pairs:
+                    parsed_value = HYV3ToolParser._parse_value(
+                        value, function_name, key, request.tools
+                    )
+                    arg_dict[key] = parsed_value
+                tool_calls.append(
+                    ToolCall(
+                        type="function",
+                        function=FunctionCall(
+                            name=function_name,
+                            arguments=json.dumps(arg_dict, ensure_ascii=False),
+                        ),
+                    )
+                )
+            return tool_calls
+        except Exception:
+            logger.exception("Error in extracting tool call from response.")
+            return []
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        # sanity check; avoid unnecessary processing
+        if self.tool_calls_start_token not in model_output:
+            return ExtractedToolCallInformation(
+                tools_called=False, tool_calls=[], content=model_output
+            )
+        else:
+            try:
+                tool_calls = self._extract_tool_calls(model_output, request)
+
+                s_index = model_output.find(self.tool_calls_start_token)
+                content = model_output[:s_index] if s_index != -1 else model_output
+                return ExtractedToolCallInformation(
+                    tools_called=True,
+                    tool_calls=tool_calls,
+                    content=content if content else None,
+                )
+
+            except Exception:
+                logger.exception("Error in extracting tool call from response.")
+                return ExtractedToolCallInformation(
+                    tools_called=False, tool_calls=[], content=model_output
+                )
+
+    def _reset_streaming_tool_state(self):
+        """Reset the streaming state for a single tool call."""
+        self._streaming_tool_name = None
+        self._completed_args = {}
+        self._current_arg_key = None
+        self._current_arg_is_string = False
+        self._streamed_json_len = 0
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> DeltaMessage | None:
+        # Check whether current tokens contain the tool_calls start token
+        if self.tool_calls_start_token_id not in current_token_ids:
+            return DeltaMessage(content=delta_text)
+
+        # Encountered tool_calls start tag; extract preceding content and buffer
+        if self.tool_calls_start_token in delta_text:
+            text_parts = delta_text.split(self.tool_calls_start_token)
+            self._buffer += text_parts[-1]
+            if text_parts[0]:
+                return DeltaMessage(content=text_parts[0])
+            # Don't return None; continue processing buffer for complete content
+        else:
+            self._buffer += delta_text
+
+        # Encountered finish, extract valid arguments
+        if (
+            current_text.find(self.tool_call_end_token + self.tool_calls_end_token)
+            != -1
+            and self._buffer.find(self.tool_call_end_token) == -1
+        ):
+            self._buffer += self.tool_call_end_token + self.tool_calls_end_token
+
+        cur_text = self._buffer
+
+        # Haven't encountered tool_call start tag yet; keep buffering
+        start_idx = cur_text.find(self.tool_call_start_token)
+        if start_idx == -1 and self._streaming_tool_name is None:
+            self._buffer = ""
+            return None
+
+        # === Phase 1: Detect tool name (send when tool_sep_token is seen) ===
+        name_delta: DeltaMessage | None = None
+        if self._streaming_tool_name is None:
+            sep_idx = cur_text.find(self.tool_sep_token)
+            if sep_idx == -1:
+                # tool_sep not yet seen; keep buffering from tool_call_start
+                self._buffer = cur_text[start_idx:]
+                return None
+
+            # Extract tool name: between tool_call_start_token and tool_sep_token
+            name_start = start_idx + len(self.tool_call_start_token)
+            tool_name = cur_text[name_start:sep_idx].strip()
+            self._streaming_tool_name = tool_name
+
+            # Update buffer: keep only content after tool_sep (i.e. the args portion)
+            self._buffer = cur_text[sep_idx + len(self.tool_sep_token) :]
+
+            # Increment tool_id and send a chunk containing only the name
+            self.current_tool_id += 1
+            self._current_tool_call_id = make_tool_call_id()
+            name_delta = DeltaMessage(
+                tool_calls=[
+                    DeltaToolCall(
+                        index=self.current_tool_id,
+                        id=self._current_tool_call_id,
+                        type="function",
+                        function=DeltaFunctionCall(
+                            name=tool_name,
+                        ),
+                    )
+                ]
+            )
+
+            # Check if buffer already has complete arguments (all-in-one-delta)
+            if self.tool_call_end_token not in self._buffer:
+                return name_delta
+            # Buffer already has a complete tool call; continue to phase 2 below
+
+        # === Phase 2: Incremental argument streaming ===
+        return self._extract_streaming_incremental(name_delta, request)
+
+    def _make_args_delta(self, argument_diff: str) -> DeltaMessage:
+        """Build a DeltaMessage containing only an arguments diff."""
+        return DeltaMessage(
+            tool_calls=[
+                DeltaToolCall(
+                    index=self.current_tool_id,
+                    function=DeltaFunctionCall(arguments=argument_diff),
+                )
+            ]
+        )
+
+    def _extract_streaming_incremental(
+        self,
+        name_delta: DeltaMessage | None,
+        request: ChatCompletionRequest,
+    ) -> DeltaMessage | None:
+        """Incremental phase-2: scan tags in buffer, emit JSON diffs.
+
+        Strategy:
+        - Track completed args and emit each one as a JSON fragment.
+        - For string-typed args, stream the value character-by-character.
+        - Withhold the closing ``}`` until ``</tool_call>`` is seen.
+
+        We build JSON manually via fragments rather than using json.dumps
+        with a cursor, because json.dumps of partial-vs-full string values
+        produces incompatible prefixes (e.g. ``""}`` vs ``"Hello"}``).
+        """
+        buf = self._buffer
+        is_complete = self.tool_call_end_token in buf
+
+        if is_complete:
+            end_idx = buf.find(self.tool_call_end_token)
+            args_text = buf[:end_idx]
+            remaining = buf[end_idx + len(self.tool_call_end_token) :]
+        else:
+            args_text = buf
+            remaining = ""
+
+        # --- scan all fully closed kv pairs ---
+        arg_pairs = self.func_args_regex.findall(args_text)
+        for key, value in arg_pairs:
+            key = key.strip()
+            if key not in self._completed_args:
+                parsed_value = HYV3ToolParser._parse_value(
+                    value, self._streaming_tool_name or "", key, request.tools
+                )
+                self._completed_args[key] = parsed_value
+
+        # --- detect partial (unclosed) kv at the tail ---
+        last_closed_end = 0
+        for m in self.func_args_regex.finditer(args_text):
+            last_closed_end = m.end()
+        tail = args_text[last_closed_end:]
+
+        partial_key: str | None = None
+        partial_value: str | None = None
+
+        ak_start = tail.find(self.arg_key_start_token)
+        if ak_start != -1:
+            ak_end = tail.find(
+                self.arg_key_end_token,
+                ak_start + len(self.arg_key_start_token),
+            )
+            if ak_end != -1:
+                partial_key = tail[
+                    ak_start + len(self.arg_key_start_token) : ak_end
+                ].strip()
+                self._current_arg_key = partial_key
+                self._current_arg_is_string = HYV3ToolParser._is_only_string_type(
+                    self._streaming_tool_name or "",
+                    partial_key,
+                    request.tools,
+                )
+
+                av_start = tail.find(self.arg_value_start_token, ak_end)
+                if av_start != -1:
+                    val_content_start = av_start + len(self.arg_value_start_token)
+                    if self._current_arg_is_string:
+                        partial_value = tail[val_content_start:]
+            else:
+                # key not yet closed
+                self._current_arg_key = None
+                self._current_arg_is_string = False
+
+        # --- build the current JSON snapshot as a string ---
+        # We construct JSON manually so we can precisely control
+        # what gets sent incrementally.
+        snapshot_parts: list[str] = []
+        for k, v in self._completed_args.items():
+            k_json = json.dumps(k, ensure_ascii=False)
+            v_json = json.dumps(v, ensure_ascii=False)
+            snapshot_parts.append(f"{k_json}: {v_json}")
+
+        if partial_key is not None and partial_value is not None:
+            k_json = json.dumps(partial_key, ensure_ascii=False)
+            # For string partial value, we build the JSON string
+            # WITHOUT the closing quote, so the prefix stays stable
+            # as the value grows.  The closing `"` and `}` will be
+            # sent when the value or tool_call closes.
+            escaped_val = (
+                partial_value.replace("\\", "\\\\")
+                .replace('"', '\\"')
+                .replace("\n", "\\n")
+                .replace("\r", "\\r")
+                .replace("\t", "\\t")
+            )
+            # Note: no closing " here – it's appended only on close
+            snapshot_parts.append(f'{k_json}: "{escaped_val}')
+
+        snapshot = "{" + ", ".join(snapshot_parts) + "}"
+
+        # --- compute diff ---
+        argument_diff: str | None = None
+
+        if is_complete:
+            # Tool call finished – send everything remaining.
+            # Build final snapshot with proper JSON (all values closed).
+            final_args = dict(self._completed_args)
+            final_json = json.dumps(final_args, ensure_ascii=False)
+            if self._streamed_json_len < len(final_json):
+                argument_diff = final_json[self._streamed_json_len :]
+            self._streamed_json_len = len(final_json)
+
+            # Record into prev_tool_call_arr
+            self.prev_tool_call_arr.append(
+                {
+                    "name": self._streaming_tool_name,
+                    "arguments": final_args,
+                }
+            )
+            self.streamed_args_for_tool.append(final_json)
+
+            self._reset_streaming_tool_state()
+            self._buffer = remaining
+        else:
+            # Still in progress – withhold the tail.
+            # For open strings: snapshot ends with ...partial_val}
+            #   we withhold "}" (1 char) – the missing closing " will
+            #   be sent when the value closes.
+            # For no open string: snapshot ends with ...value"}
+            #   we withhold "}" (1 char).
+            end = len(snapshot) - 1  # exclude trailing "}"
+            if end > self._streamed_json_len:
+                argument_diff = snapshot[self._streamed_json_len : end]
+                self._streamed_json_len = end
+
+        # --- construct return DeltaMessage ---
+        if name_delta is not None and argument_diff:
+            nd_func = name_delta.tool_calls[0].function
+            return DeltaMessage(
+                tool_calls=[
+                    DeltaToolCall(
+                        index=self.current_tool_id,
+                        id=self._current_tool_call_id,
+                        type="function",
+                        function=DeltaFunctionCall(
+                            name=nd_func.name if nd_func else None,
+                            arguments=argument_diff,
+                        ),
+                    )
+                ]
+            )
+        elif name_delta is not None:
+            return name_delta
+        elif argument_diff:
+            return self._make_args_delta(argument_diff)
+        else:
+            return None
diff --git a/vllm/tool_parsers/kimi_k2_tool_parser.py b/vllm/tool_parsers/kimi_k2_tool_parser.py
index bc995319e51b..7ddd8fa7a80d 100644
--- a/vllm/tool_parsers/kimi_k2_tool_parser.py
+++ b/vllm/tool_parsers/kimi_k2_tool_parser.py
@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-# code modified from deepseekv3_tool_parser.py
 
 from collections.abc import Sequence
 
@@ -17,12 +16,14 @@
     FunctionCall,
     ToolCall,
 )
+from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
 from vllm.logger import init_logger
 from vllm.tokenizers import TokenizerLike
 from vllm.tool_parsers.abstract_tool_parser import (
     Tool,
     ToolParser,
 )
+from vllm.tool_parsers.utils import partial_tag_overlap
 
 logger = init_logger(__name__)
 
@@ -30,124 +31,44 @@
 class KimiK2ToolParser(ToolParser):
     def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
         super().__init__(tokenizer, tools)
-        self.current_tool_name_sent: bool = False
+
+        # Streaming state
+        self._sent_content_idx: int = 0
         self.prev_tool_call_arr: list[dict] = []
-        self.current_tool_id: int = -1
-        self.streamed_args_for_tool: list[
-            str
-        ] = []  # map what has been streamed for each tool so far to a list
-
-        # Section-level state management to prevent token leakage
-        self.in_tool_section: bool = False
-        self.token_buffer: str = ""
-        # Buffer size: empirical worst-case for longest marker (~30 chars) * 2
-        # + safety margin for unicode + partial overlap. Prevents unbounded growth.
-        self.buffer_max_size: int = 1024
-        self.section_char_count: int = 0  # Track characters processed in tool section
-        self.max_section_chars: int = 8192  # Force exit if section exceeds this
-        self._buffer_overflow_logged: bool = False  # Log overflow once per session
-
-        # Support both singular and plural variants
+        self.streamed_args_for_tool: list[str] = []
+
+        # Section marker
         self.tool_calls_start_token: str = "<|tool_calls_section_begin|>"
-        self.tool_calls_end_token: str = "<|tool_calls_section_end|>"
-        self.tool_calls_start_token_variants: list[str] = [
-            "<|tool_calls_section_begin|>",
-            "<|tool_call_section_begin|>",  # singular variant
-        ]
-        self.tool_calls_end_token_variants: list[str] = [
-            "<|tool_calls_section_end|>",
-            "<|tool_call_section_end|>",  # singular variant
-        ]
 
+        # Individual tool call markers
         self.tool_call_start_token: str = "<|tool_call_begin|>"
         self.tool_call_end_token: str = "<|tool_call_end|>"
+        self.tool_call_arg_token: str = "<|tool_call_argument_begin|>"
 
+        # Regex for non-streaming extraction
         self.tool_call_regex = re.compile(
-            r"<\|tool_call_begin\|>\s*(?P<tool_call_id>[^<]+:\d+)\s*<\|tool_call_argument_begin\|>\s*(?P<function_arguments>(?:(?!<\|tool_call_begin\|>).)*?)\s*<\|tool_call_end\|>",
+            r"<\|tool_call_begin\|>\s*(?P<tool_call_id>[^<]+:\d+)\s*"
+            r"<\|tool_call_argument_begin\|>\s*"
+            r"(?P<function_arguments>(?:(?!<\|tool_call_begin\|>).)*?)\s*"
+            r"<\|tool_call_end\|>",
             re.DOTALL,
         )
 
-        self.stream_tool_call_portion_regex = re.compile(
-            r"(?P<tool_call_id>.+:\d+)\s*<\|tool_call_argument_begin\|>\s*(?P<function_arguments>.*)"
-        )
-
-        self.stream_tool_call_name_regex = re.compile(r"(?P<tool_call_id>.+:\d+)\s*")
-
         if not self.model_tokenizer:
             raise ValueError(
                 "The model tokenizer must be passed to the ToolParser "
                 "constructor during construction."
             )
-        self.tool_calls_start_token_id = self.vocab.get(self.tool_calls_start_token)
-        self.tool_calls_end_token_id = self.vocab.get(self.tool_calls_end_token)
-
-        # Get token IDs for all variants
-        self.tool_calls_start_token_ids: list[int] = [
-            tid
-            for variant in self.tool_calls_start_token_variants
-            if (tid := self.vocab.get(variant)) is not None
-        ]
-        self.tool_calls_end_token_ids: list[int] = [
-            tid
-            for variant in self.tool_calls_end_token_variants
-            if (tid := self.vocab.get(variant)) is not None
-        ]
-
-        self.tool_call_start_token_id = self.vocab.get(self.tool_call_start_token)
-        self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
-
-        if (
-            self.tool_calls_start_token_id is None
-            or self.tool_calls_end_token_id is None
-        ):
-            raise RuntimeError(
-                "Kimi-K2 Tool parser could not locate tool call start/end "
-                "tokens in the tokenizer!"
-            )
 
-    def _check_and_strip_markers(self, text: str) -> tuple[str, bool, bool]:
-        """
-        Check for section begin/end markers in text and strip them.
-        Returns: (cleaned_text, found_section_begin, found_section_end)
-        """
-        found_begin = False
-        found_end = False
-        cleaned = text
-
-        # Check for section begin markers (any variant)
-        for variant in self.tool_calls_start_token_variants:
-            if variant in cleaned:
-                cleaned = cleaned.replace(variant, "")
-                found_begin = True
-
-        # Check for section end markers (any variant)
-        for variant in self.tool_calls_end_token_variants:
-            if variant in cleaned:
-                cleaned = cleaned.replace(variant, "")
-                found_end = True
-        return cleaned, found_begin, found_end
-
-    def _reset_section_state(self) -> None:
-        """Reset state when exiting tool section."""
-        self.in_tool_section = False
-        self.token_buffer = ""
-        self.section_char_count = 0
-
-    def reset_streaming_state(self) -> None:
-        """
-        Reset all streaming state. Call this between requests to prevent
-        state leakage when parser instance is reused.
-        """
-        # Reset section state
-        self._reset_section_state()
-
-        # Reset parent class state
-        self.current_tool_name_sent = False
-        self.prev_tool_call_arr = []
-        self.current_tool_id = -1
-        self.streamed_args_for_tool = []
-
-        logger.debug("Streaming state reset")
+    def adjust_request(
+        self, request: ChatCompletionRequest | ResponsesRequest
+    ) -> ChatCompletionRequest | ResponsesRequest:
+        request = super().adjust_request(request)
+        if request.tools and request.tool_choice != "none":
+            # Ensure special-token markers appear as literal text in
+            # current_text so we can do pure text-based parsing.
+            request.skip_special_tokens = False
+        return request
 
     def extract_tool_calls(
         self,
@@ -198,6 +119,95 @@ def extract_tool_calls(
                     tools_called=False, tool_calls=[], content=model_output
                 )
 
+    def _extract_content(self, current_text: str) -> str | None:
+        """Return unsent content before the tool-calls section, or None.
+
+        Holds back any trailing suffix that partially matches
+        ``<|tool_calls_section_begin|>`` to avoid leaking marker bytes.
+        """
+        if self.tool_calls_start_token not in current_text:
+            overlap = partial_tag_overlap(current_text, self.tool_calls_start_token)
+            sendable_idx = len(current_text) - overlap
+        else:
+            sendable_idx = current_text.index(self.tool_calls_start_token)
+
+        if sendable_idx > self._sent_content_idx:
+            content = current_text[self._sent_content_idx : sendable_idx]
+            self._sent_content_idx = sendable_idx
+            return content
+        return None
+
+    def _extract_tool_calls(self, current_text: str) -> list[str]:
+        """Extract raw bodies from ``<|tool_call_begin|>…<|tool_call_end|>`` blocks."""
+        if self.tool_calls_start_token not in current_text:
+            return []
+
+        results: list[str] = []
+        pos = current_text.index(self.tool_calls_start_token)
+        while True:
+            start = current_text.find(self.tool_call_start_token, pos)
+            if start == -1:
+                break
+            tc_start = start + len(self.tool_call_start_token)
+            end = current_text.find(self.tool_call_end_token, tc_start)
+
+            if end != -1:
+                tool_call = current_text[tc_start:end]
+                pos = end + len(self.tool_call_end_token)
+            else:
+                tool_call = current_text[tc_start:]
+                overlap = partial_tag_overlap(tool_call, self.tool_call_end_token)
+                if overlap:
+                    tool_call = tool_call[:-overlap]
+
+            results.append(tool_call)
+
+            if end == -1:
+                break
+        return results
+
+    @staticmethod
+    def _extract_tool_id_and_name(
+        header: str | None,
+    ) -> tuple[str | None, str | None]:
+        """Parse ``(tool_id, tool_name)`` from a header
+        like ``"functions.get_weather:0"``."""
+        if header is None:
+            return None, None
+        match = re.match(r"(.+:\d+)", header)
+        if not match:
+            return None, None
+
+        tool_id = match.group(1).strip()
+        tool_name = tool_id.split(":")[0].split(".")[-1]
+        return tool_id, tool_name
+
+    def _split_tool_call(self, tool_call: str) -> tuple[str | None, str | None]:
+        """Split a tool-call body into ``(header, arguments)`` at the argument marker.
+
+        Example::
+            'get_weather:0 <|tool_call_argument_begin|>{"c'
+            -> ("get_weather:0", '{"c')
+        """
+        arg_pos = tool_call.find(self.tool_call_arg_token)
+        if arg_pos == -1:
+            return None, None
+        header = tool_call[:arg_pos].strip()
+        tool_args = tool_call[arg_pos + len(self.tool_call_arg_token) :]
+        return header, tool_args
+
+    def _compute_args_diff(self, index: int, tool_args: str | None) -> str | None:
+        """Return new argument text not yet sent for tool `index`, or None."""
+        if tool_args is None:
+            return None
+        prev = self.streamed_args_for_tool[index]
+        if len(tool_args) <= len(prev):
+            return None
+        diff = tool_args[len(prev) :]
+        self.streamed_args_for_tool[index] = tool_args
+        self.prev_tool_call_arr[index]["arguments"] = tool_args
+        return diff
+
     def extract_tool_calls_streaming(
         self,
         previous_text: str,
@@ -208,394 +218,59 @@ def extract_tool_calls_streaming(
         delta_token_ids: Sequence[int],
         request: ChatCompletionRequest,
     ) -> DeltaMessage | None:
-        logger.debug("delta_text: %s", delta_text)
-        logger.debug("delta_token_ids: %s", delta_token_ids)
-
-        # Flag to defer section exit until after tool parsing completes
-        deferred_section_exit = False
-
-        # Add delta to buffer for split marker detection
-        self.token_buffer += delta_text
-
-        # Enforce buffer size limit to prevent memory issues
-        if len(self.token_buffer) > self.buffer_max_size:
-            if not self._buffer_overflow_logged:
-                logger.warning(
-                    "Token buffer exceeded max size (%d bytes), flushing excess. "
-                    "This may indicate very long markers or unusual tokenization.",
-                    self.buffer_max_size,
-                )
-                self._buffer_overflow_logged = True
-            # Keep only the most recent content that might contain partial markers
-            self.token_buffer = self.token_buffer[-self.buffer_max_size // 2 :]
-
-        # Check buffer for section markers (handles split tokens)
-        buffered_text, found_section_begin, found_section_end = (
-            self._check_and_strip_markers(self.token_buffer)
-        )
-
-        # Track section state transitions
-        if found_section_begin and not self.in_tool_section:
-            logger.debug("Entering tool section")
-            self.in_tool_section = True
-            self.token_buffer = buffered_text  # Use cleaned buffer
-            self.section_char_count = 0  # Reset counter for new section
-
-        if found_section_end and self.in_tool_section:
-            logger.debug("Detected section end marker")
-            # CRITICAL: Don't exit early if tool_call_end is in this chunk.
-            # Tool parser must emit final arguments/close first to avoid dropping
-            # the final tool update and leaking tokens into reasoning channel.
-            has_tool_end = self.tool_call_end_token_id in delta_token_ids
-            if has_tool_end:
-                # Defer exit until after tool parsing completes
-                deferred_section_exit = True
-                logger.debug("Deferring section exit: tool_call_end in same chunk")
-                self.token_buffer = buffered_text
-            else:
-                # No tool call ending, safe to exit immediately
-                logger.debug("Exiting tool section")
-                self._reset_section_state()
-                # Extract any content AFTER the section end marker in delta_text
-                # (don't use buffered_text as it contains tool call data)
-                post_section_content = ""
-                for variant in self.tool_calls_end_token_variants:
-                    if variant in delta_text:
-                        parts = delta_text.split(variant, 1)
-                        if len(parts) > 1:
-                            post_section_content = parts[1]
-                        break
-                if post_section_content.strip():
-                    return DeltaMessage(content=post_section_content)
-                return DeltaMessage(content="")
-        else:
-            self.token_buffer = buffered_text
-
-        # Check if any variant of section start token is in current_token_ids
-        has_section_token = any(
-            tid in current_token_ids for tid in self.tool_calls_start_token_ids
-        )
-
-        # Early return: if no section token detected yet, return as reasoning content
-        if not has_section_token and not self.in_tool_section:
-            logger.debug("No tool call tokens found!")
-            # Don't clear buffer - it needs to accumulate partial markers across deltas
-            # Buffer overflow is already protected by lines 215-224
-            return DeltaMessage(content=delta_text)
-
-        # Strip section markers from delta_text for subsequent processing
-        # NOTE: This preprocessing happens BEFORE the regex-based tool call
-        # parsing (from PR #24847) to ensure markers are removed cleanly
-        # before pattern matching. No double-stripping occurs because
-        # section markers and tool call markers are distinct.
-        delta_text, _, _ = self._check_and_strip_markers(delta_text)
-
-        # Error recovery: If in tool section for too long, force exit
-        if self.in_tool_section:
-            self.section_char_count += len(delta_text)
-            if self.section_char_count > self.max_section_chars:
-                logger.warning(
-                    "Tool section exceeded max length (%d chars), forcing exit. "
-                    "This may indicate malformed model output.",
-                    self.max_section_chars,
-                )
-                self._reset_section_state()
-                # Deferred exit already handled by forced exit above
-                # Return remaining content as reasoning (or empty delta if no content)
-                return DeltaMessage(content=delta_text if delta_text.strip() else "")
-
         try:
-            # figure out where we are in the parsing by counting tool call
-            # start & end tags
-            prev_tool_start_count = previous_token_ids.count(
-                self.tool_call_start_token_id
-            )
-            prev_tool_end_count = previous_token_ids.count(self.tool_call_end_token_id)
-            cur_tool_start_count = current_token_ids.count(
-                self.tool_call_start_token_id
-            )
-            cur_tool_end_count = current_token_ids.count(self.tool_call_end_token_id)
-            tool_call_portion = None
-            text_portion = None
-
-            # case: if we're generating text, OR rounding out a tool call
-            if (
-                cur_tool_start_count == cur_tool_end_count
-                and prev_tool_end_count == cur_tool_end_count
-                and self.tool_call_end_token not in delta_text
-            ):
-                # Suppress content between section begin and first tool begin
-                # (header noise). Don't suppress content between tools to avoid
-                # breaking potential delimiter characters.
-                if self.in_tool_section and cur_tool_start_count == 0:
-                    logger.debug(
-                        "In tool section before first tool, suppressing: %s",
-                        delta_text,
-                    )
-                    # Return empty delta to maintain iterator contract
-                    return DeltaMessage(content="")
-                logger.debug("Generating text content! skipping tool parsing.")
-                return DeltaMessage(content=delta_text)
-
-            if self.tool_call_end_token in delta_text:
-                logger.debug("tool_call_end_token in delta_text")
-                full_text = current_text + delta_text
-                tool_call_portion = (
-                    full_text.split(self.tool_call_start_token)[-1]
-                    .split(self.tool_call_end_token)[0]
-                    .rstrip()
-                )
-                delta_text = delta_text.split(self.tool_call_end_token)[0].rstrip()
-                text_portion = delta_text.split(self.tool_call_end_token)[-1].lstrip()
-
-            # case -- we're starting a new tool call
-            if (
-                cur_tool_start_count > cur_tool_end_count
-                and cur_tool_start_count > prev_tool_start_count
-            ):
-                if len(delta_token_ids) > 1:
-                    tool_call_portion = current_text.split(self.tool_call_start_token)[
-                        -1
-                    ]
-                else:
-                    tool_call_portion = None
-                    delta = None
-
-                text_portion = None
-
-                # set cursors and state appropriately
-                self.current_tool_id += 1
-                self.current_tool_name_sent = False
-                self.streamed_args_for_tool.append("")
-                logger.debug("Starting on a new tool %s", self.current_tool_id)
-
-            # case -- we're updating an existing tool call
-            elif (
-                cur_tool_start_count > cur_tool_end_count
-                and cur_tool_start_count == prev_tool_start_count
-            ):
-                # get the portion of the text that's the tool call
-                tool_call_portion = current_text.split(self.tool_call_start_token)[-1]
-                text_portion = None
-
-            # case -- the current tool call is being closed.
-            elif (
-                cur_tool_start_count == cur_tool_end_count
-                and cur_tool_end_count >= prev_tool_end_count
-            ):
-                if self.prev_tool_call_arr is None or len(self.prev_tool_call_arr) == 0:
-                    logger.debug("attempting to close tool call, but no tool call")
-                    # Handle deferred section exit before returning
-                    if deferred_section_exit and self.in_tool_section:
-                        self._reset_section_state()
-                    return None
-                diff = self.prev_tool_call_arr[self.current_tool_id].get("arguments")
-                if diff:
-                    diff = (
-                        diff.encode("utf-8").decode("unicode_escape")
-                        if diff is str
-                        else diff
-                    )
-                    if '"}' not in delta_text:
-                        # Handle deferred section exit before returning
-                        if deferred_section_exit and self.in_tool_section:
-                            self._reset_section_state()
-                        return None
-                    end_loc = delta_text.rindex('"}')
-                    diff = delta_text[:end_loc] + '"}'
-                    logger.debug(
-                        "Finishing tool and found diff that had not "
-                        "been streamed yet: %s",
-                        diff,
-                    )
-                    self.streamed_args_for_tool[self.current_tool_id] += diff
-                    # Handle deferred section exit before returning
-                    if deferred_section_exit and self.in_tool_section:
-                        logger.debug("Completing deferred section exit")
-                        self._reset_section_state()
-                    return DeltaMessage(
-                        tool_calls=[
-                            DeltaToolCall(
-                                index=self.current_tool_id,
-                                function=DeltaFunctionCall(arguments=diff).model_dump(
-                                    exclude_none=True
-                                ),
-                            )
-                        ]
-                    )
-
-            # case -- otherwise we're just generating text
-            else:
-                # Check if we're in tool section - if so, suppress
-                if self.in_tool_section:
-                    logger.debug("In tool section, suppressing text generation")
-                    # Handle deferred section exit before returning
-                    if deferred_section_exit:
-                        self._reset_section_state()
-                    return DeltaMessage(content="")
-                text = delta_text.replace(self.tool_call_start_token, "")
-                text = text.replace(self.tool_call_end_token, "")
-                delta = DeltaMessage(tool_calls=[], content=text)
-                # Handle deferred section exit before returning
-                if deferred_section_exit and self.in_tool_section:
-                    self._reset_section_state()
-                return delta
-
-            current_tool_call = dict()
-            if tool_call_portion:
-                current_tool_call_matches = self.stream_tool_call_portion_regex.match(
-                    tool_call_portion
-                )
-                if current_tool_call_matches:
-                    tool_id, tool_args = current_tool_call_matches.groups()
-                    tool_name = tool_id.split(":")[0].split(".")[-1]
-                    current_tool_call["id"] = tool_id.strip()
-                    current_tool_call["name"] = tool_name
-                    current_tool_call["arguments"] = tool_args
-                else:
-                    current_tool_call_name_matches = (
-                        self.stream_tool_call_name_regex.match(tool_call_portion)
-                    )
-                    if current_tool_call_name_matches:
-                        (tool_id_str,) = current_tool_call_name_matches.groups()
-                        tool_name = tool_id_str.split(":")[0].split(".")[-1]
-                        current_tool_call["id"] = tool_id_str.strip()
-                        current_tool_call["name"] = tool_name
-                        current_tool_call["arguments"] = ""
-                    else:
-                        logger.debug("Not enough token")
-                        return None
-
-            # case - we haven't sent the tool name yet. If it's available, send
-            #   it. otherwise, wait until it's available.
-            if not self.current_tool_name_sent:
-                if current_tool_call is None:
-                    return None
-                function_name: str | None = current_tool_call.get("name")
-                tool_id = current_tool_call.get("id")
-                if function_name:
-                    self.current_tool_name_sent = True
-                    return DeltaMessage(
-                        tool_calls=[
-                            DeltaToolCall(
-                                index=self.current_tool_id,
-                                type="function",
-                                id=tool_id,
-                                function=DeltaFunctionCall(
-                                    name=function_name
-                                ).model_dump(exclude_none=True),
-                            )
-                        ]
+            # Extract any content before tool calls.
+            content = self._extract_content(current_text)
+            tool_calls = self._extract_tool_calls(current_text)
+            tool_call_deltas: list[DeltaToolCall] = []
+
+            for i, tool_call in enumerate(tool_calls):
+                # First time seeing tool call at index i.
+                if i >= len(self.prev_tool_call_arr):
+                    # Initialize streaming state.
+                    self.prev_tool_call_arr.append({})
+                    self.streamed_args_for_tool.append("")
+
+                header, tool_args = self._split_tool_call(tool_call)
+
+                # Stream back tool name.
+                if "name" not in self.prev_tool_call_arr[i]:
+                    tool_id, tool_name = self._extract_tool_id_and_name(header)
+                    if not tool_name:
+                        # Can't skip to tool i+1 if i isn't ready
+                        break
+                    self.prev_tool_call_arr[i]["name"] = tool_name
+                    self.prev_tool_call_arr[i]["id"] = tool_id
+                    tool_call_deltas.append(
+                        DeltaToolCall(
+                            index=i,
+                            type="function",
+                            id=tool_id,
+                            function=DeltaFunctionCall(name=tool_name).model_dump(
+                                exclude_none=True
+                            ),
+                        )
                     )
-                else:
-                    return None
-
-            # case -- otherwise, send the tool call delta
-
-            # if the tool call portion is None, send the delta as text
-            if tool_call_portion is None:
-                # if there's text but not tool calls, send that -
-                # otherwise None to skip chunk
-                # CRITICAL: Never return content if we're in a tool section
-                if self.in_tool_section:
-                    return None
-                delta = (
-                    DeltaMessage(content=delta_text)
-                    if text_portion is not None
-                    else None
-                )
-                return delta
-
-            # now, the nitty-gritty of tool calls
-            # now we have the portion to parse as tool call.
-
-            logger.debug(
-                "Trying to parse current tool call with ID %s", self.current_tool_id
-            )
-
-            # if we're starting a new tool call, push an empty object in as
-            #   a placeholder for the arguments
-            if len(self.prev_tool_call_arr) <= self.current_tool_id:
-                self.prev_tool_call_arr.append({})
-
-            # main logic for tool parsing here - compare prev. partially-parsed
-            #   JSON to the current partially-parsed JSON
-            prev_arguments = self.prev_tool_call_arr[self.current_tool_id].get(
-                "arguments"
-            )
-            cur_arguments = current_tool_call.get("arguments")
-
-            logger.debug("diffing old arguments: %s", prev_arguments)
-            logger.debug("against new ones: %s", cur_arguments)
-
-            # case -- no arguments have been created yet. skip sending a delta.
-            if not cur_arguments and not prev_arguments:
-                logger.debug("Skipping text %s - no arguments", delta_text)
-                delta = None
-
-            # case -- prev arguments are defined, but non are now.
-            #   probably impossible, but not a fatal error - just keep going
-            elif not cur_arguments and prev_arguments:
-                logger.error(
-                    "should be impossible to have arguments reset "
-                    "mid-call. skipping streaming anything."
-                )
-                delta = None
 
-            # case -- we now have the first info about arguments available from
-            #   autocompleting the JSON
-            elif cur_arguments and not prev_arguments:
-                delta = DeltaMessage(
-                    tool_calls=[
+                # Stream back new tool args by diffing against what was sent.
+                args_diff = self._compute_args_diff(i, tool_args)
+                if args_diff:
+                    tool_call_deltas.append(
                         DeltaToolCall(
-                            index=self.current_tool_id,
-                            function=DeltaFunctionCall(
-                                arguments=cur_arguments
-                            ).model_dump(exclude_none=True),
+                            index=i,
+                            function=DeltaFunctionCall(arguments=args_diff).model_dump(
+                                exclude_none=True
+                            ),
                         )
-                    ]
-                )
-                self.streamed_args_for_tool[self.current_tool_id] = cur_arguments
-
-            # last case -- we have an update to existing arguments.
-            elif cur_arguments and prev_arguments:
-                if (
-                    isinstance(delta_text, str)
-                    and cur_arguments != prev_arguments
-                    and len(cur_arguments) > len(prev_arguments)
-                    and cur_arguments.startswith(prev_arguments)
-                ):
-                    delta_arguments = cur_arguments[len(prev_arguments) :]
-                    logger.debug("got diff %s", delta_text)
-
-                    delta = DeltaMessage(
-                        tool_calls=[
-                            DeltaToolCall(
-                                index=self.current_tool_id,
-                                function=DeltaFunctionCall(
-                                    arguments=delta_arguments
-                                ).model_dump(exclude_none=True),
-                            )
-                        ]
                     )
-                    self.streamed_args_for_tool[self.current_tool_id] = cur_arguments
-                else:
-                    delta = None
-
-            # handle saving the state for the current tool into
-            # the "prev" list for use in diffing for the next iteration
-            if self.current_tool_id == len(self.prev_tool_call_arr) - 1:
-                self.prev_tool_call_arr[self.current_tool_id] = current_tool_call
-            else:
-                self.prev_tool_call_arr.append(current_tool_call)
-
-            # Handle deferred section exit after tool parsing completes
-            if deferred_section_exit and self.in_tool_section:
-                logger.debug("Completing deferred section exit")
-                self._reset_section_state()
 
-            return delta
+            if content or tool_call_deltas:
+                return DeltaMessage(
+                    content=content,
+                    tool_calls=tool_call_deltas,
+                )
+            return None
 
         except Exception:
             logger.exception("Error trying to handle streaming tool call.")
-            return None  # do not stream a delta. skip this token ID.
+            return None
diff --git a/vllm/tool_parsers/lfm2_tool_parser.py b/vllm/tool_parsers/lfm2_tool_parser.py
new file mode 100644
index 000000000000..ee92d060fbea
--- /dev/null
+++ b/vllm/tool_parsers/lfm2_tool_parser.py
@@ -0,0 +1,343 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import ast
+from collections.abc import Sequence
+
+import regex as re
+
+import vllm.envs as envs
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+)
+from vllm.entrypoints.openai.engine.protocol import (
+    DeltaMessage,
+    ExtractedToolCallInformation,
+)
+from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
+from vllm.logger import init_logger
+from vllm.tokenizers import TokenizerLike
+from vllm.tool_parsers.abstract_tool_parser import (
+    Tool,
+    ToolParser,
+)
+from vllm.tool_parsers.utils import (
+    UnexpectedAstError,
+    compute_tool_delta,
+    handle_single_tool,
+    make_valid_python,
+)
+
+logger = init_logger(__name__)
+
+TOOL_CALL_START = "<|tool_call_start|>"
+TOOL_CALL_END = "<|tool_call_end|>"
+
+
+class Lfm2ToolParser(ToolParser):
+    """
+    Tool call parser for LiquidAI LFM2/LFM2.5 models that produce pythonic
+    tool calls wrapped in <|tool_call_start|> and <|tool_call_end|> tokens.
+
+    Example model output:
+        <|tool_call_start|>[get_weather(location="Paris")]<|tool_call_end|>
+        The weather in Paris is sunny.
+
+    Used when --enable-auto-tool-choice --tool-call-parser lfm2 are all set.
+    """
+
+    TOOL_CALL_REGEX = re.compile(r"\[.*\]$", re.DOTALL)
+
+    def __init__(
+        self,
+        tokenizer: TokenizerLike,
+        tools: list[Tool] | None = None,
+    ):
+        super().__init__(tokenizer, tools)
+
+        self.tool_call_start_token_id = self.vocab.get(TOOL_CALL_START)
+        self.tool_call_end_token_id = self.vocab.get(TOOL_CALL_END)
+
+        if self.tool_call_start_token_id is None or self.tool_call_end_token_id is None:
+            raise RuntimeError(
+                "LFM2 tool parser could not locate "
+                "<|tool_call_start|>/<|tool_call_end|> tokens in the "
+                "tokenizer!"
+            )
+
+        # Trailing content already emitted to the client. Used by the
+        # streaming path to suppress LFM2's frequent echo of the tool
+        # call body after the first <|tool_call_end|> while still
+        # allowing legitimate post-call prose through.
+        self._trailing_emitted: str = ""
+
+    def adjust_request(
+        self, request: ChatCompletionRequest | ResponsesRequest
+    ) -> ChatCompletionRequest | ResponsesRequest:
+        request = super().adjust_request(request)
+        if request.tools and request.tool_choice != "none":
+            # The <|tool_call_start|>/<|tool_call_end|> sentinels are
+            # registered as special tokens in the LFM2/LFM2.5 tokenizer.
+            # With the default ``skip_special_tokens=True`` they are
+            # stripped from the decoded text before reaching this parser,
+            # so the tool block becomes invisible. Force the engine to
+            # preserve them when tool calling is enabled.
+            request.skip_special_tokens = False
+        return request
+
+    # Rename for readability. This is NOT a tool id.
+    @property
+    def current_tool_index(self) -> int:
+        return self.current_tool_id
+
+    @current_tool_index.setter
+    def current_tool_index(self, value: int) -> None:
+        self.current_tool_id = value
+
+    @staticmethod
+    def _strip_echo(raw_after: str) -> str:
+        """Drop any orphan <|tool_call_end|> (and the preceding text) from
+        trailing content. LFM2 occasionally echoes the call body after the
+        first end token and caps it with a second end token; everything
+        through the last such orphan is model garbage, not user content."""
+        last_orphan = raw_after.rfind(TOOL_CALL_END)
+        if last_orphan != -1:
+            return raw_after[last_orphan + len(TOOL_CALL_END) :]
+        return raw_after
+
+    @classmethod
+    def _extract_tool_call_text(
+        cls, model_output: str
+    ) -> tuple[str | None, str | None]:
+        """Extract the pythonic call text and surrounding content.
+
+        Returns (tool_text, content) where tool_text is the text between
+        the sentinel tokens and content is everything outside them.
+        """
+        start_idx = model_output.find(TOOL_CALL_START)
+        if start_idx == -1:
+            return None, model_output
+
+        end_idx = model_output.find(TOOL_CALL_END, start_idx)
+        if end_idx == -1:
+            # Incomplete — treat entire text after start as tool call
+            tool_text = model_output[start_idx + len(TOOL_CALL_START) :]
+            content_before = model_output[:start_idx].strip()
+            content = content_before or None
+            return tool_text, content
+
+        tool_text = model_output[start_idx + len(TOOL_CALL_START) : end_idx]
+        content_before = model_output[:start_idx].strip()
+        content_after = cls._strip_echo(
+            model_output[end_idx + len(TOOL_CALL_END) :]
+        ).strip()
+
+        content_parts = []
+        if content_before:
+            content_parts.append(content_before)
+        if content_after:
+            content_parts.append(content_after)
+        content = "\n".join(content_parts) if content_parts else None
+
+        return tool_text, content
+
+    def extract_tool_calls(
+        self, model_output: str, request: ChatCompletionRequest
+    ) -> ExtractedToolCallInformation:
+        tool_text, content = self._extract_tool_call_text(model_output)
+
+        if tool_text is None:
+            return ExtractedToolCallInformation(
+                tools_called=False, tool_calls=[], content=model_output
+            )
+
+        tool_text = tool_text.strip()
+
+        is_tool_call_pattern = False
+        try:
+            is_tool_call_pattern = (
+                self.TOOL_CALL_REGEX.match(
+                    tool_text,
+                    timeout=envs.VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS,
+                )
+                is not None
+            )
+        except TimeoutError:
+            logger.warning("Regex timeout occurred when matching tool call pattern.")
+
+        if not is_tool_call_pattern:
+            return ExtractedToolCallInformation(
+                tools_called=False, tool_calls=[], content=model_output
+            )
+
+        try:
+            module = ast.parse(tool_text)
+            parsed = getattr(module.body[0], "value", None)
+            if isinstance(parsed, ast.List) and all(
+                isinstance(e, ast.Call) for e in parsed.elts
+            ):
+                return ExtractedToolCallInformation(
+                    tools_called=True,
+                    tool_calls=[
+                        handle_single_tool(e)  # type: ignore
+                        for e in parsed.elts
+                    ],
+                    content=content,
+                )
+            else:
+                raise UnexpectedAstError("Tool output must be a list of function calls")
+        except Exception:
+            logger.exception("Error in extracting tool call from response.")
+            return ExtractedToolCallInformation(
+                tools_called=False, tool_calls=[], content=model_output
+            )
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> DeltaMessage | None:
+        # If the tool call start token hasn't appeared yet, stream as content.
+        if TOOL_CALL_START not in current_text:
+            return DeltaMessage(content=delta_text)
+
+        # Compute leading content (before <|tool_call_start|>) that arrived
+        # in this delta and hasn't been streamed yet. Without this, when the
+        # prefix and the start token land in the same delta the prefix is
+        # silently dropped — token-by-token streaming masked the bug because
+        # the prefix tokens always arrived in earlier deltas.
+        leading_content = ""
+        if TOOL_CALL_START not in previous_text:
+            start_idx = current_text.find(TOOL_CALL_START)
+            # previous_text contained no start token, so it has already been
+            # streamed via the no-start-token branch above.
+            leading_content = current_text[len(previous_text) : start_idx]
+
+        has_end_in_current = TOOL_CALL_END in current_text
+        has_end_in_previous = TOOL_CALL_END in previous_text
+
+        # Compute trailing content (after <|tool_call_end|>) not yet
+        # streamed. LFM2 frequently echoes the tool call body again
+        # after the first end token, capped with a second end token.
+        # Suppress that echo:
+        #   - If a second <|tool_call_end|> has appeared, treat
+        #     everything through the last one as garbage.
+        #   - If the trailing starts with `[` or `<` (potential echo
+        #     body or another sentinel) and no second end token has
+        #     arrived yet, buffer it instead of emitting.
+        trailing_content = ""
+        if has_end_in_current:
+            end_idx = current_text.find(TOOL_CALL_END) + len(TOOL_CALL_END)
+            full_trailing = current_text[end_idx:]
+            stripped_trailing = self._strip_echo(full_trailing)
+            if stripped_trailing == full_trailing:
+                # No second end token yet — possibly mid-echo.
+                lstripped = full_trailing.lstrip()
+                if lstripped.startswith("[") or lstripped.startswith("<"):
+                    # Suspect echo; hold off until resolved.
+                    final_trailing = self._trailing_emitted
+                else:
+                    final_trailing = full_trailing
+            else:
+                final_trailing = stripped_trailing
+            if final_trailing.startswith(self._trailing_emitted):
+                trailing_content = final_trailing[len(self._trailing_emitted) :]
+            self._trailing_emitted = final_trailing
+
+        # If tools were already parsed in a prior delta, just stream any
+        # newly arrived trailing content.
+        if has_end_in_current and self.prev_tool_call_arr and has_end_in_previous:
+            if trailing_content:
+                return DeltaMessage(content=trailing_content)
+            return DeltaMessage(content="")
+
+        # Extract the pythonic text between start and end tokens.
+        tool_text = current_text.split(TOOL_CALL_START, 1)[1]
+        # Strip the end token if present (entire call arrived at once).
+        if TOOL_CALL_END in tool_text:
+            tool_text = tool_text.split(TOOL_CALL_END, 1)[0]
+
+        def _content_only_or_none() -> DeltaMessage | None:
+            """Return a content-only delta if any content arrived in this
+            chunk, otherwise None. Used on incremental-parse failure paths
+            so leading/trailing content is never silently dropped.
+            """
+            combined = leading_content + trailing_content
+            return DeltaMessage(content=combined) if combined else None
+
+        try:
+            valid_and_added_text = make_valid_python(tool_text)
+            if valid_and_added_text is None:
+                return _content_only_or_none()
+            valid_text, added_text = valid_and_added_text
+
+            module = ast.parse(valid_text)
+            parsed = getattr(module.body[0], "value", None)
+            if not isinstance(parsed, ast.List) or not all(
+                isinstance(e, ast.Call) for e in parsed.elts
+            ):
+                raise UnexpectedAstError("Tool output must be a list of function calls")
+            tool_calls = [
+                handle_single_tool(e)  # type: ignore
+                for e in parsed.elts
+            ]
+
+            tool_deltas = []
+            for index, new_call in enumerate(tool_calls):
+                if index < self.current_tool_index:
+                    continue
+
+                self.current_tool_index = index
+                if len(self.streamed_args_for_tool) == index:
+                    self.streamed_args_for_tool.append("")
+
+                new_call_complete = (
+                    index < len(tool_calls) - 1 or ")]" not in added_text
+                )
+                if new_call_complete:
+                    self.current_tool_index += 1
+
+                withheld_suffix = added_text[:-2] if not new_call_complete else ""
+                if not new_call_complete and added_text[-2] == ")":
+                    withheld_suffix = withheld_suffix + "}"
+                withheld_suffix = withheld_suffix.replace("'", '"')
+                delta = compute_tool_delta(
+                    self.streamed_args_for_tool[index],
+                    new_call,
+                    index,
+                    withheld_suffix,
+                )
+
+                if delta is not None:
+                    tool_deltas.append(delta)
+                    if (
+                        delta.function is not None
+                        and delta.function.arguments is not None
+                    ):
+                        self.streamed_args_for_tool[index] += delta.function.arguments
+
+            if tool_deltas and not self.prev_tool_call_arr:
+                self.prev_tool_call_arr = [{"arguments": {}}]
+
+            combined_content = leading_content + trailing_content
+
+            if tool_deltas or combined_content:
+                return DeltaMessage(
+                    content=combined_content if combined_content else None,
+                    tool_calls=tool_deltas,
+                )
+            elif not added_text and self.current_tool_id > 0:
+                return DeltaMessage(content="")
+            else:
+                return None
+        except Exception:
+            logger.exception("Error trying to handle streaming tool call.")
+            logger.debug(
+                "Skipping chunk as a result of tool streaming extraction error"
+            )
+            return _content_only_or_none()
diff --git a/vllm/tool_parsers/llama_tool_parser.py b/vllm/tool_parsers/llama_tool_parser.py
index be3d47acd97f..4a041041f096 100644
--- a/vllm/tool_parsers/llama_tool_parser.py
+++ b/vllm/tool_parsers/llama_tool_parser.py
@@ -45,6 +45,12 @@ class Llama3JsonToolParser(ToolParser):
     llama4_json are set.
     """
 
+    bot_token: str = "<|python_tag|>"
+    # Simple regex to find opening braces - we'll use JSON decoder for parsing
+    # This handles arbitrary nesting depth correctly
+    tool_call_start_regex: re.Pattern = re.compile(r"\{")
+    json_decoder: json.JSONDecoder = json.JSONDecoder()
+
     def __init__(
         self,
         tokenizer: PreTrainedTokenizerBase,
@@ -60,14 +66,12 @@ def __init__(
         self.streamed_args_for_tool: list[
             str
         ] = []  # map what has been streamed for each tool so far to a list
-        self.bot_token = "<|python_tag|>"
-        self.bot_token_id = tokenizer.encode(self.bot_token, add_special_tokens=False)[
-            0
-        ]
-        # Simple regex to find opening braces - we'll use JSON decoder for parsing
-        # This handles arbitrary nesting depth correctly
-        self.tool_call_start_regex = re.compile(r"\{")
-        self.json_decoder = json.JSONDecoder()
+        self.bot_token_id = self.vocab.get(self.bot_token)
+        if self.bot_token_id is None:
+            raise RuntimeError(
+                "Llama3JsonToolParser could not locate the bot token "
+                f"'{self.bot_token}' in the tokenizer."
+            )
 
     def extract_tool_calls(
         self, model_output: str, request: ChatCompletionRequest
diff --git a/vllm/tool_parsers/mistral_tool_parser.py b/vllm/tool_parsers/mistral_tool_parser.py
index 4d1aaffedd0e..0a057a3af468 100644
--- a/vllm/tool_parsers/mistral_tool_parser.py
+++ b/vllm/tool_parsers/mistral_tool_parser.py
@@ -1,12 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from __future__ import annotations
+
 import json
 from collections.abc import Sequence
+from dataclasses import dataclass
 from enum import Enum, auto
 from random import choices
 from string import ascii_letters, digits
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import ijson
 import regex as re
@@ -37,14 +40,19 @@
 )
 from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
 from vllm.logger import init_logger
+from vllm.reasoning.mistral_reasoning_parser import MistralReasoningParser
 from vllm.sampling_params import StructuredOutputsParams
 from vllm.tokenizers import TokenizerLike
+from vllm.tokenizers.mistral import MistralTokenizer
 from vllm.tool_parsers.abstract_tool_parser import (
     Tool,
     ToolParser,
 )
 from vllm.utils.mistral import is_mistral_tokenizer
 
+if TYPE_CHECKING:
+    from vllm.reasoning import ReasoningParser
+
 logger = init_logger(__name__)
 
 ALPHANUMERIC = ascii_letters + digits
@@ -83,18 +91,40 @@ def is_valid_id(id: str) -> bool:
 
 
 def _is_pre_v11_tokeniser(model_tokenizer: TokenizerLike) -> bool:
-    return not (is_mistral_tokenizer(model_tokenizer) and model_tokenizer.version >= 11)
+    if is_mistral_tokenizer(model_tokenizer):
+        return model_tokenizer.version < 11
+    # For HF tokenizers, check if [ARGS] token exists in vocab
+    # which indicates a v11+ equivalent tokenizer
+    vocab: dict[str, int] = getattr(model_tokenizer, "get_vocab", lambda: {})()
+    return "[ARGS]" not in vocab
+
+
+@dataclass
+class MistralStreamingResult:
+    r"""Encapsulates the mutable state returned from
+    `MistralToolParser.extract_maybe_reasoning_and_tool_streaming`.
+    """
+
+    delta_message: DeltaMessage | None
+    reasoning_ended: bool
+    tools_called: bool
+    current_text: str
+    current_token_ids: list[int]
 
 
 class MistralToolParser(ToolParser):
-    """
-    Tool call parser for Mistral 7B Instruct v0.3, intended for use with
-    - [`mistral_common`](https://github.com/mistralai/mistral-common/)
-    - the examples/tool_chat_template_mistral.jinja template.
+    r"""Tool call parser for Mistral models, intended for use with either:
 
-    Used when --enable-auto-tool-choice --tool-call-parser mistral are all set
+    - `mistral_common <https://github.com/mistralai/mistral-common/>`_
+      (recommended)
+    - the `examples/tool_chat_template_mistral.jinja` template.
+
+    Used when `--enable-auto-tool-choice --tool-call-parser mistral` are all
+    set.
     """
 
+    IS_MISTRAL_TOOL_PARSER = True  # used by vllm.utils.mistral
+
     # Used to generate correct grammar in `adjust_request`
     model_can_reason: bool = False
 
@@ -114,7 +144,8 @@ def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
         self.current_tool_name: str | None = None
         self.current_tool_mistral_id: str | None = None
         self.starting_new_tool = False
-        if _is_pre_v11_tokeniser(self.model_tokenizer):
+        self._is_pre_v11 = _is_pre_v11_tokeniser(self.model_tokenizer)
+        if self._is_pre_v11:
             self.parse_coro = ijson.parse_coro(
                 self.update_stream_state_pre_v11_tokenizer()
             )
@@ -122,7 +153,6 @@ def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
         self.bot_token = "[TOOL_CALLS]"
         self.bot_token_id = self.vocab.get(self.bot_token)
         self.tool_call_regex = re.compile(r"\[{.*}\]", re.DOTALL)
-        self._is_pre_v11 = _is_pre_v11_tokeniser(self.model_tokenizer)
 
         if self.bot_token_id is None:
             raise RuntimeError(
@@ -210,11 +240,8 @@ def adjust_request(
             reasoning=self.model_can_reason
         )
 
-        tools = (
-            [
-                MistralTool.from_openai(openai_tool=tool.model_dump())
-                for tool in request.tools
-            ]
+        mistral_tools = (
+            [MistralTool.from_openai(tool.model_dump()) for tool in request.tools]
             if request.tools is not None
             else None
         )
@@ -244,15 +271,158 @@ def adjust_request(
                 lark_grammar = grammar_factory.get_lark_from_jinja(
                     template=template,
                     mode=tool_choice,
-                    tools=tools,
+                    tools=mistral_tools,
                     json_schema=json_schema,
                     parallel_tool_calls=request.parallel_tool_calls,
                     json_only=False,
                 )
 
         request.structured_outputs = StructuredOutputsParams(grammar=lark_grammar)
+        request._grammar_from_tool_parser = True
         return request
 
+    def extract_maybe_reasoning_and_tool_streaming(
+        self,
+        *,
+        reasoning_parser: ReasoningParser | None,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: list[int],
+        current_token_ids: list[int],
+        output_token_ids: Sequence[int],
+        reasoning_ended: bool,
+        prompt_is_reasoning_end: bool | None,
+        request: ChatCompletionRequest,
+    ) -> MistralStreamingResult:
+        r"""Streaming extraction with reasoning followed by tool-call parsing.
+
+        This method encapsulates the combined reasoning extraction and
+        tool-call streaming logic so that the serving layer only needs a
+        thin routing branch.
+
+        The flow is:
+
+        1. If a *reasoning_parser* is present and reasoning has **not** ended,
+           extract reasoning tokens.  Pre-v15 models may have pre-filled
+           `[THINK]...[/THINK]` in system prompts, so we skip the
+           prompt-level reasoning-end check for those.
+        2. Once reasoning ends (or if there is no reasoning parser), delegate
+           to `extract_tool_calls_streaming` and track whether tools were
+           called.
+
+        Args:
+            reasoning_parser: Optional reasoning parser instance.
+            previous_text: Accumulated text from prior chunks.
+            current_text: Full accumulated text including current chunk.
+            delta_text: New text in this chunk.
+            previous_token_ids: Token ids from prior chunks.
+            current_token_ids: Full token ids including current chunk.
+            output_token_ids: Raw output token ids from the engine.
+            reasoning_ended: Whether reasoning has already ended.
+            prompt_is_reasoning_end: Whether the prompt itself ends reasoning.
+            request: The originating chat completion request.
+        """
+        delta_message: DeltaMessage | None = None
+        tools_called = False
+        reasoning_ended_at_entry = reasoning_ended
+
+        # For MistralReasoningParser, only enter the reasoning block when
+        # the model has actually emitted a [THINK] token.  Other reasoning
+        # parsers always expect thinking to be present.
+        expect_thinking = (
+            not isinstance(reasoning_parser, MistralReasoningParser)
+            or reasoning_parser.start_token_id in current_token_ids
+        )
+        if reasoning_parser is not None and not reasoning_ended and expect_thinking:
+            # Pre-v15 models may have pre-filled [THINK]...[/THINK] in
+            # system prompts, so skip the prompt-level reasoning-end
+            # check and wait for the output's own end-of-think.
+            is_pre_v15 = (
+                isinstance(self.model_tokenizer, MistralTokenizer)
+                and self.model_tokenizer.version < 15
+            )
+
+            if not is_pre_v15 and prompt_is_reasoning_end:
+                reasoning_ended = True
+                current_token_ids = list(output_token_ids)
+            else:
+                delta_message = reasoning_parser.extract_reasoning_streaming(
+                    previous_text,
+                    current_text,
+                    delta_text,
+                    previous_token_ids,
+                    current_token_ids,
+                    output_token_ids,
+                )
+                if reasoning_parser.is_reasoning_end_streaming(
+                    current_token_ids, output_token_ids
+                ):
+                    reasoning_ended = True
+                    current_token_ids = reasoning_parser.extract_content_ids(
+                        list(output_token_ids)
+                    )
+                    if delta_message and delta_message.content:
+                        current_text = delta_message.content
+                        delta_message.content = None
+                    else:
+                        current_text = ""
+
+            if not reasoning_ended:
+                return MistralStreamingResult(
+                    delta_message=delta_message,
+                    reasoning_ended=False,
+                    tools_called=False,
+                    current_text=current_text,
+                    current_token_ids=current_token_ids,
+                )
+
+        delta_token_ids = list(output_token_ids)
+
+        # On the iteration where reasoning just ended, reset the text/token
+        # state so the tool parser sees a clean history instead of the
+        # accumulated reasoning text.
+        if not reasoning_ended_at_entry and reasoning_ended:
+            previous_text = ""
+            previous_token_ids = []
+            delta_text = current_text
+            delta_token_ids = current_token_ids
+
+        delta_message = self.extract_tool_calls_streaming(
+            previous_text=previous_text,
+            current_text=current_text,
+            delta_text=delta_text,
+            previous_token_ids=previous_token_ids,
+            current_token_ids=current_token_ids,
+            delta_token_ids=delta_token_ids,
+            request=request,
+        )
+        if delta_message and delta_message.tool_calls:
+            tools_called = True
+
+        return MistralStreamingResult(
+            delta_message=delta_message,
+            reasoning_ended=reasoning_ended,
+            tools_called=tools_called,
+            current_text=current_text,
+            current_token_ids=current_token_ids,
+        )
+
+    @staticmethod
+    def build_non_streaming_tool_calls(
+        tool_calls: list[FunctionCall] | None,
+    ) -> list[ToolCall]:
+        r"""Build `MistralToolCall` items for non-streaming responses."""
+        if not tool_calls:
+            return []
+
+        return [
+            MistralToolCall(id=tc.id, function=tc)
+            if tc.id
+            else MistralToolCall(function=tc)
+            for tc in tool_calls
+        ]
+
     def extract_tool_calls(
         self,
         model_output: str,
@@ -300,6 +470,8 @@ def extract_tool_calls(
                     raw_tool_call[end_name:],
                 )
 
+                # HF tokenizers may include [ARGS] in the text
+                tool_name = tool_name.replace("[ARGS]", "")
                 tool_calls.append({"name": tool_name, "arguments": args})
 
         # < v11: content[BOT] [{tool_call1},{tool_call2}]
@@ -311,21 +483,28 @@ def extract_tool_calls(
                 )
             stringified_tool_calls = raw_tool_calls[0].strip()
             try:
-                tool_calls = json.loads(stringified_tool_calls)
+                # Use raw_decode to parse the first valid JSON value,
+                # ignoring trailing tokens the model may emit after
+                # the tool call array.
+                tool_calls, _ = json.JSONDecoder().raw_decode(stringified_tool_calls)
             except json.JSONDecodeError:
-                # use a regex to find the part corresponding to the tool call.
-                # NOTE: This use case should not happen if the model is trained
-                # correctly. It's an easy possible fix so it's included, but
-                # can be brittle for very complex / highly nested tool calls
                 try:
                     raw_tool_call = self.tool_call_regex.findall(
                         stringified_tool_calls
                     )[0]
                     tool_calls = json.loads(raw_tool_call)
+                    tool_calls = [
+                        {
+                            "name": tool_call["name"],
+                            "arguments": json.dumps(
+                                tool_call.get("arguments", {}),
+                                ensure_ascii=False,
+                            ),
+                        }
+                        for tool_call in tool_calls
+                    ]
                 except (IndexError, json.JSONDecodeError):
-                    logger.exception("Error in extracting tool call from response: {e}")
-                    # If raw decoding and decoding post regex rule fails, then just
-                    # return content.
+                    logger.exception("Error in extracting tool call from response.")
                     return ExtractedToolCallInformation(
                         tools_called=False,
                         tool_calls=[],
@@ -336,7 +515,8 @@ def extract_tool_calls(
                     {
                         "name": tool_call["name"],
                         "arguments": json.dumps(
-                            tool_call["arguments"], ensure_ascii=False
+                            tool_call.get("arguments", {}),
+                            ensure_ascii=False,
                         ),
                     }
                     for tool_call in tool_calls
@@ -347,7 +527,7 @@ def extract_tool_calls(
                 type="function",
                 function=FunctionCall(
                     name=tool_call["name"],
-                    arguments=tool_call["arguments"],
+                    arguments=tool_call.get("arguments", "{}"),
                 ),
             )
             for tool_call in tool_calls
@@ -380,7 +560,7 @@ def extract_tool_calls_streaming(
         # if the tool call token IS in the tokens generated so far, that
         # means we're parsing as tool calls now
         try:
-            if _is_pre_v11_tokeniser(self.model_tokenizer):
+            if self._is_pre_v11:
                 return self._extract_tool_calls_streaming_pre_v11_tokenizer(
                     delta_text=delta_text,
                     delta_token_ids=delta_token_ids,
@@ -438,13 +618,6 @@ def _extract_tool_calls_streaming(
         if len(delta_tool_calls) > 0:
             delta.tool_calls = delta_tool_calls
 
-        # HACK: serving_chat.py inspects the internal state of tool parsers
-        # when determining its final streaming delta, automatically
-        # adding autocompleted JSON.
-        # These two lines avoid that nonsense while ensuring finish_reason
-        # is set to tool_calls when at least one tool is called.
-        if delta_tool_calls and not self.prev_tool_call_arr:
-            self.prev_tool_call_arr = [{"arguments": {}}]
         return delta
 
     def _generate_delta_tool_call(self, delta_text: str) -> list[DeltaToolCall]:
@@ -457,6 +630,8 @@ def _generate_delta_tool_call(self, delta_text: str) -> list[DeltaToolCall]:
             StreamingState.PARSING_ARGUMENTS,
         ] and delta_text.startswith(self.bot_token):
             self.current_tool_id += 1
+            self.streamed_args_for_tool.append("")
+            self.prev_tool_call_arr.append({})
             self.streaming_state = StreamingState.PARSING_NAME
             delta_text = delta_text.replace(self.bot_token, "", 1)
         if self.streaming_state == StreamingState.PARSING_NAME:
@@ -468,6 +643,11 @@ def _generate_delta_tool_call(self, delta_text: str) -> list[DeltaToolCall]:
                 tool_id = MistralToolCall.generate_random_id()
                 delta_function_name = delta_text.split("{")[0]
                 self.current_tool_name += delta_function_name
+                # HF tokenizers may include [ARGS] in the text
+                self.current_tool_name = self.current_tool_name.replace("[ARGS]", "")
+                self.prev_tool_call_arr[self.current_tool_id]["name"] = (
+                    self.current_tool_name
+                )
                 delta_text = delta_text[len(delta_function_name) :]
                 self.streaming_state = StreamingState.PARSING_ARGUMENTS
             else:
@@ -484,6 +664,10 @@ def _generate_delta_tool_call(self, delta_text: str) -> list[DeltaToolCall]:
                 self.streaming_state = StreamingState.TOOL_COMPLETE
             else:
                 delta_arguments = delta_text
+            self.streamed_args_for_tool[self.current_tool_id] += delta_arguments
+            self.prev_tool_call_arr[self.current_tool_id]["arguments"] = (
+                self.streamed_args_for_tool[self.current_tool_id]
+            )
             ret = []
             if self.current_tool_name or delta_arguments:
                 ret += [
@@ -633,9 +817,12 @@ def _extract_tool_calls_streaming_pre_v11_tokenizer(
                     if self.current_tool_mistral_id is not None:
                         current_tool_call.id = self.current_tool_mistral_id
                         self.current_tool_mistral_id = None
+                    self._track_streamed_args_pre_v11(current_tool_call)
                     delta_tool_calls.append(current_tool_call)
                 current_tool_call_modified = False
                 self.current_tool_id += 1
+                self.streamed_args_for_tool.append("")
+                self.prev_tool_call_arr.append({})
                 self.current_tool_mistral_id = MistralToolCall.generate_random_id()
                 current_tool_call = DeltaToolCall(
                     index=self.current_tool_id,
@@ -648,6 +835,9 @@ def _extract_tool_calls_streaming_pre_v11_tokenizer(
                 # we have the complete tool name
                 current_tool_call_modified = True
                 current_tool_call.function.name = self.current_tool_name
+                self.prev_tool_call_arr[self.current_tool_id]["name"] = (
+                    self.current_tool_name
+                )
                 self.current_tool_name = None
             if self.streaming_state == StreamingState.PARSING_NAME_COMPLETED:
                 self.streaming_state = StreamingState.WAITING_FOR_TOOL_KEY
@@ -673,16 +863,9 @@ def _extract_tool_calls_streaming_pre_v11_tokenizer(
             if self.current_tool_mistral_id is not None:
                 current_tool_call.id = self.current_tool_mistral_id
                 self.current_tool_mistral_id = None
+            self._track_streamed_args_pre_v11(current_tool_call)
             delta_tool_calls.append(current_tool_call)
 
-        # HACK: serving_chat.py inspects the internal state of tool parsers
-        # when determining it's final streaming delta, automatically
-        # adding autocompleted JSON.
-        # These two lines avoid that nonsense while ensuring finish_reason
-        # is set to tool_calls when at least one tool is called.
-        if delta_tool_calls and not self.prev_tool_call_arr:
-            self.prev_tool_call_arr = [{"arguments": {}}]
-
         if content or len(delta_tool_calls) > 0:
             delta_message = DeltaMessage()
             if content:
@@ -696,6 +879,16 @@ def _extract_tool_calls_streaming_pre_v11_tokenizer(
             else:
                 return None
 
+    def _track_streamed_args_pre_v11(self, tool_call: DeltaToolCall) -> None:
+        r"""Accumulate `tool_call` arguments into the streaming state."""
+        if tool_call.function is not None and tool_call.function.arguments is not None:
+            self.streamed_args_for_tool[self.current_tool_id] += (
+                tool_call.function.arguments
+            )
+            self.prev_tool_call_arr[self.current_tool_id]["arguments"] = (
+                self.streamed_args_for_tool[self.current_tool_id]
+            )
+
     def _split_delta(
         self,
         delta_text: str,
diff --git a/vllm/tool_parsers/poolside_v1_tool_parser.py b/vllm/tool_parsers/poolside_v1_tool_parser.py
new file mode 100644
index 000000000000..f14b47362917
--- /dev/null
+++ b/vllm/tool_parsers/poolside_v1_tool_parser.py
@@ -0,0 +1,583 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+GLM-4 Tool Call Parser with incremental string streaming support.
+
+This parser fixes the streaming issue reported in Issue #32829 where long string
+parameters (e.g., file content with 4000+ characters of code) are buffered until
+complete, causing multi-second delays before the user sees any content.
+
+The fix streams string values incrementally as they arrive, providing a true
+streaming experience for long content.
+"""
+
+import ast
+import json
+from collections.abc import Sequence
+from typing import Any
+
+import partial_json_parser.core.complete
+import regex as re
+from partial_json_parser.core.options import Allow
+
+from vllm.entrypoints.chat_utils import make_tool_call_id
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+)
+from vllm.entrypoints.openai.engine.protocol import (
+    DeltaFunctionCall,
+    DeltaMessage,
+    DeltaToolCall,
+    ExtractedToolCallInformation,
+    FunctionCall,
+    ToolCall,
+)
+from vllm.entrypoints.openai.responses.protocol import (
+    ResponsesRequest,
+)
+from vllm.logger import init_logger
+from vllm.tokenizers import TokenizerLike
+from vllm.tool_parsers.abstract_tool_parser import (
+    Tool,
+    ToolParser,
+)
+
+logger = init_logger(__name__)
+
+
+class PoolsideV1ToolParser(ToolParser):
+    """Tool parser for GLM-4 models with incremental string streaming.
+
+    This parser emits tool-call deltas incrementally as arguments arrive.
+    For string-type parameters, content is streamed character-by-character
+    rather than waiting for the complete </arg_value> tag.
+    """
+
+    def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
+        super().__init__(tokenizer, tools)
+        # Stateful streaming fields
+        self.current_tool_name_sent: bool = False
+        self.prev_tool_call_arr: list[dict[str, Any]] = []
+        self.current_tool_id: int = -1
+        self.streamed_args_for_tool: list[str] = []
+
+        self.tool_call_start_token: str = "<tool_call>"
+        self.tool_call_end_token: str = "</tool_call>"
+        self.arg_key_start: str = "<arg_key>"
+        self.arg_key_end: str = "</arg_key>"
+        self.arg_val_start: str = "<arg_value>"
+        self.arg_val_end: str = "</arg_value>"
+
+        self.tool_calls_start_token = self.tool_call_start_token
+
+        self.func_call_regex = re.compile(r"<tool_call>.*?</tool_call>", re.DOTALL)
+        self.func_detail_regex = re.compile(
+            r"<tool_call>([^\n]*)\n(.*)</tool_call>", re.DOTALL
+        )
+        self.func_arg_regex = re.compile(
+            r"<arg_key>(.*?)</arg_key>\s*<arg_value>(.*?)</arg_value>", re.DOTALL
+        )
+
+        if not self.model_tokenizer:
+            raise ValueError(
+                "The model tokenizer must be passed to the ToolParser "
+                "constructor during construction."
+            )
+
+        self.tool_call_start_token_id = self.vocab.get(self.tool_call_start_token)
+        self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
+        self._buffer: str = ""
+
+        # Streaming state for incremental tool-call streaming
+        self._in_tool_call: bool = False
+        self._current_tool_name: str | None = None
+        self._pending_key: str | None = None
+        self._streaming_string_value: bool = False
+        self._tool_call_ids: list[str] = []
+        self._args_started: list[bool] = []
+        self._args_closed: list[bool] = []
+        self._seen_keys: list[set[str]] = []
+
+    @staticmethod
+    def _deserialize(value: str) -> Any:
+        try:
+            return json.loads(value)
+        except json.JSONDecodeError:
+            pass
+
+        try:
+            return ast.literal_eval(value)
+        except (ValueError, SyntaxError):
+            pass
+
+        return value
+
+    @staticmethod
+    def _json_escape_string_content(s: str) -> str:
+        """JSON-escape string content for incremental streaming.
+
+        This escapes the content that goes INSIDE a JSON string (between quotes),
+        not including the surrounding quotes themselves.
+        """
+        if not s:
+            return ""
+        return json.dumps(s, ensure_ascii=False)[1:-1]
+
+    @staticmethod
+    def _is_string_type(
+        tool_name: str,
+        arg_name: str,
+        tools: list[Tool] | None,
+    ) -> bool:
+        if tools is None:
+            return False
+        for tool in tools:
+            if tool.function.name != tool_name:
+                continue
+            if tool.function.parameters is None:
+                return False
+            arg_type = (
+                tool.function.parameters.get("properties", {})
+                .get(arg_name, {})
+                .get("type", None)
+            )
+            return arg_type == "string"
+        logger.debug("No tool named '%s'.", tool_name)
+        return False
+
+    @staticmethod
+    def _tools_enabled(request: ChatCompletionRequest) -> bool:
+        """Return whether tool parsing should be applied for this request."""
+        try:
+            tools = getattr(request, "tools", None)
+            tool_choice = getattr(request, "tool_choice", None)
+            return bool(tools) and tool_choice != "none"
+        except Exception:
+            logger.exception("Failed to determine if tools are enabled.")
+            return False
+
+    def adjust_request(
+        self, request: ChatCompletionRequest | ResponsesRequest
+    ) -> ChatCompletionRequest | ResponsesRequest:
+        """Adjust request parameters for tool call token handling."""
+        request = super().adjust_request(request)
+        if request.tools and request.tool_choice != "none":
+            # Ensure tool call tokens (<tool_call>, </tool_call>) are not skipped
+            # during decoding. Even though they are not marked as special tokens,
+            # setting skip_special_tokens=False ensures proper handling in
+            # transformers 5.x where decoding behavior may have changed.
+            request.skip_special_tokens = False
+        return request
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        matched_tool_calls = self.func_call_regex.findall(model_output)
+        logger.debug("model_output: %s", model_output)
+        try:
+            tool_calls: list[ToolCall] = []
+            for match in matched_tool_calls:
+                tc_detail = self.func_detail_regex.search(match)
+                if not tc_detail:
+                    logger.warning(
+                        "Failed to parse tool call details from: %s",
+                        match,
+                    )
+                    continue
+                tc_name = tc_detail.group(1).strip()
+                tc_args = tc_detail.group(2)
+                pairs = self.func_arg_regex.findall(tc_args) if tc_args else []
+                arg_dct: dict[str, Any] = {}
+                for key, value in pairs:
+                    arg_key = key.strip()
+                    arg_val = value.strip()
+                    if not self._is_string_type(tc_name, arg_key, request.tools):
+                        arg_val = self._deserialize(arg_val)
+                    logger.debug("arg_key = %s, arg_val = %s", arg_key, arg_val)
+                    arg_dct[arg_key] = arg_val
+                tool_calls.append(
+                    ToolCall(
+                        type="function",
+                        function=FunctionCall(
+                            name=tc_name,
+                            arguments=json.dumps(arg_dct, ensure_ascii=False),
+                        ),
+                    )
+                )
+        except Exception:
+            logger.exception("Failed to extract tool call spec")
+            return ExtractedToolCallInformation(
+                tools_called=False, tool_calls=[], content=model_output
+            )
+        else:
+            if len(tool_calls) > 0:
+                content: str | None = model_output[
+                    : model_output.find(self.tool_calls_start_token)
+                ]
+                # Normalize empty/whitespace-only content to None
+                if not content or not content.strip():
+                    content = None
+                return ExtractedToolCallInformation(
+                    tools_called=True, tool_calls=tool_calls, content=content
+                )
+            return ExtractedToolCallInformation(
+                tools_called=False, tool_calls=[], content=model_output
+            )
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> DeltaMessage | None:
+        if not self._tools_enabled(request):
+            return DeltaMessage(content=delta_text) if delta_text else None
+
+        self._buffer += delta_text
+
+        pending_deltas: dict[int, DeltaToolCall] = {}
+        content: str | None = None
+
+        while True:
+            if not self._in_tool_call:
+                start_idx = self._buffer.find(self.tool_call_start_token)
+                if start_idx == -1:
+                    # Check for partial start token at end of buffer
+                    for i in range(1, len(self.tool_call_start_token)):
+                        if self._buffer.endswith(self.tool_call_start_token[:i]):
+                            out = self._buffer[:-i]
+                            self._buffer = self._buffer[-i:]
+                            if out:
+                                content = (content or "") + out
+                            break
+                    else:
+                        out = self._buffer
+                        self._buffer = ""
+                        if out:
+                            content = (content or "") + out
+                    break
+
+                if start_idx > 0:
+                    content = (content or "") + self._buffer[:start_idx]
+                    self._buffer = self._buffer[start_idx:]
+
+                self._buffer = self._buffer[len(self.tool_call_start_token) :]
+                self._begin_tool_call()
+                continue
+
+            # Parse tool name first
+            if not self.current_tool_name_sent:
+                nl = self._buffer.find("\n")
+                ak = self._buffer.find(self.arg_key_start)
+                end = self._buffer.find(self.tool_call_end_token)
+                candidates = [i for i in [nl, ak, end] if i != -1]
+                if not candidates:
+                    break
+                cut = min(candidates)
+                tool_name = self._buffer[:cut].strip()
+                if tool_name == "" and cut == end:
+                    # Handle empty tool call like `<tool_call></tool_call>`.
+                    # Consume the tokens and reset state to avoid infinite loop.
+                    self._buffer = self._buffer[end + len(self.tool_call_end_token) :]
+                    self._finish_tool_call()
+                    self._revert_last_tool_call_state()
+                    continue
+
+                if cut == nl:
+                    self._buffer = self._buffer[nl + 1 :]
+                else:
+                    self._buffer = self._buffer[cut:]
+
+                self._current_tool_name = tool_name
+                self.current_tool_name_sent = True
+                self._update_tool_name(pending_deltas, tool_name)
+                continue
+
+            assert self._current_tool_name is not None
+
+            # Handle incremental string value streaming
+            if self._streaming_string_value:
+                val_end = self._buffer.find(self.arg_val_end)
+                if val_end != -1:
+                    raw_content = self._buffer[:val_end]
+                    self._buffer = self._buffer[val_end + len(self.arg_val_end) :]
+                    self._streaming_string_value = False
+                    self._pending_key = None
+
+                    escaped = self._json_escape_string_content(raw_content)
+                    frag = escaped + '"'
+                    self.streamed_args_for_tool[self.current_tool_id] += frag
+                    self._update_tool_args(pending_deltas, frag)
+                    continue
+
+                # Check for partial </arg_value> at end
+                safe_len = len(self._buffer)
+                for i in range(1, len(self.arg_val_end)):
+                    if self._buffer.endswith(self.arg_val_end[:i]):
+                        safe_len = len(self._buffer) - i
+                        break
+
+                if safe_len > 0:
+                    to_emit = self._buffer[:safe_len]
+                    self._buffer = self._buffer[safe_len:]
+                    escaped = self._json_escape_string_content(to_emit)
+                    if escaped:
+                        self.streamed_args_for_tool[self.current_tool_id] += escaped
+                        self._update_tool_args(pending_deltas, escaped)
+                break
+
+            # If we have a pending key, parse its value
+            if self._pending_key is not None:
+                val_pos = self._buffer.find(self.arg_val_start)
+                if val_pos == -1:
+                    break
+                if val_pos > 0:
+                    self._buffer = self._buffer[val_pos:]
+
+                key = (self._pending_key or "").strip()
+
+                is_string = self._is_string_type(
+                    self._current_tool_name, key, request.tools
+                )
+
+                if is_string:
+                    # String type: stream incrementally
+                    self._buffer = self._buffer[len(self.arg_val_start) :]
+
+                    if key in self._seen_keys[self.current_tool_id]:
+                        self._pending_key = None
+                        continue
+
+                    self._seen_keys[self.current_tool_id].add(key)
+                    key_json = json.dumps(key, ensure_ascii=False)
+
+                    if not self._args_started[self.current_tool_id]:
+                        frag = "{" + key_json + ': "'
+                        self._args_started[self.current_tool_id] = True
+                    else:
+                        frag = ", " + key_json + ': "'
+
+                    self.streamed_args_for_tool[self.current_tool_id] += frag
+                    self._streaming_string_value = True
+                    self._update_tool_args(pending_deltas, frag)
+                    continue
+
+                # Non-string type: wait for complete value
+                val_end = self._buffer.find(self.arg_val_end)
+                if val_end == -1:
+                    break
+
+                raw_val = self._buffer[len(self.arg_val_start) : val_end].strip()
+                self._buffer = self._buffer[val_end + len(self.arg_val_end) :]
+                self._pending_key = None
+
+                frag_or_none = self._append_arg_fragment(key=key, raw_val=raw_val)
+                if frag_or_none:
+                    self._update_tool_args(pending_deltas, frag_or_none)
+                continue
+
+            # Parse next arg or close
+            end_pos = self._buffer.find(self.tool_call_end_token)
+            key_pos = self._buffer.find(self.arg_key_start)
+            if end_pos != -1 and (key_pos == -1 or end_pos < key_pos):
+                self._buffer = self._buffer[end_pos + len(self.tool_call_end_token) :]
+                frag_or_none = self._close_args_if_needed()
+                # Finalize prev_tool_call_arr with complete parsed arguments
+                if self._current_tool_name:
+                    try:
+                        full_args_str = self.streamed_args_for_tool[
+                            self.current_tool_id
+                        ]
+                        args_dict = json.loads(full_args_str)
+                        self.prev_tool_call_arr[self.current_tool_id] = {
+                            "name": self._current_tool_name,
+                            "arguments": args_dict,
+                        }
+                    except (json.JSONDecodeError, IndexError) as e:
+                        logger.warning(
+                            "Failed to finalize tool call state for tool %d: %s",
+                            self.current_tool_id,
+                            e,
+                        )
+                self._finish_tool_call()
+                if frag_or_none:
+                    self._update_tool_args(pending_deltas, frag_or_none)
+                continue
+
+            if key_pos == -1:
+                break
+            if key_pos > 0:
+                self._buffer = self._buffer[key_pos:]
+            key_end = self._buffer.find(self.arg_key_end)
+            if key_end == -1:
+                break
+            key = self._buffer[len(self.arg_key_start) : key_end]
+            self._buffer = self._buffer[key_end + len(self.arg_key_end) :]
+            self._pending_key = key
+            continue
+
+        tool_calls = list(pending_deltas.values())
+        if content is None and len(tool_calls) == 0:
+            if request.logprobs:
+                return DeltaMessage(content="")
+            return None
+        return DeltaMessage(content=content, tool_calls=tool_calls)
+
+    def _ensure_tool_state(self) -> None:
+        while len(self._tool_call_ids) <= self.current_tool_id:
+            self._tool_call_ids.append(
+                make_tool_call_id(id_type="random", func_name=None, idx=None)
+            )
+        while len(self.streamed_args_for_tool) <= self.current_tool_id:
+            self.streamed_args_for_tool.append("")
+        while len(self.prev_tool_call_arr) <= self.current_tool_id:
+            self.prev_tool_call_arr.append({})
+        while len(self._args_started) <= self.current_tool_id:
+            self._args_started.append(False)
+        while len(self._args_closed) <= self.current_tool_id:
+            self._args_closed.append(False)
+        while len(self._seen_keys) <= self.current_tool_id:
+            self._seen_keys.append(set())
+
+    def _begin_tool_call(self) -> None:
+        if self.current_tool_id == -1:
+            self.current_tool_id = 0
+        else:
+            self.current_tool_id += 1
+        self._ensure_tool_state()
+        self.current_tool_name_sent = False
+        self._current_tool_name = None
+        self._pending_key = None
+        self._streaming_string_value = False
+        self._in_tool_call = True
+
+    def _finish_tool_call(self) -> None:
+        self._in_tool_call = False
+        self._current_tool_name = None
+        self._pending_key = None
+        self._streaming_string_value = False
+
+    def _revert_last_tool_call_state(self) -> None:
+        """Revert the state allocation for the last tool call."""
+        if self.current_tool_id < 0:
+            return
+        self._tool_call_ids.pop()
+        self.streamed_args_for_tool.pop()
+        self.prev_tool_call_arr.pop()
+        self._args_started.pop()
+        self._args_closed.pop()
+        self._seen_keys.pop()
+        self.current_tool_id -= 1
+
+    def _get_or_create_delta(self, pending: dict[int, DeltaToolCall]) -> DeltaToolCall:
+        idx = self.current_tool_id
+        if idx not in pending:
+            pending[idx] = DeltaToolCall(
+                index=idx,
+                function=DeltaFunctionCall(),
+            )
+        delta = pending[idx]
+        assert delta.function is not None
+        return delta
+
+    def _update_tool_name(
+        self, pending: dict[int, DeltaToolCall], tool_name: str
+    ) -> None:
+        self.prev_tool_call_arr[self.current_tool_id] = {
+            "name": self._current_tool_name,
+            "arguments": {},
+        }
+        delta = self._get_or_create_delta(pending)
+        delta.id = self._tool_call_ids[self.current_tool_id]
+        delta.type = "function"
+        assert delta.function is not None
+        delta.function.name = tool_name
+        if delta.function.arguments is None:
+            delta.function.arguments = ""
+
+    @staticmethod
+    def _complete_json_prefix(
+        json_prefix: str,
+        allowed_partial_types: Allow,
+    ) -> dict | None:
+        """Complete a partial JSON prefix into a valid JSON object.
+
+        Returns (formatted_prefix, parsed_dict) or None on failure.
+
+        Note: ``partial_json_parser`` strips trailing whitespace before
+        parsing (``complete.py:20``), which means the returned slice is
+        shorter than ``json_prefix`` when it has trailing whitespace.
+        Since the parser controls the construction of the json_prefix value,
+        this code relies on it being a valid prefix and we only use the fix for
+        the completion of the JSON object.
+        """
+        try:
+            _, partial_str_completion = partial_json_parser.core.complete.fix(
+                json_prefix,
+                allowed_partial_types,
+            )
+            return json.loads(json_prefix + partial_str_completion)
+        except Exception:
+            return None
+
+    def _update_tool_args(
+        self, pending: dict[int, DeltaToolCall], fragment: str
+    ) -> None:
+        result = self._complete_json_prefix(
+            self.streamed_args_for_tool[self.current_tool_id],
+            Allow.ALL,
+        )
+        if result is not None:
+            self.prev_tool_call_arr[self.current_tool_id]["arguments"] = result
+        delta = self._get_or_create_delta(pending)
+        assert delta.function is not None
+        if delta.function.arguments is None:
+            delta.function.arguments = ""
+        delta.function.arguments += fragment
+
+    def _append_arg_fragment(
+        self,
+        *,
+        key: str,
+        raw_val: str,
+    ) -> str | None:
+        key = key.strip()
+        if not key:
+            return None
+        if key in self._seen_keys[self.current_tool_id]:
+            return None
+
+        # This function is only called for non-string types (already checked
+        # by _is_string_type in the caller), so we always deserialize.
+        val_obj: Any = self._deserialize(raw_val)
+
+        key_json = json.dumps(key, ensure_ascii=False)
+        val_json = json.dumps(val_obj, ensure_ascii=False)
+
+        if not self._args_started[self.current_tool_id]:
+            fragment = "{" + key_json + ": " + val_json
+            self._args_started[self.current_tool_id] = True
+        else:
+            fragment = ", " + key_json + ": " + val_json
+
+        self._seen_keys[self.current_tool_id].add(key)
+        self.streamed_args_for_tool[self.current_tool_id] += fragment
+        return fragment
+
+    def _close_args_if_needed(self) -> str | None:
+        if self._args_closed[self.current_tool_id]:
+            return None
+        self._args_closed[self.current_tool_id] = True
+        if not self._args_started[self.current_tool_id]:
+            fragment = "{}"
+            self.streamed_args_for_tool[self.current_tool_id] = fragment
+        else:
+            fragment = "}"
+            self.streamed_args_for_tool[self.current_tool_id] += fragment
+        return fragment
diff --git a/vllm/tool_parsers/qwen3coder_tool_parser.py b/vllm/tool_parsers/qwen3coder_tool_parser.py
index 7b089ceffbc0..73850b2ab0c5 100644
--- a/vllm/tool_parsers/qwen3coder_tool_parser.py
+++ b/vllm/tool_parsers/qwen3coder_tool_parser.py
@@ -25,12 +25,18 @@
     Tool,
     ToolParser,
 )
+from vllm.tool_parsers.structural_tag_registry import (
+    get_enable_structured_outputs_in_reasoning,
+    get_model_structural_tag,
+)
 from vllm.tool_parsers.utils import find_tool_properties
 
 logger = init_logger(__name__)
 
 
 class Qwen3CoderToolParser(ToolParser):
+    supports_required_and_named: bool = False
+
     def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
         super().__init__(tokenizer, tools)
 
@@ -681,3 +687,11 @@ def extract_tool_calls_streaming(
                 return result
 
         return None
+
+    def get_structural_tag(self, request: ChatCompletionRequest):
+        return get_model_structural_tag(
+            model="qwen_3_5",
+            tools=request.tools,
+            tool_choice=request.tool_choice,
+            reasoning=get_enable_structured_outputs_in_reasoning(),
+        )
diff --git a/vllm/tool_parsers/qwen3xml_tool_parser.py b/vllm/tool_parsers/qwen3xml_tool_parser.py
index 4ecb9666886f..8ee10dcbc9e6 100644
--- a/vllm/tool_parsers/qwen3xml_tool_parser.py
+++ b/vllm/tool_parsers/qwen3xml_tool_parser.py
@@ -1258,11 +1258,11 @@ def extract_tool_calls_streaming(
             return None
 
         # Parse the delta text and get the result
-        result = self.parser.parse_single_streaming_chunks(delta_text)
+        delta = self.parser.parse_single_streaming_chunks(delta_text)
 
         # Update tool call tracking arrays based on incremental parsing results
-        if result and result.tool_calls:
-            for tool_call in result.tool_calls:
+        if delta and delta.tool_calls:
+            for tool_call in delta.tool_calls:
                 if tool_call.function:
                     tool_index = (
                         tool_call.index
@@ -1292,4 +1292,7 @@ def extract_tool_calls_streaming(
                         self.streamed_args_for_tool[tool_index] += (
                             tool_call.function.arguments
                         )
-        return result
+        if delta.content is None and not delta.tool_calls and delta.reasoning is None:
+            # If no content and no tool calls, return None to indicate no update
+            return None
+        return delta
diff --git a/vllm/tool_parsers/streaming.py b/vllm/tool_parsers/streaming.py
new file mode 100644
index 000000000000..7f6638dcb94e
--- /dev/null
+++ b/vllm/tool_parsers/streaming.py
@@ -0,0 +1,195 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import json
+from typing import TYPE_CHECKING
+
+import partial_json_parser
+import regex as re
+from partial_json_parser.core.options import Allow
+
+from vllm.entrypoints.chat_utils import make_tool_call_id
+from vllm.entrypoints.openai.engine.protocol import (
+    DeltaFunctionCall,
+    DeltaMessage,
+    DeltaToolCall,
+)
+from vllm.tool_parsers.mistral_tool_parser import MistralToolCall
+from vllm.tool_parsers.utils import partial_json_loads
+from vllm.utils.mistral import is_mistral_tokenizer
+
+if TYPE_CHECKING:
+    from vllm.tokenizers import TokenizerLike
+else:
+    TokenizerLike = object
+
+
+def _bracket_level(s: str, opening: str = "{", closing: str = "}") -> int:
+    """Calculate the current level of nested brackets in a string."""
+    level = 0
+    for char in s:
+        if char == opening:
+            level += 1
+        elif char == closing:
+            level -= 1
+    return level
+
+
+def filter_delta_text(
+    delta_text: str,
+    previous_text: str,
+) -> tuple[str, bool]:
+    """Trim trailing tool-list delimiters from required-tool streaming text."""
+    bracket_level = _bracket_level(previous_text)
+    updated_delta = ""
+    passed_zero = False
+    for char in delta_text:
+        if char == "{":
+            bracket_level += 1
+            passed_zero = bracket_level == 0
+        elif char == "}":
+            bracket_level -= 1
+            passed_zero = bracket_level == 0
+
+        if bracket_level != 0:
+            updated_delta += char
+        else:
+            if char == ",":
+                break
+    return updated_delta, passed_zero
+
+
+def extract_named_tool_call_streaming(
+    *,
+    delta_text: str,
+    function_name: str,
+    function_name_returned: bool,
+    tool_call_idx: int | None,
+    tool_call_id_type: str,
+    tokenizer: "TokenizerLike",
+    tool_call_array_index: int = 0,
+) -> tuple[DeltaMessage | None, bool]:
+    """Build a streaming tool-call delta for forced named tool choice."""
+    if function_name_returned:
+        delta_tool_call = DeltaToolCall(
+            function=DeltaFunctionCall(arguments=delta_text),
+            index=tool_call_array_index,
+        )
+    else:
+        if is_mistral_tokenizer(tokenizer):
+            tool_call_id = MistralToolCall.generate_random_id()
+        else:
+            tool_call_id = make_tool_call_id(
+                id_type=tool_call_id_type,
+                func_name=function_name,
+                idx=tool_call_idx,
+            )
+        delta_tool_call = DeltaToolCall(
+            id=tool_call_id,
+            type="function",
+            function=DeltaFunctionCall(
+                name=function_name,
+                arguments=delta_text,
+            ),
+            index=tool_call_array_index,
+        )
+        function_name_returned = True
+    return (
+        DeltaMessage(tool_calls=[delta_tool_call]),
+        function_name_returned,
+    )
+
+
+def extract_required_tool_call_streaming(
+    *,
+    previous_text: str,
+    current_text: str | None,
+    delta_text: str,
+    function_name_returned: bool,
+    tool_call_idx: int | None,
+    tool_call_id_type: str,
+) -> tuple[DeltaMessage | None, bool]:
+    if current_text is None or current_text == "":
+        # if the current text is empty, we cannot parse it
+        return None, function_name_returned
+    try:
+        flags = Allow.ALL
+        obj, _ = partial_json_loads(current_text, flags)
+    except (
+        partial_json_parser.core.exceptions.MalformedJSON,
+        json.JSONDecodeError,
+    ):
+        obj = None
+
+    # check if the current text is a valid array
+    # containing a partial tool calling object
+    # if not repeat
+    if obj is None or not isinstance(obj, list) or not len(obj) > 0:
+        function_name_returned = False
+        delta_message = None
+    else:
+        _, finishes_previous_tool = filter_delta_text(delta_text, previous_text)
+        # take the last tool call from the generated list
+        current_tool_call = obj[-1]
+
+        # once parameters have been generated the name is complete as well
+        if not finishes_previous_tool and (
+            "name" not in current_tool_call or "parameters" not in current_tool_call
+        ):
+            function_name_returned = False
+            delta_message = None
+        else:
+            if not function_name_returned:
+                # get partly generated arguments from the latest tool call
+                param_match = re.search(
+                    r'.*"parameters":\s*(.*)', current_text, re.DOTALL
+                )
+                arguments = param_match.group(1) if param_match else ""
+                arguments, _ = filter_delta_text(arguments, previous_text)
+
+                # if this iteration finishes a previous tool call but a
+                # new incomplete tool is already generated, take the
+                # previous from the list
+                if finishes_previous_tool and "parameters" not in current_tool_call:
+                    current_tool_call = obj[-2]
+
+                function_name_returned = True
+                tool_call_id = make_tool_call_id(
+                    id_type=tool_call_id_type,
+                    func_name=current_tool_call["name"],
+                    idx=tool_call_idx,
+                )
+                delta_message = DeltaMessage(
+                    tool_calls=[
+                        DeltaToolCall(
+                            id=tool_call_id,
+                            function=DeltaFunctionCall(
+                                name=current_tool_call["name"], arguments=arguments
+                            ),
+                            index=len(obj) - 1,
+                            type="function",
+                        )
+                    ]
+                )
+
+            else:
+                delta_text, _ = filter_delta_text(delta_text, previous_text)
+
+                if delta_text != "":
+                    delta_message = DeltaMessage(
+                        tool_calls=[
+                            DeltaToolCall(
+                                function=DeltaFunctionCall(
+                                    # OpenAI API returns None
+                                    # instead of name every time
+                                    name=None,
+                                    arguments=delta_text,
+                                ),
+                                index=len(obj) - 1,
+                            )
+                        ]
+                    )
+                else:
+                    delta_message = None
+
+    return delta_message, function_name_returned
diff --git a/vllm/tool_parsers/structural_tag_registry.py b/vllm/tool_parsers/structural_tag_registry.py
new file mode 100644
index 000000000000..754cc52361c5
--- /dev/null
+++ b/vllm/tool_parsers/structural_tag_registry.py
@@ -0,0 +1,330 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Model-specific structural tag builders adapted from XGrammar's
+# builtin structural tag implementations:
+# https://github.com/mlc-ai/xgrammar/blob/main/python/xgrammar/builtin_structural_tag.py
+
+from collections.abc import Callable
+from typing import Any, Literal
+
+from xgrammar import StructuralTag
+from xgrammar.structural_tag import (
+    AnyTextFormat,
+    ConstStringFormat,
+    JSONSchemaFormat,
+    SequenceFormat,
+    TagFormat,
+    TagsWithSeparatorFormat,
+    TriggeredTagsFormat,
+)
+
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionNamedToolChoiceParam,
+    ChatCompletionToolsParam,
+)
+
+SimplifiedToolChoice = Literal["auto", "required", "forced"]
+ToolChoice = (
+    Literal["none", "auto", "required"] | ChatCompletionNamedToolChoiceParam | None
+)
+StructuralTagBuilder = Callable[
+    [list[ChatCompletionToolsParam], SimplifiedToolChoice, bool],
+    StructuralTag,
+]
+
+_structural_tag_registry: dict[str, StructuralTagBuilder] = {}
+
+
+def register_model_structural_tag(name: str):
+    """Register a vLLM-owned model-specific structural tag builder."""
+
+    def decorator(func: StructuralTagBuilder) -> StructuralTagBuilder:
+        _structural_tag_registry[name] = func
+        return func
+
+    return decorator
+
+
+def get_model_structural_tag(
+    model: str,
+    tools: list[ChatCompletionToolsParam] | None,
+    tool_choice: ToolChoice,
+    reasoning: bool,
+) -> StructuralTag | None:
+    """Build a structural tag from vLLM-owned model-specific builders."""
+
+    builder = _structural_tag_registry.get(model)
+    if builder is None:
+        supported = list(_structural_tag_registry.keys())
+        raise ValueError(f"Unknown format type: {model}, supported types: {supported}")
+
+    normalized_tools, simplified_tool_choice = _normalize_tool_choice(
+        tools=tools,
+        tool_choice=tool_choice,
+    )
+    if not normalized_tools:
+        return None
+
+    return builder(normalized_tools, simplified_tool_choice, reasoning)
+
+
+def _normalize_tool_choice(
+    tools: list[ChatCompletionToolsParam] | None,
+    tool_choice: ToolChoice,
+) -> tuple[list[ChatCompletionToolsParam], SimplifiedToolChoice]:
+    """Normalize vLLM ChatCompletion tool_choice for structural tag builders."""
+
+    if not tools:
+        return [], "auto"
+
+    if tool_choice is None or tool_choice == "none":
+        return [], "auto"
+
+    if tool_choice == "auto":
+        return tools, "auto"
+
+    if tool_choice == "required":
+        return tools, "required"
+
+    if isinstance(tool_choice, ChatCompletionNamedToolChoiceParam):
+        tool_name = tool_choice.function.name
+        filtered_tools = [tool for tool in tools if tool.function.name == tool_name]
+        if not filtered_tools:
+            raise ValueError(
+                f"The tool with name '{tool_name}' is not found in the tools list."
+            )
+        return filtered_tools, "forced"
+
+    raise ValueError(f"Unsupported tool_choice for structural tag: {tool_choice}")
+
+
+def _get_function_parameters(function: Any) -> dict[str, Any] | bool:
+    """Return the JSON schema used for constrained tool arguments."""
+
+    if getattr(function, "strict", None) is False:
+        return True
+    if function.parameters is None:
+        return True
+    return function.parameters
+
+
+_enable_structured_outputs_in_reasoning: bool = False
+
+
+def set_enable_structured_outputs_in_reasoning(enabled: bool) -> None:
+    """Publish the engine's ``enable_in_reasoning`` flag to tool parsers.
+
+    Called once during APIServer startup so request-time parsers can read
+    it without going through the EngineCore-only contextvar.
+    """
+
+    global _enable_structured_outputs_in_reasoning
+    _enable_structured_outputs_in_reasoning = bool(enabled)
+
+
+def get_enable_structured_outputs_in_reasoning() -> bool:
+    """Whether structured outputs are active during the reasoning phase.
+
+    When ``True``, the structural tag will cover the reasoning part:
+    ``<think>...</think>`` prefix (if available); when ``False`` (default), the tag only
+    constrains the post-reasoning suffix.
+    """
+
+    return _enable_structured_outputs_in_reasoning
+
+
+@register_model_structural_tag("deepseek_v4")
+def get_deepseek_v4_structural_tag(
+    tools: list[ChatCompletionToolsParam],
+    tool_choice: SimplifiedToolChoice,
+    reasoning: bool,
+) -> StructuralTag:
+    """Build DeepSeek V4 structural tags."""
+
+    invoke_begin_prefix = '<｜DSML｜invoke name="'
+    invoke_begin_suffix = '">\n'
+    invoke_end = "</｜DSML｜invoke>\n"
+    tool_calls_prefix = "\n\n"
+    function_calls_begin = "<｜DSML｜tool_calls>\n"
+    function_calls_end = "</｜DSML｜tool_calls>"
+    function_calls_trigger = "<｜DSML｜tool_calls>"
+    think_tag_end = "</think>"
+    think_exclude_tokens = ["<think>", "</think>"]
+    xml_style = "deepseek_xml"
+
+    if tool_choice == "auto":
+        tags = []
+        for tool in tools:
+            function = tool.function
+            parameters = _get_function_parameters(function)
+            tags.append(
+                TagFormat(
+                    begin=invoke_begin_prefix + function.name + invoke_begin_suffix,
+                    content=JSONSchemaFormat(
+                        json_schema=parameters,
+                        style=xml_style,
+                    ),
+                    end=invoke_end,
+                )
+            )
+
+        if tags:
+            function_calling_tags = TagsWithSeparatorFormat(
+                tags=tags,
+                separator="\n",
+                at_least_one=True,
+            )
+            suffix_tag = TriggeredTagsFormat(
+                triggers=[function_calls_trigger],
+                tags=[
+                    TagFormat(
+                        begin=function_calls_begin,
+                        content=function_calling_tags,
+                        end=function_calls_end,
+                    )
+                ],
+                excludes=think_exclude_tokens,
+            )
+        else:
+            suffix_tag = AnyTextFormat(excludes=think_exclude_tokens)
+
+    elif tool_choice == "forced":
+        if not tools:
+            raise ValueError("Forced tool choice must resolve to exactly one tool.")
+        function = tools[0].function
+        suffix_tag = SequenceFormat(
+            elements=[
+                ConstStringFormat(value=tool_calls_prefix + function_calls_begin),
+                TagFormat(
+                    begin=invoke_begin_prefix + function.name + invoke_begin_suffix,
+                    content=JSONSchemaFormat(
+                        json_schema=_get_function_parameters(function),
+                        style=xml_style,
+                    ),
+                    end=invoke_end,
+                ),
+                ConstStringFormat(value=function_calls_end),
+            ]
+        )
+
+    elif tool_choice == "required":
+        tags = []
+        for tool in tools:
+            function = tool.function
+            parameters = _get_function_parameters(function)
+            tags.append(
+                TagFormat(
+                    begin=invoke_begin_prefix + function.name + invoke_begin_suffix,
+                    content=JSONSchemaFormat(
+                        json_schema=parameters,
+                        style=xml_style,
+                    ),
+                    end=invoke_end,
+                )
+            )
+        assert len(tags) > 0
+        suffix_tag = SequenceFormat(
+            elements=[
+                ConstStringFormat(value=tool_calls_prefix + function_calls_begin),
+                TagsWithSeparatorFormat(
+                    tags=tags,
+                    separator="\n",
+                    at_least_one=True,
+                ),
+                ConstStringFormat(value=function_calls_end),
+            ]
+        )
+
+    if not reasoning:
+        return StructuralTag(format=suffix_tag)
+
+    prefix_tag = TagFormat(begin="", content=AnyTextFormat(), end=think_tag_end)
+    return StructuralTag(format=SequenceFormat(elements=[prefix_tag, suffix_tag]))
+
+
+@register_model_structural_tag("qwen_3_5")
+def get_qwen_3_5_structural_tag(
+    tools: list[ChatCompletionToolsParam],
+    tool_choice: SimplifiedToolChoice,
+    reasoning: bool,
+) -> StructuralTag:
+    """Build Qwen XML structural tags.
+
+    This format is used for Qwen3-Coder/Qwen3.5/Qwen3.6 and is compatible with
+    Qwen variants that use the same XML tool-call format.
+    """
+    tool_call_begin_prefix = "<tool_call>\n<function="
+    tool_call_begin_suffix = ">\n"
+    tool_call_end = "\n</function>\n</tool_call>"
+    tool_call_trigger = "<tool_call>\n<function="
+    think_tag_end = "</think>"
+    think_suffix = "\n\n"
+    think_exclude_tokens = ["<think>", "</think>"]
+
+    if tool_choice == "auto":
+        tags = []
+        for tool in tools:
+            function = tool.function
+            parameters = _get_function_parameters(function)
+            tags.append(
+                TagFormat(
+                    begin=f"{tool_call_begin_prefix}{function.name}{tool_call_begin_suffix}",
+                    content=JSONSchemaFormat(json_schema=parameters, style="qwen_xml"),
+                    end=tool_call_end,
+                )
+            )
+
+        if tags:
+            suffix_tag = TriggeredTagsFormat(
+                triggers=[tool_call_trigger],
+                tags=tags,
+                excludes=think_exclude_tokens,
+            )
+        else:
+            suffix_tag = AnyTextFormat(excludes=think_exclude_tokens)
+
+    elif tool_choice == "forced":
+        if not tools:
+            raise ValueError("Forced tool choice must resolve to exactly one tool.")
+        function = tools[0].function
+        suffix_tag = TagFormat(
+            begin=f"{tool_call_begin_prefix}{function.name}{tool_call_begin_suffix}",
+            content=JSONSchemaFormat(
+                json_schema=_get_function_parameters(function),
+                style="qwen_xml",
+            ),
+            end=tool_call_end,
+        )
+
+    elif tool_choice == "required":
+        tags = []
+        for tool in tools:
+            function = tool.function
+            parameters = _get_function_parameters(function)
+            tags.append(
+                TagFormat(
+                    begin=f"{tool_call_begin_prefix}{function.name}{tool_call_begin_suffix}",
+                    content=JSONSchemaFormat(json_schema=parameters, style="qwen_xml"),
+                    end=tool_call_end,
+                )
+            )
+        assert len(tags) > 0
+        suffix_tag = TagsWithSeparatorFormat(
+            tags=tags,
+            separator="",
+            at_least_one=True,
+        )
+
+    if not reasoning:
+        result = StructuralTag(format=suffix_tag)
+    else:
+        prefix_tag = SequenceFormat(
+            elements=[
+                TagFormat(begin="", content=AnyTextFormat(), end=think_tag_end),
+                ConstStringFormat(value=think_suffix),
+            ]
+        )
+        result = StructuralTag(format=SequenceFormat(elements=[prefix_tag, suffix_tag]))
+
+    return result
diff --git a/vllm/tool_parsers/utils.py b/vllm/tool_parsers/utils.py
index 439441690d04..464ed40f948b 100644
--- a/vllm/tool_parsers/utils.py
+++ b/vllm/tool_parsers/utils.py
@@ -308,20 +308,43 @@ def get_parameter_value(val: ast.expr) -> Any:
         raise UnexpectedAstError("Tool call arguments must be literals")
 
 
+def _ast_callable_dotted_name(node: ast.expr) -> str:
+    """Return the dotted name for a call target, walking ``ast.Attribute``
+    chains so ``a.b.c(...)`` becomes ``"a.b.c"``.
+
+    Raises:
+        UnexpectedAstError: If the chain does not bottom out in an
+            ``ast.Name`` (e.g. subscript or call expression as receiver).
+    """
+    parts: list[str] = []
+    current: ast.expr = node
+    while isinstance(current, ast.Attribute):
+        parts.append(current.attr)
+        current = current.value
+    if not isinstance(current, ast.Name):
+        raise UnexpectedAstError("Invalid tool call name")
+    parts.append(current.id)
+    return ".".join(reversed(parts))
+
+
 def handle_single_tool(call: ast.Call) -> ToolCall:
     """Convert a single AST function call node into a ToolCall object.
 
+    Accepts both bare names (``foo(...)``) and dotted attribute chains
+    (``a.b.c(...)``); the resulting tool call ``name`` field preserves the
+    dotted form.
+
     Raises:
-        UnexpectedAstError: If the call node does not have a simple
-            function name (e.g. it's an attribute access or subscript).
+        UnexpectedAstError: If the call target is neither a simple name
+            nor a chain of attribute accesses bottoming out in a name.
     """
-    if not isinstance(call.func, ast.Name):
+    if not isinstance(call.func, (ast.Name, ast.Attribute)):
         logger.warning(
             "Tool call has non-simple function name: %s",
             ast.dump(call.func),
         )
         raise UnexpectedAstError("Invalid tool call name")
-    function_name = call.func.id
+    function_name = _ast_callable_dotted_name(call.func)
     arguments = {}
     for keyword in call.keywords:
         arguments[keyword.arg] = get_parameter_value(keyword.value)
@@ -403,7 +426,28 @@ def make_valid_python(text: str) -> tuple[str, str] | None:
     for char in reversed(bracket_stack):
         added_text += _CLOSING[char]
 
-    return text + added_text, added_text
+    candidate = text + added_text
+
+    # Streaming partial text can land in shapes the bracket-counting
+    # heuristics above don't catch. Two failure modes:
+    #   1. Mid-key inside a dict (`..., "k`) closes to `..., "k"}` — a
+    #      syntactically invalid mixed dict/set.
+    #   2. A bare string inside a dict (`{"k`) closes to `{"k"}` — valid
+    #      Python but a *set* literal, which downstream tool-call AST
+    #      handling rejects.
+    # Validate the candidate parses, has a body, and contains no Set
+    # nodes (pythonic tool calls always use dicts for `{...}`).
+    try:
+        module = ast.parse(candidate)
+    except SyntaxError:
+        return None
+    if not module.body:
+        return None
+    for node in ast.walk(module):
+        if isinstance(node, ast.Set):
+            return None
+
+    return candidate, added_text
 
 
 def compute_tool_delta(
diff --git a/vllm/transformers_utils/config.py b/vllm/transformers_utils/config.py
index 5f4b5a3b2a48..e6c497c0b450 100644
--- a/vllm/transformers_utils/config.py
+++ b/vllm/transformers_utils/config.py
@@ -66,6 +66,13 @@
 
 logger = init_logger(__name__)
 
+if Version(version("transformers")) < Version("5.0.0"):
+    logger.warning(
+        "Support for Transformers v4 is deprecated. The Transformers v4 codepath will "
+        "become unmaintained in vLLM v0.22.0 and will be removed in vLLM v0.24.0. "
+        "Please upgrade to Transformers v5: pip install --upgrade transformers"
+    )
+
 
 class LazyConfigDict(dict):
     def __getitem__(self, key):
@@ -89,10 +96,14 @@ def __getitem__(self, key):
     qwen3_vl_nemotron_embed="Qwen3VLNemotronEmbedConfig",
     deepseek_vl_v2="DeepseekVLV2Config",
     deepseek_v32="DeepseekV3Config",
+    deepseek_v4="DeepseekV4Config",
     flex_olmo="FlexOlmoConfig",
     fireredlid="FireRedLIDConfig",
     funaudiochat="FunAudioChatConfig",
+    granite4_vision="Granite4VisionConfig",
+    hyperclovax_vlm="HCXVisionConfig",
     hunyuan_vl="HunYuanVLConfig",
+    hy_v3="HYV3Config",
     isaac="IsaacConfig",
     kimi_k2="DeepseekV3Config",  # Kimi K2 uses same architecture as DeepSeek V3
     kimi_linear="KimiLinearConfig",
@@ -104,6 +115,7 @@ def __getitem__(self, key):
     mlp_speculator="MLPSpeculatorConfig",
     medusa="MedusaConfig",
     midashenglm="MiDashengLMConfig",
+    moondream3="Moondream3Config",
     eagle="EAGLEConfig",
     speculators="SpeculatorsConfig",
     nemotron="NemotronConfig",
@@ -113,10 +125,12 @@ def __getitem__(self, key):
     step3_vl="Step3VLConfig",
     step3_text="Step3TextConfig",
     step3p5="Step3p5Config",
+    qianfan_ocr="QianfanOCRConfig",
     qwen3_asr="Qwen3ASRConfig",
     qwen3_next="Qwen3NextConfig",
     qwen3_5="Qwen3_5Config",
     qwen3_5_moe="Qwen3_5MoeConfig",
+    laguna="LagunaConfig",
     lfm2_moe="Lfm2MoeConfig",
     tarsier2="Tarsier2Config",
 )
@@ -205,11 +219,22 @@ def parse(
             )
         else:
             if model_type in _CONFIG_REGISTRY:
-                # Register the config class to AutoConfig to ensure it's used in future
-                # calls to `from_pretrained`
+                # Register the config class to AutoConfig to ensure it's used
+                # in future calls to `from_pretrained` (e.g. from
+                # AutoTokenizer or AutoProcessor).
                 config_class = _CONFIG_REGISTRY[model_type]
                 config_class.model_type = model_type
                 AutoConfig.register(model_type, config_class, exist_ok=True)
+                # If the on-disk model_type differs from the overridden
+                # one, register under both so AutoConfig.from_pretrained
+                # returns the correct class regardless of what the
+                # checkpoint says
+                if (
+                    config_model_type := config_dict.get("model_type")
+                ) and config_model_type != model_type:
+                    config_class.model_type = config_model_type
+                    AutoConfig.register(config_model_type, config_class, exist_ok=True)
+                    config_class.model_type = model_type
                 # Now that it is registered, it is not considered remote code anymore
                 trust_remote_code = False
             try:
@@ -376,6 +401,57 @@ def set_default_rope_theta(config: PretrainedConfig, default_theta: float) -> No
         config.rope_parameters["rope_theta"] = default_theta
 
 
+def patch_legacy_rope_type(rope_parameters: dict[str, Any] | None) -> None:
+    """Patch legacy RoPE type fields for backwards compatibility with
+    older custom models which would otherwise fail to load."""
+
+    # No RoPE parameters to patch
+    if rope_parameters is None:
+        return
+
+    def _patch_legacy_rope_type(rope_parameters: dict[str, Any]) -> None:
+        # Case 1: Both legacy and modern fields present - check for conflicts
+        if "rope_type" in rope_parameters and "type" in rope_parameters:
+            rope_type = rope_parameters["rope_type"]
+            rope_type_legacy = rope_parameters["type"]
+            if (rope_type_legacy == "su" and rope_type == "longrope") or (
+                rope_type_legacy == "mrope" and rope_type == "default"
+            ):
+                pass  # No action needed
+            elif rope_type != rope_type_legacy:
+                raise ValueError(
+                    f"Found conflicts between 'rope_type={rope_type}' (modern "
+                    f"field) and 'type={rope_type_legacy}' (legacy field). "
+                    "You should only specify one of them."
+                )
+        # Case 2: Only legacy field present - patch to modern format with warning
+        if "rope_type" not in rope_parameters and "type" in rope_parameters:
+            rope_parameters["rope_type"] = rope_parameters["type"]
+            logger.info("Replacing legacy 'type' key with 'rope_type'")
+        # Case 3: No rope_type field at all - cannot determine RoPE type, raise error
+        if "rope_type" not in rope_parameters:
+            raise ValueError("rope_parameters should have a 'rope_type' key")
+        # Patch legacy rope_type values with warning
+        if rope_parameters["rope_type"] == "su":
+            rope_parameters["rope_type"] = "longrope"
+            logger.warning("Replacing legacy rope_type 'su' with 'longrope'")
+        elif rope_parameters["rope_type"] == "mrope":
+            if "mrope_section" not in rope_parameters:
+                raise ValueError(
+                    "Legacy rope_type 'mrope' requires "
+                    "'mrope_section' in rope_parameters"
+                )
+            rope_parameters["rope_type"] = "default"
+            logger.warning("Replacing legacy rope_type 'mrope' with 'default'")
+
+    # Handle nested rope_parameters in interleaved sliding attention models
+    if is_rope_parameters_nested(rope_parameters):
+        for rope_parameters_layer_type in rope_parameters.values():
+            _patch_legacy_rope_type(rope_parameters_layer_type)
+    else:
+        _patch_legacy_rope_type(rope_parameters)
+
+
 def patch_rope_parameters(config: PretrainedConfig) -> None:
     """Provide backwards compatibility for RoPE."""
     from vllm.config.utils import getattr_iter
@@ -389,22 +465,28 @@ def patch_rope_parameters(config: PretrainedConfig) -> None:
     ompe = getattr(config, "original_max_position_embeddings", None)
 
     if Version(version("transformers")) < Version("5.0.0"):
-        # Transformers v4 installed, legacy config fields may be present
-        if (rope_scaling := getattr(config, "rope_scaling", None)) is not None:
-            config.rope_parameters = rope_scaling
-        if (
-            rope_theta is not None
-            or partial_rotary_factor is not None
-            or ompe is not None
-        ) and not getattr(config, "rope_parameters", None):
-            config.rope_parameters = {"rope_type": "default"}
-        # Patch legacy fields into rope_parameters
-        if rope_theta is not None:
-            config.rope_parameters["rope_theta"] = rope_theta
-        if partial_rotary_factor is not None:
-            config.rope_parameters["partial_rotary_factor"] = partial_rotary_factor
-        if ompe is not None:
-            config.rope_parameters["original_max_position_embeddings"] = ompe
+        # Transformers v4 installed, legacy config fields may be present.
+        if is_rope_parameters_nested(getattr(config, "rope_parameters", {})):
+            # Loading nested rope_parameters (from Transformers v5) in Transformers v4.
+            # Skip legacy patching since it should already be in the correct format.
+            pass
+        else:
+            if (rope_scaling := getattr(config, "rope_scaling", None)) is not None:
+                config.rope_parameters = rope_scaling
+            if (
+                rope_theta is not None
+                or partial_rotary_factor is not None
+                or ompe is not None
+            ) and not getattr(config, "rope_parameters", None):
+                config.rope_parameters = {"rope_type": "default"}
+            # Patch legacy fields into rope_parameters
+            if rope_theta is not None:
+                config.rope_parameters["rope_theta"] = rope_theta
+            if partial_rotary_factor is not None:
+                config.rope_parameters["partial_rotary_factor"] = partial_rotary_factor
+            if ompe is not None:
+                config.rope_parameters["original_max_position_embeddings"] = ompe
+            patch_legacy_rope_type(getattr(config, "rope_parameters", None))
     elif rope_theta is not None or getattr(config, "rope_parameters", None):
         # Transformers v5 installed
         # Patch these fields in case they used non-standard names
@@ -413,54 +495,10 @@ def patch_rope_parameters(config: PretrainedConfig) -> None:
         if partial_rotary_factor is not None:
             config.partial_rotary_factor = partial_rotary_factor
         # Standardize and validate RoPE parameters
+        patch_legacy_rope_type(getattr(config, "rope_parameters", None))
         config.standardize_rope_params()
         config.validate_rope()
 
-    # No RoPE parameters to patch
-    if getattr(config, "rope_parameters", None) is None:
-        return
-
-    # Handle nested rope_parameters in interleaved sliding attention models
-    if is_rope_parameters_nested(config.rope_parameters):
-        for rope_parameters_layer_type in config.rope_parameters.values():
-            patch_rope_parameters_dict(rope_parameters_layer_type)
-    else:
-        patch_rope_parameters_dict(config.rope_parameters)
-
-
-def patch_rope_parameters_dict(rope_parameters: dict[str, Any]) -> None:
-    if "rope_type" in rope_parameters and "type" in rope_parameters:
-        rope_type = rope_parameters["rope_type"]
-        rope_type_legacy = rope_parameters["type"]
-        if (rope_type_legacy == "su" and rope_type == "longrope") or (
-            rope_type_legacy == "mrope" and rope_type == "default"
-        ):
-            pass  # No action needed
-        elif rope_type != rope_type_legacy:
-            raise ValueError(
-                f"Found conflicts between 'rope_type={rope_type}' (modern "
-                f"field) and 'type={rope_type_legacy}' (legacy field). "
-                "You should only specify one of them."
-            )
-
-    if "rope_type" not in rope_parameters and "type" in rope_parameters:
-        rope_parameters["rope_type"] = rope_parameters["type"]
-        logger.info("Replacing legacy 'type' key with 'rope_type'")
-
-    if "rope_type" not in rope_parameters:
-        raise ValueError("rope_parameters should have a 'rope_type' key")
-
-    if rope_parameters["rope_type"] == "su":
-        rope_parameters["rope_type"] = "longrope"
-        logger.warning("Replacing legacy rope_type 'su' with 'longrope'")
-    elif rope_parameters["rope_type"] == "mrope":
-        if "mrope_section" not in rope_parameters:
-            raise ValueError(
-                "Legacy rope_type 'mrope' requires 'mrope_section' in rope_parameters"
-            )
-        rope_parameters["rope_type"] = "default"
-        logger.warning("Replacing legacy rope_type 'mrope' with 'default'")
-
 
 def _uses_mrope(config: PretrainedConfig) -> bool:
     rope_parameters = getattr(config, "rope_parameters", None)
diff --git a/vllm/transformers_utils/configs/__init__.py b/vllm/transformers_utils/configs/__init__.py
index 5f0d95d7b8f6..c3466fddd65a 100644
--- a/vllm/transformers_utils/configs/__init__.py
+++ b/vllm/transformers_utils/configs/__init__.py
@@ -26,15 +26,19 @@
     "OpsColQwen3Config": "vllm.transformers_utils.configs.colqwen3",
     "Qwen3VLNemotronEmbedConfig": "vllm.transformers_utils.configs.colqwen3",
     "DeepseekVLV2Config": "vllm.transformers_utils.configs.deepseek_vl2",
+    "DeepseekV4Config": "vllm.transformers_utils.configs.deepseek_v4",
     "DotsOCRConfig": "vllm.transformers_utils.configs.dotsocr",
     "EAGLEConfig": "vllm.transformers_utils.configs.eagle",
     "FireRedLIDConfig": "vllm.transformers_utils.configs.fireredlid",
     "FlexOlmoConfig": "vllm.transformers_utils.configs.flex_olmo",
     "FunAudioChatConfig": "vllm.transformers_utils.configs.funaudiochat",
     "FunAudioChatAudioEncoderConfig": "vllm.transformers_utils.configs.funaudiochat",
+    "Granite4VisionConfig": "vllm.transformers_utils.configs.granite4_vision",
     "HunYuanVLConfig": "vllm.transformers_utils.configs.hunyuan_vl",
     "HunYuanVLTextConfig": "vllm.transformers_utils.configs.hunyuan_vl",
     "HunYuanVLVisionConfig": "vllm.transformers_utils.configs.hunyuan_vl",
+    "HCXVisionConfig": "vllm.transformers_utils.configs.hyperclovax",
+    "HYV3Config": "vllm.transformers_utils.configs.hy_v3",
     "HyperCLOVAXConfig": "vllm.transformers_utils.configs.hyperclovax",
     "IsaacConfig": "vllm.transformers_utils.configs.isaac",
     # RWConfig is for the original tiiuae/falcon-40b(-instruct) and
@@ -42,10 +46,14 @@
     # `FalconConfig` class from the official HuggingFace transformers library.
     "RWConfig": "vllm.transformers_utils.configs.falcon",
     "JAISConfig": "vllm.transformers_utils.configs.jais",
+    "LagunaConfig": "vllm.transformers_utils.configs.laguna",
     "Lfm2MoeConfig": "vllm.transformers_utils.configs.lfm2_moe",
     "MedusaConfig": "vllm.transformers_utils.configs.medusa",
     "MiDashengLMConfig": "vllm.transformers_utils.configs.midashenglm",
     "MLPSpeculatorConfig": "vllm.transformers_utils.configs.mlp_speculator",
+    "Moondream3Config": "vllm.transformers_utils.configs.moondream3",
+    "Moondream3TextConfig": "vllm.transformers_utils.configs.moondream3",
+    "Moondream3VisionConfig": "vllm.transformers_utils.configs.moondream3",
     "MoonViTConfig": "vllm.transformers_utils.configs.moonvit",
     "KimiLinearConfig": "vllm.transformers_utils.configs.kimi_linear",
     "KimiVLConfig": "vllm.transformers_utils.configs.kimi_vl",
@@ -62,6 +70,8 @@
     "Step3VisionEncoderConfig": "vllm.transformers_utils.configs.step3_vl",
     "Step3TextConfig": "vllm.transformers_utils.configs.step3_vl",
     "Step3p5Config": "vllm.transformers_utils.configs.step3p5",
+    "QianfanOCRConfig": "vllm.transformers_utils.configs.qianfan_ocr",
+    "QianfanOCRVisionConfig": "vllm.transformers_utils.configs.qianfan_ocr",
     "Qwen3ASRConfig": "vllm.transformers_utils.configs.qwen3_asr",
     "Qwen3NextConfig": "vllm.transformers_utils.configs.qwen3_next",
     "Qwen3_5Config": "vllm.transformers_utils.configs.qwen3_5",
@@ -86,23 +96,31 @@
     "Qwen3VLNemotronEmbedConfig",
     "DeepseekVLV2Config",
     "DeepseekV3Config",
+    "DeepseekV4Config",
     "DotsOCRConfig",
     "EAGLEConfig",
     "FlexOlmoConfig",
     "FireRedLIDConfig",
     "FunAudioChatConfig",
     "FunAudioChatAudioEncoderConfig",
+    "Granite4VisionConfig",
     "HunYuanVLConfig",
     "HunYuanVLTextConfig",
     "HunYuanVLVisionConfig",
+    "HCXVisionConfig",
+    "HYV3Config",
     "HyperCLOVAXConfig",
     "IsaacConfig",
     "RWConfig",
     "JAISConfig",
+    "LagunaConfig",
     "Lfm2MoeConfig",
     "MedusaConfig",
     "MiDashengLMConfig",
     "MLPSpeculatorConfig",
+    "Moondream3Config",
+    "Moondream3TextConfig",
+    "Moondream3VisionConfig",
     "MoonViTConfig",
     "KimiLinearConfig",
     "KimiVLConfig",
@@ -119,6 +137,8 @@
     "Step3VisionEncoderConfig",
     "Step3TextConfig",
     "Step3p5Config",
+    "QianfanOCRConfig",
+    "QianfanOCRVisionConfig",
     "Qwen3ASRConfig",
     "Qwen3NextConfig",
     "Qwen3_5Config",
diff --git a/vllm/transformers_utils/configs/deepseek_v4.py b/vllm/transformers_utils/configs/deepseek_v4.py
new file mode 100755
index 000000000000..7708272c3bd4
--- /dev/null
+++ b/vllm/transformers_utils/configs/deepseek_v4.py
@@ -0,0 +1,23 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+from transformers import PretrainedConfig
+
+
+class DeepseekV4Config(PretrainedConfig):
+    model_type = "deepseek_v4"
+
+    def __init__(
+        self,
+        max_position_embeddings: int = 1048576,
+        rope_scaling: dict[str, Any] | None = None,
+        rope_parameters: dict[str, Any] | None = None,
+        rope_theta: float = 10000.0,
+        **kwargs,
+    ):
+        self.max_position_embeddings = max_position_embeddings
+        self.rope_scaling = rope_scaling
+        self.rope_theta = rope_theta
+        self.rope_parameters = rope_scaling or rope_parameters
+        super().__init__(**kwargs)
diff --git a/vllm/transformers_utils/configs/deepseek_vl2.py b/vllm/transformers_utils/configs/deepseek_vl2.py
index 03f24319e287..3d3e20fea856 100644
--- a/vllm/transformers_utils/configs/deepseek_vl2.py
+++ b/vllm/transformers_utils/configs/deepseek_vl2.py
@@ -101,7 +101,6 @@ class DeepseekVLV2TextConfig(DeepseekV2Config):
 
 class DeepseekVLV2Config(PretrainedConfig):
     model_type = "deepseek_vl_v2"
-    architectures: list[str] | None = None
 
     tile_tag: str = "2D"
     global_view_pos: str = "head"
@@ -114,17 +113,11 @@ def __init__(
         candidate_resolutions: tuple[tuple[int, int]] = ((384, 384),),
         **kwargs,
     ):
-        if "architectures" not in kwargs:
-            kwargs["architectures"] = ["DeepseekVLV2ForCausalLM"]
+        architectures = kwargs.setdefault("architectures", ["DeepseekVLV2ForCausalLM"])
 
-        vision_config = kwargs.pop("vision_config", {})
-        self.vision_config = VisionEncoderConfig(**vision_config)
-
-        projector_config = kwargs.pop("projector_config", {})
-        self.projector_config = MlpProjectorConfig(**projector_config)
-
-        language_config = kwargs.pop("language_config", {})
-        self.text_config = DeepseekVLV2TextConfig(**language_config)
+        self.vision_config = VisionEncoderConfig(**kwargs.pop("vision_config", {}))
+        self.projector_config = MlpProjectorConfig(**kwargs.pop("projector_config", {}))
+        self.text_config = DeepseekVLV2TextConfig(**kwargs.pop("language_config", {}))
 
         self.tile_tag = tile_tag
         self.global_view_pos = global_view_pos
@@ -132,8 +125,8 @@ def __init__(
         self.vocab_size = self.text_config.vocab_size
 
         # update model_type for OCR models
-        if "DeepseekOCRForCausalLM" in kwargs["architectures"]:
-            self.model_type = "deepseek_ocr"
-        elif "DeepseekOCR2ForCausalLM" in kwargs["architectures"]:
-            self.model_type = "deepseek_ocr2"
+        if "DeepseekOCRForCausalLM" in architectures:
+            kwargs["model_type"] = "deepseek_ocr"
+        elif "DeepseekOCR2ForCausalLM" in architectures:
+            kwargs["model_type"] = "deepseek_ocr2"
         super().__init__(**kwargs)
diff --git a/vllm/transformers_utils/configs/granite4_vision.py b/vllm/transformers_utils/configs/granite4_vision.py
new file mode 100644
index 000000000000..a67e6ffeb8ee
--- /dev/null
+++ b/vllm/transformers_utils/configs/granite4_vision.py
@@ -0,0 +1,94 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Any
+
+import transformers
+
+
+class Granite4VisionConfig(transformers.PretrainedConfig):
+    """Configuration for Granite 4 Vision model.
+
+    This config is needed because the granite4_vision model type is not yet
+    in the transformers version pinned by vLLM.  Once transformers adds native
+    support, this file can be removed and the _CONFIG_REGISTRY entry dropped.
+    """
+
+    model_type = "granite4_vision"
+    is_composition = False
+
+    def __init__(
+        self,
+        vision_config: dict[str, Any] | None = None,
+        text_config: dict[str, Any] | None = None,
+        image_token_index: int = 100352,
+        image_seq_length: int = 576,
+        image_grid_pinpoints: list[list[int]] | None = None,
+        vision_feature_select_strategy: str = "full",
+        vision_feature_layer: int | list[int] = -2,
+        projector_hidden_act: str = "gelu",
+        projector_dropout: float = 0.1,
+        downsample_rate: str | None = None,
+        use_image_newline_parameter: bool = True,
+        deepstack_layer_map: list[list[int]] | None = None,
+        use_spatial_sampling: bool = False,
+        spatial_stride: int = 2,
+        spatial_vision_layer: int = -1,
+        spatial_target_layers: list[int] | None = None,
+        # Hub aliases — base model config uses different field names
+        vision_layer_to_llm_layer: list[list[int]] | None = None,
+        use_checkerboard_sampling: bool | None = None,
+        checkerboard_stride: int | None = None,
+        checkerboard_vision_layer: int | None = None,
+        checkerboard_llm_layers: list[int] | None = None,
+        **kwargs: Any,
+    ):
+        self.image_token_index = image_token_index
+        self.image_seq_length = image_seq_length
+        self.image_grid_pinpoints = image_grid_pinpoints or []
+        self.vision_feature_select_strategy = vision_feature_select_strategy
+        self.vision_feature_layer = vision_feature_layer
+        self.projector_hidden_act = projector_hidden_act
+        self.projector_dropout = projector_dropout
+        self.downsample_rate = downsample_rate
+        self.use_image_newline_parameter = use_image_newline_parameter
+        self.deepstack_layer_map = deepstack_layer_map or vision_layer_to_llm_layer
+        self.use_spatial_sampling = (
+            use_spatial_sampling
+            if use_checkerboard_sampling is None
+            else use_checkerboard_sampling
+        )
+        self.spatial_stride = (
+            spatial_stride if checkerboard_stride is None else checkerboard_stride
+        )
+        self.spatial_vision_layer = (
+            spatial_vision_layer
+            if checkerboard_vision_layer is None
+            else checkerboard_vision_layer
+        )
+        self.spatial_target_layers = (
+            spatial_target_layers or checkerboard_llm_layers or [0, 10, 20, 30]
+        )
+
+        if vision_config is None:
+            vision_config = {}
+        if text_config is None:
+            text_config = {}
+
+        vision_model_type = vision_config.get("model_type", "siglip_vision_model")
+        if vision_model_type in transformers.CONFIG_MAPPING:
+            self.vision_config = transformers.CONFIG_MAPPING[vision_model_type](
+                **vision_config
+            )
+        else:
+            self.vision_config = transformers.PretrainedConfig(**vision_config)
+
+        text_model_type = text_config.get("model_type", "granite")
+        if text_model_type in transformers.CONFIG_MAPPING:
+            self.text_config = transformers.CONFIG_MAPPING[text_model_type](
+                **text_config
+            )
+        else:
+            self.text_config = transformers.PretrainedConfig(**text_config)
+
+        super().__init__(**kwargs)
diff --git a/vllm/transformers_utils/configs/hy_v3.py b/vllm/transformers_utils/configs/hy_v3.py
new file mode 100644
index 000000000000..9425caf4e03f
--- /dev/null
+++ b/vllm/transformers_utils/configs/hy_v3.py
@@ -0,0 +1,185 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class HYV3Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`HYV3Model`].
+    It is used to instantiate a HYV3 model (HY V3 MoE language model) according to
+    the specified arguments.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to
+    control the model outputs. Read the documentation from [`PretrainedConfig`]
+    for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 120832):
+            Vocabulary size of the model.
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 13312):
+            Dimension of the dense FFN intermediate representations.
+        num_hidden_layers (`int`, *optional*, defaults to 80):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 64):
+            Number of attention heads for each attention layer.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            Number of key-value heads for grouped-query attention.
+        head_dim (`int`, *optional*, defaults to 128):
+            Dimension per attention head.
+        hidden_act (`str`, *optional*, defaults to `"silu"`):
+            Activation function used in FFN layers.
+        max_position_embeddings (`int`, *optional*, defaults to 131072):
+            Maximum sequence length supported by the model.
+        initializer_range (`float`, *optional*, defaults to 0.006):
+            Standard deviation of the truncated normal initializer for weight
+            initialization.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-5):
+            Epsilon for RMS normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether to use KV cache for decoding.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*):
+            Beginning-of-sequence token id.
+        eos_token_id (`int` or `List[int]`, *optional*):
+            End-of-sequence token id(s).
+        rope_parameters (`dict`, *optional*):
+            The parameters of the RoPE embeddings.
+        qk_norm (`bool`, *optional*, defaults to `True`):
+            Whether to apply RMSNorm to query and key states before attention.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie input and output embedding weights.
+        enable_attention_fp32_softmax (`bool`, *optional*, defaults to `False`):
+            Whether to upcast attention softmax to float32. Note: the eager attention
+            path always computes softmax in float32 regardless of this setting; this
+            flag is reserved for future use with custom attention backends.
+        enable_lm_head_fp32 (`bool`, *optional*, defaults to `True`):
+            Whether to upcast the LM head computation to float32.
+        num_experts (`int`, *optional*, defaults to 192):
+            Total number of MoE experts.
+        num_experts_per_tok (`int`, *optional*, defaults to 8):
+            Number of experts selected per token (top-k routing).
+        num_shared_experts (`int`, *optional*, defaults to 1):
+            Number of always-active shared experts combined into a single MLP.
+        expert_hidden_dim (`int`, *optional*, defaults to 1536):
+            Intermediate dimension of each individual MoE expert.
+        moe_router_enable_expert_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use per-expert load-balancing bias in the router.
+        moe_router_use_sigmoid (`bool`, *optional*, defaults to `True`):
+            Whether to use sigmoid (instead of softmax) for router scoring.
+        route_norm (`bool`, *optional*, defaults to `True`):
+            Whether to normalize routing scores when using sigmoid routing.
+        router_scaling_factor (`float`, *optional*):
+            Optional multiplicative scaling factor applied to routing scores.
+        use_grouped_mm (`bool`, *optional*, defaults to `False`):
+            Whether to use grouped GEMM for expert computation (not yet implemented).
+        enable_moe_fp32_combine (`bool`, *optional*, defaults to `False`):
+            Whether to accumulate expert outputs in float32.
+        first_k_dense_replace (`int`, *optional*, defaults to 1):
+            Number of initial decoder layers that use a dense FFN instead of MoE.
+        output_router_logits (`bool`, *optional*, defaults to `False`):
+            Whether to output router logits from each MoE layer. Useful for computing
+            auxiliary load-balancing loss during training. Disabled by default to avoid
+            the memory overhead of storing per-layer router tensors during inference.
+
+    Example:
+        ```python
+        >>> from transformers import HYV3Config, HYV3Model
+
+        >>> config = HYV3Config()
+        >>> model = HYV3Model(config)
+        ```
+    """
+
+    model_type = "hy_v3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=120832,
+        hidden_size=4096,
+        intermediate_size=13312,
+        num_hidden_layers=80,
+        num_attention_heads=64,
+        num_key_value_heads=8,
+        head_dim=128,
+        hidden_act="silu",
+        max_position_embeddings=131072,
+        initializer_range=0.006,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=None,
+        eos_token_id=None,
+        rope_parameters: dict[str, Any] | None = None,
+        qk_norm=True,
+        tie_word_embeddings=False,
+        enable_attention_fp32_softmax=False,
+        enable_lm_head_fp32=True,
+        # MoE specific
+        num_experts=192,
+        num_experts_per_tok=8,
+        num_shared_experts=1,
+        expert_hidden_dim=1536,
+        moe_router_enable_expert_bias=True,
+        moe_router_use_sigmoid=True,
+        route_norm=True,
+        router_scaling_factor=None,
+        use_grouped_mm=False,
+        enable_moe_fp32_combine=False,
+        # Dense/MoE layer control
+        first_k_dense_replace=1,
+        output_router_logits=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        rope_theta = kwargs.pop("rope_theta", 11158840.0)
+        if rope_parameters is None:
+            rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
+        self.rope_parameters = rope_parameters
+        self.qk_norm = qk_norm
+        self.tie_word_embeddings = tie_word_embeddings
+        self.enable_lm_head_fp32 = enable_lm_head_fp32
+        self.enable_attention_fp32_softmax = enable_attention_fp32_softmax
+
+        # MoE specific
+        self.num_experts = num_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_shared_experts = num_shared_experts
+        self.expert_hidden_dim = expert_hidden_dim
+        self.moe_router_enable_expert_bias = moe_router_enable_expert_bias
+        self.moe_router_use_sigmoid = moe_router_use_sigmoid
+        self.route_norm = route_norm
+        self.use_grouped_mm = use_grouped_mm
+        self.router_scaling_factor = router_scaling_factor
+        self.enable_moe_fp32_combine = enable_moe_fp32_combine
+
+        # Dense/MoE layer control
+        self.first_k_dense_replace = first_k_dense_replace
+        self.output_router_logits = output_router_logits
+
+        if eos_token_id is not None and isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
diff --git a/vllm/transformers_utils/configs/hyperclovax.py b/vllm/transformers_utils/configs/hyperclovax.py
index 9fa823743d66..d1a3218fe4dd 100644
--- a/vllm/transformers_utils/configs/hyperclovax.py
+++ b/vllm/transformers_utils/configs/hyperclovax.py
@@ -17,6 +17,7 @@
 # limitations under the License.
 """HyperCLOVA X model configuration."""
 
+from transformers import AutoConfig
 from transformers.configuration_utils import PretrainedConfig
 
 
@@ -275,3 +276,74 @@ def __init__(
             auto_map=auto_map,
             **kwargs,
         )
+
+
+class HCXVisionConfig(PretrainedConfig):
+    """Vendored HyperCLOVAX Vision config with transformers v5 fix.
+
+    The original remote code config does not handle empty initialization
+    (text_config=None), which breaks transformers v5's @strict validation.
+
+    TODO: Remove this class once HyperCLOVAX is upstreamed to transformers.
+    Tracking PR: https://github.com/huggingface/transformers/pull/44956
+    """
+
+    model_type = "hyperclovax_vlm"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    text_config_attribute_map = {
+        "n_embd": "hidden_size",
+        "n_positions": "max_position_embeddings",
+        "n_head": "num_attention_heads",
+        "n_layer": "num_hidden_layers",
+    }
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        use_nth_layer=-2,
+        img_start_id=100009,
+        decoder_max_length=4096,
+        anyres=False,
+        unpad=False,
+        max_num_grids=-1,
+        num_queries_vis_abstractor=-1,
+        ignore_index=-100,
+        proj_pos_emb=True,
+        proj_prenorm=False,
+        use_1x1_grid=False,
+        **kwargs,
+    ):
+        for key, val in self.text_config_attribute_map.items():
+            if text_config is not None and key in text_config:
+                text_config[val] = text_config.pop(key)
+
+        self.text_config = None
+        if text_config is not None:
+            _text_config = AutoConfig.for_model(text_config["model_type"])
+            self.text_config = _text_config.from_dict(text_config)
+            self.hidden_size = self.text_config.hidden_size
+
+        self.vision_config = None
+        if vision_config is not None:
+            _vision_config = AutoConfig.for_model(vision_config["model_type"])
+            self.vision_config = _vision_config.from_dict(vision_config)
+
+        self.use_nth_layer = use_nth_layer
+        self.decoder_max_length = decoder_max_length
+        self.anyres = anyres
+        self.unpad = unpad
+        self.max_num_grids = max_num_grids
+        self.num_queries_vis_abstractor = num_queries_vis_abstractor
+        self.img_start_id = img_start_id
+        self.ignore_index = ignore_index
+        self.proj_pos_emb = proj_pos_emb
+        self.proj_prenorm = proj_prenorm
+        self.use_1x1_grid = use_1x1_grid
+        super().__init__(**kwargs)
+
+    def get_text_config(self, decoder=False):
+        if self.text_config is not None:
+            return self.text_config
+        return self
diff --git a/vllm/transformers_utils/configs/laguna.py b/vllm/transformers_utils/configs/laguna.py
new file mode 100644
index 000000000000..2702d3af5aa1
--- /dev/null
+++ b/vllm/transformers_utils/configs/laguna.py
@@ -0,0 +1,120 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class LagunaConfig(PretrainedConfig):
+    model_type = "laguna"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.g_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size: int = 100352,
+        hidden_size: int = 2048,
+        intermediate_size: int = 8192,
+        num_hidden_layers: int = 40,
+        num_attention_heads: int = 48,
+        num_key_value_heads: int = 8,
+        head_dim: int = 128,
+        qkv_bias: bool = False,
+        attention_bias: bool = False,
+        gating: bool | str = True,
+        hidden_act: str = "silu",
+        max_position_embeddings: int = 131072,
+        initializer_range: float = 0.02,
+        rms_norm_eps: float = 1e-6,
+        use_cache: bool = True,
+        tie_word_embeddings: bool = False,
+        rope_theta: float = 500000.0,
+        rope_scaling: dict | None = None,
+        rope_parameters: dict | None = None,
+        partial_rotary_factor: float = 1.0,
+        attention_dropout: float = 0.0,
+        sliding_window: int | None = None,
+        layer_types: list[str] | None = None,
+        swa_attention_sink_enabled: bool = False,
+        swa_rope_parameters: dict | None = None,
+        num_attention_heads_per_layer: list[int] | None = None,
+        num_experts: int = 256,
+        num_experts_per_tok: int = 8,
+        moe_intermediate_size: int = 512,
+        shared_expert_intermediate_size: int = 512,
+        norm_topk_prob: bool = True,
+        decoder_sparse_step: int = 1,
+        mlp_only_layers: list[int] | None = None,
+        router_aux_loss_coef: float = 0.001,
+        output_router_logits: bool = False,
+        moe_routed_scaling_factor: float = 1.0,
+        moe_apply_router_weight_on_input: bool = False,
+        **kwargs,
+    ):
+        if mlp_only_layers is None:
+            mlp_only_layers = [0]
+
+        # Accept either v4-style (rope_theta + rope_scaling) or v5-style
+        # (rope_parameters). Translate v5 → v4 so downstream code has one path.
+        if rope_parameters is not None:
+            rp = dict(rope_parameters)
+            rope_theta = float(rp.pop("rope_theta", rope_theta))
+            rt = rp.pop("rope_type", None)
+            if rt is not None and rt != "default":
+                rope_scaling = {"rope_type": rt, **rp}
+            elif rp and rope_scaling is None:
+                rope_scaling = {"rope_type": "default", **rp}
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.qkv_bias = qkv_bias
+        self.attention_bias = attention_bias
+        self.gating = gating
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.partial_rotary_factor = partial_rotary_factor
+        self.attention_dropout = attention_dropout
+        self.sliding_window = sliding_window
+        self.layer_types = layer_types
+        self.swa_attention_sink_enabled = swa_attention_sink_enabled
+        self.swa_rope_parameters = swa_rope_parameters
+        self.num_attention_heads_per_layer = num_attention_heads_per_layer
+        self.num_experts = num_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.moe_intermediate_size = moe_intermediate_size
+        self.shared_expert_intermediate_size = shared_expert_intermediate_size
+        self.norm_topk_prob = norm_topk_prob
+        self.decoder_sparse_step = decoder_sparse_step
+        self.mlp_only_layers = mlp_only_layers
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.output_router_logits = output_router_logits
+        self.moe_routed_scaling_factor = moe_routed_scaling_factor
+        self.moe_apply_router_weight_on_input = moe_apply_router_weight_on_input
+
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+__all__ = ["LagunaConfig"]
diff --git a/vllm/transformers_utils/configs/mimo_v2_omni.py b/vllm/transformers_utils/configs/mimo_v2_omni.py
new file mode 100644
index 000000000000..b87ca22a9a83
--- /dev/null
+++ b/vllm/transformers_utils/configs/mimo_v2_omni.py
@@ -0,0 +1,65 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+
+from transformers import PretrainedConfig
+
+
+class Mimo_VLVisionConfig(PretrainedConfig):
+    model_type = "mimovl"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        depth=28,
+        hidden_size=1280,
+        hidden_act="silu",
+        intermediate_size=4608,
+        num_heads=32,
+        in_channels=3,
+        patch_size=16,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        tokens_per_second=2,
+        window_size=128,
+        out_hidden_size=2048,
+        fullatt_block_indexes=None,
+        initializer_range=0.02,
+        kv_channels=64,  # HACK
+        qk_channels=64,
+        num_query_groups=4,
+        num_key_value_heads=8,
+        vit_window_attn_types=None,
+        visual_token_window_size=64,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.depth = depth
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.num_heads = num_heads
+        # Support GQA: if num_key_value_heads is not provided,
+        # default to num_heads (MHA)
+        if num_key_value_heads is None:
+            num_key_value_heads = num_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.in_channels = in_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.tokens_per_second = tokens_per_second
+        self.window_size = window_size
+        self.fullatt_block_indexes = (
+            fullatt_block_indexes
+            if fullatt_block_indexes is not None
+            else [7, 15, 23, 31]
+        )
+        self.out_hidden_size = out_hidden_size
+        self.initializer_range = initializer_range
+        self.kv_channels = kv_channels
+        self.qk_channels = qk_channels
+        self.num_query_groups = num_query_groups
+        self.vit_window_attn_types = vit_window_attn_types or [-1] * depth
+        self.visual_token_window_size = visual_token_window_size
diff --git a/vllm/transformers_utils/configs/moondream3.py b/vllm/transformers_utils/configs/moondream3.py
new file mode 100644
index 000000000000..307bb2977206
--- /dev/null
+++ b/vllm/transformers_utils/configs/moondream3.py
@@ -0,0 +1,152 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Configuration for Moondream3 model."""
+
+from transformers import PretrainedConfig
+
+
+class Moondream3VisionConfig(PretrainedConfig):
+    """Vision encoder configuration for Moondream3."""
+
+    model_type = "moondream3_vision"
+
+    def __init__(
+        self,
+        enc_dim: int = 1152,
+        enc_patch_size: int = 14,
+        enc_n_layers: int = 27,
+        enc_ff_dim: int = 4304,
+        enc_n_heads: int = 16,
+        proj_inner_dim: int = 8192,
+        crop_size: int = 378,
+        max_crops: int = 12,
+        overlap_margin: int = 4,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.enc_dim = enc_dim
+        self.enc_patch_size = enc_patch_size
+        self.enc_n_layers = enc_n_layers
+        self.enc_ff_dim = enc_ff_dim
+        self.enc_n_heads = enc_n_heads
+        self.proj_inner_dim = proj_inner_dim
+        self.crop_size = crop_size
+        self.max_crops = max_crops
+        self.overlap_margin = overlap_margin
+
+        # Standard HuggingFace attributes for vision config
+        self.hidden_size = enc_dim
+        self.num_attention_heads = enc_n_heads
+        self.num_hidden_layers = enc_n_layers
+        self.intermediate_size = enc_ff_dim
+        self.patch_size = enc_patch_size
+        self.image_size = crop_size
+
+
+class Moondream3TextConfig(PretrainedConfig):
+    """Text decoder configuration for Moondream3."""
+
+    model_type = "moondream3_text"
+
+    def __init__(
+        self,
+        dim: int = 2048,
+        ff_dim: int = 8192,
+        n_layers: int = 24,
+        vocab_size: int = 51200,
+        max_context: int = 4096,
+        n_heads: int = 32,
+        n_kv_heads: int = 32,
+        prefix_attn: int = 730,
+        rope_theta: float = 1500000.0,
+        moe: dict | None = None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        # Store original moondream3 config names
+        self.dim = dim
+        self.ff_dim = ff_dim
+        self.n_layers = n_layers
+        self.n_heads = n_heads
+        self.n_kv_heads = n_kv_heads
+        self.prefix_attn = prefix_attn
+        self.max_context = max_context
+        self.rope_theta = rope_theta
+
+        # MoE config
+        moe = moe or {}
+        self.moe_start_layer = moe.get("start_layer", 4)
+        self.moe_num_experts = moe.get("n_experts", 64)
+        self.moe_experts_per_token = moe.get("n_experts_per_tok", 8)
+        self.moe_expert_inner_dim = moe.get("expert_inner_dim", 1024)
+
+        # Standard HuggingFace attributes (required by vLLM)
+        self.hidden_size = dim
+        self.num_attention_heads = n_heads
+        self.num_key_value_heads = n_kv_heads
+        self.num_hidden_layers = n_layers
+        self.intermediate_size = ff_dim
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_context
+
+        # Moondream3 uses token 0 (<|endoftext|>) as both BOS and EOS.
+        # Token 3 (<|md_reserved_2|>) is an answer delimiter that the model
+        # implementation suppresses during generation.
+        self.bos_token_id = 0
+        self.eos_token_id = 0
+
+        # MoE standard attributes
+        self.num_local_experts = self.moe_num_experts
+        self.num_experts_per_tok = self.moe_experts_per_token
+
+
+class Moondream3Config(PretrainedConfig):
+    """Combined configuration for Moondream3 multimodal model."""
+
+    model_type = "moondream3"
+    is_composition = True
+
+    def __init__(
+        self,
+        config: dict | None = None,
+        **kwargs,
+    ):
+        config = config or {}
+
+        # Parse text config
+        text_config = config.get("text", {})
+        self.text_config: Moondream3TextConfig = Moondream3TextConfig(**text_config)
+
+        # Parse vision config
+        vision_config = config.get("vision", {})
+        self.vision_config = Moondream3VisionConfig(**vision_config)
+
+        # Store the original config dict for model access
+        self.config = config
+        tokenizer_config = config.get("tokenizer", {})
+        self.answer_token_id = tokenizer_config.get("answer_id", 3)
+
+        super().__init__(**kwargs)
+
+        # Expose key attributes at top level for vLLM compatibility
+        self.hidden_size = self.text_config.hidden_size
+        self.num_attention_heads = self.text_config.num_attention_heads
+        self.num_key_value_heads = self.text_config.num_key_value_heads
+        self.num_hidden_layers = self.text_config.num_hidden_layers
+        self.vocab_size = self.text_config.vocab_size
+        self.intermediate_size = self.text_config.intermediate_size
+
+        # Moondream3 uses token 0 (<|endoftext|>) as both BOS and EOS.
+        # Token 3 (<|md_reserved_2|>) is an answer delimiter that the model
+        # implementation suppresses during generation.
+        self.bos_token_id = 0
+        self.eos_token_id = 0
+
+    def get_text_config(self, decoder: bool = False) -> "Moondream3TextConfig":
+        """Return the text config for vLLM's text_config detection.
+
+        Args:
+            decoder: Ignored. Only used for encoder-decoder models.
+        """
+        return self.text_config
diff --git a/vllm/transformers_utils/configs/qianfan_ocr.py b/vllm/transformers_utils/configs/qianfan_ocr.py
new file mode 100644
index 000000000000..da004bb90f4f
--- /dev/null
+++ b/vllm/transformers_utils/configs/qianfan_ocr.py
@@ -0,0 +1,105 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Any
+
+from transformers import PretrainedConfig
+from transformers.models.auto import CONFIG_MAPPING
+
+
+class QianfanOCRVisionConfig(PretrainedConfig):
+    model_type = "qianfan_ocr_vision"
+
+    def __init__(
+        self,
+        hidden_size: int = 1024,
+        intermediate_size: int = 4096,
+        num_hidden_layers: int = 24,
+        num_attention_heads: int = 16,
+        num_channels: int = 3,
+        image_size: int = 448,
+        patch_size: int = 14,
+        hidden_act: str = "gelu",
+        layer_norm_eps: float = 1e-6,
+        attention_dropout: float = 0.0,
+        drop_path_rate: float = 0.1,
+        qkv_bias: bool = True,
+        qk_normalization: bool = False,
+        norm_type: str = "layer_norm",
+        initializer_range: float = 0.02,
+        initializer_factor: float = 0.1,
+        use_mask_token: bool = False,
+        use_mean_pooling: bool = True,
+        **kwargs: Any,
+    ):
+        super().__init__(**kwargs)
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.hidden_act = hidden_act
+        self.layer_norm_eps = layer_norm_eps
+        self.attention_dropout = attention_dropout
+        self.drop_path_rate = drop_path_rate
+        self.qkv_bias = qkv_bias
+        self.qk_normalization = qk_normalization
+        self.norm_type = norm_type
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.use_mask_token = use_mask_token
+        self.use_mean_pooling = use_mean_pooling
+
+
+class QianfanOCRConfig(PretrainedConfig):
+    model_type = "qianfan_ocr"
+
+    def __init__(
+        self,
+        vision_config: dict | None = None,
+        text_config: dict | None = None,
+        downsample_ratio: float = 0.5,
+        dynamic_image_size: bool = True,
+        force_image_size: int = 448,
+        image_token_id: int = 151671,
+        max_dynamic_patch: int = 12,
+        min_dynamic_patch: int = 1,
+        pad2square: bool = False,
+        ps_version: str = "v2",
+        select_layer: int = -1,
+        template: str = "internvl2_5",
+        use_thumbnail: bool = True,
+        tie_word_embeddings: bool = False,
+        **kwargs: Any,
+    ):
+        super().__init__(**kwargs)
+
+        if isinstance(vision_config, dict):
+            self.vision_config = QianfanOCRVisionConfig(**vision_config)
+        elif vision_config is None:
+            self.vision_config = QianfanOCRVisionConfig()
+        else:
+            self.vision_config = vision_config
+
+        if isinstance(text_config, dict):
+            model_type = text_config.get("model_type", "qwen3")
+            self.text_config = CONFIG_MAPPING[model_type](**text_config)
+        elif text_config is None:
+            self.text_config = CONFIG_MAPPING["qwen3"]()
+        else:
+            self.text_config = text_config
+
+        self.downsample_ratio = downsample_ratio
+        self.dynamic_image_size = dynamic_image_size
+        self.force_image_size = force_image_size
+        self.image_token_id = image_token_id
+        self.max_dynamic_patch = max_dynamic_patch
+        self.min_dynamic_patch = min_dynamic_patch
+        self.pad2square = pad2square
+        self.ps_version = ps_version
+        self.select_layer = select_layer
+        self.template = template
+        self.use_thumbnail = use_thumbnail
+        self.tie_word_embeddings = tie_word_embeddings
diff --git a/vllm/transformers_utils/configs/speculators/algos.py b/vllm/transformers_utils/configs/speculators/algos.py
index f4dffab8b3e3..405d5f5de1d1 100644
--- a/vllm/transformers_utils/configs/speculators/algos.py
+++ b/vllm/transformers_utils/configs/speculators/algos.py
@@ -41,3 +41,34 @@ def update_eagle3(config_dict: dict, pre_trained_config: dict) -> None:
         pre_trained_config["eagle_aux_hidden_state_layer_ids"] = config_dict[
             "eagle_aux_hidden_state_layer_ids"
         ]
+
+
+@register_speculator("dflash")
+def update_dflash(config_dict: dict, pre_trained_config: dict) -> None:
+    """
+    Apply DFlash specific configuration transformations to the `dict` used to
+    construct the Transformers PreTrainedConfig.
+
+    DFlash specific fields:
+    - draft_vocab_size: Size of the draft model's vocabulary
+    - target_hidden_size: Hidden size of the target model
+    - mask_token_id (required): Token ID used for parallel drafting mask
+        placeholders
+    - aux_hidden_state_layer_ids (required): Layer indices from the target
+        model whose intermediate hidden states are used as context for the
+        DFlash drafter. Mapped to both eagle_aux_hidden_state_layer_ids
+        (for gpu_model_runner) and dflash_config.target_layer_ids (for the
+        DFlash model).
+    """
+    pre_trained_config["architectures"] = ["DFlashDraftModel"]
+    pre_trained_config["draft_vocab_size"] = config_dict.get("draft_vocab_size")
+    if config_dict.get("target_hidden_size") is not None:
+        pre_trained_config["target_hidden_size"] = config_dict["target_hidden_size"]
+
+    aux_layer_ids = config_dict["aux_hidden_state_layer_ids"]
+    pre_trained_config["eagle_aux_hidden_state_layer_ids"] = aux_layer_ids
+
+    pre_trained_config["dflash_config"] = {
+        "mask_token_id": config_dict["mask_token_id"],
+        "target_layer_ids": aux_layer_ids,
+    }
diff --git a/vllm/transformers_utils/model_arch_config_convertor.py b/vllm/transformers_utils/model_arch_config_convertor.py
index ea7096ae8bde..35fa1313d1e7 100644
--- a/vllm/transformers_utils/model_arch_config_convertor.py
+++ b/vllm/transformers_utils/model_arch_config_convertor.py
@@ -47,6 +47,9 @@ def get_hidden_size(self) -> int:
 
     def get_head_size(self) -> int:
         if self.is_deepseek_mla():
+            # special case for deepseek_v4
+            if hasattr(self.hf_text_config, "compress_ratios"):
+                return self.hf_text_config.head_dim
             qk_rope_head_dim = getattr(self.hf_text_config, "qk_rope_head_dim", 0)
             if not envs.VLLM_MLA_DISABLE:
                 return self.hf_text_config.kv_lora_rank + qk_rope_head_dim
@@ -77,6 +80,8 @@ def get_total_num_kv_heads(self) -> int:
             "num_key_value_heads",
             # For ChatGLM:
             "multi_query_group_num",
+            # For Step3p5:
+            "num_attention_groups",
         ]
         # For non-grouped-query attention models, the number of KV heads is
         # equal to the number of attention heads.
@@ -220,6 +225,7 @@ def is_deepseek_mla(self) -> bool:
             "deepseek_v2",
             "deepseek_v3",
             "deepseek_v32",
+            "deepseek_v4",
             "deepseek_mtp",
             "glm_moe_dsa",
             "glm4_moe_lite",
@@ -231,7 +237,11 @@ def is_deepseek_mla(self) -> bool:
             "pangu_ultra_moe_mtp",
             "bailing_hybrid",
         ):
-            return getattr(self.hf_text_config, "kv_lora_rank", None) is not None
+            # check is deepseek_v4 model
+            if hasattr(self.hf_text_config, "compress_ratios"):
+                return getattr(self.hf_text_config, "head_dim", None) is not None
+            else:
+                return getattr(self.hf_text_config, "kv_lora_rank", None) is not None
         elif self.hf_text_config.model_type == "eagle":
             # if the model is an EAGLE module, check for the
             # underlying architecture
@@ -248,6 +258,23 @@ def is_deepseek_mla(self) -> bool:
             )
         return False
 
+    def is_mm_prefix_lm(self) -> bool:
+        """Whether to use bidirectional attention for mm positions."""
+        if hasattr(self.hf_config, "is_mm_prefix_lm"):
+            return bool(self.hf_config.is_mm_prefix_lm)
+        # fallback to list of known models
+        MM_PREFIX_LM_MODELS = (
+            "bagel",
+            "gemma3",
+            "molmo2",
+            "moondream3",
+            "paligemma",
+            "umm",
+        )
+        if not hasattr(self.hf_config, "model_type"):
+            return False
+        return self.hf_config.model_type in MM_PREFIX_LM_MODELS
+
     def derive_max_model_len_and_key(self) -> tuple[float, str | None]:
         derived_max_model_len = float("inf")
         possible_keys = [
@@ -297,6 +324,7 @@ def convert(self) -> ModelArchitectureConfig:
             num_experts=self.get_num_experts(),
             quantization_config=self.get_quantization_config(),
             is_deepseek_mla=self.is_deepseek_mla(),
+            is_mm_prefix_lm=self.is_mm_prefix_lm(),
             derived_max_model_len_and_key=self.derive_max_model_len_and_key(),
         )
 
@@ -324,6 +352,9 @@ def get_total_num_kv_heads(self) -> int:
         )
         return enc_num_kv_heads
 
+    def is_mm_prefix_lm(self) -> bool:
+        return False
+
 
 class MambaModelArchConfigConvertor(ModelArchConfigConvertorBase):
     def get_head_size(self) -> int:
@@ -418,6 +449,39 @@ def get_num_hidden_layers(self) -> int:
         return getattr(self.hf_text_config, "num_nextn_predict_layers", 0)
 
 
+def _strip_mimo_v2_attention_chunk_size(
+    hf_config: PretrainedConfig, hf_text_config: PretrainedConfig
+) -> None:
+    # MiMo-V2-Flash's config.json sets `attention_chunk_size=128` but the
+    # architecture does not actually use chunked local attention. Leaving it
+    # set makes vLLM disable the hybrid KV cache manager
+    for cfg in (hf_text_config, hf_config):
+        if cfg is not None and hasattr(cfg, "attention_chunk_size"):
+            delattr(cfg, "attention_chunk_size")
+
+
+class MimoV2ModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def __init__(self, hf_config: PretrainedConfig, hf_text_config: PretrainedConfig):
+        if getattr(hf_config, "vision_config", None):
+            hf_config.architectures = ["MiMoV2OmniForCausalLM"]
+        super().__init__(hf_config, hf_text_config)
+        _strip_mimo_v2_attention_chunk_size(hf_config, hf_text_config)
+
+
+class MimoV2MTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def __init__(self, hf_config: PretrainedConfig, hf_text_config: PretrainedConfig):
+        super().__init__(hf_config, hf_text_config)
+        _strip_mimo_v2_attention_chunk_size(hf_config, hf_text_config)
+
+    def get_num_hidden_layers(self) -> int:
+        n = getattr(self.hf_text_config, "num_nextn_predict_layers", None)
+        if n is not None:
+            return n
+        # Fall back to n_predict set by hf_config_override
+        n = getattr(self.hf_text_config, "n_predict", None)
+        return n if n is not None else 0
+
+
 class GLM4MoeMTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
     def get_num_hidden_layers(self) -> int:
         return getattr(self.hf_text_config, "num_nextn_predict_layers", 0)
@@ -448,7 +512,25 @@ def get_num_hidden_layers(self) -> int:
         return getattr(self.hf_text_config, "num_nextn_predict_layers", 1)
 
 
+class Gemma4MTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_hidden_size(self) -> int:
+        # The speculator buffer must match the backbone (target) model's
+        # hidden dimension, not the draft model's smaller dimension.
+        return getattr(
+            self.hf_config, "backbone_hidden_size", super().get_hidden_size()
+        )
+
+    def get_num_hidden_layers(self) -> int:
+        return getattr(self.hf_text_config, "num_hidden_layers", 0)
+
+
 class Gemma4ModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def is_mm_prefix_lm(self) -> bool:
+        return (
+            getattr(self.hf_text_config, "use_bidirectional_attention", None)
+            == "vision"
+        )
+
     def get_head_size(self) -> int:
         # Gemma4 uses dual head dimensions: head_dim (sliding attention)
         # and global_head_dim (full attention).  Return the largest so
@@ -471,6 +553,7 @@ def get_head_size(self) -> int:
     "falcon": FalconModelArchConfigConvertor,
     "gemma4": Gemma4ModelArchConfigConvertor,
     "gemma4_text": Gemma4ModelArchConfigConvertor,
+    "gemma4_mtp": Gemma4MTPModelArchConfigConvertor,
     "RefinedWeb": FalconModelArchConfigConvertor,
     "RefinedWebModel": FalconModelArchConfigConvertor,
     "nemotron-nas": NemotronNasModelArchConfigConvertor,
@@ -478,6 +561,10 @@ def get_head_size(self) -> int:
     "qwen3_next_mtp": Qwen3NextMTPModelArchConfigConvertor,
     "qwen3_5_mtp": Qwen3_5MTPModelArchConfigConvertor,
     "mimo_mtp": MimoMTPModelArchConfigConvertor,
+    "mimo_v2": MimoV2ModelArchConfigConvertor,
+    "mimo_v2_flash": MimoV2ModelArchConfigConvertor,
+    "mimo_v2_mtp": MimoV2MTPModelArchConfigConvertor,
+    "mimo_v2_omni_mtp": MimoV2MTPModelArchConfigConvertor,
     "glm4_moe_mtp": GLM4MoeMTPModelArchConfigConvertor,
     "glm_ocr_mtp": GLM4MoeMTPModelArchConfigConvertor,
     "ernie_mtp": ErnieMTPModelArchConfigConvertor,
diff --git a/vllm/transformers_utils/processors/__init__.py b/vllm/transformers_utils/processors/__init__.py
index 750d6291e0f1..4f44591b16d1 100644
--- a/vllm/transformers_utils/processors/__init__.py
+++ b/vllm/transformers_utils/processors/__init__.py
@@ -19,13 +19,16 @@
     "FireRedLIDProcessor",
     "FunASRProcessor",
     "GLM4VProcessor",
+    "Granite4VisionProcessor",
     "H2OVLProcessor",
     "HunYuanVLProcessor",
     "HunYuanVLImageProcessor",
+    "Moondream3Processor",
     "InternVLProcessor",
     "IsaacProcessor",
     "KimiAudioProcessor",
     "KimiK25Processor",
+    "MiMoOmniProcessor",
     "MistralCommonPixtralProcessor",
     "MistralCommonVoxtralProcessor",
     "NanoNemotronVLProcessor",
@@ -48,6 +51,7 @@
     "FireRedLIDProcessor": "vllm.transformers_utils.processors.fireredlid",
     "FunASRProcessor": "vllm.transformers_utils.processors.funasr",
     "GLM4VProcessor": "vllm.transformers_utils.processors.glm4v",
+    "Granite4VisionProcessor": "vllm.transformers_utils.processors.granite4_vision",
     "H2OVLProcessor": "vllm.transformers_utils.processors.h2ovl",
     "HunYuanVLProcessor": "vllm.transformers_utils.processors.hunyuan_vl",
     "HunYuanVLImageProcessor": "vllm.transformers_utils.processors.hunyuan_vl_image",
@@ -55,8 +59,10 @@
     "IsaacProcessor": "vllm.transformers_utils.processors.isaac",
     "KimiAudioProcessor": "vllm.transformers_utils.processors.kimi_audio",
     "KimiK25Processor": "vllm.transformers_utils.processors.kimi_k25",
+    "MiMoOmniProcessor": "vllm.transformers_utils.processors.mimo_v2_omni",
     "MistralCommonPixtralProcessor": "vllm.transformers_utils.processors.pixtral",
     "MistralCommonVoxtralProcessor": "vllm.transformers_utils.processors.voxtral",
+    "Moondream3Processor": "vllm.transformers_utils.processors.moondream3",
     "NanoNemotronVLProcessor": "vllm.transformers_utils.processors.nano_nemotron_vl",
     "NemotronVLProcessor": "vllm.transformers_utils.processors.nemotron_vl",
     "LlamaNemotronVLEmbedProcessor": "vllm.transformers_utils.processors.nemotron_vl",
diff --git a/vllm/transformers_utils/processors/cohere_asr.py b/vllm/transformers_utils/processors/cohere_asr.py
index f742074a4e3d..e1257de4e735 100644
--- a/vllm/transformers_utils/processors/cohere_asr.py
+++ b/vllm/transformers_utils/processors/cohere_asr.py
@@ -4,11 +4,11 @@
 import math
 import random
 
-import librosa
 import numpy as np
 import torch
 import torch.nn.functional as F
 from torch import nn
+from torchaudio.functional import melscale_fbanks
 from transformers import AutoFeatureExtractor, AutoProcessor, BatchFeature
 from transformers.feature_extraction_sequence_utils import (
     SequenceFeatureExtractor,
@@ -129,17 +129,15 @@ def __init__(
         self.pad_min_duration = 0.0
         self.pad_direction = "both"
 
-        filterbanks = torch.tensor(
-            librosa.filters.mel(
-                sr=sample_rate,
-                n_fft=self.n_fft,
-                n_mels=nfilt,
-                fmin=lowfreq,
-                fmax=highfreq,
-                norm=mel_norm,
-            ),
-            dtype=torch.float,
-        ).unsqueeze(0)
+        filterbanks = melscale_fbanks(
+            n_freqs=self.n_fft // 2 + 1,
+            f_min=lowfreq,
+            f_max=highfreq,
+            n_mels=nfilt,
+            sample_rate=sample_rate,
+            norm=mel_norm,
+            mel_scale="slaney",
+        ).T.unsqueeze(0)
         self.register_buffer("fb", filterbanks)
 
         # Calculate maximum sequence length
diff --git a/vllm/transformers_utils/processors/granite4_vision.py b/vllm/transformers_utils/processors/granite4_vision.py
new file mode 100644
index 000000000000..7972015adf9d
--- /dev/null
+++ b/vllm/transformers_utils/processors/granite4_vision.py
@@ -0,0 +1,78 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from fractions import Fraction
+
+from transformers import LlavaNextProcessor
+from transformers.image_processing_utils import select_best_resolution
+
+
+class Granite4VisionProcessor(LlavaNextProcessor):
+    """Processor for Granite 4 Vision.
+
+    Extends LlavaNextProcessor to account for the Window Q-Former
+    downsampling when computing the number of image features.
+
+    This processor is needed because the granite4_vision processor type
+    is not yet in the transformers version pinned by vLLM.
+    """
+
+    model_type = "granite4_vision"
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        patch_size=None,
+        vision_feature_select_strategy=None,
+        chat_template=None,
+        image_token="<image>",
+        num_additional_image_tokens=0,
+        downsample_rate=None,
+        **kwargs,
+    ):
+        super().__init__(
+            image_processor=image_processor,
+            tokenizer=tokenizer,
+            patch_size=patch_size,
+            vision_feature_select_strategy=vision_feature_select_strategy,
+            chat_template=chat_template,
+            image_token=image_token,
+            num_additional_image_tokens=num_additional_image_tokens,
+        )
+        self.downsample_rate = downsample_rate
+
+    def _get_number_of_features(
+        self,
+        orig_height: int,
+        orig_width: int,
+        height: int,
+        width: int,
+    ) -> int:
+        image_grid_pinpoints = self.image_processor.image_grid_pinpoints
+
+        height_best_resolution, width_best_resolution = select_best_resolution(
+            [orig_height, orig_width], image_grid_pinpoints
+        )
+        scale_height = height_best_resolution // height
+        scale_width = width_best_resolution // width
+
+        patches_height = height // self.patch_size
+        patches_width = width // self.patch_size
+        if self.downsample_rate is not None:
+            ds_rate = Fraction(self.downsample_rate)
+            patches_height = int(patches_height * ds_rate)
+            patches_width = int(patches_width * ds_rate)
+
+        unpadded_features, newline_features = self._get_unpadded_features(
+            orig_height,
+            orig_width,
+            patches_height,
+            patches_width,
+            scale_height,
+            scale_width,
+        )
+        base_features = (
+            patches_height * patches_width + self.num_additional_image_tokens
+        )
+        return unpadded_features + newline_features + base_features
diff --git a/vllm/transformers_utils/processors/mimo_v2_omni.py b/vllm/transformers_utils/processors/mimo_v2_omni.py
new file mode 100644
index 000000000000..97df3184113e
--- /dev/null
+++ b/vllm/transformers_utils/processors/mimo_v2_omni.py
@@ -0,0 +1,1285 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# mypy: ignore-errors
+"""MiMo-Omni multimodal processor for vLLM.
+
+Ported from SGLang's MiMoV2OmniProcessor / MiMoVLProcessor implementations.
+"""
+
+import contextlib
+import copy
+import io
+import logging
+import math
+from collections import OrderedDict
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from dataclasses import dataclass, field
+from io import BytesIO
+from typing import Any, Literal
+
+import numpy as np
+import regex as re
+import requests
+import torch
+import torch.nn.functional as F
+from PIL import Image
+from transformers import BatchFeature, TensorType
+from transformers.processing_utils import ProcessorMixin
+
+try:
+    from torchcodec.decoders import AudioDecoder
+
+    _HAS_TORCHCODEC = True
+except ImportError:
+    AudioDecoder = None
+    _HAS_TORCHCODEC = False
+
+try:
+    import torchaudio
+    from torchaudio.transforms import MelSpectrogram as _MelSpectrogram
+
+    _HAS_TORCHAUDIO = True
+except ImportError:
+    torchaudio = None  # type: ignore[assignment]
+    _MelSpectrogram = None  # type: ignore[assignment,misc]
+    _HAS_TORCHAUDIO = False
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+_PIXEL_MEAN = [123.675, 116.28, 103.53]
+_PIXEL_STD = [58.395, 57.12, 57.375]
+_mean_std_cache: dict[str, tuple[torch.Tensor, torch.Tensor]] = {}
+
+
+# ---------------------------------------------------------------------------
+# Data classes
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class ImageInput:
+    # PIL.Image | str (path/url/base64) | bytes | torch.Tensor (C,H,W)
+    image: Any
+    max_pixels: int | None = None
+    min_pixels: int | None = None
+
+
+@dataclass
+class VideoInput:
+    # tuple[frames_TCHW: torch.Tensor, timestamps_T: torch.Tensor]
+    video: Any
+    min_pixels: int | None = None
+    max_pixels: int | None = None
+    total_max_pixels: int | None = None
+    fps: float | None = None
+    num_frames: int | None = None
+    max_frames: int | None = None
+    min_frames: int | None = None
+    do_include_last_frame: bool | None = False
+    start_time: float | None = None
+    end_time: float | None = None
+    segment_type: Literal["individual", "partial"] = "individual"
+
+
+@dataclass
+class AudioInput:
+    # str (path/url/base64) | bytes | tuple[waveform_1D, sr]
+    # | np.ndarray | torch.Tensor (T,n_vq)
+    audio: Any
+
+
+@dataclass
+class VideoAudioInput:
+    video: Any  # same as VideoInput.video
+    audio: Any  # same as AudioInput.audio
+    min_pixels: int | None = None
+    max_pixels: int | None = None
+    total_max_pixels: int | None = None
+    fps: float | None = None
+    num_frames: int | None = None
+    max_frames: int | None = None
+    min_frames: int | None = None
+    do_include_last_frame: bool | None = False
+    start_time: float | None = None
+    end_time: float | None = None
+    segment_type: Literal["individual", "partial"] = "individual"
+
+
+@dataclass
+class Content:
+    type: Literal["text", "image", "video", "audio", "video_audio"]
+    content: Any
+    is_target: bool | None = None
+
+
+@dataclass
+class MiMoVLInputSample:
+    input_ids: torch.Tensor
+    labels: torch.Tensor | None
+    pixel_values: list[torch.Tensor]
+    pixel_values_videos: list[torch.Tensor]
+    image_thw_grids: list[torch.Tensor]
+    video_thw_grids: list[torch.Tensor]
+    audio_inputs: list[torch.Tensor]
+    second_per_grid_ts: list[float] = field(default_factory=list)
+    video_start_times: list[float] = field(default_factory=list)
+    audio_token_lens: list[int] = field(default_factory=list)
+    va_audio_inputs: list[torch.Tensor] = field(default_factory=list)
+    video_audio_n_segs: list[int] = field(default_factory=list)
+    video_audio_seg_lens: list[int] = field(default_factory=list)
+    position_ids: torch.Tensor | None = None
+    rope_deltas: torch.Tensor | None = None
+    extra: dict = field(default_factory=dict)
+
+
+# ---------------------------------------------------------------------------
+# Vision utilities
+# ---------------------------------------------------------------------------
+
+
+def _format_timestamp(ts: float) -> str:
+    return f"{int(ts // 60):02d}:{int(ts % 60):02d}"
+
+
+def _smart_resize(
+    h: int, w: int, factor: int, min_px: int, max_px: int
+) -> tuple[int, int]:
+    if min(h, w) < factor:
+        if h < w:
+            h, w = factor, int(w * factor / h)
+        else:
+            w, h = factor, int(h * factor / w)
+    elif max(h, w) / min(h, w) > 200:
+        raise ValueError(f"Aspect ratio > 200 not allowed: {h}x{w}")
+    h_bar = round(h / factor) * factor
+    w_bar = round(w / factor) * factor
+    if h_bar * w_bar > max_px:
+        beta = math.sqrt((h * w) / max_px)
+        h_bar = math.floor(h / beta / factor) * factor
+        w_bar = math.floor(w / beta / factor) * factor
+    elif h_bar * w_bar < min_px:
+        beta = math.sqrt(min_px / (h * w))
+        h_bar = math.ceil(h * beta / factor) * factor
+        w_bar = math.ceil(w * beta / factor) * factor
+    return int(h_bar), int(w_bar)
+
+
+def _to_rgb(img: Image.Image) -> Image.Image:
+    if img.mode == "RGBA":
+        bg = Image.new("RGB", img.size, (255, 255, 255))
+        bg.paste(img, mask=img.split()[3])
+        return bg
+    return img.convert("RGB")
+
+
+def _standardize(images: torch.Tensor) -> torch.Tensor:
+    key = str(images.device)
+    if key not in _mean_std_cache:
+        mean = torch.tensor(_PIXEL_MEAN, device=images.device).view(1, -1, 1, 1)
+        std = torch.tensor(_PIXEL_STD, device=images.device).view(1, -1, 1, 1)
+        _mean_std_cache[key] = (mean, std)
+    mean, std = _mean_std_cache[key]
+    return (images - mean) / std
+
+
+def _transform_batch(
+    frames: torch.Tensor,
+    factor: int,
+    min_px: int,
+    max_px: int,
+    device: torch.device | None = None,
+) -> tuple[torch.Tensor, int, int]:
+    if device is not None:
+        frames = frames.to(device)
+    _, _, h, w = frames.shape
+    h_bar, w_bar = _smart_resize(h, w, factor, min_px, max_px)
+    resized = F.interpolate(
+        frames.float(), (h_bar, w_bar), mode="bilinear", align_corners=False
+    )
+    return _standardize(resized), w_bar, h_bar
+
+
+def _transform_single(
+    img: Any,
+    factor: int,
+    min_px: int,
+    max_px: int,
+    device: torch.device | None = None,
+) -> tuple[torch.Tensor, int, int]:
+    if isinstance(img, torch.Tensor):
+        t = img.float()
+        _, h, w = t.shape
+    elif isinstance(img, Image.Image):
+        img = img.convert("RGB")
+        w, h = img.size
+        t = torch.from_numpy(np.array(img)).permute(2, 0, 1).float()
+    else:
+        raise TypeError(f"Expected Tensor or PIL.Image, got {type(img)}")
+    if device is not None:
+        t = t.to(device)
+    h_bar, w_bar = _smart_resize(h, w, factor, min_px, max_px)
+    out = F.interpolate(
+        t.unsqueeze(0), (h_bar, w_bar), mode="bilinear", align_corners=False
+    )
+    return _standardize(out).squeeze(0), w_bar, h_bar
+
+
+def _fetch_image(src: Any) -> Image.Image:
+    if isinstance(src, Image.Image):
+        return _to_rgb(src)
+    if isinstance(src, bytes):
+        return _to_rgb(copy.deepcopy(Image.open(BytesIO(src))))
+    if isinstance(src, str):
+        if src.startswith(("http://", "https://")):
+            r = requests.get(src, timeout=30)
+            r.raise_for_status()
+            return _to_rgb(copy.deepcopy(Image.open(BytesIO(r.content))))
+        if src.startswith("file://"):
+            return _to_rgb(Image.open(src[7:]))
+        if src.startswith("data:image"):
+            import pybase64 as _b64
+
+            _, b64 = src.split("base64,", 1)
+            return _to_rgb(copy.deepcopy(Image.open(BytesIO(_b64.b64decode(b64)))))
+        return _to_rgb(Image.open(src))
+    raise ValueError(f"Unrecognized image source: {type(src)}")
+
+
+# ---------------------------------------------------------------------------
+# Core processor
+# ---------------------------------------------------------------------------
+
+
+class MiMoVLProcessor:
+    """Core MiMo-VL multimodal processor.
+
+    Handles image/video/audio preprocessing and token sequence construction.
+    Ported from SGLang's MiMoVLProcessor.
+    """
+
+    def __init__(
+        self,
+        tokenizer: Any,
+        patch_size: int = 14,
+        merge_size: int = 2,
+        temporal_patch_size: int = 2,
+        temporal_compression_ratio: int = 1,
+        use_video_timestamps: bool = True,
+        video_audio_interleave_length: int = 0,
+        audio_kernel_size: int = 3,
+        audio_stride_size: int = 2,
+        audio_avg_pooler: int = 2,
+        audio_sampling_rate: int = 24000,
+        audio_nfft: int = 960,
+        audio_hop_length: int = 240,
+        audio_window_size: int = 960,
+        audio_fmin: float = 0.0,
+        audio_fmax: float | None = None,
+        audio_n_mels: int = 128,
+        audio_segment_size: int = 6000,
+        audio_channels: int = 8,
+        audio_group_size: int = 4,
+        audio_input_id_per_second: float = 25.0,
+        audio_zeroemb_idx: int = 4096,
+        image_min_pixels: int | None = None,
+        image_max_pixels: int | None = None,
+        video_min_pixels: int | None = None,
+        video_max_pixels: int | None = None,
+        video_total_max_pixels: int | None = None,
+        fps: float | None = None,
+        num_frames: int | None = None,
+        max_frames: int | None = None,
+        min_frames: int | None = None,
+        image_token_id: int | None = None,
+        video_token_id: int | None = None,
+        audio_token_id: int | None = None,
+        vision_start_token_id: int | None = None,
+        vision_end_token_id: int | None = None,
+        audio_start_token_id: int | None = None,
+        audio_end_token_id: int | None = None,
+        video_start_token_id: int | None = None,
+        video_end_token_id: int | None = None,
+        pad_token_id: int | None = None,
+        rope_type: str = "rope",
+        video_process_num_threads: int = 16,
+        device: Any | None = None,
+        **kwargs: Any,
+    ) -> None:
+        self.tokenizer = tokenizer
+        self.video_process_num_threads = video_process_num_threads
+        self.device = torch.device(device) if isinstance(device, str) else device
+
+        self.rope_type = "rope" if rope_type == "1d" else rope_type
+        assert self.rope_type in ("rope", "mrope"), (
+            f"Unknown rope_type: {self.rope_type}"
+        )
+
+        # video timestamps require 1-D rope
+        assert use_video_timestamps, "use_video_timestamps must be True"
+        assert self.rope_type == "rope", (
+            "use_video_timestamps requires rope_type='rope'"
+        )
+        self.use_video_timestamps = use_video_timestamps
+        self.video_audio_interleave_length = video_audio_interleave_length
+
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.audio_token_id = audio_token_id
+        self.vision_start_token_id = vision_start_token_id
+        self.vision_end_token_id = vision_end_token_id
+        self.audio_start_token_id = audio_start_token_id
+        self.audio_end_token_id = audio_end_token_id
+        self.video_start_token_id = video_start_token_id
+        self.video_end_token_id = video_end_token_id
+        self.pad_token_id = pad_token_id
+
+        self.patch_size = patch_size
+        self.merge_size = merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.temporal_compression_ratio = temporal_compression_ratio
+
+        self.audio_sampling_rate = audio_sampling_rate
+        self.audio_nfft = audio_nfft
+        self.audio_hop_length = audio_hop_length
+        self.audio_window_size = audio_window_size
+        self.audio_fmin = audio_fmin
+        self.audio_fmax = audio_fmax
+        self.audio_n_mels = audio_n_mels
+        self.audio_segment_size = audio_segment_size
+        self.audio_kernel_size = audio_kernel_size
+        self.audio_stride_size = audio_stride_size
+        self.audio_avg_pooler = audio_avg_pooler
+        self.audio_channels = audio_channels
+        self.audio_group_size = audio_group_size
+        self.audio_input_id_per_second = audio_input_id_per_second
+
+        self._mel_spec_kwargs = dict(
+            sample_rate=audio_sampling_rate,
+            n_fft=audio_nfft,
+            hop_length=audio_hop_length,
+            win_length=audio_window_size,
+            f_min=audio_fmin,
+            f_max=audio_fmax,
+            n_mels=audio_n_mels,
+            power=1.0,
+            center=True,
+        )
+        self._mel_spectrogram: Any | None = None
+        self._resamplers: OrderedDict = OrderedDict()
+        self._resamplers_max = 16
+
+        if isinstance(audio_zeroemb_idx, int):
+            self.audio_zeroemb_idxs = torch.tensor(
+                [audio_zeroemb_idx] * audio_channels, dtype=torch.int32
+            )
+        else:
+            self.audio_zeroemb_idxs = torch.tensor(audio_zeroemb_idx, dtype=torch.int32)
+
+        assert image_min_pixels is not None, "image_min_pixels must be set"
+        assert image_max_pixels is not None, "image_max_pixels must be set"
+        assert video_min_pixels is not None, "video_min_pixels must be set"
+        assert video_max_pixels is not None, "video_max_pixels must be set"
+        assert video_total_max_pixels is not None, "video_total_max_pixels must be set"
+        assert fps is not None or num_frames is not None, (
+            "fps or num_frames must be set"
+        )
+
+        self._img_kw = {"min_pixels": image_min_pixels, "max_pixels": image_max_pixels}
+        self._vid_kw = {
+            "min_pixels": video_min_pixels,
+            "max_pixels": video_max_pixels,
+            "total_max_pixels": video_total_max_pixels,
+            "fps": fps,
+            "num_frames": num_frames,
+            "max_frames": max_frames,
+            "min_frames": min_frames,
+        }
+
+    @property
+    def mel_spectrogram(self) -> Any:
+        if self._mel_spectrogram is None:
+            if _MelSpectrogram is None:
+                raise RuntimeError(
+                    "torchaudio is required for audio. "
+                    "Install with: pip install torchaudio"
+                )
+            self._mel_spectrogram = _MelSpectrogram(**self._mel_spec_kwargs)
+        return self._mel_spectrogram
+
+    def _resolve_img_kw(self, img: ImageInput) -> dict:
+        return {
+            "min_px": (
+                img.min_pixels
+                if img.min_pixels is not None
+                else self._img_kw["min_pixels"]
+            ),
+            "max_px": (
+                img.max_pixels
+                if img.max_pixels is not None
+                else self._img_kw["max_pixels"]
+            ),
+        }
+
+    def _resolve_vid_kw(self, vid: VideoInput) -> dict:
+        kw: dict = {}
+        for k in ("min_pixels", "max_pixels", "total_max_pixels"):
+            kw[k] = getattr(vid, k) or self._vid_kw[k]
+        if vid.num_frames is not None:
+            kw["num_frames"] = vid.num_frames
+        elif vid.fps is not None:
+            kw["fps"] = vid.fps
+            if vid.max_frames is not None:
+                kw["max_frames"] = vid.max_frames
+            if vid.min_frames is not None:
+                kw["min_frames"] = vid.min_frames
+        elif self._vid_kw["num_frames"] is not None:
+            kw["num_frames"] = self._vid_kw["num_frames"]
+        elif self._vid_kw["fps"] is not None:
+            kw["fps"] = self._vid_kw["fps"]
+            if self._vid_kw["max_frames"] is not None:
+                kw["max_frames"] = self._vid_kw["max_frames"]
+            if self._vid_kw["min_frames"] is not None:
+                kw["min_frames"] = self._vid_kw["min_frames"]
+        else:
+            raise ValueError(
+                "No video sampling strategy specified (fps or num_frames)."
+            )
+        return kw
+
+    def preprocess_audio(self, audio: Any) -> tuple[torch.Tensor, int]:
+        """Decode audio bytes/path/tuple → (mel_spec (T, n_mels), token_len)."""
+        if isinstance(audio, tuple):
+            waveform, original_sr = audio
+        else:
+            if AudioDecoder is None:
+                raise RuntimeError(
+                    "torchcodec is required for audio. "
+                    "Install with: pip install torchcodec"
+                )
+            if isinstance(audio, bytes):
+                file_obj: Any = io.BytesIO(audio)
+            elif isinstance(audio, str):
+                if audio.startswith("data:"):
+                    import pybase64 as _b64
+
+                    file_obj = io.BytesIO(_b64.b64decode(audio.split(",")[1]))
+                elif audio.startswith(("http://", "https://")):
+                    r = requests.get(audio, timeout=30)
+                    r.raise_for_status()
+                    file_obj = io.BytesIO(r.content)
+                else:
+                    file_obj = audio
+            else:
+                raise ValueError(f"Unsupported audio source type: {type(audio)}")
+            samples = AudioDecoder(file_obj).get_all_samples()
+            waveform = samples.data
+            original_sr = samples.sample_rate
+
+        if original_sr != self.audio_sampling_rate:
+            if original_sr not in self._resamplers:
+                if len(self._resamplers) >= self._resamplers_max:
+                    self._resamplers.popitem(last=False)
+                self._resamplers[original_sr] = torchaudio.transforms.Resample(
+                    orig_freq=original_sr, new_freq=self.audio_sampling_rate
+                )
+            self._resamplers.move_to_end(original_sr)
+            waveform = self._resamplers[original_sr](waveform)
+
+        if waveform.ndim == 2:
+            waveform = waveform.mean(dim=0)
+        spec = self.mel_spectrogram(waveform[None, :])
+        spec = torch.log(torch.clip(spec, min=1e-7)).squeeze().transpose(0, 1)
+
+        n = spec.shape[0]
+        n = n + 3 - self.audio_kernel_size
+        n = (n + 2 - self.audio_kernel_size) // self.audio_stride_size + 1
+        n = n // self.audio_avg_pooler + int(n % self.audio_avg_pooler != 0)
+        token_len = math.ceil(n / self.audio_group_size)
+        return spec, token_len
+
+    def process_image(self, image: ImageInput) -> torch.Tensor:
+        kw = self._resolve_img_kw(image)
+        src = image.image
+        if isinstance(src, (str, bytes)):
+            src = _fetch_image(src)
+        tensor, _, _ = _transform_single(
+            src,
+            factor=self.patch_size * self.merge_size,
+            device=self.device,
+            **kw,
+        )
+        return tensor
+
+    def process_video(
+        self, video_input: VideoInput
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, dict]:
+        kw = self._resolve_vid_kw(video_input)
+        video = video_input.video
+        if not isinstance(video, tuple):
+            raise ValueError(
+                f"video must be a (frames_TCHW, timestamps_T) tuple, "
+                f"got {type(video)}. "
+                "Decode the video before calling the processor."
+            )
+        frames, timestamps = video
+
+        fps = (
+            1.0
+            if len(timestamps) < 2
+            else float(1.0 / (float(timestamps[1]) - float(timestamps[0])))
+        )
+        start = (
+            video_input.start_time
+            if video_input.start_time is not None
+            else float(timestamps[0])
+        )
+        end = (
+            video_input.end_time
+            if video_input.end_time is not None
+            else float(timestamps[-1]) + 1.0 / fps
+        )
+
+        if video_input.segment_type != "individual":
+            mask = (timestamps >= start) & (timestamps < end)
+            idxs = torch.where(mask)[0]
+            if len(idxs) == 0:
+                idxs = torch.where(timestamps <= start)[0][-1:]
+            frames, timestamps = frames[idxs], timestamps[idxs]
+
+        tp = self.temporal_patch_size * self.temporal_compression_ratio
+        n = frames.shape[0]
+        total_px = kw["total_max_pixels"]
+        max_px = max(
+            kw["min_pixels"], min(total_px * tp // max(n, 1), kw["max_pixels"])
+        )
+
+        if n % tp != 0:
+            pad = tp - n % tp
+            frames = torch.cat(
+                [frames, frames[-1:].repeat(pad, *([1] * (frames.ndim - 1)))],
+                dim=0,
+            )
+            timestamps = torch.cat([timestamps, timestamps[-1:].repeat(pad)], dim=0)
+
+        transformed, _, _ = _transform_batch(
+            frames,
+            factor=self.patch_size * self.merge_size,
+            min_px=kw["min_pixels"],
+            max_px=max_px,
+            device=self.device,
+        )
+        patches, thw = self._flatten_visual(transformed, "video")
+        meta = {
+            "fps_sampled": fps,
+            "segment_start_time": start,
+            "segment_end_time": end,
+        }
+        return patches, thw, timestamps, meta
+
+    def process_audio(self, audio: AudioInput) -> Any:
+        src = audio.audio
+        if isinstance(src, np.ndarray):
+            src = (torch.from_numpy(src).float(), self.audio_sampling_rate)
+        if isinstance(src, (str, bytes, tuple)):
+            return self.preprocess_audio(src)
+        # Pre-tokenized tensor (T, n_vq)
+        assert isinstance(src, torch.Tensor) and src.ndim == 2
+        T = src.shape[0]
+        src = src[:, : self.audio_channels].to(torch.long)
+        pad_T = (
+            (T + self.audio_group_size - 1)
+            // self.audio_group_size
+            * self.audio_group_size
+        )
+        padding = (
+            torch.zeros(pad_T - T, self.audio_channels, dtype=torch.long) + src[-1]
+        )
+        src = torch.cat([src, padding], dim=0)
+        return src.reshape(
+            pad_T // self.audio_group_size, self.audio_group_size, self.audio_channels
+        )
+
+    def _flatten_visual(
+        self, visual: torch.Tensor, kind: str
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if kind == "image":
+            h, w = visual.shape[-2:]
+            patches = visual.unsqueeze(0).repeat(self.temporal_patch_size, 1, 1, 1)
+        else:  # video / video_audio
+            temporal_stride = self.temporal_compression_ratio * self.temporal_patch_size
+            assert visual.shape[0] % temporal_stride == 0
+            patches = visual
+            h, w = patches.shape[-2:]
+
+        C = patches.shape[1]
+        grid_t = patches.shape[0] // self.temporal_patch_size
+        grid_h, grid_w = h // self.patch_size, w // self.patch_size
+
+        patches = (
+            patches.contiguous()
+            .view(
+                grid_t,
+                self.temporal_patch_size,
+                C,
+                grid_h // self.merge_size,
+                self.merge_size,
+                self.patch_size,
+                grid_w // self.merge_size,
+                self.merge_size,
+                self.patch_size,
+            )
+            .permute(0, 3, 6, 4, 7, 2, 1, 5, 8)
+            .contiguous()
+            .view(
+                grid_t * grid_h * grid_w,
+                C * self.temporal_patch_size * self.patch_size * self.patch_size,
+            )
+        )
+        thw = torch.tensor([grid_t, grid_h, grid_w], dtype=torch.int32)
+        return patches, thw
+
+    def process(
+        self, contents: list[Content], verbose: bool = False
+    ) -> MiMoVLInputSample:
+        input_ids: list[int] = []
+        labels: list[int] = []
+        img_pv: list[torch.Tensor] = []
+        img_grids: list[torch.Tensor] = []
+        vid_pv: list[torch.Tensor] = []
+        vid_grids: list[torch.Tensor] = []
+        audio_inputs: list[torch.Tensor] = []
+        is_audio_tokenized: list[bool] = []
+        audio_token_lens: list[int] = []
+        second_per_grid_ts: list[float] = []
+        video_start_times: list[float] = []
+        va_audio_inputs: list[torch.Tensor] = []
+        video_audio_n_segs: list[int] = []
+        video_audio_seg_lens: list[int] = []
+
+        # Pre-decode videos in parallel
+        vid_info = [
+            (i, c.content, c.type == "video_audio")
+            for i, c in enumerate(contents)
+            if c.type in ("video", "video_audio")
+        ]
+        vid_results: dict[int, tuple] = {}
+        if vid_info:
+            n_t = min(self.video_process_num_threads, len(vid_info))
+            if n_t > 1 and len(vid_info) > 1:
+                with ThreadPoolExecutor(max_workers=n_t) as ex:
+                    fut_map = {
+                        ex.submit(self.process_video, vi): idx
+                        for idx, vi, _ in vid_info
+                    }
+                    for fut in as_completed(fut_map):
+                        vid_results[fut_map[fut]] = fut.result()
+            else:
+                for idx, vi, _ in vid_info:
+                    vid_results[idx] = self.process_video(vi)
+
+        for ci, content in enumerate(contents):
+            _ids: list[int] = []
+            _lbls: list[int] | None = None
+
+            if content.type == "text":
+                _ids = (
+                    self.tokenizer.encode(content.content)
+                    if isinstance(content.content, str)
+                    else list(content.content)
+                )
+                if content.is_target:
+                    _lbls = _ids
+
+            elif content.type == "image":
+                tensor = self.process_image(content.content)
+                patches, thw = self._flatten_visual(tensor, "image")
+                t, h, w = thw.tolist()
+                n_tok = (t * h * w) // (self.merge_size**2)
+                img_pv.append(patches)
+                img_grids.append(thw)
+                _ids = (
+                    [self.vision_start_token_id]
+                    + [self.image_token_id] * n_tok
+                    + [self.vision_end_token_id]
+                )
+
+            elif content.type == "video":
+                patches, thw, ts, meta = vid_results[ci]
+                t, h, w = thw.tolist()
+                n_per_grid = h * w // (self.merge_size**2)
+                vid_pv.append(patches)
+                vid_grids.append(thw)
+                second_per_grid_ts.append(
+                    self.temporal_patch_size / meta["fps_sampled"]
+                )
+                video_start_times.append(float(ts[0]))
+                video_audio_n_segs.append(0)
+
+                stride = self.temporal_patch_size * self.temporal_compression_ratio
+                ts_texts = [_format_timestamp(float(x)) for x in ts[::stride]]
+                ts_ids_list = [self.tokenizer.encode(s) for s in ts_texts]
+
+                _ids = [self.video_start_token_id]
+                for ts_ids in ts_ids_list:
+                    _ids += (
+                        ts_ids
+                        + [self.vision_start_token_id]
+                        + [self.video_token_id] * n_per_grid
+                        + [self.vision_end_token_id]
+                    )
+                _ids += [self.video_end_token_id]
+
+            elif content.type == "audio":
+                processed = self.process_audio(content.content)
+                if isinstance(processed, tuple):
+                    is_audio_tokenized.append(False)
+                    spec, tok_len = processed
+                    audio_inputs.append(spec)
+                else:
+                    is_audio_tokenized.append(True)
+                    tok_len = processed.shape[0]
+                    audio_inputs.append(processed)
+                audio_token_lens.append(tok_len)
+                _ids = (
+                    [self.audio_start_token_id]
+                    + [self.audio_token_id] * tok_len
+                    + [self.audio_end_token_id]
+                )
+
+            elif content.type == "video_audio":
+                patches, thw, ts, meta = vid_results[ci]
+                second_per_grid_ts.append(
+                    self.temporal_patch_size / meta["fps_sampled"]
+                )
+                video_start_times.append(float(ts[0]))
+                processed_audio = self.process_audio(content.content)
+                tok_per_sec = self.audio_input_id_per_second / self.audio_group_size
+
+                t, h, w = thw.tolist()
+                vid_pv.append(patches)
+                vid_grids.append(thw)
+
+                if isinstance(processed_audio, tuple):
+                    # Mel spec (not pre-tokenized): store in va_audio_inputs separately
+                    spec, total_atok = processed_audio
+                    va_audio_inputs.append(spec)
+                    _va_is_tokenized = False
+                else:
+                    # Pre-tokenized: not expected in vLLM, but handle defensively
+                    total_atok = processed_audio.shape[0]
+                    _va_is_tokenized = True
+
+                n_per_grid = h * w // (self.merge_size**2)
+                stride = self.temporal_patch_size * self.temporal_compression_ratio
+                grid_ts = ts[::stride]
+                ts_texts = [_format_timestamp(float(x)) for x in grid_ts]
+                ts_ids_list = [self.tokenizer.encode(s) for s in ts_texts]
+
+                units: list[tuple] = []
+                for i in range(len(grid_ts)):
+                    a_start = int(float(grid_ts[i]) * tok_per_sec)
+                    a_end = (
+                        int(float(grid_ts[i + 1]) * tok_per_sec)
+                        if i < len(grid_ts) - 1
+                        else int(meta["segment_end_time"] * tok_per_sec)
+                    )
+                    seg_len = min(a_end, total_atok) - a_start
+                    assert seg_len > 0, f"Zero-length audio segment at grid index {i}"
+                    seg = (
+                        processed_audio[a_start : a_start + seg_len]
+                        if _va_is_tokenized
+                        else None
+                    )
+                    units.append(
+                        (
+                            float(grid_ts[i]),
+                            ts_texts[i],
+                            ts_ids_list[i],
+                            n_per_grid,
+                            seg_len,
+                            seg,
+                        )
+                    )
+
+                il = self.video_audio_interleave_length
+                if il == -1:
+                    groups: list[list] = [list(enumerate(units))]
+                elif il == 0:
+                    groups = [[(i, u)] for i, u in enumerate(units)]
+                else:
+                    groups, cur, t_ptr = [], [], 0.0
+                    for i, u in enumerate(units):
+                        while u[0] >= t_ptr + il:
+                            if cur:
+                                groups.append(cur)
+                                cur = []
+                            t_ptr += il
+                        cur.append((i, u))
+                    if cur:
+                        groups.append(cur)
+
+                # Track n_segs (= num groups) and per-group audio token counts
+                video_audio_n_segs.append(len(groups))
+                for group in groups:
+                    group_seg_len = sum(u[4] for _, u in group)
+                    video_audio_seg_lens.append(group_seg_len)
+
+                _ids = [self.video_start_token_id]
+                for group in groups:
+                    _ids += group[0][1][2]  # first-unit timestamp token ids
+                    _vid_tok: list[int] = []
+                    _aud_tok: list[int] = []
+                    for _, u in group:
+                        _, _, _, vid_n, seg_n, seg_audio = u
+                        _vid_tok += (
+                            [self.vision_start_token_id]
+                            + [self.video_token_id] * vid_n
+                            + [self.vision_end_token_id]
+                        )
+                        _aud_tok += [self.audio_token_id] * seg_n
+                        if seg_audio is not None:
+                            # Pre-tokenized per-frame segments (rare in vLLM)
+                            audio_inputs.append(seg_audio)
+                    _ids += (
+                        _vid_tok
+                        + [self.audio_start_token_id]
+                        + _aud_tok
+                        + [self.audio_end_token_id]
+                    )
+                _ids += [self.video_end_token_id]
+
+            input_ids.extend(_ids)
+            labels.extend(
+                _lbls if _lbls is not None else [self.pad_token_id] * len(_ids)
+            )
+
+        ids_t = torch.tensor(input_ids)
+        lbl_arr = np.roll(labels, shift=-1)
+        lbl_arr[-1] = self.pad_token_id
+        lbl_t = torch.tensor(lbl_arr)
+
+        extra: dict = {}
+        if is_audio_tokenized:
+            assert all(is_audio_tokenized) or not any(is_audio_tokenized)
+            extra["is_audio_tokenized"] = is_audio_tokenized[0]
+
+        position_ids = torch.arange(ids_t.shape[0]).expand(3, -1)
+        rope_deltas = torch.zeros((1, 1), dtype=torch.int32)
+
+        return MiMoVLInputSample(
+            input_ids=ids_t,
+            labels=lbl_t,
+            pixel_values=img_pv,
+            pixel_values_videos=vid_pv,
+            image_thw_grids=img_grids,
+            video_thw_grids=vid_grids,
+            audio_inputs=audio_inputs,
+            second_per_grid_ts=second_per_grid_ts,
+            video_start_times=video_start_times,
+            audio_token_lens=audio_token_lens,
+            va_audio_inputs=va_audio_inputs,
+            video_audio_n_segs=video_audio_n_segs,
+            video_audio_seg_lens=video_audio_seg_lens,
+            position_ids=position_ids,
+            rope_deltas=rope_deltas,
+            extra=extra,
+        )
+
+
+# ---------------------------------------------------------------------------
+# vLLM ProcessorMixin wrapper
+# ---------------------------------------------------------------------------
+
+
+class MiMoOmniProcessor(ProcessorMixin):
+    """HuggingFace-compatible ProcessorMixin wrapper for MiMo-Omni.
+
+    Accepts PIL images, pre-decoded video tuples (frames_TCHW, timestamps_T),
+    and audio (file path / bytes / (waveform, sr) tuple / numpy array).
+    """
+
+    attributes = ["tokenizer"]
+    tokenizer_class = "AutoTokenizer"
+
+    # Single or multi-pad placeholders produced by the chat template / prior expansion
+    _IMG_RE = re.compile(r"<\|vision_start\|>(?:<\|image_pad\|>)+<\|vision_end\|>")
+    _VID_RE = re.compile(r"<\|vision_start\|>(?:<\|video_pad\|>)+<\|vision_end\|>")
+    _AUD_RE = re.compile(
+        r"<\|mimo_audio_start\|>(?:<\|audio_pad\|>)+<\|mimo_audio_end\|>"
+    )
+
+    _MM_RE = re.compile(
+        r"(<\|vision_start\|>(?:<\|image_pad\|>)+<\|vision_end\|>"
+        r"|<\|vision_start\|>(?:<\|video_pad\|>)+<\|vision_end\|>"
+        r"|<\|mimo_audio_start\|>(?:<\|audio_pad\|>)+<\|mimo_audio_end\|>)"
+    )
+
+    def __init__(
+        self,
+        tokenizer: Any,
+        *,
+        patch_size: int = 14,
+        merge_size: int = 2,
+        temporal_patch_size: int = 2,
+        temporal_compression_ratio: int = 1,
+        image_min_pixels: int | None = None,
+        image_max_pixels: int | None = None,
+        video_min_pixels: int | None = None,
+        video_max_pixels: int | None = None,
+        video_total_max_pixels: int | None = None,
+        fps: float = 2.0,
+        num_frames: int | None = None,
+        max_frames: int = 256,
+        min_frames: int = 8,
+        video_audio_interleave_length: int = 0,
+        audio_sampling_rate: int = 24000,
+        audio_nfft: int = 960,
+        audio_hop_length: int = 240,
+        audio_window_size: int = 960,
+        audio_fmin: float = 0.0,
+        audio_fmax: float | None = None,
+        audio_n_mels: int = 128,
+        audio_segment_size: int = 6000,
+        audio_kernel_size: int = 3,
+        audio_stride_size: int = 2,
+        audio_avg_pooler: int = 2,
+        audio_channels: int = 8,
+        audio_group_size: int = 4,
+        audio_input_id_per_second: float = 25.0,
+        audio_zeroemb_idx: int = 4096,
+        image_token_id: int | None = None,
+        video_token_id: int | None = None,
+        audio_token_id: int | None = None,
+        vision_start_token_id: int | None = None,
+        vision_end_token_id: int | None = None,
+        audio_start_token_id: int | None = None,
+        audio_end_token_id: int | None = None,
+        video_start_token_id: int | None = None,
+        video_end_token_id: int | None = None,
+        rope_type: str = "rope",
+    ) -> None:
+        self.tokenizer = tokenizer
+
+        unit = patch_size * merge_size
+        self.mimo_processor = MiMoVLProcessor(
+            tokenizer=tokenizer,
+            patch_size=patch_size,
+            merge_size=merge_size,
+            temporal_patch_size=temporal_patch_size,
+            temporal_compression_ratio=temporal_compression_ratio,
+            use_video_timestamps=True,
+            video_audio_interleave_length=video_audio_interleave_length,
+            audio_sampling_rate=audio_sampling_rate,
+            audio_nfft=audio_nfft,
+            audio_hop_length=audio_hop_length,
+            audio_window_size=audio_window_size,
+            audio_fmin=audio_fmin,
+            audio_fmax=audio_fmax,
+            audio_n_mels=audio_n_mels,
+            audio_segment_size=audio_segment_size,
+            audio_kernel_size=audio_kernel_size,
+            audio_stride_size=audio_stride_size,
+            audio_avg_pooler=audio_avg_pooler,
+            audio_channels=audio_channels,
+            audio_group_size=audio_group_size,
+            audio_input_id_per_second=audio_input_id_per_second,
+            audio_zeroemb_idx=audio_zeroemb_idx,
+            image_min_pixels=image_min_pixels or (4 * unit * unit),
+            image_max_pixels=image_max_pixels or (4096 * unit * unit),
+            video_min_pixels=video_min_pixels or (4 * unit * unit),
+            video_max_pixels=video_max_pixels or (4096 * unit * unit),
+            video_total_max_pixels=video_total_max_pixels or (16384 * unit * unit),
+            fps=fps,
+            num_frames=num_frames,
+            max_frames=max_frames,
+            min_frames=min_frames,
+            image_token_id=image_token_id,
+            video_token_id=video_token_id,
+            audio_token_id=audio_token_id,
+            vision_start_token_id=vision_start_token_id,
+            vision_end_token_id=vision_end_token_id,
+            audio_start_token_id=audio_start_token_id,
+            audio_end_token_id=audio_end_token_id,
+            video_start_token_id=video_start_token_id,
+            video_end_token_id=video_end_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+            rope_type=rope_type,
+        )
+
+    @classmethod
+    def from_hf_config(cls, tokenizer: Any, hf_config: Any) -> "MiMoOmniProcessor":
+        """Convenience factory: instantiate directly from an HF model config object."""
+        vc = hf_config.vision_config
+        if isinstance(vc, dict):
+            patch_size = vc.get("patch_size", 14)
+            merge_size = vc.get("spatial_merge_size", 2)
+            temporal_patch_size = vc.get("temporal_patch_size", 2)
+        else:
+            patch_size = getattr(vc, "patch_size", 14)
+            merge_size = getattr(vc, "spatial_merge_size", 2)
+            temporal_patch_size = getattr(vc, "temporal_patch_size", 2)
+
+        pc: dict = getattr(hf_config, "processor_config", {}) or {}
+        ac = getattr(hf_config, "audio_config", None)
+        audio_sr: int | None = pc.get("audio_sampling_rate")
+        if audio_sr is None and ac is not None:
+            if isinstance(ac, dict):
+                audio_sr = ac.get("sampling_rate") or ac.get("sample_rate")
+            else:
+                audio_sr = getattr(ac, "sampling_rate", None) or getattr(
+                    ac, "sample_rate", None
+                )
+
+        rope_type = "rope"
+        rs = getattr(hf_config, "rope_scaling", None)
+        if rs and rs.get("type") == "default" and rs.get("mrope_section") is not None:
+            rope_type = "mrope"
+
+        unit = patch_size * merge_size
+        return cls(
+            tokenizer,
+            patch_size=patch_size,
+            merge_size=merge_size,
+            temporal_patch_size=temporal_patch_size,
+            image_min_pixels=pc.get("image_min_pixels") or (4 * unit * unit),
+            image_max_pixels=pc.get("image_max_pixels") or (4096 * unit * unit),
+            video_min_pixels=pc.get("video_min_pixels") or (4 * unit * unit),
+            video_max_pixels=pc.get("video_max_pixels") or (4096 * unit * unit),
+            video_total_max_pixels=(
+                pc.get("video_total_max_pixels") or (16384 * unit * unit)
+            ),
+            fps=pc.get("fps") or 2.0,
+            num_frames=pc.get("num_frames"),
+            max_frames=pc.get("max_frames") or 256,
+            min_frames=pc.get("min_frames") or 8,
+            video_audio_interleave_length=pc.get("video_audio_interleave_length", 0),
+            audio_sampling_rate=audio_sr or 24000,
+            image_token_id=pc.get("image_token_id"),
+            video_token_id=pc.get("video_token_id"),
+            audio_token_id=pc.get("audio_token_id"),
+            vision_start_token_id=pc.get("vision_start_token_id"),
+            vision_end_token_id=pc.get("vision_end_token_id"),
+            audio_start_token_id=pc.get("audio_start_token_id"),
+            audio_end_token_id=pc.get("audio_end_token_id"),
+            video_start_token_id=pc.get("video_start_token_id"),
+            video_end_token_id=pc.get("video_end_token_id"),
+            rope_type=rope_type,
+        )
+
+    @property
+    def image_token(self) -> str:
+        """Token string used as image placeholder (for vLLM integration)."""
+        return "<|image_pad|>"
+
+    @property
+    def video_token(self) -> str:
+        """Token string used as video placeholder (for vLLM integration)."""
+        return "<|video_pad|>"
+
+    @property
+    def image_processor(self) -> Any:
+        """Minimal image-processor-like object for vLLM processing-info compat."""
+        p = self.mimo_processor
+
+        class _ImageProcessor:
+            merge_size = p.merge_size
+            size = {
+                "shortest_edge": p._img_kw["min_pixels"],
+                "longest_edge": p._img_kw["max_pixels"],
+            }
+
+        return _ImageProcessor()
+
+    def _modality(self, token: str) -> str:
+        if self._IMG_RE.fullmatch(token):
+            return "image"
+        if self._VID_RE.fullmatch(token):
+            return "video"
+        if self._AUD_RE.fullmatch(token):
+            return "audio"
+        return "unknown"
+
+    def __call__(
+        self,
+        text: str | list[str] | None = None,
+        images: Any = None,
+        videos: Any = None,
+        audio: Any = None,
+        video_audio: Any = None,
+        return_tensors: str | TensorType | None = None,
+        **kwargs: Any,
+    ) -> BatchFeature:
+        """Process multimodal inputs into model-ready tensors.
+
+        Args:
+            text: Prompt string(s) containing multimodal placeholders
+                  ``<|vision_start|><|image_pad|><|vision_end|>``,
+                  ``<|vision_start|><|video_pad|><|vision_end|>``, or
+                  ``<|mimo_audio_start|><|audio_pad|><|mimo_audio_end|>``.
+            images: PIL.Image or list[PIL.Image].
+            videos: list of ``(frames_TCHW: torch.Tensor, timestamps_T: torch.Tensor)``
+                    tuples (pre-decoded).
+            audio: list of ``str`` (path/url/base64), ``bytes``,
+                   ``(waveform_1D, sample_rate)`` tuples, or ``np.ndarray``.
+            return_tensors: Passed to :class:`BatchFeature`.
+
+        Returns:
+            :class:`BatchFeature` with keys:
+            - ``input_ids``
+            - ``pixel_values`` + ``image_grid_thw``
+            - ``pixel_values_videos`` + ``video_grid_thw`` + ``second_per_grid_ts``
+            - ``audio_features``
+        """
+        if isinstance(text, list):
+            text = text[0] if len(text) == 1 else "\n".join(text)
+
+        imgs: list = (
+            ([images] if isinstance(images, Image.Image) else list(images))
+            if images is not None
+            else []
+        )
+        vids: list = list(videos) if videos is not None else []
+        auds: list = list(audio) if audio is not None else []
+        va_items: list = list(video_audio) if video_audio is not None else []
+
+        # If audio exists but text has no audio placeholder, prepend one
+        _aud_placeholder = "<|mimo_audio_start|><|audio_pad|><|mimo_audio_end|>"
+        if auds and text is not None and not self._AUD_RE.search(text):
+            text = _aud_placeholder + text
+
+        # Build Content list
+        contents: list[Content] = []
+
+        if text and (imgs or vids or auds or va_items):
+            parts = self._MM_RE.split(text)
+            img_it = iter(imgs)
+            vid_it = iter(vids)
+            aud_it = iter(auds)
+            va_it = iter(va_items)
+            for part in parts:
+                if self._MM_RE.fullmatch(part):
+                    mod = self._modality(part)
+                    if mod == "image":
+                        with contextlib.suppress(StopIteration):
+                            contents.append(
+                                Content(
+                                    type="image",
+                                    content=ImageInput(image=next(img_it)),
+                                )
+                            )
+                    elif mod == "video":
+                        # Try regular video first, fall back to video_audio
+                        vid_item = None
+                        vid_type = "video"
+                        with contextlib.suppress(StopIteration):
+                            vid_item = next(vid_it)
+                        if vid_item is None:
+                            with contextlib.suppress(StopIteration):
+                                vid_item = next(va_it)
+                                vid_type = "video_audio"
+                        if vid_item is not None:
+                            if vid_type == "video":
+                                contents.append(
+                                    Content(
+                                        type="video",
+                                        content=VideoInput(video=vid_item),
+                                    )
+                                )
+                            else:
+                                contents.append(
+                                    Content(
+                                        type="video_audio",
+                                        content=vid_item,
+                                    )
+                                )
+                    elif mod == "audio":
+                        with contextlib.suppress(StopIteration):
+                            contents.append(
+                                Content(
+                                    type="audio",
+                                    content=AudioInput(audio=next(aud_it)),
+                                )
+                            )
+                elif part:
+                    contents.append(Content(type="text", content=part))
+        elif text:
+            contents.append(Content(type="text", content=text))
+        else:
+            for img in imgs:
+                contents.append(Content(type="image", content=ImageInput(image=img)))
+            for vid in vids:
+                contents.append(Content(type="video", content=VideoInput(video=vid)))
+            for aud in auds:
+                contents.append(Content(type="audio", content=AudioInput(audio=aud)))
+            for va_item in va_items:
+                contents.append(Content(type="video_audio", content=va_item))
+
+        if not contents:
+            ids = self.tokenizer(text or "", return_tensors=return_tensors)["input_ids"]
+            return BatchFeature(data={"input_ids": ids}, tensor_type=return_tensors)
+
+        sample = self.mimo_processor.process(contents, verbose=False)
+
+        # vLLM expects input_ids to have a batch dimension [1, seq_len].
+        data: dict = {"input_ids": sample.input_ids.unsqueeze(0)}
+
+        if sample.pixel_values:
+            data["pixel_values"] = torch.cat(sample.pixel_values, dim=0)
+            data["image_grid_thw"] = torch.stack(sample.image_thw_grids)
+
+        if sample.pixel_values_videos:
+            data["pixel_values_videos"] = torch.cat(sample.pixel_values_videos, dim=0)
+            data["video_grid_thw"] = torch.stack(sample.video_thw_grids)
+            if sample.second_per_grid_ts:
+                data["second_per_grid_ts"] = torch.tensor(
+                    sample.second_per_grid_ts, dtype=torch.float32
+                )
+            if sample.video_start_times:
+                data["video_start_times"] = torch.tensor(
+                    sample.video_start_times, dtype=torch.float32
+                )
+            if sample.video_audio_n_segs:
+                data["video_audio_n_segs"] = torch.tensor(
+                    sample.video_audio_n_segs, dtype=torch.long
+                )
+            # video_audio_seg_lens: 2D padded tensor (num_videos, max_T).
+            # Row i has the per-group audio token lengths for video i
+            # (zeros for regular videos; valid values for video_audio videos).
+            n_segs_list = sample.video_audio_n_segs
+            max_segs = max(n_segs_list) if n_segs_list else 0
+            if max_segs > 0:
+                seg_lens_2d = torch.zeros(len(n_segs_list), max_segs, dtype=torch.long)
+                flat_cursor = 0
+                for vi, n in enumerate(n_segs_list):
+                    if n > 0:
+                        seg_lens_2d[vi, :n] = torch.tensor(
+                            sample.video_audio_seg_lens[flat_cursor : flat_cursor + n],
+                            dtype=torch.long,
+                        )
+                        flat_cursor += n
+                data["video_audio_seg_lens"] = seg_lens_2d
+
+        # audio_features is a list of variable-length mel-spec tensors; pop it
+        # before BatchFeature conversion to avoid "batched tensors of the same
+        # length" errors, then re-attach it after.
+        audio_features = None
+        if sample.audio_inputs:
+            audio_features = sample.audio_inputs
+            if "is_audio_tokenized" in sample.extra:
+                data["is_audio_tokenized"] = sample.extra["is_audio_tokenized"]
+            if sample.audio_token_lens:
+                data["audio_token_lens"] = torch.tensor(
+                    sample.audio_token_lens, dtype=torch.long
+                )
+
+        bf = BatchFeature(data=data, tensor_type=return_tensors)
+        if audio_features is not None:
+            bf["audio_features"] = audio_features
+        # va_audio_features: list of mel-spec tensors (one per video_audio item)
+        if sample.va_audio_inputs:
+            bf["va_audio_features"] = sample.va_audio_inputs
+        return bf
diff --git a/vllm/transformers_utils/processors/moondream3.py b/vllm/transformers_utils/processors/moondream3.py
new file mode 100644
index 000000000000..289c40dd175e
--- /dev/null
+++ b/vllm/transformers_utils/processors/moondream3.py
@@ -0,0 +1,541 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Custom processor for Moondream3 model."""
+
+import math
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers import AutoProcessor, BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from transformers.tokenization_utils_base import (
+    PreTokenizedInput,
+    PreTrainedTokenizerBase,
+    TextInput,
+)
+
+from vllm.multimodal.image import convert_image_mode
+
+__all__ = ["Moondream3Processor"]
+
+
+class Moondream3ProcessorKwargs(ProcessingKwargs, total=False):  # type: ignore[call-arg]
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "images_kwargs": {
+            "max_crops": 12,
+            "overlap_margin": 4,
+            "crop_size": 378,
+            "patch_size": 14,
+            "convert_to_rgb": True,
+            "return_tensors": "pt",
+        },
+    }
+
+
+def select_tiling(
+    height: int, width: int, crop_size: int, max_crops: int
+) -> tuple[int, int]:
+    """Determine the optimal number of tiles to cover an image."""
+    if height <= crop_size or width <= crop_size:
+        return (1, 1)
+
+    min_h = math.ceil(height / crop_size)
+    min_w = math.ceil(width / crop_size)
+
+    if min_h * min_w > max_crops:
+        ratio = math.sqrt(max_crops / (min_h * min_w))
+        return (max(1, math.floor(min_h * ratio)), max(1, math.floor(min_w * ratio)))
+
+    h_tiles = math.floor(math.sqrt(max_crops * height / width))
+    w_tiles = math.floor(math.sqrt(max_crops * width / height))
+
+    h_tiles = max(h_tiles, min_h)
+    w_tiles = max(w_tiles, min_w)
+
+    if h_tiles * w_tiles > max_crops:
+        if w_tiles > h_tiles:
+            w_tiles = math.floor(max_crops / h_tiles)
+        else:
+            h_tiles = math.floor(max_crops / w_tiles)
+
+    return (max(1, h_tiles), max(1, w_tiles))
+
+
+class Moondream3Processor(ProcessorMixin):
+    """
+    Constructs a Moondream3 processor which handles image preprocessing
+    and tokenization for the Moondream3 multimodal model.
+
+    Args:
+        tokenizer: The tokenizer to use for text processing.
+        chat_template: Optional chat template string.
+        crop_size: Size of each image crop.
+        max_crops: Maximum number of crops per image.
+        overlap_margin: Margin for overlapping crops in patches.
+        patch_size: Size of each patch.
+    """
+
+    attributes = ["tokenizer"]
+    valid_kwargs = [
+        "chat_template",
+        "crop_size",
+        "max_crops",
+        "overlap_margin",
+        "patch_size",
+    ]
+
+    tokenizer_class = "AutoTokenizer"
+    # Use separate tokenizer repo
+    _tokenizer_repo = "moondream/starmie-v1"
+
+    # Default chat template for Moondream3
+    # Moondream uses special tokens for prompting:
+    # - Token 0 (<|endoftext|>): BOS token (ALWAYS present at position 0)
+    # - Token 1 (<|md_reserved_0|>): Start of instruction
+    # - Token 2 (<|md_reserved_1|>): Separator before question
+    # - Token 3 (<|md_reserved_2|>): End of question / start of answer
+    #
+    # Task routing based on text prefix:
+    #   "caption [short|normal|long]" → describe<|md_reserved_1|>{length}
+    #   "describe [short|normal|long]" → describe<|md_reserved_1|>{length}
+    #   otherwise                      → query<|md_reserved_1|><text>
+    #
+    # Format with image:
+    #   <|endoftext|><image><|md_reserved_0|>{task}<|md_reserved_1|>{q}<|md_reserved_2|>
+    # Format without image:
+    #   <|endoftext|><|md_reserved_0|>{task}<|md_reserved_1|>{q}<|md_reserved_2|>
+    _default_chat_template = (
+        "{% for message in messages %}"
+        "{% if message['role'] == 'user' %}"
+        "{% if message['content'] is string %}"
+        # Simple string content (with image assumed) - route by prefix
+        "<|endoftext|><image><|md_reserved_0|>"
+        "{% if message['content'] == 'caption' %}"
+        "describe<|md_reserved_1|>normal<|md_reserved_2|>"
+        "{% elif message['content'].startswith('caption ') %}"
+        "describe<|md_reserved_1|>{{ message['content'][8:] }}<|md_reserved_2|>"
+        "{% elif message['content'] == 'describe' %}"
+        "describe<|md_reserved_1|>normal<|md_reserved_2|>"
+        "{% elif message['content'].startswith('describe ') %}"
+        "describe<|md_reserved_1|>{{ message['content'][9:] }}<|md_reserved_2|>"
+        "{% else %}"
+        "query<|md_reserved_1|>{{ message['content'] }}<|md_reserved_2|>"
+        "{% endif %}"
+        "{% else %}"
+        # List content - build Moondream's image prefix independently of
+        # OpenAI-style content part order, then render the text task.
+        "<|endoftext|>"
+        "{% for content in message['content'] %}"
+        "{% if content['type'] in ['image', 'image_url', 'input_image', 'image_pil'] %}"  # noqa: E501
+        "<image>"
+        "{% endif %}"
+        "{% endfor %}"
+        "{% for content in message['content'] %}"
+        "{% if content['type'] == 'text' %}"
+        "<|md_reserved_0|>"
+        "{% if content['text'] == 'caption' %}"
+        "describe<|md_reserved_1|>normal<|md_reserved_2|>"
+        "{% elif content['text'].startswith('caption ') %}"
+        "describe<|md_reserved_1|>{{ content['text'][8:] }}<|md_reserved_2|>"
+        "{% elif content['text'] == 'describe' %}"
+        "describe<|md_reserved_1|>normal<|md_reserved_2|>"
+        "{% elif content['text'].startswith('describe ') %}"
+        "describe<|md_reserved_1|>{{ content['text'][9:] }}<|md_reserved_2|>"
+        "{% else %}"
+        "query<|md_reserved_1|>{{ content['text'] }}<|md_reserved_2|>"
+        "{% endif %}"
+        "{% endif %}"
+        "{% endfor %}"
+        "{% endif %}"
+        "{% elif message['role'] == 'assistant' %}"
+        "{{ message['content'] }}"
+        "{% endif %}"
+        "{% endfor %}"
+    )
+
+    def __init__(
+        self,
+        tokenizer: PreTrainedTokenizerBase | None = None,
+        chat_template: str | None = None,
+        crop_size: int = 378,
+        max_crops: int = 12,
+        overlap_margin: int = 4,
+        patch_size: int = 14,
+        **kwargs,
+    ):
+        self.image_token = "<image>"
+        self.crop_size = crop_size
+        self.max_crops = max_crops
+        self.overlap_margin = overlap_margin
+        self.patch_size = patch_size
+
+        # Number of patches per crop (27x27 = 729 for 378/14)
+        self.patches_per_crop = (crop_size // patch_size) ** 2
+
+        # Use default chat template if none provided
+        if chat_template is None:
+            chat_template = self._default_chat_template
+
+        super().__init__(tokenizer, chat_template=chat_template)
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path,
+        **kwargs,
+    ):
+        """
+        Load the processor, using a separate tokenizer repo.
+
+        The moondream3 model uses a custom tokenizer from 'moondream/starmie-v1'
+        instead of having tokenizer files in the model repo.
+        """
+        from transformers import AutoTokenizer, PreTrainedTokenizerFast
+        from transformers.utils import cached_file
+
+        tokenizer = kwargs.pop("tokenizer", None)
+
+        tokenizer_kwargs = {
+            "trust_remote_code": kwargs.get("trust_remote_code", False),
+        }
+        for key in (
+            "cache_dir",
+            "force_download",
+            "local_files_only",
+            "revision",
+            "subfolder",
+            "token",
+            "use_fast",
+        ):
+            if key in kwargs:
+                tokenizer_kwargs[key] = kwargs[key]
+
+        cached_file_kwargs = {
+            key: tokenizer_kwargs[key]
+            for key in (
+                "cache_dir",
+                "force_download",
+                "local_files_only",
+                "revision",
+                "subfolder",
+                "token",
+            )
+            if key in tokenizer_kwargs
+        }
+
+        def load_tokenizer(repo_or_path):
+            try:
+                return AutoTokenizer.from_pretrained(repo_or_path, **tokenizer_kwargs)
+            except Exception:
+                tokenizer_file = cached_file(
+                    repo_or_path,
+                    "tokenizer.json",
+                    **cached_file_kwargs,
+                )
+                return PreTrainedTokenizerFast(
+                    tokenizer_file=tokenizer_file,
+                    clean_up_tokenization_spaces=False,
+                )
+
+        if isinstance(tokenizer, str):
+            tokenizer = load_tokenizer(tokenizer)
+
+        if tokenizer is None:
+            # Prefer model-local tokenizer files first. If unavailable, fall
+            # back to moondream's dedicated tokenizer repository.
+            try:
+                tokenizer = load_tokenizer(pretrained_model_name_or_path)
+            except Exception:
+                tokenizer = load_tokenizer(cls._tokenizer_repo)
+
+        # Configure special tokens for Moondream3
+        # BOS and EOS are both token 0 (<|endoftext|>), matching the native
+        # config (TokenizerConfig.bos_id=0, eos_id=0). This is standard for
+        # GPT-2 style models where <|endoftext|> signals both start and end.
+        # Token 1 (<|md_reserved_0|>) is a template delimiter, NOT the EOS.
+        tokenizer.bos_token = "<|endoftext|>"
+        tokenizer.bos_token_id = 0
+        tokenizer.eos_token = "<|endoftext|>"
+        tokenizer.eos_token_id = 0
+
+        # Extract processor-specific kwargs
+        crop_size = kwargs.pop("crop_size", 378)
+        max_crops = kwargs.pop("max_crops", 12)
+        overlap_margin = kwargs.pop("overlap_margin", 4)
+        patch_size = kwargs.pop("patch_size", 14)
+        chat_template = kwargs.pop("chat_template", None)
+
+        # Set default chat template on tokenizer if not already set
+        if chat_template is None:
+            chat_template = cls._default_chat_template
+        if tokenizer.chat_template is None:
+            tokenizer.chat_template = chat_template
+
+        return cls(
+            tokenizer=tokenizer,
+            chat_template=chat_template,
+            crop_size=crop_size,
+            max_crops=max_crops,
+            overlap_margin=overlap_margin,
+            patch_size=patch_size,
+        )
+
+    def __call__(
+        self,
+        images: ImageInput = None,
+        text: TextInput
+        | PreTokenizedInput
+        | list[TextInput]
+        | list[PreTokenizedInput] = None,
+        **kwargs: Unpack[Moondream3ProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Process images and text for Moondream3 model.
+
+        Args:
+            images: Input images (PIL Image, numpy array, or list thereof).
+            text: Input text or list of texts.
+            **kwargs: Additional processing arguments.
+
+        Returns:
+            BatchFeature with processed inputs.
+        """
+        output_kwargs = self._merge_kwargs(
+            Moondream3ProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        # Process images
+        image_features = {}
+        if images is not None:
+            processed_images = []
+            tilings = []
+
+            images_list = images if isinstance(images, list) else [images]
+            for image in images_list:
+                pixel_values, tiling = self.preprocess_image(
+                    image, **output_kwargs["images_kwargs"]
+                )
+                processed_images.append(pixel_values)
+                tilings.append(tiling)
+
+            if processed_images:
+                image_features["pixel_values"] = processed_images
+                image_features["tilings"] = tilings
+
+        # Process text
+        if text is not None:
+            if not isinstance(text, list):
+                text = [text]
+
+            # Get text kwargs, remove keys we set ourselves
+            text_kwargs = output_kwargs.get("text_kwargs", {}).copy()
+            text_kwargs.pop("return_tensors", None)
+            text_kwargs.pop("add_special_tokens", None)
+
+            # Tokenize text
+            tokenized = self.tokenizer(
+                text,
+                add_special_tokens=True,
+                return_tensors="pt",
+                **text_kwargs,
+            )
+
+            output = BatchFeature(data=dict(tokenized))
+
+            # Add image features
+            if image_features:
+                output["pixel_values"] = image_features["pixel_values"]
+                output["tilings"] = image_features["tilings"]
+
+            return output
+
+        # If only images were provided
+        return BatchFeature(data=image_features)
+
+    @staticmethod
+    def _image_array_to_uint8(array: np.ndarray) -> np.ndarray:
+        if array.dtype == np.uint8:
+            return np.ascontiguousarray(array)
+
+        if array.dtype == np.bool_:
+            return np.ascontiguousarray(array.astype(np.uint8) * 255)
+
+        if np.issubdtype(array.dtype, np.floating):
+            array = np.nan_to_num(array, nan=0.0, posinf=255.0, neginf=0.0)
+            if array.size > 0 and array.max() <= 1.0:
+                array = array * 255.0
+            array = np.rint(array)
+
+        return np.ascontiguousarray(np.clip(array, 0, 255).astype(np.uint8))
+
+    @staticmethod
+    def _to_pil_image(image: ImageInput) -> Image.Image:
+        if isinstance(image, Image.Image):
+            return image
+
+        if isinstance(image, torch.Tensor):
+            tensor = image.detach().cpu()
+            if tensor.dtype == torch.bfloat16:
+                tensor = tensor.to(torch.float32)
+            image_array = tensor.numpy()
+        elif isinstance(image, np.ndarray):
+            image_array = image
+        else:
+            raise TypeError(
+                "Moondream3 images must be PIL images, numpy arrays, "
+                f"or torch tensors, got {type(image)!r}."
+            )
+
+        if image_array.ndim == 2:
+            image_array = Moondream3Processor._image_array_to_uint8(image_array)
+            return Image.fromarray(image_array)
+
+        if image_array.ndim != 3:
+            raise ValueError(
+                "Moondream3 image arrays must have 2 or 3 dimensions, "
+                f"got shape {image_array.shape}."
+            )
+
+        channel_dims = (1, 3, 4)
+        if image_array.shape[-1] not in channel_dims:
+            if image_array.shape[0] not in channel_dims:
+                raise ValueError(
+                    "Moondream3 image arrays must be HWC or CHW with 1, 3, "
+                    f"or 4 channels, got shape {image_array.shape}."
+                )
+            image_array = np.transpose(image_array, (1, 2, 0))
+
+        image_array = Moondream3Processor._image_array_to_uint8(image_array)
+        if image_array.shape[-1] == 1:
+            image_array = image_array[..., 0]
+
+        return Image.fromarray(image_array)
+
+    def preprocess_image(
+        self,
+        image: ImageInput,
+        max_crops: int = 12,
+        overlap_margin: int = 4,
+        crop_size: int = 378,
+        patch_size: int = 14,
+        convert_to_rgb: bool = True,
+        return_tensors: str = "pt",
+    ) -> tuple[torch.Tensor, tuple[int, int]]:
+        """
+        Preprocess an image using overlap-and-resize cropping strategy.
+
+        Args:
+            image: Input PIL image, numpy array, or torch tensor.
+            max_crops: Maximum number of crops.
+            overlap_margin: Margin for overlapping in patches.
+            crop_size: Size of each crop.
+            patch_size: Size of each patch.
+            convert_to_rgb: Whether to convert to RGB.
+            return_tensors: Return type ("pt" for PyTorch).
+
+        Returns:
+            Tuple of (pixel_values tensor, tiling tuple).
+        """
+        image = self._to_pil_image(image)
+        if convert_to_rgb:
+            image = convert_image_mode(image, "RGB")
+
+        # Convert to numpy array
+        image_array = np.array(image)
+        original_h, original_w = image_array.shape[:2]
+
+        margin_pixels = patch_size * overlap_margin
+        total_margin_pixels = margin_pixels * 2
+
+        crop_patches = crop_size // patch_size
+        crop_window_patches = crop_patches - (2 * overlap_margin)
+        crop_window_size = crop_window_patches * patch_size
+
+        tiling = select_tiling(
+            original_h - total_margin_pixels,
+            original_w - total_margin_pixels,
+            crop_window_size,
+            max_crops,
+        )
+
+        n_crops = tiling[0] * tiling[1] + 1
+        crops = np.zeros((n_crops, crop_size, crop_size, 3), dtype=np.uint8)
+
+        target_size = (
+            tiling[0] * crop_window_size + total_margin_pixels,
+            tiling[1] * crop_window_size + total_margin_pixels,
+        )
+
+        # Resize image
+        pil_img = Image.fromarray(image_array)
+        resized = pil_img.resize(
+            (int(target_size[1]), int(target_size[0])),
+            resample=Image.Resampling.LANCZOS,
+        )
+        resized_array = np.asarray(resized)
+
+        # Create global crop
+        global_pil = pil_img.resize(
+            (crop_size, crop_size), resample=Image.Resampling.LANCZOS
+        )
+        crops[0] = np.asarray(global_pil)
+
+        # Create local crops
+        for i in range(tiling[0]):
+            for j in range(tiling[1]):
+                y0 = i * crop_window_size
+                x0 = j * crop_window_size
+                y_end = min(y0 + crop_size, resized_array.shape[0])
+                x_end = min(x0 + crop_size, resized_array.shape[1])
+
+                crop_region = resized_array[y0:y_end, x0:x_end]
+                crop_idx = 1 + i * tiling[1] + j
+                h_slice = slice(None, crop_region.shape[0])
+                w_slice = slice(None, crop_region.shape[1])
+                crops[crop_idx, h_slice, w_slice] = crop_region
+
+        # Convert to tensor: (n_crops, H, W, C) -> (n_crops, C, H, W)
+        pixel_values = np.transpose(crops, (0, 3, 1, 2))
+
+        if return_tensors == "pt":
+            # Match HF reference preprocessing exactly: convert uint8 crops to
+            # bfloat16 before in-place normalization.
+            pixel_values = (
+                torch.from_numpy(pixel_values)
+                .to(dtype=torch.bfloat16)
+                .div_(255.0)
+                .sub_(0.5)
+                .div_(0.5)
+            )
+        else:
+            pixel_values = pixel_values.astype(np.float32) / 255.0
+            pixel_values = (pixel_values - 0.5) / 0.5
+
+        return pixel_values, tiling
+
+    def get_num_image_tokens(self) -> int:
+        """Return the number of image tokens (729 = 27x27 patches)."""
+        return self.patches_per_crop
+
+    def batch_decode(self, *args, **kwargs):
+        """Forward to tokenizer's batch_decode."""
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """Forward to tokenizer's decode."""
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        return tokenizer_input_names + ["pixel_values", "tilings"]
+
+
+AutoProcessor.register("Moondream3Processor", Moondream3Processor)
diff --git a/vllm/transformers_utils/processors/nano_nemotron_vl.py b/vllm/transformers_utils/processors/nano_nemotron_vl.py
index 57e874be4ec1..76b73d21635c 100644
--- a/vllm/transformers_utils/processors/nano_nemotron_vl.py
+++ b/vllm/transformers_utils/processors/nano_nemotron_vl.py
@@ -8,7 +8,6 @@
 # --------------------------------------------------------
 
 import math
-import warnings
 from abc import ABC, abstractmethod
 from collections.abc import Sequence
 from dataclasses import dataclass
@@ -26,7 +25,7 @@
 from vllm.model_executor.models.parakeet import ParakeetExtractor
 from vllm.multimodal.evs import compute_retained_tokens_count
 from vllm.multimodal.inputs import AudioItem
-from vllm.multimodal.processing.processor import PromptUpdateDetails, _seq2tokens
+from vllm.multimodal.processing.processor import PromptUpdateDetails
 from vllm.tokenizers.hf import HfTokenizer
 
 from .internvl import calculate_internvl_targets, get_internvl_target_ratios
@@ -63,42 +62,50 @@ def calculate_timestamps(
     return timestamps
 
 
-def input_conditioner(x: torch.Tensor, norm_mean: torch.Tensor, norm_std: torch.Tensor):
-    return (x - norm_mean) / norm_std
+@torch.compile(dynamic=True)
+def _bicubic_resize_and_normalize(
+    tensor: torch.Tensor,
+    size: tuple[int, int] | None = None,
+    norm_mean: torch.Tensor | None = None,
+    norm_std: torch.Tensor | None = None,
+    dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
+    """Permute NHWC→NCHW, optional bicubic resize, rescale + normalize.
 
+    Input must be a raw 4-D **NHWC** tensor.
 
-def _bicubic_from_ndarray(
-    array: npt.NDArray[Any], *, size: tuple[int, int]
-) -> torch.Tensor:
-    """
-    Convert a 4D NHWC ndarray to NCHW and interpolate with bicubic.
-    Suppresses PyTorch's non-writable NumPy warning because interpolate copies,
-    and torch.from_numpy(array) is discarded at the end of function scope.
+    *size*: target ``(H, W)``; skips interpolation when ``None``.
+    *norm_mean* / *norm_std*: when both provided, fused
+    ``(x/255 - mean) / std`` + dtype cast; otherwise ``x/255`` + cast.
     """
-
-    with warnings.catch_warnings():
-        msg = "The given NumPy array is not writ.*"
-        # Apparently, different versions of PyTorch use writable or writeable.
-        warnings.filterwarnings("ignore", message=msg, category=UserWarning)
-        tensor = torch.from_numpy(array)
-    assert tensor.ndim == 4, f"{tensor.ndim=}"
-    tensor = tensor.permute(0, 3, 1, 2)
-    return (
-        torch.nn.functional.interpolate(
+    tensor = tensor.permute(0, 3, 1, 2).to(dtype=torch.float32)
+    if size is not None:
+        tensor = torch.nn.functional.interpolate(
             tensor, size=size, mode="bicubic", align_corners=False, antialias=True
         )
-        / 255.0
+    if norm_mean is not None and norm_std is not None:
+        return ((tensor / 255.0 - norm_mean) / norm_std).to(dtype=dtype).contiguous()
+    return (tensor / 255.0).to(dtype=dtype).contiguous()
+
+
+def _pil_to_nhwc_tensor(image: Image.Image) -> torch.Tensor:
+    """Convert a PIL image to a 4-D NHWC tensor suitable for compiled ops."""
+    array = np.asarray(
+        image.convert("RGB") if image.mode != "RGB" else image, dtype=np.uint8
     )
+    return torch.from_numpy(np.expand_dims(array, axis=0))
 
 
 def dynamic_preprocess(
-    image,
+    image: Image.Image,
     *,
-    image_size=512,
-    max_num_tiles=12,
-    use_thumbnail=True,
-    idx=0,
-):
+    image_size: int = 512,
+    max_num_tiles: int = 12,
+    use_thumbnail: bool = True,
+    norm_mean: torch.Tensor | None = None,
+    norm_std: torch.Tensor | None = None,
+    dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
     orig_width, orig_height = image.size
 
     target_ratios = get_internvl_target_ratios(1, max_num_tiles)
@@ -111,13 +118,15 @@ def dynamic_preprocess(
         use_thumbnail=False,
     )
 
-    image = np.asarray(
-        image.convert("RGB") if image.mode != "RGB" else image, dtype=np.uint8
-    )
+    tensor = _pil_to_nhwc_tensor(image)
 
-    image = np.expand_dims(image, axis=0)
-
-    resized_img = _bicubic_from_ndarray(image, size=(target_height, target_width))
+    resized_img = _bicubic_resize_and_normalize(
+        tensor,
+        size=(target_height, target_width),
+        norm_mean=norm_mean,
+        norm_std=norm_std,
+        dtype=dtype,
+    )
     B, C, H, W = resized_img.shape
     hp, wp = H // image_size, W // image_size
     patches = (
@@ -127,30 +136,16 @@ def dynamic_preprocess(
     )
 
     if use_thumbnail and patches.shape[0] > 1:
-        thumb = _bicubic_from_ndarray(image, size=(image_size, image_size))
+        thumb = _bicubic_resize_and_normalize(
+            tensor,
+            size=(image_size, image_size),
+            norm_mean=norm_mean,
+            norm_std=norm_std,
+            dtype=dtype,
+        )
         patches = torch.cat([patches, thumb], dim=0)
 
-    return list(patches)
-
-
-def image_to_pixel_values(
-    image: Image.Image,
-    *,
-    input_size: int,
-    max_num: int,
-    use_thumbnail: bool,
-    idx: int,
-) -> torch.Tensor:
-    images = dynamic_preprocess(
-        image,
-        image_size=input_size,
-        max_num_tiles=max_num,
-        use_thumbnail=use_thumbnail,
-        idx=idx,
-    )
-
-    pixel_values = torch.stack(images)
-    return pixel_values
+    return patches
 
 
 def _compute_aspect_preserving_size(
@@ -233,14 +228,16 @@ def video_to_pixel_values(
     video_maintain_aspect_ratio: bool = False,
     patch_size: int = 16,
     downsample_ratio: float = 0.5,
+    norm_mean: torch.Tensor | None = None,
+    norm_std: torch.Tensor | None = None,
+    dtype: torch.dtype = torch.float32,
 ) -> torch.Tensor:
-    # (num_frames, H, W, C) -> (num_frames, C, H, W)
-    video_tensor = torch.from_numpy(video).permute(0, 3, 1, 2)
+    """Convert video ndarray (T, H, W, C) to normalized pixel tensor (T, C, H, W)."""
+    orig_h, orig_w = video.shape[1], video.shape[2]
+    size: tuple[int, int] | None = None
 
     if video_target_num_patches is not None:
-        # Resize to target patch count (aspect-preserving or square).
-        orig_h, orig_w = video_tensor.shape[2], video_tensor.shape[3]
-        target_w, target_h, _ = get_video_target_size_and_feature_size(
+        tw, th, _ = get_video_target_size_and_feature_size(
             orig_w=orig_w,
             orig_h=orig_h,
             target_patches=video_target_num_patches,
@@ -248,14 +245,13 @@ def video_to_pixel_values(
             patch_size=patch_size,
             downsample_ratio=downsample_ratio,
         )
-        if video_tensor.shape[2] != target_h or video_tensor.shape[3] != target_w:
-            return _bicubic_from_ndarray(video, size=(target_h, target_w))
-    elif video_tensor.shape[2] != input_size or video_tensor.shape[3] != input_size:
-        return _bicubic_from_ndarray(video, size=(input_size, input_size))
+        if orig_h != th or orig_w != tw:
+            size = (th, tw)
+    elif orig_h != input_size or orig_w != input_size:
+        size = (input_size, input_size)
 
-    video_tensor = video_tensor / 255.0
-
-    return video_tensor
+    tensor = torch.from_numpy(video)
+    return _bicubic_resize_and_normalize(tensor, size, norm_mean, norm_std, dtype)
 
 
 class DynamicResolutionImageTiler:
@@ -343,6 +339,7 @@ def _images_to_pixel_values_lst(
         self,
         text_prompt_length: int,
         images: list[Image.Image],
+        dtype: torch.dtype = torch.float32,
     ) -> tuple[list[torch.Tensor], list[int]]:
         num_tokens_available = self.max_num_tokens_available(text_prompt_length)
         params_per_image = self.compute_params(images, num_tokens_available)
@@ -350,7 +347,7 @@ def _images_to_pixel_values_lst(
         feature_sizes = []
         images = []
         for param in params_per_image:
-            for t in self.apply_params(param):
+            for t in self.apply_params(param, dtype=dtype):
                 assert t.ndim == 3, f"{t.ndim=}: expected 3 dim tensor"
                 images.append(t)
                 feature_sizes.append(param.num_embeddings)
@@ -363,17 +360,23 @@ class DynamicResolutionParams:
         num_embeddings: int
         patch_size: tuple[int, int]
 
-    def apply_params(self, params: DynamicResolutionParams) -> list[torch.Tensor]:
+    def apply_params(
+        self,
+        params: DynamicResolutionParams,
+        dtype: torch.dtype = torch.float32,
+    ) -> list[torch.Tensor]:
         target_size = (
             params.patch_size[1] * self._patch_size,
             params.patch_size[0] * self._patch_size,
         )
-        image = np.asarray(
-            params.media.convert("RGB") if params.media.mode != "RGB" else params.media,
-            dtype=np.uint8,
+        tensor = _pil_to_nhwc_tensor(params.media)
+        resized_img = _bicubic_resize_and_normalize(
+            tensor,
+            size=target_size,
+            norm_mean=self.norm_mean,
+            norm_std=self.norm_std,
+            dtype=dtype,
         )
-        image = np.expand_dims(image, axis=0)
-        resized_img = _bicubic_from_ndarray(image, size=target_size)
         return list(resized_img)
 
     def process_media(
@@ -619,6 +622,7 @@ def __init__(
                 norm_mean=config.norm_mean,
                 norm_std=config.norm_std,
             )
+        self.dtype: torch.dtype = getattr(config, "dtype", torch.float32)
 
     @staticmethod
     def use_dynamic_resolution(config: PretrainedConfig) -> bool:
@@ -662,14 +666,16 @@ def _images_to_pixel_values_lst(
         max_num_tiles: int,
     ) -> list[torch.Tensor]:
         return [
-            image_to_pixel_values(
+            dynamic_preprocess(
                 image,
-                input_size=self.image_size,
-                max_num=max_num_tiles,
+                image_size=self.image_size,
+                max_num_tiles=max_num_tiles,
                 use_thumbnail=self.use_thumbnail,
-                idx=idx,
+                norm_mean=self.norm_mean,
+                norm_std=self.norm_std,
+                dtype=self.dtype,
             )
-            for idx, image in enumerate(images)
+            for image in images
         ]
 
     def _preprocess_image(
@@ -690,23 +696,22 @@ def _preprocess_image(
             pixel_values_lst, num_tokens_per_image = tiler._images_to_pixel_values_lst(
                 text_prompt_length=text_prompt_length,
                 images=images,
+                dtype=self.dtype,
             )
             imgs_sizes = [(pv.shape[-2], pv.shape[-1]) for pv in pixel_values_lst]
-            normalized = [
-                input_conditioner(img, tiler.norm_mean, tiler.norm_std)
-                for img in pixel_values_lst
-            ]
             image_num_patches = torch.tensor([1] * len(num_tokens_per_image))
             image_inputs = {
-                "pixel_values_flat": normalized,
+                "pixel_values_flat": pixel_values_lst,
                 "imgs_sizes": imgs_sizes,
                 "num_tokens_per_image": num_tokens_per_image,
             }
         else:
             pixel_values_lst = self._images_to_pixel_values_lst(images, max_num_tiles)
             image_num_patches = torch.tensor([len(item) for item in pixel_values_lst])
-            pixel_values_flat = input_conditioner(
-                torch.cat(pixel_values_lst), self.norm_mean, self.norm_std
+            pixel_values_flat = (
+                torch.cat(pixel_values_lst)
+                if len(pixel_values_lst) > 1
+                else pixel_values_lst[0]
             )
             image_inputs = {
                 "pixel_values_flat": pixel_values_flat,
@@ -863,6 +868,8 @@ def image_token_id(self) -> int:
     def _videos_to_pixel_values_lst(
         self,
         videos: list[npt.NDArray],
+        *,
+        dtype: torch.dtype = torch.float32,
     ) -> list[torch.Tensor]:
         return [
             video_to_pixel_values(
@@ -872,6 +879,9 @@ def _videos_to_pixel_values_lst(
                 video_maintain_aspect_ratio=self.video_maintain_aspect_ratio,
                 patch_size=self.config.patch_size,
                 downsample_ratio=self.config.downsample_ratio,
+                norm_mean=self.norm_mean,
+                norm_std=self.norm_std,
+                dtype=dtype,
             )
             for video in videos
         ]
@@ -886,8 +896,10 @@ def _preprocess_video(
 
         videos_lst = [v[0] for v in videos]
         video_metadata_lst = [v[1] for v in videos]
+
         pixel_values_lst_video = self._videos_to_pixel_values_lst(
             videos_lst,
+            dtype=self.dtype,
         )
 
         # We use frame duration in milliseconds (as integer) to ensure
@@ -903,10 +915,15 @@ def _preprocess_video(
             metadata["frames_indices"] for metadata in video_metadata_lst
         ]
         video_num_patches = torch.tensor([len(item) for item in pixel_values_lst_video])
+
+        # Normalization already fused into resize above.
+        # Skip the torch.cat copy when there is exactly one video
+        if len(pixel_values_lst_video) == 1:
+            pixel_values_flat = pixel_values_lst_video[0]
+        else:
+            pixel_values_flat = torch.cat(pixel_values_lst_video)
         video_inputs = {
-            "pixel_values_flat_video": input_conditioner(
-                torch.cat(pixel_values_lst_video), self.norm_mean, self.norm_std
-            ),
+            "pixel_values_flat_video": pixel_values_flat,
             "video_num_patches": video_num_patches,
             "frames_indices": frames_indices_lst,
             "frame_duration_ms": torch.tensor(frame_duration_ms_lst),
@@ -1168,20 +1185,21 @@ def get_video_repl(
                 for i, _ in enumerate(tokens_per_frame)
             ]
 
-        # Tokenize frame separator independently
-        frame_separators_tokenized = [
-            _seq2tokens(tokenizer, sep) for sep in frame_separators
-        ]
+        # Batch-tokenize all frame separators at once — the HuggingFace
+        # tokenizers Rust backend parallelizes batch encoding across threads.
+        batch_encoded = tokenizer(
+            frame_separators,
+            add_special_tokens=False,
+            return_attention_mask=False,
+        )
+        frame_separators_tokenized: list[list[int]] = batch_encoded["input_ids"]
 
         # Tokenize each component independently to avoid tokenizer merging tokens
         # across boundaries. This ensures consistent tokenization regardless of
         # num_tokens_per_frame values.
         all_token_ids = []
         for i, num_tokens in enumerate(tokens_per_frame):
-            frame_sep_token_ids = frame_separators_tokenized[i]
-            all_token_ids.extend(frame_sep_token_ids)
-
-            # Add pre-tokenized special tokens
+            all_token_ids.extend(frame_separators_tokenized[i])
             all_token_ids.extend(img_start_token_ids)
             all_token_ids.extend(img_context_token_ids * num_tokens)
             all_token_ids.extend(img_end_token_ids)
diff --git a/vllm/triton_utils/importing.py b/vllm/triton_utils/importing.py
index f05bc555bfdc..9b2634c90982 100644
--- a/vllm/triton_utils/importing.py
+++ b/vllm/triton_utils/importing.py
@@ -6,6 +6,7 @@
 from importlib.util import find_spec
 
 from vllm.logger import init_logger
+from vllm.utils.math_utils import cdiv
 
 logger = init_logger(__name__)
 
@@ -79,6 +80,7 @@ def __init__(self):
         self.autotune = self._dummy_decorator("autotune")
         self.heuristics = self._dummy_decorator("heuristics")
         self.Config = self._dummy_decorator("Config")
+        self.cdiv = cdiv
         self.language = TritonLanguagePlaceholder()
 
     def _dummy_decorator(self, name):
diff --git a/vllm/triton_utils/jit_monitor.py b/vllm/triton_utils/jit_monitor.py
new file mode 100644
index 000000000000..5ee33fc51dc4
--- /dev/null
+++ b/vllm/triton_utils/jit_monitor.py
@@ -0,0 +1,113 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Monitor unexpected Triton kernel JIT compilation during inference.
+
+After server warmup completes, any Triton JIT compilation or autotuning
+event indicates a cache miss or unexpected input shape that causes a
+latency spike. This module registers hooks in the Triton runtime to
+detect and log such events so they can be investigated.
+
+Currently monitors:
+- Triton ``@triton.autotune`` cache misses (via ``knobs.autotuning.print``)
+- Triton ``@triton.jit`` first-time compilations
+  (via ``knobs.runtime.jit_post_compile_hook``)
+"""
+
+import os
+
+from vllm.logger import init_logger
+from vllm.triton_utils.importing import HAS_TRITON
+
+logger = init_logger(__name__)
+
+_active: bool = False
+
+
+def is_active() -> bool:
+    """Return whether the JIT compilation monitor is currently active."""
+    return _active
+
+
+def activate() -> None:
+    """Enable JIT compilation monitoring after warmup.
+
+    Call once per worker process at the end of
+    :func:`compile_or_warm_up_model`.  After activation every Triton
+    kernel compilation or autotuning benchmark that happens during
+    inference will be logged as a warning.
+
+    Safe to call multiple times — subsequent calls are no-ops.
+
+    If the user has explicitly set ``TRITON_PRINT_AUTOTUNING=0`` in
+    their environment, autotuning printing is left disabled; the JIT
+    compilation hook is still registered regardless.
+    """
+    global _active
+    if _active:
+        return
+    _active = True
+
+    _setup_triton_autotuning_print()
+    _setup_triton_jit_hook()
+
+    logger.info(
+        "Kernel JIT monitor activated — Triton JIT compilations "
+        "during inference will be logged as warnings."
+    )
+
+
+# ------------------------------------------------------------------
+# Triton autotuning print
+# ------------------------------------------------------------------
+
+
+def _setup_triton_autotuning_print() -> None:
+    """Enable ``TRITON_PRINT_AUTOTUNING`` unless the user opted out."""
+    if not HAS_TRITON:
+        return
+    from triton import knobs  # type: ignore[import-untyped]
+
+    user_val = os.environ.get("TRITON_PRINT_AUTOTUNING")
+    if user_val == "0":
+        logger.debug(
+            "TRITON_PRINT_AUTOTUNING=0 set by user — "
+            "autotuning messages will stay suppressed."
+        )
+        return
+
+    knobs.autotuning.print = True
+
+
+# ------------------------------------------------------------------
+# Triton JIT compilation hook
+# ------------------------------------------------------------------
+
+
+def _setup_triton_jit_hook() -> None:
+    """Register a ``jit_post_compile_hook`` that warns on compilation."""
+    if not HAS_TRITON:
+        return
+    from triton import knobs  # type: ignore[import-untyped]
+
+    existing_hook = knobs.runtime.jit_post_compile_hook
+
+    def _on_jit_compile(**kwargs):
+        # `jit_post_compile_hook` is Triton internal API and its
+        # signature has changed across releases (kwargs added/renamed).
+        # Accept **kwargs so an upstream change cannot crash this hook
+        # with TypeError, and forward the full kwarg set to any
+        # pre-existing hook unchanged.
+        fn = kwargs.get("fn")
+        fn_name = getattr(fn, "name", "<unknown>")
+        logger.warning_once(
+            "Triton kernel JIT compilation during inference: %s. "
+            "This causes a latency spike; consider extending warmup "
+            "to cover this shape/config.",
+            fn_name,
+        )
+        if existing_hook is not None:
+            return existing_hook(**kwargs)
+        return None
+
+    knobs.runtime.jit_post_compile_hook = _on_jit_compile
diff --git a/vllm/utils/__init__.py b/vllm/utils/__init__.py
index 9b481d63990b..bf455c261f4f 100644
--- a/vllm/utils/__init__.py
+++ b/vllm/utils/__init__.py
@@ -34,3 +34,16 @@ def length_from_prompt_token_ids_or_embeds(
                 f" prompt_embeds={prompt_embeds_len}"
             )
         return prompt_token_len
+
+
+def is_moe_layer(module: torch.nn.Module) -> bool:
+    # TODO(bnell): Should use isinstance but can't due to circular dependencies.
+    def _check_bases(cls):
+        if cls.__name__ == "FusedMoE":
+            return True
+
+        for b in cls.__bases__:
+            if _check_bases(b):
+                return True
+
+    return _check_bases(module.__class__)
diff --git a/vllm/utils/argparse_utils.py b/vllm/utils/argparse_utils.py
index 04c70bf79e62..84c853757192 100644
--- a/vllm/utils/argparse_utils.py
+++ b/vllm/utils/argparse_utils.py
@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Argument parsing utilities for vLLM."""
 
+import argparse
 import json
 import sys
 import textwrap
@@ -25,6 +26,71 @@
 logger = init_logger(__name__)
 
 
+def human_readable_int(value: str) -> int:
+    """Parse human-readable integers like '1k', '2M', etc.
+    Including decimal values with decimal multipliers.
+
+    Examples:
+    - '1k' -> 1,000
+    - '1K' -> 1,024
+    - '25.6k' -> 25,600
+    """
+    value = value.strip()
+
+    match = re.fullmatch(r"(\d+(?:\.\d+)?)([kKmMgGtT])", value)
+    if match:
+        decimal_multiplier = {
+            "k": 10**3,
+            "m": 10**6,
+            "g": 10**9,
+            "t": 10**12,
+        }
+        binary_multiplier = {
+            "K": 2**10,
+            "M": 2**20,
+            "G": 2**30,
+            "T": 2**40,
+        }
+
+        number, suffix = match.groups()
+        if suffix in decimal_multiplier:
+            mult = decimal_multiplier[suffix]
+            return int(float(number) * mult)
+        elif suffix in binary_multiplier:
+            mult = binary_multiplier[suffix]
+            # Do not allow decimals with binary multipliers
+            try:
+                return int(number) * mult
+            except ValueError as e:
+                raise argparse.ArgumentTypeError(
+                    "Decimals are not allowed "
+                    f"with binary suffixes like {suffix}. Did you mean to use "
+                    f"{number}{suffix.lower()} instead?"
+                ) from e
+
+    # Regular plain number.
+    return int(value)
+
+
+def human_readable_int_or_auto(value: str) -> int:
+    """Parse human-readable integers like '1k', '2M', etc.
+    Including decimal values with decimal multipliers.
+    Also accepts -1 or 'auto' as a special value for auto-detection.
+
+    Examples:
+    - '1k' -> 1,000
+    - '1K' -> 1,024
+    - '25.6k' -> 25,600
+    - '-1' or 'auto' -> -1 (special value for auto-detection)
+    """
+    value = value.strip()
+
+    if value == "-1" or value.lower() == "auto":
+        return -1
+
+    return human_readable_int(value)
+
+
 class SortedHelpFormatter(ArgumentDefaultsHelpFormatter, RawDescriptionHelpFormatter):
     """SortedHelpFormatter that sorts arguments by their option strings."""
 
@@ -338,7 +404,12 @@ def recursive_dict_update(
                 try:
                     value = json.loads(value_str)
                 except json.decoder.JSONDecodeError:
-                    value = value_str
+                    # Support human-readable suffixes (e.g. 1k, 80g) for
+                    # dotted config args like --config.field 80g
+                    try:
+                        value = human_readable_int(value_str)  # type: ignore[assignment]
+                    except (ValueError, ArgumentTypeError):
+                        value = value_str
 
                 # Merge all values with the same key into a single dict
                 arg_dict = create_nested_dict(keys, value)
diff --git a/vllm/utils/cpu_resource_utils.py b/vllm/utils/cpu_resource_utils.py
new file mode 100644
index 000000000000..6baf84266195
--- /dev/null
+++ b/vllm/utils/cpu_resource_utils.py
@@ -0,0 +1,198 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import json
+import os
+import subprocess
+import sys
+from dataclasses import dataclass
+from functools import cache
+
+import psutil
+import regex as re
+
+DEVICE_CONTROL_ENV_VAR = "CPU_VISIBLE_MEMORY_NODES"
+
+
+@dataclass
+class LogicalCPUInfo:
+    id: int = -1
+    physical_core: int = -1
+    numa_node: int = -1
+
+    @classmethod
+    def _int(cls, value: str) -> int:
+        try:
+            int_value = int(value)
+        except Exception:
+            int_value = -1
+        return int_value
+
+    @staticmethod
+    def json_decoder(obj_dict: dict):
+        id = obj_dict.get("cpu")
+        physical_core = obj_dict.get("core")
+        numa_node = obj_dict.get("node")
+
+        if not (id is None or physical_core is None or numa_node is None):
+            return LogicalCPUInfo(
+                id=LogicalCPUInfo._int(id),
+                physical_core=LogicalCPUInfo._int(physical_core),
+                numa_node=LogicalCPUInfo._int(numa_node),
+            )
+        else:
+            return obj_dict
+
+
+@dataclass
+class MemoryNodeInfo:
+    total_memory: int = -1
+    available_memory: int = -1
+
+
+def get_memory_affinity(pid: int = 0) -> list[int]:
+    pid = os.getpid() if pid == 0 else pid
+    path = f"/proc/{pid}/status"
+    with open(path) as f:
+        for line in f:
+            if line.startswith("Mems_allowed_list:"):
+                # Extract the string part (e.g., "0-1,3")
+                raw_list = line.split(":")[1].strip()
+                return parse_id_list(raw_list)
+    return []
+
+
+def parse_id_list(raw_str: str) -> list[int]:
+    """Parses strings like '0-2,4,7-8' into [0, 1, 2, 4, 7, 8]"""
+    result: list[int] = []
+    if not raw_str:
+        return result
+
+    for part in raw_str.split(","):
+        if "-" in part:
+            start, end = map(int, part.split("-"))
+            result.extend(range(start, end + 1))
+        else:
+            result.append(int(part))
+    return sorted(list(set(result)))
+
+
+def get_memory_node_info(node_id: int = 0) -> MemoryNodeInfo:
+    if sys.platform == "darwin":
+        # MacOS has no memory node
+        return MemoryNodeInfo(
+            total_memory=psutil.virtual_memory().total,
+            available_memory=psutil.virtual_memory().available,
+        )
+
+    meminfo_path = f"/sys/devices/system/node/node{node_id}/meminfo"
+    if not os.path.exists(meminfo_path):
+        # Non-NUMA systems (e.g. many RISC-V boards) don't expose per-node
+        # meminfo. Fall back to system-wide numbers from psutil.
+        vm = psutil.virtual_memory()
+        return MemoryNodeInfo(
+            total_memory=vm.total,
+            available_memory=vm.available,
+        )
+
+    meminfo = {}
+    with open(meminfo_path) as f:
+        for line in f:
+            # Each line looks like: "Node 0 MemTotal: 97421888 kB"
+            parts = line.split()
+            key = parts[2].rstrip(":")
+            # convert to Bytes
+            value = int(parts[3]) * 1024
+            meminfo[key] = value
+
+    total_memory = meminfo["MemTotal"]
+    free_memory = meminfo["MemFree"]
+    active_file_memory = meminfo["Active(file)"]
+    inactive_file_memory = meminfo["Inactive(file)"]
+    reclaimable_memory = meminfo["SReclaimable"]
+    available_memory = (
+        free_memory + active_file_memory + inactive_file_memory + reclaimable_memory
+    )
+
+    return MemoryNodeInfo(
+        total_memory=total_memory,
+        available_memory=available_memory,
+    )
+
+
+def get_allowed_cpu_list() -> list[LogicalCPUInfo]:
+    cpu_list = _get_cpu_list()
+    if sys.platform == "linux":
+        allowed = os.sched_getaffinity(0)
+        return [x for x in cpu_list if x.id in allowed]
+    return cpu_list
+
+
+def get_visible_memory_node() -> list[int]:
+    if sys.platform == "darwin":
+        return [0]
+
+    allowed_memory_node_list = get_memory_affinity()
+
+    env_key = DEVICE_CONTROL_ENV_VAR
+    if (
+        ("VLLM_CPU_SIM_MULTI_NUMA" not in os.environ)
+        and env_key in os.environ
+        and os.environ[env_key] != ""
+    ):
+        visible_nodes = [int(s) for s in os.environ[env_key].split(",")]
+        visible_nodes = [
+            node for node in visible_nodes if node in allowed_memory_node_list
+        ]
+        return visible_nodes
+
+    return allowed_memory_node_list
+
+
+@cache
+def _synthesize_cpu_list() -> list[LogicalCPUInfo]:
+    """Synthesize a flat CPU list: each logical CPU is its own core on
+    NUMA node 0.  Used when lscpu output is unavailable or unparsable
+    (e.g. macOS, RISC-V)."""
+    cpu_count = os.cpu_count()
+    assert cpu_count
+    return [LogicalCPUInfo(i, i, 0) for i in range(cpu_count)]
+
+
+def _get_cpu_list() -> list[LogicalCPUInfo]:
+    if sys.platform == "darwin":
+        # For MacOS, no user-level CPU affinity and SMT, return all CPUs
+        return _synthesize_cpu_list()
+
+    lscpu_output = subprocess.check_output(
+        "lscpu --json --extended=CPU,CORE,NODE --online", shell=True, text=True
+    )
+
+    # For platforms without NUMA, map bare `-` node to 0 so non-NUMA
+    # systems keep the existing behavior from #39781.
+    lscpu_output = re.sub(r'"node":\s*-\s*(,|\n|\})', r'"node": 0\1', lscpu_output)
+
+    # On some architectures (notably RISC-V), lscpu also emits bare `-`
+    # for cpu/core.  Quote them so the JSON parses; they will decode to
+    # -1 and be filtered out below, triggering the synthesized fallback.
+    lscpu_output = re.sub(
+        r'("(?:cpu|core)":\s*)-\s*(,|\n|\})',
+        r'\1"-"\2',
+        lscpu_output,
+    )
+
+    logical_cpu_list: list[LogicalCPUInfo] = json.loads(
+        lscpu_output, object_hook=LogicalCPUInfo.json_decoder
+    )["cpus"]
+
+    # Filter CPUs with invalid attributes
+    logical_cpu_list = [
+        x for x in logical_cpu_list if -1 not in (x.id, x.physical_core, x.numa_node)
+    ]
+
+    # If lscpu returned no valid entries (e.g. RISC-V where all fields
+    # are bare `-`), fall back to synthesized topology.
+    if not logical_cpu_list:
+        logical_cpu_list = _synthesize_cpu_list()
+
+    return logical_cpu_list
diff --git a/vllm/utils/cpu_triton_utils.py b/vllm/utils/cpu_triton_utils.py
index d823e8b14aba..ea0383a9d4b9 100644
--- a/vllm/utils/cpu_triton_utils.py
+++ b/vllm/utils/cpu_triton_utils.py
@@ -204,8 +204,16 @@ def _rejection_greedy_sample_kernel_impl(
     bonus_token_ids,
     is_greedy,
     max_spec_len,
+    uniform_probs=None,
+    synthetic_conditional_rates=None,
+    SYNTHETIC_MODE=False,
 ):
     # C++ kernel expects int64 for all integer tensors.
+    # Note: uniform_probs, synthetic_conditional_rates, and SYNTHETIC_MODE are
+    # passed by the rejection sampler for synthetic mode support, but are not
+    # yet implemented in the C++ CPU kernel. We accept them here to maintain
+    # compatibility with the kernel calling convention.
+    assert not SYNTHETIC_MODE, "Synthetic acceptance not supported with CPU sampling"
     orig_dtype = output_token_ids.dtype
     output_token_ids_i64 = _ensure_int64(output_token_ids)
     torch.ops._C.rejection_greedy_sample_kernel_impl(
@@ -233,11 +241,18 @@ def _rejection_random_sample_kernel_impl(
     is_greedy,
     max_spec_len,
     vocab_size,
+    synthetic_conditional_rates=None,
     NO_DRAFT_PROBS=False,
+    SYNTHETIC_MODE=False,
 ):
     # C++ kernel expects int64 for all integer tensors and float32 for probs.
     # uniform_probs is intentionally float64 in Python to avoid exact-zero
     # samples; cast to float32 here for C++ compatibility.
+    # Note: synthetic_conditional_rates and SYNTHETIC_MODE are passed by the
+    # rejection sampler for synthetic mode support, but are not yet implemented
+    # in the C++ CPU kernel. We accept them here to maintain compatibility with
+    # the kernel calling convention.
+    assert not SYNTHETIC_MODE, "Synthetic acceptance not supported with CPU sampling"
     orig_dtype = output_token_ids.dtype
     output_token_ids_i64 = _ensure_int64(output_token_ids)
     torch.ops._C.rejection_random_sample_kernel_impl(
diff --git a/vllm/utils/deep_gemm.py b/vllm/utils/deep_gemm.py
index a2e10ea3951f..6b89f5c33203 100644
--- a/vllm/utils/deep_gemm.py
+++ b/vllm/utils/deep_gemm.py
@@ -106,16 +106,14 @@ def is_deep_gemm_e8m0_used() -> bool:
     _lazy_init()
 
     if _fp8_gemm_nt_impl is None:
-        logger.info_once(
-            "DeepGEMM E8M0 disabled: _fp8_gemm_nt_impl not found", scope="local"
-        )
+        logger.info_once("DeepGEMM E8M0 disabled: _fp8_gemm_nt_impl not found")
         return False
 
     if envs.VLLM_USE_DEEP_GEMM_E8M0:
-        logger.info_once("DeepGEMM E8M0 enabled on current platform.", scope="local")
+        logger.info_once("DeepGEMM E8M0 enabled on current platform.")
         return True
 
-    logger.info_once("DeepGEMM E8M0 disabled on current configuration.", scope="local")
+    logger.info_once("DeepGEMM E8M0 disabled on current configuration.")
     return False
 
 
@@ -127,12 +125,16 @@ def _missing(*_: Any, **__: Any) -> NoReturn:
     )
 
 
+_cublaslt_gemm_nt_impl: Callable[..., Any] | None = None
 _fp8_gemm_nt_impl: Callable[..., Any] | None = None
+_fp8_einsum_impl: Callable[..., Any] | None = None
 _grouped_impl: Callable[..., Any] | None = None
 _grouped_masked_impl: Callable[..., Any] | None = None
-_fp8_mqa_logits_impl: Callable[..., Any] | None = None
-_fp8_paged_mqa_logits_impl: Callable[..., Any] | None = None
+_grouped_fp4_impl: Callable[..., Any] | None = None
+_fp8_fp4_mqa_logits_impl: Callable[..., Any] | None = None
+_fp8_fp4_paged_mqa_logits_impl: Callable[..., Any] | None = None
 _get_paged_mqa_logits_metadata_impl: Callable[..., Any] | None = None
+_tf32_hc_prenorm_gemm_impl: Callable[..., Any] | None = None
 _get_mn_major_tma_aligned_tensor_impl: Callable[..., Any] | None = None
 _get_mk_alignment_for_contiguous_layout_impl: Callable[..., Any] | None = None
 _transform_sf_into_required_layout_impl: Callable[..., Any] | None = None
@@ -175,20 +177,27 @@ def _import_deep_gemm():
 
 def _lazy_init() -> None:
     """Import deep_gemm and resolve symbols on first use."""
-    global _fp8_gemm_nt_impl, _grouped_impl, _grouped_masked_impl
-    global _fp8_mqa_logits_impl, _fp8_paged_mqa_logits_impl
+    global _cublaslt_gemm_nt_impl
+    global _fp8_gemm_nt_impl, _fp8_einsum_impl
+    global _grouped_impl, _grouped_masked_impl, _grouped_fp4_impl
+    global _fp8_fp4_mqa_logits_impl, _fp8_fp4_paged_mqa_logits_impl
     global _get_paged_mqa_logits_metadata_impl
+    global _tf32_hc_prenorm_gemm_impl
     global _get_mn_major_tma_aligned_tensor_impl
     global _get_mk_alignment_for_contiguous_layout_impl
     global _transform_sf_into_required_layout_impl
     # fast path
     if (
-        _fp8_gemm_nt_impl is not None
+        _cublaslt_gemm_nt_impl is not None
+        or _fp8_gemm_nt_impl is not None
+        or _fp8_einsum_impl is not None
         or _grouped_impl is not None
         or _grouped_masked_impl is not None
-        or _fp8_mqa_logits_impl is not None
-        or _fp8_paged_mqa_logits_impl is not None
+        or _grouped_fp4_impl is not None
+        or _fp8_fp4_mqa_logits_impl is not None
+        or _fp8_fp4_paged_mqa_logits_impl is not None
         or _get_paged_mqa_logits_metadata_impl is not None
+        or _tf32_hc_prenorm_gemm_impl is not None
         or _get_mk_alignment_for_contiguous_layout_impl is not None
         or _transform_sf_into_required_layout_impl is not None
     ):
@@ -208,14 +217,20 @@ def _lazy_init() -> None:
     if _dg is None:
         return
 
+    _cublaslt_gemm_nt_impl = getattr(_dg, "cublaslt_gemm_nt", None)
     _fp8_gemm_nt_impl = getattr(_dg, "fp8_gemm_nt", None)
+    _fp8_einsum_impl = getattr(_dg, "fp8_einsum", None)
     _grouped_impl = getattr(_dg, "m_grouped_fp8_gemm_nt_contiguous", None)
     _grouped_masked_impl = getattr(_dg, "fp8_m_grouped_gemm_nt_masked", None)
-    _fp8_mqa_logits_impl = getattr(_dg, "fp8_mqa_logits", None)
-    _fp8_paged_mqa_logits_impl = getattr(_dg, "fp8_paged_mqa_logits", None)
+    _grouped_fp4_impl = getattr(_dg, "m_grouped_fp8_fp4_gemm_nt_contiguous", None)
+    # DeepGEMM exposes fp8_fp4_*_mqa_logits as the canonical symbols that
+    # handle both the FP8 and FP4 Q/K paths via a tuple-typed `q`.
+    _fp8_fp4_mqa_logits_impl = getattr(_dg, "fp8_fp4_mqa_logits", None)
+    _fp8_fp4_paged_mqa_logits_impl = getattr(_dg, "fp8_fp4_paged_mqa_logits", None)
     _get_paged_mqa_logits_metadata_impl = getattr(
         _dg, "get_paged_mqa_logits_metadata", None
     )
+    _tf32_hc_prenorm_gemm_impl = getattr(_dg, "tf32_hc_prenorm_gemm", None)
     _get_mn_major_tma_aligned_tensor_impl = getattr(
         _dg, "get_mn_major_tma_aligned_tensor", None
     )
@@ -261,6 +276,13 @@ def get_col_major_tma_aligned_tensor(x: torch.Tensor) -> torch.Tensor:
     return _get_mn_major_tma_aligned_tensor_impl(x)
 
 
+def cublaslt_gemm_nt(*args, **kwargs):
+    _lazy_init()
+    if _cublaslt_gemm_nt_impl is None:
+        return _missing(*args, **kwargs)
+    return _cublaslt_gemm_nt_impl(*args, **kwargs)
+
+
 def fp8_gemm_nt(*args, **kwargs):
     _lazy_init()
     if _fp8_gemm_nt_impl is None:
@@ -273,6 +295,13 @@ def fp8_gemm_nt(*args, **kwargs):
     return _fp8_gemm_nt_impl(*args, disable_ue8m0_cast=not use_ue8m0, **kwargs)
 
 
+def fp8_einsum(*args, **kwargs):
+    _lazy_init()
+    if _fp8_einsum_impl is None:
+        return _missing(*args, **kwargs)
+    return _fp8_einsum_impl(*args, **kwargs)
+
+
 def m_grouped_fp8_gemm_nt_contiguous(*args, **kwargs):
     _lazy_init()
     if _grouped_impl is None:
@@ -282,6 +311,15 @@ def m_grouped_fp8_gemm_nt_contiguous(*args, **kwargs):
     )
 
 
+def m_grouped_fp8_fp4_gemm_nt_contiguous(*args, **kwargs):
+    _lazy_init()
+    if _grouped_fp4_impl is None:
+        return _missing(*args, **kwargs)
+    return _grouped_fp4_impl(
+        *args, disable_ue8m0_cast=not is_deep_gemm_e8m0_used(), **kwargs
+    )
+
+
 def fp8_m_grouped_gemm_nt_masked(*args, **kwargs):
     _lazy_init()
     if _grouped_masked_impl is None:
@@ -300,37 +338,48 @@ def transform_sf_into_required_layout(*args, **kwargs):
     )
 
 
-def fp8_mqa_logits(
-    q: torch.Tensor,
+def fp8_fp4_mqa_logits(
+    q: tuple[torch.Tensor, torch.Tensor | None],
     kv: tuple[torch.Tensor, torch.Tensor],
     weights: torch.Tensor,
     cu_seqlen_ks: torch.Tensor,
     cu_seqlen_ke: torch.Tensor,
     clean_logits: bool,
 ) -> torch.Tensor:
-    """Compute FP8 MQA logits for a single sequence without KV paging.
+    """Compute MQA logits for a single sequence without KV paging.
+
+    Unified FP8/FP4 dispatch — the underlying DeepGEMM kernel takes
+    ``q = (values, scales_or_None)`` where ``scales`` is None for FP8 Q
+    (per-token scale is folded into ``weights``) and a packed block-scale
+    tensor for MXFP4 Q.
 
     Args:
-        q: Query tensor of shape [M, H, D]. Casted to
-            `torch.float8_e4m3fn` by caller.
-        kv: Tuple `(k_fp8, k_scales)` where `k_fp8` has shape [N, D] with
-            dtype `torch.float8_e4m3fn` and `k_scales` has shape [N])
-            with dtype `torch.float32`.
+        q: Tuple ``(q_values, q_scale)``. FP8 path: q_values is [M, H, D]
+            float8_e4m3fn and q_scale is None (per-token scale is folded
+            into ``weights``). FP4 path: q_values is packed uint8 and
+            q_scale is the companion block-scale tensor.
+        kv: Tuple `(k_packed, k_scales)` — FP8 layout is [N, D]
+            float8_e4m3fn plus fp32 scales [N]; FP4 layout is packed uint8.
         weights: weights of shape [M, H], dtype `torch.float32`.
-        cu_seqlen_ks: Start indices (inclusive) for valid K per query position,
-            shape [M], dtype int32.
-        cu_seqlen_ke: End indices (exclusive) for valid K per query position,
-            shape [M], dtype int32.
+        cu_seqlen_ks: Start indices (inclusive) for valid K per query
+            position, shape [M], dtype int32.
+        cu_seqlen_ke: End indices (exclusive) for valid K per query
+            position, shape [M], dtype int32.
         clean_logits: Whether to clean the unfilled logits into `-inf`.
 
     Returns:
         Logits tensor of shape [M, N], dtype `torch.float32`.
     """
     _lazy_init()
-    if _fp8_mqa_logits_impl is None:
+    if _fp8_fp4_mqa_logits_impl is None:
         return _missing()
-    return _fp8_mqa_logits_impl(
-        q, kv, weights, cu_seqlen_ks, cu_seqlen_ke, clean_logits=clean_logits
+    return _fp8_fp4_mqa_logits_impl(
+        q,
+        kv,
+        weights,
+        cu_seqlen_ks,
+        cu_seqlen_ke,
+        clean_logits=clean_logits,
     )
 
 
@@ -346,7 +395,7 @@ def get_paged_mqa_logits_metadata(
         num_sms: Number of SMs available. 132 for Hopper
 
     Returns:
-        Backend-specific tensor consumed by `fp8_paged_mqa_logits` to
+        Backend-specific tensor consumed by `fp8_fp4_paged_mqa_logits` to
         schedule work across SMs.
     """
     _lazy_init()
@@ -355,9 +404,9 @@ def get_paged_mqa_logits_metadata(
     return _get_paged_mqa_logits_metadata_impl(context_lens, block_size, num_sms)
 
 
-def fp8_paged_mqa_logits(
-    q_fp8: torch.Tensor,
-    kv_cache_fp8: torch.Tensor,
+def fp8_fp4_paged_mqa_logits(
+    q: tuple[torch.Tensor, torch.Tensor | None],
+    kv_cache: torch.Tensor,
     weights: torch.Tensor,
     context_lens: torch.Tensor,
     block_tables: torch.Tensor,
@@ -365,14 +414,20 @@ def fp8_paged_mqa_logits(
     max_model_len: int,
     clean_logits: bool,
 ) -> torch.Tensor:
-    """Compute FP8 MQA logits using paged KV-cache.
+    """Compute MQA logits using a paged KV-cache.
+
+    Unified FP8/FP4 dispatch — the underlying DeepGEMM kernel takes
+    ``q = (values, scales_or_None)``; pass ``(q_tensor, None)`` for the FP8
+    path and ``(q_values, q_scale)`` for MXFP4.
 
     Args:
-        q_fp8: Query tensor of shape [B, next_n, H, D]. Casted to
-            `torch.float8_e4m3fn` by caller.
-        kv_cache_fp8: Paged KV-cache in packed FP8+scale layout with shape
-            [num_blocks, block_size, 1, D+4], dtype `torch.uint8`. The last
-            4 bytes per (block,pos) store the `float` dequant scale.
+        q: Tuple ``(q_values, q_scale)``. FP8 path: q_values is
+            [B, next_n, H, D] float8_e4m3fn and q_scale is None. FP4 path:
+            q_values is packed uint8 and q_scale is the companion
+            block-scale tensor.
+        kv_cache: Paged KV-cache. FP8 layout is [num_blocks, block_size, 1,
+            D+4], dtype `torch.uint8`, with the last 4 bytes per (block, pos)
+            storing the float dequant scale.
         weights: Tensor of shape [B * next_n, H], dtype `torch.float32`.
         context_lens: Tensor of shape [B], dtype int32; effective context length
             for each batch element.
@@ -388,11 +443,11 @@ def fp8_paged_mqa_logits(
         `torch.float32`.
     """
     _lazy_init()
-    if _fp8_paged_mqa_logits_impl is None:
+    if _fp8_fp4_paged_mqa_logits_impl is None:
         return _missing()
-    return _fp8_paged_mqa_logits_impl(
-        q_fp8,
-        kv_cache_fp8,
+    return _fp8_fp4_paged_mqa_logits_impl(
+        q,
+        kv_cache,
         weights,
         context_lens,
         block_tables,
@@ -402,6 +457,32 @@ def fp8_paged_mqa_logits(
     )
 
 
+def tf32_hc_prenorm_gemm(
+    x: torch.Tensor,
+    fn: torch.Tensor,
+    out: torch.Tensor,
+    sqrsum: torch.Tensor,
+    num_split: int,
+) -> torch.Tensor:
+    """
+    Perform the following computation:
+        out = x.float() @ fn.T
+        sqrsum = x.float().square().sum(-1)
+
+    See the caller function for shape requirement
+    """
+    _lazy_init()
+    if _tf32_hc_prenorm_gemm_impl is None:
+        return _missing()
+    return _tf32_hc_prenorm_gemm_impl(
+        x,
+        fn,
+        out,
+        sqrsum,
+        num_split,
+    )
+
+
 def _ceil_to_ue8m0(x: torch.Tensor):
     return torch.pow(2.0, torch.ceil(torch.log2(x.abs())))
 
@@ -484,10 +565,12 @@ def should_use_deepgemm_for_fp8_linear(
     "calc_diff",
     "DeepGemmQuantScaleFMT",
     "fp8_gemm_nt",
+    "fp8_einsum",
     "m_grouped_fp8_gemm_nt_contiguous",
+    "m_grouped_fp8_fp4_gemm_nt_contiguous",
     "fp8_m_grouped_gemm_nt_masked",
-    "fp8_mqa_logits",
-    "fp8_paged_mqa_logits",
+    "fp8_fp4_mqa_logits",
+    "fp8_fp4_paged_mqa_logits",
     "get_paged_mqa_logits_metadata",
     "per_block_cast_to_fp8",
     "is_deep_gemm_e8m0_used",
diff --git a/vllm/utils/flashinfer.py b/vllm/utils/flashinfer.py
index ed171db96e73..44fcc19c2d2b 100644
--- a/vllm/utils/flashinfer.py
+++ b/vllm/utils/flashinfer.py
@@ -305,10 +305,10 @@ def supports_trtllm_attention() -> bool:
     if envs.VLLM_BATCH_INVARIANT:
         return False
 
-    # TRTLLM attention is currently only validated on SM100 (CC 10.0).
-    # SM103 (GB300) hangs with FlashInfer >= 0.6.7.
-    # See: https://github.com/flashinfer-ai/flashinfer/issues/2939
-    return current_platform.is_device_capability(100) and has_nvidia_artifactory()
+    # Requires SM100 and NVIDIA artifactory to be accessible to download cubins
+    return (
+        current_platform.is_device_capability_family(100) and has_nvidia_artifactory()
+    )
 
 
 def force_use_trtllm_attention() -> bool | None:
@@ -685,6 +685,47 @@ def flashinfer_scaled_fp4_mm(
     )
 
 
+def flashinfer_scaled_fp4_mm_out(
+    a: torch.Tensor,
+    b: torch.Tensor,
+    block_scale_a: torch.Tensor,
+    block_scale_b: torch.Tensor,
+    alpha: torch.Tensor,
+    out: torch.Tensor,
+    out_dtype: torch.dtype | None,
+    use_8x4_sf_layout: bool,
+    backend: str,
+) -> torch.Tensor:
+    assert a.ndim == 2 and b.ndim == 2 and out.ndim == 2
+    assert block_scale_a.ndim == 2 and block_scale_b.ndim == 2
+    assert a.stride(-1) == 1
+    assert a.shape[1] == b.shape[0]
+    assert out.shape == (a.shape[0], b.shape[1])
+    assert out.device.type == "cuda"
+
+    if backend in ("cutlass", "cudnn"):
+        if block_scale_a.dtype != torch.uint8:
+            block_scale_a = block_scale_a.view(torch.uint8)
+        if block_scale_b.dtype != torch.uint8:
+            block_scale_b = block_scale_b.view(torch.uint8)
+
+    from flashinfer import mm_fp4 as flashinfer_mm_fp4_
+
+    flashinfer_mm_fp4_(
+        a,
+        b,
+        block_scale_a,
+        block_scale_b,
+        alpha,
+        out_dtype or out.dtype,
+        out=out,
+        block_size=16,
+        use_8x4_sf_layout=use_8x4_sf_layout,
+        backend=backend,
+    )
+    return out
+
+
 def flashinfer_scaled_fp8_mm(
     a: torch.Tensor,
     b: torch.Tensor,
@@ -715,6 +756,38 @@ def flashinfer_scaled_fp8_mm(
     return output
 
 
+def flashinfer_scaled_fp8_mm_out(
+    a: torch.Tensor,
+    b: torch.Tensor,
+    scale_a: torch.Tensor,
+    scale_b: torch.Tensor,
+    out: torch.Tensor,
+    out_dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    assert a.ndim == 2 and b.ndim == 2 and out.ndim == 2
+    assert a.shape[1] == b.shape[0]
+    assert out.shape == (a.shape[0], b.shape[1])
+    assert scale_a.numel() == 1 and scale_b.numel() == 1
+    assert a.dtype == torch.float8_e4m3fn and b.dtype == torch.float8_e4m3fn
+    assert out.device.type == "cuda"
+    assert a.is_contiguous()
+
+    from flashinfer import bmm_fp8 as bmm_fp8_
+
+    bmm_fp8_(
+        a.unsqueeze(0),
+        # FlashInfer expects the weight in the same column-major view layout
+        # consumed by flashinfer_scaled_fp8_mm, so keep the transposed view.
+        b.unsqueeze(0),
+        scale_a,
+        scale_b,
+        out_dtype or out.dtype,
+        out.unsqueeze(0),
+        "auto",
+    )
+    return out
+
+
 def flashinfer_quant_nvfp4_8x4_sf_layout(
     a: torch.Tensor, a_global_sf: torch.Tensor
 ) -> tuple[torch.Tensor, torch.Tensor]:
@@ -772,6 +845,40 @@ def should_use_flashinfer_for_blockscale_fp8_gemm(
     return should_use_flashinfer
 
 
+_MIN_CUDNN_FP8 = 91701  # cuDNN >= 9.17.1 required for FP8 attention
+
+
+@functools.cache
+def is_flashinfer_cudnn_fp8_prefill_attn_supported() -> bool:
+    """Check if FP8 ViT attention is supported on this platform.
+
+    Requires native FP8 hardware support, the FlashInfer cuDNN backend,
+    and cuDNN >= 9.17.1.
+    """
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+    # cuDNN SDPA FP8 requires Hopper (SM 90) or newer.
+    if not current_platform.has_device_capability(90):
+        return False
+
+    try:
+        supported = current_platform.get_supported_vit_attn_backends()
+        if AttentionBackendEnum.FLASHINFER not in supported:
+            return False
+    except (ImportError, AttributeError):
+        return False
+
+    try:
+        import torch.backends.cudnn as cudnn
+
+        if cudnn.is_available() and cudnn.version() < _MIN_CUDNN_FP8:
+            return False
+    except (ImportError, AttributeError):
+        pass
+
+    return True
+
+
 __all__ = [
     "has_flashinfer",
     "flashinfer_trtllm_fp8_block_scale_moe",
@@ -798,9 +905,12 @@ def should_use_flashinfer_for_blockscale_fp8_gemm(
     "can_use_trtllm_attention",
     "use_trtllm_attention",
     "flashinfer_scaled_fp4_mm",
+    "flashinfer_scaled_fp4_mm_out",
     "flashinfer_scaled_fp8_mm",
+    "flashinfer_scaled_fp8_mm_out",
     "flashinfer_quant_nvfp4_8x4_sf_layout",
     "flashinfer_fp8_blockscale_gemm",
     "should_use_flashinfer_for_blockscale_fp8_gemm",
     "is_flashinfer_fp8_blockscale_gemm_supported",
+    "is_flashinfer_cudnn_fp8_prefill_attn_supported",
 ]
diff --git a/vllm/utils/func_utils.py b/vllm/utils/func_utils.py
index 82eab043b0db..5ce23e6a0074 100644
--- a/vllm/utils/func_utils.py
+++ b/vllm/utils/func_utils.py
@@ -45,16 +45,14 @@ def wrapper(*args: P.args, **kwargs: P.kwargs) -> None:
 
 
 @lru_cache
-def supports_kw(
+def _supports_kw(
     callable: Callable[..., object],
     kw_name: str,
     *,
     requires_kw_only: bool = False,
     allow_var_kwargs: bool = True,
 ) -> bool:
-    """Check if a keyword is a valid kwarg for a callable; if requires_kw_only
-    disallows kwargs names that can also be positional arguments.
-    """
+    """Internal cached implementation of supports_kw."""
     params = inspect.signature(callable).parameters
     if not params:
         return False
@@ -99,6 +97,29 @@ def supports_kw(
     return False
 
 
+def supports_kw(
+    callable: Callable[..., object],
+    kw_name: str,
+    *,
+    requires_kw_only: bool = False,
+    allow_var_kwargs: bool = True,
+) -> bool:
+    """Check if a keyword is a valid kwarg for a callable; if requires_kw_only
+    disallows kwargs names that can also be positional arguments.
+    """
+    # Unwrap bound methods so that the lru_cache key is the underlying
+    # function, not the instance. Caching bound methods pins the object
+    # (and all its GPU tensors) for the lifetime of the cache.
+    if hasattr(callable, "__func__"):
+        callable = callable.__func__
+    return _supports_kw(
+        callable,
+        kw_name,
+        requires_kw_only=requires_kw_only,
+        allow_var_kwargs=allow_var_kwargs,
+    )
+
+
 def get_allowed_kwarg_only_overrides(
     callable: Callable[..., object],
     overrides: Mapping[str, object] | None,
diff --git a/vllm/utils/import_utils.py b/vllm/utils/import_utils.py
index 31b63d1e6b49..5822e5840afc 100644
--- a/vllm/utils/import_utils.py
+++ b/vllm/utils/import_utils.py
@@ -66,14 +66,12 @@ def import_triton_kernels():
 
         logger.debug_once(
             f"Loading module triton_kernels from {triton_kernels.__file__}.",
-            scope="local",
         )
     elif _has_module("vllm.third_party.triton_kernels"):
         import vllm.third_party.triton_kernels as triton_kernels
 
         logger.debug_once(
             f"Loading module triton_kernels from {triton_kernels.__file__}.",
-            scope="local",
         )
         sys.modules["triton_kernels"] = triton_kernels
     else:
@@ -471,3 +469,8 @@ def has_mori() -> bool:
 def has_fbgemm_gpu() -> bool:
     """Whether the optional `fbgemm_gpu` package is available."""
     return _has_module("fbgemm_gpu")
+
+
+def has_cutedsl() -> bool:
+    """Whether the optional `cutelass` package is available."""
+    return _has_module("cutlass")
diff --git a/vllm/utils/mistral.py b/vllm/utils/mistral.py
index c9c24a2e306c..276ca8170f1d 100644
--- a/vllm/utils/mistral.py
+++ b/vllm/utils/mistral.py
@@ -12,8 +12,10 @@
 if TYPE_CHECKING:
     # if type checking, eagerly import the module
     import vllm.tokenizers.mistral as mt
+    import vllm.tool_parsers.mistral_tool_parser as mtp
 else:
     mt = LazyLoader("mt", globals(), "vllm.tokenizers.mistral")
+    mtp = LazyLoader("mtp", globals(), "vllm.tool_parsers.mistral_tool_parser")
 
 
 def is_mistral_tokenizer(obj: TokenizerLike | None) -> TypeGuard[mt.MistralTokenizer]:
@@ -26,3 +28,16 @@ def is_mistral_tokenizer(obj: TokenizerLike | None) -> TypeGuard[mt.MistralToken
         getattr(cls, "IS_MISTRAL_TOKENIZER", False)
         and isinstance(obj, mt.MistralTokenizer)
     )
+
+
+def is_mistral_tool_parser(cls: type | None) -> bool:
+    """Return true if *cls* is (a subclass of) MistralToolParser.
+
+    Uses a class attribute check so that importing
+    ``vllm.tool_parsers.mistral_tool_parser`` — and transitively
+    ``mistral_common`` — is not required.
+    """
+    return bool(
+        getattr(cls, "IS_MISTRAL_TOOL_PARSER", False)
+        and issubclass(cls, mtp.MistralToolParser)  # type: ignore[arg-type]
+    )
diff --git a/vllm/utils/multi_stream_utils.py b/vllm/utils/multi_stream_utils.py
index 3ade910bf99c..2203221c5a14 100644
--- a/vllm/utils/multi_stream_utils.py
+++ b/vllm/utils/multi_stream_utils.py
@@ -2,11 +2,21 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from collections.abc import Callable
+from enum import Enum
 from typing import Any
 
 import torch
 
 
+class AuxStreamType(Enum):
+    Attention = 1
+
+
+class EventType(Enum):
+    Main = 0
+    Attention = 1
+
+
 def maybe_execute_in_parallel(
     fn0: Callable[[], Any],
     fn1: Callable[[], Any],
@@ -46,3 +56,73 @@ def maybe_execute_in_parallel(
         result0 = fn0()
         result1 = fn1()
     return (result0, result1)
+
+
+def execute_in_parallel(
+    default_fn: Callable[[], Any],
+    aux_fns: list[Callable[[], Any] | None],
+    start_event: torch.cuda.Event,
+    done_events: list[torch.cuda.Event],
+    aux_streams: list[torch.cuda.Stream] | None = None,
+    enable: bool = False,
+) -> tuple[Any, list[Any]]:
+    """Run default_fn on the current stream and aux_fns concurrently on
+    aux_streams.
+
+    Generalizes maybe_execute_in_parallel to N aux callables. Slots where
+    aux_fns[i] is None are skipped (no stream switch, no event record); their
+    corresponding entry in the returned aux_results list is None.
+
+    start_event fans out from the current stream to every launched aux stream;
+    done_events[i] is recorded after aux_fns[i] so the current stream joins
+    before returning. Falls back to sequential execution on the current stream
+    when aux_streams is None or enable is False; in that case default_fn runs
+    first, then aux_fns in order.
+
+    Args:
+        default_fn: Callable for the default (current) stream.
+        aux_fns: Per-aux callables; entries may be None to skip.
+        start_event: CUDA event recorded on the current stream before
+            default_fn so each launched aux stream can wait on it.
+        done_events: One CUDA event per aux slot, recorded after the
+            corresponding aux_fn. Length must match aux_fns.
+        aux_streams: Per-aux CUDA streams. Length must match aux_fns.
+            Multi-stream is disabled when None.
+        enable: Opt-in switch for the multi-stream path. Defaults to False,
+            so callers that pass aux_streams must also pass enable=True
+            (typically gated by an env var) to actually overlap. When False,
+            execution falls back to sequential on the current stream.
+
+    Returns:
+        Tuple of (default_result, aux_results) where aux_results[i] is the
+        result of aux_fns[i] (or None when skipped).
+    """
+    aux_results: list[Any]
+    if aux_streams is None or not enable:
+        default_result = default_fn()
+        aux_results = [fn() if fn is not None else None for fn in aux_fns]
+        return default_result, aux_results
+
+    assert len(aux_fns) == len(aux_streams) == len(done_events), (
+        "aux_fns, aux_streams, and done_events must be the same length"
+    )
+
+    aux_results = [None] * len(aux_fns)
+    pending: list[torch.cuda.Event] = []
+
+    start_event.record()
+    for i, fn in enumerate(aux_fns):
+        if fn is None:
+            continue
+        with torch.cuda.stream(aux_streams[i]):
+            start_event.wait()
+            aux_results[i] = fn()
+            done_events[i].record()
+        pending.append(done_events[i])
+
+    default_result = default_fn()
+
+    for ev in pending:
+        ev.wait()
+
+    return default_result, aux_results
diff --git a/vllm/utils/network_utils.py b/vllm/utils/network_utils.py
index 6152bb0b2d9d..cf3141a0ced6 100644
--- a/vllm/utils/network_utils.py
+++ b/vllm/utils/network_utils.py
@@ -64,7 +64,7 @@ def get_ip() -> str:
         pass
 
     warnings.warn(
-        "Failed to get the IP address, using 0.0.0.0 by default."
+        "Failed to get the IP address, using 0.0.0.0 by default. "
         "The value can be set by the environment variable"
         " VLLM_HOST_IP or HOST_IP.",
         stacklevel=2,
diff --git a/vllm/utils/ompmultiprocessing.py b/vllm/utils/ompmultiprocessing.py
index d601e39e15f5..6e7b8c3496c9 100644
--- a/vllm/utils/ompmultiprocessing.py
+++ b/vllm/utils/ompmultiprocessing.py
@@ -5,196 +5,287 @@
 Copyright (c) 2026 Cambridge Greys Ltd
 """
 
-import json
 import os
-import platform
-import subprocess
-
-
-def _int(arg):
-    """Relaxed parsing of ints which handles a - instead of a number.
-    The lscpu json may contain that for nodes in some cases. If that
-    is the case we parse it to zero
-    """
-    try:
-        if int(arg) >= 0:
-            return int(arg)
-    except ValueError:
-        pass
-    return 0
-
-
-def parse_mask(mask):
-    """Expand a X-Y,Z list"""
-    result = []
-    for token in mask.split(","):
-        try:
-            start, finish = token.split("-")
-            if int(start) > int(finish):
-                raise IndexError("Invalid Indexes for cpu ranges")
-            for cpu in range(int(start), int(finish) + 1):
-                result.append(cpu)
-        except ValueError:
-            result.append(int(token))
-    return set(result)
-
-
-def _get_default_affinity() -> set[int]:
-    """Get the set of CPUs the process is allowed to run on."""
-    if hasattr(os, "sched_getaffinity"):
-        return os.sched_getaffinity(0)
-    # macOS does not support sched_getaffinity; fall back to cpu_count
-    cpu_count = os.cpu_count() or 1
-    return set(range(cpu_count))
-
-
-def _get_cpu_topology_json() -> bytes:
-    """Get CPU topology as JSON.
-
-    On Linux this uses ``lscpu -Je``.  On other platforms (e.g. macOS) we
-    synthesize a simple topology where every logical CPU is its own core
-    on NUMA node 0, which is sufficient for the OMP place-list builder.
-    """
-    if platform.system() == "Linux":
-        return subprocess.run(["lscpu", "-Je"], check=True, capture_output=True).stdout
-
-    # Fallback for non-Linux (macOS, etc.)
-    cpu_count = os.cpu_count() or 1
-    cpus = []
-    for i in range(cpu_count):
-        cpus.append({"cpu": str(i), "core": str(i), "node": "0"})
-    return json.dumps({"cpus": cpus}).encode()
-
-
-def enumerate_resources(resource_map, mask=None, allowed=None):
-    """Enumerate system resources"""
-    if allowed is None:
-        allowed = _get_default_affinity()
-    if mask is not None:
-        allowed = allowed & mask
-
-    try:
-        allowed_nodes = parse_mask(os.environ["CPU_VISIBLE_MEMORY_NODES"])
-    except KeyError:
-        allowed_nodes = None
-
-    lscpu: dict[str, dict] = {"cpus": {}, "cores": {}, "nodes": {}}
-    for cpu in resource_map["cpus"]:
-        cpunum = int(cpu["cpu"])
-        if (
-            cpunum in allowed
-            and cpunum >= 0
-            and (allowed_nodes is None or _int(cpu["node"]) in allowed_nodes)
-        ):
-            lscpu["cpus"][cpunum] = [cpu]
-            core = _int(cpu["core"])
-            if lscpu["cores"].get(core, None) is None:
-                lscpu["cores"][core] = [cpu]
-            else:
-                lscpu["cores"][core].append(cpu)
-            node = _int(cpu["node"])
-            if lscpu["nodes"].get(node, None) is None:
-                lscpu["nodes"][node] = [cpu]
-            else:
-                lscpu["nodes"][node].append(cpu)
-    return lscpu
-
-
-def produce_cpu_list(cpus, smt=1):
-    """Produce a CPU list with/without SMT pairs - main cpu list case"""
-    mask: list[int] = []
-    for key, value in cpus.items():
-        exists = 0
-        for cpu in mask:
-            if cpu == value[0]["core"]:
-                exists += 1
-                break
-        if exists < smt:
-            mask.append(int(key))
-    return {"mask": set(mask), "available": True}
-
-
-def produce_cpu_sublist(scpus, smt=1):
-    """Produce a CPU list with/without SMT pairs - resource leaf case"""
-    cpu_list: list[dict] = []
-    for value in scpus:
-        exists = 0
-        for cpu in cpu_list:
-            if int(cpu["core"]) == int(value["core"]):
-                exists += 1
-                break
-        if exists < smt:
-            cpu_list.append(value)
-    mask = []
-    for cpu in cpu_list:
-        mask.append(int(cpu["cpu"]))
-
-    return {"mask": set(mask), "available": True}
-
-
-def create_omp_places(resources, strategy, smt=True):
-    """Parse CPU topology and generate possible CPU masks"""
-    omp_places = []
-    if strategy == "all":
-        omp_places.append(produce_cpu_list(resources["cpus"], smt))
-    elif strategy == "cores":
-        for value in resources["cores"].values():
-            omp_places.append(produce_cpu_sublist(value, smt))
-    elif strategy == "nodes":
-        for value in resources["nodes"].values():
-            omp_places.append(produce_cpu_sublist(value, smt))
-    else:
-        raise NotImplementedError("Unknown strategy")
-
-    return omp_places
-
-
-# pylint: disable=too-few-public-methods
+from collections.abc import Callable
+from contextlib import contextmanager
+from typing import TYPE_CHECKING
+
+import vllm.utils.cpu_resource_utils as cr_utils
+from vllm import envs
+from vllm.logger import init_logger
+from vllm.platforms import CpuArchEnum, current_platform
+from vllm.utils.cpu_resource_utils import LogicalCPUInfo
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+
+logger = init_logger(__name__)
+
+
 class OMPProcessManager:
-    """OMP aware wrapper to run mp Process()"""
-
-    def __init__(self, strategy="nodes", smt=1, mock=None, affinity=None):
-        self.strategy = strategy
-        self.smt = smt
-        self.omp_places = []
-        vllm_mask = os.environ.get("VLLM_CPU_OMP_THREADS_BIND", None)
-        self.setup_omp = vllm_mask != "nobind"
-        if self.setup_omp:
-            omp_places = []
-            if vllm_mask is not None:
-                masks = []
-                for spec in vllm_mask.split("|"):
-                    masks.append(parse_mask(spec))
+    def __init__(self, config: "VllmConfig"):
+        if not current_platform.is_cpu():
+            return
+
+        self.local_world_size = config.parallel_config.local_world_size
+        self.local_dp_rank = config.parallel_config.data_parallel_rank_local
+        # This is a bit tricky because the internal DP size
+        # is always 1 for non-MoE models
+        self.internal_dp_size = config.parallel_config._api_process_count
+
+        self.simulate_multi_node = os.environ.get("VLLM_CPU_SIM_MULTI_NUMA", "0") != "0"
+        ld_preload_str = os.getenv("LD_PRELOAD", "")
+        self.use_iomp = "libiomp" in ld_preload_str or "libomp" in ld_preload_str
+        self.use_gomp = "libgomp" in ld_preload_str
+
+        assert not (self.use_iomp and self.use_gomp)
+
+        # at least reserve 1/local_world_size(for ARM/RISC-V) core for scheduler
+        # proc as always use MP executor
+        # TODO: make scheduler proc sleep when idle
+        self.reserve_cpu_num = (
+            self.local_world_size
+            if current_platform.get_cpu_architecture()
+            in (CpuArchEnum.ARM, CpuArchEnum.RISCV)
+            else 1
+        )
+        # reserve at one more core for nixl_connector under p/d case
+        if config.kv_transfer_config:
+            self.reserve_cpu_num += 1
+
+        if envs.VLLM_CPU_NUM_OF_RESERVED_CPU is not None:
+            if self.reserve_cpu_num > envs.VLLM_CPU_NUM_OF_RESERVED_CPU:
+                msg = (
+                    f"VLLM_CPU_NUM_OF_RESERVED_CPU is less than "
+                    "the minimum requirement"
+                    f": {self.reserve_cpu_num} cores"
+                )
+                logger.warning(msg=msg)
+            self.reserve_cpu_num = envs.VLLM_CPU_NUM_OF_RESERVED_CPU
+
+        self._parse_omp_threads_bind_env()
+
+        assert not self.simulate_multi_node or self.auto_setup
+
+    @contextmanager
+    def configure_omp_envs(self, rank: int, local_rank: int):
+        if not current_platform.is_cpu() or self.skip_setup:
+            yield
+            return
+
+        envs_dict = {}
+        cpu_list = [str(i) for i in self.cpu_lists[local_rank]]
+        envs_dict["OMP_NUM_THREADS"] = str(len(cpu_list))
+        if self.use_iomp:
+            # set IOMP envs
+            cpu_list_str = ",".join(cpu_list)
+            envs_dict["KMP_AFFINITY"] = (
+                f"granularity=fine,explicit,proclist=[{cpu_list_str}]"
+            )
+            # The time(milliseconds) that a thread should wait after
+            # completing the execution of a parallel region, before sleeping.
+            envs_dict["KMP_BLOCKTIME"] = "1"
+            # Prevents the CPU to run into low performance state
+            envs_dict["KMP_TPAUSE"] = "0"
+            # Provides fine granularity parallelism
+            envs_dict["KMP_FORKJOIN_BARRIER_PATTERN"] = "dist,dist"
+            envs_dict["KMP_PLAIN_BARRIER_PATTERN"] = "dist,dist"
+            envs_dict["KMP_REDUCTION_BARRIER_PATTERN"] = "dist,dist"
+        elif self.use_gomp:
+            # set GOMP envs
+            # likes '0 1 2 ...'
+            cpu_list_str = " ".join(cpu_list)
+            envs_dict["GOMP_CPU_AFFINITY"] = cpu_list_str
+        else:
+            # set OMP envs
+            # likes '{0,1,2,...}'
+            cpu_list_str = ",".join(cpu_list)
+            envs_dict["OMP_PLACES"] = f"{{{cpu_list_str}}}"
+            envs_dict["OMP_PROC_BIND"] = "true"
+
+        # backup envs
+        old_envs_dict = {}
+        for k in envs_dict:
+            old_envs_dict[k] = os.environ.get(k)
+
+        try:
+            # set envs
+            for k, v in envs_dict.items():
+                os.environ[k] = v
+            yield
+        finally:
+            # restore old envs
+            for k, v in old_envs_dict.items():  # type: ignore
+                if v is None:
+                    os.environ.pop(k, None)
+                else:
+                    os.environ[k] = v
+
+    def _parse_omp_threads_bind_env(self):
+        vllm_mask = envs.VLLM_CPU_OMP_THREADS_BIND
+        self.skip_setup = vllm_mask == "nobind"
+        self.auto_setup = vllm_mask == "auto"
+        self.reserved_cpu_list = []
+        self.cpu_lists = []
+
+        if self.auto_setup:
+            # auto generate CPU lists
+            cpu_arch = current_platform.get_cpu_architecture()
+            if cpu_arch == CpuArchEnum.POWERPC:
+                # For POWERPC SMT-8/4/2
+                cpu_list, reserve_list = self._get_autobind_cpu_ids(
+                    lambda cpus: [cpu for cpu in cpus if cpu.id % 8 < 4]
+                )
+            elif cpu_arch in (CpuArchEnum.X86, CpuArchEnum.S390X):
+                # For x86/S390X SMT-2, use 1 logical CPU per physical core
+                cpu_list, reserve_list = self._get_autobind_cpu_ids(
+                    lambda cpus: cpus[-1:]
+                )
+            elif cpu_arch in (CpuArchEnum.ARM, CpuArchEnum.RISCV):
+                # For AArch64 / RISC-V, no SMT, use all logical CPUs
+                cpu_list, reserve_list = self._get_autobind_cpu_ids(lambda cpus: cpus)
             else:
-                masks = [None]
-            if mock is None:
-                data = _get_cpu_topology_json()
+                cpu_list, reserve_list = [], []
+                raise RuntimeError(f"{cpu_arch} doesn't support auto CPU binding.")
+
+            for item in cpu_list:
+                self.cpu_lists.append([x.id for x in item])
+            self.reserved_cpu_list = [x.id for x in reserve_list]
+        elif not self.skip_setup:
+            # user defined CPU lists
+            omp_cpuids_list = vllm_mask.split("|")
+            if self.local_dp_rank is not None:
+                local_dp_rank = self.local_dp_rank
+                world_size = self.local_world_size
+                # Rank mapping [DP, PP, TP]
+                omp_cpuids_list = omp_cpuids_list[
+                    local_dp_rank * world_size : (local_dp_rank + 1) * world_size
+                ]
+
+            assert len(omp_cpuids_list) == self.local_world_size, (
+                "Given "
+                f"number of CPU id list {omp_cpuids_list} doesn't match "
+                f"local world size {self.local_world_size}."
+            )
+
+            # parse CPU list strings like "5,2-4" to [5, 2, 3, 4]
+            self.cpu_lists = [cr_utils.parse_id_list(s) for s in omp_cpuids_list]
+        else:
+            # skip
+            self.cpu_lists = []
+
+        msg = (
+            "OpenMP thread binding info: \n"
+            f"\tVLLM_CPU_OMP_THREADS_BIND={vllm_mask!r}, "
+            f"auto_setup={self.auto_setup}, skip_setup={self.skip_setup}\n"
+            f"\tlocal_world_size={self.local_world_size}, "
+            f"reserve_cpu_num={self.reserve_cpu_num}\n"
+        )
+        for i, cpus in enumerate(self.cpu_lists):
+            msg += f"\tlocal_rank={i}, core ids={cpus}\n"
+        msg += f"\treserved_cpus={self.reserved_cpu_list}"
+        logger.info(msg)
+
+    def _get_autobind_cpu_ids(
+        self, cpu_selector: Callable[[list[LogicalCPUInfo]], list[LogicalCPUInfo]]
+    ) -> tuple[list[list[LogicalCPUInfo]], list[LogicalCPUInfo]]:
+        """
+        Return CPU ids to bind based on NUMA nodes, and CPU ids reserved for
+        other processes.
+        Currently for rank N, only CPU ids on the N-th node in available NUMA
+        node list will be selected.
+        Args:
+            cpu_selector: a callable object to select CPUs from a CPU list
+            of a physical core. The input is a LogicalCPUInfo list contains
+            logical CPUs of a physical CPU, sorted by the LogicalCPUInfo.id.
+            A selected LogicalCPUInfo list should be returned.
+        """
+
+        # this memory node list has been sliced for DP offset
+        allowed_numa_nodes = cr_utils.get_visible_memory_node()
+        logical_cpu_list = cr_utils.get_allowed_cpu_list()
+
+        local_world_size = self.local_world_size
+        assert (
+            len(allowed_numa_nodes) >= local_world_size or self.simulate_multi_node
+        ), (
+            f"Not enough allowed NUMA nodes to bind threads of "
+            f"{local_world_size} local CPUWorkers. "
+            f"Allowed NUMA nodes are {allowed_numa_nodes}. "
+            "Please try to bind threads manually or decrease DP/TP/PP."
+        )
+
+        # Generate OMP CPU list for each rank
+        cpu_lists_of_ranks = []
+        reserved_cpu_list = []
+        total_cpu_num = 0
+        for local_rank in range(self.local_world_size):
+            if not self.simulate_multi_node:
+                selected_numa_node = allowed_numa_nodes[local_rank]
+                selected_logical_cpu_list = [
+                    x for x in logical_cpu_list if x.numa_node == selected_numa_node
+                ]
             else:
-                with open(mock, mode="rb") as jf:
-                    data = jf.read()
-            lscpu = json.loads(data)
-            for mask in masks:
-                resources = enumerate_resources(lscpu, mask, affinity)
-                omp_places.extend(create_omp_places(resources, strategy, smt))
-            self.omp_places = sorted(
-                omp_places,
-                key=lambda p: "{:04d}-{:04d}".format(len(p["mask"]), max(p["mask"])),
-                reverse=True,
+                world_size_across_dp = self.local_world_size * self.internal_dp_size
+                assert len(logical_cpu_list) >= world_size_across_dp
+                selected_logical_cpu_list = sorted(
+                    logical_cpu_list, key=lambda x: x.numa_node
+                )
+                sim_cpu_num_per_node = (
+                    len(selected_logical_cpu_list) // world_size_across_dp
+                )
+                assert self.local_dp_rank is not None
+                start_idx = (
+                    local_rank + self.local_world_size * self.local_dp_rank
+                ) * sim_cpu_num_per_node
+                selected_logical_cpu_list = selected_logical_cpu_list[
+                    start_idx : (start_idx + sim_cpu_num_per_node)
+                ]
+
+            # Select logical CPUs on same physical cores via cpu_selector
+            core_to_cpus: dict[int, list[LogicalCPUInfo]] = {}
+            for cpu_info in selected_logical_cpu_list:
+                if cpu_info.physical_core not in core_to_cpus:
+                    core_to_cpus[cpu_info.physical_core] = []
+                core_to_cpus[cpu_info.physical_core].append(cpu_info)
+            selected_logical_cpu_list = []
+            for cpu_list in core_to_cpus.values():
+                cpu_list = sorted(cpu_list, key=lambda x: x.id)
+                selected_logical_cpu_list.extend(cpu_selector(cpu_list))
+
+            # sort selected cores based on core id
+            selected_logical_cpu_list = sorted(
+                selected_logical_cpu_list, key=lambda x: x.id
             )
 
-    def run(self, what, *args, **kwargs):
-        """Run arg with correct OMP environment"""
-        if self.setup_omp:
-            for place in self.omp_places:
-                if place["available"]:
-                    reserve = int(os.environ.get("VLLM_CPU_NUM_OF_RESERVED_CPU", 0))
-                    place["available"] = False
-                    # pylint: disable=consider-using-f-string
-                    os.environ["OMP_PLACES"] = "{}".format(place["mask"])
-                    os.environ["OMP_NUM_THREADS"] = "{}".format(
-                        len(place["mask"]) - reserve
-                    )
-                    os.environ["OMP_PROC_BIND"] = "TRUE"
-                    return what(*args, **kwargs)
-            raise IndexError("Out of OMP places")
-        return what(*args, **kwargs)
+            cpu_lists_of_ranks.append(selected_logical_cpu_list)
+            total_cpu_num += len(selected_logical_cpu_list)
+
+        # Reserve CPUs for other processes
+        if total_cpu_num <= self.reserve_cpu_num:
+            logger.warning(
+                "Selected CPU core number (%s) "
+                "should be greater than reserved CPU core "
+                "number (%s).",
+                total_cpu_num,
+                self.reserve_cpu_num,
+            )
+            return cpu_lists_of_ranks, []
+
+        reserve_num_per_rank = [
+            self.reserve_cpu_num // self.local_world_size
+        ] * self.local_world_size
+        # last rank first
+        for i in range(
+            self.local_world_size - 1,
+            self.local_world_size - 1 - self.reserve_cpu_num % self.local_world_size,
+            -1,
+        ):
+            reserve_num_per_rank[i] += 1
+        for i in range(self.local_world_size):
+            num = reserve_num_per_rank[i]
+            if num > 0:
+                reserved_cpu_list.extend(cpu_lists_of_ranks[i][-num:])
+                cpu_lists_of_ranks[i] = cpu_lists_of_ranks[i][:-num]
+
+        return cpu_lists_of_ranks, reserved_cpu_list
diff --git a/vllm/utils/profiling.py b/vllm/utils/profiling.py
index b66910693957..ce2a5ba3993a 100644
--- a/vllm/utils/profiling.py
+++ b/vllm/utils/profiling.py
@@ -8,7 +8,13 @@
 from functools import wraps
 from typing import Any
 
+from typing_extensions import deprecated
 
+
+@deprecated(
+    "vllm.utils.profiling.cprofile_context() is deprecated and will be removed "
+    "in v0.21. Use Python's cProfile module directly instead."
+)
 @contextlib.contextmanager
 def cprofile_context(save_file: str | None = None):
     """Run a cprofile
@@ -32,6 +38,10 @@ def cprofile_context(save_file: str | None = None):
             prof.print_stats(sort="cumtime")
 
 
+@deprecated(
+    "vllm.utils.profiling.cprofile() is deprecated and will be removed in "
+    "v0.21. Use Python's cProfile module directly instead."
+)
 def cprofile(save_file: str | None = None, enabled: bool = True):
     """Decorator to profile a Python method using cProfile.
 
diff --git a/vllm/utils/serial_utils.py b/vllm/utils/serial_utils.py
index 596a71935107..5fde5ac7105d 100644
--- a/vllm/utils/serial_utils.py
+++ b/vllm/utils/serial_utils.py
@@ -27,6 +27,7 @@ def nbytes(self) -> int:
 
 
 EmbedDType = Literal["float32", "float16", "bfloat16", "fp8_e4m3", "fp8_e5m2"]
+MmMetadataDType = Literal["int32", "int64", "uint8", "bool"]
 Endianness = Literal["native", "big", "little"]
 EncodingFormat = Literal["float", "base64", "bytes", "bytes_only"]
 
@@ -42,6 +43,15 @@ def nbytes(self) -> int:
     "fp8_e4m3": DTypeInfo(torch.float8_e4m3fn, torch.uint8, np.uint8),
     "fp8_e5m2": DTypeInfo(torch.float8_e5m2, torch.uint8, np.uint8),
 }
+MM_METADATA_DTYPES: Mapping[MmMetadataDType, DTypeInfo] = {
+    "int32": DTypeInfo(torch.int32, torch.int32, np.int32),
+    "int64": DTypeInfo(torch.int64, torch.int64, np.int64),
+    "uint8": DTypeInfo(torch.uint8, torch.uint8, np.uint8),
+    "bool": DTypeInfo(torch.bool, torch.uint8, np.uint8),
+}
+_ALL_SERIAL_DTYPES: Mapping[str, DTypeInfo] = {
+    k: v for d in (EMBED_DTYPES, MM_METADATA_DTYPES) for k, v in d.items()
+}
 ENDIANNESS: tuple[Endianness, ...] = get_args(Endianness)
 
 
@@ -56,14 +66,14 @@ def tensor2base64(x: torch.Tensor) -> str:
 
 def tensor2binary(
     tensor: torch.Tensor,
-    embed_dtype: EmbedDType,
+    embed_dtype: "EmbedDType | MmMetadataDType",
     endianness: Endianness,
 ) -> bytes:
     assert isinstance(tensor, torch.Tensor)
-    assert embed_dtype in EMBED_DTYPES
+    assert embed_dtype in _ALL_SERIAL_DTYPES
     assert endianness in ENDIANNESS
 
-    dtype_info = EMBED_DTYPES[embed_dtype]
+    dtype_info = _ALL_SERIAL_DTYPES[embed_dtype]
 
     np_array = (
         tensor.to(dtype_info.torch_dtype)
@@ -82,13 +92,13 @@ def tensor2binary(
 def binary2tensor(
     binary: bytes,
     shape: tuple[int, ...],
-    embed_dtype: EmbedDType,
+    embed_dtype: "EmbedDType | MmMetadataDType",
     endianness: Endianness,
 ) -> torch.Tensor:
-    assert embed_dtype in EMBED_DTYPES
+    assert embed_dtype in _ALL_SERIAL_DTYPES
     assert endianness in ENDIANNESS
 
-    dtype_info = EMBED_DTYPES[embed_dtype]
+    dtype_info = _ALL_SERIAL_DTYPES[embed_dtype]
 
     np_array = np.frombuffer(binary, dtype=dtype_info.numpy_view_dtype).reshape(shape)
 
diff --git a/vllm/utils/torch_utils.py b/vllm/utils/torch_utils.py
index 60b40855b5d3..12ec5b0fcc66 100644
--- a/vllm/utils/torch_utils.py
+++ b/vllm/utils/torch_utils.py
@@ -3,6 +3,7 @@
 import contextlib
 import importlib.metadata
 import os
+import platform
 import random
 import threading
 from collections.abc import Callable, Collection
@@ -42,6 +43,11 @@
     "fp8_per_token_head": torch.uint8,
     "fp8_inc": torch.float8_e4m3fn,
     "fp8_ds_mla": torch.uint8,
+    "turboquant_k8v4": torch.uint8,
+    "turboquant_4bit_nc": torch.uint8,
+    "turboquant_k3v4_nc": torch.uint8,
+    "turboquant_3bit_nc": torch.uint8,
+    "nvfp4": torch.uint8,
 }
 
 TORCH_DTYPE_TO_NUMPY_DTYPE = {
@@ -55,17 +61,24 @@
 
 
 MODELOPT_TO_VLLM_KV_CACHE_DTYPE_MAP = {
-    # TODO: Add more modelopt kv cache dtype
-    # mappings here when it supported by some attention backend
-    # (for example supports nvfp4).
     "fp8": "fp8_e4m3",
+    "nvfp4": "nvfp4",
 }
 
 T = TypeVar("T")
 
 
+# Pin memory in non-WSL case.
+# Logic duplicated here for now to avoid circular import.
+PIN_MEMORY = "microsoft" not in " ".join(platform.uname()).lower()
+
+
 def is_quantized_kv_cache(kv_cache_dtype: str) -> bool:
-    return kv_cache_dtype.startswith("fp8") or kv_cache_dtype.endswith("per_token_head")
+    return (
+        kv_cache_dtype.startswith("fp8")
+        or kv_cache_dtype.endswith("per_token_head")
+        or kv_cache_dtype == "nvfp4"
+    )
 
 
 def kv_cache_uses_per_token_head_scales(kv_cache_dtype: str) -> bool:
@@ -103,6 +116,32 @@ def is_strictly_contiguous(t: torch.Tensor) -> bool:
     return True
 
 
+def canonicalize_singleton_dim_strides(t: torch.Tensor) -> torch.Tensor:
+    """Fix degenerate strides on size=1 dimensions for CUDA TMA compatibility.
+
+    PyTorch allows any stride on a size=1 dim (is_contiguous() is always True
+    there), so a size=1 dim may have stride=1 (2 bytes for bf16) instead of
+    the canonical product(shape[i+1:]).  CUDA TMA on H100+ requires all
+    non-outermost strides to be ≥16-byte aligned; stride=1 triggers
+    cudaErrorIllegalInstruction.  Zero-copy: patches stride metadata only via
+    as_strided; returns t unchanged if all size=1 strides are already canonical.
+    """
+    if 1 not in t.shape:
+        return t
+    strides = list(t.stride())
+    shape = t.shape
+    prev_stride = 1
+    changed = False
+    for i in range(len(shape) - 1, -1, -1):
+        if shape[i] == 1 and strides[i] != prev_stride:
+            strides[i] = prev_stride
+            changed = True
+        prev_stride = strides[i] * shape[i]
+    if not changed:
+        return t
+    return t.as_strided(t.shape, strides)
+
+
 @contextlib.contextmanager
 def set_default_torch_dtype(dtype: torch.dtype):
     """Sets the default torch dtype to the given dtype."""
@@ -295,6 +334,8 @@ def get_kv_cache_quant_algo_string(quant_cfg: dict[str, Any]) -> str | None:
                 and kv_algo.get("type") == "float"
             ):
                 kv_algo = "fp8"
+            elif kv_algo.get("num_bits") == 4 and kv_algo.get("type") == "float":
+                kv_algo = "nvfp4"
             else:
                 # Unknown/unsupported format - return "auto" as safe fallback
                 logger.warning(
@@ -371,6 +412,95 @@ def set_random_seed(seed: int | None) -> None:
         current_platform.manual_seed_all(seed)
 
 
+def nvfp4_kv_cache_full_dim(head_size: int) -> int:
+    """Packed last dim for NVFP4 KV cache: fp4 data + fp8 block scales."""
+    return head_size // 2 + head_size // 16
+
+
+def _nvfp4_split_data_scale(
+    kv_side: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Split a single NVFP4 KV-side buffer into data and scale views.
+
+    The input is a 4D tensor for one KV side (K or V) whose last
+    dimension is ``full_dim = data_dim + scale_dim``.  The physical
+    layout within each side is [data | scale], both packed contiguously.
+
+    Args:
+        kv_side: 4D uint8 tensor with shape
+            ``(num_pages, dim_1, dim_2, full_dim)``.
+            May be in any permutation order (NHD or HND).
+
+    Returns:
+        ``(data, scale)`` where
+        ``data`` is a uint8 view with shape
+        ``(num_pages, dim_1, dim_2, data_dim)``.
+        ``scale`` is a float8_e4m3fn view with shape
+        ``(num_pages, dim_1, dim_2, scale_dim)``.
+    """
+    num_pages = kv_side.shape[0]
+    dim_1, dim_2 = kv_side.shape[1], kv_side.shape[2]
+    full_dim = kv_side.shape[3]
+    data_dim = full_dim * 8 // 9
+    scale_dim = full_dim - data_dim
+
+    data_per_kv = dim_1 * dim_2 * data_dim
+    page_bytes = kv_side.stride(0)
+
+    # Derive inner strides from the kv_side strides, scaling by the
+    # ratio of the target dim to full_dim.  This preserves the physical
+    # layout (NHD vs HND) encoded in the input tensor's strides.
+    s1 = kv_side.stride(1) * data_dim // full_dim
+    s2 = kv_side.stride(2) * data_dim // full_dim
+    data_shape = (num_pages, dim_1, dim_2, data_dim)
+    data_strides = (page_bytes, s1, s2, 1)
+
+    s1_s = kv_side.stride(1) * scale_dim // full_dim
+    s2_s = kv_side.stride(2) * scale_dim // full_dim
+    scale_shape = (num_pages, dim_1, dim_2, scale_dim)
+    scale_strides = (page_bytes, s1_s, s2_s, 1)
+
+    base = kv_side.storage_offset()
+    data = torch.as_strided(kv_side, data_shape, data_strides, storage_offset=base)
+    scale = torch.as_strided(
+        kv_side, scale_shape, scale_strides, storage_offset=base + data_per_kv
+    ).view(torch.float8_e4m3fn)
+
+    return data, scale
+
+
+def nvfp4_kv_cache_split_views(kv_cache: torch.Tensor) -> tuple[tuple, tuple]:
+    """Split an NVFP4 KV cache tensor into data and scale views.
+
+    Accepts either a 5D tensor ``(num_pages, 2, dim_2, dim_3, full_dim)``
+    or a 4D single-side tensor ``(num_pages, dim_2, dim_3, full_dim)``.
+
+    Per-page layout: [K_data | K_scale | V_data | V_scale].
+    Each KV side is self-contained (data followed by its scale), so the
+    5D case simply splits each side independently.
+
+    The returned views are in the same dim order as the input (NHD or
+    HND), so callers get views matching whichever order they passed in.
+
+    Args:
+        kv_cache: 5D or 4D uint8 tensor where the last dimension is
+            ``full_dim = data_dim + scale_dim = 9 * head_size / 16``.
+
+    Returns:
+        For 5D input:
+            ``(k_data, v_data), (k_scale, v_scale)``
+        For 4D input (single KV side):
+            ``(data,), (scale,)``
+    """
+    if kv_cache.dim() == 4:
+        data, scale = _nvfp4_split_data_scale(kv_cache)
+        return (data,), (scale,)
+
+    k_data, k_scale = _nvfp4_split_data_scale(kv_cache[:, 0])
+    v_data, v_scale = _nvfp4_split_data_scale(kv_cache[:, 1])
+    return (k_data, v_data), (k_scale, v_scale)
+
+
 def create_kv_caches_with_random_flash(
     num_blocks: int,
     block_size: int,
@@ -397,15 +527,31 @@ def create_kv_caches_with_random_flash(
     value_caches: list[torch.Tensor] = []
 
     for _ in range(num_layers):
-        key_value_cache = torch.empty(
-            size=kv_cache_allocation_shape, dtype=dtype, device=device
-        ).permute(*stride_order)
-        if cache_dtype in ["auto", "half", "bfloat16", "float"]:
-            key_value_cache.uniform_(-scale, scale)
-        elif cache_dtype == "fp8":
-            _generate_random_fp8(key_value_cache, -scale, scale)
+        if cache_dtype == "nvfp4":
+            # Full page dim: fp4 data + fp8 block scales per head.
+            # Per page layout: [K_data | K_scale | V_data | V_scale]
+            # Returns [:, 0] and [:, 1] like all other dtypes.
+            full_dim = nvfp4_kv_cache_full_dim(head_size)
+            nvfp4_shape = (num_blocks, 2, block_size, num_heads, full_dim)
+            nvfp4_phys = tuple(nvfp4_shape[i] for i in stride_order)
+            inv = [stride_order.index(i) for i in range(len(stride_order))]
+            key_value_cache = torch.randint(
+                0,
+                256,
+                nvfp4_phys,
+                dtype=dtype,
+                device=device,
+            ).permute(*inv)
         else:
-            raise ValueError(f"Does not support key cache of type {cache_dtype}")
+            key_value_cache = torch.empty(
+                size=kv_cache_allocation_shape, dtype=dtype, device=device
+            ).permute(*stride_order)
+            if cache_dtype in ["auto", "half", "bfloat16", "float"]:
+                key_value_cache.uniform_(-scale, scale)
+            elif cache_dtype == "fp8":
+                _generate_random_fp8(key_value_cache, -scale, scale)
+            else:
+                raise ValueError(f"Does not support key cache of type {cache_dtype}")
         key_caches.append(key_value_cache[:, 0])
         value_caches.append(key_value_cache[:, 1])
     return key_caches, value_caches
@@ -462,12 +608,12 @@ def create_kv_caches_with_random(
 def async_tensor_h2d(
     data: list,
     dtype: torch.dtype,
-    target_device: str | torch.device,
-    pin_memory: bool,
+    device: str | torch.device,
+    pin_memory: bool = PIN_MEMORY,
 ) -> torch.Tensor:
     """Asynchronously create a tensor and copy it from host to device."""
     t = torch.tensor(data, dtype=dtype, pin_memory=pin_memory, device="cpu")
-    return t.to(device=target_device, non_blocking=True)
+    return t.to(device=device, non_blocking=True)
 
 
 def make_ndarray_with_pad(
diff --git a/vllm/v1/attention/backend.py b/vllm/v1/attention/backend.py
index 28d077fcb771..d83489238d33 100644
--- a/vllm/v1/attention/backend.py
+++ b/vllm/v1/attention/backend.py
@@ -11,6 +11,8 @@
 from typing_extensions import deprecated
 
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    kFp8Dynamic64Sym,
+    kFp8Dynamic128Sym,
     kFp8StaticTensorSym,
     kNvfp4Dynamic,
 )
@@ -234,6 +236,10 @@ def supports_non_causal(cls) -> bool:
         """
         return False
 
+    @classmethod
+    def supports_batch_invariance(cls) -> bool:
+        return False
+
     @classmethod
     def supports_attn_type(cls, attn_type: str) -> bool:
         """Check if backend supports a given attention type.
@@ -276,6 +282,7 @@ def validate_configuration(
         device_capability: "DeviceCapability",
         attn_type: str,
         use_non_causal: bool = False,
+        use_batch_invariant: bool = False,
     ) -> list[str]:
         invalid_reasons = []
         if not cls.supports_head_size(head_size):
@@ -310,6 +317,8 @@ def validate_configuration(
             invalid_reasons.append(f"attention type {attn_type} not supported")
         if use_non_causal and not cls.supports_non_causal():
             invalid_reasons.append("non-causal attention not supported")
+        if use_batch_invariant and not cls.supports_batch_invariance():
+            invalid_reasons.append("batch invariance not supported")
         combination_reason = cls.supports_combination(
             head_size,
             dtype,
@@ -383,11 +392,22 @@ class CommonAttentionMetadata:
     dcp_local_seq_lens_cpu: torch.Tensor | None = None
     """Sequence lengths of the local rank in decode context parallelism world"""
 
+    positions: torch.Tensor | None = None
+    """(num_actual_tokens,) token positions.  Optional; set when the caller
+    has positions available so that builders can pre-compute position-dependent
+    metadata (e.g. C128A topk indices for DeepSeek V4)."""
+
     is_prefilling: torch.Tensor | None = None
     """(batch_size,) bool tensor: True if request is still in prefill phase
     (num_computed_tokens < num_prompt_tokens). Used by some backends to
     distinguish actual decodes from short extends."""
 
+    seq_lens_cpu_upper_bound: torch.Tensor | None = None
+    """(batch_size,) CPU upper bound on seq_lens. Precise for prefill rows
+    and for all rows outside async spec decode; optimistic for async-spec
+    decode rows (assumes every draft was accepted). Not safe for kernels
+    that need exact per-row context lengths on decode rows."""
+
     # WARNING: Deprecated fields. Will be removed in a future release (v0.15.0)
     _seq_lens_cpu: torch.Tensor | None = None
     _num_computed_tokens_cpu: torch.Tensor | None = None
@@ -880,7 +900,12 @@ def fused_output_quant_supported(self, quant_key: "QuantKey"):
         Since MLA quantization is done manually in forward_impl (common code),
         all MLA backends support it by default.
         """
-        return quant_key in (kFp8StaticTensorSym, kNvfp4Dynamic)
+        return quant_key in (
+            kFp8StaticTensorSym,
+            kNvfp4Dynamic,
+            kFp8Dynamic128Sym,
+            kFp8Dynamic64Sym,
+        )
 
     def do_kv_cache_update(
         self,
@@ -918,7 +943,12 @@ def fused_output_quant_supported(self, quant_key: "QuantKey"):
         Since MLA quantization is done manually in forward_impl (common code),
         all MLA backends support it by default.
         """
-        return quant_key in (kFp8StaticTensorSym, kNvfp4Dynamic)
+        return quant_key in (
+            kFp8StaticTensorSym,
+            kNvfp4Dynamic,
+            kFp8Dynamic128Sym,
+            kFp8Dynamic64Sym,
+        )
 
     @abstractmethod
     def __init__(
diff --git a/vllm/v1/attention/backends/cpu_attn.py b/vllm/v1/attention/backends/cpu_attn.py
index 76c78fed46a3..51e3e865d52f 100644
--- a/vllm/v1/attention/backends/cpu_attn.py
+++ b/vllm/v1/attention/backends/cpu_attn.py
@@ -1,7 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from dataclasses import dataclass
-from typing import ClassVar
+from typing import TYPE_CHECKING, ClassVar
+
+if TYPE_CHECKING:
+    from vllm.config.cache import CacheDType
 
 import torch
 
@@ -18,6 +21,7 @@
     AttentionMetadataBuilder,
     AttentionType,
     CommonAttentionMetadata,
+    MultipleOf,
 )
 from vllm.v1.attention.backends.utils import (
     split_decodes_and_prefills,
@@ -26,7 +30,12 @@
 
 logger = init_logger(__name__)
 
-_CPU_ARCH_PREFER_MIXED_BATCH = (CpuArchEnum.X86, CpuArchEnum.ARM, CpuArchEnum.S390X)
+_CPU_ARCH_PREFER_MIXED_BATCH = (
+    CpuArchEnum.X86,
+    CpuArchEnum.ARM,
+    CpuArchEnum.S390X,
+    CpuArchEnum.POWERPC,
+)
 
 
 class CPUAttentionBackend(AttentionBackend):
@@ -35,6 +44,16 @@ class CPUAttentionBackend(AttentionBackend):
         torch.bfloat16,
         torch.float32,
     ]
+    supported_kv_cache_dtypes: ClassVar[list["CacheDType"]] = [
+        "auto",
+        "fp8",
+        "fp8_e4m3",
+        "fp8_e5m2",
+    ]
+
+    @staticmethod
+    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
+        return [MultipleOf(16)]
 
     @classmethod
     def get_supported_head_sizes(cls) -> list[int]:
@@ -133,7 +152,13 @@ def __init__(
         if self.window_size is None:
             self.window_size = -1
         self.block_size = vllm_config.cache_config.block_size
-        self.isa = _get_attn_isa(self.dtype, self.block_size, self.head_dim)
+        kv_cache_dtype_str = vllm_config.cache_config.cache_dtype
+        self.isa = _get_attn_isa(
+            self.dtype,
+            self.block_size,
+            self.head_dim,
+            kv_cache_dtype_str,
+        )
         self.is_cross_attention = isinstance(kv_cache_spec, CrossAttentionSpec)
 
     def build(
@@ -247,8 +272,7 @@ def __init__(
         self.kv_cache_dtype = kv_cache_dtype
         self.num_queries_per_kv = self.num_heads // self.num_kv_heads
 
-        if is_quantized_kv_cache(kv_cache_dtype):
-            raise NotImplementedError("FP8 KV cache is unsupported in CPU_ATTN")
+        self.is_fp8_kv_cache = is_quantized_kv_cache(kv_cache_dtype)
         self.attn_type = attn_type
 
         self.sinks = sinks
@@ -325,6 +349,9 @@ def forward(
                 value_cache,
                 attn_metadata.slot_mapping,
                 attn_metadata.isa,
+                k_scale=layer._k_scale_float,
+                v_scale=layer._v_scale_float,
+                kv_cache_dtype=self.kv_cache_dtype,
             )
 
         if attn_metadata.use_sdpa_prefill:
@@ -356,6 +383,9 @@ def forward(
                 softcap=self.logits_soft_cap,
                 scheduler_metadata=attn_metadata.scheduler_metadata,
                 s_aux=self.sinks,
+                k_scale=layer._k_scale_float,
+                v_scale=layer._v_scale_float,
+                kv_cache_dtype=self.kv_cache_dtype,
             )
 
         return output
@@ -477,13 +507,28 @@ def _make_sliding_window_bias(
 
 
 def _get_attn_isa(
-    dtype: torch.dtype, block_size: int, head_size: int | None = None
+    dtype: torch.dtype,
+    block_size: int,
+    head_size: int | None = None,
+    kv_cache_dtype: str | None = None,
 ) -> str:
+    fp8_kv = is_quantized_kv_cache(kv_cache_dtype) if kv_cache_dtype else False
     if head_size is not None and head_size % 32 != 0 and head_size % 16 == 0:
+        if fp8_kv:
+            raise NotImplementedError(
+                "FP8 KV cache requires head_size divisible by 32 on CPU."
+            )
         return "vec16"
     supports_amx = torch.cpu._is_amx_tile_supported()
-    supports_arm = current_platform.get_cpu_architecture() == CpuArchEnum.ARM
-    supports_vxe = current_platform.get_cpu_architecture() == CpuArchEnum.S390X
+    arch = current_platform.get_cpu_architecture()
+    supports_arm = arch == CpuArchEnum.ARM
+    supports_vxe = arch == CpuArchEnum.S390X
+    supports_vsx = arch == CpuArchEnum.POWERPC
+    supports_avx512 = torch.cpu._is_avx512_supported()
+    if fp8_kv and not supports_amx and not supports_avx512:
+        raise NotImplementedError(
+            "FP8 KV cache on CPU requires x86 with AVX-512 or AMX."
+        )
     if supports_amx and dtype in (torch.bfloat16,) and block_size % 32 == 0:
         return "amx"
     elif block_size % 32 == 0:
@@ -492,6 +537,8 @@ def _get_attn_isa(
             return "neon"
         elif supports_vxe:
             return "vxe"
+        elif supports_vsx:
+            return "vsx"
         else:
             return "vec"
     else:
diff --git a/vllm/v1/attention/backends/fa_utils.py b/vllm/v1/attention/backends/fa_utils.py
index db8cafeb7487..1e74e4c48eda 100644
--- a/vllm/v1/attention/backends/fa_utils.py
+++ b/vllm/v1/attention/backends/fa_utils.py
@@ -54,7 +54,10 @@ def get_scheduler_metadata(*args: Any, **kwargs: Any) -> None:  # type: ignore[m
 
 
 def get_flash_attn_version(
-    requires_alibi: bool = False, head_size: int | None = None
+    requires_alibi: bool = False,
+    head_size: int | None = None,
+    head_size_v: int | None = None,
+    has_sinks: bool = False,
 ) -> int | None:
     if current_platform.is_xpu():
         return 2
@@ -112,13 +115,36 @@ def get_flash_attn_version(
             )
             fa_version = 2
 
+        # Some FA3 unsupported SM90 cases can use FA4 when available.
+        if (
+            fa_version == 3
+            and device_capability.major == 9
+            and is_fa_version_supported(4)
+        ):
+            upgrade_reason = None
+            if head_size is not None and head_size > 256:
+                upgrade_reason = f"FA3 does not support head_size={head_size} on SM90"
+            elif (
+                has_sinks
+                and head_size is not None
+                and head_size_v is not None
+                and head_size != head_size_v
+            ):
+                upgrade_reason = "Diff-KV with sinks"
+            if upgrade_reason:
+                logger.info_once(
+                    "%s: upgrading FlashAttention 3 -> 4",
+                    upgrade_reason,
+                    scope="local",
+                )
+                fa_version = 4
+
         # FA4 currently uses batch-shape-dependent scheduling
         # heuristics on SM100+, which breaks batch invariance.
         if envs.VLLM_BATCH_INVARIANT and fa_version == 4:
             logger.warning_once(
                 "Cannot use FA version 4 with batch invariance, "
                 "defaulting to FA version 2.",
-                scope="local",
             )
             fa_version = 2
 
@@ -181,8 +207,7 @@ def flash_attn_supports_quant_query_input() -> bool:
 def flash_attn_supports_sinks() -> bool:
     if current_platform.is_xpu():
         return True
-    else:
-        return get_flash_attn_version() == 3
+    return get_flash_attn_version() in (3, 4)
 
 
 def flash_attn_supports_mla():
diff --git a/vllm/v1/attention/backends/flash_attn.py b/vllm/v1/attention/backends/flash_attn.py
index 0e926a99b8c1..4b8b86d864be 100755
--- a/vllm/v1/attention/backends/flash_attn.py
+++ b/vllm/v1/attention/backends/flash_attn.py
@@ -11,7 +11,10 @@
 
 from vllm.model_executor.layers.attention import Attention
 from vllm.platforms import current_platform
-from vllm.utils.torch_utils import is_quantized_kv_cache
+from vllm.utils.torch_utils import (
+    canonicalize_singleton_dim_strides,
+    is_quantized_kv_cache,
+)
 from vllm.v1.attention.backend import (
     AttentionBackend,
     AttentionImpl,
@@ -103,6 +106,10 @@ def get_preferred_block_size(cls, default_block_size: int) -> int:
     def get_name() -> str:
         return "FLASH_ATTN"
 
+    @classmethod
+    def supports_batch_invariance(cls) -> bool:
+        return True
+
     @classmethod
     def supports_non_causal(cls) -> bool:
         return True
@@ -255,11 +262,16 @@ class FlashAttentionMetadata:
 def _get_sliding_window_configs(
     vllm_config: VllmConfig,
 ) -> set[tuple[int, int] | None]:
-    """Get the set of all sliding window configs used in the model."""
+    """Get the set of all sliding window configs used in the model.
+
+    Only inspects FlashAttentionImpl layers. Other backends (e.g.
+    TurboQuant, MLA) use their own metadata builders and are skipped.
+    """
     sliding_window_configs: set[tuple[int, int] | None] = set()
     layers = get_layers_from_vllm_config(vllm_config, Attention)
     for layer in layers.values():
-        assert isinstance(layer.impl, FlashAttentionImpl)
+        if not isinstance(layer.impl, FlashAttentionImpl):
+            continue
         sliding_window_configs.add(layer.impl.sliding_window)
     return sliding_window_configs
 
@@ -626,18 +638,9 @@ def __init__(
             requires_alibi=alibi_slopes is not None,
             head_size=head_size,
         )
-        # head_size > 256 requires FA4 on SM90+; force upgrade from FA3
-        if (
-            head_size > 256
-            and self.vllm_flash_attn_version == 3
-            and current_platform.is_cuda()
-            and current_platform.is_device_capability_family(90)
-        ):
-            self.vllm_flash_attn_version = 4
         logger.info_once(
             "Using FlashAttention version %s",
             self.vllm_flash_attn_version,
-            scope="local",
         )
         # Cache the batch invariant result for use in forward passes
         self.batch_invariant_enabled = envs.VLLM_BATCH_INVARIANT
@@ -739,6 +742,23 @@ def forward(
 
         # For decoder and cross-attention, use KV cache as before
         key_cache, value_cache = kv_cache.unbind(0)
+        # Fix degenerate strides on size-1 dims (e.g. num_kv_heads=1 with TP).
+        # FA3/4 on H100+ uses TMA, which requires ≥16-byte stride alignment.
+        # See vllm.utils.torch_utils.canonicalize_singleton_dim_strides.
+        fixed_k = canonicalize_singleton_dim_strides(key_cache)
+        fixed_v = canonicalize_singleton_dim_strides(value_cache)
+        if fixed_k is not key_cache or fixed_v is not value_cache:
+            logger.debug(
+                "Canonicalized degenerate KV cache strides (FlashAttention): "
+                "shape=%s, key strides before=%s after=%s, "
+                "value strides before=%s after=%s",
+                key_cache.shape,
+                key_cache.stride(),
+                fixed_k.stride(),
+                value_cache.stride(),
+                fixed_v.stride(),
+            )
+        key_cache, value_cache = fixed_k, fixed_v
 
         if is_quantized_kv_cache(self.kv_cache_dtype):
             # queries are quantized in the attention layer
@@ -853,6 +873,8 @@ def do_kv_cache_update(
             # we use direct Q, K, V tensors without caching
             return
 
+        # Scatter write into the KV cache using slot_mapping indices.
+        # No TMA kernel is invoked here, so stride canonicalization is not needed.
         key_cache, value_cache = kv_cache.unbind(0)
 
         # Reshape the input keys and values and store them in the cache.
@@ -1031,7 +1053,9 @@ def _forward_encoder_attention(
             window_size=sliding_window_size,
             softcap=self.logits_soft_cap,
             fa_version=self.vllm_flash_attn_version,
-            q_descale=layer._q_scale.expand(descale_shape),
+            q_descale=layer._q_scale.expand(descale_shape)
+            if self.supports_quant_query_input
+            else None,
             k_descale=layer._k_scale.expand(descale_shape),
             v_descale=layer._v_scale.expand(descale_shape),
             num_splits=1 if self.batch_invariant_enabled else 0,
diff --git a/vllm/v1/attention/backends/flash_attn_diffkv.py b/vllm/v1/attention/backends/flash_attn_diffkv.py
index cf98d3d09c71..82a9f07a4e59 100644
--- a/vllm/v1/attention/backends/flash_attn_diffkv.py
+++ b/vllm/v1/attention/backends/flash_attn_diffkv.py
@@ -4,16 +4,22 @@
 
 import torch
 
-from vllm.utils.torch_utils import is_quantized_kv_cache
+from vllm.logger import init_logger
+from vllm.utils.torch_utils import (
+    canonicalize_singleton_dim_strides,
+    is_quantized_kv_cache,
+)
 from vllm.v1.attention.backend import AttentionType
-from vllm.v1.attention.backends.fa_utils import is_flash_attn_varlen_func_available
+from vllm.v1.attention.backends.fa_utils import (
+    get_flash_attn_version,
+    is_flash_attn_varlen_func_available,
+)
 from vllm.v1.attention.ops.triton_reshape_and_cache_flash import (
     triton_reshape_and_cache_flash_diffkv,
 )
 
 if is_flash_attn_varlen_func_available():
     from vllm.v1.attention.backends.fa_utils import flash_attn_varlen_func
-from vllm.logger import init_logger
 from vllm.v1.attention.backends.utils import get_kv_cache_layout
 
 from .flash_attn import (
@@ -86,6 +92,20 @@ def get_kv_cache_stride_order(
 
 
 class FlashAttentionDiffKVImpl(FlashAttentionImpl):
+    vllm_flash_attn_version: int | None
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        # Re-derive the FA version with diff-kv context so that
+        # get_flash_attn_version can apply the FA3 -> FA4 upgrade rule
+        # for sinks + hdim != hdim_v.
+        self.vllm_flash_attn_version = get_flash_attn_version(
+            requires_alibi=self.alibi_slopes is not None,
+            head_size=self.head_size,
+            head_size_v=FlashAttentionDiffKVBackend.head_size_v,
+            has_sinks=self.sinks is not None,
+        )
+
     def do_kv_cache_update(
         self,
         layer: torch.nn.Module,
@@ -190,6 +210,23 @@ def forward(
         # Different head_size for K and V
         key_cache = kv_cache[..., : self.head_size]
         value_cache = kv_cache[..., self.head_size :]
+        # Fix degenerate strides on size-1 dims (e.g. num_kv_heads=1 with TP).
+        # FA3/4 on H100+ uses TMA, which requires ≥16-byte stride alignment.
+        # See vllm.utils.torch_utils.canonicalize_singleton_dim_strides.
+        fixed_k = canonicalize_singleton_dim_strides(key_cache)
+        fixed_v = canonicalize_singleton_dim_strides(value_cache)
+        if fixed_k is not key_cache or fixed_v is not value_cache:
+            logger.debug(
+                "Canonicalized degenerate KV cache strides (FlashAttentionDiffKV): "
+                "shape=%s, key strides before=%s after=%s, "
+                "value strides before=%s after=%s",
+                key_cache.shape,
+                key_cache.stride(),
+                fixed_k.stride(),
+                value_cache.stride(),
+                fixed_v.stride(),
+            )
+        key_cache, value_cache = fixed_k, fixed_v
 
         if is_quantized_kv_cache(self.kv_cache_dtype):
             # queries are quantized in the attention layer
diff --git a/vllm/v1/attention/backends/flashinfer.py b/vllm/v1/attention/backends/flashinfer.py
index 4adad61c2fab..7c1a784888eb 100755
--- a/vllm/v1/attention/backends/flashinfer.py
+++ b/vllm/v1/attention/backends/flashinfer.py
@@ -42,7 +42,13 @@
 )
 from vllm.utils.math_utils import cdiv
 from vllm.utils.platform_utils import is_pin_memory_available
-from vllm.utils.torch_utils import is_quantized_kv_cache, is_strictly_contiguous
+from vllm.utils.torch_utils import (
+    canonicalize_singleton_dim_strides,
+    is_quantized_kv_cache,
+    is_strictly_contiguous,
+    nvfp4_kv_cache_full_dim,
+    nvfp4_kv_cache_split_views,
+)
 from vllm.v1.attention.backend import (
     AttentionBackend,
     AttentionCGSupport,
@@ -327,6 +333,7 @@ class FlashInferBackend(AttentionBackend):
         "fp8",
         "fp8_e4m3",
         "fp8_e5m2",
+        "nvfp4",
     ]
 
     @staticmethod
@@ -355,6 +362,10 @@ def get_kv_cache_shape(
         head_size: int,
         cache_dtype_str: str = "auto",
     ) -> tuple[int, ...]:
+        if cache_dtype_str == "nvfp4":
+            # Packed layout: fp4 data + fp8 block scales in last dim
+            last_dim = nvfp4_kv_cache_full_dim(head_size)
+            return (num_blocks, 2, block_size, num_kv_heads, last_dim)
         return (num_blocks, 2, block_size, num_kv_heads, head_size)
 
     @staticmethod
@@ -379,13 +390,15 @@ def get_kv_cache_stride_order(
         return stride_order
 
     @staticmethod
-    def get_fp8_dtype_for_flashinfer(kv_cache_dtype: str) -> torch.dtype:
+    def get_dtype_for_flashinfer(kv_cache_dtype: str) -> torch.dtype:
         if kv_cache_dtype in ("fp8", "fp8_e4m3"):
             return torch.float8_e4m3fn
         elif kv_cache_dtype == "fp8_e5m2":
             return torch.float8_e5m2
+        elif kv_cache_dtype == "nvfp4":
+            return torch.uint8
         else:
-            raise ValueError(f"Unrecognized FP8 dtype: {kv_cache_dtype}")
+            raise ValueError(f"Unrecognized dtype: {kv_cache_dtype}")
 
     @classmethod
     def get_supported_head_sizes(cls) -> list[int]:
@@ -608,11 +621,18 @@ def __init__(
             self.cache_dtype = self.cache_config.cache_dtype
             # Cannot use self.kv_cache_spec.dtype here because kv_cache_spec
             # storage dtype may not be the same as the op dtype (uint8 vs fp8_e4m3)
-            self.kv_cache_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
-                self.cache_dtype
-            )
+            self.is_kvcache_nvfp4 = self.cache_dtype == "nvfp4"
+            if self.is_kvcache_nvfp4:
+                # For NVFP4, kv_cache_dtype stays as the string "nvfp4"
+                # which is passed to FlashInferImpl
+                self.kv_cache_dtype = self.cache_dtype
+            else:
+                self.kv_cache_dtype = FlashInferBackend.get_dtype_for_flashinfer(
+                    self.cache_dtype
+                )
         else:
             self.cache_dtype = "auto"
+            self.is_kvcache_nvfp4 = False
             assert self.kv_cache_spec.dtype == self.model_config.dtype
             self.kv_cache_dtype = self.kv_cache_spec.dtype
 
@@ -626,7 +646,13 @@ def __init__(
             can_use_trtllm
             and not vllm_config.attention_config.disable_flashinfer_q_quantization
         ):
-            self.q_data_type = self.kv_cache_dtype
+            if self.is_kvcache_nvfp4:
+                # NVFP4 KV cache uses FP8 quantized queries
+                self.q_data_type = FlashInferBackend.get_dtype_for_flashinfer(
+                    "fp8_e4m3"
+                )
+            else:
+                self.q_data_type = self.kv_cache_dtype
         else:
             self.q_data_type = self.model_config.dtype
 
@@ -742,8 +768,13 @@ def _get_prefill_wrapper(
                     dcp_a2a=self.dcp_a2a,
                 )
             else:
+                # NVFP4 KV cache requires the trtllm-gen backend inside
+                # the wrapper; fa2/fa3 do not support nvfp4.
+                backend = "trtllm-gen" if self.is_kvcache_nvfp4 else "auto"
                 self._prefill_wrapper = BatchPrefillWithPagedKVCacheWrapper(
-                    self._get_workspace_buffer(), get_kv_cache_layout()
+                    self._get_workspace_buffer(),
+                    get_kv_cache_layout(),
+                    backend=backend,
                 )
         assert self._prefill_wrapper is not None
         return self._prefill_wrapper
@@ -763,6 +794,9 @@ def _get_decode_wrapper(self, batch_size: int, use_cudagraph: bool = False):
                 paged_kv_indptr = None
                 paged_kv_indices = None
                 paged_kv_last_page_len = None
+            # NVFP4 KV cache requires the trtllm-gen backend inside
+            # the wrapper; fa2/fa3 do not support nvfp4.
+            backend = "trtllm-gen" if self.is_kvcache_nvfp4 else "auto"
             decode_wrapper = BatchDecodeWithPagedKVCacheWrapper(
                 self._get_workspace_buffer(),
                 get_kv_cache_layout(),
@@ -774,6 +808,7 @@ def _get_decode_wrapper(self, batch_size: int, use_cudagraph: bool = False):
                 # at least as good as cuda cores for all attention ops in latest
                 # gpus.
                 use_tensor_cores=True,
+                backend=backend,
             )
 
             # save the decode wrapper
@@ -897,9 +932,6 @@ def build(
         all_uses_trtllm = (num_prefills == 0 or prefill_use_trtllm) and (
             num_decodes == 0 or decode_use_trtllm
         )
-        is_only_trtllm_decode = num_prefills == 0 and (
-            num_decodes > 0 and decode_use_trtllm
-        )
 
         if not all_uses_trtllm:
             if self.has_sinks:
@@ -945,7 +977,10 @@ def build(
 
         # Guard access to seq_lens_cpu, which may not always be needed
         # and can be expensive to retrieve in async mode.
-        needs_seq_lens_cpu = self.use_dcp or use_cascade or not is_only_trtllm_decode
+        # When all attention (both prefill and decode) uses TRTLLM,
+        # seq_lens_cpu is not needed since TRTLLM paths use GPU tensors
+        # (block_tables, seq_lens) directly.
+        needs_seq_lens_cpu = self.use_dcp or use_cascade or not all_uses_trtllm
         seq_lens_cpu = common_attn_metadata.seq_lens_cpu if needs_seq_lens_cpu else None
         seq_lens_np = seq_lens_cpu.numpy() if seq_lens_cpu is not None else None
         num_blocks_np = (
@@ -983,7 +1018,9 @@ def build(
             num_blocks_np -= num_common_kv_blocks
 
         # Compute paged_kv_indices if necessary
-        needs_paged_kv_indices = use_cascade or not is_only_trtllm_decode
+        # paged_kv_indices is only needed for FlashInfer native paths;
+        # TRTLLM paths use block_tables directly on GPU.
+        needs_paged_kv_indices = use_cascade or not all_uses_trtllm
         if needs_paged_kv_indices:
             assert num_blocks_np is not None
             assert seq_lens_np is not None
@@ -1060,9 +1097,21 @@ def build(
                 qo_indptr_prefill_gpu = (
                     qo_indptr[prefill_start:] - qo_indptr[prefill_start]
                 )
+                # Compute cum_seq_lens_kv on GPU to avoid CPU sync.
+                # This is the cumulative sum of the number of KV cache
+                # blocks per prefill request.
+                prefill_seq_lens = seq_lens[prefill_start:]
+                num_blocks_per_req = (prefill_seq_lens + page_size - 1) // page_size
                 paged_kv_indptr_prefill_gpu = self.paged_kv_indptr.gpu[
                     prefill_start : num_reqs + 1
                 ]
+                # Assign to slice to avoid cpu sync.
+                paged_kv_indptr_prefill_gpu[:1] = 0
+                torch.cumsum(
+                    num_blocks_per_req,
+                    dim=0,
+                    out=paged_kv_indptr_prefill_gpu[1:],
+                )
                 # Compute max_q_len for prefill requests
                 query_lens_prefill_cpu = (
                     qo_indptr_prefill_cpu[1:] - qo_indptr_prefill_cpu[:-1]
@@ -1112,6 +1161,12 @@ def build(
                         prefill_wrapper,
                         BatchPrefillWithPagedKVCacheWrapper,
                     )
+                    # NVFP4 trtllm kernel only supports FP8 output;
+                    # use FP8 o_data_type so the wrapper matches the
+                    # FP8 output buffer allocated in forward().
+                    o_dtype = (
+                        FP8_DTYPE if self.is_kvcache_nvfp4 else self.model_config.dtype
+                    )
                     prefill_wrapper.plan(
                         qo_indptr=qo_indptr_prefill_cpu,
                         paged_kv_indptr=paged_kv_indptr_prefill_cpu,
@@ -1127,7 +1182,7 @@ def build(
                         logits_soft_cap=self.logits_soft_cap,
                         q_data_type=self.q_data_type,
                         kv_data_type=self.kv_cache_dtype,
-                        o_data_type=self.model_config.dtype,
+                        o_data_type=o_dtype,
                         fixed_split_size=self.prefill_fixed_split_size,
                         disable_split_kv=self.disable_split_kv,
                     )
@@ -1161,6 +1216,12 @@ def build(
                 # Use the persistent buffer with padding length,
                 # instead of the same address but chunked version
                 # in atten_metadata when using cudagraph.
+                # NVFP4 trtllm kernel only supports FP8 output;
+                # use FP8 o_data_type so the wrapper matches the
+                # FP8 output buffer allocated in forward().
+                o_dtype = (
+                    FP8_DTYPE if self.is_kvcache_nvfp4 else self.model_config.dtype
+                )
                 fast_plan_decode(
                     decode_wrapper,
                     indptr_cpu=self.paged_kv_indptr.cpu[: num_input_tokens + 1],
@@ -1179,7 +1240,7 @@ def build(
                     logits_soft_cap=self.logits_soft_cap,
                     q_data_type=self.q_data_type,
                     kv_data_type=self.kv_cache_dtype,
-                    o_data_type=self.model_config.dtype,
+                    o_data_type=o_dtype,
                     fixed_split_size=self.decode_fixed_split_size,
                     disable_split_kv=self.disable_split_kv,
                 )
@@ -1228,6 +1289,8 @@ def __init__(
             self.sliding_window[0] if self.sliding_window is not None else -1
         )
         self.kv_cache_dtype = kv_cache_dtype
+        self.is_kvcache_nvfp4 = kv_cache_dtype == "nvfp4"
+        self.fp4_data_dim = head_size // 2 if self.is_kvcache_nvfp4 else 0
         self.logits_soft_cap = logits_soft_cap
         self.kv_sharing_target_layer_name = kv_sharing_target_layer_name
 
@@ -1262,6 +1325,17 @@ def __init__(
         self.bmm2_scale: float | None = None
         self.o_sf_scale: float | None = None
 
+        # Pre-allocated FP8 output buffer for NVFP4 without fused output quant.
+        if self.is_kvcache_nvfp4 and vllm_config is not None:
+            max_num_tokens = vllm_config.scheduler_config.max_num_batched_tokens
+            self._nvfp4_fp8_out = torch.empty(
+                (max_num_tokens, num_heads, head_size),
+                dtype=FP8_DTYPE,
+                device="cuda",
+            )
+        else:
+            self._nvfp4_fp8_out = None
+
         dcp_a2a = (
             vllm_config is not None
             and vllm_config.parallel_config.decode_context_parallel_size > 1
@@ -1382,7 +1456,7 @@ def forward(
         if self.kv_sharing_target_layer_name is None and is_quantized_kv_cache(
             self.kv_cache_dtype
         ):
-            torch_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
+            torch_dtype = FlashInferBackend.get_dtype_for_flashinfer(
                 self.kv_cache_dtype
             )
             kv_cache = kv_cache.view(torch_dtype)
@@ -1406,7 +1480,31 @@ def forward(
         num_prefill_tokens = attn_metadata.num_prefill_tokens
 
         stride_order = FlashInferBackend.get_kv_cache_stride_order()
-        kv_cache_permute = kv_cache.permute(*stride_order)
+        kv_cache_permute = kv_cache.permute(*stride_order)  # HND and contiguous
+        # Fix degenerate strides on any size-1 dimension (e.g. num_kv_heads=1
+        # with TP=8).  PyTorch permits non-canonical strides on size-1 dims;
+        # CUDA TMA requires ≥16-byte alignment on all non-outermost strides.
+        # canonicalize_singleton_dim_strides patches metadata via as_strided —
+        # zero-copy.  See vllm.utils.torch_utils.
+        fixed = canonicalize_singleton_dim_strides(kv_cache_permute)
+        if fixed is not kv_cache_permute:
+            logger.debug(
+                "Canonicalized degenerate KV cache strides (FlashInfer): "
+                "shape=%s, strides before=%s, strides after=%s",
+                kv_cache_permute.shape,
+                kv_cache_permute.stride(),
+                fixed.stride(),
+            )
+        kv_cache_permute = fixed
+
+        # For NVFP4, the kv_cache last dim is full_dim (data + scale packed).
+        # Split into correctly-strided data and scale views.
+        nvfp4_kv_data = None
+        nvfp4_kv_block_scales = None
+        if self.is_kvcache_nvfp4:
+            nvfp4_kv_data, nvfp4_kv_block_scales = nvfp4_kv_cache_split_views(
+                kv_cache_permute
+            )
 
         use_dcp = self.dcp_world_size > 1
 
@@ -1453,20 +1551,45 @@ def forward(
                     )
                     assert prefill_wrapper._sm_scale == self.scale
                     assert prefill_wrapper._causal
+
+                    if self.is_kvcache_nvfp4:
+                        kv_cache_permute = nvfp4_kv_data
+                    kv_cache_sf = (
+                        nvfp4_kv_block_scales if self.is_kvcache_nvfp4 else None
+                    )
+
+                    # NVFP4 trtllm kernel only supports FP8 output.
+                    # Use a pre-allocated FP8 buffer and dequantize
+                    # afterwards.
+                    needs_fp8_out_prefill = (
+                        self.is_kvcache_nvfp4 and output.dtype != FP8_DTYPE
+                    )
+                    if needs_fp8_out_prefill:
+                        out_prefill = self._nvfp4_fp8_out[:num_prefill_tokens]
+                    else:
+                        out_prefill = output[num_decode_tokens:]
+
                     prefill_wrapper.run(
                         prefill_query,
                         kv_cache_permute,
                         k_scale=layer._k_scale_float,
                         v_scale=layer._v_scale_float,
-                        out=output[num_decode_tokens:],
+                        out=out_prefill,
+                        kv_cache_sf=kv_cache_sf,
                     )
+
+                    if needs_fp8_out_prefill:
+                        output[
+                            num_decode_tokens : num_decode_tokens + num_prefill_tokens
+                        ].copy_(out_prefill.to(output.dtype))
             else:
                 assert isinstance(attn_metadata.prefill, TRTLLMPrefill)
                 # prefill_query may be non-contiguous or have degenerate strides
-                # First ensure memory contiguity, then fix degenerate strides
-                # with reshape. contiguous() alone doesn't fix degenerate
-                # strides when a dimension has size 1.
-                prefill_query = prefill_query.contiguous().reshape(prefill_query.shape)
+                # on size=1 dims. contiguous() ensures memory layout; then
+                # canonicalize_singleton_dim_strides fixes any remaining
+                # degenerate strides on size=1 dims for TMA alignment.
+                prefill_query = prefill_query.contiguous()
+                prefill_query = canonicalize_singleton_dim_strides(prefill_query)
                 workspace_buffer = _get_trtllm_gen_workspace_buffer()
                 block_tables_prefill = attn_metadata.prefill.block_tables
                 seq_lens_prefill = attn_metadata.prefill.seq_lens
@@ -1490,19 +1613,35 @@ def forward(
                     assert self.o_sf_scale is None
                     out = output[num_decode_tokens:]
 
-                if attn_metadata.q_data_type != FP8_DTYPE and is_quantized_kv_cache(
-                    self.kv_cache_dtype
+                # NVFP4 trtllm kernel only supports FP8 output.
+                # Use a pre-allocated FP8 buffer and dequantize afterwards.
+                needs_fp8_out = self.is_kvcache_nvfp4 and output.dtype != FP8_DTYPE
+                if needs_fp8_out:
+                    out = self._nvfp4_fp8_out[:num_prefill_tokens]
+
+                prefill_kv_block_scales = None
+                if self.is_kvcache_nvfp4:
+                    # NVFP4 trtllm-gen kernel requires FP8 query.
+                    assert attn_metadata.q_data_type == FP8_DTYPE, (
+                        "NVFP4 KV cache requires FP8 quantized queries for "
+                        "trtllm-gen prefill. Set "
+                        "disable_flashinfer_q_quantization=False."
+                    )
+                    mock_kv_cache = nvfp4_kv_data
+                    mock_block_table = block_tables_prefill
+                    prefill_kv_block_scales = nvfp4_kv_block_scales
+                elif (
+                    attn_metadata.q_data_type != FP8_DTYPE
+                    and self.kv_cache_dtype.startswith("fp8")
                 ):
                     # TRTLLM prefill attention does not support BF16 Q
                     # and fp8 kv cache. So to enable prefill attention
                     # with fp8 kv cache, we can construct a mock block
                     # and mock kv cache with BF16 KV involved in the prefill
                     #
-                    # The inner (block_size, head_size) dims must be
-                    # contiguous; outer dims may have non-canonical strides
-                    # (e.g. cross-layer unified allocation).
-                    # Degenerate strides on outer dims break TMA descriptors
-                    # (see flashinfer-ai/flashinfer#2232).
+                    kv_cache_permute = canonicalize_singleton_dim_strides(
+                        kv_cache_permute
+                    )
                     kv_strides = kv_cache_permute.stride()
                     assert (
                         kv_strides[-1] == 1
@@ -1539,8 +1678,14 @@ def forward(
                     sinks=self.sinks,
                     o_sf_scale=self.o_sf_scale,
                     out=out,
+                    kv_cache_sf=prefill_kv_block_scales,
                 )
 
+                if needs_fp8_out:
+                    output[
+                        num_decode_tokens : num_decode_tokens + num_prefill_tokens
+                    ].copy_(out[:num_prefill_tokens].to(output.dtype))
+
         if num_decode_tokens > 0:
             decode_query = query[:num_decode_tokens]
             assert decode_query.shape[0] == num_decode_tokens
@@ -1553,6 +1698,18 @@ def forward(
                 assert decode_wrapper._logits_soft_cap == (self.logits_soft_cap or 0.0)
                 assert decode_wrapper._sm_scale == self.scale
 
+                if self.is_kvcache_nvfp4:
+                    kv_cache_permute = nvfp4_kv_data
+                kv_cache_sf = nvfp4_kv_block_scales if self.is_kvcache_nvfp4 else None
+
+                # NVFP4 kernel only supports FP8 output.
+                # Use a pre-allocated FP8 buffer and dequantize afterwards.
+                needs_fp8_out = self.is_kvcache_nvfp4 and output.dtype != FP8_DTYPE
+                if needs_fp8_out:
+                    out_decode = self._nvfp4_fp8_out[:num_decode_tokens]
+                else:
+                    out_decode = output[:num_decode_tokens]
+
                 if use_dcp:
                     decode_query = get_dcp_group().all_gather(
                         decode_query.contiguous(), dim=-2
@@ -1571,6 +1728,7 @@ def forward(
                         out=output_tmp,
                         lse=lse,
                         return_lse=True,
+                        kv_cache_sf=kv_cache_sf,
                     )
                     output[:num_decode_tokens] = self.dcp_combine(
                         output_tmp,
@@ -1583,15 +1741,20 @@ def forward(
                         kv_cache_permute,
                         k_scale=layer._k_scale_float,
                         v_scale=layer._v_scale_float,
-                        out=output[:num_decode_tokens],
+                        out=out_decode,
+                        kv_cache_sf=kv_cache_sf,
                     )
+
+                if needs_fp8_out:
+                    output[:num_decode_tokens].copy_(out_decode.to(output.dtype))
             else:
-                # decode_query may be non-contiguous or have degenerate strides
                 assert isinstance(attn_metadata.decode, TRTLLMDecode)
-                # First ensure memory contiguity, then fix degenerate strides
-                # with reshape. contiguous() alone doesn't fix degenerate
-                # strides when a dimension has size 1.
-                decode_query = decode_query.contiguous().reshape(decode_query.shape)
+                # decode_query may be non-contiguous or have degenerate strides
+                # on size=1 dims. contiguous() ensures memory layout; then
+                # canonicalize_singleton_dim_strides fixes any remaining
+                # degenerate strides on size=1 dims for TMA alignment.
+                decode_query = decode_query.contiguous()
+                decode_query = canonicalize_singleton_dim_strides(decode_query)
                 workspace_buffer = _get_trtllm_gen_workspace_buffer()
                 block_tables_decode = attn_metadata.decode.block_tables
                 seq_lens_decode = attn_metadata.decode.seq_lens
@@ -1602,11 +1765,7 @@ def forward(
                 assert is_strictly_contiguous(workspace_buffer)
                 assert is_strictly_contiguous(block_tables_decode)
                 assert is_strictly_contiguous(seq_lens_decode)
-                # kv_cache outer dims may be non-contiguous (e.g.
-                # cross-layer unified allocation), but inner dims
-                # (block_size, head_size) must be contiguous and
-                # strides must be canonical to avoid TMA descriptor
-                # failures (see flashinfer-ai/flashinfer#2232).
+                kv_cache_permute = canonicalize_singleton_dim_strides(kv_cache_permute)
                 kv_strides = kv_cache_permute.stride()
                 assert (
                     kv_strides[-1] == 1 and kv_strides[-2] == kv_cache_permute.shape[-1]
@@ -1627,6 +1786,12 @@ def forward(
                     assert self.o_sf_scale is None
                     out = output[:num_decode_tokens]
 
+                # NVFP4 trtllm kernel only supports FP8 output.
+                # Use a pre-allocated FP8 buffer and dequantize afterwards.
+                needs_fp8_out = self.is_kvcache_nvfp4 and output.dtype != FP8_DTYPE
+                if needs_fp8_out:
+                    out = self._nvfp4_fp8_out[:num_decode_tokens]
+
                 if num_decode_tokens % attn_metadata.num_decodes != 0:
                     # This gets triggered when the dummy_run forces
                     # attention to be initialized with q_len = 0
@@ -1636,7 +1801,9 @@ def forward(
 
                 trtllm_batch_decode_with_kv_cache(
                     query=decode_query,
-                    kv_cache=kv_cache_permute,
+                    kv_cache=(
+                        nvfp4_kv_data if self.is_kvcache_nvfp4 else kv_cache_permute
+                    ),
                     workspace_buffer=workspace_buffer,
                     block_tables=block_tables_decode,
                     seq_lens=seq_lens_decode,
@@ -1648,7 +1815,13 @@ def forward(
                     o_sf_scale=self.o_sf_scale,
                     out=out,
                     q_len_per_req=q_len_per_req,
+                    kv_cache_sf=(
+                        nvfp4_kv_block_scales if self.is_kvcache_nvfp4 else None
+                    ),
                 )
+
+                if needs_fp8_out:
+                    output[:num_decode_tokens].copy_(out.to(output.dtype))
         return output_padded
 
     def do_kv_cache_update(
@@ -1667,11 +1840,13 @@ def do_kv_cache_update(
             # and value[:num_actual_tokens] because the reshape_and_cache_flash
             # op uses the slot_mapping's shape to determine the number of
             # actual tokens.
+            k_cache = kv_cache[:, 0]
+            v_cache = kv_cache[:, 1]
             torch.ops._C_cache_ops.reshape_and_cache_flash(
                 key,
                 value,
-                kv_cache[:, 0],
-                kv_cache[:, 1],
+                k_cache,
+                v_cache,
                 slot_mapping,
                 self.kv_cache_dtype,
                 layer._k_scale,
diff --git a/vllm/v1/attention/backends/flex_attention.py b/vllm/v1/attention/backends/flex_attention.py
index 3c5b99904b62..0ce5baf1a341 100644
--- a/vllm/v1/attention/backends/flex_attention.py
+++ b/vllm/v1/attention/backends/flex_attention.py
@@ -35,8 +35,9 @@
     AttentionMetadataBuilder,
     AttentionType,
     CommonAttentionMetadata,
+    MultipleOf,
 )
-from vllm.v1.kv_cache_interface import AttentionSpec
+from vllm.v1.kv_cache_interface import AttentionSpec, EncoderOnlyAttentionSpec
 
 logger = init_logger(__name__)
 
@@ -47,12 +48,16 @@
 flex_attention_compiled = torch.compile(flex_attention, fullgraph=True)
 
 
-def _offsets_to_doc_ids_tensor(offsets: torch.Tensor) -> torch.Tensor:
-    device = offsets.device
-    counts = offsets[1:] - offsets[:-1]
-    return torch.repeat_interleave(
-        torch.arange(len(counts), device=device, dtype=torch.int32), counts
+def _offsets_to_doc_ids_tensor(
+    offsets_cpu: torch.Tensor, device: torch.device
+) -> torch.Tensor:
+    # Build on CPU (so `repeat_interleave` doesn't force a GPU->CPU sync to
+    # learn the data-dependent output length) and upload non-blocking.
+    counts = offsets_cpu[1:] - offsets_cpu[:-1]
+    doc_ids = torch.repeat_interleave(
+        torch.arange(len(counts), dtype=torch.int32), counts
     )
+    return doc_ids.to(device, non_blocking=True)
 
 
 def pad_to_multiple(x: torch.Tensor, multiple: int, dim: int):
@@ -90,11 +95,19 @@ class FlexAttentionBackend(AttentionBackend):
     def get_name() -> str:
         return "FLEX_ATTENTION"
 
+    @classmethod
+    def supports_non_causal(cls) -> bool:
+        return True
+
     @classmethod
     def supports_attn_type(cls, attn_type: str) -> bool:
         """FlexAttention supports both decoder and encoder-only attention."""
         return attn_type in (AttentionType.DECODER, AttentionType.ENCODER_ONLY)
 
+    @classmethod
+    def supports_batch_invariance(cls) -> bool:
+        return True
+
     @classmethod
     def supports_mm_prefix(cls) -> bool:
         """FlexAttention supports full attention for image tokens."""
@@ -126,6 +139,10 @@ def use_cascade_attention(*args, **kwargs) -> bool:
     def get_supported_head_sizes(cls) -> list[int]:
         return []
 
+    @staticmethod
+    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
+        return [MultipleOf(16)]
+
 
 # @torch.compile(fullgraph=True, mode="reduce-overhead")
 def physical_to_logical_mapping(
@@ -277,11 +294,13 @@ def unique_static_unsorted(
     keep = (x_flat != ignored_val) & (idx == first_idx.gather(1, x_flat))  # [B, N]
 
     # ── left-pack uniques into a fresh tensor ───────────────────────────
+    # Route non-kept entries to a garbage slot at column N so we can do a
+    # single scatter rather than using torch.nonzero (which would force a
+    # GPU->CPU sync to enumerate kept positions).
     dest_pos = torch.cumsum(keep.to(torch.long), dim=1) - 1  # where to go
-    packed_flat = torch.full_like(x_flat, pad_val)
-
-    rows, src_cols = torch.nonzero(keep, as_tuple=True)
-    packed_flat[rows, dest_pos[rows, src_cols]] = x_flat[rows, src_cols]
+    dest_pos = torch.where(keep, dest_pos, N)
+    packed_extended = torch.full((B, N + 1), pad_val, device=device, dtype=x_flat.dtype)
+    packed_flat = packed_extended.scatter_(1, dest_pos, x_flat)[:, :N]
 
     # ── restore original layout ─────────────────────────────────────────
     packed = packed_flat.reshape(x_perm.shape).movedim(-1, dim)
@@ -294,6 +313,12 @@ def causal_mask_mod(
     return q_idx >= kv_idx
 
 
+def bidirectional_mask_mod(
+    b: torch.Tensor, h: torch.Tensor, q_idx: torch.Tensor, kv_idx: torch.Tensor
+):
+    return q_idx >= 0
+
+
 # Type alias for the block sparsity hint callable signature.
 _block_sparsity_hint_signature = Callable[
     [torch.Tensor, torch.Tensor, int], torch.Tensor
@@ -316,15 +341,9 @@ class BlockSparsityHint(NamedTuple):
 
 
 def copy_to_persistent(dst, src):
-    try:
-        dst = dst.as_strided(src.shape, src.stride())
-    except RuntimeError as e:
-        raise RuntimeError(
-            f"Fail to re-stride a persistent tensor of shape {dst.shape} "
-            f"for a tensor of shape {src.shape}"
-        ) from e
-    dst.copy_(src)
-    return dst
+    sliced = dst[tuple(slice(0, s) for s in src.shape)]
+    sliced.copy_(src)
+    return sliced
 
 
 @dataclass
@@ -333,6 +352,9 @@ class FlexAttentionMetadata:
     num_actual_tokens: int  # Number of tokens excluding padding.
     max_query_len: int
     query_start_loc: torch.Tensor
+    # CPU-resident copy of query_start_loc used to derive doc_ids without a
+    # GPU->CPU sync from repeat_interleave's data-dependent output size.
+    query_start_loc_cpu: torch.Tensor
     max_seq_len: int
     seq_lens: torch.Tensor
     block_table: torch.Tensor
@@ -364,6 +386,7 @@ class FlexAttentionMetadata:
     block_mask: BlockMask | None = None
     score_mod: _score_mod_signature | None = None
     logical_mask_mod: _mask_mod_signature = causal_mask_mod
+    uses_paged_kv: bool = True
     doc_ids: torch.Tensor | None = None
     direct_build: bool = True
     q_block_size: int = 16
@@ -438,12 +461,7 @@ def final_mask_mod(
             (is_valid, logical_q_idx, logical_kv_idx) = (
                 self._convert_physical_to_logical(self.doc_ids, q_idx, physical_kv_idx)
             )
-            # Apply mask modification only for valid indices
-            return torch.where(
-                is_valid,
-                self.logical_mask_mod(b, h, logical_q_idx, logical_kv_idx),
-                False,
-            )
+            return is_valid & self.logical_mask_mod(b, h, logical_q_idx, logical_kv_idx)
 
         return final_mask_mod
 
@@ -455,7 +473,9 @@ def get_bidirectional_mask_mod(self) -> _mask_mod_signature:
         packed query sequences.
         """
         # Create a lookup mapping from query indices -> request number
-        request_lookup = _offsets_to_doc_ids_tensor(self.query_start_loc)
+        request_lookup = _offsets_to_doc_ids_tensor(
+            self.query_start_loc_cpu, self.query_start_loc.device
+        )
 
         def final_mask_mod(
             b: torch.Tensor,
@@ -497,7 +517,7 @@ def final_mask_mod(
                 False,
             )
 
-        return final_mask_mod if self.causal else sliding_window_mask_mod
+        return final_mask_mod if self.uses_paged_kv else sliding_window_mask_mod
 
     def get_prefix_lm_mask_mod(self) -> _mask_mod_signature:
         """Creates the prefix LM mask_mod function for FlexAttention."""
@@ -541,8 +561,7 @@ def final_mask_mod(
     def get_mask_mod(self):
         # Stage-1: initialize the base mask_mod
         # (causal mask for decoder or bidirectional mask for encoder)
-        has_custom_mask = self.logical_mask_mod is not causal_mask_mod
-        if self.causal or has_custom_mask:
+        if self.uses_paged_kv:
             mask_mod = self.get_paged_mask_mod()
         else:
             mask_mod = self.get_bidirectional_mask_mod()
@@ -568,7 +587,9 @@ def get_transformed_score_mod(self) -> _score_mod_signature | None:
             return None
 
         # Create a lookup mapping from query indices -> request number
-        request_lookup = _offsets_to_doc_ids_tensor(self.query_start_loc)
+        request_lookup = _offsets_to_doc_ids_tensor(
+            self.query_start_loc_cpu, self.query_start_loc.device
+        )
         user_score_mod = self.score_mod
 
         def transformed_score_mod(
@@ -595,7 +616,7 @@ def transformed_score_mod(
         return transformed_score_mod
 
     def _build_block_mask_direct(self) -> BlockMask:
-        """Direct block mask construction for standard causal attention.
+        """Direct block mask construction for paged KV cache attention.
 
         This method constructs the block mask directly using
         BlockMask.from_kv_blocks which is much more efficient than the
@@ -693,7 +714,9 @@ def _build_block_mask_direct(self) -> BlockMask:
 
     def build_block_mask(self) -> BlockMask:
         mask_mod = self.get_mask_mod()
-        kv_len = self.total_cache_tokens if self.causal else self.num_actual_tokens
+        kv_len = (
+            self.total_cache_tokens if self.uses_paged_kv else self.num_actual_tokens
+        )
         return create_block_mask_compiled(
             mask_mod,
             None,
@@ -711,7 +734,9 @@ def __post_init__(self):
         assert self.prefix_kv_lens is None, "Not implemented yet."
         assert self.suffix_kv_lens is None, "Not implemented yet."
         # Create a lookup mapping from query indices -> request number
-        self.doc_ids = _offsets_to_doc_ids_tensor(self.query_start_loc)
+        self.doc_ids = _offsets_to_doc_ids_tensor(
+            self.query_start_loc_cpu, self.query_start_loc.device
+        )
         self.doc_ids = copy_to_persistent(self.persistent_doc_ids, self.doc_ids)
         self.num_blocks = self.total_cache_tokens // self.block_size
 
@@ -770,10 +795,11 @@ def __init__(
     def build_for_cudagraph_capture(
         self, common_attn_metadata: CommonAttentionMetadata
     ) -> FlexAttentionMetadata:
-        # Use actual max_seq_len instead of max_model_len to avoid
-        # torch.compile recompilation during CUDA graph capture.
-        common_attn_metadata.max_seq_len = (
-            common_attn_metadata.seq_lens_cpu.max().item()
+        # Use actual max_seq_len (not max_model_len) to avoid torch.compile
+        # recompilation during CUDA graph capture.
+        assert common_attn_metadata.seq_lens_cpu_upper_bound is not None
+        common_attn_metadata.max_seq_len = int(
+            common_attn_metadata.seq_lens_cpu_upper_bound.max().item()
         )
         return self.build(
             common_prefix_len=0, common_attn_metadata=common_attn_metadata
@@ -791,6 +817,7 @@ def build(
 
         max_seq_len = common_attn_metadata.max_seq_len
         query_start_loc = common_attn_metadata.query_start_loc
+        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
         seq_lens = common_attn_metadata.seq_lens
         block_table_tensor = common_attn_metadata.block_table_tensor
         slot_mapping = common_attn_metadata.slot_mapping
@@ -842,11 +869,20 @@ def build(
         offset_tensor = common_attn_metadata.compute_num_computed_tokens()
         offset_tensor = copy_to_persistent(self.persistent_offset_tensor, offset_tensor)
 
+        uses_paged_kv = not isinstance(self.kv_cache_spec, EncoderOnlyAttentionSpec)
+        logical_mask_mod = (
+            bidirectional_mask_mod
+            if uses_paged_kv and not common_attn_metadata.causal
+            else causal_mask_mod
+        )
+
         out = FlexAttentionMetadata(
             causal=common_attn_metadata.causal,
+            logical_mask_mod=logical_mask_mod,
             num_actual_tokens=num_actual_tokens,
             max_query_len=max_query_len,
             query_start_loc=query_start_loc,
+            query_start_loc_cpu=query_start_loc_cpu,
             max_seq_len=max_seq_len,
             seq_lens=seq_lens,
             block_table=block_table_tensor,
@@ -863,10 +899,11 @@ def build(
             total_cache_tokens=total_cache_tokens,
             decode_offset=offset_tensor,
             num_blocks_per_seq=num_blocks_per_seq,
+            uses_paged_kv=uses_paged_kv,
             # FIXME(Isotr0py): direct build has issue to build bidirectional
             # attention block mask for encoder-only models, disable it temporarily.
             # see: https://github.com/vllm-project/vllm/pull/27329#issuecomment-3431484053
-            direct_build=(self.direct_build and common_attn_metadata.causal),
+            direct_build=self.direct_build and uses_paged_kv,
             q_block_size=self.q_block_size,
             kv_block_size=self.kv_block_size,
             persistent_kv_indices=self.persistent_kv_indices,
@@ -1055,9 +1092,7 @@ def forward(
             else:
                 attn_metadata.block_mask = attn_metadata.build_block_mask()
 
-        if not attn_metadata.causal:
-            assert self.attn_type == AttentionType.ENCODER_ONLY
-
+        if self.attn_type == AttentionType.ENCODER_ONLY:
             query, key_tensor, value_tensor = map(
                 lambda x: self.view_as_4d(x).permute(0, 2, 1, 3),
                 (query, key, value),
diff --git a/vllm/v1/attention/backends/mamba_attn.py b/vllm/v1/attention/backends/mamba_attn.py
index eec53032288d..716dfcde592f 100644
--- a/vllm/v1/attention/backends/mamba_attn.py
+++ b/vllm/v1/attention/backends/mamba_attn.py
@@ -9,6 +9,7 @@
 
 from vllm.config import VllmConfig
 from vllm.utils.math_utils import cdiv
+from vllm.utils.torch_utils import async_tensor_h2d
 from vllm.v1.attention.backend import (
     AttentionCGSupport,
     AttentionMetadataBuilder,
@@ -270,16 +271,20 @@ def _build_chunk_metadata_tensors(
         num_prefills = common.num_prefills
         num_decode_tokens = common.num_decode_tokens
 
-        num_computed_tokens_cpu = (
-            common_attn_metadata.compute_num_computed_tokens().cpu()
-        )
-        num_computed_tokens_p_cpu = num_computed_tokens_cpu[
-            num_reqs - num_prefills : num_reqs
-        ]
+        # Derive prefill context lengths from CPU data only.
+        # `seq_lens_cpu_upper_bound` is precise for prefill rows in all modes
+        # (including async spec decode), so this avoids the D2H sync that
+        # `compute_num_computed_tokens().cpu()` would force.
+        seq_lens_cpu = common_attn_metadata.seq_lens_cpu_upper_bound
+        assert seq_lens_cpu is not None
         query_start_loc_p_cpu = (
             common_attn_metadata.query_start_loc_cpu[-num_prefills - 1 :]
             - num_decode_tokens
         )
+        prefill_query_lens_cpu = query_start_loc_p_cpu[1:] - query_start_loc_p_cpu[:-1]
+        num_computed_tokens_p_cpu = (
+            seq_lens_cpu[num_reqs - num_prefills : num_reqs] - prefill_query_lens_cpu
+        )
 
         cu_chunk_seqlen, seq_idx, last_chunk_indices = self._compute_chunk_metadata(
             chunk_size,
@@ -289,20 +294,14 @@ def _build_chunk_metadata_tensors(
         )
 
         device = common_attn_metadata.query_start_loc.device
-        cu_chunk_seqlen_p = torch.as_tensor(
-            cu_chunk_seqlen,
-            device=device,
-            dtype=torch.int32,
-        )
-        seq_idx_p = torch.as_tensor(
-            seq_idx,
-            device=device,
-            dtype=torch.int32,
+        # Build on pinned CPU and upload non-blocking to avoid the synchronous
+        # H2D copy that `torch.as_tensor(list, device=cuda)` would force.
+        cu_chunk_seqlen_p = async_tensor_h2d(
+            cu_chunk_seqlen, dtype=torch.int32, device=device
         )
-        last_chunk_indices_p = torch.as_tensor(
-            last_chunk_indices,
-            device=device,
-            dtype=torch.int32,
+        seq_idx_p = async_tensor_h2d(seq_idx, dtype=torch.int32, device=device)
+        last_chunk_indices_p = async_tensor_h2d(
+            last_chunk_indices, dtype=torch.int32, device=device
         )
         return cu_chunk_seqlen_p, seq_idx_p, last_chunk_indices_p
 
diff --git a/vllm/v1/attention/backends/mla/compressor_utils.py b/vllm/v1/attention/backends/mla/compressor_utils.py
new file mode 100644
index 000000000000..36b115f64444
--- /dev/null
+++ b/vllm/v1/attention/backends/mla/compressor_utils.py
@@ -0,0 +1,86 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+
+from vllm.triton_utils import tl, triton
+
+
+@triton.jit
+def _compressed_slot_mapping_kernel(
+    # [num_tokens]
+    slot_mapping_ptr,
+    # [num_reqs + 1]
+    query_start_loc_ptr,
+    # [num_reqs]
+    seq_lens_ptr,
+    # [num_reqs, max_num_blocks]
+    block_table_ptr,
+    block_table_stride,
+    block_size,
+    COMPRESS_RATIO: tl.constexpr,
+    PAD_ID: tl.constexpr,
+    TRITON_BLOCK_SIZE: tl.constexpr,
+):
+    batch_idx = tl.program_id(0)
+
+    query_start = tl.load(query_start_loc_ptr + batch_idx)
+    query_end = tl.load(query_start_loc_ptr + batch_idx + 1)
+    query_len = query_end - query_start
+
+    seq_len = tl.load(seq_lens_ptr + batch_idx)
+    start_pos = seq_len - query_len
+
+    for i in range(0, query_len, TRITON_BLOCK_SIZE):
+        offset = i + tl.arange(0, TRITON_BLOCK_SIZE)
+        mask = offset < query_len
+
+        pos = start_pos + i + tl.arange(0, TRITON_BLOCK_SIZE)
+        is_valid = (pos + 1) % COMPRESS_RATIO == 0
+        pos_after_compress = pos // COMPRESS_RATIO
+
+        block_ids = pos_after_compress // block_size
+        block_numbers = tl.load(
+            block_table_ptr + batch_idx * block_table_stride + block_ids,
+            mask=mask & is_valid,
+        )
+        slot_ids = block_numbers * block_size + pos_after_compress % block_size
+
+        # NOTE
+        slot_ids = tl.where(is_valid, slot_ids, PAD_ID)
+        tl.store(slot_mapping_ptr + query_start + offset, slot_ids, mask=mask)
+
+
+def get_compressed_slot_mapping(
+    num_tokens: int,
+    query_start_loc: torch.Tensor,
+    seq_lens: torch.Tensor,
+    block_table: torch.Tensor,
+    block_size: int,
+    compress_ratio: int,
+    out: torch.Tensor | None = None,
+) -> torch.Tensor:
+    if out is not None:
+        # Guard: for padded / invalid sequences.
+        # Negative positions produce bogus block indices that lead to illegal memory
+        # accesses inside the block_table load.
+        # NOTE: Fill -1 to the whole tensor, not just the first `num_tokens`.
+        out.fill_(-1)
+        slot_mapping = out[:num_tokens]
+    else:
+        slot_mapping = torch.full(
+            (num_tokens,), -1, dtype=torch.int64, device=query_start_loc.device
+        )
+
+    num_reqs = block_table.shape[0]
+    _compressed_slot_mapping_kernel[(num_reqs,)](
+        slot_mapping,
+        query_start_loc,
+        seq_lens,
+        block_table,
+        block_table.stride(0),
+        block_size,
+        compress_ratio,
+        PAD_ID=-1,
+        TRITON_BLOCK_SIZE=1024,
+    )
+    return slot_mapping
diff --git a/vllm/v1/attention/backends/mla/flashattn_mla.py b/vllm/v1/attention/backends/mla/flashattn_mla.py
index f58d9aeb302b..bd947296e8bc 100644
--- a/vllm/v1/attention/backends/mla/flashattn_mla.py
+++ b/vllm/v1/attention/backends/mla/flashattn_mla.py
@@ -56,6 +56,10 @@ def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
     def get_name() -> str:
         return "FLASH_ATTN_MLA"
 
+    @classmethod
+    def supports_batch_invariance(cls) -> bool:
+        return True
+
     @staticmethod
     def get_builder_cls() -> type["FlashAttnMLAMetadataBuilder"]:
         return FlashAttnMLAMetadataBuilder
diff --git a/vllm/v1/attention/backends/mla/flashmla_sparse.py b/vllm/v1/attention/backends/mla/flashmla_sparse.py
index 1d981717cbff..474a5b2d421e 100644
--- a/vllm/v1/attention/backends/mla/flashmla_sparse.py
+++ b/vllm/v1/attention/backends/mla/flashmla_sparse.py
@@ -15,6 +15,8 @@
 )
 from vllm.platforms import current_platform
 from vllm.platforms.interface import DeviceCapability
+from vllm.triton_utils import tl, triton
+from vllm.utils.math_utils import cdiv
 from vllm.utils.platform_utils import num_compute_units
 from vllm.utils.torch_utils import is_quantized_kv_cache
 from vllm.v1.attention.backend import (
@@ -27,6 +29,7 @@
     MultipleOf,
     SparseMLAAttentionImpl,
 )
+from vllm.v1.attention.backends.mla.compressor_utils import get_compressed_slot_mapping
 from vllm.v1.attention.backends.mla.sparse_utils import (
     triton_convert_req_index_to_global_index,
 )
@@ -65,8 +68,8 @@
 """
 NOTE: FlashMLA Sparse uses an fp8 cache with the following format
 
-In the "FP8 with scale" format, each token's KV cache is 656 Bytes,
-structured as:
+For DeepSeek V3.2, in the "FP8 with scale" format, each token's KV cache is 656
+Bytes, structured as:
 -   **First 512 bytes:** The "quantized NoPE" part, containing 512
     `float8_e4m3` values.
 -   **Next 16 bytes:** Scale factors, containing 4 `float32` values.
@@ -74,6 +77,16 @@
     the second for the next 128, and so on.
 -   **Last 128 bytes:** The "RoPE" part, containing 64 `bfloat16` values. This
     part is not quantized for accuracy.
+
+For DeepSeek V4, in the "FP8 with scale" format, each token's KV cache is 584
+Bytes, structured as:
+-   **First 448 bytes:** The "quantized NoPE" part, containing 448
+    `float8_e4m3` values.
+-   **Next 128 bytes:** The "RoPE" part, containing 64 `bfloat16` values. This
+    part is not quantized for accuracy.
+-   **Last 8 bytes:** Scale factors, containing 7 `ue8m0` values + 1B pad.
+    The first `ue8m0` is the scale for the first 64 `float8_e4m3` values,
+    the second for the next 64, and so on.
 """
 
 
@@ -104,7 +117,8 @@ def get_impl_cls() -> type["FlashMLASparseImpl"]:
 
     @classmethod
     def get_supported_head_sizes(cls) -> list[int]:
-        return [576]
+        # V3.2: 576 (512 NoPE + 64 RoPE); DeepseekV4: 512 (448 NoPE + 64 RoPE)
+        return [512, 576]
 
     @classmethod
     def is_mla(cls) -> bool:
@@ -127,13 +141,37 @@ def get_kv_cache_shape(
         cache_dtype_str: str = "auto",
     ) -> tuple[int, ...]:
         if cache_dtype_str == "fp8_ds_mla":
-            # custom storage format is 656 bytes
-            #  see FlashMLA readme.md for details
+            # V3.2 main MLA: 656-byte custom storage format. See module docstring.
             return (num_blocks, block_size, 656)
         else:
             return (num_blocks, block_size, head_size)
 
 
+class DeepseekV4FlashMLASparseBackend(FlashMLASparseBackend):
+    @staticmethod
+    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
+        return [256]
+
+    @staticmethod
+    def get_name() -> str:
+        return "V4_FLASHMLA_SPARSE"
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+        cache_dtype_str: str = "auto",
+    ) -> tuple[int, ...]:
+        if cache_dtype_str == "fp8_ds_mla":
+            # DeepseekV4 main MLA: 584B per token (448 NoPE + 128 RoPE + 8 fp8 scale).
+            # head_size passed in is the semantic head_dim (512).
+            return (num_blocks, block_size, 584)
+        else:
+            return (num_blocks, block_size, head_size)
+
+
 @dataclass
 class FlashMLASparseMetadata(AttentionMetadata):
     num_reqs: int
@@ -159,6 +197,7 @@ class FP8KernelMetadata:
     class FP8SeparatePrefillDecode:
         @dataclass
         class Decode:
+            seq_lens: torch.Tensor
             kernel_metadata: "FlashMLASparseMetadata.FP8KernelMetadata"
             decode_query_len: int  # needed for reshape in spec decode
 
@@ -206,6 +245,13 @@ class Chunk:
     fp8_extra_metadata: FP8SeparatePrefillDecode | FP8KernelMetadata | None = None
     fp8_use_mixed_batch: bool = False
 
+    # Pre-computed C128A metadata (DeepseekV4 only, compress_ratio == 128).
+    # Decode: global slot ids + valid-entry counts (fused from positions).
+    c128a_global_decode_topk_indices: torch.Tensor | None = None
+    c128a_decode_topk_lens: torch.Tensor | None = None
+    # Prefill: local topk indices (used by combine_topk_swa_indices).
+    c128a_prefill_topk_indices: torch.Tensor | None = None
+
 
 def get_prefill_workspace_size(max_model_len: int):
     # NOTE(Lucas): 5 is a magic number for controlling the prefill buffer size.
@@ -235,8 +281,9 @@ def __init__(
         parallel_config = vllm_config.parallel_config
         self.device = device
 
-        # Treat requests with query length <= 1 as decodes to match the
-        # DeepGEMM indexer constraint (fp8_paged_mqa_logits only supports next_n <= 2)
+        # Classify single-token queries (plus num_speculative_tokens via
+        # supports_spec_as_decode=True) as decodes; longer queries go to
+        # prefill.
         self._init_reorder_batch_threshold(1, supports_spec_as_decode=True)
 
         sm_count = num_compute_units(device.index)
@@ -300,6 +347,68 @@ def __init__(
             device=device,
         )
 
+        # DeepseekV4: has compress_ratios in hf_config.
+        hf_config = vllm_config.model_config.hf_config
+        self.is_deepseek_v4 = (
+            hasattr(hf_config, "compress_ratios") and len(hf_config.compress_ratios) > 0
+        )
+        self.compress_ratio = 1
+        if self.is_deepseek_v4:
+            assert hasattr(self.kv_cache_spec, "compress_ratio")
+            self.compress_ratio = self.kv_cache_spec.compress_ratio
+            # Pre-allocate compressed slot mapping buffer for CUDA graph
+            # address stability when compress_ratio > 1.
+            if self.compress_ratio > 1:
+                max_num_batched_tokens = (
+                    vllm_config.scheduler_config.max_num_batched_tokens
+                )
+                self.compressed_slot_mapping_buffer = torch.empty(
+                    max_num_batched_tokens,
+                    dtype=torch.int64,
+                    device=self.device,
+                )
+
+            # Pre-allocate C128A topk buffers for CUDA graph address stability.
+            if self.compress_ratio == 128:
+                max_num_batched_tokens = (
+                    vllm_config.scheduler_config.max_num_batched_tokens
+                )
+                # Pad to B_TOPK alignment (128 covers both h_q=64 B_TOPK=64 and
+                # h_q=128 B_TOPK=128). FlashMLA decode asserts extra_topk % B_TOPK
+                # == 0; unaligned widths (e.g. 17 = ceil(2136/128)) crash the
+                # sm100 head64 kernel. Padded slots stay -1 and decode_lens caps
+                # them via topk_length, so the pad is a no-op at kernel level.
+                # Mirrors _SPARSE_PREFILL_TOPK_ALIGNMENT in cache_utils.py.
+                _C128A_TOPK_ALIGNMENT = 128
+                c128a_max_compressed = cdiv(
+                    self.model_config.max_model_len, self.compress_ratio
+                )
+                c128a_max_compressed = (
+                    cdiv(c128a_max_compressed, _C128A_TOPK_ALIGNMENT)
+                    * _C128A_TOPK_ALIGNMENT
+                )
+                # Stored so _build_c128a_metadata passes it as the kernel's
+                # max_compressed_tokens, matching the buffer stride. Otherwise
+                # the kernel's default 8192 iterates past row width and spills
+                # writes into adjacent rows (present in both decode and prefill
+                # branches of _build_c128a_topk_metadata_kernel).
+                self.c128a_max_compressed = c128a_max_compressed
+                self.c128a_global_decode_buffer = torch.empty(
+                    (max_num_batched_tokens, c128a_max_compressed),
+                    dtype=torch.int32,
+                    device=self.device,
+                )
+                self.c128a_decode_lens_buffer = torch.empty(
+                    max_num_batched_tokens,
+                    dtype=torch.int32,
+                    device=self.device,
+                )
+                self.c128a_prefill_buffer = torch.empty(
+                    (max_num_batched_tokens, c128a_max_compressed),
+                    dtype=torch.int32,
+                    device=self.device,
+                )
+
     def _build_fp8_mixed_decode_prefill(
         self,
         common_attn_metadata: CommonAttentionMetadata,
@@ -364,7 +473,10 @@ def _build_fp8_separate_prefill_decode(
         # For pure decode batches, prefill_request_id will be None
         # For mixed batches, it will have -1 for decode and request_id for prefill
         if num_prefills > 0:
-            seq_lens_cpu = common_attn_metadata.seq_lens.cpu()
+            # Upper bound is exact for prefill rows (the `[num_decodes:]`
+            # slice below), so no D2H sync is needed.
+            seq_lens_cpu = common_attn_metadata.seq_lens_cpu_upper_bound
+            assert seq_lens_cpu is not None
             seq_lens = common_attn_metadata.seq_lens
             query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
 
@@ -457,15 +569,7 @@ def _build_fp8_separate_prefill_decode(
             decode_query_len = (query_start_loc_cpu[1] - query_start_loc_cpu[0]).item()
 
             # Use padded head count since that's what the kernel will see
-            padded_heads = self.fp8_decode_padded_heads
-            scheduler_metadata, _ = get_mla_metadata(
-                cache_seqlens=self.topk_tokens_tensor[:num_decodes],
-                num_q_tokens_per_head_k=decode_query_len * padded_heads,
-                topk=self.topk_tokens,
-                num_heads_q=padded_heads,
-                num_heads_k=1,
-                is_fp8_kvcache=True,
-            )
+            scheduler_metadata, _ = get_mla_metadata()
 
             kernel_meta = FlashMLASparseMetadata.FP8KernelMetadata(
                 scheduler_metadata=scheduler_metadata,
@@ -473,6 +577,7 @@ def _build_fp8_separate_prefill_decode(
                 cache_lens=self.max_model_len_tensor[:num_decodes],
             )
             fp8_metadata.decode = FP8Meta.Decode(
+                seq_lens=common_attn_metadata.seq_lens[:num_decodes],
                 kernel_metadata=kernel_meta,
                 decode_query_len=decode_query_len,
             )
@@ -499,35 +604,109 @@ def build(
         )
         req_id_per_token = self.req_id_per_token_buffer[:num_tokens]
 
+        slot_mapping = cm.slot_mapping
+        if self.compress_ratio > 1:
+            slot_mapping = get_compressed_slot_mapping(
+                common_attn_metadata.num_actual_tokens,
+                common_attn_metadata.query_start_loc,
+                common_attn_metadata.seq_lens,
+                common_attn_metadata.block_table_tensor.clamp(min=0),
+                int(self.kv_cache_spec.storage_block_size),
+                self.compress_ratio,
+                out=self.compressed_slot_mapping_buffer,
+            )
+
         fp8_extra_metadata: (
             FlashMLASparseMetadata.FP8SeparatePrefillDecode
             | FlashMLASparseMetadata.FP8KernelMetadata
             | None
         ) = None
-        fp8_use_mixed_batch = self.num_heads < MIN_HEADS_FOR_BF16_PREFILL
-        if self.use_fp8_kv_cache:
+        fp8_use_mixed_batch = (
+            self.num_heads < MIN_HEADS_FOR_BF16_PREFILL and not self.is_deepseek_v4
+        )
+        # DeepseekV4 has its own attention impl (DeepseekV4MLAAttention) that does not
+        # consume fp8_extra_metadata. Skipping the build here avoids a
+        # forced D2H sync on seq_lens that would otherwise fire on every
+        # prefill-bearing step, lifting GPU utilization on long-prefill
+        # workloads (e.g. LongBench) from ~83% to ~100%.
+        if self.use_fp8_kv_cache and not self.is_deepseek_v4:
             if fp8_use_mixed_batch:
                 fp8_extra_metadata = self._build_fp8_mixed_decode_prefill(cm)
             else:
                 fp8_extra_metadata = self._build_fp8_separate_prefill_decode(cm)
 
+        # Pre-compute C128A topk indices for DeepseekV4.
+        c128a_fields = {}
+        if self.is_deepseek_v4 and self.compress_ratio == 128:
+            c128a_fields = self._build_c128a_metadata(cm, req_id_per_token)
+
         metadata = FlashMLASparseMetadata(
             num_reqs=cm.num_reqs,
             max_query_len=cm.max_query_len,
             max_seq_len=cm.max_seq_len,
             num_actual_tokens=cm.num_actual_tokens,
             query_start_loc=cm.query_start_loc,
-            slot_mapping=cm.slot_mapping,
+            slot_mapping=slot_mapping,
             block_table=cm.block_table_tensor,
             req_id_per_token=req_id_per_token,
             block_size=self.kv_cache_spec.block_size,
             topk_tokens=self.topk_tokens,
             fp8_extra_metadata=fp8_extra_metadata,
             fp8_use_mixed_batch=fp8_use_mixed_batch,
+            **c128a_fields,
         )
 
         return metadata
 
+    def _build_c128a_metadata(
+        self,
+        cm: CommonAttentionMetadata,
+        req_id_per_token: torch.Tensor,
+    ) -> dict[str, torch.Tensor | None]:
+        """Pre-compute C128A topk indices for DeepseekV4 (compress_ratio >= 128)."""
+        # Must match SWA's decode split (no `require_uniform=True`) so
+        # `c128a_global_decode_topk_indices.shape[0]` lines up with q in
+        # `_forward_decode`. The per-token C128A kernel handles non-uniform
+        # query lengths.
+        (num_decodes, _, num_decode_tokens, num_prefill_tokens) = (
+            split_decodes_and_prefills(
+                cm,
+                decode_threshold=self.reorder_batch_threshold or 1,
+            )
+        )
+
+        num_total = num_decode_tokens + num_prefill_tokens
+        if num_total == 0:
+            return {}
+
+        assert cm.positions is not None, (
+            "positions is required for C128A metadata build"
+        )
+        block_size = self.kv_cache_spec.block_size // self.compress_ratio
+        global_decode, decode_lens, prefill_local = build_c128a_topk_metadata(
+            cm.positions[:num_total],
+            self.compress_ratio,
+            num_decode_tokens,
+            req_id_per_token,
+            cm.block_table_tensor[:num_decodes],
+            block_size,
+            cm.slot_mapping,
+            self.c128a_global_decode_buffer,
+            self.c128a_decode_lens_buffer,
+            self.c128a_prefill_buffer,
+            max_compressed_tokens=self.c128a_max_compressed,
+        )
+
+        result: dict[str, torch.Tensor | None] = {}
+        if num_decode_tokens > 0:
+            result["c128a_global_decode_topk_indices"] = global_decode.view(
+                num_decode_tokens, 1, -1
+            )
+            result["c128a_decode_topk_lens"] = decode_lens
+        if num_prefill_tokens > 0:
+            result["c128a_prefill_topk_indices"] = prefill_local
+        return result
+
 
 class FlashMLASparseImpl(SparseMLAAttentionImpl[FlashMLASparseMetadata]):
     @staticmethod
@@ -549,7 +728,7 @@ def __init__(
         attn_type: str,
         kv_sharing_target_layer_name: str | None,
         # MLA Specific Arguments
-        topk_indice_buffer: torch.Tensor | None = None,
+        topk_indices_buffer: torch.Tensor | None = None,
         indexer: "Indexer | None" = None,
         **mla_args,
     ) -> None:
@@ -612,7 +791,11 @@ def _forward_bf16_kv(
             NUM_TOPK_TOKENS=topk_indices.shape[1],
         )
 
-        return self._bf16_flash_mla_kernel(q, kv_c_and_k_pe_cache, topk_indices)
+        return self._bf16_flash_mla_kernel(
+            q,
+            kv_c_and_k_pe_cache,
+            topk_indices,
+        )
 
     def _forward_fp8_kv_separate_prefill_decode(
         self,
@@ -653,7 +836,10 @@ def _forward_fp8_kv_separate_prefill_decode(
         fp8_metadata = attn_metadata.fp8_extra_metadata
         assert isinstance(fp8_metadata, FlashMLASparseMetadata.FP8SeparatePrefillDecode)
 
-        def _fp8_decode(q: torch.Tensor, topk_indices: torch.Tensor) -> torch.Tensor:
+        def _fp8_decode(
+            q: torch.Tensor,
+            topk_indices: torch.Tensor,
+        ) -> torch.Tensor:
             # Reshape q: (num_decode_tokens, num_heads, head_dim)
             #         -> (num_decodes, seq_len, num_heads, head_dim)
             q = reshape_query_for_spec_decode(q, num_decodes)
@@ -689,7 +875,8 @@ def _fp8_decode(q: torch.Tensor, topk_indices: torch.Tensor) -> torch.Tensor:
 
             if num_decode_tokens > 0:
                 attn_out[:num_decode_tokens] = _fp8_decode(
-                    q[:num_decode_tokens], topk_indices[:num_decode_tokens]
+                    q[:num_decode_tokens],
+                    topk_indices[:num_decode_tokens],
                 )
 
             assert fp8_metadata.prefill is not None
@@ -820,6 +1007,7 @@ def _bf16_flash_mla_kernel(
         output = flash_mla_sparse_fwd(
             q, kv_c_and_k_pe_cache, topk_indices, self.softmax_scale
         )[0]
+
         output = output[:, : self.num_heads, :]
         return output
 
@@ -861,3 +1049,123 @@ def forward_mqa(
             )
 
         return attn_out, None
+
+
+def build_c128a_topk_metadata(
+    positions: torch.Tensor,
+    compress_ratio: int,
+    num_decode_tokens: int,
+    token_to_req_indices: torch.Tensor,
+    block_table: torch.Tensor,
+    block_size: int,
+    slot_mapping: torch.Tensor,
+    global_decode_buffer: torch.Tensor,
+    decode_lens_buffer: torch.Tensor,
+    prefill_buffer: torch.Tensor,
+    max_compressed_tokens: int = 8192,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Single kernel for all C128A tokens (decode + prefill).
+
+    Decode tokens: position → block_table lookup → global slot ids + topk_lens.
+    Prefill tokens: position → local indices [0, ..., n-1, -1, ...].
+
+    Writes into pre-allocated buffers for CUDA graph address stability.
+    Returns slices of the buffers.
+    """
+    num_tokens = positions.shape[0]
+    num_prefill_tokens = num_tokens - num_decode_tokens
+
+    global_decode = global_decode_buffer[:num_decode_tokens]
+    decode_lens = decode_lens_buffer[:num_decode_tokens]
+    prefill_local = prefill_buffer[:num_prefill_tokens]
+
+    if num_tokens == 0:
+        return global_decode, decode_lens, prefill_local
+
+    _build_c128a_topk_metadata_kernel[(num_tokens,)](
+        global_decode_buffer,
+        global_decode_buffer.stride(0),
+        decode_lens_buffer,
+        prefill_buffer,
+        prefill_buffer.stride(0),
+        positions,
+        compress_ratio,
+        max_compressed_tokens,
+        num_decode_tokens,
+        token_to_req_indices,
+        block_table,
+        block_table.stride(0),
+        block_size,
+        slot_mapping,
+        BLOCK_SIZE=1024,
+    )
+    return global_decode, decode_lens, prefill_local
+
+
+@triton.jit
+def _build_c128a_topk_metadata_kernel(
+    # Decode outputs
+    global_decode_ptr,
+    global_decode_stride,
+    decode_lens_ptr,
+    # Prefill output
+    prefill_local_ptr,
+    prefill_local_stride,
+    # Inputs
+    positions_ptr,
+    compress_ratio,
+    max_compressed_tokens,
+    num_decode_tokens,
+    token_to_req_indices_ptr,
+    block_table_ptr,
+    block_table_stride,
+    block_size,
+    slot_mapping_ptr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    token_idx = tl.program_id(0)
+    position = tl.load(positions_ptr + token_idx)
+    num_compressed = (position + 1) // compress_ratio
+    num_compressed = tl.minimum(num_compressed, max_compressed_tokens)
+    is_decode = token_idx < num_decode_tokens
+
+    if is_decode:
+        # --- Decode: block-table lookup → global slot ids + count ---
+        is_valid_token = tl.load(slot_mapping_ptr + token_idx) >= 0
+        req_idx = tl.load(token_to_req_indices_ptr + token_idx)
+        count = tl.zeros((), dtype=tl.int32)
+        for i in range(0, max_compressed_tokens, BLOCK_SIZE):
+            offset = i + tl.arange(0, BLOCK_SIZE)
+            mask = offset < max_compressed_tokens
+            is_valid = offset < num_compressed
+
+            block_indices = offset // block_size
+            block_numbers = tl.load(
+                block_table_ptr + req_idx * block_table_stride + block_indices,
+                mask=mask & is_valid,
+            )
+            block_offsets = offset % block_size
+            slot_ids = block_numbers * block_size + block_offsets
+            slot_ids = tl.where(is_valid, slot_ids, -1)
+            tl.store(
+                global_decode_ptr + token_idx * global_decode_stride + offset,
+                slot_ids,
+                mask=mask,
+            )
+            count += tl.sum(is_valid.to(tl.int32), axis=0)
+
+        tl.store(
+            decode_lens_ptr + token_idx,
+            tl.where(is_valid_token, count, 0),
+        )
+    else:
+        # --- Prefill: write local indices ---
+        pfx_idx = token_idx - num_decode_tokens
+        for i in range(0, max_compressed_tokens, BLOCK_SIZE):
+            offset = i + tl.arange(0, BLOCK_SIZE)
+            mask = offset < max_compressed_tokens
+            tl.store(
+                prefill_local_ptr + pfx_idx * prefill_local_stride + offset,
+                tl.where(offset < num_compressed, offset, -1),
+                mask=mask,
+            )
diff --git a/vllm/v1/attention/backends/mla/indexer.py b/vllm/v1/attention/backends/mla/indexer.py
index 402dfc0c74ff..7c0715a9e8b6 100644
--- a/vllm/v1/attention/backends/mla/indexer.py
+++ b/vllm/v1/attention/backends/mla/indexer.py
@@ -8,6 +8,7 @@
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
 from vllm.utils.deep_gemm import (
     get_paged_mqa_logits_metadata,
     has_deep_gemm,
@@ -21,15 +22,50 @@
     CommonAttentionMetadata,
     MultipleOf,
 )
+from vllm.v1.attention.backends.mla.compressor_utils import get_compressed_slot_mapping
 from vllm.v1.attention.backends.utils import (
     split_decodes_and_prefills,
 )
-from vllm.v1.kv_cache_interface import AttentionSpec
+from vllm.v1.kv_cache_interface import AttentionSpec, MLAAttentionSpec
 from vllm.v1.worker.cp_utils import get_total_cp_world_size
 
 logger = init_logger(__name__)
 
 
+@triton.jit
+def _prepare_uniform_decode_kernel(
+    seq_lens_ptr,
+    decode_seq_lens_ptr,
+    block_table_ptr,
+    block_table_stride,
+    expanded_block_table_ptr,
+    expanded_bt_stride,
+    decode_lens_ptr,
+    max_decode_len,
+    BLOCK_SIZE: tl.constexpr,
+):
+    idx = tl.program_id(0)
+    req_id = idx // max_decode_len
+    local_idx = idx % max_decode_len
+
+    # Compute number of KVs attended to by this token.
+    seq_len = tl.load(seq_lens_ptr + req_id)
+    per_token_seq_len = seq_len - max_decode_len + local_idx + 1
+    tl.store(decode_seq_lens_ptr + idx, per_token_seq_len)
+
+    # Copy block table row.
+    src = block_table_ptr + req_id * block_table_stride
+    dst = expanded_block_table_ptr + idx * expanded_bt_stride
+    for i in tl.range(0, expanded_bt_stride, BLOCK_SIZE):
+        off = i + tl.arange(0, BLOCK_SIZE)
+        mask = off < expanded_bt_stride
+        src_block = tl.load(src + off, mask=mask)
+        tl.store(dst + off, src_block, mask=mask)
+
+    # All reqs now have decode_len = 1.
+    tl.store(decode_lens_ptr + idx, 1)
+
+
 def split_indexer_prefill_chunks(
     seq_lens_cpu: torch.Tensor,
     query_lens_cpu: torch.Tensor,
@@ -86,7 +122,7 @@ def get_name() -> str:
 
     @staticmethod
     def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
-        return [1 if current_platform.is_rocm() else 64]
+        return [1, 64] if current_platform.is_rocm() else [64]
 
     @classmethod
     def get_supported_head_sizes(cls) -> list[int]:
@@ -119,6 +155,16 @@ def get_kv_cache_stride_order(
         return (0, 1, 2)
 
 
+class DeepseekV4IndexerBackend(DeepseekV32IndexerBackend):
+    @staticmethod
+    def get_name() -> str:
+        return "DEEPSEEK_V4_INDEXER"
+
+    @staticmethod
+    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
+        return [256]
+
+
 @dataclass
 class DeepseekV32IndexerPrefillChunkMetadata:
     block_table: torch.Tensor
@@ -144,7 +190,7 @@ class DeepSeekV32IndexerDecodeMetadata:
     # seq_lens: per-token effective context lengths.
     #   - flatten path / plain decode: 1D (batch_size,)
     #   - native MTP path: 2D (B, next_n) where [b,j] = L_b - next_n + j + 1
-    # Both fp8_paged_mqa_logits and the topk kernels accept both shapes.
+    # Both fp8_fp4_paged_mqa_logits and the topk kernels accept both shapes.
     seq_lens: torch.Tensor
     decode_lens: torch.Tensor
     requires_padding: bool
@@ -156,16 +202,8 @@ class DeepseekV32IndexerMetadata:
     # FIXME (zyongye)
     # hacky way to access the data now, need to be in chunked meta
     seq_lens: torch.Tensor
-
-    num_reqs: int
-    max_query_len: int
     max_seq_len: int
-
-    num_actual_tokens: int  # Number of tokens excluding padding.
-    query_start_loc: torch.Tensor
     slot_mapping: torch.Tensor
-    # The dimension of the attention heads
-    head_dim: int
 
     # New for MLA (compared to FlashAttention)
     # For handling prefill decode split
@@ -178,71 +216,6 @@ class DeepseekV32IndexerMetadata:
     prefill: DeepseekV32IndexerPrefillMetadata | None = None
 
 
-# TODO (zyongye) optimize this, this is now vibe coded
-def kv_spans_from_batches(
-    start_seq_loc: torch.Tensor, seq_len_per_batch: torch.Tensor, device: torch.device
-) -> tuple[torch.Tensor, torch.Tensor]:
-    """
-    Args:
-      start_seq_loc: 1D long tensor [B+1], cumulative counts of
-                     selected tokens per batch.
-            Example: [0, 2, 4, 7] ->
-                     batch sizes (selected) [2, 2, 3], N=7 tokens total.
-      seq_len_per_batch: 1D long tensor [B],
-                         full sequence length (KV length) of each batch.
-                         Example: [5, 9, 4].
-
-    Returns:
-      start_tensor: 1D long tensor [N], start offset in the
-                    concatenated KV cache for each token's batch.
-      end_location: 1D long tensor [N],
-                    **exclusive** end = start + token's local position.
-                    (So the attended KV slice is kv[start:end].)
-
-    Assumes each batch contributes its full `seq_len_per_batch[i]`
-    keys to the KV cache, andthe selected tokens within a batch
-    are the **last** `counts[i]` positions of that sequence.
-    """
-    q = start_seq_loc.to(dtype=torch.long)
-    L = seq_len_per_batch.to(dtype=torch.long)
-    assert q.dim() == 1 and L.dim() == 1
-    assert q.numel() == L.numel() + 1, "start_seq_loc must have length B+1"
-
-    # Selected tokens per batch and totals
-    counts = q[1:] - q[:-1]  # [B]
-    N = int(q[-1].item())  # total selected tokens
-    B = L.numel()
-
-    if N == 0:
-        return (
-            torch.empty(0, dtype=torch.long, device=device),
-            torch.empty(0, dtype=torch.long, device=device),
-        )
-
-    # KV start offsets per batch in the concatenated KV cache
-    kv_starts_per_batch = torch.cumsum(L, dim=0) - L  # [B]
-
-    # For each selected token, which batch does it belong to?
-    batch_id = torch.repeat_interleave(torch.arange(B), counts)  # [N]
-
-    # Map batch KV start to each token
-    start_tensor = kv_starts_per_batch[batch_id]  # [N]
-
-    # End-align local positions inside each batch:
-    # local_pos = L[b] - counts[b] + (1..counts[b])  for each batch b
-    L_expand = torch.repeat_interleave(L, counts)  # [N]
-    m_expand = torch.repeat_interleave(counts, counts)  # [N]
-    # position within the selected block: 1..counts[b]
-    pos_within = (
-        torch.arange(N, dtype=torch.long) - torch.repeat_interleave(q[:-1], counts) + 1
-    )
-
-    local_pos = L_expand - m_expand + pos_within  # [N], 1-based
-    end_location = start_tensor + local_pos  # exclusive end
-
-    return start_tensor.int().to(device), end_location.int().to(device)
-
-
 def get_max_prefill_buffer_size(vllm_config: VllmConfig):
     max_model_len = vllm_config.model_config.max_model_len
     # NOTE(Chen): 40 is a magic number for controlling the prefill buffer size.
@@ -258,7 +231,7 @@ def get_max_prefill_buffer_size(vllm_config: VllmConfig):
 
 class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
     reorder_batch_threshold: int = 1
-    natively_supported_next_n: list[int] = [1, 2]
+    natively_supported_next_n_fp4: list[int] = [1, 2]
     # TODO (matt): integrate kernel with next_n = 4 support
 
     @classmethod
@@ -279,9 +252,30 @@ def __init__(self, *args, **kwargs):
             if self.vllm_config.speculative_config
             else 0
         )
+        self.use_fp4_indexer_cache = (
+            self.vllm_config.attention_config.use_fp4_indexer_cache
+        )
+
+        assert (
+            current_platform.is_device_capability_family(100)
+            or not self.use_fp4_indexer_cache
+        ), (
+            "use_fp4_indexer_cache requires Blackwell datacenter GPUs "
+            "(sm_10x, e.g. B200/GB200); sm_120 (consumer Blackwell) and "
+            "earlier architectures are not supported."
+        )
+
         next_n = self.num_speculative_tokens + 1
         self.reorder_batch_threshold += self.num_speculative_tokens
-        self.use_flattening = next_n not in self.natively_supported_next_n
+        # NOTE(zyongye) fp4 indexer cache only natively supports next_n in
+        # natively_supported_next_n_fp4; for other next_n values we fall back
+        # to the flattening path. Outside the SM100 datacenter family the FP8
+        # paged MQA logits kernel has the same [1, 2] constraint (deepgemm
+        # smxx_fp8_fp4_paged_mqa_logits.hpp:233), so flatten there too.
+        self.use_flattening = (
+            self.use_fp4_indexer_cache
+            or not current_platform.is_device_capability_family(100)
+        ) and next_n not in self.natively_supported_next_n_fp4
 
         sm_count = num_compute_units(self.device.index)
         self.num_sms = sm_count
@@ -296,7 +290,6 @@ def __init__(self, *args, **kwargs):
         )
         if not self.use_flattening and next_n > 1:
             # Native MTP: 2D buffer for per-token seq_lens.
-            # Flattening path is never used, so no expanded_seq_lens_buffer.
             self.decode_seq_lens_buffer = torch.zeros(
                 (scheduler_config.max_num_seqs, next_n),
                 dtype=torch.int32,
@@ -310,7 +303,10 @@ def __init__(self, *args, **kwargs):
                 device=self.device,
             )
         self.arange_buffer = torch.arange(
-            scheduler_config.max_num_seqs * next_n,
+            max(
+                scheduler_config.max_num_seqs * next_n,
+                scheduler_config.max_num_batched_tokens,
+            ),
             dtype=torch.int32,
             device=self.device,
         )
@@ -332,53 +328,27 @@ def __init__(self, *args, **kwargs):
             (self.num_sms + 1, 2), dtype=torch.int32, device=self.device
         )
 
-    def build_one_prefill_chunk(
-        self,
-        req_slice: slice,
-        query_slice: slice,
-        query_start_loc_cpu,
-        seq_lens_cpu,
-        block_table,
-        skip_kv_gather: bool = False,
-    ) -> DeepseekV32IndexerPrefillChunkMetadata:
-        prefill_query_start_loc = (
-            query_start_loc_cpu[req_slice.start : req_slice.stop + 1]
-            - query_start_loc_cpu[req_slice.start]
-        )
-        cu_seqlen_ks, cu_seqlen_ke = kv_spans_from_batches(
-            prefill_query_start_loc, seq_lens_cpu[req_slice], self.device
-        )
-        token_start = query_start_loc_cpu[req_slice.start].item()
-        total_seq_lens = seq_lens_cpu[req_slice].sum()
-        num_reqs = req_slice.stop - req_slice.start
-        seq_idx = torch.arange(0, num_reqs, dtype=torch.int32)
-        token_to_seq = torch.repeat_interleave(seq_idx, seq_lens_cpu[req_slice]).to(
-            self.device
-        )
-        assert total_seq_lens <= self.max_prefill_buffer_size
-        cu_seq_lens = (
-            torch.cat(
-                [
-                    torch.zeros(1, dtype=torch.int32),
-                    seq_lens_cpu[req_slice].cumsum(dim=0),
-                ]
+        # KV compression. Default to 1 for no compression.
+        self.compress_ratio = 1
+        # Get compress_ratio for DeepseekV4 support
+        if isinstance(self.kv_cache_spec, MLAAttentionSpec):
+            self.compress_ratio = self.kv_cache_spec.compress_ratio
+
+        # Pre-allocate buffers for CUDA graph compatibility when
+        if self.compress_ratio > 1:
+            # compress_ratio > 1 (DeepseekV4)
+            # Compressed slot mapping output buffer
+            self.compressed_slot_mapping_buffer = torch.zeros(
+                (scheduler_config.max_num_batched_tokens,),
+                dtype=torch.int64,
+                device=self.device,
+            )
+            # Buffer for compressed seq_lens in decode path
+            self.expanded_seq_lens_buffer = torch.zeros(
+                (scheduler_config.max_num_batched_tokens,),
+                dtype=torch.int32,
+                device=self.device,
             )
-            .to(torch.int32)
-            .to(self.device)
-        )
-
-        return DeepseekV32IndexerPrefillChunkMetadata(
-            cu_seqlen_ks=cu_seqlen_ks[query_slice],
-            cu_seqlen_ke=cu_seqlen_ke[query_slice],
-            cu_seq_lens=cu_seq_lens,
-            token_to_seq=token_to_seq,
-            total_seq_lens=total_seq_lens,
-            block_table=block_table[req_slice],
-            token_start=token_start + query_slice.start,
-            token_end=token_start + query_slice.stop,
-            num_reqs=num_reqs,
-            skip_kv_gather=skip_kv_gather,
-        )
 
     def _prepare_decode_tensors(
         self,
@@ -405,52 +375,75 @@ def _prepare_decode_tensors(
         Returns (seq_lens, block_table, decode_lens, batch_size, requires_padding).
         seq_lens is 1D (batch_size,) for flatten/plain, 2D (B, next_n) for native MTP.
         """
+        min_decode_len = int(decode_lens_cpu.min().item())
         if not use_native and max_decode_len > 1:
             assert self.decode_seq_lens_buffer.dim() == 1
-            # Assume 4 requests with seq_lens [10, 7, 12, 0] (the final req is
-            # padding) and decode_lens [3, 1, 4, 0] in the below example comments.
-            # The context lengths are therefore
-            # [10-3, 7-1, 12-4, 0-0] = [7, 6, 8, 0].
-
-            # 3 + 1 + 4 + 0 = 8
-            actual_expanded = int(decode_lens_cpu.sum().item())
-
-            # Fuse expanded_base and expanded_starts into a single repeat_interleave:
-            # seq_len_i = (context_start[b] - query_start_loc[b]) + arange[i] + 1
-            # where context_start[b] = seq_lens[b] - decode_lens[b].
-            # Example: offsets = [7-0, 6-3, 8-4, 0-8] = [7, 3, 4, -8]
-            # expanded_offsets  = [7, 7, 7, 3, 4, 4, 4, 4]
-            # result            = [8, 9, 10, 7, 9, 10, 11, 12]
-            expanded_offsets = torch.repeat_interleave(
-                seq_lens - decode_lens - query_start_loc,
-                decode_lens,
-                output_size=actual_expanded,
-            )
+            if min_decode_len == max_decode_len:
+                # Uniform decode lengths.
+                num_decode_tokens = num_decodes * max_decode_len
+                _prepare_uniform_decode_kernel[(num_decode_tokens,)](
+                    seq_lens,
+                    self.decode_seq_lens_buffer,
+                    block_table,
+                    block_table.stride(0),
+                    self.expanded_block_table_buffer,
+                    self.expanded_block_table_buffer.stride(0),
+                    self.decode_lens_buffer,
+                    max_decode_len,
+                    BLOCK_SIZE=1024,
+                )
+                self.decode_seq_lens_buffer[num_decode_tokens:] = 0
+                seq_lens = self.decode_seq_lens_buffer[:num_decode_tokens]
+                block_table = self.expanded_block_table_buffer[:num_decode_tokens]
+                decode_lens = self.decode_lens_buffer[:num_decode_tokens]
+                return seq_lens, block_table, decode_lens, num_decode_tokens, False
+            else:
+                # Variable decode lengths.
+                # Assume 4 requests with seq_lens [10, 7, 12, 0] (the final req is
+                # padding) and decode_lens [3, 1, 4, 0] in the below example comments.
+                # The context lengths are therefore
+                # [10-3, 7-1, 12-4, 0-0] = [7, 6, 8, 0].
+
+                # 3 + 1 + 4 + 0 = 8
+                actual_expanded = int(decode_lens_cpu.sum().item())
+
+                # Fuse expanded_base and expanded_starts into a single
+                # repeat_interleave:
+                # seq_len_i = (context_start[b] - query_start_loc[b]) + arange[i] + 1
+                # where context_start[b] = seq_lens[b] - decode_lens[b].
+                # Example: offsets = [7-0, 6-3, 8-4, 0-8] = [7, 3, 4, -8]
+                # expanded_offsets  = [7, 7, 7, 3, 4, 4, 4, 4]
+                # result            = [8, 9, 10, 7, 9, 10, 11, 12]
+                expanded_offsets = torch.repeat_interleave(
+                    seq_lens - decode_lens - query_start_loc,
+                    decode_lens,
+                    output_size=actual_expanded,
+                )
 
-            # [8, 9, 10, 7, 9, 10, 11, 12, ...] where ... is unused buffer space
-            self.decode_seq_lens_buffer[:actual_expanded] = (
-                expanded_offsets + self.arange_buffer[:actual_expanded] + 1
-            )
-            self.decode_seq_lens_buffer[actual_expanded:] = 0
-            seq_lens = self.decode_seq_lens_buffer[:num_decode_tokens]
-
-            # Give each of the flattened entries the same block table row as the
-            # original request.
-            self.expanded_block_table_buffer[:actual_expanded] = (
-                torch.repeat_interleave(
-                    block_table, decode_lens, dim=0, output_size=actual_expanded
+                # [8, 9, 10, 7, 9, 10, 11, 12, ...] where ... is unused buffer space
+                self.decode_seq_lens_buffer[:actual_expanded] = (
+                    expanded_offsets + self.arange_buffer[:actual_expanded] + 1
                 )
-            )
-            if actual_expanded < num_decode_tokens:
-                self.expanded_block_table_buffer[
-                    actual_expanded:num_decode_tokens, 0
-                ] = 0
-            block_table = self.expanded_block_table_buffer[:num_decode_tokens]
-
-            # All reqs now have decode_len=1
-            self.decode_lens_buffer[:num_decode_tokens] = 1
-            decode_lens = self.decode_lens_buffer[:num_decode_tokens]
-            return seq_lens, block_table, decode_lens, num_decode_tokens, False
+                self.decode_seq_lens_buffer[actual_expanded:] = 0
+                seq_lens = self.decode_seq_lens_buffer[:num_decode_tokens]
+
+                # Give each of the flattened entries the same block table row as the
+                # original request.
+                self.expanded_block_table_buffer[:actual_expanded] = (
+                    torch.repeat_interleave(
+                        block_table, decode_lens, dim=0, output_size=actual_expanded
+                    )
+                )
+                if actual_expanded < num_decode_tokens:
+                    self.expanded_block_table_buffer[
+                        actual_expanded:num_decode_tokens, 0
+                    ] = 0
+                block_table = self.expanded_block_table_buffer[:num_decode_tokens]
+
+                # All reqs now have decode_len=1
+                self.decode_lens_buffer[:num_decode_tokens] = 1
+                decode_lens = self.decode_lens_buffer[:num_decode_tokens]
+                return seq_lens, block_table, decode_lens, num_decode_tokens, False
         else:
             # Native path: plain decode (next_n==1) or spec decode
             # with 2D per-token context lengths (next_n > 1).
@@ -459,15 +452,18 @@ def _prepare_decode_tensors(
             # decode_len < next_n due to padding or short prefills), the simple
             # reshape in sparse_attn_indexer won't work. Use pack_seq_triton
             # (requires_padding) instead.
-            min_decode_len = int(decode_lens_cpu.min().item())
             requires_padding = min_decode_len != max_decode_len
             if use_native and next_n > 1:
                 assert self.decode_seq_lens_buffer.dim() == 2
-                # (B, next_n): token j attends to L - next_n + j + 1 KV tokens
-                self.decode_seq_lens_buffer[:num_decodes] = (
-                    seq_lens.unsqueeze(1) - next_n + 1 + self.offsets_buffer
+                # (B, max_decode_len): token j attends to
+                # L - max_decode_len + j + 1 KV tokens.
+                self.decode_seq_lens_buffer[:num_decodes, :max_decode_len] = (
+                    seq_lens.unsqueeze(1)
+                    - max_decode_len
+                    + 1
+                    + self.offsets_buffer[:max_decode_len]
                 )
-                seq_lens = self.decode_seq_lens_buffer[:num_decodes]
+                seq_lens = self.decode_seq_lens_buffer[:num_decodes, :max_decode_len]
             return seq_lens, block_table, decode_lens, num_decodes, requires_padding
 
     def build(
@@ -478,8 +474,12 @@ def build(
     ) -> DeepseekV32IndexerMetadata:
         num_reqs = common_attn_metadata.num_reqs
         num_tokens = common_attn_metadata.num_actual_tokens
-
+        query_start_loc = common_attn_metadata.query_start_loc
         query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
+        seq_lens = common_attn_metadata.seq_lens
+        slot_mapping = common_attn_metadata.slot_mapping
+        block_table = common_attn_metadata.block_table_tensor
+
         num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
             split_decodes_and_prefills(
                 common_attn_metadata,
@@ -491,33 +491,67 @@ def build(
         assert num_decodes + num_prefills == num_reqs
         assert num_decode_tokens + num_prefill_tokens == num_tokens
 
+        compressed_slot_mapping = slot_mapping
+        compressed_seq_lens = seq_lens
+        if self.compress_ratio > 1:
+            compressed_slot_mapping = get_compressed_slot_mapping(
+                num_tokens,
+                query_start_loc,
+                seq_lens,
+                block_table,
+                self.kv_cache_spec.storage_block_size,
+                self.compress_ratio,
+                out=self.compressed_slot_mapping_buffer,
+            )
+            compressed_seq_lens = seq_lens // self.compress_ratio
+
         prefill_metadata = None
         if num_prefills > 0:
+            # This CPU value is an upper bound for async-spec extend rows.  It
+            # is safe for chunking/allocation because CUDA metadata below is
+            # built from exact device seq_lens and gather ignores the tail.
+            assert common_attn_metadata.seq_lens_cpu_upper_bound is not None
+            seq_lens_cpu = common_attn_metadata.seq_lens_cpu_upper_bound
+            compressed_seq_lens_cpu = (
+                seq_lens_cpu // self.compress_ratio
+                if self.compress_ratio > 1
+                else seq_lens_cpu
+            )
             prefill_query_lens_cpu = torch.diff(
                 query_start_loc_cpu[num_decodes : num_decodes + num_prefills + 1]
             )
             max_logits_bytes = envs.VLLM_SPARSE_INDEXER_MAX_LOGITS_MB * 1024 * 1024
+            # Upper bound is exact for prefill rows (the `[num_decodes:]`
+            # slice below).
+            assert common_attn_metadata.seq_lens_cpu_upper_bound is not None
+            seq_lens_cpu = common_attn_metadata.seq_lens_cpu_upper_bound
             chunk_specs = split_indexer_prefill_chunks(
-                common_attn_metadata.seq_lens_cpu[num_decodes:],
+                compressed_seq_lens_cpu[num_decodes:],
                 prefill_query_lens_cpu,
                 self.max_prefill_buffer_size,
                 max_logits_bytes,
                 request_offset=num_decodes,
             )
-            chunks = [
-                self.build_one_prefill_chunk(
-                    req_slice,
-                    query_slice,
+
+            chunks = []
+            for req_slice, query_slice in chunk_specs:
+                metadata = build_prefill_chunk_metadata(
+                    req_slice.start,
+                    req_slice.stop,
+                    query_start_loc,
                     query_start_loc_cpu,
-                    common_attn_metadata.seq_lens_cpu,
+                    seq_lens,
+                    compressed_seq_lens,
+                    compressed_seq_lens_cpu,
                     common_attn_metadata.block_table_tensor,
+                    self.compress_ratio,
+                    query_slice=query_slice,
                     skip_kv_gather=query_slice.start > 0,
                 )
-                for req_slice, query_slice in chunk_specs
-            ]
-            prefill_metadata = DeepseekV32IndexerPrefillMetadata(
-                chunks=chunks,
-            )
+                # Skip when total_seq_lens is 0 (i.e., no compressed token).
+                if metadata is not None:
+                    chunks.append(metadata)
+            prefill_metadata = DeepseekV32IndexerPrefillMetadata(chunks)
 
         decode_metadata = None
         if num_decodes > 0:
@@ -535,7 +569,7 @@ def build(
 
             max_decode_len = int(decode_lens_cpu.max().item())
             next_n = 1 + self.num_speculative_tokens
-            use_native = not self.use_flattening and max_decode_len == next_n
+            use_native = not self.use_flattening and max_decode_len <= next_n
 
             seq_lens, block_table, decode_lens, batch_size, requires_padding = (
                 self._prepare_decode_tensors(
@@ -552,11 +586,35 @@ def build(
                 )
             )
 
+            # For DeepseekV4 (compress_ratio > 1), the indexer KV cache stores
+            # compressed tokens. Convert uncompressed seq_lens to compressed.
+            if self.compress_ratio > 1:
+                # True iff seq_lens aliases decode_seq_lens_buffer (flatten or
+                # native wrote it); False iff it aliases common_attn_metadata.
+                seq_lens_is_local_view = (use_native and next_n > 1) or (
+                    not use_native and max_decode_len > 1
+                )
+                if seq_lens_is_local_view:
+                    seq_lens //= self.compress_ratio
+                else:
+                    # Copy to avoid mutating shared state; keeps CG address stable.
+                    self.expanded_seq_lens_buffer[:num_decodes] = (
+                        seq_lens // self.compress_ratio
+                    )
+                    self.expanded_seq_lens_buffer[num_decodes:num_decode_tokens] = 0
+                    seq_lens = self.expanded_seq_lens_buffer[:num_decode_tokens]
+
+            # Non-MTP: deep_gemm paged MQA logits requires 2D context_lens
+            # (csrc/apis/attention.hpp). Unsqueeze to (B, 1) so downstream
+            # kernels see the same (B, next_n) layout as the MTP path.
+            if seq_lens.dim() == 1:
+                seq_lens = seq_lens.unsqueeze(-1)
+
             # DeepGEMM is required for the paged MQA logits on CUDA devices
             if current_platform.is_cuda() and has_deep_gemm():
                 self.scheduler_metadata_buffer[:] = get_paged_mqa_logits_metadata(
                     seq_lens,
-                    self.kv_cache_spec.block_size,
+                    self.kv_cache_spec.storage_block_size,
                     self.num_sms,
                 )
 
@@ -570,13 +628,8 @@ def build(
 
         attn_metadata = DeepseekV32IndexerMetadata(
             seq_lens=common_attn_metadata.seq_lens,
-            num_reqs=common_attn_metadata.num_reqs,
-            max_query_len=common_attn_metadata.max_query_len,
             max_seq_len=common_attn_metadata.max_seq_len,
-            num_actual_tokens=common_attn_metadata.num_actual_tokens,
-            query_start_loc=common_attn_metadata.query_start_loc,
-            slot_mapping=common_attn_metadata.slot_mapping,
-            head_dim=128,
+            slot_mapping=compressed_slot_mapping,
             num_decodes=num_decodes,
             num_decode_tokens=num_decode_tokens,
             num_prefills=num_prefills,
@@ -586,3 +639,138 @@ def build(
         )
 
         return attn_metadata
+
+
+def build_prefill_chunk_metadata(
+    start_idx: int,
+    end_idx: int,
+    query_start_loc: torch.Tensor,
+    query_start_loc_cpu: torch.Tensor,
+    uncompressed_seq_lens: torch.Tensor,
+    compressed_seq_lens: torch.Tensor,
+    compressed_seq_lens_cpu: torch.Tensor,
+    block_table: torch.Tensor,
+    compress_ratio: int,
+    query_slice: slice | None = None,
+    skip_kv_gather: bool = False,
+) -> DeepseekV32IndexerPrefillChunkMetadata | None:
+    total_seq_lens = compressed_seq_lens_cpu[start_idx:end_idx].sum().item()
+    if total_seq_lens == 0:
+        return None
+
+    num_reqs = end_idx - start_idx
+    device = block_table.device
+    token_to_seq = torch.empty(total_seq_lens, dtype=torch.int32, device=device)
+
+    cu_seq_lens = torch.empty(num_reqs + 1, dtype=torch.int32, device=device)
+    # Assigning to slice avoids cpu sync.
+    cu_seq_lens[:1] = 0
+    torch.cumsum(compressed_seq_lens[start_idx:end_idx], dim=0, out=cu_seq_lens[1:])
+
+    query_start_loc = (
+        query_start_loc[start_idx : end_idx + 1] - query_start_loc[start_idx]
+    )
+
+    total_query_len = int(
+        (query_start_loc_cpu[end_idx] - query_start_loc_cpu[start_idx]).item()
+    )
+    if query_slice is not None:
+        qs_start = query_slice.start
+        qs_stop = query_slice.stop
+    else:
+        qs_start = 0
+        qs_stop = total_query_len
+    output_query_len = qs_stop - qs_start
+
+    cu_seq_len_ks = torch.empty(output_query_len, dtype=torch.int32, device=device)
+    cu_seq_len_ke = torch.empty(output_query_len, dtype=torch.int32, device=device)
+
+    _build_prefill_chunk_metadata_kernel[(num_reqs,)](
+        query_start_loc,
+        uncompressed_seq_lens[start_idx:end_idx],
+        cu_seq_lens,
+        token_to_seq,
+        cu_seq_len_ks,
+        cu_seq_len_ke,
+        qs_start,
+        qs_stop,
+        BLOCK_SIZE=1024,
+        COMPRESS_RATIO=compress_ratio,
+    )
+
+    token_start = query_start_loc_cpu[start_idx].item()
+    if query_slice is not None:
+        token_end = token_start + qs_stop
+        token_start = token_start + qs_start
+        skip_kv_gather = skip_kv_gather or qs_start > 0
+    else:
+        token_end = query_start_loc_cpu[end_idx].item()
+
+    return DeepseekV32IndexerPrefillChunkMetadata(
+        cu_seqlen_ks=cu_seq_len_ks,
+        cu_seqlen_ke=cu_seq_len_ke,
+        cu_seq_lens=cu_seq_lens,
+        token_to_seq=token_to_seq,
+        total_seq_lens=total_seq_lens,
+        block_table=block_table[start_idx:end_idx],
+        token_start=token_start,
+        token_end=token_end,
+        num_reqs=num_reqs,
+        skip_kv_gather=skip_kv_gather,
+    )
+
+
+@triton.jit
+def _build_prefill_chunk_metadata_kernel(
+    # Inputs
+    query_start_loc_ptr,
+    uncompressed_seq_lens_ptr,
+    cu_compressed_seq_lens_ptr,
+    # Outputs
+    token_to_seq_ptr,
+    cu_compressed_seq_len_ks_ptr,
+    cu_compressed_seq_len_ke_ptr,
+    query_slice_start,
+    query_slice_stop,
+    BLOCK_SIZE: tl.constexpr,
+    COMPRESS_RATIO: tl.constexpr,
+):
+    batch_idx = tl.program_id(0)
+
+    query_start = tl.load(query_start_loc_ptr + batch_idx)
+    query_end = tl.load(query_start_loc_ptr + batch_idx + 1)
+    query_len = query_end - query_start
+
+    seq_start = tl.load(cu_compressed_seq_lens_ptr + batch_idx)
+    seq_end = tl.load(cu_compressed_seq_lens_ptr + batch_idx + 1)
+    compressed_seq_len = seq_end - seq_start
+
+    uncompressed_seq_len = tl.load(uncompressed_seq_lens_ptr + batch_idx)
+    start_pos = uncompressed_seq_len - query_len
+
+    for i in range(0, query_len, BLOCK_SIZE):
+        offset = i + tl.arange(0, BLOCK_SIZE)
+        abs_pos = query_start + offset
+        mask = (
+            (offset < query_len)
+            & (abs_pos >= query_slice_start)
+            & (abs_pos < query_slice_stop)
+        )
+        out_pos = abs_pos - query_slice_start
+
+        # Compute cu_seq_len_ks
+        tl.store(cu_compressed_seq_len_ks_ptr + out_pos, seq_start, mask=mask)
+
+        # Compute cu_seq_len_ke
+        seq_len_per_token = (start_pos + 1 + offset) // COMPRESS_RATIO
+        tl.store(
+            cu_compressed_seq_len_ke_ptr + out_pos,
+            seq_start + seq_len_per_token,
+            mask=mask,
+        )
+
+    # Compute token_to_seq
+    for i in range(0, compressed_seq_len, BLOCK_SIZE):
+        offset = i + tl.arange(0, BLOCK_SIZE)
+        mask = offset < compressed_seq_len
+        tl.store(token_to_seq_ptr + seq_start + offset, batch_idx, mask=mask)
diff --git a/vllm/v1/attention/backends/mla/prefill/__init__.py b/vllm/v1/attention/backends/mla/prefill/__init__.py
new file mode 100644
index 000000000000..ae5b7ae82598
--- /dev/null
+++ b/vllm/v1/attention/backends/mla/prefill/__init__.py
@@ -0,0 +1,11 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from vllm.v1.attention.backends.mla.prefill.base import MLAPrefillBackend
+from vllm.v1.attention.backends.mla.prefill.registry import MLAPrefillBackendEnum
+from vllm.v1.attention.backends.mla.prefill.selector import get_mla_prefill_backend
+
+__all__ = [
+    "MLAPrefillBackend",
+    "MLAPrefillBackendEnum",
+    "get_mla_prefill_backend",
+]
diff --git a/vllm/v1/attention/backends/mla/prefill/base.py b/vllm/v1/attention/backends/mla/prefill/base.py
new file mode 100644
index 000000000000..91d668826fd9
--- /dev/null
+++ b/vllm/v1/attention/backends/mla/prefill/base.py
@@ -0,0 +1,121 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Abstract base class for MLA prefill backends."""
+
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, ClassVar
+
+import torch
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+    from vllm.model_executor.layers.attention.mla_attention import (
+        MLACommonPrefillMetadata,
+    )
+    from vllm.platforms.interface import DeviceCapability
+    from vllm.v1.attention.backends.mla.prefill.selector import (
+        MLAPrefillSelectorConfig,
+    )
+
+
+class MLAPrefillBackend(ABC):
+    """Abstract base class for MLA prefill backends."""
+
+    supported_dtypes: ClassVar[list[torch.dtype]] = [
+        torch.float16,
+        torch.bfloat16,
+    ]
+    requires_r1_mla_dimensions: ClassVar[bool] = False
+
+    @staticmethod
+    @abstractmethod
+    def get_name() -> str:
+        raise NotImplementedError
+
+    @classmethod
+    def supports_compute_capability(cls, device_capability: "DeviceCapability") -> bool:
+        return True
+
+    @classmethod
+    def supports_dtype(cls, dtype: torch.dtype) -> bool:
+        return dtype in cls.supported_dtypes
+
+    @classmethod
+    def is_available(cls) -> bool:
+        return True
+
+    @classmethod
+    def validate_configuration(
+        cls,
+        device_capability: "DeviceCapability",
+        selector_config: "MLAPrefillSelectorConfig",
+    ) -> list[str]:
+        invalid_reasons: list[str] = []
+
+        if not cls.supports_compute_capability(device_capability):
+            invalid_reasons.append(
+                f"compute capability {device_capability.major}."
+                f"{device_capability.minor} not supported"
+            )
+
+        if not cls.supports_dtype(selector_config.dtype):
+            invalid_reasons.append(f"dtype {selector_config.dtype} not supported")
+
+        if not cls.is_available():
+            invalid_reasons.append("required dependencies not available")
+
+        if cls.requires_r1_mla_dimensions and not selector_config.is_r1_compatible:
+            invalid_reasons.append(
+                "model does not have DeepSeek R1 MLA dimensions "
+                "(qk_nope_head_dim=128, qk_rope_head_dim=64, v_head_dim=128)"
+            )
+
+        return invalid_reasons
+
+    def __init__(
+        self,
+        num_heads: int,
+        scale: float,
+        kv_lora_rank: int,
+        qk_nope_head_dim: int,
+        qk_rope_head_dim: int,
+        v_head_dim: int,
+        vllm_config: "VllmConfig",
+    ) -> None:
+        self.num_heads = num_heads
+        self.scale = scale
+        self.kv_lora_rank = kv_lora_rank
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.vllm_config = vllm_config
+
+    def prepare_metadata(  # noqa: B027
+        self,
+        prefill_metadata: "MLACommonPrefillMetadata",
+    ) -> None:
+        """Prepare backend-specific metadata before the forward pass.
+
+        Called by the metadata builder after constructing the prefill metadata.
+        """
+        self._prefill_metadata = prefill_metadata
+
+    @abstractmethod
+    def run_prefill_new_tokens(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        return_softmax_lse: bool,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        raise NotImplementedError
+
+    @abstractmethod
+    def run_prefill_context_chunk(
+        self,
+        chunk_idx: int,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        raise NotImplementedError
diff --git a/vllm/v1/attention/backends/mla/prefill/flash_attn.py b/vllm/v1/attention/backends/mla/prefill/flash_attn.py
new file mode 100644
index 000000000000..029bd8ec9560
--- /dev/null
+++ b/vllm/v1/attention/backends/mla/prefill/flash_attn.py
@@ -0,0 +1,176 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""FlashAttention backend for MLA prefill."""
+
+import functools
+from typing import TYPE_CHECKING
+
+import torch
+
+import vllm.envs as envs
+from vllm.platforms import current_platform
+from vllm.v1.attention.backends.fa_utils import (
+    get_flash_attn_version,
+    is_flash_attn_varlen_func_available,
+)
+from vllm.v1.attention.backends.mla.prefill.base import MLAPrefillBackend
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+
+if is_flash_attn_varlen_func_available():
+    from vllm.v1.attention.backends.fa_utils import flash_attn_varlen_func
+else:
+    flash_attn_varlen_func = None  # type: ignore[assignment]
+
+
+class FlashAttnPrefillBackend(MLAPrefillBackend):
+    """FlashAttention backend for MLA prefill."""
+
+    @staticmethod
+    def get_name() -> str:
+        return "FLASH_ATTN"
+
+    @classmethod
+    def is_available(cls) -> bool:
+        return is_flash_attn_varlen_func_available()
+
+    def __init__(
+        self,
+        num_heads: int,
+        scale: float,
+        kv_lora_rank: int,
+        qk_nope_head_dim: int,
+        qk_rope_head_dim: int,
+        v_head_dim: int,
+        vllm_config: "VllmConfig",
+    ) -> None:
+        super().__init__(
+            num_heads=num_heads,
+            scale=scale,
+            kv_lora_rank=kv_lora_rank,
+            qk_nope_head_dim=qk_nope_head_dim,
+            qk_rope_head_dim=qk_rope_head_dim,
+            v_head_dim=v_head_dim,
+            vllm_config=vllm_config,
+        )
+
+        # Handle the differences between the flash_attn_varlen from
+        # flash_attn and the one from vllm_flash_attn
+        assert flash_attn_varlen_func is not None, (
+            "FlashAttnPrefillBackend requires flash_attn_varlen_func. "
+            "Ensure FlashAttnPrefillBackend.is_available() is checked first."
+        )
+        qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
+        self.flash_attn_varlen_func = flash_attn_varlen_func
+        self.vllm_flash_attn_version = get_flash_attn_version(head_size=qk_head_dim)
+        if self.vllm_flash_attn_version is not None:
+            self.flash_attn_varlen_func = functools.partial(
+                flash_attn_varlen_func, fa_version=self.vllm_flash_attn_version
+            )
+
+        # Determine if we need to pad V
+        # For MLA the v head dim is smaller than qk head dim so we pad out
+        # v with 0s to match the qk head dim for attention backends that do
+        # not support different headdims.
+        # FA3 on Hopper (SM90) and FA4 natively handle diff headdims.
+        device_capability = current_platform.get_device_capability()
+        self.requires_v_padding = self.vllm_flash_attn_version is None or not (
+            (
+                self.vllm_flash_attn_version == 3
+                and device_capability is not None
+                and device_capability[0] == 9
+            )
+            or self.vllm_flash_attn_version == 4
+        )
+
+        # Track whether we're using vllm's FA or upstream (for ROCm)
+        self._is_vllm_fa = current_platform.is_cuda() or current_platform.is_xpu()
+
+    def _flash_attn_varlen_diff_headdims(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        return_softmax_lse: bool = False,
+        softmax_scale: float | None = None,
+        **kwargs,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        maybe_padded_v = v
+        if self.requires_v_padding:
+            maybe_padded_v = torch.nn.functional.pad(
+                v, [0, q.shape[-1] - v.shape[-1]], value=0
+            )
+
+        if self._is_vllm_fa:
+            kwargs["return_softmax_lse"] = return_softmax_lse
+        else:
+            # ROCm leverages the upstream flash_attn, which takes a parameter
+            # called "return_attn_probs" instead of return_softmax_lse
+            kwargs["return_attn_probs"] = return_softmax_lse
+        if envs.VLLM_BATCH_INVARIANT:
+            kwargs["num_splits"] = 1
+
+        attn_out = self.flash_attn_varlen_func(
+            q=q,
+            k=k,
+            v=maybe_padded_v,
+            softmax_scale=softmax_scale,
+            **kwargs,
+        )
+
+        # Unpack the output if there are multiple results
+        lse = None
+        if isinstance(attn_out, tuple):
+            attn_out, lse = attn_out[0], attn_out[1]
+
+        # Unpad output back to v_head_dim if we padded V
+        if self.requires_v_padding:
+            attn_out = attn_out[..., : v.shape[-1]]
+
+        # Remain consistent with old `flash_attn_varlen_func` where there
+        # is only one output tensor if `return_softmax_lse` is False.
+        if return_softmax_lse:
+            return attn_out, lse
+        return attn_out
+
+    def run_prefill_new_tokens(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        return_softmax_lse: bool,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        return self._flash_attn_varlen_diff_headdims(
+            q=q,
+            k=k,
+            v=v,
+            cu_seqlens_q=self._prefill_metadata.query_start_loc,
+            cu_seqlens_k=self._prefill_metadata.query_start_loc,
+            max_seqlen_q=self._prefill_metadata.max_query_len,
+            max_seqlen_k=self._prefill_metadata.max_query_len,
+            softmax_scale=self.scale,
+            causal=True,
+            return_softmax_lse=return_softmax_lse,
+        )
+
+    def run_prefill_context_chunk(
+        self,
+        chunk_idx: int,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        assert self._prefill_metadata.chunked_context is not None
+        return self._flash_attn_varlen_diff_headdims(
+            q=q,
+            k=k,
+            v=v,
+            cu_seqlens_q=self._prefill_metadata.query_start_loc,
+            cu_seqlens_k=self._prefill_metadata.chunked_context.cu_seq_lens[chunk_idx],
+            max_seqlen_q=self._prefill_metadata.max_query_len,
+            max_seqlen_k=self._prefill_metadata.chunked_context.max_seq_lens[chunk_idx],
+            softmax_scale=self.scale,
+            causal=False,  # Context is unmasked
+            return_softmax_lse=True,
+        )
diff --git a/vllm/v1/attention/backends/mla/prefill/flashinfer.py b/vllm/v1/attention/backends/mla/prefill/flashinfer.py
new file mode 100644
index 000000000000..77199fb5238a
--- /dev/null
+++ b/vllm/v1/attention/backends/mla/prefill/flashinfer.py
@@ -0,0 +1,222 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""FlashInfer backend for MLA prefill."""
+
+from typing import TYPE_CHECKING
+
+import torch
+
+import vllm.envs as envs
+from vllm.v1.attention.backends.mla.prefill.base import MLAPrefillBackend
+from vllm.v1.attention.backends.utils import (
+    PerLayerParameters,
+    get_per_layer_parameters,
+    infer_global_hyperparameters,
+)
+from vllm.v1.worker.workspace import current_workspace_manager
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+    from vllm.model_executor.layers.attention.mla_attention import (
+        MLACommonPrefillMetadata,
+    )
+    from vllm.platforms.interface import DeviceCapability
+
+try:
+    from flashinfer import BatchPrefillWithRaggedKVCacheWrapper
+except ImportError:
+    BatchPrefillWithRaggedKVCacheWrapper = object  # type: ignore[misc,assignment]
+
+_DEFAULT_NUM_CHUNKS = 32
+
+
+class FlashInferPrefillBackend(MLAPrefillBackend):
+    """FlashInfer backend for MLA prefill."""
+
+    requires_r1_mla_dimensions = True
+
+    @staticmethod
+    def get_name() -> str:
+        return "FLASHINFER"
+
+    @classmethod
+    def supports_compute_capability(cls, device_capability: "DeviceCapability") -> bool:
+        return device_capability.major == 10
+
+    @classmethod
+    def is_available(cls) -> bool:
+        try:
+            from flashinfer import (
+                BatchPrefillWithRaggedKVCacheWrapper,  # noqa: F401
+            )
+
+            return True
+        except ImportError:
+            return False
+
+    def __init__(
+        self,
+        num_heads: int,
+        scale: float,
+        kv_lora_rank: int,
+        qk_nope_head_dim: int,
+        qk_rope_head_dim: int,
+        v_head_dim: int,
+        vllm_config: "VllmConfig",
+    ) -> None:
+        super().__init__(
+            num_heads=num_heads,
+            scale=scale,
+            kv_lora_rank=kv_lora_rank,
+            qk_nope_head_dim=qk_nope_head_dim,
+            qk_rope_head_dim=qk_rope_head_dim,
+            v_head_dim=v_head_dim,
+            vllm_config=vllm_config,
+        )
+
+        self._prefill_main: BatchPrefillWithRaggedKVCacheWrapper | None = None
+        self._prefill_chunks: list[BatchPrefillWithRaggedKVCacheWrapper] = []
+        self._global_hyperparameters: PerLayerParameters | None = None
+
+    def _ensure_chunks(
+        self,
+        num_chunks: int,
+        workspace_buffer: torch.Tensor,
+    ) -> None:
+        if len(self._prefill_chunks) < num_chunks:
+            for _ in range(len(self._prefill_chunks), num_chunks):
+                self._prefill_chunks.append(
+                    BatchPrefillWithRaggedKVCacheWrapper(
+                        workspace_buffer, "NHD", backend="cutlass"
+                    )
+                )
+
+    def _resolve_global_hyperparameters(self) -> PerLayerParameters:
+        if self._global_hyperparameters is not None:
+            return self._global_hyperparameters
+
+        from vllm.model_executor.layers.attention.mla_attention import (
+            MLAAttention,
+            MLACommonImpl,
+        )
+
+        forward_context = self.vllm_config.compilation_config.static_forward_context
+        layer_names = [
+            name
+            for name, layer in forward_context.items()
+            if isinstance(layer, MLAAttention)
+        ]
+
+        self._global_hyperparameters = infer_global_hyperparameters(
+            get_per_layer_parameters(
+                self.vllm_config,
+                layer_names,
+                MLACommonImpl,  # type: ignore[type-abstract]
+            )
+        )
+        return self._global_hyperparameters
+
+    def prepare_metadata(
+        self,
+        prefill_metadata: "MLACommonPrefillMetadata",
+    ) -> None:
+        global_hyperparameters = self._resolve_global_hyperparameters()
+        qo_indptr = prefill_metadata.query_start_loc
+        has_context = prefill_metadata.chunked_context is not None
+        (workspace_buffer,) = current_workspace_manager().get_simultaneous(
+            ((envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE,), torch.uint8),
+        )
+
+        if self._prefill_main is None:
+            self._prefill_main = BatchPrefillWithRaggedKVCacheWrapper(
+                workspace_buffer, "NHD", backend="cutlass"
+            )
+            self._ensure_chunks(_DEFAULT_NUM_CHUNKS, workspace_buffer)
+
+        if has_context:
+            chunked_context = prefill_metadata.chunked_context
+            assert chunked_context is not None
+            num_chunks = chunked_context.cu_seq_lens.shape[0]
+            self._ensure_chunks(num_chunks, workspace_buffer)
+
+        num_qo_heads = self.num_heads
+        num_kv_heads = num_qo_heads
+
+        head_dim_qk = self.qk_nope_head_dim + self.qk_rope_head_dim
+        head_dim_vo = self.v_head_dim
+        kv_indptr = qo_indptr.clone()
+
+        assert self._prefill_main is not None
+        self._prefill_main.plan(
+            qo_indptr=qo_indptr,
+            kv_indptr=kv_indptr,
+            num_qo_heads=num_qo_heads,
+            num_kv_heads=num_kv_heads,
+            head_dim_qk=head_dim_qk,
+            head_dim_vo=head_dim_vo,
+            causal=True,
+            sm_scale=global_hyperparameters.sm_scale,
+            window_left=global_hyperparameters.window_left,
+            logits_soft_cap=global_hyperparameters.logits_soft_cap,
+            q_data_type=prefill_metadata.q_data_type,
+            o_data_type=prefill_metadata.output_dtype,
+        )
+
+        if has_context:
+            chunked_context = prefill_metadata.chunked_context
+            assert chunked_context is not None
+            for i in range(num_chunks):
+                kv_indptr_chunk = chunked_context.cu_seq_lens[i]
+
+                self._prefill_chunks[i].plan(
+                    qo_indptr=qo_indptr,
+                    kv_indptr=kv_indptr_chunk,
+                    num_qo_heads=num_qo_heads,
+                    num_kv_heads=num_kv_heads,
+                    head_dim_qk=head_dim_qk,
+                    head_dim_vo=head_dim_vo,
+                    causal=False,
+                    sm_scale=global_hyperparameters.sm_scale,
+                    window_left=global_hyperparameters.window_left,
+                    logits_soft_cap=global_hyperparameters.logits_soft_cap,
+                    q_data_type=prefill_metadata.q_data_type,
+                    o_data_type=prefill_metadata.output_dtype,
+                )
+
+    def run_prefill_new_tokens(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        return_softmax_lse: bool,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        assert self._prefill_main is not None
+
+        ret = self._prefill_main.run(
+            q=q,
+            k=k,
+            v=v,
+            return_lse=return_softmax_lse,
+        )
+
+        if isinstance(ret, tuple):
+            # Convert from (q_len, num_heads) to (num_heads, q_len)
+            return ret[0], ret[1].transpose(0, 1).contiguous()
+        return ret
+
+    def run_prefill_context_chunk(
+        self,
+        chunk_idx: int,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        attn_out, lse = self._prefill_chunks[chunk_idx].run(
+            q=q,
+            k=k,
+            v=v,
+            return_lse=True,
+        )
+
+        # Convert from (q_len, num_heads) to (num_heads, q_len)
+        return attn_out, lse.transpose(0, 1).contiguous()
diff --git a/vllm/v1/attention/backends/mla/prefill/registry.py b/vllm/v1/attention/backends/mla/prefill/registry.py
new file mode 100644
index 000000000000..3a3242f60365
--- /dev/null
+++ b/vllm/v1/attention/backends/mla/prefill/registry.py
@@ -0,0 +1,53 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Registry for MLA prefill backends.
+
+This module provides an enumeration of all available MLA prefill backends
+and utilities for loading them.
+"""
+
+from enum import Enum, EnumMeta
+from typing import TYPE_CHECKING
+
+from vllm.utils.import_utils import resolve_obj_by_qualname
+
+if TYPE_CHECKING:
+    from vllm.v1.attention.backends.mla.prefill.base import MLAPrefillBackend
+
+
+class _MLAPrefillBackendEnumMeta(EnumMeta):
+    """Metaclass for MLAPrefillBackendEnum to provide better error messages."""
+
+    def __getitem__(cls, name: str):
+        try:
+            return super().__getitem__(name)
+        except KeyError:
+            members = cls.__members__.keys()
+            valid_backends = ", ".join(members)
+            raise ValueError(
+                f"Unknown MLA prefill backend: '{name}'. "
+                f"Valid options are: {valid_backends}"
+            ) from None
+
+
+class MLAPrefillBackendEnum(Enum, metaclass=_MLAPrefillBackendEnumMeta):
+    """Enumeration of all supported MLA prefill backends."""
+
+    FLASH_ATTN = (
+        "vllm.v1.attention.backends.mla.prefill.flash_attn.FlashAttnPrefillBackend"
+    )
+    FLASHINFER = (
+        "vllm.v1.attention.backends.mla.prefill.flashinfer.FlashInferPrefillBackend"
+    )
+    TRTLLM_RAGGED = (
+        "vllm.v1.attention.backends.mla.prefill.trtllm_ragged."
+        "TrtllmRaggedPrefillBackend"
+    )
+
+    def get_path(self) -> str:
+        """Get the fully qualified class path for this backend."""
+        return self.value
+
+    def get_class(self) -> "type[MLAPrefillBackend]":
+        """Lazy load and return the backend class."""
+        return resolve_obj_by_qualname(self.get_path())
diff --git a/vllm/v1/attention/backends/mla/prefill/selector.py b/vllm/v1/attention/backends/mla/prefill/selector.py
new file mode 100644
index 000000000000..fdb8be6d65d9
--- /dev/null
+++ b/vllm/v1/attention/backends/mla/prefill/selector.py
@@ -0,0 +1,183 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Selector for MLA prefill backends.
+
+This module provides functions for selecting the appropriate MLA prefill
+backend based on device capabilities and configuration.
+"""
+
+from functools import cache
+from typing import TYPE_CHECKING, NamedTuple
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.platforms.interface import DeviceCapability
+from vllm.v1.attention.backends.mla.prefill.registry import MLAPrefillBackendEnum
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+    from vllm.v1.attention.backends.mla.prefill.base import MLAPrefillBackend
+
+logger = init_logger(__name__)
+
+
+class MLAPrefillSelectorConfig(NamedTuple):
+    """Hashable configuration for MLA prefill backend selection.
+
+    This is analogous to AttentionSelectorConfig and contains model-specific
+    configuration needed to select an MLA prefill backend, extracted from
+    VllmConfig into a hashable form for caching.
+    """
+
+    dtype: torch.dtype
+    is_r1_compatible: bool
+
+
+def is_deepseek_r1_mla_compatible(vllm_config: "VllmConfig") -> bool:
+    """Check if model has DeepSeek R1 compatible MLA dimensions.
+
+    DeepSeek R1 MLA dimensions are:
+    - qk_nope_head_dim = 128
+    - qk_rope_head_dim = 64
+    - v_head_dim = 128
+    """
+    if vllm_config.model_config is None:
+        return False
+    hf_text_config = vllm_config.model_config.hf_text_config
+    qk_nope_head_dim = getattr(hf_text_config, "qk_nope_head_dim", 1)
+    qk_rope_head_dim = getattr(hf_text_config, "qk_rope_head_dim", 1)
+    v_head_dim = getattr(hf_text_config, "v_head_dim", 1)
+    return qk_nope_head_dim == 128 and qk_rope_head_dim == 64 and v_head_dim == 128
+
+
+def _get_mla_prefill_backend_priorities(
+    device_capability: DeviceCapability,
+) -> list[MLAPrefillBackendEnum]:
+    """Get MLA prefill backend priorities based on device capability.
+
+    Args:
+        device_capability: The device's compute capability.
+
+    Returns:
+        List of backends in priority order (highest priority first).
+    """
+    if device_capability.major == 10:  # Blackwell
+        return [
+            MLAPrefillBackendEnum.FLASH_ATTN,
+            MLAPrefillBackendEnum.TRTLLM_RAGGED,
+            MLAPrefillBackendEnum.FLASHINFER,
+        ]
+    else:  # Hopper (SM90) and older
+        return [
+            MLAPrefillBackendEnum.FLASH_ATTN,
+        ]
+
+
+def get_mla_prefill_backend(
+    vllm_config: "VllmConfig",
+) -> "type[MLAPrefillBackend]":
+    """Select the MLA prefill backend based on configuration and device.
+
+    This function first checks for explicit user preferences via
+    mla_prefill_backend in AttentionConfig, then falls back to automatic
+    priority-based selection.
+
+    Args:
+        vllm_config: The vLLM configuration.
+
+    Returns:
+        The selected prefill backend class.
+    """
+    from vllm.platforms import current_platform
+
+    device_capability = current_platform.get_device_capability()
+    if device_capability is None:
+        logger.info_once(
+            "Device capability not available, using FlashAttention MLA prefill backend."
+        )
+        return MLAPrefillBackendEnum.FLASH_ATTN.get_class()
+
+    attention_config = vllm_config.attention_config
+
+    selector_config = MLAPrefillSelectorConfig(
+        dtype=vllm_config.model_config.dtype,
+        is_r1_compatible=is_deepseek_r1_mla_compatible(vllm_config),
+    )
+
+    if attention_config.mla_prefill_backend is not None:
+        selected_backend = attention_config.mla_prefill_backend
+        backend_cls: type[MLAPrefillBackend] | None = None
+        try:
+            backend_cls = selected_backend.get_class()
+            invalid_reasons = backend_cls.validate_configuration(
+                device_capability, selector_config
+            )
+        except ImportError:
+            invalid_reasons = ["ImportError"]
+        if invalid_reasons:
+            raise ValueError(
+                f"Selected MLA prefill backend {selected_backend.name} "
+                f"is not valid for this configuration. "
+                f"Reason: {invalid_reasons}"
+            )
+        assert backend_cls is not None
+        logger.info("Using %s MLA prefill backend.", selected_backend.name)
+        return backend_cls
+
+    return _auto_select_mla_prefill_backend(
+        device_capability,
+        selector_config,
+    )
+
+
+@cache
+def _auto_select_mla_prefill_backend(
+    device_capability: DeviceCapability,
+    selector_config: MLAPrefillSelectorConfig,
+) -> "type[MLAPrefillBackend]":
+    """Auto-select the best available MLA prefill backend.
+
+    Args:
+        device_capability: The device's compute capability.
+        selector_config: Hashable configuration for backend selection.
+
+    Returns:
+        The selected prefill backend class.
+    """
+    priorities = _get_mla_prefill_backend_priorities(device_capability)
+    all_invalid_reasons: dict[str, list[str]] = {}
+
+    for backend_enum in priorities:
+        backend_cls: type[MLAPrefillBackend] | None = None
+        try:
+            backend_cls = backend_enum.get_class()
+            invalid_reasons = backend_cls.validate_configuration(
+                device_capability, selector_config
+            )
+        except ImportError:
+            invalid_reasons = ["ImportError"]
+        if not invalid_reasons:
+            assert backend_cls is not None
+            logger.info_once("Using %s MLA prefill backend.", backend_enum.name)
+            return backend_cls
+        all_invalid_reasons[backend_enum.name] = invalid_reasons
+
+    reasons_str = (
+        "{"
+        + ", ".join(
+            f"{name}: [{', '.join(reasons)}]"
+            for name, reasons in all_invalid_reasons.items()
+        )
+        + "}"
+    )
+    config_str = repr(selector_config)
+    logger.debug_once(
+        "Some MLA prefill backends are not valid with %s. Reasons: %s.",
+        config_str,
+        reasons_str,
+    )
+
+    raise ValueError(
+        f"No valid MLA prefill backend found with {config_str}. Reasons: {reasons_str}."
+    )
diff --git a/vllm/v1/attention/backends/mla/prefill/trtllm_ragged.py b/vllm/v1/attention/backends/mla/prefill/trtllm_ragged.py
new file mode 100644
index 000000000000..7462dc39e7bc
--- /dev/null
+++ b/vllm/v1/attention/backends/mla/prefill/trtllm_ragged.py
@@ -0,0 +1,174 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""TRT-LLM Ragged backend for MLA prefill."""
+
+from typing import TYPE_CHECKING
+
+import torch
+
+import vllm.envs as envs
+from vllm.v1.attention.backends.mla.prefill.base import MLAPrefillBackend
+from vllm.v1.worker.workspace import current_workspace_manager
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+    from vllm.model_executor.layers.attention.mla_attention import (
+        MLACommonPrefillMetadata,
+    )
+    from vllm.platforms.interface import DeviceCapability
+
+
+class TrtllmRaggedPrefillBackend(MLAPrefillBackend):
+    """TRT-LLM Ragged backend for MLA prefill."""
+
+    requires_r1_mla_dimensions = True
+
+    @staticmethod
+    def get_name() -> str:
+        return "TRTLLM_RAGGED"
+
+    @classmethod
+    def supports_compute_capability(cls, device_capability: "DeviceCapability") -> bool:
+        return device_capability.major == 10
+
+    @classmethod
+    def is_available(cls) -> bool:
+        try:
+            from flashinfer.prefill import (
+                trtllm_ragged_attention_deepseek,  # noqa: F401
+            )
+
+            return True
+        except ImportError:
+            return False
+
+    def __init__(
+        self,
+        num_heads: int,
+        scale: float,
+        kv_lora_rank: int,
+        qk_nope_head_dim: int,
+        qk_rope_head_dim: int,
+        v_head_dim: int,
+        vllm_config: "VllmConfig",
+    ) -> None:
+        super().__init__(
+            num_heads=num_heads,
+            scale=scale,
+            kv_lora_rank=kv_lora_rank,
+            qk_nope_head_dim=qk_nope_head_dim,
+            qk_rope_head_dim=qk_rope_head_dim,
+            v_head_dim=v_head_dim,
+            vllm_config=vllm_config,
+        )
+
+    def _get_workspace_buffer(self) -> torch.Tensor:
+        (workspace_buffer,) = current_workspace_manager().get_simultaneous(
+            (
+                (envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE,),
+                torch.uint8,
+            ),
+        )
+        return workspace_buffer
+
+    def prepare_metadata(
+        self,
+        prefill_metadata: "MLACommonPrefillMetadata",
+    ) -> None:
+        super().prepare_metadata(prefill_metadata)
+        self._query_seq_lens = (
+            prefill_metadata.query_start_loc[1:] - prefill_metadata.query_start_loc[:-1]
+        )
+
+    def run_prefill_new_tokens(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        return_softmax_lse: bool,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        from flashinfer.prefill import trtllm_ragged_attention_deepseek
+
+        workspace_buffer = self._get_workspace_buffer()
+        out = torch.empty(
+            q.shape[0],
+            q.shape[1],
+            v.shape[2],
+            device=q.device,
+            dtype=self._prefill_metadata.output_dtype,
+        )
+
+        ret = trtllm_ragged_attention_deepseek(
+            query=q,
+            key=k,
+            value=v,
+            workspace_buffer=workspace_buffer,
+            seq_lens=self._query_seq_lens,
+            max_q_len=self._prefill_metadata.max_query_len,
+            max_kv_len=self._prefill_metadata.max_query_len,
+            bmm1_scale=self.scale,
+            bmm2_scale=1.0,
+            o_sf_scale=1.0,
+            batch_size=self._query_seq_lens.shape[0],
+            window_left=-1,
+            cum_seq_lens_q=self._prefill_metadata.query_start_loc,
+            cum_seq_lens_kv=self._prefill_metadata.query_start_loc,
+            enable_pdl=False,
+            is_causal=True,
+            return_lse=return_softmax_lse,
+            out=out,
+        )
+
+        if isinstance(ret, tuple):
+            # Convert from (q_len, num_heads) to (num_heads, q_len)
+            return ret[0], ret[1].transpose(0, 1).contiguous()
+        return ret
+
+    def run_prefill_context_chunk(
+        self,
+        chunk_idx: int,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        from flashinfer.prefill import trtllm_ragged_attention_deepseek
+
+        assert self._prefill_metadata.chunked_context is not None
+        assert self._prefill_metadata.chunked_context.seq_lens[chunk_idx] is not None
+        workspace_buffer = self._get_workspace_buffer()
+
+        out = torch.empty(
+            q.shape[0],
+            q.shape[1],
+            v.shape[2],
+            device=q.device,
+            dtype=self._prefill_metadata.output_dtype,
+        )
+
+        attn_out, lse = trtllm_ragged_attention_deepseek(
+            query=q,
+            key=k,
+            value=v,
+            workspace_buffer=workspace_buffer,
+            seq_lens=self._prefill_metadata.chunked_context.seq_lens[chunk_idx],
+            max_q_len=self._prefill_metadata.max_query_len,
+            max_kv_len=self._prefill_metadata.chunked_context.max_seq_lens[chunk_idx],
+            bmm1_scale=self.scale,
+            bmm2_scale=1.0,
+            o_sf_scale=1.0,
+            batch_size=self._prefill_metadata.chunked_context.seq_lens[chunk_idx].shape[
+                0
+            ],
+            window_left=-1,
+            cum_seq_lens_q=self._prefill_metadata.query_start_loc,
+            cum_seq_lens_kv=self._prefill_metadata.chunked_context.cu_seq_lens[
+                chunk_idx
+            ],
+            enable_pdl=False,
+            is_causal=False,
+            return_lse=True,
+            out=out,
+        )
+
+        # Convert from (q_len, num_heads) to (num_heads, q_len)
+        return attn_out, lse.transpose(0, 1).contiguous()
diff --git a/vllm/v1/attention/backends/mla/rocm_aiter_mla.py b/vllm/v1/attention/backends/mla/rocm_aiter_mla.py
index 351fdba085ad..f16fd5dc14f0 100644
--- a/vllm/v1/attention/backends/mla/rocm_aiter_mla.py
+++ b/vllm/v1/attention/backends/mla/rocm_aiter_mla.py
@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from dataclasses import dataclass
-from typing import ClassVar
+from typing import ClassVar, Final
 
 import torch
 
@@ -44,7 +44,11 @@ def get_supported_head_sizes(cls) -> list[int]:
 
     @staticmethod
     def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
-        return [1]
+        # The aiter MLA decode kernel always operates with page_size=1
+        # internally (the wrapper flattens kv_buffer via .view(-1, 1, 1, H)).
+        # We support any kernel_block_size by expanding block-level indices
+        # into per-token flat indices in the metadata builder.
+        return [MultipleOf(1)]
 
     @staticmethod
     def get_name() -> str:
@@ -74,6 +78,8 @@ class AiterMLADecodeMetadata(MLACommonDecodeMetadata):
     attn_out_dtype: torch.dtype = torch.bfloat16
     # The max query output length: int
     max_qo_len: int | None = None
+    # Whether persistent MLA metadata was computed (only for qseqlen=1)
+    has_persistent_metadata: bool = False
 
 
 @dataclass
@@ -105,7 +111,16 @@ def __init__(
 
         self.compilation_config = vllm_config.compilation_config
         self.decode_attn_out_dtype = vllm_config.model_config.dtype
-        # kernel block size is always 1.
+
+        # Store the kernel block size from the spec. When kernel_block_size=1
+        # (no spec-dec), behavior is identical to the original. When > 1
+        # (e.g. 16 with Eagle3), we expand block-level indices into per-token
+        # flat indices since the aiter kernel always uses page_size=1 internally.
+        self.kernel_block_size = kv_cache_spec.block_size
+
+        # In the flat view (.view(-1,1,1,H)), each token is its own page,
+        # so max_num_pages_per_req = max_model_len regardless of
+        # kernel_block_size.
         max_num_pages_per_req = vllm_config.model_config.max_model_len
         max_num_reqs = vllm_config.scheduler_config.max_num_seqs
         max_num_pages = max_num_reqs * max_num_pages_per_req
@@ -115,8 +130,9 @@ def __init__(
         # so we can only use the persistent buffer if a cudagraph is actually
         # being used.
 
-        # paged_kv_last_page_len is always 1s (kernel block size is always 1),
-        # so we create it once and reuse slices in both eager and cudagraph modes.
+        # paged_kv_last_page_len is always 1s (the aiter kernel always sees
+        # page_size=1 after .view(-1,1,1,H) flattening), so we create it
+        # once and reuse slices in both eager and cudagraph modes.
         self.paged_kv_last_page_len = torch.ones(
             max_num_reqs, dtype=torch.int32, device=device
         )
@@ -196,14 +212,14 @@ def _build_decode(
         num_decode_tokens: int,
         dcp_tot_seq_lens_device: torch.Tensor | None,
     ) -> AiterMLADecodeMetadata:
-        # kernel block size is always 1, although the kv block size is not 1.
         device = self.device
         num_reqs = seq_lens_device.size(0)
 
-        # kernel block size is always 1, so each page has exactly 1 token.
-        # last_page_len is always 1 - just slice the pre-initialized buffer.
+        # The aiter kernel always operates with page_size=1 (the wrapper
+        # flattens kv_buffer). last_page_len is always 1.
         paged_kv_last_page_len = self.paged_kv_last_page_len[:num_reqs]
 
+        # indptr: cumsum of seq_lens (one page per token in the flat view)
         paged_kv_indptr = torch.cat(
             [
                 torch.zeros(1, dtype=seq_lens_device.dtype, device=device),
@@ -215,11 +231,19 @@ def _build_decode(
 
         if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
             self.paged_kv_indices.fill_(-1)
-        _copy_page_indices_kernel[(num_reqs,)](
+
+        # Expand block_table entries into per-token flat indices.
+        # When kernel_block_size=1, this degrades to a direct copy (identical
+        # to the original _copy_page_indices_kernel).
+        # When kernel_block_size=K>1, block_table entry b covering K tokens
+        # gets expanded to flat indices b*K, b*K+1, ..., b*K+(K-1).
+        _expand_page_indices_kernel[(num_reqs,)](
             self.paged_kv_indices,
             block_table_tensor,
             block_table_tensor.stride(0),
             paged_kv_indptr,
+            seq_lens_device,
+            KERNEL_BLOCK_SIZE=self.kernel_block_size,
             BLOCK_SIZE=1024,
         )
         paged_kv_indices = self.paged_kv_indices
@@ -245,27 +269,37 @@ def _build_decode(
                 0, num_reqs + 1, step=1, dtype=torch.int32, device=device
             )
 
-        from aiter import get_mla_metadata_v1
-
-        get_mla_metadata_v1(
-            qo_indptr,
-            paged_kv_indptr,
-            paged_kv_last_page_len,
-            self._num_attention_heads,
-            1,
-            True,
-            self._mla_work_meta_data,
-            self._mla_work_info_set,
-            self._mla_work_indptr,
-            self._mla_reduce_indptr,
-            self._mla_reduce_final_map,
-            self._mla_reduce_partial_map,
-            page_size=1,
-            kv_granularity=16,
-            max_seqlen_qo=max_qo_len,
-            uni_seqlen_qo=max_qo_len,
-            fast_mode=True,
-        )
+        # The aiter MLA ASM kernel only supports qseqlen=1 (single-token
+        # decode). With speculative decoding, the verification step has
+        # qseqlen > 1 (e.g. 8 for spec7). get_mla_metadata_v1 calls
+        # get_heuristic_kernel_mla which fails for qseqlen > 1.
+        # We track whether persistent metadata was successfully computed
+        # so forward_mqa can skip passing it (falling back to the kernel
+        # computing its own metadata internally, like v0.18.0).
+        has_persistent_metadata = False
+        if max_qo_len == 1:
+            from aiter import get_mla_metadata_v1
+
+            get_mla_metadata_v1(
+                qo_indptr,
+                paged_kv_indptr,
+                paged_kv_last_page_len,
+                self._num_attention_heads,
+                1,
+                True,
+                self._mla_work_meta_data,
+                self._mla_work_info_set,
+                self._mla_work_indptr,
+                self._mla_reduce_indptr,
+                self._mla_reduce_final_map,
+                self._mla_reduce_partial_map,
+                page_size=1,
+                kv_granularity=16,
+                max_seqlen_qo=max_qo_len,
+                uni_seqlen_qo=max_qo_len,
+                fast_mode=True,
+            )
+            has_persistent_metadata = True
 
         attn_metadata = AiterMLADecodeMetadata(
             block_table=block_table_tensor,
@@ -277,6 +311,7 @@ def _build_decode(
             dcp_tot_seq_lens=dcp_tot_seq_lens_device,
             max_qo_len=max_qo_len,
             attn_out_dtype=self.decode_attn_out_dtype,
+            has_persistent_metadata=has_persistent_metadata,
         )
 
         return attn_metadata
@@ -290,41 +325,115 @@ def build(
         attn_metadata = super().build(
             common_prefix_len, common_attn_metadata, fast_build
         )
-        attn_metadata.work_meta_data = self._mla_work_meta_data
-        attn_metadata.work_indptr = self._mla_work_indptr
-        attn_metadata.work_info_set = self._mla_work_info_set
-        attn_metadata.reduce_indptr = self._mla_reduce_indptr
-        attn_metadata.reduce_final_map = self._mla_reduce_final_map
-        attn_metadata.reduce_partial_map = self._mla_reduce_partial_map
+        if (
+            attn_metadata.decode is not None
+            and attn_metadata.decode.has_persistent_metadata
+        ):
+            attn_metadata.work_meta_data = self._mla_work_meta_data
+            attn_metadata.work_indptr = self._mla_work_indptr
+            attn_metadata.work_info_set = self._mla_work_info_set
+            attn_metadata.reduce_indptr = self._mla_reduce_indptr
+            attn_metadata.reduce_final_map = self._mla_reduce_final_map
+            attn_metadata.reduce_partial_map = self._mla_reduce_partial_map
         return attn_metadata
 
 
 @triton.jit
-def _copy_page_indices_kernel(
+def _expand_page_indices_kernel(
     page_indices,
     block_table,
     block_table_stride,
-    cu_num_blocks,
+    cu_num_tokens,
+    seq_lens,
+    KERNEL_BLOCK_SIZE: tl.constexpr,
     BLOCK_SIZE: tl.constexpr,
 ):
-    """Copy block table rows into a flat page_indices buffer using indptr.
-    Avoids blocking boolean mask indexing (tensor[mask]) which has
-    data-dependent output size and forces sync.
-    This is the same kernel as introduced in backends/flashinfer.py.
+    """Expand block table entries into per-token flat page indices.
+
+    The aiter MLA kernel always operates with page_size=1 internally
+    (kv_buffer is flattened via .view(-1, 1, 1, H)). This kernel converts
+    block-level indices from the block table into individual token positions
+    in the flattened KV buffer.
+
+    When KERNEL_BLOCK_SIZE=1: block_idx=t, offset=0, flat=block_id
+    (equivalent to a direct copy -- no regression from the original kernel).
+
+    When KERNEL_BLOCK_SIZE=K: block table entry b (covering K tokens)
+    is expanded to flat indices b*K, b*K+1, ..., b*K+(K-1).
     """
     req_idx = tl.program_id(0)
     row_ptr = block_table + req_idx * block_table_stride
-    start_idx = tl.load(cu_num_blocks + req_idx)
-    end_idx = tl.load(cu_num_blocks + req_idx + 1)
-    num_blocks = end_idx - start_idx
+    start_idx = tl.load(cu_num_tokens + req_idx)
+    num_tokens = tl.load(seq_lens + req_idx)
 
     offset = tl.arange(0, BLOCK_SIZE)
-    for i in tl.range(0, num_blocks, BLOCK_SIZE):
-        block_ids = tl.load(row_ptr + i + offset, mask=i + offset < num_blocks)
+    for i in tl.range(0, num_tokens, BLOCK_SIZE):
+        token_offsets = i + offset
+        mask = token_offsets < num_tokens
+
+        # Which block in the block table does this token belong to?
+        block_idx = token_offsets // KERNEL_BLOCK_SIZE
+        # Offset within that block
+        offset_in_block = token_offsets % KERNEL_BLOCK_SIZE
+
+        # Load the block ID from the block table
+        block_ids = tl.load(row_ptr + block_idx, mask=mask)
+
+        # Compute flat index in the flattened kv_buffer
+        flat_indices = block_ids * KERNEL_BLOCK_SIZE + offset_in_block
+
         tl.store(
-            page_indices + start_idx + i + offset,
-            block_ids,
-            mask=i + offset < num_blocks,
+            page_indices + start_idx + token_offsets,
+            flat_indices,
+            mask=mask,
+        )
+
+
+class AiterMLAHelper:
+    """
+    AITER MLA implementation requires num_heads >= 16. If num_heads < 16 and
+    16 % num_heads == 0, we can pad q to 16 heads; otherwise AITER has to fail.
+    """
+
+    _AITER_MIN_MLA_HEADS: Final = 16
+    _AITER_UNSUPPORTED_HEADS: ClassVar[tuple[int, ...]] = ()
+
+    @staticmethod
+    def check_num_heads_validity(num_heads: int):
+        assert AiterMLAHelper.is_valid_num_heads(num_heads), (
+            f"Aiter MLA requires that num_heads be multiples or divisors of 16, "
+            f"but provided {num_heads} number of heads.\n"
+            f"Try adjusting tensor_parallel_size value."
+        )
+
+    @staticmethod
+    def is_valid_num_heads(num_heads: int) -> bool:
+        return (
+            num_heads % AiterMLAHelper._AITER_MIN_MLA_HEADS == 0
+            if num_heads >= AiterMLAHelper._AITER_MIN_MLA_HEADS
+            else AiterMLAHelper._AITER_MIN_MLA_HEADS % num_heads == 0
+        )
+
+    @staticmethod
+    def get_actual_mla_num_heads(num_heads: int) -> int:
+        return max(num_heads, AiterMLAHelper._AITER_MIN_MLA_HEADS)
+
+    @staticmethod
+    def get_mla_padded_q(num_heads: int, q: torch.Tensor) -> torch.Tensor:
+        return (
+            q
+            if num_heads >= AiterMLAHelper._AITER_MIN_MLA_HEADS
+            else q.repeat_interleave(
+                AiterMLAHelper._AITER_MIN_MLA_HEADS // num_heads, dim=1
+            )
+        )
+
+    @staticmethod
+    def get_mla_unpadded_o(num_heads: int, o: torch.Tensor) -> torch.Tensor:
+        return (
+            o
+            if num_heads >= AiterMLAHelper._AITER_MIN_MLA_HEADS
+            else o[:, :: AiterMLAHelper._AITER_MIN_MLA_HEADS // num_heads, :]
         )
 
 
@@ -357,17 +466,8 @@ def __init__(
             kv_sharing_target_layer_name,
             **mla_args,
         )
-        _valid_heads = num_heads in (4, 8) or (
-            num_heads % 16 == 0 and 16 <= num_heads <= 128
-        )
-        assert _valid_heads, (
-            f"Aiter MLA supports num_heads of 4, 8, or multiples of 16 "
-            f"in [16, 128].\n"
-            f"Provided {num_heads} number of heads.\n"
-            "Try adjusting tensor_parallel_size value."
-        )
-        self._needs_head_repeat = num_heads < 16
-        self._head_repeat_factor = 16 // num_heads if num_heads < 16 else 1
+        AiterMLAHelper.check_num_heads_validity(num_heads)
+
         unsupported_features = [alibi_slopes, sliding_window, logits_soft_cap]
         if any(unsupported_features):
             raise NotImplementedError(
@@ -410,15 +510,11 @@ def forward_mqa(
         assert isinstance(q, torch.Tensor)
         B = q.shape[0]
 
-        if self._needs_head_repeat:
-            q = q.repeat_interleave(self._head_repeat_factor, dim=1)
-            kernel_num_heads = 16
-        else:
-            kernel_num_heads = self.num_heads
-
+        mla_padded_q = AiterMLAHelper.get_mla_padded_q(self.num_heads, q)
+        mla_num_heads = AiterMLAHelper.get_actual_mla_num_heads(self.num_heads)
         o = torch.empty(
             B,
-            kernel_num_heads,
+            mla_num_heads,
             self.kv_lora_rank,
             dtype=attn_metadata.decode.attn_out_dtype,
             device=q.device,
@@ -426,8 +522,26 @@ def forward_mqa(
 
         kv_buffer = kv_c_and_k_pe_cache.unsqueeze(2)
 
+        # Build kwargs for mla_decode_fwd. Pass persistent metadata only
+        # when it was successfully computed (qseqlen=1 decode steps).
+        # For multi-token verification steps (spec-dec), the kernel falls
+        # back to computing metadata internally.
+        mla_kwargs = dict(
+            q_scale=layer._q_scale,
+            kv_scale=layer._k_scale,
+        )
+        if attn_metadata.work_meta_data is not None:
+            mla_kwargs.update(
+                work_meta_data=attn_metadata.work_meta_data,
+                work_indptr=attn_metadata.work_indptr,
+                work_info_set=attn_metadata.work_info_set,
+                reduce_indptr=attn_metadata.reduce_indptr,
+                reduce_final_map=attn_metadata.reduce_final_map,
+                reduce_partial_map=attn_metadata.reduce_partial_map,
+            )
+
         rocm_aiter_ops.mla_decode_fwd(
-            q,
+            mla_padded_q,
             kv_buffer,
             o,
             self.scale,
@@ -436,17 +550,7 @@ def forward_mqa(
             attn_metadata.decode.paged_kv_indptr,
             attn_metadata.decode.paged_kv_indices,
             attn_metadata.decode.paged_kv_last_page_len,
-            q_scale=layer._q_scale,
-            kv_scale=layer._k_scale,
-            work_meta_data=attn_metadata.work_meta_data,
-            work_indptr=attn_metadata.work_indptr,
-            work_info_set=attn_metadata.work_info_set,
-            reduce_indptr=attn_metadata.reduce_indptr,
-            reduce_final_map=attn_metadata.reduce_final_map,
-            reduce_partial_map=attn_metadata.reduce_partial_map,
+            **mla_kwargs,
         )
 
-        if self._needs_head_repeat:
-            o = o[:, :: self._head_repeat_factor, :]
-
-        return o, None
+        return AiterMLAHelper.get_mla_unpadded_o(self.num_heads, o), None
diff --git a/vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py b/vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py
index f24aa6055e2a..dd25e721d33e 100644
--- a/vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py
+++ b/vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py
@@ -7,6 +7,7 @@
 import numpy as np
 import torch
 
+from vllm import _custom_ops as ops
 from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import VllmConfig
 from vllm.config.cache import CacheDType
@@ -14,6 +15,7 @@
 from vllm.model_executor.layers.attention.mla_attention import (
     get_mla_dims,
 )
+from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.v1.attention.backend import (
     AttentionBackend,
@@ -25,8 +27,8 @@
     MultipleOf,
     SparseMLAAttentionImpl,
 )
-from vllm.v1.attention.backends.mla.flashmla_sparse import (
-    triton_convert_req_index_to_global_index,
+from vllm.v1.attention.backends.mla.rocm_aiter_mla import (
+    AiterMLAHelper,
 )
 from vllm.v1.kv_cache_interface import AttentionSpec
 
@@ -35,6 +37,188 @@
 logger = init_logger(__name__)
 
 
+@triton.jit
+def _convert_req_index_to_global_index_kernel(
+    req_id_ptr,  # int32 [num_tokens]
+    block_table_ptr,  # int32 [num_requests, max_num_blocks_per_req]
+    token_indices_ptr,  # int32 [num_tokens, NUM_TOPK_TOKENS]
+    cu_seqlens_ptr,  # int32 [num_tokens + 1]
+    out_ptr,  # int32 [num_tokens, NUM_TOPK_TOKENS]
+    # shapes (compile-time where possible)
+    max_num_blocks_per_req: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+    BLOCK_N: tl.constexpr,  # tile width along columns
+    # strides (in elements)
+    bt_stride0,
+    bt_stride1,
+    ti_stride0,
+    ti_stride1,
+):
+    # program_id(0) -> token_id (row)
+    # program_id(1) -> tile index along columns
+    token_id = tl.program_id(0)
+    tile_id = tl.program_id(1)
+
+    # Each program covers BLOCK_N consecutive columns
+    indice_id = tile_id * BLOCK_N + tl.arange(0, BLOCK_N)
+
+    # Load request id for this token (no mask: grid is exact)
+    req = tl.load(req_id_ptr + token_id)
+
+    # Load cumulative sequence lengths to get starting index of this request
+    seq_start = tl.load(cu_seqlens_ptr + token_id)
+    seq_end = tl.load(cu_seqlens_ptr + token_id + 1)
+
+    if tile_id * BLOCK_N + seq_start >= seq_end:
+        return
+
+    # Load token indices for this tile
+    ti_ptr = token_indices_ptr + token_id * ti_stride0 + indice_id * ti_stride1
+    tok = tl.load(ti_ptr)  # int32
+
+    # Only token == -1 should propagate as -1
+    is_invalid_tok = tok < 0
+
+    # Compute block id and in-block offset
+    block_id = tok // BLOCK_SIZE
+    inblock_off = tok % BLOCK_SIZE
+
+    # Guard block_table access
+    valid_block = (block_id < max_num_blocks_per_req) & (block_id >= 0)
+    bt_ptr = block_table_ptr + req * bt_stride0 + block_id * bt_stride1
+    base = tl.load(bt_ptr, mask=valid_block, other=0)
+
+    # # If token == -1 OR block_id OOB, output 0; else base * BLOCK_SIZE + offset
+    out_val = tl.where(
+        is_invalid_tok | (~valid_block), 0, base * BLOCK_SIZE + inblock_off
+    )
+    out_ptr_ij = out_ptr + seq_start + indice_id
+    out_ptr_ij_mask = (seq_start + indice_id) < seq_end
+
+    # store the results with mask
+    tl.store(out_ptr_ij, out_val, mask=out_ptr_ij_mask)
+
+
+def triton_convert_req_index_to_global_index(
+    req_id: torch.Tensor,  # int32 [num_tokens]
+    block_table: torch.Tensor,  # int32 [num_requests, max_num_blocks_per_req]
+    token_indices: torch.Tensor,  # int32 [num_tokens, NUM_TOPK_TOKENS]
+    cu_seqlens: torch.Tensor,  # int32 [num_tokens + 1]
+    paged_kv_indices: torch.Tensor,  # int32 [num_tokens * topk] out_buffer
+    BLOCK_SIZE: int = 64,
+    NUM_TOPK_TOKENS: int = 2048,
+    BLOCK_N: int = 128,  # tile width along columns
+):
+    """
+    out[token_id, indice_id] =
+        block_table[req_id[token_id],
+            token_indices[token_id, indice_id] // BLOCK_SIZE] * BLOCK_SIZE
+        + token_indices[token_id, indice_id] % BLOCK_SIZE
+
+    Only when token_indices[token_id, indice_id] == -1 do we output -1.
+    For safety, we also output -1 if the derived block_id would be
+        out-of-bounds.
+    """
+    assert req_id.dtype == torch.int32
+    assert block_table.dtype == torch.int32
+    assert token_indices.dtype == torch.int32
+    assert token_indices.shape[1] == NUM_TOPK_TOKENS
+    assert NUM_TOPK_TOKENS % BLOCK_N == 0, (
+        f"NUM_TOPK_TOKENS ({NUM_TOPK_TOKENS}) must be divisible byBLOCK_N ({BLOCK_N})"
+    )
+    # print("req_id: ", req_id, flush=True)
+    num_tokens = req_id.shape[0]
+    _, max_num_blocks_per_req = block_table.shape
+    tiles_per_row = NUM_TOPK_TOKENS // BLOCK_N
+
+    # Ensure contiguous tensors on the same device
+    req_id_c = req_id.contiguous()
+    block_table_c = block_table.contiguous()
+    token_indices_c = token_indices.contiguous()
+
+    # Strides in elements
+    bt_stride0, bt_stride1 = block_table_c.stride()
+    ti_stride0, ti_stride1 = token_indices_c.stride()
+
+    # Exact 2D grid: tokens × column tiles
+    grid = (num_tokens, tiles_per_row)
+
+    _convert_req_index_to_global_index_kernel[grid](
+        req_id_c,
+        block_table_c,
+        token_indices_c,
+        cu_seqlens,
+        paged_kv_indices,
+        # shapes / constexprs
+        max_num_blocks_per_req,
+        BLOCK_SIZE,
+        BLOCK_N,
+        # strides
+        bt_stride0,
+        bt_stride1,
+        ti_stride0,
+        ti_stride1,
+    )
+    return
+
+
+@triton.jit
+def generate_sparse_seqlen_kernel(
+    seq_len_ptr,  # [num_seq]
+    cu_query_lens_ptr,  # [num_seq]
+    out_ptr,  # [num_query_tokens]
+    topk_token: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    seq_id = tl.program_id(0)
+    query_offset = tl.program_id(1) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    query_start = tl.load(cu_query_lens_ptr + seq_id)
+    query_end = tl.load(cu_query_lens_ptr + seq_id + 1)
+    if query_start + tl.program_id(1) * BLOCK_SIZE > query_end:
+        return
+    query_len = query_end - query_start
+    query_mask = query_offset + query_start < query_end
+    seq_len = tl.load(seq_len_ptr + seq_id)
+    # Just return since the out_ptr is zero initialized.
+    if seq_len == 0:
+        return
+    context_start_point = seq_len - query_len
+    sparse_seqlen = context_start_point + query_offset
+    sparse_seqlen_masked = tl.where(
+        sparse_seqlen + 1 < topk_token, sparse_seqlen + 1, topk_token
+    )
+    tl.store(
+        out_ptr + query_start + query_offset, sparse_seqlen_masked, mask=query_mask
+    )
+
+
+def generate_sparse_seqlen_triton(
+    query_lens: torch.Tensor,
+    seq_lens: torch.Tensor,
+    cu_query_lens: torch.Tensor,
+    topk_token: int,
+    num_tokens: int,
+    max_query_len: int,
+):
+    num_seqs = query_lens.size(0)
+    # zero initialize the tensor to make sure invalid positions will be zero
+    out = torch.zeros([num_tokens], dtype=torch.int32, device=query_lens.device)
+    block_size = 64
+    num_block_per_row = triton.cdiv(max_query_len, block_size)
+    grid = (
+        num_seqs,
+        num_block_per_row,
+    )
+    generate_sparse_seqlen_kernel[grid](
+        seq_lens,
+        cu_query_lens,
+        out,
+        topk_token,
+        block_size,
+    )
+    return out
+
+
 @triton.jit
 def fetch_id_to_ragged_kernel(
     in_tensor_ptr,  # [num_seq, topk]
@@ -83,11 +267,13 @@ class ROCMAiterMLASparseBackend(AttentionBackend):
         "auto",
         "float16",
         "bfloat16",
+        "fp8",
+        "fp8_e4m3",
     ]
 
     @staticmethod
     def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
-        return [1]
+        return [1, 64]
 
     @staticmethod
     def get_name() -> str:
@@ -141,11 +327,20 @@ class ROCMAiterMLASparseMetadata(AttentionMetadata):
     paged_kv_last_page_len: torch.Tensor
     paged_kv_indices: torch.Tensor
     paged_kv_indptr: torch.Tensor
-    paged_kv_indptr_rest: torch.Tensor
+    attn_out_dtype: torch.dtype
 
     block_size: int = 1
     topk_tokens: int = 2048
 
+    # Persistent MLA metadata (only populated when persistent mode is enabled,
+    # i.e. when the aiter sparse decode kernel supports work-stealing splits).
+    work_meta_data: torch.Tensor | None = None
+    work_indptr: torch.Tensor | None = None
+    work_info_set: torch.Tensor | None = None
+    reduce_indptr: torch.Tensor | None = None
+    reduce_final_map: torch.Tensor | None = None
+    reduce_partial_map: torch.Tensor | None = None
+
 
 @dataclass
 class ROCMAiterMLASparseMetadataBuilder(
@@ -164,6 +359,7 @@ def __init__(
     ):
         self.kv_cache_spec = kv_cache_spec
         self.model_config = vllm_config.model_config
+        self.model_dtype = vllm_config.model_config.dtype
         parallel_config = vllm_config.parallel_config
         self.device = device
         max_num_batched_tokens = vllm_config.scheduler_config.max_num_batched_tokens
@@ -171,9 +367,6 @@ def __init__(
         self.num_heads = self.model_config.get_num_attention_heads(parallel_config)
         self.mla_dims = get_mla_dims(self.model_config)
         self.topk_tokens = vllm_config.model_config.hf_config.index_topk
-        self.topk_tokens_tensor = torch.tensor(
-            [self.topk_tokens], device=device, dtype=torch.int32
-        )
         self.max_model_len_tensor = torch.tensor(
             [self.model_config.max_model_len], device=device, dtype=torch.int32
         )
@@ -205,6 +398,66 @@ def __init__(
             [max_num_batched_tokens + 1], dtype=torch.int32, device=device
         )
 
+        # ----- Persistent MLA metadata buffers -----
+        # The aiter sparse decode kernel supports a "persistent" path that
+        # uses precomputed work-splitting metadata for better load balancing
+        # across CUs. Mirrors the approach used in rocm_aiter_mla.py.
+        #
+        # In the sparse case each query token is its own "batch" entry in the
+        # qo_indptr (qo_indptr = [0, 1, 2, ..., num_tokens]) and max_qo_len=1.
+        # We pad get_mla_metadata_info_v1's batch_size to max_num_batched_tokens
+        # so the buffers are large enough for any decode shape we might see.
+        from aiter import dtypes, get_mla_metadata_info_v1
+
+        # Aiter sparse MLA also requires num_heads >= 16 (will be padded by
+        # AiterMLAHelper.get_mla_padded_q in forward).
+        self._num_attention_heads = max(16, self.num_heads)
+
+        q_dtype = self.model_dtype
+        kv_cache_dtype_str = getattr(vllm_config.cache_config, "cache_dtype", "auto")
+        if kv_cache_dtype_str in ("fp8", "fp8_e4m3", "fp8_e5m2"):
+            kv_cache_dtype_str = "fp8"
+        else:
+            kv_cache_dtype_str = "bf16"
+        kv_dtype = dtypes.d_dtypes.get(kv_cache_dtype_str, dtypes.bf16)
+
+        (
+            (work_meta_data_size, work_meta_data_type),
+            (work_indptr_size, work_indptr_type),
+            (work_info_set_size, work_info_set_type),
+            (reduce_indptr_size, reduce_indptr_type),
+            (reduce_final_map_size, reduce_final_map_type),
+            (reduce_partial_map_size, reduce_partial_map_type),
+        ) = get_mla_metadata_info_v1(
+            max_num_batched_tokens,
+            1,
+            self._num_attention_heads,
+            q_dtype,
+            kv_dtype,
+            is_sparse=True,
+            fast_mode=True,
+        )
+        self._mla_work_meta_data = torch.empty(
+            work_meta_data_size, dtype=work_meta_data_type, device=device
+        )
+        self._mla_work_indptr = torch.empty(
+            work_indptr_size, dtype=work_indptr_type, device=device
+        )
+        self._mla_work_info_set = torch.empty(
+            work_info_set_size, dtype=work_info_set_type, device=device
+        )
+        self._mla_reduce_indptr = torch.empty(
+            reduce_indptr_size, dtype=reduce_indptr_type, device=device
+        )
+        self._mla_reduce_final_map = torch.empty(
+            reduce_final_map_size, dtype=reduce_final_map_type, device=device
+        )
+        self._mla_reduce_partial_map = torch.empty(
+            reduce_partial_map_size,
+            dtype=reduce_partial_map_type,
+            device=device,
+        )
+
     def build(
         self,
         common_prefix_len: int,
@@ -219,18 +472,60 @@ def build(
         )
         # Zero-fill for cudagraphs
         self.req_id_per_token_buffer.fill_(0)
+        self.paged_kv_indices.fill_(0)
+        self.paged_kv_indptr.fill_(0)
         self.req_id_per_token_buffer[: req_id_per_token.shape[0]].copy_(
             torch.from_numpy(req_id_per_token), non_blocking=True
         )
-        self.paged_kv_indices.fill_(0)
-        self.paged_kv_indptr.fill_(0)
+        query_lens = (
+            common_attn_metadata.query_start_loc[1:]
+            - common_attn_metadata.query_start_loc[:-1]
+        )
+        seq_lens = common_attn_metadata.seq_lens
+        sparse_seqlen = generate_sparse_seqlen_triton(
+            query_lens,
+            seq_lens,
+            common_attn_metadata.query_start_loc,
+            self.topk_tokens,
+            num_tokens,
+            common_attn_metadata.max_query_len,
+        )
+
+        torch.cumsum(sparse_seqlen, dim=0, out=self.paged_kv_indptr[1 : num_tokens + 1])
+        self.paged_kv_indptr[num_tokens + 1 :].fill_(self.paged_kv_indptr[num_tokens])
 
         req_id_per_token = self.req_id_per_token_buffer[:num_tokens]
         qo_indptr = self.qo_indptr[: num_tokens + 1]
         paged_kv_last_page_len = self.paged_kv_last_page_len[:num_tokens]
-        paged_kv_indices = self.paged_kv_indices[: num_tokens * self.topk_tokens]
         paged_kv_indptr = self.paged_kv_indptr[: num_tokens + 1]
-        paged_kv_indptr_rest = self.paged_kv_indptr[num_tokens + 1 :]
+        paged_kv_indices = self.paged_kv_indices[: num_tokens * self.topk_tokens]
+
+        # ----- Compute persistent MLA metadata -----
+        # The aiter sparse decode kernel uses qseqlen=1 (each query token is
+        # treated as its own batch entry), so persistent metadata can always
+        # be precomputed here. The kernel switches to the persistent
+        # work-stealing path automatically when work_meta_data is non-None.
+        from aiter import get_mla_metadata_v1
+
+        get_mla_metadata_v1(
+            qo_indptr,
+            paged_kv_indptr,
+            paged_kv_last_page_len,
+            self._num_attention_heads,
+            1,
+            True,
+            self._mla_work_meta_data,
+            self._mla_work_info_set,
+            self._mla_work_indptr,
+            self._mla_reduce_indptr,
+            self._mla_reduce_final_map,
+            self._mla_reduce_partial_map,
+            page_size=1,
+            kv_granularity=16,
+            max_seqlen_qo=1,
+            uni_seqlen_qo=1,
+            fast_mode=True,
+        )
 
         metadata = ROCMAiterMLASparseMetadata(
             num_reqs=common_attn_metadata.num_reqs,
@@ -242,12 +537,18 @@ def build(
             block_table=common_attn_metadata.block_table_tensor,
             req_id_per_token=req_id_per_token,
             block_size=self.kv_cache_spec.block_size,
+            attn_out_dtype=self.model_dtype,
             topk_tokens=self.topk_tokens,
             qo_indptr=qo_indptr,
             paged_kv_last_page_len=paged_kv_last_page_len,
             paged_kv_indices=paged_kv_indices,
             paged_kv_indptr=paged_kv_indptr,
-            paged_kv_indptr_rest=paged_kv_indptr_rest,
+            work_meta_data=self._mla_work_meta_data,
+            work_indptr=self._mla_work_indptr,
+            work_info_set=self._mla_work_info_set,
+            reduce_indptr=self._mla_reduce_indptr,
+            reduce_final_map=self._mla_reduce_final_map,
+            reduce_partial_map=self._mla_reduce_partial_map,
         )
         return metadata
 
@@ -299,6 +600,8 @@ def __init__(
         indexer: "Indexer | None" = None,
         **mla_args,
     ) -> None:
+        AiterMLAHelper.check_num_heads_validity(num_heads)
+
         self.num_heads = num_heads
         self.head_size = head_size
         self.scale = float(scale)
@@ -309,28 +612,37 @@ def __init__(
         assert indexer is not None
         self.topk_indices_buffer: torch.Tensor | None = indexer.topk_indices_buffer
 
-    def _forward_bf16_kv(
+    def _forward_mla(
         self,
+        layer: AttentionLayer,
         q: torch.Tensor,  # [sq, heads, d_qk]
         kv_c_and_k_pe_cache: torch.Tensor,  # [blocks, heads, d_qk]
-        topk_indices: torch.Tensor,  # [sq, topk]
         attn_metadata: ROCMAiterMLASparseMetadata,
     ) -> torch.Tensor:
         num_tokens = q.shape[0]
+        mla_num_heads = AiterMLAHelper.get_actual_mla_num_heads(self.num_heads)
         output = torch.empty(
-            [num_tokens, self.num_heads, self.kv_lora_rank],
-            dtype=q.dtype,
+            [num_tokens, mla_num_heads, self.kv_lora_rank],
+            dtype=attn_metadata.attn_out_dtype,
             device=q.device,
         )
-        seq_len = (topk_indices != -1).sum(dim=-1)
-        torch.cumsum(seq_len, dim=0, out=attn_metadata.paged_kv_indptr[1:])
-        attn_metadata.paged_kv_indptr_rest.fill_(attn_metadata.paged_kv_indptr[-1])
-        fetch_id_to_ragged_triton(
-            topk_indices,
-            attn_metadata.paged_kv_indptr,
-            attn_metadata.paged_kv_indices,
-            attn_metadata.topk_tokens,
+
+        # Build kwargs and forward the persistent MLA metadata when it has
+        # been computed. The aiter mla_decode_fwd switches to its
+        # work-stealing persistent kernel path when work_meta_data is given.
+        mla_kwargs: dict = dict(
+            q_scale=layer._q_scale,
+            kv_scale=layer._k_scale,
         )
+        if attn_metadata.work_meta_data is not None:
+            mla_kwargs.update(
+                work_meta_data=attn_metadata.work_meta_data,
+                work_indptr=attn_metadata.work_indptr,
+                work_info_set=attn_metadata.work_info_set,
+                reduce_indptr=attn_metadata.reduce_indptr,
+                reduce_final_map=attn_metadata.reduce_final_map,
+                reduce_partial_map=attn_metadata.reduce_partial_map,
+            )
 
         rocm_aiter_ops.mla_decode_fwd(
             q,
@@ -342,9 +654,10 @@ def _forward_bf16_kv(
             attn_metadata.paged_kv_indptr,
             attn_metadata.paged_kv_indices,
             attn_metadata.paged_kv_last_page_len,
+            **mla_kwargs,
         )
 
-        return output[:, : self.num_heads, :]
+        return AiterMLAHelper.get_mla_unpadded_o(self.num_heads, output)
 
     def forward_mqa(
         self,
@@ -360,22 +673,32 @@ def forward_mqa(
         if isinstance(q, tuple):
             q = torch.cat(q, dim=-1)
 
-        num_actual_toks = q.shape[0]
+        num_actual_toks = attn_metadata.num_actual_tokens
 
         # Get topk indices
         assert self.topk_indices_buffer is not None
         topk_indices = self.topk_indices_buffer[:num_actual_toks]
 
-        topk_indices_global = triton_convert_req_index_to_global_index(
+        triton_convert_req_index_to_global_index(
             attn_metadata.req_id_per_token,
             attn_metadata.block_table,
             topk_indices,
+            attn_metadata.paged_kv_indptr,
+            attn_metadata.paged_kv_indices,
             BLOCK_SIZE=attn_metadata.block_size,
             NUM_TOPK_TOKENS=attn_metadata.topk_tokens,
         )
 
-        attn_out = self._forward_bf16_kv(
-            q, kv_c_and_k_pe_cache, topk_indices_global, attn_metadata
+        # write the latent and rope to kv cache
+        fp8_attention = self.kv_cache_dtype.startswith("fp8")
+        if fp8_attention:
+            original_q_shape = q.shape
+            kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.view(current_platform.fp8_dtype())
+            q, _ = ops.scaled_fp8_quant(q.view(q.shape[0], -1), layer._q_scale)
+            q = q.view(original_q_shape)
+        mla_padded_q = AiterMLAHelper.get_mla_padded_q(self.num_heads, q)
+        attn_out = self._forward_mla(
+            layer, mla_padded_q, kv_c_and_k_pe_cache, attn_metadata
         )
 
         return attn_out, None
diff --git a/vllm/v1/attention/backends/mla/sparse_swa.py b/vllm/v1/attention/backends/mla/sparse_swa.py
new file mode 100644
index 000000000000..28564e6a97d3
--- /dev/null
+++ b/vllm/v1/attention/backends/mla/sparse_swa.py
@@ -0,0 +1,493 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+from typing import ClassVar, cast
+
+import torch
+
+from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
+from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
+from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
+from vllm.v1.attention.backend import (
+    AttentionBackend,
+    AttentionCGSupport,
+    AttentionMetadataBuilder,
+    CommonAttentionMetadata,
+    MultipleOf,
+)
+from vllm.v1.attention.backends.utils import split_decodes_and_prefills
+from vllm.v1.attention.ops.flashmla import FlashMLASchedMeta, get_mla_metadata
+from vllm.v1.kv_cache_interface import (
+    KVCacheSpec,
+    MLAAttentionSpec,
+    SlidingWindowMLASpec,
+)
+
+# DeepseekV4 decode layer types, keyed by compress_ratio. Each type has a distinct
+# (topk, extra_topk, extra_page_block_size) config, so they cannot share a
+# FlashMLA tile-scheduler plan. Within a type, all ~60 DeepseekV4 layers share one
+# plan per step because b / s_q / h_q / page_block_sizes / topks are identical.
+_LAYER_TYPE_SWAONLY = "swaonly"
+_LAYER_TYPE_C4A = "c4a"
+_LAYER_TYPE_C128A = "c128a"
+
+
+def _layer_type_for(compress_ratio: int) -> str:
+    if compress_ratio <= 1:
+        return _LAYER_TYPE_SWAONLY
+    if compress_ratio == 4:
+        return _LAYER_TYPE_C4A
+    if compress_ratio == 128:
+        return _LAYER_TYPE_C128A
+    raise ValueError(
+        f"Unsupported DeepseekV4 compress_ratio={compress_ratio}; "
+        "expected 1, 4, or 128."
+    )
+
+
+class DeepseekV4SWACache(torch.nn.Module, AttentionLayerBase):
+    def __init__(
+        self,
+        head_dim: int,
+        window_size: int,
+        dtype: torch.dtype,
+        prefix: str,
+        cache_config: CacheConfig,
+    ):
+        super().__init__()
+        self.kv_cache = torch.tensor([])
+        self.head_dim = head_dim
+        self.window_size = window_size
+        self.prefix = prefix
+        self.cache_config = cache_config
+        self.dtype = dtype
+        compilation_config = get_current_vllm_config().compilation_config
+        if prefix in compilation_config.static_forward_context:
+            raise ValueError(f"Duplicate layer name: {prefix}")
+        compilation_config.static_forward_context[prefix] = self
+
+        # Block size is constrained by tensor sharing between SWA and C4A KV blocks.
+        # Since both block types share the same physical tensor, they must use the
+        # same page size. The C4A KV block shape [256//4, head_dim] = [64, head_dim]
+        # determines the SWA block size of 64 tokens per block.
+        # TODO(yifan): make SWA block size automatically determined and configurable.
+        self.block_size = 64
+        assert self.dtype == torch.uint8
+
+    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec:
+        return SlidingWindowMLASpec(
+            block_size=self.block_size,
+            num_kv_heads=1,
+            head_size=self.head_dim,
+            dtype=self.dtype,
+            sliding_window=self.window_size,
+            cache_dtype_str=self.cache_config.cache_dtype,
+            alignment=576,  # NOTE: FlashMLA requires 576B alignment
+            model_version="deepseek_v4",
+        )
+
+    def forward(self): ...
+
+    def get_attn_backend(self) -> type[AttentionBackend]:
+        return DeepseekSparseSWABackend
+
+
+class DeepseekSparseSWABackend(AttentionBackend):
+    @staticmethod
+    def get_name() -> str:
+        return "DEEPSEEK_SPARSE_SWA"
+
+    @staticmethod
+    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
+        return [MultipleOf(64)]
+
+    @classmethod
+    def get_preferred_block_size(cls, default_block_size: int) -> int:
+        return 256
+
+    @classmethod
+    def get_supported_head_sizes(cls) -> list[int]:
+        return [512]
+
+    @staticmethod
+    def get_builder_cls() -> type["DeepseekSparseSWAMetadataBuilder"]:
+        return DeepseekSparseSWAMetadataBuilder
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+        cache_dtype_str: str = "auto",
+    ) -> tuple[int, ...]:
+        assert num_kv_heads == 1
+        if cache_dtype_str == "fp8_ds_mla":
+            # DeepseekV4 SWA: 584B per token (448 NoPE + 128 RoPE + 8 fp8 scale).
+            # head_size passed in is the semantic head_dim (512).
+            return (num_blocks, block_size, 584)
+        else:
+            return (num_blocks, block_size, head_size)
+
+    @staticmethod
+    def get_kv_cache_stride_order(
+        include_num_layers_dimension: bool = False,
+    ) -> tuple[int, ...]:
+        if include_num_layers_dimension:
+            return (0, 1, 2, 3)
+        return (0, 1, 2)
+
+
+@dataclass
+class DeepseekSparseSWAMetadata:
+    block_table: torch.Tensor
+    slot_mapping: torch.Tensor
+    block_size: int
+    seq_lens: torch.Tensor | None = None  # [num_seqs]
+    query_start_loc: torch.Tensor | None = None  # [num_seqs + 1]
+    query_start_loc_cpu: torch.Tensor | None = None  # [num_seqs + 1]
+
+    is_valid_token: torch.Tensor | None = None  # [num_tokens]
+    token_to_req_indices: torch.Tensor | None = None  # [num_tokens]
+    decode_swa_indices: torch.Tensor | None = None  # [num_decode_tokens, window_size]
+    decode_swa_lens: torch.Tensor | None = None  # [num_decode_tokens]
+
+    # Number of decode/prefill requests/tokens (batch is reordered: decodes first)
+    num_decodes: int = 0
+    num_prefills: int = 0
+    num_decode_tokens: int = 0
+    num_prefill_tokens: int = 0
+
+    # Pre-computed prefill metadata shared across all DeepseekV4 attention layers.
+    prefill_seq_lens: torch.Tensor | None = None
+    prefill_gather_lens: torch.Tensor | None = None
+
+    # Per-layer-type FlashMLA tile-scheduler metadata. One FlashMLASchedMeta
+    # per present DeepseekV4 layer type, shared across all ~60 layers of that type
+    # within a decode step. The first forward call of a given type triggers
+    # the in-kernel planner (which also allocates tile_scheduler_metadata and
+    # num_splits via PyTorch's graph-aware allocator); subsequent same-type
+    # calls skip planning and reuse the plan. Fresh instance per build(), so
+    # have_initialized is always False at the start of a step and the plan
+    # is re-derived from current seq_lens / topk_length on replay.
+    # None for layer types the model does not use (or when num_decode_tokens
+    # is zero).
+    tile_sched_swaonly: "FlashMLASchedMeta | None" = None
+    tile_sched_c4a: "FlashMLASchedMeta | None" = None
+    tile_sched_c128a: "FlashMLASchedMeta | None" = None
+
+
+class DeepseekSparseSWAMetadataBuilder(AttentionMetadataBuilder):
+    """Builds metadata for DeepseekV4 SWA cache.
+
+    Similar to the indexer, this handles mixed batches by:
+    1. Using split_decodes_and_prefills() to determine the boundary
+    2. Building separate metadata for decode and prefill portions
+
+    Supports:
+    - Mixed decode/prefill batches
+    - MTP (Multi-Token Prediction) where decode has query_len > 1
+    - Chunked prefill (aligns with the indexer's chunking)
+    """
+
+    # Base threshold: query_len <= 1 is decode
+    reorder_batch_threshold: int = 1
+    _cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.UNIFORM_BATCH
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert isinstance(self.kv_cache_spec, SlidingWindowMLASpec | MLAAttentionSpec)
+        mla_spec = cast(SlidingWindowMLASpec | MLAAttentionSpec, self.kv_cache_spec)
+        self.head_size = mla_spec.head_size  # Already considered quantization.
+        self.compress_ratio = mla_spec.compress_ratio
+        self.block_size = mla_spec.block_size
+
+        # Handle MTP: adjust decode_threshold like the indexer does
+        self.num_speculative_tokens = (
+            self.vllm_config.speculative_config.num_speculative_tokens
+            if self.vllm_config.speculative_config
+            else 0
+        )
+        # With MTP, decode can have query_len up to 1 + num_speculative_tokens.
+        # Must match the threshold used by the indexer and flashmla_sparse so
+        # that all backends agree on the decode/prefill split.
+        self.decode_threshold = (
+            self.reorder_batch_threshold + self.num_speculative_tokens
+        )
+
+        hf_config = self.vllm_config.model_config.hf_config
+        assert hasattr(hf_config, "sliding_window")
+        self.window_size = hf_config.sliding_window
+
+        # Detect which DeepseekV4 layer types this model uses so we only build a
+        # FlashMLA tile-scheduler plan for types that will actually be called.
+        # Models without compress_ratios (pure SWA) fall back to swaonly.
+        compress_ratios = getattr(hf_config, "compress_ratios", None) or [1]
+        self._layer_types: set[str] = set()
+        for ratio in compress_ratios:
+            self._layer_types.add(_layer_type_for(int(ratio)))
+
+        max_tokens = self.vllm_config.scheduler_config.max_num_batched_tokens
+        self.token_to_req_indices = torch.zeros(
+            max_tokens,
+            dtype=torch.int32,
+            device=self.device,
+        )
+        self.decode_swa_indices = torch.zeros(
+            max_tokens,
+            1,
+            self.window_size,
+            dtype=torch.int32,
+            device=self.device,
+        )
+        self.decode_swa_lens = torch.zeros(
+            max_tokens,
+            dtype=torch.int32,
+            device=self.device,
+        )
+        self.is_valid_token = torch.zeros(
+            max_tokens,
+            dtype=torch.bool,
+            device=self.device,
+        )
+
+    def build(
+        self,
+        common_prefix_len: int,
+        common_attn_metadata: CommonAttentionMetadata,
+        fast_build: bool = False,
+    ) -> DeepseekSparseSWAMetadata:
+        """Build SWA metadata for mixed decode/prefill batches.
+
+        The batch is assumed to be reordered with decodes first (by vLLM scheduler).
+        We use split_decodes_and_prefills() to find the boundary, then build
+        separate window_topk_idxs for each portion.
+
+        For prefill, we use chunked prefill to align with the indexer's chunking.
+        """
+        num_reqs = common_attn_metadata.num_reqs
+        seq_lens = common_attn_metadata.seq_lens
+        query_start_loc = common_attn_metadata.query_start_loc
+        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
+        block_table = common_attn_metadata.block_table_tensor
+        slot_mapping = common_attn_metadata.slot_mapping
+
+        # Split into decode and prefill portions using configurable threshold
+        (num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens) = (
+            split_decodes_and_prefills(
+                common_attn_metadata, decode_threshold=self.decode_threshold
+            )
+        )
+
+        # NOTE: Ensure all metadata tensors maintain fixed memory addresses
+        # for CUDA graph compatibility.
+        query_lens = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
+        x = torch.repeat_interleave(torch.arange(num_reqs), query_lens).pin_memory()
+        token_to_req_indices = self.token_to_req_indices[: x.shape[0]]
+        token_to_req_indices.copy_(x, non_blocking=True)
+
+        is_valid_token = self.is_valid_token[: slot_mapping.shape[0]]
+        is_valid_token.copy_(slot_mapping >= 0)
+
+        if num_decode_tokens > 0:
+            self.decode_swa_lens[num_decode_tokens:] = 0
+            _compute_swa_indices_and_lens_kernel[(num_decode_tokens,)](
+                self.decode_swa_indices,
+                self.decode_swa_indices.stride(0),
+                self.decode_swa_lens,
+                self.window_size,
+                query_start_loc,
+                seq_lens,
+                token_to_req_indices,
+                is_valid_token,
+                block_table,
+                block_table.stride(0),
+                self.block_size,
+                TRITON_BLOCK_SIZE=1024,
+            )
+
+        # Pre-compute DeepseekV4 prefill metadata shared across all attention layers.
+        deepseek_v4_fields = self._build_deepseek_v4_metadata(
+            num_decodes,
+            num_prefills,
+            seq_lens,
+            query_start_loc,
+        )
+
+        # Per-layer-type tile-scheduler plan holders. Empty FlashMLASchedMeta
+        # per present DeepseekV4 layer type; the first flash_mla_with_kvcache call of
+        # each type triggers the planner and all same-type layers reuse the
+        # resulting plan for the rest of the step.
+        tile_sched = self.build_tile_scheduler(num_decode_tokens)
+
+        return DeepseekSparseSWAMetadata(
+            seq_lens=seq_lens,
+            query_start_loc=query_start_loc,
+            query_start_loc_cpu=query_start_loc_cpu,
+            block_table=block_table,
+            slot_mapping=slot_mapping,
+            is_valid_token=is_valid_token,
+            token_to_req_indices=token_to_req_indices,
+            decode_swa_indices=self.decode_swa_indices[:num_decode_tokens],
+            decode_swa_lens=self.decode_swa_lens[:num_decode_tokens],
+            block_size=self.block_size,
+            num_decodes=num_decodes,
+            num_prefills=num_prefills,
+            num_decode_tokens=num_decode_tokens,
+            num_prefill_tokens=num_prefill_tokens,
+            tile_sched_swaonly=tile_sched[_LAYER_TYPE_SWAONLY],
+            tile_sched_c4a=tile_sched[_LAYER_TYPE_C4A],
+            tile_sched_c128a=tile_sched[_LAYER_TYPE_C128A],
+            **deepseek_v4_fields,
+        )
+
+    def build_tile_scheduler(
+        self, num_decode_tokens: int
+    ) -> dict[str, FlashMLASchedMeta | None]:
+        """Allocate one empty ``FlashMLASchedMeta`` per present DeepseekV4 layer type.
+
+        Returned instances have ``tile_scheduler_metadata`` / ``num_splits``
+        set to ``None``; the FlashMLA C++ decode path will allocate them and
+        run the tile-scheduler planner on the first ``flash_mla_with_kvcache``
+        call of each type. Subsequent same-type calls reuse the plan because
+        the tensors (and ``have_initialized``) are populated on the struct.
+
+        Returns all-``None`` when there are no decode tokens this step, so
+        ``_forward_decode`` sees a clean sentinel.
+        """
+        out: dict[str, FlashMLASchedMeta | None] = {
+            _LAYER_TYPE_SWAONLY: None,
+            _LAYER_TYPE_C4A: None,
+            _LAYER_TYPE_C128A: None,
+        }
+        if num_decode_tokens == 0 or current_platform.is_rocm():
+            return out
+        for layer_type in self._layer_types:
+            # get_mla_metadata() is the official FlashMLA entry point that
+            # returns a fresh empty FlashMLASchedMeta; using it keeps this
+            # call site aligned with the rest of the vLLM FlashMLA backends
+            # that already go through the same stub.
+            out[layer_type] = get_mla_metadata()[0]
+        return out
+
+    def _build_deepseek_v4_metadata(
+        self,
+        num_decodes: int,
+        num_prefills: int,
+        seq_lens: torch.Tensor,
+        query_start_loc: torch.Tensor,
+    ) -> dict[str, torch.Tensor | None]:
+        """Pre-compute DeepseekV4 prefill metadata during the metadata build phase.
+
+        Returns a dict of keyword arguments to pass to the
+        DeepseekSparseSWAMetadata constructor.
+
+        Note: C128A topk indices are computed by the FlashMLASparse builder
+        (which owns the C128A block_table), not here.
+        """
+        result: dict[str, torch.Tensor | None] = {}
+
+        # --- Prefill query metadata (single Triton kernel + CPU slicing) ---
+        if num_prefills > 0:
+            pfx_gather_lens = torch.empty(
+                num_prefills, dtype=torch.int32, device=seq_lens.device
+            )
+            _compute_prefill_metadata_kernel[(1,)](
+                pfx_gather_lens,
+                seq_lens,
+                query_start_loc,
+                num_prefills,
+                num_decodes,
+                self.window_size,
+                BLOCK_SIZE=triton.next_power_of_2(num_prefills),
+            )
+
+            result["prefill_seq_lens"] = seq_lens[num_decodes:]
+            result["prefill_gather_lens"] = pfx_gather_lens
+
+        return result
+
+
+@triton.jit
+def _compute_prefill_metadata_kernel(
+    # Outputs
+    prefill_gather_lens_ptr,
+    # Inputs
+    seq_lens_ptr,
+    query_start_loc_ptr,
+    num_prefills,
+    num_decodes,
+    window_size,
+    BLOCK_SIZE: tl.constexpr,
+):
+    """Compute prefill gather_lens in a single pass."""
+    offset = tl.arange(0, BLOCK_SIZE)
+    mask = offset < num_prefills
+
+    seq_len = tl.load(seq_lens_ptr + num_decodes + offset, mask=mask)
+    qsl_start = tl.load(query_start_loc_ptr + num_decodes + offset, mask=mask)
+    qsl_end = tl.load(query_start_loc_ptr + num_decodes + offset + 1, mask=mask)
+
+    query_len = qsl_end - qsl_start
+    prefix_len = seq_len - query_len
+    gather_len = query_len + tl.minimum(prefix_len, window_size - 1)
+
+    tl.store(prefill_gather_lens_ptr + offset, gather_len, mask=mask)
+
+
+@triton.jit
+def _compute_swa_indices_and_lens_kernel(
+    swa_indices_ptr,
+    swa_indices_stride,
+    swa_lens_ptr,
+    window_size,
+    query_start_loc_ptr,
+    seq_lens_ptr,
+    token_to_req_indices_ptr,
+    is_valid_token_ptr,
+    block_table_ptr,
+    block_table_stride,
+    block_size,
+    TRITON_BLOCK_SIZE: tl.constexpr,
+):
+    token_idx = tl.program_id(0)
+    is_valid = tl.load(is_valid_token_ptr + token_idx)
+    if not is_valid:
+        tl.store(swa_lens_ptr + token_idx, 0)
+        return
+
+    req_idx = tl.load(token_to_req_indices_ptr + token_idx)
+
+    query_start = tl.load(query_start_loc_ptr + req_idx)
+    query_end = tl.load(query_start_loc_ptr + req_idx + 1)
+    query_len = query_end - query_start
+
+    seq_len = tl.load(seq_lens_ptr + req_idx)
+    prefix_len = seq_len - query_len
+
+    pos = prefix_len + token_idx - query_start
+    start_pos = tl.maximum(pos - window_size + 1, 0)
+    end_pos = pos + 1
+
+    swa_len = end_pos - start_pos
+    tl.store(swa_lens_ptr + token_idx, swa_len)
+
+    for i in range(0, window_size, TRITON_BLOCK_SIZE):
+        offset = i + tl.arange(0, TRITON_BLOCK_SIZE)
+
+        pos_offset = start_pos + offset
+        block_indices = pos_offset // block_size
+        block_numbers = tl.load(
+            block_table_ptr + req_idx * block_table_stride + block_indices,
+            mask=pos_offset < end_pos,
+        )
+        block_offsets = pos_offset % block_size
+        slot_ids = block_numbers * block_size + block_offsets
+
+        slot_ids = tl.where(offset < swa_len, slot_ids, -1)
+        tl.store(
+            swa_indices_ptr + token_idx * swa_indices_stride + offset,
+            slot_ids,
+            mask=offset < window_size,
+        )
diff --git a/vllm/v1/attention/backends/mla/triton_mla.py b/vllm/v1/attention/backends/mla/triton_mla.py
index 0f8eb1c49a55..c45a631008e5 100644
--- a/vllm/v1/attention/backends/mla/triton_mla.py
+++ b/vllm/v1/attention/backends/mla/triton_mla.py
@@ -55,6 +55,10 @@ def supports_block_size(cls, block_size: int | None) -> bool:
     def get_name() -> str:
         return "TRITON_MLA"
 
+    @classmethod
+    def supports_batch_invariance(cls) -> bool:
+        return True
+
     @staticmethod
     def get_impl_cls() -> type["TritonMLAImpl"]:
         return TritonMLAImpl
@@ -119,18 +123,6 @@ def __init__(
 
         self._sm_count = current_platform.num_compute_units()
 
-    def _flash_attn_varlen_diff_headdims(
-        self, q, k, v, return_softmax_lse=False, softmax_scale=None, **kwargs
-    ):
-        return super()._flash_attn_varlen_diff_headdims(
-            q,
-            k,
-            v,
-            return_softmax_lse=return_softmax_lse,
-            softmax_scale=softmax_scale,
-            **kwargs,
-        )
-
     def forward_mqa(
         self,
         q: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
diff --git a/vllm/v1/attention/backends/registry.py b/vllm/v1/attention/backends/registry.py
index 4744ead4f54b..552b7f3373c7 100644
--- a/vllm/v1/attention/backends/registry.py
+++ b/vllm/v1/attention/backends/registry.py
@@ -76,12 +76,12 @@ class AttentionBackendEnum(Enum, metaclass=_AttentionBackendEnumMeta):
     FLASH_ATTN_MLA = "vllm.v1.attention.backends.mla.flashattn_mla.FlashAttnMLABackend"
     NO_ATTENTION = "vllm.v1.attention.backends.no_attention.NoAttentionBackend"
     FLEX_ATTENTION = "vllm.v1.attention.backends.flex_attention.FlexAttentionBackend"
-    TREE_ATTN = "vllm.v1.attention.backends.tree_attn.TreeAttentionBackend"
     ROCM_AITER_UNIFIED_ATTN = (
         "vllm.v1.attention.backends.rocm_aiter_unified_attn."
         "RocmAiterUnifiedAttentionBackend"
     )
     CPU_ATTN = "vllm.v1.attention.backends.cpu_attn.CPUAttentionBackend"
+    TURBOQUANT = "vllm.v1.attention.backends.turboquant_attn.TurboQuantAttentionBackend"
     # Placeholder for third-party/custom backends - must be registered before use
     # set to None to avoid alias with other backend, whose value is an empty string
     CUSTOM = None
diff --git a/vllm/v1/attention/backends/rocm_aiter_fa.py b/vllm/v1/attention/backends/rocm_aiter_fa.py
index 14c473a70ab5..01280cd4f48b 100644
--- a/vllm/v1/attention/backends/rocm_aiter_fa.py
+++ b/vllm/v1/attention/backends/rocm_aiter_fa.py
@@ -389,6 +389,7 @@ class AiterFlashAttentionMetadata:
     seq_lens: torch.Tensor
     slot_mapping: torch.Tensor
     block_table: torch.Tensor
+    causal: bool
 
     # prefill and decode split
     num_decodes: int
@@ -676,6 +677,7 @@ def build(
             max_seq_len=common_attn_metadata.max_seq_len,
             seq_lens=common_attn_metadata.seq_lens,
             block_table=common_attn_metadata.block_table_tensor,
+            causal=common_attn_metadata.causal,
             slot_mapping=common_attn_metadata.slot_mapping,
             num_decodes=num_decodes,
             num_decode_tokens=num_decode_tokens,
@@ -724,6 +726,7 @@ def build_for_drafting(
             max_seq_len=common_attn_metadata.max_seq_len,
             seq_lens=common_attn_metadata.seq_lens,
             block_table=common_attn_metadata.block_table_tensor,
+            causal=common_attn_metadata.causal,
             slot_mapping=common_attn_metadata.slot_mapping,
             num_decodes=num_reqs,
             num_decode_tokens=num_tokens,
@@ -808,6 +811,10 @@ def supports_compute_capability(cls, capability: DeviceCapability) -> bool:
         # more reliable.
         return on_mi3xx()
 
+    @classmethod
+    def supports_non_causal(cls) -> bool:
+        return True
+
 
 class AiterFlashAttentionImpl(AttentionImpl):
     def __init__(
@@ -1122,7 +1129,7 @@ def forward(
                     min_seqlen_q=1,
                     dropout_p=0.0,
                     softmax_scale=self.scale,
-                    causal=True,
+                    causal=attn_metadata.causal,
                     window_size=self.sliding_window,
                     alibi_slopes=self.alibi_slopes,
                     out=output_actual_tokens[num_decode_tokens + num_extend_tokens :],
@@ -1170,39 +1177,78 @@ def forward(
                 assert attn_metadata.decode_metadata is not None
                 decode_max_query_len = attn_metadata.decode_metadata.max_query_len
 
-                # Use unified_attention for speculative decoding (multi-token)
+                # Multi-token speculative decode path.
                 if decode_max_query_len > 1:
                     assert not rocm_aiter_ops.is_shuffle_kv_cache_enabled(), (
                         "Shuffle KV cache layout is not supported with "
                         "speculative decoding (multi-token decode)."
                     )
-                    from aiter.ops.triton.unified_attention import (
-                        unified_attention,
-                    )
-
-                    descale_shape = (
-                        attn_metadata.query_start_loc[:num_decodes].shape[0] - 1,
-                        key_cache.shape[2],
-                    )
-                    unified_attention(
-                        q=query[:num_decode_tokens],
-                        k=key_cache,
-                        v=value_cache,
-                        out=output[:num_decode_tokens],
-                        cu_seqlens_q=attn_metadata.query_start_loc[:num_decodes],
-                        max_seqlen_q=decode_max_query_len,
-                        seqused_k=attn_metadata.seq_lens[:num_decodes],
-                        max_seqlen_k=attn_metadata.max_seq_len,
-                        softmax_scale=self.scale,
-                        causal=True,
-                        alibi_slopes=self.alibi_slopes,
-                        window_size=self.sliding_window,
-                        block_table=attn_metadata.block_table[:num_decodes],
-                        softcap=self.logits_soft_cap,
-                        q_descale=None,
-                        k_descale=layer._k_scale.expand(descale_shape),
-                        v_descale=layer._v_scale.expand(descale_shape),
-                    )
+                    if not attn_metadata.causal:
+                        from aiter.ops.triton.attention.mha_v3 import (
+                            flash_attn_with_kvcache,
+                        )
+
+                        descale_shape = (num_decodes, key_cache.shape[2])
+                        decode_query = query[:num_decode_tokens].reshape(
+                            num_decodes,
+                            decode_max_query_len,
+                            query.shape[1],
+                            query.shape[2],
+                        )
+                        decode_out = flash_attn_with_kvcache(
+                            q=decode_query,
+                            k_cache=key_cache,
+                            v_cache=value_cache,
+                            cache_seqlens=attn_metadata.seq_lens[:num_decodes],
+                            softmax_scale=self.scale,
+                            causal=attn_metadata.causal,
+                            window_size=self.sliding_window,
+                            softcap=self.logits_soft_cap,
+                            q_descale=None,
+                            k_descale=layer._k_scale.expand(descale_shape),
+                            v_descale=layer._v_scale.expand(descale_shape),
+                            page_table=attn_metadata.block_table[:num_decodes],
+                        )
+                        output[:num_decode_tokens].copy_(
+                            decode_out.reshape(
+                                num_decode_tokens,
+                                query.shape[1],
+                                query.shape[2],
+                            )
+                        )
+                    else:
+                        # Non-uniform query lengths can appear in real serving
+                        # traffic (e.g. mixed datasets). Fall back to varlen
+                        # unified_attention instead of asserting.
+                        from aiter.ops.triton.unified_attention import (
+                            unified_attention,
+                        )
+
+                        descale_shape = (
+                            num_decodes,
+                            key_cache.shape[2],
+                        )
+                        unified_attention(
+                            q=query[:num_decode_tokens],
+                            k=key_cache,
+                            v=value_cache,
+                            out=output[:num_decode_tokens],
+                            cu_seqlens_q=attn_metadata.query_start_loc[
+                                : num_decodes + 1
+                            ],
+                            max_seqlen_q=decode_max_query_len,
+                            seqused_k=attn_metadata.seq_lens[:num_decodes],
+                            max_seqlen_k=attn_metadata.max_seq_len,
+                            softmax_scale=self.scale,
+                            causal=True,
+                            alibi_slopes=self.alibi_slopes,
+                            window_size=self.sliding_window,
+                            block_table=attn_metadata.block_table[:num_decodes],
+                            softcap=self.logits_soft_cap,
+                            q_descale=None,
+                            k_descale=layer._k_scale.expand(descale_shape),
+                            v_descale=layer._v_scale.expand(descale_shape),
+                        )
                     return
 
                 # The ll4mi kernel in paged_attention_v1 requires
diff --git a/vllm/v1/attention/backends/rocm_aiter_unified_attn.py b/vllm/v1/attention/backends/rocm_aiter_unified_attn.py
index eb0fe046e343..f56b58c43e7f 100644
--- a/vllm/v1/attention/backends/rocm_aiter_unified_attn.py
+++ b/vllm/v1/attention/backends/rocm_aiter_unified_attn.py
@@ -13,10 +13,10 @@
 )
 from vllm.utils.torch_utils import is_quantized_kv_cache
 from vllm.v1.attention.backend import AttentionLayer, AttentionType, MultipleOf
-from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
 from vllm.v1.attention.backends.rocm_attn import (
     RocmAttentionBackend,
     RocmAttentionImpl,
+    RocmAttentionMetadata,
     RocmAttentionMetadataBuilder,
 )
 
@@ -53,6 +53,10 @@ def supports_mm_prefix(cls) -> bool:
     def supports_sink(cls) -> bool:
         return True
 
+    @classmethod
+    def supports_non_causal(cls) -> bool:
+        return False
+
     forward_includes_kv_cache_update: bool = False
 
     @staticmethod
@@ -140,7 +144,7 @@ def forward(
         key: torch.Tensor,
         value: torch.Tensor,
         kv_cache: torch.Tensor,
-        attn_metadata: FlashAttentionMetadata,
+        attn_metadata: RocmAttentionMetadata,
         output: torch.Tensor,
         output_scale: torch.Tensor | None = None,
         output_block_scale: torch.Tensor | None = None,
@@ -196,19 +200,9 @@ def forward(
         key_cache, value_cache = kv_cache.unbind(0)
 
         softmax_scale = self.scale
-        fp8_post_attn_v_rescale = False
         if is_quantized_kv_cache(self.kv_cache_dtype):
             key_cache = key_cache.view(self.fp8_dtype)
             value_cache = value_cache.view(self.fp8_dtype)
-            # When Q is FP8, triton kernel skips K/V dequant (for fp8xfp8 matmul).
-            # Compensate by absorbing q_scale and k_scale into softmax_scale, and
-            # v_scale into output_scale (or post-multiplying if no fusion).
-            if query.dtype == self.fp8_dtype:
-                softmax_scale = self.scale * layer._q_scale_float * layer._k_scale_float
-                if output_scale is not None:
-                    output_scale = output_scale / layer._v_scale_float
-                else:
-                    fp8_post_attn_v_rescale = True
 
         cu_seqlens_q = attn_metadata.query_start_loc
         seqused_k = attn_metadata.seq_lens
@@ -216,11 +210,6 @@ def forward(
         max_seqlen_k = attn_metadata.max_seq_len
         block_table = attn_metadata.block_table
 
-        descale_shape = (
-            cu_seqlens_q.shape[0] - 1,
-            key.shape[1] if key is not None else self.num_kv_heads,
-        )
-
         self.unified_attention(
             q=query[:num_actual_tokens],
             k=key_cache,
@@ -236,16 +225,13 @@ def forward(
             window_size=self.sliding_window,
             block_table=block_table,
             softcap=self.logits_soft_cap,
-            q_descale=None,  # q_scale absorbed into softmax_scale
-            k_descale=layer._k_scale.expand(descale_shape),
-            v_descale=layer._v_scale.expand(descale_shape),
+            q_descale=layer._q_scale if query.dtype == self.fp8_dtype else None,
+            k_descale=layer._k_scale,
+            v_descale=layer._v_scale,
             sinks=self.sinks,
             output_scale=output_scale,
         )
 
-        if fp8_post_attn_v_rescale:
-            output[:num_actual_tokens].mul_(layer._v_scale_float)
-
         return output
 
     def do_kv_cache_update(
diff --git a/vllm/v1/attention/backends/rocm_attn.py b/vllm/v1/attention/backends/rocm_attn.py
index 3a906233272a..d533268e2176 100644
--- a/vllm/v1/attention/backends/rocm_attn.py
+++ b/vllm/v1/attention/backends/rocm_attn.py
@@ -27,9 +27,9 @@
     CommonAttentionMetadata,
     MultipleOf,
 )
-from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
 from vllm.v1.attention.ops.chunked_prefill_paged_decode import (
     chunked_prefill_paged_decode,
+    has_native_kv_cache_layout,
 )
 from vllm.v1.attention.ops.paged_attn import PagedAttention
 from vllm.v1.attention.ops.triton_reshape_and_cache_flash import (
@@ -69,6 +69,9 @@ class RocmAttentionMetadata:
     scheduler_metadata: torch.Tensor | None = None
     prefix_scheduler_metadata: torch.Tensor | None = None
 
+    # DFlash drafting sets this to False via CommonAttentionMetadata.
+    causal: bool = True
+
 
 class RocmAttentionMetadataBuilder(AttentionMetadataBuilder[RocmAttentionMetadata]):
     _cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.ALWAYS
@@ -154,6 +157,7 @@ def build(
             prefix_kv_lens=prefix_kv_lens,
             suffix_kv_lens=suffix_kv_lens,
             prefix_scheduler_metadata=prefix_scheduler_metadata,
+            causal=common_attn_metadata.causal,
         )
         return attn_metadata
 
@@ -200,6 +204,10 @@ def supports_sink(cls) -> bool:
         # kernel, which is less efficient than the proper triton backends.
         return False
 
+    @classmethod
+    def supports_non_causal(cls) -> bool:
+        return True
+
     forward_includes_kv_cache_update: bool = False
 
     @staticmethod
@@ -301,7 +309,7 @@ def _forward_encoder_attention(
         key: torch.Tensor,
         value: torch.Tensor,
         output: torch.Tensor,
-        attn_metadata: FlashAttentionMetadata,
+        attn_metadata: RocmAttentionMetadata,
         layer: torch.nn.Module,
     ) -> torch.Tensor:
         """Forward pass for encoder attention without KV cache.
@@ -350,7 +358,7 @@ def forward(
         key: torch.Tensor,
         value: torch.Tensor,
         kv_cache: torch.Tensor,
-        attn_metadata: FlashAttentionMetadata,
+        attn_metadata: RocmAttentionMetadata,
         output: torch.Tensor,
         output_scale: torch.Tensor | None = None,
         output_block_scale: torch.Tensor | None = None,
@@ -438,6 +446,7 @@ def forward(
             sm_scale=self.scale,
             output_scale=output_scale,
             sinks=self.sinks,
+            causal=attn_metadata.causal,
         )
 
         return output
@@ -460,9 +469,10 @@ def do_kv_cache_update(
         # Get the actual block_size from value_cache
         # value_cache shape: [num_blocks, num_heads, head_size, block_size]
         block_size = value_cache.shape[3]
+        has_native_layout = has_native_kv_cache_layout(key_cache, value_cache)
 
-        if block_size in (16, 32):
-            # Normal 16, 32, use vLLM native HIP C++ logic
+        if block_size in (16, 32) and has_native_layout:
+            # Normal 16, 32 with contiguous blocks: use vLLM native HIP C++ logic.
             PagedAttention.write_to_paged_cache(
                 key,
                 value,
@@ -474,8 +484,10 @@ def do_kv_cache_update(
                 layer._v_scale,
             )
         else:
-            # Case B: Non-standard blocks (e.g., 64, 128, 544 in Qwen3Next or Qwen3.5 ),
-            # force using our modified Triton logic
+            # Non-standard blocks and hybrid attention/Mamba layouts need the
+            # stride-aware Triton writer. The native reshape_and_cache kernel
+            # assumes contiguous block storage and writes to the wrong hybrid
+            # cache blocks.
             triton_reshape_and_cache_flash(
                 key,
                 value,
diff --git a/vllm/v1/attention/backends/tree_attn.py b/vllm/v1/attention/backends/tree_attn.py
deleted file mode 100644
index ceee8d5499ea..000000000000
--- a/vllm/v1/attention/backends/tree_attn.py
+++ /dev/null
@@ -1,442 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Attention layer with TreeAttention."""
-
-import ast
-from dataclasses import dataclass
-from typing import ClassVar
-
-import torch
-
-from vllm import _custom_ops as ops
-from vllm.config import VllmConfig
-from vllm.config.cache import CacheDType
-from vllm.logger import init_logger
-from vllm.v1.attention.backend import (
-    AttentionBackend,
-    AttentionImpl,
-    AttentionMetadataBuilder,
-    AttentionType,
-    CommonAttentionMetadata,
-    MultipleOf,
-)
-from vllm.v1.attention.backends.utils import (
-    split_decodes_and_prefills,
-)
-from vllm.v1.attention.ops.triton_unified_attention import unified_attention
-from vllm.v1.kv_cache_interface import AttentionSpec
-
-logger = init_logger(__name__)
-
-
-class TreeAttentionBackend(AttentionBackend):
-    supported_dtypes: ClassVar[list[torch.dtype]] = [torch.float16, torch.bfloat16]
-    supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = [
-        "auto",
-        "float16",
-        "bfloat16",
-    ]
-    forward_includes_kv_cache_update: bool = False
-
-    @staticmethod
-    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
-        return [MultipleOf(16)]
-
-    @classmethod
-    def get_supported_head_sizes(cls) -> list[int]:
-        return [32, 64, 96, 128, 160, 192, 224, 256]
-
-    @staticmethod
-    def get_name() -> str:
-        return "TREE_ATTN"
-
-    @staticmethod
-    def get_impl_cls() -> type["TreeAttentionImpl"]:
-        return TreeAttentionImpl
-
-    @staticmethod
-    def get_kv_cache_shape(
-        num_blocks: int,
-        block_size: int,
-        num_kv_heads: int,
-        head_size: int,
-        cache_dtype_str: str = "auto",
-    ) -> tuple[int, ...]:
-        if block_size % 16 != 0:
-            raise ValueError("Block size must be a multiple of 16.")
-        return (2, num_blocks, block_size, num_kv_heads, head_size)
-
-    @staticmethod
-    def get_builder_cls() -> type["TreeAttentionMetadataBuilder"]:
-        return TreeAttentionMetadataBuilder
-
-    @staticmethod
-    def use_cascade_attention(*args, **kwargs) -> bool:
-        return False
-
-
-@dataclass
-class TreeAttentionMetadata:
-    num_actual_tokens: int  # Number of tokens excluding padding.
-    max_query_len: int
-    query_start_loc: torch.Tensor
-    max_seq_len: int
-    seq_lens: torch.Tensor
-    block_table: torch.Tensor
-    slot_mapping: torch.Tensor
-
-    num_prefill_tokens: int = 0
-    num_decode_tokens: int = 0
-    num_prefills: int = 0
-    num_decodes: int = 0
-
-    tree_attn_bias: torch.Tensor | None = None
-
-    # Cached Prefill/decode metadata.
-    _cached_prefill_metadata: "TreeAttentionMetadata | None" = None
-    _cached_decode_metadata: "TreeAttentionMetadata | None" = None
-
-    @property
-    def prefill_metadata(self) -> "TreeAttentionMetadata | None":
-        if self.num_prefills == 0:
-            return None
-
-        if self._cached_prefill_metadata is not None:
-            # Recover cached prefill-phase attention
-            # metadata structure
-            return self._cached_prefill_metadata
-
-        q_start_loc = self.query_start_loc[self.num_decodes :]
-        q_seqlens = torch.diff(q_start_loc)
-        kv_seqlens = self.seq_lens[self.num_decodes :]
-        # Construct & cache prefill-phase attention metadata structure
-        self._cached_prefill_metadata = TreeAttentionMetadata(
-            num_actual_tokens=self.num_prefill_tokens,
-            max_query_len=int(q_seqlens.max().item()),
-            query_start_loc=q_start_loc - q_start_loc[0],
-            max_seq_len=int(kv_seqlens.max().item()),
-            seq_lens=kv_seqlens,
-            block_table=self.block_table[self.num_decodes :],
-            slot_mapping=self.slot_mapping[self.num_decode_tokens :],
-        )
-        return self._cached_prefill_metadata
-
-    @property
-    def decode_metadata(self) -> "TreeAttentionMetadata | None":
-        if self.num_decode_tokens == 0:
-            return None
-
-        if self._cached_decode_metadata is not None:
-            # Recover cached decode-phase attention
-            # metadata structure
-            return self._cached_decode_metadata
-
-        q_start_loc = self.query_start_loc[: self.num_decodes + 1]
-        q_seqlens = torch.diff(q_start_loc)
-        kv_seqlens = self.seq_lens[: self.num_decodes]
-        # Construct & cache decode-phase attention metadata structure
-        self._cached_decode_metadata = TreeAttentionMetadata(
-            num_actual_tokens=self.num_decode_tokens,
-            max_query_len=int(q_seqlens.max().item()),
-            query_start_loc=q_start_loc,
-            max_seq_len=int(kv_seqlens.max().item()),
-            seq_lens=kv_seqlens,
-            block_table=self.block_table[: self.num_decodes],
-            slot_mapping=self.slot_mapping[: self.num_decode_tokens],
-            tree_attn_bias=self.tree_attn_bias,
-        )
-        return self._cached_decode_metadata
-
-
-class TreeAttentionMetadataBuilder(AttentionMetadataBuilder[TreeAttentionMetadata]):
-    def __init__(
-        self,
-        kv_cache_spec: AttentionSpec,
-        layer_names: list[str],
-        vllm_config: VllmConfig,
-        device: torch.device,
-    ):
-        super().__init__(kv_cache_spec, layer_names, vllm_config, device)
-
-        self.block_size = kv_cache_spec.block_size
-
-        spec_config = vllm_config.speculative_config
-        spec_token_tree: str | None = None
-        if spec := spec_config:
-            spec_token_tree = spec.speculative_token_tree
-        tree_choices: list[tuple[int, ...]] = (
-            ast.literal_eval(spec_token_tree) if spec_token_tree is not None else [(0,)]
-        )
-        # Construct the tree attention bias.
-        depth_counts = _get_depth_counts(tree_choices)
-        self.tree_attn_bias = _prepare_tree_attn_bias(
-            tree_choices,
-            depth_counts,
-            dtype=torch.float32,
-            device=device,
-        )
-
-        self.reorder_batch_threshold = self.tree_attn_bias.shape[0]
-
-    def build(
-        self,
-        common_prefix_len: int,
-        common_attn_metadata: CommonAttentionMetadata,
-        fast_build: bool = False,
-    ) -> TreeAttentionMetadata:
-        decode_threshold = self.tree_attn_bias.shape[0]
-        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
-            split_decodes_and_prefills(
-                common_attn_metadata, decode_threshold=decode_threshold
-            )
-        )
-
-        num_actual_tokens = common_attn_metadata.num_actual_tokens
-        q_start_loc = common_attn_metadata.query_start_loc
-        max_query_len = common_attn_metadata.max_query_len
-        kv_seqlens = common_attn_metadata.seq_lens
-        max_seq_len = common_attn_metadata.max_seq_len
-        block_table = common_attn_metadata.block_table_tensor
-        slot_mapping = common_attn_metadata.slot_mapping
-
-        return TreeAttentionMetadata(
-            num_actual_tokens=num_actual_tokens,
-            num_prefill_tokens=num_prefill_tokens,
-            num_decode_tokens=num_decode_tokens,
-            num_prefills=num_prefills,
-            num_decodes=num_decodes,
-            max_query_len=max_query_len,
-            query_start_loc=q_start_loc,
-            max_seq_len=max_seq_len,
-            seq_lens=kv_seqlens,
-            block_table=block_table,
-            slot_mapping=slot_mapping,
-            tree_attn_bias=self.tree_attn_bias,
-        )
-
-    def build_for_drafting(
-        self,
-        common_attn_metadata: CommonAttentionMetadata,
-        draft_index: int,
-    ) -> TreeAttentionMetadata:
-        # Cache the original tree attention bias.
-        orig_tree_attn_bias = self.tree_attn_bias
-
-        if draft_index == 0:
-            # Use prefill for drafting at the root level.
-            self.tree_attn_bias = torch.empty(0)
-        else:
-            # Slice the tree attention bias for drafting. Exclude
-            # the root level.
-            start, end = 1, 1 + common_attn_metadata.max_query_len
-            self.tree_attn_bias = self.tree_attn_bias[start:end, start:end].contiguous()
-
-        # Build attention bias.
-        attn_metadata = self.build(0, common_attn_metadata, fast_build=True)
-
-        # Reset the tree attention bias to the original value.
-        self.tree_attn_bias = orig_tree_attn_bias
-        return attn_metadata
-
-
-def _get_depth_counts(sorted_tree_choices: list[tuple[int, ...]]) -> list[int]:
-    # Count the number of choices at each depth of the tree.
-    depth_counts = []
-    prev_depth = 0
-    for path in sorted_tree_choices:
-        depth = len(path)
-        if depth != prev_depth:
-            depth_counts.append(0)
-        depth_counts[depth - 1] += 1
-        prev_depth = depth
-    return depth_counts
-
-
-def _prepare_tree_attn_bias(
-    sorted_tree_choices: list[tuple[int, ...]],
-    depth_counts: list[int],
-    dtype: torch.dtype | None,
-    device: torch.device | None,
-) -> torch.Tensor:
-    # +1 comes from the additional root node.
-    tree_len = len(sorted_tree_choices) + 1
-    tree_attn_mask = torch.full(
-        (tree_len, tree_len), -torch.inf, device=device, dtype=dtype
-    )
-
-    # Set diagonal to all zeros. Each token should
-    # attend to itself.
-    mask_val = 0
-    for i in range(tree_len):
-        tree_attn_mask[i, i] = mask_val
-
-    # Set root to all zeros. All tokens attend to it.
-    tree_attn_mask[:, 0] = mask_val
-
-    # Set all ancestors to zeros.
-    start = 0
-    for i in range(len(depth_counts)):
-        for j in range(depth_counts[i]):
-            cur_tree_choice = sorted_tree_choices[start + j]
-            # Retrieve ancestor position.
-            if len(cur_tree_choice) == 1:
-                continue
-            ancestor_idx = []
-            for c in range(len(cur_tree_choice) - 1):
-                ancestor_idx.append(
-                    sorted_tree_choices.index(cur_tree_choice[: c + 1]) + 1
-                )
-            tree_attn_mask[j + start + 1, ancestor_idx] = mask_val
-        start += depth_counts[i]
-    return tree_attn_mask
-
-
-class TreeAttentionImpl(AttentionImpl):
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        scale: float,
-        num_kv_heads: int,
-        alibi_slopes: list[float] | None,
-        sliding_window: int | None,
-        kv_cache_dtype: str,
-        logits_soft_cap: float | None = None,
-        attn_type: AttentionType = AttentionType.DECODER,
-        kv_sharing_target_layer_name: str | None = None,
-    ) -> None:
-        self.num_heads = num_heads
-        self.head_size = head_size
-        self.scale = float(scale)
-        self.num_kv_heads = num_kv_heads
-        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
-        self.kv_cache_dtype = kv_cache_dtype
-        self.kv_sharing_target_layer_name = kv_sharing_target_layer_name
-        if alibi_slopes is not None:
-            alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
-        self.alibi_slopes = alibi_slopes
-        if logits_soft_cap is None:
-            # Setting logits_soft_cap to 0 means no soft cap.
-            logits_soft_cap = 0
-        self.logits_soft_cap = logits_soft_cap
-        if sliding_window is None:
-            self.sliding_window = (-1, -1)
-        else:
-            self.sliding_window = (sliding_window - 1, 0)
-
-        if attn_type != AttentionType.DECODER:
-            raise NotImplementedError(
-                "Encoder self-attention and "
-                "encoder/decoder cross-attention "
-                "are not implemented for "
-                "TreeAttentionImpl."
-            )
-
-    def do_kv_cache_update(
-        self,
-        layer: torch.nn.Module,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        kv_cache: torch.Tensor,
-        slot_mapping: torch.Tensor,
-    ) -> None:
-        key_cache, value_cache = kv_cache.unbind(0)
-
-        # Reshape the input keys and values and store them in the cache.
-        # NOTE(woosuk): Here, key and value are padded while slot_mapping is
-        # not padded. However, we don't need to do key[:num_actual_tokens]
-        # and value[:num_actual_tokens] because the reshape_and_cache_flash
-        # op uses the slot_mapping's shape to determine the number of
-        # actual tokens.
-        ops.reshape_and_cache_flash(
-            key,
-            value,
-            key_cache,
-            value_cache,
-            slot_mapping,
-            self.kv_cache_dtype,
-            layer._k_scale,
-            layer._v_scale,
-        )
-
-    def forward(
-        self,
-        layer: torch.nn.Module,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: TreeAttentionMetadata,
-        output: torch.Tensor,
-        output_scale: torch.Tensor | None = None,
-        output_block_scale: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        """Forward pass with TreeAttention.
-
-        Args:
-            query: shape = [num_tokens, num_heads, head_size]
-            key: shape = [num_tokens, num_kv_heads, head_size]
-            value: shape = [num_tokens, num_kv_heads, head_size]
-            kv_cache: shape =
-                [2, num_blocks, block_size, num_kv_heads, head_size]
-            attn_metadata: Metadata for attention.
-        Returns:
-            shape = [num_tokens, num_heads * head_size]
-        """
-        if output_scale is not None or output_block_scale is not None:
-            raise NotImplementedError(
-                "fused output quantization is not yet supported for TreeAttentionImpl"
-            )
-
-        if attn_metadata is None:
-            # Profiling run.
-            return output.fill_(0)
-
-        key_cache, value_cache = kv_cache.unbind(0)
-
-        num_actual_tokens = attn_metadata.num_actual_tokens
-        num_decode_tokens = attn_metadata.num_decode_tokens
-        descale_shape = (attn_metadata.query_start_loc.shape[0] - 1, key.shape[1])
-        if prefill_meta := attn_metadata.prefill_metadata:
-            unified_attention(
-                q=query[num_decode_tokens:num_actual_tokens],
-                k=key_cache,
-                v=value_cache,
-                out=output[num_decode_tokens:num_actual_tokens],
-                cu_seqlens_q=prefill_meta.query_start_loc,
-                max_seqlen_q=prefill_meta.max_query_len,
-                seqused_k=prefill_meta.seq_lens,
-                max_seqlen_k=prefill_meta.max_seq_len,
-                softmax_scale=self.scale,
-                causal=True,
-                alibi_slopes=self.alibi_slopes,
-                window_size=self.sliding_window,
-                block_table=prefill_meta.block_table,
-                softcap=self.logits_soft_cap,
-                q_descale=None,  # Not supported
-                k_descale=layer._k_scale.expand(descale_shape),
-                v_descale=layer._v_scale.expand(descale_shape),
-            )
-
-        if decode_meta := attn_metadata.decode_metadata:
-            unified_attention(
-                q=query[:num_decode_tokens],
-                k=key_cache,
-                v=value_cache,
-                out=output[:num_decode_tokens],
-                cu_seqlens_q=decode_meta.query_start_loc,
-                max_seqlen_q=decode_meta.max_query_len,
-                seqused_k=decode_meta.seq_lens,
-                max_seqlen_k=decode_meta.max_seq_len,
-                softmax_scale=self.scale,
-                causal=True,
-                alibi_slopes=self.alibi_slopes,
-                qq_bias=decode_meta.tree_attn_bias,
-                window_size=self.sliding_window,
-                block_table=decode_meta.block_table,
-                softcap=self.logits_soft_cap,
-                q_descale=None,  # Not supported
-                k_descale=layer._k_scale.expand(descale_shape),
-                v_descale=layer._v_scale.expand(descale_shape),
-            )
-        return output
diff --git a/vllm/v1/attention/backends/triton_attn.py b/vllm/v1/attention/backends/triton_attn.py
index 76cae14aedb1..0aeb33ae6fd9 100644
--- a/vllm/v1/attention/backends/triton_attn.py
+++ b/vllm/v1/attention/backends/triton_attn.py
@@ -18,7 +18,7 @@
 from vllm.platforms import current_platform
 from vllm.platforms.interface import DeviceCapability
 from vllm.utils.math_utils import next_power_of_2
-from vllm.utils.torch_utils import is_quantized_kv_cache
+from vllm.utils.torch_utils import async_tensor_h2d, is_quantized_kv_cache
 from vllm.v1.attention.backend import (
     AttentionBackend,
     AttentionCGSupport,
@@ -117,10 +117,9 @@ def compute_mm_prefix_range_tensor(
         for r in range_lists:
             padded_r = list(r) + [(0, 0)] * (max_ranges - len(r))
             padded.append(padded_r)
-        # Create tensor with efficient H2D transfer
-        return torch.tensor(padded, dtype=torch.int32, device=device).view(
-            num_seqs, max_ranges, 2
-        )
+        # Build on pinned CPU memory so the H2D transfer is non-blocking.
+        padded = async_tensor_h2d(padded, dtype=torch.int32, device=device)
+        return padded.view(num_seqs, max_ranges, 2)
 
 
 class TritonAttentionMetadataBuilder(AttentionMetadataBuilder[TritonAttentionMetadata]):
@@ -296,6 +295,10 @@ def supports_block_size(cls, block_size: int | None) -> bool:
     def get_name() -> str:
         return "TRITON_ATTN"
 
+    @classmethod
+    def supports_batch_invariance(cls) -> bool:
+        return True
+
     @staticmethod
     def get_impl_cls() -> type["TritonAttentionImpl"]:
         return TritonAttentionImpl
@@ -458,6 +461,7 @@ def __init__(
         kv_sharing_target_layer_name: int | None = None,
         sinks: torch.Tensor | None = None,
         use_alibi_sqrt: bool = False,
+        chunk_lookback: int = -1,
     ) -> None:
         self.num_heads = num_heads
         self.head_size = head_size
@@ -492,6 +496,7 @@ def __init__(
                 f"num_heads: {num_heads}."
             )
         self.use_alibi_sqrt = use_alibi_sqrt
+        self.chunk_lookback = chunk_lookback
         self.supports_quant_query_input = current_platform.is_cuda()
 
         self._kv_quant_mode = get_kv_quant_mode(kv_cache_dtype)
@@ -631,6 +636,7 @@ def forward(
             kv_quant_mode=self._kv_quant_mode,
             k_scale_cache=k_scale_cache,
             v_scale_cache=v_scale_cache,
+            chunk_lookback=self.chunk_lookback,
         )
 
         return output
diff --git a/vllm/v1/attention/backends/turboquant_attn.py b/vllm/v1/attention/backends/turboquant_attn.py
new file mode 100644
index 000000000000..53684b4360f7
--- /dev/null
+++ b/vllm/v1/attention/backends/turboquant_attn.py
@@ -0,0 +1,906 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""TurboQuant attention backend for vLLM.
+
+Prefill: Standard scaled dot-product attention on uncompressed K/V,
+         then quantize K and store K+V into combined cache slot.
+Decode:  Compute TQ attention scores from compressed cache,
+         unpack FP16 values, softmax + weighted sum.
+
+Cache layout (no leading 2 dimension):
+  (num_blocks, block_size, num_kv_heads, slot_size)
+  where slot_size = key_packed_size + value_fp16_size
+
+Per-head per-position slot layout:
+  [key_packed (kps bytes) | value_fp16 (D*2 bytes)]
+  For turboquant_k3v4_nc head_dim=256: [100 bytes key | 512 bytes value] = 612
+"""
+
+import functools
+import math
+from dataclasses import dataclass
+from typing import Any, ClassVar
+
+import torch
+import torch.nn.functional as F
+
+from vllm.config import get_current_vllm_config
+from vllm.config.cache import CacheDType
+from vllm.model_executor.layers.quantization.turboquant.centroids import (
+    get_centroids,
+)
+from vllm.triton_utils import triton
+from vllm.v1.attention.backend import (
+    AttentionBackend,
+    AttentionCGSupport,
+    AttentionImpl,
+    AttentionLayer,
+    AttentionMetadata,
+    AttentionMetadataBuilder,
+    AttentionType,
+    CommonAttentionMetadata,
+    MultipleOf,
+)
+from vllm.v1.attention.backends.fa_utils import (
+    get_flash_attn_version,
+    is_flash_attn_varlen_func_available,
+)
+from vllm.v1.attention.backends.utils import split_decodes_and_prefills
+from vllm.v1.attention.ops.triton_turboquant_decode import (
+    _tq_full_dequant_kv,
+    _use_fp8_e4b15,
+    triton_turboquant_decode_attention,
+)
+from vllm.v1.attention.ops.triton_turboquant_store import triton_turboquant_store
+from vllm.v1.worker.workspace import (
+    current_workspace_manager,
+    is_workspace_manager_initialized,
+)
+
+_HAS_FLASH_ATTN = is_flash_attn_varlen_func_available()
+if _HAS_FLASH_ATTN:
+    from vllm.v1.attention.backends.fa_utils import flash_attn_varlen_func
+
+# Continuation prefill: for small continuation chunks (q_len ≤ threshold),
+# use the TQ decode kernel directly instead of full-dequant + flash_attn.
+# do_kv_cache_update already stored all tokens to TQ cache, so the decode
+# kernel can read them efficiently. This avoids O(cached_len) dequant work
+# per continuation, eliminating the O(N²/chunk_size) collapse at long context.
+_CONTINUATION_DECODE_THRESHOLD = 128
+
+
+def _build_hadamard(d: int, device_str: str) -> torch.Tensor:
+    """Orthonormal Hadamard matrix (Sylvester construction), cached per (d, device).
+
+    Precomputed D×D matrix enables matmul-based WHT — single cuBLAS GEMM
+    instead of log2(D) butterfly kernel launches. 64KB for D=128.
+    """
+    # Normalize device string so "cuda" and "cuda:0" hit the same cache entry.
+    return _build_hadamard_cached(d, str(torch.device(device_str)))
+
+
+@functools.cache
+def _build_hadamard_cached(d: int, device_str: str) -> torch.Tensor:
+    H = torch.tensor([[1.0]])
+    while H.shape[0] < d:
+        H = torch.cat([torch.cat([H, H], 1), torch.cat([H, -H], 1)], 0)
+    return (H / math.sqrt(d)).to(torch.device(device_str))
+
+
+class TurboQuantAttentionBackend(AttentionBackend):
+    """Attention backend using TurboQuant KV-cache compression."""
+
+    accept_output_buffer: bool = True
+    forward_includes_kv_cache_update: bool = False
+
+    supported_dtypes: ClassVar[list[torch.dtype]] = [
+        torch.float16,
+        torch.bfloat16,
+    ]
+    supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = [
+        "turboquant_k8v4",
+        "turboquant_4bit_nc",
+        "turboquant_k3v4_nc",
+        "turboquant_3bit_nc",
+    ]
+
+    @staticmethod
+    def get_name() -> str:
+        return "TURBOQUANT"
+
+    @staticmethod
+    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
+        return [16, 32, 64, 128]
+
+    @classmethod
+    def supports_attn_type(cls, attn_type: str) -> bool:
+        return attn_type == AttentionType.DECODER
+
+    @classmethod
+    def supports_per_head_quant_scales(cls) -> bool:
+        return False
+
+    @staticmethod
+    def get_impl_cls() -> type["TurboQuantAttentionImpl"]:
+        return TurboQuantAttentionImpl
+
+    @staticmethod
+    def get_builder_cls() -> type["TurboQuantMetadataBuilder"]:
+        return TurboQuantMetadataBuilder
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+        cache_dtype_str: str = "turboquant_4bit_nc",
+    ) -> tuple[int, ...]:
+        """Combined K+V cache shape — no leading 2 dimension.
+
+        Standard attention backends use (2, num_blocks, block_size, num_kv_heads,
+        head_dim) with a leading 2 to separate K and V. TurboQuant packs K+V
+        into a single interleaved slot per head per position, so the cache is:
+
+            (num_blocks, block_size, num_kv_heads, slot_size_aligned)
+
+        Each slot = [key_packed | value_packed | padding].
+        This is safe because TQ has its own get_kv_cache_shape override and
+        never shares cache tensors with other backends. Layers that fall back
+        to native dtype via kv_cache_dtype_skip_layers get their own
+        standard-shaped cache allocation.
+
+        head_size is the model's real head_dim. slot_size_aligned is computed
+        from the TQ config to ensure correct cache allocation for all head dims.
+        """
+        from vllm.model_executor.layers.quantization.turboquant.config import (
+            TurboQuantConfig,
+        )
+
+        tq_config = TurboQuantConfig.from_cache_dtype(cache_dtype_str, head_size)
+        return (num_blocks, block_size, num_kv_heads, tq_config.slot_size_aligned)
+
+    @classmethod
+    def supports_kv_cache_dtype(cls, kv_cache_dtype: CacheDType | None) -> bool:
+        if kv_cache_dtype is None:
+            return False
+        return kv_cache_dtype.startswith("turboquant_")
+
+    @classmethod
+    def supports_head_size(cls, head_size: int) -> bool:
+        # head_size from spec is effective_head_size (padded_slot//2),
+        # not the model's actual head_dim. Accept any positive value.
+        return head_size > 0
+
+
+@dataclass
+class TurboQuantMetadata(AttentionMetadata):
+    """Metadata for TurboQuant attention."""
+
+    seq_lens: torch.Tensor  # (num_reqs,) — total context length per request
+    slot_mapping: torch.Tensor  # (num_tokens,) — cache slot for each token
+    block_table: torch.Tensor  # (num_reqs, max_num_blocks)
+    query_start_loc: torch.Tensor  # (num_reqs + 1,) — cu_seqlens for queries
+    num_actual_tokens: int = 0  # actual tokens (excluding padding)
+    max_query_len: int = 0  # longest query in batch
+    max_seq_len: int = 0  # longest context in batch
+    is_prefill: bool = False
+    num_decodes: int = 0  # number of decode requests (first in batch)
+    num_decode_tokens: int = 0  # tokens from decode requests
+    # CPU-resident copies used by the prefill path for per-request iteration
+    # without per-step D2H syncs.
+    query_start_loc_cpu: torch.Tensor | None = None
+    seq_lens_cpu: torch.Tensor | None = None
+
+
+class TurboQuantMetadataBuilder(AttentionMetadataBuilder[TurboQuantMetadata]):
+    """Builds TurboQuantMetadata from scheduler output."""
+
+    _cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.UNIFORM_BATCH
+
+    def __init__(self, kv_cache_spec, layer_names, vllm_config, device):
+        super().__init__(kv_cache_spec, layer_names, vllm_config, device)
+        self._init_reorder_batch_threshold(1, supports_spec_as_decode=False)
+
+    def build_for_cudagraph_capture(
+        self, common_attn_metadata: CommonAttentionMetadata
+    ) -> TurboQuantMetadata:
+        attn_metadata = self.build(0, common_attn_metadata)
+        # Set seq_lens to 1 so CUDA graph capture is fast
+        # (real seq_lens are filled at replay time).
+        attn_metadata.seq_lens.fill_(1)
+        return attn_metadata
+
+    def build(self, common_prefix_len, common_attn_metadata, fast_build=False):
+        """Build TurboQuantMetadata from common attention metadata."""
+        cam = common_attn_metadata
+
+        # With reorder_batch_threshold=1, the model runner guarantees
+        # decodes come first in the batch. split_decodes_and_prefills
+        # finds the boundary (operates on CPU tensors — no GPU sync).
+        assert self.reorder_batch_threshold is not None
+        num_decodes, num_prefills, num_decode_tokens, _ = split_decodes_and_prefills(
+            cam, decode_threshold=self.reorder_batch_threshold
+        )
+
+        return TurboQuantMetadata(
+            seq_lens=cam.seq_lens,
+            slot_mapping=cam.slot_mapping,
+            block_table=cam.block_table_tensor,
+            query_start_loc=cam.query_start_loc,
+            num_actual_tokens=cam.num_actual_tokens,
+            max_query_len=cam.max_query_len,
+            max_seq_len=cam.max_seq_len,
+            is_prefill=(cam.max_query_len > 1),
+            num_decodes=num_decodes,
+            num_decode_tokens=num_decode_tokens,
+            query_start_loc_cpu=cam.query_start_loc_cpu,
+            seq_lens_cpu=cam.seq_lens_cpu_upper_bound,
+        )
+
+
+class TurboQuantAttentionImpl(AttentionImpl["TurboQuantMetadata"]):
+    """TurboQuant attention implementation.
+
+    Vectorized PyTorch: batch quantize/store, vectorized bit-unpack
+    decode with einsum scores and value gather.
+    """
+
+    supports_quant_query_input: bool = False
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        scale: float,
+        num_kv_heads: int | None = None,
+        alibi_slopes: list[float] | None = None,
+        sliding_window: int | None = None,
+        kv_cache_dtype: str = "auto",
+        logits_soft_cap: float | None = None,
+        attn_type: str = AttentionType.DECODER,
+        kv_sharing_target_layer_name: str | None = None,
+        **kwargs,
+    ):
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.scale = scale
+        self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads
+        self.num_kv_groups = num_heads // self.num_kv_heads
+        self.kv_cache_dtype = kv_cache_dtype
+
+        from vllm.model_executor.layers.quantization.turboquant.config import (
+            TurboQuantConfig,
+        )
+
+        self.tq_config = TurboQuantConfig.from_cache_dtype(kv_cache_dtype, head_size)
+
+        # Pre-compute kernel constants from config (avoid repeated arithmetic)
+        cfg = self.tq_config
+        self._mse_bytes = (
+            math.ceil(head_size * cfg.key_mse_bits / 8)
+            if not cfg.key_fp8
+            else head_size
+        )
+        self._val_data_bytes = math.ceil(head_size * cfg.effective_value_quant_bits / 8)
+        self._n_centroids = cfg.n_centroids if not cfg.key_fp8 else 1
+
+        # Detect flash-attn version (FA2/3/4) for prefill paths.
+        self.fa_version = get_flash_attn_version(head_size=head_size)
+
+        # Fixed NUM_KV_SPLITS (grid dims must be constant for cudagraph,
+        # and benchmarks show no regression vs dynamic in eager mode).
+        vllm_config = get_current_vllm_config()
+        self.max_num_kv_splits = (
+            vllm_config.attention_config.tq_max_kv_splits_for_cuda_graph
+        )
+
+    def _flash_attn_varlen(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        cu_seqlens_q: torch.Tensor,
+        cu_seqlens_k: torch.Tensor,
+        max_seqlen_q: int,
+        max_seqlen_k: int,
+    ) -> torch.Tensor:
+        # fa_utils.get_flash_attn_version() returns None on backends that
+        # should not pass an explicit fa_version kwarg.
+        if self.fa_version is None:
+            return flash_attn_varlen_func(
+                q=q,
+                k=k,
+                v=v,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_q,
+                max_seqlen_k=max_seqlen_k,
+                softmax_scale=self.scale,
+                causal=True,
+            )
+        return flash_attn_varlen_func(
+            q=q,
+            k=k,
+            v=v,
+            cu_seqlens_q=cu_seqlens_q,
+            cu_seqlens_k=cu_seqlens_k,
+            max_seqlen_q=max_seqlen_q,
+            max_seqlen_k=max_seqlen_k,
+            softmax_scale=self.scale,
+            causal=True,
+            fa_version=self.fa_version,
+        )
+
+    def _ensure_on_device(self, layer, device):
+        """One-time derivation of TQ buffers (rotation matrix, midpoints).
+
+        The Hadamard rotation is shared across all layers: random sign
+        flips do not improve Lloyd-Max quantization quality because the
+        quantizer is symmetric around zero (sign-flipping a coordinate
+        maps it to the mirror centroid with identical distortion).
+        """
+        if not hasattr(layer, "_tq_cached"):
+            D = self.head_size
+
+            # Pure Hadamard: orthonormal + symmetric (H = H^T), enabling
+            # in-kernel butterfly fusion and trivial inverse for continuation.
+            H = _build_hadamard(D, str(device))
+            layer._tq_PiT = H
+            layer._tq_Pi = H
+            # fp16 copy for rotation in continuation prefill path
+            layer._tq_Pi_half = H.to(torch.float16)
+
+            # Centroids for Lloyd-Max quantization.
+            layer._tq_centroids = get_centroids(D, self.tq_config.centroid_bits).to(
+                device=device, dtype=torch.float32
+            )
+
+            c_sorted, _ = layer._tq_centroids.sort()
+            layer._tq_midpoints = (c_sorted[:-1] + c_sorted[1:]) / 2
+            layer._tq_cached = True
+
+    def do_kv_cache_update(
+        self,
+        layer: torch.nn.Module,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        slot_mapping: torch.Tensor,
+    ) -> None:
+        """Store compressed K/V into the combined TQ cache.
+
+        Called as a separate custom op (unified_kv_cache_update) BEFORE
+        the attention forward, matching FlashAttention's split pattern.
+        slot_mapping is already sliced to num_actual_tokens by the caller.
+        """
+        N = slot_mapping.shape[0]
+        if N <= 0:
+            return
+
+        device = key.device
+        self._ensure_on_device(layer, device)
+
+        k = key[:N].view(N, self.num_kv_heads, self.head_size)
+        v = value[:N].view(N, self.num_kv_heads, self.head_size)
+        self._store_kv(k, v, kv_cache, slot_mapping, layer)
+
+    def forward(
+        self,
+        layer: AttentionLayer,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: "TurboQuantMetadata",
+        output: torch.Tensor | None = None,
+        output_scale: torch.Tensor | None = None,
+        output_block_scale: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        num_tokens = query.shape[0]
+
+        if output is None:
+            output = torch.zeros(
+                num_tokens,
+                self.num_heads * self.head_size,
+                dtype=query.dtype,
+                device=query.device,
+            )
+
+        if attn_metadata is None:
+            return output.fill_(0)
+
+        # Slice to actual tokens
+        N = attn_metadata.num_actual_tokens
+        if N <= 0:
+            return output.fill_(0)
+
+        q = query[:N].view(N, self.num_heads, self.head_size)
+
+        # Get TQ buffers, ensure on device (one-time migration).
+        # Use Any-typed alias for dynamic _tq_* attrs set by _ensure_on_device.
+        tq_layer: Any = layer
+        device = q.device
+        self._ensure_on_device(tq_layer, device)
+        Pi = tq_layer._tq_Pi
+        PiT = tq_layer._tq_PiT
+        centroids = tq_layer._tq_centroids
+
+        # Compute attention (KV cache was already updated by do_kv_cache_update)
+        # With reorder_batch_threshold=1, decodes come first in the batch.
+        # num_decodes/num_decode_tokens from metadata give the split point.
+        num_decodes = attn_metadata.num_decodes
+        num_decode_tokens = attn_metadata.num_decode_tokens
+
+        if not attn_metadata.is_prefill:
+            # Pure decode batch — fast path
+            attn_out = self._decode_attention(
+                q, kv_cache, attn_metadata, Pi, centroids, PiT, layer
+            )
+        elif num_decodes == 0:
+            # Pure prefill batch
+            k = key[:N].view(N, self.num_kv_heads, self.head_size)
+            v = value[:N].view(N, self.num_kv_heads, self.head_size)
+            attn_out = self._prefill_attention(
+                q,
+                k,
+                v,
+                kv_cache,
+                attn_metadata,
+                Pi,
+                centroids,
+                PiT,
+                layer=layer,
+            )
+        else:
+            # Mixed batch: decodes first (guaranteed by reorder_batch).
+            attn_out = torch.zeros(
+                N, self.num_heads, self.head_size, device=device, dtype=q.dtype
+            )
+
+            # --- Decode portion (first num_decodes requests) ---
+            # Use full-batch max_seq_len as safe upper bound (no GPU sync).
+            decode_meta = TurboQuantMetadata(
+                seq_lens=attn_metadata.seq_lens[:num_decodes],
+                slot_mapping=attn_metadata.slot_mapping[:num_decode_tokens],
+                block_table=attn_metadata.block_table[:num_decodes],
+                query_start_loc=attn_metadata.query_start_loc[: num_decodes + 1],
+                num_actual_tokens=num_decode_tokens,
+                max_query_len=1,
+                max_seq_len=attn_metadata.max_seq_len,
+                is_prefill=False,
+            )
+            attn_out[:num_decode_tokens] = self._decode_attention(
+                q[:num_decode_tokens], kv_cache, decode_meta, Pi, centroids, PiT, layer
+            )
+
+            # --- Prefill portion (remaining requests) ---
+            # CRITICAL: use prefill-specific max_seq_len so flash_attn's
+            # fast path (max_query_len == max_seq_len) triggers for
+            # first-chunk prefills. Using full-batch max_seq_len breaks
+            # this because decode requests inflate max_seq_len.
+            prefill_seq_lens = attn_metadata.seq_lens[num_decodes:]
+            # Use the CPU-resident `seq_lens` upper-bound from the metadata
+            # (populated in the builder) to compute the prefill sub-batch
+            # max without a GPU→CPU sync.
+            if attn_metadata.seq_lens_cpu is not None:
+                prefill_max_seq = int(attn_metadata.seq_lens_cpu[num_decodes:].max())
+            else:
+                prefill_max_seq = attn_metadata.max_seq_len
+            prefill_qsl = (
+                attn_metadata.query_start_loc[num_decodes:] - num_decode_tokens
+            )
+            prefill_qsl_cpu = None
+            if attn_metadata.query_start_loc_cpu is not None:
+                prefill_qsl_cpu = (
+                    attn_metadata.query_start_loc_cpu[num_decodes:] - num_decode_tokens
+                )
+            prefill_meta = TurboQuantMetadata(
+                seq_lens=prefill_seq_lens,
+                slot_mapping=attn_metadata.slot_mapping[num_decode_tokens:N],
+                block_table=attn_metadata.block_table[num_decodes:],
+                query_start_loc=prefill_qsl,
+                num_actual_tokens=N - num_decode_tokens,
+                max_query_len=attn_metadata.max_query_len,
+                max_seq_len=prefill_max_seq,
+                is_prefill=True,
+                query_start_loc_cpu=prefill_qsl_cpu,
+                seq_lens_cpu=attn_metadata.seq_lens_cpu[num_decodes:]
+                if attn_metadata.seq_lens_cpu is not None
+                else None,
+            )
+            k = key[:N].view(N, self.num_kv_heads, self.head_size)
+            v = value[:N].view(N, self.num_kv_heads, self.head_size)
+            attn_out[num_decode_tokens:] = self._prefill_attention(
+                q[num_decode_tokens:],
+                k[num_decode_tokens:],
+                v[num_decode_tokens:],
+                kv_cache,
+                prefill_meta,
+                Pi,
+                centroids,
+                PiT,
+                layer=layer,
+            )
+
+        # Write into output buffer: attn_out is (N, Hq, D)
+        # output may be 2D (N, Hq*D) or 3D (N, Hq, D)
+        if output.ndim == 3:
+            output[:N] = attn_out.to(output.dtype)
+        else:
+            output[:N] = attn_out.reshape(N, -1).to(output.dtype)
+        return output
+
+    # ------------------------------------------------------------------ #
+    #  Store K/V into combined cache (vectorized)                         #
+    # ------------------------------------------------------------------ #
+    def _store_kv(
+        self,
+        key: torch.Tensor,  # (N, Hk, D)
+        value: torch.Tensor,  # (N, Hk, D)
+        kv_cache: torch.Tensor,  # (num_blocks, block_size, Hk, slot_size)
+        slot_mapping: torch.Tensor,
+        layer: Any,
+    ):
+        """Quantize + store via fused Triton kernel."""
+        triton_turboquant_store(
+            key,
+            value,
+            kv_cache,
+            slot_mapping,
+            layer._tq_PiT,
+            layer._tq_midpoints,
+            mse_bits=self.tq_config.key_mse_bits,
+            key_packed_size=self.tq_config.key_packed_size,
+            value_quant_bits=self.tq_config.effective_value_quant_bits,
+            key_fp8=self.tq_config.key_fp8,
+        )
+
+    # ------------------------------------------------------------------ #
+    #  Prefill: SDPA on raw Q/K/V with causal mask                        #
+    # ------------------------------------------------------------------ #
+    def _prefill_attention(
+        self,
+        query: torch.Tensor,  # (N, Hq, D)
+        key: torch.Tensor,  # (N, Hk, D)
+        value: torch.Tensor,  # (N, Hk, D)
+        kv_cache: torch.Tensor,  # (num_blocks, block_size, Hk, slot_size)
+        attn_metadata: TurboQuantMetadata,
+        Pi: torch.Tensor,
+        centroids: torch.Tensor,
+        PiT: torch.Tensor | None = None,
+        layer: Any = None,
+    ) -> torch.Tensor:
+        N, Hq, D = query.shape
+
+        # Fast path: use flash_attn for first-chunk prefills (all K/V in batch).
+        # max_query_len == max_seq_len means no request has prior cached KV.
+        # Both are Python ints — no GPU sync.
+        if _HAS_FLASH_ATTN and attn_metadata.max_query_len == attn_metadata.max_seq_len:
+            return self._flash_attn_varlen(
+                q=query,
+                k=key,
+                v=value,
+                cu_seqlens_q=attn_metadata.query_start_loc,
+                cu_seqlens_k=attn_metadata.query_start_loc,
+                max_seqlen_q=attn_metadata.max_query_len,
+                max_seqlen_k=attn_metadata.max_query_len,
+            )
+
+        # Continuation or no flash_attn: per-request attention.
+        # For continuation chunks (seq_len > q_len), we must attend to
+        # previously cached K/V from the TQ cache, not just the current
+        # chunk's raw K/V.
+        Hk = key.shape[1]
+        use_gqa = Hk < Hq
+        query_start_loc = attn_metadata.query_start_loc
+        num_reqs = query_start_loc.shape[0] - 1
+
+        output = torch.zeros(N, Hq, D, device=query.device, dtype=query.dtype)
+
+        # Prefer the CPU-resident copies from the metadata if populated —
+        # otherwise `.tolist()` on GPU tensors forces a synchronizing copy.
+        if attn_metadata.query_start_loc_cpu is not None:
+            qsl = attn_metadata.query_start_loc_cpu.tolist()
+        else:
+            qsl = query_start_loc.tolist()
+        if attn_metadata.seq_lens_cpu is not None:
+            seq_lens_list = attn_metadata.seq_lens_cpu.tolist()
+        else:
+            seq_lens_list = attn_metadata.seq_lens.tolist()
+
+        # Pre-allocate cu_seqlens for single-request flash_attn calls
+        # to avoid per-request host→device tensor creation.
+        if not hasattr(self, "_cu_2"):
+            self._cu_2 = torch.zeros(2, device=query.device, dtype=torch.int32)
+        # Cache arange on self (avoid per-call kernel launch).
+        _max_seq = attn_metadata.max_seq_len
+        _ac: torch.Tensor | None = getattr(self, "_arange_cache", None)
+        if _ac is None or _ac.shape[0] <= _max_seq:
+            _ac = torch.arange(
+                0, _max_seq + 1, device=query.device, dtype=attn_metadata.seq_lens.dtype
+            )
+            self._arange_cache = _ac
+        _arange_cache: torch.Tensor = _ac
+
+        for i in range(num_reqs):
+            q_start = qsl[i]
+            q_end = qsl[i + 1]
+            q_len = q_end - q_start
+            if q_len <= 0:
+                continue
+
+            seq_len = seq_lens_list[i]
+            q_seq = query[q_start:q_end]  # (q_len, Hq, D)
+            k_seq = key[q_start:q_end]  # (q_len, Hk, D)
+            v_seq = value[q_start:q_end]  # (q_len, Hk, D)
+
+            if q_len == seq_len:
+                # First-chunk prefill: all K/V are in the current batch.
+                if _HAS_FLASH_ATTN:
+                    # Assign to slice to avoid gpu/cpu sync.
+                    self._cu_2[1:2] = q_len
+                    cu = self._cu_2
+                    out = self._flash_attn_varlen(
+                        q=q_seq,
+                        k=k_seq,
+                        v=v_seq,
+                        cu_seqlens_q=cu,
+                        cu_seqlens_k=cu,
+                        max_seqlen_q=q_len,
+                        max_seqlen_k=q_len,
+                    )
+                else:
+                    q_t = q_seq.transpose(0, 1).contiguous()
+                    k_t = k_seq.transpose(0, 1).contiguous()
+                    v_t = v_seq.transpose(0, 1).contiguous()
+                    out = F.scaled_dot_product_attention(
+                        q_t,
+                        k_t,
+                        v_t,
+                        is_causal=True,
+                        scale=self.scale,
+                        enable_gqa=use_gqa,
+                    ).transpose(0, 1)
+                output[q_start:q_end] = out.to(query.dtype)
+            else:
+                # Continuation chunk: tokens already stored to TQ cache
+                # by do_kv_cache_update. Use decode kernel directly to
+                # avoid O(cached_len) full-dequant per continuation.
+                # For large continuations, fall back to _continuation_prefill.
+                cached_len = seq_len - q_len
+                if q_len <= _CONTINUATION_DECODE_THRESHOLD:
+                    # Fast path: treat each query as a decode request
+                    # with incremental seq_lens for causal masking.
+                    # Slice from pre-built arange (no kernel launch)
+                    synth_seq_lens = _arange_cache[cached_len + 1 : seq_len + 1]
+                    synth_bt = attn_metadata.block_table[i : i + 1].expand(q_len, -1)
+                    out = triton_turboquant_decode_attention(
+                        query=q_seq,
+                        kv_cache=kv_cache,
+                        block_table=synth_bt,
+                        seq_lens=synth_seq_lens,
+                        Pi=Pi,
+                        centroids=centroids,
+                        scale=self.scale,
+                        mse_bits=self.tq_config.key_mse_bits,
+                        key_packed_size=self.tq_config.key_packed_size,
+                        value_quant_bits=(self.tq_config.effective_value_quant_bits),
+                        key_fp8=self.tq_config.key_fp8,
+                        norm_correction=self.tq_config.norm_correction,
+                        PiT=PiT,
+                    )
+                else:
+                    # Large continuation: dequant cached K/V and use
+                    # flash_attn for better throughput.
+                    out = self._continuation_prefill(
+                        layer,
+                        q_seq,
+                        k_seq,
+                        v_seq,
+                        kv_cache,
+                        attn_metadata.block_table[i : i + 1],
+                        cached_len,
+                        seq_len,
+                        Pi,
+                        centroids,
+                    )
+                output[q_start:q_end] = out.to(query.dtype)
+
+        return output
+
+    def _continuation_prefill(
+        self,
+        layer: Any,
+        query: torch.Tensor,  # (q_len, Hq, D)
+        key_chunk: torch.Tensor,  # (q_len, Hk, D)
+        val_chunk: torch.Tensor,  # (q_len, Hk, D)
+        kv_cache: torch.Tensor,  # (num_blocks, block_size, Hk, slot_size)
+        block_table: torch.Tensor,  # (1, max_num_blocks)
+        cached_len: int,
+        seq_len: int,
+        Pi: torch.Tensor,
+        centroids: torch.Tensor,
+    ) -> torch.Tensor:
+        """Handle continuation chunk by dequanting cached K/V from TQ cache.
+
+        Dequants previously cached K/V, concatenates with the current
+        chunk's raw K/V, then runs flash_attn with causal masking.
+        """
+        q_len, Hq, D = query.shape
+        Hk = key_chunk.shape[1]
+        device = query.device
+        block_size = kv_cache.shape[1]
+        BLOCK_D = triton.next_power_of_2(D)
+
+        mse_bytes = self._mse_bytes
+        val_data_bytes = self._val_data_bytes
+
+        # Dequant cached K/V from TQ cache
+        # Allocate slightly over to align to block_size for the grid.
+        # Reuse cached buffers to avoid per-call allocation (~16MB at 8K).
+        alloc_len = math.ceil(cached_len / block_size) * block_size
+        buf_shape = (1, Hk, alloc_len, D)
+        # Use WorkspaceManager for dequant buffers.
+        # Shared across all layers — saves 60× memory at long context.
+        # Required for CUDA Graph capture (per-layer growth incompatible with CG).
+        k_buf, v_buf = current_workspace_manager().get_simultaneous(
+            (buf_shape, torch.float16),
+            (buf_shape, torch.float16),
+        )
+        # Skip .zero_() — kernel writes all positions up to cached_len,
+        # and we only read [:cached_len] afterwards.
+        k_cached = k_buf[:, :, :alloc_len, :]
+        v_cached = v_buf[:, :, :alloc_len, :]
+
+        grid = (alloc_len, 1 * Hk)
+        _tq_full_dequant_kv[grid](
+            kv_cache,
+            block_table,
+            centroids,
+            k_cached,
+            v_cached,
+            k_cached.stride(0),
+            k_cached.stride(1),
+            k_cached.stride(2),
+            v_cached.stride(0),
+            v_cached.stride(1),
+            v_cached.stride(2),
+            kv_cache.stride(0),
+            kv_cache.stride(1),
+            kv_cache.stride(2),
+            block_table.stride(0),
+            HEAD_DIM=D,
+            BLOCK_SIZE=block_size,
+            NUM_KV_HEADS=Hk,
+            MSE_BYTES=mse_bytes,
+            KPS=self.tq_config.key_packed_size,
+            VQB=self.tq_config.effective_value_quant_bits,
+            VAL_DATA_BYTES=val_data_bytes,
+            MSE_BITS=self.tq_config.key_mse_bits,
+            KEY_FP8=1 if self.tq_config.key_fp8 else 0,
+            BLOCK_D=BLOCK_D,
+            NORM_CORRECTION=1 if self.tq_config.norm_correction else 0,
+            FP8_E4B15=_use_fp8_e4b15(device.index or 0),
+            num_warps=4,
+        )
+
+        # Inverse-rotate MSE keys back to original space
+        if not self.tq_config.key_fp8:
+            # fp16 matmul for rotation (2× less bandwidth, uses fp16 tensor cores)
+            Pi_half = layer._tq_Pi_half
+            k_flat = k_cached[0, :, :cached_len, :].reshape(-1, D)
+            k_flat = k_flat @ Pi_half
+            k_cached_trim = k_flat.reshape(Hk, cached_len, D).transpose(
+                0, 1
+            )  # (cached_len, Hk, D) — already fp16
+        else:
+            k_cached_trim = k_cached[0, :, :cached_len, :].transpose(
+                0, 1
+            )  # (cached_len, Hk, D)
+
+        # Skip .contiguous() — the copy into k_full/v_full handles layout
+        v_cached_trim = v_cached[0, :, :cached_len, :].transpose(0, 1)
+
+        # Concatenate cached + current chunk K/V (match query dtype)
+        # Pre-allocate full K/V buffer, copy into slices (no cat alloc)
+        qdtype = query.dtype
+        k_full = torch.empty(seq_len, Hk, D, dtype=qdtype, device=device)
+        v_full = torch.empty(seq_len, Hk, D, dtype=qdtype, device=device)
+        k_full[:cached_len] = k_cached_trim.to(qdtype)
+        k_full[cached_len:] = key_chunk
+        v_full[:cached_len] = v_cached_trim.to(qdtype)
+        v_full[cached_len:] = val_chunk
+
+        # Attention: q_len queries attending to seq_len K/V with causal mask
+        if _HAS_FLASH_ATTN:
+            # Reuse pre-allocated cu_seqlens (avoid host→device transfer)
+            if not hasattr(self, "_cu_2_q"):
+                self._cu_2_q = torch.zeros(2, device=device, dtype=torch.int32)
+                self._cu_2_k = torch.zeros(2, device=device, dtype=torch.int32)
+            # Assigning to slice uses fill_ which avoids cpu/gpu sync.
+            self._cu_2_q[1:2] = q_len
+            self._cu_2_k[1:2] = seq_len
+            cu_seqlens_q = self._cu_2_q
+            cu_seqlens_k = self._cu_2_k
+            return self._flash_attn_varlen(
+                q=query,
+                k=k_full,
+                v=v_full,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=q_len,
+                max_seqlen_k=seq_len,
+            )
+        else:
+            # SDPA fallback: expand KV for GQA, build causal mask
+            q_t = query.transpose(0, 1).unsqueeze(0)  # (1, Hq, q_len, D)
+            k_t = k_full.transpose(0, 1).unsqueeze(0)  # (1, Hk, seq_len, D)
+            v_t = v_full.transpose(0, 1).unsqueeze(0)  # (1, Hk, seq_len, D)
+            # Build causal mask: query position p can attend to K position j
+            # where j <= cached_len + p (p is 0-indexed within chunk)
+            q_pos = torch.arange(q_len, device=device).unsqueeze(1) + cached_len
+            k_pos = torch.arange(seq_len, device=device).unsqueeze(0)
+            mask = k_pos <= q_pos  # (q_len, seq_len)
+            out = F.scaled_dot_product_attention(
+                q_t,
+                k_t,
+                v_t,
+                attn_mask=mask,
+                scale=self.scale,
+                enable_gqa=(Hk < Hq),
+            )  # (1, Hq, q_len, D)
+            return out[0].transpose(0, 1)  # (q_len, Hq, D)
+
+    # ------------------------------------------------------------------ #
+    #  Decode: Triton TQ decode attention                                 #
+    # ------------------------------------------------------------------ #
+    def _decode_attention(
+        self,
+        query: torch.Tensor,  # (B, Hq, D)
+        kv_cache: torch.Tensor,  # (num_blocks, block_size, Hk, slot_size)
+        attn_metadata: TurboQuantMetadata,
+        Pi: torch.Tensor,
+        centroids: torch.Tensor,
+        PiT: torch.Tensor | None = None,
+        layer: torch.nn.Module | None = None,
+    ) -> torch.Tensor:
+        # Acquire shared decode scratch buffers from WorkspaceManager.
+        # Layers execute sequentially so one set of buffers is sufficient.
+        # Falls back to kernel-internal allocation if workspace unavailable.
+        B = query.shape[0]
+        D = self.head_size
+        S = self.max_num_kv_splits
+        Hq = self.num_heads
+        mid_o_buf = output_buf = lse_buf = None
+        if is_workspace_manager_initialized():
+            # output_buf in query dtype — matches the in-kernel fp16 cast in stage2.
+            mid_o_buf, output_buf, lse_buf = (
+                current_workspace_manager().get_simultaneous(
+                    ((B, Hq, S, D + 1), torch.float32),
+                    ((B, Hq, D), query.dtype),
+                    ((B, Hq), torch.float32),
+                )
+            )
+
+        result = triton_turboquant_decode_attention(
+            query=query,
+            kv_cache=kv_cache,
+            block_table=attn_metadata.block_table,
+            seq_lens=attn_metadata.seq_lens,
+            Pi=Pi,
+            centroids=centroids,
+            scale=self.scale,
+            mse_bits=self.tq_config.key_mse_bits,
+            key_packed_size=self.tq_config.key_packed_size,
+            value_quant_bits=self.tq_config.effective_value_quant_bits,
+            key_fp8=self.tq_config.key_fp8,
+            norm_correction=self.tq_config.norm_correction,
+            PiT=PiT,
+            mid_o_buf=mid_o_buf,
+            output_buf=output_buf,
+            lse_buf=lse_buf,
+            buf_holder=layer,
+            max_num_kv_splits=self.max_num_kv_splits,
+        )
+        return result
diff --git a/vllm/v1/attention/backends/utils.py b/vllm/v1/attention/backends/utils.py
index 0a36e6fd490a..43cbcfec1844 100644
--- a/vllm/v1/attention/backends/utils.py
+++ b/vllm/v1/attention/backends/utils.py
@@ -332,8 +332,10 @@ def make_local_attention_virtual_batches(
     # regression when using numpy arrays (batch and block indices) to index into
     # torch tensor (block_table). As a workaround, convert numpy arrays to torch
     # tensor first, which recovers perf.
-    batch_indices_torch = torch.from_numpy(batch_indices)
-    block_indices_torch = torch.from_numpy(block_indices)
+    # Upload the index tensors to the block_table's device up-front so that the
+    # fancy indexing below doesn't implicitly force a synchronous H2D copy.
+    batch_indices_torch = torch.from_numpy(batch_indices).to(device, non_blocking=True)
+    block_indices_torch = torch.from_numpy(block_indices).to(device, non_blocking=True)
 
     # Save as a lambda so we can return this for update_block_table
     make_block_table = lambda block_table: block_table[
@@ -356,6 +358,7 @@ def make_local_attention_virtual_batches(
         block_table_tensor=block_table_local,
         slot_mapping=common_attn_metadata.slot_mapping,
         causal=True,
+        seq_lens_cpu_upper_bound=common_attn_metadata.seq_lens_cpu_upper_bound,
         _seq_lens_cpu=seq_lens_cpu,
         _num_computed_tokens_cpu=torch.from_numpy(num_computed_tokens_local),
     ), make_block_table
@@ -390,7 +393,16 @@ def make_kv_sharing_fast_prefill_common_attn_metadata(
 
     # Figure out how many tokens are in each request
     # num_decode_tokens: [1, 2, 1]
-    num_decode_tokens = torch.bincount(request_ids, minlength=num_reqs)
+    # Avoid `torch.bincount` here — on CUDA it forces a sync to determine
+    # the output size (even with `minlength`, the kernel must confirm no
+    # value exceeds the bound). `scatter_add_` into a preallocated buffer
+    # is equivalent and stays async.
+    num_decode_tokens = torch.zeros(
+        num_reqs, dtype=request_ids.dtype, device=request_ids.device
+    )
+    num_decode_tokens.scatter_add_(
+        0, request_ids.to(num_decode_tokens.dtype), torch.ones_like(request_ids)
+    )
 
     # Calculate new query_start_loc with tokens in generation_indices
     # decode_query_start_loc: [0, 1, 3, 4]
@@ -398,7 +410,7 @@ def make_kv_sharing_fast_prefill_common_attn_metadata(
         num_reqs + 1, device=query_start_loc.device, dtype=query_start_loc.dtype
     )
 
-    decode_query_start_loc[0] = 0
+    decode_query_start_loc[:1].fill_(0)  # Avoid sync from scalar assignment.
     decode_query_start_loc[1:] = torch.cumsum(num_decode_tokens, dim=0)
     decode_max_query_len = int(num_decode_tokens.max().item())
     total_num_decode_tokens = int(num_decode_tokens.sum().item())
@@ -414,6 +426,7 @@ def make_kv_sharing_fast_prefill_common_attn_metadata(
         block_table_tensor=common_attn_metadata.block_table_tensor,
         slot_mapping=common_attn_metadata.slot_mapping,
         causal=True,
+        seq_lens_cpu_upper_bound=common_attn_metadata.seq_lens_cpu_upper_bound,
         _seq_lens_cpu=common_attn_metadata._seq_lens_cpu,
         _num_computed_tokens_cpu=common_attn_metadata._num_computed_tokens_cpu,
     )
@@ -445,7 +458,11 @@ def split_decodes_prefills_and_extends(
     num_reqs = common_attn_metadata.num_reqs
     num_tokens = common_attn_metadata.num_actual_tokens
     query_start_loc = common_attn_metadata.query_start_loc_cpu
-    seq_lens = common_attn_metadata.seq_lens_cpu
+    # Upper bound is exact for prefill rows; decode rows still satisfy
+    # seq_len > query_len under the optimistic bound, so `seq_lens ==
+    # query_lens` identifies prefills correctly either way.
+    assert common_attn_metadata.seq_lens_cpu_upper_bound is not None
+    seq_lens = common_attn_metadata.seq_lens_cpu_upper_bound
 
     if max_query_len <= decode_threshold:
         return num_reqs, 0, 0, num_tokens, 0, 0
diff --git a/vllm/v1/attention/ops/chunked_prefill_paged_decode.py b/vllm/v1/attention/ops/chunked_prefill_paged_decode.py
index 000fd4d43b93..77eb3ac60b1f 100644
--- a/vllm/v1/attention/ops/chunked_prefill_paged_decode.py
+++ b/vllm/v1/attention/ops/chunked_prefill_paged_decode.py
@@ -21,6 +21,22 @@
 float8_info = torch.finfo(current_platform.fp8_dtype())
 
 
+def has_native_kv_cache_layout(
+    key_cache: torch.Tensor,
+    value_cache: torch.Tensor,
+) -> bool:
+    """Return whether KV cache blocks can use the native ROCm pairing.
+
+    The native reshape_and_cache writer assumes packed blocks. If cache update
+    needs reshape_and_cache_flash for a stride-padded hybrid layout, decode
+    should use the matching Triton path too.
+    """
+    return (
+        key_cache.stride(0) == key_cache.shape[1:].numel()
+        and value_cache.stride(0) == value_cache.shape[1:].numel()
+    )
+
+
 @triton.jit
 def cdiv_fn(x, y):
     return (x + y - 1) // y
@@ -269,6 +285,7 @@ def chunked_prefill_paged_decode(
     # Optional tensor for sinks
     sinks=None,
     is_block_table_ptr: bool = False,
+    causal: bool = True,
 ):
     if sm_scale is None:
         sm_scale = 1.0 / (query.shape[2] ** 0.5)
@@ -300,6 +317,7 @@ def chunked_prefill_paged_decode(
             skip_decode=True,
             fp8_out_scale=output_scale,
             sinks=sinks,
+            causal=causal,
         )
 
     block_size = value_cache.shape[3]
@@ -344,14 +362,12 @@ def chunked_prefill_paged_decode(
         alibi_slopes,
         sinks,
     )
-    # Triton is only forced when encountering a non-standard block
-    # like Qwen3 with a size of 544.
-    # 1. Check if block_size is a power of 2 (16, 32, 64...)
-    # 2. If it's a power of 2, we trust the vLLM's native use_custom decision.
-    # 3. If it's not a power of 2 (such as Qwen3's 544),
-    # then our Triton path is forced.
+    has_native_layout = has_native_kv_cache_layout(key_cache, value_cache)
+    # Force Triton for non-standard blocks like Qwen3's 544 and for
+    # stride-padded hybrid layouts. The latter use reshape_and_cache_flash
+    # during cache update, so keep decode on the matching stride-aware path.
     is_pow2 = block_size > 0 and (block_size & (block_size - 1) == 0)
-    if not is_pow2:
+    if not is_pow2 or not has_native_layout:
         use_custom = False
 
     if use_custom:
@@ -402,7 +418,12 @@ def chunked_prefill_paged_decode(
         real_block_size = value_cache.shape[3]
         # The standard model directly uses the original block_size.
         # Non-standard 544 uses 32 to accommodate integer division logic.
-        TRITON_BLOCK_SIZE = block_size if is_pow2 else 32
+        # Cap at 128 to avoid exceeding GPU shared memory limits
+        # (e.g. hybrid Mamba models inflate block_size to 2048).
+        # The kernel handles TRITON_BLOCK_SIZE != PHYSICAL_BLOCK_SIZE
+        # via the l_block_idx/internal_offsets addressing logic.
+        MAX_TRITON_BLOCK_SIZE = 128
+        TRITON_BLOCK_SIZE = min(block_size, MAX_TRITON_BLOCK_SIZE) if is_pow2 else 32
         if is_block_table_ptr:
             # Using the physical base address of tensors
             kv_element_size = key_cache.element_size()
diff --git a/vllm/v1/attention/ops/common.py b/vllm/v1/attention/ops/common.py
index 46c689ce0b8f..98abc7790ea2 100644
--- a/vllm/v1/attention/ops/common.py
+++ b/vllm/v1/attention/ops/common.py
@@ -265,6 +265,7 @@ def _pack_seq_kernel(
     D: tl.constexpr,
     Lmax: tl.constexpr,
     PAD_VALUE: tl.constexpr,
+    PAD_IS_UINT8: tl.constexpr,
     BLOCK_T: tl.constexpr,  # timesteps per program
     BLOCK_D: tl.constexpr,  # features per program
 ):
@@ -294,9 +295,15 @@ def _pack_seq_kernel(
     # out_ptr: row-major [B, Lmax, D]
     out_row_ptr = out_ptr + (pid_b * Lmax + off_t)[:, None] * D + off_d[None, :]
 
-    # Initialize with PAD (cast will occur as needed based on out_ptr dtype)
+    # Initialize with PAD. PAD_IS_UINT8 selects the pad tensor's dtype so
+    # integer-typed outputs (e.g. MXFP4 packed nibbles, ue8m0 scale bytes)
+    # get an exact-byte pad rather than going through an fp32→uint8 cast
+    # that's implementation-defined outside of value 0.
     d_mask = off_d[None, :] < D
-    pad_vals = tl.full([BLOCK_T, BLOCK_D], PAD_VALUE, tl.float32)
+    if PAD_IS_UINT8:
+        pad_vals = tl.full([BLOCK_T, BLOCK_D], PAD_VALUE, tl.uint8)
+    else:
+        pad_vals = tl.full([BLOCK_T, BLOCK_D], PAD_VALUE, tl.float32)
     tl.store(out_row_ptr, pad_vals, mask=t_mask[:, None] & d_mask)
 
     # Load & write only where within seq_len
@@ -307,23 +314,36 @@ def _pack_seq_kernel(
 def pack_seq_triton(
     x: torch.Tensor,
     lengths: torch.Tensor,
-    pad_value: float = -float("inf"),
+    pad_value: float | int = -float("inf"),
     block_t: int = 64,
     block_d: int = 64,
 ) -> torch.Tensor:
-    """
-    Pack sequences of different lengths into a batched tensor.
+    """Pack sequences of different lengths into a batched tensor.
+
+    Supports float dtypes (any, via fp32 pad) and ``torch.uint8`` (exact-byte
+    pad — e.g. MXFP4 packed nibbles or ue8m0 scale bytes). For uint8 inputs
+    ``pad_value`` must be an integer in ``[0, 255]``.
 
     Args:
-        x: [N, ...] - input tensor where N is total number of tokens
-        lengths: [B] - sequence lengths for each batch
-        pad_value: value to use for padding
-        block_t: block size for time dimension
-        block_d: block size for feature dimension
+        x: [N, ...] — input tensor where N is total number of tokens.
+        lengths: [B] — sequence lengths for each batch.
+        pad_value: value to use for padding. Defaults to ``-inf`` which is
+            only sensible for float dtypes; pass ``0`` (or any byte) for
+            uint8 inputs.
+        block_t: block size for time dimension.
+        block_d: block size for feature dimension.
 
     Returns:
-        packed: [B, Lmax, ...] - packed tensor
+        packed: [B, Lmax, ...] — packed tensor.
     """
+    is_uint8 = x.dtype == torch.uint8
+    if is_uint8:
+        assert isinstance(pad_value, int) and 0 <= pad_value <= 255, (
+            f"uint8 pack requires an integer pad in [0, 255], got {pad_value!r}"
+        )
+        pad_constexpr: int | float = int(pad_value)
+    else:
+        pad_constexpr = float(pad_value)
 
     # Handle multi-dimensional input by reshaping to (N, -1)
     original_shape = x.shape
@@ -338,8 +358,6 @@ def pack_seq_triton(
     B = lengths.numel()
     Lmax = int(lengths.max().item())
 
-    # Starts are computed inside the kernel from lengths
-
     out = torch.empty((B, Lmax, D), device=x.device, dtype=x.dtype)
 
     grid = (B, triton.cdiv(Lmax, block_t), triton.cdiv(D, block_d))
@@ -350,17 +368,16 @@ def pack_seq_triton(
         N,
         D,
         Lmax,
-        PAD_VALUE=float(pad_value),
+        PAD_VALUE=pad_constexpr,
+        PAD_IS_UINT8=is_uint8,
         BLOCK_T=block_t,
         BLOCK_D=block_d,
         num_warps=4,
         num_stages=2,
     )
 
-    # Reshape output back to original dimensions (except first dimension)
     if len(original_shape) > 2:
-        output_shape = (B, Lmax) + original_shape[1:]
-        out = out.reshape(output_shape)
+        out = out.reshape((B, Lmax) + original_shape[1:])
 
     return out
 
diff --git a/vllm/v1/attention/ops/dcp_alltoall.py b/vllm/v1/attention/ops/dcp_alltoall.py
index 92f50f63e3ef..1469a5c754d6 100644
--- a/vllm/v1/attention/ops/dcp_alltoall.py
+++ b/vllm/v1/attention/ops/dcp_alltoall.py
@@ -9,10 +9,8 @@
 A2A exchanges partial attention outputs and their LSE values across
 ranks, then combines them with exact LSE-weighted reduction.
 
-This reduces the number of NCCL calls per attention layer from 3
-(AG for Q, AG for K metadata, RS for output) to 2 (A2A for output,
-A2A for LSE), lowering per-step communication overhead for long-context
-decode where NCCL latency is a significant fraction of step time.
+This reduces the number of NCCL calls per attention layer by exchanging
+the partial output and LSE in a single packed All-to-All payload.
 
 Usage:
     vllm serve model --tp 16 --dcp 16 --dcp-comm-backend a2a
@@ -28,6 +26,10 @@
 import torch.distributed as dist
 
 from vllm.triton_utils import tl, triton
+from vllm.v1.worker.workspace import (
+    current_workspace_manager,
+    is_workspace_manager_initialized,
+)
 
 if TYPE_CHECKING:
     from vllm.distributed.parallel_state import GroupCoordinator
@@ -44,7 +46,6 @@ def _lse_weighted_combine(
     CPU reference implementation for LSE-weighted combination.
 
     This is a pure PyTorch implementation used for testing and validation.
-    For GPU execution, use dcp_lse_combine_triton instead.
 
     Args:
         outputs: Partial attention outputs [N, B, H, D]
@@ -102,57 +103,137 @@ def _lse_weighted_combine(
     return result
 
 
+def _dcp_a2a_lse_pack_dim(output_dtype: torch.dtype) -> int:
+    bits = torch.finfo(output_dtype).bits
+    if bits == 16:
+        return 2
+    if bits == 32:
+        return 1
+    raise ValueError(f"Cannot pack fp32 LSE into output dtype {output_dtype}.")
+
+
+def _dcp_a2a_send_recv_buffers(
+    shape: tuple[int, ...],
+    device: torch.device,
+    dtype: torch.dtype,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    if is_workspace_manager_initialized():
+        send_buffer, recv_buffer = current_workspace_manager().get_simultaneous(
+            (shape, dtype),
+            (shape, dtype),
+        )
+        return send_buffer, recv_buffer
+
+    return (
+        torch.empty(shape, device=device, dtype=dtype),
+        torch.empty(shape, device=device, dtype=dtype),
+    )
+
+
 @triton.jit
-def _dcp_lse_combine_kernel(
-    # Input pointers
-    recv_output_ptr,
-    recv_lse_ptr,
-    # Output pointers
+def _dcp_a2a_pack_send_kernel(
     out_ptr,
-    out_lse_ptr,
-    # Strides for recv_output [N, B, H_local, D]
-    ro_stride_N,
-    ro_stride_B,
-    ro_stride_H,
-    ro_stride_D,
-    # Strides for recv_lse [N, B, H_local]
-    rl_stride_N,
-    rl_stride_B,
-    rl_stride_H,
-    # Strides for output [B, H_local, D]
-    o_stride_B,
-    o_stride_H,
-    o_stride_D,
-    # Constants
+    lse_ptr,
+    send_ptr,
+    out_stride_B,
+    out_stride_H,
+    out_stride_D,
+    lse_stride_B,
+    lse_stride_H,
+    send_stride_N,
+    send_stride_B,
+    send_stride_H,
+    send_stride_D,
     N: tl.constexpr,
     HEAD_DIM: tl.constexpr,
-    IS_BASE_E: tl.constexpr,
-    RETURN_LSE: tl.constexpr,
+    H_PER_RANK: tl.constexpr,
+    LSE_PACK_DIM: tl.constexpr,
 ):
-    """
-    Triton kernel for LSE-weighted combination of partial attention outputs.
+    batch_idx = tl.program_id(0).to(tl.int64)
+    local_head_idx = tl.program_id(1).to(tl.int64)
+    d_offsets = tl.arange(0, HEAD_DIM)
 
-    After All-to-All, each rank has:
-    - recv_output [N, B, H_local, D]: partial outputs from all KV shards
-    - recv_lse [N, B, H_local]: partial LSEs from all KV shards
+    for rank_idx in tl.static_range(N):
+        src_head_idx = rank_idx * H_PER_RANK + local_head_idx
+        send_base = (
+            rank_idx * send_stride_N
+            + batch_idx * send_stride_B
+            + local_head_idx * send_stride_H
+        )
 
-    This kernel computes the weighted combination locally (no communication).
+        out_offsets = (
+            batch_idx * out_stride_B
+            + src_head_idx * out_stride_H
+            + d_offsets * out_stride_D
+        )
+        tl.store(
+            send_ptr + send_base + d_offsets * send_stride_D,
+            tl.load(out_ptr + out_offsets),
+        )
 
-    Grid: (B, H_local)
-    Each program handles one (batch, head) and processes all D elements.
-    """
+        lse_val = tl.load(
+            lse_ptr + batch_idx * lse_stride_B + src_head_idx * lse_stride_H
+        )
+        if LSE_PACK_DIM == 1:
+            tl.store(
+                send_ptr + send_base + HEAD_DIM * send_stride_D,
+                lse_val.to(send_ptr.dtype.element_ty),
+            )
+        else:
+            lse_bits = lse_val.to(tl.uint32, bitcast=True)
+            lo = (lse_bits & 0xFFFF).to(tl.uint16)
+            hi = ((lse_bits >> 16) & 0xFFFF).to(tl.uint16)
+            tl.store(
+                send_ptr + send_base + HEAD_DIM * send_stride_D,
+                lo.to(send_ptr.dtype.element_ty, bitcast=True),
+            )
+            tl.store(
+                send_ptr + send_base + (HEAD_DIM + 1) * send_stride_D,
+                hi.to(send_ptr.dtype.element_ty, bitcast=True),
+            )
+
+
+@triton.jit
+def _dcp_a2a_unpack_combine_kernel(
+    recv_ptr,
+    out_ptr,
+    out_lse_ptr,
+    recv_stride_N,
+    recv_stride_B,
+    recv_stride_H,
+    recv_stride_D,
+    out_stride_B,
+    out_stride_H,
+    out_stride_D,
+    out_lse_stride_B,
+    out_lse_stride_H,
+    N: tl.constexpr,
+    HEAD_DIM: tl.constexpr,
+    IS_BASE_E: tl.constexpr,
+    RETURN_LSE: tl.constexpr,
+    LSE_PACK_DIM: tl.constexpr,
+):
     batch_idx = tl.program_id(0).to(tl.int64)
     head_idx = tl.program_id(1).to(tl.int64)
+    d_offsets = tl.arange(0, HEAD_DIM)
 
-    # Base offset for this (batch, head)
-    base_lse_offset = batch_idx * rl_stride_B + head_idx * rl_stride_H
-    base_out_offset = batch_idx * ro_stride_B + head_idx * ro_stride_H
-
-    # First pass: find max LSE for numerical stability
     lse_max = -float("inf")
-    for n in tl.static_range(N):
-        lse_offset = n * rl_stride_N + base_lse_offset
-        lse_val = tl.load(recv_lse_ptr + lse_offset)
+    for rank_idx in tl.static_range(N):
+        recv_base = (
+            rank_idx * recv_stride_N
+            + batch_idx * recv_stride_B
+            + head_idx * recv_stride_H
+        )
+        if LSE_PACK_DIM == 1:
+            lse_val = tl.load(recv_ptr + recv_base + HEAD_DIM * recv_stride_D).to(
+                tl.float32
+            )
+        else:
+            lo_raw = tl.load(recv_ptr + recv_base + HEAD_DIM * recv_stride_D)
+            hi_raw = tl.load(recv_ptr + recv_base + (HEAD_DIM + 1) * recv_stride_D)
+            lo = lo_raw.to(tl.uint16, bitcast=True).to(tl.uint32)
+            hi = hi_raw.to(tl.uint16, bitcast=True).to(tl.uint32)
+            lse_val = (lo | (hi << 16)).to(tl.float32, bitcast=True)
         lse_val = tl.where(
             (lse_val != lse_val) | (lse_val == float("inf")),
             -float("inf"),
@@ -162,11 +243,23 @@ def _dcp_lse_combine_kernel(
 
     lse_max = tl.where(lse_max == -float("inf"), 0.0, lse_max)
 
-    # Second pass: compute sum of exp(lse - max)
     lse_sum = 0.0
-    for n in tl.static_range(N):
-        lse_offset = n * rl_stride_N + base_lse_offset
-        lse_val = tl.load(recv_lse_ptr + lse_offset)
+    for rank_idx in tl.static_range(N):
+        recv_base = (
+            rank_idx * recv_stride_N
+            + batch_idx * recv_stride_B
+            + head_idx * recv_stride_H
+        )
+        if LSE_PACK_DIM == 1:
+            lse_val = tl.load(recv_ptr + recv_base + HEAD_DIM * recv_stride_D).to(
+                tl.float32
+            )
+        else:
+            lo_raw = tl.load(recv_ptr + recv_base + HEAD_DIM * recv_stride_D)
+            hi_raw = tl.load(recv_ptr + recv_base + (HEAD_DIM + 1) * recv_stride_D)
+            lo = lo_raw.to(tl.uint16, bitcast=True).to(tl.uint32)
+            hi = hi_raw.to(tl.uint16, bitcast=True).to(tl.uint32)
+            lse_val = (lo | (hi << 16)).to(tl.float32, bitcast=True)
         lse_val = tl.where(
             (lse_val != lse_val) | (lse_val == float("inf")),
             -float("inf"),
@@ -177,19 +270,28 @@ def _dcp_lse_combine_kernel(
         else:
             lse_sum += tl.exp2(lse_val - lse_max)
 
-    # Compute global LSE
     if IS_BASE_E:  # noqa: SIM108
         global_lse = tl.log(lse_sum) + lse_max
     else:
         global_lse = tl.log2(lse_sum) + lse_max
 
-    # Third pass: weighted combination across D dimension
-    d_offsets = tl.arange(0, HEAD_DIM)
     acc = tl.zeros([HEAD_DIM], dtype=tl.float32)
-
-    for n in tl.static_range(N):
-        lse_offset = n * rl_stride_N + base_lse_offset
-        lse_val = tl.load(recv_lse_ptr + lse_offset)
+    for rank_idx in tl.static_range(N):
+        recv_base = (
+            rank_idx * recv_stride_N
+            + batch_idx * recv_stride_B
+            + head_idx * recv_stride_H
+        )
+        if LSE_PACK_DIM == 1:
+            lse_val = tl.load(recv_ptr + recv_base + HEAD_DIM * recv_stride_D).to(
+                tl.float32
+            )
+        else:
+            lo_raw = tl.load(recv_ptr + recv_base + HEAD_DIM * recv_stride_D)
+            hi_raw = tl.load(recv_ptr + recv_base + (HEAD_DIM + 1) * recv_stride_D)
+            lo = lo_raw.to(tl.uint16, bitcast=True).to(tl.uint32)
+            hi = hi_raw.to(tl.uint16, bitcast=True).to(tl.uint32)
+            lse_val = (lo | (hi << 16)).to(tl.float32, bitcast=True)
         lse_val = tl.where(
             (lse_val != lse_val) | (lse_val == float("inf")),
             -float("inf"),
@@ -200,80 +302,89 @@ def _dcp_lse_combine_kernel(
         else:
             weight = tl.exp2(lse_val - global_lse)
         weight = tl.where(weight != weight, 0.0, weight)
+        acc += (
+            tl.load(recv_ptr + recv_base + d_offsets * recv_stride_D).to(tl.float32)
+            * weight
+        )
 
-        out_offsets = n * ro_stride_N + base_out_offset + d_offsets * ro_stride_D
-        out_vals = tl.load(recv_output_ptr + out_offsets)
-        acc += out_vals.to(tl.float32) * weight
-
-    # Store result
     final_offsets = (
-        batch_idx * o_stride_B + head_idx * o_stride_H + d_offsets * o_stride_D
+        batch_idx * out_stride_B + head_idx * out_stride_H + d_offsets * out_stride_D
     )
     tl.store(out_ptr + final_offsets, acc)
 
     if RETURN_LSE:
-        tl.store(out_lse_ptr + base_lse_offset, global_lse)
-
+        out_lse_offset = batch_idx * out_lse_stride_B + head_idx * out_lse_stride_H
+        tl.store(out_lse_ptr + out_lse_offset, global_lse)
 
-def dcp_lse_combine_triton(
-    recv_output: torch.Tensor,
-    recv_lse: torch.Tensor,
-    return_lse: bool = False,
-    is_lse_base_on_e: bool = True,
-) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-    """
-    Triton-accelerated LSE-weighted combination for DCP A2A.
 
-    Args:
-        recv_output: [N, B, H_local, D] - partial outputs from all KV shards
-        recv_lse: [N, B, H_local] - partial LSEs from all KV shards
-        return_lse: If True, also return the global LSE
-        is_lse_base_on_e: If True, LSE is base e; if False, base 2
+def _dcp_a2a_pack_send(
+    cp_attn_out: torch.Tensor,
+    cp_attn_lse: torch.Tensor,
+    send_buffer: torch.Tensor,
+    world_size: int,
+    h_per_rank: int,
+    head_dim: int,
+    lse_pack_dim: int,
+) -> None:
+    grid = (cp_attn_out.shape[0], h_per_rank, 1)
+    _dcp_a2a_pack_send_kernel[grid](
+        cp_attn_out,
+        cp_attn_lse,
+        send_buffer,
+        cp_attn_out.stride(0),
+        cp_attn_out.stride(1),
+        cp_attn_out.stride(2),
+        cp_attn_lse.stride(0),
+        cp_attn_lse.stride(1),
+        send_buffer.stride(0),
+        send_buffer.stride(1),
+        send_buffer.stride(2),
+        send_buffer.stride(3),
+        N=world_size,
+        HEAD_DIM=head_dim,
+        H_PER_RANK=h_per_rank,
+        LSE_PACK_DIM=lse_pack_dim,
+    )
 
-    Returns:
-        Combined output [B, H_local, D]
-        If return_lse=True, also returns global_lse [B, H_local]
-    """
-    N, B, H_local, D = recv_output.shape
 
+def _dcp_a2a_unpack_combine(
+    recv_buffer: torch.Tensor,
+    head_dim: int,
+    lse_pack_dim: int,
+    return_lse: bool,
+    is_lse_base_on_e: bool,
+) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+    world_size, num_tokens, h_per_rank, _ = recv_buffer.shape
     out = torch.empty(
-        (B, H_local, D), device=recv_output.device, dtype=recv_output.dtype
+        (num_tokens, h_per_rank, head_dim),
+        device=recv_buffer.device,
+        dtype=recv_buffer.dtype,
     )
-
-    if return_lse:
-        out_lse = torch.empty(
-            (B, H_local), device=recv_lse.device, dtype=recv_lse.dtype
-        )
-    else:
-        out_lse = torch.empty(1, device=recv_lse.device, dtype=recv_lse.dtype)
-
-    ro_stride_N, ro_stride_B, ro_stride_H, ro_stride_D = recv_output.stride()
-    rl_stride_N, rl_stride_B, rl_stride_H = recv_lse.stride()
-    o_stride_B, o_stride_H, o_stride_D = out.stride()
-
-    grid = (B, H_local, 1)
-
-    _dcp_lse_combine_kernel[grid](
-        recv_output,
-        recv_lse,
+    out_lse = torch.empty(
+        (num_tokens, h_per_rank) if return_lse else (1, 1),
+        device=recv_buffer.device,
+        dtype=torch.float32 if return_lse else recv_buffer.dtype,
+    )
+    grid = (num_tokens, h_per_rank, 1)
+    _dcp_a2a_unpack_combine_kernel[grid](
+        recv_buffer,
         out,
         out_lse,
-        ro_stride_N,
-        ro_stride_B,
-        ro_stride_H,
-        ro_stride_D,
-        rl_stride_N,
-        rl_stride_B,
-        rl_stride_H,
-        o_stride_B,
-        o_stride_H,
-        o_stride_D,
-        N=N,
-        HEAD_DIM=D,
+        recv_buffer.stride(0),
+        recv_buffer.stride(1),
+        recv_buffer.stride(2),
+        recv_buffer.stride(3),
+        out.stride(0),
+        out.stride(1),
+        out.stride(2),
+        out_lse.stride(0),
+        out_lse.stride(1),
+        N=world_size,
+        HEAD_DIM=head_dim,
         IS_BASE_E=is_lse_base_on_e,
         RETURN_LSE=return_lse,
+        LSE_PACK_DIM=lse_pack_dim,
     )
-
     if return_lse:
         return out, out_lse
     return out
@@ -290,17 +401,8 @@ def dcp_a2a_lse_reduce(
     """
     Combine partial attention outputs across DCP ranks using All-to-All.
 
-    Each rank holds attention output for all heads but only a local shard
-    of the KV cache. This function:
-    1. Exchanges partial outputs across ranks via All-to-All
-    2. Exchanges LSE values via All-to-All
-    3. Combines them with exact LSE-weighted reduction (Triton kernel)
-
-    Tensor flow:
-        Input:  cp_attn_out [B, H, D] - all heads, local KV shard
-        Reshape: [N, B, H/N, D] - split heads across ranks
-        A2A:    Two all_to_all_single calls (output and LSE)
-        Combine: recv [N, B, H/N, D] + lse [N, B, H/N] -> [B, H/N, D]
+    The output and fp32 LSE are packed into a single output-dtype buffer, sent
+    with one All-to-All, then unpacked and combined with exact LSE weighting.
 
     Args:
         cp_attn_out: [B, H, D] where B=num_tokens, H=total_heads, D=head_dim
@@ -321,43 +423,36 @@ def dcp_a2a_lse_reduce(
             return cp_attn_out, cp_attn_lse
         return cp_attn_out
 
-    local_output = cp_attn_out.contiguous()
-    local_lse = cp_attn_lse.contiguous()
-
-    B, H, D = local_output.shape
+    B, H, D = cp_attn_out.shape
+    if H % world_size != 0:
+        raise ValueError(f"H={H} must be divisible by DCP world size {world_size}.")
     H_per_rank = H // world_size
+    lse_pack_dim = _dcp_a2a_lse_pack_dim(cp_attn_out.dtype)
 
-    # Reshape for All-to-All: [B, H, D] -> [N, B, H/N, D]
-    # Split heads into N chunks, each destined for a different rank
-    send_output = (
-        local_output.view(B, world_size, H_per_rank, D).permute(1, 0, 2, 3).contiguous()
+    send_buffer, recv_buffer = _dcp_a2a_send_recv_buffers(
+        (world_size, B, H_per_rank, D + lse_pack_dim),
+        device=cp_attn_out.device,
+        dtype=cp_attn_out.dtype,
     )
-    recv_output = torch.empty_like(send_output)
-
-    # Same for LSE: [B, H] -> [N, B, H/N]
-    send_lse = local_lse.view(B, world_size, H_per_rank).permute(1, 0, 2).contiguous()
-    recv_lse = torch.empty_like(send_lse)
 
-    # All-to-All for partial attention outputs and LSE values (async overlap)
-    work_output = dist.all_to_all_single(
-        recv_output.view(-1),
-        send_output.view(-1),
-        group=cp_group.device_group,
-        async_op=True,
+    _dcp_a2a_pack_send(
+        cp_attn_out,
+        cp_attn_lse,
+        send_buffer,
+        world_size,
+        H_per_rank,
+        D,
+        lse_pack_dim,
     )
-    work_lse = dist.all_to_all_single(
-        recv_lse.view(-1),
-        send_lse.view(-1),
+
+    work = dist.all_to_all_single(
+        recv_buffer.view(-1),
+        send_buffer.view(-1),
         group=cp_group.device_group,
         async_op=True,
     )
-    work_output.wait()
-    work_lse.wait()
-
-    # LSE-weighted combination via Triton kernel (local, no communication)
-    return dcp_lse_combine_triton(
-        recv_output,
-        recv_lse,
-        return_lse=return_lse,
-        is_lse_base_on_e=is_lse_base_on_e,
+    work.wait()
+
+    return _dcp_a2a_unpack_combine(
+        recv_buffer, D, lse_pack_dim, return_lse, is_lse_base_on_e
     )
diff --git a/vllm/v1/attention/ops/deepseek_v4_ops/__init__.py b/vllm/v1/attention/ops/deepseek_v4_ops/__init__.py
new file mode 100644
index 000000000000..959a79f292a5
--- /dev/null
+++ b/vllm/v1/attention/ops/deepseek_v4_ops/__init__.py
@@ -0,0 +1,23 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from .cache_utils import (
+    combine_topk_swa_indices,
+    compute_global_topk_indices_and_lens,
+    dequantize_and_gather_k_cache,
+    quantize_and_insert_k_cache,
+)
+from .fused_indexer_q import MXFP4_BLOCK_SIZE, fused_indexer_q_rope_quant
+from .fused_inv_rope_fp8_quant import fused_inv_rope_fp8_quant
+from .fused_qk_rmsnorm import fused_q_kv_rmsnorm
+
+__all__ = [
+    "MXFP4_BLOCK_SIZE",
+    "combine_topk_swa_indices",
+    "compute_global_topk_indices_and_lens",
+    "dequantize_and_gather_k_cache",
+    "fused_indexer_q_rope_quant",
+    "fused_inv_rope_fp8_quant",
+    "fused_q_kv_rmsnorm",
+    "quantize_and_insert_k_cache",
+]
diff --git a/vllm/v1/attention/ops/deepseek_v4_ops/cache_utils.py b/vllm/v1/attention/ops/deepseek_v4_ops/cache_utils.py
new file mode 100644
index 000000000000..69d20c107e11
--- /dev/null
+++ b/vllm/v1/attention/ops/deepseek_v4_ops/cache_utils.py
@@ -0,0 +1,563 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Triton kernels for DeepseekV4 paged K-cache management and sparse-attention index
+preparation.
+
+- quantize_and_insert_k_cache: quantize bf16 K to UE8M0 FP8 and insert into
+  the paged cache.
+- dequantize_and_gather_k_cache: gather and dequantize FP8 K from the paged
+  cache for sparse/SWA prefill.
+- compute_global_topk_indices_and_lens: map local topk indices to global KV
+  cache slots and count valid entries.
+- combine_topk_swa_indices: concatenate topk compressed indices with SWA
+  window indices for sparse prefill.
+"""
+
+import torch
+
+from vllm.triton_utils import tl, triton
+
+
+@triton.jit
+def quantize_and_insert_k_kernel(
+    # Input tensors
+    k_ptr,  # [num_tokens, 512] bf16
+    slot_mapping_ptr,  # [num_tokens] int64
+    # Output tensor
+    k_cache_ptr,  # [num_blocks, block_bytes] as uint8 (flattened view)
+    # Dimensions
+    num_tokens,
+    input_dim: tl.constexpr,  # 512
+    fp8_dim: tl.constexpr,  # 448
+    bf16_dim: tl.constexpr,  # 64
+    scale_dim: tl.constexpr,  # 8
+    quant_block: tl.constexpr,  # 64 (quantization block size)
+    cache_block_size: tl.constexpr,  # 64 (paged cache block size)
+    token_data_size: tl.constexpr,  # 576 bytes per token data
+    block_stride: tl.constexpr,  # total bytes per block (padded)
+    fp8_max: tl.constexpr,
+    n_quant_blocks: tl.constexpr,  # 8 (7 real + 1 padding)
+):
+    """
+    Quantize K tensor and insert into paged K cache.
+
+    K Cache block layout (block_size=64 tokens):
+    - [0, 64*576): Token data, each token has 448 fp8 + 128 bf16
+    - [64*576, 64*576 + 64*8): Scales, each token has 8 uint8 scales
+    - [64*576 + 64*8, block_stride): Padding
+
+    One program per token.
+    """
+    pid = tl.program_id(0)
+
+    if pid >= num_tokens:
+        return
+
+    # Get slot mapping
+    slot_idx = tl.load(slot_mapping_ptr + pid)
+    if slot_idx == -1:
+        return
+
+    block_idx = slot_idx // cache_block_size
+    pos_in_block = slot_idx % cache_block_size
+
+    # Input pointer for this token
+    input_row_ptr = k_ptr + pid * input_dim
+
+    # int64: block_idx * block_stride can exceed 2^31 with many KV-cache blocks
+    # (e.g. >= 57K at block_stride ~37K). Matches gather path below.
+    cache_block_ptr = k_cache_ptr + block_idx.to(tl.int64) * block_stride
+
+    # Token data pointer: token data is stored contiguously at start of block
+    # Each token's data is at offset pos_in_block * token_data_size
+    token_data_ptr = cache_block_ptr + pos_in_block * token_data_size
+
+    # Scale pointer: scales are stored after ALL token data in the block
+    # Scale for this token is at offset (64 * 576) + pos_in_block * 8
+    token_scale_ptr = (
+        cache_block_ptr + cache_block_size * token_data_size + pos_in_block * scale_dim
+    )
+
+    # Token data layout: [0:448] fp8, [448:576] bf16
+    token_fp8_ptr = token_data_ptr
+    token_bf16_ptr = token_data_ptr + fp8_dim
+
+    # ========== Quantize and store FP8 portion (first 448 elements) ==========
+    # Using UE8M0 quantization strategy (scale is power of 2, stored as uint8 exponent)
+    for qblock_idx in tl.static_range(n_quant_blocks):
+        qblock_start = qblock_idx * quant_block
+
+        if qblock_start < fp8_dim:
+            offsets = qblock_start + tl.arange(0, quant_block)
+            mask = offsets < fp8_dim
+
+            # Load bf16 input
+            x = tl.load(input_row_ptr + offsets, mask=mask, other=0.0)
+
+            # Compute absmax scale (same as CUDA kernel)
+            abs_x = tl.abs(x)
+            block_max = tl.max(abs_x, axis=0)
+            block_max = tl.maximum(block_max, 1e-4)  # Match CUDA: fmaxf(amax, 1e-4)
+
+            # UE8M0: Round scale UP to next power of 2
+            # scale = 2^ceil(log2(block_max / fp8_max))
+            raw_scale = block_max / fp8_max
+            log_scale = tl.log2(raw_scale)
+            exponent = tl.ceil(log_scale)  # Round UP to next integer exponent
+            scale = tl.exp2(exponent)  # scale = 2^exponent (power of 2)
+
+            # Quantize to fp8: fp8_value = bf16_value / scale
+            x_scaled = x / scale
+            x_clamped = tl.clamp(x_scaled, -fp8_max, fp8_max)
+
+            # Convert to fp8, then bitcast to uint8 for storage
+            x_fp8 = x_clamped.to(tl.float8e4nv)
+            x_uint8 = x_fp8.to(tl.uint8, bitcast=True)
+
+            # Store as uint8 (1 byte each)
+            tl.store(token_fp8_ptr + offsets, x_uint8, mask=mask)
+
+            # UE8M0 scale encoding: stored_value = exponent + 127 (bias)
+            # During dequant: scale = 2^(stored_value - 127)
+            encoded_scale = exponent + 127.0
+            encoded_scale = tl.maximum(tl.minimum(encoded_scale, 255.0), 0.0)
+            tl.store(token_scale_ptr + qblock_idx, encoded_scale.to(tl.uint8))
+
+    # Padding scale at index 7
+    tl.store(token_scale_ptr + 7, tl.zeros((), dtype=tl.uint8))
+
+    # ========== Store BF16 portion (last 64 elements, no quantization) ==========
+    bf16_input_offset = fp8_dim
+
+    # Process bf16 in chunks of 16
+    bf16_out_ptr = token_bf16_ptr.to(tl.pointer_type(tl.bfloat16))
+    for i in tl.static_range(bf16_dim // 16):
+        chunk_offsets = i * 16 + tl.arange(0, 16)
+        bf16_vals = tl.load(input_row_ptr + bf16_input_offset + chunk_offsets)
+        tl.store(bf16_out_ptr + chunk_offsets, bf16_vals)
+
+
+def quantize_and_insert_k_cache(
+    k: torch.Tensor,  # [num_tokens, 512] bf16
+    k_cache: torch.Tensor,  # [num_blocks, block_bytes] uint8
+    slot_mapping: torch.Tensor,  # [num_tokens] int64
+    block_size: int = 64,
+    is_ue8m0: bool = True,
+):
+    """
+    Quantize K tensor and insert into paged K cache.
+
+    K Cache block layout (block_size=64 tokens):
+    - First 64 * 576 = 36864 bytes: Token data
+      - Each token: 448 bytes (fp8) + 128 bytes (bf16)
+    - Next 64 * 8 = 512 bytes: Scales
+      - Each token: 8 bytes (uint8 scales, 7 real + 1 padding)
+    - Padded to multiple of 576
+    """
+    assert k.dim() == 2 and k.shape[1] == 512, (
+        f"K must be [num_tokens, 512], got {k.shape}"
+    )
+    assert k.dtype == torch.bfloat16, f"K must be bf16, got {k.dtype}"
+    assert is_ue8m0, "Only support ue8m0 quantization."
+
+    # NOTE: When using DP, slot_mapping.shape[0] can be less than k.shape[0] due to
+    # padding. Always use slot_mapping.shape[0] as the token count.
+    num_tokens = slot_mapping.shape[0]
+    block_stride = k_cache.stride(0)  # bytes per block
+
+    TOKEN_FP8_DIM = 448
+    TOKEN_BF16_DIM = 64
+    TOKEN_SCALE_DIM = 8
+    QUANT_BLOCK_SIZE = 64
+    FP8_MAX = 448.0
+    TOKEN_DATA_SIZE = TOKEN_FP8_DIM + TOKEN_BF16_DIM * 2
+
+    grid = (num_tokens,)
+
+    quantize_and_insert_k_kernel[grid](
+        k,
+        slot_mapping,
+        k_cache,
+        num_tokens,
+        input_dim=512,
+        fp8_dim=TOKEN_FP8_DIM,
+        bf16_dim=TOKEN_BF16_DIM,
+        scale_dim=TOKEN_SCALE_DIM,
+        quant_block=QUANT_BLOCK_SIZE,
+        cache_block_size=block_size,
+        token_data_size=TOKEN_DATA_SIZE,
+        block_stride=block_stride,
+        fp8_max=FP8_MAX,
+        n_quant_blocks=8,
+    )
+
+
+@triton.jit
+def _dequantize_and_gather_k_kernel(
+    out_ptr,
+    out_stride0,
+    out_stride1,
+    k_cache_ptr,
+    seq_lens_ptr,
+    block_table_ptr,
+    offset,
+    gather_lens_ptr,
+    # Constants
+    max_blocks_per_seq: tl.constexpr,
+    fp8_dim: tl.constexpr,  # 448
+    bf16_dim: tl.constexpr,  # 64
+    scale_dim: tl.constexpr,  # 8
+    quant_block: tl.constexpr,  # 64 (quantization block size)
+    cache_block_size: tl.constexpr,  # 64 or 128 (paged cache block size)
+    token_data_size: tl.constexpr,  # 576 bytes per token data
+    block_stride: tl.constexpr,  # total bytes per block (padded) int32
+    output_dim: tl.constexpr,  # 512
+    fp8_max: tl.constexpr,
+    n_quant_blocks: tl.constexpr,  # 7 real blocks
+):
+    batch_idx = tl.program_id(0)
+    worker_id = tl.program_id(1)
+    num_workers = tl.num_programs(1)
+
+    seq_len = tl.load(seq_lens_ptr + batch_idx)
+    if gather_lens_ptr is not None:  # noqa: SIM108
+        gather_len = tl.load(gather_lens_ptr + batch_idx)
+    else:
+        # Gather all tokens
+        gather_len = seq_len
+    start_pos = seq_len - gather_len
+
+    for i in range(worker_id, gather_len, num_workers):
+        # Calculate the actual token index in the sequence
+        pos = start_pos + i
+
+        # Calculate which block and position within block
+        block_in_seq = pos // cache_block_size
+        pos_in_block = pos % cache_block_size
+
+        # Get physical block index from block table
+        block_table_row_ptr = block_table_ptr + batch_idx * max_blocks_per_seq
+        physical_block_idx = tl.load(block_table_row_ptr + block_in_seq)  # int32
+
+        # int64: physical_block_idx * block_stride can exceed 2^31 with many
+        # KV-cache blocks (e.g. >= 57K at block_stride ~37K).
+        cache_block_ptr = k_cache_ptr + physical_block_idx.to(tl.int64) * block_stride
+
+        # Token data pointer
+        token_data_ptr = cache_block_ptr + pos_in_block * token_data_size
+
+        # Scale pointer: after all token data
+        token_scale_ptr = (
+            cache_block_ptr
+            + cache_block_size * token_data_size
+            + pos_in_block * scale_dim
+        )
+
+        # Token data layout: [0:448] fp8, [448:576] bf16
+        token_fp8_ptr = token_data_ptr
+        token_bf16_ptr = token_data_ptr + fp8_dim
+
+        # Output pointer for this token (flattened)
+        output_row_ptr = out_ptr + batch_idx * out_stride0 + (offset + i) * out_stride1
+
+        # ========== Dequantize FP8 portion using UE8M0 ==========
+        for qblock_idx in tl.static_range(n_quant_blocks):
+            qblock_start = qblock_idx * quant_block
+
+            if qblock_start < fp8_dim:
+                offsets = qblock_start + tl.arange(0, quant_block)
+                mask = offsets < fp8_dim
+
+                # Load quantized fp8 values (stored as uint8)
+                x_uint8 = tl.load(token_fp8_ptr + offsets, mask=mask, other=0)
+
+                # Bitcast uint8 back to fp8
+                x_fp8 = x_uint8.to(tl.float8e4nv, bitcast=True)
+
+                # Convert fp8 to float32 for computation
+                x_float = x_fp8.to(tl.float32)
+
+                # Load and decode UE8M0 scale
+                # UE8M0: scale = 2^(stored_value - 127)
+                encoded_scale = tl.load(token_scale_ptr + qblock_idx)
+                exponent = encoded_scale.to(tl.float32) - 127.0
+                scale = tl.exp2(exponent)
+
+                # Dequantize: bf16_value = fp8_value * scale
+                x_dequant = x_float * scale
+
+                # Store as bf16
+                tl.store(output_row_ptr + offsets, x_dequant.to(tl.bfloat16), mask=mask)
+
+        # ========== Copy BF16 portion directly ==========
+        bf16_output_offset = fp8_dim  # After 448 elements in output
+
+        # Read bf16 from cache
+        bf16_cache_ptr = token_bf16_ptr.to(tl.pointer_type(tl.bfloat16))
+
+        # Process in chunks of 16
+        for j in tl.static_range(bf16_dim // 16):
+            chunk_offsets = j * 16 + tl.arange(0, 16)
+            bf16_vals = tl.load(bf16_cache_ptr + chunk_offsets)
+            tl.store(output_row_ptr + bf16_output_offset + chunk_offsets, bf16_vals)
+
+
+def dequantize_and_gather_k_cache(
+    # [num_reqs, max_num_tokens, head_size]
+    out: torch.Tensor,
+    # [num_blocks, block_size, head_bytes]
+    k_cache: torch.Tensor,
+    # [num_reqs]
+    seq_lens: torch.Tensor,
+    # [num_reqs]
+    gather_lens: torch.Tensor | None,
+    # [num_reqs, max_blocks_per_seq]
+    block_table: torch.Tensor,
+    block_size: int,
+    offset: int,
+) -> None:
+    TOKEN_FP8_DIM = 448
+    TOKEN_BF16_DIM = 64
+    TOKEN_SCALE_DIM = 8
+    QUANT_BLOCK_SIZE = 64
+    FP8_MAX = 448.0
+    TOKEN_DATA_SIZE = TOKEN_FP8_DIM + TOKEN_BF16_DIM * 2
+
+    num_reqs = seq_lens.shape[0]
+    NUM_WORKERS = 128
+    _dequantize_and_gather_k_kernel[(num_reqs, NUM_WORKERS)](
+        out,
+        out.stride(0),
+        out.stride(1),
+        k_cache,
+        seq_lens,
+        block_table,
+        offset,
+        gather_lens,
+        max_blocks_per_seq=block_table.shape[-1],
+        fp8_dim=TOKEN_FP8_DIM,
+        bf16_dim=TOKEN_BF16_DIM,
+        scale_dim=TOKEN_SCALE_DIM,
+        quant_block=QUANT_BLOCK_SIZE,
+        cache_block_size=block_size,
+        token_data_size=TOKEN_DATA_SIZE,
+        block_stride=k_cache.stride(0),
+        output_dim=512,
+        fp8_max=FP8_MAX,
+        n_quant_blocks=7,
+    )
+
+
+def compute_global_topk_indices_and_lens(
+    topk_indices: torch.Tensor,
+    token_to_req_indices: torch.Tensor,
+    block_table: torch.Tensor,
+    block_size: int,
+    is_valid_token: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Map local topk indices to global KV cache slots and count valid entries.
+
+    Fuses three operations into a single kernel:
+    1. Block-table lookup (local index → global slot id)
+    2. Valid-entry counting (topk_lens per token)
+    3. Masking padding tokens to length 0
+    """
+    num_tokens = topk_indices.shape[0]
+    global_topk_indices = torch.empty_like(topk_indices)
+    topk_lens = torch.empty(num_tokens, dtype=torch.int32, device=topk_indices.device)
+    _compute_global_topk_indices_and_lens_kernel[(num_tokens,)](
+        global_topk_indices,
+        global_topk_indices.stride(0),
+        topk_lens,
+        topk_indices,
+        topk_indices.stride(0),
+        topk_indices.shape[-1],
+        token_to_req_indices,
+        block_table,
+        block_table.stride(0),
+        block_size,
+        is_valid_token,
+        TRITON_BLOCK_SIZE=1024,
+    )
+    return global_topk_indices, topk_lens
+
+
+@triton.jit
+def _compute_global_topk_indices_and_lens_kernel(
+    global_topk_indices_ptr,
+    global_topk_indices_stride,
+    topk_lens_ptr,
+    topk_indices_ptr,
+    topk_indices_stride,
+    topk,
+    token_to_req_indices_ptr,
+    block_table_ptr,
+    block_table_stride,
+    block_size,
+    is_valid_token_ptr,
+    TRITON_BLOCK_SIZE: tl.constexpr,
+):
+    token_idx = tl.program_id(0)
+    is_valid_token = tl.load(is_valid_token_ptr + token_idx)
+    req_idx = tl.load(token_to_req_indices_ptr + token_idx)
+
+    count = tl.zeros((), dtype=tl.int32)
+    for i in range(0, topk, TRITON_BLOCK_SIZE):
+        offset = i + tl.arange(0, TRITON_BLOCK_SIZE)
+        mask = offset < topk
+
+        local_idx = tl.load(
+            topk_indices_ptr + token_idx * topk_indices_stride + offset,
+            mask=mask,
+            other=-1,
+        )
+        is_valid = local_idx >= 0
+
+        block_indices = local_idx // block_size
+        block_numbers = tl.load(
+            block_table_ptr + req_idx * block_table_stride + block_indices,
+            mask=mask & is_valid,
+        )
+        block_offsets = local_idx % block_size
+
+        slot_ids = block_numbers * block_size + block_offsets
+        slot_ids = tl.where(is_valid, slot_ids, -1)
+        tl.store(
+            global_topk_indices_ptr + token_idx * global_topk_indices_stride + offset,
+            slot_ids,
+            mask=mask,
+        )
+        count += tl.sum(is_valid.to(tl.int32), axis=0)
+
+    # Zero out length for padding tokens.
+    tl.store(topk_lens_ptr + token_idx, tl.where(is_valid_token, count, 0))
+
+
+# FlashMLA sparse prefill asserts `params.topk % B_TOPK == 0` (see
+# flashmla/csrc/sm100/prefill/sparse/fwd/head{64,128}/phase1.cuh). B_TOPK is
+# 64 for the h_q=64 kernel and 128 for h_q=128; pad to 128 to satisfy both.
+# The extra slots stay as -1 sentinels and `combined_lens` caps the valid
+# range via `topk_length`, so padding is a no-op at kernel level.
+_SPARSE_PREFILL_TOPK_ALIGNMENT = 128
+
+
+def combine_topk_swa_indices(
+    topk_indices: torch.Tensor,
+    query_start_loc: torch.Tensor,
+    seq_lens: torch.Tensor,
+    gather_lens: torch.Tensor,
+    window_size: int,
+    compress_ratio: int,
+    topk: int,
+    M: int,
+    N: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    num_tokens = topk_indices.shape[0]
+    num_reqs = seq_lens.shape[0]
+    combined_topk = (
+        (topk + window_size + _SPARSE_PREFILL_TOPK_ALIGNMENT - 1)
+        // _SPARSE_PREFILL_TOPK_ALIGNMENT
+        * _SPARSE_PREFILL_TOPK_ALIGNMENT
+    )
+    combined_indices = torch.full(
+        (num_tokens, combined_topk),
+        fill_value=-1,
+        dtype=torch.int32,
+        device=topk_indices.device,
+    )
+    combined_lens = torch.empty(
+        num_tokens, dtype=torch.int32, device=topk_indices.device
+    )
+
+    NUM_WORKERS = 128
+    _combine_topk_swa_indices_kernel[(num_reqs, NUM_WORKERS)](
+        combined_indices,
+        combined_indices.stride(0),
+        combined_lens,
+        topk_indices,
+        topk_indices.stride(0),
+        query_start_loc,
+        seq_lens,
+        gather_lens,
+        M,
+        N,
+        TOP_K=topk,
+        COMPRESS_RATIO=compress_ratio,
+        WINDOW_SIZE=window_size,
+        PADDED_TOP_K=triton.next_power_of_2(topk_indices.shape[-1]),
+    )
+    return combined_indices, combined_lens
+
+
+@triton.jit
+def _combine_topk_swa_indices_kernel(
+    combined_indices_ptr,
+    combined_indices_stride,
+    combined_lens_ptr,
+    topk_indices_ptr,
+    topk_indices_stride,
+    query_start_loc_ptr,
+    seq_lens_ptr,
+    gather_lens_ptr,
+    M,
+    N,
+    TOP_K: tl.constexpr,
+    COMPRESS_RATIO: tl.constexpr,
+    WINDOW_SIZE: tl.constexpr,
+    PADDED_TOP_K: tl.constexpr,
+):
+    batch_idx = tl.program_id(0)
+    worker_id = tl.program_id(1)
+    num_workers = tl.num_programs(1)
+
+    # query_start_loc is a global tensor; rebase to chunk-local offsets
+    # by subtracting the chunk's starting value.
+    base = tl.load(query_start_loc_ptr)
+    query_start = tl.load(query_start_loc_ptr + batch_idx) - base
+    query_end = tl.load(query_start_loc_ptr + batch_idx + 1) - base
+    query_len = query_end - query_start
+    seq_len = tl.load(seq_lens_ptr + batch_idx)
+    gather_len = tl.load(gather_lens_ptr + batch_idx)
+    start_pos = seq_len - query_len
+    # The SWA portion of the gathered buffer starts from position
+    # (seq_len - gather_len), not position 0. We need this offset
+    # to correctly index into the gathered buffer.
+    gather_start = seq_len - gather_len
+
+    for token_idx in range(query_start + worker_id, query_end, num_workers):
+        # topk_len is fully determined by the query token's absolute position:
+        # both the C4A indexer and the C128A metadata builder emit
+        # min((pos + 1) // compress_ratio, topk_tokens) valid entries.
+        # Caller passes TOP_K=0 for SWA-only layers to zero this out.
+        token_idx_in_query = token_idx - query_start
+        pos = start_pos + token_idx_in_query
+        topk_len = tl.minimum((pos + 1) // COMPRESS_RATIO, TOP_K)
+        swa_len = tl.minimum(pos + 1, WINDOW_SIZE)
+
+        offset = tl.arange(0, PADDED_TOP_K)
+        mask = offset < topk_len
+        topk_indices = tl.load(
+            topk_indices_ptr + token_idx * topk_indices_stride + offset,
+            mask=mask,
+        )
+        tl.store(
+            combined_indices_ptr + token_idx * combined_indices_stride + offset,
+            topk_indices + M * batch_idx,
+            mask=mask,
+        )
+        offset = tl.arange(0, WINDOW_SIZE)
+        # Index into gathered buffer: N + (position - gather_start)
+        # For positions [pos - swa_len + 1, pos], the buffer indices are:
+        # [N + pos - swa_len + 1 - gather_start, N + pos - gather_start]
+        tl.store(
+            combined_indices_ptr
+            + token_idx * combined_indices_stride
+            + topk_len
+            + offset,
+            M * batch_idx + N + offset + pos - swa_len + 1 - gather_start,
+            mask=offset < swa_len,
+        )
+
+        combined_len = topk_len + swa_len
+        tl.store(combined_lens_ptr + token_idx, combined_len)
diff --git a/vllm/v1/attention/ops/deepseek_v4_ops/fused_compress_quant_cache.py b/vllm/v1/attention/ops/deepseek_v4_ops/fused_compress_quant_cache.py
new file mode 100644
index 000000000000..2f97d8733c95
--- /dev/null
+++ b/vllm/v1/attention/ops/deepseek_v4_ops/fused_compress_quant_cache.py
@@ -0,0 +1,584 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Fused compressor + FP8/MXFP4 UE8M0 quantization + KV cache insert kernels.
+
+Three specialized kernels:
+  - _fused_kv_compress_norm_rope_insert_sparse_attn:
+        head=512, nope=448 FP8 + rope=64 bf16
+  - _fused_kv_compress_norm_rope_insert_indexer_attn:
+        head=128, all FP8, 1 block/token
+  - _fused_kv_compress_norm_rope_insert_indexer_mxfp4_attn:
+        head=128, MXFP4 (block=32), 4 ue8m0 bytes
+
+RoPE is register-based via tl.reshape -> tl.split -> tl.interleave (or the
+even/odd halves are consumed directly for MXFP4, no interleave needed).
+FP8 UE8M0 quant uses tl.reshape to tile [N_QUANT_BLOCKS, QUANT_BLOCK] for
+per-block absmax entirely in registers. MXFP4 does the same tiling on the
+even/odd halves, producing (N_QUANT_BLOCKS, MXFP4_BLOCK/2) packed nibbles
+and N_QUANT_BLOCKS ue8m0 bytes.
+"""
+
+from vllm.triton_utils import tl, triton
+
+from .fused_indexer_q import _fp32x2_to_fp4x2
+
+
+# =============================================================================
+# DeepseekV4 Attention path (head=512, nope=448 FP8 + rope=64 bf16)
+# =============================================================================
+@triton.jit
+def _fused_kv_compress_norm_rope_insert_sparse_attn(
+    # ── state cache (compressor internal state) ──
+    state_cache_ptr,
+    state_cache_stride0,
+    state_cache_stride1,
+    # ── metadata ──
+    token_to_req_indices_ptr,
+    positions_ptr,
+    slot_mapping_ptr,
+    block_table_ptr,
+    block_table_stride,
+    block_size,
+    # ── RMSNorm ──
+    rms_norm_weight_ptr,
+    rms_norm_eps,
+    # ── RoPE ──
+    cos_sin_cache_ptr,
+    cos_sin_stride,
+    # ── KV cache output ──
+    k_cache_ptr,
+    kv_slot_mapping_ptr,
+    kv_cache_block_size,
+    # ── constexprs ──
+    HEAD_SIZE: tl.constexpr,
+    TRITON_BLOCK_SIZE: tl.constexpr,
+    STATE_WIDTH: tl.constexpr,
+    COMPRESS_RATIO: tl.constexpr,
+    OVERLAP: tl.constexpr,
+    ROPE_HEAD_DIM: tl.constexpr,
+    FP8_MAX: tl.constexpr,  # 448.0
+    QUANT_BLOCK: tl.constexpr,  # 64 for DeepseekV4
+    TOKEN_STRIDE: tl.constexpr,  # 576 for DeepseekV4
+    SCALE_DIM: tl.constexpr,  # 8 for DeepseekV4 (7 real + 1 pad)
+    KV_BLOCK_STRIDE: tl.constexpr,
+):
+    """Fused compress → RMSNorm → FP8 quant (nope) → RoPE → bf16 store (rope).
+
+    One program per token; early-exits for non-boundary positions.
+
+    Cache block layout (``block_size`` tokens):
+      [0, bs*576):       token data (448 fp8 + 128 bf16 each)
+      [bs*576, +bs*8):   uint8 UE8M0 scales (7 real + 1 pad each)
+    """
+    token_idx = tl.program_id(0)
+
+    slot_id = tl.load(slot_mapping_ptr + token_idx)
+    if slot_id < 0:
+        return
+
+    position = tl.load(positions_ptr + token_idx)
+    if (position + 1) % COMPRESS_RATIO != 0:
+        return
+
+    req_idx = tl.load(token_to_req_indices_ptr + token_idx)
+
+    # ── Gather state cache entries ────────────────────────────────────
+    start = position - (1 + OVERLAP) * COMPRESS_RATIO + 1
+    tokens = tl.arange(0, (1 + OVERLAP) * COMPRESS_RATIO)
+    pos = start + tokens
+    mask_pos = pos >= 0
+
+    block_indices = pos // block_size
+    block_numbers = tl.load(
+        block_table_ptr + req_idx * block_table_stride + block_indices,
+        mask=mask_pos,
+        other=0,
+    )
+    block_offsets = pos % block_size
+    head_offset = (tokens >= COMPRESS_RATIO).to(tl.int32) * HEAD_SIZE
+
+    block = tl.arange(0, TRITON_BLOCK_SIZE)
+    mask = block < HEAD_SIZE
+    block_numbers_i64 = block_numbers.to(tl.int64)
+
+    # Precomputed row base shared by score and kv loads
+    row_base = (
+        state_cache_ptr
+        + block_numbers_i64 * state_cache_stride0
+        + block_offsets * state_cache_stride1
+        + head_offset
+    )
+
+    combined_mask = mask_pos[:, None] & mask[None, :]
+
+    # ── Softmax + weighted sum ───────────────────────────────────────
+    score = tl.load(
+        row_base[:, None] + STATE_WIDTH + block[None, :],
+        mask=combined_mask,
+        other=float("-inf"),
+    )
+    score = tl.softmax(score, dim=0)
+
+    kv = tl.load(
+        row_base[:, None] + block[None, :],
+        mask=combined_mask,
+        other=0.0,
+    )
+
+    compressed_kv = tl.sum(kv * score, axis=0)  # [TRITON_BLOCK_SIZE] fp32
+
+    # ── RMSNorm (fp32 throughout) ──────────────────────────────────────
+    rms_w = tl.load(rms_norm_weight_ptr + block, mask=mask, other=0.0)
+    variance = tl.sum(compressed_kv * compressed_kv, axis=0) / HEAD_SIZE
+    rrms = tl.rsqrt(variance + rms_norm_eps)
+    normed = compressed_kv * rrms * rms_w
+
+    # ── KV cache pointers ────────────────────────────────────────────
+    kv_slot_idx = tl.load(kv_slot_mapping_ptr + token_idx)
+    if kv_slot_idx < 0:
+        return
+    kv_block_idx = kv_slot_idx // kv_cache_block_size
+    kv_pos_in_block = kv_slot_idx % kv_cache_block_size
+
+    cache_block_ptr = k_cache_ptr + kv_block_idx.to(tl.int64) * KV_BLOCK_STRIDE
+    fp8_ptr = cache_block_ptr + kv_pos_in_block * TOKEN_STRIDE
+    scale_ptr = (
+        cache_block_ptr
+        + kv_cache_block_size * TOKEN_STRIDE
+        + kv_pos_in_block * SCALE_DIM
+    )
+
+    NOPE_HEAD_DIM: tl.constexpr = HEAD_SIZE - ROPE_HEAD_DIM  # 448
+    HALF_ROPE: tl.constexpr = ROPE_HEAD_DIM // 2  # 32
+
+    # FP8 UE8M0 quant: cast fp32 → bf16 → fp32 before quant to match reference.
+    N_QUANT_BLOCKS: tl.constexpr = TRITON_BLOCK_SIZE // QUANT_BLOCK
+    N_NOPE_BLOCKS: tl.constexpr = NOPE_HEAD_DIM // QUANT_BLOCK  # 7
+    INV_FP8_MAX: tl.constexpr = 1.0 / FP8_MAX
+
+    quant_input = normed.to(tl.bfloat16).to(tl.float32)
+    quant_2d = tl.reshape(quant_input, (N_QUANT_BLOCKS, QUANT_BLOCK))
+    abs_2d = tl.abs(quant_2d)
+    block_absmax = tl.max(abs_2d, axis=1)  # [N_QUANT_BLOCKS] fp32
+    block_absmax = tl.maximum(block_absmax, 1e-4)
+
+    raw_scales = block_absmax * INV_FP8_MAX
+    exponents = tl.ceil(tl.log2(raw_scales))
+    inv_scales = tl.exp2(-exponents)
+    inv_scales_col = tl.reshape(inv_scales, (N_QUANT_BLOCKS, 1))
+    x_scaled = quant_2d * inv_scales_col
+    x_clamped = tl.clamp(x_scaled, -FP8_MAX, FP8_MAX)
+    x_fp8 = x_clamped.to(tl.float8e4nv)
+    x_uint8 = x_fp8.to(tl.uint8, bitcast=True)
+    x_uint8_flat = tl.reshape(x_uint8, (TRITON_BLOCK_SIZE,))
+
+    nope_mask = block < NOPE_HEAD_DIM
+    tl.store(fp8_ptr + block, x_uint8_flat, mask=nope_mask)
+
+    scale_idx = tl.arange(0, N_QUANT_BLOCKS)
+    encoded = exponents + 127.0
+    encoded = tl.maximum(tl.minimum(encoded, 255.0), 0.0)
+    tl.store(
+        scale_ptr + scale_idx,
+        encoded.to(tl.uint8),
+        mask=scale_idx < N_NOPE_BLOCKS,
+    )
+    tl.store(scale_ptr + N_NOPE_BLOCKS, tl.zeros((), dtype=tl.uint8))
+
+    # Register-based GPT-J RoPE in fp32.
+    NUM_PAIRS: tl.constexpr = TRITON_BLOCK_SIZE // 2
+    NOPE_PAIRS: tl.constexpr = NOPE_HEAD_DIM // 2
+
+    pair_2d = tl.reshape(normed, (NUM_PAIRS, 2))
+    even, odd = tl.split(pair_2d)  # each [NUM_PAIRS] fp32
+
+    pair_idx = tl.arange(0, NUM_PAIRS)
+    rope_pair_local = pair_idx - NOPE_PAIRS
+    is_rope_pair = rope_pair_local >= 0
+    cs_idx = tl.maximum(rope_pair_local, 0)
+
+    compressed_pos = (position // COMPRESS_RATIO) * COMPRESS_RATIO
+    cache_base = cos_sin_cache_ptr + compressed_pos * cos_sin_stride
+    cos_v = tl.load(cache_base + cs_idx, mask=is_rope_pair, other=1.0)
+    sin_v = tl.load(cache_base + HALF_ROPE + cs_idx, mask=is_rope_pair, other=0.0)
+
+    new_even = even * cos_v - odd * sin_v
+    new_odd = odd * cos_v + even * sin_v
+    result = tl.interleave(new_even, new_odd)  # [TRITON_BLOCK_SIZE] fp32
+
+    # Store rotated rope portion as bf16 into the cache's bf16 area.
+    bf16_ptr = (fp8_ptr + NOPE_HEAD_DIM).to(tl.pointer_type(tl.bfloat16))
+    rope_local = block - NOPE_HEAD_DIM
+    is_rope = (block >= NOPE_HEAD_DIM) & mask
+    tl.store(bf16_ptr + rope_local, result.to(tl.bfloat16), mask=is_rope)
+
+
+# =============================================================================
+# Indexer path (head=128, all FP8, single quant block)
+# =============================================================================
+@triton.jit
+def _fused_kv_compress_norm_rope_insert_indexer_attn(
+    # ── state cache (compressor internal state) ──
+    state_cache_ptr,
+    state_cache_stride0,
+    state_cache_stride1,
+    # ── metadata ──
+    token_to_req_indices_ptr,
+    positions_ptr,
+    slot_mapping_ptr,
+    block_table_ptr,
+    block_table_stride,
+    block_size,
+    # ── RMSNorm ──
+    rms_norm_weight_ptr,
+    rms_norm_eps,
+    # ── RoPE ──
+    cos_sin_cache_ptr,
+    cos_sin_stride,
+    # ── KV cache output ──
+    k_cache_ptr,
+    kv_slot_mapping_ptr,
+    kv_cache_block_size,
+    # ── constexprs ──
+    HEAD_SIZE: tl.constexpr,
+    TRITON_BLOCK_SIZE: tl.constexpr,
+    STATE_WIDTH: tl.constexpr,
+    COMPRESS_RATIO: tl.constexpr,
+    OVERLAP: tl.constexpr,
+    ROPE_HEAD_DIM: tl.constexpr,
+    FP8_MAX: tl.constexpr,  # 448.0
+    QUANT_BLOCK: tl.constexpr,  # 128 for indexer
+    TOKEN_STRIDE: tl.constexpr,  # 128 for indexer
+    SCALE_DIM: tl.constexpr,  # 4 for indexer (1 float32)
+    KV_BLOCK_STRIDE: tl.constexpr,
+):
+    """Fused compress → RMSNorm → RoPE → FP8 quant → store.
+
+    One program per token; early-exits for non-boundary positions.
+
+    Cache block layout:
+      [0, bs*128):       FP8 data (128 bytes/token)
+      [bs*128, +bs*4):   float32 scales (4 bytes/token)
+
+    For head_dim=128 we have exactly one quant block, so we skip the
+    [N_QUANT_BLOCKS, QUANT_BLOCK] reshape entirely and use a flat
+    ``tl.max`` reduction.
+    """
+    token_idx = tl.program_id(0)
+
+    slot_id = tl.load(slot_mapping_ptr + token_idx)
+    if slot_id < 0:
+        return
+
+    position = tl.load(positions_ptr + token_idx)
+    if (position + 1) % COMPRESS_RATIO != 0:
+        return
+
+    req_idx = tl.load(token_to_req_indices_ptr + token_idx)
+
+    # ── Gather state cache entries ────────────────────────────────────
+    start = position - (1 + OVERLAP) * COMPRESS_RATIO + 1
+    tokens = tl.arange(0, (1 + OVERLAP) * COMPRESS_RATIO)
+    pos = start + tokens
+    mask_pos = pos >= 0
+
+    block_indices = pos // block_size
+    block_numbers = tl.load(
+        block_table_ptr + req_idx * block_table_stride + block_indices,
+        mask=mask_pos,
+        other=0,
+    )
+    block_offsets = pos % block_size
+    head_offset = (tokens >= COMPRESS_RATIO).to(tl.int32) * HEAD_SIZE
+
+    block = tl.arange(0, TRITON_BLOCK_SIZE)
+    mask = block < HEAD_SIZE
+    block_numbers_i64 = block_numbers.to(tl.int64)
+
+    row_base = (
+        state_cache_ptr
+        + block_numbers_i64 * state_cache_stride0
+        + block_offsets * state_cache_stride1
+        + head_offset
+    )
+
+    combined_mask = mask_pos[:, None] & mask[None, :]
+
+    score = tl.load(
+        row_base[:, None] + STATE_WIDTH + block[None, :],
+        mask=combined_mask,
+        other=float("-inf"),
+    )
+    score = tl.softmax(score, dim=0)
+
+    kv = tl.load(
+        row_base[:, None] + block[None, :],
+        mask=combined_mask,
+        other=0.0,
+    )
+
+    compressed_kv = tl.sum(kv * score, axis=0)  # [TRITON_BLOCK_SIZE] fp32
+
+    # ── RMSNorm (fp32 throughout) ──────────────────────────────────────
+    rms_w = tl.load(rms_norm_weight_ptr + block, mask=mask, other=0.0)
+    variance = tl.sum(compressed_kv * compressed_kv, axis=0) / HEAD_SIZE
+    rrms = tl.rsqrt(variance + rms_norm_eps)
+    normed = compressed_kv * rrms * rms_w
+
+    # ── KV cache pointers ────────────────────────────────────────────
+    kv_slot_idx = tl.load(kv_slot_mapping_ptr + token_idx)
+    if kv_slot_idx < 0:
+        return
+    kv_block_idx = kv_slot_idx // kv_cache_block_size
+    kv_pos_in_block = kv_slot_idx % kv_cache_block_size
+
+    cache_block_ptr = k_cache_ptr + kv_block_idx.to(tl.int64) * KV_BLOCK_STRIDE
+    fp8_ptr = cache_block_ptr + kv_pos_in_block * TOKEN_STRIDE
+    scale_ptr = (
+        cache_block_ptr
+        + kv_cache_block_size * TOKEN_STRIDE
+        + kv_pos_in_block * SCALE_DIM
+    )
+
+    NOPE_HEAD_DIM: tl.constexpr = HEAD_SIZE - ROPE_HEAD_DIM
+    HALF_ROPE: tl.constexpr = ROPE_HEAD_DIM // 2
+
+    # ── Register-based GPT-J forward RoPE in fp32 ─────────────────────
+    NUM_PAIRS: tl.constexpr = TRITON_BLOCK_SIZE // 2
+    NOPE_PAIRS: tl.constexpr = NOPE_HEAD_DIM // 2
+
+    normed_2d = tl.reshape(normed, (NUM_PAIRS, 2))
+    even, odd = tl.split(normed_2d)  # each [NUM_PAIRS] fp32
+
+    pair_idx = tl.arange(0, NUM_PAIRS)
+    rope_pair_local = pair_idx - NOPE_PAIRS
+    is_rope_pair = rope_pair_local >= 0
+    cs_idx = tl.maximum(rope_pair_local, 0)
+
+    compressed_pos = (position // COMPRESS_RATIO) * COMPRESS_RATIO
+    cache_base = cos_sin_cache_ptr + compressed_pos * cos_sin_stride
+    cos_v = tl.load(cache_base + cs_idx, mask=is_rope_pair, other=1.0)
+    sin_v = tl.load(cache_base + HALF_ROPE + cs_idx, mask=is_rope_pair, other=0.0)
+
+    new_even = even * cos_v - odd * sin_v
+    new_odd = odd * cos_v + even * sin_v
+    result = tl.interleave(new_even, new_odd)  # fp32
+
+    # ── FP8 UE8M0 quant: single block, flat reduction ────────────────
+    tl.static_assert(
+        TRITON_BLOCK_SIZE == QUANT_BLOCK,
+        "Indexer expects one quant block (QUANT_BLOCK == TRITON_BLOCK_SIZE)",
+    )
+    INV_FP8_MAX: tl.constexpr = 1.0 / FP8_MAX
+
+    result_bf16 = result.to(tl.bfloat16).to(tl.float32)
+    absmax = tl.max(tl.abs(result_bf16), axis=0)  # scalar
+    absmax = tl.maximum(absmax, 1e-4)
+    raw_scale = absmax * INV_FP8_MAX
+    exponent = tl.ceil(tl.log2(raw_scale))
+    inv_scale = tl.exp2(-exponent)
+
+    x_scaled = result_bf16 * inv_scale
+    x_clamped = tl.clamp(x_scaled, -FP8_MAX, FP8_MAX)
+    x_fp8 = x_clamped.to(tl.float8e4nv)
+    x_uint8 = x_fp8.to(tl.uint8, bitcast=True)
+
+    tl.store(fp8_ptr + block, x_uint8, mask=mask)
+
+    # Single float32 scale
+    scale_val = tl.exp2(exponent)
+    tl.store(scale_ptr.to(tl.pointer_type(tl.float32)), scale_val)
+
+
+# =============================================================================
+# Indexer path (head=128, MXFP4: 2 nibbles/byte + ue8m0 per 32-elem block)
+# =============================================================================
+@triton.jit
+def _fused_kv_compress_norm_rope_insert_indexer_mxfp4_attn(
+    # ── state cache (compressor internal state) ──
+    state_cache_ptr,
+    state_cache_stride0,
+    state_cache_stride1,
+    # ── metadata ──
+    token_to_req_indices_ptr,
+    positions_ptr,
+    slot_mapping_ptr,
+    block_table_ptr,
+    block_table_stride,
+    block_size,
+    # ── RMSNorm ──
+    rms_norm_weight_ptr,
+    rms_norm_eps,
+    # ── RoPE ──
+    cos_sin_cache_ptr,
+    cos_sin_stride,
+    # ── KV cache output ──
+    k_cache_ptr,
+    kv_slot_mapping_ptr,
+    kv_cache_block_size,
+    # ── constexprs ──
+    HEAD_SIZE: tl.constexpr,
+    TRITON_BLOCK_SIZE: tl.constexpr,
+    STATE_WIDTH: tl.constexpr,
+    COMPRESS_RATIO: tl.constexpr,
+    OVERLAP: tl.constexpr,
+    ROPE_HEAD_DIM: tl.constexpr,
+    FP8_MAX: tl.constexpr,  # unused for MXFP4 (kept for signature parity)
+    QUANT_BLOCK: tl.constexpr,  # 32 for MXFP4
+    TOKEN_STRIDE: tl.constexpr,  # HEAD_SIZE // 2 = 64 packed bytes/token
+    SCALE_DIM: tl.constexpr,  # HEAD_SIZE // QUANT_BLOCK = 4 ue8m0 bytes/token
+    KV_BLOCK_STRIDE: tl.constexpr,
+):
+    """Fused compress → RMSNorm → RoPE → MXFP4 quant → store.
+
+    One program per token; early-exits for non-boundary positions.
+
+    Cache block layout (``block_size`` tokens per cache block):
+      [0, bs*TOKEN_STRIDE):        packed MXFP4 nibbles (2 values/byte)
+      [bs*TOKEN_STRIDE, +bs*SCALE_DIM): ue8m0 scale bytes (one per 32-elem block)
+
+    MXFP4 format:
+      - E2M1 4-bit values packed two per byte (low nibble first, then high).
+      - Per-32-element block scale = 2^ceil(log2(amax / 6.0)), stored ue8m0
+        (byte = exponent + 127).
+      - Max representable magnitude = 6.0.
+    """
+    token_idx = tl.program_id(0)
+
+    slot_id = tl.load(slot_mapping_ptr + token_idx)
+    if slot_id < 0:
+        return
+
+    position = tl.load(positions_ptr + token_idx)
+    if (position + 1) % COMPRESS_RATIO != 0:
+        return
+
+    req_idx = tl.load(token_to_req_indices_ptr + token_idx)
+
+    # ── Gather state cache entries ────────────────────────────────────
+    start = position - (1 + OVERLAP) * COMPRESS_RATIO + 1
+    tokens = tl.arange(0, (1 + OVERLAP) * COMPRESS_RATIO)
+    pos = start + tokens
+    mask_pos = pos >= 0
+
+    block_indices = pos // block_size
+    block_numbers = tl.load(
+        block_table_ptr + req_idx * block_table_stride + block_indices,
+        mask=mask_pos,
+        other=0,
+    )
+    block_offsets = pos % block_size
+    head_offset = (tokens >= COMPRESS_RATIO).to(tl.int32) * HEAD_SIZE
+
+    block = tl.arange(0, TRITON_BLOCK_SIZE)
+    mask = block < HEAD_SIZE
+    block_numbers_i64 = block_numbers.to(tl.int64)
+
+    row_base = (
+        state_cache_ptr
+        + block_numbers_i64 * state_cache_stride0
+        + block_offsets * state_cache_stride1
+        + head_offset
+    )
+
+    combined_mask = mask_pos[:, None] & mask[None, :]
+
+    score = tl.load(
+        row_base[:, None] + STATE_WIDTH + block[None, :],
+        mask=combined_mask,
+        other=float("-inf"),
+    )
+    score = tl.softmax(score, dim=0)
+
+    kv = tl.load(
+        row_base[:, None] + block[None, :],
+        mask=combined_mask,
+        other=0.0,
+    )
+
+    compressed_kv = tl.sum(kv * score, axis=0)  # [TRITON_BLOCK_SIZE] fp32
+
+    # ── RMSNorm (fp32 throughout) ──────────────────────────────────────
+    rms_w = tl.load(rms_norm_weight_ptr + block, mask=mask, other=0.0)
+    variance = tl.sum(compressed_kv * compressed_kv, axis=0) / HEAD_SIZE
+    rrms = tl.rsqrt(variance + rms_norm_eps)
+    normed = compressed_kv * rrms * rms_w
+
+    # ── KV cache pointers (segregated: values first, then scales) ────
+    kv_slot_idx = tl.load(kv_slot_mapping_ptr + token_idx)
+    if kv_slot_idx < 0:
+        return
+    kv_block_idx = kv_slot_idx // kv_cache_block_size
+    kv_pos_in_block = kv_slot_idx % kv_cache_block_size
+
+    cache_block_ptr = k_cache_ptr + kv_block_idx.to(tl.int64) * KV_BLOCK_STRIDE
+    val_ptr = cache_block_ptr + kv_pos_in_block * TOKEN_STRIDE
+    scale_ptr = (
+        cache_block_ptr
+        + kv_cache_block_size * TOKEN_STRIDE
+        + kv_pos_in_block * SCALE_DIM
+    )
+
+    NOPE_HEAD_DIM: tl.constexpr = HEAD_SIZE - ROPE_HEAD_DIM
+    HALF_ROPE: tl.constexpr = ROPE_HEAD_DIM // 2
+
+    # ── Register-based GPT-J forward RoPE in fp32 ─────────────────────
+    # We keep the even/odd halves (no tl.interleave afterwards) because the
+    # MXFP4 per-block absmax / pack naturally operates on (even, odd) pairs.
+    NUM_PAIRS: tl.constexpr = TRITON_BLOCK_SIZE // 2
+    NOPE_PAIRS: tl.constexpr = NOPE_HEAD_DIM // 2
+
+    normed_2d = tl.reshape(normed, (NUM_PAIRS, 2))
+    even, odd = tl.split(normed_2d)  # each [NUM_PAIRS] fp32
+
+    pair_idx = tl.arange(0, NUM_PAIRS)
+    rope_pair_local = pair_idx - NOPE_PAIRS
+    is_rope_pair = rope_pair_local >= 0
+    cs_idx = tl.maximum(rope_pair_local, 0)
+
+    compressed_pos = (position // COMPRESS_RATIO) * COMPRESS_RATIO
+    cache_base = cos_sin_cache_ptr + compressed_pos * cos_sin_stride
+    cos_v = tl.load(cache_base + cs_idx, mask=is_rope_pair, other=1.0)
+    sin_v = tl.load(cache_base + HALF_ROPE + cs_idx, mask=is_rope_pair, other=0.0)
+
+    new_even = even * cos_v - odd * sin_v
+    new_odd = odd * cos_v + even * sin_v
+
+    # bf16 roundtrip for parity with reference / Q-side kernel numerics.
+    new_even = new_even.to(tl.bfloat16).to(tl.float32)
+    new_odd = new_odd.to(tl.bfloat16).to(tl.float32)
+
+    # ── MXFP4 quant: tile even/odd halves into (N_BLOCKS, HALF_BLOCK) ──
+    # Each MXFP4 block of QUANT_BLOCK elements = HALF_BLOCK consecutive pairs,
+    # so (N_BLOCKS, HALF_BLOCK) rows of even/odd each land exactly one block.
+    N_QUANT_BLOCKS: tl.constexpr = HEAD_SIZE // QUANT_BLOCK
+    HALF_BLOCK: tl.constexpr = QUANT_BLOCK // 2
+    tl.static_assert(TRITON_BLOCK_SIZE == HEAD_SIZE)
+    tl.static_assert(HEAD_SIZE % QUANT_BLOCK == 0)
+    tl.static_assert(TOKEN_STRIDE == HEAD_SIZE // 2)
+    tl.static_assert(SCALE_DIM == N_QUANT_BLOCKS)
+
+    even_2d = tl.reshape(new_even, (N_QUANT_BLOCKS, HALF_BLOCK))
+    odd_2d = tl.reshape(new_odd, (N_QUANT_BLOCKS, HALF_BLOCK))
+
+    amax = tl.maximum(
+        tl.max(tl.abs(even_2d), axis=1),
+        tl.max(tl.abs(odd_2d), axis=1),
+    )
+    amax = tl.maximum(amax, 6.0 * (2**-126))
+
+    # ue8m0 block scale: 2^ceil(log2(amax / 6.0)), stored as (exp + 127) byte.
+    log2_ratio = tl.ceil(tl.log2(amax * (1.0 / 6.0)))
+    log2_ratio = tl.minimum(tl.maximum(log2_ratio, -127.0), 127.0)
+    inv_scale = tl.exp2(-log2_ratio)
+    ue8m0 = (log2_ratio + 127.0).to(tl.uint8)  # [N_QUANT_BLOCKS]
+
+    inv_scale_col = tl.reshape(inv_scale, (N_QUANT_BLOCKS, 1))
+    packed = _fp32x2_to_fp4x2(
+        even_2d * inv_scale_col, odd_2d * inv_scale_col
+    )  # (N_BLOCKS, HALF_BLOCK) uint8
+    packed_flat = tl.reshape(packed, (TOKEN_STRIDE,))
+
+    tl.store(val_ptr + tl.arange(0, TOKEN_STRIDE), packed_flat)
+    tl.store(scale_ptr + tl.arange(0, SCALE_DIM), ue8m0)
diff --git a/vllm/v1/attention/ops/deepseek_v4_ops/fused_indexer_q.py b/vllm/v1/attention/ops/deepseek_v4_ops/fused_indexer_q.py
new file mode 100644
index 000000000000..ec880f7ab4c4
--- /dev/null
+++ b/vllm/v1/attention/ops/deepseek_v4_ops/fused_indexer_q.py
@@ -0,0 +1,421 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+from vllm.triton_utils import tl, triton
+from vllm.utils.import_utils import has_cutedsl
+
+# MXFP4: 32 elements per block, packed 2 nibbles per byte, ue8m0 block scale.
+MXFP4_BLOCK_SIZE = 32
+
+
+@triton.jit
+def _get_cos_sin(
+    cos_sin_cache_ptr,
+    cos_sin_cache_stride,
+    pos,
+    HALF_ROT_DIM: tl.constexpr,
+):
+    block = tl.arange(0, HALF_ROT_DIM)
+    cos = tl.load(cos_sin_cache_ptr + pos * cos_sin_cache_stride + block)
+    cos = cos.to(tl.float32)
+    sin = tl.load(cos_sin_cache_ptr + pos * cos_sin_cache_stride + block + HALF_ROT_DIM)
+    sin = sin.to(tl.float32)
+    return cos, sin
+
+
+@triton.jit
+def _fp32x2_to_fp4x2(x_lo, x_hi):
+    # NOTE: $1 is high nibble, $2 is low nibble
+    return tl.inline_asm_elementwise(
+        """
+        {
+            .reg .b8 tmp;
+            cvt.rn.satfinite.e2m1x2.f32 tmp, $1, $2;
+            cvt.u32.u8 $0, tmp;
+        }
+        """,
+        constraints="=r,f,f",
+        args=[x_hi, x_lo],
+        dtype=tl.uint32,
+        is_pure=True,
+        pack=1,
+    ).to(tl.uint8)
+
+
+@triton.jit
+def _quantize_mxfp4_pair(x_lo, x_hi):
+    """Quantize a block of MXFP4_BLOCK_SIZE fp32 values given as two
+    interleaved halves (x_lo = values at even positions in the block,
+    x_hi = values at odd positions). Returns:
+        - packed : uint8[BLOCK/2]  (low nibble = quant(x_lo), high = quant(x_hi))
+        - ue8m0  : scalar uint8    (block scale = 2^(ue8m0 - 127))
+    """
+    amax = tl.maximum(tl.max(tl.abs(x_lo)), tl.max(tl.abs(x_hi)))
+    # 6 * 2^-126 is from https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/inference/kernel.py#L163
+    amax = tl.maximum(amax, 6.0 * (2**-126))
+    # ue8m0 block scale: 2^ceil(log2(amax/6.0)).
+    log2_ratio = tl.math.ceil(tl.math.log2(amax * (1.0 / 6.0)))
+    log2_ratio = tl.minimum(tl.maximum(log2_ratio, -127.0), 127.0)
+    scale = tl.math.exp2(log2_ratio)
+    ue8m0 = (log2_ratio + 127.0).to(tl.uint8)
+
+    inv_scale = 1.0 / scale
+    packed = _fp32x2_to_fp4x2(x_lo * inv_scale, x_hi * inv_scale)
+    return packed, ue8m0
+
+
+@triton.jit
+def _fused_indexer_q_rope_quant_kernel(
+    pos_ptr,
+    # Index Q RoPE
+    index_q_ptr,
+    index_q_stride0,
+    index_q_stride1,
+    index_q_cos_sin_ptr,
+    index_q_cos_sin_stride,
+    INDEX_Q_HALF_ROT_DIM: tl.constexpr,
+    # Index Q Quantize
+    index_q_fp8_ptr,
+    index_q_fp8_stride0,
+    index_q_fp8_stride1,
+    INDEX_Q_HEAD_DIM: tl.constexpr,
+    # Index weights
+    index_weights_ptr,
+    index_weights_stride,
+    index_weights_softmax_scale,
+    index_weights_head_scale,
+    index_weights_out_ptr,
+    index_weights_out_stride,
+):
+    # Layout matches the unfused reference (DeepseekV4ScalingRotaryEmbedding
+    # + per_token_group_quant_fp8): GPT-J interleaved RoPE applied to the
+    # LAST rope_dim dims of each head; the leading [0, NOPE_DIM) is passed
+    # through unchanged.
+    INDEX_Q_ROT_DIM: tl.constexpr = 2 * INDEX_Q_HALF_ROT_DIM
+    INDEX_Q_NOPE_DIM: tl.constexpr = INDEX_Q_HEAD_DIM - INDEX_Q_ROT_DIM
+    tl.static_assert(INDEX_Q_NOPE_DIM >= 0)
+
+    tok_idx = tl.program_id(0)
+    head_idx = tl.program_id(1)
+
+    pos = tl.load(pos_ptr + tok_idx)
+    cos, sin = _get_cos_sin(
+        index_q_cos_sin_ptr,
+        index_q_cos_sin_stride,
+        pos,
+        INDEX_Q_HALF_ROT_DIM,
+    )
+    half_offset = tl.arange(0, INDEX_Q_HALF_ROT_DIM)
+    base_ptr = index_q_ptr + tok_idx * index_q_stride0 + head_idx * index_q_stride1
+
+    # Interleaved (GPT-J) RoPE on dims [NOPE_DIM, HEAD_DIM):
+    #   even = q[NOPE_DIM + 2*i],  odd = q[NOPE_DIM + 2*i + 1]
+    rot_base = base_ptr + INDEX_Q_NOPE_DIM
+    x_even = tl.load(rot_base + half_offset * 2).to(tl.float32)
+    x_odd = tl.load(rot_base + half_offset * 2 + 1).to(tl.float32)
+    r_even = x_even * cos - x_odd * sin
+    r_odd = x_odd * cos + x_even * sin
+
+    # Match reference numerics: fp32 → bf16 → fp32 before the ue8m0 absmax.
+    # Same pattern as the K-side compressor kernel (fused_compress_quant_cache.py).
+    r_even = r_even.to(tl.bfloat16).to(tl.float32)
+    r_odd = r_odd.to(tl.bfloat16).to(tl.float32)
+
+    amax = tl.maximum(tl.max(tl.abs(r_even)), tl.max(tl.abs(r_odd)))
+    if INDEX_Q_NOPE_DIM > 0:
+        nope_offset = tl.arange(0, INDEX_Q_NOPE_DIM)
+        x_nope = tl.load(base_ptr + nope_offset).to(tl.float32)
+        amax = tl.maximum(amax, tl.max(tl.abs(x_nope)))
+    index_q_scale = tl.div_rn(tl.maximum(amax, 1e-4), 448.0)
+    index_q_scale = tl.math.exp2(tl.math.ceil(tl.math.log2(index_q_scale)))
+
+    # Store quantized values to index_q_fp8
+    fp8_base_ptr = (
+        index_q_fp8_ptr + tok_idx * index_q_fp8_stride0 + head_idx * index_q_fp8_stride1
+    )
+    if INDEX_Q_NOPE_DIM > 0:
+        tl.store(
+            fp8_base_ptr + nope_offset,
+            tl.div_rn(x_nope, index_q_scale).to(tl.float8e4nv),
+        )
+    fp8_rot_base = fp8_base_ptr + INDEX_Q_NOPE_DIM
+    tl.store(
+        fp8_rot_base + half_offset * 2,
+        tl.div_rn(r_even, index_q_scale).to(tl.float8e4nv),
+    )
+    tl.store(
+        fp8_rot_base + half_offset * 2 + 1,
+        tl.div_rn(r_odd, index_q_scale).to(tl.float8e4nv),
+    )
+
+    # FP8 weight-fold contract:
+    #   index_weights_out = index_weights * q_scale * softmax_scale * head_scale
+    # The per-token-per-head q_scale (fp32) IS folded into the output weights
+    # here because FP8 Q is stored WITHOUT a companion scale tensor — the
+    # downstream fp8_fp4_mqa_logits/fp8_fp4_paged_mqa_logits kernels use `weights` to
+    # apply per-token Q scale inline. See the MXFP4 kernel below for the
+    # contrasting convention (scales live with the Q values, weights are NOT
+    # q-scaled).
+    index_weights = tl.load(
+        index_weights_ptr + tok_idx * index_weights_stride + head_idx
+    )
+    index_weights = index_weights.to(tl.float32)
+    index_weights *= index_q_scale
+    index_weights *= index_weights_softmax_scale
+    index_weights *= index_weights_head_scale
+    tl.store(
+        index_weights_out_ptr + tok_idx * index_weights_out_stride + head_idx,
+        index_weights,
+    )
+
+
+@triton.jit
+def _fused_indexer_q_rope_mxfp4_kernel(
+    pos_ptr,
+    # Index Q RoPE input (fp/bf16)
+    index_q_ptr,
+    index_q_stride0,
+    index_q_stride1,
+    index_q_cos_sin_ptr,
+    index_q_cos_sin_stride,
+    INDEX_Q_HALF_ROT_DIM: tl.constexpr,
+    # MXFP4 Q outputs
+    index_q_mxfp4_ptr,  # uint8, (T, H, HEAD_DIM // 2)
+    index_q_mxfp4_stride0,
+    index_q_mxfp4_stride1,
+    index_q_scale_ptr,  # uint8 ue8m0, (T, H, HEAD_DIM // BLOCK)
+    index_q_scale_stride0,
+    index_q_scale_stride1,
+    INDEX_Q_HEAD_DIM: tl.constexpr,
+    MXFP4_BLOCK: tl.constexpr,
+    # Weights (NO per-token q_scale fold for MXFP4; per-block scales stay
+    # with the Q values in the output scale tensor).
+    index_weights_ptr,
+    index_weights_stride,
+    index_weights_softmax_scale,
+    index_weights_head_scale,
+    index_weights_out_ptr,
+    index_weights_out_stride,
+):
+    INDEX_Q_ROT_DIM: tl.constexpr = 2 * INDEX_Q_HALF_ROT_DIM
+    INDEX_Q_NOPE_DIM: tl.constexpr = INDEX_Q_HEAD_DIM - INDEX_Q_ROT_DIM
+    NUM_NOPE_BLOCKS: tl.constexpr = INDEX_Q_NOPE_DIM // MXFP4_BLOCK
+    NUM_ROPE_BLOCKS: tl.constexpr = INDEX_Q_ROT_DIM // MXFP4_BLOCK
+    HALF_BLOCK: tl.constexpr = MXFP4_BLOCK // 2
+    tl.static_assert(INDEX_Q_NOPE_DIM >= 0)
+    tl.static_assert(INDEX_Q_NOPE_DIM % MXFP4_BLOCK == 0)
+    tl.static_assert(INDEX_Q_ROT_DIM % MXFP4_BLOCK == 0)
+    tl.static_assert(MXFP4_BLOCK % 2 == 0)
+
+    tok_idx = tl.program_id(0)
+    head_idx = tl.program_id(1)
+
+    pos = tl.load(pos_ptr + tok_idx)
+
+    q_base = index_q_ptr + tok_idx * index_q_stride0 + head_idx * index_q_stride1
+    out_base = (
+        index_q_mxfp4_ptr
+        + tok_idx * index_q_mxfp4_stride0
+        + head_idx * index_q_mxfp4_stride1
+    )
+    scale_base = (
+        index_q_scale_ptr
+        + tok_idx * index_q_scale_stride0
+        + head_idx * index_q_scale_stride1
+    )
+
+    half_off = tl.arange(0, HALF_BLOCK)
+
+    # ---- NoPE blocks: direct load, pair as (even-index, odd-index) values ----
+    for b in tl.static_range(NUM_NOPE_BLOCKS):
+        base = b * MXFP4_BLOCK
+        x_lo = tl.load(q_base + base + half_off * 2).to(tl.float32)
+        x_hi = tl.load(q_base + base + half_off * 2 + 1).to(tl.float32)
+        packed, ue8m0 = _quantize_mxfp4_pair(x_lo, x_hi)
+        tl.store(out_base + base // 2 + half_off, packed)
+        tl.store(scale_base + b, ue8m0)
+
+    # ---- RoPE blocks: apply GPT-J interleaved RoPE to the block's 16 pairs,
+    # then quantize. Each block covers HALF_BLOCK (=16) cos/sin pairs. ----
+    rot_q_base = q_base + INDEX_Q_NOPE_DIM
+    for b in tl.static_range(NUM_ROPE_BLOCKS):
+        pair_off = b * HALF_BLOCK + half_off  # indices in [0, HALF_ROT_DIM)
+        cos_b = tl.load(
+            index_q_cos_sin_ptr + pos * index_q_cos_sin_stride + pair_off
+        ).to(tl.float32)
+        sin_b = tl.load(
+            index_q_cos_sin_ptr
+            + pos * index_q_cos_sin_stride
+            + pair_off
+            + INDEX_Q_HALF_ROT_DIM
+        ).to(tl.float32)
+        x_even = tl.load(rot_q_base + pair_off * 2).to(tl.float32)
+        x_odd = tl.load(rot_q_base + pair_off * 2 + 1).to(tl.float32)
+        r_even = x_even * cos_b - x_odd * sin_b
+        r_odd = x_odd * cos_b + x_even * sin_b
+        # bf16 roundtrip for parity with the FP8 kernel / reference numerics.
+        r_even = r_even.to(tl.bfloat16).to(tl.float32)
+        r_odd = r_odd.to(tl.bfloat16).to(tl.float32)
+        packed, ue8m0 = _quantize_mxfp4_pair(r_even, r_odd)
+        rope_byte_off = (INDEX_Q_NOPE_DIM + b * MXFP4_BLOCK) // 2
+        tl.store(out_base + rope_byte_off + half_off, packed)
+        tl.store(scale_base + NUM_NOPE_BLOCKS + b, ue8m0)
+
+    # MXFP4 weight-fold contract:
+    #   index_weights_out = index_weights * softmax_scale * head_scale
+    # NOTE: q_scale is NOT folded here (contrast with the FP8 kernel above).
+    # MXFP4 Q emits a separate ue8m0 scale tensor of shape
+    # (T, H, HEAD_DIM // MXFP4_BLOCK) alongside the packed values, so each
+    # per-block scale is applied by the downstream MXFP4 logits kernel when
+    # dequantizing Q — there is no per-token scalar to fold into `weights`.
+    index_weights = tl.load(
+        index_weights_ptr + tok_idx * index_weights_stride + head_idx
+    ).to(tl.float32)
+    index_weights *= index_weights_softmax_scale
+    index_weights *= index_weights_head_scale
+    tl.store(
+        index_weights_out_ptr + tok_idx * index_weights_out_stride + head_idx,
+        index_weights,
+    )
+
+
+def fused_indexer_q_rope_quant(
+    positions: torch.Tensor,
+    index_q: torch.Tensor,
+    index_q_cos_sin_cache: torch.Tensor,
+    # Index weights
+    index_weights: torch.Tensor,
+    index_weights_softmax_scale: float,
+    index_weights_head_scale: float,
+    use_fp4: bool = False,
+) -> tuple[
+    torch.Tensor | tuple[torch.Tensor, torch.Tensor],
+    torch.Tensor,
+]:
+    """Fused RoPE + quantize Q for the sparse indexer.
+
+    Weight-fold semantics (important — the two paths differ):
+
+    FP8 path (use_fp4=False, default):
+        q_fp8      : (T, H, HEAD_DIM) float8_e4m3fn, per-token-per-head
+                     scalar scale (NOT stored — folded into weights below)
+        weights_out = weights * q_scale * softmax_scale * head_scale
+        Rationale: a single per-token q_scale is a scalar the downstream FP8
+        logits kernel would otherwise multiply in. Folding it into `weights`
+        avoids emitting a separate tensor and is free for the logits kernel.
+
+    MXFP4 path (use_fp4=True):
+        q_packed   : (T, H, HEAD_DIM // 2) uint8 (2 E2M1 nibbles per byte)
+        q_scale    : (T, H, HEAD_DIM // MXFP4_BLOCK_SIZE) uint8 ue8m0 bytes
+        weights_out = weights * softmax_scale * head_scale
+        Rationale: MXFP4 has PER-BLOCK (32-element) scales that live with
+        the Q values — they cannot be folded into a per-token weight
+        scalar, so `weights` carries only the softmax and head scales.
+
+    Returns (q_quant, weights_out) where q_quant is either a Tensor (FP8) or
+    a (values, scales) tuple (MXFP4). This matches the union type accepted
+    by `SparseAttnIndexer.forward_*`.
+    """
+    assert positions.ndim == 1
+    assert index_q.ndim == 3
+    assert index_q_cos_sin_cache.ndim == 2
+
+    num_tokens = positions.shape[0]
+    num_index_q_heads = index_q.shape[1]
+    index_q_head_dim = index_q.shape[2]
+
+    index_weights_out = torch.empty_like(index_weights, dtype=torch.float32)
+
+    if use_fp4:
+        assert index_q_head_dim % MXFP4_BLOCK_SIZE == 0, (
+            f"head_dim={index_q_head_dim} must be a multiple of MXFP4 block "
+            f"size {MXFP4_BLOCK_SIZE}"
+        )
+        num_scale_blocks = index_q_head_dim // MXFP4_BLOCK_SIZE
+        index_q_packed = torch.empty(
+            (num_tokens, num_index_q_heads, index_q_head_dim // 2),
+            dtype=torch.uint8,
+            device=index_q.device,
+        )
+        index_q_scale = torch.empty(
+            (num_tokens, num_index_q_heads, num_scale_blocks),
+            dtype=torch.uint8,
+            device=index_q.device,
+        )
+        if has_cutedsl():
+            # lazily import, otherwise some tests fail due to CUDA driver init failure.
+            from .fused_indexer_q_cutedsl import (
+                fused_indexer_q_rope_quant_mxfp4_cutedsl,
+            )
+
+            fused_indexer_q_rope_quant_mxfp4_cutedsl(
+                positions,
+                index_q,
+                index_q_cos_sin_cache,
+                index_weights,
+                index_weights_softmax_scale,
+                index_weights_head_scale,
+                index_q_packed,
+                index_q_scale,
+                index_weights_out,
+            )
+        else:
+            _fused_indexer_q_rope_mxfp4_kernel[(num_tokens, num_index_q_heads)](
+                positions,
+                index_q,
+                index_q.stride(0),
+                index_q.stride(1),
+                index_q_cos_sin_cache,
+                index_q_cos_sin_cache.stride(0),
+                index_q_cos_sin_cache.shape[-1] // 2,
+                index_q_packed,
+                index_q_packed.stride(0),
+                index_q_packed.stride(1),
+                index_q_scale,
+                index_q_scale.stride(0),
+                index_q_scale.stride(1),
+                index_q_head_dim,
+                MXFP4_BLOCK_SIZE,
+                index_weights,
+                index_weights.stride(0),
+                index_weights_softmax_scale,
+                index_weights_head_scale,
+                index_weights_out,
+                index_weights_out.stride(0),
+                num_warps=1,  # TODO: Tune this
+            )
+
+        # Values stay uint8 (2 E2M1 nibbles per byte). Scales are 4 ue8m0
+        # bytes per (token, head) reinterpreted as one int32, then squeezed
+        # from (T, H, 1) to (T, H) to match DeepGEMM's expected q_sf rank
+        # (prefill wants 2-D (seq_len, num_heads); decode reshapes this to
+        # 3-D (batch, next_n, num_heads)).
+        return (
+            index_q_packed,
+            index_q_scale.view(torch.int32).squeeze(-1),
+        ), index_weights_out
+
+    index_q_fp8 = torch.empty_like(index_q, dtype=torch.float8_e4m3fn)
+    _fused_indexer_q_rope_quant_kernel[(num_tokens, num_index_q_heads)](
+        positions,
+        index_q,
+        index_q.stride(0),
+        index_q.stride(1),
+        index_q_cos_sin_cache,
+        index_q_cos_sin_cache.stride(0),
+        index_q_cos_sin_cache.shape[-1] // 2,
+        index_q_fp8,
+        index_q_fp8.stride(0),
+        index_q_fp8.stride(1),
+        index_q_head_dim,
+        index_weights,
+        index_weights.stride(0),
+        index_weights_softmax_scale,
+        index_weights_head_scale,
+        index_weights_out,
+        index_weights_out.stride(0),
+        num_warps=1,  # TODO: Tune this
+    )
+    return index_q_fp8, index_weights_out
diff --git a/vllm/v1/attention/ops/deepseek_v4_ops/fused_indexer_q_cutedsl.py b/vllm/v1/attention/ops/deepseek_v4_ops/fused_indexer_q_cutedsl.py
new file mode 100644
index 000000000000..3ba60bb33102
--- /dev/null
+++ b/vllm/v1/attention/ops/deepseek_v4_ops/fused_indexer_q_cutedsl.py
@@ -0,0 +1,423 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# once we have more CuteDSL kernels in vLLM, we can refactor small helper functions
+# to a separate file
+from functools import cache
+
+import cutlass
+import cutlass.cute as cute
+import torch
+from cuda.bindings.driver import CUstream
+from cutlass import BFloat16, Float32, Int64, Uint8, Uint32, const_expr
+from cutlass._mlir.dialects import llvm
+from cutlass.cutlass_dsl import T, dsl_user_op
+from quack.compile_utils import make_fake_tensor
+
+from vllm.vllm_flash_attn.cute import utils as cute_utils
+
+# MXFP4: 32 elements per block, packed 2 nibbles per byte, ue8m0 block scale.
+MXFP4_BLOCK_SIZE = 32
+
+_TORCH_TO_CUTE = {
+    torch.bfloat16: BFloat16,
+    torch.float32: Float32,
+}
+
+
+def fused_indexer_q_rope_quant_mxfp4_cutedsl(
+    positions: torch.Tensor,
+    index_q: torch.Tensor,
+    index_q_cos_sin_cache: torch.Tensor,
+    index_weights: torch.Tensor,
+    index_weights_softmax_scale: float,
+    index_weights_head_scale: float,
+    index_q_packed: torch.Tensor,
+    index_q_scale: torch.Tensor,
+    index_weights_out: torch.Tensor,
+) -> None:
+    num_tokens, num_heads, head_dim = index_q.shape
+    rope_dim = index_q_cos_sin_cache.shape[-1]
+    rope_type = _TORCH_TO_CUTE[index_q_cos_sin_cache.dtype]
+
+    # compile all variants at first invocation
+    for coarsen in (1, 4):
+        IndexerQMxFp4Kernel.compile(head_dim, rope_dim, num_heads, rope_type, coarsen)
+
+    # heuristic
+    coarsen = 1 if num_tokens < 512 else 4
+    compiled = IndexerQMxFp4Kernel.compile(
+        head_dim, rope_dim, num_heads, rope_type, coarsen
+    )
+    scale = float(index_weights_softmax_scale * index_weights_head_scale)
+    compiled(
+        positions,
+        index_q,
+        index_q_cos_sin_cache,
+        index_weights,
+        index_q_packed,
+        index_q_scale,
+        index_weights_out,
+        scale,
+    )
+
+
+@dsl_user_op
+def _recast_val(x, dtype, *, loc=None, ip=None):
+    return dtype(llvm.bitcast(dtype.mlir_type, x.ir_value(loc=loc, ip=ip)))
+
+
+@dsl_user_op
+def _fp32x2_to_bf16x2(a: Float32, b: Float32, *, loc=None, ip=None) -> Uint32:
+    out = llvm.inline_asm(
+        T.i32(),
+        [a.ir_value(loc=loc, ip=ip), b.ir_value(loc=loc, ip=ip)],
+        "cvt.rn.bf16x2.f32 $0, $2, $1;",
+        "=r,f,f",
+        has_side_effects=False,
+        is_align_stack=False,
+    )
+    return Uint32(out)
+
+
+@dsl_user_op
+def _bf16x2_to_fp32(data: Uint32, *, loc=None, ip=None) -> tuple[Float32, Float32]:
+    out = llvm.inline_asm(
+        llvm.StructType.get_literal([T.f32(), T.f32()]),
+        [data.ir_value(loc=loc, ip=ip)],
+        "shl.b32 $0, $2, 16;\n\tand.b32 $1, $2, 0xFFFF0000;\n",
+        "=f,=f,r",
+        has_side_effects=False,
+        is_align_stack=False,
+    )
+    return (
+        Float32(llvm.extractvalue(T.f32(), out, [0], loc=loc, ip=ip)),
+        Float32(llvm.extractvalue(T.f32(), out, [1], loc=loc, ip=ip)),
+    )
+
+
+@dsl_user_op
+def _bf16x2_abs(a: Uint32, *, loc=None, ip=None) -> Uint32:
+    out = llvm.inline_asm(
+        T.i32(),
+        [a.ir_value(loc=loc, ip=ip)],
+        "abs.bf16x2 $0, $1;",
+        "=r,r",
+        has_side_effects=False,
+        is_align_stack=False,
+    )
+    return Uint32(out)
+
+
+@dsl_user_op
+def _bf16x2_max(a: Uint32, b: Uint32, *, loc=None, ip=None) -> Uint32:
+    out = llvm.inline_asm(
+        T.i32(),
+        [a.ir_value(loc=loc, ip=ip), b.ir_value(loc=loc, ip=ip)],
+        "max.bf16x2 $0, $1, $2;",
+        "=r,r,r",
+        has_side_effects=False,
+        is_align_stack=False,
+    )
+    return Uint32(out)
+
+
+@dsl_user_op
+def _fp32x8_to_fp4x8(
+    vals: cute.Tensor,
+    offset: cutlass.Constexpr[int],
+    *,
+    loc=None,
+    ip=None,
+) -> Uint32:
+    # Pack eight scaled FP32 values into four E2M1x2 bytes, returned as one b32.
+    assert vals.element_type is Float32
+    out = llvm.inline_asm(
+        T.i32(),
+        [vals[offset + i].ir_value(loc=loc, ip=ip) for i in range(8)],
+        "{\n\t"
+        ".reg .b8 x0, x1, x2, x3;\n\t"
+        "cvt.rn.satfinite.e2m1x2.f32 x0, $2, $1;\n\t"
+        "cvt.rn.satfinite.e2m1x2.f32 x1, $4, $3;\n\t"
+        "cvt.rn.satfinite.e2m1x2.f32 x2, $6, $5;\n\t"
+        "cvt.rn.satfinite.e2m1x2.f32 x3, $8, $7;\n\t"
+        "mov.b32 $0, {x0, x1, x2, x3};\n\t"
+        "}\n",
+        "=r,f,f,f,f,f,f,f,f",
+        has_side_effects=False,
+        is_align_stack=False,
+    )
+    return Uint32(out)
+
+
+class IndexerQMxFp4Kernel:
+    """Eight-thread subwarps process one ``(token, head)`` row."""
+
+    def __init__(
+        self,
+        head_dim: int = 128,
+        rope_dim: int = 64,
+        num_heads: int = 64,
+        cos_sin_dtype: type[cutlass.Numeric] = Float32,
+        coarsen: int = 4,
+    ):
+        self.head_dim = head_dim
+        self.rope_dim = rope_dim
+        self.nope_dim = head_dim - rope_dim
+        self.num_heads = num_heads
+        self.cos_sin_dtype = cos_sin_dtype
+
+        # process multiple heads at the same time to armotize RoPE load costs
+        assert num_heads % coarsen == 0
+        self.coarsen = coarsen
+
+        # later we will use 32B load = 16 BF16 elems
+        # thus, head_dim=128 requires 8 threads to handle.
+        # let's call subwarp = 8 threads.
+        self.subwarp_size = head_dim // 16
+        self.tb_size = 128
+        self.threads_per_token = (self.num_heads // self.coarsen) * self.subwarp_size
+
+    @cute.jit
+    def __call__(
+        self,
+        positions: cute.Tensor,
+        q: cute.Tensor,
+        cos_sin_cache: cute.Tensor,
+        weights: cute.Tensor,
+        q_fp4: cute.Tensor,
+        q_scale: cute.Tensor,
+        weights_out: cute.Tensor,
+        scale: Float32,
+        stream: CUstream,
+    ):
+        total_threads = q.shape[0] * self.threads_per_token
+        grid = (cute.ceil_div(total_threads, self.tb_size), 1, 1)
+        self.kernel(
+            positions,
+            q,
+            cos_sin_cache,
+            weights,
+            q_fp4,
+            q_scale,
+            weights_out,
+            scale,
+        ).launch(grid=grid, block=(self.tb_size, 1, 1), stream=stream)
+
+    @cute.kernel
+    def kernel(
+        self,
+        positions: cute.Tensor,
+        q: cute.Tensor,
+        cos_sin_cache: cute.Tensor,
+        weights: cute.Tensor,
+        q_fp4: cute.Tensor,
+        q_scale: cute.Tensor,
+        weights_out: cute.Tensor,
+        scale: Float32,
+    ):
+        block_id, _, _ = cute.arch.block_idx()
+        tid, _, _ = cute.arch.thread_idx()
+
+        num_token_heads = q.shape[0] * self.num_heads
+        global_tid = block_id * self.tb_size + tid
+
+        global_subwarp_id = global_tid // self.subwarp_size
+        sublane = tid % self.subwarp_size
+
+        token_id = global_subwarp_id // (self.num_heads // self.coarsen)
+        head_tile_id = global_subwarp_id % (self.num_heads // self.coarsen)
+        head_start = head_tile_id * self.coarsen
+
+        # NOTE: token_id may exceed bounds, hence we need to add load/store guards
+        # we can't do early exit because CuteDSL doesn't support it. and we also need
+        # all threads in a warp to be active since we utilize warp shuffle later.
+        # must_in_bounds is constexpr, True when 1 threadblock fit within 1 token
+        # position. the compiler will remove bounds check when that happens.
+        must_in_bounds = cutlass.const_expr(self.tb_size % self.threads_per_token == 0)
+        in_bounds = must_in_bounds or (token_id < q.shape[0])
+
+        cp_op = cute.nvgpu.CopyUniversalOp()
+
+        _layout = cute.make_layout((self.coarsen, 8), stride=(8, 1))
+        q_bf16x2 = cute.make_rmem_tensor(_layout, Uint32)
+
+        if in_bounds:
+            # we can't do cute.copy() on the whole 2D tile directly because
+            # cute.copy() wants the 1st mode to be covered by the copy atom,
+            # and other modes as for loop. there is no fast way to
+            # "transpose" the tensor view.
+            q_tile = cute.local_tile(
+                q[token_id, None, None],
+                tiler=(self.coarsen, 16),
+                coord=(head_tile_id, sublane),
+            )
+            cp_u32x8 = cute.make_copy_atom(cp_op, Uint32, num_bits_per_copy=256)
+            for i in cutlass.range_constexpr(self.coarsen):
+                src = cute.recast_tensor(q_tile[i, None], Uint32)
+                cute.copy(cp_u32x8, src, q_bf16x2[i, None])
+
+        # RoPE applies only to the trailing rope_dim values. We keep the rounded
+        # BF16 result in q_bits so the later amax and quantization see BF16.
+        # cos_sin_cache layout: [max_pos, rope_dim]
+        if in_bounds and sublane * 16 >= self.nope_dim:
+            cos_vals = cute.make_rmem_tensor((8,), Float32)
+            sin_vals = cute.make_rmem_tensor((8,), Float32)
+
+            pos = positions[token_id]
+
+            # select 8 elems from cos and sin
+            cos_id = sublane - self.nope_dim // 16
+            sin_id = cos_id + self.rope_dim // 16
+            cos_src = cute.local_tile(
+                cos_sin_cache[pos, None], tiler=(8,), coord=(cos_id,)
+            )
+            sin_src = cute.local_tile(
+                cos_sin_cache[pos, None], tiler=(8,), coord=(sin_id,)
+            )
+
+            cp_f32x8 = cute.make_copy_atom(cp_op, Float32, num_bits_per_copy=256)
+            cp_u32x4 = cute.make_copy_atom(cp_op, Uint32, num_bits_per_copy=128)
+
+            if const_expr(self.cos_sin_dtype is Float32):
+                cute.copy(cp_f32x8, cos_src, cos_vals)
+                cute.copy(cp_f32x8, sin_src, sin_vals)
+            else:
+                cos_bf16x2 = cute.make_rmem_tensor((4,), Uint32)
+                sin_bf16x2 = cute.make_rmem_tensor((4,), Uint32)
+                cute.copy(cp_u32x4, cute.recast_tensor(cos_src, Uint32), cos_bf16x2)
+                cute.copy(cp_u32x4, cute.recast_tensor(sin_src, Uint32), sin_bf16x2)
+
+                for i in cutlass.range_constexpr(4):
+                    cos0, cos1 = _bf16x2_to_fp32(cos_bf16x2[i])
+                    sin0, sin1 = _bf16x2_to_fp32(sin_bf16x2[i])
+                    cos_vals[i * 2] = cos0
+                    cos_vals[i * 2 + 1] = cos1
+                    sin_vals[i * 2] = sin0
+                    sin_vals[i * 2 + 1] = sin1
+
+            for i in cutlass.range_constexpr(self.coarsen):
+                for j in cutlass.range_constexpr(8):
+                    q0, q1 = _bf16x2_to_fp32(q_bf16x2[i, j])
+                    rot0 = q0 * cos_vals[j] - q1 * sin_vals[j]
+                    rot1 = q0 * sin_vals[j] + q1 * cos_vals[j]
+                    # convert back to BF16 to match numerics
+                    q_bf16x2[i, j] = _fp32x2_to_bf16x2(rot0, rot1)
+
+        # layout: [coarsen, 8]
+        q_fp4_tile = cute.local_tile(
+            q_fp4[token_id, None, None],
+            tiler=(self.coarsen, 8),
+            coord=(head_tile_id, sublane),
+        )
+
+        for i in cutlass.range_constexpr(self.coarsen):
+            # compute amax in packed bf16x2 to save instructions
+            # Each thread holds 16 elems. Two adjacent threads form one 32-elem
+            # MXFP4 block, so a width-2 shuffle gives the block amax.
+            amax_bf16x2 = _bf16x2_abs(q_bf16x2[i, 0])
+            for j in cutlass.range_constexpr(1, 8):
+                amax_bf16x2 = _bf16x2_max(amax_bf16x2, _bf16x2_abs(q_bf16x2[i, j]))
+            amax_bf16x2 = cute_utils.warp_reduce(
+                amax_bf16x2,
+                _bf16x2_max,
+                width=MXFP4_BLOCK_SIZE // 16,
+            )
+            amax_pair = _bf16x2_to_fp32(amax_bf16x2)
+            amax = cute_utils.fmax(amax_pair[0], amax_pair[1])
+
+            if in_bounds:
+                # compute block scale with bit manipulation
+                # UE8M0 stores ceil(log2(fp4_scale)) + 127. Adding the mantissa mask
+                # increments the exponent whenever fp4_scale is not exactly a power of 2
+                eps = cutlass.const_expr(float.fromhex("0x6p-126"))
+                fp4_scale = cute_utils.fmax(amax, eps) * Float32(1.0 / 6.0)
+                bits = _recast_val(fp4_scale, Uint32)
+                ue8m0 = cute_utils.shr_u32(
+                    bits + Uint32(0x7FFFFF), Uint32(23)
+                ) & Uint32(0xFF)
+
+                # Only one of the two threads in an MXFP4 block writes the shared scale.
+                if tid % 2 == 0:
+                    mx_block = sublane // 2
+                    q_scale[token_id, head_start + i, mx_block] = Uint8(ue8m0)
+
+                # If scale = 2^A and ue8m0 = A + 127, then inverse scale has exponent
+                # -A + 127 = 254 - ue8m0.
+                inv_scale_bits = (Uint32(254) - ue8m0) << Uint32(23)
+                inv_fp4_scale = _recast_val(inv_scale_bits, Float32)
+
+                vals = cute.make_rmem_tensor(16, Float32)
+                for j in cutlass.range_constexpr(8):
+                    q0, q1 = _bf16x2_to_fp32(q_bf16x2[i, j])
+                    vals[j * 2] = q0 * inv_fp4_scale
+                    vals[j * 2 + 1] = q1 * inv_fp4_scale
+
+                # pack to FP4
+                packed = cute.make_rmem_tensor((2,), Uint32)
+                packed[0] = _fp32x8_to_fp4x8(vals, 0)
+                packed[1] = _fp32x8_to_fp4x8(vals, 8)
+
+                dst = q_fp4_tile[i, None]
+                cp_u32x2 = cute.make_copy_atom(cp_op, Uint32, num_bits_per_copy=64)
+                cute.copy(cp_u32x2, packed, cute.recast_tensor(dst, Uint32))
+
+        # Weight scaling is independent of the Q subwarp work. The first
+        # num_tokens * num_heads logical threads cover one weight each.
+        if global_tid < num_token_heads:
+            weight_token_id = global_tid // self.num_heads
+            weight_head_id = global_tid % self.num_heads
+            weights_out[weight_token_id, weight_head_id] = (
+                weights[weight_token_id, weight_head_id].to(Float32) * scale
+            )
+
+    @cache
+    @staticmethod
+    def compile(
+        head_dim: int = 128,
+        rope_dim: int = 64,
+        num_heads: int = 64,
+        cos_sin_dtype: type[cutlass.Numeric] = Float32,
+        coarsen: int = 4,
+    ):
+        num_tokens = cute.sym_int()
+        max_pos = cute.sym_int()
+
+        q = make_fake_tensor(
+            BFloat16, (num_tokens, num_heads, head_dim), divisibility=16
+        )
+        positions = make_fake_tensor(Int64, (num_tokens,), divisibility=1)
+        cos_sin_cache = make_fake_tensor(
+            cos_sin_dtype,
+            (max_pos, rope_dim),
+            divisibility=8,
+        )
+        weights = make_fake_tensor(BFloat16, (num_tokens, num_heads), divisibility=8)
+        q_fp4 = make_fake_tensor(
+            Uint8,
+            (num_tokens, num_heads, head_dim // 2),
+            divisibility=16,
+        )
+        q_scale = make_fake_tensor(
+            Uint8,
+            (num_tokens, num_heads, head_dim // MXFP4_BLOCK_SIZE),
+            divisibility=4,
+        )
+        weights_out = make_fake_tensor(Float32, (num_tokens, num_heads), divisibility=4)
+
+        kernel = IndexerQMxFp4Kernel(
+            head_dim, rope_dim, num_heads, cos_sin_dtype, coarsen
+        )
+        stream = cute.runtime.make_fake_stream(use_tvm_ffi_env_stream=True)
+        return cute.compile(
+            kernel,
+            positions,
+            q,
+            cos_sin_cache,
+            weights,
+            q_fp4,
+            q_scale,
+            weights_out,
+            Float32(0.0),
+            stream,
+            options="--enable-tvm-ffi",
+        )
diff --git a/vllm/v1/attention/ops/deepseek_v4_ops/fused_inv_rope_fp8_quant.py b/vllm/v1/attention/ops/deepseek_v4_ops/fused_inv_rope_fp8_quant.py
new file mode 100644
index 000000000000..68d33f1aa105
--- /dev/null
+++ b/vllm/v1/attention/ops/deepseek_v4_ops/fused_inv_rope_fp8_quant.py
@@ -0,0 +1,314 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Fused inverse RoPE + block-scaled FP8 quantization kernel for DeepseekV4 attention.
+
+Output scale format is pre-transformed (MN-major TMA-aligned; FP32 on SM90,
+INT32-packed UE8M0 on SM100) so fp8_einsum skips transform_sf_into_required_layout.
+"""
+
+import torch
+
+from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
+from vllm.utils.torch_utils import direct_register_custom_op
+
+
+@triton.jit
+def _fused_inv_rope_fp8_quant_per_head(
+    o_ptr,
+    positions_ptr,
+    cos_sin_cache_ptr,
+    fp8_ptr,
+    scale_ptr,
+    num_tokens,
+    heads_per_group: tl.constexpr,
+    o_stride_token,
+    o_stride_head,
+    cache_stride_pos,
+    fp8_stride_group,
+    fp8_stride_token,
+    scale_stride_group,
+    scale_stride_k,
+    fp8_max: tl.constexpr,
+    eps: tl.constexpr,
+    QUANT_GROUP_SIZE: tl.constexpr,
+    CHUNKS_PER_HEAD: tl.constexpr,
+    ROPE_START: tl.constexpr,
+    HALF_ROPE: tl.constexpr,
+    TMA_ALIGNED_SCALES: tl.constexpr,
+):
+    # int64: stride multiply overflows int32 past num_tokens=32768 (IMA).
+    pid_token = tl.program_id(0).to(tl.int64)
+    pid_gh = tl.program_id(1).to(tl.int64)
+
+    g = pid_gh // heads_per_group
+    head_in_group = pid_gh % heads_per_group
+    global_head = pid_gh
+    qb_start = head_in_group * CHUNKS_PER_HEAD
+
+    # Padding rows in the TMA-aligned scale buffer: fill with zero and skip quant.
+    if pid_token >= num_tokens:
+        if TMA_ALIGNED_SCALES:
+            scale_addr = (
+                scale_ptr
+                + g * scale_stride_group
+                + pid_token
+                + head_in_group * scale_stride_k
+            )
+            tl.store(scale_addr, tl.zeros((), dtype=tl.int32))
+        else:
+            block_offsets = tl.arange(0, CHUNKS_PER_HEAD)
+            qb_indices = qb_start + block_offsets
+            scale_addrs = (
+                scale_ptr
+                + g * scale_stride_group
+                + pid_token
+                + qb_indices * scale_stride_k
+            )
+            tl.store(scale_addrs, tl.zeros((CHUNKS_PER_HEAD,), dtype=tl.float32))
+        return
+
+    input_base = o_ptr + pid_token * o_stride_token + global_head * o_stride_head
+
+    HEAD_DIM: tl.constexpr = CHUNKS_PER_HEAD * QUANT_GROUP_SIZE
+    offsets = tl.arange(0, HEAD_DIM)
+    x = tl.load(input_base + offsets).to(tl.float32)
+
+    rope_abs_start: tl.constexpr = (CHUNKS_PER_HEAD - 1) * QUANT_GROUP_SIZE + ROPE_START
+    pos = tl.load(positions_ptr + pid_token)
+    cache_base = cos_sin_cache_ptr + pos * cache_stride_pos
+    is_rope = offsets >= rope_abs_start
+    rope_local = offsets - rope_abs_start
+
+    x_partner = tl.load(input_base + (offsets ^ 1), mask=is_rope, other=0.0).to(
+        tl.float32
+    )
+    cs_idx = tl.maximum(rope_local >> 1, 0)
+    cos_v = tl.load(cache_base + cs_idx, mask=is_rope, other=1.0)
+    sin_v = tl.load(cache_base + HALF_ROPE + cs_idx, mask=is_rope, other=0.0)
+    x_add = x * cos_v + x_partner * sin_v
+    x_sub = x * cos_v - x_partner * sin_v
+    is_even = (rope_local & 1) == 0
+    rotated = tl.where(is_even, x_add, x_sub)
+    x = tl.where(is_rope, rotated, x)
+
+    x_2d = tl.reshape(tl.abs(x), (CHUNKS_PER_HEAD, QUANT_GROUP_SIZE))
+    block_absmax = tl.maximum(tl.max(x_2d, axis=1), eps)
+    scale_raw = block_absmax * (1.0 / fp8_max)
+    scales = tl.math.exp2(tl.ceil(tl.log2(scale_raw)))
+
+    scales_exp = tl.reshape(
+        tl.broadcast_to(
+            tl.reshape(scales, (CHUNKS_PER_HEAD, 1)),
+            (CHUNKS_PER_HEAD, QUANT_GROUP_SIZE),
+        ),
+        (HEAD_DIM,),
+    )
+    x_quant = tl.clamp(x / scales_exp, -fp8_max, fp8_max).to(tl.float8e4nv)
+
+    fp8_base = (
+        fp8_ptr
+        + g * fp8_stride_group
+        + pid_token * fp8_stride_token
+        + qb_start * QUANT_GROUP_SIZE
+    )
+    tl.store(fp8_base + offsets, x_quant)
+
+    block_offsets = tl.arange(0, CHUNKS_PER_HEAD)
+    qb_indices = qb_start + block_offsets
+    if TMA_ALIGNED_SCALES:
+        scale_bits = scales.to(tl.int32, bitcast=True)
+        ue8m0_bytes = (scale_bits >> 23) & 0xFF
+        packed_val = tl.sum(ue8m0_bytes << (block_offsets * 8))
+        scale_addr = (
+            scale_ptr
+            + g * scale_stride_group
+            + pid_token
+            + head_in_group * scale_stride_k
+        )
+        tl.store(scale_addr, packed_val)
+    else:
+        scale_addrs = (
+            scale_ptr + g * scale_stride_group + pid_token + qb_indices * scale_stride_k
+        )
+        tl.store(scale_addrs, scales)
+
+
+def fused_inv_rope_fp8_quant(
+    o: torch.Tensor,
+    positions: torch.Tensor,
+    cos_sin_cache: torch.Tensor,
+    n_groups: int,
+    heads_per_group: int,
+    nope_dim: int = 448,
+    rope_dim: int = 64,
+    quant_group_size: int = 128,
+    tma_aligned_scales: bool = False,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Fused inverse RoPE + block-scaled FP8 quantization.
+
+    Args:
+        o: Attention output [num_tokens, num_heads, head_dim] bf16.
+        positions: Token positions [num_tokens] int64.
+        cos_sin_cache: Precomputed [max_pos, rope_dim] with cos||sin.
+        n_groups: Number of output groups.
+        heads_per_group: Heads per group.
+        nope_dim: Non-RoPE dimensions per head (default 448).
+        rope_dim: RoPE dimensions per head (default 64).
+        quant_group_size: FP8 quantization block size (default 128).
+        tma_aligned_scales: Output INT32 packed UE8M0 for SM100 (True)
+                            or FP32 for SM90 (False).
+
+    Returns:
+        o_fp8: [T, G, D] float8_e4m3fn, strides (D, T*D, 1).
+        o_scale: Pre-transformed scale tensor for fp8_einsum.
+    """
+    from vllm.utils.deep_gemm import get_tma_aligned_size
+
+    num_tokens, num_heads, head_dim = o.shape
+    assert num_heads == n_groups * heads_per_group
+    assert head_dim == nope_dim + rope_dim
+    assert head_dim % quant_group_size == 0
+    assert nope_dim % quant_group_size == (quant_group_size - rope_dim)
+    assert rope_dim % 2 == 0
+    assert cos_sin_cache.shape[-1] == rope_dim
+    assert cos_sin_cache.dtype == torch.float32
+
+    d = heads_per_group * head_dim
+    num_scale_blocks = d // quant_group_size
+    chunks_per_head = head_dim // quant_group_size
+
+    fp8_dtype = torch.float8_e4m3fn
+    fp8_max = torch.finfo(fp8_dtype).max
+
+    tma_aligned_T = get_tma_aligned_size(num_tokens, 4)
+    if tma_aligned_scales:
+        packed_sf_k = (num_scale_blocks + 3) // 4
+        scale_inner = packed_sf_k
+    else:
+        scale_inner = num_scale_blocks
+
+    # Run kernel through a custom op so inductor sees an opaque boundary.
+    # It's a pytorch bug, see https://github.com/vllm-project/vllm/issues/41106
+    fp8_buf, scale_buf = torch.ops.vllm.fused_inv_rope_fp8_quant_kernel(
+        o,
+        positions,
+        cos_sin_cache,
+        heads_per_group,
+        quant_group_size,
+        chunks_per_head,
+        nope_dim % quant_group_size,
+        rope_dim // 2,
+        tma_aligned_scales,
+        fp8_max,
+        tma_aligned_T,
+        num_tokens,
+        n_groups,
+        d,
+        scale_inner,
+    )
+    return fp8_buf.transpose(0, 1), scale_buf.transpose(0, 1)
+
+
+def _fused_inv_rope_fp8_quant_kernel_impl(
+    o: torch.Tensor,
+    positions: torch.Tensor,
+    cos_sin_cache: torch.Tensor,
+    heads_per_group: int,
+    quant_group_size: int,
+    chunks_per_head: int,
+    rope_start: int,
+    half_rope: int,
+    tma_aligned_scales: bool,
+    fp8_max: float,
+    tma_aligned_T: int,
+    num_tokens: int,
+    n_groups: int,
+    d: int,
+    scale_inner: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    fp8_buf = torch.empty(
+        (n_groups, num_tokens, d),
+        dtype=torch.float8_e4m3fn,
+        device=o.device,
+    )
+    scale_dtype = torch.int32 if tma_aligned_scales else torch.float32
+    scale_buf = torch.empty(
+        n_groups * scale_inner * tma_aligned_T,
+        dtype=scale_dtype,
+        device=o.device,
+    ).as_strided(
+        (n_groups, num_tokens, scale_inner),
+        (scale_inner * tma_aligned_T, 1, tma_aligned_T),
+    )
+    grid = (tma_aligned_T, n_groups * heads_per_group)
+    pdl_kwargs = {} if current_platform.is_rocm() else {"launch_pdl": False}
+    _fused_inv_rope_fp8_quant_per_head[grid](
+        o,
+        positions,
+        cos_sin_cache,
+        fp8_buf,
+        scale_buf,
+        num_tokens,
+        heads_per_group=heads_per_group,
+        o_stride_token=o.stride(0),
+        o_stride_head=o.stride(1),
+        cache_stride_pos=cos_sin_cache.stride(0),
+        fp8_stride_group=fp8_buf.stride(0),
+        fp8_stride_token=fp8_buf.stride(1),
+        scale_stride_group=scale_buf.stride(0),
+        scale_stride_k=scale_buf.stride(2),
+        fp8_max=fp8_max,
+        eps=1e-10,
+        QUANT_GROUP_SIZE=quant_group_size,
+        CHUNKS_PER_HEAD=chunks_per_head,
+        ROPE_START=rope_start,
+        HALF_ROPE=half_rope,
+        TMA_ALIGNED_SCALES=tma_aligned_scales,
+        num_stages=1,
+        **pdl_kwargs,
+        num_warps=1,
+    )
+    return fp8_buf, scale_buf
+
+
+def _fused_inv_rope_fp8_quant_kernel_fake(
+    o: torch.Tensor,
+    positions: torch.Tensor,
+    cos_sin_cache: torch.Tensor,
+    heads_per_group: int,
+    quant_group_size: int,
+    chunks_per_head: int,
+    rope_start: int,
+    half_rope: int,
+    tma_aligned_scales: bool,
+    fp8_max: float,
+    tma_aligned_T: int,
+    num_tokens: int,
+    n_groups: int,
+    d: int,
+    scale_inner: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    fp8_buf = torch.empty(
+        (n_groups, num_tokens, d),
+        dtype=torch.float8_e4m3fn,
+        device=o.device,
+    )
+    scale_dtype = torch.int32 if tma_aligned_scales else torch.float32
+    scale_buf = torch.empty(
+        n_groups * scale_inner * tma_aligned_T,
+        dtype=scale_dtype,
+        device=o.device,
+    ).as_strided(
+        (n_groups, num_tokens, scale_inner),
+        (scale_inner * tma_aligned_T, 1, tma_aligned_T),
+    )
+    return fp8_buf, scale_buf
+
+
+direct_register_custom_op(
+    op_name="fused_inv_rope_fp8_quant_kernel",
+    op_func=_fused_inv_rope_fp8_quant_kernel_impl,
+    fake_impl=_fused_inv_rope_fp8_quant_kernel_fake,
+)
diff --git a/vllm/v1/attention/ops/deepseek_v4_ops/fused_qk_rmsnorm.py b/vllm/v1/attention/ops/deepseek_v4_ops/fused_qk_rmsnorm.py
new file mode 100644
index 000000000000..0dd348a46e26
--- /dev/null
+++ b/vllm/v1/attention/ops/deepseek_v4_ops/fused_qk_rmsnorm.py
@@ -0,0 +1,96 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+
+from vllm.triton_utils import tl, triton
+
+
+@triton.jit
+def _fused_q_kv_rmsnorm_kernel(
+    q_ptr,
+    q_out_ptr,
+    q_weight_ptr,
+    q_in_stride,
+    q_out_stride,
+    kv_ptr,
+    kv_out_ptr,
+    kv_weight_ptr,
+    kv_in_stride,
+    kv_out_stride,
+    eps,
+    Q_SIZE: tl.constexpr,
+    KV_SIZE: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    # num_tokens goes on grid-x (max 2**31 - 1); task goes on grid-y.
+    # CUDA's grid-y/z are capped at 65535, so putting num_tokens there crashes
+    # the launch at max-num-batched-tokens >= 65536 with "invalid argument".
+    # int64: q_in_stride can be ~24K (128 heads × 192) and overflows int32
+    # past num_tokens ~87K under large chunked prefill.
+    token_idx = tl.program_id(0).to(tl.int64)
+    pid_task = tl.program_id(1)
+
+    if pid_task == 0:
+        SIZE = Q_SIZE
+        row_in = q_ptr + token_idx * q_in_stride
+        weight_ptr = q_weight_ptr
+        row_out = q_out_ptr + token_idx * q_out_stride
+    else:
+        SIZE = KV_SIZE
+        row_in = kv_ptr + token_idx * kv_in_stride
+        weight_ptr = kv_weight_ptr
+        row_out = kv_out_ptr + token_idx * kv_out_stride
+
+    # RMSNorm in fp32 throughout — matches csrc/layernorm_kernels.cu's
+    # `(scalar_t)(x * s_variance * w)` and DeepseekV4's compressor kernel, which
+    # keep x, rrms, and w all in fp32 and perform a single cast at store.
+    block = tl.arange(0, BLOCK_SIZE)
+    mask = block < SIZE
+    x = tl.load(row_in + block, mask=mask, other=0.0).to(tl.float32)
+    variance = tl.sum(x * x, axis=0) / SIZE
+    rrms = tl.rsqrt(variance + eps)
+    w = tl.load(weight_ptr + block, mask=mask, other=0.0).to(tl.float32)
+    y = x * rrms * w
+    tl.store(row_out + block, y.to(row_out.dtype.element_ty), mask=mask)
+
+
+def fused_q_kv_rmsnorm(
+    qr: torch.Tensor,
+    kv: torch.Tensor,
+    q_weight: torch.Tensor,
+    kv_weight: torch.Tensor,
+    eps: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    assert qr.ndim == 2 and kv.ndim == 2
+    assert qr.shape[0] == kv.shape[0], (
+        f"token dim mismatch: qr={qr.shape}, kv={kv.shape}"
+    )
+    assert qr.stride(-1) == 1 and kv.stride(-1) == 1
+    assert q_weight.is_contiguous() and kv_weight.is_contiguous()
+
+    q_size = qr.shape[1]
+    kv_size = kv.shape[1]
+    num_tokens = qr.shape[0]
+    qr_out = torch.empty_like(qr)
+    kv_out = torch.empty_like(kv)
+    if num_tokens == 0:
+        return qr_out, kv_out
+
+    block_size = triton.next_power_of_2(max(q_size, kv_size))
+    _fused_q_kv_rmsnorm_kernel[(num_tokens, 2)](
+        qr,
+        qr_out,
+        q_weight,
+        qr.stride(0),
+        qr_out.stride(0),
+        kv,
+        kv_out,
+        kv_weight,
+        kv.stride(0),
+        kv_out.stride(0),
+        eps,
+        Q_SIZE=q_size,
+        KV_SIZE=kv_size,
+        BLOCK_SIZE=block_size,
+    )
+    return qr_out, kv_out
diff --git a/vllm/v1/attention/ops/prefix_prefill.py b/vllm/v1/attention/ops/prefix_prefill.py
index afa5f5178386..8488c72aeaf6 100644
--- a/vllm/v1/attention/ops/prefix_prefill.py
+++ b/vllm/v1/attention/ops/prefix_prefill.py
@@ -89,6 +89,7 @@ def _fwd_kernel(
     SKIP_DECODE: tl.constexpr,
     USE_SINKS: tl.constexpr,
     USE_FP8: tl.constexpr,
+    CAUSAL: tl.constexpr = True,
     MAX_Q_LEN: tl.constexpr = 0,
     MAX_CTX_LEN: tl.constexpr = 0,
     FP8_MIN: tl.constexpr = float8_info.min,
@@ -283,10 +284,17 @@ def _fwd_kernel(
     # block_mask is 0 when we're already past the current query length
     block_mask = tl.where(block_start_loc < cur_batch_query_len, 1, 0)
 
-    # compute query against itself (with causal mask)
+    # compute query against itself (causal among queries by default;
+    # CAUSAL=False for bidirectional attention over query tokens, e.g. DFlash.)
+    if CAUSAL:
+        key_range_upper = block_mask * (start_m + 1) * BLOCK_M
+    else:
+        q_len_pad = (cur_batch_query_len + BLOCK_N - 1) // BLOCK_N * BLOCK_N
+        key_range_upper = block_mask * q_len_pad
+
     for start_n in tl.range(
         0,
-        block_mask * (start_m + 1) * BLOCK_M,
+        key_range_upper,
         BLOCK_N,
         loop_unroll_factor=num_unroll_request,
     ):
@@ -302,14 +310,17 @@ def _fwd_kernel(
         qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
         qk = tl.dot(q, k, acc=qk, input_precision=IN_PRECISION)
         qk *= sm_scale
-        # apply causal mask
-        qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk, float("-inf"))
+
+        valid_kv = (start_n + offs_n[None, :]) < cur_batch_query_len
+        if CAUSAL:
+            attn_mask = valid_kv & (offs_m[:, None] >= (start_n + offs_n[None, :]))
+        else:
+            attn_mask = valid_kv
         if SLIDING_WINDOW > 0:
-            qk = tl.where(
-                offs_m[:, None] - (start_n + offs_n[None, :]) < SLIDING_WINDOW,
-                qk,
-                float("-inf"),
+            attn_mask = attn_mask & (
+                offs_m[:, None] - (start_n + offs_n[None, :]) < SLIDING_WINDOW
             )
+        qk = tl.where(attn_mask, qk, float("-inf"))
 
         # compute running maximum
         m_ij = tl.maximum(m_i, tl.max(qk, axis=1))
@@ -656,6 +667,7 @@ def context_attention_fwd(
     fp8_out_scale=None,
     sinks=None,
     is_block_table_ptr: bool = False,
+    causal: bool = True,
 ):
     q_dtype_is_f32 = q.dtype is torch.float32
 
@@ -722,6 +734,7 @@ def context_attention_fwd(
         processed_b_loc = b_loc.to(torch.int32)
 
     if alibi_slopes is not None:
+        assert causal, "Non-causal prefix attention is not supported with alibi"
         assert sinks is None, "Sinks arg is not supported with alibi"
         assert fp8_out_scale is None, "FP8 output not supported with alibi"
         # need to reduce num. blocks when using fp32
@@ -859,6 +872,7 @@ def context_attention_fwd(
         num_warps=4,
         num_stages=1,
         USE_SINKS=sinks is not None,
+        CAUSAL=causal,
         **extra_kargs,
     )
     return
diff --git a/vllm/v1/attention/ops/rocm_aiter_mla_sparse.py b/vllm/v1/attention/ops/rocm_aiter_mla_sparse.py
index 35a23b7a6057..5d0343ffd607 100644
--- a/vllm/v1/attention/ops/rocm_aiter_mla_sparse.py
+++ b/vllm/v1/attention/ops/rocm_aiter_mla_sparse.py
@@ -2,9 +2,11 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import functools
 import importlib
+import math
 from importlib.util import find_spec
 
 import torch
+import torch.nn.functional as F
 
 from vllm.forward_context import get_forward_context
 from vllm.platforms import current_platform
@@ -13,8 +15,10 @@
 from vllm.v1.attention.backends.mla.indexer import DeepseekV32IndexerMetadata
 from vllm.v1.attention.ops.common import pack_seq_triton, unpack_seq_triton
 
-if current_platform.is_cuda_alike():
-    from vllm import _custom_ops as ops
+if current_platform.is_rocm():
+    from vllm.platforms.rocm import _ON_GFX942
+else:
+    _ON_GFX942 = False
 
 
 @triton.jit
@@ -97,7 +101,8 @@ def indexer_k_quant_and_cache_triton(
     # In real layout, we store the first portion as kv cache value
     # and second portion as kv cache scale
     kv_cache = kv_cache.view(num_blocks, -1)
-    kv_cache_value = kv_cache[:, : block_size * head_dim]
+    fp8_dtype = current_platform.fp8_dtype()
+    kv_cache_value = kv_cache[:, : block_size * head_dim].view(fp8_dtype)
     kv_cache_scale = kv_cache[:, block_size * head_dim :].view(torch.float32)
     head_tile_size = head_tile_size // kv_cache.element_size()
     grid = (num_tokens,)
@@ -111,7 +116,7 @@ def indexer_k_quant_and_cache_triton(
         block_size,
         num_tokens,
         head_dim,
-        "NHD",
+        "SHUFFLE",
         block_tile_size,
         head_tile_size,
         IS_FNUZ=current_platform.fp8_dtype() == torch.float8_e4m3fnuz,
@@ -212,7 +217,7 @@ def cp_gather_indexer_k_quant_cache_triton(
         block_table_stride,
         k_cache_value.stride(0),
         k_cache_scale.stride(0),
-        "NHD",
+        "SHUFFLE",
         head_dim,
         block_tile_size,
         head_tile_size,
@@ -232,6 +237,43 @@ def fp8_paged_mqa_logits_torch(
 
     fp8_dtype = current_platform.fp8_dtype()
     batch_size, next_n, _, dim = q.size()
+    if next_n == 1:
+        block_size = kv_cache.shape[1]
+        logits = torch.full(
+            [batch_size, max_model_len],
+            float("-inf"),
+            device=q.device,
+            dtype=torch.float32,
+        )
+        if context_lens.dim() > 1:
+            context_lens = context_lens.squeeze(-1)
+        kv_cache_flat = kv_cache.view(-1, block_size * (dim + 4))
+        for i in range(batch_size):
+            q_i = q[i, 0].to(torch.float32)
+            q_scale = weights[i]
+            seq_len = int(context_lens[i].item())
+            assert seq_len <= max_model_len
+            num_pages = cdiv(seq_len, block_size)
+            padded_seq_len = num_pages * block_size
+            pages = block_tables[i, :num_pages]
+            cache = kv_cache_flat[pages]
+            scale_offset = block_size * dim
+            cache_value = (
+                cache[..., :scale_offset].view(dtype=fp8_dtype).to(torch.float32)
+            )
+            cache_scale = (
+                cache[..., scale_offset:].view(dtype=torch.float32).contiguous()
+            )
+            cache_value = cache_value.view(padded_seq_len, dim)
+            cache_scale = cache_scale.view(padded_seq_len)
+            score = F.linear(cache_value, q_i)
+            score = F.relu(score)
+            score *= q_scale[None, :]
+            score = score.sum(dim=1)
+            score *= cache_scale
+            logits[i, :seq_len] = score[:seq_len]
+        return logits
+
     kv_cache, scale = kv_cache[..., :dim], kv_cache[..., dim:]
     scale = scale.contiguous().view(torch.float)
     q = q.float()
@@ -243,20 +285,30 @@ def fp8_paged_mqa_logits_torch(
         device=q.device,
         dtype=torch.float32,
     )
-    context_lens = context_lens.tolist()
     for i in range(batch_size):
         context_len = context_lens[i]
-        q_offsets = torch.arange(context_len - next_n, context_len, device="cuda")
+        if context_len.ndim == 0:
+            context_len_i = int(context_len.item())
+            q_offsets = torch.arange(
+                context_len_i - next_n, context_len_i, device=q.device
+            )
+            context_limit = torch.full(
+                (next_n,), context_len_i, dtype=torch.int32, device=q.device
+            )
+        else:
+            context_limit = context_len.to(device=q.device, dtype=torch.int32)
+            q_offsets = context_limit - 1
         weight_slice = (
             weights[i * next_n : (i + 1) * next_n, :].transpose(0, 1).contiguous()
         )
-        for block_rk in range(cdiv(context_len, block_size)):
+        max_context_len = int(context_limit.max().item())
+        for block_rk in range(cdiv(max_context_len, block_size)):
             block_idx = block_tables[i][block_rk]
             qx, kx = q[i], kv_cache[block_idx]
             k_offsets = torch.arange(
-                block_rk * block_size, (block_rk + 1) * block_size, device="cuda"
+                block_rk * block_size, (block_rk + 1) * block_size, device=q.device
             )
-            mask = (k_offsets[None, :] < context_len) & (
+            mask = (k_offsets[None, :] < context_limit[:, None]) & (
                 k_offsets[None, :] <= q_offsets[:, None]
             )
             s = torch.where(
@@ -325,10 +377,39 @@ def rocm_fp8_paged_mqa_logits(
     from vllm._aiter_ops import rocm_aiter_ops
 
     aiter_paged_mqa_logits_module = None
+    # if rocm_aiter_ops.is_enabled():
+    batch_size, next_n, heads, head_dim = q_fp8.shape
+    num_blocks, block_size, _, _ = kv_cache_fp8.shape
+
     if rocm_aiter_ops.is_enabled():
         aiter_paged_mqa_logits_module = paged_mqa_logits_module()
 
     if aiter_paged_mqa_logits_module is not None:
+        if _ON_GFX942:
+            deepgemm_fp8_paged_mqa_logits = (
+                aiter_paged_mqa_logits_module.deepgemm_fp8_paged_mqa_logits
+            )
+            batch_size, next_n, heads, _ = q_fp8.shape
+            out_logits = torch.full(
+                [batch_size * next_n, max_model_len],
+                float("-inf"),
+                device="cuda",
+                dtype=torch.float32,
+            )
+            deepgemm_fp8_paged_mqa_logits(
+                q_fp8,
+                kv_cache_fp8,
+                weights,
+                out_logits,
+                context_lens,
+                block_tables,
+                max_model_len,
+                ChunkK=256,
+                Preshuffle=block_size == 64,
+                KVBlockSize=block_size,
+                WavePerEU=2,
+            )
+            return out_logits
         deepgemm_fp8_paged_mqa_logits_stage1 = (
             aiter_paged_mqa_logits_module.deepgemm_fp8_paged_mqa_logits_stage1
         )
@@ -347,6 +428,7 @@ def rocm_fp8_paged_mqa_logits(
             context_lens,
             block_tables,
             max_model_len,
+            ChunkQ=heads,
         )
         return out_qk.sum(dim=0)
     else:
@@ -458,6 +540,27 @@ def rocm_fp8_mqa_logits(
         return fp8_mqa_logits_torch(q, kv, weights, cu_seqlen_ks, cu_seqlen_ke)
 
 
+def _topk_indices_torch(logits: torch.Tensor, topk_tokens: int) -> torch.Tensor:
+    k = min(topk_tokens, logits.shape[-1])
+    values, indices = torch.topk(logits, k=k, dim=-1)
+    indices = indices.to(torch.int32)
+    indices = torch.where(
+        values == float("-inf"),
+        torch.full_like(indices, -1, dtype=torch.int32),
+        indices,
+    )
+    if k == topk_tokens:
+        return indices
+    padded = torch.full(
+        (logits.shape[0], topk_tokens),
+        -1,
+        dtype=torch.int32,
+        device=logits.device,
+    )
+    padded[:, :k] = indices
+    return padded
+
+
 def rocm_aiter_sparse_attn_indexer_fake(
     hidden_states: torch.Tensor,
     k_cache_prefix: LayerNameType,
@@ -476,8 +579,9 @@ def rocm_aiter_sparse_attn_indexer_fake(
     # profile run
     # NOTE(Chen): create the max possible flattened_kv. So that
     # profile_run can get correct memory usage.
+    device = hidden_states.device if k is None else k.device
     _flattened_kv = torch.empty(
-        [total_seq_lens, head_dim + 4], device=k.device, dtype=torch.uint8
+        [total_seq_lens, head_dim + 4], device=device, dtype=torch.uint8
     )
     fp8_dtype = current_platform.fp8_dtype()
     _k_fp8 = _flattened_kv[..., :head_dim].view(fp8_dtype).contiguous()
@@ -485,7 +589,7 @@ def rocm_aiter_sparse_attn_indexer_fake(
     return topk_indices_buffer
 
 
-def rocm_aiter_sparse_attn_indexer(
+def rocm_aiter_sparse_attn_indexer_native(
     hidden_states: torch.Tensor,
     k_cache_prefix: LayerNameType,
     kv_cache: torch.Tensor,
@@ -499,10 +603,12 @@ def rocm_aiter_sparse_attn_indexer(
     max_model_len: int,
     total_seq_lens: int,
     topk_indices_buffer: torch.Tensor | None,
+    skip_k_cache_insert: bool = False,
 ) -> torch.Tensor:
     # careful! this will be None in dummy run
     attn_metadata = get_forward_context().attn_metadata
     fp8_dtype = current_platform.fp8_dtype()
+    from vllm import _custom_ops as ops
     from vllm.utils.torch_utils import _resolve_layer_name
 
     k_cache_prefix = _resolve_layer_name(k_cache_prefix)
@@ -531,19 +637,33 @@ def rocm_aiter_sparse_attn_indexer(
     has_decode = layer_attn_metadata.num_decodes > 0
     has_prefill = layer_attn_metadata.num_prefills > 0
     num_decode_tokens = layer_attn_metadata.num_decode_tokens
+    device = hidden_states.device if k is None else k.device
 
     # during speculative decoding, k may be padded to the CUDA graph batch
     # size while slot_mapping only covers actual tokens.
     num_tokens = slot_mapping.shape[0]
-    k = k[:num_tokens]
+    if k is not None:
+        k = k[:num_tokens]
+    elif not skip_k_cache_insert:
+        raise ValueError("k must be provided when skip_k_cache_insert is False")
 
-    ops.indexer_k_quant_and_cache(
-        k,
-        kv_cache,
-        slot_mapping,
-        quant_block_size,
-        scale_fmt,
-    )
+    if not skip_k_cache_insert:
+        if _ON_GFX942:
+            ops.indexer_k_quant_and_cache(
+                k,
+                kv_cache,
+                slot_mapping,
+                quant_block_size,
+                scale_fmt,
+            )
+        else:
+            indexer_k_quant_and_cache_triton(
+                k,
+                kv_cache,
+                slot_mapping,
+                quant_block_size,
+                scale_fmt,
+            )
 
     topk_indices_buffer[: hidden_states.shape[0]] = -1
     if has_prefill:
@@ -552,22 +672,31 @@ def rocm_aiter_sparse_attn_indexer(
         for chunk in prefill_metadata.chunks:
             k_fp8 = torch.empty(
                 [chunk.total_seq_lens, head_dim],
-                device=k.device,
+                device=device,
                 dtype=fp8_dtype,
             )
             k_scale = torch.empty(
                 [chunk.total_seq_lens, 4],
-                device=k.device,
+                device=device,
                 dtype=torch.uint8,
             )
-
-            ops.cp_gather_indexer_k_quant_cache(
-                kv_cache,
-                k_fp8,
-                k_scale,
-                chunk.block_table,
-                chunk.cu_seq_lens,
-            )
+            if _ON_GFX942:
+                ops.cp_gather_indexer_k_quant_cache(
+                    kv_cache,
+                    k_fp8,
+                    k_scale,
+                    chunk.block_table,
+                    chunk.cu_seq_lens,
+                )
+            else:
+                cp_gather_indexer_k_quant_cache_triton(
+                    kv_cache,
+                    k_fp8,
+                    k_scale,
+                    chunk.block_table,
+                    chunk.cu_seq_lens,
+                    token_to_seq=chunk.token_to_seq,
+                )
 
             logits = rocm_fp8_mqa_logits(
                 q_fp8[chunk.token_start : chunk.token_end],
@@ -576,21 +705,10 @@ def rocm_aiter_sparse_attn_indexer(
                 chunk.cu_seqlen_ks,
                 chunk.cu_seqlen_ke,
             )
-            num_rows = logits.shape[0]
-            assert topk_tokens == 2048, "top_k_per_row assumes size 2048"
             topk_indices = topk_indices_buffer[
                 chunk.token_start : chunk.token_end, :topk_tokens
             ]
-            torch.ops._C.top_k_per_row_prefill(
-                logits,
-                chunk.cu_seqlen_ks,
-                chunk.cu_seqlen_ke,
-                topk_indices,
-                num_rows,
-                logits.stride(0),
-                logits.stride(1),
-                topk_tokens,
-            )
+            topk_indices.copy_(_topk_indices_torch(logits, topk_tokens))
 
     if has_decode:
         decode_metadata = layer_attn_metadata.decode
@@ -627,19 +745,8 @@ def rocm_aiter_sparse_attn_indexer(
             max_model_len=max_model_len,
         )
 
-        num_rows = logits.shape[0]
-        assert topk_tokens == 2048, "top_k_per_row assumes size 2048"
         topk_indices = topk_indices_buffer[:num_decode_tokens, :topk_tokens]
-        torch.ops._C.top_k_per_row_decode(
-            logits,
-            next_n,
-            decode_metadata.seq_lens,
-            topk_indices,
-            num_rows,
-            logits.stride(0),
-            logits.stride(1),
-            topk_tokens,
-        )
+        topk_indices.copy_(_topk_indices_torch(logits, topk_tokens)[:num_decode_tokens])
 
         if decode_metadata.requires_padding:
             # if padded, we need to unpack
@@ -653,3 +760,370 @@ def rocm_aiter_sparse_attn_indexer(
             )
 
     return topk_indices_buffer
+
+
+def rocm_aiter_sparse_attn_indexer(
+    hidden_states: torch.Tensor,
+    k_cache_prefix: LayerNameType,
+    kv_cache: torch.Tensor,
+    q_fp8: torch.Tensor,
+    k: torch.Tensor,
+    weights: torch.Tensor,
+    quant_block_size: int,
+    scale_fmt: str | None,
+    topk_tokens: int,
+    head_dim: int,
+    max_model_len: int,
+    total_seq_lens: int,
+    topk_indices_buffer: torch.Tensor | None,
+) -> torch.Tensor:
+    return rocm_aiter_sparse_attn_indexer_native(
+        hidden_states,
+        k_cache_prefix,
+        kv_cache,
+        q_fp8,
+        k,
+        weights,
+        quant_block_size,
+        scale_fmt,
+        topk_tokens,
+        head_dim,
+        max_model_len,
+        total_seq_lens,
+        topk_indices_buffer,
+        skip_k_cache_insert=False,
+    )
+
+
+def _decode_e8m0_scales(scale: torch.Tensor) -> torch.Tensor:
+    if scale.dtype == torch.float8_e8m0fnu:
+        from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+            _upcast_e8m0_to_fp32,
+        )
+
+        return _upcast_e8m0_to_fp32(scale).contiguous()
+    return scale.to(torch.float32)
+
+
+def _expand_2d_block_scales(
+    scale: torch.Tensor,
+    rows: int,
+    cols: int,
+) -> torch.Tensor:
+    scale = _decode_e8m0_scales(scale)
+    row_blocks, col_blocks = scale.shape[-2:]
+    row_block = math.ceil(rows / row_blocks)
+    col_block = math.ceil(cols / col_blocks)
+    scale = torch.repeat_interleave(scale, row_block, dim=-2)[..., :rows, :]
+    scale = torch.repeat_interleave(scale, col_block, dim=-1)[..., :, :cols]
+    return scale
+
+
+def _apply_gptj_inv_rope_ref(
+    x: torch.Tensor,
+    positions: torch.Tensor,
+    cos_sin_cache: torch.Tensor,
+    rope_dim: int,
+) -> torch.Tensor:
+    if rope_dim == 0 or x.numel() == 0:
+        return x
+    half_rot = rope_dim // 2
+    nope_dim = x.shape[-1] - rope_dim
+    dtype = x.dtype
+    x = x.to(torch.float32)
+    cache = cos_sin_cache.index_select(0, positions.to(torch.long))
+    cos = cache[:, :half_rot].to(torch.float32)
+    sin = cache[:, half_rot : 2 * half_rot].to(torch.float32)
+    view_shape = (positions.shape[0],) + (1,) * (x.dim() - 2) + (half_rot,)
+    cos = cos.view(view_shape)
+    sin = sin.view(view_shape)
+    rope = x[..., nope_dim:]
+    y_even = rope[..., 0::2]
+    y_odd = rope[..., 1::2]
+    rope_out = torch.stack(
+        (y_even * cos + y_odd * sin, y_odd * cos - y_even * sin),
+        dim=-1,
+    ).flatten(-2)
+    x = x.clone()
+    x[..., nope_dim:] = rope_out
+    return x.to(dtype)
+
+
+def _apply_inv_rope_ref(
+    rotary_emb: torch.nn.Module,
+    x: torch.Tensor,
+    positions: torch.Tensor,
+    rope_dim: int,
+) -> torch.Tensor:
+    if hasattr(rotary_emb, "forward_native"):
+        try:
+            query, _ = rotary_emb.forward_native(
+                positions,
+                x.clone(),
+                None,
+                inverse=True,
+            )
+            return query
+        except TypeError:
+            pass
+    return _apply_gptj_inv_rope_ref(x, positions, rotary_emb.cos_sin_cache, rope_dim)
+
+
+def rocm_inv_rope_einsum(
+    rotary_emb: torch.nn.Module,
+    o: torch.Tensor,
+    positions: torch.Tensor,
+    rope_head_dim: int,
+    n_local_groups: int,
+    o_lora_rank: int,
+    wo_a: torch.nn.Module,
+) -> torch.Tensor:
+    """Reference inverse-RoPE + WO_A einsum path used on ROCm."""
+    o_ref = _apply_inv_rope_ref(rotary_emb, o, positions, rope_head_dim).to(
+        torch.bfloat16
+    )
+    o_ref = o_ref.view(o.shape[0], n_local_groups, -1)
+
+    hidden_dim = o_ref.shape[-1]
+    if hasattr(wo_a, "weight_scale_inv"):
+        wo_a_weight = wo_a.weight.view(n_local_groups, o_lora_rank, hidden_dim).to(
+            torch.float32
+        )
+        wo_a_scale = _expand_2d_block_scales(
+            wo_a.weight_scale_inv.view(
+                n_local_groups, -1, wo_a.weight_scale_inv.shape[-1]
+            ),
+            o_lora_rank,
+            hidden_dim,
+        )
+        wo_a_weight = (wo_a_weight * wo_a_scale).to(torch.bfloat16)
+    else:
+        wo_a_weight = wo_a.weight.view(n_local_groups, o_lora_rank, hidden_dim).to(
+            torch.bfloat16
+        )
+
+    return torch.einsum("tgd,grd->tgr", o_ref, wo_a_weight)
+
+
+def rocm_ref_sparse_attn_prefill(
+    q: torch.Tensor,
+    kv: torch.Tensor,
+    indices: torch.Tensor,
+    topk_length: torch.Tensor | None,
+    scale: float,
+    head_dim: int,
+    attn_sink: torch.Tensor | None,
+) -> torch.Tensor:
+    indices = indices.clone().squeeze(1)
+    s_q, h_q, d_qk = q.shape
+    topk = indices.shape[-1]
+    s_kv = kv.shape[0]
+    if topk_length is not None:
+        mask = torch.arange(topk, device=indices.device).unsqueeze(
+            0
+        ) >= topk_length.unsqueeze(1)
+        indices[mask] = -1
+    invalid_mask = (indices < 0) | (indices >= s_kv)
+    indices[invalid_mask] = 0
+
+    qf = q.float()
+    gathered_kv = kv.index_select(0, indices.flatten()).reshape(s_q, topk, d_qk).float()
+    scores = qf @ gathered_kv.transpose(1, 2)
+    scores *= scale
+    scores[invalid_mask.unsqueeze(1).expand_as(scores)] = float("-inf")
+
+    orig_lse = torch.logsumexp(scores, dim=-1)
+    lse_for_o = orig_lse
+    if attn_sink is not None:
+        lse_for_o = torch.logsumexp(
+            torch.stack(
+                [orig_lse, attn_sink[:h_q].view(1, h_q).expand_as(orig_lse)],
+                dim=0,
+            ),
+            dim=0,
+        )
+    lse_for_o = lse_for_o.clone()
+    lse_for_o[lse_for_o == float("-inf")] = float("+inf")
+    probs = torch.exp(scores - lse_for_o.unsqueeze(-1))
+    out = probs @ gathered_kv[..., :head_dim]
+    lonely_q_mask = orig_lse == float("-inf")
+    out[lonely_q_mask.unsqueeze(-1).expand_as(out)] = 0.0
+    return out.to(torch.bfloat16)
+
+
+def rocm_sparse_attn_prefill(
+    q: torch.Tensor,
+    kv: torch.Tensor,
+    indices: torch.Tensor,
+    topk_length: torch.Tensor | None,
+    scale: float,
+    head_dim: int,
+    attn_sink: torch.Tensor | None,
+    output: torch.Tensor,
+) -> None:
+    output_chunk = rocm_ref_sparse_attn_prefill(
+        q=q,
+        kv=kv,
+        indices=indices,
+        topk_length=topk_length,
+        scale=scale,
+        head_dim=head_dim,
+        attn_sink=attn_sink,
+    )
+    output.copy_(output_chunk.to(output.dtype))
+
+
+def rocm_dequantize_blocked_k_cache(
+    quant_k_cache: torch.Tensor,
+    head_dim: int,
+    nope_head_dim: int,
+    rope_head_dim: int,
+) -> torch.Tensor:
+    fp8_dtype = current_platform.fp8_dtype()
+    tile_size = 64
+    num_tiles = nope_head_dim // tile_size
+
+    num_blocks, block_size, _ = quant_k_cache.shape
+    quant_k_cache = quant_k_cache.view(num_blocks, -1)
+    input_nope_rope = quant_k_cache[
+        :, : block_size * (nope_head_dim + 2 * rope_head_dim)
+    ].view(num_blocks, block_size, nope_head_dim + 2 * rope_head_dim)
+    input_nope = input_nope_rope[:, :, :nope_head_dim].view(fp8_dtype)
+    input_rope = input_nope_rope[:, :, nope_head_dim:].view(torch.bfloat16)
+    input_scale = (
+        quant_k_cache[:, block_size * (nope_head_dim + 2 * rope_head_dim) :]
+        .view(num_blocks, block_size, 8)[:, :, :num_tiles]
+        .view(torch.float8_e8m0fnu)
+    )
+
+    result = torch.empty(
+        (num_blocks, block_size, 1, head_dim),
+        dtype=torch.bfloat16,
+        device=quant_k_cache.device,
+    )
+    result[..., nope_head_dim:] = input_rope.unsqueeze(2)
+    for tile_idx in range(num_tiles):
+        cur_nope = input_nope[
+            ..., tile_idx * tile_size : (tile_idx + 1) * tile_size
+        ].to(torch.bfloat16)
+        cur_scales = input_scale[:, :, tile_idx].to(torch.bfloat16).unsqueeze(-1)
+        result[..., tile_idx * tile_size : (tile_idx + 1) * tile_size] = (
+            cur_nope * cur_scales
+        ).unsqueeze(2)
+    return result
+
+
+def rocm_ref_sparse_attn_decode(
+    q: torch.Tensor,
+    blocked_k: torch.Tensor,
+    indices_in_kvcache: torch.Tensor,
+    topk_length: torch.Tensor | None,
+    scale: float,
+    head_dim: int,
+    attn_sink: torch.Tensor | None,
+    extra_blocked_k: torch.Tensor | None = None,
+    extra_indices_in_kvcache: torch.Tensor | None = None,
+    extra_topk_length: torch.Tensor | None = None,
+) -> torch.Tensor:
+    b, s_q, h_q, d_qk = q.shape
+
+    def process_scope(
+        cur_blocked_k: torch.Tensor,
+        cur_indices: torch.Tensor,
+        cur_topk_length: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        cur_indices = cur_indices.reshape(b, s_q, -1)
+        topk = cur_indices.size(-1)
+        fixed_indices = torch.clamp_min(cur_indices, 0)
+        gathered_kv = (
+            cur_blocked_k.view(-1, d_qk)
+            .index_select(0, fixed_indices.view(-1))
+            .view(b, s_q, topk, d_qk)
+        )
+        invalid_mask = cur_indices == -1
+        if cur_topk_length is not None:
+            cur_topk_length = cur_topk_length.reshape(b)
+            invalid_mask |= torch.arange(0, topk, device=invalid_mask.device).view(
+                1, 1, topk
+            ) >= cur_topk_length.view(b, 1, 1)
+        return gathered_kv, invalid_mask
+
+    gathered_kv, invalid_mask = process_scope(
+        blocked_k, indices_in_kvcache, topk_length
+    )
+    if extra_blocked_k is not None:
+        assert extra_indices_in_kvcache is not None
+        gathered_kv1, invalid_mask1 = process_scope(
+            extra_blocked_k, extra_indices_in_kvcache, extra_topk_length
+        )
+        gathered_kv = torch.cat([gathered_kv, gathered_kv1], dim=2)
+        invalid_mask = torch.cat([invalid_mask, invalid_mask1], dim=2)
+
+    gathered_kv = gathered_kv.view(b * s_q, -1, d_qk).float()
+    gathered_kv[gathered_kv != gathered_kv] = 0.0
+    qf = q.float().view(b * s_q, h_q, d_qk)
+    attn_weight = qf @ gathered_kv.transpose(-1, -2)
+    attn_weight *= scale
+    attn_weight[
+        invalid_mask.view(b * s_q, 1, -1).expand(b * s_q, h_q, invalid_mask.size(-1))
+    ] = float("-inf")
+    lse = attn_weight.logsumexp(dim=-1)
+    attn_weight = torch.exp(attn_weight - lse.unsqueeze(-1))
+    output = attn_weight @ gathered_kv[..., :head_dim]
+    output = output.view(b, s_q, h_q, head_dim)
+    lse = lse.view(b, s_q, h_q)
+
+    if attn_sink is not None:
+        output *= (1.0 / (1.0 + torch.exp(attn_sink.view(1, 1, h_q) - lse))).unsqueeze(
+            -1
+        )
+
+    lonely_q_mask = lse == float("-inf")
+    output[lonely_q_mask.unsqueeze(-1).expand_as(output)] = 0.0
+    return output.squeeze(1).to(torch.bfloat16)
+
+
+def rocm_forward_decode_fallback(
+    q: torch.Tensor,
+    kv_cache: torch.Tensor | None,
+    swa_k_cache: torch.Tensor,
+    swa_only: bool,
+    topk_indices: torch.Tensor | None,
+    topk_lens: torch.Tensor | None,
+    swa_indices: torch.Tensor,
+    swa_lens: torch.Tensor,
+    attn_sink: torch.Tensor | None,
+    scale: float,
+    head_dim: int,
+    nope_head_dim: int,
+    rope_head_dim: int,
+    output: torch.Tensor,
+) -> None:
+    blocked_swa = rocm_dequantize_blocked_k_cache(
+        swa_k_cache,
+        head_dim=head_dim,
+        nope_head_dim=nope_head_dim,
+        rope_head_dim=rope_head_dim,
+    )
+    blocked_extra = None
+    if not swa_only:
+        assert kv_cache is not None
+        blocked_extra = rocm_dequantize_blocked_k_cache(
+            kv_cache,
+            head_dim=head_dim,
+            nope_head_dim=nope_head_dim,
+            rope_head_dim=rope_head_dim,
+        )
+    attn_out = rocm_ref_sparse_attn_decode(
+        q=q.unsqueeze(1),
+        blocked_k=blocked_swa,
+        indices_in_kvcache=swa_indices.unsqueeze(1),
+        topk_length=swa_lens,
+        scale=scale,
+        head_dim=head_dim,
+        attn_sink=attn_sink[: q.shape[1]] if attn_sink is not None else None,
+        extra_blocked_k=blocked_extra,
+        extra_indices_in_kvcache=topk_indices,
+        extra_topk_length=topk_lens,
+    )
+    output.copy_(attn_out.to(output.dtype))
diff --git a/vllm/v1/attention/ops/triton_attention_helpers.py b/vllm/v1/attention/ops/triton_attention_helpers.py
new file mode 100644
index 000000000000..6ed50f6a2df2
--- /dev/null
+++ b/vllm/v1/attention/ops/triton_attention_helpers.py
@@ -0,0 +1,383 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Shared ``@triton.jit`` helpers used by the unified attention kernel
+and ``reduce_segments``.
+
+These are plain attention-loop helpers — mask building, ALiBi / QQ-bias
+score post-processing, online-softmax bookkeeping, tile-loop bounds,
+sequence lookup — extracted so the 2D and 3D paths of the unified
+kernel (and any future consumer) share a single implementation.
+"""
+
+from __future__ import annotations
+
+from vllm.triton_utils import tl, triton
+
+# ===========================================================================
+# Scalar helpers (reused by every kernel + reduce_segments)
+# ===========================================================================
+
+
+@triton.jit
+def cdiv_fn(x, y):
+    """Ceiling division.  Kept as a helper to keep kernel bodies terse."""
+    return (x + y - 1) // y
+
+
+@triton.jit
+def apply_softcap(S, x):
+    """Softcap (aka tanh-style clamp) used to bound attention scores.
+
+    ``x * tanh(S / x)`` rewritten to avoid a direct ``tanh`` call.
+    """
+    Sdiv = S / x
+    p1 = tl.exp(Sdiv)
+    p2 = tl.exp(-Sdiv)
+    return x * (p1 - p2) / (p1 + p2)
+
+
+# ===========================================================================
+# Attention loop
+# ===========================================================================
+
+
+@triton.jit
+def resolve_seq_and_query_len(
+    query_start_len_ptr,
+    seq_lens_ptr,
+    q_block_global_idx,
+    num_seqs,
+    BLOCK_Q: tl.constexpr,
+):
+    """Resolve the (sequence, q-block-within-sequence) pair and load the
+    per-sequence lengths.
+
+    Shared across every attention kernel — the ``q_block_global_idx``
+    program id indexes into the flattened ``(seq, q_block_in_seq)``
+    space, and a binary search over ``query_start_len_ptr`` recovers
+    the (seq, local-q-block) pair.
+
+    Returns ``(seq_idx, q_block_local_idx, cur_batch_in_all_start_index,
+    cur_batch_query_len, seq_len)``.  Callers must still early-return
+    when ``q_block_local_idx * BLOCK_Q >= cur_batch_query_len`` (Triton
+    helpers cannot return from the caller).
+    """
+    # find_seq_idx is defined below; forward use is fine inside @triton.jit.
+    seq_idx = find_seq_idx(
+        query_start_len_ptr, q_block_global_idx, num_seqs, BLOCK_Q, True
+    )
+    q_block_start_idx = tl.load(query_start_len_ptr + seq_idx) // BLOCK_Q + seq_idx
+    q_block_local_idx = q_block_global_idx - q_block_start_idx
+    cur_start = tl.load(query_start_len_ptr + seq_idx)
+    cur_stop = tl.load(query_start_len_ptr + seq_idx + 1)
+    cur_batch_query_len = cur_stop - cur_start
+    seq_len = tl.load(seq_lens_ptr + seq_idx)
+    return seq_idx, q_block_local_idx, cur_start, cur_batch_query_len, seq_len
+
+
+@triton.jit
+def find_seq_idx(
+    query_start_len_ptr,
+    target_idx,
+    num_seqs,
+    BLOCK_Q: tl.constexpr,
+    use_q_block_mode: tl.constexpr,
+):
+    """Binary search over the cumulative query-length prefix.
+
+    When ``use_q_block_mode`` is True, the prefix values are reshaped
+    into units of ``BLOCK_Q`` plus one entry per boundary — matching
+    the q-block grid laid out by the attention kernels.  When False
+    we search the plain cumulative-length prefix (used by
+    ``reduce_segments`` which iterates over raw query tokens).
+    """
+    left: tl.int32 = 0
+    right = num_seqs
+    while left < right:
+        mid = (left + right) // 2
+        val = tl.load(query_start_len_ptr + mid)
+        mid_val = val // BLOCK_Q + mid if use_q_block_mode else val
+
+        if mid_val <= target_idx:
+            left = mid + 1
+        else:
+            right = mid
+
+    return left - 1
+
+
+@triton.jit
+def init_softmax_M(
+    sink_ptr,
+    query_offset_1,
+    query_mask_1,
+    segm_idx_or_0,
+    BLOCK_M: tl.constexpr,
+    USE_SINKS: tl.constexpr,
+    IS_3D: tl.constexpr,
+):
+    """Initial row-max ``M`` for the online softmax.
+
+    Without sinks: ``-inf``.  With sinks: load the per-head sink bias
+    once.  In 3D mode only segment 0 loads — ``reduce_segments`` adds
+    the sink contribution exactly once across segments, so other
+    segments must start from ``-inf``.
+
+    ``segm_idx_or_0`` is the 3D segment index or 0 for 2D (caller
+    passes ``0`` when ``IS_3D`` is False).
+    """
+    M = tl.full([BLOCK_M], float("-inf"), dtype=tl.float32)
+    if USE_SINKS:
+        load_sinks = (not IS_3D) or (segm_idx_or_0 == 0)
+        if load_sinks:
+            M = tl.load(
+                sink_ptr + query_offset_1,
+                mask=query_mask_1,
+                other=float("-inf"),
+            ).to(tl.float32)
+    return M
+
+
+@triton.jit
+def compute_tile_loop_bounds(
+    context_len,
+    seq_len,
+    cur_batch_query_len,
+    q_block_local_idx,
+    segm_idx_or_0,
+    tiles_per_segment_or_0,
+    TILE_SIZE: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_Q: tl.constexpr,
+    num_queries_per_kv: tl.constexpr,
+    SLIDING_WINDOW: tl.constexpr,
+    USE_MM_PREFIX: tl.constexpr,
+    IS_3D: tl.constexpr,
+    CHUNK_LOOKBACK: tl.constexpr = -1,
+    CHUNK_SIZE: tl.constexpr = -1,
+):
+    """Compute the tile-loop bounds ``(loop_lo, loop_hi)`` and the
+    derived ``max_seq_prefix_len`` used for per-tile masking.
+
+    Combines three concerns into one helper:
+
+    1. Longest prefix spanned by any query token in this q-block.
+       Clamped to ``seq_len`` (causal) or extended to it when
+       mm_prefix is active (bidirectional ranges can reach past the
+       causal prefix).
+    2. Sliding-window pruning: narrows ``[tile_start, tile_end)`` to
+       only tiles that can contain an allowed key under SWA.
+    3. 3D scoping: when ``IS_3D`` is True, further narrows to the
+       segment's slice via ``(segm_idx * tiles_per_segment,
+       (segm_idx + 1) * tiles_per_segment)``.
+    """
+    # compute the length of the longest sequence prefix spanned by any
+    # query token in the current q_block (q_block_local_idx)
+    max_seq_prefix_len = (
+        context_len
+        + q_block_local_idx * BLOCK_Q
+        + (BLOCK_M - 1) // num_queries_per_kv
+        + 1
+    )
+    if USE_MM_PREFIX:
+        # image bidirectional attention ranges require a full range
+        # including q_block padding to make sure doc mask is correct
+        max_seq_prefix_len = tl.maximum(max_seq_prefix_len, seq_len)
+    else:
+        max_seq_prefix_len = tl.minimum(max_seq_prefix_len, seq_len)
+
+    num_tiles = cdiv_fn(max_seq_prefix_len, TILE_SIZE)
+
+    # ---- Sliding-window tile pruning --------------------
+    # Default: keep previous global behavior
+    tile_start = 0
+    tile_end = num_tiles
+    # TODO(Isotr0py): sliding window pruning with image bidirectional mask
+    if SLIDING_WINDOW > 0 and not USE_MM_PREFIX:
+        # Query rows covered by this Q-block
+        qpos_lo = q_block_local_idx * BLOCK_Q
+        qpos_hi = tl.minimum(
+            qpos_lo + (BLOCK_M - 1) // num_queries_per_kv,
+            cur_batch_query_len - 1,
+        )
+        # For sliding window, each query position q can only attend to
+        # keys in the range [q_abs - SLIDING_WINDOW + 1, q_abs]
+        # where q_abs = context_len + q
+        # The union of allowed key positions for this Q-block is:
+        # [context_len + qpos_lo - SLIDING_WINDOW + 1, context_len + qpos_hi]
+        q_abs = context_len + qpos_lo
+        if CHUNK_LOOKBACK > -1:
+            # Chunked attention: align lower bound to the start of the
+            # lookback'th previous chunk.
+            first_allowed_key = ((q_abs // CHUNK_SIZE) - CHUNK_LOOKBACK) * CHUNK_SIZE
+        else:
+            first_allowed_key = q_abs - SLIDING_WINDOW + 1
+        last_allowed_key = context_len + qpos_hi
+        # Convert to tile indices and clamp
+        tile_start = tl.maximum(0, first_allowed_key // TILE_SIZE)
+        tile_end = tl.minimum((last_allowed_key // TILE_SIZE) + 1, num_tiles)
+
+    if IS_3D:
+        loop_lo = max(segm_idx_or_0 * tiles_per_segment_or_0, tile_start)
+        loop_hi = min((segm_idx_or_0 + 1) * tiles_per_segment_or_0, tile_end)
+    else:
+        loop_lo = tile_start
+        loop_hi = tile_end
+
+    return loop_lo, loop_hi, max_seq_prefix_len
+
+
+@triton.jit
+def store_segm_reduce_scalars(
+    segm_max_ptr,
+    segm_expsum_ptr,
+    query_offset_0,
+    query_offset_1,
+    segm_idx,
+    M,
+    L,
+    query_mask_0,
+    query_mask_1,
+    num_query_heads: tl.constexpr,
+    NUM_SEGMENTS_PER_SEQ: tl.constexpr,
+):
+    """Store per-segment ``M`` and ``L`` for ``reduce_segments`` to
+    combine into the final softmax.
+
+    Shared across every 3D attention epilogue; the per-token output
+    stripes are mode-specific (flat / 2-stream split / 4-stream split)
+    and stay inlined.
+    """
+    segm_offset = (
+        query_offset_0.to(tl.int64) * (num_query_heads * NUM_SEGMENTS_PER_SEQ)
+        + query_offset_1 * NUM_SEGMENTS_PER_SEQ
+        + segm_idx
+    )
+    tl.store(segm_max_ptr + segm_offset, M, mask=query_mask_0 & query_mask_1)
+    tl.store(segm_expsum_ptr + segm_offset, L, mask=query_mask_0 & query_mask_1)
+
+
+@triton.jit
+def compute_kv_seq_mask(
+    query_abs_pos,
+    seq_offset,
+    seq_idx,
+    mm_prefix_range_ptr,
+    SLIDING_WINDOW: tl.constexpr,
+    USE_MM_PREFIX: tl.constexpr,
+    MAX_MM_RANGES: tl.constexpr,
+    CHUNK_LOOKBACK: tl.constexpr = -1,
+    CHUNK_SIZE: tl.constexpr = -1,
+):
+    """Build the KV mask for one tile.
+
+    Causal (key <= query) by default; AND-ed with either chunked
+    attention (``CHUNK_LOOKBACK >= 0``) or sliding window
+    (``SLIDING_WINDOW > 0``); OR-ed with the bidirectional ranges from
+    ``mm_prefix_range`` when PrefixLM / multimodal attention is active.
+    Order matches FlexAttention: ``(causal AND window) OR mm_prefix``.
+    Chunked attention takes precedence over sliding window when both
+    are non-default — the launcher zeros ``CHUNK_LOOKBACK`` whenever
+    sliding window is disabled.
+    """
+    # Compute attention mask: causal by default (key <= query)
+    seq_mask = seq_offset[None, :] <= query_abs_pos
+
+    # Apply sliding window / chunked attention to base mask
+    # BEFORE mm_prefix OR.
+    # Order must match FlexAttention:
+    #   (causal AND sliding_window) OR mm_prefix
+    if CHUNK_LOOKBACK > -1:
+        seq_mask = seq_mask & (
+            (query_abs_pos // CHUNK_SIZE - seq_offset[None, :] // CHUNK_SIZE)
+            <= CHUNK_LOOKBACK
+        )
+    elif SLIDING_WINDOW > 0:
+        seq_mask = seq_mask & ((query_abs_pos - seq_offset) < SLIDING_WINDOW)
+
+    # PrefixLM: extend mask with bidirectional ranges for multimodal tokens.
+    # Applied AFTER sliding window so mm_prefix ranges override SW restriction.
+    if USE_MM_PREFIX:
+        for i in range(MAX_MM_RANGES):
+            range_start = tl.load(
+                mm_prefix_range_ptr + seq_idx * MAX_MM_RANGES * 2 + i * 2
+            )
+            range_end = tl.load(
+                mm_prefix_range_ptr + seq_idx * MAX_MM_RANGES * 2 + i * 2 + 1
+            )
+            is_valid = range_start < range_end
+            q_in_range = (
+                (query_abs_pos >= range_start) & (query_abs_pos <= range_end) & is_valid
+            )
+            k_in_range = (
+                (seq_offset[None, :] >= range_start)
+                & (seq_offset[None, :] <= range_end)
+                & is_valid
+            )
+            seq_mask |= q_in_range & k_in_range
+    return seq_mask
+
+
+@triton.jit
+def apply_alibi_to_score(
+    S,
+    alibi_slope,
+    seq_offset,
+    context_len,
+    query_pos,
+    USE_ALIBI_SQRT: tl.constexpr,
+):
+    """Add the ALiBi positional bias (linear or sqrt variant) to S in-place."""
+    if USE_ALIBI_SQRT:
+        relative_pos = seq_offset - (context_len + query_pos[:, None])
+        alibi_offset = tl.where(
+            relative_pos <= 0,
+            -tl.sqrt((-relative_pos).to(tl.float32)),
+            0.0,
+        )
+    else:
+        alibi_offset = seq_offset - context_len
+    return S + alibi_slope[:, None] * alibi_offset
+
+
+@triton.jit
+def load_qq_bias_tile(
+    qq_bias_row_ptrs,
+    seq_offset,
+    context_len,
+    qq_bias_stride_0,
+):
+    """Load the qq-bias slice for keys that correspond to query rows."""
+    key_rel_pos = seq_offset - context_len
+    is_query_key = key_rel_pos >= 0 and key_rel_pos < qq_bias_stride_0
+    return tl.load(
+        qq_bias_row_ptrs + key_rel_pos[None, :],
+        mask=is_query_key[None, :],
+        other=0.0,
+    )
+
+
+@triton.jit
+def softmax_step(S, M, L):
+    """Online softmax update for one tile.
+
+    Returns ``(M_new, L_new, P, alpha)``.  Caller is responsible for
+    rescaling its accumulator(s) by ``alpha[:, None]`` — done outside so
+    kernels with a different number / shape of accumulators can reuse
+    the same step.
+    """
+    # compute running maximum
+    # m_j : (BLOCK_M,)
+    m_j = tl.maximum(M, tl.max(S, axis=1))
+    # For sliding window there's a chance the max is -inf due to masking of
+    # the entire row. In this case we need to set m_j 0 to avoid NaN
+    m_j = tl.where(m_j > float("-inf"), m_j, 0.0)
+    # P : (BLOCK_M, TILE_SIZE)
+    P = tl.exp(S - m_j[:, None])
+    # l_j : (BLOCK_M,)
+    l_j = tl.sum(P, axis=1)
+    # alpha : (BLOCK_M, )
+    alpha = tl.exp(M - m_j)
+    # update constants
+    L_new = L * alpha + l_j
+    return m_j, L_new, P, alpha
diff --git a/vllm/v1/attention/ops/triton_decode_attention.py b/vllm/v1/attention/ops/triton_decode_attention.py
index 8118db0da8cf..e1059b47bcba 100644
--- a/vllm/v1/attention/ops/triton_decode_attention.py
+++ b/vllm/v1/attention/ops/triton_decode_attention.py
@@ -551,6 +551,7 @@ def _fwd_kernel_stage2(
     NUM_KV_SPLITS: tl.constexpr,
     BLOCK_DV: tl.constexpr,
     Lv: tl.constexpr,
+    OUTPUT_FP16: tl.constexpr = 0,
 ):
     cur_batch = tl.program_id(0)
     cur_head = tl.program_id(1)
@@ -587,9 +588,12 @@ def _fwd_kernel_stage2(
             e_sum = e_sum * old_scale + exp_logic
             e_max = n_e_max
 
+    result = acc / e_sum
+    if OUTPUT_FP16:
+        result = result.to(tl.float16)
     tl.store(
         o + cur_batch * stride_obs + cur_head * stride_oh + offs_d,
-        acc / e_sum,
+        result,
         mask=mask_d,
     )
     lse_val = e_max + tl.log(e_sum)
diff --git a/vllm/v1/attention/ops/triton_turboquant_decode.py b/vllm/v1/attention/ops/triton_turboquant_decode.py
new file mode 100644
index 000000000000..3adaf2610d8d
--- /dev/null
+++ b/vllm/v1/attention/ops/triton_turboquant_decode.py
@@ -0,0 +1,630 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Triton fused TurboQuant decode attention.
+
+Decode path: Triton stage1 (split-KV tiled attention scoring + value
+accumulation) + stage2 (log-sum-exp reduction across splits).
+
+Supports FP8 (E4M3) keys, 3-bit and 4-bit uniform quantized values.
+"""
+
+import math
+from typing import Any
+
+import torch
+
+from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
+from vllm.v1.attention.ops.triton_decode_attention import (
+    _fwd_kernel_stage2,
+)
+
+_FP8_E4B15: dict[int, int] = {}
+
+
+def _use_fp8_e4b15(device: int = 0) -> int:
+    """Return 1 if device needs fp8e4b15 (Ampere/Ada, SM < 8.9), else 0.
+    On non-CUDA platforms (e.g. XPU), always returns 0 (use e4nv format).
+    """
+    if device not in _FP8_E4B15:
+        if current_platform.is_cuda_alike():
+            cap = torch.cuda.get_device_capability(device)
+            _FP8_E4B15[device] = 1 if cap < (8, 9) else 0
+        else:
+            _FP8_E4B15[device] = 0
+    return _FP8_E4B15[device]
+
+
+# ---------------------------------------------------------------------------
+# Stage 1: Fused TQ score + value accumulation (BLOCK_KV tiled)
+# ---------------------------------------------------------------------------
+
+
+@triton.jit
+def _tq_decode_stage1(
+    # Precomputed query projection
+    Q_rot_ptr,  # [B, Hq, D] float32
+    # Compressed KV cache (combined K+V)
+    KV_cache_ptr,  # [num_blocks, block_size, Hk, padded_slot] uint8
+    # Block table and sequence info
+    Block_table_ptr,  # [B, max_num_blocks] int32
+    Seq_lens_ptr,  # [B] int32
+    # TQ parameters
+    Centroids_ptr,  # [n_centroids] float32
+    # Output (intermediate for stage2)
+    Mid_o_ptr,  # [B, Hq, NUM_KV_SPLITS, D+1] float32
+    # Strides
+    stride_qb,
+    stride_qh,  # Q strides: [B, Hq, D]
+    stride_cache_block,
+    stride_cache_pos,
+    stride_cache_head,  # KV cache
+    stride_bt_b,  # block_table stride per batch
+    stride_mid_b,
+    stride_mid_h,
+    stride_mid_s,  # mid_o strides
+    # Constexpr dims
+    NUM_KV_HEADS: tl.constexpr,
+    HEAD_DIM: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,  # KV cache block_size (pages)
+    NUM_KV_SPLITS: tl.constexpr,
+    KV_GROUP_SIZE: tl.constexpr,  # Hq // Hk
+    # TQ layout constants
+    MSE_BITS: tl.constexpr,  # 3 or 4
+    MSE_BYTES: tl.constexpr,  # ceil(D * mse_bits / 8)
+    KPS: tl.constexpr,  # key_packed_size
+    VQB: tl.constexpr,  # value_quant_bits (4 or 8=FP8)
+    VAL_DATA_BYTES: tl.constexpr,  # ceil(D * vqb / 8) or D for FP8
+    # Score constants
+    ATTN_SCALE: tl.constexpr,  # 1/sqrt(D)
+    # Block tile sizes
+    BLOCK_D: tl.constexpr,  # next_power_of_2(HEAD_DIM)
+    BLOCK_KV: tl.constexpr,  # tokens per tile (16)
+    KEY_FP8: tl.constexpr,  # 1 if K is stored as FP8
+    NORM_CORRECTION: tl.constexpr = 0,  # 1 = re-normalize centroids
+    FP8_E4B15: tl.constexpr = 0,  # 1 = use e4b15 (Ampere/Ada), 0 = e4nv (Hopper+)
+):
+    bid = tl.program_id(0)  # batch index
+    hid = tl.program_id(1)  # q_head index
+    sid = tl.program_id(2)  # kv_split index
+
+    kv_head = hid // KV_GROUP_SIZE
+
+    # Sequence length for this batch
+    seq_len = tl.load(Seq_lens_ptr + bid)
+
+    # KV split range
+    split_len = tl.cdiv(seq_len, NUM_KV_SPLITS)
+    split_start = split_len * sid
+    split_end = tl.minimum(split_start + split_len, seq_len)
+
+    if split_start >= split_end:
+        return
+
+    # Dimension offsets
+    d_offs = tl.arange(0, BLOCK_D)
+    d_mask = d_offs < HEAD_DIM
+    kv_range = tl.arange(0, BLOCK_KV)
+
+    # Load query vector: q_rot — [BLOCK_D] float32
+    q_base = bid * stride_qb + hid * stride_qh
+    q_rot = tl.load(Q_rot_ptr + q_base + d_offs, mask=d_mask, other=0.0).to(tl.float32)
+
+    # Precompute byte/bit index vectors for MSE gather loads
+    if not KEY_FP8:
+        mse_bit_off = d_offs * MSE_BITS
+        mse_byte_idx = mse_bit_off // 8
+        mse_bit_shift = mse_bit_off % 8
+        mse_mask = (1 << MSE_BITS) - 1
+
+    # Precompute value bit/byte index vectors (loop-invariant)
+    if VQB == 3:
+        val_bit_off = d_offs * 3
+        val_byte_idx = val_bit_off // 8
+        val_bit_shift = val_bit_off % 8
+
+    # Online softmax accumulators
+    m_prev = -float("inf")
+    l_prev = 0.0
+    acc = tl.zeros([BLOCK_D], dtype=tl.float32)
+
+    bt_base = bid * stride_bt_b
+
+    # ================================================================
+    # TILED LOOP: process BLOCK_KV tokens per iteration
+    # ================================================================
+    for start_n in range(split_start, split_end, BLOCK_KV):
+        kv_offs = start_n + kv_range
+        kv_mask = kv_offs < split_end
+
+        page_idx = kv_offs // BLOCK_SIZE
+        page_off = kv_offs % BLOCK_SIZE
+        block_nums = tl.load(
+            Block_table_ptr + bt_base + page_idx,
+            mask=kv_mask,
+            other=0,
+        ).to(tl.int64)
+
+        slot_bases = (
+            block_nums * stride_cache_block
+            + page_off.to(tl.int64) * stride_cache_pos
+            + tl.cast(kv_head, tl.int64) * stride_cache_head
+        )
+
+        # ============================================================
+        # COMPUTE ATTENTION SCORES: [BLOCK_KV]
+        # ============================================================
+        if KEY_FP8:
+            k_addrs = slot_bases[:, None] + d_offs[None, :]
+            k_raw = tl.load(
+                KV_cache_ptr + k_addrs,
+                mask=kv_mask[:, None] & d_mask[None, :],
+                other=0,
+            )
+            if FP8_E4B15:
+                k_float = k_raw.to(tl.float8e4b15, bitcast=True).to(tl.float32)
+            else:
+                k_float = k_raw.to(tl.float8e4nv, bitcast=True).to(tl.float32)
+            scores = (
+                tl.sum(
+                    tl.where(d_mask[None, :], q_rot[None, :] * k_float, 0.0),
+                    axis=1,
+                )
+                * ATTN_SCALE
+            )
+            scores = tl.where(kv_mask, scores, -float("inf"))
+        else:
+            # MSE unpack + norms
+            mse_addrs0 = slot_bases[:, None] + mse_byte_idx[None, :]
+            mse_raw0 = tl.load(
+                KV_cache_ptr + mse_addrs0,
+                mask=kv_mask[:, None] & d_mask[None, :],
+                other=0,
+            ).to(tl.int32)
+            mse_raw1 = tl.load(
+                KV_cache_ptr + mse_addrs0 + 1,
+                mask=kv_mask[:, None] & d_mask[None, :],
+                other=0,
+            ).to(tl.int32)
+            raw16 = mse_raw0 | (mse_raw1 << 8)
+            mse_idx = (raw16 >> mse_bit_shift[None, :]) & mse_mask
+
+            # Centroid gather + dot product
+            c_vals = tl.load(
+                Centroids_ptr + mse_idx,
+                mask=kv_mask[:, None] & d_mask[None, :],
+                other=0.0,
+            )
+
+            # Norm correction: re-normalize centroid vector to unit norm
+            if NORM_CORRECTION:
+                c_norm_sq = tl.sum(
+                    tl.where(d_mask[None, :], c_vals * c_vals, 0.0),
+                    axis=1,
+                )
+                c_inv_norm = 1.0 / tl.sqrt(c_norm_sq + 1e-16)
+                c_vals = c_vals * c_inv_norm[:, None]
+
+            term1 = tl.sum(
+                tl.where(d_mask[None, :], q_rot[None, :] * c_vals, 0.0),
+                axis=1,
+            )
+
+            # Load norms (fp16 -> fp32): norms are at MSE_BYTES offset
+            norm_bases = slot_bases + MSE_BYTES
+            n_lo = tl.load(KV_cache_ptr + norm_bases, mask=kv_mask, other=0).to(
+                tl.uint16
+            )
+            n_hi = tl.load(KV_cache_ptr + norm_bases + 1, mask=kv_mask, other=0).to(
+                tl.uint16
+            )
+            vec_norms = (n_lo | (n_hi << 8)).to(tl.float16, bitcast=True).to(tl.float32)
+
+            scores = vec_norms * term1 * ATTN_SCALE
+            scores = tl.where(kv_mask, scores, -float("inf"))
+
+        # ============================================================
+        # ONLINE SOFTMAX UPDATE (block-level)
+        # ============================================================
+        n_e_max = tl.maximum(tl.max(scores, 0), m_prev)
+        re_scale = tl.exp(m_prev - n_e_max)
+        p = tl.exp(scores - n_e_max)
+
+        # ============================================================
+        # VALUE LOAD + DEQUANTIZE: [BLOCK_KV, BLOCK_D]
+        # ============================================================
+        val_bases = slot_bases + KPS
+
+        if VQB == 3:
+            val_addrs0 = val_bases[:, None] + val_byte_idx[None, :]
+            val_raw0 = tl.load(
+                KV_cache_ptr + val_addrs0,
+                mask=kv_mask[:, None] & d_mask[None, :],
+                other=0,
+            ).to(tl.int32)
+            val_raw1 = tl.load(
+                KV_cache_ptr + val_addrs0 + 1,
+                mask=kv_mask[:, None] & d_mask[None, :],
+                other=0,
+            ).to(tl.int32)
+            raw16 = val_raw0 | (val_raw1 << 8)
+            v_idx = ((raw16 >> val_bit_shift[None, :]) & 0x7).to(tl.float32)
+
+            sc_bases = val_bases + VAL_DATA_BYTES
+            sc_lo = tl.load(KV_cache_ptr + sc_bases, mask=kv_mask, other=0).to(
+                tl.uint16
+            )
+            sc_hi = tl.load(KV_cache_ptr + sc_bases + 1, mask=kv_mask, other=0).to(
+                tl.uint16
+            )
+            v_scales = (
+                (sc_lo | (sc_hi << 8)).to(tl.float16, bitcast=True).to(tl.float32)
+            )
+            zr_lo = tl.load(KV_cache_ptr + sc_bases + 2, mask=kv_mask, other=0).to(
+                tl.uint16
+            )
+            zr_hi = tl.load(KV_cache_ptr + sc_bases + 3, mask=kv_mask, other=0).to(
+                tl.uint16
+            )
+            v_zeros = (zr_lo | (zr_hi << 8)).to(tl.float16, bitcast=True).to(tl.float32)
+            values = v_idx * v_scales[:, None] + v_zeros[:, None]
+        else:  # VQB == 4
+            vb_idx = d_offs // 2
+            vb_shift = (d_offs % 2) * 4
+            val_addrs = val_bases[:, None] + vb_idx[None, :]
+            val_raw = tl.load(
+                KV_cache_ptr + val_addrs,
+                mask=kv_mask[:, None] & d_mask[None, :],
+                other=0,
+            ).to(tl.int32)
+            v_idx = ((val_raw >> vb_shift[None, :]) & 0xF).to(tl.float32)
+
+            sc_bases = val_bases + VAL_DATA_BYTES
+            sc_lo = tl.load(KV_cache_ptr + sc_bases, mask=kv_mask, other=0).to(
+                tl.uint16
+            )
+            sc_hi = tl.load(KV_cache_ptr + sc_bases + 1, mask=kv_mask, other=0).to(
+                tl.uint16
+            )
+            v_scales = (
+                (sc_lo | (sc_hi << 8)).to(tl.float16, bitcast=True).to(tl.float32)
+            )
+            zr_lo = tl.load(KV_cache_ptr + sc_bases + 2, mask=kv_mask, other=0).to(
+                tl.uint16
+            )
+            zr_hi = tl.load(KV_cache_ptr + sc_bases + 3, mask=kv_mask, other=0).to(
+                tl.uint16
+            )
+            v_zeros = (zr_lo | (zr_hi << 8)).to(tl.float16, bitcast=True).to(tl.float32)
+            values = v_idx * v_scales[:, None] + v_zeros[:, None]
+
+        # ============================================================
+        # WEIGHTED VALUE ACCUMULATION
+        # ============================================================
+        acc = acc * re_scale + tl.sum(p[:, None] * values, 0)
+        l_prev = l_prev * re_scale + tl.sum(p, 0)
+        m_prev = n_e_max
+
+    # Store partial result
+    out_base = bid * stride_mid_b + hid * stride_mid_h + sid * stride_mid_s
+    safe_l = tl.where(l_prev > 0.0, l_prev, 1.0)
+    tl.store(Mid_o_ptr + out_base + d_offs, acc / safe_l, mask=d_mask)
+    lse = m_prev + tl.log(safe_l)
+    tl.store(Mid_o_ptr + out_base + HEAD_DIM, lse)
+
+
+# ---------------------------------------------------------------------------
+# Pre-dequant kernel: Bulk dequant K (MSE+norms) and V to fp16
+# ---------------------------------------------------------------------------
+
+
+@triton.jit
+def _tq_full_dequant_kv(
+    KV_cache_ptr,
+    Block_table_ptr,
+    Centroids_ptr,
+    K_out_ptr,  # [B, Hk, max_seq, D] float16
+    V_out_ptr,  # [B, Hk, max_seq, D] float16
+    stride_ko_b,
+    stride_ko_h,
+    stride_ko_s,
+    stride_vo_b,
+    stride_vo_h,
+    stride_vo_s,
+    stride_cache_block,
+    stride_cache_pos,
+    stride_cache_head,
+    stride_bt_b,
+    HEAD_DIM: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+    NUM_KV_HEADS: tl.constexpr,
+    MSE_BYTES: tl.constexpr,
+    KPS: tl.constexpr,
+    VQB: tl.constexpr,
+    VAL_DATA_BYTES: tl.constexpr,
+    MSE_BITS: tl.constexpr,
+    KEY_FP8: tl.constexpr,
+    BLOCK_D: tl.constexpr,
+    NORM_CORRECTION: tl.constexpr = 0,
+    FP8_E4B15: tl.constexpr = 0,  # 1 = use e4b15 (Ampere/Ada), 0 = e4nv (Hopper+)
+):
+    """Full dequant: reconstruct K (MSE centroids * norm or FP8) and V to fp16."""
+    pos = tl.program_id(0)
+    bh = tl.program_id(1)
+    bid = bh // NUM_KV_HEADS
+    hid = bh % NUM_KV_HEADS
+
+    page_idx = pos // BLOCK_SIZE
+    page_off = pos % BLOCK_SIZE
+    block_num = tl.load(Block_table_ptr + bid * stride_bt_b + page_idx).to(tl.int64)
+    slot_base = (
+        block_num * stride_cache_block
+        + tl.cast(page_off, tl.int64) * stride_cache_pos
+        + tl.cast(hid, tl.int64) * stride_cache_head
+    )
+
+    d_offs = tl.arange(0, BLOCK_D)
+    d_mask = d_offs < HEAD_DIM
+
+    # === K dequant ===
+    ko_base = bid * stride_ko_b + hid * stride_ko_h + pos * stride_ko_s
+    if KEY_FP8:
+        k_raw = tl.load(KV_cache_ptr + slot_base + d_offs, mask=d_mask, other=0)
+        if FP8_E4B15:
+            k_recon = k_raw.to(tl.float8e4b15, bitcast=True).to(tl.float32)
+        else:
+            k_recon = k_raw.to(tl.float8e4nv, bitcast=True).to(tl.float32)
+        tl.store(K_out_ptr + ko_base + d_offs, k_recon.to(tl.float16), mask=d_mask)
+    else:
+        # MSE unpack (3-bit or 4-bit) + norms
+        mse_bit_off = d_offs * MSE_BITS
+        mse_byte_idx = mse_bit_off // 8
+        mse_bit_shift = mse_bit_off % 8
+        mse_umask = (1 << MSE_BITS) - 1
+
+        mse_raw0 = tl.load(
+            KV_cache_ptr + slot_base + mse_byte_idx, mask=d_mask, other=0
+        ).to(tl.int32)
+        mse_raw1 = tl.load(
+            KV_cache_ptr + slot_base + mse_byte_idx + 1, mask=d_mask, other=0
+        ).to(tl.int32)
+        raw16_key = mse_raw0 | (mse_raw1 << 8)
+        mse_idx = (raw16_key >> mse_bit_shift) & mse_umask
+
+        k_mse = tl.load(Centroids_ptr + mse_idx, mask=d_mask, other=0.0)
+
+        # Norm correction: re-normalize centroid vector to unit norm
+        if NORM_CORRECTION:
+            c_norm_sq = tl.sum(tl.where(d_mask, k_mse * k_mse, 0.0), axis=0)
+            c_inv_norm = 1.0 / tl.sqrt(c_norm_sq + 1e-16)
+            k_mse = k_mse * c_inv_norm
+
+        # Norms at MSE_BYTES offset (no QJL bytes)
+        norm_base = slot_base + MSE_BYTES
+        n_lo = tl.load(KV_cache_ptr + norm_base).to(tl.uint16)
+        n_hi = tl.load(KV_cache_ptr + norm_base + 1).to(tl.uint16)
+        vec_norm = (n_lo | (n_hi << 8)).to(tl.float16, bitcast=True).to(tl.float32)
+
+        k_recon = vec_norm * k_mse
+        tl.store(K_out_ptr + ko_base + d_offs, k_recon.to(tl.float16), mask=d_mask)
+
+    # === V dequant ===
+    val_base = slot_base + KPS
+    if VQB == 4:
+        vb_idx = d_offs // 2
+        vb_shift = (d_offs % 2) * 4
+        val_raw = tl.load(KV_cache_ptr + val_base + vb_idx, mask=d_mask, other=0).to(
+            tl.int32
+        )
+        v_idx = ((val_raw >> vb_shift) & 0xF).to(tl.float32)
+
+        sc_base = val_base + VAL_DATA_BYTES
+        sc_lo = tl.load(KV_cache_ptr + sc_base).to(tl.uint16)
+        sc_hi = tl.load(KV_cache_ptr + sc_base + 1).to(tl.uint16)
+        v_scale = (sc_lo | (sc_hi << 8)).to(tl.float16, bitcast=True).to(tl.float32)
+        zr_lo = tl.load(KV_cache_ptr + sc_base + 2).to(tl.uint16)
+        zr_hi = tl.load(KV_cache_ptr + sc_base + 3).to(tl.uint16)
+        v_zero = (zr_lo | (zr_hi << 8)).to(tl.float16, bitcast=True).to(tl.float32)
+        v_vals = v_idx * v_scale + v_zero
+    elif VQB == 3:
+        # 3-bit value unpack: 8 values per 3 bytes
+        val_bit_off = d_offs * 3
+        val_byte_idx = val_bit_off // 8
+        val_bit_shift = val_bit_off % 8
+        val_raw0 = tl.load(
+            KV_cache_ptr + val_base + val_byte_idx, mask=d_mask, other=0
+        ).to(tl.int32)
+        val_raw1 = tl.load(
+            KV_cache_ptr + val_base + val_byte_idx + 1, mask=d_mask, other=0
+        ).to(tl.int32)
+        raw16_val = val_raw0 | (val_raw1 << 8)
+        v_idx = ((raw16_val >> val_bit_shift) & 0x7).to(tl.float32)
+
+        sc_base = val_base + VAL_DATA_BYTES
+        sc_lo = tl.load(KV_cache_ptr + sc_base).to(tl.uint16)
+        sc_hi = tl.load(KV_cache_ptr + sc_base + 1).to(tl.uint16)
+        v_scale = (sc_lo | (sc_hi << 8)).to(tl.float16, bitcast=True).to(tl.float32)
+        zr_lo = tl.load(KV_cache_ptr + sc_base + 2).to(tl.uint16)
+        zr_hi = tl.load(KV_cache_ptr + sc_base + 3).to(tl.uint16)
+        v_zero = (zr_lo | (zr_hi << 8)).to(tl.float16, bitcast=True).to(tl.float32)
+        v_vals = v_idx * v_scale + v_zero
+    else:
+        v_vals = tl.zeros([BLOCK_D], dtype=tl.float32)
+
+    vo_base = bid * stride_vo_b + hid * stride_vo_h + pos * stride_vo_s
+    tl.store(V_out_ptr + vo_base + d_offs, v_vals.to(tl.float16), mask=d_mask)
+
+
+# ---------------------------------------------------------------------------
+# Stage 2: Reuse from triton_decode_attention.py
+# ---------------------------------------------------------------------------
+
+# ---------------------------------------------------------------------------
+# Launcher — cached constants + fused GEMM
+# ---------------------------------------------------------------------------
+
+_layout_cache: dict = {}
+
+
+def _get_layout(D, mse_bits, value_quant_bits, key_packed_size):
+    """Get cached layout constants."""
+    key = (D, mse_bits, value_quant_bits, key_packed_size)
+    cfg = _layout_cache.get(key)
+    if cfg is None:
+        val_data_bytes = math.ceil(D * value_quant_bits / 8)
+        cfg = {
+            "mse_bytes": math.ceil(D * mse_bits / 8),
+            "val_data_bytes": val_data_bytes,
+            "mse_bits": mse_bits,
+            "n_centroids": 2**mse_bits,
+            "BLOCK_D": triton.next_power_of_2(D),
+        }
+        _layout_cache[key] = cfg
+    return cfg
+
+
+def triton_turboquant_decode_attention(
+    query: torch.Tensor,  # [B, Hq, D] — original query
+    kv_cache: torch.Tensor,  # [num_blocks, block_size, Hk, padded_slot] uint8
+    block_table: torch.Tensor,  # [B, max_num_blocks] int32
+    seq_lens: torch.Tensor,  # [B] int32
+    Pi: torch.Tensor,  # [D, D] float32
+    centroids: torch.Tensor,  # [n_centroids] float32
+    scale: float,
+    mse_bits: int,
+    key_packed_size: int,
+    value_quant_bits: int,
+    key_fp8: bool = False,
+    norm_correction: bool = False,
+    PiT: torch.Tensor | None = None,  # [D, D] pre-computed Pi.T contiguous
+    # Pre-allocated buffers (optional, avoids per-call allocation)
+    mid_o_buf: torch.Tensor | None = None,
+    output_buf: torch.Tensor | None = None,
+    lse_buf: torch.Tensor | None = None,
+    buf_holder: Any = None,
+    max_num_kv_splits: int = 32,  # fixed split count (must be constant for cudagraph)
+) -> torch.Tensor:
+    """Launch fused TQ decode attention (Triton stage1 + stage2).
+
+    Returns: output tensor [B, Hq, D] in query's dtype.
+    """
+    B, Hq, D = query.shape
+    Hk = kv_cache.shape[2]
+    block_size = kv_cache.shape[1]
+    kv_group_size = Hq // Hk
+    device = query.device
+
+    cfg = _get_layout(D, mse_bits, value_quant_bits, key_packed_size)
+
+    # Compute q_rot = q @ Pi.T (rotated query for MSE key scoring)
+    # FP8 path: pass query directly (float16); kernel casts inline.
+    # MSE path: still needs external GEMM (cuBLAS), so q_rot is float32.
+    if key_fp8:
+        q_rot = query.contiguous()
+    else:
+        q_float = query.float()
+        if PiT is None:
+            PiT = Pi.T.contiguous()
+        q_rot = (q_float @ PiT).contiguous()
+
+    NUM_KV_SPLITS = max_num_kv_splits
+
+    if (
+        mid_o_buf is not None
+        and mid_o_buf.shape[0] >= B
+        and mid_o_buf.shape[2] >= NUM_KV_SPLITS
+    ):
+        mid_o = mid_o_buf[:B, :Hq, :NUM_KV_SPLITS, :]
+    else:
+        mid_o = torch.empty(
+            B,
+            Hq,
+            NUM_KV_SPLITS,
+            D + 1,
+            dtype=torch.float32,
+            device=device,
+        )
+        if buf_holder is not None:
+            buf_holder._tq_mid_o_buf = mid_o
+
+    # Stage 1: split-KV tiled attention scoring + value accumulation
+    fp8_e4b15 = _use_fp8_e4b15(device.index or 0)
+    BLOCK_KV = 4
+    grid = (B, Hq, NUM_KV_SPLITS)
+    _tq_decode_stage1[grid](
+        q_rot,
+        kv_cache,
+        block_table,
+        seq_lens,
+        centroids,
+        mid_o,
+        q_rot.stride(0),
+        q_rot.stride(1),
+        kv_cache.stride(0),
+        kv_cache.stride(1),
+        kv_cache.stride(2),
+        block_table.stride(0),
+        mid_o.stride(0),
+        mid_o.stride(1),
+        mid_o.stride(2),
+        NUM_KV_HEADS=Hk,
+        HEAD_DIM=D,
+        BLOCK_SIZE=block_size,
+        NUM_KV_SPLITS=NUM_KV_SPLITS,
+        KV_GROUP_SIZE=kv_group_size,
+        MSE_BITS=mse_bits,
+        MSE_BYTES=cfg["mse_bytes"],
+        KPS=key_packed_size,
+        VQB=value_quant_bits,
+        VAL_DATA_BYTES=cfg["val_data_bytes"],
+        ATTN_SCALE=scale,
+        BLOCK_D=cfg["BLOCK_D"],
+        BLOCK_KV=BLOCK_KV,
+        KEY_FP8=1 if key_fp8 else 0,
+        NORM_CORRECTION=1 if norm_correction else 0,
+        FP8_E4B15=fp8_e4b15,
+        num_warps=1,
+        num_stages=1,
+    )
+
+    # Stage 2: Reduce across KV splits
+    # Output in query dtype — eliminates float16_copy kernel after stage2
+    out_dtype = query.dtype
+    if (
+        output_buf is not None
+        and output_buf.shape[0] >= B
+        and output_buf.dtype == out_dtype
+    ):
+        output = output_buf[:B, :Hq, :D]
+    else:
+        output = torch.empty(B, Hq, D, dtype=out_dtype, device=device)
+        if buf_holder is not None:
+            buf_holder._tq_output_buf = output
+    if lse_buf is not None and lse_buf.shape[0] >= B:
+        lse = lse_buf[:B, :Hq]
+    else:
+        lse = torch.empty(B, Hq, dtype=torch.float32, device=device)
+        if buf_holder is not None:
+            buf_holder._tq_lse_buf = lse
+
+    grid2 = (B, Hq)
+    _fwd_kernel_stage2[grid2](
+        mid_o,
+        output,
+        lse,
+        seq_lens,
+        mid_o.stride(0),
+        mid_o.stride(1),
+        mid_o.stride(2),
+        output.stride(0),
+        output.stride(1),
+        lse.stride(0),
+        NUM_KV_SPLITS=NUM_KV_SPLITS,
+        BLOCK_DV=cfg["BLOCK_D"],
+        Lv=D,
+        OUTPUT_FP16=1 if out_dtype == torch.float16 else 0,
+        num_warps=4,
+        num_stages=2,
+    )
+
+    return output  # already in query dtype
diff --git a/vllm/v1/attention/ops/triton_turboquant_store.py b/vllm/v1/attention/ops/triton_turboquant_store.py
new file mode 100644
index 000000000000..3ad2d41488e7
--- /dev/null
+++ b/vllm/v1/attention/ops/triton_turboquant_store.py
@@ -0,0 +1,447 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Fused Triton kernels for TurboQuant KV store.
+
+Two kernels:
+1. _tq_fused_store_fp8: FP8 key scatter + value uniform quantization.
+2. _tq_fused_store_mse: Fused binary-search bucketize + MSE index
+   packing + value quantization.
+
+The launcher `triton_turboquant_store` selects the appropriate kernel.
+"""
+
+import math
+
+import torch
+
+from vllm.triton_utils import tl, triton
+from vllm.v1.attention.ops.triton_turboquant_decode import _use_fp8_e4b15
+
+# ═══════════════════════════════════════════════════════════════════════
+# Shared: value uniform quantization + pack + scale/zero store
+# ═══════════════════════════════════════════════════════════════════════
+
+
+@triton.jit
+def _store_quantized_value(
+    Value_ptr,
+    KV_cache_ptr,
+    base,  # pid * D offset into Value_ptr
+    slot_base,  # byte offset into KV_cache_ptr for this slot+head
+    d_offs,  # tl.arange(0, BLOCK_D)
+    d_mask,  # d_offs < D
+    D: tl.constexpr,
+    KPS: tl.constexpr,
+    VQB: tl.constexpr,
+    VAL_DATA_BYTES: tl.constexpr,
+    BLOCK_D: tl.constexpr,
+    BLOCK_VAL: tl.constexpr,
+    BLOCK_GRP: tl.constexpr,
+):
+    """Uniform quantization of values to VQB bits, pack, and store with scale/zero."""
+    val_cache_offset = KPS
+
+    if VQB == 3:
+        val_vec = tl.load(Value_ptr + base + d_offs, mask=d_mask, other=0.0).to(
+            tl.float32
+        )
+        val_min = tl.min(tl.where(d_mask, val_vec, float("inf")), axis=0)
+        val_max = tl.max(tl.where(d_mask, val_vec, -float("inf")), axis=0)
+        v_scale = (val_max - val_min) / 7.0
+        v_scale = tl.where(v_scale > 1e-8, v_scale, 1e-8)
+
+        q_vals = tl.minimum(
+            tl.maximum(((val_vec - val_min) / v_scale + 0.5).to(tl.int32), 0), 7
+        )
+
+        grp_offs = tl.arange(0, BLOCK_GRP)
+        grp_mask = grp_offs < (D // 8)
+        q_grp = tl.reshape(q_vals, [BLOCK_GRP, 8])
+        shifts_3bit = tl.arange(0, 8) * 3
+        packed_24 = tl.sum(q_grp << shifts_3bit[None, :], axis=1)
+        b0 = (packed_24 & 0xFF).to(tl.uint8)
+        b1 = ((packed_24 >> 8) & 0xFF).to(tl.uint8)
+        b2 = ((packed_24 >> 16) & 0xFF).to(tl.uint8)
+        tl.store(
+            KV_cache_ptr + slot_base + val_cache_offset + grp_offs * 3,
+            b0,
+            mask=grp_mask,
+        )
+        tl.store(
+            KV_cache_ptr + slot_base + val_cache_offset + grp_offs * 3 + 1,
+            b1,
+            mask=grp_mask,
+        )
+        tl.store(
+            KV_cache_ptr + slot_base + val_cache_offset + grp_offs * 3 + 2,
+            b2,
+            mask=grp_mask,
+        )
+
+        sc_offset = val_cache_offset + VAL_DATA_BYTES
+        sc_f16 = v_scale.to(tl.float16)
+        sc_u16 = sc_f16.to(tl.uint16, bitcast=True)
+        tl.store(KV_cache_ptr + slot_base + sc_offset, (sc_u16 & 0xFF).to(tl.uint8))
+        tl.store(
+            KV_cache_ptr + slot_base + sc_offset + 1,
+            ((sc_u16 >> 8) & 0xFF).to(tl.uint8),
+        )
+        zr_f16 = val_min.to(tl.float16)
+        zr_u16 = zr_f16.to(tl.uint16, bitcast=True)
+        tl.store(KV_cache_ptr + slot_base + sc_offset + 2, (zr_u16 & 0xFF).to(tl.uint8))
+        tl.store(
+            KV_cache_ptr + slot_base + sc_offset + 3,
+            ((zr_u16 >> 8) & 0xFF).to(tl.uint8),
+        )
+
+    else:  # VQB == 4
+        val_vec = tl.load(Value_ptr + base + d_offs, mask=d_mask, other=0.0).to(
+            tl.float32
+        )
+        val_min = tl.min(tl.where(d_mask, val_vec, float("inf")), axis=0)
+        val_max = tl.max(tl.where(d_mask, val_vec, -float("inf")), axis=0)
+        v_scale = (val_max - val_min) / 15.0
+        v_scale = tl.where(v_scale > 1e-8, v_scale, 1e-8)
+
+        # Quantize all D elements from register (no re-load)
+        q_all = tl.minimum(
+            tl.maximum(((val_vec - val_min) / v_scale + 0.5).to(tl.int32), 0), 15
+        )
+        # Reshape to pairs and pack two 4-bit values per byte
+        q_pairs = tl.reshape(q_all, [BLOCK_D // 2, 2])
+        shifts_4 = tl.arange(0, 2) * 4
+        packed_val = tl.sum((q_pairs & 0xF) << shifts_4[None, :], axis=1).to(tl.uint8)
+        val_offs = tl.arange(0, BLOCK_D // 2)
+        val_mask = val_offs < VAL_DATA_BYTES
+        tl.store(
+            KV_cache_ptr + slot_base + val_cache_offset + val_offs,
+            packed_val,
+            mask=val_mask,
+        )
+
+        sc_offset = val_cache_offset + VAL_DATA_BYTES
+        sc_f16 = v_scale.to(tl.float16)
+        sc_u16 = sc_f16.to(tl.uint16, bitcast=True)
+        tl.store(KV_cache_ptr + slot_base + sc_offset, (sc_u16 & 0xFF).to(tl.uint8))
+        tl.store(
+            KV_cache_ptr + slot_base + sc_offset + 1,
+            ((sc_u16 >> 8) & 0xFF).to(tl.uint8),
+        )
+        zr_f16 = val_min.to(tl.float16)
+        zr_u16 = zr_f16.to(tl.uint16, bitcast=True)
+        tl.store(KV_cache_ptr + slot_base + sc_offset + 2, (zr_u16 & 0xFF).to(tl.uint8))
+        tl.store(
+            KV_cache_ptr + slot_base + sc_offset + 3,
+            ((zr_u16 >> 8) & 0xFF).to(tl.uint8),
+        )
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# FP8 key store + value uniform quantization
+# ═══════════════════════════════════════════════════════════════════════
+
+
+@triton.jit
+def _tq_fused_store_fp8(
+    Key_ptr,  # [NH, D] float16/bfloat16 — raw keys
+    Value_ptr,  # [NH, D] float16/bfloat16 — raw values
+    KV_cache_ptr,  # [total_bytes] uint8 (flattened view)
+    Slot_mapping_ptr,  # [N] int32 — per-token slot indices
+    # Cache strides (for computing byte offsets)
+    stride_cache_block: tl.constexpr,
+    stride_cache_pos: tl.constexpr,
+    stride_cache_head: tl.constexpr,
+    # Dimensions
+    D: tl.constexpr,
+    H: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+    BLOCK_D: tl.constexpr,
+    # TQ layout
+    KPS: tl.constexpr,
+    # Value quantization
+    VQB: tl.constexpr,
+    VAL_DATA_BYTES: tl.constexpr,
+    # Packing block sizes
+    BLOCK_VAL: tl.constexpr,
+    BLOCK_GRP: tl.constexpr = 16,
+    FP8_E4B15: tl.constexpr = 0,  # 1 = e4b15 (Ampere/Ada), 0 = e4nv (Hopper+)
+):
+    """FP8 key cast+scatter + value uniform quantization."""
+    pid = tl.program_id(0)
+    token_idx = pid // H
+    head_idx = pid % H
+
+    slot = tl.load(Slot_mapping_ptr + token_idx)
+    if slot < 0:
+        return
+    blk = (slot // BLOCK_SIZE).to(tl.int64)
+    off = (slot % BLOCK_SIZE).to(tl.int64)
+    head_idx_i64 = tl.cast(head_idx, tl.int64)
+    slot_base = (
+        blk * stride_cache_block
+        + off * stride_cache_pos
+        + head_idx_i64 * stride_cache_head
+    )
+
+    base = pid * D
+
+    # ── FP8 KEY: cast to FP8 in-kernel and store ─────────────────
+    d_offs = tl.arange(0, BLOCK_D)
+    d_mask = d_offs < D
+    k_vals = tl.load(Key_ptr + base + d_offs, mask=d_mask, other=0.0)
+    k_fp8 = k_vals.to(tl.float8e4b15) if FP8_E4B15 else k_vals.to(tl.float8e4nv)
+    k_bytes = k_fp8.to(tl.uint8, bitcast=True)
+    tl.store(KV_cache_ptr + slot_base + d_offs, k_bytes, mask=d_mask)
+
+    # ── VALUE QUANTIZE + PACK ───────────────────────────────────────
+    _store_quantized_value(
+        Value_ptr,
+        KV_cache_ptr,
+        base,
+        slot_base,
+        d_offs,
+        d_mask,
+        D=D,
+        KPS=KPS,
+        VQB=VQB,
+        VAL_DATA_BYTES=VAL_DATA_BYTES,
+        BLOCK_D=BLOCK_D,
+        BLOCK_VAL=BLOCK_VAL,
+        BLOCK_GRP=BLOCK_GRP,
+    )
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# Fused MSE store: bucketize + MSE index pack + norm store + value pack
+# (eliminates 4 PyTorch kernel launches per layer vs pack-only kernel)
+# ═══════════════════════════════════════════════════════════════════════
+
+
+@triton.jit
+def _tq_fused_store_mse(
+    # Post-rotation inputs
+    Y_ptr,  # [NH, D] float32 — rotated normalized keys (x_hat @ PiT)
+    Norms_ptr,  # [NH] float32 — key vector norms (||k||)
+    Value_ptr,  # [NH, D] float32 — raw values
+    # Quantization tables
+    Midpoints_ptr,  # [n_centroids-1] float32
+    # Cache and indexing
+    KV_cache_ptr,  # [total_bytes] uint8 (flattened view)
+    Slot_mapping_ptr,  # [N] int32 — per-token slot indices
+    # Cache strides
+    stride_cache_block: tl.constexpr,
+    stride_cache_pos: tl.constexpr,
+    stride_cache_head: tl.constexpr,
+    # Dimensions
+    D: tl.constexpr,
+    H: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+    BLOCK_D: tl.constexpr,
+    # TQ layout
+    MSE_BYTES: tl.constexpr,
+    KPS: tl.constexpr,
+    # Value quantization
+    VQB: tl.constexpr,
+    VAL_DATA_BYTES: tl.constexpr,
+    # Packing block sizes
+    BLOCK_VAL: tl.constexpr,
+    # MSE params
+    MSE_BITS: tl.constexpr,
+    N_CENTROIDS: tl.constexpr,
+    BLOCK_GRP: tl.constexpr = 16,
+):
+    """Fused MSE quantize + pack + store.
+
+    Performs binary-search bucketize, MSE index packing, norm storage,
+    and value quantization in one kernel.
+    """
+    pid = tl.program_id(0)
+    token_idx = pid // H
+    head_idx = pid % H
+
+    slot = tl.load(Slot_mapping_ptr + token_idx)
+    if slot < 0:
+        return
+    blk = (slot // BLOCK_SIZE).to(tl.int64)
+    off = (slot % BLOCK_SIZE).to(tl.int64)
+    head_idx_i64 = tl.cast(head_idx, tl.int64)
+    slot_base = (
+        blk * stride_cache_block
+        + off * stride_cache_pos
+        + head_idx_i64 * stride_cache_head
+    )
+
+    base = pid * D
+    d_offs = tl.arange(0, BLOCK_D)
+    d_mask = d_offs < D
+
+    # ── 1. BINARY SEARCH BUCKETIZE ───────────────────────────────────
+    # Midpoints are sorted (N_CENTROIDS-1 values); binary search finds
+    # insertion point in MSE_BITS iterations vs N_CENTROIDS-1 for linear.
+    y_vec = tl.load(Y_ptr + base + d_offs, mask=d_mask, other=0.0)
+    lo = tl.zeros([BLOCK_D], dtype=tl.int32)
+    hi = tl.full([BLOCK_D], N_CENTROIDS - 1, dtype=tl.int32)
+    for _ in range(MSE_BITS):
+        mid = (lo + hi) >> 1
+        # Clamp to valid midpoint index [0, N_CENTROIDS-2] for load safety;
+        # the search result (lo) is still correct since converged lanes
+        # don't change.
+        safe_mid = tl.minimum(mid, N_CENTROIDS - 2)
+        mid_val = tl.load(Midpoints_ptr + safe_mid, mask=d_mask, other=0.0)
+        lo = tl.where(y_vec >= mid_val, mid + 1, lo)
+        hi = tl.where(y_vec >= mid_val, hi, mid)
+    idx = tl.minimum(lo, N_CENTROIDS - 1)
+
+    # ── 2. PACK MSE INDICES from register idx ─────────────────────────
+    if MSE_BITS == 4:
+        idx_pairs = tl.reshape(idx, [BLOCK_D // 2, 2])
+        shifts_4 = tl.arange(0, 2) * 4
+        packed = tl.sum((idx_pairs & 0xF) << shifts_4[None, :], axis=1).to(tl.uint8)
+        mse_offs = tl.arange(0, BLOCK_D // 2)
+        mse_mask = mse_offs < MSE_BYTES
+        tl.store(KV_cache_ptr + slot_base + mse_offs, packed, mask=mse_mask)
+
+    elif MSE_BITS == 3:
+        grp_offs = tl.arange(0, BLOCK_GRP)
+        grp_mask = grp_offs < (D // 8)
+        idx_grp = tl.reshape(idx, [BLOCK_GRP, 8])
+        shifts_3 = tl.arange(0, 8) * 3
+        packed_24 = tl.sum((idx_grp & 0x7) << shifts_3[None, :], axis=1)
+        b0 = (packed_24 & 0xFF).to(tl.uint8)
+        b1 = ((packed_24 >> 8) & 0xFF).to(tl.uint8)
+        b2 = ((packed_24 >> 16) & 0xFF).to(tl.uint8)
+        tl.store(KV_cache_ptr + slot_base + grp_offs * 3, b0, mask=grp_mask)
+        tl.store(KV_cache_ptr + slot_base + grp_offs * 3 + 1, b1, mask=grp_mask)
+        tl.store(KV_cache_ptr + slot_base + grp_offs * 3 + 2, b2, mask=grp_mask)
+
+    # ── 3. STORE vec_norm (fp16, 2 bytes) ─────────────────────────────
+    norm_offset = MSE_BYTES
+
+    vn_f16 = tl.load(Norms_ptr + pid).to(tl.float16)
+    vn_u16 = vn_f16.to(tl.uint16, bitcast=True)
+    tl.store(KV_cache_ptr + slot_base + norm_offset, (vn_u16 & 0xFF).to(tl.uint8))
+    tl.store(
+        KV_cache_ptr + slot_base + norm_offset + 1, ((vn_u16 >> 8) & 0xFF).to(tl.uint8)
+    )
+
+    # ── 4. VALUE QUANTIZE + PACK ──────────────────────────────────────
+    _store_quantized_value(
+        Value_ptr,
+        KV_cache_ptr,
+        base,
+        slot_base,
+        d_offs,
+        d_mask,
+        D=D,
+        KPS=KPS,
+        VQB=VQB,
+        VAL_DATA_BYTES=VAL_DATA_BYTES,
+        BLOCK_D=BLOCK_D,
+        BLOCK_VAL=BLOCK_VAL,
+        BLOCK_GRP=BLOCK_GRP,
+    )
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# Launcher
+# ═══════════════════════════════════════════════════════════════════════
+
+
+def triton_turboquant_store(
+    key: torch.Tensor,  # [N, H, D] — raw keys (post-RoPE)
+    value: torch.Tensor,  # [N, H, D] — raw values
+    kv_cache: torch.Tensor,  # [num_blocks, block_size, Hk, padded_slot] uint8
+    slot_mapping: torch.Tensor,  # [N] int32
+    PiT: torch.Tensor,  # [D, D] float32
+    midpoints: torch.Tensor,  # [n_centroids-1] float32
+    mse_bits: int,
+    key_packed_size: int,
+    value_quant_bits: int,
+    key_fp8: bool = False,
+):
+    """Launch TQ store kernel (FP8 or MSE path)."""
+    N, H, D = key.shape
+    NH = N * H
+    block_size = kv_cache.shape[1]
+    BLOCK_D = triton.next_power_of_2(D)
+    mse_bytes = math.ceil(D * mse_bits / 8)
+    n_centroids = 2**mse_bits
+
+    val_data_bytes = math.ceil(D * value_quant_bits / 8)
+
+    BLOCK_VAL = triton.next_power_of_2(val_data_bytes)
+
+    # Cache strides (element_size=1 for uint8, so stride in bytes = stride())
+    stride_block = kv_cache.stride(0)
+    stride_pos = kv_cache.stride(1)
+    stride_head = kv_cache.stride(2)
+
+    block_grp = triton.next_power_of_2(D // 8) if D >= 8 else 1
+
+    # ── FP8 PATH: in-kernel FP8 cast + scatter via fp8 kernel ──
+    if key_fp8:
+        k_flat = key.reshape(NH, D).contiguous()
+        v_flat = value.reshape(NH, D).contiguous()
+
+        fp8_e4b15 = _use_fp8_e4b15(key.device.index or 0)
+
+        grid = (NH,)
+        _tq_fused_store_fp8[grid](
+            k_flat,
+            v_flat,
+            kv_cache.view(-1),
+            slot_mapping,
+            stride_cache_block=stride_block,
+            stride_cache_pos=stride_pos,
+            stride_cache_head=stride_head,
+            D=D,
+            H=H,
+            BLOCK_SIZE=block_size,
+            BLOCK_D=BLOCK_D,
+            KPS=key_packed_size,
+            VQB=value_quant_bits,
+            VAL_DATA_BYTES=val_data_bytes,
+            BLOCK_VAL=BLOCK_VAL,
+            BLOCK_GRP=block_grp,
+            FP8_E4B15=fp8_e4b15,
+            num_warps=4,
+            num_stages=1,
+        )
+        return
+
+    # ── MSE PATH: external GEMM + fused bucketize/pack kernel ──
+    # Normalize + rotation GEMM externally (cuBLAS is faster than in-kernel)
+    k_flat = key.float().reshape(NH, D)
+    norms = k_flat.norm(dim=1, keepdim=True)
+    x_hat = k_flat / (norms + 1e-8)
+    y = x_hat @ PiT
+
+    v_flat = value.float().reshape(NH, D)
+
+    # Fused kernel: bucketize + MSE index pack + norm store + value pack
+    grid = (NH,)
+    _tq_fused_store_mse[grid](
+        y,
+        norms.squeeze(1),
+        v_flat,
+        midpoints,
+        kv_cache.view(-1),
+        slot_mapping,
+        stride_cache_block=stride_block,
+        stride_cache_pos=stride_pos,
+        stride_cache_head=stride_head,
+        D=D,
+        H=H,
+        BLOCK_SIZE=block_size,
+        BLOCK_D=BLOCK_D,
+        MSE_BYTES=mse_bytes,
+        KPS=key_packed_size,
+        VQB=value_quant_bits,
+        VAL_DATA_BYTES=val_data_bytes,
+        BLOCK_VAL=BLOCK_VAL,
+        MSE_BITS=mse_bits,
+        N_CENTROIDS=n_centroids,
+        BLOCK_GRP=block_grp,
+        num_warps=4,
+        num_stages=1,
+    )
diff --git a/vllm/v1/attention/ops/triton_unified_attention.py b/vllm/v1/attention/ops/triton_unified_attention.py
index 150f022f848c..1774f1b30570 100644
--- a/vllm/v1/attention/ops/triton_unified_attention.py
+++ b/vllm/v1/attention/ops/triton_unified_attention.py
@@ -7,12 +7,27 @@
 #  - Chih-Chieh Yang <chih.chieh.yang@ibm.com>
 #  - Thomas Parnell <tpa@zurich.ibm.com>
 
+from typing import Any
+
 import torch
 
 import vllm.envs as envs
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
+from vllm.v1.attention.ops.triton_attention_helpers import (
+    apply_alibi_to_score,
+    apply_softcap,
+    cdiv_fn,
+    compute_kv_seq_mask,
+    compute_tile_loop_bounds,
+    find_seq_idx,
+    init_softmax_M,
+    load_qq_bias_tile,
+    resolve_seq_and_query_len,
+    softmax_step,
+    store_segm_reduce_scalars,
+)
 from vllm.v1.kv_cache_interface import KVQuantMode
 
 logger = init_logger(__name__)
@@ -21,114 +36,53 @@
 
 
 @triton.jit
-def cdiv_fn(x, y):
-    return (x + y - 1) // y
-
-
-@triton.jit
-def apply_softcap(S, x):
-    Sdiv = S / x
-    p1 = tl.exp(Sdiv)
-    p2 = tl.exp(-Sdiv)
-    return x * (p1 - p2) / (p1 + p2)
-
-
-@triton.jit
-def _prepare_kv_tile(
-    data,
-    Q,
-    tensor_scale,
-    scale_cache_ptr,
-    physical_block_idx,
-    seq_offset,
-    kv_head_idx,
-    stride_s_blk,
-    stride_s_slot,
-    stride_s_head,
-    tile_mask,
-    BLOCK_SIZE: tl.constexpr,
-    KV_QUANT_MODE: tl.constexpr,
-):
-    """Prepare a loaded KV tile for attention computation.
+def _cast_kv_tile(data, Q, tensor_scale, KV_QUANT_MODE: tl.constexpr):
+    """Cast a loaded KV tile to Q's dtype, dequantizing if needed.
 
-    Casts the raw KV data to Q's dtype and loads per-token-head scales
-    when applicable:
+    Modes handled inside the core kernel:
 
-    - ``KV_QUANT_MODE == 0``: cast only (no-op for bf16/fp16).
-    - ``KV_QUANT_MODE == 1`` (FP8 per-tensor): dequantize inline
-      using the tensor-wide scale.
-    - ``KV_QUANT_MODE >= 2`` (per-token-head int8/fp8): cast to Q's
-      dtype and return per-head scales separately — the caller applies
-      them after the dot product for better numerical efficiency.
-
-    Returns ``(data, token_head_scales)``.  *token_head_scales* is only
-    meaningful when ``KV_QUANT_MODE >= 2``; callers gate its use on
-    the same constexpr so the compiler eliminates dead code.
+    - ``KV_QUANT_MODE == 0`` (NONE) and ``2`` (INT8 per-token-head) and
+      ``3`` (FP8 per-token-head): plain cast.  Per-token-head modes apply
+      their scales separately on S/P inside the loop.
+    - ``KV_QUANT_MODE == 1`` (FP8 per-tensor): dequantize using the
+      tensor-wide scale.
     """
-    # KV_QUANT_MODE values: 0=none, 1=fp8 per-tensor,
-    #                       2=int8 per-token-head, 3=fp8 per-token-head
-
-    # Placeholder scales (float32) — never read when KV_QUANT_MODE < 2.
-    unused_scales = tile_mask.to(tl.float32)
-
-    if KV_QUANT_MODE == 1:  # FP8 per-tensor
+    if KV_QUANT_MODE == 1:
         if Q.dtype.is_fp8():
-            return data.to(Q.dtype), unused_scales
-        return (data.to(tl.float32) * tl.load(tensor_scale)).to(Q.dtype), unused_scales
-    if KV_QUANT_MODE >= 2:  # per-token-head (int8 or fp8)
-        scale_idx = (
-            physical_block_idx * stride_s_blk
-            + (seq_offset % BLOCK_SIZE) * stride_s_slot
-            + kv_head_idx * stride_s_head
-        )
-        token_head_scales = tl.load(
-            scale_cache_ptr + scale_idx, mask=tile_mask, other=1.0
-        )
-        return data.to(Q.dtype), token_head_scales
-    # .to(Q.dtype) is a no-op when data is already Q's type (bf16/fp16),
-    # but required so Triton sees consistent return types across branches.
-    return data.to(Q.dtype), unused_scales
+            return data.to(Q.dtype)
+        return (data.to(tl.float32) * tl.load(tensor_scale)).to(Q.dtype)
+    return data.to(Q.dtype)
 
 
 @triton.jit
-def find_seq_idx(
-    query_start_len_ptr,
-    target_idx,
-    num_seqs,
-    BLOCK_Q: tl.constexpr,
-    use_q_block_mode: tl.constexpr,
-):
-    left: tl.int32 = 0
-    right = num_seqs
-    while left < right:
-        mid = (left + right) // 2
-        val = tl.load(query_start_len_ptr + mid)
-        mid_val = val // BLOCK_Q + mid if use_q_block_mode else val
-
-        if mid_val <= target_idx:
-            left = mid + 1
-        else:
-            right = mid
-
-    return left - 1
-
-
-@triton.jit
-def kernel_unified_attention_2d(
-    output_ptr,  # [num_tokens, num_query_heads, head_size]
-    query_ptr,  # [num_tokens, num_query_heads, head_size]
-    key_cache_ptr,  # [num_blks, blk_size, num_kv_heads, head_size]
-    value_cache_ptr,  # [num_blks, blk_size, num_kv_heads, head_size]
-    sink_ptr,  # [num_query_heads]
-    block_tables_ptr,  # [num_seqs, max_num_blocks_per_seq]
-    seq_lens_ptr,  # [num_seqs]
-    alibi_slopes_ptr,  # [num_query_heads]
-    qq_bias_ptr,  # [num_query_tokens, num_query_tokens]
-    scale,  # float32
-    k_scale,  # float32
-    v_scale,  # float32
-    out_scale,  # float32
-    softcap,  # float32
+def kernel_unified_attention(
+    # Output destinations.  In 2D mode we write the final result into
+    # ``output_ptr``; in 3D mode we write per-segment partials into the
+    # three ``segm_*`` tensors and ``output_ptr`` is unused (callers may
+    # pass any non-null pointer).
+    output_ptr,
+    segm_output_ptr,
+    segm_max_ptr,
+    segm_expsum_ptr,
+    # Inputs
+    query_ptr,
+    key_cache_ptr,
+    value_cache_ptr,
+    sink_ptr,
+    block_tables_ptr,
+    seq_lens_ptr,
+    alibi_slopes_ptr,
+    qq_bias_ptr,
+    # Per-(token, head) scale caches (used iff KV_QUANT_MODE in {2, 3}).
+    # For other modes callers may pass any non-null pointer.
+    k_scale_cache_ptr,
+    v_scale_cache_ptr,
+    # Scalars
+    scale,
+    k_scale,
+    v_scale,
+    out_scale,
+    softcap,
     num_query_heads: tl.constexpr,  # int
     num_queries_per_kv: tl.constexpr,  # int
     block_table_stride: tl.int64,  # int
@@ -149,7 +103,7 @@ def kernel_unified_attention_2d(
     SLIDING_WINDOW: tl.constexpr,  # int
     USE_MM_PREFIX: tl.constexpr,  # bool
     MAX_MM_RANGES: tl.constexpr,  # int
-    mm_prefix_range_ptr,  # [num_seqs] - prefix length for each sequence
+    mm_prefix_range_ptr,
     stride_k_cache_0: tl.int64,  # int
     stride_k_cache_1: tl.int64,  # int
     stride_k_cache_2: tl.int64,  # int
@@ -158,45 +112,61 @@ def kernel_unified_attention_2d(
     stride_v_cache_1: tl.int64,  # int
     stride_v_cache_2: tl.int64,  # int
     stride_v_cache_3: tl.constexpr,  # int
-    query_start_len_ptr,  # [num_seqs+1]
-    BLOCK_Q: tl.constexpr,  # int
+    stride_ks_blk: tl.int64,
+    stride_ks_slot: tl.int64,
+    stride_ks_head: tl.int64,
+    stride_vs_blk: tl.int64,
+    stride_vs_slot: tl.int64,
+    stride_vs_head: tl.int64,
+    query_start_len_ptr,
+    BLOCK_Q: tl.constexpr,
     num_seqs: tl.int32,
-    BLOCK_M: tl.constexpr,  # int
-    USE_FP8: tl.constexpr,  # bool
-    # KV cache quantization: 0=none, 1=fp8, 2=per-token-head
+    BLOCK_M: tl.constexpr,
+    NUM_SEGMENTS_PER_SEQ: tl.constexpr,
+    USE_FP8: tl.constexpr,
+    # Toggles 2D vs 3D layout.  The 2D path runs the full sequence in one
+    # tile loop and writes to ``output_ptr``.  The 3D path scopes the loop
+    # to ``[segm_idx, segm_idx+1) × tiles_per_segment`` and writes
+    # per-segment partials, finalized by ``reduce_segments``.
+    IS_3D: tl.constexpr,
+    # KV cache quantization mode handled inside this kernel via constexpr
+    # branches: NONE (0), FP8_PER_TENSOR (1), INT8_PER_TOKEN_HEAD (2),
+    # FP8_PER_TOKEN_HEAD (3).
     KV_QUANT_MODE: tl.constexpr = 0,
     FP8_MIN: tl.constexpr = float8_info.min,
     FP8_MAX: tl.constexpr = float8_info.max,
-    # Per-token-head scale caches (KV_QUANT_MODE >= 2)
-    # Shape: [num_blocks, block_size, num_kv_heads]
-    k_scale_cache_ptr=None,
-    v_scale_cache_ptr=None,
-    stride_ks_blk=0,
-    stride_ks_slot=0,
-    stride_ks_head=0,
-    stride_vs_blk=0,
-    stride_vs_slot=0,
-    stride_vs_head=0,
+    # Chunked / block-local attention.  ``CHUNK_LOOKBACK >= 0`` enables
+    # chunked masking (used by Gemma3 block-local layers); takes precedence
+    # over ``SLIDING_WINDOW`` inside the helpers.  ``-1`` disables.
+    CHUNK_LOOKBACK: tl.constexpr = -1,
+    CHUNK_SIZE: tl.constexpr = -1,
 ):
+    USE_PER_TOKEN_HEAD_SCALES: tl.constexpr = KV_QUANT_MODE >= 2
+
     q_block_global_idx = tl.program_id(0)
     kv_head_idx = tl.program_id(1)
-
-    seq_idx = find_seq_idx(
-        query_start_len_ptr, q_block_global_idx, num_seqs, BLOCK_Q, True
+    segm_idx = tl.program_id(2) if IS_3D else 0
+
+    (
+        seq_idx,
+        q_block_local_idx,
+        cur_batch_in_all_start_index,
+        cur_batch_query_len,
+        seq_len,
+    ) = resolve_seq_and_query_len(
+        query_start_len_ptr, seq_lens_ptr, q_block_global_idx, num_seqs, BLOCK_Q
     )
 
-    q_block_start_idx = tl.load(query_start_len_ptr + seq_idx) // BLOCK_Q + seq_idx
-
-    q_block_local_idx = q_block_global_idx - q_block_start_idx
-
-    cur_batch_in_all_start_index = tl.load(query_start_len_ptr + seq_idx)
-    cur_batch_in_all_stop_index = tl.load(query_start_len_ptr + seq_idx + 1)
-
-    cur_batch_query_len = cur_batch_in_all_stop_index - cur_batch_in_all_start_index
-
     if q_block_local_idx * BLOCK_Q >= cur_batch_query_len:
         return
 
+    if IS_3D:
+        tiles_per_segment = cdiv_fn(seq_len, NUM_SEGMENTS_PER_SEQ * TILE_SIZE)
+        if segm_idx * tiles_per_segment * TILE_SIZE >= seq_len:
+            return
+    else:
+        tiles_per_segment = 0
+
     offs_m = tl.arange(0, BLOCK_M)
     offs_d = tl.arange(0, HEAD_SIZE_PADDED)
     offs_t = tl.arange(0, TILE_SIZE)
@@ -223,84 +193,43 @@ def kernel_unified_attention_2d(
 
     block_table_offset = seq_idx * block_table_stride
 
-    if not USE_SINKS:
-        M = tl.full([BLOCK_M], float("-inf"), dtype=tl.float32)
-    else:
-        M = tl.load(
-            sink_ptr + query_offset_1,
-            mask=query_mask_1,
-            other=float("-inf"),
-        ).to(dtype=tl.float32)
-
+    M = init_softmax_M(
+        sink_ptr, query_offset_1, query_mask_1, segm_idx, BLOCK_M, USE_SINKS, IS_3D
+    )
     L = tl.full([BLOCK_M], 1.0, dtype=tl.float32)
+    # acc : (BLOCK_M, HEAD_SIZE_PADDED)
     acc = tl.zeros([BLOCK_M, HEAD_SIZE_PADDED], dtype=tl.float32)
 
-    # sequence len for this particular sequence
-    seq_len = tl.load(seq_lens_ptr + seq_idx)
-
-    # context length for this particular sequences
     context_len = seq_len - cur_batch_query_len
 
-    # alibi slope for this head
     if USE_ALIBI_SLOPES:
         alibi_slope = tl.load(
             alibi_slopes_ptr + query_offset_1, mask=query_mask_1, other=0.0
         )
 
-    # query-query attention bias
     if USE_QQ_BIAS:
-        qq_bias_row_ptrs = (
-            qq_bias_ptr + query_pos[:, None] * qq_bias_stride_0
-        )  # shape: [BLOCK_M]
-
-    # compute the length of the longest sequence prefix spanned by any
-    # query token in the current q_block (q_block_local_idx)
-    max_seq_prefix_len = (
-        context_len
-        + q_block_local_idx * BLOCK_Q
-        + (BLOCK_M - 1) // num_queries_per_kv
-        + 1
+        qq_bias_row_ptrs = qq_bias_ptr + query_pos[:, None] * qq_bias_stride_0
+
+    loop_lo, loop_hi, max_seq_prefix_len = compute_tile_loop_bounds(
+        context_len,
+        seq_len,
+        cur_batch_query_len,
+        q_block_local_idx,
+        segm_idx,
+        tiles_per_segment,
+        TILE_SIZE,
+        BLOCK_M,
+        BLOCK_Q,
+        num_queries_per_kv,
+        SLIDING_WINDOW,
+        USE_MM_PREFIX,
+        IS_3D,
+        CHUNK_LOOKBACK,
+        CHUNK_SIZE,
     )
 
-    if USE_MM_PREFIX:
-        # image bidirectional attention ranges require a full range
-        # including q_block padding to make sure doc mask is correct
-        max_seq_prefix_len = tl.maximum(max_seq_prefix_len, seq_len)
-    else:
-        # adjust for potential padding in the last q_block by considering the
-        # actual sequence length
-        max_seq_prefix_len = tl.minimum(max_seq_prefix_len, seq_len)
-
-    # calculate the number of tiles that need to be processed to
-    # cover the longest sequence prefix (due to causal masking, tiles beyond
-    # this prefix can be skipped)
-    num_tiles = cdiv_fn(max_seq_prefix_len, TILE_SIZE)
-
-    # ---- Sliding-window tile pruning --------------------
-    # Default: keep previous global behavior
-    tile_start = 0
-    tile_end = num_tiles
-    # TODO(Isotr0py): sliding window pruning with image bidirectional mask
-    if SLIDING_WINDOW > 0 and not USE_MM_PREFIX:
-        # Query rows covered by this Q-block
-        qpos_lo = q_block_local_idx * BLOCK_Q
-        qpos_hi = tl.minimum(
-            qpos_lo + (BLOCK_M - 1) // num_queries_per_kv,
-            cur_batch_query_len - 1,
-        )
-        # For sliding window, each query position q can only attend to
-        # keys in the range [q_abs - SLIDING_WINDOW + 1, q_abs]
-        # where q_abs = context_len + q
-        # The union of allowed key positions for this Q-block is:
-        # [context_len + qpos_lo - SLIDING_WINDOW + 1, context_len + qpos_hi]
-        first_allowed_key = context_len + qpos_lo - SLIDING_WINDOW + 1
-        last_allowed_key = context_len + qpos_hi
-        # Convert to tile indices and clamp
-        tile_start = tl.maximum(0, first_allowed_key // TILE_SIZE)
-        tile_end = tl.minimum((last_allowed_key // TILE_SIZE) + 1, num_tiles)
-
     # iterate through tiles (now limited to the sliding window range)
-    for j in range(tile_start, tile_end):
+    for j in range(loop_lo, loop_hi):
         seq_offset = j * TILE_SIZE + offs_t
         tile_mask = seq_offset < max_seq_prefix_len
 
@@ -314,97 +243,64 @@ def kernel_unified_attention_2d(
             + offs_d[None, :] * stride_v_cache_3
             + (seq_offset % BLOCK_SIZE)[:, None] * stride_v_cache_1
         )
-
         k_offset = (
             physical_block_idx[None, :] * stride_k_cache_0
             + kv_head_idx * stride_k_cache_2
             + offs_d[:, None] * stride_k_cache_3
             + (seq_offset % BLOCK_SIZE)[None, :] * stride_k_cache_1
         )
-
         # K : (HEAD_SIZE, TILE_SIZE)
         K_load = tl.load(
             key_cache_ptr + k_offset,
             mask=dim_mask[:, None] & tile_mask[None, :],
             other=0.0,
         )
-        K, k_token_head_scales = _prepare_kv_tile(
-            K_load,
-            Q,
-            k_scale,
-            k_scale_cache_ptr,
-            physical_block_idx,
-            seq_offset,
-            kv_head_idx,
-            stride_ks_blk,
-            stride_ks_slot,
-            stride_ks_head,
-            tile_mask,
-            BLOCK_SIZE,
-            KV_QUANT_MODE,
-        )
-
+        K = _cast_kv_tile(K_load, Q, k_scale, KV_QUANT_MODE)
         # V : (TILE_SIZE, HEAD_SIZE)
         V_load = tl.load(
             value_cache_ptr + v_offset,
             mask=dim_mask[None, :] & tile_mask[:, None],
             other=0.0,
         )
-        V, v_token_head_scales = _prepare_kv_tile(
-            V_load,
-            Q,
-            v_scale,
-            v_scale_cache_ptr,
-            physical_block_idx,
-            seq_offset,
-            kv_head_idx,
-            stride_vs_blk,
-            stride_vs_slot,
-            stride_vs_head,
-            tile_mask,
-            BLOCK_SIZE,
-            KV_QUANT_MODE,
-        )
+        V = _cast_kv_tile(V_load, Q, v_scale, KV_QUANT_MODE)
+
+        # Per-(token, head) scales for INT8 / FP8 per-token-head modes.
+        if USE_PER_TOKEN_HEAD_SCALES:
+            scale_idx = (
+                physical_block_idx * stride_ks_blk
+                + (seq_offset % BLOCK_SIZE) * stride_ks_slot
+                + kv_head_idx * stride_ks_head
+            )
+            k_token_head_scales = tl.load(
+                k_scale_cache_ptr + scale_idx, mask=tile_mask, other=1.0
+            )
+            v_scale_idx = (
+                physical_block_idx * stride_vs_blk
+                + (seq_offset % BLOCK_SIZE) * stride_vs_slot
+                + kv_head_idx * stride_vs_head
+            )
+            v_token_head_scales = tl.load(
+                v_scale_cache_ptr + v_scale_idx, mask=tile_mask, other=1.0
+            )
 
-        # Compute attention mask: causal by default (key <= query)
         query_abs_pos = context_len + query_pos[:, None]
-        seq_mask = seq_offset[None, :] <= query_abs_pos
-
-        # Apply sliding window to base mask BEFORE mm_prefix OR.
-        # Order must match FlexAttention: (causal AND sliding_window) OR mm_prefix
-        if SLIDING_WINDOW > 0:
-            seq_mask = seq_mask & ((query_abs_pos - seq_offset) < SLIDING_WINDOW)
-
-        # PrefixLM: extend mask with bidirectional ranges for multimodal tokens.
-        # Applied AFTER sliding window so mm_prefix ranges override SW restriction.
-        if USE_MM_PREFIX:
-            for i in range(MAX_MM_RANGES):
-                range_start = tl.load(
-                    mm_prefix_range_ptr + seq_idx * MAX_MM_RANGES * 2 + i * 2
-                )
-                range_end = tl.load(
-                    mm_prefix_range_ptr + seq_idx * MAX_MM_RANGES * 2 + i * 2 + 1
-                )
-
-                is_valid = range_start < range_end
-                q_in_range = (
-                    (query_abs_pos >= range_start)
-                    & (query_abs_pos <= range_end)
-                    & is_valid
-                )
-                k_in_range = (
-                    (seq_offset[None, :] >= range_start)
-                    & (seq_offset[None, :] <= range_end)
-                    & is_valid
-                )
-                seq_mask |= q_in_range & k_in_range
+        seq_mask = compute_kv_seq_mask(
+            query_abs_pos,
+            seq_offset,
+            seq_idx,
+            mm_prefix_range_ptr,
+            SLIDING_WINDOW,
+            USE_MM_PREFIX,
+            MAX_MM_RANGES,
+            CHUNK_LOOKBACK,
+            CHUNK_SIZE,
+        )
 
         # S : (BLOCK_M, TILE_SIZE)
         S = tl.zeros(shape=(BLOCK_M, TILE_SIZE), dtype=tl.float32)
-
-        # Per-token-head quant: fuse softmax_scale with per-head k_scale
-        # to avoid a separate BLOCK_M × TILE_SIZE multiply on S.
-        if KV_QUANT_MODE >= 2:
+        if USE_PER_TOKEN_HEAD_SCALES:
+            # Per-token-head quant: fuse softmax_scale with per-head k_scale
+            # to avoid a separate BLOCK_M × TILE_SIZE multiply on S.
             S += tl.dot(Q, K) * (scale * k_token_head_scales[None, :])
         else:
             S += scale * tl.dot(Q, K)
@@ -417,478 +313,76 @@ def kernel_unified_attention_2d(
         )
 
         if USE_ALIBI_SLOPES:
-            if USE_ALIBI_SQRT:
-                relative_pos = seq_offset - (context_len + query_pos[:, None])
-                alibi_offset = tl.where(
-                    relative_pos <= 0,
-                    -tl.sqrt((-relative_pos).to(tl.float32)),
-                    0.0,
-                )
-            else:
-                alibi_offset = seq_offset - context_len
-            S += alibi_slope[:, None] * alibi_offset
+            S = apply_alibi_to_score(
+                S, alibi_slope, seq_offset, context_len, query_pos, USE_ALIBI_SQRT
+            )
 
         if USE_QQ_BIAS:
-            # compute key positions relative to query section
-            key_rel_pos = seq_offset - context_len  # shape: [BLOCK_SIZE]
-            # load bias only for keys that correspond to queries
-            is_query_key = key_rel_pos >= 0 and key_rel_pos < qq_bias_stride_0
-            qq_bias = tl.load(
-                qq_bias_row_ptrs + key_rel_pos[None, :],
-                mask=is_query_key[None, :],  # avoid OOB for context keys
-                other=0.0,
+            S += load_qq_bias_tile(
+                qq_bias_row_ptrs, seq_offset, context_len, qq_bias_stride_0
             )
-            S += qq_bias
-
-        # compute running maximum
-        # m_j : (BLOCK_M,)
-        m_j = tl.maximum(M, tl.max(S, axis=1))
-
-        # For sliding window there's a chance the max is -inf due to masking of
-        # the entire row. In this case we need to set m_j 0 to avoid NaN
-        m_j = tl.where(m_j > float("-inf"), m_j, 0.0)
-
-        # P : (BLOCK_M, TILE_SIZE)
-        P = tl.exp(S - m_j[:, None])
-
-        # l_j : (BLOCK_M,)
-        l_j = tl.sum(P, axis=1)
-
-        # alpha : (BLOCK_M, )
-        alpha = tl.exp(M - m_j)
 
-        # acc : (BLOCK_M, HEAD_SIZE_PADDED)
+        M, L, P, alpha = softmax_step(S, M, L)
         acc = acc * alpha[:, None]
 
-        # update constants
-        L = L * alpha + l_j
-        M = m_j
-
         if SLIDING_WINDOW:
             qpos_lo = q_block_local_idx * BLOCK_Q
             V = tl.where(
-                (context_len + qpos_lo - seq_offset[:, None]) < SLIDING_WINDOW, V, 0.0
+                (context_len + qpos_lo - seq_offset[:, None]) < SLIDING_WINDOW,
+                V,
+                0.0,
             )
-
-        # acc : (BLOCK_M, HEAD_SIZE_PADDED)
-        # Per-token-head quant: apply v_scale to P instead of V.
-        if KV_QUANT_MODE >= 2:
+        if USE_PER_TOKEN_HEAD_SCALES:
+            # Per-token-head quant: apply v_scale to P instead of V.
             P_v = (P * v_token_head_scales[None, :]).to(V.dtype)
             acc += tl.dot(P_v, V)
         else:
             acc += tl.dot(P.to(V.dtype), V)
 
-    # epilogue
-    acc = acc / L[:, None]
-    if USE_FP8:
-        acc = acc * tl.load(out_scale)
-        acc = tl.clamp(acc, FP8_MIN, FP8_MAX)
-
-    output_offset = (
-        query_offset_0[:, None] * output_stride_0
-        + query_offset_1[:, None] * output_stride_1
-        + offs_d[None, :]
-    )
-
-    tl.store(
-        output_ptr + output_offset,
-        acc,
-        mask=dim_mask[None, :] & query_mask_0[:, None] & query_mask_1[:, None],
-    )
-
-
-@triton.jit
-def kernel_unified_attention_3d(
-    segm_output_ptr,
-    # [num_tokens, num_query_heads, num_segments, head_size_padded]
-    segm_max_ptr,  # [num_tokens, num_query_heads, num_segments]
-    segm_expsum_ptr,  # [num_tokens, num_query_heads, num_segments]
-    query_ptr,  # [num_tokens, num_query_heads, head_size]
-    key_cache_ptr,  # [num_blks, num_kv_heads, head_size // x, blk_size, x]
-    value_cache_ptr,  # [num_blks, num_kv_heads, head_size, blk_size]
-    sink_ptr,  # [num_query_heads]
-    block_tables_ptr,  # [num_seqs, max_num_blocks_per_seq]
-    seq_lens_ptr,  # [num_seqs]
-    alibi_slopes_ptr,  # [num_query_heads]
-    qq_bias_ptr,  # [num_query_tokens, num_query_tokens]
-    scale,  # float32
-    k_scale,  # float32
-    v_scale,  # float32
-    softcap,  # float32
-    num_query_heads: tl.constexpr,  # int
-    num_queries_per_kv: tl.constexpr,  # int
-    block_table_stride: tl.int64,  # int
-    query_stride_0: tl.int64,  # int
-    query_stride_1: tl.int64,  # int, should be equal to head_size
-    qq_bias_stride_0: tl.int64,  # int
-    BLOCK_SIZE: tl.constexpr,  # int
-    TILE_SIZE: tl.constexpr,  # int, must be power of 2
-    HEAD_SIZE: tl.constexpr,  # int
-    HEAD_SIZE_PADDED: tl.constexpr,  # int, must be power of 2
-    USE_ALIBI_SLOPES: tl.constexpr,  # bool
-    USE_ALIBI_SQRT: tl.constexpr,  # bool
-    USE_QQ_BIAS: tl.constexpr,  # bool
-    USE_SOFTCAP: tl.constexpr,  # bool
-    USE_SINKS: tl.constexpr,  # bool
-    SLIDING_WINDOW: tl.constexpr,  # int
-    stride_k_cache_0: tl.int64,  # int
-    stride_k_cache_1: tl.int64,  # int
-    stride_k_cache_2: tl.int64,  # int
-    stride_k_cache_3: tl.constexpr,  # int
-    stride_v_cache_0: tl.int64,  # int
-    stride_v_cache_1: tl.int64,  # int
-    stride_v_cache_2: tl.int64,  # int
-    stride_v_cache_3: tl.constexpr,  # int
-    query_start_len_ptr,  # [num_seqs+1]
-    BLOCK_Q: tl.constexpr,  # int
-    num_seqs: tl.int32,
-    BLOCK_M: tl.constexpr,  # int
-    NUM_SEGMENTS_PER_SEQ: tl.constexpr,  # int
-    USE_MM_PREFIX: tl.constexpr,  # bool
-    MAX_MM_RANGES: tl.constexpr,  # int
-    mm_prefix_range_ptr,  # [num_seqs] - prefix length for each sequence
-    # KV cache quantization: 0=none, 1=fp8, 2=per-token-head
-    KV_QUANT_MODE: tl.constexpr = 0,
-    # Per-token-head scale caches (KV_QUANT_MODE >= 2)
-    # Shape: [num_blocks, block_size, num_kv_heads]
-    k_scale_cache_ptr=None,
-    v_scale_cache_ptr=None,
-    stride_ks_blk=0,
-    stride_ks_slot=0,
-    stride_ks_head=0,
-    stride_vs_blk=0,
-    stride_vs_slot=0,
-    stride_vs_head=0,
-):
-    q_block_global_idx = tl.program_id(0)
-    kv_head_idx = tl.program_id(1)
-    segm_idx = tl.program_id(2)
-
-    seq_idx = find_seq_idx(
-        query_start_len_ptr, q_block_global_idx, num_seqs, BLOCK_Q, True
-    )
-
-    q_block_start_idx = tl.load(query_start_len_ptr + seq_idx) // BLOCK_Q + seq_idx
-
-    q_block_local_idx = q_block_global_idx - q_block_start_idx
-
-    cur_batch_in_all_start_index = tl.load(query_start_len_ptr + seq_idx)
-    cur_batch_in_all_stop_index = tl.load(query_start_len_ptr + seq_idx + 1)
-
-    cur_batch_query_len = cur_batch_in_all_stop_index - cur_batch_in_all_start_index
-
-    if q_block_local_idx * BLOCK_Q >= cur_batch_query_len:
-        return
-
-    # sequence len for this particular sequence
-    seq_len = tl.load(seq_lens_ptr + seq_idx)
-
-    # number of segments for this particular sequence
-    num_segments = NUM_SEGMENTS_PER_SEQ
-    tiles_per_segment = cdiv_fn(seq_len, num_segments * TILE_SIZE)
-
-    if segm_idx * tiles_per_segment * TILE_SIZE >= seq_len:
-        return
-
-    offs_m = tl.arange(0, BLOCK_M)
-    offs_d = tl.arange(0, HEAD_SIZE_PADDED)
-    offs_t = tl.arange(0, TILE_SIZE)
-    query_pos = q_block_local_idx * BLOCK_Q + offs_m // num_queries_per_kv
-
-    query_offset_0 = cur_batch_in_all_start_index + query_pos
-    query_offset_1 = kv_head_idx * num_queries_per_kv + offs_m % num_queries_per_kv
-    query_offset = (
-        query_offset_0[:, None] * query_stride_0
-        + query_offset_1[:, None] * query_stride_1
-        + offs_d[None, :]
-    )
-
-    dim_mask = tl.where(offs_d < HEAD_SIZE, 1, 0).to(tl.int1)
-    query_mask_0 = tl.where(query_pos < cur_batch_query_len, 1, 0).to(tl.int1)
-    query_mask_1 = tl.where(query_offset_1 < num_query_heads, 1, 0).to(tl.int1)
-
-    # Q : (BLOCK_M, HEAD_SIZE_PADDED)
-    Q = tl.load(
-        query_ptr + query_offset,
-        mask=dim_mask[None, :] & query_mask_0[:, None] & query_mask_1[:, None],
-        other=0.0,
-    )
-
-    block_table_offset = seq_idx * block_table_stride
-
-    if USE_SINKS:
-        if segm_idx == 0:
-            M = tl.load(
-                sink_ptr + query_offset_1,
-                mask=query_mask_1,
-                other=float("-inf"),
-            ).to(dtype=tl.float32)
-        else:
-            M = tl.full([BLOCK_M], float("-inf"), dtype=tl.float32)
-    else:
-        M = tl.full([BLOCK_M], float("-inf"), dtype=tl.float32)
-
-    L = tl.full([BLOCK_M], 1.0, dtype=tl.float32)
-    acc = tl.zeros([BLOCK_M, HEAD_SIZE_PADDED], dtype=tl.float32)
-
-    # context length for this particular sequences
-    context_len = seq_len - cur_batch_query_len
-
-    # alibi slope for this head
-    if USE_ALIBI_SLOPES:
-        alibi_slope = tl.load(
-            alibi_slopes_ptr + query_offset_1, mask=query_mask_1, other=0.0
+    # ---- Epilogue ---------------------------------------------------------
+    if IS_3D:
+        # Store per-segment partials; finalized by ``reduce_segments``.
+        segm_output_offset = (
+            query_offset_0[:, None].to(tl.int64)
+            * (num_query_heads * NUM_SEGMENTS_PER_SEQ * HEAD_SIZE_PADDED)
+            + query_offset_1[:, None] * (NUM_SEGMENTS_PER_SEQ * HEAD_SIZE_PADDED)
+            + segm_idx * HEAD_SIZE_PADDED
+            + tl.arange(0, HEAD_SIZE_PADDED)[None, :]
         )
-
-    # query-query attention bias
-    if USE_QQ_BIAS:
-        qq_bias_row_ptrs = (
-            qq_bias_ptr + query_pos[:, None] * qq_bias_stride_0
-        )  # shape: [BLOCK_M]
-
-    # compute the length of the longest sequence prefix spanned by any
-    # query token in the current q_block (q_block_local_idx)
-    max_seq_prefix_len = (
-        context_len
-        + q_block_local_idx * BLOCK_Q
-        + (BLOCK_M - 1) // num_queries_per_kv
-        + 1
-    )
-
-    # adjust for potential padding in the last q_block by considering the
-    # actual sequence length
-    max_seq_prefix_len = tl.minimum(max_seq_prefix_len, seq_len)
-
-    # calculate the number of tiles that need to be processed to
-    # cover the longest sequence prefix (due to causal masking, tiles beyond
-    # this prefix can be skipped)
-    num_tiles = cdiv_fn(max_seq_prefix_len, TILE_SIZE)
-
-    # ---- Sliding-window tile pruning --------------------
-    # Default: keep previous global behavior
-    tile_start = 0
-    tile_end = num_tiles
-    # TODO(Isotr0py): sliding window pruning with image bidirectional mask
-    if SLIDING_WINDOW > 0 and not USE_MM_PREFIX:
-        # Query rows covered by this Q-block
-        qpos_lo = q_block_local_idx * BLOCK_Q
-        qpos_hi = tl.minimum(
-            qpos_lo + (BLOCK_M - 1) // num_queries_per_kv,
-            cur_batch_query_len - 1,
+        tl.store(
+            segm_output_ptr + segm_output_offset,
+            acc,
+            mask=dim_mask[None, :] & query_mask_0[:, None] & query_mask_1[:, None],
         )
-        # For sliding window, each query position q can only attend to
-        # keys in the range [q_abs - SLIDING_WINDOW + 1, q_abs]
-        # where q_abs = context_len + q
-        # The union of allowed key positions for this Q-block is:
-        # [context_len + qpos_lo - SLIDING_WINDOW + 1, context_len + qpos_hi]
-        first_allowed_key = context_len + qpos_lo - SLIDING_WINDOW + 1
-        last_allowed_key = context_len + qpos_hi
-        # Convert to tile indices and clamp
-        tile_start = tl.maximum(0, first_allowed_key // TILE_SIZE)
-        tile_end = tl.minimum((last_allowed_key // TILE_SIZE) + 1, num_tiles)
-
-    # iterate through tiles (now limited to the sliding window range)
-    for j in range(
-        max(segm_idx * tiles_per_segment, tile_start),
-        min((segm_idx + 1) * tiles_per_segment, tile_end),
-    ):
-        seq_offset = j * TILE_SIZE + offs_t
-        tile_mask = seq_offset < max_seq_prefix_len
-
-        physical_block_idx = tl.load(
-            block_tables_ptr + block_table_offset + seq_offset // BLOCK_SIZE
-        ).to(tl.int64)
-
-        v_offset = (
-            physical_block_idx[:, None] * stride_v_cache_0
-            + kv_head_idx * stride_v_cache_2
-            + offs_d[None, :] * stride_v_cache_3
-            + (seq_offset % BLOCK_SIZE)[:, None] * stride_v_cache_1
+        store_segm_reduce_scalars(
+            segm_max_ptr,
+            segm_expsum_ptr,
+            query_offset_0,
+            query_offset_1,
+            segm_idx,
+            M,
+            L,
+            query_mask_0,
+            query_mask_1,
+            num_query_heads,
+            NUM_SEGMENTS_PER_SEQ,
         )
-
-        k_offset = (
-            physical_block_idx[None, :] * stride_k_cache_0
-            + kv_head_idx * stride_k_cache_2
-            + offs_d[:, None] * stride_k_cache_3
-            + (seq_offset % BLOCK_SIZE)[None, :] * stride_k_cache_1
-        )
-
-        # K : (HEAD_SIZE, TILE_SIZE)
-        K_load = tl.load(
-            key_cache_ptr + k_offset,
-            mask=dim_mask[:, None] & tile_mask[None, :],
-            other=0.0,
-        )
-        K, k_token_head_scales = _prepare_kv_tile(
-            K_load,
-            Q,
-            k_scale,
-            k_scale_cache_ptr,
-            physical_block_idx,
-            seq_offset,
-            kv_head_idx,
-            stride_ks_blk,
-            stride_ks_slot,
-            stride_ks_head,
-            tile_mask,
-            BLOCK_SIZE,
-            KV_QUANT_MODE,
-        )
-
-        # V : (TILE_SIZE, HEAD_SIZE)
-        V_load = tl.load(
-            value_cache_ptr + v_offset,
-            mask=dim_mask[None, :] & tile_mask[:, None],
-            other=0.0,
+    else:
+        acc = acc / L[:, None]
+        if USE_FP8:
+            acc = acc * tl.load(out_scale)
+            acc = tl.clamp(acc, FP8_MIN, FP8_MAX)
+        output_offset = (
+            query_offset_0[:, None] * output_stride_0
+            + query_offset_1[:, None] * output_stride_1
+            + offs_d[None, :]
         )
-        V, v_token_head_scales = _prepare_kv_tile(
-            V_load,
-            Q,
-            v_scale,
-            v_scale_cache_ptr,
-            physical_block_idx,
-            seq_offset,
-            kv_head_idx,
-            stride_vs_blk,
-            stride_vs_slot,
-            stride_vs_head,
-            tile_mask,
-            BLOCK_SIZE,
-            KV_QUANT_MODE,
+        tl.store(
+            output_ptr + output_offset,
+            acc,
+            mask=dim_mask[None, :] & query_mask_0[:, None] & query_mask_1[:, None],
         )
 
-        # Compute attention mask: causal by default (key <= query)
-        query_abs_pos = context_len + query_pos[:, None]
-        seq_mask = seq_offset[None, :] <= query_abs_pos
-
-        # Apply sliding window to base mask BEFORE mm_prefix OR.
-        # Order must match FlexAttention: (causal AND sliding_window) OR mm_prefix
-        if SLIDING_WINDOW > 0:
-            seq_mask = seq_mask & ((query_abs_pos - seq_offset) < SLIDING_WINDOW)
-
-        # PrefixLM: extend mask with bidirectional ranges for multimodal tokens.
-        # Applied AFTER sliding window so mm_prefix ranges override SW restriction.
-        if USE_MM_PREFIX:
-            for i in range(MAX_MM_RANGES):
-                range_start = tl.load(
-                    mm_prefix_range_ptr + seq_idx * MAX_MM_RANGES * 2 + i * 2
-                )
-                range_end = tl.load(
-                    mm_prefix_range_ptr + seq_idx * MAX_MM_RANGES * 2 + i * 2 + 1
-                )
-
-                is_valid = range_start < range_end
-                q_in_range = (
-                    (query_abs_pos >= range_start)
-                    & (query_abs_pos <= range_end)
-                    & is_valid
-                )
-                k_in_range = (
-                    (seq_offset[None, :] >= range_start)
-                    & (seq_offset[None, :] <= range_end)
-                    & is_valid
-                )
-                seq_mask |= q_in_range & k_in_range
-
-        # S : (BLOCK_M, TILE_SIZE)
-        S = tl.zeros(shape=(BLOCK_M, TILE_SIZE), dtype=tl.float32)
-
-        # Per-token-head quant: fuse softmax_scale with per-head k_scale
-        # to avoid a separate BLOCK_M × TILE_SIZE multiply on S.
-        if KV_QUANT_MODE >= 2:
-            S += tl.dot(Q, K) * (scale * k_token_head_scales[None, :])
-        else:
-            S += scale * tl.dot(Q, K)
-
-        if USE_SOFTCAP:
-            S = apply_softcap(S, softcap)
-
-        S = tl.where(
-            query_mask_1[:, None] & query_mask_0[:, None] & seq_mask, S, float("-inf")
-        )
-
-        if USE_ALIBI_SLOPES:
-            if USE_ALIBI_SQRT:
-                relative_pos = seq_offset - (context_len + query_pos[:, None])
-                alibi_offset = tl.where(
-                    relative_pos <= 0,
-                    -tl.sqrt((-relative_pos).to(tl.float32)),
-                    0.0,
-                )
-            else:
-                alibi_offset = seq_offset - context_len
-            S += alibi_slope[:, None] * alibi_offset
-
-        if USE_QQ_BIAS:
-            # compute key positions relative to query section
-            key_rel_pos = seq_offset - context_len  # shape: [BLOCK_SIZE]
-            # load bias only for keys that correspond to queries
-            is_query_key = key_rel_pos >= 0 and key_rel_pos < qq_bias_stride_0
-            qq_bias = tl.load(
-                qq_bias_row_ptrs + key_rel_pos[None, :],
-                mask=is_query_key[None, :],  # avoid OOB for context keys
-                other=0.0,
-            )
-            S += qq_bias
-
-        # compute running maximum
-        # m_j : (BLOCK_M,)
-        m_j = tl.maximum(M, tl.max(S, axis=1))
-
-        # For sliding window there's a chance the max is -inf due to masking of
-        # the entire row. In this case we need to set m_j 0 to avoid NaN
-        m_j = tl.where(m_j > float("-inf"), m_j, 0.0)
-
-        # P : (BLOCK_M, TILE_SIZE,)
-        P = tl.exp(S - m_j[:, None])
-
-        # l_j : (BLOCK_M,)
-        l_j = tl.sum(P, axis=1)
-
-        # alpha : (BLOCK_M, )
-        alpha = tl.exp(M - m_j)
-
-        # acc : (BLOCK_M, HEAD_SIZE_PADDED)
-        acc = acc * alpha[:, None]
-
-        # update constants
-        L = L * alpha + l_j
-        M = m_j
-
-        if SLIDING_WINDOW:
-            qpos_lo = q_block_local_idx * BLOCK_Q
-            V = tl.where(
-                (context_len + qpos_lo - seq_offset[:, None]) < SLIDING_WINDOW, V, 0.0
-            )
-
-        # acc : (BLOCK_M, HEAD_SIZE_PADDED)
-        # Per-token-head quant: apply v_scale to P instead of V.
-        if KV_QUANT_MODE >= 2:
-            P_v = (P * v_token_head_scales[None, :]).to(V.dtype)
-            acc += tl.dot(P_v, V)
-        else:
-            acc += tl.dot(P.to(V.dtype), V)
-
-    segm_output_offset = (
-        query_offset_0[:, None].to(tl.int64)
-        * (num_query_heads * NUM_SEGMENTS_PER_SEQ * HEAD_SIZE_PADDED)
-        + query_offset_1[:, None] * (NUM_SEGMENTS_PER_SEQ * HEAD_SIZE_PADDED)
-        + segm_idx * HEAD_SIZE_PADDED
-        + tl.arange(0, HEAD_SIZE_PADDED)[None, :]
-    )
-    tl.store(
-        segm_output_ptr + segm_output_offset,
-        acc,
-        mask=dim_mask[None, :] & query_mask_0[:, None] & query_mask_1[:, None],
-    )
-    segm_offset = (
-        query_offset_0.to(tl.int64) * (num_query_heads * NUM_SEGMENTS_PER_SEQ)
-        + query_offset_1 * NUM_SEGMENTS_PER_SEQ
-        + segm_idx
-    )
-    tl.store(segm_max_ptr + segm_offset, M, mask=query_mask_0 & query_mask_1)
-    tl.store(segm_expsum_ptr + segm_offset, L, mask=query_mask_0 & query_mask_1)
-
 
 @triton.jit
 def reduce_segments(
@@ -996,12 +490,7 @@ def _get_tile_size(
     element_size: int,
     is_prefill: bool,
 ) -> int:
-    """Select tile size with Gemma3-specific optimization.
-
-    For Gemma3, use 32 for both prefill and decode to better utilize
-    the larger head dimension (128/256). For other models, use
-    the default vLLM behavior.
-    """
+    """Select tile size with Gemma3-specific optimization."""
     if _is_gemma3_attention(head_size, sliding_window):
         # Gemma3: use 32 for decode (default is 16)
         return 32
@@ -1009,6 +498,7 @@ def _get_tile_size(
     # Default behavior
     if is_prefill:
         return 32
+    # Note: tile size must be at least 32 for fp8 (element_size == 1).
     return 16 if element_size >= 2 else 32
 
 
@@ -1046,6 +536,8 @@ def unified_attention(
     kv_quant_mode: KVQuantMode = KVQuantMode.NONE,
     k_scale_cache=None,  # [num_blocks, block_size, num_kv_heads] float32
     v_scale_cache=None,  # [num_blocks, block_size, num_kv_heads] float32
+    # Chunked attention: restrict attention to aligned blocks with lookback.
+    chunk_lookback=-1,
 ):
     assert causal, "Only causal attention is supported"
     assert q_descale is None, "Q scales not supported"
@@ -1053,6 +545,15 @@ def unified_attention(
     if sinks is not None:
         assert sinks.shape[0] == q.shape[1], "Sinks must be num_query_heads size"
 
+    use_per_token_head_scales = kv_quant_mode in (
+        KVQuantMode.INT8_PER_TOKEN_HEAD,
+        KVQuantMode.FP8_PER_TOKEN_HEAD,
+    )
+    if use_per_token_head_scales:
+        assert k_scale_cache is not None and v_scale_cache is not None, (
+            f"{kv_quant_mode.name} requires k_scale_cache / v_scale_cache"
+        )
+
     use_mm_prefix = False
     max_mm_ranges = 0
     if mm_prefix_range is not None:
@@ -1090,20 +591,21 @@ def unified_attention(
     #    = floor(q.shape[0] / BLOCK_Q) + num_seqs
     total_num_q_blocks = q.shape[0] // BLOCK_Q + num_seqs
 
-    # Tile sizes for prefill and decode. Gemma3 models use optimized values.
-    # Note: tile size must be at least 32 for fp8 (element_size == 1).
     sliding_window_val = 1 + window_size[0] if window_size[0] >= 0 else 0
+
+    # Compute chunked block size from sliding window if needed.
+    chunk_size = -1
+    if sliding_window_val > 0 and chunk_lookback > -1:
+        chunk_size = sliding_window_val // (chunk_lookback + 1)
+        assert chunk_size > 0, "sliding_window must be > chunk_lookback+1"
+    elif sliding_window_val <= 0:
+        chunk_lookback = -1
+
     TILE_SIZE_PREFILL = _get_tile_size(
-        head_size,
-        sliding_window_val,
-        q.element_size(),
-        is_prefill=True,
+        head_size, sliding_window_val, q.element_size(), is_prefill=True
     )
     TILE_SIZE_DECODE = _get_tile_size(
-        head_size,
-        sliding_window_val,
-        q.element_size(),
-        is_prefill=False,
+        head_size, sliding_window_val, q.element_size(), is_prefill=False
     )
 
     # Launch the 2D kernel if
@@ -1111,7 +613,7 @@ def unified_attention(
     # 2. The batch includes at least one prefill request, or
     # 3. The number of sequences exceeds the configured threshold, or
     # 4. Batch invariance is enabled
-    if (
+    use_3d = not (
         seq_threshold_3D is None
         or num_par_softmax_segments is None
         or softmax_segm_output is None
@@ -1120,132 +622,110 @@ def unified_attention(
         or max_seqlen_q > 1
         or num_seqs > seq_threshold_3D
         or is_batch_invariant
-    ):
-        kernel_unified_attention_2d[
-            (
-                total_num_q_blocks,
-                num_kv_heads,
-            )
-        ](
-            output_ptr=out,
-            query_ptr=q,
-            key_cache_ptr=k,
-            value_cache_ptr=v,
-            sink_ptr=sinks,
-            block_tables_ptr=block_table,
-            seq_lens_ptr=seqused_k,
-            alibi_slopes_ptr=alibi_slopes,
-            qq_bias_ptr=qq_bias,
-            scale=softmax_scale,
-            k_scale=k_descale,
-            v_scale=v_descale,
-            out_scale=1 / output_scale if output_scale is not None else 1.0,
-            softcap=softcap,
-            num_query_heads=num_query_heads,
-            num_queries_per_kv=num_queries_per_kv,
-            block_table_stride=block_table.stride(0),
-            query_stride_0=q.stride(0),
-            query_stride_1=q.stride(1),
-            output_stride_0=out.stride(0),
-            output_stride_1=out.stride(1),
-            qq_bias_stride_0=qq_bias.stride(0) if use_qq_bias else 0,
-            BLOCK_SIZE=block_size,
-            TILE_SIZE=TILE_SIZE_PREFILL,
-            HEAD_SIZE=head_size,
-            HEAD_SIZE_PADDED=triton.next_power_of_2(head_size),
-            USE_ALIBI_SLOPES=use_alibi_slopes,
-            USE_ALIBI_SQRT=use_alibi_sqrt,
-            USE_QQ_BIAS=use_qq_bias,
-            USE_SOFTCAP=(softcap > 0),
-            USE_SINKS=(sinks is not None),
-            USE_MM_PREFIX=use_mm_prefix,
-            MAX_MM_RANGES=max_mm_ranges,
-            mm_prefix_range_ptr=mm_prefix_range,
-            SLIDING_WINDOW=(1 + window_size[0]),
-            stride_k_cache_0=k.stride(0),
-            stride_k_cache_1=k.stride(1),
-            stride_k_cache_2=k.stride(2),
-            stride_k_cache_3=k.stride(3),
-            stride_v_cache_0=v.stride(0),
-            stride_v_cache_1=v.stride(1),
-            stride_v_cache_2=v.stride(2),
-            stride_v_cache_3=v.stride(3),
-            query_start_len_ptr=cu_seqlens_q,
-            BLOCK_Q=BLOCK_Q,
-            num_seqs=num_seqs,
-            BLOCK_M=BLOCK_M,
-            USE_FP8=output_scale is not None,
-            KV_QUANT_MODE=kv_quant_mode,
-            k_scale_cache_ptr=k_scale_cache,
-            v_scale_cache_ptr=v_scale_cache,
-            stride_ks_blk=k_scale_cache.stride(0) if k_scale_cache is not None else 0,
-            stride_ks_slot=k_scale_cache.stride(1) if k_scale_cache is not None else 0,
-            stride_ks_head=k_scale_cache.stride(2) if k_scale_cache is not None else 0,
-            stride_vs_blk=v_scale_cache.stride(0) if v_scale_cache is not None else 0,
-            stride_vs_slot=v_scale_cache.stride(1) if v_scale_cache is not None else 0,
-            stride_vs_head=v_scale_cache.stride(2) if v_scale_cache is not None else 0,
-        )
+    )
+
+    # The kernel signature is the same for 2D and 3D — only the launch
+    # grid + a handful of constexpr toggles differ.  Per-token-head scale
+    # caches and their strides are required arguments; non-per-token-head
+    # modes pass dummy zeros (the code path is dead-code eliminated by
+    # the ``USE_PER_TOKEN_HEAD_SCALES`` constexpr branch in the kernel).
+    if use_per_token_head_scales:
+        ks_strides = k_scale_cache.stride()
+        vs_strides = v_scale_cache.stride()
+        ks_blk, ks_slot, ks_head = ks_strides[0], ks_strides[1], ks_strides[2]
+        vs_blk, vs_slot, vs_head = vs_strides[0], vs_strides[1], vs_strides[2]
+        k_scale_ptr = k_scale_cache
+        v_scale_ptr = v_scale_cache
     else:
-        kernel_unified_attention_3d[
-            (total_num_q_blocks, num_kv_heads, num_par_softmax_segments)
-        ](
-            segm_output_ptr=softmax_segm_output,
-            segm_max_ptr=softmax_segm_max,
-            segm_expsum_ptr=softmax_segm_expsum,
-            query_ptr=q,
-            key_cache_ptr=k,
-            value_cache_ptr=v,
-            sink_ptr=sinks,
-            block_tables_ptr=block_table,
-            seq_lens_ptr=seqused_k,
-            alibi_slopes_ptr=alibi_slopes,
-            qq_bias_ptr=qq_bias,
-            scale=softmax_scale,
-            k_scale=k_descale,
-            v_scale=v_descale,
-            softcap=softcap,
-            num_query_heads=num_query_heads,
-            num_queries_per_kv=num_queries_per_kv,
-            block_table_stride=block_table.stride(0),
-            query_stride_0=q.stride(0),
-            query_stride_1=q.stride(1),
-            qq_bias_stride_0=qq_bias.stride(0) if use_qq_bias else 0,
-            BLOCK_SIZE=block_size,
-            TILE_SIZE=TILE_SIZE_DECODE,
-            HEAD_SIZE=head_size,
-            HEAD_SIZE_PADDED=triton.next_power_of_2(head_size),
-            USE_ALIBI_SLOPES=use_alibi_slopes,
-            USE_ALIBI_SQRT=use_alibi_sqrt,
-            USE_QQ_BIAS=use_qq_bias,
-            USE_SOFTCAP=(softcap > 0),
-            USE_SINKS=(sinks is not None),
-            USE_MM_PREFIX=use_mm_prefix,
-            MAX_MM_RANGES=max_mm_ranges,
-            mm_prefix_range_ptr=mm_prefix_range,
-            SLIDING_WINDOW=(1 + window_size[0]),
-            stride_k_cache_0=k.stride(0),
-            stride_k_cache_1=k.stride(1),
-            stride_k_cache_2=k.stride(2),
-            stride_k_cache_3=k.stride(3),
-            stride_v_cache_0=v.stride(0),
-            stride_v_cache_1=v.stride(1),
-            stride_v_cache_2=v.stride(2),
-            stride_v_cache_3=v.stride(3),
-            query_start_len_ptr=cu_seqlens_q,
-            BLOCK_Q=BLOCK_Q,
-            num_seqs=num_seqs,
-            BLOCK_M=BLOCK_M,
-            NUM_SEGMENTS_PER_SEQ=num_par_softmax_segments,
-            KV_QUANT_MODE=kv_quant_mode,
-            k_scale_cache_ptr=k_scale_cache,
-            v_scale_cache_ptr=v_scale_cache,
-            stride_ks_blk=k_scale_cache.stride(0) if k_scale_cache is not None else 0,
-            stride_ks_slot=k_scale_cache.stride(1) if k_scale_cache is not None else 0,
-            stride_ks_head=k_scale_cache.stride(2) if k_scale_cache is not None else 0,
-            stride_vs_blk=v_scale_cache.stride(0) if v_scale_cache is not None else 0,
-            stride_vs_slot=v_scale_cache.stride(1) if v_scale_cache is not None else 0,
-            stride_vs_head=v_scale_cache.stride(2) if v_scale_cache is not None else 0,
-        )
+        ks_blk = ks_slot = ks_head = 0
+        vs_blk = vs_slot = vs_head = 0
+        # Pass the K cache as a stand-in pointer; never dereferenced.
+        k_scale_ptr = k
+        v_scale_ptr = v
+
+    # 3D needs real segm tensors; 2D never touches them but Triton wants
+    # a non-null pointer.  Reuse ``out`` as the placeholder.
+    segm_output_ptr = softmax_segm_output if use_3d else out
+    segm_max_ptr = softmax_segm_max if use_3d else out
+    segm_expsum_ptr = softmax_segm_expsum if use_3d else out
+    num_segments = num_par_softmax_segments if use_3d else 1
+
+    grid: tuple[Any, ...]
+    if not use_3d:
+        grid = (total_num_q_blocks, num_kv_heads)
+        tile_size = TILE_SIZE_PREFILL
+    else:
+        grid = (total_num_q_blocks, num_kv_heads, num_par_softmax_segments)
+        tile_size = TILE_SIZE_DECODE
+
+    kernel_unified_attention[grid](
+        output_ptr=out,
+        segm_output_ptr=segm_output_ptr,
+        segm_max_ptr=segm_max_ptr,
+        segm_expsum_ptr=segm_expsum_ptr,
+        query_ptr=q,
+        key_cache_ptr=k,
+        value_cache_ptr=v,
+        sink_ptr=sinks,
+        block_tables_ptr=block_table,
+        seq_lens_ptr=seqused_k,
+        alibi_slopes_ptr=alibi_slopes,
+        qq_bias_ptr=qq_bias,
+        k_scale_cache_ptr=k_scale_ptr,
+        v_scale_cache_ptr=v_scale_ptr,
+        scale=softmax_scale,
+        k_scale=k_descale,
+        v_scale=v_descale,
+        out_scale=1 / output_scale if output_scale is not None else 1.0,
+        softcap=softcap,
+        num_query_heads=num_query_heads,
+        num_queries_per_kv=num_queries_per_kv,
+        block_table_stride=block_table.stride(0),
+        query_stride_0=q.stride(0),
+        query_stride_1=q.stride(1),
+        output_stride_0=out.stride(0),
+        output_stride_1=out.stride(1),
+        qq_bias_stride_0=qq_bias.stride(0) if use_qq_bias else 0,
+        BLOCK_SIZE=block_size,
+        TILE_SIZE=tile_size,
+        HEAD_SIZE=head_size,
+        HEAD_SIZE_PADDED=triton.next_power_of_2(head_size),
+        USE_ALIBI_SLOPES=use_alibi_slopes,
+        USE_ALIBI_SQRT=use_alibi_sqrt,
+        USE_QQ_BIAS=use_qq_bias,
+        USE_SOFTCAP=(softcap > 0),
+        USE_SINKS=(sinks is not None),
+        USE_MM_PREFIX=use_mm_prefix,
+        MAX_MM_RANGES=max_mm_ranges,
+        mm_prefix_range_ptr=mm_prefix_range,
+        SLIDING_WINDOW=(1 + window_size[0]),
+        stride_k_cache_0=k.stride(0),
+        stride_k_cache_1=k.stride(1),
+        stride_k_cache_2=k.stride(2),
+        stride_k_cache_3=k.stride(3),
+        stride_v_cache_0=v.stride(0),
+        stride_v_cache_1=v.stride(1),
+        stride_v_cache_2=v.stride(2),
+        stride_v_cache_3=v.stride(3),
+        stride_ks_blk=ks_blk,
+        stride_ks_slot=ks_slot,
+        stride_ks_head=ks_head,
+        stride_vs_blk=vs_blk,
+        stride_vs_slot=vs_slot,
+        stride_vs_head=vs_head,
+        query_start_len_ptr=cu_seqlens_q,
+        BLOCK_Q=BLOCK_Q,
+        num_seqs=num_seqs,
+        BLOCK_M=BLOCK_M,
+        NUM_SEGMENTS_PER_SEQ=num_segments,
+        USE_FP8=output_scale is not None,
+        IS_3D=use_3d,
+        KV_QUANT_MODE=kv_quant_mode,
+        CHUNK_LOOKBACK=chunk_lookback,
+        CHUNK_SIZE=chunk_size,
+    )
+
+    if use_3d:
         reduce_segments[(q.shape[0], num_query_heads)](
             output_ptr=out,
             segm_output_ptr=softmax_segm_output,
diff --git a/vllm/v1/attention/ops/vit_attn_wrappers.py b/vllm/v1/attention/ops/vit_attn_wrappers.py
index 2d3505f51b76..4506f452cf9a 100644
--- a/vllm/v1/attention/ops/vit_attn_wrappers.py
+++ b/vllm/v1/attention/ops/vit_attn_wrappers.py
@@ -279,6 +279,10 @@ def flashinfer_wrapper(
     cu_seqlens: torch.Tensor | None = None,
     max_seqlen: torch.Tensor | None = None,
     sequence_lengths: torch.Tensor | None = None,
+    q_scale: torch.Tensor | None = None,
+    k_scale: torch.Tensor | None = None,
+    v_scale: torch.Tensor | None = None,
+    o_data_type: torch.dtype | None = None,
 ) -> torch.Tensor:
     from flashinfer.prefill import cudnn_batch_prefill_with_kv_cache
 
@@ -318,6 +322,10 @@ def flashinfer_wrapper(
         batch_offsets_k=batch_offsets_qko,
         batch_offsets_v=batch_offsets_v,
         batch_offsets_o=batch_offsets_qko,
+        q_scale=q_scale,
+        k_scale=k_scale,
+        v_scale=v_scale,
+        o_data_type=o_data_type,
     )
 
     if is_reshaped:
@@ -335,8 +343,12 @@ def vit_flashinfer_wrapper_fake(
     cu_seqlens: torch.Tensor | None = None,
     max_seqlen: torch.Tensor | None = None,
     sequence_lengths: torch.Tensor | None = None,
+    q_scale: torch.Tensor | None = None,
+    k_scale: torch.Tensor | None = None,
+    v_scale: torch.Tensor | None = None,
+    o_data_type: torch.dtype | None = None,
 ) -> torch.Tensor:
-    return torch.empty_like(q)
+    return torch.empty_like(q, dtype=o_data_type or q.dtype)
 
 
 direct_register_custom_op(
@@ -355,7 +367,22 @@ def vit_flashinfer_wrapper(
     cu_seqlens: torch.Tensor | None = None,
     max_seqlen: torch.Tensor | None = None,
     sequence_lengths: torch.Tensor | None = None,
+    q_scale: torch.Tensor | None = None,
+    k_scale: torch.Tensor | None = None,
+    v_scale: torch.Tensor | None = None,
+    o_data_type: torch.dtype | None = None,
 ) -> torch.Tensor:
     return torch.ops.vllm.flashinfer_wrapper(
-        q, k, v, scale, workspace_buffer, cu_seqlens, max_seqlen, sequence_lengths
+        q,
+        k,
+        v,
+        scale,
+        workspace_buffer,
+        cu_seqlens,
+        max_seqlen,
+        sequence_lengths,
+        q_scale,
+        k_scale,
+        v_scale,
+        o_data_type,
     )
diff --git a/vllm/v1/attention/selector.py b/vllm/v1/attention/selector.py
index 066c5fcc9c2a..a328b1830103 100644
--- a/vllm/v1/attention/selector.py
+++ b/vllm/v1/attention/selector.py
@@ -6,6 +6,7 @@
 
 import torch
 
+import vllm.envs as envs
 from vllm.config.cache import CacheDType
 from vllm.logger import init_logger
 from vllm.utils.import_utils import resolve_obj_by_qualname
@@ -30,6 +31,7 @@ class AttentionSelectorConfig(NamedTuple):
     use_per_head_quant_scales: bool = False
     attn_type: str = AttentionType.DECODER
     use_non_causal: bool = False
+    use_batch_invariant: bool = False
 
     def __repr__(self):
         return (
@@ -43,7 +45,8 @@ def __repr__(self):
             f"use_mm_prefix={self.use_mm_prefix}, "
             f"use_per_head_quant_scales={self.use_per_head_quant_scales}, "
             f"attn_type={self.attn_type}, "
-            f"use_non_causal={self.use_non_causal})"
+            f"use_non_causal={self.use_non_causal}, "
+            f"use_batch_invariant={self.use_batch_invariant})"
         )
 
 
@@ -78,11 +81,6 @@ def get_attn_backend(
     else:
         block_size = None
 
-    speculative_config = vllm_config.speculative_config
-    use_non_causal = (
-        speculative_config is not None and speculative_config.method == "dflash"
-    )
-
     attn_selector_config = AttentionSelectorConfig(
         head_size=head_size,
         dtype=dtype,
@@ -94,7 +92,8 @@ def get_attn_backend(
         use_mm_prefix=use_mm_prefix,
         use_per_head_quant_scales=use_per_head_quant_scales,
         attn_type=attn_type or AttentionType.DECODER,
-        use_non_causal=use_non_causal,
+        use_non_causal=vllm_config.attention_config.use_non_causal,
+        use_batch_invariant=envs.VLLM_BATCH_INVARIANT,
     )
 
     return _cached_get_attn_backend(
@@ -162,4 +161,9 @@ def _cached_get_mamba_attn_backend(
         ) from e
 
     mamba_attn_backend = selected_backend.get_class()
+    if envs.VLLM_BATCH_INVARIANT and not mamba_attn_backend.supports_batch_invariance():
+        raise RuntimeError(
+            "VLLM batch_invariant mode is not supported for "
+            f"{mamba_attn_backend.get_name()}."
+        )
     return mamba_attn_backend
diff --git a/vllm/v1/core/kv_cache_coordinator.py b/vllm/v1/core/kv_cache_coordinator.py
index eaa95dfe49f7..65993e804153 100644
--- a/vllm/v1/core/kv_cache_coordinator.py
+++ b/vllm/v1/core/kv_cache_coordinator.py
@@ -34,6 +34,7 @@ def __init__(
         self,
         kv_cache_config: KVCacheConfig,
         max_model_len: int,
+        max_num_batched_tokens: int,
         use_eagle: bool,
         enable_caching: bool,
         enable_kv_cache_events: bool,
@@ -54,11 +55,19 @@ def __init__(
             metrics_collector,
         )
 
-        # Needs special handling for find_longest_cache_hit if eagle is enabled
-        self.use_eagle = use_eagle
+        # KV cache group indices that get the EAGLE last-block drop.
+        self.eagle_group_ids: set[int] = {
+            i for i, g in enumerate(kv_cache_config.kv_cache_groups) if g.is_eagle_group
+        }
+        # Conservatively fall back to flag all groups when no group is flagged.
+        if use_eagle and not self.eagle_group_ids:
+            self.eagle_group_ids = set(range(len(kv_cache_config.kv_cache_groups)))
+
         self.single_type_managers = tuple(
             get_manager_for_kv_cache_spec(
                 kv_cache_spec=kv_cache_group.kv_cache_spec,
+                max_num_batched_tokens=max_num_batched_tokens,
+                max_model_len=max_model_len,
                 block_pool=self.block_pool,
                 enable_caching=enable_caching,
                 kv_cache_group_id=i,
@@ -76,6 +85,7 @@ def get_num_blocks_to_allocate(
         num_encoder_tokens: int,
         total_computed_tokens: int,
         num_tokens_main_model: int,
+        apply_admission_cap: bool = False,
     ) -> int:
         """
         Get the number of blocks needed to be allocated for the request.
@@ -92,6 +102,10 @@ def get_num_blocks_to_allocate(
             num_tokens_main_model: The number of tokens for the main model (aka target
                 model in spec decode). w/o spec decode, it is num_tokens;
                 with spec decode, it is num_tokens - num_lookahead_tokens.
+            apply_admission_cap: If True, apply the recycling-aware
+                per-request admission cap (SWA / chunked-local). Set only by
+                the full-sequence admission gate; per-step allocation must
+                leave it False so the predictor matches `allocate_new_blocks`.
 
         Returns:
             The number of blocks to allocate.
@@ -102,7 +116,12 @@ def get_num_blocks_to_allocate(
                 # For cross-attention, we issue a single static allocation
                 # of blocks based on the number of encoder input tokens.
                 num_blocks_to_allocate += manager.get_num_blocks_to_allocate(
-                    request_id, num_encoder_tokens, [], 0, num_encoder_tokens
+                    request_id,
+                    num_encoder_tokens,
+                    [],
+                    0,
+                    num_encoder_tokens,
+                    apply_admission_cap=apply_admission_cap,
                 )
             else:
                 num_blocks_to_allocate += manager.get_num_blocks_to_allocate(
@@ -111,6 +130,7 @@ def get_num_blocks_to_allocate(
                     new_computed_blocks[i],
                     total_computed_tokens,
                     num_tokens_main_model,
+                    apply_admission_cap=apply_admission_cap,
                 )
         return num_blocks_to_allocate
 
@@ -265,6 +285,7 @@ def __init__(
         self,
         kv_cache_config: KVCacheConfig,
         max_model_len: int,
+        max_num_batched_tokens: int,
         use_eagle: bool,
         enable_kv_cache_events: bool,
         dcp_world_size: int,
@@ -275,6 +296,7 @@ def __init__(
         super().__init__(
             kv_cache_config,
             max_model_len,
+            max_num_batched_tokens,
             use_eagle,
             False,
             enable_kv_cache_events,
@@ -310,6 +332,7 @@ def __init__(
         self,
         kv_cache_config: KVCacheConfig,
         max_model_len: int,
+        max_num_batched_tokens: int,
         use_eagle: bool,
         enable_caching: bool,
         enable_kv_cache_events: bool,
@@ -321,6 +344,7 @@ def __init__(
         super().__init__(
             kv_cache_config,
             max_model_len,
+            max_num_batched_tokens,
             use_eagle,
             enable_caching,
             enable_kv_cache_events,
@@ -357,7 +381,7 @@ def find_longest_cache_hit(
             kv_cache_group_ids=[0],
             block_pool=self.block_pool,
             kv_cache_spec=self.kv_cache_spec,
-            use_eagle=self.use_eagle,
+            use_eagle=0 in self.eagle_group_ids,
             alignment_tokens=self.block_size,
             dcp_world_size=self.dcp_world_size,
             pcp_world_size=self.pcp_world_size,
@@ -375,6 +399,7 @@ def __init__(
         self,
         kv_cache_config: KVCacheConfig,
         max_model_len: int,
+        max_num_batched_tokens: int,
         use_eagle: bool,
         enable_caching: bool,
         enable_kv_cache_events: bool,
@@ -386,6 +411,7 @@ def __init__(
         super().__init__(
             kv_cache_config,
             max_model_len,
+            max_num_batched_tokens,
             use_eagle,
             enable_caching,
             enable_kv_cache_events,
@@ -450,6 +476,14 @@ def verify_and_split_kv_cache_groups(self) -> None:
         block_sizes = [spec.block_size for spec, _, _ in attention_groups]
         self.lcm_block_size = lcm(*block_sizes)
 
+        # Attention-group indices (into ``self.attention_groups``) that
+        # contain at least one EAGLE/MTP KV cache group.
+        self.eagle_attn_group_indices: set[int] = {
+            i
+            for i, (_, group_ids, _) in enumerate(self.attention_groups)
+            if any(gid in self.eagle_group_ids for gid in group_ids)
+        }
+
     def find_longest_cache_hit(
         self,
         block_hashes: list[BlockHash],
@@ -485,49 +519,62 @@ def _get_block_hashes(kv_cache_spec: KVCacheSpec) -> BlockHashList:
         hit_blocks_by_group: list[list[KVCacheBlock] | None] = [None] * num_groups
 
         # Simple hybrid (1 full attn + 1 other): one iteration suffices.
-        # Full attn is always first if it exists. This avoids EAGLE drops
-        # being applied multiple times to non-full-attn groups.
-        # FIXME (yifan): However, for complex hybrid models with multiple attn
-        # groups, we still have the EAGLE spiral block dropping problem. See
-        # discussion in issue https://github.com/vllm-project/vllm/issues/32802.
+        # Full attn is always first if it exists.
         is_simple_hybrid = len(self.attention_groups) == 2 and isinstance(
             self.attention_groups[0][0], FullAttentionSpec
         )
 
+        # Attention-group indices whose EAGLE drop is verified at the current
+        # ``curr_hit_length``. Each eagle group applies the drop at most once
+        # per candidate length (see issue #32802).
+        eagle_verified: set[int] = set()
+
         while True:
             curr_hit_length = hit_length
 
-            for spec, group_ids, manager_cls in self.attention_groups:
-                is_full_attn = isinstance(spec, FullAttentionSpec)
-
-                # Full attention: reuse cached blocks (downward-closed property)
+            for idx, (spec, group_ids, manager_cls) in enumerate(self.attention_groups):
                 cached_blocks = hit_blocks_by_group[group_ids[0]]
-                if is_full_attn and cached_blocks is not None:
-                    # For full attention, we only need to compute the cache hit
-                    # length once. Starting from the second iteration, if the
-                    # curr_hit_length is reduced by other groups, we can simply
-                    # keep the first (curr_hit_length // block_size) blocks from
-                    # the last iteration.
-                    num_blocks = curr_hit_length // spec.block_size
-                    curr_hit_length = num_blocks * spec.block_size
-                else:
-                    hit_blocks = manager_cls.find_longest_cache_hit(
-                        block_hashes=_get_block_hashes(spec),
-                        max_length=curr_hit_length,
-                        kv_cache_group_ids=group_ids,
-                        block_pool=self.block_pool,
-                        kv_cache_spec=spec,
-                        use_eagle=self.use_eagle,
-                        alignment_tokens=self.lcm_block_size,
+                if isinstance(spec, FullAttentionSpec) and cached_blocks is not None:
+                    # Full attention is downward-closed: we only need to look
+                    # up cached blocks once; on subsequent iterations just trim
+                    # to the (reduced) current hit length.
+                    curr_hit_length = (
+                        curr_hit_length // spec.block_size * spec.block_size
                     )
-                    curr_hit_length = len(hit_blocks[0]) * spec.block_size
-                    for group_id, blocks in zip(group_ids, hit_blocks):
-                        hit_blocks_by_group[group_id] = blocks
+                    continue
+
+                use_eagle = (
+                    idx in self.eagle_attn_group_indices and idx not in eagle_verified
+                )
+
+                _max_length = curr_hit_length
+                if use_eagle:
+                    # Eagle needs to match one more block and then pop the last.
+                    _max_length = min(
+                        curr_hit_length + spec.block_size, max_cache_hit_length
+                    )
+                hit_blocks = manager_cls.find_longest_cache_hit(
+                    block_hashes=_get_block_hashes(spec),
+                    max_length=_max_length,
+                    kv_cache_group_ids=group_ids,
+                    block_pool=self.block_pool,
+                    kv_cache_spec=spec,
+                    use_eagle=use_eagle,
+                    alignment_tokens=self.lcm_block_size,
+                )
+                _new_hit_length = len(hit_blocks[0]) * spec.block_size
+                if use_eagle:
+                    eagle_verified.add(idx)
+                elif _new_hit_length < curr_hit_length:
+                    # length shrunk; invalidate previous eagle verifications
+                    eagle_verified.clear()
+                curr_hit_length = _new_hit_length
+                for group_id, blocks in zip(group_ids, hit_blocks):
+                    hit_blocks_by_group[group_id] = blocks
 
             if curr_hit_length >= hit_length:
                 break
             hit_length = curr_hit_length
-            # Simple hybrid: exit after one iteration
             if is_simple_hybrid:
                 break
 
@@ -547,6 +594,7 @@ def _get_block_hashes(kv_cache_spec: KVCacheSpec) -> BlockHashList:
 def get_kv_cache_coordinator(
     kv_cache_config: KVCacheConfig,
     max_model_len: int,
+    max_num_batched_tokens: int,
     use_eagle: bool,
     enable_caching: bool,
     enable_kv_cache_events: bool,
@@ -559,6 +607,7 @@ def get_kv_cache_coordinator(
         return KVCacheCoordinatorNoPrefixCache(
             kv_cache_config,
             max_model_len,
+            max_num_batched_tokens,
             use_eagle,
             enable_kv_cache_events,
             dcp_world_size=dcp_world_size,
@@ -570,6 +619,7 @@ def get_kv_cache_coordinator(
         return UnitaryKVCacheCoordinator(
             kv_cache_config,
             max_model_len,
+            max_num_batched_tokens,
             use_eagle,
             enable_caching,
             enable_kv_cache_events,
@@ -581,6 +631,7 @@ def get_kv_cache_coordinator(
     return HybridKVCacheCoordinator(
         kv_cache_config,
         max_model_len,
+        max_num_batched_tokens,
         use_eagle,
         enable_caching,
         enable_kv_cache_events,
diff --git a/vllm/v1/core/kv_cache_manager.py b/vllm/v1/core/kv_cache_manager.py
index dcec5e05bf97..431776870cf4 100644
--- a/vllm/v1/core/kv_cache_manager.py
+++ b/vllm/v1/core/kv_cache_manager.py
@@ -109,6 +109,7 @@ def __init__(
         kv_cache_config: KVCacheConfig,
         max_model_len: int,
         hash_block_size: int,
+        max_num_batched_tokens: int | None = None,
         enable_caching: bool = True,
         use_eagle: bool = False,
         log_stats: bool = False,
@@ -118,6 +119,11 @@ def __init__(
         metrics_collector: KVCacheMetricsCollector | None = None,
     ) -> None:
         self.max_model_len = max_model_len
+        # When unset, fall back to `max_model_len` so the recycling-aware cap
+        # collapses to the prior (uncapped) admission behavior. The scheduler
+        # always supplies the real value at runtime.
+        if max_num_batched_tokens is None:
+            max_num_batched_tokens = max_model_len
 
         self.enable_caching = enable_caching
         self.use_eagle = use_eagle
@@ -131,6 +137,7 @@ def __init__(
         self.coordinator = get_kv_cache_coordinator(
             kv_cache_config=kv_cache_config,
             max_model_len=self.max_model_len,
+            max_num_batched_tokens=max_num_batched_tokens,
             use_eagle=self.use_eagle,
             enable_caching=self.enable_caching,
             enable_kv_cache_events=enable_kv_cache_events,
@@ -215,45 +222,6 @@ def get_computed_blocks(self, request: Request) -> tuple[KVCacheBlocks, int]:
 
         return self.create_kv_cache_blocks(computed_blocks), num_new_computed_tokens
 
-    def can_fit_full_sequence(
-        self,
-        request: Request,
-        num_new_computed_tokens: int = 0,
-        new_computed_blocks: KVCacheBlocks | None = None,
-        num_external_computed_tokens: int = 0,
-        num_encoder_tokens: int = 0,
-    ) -> bool:
-        """Check if the KV cache has enough free blocks to hold the full
-        sequence, accounting for prefix cache hits and sliding window.
-
-        This is used as an admission gate to prevent over-admitting requests
-        when chunked prefill would otherwise only check the first chunk.
-        """
-        if new_computed_blocks is not None:
-            new_computed_block_list = new_computed_blocks.blocks
-        else:
-            new_computed_block_list = self.empty_kv_cache_blocks.blocks
-
-        num_local_computed_tokens = (
-            request.num_computed_tokens + num_new_computed_tokens
-        )
-        total_computed_tokens = min(
-            num_local_computed_tokens + num_external_computed_tokens,
-            self.max_model_len,
-        )
-        full_num_tokens = min(request.num_tokens, self.max_model_len)
-
-        num_blocks_to_allocate = self.coordinator.get_num_blocks_to_allocate(
-            request_id=request.request_id,
-            num_tokens=full_num_tokens,
-            new_computed_blocks=new_computed_block_list,
-            num_encoder_tokens=num_encoder_tokens,
-            total_computed_tokens=total_computed_tokens,
-            num_tokens_main_model=full_num_tokens,
-        )
-
-        return num_blocks_to_allocate <= self.block_pool.get_num_free_blocks()
-
     def allocate_slots(
         self,
         request: Request,
@@ -264,6 +232,7 @@ def allocate_slots(
         num_external_computed_tokens: int = 0,
         delay_cache_blocks: bool = False,
         num_encoder_tokens: int = 0,
+        full_sequence_must_fit: bool = False,
     ) -> KVCacheBlocks | None:
         """Add slots for a request with new tokens to append.
 
@@ -285,6 +254,10 @@ def allocate_slots(
             num_encoder_tokens: The number of encoder tokens to allocate for
                 cross-attention in encoder-decoder models(e.g., Whisper).
                 For decoder-only models, this should be 0.
+            full_sequence_must_fit: Only allocate blocks if the KV cache has enough
+                free blocks to hold the full sequence, accounting for prefix cache hits
+                and sliding window. Used as an admission gate to prevent over-admitting
+                requests when chunked prefill would otherwise only check the first chunk
 
         Blocks layout:
         ```
@@ -358,10 +331,26 @@ def allocate_slots(
             num_local_computed_tokens + num_external_computed_tokens,
             self.max_model_len,
         )
+
+        if full_sequence_must_fit:
+            # First check and fail if the full request sequence won't fit.
+            full_num_tokens = min(request.num_tokens, self.max_model_len)
+
+            num_blocks_to_allocate = self.coordinator.get_num_blocks_to_allocate(
+                request_id=request.request_id,
+                num_tokens=full_num_tokens,
+                new_computed_blocks=new_computed_block_list,
+                num_encoder_tokens=num_encoder_tokens,
+                total_computed_tokens=total_computed_tokens,
+                num_tokens_main_model=full_num_tokens,
+                apply_admission_cap=True,
+            )
+            if num_blocks_to_allocate > self.block_pool.get_num_free_blocks():
+                return None
+
         num_tokens_main_model = total_computed_tokens + num_new_tokens
         num_tokens_need_slot = min(
-            num_tokens_main_model + num_lookahead_tokens,
-            self.max_model_len,
+            num_tokens_main_model + num_lookahead_tokens, self.max_model_len
         )
 
         # Free the blocks that are skipped during the attention computation
diff --git a/vllm/v1/core/kv_cache_utils.py b/vllm/v1/core/kv_cache_utils.py
index 9ab5af0f6fb0..b57e10b67faa 100644
--- a/vllm/v1/core/kv_cache_utils.py
+++ b/vllm/v1/core/kv_cache_utils.py
@@ -4,18 +4,19 @@
 
 import copy
 import hashlib
+import math
 import os
 from collections import defaultdict
 from collections.abc import Callable, Iterable, Iterator, Sequence
 from dataclasses import dataclass, replace
 from functools import partial
-from typing import Any, NewType, TypeAlias, overload
+from typing import Any, NewType, TypeAlias, cast, overload
 
 from vllm import envs
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.utils.hashing import sha256_cbor, xxhash_cbor
-from vllm.utils.math_utils import cdiv
+from vllm.utils.math_utils import cdiv, round_up
 from vllm.utils.mem_utils import format_gib
 from vllm.v1.kv_cache_interface import (
     ChunkedLocalAttentionSpec,
@@ -24,6 +25,9 @@
     KVCacheGroupSpec,
     KVCacheSpec,
     KVCacheTensor,
+    MambaSpec,
+    MLAAttentionSpec,
+    SlidingWindowMLASpec,
     SlidingWindowSpec,
     UniformTypeKVCacheSpecs,
 )
@@ -562,6 +566,72 @@ def hash_block_tokens(
     )
 
 
+def resolve_kv_cache_block_sizes(
+    kv_cache_config: KVCacheConfig,
+    vllm_config: VllmConfig,
+) -> tuple[int, int]:
+    """Resolve (scheduler_block_size, hash_block_size).
+
+    - ``scheduler_block_size`` is the token-alignment invariant used by the
+      scheduler (e.g. for ``num_computed_tokens`` rounding). Single group:
+      ``cache_config.block_size * dcp * pcp``. Multiple groups: LCM of every
+      group's block size — context parallelism is not supported here.
+    - ``hash_block_size`` is the granularity at which ``Request.block_hashes``
+      is computed. Single group: equals scheduler block size. Multiple groups:
+      ``cache_config.hash_block_size`` override if set, else the GCD of group
+      block sizes; every group's block size must be divisible by it. Returns
+      the scheduler block size (i.e. disables finer hashing) if block hashing
+      is inactive or a mamba group's block size diverges from the cache
+      block size (mamba_cache_mode != "align").
+    """
+    cache_config = vllm_config.cache_config
+    dcp = vllm_config.parallel_config.decode_context_parallel_size
+    pcp = vllm_config.parallel_config.prefill_context_parallel_size
+    groups = kv_cache_config.kv_cache_groups
+
+    if len(groups) <= 1:  # Single group: block_size * dcp * pcp
+        bs = cache_config.block_size * dcp * pcp
+        return bs, bs
+
+    if dcp != 1 or pcp != 1:
+        raise ValueError(
+            "Hybrid KV cache groups with multiple block sizes do not "
+            "support context parallelism (dcp_world_size/pcp_world_size > 1)."
+        )
+
+    group_block_sizes = [g.kv_cache_spec.block_size for g in groups]
+    scheduler_block_size = math.lcm(*group_block_sizes)
+
+    # Block hashes are only consumed by prefix caching and KV connectors
+    # (P/D, offloading); when neither is active, keep hash_block_size equal
+    # to the scheduler block size.
+    connector_enabled = vllm_config.kv_transfer_config is not None
+    if not (cache_config.enable_prefix_caching or connector_enabled):
+        return scheduler_block_size, scheduler_block_size
+
+    # Mamba groups with block_size != cache_config.block_size
+    # (mamba_cache_mode != "align") break divisibility; back off to the
+    # scheduler block size.
+    if any(
+        isinstance(g.kv_cache_spec, MambaSpec)
+        and g.kv_cache_spec.block_size != cache_config.block_size
+        for g in groups
+    ):
+        return scheduler_block_size, scheduler_block_size
+
+    requested = cache_config.hash_block_size
+    hash_block_size = (
+        requested if requested is not None else math.gcd(*group_block_sizes)
+    )
+    if any(bs % hash_block_size != 0 for bs in group_block_sizes):
+        raise ValueError(
+            f"Invalid hash_block_size={hash_block_size}; all KV cache group "
+            f"block sizes must be divisible by hash_block_size. "
+            f"Got group block sizes={group_block_sizes}."
+        )
+    return scheduler_block_size, hash_block_size
+
+
 def get_request_block_hasher(
     block_size: int,
     caching_hash_fn: Callable[[Any], bytes],
@@ -823,21 +893,45 @@ def get_max_concurrency_for_kv_cache_config(
 def may_override_num_blocks(vllm_config: VllmConfig, num_blocks: int) -> int:
     """
     Override the number of kv cache blocks if `num_gpu_blocks_override` is set.
+    The override is logged once, at the call site in `get_kv_cache_configs`.
     """
     if vllm_config.cache_config.num_gpu_blocks_override is not None:
-        num_gpu_blocks_override = vllm_config.cache_config.num_gpu_blocks_override
-        logger.info(
-            "Overriding num_gpu_blocks=%d with num_gpu_blocks_override=%d",
-            num_blocks,
-            num_gpu_blocks_override,
-        )
-        num_blocks = num_gpu_blocks_override
-
+        num_blocks = vllm_config.cache_config.num_gpu_blocks_override
     return num_blocks
 
 
+def _pool_bytes_per_block(kv_cache_groups: list[KVCacheGroupSpec]) -> int:
+    """
+    Bytes consumed by one block in the worker's shared KV cache pool, mirroring
+    the divisor used by `get_kv_cache_config_from_groups` to convert
+    `available_memory` into `num_blocks`. Used to compute the effective KV cache
+    capacity once `num_gpu_blocks_override` is applied.
+    """
+    if len(kv_cache_groups) == 1 and isinstance(
+        kv_cache_groups[0].kv_cache_spec, UniformTypeKVCacheSpecs
+    ):
+        return kv_cache_groups[0].kv_cache_spec.page_size_bytes
+    if all(
+        isinstance(g.kv_cache_spec, UniformTypeKVCacheSpecs) for g in kv_cache_groups
+    ):
+        # DeepseekV4: shared layout sized by the largest per-page-size bucket.
+        full_mla_spec = cast(UniformTypeKVCacheSpecs, kv_cache_groups[0].kv_cache_spec)
+        layer_tuple_page_bytes = sum(full_mla_spec.get_page_sizes())
+        num_layer_tuples = max(
+            cast(UniformTypeKVCacheSpecs, g.kv_cache_spec).get_num_layer_tuples()
+            for g in kv_cache_groups
+        )
+        return layer_tuple_page_bytes * num_layer_tuples
+    group_size = max(len(g.layer_names) for g in kv_cache_groups)
+    page_size = get_uniform_page_size([g.kv_cache_spec for g in kv_cache_groups])
+    return page_size * group_size
+
+
 def get_num_blocks(
-    vllm_config: VllmConfig, num_layers: int, available_memory: int, page_size: int
+    vllm_config: VllmConfig,
+    num_layers: int,
+    available_memory: int,
+    page_size: int,
 ) -> int:
     """
     Get the number of kv cache blocks.
@@ -850,8 +944,7 @@ def get_num_blocks(
     """
     num_blocks = int(available_memory // page_size // num_layers)
     num_blocks = max(num_blocks, 0)
-    num_blocks = may_override_num_blocks(vllm_config, num_blocks)
-    return num_blocks
+    return may_override_num_blocks(vllm_config, num_blocks)
 
 
 def get_uniform_page_size(kv_cache_specs: Iterable[KVCacheSpec]) -> int:
@@ -1078,6 +1171,63 @@ def _get_kv_cache_groups_uniform_page_size(
     return create_kv_cache_group_specs(kv_cache_spec, grouped_layers)
 
 
+def _get_kv_cache_config_deepseek_v4(
+    vllm_config: VllmConfig,
+    kv_cache_groups: list[KVCacheGroupSpec],
+    available_memory: int,
+) -> tuple[int, list[KVCacheTensor]]:
+    """DeepseekV4 KV cache tensor layout planning.
+
+    Precondition: kv_cache_groups[0] is the full-MLA group; its page sizes
+    define the canonical bucket set. Non-full-MLA groups must have been
+    page_size-padded upstream (see _get_kv_cache_groups_uniform_groups) so
+    every layer's page_size matches one of the full-MLA bucket sizes.
+
+    For each group, bucket its layers by page_size_bytes and place each
+    layer at tuple_idx = position-within-bucket. Emit one KVCacheTensor
+    per (tuple_idx, bucket) whose shared_by is the union of per-group
+    layers at that slot.
+    """
+    full_mla_spec = kv_cache_groups[0].kv_cache_spec
+    assert isinstance(full_mla_spec, UniformTypeKVCacheSpecs)
+    page_sizes = sorted(full_mla_spec.get_page_sizes())
+    layer_tuple_page_bytes = sum(page_sizes)
+
+    # Pre-bucket each group's layers by page_size (registration order within
+    # bucket). bucketed[g_idx][page_size] = [layer_name, ...].
+    bucketed: list[dict[int, list[str]]] = []
+    for group in kv_cache_groups:
+        assert isinstance(group.kv_cache_spec, UniformTypeKVCacheSpecs)
+        specs = group.kv_cache_spec.kv_cache_specs
+        b: dict[int, list[str]] = defaultdict(list)
+        for name in group.layer_names:
+            b[specs[name].page_size_bytes].append(name)
+        bucketed.append(b)
+
+    # num_layer_tuples = longest bucket list across all groups. For the
+    # full-MLA group this equals the count of layers in the largest
+    # per-page-size bucket (= get_num_layer_tuples()); for SWA sub-groups
+    # this equals the sub-group size (each has a single page_size).
+    num_layer_tuples = max(len(layers) for b in bucketed for layers in b.values())
+
+    num_blocks = available_memory // (layer_tuple_page_bytes * num_layer_tuples)
+    num_blocks = may_override_num_blocks(vllm_config, num_blocks)
+
+    kv_cache_tensors: list[KVCacheTensor] = []
+    for tuple_idx in range(num_layer_tuples):
+        for ps in page_sizes:
+            shared_by: list[str] = []
+            for b in bucketed:
+                bucket = b.get(ps)
+                if bucket is not None and tuple_idx < len(bucket):
+                    shared_by.append(bucket[tuple_idx])
+            kv_cache_tensors.append(
+                KVCacheTensor(size=ps * num_blocks, shared_by=shared_by)
+            )
+
+    return num_blocks, kv_cache_tensors
+
+
 def get_kv_cache_config_from_groups(
     vllm_config: VllmConfig,
     kv_cache_groups: list[KVCacheGroupSpec],
@@ -1108,7 +1258,7 @@ def get_kv_cache_config_from_groups(
         kv_cache_groups[0].kv_cache_spec, UniformTypeKVCacheSpecs
     ):
         # Special case: all layers have the same type of KV cache but with
-        # different hidden size. Allocate different amount of memory for each
+        # different hidden sizes. Allocate different amount of memory for each
         # layer based on its hidden size.
         num_blocks = (
             available_memory // kv_cache_groups[0].kv_cache_spec.page_size_bytes
@@ -1122,6 +1272,15 @@ def get_kv_cache_config_from_groups(
             )
             for layer_name in kv_cache_groups[0].layer_names
         ]
+    elif all(
+        isinstance(group.kv_cache_spec, UniformTypeKVCacheSpecs)
+        for group in kv_cache_groups
+    ):
+        # DeepseekV4: UniformTypeKVCacheSpecs but multiple groups.
+        # Delegate to the DeepseekV4-specific allocator.
+        num_blocks, kv_cache_tensors = _get_kv_cache_config_deepseek_v4(
+            vllm_config, kv_cache_groups, available_memory
+        )
     else:
         # General case:
         # We will have group_size memory pools, each is shared by one layer from
@@ -1188,14 +1347,48 @@ def unify_hybrid_kv_cache_specs(kv_cache_spec: dict[str, KVCacheSpec]):
     has_chunked_local_attention = any(
         isinstance(spec, ChunkedLocalAttentionSpec) for spec in kv_cache_spec.values()
     )
+    has_swa_mla = any(
+        isinstance(spec, SlidingWindowMLASpec) for spec in kv_cache_spec.values()
+    )
+
+    uniform_block_size: int | None = None
+    if has_swa_mla:
+        # For DeepseekV4, block sizes can be different for different KV cache groups.
+        # E.g., Full MLA: 256; SWA MLA: 64; C4 partial states: 4, C128 states: 8.
+        assert has_full_attention
+        any_full_spec = next(
+            iter(
+                spec
+                for spec in kv_cache_spec.values()
+                if isinstance(spec, FullAttentionSpec)
+            )
+        )
+        uniform_block_size = any_full_spec.block_size
+
     if has_full_attention and (has_sliding_window or has_chunked_local_attention):
         for layer_name, spec in kv_cache_spec.items():
-            if isinstance(spec, SlidingWindowSpec):
+            if isinstance(spec, SlidingWindowMLASpec):
+                kv_cache_spec[layer_name] = MLAAttentionSpec(
+                    block_size=uniform_block_size
+                    if uniform_block_size is not None
+                    else spec.block_size,
+                    num_kv_heads=spec.num_kv_heads,
+                    head_size=spec.head_size,
+                    dtype=spec.dtype,
+                    page_size_padded=spec.page_size_padded,
+                    cache_dtype_str=spec.cache_dtype_str,
+                    alignment=spec.alignment,
+                    compress_ratio=spec.compress_ratio,
+                    model_version=spec.model_version,
+                )
+            elif isinstance(spec, SlidingWindowSpec):
                 kv_cache_spec[layer_name] = FullAttentionSpec(
                     block_size=spec.block_size,
                     num_kv_heads=spec.num_kv_heads,
                     head_size=spec.head_size,
+                    head_size_v=spec.head_size_v,
                     dtype=spec.dtype,
+                    kv_quant_mode=spec.kv_quant_mode,
                     sliding_window=spec.sliding_window,
                     page_size_padded=spec.page_size_padded,
                 )
@@ -1219,6 +1412,204 @@ def unify_hybrid_kv_cache_specs(kv_cache_spec: dict[str, KVCacheSpec]):
         )
 
 
+def group_and_unify_kv_cache_specs(
+    kv_cache_spec: dict[str, KVCacheSpec],
+) -> list[UniformTypeKVCacheSpecs] | None:
+    """
+    Group the KV cache specs and unify each group into one UniformTypeKVCacheSpecs.
+    Currently, this is only used for DeepseekV4.
+    """
+    if not any(
+        isinstance(spec, SlidingWindowMLASpec) for spec in kv_cache_spec.values()
+    ):
+        return None
+
+    mla_specs: dict[str, KVCacheSpec] = {}
+    grouped_swa_mla_specs: dict[tuple[int, int], dict[str, KVCacheSpec]] = defaultdict(
+        dict
+    )
+    # NOTE: Here we group SWA layers by (block_size, sliding_window), which separates
+    # SWA layers, C4I+C4A layers, and C128A layers into three different groups. It can
+    # be fragile with only block_size and sliding_window as keys, but fine for now.
+    for name, spec in kv_cache_spec.items():
+        if isinstance(spec, SlidingWindowMLASpec):
+            grouped_swa_mla_specs[(spec.block_size, spec.sliding_window)][name] = spec
+        elif isinstance(spec, MLAAttentionSpec):
+            mla_specs[name] = spec
+
+    assert len(mla_specs) > 0
+    mla_uniform_spec = UniformTypeKVCacheSpecs.from_specs(mla_specs)
+    assert mla_uniform_spec is not None
+
+    swa_uniform_specs: list[UniformTypeKVCacheSpecs] = []
+    for spec_dict in grouped_swa_mla_specs.values():
+        uniform_spec = UniformTypeKVCacheSpecs.from_specs(spec_dict)
+        assert uniform_spec is not None
+        swa_uniform_specs.append(uniform_spec)
+
+    return [mla_uniform_spec, *swa_uniform_specs]
+
+
+def _approximate_gcd(values: Sequence[int], *, lower_bound: int | None = None) -> int:
+    """Pick a chunk size that minimizes total upward padding.
+
+    Each x is rounded up to a multiple of d:
+
+      x -> ceil(x / d) * d
+
+    Total padding is:
+
+      pad(d) = sum_i (ceil(x_i / d) * d - x_i)
+
+    We brute-force d in [lower_bound, max(values)] (fine for small lists / small
+    maxima) and return the d with minimum padding. Ties prefer larger d.
+    """
+    if not values:
+        raise ValueError("values must be non-empty")
+    if any(x <= 0 for x in values):
+        raise ValueError(f"values must be positive, got: {list(values)!r}")
+
+    min_d = max(1, lower_bound if lower_bound is not None else 1)
+    max_d = max(values)
+    if min_d > max_d:
+        return min_d
+
+    best_d = min_d
+    best_pad: int | None = None
+    for d in range(min_d, max_d + 1):
+        pad = sum((d - (x % d)) % d for x in values)
+        if best_pad is None or pad < best_pad or (pad == best_pad and d > best_d):
+            best_pad = pad
+            best_d = d
+
+    return best_d
+
+
+def _get_kv_cache_groups_uniform_groups(
+    grouped_specs: list[UniformTypeKVCacheSpecs],
+) -> list[KVCacheGroupSpec]:
+    """
+    Generate the KV cache groups from the grouped specs.
+    """
+    assert len(grouped_specs) > 0 and all(
+        isinstance(spec, UniformTypeKVCacheSpecs) for spec in grouped_specs
+    )
+    # For now, we restrict the first grouped_spec to be UniformTypeKVCacheSpecs
+    # containing only MLAAttentionSpec.
+    full_mla_spec = grouped_specs[0]
+    assert all(
+        isinstance(spec, MLAAttentionSpec)
+        for spec in full_mla_spec.kv_cache_specs.values()
+    )
+    full_mla_group = KVCacheGroupSpec(
+        layer_names=list(full_mla_spec.kv_cache_specs.keys()),
+        kv_cache_spec=full_mla_spec,
+    )
+
+    # We define a layer tuple as a group of layers with different page sizes, and
+    # one UniformTypeKVCacheSpecs contains a list of layer tuples.
+    # For example, if we have 11 C4 layers and 10 C128 layers, we can define a layer
+    # tuple as [C4I, C4A, C128], and the full_mla_group will contain "11" layer tuples.
+    # The other uniform KV cache specs will be similarly partitioned into layer tuples.
+    # Say we have 21 SWA layers, all with the same page size, then we will have "21"
+    # layer tuples.
+    num_layer_tuples_per_group: list[int] = [
+        g_spec.get_num_layer_tuples() for g_spec in grouped_specs
+    ]
+    # Choose `num_layer_tuples` to minimize total padding across groups.
+    num_layer_tuples = _approximate_gcd(
+        num_layer_tuples_per_group, lower_bound=num_layer_tuples_per_group[0]
+    )
+    # Round up to the nearest multiple of `num_layer_tuples` (i.e., padding)
+    num_layer_tuples_per_group = [
+        round_up(x, num_layer_tuples) for x in num_layer_tuples_per_group
+    ]
+
+    swa_mla_specs = grouped_specs[1:]
+    assert all(
+        isinstance(spec, SlidingWindowMLASpec)
+        for group in swa_mla_specs
+        for spec in group.kv_cache_specs.values()
+    )
+
+    # Split each SWA UniformKV group into smaller groups to align their #(layer tuples)
+    # Possibly padding layer tuples for this.
+    # Additionally, we also pad KV blocks in each SWA layer, to align the page size
+    # with the corresponding layer in the full-MLA group.
+    all_page_sizes = full_mla_spec.get_page_sizes()
+    swa_mla_groups = []
+    for sm_spec in swa_mla_specs:
+        sm_page_sizes = sm_spec.get_page_sizes()
+        layers_per_size: dict[int, list[str]] = defaultdict(list)
+        assert max(sm_page_sizes) <= max(all_page_sizes)
+
+        # Unify page size by padding layers' page_size to the nearest larger page_size.
+        # Compute candidate (nearest larger page_size) for each unique page size.
+        size_to_candidate: dict[int, int] = {}
+        for ps in sm_page_sizes:
+            size_to_candidate[ps] = min(x for x in all_page_sizes if x >= ps)
+        # Pad and collect layer names per page size.
+        for layer_name, layer_spec in sm_spec.kv_cache_specs.items():
+            current_size = layer_spec.page_size_bytes
+            candidate = size_to_candidate[current_size]
+            if current_size < candidate:
+                object.__setattr__(layer_spec, "page_size_padded", candidate)
+            layers_per_size[candidate].append(layer_name)
+        # NOTE(yifan): for now, inside a UniformKV group, each page_size should
+        # have the same number of layers. This also means we don't need to pad layers
+        # inside a partial-full layer tuple.
+        assert len(set(len(layers) for layers in layers_per_size.values())) == 1
+        num_layers_per_size = len(next(iter(layers_per_size.values())))
+
+        # Split layers inside each UniformKV group for aligned #(layers).
+        # See `_get_kv_cache_groups_uniform_page_size` for more details.
+        num_tuple_groups = cdiv(num_layers_per_size, num_layer_tuples)
+        layer_tuples = list(zip(*layers_per_size.values()))
+        for i in range(num_tuple_groups):
+            group_layer_tuples = layer_tuples[i::num_tuple_groups]
+            # Flatten tuples and build dict for from_specs
+            group_layer_names = [
+                name for layer_tuple in group_layer_tuples for name in layer_tuple
+            ]
+            group_layer_specs = {
+                name: sm_spec.kv_cache_specs[name] for name in group_layer_names
+            }
+            sub_sm_spec = UniformTypeKVCacheSpecs.from_specs(group_layer_specs)
+            assert sub_sm_spec is not None
+            swa_mla_groups.append(
+                KVCacheGroupSpec(
+                    layer_names=group_layer_names,
+                    kv_cache_spec=sub_sm_spec,
+                )
+            )
+
+    return [full_mla_group, *swa_mla_groups]
+
+
+def _annotate_eagle_groups_deepseek_v4(
+    vllm_config: VllmConfig,
+    kv_cache_spec: dict[str, KVCacheSpec],
+    kv_cache_groups: list[KVCacheGroupSpec],
+) -> None:
+    spec_config = vllm_config.speculative_config
+    if spec_config is None or not spec_config.use_eagle():
+        return
+    # Detection uses the merged MLA spec's model_version.
+    if not any(
+        getattr(spec, "model_version", None) == "deepseek_v4"
+        for spec in kv_cache_spec.values()
+    ):
+        return
+    # DeepseekV4's MTP attention layer is always the last layer, and we flag whichever
+    # group contains it.
+    # FIXME(yifan): avoid/generalize this hacky check.
+    last_layer = next(reversed(kv_cache_spec))
+    for group in kv_cache_groups:
+        if last_layer in group.layer_names:
+            group.is_eagle_group = True
+            break
+
+
 def get_kv_cache_groups(
     vllm_config: VllmConfig, kv_cache_spec: dict[str, KVCacheSpec]
 ) -> list[KVCacheGroupSpec]:
@@ -1250,6 +1641,14 @@ def get_kv_cache_groups(
         # full attention, or all layers are sliding window attention with the
         # same window size). Put all layers into one group.
         return _get_kv_cache_groups_uniform_type(uniform_spec)
+    elif grouped_specs := group_and_unify_kv_cache_specs(kv_cache_spec):
+        # DeepseekV4 case: All layers need the same number of token slots,
+        # yet some layers are full attention while others are sliding window
+        # attention in different sizes. Need to group layers into multiple
+        # UniformTypeKVCacheSpecs.
+        kv_cache_groups = _get_kv_cache_groups_uniform_groups(grouped_specs)
+        _annotate_eagle_groups_deepseek_v4(vllm_config, kv_cache_spec, kv_cache_groups)
+        return kv_cache_groups
 
     # As KVCacheManager can only allocate memory of one size, we need to unify
     # the page size of the layers. For cases cannot be unified, this function
@@ -1294,38 +1693,25 @@ def _report_kv_cache_config(
         vllm_config: The global VllmConfig
         kv_cache_config: The resolved KV cache configuration
     """
-    min_block_size = min(
-        [group.kv_cache_spec.block_size for group in kv_cache_config.kv_cache_groups]
-    )
-
-    # Log the KV cache size and maximum concurrency.
-    num_tokens = (
-        kv_cache_config.num_blocks
-        // len(kv_cache_config.kv_cache_groups)
-        * min_block_size
-    )
-    dcp_size = vllm_config.parallel_config.decode_context_parallel_size
-    pcp_size = vllm_config.parallel_config.prefill_context_parallel_size
-    if pcp_size * dcp_size > 1:
-        num_tokens *= pcp_size * dcp_size
-        logger.info(
-            "Multiplying the GPU KV cache size by the cp_world_size %d "
-            "(pcp_world_size %d * dcp_world_size %d).",
-            pcp_size * dcp_size,
-            pcp_size,
-            dcp_size,
-        )
-    num_tokens_str = f"{num_tokens:,}"
-    logger.info_once("GPU KV cache size: %s tokens", num_tokens_str, scope="local")
-    max_model_len_str = f"{vllm_config.model_config.max_model_len:,}"
+    max_model_len = vllm_config.model_config.max_model_len
     max_concurrency = get_max_concurrency_for_kv_cache_config(
         vllm_config, kv_cache_config
     )
+
+    # GPU KV cache size in tokens = max_concurrency * max_model_len: the total
+    # tokens of context the pool can hold at peak utilization. Sourcing this
+    # from the concurrency calculation handles hybrid layouts correctly: SWA /
+    # chunked-local groups have a per-request block count that's capped by
+    # their window, so a naive `num_blocks // num_groups * block_size` formula
+    # underestimates capacity for these models. DCP/PCP sharding is already
+    # accounted for in each spec's `max_memory_usage_bytes`.
+    num_tokens = int(max_concurrency * max_model_len)
+
+    logger.info_once("GPU KV cache size: %s tokens", f"{num_tokens:,}")
     logger.info_once(
         "Maximum concurrency for %s tokens per request: %.2fx",
-        max_model_len_str,
+        f"{max_model_len:,}",
         max_concurrency,
-        scope="local",
     )
 
 
@@ -1343,15 +1729,40 @@ def _max_memory_usage_bytes_from_groups(
     if not kv_cache_groups:
         return 0
 
-    # UniformTypeKVCacheSpecs special case (single group, per-layer specs)
     if len(kv_cache_groups) == 1 and isinstance(
         kv_cache_groups[0].kv_cache_spec, UniformTypeKVCacheSpecs
     ):
+        # UniformTypeKVCacheSpecs special case (single group, per-layer specs)
         per_layer_specs = kv_cache_groups[0].kv_cache_spec.kv_cache_specs
         return sum(
             spec.max_memory_usage_bytes(vllm_config)
             for spec in per_layer_specs.values()
         )
+    elif all(
+        isinstance(group.kv_cache_spec, UniformTypeKVCacheSpecs)
+        for group in kv_cache_groups
+    ):
+        # Special case (only DeepseekV4 for now): all groups are
+        # UniformTypeKVCacheSpecs.
+        # They must already be page_size aligned and share a common padded
+        # layer-tuple layout. Even groups with fewer actual tuples still reserve
+        # the global number of tuple slots in the shared tensor layout.
+        full_mla_spec = cast(UniformTypeKVCacheSpecs, kv_cache_groups[0].kv_cache_spec)
+        layer_tuple_bytes = sum(full_mla_spec.get_page_sizes())
+        num_layer_tuples = max(
+            cast(UniformTypeKVCacheSpecs, group.kv_cache_spec).get_num_layer_tuples()
+            for group in kv_cache_groups
+        )
+
+        total_max_mem_usage_bytes = 0
+        for group in kv_cache_groups:
+            group_spec = cast(UniformTypeKVCacheSpecs, group.kv_cache_spec)
+            g_max_mem_usage_pages = group_spec.max_memory_usage_pages(vllm_config)
+            g_max_mem_usage_page_bytes = (
+                num_layer_tuples * g_max_mem_usage_pages * layer_tuple_bytes
+            )
+            total_max_mem_usage_bytes += g_max_mem_usage_page_bytes
+        return total_max_mem_usage_bytes
 
     # General case: group_size pools, each shared by one layer per group
     # Memory = group_size * page_size * blocks_for_max_len
@@ -1427,7 +1838,6 @@ def _auto_fit_max_model_len(
             "Auto-fit max_model_len: attention-free model, "
             "using derived max_model_len=%d",
             original_max,
-            scope="local",
         )
         return
 
@@ -1454,7 +1864,6 @@ def _auto_fit_max_model_len(
             "Auto-fit max_model_len: full model context length %d fits in "
             "available GPU memory",
             original_max,
-            scope="local",
         )
     else:
         # Need to reduce max_model_len to fit in memory
@@ -1465,7 +1874,6 @@ def _auto_fit_max_model_len(
             original_max,
             auto_fit_max,
             format_gib(limiting_worker_mem),
-            scope="local",
         )
 
 
@@ -1501,7 +1909,13 @@ def _project_kv_cache_groups_to_worker(
                     for layer_name in worker_layer_names
                 },
             )
-        projected_groups.append(KVCacheGroupSpec(worker_layer_names, group_spec))
+        projected_groups.append(
+            KVCacheGroupSpec(
+                worker_layer_names,
+                group_spec,
+                is_eagle_group=group.is_eagle_group and bool(worker_layer_names),
+            )
+        )
     return projected_groups
 
 
@@ -1567,6 +1981,28 @@ def get_kv_cache_configs(
         for worker_spec in kv_cache_specs
     ]
 
+    # If `num_gpu_blocks_override` is set, the cache size that will actually
+    # be allocated is decoupled from the profiled `available_memory`:
+    # `may_override_num_blocks` in `get_kv_cache_config_from_groups` clamps
+    # `num_blocks` to the override. Reflect that in `available_memory` here so
+    # auto-fit, the admission check, and the per-worker config builder all
+    # plan against the same effective capacity.
+    override = vllm_config.cache_config.num_gpu_blocks_override
+    if override is not None:
+        adjusted_memory: list[int] = []
+        for groups, avail_mem in zip(projected_groups_per_worker, available_memory):
+            if not groups:
+                adjusted_memory.append(avail_mem)
+                continue
+            bytes_per_block = _pool_bytes_per_block(groups)
+            logger.info(
+                "Overriding num_gpu_blocks=%d with num_gpu_blocks_override=%d",
+                avail_mem // bytes_per_block,
+                override,
+            )
+            adjusted_memory.append(override * bytes_per_block)
+        available_memory = adjusted_memory
+
     if vllm_config.model_config.original_max_model_len == -1:
         _auto_fit_max_model_len(
             vllm_config, projected_groups_per_worker, available_memory
@@ -1684,10 +2120,7 @@ def __iter__(self) -> Iterator[BlockHash]:
     def _get_value_at(self, idx: int) -> BlockHash:
         base = idx * self.scale_factor
         end = base + self.scale_factor
-        merged_hash: bytes = self.block_hashes[base]
-        for i in range(base + 1, end):
-            merged_hash += self.block_hashes[i]
-        return BlockHash(merged_hash)
+        return BlockHash(b"".join(self.block_hashes[base:end]))
 
 
 BlockHashList = list[BlockHash] | BlockHashListWithBlockSize
diff --git a/vllm/v1/core/sched/interface.py b/vllm/v1/core/sched/interface.py
index b44f2db1926b..264811a556d3 100644
--- a/vllm/v1/core/sched/interface.py
+++ b/vllm/v1/core/sched/interface.py
@@ -41,6 +41,7 @@ def __init__(
         kv_cache_config: "KVCacheConfig",
         structured_output_manager: "StructuredOutputManager",
         block_size: int,
+        hash_block_size: int,
         mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
         include_finished_set: bool = False,
         log_stats: bool = False,
diff --git a/vllm/v1/core/sched/output.py b/vllm/v1/core/sched/output.py
index bdb97decadfe..b2e9dd8b1719 100644
--- a/vllm/v1/core/sched/output.py
+++ b/vllm/v1/core/sched/output.py
@@ -38,6 +38,7 @@ class NewRequestData:
     num_computed_tokens: int
     lora_request: LoRARequest | None
     prompt_embeds: "torch.Tensor | None" = None
+    prompt_is_token_ids: list[bool] | None = None
 
     # Only used for v2 model runner.
     prefill_token_ids: list[int] | None = None
@@ -59,6 +60,7 @@ def from_request(
             num_computed_tokens=request.num_computed_tokens,
             lora_request=request.lora_request,
             prompt_embeds=request.prompt_embeds,
+            prompt_is_token_ids=request.prompt_is_token_ids,
             prefill_token_ids=prefill_token_ids,
         )
 
diff --git a/vllm/v1/core/sched/scheduler.py b/vllm/v1/core/sched/scheduler.py
index 40b5899f0457..8aaeb3970079 100644
--- a/vllm/v1/core/sched/scheduler.py
+++ b/vllm/v1/core/sched/scheduler.py
@@ -7,8 +7,6 @@
 from dataclasses import replace
 from typing import Any
 
-import numpy as np
-
 from vllm import envs
 from vllm.compilation.cuda_graph import CUDAGraphStat
 from vllm.config import VllmConfig
@@ -27,9 +25,6 @@
 from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorMetadata
 from vllm.distributed.kv_transfer.kv_connector.v1.metrics import KVConnectorStats
 from vllm.logger import init_logger
-from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
-    RoutedExpertsReader,
-)
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
 from vllm.multimodal.encoder_budget import MultiModalBudget
 from vllm.v1.core.encoder_cache_manager import (
@@ -52,7 +47,7 @@
 )
 from vllm.v1.core.sched.utils import check_stop, remove_all
 from vllm.v1.engine import EngineCoreEventType, EngineCoreOutput, EngineCoreOutputs
-from vllm.v1.kv_cache_interface import AttentionSpec, KVCacheConfig
+from vllm.v1.kv_cache_interface import KVCacheConfig
 from vllm.v1.metrics.perf import ModelMetrics, PerfStats
 from vllm.v1.metrics.stats import PrefixCacheStats, SchedulerStats
 from vllm.v1.outputs import DraftTokenIds, KVConnectorOutput, ModelRunnerOutput
@@ -71,6 +66,7 @@ def __init__(
         kv_cache_config: KVCacheConfig,
         structured_output_manager: StructuredOutputManager,
         block_size: int,
+        hash_block_size: int | None = None,
         mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
         include_finished_set: bool = False,
         log_stats: bool = False,
@@ -222,16 +218,19 @@ def __init__(
                 self.num_lookahead_tokens = self.num_spec_tokens
 
         # Create the KV cache manager.
+        if hash_block_size is None:
+            hash_block_size = block_size
         self.kv_cache_manager = KVCacheManager(
             kv_cache_config=kv_cache_config,
             max_model_len=self.max_model_len,
+            max_num_batched_tokens=self.scheduler_config.max_num_batched_tokens,
             enable_caching=self.cache_config.enable_prefix_caching,
             use_eagle=self.use_eagle,
             log_stats=self.log_stats,
             enable_kv_cache_events=self.enable_kv_cache_events,
             dcp_world_size=self.dcp_world_size,
             pcp_world_size=self.pcp_world_size,
-            hash_block_size=self.block_size,
+            hash_block_size=hash_block_size,
             metrics_collector=self.kv_metrics_collector,
         )
         # Bind GPU block pool to the KV connector. This must happen after
@@ -256,43 +255,6 @@ def __init__(
         if self.log_stats and vllm_config.observability_config.enable_mfu_metrics:
             self.perf_metrics = ModelMetrics(vllm_config)
 
-        if self.vllm_config.model_config.enable_return_routed_experts:
-            assert self.dcp_world_size == 1 and self.pcp_world_size == 1, (
-                "enable_return_routed_experts does not support context parallelism "
-                "(dcp_world_size > 1 or pcp_world_size > 1)"
-            )
-
-            self.routed_experts_reader = RoutedExpertsReader.create()
-
-            assert len(kv_cache_config.kv_cache_groups) > 0, (
-                "enable_return_routed_experts requires at least one kv cache group"
-            )
-            # Find the attention group for routed experts indexing.
-            self.routed_experts_attn_gid = 0
-            for gid, group in enumerate(kv_cache_config.kv_cache_groups):
-                if isinstance(group.kv_cache_spec, AttentionSpec):
-                    self.routed_experts_attn_gid = gid
-                    break
-            min_block_size = min(
-                [
-                    group.kv_cache_spec.block_size
-                    for group in kv_cache_config.kv_cache_groups
-                ]
-            )
-            num_groups = len(kv_cache_config.kv_cache_groups)
-            self.max_num_kv_tokens = (
-                kv_cache_config.num_blocks // num_groups
-            ) * min_block_size
-            dcp_size = self.vllm_config.parallel_config.decode_context_parallel_size
-            pcp_size = self.vllm_config.parallel_config.prefill_context_parallel_size
-            if pcp_size * dcp_size > 1:
-                self.max_num_kv_tokens *= pcp_size * dcp_size
-
-            self.routed_experts_reader.attach_buffer(
-                max_num_kv_tokens=self.max_num_kv_tokens,
-                vllm_config=self.vllm_config,
-            )
-
         self._pause_state: PauseState = PauseState.UNPAUSED
 
     def _mamba_block_aligned_split(
@@ -737,20 +699,6 @@ def schedule(self) -> SchedulerOutput:
                         for i in encoder_inputs_to_schedule
                     )
 
-                if (
-                    self.scheduler_reserve_full_isl
-                    and not self.kv_cache_manager.can_fit_full_sequence(
-                        request,
-                        num_new_computed_tokens=num_new_local_computed_tokens,
-                        new_computed_blocks=new_computed_blocks,
-                        num_external_computed_tokens=num_external_computed_tokens,
-                        num_encoder_tokens=num_encoder_tokens,
-                    )
-                ):
-                    if request.has_encoder_inputs:
-                        self.encoder_cache_manager.free(request)
-                    break
-
                 new_blocks = self.kv_cache_manager.allocate_slots(
                     request,
                     num_new_tokens,
@@ -760,6 +708,7 @@ def schedule(self) -> SchedulerOutput:
                     num_external_computed_tokens=num_external_computed_tokens,
                     delay_cache_blocks=load_kv_async,
                     num_encoder_tokens=num_encoder_tokens,
+                    full_sequence_must_fit=self.scheduler_reserve_full_isl,
                 )
 
                 if new_blocks is None:
@@ -1424,11 +1373,15 @@ def update_from_output(
                     request.resumable = False
                     stopped = True
 
+            # Get routing data from ModelRunnerOutput (via worker D2H pipeline)
             routed_experts = None
+            if (
+                model_runner_output.routed_experts_dict is not None
+                and req_id in model_runner_output.routed_experts_dict
+            ):
+                routed_experts = model_runner_output.routed_experts_dict[req_id]
             finish_reason = None
             if stopped:
-                routed_experts = self._get_routed_experts(request)
-
                 # Capture finish_reason BEFORE _handle_stopped_request, which may
                 # reset the status to WAITING for streaming requests that continue.
                 finish_reason = request.get_finished_reason()
@@ -1444,7 +1397,7 @@ def update_from_output(
             # Extract sample logprobs if needed.
             if (
                 request.sampling_params is not None
-                and request.sampling_params.logprobs is not None
+                and request.sampling_params.num_logprobs is not None
                 and logprobs
             ):
                 new_logprobs = logprobs.slice_request(req_index, len(new_token_ids))
@@ -1603,31 +1556,6 @@ def _handle_stopped_request(self, request: Request) -> bool:
         self._enqueue_waiting_request(request)
         return False
 
-    def _get_routed_experts(self, request: Request) -> np.ndarray | None:
-        if not self.vllm_config.model_config.enable_return_routed_experts:
-            return None
-
-        kv_blocks = self.kv_cache_manager.get_blocks(request.request_id)
-        block_ids = kv_blocks.get_block_ids()[self.routed_experts_attn_gid]
-        num_tokens = request.num_tokens - 1
-
-        # compute slot mapping using attention group's block_size
-        block_ids_array = np.array(block_ids, dtype=np.int32)
-        num_blocks = len(block_ids)
-        attn_group = self.kv_cache_config.kv_cache_groups[self.routed_experts_attn_gid]
-        block_size = attn_group.kv_cache_spec.block_size
-
-        # generate block offsets
-        block_offsets = np.arange(0, block_size)
-
-        # compute slot mapping: slot = block_id * block_size + offset
-        slot_mapping = (
-            block_offsets.reshape((1, block_size))
-            + block_ids_array.reshape((num_blocks, 1)) * block_size
-        ).flatten()[:num_tokens]
-
-        return self.routed_experts_reader.get_routed_experts(indices=slot_mapping)
-
     def _update_request_with_output(
         self, request: Request, new_token_ids: list[int]
     ) -> tuple[list[int], bool]:
@@ -2018,7 +1946,7 @@ def _connector_finished(
         # the connector.
         self.kv_cache_manager.remove_skipped_blocks(
             request_id=request.request_id,
-            total_computed_tokens=request.num_tokens,
+            total_computed_tokens=request.num_computed_tokens,
         )
 
         block_ids = self.kv_cache_manager.get_block_ids(request.request_id)
diff --git a/vllm/v1/core/single_type_kv_cache_manager.py b/vllm/v1/core/single_type_kv_cache_manager.py
index fa5395685e6a..e8d3a6f75688 100644
--- a/vllm/v1/core/single_type_kv_cache_manager.py
+++ b/vllm/v1/core/single_type_kv_cache_manager.py
@@ -20,7 +20,9 @@
     MambaSpec,
     MLAAttentionSpec,
     SinkFullAttentionSpec,
+    SlidingWindowMLASpec,
     SlidingWindowSpec,
+    TQFullAttentionSpec,
 )
 from vllm.v1.request import Request
 
@@ -39,6 +41,7 @@ def __init__(
         kv_cache_group_id: int,
         dcp_world_size: int = 1,
         pcp_world_size: int = 1,
+        max_admission_blocks_per_request: int | None = None,
     ) -> None:
         """
         Initializes the SingleTypeKVCacheManager.
@@ -46,6 +49,12 @@ def __init__(
             kv_cache_spec: The kv_cache_spec for this manager.
             block_pool: The block pool.
             kv_cache_group_id: The id of the kv cache group of this manager.
+            max_admission_blocks_per_request: Recycling-aware per-request
+                block cap used by `get_num_blocks_to_allocate`. Only set for
+                spec types that recycle blocks across chunks (SWA,
+                chunked-local); `None` (the default) means no cap, which is
+                correct for full-attention-style specs that hold every
+                block until the request finishes.
         """
         self.block_size = kv_cache_spec.block_size
         self.dcp_world_size = dcp_world_size
@@ -55,6 +64,7 @@ def __init__(
         self.kv_cache_spec = kv_cache_spec
         self.block_pool = block_pool
         self.enable_caching = enable_caching
+        self._max_admission_blocks_per_request = max_admission_blocks_per_request
         self.new_block_ids: list[int] = []
 
         # Mapping from request ID to blocks to track the blocks allocated
@@ -82,6 +92,7 @@ def get_num_blocks_to_allocate(
         new_computed_blocks: Sequence[KVCacheBlock],
         total_computed_tokens: int,
         num_tokens_main_model: int,
+        apply_admission_cap: bool = False,
     ) -> int:
         """
         Get the number of blocks needed to be allocated for the request.
@@ -97,12 +108,28 @@ def get_num_blocks_to_allocate(
             num_tokens_main_model: The number of tokens for the main model (aka target
                 model in spec decode). w/o spec decode, it is num_tokens;
                 with spec decode, it is num_tokens - num_lookahead_tokens.
+            apply_admission_cap: If True, clamp by `num_required_blocks` by
+                `_max_admission_blocks_per_request`for recycling-aware specs
+                (SWA, chunked-local).
 
         Returns:
             The number of blocks to allocate.
         """
 
         num_required_blocks = cdiv(num_tokens, self.block_size)
+        if apply_admission_cap and self._max_admission_blocks_per_request is not None:
+            # Recycling-aware specs (SWA, chunked-local) cap the per-request
+            # reservation here so admission matches the startup pool sizer
+            # (`SlidingWindowSpec.max_admission_blocks_per_request` / its
+            # chunked-local counterpart). `remove_skipped_blocks` runs from
+            # `allocate_slots` before each chunk's `get_num_blocks_to_allocate`,
+            # so per-request peak real-held blocks <= this cap, which keeps
+            # `sum(reservations) <= pool` <=> `sum(peak_real_held) <= pool`.
+            # Drift between the two would re-introduce the deadlock from
+            # issue #39734 or, worse, mid-prefill OOM.
+            num_required_blocks = min(
+                num_required_blocks, self._max_admission_blocks_per_request
+            )
         num_req_blocks = len(self.req_to_blocks.get(request_id, ()))
 
         if request_id in self.num_cached_block:
@@ -209,7 +236,7 @@ def allocate_new_computed_blocks(
                 cdiv(num_total_computed_tokens, self.block_size) - len(req_blocks)
             )
             req_blocks.extend(allocated_blocks)
-            if type(self.kv_cache_spec) is FullAttentionSpec:
+            if type(self.kv_cache_spec) in (FullAttentionSpec, TQFullAttentionSpec):
                 self.new_block_ids.extend(b.block_id for b in allocated_blocks)
 
     def allocate_new_blocks(
@@ -237,7 +264,7 @@ def allocate_new_blocks(
         else:
             new_blocks = self.block_pool.get_new_blocks(num_new_blocks)
             req_blocks.extend(new_blocks)
-            if type(self.kv_cache_spec) is FullAttentionSpec:
+            if type(self.kv_cache_spec) in (FullAttentionSpec, TQFullAttentionSpec):
                 self.new_block_ids.extend(b.block_id for b in new_blocks)
             return new_blocks
 
@@ -533,12 +560,10 @@ def find_longest_cache_hit(
             ):
                 # Skip prefix matching check if the block is not aligned with
                 # `alignment_tokens`.
-                if (
-                    num_contiguous_blocks == 0
-                    and block_size != alignment_tokens  # Faster for common case.
-                    and (i + 1) * block_size % alignment_tokens != 0
-                ):
-                    continue
+                if num_contiguous_blocks == 0 and block_size != alignment_tokens:
+                    post_pop_blocks = i if use_eagle else i + 1
+                    if (post_pop_blocks * block_size) % alignment_tokens != 0:
+                        continue
                 # Add the cached block to the computed blocks.
                 for computed, cached in zip(computed_blocks, cached_block):
                     computed[i] = cached
@@ -872,6 +897,7 @@ def get_num_blocks_to_allocate(
         new_computed_blocks: Sequence[KVCacheBlock],
         total_computed_tokens: int,
         num_tokens_main_model: int,
+        apply_admission_cap: bool = False,
     ) -> int:
         assert isinstance(self.kv_cache_spec, MambaSpec)
         if (
@@ -896,6 +922,7 @@ def get_num_blocks_to_allocate(
                 new_computed_blocks,
                 total_computed_tokens,
                 num_tokens_main_model,
+                apply_admission_cap=apply_admission_cap,
             )
         else:
             # We don't allocate blocks for lookahead tokens in align mode, because if
@@ -1114,8 +1141,10 @@ def __init__(
 
 spec_manager_map: dict[type[KVCacheSpec], type[SingleTypeKVCacheManager]] = {
     FullAttentionSpec: FullAttentionManager,
+    TQFullAttentionSpec: FullAttentionManager,
     MLAAttentionSpec: FullAttentionManager,
     SlidingWindowSpec: SlidingWindowManager,
+    SlidingWindowMLASpec: SlidingWindowManager,
     ChunkedLocalAttentionSpec: ChunkedLocalAttentionManager,
     MambaSpec: MambaManager,
     CrossAttentionSpec: CrossAttentionManager,
@@ -1124,8 +1153,21 @@ def __init__(
 
 
 def get_manager_for_kv_cache_spec(
-    kv_cache_spec: KVCacheSpec, **kwargs
+    kv_cache_spec: KVCacheSpec,
+    max_num_batched_tokens: int,
+    max_model_len: int,
+    **kwargs,
 ) -> SingleTypeKVCacheManager:
     manager_class = spec_manager_map[type(kv_cache_spec)]
+    # SlidingWindow / ChunkedLocalAttention managers recycle blocks across
+    # chunks; the runtime admission cap must match the recycling-aware bound
+    # the startup pool sizer uses (single source of truth: the spec method).
+    if isinstance(kv_cache_spec, (SlidingWindowSpec, ChunkedLocalAttentionSpec)):
+        kwargs["max_admission_blocks_per_request"] = (
+            kv_cache_spec.max_admission_blocks_per_request(
+                max_num_batched_tokens=max_num_batched_tokens,
+                max_model_len=max_model_len,
+            )
+        )
     manager = manager_class(kv_cache_spec, **kwargs)
     return manager
diff --git a/vllm/v1/engine/__init__.py b/vllm/v1/engine/__init__.py
index d5c5dba63475..f6c80055b651 100644
--- a/vllm/v1/engine/__init__.py
+++ b/vllm/v1/engine/__init__.py
@@ -94,6 +94,12 @@ class EngineCoreRequest(
     data_parallel_rank: int | None
     prompt_embeds: torch.Tensor | None = None
 
+    # Per-position mask for mixed-mode inputs (e.g chat completion with
+    # prompt_embeds content parts). `True` means the position is a real
+    # token ID; `False` means the position uses a pre-computed entry from
+    # `prompt_embeds`. `None` for pure-tokens and pure-embeds requests.
+    prompt_is_token_ids: list[bool] | None = None
+
     # Index of the client, used to ensure outputs are sent back to the same
     # client for this request when scaling out the front-end.
     client_index: int = 0
@@ -114,6 +120,13 @@ class EngineCoreRequest(
     external_req_id: str | None = None
 
     reasoning_ended: bool | None = None
+    reasoning_parser_kwargs: dict[str, Any] | None = None
+
+    # If True, the request should be added to the scheduler's waiting queue
+    # and immediately aborted, so connector-side cleanup runs via the standard
+    # request_finished hook. Used to free P-side prefill blocks when a
+    # KV-transfer request is rejected on the D node before engine admission.
+    abort_immediately: bool = False
 
     @property
     def params(self) -> SamplingParams | PoolingParams:
diff --git a/vllm/v1/engine/async_llm.py b/vllm/v1/engine/async_llm.py
index 1c87d9ec0944..32cc3c0d2d05 100644
--- a/vllm/v1/engine/async_llm.py
+++ b/vllm/v1/engine/async_llm.py
@@ -77,7 +77,6 @@ def __init__(
         log_stats: bool,
         usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
         mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
-        use_cached_outputs: bool = False,
         log_requests: bool = True,
         start_engine_loop: bool = True,
         stat_loggers: list[StatLoggerFactory] | None = None,
@@ -95,7 +94,6 @@ def __init__(
             log_stats: Whether to log stats.
             usage_context: Usage context of the LLM.
             mm_registry: Multi-modal registry.
-            use_cached_outputs: Whether to use cached outputs.
             log_requests: Whether to log requests.
             start_engine_loop: Whether to start the engine loop.
             stat_loggers: customized stat loggers for the engine.
@@ -295,6 +293,7 @@ async def add_request(
         data_parallel_rank: int | None = None,
         prompt_text: str | None = None,
         reasoning_ended: bool | None = None,
+        reasoning_parser_kwargs: dict[str, Any] | None = None,
     ) -> RequestOutputCollector:
         """Add new request to the AsyncLLM."""
 
@@ -315,7 +314,7 @@ async def add_request(
             )
 
         if isinstance(prompt, AsyncGenerator):
-            if reasoning_ended is not None:
+            if reasoning_ended is not None or reasoning_parser_kwargs is not None:
                 raise NotImplementedError
 
             # Streaming input case.
@@ -363,6 +362,8 @@ async def add_request(
 
         if reasoning_ended is not None:
             request.reasoning_ended = reasoning_ended
+        if reasoning_parser_kwargs is not None:
+            request.reasoning_parser_kwargs = reasoning_parser_kwargs
 
         self.input_processor.assign_request_id(request)
 
@@ -536,6 +537,7 @@ async def generate(
         priority: int = 0,
         data_parallel_rank: int | None = None,
         reasoning_ended: bool | None = None,
+        reasoning_parser_kwargs: dict[str, Any] | None = None,
     ) -> AsyncGenerator[RequestOutput, None]:
         """
         Main function called by the API server to kick off a request
@@ -565,6 +567,7 @@ async def generate(
                 data_parallel_rank=data_parallel_rank,
                 prompt_text=prompt_text,
                 reasoning_ended=reasoning_ended,
+                reasoning_parser_kwargs=reasoning_parser_kwargs,
             )
 
             # The output_handler task pushes items into the queue.
@@ -717,6 +720,33 @@ async def abort(
         if self.log_requests:
             logger.info("Aborted request(s) %s.", ",".join(request_ids))
 
+    async def notify_kv_transfer_request_rejected(
+        self,
+        request_id: str,
+        kv_transfer_params: dict[str, Any],
+        *,
+        data_parallel_rank: int | None = None,
+    ) -> None:
+        """Submit a pre-aborted request so the connector's request_finished
+        hook runs to free any pre-admission KV-transfer resources (e.g. NIXL
+        prefill blocks pinned on the P node)."""
+        request = EngineCoreRequest(
+            request_id=request_id,
+            prompt_token_ids=[0],
+            mm_features=None,
+            sampling_params=SamplingParams(
+                max_tokens=1,
+                extra_args={"kv_transfer_params": dict(kv_transfer_params)},
+            ),
+            pooling_params=None,
+            arrival_time=time.time(),
+            lora_request=None,
+            cache_salt=None,
+            data_parallel_rank=data_parallel_rank,
+            abort_immediately=True,
+        )
+        await self.engine_core.add_request_async(request)
+
     async def pause_generation(
         self,
         *,
@@ -1046,6 +1076,13 @@ async def init_weight_transfer_engine(
             "init_weight_transfer_engine", kwargs={"init_info": init_info_dict}
         )
 
+    async def start_weight_update(self, is_checkpoint_format: bool = True) -> None:
+        """Start a new weight update."""
+        await self.collective_rpc(
+            "start_weight_update",
+            kwargs={"is_checkpoint_format": is_checkpoint_format},
+        )
+
     async def update_weights(self, request: WeightTransferUpdateRequest) -> None:
         """
         Batched weight update for RL training.
@@ -1064,3 +1101,7 @@ async def update_weights(self, request: WeightTransferUpdateRequest) -> None:
         await self.collective_rpc(
             "update_weights", kwargs={"update_info": update_info_dict}
         )
+
+    async def finish_weight_update(self) -> None:
+        """Finish the current weight update."""
+        await self.collective_rpc("finish_weight_update")
diff --git a/vllm/v1/engine/core.py b/vllm/v1/engine/core.py
index c2c1a239adb2..122cdd8f965d 100644
--- a/vllm/v1/engine/core.py
+++ b/vllm/v1/engine/core.py
@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import gc
 import os
 import queue
 import signal
@@ -44,6 +45,7 @@
     get_kv_cache_configs,
     get_request_block_hasher,
     init_none_hash,
+    resolve_kv_cache_block_sizes,
 )
 from vllm.v1.core.sched.interface import PauseState, SchedulerInterface
 from vllm.v1.core.sched.output import SchedulerOutput
@@ -136,10 +138,8 @@ def __init__(
                 logger.warning("Disabling chunked prefill for model without KVCache")
                 vllm_config.scheduler_config.enable_chunked_prefill = False
 
-        scheduler_block_size = (
-            vllm_config.cache_config.block_size
-            * vllm_config.parallel_config.decode_context_parallel_size
-            * vllm_config.parallel_config.prefill_context_parallel_size
+        scheduler_block_size, hash_block_size = resolve_kv_cache_block_sizes(
+            kv_cache_config, vllm_config
         )
 
         self.scheduler: SchedulerInterface = Scheduler(
@@ -149,6 +149,7 @@ def __init__(
             include_finished_set=include_finished_set,
             log_stats=self.log_stats,
             block_size=scheduler_block_size,
+            hash_block_size=hash_block_size,
         )
         self.use_spec_decode = vllm_config.speculative_config is not None
         if self.scheduler.connector is not None:  # type: ignore
@@ -206,7 +207,7 @@ def __init__(
             init_none_hash(caching_hash_fn)
 
             self.request_block_hasher = get_request_block_hasher(
-                scheduler_block_size, caching_hash_fn
+                hash_block_size, caching_hash_fn
             )
 
         self.step_fn = (
@@ -293,7 +294,6 @@ def _initialize_kv_caches(self, vllm_config: VllmConfig) -> KVCacheConfig:
                 compile_time + encoder_compile_time,
                 compile_time,
                 encoder_compile_time,
-                scope="local",
             )
         elif compile_time > 0:
             logger.info_once(
@@ -301,13 +301,11 @@ def _initialize_kv_caches(self, vllm_config: VllmConfig) -> KVCacheConfig:
                 "%.2f s (compilation: %.2f s)",
                 elapsed,
                 compile_time,
-                scope="local",
             )
         else:
             logger.info_once(
                 "init engine (profile, create kv cache, warmup model) took %.2f s",
                 elapsed,
-                scope="local",
             )
         return scheduler_kv_cache_config
 
@@ -346,6 +344,10 @@ def add_request(self, request: Request, request_wave: int = 0):
             )
 
         self.scheduler.add_request(request)
+        if request.abort_immediately:
+            # Immediately abort so the connector's request_finished hook runs
+            # to free any pre-admission KV-transfer resources.
+            self.abort_requests([request.request_id])
 
     def abort_requests(self, request_ids: list[str]):
         """Abort requests from the scheduler."""
@@ -577,6 +579,12 @@ def shutdown(self):
         if self.scheduler:
             self.scheduler.shutdown()
 
+        # Undo the gc.freeze() from __init__ so that the objects allocated
+        # during engine startup (model weights, KV caches, etc.) become
+        # visible to the garbage collector again. Without this, deleting
+        # the engine in-process (e.g. unit tests) leaks GPU memory.
+        gc.unfreeze()
+
     def profile(self, is_start: bool = True, profile_prefix: str | None = None):
         self.model_executor.profile(is_start, profile_prefix)
 
@@ -1567,7 +1575,8 @@ def engine_idle_callback(engine: "EngineCoreProc", future: Future[Any]) -> None:
 
         pause_state = PauseState.PAUSED_ALL if mode == "keep" else PauseState.PAUSED_NEW
         self.scheduler.set_pause_state(pause_state)
-        if not self.has_work():
+
+        if self._pause_complete():
             if clear_cache:
                 self._reset_caches()
             return None
@@ -1576,6 +1585,13 @@ def engine_idle_callback(engine: "EngineCoreProc", future: Future[Any]) -> None:
         self._idle_state_callbacks.append(partial(engine_idle_callback, future=future))
         return future
 
+    def _pause_complete(self) -> bool:
+        """Returns True if the pause has fully completed and the caller can
+        return ``None`` synchronously; False if the pause is still pending
+        and the caller should register an idle-state callback to finish it.
+        """
+        return not self.has_work()
+
     def _send_finish_outputs_to_client(
         self, req_ids: list[str], client_index: int, finish_reason: FinishReason
     ) -> None:
@@ -1631,6 +1647,14 @@ def __init__(
         self.current_wave = 0
         self.last_counts = (0, 0)
 
+        # Two-phase pause protocol state. When pending_pause is True, the
+        # engine keeps stepping (dummy batches) while waiting for all DP
+        # ranks to also set pending_pause. Once all ranks agree via
+        # all-reduce, ignore_start_dp_wave is set so that stale
+        # START_DP_WAVE messages cannot re-wake the engines.
+        self.pending_pause = False
+        self.ignore_start_dp_wave = False
+
         from vllm.distributed.elastic_ep.elastic_state import ElasticEPScalingState
 
         self.eep_scaling_state: ElasticEPScalingState | None = None
@@ -1660,6 +1684,7 @@ def _init_data_parallel(self, vllm_config: VllmConfig):
         assert 0 <= local_dp_rank <= dp_rank < dp_size
 
         self.dp_rank = dp_rank
+        self.dp_size = dp_size
         dp_group, dp_store = parallel_config.stateless_init_dp_group(return_store=True)
         self.dp_group, self.dp_store = dp_group, dp_store
 
@@ -1668,6 +1693,24 @@ def shutdown(self):
         if dp_group := getattr(self, "dp_group", None):
             stateless_destroy_torch_distributed_process_group(dp_group)
 
+    def _pause_complete(self) -> bool:
+        """Two-phase DP-aware pause.
+
+        Phase 1: Set local pause state and ``pending_pause`` flag. If the
+        engines are idle, kick-start them by setting ``engines_running`` to
+        True so ranks enter the stepping loop and reach the all-reduce
+        consensus checkpoint in ``_has_global_unfinished_reqs``.
+
+        Phase 2 (in ``_has_global_unfinished_reqs``): Once the all-reduce
+        confirms that **all** ranks have ``pending_pause`` set, collectively
+        stop stepping and set ``ignore_start_dp_wave`` so that stale
+        ``START_DP_WAVE`` messages cannot re-wake any engine.
+        """
+        self.pending_pause = True
+        self.engines_running = True
+
+        return False
+
     def add_request(self, request: Request, request_wave: int = 0):
         super().add_request(request, request_wave)
         if self.has_coordinator and request_wave != self.current_wave:
@@ -1677,36 +1720,60 @@ def add_request(self, request: Request, request_wave: int = 0):
                 not self.engines_running
                 and self.scheduler.pause_state == PauseState.UNPAUSED
             ):
-                self.engines_running = True
                 # Request received for an already-completed wave, notify
                 # front-end that we need to start the next one.
+                self.engines_running = True
                 self.output_queue.put_nowait(
                     (-1, EngineCoreOutputs(start_wave=self.current_wave))
                 )
 
     def resume_scheduler(self):
-        super().resume_scheduler()
-        if (
-            self.has_coordinator
-            and not self.engines_running
-            and self.scheduler.has_unfinished_requests()
-        ):
-            # Wake up other DP engines.
-            self.output_queue.put_nowait(
-                (-1, EngineCoreOutputs(start_wave=self.current_wave))
+        if self.pending_pause or (self.engines_running and self.ignore_start_dp_wave):
+            raise RuntimeError(
+                "resume_scheduler called while pause is still in "
+                "flight. Wait for the pause future to resolve before "
+                "resuming."
             )
+        if self.engines_running:
+            logger.debug("Resume called while engines are not paused, ignoring.")
+            return
+
+        super().resume_scheduler()
+        self.ignore_start_dp_wave = False
+
+        # Barrier: wait for all DP ranks to have resumed (and cleared
+        # ignore_start_dp_wave) before any rank starts stepping. Uses
+        # the existing all-reduce which is safe because engines are
+        # stopped.
+        has_global_unfinished = ParallelConfig.has_unfinished_dp(
+            self.dp_group, self.scheduler.has_unfinished_requests()
+        )
+
+        if has_global_unfinished:
+            self.engines_running = True
+
+    def barrier(self):
+        """Blocking barrier on the DP process group (test-only utility)."""
+        import torch.distributed as dist
+
+        dist.barrier(group=self.dp_group)
 
     def _handle_client_request(
         self, request_type: EngineCoreRequestType, request: Any
     ) -> None:
         if request_type == EngineCoreRequestType.START_DP_WAVE:
+            if self.ignore_start_dp_wave:
+                return
             new_wave, exclude_eng_index = request
             if exclude_eng_index != self.engine_index and (
                 new_wave >= self.current_wave
             ):
                 self.current_wave = new_wave
                 if not self.engines_running:
-                    logger.debug("EngineCore starting idle loop for wave %d.", new_wave)
+                    logger.debug(
+                        "EngineCore starting idle loop for wave %d.",
+                        new_wave,
+                    )
                     self.engines_running = True
         else:
             super()._handle_client_request(request_type, request)
@@ -1786,7 +1853,18 @@ def _has_global_unfinished_reqs(self, local_unfinished: bool) -> bool:
         if self.step_counter % 32 != 0:
             return True
 
-        return ParallelConfig.has_unfinished_dp(self.dp_group, local_unfinished)
+        has_unfinished, pause_consensus = ParallelConfig.sync_dp_state(
+            self.dp_group,
+            has_unfinished=local_unfinished,
+            pending_pause=self.pending_pause,
+        )
+
+        if pause_consensus:
+            self.ignore_start_dp_wave = True
+            self.pending_pause = False
+            logger.debug("DP pause consensus reached, ignoring START_DP_WAVE.")
+
+        return has_unfinished
 
     def reinitialize_distributed(
         self, reconfig_request: ReconfigureDistributedRequest
diff --git a/vllm/v1/engine/detokenizer.py b/vllm/v1/engine/detokenizer.py
index 2f81ba4f6c78..4700eecb59a7 100644
--- a/vllm/v1/engine/detokenizer.py
+++ b/vllm/v1/engine/detokenizer.py
@@ -3,9 +3,9 @@
 from abc import ABC, abstractmethod
 
 import tokenizers
+import tokenizers.decoders
 from packaging import version
 from tokenizers import Tokenizer
-from tokenizers.decoders import DecodeStream
 from transformers import PreTrainedTokenizerFast
 
 from vllm.logger import init_logger
@@ -177,7 +177,10 @@ def __init__(self, tokenizer: PreTrainedTokenizerFast, request: EngineCoreReques
         self.tokenizer: Tokenizer = tokenizer._tokenizer
 
         # Use native prefill to prime the decode stream with prompt tokens.
-        self.stream = DecodeStream(
+        # Look up DecodeStream on the module so backend patches (e.g. the
+        # fastokens shim that replaces ``tokenizers.decoders.DecodeStream``)
+        # are honored regardless of import order.
+        self.stream = tokenizers.decoders.DecodeStream(
             ids=request.prompt_token_ids,
             skip_special_tokens=self.skip_special_tokens,
         )
@@ -237,7 +240,9 @@ def _protected_step(self, next_token_id: int) -> str | None:
                 " for request %s, resetting decode stream.",
                 self.request_id,
             )
-            self.stream = DecodeStream(skip_special_tokens=self.skip_special_tokens)
+            self.stream = tokenizers.decoders.DecodeStream(
+                skip_special_tokens=self.skip_special_tokens
+            )
             token = self.stream.step(self.tokenizer, next_token_id)
         return token
 
diff --git a/vllm/v1/engine/input_processor.py b/vllm/v1/engine/input_processor.py
index 1aab90c14581..c579c92baf37 100644
--- a/vllm/v1/engine/input_processor.py
+++ b/vllm/v1/engine/input_processor.py
@@ -172,6 +172,45 @@ def _get_mm_identifier(
             return mm_hash
         return f"{lora_request.lora_name}:{mm_hash}"
 
+    def inject_into_mm_cache(
+        self,
+        mm_hashes: dict[str, list[str]],
+        mm_kwargs: dict[str, list],
+    ) -> None:
+        """Inject pre-processed mm_kwargs into the processor cache.
+
+        Call this when mm_kwargs have already been through the HF processor
+        externally (e.g. by a frontend that transfers pre-processed tensors
+        to the backend).  This ensures MM cache hit rate metrics are reported
+        accurately and avoids redundant processing on subsequent requests
+        with the same images.
+
+        Uses ``get_and_update_item()`` with an empty prompt_updates list,
+        since token expansion has already been handled externally.
+        """
+        cache = self.renderer.mm_processor_cache
+        if cache is None:
+            return
+        try:
+            for modality, hashes in mm_hashes.items():
+                items = mm_kwargs.get(modality, [])
+                for i, mm_hash in enumerate(hashes):
+                    if i < len(items) and items[i] is not None:
+                        # Insert into cache via get_and_update_item.
+                        # Use the returned item (may be an address for SHM
+                        # cache or the original item for LRU cache).
+                        items[i], _ = cache.get_and_update_item(
+                            (items[i], []),
+                            mm_hash,
+                        )
+            # Update cache stats to reflect the externally processed items
+            self.renderer.update_mm_cache_stats()
+        except Exception:
+            logger.warning(
+                "Failed to inject mm_kwargs into processor cache",
+                exc_info=True,
+            )
+
     @staticmethod
     def assign_request_id(request: EngineCoreRequest):
         """Replace the externally supplied request ID with an internal request ID
@@ -253,11 +292,13 @@ def process_inputs(
 
         # Mypy can be conservative for TypedDict unions; normalize access.
         if decoder_inputs["type"] == "embeds":
-            prompt_token_ids = None
             prompt_embeds = decoder_inputs["prompt_embeds"]
+            prompt_token_ids = decoder_inputs.get("prompt_token_ids")
+            prompt_is_token_ids = decoder_inputs.get("is_token_ids")
         else:
             prompt_token_ids = decoder_inputs["prompt_token_ids"]
             prompt_embeds = None
+            prompt_is_token_ids = None
 
         sampling_params = None
         pooling_params = None
@@ -322,6 +363,7 @@ def process_inputs(
             request_id=request_id,
             prompt_token_ids=prompt_token_ids,
             prompt_embeds=prompt_embeds,
+            prompt_is_token_ids=prompt_is_token_ids,
             mm_features=mm_features,
             sampling_params=sampling_params,
             pooling_params=pooling_params,
diff --git a/vllm/v1/engine/llm_engine.py b/vllm/v1/engine/llm_engine.py
index d0545651b966..62c7d0e2a819 100644
--- a/vllm/v1/engine/llm_engine.py
+++ b/vllm/v1/engine/llm_engine.py
@@ -56,7 +56,6 @@ def __init__(
         usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
         stat_loggers: list[StatLoggerFactory] | None = None,
         mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
-        use_cached_outputs: bool = False,
         multiprocess_mode: bool = False,
     ) -> None:
         self.vllm_config = vllm_config
diff --git a/vllm/v1/engine/logprobs.py b/vllm/v1/engine/logprobs.py
index 9ada6eda48ce..74a45ab1e4d4 100644
--- a/vllm/v1/engine/logprobs.py
+++ b/vllm/v1/engine/logprobs.py
@@ -47,7 +47,7 @@ def from_new_request(
     ) -> "LogprobsProcessor":
         sampling_params = request.sampling_params
         assert sampling_params is not None
-        num_logprobs = sampling_params.logprobs
+        num_logprobs = sampling_params.num_logprobs
         num_prompt_logprobs = sampling_params.prompt_logprobs
         return cls(
             tokenizer=tokenizer,
diff --git a/vllm/v1/engine/output_processor.py b/vllm/v1/engine/output_processor.py
index 3d1d7a82db30..e0e53694c400 100644
--- a/vllm/v1/engine/output_processor.py
+++ b/vllm/v1/engine/output_processor.py
@@ -11,6 +11,9 @@
 import torch
 
 from vllm.lora.request import LoRARequest
+from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
+    split_routed_experts,
+)
 from vllm.outputs import (
     STREAM_FINISHED,
     CompletionOutput,
@@ -314,8 +317,24 @@ def make_request_output(
                 finished,
             )
 
+        # Split routing data into prompt and generation portions.
+        # Prompt routing lives on RequestOutput (shared across n>1
+        # completions); generation routing lives on each CompletionOutput.
+        prompt_routed_experts = None
+        gen_routed_experts = None
+        if routed_experts is not None:
+            prompt_len = len(self.prompt_token_ids) if self.prompt_token_ids else 0
+            num_gen = (
+                self.detokenizer.num_output_tokens()
+                if self.detokenizer is not None
+                else None
+            )
+            prompt_routed_experts, gen_routed_experts = split_routed_experts(
+                routed_experts, prompt_len, num_gen
+            )
+
         output = self._new_completion_output(
-            new_token_ids, finish_reason, stop_reason, routed_experts
+            new_token_ids, finish_reason, stop_reason, gen_routed_experts
         )
 
         if self.parent_req is None:
@@ -327,7 +346,11 @@ def make_request_output(
             external_req_id = self.parent_req.external_req_id
 
         return self._new_request_output(
-            external_req_id, outputs, finished, kv_transfer_params
+            external_req_id,
+            outputs,
+            finished,
+            kv_transfer_params,
+            prompt_routed_experts,
         )
 
     def _new_request_output(
@@ -336,6 +359,7 @@ def _new_request_output(
         outputs: list[CompletionOutput] | list[PoolingOutput],
         finished: bool,
         kv_transfer_params: dict[str, Any] | None = None,
+        prompt_routed_experts: np.ndarray | None = None,
     ) -> RequestOutput | PoolingRequestOutput:
         # If prompt embeds were used, put placeholder prompt token ids
         prompt_token_ids = self.prompt_token_ids
@@ -371,6 +395,7 @@ def _new_request_output(
             kv_transfer_params=kv_transfer_params,
             num_cached_tokens=self.num_cached_tokens,
             metrics=self.stats,
+            prompt_routed_experts=prompt_routed_experts,
         )
 
     def _new_completion_output(
@@ -799,6 +824,7 @@ def _update_stats_from_finished(
         assert req_state.stats is not None
         iteration_stats.update_from_finished_request(
             finish_reason=finish_reason,
+            request_id=req_state.external_req_id,
             num_prompt_tokens=req_state.prompt_len,
             max_tokens_param=req_state.max_tokens_param,
             req_stats=req_state.stats,
diff --git a/vllm/v1/engine/utils.py b/vllm/v1/engine/utils.py
index 0de9b9ba4d94..3951e0184730 100644
--- a/vllm/v1/engine/utils.py
+++ b/vllm/v1/engine/utils.py
@@ -80,6 +80,21 @@ class EngineHandshakeMetadata:
     parallel_config: dict[str, int | str | list[int]]
 
 
+def _make_control_bundle(node_ip: str) -> dict[str, float]:
+    # The engine actor is scheduled on the final CPU-only bundle. Keep that
+    # bundle colocated with the group's first GPU bundle so the actor does not
+    # float to an unrelated node and reorder worker ranks away from the
+    # advertised DP bootstrap host.
+    return {"CPU": 1.0, "node:" + node_ip: 0.001}
+
+
+def _get_bundle_node_ip(bundle: dict[str, float]) -> str:
+    for key in bundle:
+        if key.startswith("node:"):
+            return key.split(":", 1)[1]
+    raise ValueError(f"Missing node affinity in placement bundle: {bundle}")
+
+
 class CoreEngineProcManager:
     """
     Utility class to handle creation, readiness, and shutdown
@@ -139,15 +154,16 @@ def __init__(
 
         try:
             for proc, local_dp_rank in zip(self.processes, local_dp_ranks):
-                # Adjust device control in DP for non-CUDA platforms
-                # as well as external and ray launchers
-                # For CUDA platforms, we use torch.accelerator.set_device_index()()
+                # Adjust device control in DP for platforms that cannot rely
+                # on torch.accelerator.set_device_index(), and for Ray launchers.
                 device_control_context: contextlib.AbstractContextManager[None] = (
                     contextlib.nullcontext()
                 )
+                needs_device_env_isolation = not (
+                    current_platform.is_cuda_alike() or current_platform.is_xpu()
+                )
                 if is_dp and (
-                    not current_platform.is_cuda_alike()
-                    or vllm_config.parallel_config.use_ray
+                    needs_device_env_isolation or vllm_config.parallel_config.use_ray
                 ):
                     device_control_context = set_device_control_env_var(
                         vllm_config, local_dp_rank
@@ -293,6 +309,18 @@ def get_device_indices(
     return value
 
 
+def _apply_dp_identity_suffix(dp_vllm_config, dp_rank: int) -> None:
+    # Ray actor names (RayExecutorV2) and KV-connector engine_ids must
+    # be unique across sibling DP engines or registration collides.
+    # Use the global DP rank, not a node-local rank, since sibling DP
+    # engines can span multiple nodes.
+    dp_vllm_config.instance_id = f"{dp_vllm_config.instance_id}_dp{dp_rank}"
+    if dp_vllm_config.kv_transfer_config is not None:
+        dp_vllm_config.kv_transfer_config.engine_id = (
+            f"{dp_vllm_config.kv_transfer_config.engine_id}_dp{dp_rank}"
+        )
+
+
 class CoreEngineActorManager:
     """
     Utility class to handle creation, readiness, and shutdown
@@ -388,16 +416,11 @@ def __init__(
             range(dp_size), local_dp_ranks, placement_groups
         ):
             dp_vllm_config = copy.deepcopy(vllm_config)
+            if dp_size > 1:
+                _apply_dp_identity_suffix(dp_vllm_config, index)
             dp_vllm_config.parallel_config.placement_group = pg
             local_client = index < local_engine_count
 
-            if dp_size > 1 and dp_vllm_config.kv_transfer_config is not None:
-                # modify the engine_id and append the local_dp_rank to it to ensure
-                # that the kv_transfer_config is unique for each DP rank.
-                dp_vllm_config.kv_transfer_config.engine_id = (
-                    f"{dp_vllm_config.kv_transfer_config.engine_id}_dp{local_index}"
-                )
-
             # Ray XPU known issue: dpctl initializes the GPU runtime early, so
             # setting device env vars in Ray actor's initialization method
             # will not affect device selection. See:
@@ -597,10 +620,20 @@ def create_dp_placement_groups(
                     if len(collected_bundles) < world_size:
                         continue
 
-                    bundles = collected_bundles + [{"CPU": 1.0}]
+                    control_node_ip = _get_bundle_node_ip(collected_bundles[0])
+                    bundles = collected_bundles + [
+                        _make_control_bundle(control_node_ip)
+                    ]
                     collected_bundles = []
                 else:
-                    bundles = device_bundle * world_size + [{"CPU": 1.0}]
+                    # STRICT_PACK already keeps every bundle in the placement
+                    # group on one node, so the explicit node affinity on the
+                    # control bundle is redundant for correctness here. Keep it
+                    # anyway for consistency with the span path and to preserve
+                    # intent if this scheduling strategy changes later.
+                    bundles = device_bundle * world_size + [
+                        _make_control_bundle(node_ip)
+                    ]
 
                 pg = ray.util.placement_group(
                     name=f"dp_rank_{len(placement_groups)}",
@@ -759,6 +792,8 @@ def scale_up_elastic_ep(
         for i, (pg, local_rank) in enumerate(zip(placement_groups, local_dp_ranks)):
             rank = cur_data_parallel_size + i
             dp_vllm_config = copy.deepcopy(cur_vllm_config)
+            if new_data_parallel_size > 1:
+                _apply_dp_identity_suffix(dp_vllm_config, rank)
             dp_vllm_config.parallel_config.data_parallel_size = new_data_parallel_size
             dp_vllm_config.parallel_config.placement_group = pg
 
@@ -927,7 +962,7 @@ def get_engine_zmq_addresses(
 
     # In offline mode there is an LLM instance per DP rank and
     # one core engine per LLM, see
-    # examples/offline_inference/data_parallel.py.
+    # examples/features/data_parallel/data_parallel_offline.py.
     offline_mode = local_start_index is not None
 
     # client_local_only = True for cases where this front-end
diff --git a/vllm/v1/executor/multiproc_executor.py b/vllm/v1/executor/multiproc_executor.py
index 3eccce722b56..db21d7cee779 100644
--- a/vllm/v1/executor/multiproc_executor.py
+++ b/vllm/v1/executor/multiproc_executor.py
@@ -51,6 +51,7 @@
     get_loopback_ip,
     get_open_port,
 )
+from vllm.utils.ompmultiprocessing import OMPProcessManager
 from vllm.utils.system_utils import (
     _maybe_force_spawn,
     decorate_logs,
@@ -169,24 +170,14 @@ def _init_executor(self) -> None:
                 [] if context.get_start_method() == "fork" else None
             )
 
+            # For CPU backend only, to setup OpenMP threads affinity
+            cpu_omp_manager = OMPProcessManager(self.vllm_config)
             for local_rank in range(self.local_world_size):
                 global_rank = global_start_rank + local_rank
                 is_driver_worker = self._is_driver_worker(global_rank)
-                if current_platform.is_cpu():
-                    om = current_platform.get_omp_manager()
-                    logger.info("Configured OMP PLACES %s", str(om.omp_places))
-                    unready_worker_handle = om.run(
-                        WorkerProc.make_worker_process,
-                        vllm_config=self.vllm_config,
-                        local_rank=local_rank,
-                        rank=global_rank,
-                        distributed_init_method=distributed_init_method,
-                        input_shm_handle=scheduler_output_handle,
-                        shared_worker_lock=shared_worker_lock,
-                        is_driver_worker=is_driver_worker,
-                        inherited_fds=inherited_fds,
-                    )
-                else:
+                with cpu_omp_manager.configure_omp_envs(
+                    rank=global_rank, local_rank=local_rank
+                ):
                     unready_worker_handle = WorkerProc.make_worker_process(
                         vllm_config=self.vllm_config,
                         local_rank=local_rank,
@@ -1041,7 +1032,6 @@ def set_multiprocessing_worker_envs():
                 "external environment to tune this value as needed.",
                 current_parallelism,
                 default_omp_num_threads,
-                scope="local",
             )
             os.environ["OMP_NUM_THREADS"] = str(default_omp_num_threads)
             torch.set_num_threads(default_omp_num_threads)
diff --git a/vllm/v1/executor/ray_executor.py b/vllm/v1/executor/ray_executor.py
index 1dda2c294e4e..cfeebb5e09de 100644
--- a/vllm/v1/executor/ray_executor.py
+++ b/vllm/v1/executor/ray_executor.py
@@ -26,6 +26,7 @@
     WORKER_SPECIFIC_ENV_VARS,
     FutureWrapper,
     RayWorkerWrapper,
+    detach_zero_copy_from_model_runner_output,
     initialize_ray_cluster,
     ray,
 )
@@ -463,7 +464,9 @@ def _execute_dag(
             # Get output only from a single worker (output_rank)
             # When PP is not used, we block here until the result is available.
             if not non_block:
-                return refs[0].get()
+                output = refs[0].get()
+                detach_zero_copy_from_model_runner_output(output)
+                return output
 
             # When PP is used, we return a FutureWrapper immediately so that
             # the scheduler can yield to the next batch.
@@ -473,7 +476,10 @@ def _execute_dag(
         assert self.kv_output_aggregator is not None
         if not non_block:
             # Block and get results from all workers
-            return self.kv_output_aggregator.aggregate(ray.get(refs))
+            outputs = ray.get(refs)
+            for output in outputs:
+                detach_zero_copy_from_model_runner_output(output)
+            return self.kv_output_aggregator.aggregate(outputs)
 
         # Return a future that will aggregate outputs from all workers
         return FutureWrapper(refs, self.kv_output_aggregator)
diff --git a/vllm/v1/executor/ray_utils.py b/vllm/v1/executor/ray_utils.py
index 67b43bdc11d2..1541b24deaaf 100644
--- a/vllm/v1/executor/ray_utils.py
+++ b/vllm/v1/executor/ray_utils.py
@@ -7,6 +7,8 @@
 from concurrent.futures import Future
 from typing import TYPE_CHECKING, Union
 
+import numpy as np
+
 import vllm.platforms
 from vllm.config import ParallelConfig
 from vllm.distributed import get_pp_group
@@ -190,6 +192,48 @@ def _is_last_rank(self) -> bool:
     RayWorkerWrapper = None  # type: ignore
 
 
+def detach_zero_copy_from_model_runner_output(output: "ModelRunnerOutput") -> None:
+    """Detach Ray SHM-channel zero-copy buffers from a ModelRunnerOutput in-place.
+
+    Ray compiled DAG SHM channels may return zero-copy objects (e.g. `np.ndarray`)
+    backed by Ray's shared-memory object store. Ray's channel docs explicitly
+    warn that subsequent reads may block if such an object is still in scope.
+
+    vLLM can return numpy-backed logprobs in `ModelRunnerOutput.logprobs`. If
+    those arrays are backed by Ray SHM (commonly read-only), retaining them in
+    scope across scheduler iterations can stall the channel and eventually hit
+    `RAY_CGRAPH_get_timeout`.
+
+    Copy read-only numpy arrays so the returned output no longer retains
+    references to Ray's shared-memory buffers.
+
+    We intentionally do not touch `prompt_logprobs_dict`: those entries are
+    `LogprobsTensors` backed by PyTorch-owned CPU tensors (`to_cpu_nonblocking`
+    or `empty_cpu`), not NumPy views decoded from Ray channels.
+    """
+    if output.logprobs is None:
+        return
+
+    token_ids, logprobs, ranks, cu_num_generated_tokens = output.logprobs
+
+    def _copy_if_readonly(arr):
+        if isinstance(arr, np.ndarray) and not arr.flags.writeable:
+            return arr.copy()
+        return arr
+
+    # `cu_num_generated_tokens` is already a plain Python list (or None), so it
+    # never aliases Ray SHM buffers and can be reused as-is.
+    token_ids_c = _copy_if_readonly(token_ids)
+    logprobs_c = _copy_if_readonly(logprobs)
+    ranks_c = _copy_if_readonly(ranks)
+    if token_ids_c is token_ids and logprobs_c is logprobs and ranks_c is ranks:
+        return
+
+    output.logprobs = type(output.logprobs)(
+        token_ids_c, logprobs_c, ranks_c, cu_num_generated_tokens
+    )
+
+
 class FutureWrapper(Future):
     """A wrapper around Ray output reference to meet the interface
     of .execute_model(): The top level (core busy loop) expects .result() api
@@ -207,8 +251,11 @@ def __init__(self, ref_or_refs, aggregator: KVOutputAggregator | None = None):
     def result(self, timeout=None):
         outputs = ray.get(self.ref_or_refs, timeout=timeout)
         if self.aggregator is None:
+            detach_zero_copy_from_model_runner_output(outputs)
             return outputs
 
+        for output in outputs:
+            detach_zero_copy_from_model_runner_output(output)
         return self.aggregator.aggregate(outputs, output_rank=0)
 
 
diff --git a/vllm/v1/executor/uniproc_executor.py b/vllm/v1/executor/uniproc_executor.py
index b616c3b7b8ad..92e668406f98 100644
--- a/vllm/v1/executor/uniproc_executor.py
+++ b/vllm/v1/executor/uniproc_executor.py
@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import os
 from collections.abc import Callable
-from concurrent.futures import Future, ThreadPoolExecutor
+from concurrent.futures import Future
 from functools import cached_property
 from multiprocessing import Lock
 from typing import Any
@@ -23,6 +23,25 @@
 logger = init_logger(__name__)
 
 
+class AsyncOutputFuture(Future):
+    def __init__(self, async_output: AsyncModelRunnerOutput, single_value: bool):
+        self.async_output = async_output
+        self.single_value = single_value
+        super().__init__()
+
+    def result(self, timeout=None):
+        if timeout is not None:
+            raise RuntimeError("timeout not implemented")
+
+        if not super().done():
+            try:
+                output = self.async_output.get_output()
+                self.set_result(output if self.single_value else [output])
+            except Exception as e:
+                self.set_exception(e)
+        return super().result()
+
+
 class UniProcExecutor(Executor):
     def _init_executor(self) -> None:
         """Initialize the worker and load the model."""
@@ -37,12 +56,6 @@ def _init_executor(self) -> None:
             shared_worker_lock=Lock(),
         )
 
-        self.async_output_thread: ThreadPoolExecutor | None = None
-        if self.max_concurrent_batches > 1:
-            self.async_output_thread = ThreadPoolExecutor(
-                max_workers=1, thread_name_prefix="WorkerAsyncOutput"
-            )
-
         self.driver_worker.init_worker(all_kwargs=[kwargs])
         self.driver_worker.init_device()
 
@@ -83,15 +96,7 @@ def collective_rpc(  # type: ignore[override]
         try:
             result = run_method(self.driver_worker, method, args, kwargs)
             if isinstance(result, AsyncModelRunnerOutput):
-                if (async_thread := self.async_output_thread) is not None:
-                    if single_value:
-                        return async_thread.submit(result.get_output)
-
-                    def get_output_list() -> list[Any]:
-                        return [result.get_output()]
-
-                    return async_thread.submit(get_output_list)
-                result = result.get_output()
+                return AsyncOutputFuture(result, single_value)
             future = Future[Any]()
             future.set_result(result if single_value else [result])
         except Exception as e:
@@ -147,7 +152,7 @@ class ExecutorWithExternalLauncher(UniProcExecutor):
     offline inference with tensor parallelism.
 
     see https://github.com/vllm-project/vllm/issues/11400 for
-    the motivation, and examples/offline_inference/torchrun_example.py
+    the motivation, and examples/features/torchrun/torchrun_example_offline.py
     for the usage example.
 
     The key idea: although it is tensor-parallel inference, we only
diff --git a/vllm/v1/kv_cache_interface.py b/vllm/v1/kv_cache_interface.py
index 6f8ad8e7d8ef..19438fb1e42d 100644
--- a/vllm/v1/kv_cache_interface.py
+++ b/vllm/v1/kv_cache_interface.py
@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 import copy
+from collections import Counter
 from dataclasses import dataclass, fields, replace
 from enum import IntEnum
 from math import prod
@@ -13,11 +14,11 @@
 from typing_extensions import Self
 
 from vllm.logger import init_logger
+from vllm.utils.math_utils import cdiv, round_up
+from vllm.utils.torch_utils import get_dtype_size, nvfp4_kv_cache_full_dim
 
 if TYPE_CHECKING:
     from vllm.config import VllmConfig
-from vllm.utils.math_utils import cdiv
-from vllm.utils.torch_utils import get_dtype_size
 
 logger = init_logger(__name__)
 
@@ -38,11 +39,20 @@ class KVQuantMode(IntEnum):
     FP8_PER_TENSOR = 1  # per-tensor scales (current fp8 path)
     INT8_PER_TOKEN_HEAD = 2  # per-token-head dynamic scales for int8
     FP8_PER_TOKEN_HEAD = 3  # per-token-head dynamic scales for fp8
+    NVFP4 = 4  # packed fp4 data + fp8 block scales
 
     @property
     def is_per_token_head(self) -> bool:
         """True for any per-token-head quantization mode."""
-        return self >= 2
+        return self in (
+            KVQuantMode.INT8_PER_TOKEN_HEAD,
+            KVQuantMode.FP8_PER_TOKEN_HEAD,
+        )
+
+    @property
+    def is_nvfp4(self) -> bool:
+        """True for NVFP4 packed quantization mode."""
+        return self == KVQuantMode.NVFP4
 
 
 def get_kv_quant_mode(kv_cache_dtype: str) -> KVQuantMode:
@@ -51,7 +61,9 @@ def get_kv_quant_mode(kv_cache_dtype: str) -> KVQuantMode:
         return KVQuantMode.INT8_PER_TOKEN_HEAD
     if kv_cache_dtype == "fp8_per_token_head":
         return KVQuantMode.FP8_PER_TOKEN_HEAD
-    if kv_cache_dtype.startswith("fp8"):
+    if kv_cache_dtype == "nvfp4":
+        return KVQuantMode.NVFP4
+    if isinstance(kv_cache_dtype, str) and kv_cache_dtype.startswith("fp8"):
         return KVQuantMode.FP8_PER_TENSOR
     return KVQuantMode.NONE
 
@@ -84,6 +96,10 @@ def page_size_bytes(self) -> int:
         """
         raise NotImplementedError
 
+    @property
+    def storage_block_size(self) -> int:
+        return self.block_size
+
     def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int:
         """
         The maximum possible memory usage of this KV cache in bytes.
@@ -135,6 +151,16 @@ def page_size_bytes(self) -> int:
 
     @property
     def real_page_size_bytes(self) -> int:
+        if self.kv_quant_mode.is_nvfp4:
+            # Packed layout: fp4 data + fp8 block scales per head.
+            full_dim = nvfp4_kv_cache_full_dim(self.head_size)
+            return (
+                2
+                * self.block_size
+                * self.num_kv_heads
+                * full_dim
+                * get_dtype_size(self.dtype)
+            )
         return (
             2
             * self.block_size
@@ -237,6 +263,19 @@ def merge(cls, specs: list[Self]) -> Self:
 
     @property
     def real_page_size_bytes(self) -> int:
+        if self.kv_quant_mode.is_nvfp4:
+            # Packed layout per head: fp4 data + fp8 block scales.
+            # fp4 data: head_size//2 bytes (2 fp4 values per byte)
+            # fp8 block scale: head_size//16 bytes (1 scale per 16 elements)
+            last_dim = nvfp4_kv_cache_full_dim(
+                self.head_size
+            ) + nvfp4_kv_cache_full_dim(self.head_size_v)
+            return (
+                self.block_size
+                * self.num_kv_heads
+                * last_dim
+                * get_dtype_size(self.dtype)
+            )
         return (
             self.block_size
             * self.num_kv_heads
@@ -245,19 +284,70 @@ def real_page_size_bytes(self) -> int:
         )
 
 
+def _apply_alignment_padding(spec: MLAAttentionSpec | SlidingWindowMLASpec):
+    if spec.alignment is None:
+        return
+    actual_page_size = spec.real_page_size_bytes
+    padded_page_size = round_up(actual_page_size, spec.alignment)
+    if padded_page_size != actual_page_size:
+        object.__setattr__(spec, "page_size_padded", padded_page_size)
+
+
+@dataclass(frozen=True, kw_only=True)
+class TQFullAttentionSpec(FullAttentionSpec):
+    """FullAttentionSpec with TQ-aware page size.
+
+    Python equivalent of the C++ TQ4FullAttentionSpec. Overrides
+    real_page_size_bytes to use TQ slot bytes instead of the raw
+    head_size * dtype formula.
+    """
+
+    tq_slot_size: int = 0
+
+    @property
+    def real_page_size_bytes(self) -> int:
+        if self.tq_slot_size > 0:
+            return self.block_size * self.num_kv_heads * self.tq_slot_size
+        return super().real_page_size_bytes
+
+    @classmethod
+    def merge(cls, specs: list[Self]) -> Self:
+        merged = super().merge(specs)
+        assert all(s.tq_slot_size == specs[0].tq_slot_size for s in specs), (
+            "All TQ layers in the same KV cache group must use the same tq_slot_size."
+        )
+        return replace(merged, tq_slot_size=specs[0].tq_slot_size)
+
+
 @dataclass(frozen=True, kw_only=True)
 class MLAAttentionSpec(FullAttentionSpec):
     # TODO(Lucas/Chen): less hacky way to do this
     cache_dtype_str: str | None = None
+    # DeepseekV4 only fields. Non-DeepseekV4 MLA models leave these at defaults.
+    alignment: int | None = None  # Default to None for no padding.
+    compress_ratio: int = 1  # Default to 1 for no compression.
+    model_version: str | None = None
+
+    def __post_init__(self):
+        super().__post_init__()
+        _apply_alignment_padding(self)
+
+    @property
+    def storage_block_size(self) -> int:
+        return self.block_size // self.compress_ratio
 
     @property
     def real_page_size_bytes(self) -> int:
         if self.cache_dtype_str == "fp8_ds_mla":
-            # See `vllm/v1/attention/backends/mla/flashmla_sparse.py`
-            #  for details.
+            if self.model_version == "deepseek_v4":
+                # DeepseekV4: 448B NoPE + 128B RoPE + 8B fp8 scale = 584B per token.
+                # head_size stays semantic (512); bytes are determined here.
+                return self.storage_block_size * 584
+            # V3.2 main MLA: 656-byte custom layout (kv_lora_rank=512 +
+            # qk_rope_head_dim=64, head_size=576). See flashmla_sparse.py.
             return self.block_size * 656
         return (
-            self.block_size
+            self.storage_block_size
             * self.num_kv_heads
             * self.head_size
             * get_dtype_size(self.dtype)
@@ -269,9 +359,15 @@ def merge(cls, specs: list[Self]) -> Self:
             "All attention layers in the same KV cache group must be MLAAttentionSpec."
         )
         cache_dtype_str_set = set(spec.cache_dtype_str for spec in specs)
-        assert len(cache_dtype_str_set) == 1, (
+        compress_ratio_set = set(spec.compress_ratio for spec in specs)
+        model_version_set = set(spec.model_version for spec in specs)
+        assert (
+            len(cache_dtype_str_set) == 1
+            and len(compress_ratio_set) == 1
+            and len(model_version_set) == 1
+        ), (
             "All attention layers in the same KV cache group must use the same "
-            "quantization method."
+            "quantization method, compress ratio, and model version."
         )
         return cls(
             block_size=specs[0].block_size,
@@ -281,6 +377,8 @@ def merge(cls, specs: list[Self]) -> Self:
             kv_quant_mode=specs[0].kv_quant_mode,
             page_size_padded=specs[0].page_size_padded,
             cache_dtype_str=cache_dtype_str_set.pop(),
+            compress_ratio=compress_ratio_set.pop(),
+            model_version=model_version_set.pop(),
         )
 
 
@@ -288,24 +386,69 @@ def merge(cls, specs: list[Self]) -> Self:
 class ChunkedLocalAttentionSpec(AttentionSpec):
     attention_chunk_size: int
 
-    def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int:
-        max_model_len = vllm_config.model_config.max_model_len
-        max_num_batched_tokens = vllm_config.scheduler_config.max_num_batched_tokens
+    def max_admission_blocks_per_request(
+        self, max_num_batched_tokens: int, max_model_len: int
+    ) -> int:
+        """Per-request admission cap, in blocks.
 
-        # During chunked prefill, we allocate KV cache for at most
-        # `self.attention_chunk_size` computed tokens plus the newly scheduled
-        # tokens. And we won't allocate KV cache for more than `max_model_len`
-        # tokens.
+        Single source of truth for both startup pool sizing
+        (`max_memory_usage_bytes`) and the runtime admission gate, so requests
+        admitted by startup can also be admitted at runtime.
+        """
+        # During chunked prefill, we hold KV for at most one chunk window.
         num_tokens = min(
             self.attention_chunk_size + max_num_batched_tokens, max_model_len
         )
+        return cdiv(num_tokens, self.block_size)
 
-        return cdiv(num_tokens, self.block_size) * self.page_size_bytes
+    def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int:
+        max_model_len = vllm_config.model_config.max_model_len
+        max_num_batched_tokens = vllm_config.scheduler_config.max_num_batched_tokens
+        max_blocks = self.max_admission_blocks_per_request(
+            max_num_batched_tokens=max_num_batched_tokens, max_model_len=max_model_len
+        )
+        return max_blocks * self.page_size_bytes
 
 
 @dataclass(frozen=True, kw_only=True)
 class SlidingWindowSpec(AttentionSpec):
     sliding_window: int
+    head_size_v: int = None  # type: ignore[assignment]
+
+    def __post_init__(self):
+        if self.head_size_v is None:
+            object.__setattr__(self, "head_size_v", self.head_size)
+
+    @property
+    def real_page_size_bytes(self) -> int:
+        return (
+            self.block_size
+            * self.num_kv_heads
+            * (self.head_size + self.head_size_v)
+            * get_dtype_size(self.dtype)
+        )
+
+    def max_admission_blocks_per_request(
+        self, max_num_batched_tokens: int, max_model_len: int
+    ) -> int:
+        """Per-request admission cap, in blocks.
+
+        Single source of truth for both startup pool sizing
+        (`max_memory_usage_bytes`) and the runtime admission gate. Per-request
+        real-held blocks plateau at this bound because
+        `SlidingWindowManager.remove_skipped_blocks` runs from `allocate_slots`
+        before each chunk's `get_num_blocks_to_allocate`.
+        """
+        # During chunked prefill, we hold KV for the last `sliding_window-1`
+        # computed tokens plus the newly scheduled tokens, and never more
+        # than `max_model_len`.
+        num_tokens = min(
+            self.sliding_window - 1 + max_num_batched_tokens, max_model_len
+        )
+        # +1 because the sliding window may not start from the beginning of
+        # the block. E.g. block size 4 and num_token 4 needs two blocks
+        # [XXCD][EF] to store the 6-token window [CDEF].
+        return cdiv(num_tokens, self.block_size) + 1
 
     def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int:
         assert vllm_config.parallel_config.decode_context_parallel_size == 1, (
@@ -313,20 +456,75 @@ def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int:
         )
         max_model_len = vllm_config.model_config.max_model_len
         max_num_batched_tokens = vllm_config.scheduler_config.max_num_batched_tokens
+        max_blocks = self.max_admission_blocks_per_request(
+            max_num_batched_tokens=max_num_batched_tokens, max_model_len=max_model_len
+        )
+        return max_blocks * self.page_size_bytes
 
-        # During chunked prefill, we allocate KV cache for the last
-        # `self.sliding_window-1` computed tokens plus the newly scheduled
-        # tokens. And we won't allocate KV cache for more than `max_model_len`
-        # tokens.
-        num_tokens = min(
-            self.sliding_window - 1 + max_num_batched_tokens, max_model_len
+
+@dataclass(frozen=True, kw_only=True)
+class SlidingWindowMLASpec(SlidingWindowSpec):
+    """Sliding window attention with MLA cache format."""
+
+    cache_dtype_str: str | None = None
+    # DeepseekV4-only: see MLAAttentionSpec.model_version.
+    alignment: int | None = None  # Default to None for no padding.
+    compress_ratio: int = 1
+    model_version: str | None = None
+
+    def __post_init__(self):
+        _apply_alignment_padding(self)
+
+    @property
+    def storage_block_size(self) -> int:
+        return self.block_size // self.compress_ratio
+
+    @property
+    def real_page_size_bytes(self) -> int:
+        if self.model_version == "deepseek_v4":
+            # DeepseekV4: 448B NoPE + 128B RoPE + 8B fp8 scale = 584B per token.
+            return self.storage_block_size * 584
+        assert self.model_version is None, (
+            f"Unsupported model version: {self.model_version}"
+        )
+        return (
+            self.storage_block_size
+            * self.num_kv_heads
+            * self.head_size
+            * get_dtype_size(self.dtype)
         )
 
-        # +1 here because the sliding window may not start from the beginning
-        # of the block. For example, if the block size is 4 and num_token
-        # is 4, we need two blocks [XXCD] [EF] to store the sliding
-        # window [CDEF] of 6 tokens.
-        return (cdiv(num_tokens, self.block_size) + 1) * self.page_size_bytes
+    @classmethod
+    def merge(cls, specs: list[Self]) -> Self:
+        assert all(isinstance(spec, SlidingWindowMLASpec) for spec in specs), (
+            "All attention layers in the same KV cache group must be "
+            "SlidingWindowMLASpec."
+        )
+        cache_dtype_str_set = set(spec.cache_dtype_str for spec in specs)
+        compress_ratio_set = set(spec.compress_ratio for spec in specs)
+        model_version_set = set(spec.model_version for spec in specs)
+        sliding_window_set = set(spec.sliding_window for spec in specs)
+        assert (
+            len(cache_dtype_str_set) == 1
+            and len(compress_ratio_set) == 1
+            and len(model_version_set) == 1
+            and len(sliding_window_set) == 1
+        ), (
+            "All attention layers in the same KV cache group must use the same "
+            "quantization method, compress ratio, model version and sliding "
+            "window size."
+        )
+        return cls(
+            block_size=specs[0].block_size,
+            num_kv_heads=specs[0].num_kv_heads,
+            head_size=specs[0].head_size,
+            dtype=specs[0].dtype,
+            page_size_padded=specs[0].page_size_padded,
+            sliding_window=sliding_window_set.pop(),
+            cache_dtype_str=cache_dtype_str_set.pop(),
+            compress_ratio=compress_ratio_set.pop(),
+            model_version=model_version_set.pop(),
+        )
 
 
 @dataclass(frozen=True)
@@ -463,7 +661,17 @@ def is_uniform_type(cls, kv_cache_specs: dict[str, KVCacheSpec]) -> bool:
             # Different block sizes, not uniform.
             return False
         one_spec = next(iter(kv_cache_specs.values()))
-        if isinstance(one_spec, FullAttentionSpec):
+        # NOTE: Check subclasses before parent classes since isinstance()
+        # returns True for subclasses.
+        if isinstance(one_spec, SlidingWindowMLASpec):
+            # SlidingWindowMLASpec is uniform if all specs are SlidingWindowMLASpec
+            # with the same sliding_window size.
+            return all(
+                isinstance(spec, SlidingWindowMLASpec)
+                and spec.sliding_window == one_spec.sliding_window
+                for spec in kv_cache_specs.values()
+            )
+        elif isinstance(one_spec, FullAttentionSpec):
             return all(
                 isinstance(spec, FullAttentionSpec) for spec in kv_cache_specs.values()
             )
@@ -507,6 +715,21 @@ def from_specs(cls, kv_cache_specs: dict[str, KVCacheSpec]) -> Self | None:
         else:
             return None
 
+    # NOTE: below util functions are only used by DeepseekV4 for now.
+    def get_page_sizes(self) -> list[int]:
+        return list(set(spec.page_size_bytes for spec in self.kv_cache_specs.values()))
+
+    def get_num_layer_tuples(self) -> int:
+        return Counter(
+            spec.page_size_bytes for spec in self.kv_cache_specs.values()
+        ).most_common(1)[0][1]
+
+    def max_memory_usage_pages(self, vllm_config: VllmConfig) -> int:
+        return max(
+            cdiv(spec.max_memory_usage_bytes(vllm_config), spec.page_size_bytes)
+            for spec in self.kv_cache_specs.values()
+        )
+
 
 @dataclass
 class KVCacheTensor:
@@ -529,6 +752,8 @@ class KVCacheGroupSpec:
     layer_names: list[str]
     # The KV cache spec of this manager layer
     kv_cache_spec: KVCacheSpec
+    # Whether this group contains EAGLE/MTP draft attention layers.
+    is_eagle_group: bool = False
 
 
 @dataclass
diff --git a/vllm/v1/kv_offload/abstract.py b/vllm/v1/kv_offload/abstract.py
deleted file mode 100644
index 5b3278403a01..000000000000
--- a/vllm/v1/kv_offload/abstract.py
+++ /dev/null
@@ -1,184 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-OffloadingManager class for managing KV data offloading in vLLM v1
-
-This class runs in the scheduler, tracks which blocks are offloaded
-and their address.
-
-The class provides the following primitives:
-    lookup() - find the length of the maximal series of blocks,
-        starting from the first one, that are all offloaded.
-    prepare_load() - prepare given blocks to be read.
-        The given blocks will be protected from eviction.
-        This function returns a LoadSpec which encapsulates
-        information required for performing the load.
-    touch() - marks the give blocks as recently used. Can be used
-        to track block's LRU. This function is separated from the
-        prepare_load function to allow setting block recency even
-        for blocks which do not need reading from the cache, such as
-        blocks that are cached by the GPU prefix cache.
-    complete_load() - mark blocks which were previously prepared to be
-        loaded as done loading. This is to re-allow their eviction.
-    prepare_store() - prepare the given blocks to be written.
-        Returns a StoreSpec encapsulating offloading information,
-        as well as a list of blocks that were evicted as a result.
-    complete_store() - marks a previous store as completed.
-        Following this call, the given blocks will become loadable.
-"""
-
-from abc import ABC, abstractmethod
-from collections.abc import Iterable
-from dataclasses import dataclass
-from typing import NewType
-
-# `OffloadKey` identifies an offloaded block. It combines a block hash with
-# its KV cache group index, encoded as raw bytes to avoid tuple GC overhead.
-# Use the helper functions below to construct / decompose keys.
-OffloadKey = NewType("OffloadKey", bytes)
-
-
-def make_offload_key(block_hash: bytes, group_idx: int) -> OffloadKey:
-    """Pack a block hash and group index into an `OffloadKey`."""
-    return OffloadKey(block_hash + group_idx.to_bytes(4, "big", signed=False))
-
-
-def get_offload_block_hash(key: OffloadKey) -> bytes:
-    """Extract the block hash from an `OffloadKey`."""
-    return key[:-4]
-
-
-def get_offload_group_idx(key: OffloadKey) -> int:
-    """Extract the group index from an `OffloadKey`."""
-    return int.from_bytes(key[-4:], "big", signed=False)
-
-
-class LoadStoreSpec(ABC):
-    """
-    Abstract metadata that encapsulates information allowing a worker
-    to load, and optionally also to store, blocks of KV data.
-    """
-
-    @staticmethod
-    @abstractmethod
-    def medium() -> str:
-        """
-        Returns a string representation of the medium type
-        this store/load targets.
-        """
-        pass
-
-
-@dataclass
-class PrepareStoreOutput:
-    keys_to_store: list[OffloadKey]
-    store_spec: LoadStoreSpec
-    evicted_keys: list[OffloadKey]
-
-
-@dataclass
-class OffloadingEvent:
-    keys: list[OffloadKey]
-    block_size: int
-    medium: str
-    # True if blocks are removed, False if stored
-    removed: bool
-
-
-class OffloadingManager(ABC):
-    @abstractmethod
-    def lookup(self, keys: Iterable[OffloadKey]) -> int | None:
-        """
-        Finds the length of the maximal series of blocks, starting from the
-        first one, that are all offloaded.
-
-        Args:
-            keys: the keys identifying the blocks to lookup.
-
-        Returns:
-            An integer representing the maximal number of blocks that
-            are currently offloaded, or None if the lookup should be retried
-            later. Returning None will delay the request handling by the vLLM
-            scheduler.
-        """
-        pass
-
-    @abstractmethod
-    def prepare_load(self, keys: Iterable[OffloadKey]) -> LoadStoreSpec:
-        """
-        Prepare the given blocks to be read.
-        The given blocks will be protected from eviction until
-        complete_load is called.
-        It assumes all given blocks are offloaded.
-
-        Args:
-            keys: the keys identifying the blocks.
-
-        Returns:
-            A LoadStoreSpec that can be used by a worker to locate and load
-            the actual offloaded KV data.
-        """
-        pass
-
-    def touch(self, keys: Iterable[OffloadKey]):
-        """
-        Mark the given blocks as recently used.
-        This could in practice mean moving them to the end of an LRU list.
-
-        Args:
-            keys: the keys identifying the blocks.
-        """
-        return
-
-    def complete_load(self, keys: Iterable[OffloadKey]):
-        """
-        Marks previous blocks that were prepared to load as done loading.
-
-        Args:
-            keys: the keys identifying the blocks.
-        """
-        return
-
-    @abstractmethod
-    def prepare_store(self, keys: Iterable[OffloadKey]) -> PrepareStoreOutput | None:
-        """
-        Prepare the given blocks to be offloaded.
-        The given blocks will be protected from eviction until
-        complete_store is called.
-
-        Args:
-            keys: the keys identifying the blocks.
-
-        Returns:
-            A PrepareStoreOutput indicating which blocks need storing,
-            where to store them (LoadStoreSpec), and list of blocks that
-            were evicted as a result.
-            None is returned if the blocks cannot be stored.
-        """
-        pass
-
-    def complete_store(self, keys: Iterable[OffloadKey], success: bool = True):
-        """
-        Marks blocks which were previously prepared to be stored, as stored.
-        Following this call, the blocks become loadable.
-        If if_success is False, blocks that were not marked as stored will be
-        removed.
-
-        Args:
-            keys: the keys identifying the blocks.
-            success: whether the blocks were stored successfully.
-        """
-        return
-
-    def take_events(self) -> Iterable[OffloadingEvent]:
-        """
-        Take the offloading events from the manager.
-
-        Yields:
-            New OffloadingEvents collected since the last call.
-        """
-        return ()
-
-    def shutdown(self) -> None:
-        """Shutdown the manager and release any resources."""
-        return
diff --git a/vllm/v1/kv_offload/base.py b/vllm/v1/kv_offload/base.py
new file mode 100644
index 000000000000..fed47cf5993a
--- /dev/null
+++ b/vllm/v1/kv_offload/base.py
@@ -0,0 +1,406 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Core abstractions for KV cache offloading in vLLM v1.
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from collections.abc import Collection, Iterable, Iterator, Sequence
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, NewType
+
+import numpy as np
+import torch
+
+from vllm.logger import init_logger
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+    from vllm.v1.kv_cache_interface import KVCacheConfig
+    from vllm.v1.kv_offload.worker.worker import OffloadingHandler
+
+# `OffloadKey` identifies an offloaded block. It combines a block hash with
+# its KV cache group index, encoded as raw bytes to avoid tuple GC overhead.
+# Use the helper functions below to construct / decompose keys.
+OffloadKey = NewType("OffloadKey", bytes)
+
+logger = init_logger(__name__)
+
+
+def make_offload_key(block_hash: bytes, group_idx: int) -> OffloadKey:
+    """Pack a block hash and group index into an `OffloadKey`."""
+    return OffloadKey(block_hash + group_idx.to_bytes(4, "big", signed=False))
+
+
+def get_offload_block_hash(key: OffloadKey) -> bytes:
+    """Extract the block hash from an `OffloadKey`."""
+    return key[:-4]
+
+
+def get_offload_group_idx(key: OffloadKey) -> int:
+    """Extract the group index from an `OffloadKey`."""
+    return int.from_bytes(key[-4:], "big", signed=False)
+
+
+@dataclass
+class ReqContext:
+    kv_transfer_params: dict[str, Any] | None = None
+
+
+class LoadStoreSpec(ABC):
+    """
+    Abstract metadata that encapsulates information allowing a worker
+    to load, and optionally also to store, blocks of KV data.
+    """
+
+    @staticmethod
+    @abstractmethod
+    def medium() -> str:
+        """
+        Returns a string representation of the medium type
+        this store/load targets.
+        """
+        pass
+
+
+@dataclass
+class PrepareStoreOutput:
+    keys_to_store: list[OffloadKey]
+    store_spec: LoadStoreSpec
+    evicted_keys: list[OffloadKey]
+
+
+@dataclass
+class OffloadingEvent:
+    keys: list[OffloadKey]
+    medium: str
+    # True if blocks are removed, False if stored
+    removed: bool
+
+
+"""
+OffloadingManager class for managing KV data offloading in vLLM v1
+
+This class runs in the scheduler, tracks which blocks are offloaded
+and their address.
+
+The class provides the following primitives:
+    lookup() - check whether a single block is offloaded and ready.
+    prepare_load() - prepare given blocks to be read.
+        The given blocks will be protected from eviction.
+        This function returns a LoadSpec which encapsulates
+        information required for performing the load.
+    touch() - marks the give blocks as recently used. Can be used
+        to track block's LRU. This function is separated from the
+        prepare_load function to allow setting block recency even
+        for blocks which do not need reading from the cache, such as
+        blocks that are cached by the GPU prefix cache.
+    complete_load() - mark blocks which were previously prepared to be
+        loaded as done loading. This is to re-allow their eviction.
+    prepare_store() - prepare the given blocks to be written.
+        Returns a StoreSpec encapsulating offloading information,
+        as well as a list of blocks that were evicted as a result.
+    complete_store() - marks a previous store as completed.
+        Following this call, the given blocks will become loadable.
+"""
+
+
+class OffloadingManager(ABC):
+    @abstractmethod
+    def lookup(self, key: OffloadKey, req_context: ReqContext) -> bool | None:
+        """
+        Checks whether a single block is offloaded and ready to be read.
+
+        Args:
+            key: the key identifying the block to lookup.
+            req_context: per-request context (e.g. kv_transfer_params).
+
+        Returns:
+            True if the block is offloaded and ready, False if not,
+            or None if the lookup should be retried later.
+            Returning None will delay the request handling by the vLLM
+            scheduler.
+        """
+        pass
+
+    @abstractmethod
+    def prepare_load(
+        self,
+        keys: Collection[OffloadKey],
+        req_context: ReqContext,
+    ) -> LoadStoreSpec:
+        """
+        Prepare the given blocks to be read.
+        The given blocks will be protected from eviction until
+        complete_load is called.
+        It assumes all given blocks are offloaded.
+
+        Args:
+            keys: the keys identifying the blocks.
+            req_context: per-request context (e.g. kv_transfer_params).
+
+        Returns:
+            A LoadStoreSpec that can be used by a worker to locate and load
+            the actual offloaded KV data.
+        """
+        pass
+
+    def touch(self, keys: Collection[OffloadKey], req_context: ReqContext):
+        """
+        Mark the given blocks as recently used.
+        This could in practice mean moving them to the end of an LRU list.
+
+        Args:
+            keys: the keys identifying the blocks.
+            req_context: per-request context (e.g. kv_transfer_params).
+        """
+        return
+
+    def complete_load(self, keys: Collection[OffloadKey], req_context: ReqContext):
+        """
+        Marks previous blocks that were prepared to load as done loading.
+
+        Args:
+            keys: the keys identifying the blocks.
+            req_context: per-request context (e.g. kv_transfer_params).
+        """
+        return
+
+    @abstractmethod
+    def prepare_store(
+        self,
+        keys: Collection[OffloadKey],
+        req_context: ReqContext,
+    ) -> PrepareStoreOutput | None:
+        """
+        Prepare the given blocks to be offloaded.
+        The given blocks will be protected from eviction until
+        complete_store is called.
+
+        Args:
+            keys: the keys identifying the blocks.
+            req_context: per-request context (e.g. kv_transfer_params).
+
+        Returns:
+            A PrepareStoreOutput indicating which blocks need storing,
+            where to store them (LoadStoreSpec), and list of blocks that
+            were evicted as a result.
+            None is returned if the blocks cannot be stored.
+        """
+        pass
+
+    def complete_store(
+        self,
+        keys: Collection[OffloadKey],
+        req_context: ReqContext,
+        success: bool = True,
+    ):
+        """
+        Marks blocks which were previously prepared to be stored, as stored.
+        Following this call, the blocks become loadable.
+        If if_success is False, blocks that were not marked as stored will be
+        removed.
+
+        Args:
+            keys: the keys identifying the blocks.
+            req_context: per-request context (e.g. kv_transfer_params).
+            success: whether the blocks were stored successfully.
+        """
+        return
+
+    def take_events(self) -> Iterable[OffloadingEvent]:
+        """
+        Take the offloading events from the manager.
+
+        Yields:
+            New OffloadingEvents collected since the last call.
+        """
+        return ()
+
+    def shutdown(self) -> None:
+        """Shutdown the manager and release any resources."""
+        return
+
+
+class BlockIDsLoadStoreSpec(LoadStoreSpec, ABC):
+    """
+    Spec for loading/storing KV blocks from given block numbers.
+    """
+
+    def __init__(self, block_ids: list[int]):
+        self.block_ids = np.array(block_ids, dtype=np.int64)
+
+    def __repr__(self) -> str:
+        return repr(self.block_ids)
+
+
+class GPULoadStoreSpec(BlockIDsLoadStoreSpec):
+    """
+    Spec for loading/storing a KV block to GPU memory.
+
+    If there are multiple KV groups, the blocks are expected to be
+    ordered by the group index.
+    In that case, group_sizes[i] determines the number of blocks
+    per the i-th KV group, and thus sum(group_sizes) == len(block_ids).
+    group_sizes=None indicates a single KV group.
+
+    If block_indices is given, each group (determined by group_sizes) of block IDs
+    will correspond to logically contiguous blocks, e.g. blocks 5-10 of a some request.
+    block_indices[i] will represent the block index of the first block in group #i.
+    Thus, len(block_indices) == len(group_sizes) = number of KV cache groups.
+    This information is required in order to support off/loading from offloaded blocks
+    which are larger than GPU blocks.
+    In such cases, the first GPU block per each group may be unaligned to the offloaded
+    block size, and so knowing block_indices[i] allows the worker to correctly
+    skip part of the first matching offloaded block.
+    """
+
+    def __init__(
+        self,
+        block_ids: list[int],
+        group_sizes: Sequence[int],
+        block_indices: Sequence[int],
+    ):
+        super().__init__(block_ids)
+        assert sum(group_sizes) == len(block_ids)
+        assert len(block_indices) == len(group_sizes)
+        self.group_sizes: Sequence[int] = group_sizes
+        self.block_indices: Sequence[int] = block_indices
+
+    @staticmethod
+    def medium() -> str:
+        return "GPU"
+
+
+@dataclass
+class CanonicalKVCacheTensor:
+    """
+    A canonicalized KV cache tensor whose first dimension is num_blocks.
+
+    For attention backends where the raw tensor has num_blocks at a
+    non-leading physical dimension (e.g. FlashAttention's
+    (2, num_blocks, ...) layout), the tensor is split so that each
+    resulting CanonicalKVCacheTensor starts with (num_blocks, ...).
+    """
+
+    # The KV cache tensor with shape (num_blocks, ...)
+    tensor: torch.Tensor
+    # The (possibly padded) page size per block in bytes
+    page_size_bytes: int
+
+
+@dataclass
+class CanonicalKVCacheRef:
+    """
+    Per-layer (or group of layers) reference to a specific (by index)
+    CanonicalKVCacheTensor and records the un-padded page size used by that layer.
+    """
+
+    # Index into the list of CanonicalKVCacheTensor objects
+    tensor_idx: int
+    # The un-padded page size per block in bytes
+    page_size_bytes: int
+
+
+@dataclass
+class CanonicalKVCaches:
+    """
+    Canonicalized block-level representation of the KV caches.
+
+    Composed of:
+        - Unique list of KV cache data tensors,
+          each with shape (num_blocks, page_size_in_bytes) and int8 dtype.
+        - Per-group data references of the tensors.
+          i.e. how each KV cache group maps to the tensors.
+    """
+
+    # Ordered list of unique block tensors, each with shape
+    # (num_blocks, ...).
+    tensors: list[CanonicalKVCacheTensor]
+    # Per-KV-cache-group list of data references that map each layer
+    # in the group to the appropriate entry in the tensors list.
+    group_data_refs: list[list[CanonicalKVCacheRef]]
+
+
+class OffloadingSpec(ABC):
+    """Spec for an offloading connector"""
+
+    def __init__(self, vllm_config: VllmConfig, kv_cache_config: KVCacheConfig):
+        logger.warning(
+            "Initializing OffloadingSpec. This API is experimental and "
+            "subject to change in the future as we iterate the design."
+        )
+        self.vllm_config = vllm_config
+        self.kv_cache_config = kv_cache_config
+
+        kv_transfer_config = vllm_config.kv_transfer_config
+        assert kv_transfer_config is not None
+        self.extra_config = kv_transfer_config.kv_connector_extra_config
+
+        parallel_config = vllm_config.parallel_config
+        context_parallel_factor = (
+            parallel_config.decode_context_parallel_size
+            * parallel_config.prefill_context_parallel_size
+        )
+
+        # block size used by vLLM for hashing request tokens for the sake
+        # of enabling prefix caching
+        self.hash_block_size = (
+            vllm_config.cache_config.block_size * context_parallel_factor
+        )
+        # gpu block size per group
+        self.gpu_block_size: tuple[int, ...] = tuple(
+            kv_cache_group.kv_cache_spec.block_size * context_parallel_factor
+            for kv_cache_group in kv_cache_config.kv_cache_groups
+        )
+
+        for block_size in self.gpu_block_size:
+            assert block_size % self.hash_block_size == 0, (
+                f"gpu_block_size={block_size} not divisible by "
+                f"hash_block_size={self.hash_block_size}. "
+                f"Hybrid models (e.g. Mamba+Attention) need "
+                f"--enable-prefix-caching to align block sizes."
+            )
+
+        # offloaded_block_size / gpu_block_size
+        self.block_size_factor: int = 1
+
+        offloaded_block_size = self.extra_config.get("block_size")
+        if offloaded_block_size is not None:
+            offloaded_block_size_int = int(offloaded_block_size)
+            gpu_block_sizes = set(self.gpu_block_size)
+            assert len(gpu_block_sizes) == 1, (
+                "If 'block_size' is specified in kv_connector_extra_config, "
+                "there must be at least one KV cache group, "
+                "and all groups must have the same block size."
+            )
+            gpu_block_size = gpu_block_sizes.pop()
+
+            assert offloaded_block_size_int % gpu_block_size == 0
+            self.block_size_factor = offloaded_block_size_int // gpu_block_size
+
+    @abstractmethod
+    def get_manager(self) -> OffloadingManager:
+        """
+        Get an OffloadingManager that will be used
+        by the scheduler-side offloading connector to track
+        offloaded blocks and manage evictions.
+        """
+        pass
+
+    @abstractmethod
+    def get_handlers(
+        self, kv_caches: CanonicalKVCaches
+    ) -> Iterator[tuple[type[LoadStoreSpec], type[LoadStoreSpec], OffloadingHandler]]:
+        """
+        Get offloading handlers along with their respective src and dst types.
+
+        Args:
+            kv_caches: Canonicalized KV caches.
+
+        Yields:
+            Tuples of (src_type, dst_type, offloading_handler).
+        """
+        pass
diff --git a/vllm/v1/kv_offload/cpu/common.py b/vllm/v1/kv_offload/cpu/common.py
new file mode 100644
index 000000000000..cf5b2b39dd6b
--- /dev/null
+++ b/vllm/v1/kv_offload/cpu/common.py
@@ -0,0 +1,13 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from vllm.v1.kv_offload.base import BlockIDsLoadStoreSpec
+
+
+class CPULoadStoreSpec(BlockIDsLoadStoreSpec):
+    """
+    Spec for loading/storing a KV block to CPU memory.
+    """
+
+    @staticmethod
+    def medium() -> str:
+        return "CPU"
diff --git a/vllm/v1/kv_offload/worker/cpu_gpu.py b/vllm/v1/kv_offload/cpu/gpu_worker.py
similarity index 69%
rename from vllm/v1/kv_offload/worker/cpu_gpu.py
rename to vllm/v1/kv_offload/cpu/gpu_worker.py
index dd12a533ede9..2da3f1038e96 100644
--- a/vllm/v1/kv_offload/worker/cpu_gpu.py
+++ b/vllm/v1/kv_offload/cpu/gpu_worker.py
@@ -9,10 +9,15 @@
 
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
+from vllm.utils.math_utils import cdiv
 from vllm.utils.platform_utils import is_pin_memory_available
+from vllm.v1.kv_offload.base import (
+    BlockIDsLoadStoreSpec,
+    CanonicalKVCacheRef,
+    CanonicalKVCaches,
+    GPULoadStoreSpec,
+)
 from vllm.v1.kv_offload.cpu.shared_offload_region import SharedOffloadRegion
-from vllm.v1.kv_offload.mediums import BlockIDsLoadStoreSpec
-from vllm.v1.kv_offload.spec import CanonicalKVCacheRef, CanonicalKVCaches
 from vllm.v1.kv_offload.worker.worker import (
     OffloadingHandler,
     TransferResult,
@@ -135,9 +140,6 @@ def __init__(
         assert len(gpu_tensors) == len(cpu_tensors)
         assert len(gpu_tensors) > 0
 
-        # assert a single KV group until transfer_async supports multiple groups
-        assert len(kv_cache_groups_data_refs) == 1
-
         # assert input tensors are as expected
         for gpu_tensor, cpu_tensor in zip(gpu_tensors, cpu_tensors):
             assert gpu_tensor.dtype == torch.int8
@@ -157,29 +159,13 @@ def __init__(
             cpu_tensors if gpu_to_cpu else gpu_tensors
         )
         self.gpu_to_cpu: bool = gpu_to_cpu
+        self.kv_cache_groups_data_refs = kv_cache_groups_data_refs
 
         # GPU blocks may be smaller
         # cpu_page_size = gpu_page_size * block_size_factor.
         self.src_block_size_factor = 1 if self.gpu_to_cpu else block_size_factor
         self.dst_block_size_factor = block_size_factor if self.gpu_to_cpu else 1
 
-        # per-tensor block size in byte
-        self.tensor_block_size_in_bytes = [
-            gpu_tensor.shape[1] for gpu_tensor in gpu_tensors
-        ]
-
-        # per-group block size in bytes
-        self.group_block_size_in_bytes = []
-        for kv_cache_group_data_refs in kv_cache_groups_data_refs:
-            group_block_size_in_bytes = 0
-            for kv_cache_data_ref in kv_cache_group_data_refs:
-                # TODO(orozery): use kv_cache_data_ref.page_size_bytes
-                # once swap_blocks support it
-                group_block_size_in_bytes += self.tensor_block_size_in_bytes[
-                    kv_cache_data_ref.tensor_idx
-                ]
-            self.group_block_size_in_bytes.append(group_block_size_in_bytes)
-
         self.transfer_type = ("GPU", "CPU") if self.gpu_to_cpu else ("CPU", "GPU")
         # job_id -> event
         self._transfer_events: dict[int, torch.Event] = {}
@@ -190,11 +176,6 @@ def __init__(
         # list of CUDA events available for re-use
         self._event_pool: list[torch.Event] = []
 
-        # Pre-compute block sizes for batch copies.
-        self._block_size_in_bytes_arr = np.array(
-            self.tensor_block_size_in_bytes, dtype=np.int64
-        )
-
     def transfer_async(self, job_id: int, transfer_spec: TransferSpec) -> bool:
         src_spec, dst_spec = transfer_spec
         assert isinstance(src_spec, BlockIDsLoadStoreSpec)
@@ -205,37 +186,108 @@ def transfer_async(self, job_id: int, transfer_spec: TransferSpec) -> bool:
         assert src_blocks.ndim == 1
         assert dst_blocks.ndim == 1
 
-        src_sub_block_count = src_blocks.size * self.src_block_size_factor
-        dst_sub_block_count = dst_blocks.size * self.dst_block_size_factor
-        src_sub_blocks_to_skip = -dst_blocks.size % self.src_block_size_factor
-
-        assert dst_sub_block_count == src_sub_block_count - src_sub_blocks_to_skip
-
-        num_pairs = dst_sub_block_count
-        num_tensors = len(self.src_tensors)
-        total = num_pairs * num_tensors
-
-        all_src = np.empty(total, dtype=np.int64)
-        all_dst = np.empty(total, dtype=np.int64)
-        all_sizes = np.empty(total, dtype=np.int64)
-
-        for t_idx, bsz in enumerate(self._block_size_in_bytes_arr):
-            start = t_idx * num_pairs
-            end = start + num_pairs
-            compute_sub_block_ptrs(
-                block_ids=src_blocks,
-                block_size_factor=self.src_block_size_factor,
-                output=all_src[start:end],
-                tensor=self.src_tensors[t_idx],
-                skip_count=src_sub_blocks_to_skip,
+        num_src_blocks = len(src_blocks)
+        num_dst_blocks = len(dst_blocks)
+
+        # There are 2 types of transfers:
+        # 1. GPU -> CPU
+        # 2. CPU -> GPU
+        #
+        # transfers are also to CPU blocks, EXCEPT MAYBE for the first and last block.
+        # i.e. the first and last CPU blocks in src_blocks can match against
+        # a smaller (byte-wise) set of GPU blocks in dst_blocks.
+        # In such cases, we may need to skip some gpu-sized sub-blocks,
+        # and start reading/writing from the middle of the first CPU block.
+        # If we have multiple KV cache groups (when using HMA with hybrid models),
+        # we may have a partial first/last CPU block per each group.
+        # The group_sizes parameter encodes the size of each group of blocks
+        # in the GPU dst_blocks.
+        # If group_sizes is None, we assume all blocks belong to a single group.
+        # The logical_offset parameter maps each group of blocks to its logical
+        # offset inside the request, counting in GPU blocks.
+        # This allows us to find the correct starting position
+        # in the matching first CPU block.
+
+        # extract group_sizes from the GPU spec
+        gpu_spec = src_spec if self.gpu_to_cpu else dst_spec
+        assert isinstance(gpu_spec, GPULoadStoreSpec)
+        group_sizes = gpu_spec.group_sizes
+        assert len(group_sizes) == len(self.kv_cache_groups_data_refs)
+
+        # extract block indices from the GPU spec
+        block_indices = gpu_spec.block_indices
+        assert len(block_indices) == len(self.kv_cache_groups_data_refs)
+
+        num_copy_ops = 0
+        for group_size, group_data_refs in zip(
+            group_sizes, self.kv_cache_groups_data_refs
+        ):
+            num_copy_ops += group_size * len(group_data_refs)
+
+        all_src = np.empty(num_copy_ops, dtype=np.int64)
+        all_dst = np.empty(num_copy_ops, dtype=np.int64)
+        all_sizes = np.empty(num_copy_ops, dtype=np.int64)
+
+        src_offset = 0
+        dst_offset = 0
+        op_idx = 0
+        # count total number of bytes copied
+        num_transfer_bytes = 0
+        for group_size, block_idx, group_data_refs in zip(
+            group_sizes, block_indices, self.kv_cache_groups_data_refs
+        ):
+            if group_size == 0:
+                continue
+
+            src_logical_blocks_to_skip = block_idx % self.src_block_size_factor
+            dst_logical_blocks_to_skip = block_idx % self.dst_block_size_factor
+            src_logical_blocks_count = group_size + src_logical_blocks_to_skip
+            dst_logical_blocks_count = group_size + dst_logical_blocks_to_skip
+
+            dst_blocks_count = cdiv(
+                dst_logical_blocks_count, self.dst_block_size_factor
             )
-            compute_sub_block_ptrs(
-                block_ids=dst_blocks,
-                block_size_factor=self.dst_block_size_factor,
-                output=all_dst[start:end],
-                tensor=self.dst_tensors[t_idx],
+            dst_end_offset = dst_offset + dst_blocks_count
+            assert dst_end_offset <= num_dst_blocks
+
+            src_blocks_count = cdiv(
+                src_logical_blocks_count, self.src_block_size_factor
             )
-            all_sizes[start:end] = bsz
+            src_end_offset = src_offset + src_blocks_count
+            assert src_end_offset <= num_src_blocks
+
+            group_src = src_blocks[src_offset:src_end_offset]
+            group_dst = dst_blocks[dst_offset:dst_end_offset]
+
+            for data_ref in group_data_refs:
+                t_idx = data_ref.tensor_idx
+                end_idx = op_idx + group_size
+
+                compute_sub_block_ptrs(
+                    group_src,
+                    self.src_block_size_factor,
+                    all_src[op_idx:end_idx],
+                    self.src_tensors[t_idx],
+                    skip_count=src_logical_blocks_to_skip,
+                )
+                compute_sub_block_ptrs(
+                    group_dst,
+                    self.dst_block_size_factor,
+                    all_dst[op_idx:end_idx],
+                    self.dst_tensors[t_idx],
+                    skip_count=dst_logical_blocks_to_skip,
+                )
+
+                all_sizes[op_idx:end_idx] = data_ref.page_size_bytes
+                num_transfer_bytes += group_size * data_ref.page_size_bytes
+                op_idx = end_idx
+
+            src_offset = src_end_offset
+            dst_offset = dst_end_offset
+
+        assert src_offset == num_src_blocks
+        assert dst_offset == num_dst_blocks
+        assert op_idx == num_copy_ops
 
         batch_src = torch.from_numpy(all_src)
         batch_dst = torch.from_numpy(all_dst)
@@ -261,10 +313,22 @@ def transfer_async(self, job_id: int, transfer_spec: TransferSpec) -> bool:
             last_event = last_transfer.end_event
             # assure job will start only after the previous one completes
             stream.wait_event(last_event)
+        # CPU->GPU reads from host pinned memory, which is never written
+        # by a concurrent GPU stream, so CU_MEMCPY_SRC_ACCESS_ORDER_ANY is
+        # safe and lets the driver pipeline source reads. GPU->CPU reads
+        # from the live GPU KV cache, which the compute stream keeps
+        # writing; we must keep STREAM ordering so source reads are gated
+        # by the transfer stream's wait_stream(compute) barrier.
+        is_src_access_order_any = not self.gpu_to_cpu
         with torch.cuda.stream(stream):
             start_event.record(stream)
-            if total > 0:
-                ops.swap_blocks_batch(batch_src, batch_dst, batch_sizes)
+            if num_copy_ops > 0:
+                ops.swap_blocks_batch(
+                    batch_src,
+                    batch_dst,
+                    batch_sizes,
+                    is_src_access_order_any=is_src_access_order_any,
+                )
             end_event.record(stream)
 
         self._transfer_events[job_id] = end_event
@@ -274,7 +338,7 @@ def transfer_async(self, job_id: int, transfer_spec: TransferSpec) -> bool:
                 stream=stream,
                 start_event=start_event,
                 end_event=end_event,
-                num_bytes=dst_sub_block_count * self.group_block_size_in_bytes[0],
+                num_bytes=num_transfer_bytes,
             )
         )
 
diff --git a/vllm/v1/kv_offload/cpu/manager.py b/vllm/v1/kv_offload/cpu/manager.py
index 05f97df5b100..dc501d745683 100644
--- a/vllm/v1/kv_offload/cpu/manager.py
+++ b/vllm/v1/kv_offload/cpu/manager.py
@@ -1,19 +1,20 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from collections.abc import Iterable
+from collections.abc import Collection, Iterable
 from typing import Literal
 
-from vllm.v1.kv_offload.abstract import (
+from vllm.v1.kv_offload.base import (
     LoadStoreSpec,
     OffloadingEvent,
     OffloadingManager,
     OffloadKey,
     PrepareStoreOutput,
+    ReqContext,
 )
-from vllm.v1.kv_offload.cpu.policies.abstract import BlockStatus, CachePolicy
+from vllm.v1.kv_offload.cpu.common import CPULoadStoreSpec
 from vllm.v1.kv_offload.cpu.policies.arc import ARCCachePolicy
+from vllm.v1.kv_offload.cpu.policies.base import BlockStatus, CachePolicy
 from vllm.v1.kv_offload.cpu.policies.lru import LRUCachePolicy
-from vllm.v1.kv_offload.mediums import CPULoadStoreSpec
 
 _CACHE_POLICIES: dict[str, type[CachePolicy]] = {
     "lru": LRUCachePolicy,
@@ -33,12 +34,10 @@ class CPUOffloadingManager(OffloadingManager):
 
     def __init__(
         self,
-        block_size: int,
         num_blocks: int,
         cache_policy: Literal["lru", "arc"] = "lru",
         enable_events: bool = False,
     ):
-        self.block_size: int = block_size
         self.medium: str = CPULoadStoreSpec.medium()
         self._num_blocks: int = num_blocks
         self._num_allocated_blocks: int = 0
@@ -85,16 +84,19 @@ def _get_load_store_spec(
 
     # --- OffloadingManager interface ---
 
-    def lookup(self, keys: Iterable[OffloadKey]) -> int | None:
-        hit_count = 0
-        for key in keys:
-            block = self._policy.get(key)
-            if block is None or not block.is_ready:
-                break
-            hit_count += 1
-        return hit_count
+    def lookup(self, key: OffloadKey, req_context: ReqContext) -> bool | None:
+        block = self._policy.get(key)
+        if block is None:
+            return False
+        if not block.is_ready:
+            return None  # write in-flight; caller should retry
+        return True
 
-    def prepare_load(self, keys: Iterable[OffloadKey]) -> LoadStoreSpec:
+    def prepare_load(
+        self,
+        keys: Collection[OffloadKey],
+        req_context: ReqContext,
+    ) -> LoadStoreSpec:
         blocks = []
         for key in keys:
             block = self._policy.get(key)
@@ -104,21 +106,25 @@ def prepare_load(self, keys: Iterable[OffloadKey]) -> LoadStoreSpec:
             blocks.append(block)
         return self._get_load_store_spec(keys, blocks)
 
-    def touch(self, keys: Iterable[OffloadKey]) -> None:
+    def touch(self, keys: Collection[OffloadKey], req_context: ReqContext) -> None:
         self._policy.touch(keys)
 
-    def complete_load(self, keys: Iterable[OffloadKey]) -> None:
+    def complete_load(
+        self, keys: Collection[OffloadKey], req_context: ReqContext
+    ) -> None:
         for key in keys:
             block = self._policy.get(key)
             assert block is not None, f"Block {key!r} not found"
             assert block.ref_cnt > 0, f"Block {key!r} ref_cnt is already 0"
             block.ref_cnt -= 1
 
-    def prepare_store(self, keys: Iterable[OffloadKey]) -> PrepareStoreOutput | None:
-        keys_list = list(keys)
-
+    def prepare_store(
+        self,
+        keys: Collection[OffloadKey],
+        req_context: ReqContext,
+    ) -> PrepareStoreOutput | None:
         # filter out blocks that are already stored
-        keys_to_store = [k for k in keys_list if self._policy.get(k) is None]
+        keys_to_store = [k for k in keys if self._policy.get(k) is None]
 
         if not keys_to_store:
             return PrepareStoreOutput(
@@ -133,7 +139,7 @@ def prepare_store(self, keys: Iterable[OffloadKey]) -> PrepareStoreOutput | None
         if num_blocks_to_evict > 0:
             # Blocks from the original input are excluded from eviction candidates:
             # a block that was already stored must remain in the cache after this call.
-            protected = set(keys_list)
+            protected = set(keys)
             evicted = self._policy.evict(num_blocks_to_evict, protected)
             if evicted is None:
                 return None
@@ -145,7 +151,6 @@ def prepare_store(self, keys: Iterable[OffloadKey]) -> PrepareStoreOutput | None
             self.events.append(
                 OffloadingEvent(
                     keys=to_evict,
-                    block_size=self.block_size,
                     medium=self.medium,
                     removed=True,
                 )
@@ -168,7 +173,12 @@ def prepare_store(self, keys: Iterable[OffloadKey]) -> PrepareStoreOutput | None
             evicted_keys=to_evict,
         )
 
-    def complete_store(self, keys: Iterable[OffloadKey], success: bool = True) -> None:
+    def complete_store(
+        self,
+        keys: Collection[OffloadKey],
+        req_context: ReqContext,
+        success: bool = True,
+    ) -> None:
         stored_keys: list[OffloadKey] = []
 
         if success:
@@ -188,7 +198,6 @@ def complete_store(self, keys: Iterable[OffloadKey], success: bool = True) -> No
             self.events.append(
                 OffloadingEvent(
                     keys=stored_keys,
-                    block_size=self.block_size,
                     medium=self.medium,
                     removed=False,
                 )
diff --git a/vllm/v1/kv_offload/cpu/policies/arc.py b/vllm/v1/kv_offload/cpu/policies/arc.py
index 07dfa4af4b30..e2af991c0cc0 100644
--- a/vllm/v1/kv_offload/cpu/policies/arc.py
+++ b/vllm/v1/kv_offload/cpu/policies/arc.py
@@ -3,8 +3,8 @@
 from collections import OrderedDict
 from collections.abc import Iterable
 
-from vllm.v1.kv_offload.abstract import OffloadKey
-from vllm.v1.kv_offload.cpu.policies.abstract import BlockStatus, CachePolicy
+from vllm.v1.kv_offload.base import OffloadKey
+from vllm.v1.kv_offload.cpu.policies.base import BlockStatus, CachePolicy
 
 
 class ARCCachePolicy(CachePolicy):
diff --git a/vllm/v1/kv_offload/cpu/policies/abstract.py b/vllm/v1/kv_offload/cpu/policies/base.py
similarity index 97%
rename from vllm/v1/kv_offload/cpu/policies/abstract.py
rename to vllm/v1/kv_offload/cpu/policies/base.py
index 181df36baa06..ee4916956e65 100644
--- a/vllm/v1/kv_offload/cpu/policies/abstract.py
+++ b/vllm/v1/kv_offload/cpu/policies/base.py
@@ -4,7 +4,7 @@
 from abc import ABC, abstractmethod
 from collections.abc import Iterable
 
-from vllm.v1.kv_offload.abstract import OffloadKey
+from vllm.v1.kv_offload.base import OffloadKey
 
 
 class BlockStatus(ctypes.Structure):
diff --git a/vllm/v1/kv_offload/cpu/policies/lru.py b/vllm/v1/kv_offload/cpu/policies/lru.py
index be04831cf5f0..bf9a8b66e657 100644
--- a/vllm/v1/kv_offload/cpu/policies/lru.py
+++ b/vllm/v1/kv_offload/cpu/policies/lru.py
@@ -3,8 +3,8 @@
 from collections import OrderedDict
 from collections.abc import Iterable
 
-from vllm.v1.kv_offload.abstract import OffloadKey
-from vllm.v1.kv_offload.cpu.policies.abstract import BlockStatus, CachePolicy
+from vllm.v1.kv_offload.base import OffloadKey
+from vllm.v1.kv_offload.cpu.policies.base import BlockStatus, CachePolicy
 
 
 class LRUCachePolicy(CachePolicy):
diff --git a/vllm/v1/kv_offload/cpu/spec.py b/vllm/v1/kv_offload/cpu/spec.py
index 4feae8cf7d5a..54046d98f452 100644
--- a/vllm/v1/kv_offload/cpu/spec.py
+++ b/vllm/v1/kv_offload/cpu/spec.py
@@ -5,12 +5,17 @@
 from vllm.config import VllmConfig
 from vllm.platforms import current_platform
 from vllm.v1.kv_cache_interface import KVCacheConfig
-from vllm.v1.kv_offload.abstract import LoadStoreSpec, OffloadingManager
+from vllm.v1.kv_offload.base import (
+    CanonicalKVCaches,
+    GPULoadStoreSpec,
+    LoadStoreSpec,
+    OffloadingManager,
+    OffloadingSpec,
+)
+from vllm.v1.kv_offload.cpu.common import CPULoadStoreSpec
+from vllm.v1.kv_offload.cpu.gpu_worker import CpuGpuOffloadingHandlers
 from vllm.v1.kv_offload.cpu.manager import CPUOffloadingManager
-from vllm.v1.kv_offload.mediums import CPULoadStoreSpec, GPULoadStoreSpec
 from vllm.v1.kv_offload.reuse_manager import FilterReusedOffloadingManager
-from vllm.v1.kv_offload.spec import CanonicalKVCaches, OffloadingSpec
-from vllm.v1.kv_offload.worker.cpu_gpu import CpuGpuOffloadingHandlers
 from vllm.v1.kv_offload.worker.worker import OffloadingHandler
 
 
@@ -26,17 +31,13 @@ def __init__(self, vllm_config: VllmConfig, kv_cache_config: KVCacheConfig):
 
         # calculate kv_bytes_per_offloaded_block
         assert kv_cache_config is not None
-        page_sizes = {
-            kv_cache_group.kv_cache_spec.page_size_bytes
-            for kv_cache_group in kv_cache_config.kv_cache_groups
-        }
-        assert len(page_sizes) == 1
-        page_size_bytes = page_sizes.pop()
-        kv_bytes_per_block = (
-            page_size_bytes
-            * len(kv_cache_config.kv_cache_tensors)
-            * vllm_config.parallel_config.world_size
-        )
+        if kv_cache_config.num_blocks > 0:
+            total_gpu_kv_bytes = sum(t.size for t in kv_cache_config.kv_cache_tensors)
+            kv_bytes_per_block = (
+                total_gpu_kv_bytes // kv_cache_config.num_blocks
+            ) * vllm_config.parallel_config.world_size
+        else:
+            kv_bytes_per_block = 0
 
         kv_bytes_per_offloaded_block = kv_bytes_per_block * self.block_size_factor
         self.num_blocks = (
@@ -60,12 +61,7 @@ def get_manager(self) -> OffloadingManager:
                 kv_events_config is not None and kv_events_config.enable_kv_cache_events
             )
 
-            assert len(self.gpu_block_size) == 1
-            gpu_block_size = self.gpu_block_size[0]
-            offloaded_block_size = gpu_block_size * self.block_size_factor
-
             self._manager = CPUOffloadingManager(
-                block_size=offloaded_block_size,
                 num_blocks=self.num_blocks,
                 cache_policy=self.eviction_policy,  # type: ignore[arg-type]
                 enable_events=enable_events,
diff --git a/vllm/v1/kv_offload/factory.py b/vllm/v1/kv_offload/factory.py
index ecbaebb0d967..ecfc270b6432 100644
--- a/vllm/v1/kv_offload/factory.py
+++ b/vllm/v1/kv_offload/factory.py
@@ -5,7 +5,7 @@
 from typing import TYPE_CHECKING
 
 from vllm.logger import init_logger
-from vllm.v1.kv_offload.spec import OffloadingSpec
+from vllm.v1.kv_offload.base import OffloadingSpec
 
 if TYPE_CHECKING:
     from vllm.config import VllmConfig
diff --git a/vllm/v1/kv_offload/mediums.py b/vllm/v1/kv_offload/mediums.py
deleted file mode 100644
index 85ef2a95a6bd..000000000000
--- a/vllm/v1/kv_offload/mediums.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from abc import ABC
-from collections.abc import Sequence
-
-import numpy as np
-
-from vllm.v1.kv_offload.abstract import LoadStoreSpec
-
-
-class BlockIDsLoadStoreSpec(LoadStoreSpec, ABC):
-    """
-    Spec for loading/storing KV blocks from given block numbers.
-    """
-
-    def __init__(self, block_ids: list[int]):
-        self.block_ids = np.array(block_ids, dtype=np.int64)
-
-    def __repr__(self) -> str:
-        return repr(self.block_ids)
-
-
-class GPULoadStoreSpec(BlockIDsLoadStoreSpec):
-    """
-    Spec for loading/storing a KV block to GPU memory.
-
-    If there are multiple KV groups, the blocks are expected to be
-    ordered by the group index.
-    In that case, group_sizes[i] determines the number of blocks
-    per the i-th KV group, and thus sum(group_sizes) == len(block_ids).
-    group_sizes=None indicates a single KV group.
-
-    If block_indices is given, each group (determined by group_sizes) of block IDs
-    will correspond to logically contiguous blocks, e.g. blocks 5-10 of a some request.
-    block_indices[i] will represent the block index of the first block in group #i.
-    Thus, len(block_indices) == len(group_sizes) = number of KV cache groups.
-    This information is required in order to support loading from offloaded blocks
-    which are larger than GPU blocks.
-    In such cases, the first GPU block per each group may be unaligned to the offloaded
-    block size, and so knowing block_indices[i] allows the worker to correctly
-    skip part of the first matching offloaded block.
-    Offloading from GPU is always aligned to offloaded block size, and so
-    block_indices will only be set by the offloading connector when loading into GPU.
-    """
-
-    def __init__(
-        self,
-        block_ids: list[int],
-        group_sizes: Sequence[int],
-        block_indices: Sequence[int] | None = None,
-    ):
-        super().__init__(block_ids)
-        assert sum(group_sizes) == len(block_ids)
-        assert block_indices is None or len(block_indices) == len(group_sizes)
-        self.group_sizes: Sequence[int] = group_sizes
-        self.block_indices: Sequence[int] | None = block_indices
-
-    @staticmethod
-    def medium() -> str:
-        return "GPU"
-
-
-class CPULoadStoreSpec(BlockIDsLoadStoreSpec):
-    """
-    Spec for loading/storing a KV block to CPU memory.
-    """
-
-    @staticmethod
-    def medium() -> str:
-        return "CPU"
diff --git a/vllm/v1/kv_offload/reuse_manager.py b/vllm/v1/kv_offload/reuse_manager.py
index 95de602976aa..d8c20b9dec7b 100644
--- a/vllm/v1/kv_offload/reuse_manager.py
+++ b/vllm/v1/kv_offload/reuse_manager.py
@@ -8,14 +8,15 @@
 """
 
 from collections import OrderedDict
-from collections.abc import Iterable
+from collections.abc import Collection, Iterable
 
-from vllm.v1.kv_offload.abstract import (
+from vllm.v1.kv_offload.base import (
     LoadStoreSpec,
     OffloadingEvent,
     OffloadingManager,
     OffloadKey,
     PrepareStoreOutput,
+    ReqContext,
 )
 
 
@@ -26,8 +27,9 @@ class FilterReusedOffloadingManager(OffloadingManager):
     All methods are delegated to the *backing* manager.  Two methods are
     intercepted:
 
-    * ``lookup`` — records each visited key in an internal LRU counter.
     * ``prepare_store`` — filters out keys that have not yet
+    * ``lookup`` — records the visited key in an internal LRU
+      counter, then delegates to the backing manager.
       crossed the threshold *before* calling the backing
       ``prepare_store``.
 
@@ -65,27 +67,26 @@ def __init__(
     # Intercepted methods
     # ------------------------------------------------------------------
 
-    def lookup(self, keys: Iterable[OffloadKey]) -> int | None:
-        """Record each key, then delegate lookup to backing manager."""
-        keys = list(keys)
-        for key in keys:
-            if key in self.counts:
-                self.counts.move_to_end(key)
-                self.counts[key] += 1
-            else:
-                if len(self.counts) >= self.max_tracker_size:
-                    self.counts.popitem(last=False)  # evict LRU
-                self.counts[key] = 1
-        return self._backing.lookup(keys)
-
-    def prepare_store(self, keys: Iterable[OffloadKey]) -> PrepareStoreOutput | None:
+    def lookup(self, key: OffloadKey, req_context: ReqContext) -> bool | None:
+        """Record the key, then delegate lookup to backing manager."""
+        if key in self.counts:
+            self.counts.move_to_end(key)
+            self.counts[key] += 1
+        else:
+            if len(self.counts) >= self.max_tracker_size:
+                self.counts.popitem(last=False)  # evict LRU
+            self.counts[key] = 1
+        return self._backing.lookup(key, req_context)
+
+    def prepare_store(
+        self, keys: Collection[OffloadKey], req_context: ReqContext
+    ) -> PrepareStoreOutput | None:
         """Filter out blocks below threshold, then delegate to backing.
 
         Filtering is evaluated *before* calling the backing manager's
         ``prepare_store`` so that blocks that would be skipped do not
         consume any CPU offload capacity.
         """
-        keys = list(keys)
         eligible = [
             key for key in keys if self.counts.get(key, 0) >= self.store_threshold
         ]
@@ -93,23 +94,32 @@ def prepare_store(self, keys: Iterable[OffloadKey]) -> PrepareStoreOutput | None
         # Passing an empty list is intentional and safe — CPUOffloadingManager
         # handles it correctly, returning a PrepareStoreOutput with empty lists.
         # Delegate to the backing manager with only the eligible keys.
-        return self._backing.prepare_store(eligible)
+        return self._backing.prepare_store(eligible, req_context)
 
     # ------------------------------------------------------------------
     # Delegated methods
     # ------------------------------------------------------------------
 
-    def prepare_load(self, keys: Iterable[OffloadKey]) -> LoadStoreSpec:
-        return self._backing.prepare_load(keys)
+    def prepare_load(
+        self, keys: Collection[OffloadKey], req_context: ReqContext
+    ) -> LoadStoreSpec:
+        return self._backing.prepare_load(keys, req_context)
 
-    def touch(self, keys: Iterable[OffloadKey]) -> None:
-        return self._backing.touch(keys)
+    def touch(self, keys: Collection[OffloadKey], req_context: ReqContext) -> None:
+        return self._backing.touch(keys, req_context)
 
-    def complete_load(self, keys: Iterable[OffloadKey]) -> None:
-        return self._backing.complete_load(keys)
+    def complete_load(
+        self, keys: Collection[OffloadKey], req_context: ReqContext
+    ) -> None:
+        return self._backing.complete_load(keys, req_context)
 
-    def complete_store(self, keys: Iterable[OffloadKey], success: bool = True) -> None:
-        return self._backing.complete_store(keys, success)
+    def complete_store(
+        self,
+        keys: Collection[OffloadKey],
+        req_context: ReqContext,
+        success: bool = True,
+    ) -> None:
+        return self._backing.complete_store(keys, req_context, success)
 
     def take_events(self) -> Iterable[OffloadingEvent]:
         return self._backing.take_events()
diff --git a/vllm/v1/kv_offload/spec.py b/vllm/v1/kv_offload/spec.py
deleted file mode 100644
index b66b04ffbd65..000000000000
--- a/vllm/v1/kv_offload/spec.py
+++ /dev/null
@@ -1,142 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from abc import ABC, abstractmethod
-from collections.abc import Iterator
-from dataclasses import dataclass
-from typing import TYPE_CHECKING
-
-import torch
-
-from vllm.logger import init_logger
-from vllm.v1.kv_offload.abstract import LoadStoreSpec, OffloadingManager
-from vllm.v1.kv_offload.worker.worker import OffloadingHandler
-
-if TYPE_CHECKING:
-    from vllm.config import VllmConfig
-    from vllm.v1.kv_cache_interface import KVCacheConfig
-
-logger = init_logger(__name__)
-
-
-@dataclass
-class CanonicalKVCacheTensor:
-    """
-    A canonicalized KV cache tensor whose first dimension is num_blocks.
-
-    For attention backends where the raw tensor has num_blocks at a
-    non-leading physical dimension (e.g. FlashAttention's
-    (2, num_blocks, ...) layout), the tensor is split so that each
-    resulting CanonicalKVCacheTensor starts with (num_blocks, ...).
-    """
-
-    # The KV cache tensor with shape (num_blocks, ...)
-    tensor: torch.Tensor
-    # The (possibly padded) page size per block in bytes
-    page_size_bytes: int
-
-
-@dataclass
-class CanonicalKVCacheRef:
-    """
-    Per-layer (or group of layers) reference to a specific (by index)
-    CanonicalKVCacheTensor and records the un-padded page size used by that layer.
-    """
-
-    # Index into the list of CanonicalKVCacheTensor objects
-    tensor_idx: int
-    # The un-padded page size per block in bytes
-    page_size_bytes: int
-
-
-@dataclass
-class CanonicalKVCaches:
-    """
-    Canonicalized block-level representation of the KV caches.
-
-    Composed of:
-        - Unique list of KV cache data tensors,
-          each with shape (num_blocks, page_size_in_bytes) and int8 dtype.
-        - Per-group data references of the tensors.
-          i.e. how each KV cache group maps to the tensors.
-    """
-
-    # Ordered list of unique block tensors, each with shape
-    # (num_blocks, ...).
-    tensors: list[CanonicalKVCacheTensor]
-    # Per-KV-cache-group list of data references that map each layer
-    # in the group to the appropriate entry in the tensors list.
-    group_data_refs: list[list[CanonicalKVCacheRef]]
-
-
-class OffloadingSpec(ABC):
-    """Spec for an offloading connector"""
-
-    def __init__(self, vllm_config: "VllmConfig", kv_cache_config: "KVCacheConfig"):
-        logger.warning(
-            "Initializing OffloadingSpec. This API is experimental and "
-            "subject to change in the future as we iterate the design."
-        )
-        self.vllm_config = vllm_config
-        self.kv_cache_config = kv_cache_config
-
-        kv_transfer_config = vllm_config.kv_transfer_config
-        assert kv_transfer_config is not None
-        self.extra_config = kv_transfer_config.kv_connector_extra_config
-
-        # block size used by vLLM for hashing request tokens for the sake
-        # of enabling prefix caching
-        self.hash_block_size = vllm_config.cache_config.block_size
-        # gpu block size per group
-        self.gpu_block_size: tuple[int, ...] = tuple(
-            kv_cache_group.kv_cache_spec.block_size
-            for kv_cache_group in kv_cache_config.kv_cache_groups
-        )
-
-        for block_size in self.gpu_block_size:
-            assert block_size % self.hash_block_size == 0, (
-                f"gpu_block_size={block_size} not divisible by "
-                f"hash_block_size={self.hash_block_size}. "
-                f"Hybrid models (e.g. Mamba+Attention) need "
-                f"--enable-prefix-caching to align block sizes."
-            )
-
-        # offloaded_block_size / gpu_block_size
-        self.block_size_factor: int = 1
-
-        offloaded_block_size = self.extra_config.get("block_size")
-        if offloaded_block_size is not None:
-            offloaded_block_size_int = int(offloaded_block_size)
-            gpu_block_sizes = set(self.gpu_block_size)
-            assert len(gpu_block_sizes) == 1, (
-                "If 'block_size' is specified in kv_connector_extra_config, "
-                "there must be at least one KV cache group, "
-                "and all groups must have the same block size."
-            )
-            gpu_block_size = gpu_block_sizes.pop()
-
-            assert offloaded_block_size_int % gpu_block_size == 0
-            self.block_size_factor = offloaded_block_size_int // gpu_block_size
-
-    @abstractmethod
-    def get_manager(self) -> OffloadingManager:
-        """
-        Get an OffloadingManager that will be used
-        by the scheduler-side offloading connector to track
-        offloaded blocks and manage evictions.
-        """
-        pass
-
-    @abstractmethod
-    def get_handlers(
-        self, kv_caches: CanonicalKVCaches
-    ) -> Iterator[tuple[type[LoadStoreSpec], type[LoadStoreSpec], OffloadingHandler]]:
-        """
-        Get offloading handlers along with their respective src and dst types.
-
-        Args:
-            kv_caches: Canonicalized KV caches.
-
-        Yields:
-            Tuples of (src_type, dst_type, offloading_handler).
-        """
-        pass
diff --git a/vllm/v1/kv_offload/worker/worker.py b/vllm/v1/kv_offload/worker/worker.py
index 2a47cc8e9d07..2f0dd2471631 100644
--- a/vllm/v1/kv_offload/worker/worker.py
+++ b/vllm/v1/kv_offload/worker/worker.py
@@ -4,7 +4,7 @@
 from dataclasses import dataclass
 
 from vllm.logger import init_logger
-from vllm.v1.kv_offload.abstract import LoadStoreSpec
+from vllm.v1.kv_offload.base import LoadStoreSpec
 
 # a single transfer spec (src_blocks_spec, dst_blocks_spec)
 TransferSpec = tuple[LoadStoreSpec, LoadStoreSpec]
diff --git a/vllm/v1/metrics/ray_wrappers.py b/vllm/v1/metrics/ray_wrappers.py
index a11b92680779..7e2100546e82 100644
--- a/vllm/v1/metrics/ray_wrappers.py
+++ b/vllm/v1/metrics/ray_wrappers.py
@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import copy
 import time
 
 from vllm.distributed.kv_transfer.kv_connector.v1.metrics import KVConnectorProm
@@ -28,10 +29,13 @@ def _get_replica_id() -> str | None:
 
 
 class RayPrometheusMetric:
+    _is_labeled: bool = False
+
     def __init__(self):
         if ray_metrics is None:
             raise ImportError("RayPrometheusMetric requires Ray to be installed.")
         self.metric: Metric = None
+        self._tags: dict[str, str] = {"ReplicaId": _get_replica_id() or ""}
 
     @staticmethod
     def _get_tag_keys(labelnames: list[str] | None) -> tuple[str, ...]:
@@ -39,7 +43,7 @@ def _get_tag_keys(labelnames: list[str] | None) -> tuple[str, ...]:
         labels.append("ReplicaId")
         return tuple(labels)
 
-    def labels(self, *labels, **labelskwargs):
+    def _build_tags(self, *labels, **labelskwargs) -> dict[str, str]:
         if labels:
             # -1 because ReplicaId was added automatically
             expected = len(self.metric._tag_keys) - 1
@@ -52,12 +56,15 @@ def labels(self, *labels, **labelskwargs):
 
         labelskwargs["ReplicaId"] = _get_replica_id() or ""
 
-        if labelskwargs:
-            for k, v in labelskwargs.items():
-                if not isinstance(v, str):
-                    labelskwargs[k] = str(v)
-            self.metric.set_default_tags(labelskwargs)
-        return self
+        return {k: v if isinstance(v, str) else str(v) for k, v in labelskwargs.items()}
+
+    def labels(self, *labels, **labelskwargs) -> "RayPrometheusMetric":
+        if self._is_labeled:
+            raise ValueError("labels() cannot be called on an already-labeled metric.")
+        clone = copy.copy(self)
+        clone._tags = self._build_tags(*labels, **labelskwargs)
+        clone._is_labeled = True
+        return clone
 
     @staticmethod
     def _get_sanitized_opentelemetry_name(name: str) -> str:
@@ -91,6 +98,7 @@ def __init__(
         # implemented at the observability layer (Prometheus/Grafana).
         del multiprocess_mode
 
+        super().__init__()
         tag_keys = self._get_tag_keys(labelnames)
         name = self._get_sanitized_opentelemetry_name(name)
 
@@ -101,11 +109,11 @@ def __init__(
         )
 
     def set(self, value: int | float):
-        return self.metric.set(value)
+        return self.metric.set(value, tags=self._tags)
 
     def set_to_current_time(self):
         # ray metrics doesn't have set_to_current time, https://docs.ray.io/en/latest/_modules/ray/util/metrics.html
-        return self.metric.set(time.time())
+        return self.set(time.time())
 
 
 class RayCounterWrapper(RayPrometheusMetric):
@@ -118,6 +126,7 @@ def __init__(
         documentation: str | None = "",
         labelnames: list[str] | None = None,
     ):
+        super().__init__()
         tag_keys = self._get_tag_keys(labelnames)
         name = self._get_sanitized_opentelemetry_name(name)
         self.metric = ray_metrics.Counter(
@@ -129,7 +138,7 @@ def __init__(
     def inc(self, value: int | float = 1.0):
         if value == 0:
             return
-        return self.metric.inc(value)
+        return self.metric.inc(value, tags=self._tags)
 
 
 class RayHistogramWrapper(RayPrometheusMetric):
@@ -143,6 +152,7 @@ def __init__(
         labelnames: list[str] | None = None,
         buckets: list[float] | None = None,
     ):
+        super().__init__()
         tag_keys = self._get_tag_keys(labelnames)
         name = self._get_sanitized_opentelemetry_name(name)
 
@@ -155,7 +165,7 @@ def __init__(
         )
 
     def observe(self, value: int | float):
-        return self.metric.observe(value)
+        return self.metric.observe(value, tags=self._tags)
 
 
 class RaySpecDecodingProm(SpecDecodingProm):
diff --git a/vllm/v1/metrics/stats.py b/vllm/v1/metrics/stats.py
index 78faa73a88d9..a7a5fb7a2d2f 100644
--- a/vllm/v1/metrics/stats.py
+++ b/vllm/v1/metrics/stats.py
@@ -225,6 +225,7 @@ class FinishedRequestStats:
     """Stats associated with a finished request."""
 
     finish_reason: "FinishReason"
+    request_id: str | None = None
     e2e_latency: float = 0.0
     num_prompt_tokens: int = 0
     num_generation_tokens: int = 0
@@ -427,6 +428,7 @@ def update_from_events(
     def update_from_finished_request(
         self,
         finish_reason: "FinishReason",
+        request_id: str,
         num_prompt_tokens: int,
         max_tokens_param: int | None,
         req_stats: RequestStateStats,
@@ -458,6 +460,7 @@ def update_from_finished_request(
 
         finished_req = FinishedRequestStats(
             finish_reason=finish_reason,
+            request_id=request_id,
             e2e_latency=e2e_latency,
             num_prompt_tokens=num_prompt_tokens,
             num_generation_tokens=req_stats.num_generation_tokens,
diff --git a/vllm/v1/outputs.py b/vllm/v1/outputs.py
index 1f102ec61783..4a7c7a5f381a 100644
--- a/vllm/v1/outputs.py
+++ b/vllm/v1/outputs.py
@@ -198,6 +198,9 @@ class ModelRunnerOutput:
     # req_id -> num_nans_in_logits
     num_nans_in_logits: dict[str, int] | None = None
 
+    # req_id -> routed experts ndarray of shape (seq_len, num_moe_layers, top_k)
+    routed_experts_dict: dict[str, np.ndarray] | None = None
+
     # information related to cudagraph execution
     cudagraph_stats: CUDAGraphStat | None = None
 
diff --git a/vllm/v1/request.py b/vllm/v1/request.py
index 678d57580cc7..0d435deb3f05 100644
--- a/vllm/v1/request.py
+++ b/vllm/v1/request.py
@@ -66,6 +66,7 @@ def __init__(
         client_index: int = 0,
         arrival_time: float | None = None,
         prompt_embeds: torch.Tensor | None = None,
+        prompt_is_token_ids: list[bool] | None = None,
         mm_features: list[MultiModalFeatureSpec] | None = None,
         lora_request: "LoRARequest | None" = None,
         cache_salt: str | None = None,
@@ -74,6 +75,8 @@ def __init__(
         block_hasher: Callable[["Request"], list["BlockHash"]] | None = None,
         resumable: bool = False,
         reasoning_ended: bool | None = None,
+        reasoning_parser_kwargs: dict[str, Any] | None = None,
+        abort_immediately: bool = False,
     ) -> None:
         self.request_id = request_id
         self.client_index = client_index
@@ -86,6 +89,9 @@ def __init__(
         )
         if self.structured_output_request is not None:
             self.structured_output_request.reasoning_ended = reasoning_ended
+            self.structured_output_request.reasoning_parser_kwargs = (
+                reasoning_parser_kwargs
+            )
         self.arrival_time = arrival_time if arrival_time is not None else time.time()
 
         self.status = RequestStatus.WAITING
@@ -114,6 +120,10 @@ def __init__(
 
         self.prompt_token_ids = prompt_token_ids
         self.prompt_embeds = prompt_embeds
+        # Per-position mask used in mixed-mode (chat completion with
+        # prompt_embeds). `None` except when both `prompt_token_ids` and
+        # `prompt_embeds` are set and their positions are interleaved.
+        self.prompt_is_token_ids = prompt_is_token_ids
         # Cache per-block prompt-embed hashes to avoid rehashing the same
         # tensor slices when generating extra keys.
         self._prompt_embeds_per_block_hashes: dict[tuple[int, int], bytes] = {}
@@ -173,6 +183,10 @@ def __init__(
         # None entry in the queue means finished.
         self.streaming_queue: deque[StreamingUpdate | None] | None = None
 
+        # If True, request should be aborted immediately after being added to
+        # the scheduler so the connector's request_finished hook runs.
+        self.abort_immediately = abort_immediately
+
     @classmethod
     def from_engine_core_request(
         cls,
@@ -184,6 +198,7 @@ def from_engine_core_request(
             client_index=request.client_index,
             prompt_token_ids=request.prompt_token_ids,
             prompt_embeds=request.prompt_embeds,
+            prompt_is_token_ids=request.prompt_is_token_ids,
             mm_features=request.mm_features,
             sampling_params=request.sampling_params,
             pooling_params=request.pooling_params,
@@ -195,6 +210,8 @@ def from_engine_core_request(
             block_hasher=block_hasher,
             resumable=request.resumable,
             reasoning_ended=request.reasoning_ended,
+            reasoning_parser_kwargs=request.reasoning_parser_kwargs,
+            abort_immediately=request.abort_immediately,
         )
 
     def append_output_token_ids(
diff --git a/vllm/v1/sample/logits_processor/__init__.py b/vllm/v1/sample/logits_processor/__init__.py
index fb4a046fc057..2cb89e1ea950 100644
--- a/vllm/v1/sample/logits_processor/__init__.py
+++ b/vllm/v1/sample/logits_processor/__init__.py
@@ -18,7 +18,6 @@
     LogitBiasLogitsProcessor,
     MinPLogitsProcessor,
     MinTokensLogitsProcessor,
-    ThinkingTokenBudgetLogitsProcessor,
     process_dict_updates,
 )
 from vllm.v1.sample.logits_processor.interface import (
@@ -51,7 +50,6 @@
     MinTokensLogitsProcessor,
     LogitBiasLogitsProcessor,
     MinPLogitsProcessor,
-    ThinkingTokenBudgetLogitsProcessor,
 ]
 
 
@@ -356,5 +354,4 @@ def apply(self, logits: torch.Tensor) -> torch.Tensor:
     "STR_POOLING_REJECTS_LOGITSPROCS",
     "LOGITSPROCS_GROUP",
     "AdapterLogitsProcessor",
-    "ThinkingTokenBudgetLogitsProcessor",
 ]
diff --git a/vllm/v1/sample/logits_processor/builtin.py b/vllm/v1/sample/logits_processor/builtin.py
index 1739452b44a0..11a52711d671 100644
--- a/vllm/v1/sample/logits_processor/builtin.py
+++ b/vllm/v1/sample/logits_processor/builtin.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from collections.abc import Callable, Sequence
-from typing import TYPE_CHECKING, Any, TypeVar
+from typing import TYPE_CHECKING, TypeVar
 
 import numpy as np
 import torch
@@ -291,267 +291,6 @@ def apply_with_spec_decode(
         return logits
 
 
-class ThinkingTokenBudgetLogitsProcessor(LogitsProcessor):
-    """Limits the number of tokens allowed inside a 'thinking' section."""
-
-    def __init__(
-        self, vllm_config: "VllmConfig", device: torch.device, is_pin_memory: bool
-    ):
-        reasoning_config = vllm_config.reasoning_config
-        max_num_reqs = vllm_config.scheduler_config.max_num_seqs
-
-        # Check if thinking is enabled
-        self.is_enabled = reasoning_config is not None and reasoning_config.enabled
-
-        self.reasoning_start_token_ids = getattr(
-            reasoning_config, "reasoning_start_token_ids", []
-        )
-        self.reasoning_end_token_ids = getattr(
-            reasoning_config, "reasoning_end_token_ids", []
-        )
-
-        self.pin_memory = is_pin_memory
-        self.device = device
-        # Per-request state tracking for thinking token management
-        # Key: request_index, Value: state dict containing:
-        # "in_think": bool - currently in thinking mode
-        # "in_end": bool - currently forcing end tokens output
-        # "check_count_down": int - steps remaining until next think
-        #                            start/end token parsing
-        # "think_count": int - number of thinking tokens generated
-        # "end_count": int - number of end tokens forced so far
-        # "thinking_token_budget": int - max allowed thinking tokens
-        # "output_tok_ids": list[int] - generated output tokens
-        # "prev_output_length": int - previous output length for
-        #                               incremental processing
-        self._state: dict[int, dict[str, Any]] = {}
-
-        # Preallocate reusable tensors
-        self.mask = torch.zeros(max_num_reqs, dtype=torch.bool, device=device)
-        self.force_token_ids = torch.full(
-            (max_num_reqs,), -1, dtype=torch.long, device=device
-        )
-
-    @staticmethod
-    def _find_last_sequence_index(target_list: list[int], token_ids: list[int]) -> int:
-        """
-        Returns the index of the last occurrence of token_ids in target_list.
-
-        Args:
-          target_list (list[int]): The list of token IDs.
-          token_ids (list[int]): The sequence of token IDs to find.
-        """
-        if not token_ids:
-            return -1
-        for i in range(len(target_list) - len(token_ids), -1, -1):
-            if target_list[i : i + len(token_ids)] == token_ids:
-                return i
-        return -1
-
-    def _init_state_entry(
-        self, prompt_tok_ids: list[int] | None, thinking_token_budget: int
-    ) -> dict[str, Any]:
-        """Initializes the tracking state for a given sequence index."""
-        if prompt_tok_ids is None:
-            last_start = -1
-            last_end = -1
-            in_think = False
-            think_count = 0
-        else:
-            last_start = self._find_last_sequence_index(
-                prompt_tok_ids, self.reasoning_start_token_ids
-            )
-            last_end = self._find_last_sequence_index(
-                prompt_tok_ids, self.reasoning_end_token_ids
-            )
-            in_think = last_start > last_end
-            if in_think:
-                think_count = len(prompt_tok_ids) - (
-                    last_start + len(self.reasoning_start_token_ids)
-                )
-            else:
-                think_count = 0
-
-        return {
-            "in_think": in_think,  # Currently in thinking mode
-            "in_end": in_think and thinking_token_budget == 0,
-            "check_count_down": thinking_token_budget,
-            "think_count": think_count,  # Number of tokens in thinking section
-            "end_count": 0,  # Number of end tokens forced so far
-            "prompt_tok_ids": prompt_tok_ids,
-            "output_tok_ids": [],
-            "thinking_token_budget": thinking_token_budget,
-            "prev_output_length": 0,
-            # Track previous output length for incremental updates
-        }
-
-    def _update_think_state(self, state: dict[str, Any]):
-        """Updates the state based on newly generated output tokens."""
-        if not state.get("in_end", False) and state.get("check_count_down", 0) > 0:
-            state["check_count_down"] -= 1
-            return
-
-        output = state.get("output_tok_ids", [])
-        if not output:
-            return
-
-        # Track previous output length for incremental processing
-        prev_length = state.get("prev_output_length", 0)
-        current_length = len(output)
-
-        if current_length <= prev_length:
-            return
-
-        # Process only newly added tokens
-        new_tokens = output[prev_length:]
-        state["prev_output_length"] = current_length
-
-        # Check if new tokens contain think start or end sequences
-        start_len = len(self.reasoning_start_token_ids)
-        end_len = len(self.reasoning_end_token_ids)
-
-        # Look for think sequences in recent tokens (including boundary)
-        # Check overlapping regions where sequences might span boundaries
-        check_start_idx = max(0, prev_length - max(start_len, end_len) + 1)
-        recent_tokens = output[check_start_idx:]
-
-        # Find any think start/end sequences in recent tokens
-        recent_start_pos = self._find_last_sequence_index(
-            recent_tokens, self.reasoning_start_token_ids
-        )
-        recent_end_pos = self._find_last_sequence_index(
-            recent_tokens, self.reasoning_end_token_ids
-        )
-
-        # Update state based on recent sequences
-        if not state["in_end"]:
-            if recent_start_pos >= 0 and recent_end_pos >= 0:
-                if recent_start_pos > recent_end_pos:
-                    # Case: ...<end>...<start>... - entering think mode
-                    absolute_start_pos = check_start_idx + recent_start_pos
-                    new_think_count = current_length - (absolute_start_pos + start_len)
-                    state["in_think"] = True
-                    state["think_count"] = new_think_count
-                else:
-                    # Case: ...<start>...<end>... - exiting think mode
-                    state["in_think"] = False
-                    state["think_count"] = 0
-            elif recent_start_pos >= 0:
-                # Found think start - entering think mode
-                absolute_start_pos = check_start_idx + recent_start_pos
-                new_think_count = current_length - (absolute_start_pos + start_len)
-                state["in_think"] = True
-                state["think_count"] = new_think_count
-            elif recent_end_pos >= 0:
-                # Found think end - exiting think mode
-                state["in_think"] = False
-                state["think_count"] = 0
-            elif state["in_think"]:
-                # Continue thinking mode, increment count by new tokens
-                state["think_count"] += len(new_tokens)
-
-            # Set countdown based on current state
-            if state["in_think"]:
-                remaining_budget = max(
-                    0, state["thinking_token_budget"] - state["think_count"]
-                )
-                state["check_count_down"] = max(0, remaining_budget - 1)
-            else:
-                state["check_count_down"] = state["thinking_token_budget"]
-
-            # Check if need to transition to end mode
-            if (
-                state["in_think"]
-                and state["think_count"] >= state["thinking_token_budget"]
-            ):
-                state["in_think"] = False
-                state["in_end"] = True
-                state["end_count"] = 0
-                state["check_count_down"] = state["thinking_token_budget"]
-        else:
-            # In end mode
-            state["end_count"] += 1
-            if state["end_count"] >= len(self.reasoning_end_token_ids):
-                state.update(
-                    {
-                        "in_end": False,
-                        "end_count": 0,
-                        "check_count_down": state["thinking_token_budget"],
-                    }
-                )
-
-    def is_argmax_invariant(self) -> bool:
-        """This logits processor can change the outcome of
-        greedy sampling by forcing that the thinking section
-        ends after a certain number of tokens."""
-        return False
-
-    def update_state(self, batch_update: BatchUpdate | None):
-        if not self.is_enabled:
-            return
-        if batch_update:
-            for index, params, prompt_tok_ids, output_tok_ids in batch_update.added:
-                thinking_token_budget = params.thinking_token_budget
-
-                if thinking_token_budget is not None:
-                    self._state[index] = self._init_state_entry(
-                        prompt_tok_ids, thinking_token_budget
-                    )
-                    self._state[index]["output_tok_ids"] = output_tok_ids
-                else:
-                    # Remove state if no thinking budget
-                    self._state.pop(index, None)
-
-            for index in batch_update.removed:
-                self._state.pop(index, {})
-
-            for i1, i2, direction in batch_update.moved:
-                if direction == MoveDirectionality.SWAP:
-                    state1 = self._state.pop(i1, None)
-                    state2 = self._state.pop(i2, None)
-                    if state1 is not None:
-                        self._state[i2] = state1
-                    if state2 is not None:
-                        self._state[i1] = state2
-                else:
-                    state = self._state.pop(i1, None)
-                    if state is not None:
-                        self._state[i2] = state
-
-        for state in self._state.values():
-            self._update_think_state(state)
-
-    def apply(self, logits: torch.Tensor) -> torch.Tensor:
-        if not self.is_enabled or not self._state:
-            return logits
-
-        batch_size = logits.size(0)
-        self.mask[:batch_size] = False
-
-        for i in range(batch_size):
-            state = self._state.get(i)
-            if state and state["in_end"]:
-                self.mask[i] = True
-                self.force_token_ids[i] = self.reasoning_end_token_ids[
-                    state["end_count"]
-                ]
-
-        # Check in CPU first not to sync with GPU
-        has_active_thinking = any(
-            state.get("in_end", False) for state in self._state.values()
-        )
-
-        if has_active_thinking:
-            current_mask = self.mask[:batch_size]
-            active_indices = current_mask.nonzero(as_tuple=False).view(-1)
-            if len(active_indices) > 0:
-                force_tokens = self.force_token_ids[active_indices]
-                # Apply a large value for the end thinking token id index
-                logits[active_indices, force_tokens] = 1e9
-
-        return logits
-
-
 def process_dict_updates(
     req_entries: dict[int, T],
     batch_update: BatchUpdate | None,
diff --git a/vllm/v1/sample/metadata.py b/vllm/v1/sample/metadata.py
index 4682cde1098b..fa4ceac8e71e 100644
--- a/vllm/v1/sample/metadata.py
+++ b/vllm/v1/sample/metadata.py
@@ -1,11 +1,14 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from __future__ import annotations
+
 from dataclasses import dataclass
 
 import torch
 
 from vllm.v1.sample.logits_processor import LogitsProcessors
+from vllm.v1.sample.thinking_budget_state import ThinkingBudgetStateHolder
 
 
 @dataclass
@@ -47,3 +50,6 @@ class SamplingMetadata:
 
     # Speculative token ids
     spec_token_ids: list[list[int]] | None = None
+    # When non-None, use ``holder.has_tracked_requests()`` to see if this batch applies
+    # thinking-token-budget logits (holder may exist with an empty tracking set).
+    thinking_budget_state_holder: ThinkingBudgetStateHolder | None = None
diff --git a/vllm/v1/sample/ops/topk_topp_sampler.py b/vllm/v1/sample/ops/topk_topp_sampler.py
index 33f7090e4e3d..363b113f0a4f 100644
--- a/vllm/v1/sample/ops/topk_topp_sampler.py
+++ b/vllm/v1/sample/ops/topk_topp_sampler.py
@@ -41,23 +41,35 @@ def __init__(self, logprobs_mode: LogprobsMode = "raw_logprobs") -> None:
 
                 capability = current_platform.get_device_capability()
                 assert capability is not None
-                if not FlashInferBackend.supports_compute_capability(capability):
+                if FlashInferBackend.supports_compute_capability(capability):
+                    logger.info_once(
+                        "Using FlashInfer for top-p & top-k sampling.",
+                        scope="global",
+                    )
+                    self.forward = self.forward_cuda
+                elif envs.is_set("VLLM_USE_FLASHINFER_SAMPLER"):
+                    # User explicitly opted in but the GPU can't run FlashInfer.
                     capability_str = capability.as_version_str()
                     raise RuntimeError(
                         "FlashInfer does not support compute capability "
                         f"{capability_str}, unset VLLM_USE_FLASHINFER_SAMPLER=1."
                     )
-                # Users must opt in explicitly via VLLM_USE_FLASHINFER_SAMPLER=1.
-                logger.info_once(
-                    "Using FlashInfer for top-p & top-k sampling.",
-                    scope="global",
-                )
-                self.forward = self.forward_cuda
+                else:
+                    # Default-on path; hardware can't run FlashInfer →
+                    # quietly fall back to the PyTorch-native sampler
+                    # instead of failing server startup.
+                    logger.warning_once(
+                        "FlashInfer top-p/top-k sampling not supported on "
+                        "compute capability %s; falling back to PyTorch-native "
+                        "sampler. Set VLLM_USE_FLASHINFER_SAMPLER=0 to silence.",
+                        capability.as_version_str(),
+                    )
+                    self.forward = self.forward_native
             else:
-                logger.debug_once(
-                    "FlashInfer top-p/top-k sampling is available but disabled "
-                    "by default. Set VLLM_USE_FLASHINFER_SAMPLER=1 to opt in "
-                    "after verifying accuracy for your workloads."
+                # User explicitly set VLLM_USE_FLASHINFER_SAMPLER=0.
+                logger.info_once(
+                    "FlashInfer top-p/top-k sampling disabled via "
+                    "VLLM_USE_FLASHINFER_SAMPLER=0; using PyTorch-native sampler."
                 )
                 self.forward = self.forward_native
 
@@ -70,6 +82,11 @@ def __init__(self, logprobs_mode: LogprobsMode = "raw_logprobs") -> None:
                 self.forward = self.forward_native
             else:
                 self.forward = self.forward_cpu
+        elif current_platform.is_xpu():
+            if envs.VLLM_XPU_USE_SAMPLER_KERNEL:
+                self.forward = self.forward_xpu
+            else:
+                self.forward = self.forward_native
         elif (
             logprobs_mode not in ("processed_logits", "processed_logprobs")
             and rocm_aiter_ops.is_enabled()
@@ -120,9 +137,9 @@ def forward_cuda(
         p: torch.Tensor | None,
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
         """More optimized implementation for top-k and top-p sampling."""
-        # We prefer `random_sample` over `flashinfer_sample` when sorting is
-        # not needed. This is because `random_sample` does not require
-        # CPU-GPU synchronization while `flashinfer_sample` does.
+        # Fall back to the PyTorch-native path when FlashInfer has nothing
+        # to do (no top-k / top-p filter) or when per-request generators
+        # are present (unsupported by FlashInfer 0.2.3+).
         if (k is None and p is None) or generators:
             if generators:
                 logger.debug_once(
@@ -231,6 +248,49 @@ def aiter_sample(
             return torch.multinomial(renorm_probs, num_samples=1).view(-1)
         raise RuntimeError("aiter_sample was called with no active top-k or top-p.")
 
+    def forward_xpu(
+        self,
+        logits: torch.Tensor,
+        generators: dict[int, torch.Generator],
+        k: torch.Tensor | None,
+        p: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        if generators:
+            logger.warning_once(
+                "xpu kernel topk_topp_sampler does not support "
+                "per-request generators. Falling back to "
+                "PyTorch-native implementation."
+            )
+            return self.forward_native(logits, generators, k, p)
+        random_sampled = torch.empty(
+            logits.shape[0], dtype=torch.int64, device=logits.device
+        )
+        logits_to_return = None
+        if (
+            self.logprobs_mode == "processed_logits"
+            or self.logprobs_mode == "processed_logprobs"
+        ):
+            logits_to_return = torch.empty_like(logits)
+
+        assert len(generators) != logits.shape[0], (
+            "xpu kernel topk_topp_sampler does not support batch-wise generators."
+        )
+        generator = torch.xpu.default_generators[logits.device.index]
+
+        state = generator.get_state()
+        seed, offset = state.view(torch.int64)
+        seeds = torch.tensor(
+            [seed, offset], dtype=torch.int64, device=torch.device("cpu")
+        )
+        # The XPU kernel expects k as int64 (Long), but the input batch
+        # stores top_k as int32. Cast here to avoid dtype mismatch.
+        if k is not None:
+            k = k.to(torch.int64)
+        torch.ops.vllm.xpu_topk_topp_sampler(
+            random_sampled, logits_to_return, logits, k, p, self.logprobs_mode, seeds
+        )
+        return random_sampled, logits_to_return
+
 
 # Note: this is a workaround for
 # https://github.com/pytorch/pytorch/pull/151218
@@ -361,10 +421,6 @@ def flashinfer_sample(
     NOTE: The outputs of this function do not necessarily match the outputs of
     the `random_sample` function. It only guarantees that the outputs are
     statistically equivalent.
-
-    NOTE: This function includes CPU-GPU synchronization, while `random_sample`
-    does not. Call this function at the end of the forward pass to minimize
-    the synchronization overhead.
     """
     import flashinfer
 
diff --git a/vllm/v1/sample/rejection_sampler.py b/vllm/v1/sample/rejection_sampler.py
index d3e8573458b1..678654cb78a4 100644
--- a/vllm/v1/sample/rejection_sampler.py
+++ b/vllm/v1/sample/rejection_sampler.py
@@ -1,8 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from __future__ import annotations
+
 from collections.abc import Sequence
 from dataclasses import replace
+from typing import TYPE_CHECKING
 
 import torch
 import torch.nn as nn
@@ -17,6 +20,10 @@
 from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p
 from vllm.v1.sample.sampler import Sampler
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
+from vllm.v1.spec_decode.utils import unconditional_to_conditional_rates
+
+if TYPE_CHECKING:
+    from vllm.config.speculative import SpeculativeConfig
 
 logger = init_logger(__name__)
 
@@ -50,13 +57,33 @@ class RejectionSampler(nn.Module):
         output tokens = accepted tokens + recovered tokens + bonus tokens
     """
 
-    def __init__(self, sampler: Sampler):
+    def __init__(
+        self,
+        sampler: Sampler,
+        spec_config: SpeculativeConfig | None = None,
+        device: torch.device | None = None,
+    ):
         super().__init__()
         self.sampler = sampler
         logprobs_mode = self.sampler.logprobs_mode
         self.is_processed_logprobs_mode = logprobs_mode.startswith("processed")
         self.is_logits_logprobs_mode = logprobs_mode.endswith("logits")
 
+        self.synthetic_conditional_rates: torch.Tensor | None = None
+        if (
+            spec_config is not None
+            and spec_config.rejection_sample_method == "synthetic"
+        ):
+            assert spec_config.synthetic_acceptance_rates is not None
+            self.synthetic_conditional_rates = torch.tensor(
+                unconditional_to_conditional_rates(
+                    spec_config.synthetic_acceptance_rates
+                ),
+                dtype=torch.float32,
+                device=device,
+            )
+        self.synthetic_mode = self.synthetic_conditional_rates is not None
+
     def forward(
         self,
         metadata: SpecDecodeMetadata,
@@ -147,6 +174,8 @@ def forward(
             target_logits,
             bonus_token_ids,
             sampling_metadata,
+            synthetic_mode=self.synthetic_mode,
+            synthetic_conditional_rates=self.synthetic_conditional_rates,
         )
 
         logprobs_tensors = None
@@ -261,16 +290,24 @@ def apply_logits_processors(
         any_penalties_or_bad_words = (
             sampling_metadata.bad_words_token_ids or has_penalties
         )
+        holder = sampling_metadata.thinking_budget_state_holder
+        needs_thinking = holder is not None and holder.has_tracked_requests()
 
         output_token_ids = sampling_metadata.output_token_ids
-        if any_penalties_or_bad_words:
+        if any_penalties_or_bad_words or needs_thinking:
             output_token_ids = self._combine_outputs_with_spec_tokens(
                 output_token_ids,
                 sampling_metadata.spec_token_ids,
             )
 
         # Calculate indices of target logits.
-        if sampling_metadata.allowed_token_ids_mask is not None or has_penalties:
+        repeat_indices: torch.Tensor | None = None
+        need_repeat_indices = (
+            sampling_metadata.allowed_token_ids_mask is not None
+            or has_penalties
+            or needs_thinking
+        )
+        if need_repeat_indices:
             num_requests = len(metadata.num_draft_tokens)
             num_draft_tokens = torch.tensor(metadata.num_draft_tokens, device="cpu")
             original_indices = torch.arange(num_requests, device="cpu")
@@ -298,7 +335,12 @@ def apply_logits_processors(
                 logits = processor.apply_with_spec_decode(
                     logits, metadata.num_draft_tokens
                 )
-
+        if holder is not None and holder.has_tracked_requests():
+            logits = holder.apply_to_logits(
+                logits,
+                predict_bonus_token=False,
+                spec_token_ids=sampling_metadata.spec_token_ids,
+            )
         return logits
 
     @staticmethod
@@ -362,6 +404,8 @@ def rejection_sample(
     # [batch_size, 1]
     bonus_token_ids: torch.Tensor,
     sampling_metadata: SamplingMetadata,
+    synthetic_mode: bool = False,
+    synthetic_conditional_rates: torch.Tensor | None = None,
 ) -> torch.Tensor:
     assert draft_token_ids.ndim == 1
     assert draft_probs is None or draft_probs.ndim == 2
@@ -389,6 +433,20 @@ def rejection_sample(
         is_greedy = None
     else:
         is_greedy = sampling_metadata.temperature == GREEDY_TEMPERATURE
+
+    # Generate uniform probabilities before either kernel because synthetic
+    # mode needs them in the greedy kernel too.  Skip only when all requests
+    # are greedy *and* synthetic mode is off (the standard fast-path).
+    # [num_tokens]
+    uniform_probs: torch.Tensor | None = None
+    if synthetic_mode or not sampling_metadata.all_greedy:
+        uniform_probs = generate_uniform_probs(
+            num_tokens,
+            num_draft_tokens,
+            sampling_metadata.generators,
+            device,
+        )
+
     if not sampling_metadata.all_random:
         # Rejection sampling for greedy sampling requests.
         target_argmax = target_logits.argmax(dim=-1)
@@ -400,6 +458,9 @@ def rejection_sample(
             bonus_token_ids,
             is_greedy,
             max_spec_len,
+            uniform_probs,
+            synthetic_conditional_rates,
+            SYNTHETIC_MODE=synthetic_mode,
         )
         if sampling_metadata.all_greedy:
             return output_token_ids
@@ -408,15 +469,6 @@ def rejection_sample(
     target_probs = target_logits.softmax(dim=-1, dtype=torch.float32)
     assert target_probs.is_contiguous()
 
-    # Generate uniform probabilities for rejection sampling.
-    # [num_tokens]
-    uniform_probs = generate_uniform_probs(
-        num_tokens,
-        num_draft_tokens,
-        sampling_metadata.generators,
-        device,
-    )
-
     # Sample recovered tokens for each position.
     # [num_tokens]
     recovered_token_ids = sample_recovered_tokens(
@@ -431,6 +483,7 @@ def rejection_sample(
     )
 
     # Rejection sampling for random sampling requests.
+    assert uniform_probs is not None
     rejection_random_sample_kernel[(batch_size,)](
         output_token_ids,
         cu_num_draft_tokens,
@@ -443,7 +496,9 @@ def rejection_sample(
         is_greedy,
         max_spec_len,
         vocab_size,
+        synthetic_conditional_rates,
         NO_DRAFT_PROBS=draft_probs is None,
+        SYNTHETIC_MODE=synthetic_mode,
     )
     return output_token_ids
 
@@ -658,6 +713,9 @@ def rejection_greedy_sample_kernel(
     bonus_token_ids_ptr,  # [batch_size]
     is_greedy_ptr,  # [batch_size] or None
     max_spec_len,
+    uniform_probs_ptr,  # [num_tokens] or None (synthetic mode only)
+    synthetic_conditional_rates_ptr,  # [num_speculative_tokens] or None
+    SYNTHETIC_MODE: tl.constexpr,
 ):
     req_idx = tl.program_id(0)
     # FIXME(woosuk): Because is_greedy_ptr is not None at profiling run,
@@ -675,14 +733,20 @@ def rejection_greedy_sample_kernel(
     for pos in range(num_draft_tokens):
         if not rejected:
             draft_token_id = tl.load(draft_token_ids_ptr + start_idx + pos)
-            target_argmax_id = tl.load(target_argmax_ptr + start_idx + pos)
+            target_argmax_id = tl.load(target_argmax_ptr + start_idx + pos).to(tl.int32)
+            if SYNTHETIC_MODE:
+                uniform_prob = tl.load(uniform_probs_ptr + start_idx + pos)
+                rate = tl.load(synthetic_conditional_rates_ptr + pos)
+                accepted = uniform_prob < rate
+                token_id = draft_token_id if accepted else target_argmax_id
+                rejected = not accepted
+            else:
+                token_id = target_argmax_id
+                rejected = draft_token_id != target_argmax_id
             tl.store(
                 output_token_ids_ptr + req_idx * (max_spec_len + 1) + pos,
-                target_argmax_id,
+                token_id,
             )
-            if draft_token_id != target_argmax_id:
-                # Reject.
-                rejected = True
 
     if not rejected:
         # If all tokens are accepted, append the bonus token.
@@ -707,7 +771,9 @@ def rejection_random_sample_kernel(
     is_greedy_ptr,  # [batch_size]
     max_spec_len,
     vocab_size,
+    synthetic_conditional_rates_ptr,  # [num_speculative_tokens] or None
     NO_DRAFT_PROBS: tl.constexpr,
+    SYNTHETIC_MODE: tl.constexpr,
 ):
     req_idx = tl.program_id(0)
     is_greedy = tl.load(is_greedy_ptr + req_idx)
@@ -723,23 +789,28 @@ def rejection_random_sample_kernel(
     for pos in range(num_draft_tokens):
         if not rejected:
             draft_token_id = tl.load(draft_token_ids_ptr + start_idx + pos)
-            if NO_DRAFT_PROBS:
-                draft_prob = 1
+            uniform_prob = tl.load(uniform_probs_ptr + start_idx + pos)
+            if SYNTHETIC_MODE:
+                rate = tl.load(synthetic_conditional_rates_ptr + pos)
+                accepted = uniform_prob < rate
             else:
-                draft_prob = tl.load(
-                    draft_probs_ptr + (start_idx + pos) * vocab_size + draft_token_id
+                if NO_DRAFT_PROBS:
+                    draft_prob = 1
+                else:
+                    draft_prob = tl.load(
+                        draft_probs_ptr
+                        + (start_idx + pos) * vocab_size
+                        + draft_token_id
+                    )
+                target_prob = tl.load(
+                    target_probs_ptr + (start_idx + pos) * vocab_size + draft_token_id
                 )
-            target_prob = tl.load(
-                target_probs_ptr + (start_idx + pos) * vocab_size + draft_token_id
-            )
-            uniform_prob = tl.load(uniform_probs_ptr + start_idx + pos)
-            # NOTE(woosuk): While the draft probability should never be 0,
-            # we check it to avoid NaNs. If it happens to be 0, we reject.
-            if draft_prob > 0 and target_prob / draft_prob >= uniform_prob:
-                # Accept.
+                # NOTE(woosuk): While the draft probability should never be 0,
+                # we check it to avoid NaNs. If it happens to be 0, we reject.
+                accepted = draft_prob > 0 and target_prob / draft_prob >= uniform_prob
+            if accepted:
                 token_id = draft_token_id
             else:
-                # Reject. Use recovered token.
                 rejected = True
                 token_id = tl.load(recovered_token_ids_ptr + start_idx + pos)
             tl.store(
diff --git a/vllm/v1/sample/sampler.py b/vllm/v1/sample/sampler.py
index 5341351352e3..a77eafba2556 100644
--- a/vllm/v1/sample/sampler.py
+++ b/vllm/v1/sample/sampler.py
@@ -364,9 +364,13 @@ def apply_logits_processors(
         any_penalties_or_bad_words = (
             bool(bad_words_token_ids) or not sampling_metadata.no_penalties
         )
+        holder = sampling_metadata.thinking_budget_state_holder
+        needs_thinking_combine = holder is not None and holder.has_tracked_requests()
 
         output_token_ids = sampling_metadata.output_token_ids
-        if predict_bonus_token and any_penalties_or_bad_words:
+        if predict_bonus_token and (
+            any_penalties_or_bad_words or needs_thinking_combine
+        ):
             # Combine base outputs with spec tokens when speculative decoding
             # is enabled.
             output_token_ids = self._combine_outputs_with_spec_tokens(
@@ -388,6 +392,17 @@ def apply_logits_processors(
 
         # Apply penalties (e.g., freq_penalties).
         logits = self.apply_penalties(logits, sampling_metadata, output_token_ids)
+        if holder is not None and holder.has_tracked_requests():
+            holder.update_state(
+                output_token_ids,
+                sampling_metadata.spec_token_ids,
+                repeat_indices=None,
+            )
+            logits = holder.apply_to_logits(
+                logits,
+                predict_bonus_token,
+                sampling_metadata.spec_token_ids,
+            )
         return logits
 
     @staticmethod
diff --git a/vllm/v1/sample/thinking_budget_state.py b/vllm/v1/sample/thinking_budget_state.py
new file mode 100644
index 000000000000..74599a1e8c55
--- /dev/null
+++ b/vllm/v1/sample/thinking_budget_state.py
@@ -0,0 +1,528 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Per-batch thinking token budget state; applied after penalties at sample time."""
+
+from typing import TYPE_CHECKING, Any
+
+import torch
+
+from vllm.v1.sample.logits_processor.interface import (
+    BatchUpdate,
+    MoveDirectionality,
+)
+
+if TYPE_CHECKING:
+    from vllm.config.reasoning import ReasoningConfig
+
+
+def maybe_create_thinking_budget_state_holder(
+    reasoning_config: "ReasoningConfig | None",
+    max_num_seqs: int,
+    num_spec_tokens: int,
+    device: torch.device,
+    is_pin_memory: bool,
+) -> "ThinkingBudgetStateHolder | None":
+    if reasoning_config is None:
+        return None
+    return ThinkingBudgetStateHolder(
+        reasoning_config, max_num_seqs, num_spec_tokens, device, is_pin_memory
+    )
+
+
+class ThinkingBudgetStateHolder:
+    """Tracks thinking sections and forces end tokens when budget is exceeded."""
+
+    think_start_token_ids: list[int]
+    think_end_token_ids: list[int]
+
+    def __init__(
+        self,
+        reasoning_config: "ReasoningConfig | None",
+        max_num_seqs: int,
+        num_spec_tokens: int,
+        device: torch.device,
+        is_pin_memory: bool,
+    ):
+        _ = is_pin_memory  # API parity with logits processors
+        max_num_reqs = max_num_seqs
+        self.in_spec_mode = num_spec_tokens > 0
+        self.num_spec_tokens = num_spec_tokens
+
+        # No separate enable flag: a non-``None`` ``reasoning_config`` is the switch.
+        self.is_enabled = reasoning_config is not None
+
+        if reasoning_config is None:
+            self.think_start_token_ids = []
+            self.think_end_token_ids = []
+        else:
+            rs = reasoning_config.reasoning_start_token_ids
+            re = reasoning_config.reasoning_end_token_ids
+            self.think_start_token_ids = rs if rs else []
+            self.think_end_token_ids = re if re else []
+
+        self.device = device
+        self._state: dict[int, dict[str, Any]] = {}
+        self.cu_num_tokens: dict[int, int] = {}
+
+        if self.num_spec_tokens > 0:
+            self.mask = torch.zeros(
+                max_num_reqs * (self.num_spec_tokens + 1),
+                dtype=torch.bool,
+                device=device,
+            )
+            self.force_token_ids = torch.full(
+                (max_num_reqs * (self.num_spec_tokens + 1),),
+                -1,
+                dtype=torch.long,
+                device=device,
+            )
+        else:
+            self.mask = torch.zeros(max_num_reqs, dtype=torch.bool, device=device)
+            self.force_token_ids = torch.full(
+                (max_num_reqs,), -1, dtype=torch.long, device=device
+            )
+
+    def has_tracked_requests(self) -> bool:
+        """True when ``sync_batch`` has state for a ``thinking_token_budget`` row.
+
+        Used to decide whether sampling needs output-token rows and spec combining;
+        distinct from merely having a holder instance (reasoning may be on with no
+        budgeted requests in this batch).
+        """
+        return bool(self._state)
+
+    def sync_batch(self, batch_update: BatchUpdate | None) -> None:
+        """Add/remove/move per-request state only (no _update_think_state)."""
+        if not self.is_enabled or not batch_update:
+            return
+        for index in batch_update.removed:
+            self._state.pop(index, None)
+
+        for index, params, prompt_tok_ids, output_tok_ids in batch_update.added:
+            thinking_token_budget = params.thinking_token_budget
+            if thinking_token_budget is not None:
+                self._state[index] = self._init_state_entry(
+                    prompt_tok_ids, thinking_token_budget
+                )
+                self._state[index]["output_tok_ids"] = output_tok_ids
+                self._state[index]["spec_token_ids"] = []
+            else:
+                self._state.pop(index, None)
+
+        for i1, i2, direction in batch_update.moved:
+            if direction == MoveDirectionality.SWAP:
+                state1 = self._state.get(i1)
+                state2 = self._state.get(i2)
+                if state1 is not None:
+                    self._state[i2] = state1
+                if state2 is not None:
+                    self._state[i1] = state2
+            else:
+                state = self._state.pop(i1, None)
+                if state is not None:
+                    self._state[i2] = state
+
+    def update_state(
+        self,
+        output_token_ids: list[list[int]],
+        spec_token_ids: list[list[int]] | None,
+        repeat_indices: torch.Tensor | None = None,
+    ) -> None:
+        """Refresh output/spec from sampling rows and recompute think state."""
+        if not self.is_enabled or not self._state:
+            return
+
+        spec_lists = spec_token_ids or []
+        last_row_for_req: dict[int, int] | None = None
+        if repeat_indices is not None:
+            last_row_for_req = {}
+            rpt = repeat_indices.cpu().tolist()
+            for batch_row, req_i in enumerate(rpt):
+                last_row_for_req[req_i] = batch_row
+
+        for seq_idx, state in list(self._state.items()):
+            if last_row_for_req is not None:
+                output_row: int | None = last_row_for_req.get(seq_idx)
+                if output_row is None or output_row >= len(output_token_ids):
+                    continue
+                state["output_tok_ids"] = output_token_ids[output_row]
+            elif seq_idx >= len(output_token_ids):
+                continue
+            else:
+                state["output_tok_ids"] = output_token_ids[seq_idx]
+            if seq_idx < len(spec_lists):
+                state["spec_token_ids"] = list(spec_lists[seq_idx])
+            else:
+                state["spec_token_ids"] = []
+            state["in_spec_mode"] = self.in_spec_mode
+            state["force_index"] = []
+            if len(state["output_tok_ids"]) > 0:
+                spec_len = len(state["spec_token_ids"])
+                # Only strip draft suffix when there are spec tokens; ``[:-0]`` would
+                # clear the whole list (Python treats stop index 0 as "up to empty").
+                if spec_len > 0 and len(state["output_tok_ids"]) >= spec_len:
+                    state["output_tok_ids"] = state["output_tok_ids"][:-spec_len]
+            self._update_think_state(state)
+
+    def apply_to_logits(
+        self,
+        logits: torch.Tensor,
+        predict_bonus_token: bool,
+        spec_token_ids: list[list[int]] | None,
+    ) -> torch.Tensor:
+        """Mask and bump logits for forced end-of-thinking tokens."""
+        if not self.is_enabled or not self._state:
+            return logits
+        spec_lists = spec_token_ids or []
+        return self._apply_forcing_to_logits(logits, predict_bonus_token, spec_lists)
+
+    @staticmethod
+    def _find_last_sequence_index(target_list: list[int], token_ids: list[int]) -> int:
+        if not token_ids:
+            return -1
+        for i in range(len(target_list) - len(token_ids), -1, -1):
+            if target_list[i : i + len(token_ids)] == token_ids:
+                return i
+        return -1
+
+    def _init_state_entry(
+        self, prompt_tok_ids: list[int] | None, thinking_token_budget: int
+    ) -> dict[str, Any]:
+        if prompt_tok_ids is None:
+            last_start = -1
+            last_end = -1
+            in_think = False
+            think_count = 0
+            start_thinking = -1
+            countdown = thinking_token_budget
+            continue_thinking = False
+            in_end = False
+        else:
+            start_thinking = -1
+            countdown = thinking_token_budget
+            continue_thinking = False
+            in_end = False
+            last_start = self._find_last_sequence_index(
+                prompt_tok_ids, self.think_start_token_ids
+            )
+            last_end = self._find_last_sequence_index(
+                prompt_tok_ids, self.think_end_token_ids
+            )
+            in_think = last_start > last_end
+            # load metrics such as think count, start thinking
+            # if request is in thinking mode, already
+            if in_think:
+                think_count = len(prompt_tok_ids) - (
+                    last_start + len(self.think_start_token_ids)
+                )
+                start_thinking = len(prompt_tok_ids) - think_count - 1
+                countdown -= think_count
+                continue_thinking = True
+                # check if the token is exhausted within prompt
+                token_exhausted = thinking_token_budget - think_count
+                in_end = token_exhausted <= 0
+            else:
+                think_count = 0
+
+        return {
+            "in_think": in_think,
+            "in_end": in_end,
+            "check_count_down": countdown,
+            "think_count": think_count,
+            "end_count": 0,
+            "prompt_tok_ids": prompt_tok_ids,
+            "output_tok_ids": [],
+            "thinking_token_budget": thinking_token_budget,
+            "prev_output_length": 0,
+            "spec_token_ids": [],
+            "force_index": [],
+            "start_thinking": start_thinking,
+            "end_thinking": -1,
+            "in_spec_mode": False,
+            "bonus_token_forced": False,
+            "continue_thinking": continue_thinking,
+        }
+
+    def _update_think_state(self, state: dict[str, Any]) -> None:
+        if state.get("thinking_token_budget", -1) == -1:
+            return
+        if len(self.think_end_token_ids) == 0:
+            state["thinking_token_budget"] = -1
+            state["in_end"] = False
+            state["force_index"] = []
+            return
+
+        if state["start_thinking"] == -1:
+            start_thinking = self._find_last_sequence_index(
+                state.get("output_tok_ids", []), self.think_start_token_ids
+            )
+            state["start_thinking"] = start_thinking
+        if state["end_thinking"] == -1:
+            end_thinking = self._find_last_sequence_index(
+                state.get("output_tok_ids", []), self.think_end_token_ids
+            )
+            state["end_thinking"] = end_thinking
+
+        if state["start_thinking"] == -1:
+            return
+
+        if state["continue_thinking"]:
+            sampled_tokens_from_previous_step = len(
+                state.get("output_tok_ids", [])
+            ) - state.get("prev_output_length", 0)
+        else:
+            if state["prev_output_length"] == 0:
+                sampled_tokens_from_previous_step = len(
+                    state.get("output_tok_ids", [])
+                ) - len(self.think_start_token_ids)
+            else:
+                sampled_tokens_from_previous_step = (
+                    len(state.get("output_tok_ids", [])) - state["prev_output_length"]
+                )
+        current_step_countdown = (
+            state["check_count_down"] - sampled_tokens_from_previous_step
+        )
+        predicted_countdown = current_step_countdown - len(state["spec_token_ids"]) - 1
+        # We only proceed further if we have counted down the thinking budget
+        # to 0 or less and when we are in the "in think" mode.
+        if (
+            not state.get("in_end", False)
+            and predicted_countdown >= 0
+            and state["start_thinking"] > -1
+        ):
+            state["check_count_down"] = current_step_countdown
+            state["prev_output_length"] = len(state.get("output_tok_ids", []))
+            return
+        output = state.get("output_tok_ids", [])
+        if not output:
+            # When in_end was set at init (budget=0, prompt already in think),
+            # we must force the first generated token to be the end token;
+            # otherwise apply() sees in_end=True but force_index=[] and
+            # allows an extra thinking token.
+            if state.get("in_end", False):
+                state["force_index"] = [0]
+            return
+
+        # Track previous output length for incremental processing
+        prev_length = state.get("prev_output_length", 0)
+        current_length = len(output)
+
+        if current_length <= prev_length:
+            if state.get("in_end", False):
+                remaining_budget = state["thinking_token_budget"] - state["think_count"]
+                spec_len = len(state["spec_token_ids"])
+                if spec_len > 0:
+                    if 0 < remaining_budget < spec_len:
+                        state["force_index"] = [remaining_budget]
+                    elif remaining_budget <= 0:
+                        state["force_index"] = [0]
+                    else:
+                        state["force_index"] = [spec_len]
+                else:
+                    state["force_index"] = [0]
+            return
+
+        state["prev_output_length"] = current_length
+
+        start_len = len(self.think_start_token_ids)
+        absolute_start_pos = state["start_thinking"]
+
+        if state["continue_thinking"] and state["end_thinking"] > -1:
+            absolute_end_pos = state["end_thinking"] + len(
+                state.get("prompt_tok_ids") or []
+            )
+        else:
+            absolute_end_pos = state["end_thinking"]
+        # Update state based on recent sequences
+        # This is the case where we are in end mode, but the rejection sampler
+        # rejected a token before the end token,
+        # so we need to go back to think mode and wait for the next end token
+        # eg with 999: [2,4,5,999] -> [3,-1,-1,-1]
+        if state["in_end"] and state["end_count"] == 0:
+            new_tokens = output[prev_length:]
+            stopping_thinking = (
+                self.think_end_token_ids[state["end_count"]] in new_tokens
+            )
+            if not stopping_thinking:
+                state["in_think"] = True
+                state["in_end"] = False
+                state["end_count"] = 0
+                state["bonus_token_forced"] = False
+
+        if not state["in_end"]:
+            if absolute_start_pos >= 0 and absolute_end_pos >= 0:
+                # Case: ...<end>...<start>... - entering think mode
+                if absolute_start_pos > absolute_end_pos:
+                    new_think_count = current_length - (absolute_start_pos + start_len)
+                    state["in_think"] = True
+                    state["think_count"] = new_think_count
+                else:
+                    # Case: ...<start>...<end>... - exiting think mode
+                    state["in_think"] = False
+                    state["think_count"] = 0
+
+            elif absolute_start_pos >= 0 and not state["continue_thinking"]:
+                # Found think start - entering think mode
+                new_think_count = current_length - (absolute_start_pos + start_len)
+                state["in_think"] = True
+                state["think_count"] = new_think_count
+
+            elif absolute_end_pos >= 0:
+                # Found think end - exiting think mode
+                state["in_think"] = False
+                state["think_count"] = 0
+
+            elif state["in_think"]:
+                # Continue thinking mode, increment count by new tokens
+                prompt_tok_ids = state.get("prompt_tok_ids") or []
+                think_tokens_in_prompt = len(prompt_tok_ids) - (
+                    absolute_start_pos + start_len
+                )
+                state["think_count"] = (
+                    len(state["output_tok_ids"]) + think_tokens_in_prompt
+                )
+            if state["in_think"]:
+                remaining_budget = max(
+                    0, state["thinking_token_budget"] - state["think_count"]
+                )
+                state["check_count_down"] = remaining_budget
+            else:
+                state["check_count_down"] = state["thinking_token_budget"]
+
+            total_thinking_tokens = (
+                state["think_count"] + len(state["spec_token_ids"]) + 1
+            )
+            # Check if need to transition to end mode
+            # If we have more thinking tokens than the budget,
+            # we need to transition to end mode
+            if (
+                state["in_think"]
+                and total_thinking_tokens > state["thinking_token_budget"]
+            ):
+                # Calculate force_index: position within spec_token_ids where
+                # forcing starts. If we're already over budget without spec
+                # tokens, force from position 0. Force from the position
+                # where budget is exceeded.
+                state["in_think"] = False
+                state["in_end"] = True
+                state["end_count"] = 0
+                state["check_count_down"] = state["thinking_token_budget"]
+                remaining_budget = state["thinking_token_budget"] - state["think_count"]
+                spec_len = len(state["spec_token_ids"])
+                if 0 < remaining_budget < spec_len:
+                    state["force_index"] = [remaining_budget]
+
+                elif remaining_budget <= 0:
+                    state["force_index"] = [0]
+
+                else:
+                    # remaining_budget >= spec_len: all spec tokens are within
+                    # budget; force the bonus token position
+                    state["force_index"] = [len(state["spec_token_ids"])]
+
+        else:
+            state["force_index"] = []
+            if len(state["spec_token_ids"]) > 0:
+                for i, token_id in enumerate(state["spec_token_ids"]):
+                    if state["end_count"] + 1 < len(self.think_end_token_ids):
+                        if token_id == self.think_end_token_ids[state["end_count"] + 1]:
+                            state["end_count"] += 1
+                        else:
+                            state["end_count"] += 1
+                            state["force_index"] = [i]
+                            break
+                    else:
+                        state["end_count"] += 1
+                if len(state["force_index"]) == 0:
+                    state["end_count"] += 1
+                    state["force_index"] = [len(state["spec_token_ids"])]
+            else:
+                state["end_count"] += 1
+                state["force_index"] = [0]
+            if state["end_count"] >= len(self.think_end_token_ids):
+                state.update(
+                    {
+                        "in_end": False,
+                        "end_count": 0,
+                        "check_count_down": state["thinking_token_budget"],
+                    }
+                )
+
+    def _apply_forcing_to_logits(
+        self,
+        logits: torch.Tensor,
+        predict_bonus_token: bool,
+        spec_token_ids_for_layout: list[list[int]],
+    ) -> torch.Tensor:
+        self.mask[:] = False
+        cumulative_total = 0
+        self.cu_num_tokens.clear()
+
+        n_layout = len(spec_token_ids_for_layout)
+        if self._state:
+            n_layout = max(n_layout, max(self._state.keys()) + 1)
+
+        for index in range(n_layout):
+            self.cu_num_tokens[index] = cumulative_total
+            spec_tokens = (
+                spec_token_ids_for_layout[index]
+                if index < len(spec_token_ids_for_layout)
+                else []
+            )
+            if self.in_spec_mode:
+                cumulative_total += len(spec_tokens) if not predict_bonus_token else 1
+            else:
+                cumulative_total += 1
+
+        for seq_idx in sorted(self._state.keys()):
+            if seq_idx not in self.cu_num_tokens:
+                continue
+            state = self._state[seq_idx]
+            if state.get("in_end", False):
+                # logits processor in spec mode are called twice
+                # once for bonus token logits and
+                # second time for the target logits
+                # in case the force index is bonus token index
+                # we change the force index to 0
+                if predict_bonus_token:
+                    if state.get("force_index") and state["force_index"][0] < len(
+                        state["spec_token_ids"]
+                    ):
+                        continue
+                    else:
+                        state["force_index"] = [0]
+                # continue enforcing the end thinking tokens
+                if state["end_count"] > 0:
+                    state["bonus_token_forced"] = False
+                if state and not state["bonus_token_forced"]:
+                    force_index = state.get("force_index", [])
+                    if len(force_index) == 0:
+                        continue
+                    end_count = state.get("end_count", 0)
+                    for force_idx in force_index:
+                        if end_count < len(self.think_end_token_ids):
+                            mask_idx = self.cu_num_tokens[seq_idx] + force_idx
+                            if mask_idx < len(self.mask) and mask_idx < logits.shape[0]:
+                                self.mask[mask_idx] = True
+                                self.force_token_ids[mask_idx] = (
+                                    self.think_end_token_ids[end_count]
+                                )
+                            if predict_bonus_token:
+                                if state["end_count"] > 0:
+                                    state["bonus_token_forced"] = False
+                                    state["force_index"] = []
+                                else:
+                                    state["bonus_token_forced"] = True
+
+        has_active_thinking = any(
+            state.get("in_end", False) for state in self._state.values()
+        )
+
+        if has_active_thinking:
+            active_indices = self.mask.nonzero(as_tuple=False).view(-1)
+
+            if len(active_indices) > 0:
+                force_tokens = self.force_token_ids[active_indices]
+                logits[active_indices, force_tokens] = 1e9
+
+        return logits
diff --git a/vllm/v1/simple_kv_offload/cuda_mem_ops.py b/vllm/v1/simple_kv_offload/cuda_mem_ops.py
index 03338421c457..b4c68aff3ca9 100644
--- a/vllm/v1/simple_kv_offload/cuda_mem_ops.py
+++ b/vllm/v1/simple_kv_offload/cuda_mem_ops.py
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Low-level CUDA memory helpers: pinning and batch DMA transfers."""
+"""Low-level CUDA/HIP memory helpers: pinning and batch DMA transfers."""
 
 import ctypes
 from typing import Any, NamedTuple
@@ -9,6 +9,7 @@
 import torch
 
 from vllm.logger import init_logger
+from vllm.platforms import current_platform
 
 logger = init_logger(__name__)
 
@@ -39,7 +40,7 @@ class _CUmemcpyAttributes(ctypes.Structure):
 
 
 _BATCH_MEMCPY_FUNC_TYPE = ctypes.CFUNCTYPE(
-    ctypes.c_uint,  # CUresult
+    ctypes.c_uint,  # CUresult / hipError_t
     ctypes.c_void_p,
     ctypes.c_void_p,
     ctypes.c_void_p,
@@ -56,7 +57,42 @@ class _CUmemcpyAttributes(ctypes.Structure):
 
 
 def _resolve_batch_memcpy():
-    """Resolve cuMemcpyBatchAsync via cuGetProcAddress (one-time)."""
+    """Resolve the platform batch-memcpy entry point (one-time).
+
+    * CUDA: ``cuMemcpyBatchAsync`` via ``cuGetProcAddress`` (uses
+      srcAccessOrder=STREAM via one attributes entry).
+    * ROCm: ``hipMemcpyBatchAsync`` from libamdhip64 (ROCm 7.1+). ROCm
+      7.2.1 or 7.2.2 rejects any call with ``numAttrs > 0``
+      (see ROCm/clr @ rocm-7.2.1 hipamd/src/hip_memory.cpp:2819-2822), so
+      we call with ``numAttrs=0``.
+
+    Raises ``RuntimeError`` if the symbol is unavailable (older CUDA
+    driver, ROCm < 7.1, unusual install). The connector requires the
+    batch API.
+    """
+    if current_platform.is_rocm():
+        try:
+            lib = ctypes.CDLL("libamdhip64.so", mode=ctypes.RTLD_GLOBAL)
+            fn = lib.hipMemcpyBatchAsync
+        except (OSError, AttributeError) as e:
+            raise RuntimeError(
+                "hipMemcpyBatchAsync is unavailable in this ROCm install; "
+                "SimpleCPUOffloadConnector requires ROCm 7.1+."
+            ) from e
+        fn.restype = ctypes.c_uint
+        fn.argtypes = [
+            ctypes.c_void_p,  # dsts
+            ctypes.c_void_p,  # srcs
+            ctypes.c_void_p,  # sizes
+            ctypes.c_size_t,  # count
+            ctypes.c_void_p,  # attrs
+            ctypes.c_void_p,  # attrIdxs
+            ctypes.c_size_t,  # numAttrs
+            ctypes.c_void_p,  # failIdx
+            ctypes.c_void_p,  # stream
+        ]
+        return fn
+
     from cuda.bindings import driver as drv
 
     err, ptr, _ = drv.cuGetProcAddress(b"cuMemcpyBatchAsync", 12080, 0)
@@ -70,6 +106,8 @@ class BatchMemcpyParams(NamedTuple):
     dst_bases: np.ndarray  # [num_layers] uint64
     bpb: np.ndarray  # [num_layers] uint64 — bytes per block
     num_layers: int
+    # CUDA only: one attributes entry with srcAccessOrder=ANY. Unused on
+    # ROCm (7.2.1 or 7.2.2) because the current runtime rejects numAttrs > 0.
     attrs: _CUmemcpyAttributes
     attrs_idx: ctypes.c_size_t
     # NOTE: cuMemcpyBatchAsync_v2() removed fail_idx field, but we use
@@ -99,8 +137,10 @@ def build_params(
         dst_bases.append(d.data_ptr())
         bpb.append(s_bpb)
 
-    # Refer to https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__MEM.html#group__CUDA__MEM_1g6f1ff58e3065df3eb4b573dba77ad31f for details.  # noqa: E501
-    attrs = _CUmemcpyAttributes(srcAccessOrder=3)  # ANY
+    # ``srcAccessOrder=3`` == CU_MEMCPY_SRC_ACCESS_ORDER_ANY /
+    # hipMemcpySrcAccessOrderAny. See
+    # https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__MEM.html#group__CUDA__MEM_1g6f1ff58e3065df3eb4b573dba77ad31f  # noqa: E501
+    attrs = _CUmemcpyAttributes(srcAccessOrder=3)
 
     return BatchMemcpyParams(
         src_bases=np.array(src_bases, dtype=np.uint64),
@@ -119,7 +159,7 @@ def copy_blocks(
     dst_block_ids: list[int],
     params: BatchMemcpyParams,
 ) -> None:
-    """Copy blocks via cuMemcpyBatchAsync."""
+    """Copy blocks via cuMemcpyBatchAsync / hipMemcpyBatchAsync."""
     n = len(src_block_ids)
     if n == 0:
         return
@@ -134,8 +174,13 @@ def copy_blocks(
         params.dst_bases[:, None] + dst_ids[None, :] * params.bpb[:, None]
     ).ravel()
     sz_all = np.repeat(params.bpb, n)
-
     total = n * params.num_layers
+
+    # ROCm 7.2.1/7.2.2 rejects any call with numAttrs>0 (hipMemcpyBatchAsync
+    # hipamd/src/hip_memory.cpp:2819-2822); CUDA uses one attrs entry so
+    # srcAccessOrder is honored. attrs / attrsIdxs are ignored when
+    # numAttrs==0, so we pass the same values from both paths.
+    num_attrs = 0 if current_platform.is_rocm() else 1
     err = _batch_memcpy_fn(
         dst_all.ctypes.data,
         src_all.ctypes.data,
@@ -143,11 +188,11 @@ def copy_blocks(
         total,
         ctypes.addressof(params.attrs),
         ctypes.byref(params.attrs_idx),
-        1,
+        num_attrs,
         ctypes.byref(params.fail_idx),
         params.stream_handle,
     )
     if err != 0:
         raise RuntimeError(
-            f"cuMemcpyBatchAsync failed: err={err} failIdx={params.fail_idx.value}"
+            f"batch memcpy failed: err={err} failIdx={params.fail_idx.value}"
         )
diff --git a/vllm/v1/simple_kv_offload/manager.py b/vllm/v1/simple_kv_offload/manager.py
index 5eedc07f717e..846526e5bee4 100644
--- a/vllm/v1/simple_kv_offload/manager.py
+++ b/vllm/v1/simple_kv_offload/manager.py
@@ -110,6 +110,9 @@ def __init__(
         self.cpu_coordinator: KVCacheCoordinator = get_kv_cache_coordinator(
             kv_cache_config=self.cpu_kv_cache_config,
             max_model_len=vllm_config.model_config.max_model_len,
+            max_num_batched_tokens=(
+                vllm_config.scheduler_config.max_num_batched_tokens
+            ),
             use_eagle=False,
             enable_caching=True,
             enable_kv_cache_events=self.enable_kv_cache_events,
diff --git a/vllm/v1/spec_decode/dflash.py b/vllm/v1/spec_decode/dflash.py
index 51916053c7d3..db74044f4fde 100644
--- a/vllm/v1/spec_decode/dflash.py
+++ b/vllm/v1/spec_decode/dflash.py
@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from dataclasses import replace
 from typing import Any
 
 import torch
@@ -11,7 +12,7 @@
 from vllm.logger import init_logger
 from vllm.triton_utils import triton
 from vllm.v1.attention.backend import CommonAttentionMetadata
-from vllm.v1.spec_decode.eagle import SpecDecodeBaseProposer
+from vllm.v1.spec_decode.llm_base_proposer import SpecDecodeBaseProposer
 from vllm.v1.spec_decode.utils import copy_and_expand_dflash_inputs_kernel
 
 logger = init_logger(__name__)
@@ -68,9 +69,19 @@ def __init__(
         self.parallel_drafting_hidden_state_tensor = None
 
     @override
-    def _raise_if_multimodal(self):
+    def _create_draft_vllm_config(self) -> VllmConfig:
+        base = super()._create_draft_vllm_config()
+        return replace(
+            base,
+            attention_config=replace(
+                base.attention_config,
+                use_non_causal=True,
+            ),
+        )
+
+    @override
+    def _warn_if_multimodal(self):
         # Override to allow multimodal inputs since DFlash supports Qwen3.5 models
-        # Support for multimodal inputs has not been tested.
         pass
 
     @override
@@ -151,6 +162,12 @@ def set_inputs_first_pass(
         if has_num_rejected:
             effective_seq_lens = effective_seq_lens - num_rejected_tokens_gpu
 
+        # Skip num_rejected_tokens (GPU-only); overestimating is fine here.
+        new_seq_lens_cpu_upper_bound = (
+            cad.seq_lens_cpu_upper_bound + num_query_per_req
+            if cad.seq_lens_cpu_upper_bound is not None
+            else None
+        )
         new_cad = CommonAttentionMetadata(
             query_start_loc=new_query_start_loc,
             seq_lens=effective_seq_lens + num_query_per_req,
@@ -160,6 +177,7 @@ def set_inputs_first_pass(
             ),
             _seq_lens_cpu=None,
             _num_computed_tokens_cpu=None,
+            seq_lens_cpu_upper_bound=new_seq_lens_cpu_upper_bound,
             num_reqs=cad.num_reqs,
             num_actual_tokens=num_query_total,
             max_query_len=num_query_per_req,
diff --git a/vllm/v1/spec_decode/draft_model.py b/vllm/v1/spec_decode/draft_model.py
index 9633e2ef6ca2..a8c8ab03b615 100644
--- a/vllm/v1/spec_decode/draft_model.py
+++ b/vllm/v1/spec_decode/draft_model.py
@@ -9,7 +9,7 @@
 from vllm.config.utils import replace
 from vllm.logger import init_logger
 from vllm.model_executor.model_loader import get_model
-from vllm.v1.spec_decode.eagle import SpecDecodeBaseProposer
+from vllm.v1.spec_decode.llm_base_proposer import SpecDecodeBaseProposer
 
 logger = init_logger(__name__)
 
diff --git a/vllm/v1/spec_decode/eagle.py b/vllm/v1/spec_decode/eagle.py
index f22e15b79f6f..002d0b7833a4 100644
--- a/vllm/v1/spec_decode/eagle.py
+++ b/vllm/v1/spec_decode/eagle.py
@@ -1,1735 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import ast
-from importlib.util import find_spec
-from typing import Any, cast
 
-import numpy as np
 import torch
-import torch.nn as nn
 
-from vllm.config import (
-    CUDAGraphMode,
-    VllmConfig,
-    get_layers_from_vllm_config,
-    replace,
-)
-from vllm.distributed.parallel_state import get_pp_group
-from vllm.forward_context import set_forward_context
-from vllm.logger import init_logger
-from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
-from vllm.model_executor.model_loader import get_model
-from vllm.model_executor.models import supports_multimodal
-from vllm.model_executor.models.deepseek_eagle3 import Eagle3DeepseekV2ForCausalLM
-from vllm.model_executor.models.interfaces import SupportsMultiModal
-from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
-from vllm.model_executor.models.qwen3_dflash import DFlashQwen3ForCausalLM
-from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.platforms import current_platform
-from vllm.utils.platform_utils import is_pin_memory_available
-from vllm.v1.attention.backend import CommonAttentionMetadata
-from vllm.v1.attention.backends.registry import AttentionBackendEnum
-from vllm.v1.attention.backends.tree_attn import (
-    TreeAttentionMetadata,
-    TreeAttentionMetadataBuilder,
-)
-from vllm.v1.attention.backends.triton_attn import TritonAttentionMetadata
-from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
-from vllm.v1.kv_cache_interface import KVCacheConfig, UniformTypeKVCacheSpecs
-from vllm.v1.sample.metadata import SamplingMetadata
-from vllm.v1.sample.sampler import _SAMPLING_EPS
-from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
-from vllm.v1.spec_decode.utils import (
-    PADDING_SLOT_ID,
-    compute_new_slot_mapping,
-    copy_and_expand_eagle_inputs_kernel,
-    eagle_prepare_inputs_padded_kernel,
-    eagle_prepare_next_token_padded_kernel,
-    eagle_step_update_slot_mapping_and_metadata,
-    extend_all_queries_by_N,
-    next_power_of_2,
-)
-from vllm.v1.utils import CpuGpuBuffer
-from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
-from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
-from vllm.v1.worker.utils import AttentionGroup
-
-logger = init_logger(__name__)
-
-
-class SpecDecodeBaseProposer:
-    def __init__(
-        self,
-        vllm_config: VllmConfig,
-        device: torch.device,
-        pass_hidden_states_to_model: bool,
-        runner=None,
-    ):
-        self.vllm_config = vllm_config
-        assert vllm_config.speculative_config is not None
-        self.speculative_config = vllm_config.speculative_config
-        self.draft_model_config = self.speculative_config.draft_model_config
-        self.method = self.speculative_config.method
-        self.pass_hidden_states_to_model = pass_hidden_states_to_model
-
-        self.device = device
-        self.dtype = vllm_config.model_config.dtype
-        self.max_model_len = vllm_config.model_config.max_model_len
-        self.dp_rank = vllm_config.parallel_config.data_parallel_rank
-        self.num_speculative_tokens = self.speculative_config.num_speculative_tokens
-
-        # We need to get the hidden size from the draft model config because
-        # the draft model's hidden size can be different from the target model's
-        # hidden size (e.g., Llama 3.3 70B).
-        self.hidden_size = self.draft_model_config.get_hidden_size()
-        self.inputs_embeds_size = self.draft_model_config.get_inputs_embeds_size()
-
-        # Unifying eagle, draft model, and parallel drafting support.
-        # DFlash always uses parallel drafting (all tokens in one pass),
-        # but has an additional slot for the next_token_id (does not shift like EAGLE)
-        self.parallel_drafting: bool = self.speculative_config.parallel_drafting
-        self.extra_slots_per_request = (
-            1 if not self.parallel_drafting else self.num_speculative_tokens
-        )
-        self.net_num_new_slots_per_request = self.extra_slots_per_request - (
-            1 if (self.pass_hidden_states_to_model and self.method != "dflash") else 0
-        )
-        self.needs_extra_input_slots = self.net_num_new_slots_per_request > 0
-
-        self.parallel_drafting_token_id: int = 0
-        self.parallel_drafting_hidden_state_tensor: torch.Tensor | None = None
-        if self.parallel_drafting:
-            self._init_parallel_drafting_params()
-        self.use_local_argmax_reduction: bool = (
-            self.speculative_config.use_local_argmax_reduction
-        )
-
-        self.max_batch_size = vllm_config.scheduler_config.max_num_seqs
-        self.max_num_tokens = vllm_config.scheduler_config.max_num_batched_tokens
-        self.token_arange_np = np.arange(self.max_num_tokens)
-
-        # Can be specialized by methods like DFlash to reduce the limit
-        self.max_query_tokens = self.max_num_tokens
-        self.max_positions = self.max_num_tokens
-
-        # Multi-modal data support
-        self.mm_registry = MULTIMODAL_REGISTRY
-        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
-            vllm_config.model_config
-        )
-
-        self.draft_attn_groups: list[AttentionGroup] = []
-        self.kv_cache_gid: int = -1
-        self.eagle3_use_aux_hidden_state: bool = (
-            self._get_eagle3_use_aux_hidden_state_from_config()
-        )
-
-        self.compilation_config = self.vllm_config.compilation_config
-
-        # Cudagraph dispatcher for PIECEWISE-only dispatching in eagle.
-        # Keys are initialized later via initialize_cudagraph_keys() called from
-        # gpu_model_runner._check_and_update_cudagraph_mode after
-        # adjust_cudagraph_sizes_for_spec_decode is called.
-        self.cudagraph_dispatcher = CudagraphDispatcher(self.vllm_config)
-
-        # persistent buffers for cuda graph
-        self.input_ids = torch.zeros(
-            self.max_num_tokens, dtype=torch.int32, device=device
-        )
-        # Use draft model's M-RoPE setting, not target model's
-        # Draft models may be text-only even if target is multimodal
-        self.uses_mrope = self.draft_model_config.uses_mrope
-        self.uses_xdrope_dim = self.vllm_config.model_config.uses_xdrope_dim
-        self.draft_uses_xdrope_dim = self.draft_model_config.uses_xdrope_dim
-        if self.uses_mrope:
-            # NOTE: `mrope_positions` is implemented with one additional dummy
-            # position on purpose to make it non-contiguous so that it can work
-            # with torch compile.
-            # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
-
-            # NOTE: When M-RoPE is enabled, position ids are 3D regardless of
-            # the modality of inputs. For text-only inputs, each dimension has
-            # identical position IDs, making M-RoPE functionally equivalent to
-            # 1D-RoPE.
-            # See page 5 of https://arxiv.org/abs/2409.12191
-            self.mrope_positions = torch.zeros(
-                (3, self.max_positions + 1), dtype=torch.int64, device=device
-            )
-        elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
-            self.xdrope_positions = torch.zeros(
-                (self.uses_xdrope_dim, self.max_positions + 1),
-                dtype=torch.int64,
-                device=device,
-            )
-        else:
-            # RoPE need (max_num_tokens,)
-            self.positions = torch.zeros(
-                self.max_positions,
-                dtype=torch.int64,
-                device=device,
-            )
-        self.hidden_states = torch.zeros(
-            (self.max_num_tokens, self.hidden_size), dtype=self.dtype, device=device
-        )
-
-        # Will be set when we initialize the attention backend
-        self.block_size: int = -1
-
-        # We need +1 here because the arange is used to set query_start_loc,
-        # which has one more element than batch_size.
-        max_num_slots_for_arange = max(self.max_batch_size + 1, self.max_num_tokens)
-        self.arange = torch.arange(
-            max_num_slots_for_arange, device=device, dtype=torch.int32
-        )
-
-        if self.needs_extra_input_slots:
-            self._raise_if_padded_drafter_batch_disabled()
-            self._raise_if_multimodal()
-            self._raise_if_mrope()
-
-        self.is_rejected_token_mask: torch.Tensor | None = None
-        self.is_masked_token_mask: torch.Tensor | None = None
-        if self.needs_extra_input_slots:
-            # For draft models and parallel drafting, we need to keep track of
-            # which tokens are rejected to update the slot mapping with padding slots.
-            self.is_rejected_token_mask = torch.zeros(
-                (self.max_num_tokens,), dtype=torch.bool, device=device
-            )
-            # For parallel drafting, we also need to keep track of which tokens
-            # are parallel-padding tokens used to sample at later positions.
-            # We populate this tensor even when using draft models for simplicity.
-            self.is_masked_token_mask = torch.zeros(
-                (self.max_num_tokens,), dtype=torch.bool, device=device
-            )
-
-        self.inputs_embeds = torch.zeros(
-            (self.max_num_tokens, self.inputs_embeds_size),
-            dtype=self.dtype,
-            device=device,
-        )
-
-        self.backup_next_token_ids = CpuGpuBuffer(
-            self.max_batch_size,
-            dtype=torch.int32,
-            pin_memory=is_pin_memory_available(),
-            device=device,
-            with_numpy=True,
-        )
-
-        self._slot_mapping_buffer = torch.zeros(
-            self.max_positions,
-            dtype=torch.int64,
-            device=device,
-        )
-
-        # Determine allowed attention backends once during initialization.
-        self.allowed_attn_types: tuple | None = None
-        if current_platform.is_rocm():
-            from vllm.v1.attention.backends.mla.indexer import (
-                DeepseekV32IndexerMetadata,
-            )
-            from vllm.v1.attention.backends.mla.rocm_aiter_mla_sparse import (
-                ROCMAiterMLASparseMetadata,
-            )
-            from vllm.v1.attention.backends.rocm_attn import RocmAttentionMetadata
-
-            rocm_types = [
-                TritonAttentionMetadata,
-                RocmAttentionMetadata,
-                ROCMAiterMLASparseMetadata,
-                DeepseekV32IndexerMetadata,
-            ]
-            # ROCM_AITER_FA is an optional backend
-            # We check is_enabled() here to avoid importing the backend module during
-            # auto-discovery when VLLM_ROCM_USE_AITER=0, which would trigger aiter
-            # import and JIT compilation warnings. Explicit backend selection via
-            # attention_config still works because the backend module is loaded
-            # directly when selected, not through this auto-discovery path.
-            # Check if backend module exists to allow explicit selection
-            if find_spec(
-                AttentionBackendEnum.ROCM_AITER_FA.get_path(include_classname=False)
-            ):
-                from vllm.v1.attention.backends.rocm_aiter_fa import (
-                    AiterFlashAttentionMetadata,
-                )
-
-                rocm_types.append(AiterFlashAttentionMetadata)
-
-            # TRITON_MLA backend support for MLA models (e.g., DeepSeek)
-            from vllm.model_executor.layers.attention.mla_attention import (
-                MLACommonMetadata,
-            )
-
-            rocm_types.append(MLACommonMetadata)
-
-            # FlexAttention backend support
-            from vllm.v1.attention.backends.flex_attention import FlexAttentionMetadata
-
-            rocm_types.append(FlexAttentionMetadata)
-
-            self.allowed_attn_types = tuple(rocm_types)
-
-        # Parse the speculative token tree.
-        spec_token_tree = self.speculative_config.speculative_token_tree
-        assert spec_token_tree is not None
-        self.tree_choices: list[tuple[int, ...]] = ast.literal_eval(spec_token_tree)
-        tree_depth = len(self.tree_choices[-1])
-        # Precompute per-level properties of the tree.
-        num_drafts_per_level = [0] * tree_depth
-        for node in self.tree_choices:
-            num_drafts_per_level[len(node) - 1] += 1
-        self.cu_drafts_per_level = [num_drafts_per_level[0]]
-        self.child_drafts_per_level = [num_drafts_per_level[0]]
-        for level in range(1, tree_depth):
-            self.cu_drafts_per_level.append(
-                self.cu_drafts_per_level[-1] + num_drafts_per_level[level]
-            )
-            self.child_drafts_per_level.append(
-                num_drafts_per_level[level] // num_drafts_per_level[level - 1]
-            )
-        # Precompute draft position offsets in flattened tree.
-        self.tree_draft_pos_offsets = torch.arange(
-            1, len(self.tree_choices) + 1, device=device, dtype=torch.int32
-        ).repeat(self.max_batch_size, 1)
-
-    def _raise_if_padded_drafter_batch_disabled(self):
-        if self.speculative_config.disable_padded_drafter_batch:
-            raise NotImplementedError(
-                "Speculative Decoding with draft models or parallel drafting only "
-                "supports padded drafter batch. Please unset "
-                "disable_padded_drafter_batch in the speculative_config."
-            )
-
-    def _raise_if_multimodal(self):
-        if self.supports_mm_inputs:
-            raise NotImplementedError(
-                "Speculative Decoding with draft models or parallel drafting "
-                "does not support multimodal models yet"
-            )
-
-    def _raise_if_mrope(self):
-        if self.draft_model_config.uses_mrope:
-            raise NotImplementedError(
-                "Speculative Decoding with draft models or parallel drafting "
-                "does not support M-RoPE yet"
-            )
-
-    def _init_parallel_drafting_params(self):
-        # For parallel drafting, we need the token ID to use for masked slots
-        # And for EAGLE + parallel drafting, we need the hidden state tensor to use
-        # for those masked slots.
-
-        model_hf_config = self.draft_model_config.hf_config
-        # DFlash stores mask_token_id in dflash_config
-        dflash_config = getattr(model_hf_config, "dflash_config", None)
-        if dflash_config and "mask_token_id" in dflash_config:
-            self.parallel_drafting_token_id = dflash_config["mask_token_id"]
-        elif hasattr(model_hf_config, "pard_token"):
-            self.parallel_drafting_token_id = model_hf_config.pard_token
-        elif hasattr(model_hf_config, "ptd_token_id"):
-            self.parallel_drafting_token_id = model_hf_config.ptd_token_id
-        else:
-            raise ValueError(
-                "For parallel drafting, the draft model config must have "
-                "`pard_token`, `ptd_token_id`, or "
-                "`dflash_config.mask_token_id` specified in its config.json."
-            )
-
-        if self.pass_hidden_states_to_model:
-            self.parallel_drafting_hidden_state_tensor = torch.empty(
-                self.hidden_size, dtype=self.dtype, device=self.device
-            )
-
-    def _get_positions(self, num_tokens: int):
-        if self.uses_mrope:
-            return self.mrope_positions[:, :num_tokens]
-        if self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
-            return self.xdrope_positions[:, :num_tokens]
-        return self.positions[:num_tokens]
-
-    def _set_positions(self, num_tokens: int, positions: torch.Tensor):
-        if self.uses_mrope:
-            self.mrope_positions[:, :num_tokens] = positions
-        elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
-            self.xdrope_positions[:, :num_tokens] = positions
-        else:
-            # Convert M-RoPE positions if target model uses M-RoPE
-            # but draft doesn't, For text inputs, all M-RoPE
-            # dimensions are identical
-            if self.vllm_config.model_config.uses_mrope:
-                positions = positions[0]
-            self.positions[:num_tokens] = positions
-
-    def _get_slot_mapping(
-        self,
-        num_tokens: int,
-        slot_mapping: torch.Tensor | None = None,
-    ) -> dict[str, torch.Tensor]:
-        """Return slot_mapping dict for EAGLE layers.
-
-        If slot_mapping is provided, copies it into the buffer first.
-        """
-        if slot_mapping is not None:
-            num_actual = slot_mapping.shape[0]
-            self._slot_mapping_buffer[:num_actual].copy_(slot_mapping)
-            if num_tokens > num_actual:
-                self._slot_mapping_buffer[num_actual:num_tokens].fill_(PADDING_SLOT_ID)
-
-        view = self._slot_mapping_buffer[:num_tokens]
-        return {name: view for name in self._draft_attn_layer_names}
-
-    def initialize_cudagraph_keys(self, cudagraph_mode: CUDAGraphMode) -> None:
-        """Initialize cudagraph dispatcher keys for eagle.
-
-        Eagle only supports PIECEWISE cudagraphs (via mixed_mode).
-        This should be called after adjust_cudagraph_sizes_for_spec_decode.
-        """
-        if (
-            not self.speculative_config.enforce_eager
-            and cudagraph_mode.mixed_mode()
-            in [CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL]
-        ):
-            eagle_cudagraph_mode = CUDAGraphMode.PIECEWISE
-        else:
-            eagle_cudagraph_mode = CUDAGraphMode.NONE
-
-        self.cudagraph_dispatcher.initialize_cudagraph_keys(eagle_cudagraph_mode)
-
-    def _greedy_sample(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        """Greedy-sample draft tokens from hidden states."""
-        if self.use_local_argmax_reduction:
-            return self.model.get_top_tokens(hidden_states)
-        return self.model.compute_logits(hidden_states).argmax(dim=-1)
-
-    def propose(
-        self,
-        # [num_tokens]
-        target_token_ids: torch.Tensor,
-        # [num_tokens] or [3, num_tokens] when M-RoPE is enabled
-        target_positions: torch.Tensor,
-        # [num_tokens, hidden_size]
-        target_hidden_states: torch.Tensor,
-        # [batch_size]
-        next_token_ids: torch.Tensor,
-        token_indices_to_sample: torch.Tensor | None,
-        common_attn_metadata: CommonAttentionMetadata,
-        sampling_metadata: SamplingMetadata,
-        mm_embed_inputs: tuple[list[torch.Tensor], torch.Tensor] | None = None,
-        num_rejected_tokens_gpu: torch.Tensor | None = None,
-        slot_mappings: dict[str, torch.Tensor]
-        | list[dict[str, torch.Tensor]]
-        | None = None,
-    ) -> torch.Tensor:
-        batch_size = common_attn_metadata.batch_size()
-
-        if self.method in ("eagle3", "dflash"):
-            assert isinstance(
-                self.model,
-                (
-                    Eagle3LlamaForCausalLM,
-                    Eagle3DeepseekV2ForCausalLM,
-                    DFlashQwen3ForCausalLM,
-                ),
-            )
-            target_hidden_states = self.model.combine_hidden_states(
-                target_hidden_states
-            )
-            assert target_hidden_states.shape[-1] == self.hidden_size
-
-        num_tokens, token_indices_to_sample, common_attn_metadata = (
-            self.set_inputs_first_pass(
-                target_token_ids=target_token_ids,
-                next_token_ids=next_token_ids,
-                target_positions=target_positions,
-                target_hidden_states=target_hidden_states,
-                token_indices_to_sample=token_indices_to_sample,
-                cad=common_attn_metadata,
-                num_rejected_tokens_gpu=num_rejected_tokens_gpu,
-            )
-        )
-
-        per_group_attn_metadata, per_layer_attn_metadata = (
-            self.build_per_group_and_layer_attn_metadata(common_attn_metadata)
-        )
-
-        cudagraph_runtime_mode, num_input_tokens, num_tokens_across_dp = (
-            self._determine_batch_execution_and_padding(num_tokens)
-        )
-
-        model_kwargs, slot_mapping_size = self.build_model_inputs_first_pass(
-            num_tokens, num_input_tokens, mm_embed_inputs
-        )
-
-        with set_forward_context(
-            per_layer_attn_metadata,
-            self.vllm_config,
-            num_tokens=num_input_tokens,
-            num_tokens_across_dp=num_tokens_across_dp,
-            cudagraph_runtime_mode=cudagraph_runtime_mode,
-            slot_mapping=self._get_slot_mapping(
-                slot_mapping_size, common_attn_metadata.slot_mapping
-            ),
-        ):
-            ret_hidden_states = self.model(**model_kwargs)
-            if not self.model_returns_tuple():
-                last_hidden_states = ret_hidden_states
-                hidden_states = last_hidden_states
-            else:
-                last_hidden_states, hidden_states = ret_hidden_states
-
-        sample_hidden_states = last_hidden_states[token_indices_to_sample]
-
-        # Early exit if there is only one draft token to be generated.
-        if self.num_speculative_tokens == 1 or self.parallel_drafting:
-            draft_token_ids = self._greedy_sample(sample_hidden_states)
-            return draft_token_ids.view(-1, self.num_speculative_tokens)
-
-        if self.uses_mrope:
-            positions = self.mrope_positions[:, token_indices_to_sample]
-        else:
-            positions = self.positions[token_indices_to_sample]
-        hidden_states = hidden_states[token_indices_to_sample]
-
-        if any(isinstance(md, TreeAttentionMetadata) for md in per_group_attn_metadata):
-            # Draft using tree attention - requires full logits for top-k
-            logits = self.model.compute_logits(sample_hidden_states)
-            draft_token_ids_list = self.propose_tree(
-                batch_size=batch_size,
-                logits=logits,
-                positions=positions,
-                hidden_states=hidden_states,
-                common_attn_metadata=common_attn_metadata,
-                slot_mappings=slot_mappings,
-            )
-            # [batch_size, num_tree_tokens]
-            return torch.cat(draft_token_ids_list, dim=1)
-
-        draft_token_ids = self._greedy_sample(sample_hidden_states)
-
-        if self.allowed_attn_types is not None:
-            for group_md in per_group_attn_metadata:
-                if not isinstance(group_md, self.allowed_attn_types):
-                    raise ValueError(
-                        f"Unsupported attention metadata type for speculative "
-                        "decoding with num_speculative_tokens > 1: "
-                        f"{type(group_md)}. Supported types are: "
-                        f"{self.allowed_attn_types}"
-                    )
-
-        # Generate the remaining draft tokens.
-        draft_token_ids_list = [draft_token_ids]
-
-        cudagraph_runtime_mode, input_batch_size, batch_size_across_dp = (
-            self._determine_batch_execution_and_padding(batch_size)
-        )
-
-        common_attn_metadata.num_actual_tokens = batch_size
-        common_attn_metadata.max_query_len = 1
-        common_attn_metadata.query_start_loc = self.arange[: batch_size + 1]
-        common_attn_metadata.query_start_loc_cpu = torch.from_numpy(
-            self.token_arange_np[: batch_size + 1]
-        ).clone()
-
-        # In padded drafter batch, we need to adjust the sequence lengths
-        # to remove the "padding" (i.e. rejected tokens).
-        # Only apply this adjustment when we have rejected tokens
-        # (i.e., not the first proposal).
-        if self.num_speculative_tokens > 1 and num_rejected_tokens_gpu is not None:
-            common_attn_metadata.seq_lens -= num_rejected_tokens_gpu
-            # Invalidate the CPU-side shadows to avoid H<>D sync.
-            common_attn_metadata._seq_lens_cpu = None
-            common_attn_metadata._num_computed_tokens_cpu = None
-
-        block_size = self.block_size
-        assert block_size > 0, "block_size has not been initialized."
-        for token_index in range(self.num_speculative_tokens - 1):
-            # Update the inputs.
-            # cast to int32 is crucial when eagle model is compiled.
-            # tensor.argmax() returns int64 by default.
-            input_ids = draft_token_ids_list[-1].int()
-            # Use fused kernel for slot mapping and metadata updates.
-            # Write clamped positions directly into the positions buffer to
-            # avoid an extra D2D copy for the common (non-mrope) case.
-            positions_1d = positions[0] if self.uses_mrope else positions
-            if self.uses_mrope:
-                out_pos = self.mrope_positions[0, :batch_size]
-            elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
-                out_pos = self.xdrope_positions[0, :batch_size]
-            else:
-                out_pos = self.positions[:batch_size]
-            eagle_step_update_slot_mapping_and_metadata(
-                positions_1d=positions_1d,
-                block_table_tensor=common_attn_metadata.block_table_tensor,
-                seq_lens=common_attn_metadata.seq_lens,
-                block_size=block_size,
-                max_model_len=self.max_model_len,
-                out_clamped_positions=out_pos,
-                out_slot_mapping=self._slot_mapping_buffer[:input_batch_size],
-                input_batch_size=input_batch_size,
-            )
-            common_attn_metadata.slot_mapping = self._slot_mapping_buffer[:batch_size]
-            if self.uses_mrope:
-                self.mrope_positions[1:, :batch_size] = self.mrope_positions[
-                    0, :batch_size
-                ]
-                positions = self.mrope_positions[:, :batch_size]
-            elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
-                self.xdrope_positions[1:, :batch_size] = self.xdrope_positions[
-                    0, :batch_size
-                ]
-                positions = self.xdrope_positions[0, :batch_size]
-            else:
-                positions = self.positions[:batch_size]
-            # Increment the maximum sequence length. We increment max_seq_len
-            # unconditionally even though some seq_lens may have been capped above,
-            # as max_seq_len serves as an upper bound for sequence lengths.
-            common_attn_metadata.max_seq_len = min(
-                common_attn_metadata.max_seq_len + 1, self.max_model_len
-            )
-
-            # Also update the CPU-side shadow; NOTE: this is hacky and should be
-            # removed in when common_attn_metadata.seq_lens_cpu is deprecated.
-            if common_attn_metadata._seq_lens_cpu is not None:
-                common_attn_metadata._seq_lens_cpu += 1
-            if common_attn_metadata._num_computed_tokens_cpu is not None:
-                common_attn_metadata._num_computed_tokens_cpu += 1
-
-            # Rebuild attention metadata
-            _, per_layer_attn_metadata = self.build_per_group_and_layer_attn_metadata(
-                common_attn_metadata, draft_index=token_index + 1
-            )
-
-            # copy inputs to buffer for cudagraph
-            self.input_ids[:batch_size] = input_ids
-            self.hidden_states[:batch_size] = hidden_states
-            if self.supports_mm_inputs:
-                self.inputs_embeds[:batch_size] = self.model.embed_input_ids(input_ids)
-
-                input_ids = None
-                inputs_embeds = self.inputs_embeds[:input_batch_size]
-            else:
-                input_ids = self.input_ids[:input_batch_size]
-                inputs_embeds = None
-
-            # Run the model.
-            model_kwargs = {
-                "input_ids": input_ids,
-                "positions": self._get_positions(input_batch_size),
-                "inputs_embeds": inputs_embeds,
-            }
-            if self.pass_hidden_states_to_model:
-                model_kwargs["hidden_states"] = self.hidden_states[:input_batch_size]
-
-            with set_forward_context(
-                per_layer_attn_metadata,
-                self.vllm_config,
-                num_tokens=input_batch_size,
-                num_tokens_across_dp=batch_size_across_dp,
-                cudagraph_runtime_mode=cudagraph_runtime_mode,
-                slot_mapping=self._get_slot_mapping(input_batch_size),
-            ):
-                ret_hidden_states = self.model(**model_kwargs)
-                if not self.model_returns_tuple():
-                    last_hidden_states = ret_hidden_states
-                    hidden_states = ret_hidden_states
-                else:
-                    last_hidden_states, hidden_states = ret_hidden_states
-
-            hidden_states = hidden_states[:batch_size]
-            draft_token_ids = self._greedy_sample(last_hidden_states[:batch_size])
-            draft_token_ids_list.append(draft_token_ids)
-
-        # [batch_size, num_speculative_tokens]
-        draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
-        return draft_token_ids
-
-    def set_inputs_first_pass(
-        self,
-        target_token_ids: torch.Tensor,
-        next_token_ids: torch.Tensor,
-        target_positions: torch.Tensor,
-        target_hidden_states: torch.Tensor,
-        token_indices_to_sample: torch.Tensor | None,
-        cad: CommonAttentionMetadata,
-        num_rejected_tokens_gpu: torch.Tensor | None,
-    ) -> tuple[int, torch.Tensor, CommonAttentionMetadata]:
-        if not self.needs_extra_input_slots:
-            # Default EAGLE pathway: no reshaping of input tensors needed.
-            # Simply rotate the input ids and leave the positions unchanged,
-            # Inserting the next token ids at the last slot in each request.
-            if token_indices_to_sample is None:
-                token_indices_to_sample = cad.query_start_loc[1:] - 1
-
-            num_tokens = target_token_ids.shape[0]
-            # Shift the input ids by one token.
-            # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
-            self.input_ids[: num_tokens - 1] = target_token_ids[1:]
-            # Replace the last token with the next token.
-            # E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
-            self.input_ids[token_indices_to_sample] = next_token_ids
-
-            # copy inputs to buffer for cudagraph
-            if self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim == 0:
-                target_positions = target_positions[0]
-            self._set_positions(num_tokens, target_positions)
-
-            self.hidden_states[:num_tokens] = target_hidden_states
-
-            return num_tokens, token_indices_to_sample, cad
-        else:
-            assert self.is_rejected_token_mask is not None
-            assert self.is_masked_token_mask is not None
-            # 1.
-            # Call a custom triton kernel to copy input_ids and positions
-            # into the correct slots in the preallocated buffers self.input_ids,
-            # self.positions.
-            batch_size = cad.batch_size()
-            # Since we might have to copy a lot of data for prefills, we select the
-            # block size based on the max query length and limit to max 256 slots/block.
-            max_num_tokens_per_request = (
-                cad.max_query_len + self.net_num_new_slots_per_request
-            )
-            BLOCK_SIZE_TOKENS = min(256, next_power_of_2(max_num_tokens_per_request))
-            num_blocks = (
-                max_num_tokens_per_request + BLOCK_SIZE_TOKENS - 1
-            ) // BLOCK_SIZE_TOKENS
-            total_num_input_tokens = target_token_ids.shape[0]
-            total_num_output_tokens = total_num_input_tokens + (
-                self.net_num_new_slots_per_request * batch_size
-            )
-
-            token_indices_to_sample = torch.empty(
-                batch_size * self.extra_slots_per_request,
-                dtype=torch.int32,
-                device=self.device,
-            )
-
-            # Destination indices to write target_hidden_states into drafting buffer.
-            out_hidden_state_mapping = torch.empty(
-                total_num_input_tokens, dtype=torch.int32, device=self.device
-            )
-
-            # Kernel grid: one program per request (row)
-            grid = (batch_size, num_blocks)
-            query_start_loc = cad.query_start_loc
-            query_end_loc = cad.query_start_loc[1:] - 1
-            if num_rejected_tokens_gpu is not None:
-                query_end_loc = query_end_loc - num_rejected_tokens_gpu
-
-            copy_and_expand_eagle_inputs_kernel[grid](
-                # (Padded) Inputs from the target model
-                target_token_ids_ptr=target_token_ids,
-                target_positions_ptr=target_positions,
-                next_token_ids_ptr=next_token_ids,  # sampled tokens, one per request
-                # Outputs to the drafting buffers
-                out_input_ids_ptr=self.input_ids,
-                out_positions_ptr=self.positions,  # Doesn't support mrope for now
-                out_is_rejected_token_mask_ptr=self.is_rejected_token_mask,
-                out_is_masked_token_mask_ptr=self.is_masked_token_mask,
-                out_new_token_indices_ptr=token_indices_to_sample,
-                out_hidden_state_mapping_ptr=out_hidden_state_mapping,
-                # Input metadata
-                query_start_loc_ptr=query_start_loc,
-                query_end_loc_ptr=query_end_loc,
-                padding_token_id=0,
-                parallel_drafting_token_id=self.parallel_drafting_token_id,
-                # Sizing info
-                # Note that we can deduce batch_size for free from the grid size
-                total_input_tokens=total_num_input_tokens,
-                num_padding_slots_per_request=self.extra_slots_per_request,
-                shift_input_ids=self.pass_hidden_states_to_model,
-                BLOCK_SIZE_TOKENS=BLOCK_SIZE_TOKENS,
-            )
-            if self.pass_hidden_states_to_model:
-                assert self.parallel_drafting_hidden_state_tensor is not None
-                self.hidden_states[out_hidden_state_mapping] = target_hidden_states
-                # Use torch.where to avoid DtoH sync from boolean indexing
-                mask = self.is_masked_token_mask[:total_num_output_tokens]
-                torch.where(
-                    mask.unsqueeze(1),
-                    self.parallel_drafting_hidden_state_tensor,
-                    self.hidden_states[:total_num_output_tokens],
-                    out=self.hidden_states[:total_num_output_tokens],
-                )
-
-            # 2.
-            # Recompute the slot mapping based on the new positions and
-            # rejection mask.
-            assert self.block_size > 0, "block_size has not been initialized."
-            new_slot_mapping = compute_new_slot_mapping(
-                cad=cad,
-                new_positions=self.positions[:total_num_output_tokens],
-                is_rejected_token_mask=self.is_rejected_token_mask[
-                    :total_num_output_tokens
-                ],
-                block_size=self.block_size,
-                num_new_tokens=self.net_num_new_slots_per_request,
-                max_model_len=self.max_model_len,
-            )
-
-            # 3. Update the common attention metadata with the new (meta)data
-            new_cad = extend_all_queries_by_N(
-                cad,
-                N=self.net_num_new_slots_per_request,
-                arange=self.arange,
-                new_slot_mapping=new_slot_mapping,
-            )
-
-            return total_num_output_tokens, token_indices_to_sample, new_cad
-
-    def build_model_inputs_first_pass(
-        self,
-        num_tokens: int,
-        num_input_tokens: int,
-        mm_embed_inputs: tuple[list[torch.Tensor], torch.Tensor] | None,
-    ) -> tuple[dict[str, Any], int]:
-        if self.supports_mm_inputs:
-            mm_embeds, is_mm_embed = mm_embed_inputs or (None, None)
-
-            self.inputs_embeds[:num_tokens] = self.model.embed_input_ids(
-                self.input_ids[:num_tokens],
-                multimodal_embeddings=mm_embeds,
-                is_multimodal=is_mm_embed,
-            )
-
-            input_ids = None
-            inputs_embeds = self.inputs_embeds[:num_input_tokens]
-        else:
-            input_ids = self.input_ids[:num_input_tokens]
-            inputs_embeds = None
-
-        model_kwargs = {
-            "input_ids": input_ids,
-            "positions": self._get_positions(num_input_tokens),
-            "inputs_embeds": inputs_embeds,
-        }
-        if self.pass_hidden_states_to_model:
-            model_kwargs["hidden_states"] = self.hidden_states[:num_input_tokens]
-
-        return model_kwargs, num_input_tokens
-
-    def build_per_group_and_layer_attn_metadata(
-        self, common_attn_metadata: CommonAttentionMetadata, draft_index: int = 0
-    ) -> tuple[list[object], dict[str, object]]:
-        per_group_attn_metadata: list[object] = []
-        per_layer_attn_metadata: dict[str, object] = {}
-        for attn_group in self.draft_attn_groups:
-            attn_metadata = attn_group.get_metadata_builder().build_for_drafting(
-                common_attn_metadata=common_attn_metadata, draft_index=draft_index
-            )
-            per_group_attn_metadata.append(attn_metadata)
-            for layer_name in attn_group.layer_names:
-                per_layer_attn_metadata[layer_name] = attn_metadata
-        return per_group_attn_metadata, per_layer_attn_metadata
-
-    def model_returns_tuple(self) -> bool:
-        return self.method not in ("mtp", "draft_model", "dflash")
-
-    def prepare_next_token_ids_cpu(
-        self,
-        sampled_token_ids: list[list[int]],
-        requests: dict[str, CachedRequestState],
-        gpu_input_batch: InputBatch,
-        num_scheduled_tokens: dict[str, int],
-    ) -> torch.Tensor:
-        """
-        This function is used to prepare the inputs for speculative decoding.
-        It calculates the next token ids for each request based on the sampled
-        token ids from the CPU. If a request has no sampled token ids (e.g.,
-        during the initial decoding steps), it falls back to using the request
-        state to get the next token id.
-        """
-        req_ids = gpu_input_batch.req_ids
-        next_token_ids: list[int] = []
-        for i, token_ids in enumerate(sampled_token_ids):
-            if token_ids:
-                # Common case.
-                next_token_id = token_ids[-1]
-            else:
-                # Partial prefill (rare case).
-                # Get the next token id from the request state.
-                req_id = req_ids[i]
-                req_state = requests[req_id]
-                seq_len = req_state.num_computed_tokens + num_scheduled_tokens[req_id]
-                next_token_id = req_state.get_token_id(seq_len)
-            next_token_ids.append(next_token_id)
-        next_token_ids = torch.tensor(
-            next_token_ids, dtype=torch.int32, device=self.input_ids.device
-        )
-        return next_token_ids
-
-    def prepare_next_token_ids_padded(
-        self,
-        sampled_token_ids: torch.Tensor,
-        requests: dict[str, CachedRequestState],
-        gpu_input_batch: InputBatch,
-        discard_request_mask: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """
-        This function is used to prepare the inputs for speculative decoding.
-        It calculates the next token ids and the number of valid sampled tokens
-        for each request, considering the "discarded" requests whose next token
-        is not sampled and comes from `request.get_token_id()` instead. This is denoted
-        the "backup" token id. It also counts rejected tokens via `sampled_token_ids`.
-        """
-        # Precompute get_token_id for when there is no valid next token
-        num_reqs = gpu_input_batch.num_reqs
-        seq_lens_list = (gpu_input_batch.num_tokens_no_spec[:num_reqs] - 1).tolist()
-        self.backup_next_token_ids.np[:num_reqs] = np.array(
-            [
-                requests[gpu_input_batch.req_ids[i]].get_token_id(seq_lens_list[i])
-                for i in range(num_reqs)
-            ],
-            dtype=np.int32,
-        )
-        self.backup_next_token_ids.copy_to_gpu(num_reqs)
-        backup_tokens_gpu = self.backup_next_token_ids.gpu
-
-        batch_size, num_tokens = sampled_token_ids.shape
-        device = sampled_token_ids.device
-
-        assert discard_request_mask.dtype == torch.bool
-        assert backup_tokens_gpu.dtype == torch.int32
-
-        next_token_ids = torch.empty(batch_size, dtype=torch.int32, device=device)
-        valid_sampled_tokens_count = next_token_ids.new_empty(batch_size)
-
-        # Kernel grid: one program per request (row)
-        grid = (batch_size,)
-
-        # Find the next power of 2 for block sizes
-        BLOCK_SIZE_TOKENS = next_power_of_2(num_tokens)
-        eagle_prepare_next_token_padded_kernel[grid](
-            sampled_token_ids,
-            discard_request_mask,
-            backup_tokens_gpu,
-            next_token_ids,
-            valid_sampled_tokens_count,
-            gpu_input_batch.vocab_size,
-            num_tokens,
-            batch_size,
-            sampled_token_ids.stride(0),
-            BLOCK_SIZE_TOKENS=BLOCK_SIZE_TOKENS,
-        )
-
-        return next_token_ids, valid_sampled_tokens_count
-
-    def prepare_inputs_padded(
-        self,
-        common_attn_metadata: CommonAttentionMetadata,
-        spec_decode_metadata: SpecDecodeMetadata,
-        valid_sampled_tokens_count: torch.Tensor,
-    ) -> tuple[CommonAttentionMetadata, torch.Tensor, torch.Tensor]:
-        """
-        This function is used to prepare the inputs for speculative decoding
-        It updates the common_attn_metadata for speculative decoding,
-        but does not consider the rejected tokens. Instead, all tokens
-        are included as inputs to the speculator, with the rejected tokens
-        used as padding and filtered out later by `token_indices_to_sample`.
-        No blocking CPU operations should be introduced in this function.
-        """
-        num_reqs = common_attn_metadata.num_reqs
-        device = valid_sampled_tokens_count.device
-
-        token_indices_to_sample = torch.empty(
-            (num_reqs,), dtype=torch.int32, device=device
-        )
-        num_rejected_tokens_gpu = torch.empty(
-            (num_reqs,), dtype=torch.int32, device=device
-        )
-
-        grid = (num_reqs,)
-        eagle_prepare_inputs_padded_kernel[grid](
-            spec_decode_metadata.cu_num_draft_tokens,
-            valid_sampled_tokens_count,
-            common_attn_metadata.query_start_loc,
-            token_indices_to_sample,
-            num_rejected_tokens_gpu,
-            num_reqs,
-        )
-
-        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
-        new_query_len_per_req = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
-
-        total_num_tokens = query_start_loc_cpu[-1].item()
-
-        spec_common_attn_metadata = CommonAttentionMetadata(
-            query_start_loc=common_attn_metadata.query_start_loc,
-            seq_lens=common_attn_metadata.seq_lens,
-            query_start_loc_cpu=query_start_loc_cpu,
-            _seq_lens_cpu=common_attn_metadata._seq_lens_cpu,
-            _num_computed_tokens_cpu=common_attn_metadata._num_computed_tokens_cpu,
-            num_reqs=common_attn_metadata.num_reqs,
-            num_actual_tokens=total_num_tokens,
-            max_query_len=new_query_len_per_req.max().item(),
-            max_seq_len=common_attn_metadata.max_seq_len,
-            block_table_tensor=common_attn_metadata.block_table_tensor,
-            slot_mapping=common_attn_metadata.slot_mapping[:total_num_tokens],
-            causal=True,
-            dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
-        )
-
-        return (
-            spec_common_attn_metadata,
-            token_indices_to_sample,
-            num_rejected_tokens_gpu,
-        )
-
-    def propose_tree(
-        self,
-        batch_size: int,
-        # [num_tokens, vocab_size]
-        logits: torch.Tensor,
-        # [num_tokens]
-        positions: torch.Tensor,
-        # [num_tokens, hidden_size]
-        hidden_states: torch.Tensor,
-        common_attn_metadata: CommonAttentionMetadata,
-        slot_mappings: dict[str, torch.Tensor]
-        | list[dict[str, torch.Tensor]]
-        | None = None,
-    ) -> list[torch.Tensor]:
-        tree_attn_metadata_builder = self.draft_attn_groups[0].get_metadata_builder()
-        assert isinstance(tree_attn_metadata_builder, TreeAttentionMetadataBuilder)
-
-        total_num_drafts = self.cu_drafts_per_level[0]
-        level_num_drafts = total_num_drafts
-        # Sample a draft token for each child at the tree root level.
-        num_children = self.child_drafts_per_level[0]
-        if num_children == 1:
-            draft_token_ids = logits.argmax(dim=-1).view(batch_size, -1)
-        else:
-            draft_token_ids = torch.topk(logits, num_children, dim=-1).indices.view(
-                batch_size, -1
-            )
-        draft_token_ids_list = [draft_token_ids]
-        draft_hidden_states = hidden_states.view(batch_size, 1, -1)
-
-        # Initialize empty tensors for concatenation with the level outputs.
-        tree_input_ids = torch.empty(
-            0, device=self.input_ids.device, dtype=self.input_ids.dtype
-        )
-        tree_positions = torch.empty(
-            0, device=self.positions.device, dtype=self.positions.dtype
-        )
-        tree_hidden_states = torch.empty(
-            0, device=self.hidden_states.device, dtype=self.hidden_states.dtype
-        )
-        # Precompute the draft token positions.
-        flattened_draft_positions = (
-            positions.view(batch_size, -1) + self.tree_draft_pos_offsets[:batch_size, :]
-        )
-        tree_depth = len(self.cu_drafts_per_level)
-        for level in range(tree_depth - 1):
-            # Get draft positions for RoPE.
-            draft_positions = positions + (level + 1)
-            exceeds_max_model_len = (positions + total_num_drafts) >= self.max_model_len
-            # Mask out the position ids that exceed the max model length.
-            # Otherwise, we may get out-of-range error in RoPE.
-            draft_positions = torch.where(
-                exceeds_max_model_len,
-                0,
-                draft_positions,
-            ).view(batch_size, -1)
-
-            if level_num_drafts > 1:
-                # Repeat the positions for each draft at this level.
-                draft_positions = draft_positions.repeat_interleave(
-                    level_num_drafts, dim=1
-                )
-
-            if num_children > 1:
-                # Repeat draft hidden states for each child.
-                draft_hidden_states = draft_hidden_states.repeat_interleave(
-                    num_children, dim=1
-                )
-
-            # Concatenate the draft tokens, positions, and hidden states.
-            tree_input_ids = torch.cat([tree_input_ids, draft_token_ids], dim=1)
-            tree_positions = torch.cat([tree_positions, draft_positions], dim=1)
-            tree_hidden_states = torch.cat(
-                [tree_hidden_states, draft_hidden_states], dim=1
-            )
-
-            # Build new attention metadata for the next level of drafts.
-            # This is necessary to support tree attention.
-            query_len = total_num_drafts
-            common_attn_metadata = replace(
-                common_attn_metadata,
-                query_start_loc=query_len * self.arange[: batch_size + 1],
-                seq_lens=common_attn_metadata.seq_lens + level_num_drafts,
-                num_actual_tokens=batch_size * query_len,
-                max_query_len=query_len,
-            )
-            attn_metadata = tree_attn_metadata_builder.build_for_drafting(
-                common_attn_metadata=common_attn_metadata, draft_index=level + 1
-            )
-
-            # Apply new attention metadata to all draft layers.
-            per_layer_attn_metadata = {}
-            for attn_group in self.draft_attn_groups:
-                for layer_name in attn_group.layer_names:
-                    per_layer_attn_metadata[layer_name] = attn_metadata
-
-            # Consider max model length.
-            attn_metadata.max_seq_len = min(
-                attn_metadata.max_seq_len, self.max_model_len
-            )
-            # For the requests that exceed the max model length, we set the
-            # sequence length to 1 to minimize their overheads in attention.
-            attn_metadata.seq_lens.masked_fill_(exceeds_max_model_len, 1)
-
-            # Compute the slot mapping.
-            block_size = tree_attn_metadata_builder.kv_cache_spec.block_size
-            query_positions = flattened_draft_positions[:, level : level + query_len]
-            block_numbers = query_positions // block_size
-            block_ids = attn_metadata.block_table.gather(dim=1, index=block_numbers)
-            slot_mapping = block_ids * block_size + query_positions % block_size
-            # Mask out the slot mappings that exceed the max model length.
-            # Otherwise, the KV cache will be inadvertently updated with the
-            # padding tokens.
-            slot_mapping[exceeds_max_model_len] = PADDING_SLOT_ID
-            attn_metadata.slot_mapping = slot_mapping.view(-1)
-
-            # Copy inputs to buffer for cudagraph.
-            num_tokens = attn_metadata.num_actual_tokens
-            input_ids = tree_input_ids.view(-1)
-            self.input_ids[:num_tokens] = input_ids
-            self.positions[:num_tokens] = tree_positions.view(-1)
-            self.hidden_states[:num_tokens] = tree_hidden_states.view(num_tokens, -1)
-
-            cudagraph_runtime_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
-                num_tokens
-            )
-            num_input_tokens = batch_desc.num_tokens
-            # Run the model.
-            with set_forward_context(
-                per_layer_attn_metadata,
-                self.vllm_config,
-                num_tokens=num_input_tokens,
-                cudagraph_runtime_mode=cudagraph_runtime_mode,
-                slot_mapping=self._get_slot_mapping(
-                    num_input_tokens, attn_metadata.slot_mapping
-                ),
-            ):
-                last_hidden_states, hidden_states = self.model(
-                    input_ids=self.input_ids[:num_input_tokens],
-                    positions=self.positions[:num_input_tokens],
-                    hidden_states=self.hidden_states[:num_input_tokens],
-                    inputs_embeds=None,
-                )
-
-            # Get the output hidden states for the draft tokens.
-            draft_hidden_states = hidden_states[:num_tokens].view(
-                batch_size, query_len, -1
-            )[:, -level_num_drafts:]
-            draft_last_hidden_states = last_hidden_states[:num_tokens].view(
-                batch_size, query_len, -1
-            )[:, -level_num_drafts:]
-
-            # Get the output logits for the draft tokens.
-            logits = self.model.compute_logits(
-                draft_last_hidden_states.reshape(batch_size * level_num_drafts, -1)
-            )
-
-            # Sample a draft token for each child at the next tree level.
-            num_children = self.child_drafts_per_level[level + 1]
-            if num_children == 1:
-                draft_token_ids = logits.argmax(dim=-1).view(batch_size, -1)
-            else:
-                draft_token_ids = torch.topk(logits, num_children, dim=-1).indices.view(
-                    batch_size, -1
-                )
-            draft_token_ids_list.append(draft_token_ids)
-
-            # Update the # drafts counters for the next tree level.
-            level_num_drafts = self.cu_drafts_per_level[level + 1] - total_num_drafts
-            total_num_drafts = self.cu_drafts_per_level[level + 1]
-        return draft_token_ids_list
-
-    def prepare_inputs(
-        self,
-        common_attn_metadata: CommonAttentionMetadata,
-        sampled_token_ids: list[list[int]],
-        num_draft_tokens: list[int],
-    ) -> tuple[CommonAttentionMetadata, torch.Tensor]:
-        """
-        This function is used to prepare the inputs for speculative decoding.
-        It updates to the common_attn_metadata to account for the rejected
-        tokens (and newly sampled tokens). It also returns the token indices
-        of the tokens that should be fed to the speculator.
-        """
-        # E.g.
-        #  common_attn_metadata.query_start_loc{_cpu}:
-        #       [0, q1, q1 + q2, q1 + q2 + q3]
-        #  common_attn_metadata.seq_lens{_cpu}: [s1, s2, s3]
-        #  num_rejected_tokens: [n1, n2, n3]
-        # This function computes the intermediate values:
-        #  num_tokens_per_req: [q1 - n1, q2 - n2, q3 - n3]
-        # And returns:
-        #  common_attn_metadata.query_start_loc{_cpu}:
-        #       [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
-        #  common_attn_metadata.seq_lens{_cpu}:
-        #       [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
-        #  token_indices: [0, 1, ..., q1 - n1 - 1,
-        #                 q1, q1 + 1, ..., q1 + q2 - n2 - 1,
-        #                 q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
-
-        num_rejected_tokens = [
-            n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
-            for i, n in enumerate(num_draft_tokens)
-        ]
-        num_rejected_tokens = torch.tensor(num_rejected_tokens, dtype=torch.int32)
-
-        device = common_attn_metadata.query_start_loc.device
-        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
-        new_seq_lens_cpu = common_attn_metadata.seq_lens_cpu - num_rejected_tokens
-
-        # [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
-        new_query_len_per_req = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
-        # [q1, q2, q3] -> [q1 - n1, q2 - n2, q3 - n3]
-        new_num_tokens_per_req = new_query_len_per_req - num_rejected_tokens
-        new_num_tokens_per_req_np = new_num_tokens_per_req.numpy()
-
-        # [q1 - n1, q2 - n2, q3 - n3] ->
-        # [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
-        new_query_start_loc_cpu = torch.zeros(
-            query_start_loc_cpu.shape,
-            dtype=torch.int32,
-            pin_memory=is_pin_memory_available(),
-        )
-        new_query_start_loc_np = new_query_start_loc_cpu.numpy()
-        np.cumsum(new_num_tokens_per_req_np, out=new_query_start_loc_np[1:])
-
-        total_num_tokens = new_query_start_loc_np[-1]
-        # Example assuming num_tokens_per_req_np = [2, 4, 3]
-        # this implies that `new_query_start_locs` is:
-        # [0, 2, 6, 9] ->
-        # [0, 0, 2, 2, 2, 2, 6, 6, 6]
-        #  _r1_  ____r2____  ___r3__
-        new_query_start_locs_expanded = np.repeat(
-            new_query_start_loc_np[:-1], new_num_tokens_per_req_np
-        )
-        # [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
-        # [0, 1, 0, 1, 2, 3, 0, 1, 2]
-        #  _r1_  ____r2____  ___r3__
-        token_offsets = (
-            self.token_arange_np[:total_num_tokens] - new_query_start_locs_expanded
-        )
-
-        # Expand starting positions to match token pattern
-        # [0, q1, q1 + q2] ->
-        # [0, 0, q1, q1, q1, q1, q1 + q2, q1 + q2, q1 + q2]
-        #  _r1_  _____r2_______  ___________r3____________
-        old_query_start_locs_expanded = np.repeat(
-            query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np
-        )
-        # Final token indices are:
-        # [0, 1,                                // req 1
-        #  q1 + 0, q1 + 1, q1 + 2, q1 + 3,       // req 2
-        #  q1 + q2 + 0, q1 + q2 + 1, q1 + q2 + 2] // req 3
-        token_indices_np = token_offsets + old_query_start_locs_expanded
-        token_indices = torch.from_numpy(token_indices_np).to(device, non_blocking=True)
-
-        spec_common_attn_metadata = CommonAttentionMetadata(
-            query_start_loc=new_query_start_loc_cpu.to(device, non_blocking=True),
-            seq_lens=new_seq_lens_cpu.to(device, non_blocking=True),
-            query_start_loc_cpu=new_query_start_loc_cpu,
-            _seq_lens_cpu=new_seq_lens_cpu,
-            _num_computed_tokens_cpu=common_attn_metadata._num_computed_tokens_cpu,
-            num_reqs=common_attn_metadata.num_reqs,
-            num_actual_tokens=total_num_tokens,
-            max_query_len=new_query_len_per_req.max().item(),
-            max_seq_len=new_seq_lens_cpu.max().item(),
-            block_table_tensor=common_attn_metadata.block_table_tensor,
-            slot_mapping=common_attn_metadata.slot_mapping[token_indices],
-            causal=True,
-            dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
-        )
-
-        return spec_common_attn_metadata, token_indices
-
-    def get_model_name(self, model: nn.Module) -> str:
-        if hasattr(model, "module"):  # multi-GPU
-            model = model.module
-        return model.__class__.__name__
-
-    def _create_draft_vllm_config(self) -> VllmConfig:
-        """Return a VllmConfig with kernel-level overrides for the proposer.
-        Subclasses may override to apply additional config changes.
-        """
-        spec_cfg = self.speculative_config
-        if spec_cfg.moe_backend is not None:
-            return replace(
-                self.vllm_config,
-                kernel_config=replace(
-                    self.vllm_config.kernel_config,
-                    moe_backend=spec_cfg.moe_backend,
-                ),
-            )
-        return self.vllm_config
-
-    def _get_model(self) -> nn.Module:
-        """
-        Default method to call get_model(). Can be overridden by subclasses which
-        need to customize model loading.
-        """
-        from vllm.compilation.backends import set_model_tag
-
-        draft_vllm_config = self._create_draft_vllm_config()
-        with set_model_tag("eagle_head"):
-            model = get_model(
-                vllm_config=draft_vllm_config,
-                model_config=self.speculative_config.draft_model_config,
-                load_config=self.speculative_config.draft_load_config,
-            )
-        return model
-
-    def load_model(self, target_model: nn.Module) -> None:
-        target_attn_layer_names = set(
-            get_layers_from_vllm_config(
-                self.vllm_config,
-                AttentionLayerBase,  # type: ignore[type-abstract]
-            ).keys()
-        )
-
-        self.model = self._get_model()
-
-        # Find draft layers (attention layers added by draft model)
-        all_attn_layers = get_layers_from_vllm_config(
-            self.vllm_config,
-            AttentionLayerBase,  # type: ignore[type-abstract]
-        )
-        self._draft_attn_layer_names = (
-            set(all_attn_layers.keys()) - target_attn_layer_names
-        )
-
-        if self.supports_mm_inputs:
-            # Even if the target model is multimodal, we can also use
-            # text-only draft models
-            try:
-                dummy_input_ids = torch.tensor([[1]], device=self.input_ids.device)
-                self.model.embed_input_ids(dummy_input_ids, multimodal_embeddings=None)
-            except (NotImplementedError, AttributeError, TypeError):
-                logger.warning(
-                    "Draft model does not support multimodal inputs, "
-                    "falling back to text-only mode"
-                )
-                self.supports_mm_inputs = False
-
-        if supports_multimodal(target_model):
-            # handle multimodality
-            assert hasattr(target_model, "config")
-            if self.get_model_name(target_model) in [
-                "Exaone4_5_ForConditionalGeneration",
-                "GlmOcrForConditionalGeneration",
-                "HunYuanVLForConditionalGeneration",
-                "Qwen2_5_VLForConditionalGeneration",
-                "Qwen3_5ForConditionalGeneration",
-                "Qwen3_5MoeForConditionalGeneration",
-                "Qwen3VLForConditionalGeneration",
-                "Qwen3VLMoeForConditionalGeneration",
-                "Gemma4ForConditionalGeneration",
-            ]:
-                self.model.config.image_token_index = target_model.config.image_token_id
-            elif self.get_model_name(target_model) == "PixtralForConditionalGeneration":
-                self.model.config.image_token_index = (
-                    target_model.config.vision_config.image_token_id
-                )
-            elif self.get_model_name(target_model) == "KimiK25ForConditionalGeneration":
-                self.model.config.image_token_index = (
-                    target_model.config.media_placeholder_token_id
-                )
-            else:
-                self.model.config.image_token_index = (
-                    target_model.config.image_token_index
-                )
-            target_language_model = cast(
-                SupportsMultiModal, target_model
-            ).get_language_model()
-        else:
-            target_language_model = target_model
-
-        self._maybe_share_embeddings(target_language_model)
-        self._maybe_share_lm_head(target_language_model)
-
-        if (
-            self.parallel_drafting
-            and self.pass_hidden_states_to_model
-            and self.parallel_drafting_hidden_state_tensor is not None
-        ):
-            flat_mask = self.model.mask_hidden.view(-1)
-            if self.eagle3_use_aux_hidden_state:
-                # EAGLE3: mask_hidden stores all aux hidden states,
-                # project through combine_hidden_states
-                self.parallel_drafting_hidden_state_tensor.copy_(
-                    self.model.combine_hidden_states(flat_mask)
-                )
-            else:
-                self.parallel_drafting_hidden_state_tensor.copy_(flat_mask)
-
-    def _maybe_share_embeddings(self, target_language_model: nn.Module) -> None:
-        """
-        Some draft models may not have their own embedding layers, and some may
-        have a duplicate copy of the target model's embedding layers. In these cases,
-        we share the target model's embedding layers with the draft model to save
-        memory.
-        """
-        if get_pp_group().world_size == 1:
-            inner_model = getattr(target_language_model, "model", None)
-            if inner_model is None:
-                raise AttributeError("Target model does not have 'model' attribute")
-            if hasattr(inner_model, "embed_tokens"):
-                target_embed_tokens = inner_model.embed_tokens
-            elif hasattr(inner_model, "embedding"):
-                target_embed_tokens = inner_model.embedding
-            else:
-                raise AttributeError(
-                    "Target model does not have 'embed_tokens' or 'embedding' attribute"
-                )
-
-            share_embeddings = False
-            if hasattr(self.model, "has_own_embed_tokens"):
-                # EAGLE model
-                if not self.model.has_own_embed_tokens:
-                    share_embeddings = True
-                    logger.info(
-                        "Detected EAGLE model without its own embed_tokens in the"
-                        " checkpoint. Sharing target model embedding weights with the"
-                        " draft model."
-                    )
-                elif (
-                    isinstance(target_embed_tokens.weight, torch.Tensor)
-                    and isinstance(self.model.model.embed_tokens.weight, torch.Tensor)
-                    # TODO: Offload to CPU for comparison to avoid extra GPU memory
-                    # usage in CI testing environments with limited GPU memory
-                    and torch.equal(
-                        target_embed_tokens.weight.cpu(),
-                        self.model.model.embed_tokens.weight.cpu(),
-                    )
-                ):
-                    share_embeddings = True
-                    logger.info(
-                        "Detected EAGLE model with embed_tokens identical to the target"
-                        " model. Sharing target model embedding weights with the draft"
-                        " model."
-                    )
-                else:
-                    logger.info(
-                        "Detected EAGLE model with distinct embed_tokens weights. "
-                        "Keeping separate embedding weights from the target model."
-                    )
-            else:
-                # MTP model
-                share_embeddings = True
-                logger.info(
-                    "Detected MTP model. "
-                    "Sharing target model embedding weights with the draft model."
-                )
-
-            if share_embeddings:
-                if hasattr(self.model.model, "embed_tokens"):
-                    del self.model.model.embed_tokens
-                self.model.model.embed_tokens = target_embed_tokens
-        else:
-            logger.info(
-                "The draft model's vocab embedding will be loaded separately"
-                " from the target model."
-            )
-
-    def _maybe_share_lm_head(self, target_language_model: nn.Module) -> None:
-        """
-        Some draft models may not have their own LM head, and some may have a
-        duplicate copy of the target model's LM head. In these cases, we share
-        the target model's LM head with the draft model to save memory.
-        """
-        share_lm_head = False
-        if hasattr(self.model, "has_own_lm_head"):
-            # EAGLE model
-            if not self.model.has_own_lm_head:
-                share_lm_head = True
-                logger.info(
-                    "Detected EAGLE model without its own lm_head in the checkpoint. "
-                    "Sharing target model lm_head weights with the draft model."
-                )
-            elif (
-                hasattr(target_language_model, "lm_head")
-                and hasattr(target_language_model.lm_head, "weight")
-                and hasattr(self.model.lm_head, "weight")
-                and isinstance(target_language_model.lm_head.weight, torch.Tensor)
-                and isinstance(self.model.lm_head.weight, torch.Tensor)
-                # TODO: Offload to CPU for comparison to avoid extra GPU memory
-                # usage in CI testing environments with limited GPU memory
-                and torch.equal(
-                    target_language_model.lm_head.weight.cpu(),
-                    self.model.lm_head.weight.cpu(),
-                )
-            ):
-                share_lm_head = True
-                logger.info(
-                    "Detected EAGLE model with lm_head identical to the target model. "
-                    "Sharing target model lm_head weights with the draft model."
-                )
-            else:
-                logger.info(
-                    "Detected EAGLE model with distinct lm_head weights. "
-                    "Keeping separate lm_head weights from the target model."
-                )
-        else:
-            # MTP model
-            share_lm_head = True
-            logger.info(
-                "Detected MTP model. "
-                "Sharing target model lm_head weights with the draft model."
-            )
-
-        if share_lm_head and hasattr(target_language_model, "lm_head"):
-            if hasattr(self.model, "lm_head"):
-                del self.model.lm_head
-            self.model.lm_head = target_language_model.lm_head
-
-            # MTP models call compute_logits via shared_head.head (a
-            # ParallelLMHead inside each MTP layer), not self.model.lm_head.
-            # If the checkpoint omits a copy of the lm_head weights at the
-            # MTP layer path, shared_head.head stays uninitialised and
-            # produces NaN logits. Always share it explicitly.
-            inner = getattr(self.model, "model", None)
-            layers = getattr(inner, "layers", None) if inner else None
-            if layers is not None:
-                items = layers.values() if isinstance(layers, nn.ModuleDict) else layers
-                for layer in items:
-                    sh = getattr(layer, "shared_head", None)
-                    if sh is not None and hasattr(sh, "head"):
-                        del sh.head
-                        sh.head = target_language_model.lm_head
-                        logger.info(
-                            "Shared target model lm_head with MTP shared_head.head."
-                        )
-
-        if self.use_local_argmax_reduction:
-            if not hasattr(self.model, "get_top_tokens"):
-                raise ValueError(
-                    "use_local_argmax_reduction is enabled but draft model "
-                    f"{self.model.__class__.__name__} does not implement "
-                    "get_top_tokens()."
-                )
-            # Warn if draft model has vocab remapping, which forces fallback
-            # to the full-logits path (negating the optimization).
-            if (
-                hasattr(self.model, "draft_id_to_target_id")
-                and self.model.draft_id_to_target_id is not None
-            ):
-                logger.warning(
-                    "use_local_argmax_reduction is enabled but draft model "
-                    "uses draft_id_to_target_id vocab remapping. The "
-                    "optimization will be bypassed (falling back to full "
-                    "logits gather + argmax)."
-                )
-            else:
-                logger.info(
-                    "Using local argmax reduction for draft token generation "
-                    "(communication: O(2*tp_size) vs O(vocab_size))."
-                )
-
-    @torch.inference_mode()
-    def dummy_run(
-        self,
-        num_tokens: int,
-        use_cudagraphs: bool = True,
-        is_graph_capturing: bool = False,
-        slot_mappings: dict[str, torch.Tensor] | None = None,
-    ) -> None:
-        # FIXME: when using tree-based specdec, adjust number of forward-passes
-        # according to the depth of the tree.
-        only_one_forward_pass = is_graph_capturing or self.parallel_drafting
-        for fwd_idx in range(
-            1 if only_one_forward_pass else self.num_speculative_tokens
-        ):
-            if fwd_idx <= 1:
-                cudagraph_runtime_mode, num_input_tokens, num_tokens_across_dp = (
-                    self._determine_batch_execution_and_padding(
-                        num_tokens, use_cudagraphs=use_cudagraphs
-                    )
-                )
-
-            # Make sure to use EAGLE's own buffer during cudagraph capture.
-            if (
-                self._draft_attn_layer_names
-                and slot_mappings is not None
-                and next(iter(self._draft_attn_layer_names)) in slot_mappings
-            ):
-                slot_mapping_dict = self._get_slot_mapping(num_input_tokens)
-            else:
-                slot_mapping_dict = slot_mappings or {}
-
-            with set_forward_context(
-                None,
-                self.vllm_config,
-                num_tokens=num_input_tokens,
-                num_tokens_across_dp=num_tokens_across_dp,
-                cudagraph_runtime_mode=cudagraph_runtime_mode,
-                slot_mapping=slot_mapping_dict,
-            ):
-                if self.supports_mm_inputs:
-                    input_ids = None
-                    inputs_embeds = self.inputs_embeds[:num_input_tokens]
-                else:
-                    input_ids = self.input_ids[:num_input_tokens]
-                    inputs_embeds = None
-
-                kwargs = dict(
-                    input_ids=input_ids,
-                    positions=self._get_positions(num_input_tokens),
-                    inputs_embeds=inputs_embeds,
-                )
-                if self.pass_hidden_states_to_model:
-                    kwargs["hidden_states"] = self.hidden_states[:num_input_tokens]
-                self.model(**kwargs)
-
-    def _get_eagle3_use_aux_hidden_state_from_config(self) -> bool:
-        """
-        Some eagle3 heads (e.g., nvidia/gpt-oss-120b-Eagle3-v2) do not use auxiliary
-        hidden states and directly uses the last layer output just like eagle1.
-        They might indicate this by setting "use_aux_hidden_state" to False
-        inside the "eagle_config" dict of their hf_config.
-        """
-        if self.method != "eagle3":
-            return False
-        # Assume that eagle3 heads use aux hidden states by default
-        use_aux_hidden_state = True
-        eagle_config = getattr(self.draft_model_config.hf_config, "eagle_config", None)
-        if eagle_config is not None:
-            use_aux_hidden_state = eagle_config.get("use_aux_hidden_state", True)
-        return use_aux_hidden_state
-
-    def validate_same_kv_cache_group(self, kv_cache_config: KVCacheConfig) -> None:
-        """
-        Validate that all drafting layers belong to the same KVCacheGroup.
-        Need this assumption to ensure all drafting layers can use the
-        same AttentionMetadata.
-        May extend to multiple AttentionMetadata in the future.
-        """
-        kv_cache_groups: dict[str, int] = {}
-        for id, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
-            for layer_name in kv_cache_group.layer_names:
-                kv_cache_groups[layer_name] = id
-        assert (
-            len(
-                set(
-                    [
-                        kv_cache_groups[layer_name]
-                        for layer_name in self._draft_attn_layer_names
-                    ]
-                )
-            )
-            == 1
-        ), "All drafting layers should belong to the same kv cache group"
-
-    def initialize_attn_backend(
-        self,
-        kv_cache_config: KVCacheConfig,
-        kernel_block_sizes: list[int] | None = None,
-    ) -> None:
-        """
-        Initialize AttentionGroups for draft layers using kv_cache_config.
-        Called from the model runner's initialize_metadata_builders.
-        """
-        all_attn_layers = get_layers_from_vllm_config(
-            self.vllm_config,
-            AttentionLayerBase,  # type: ignore[type-abstract]
-        )
-
-        # Find which kv_cache_group the draft layers belong to
-        self.validate_same_kv_cache_group(kv_cache_config)
-        kv_cache_spec = None
-        for gid, group in enumerate(kv_cache_config.kv_cache_groups):
-            if self._draft_attn_layer_names & set(group.layer_names):
-                self.kv_cache_gid = gid
-                kv_cache_spec = group.kv_cache_spec
-                break
-
-        attention_groups: dict[tuple[str, str], AttentionGroup] = {}
-        if kv_cache_spec is not None:
-            for layer_name in self._draft_attn_layer_names:
-                attn_backend = all_attn_layers[layer_name].get_attn_backend()
-                backend_key = attn_backend.full_cls_name()
-                if backend_key not in attention_groups:
-                    layer_kv_cache_spec = kv_cache_spec
-                    if isinstance(layer_kv_cache_spec, UniformTypeKVCacheSpecs):
-                        layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[
-                            layer_name
-                        ]
-
-                    kernel_block_size = (
-                        kernel_block_sizes[self.kv_cache_gid]
-                        if kernel_block_sizes is not None
-                        and self.kv_cache_gid < len(kernel_block_sizes)
-                        else None
-                    )
-                    attn_group = AttentionGroup(
-                        backend=attn_backend,
-                        layer_names=[layer_name],
-                        kv_cache_spec=layer_kv_cache_spec,
-                        kv_cache_group_id=self.kv_cache_gid,
-                    )
-                    attn_group.create_metadata_builders(
-                        self.vllm_config,
-                        self.device,
-                        kernel_block_size=kernel_block_size,
-                    )
-                    attention_groups[backend_key] = attn_group
-                else:
-                    attention_groups[backend_key].layer_names.append(layer_name)
-
-        self.draft_attn_groups = list(attention_groups.values())
-        self.block_size = (
-            self.draft_attn_groups[0].get_metadata_builder().kv_cache_spec.block_size
-        )
-        logger.debug("Using block size %d for drafting layers", self.block_size)
-
-    def _determine_batch_execution_and_padding(
-        self,
-        num_tokens: int,
-        use_cudagraphs: bool = True,
-    ) -> tuple[CUDAGraphMode, int, torch.Tensor | None]:
-        cudagraph_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
-            num_tokens,
-            valid_modes=({CUDAGraphMode.NONE} if not use_cudagraphs else None),
-        )
-        num_tokens_padded = batch_desc.num_tokens
-
-        # Extra coordination when running data-parallel since we need to
-        # coordinate across ranks
-        # TODO(Flechman): support DBO ubatching
-        should_ubatch, num_tokens_across_dp = False, None
-        if self.vllm_config.parallel_config.data_parallel_size > 1:
-            should_ubatch, num_tokens_across_dp, synced_cudagraph_mode = (
-                coordinate_batch_across_dp(
-                    num_tokens_unpadded=num_tokens,
-                    parallel_config=self.vllm_config.parallel_config,
-                    allow_microbatching=False,
-                    num_tokens_padded=num_tokens_padded,
-                    cudagraph_mode=cudagraph_mode.value,
-                )
-            )
-            assert not should_ubatch, "DBO ubatching not implemented for EAGLE"
-
-            # Extract DP-synced values
-            if num_tokens_across_dp is not None:
-                dp_rank = self.dp_rank
-                num_tokens_padded = int(num_tokens_across_dp[dp_rank].item())
-                # Re-dispatch with DP padding so we have the correct
-                # batch_descriptor
-                cudagraph_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
-                    num_tokens_padded,
-                    valid_modes={CUDAGraphMode(synced_cudagraph_mode)},
-                )
-                # Assert to make sure the agreed upon token count is correct
-                # otherwise num_tokens_across_dp will no-longer be valid
-                assert batch_desc.num_tokens == num_tokens_padded
-                num_tokens_across_dp[dp_rank] = num_tokens_padded
-
-        return cudagraph_mode, num_tokens_padded, num_tokens_across_dp
+from vllm.config import VllmConfig
+from vllm.v1.spec_decode.llm_base_proposer import SpecDecodeBaseProposer
 
 
 class EagleProposer(SpecDecodeBaseProposer):
@@ -1745,49 +20,3 @@ def __init__(
             pass_hidden_states_to_model=True,
             runner=runner,
         )
-
-
-# NOTE(woosuk): Currently, the below code is not used and we always use argmax
-# to sample the draft tokens. We will use this after we find a way to manage
-# the draft prob tensor.
-# Refer to https://github.com/vllm-project/vllm/pull/16899 for the details.
-# FIXME(woosuk): The logic here is duplicated with the main sampling code.
-# We should refactor this to reuse the same sampling implementation.
-def compute_probs_and_sample_next_token(
-    logits: torch.Tensor,
-    sampling_metadata: SamplingMetadata,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    if sampling_metadata.all_greedy:
-        # For greedy requests, draft_probs is not used in rejection sampling.
-        # Therefore, we can just return the logits.
-        probs = logits
-        next_token_ids = logits.argmax(dim=-1)
-        return next_token_ids, probs
-
-    assert sampling_metadata.temperature is not None
-
-    # Use epsilon comparison to detect greedy sampling (temperature ~ 0.0)
-    # consistent with sampler.py's _SAMPLING_EPS threshold
-    temperature = sampling_metadata.temperature
-    # Avoid division by zero if there are greedy requests.
-    if not sampling_metadata.all_random:
-        is_greedy = temperature < _SAMPLING_EPS
-        temperature = torch.where(is_greedy, 1.0, temperature)
-    logits.div_(temperature.view(-1, 1))
-    probs = logits.softmax(dim=-1, dtype=torch.float32)
-
-    # NOTE(woosuk): Currently, we ignore most of the sampling parameters in
-    # generating the draft tokens. We only use the temperature. While this
-    # could degrade the acceptance rate, it does not affect the distribution
-    # of the generated tokens after rejection sampling.
-
-    # TODO(woosuk): Consider seeds.
-    q = torch.empty_like(probs)
-    q.exponential_()
-    # NOTE(woosuk): We shouldn't use `probs.div_(q)` because the draft_probs
-    # will be used later for rejection sampling.
-    next_token_ids = probs.div(q).argmax(dim=-1).view(-1)
-    if not sampling_metadata.all_random:
-        greedy_token_ids = probs.argmax(dim=-1)
-        next_token_ids = torch.where(is_greedy, greedy_token_ids, next_token_ids)
-    return next_token_ids, probs
diff --git a/vllm/v1/spec_decode/gemma4.py b/vllm/v1/spec_decode/gemma4.py
new file mode 100644
index 000000000000..b0a02774faf6
--- /dev/null
+++ b/vllm/v1/spec_decode/gemma4.py
@@ -0,0 +1,335 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Gemma4 MTP (Multi-Token Prediction) proposer for speculative decoding.
+
+The Gemma4 assistant model runs all decoder layers per draft step
+(producing one token), and all its attention layers share KV cache
+with the target model via cross-model KV sharing.
+"""
+
+from collections import defaultdict
+from copy import copy
+
+import torch
+import torch.nn as nn
+
+from vllm.config import VllmConfig, get_layers_from_vllm_config, replace
+from vllm.logger import init_logger
+from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
+from vllm.v1.attention.backend import CommonAttentionMetadata
+from vllm.v1.kv_cache_interface import (
+    KVCacheConfig,
+    KVCacheSpec,
+    UniformTypeKVCacheSpecs,
+)
+from vllm.v1.spec_decode.llm_base_proposer import SpecDecodeBaseProposer
+from vllm.v1.worker.utils import AttentionGroup
+
+logger = init_logger(__name__)
+
+
+class Gemma4Proposer(SpecDecodeBaseProposer):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        device: torch.device,
+        runner=None,
+    ):
+        super().__init__(
+            vllm_config,
+            device,
+            pass_hidden_states_to_model=True,
+            runner=runner,
+        )
+        # All draft steps predict from the same position (the last
+        # target-model position), so positions and seq_lens must not
+        # advance between steps.
+        self.constant_draft_positions = True
+
+        # Per-group block tables for multi-group KV cache models.
+        # Populated by gpu_model_runner during _prepare_inputs.
+        self._per_group_block_tables: dict[int, torch.Tensor] = {}
+
+        # Centroids CUDA graphs — populated in load_model if centroids
+        # masking is active. _centroids_sizes is pre-sorted for fast
+        # lookup in _greedy_sample.
+        self._centroids_sizes: list[int] = []
+        self._centroids_graphs: dict[int, torch.cuda.CUDAGraph] = {}
+        self._centroids_inputs: dict[int, torch.Tensor] = {}
+        self._centroids_outputs: dict[int, torch.Tensor] = {}
+
+    def set_per_group_block_table(self, gid: int, block_table: torch.Tensor) -> None:
+        self._per_group_block_tables[gid] = block_table
+
+    def model_returns_tuple(self) -> bool:
+        # forward() returns (draft_hidden_states, backbone_hidden_states).
+        # The proposer uses draft_hidden_states for compute_logits and
+        # backbone_hidden_states for the hidden-state feedback buffer.
+        return True
+
+    def build_per_group_and_layer_attn_metadata(
+        self,
+        common_attn_metadata: CommonAttentionMetadata,
+        draft_index: int = 0,
+    ) -> tuple[list[object], dict[str, object]]:
+        """Build attention metadata using the correct block table per group.
+
+        Gemma4 has multiple KV cache groups (sliding vs full attention)
+        with different block tables.  The base class receives a single
+        common_attn_metadata whose block_table belongs to one group.
+        We swap in the correct block table for each draft attention group.
+        """
+        per_group_attn_metadata: list[object] = []
+        per_layer_attn_metadata: dict[str, object] = {}
+        for attn_group in self.draft_attn_groups:
+            gid = attn_group.kv_cache_group_id
+            if gid in self._per_group_block_tables:
+                cm = copy(common_attn_metadata)
+                cm.block_table_tensor = self._per_group_block_tables[gid]
+            else:
+                cm = common_attn_metadata
+            attn_metadata = attn_group.get_metadata_builder().build_for_drafting(
+                common_attn_metadata=cm, draft_index=draft_index
+            )
+            per_group_attn_metadata.append(attn_metadata)
+            for layer_name in attn_group.layer_names:
+                per_layer_attn_metadata[layer_name] = attn_metadata
+        return per_group_attn_metadata, per_layer_attn_metadata
+
+    def _greedy_sample(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self._centroids_sizes:
+            T = hidden_states.shape[0]
+            for size in self._centroids_sizes:
+                if size >= T:
+                    self._centroids_inputs[size][:T].copy_(hidden_states)
+                    self._centroids_graphs[size].replay()
+                    return self._centroids_outputs[size][:T].clone()
+            return self.model.get_top_tokens(hidden_states)
+        return super()._greedy_sample(hidden_states)
+
+    def _setup_centroids_cuda_graphs(self) -> None:
+        """Capture CUDA graphs for centroids get_top_tokens at key sizes."""
+        masked_emb = self.model.masked_embedding
+        lm_head_weight = self.model._get_full_lm_head_weight()
+
+        for size in [1, 2, 4, 8, 16, 32, 64]:
+            static_input = torch.zeros(
+                size,
+                masked_emb.hidden_size,
+                dtype=self.dtype,
+                device=self.device,
+            )
+            for _ in range(3):
+                masked_emb.get_top_tokens(static_input, lm_head_weight)
+            torch.accelerator.synchronize()
+
+            g = torch.cuda.CUDAGraph()
+            with torch.cuda.graph(g):
+                static_output = masked_emb.get_top_tokens(
+                    static_input,
+                    lm_head_weight,
+                )
+            self._centroids_graphs[size] = g
+            self._centroids_inputs[size] = static_input
+            self._centroids_outputs[size] = static_output
+
+        self._centroids_sizes = sorted(self._centroids_graphs)
+        logger.info(
+            "Gemma4 MTP: captured centroids CUDA graphs for sizes %s.",
+            self._centroids_sizes,
+        )
+
+    def _create_draft_vllm_config(self) -> VllmConfig:
+        """Preserve the target's forced TRITON_ATTN backend for draft layers.
+
+        Gemma4 forces TRITON_ATTN due to heterogeneous head dimensions
+        (head_dim=256 sliding, global_head_dim=512 full). The base class
+        resets attention_config.backend to None for draft models, causing
+        sliding layers to fall back to FLASH_ATTN which cannot handle
+        KV-shared cache. Override to carry the target's backend through.
+        """
+        base = super()._create_draft_vllm_config()
+        target_backend = self.vllm_config.attention_config.backend
+        if target_backend is not None:
+            base = replace(
+                base,
+                attention_config=replace(
+                    base.attention_config,
+                    backend=target_backend,
+                ),
+            )
+        return base
+
+    def _maybe_share_lm_head(self, target_language_model: nn.Module) -> None:
+        """Gemma4 MTP always keeps its own draft-dim lm_head.
+
+        The draft model's lm_head operates in draft hidden_size (e.g. 256),
+        which differs from the target's backbone hidden_size (e.g. 1536).
+        Sharing would break compute_logits (and centroids masking when
+        use_ordered_embeddings is enabled).
+        """
+        logger.info(
+            "Gemma4 MTP: keeping draft model's own lm_head (draft_dim != backbone_dim)."
+        )
+
+    def load_model(self, target_model: nn.Module) -> None:
+        target_attn_layer_names = set(
+            get_layers_from_vllm_config(
+                self.vllm_config,
+                AttentionLayerBase,  # type: ignore[type-abstract]
+            ).keys()
+        )
+
+        super().load_model(target_model)
+
+        self._setup_gemma4_kv_sharing(target_attn_layer_names)
+
+        if getattr(self.model, "masked_embedding", None) is not None:
+            self._setup_centroids_cuda_graphs()
+
+    def validate_same_kv_cache_group(self, kv_cache_config: KVCacheConfig) -> None:
+        """Draft layers span multiple KV cache groups (sliding + full
+        attention with different head dimensions), so skip the base
+        class single-group assertion."""
+
+    def initialize_attn_backend(
+        self,
+        kv_cache_config: KVCacheConfig,
+        kernel_block_sizes: list[int] | None = None,
+    ) -> None:
+        """Create separate AttentionGroup objects per KV cache spec
+        so that each head-dim variant gets its own metadata builder."""
+        all_attn_layers = get_layers_from_vllm_config(
+            self.vllm_config,
+            AttentionLayerBase,  # type: ignore[type-abstract]
+        )
+
+        layer_to_gid: dict[str, int] = {}
+        layer_to_spec: dict[str, KVCacheSpec] = {}
+        for gid, group in enumerate(kv_cache_config.kv_cache_groups):
+            group_spec = group.kv_cache_spec
+            for ln in group.layer_names:
+                layer_to_gid[ln] = gid
+                if isinstance(group_spec, UniformTypeKVCacheSpecs):
+                    if ln in group_spec.kv_cache_specs:
+                        layer_to_spec[ln] = group_spec.kv_cache_specs[ln]
+                    else:
+                        tgt = getattr(
+                            all_attn_layers.get(ln),
+                            "kv_sharing_target_layer_name",
+                            None,
+                        )
+                        if tgt and tgt in group_spec.kv_cache_specs:
+                            layer_to_spec[ln] = group_spec.kv_cache_specs[tgt]
+                        else:
+                            layer_to_spec[ln] = group_spec
+                else:
+                    layer_to_spec[ln] = group_spec
+
+        attention_groups: dict[tuple[tuple[str, str], KVCacheSpec], AttentionGroup] = {}
+        for layer_name in self._draft_attn_layer_names:
+            if layer_name not in layer_to_spec:
+                continue
+            attn_layer = all_attn_layers[layer_name]
+            attn_backend = attn_layer.get_attn_backend()
+            spec = layer_to_spec[layer_name]
+            gid = layer_to_gid[layer_name]
+            group_key = (attn_backend.full_cls_name(), spec)
+
+            if group_key not in attention_groups:
+                kernel_block_size = (
+                    kernel_block_sizes[gid]
+                    if kernel_block_sizes is not None and gid < len(kernel_block_sizes)
+                    else None
+                )
+                attn_group = AttentionGroup(
+                    backend=attn_backend,
+                    layer_names=[layer_name],
+                    kv_cache_spec=spec,
+                    kv_cache_group_id=gid,
+                )
+                attn_group.create_metadata_builders(
+                    self.vllm_config,
+                    self.device,
+                    kernel_block_size=kernel_block_size,
+                )
+                attention_groups[group_key] = attn_group
+            else:
+                attention_groups[group_key].layer_names.append(layer_name)
+
+        self.draft_attn_groups = list(attention_groups.values())
+        if self.draft_attn_groups:
+            self.kv_cache_gid = self.draft_attn_groups[0].kv_cache_group_id
+            self.block_size = (
+                self.draft_attn_groups[0]
+                .get_metadata_builder()
+                .kv_cache_spec.block_size
+            )
+        else:
+            self.kv_cache_gid = 0
+            self.block_size = kv_cache_config.kv_cache_groups[
+                0
+            ].kv_cache_spec.block_size
+        logger.debug("Using block size %d for drafting layers", self.block_size)
+
+    def _setup_gemma4_kv_sharing(
+        self,
+        target_attn_layer_names: set[str],
+    ) -> None:
+        """Wire draft layers to share KV with the target model.
+
+        Each draft decoder layer is mapped to the last non-KV-shared
+        target layer of the same attention type (sliding or full).
+        """
+        draft_config = self.speculative_config.draft_model_config.hf_config
+        draft_text_config = draft_config.get_text_config()
+        target_config = self.vllm_config.model_config.hf_config
+        target_text_config = target_config.get_text_config()
+        target_layer_types = getattr(target_text_config, "layer_types", [])
+
+        if not (hasattr(self.model, "model") and hasattr(self.model.model, "layers")):
+            return
+
+        target_num_kv_shared = getattr(target_text_config, "num_kv_shared_layers", 0)
+        num_non_shared = len(target_layer_types) - target_num_kv_shared
+        type_to_target_indices: dict[str, list[int]] = defaultdict(list)
+        for idx, lt in enumerate(target_layer_types[:num_non_shared]):
+            type_to_target_indices[lt].append(idx)
+
+        target_prefix = "model.layers"
+        for name in target_attn_layer_names:
+            if ".layers." in name:
+                target_prefix = name.split(".layers.")[0] + ".layers"
+                break
+
+        draft_layer_types = getattr(draft_text_config, "layer_types", [])
+        for draft_idx, layer in enumerate(self.model.model.layers):
+            if not hasattr(layer, "self_attn"):
+                continue
+            attn = getattr(layer.self_attn, "attn", None)
+            if attn is None:
+                continue
+
+            draft_layer_type = (
+                draft_layer_types[draft_idx]
+                if draft_idx < len(draft_layer_types)
+                else "full_attention"
+            )
+            candidates = type_to_target_indices.get(draft_layer_type, [])
+            if not candidates:
+                logger.warning(
+                    "No target layer of type '%s' for draft layer %d",
+                    draft_layer_type,
+                    draft_idx,
+                )
+                continue
+
+            target_idx = candidates[-1]
+            target_layer_name = f"{target_prefix}.{target_idx}.self_attn.attn"
+            attn.kv_sharing_target_layer_name = target_layer_name
+            logger.info(
+                "Gemma4 MTP: draft layer %d (%s) -> %s",
+                draft_idx,
+                draft_layer_type,
+                target_layer_name,
+            )
diff --git a/vllm/v1/spec_decode/llm_base_proposer.py b/vllm/v1/spec_decode/llm_base_proposer.py
new file mode 100644
index 000000000000..cc113025c129
--- /dev/null
+++ b/vllm/v1/spec_decode/llm_base_proposer.py
@@ -0,0 +1,1638 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from importlib.util import find_spec
+from typing import Any, cast
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from vllm.config import (
+    CUDAGraphMode,
+    VllmConfig,
+    get_layers_from_vllm_config,
+    replace,
+)
+from vllm.distributed.parallel_state import get_pp_group
+from vllm.forward_context import set_forward_context
+from vllm.logger import init_logger
+from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
+from vllm.model_executor.model_loader import get_model
+from vllm.model_executor.models import supports_multimodal
+from vllm.model_executor.models.deepseek_eagle3 import Eagle3DeepseekV2ForCausalLM
+from vllm.model_executor.models.interfaces import SupportsMultiModal
+from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
+from vllm.model_executor.models.qwen3_dflash import DFlashQwen3ForCausalLM
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.platforms import current_platform
+from vllm.utils.platform_utils import is_pin_memory_available
+from vllm.v1.attention.backend import CommonAttentionMetadata
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
+from vllm.v1.attention.backends.triton_attn import TritonAttentionMetadata
+from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
+from vllm.v1.kv_cache_interface import KVCacheConfig, UniformTypeKVCacheSpecs
+from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.sample.sampler import _SAMPLING_EPS
+from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
+from vllm.v1.spec_decode.utils import (
+    PADDING_SLOT_ID,
+    compute_new_slot_mapping,
+    copy_and_expand_eagle_inputs_kernel,
+    eagle_prepare_inputs_padded_kernel,
+    eagle_prepare_next_token_padded_kernel,
+    eagle_step_update_slot_mapping_and_metadata,
+    extend_all_queries_by_N,
+    next_power_of_2,
+)
+from vllm.v1.utils import CpuGpuBuffer
+from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
+from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
+from vllm.v1.worker.utils import AttentionGroup
+
+logger = init_logger(__name__)
+
+
+class SpecDecodeBaseProposer:
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        device: torch.device,
+        pass_hidden_states_to_model: bool,
+        runner=None,
+    ):
+        self.vllm_config = vllm_config
+        assert vllm_config.speculative_config is not None
+        self.speculative_config = vllm_config.speculative_config
+        self.draft_model_config = self.speculative_config.draft_model_config
+        self.method = self.speculative_config.method
+        self.pass_hidden_states_to_model = pass_hidden_states_to_model
+
+        self.device = device
+        self.dtype = vllm_config.model_config.dtype
+        self.max_model_len = vllm_config.model_config.max_model_len
+        self.dp_rank = vllm_config.parallel_config.data_parallel_rank
+        self.num_speculative_tokens = self.speculative_config.num_speculative_tokens
+
+        # We need to get the hidden size from the draft model config because
+        # the draft model's hidden size can be different from the target model's
+        # hidden size (e.g., Llama 3.3 70B).
+        self.hidden_size = self.draft_model_config.get_hidden_size()
+        self.inputs_embeds_size = self.draft_model_config.get_inputs_embeds_size()
+
+        # DeepSeek V4 MTP consumes the target's pre-hc_head residual stream,
+        # shape (T, hc_mult * hidden_size). Expand the hidden_states buffer
+        # so target_hidden_states fits; detect DeepseekV4 via draft hf_config.
+        draft_hf_config = self.draft_model_config.hf_config
+        if hasattr(draft_hf_config, "compress_ratios") and hasattr(
+            draft_hf_config, "hc_mult"
+        ):
+            self.hidden_size = self.hidden_size * draft_hf_config.hc_mult
+
+        # Unifying eagle, draft model, and parallel drafting support.
+        # DFlash always uses parallel drafting (all tokens in one pass),
+        # but has an additional slot for the next_token_id (does not shift like EAGLE)
+        self.parallel_drafting: bool = self.speculative_config.parallel_drafting
+        self.extra_slots_per_request = (
+            1 if not self.parallel_drafting else self.num_speculative_tokens
+        )
+        self.net_num_new_slots_per_request = self.extra_slots_per_request - (
+            1 if (self.pass_hidden_states_to_model and self.method != "dflash") else 0
+        )
+        self.needs_extra_input_slots = self.net_num_new_slots_per_request > 0
+
+        # When True, all draft steps reuse the same position as the
+        # first step instead of advancing by one each iteration.
+        # Used by draft models with Q-only attention that share KV
+        # with the target and always predict from the same position.
+        self.constant_draft_positions: bool = False
+
+        self.parallel_drafting_token_id: int = 0
+        self.parallel_drafting_hidden_state_tensor: torch.Tensor | None = None
+        if self.parallel_drafting:
+            self._init_parallel_drafting_params()
+        self.use_local_argmax_reduction: bool = (
+            self.speculative_config.use_local_argmax_reduction
+        )
+
+        self.max_batch_size = vllm_config.scheduler_config.max_num_seqs
+        self.max_num_tokens = vllm_config.scheduler_config.max_num_batched_tokens
+        self.token_arange_np = np.arange(self.max_num_tokens)
+
+        # Can be specialized by methods like DFlash to reduce the limit
+        self.max_query_tokens = self.max_num_tokens
+        self.max_positions = self.max_num_tokens
+
+        # Multi-modal data support
+        self.mm_registry = MULTIMODAL_REGISTRY
+        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
+            vllm_config.model_config
+        )
+
+        self.draft_attn_groups: list[AttentionGroup] = []
+        self.kv_cache_gid: int = -1
+        self.eagle3_use_aux_hidden_state: bool = (
+            self._get_eagle3_use_aux_hidden_state_from_config()
+        )
+
+        self.compilation_config = self.vllm_config.compilation_config
+
+        # Cudagraph dispatcher for PIECEWISE-only dispatching in eagle.
+        # Keys are initialized later via initialize_cudagraph_keys() called from
+        # gpu_model_runner._check_and_update_cudagraph_mode after
+        # adjust_cudagraph_sizes_for_spec_decode is called.
+        self.cudagraph_dispatcher = CudagraphDispatcher(self.vllm_config)
+
+        # persistent buffers for cuda graph
+        self.input_ids = torch.zeros(
+            self.max_num_tokens, dtype=torch.int32, device=device
+        )
+        # Use draft model's M-RoPE setting, not target model's
+        # Draft models may be text-only even if target is multimodal
+        self.uses_mrope = self.draft_model_config.uses_mrope
+        self.uses_xdrope_dim = self.vllm_config.model_config.uses_xdrope_dim
+        self.draft_uses_xdrope_dim = self.draft_model_config.uses_xdrope_dim
+        if self.uses_mrope:
+            # NOTE: `mrope_positions` is implemented with one additional dummy
+            # position on purpose to make it non-contiguous so that it can work
+            # with torch compile.
+            # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
+
+            # NOTE: When M-RoPE is enabled, position ids are 3D regardless of
+            # the modality of inputs. For text-only inputs, each dimension has
+            # identical position IDs, making M-RoPE functionally equivalent to
+            # 1D-RoPE.
+            # See page 5 of https://arxiv.org/abs/2409.12191
+            self.mrope_positions = torch.zeros(
+                (3, self.max_positions + 1), dtype=torch.int64, device=device
+            )
+        elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
+            self.xdrope_positions = torch.zeros(
+                (self.uses_xdrope_dim, self.max_positions + 1),
+                dtype=torch.int64,
+                device=device,
+            )
+        else:
+            # RoPE need (max_num_tokens,)
+            self.positions = torch.zeros(
+                self.max_positions,
+                dtype=torch.int64,
+                device=device,
+            )
+        self.hidden_states = torch.zeros(
+            (self.max_num_tokens, self.hidden_size), dtype=self.dtype, device=device
+        )
+
+        # Will be set when we initialize the attention backend
+        self.block_size: int = -1
+
+        # We need +1 here because the arange is used to set query_start_loc,
+        # which has one more element than batch_size.
+        max_num_slots_for_arange = max(self.max_batch_size + 1, self.max_num_tokens)
+        self.arange = torch.arange(
+            max_num_slots_for_arange, device=device, dtype=torch.int32
+        )
+
+        if self.needs_extra_input_slots:
+            self._raise_if_padded_drafter_batch_disabled()
+            self._warn_if_multimodal()
+            self._raise_if_mrope()
+
+        self.is_rejected_token_mask: torch.Tensor | None = None
+        self.is_masked_token_mask: torch.Tensor | None = None
+        if self.needs_extra_input_slots:
+            # For draft models and parallel drafting, we need to keep track of
+            # which tokens are rejected to update the slot mapping with padding slots.
+            self.is_rejected_token_mask = torch.zeros(
+                (self.max_num_tokens,), dtype=torch.bool, device=device
+            )
+            # For parallel drafting, we also need to keep track of which tokens
+            # are parallel-padding tokens used to sample at later positions.
+            # We populate this tensor even when using draft models for simplicity.
+            self.is_masked_token_mask = torch.zeros(
+                (self.max_num_tokens,), dtype=torch.bool, device=device
+            )
+
+        self.inputs_embeds = torch.zeros(
+            (self.max_num_tokens, self.inputs_embeds_size),
+            dtype=self.dtype,
+            device=device,
+        )
+
+        self.backup_next_token_ids = CpuGpuBuffer(
+            self.max_batch_size,
+            dtype=torch.int32,
+            pin_memory=is_pin_memory_available(),
+            device=device,
+            with_numpy=True,
+        )
+
+        self._slot_mapping_buffer = torch.zeros(
+            self.max_positions,
+            dtype=torch.int64,
+            device=device,
+        )
+
+        # Determine allowed attention backends once during initialization.
+        self.allowed_attn_types: tuple | None = None
+        if current_platform.is_rocm():
+            from vllm.v1.attention.backends.mla.indexer import (
+                DeepseekV32IndexerMetadata,
+            )
+            from vllm.v1.attention.backends.mla.rocm_aiter_mla_sparse import (
+                ROCMAiterMLASparseMetadata,
+            )
+            from vllm.v1.attention.backends.rocm_attn import RocmAttentionMetadata
+
+            rocm_types = [
+                TritonAttentionMetadata,
+                RocmAttentionMetadata,
+                ROCMAiterMLASparseMetadata,
+                DeepseekV32IndexerMetadata,
+            ]
+            # ROCM_AITER_FA is an optional backend
+            # We check is_enabled() here to avoid importing the backend module during
+            # auto-discovery when VLLM_ROCM_USE_AITER=0, which would trigger aiter
+            # import and JIT compilation warnings. Explicit backend selection via
+            # attention_config still works because the backend module is loaded
+            # directly when selected, not through this auto-discovery path.
+            # Check if backend module exists to allow explicit selection
+            if find_spec(
+                AttentionBackendEnum.ROCM_AITER_FA.get_path(include_classname=False)
+            ):
+                from vllm.v1.attention.backends.rocm_aiter_fa import (
+                    AiterFlashAttentionMetadata,
+                )
+
+                rocm_types.append(AiterFlashAttentionMetadata)
+
+            # TRITON_MLA backend support for MLA models (e.g., DeepSeek)
+            from vllm.model_executor.layers.attention.mla_attention import (
+                MLACommonMetadata,
+            )
+
+            rocm_types.append(MLACommonMetadata)
+
+            # FlexAttention backend support
+            from vllm.v1.attention.backends.flex_attention import FlexAttentionMetadata
+
+            rocm_types.append(FlexAttentionMetadata)
+
+            self.allowed_attn_types = tuple(rocm_types)
+
+    def _raise_if_padded_drafter_batch_disabled(self):
+        if self.speculative_config.disable_padded_drafter_batch:
+            raise NotImplementedError(
+                "Speculative Decoding with draft models or parallel drafting only "
+                "supports padded drafter batch. Please unset "
+                "disable_padded_drafter_batch in the speculative_config."
+            )
+
+    def _warn_if_multimodal(self):
+        if self.supports_mm_inputs:
+            logger.warning(
+                "Speculative Decoding with draft models or parallel drafting "
+                "does not fully support multimodal models yet. "
+                "Proceeding with text-only speculative decoding."
+            )
+
+    def _raise_if_mrope(self):
+        if self.draft_model_config.uses_mrope:
+            raise NotImplementedError(
+                "Speculative Decoding with draft models or parallel drafting "
+                "does not support M-RoPE yet"
+            )
+
+    def _init_parallel_drafting_params(self):
+        # For parallel drafting, we need the token ID to use for masked slots
+        # And for EAGLE + parallel drafting, we need the hidden state tensor to use
+        # for those masked slots.
+
+        model_hf_config = self.draft_model_config.hf_config
+        # DFlash stores mask_token_id in dflash_config
+        dflash_config = getattr(model_hf_config, "dflash_config", None)
+        if dflash_config and "mask_token_id" in dflash_config:
+            self.parallel_drafting_token_id = dflash_config["mask_token_id"]
+        elif hasattr(model_hf_config, "pard_token"):
+            self.parallel_drafting_token_id = model_hf_config.pard_token
+        elif hasattr(model_hf_config, "ptd_token_id"):
+            self.parallel_drafting_token_id = model_hf_config.ptd_token_id
+        else:
+            raise ValueError(
+                "For parallel drafting, the draft model config must have "
+                "`pard_token`, `ptd_token_id`, or "
+                "`dflash_config.mask_token_id` specified in its config.json."
+            )
+
+        if self.pass_hidden_states_to_model:
+            self.parallel_drafting_hidden_state_tensor = torch.empty(
+                self.hidden_size, dtype=self.dtype, device=self.device
+            )
+
+    def _get_positions(self, num_tokens: int):
+        if self.uses_mrope:
+            return self.mrope_positions[:, :num_tokens]
+        if self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
+            return self.xdrope_positions[:, :num_tokens]
+        return self.positions[:num_tokens]
+
+    def _set_positions(self, num_tokens: int, positions: torch.Tensor):
+        if self.uses_mrope:
+            self.mrope_positions[:, :num_tokens] = positions
+        elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
+            self.xdrope_positions[:, :num_tokens] = positions
+        else:
+            # Convert M-RoPE positions if target model uses M-RoPE
+            # but draft doesn't, For text inputs, all M-RoPE
+            # dimensions are identical
+            if self.vllm_config.model_config.uses_mrope:
+                positions = positions[0]
+            self.positions[:num_tokens] = positions
+
+    def _get_slot_mapping(
+        self,
+        num_tokens: int,
+        slot_mapping: torch.Tensor | None = None,
+    ) -> dict[str, torch.Tensor]:
+        """Return slot_mapping dict for EAGLE layers.
+
+        If slot_mapping is provided, copies it into the buffer first.
+        """
+        if slot_mapping is not None:
+            num_actual = slot_mapping.shape[0]
+            self._slot_mapping_buffer[:num_actual].copy_(slot_mapping)
+            if num_tokens > num_actual:
+                self._slot_mapping_buffer[num_actual:num_tokens].fill_(PADDING_SLOT_ID)
+
+        view = self._slot_mapping_buffer[:num_tokens]
+        return {name: view for name in self._draft_attn_layer_names}
+
+    def initialize_cudagraph_keys(self, cudagraph_mode: CUDAGraphMode) -> None:
+        """Initialize cudagraph dispatcher keys for the drafter.
+
+        Only supports PIECEWISE cudagraphs (via mixed_mode).
+        This should be called after adjust_cudagraph_sizes_for_spec_decode.
+        """
+        if (
+            not self.speculative_config.enforce_eager
+            and cudagraph_mode.mixed_mode()
+            in [CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL]
+        ):
+            eagle_cudagraph_mode = CUDAGraphMode.PIECEWISE
+        else:
+            eagle_cudagraph_mode = CUDAGraphMode.NONE
+
+        self.cudagraph_dispatcher.initialize_cudagraph_keys(eagle_cudagraph_mode)
+
+    def _greedy_sample(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """Greedy-sample draft tokens from hidden states."""
+        if self.use_local_argmax_reduction:
+            return self.model.get_top_tokens(hidden_states)
+        return self.model.compute_logits(hidden_states).argmax(dim=-1)
+
+    def propose(
+        self,
+        # [num_tokens]
+        target_token_ids: torch.Tensor,
+        # [num_tokens] or [3, num_tokens] when M-RoPE is enabled
+        target_positions: torch.Tensor,
+        # [num_tokens, hidden_size]
+        target_hidden_states: torch.Tensor,
+        # [batch_size]
+        next_token_ids: torch.Tensor,
+        token_indices_to_sample: torch.Tensor | None,
+        common_attn_metadata: CommonAttentionMetadata,
+        sampling_metadata: SamplingMetadata,
+        mm_embed_inputs: tuple[list[torch.Tensor], torch.Tensor] | None = None,
+        num_rejected_tokens_gpu: torch.Tensor | None = None,
+        slot_mappings: dict[str, torch.Tensor]
+        | list[dict[str, torch.Tensor]]
+        | None = None,
+    ) -> torch.Tensor:
+        batch_size = common_attn_metadata.batch_size()
+
+        if self.method in ("eagle3", "dflash"):
+            assert isinstance(
+                self.model,
+                (
+                    Eagle3LlamaForCausalLM,
+                    Eagle3DeepseekV2ForCausalLM,
+                    DFlashQwen3ForCausalLM,
+                ),
+            )
+            target_hidden_states = self.model.combine_hidden_states(
+                target_hidden_states
+            )
+            assert target_hidden_states.shape[-1] == self.hidden_size
+
+        num_tokens, token_indices_to_sample, common_attn_metadata = (
+            self.set_inputs_first_pass(
+                target_token_ids=target_token_ids,
+                next_token_ids=next_token_ids,
+                target_positions=target_positions,
+                target_hidden_states=target_hidden_states,
+                token_indices_to_sample=token_indices_to_sample,
+                cad=common_attn_metadata,
+                num_rejected_tokens_gpu=num_rejected_tokens_gpu,
+            )
+        )
+
+        per_group_attn_metadata, per_layer_attn_metadata = (
+            self.build_per_group_and_layer_attn_metadata(common_attn_metadata)
+        )
+
+        cudagraph_runtime_mode, num_input_tokens, num_tokens_across_dp = (
+            self._determine_batch_execution_and_padding(num_tokens)
+        )
+
+        model_kwargs, slot_mapping_size = self.build_model_inputs_first_pass(
+            num_tokens, num_input_tokens, mm_embed_inputs
+        )
+
+        with set_forward_context(
+            per_layer_attn_metadata,
+            self.vllm_config,
+            num_tokens=num_input_tokens,
+            num_tokens_across_dp=num_tokens_across_dp,
+            cudagraph_runtime_mode=cudagraph_runtime_mode,
+            slot_mapping=self._get_slot_mapping(
+                slot_mapping_size, common_attn_metadata.slot_mapping
+            ),
+        ):
+            ret_hidden_states = self.model(**model_kwargs)
+            if not self.model_returns_tuple():
+                last_hidden_states = ret_hidden_states
+                hidden_states = last_hidden_states
+            else:
+                last_hidden_states, hidden_states = ret_hidden_states
+
+        sample_hidden_states = last_hidden_states[token_indices_to_sample]
+
+        # Early exit if there is only one draft token to be generated.
+        if self.num_speculative_tokens == 1 or self.parallel_drafting:
+            draft_token_ids = self._greedy_sample(sample_hidden_states)
+            return draft_token_ids.view(-1, self.num_speculative_tokens)
+
+        if self.uses_mrope:
+            positions = self.mrope_positions[:, token_indices_to_sample]
+        else:
+            positions = self.positions[token_indices_to_sample]
+        hidden_states = hidden_states[token_indices_to_sample]
+
+        if self.constant_draft_positions:
+            # Write the sampling positions into the front of the
+            # positions buffer so that subsequent loop iterations
+            # (which read via _get_positions) use the correct values.
+            self.positions[:batch_size] = positions
+
+        draft_token_ids = self._greedy_sample(sample_hidden_states)
+
+        if self.allowed_attn_types is not None:
+            for group_md in per_group_attn_metadata:
+                if not isinstance(group_md, self.allowed_attn_types):
+                    raise ValueError(
+                        f"Unsupported attention metadata type for speculative "
+                        "decoding with num_speculative_tokens > 1: "
+                        f"{type(group_md)}. Supported types are: "
+                        f"{self.allowed_attn_types}"
+                    )
+
+        # Generate the remaining draft tokens.
+        draft_token_ids_list = [draft_token_ids]
+
+        cudagraph_runtime_mode, input_batch_size, batch_size_across_dp = (
+            self._determine_batch_execution_and_padding(batch_size)
+        )
+
+        common_attn_metadata.num_actual_tokens = batch_size
+        common_attn_metadata.max_query_len = 1
+        common_attn_metadata.query_start_loc = self.arange[: batch_size + 1]
+        common_attn_metadata.query_start_loc_cpu = torch.from_numpy(
+            self.token_arange_np[: batch_size + 1]
+        ).clone()
+
+        # In padded drafter batch, we need to adjust the sequence lengths
+        # to remove the "padding" (i.e. rejected tokens).
+        # Only apply this adjustment when we have rejected tokens
+        # (i.e., not the first proposal).
+        if self.num_speculative_tokens > 1 and num_rejected_tokens_gpu is not None:
+            common_attn_metadata.seq_lens -= num_rejected_tokens_gpu
+            # Invalidate the CPU-side shadows to avoid H<>D sync.
+            common_attn_metadata._seq_lens_cpu = None
+            common_attn_metadata._num_computed_tokens_cpu = None
+
+        block_size = self.block_size
+        assert block_size > 0, "block_size has not been initialized."
+        for token_index in range(self.num_speculative_tokens - 1):
+            # Update the inputs.
+            # cast to int32 is crucial when eagle model is compiled.
+            # tensor.argmax() returns int64 by default.
+            input_ids = draft_token_ids_list[-1].int()
+
+            if not self.constant_draft_positions:
+                positions = self._update_positions_dependent_metadata(
+                    positions,
+                    common_attn_metadata,
+                    batch_size,
+                    input_batch_size,
+                    block_size,
+                )
+
+            # Rebuild attention metadata. When draft positions are constant
+            # (e.g. Gemma4 MTP), common_attn_metadata is invariant across
+            # loop iterations so we build once and reuse.
+            if not self.constant_draft_positions or token_index == 0:
+                _, per_layer_attn_metadata = (
+                    self.build_per_group_and_layer_attn_metadata(
+                        common_attn_metadata, draft_index=token_index + 1
+                    )
+                )
+
+            # copy inputs to buffer for cudagraph
+            self.input_ids[:batch_size] = input_ids
+            self.hidden_states[:batch_size] = hidden_states
+            if self.supports_mm_inputs:
+                self.inputs_embeds[:batch_size] = self.model.embed_input_ids(input_ids)
+
+                input_ids = None
+                inputs_embeds = self.inputs_embeds[:input_batch_size]
+            else:
+                input_ids = self.input_ids[:input_batch_size]
+                inputs_embeds = None
+
+            # Run the model.
+            model_kwargs = {
+                "input_ids": input_ids,
+                "positions": self._get_positions(input_batch_size),
+                "inputs_embeds": inputs_embeds,
+            }
+            if self.pass_hidden_states_to_model:
+                model_kwargs["hidden_states"] = self.hidden_states[:input_batch_size]
+
+            with set_forward_context(
+                per_layer_attn_metadata,
+                self.vllm_config,
+                num_tokens=input_batch_size,
+                num_tokens_across_dp=batch_size_across_dp,
+                cudagraph_runtime_mode=cudagraph_runtime_mode,
+                slot_mapping=self._get_slot_mapping(input_batch_size),
+            ):
+                ret_hidden_states = self.model(**model_kwargs)
+                if not self.model_returns_tuple():
+                    last_hidden_states = ret_hidden_states
+                    hidden_states = ret_hidden_states
+                else:
+                    last_hidden_states, hidden_states = ret_hidden_states
+
+            hidden_states = hidden_states[:batch_size]
+            draft_token_ids = self._greedy_sample(last_hidden_states[:batch_size])
+            draft_token_ids_list.append(draft_token_ids)
+
+        # [batch_size, num_speculative_tokens]
+        draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
+        return draft_token_ids
+
+    def _update_positions_dependent_metadata(
+        self,
+        positions: torch.Tensor,
+        common_attn_metadata,
+        batch_size: int,
+        input_batch_size: int,
+        block_size: int,
+    ) -> torch.Tensor:
+        """Update positions, slot mappings, and sequence metadata for the
+        next draft step. Returns the updated positions tensor."""
+        positions_1d = positions[0] if self.uses_mrope else positions
+        if self.uses_mrope:
+            out_pos = self.mrope_positions[0, :batch_size]
+        elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
+            out_pos = self.xdrope_positions[0, :batch_size]
+        else:
+            out_pos = self.positions[:batch_size]
+        eagle_step_update_slot_mapping_and_metadata(
+            positions_1d=positions_1d,
+            block_table_tensor=common_attn_metadata.block_table_tensor,
+            seq_lens=common_attn_metadata.seq_lens,
+            block_size=block_size,
+            max_model_len=self.max_model_len,
+            out_clamped_positions=out_pos,
+            out_slot_mapping=self._slot_mapping_buffer[:input_batch_size],
+            input_batch_size=input_batch_size,
+        )
+        common_attn_metadata.slot_mapping = self._slot_mapping_buffer[:batch_size]
+        if self.uses_mrope:
+            self.mrope_positions[1:, :batch_size] = self.mrope_positions[0, :batch_size]
+            positions = self.mrope_positions[:, :batch_size]
+        elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
+            self.xdrope_positions[1:, :batch_size] = self.xdrope_positions[
+                0, :batch_size
+            ]
+            positions = self.xdrope_positions[0, :batch_size]
+        else:
+            positions = self.positions[:batch_size]
+        common_attn_metadata.max_seq_len = min(
+            common_attn_metadata.max_seq_len + 1,
+            self.max_model_len,
+        )
+
+        if common_attn_metadata._seq_lens_cpu is not None:
+            common_attn_metadata._seq_lens_cpu += 1
+        if common_attn_metadata._num_computed_tokens_cpu is not None:
+            common_attn_metadata._num_computed_tokens_cpu += 1
+        if common_attn_metadata.seq_lens_cpu_upper_bound is not None:
+            common_attn_metadata.seq_lens_cpu_upper_bound += 1
+
+        return positions
+
+    def set_inputs_first_pass(
+        self,
+        target_token_ids: torch.Tensor,
+        next_token_ids: torch.Tensor,
+        target_positions: torch.Tensor,
+        target_hidden_states: torch.Tensor,
+        token_indices_to_sample: torch.Tensor | None,
+        cad: CommonAttentionMetadata,
+        num_rejected_tokens_gpu: torch.Tensor | None,
+    ) -> tuple[int, torch.Tensor, CommonAttentionMetadata]:
+        if not self.needs_extra_input_slots:
+            # Default EAGLE pathway: no reshaping of input tensors needed.
+            # Simply rotate the input ids and leave the positions unchanged,
+            # Inserting the next token ids at the last slot in each request.
+            if token_indices_to_sample is None:
+                token_indices_to_sample = cad.query_start_loc[1:] - 1
+
+            num_tokens = target_token_ids.shape[0]
+            # Shift the input ids by one token.
+            # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
+            self.input_ids[: num_tokens - 1] = target_token_ids[1:]
+            # Replace the last token with the next token.
+            # E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
+            self.input_ids[token_indices_to_sample] = next_token_ids
+
+            # copy inputs to buffer for cudagraph
+            if self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim == 0:
+                target_positions = target_positions[0]
+            self._set_positions(num_tokens, target_positions)
+
+            self.hidden_states[:num_tokens] = target_hidden_states
+
+            return num_tokens, token_indices_to_sample, cad
+        else:
+            assert self.is_rejected_token_mask is not None
+            assert self.is_masked_token_mask is not None
+            # 1.
+            # Call a custom triton kernel to copy input_ids and positions
+            # into the correct slots in the preallocated buffers self.input_ids,
+            # self.positions.
+            batch_size = cad.batch_size()
+            # Since we might have to copy a lot of data for prefills, we select the
+            # block size based on the max query length and limit to max 256 slots/block.
+            max_num_tokens_per_request = (
+                cad.max_query_len + self.net_num_new_slots_per_request
+            )
+            BLOCK_SIZE_TOKENS = min(256, next_power_of_2(max_num_tokens_per_request))
+            num_blocks = (
+                max_num_tokens_per_request + BLOCK_SIZE_TOKENS - 1
+            ) // BLOCK_SIZE_TOKENS
+            total_num_input_tokens = target_token_ids.shape[0]
+            total_num_output_tokens = total_num_input_tokens + (
+                self.net_num_new_slots_per_request * batch_size
+            )
+
+            token_indices_to_sample = torch.empty(
+                batch_size * self.extra_slots_per_request,
+                dtype=torch.int32,
+                device=self.device,
+            )
+
+            # Destination indices to write target_hidden_states into drafting buffer.
+            out_hidden_state_mapping = torch.empty(
+                total_num_input_tokens, dtype=torch.int32, device=self.device
+            )
+
+            # Kernel grid: one program per request (row)
+            grid = (batch_size, num_blocks)
+            query_start_loc = cad.query_start_loc
+            query_end_loc = cad.query_start_loc[1:] - 1
+            if num_rejected_tokens_gpu is not None:
+                query_end_loc = query_end_loc - num_rejected_tokens_gpu
+
+            copy_and_expand_eagle_inputs_kernel[grid](
+                # (Padded) Inputs from the target model
+                target_token_ids_ptr=target_token_ids,
+                target_positions_ptr=target_positions,
+                next_token_ids_ptr=next_token_ids,  # sampled tokens, one per request
+                # Outputs to the drafting buffers
+                out_input_ids_ptr=self.input_ids,
+                out_positions_ptr=self.positions,  # Doesn't support mrope for now
+                out_is_rejected_token_mask_ptr=self.is_rejected_token_mask,
+                out_is_masked_token_mask_ptr=self.is_masked_token_mask,
+                out_new_token_indices_ptr=token_indices_to_sample,
+                out_hidden_state_mapping_ptr=out_hidden_state_mapping,
+                # Input metadata
+                query_start_loc_ptr=query_start_loc,
+                query_end_loc_ptr=query_end_loc,
+                padding_token_id=0,
+                parallel_drafting_token_id=self.parallel_drafting_token_id,
+                # Sizing info
+                # Note that we can deduce batch_size for free from the grid size
+                total_input_tokens=total_num_input_tokens,
+                num_padding_slots_per_request=self.extra_slots_per_request,
+                shift_input_ids=self.pass_hidden_states_to_model,
+                BLOCK_SIZE_TOKENS=BLOCK_SIZE_TOKENS,
+            )
+            if self.pass_hidden_states_to_model:
+                assert self.parallel_drafting_hidden_state_tensor is not None
+                self.hidden_states[out_hidden_state_mapping] = target_hidden_states
+                # Use torch.where to avoid DtoH sync from boolean indexing
+                mask = self.is_masked_token_mask[:total_num_output_tokens]
+                torch.where(
+                    mask.unsqueeze(1),
+                    self.parallel_drafting_hidden_state_tensor,
+                    self.hidden_states[:total_num_output_tokens],
+                    out=self.hidden_states[:total_num_output_tokens],
+                )
+
+            # 2.
+            # Recompute the slot mapping based on the new positions and
+            # rejection mask.
+            assert self.block_size > 0, "block_size has not been initialized."
+            new_slot_mapping = compute_new_slot_mapping(
+                cad=cad,
+                new_positions=self.positions[:total_num_output_tokens],
+                is_rejected_token_mask=self.is_rejected_token_mask[
+                    :total_num_output_tokens
+                ],
+                block_size=self.block_size,
+                num_new_tokens=self.net_num_new_slots_per_request,
+                max_model_len=self.max_model_len,
+            )
+
+            # 3. Update the common attention metadata with the new (meta)data
+            new_cad = extend_all_queries_by_N(
+                cad,
+                N=self.net_num_new_slots_per_request,
+                arange=self.arange,
+                new_slot_mapping=new_slot_mapping,
+            )
+
+            return total_num_output_tokens, token_indices_to_sample, new_cad
+
+    def build_model_inputs_first_pass(
+        self,
+        num_tokens: int,
+        num_input_tokens: int,
+        mm_embed_inputs: tuple[list[torch.Tensor], torch.Tensor] | None,
+    ) -> tuple[dict[str, Any], int]:
+        if self.supports_mm_inputs:
+            mm_embeds, is_mm_embed = mm_embed_inputs or (None, None)
+
+            self.inputs_embeds[:num_tokens] = self.model.embed_input_ids(
+                self.input_ids[:num_tokens],
+                multimodal_embeddings=mm_embeds,
+                is_multimodal=is_mm_embed,
+            )
+
+            input_ids = None
+            inputs_embeds = self.inputs_embeds[:num_input_tokens]
+        else:
+            input_ids = self.input_ids[:num_input_tokens]
+            inputs_embeds = None
+
+        model_kwargs = {
+            "input_ids": input_ids,
+            "positions": self._get_positions(num_input_tokens),
+            "inputs_embeds": inputs_embeds,
+        }
+        if self.pass_hidden_states_to_model:
+            model_kwargs["hidden_states"] = self.hidden_states[:num_input_tokens]
+
+        return model_kwargs, num_input_tokens
+
+    def build_per_group_and_layer_attn_metadata(
+        self, common_attn_metadata: CommonAttentionMetadata, draft_index: int = 0
+    ) -> tuple[list[object], dict[str, object]]:
+        per_group_attn_metadata: list[object] = []
+        per_layer_attn_metadata: dict[str, object] = {}
+        for attn_group in self.draft_attn_groups:
+            attn_metadata = attn_group.get_metadata_builder().build_for_drafting(
+                common_attn_metadata=common_attn_metadata, draft_index=draft_index
+            )
+            per_group_attn_metadata.append(attn_metadata)
+            for layer_name in attn_group.layer_names:
+                per_layer_attn_metadata[layer_name] = attn_metadata
+        return per_group_attn_metadata, per_layer_attn_metadata
+
+    def model_returns_tuple(self) -> bool:
+        return self.method not in ("mtp", "draft_model", "dflash")
+
+    def prepare_next_token_ids_cpu(
+        self,
+        sampled_token_ids: list[list[int]],
+        requests: dict[str, CachedRequestState],
+        gpu_input_batch: InputBatch,
+        num_scheduled_tokens: dict[str, int],
+    ) -> torch.Tensor:
+        """
+        This function is used to prepare the inputs for speculative decoding.
+        It calculates the next token ids for each request based on the sampled
+        token ids from the CPU. If a request has no sampled token ids (e.g.,
+        during the initial decoding steps), it falls back to using the request
+        state to get the next token id.
+        """
+        req_ids = gpu_input_batch.req_ids
+        next_token_ids: list[int] = []
+        for i, token_ids in enumerate(sampled_token_ids):
+            if token_ids:
+                # Common case.
+                next_token_id = token_ids[-1]
+            else:
+                # Partial prefill (rare case).
+                # Get the next token id from the request state.
+                req_id = req_ids[i]
+                req_state = requests[req_id]
+                seq_len = req_state.num_computed_tokens + num_scheduled_tokens[req_id]
+                next_token_id = req_state.get_token_id(seq_len)
+            next_token_ids.append(next_token_id)
+        next_token_ids = torch.tensor(
+            next_token_ids, dtype=torch.int32, device=self.input_ids.device
+        )
+        return next_token_ids
+
+    def prepare_next_token_ids_padded(
+        self,
+        sampled_token_ids: torch.Tensor,
+        requests: dict[str, CachedRequestState],
+        gpu_input_batch: InputBatch,
+        discard_request_mask: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        This function is used to prepare the inputs for speculative decoding.
+        It calculates the next token ids and the number of valid sampled tokens
+        for each request, considering the "discarded" requests whose next token
+        is not sampled and comes from `request.get_token_id()` instead. This is denoted
+        the "backup" token id. It also counts rejected tokens via `sampled_token_ids`.
+        """
+        # Precompute backup token IDs for discarded requests.
+        num_reqs = gpu_input_batch.num_reqs
+        for i in range(num_reqs):
+            self.backup_next_token_ids.np[i] = requests[
+                gpu_input_batch.req_ids[i]
+            ].get_token_id(gpu_input_batch.num_tokens_no_spec[i] - 1)
+        self.backup_next_token_ids.copy_to_gpu(num_reqs)
+        backup_tokens_gpu = self.backup_next_token_ids.gpu
+
+        batch_size, num_tokens = sampled_token_ids.shape
+        device = sampled_token_ids.device
+
+        assert discard_request_mask.dtype == torch.bool
+        assert backup_tokens_gpu.dtype == torch.int32
+
+        next_token_ids = torch.empty(batch_size, dtype=torch.int32, device=device)
+        valid_sampled_tokens_count = next_token_ids.new_empty(batch_size)
+
+        # Kernel grid: one program per request (row)
+        grid = (batch_size,)
+
+        # Find the next power of 2 for block sizes
+        BLOCK_SIZE_TOKENS = next_power_of_2(num_tokens)
+        eagle_prepare_next_token_padded_kernel[grid](
+            sampled_token_ids,
+            discard_request_mask,
+            backup_tokens_gpu,
+            next_token_ids,
+            valid_sampled_tokens_count,
+            gpu_input_batch.vocab_size,
+            num_tokens,
+            batch_size,
+            sampled_token_ids.stride(0),
+            BLOCK_SIZE_TOKENS=BLOCK_SIZE_TOKENS,
+        )
+
+        return next_token_ids, valid_sampled_tokens_count
+
+    def prepare_inputs_padded(
+        self,
+        common_attn_metadata: CommonAttentionMetadata,
+        spec_decode_metadata: SpecDecodeMetadata,
+        valid_sampled_tokens_count: torch.Tensor,
+    ) -> tuple[CommonAttentionMetadata, torch.Tensor, torch.Tensor]:
+        """
+        This function is used to prepare the inputs for speculative decoding
+        It updates the common_attn_metadata for speculative decoding,
+        but does not consider the rejected tokens. Instead, all tokens
+        are included as inputs to the speculator, with the rejected tokens
+        used as padding and filtered out later by `token_indices_to_sample`.
+        No blocking CPU operations should be introduced in this function.
+        """
+        num_reqs = common_attn_metadata.num_reqs
+        device = valid_sampled_tokens_count.device
+
+        token_indices_to_sample = torch.empty(
+            (num_reqs,), dtype=torch.int32, device=device
+        )
+        num_rejected_tokens_gpu = torch.empty(
+            (num_reqs,), dtype=torch.int32, device=device
+        )
+
+        grid = (num_reqs,)
+        eagle_prepare_inputs_padded_kernel[grid](
+            spec_decode_metadata.cu_num_draft_tokens,
+            valid_sampled_tokens_count,
+            common_attn_metadata.query_start_loc,
+            token_indices_to_sample,
+            num_rejected_tokens_gpu,
+            num_reqs,
+        )
+
+        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
+        new_query_len_per_req = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
+
+        total_num_tokens = query_start_loc_cpu[-1].item()
+
+        spec_common_attn_metadata = CommonAttentionMetadata(
+            query_start_loc=common_attn_metadata.query_start_loc,
+            seq_lens=common_attn_metadata.seq_lens,
+            query_start_loc_cpu=query_start_loc_cpu,
+            _seq_lens_cpu=common_attn_metadata._seq_lens_cpu,
+            _num_computed_tokens_cpu=common_attn_metadata._num_computed_tokens_cpu,
+            seq_lens_cpu_upper_bound=common_attn_metadata.seq_lens_cpu_upper_bound,
+            num_reqs=common_attn_metadata.num_reqs,
+            num_actual_tokens=total_num_tokens,
+            max_query_len=new_query_len_per_req.max().item(),
+            max_seq_len=common_attn_metadata.max_seq_len,
+            block_table_tensor=common_attn_metadata.block_table_tensor,
+            slot_mapping=common_attn_metadata.slot_mapping[:total_num_tokens],
+            causal=True,
+            dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
+        )
+
+        return (
+            spec_common_attn_metadata,
+            token_indices_to_sample,
+            num_rejected_tokens_gpu,
+        )
+
+    def prepare_inputs(
+        self,
+        common_attn_metadata: CommonAttentionMetadata,
+        sampled_token_ids: list[list[int]],
+        num_draft_tokens: list[int],
+    ) -> tuple[CommonAttentionMetadata, torch.Tensor]:
+        """
+        This function is used to prepare the inputs for speculative decoding.
+        It updates to the common_attn_metadata to account for the rejected
+        tokens (and newly sampled tokens). It also returns the token indices
+        of the tokens that should be fed to the speculator.
+        """
+        # E.g.
+        #  common_attn_metadata.query_start_loc{_cpu}:
+        #       [0, q1, q1 + q2, q1 + q2 + q3]
+        #  common_attn_metadata.seq_lens{_cpu}: [s1, s2, s3]
+        #  num_rejected_tokens: [n1, n2, n3]
+        # This function computes the intermediate values:
+        #  num_tokens_per_req: [q1 - n1, q2 - n2, q3 - n3]
+        # And returns:
+        #  common_attn_metadata.query_start_loc{_cpu}:
+        #       [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
+        #  common_attn_metadata.seq_lens{_cpu}:
+        #       [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
+        #  token_indices: [0, 1, ..., q1 - n1 - 1,
+        #                 q1, q1 + 1, ..., q1 + q2 - n2 - 1,
+        #                 q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
+
+        num_rejected_tokens = [
+            n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
+            for i, n in enumerate(num_draft_tokens)
+        ]
+        num_rejected_tokens = torch.tensor(num_rejected_tokens, dtype=torch.int32)
+
+        device = common_attn_metadata.query_start_loc.device
+        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
+        # upper_bound - rejected = actual post-rejection seq_lens (no D2H sync).
+        assert common_attn_metadata.seq_lens_cpu_upper_bound is not None
+        new_seq_lens_cpu = (
+            common_attn_metadata.seq_lens_cpu_upper_bound - num_rejected_tokens
+        )
+
+        # [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
+        new_query_len_per_req = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
+        # [q1, q2, q3] -> [q1 - n1, q2 - n2, q3 - n3]
+        new_num_tokens_per_req = new_query_len_per_req - num_rejected_tokens
+        new_num_tokens_per_req_np = new_num_tokens_per_req.numpy()
+
+        # [q1 - n1, q2 - n2, q3 - n3] ->
+        # [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
+        new_query_start_loc_cpu = torch.zeros(
+            query_start_loc_cpu.shape,
+            dtype=torch.int32,
+            pin_memory=is_pin_memory_available(),
+        )
+        new_query_start_loc_np = new_query_start_loc_cpu.numpy()
+        np.cumsum(new_num_tokens_per_req_np, out=new_query_start_loc_np[1:])
+
+        total_num_tokens = new_query_start_loc_np[-1]
+        # Example assuming num_tokens_per_req_np = [2, 4, 3]
+        # this implies that `new_query_start_locs` is:
+        # [0, 2, 6, 9] ->
+        # [0, 0, 2, 2, 2, 2, 6, 6, 6]
+        #  _r1_  ____r2____  ___r3__
+        new_query_start_locs_expanded = np.repeat(
+            new_query_start_loc_np[:-1], new_num_tokens_per_req_np
+        )
+        # [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
+        # [0, 1, 0, 1, 2, 3, 0, 1, 2]
+        #  _r1_  ____r2____  ___r3__
+        token_offsets = (
+            self.token_arange_np[:total_num_tokens] - new_query_start_locs_expanded
+        )
+
+        # Expand starting positions to match token pattern
+        # [0, q1, q1 + q2] ->
+        # [0, 0, q1, q1, q1, q1, q1 + q2, q1 + q2, q1 + q2]
+        #  _r1_  _____r2_______  ___________r3____________
+        old_query_start_locs_expanded = np.repeat(
+            query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np
+        )
+        # Final token indices are:
+        # [0, 1,                                // req 1
+        #  q1 + 0, q1 + 1, q1 + 2, q1 + 3,       // req 2
+        #  q1 + q2 + 0, q1 + q2 + 1, q1 + q2 + 2] // req 3
+        token_indices_np = token_offsets + old_query_start_locs_expanded
+        token_indices = torch.from_numpy(token_indices_np).to(device, non_blocking=True)
+
+        spec_common_attn_metadata = CommonAttentionMetadata(
+            query_start_loc=new_query_start_loc_cpu.to(device, non_blocking=True),
+            seq_lens=new_seq_lens_cpu.to(device, non_blocking=True),
+            query_start_loc_cpu=new_query_start_loc_cpu,
+            _seq_lens_cpu=new_seq_lens_cpu,
+            _num_computed_tokens_cpu=common_attn_metadata._num_computed_tokens_cpu,
+            seq_lens_cpu_upper_bound=new_seq_lens_cpu,
+            num_reqs=common_attn_metadata.num_reqs,
+            num_actual_tokens=total_num_tokens,
+            max_query_len=new_query_len_per_req.max().item(),
+            max_seq_len=new_seq_lens_cpu.max().item(),
+            block_table_tensor=common_attn_metadata.block_table_tensor,
+            slot_mapping=common_attn_metadata.slot_mapping[token_indices],
+            causal=True,
+            dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
+        )
+
+        return spec_common_attn_metadata, token_indices
+
+    def get_model_name(self, model: nn.Module) -> str:
+        if hasattr(model, "module"):  # multi-GPU
+            model = model.module
+        return model.__class__.__name__
+
+    def _create_draft_vllm_config(self) -> VllmConfig:
+        """Return a VllmConfig with kernel-level overrides for the proposer.
+        Subclasses may override to apply additional config changes.
+        """
+        spec_cfg = self.speculative_config
+        base = self.vllm_config
+
+        if spec_cfg.moe_backend is not None:
+            base = replace(
+                base,
+                kernel_config=replace(
+                    base.kernel_config,
+                    moe_backend=spec_cfg.moe_backend,
+                ),
+            )
+
+        # Note (matt): Never inherit the attention backend from base, because there are
+        # many opportunities for incompatibility, so we always independently autoselect
+        # unless explicitly specified in the speculative config.
+        base = replace(
+            base,
+            attention_config=replace(
+                base.attention_config,
+                backend=spec_cfg.attention_backend,
+            ),
+        )
+
+        return base
+
+    def _get_model(self) -> nn.Module:
+        """
+        Default method to call get_model(). Can be overridden by subclasses which
+        need to customize model loading.
+        """
+        from vllm.compilation.backends import set_model_tag
+
+        draft_vllm_config = self._create_draft_vllm_config()
+        with set_model_tag("eagle_head"):
+            model = get_model(
+                vllm_config=draft_vllm_config,
+                model_config=self.speculative_config.draft_model_config,
+                load_config=self.speculative_config.draft_load_config,
+            )
+        return model
+
+    def load_model(self, target_model: nn.Module) -> None:
+        target_attn_layer_names = set(
+            get_layers_from_vllm_config(
+                self.vllm_config,
+                AttentionLayerBase,  # type: ignore[type-abstract]
+            ).keys()
+        )
+
+        self.model = self._get_model()
+
+        # Find draft layers (attention layers added by draft model)
+        all_attn_layers = get_layers_from_vllm_config(
+            self.vllm_config,
+            AttentionLayerBase,  # type: ignore[type-abstract]
+        )
+        # Filter to only layers that have KV cache specs.
+        self._draft_attn_layer_names = {
+            name
+            for name in (set(all_attn_layers.keys()) - target_attn_layer_names)
+            if all_attn_layers[name].get_kv_cache_spec(self.vllm_config) is not None
+        }
+
+        if self.supports_mm_inputs:
+            # Even if the target model is multimodal, we can also use
+            # text-only draft models
+            try:
+                dummy_input_ids = torch.tensor([[1]], device=self.input_ids.device)
+                self.model.embed_input_ids(dummy_input_ids, multimodal_embeddings=None)
+            except (NotImplementedError, AttributeError, TypeError):
+                logger.warning(
+                    "Draft model does not support multimodal inputs, "
+                    "falling back to text-only mode"
+                )
+                self.supports_mm_inputs = False
+
+        if supports_multimodal(target_model):
+            # handle multimodality
+            assert hasattr(target_model, "config")
+            if self.get_model_name(target_model) in [
+                "Cohere2VisionForConditionalGeneration",
+                "Exaone4_5_ForConditionalGeneration",
+                "GlmOcrForConditionalGeneration",
+                "HunYuanVLForConditionalGeneration",
+                "MiMoV2OmniForCausalLM",
+                "Qwen2_5_VLForConditionalGeneration",
+                "Qwen3_5ForConditionalGeneration",
+                "Qwen3_5MoeForConditionalGeneration",
+                "Qwen3VLForConditionalGeneration",
+                "Qwen3VLMoeForConditionalGeneration",
+                "Gemma4ForConditionalGeneration",
+            ]:
+                self.model.config.image_token_index = target_model.config.image_token_id
+            elif self.get_model_name(target_model) == "PixtralForConditionalGeneration":
+                self.model.config.image_token_index = (
+                    target_model.config.vision_config.image_token_id
+                )
+            elif self.get_model_name(target_model) == "KimiK25ForConditionalGeneration":
+                self.model.config.image_token_index = (
+                    target_model.config.media_placeholder_token_id
+                )
+            else:
+                self.model.config.image_token_index = (
+                    target_model.config.image_token_index
+                )
+            target_language_model = cast(
+                SupportsMultiModal, target_model
+            ).get_language_model()
+        else:
+            target_language_model = target_model
+
+        self._maybe_share_embeddings(target_language_model)
+        self._maybe_share_lm_head(target_language_model)
+
+        if (
+            self.parallel_drafting
+            and self.pass_hidden_states_to_model
+            and self.parallel_drafting_hidden_state_tensor is not None
+        ):
+            flat_mask = self.model.mask_hidden.view(-1)
+            if self.eagle3_use_aux_hidden_state:
+                # EAGLE3: mask_hidden stores all aux hidden states,
+                # project through combine_hidden_states
+                self.parallel_drafting_hidden_state_tensor.copy_(
+                    self.model.combine_hidden_states(flat_mask)
+                )
+            else:
+                self.parallel_drafting_hidden_state_tensor.copy_(flat_mask)
+
+    def _maybe_share_embeddings(self, target_language_model: nn.Module) -> None:
+        """
+        Some draft models may not have their own embedding layers, and some may
+        have a duplicate copy of the target model's embedding layers. In these cases,
+        we share the target model's embedding layers with the draft model to save
+        memory.
+        """
+        if get_pp_group().world_size == 1:
+            inner_model = getattr(target_language_model, "model", None)
+            if inner_model is None:
+                raise AttributeError("Target model does not have 'model' attribute")
+            if hasattr(inner_model, "embed_tokens"):
+                target_embed_tokens = inner_model.embed_tokens
+            elif hasattr(inner_model, "embedding"):
+                target_embed_tokens = inner_model.embedding
+            else:
+                raise AttributeError(
+                    "Target model does not have 'embed_tokens' or 'embedding' attribute"
+                )
+
+            share_embeddings = False
+            if hasattr(self.model, "has_own_embed_tokens"):
+                # EAGLE model
+                if not self.model.has_own_embed_tokens:
+                    share_embeddings = True
+                    logger.info(
+                        "Detected EAGLE model without its own embed_tokens in the"
+                        " checkpoint. Sharing target model embedding weights with the"
+                        " draft model."
+                    )
+                elif (
+                    isinstance(target_embed_tokens.weight, torch.Tensor)
+                    and isinstance(self.model.model.embed_tokens.weight, torch.Tensor)
+                    # TODO: Offload to CPU for comparison to avoid extra GPU memory
+                    # usage in CI testing environments with limited GPU memory
+                    and torch.equal(
+                        target_embed_tokens.weight.cpu(),
+                        self.model.model.embed_tokens.weight.cpu(),
+                    )
+                ):
+                    share_embeddings = True
+                    logger.info(
+                        "Detected EAGLE model with embed_tokens identical to the target"
+                        " model. Sharing target model embedding weights with the draft"
+                        " model."
+                    )
+                else:
+                    logger.info(
+                        "Detected EAGLE model with distinct embed_tokens weights. "
+                        "Keeping separate embedding weights from the target model."
+                    )
+            else:
+                # MTP model
+                share_embeddings = True
+                logger.info(
+                    "Detected MTP model. "
+                    "Sharing target model embedding weights with the draft model."
+                )
+
+            if share_embeddings:
+                if hasattr(self.model.model, "embed_tokens"):
+                    del self.model.model.embed_tokens
+                self.model.model.embed_tokens = target_embed_tokens
+        else:
+            logger.info(
+                "The draft model's vocab embedding will be loaded separately"
+                " from the target model."
+            )
+
+    def _maybe_share_lm_head(self, target_language_model: nn.Module) -> None:
+        """
+        Some draft models may not have their own LM head, and some may have a
+        duplicate copy of the target model's LM head. In these cases, we share
+        the target model's LM head with the draft model to save memory.
+        """
+        share_lm_head = False
+        if hasattr(self.model, "has_own_lm_head"):
+            # EAGLE model
+            if not self.model.has_own_lm_head:
+                share_lm_head = True
+                logger.info(
+                    "Detected EAGLE model without its own lm_head in the checkpoint. "
+                    "Sharing target model lm_head weights with the draft model."
+                )
+            elif (
+                hasattr(target_language_model, "lm_head")
+                and hasattr(target_language_model.lm_head, "weight")
+                and hasattr(self.model.lm_head, "weight")
+                and isinstance(target_language_model.lm_head.weight, torch.Tensor)
+                and isinstance(self.model.lm_head.weight, torch.Tensor)
+                # TODO: Offload to CPU for comparison to avoid extra GPU memory
+                # usage in CI testing environments with limited GPU memory
+                and torch.equal(
+                    target_language_model.lm_head.weight.cpu(),
+                    self.model.lm_head.weight.cpu(),
+                )
+            ):
+                share_lm_head = True
+                logger.info(
+                    "Detected EAGLE model with lm_head identical to the target model. "
+                    "Sharing target model lm_head weights with the draft model."
+                )
+            else:
+                logger.info(
+                    "Detected EAGLE model with distinct lm_head weights. "
+                    "Keeping separate lm_head weights from the target model."
+                )
+        else:
+            # MTP model
+            share_lm_head = True
+            logger.info(
+                "Detected MTP model. "
+                "Sharing target model lm_head weights with the draft model."
+            )
+
+        if share_lm_head and hasattr(target_language_model, "lm_head"):
+            if hasattr(self.model, "lm_head"):
+                del self.model.lm_head
+            self.model.lm_head = target_language_model.lm_head
+
+            # MTP models call compute_logits via shared_head.head (a
+            # ParallelLMHead inside each MTP layer), not self.model.lm_head.
+            # If the checkpoint omits a copy of the lm_head weights at the
+            # MTP layer path, shared_head.head stays uninitialised and
+            # produces NaN logits. Always share it explicitly.
+            inner = getattr(self.model, "model", None)
+            layers = getattr(inner, "layers", None) if inner else None
+            if layers is not None:
+                items = layers.values() if isinstance(layers, nn.ModuleDict) else layers
+                for layer in items:
+                    sh = getattr(layer, "shared_head", None)
+                    if sh is not None and hasattr(sh, "head"):
+                        del sh.head
+                        sh.head = target_language_model.lm_head
+                        logger.info(
+                            "Shared target model lm_head with MTP shared_head.head."
+                        )
+
+        if hasattr(target_language_model.model, "topk_indices_buffer"):
+            if hasattr(self.model.model, "topk_indices_buffer"):
+                del self.model.model.topk_indices_buffer
+            self.model.model.topk_indices_buffer = (
+                target_language_model.model.topk_indices_buffer
+            )
+            logger.info(
+                "Detected MTP model with topk_indices_buffer. "
+                "Sharing target model topk_indices_buffer with the draft model."
+            )
+
+        if self.use_local_argmax_reduction:
+            if not hasattr(self.model, "get_top_tokens"):
+                raise ValueError(
+                    "use_local_argmax_reduction is enabled but draft model "
+                    f"{self.model.__class__.__name__} does not implement "
+                    "get_top_tokens()."
+                )
+            # Warn if draft model has vocab remapping, which forces fallback
+            # to the full-logits path (negating the optimization).
+            if (
+                hasattr(self.model, "draft_id_to_target_id")
+                and self.model.draft_id_to_target_id is not None
+            ):
+                logger.warning(
+                    "use_local_argmax_reduction is enabled but draft model "
+                    "uses draft_id_to_target_id vocab remapping. The "
+                    "optimization will be bypassed (falling back to full "
+                    "logits gather + argmax)."
+                )
+            else:
+                logger.info(
+                    "Using local argmax reduction for draft token generation "
+                    "(communication: O(2*tp_size) vs O(vocab_size))."
+                )
+
+    @torch.inference_mode()
+    def dummy_run(
+        self,
+        num_tokens: int,
+        use_cudagraphs: bool = True,
+        is_graph_capturing: bool = False,
+        slot_mappings: dict[str, torch.Tensor] | None = None,
+    ) -> None:
+        # FIXME: when using tree-based specdec, adjust number of forward-passes
+        # according to the depth of the tree.
+        only_one_forward_pass = is_graph_capturing or self.parallel_drafting
+        for fwd_idx in range(
+            1 if only_one_forward_pass else self.num_speculative_tokens
+        ):
+            if fwd_idx <= 1:
+                cudagraph_runtime_mode, num_input_tokens, num_tokens_across_dp = (
+                    self._determine_batch_execution_and_padding(
+                        num_tokens, use_cudagraphs=use_cudagraphs
+                    )
+                )
+
+            # Make sure to use EAGLE's own buffer during cudagraph capture.
+            if (
+                self._draft_attn_layer_names
+                and slot_mappings is not None
+                and next(iter(self._draft_attn_layer_names)) in slot_mappings
+            ):
+                slot_mapping_dict = self._get_slot_mapping(num_input_tokens)
+            else:
+                slot_mapping_dict = slot_mappings or {}
+
+            with set_forward_context(
+                None,
+                self.vllm_config,
+                num_tokens=num_input_tokens,
+                num_tokens_across_dp=num_tokens_across_dp,
+                cudagraph_runtime_mode=cudagraph_runtime_mode,
+                slot_mapping=slot_mapping_dict,
+            ):
+                if self.supports_mm_inputs:
+                    input_ids = None
+                    inputs_embeds = self.inputs_embeds[:num_input_tokens]
+                else:
+                    input_ids = self.input_ids[:num_input_tokens]
+                    inputs_embeds = None
+
+                kwargs = dict(
+                    input_ids=input_ids,
+                    positions=self._get_positions(num_input_tokens),
+                    inputs_embeds=inputs_embeds,
+                )
+                if self.pass_hidden_states_to_model:
+                    kwargs["hidden_states"] = self.hidden_states[:num_input_tokens]
+                self.model(**kwargs)
+
+    def _get_eagle3_use_aux_hidden_state_from_config(self) -> bool:
+        """
+        Some eagle3 heads (e.g., nvidia/gpt-oss-120b-Eagle3-v2) do not use auxiliary
+        hidden states and directly uses the last layer output just like eagle1.
+        They might indicate this by setting "use_aux_hidden_state" to False
+        inside the "eagle_config" dict of their hf_config.
+        """
+        if self.method != "eagle3":
+            return False
+        # Assume that eagle3 heads use aux hidden states by default
+        use_aux_hidden_state = True
+        eagle_config = getattr(self.draft_model_config.hf_config, "eagle_config", None)
+        if eagle_config is not None:
+            use_aux_hidden_state = eagle_config.get("use_aux_hidden_state", True)
+        return use_aux_hidden_state
+
+    def validate_same_kv_cache_group(self, kv_cache_config: KVCacheConfig) -> None:
+        """
+        Validate that all drafting layers belong to the same KVCacheGroup.
+        Need this assumption to ensure all drafting layers can use the
+        same AttentionMetadata.
+        May extend to multiple AttentionMetadata in the future.
+        """
+        kv_cache_groups: dict[str, int] = {}
+        for id, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
+            for layer_name in kv_cache_group.layer_names:
+                kv_cache_groups[layer_name] = id
+        assert (
+            len(
+                set(
+                    [
+                        kv_cache_groups[layer_name]
+                        for layer_name in self._draft_attn_layer_names
+                    ]
+                )
+            )
+            == 1
+        ), "All drafting layers should belong to the same kv cache group"
+
+    def initialize_attn_backend(
+        self,
+        kv_cache_config: KVCacheConfig,
+        kernel_block_sizes: list[int] | None = None,
+    ) -> None:
+        """
+        Initialize AttentionGroups for draft layers using kv_cache_config.
+        Called from the model runner's initialize_metadata_builders.
+        """
+        all_attn_layers = get_layers_from_vllm_config(
+            self.vllm_config,
+            AttentionLayerBase,  # type: ignore[type-abstract]
+        )
+
+        # Find which kv_cache_group the draft layers belong to
+        self.validate_same_kv_cache_group(kv_cache_config)
+        kv_cache_spec = None
+        for gid, group in enumerate(kv_cache_config.kv_cache_groups):
+            if self._draft_attn_layer_names & set(group.layer_names):
+                self.kv_cache_gid = gid
+                kv_cache_spec = group.kv_cache_spec
+                break
+
+        attention_groups: dict[tuple[str, str], AttentionGroup] = {}
+        if kv_cache_spec is not None:
+            for layer_name in self._draft_attn_layer_names:
+                attn_backend = all_attn_layers[layer_name].get_attn_backend()
+                backend_key = attn_backend.full_cls_name()
+                if backend_key not in attention_groups:
+                    layer_kv_cache_spec = kv_cache_spec
+                    if isinstance(layer_kv_cache_spec, UniformTypeKVCacheSpecs):
+                        layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[
+                            layer_name
+                        ]
+
+                    kernel_block_size = (
+                        kernel_block_sizes[self.kv_cache_gid]
+                        if kernel_block_sizes is not None
+                        and self.kv_cache_gid < len(kernel_block_sizes)
+                        else None
+                    )
+                    attn_group = AttentionGroup(
+                        backend=attn_backend,
+                        layer_names=[layer_name],
+                        kv_cache_spec=layer_kv_cache_spec,
+                        kv_cache_group_id=self.kv_cache_gid,
+                    )
+                    attn_group.create_metadata_builders(
+                        self.vllm_config,
+                        self.device,
+                        kernel_block_size=kernel_block_size,
+                    )
+                    attention_groups[backend_key] = attn_group
+                else:
+                    attention_groups[backend_key].layer_names.append(layer_name)
+
+        self.draft_attn_groups = list(attention_groups.values())
+        self.block_size = (
+            self.draft_attn_groups[0].get_metadata_builder().kv_cache_spec.block_size
+        )
+        logger.debug("Using block size %d for drafting layers", self.block_size)
+
+    def _determine_batch_execution_and_padding(
+        self,
+        num_tokens: int,
+        use_cudagraphs: bool = True,
+    ) -> tuple[CUDAGraphMode, int, torch.Tensor | None]:
+        cudagraph_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
+            num_tokens,
+            valid_modes=({CUDAGraphMode.NONE} if not use_cudagraphs else None),
+        )
+        num_tokens_padded = batch_desc.num_tokens
+
+        # Extra coordination when running data-parallel since we need to
+        # coordinate across ranks
+        # TODO(Flechman): support DBO ubatching
+        should_ubatch, num_tokens_across_dp = False, None
+        if self.vllm_config.parallel_config.data_parallel_size > 1:
+            should_ubatch, num_tokens_across_dp, synced_cudagraph_mode = (
+                coordinate_batch_across_dp(
+                    num_tokens_unpadded=num_tokens,
+                    parallel_config=self.vllm_config.parallel_config,
+                    allow_microbatching=False,
+                    num_tokens_padded=num_tokens_padded,
+                    cudagraph_mode=cudagraph_mode.value,
+                )
+            )
+            assert not should_ubatch, "DBO ubatching not implemented for EAGLE"
+
+            # Extract DP-synced values
+            if num_tokens_across_dp is not None:
+                dp_rank = self.dp_rank
+                num_tokens_padded = int(num_tokens_across_dp[dp_rank].item())
+                # Re-dispatch with DP padding so we have the correct
+                # batch_descriptor
+                cudagraph_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
+                    num_tokens_padded,
+                    valid_modes={CUDAGraphMode(synced_cudagraph_mode)},
+                )
+                # Assert to make sure the agreed upon token count is correct
+                # otherwise num_tokens_across_dp will no-longer be valid
+                assert batch_desc.num_tokens == num_tokens_padded
+                num_tokens_across_dp[dp_rank] = num_tokens_padded
+
+        return cudagraph_mode, num_tokens_padded, num_tokens_across_dp
+
+
+# NOTE(woosuk): Currently, the below code is not used and we always use argmax
+# to sample the draft tokens. We will use this after we find a way to manage
+# the draft prob tensor.
+# Refer to https://github.com/vllm-project/vllm/pull/16899 for the details.
+# FIXME(woosuk): The logic here is duplicated with the main sampling code.
+# We should refactor this to reuse the same sampling implementation.
+def compute_probs_and_sample_next_token(
+    logits: torch.Tensor,
+    sampling_metadata: SamplingMetadata,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    if sampling_metadata.all_greedy:
+        # For greedy requests, draft_probs is not used in rejection sampling.
+        # Therefore, we can just return the logits.
+        probs = logits
+        next_token_ids = logits.argmax(dim=-1)
+        return next_token_ids, probs
+
+    assert sampling_metadata.temperature is not None
+
+    # Use epsilon comparison to detect greedy sampling (temperature ~ 0.0)
+    # consistent with sampler.py's _SAMPLING_EPS threshold
+    temperature = sampling_metadata.temperature
+    # Avoid division by zero if there are greedy requests.
+    if not sampling_metadata.all_random:
+        is_greedy = temperature < _SAMPLING_EPS
+        temperature = torch.where(is_greedy, 1.0, temperature)
+    logits.div_(temperature.view(-1, 1))
+    probs = logits.softmax(dim=-1, dtype=torch.float32)
+
+    # NOTE(woosuk): Currently, we ignore most of the sampling parameters in
+    # generating the draft tokens. We only use the temperature. While this
+    # could degrade the acceptance rate, it does not affect the distribution
+    # of the generated tokens after rejection sampling.
+
+    # TODO(woosuk): Consider seeds.
+    q = torch.empty_like(probs)
+    q.exponential_()
+    # NOTE(woosuk): We shouldn't use `probs.div_(q)` because the draft_probs
+    # will be used later for rejection sampling.
+    next_token_ids = probs.div(q).argmax(dim=-1).view(-1)
+    if not sampling_metadata.all_random:
+        greedy_token_ids = probs.argmax(dim=-1)
+        next_token_ids = torch.where(is_greedy, greedy_token_ids, next_token_ids)
+    return next_token_ids, probs
diff --git a/vllm/v1/spec_decode/utils.py b/vllm/v1/spec_decode/utils.py
index cdcb3e05bfad..e046f0136152 100644
--- a/vllm/v1/spec_decode/utils.py
+++ b/vllm/v1/spec_decode/utils.py
@@ -594,3 +594,9 @@ def update_num_computed_tokens_for_batch_change(
     num_accepted_tokens.copy_(
         torch.where(participating, valid_counts, num_accepted_tokens)
     )
+
+
+def unconditional_to_conditional_rates(rates: list[float]) -> list[float]:
+    """Convert per-position unconditional rates to per-position conditional
+    rates for the early-terminating rejection loop (c_i = p_i / p_{i-1})."""
+    return [p / q if q > 0.0 else 0.0 for p, q in zip(rates, [1.0, *rates[:-1]])]
diff --git a/vllm/v1/structured_output/__init__.py b/vllm/v1/structured_output/__init__.py
index 213b49f28d91..3080fe30b0b5 100644
--- a/vllm/v1/structured_output/__init__.py
+++ b/vllm/v1/structured_output/__init__.py
@@ -37,7 +37,10 @@ class StructuredOutputManager:
 
     def __init__(self, vllm_config: VllmConfig):
         self.backend: StructuredOutputBackend | None = None
-        self.reasoner: ReasoningParser | None = None
+        # We only store the class of the reasoner in the manager.
+        # The parser instance is request-scoped because some reasoning parsers
+        # depend on per-request chat-template kwargs.
+        self.reasoner_cls: type[ReasoningParser] | None = None
         self.vllm_config = vllm_config
 
         # When in external_launcher mode, async grammar compilation causes deadlocks
@@ -85,15 +88,29 @@ def __init__(self, vllm_config: VllmConfig):
                 self.vllm_config.structured_outputs_config.reasoning_parser
             )
             if reasoning_parser:
-                reasoner_cls = ReasoningParserManager.get_reasoning_parser(
+                self.reasoner_cls = ReasoningParserManager.get_reasoning_parser(
                     reasoning_parser
                 )
-                self.reasoner = reasoner_cls(tokenizer=self.tokenizer)
 
         self.enable_in_reasoning = (
             self.vllm_config.structured_outputs_config.enable_in_reasoning
         )
 
+    def _get_reasoner(self, request: "Request") -> "ReasoningParser | None":
+        structured_req = request.structured_output_request
+        if structured_req is None or self.reasoner_cls is None:
+            return None
+
+        if structured_req.reasoner is None:
+            # Lazily build the request-local parser so the structured-output
+            # gate observes the same template kwargs used by the frontend.
+            parser_kwargs = structured_req.reasoning_parser_kwargs or {}
+            structured_req.reasoner = self.reasoner_cls(
+                tokenizer=self.tokenizer,
+                **parser_kwargs,
+            )
+        return structured_req.reasoner
+
     def grammar_init(self, request: "Request") -> None:
         if request.structured_output_request is None:
             return
@@ -285,7 +302,8 @@ def should_fill_bitmask(self, request: "Request") -> bool:
         # NOTE (Hanchen) if enable_in_reasoning is True, it means that
         # the model needs to be constrained in reasoning. So we should always
         # enable the bitmask filling.
-        if self.reasoner is not None:
+        reasoner = self._get_reasoner(request)
+        if reasoner is not None:
             if self.enable_in_reasoning:
                 return True
             assert request.structured_output_request is not None
@@ -295,7 +313,7 @@ def should_fill_bitmask(self, request: "Request") -> bool:
                 # After unifying the `openai_gptoss` and non-`openai_gptoss` styles,
                 # it can be removed.
                 request.structured_output_request.reasoning_ended = (
-                    self.reasoner.is_reasoning_end(request.prompt_token_ids or [])
+                    reasoner.is_reasoning_end(request.prompt_token_ids or [])
                 )
             return request.structured_output_request.reasoning_ended
         return True
@@ -311,7 +329,8 @@ def should_advance(self, request: "Request") -> bool:
             assert request.structured_output_request.grammar is not None
         # by default, we should always advance
         # for cases that don't use thinking mode.
-        if self.reasoner is None:
+        reasoner = self._get_reasoner(request)
+        if reasoner is None:
             return True
 
         # if the model needs structured in reasoning, we should advance
@@ -328,7 +347,7 @@ def should_advance(self, request: "Request") -> bool:
         start = (
             delta_from if delta_from >= 0 else max(len(all_token_ids) + delta_from, 0)
         )
-        if self.reasoner.is_reasoning_end_streaming(
+        if reasoner.is_reasoning_end_streaming(
             all_token_ids, itertools.islice(all_token_ids, start, None)
         ):
             # Reasoning just ended, so we shouldn't advance til
diff --git a/vllm/v1/structured_output/request.py b/vllm/v1/structured_output/request.py
index b921a71b3cf1..dfa8c7efcae4 100644
--- a/vllm/v1/structured_output/request.py
+++ b/vllm/v1/structured_output/request.py
@@ -5,7 +5,7 @@
 import json
 from concurrent.futures import Future
 from concurrent.futures._base import TimeoutError
-from typing import cast
+from typing import TYPE_CHECKING, Any, cast
 
 from vllm.sampling_params import SamplingParams, StructuredOutputsParams
 from vllm.v1.structured_output.backend_types import (
@@ -14,12 +14,19 @@
     StructuredOutputOptions,
 )
 
+if TYPE_CHECKING:
+    from vllm.reasoning import ReasoningParser
+
 
 @dataclasses.dataclass
 class StructuredOutputRequest:
     params: StructuredOutputsParams
     _grammar: Future[StructuredOutputGrammar] | StructuredOutputGrammar | None = None
     reasoning_ended: bool | None = None
+    reasoning_parser_kwargs: dict[str, Any] | None = None
+    # Cached per request; do not share reasoning parsers across requests because
+    # their behavior can depend on reasoning_parser_kwargs.
+    reasoner: "ReasoningParser | None" = None
 
     @staticmethod
     def from_sampling_params(
diff --git a/vllm/v1/worker/block_table.py b/vllm/v1/worker/block_table.py
index f46e8a8ed63c..87a2aac9d4ca 100644
--- a/vllm/v1/worker/block_table.py
+++ b/vllm/v1/worker/block_table.py
@@ -257,6 +257,13 @@ def __init__(
                 f"must match block_sizes length ({len(block_sizes)})"
             )
 
+        # Align to a multiple of (128 / block_size) as required
+        # by some attention backends such as TRTLLM (#39324)
+        max_num_blocks = [
+            cdiv(n, 128 // bs) * (128 // bs) if bs <= 128 else n
+            for n, bs in zip(max_num_blocks, block_sizes)
+        ]
+
         self.block_tables = [
             BlockTable(
                 block_size,
diff --git a/vllm/v1/worker/cp_utils.py b/vllm/v1/worker/cp_utils.py
index c5febda691d7..05cca52fc0db 100644
--- a/vllm/v1/worker/cp_utils.py
+++ b/vllm/v1/worker/cp_utils.py
@@ -33,7 +33,7 @@ def check_attention_cp_compatibility(vllm_config: VllmConfig) -> None:
                     "implementations to return the softmax LSE during decode, "
                     f"but {layer_impl.__class__.__name__} does not. "
                     "Try a different backend by setting "
-                    "VLLM_ATTENTION_BACKEND or disable DCP."
+                    "--attention-backend or disable DCP."
                 )
 
             if pcp_size > 1:
diff --git a/vllm/v1/worker/cpu_model_runner.py b/vllm/v1/worker/cpu_model_runner.py
index 7067ef62a75a..22c1b5bd0917 100644
--- a/vllm/v1/worker/cpu_model_runner.py
+++ b/vllm/v1/worker/cpu_model_runner.py
@@ -21,6 +21,9 @@
 
 class CPUModelRunner(GPUModelRunner):
     def __init__(self, vllm_config: VllmConfig, device: torch.device):
+        # avoid calling accelerator APIs for methods inherited from super class
+        _set_torch_accelerator_to_noop()
+
         with _torch_cuda_wrapper():
             super().__init__(vllm_config, device)
 
@@ -65,16 +68,16 @@ def _postprocess_triton(self) -> None:
 
         # Speculative decoding fallbacks
         import vllm.v1.sample.rejection_sampler
-        import vllm.v1.spec_decode.eagle
+        import vllm.v1.spec_decode.llm_base_proposer
         import vllm.v1.spec_decode.utils
 
-        vllm.v1.spec_decode.eagle.eagle_prepare_inputs_padded_kernel = (
+        vllm.v1.spec_decode.llm_base_proposer.eagle_prepare_inputs_padded_kernel = (
             cpu_tl.eagle_prepare_inputs_padded_kernel
         )
-        vllm.v1.spec_decode.eagle.eagle_prepare_next_token_padded_kernel = (
+        vllm.v1.spec_decode.llm_base_proposer.eagle_prepare_next_token_padded_kernel = (
             cpu_tl.eagle_prepare_next_token_padded_kernel
         )
-        vllm.v1.spec_decode.eagle.copy_and_expand_eagle_inputs_kernel = (
+        vllm.v1.spec_decode.llm_base_proposer.copy_and_expand_eagle_inputs_kernel = (
             cpu_tl.copy_and_expand_eagle_inputs_kernel
         )
         vllm.v1.spec_decode.utils.eagle_step_slot_mapping_metadata_kernel = (
@@ -116,21 +119,7 @@ def warming_up_model(self) -> None:
         logger.info("Warming up model for the compilation...")
         # Only generate graph for the generic shape
         with _set_global_compilation_settings(self.vllm_config):
-            self._dummy_run(
-                min(
-                    max(16, self.max_num_reqs),
-                    self.scheduler_config.max_num_batched_tokens,
-                )
-            )
-
-        # Warm up drafter for speculative decoding
-        if self.speculative_config and (self.speculative_config.uses_draft_model()):
-            from vllm.v1.spec_decode.draft_model import DraftModelProposer
-
-            if isinstance(self.drafter, (DraftModelProposer)):
-                logger.info("Warming up drafter model...")
-                self.drafter.dummy_run(max(16, self.max_num_reqs))
-
+            self.profile_run()
         logger.info("Warming up done.")
 
     def initialize_kv_cache(
@@ -258,3 +247,11 @@ def _set_global_compilation_settings(config: VllmConfig):
         yield
     finally:
         torch_inductor_config.freezing = freezing_value
+
+
+def _set_torch_accelerator_to_noop() -> None:
+    def noop(*args: Any, **kwargs: Any) -> None:
+        pass
+
+    torch.accelerator.synchronize = noop
+    torch.accelerator.empty_cache = noop
diff --git a/vllm/v1/worker/cpu_worker.py b/vllm/v1/worker/cpu_worker.py
index 1d85a94a4e1d..ce144c3aa215 100644
--- a/vllm/v1/worker/cpu_worker.py
+++ b/vllm/v1/worker/cpu_worker.py
@@ -1,15 +1,23 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import math
 import os
 import sys
 from typing import Any
 
+import psutil
 import torch
 
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.platforms import CpuArchEnum, current_platform
 from vllm.profiler.wrapper import TorchProfilerWrapper
+from vllm.utils.cpu_resource_utils import (
+    get_allowed_cpu_list,
+    get_memory_node_info,
+    get_visible_memory_node,
+)
+from vllm.utils.mem_utils import format_gib
 from vllm.utils.torch_utils import set_random_seed
 from vllm.v1.worker.cpu_model_runner import CPUModelRunner
 from vllm.v1.worker.gpu_worker import Worker, init_worker_distributed_environment
@@ -27,6 +35,46 @@ def __init__(
         distributed_init_method: str,
         is_driver_worker: bool = False,
     ):
+        # TODO: use numactl for process setup
+        # TODO: optimize for `interleaved` policy
+        # Bind memory node
+        allowed_memory_nodes = get_visible_memory_node()
+        allowed_cpu_list = get_allowed_cpu_list()
+        cpu_core = allowed_cpu_list[0]
+
+        # TODO: some CI hosts are not correctly set, change to assertion
+        # after fix
+        if cpu_core.numa_node not in allowed_memory_nodes:
+            logger.warning(
+                "Node %s is not in available memory nodes %s.",
+                cpu_core.numa_node,
+                allowed_memory_nodes,
+            )
+
+        torch.ops._C.init_cpu_memory_env([cpu_core.numa_node])
+
+        memory_status = get_memory_node_info(cpu_core.numa_node)
+        memory_fraction = vllm_config.cache_config.gpu_memory_utilization
+        self.requested_cpu_memory = math.ceil(
+            memory_status.total_memory * memory_fraction
+        )
+        available_memory = memory_status.available_memory
+
+        if (
+            vllm_config.cache_config.kv_cache_memory_bytes is None
+            and self.requested_cpu_memory > available_memory
+        ):
+            raise ValueError(
+                f"Available memory on node {cpu_core.numa_node} "
+                f"({format_gib(available_memory)}/"
+                f"{format_gib(memory_status.total_memory)} GiB) on startup "
+                f"is less than desired CPU memory utilization "
+                f"({vllm_config.cache_config.gpu_memory_utilization}, "
+                f"{format_gib(self.requested_cpu_memory)} GiB). "
+                "Decrease --gpu-memory-utilization"
+                f" or reduce CPU memory used by other processes."
+            )
+
         super().__init__(
             vllm_config,
             local_rank,
@@ -103,13 +151,69 @@ def wake_up(self, tags: list[str] | None = None) -> None:
         pass
 
     def determine_available_memory(self) -> int:
-        return self.cache_config.cpu_kvcache_space_bytes or 0
+        self.model_runner.warming_up_model()
+
+        allowed_cpu_list = get_allowed_cpu_list()
+        cpu_core = allowed_cpu_list[0]
+
+        memory_status = get_memory_node_info(cpu_core.numa_node)
+        available_memory = memory_status.available_memory
+        explicit_kv_cache_size = self.cache_config.kv_cache_memory_bytes
+
+        kv_cache_size = None
+        msg = None
+        if explicit_kv_cache_size is not None:
+            if explicit_kv_cache_size > available_memory:
+                raise ValueError(
+                    f"Available memory on node {cpu_core.numa_node} "
+                    f"({format_gib(available_memory)}/"
+                    f"{format_gib(memory_status.total_memory)} GiB) on kv cache"
+                    f" allocation is less than requested memory for kv "
+                    f"({format_gib(explicit_kv_cache_size)} GiB). "
+                    "Decrease --kv-cache-memory-bytes, VLLM_CPU_KVCACHE_SPACE, "
+                    "or reduce CPU memory used by other processes."
+                )
+            kv_cache_size = explicit_kv_cache_size
+            msg = (
+                f"Explicitly set ({format_gib(kv_cache_size)}/"
+                f"{format_gib(memory_status.total_memory)}) GiB for KV cache "
+                f"on node {cpu_core.numa_node}."
+            )
+        else:
+            consumed_memory = psutil.Process(os.getpid()).memory_info().rss
+            requested_memory_for_kv = int(self.requested_cpu_memory - consumed_memory)
+            if (
+                requested_memory_for_kv <= 0
+                or requested_memory_for_kv > available_memory
+            ):
+                raise ValueError(
+                    f"Available memory on node {cpu_core.numa_node} "
+                    f"({format_gib(available_memory)}/"
+                    f"{format_gib(memory_status.total_memory)} GiB) on kv cache"
+                    f" allocation is less than requested memory for kv "
+                    f"({format_gib(requested_memory_for_kv)}/"
+                    f"{format_gib(self.requested_cpu_memory)} GiB). "
+                    "Reduce CPU memory used by other processes."
+                )
+            kv_cache_size = requested_memory_for_kv
+            msg = (
+                f"Auto set ({format_gib(kv_cache_size)}/"
+                f"{format_gib(memory_status.total_memory)}) GiB for KV cache "
+                f"on node {cpu_core.numa_node}, with "
+                f"{format_gib(self.requested_cpu_memory)} GiB requested memory"
+                f" for the worker. {format_gib(consumed_memory)} GiB"
+                f" memory was consumed by non-kv usages."
+            )
+
+        logger.info(msg)
+
+        return kv_cache_size
 
     def compile_or_warm_up_model(self) -> CompilationTimes:
         # Reset the seed to ensure that the random state is not affected by
         # the model initialization and profiling.
         set_random_seed(self.model_config.seed)
-        self.model_runner.warming_up_model()
+        # Note: the model has been compiled in determine_available_memory()
         return CompilationTimes(
             language_model=self.compilation_config.compilation_time,
             encoder=self.compilation_config.encoder_compilation_time,
diff --git a/vllm/v1/worker/dp_utils.py b/vllm/v1/worker/dp_utils.py
index 051fe42155ee..64f4e59031e6 100644
--- a/vllm/v1/worker/dp_utils.py
+++ b/vllm/v1/worker/dp_utils.py
@@ -1,8 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-
-import numpy as np
 import torch
 import torch.distributed as dist
 
@@ -29,7 +27,6 @@ def _get_device_and_group(parallel_config: ParallelConfig):
     if parallel_config.disable_nccl_for_dp_synchronization:
         logger.info_once(
             "Using CPU all reduce to synchronize DP padding between ranks.",
-            scope="local",
         )
         device = "cpu"
         group = get_dp_group().cpu_group
@@ -168,7 +165,6 @@ def coordinate_batch_across_dp(
     parallel_config: ParallelConfig,
     num_tokens_padded: int | None = None,
     uniform_decode: bool | None = None,
-    num_scheduled_tokens_per_request: np.ndarray | None = None,
     cudagraph_mode: int = 0,
 ) -> tuple[bool, torch.Tensor | None, int]:
     """
@@ -183,8 +179,6 @@ def coordinate_batch_across_dp(
             TP, etc)
         uniform_decode: Only used if allow_microbatching is True. True if the batch
             only contains single token decodes
-        num_scheduled_tokens_per_request: Only used if allow_microbatching is True. The
-            number of tokens per request.
         cudagraph_mode: The cudagraph mode for this rank (0=NONE, 1=PIECEWISE, 2=FULL).
             DP padding is enabled when synced cudagraph mode across ranks is not NONE.
 
diff --git a/vllm/v1/worker/encoder_cudagraph.py b/vllm/v1/worker/encoder_cudagraph.py
index 65ad8e2cec16..bb805a5d6ce2 100644
--- a/vllm/v1/worker/encoder_cudagraph.py
+++ b/vllm/v1/worker/encoder_cudagraph.py
@@ -67,13 +67,15 @@ def __init__(
 
         comp_config = vllm_config.compilation_config
         user_budgets = comp_config.encoder_cudagraph_token_budgets
-        user_max_mm_items = comp_config.encoder_cudagraph_max_vision_items_per_batch
+        user_max_vision_items = comp_config.encoder_cudagraph_max_vision_items_per_batch
         user_max_frames = comp_config.encoder_cudagraph_max_frames_per_batch
 
-        if user_budgets and user_max_mm_items > 0:
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        if user_budgets and user_max_vision_items > 0:
             # Fully user-specified
             self.token_budgets = sorted(user_budgets)
-            self.max_batch_size = user_max_mm_items
+            self.max_batch_size = user_max_vision_items
         else:
             # Auto-infer missing values from model
             min_budget, max_budget = model.get_encoder_cudagraph_budget_range(
@@ -85,14 +87,20 @@ def __init__(
                 else self._generate_budgets(min_budget, max_budget)
             )
             self.max_batch_size = (
-                user_max_mm_items if user_max_mm_items > 0 else max_budget // min_budget
+                user_max_vision_items
+                if user_max_vision_items > 0
+                else max_budget // min_budget
             )
 
-        if user_max_frames > 0:
+        assert multimodal_config is not None
+        if multimodal_config.get_limit_per_prompt("video") == 0:
+            self.max_frames_per_batch = 0
+        elif user_max_frames is not None:
             self.max_frames_per_batch = user_max_frames
         else:
-            # TODO(shen-shanshan): optimize this auto-infer for max_frames_per_batch.
-            self.max_frames_per_batch = self.max_batch_size * 2
+            # Set it to the model-specific value according to its `processing_info`.
+            max_frames_per_video = self.model.get_max_frames_per_video()
+            self.max_frames_per_batch = self.max_batch_size * max_frames_per_video
 
         mm_config = vllm_config.model_config.multimodal_config
         self.use_dp = (
@@ -111,7 +119,7 @@ def __init__(
             "budgets=%s, max_batch_size=%d, max_frames_per_batch=%s, use_dp=%s",
             self.token_budgets,
             self.max_batch_size,
-            self.max_frames_per_batch if self.max_frames_per_batch > 0 else "auto",
+            self.max_frames_per_batch,
             self.use_dp,
         )
 
diff --git a/vllm/v1/worker/gpu/attn_utils.py b/vllm/v1/worker/gpu/attn_utils.py
index ee6244c42a04..5930777de6fa 100644
--- a/vllm/v1/worker/gpu/attn_utils.py
+++ b/vllm/v1/worker/gpu/attn_utils.py
@@ -4,11 +4,11 @@
 from dataclasses import dataclass
 from typing import Any, cast
 
-import numpy as np
 import torch
 
 from vllm.config import VllmConfig, get_layers_from_vllm_config
 from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
+from vllm.utils.torch_utils import get_dtype_size
 from vllm.v1.attention.backend import (
     AttentionBackend,
     AttentionCGSupport,
@@ -18,8 +18,10 @@
     AttentionSpec,
     KVCacheConfig,
     KVCacheSpec,
+    MambaSpec,
     UniformTypeKVCacheSpecs,
 )
+from vllm.v1.worker.gpu.model_states.interface import ModelSpecificAttnMetadata
 from vllm.v1.worker.utils import AttentionGroup, bind_kv_cache
 
 
@@ -146,48 +148,126 @@ def _reshape_kv_cache(
     kv_cache_raw_tensors: dict[str, torch.Tensor],
     attn_backends: dict[str, type[AttentionBackend]],
     cache_dtype: str,
-) -> dict[str, torch.Tensor]:
-    kv_caches: dict[str, torch.Tensor] = {}
+) -> dict[str, Any]:
+    kv_caches: dict[str, Any] = {}
+    has_attn, has_mamba = False, False
     for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
         for layer_name in kv_cache_group_spec.layer_names:
             kv_cache_spec = kv_cache_group_spec.kv_cache_spec
             if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
                 kv_cache_spec = kv_cache_spec.kv_cache_specs[layer_name]
-            assert isinstance(kv_cache_spec, AttentionSpec)
-
-            raw_tensor = kv_cache_raw_tensors[layer_name]
-            assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
-            num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
-
-            attn_backend = attn_backends[layer_name]
-            kv_cache_shape = attn_backend.get_kv_cache_shape(
-                num_blocks,
-                kv_cache_spec.block_size,
-                kv_cache_spec.num_kv_heads,
-                kv_cache_spec.head_size,
-                cache_dtype,
-            )
 
-            # FIXME(woosuk): Add kv_cache_stride_order to all attention backends.
-            try:
-                kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
-                assert len(kv_cache_stride_order) == len(kv_cache_shape)
-            except (AttributeError, NotImplementedError):
-                kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
-
-            kv_cache_shape = tuple(kv_cache_shape[i] for i in kv_cache_stride_order)
-            inv_order = [
-                kv_cache_stride_order.index(i)
-                for i in range(len(kv_cache_stride_order))
-            ]
-
-            dtype = kv_cache_spec.dtype
-            raw_tensor = raw_tensor.view(dtype)
-            raw_tensor = raw_tensor.view(kv_cache_shape)
-            kv_caches[layer_name] = raw_tensor.permute(*inv_order)
+            kv_raw_tensor = kv_cache_raw_tensors[layer_name]
+            assert kv_raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
+            num_blocks = kv_raw_tensor.numel() // kv_cache_spec.page_size_bytes
+
+            if isinstance(kv_cache_spec, AttentionSpec):
+                has_attn = True
+                attn_backend = attn_backends[layer_name]
+                kv_cache_shape = attn_backend.get_kv_cache_shape(
+                    num_blocks,
+                    kv_cache_spec.storage_block_size,
+                    kv_cache_spec.num_kv_heads,
+                    kv_cache_spec.head_size,
+                    cache_dtype_str=cache_dtype,
+                )
+
+                # FIXME(woosuk): Add kv_cache_stride_order to all attention backends.
+                try:
+                    kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
+                    assert len(kv_cache_stride_order) == len(kv_cache_shape)
+                except (AttributeError, NotImplementedError):
+                    kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
+
+                kv_cache_shape = tuple(kv_cache_shape[i] for i in kv_cache_stride_order)
+                inv_order = [
+                    kv_cache_stride_order.index(i)
+                    for i in range(len(kv_cache_stride_order))
+                ]
+
+                dtype = kv_cache_spec.dtype
+                kv_tensor = kv_raw_tensor.view(dtype)
+                if kv_cache_spec.page_size_padded is not None:
+                    # Use strided view to handle page_size_bytes that
+                    # include padding. This follows the same pattern as
+                    # MambaSpec handling in gpu_model_runner.py.
+                    # NOTE: This assumes kv_cache_shape[0] == num_blocks
+                    # (i.e. the first physical dimension is the block
+                    # index), which holds for MLA backends but NOT for
+                    # standard attention backends whose shape starts with
+                    # a K/V dimension of size 2.
+                    dtype_size = get_dtype_size(dtype)
+                    page_stride = kv_cache_spec.page_size_bytes // dtype_size
+                    strides = list(torch.empty(kv_cache_shape).stride())
+                    strides[inv_order[0]] = page_stride
+                    kv_cache = torch.as_strided(
+                        kv_tensor,
+                        size=kv_cache_shape,
+                        stride=tuple(strides),
+                    )
+                else:
+                    # No padding — safe to use a contiguous view.
+                    kv_cache = kv_tensor.view(kv_cache_shape)
+                kv_caches[layer_name] = kv_cache.permute(*inv_order)
+
+            elif isinstance(kv_cache_spec, MambaSpec):
+                has_mamba = True
+                state_tensors = []
+                storage_offset_bytes = 0
+                for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
+                    dtype_size = get_dtype_size(dtype)
+                    num_element_per_page = kv_cache_spec.page_size_bytes // dtype_size
+                    target_shape = (num_blocks, *shape)
+                    stride = torch.empty(target_shape).stride()
+                    target_stride = (num_element_per_page, *stride[1:])
+                    assert storage_offset_bytes % dtype_size == 0
+                    tensor = torch.as_strided(
+                        kv_raw_tensor.view(dtype),
+                        size=target_shape,
+                        stride=target_stride,
+                        storage_offset=storage_offset_bytes // dtype_size,
+                    )
+                    state_tensors.append(tensor)
+                    storage_offset_bytes += stride[0] * dtype_size
+                kv_caches[layer_name] = state_tensors
+            else:
+                raise NotImplementedError(
+                    f"Unsupported KV cache spec type: {type(kv_cache_spec)}"
+                )
+
+    if has_attn and has_mamba:
+        _update_hybrid_attention_layout(kv_caches, kv_cache_config)
+
     return kv_caches
 
 
+def _update_hybrid_attention_layout(
+    kv_caches: dict[str, Any],
+    kv_cache_config: KVCacheConfig,
+) -> None:
+    for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
+        for layer_name in kv_cache_group_spec.layer_names:
+            kv_cache_spec = kv_cache_group_spec.kv_cache_spec
+            if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
+                kv_cache_spec = kv_cache_spec.kv_cache_specs[layer_name]
+            if not isinstance(kv_cache_spec, AttentionSpec):
+                continue
+            kv_cache = kv_caches[layer_name]
+            if kv_cache.shape[0] == 2:
+                assert kv_cache.shape[1] != 2, (
+                    f"Cannot determine layout for tensor of shape {kv_cache.shape}"
+                )
+                hidden_size = kv_cache.shape[2:].numel()
+                kv_cache.as_strided_(
+                    size=kv_cache.shape,
+                    stride=(
+                        hidden_size,
+                        2 * hidden_size,
+                        *kv_cache.stride()[2:],
+                    ),
+                )
+
+
 def init_kv_cache(
     runner_kv_caches: list[torch.Tensor],
     forward_context: dict[str, Any],
@@ -195,7 +275,7 @@ def init_kv_cache(
     attn_backends: dict[str, type[AttentionBackend]],
     device: torch.device,
     cache_dtype: str,
-) -> dict[str, torch.Tensor]:
+) -> dict[str, Any]:
     kv_cache_raw_tensors = _allocate_kv_cache(kv_cache_config, device)
     kv_caches = _reshape_kv_cache(
         kv_cache_config, kv_cache_raw_tensors, attn_backends, cache_dtype
@@ -227,12 +307,17 @@ def build_attn_metadata(
     block_tables: Sequence[torch.Tensor],
     slot_mappings: torch.Tensor,
     kv_cache_config: KVCacheConfig,
+    seq_lens_cpu_upper_bound: torch.Tensor | None = None,
     dcp_local_seq_lens: torch.Tensor | None = None,
-    encoder_seq_lens: dict[int, tuple[torch.Tensor, np.ndarray]] | None = None,
+    positions: torch.Tensor | None = None,
+    model_specific_attn_metadata: ModelSpecificAttnMetadata | None = None,
+    for_cudagraph_capture: bool = False,
 ) -> dict[str, Any]:
     seq_lens = seq_lens[:num_reqs]
     if dcp_local_seq_lens is not None:
         dcp_local_seq_lens = dcp_local_seq_lens[:num_reqs]
+    if seq_lens_cpu_upper_bound is not None:
+        seq_lens_cpu_upper_bound = seq_lens_cpu_upper_bound[:num_reqs]
 
     attn_metadata: dict[str, Any] = {}
     num_kv_cache_groups = len(kv_cache_config.kv_cache_groups)
@@ -240,10 +325,16 @@ def build_attn_metadata(
         block_table = block_tables[i]
         slot_mapping = slot_mappings[i]
 
+        common_attn_metadata_extra_kwargs = (
+            model_specific_attn_metadata.get_extra_common_attn_kwargs(i, num_reqs)
+            if model_specific_attn_metadata is not None
+            else {}
+        )
         common_attn_metadata = CommonAttentionMetadata(
             query_start_loc=query_start_loc_gpu,
             query_start_loc_cpu=query_start_loc_cpu,
             seq_lens=seq_lens,
+            seq_lens_cpu_upper_bound=seq_lens_cpu_upper_bound,
             max_seq_len=max_seq_len,
             num_reqs=num_reqs,
             num_actual_tokens=num_tokens,
@@ -252,17 +343,30 @@ def build_attn_metadata(
             slot_mapping=slot_mapping,
             causal=True,
             dcp_local_seq_lens=dcp_local_seq_lens,
+            positions=positions,
+            **common_attn_metadata_extra_kwargs,
         )
-        if encoder_seq_lens and i in encoder_seq_lens:
-            encoder_seq_lens_gpu, encoder_seq_lens_cpu = encoder_seq_lens[i]
-            common_attn_metadata.encoder_seq_lens = encoder_seq_lens_gpu
-            common_attn_metadata.encoder_seq_lens_cpu = encoder_seq_lens_cpu
 
         for attn_group in attn_groups[i]:
             attn_metadata_builder = attn_group.get_metadata_builder(0)
-            metadata = attn_metadata_builder.build(
-                common_prefix_len=0, common_attn_metadata=common_attn_metadata
-            )
+            if for_cudagraph_capture:
+                metadata = attn_metadata_builder.build_for_cudagraph_capture(
+                    common_attn_metadata
+                )
+            else:
+                attn_metadata_extra_kwargs = (
+                    model_specific_attn_metadata.get_extra_attn_kwargs(
+                        attn_metadata_builder,
+                        num_reqs,
+                    )
+                    if model_specific_attn_metadata is not None
+                    else {}
+                )
+                metadata = attn_metadata_builder.build(
+                    common_prefix_len=0,
+                    common_attn_metadata=common_attn_metadata,
+                    **attn_metadata_extra_kwargs,
+                )
             for layer_name in attn_group.layer_names:
                 attn_metadata[layer_name] = metadata
     return attn_metadata
diff --git a/vllm/v1/worker/gpu/block_table.py b/vllm/v1/worker/gpu/block_table.py
index e79a7afbd81e..62454f70304d 100644
--- a/vllm/v1/worker/gpu/block_table.py
+++ b/vllm/v1/worker/gpu/block_table.py
@@ -5,7 +5,6 @@
 import torch
 
 from vllm.triton_utils import tl, triton
-from vllm.utils.math_utils import cdiv
 from vllm.v1.attention.backends.utils import PAD_SLOT_ID
 from vllm.v1.worker.gpu.buffer_utils import StagedWriteTensor, UvaBackedTensor
 
@@ -16,7 +15,7 @@ def __init__(
         block_sizes: list[int],
         max_num_reqs: int,
         max_num_batched_tokens: int,
-        max_model_len: int,
+        max_num_blocks_per_group: list[int],
         device: torch.device,
         cp_size: int = 1,
         cp_rank: int = 0,
@@ -25,7 +24,6 @@ def __init__(
         self.block_sizes = block_sizes
         self.max_num_reqs = max_num_reqs
         self.max_num_batched_tokens = max_num_batched_tokens
-        self.max_model_len = max_model_len
         self.device = device
 
         self.cp_size = cp_size
@@ -33,32 +31,18 @@ def __init__(
         self.cp_interleave = cp_interleave
 
         self.num_kv_cache_groups = len(self.block_sizes)
+        assert len(max_num_blocks_per_group) == self.num_kv_cache_groups
         # num_kv_cache_groups x [max_num_reqs, max_num_blocks]
         self.block_tables: list[StagedWriteTensor] = []
         for i in range(self.num_kv_cache_groups):
-            block_size = self.block_sizes[i]
-            # When using DCP, each request's KV cache is sharded among different ranks.
-            # As a result, one block on the current rank covers `block_size * cp_size`
-            # tokens in the full, global (unsharded) sequence.
-            max_num_blocks = cdiv(self.max_model_len, block_size * self.cp_size)
+            max_num_blocks = max_num_blocks_per_group[i]
             block_table = StagedWriteTensor(
                 (self.max_num_reqs, max_num_blocks),
                 dtype=torch.int32,
                 device=device,
             )
             self.block_tables.append(block_table)
-        self.block_table_ptrs = self._make_ptr_tensor(
-            [b.gpu for b in self.block_tables]
-        )
-        self.block_table_strides = torch.tensor(
-            [b.gpu.stride(0) for b in self.block_tables],
-            dtype=torch.int64,
-            device=self.device,
-        )
 
-        self.block_sizes_tensor = torch.tensor(
-            self.block_sizes, dtype=torch.int32, device=self.device
-        )
         self.num_blocks = UvaBackedTensor(
             (self.num_kv_cache_groups, self.max_num_reqs),
             dtype=torch.int32,
@@ -69,7 +53,6 @@ def __init__(
         self.input_block_tables: list[torch.Tensor] = [
             torch.zeros_like(b.gpu) for b in self.block_tables
         ]
-        self.input_block_table_ptrs = self._make_ptr_tensor(self.input_block_tables)
 
         self.slot_mappings = torch.zeros(
             self.num_kv_cache_groups,
@@ -78,12 +61,33 @@ def __init__(
             device=self.device,
         )
 
+        self.init_block_table_layout_tensors()
+
     def _make_ptr_tensor(self, x: Iterable[torch.Tensor]) -> torch.Tensor:
         # NOTE(woosuk): Use uint64 instead of int64 to cover all possible addresses.
         return torch.tensor(
             [t.data_ptr() for t in x], dtype=torch.uint64, device=self.device
         )
 
+    def init_block_table_layout_tensors(self) -> None:
+        # Called at init and after a CuMem kv_cache wake-up. The ptr tensors
+        # cache raw data_ptr() values that go stale once the underlying tensors
+        # are reallocated on wake; block_sizes_tensor needs re-populating
+        # because its storage lives under the kv_cache pool tag and comes back
+        # with undefined contents.
+        self.block_table_ptrs = self._make_ptr_tensor(
+            [b.gpu for b in self.block_tables]
+        )
+        self.block_table_strides = torch.tensor(
+            [b.gpu.stride(0) for b in self.block_tables],
+            dtype=torch.int64,
+            device=self.device,
+        )
+        self.block_sizes_tensor = torch.tensor(
+            self.block_sizes, dtype=torch.int32, device=self.device
+        )
+        self.input_block_table_ptrs = self._make_ptr_tensor(self.input_block_tables)
+
     def append_block_ids(
         self,
         req_index: int,
diff --git a/vllm/v1/worker/gpu/buffer_utils.py b/vllm/v1/worker/gpu/buffer_utils.py
index a653c262556c..5963790a7792 100644
--- a/vllm/v1/worker/gpu/buffer_utils.py
+++ b/vllm/v1/worker/gpu/buffer_utils.py
@@ -167,7 +167,7 @@ def apply_write(self) -> None:
 
         # Special handling for write_contents
         write_contents = async_tensor_h2d(
-            self._staged_write_contents, self.dtype, self.device, pin_memory=True
+            self._staged_write_contents, self.dtype, self.device
         )
 
         # Write diffs to the GPU buffer
diff --git a/vllm/v1/worker/gpu/cudagraph_utils.py b/vllm/v1/worker/gpu/cudagraph_utils.py
index d918131c68d4..71a15c3bd95a 100644
--- a/vllm/v1/worker/gpu/cudagraph_utils.py
+++ b/vllm/v1/worker/gpu/cudagraph_utils.py
@@ -3,12 +3,13 @@
 from collections import defaultdict
 from collections.abc import Callable
 from dataclasses import dataclass
-from typing import Any
+from typing import Any, NamedTuple
 
 import torch
 import torch.nn as nn
 from tqdm import tqdm
 
+from vllm.compilation.counter import compilation_counter
 from vllm.config import VllmConfig
 from vllm.config.compilation import CUDAGraphMode
 from vllm.distributed.parallel_state import (
@@ -32,6 +33,11 @@
 logger = init_logger(__name__)
 
 
+class CapturedAttentionState(NamedTuple):
+    attn_metadata: dict[str, Any] | None
+    slot_mappings: dict[str, torch.Tensor]
+
+
 @dataclass(frozen=True)
 class BatchExecutionDescriptor:
     """Describes the shape of the batch and CG mode to run; this is used to make shape
@@ -94,6 +100,7 @@ def __init__(
         self.decode_query_len = decode_query_len
 
         self.dp_size = vllm_config.parallel_config.data_parallel_size
+        self.tp_size = vllm_config.parallel_config.tensor_parallel_size
         self.is_first_pp_rank = get_pp_group().is_first_rank
         self.is_last_pp_rank = get_pp_group().is_last_rank
 
@@ -103,6 +110,10 @@ def __init__(
         self._graphs_captured = False
         self._candidates: list[list[BatchExecutionDescriptor]] = []
         self._capture_descs: dict[CUDAGraphMode, list[BatchExecutionDescriptor]] = {}
+        # adjust the cudagraph sizes to be a multiple of the uniform decode query length
+        self.compilation_config.adjust_cudagraph_sizes_for_spec_decode(
+            self.decode_query_len, self.tp_size
+        )
         self._init_candidates()
 
     def _init_candidates(self) -> None:
@@ -177,16 +188,20 @@ def needs_capture(self) -> bool:
     def capture(
         self,
         create_forward_fn: Callable[
-            [BatchExecutionDescriptor], Callable[[CUDAGraphMode], None]
+            [BatchExecutionDescriptor],
+            tuple[Callable[[CUDAGraphMode], None], CapturedAttentionState],
         ],
         progress_bar_desc: str = "Capturing CUDA graphs",
-    ) -> None:
+    ) -> dict[BatchExecutionDescriptor, CapturedAttentionState]:
         """Capture CUDA graphs.
 
         Args:
             create_forward_fn: Factory that prepares inputs (OUTSIDE graph) and
-                returns a function that runs forward with a given CUDAGraphMode.
+                returns a tuple of (forward_fn, captured_attn_state).
         """
+        captured_attn_states: dict[
+            BatchExecutionDescriptor, CapturedAttentionState
+        ] = {}
         with graph_capture(device=self.device):
             # Capture in order: PIECEWISE first, then FULL. PIECEWISE has larger
             # activations so FULL activations should fit in already allocated
@@ -200,7 +215,8 @@ def capture(
                     descs = tqdm(descs, desc=f"{progress_bar_desc} ({mode.name})")
                 for desc in descs:
                     # Prepare inputs and get forward function
-                    forward_fn = create_forward_fn(desc)
+                    forward_fn, attn_state = create_forward_fn(desc)
+                    captured_attn_states[desc] = attn_state
 
                     # Warmup
                     forward_fn(CUDAGraphMode.NONE)
@@ -227,7 +243,9 @@ def capture(
                             # the next forward pass.
                             get_offloader().join_after_forward()
                         self.graphs[desc] = graph
+                        compilation_counter.num_cudagraph_captured += 1
         self._graphs_captured = True
+        return captured_attn_states
 
     def dispatch(
         self,
@@ -289,13 +307,16 @@ def capture(
         has_lora: bool = False,
         use_aux_hidden_state_outputs: bool = False,
         progress_bar_desc: str = "Capturing CUDA graphs",
-    ) -> None:
+    ) -> dict[BatchExecutionDescriptor, CapturedAttentionState]:
         """Capture CUDA graphs for model forward pass."""
         self.use_aux_hidden_state_outputs = use_aux_hidden_state_outputs
 
         def create_forward_fn(
             desc: BatchExecutionDescriptor,
-        ) -> Callable[[CUDAGraphMode], None]:
+        ) -> tuple[
+            Callable[[CUDAGraphMode], None],
+            CapturedAttentionState,
+        ]:
             num_tokens = desc.num_tokens
             num_reqs = desc.num_reqs or min(num_tokens, self.max_num_reqs)
             num_tokens_across_dp = (
@@ -324,16 +345,16 @@ def create_forward_fn(
                 block_tables,
                 attn_groups,
                 kv_cache_config,
+                skip_attn=(desc.cg_mode == CUDAGraphMode.PIECEWISE),
             )
 
             def forward_fn(cg_mode: CUDAGraphMode) -> None:
-                batch_descriptor = (
-                    BatchDescriptor(num_tokens=num_tokens)
-                    if cg_mode == CUDAGraphMode.PIECEWISE
-                    else None
-                )
+                batch_descriptor = None
+                if cg_mode == CUDAGraphMode.PIECEWISE:
+                    assert attn_metadata is None
+                    batch_descriptor = BatchDescriptor(num_tokens=num_tokens)
                 with set_forward_context(
-                    attn_metadata if cg_mode != CUDAGraphMode.PIECEWISE else None,
+                    attn_metadata,
                     self.vllm_config,
                     num_tokens=num_tokens,
                     cudagraph_runtime_mode=cg_mode,
@@ -375,9 +396,9 @@ def forward_fn(cg_mode: CUDAGraphMode) -> None:
                     for k, v in intermediate_tensors.tensors.items():
                         self.intermediate_tensors[k][:num_tokens] = v
 
-            return forward_fn
+            return forward_fn, CapturedAttentionState(attn_metadata, slot_mappings)
 
-        super().capture(create_forward_fn, progress_bar_desc)
+        return super().capture(create_forward_fn, progress_bar_desc)
 
     def run_fullgraph(
         self, desc: BatchExecutionDescriptor
@@ -403,7 +424,8 @@ def prepare_inputs_to_capture(
     block_tables: BlockTables,
     attn_groups: list[list[AttentionGroup]],
     kv_cache_config: KVCacheConfig,
-) -> tuple[dict[str, Any], dict[str, torch.Tensor]]:
+    skip_attn: bool = False,
+) -> CapturedAttentionState:
     input_batch = InputBatch.make_dummy(num_reqs, num_tokens, input_buffers)
     input_block_tables = block_tables.get_dummy_block_tables(num_reqs)
     slot_mappings = block_tables.get_dummy_slot_mappings(num_tokens)
@@ -423,13 +445,15 @@ def prepare_inputs_to_capture(
         )
         input_batch.dcp_local_seq_lens = input_buffers.dcp_local_seq_lens[:num_reqs]
 
-    attn_metadata = model_state.prepare_attn(
-        input_batch,
-        CUDAGraphMode.NONE,
-        input_block_tables,
-        slot_mappings,
-        attn_groups,
-        kv_cache_config,
-        for_capture=True,
-    )
-    return attn_metadata, slot_mappings_by_layer
+    attn_metadata = None
+    if not skip_attn:
+        attn_metadata = model_state.prepare_attn(
+            input_batch,
+            CUDAGraphMode.NONE,
+            input_block_tables,
+            slot_mappings,
+            attn_groups,
+            kv_cache_config,
+            for_capture=True,
+        )
+    return CapturedAttentionState(attn_metadata, slot_mappings_by_layer)
diff --git a/vllm/v1/worker/gpu/dp_utils.py b/vllm/v1/worker/gpu/dp_utils.py
index 09ac5b5af640..b3c172738c3a 100644
--- a/vllm/v1/worker/gpu/dp_utils.py
+++ b/vllm/v1/worker/gpu/dp_utils.py
@@ -13,12 +13,6 @@
 )
 
 
-def make_num_tokens_across_dp(dp_size: int, num_tokens: int) -> torch.Tensor | None:
-    if dp_size == 1:
-        return None
-    return torch.full((dp_size,), num_tokens, dtype=torch.int32, device="cpu")
-
-
 def sync_cudagraph_and_dp_padding(
     cudagraph_manager: CudaGraphManager | None,
     desired_batch_desc: BatchExecutionDescriptor,
diff --git a/vllm/v1/worker/gpu/eplb_utils.py b/vllm/v1/worker/gpu/eplb_utils.py
index 61d70fafea33..4ffb081ca303 100644
--- a/vllm/v1/worker/gpu/eplb_utils.py
+++ b/vllm/v1/worker/gpu/eplb_utils.py
@@ -92,9 +92,7 @@ def maybe_register_model(
         if not is_mixture_of_experts(model):
             return False
 
-        logger.info_once(
-            "EPLB is enabled for model %s.", model_config.model, scope="local"
-        )
+        logger.info_once("EPLB is enabled for model %s.", model_config.model)
         assert self.state is not None
         self.state.add_model(model, model_config)
         self._has_registered_models = True
diff --git a/vllm/v1/worker/gpu/input_batch.py b/vllm/v1/worker/gpu/input_batch.py
index 24df137cb31e..b253d7d8c063 100644
--- a/vllm/v1/worker/gpu/input_batch.py
+++ b/vllm/v1/worker/gpu/input_batch.py
@@ -53,15 +53,22 @@ class InputBatch:
     # sum(num_scheduled_tokens)
     num_tokens: int
     num_tokens_after_padding: int
+    # Sum of draft tokens scheduled across requests.
     num_draft_tokens: int
+    # [num_reqs] number of draft tokens scheduled for each request, if any.
+    num_draft_tokens_per_req: np.ndarray | None
 
     # [num_reqs + 1]
     query_start_loc: torch.Tensor
     query_start_loc_np: np.ndarray
     # [num_reqs]
     seq_lens: torch.Tensor
+    # [num_reqs] CPU upper bound on seq_lens (see CommonAttentionMetadata).
+    seq_lens_cpu_upper_bound: torch.Tensor
     # [num_reqs]
     dcp_local_seq_lens: torch.Tensor | None
+    # [num_reqs] CPU bool array.
+    is_prefilling_np: np.ndarray
 
     # [num_tokens_after_padding]
     input_ids: torch.Tensor
@@ -121,6 +128,8 @@ def make_dummy(
         logits_indices = query_start_loc[1:] - 1
         cu_num_logits = torch.arange(num_reqs + 1, device=device, dtype=torch.int32)
         cu_num_logits_np = np.arange(num_reqs + 1, dtype=np.int32)
+        # Dummy: seq_len == query_len (fresh-prefill shape).
+        seq_lens_cpu_upper_bound = torch.from_numpy(num_scheduled_tokens.copy())
         return cls(
             req_ids=req_ids,
             num_reqs=num_reqs,
@@ -133,10 +142,13 @@ def make_dummy(
             num_tokens=num_tokens,
             num_tokens_after_padding=num_tokens,
             num_draft_tokens=0,
+            num_draft_tokens_per_req=None,
             query_start_loc=query_start_loc,
             query_start_loc_np=query_start_loc_np,
             seq_lens=seq_lens,
+            seq_lens_cpu_upper_bound=seq_lens_cpu_upper_bound,
             dcp_local_seq_lens=None,
+            is_prefilling_np=np.zeros(num_reqs, dtype=np.bool_),
             input_ids=input_ids,
             positions=positions,
             logits_indices=logits_indices,
diff --git a/vllm/v1/worker/gpu/model_runner.py b/vllm/v1/worker/gpu/model_runner.py
index 9b79a13ee38c..b81bd0dc59fc 100644
--- a/vllm/v1/worker/gpu/model_runner.py
+++ b/vllm/v1/worker/gpu/model_runner.py
@@ -27,6 +27,7 @@
 import torch
 import torch.nn as nn
 
+from vllm.compilation.counter import compilation_counter
 from vllm.config import VllmConfig
 from vllm.config.compilation import CUDAGraphMode
 from vllm.distributed.parallel_state import (
@@ -43,10 +44,11 @@
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.sequence import IntermediateTensors
 from vllm.tasks import SupportedTask
+from vllm.utils.math_utils import cdiv
 from vllm.utils.mem_utils import DeviceMemoryProfiler, format_gib
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
-from vllm.v1.kv_cache_interface import KVCacheConfig
+from vllm.v1.kv_cache_interface import KVCacheConfig, MambaSpec
 from vllm.v1.outputs import DraftTokenIds, KVConnectorOutput, ModelRunnerOutput
 from vllm.v1.worker.cp_utils import check_attention_cp_compatibility
 from vllm.v1.worker.gpu.async_utils import AsyncOutput, AsyncPoolingOutput
@@ -90,6 +92,7 @@
 from vllm.v1.worker.gpu.sample.output import SamplerOutput
 from vllm.v1.worker.gpu.sample.prompt_logprob import PromptLogprobsWorker
 from vllm.v1.worker.gpu.sample.sampler import Sampler
+from vllm.v1.worker.gpu.shutdown import free_before_shutdown
 from vllm.v1.worker.gpu.spec_decode import init_speculator
 from vllm.v1.worker.gpu.spec_decode.eagle.eagle3_utils import (
     set_eagle3_aux_hidden_state_layers,
@@ -220,6 +223,7 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
                 self.rejection_sampler = RejectionSampler(
                     self.sampler,
                     self.speculative_config,
+                    self.device,
                 )
             self.prompt_logprobs_worker = PromptLogprobsWorker(self.max_num_reqs)
             self.structured_outputs_worker = StructuredOutputsWorker(
@@ -335,10 +339,6 @@ def get_kv_cache_spec(self):
     def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
         kv_cache_config = deepcopy(kv_cache_config)
         self.kv_cache_config = kv_cache_config
-        block_sizes = [
-            kv_cache_group.kv_cache_spec.block_size
-            for kv_cache_group in kv_cache_config.kv_cache_groups
-        ]
 
         block_table_max_model_len = self.max_model_len
         if self.is_encoder_decoder:
@@ -349,11 +349,34 @@ def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
                 getattr(self.model_config.hf_config, "max_source_positions", 0),
             )
 
+        block_sizes = []
+        max_num_blocks_per_group = []
+        for kv_cache_group in kv_cache_config.kv_cache_groups:
+            spec = kv_cache_group.kv_cache_spec
+            block_sizes.append(spec.block_size)
+            # When using DCP, each request's KV cache is sharded among different ranks.
+            # As a result, one block on the current rank covers `block_size * cp_size`
+            # tokens in the full, global (unsharded) sequence.
+            max_num_blocks = cdiv(
+                block_table_max_model_len, spec.block_size * self.dcp_size
+            )
+            # Align to a multiple of (128 / block_size) as required by some attention
+            # backends such as TRTLLM (#39324)
+            if spec.block_size <= 128:
+                alignment = 128 // spec.block_size
+                max_num_blocks = cdiv(max_num_blocks, alignment) * alignment
+            # For Mamba/Hybrid Model, KVCaches need extra blocks for speculative tokens
+            if isinstance(spec, MambaSpec):
+                max_num_blocks = (
+                    max_num_blocks if self.cache_config.enable_prefix_caching else 1
+                ) + spec.num_speculative_blocks
+            max_num_blocks_per_group.append(max_num_blocks)
+
         self.block_tables = BlockTables(
             block_sizes=block_sizes,
             max_num_reqs=self.max_num_reqs,
             max_num_batched_tokens=self.max_num_tokens,
-            max_model_len=block_table_max_model_len,
+            max_num_blocks_per_group=max_num_blocks_per_group,
             device=self.device,
             cp_size=self.dcp_size,
             cp_rank=self.dcp_rank,
@@ -363,7 +386,9 @@ def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
         self.attn_backends, self.attn_groups, attn_cg_support = init_attn_backend(
             self.kv_cache_config, self.vllm_config, self.device
         )
-        initialize_mamba_ssu_backend(self.vllm_config.mamba_config)
+        initialize_mamba_ssu_backend(
+            self.vllm_config.mamba_config, self.kv_cache_config
+        )
         cudagraph_mode = self.compilation_config.resolve_cudagraph_mode_and_sizes(
             attn_cg_support.min_cg_support,
             attn_cg_support.min_cg_attn_backend,
@@ -483,11 +508,20 @@ def _dummy_run(
                         device=self.device,
                     ),
                 )
+
+            # Let the target override the hidden state fed to the drafter
+            # (e.g. DeepSeek V4 MTP needs the pre-hc_head residual). The
+            # target returns a persistent buffer sized at max_num_batched_tokens;
+            # slice to the active token count that propose() expects.
+            spec_hidden_states = hidden_states
+            if hasattr(self.model, "get_mtp_target_hidden_states"):
+                pre_hc_hidden_states = self.model.get_mtp_target_hidden_states()
+                spec_hidden_states = pre_hc_hidden_states[: hidden_states.shape[0]]  # type: ignore[union-attr]
             self.speculator.propose(
                 input_batch=input_batch,
                 attn_metadata=attn_metadata,
                 slot_mappings=slot_mappings_by_layer,
-                last_hidden_states=hidden_states,
+                last_hidden_states=spec_hidden_states,
                 aux_hidden_states=aux_hidden_states,
                 num_sampled=torch.ones(
                     input_batch.num_reqs, dtype=torch.int32, device=self.device
@@ -546,6 +580,9 @@ def profile_run(self) -> None:
         del hidden_states, sample_hidden_states
         gc.collect()
 
+    def post_kv_cache_wake_up(self) -> None:
+        self.block_tables.init_block_table_layout_tensors()
+
     def reset_mm_cache(self) -> None:
         if self.encoder_cache is not None:
             self.encoder_cache.reset_mm_cache()
@@ -572,13 +609,15 @@ def capture_model(self) -> int:
             )
             return 0
 
+        compilation_counter.num_gpu_runner_capture_triggers += 1
+
         start_time = time.perf_counter()
         gc.collect()
         torch.accelerator.empty_cache()
         start_free_gpu_memory = torch.cuda.mem_get_info()[0]
 
         with self.maybe_setup_dummy_loras(self.lora_config):
-            self.cudagraph_manager.capture(
+            captured_attn_states = self.cudagraph_manager.capture(
                 self.model,
                 self.model_state,
                 self.input_buffers,
@@ -590,7 +629,7 @@ def capture_model(self) -> int:
                 use_aux_hidden_state_outputs=self.use_aux_hidden_state_outputs,
             )
             if self.speculator is not None:
-                self.speculator.capture_model()
+                self.speculator.capture(captured_attn_states)
 
         end_time = time.perf_counter()
         end_free_gpu_memory = torch.cuda.mem_get_info()[0]
@@ -703,6 +742,7 @@ def prepare_inputs(
 
         # Get the number of draft tokens for each request.
         draft_tokens = scheduler_output.scheduled_spec_decode_tokens
+        num_draft_tokens_per_req = None
         if not draft_tokens:
             # No draft token scheduled (common case).
             total_num_draft_tokens = 0
@@ -716,15 +756,15 @@ def prepare_inputs(
                 num_reqs, dtype=torch.int32, device=self.device
             )
         else:
-            num_draft_tokens = np.fromiter(
+            num_draft_tokens_per_req = np.fromiter(
                 (len(draft_tokens.get(req_id, ())) for req_id in req_ids),
                 dtype=np.int32,
                 count=num_reqs,
             )
-            total_num_draft_tokens = int(num_draft_tokens.sum())
+            total_num_draft_tokens = int(num_draft_tokens_per_req.sum())
             total_num_logits = num_reqs + total_num_draft_tokens
 
-            num_logits = num_draft_tokens + 1
+            num_logits = num_draft_tokens_per_req + 1
             cu_num_logits_np = np.empty(num_reqs + 1, dtype=np.int32)
             cu_num_logits_np[0] = 0
             np.cumsum(num_logits, out=cu_num_logits_np[1:])
@@ -747,9 +787,13 @@ def prepare_inputs(
         async_copy_to_gpu(query_start_loc_np, out=self.input_buffers.query_start_loc)
         query_start_loc_np = query_start_loc_np[: num_reqs_padded + 1]
         query_start_loc = self.input_buffers.query_start_loc[: num_reqs_padded + 1]
+        is_prefilling_np = (
+            self.req_states.num_computed_prefill_tokens[idx_mapping_np]
+            < self.req_states.prefill_len.np[idx_mapping_np]
+        )
 
         # Get prefill tokens if any.
-        if self.req_states.any_prefills(idx_mapping_np):
+        if np.any(is_prefilling_np):
             prepare_prefill_inputs(
                 self.input_buffers.input_ids,
                 self.req_states.next_prefill_tokens,
@@ -797,6 +841,14 @@ def prepare_inputs(
             total_num_logits,
         )
 
+        # CPU upper bound on seq_lens; padded entries left at zero.
+        seq_lens_cpu_upper_bound_np = np.zeros(num_reqs_padded, dtype=np.int32)
+        np.add(
+            self.req_states.num_computed_tokens_np[idx_mapping_np],
+            num_scheduled_tokens,
+            out=seq_lens_cpu_upper_bound_np[:num_reqs],
+        )
+        seq_lens_cpu_upper_bound = torch.from_numpy(seq_lens_cpu_upper_bound_np)
         return InputBatch(
             req_ids=req_ids,
             num_reqs=num_reqs,
@@ -809,10 +861,13 @@ def prepare_inputs(
             num_tokens=num_tokens,
             num_tokens_after_padding=num_tokens_after_padding,
             num_draft_tokens=total_num_draft_tokens,
+            num_draft_tokens_per_req=num_draft_tokens_per_req,
             query_start_loc=query_start_loc,
             query_start_loc_np=query_start_loc_np,
             seq_lens=seq_lens,
+            seq_lens_cpu_upper_bound=seq_lens_cpu_upper_bound,
             dcp_local_seq_lens=dcp_local_seq_lens,
+            is_prefilling_np=is_prefilling_np,
             input_ids=self.input_buffers.input_ids[:num_tokens_after_padding],
             positions=self.input_buffers.positions[:num_tokens_after_padding],
             logits_indices=logits_indices,
@@ -925,6 +980,12 @@ def postprocess(
         np.minimum(
             computed_prefill, self.req_states.prefill_len.np, out=computed_prefill
         )
+        # Advance the CPU mirror optimistically (assume all scheduled accepted).
+        self.req_states.num_computed_tokens_np[idx_mapping_np] += (
+            input_batch.num_scheduled_tokens
+        )
+
+        self.model_state.postprocess_state(input_batch, num_sampled)
 
     @torch.inference_mode()
     def execute_model(
@@ -1216,11 +1277,19 @@ def sample_tokens(
 
         if self.speculator is not None:
             assert self.sampler is not None
+            # Let the target override the hidden state fed to the drafter
+            # (e.g. DeepSeek V4 MTP needs the pre-hc_head residual). The
+            # target returns a persistent buffer sized at max_num_batched_tokens;
+            # slice to the active token count that propose() expects.
+            spec_hidden_states = hidden_states
+            if hasattr(self.model, "get_mtp_target_hidden_states"):
+                pre_hc_hidden_states = self.model.get_mtp_target_hidden_states()
+                spec_hidden_states = pre_hc_hidden_states[: hidden_states.shape[0]]  # type: ignore[union-attr]
             draft_tokens = self.speculator.propose(
                 input_batch,
                 attn_metadata,
                 slot_mappings_by_layer,
-                hidden_states,
+                spec_hidden_states,
                 aux_hidden_states,
                 num_sampled,
                 num_rejected,
@@ -1295,6 +1364,28 @@ def postprocess_pool(self, input_batch: InputBatch) -> None:
         np.minimum(
             computed_prefill, self.req_states.prefill_len.np, out=computed_prefill
         )
+        # Advance the CPU mirror optimistically (assume all scheduled accepted).
+        self.req_states.num_computed_tokens_np[idx_mapping_np] += (
+            input_batch.num_scheduled_tokens
+        )
+
+    def shutdown(self) -> None:
+        """Release GPU tensors (model weights, KV caches, workspace) so that
+        memory is reclaimable when running in the same process."""
+        torch.accelerator.synchronize()
+        if hasattr(self, "kv_caches"):
+            self.kv_caches.clear()
+        if hasattr(self, "attn_groups"):
+            self.attn_groups.clear()
+        if hasattr(self, "kv_cache_config"):
+            del self.kv_cache_config
+        free_before_shutdown(self.vllm_config)
+        if hasattr(self, "model"):
+            del self.model
+
+        gc.collect()
+        torch.accelerator.empty_cache()
+        logger.debug("Cleaned up model weights, KV caches, and workspace")
 
     ########### EPLB methods start ###########
     @property
diff --git a/vllm/v1/worker/gpu/model_states/__init__.py b/vllm/v1/worker/gpu/model_states/__init__.py
index 651452553332..06b5a92c3952 100644
--- a/vllm/v1/worker/gpu/model_states/__init__.py
+++ b/vllm/v1/worker/gpu/model_states/__init__.py
@@ -18,6 +18,11 @@ def init_model_state(
 
         return WhisperModelState(vllm_config, model, encoder_cache, device)
 
+    if vllm_config.model_config.is_hybrid:
+        from vllm.v1.worker.gpu.model_states.mamba_hybrid import MambaHybridModelState
+
+        return MambaHybridModelState(vllm_config, model, encoder_cache, device)
+
     from vllm.v1.worker.gpu.model_states.default import DefaultModelState
 
     return DefaultModelState(vllm_config, model, encoder_cache, device)
diff --git a/vllm/v1/worker/gpu/model_states/default.py b/vllm/v1/worker/gpu/model_states/default.py
index 8e73867deb2e..7f7955a58ab3 100644
--- a/vllm/v1/worker/gpu/model_states/default.py
+++ b/vllm/v1/worker/gpu/model_states/default.py
@@ -173,6 +173,12 @@ def prepare_attn(
             num_tokens = input_batch.num_tokens
         query_start_loc_cpu = torch.from_numpy(input_batch.query_start_loc_np)
         max_query_len = input_batch.num_scheduled_tokens.max().item()
+        seq_lens_cpu_upper_bound = input_batch.seq_lens_cpu_upper_bound
+        if for_capture:
+            # Capture with worst-case max_seq_len so the graph is valid at any replay.
+            max_seq_len = self.max_model_len
+        else:
+            max_seq_len = int(seq_lens_cpu_upper_bound[:num_reqs].max().item())
         attn_metadata = build_attn_metadata(
             attn_groups=attn_groups,
             num_reqs=num_reqs,
@@ -181,10 +187,13 @@ def prepare_attn(
             query_start_loc_cpu=query_start_loc_cpu,
             max_query_len=max_query_len,
             seq_lens=input_batch.seq_lens,
-            max_seq_len=self.max_model_len,
+            max_seq_len=max_seq_len,
             block_tables=block_tables,
             slot_mappings=slot_mappings,
             kv_cache_config=kv_cache_config,
+            seq_lens_cpu_upper_bound=seq_lens_cpu_upper_bound,
             dcp_local_seq_lens=input_batch.dcp_local_seq_lens,
+            positions=input_batch.positions,
+            for_cudagraph_capture=for_capture,
         )
         return attn_metadata
diff --git a/vllm/v1/worker/gpu/model_states/interface.py b/vllm/v1/worker/gpu/model_states/interface.py
index d83ab2fc515f..721e5c2013de 100644
--- a/vllm/v1/worker/gpu/model_states/interface.py
+++ b/vllm/v1/worker/gpu/model_states/interface.py
@@ -17,6 +17,24 @@
 from vllm.v1.worker.utils import AttentionGroup
 
 
+class ModelSpecificAttnMetadata:
+    """Base class for model-specific attention metadata."""
+
+    def get_extra_common_attn_kwargs(
+        self,
+        kv_cache_group_id: int,
+        num_reqs: int,
+    ) -> dict[str, Any]:
+        return {}
+
+    def get_extra_attn_kwargs(
+        self,
+        attn_metadata_builder: Any,
+        num_reqs: int,
+    ) -> dict[str, Any]:
+        return {}
+
+
 class ModelState(ABC):
     @abstractmethod
     def __init__(
@@ -38,6 +56,13 @@ def add_request(self, req_index: int, new_req_data: NewRequestData) -> None:
     def apply_staged_writes(self) -> None:
         return None
 
+    def postprocess_state(
+        self,
+        input_batch: InputBatch,
+        num_sampled: torch.Tensor,
+    ) -> None:
+        return None
+
     @abstractmethod
     def get_mm_embeddings(
         self,
diff --git a/vllm/v1/worker/gpu/model_states/mamba_hybrid.py b/vllm/v1/worker/gpu/model_states/mamba_hybrid.py
new file mode 100644
index 000000000000..93115fdf64dd
--- /dev/null
+++ b/vllm/v1/worker/gpu/model_states/mamba_hybrid.py
@@ -0,0 +1,150 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+from typing import Any
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from vllm.config import VllmConfig
+from vllm.config.compilation import CUDAGraphMode
+from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
+from vllm.v1.attention.backends.mamba2_attn import Mamba2AttentionMetadataBuilder
+from vllm.v1.kv_cache_interface import KVCacheConfig
+from vllm.v1.worker.gpu.attn_utils import build_attn_metadata
+from vllm.v1.worker.gpu.input_batch import InputBatch
+from vllm.v1.worker.gpu.mm.encoder_cache import EncoderCache
+from vllm.v1.worker.gpu.model_states.default import DefaultModelState
+from vllm.v1.worker.gpu.model_states.interface import ModelSpecificAttnMetadata
+from vllm.v1.worker.utils import AttentionGroup
+
+
+@dataclass
+class MambaHybridAttnMetadata(ModelSpecificAttnMetadata):
+    is_prefilling: torch.Tensor
+    num_accepted_tokens: torch.Tensor | None = None
+    num_decode_draft_tokens_cpu: torch.Tensor | None = None
+
+    def get_extra_common_attn_kwargs(
+        self,
+        kv_cache_group_id: int,
+        num_reqs: int,
+    ) -> dict[str, Any]:
+        return {"is_prefilling": self.is_prefilling[:num_reqs]}
+
+    def get_extra_attn_kwargs(
+        self,
+        attn_metadata_builder: Any,
+        num_reqs: int,
+    ) -> dict[str, Any]:
+        if not isinstance(
+            attn_metadata_builder,
+            (Mamba2AttentionMetadataBuilder, GDNAttentionMetadataBuilder),
+        ):
+            return {}
+        return {
+            "num_accepted_tokens": None
+            if self.num_accepted_tokens is None
+            else self.num_accepted_tokens[:num_reqs],
+            "num_decode_draft_tokens_cpu": None
+            if self.num_decode_draft_tokens_cpu is None
+            else self.num_decode_draft_tokens_cpu[:num_reqs],
+        }
+
+
+class MambaHybridModelState(DefaultModelState):
+    """Model state for hybrid attention + Mamba / linear-attention models."""
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        model: nn.Module,
+        encoder_cache: EncoderCache | None,
+        device: torch.device,
+    ) -> None:
+        super().__init__(vllm_config, model, encoder_cache, device)
+        self.num_accepted_tokens_gpu = torch.ones(
+            self.max_num_reqs, dtype=torch.int32, device=self.device
+        )
+
+    def prepare_attn(
+        self,
+        input_batch: InputBatch,
+        cudagraph_mode: CUDAGraphMode,
+        block_tables: tuple[torch.Tensor, ...],
+        slot_mappings: torch.Tensor,
+        attn_groups: list[list[AttentionGroup]],
+        kv_cache_config: KVCacheConfig,
+        for_capture: bool = False,
+    ) -> dict[str, Any]:
+        if cudagraph_mode == CUDAGraphMode.FULL:
+            num_reqs = input_batch.num_reqs_after_padding
+            num_tokens = input_batch.num_tokens_after_padding
+        else:
+            num_reqs = input_batch.num_reqs
+            num_tokens = input_batch.num_tokens
+        query_start_loc_cpu = torch.from_numpy(input_batch.query_start_loc_np)
+        max_query_len = input_batch.num_scheduled_tokens.max().item()
+
+        is_prefilling = torch.zeros(num_reqs, dtype=torch.bool, device="cpu")
+        is_prefilling[: input_batch.num_reqs] = torch.from_numpy(
+            input_batch.is_prefilling_np
+        )
+        # During CUDAGraph capture, num_decode_draft_tokens_cpu and num_accepted_tokens
+        # are created by attn_metadata_builder.build_for_cudagraph_capture, so we only
+        # compute them during actual (non-capture) forward execution.
+        num_accepted_tokens = None
+        num_decode_draft_tokens_cpu = None
+        if not for_capture:
+            num_accepted_tokens = self.num_accepted_tokens_gpu.new_ones(num_reqs)
+            num_accepted_tokens[: input_batch.num_reqs] = self.num_accepted_tokens_gpu[
+                input_batch.idx_mapping
+            ]
+
+            # GDN uses >= 0 to select spec-decode rows, so non-decode rows
+            # need the -1 sentinel rather than a raw zero draft count.
+            num_decode_draft_tokens_np = np.full(num_reqs, -1, dtype=np.int32)
+            if input_batch.num_draft_tokens_per_req is not None:
+                spec_decode_mask = (
+                    input_batch.num_draft_tokens_per_req > 0
+                ) & ~input_batch.is_prefilling_np
+                num_decode_draft_tokens_np[: input_batch.num_reqs] = np.where(
+                    spec_decode_mask,
+                    input_batch.num_draft_tokens_per_req,
+                    -1,
+                )
+            num_decode_draft_tokens_cpu = torch.from_numpy(num_decode_draft_tokens_np)
+
+        mamba_attn_metadata = MambaHybridAttnMetadata(
+            is_prefilling=is_prefilling,
+            num_accepted_tokens=num_accepted_tokens,
+            num_decode_draft_tokens_cpu=num_decode_draft_tokens_cpu,
+        )
+        return build_attn_metadata(
+            attn_groups=attn_groups,
+            num_reqs=num_reqs,
+            num_tokens=num_tokens,
+            query_start_loc_gpu=input_batch.query_start_loc,
+            query_start_loc_cpu=query_start_loc_cpu,
+            max_query_len=max_query_len,
+            seq_lens=input_batch.seq_lens,
+            max_seq_len=self.max_model_len,
+            block_tables=block_tables,
+            slot_mappings=slot_mappings,
+            kv_cache_config=kv_cache_config,
+            dcp_local_seq_lens=input_batch.dcp_local_seq_lens,
+            model_specific_attn_metadata=mamba_attn_metadata,
+            for_cudagraph_capture=for_capture,
+        )
+
+    def postprocess_state(
+        self,
+        input_batch: InputBatch,
+        num_sampled: torch.Tensor,
+    ) -> None:
+        # Chunked prefill does not sample a token, so num_sampled can be 0.
+        # Mamba treats num_accepted_tokens=1 as the neutral non-spec value.
+        self.num_accepted_tokens_gpu[input_batch.idx_mapping] = torch.clamp(
+            num_sampled, min=1
+        )
diff --git a/vllm/v1/worker/gpu/model_states/whisper.py b/vllm/v1/worker/gpu/model_states/whisper.py
index 1268fee88210..0ef3cadc87ab 100644
--- a/vllm/v1/worker/gpu/model_states/whisper.py
+++ b/vllm/v1/worker/gpu/model_states/whisper.py
@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
 from typing import Any
 
 import numpy as np
@@ -13,11 +14,33 @@
 from vllm.v1.worker.gpu.input_batch import InputBatch
 from vllm.v1.worker.gpu.mm.encoder_cache import EncoderCache
 from vllm.v1.worker.gpu.mm.encoder_runner import EncoderRunner
-from vllm.v1.worker.gpu.model_states.interface import ModelState
+from vllm.v1.worker.gpu.model_states.interface import (
+    ModelSpecificAttnMetadata,
+    ModelState,
+)
 from vllm.v1.worker.gpu.states import RequestState
 from vllm.v1.worker.utils import AttentionGroup
 
 
+@dataclass
+class WhisperAttnMetadata(ModelSpecificAttnMetadata):
+    encoder_seq_lens: dict[int, tuple[torch.Tensor, np.ndarray]]
+
+    def get_extra_common_attn_kwargs(
+        self,
+        kv_cache_group_id: int,
+        num_reqs: int,
+    ) -> dict[str, Any]:
+        encoder_seq_lens = self.encoder_seq_lens.get(kv_cache_group_id)
+        if encoder_seq_lens is None:
+            return {}
+        encoder_seq_lens_gpu, encoder_seq_lens_cpu = encoder_seq_lens
+        return {
+            "encoder_seq_lens": encoder_seq_lens_gpu[:num_reqs],
+            "encoder_seq_lens_cpu": encoder_seq_lens_cpu[:num_reqs],
+        }
+
+
 class WhisperModelState(ModelState):
     def __init__(
         self,
@@ -111,12 +134,17 @@ def prepare_attn(
         else:
             num_reqs = input_batch.num_reqs
             num_tokens = input_batch.num_tokens
-        encoder_seq_lens = self._get_encoder_seq_lens(
-            input_batch.req_ids, attn_groups, for_capture
+        whisper_attn_metadata = WhisperAttnMetadata(
+            self._get_encoder_seq_lens(input_batch.req_ids, attn_groups, for_capture)
         )
 
         query_start_loc_cpu = torch.from_numpy(input_batch.query_start_loc_np)
         max_query_len = input_batch.num_scheduled_tokens.max().item()
+        seq_lens_cpu_upper_bound = input_batch.seq_lens_cpu_upper_bound
+        if for_capture:
+            max_seq_len = self.max_model_len
+        else:
+            max_seq_len = int(seq_lens_cpu_upper_bound[:num_reqs].max().item())
         attn_metadata = build_attn_metadata(
             attn_groups=attn_groups,
             num_reqs=num_reqs,
@@ -125,12 +153,14 @@ def prepare_attn(
             query_start_loc_cpu=query_start_loc_cpu,
             max_query_len=max_query_len,
             seq_lens=input_batch.seq_lens,
-            max_seq_len=self.max_model_len,
+            max_seq_len=max_seq_len,
             block_tables=block_tables,
             slot_mappings=slot_mappings,
             kv_cache_config=kv_cache_config,
+            seq_lens_cpu_upper_bound=seq_lens_cpu_upper_bound,
             dcp_local_seq_lens=input_batch.dcp_local_seq_lens,
-            encoder_seq_lens=encoder_seq_lens,
+            model_specific_attn_metadata=whisper_attn_metadata,
+            for_cudagraph_capture=for_capture,
         )
         return attn_metadata
 
diff --git a/vllm/v1/worker/gpu/sample/gumbel.py b/vllm/v1/worker/gpu/sample/gumbel.py
index 62912491492e..a02dd62026ad 100644
--- a/vllm/v1/worker/gpu/sample/gumbel.py
+++ b/vllm/v1/worker/gpu/sample/gumbel.py
@@ -76,6 +76,8 @@ def gumbel_block_argmax(
     pos_ptr,
     processed_logits_ptr,
     processed_logits_stride,
+    processed_logits_col_ptr,
+    vocab_size,
     APPLY_TEMPERATURE: tl.constexpr,
 ):
     req_state_idx = tl.load(expanded_idx_mapping_ptr + token_idx)
@@ -88,8 +90,15 @@ def gumbel_block_argmax(
 
     if processed_logits_ptr is not None:
         # Store the temperature-applied logits.
+        if processed_logits_col_ptr is not None:
+            col = tl.load(processed_logits_col_ptr)
+        else:
+            col = 0
         tl.store(
-            processed_logits_ptr + req_state_idx * processed_logits_stride + block,
+            processed_logits_ptr
+            + req_state_idx * processed_logits_stride
+            + col * vocab_size
+            + block,
             logits,
             mask=mask,
         )
@@ -121,6 +130,7 @@ def _gumbel_sample_kernel(
     local_max_stride,
     processed_logits_ptr,
     processed_logits_stride,
+    processed_logits_col_ptr,
     logits_ptr,
     logits_stride,
     expanded_idx_mapping_ptr,
@@ -153,6 +163,8 @@ def _gumbel_sample_kernel(
         pos_ptr,
         processed_logits_ptr,
         processed_logits_stride,
+        processed_logits_col_ptr,
+        vocab_size,
         APPLY_TEMPERATURE=APPLY_TEMPERATURE,
     )
     token_id = block_idx * BLOCK_SIZE + idx
@@ -167,7 +179,8 @@ def gumbel_sample(
     seed: torch.Tensor,  # [max_num_reqs]
     pos: torch.Tensor,  # [num_tokens]
     apply_temperature: bool,
-    processed_logits_out: torch.Tensor | None = None,  # [num_reqs, vocab_size]
+    output_processed_logits: torch.Tensor | None = None,
+    output_processed_logits_col: torch.Tensor | None = None,
 ) -> torch.Tensor:
     num_tokens, vocab_size = logits.shape
     BLOCK_SIZE = 1024
@@ -179,8 +192,9 @@ def gumbel_sample(
         local_argmax.stride(0),
         local_max,
         local_max.stride(0),
-        processed_logits_out,
-        processed_logits_out.stride(0) if processed_logits_out is not None else 0,
+        output_processed_logits,
+        output_processed_logits.stride(0) if output_processed_logits is not None else 0,
+        output_processed_logits_col,
         logits,
         logits.stride(0),
         expanded_idx_mapping,
diff --git a/vllm/v1/worker/gpu/sample/logprob.py b/vllm/v1/worker/gpu/sample/logprob.py
index 4317cad9ce7f..7530337fcd12 100644
--- a/vllm/v1/worker/gpu/sample/logprob.py
+++ b/vllm/v1/worker/gpu/sample/logprob.py
@@ -1,10 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import numpy as np
 import torch
 
+from vllm.sampling_params import MAX_LOGPROB_TOKEN_IDS, SamplingParams
 from vllm.triton_utils import tl, triton
 from vllm.v1.outputs import LogprobsTensors
+from vllm.v1.worker.gpu.buffer_utils import StagedWriteTensor, UvaBackedTensor
 
 
 @triton.jit
@@ -75,6 +78,9 @@ def _ranks_kernel(
 def compute_token_logprobs(
     logits: torch.Tensor, token_ids: torch.Tensor
 ) -> torch.Tensor:
+    # NOTE(woosuk): To save GPU memory, we do not materialize the full
+    # [batch_size, vocab_size] logprobs tensor. The kernel computes
+    # max + logsumexp per row and only emits logprobs at `token_ids`.
     batch_size, vocab_size = logits.shape
     token_ids = token_ids.to(torch.int64)
     num_logprobs = token_ids.shape[1]
@@ -97,18 +103,52 @@ def compute_topk_logprobs(
     num_logprobs: int,
     sampled_token_ids: torch.Tensor,
     cu_num_logits: list[int] | None = None,
+    logprob_token_ids_state: "LogprobTokenIdsState | None" = None,
+    expanded_idx_mapping: torch.Tensor | None = None,
+    max_per_req_token_ids: int = 0,
 ) -> LogprobsTensors:
     assert num_logprobs >= 0
     batch_size, vocab_size = logits.shape
-    logprob_token_ids = sampled_token_ids.unsqueeze(-1)
-    if num_logprobs > 0:
-        topk_indices = torch.topk(logits, num_logprobs, dim=-1).indices
-        logprob_token_ids = torch.cat((logprob_token_ids, topk_indices), dim=1)
-
-    # NOTE(woosuk): Here, to save GPU memory, we do not materialize the full
-    # logprobs tensor. Instead, we only compute and return the logprobs of
-    # the topk + 1 tokens.
-    logprobs = compute_token_logprobs(logits, logprob_token_ids)
+
+    if max_per_req_token_ids == 0:
+        # Fast path: no request asked for custom logprob_token_ids.
+        logprob_token_ids = sampled_token_ids.unsqueeze(-1)
+        if num_logprobs > 0:
+            topk_indices = torch.topk(logits, num_logprobs, dim=-1).indices
+            logprob_token_ids = torch.cat((logprob_token_ids, topk_indices), dim=1)
+        logprobs = compute_token_logprobs(logits, logprob_token_ids)
+    else:
+        # Some requests specified logprob_token_ids. Build the [batch_size,
+        # 1 + max_cols] token_ids matrix and validity mask on the GPU via a
+        # single triton kernel, overriding the topk columns with per-request
+        # tokens where applicable.
+        assert logprob_token_ids_state is not None
+        assert expanded_idx_mapping is not None
+        topk_indices = None
+        if num_logprobs > 0:
+            topk_indices = torch.topk(logits, num_logprobs, dim=-1).indices
+
+        num_cols = max(num_logprobs, max_per_req_token_ids)
+        logprob_token_ids = sampled_token_ids.new_zeros((batch_size, 1 + num_cols))
+        valid_mask = torch.zeros_like(logprob_token_ids, dtype=torch.bool)
+        _fill_logprob_token_ids_kernel[(batch_size,)](
+            logprob_token_ids,
+            logprob_token_ids.stride(0),
+            valid_mask,
+            valid_mask.stride(0),
+            sampled_token_ids,
+            topk_indices if topk_indices is not None else logprob_token_ids,
+            topk_indices.stride(0) if topk_indices is not None else 0,
+            expanded_idx_mapping,
+            logprob_token_ids_state.num_token_ids.gpu,
+            logprob_token_ids_state.token_ids.gpu,
+            logprob_token_ids_state.token_ids.gpu.stride(0),
+            NUM_TOPK=num_logprobs,
+            PADDED_COLS=triton.next_power_of_2(num_cols),
+        )
+        logprobs = compute_token_logprobs(logits, logprob_token_ids)
+        logprobs = logprobs.masked_fill(~valid_mask, float("-inf"))
+
     token_ranks = torch.empty(batch_size, dtype=torch.int64, device=logits.device)
     _ranks_kernel[(batch_size,)](
         token_ranks,
@@ -124,3 +164,87 @@ def compute_topk_logprobs(
         selected_token_ranks=token_ranks,
         cu_num_generated_tokens=cu_num_logits,
     )
+
+
+@triton.jit
+def _fill_logprob_token_ids_kernel(
+    # [batch_size, 1 + num_cols]
+    out_token_ids_ptr,
+    out_token_ids_stride,
+    # [batch_size, 1 + num_cols]
+    out_valid_mask_ptr,
+    out_valid_mask_stride,
+    sampled_token_ids_ptr,  # [batch_size]
+    topk_indices_ptr,  # [batch_size, NUM_TOPK] (unused when NUM_TOPK == 0)
+    topk_indices_stride,
+    expanded_idx_mapping_ptr,  # [batch_size] -> req_state_idx
+    num_per_req_token_ids_ptr,  # [max_num_reqs]
+    per_req_token_ids_ptr,  # [max_num_reqs, MAX_LOGPROB_TOKEN_IDS]
+    per_req_token_ids_stride,
+    NUM_TOPK: tl.constexpr,
+    PADDED_COLS: tl.constexpr,
+):
+    batch_idx = tl.program_id(0)
+
+    # Column 0: always the sampled token, always valid.
+    sampled = tl.load(sampled_token_ids_ptr + batch_idx)
+    tl.store(out_token_ids_ptr + batch_idx * out_token_ids_stride, sampled)
+    tl.store(out_valid_mask_ptr + batch_idx * out_valid_mask_stride, 1)
+
+    req_state_idx = tl.load(expanded_idx_mapping_ptr + batch_idx)
+    num_custom = tl.load(num_per_req_token_ids_ptr + req_state_idx)
+
+    col = tl.arange(0, PADDED_COLS)
+    tid_base = out_token_ids_ptr + batch_idx * out_token_ids_stride + 1
+    mask_base = out_valid_mask_ptr + batch_idx * out_valid_mask_stride + 1
+
+    if num_custom > 0:
+        # Override topk with per-request custom tokens.
+        src = per_req_token_ids_ptr + req_state_idx * per_req_token_ids_stride
+        valid = col < num_custom
+        # per_req_token_ids is int32; output is int64.
+        tokens = tl.load(src + col, mask=valid, other=0).to(tl.int64)
+    else:
+        # Fill with topk indices (no-op when NUM_TOPK == 0).
+        src = topk_indices_ptr + batch_idx * topk_indices_stride
+        valid = col < NUM_TOPK
+        tokens = tl.load(src + col, mask=valid, other=0)
+
+    tl.store(tid_base + col, tokens, mask=valid)
+    tl.store(mask_base + col, tl.full([PADDED_COLS], 1, tl.int1), mask=valid)
+
+
+class LogprobTokenIdsState:
+    """Per-request override of which token ids' logprobs to return.
+
+    See `SamplingParams.logprob_token_ids`.
+    """
+
+    def __init__(self, max_num_reqs: int, device: torch.device):
+        self.max_num_reqs = max_num_reqs
+        self.num_token_ids = UvaBackedTensor(max_num_reqs, dtype=torch.int32)
+        self.token_ids = StagedWriteTensor(
+            (max_num_reqs, MAX_LOGPROB_TOKEN_IDS),
+            dtype=torch.int32,
+            device=device,
+        )
+
+    def add_request(self, req_idx: int, sampling_params: SamplingParams) -> None:
+        token_ids = sampling_params.logprob_token_ids
+        if not token_ids:
+            self.num_token_ids.np[req_idx] = 0
+            return
+        n = len(token_ids)
+        if n > MAX_LOGPROB_TOKEN_IDS:
+            raise ValueError(
+                f"Too many logprob_token_ids: {n}. The max is {MAX_LOGPROB_TOKEN_IDS}."
+            )
+        self.num_token_ids.np[req_idx] = n
+        self.token_ids.stage_write(req_idx, 0, token_ids)
+
+    def apply_staged_writes(self) -> None:
+        self.num_token_ids.copy_to_uva()
+        self.token_ids.apply_write()
+
+    def max_num_token_ids(self, idx_mapping_np: np.ndarray) -> int:
+        return int(self.num_token_ids.np[idx_mapping_np].max(initial=0))
diff --git a/vllm/v1/worker/gpu/sample/penalties.py b/vllm/v1/worker/gpu/sample/penalties.py
index 04adf9369233..057517479b51 100644
--- a/vllm/v1/worker/gpu/sample/penalties.py
+++ b/vllm/v1/worker/gpu/sample/penalties.py
@@ -58,8 +58,7 @@ def apply_staged_writes(self) -> None:
             idx_mapping = async_tensor_h2d(
                 self._new_penalties_reqs,
                 dtype=torch.int32,
-                target_device=self.device,
-                pin_memory=True,
+                device=self.device,
             )
 
             prefill_lens = self.req_states.prefill_len.np[self._new_penalties_reqs]
diff --git a/vllm/v1/worker/gpu/sample/prompt_logprob.py b/vllm/v1/worker/gpu/sample/prompt_logprob.py
index 1915a0539790..baa48ebf900c 100644
--- a/vllm/v1/worker/gpu/sample/prompt_logprob.py
+++ b/vllm/v1/worker/gpu/sample/prompt_logprob.py
@@ -17,13 +17,14 @@ def __init__(self, max_num_reqs: int):
         self.max_num_reqs = max_num_reqs
 
         self.uses_prompt_logprobs = np.zeros(self.max_num_reqs, dtype=bool)
+        self.num_prompt_logprobs = np.zeros(self.max_num_reqs, dtype=np.int32)
         # req_idx -> list of in-progress LogprobsTensors
         self.in_progress_prompt_logprobs: dict[str, list[LogprobsTensors]] = {}
 
     def add_request(self, req_id: str, req_idx: int, sampling_params: SamplingParams):
-        # For now, only support prompt logprobs for the prompt tokens (not top-k).
         uses_prompt_logprobs = sampling_params.prompt_logprobs is not None
         self.uses_prompt_logprobs[req_idx] = uses_prompt_logprobs
+        self.num_prompt_logprobs[req_idx] = sampling_params.prompt_logprobs or 0
         if uses_prompt_logprobs:
             self.in_progress_prompt_logprobs[req_id] = []
 
@@ -52,11 +53,10 @@ def compute_prompt_logprobs(
             # Common case: No request asks for prompt logprobs.
             return {}
 
+        num_prompt_logprobs = self.num_prompt_logprobs[idx_mapping_np]
         prompt_lens = prompt_lens[idx_mapping_np]
-        # NOTE(woosuk): -1 because the last prompt token's hidden state is not
-        # needed for prompt logprobs.
         computed_prefill = num_computed_prefill_tokens[idx_mapping_np]
-        includes_prompt = computed_prefill < prompt_lens - 1
+        includes_prompt = computed_prefill < prompt_lens
         # NOTE(woosuk): If the request was resumed after preemption, its prompt
         # logprobs must have been computed before preemption. Skip.
         resumed_after_prompt = prompt_lens < prefill_lens[idx_mapping_np]
@@ -64,6 +64,14 @@ def compute_prompt_logprobs(
         if not np.any(needs_prompt_logprobs):
             return {}
 
+        # get the maximum number in this batch
+        requested_num_prompt_logprobs = num_prompt_logprobs[needs_prompt_logprobs]
+        max_num_prompt_logprobs = (
+            -1
+            if np.any(requested_num_prompt_logprobs == -1)
+            else int(requested_num_prompt_logprobs.max())
+        )
+
         # Get the prompt logprobs token_ids.
         prompt_logprobs_token_ids = get_prompt_logprobs_token_ids(
             input_batch.num_tokens,
@@ -72,45 +80,53 @@ def compute_prompt_logprobs(
             num_computed_tokens,
             all_token_ids,
         )
-        # Compute the prompt logprobs.
-        prompt_logprobs, prompt_ranks = compute_prompt_logprobs_with_chunking(
-            prompt_logprobs_token_ids,
-            hidden_states[: input_batch.num_tokens],
-            logits_fn,
+        prompt_token_ids, prompt_logprobs, prompt_ranks = (
+            compute_prompt_logprobs_with_chunking(
+                prompt_logprobs_token_ids,
+                hidden_states[: input_batch.num_tokens],
+                logits_fn,
+                max_num_prompt_logprobs,
+            )
         )
 
         pos_after_step = computed_prefill + input_batch.num_scheduled_tokens
         is_prompt_chunked = pos_after_step < prompt_lens
 
         query_start_loc_np = input_batch.query_start_loc_np
-        prompt_token_ids = prompt_logprobs_token_ids.unsqueeze(-1)
         prompt_logprobs_dict: dict[str, LogprobsTensors] = {}
         for i, req_id in enumerate(input_batch.req_ids):
             if not needs_prompt_logprobs[i]:
                 continue
 
+            req_is_prompt_chunked = is_prompt_chunked[i]
             start_idx = query_start_loc_np[i]
             end_idx = query_start_loc_np[i + 1]
             assert start_idx < end_idx, (
                 f"start_idx ({start_idx}) >= end_idx ({end_idx})"
             )
-            if not is_prompt_chunked[i]:
+            if not req_is_prompt_chunked:
                 end_idx -= 1
-            logprobs = LogprobsTensors(
-                logprob_token_ids=prompt_token_ids[start_idx:end_idx],
-                logprobs=prompt_logprobs[start_idx:end_idx],
-                selected_token_ranks=prompt_ranks[start_idx:end_idx],
+
+            # no logprobs if start_idx >= end_idx
+            logprobs = (
+                None
+                if start_idx >= end_idx
+                else LogprobsTensors(
+                    logprob_token_ids=prompt_token_ids[start_idx:end_idx],
+                    logprobs=prompt_logprobs[start_idx:end_idx],
+                    selected_token_ranks=prompt_ranks[start_idx:end_idx],
+                )
             )
 
             prompt_logprobs_list = self.in_progress_prompt_logprobs[req_id]
-            if is_prompt_chunked[i]:
-                # Prompt is chunked. Do not return the logprobs yet.
+            if logprobs is not None and (req_is_prompt_chunked or prompt_logprobs_list):
                 prompt_logprobs_list.append(logprobs)
+            if req_is_prompt_chunked:
+                # Prompt is chunked. Do not return the logprobs yet.
                 continue
 
             if prompt_logprobs_list:
                 # Merge the in-progress logprobs.
-                prompt_logprobs_list.append(logprobs)
                 logprobs = LogprobsTensors(
                     logprob_token_ids=torch.cat(
                         [x.logprob_token_ids for x in prompt_logprobs_list]
@@ -122,6 +138,9 @@ def compute_prompt_logprobs(
                 )
                 prompt_logprobs_list.clear()
 
+            if logprobs is None:
+                continue
+
             prompt_logprobs_dict[req_id] = logprobs
         return prompt_logprobs_dict
 
@@ -184,10 +203,12 @@ def compute_prompt_logprobs_with_chunking(
     prompt_token_ids: torch.Tensor,
     prompt_hidden_states: torch.Tensor,
     logits_fn: Callable[[torch.Tensor], torch.Tensor],
-) -> tuple[torch.Tensor, torch.Tensor]:
+    num_prompt_logprobs: int,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     # Since materializing the full prompt logits can take too much memory,
     # we compute it in chunks.
     CHUNK_SIZE = 1024
+    token_ids = []
     logprobs = []
     ranks = []
     prompt_token_ids = prompt_token_ids.to(torch.int64)
@@ -195,14 +216,21 @@ def compute_prompt_logprobs_with_chunking(
         end_idx = start_idx + CHUNK_SIZE
         # NOTE(woosuk): logits_fn can be slow because it involves all-gather.
         prompt_logits = logits_fn(prompt_hidden_states[start_idx:end_idx])
+        requested_num_prompt_logprobs = (
+            prompt_logits.shape[-1]
+            if num_prompt_logprobs == -1
+            else num_prompt_logprobs
+        )
         prompt_logprobs = compute_topk_logprobs(
             prompt_logits,
-            0,  # num_logprobs
+            requested_num_prompt_logprobs,
             prompt_token_ids[start_idx:end_idx],
         )
+        token_ids.append(prompt_logprobs.logprob_token_ids)
         logprobs.append(prompt_logprobs.logprobs)
         ranks.append(prompt_logprobs.selected_token_ranks)
 
+    token_ids = torch.cat(token_ids, dim=0) if len(token_ids) > 1 else token_ids[0]
     logprobs = torch.cat(logprobs, dim=0) if len(logprobs) > 1 else logprobs[0]
     ranks = torch.cat(ranks, dim=0) if len(ranks) > 1 else ranks[0]
-    return logprobs, ranks
+    return token_ids, logprobs, ranks
diff --git a/vllm/v1/worker/gpu/sample/sampler.py b/vllm/v1/worker/gpu/sample/sampler.py
index 6f73ca87ac67..5d91d5b2f097 100644
--- a/vllm/v1/worker/gpu/sample/sampler.py
+++ b/vllm/v1/worker/gpu/sample/sampler.py
@@ -12,7 +12,10 @@
 from vllm.v1.worker.gpu.sample.bad_words import BadWordsState
 from vllm.v1.worker.gpu.sample.gumbel import gumbel_sample
 from vllm.v1.worker.gpu.sample.logit_bias import LogitBiasState
-from vllm.v1.worker.gpu.sample.logprob import compute_topk_logprobs
+from vllm.v1.worker.gpu.sample.logprob import (
+    LogprobTokenIdsState,
+    compute_topk_logprobs,
+)
 from vllm.v1.worker.gpu.sample.output import SamplerOutput
 from vllm.v1.worker.gpu.sample.penalties import PenaltiesState
 from vllm.v1.worker.gpu.sample.states import NO_LOGPROBS, SamplingStates
@@ -38,6 +41,7 @@ def __init__(
         self.penalties_state = PenaltiesState(req_states)
         self.logit_bias_state = LogitBiasState(max_num_reqs, device)
         self.bad_words_state = BadWordsState(req_states)
+        self.logprob_token_ids_state = LogprobTokenIdsState(max_num_reqs, device)
         self.num_speculative_tokens = num_speculative_tokens
 
     def add_request(
@@ -47,12 +51,14 @@ def add_request(
         self.penalties_state.add_request(req_idx, sampling_params)
         self.logit_bias_state.add_request(req_idx, prompt_len, sampling_params)
         self.bad_words_state.add_request(req_idx, sampling_params)
+        self.logprob_token_ids_state.add_request(req_idx, sampling_params)
 
     def apply_staged_writes(self) -> None:
         self.sampling_states.apply_staged_writes()
         self.penalties_state.apply_staged_writes()
         self.logit_bias_state.apply_staged_writes()
         self.bad_words_state.apply_staged_writes()
+        self.logprob_token_ids_state.apply_staged_writes()
 
     def __call__(
         self,
@@ -79,13 +85,23 @@ def __call__(
         )
 
         max_num_logprobs = self.sampling_states.max_num_logprobs(idx_mapping_np)
-        if max_num_logprobs != NO_LOGPROBS:
+        max_per_req_token_ids = self.logprob_token_ids_state.max_num_token_ids(
+            idx_mapping_np
+        )
+        if max_num_logprobs != NO_LOGPROBS or max_per_req_token_ids > 0:
             if self.logprobs_mode == "processed_logprobs":
                 logits = processed_logits
             expanded_logits = logits.shape[0] != idx_mapping_np.shape[0]
             cu_num_logits = cu_num_logits_np.tolist() if expanded_logits else None
+            num_logprobs = max_num_logprobs if max_num_logprobs != NO_LOGPROBS else 0
             logprobs_tensors = compute_topk_logprobs(
-                logits, max_num_logprobs, sampled, cu_num_logits
+                logits,
+                num_logprobs,
+                sampled,
+                cu_num_logits,
+                logprob_token_ids_state=self.logprob_token_ids_state,
+                expanded_idx_mapping=input_batch.expanded_idx_mapping,
+                max_per_req_token_ids=max_per_req_token_ids,
             )
         else:
             logprobs_tensors = None
diff --git a/vllm/v1/worker/gpu/shutdown.py b/vllm/v1/worker/gpu/shutdown.py
new file mode 100644
index 000000000000..830083962347
--- /dev/null
+++ b/vllm/v1/worker/gpu/shutdown.py
@@ -0,0 +1,20 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+def free_before_shutdown(vllm_config: VllmConfig) -> None:
+    from vllm.model_executor.layers.rotary_embedding import _ROPE_DICT
+    from vllm.v1.worker.workspace import reset_workspace_manager
+
+    cache_config = vllm_config.cache_config
+    cache_config.num_gpu_blocks = None
+
+    compilation_config = vllm_config.compilation_config
+    compilation_config.static_forward_context.clear()
+
+    _ROPE_DICT.clear()
+    reset_workspace_manager()
diff --git a/vllm/v1/worker/gpu/spec_decode/eagle/cudagraph.py b/vllm/v1/worker/gpu/spec_decode/eagle/cudagraph.py
index 655007109e56..43bece01d0ec 100644
--- a/vllm/v1/worker/gpu/spec_decode/eagle/cudagraph.py
+++ b/vllm/v1/worker/gpu/spec_decode/eagle/cudagraph.py
@@ -10,6 +10,7 @@
 from vllm.v1.worker.gpu.block_table import BlockTables
 from vllm.v1.worker.gpu.cudagraph_utils import (
     BatchExecutionDescriptor,
+    CapturedAttentionState,
     CudaGraphManager,
     prepare_inputs_to_capture,
 )
@@ -18,8 +19,8 @@
 from vllm.v1.worker.utils import AttentionGroup
 
 
-class EagleCudaGraphManager(CudaGraphManager):
-    """CudaGraphManager for Eagle speculative decoding."""
+class EagleCudaGraphManagerBase(CudaGraphManager):
+    """Base CudaGraphManager for Eagle with a dedicated graph pool."""
 
     def __init__(
         self,
@@ -37,6 +38,46 @@ def __init__(
         if cudagraph_mode:
             self.pool = torch.cuda.graph_pool_handle()
 
+
+class PrefillEagleCudaGraphManager(EagleCudaGraphManagerBase):
+    """Eagle CudaGraphManager for prefill, using pre-built attention states
+    from the target model's capture."""
+
+    def capture(
+        self,
+        forward_fn: Callable,
+        full_cg_attn_states: dict[BatchExecutionDescriptor, CapturedAttentionState],
+        progress_bar_desc: str = "Capturing CUDA graphs",
+    ) -> None:
+        def create_forward_fn(
+            desc: BatchExecutionDescriptor,
+        ) -> tuple[Callable[[CUDAGraphMode], None], CapturedAttentionState]:
+            num_tokens = desc.num_tokens
+            num_reqs = desc.num_reqs or min(num_tokens, self.max_num_reqs)
+            num_tokens_across_dp = (
+                torch.full((self.dp_size,), num_tokens, dtype=torch.int32, device="cpu")
+                if self.dp_size > 1
+                else None
+            )
+            attn_state = full_cg_attn_states[desc]
+            attn_metadata, slot_mappings = attn_state
+            fwd = lambda cg_mode: forward_fn(
+                num_reqs,
+                num_tokens,
+                attn_metadata,
+                slot_mappings,
+                num_tokens_across_dp,
+                cg_mode,
+            )
+            return fwd, attn_state
+
+        super().capture(create_forward_fn, progress_bar_desc)
+
+
+class DecodeEagleCudaGraphManager(EagleCudaGraphManagerBase):
+    """Eagle CudaGraphManager for decode draft generation, building its own
+    attention metadata from scratch."""
+
     def capture(
         self,
         forward_fn: Callable,
@@ -47,11 +88,9 @@ def capture(
         kv_cache_config: KVCacheConfig,
         progress_bar_desc: str = "Capturing CUDA graphs",
     ) -> None:
-        """Capture CUDA graphs for Eagle."""
-
         def create_forward_fn(
             desc: BatchExecutionDescriptor,
-        ) -> Callable[[CUDAGraphMode], None]:
+        ) -> tuple[Callable[[CUDAGraphMode], None], CapturedAttentionState]:
             num_tokens = desc.num_tokens
             num_reqs = desc.num_reqs or min(num_tokens, self.max_num_reqs)
             num_tokens_across_dp = (
@@ -59,7 +98,7 @@ def create_forward_fn(
                 if self.dp_size > 1
                 else None
             )
-            attn_metadata, slot_mappings = prepare_inputs_to_capture(
+            attn_state = prepare_inputs_to_capture(
                 num_reqs,
                 num_tokens,
                 model_state,
@@ -67,9 +106,11 @@ def create_forward_fn(
                 block_tables,
                 attn_groups,
                 kv_cache_config,
+                skip_attn=(desc.cg_mode == CUDAGraphMode.PIECEWISE),
             )
+            attn_metadata, slot_mappings = attn_state
 
-            return lambda cg_mode: forward_fn(
+            fwd = lambda cg_mode: forward_fn(
                 num_reqs,
                 num_tokens,
                 attn_metadata,
@@ -77,5 +118,6 @@ def create_forward_fn(
                 num_tokens_across_dp,
                 cg_mode,
             )
+            return fwd, attn_state
 
         super().capture(create_forward_fn, progress_bar_desc)
diff --git a/vllm/v1/worker/gpu/spec_decode/eagle/speculator.py b/vllm/v1/worker/gpu/spec_decode/eagle/speculator.py
index b1126983539f..efe510f16e22 100644
--- a/vllm/v1/worker/gpu/spec_decode/eagle/speculator.py
+++ b/vllm/v1/worker/gpu/spec_decode/eagle/speculator.py
@@ -20,6 +20,8 @@
 )
 from vllm.v1.worker.gpu.block_table import BlockTables
 from vllm.v1.worker.gpu.cudagraph_utils import (
+    BatchExecutionDescriptor,
+    CapturedAttentionState,
     get_uniform_token_count,
 )
 from vllm.v1.worker.gpu.dp_utils import dispatch_cg_and_sync_dp
@@ -27,7 +29,8 @@
 from vllm.v1.worker.gpu.model_states.interface import ModelState
 from vllm.v1.worker.gpu.sample.gumbel import gumbel_sample
 from vllm.v1.worker.gpu.spec_decode.eagle.cudagraph import (
-    EagleCudaGraphManager,
+    DecodeEagleCudaGraphManager,
+    PrefillEagleCudaGraphManager,
 )
 from vllm.v1.worker.gpu.spec_decode.eagle.utils import load_eagle_model
 
@@ -53,6 +56,11 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
         # the draft model's hidden size can be different from the target model's
         # hidden size (e.g., Llama 3.3 70B).
         self.hidden_size = self.draft_model_config.get_hidden_size()
+        # Widen for HC-multiplexed residuals (e.g. DeepSeek V4 feeds the MTP
+        # draft the target's pre-hc_head (T, hc_mult * hidden_size) residual).
+        # Non-HC models default to hc_mult=1 and are unaffected.
+        hc_mult = getattr(self.draft_model_config.hf_config, "hc_mult", 1)
+        self.hidden_size = self.hidden_size * hc_mult
         self.vocab_size = self.draft_model_config.get_vocab_size()
         self.dtype = vllm_config.model_config.dtype
 
@@ -81,9 +89,13 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
             dtype=torch.int64,
             device=device,
         )
+        self.current_draft_step = torch.tensor(0, dtype=torch.int64, device=device)
         self.last_token_indices = torch.zeros(
             self.max_num_reqs, dtype=torch.int64, device=device
         )
+        self.arange = torch.arange(
+            self.max_num_reqs + 1, dtype=torch.int32, device="cpu"
+        )
 
         self.supports_mm_inputs = MULTIMODAL_REGISTRY.supports_multimodal_inputs(
             self.draft_model_config
@@ -94,7 +106,7 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
             )
 
         self.draft_logits: torch.Tensor | None = None
-        if self.speculative_config.rejection_sample_method == "probabilistic":
+        if self.speculative_config.draft_sample_method == "gumbel":
             self.draft_logits = torch.zeros(
                 self.max_num_reqs,
                 self.num_speculative_steps,
@@ -103,13 +115,13 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
                 device=device,
             )
 
-        self.prefill_cudagraph_manager: EagleCudaGraphManager | None = None
-        self.decode_cudagraph_manager: EagleCudaGraphManager | None = None
+        self.prefill_cudagraph_manager: PrefillEagleCudaGraphManager | None = None
+        self.decode_cudagraph_manager: DecodeEagleCudaGraphManager | None = None
 
     def init_cudagraph_manager(self, cudagraph_mode: CUDAGraphMode) -> None:
         cudagraph_mode = self.vllm_config.compilation_config.cudagraph_mode
         # Initialize cudagraph manager for draft prefill (draft position 0).
-        self.prefill_cudagraph_manager = EagleCudaGraphManager(
+        self.prefill_cudagraph_manager = PrefillEagleCudaGraphManager(
             self.vllm_config,
             self.device,
             cudagraph_mode,
@@ -126,7 +138,7 @@ def init_cudagraph_manager(self, cudagraph_mode: CUDAGraphMode) -> None:
             cudagraph_mode = CUDAGraphMode.NONE
 
         # Initialize cudagraph manager for draft decodes (draft positions > 0).
-        self.decode_cudagraph_manager = EagleCudaGraphManager(
+        self.decode_cudagraph_manager = DecodeEagleCudaGraphManager(
             self.vllm_config,
             self.device,
             cudagraph_mode,
@@ -215,6 +227,30 @@ def run_model(
             last_hidden_states, hidden_states = ret_hidden_states
         return last_hidden_states, hidden_states
 
+    def _sample_draft(
+        self,
+        logits: torch.Tensor,
+        idx_mapping: torch.Tensor,
+        pos: torch.Tensor,
+        draft_step: torch.Tensor,
+        draft_logits: torch.Tensor | None,
+    ) -> torch.Tensor:
+        if draft_logits is not None:
+            # NOTE(woosuk): We must add 1 to the positions to match the Gumbel noise
+            # used for draft and target sampling.
+            return gumbel_sample(
+                logits,
+                idx_mapping,
+                self.temperature,
+                self.seeds,
+                pos + 1,
+                apply_temperature=True,
+                output_processed_logits=draft_logits,
+                output_processed_logits_col=draft_step,
+            )
+        else:
+            return logits.argmax(dim=-1)
+
     def prefill(
         self,
         num_reqs: int,
@@ -240,22 +276,67 @@ def prefill(
         sample_hidden_states = last_hidden_states[last_token_indices]
         logits = self.model.compute_logits(sample_hidden_states)
 
-        # NOTE(woosuk): We must add 1 to the positions to match the Gumbel noise
-        # used for draft and target sampling.
-        self.draft_tokens[:num_reqs, 0] = gumbel_sample(
+        self.draft_tokens[:num_reqs, 0] = self._sample_draft(
             logits,
             idx_mapping,
-            self.temperature,
-            self.seeds,
-            pos + 1,
-            apply_temperature=True,
-            processed_logits_out=self.draft_logits[:, 0]
-            if self.draft_logits is not None
-            else None,
+            pos,
+            self.current_draft_step,
+            self.draft_logits,
         )
         self.hidden_states[:num_reqs] = hidden_states[last_token_indices]
         self.input_buffers.positions[:num_reqs] = pos
 
+    def multi_step_decode(
+        self,
+        num_reqs: int,
+        skip_attn: bool,
+        batch_desc: BatchExecutionDescriptor,
+        num_tokens_across_dp: torch.Tensor | None,
+    ) -> None:
+        positions = self.input_buffers.positions[:num_reqs]
+        query_start_loc = self.input_buffers.query_start_loc[: num_reqs + 1]
+        idx_mapping = self.idx_mapping[:num_reqs]
+
+        for step in range(1, self.num_speculative_steps):
+            attn_metadata = None
+            slot_mappings_by_layer = None
+            if not skip_attn:
+                # Build attention metadata and slot mappings for each draft
+                # decode step. It is necessary to rebuild the attention
+                # metadata even when replaying the FULL graph so that any
+                # attention metadata builder state is updated.
+                slot_mappings = self.block_tables.compute_slot_mappings(
+                    idx_mapping,
+                    query_start_loc,
+                    positions,
+                    batch_desc.num_tokens,
+                )
+                slot_mappings_by_layer = build_slot_mappings_by_layer(
+                    slot_mappings, self.kv_cache_config
+                )
+                attn_metadata = self._build_draft_attn_metadata(
+                    num_reqs=num_reqs,
+                    num_reqs_padded=batch_desc.num_reqs or num_reqs,
+                    num_tokens_padded=batch_desc.num_tokens,
+                )
+
+            # Update the current draft step.
+            self.current_draft_step.fill_(step)
+
+            # Generate draft tokens for the current step.
+            if batch_desc.cg_mode == CUDAGraphMode.FULL:
+                assert self.decode_cudagraph_manager is not None
+                self.decode_cudagraph_manager.run_fullgraph(batch_desc)
+            else:
+                self.generate_draft(
+                    num_reqs,
+                    batch_desc.num_tokens,
+                    attn_metadata,
+                    slot_mappings_by_layer,
+                    num_tokens_across_dp=num_tokens_across_dp,
+                    cudagraph_runtime_mode=batch_desc.cg_mode,
+                )
+
     def generate_draft(
         self,
         num_reqs: int,
@@ -265,66 +346,52 @@ def generate_draft(
         num_tokens_across_dp: torch.Tensor | None,
         cudagraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
     ) -> None:
-        pos = self.input_buffers.positions[:num_reqs]
-        query_start_loc = self.input_buffers.query_start_loc[: num_reqs + 1]
         idx_mapping = self.idx_mapping[:num_reqs]
-        for step in range(1, self.num_speculative_steps):
-            # Run the eagle model.
-            last_hidden_states, hidden_states = self.run_model(
-                num_tokens_padded,
-                attn_metadata,
-                slot_mappings,
-                num_tokens_across_dp,
-                cudagraph_runtime_mode,
-            )
-            last_hidden_states = last_hidden_states[:num_reqs]
-            hidden_states = hidden_states[:num_reqs]
-            logits = self.model.compute_logits(last_hidden_states)
+        positions = self.input_buffers.positions[:num_reqs]
+        # Run the eagle model forward pass.
+        last_hidden_states, hidden_states = self.run_model(
+            num_tokens_padded,
+            attn_metadata,
+            slot_mappings,
+            num_tokens_across_dp,
+            cudagraph_runtime_mode,
+        )
+        last_hidden_states = last_hidden_states[:num_reqs]
 
-            # NOTE(woosuk): We must add 1 to the positions to match the Gumbel noise
-            # used for draft and target sampling.
-            draft_tokens = gumbel_sample(
-                logits,
-                idx_mapping,
-                self.temperature,
-                self.seeds,
-                pos + 1,
-                apply_temperature=True,
-                processed_logits_out=self.draft_logits[:, step]
-                if self.draft_logits is not None
-                else None,
-            )
-            self.draft_tokens[:num_reqs, step] = draft_tokens
-
-            if step < self.num_speculative_steps - 1:
-                # Update the inputs for the next step.
-                update_eagle_inputs(
-                    draft_tokens,
-                    hidden_states,
-                    self.input_buffers,
-                    self.hidden_states,
-                    self.max_model_len,
-                )
-                if attn_metadata is not None:
-                    self.block_tables.compute_slot_mappings(
-                        idx_mapping, query_start_loc, pos, num_tokens_padded
-                    )
+        # Sample the draft tokens.
+        logits = self.model.compute_logits(last_hidden_states)
+        draft_tokens = self._sample_draft(
+            logits,
+            idx_mapping,
+            positions,
+            self.current_draft_step,
+            self.draft_logits,
+        )
+
+        # Update the inputs for the next step.
+        update_eagle_draft_inputs(
+            draft_tokens,
+            self.current_draft_step,
+            hidden_states,
+            self.draft_tokens,
+            self.hidden_states,
+            self.input_buffers,
+            num_reqs,
+            self.max_model_len,
+            self.num_speculative_steps,
+        )
 
     def _build_draft_attn_metadata(
         self,
         num_reqs: int,
         num_reqs_padded: int,
         num_tokens_padded: int,
-        max_query_len: int,
     ) -> dict[str, Any] | None:
         if not self.draft_attn_layer_names:
             return None
 
-        query_start_loc_cpu = (
-            torch.arange(num_reqs_padded + 1, dtype=torch.int32, device="cpu").clamp_(
-                max=num_reqs
-            )
-            * max_query_len
+        query_start_loc_cpu = torch.clamp(
+            self.arange[: num_reqs_padded + 1], max=num_reqs
         )
         block_tables = [
             x[:num_reqs_padded] for x in self.block_tables.input_block_tables
@@ -338,7 +405,7 @@ def _build_draft_attn_metadata(
                 : num_reqs_padded + 1
             ],
             query_start_loc_cpu=query_start_loc_cpu,
-            max_query_len=max_query_len,
+            max_query_len=1,
             seq_lens=self.input_buffers.seq_lens[:num_reqs_padded],
             max_seq_len=self.max_model_len,
             block_tables=block_tables,
@@ -347,35 +414,33 @@ def _build_draft_attn_metadata(
         )
         return attn_metadata
 
-    def capture_model(self) -> None:
+    def capture(
+        self,
+        attn_states: dict[BatchExecutionDescriptor, CapturedAttentionState],
+    ) -> None:
         logger.info("Capturing model for Eagle speculator...")
         # Reset indices to zeros to prevent stale values from prior
         # dummy runs to cause out-of-bounds indexing during capture.
         self.last_token_indices.zero_()
 
         # Capture the prefill routine (model forward + compute_logits +
-        # gumbel_sample).
+        # sample).
         # For FULL graphs, the entire routine is recorded as one graph.
         # For PIECEWISE, only the model's compiled regions are captured
         # and the rest (compute_logits, gumbel_sample) runs eagerly.
         assert self.prefill_cudagraph_manager is not None
         self.prefill_cudagraph_manager.capture(
             self.prefill,
-            self.model_state,
-            self.input_buffers,
-            self.block_tables,
-            self.attn_groups,
-            self.kv_cache_config,
+            attn_states,
             progress_bar_desc="Capturing eagle prefill CUDA graphs",
         )
 
         if self.num_speculative_steps == 1:
             return
 
-        # Capture the decode draft generation loop (model forward +
-        # compute_logits + gumbel_sample + update_eagle_inputs, for
-        # each step). For FULL graphs, the entire multi-step loop is
-        # recorded as one graph.
+        # Capture the decode draft generation routine (model forward +
+        # compute_logits + sample + update_eagle_inputs) for a single
+        # step.
         assert self.decode_cudagraph_manager is not None
         self.decode_cudagraph_manager.capture(
             self.generate_draft,
@@ -446,9 +511,10 @@ def propose(
 
         # Get the input ids and last token indices for the speculator.
         prepare_eagle_inputs(
+            self.last_token_indices,
+            self.current_draft_step,
             self.input_buffers,
             input_batch,
-            self.last_token_indices,
             num_sampled,
             num_rejected,
             last_sampled,
@@ -458,27 +524,24 @@ def propose(
 
         # When all requests are decoding (no true prefills), each has
         # num_speculative_steps + 1 tokens, enabling FULL graph replay.
-        # Mixed or prefill-only batches fall back to PIECEWISE.
+        uniform_token_count = get_uniform_token_count(
+            num_reqs,
+            # Use the actual number of tokens without padding added by
+            # the target model during FULL cudagraph.
+            input_batch.num_tokens,
+            max_query_len,
+        )
         prefill_batch_desc, num_tokens_across_dp = dispatch_cg_and_sync_dp(
             self.prefill_cudagraph_manager,
             num_reqs,
             num_tokens,
-            get_uniform_token_count(num_reqs, num_tokens, max_query_len),
+            uniform_token_count,
             dp_size=self.dp_size,
             dp_rank=self.dp_rank,
             need_eager=is_profile,
         )
 
         if prefill_batch_desc.cg_mode == CUDAGraphMode.FULL:
-            # It is necessary to rebuild the attention metadata when
-            # replaying the FULL graph so that any attention metadata
-            # builder state is updated.
-            self._build_draft_attn_metadata(
-                num_reqs=num_reqs,
-                num_reqs_padded=prefill_batch_desc.num_reqs or num_reqs,
-                num_tokens_padded=prefill_batch_desc.num_tokens,
-                max_query_len=self.num_speculative_steps + 1,
-            )
             # Replay the full graph for draft prefill.
             assert self.prefill_cudagraph_manager is not None
             self.prefill_cudagraph_manager.run_fullgraph(prefill_batch_desc)
@@ -522,48 +585,21 @@ def propose(
             need_eager=is_profile,
         )
 
-        attn_metadata_updated = None
-        slot_mappings_updated = None
-        if not (dummy_run and skip_attn_for_dummy_run):
-            # Build attention metadata and slot mappings for the draft
-            # decode steps. It is necessary to rebuild the attention
-            # metadata even when replaying the FULL graph so that any
-            # attention metadata builder state is updated.
-            slot_mappings = self.block_tables.compute_slot_mappings(
-                self.idx_mapping[:num_reqs],
-                self.input_buffers.query_start_loc[: num_reqs + 1],
-                self.input_buffers.positions[:num_reqs],
-                decode_batch_desc.num_tokens,
-            )
-            slot_mappings_updated = build_slot_mappings_by_layer(
-                slot_mappings, self.kv_cache_config
-            )
-            attn_metadata_updated = self._build_draft_attn_metadata(
-                num_reqs=num_reqs,
-                num_reqs_padded=decode_batch_desc.num_reqs or num_reqs,
-                num_tokens_padded=decode_batch_desc.num_tokens,
-                max_query_len=1,
-            )
+        # Generate the remaining num_speculative_steps - 1 draft tokens.
+        self.multi_step_decode(
+            num_reqs,
+            dummy_run and skip_attn_for_dummy_run,
+            decode_batch_desc,
+            num_tokens_across_dp,
+        )
 
-        if decode_batch_desc.cg_mode == CUDAGraphMode.FULL:
-            # Replay the full graph for draft generation.
-            assert self.decode_cudagraph_manager is not None
-            self.decode_cudagraph_manager.run_fullgraph(decode_batch_desc)
-        else:
-            self.generate_draft(
-                num_reqs,
-                decode_batch_desc.num_tokens,
-                attn_metadata_updated,
-                slot_mappings_updated,
-                num_tokens_across_dp=num_tokens_across_dp,
-                cudagraph_runtime_mode=decode_batch_desc.cg_mode,
-            )
         return self.draft_tokens[:num_reqs]
 
 
 @triton.jit
 def _prepare_eagle_inputs_kernel(
     last_token_indices_ptr,
+    eagle_current_draft_step_ptr,
     eagle_input_ids_ptr,
     eagle_positions_ptr,
     eagle_query_start_loc_ptr,
@@ -624,6 +660,8 @@ def _prepare_eagle_inputs_kernel(
     # Copy sequence lengths.
     tl.store(eagle_seq_lens_ptr + req_idx, seq_len)
     if req_idx == (num_reqs - 1):
+        # Reset the current draft step to 0.
+        tl.store(eagle_current_draft_step_ptr, 0)
         # Pad query_start_loc for CUDA graphs.
         for i in range(num_reqs, max_num_reqs + 1, BLOCK_SIZE):
             block = i + tl.arange(0, BLOCK_SIZE)
@@ -642,10 +680,11 @@ def _prepare_eagle_inputs_kernel(
 
 
 def prepare_eagle_inputs(
-    input_buffers: InputBuffers,
-    input_batch: InputBatch,
     # [num_reqs]
     last_token_indices: torch.Tensor,
+    current_draft_step: torch.Tensor,
+    input_buffers: InputBuffers,
+    input_batch: InputBatch,
     # [num_reqs]
     num_sampled: torch.Tensor,
     # [num_reqs]
@@ -659,6 +698,7 @@ def prepare_eagle_inputs(
     num_reqs = input_batch.num_reqs
     _prepare_eagle_inputs_kernel[(num_reqs,)](
         last_token_indices,
+        current_draft_step,
         input_buffers.input_ids,
         input_buffers.positions,
         input_buffers.query_start_loc,
@@ -679,7 +719,7 @@ def prepare_eagle_inputs(
 
 
 @triton.jit
-def _prepare_eagle_docode_kernel(
+def _prepare_eagle_decode_kernel(
     draft_tokens_ptr,
     draft_tokens_stride,
     target_seq_lens_ptr,
@@ -736,7 +776,7 @@ def prepare_eagle_decode(
     max_num_reqs: int,
 ):
     num_reqs = draft_tokens.shape[0]
-    _prepare_eagle_docode_kernel[(num_reqs + 1,)](
+    _prepare_eagle_decode_kernel[(num_reqs + 1,)](
         draft_tokens,
         draft_tokens.stride(0),
         target_seq_lens,
@@ -752,36 +792,55 @@ def prepare_eagle_decode(
 
 
 @triton.jit
-def _update_eagle_inputs_kernel(
+def _update_eagle_draft_inputs_kernel(
+    output_draft_tokens_ptr,
+    output_draft_tokens_stride,
+    next_input_hidden_states_ptr,
+    next_input_hidden_states_stride,
     input_ids_ptr,
     positions_ptr,
-    input_hidden_states_ptr,
-    input_hidden_states_stride,
     seq_lens_ptr,
-    max_model_len,
     draft_tokens_ptr,
-    output_hidden_states_ptr,
-    output_hidden_states_stride,
+    current_draft_step_ptr,
+    hidden_states_ptr,
+    hidden_states_stride,
     hidden_size,
+    max_model_len,
+    num_speculative_steps,
     BLOCK_SIZE: tl.constexpr,
 ):
     req_idx = tl.program_id(0)
 
-    # Draft token -> Input ID.
+    # Write the sampled draft token into self.draft_tokens[req_idx, step].
     draft_token = tl.load(draft_tokens_ptr + req_idx)
+    step = tl.load(current_draft_step_ptr)
+    tl.store(
+        output_draft_tokens_ptr + req_idx * output_draft_tokens_stride + step,
+        draft_token,
+    )
+
+    if step >= num_speculative_steps - 1:
+        # This is the final step. Skip updating draft forward inputs.
+        return
+
+    # Write the sampled draft token into the input ids tensor for the next
+    # forward pass.
     tl.store(input_ids_ptr + req_idx, draft_token)
 
-    # Output hidden states -> Input hidden states.
+    # Copy hidden states into the input hidden states tensor for the next
+    # forward pass.
     for i in range(0, hidden_size, BLOCK_SIZE):
         block = i + tl.arange(0, BLOCK_SIZE)
         mask = block < hidden_size
-        output_hidden_states = tl.load(
-            output_hidden_states_ptr + req_idx * output_hidden_states_stride + block,
+        hidden_states = tl.load(
+            hidden_states_ptr + req_idx * hidden_states_stride + block,
             mask=mask,
         )
         tl.store(
-            input_hidden_states_ptr + req_idx * input_hidden_states_stride + block,
-            output_hidden_states,
+            next_input_hidden_states_ptr
+            + req_idx * next_input_hidden_states_stride
+            + block,
+            hidden_states,
             mask=mask,
         )
 
@@ -797,24 +856,32 @@ def _update_eagle_inputs_kernel(
     tl.store(seq_lens_ptr + req_idx, seq_len)
 
 
-def update_eagle_inputs(
+def update_eagle_draft_inputs(
     draft_tokens: torch.Tensor,
-    output_hidden_states: torch.Tensor,
-    input_buffers: InputBuffers,
+    current_draft_step: torch.Tensor,
     hidden_states: torch.Tensor,
+    output_draft_tokens: torch.Tensor,
+    next_input_hidden_states: torch.Tensor,
+    input_buffers: InputBuffers,
+    num_reqs: int,
     max_model_len: int,
+    num_speculative_steps: int,
 ):
-    num_reqs, hidden_size = output_hidden_states.shape
-    _update_eagle_inputs_kernel[(num_reqs,)](
+    _, hidden_size = hidden_states.shape
+    _update_eagle_draft_inputs_kernel[(num_reqs,)](
+        output_draft_tokens,
+        output_draft_tokens.stride(0),
+        next_input_hidden_states,
+        next_input_hidden_states.stride(0),
         input_buffers.input_ids,
         input_buffers.positions,
-        hidden_states,
-        hidden_states.stride(0),
         input_buffers.seq_lens,
-        max_model_len,
         draft_tokens,
-        output_hidden_states,
-        output_hidden_states.stride(0),
+        current_draft_step,
+        hidden_states,
+        hidden_states.stride(0),
         hidden_size,
+        max_model_len,
+        num_speculative_steps,
         BLOCK_SIZE=1024,
     )
diff --git a/vllm/v1/worker/gpu/spec_decode/eagle/utils.py b/vllm/v1/worker/gpu/spec_decode/eagle/utils.py
index ee37eadb2a8e..39514ee7e91f 100644
--- a/vllm/v1/worker/gpu/spec_decode/eagle/utils.py
+++ b/vllm/v1/worker/gpu/spec_decode/eagle/utils.py
@@ -49,4 +49,19 @@ def load_eagle_model(target_model: nn.Module, vllm_config: VllmConfig) -> nn.Mod
             del eagle_model.lm_head
         eagle_model.lm_head = target_model.lm_head
 
+        # MTP models call compute_logits via shared_head.head (a
+        # ParallelLMHead inside each MTP layer), not self.model.lm_head.
+        # If the checkpoint omits a copy of the lm_head weights at the
+        # MTP layer path, shared_head.head stays uninitialised and
+        # produces zero/NaN logits. Share it explicitly from the target.
+        inner = getattr(eagle_model, "model", None)
+        layers = getattr(inner, "layers", None) if inner is not None else None
+        if layers is not None:
+            items = layers.values() if isinstance(layers, nn.ModuleDict) else layers
+            for layer in items:
+                sh = getattr(layer, "shared_head", None)
+                if sh is not None and hasattr(sh, "head"):
+                    del sh.head
+                    sh.head = target_model.lm_head
+
     return eagle_model
diff --git a/vllm/v1/worker/gpu/spec_decode/probabilistic_rejection_sampler_utils.py b/vllm/v1/worker/gpu/spec_decode/probabilistic_rejection_sampler_utils.py
index 22f5d9df0d0c..10b29433efb2 100644
--- a/vllm/v1/worker/gpu/spec_decode/probabilistic_rejection_sampler_utils.py
+++ b/vllm/v1/worker/gpu/spec_decode/probabilistic_rejection_sampler_utils.py
@@ -45,7 +45,7 @@ def _compute_global_lse(
 
 
 @triton.jit
-def _compute_block_max_and_sumexp_kernel(
+def _compute_block_stats_kernel(
     # [num_logits, num_blocks]
     target_local_argmax_ptr,
     target_local_argmax_stride,
@@ -77,6 +77,7 @@ def _compute_block_max_and_sumexp_kernel(
     vocab_size,
     num_speculative_steps,
     BLOCK_SIZE: tl.constexpr,
+    HAS_DRAFT_LOGITS: tl.constexpr,
 ):
     logit_idx = tl.program_id(0)
     draft_step_idx = tl.load(expanded_local_pos_ptr + logit_idx)
@@ -112,24 +113,6 @@ def _compute_block_max_and_sumexp_kernel(
             value,
         )
     else:
-        # Get local draft max and summed exponentials.
-        draft_logits = tl.load(
-            draft_logits_ptr
-            + req_state_idx * draft_logits_stride_0
-            + draft_step_idx * draft_logits_stride_1
-            + block_offsets,
-            mask=mask,
-            other=float("-inf"),
-        ).to(tl.float32)
-        draft_max, draft_sumexp = _compute_block_max_and_sumexp(draft_logits)
-        tl.store(
-            draft_local_max_ptr + logit_idx * draft_local_max_stride + block_idx,
-            draft_max,
-        )
-        tl.store(
-            draft_local_sumexp_ptr + logit_idx * draft_local_sumexp_stride + block_idx,
-            draft_sumexp,
-        )
         # Get local target max and summed exponentials.
         target_logits = tl.load(
             target_logits_ptr + logit_idx * target_logits_stride + block_offsets,
@@ -147,6 +130,27 @@ def _compute_block_max_and_sumexp_kernel(
             + block_idx,
             target_sumexp,
         )
+        if HAS_DRAFT_LOGITS:
+            # Get local draft max and summed exponentials.
+            draft_logits = tl.load(
+                draft_logits_ptr
+                + req_state_idx * draft_logits_stride_0
+                + draft_step_idx * draft_logits_stride_1
+                + block_offsets,
+                mask=mask,
+                other=float("-inf"),
+            ).to(tl.float32)
+            draft_max, draft_sumexp = _compute_block_max_and_sumexp(draft_logits)
+            tl.store(
+                draft_local_max_ptr + logit_idx * draft_local_max_stride + block_idx,
+                draft_max,
+            )
+            tl.store(
+                draft_local_sumexp_ptr
+                + logit_idx * draft_local_sumexp_stride
+                + block_idx,
+                draft_sumexp,
+            )
 
 
 @triton.jit
@@ -196,6 +200,7 @@ def _probabilistic_rejection_kernel(
     pos_ptr,
     vocab_num_blocks,
     PADDED_VOCAB_NUM_BLOCKS: tl.constexpr,
+    HAS_DRAFT_LOGITS: tl.constexpr,
 ):
     req_idx = tl.program_id(0)
     req_state_idx = tl.load(idx_mapping_ptr + req_idx)
@@ -238,12 +243,6 @@ def _probabilistic_rejection_kernel(
                 target_logit = tl.load(
                     target_logits_ptr + logit_idx * target_logits_stride + draft_sampled
                 ).to(tl.float32)
-                draft_logit = tl.load(
-                    draft_logits_ptr
-                    + req_state_idx * draft_logits_stride_0
-                    + i * draft_logits_stride_1
-                    + draft_sampled
-                ).to(tl.float32)
                 target_lse = _compute_global_lse(
                     target_local_max_ptr,
                     target_local_max_stride,
@@ -253,19 +252,29 @@ def _probabilistic_rejection_kernel(
                     vocab_num_blocks,
                     PADDED_VOCAB_NUM_BLOCKS,
                 )
-                draft_lse = _compute_global_lse(
-                    draft_local_max_ptr,
-                    draft_local_max_stride,
-                    draft_local_sumexp_ptr,
-                    draft_local_sumexp_stride,
-                    logit_idx,
-                    vocab_num_blocks,
-                    PADDED_VOCAB_NUM_BLOCKS,
-                )
                 target_log_prob = target_logit - target_lse
-                draft_log_prob = draft_logit - draft_lse
                 pos = tl.load(pos_ptr + logit_idx)
                 u = tl_rand64(seed, pos, includes_zero=False)
+                if HAS_DRAFT_LOGITS:
+                    draft_logit = tl.load(
+                        draft_logits_ptr
+                        + req_state_idx * draft_logits_stride_0
+                        + i * draft_logits_stride_1
+                        + draft_sampled
+                    ).to(tl.float32)
+                    draft_lse = _compute_global_lse(
+                        draft_local_max_ptr,
+                        draft_local_max_stride,
+                        draft_local_sumexp_ptr,
+                        draft_local_sumexp_stride,
+                        logit_idx,
+                        vocab_num_blocks,
+                        PADDED_VOCAB_NUM_BLOCKS,
+                    )
+                    draft_log_prob = draft_logit - draft_lse
+                else:
+                    # One-hot draft: q(draft_token) = 1, log_q = 0.
+                    draft_log_prob = 0
                 # Probability ratio test: p(x) > u * q(x)
                 # Equivalent log form: log_p(x) > log(u) + log_q(x)
                 accepted &= target_log_prob > tl.log(u) + draft_log_prob
@@ -301,6 +310,8 @@ def _resample_kernel(
     cu_num_logits_ptr,
     # [num_logits]
     expanded_idx_mapping_ptr,
+    # [num_logits]
+    draft_sampled_ptr,
     # [max_num_reqs]
     temp_ptr,
     # [max_num_reqs]
@@ -309,6 +320,7 @@ def _resample_kernel(
     pos_ptr,
     vocab_size,
     BLOCK_SIZE: tl.constexpr,
+    HAS_DRAFT_LOGITS: tl.constexpr,
 ):
     req_idx = tl.program_id(0)
     resample_idx = tl.load(rejected_step_ptr + req_idx)
@@ -327,22 +339,17 @@ def _resample_kernel(
     block_idx = tl.program_id(1)
     block = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
     mask = block < vocab_size
+    target_logits = tl.load(
+        target_logits_ptr + resample_token_idx * target_logits_stride + block,
+        mask=mask,
+        other=float("-inf"),
+    ).to(tl.float32)
 
-    # Compute the residual logits to resample the rejected token
-    # from. In the case of no rejections (bonus token), we directly
-    # use the target logits.
+    # Compute the residual logits to resample the rejected token from.
     if is_bonus:
-        residual_logits = tl.load(
-            target_logits_ptr + resample_token_idx * target_logits_stride + block,
-            mask=mask,
-            other=float("-inf"),
-        ).to(tl.float32)
-    else:
-        target_logits = tl.load(
-            target_logits_ptr + resample_token_idx * target_logits_stride + block,
-            mask=mask,
-            other=float("-inf"),
-        ).to(tl.float32)
+        # Bonus token (no rejections). Directly use the target logits.
+        residual_logits = target_logits
+    elif HAS_DRAFT_LOGITS:
         draft_logits = tl.load(
             draft_logits_ptr
             + req_state_idx * draft_logits_stride_0
@@ -365,6 +372,15 @@ def _resample_kernel(
             target_log_probs + tl.log(1 - ratio),
             float("-inf"),
         ).to(tl.float32)
+    else:
+        # One-hot draft. The residual is just the target distribution with
+        # the rejected draft token probability zeroed out.
+        rejected_draft_token = tl.load(draft_sampled_ptr + resample_token_idx + 1)
+        residual_logits = tl.where(
+            block != rejected_draft_token,
+            target_logits,
+            float("-inf"),
+        ).to(tl.float32)
 
     # Resample the rejected/bonus token.
     value, idx = gumbel_block_argmax(
@@ -376,8 +392,10 @@ def _resample_kernel(
         temp_ptr,
         seed_ptr,
         pos_ptr,
-        None,
-        0,
+        None,  # processed_logits_ptr
+        0,  # processed_logits_stride
+        None,  # processed_logits_col_ptr
+        vocab_size,
         APPLY_TEMPERATURE=False,
     )
     token_id = block_idx * BLOCK_SIZE + idx
@@ -456,7 +474,7 @@ def probabilistic_rejection_sample(
     # [num_logits, V]
     target_logits: torch.Tensor,
     # [max_num_reqs, num_speculative_steps, V]
-    draft_logits: torch.Tensor,
+    draft_logits: torch.Tensor | None,
     # [num_logits]
     draft_sampled: torch.Tensor,
     # [num_reqs + 1]
@@ -477,9 +495,17 @@ def probabilistic_rejection_sample(
 ) -> tuple[torch.Tensor, torch.Tensor]:
     num_reqs = cu_num_logits.shape[0] - 1
     num_logits, vocab_size = target_logits.shape
+    has_draft_logits = draft_logits is not None
 
-    # Gather draft logits, compute target argmax for greedy, and
-    # compute per-block LSE and max for non-greedy requests.
+    if draft_logits is None:
+        # When draft_logits is None, create a dummy tensor so that Triton
+        # kernel signatures receive valid pointers/strides. The kernels
+        # will never read from it when HAS_DRAFT_LOGITS=False.
+        draft_logits = target_logits.new_empty(1, 1, 1)
+
+    # Compute the block-level logits stats, such as target argmax
+    # (for greedy requests), and target max + softmax exponential
+    # (for non-greedy requests).
     VOCAB_BLOCK_SIZE = 8192
     vocab_num_blocks = triton.cdiv(vocab_size, VOCAB_BLOCK_SIZE)
     padded_vocab_num_blocks = triton.next_power_of_2(vocab_num_blocks)
@@ -498,7 +524,7 @@ def probabilistic_rejection_sample(
     draft_local_sumexp = target_logits.new_empty(
         num_logits, vocab_num_blocks, dtype=torch.float32
     )
-    _compute_block_max_and_sumexp_kernel[(num_logits, vocab_num_blocks)](
+    _compute_block_stats_kernel[(num_logits, vocab_num_blocks)](
         target_local_argmax,
         target_local_argmax.stride(0),
         target_local_max,
@@ -520,6 +546,7 @@ def probabilistic_rejection_sample(
         vocab_size,
         num_speculative_steps,
         BLOCK_SIZE=VOCAB_BLOCK_SIZE,
+        HAS_DRAFT_LOGITS=has_draft_logits,
     )
 
     # Sample up until the first rejected/bonus token, and store
@@ -527,7 +554,7 @@ def probabilistic_rejection_sample(
     sampled = draft_sampled.new_empty(
         num_reqs, num_speculative_steps + 1, dtype=torch.int64
     )
-    num_sampled = sampled.new_empty(num_reqs)
+    num_sampled = sampled.new_empty(num_reqs, dtype=torch.int32)
     target_rejected_logsumexp = target_logits.new_empty(num_reqs, dtype=torch.float32)
     draft_rejected_logsumexp = target_logits.new_empty(num_reqs, dtype=torch.float32)
     _probabilistic_rejection_kernel[(num_reqs,)](
@@ -559,6 +586,7 @@ def probabilistic_rejection_sample(
         pos,
         vocab_num_blocks,
         PADDED_VOCAB_NUM_BLOCKS=padded_vocab_num_blocks,
+        HAS_DRAFT_LOGITS=has_draft_logits,
         num_warps=1,
     )
 
@@ -587,11 +615,13 @@ def probabilistic_rejection_sample(
         num_sampled,
         cu_num_logits,
         expanded_idx_mapping,
+        draft_sampled,
         temperature,
         seed,
         pos,
         vocab_size,
         BLOCK_SIZE=RESAMPLE_BLOCK_SIZE,
+        HAS_DRAFT_LOGITS=has_draft_logits,
     )
 
     # Insert the resampled tokens into the output sampled.
diff --git a/vllm/v1/worker/gpu/spec_decode/rejection_sampler.py b/vllm/v1/worker/gpu/spec_decode/rejection_sampler.py
index 2f92b0c093d6..d41a7d8c84fb 100644
--- a/vllm/v1/worker/gpu/spec_decode/rejection_sampler.py
+++ b/vllm/v1/worker/gpu/spec_decode/rejection_sampler.py
@@ -5,6 +5,7 @@
 from vllm.config import SpeculativeConfig
 from vllm.triton_utils import tl, triton
 from vllm.v1.outputs import LogprobsTensors
+from vllm.v1.spec_decode.utils import unconditional_to_conditional_rates
 from vllm.v1.worker.gpu.input_batch import InputBatch
 from vllm.v1.worker.gpu.metrics.logits import get_num_nans
 from vllm.v1.worker.gpu.sample.logprob import compute_topk_logprobs
@@ -15,68 +16,10 @@
     probabilistic_rejection_sample,
 )
 from vllm.v1.worker.gpu.spec_decode.synthetic_rejection_sampler_utils import (
-    compute_synthetic_rejection_sampler_params,
     synthetic_rejection_sample,
 )
 
 
-@triton.jit
-def _strict_rejection_sample_kernel(
-    sampled_ptr,  # [num_reqs, num_speculative_steps + 1]
-    sampled_stride,
-    num_sampled_ptr,  # [num_reqs]
-    target_sampled_ptr,  # [num_draft_tokens + num_reqs]
-    input_ids_ptr,  # [num_draft_tokens + num_reqs]
-    cu_num_logits_ptr,  # [num_reqs + 1]
-):
-    req_idx = tl.program_id(0)
-    start_idx = tl.load(cu_num_logits_ptr + req_idx)
-    end_idx = tl.load(cu_num_logits_ptr + req_idx + 1)
-    num_tokens = end_idx - start_idx
-
-    num_sampled = 0
-    rejected = False
-    for i in range(num_tokens - 1):
-        if not rejected:
-            target_sampled = tl.load(target_sampled_ptr + start_idx + i)
-            draft_sampled = tl.load(input_ids_ptr + start_idx + i + 1)
-            tl.store(sampled_ptr + req_idx * sampled_stride + i, target_sampled)
-            num_sampled += 1
-            if target_sampled != draft_sampled:
-                rejected = True
-    if not rejected:
-        target_sampled = tl.load(target_sampled_ptr + start_idx + num_tokens - 1)
-        tl.store(
-            sampled_ptr + req_idx * sampled_stride + num_tokens - 1, target_sampled
-        )
-        num_sampled += 1
-    tl.store(num_sampled_ptr + req_idx, num_sampled)
-
-
-def strict_rejection_sample(
-    # [num_draft_tokens + num_reqs]
-    target_sampled: torch.Tensor,
-    # [num_draft_tokens + num_reqs]
-    draft_sampled: torch.Tensor,
-    # [num_reqs + 1]
-    cu_num_logits: torch.Tensor,
-    num_speculative_steps,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    num_reqs = cu_num_logits.shape[0] - 1
-    sampled = target_sampled.new_empty(num_reqs, num_speculative_steps + 1)
-    num_sampled = target_sampled.new_empty(num_reqs, dtype=torch.int32)
-    _strict_rejection_sample_kernel[(num_reqs,)](
-        sampled,
-        sampled.stride(0),
-        num_sampled,
-        target_sampled,
-        draft_sampled,
-        cu_num_logits,
-        num_warps=1,
-    )
-    return sampled, num_sampled
-
-
 @triton.jit
 def _flatten_sampled_kernel(
     # [num_logits]
@@ -102,24 +45,20 @@ def __init__(
         self,
         sampler: Sampler,
         spec_config: SpeculativeConfig,
+        device: torch.device,
     ):
         self.sampler = sampler
         self.num_speculative_steps = spec_config.num_speculative_tokens
         self.rejection_sample_method = spec_config.rejection_sample_method
+        self.synthetic_conditional_rates: torch.Tensor | None = None
         if self.rejection_sample_method == "synthetic":
-            synthetic_acceptance_rate = spec_config.synthetic_acceptance_rate
-            if (
-                synthetic_acceptance_rate is None
-                or not 0.0 <= synthetic_acceptance_rate <= 1.0
-            ):
-                raise ValueError(
-                    f"synthetic_acceptance_rate must be in [0, 1], "
-                    f"but got {synthetic_acceptance_rate}"
-                )
-            self.base_acceptance_rate, self.decay_factor = (
-                compute_synthetic_rejection_sampler_params(
-                    synthetic_acceptance_rate, self.num_speculative_steps
-                )
+            assert spec_config.synthetic_acceptance_rates is not None
+            self.synthetic_conditional_rates = torch.tensor(
+                unconditional_to_conditional_rates(
+                    spec_config.synthetic_acceptance_rates
+                ),
+                dtype=torch.float32,
+                device=device,
             )
 
     def _get_logprobs_tensors(
@@ -167,17 +106,7 @@ def __call__(
         # that num_nans is computed before applying penalties and temperature.
         num_nans = get_num_nans(logits) if self.sampler.compute_nans else None
 
-        if self.rejection_sample_method == "strict":
-            sampler_output = self.sampler(logits, input_batch)
-            logprobs_tensors = sampler_output.logprobs_tensors
-            sampled, num_sampled = strict_rejection_sample(
-                sampler_output.sampled_token_ids.view(-1),
-                draft_sampled,
-                input_batch.cu_num_logits,
-                self.num_speculative_steps,
-            )
-        elif self.rejection_sample_method == "probabilistic":
-            assert draft_logits is not None
+        if self.rejection_sample_method == "standard":
             pos = input_batch.positions[input_batch.logits_indices]
             processed_logits = self.sampler.apply_sampling_params(
                 logits,
@@ -218,8 +147,7 @@ def __call__(
                 input_batch.positions[input_batch.logits_indices],
                 input_batch.idx_mapping,
                 self.sampler.sampling_states.seeds.gpu,
-                self.base_acceptance_rate,
-                self.decay_factor,
+                self.synthetic_conditional_rates,
                 self.num_speculative_steps,
             )
         else:
diff --git a/vllm/v1/worker/gpu/spec_decode/synthetic_rejection_sampler_utils.py b/vllm/v1/worker/gpu/spec_decode/synthetic_rejection_sampler_utils.py
index f5388575baed..7e91075bb1a7 100644
--- a/vllm/v1/worker/gpu/spec_decode/synthetic_rejection_sampler_utils.py
+++ b/vllm/v1/worker/gpu/spec_decode/synthetic_rejection_sampler_utils.py
@@ -5,8 +5,6 @@
 from vllm.triton_utils import tl, triton
 from vllm.v1.worker.gpu.sample.gumbel import tl_rand64
 
-MIN_ACCEPTANCE_DECAY_FACTOR = 0.85
-
 
 @triton.jit
 def _synthetic_rejection_sample_kernel(
@@ -27,8 +25,8 @@ def _synthetic_rejection_sample_kernel(
     idx_mapping_ptr,
     # [max_num_reqs]
     seeds_ptr,
-    base_acceptance_rate,
-    decay_factor,
+    # [num_speculative_steps]
+    acceptance_rates_ptr,
 ):
     req_idx = tl.program_id(0)
     start_idx = tl.load(cu_num_logits_ptr + req_idx)
@@ -38,13 +36,13 @@ def _synthetic_rejection_sample_kernel(
     seed = tl.load(seeds_ptr + req_state_idx)
 
     num_sampled = 0
-    acceptance_rate = base_acceptance_rate
     rejected = False
     for i in range(num_tokens - 1):
         if not rejected:
             logit_idx = start_idx + i
             pos = tl.load(pos_ptr + logit_idx)
             u = tl_rand64(seed, pos, includes_zero=False)
+            acceptance_rate = tl.load(acceptance_rates_ptr + i)
             if u < acceptance_rate:
                 sampled = tl.load(input_ids_ptr + logit_idx + 1).to(tl.int64)
             else:
@@ -52,7 +50,6 @@ def _synthetic_rejection_sample_kernel(
                 rejected = True
             tl.store(sampled_ptr + req_idx * sampled_stride + i, sampled)
             num_sampled += 1
-            acceptance_rate *= decay_factor
     if not rejected:
         target_sampled = tl.load(target_sampled_ptr + start_idx + num_tokens - 1)
         tl.store(
@@ -75,8 +72,8 @@ def synthetic_rejection_sample(
     idx_mapping: torch.Tensor,
     # [max_num_reqs]
     seed: torch.Tensor,
-    base_acceptance_rate: float,
-    decay_factor: float,
+    # [num_speculative_steps]
+    acceptance_rates: torch.Tensor,
     num_speculative_steps: int,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     num_reqs = cu_num_logits.shape[0] - 1
@@ -92,56 +89,7 @@ def synthetic_rejection_sample(
         pos,
         idx_mapping,
         seed,
-        base_acceptance_rate,
-        decay_factor,
+        acceptance_rates,
         num_warps=1,
     )
     return sampled, num_sampled
-
-
-def compute_synthetic_rejection_sampler_params(
-    p_avg: float, n: int, tol: float = 1e-9
-) -> tuple[float, float]:
-    def mean_joint_prob(a_0: float, gamma: float, n: int):
-        total = 0.0
-        for i in range(n):
-            total += a_0 ** (i + 1) * gamma ** (i * (i + 1) // 2)
-        return total / n
-
-    def min_valid_decay_factor(p: float, n: int, tol: float = 1e-9) -> float:
-        low, high = MIN_ACCEPTANCE_DECAY_FACTOR, 1.0
-        if mean_joint_prob(1, low, n) >= p:
-            return low
-
-        # Sweep for a gamma decay factor that is guaranteed
-        # to yield a base acceptance rate <= 1.
-        while (high - low) > tol:
-            mid = (low + high) / 2
-            if mean_joint_prob(1, mid, n) >= p:
-                high = mid
-            else:
-                low = mid
-        return high
-
-    def compute_base_acceptance_rate(
-        p_avg: float, gamma: float, n: int, tol: float = 1e-9
-    ) -> float:
-        if p_avg <= 0.0:
-            return 0.0
-        if p_avg >= 1.0:
-            return 1.0
-
-        # Sweep for a base acceptance rate that yields
-        # the desired mean joint probability.
-        low, high = 0.0, 1.0
-        while (high - low) > tol:
-            mid = (low + high) / 2
-            if mean_joint_prob(mid, gamma, n) >= p_avg:
-                high = mid
-            else:
-                low = mid
-        return high
-
-    decay_factor = min_valid_decay_factor(p_avg, n)
-    base_rate = compute_base_acceptance_rate(p_avg, decay_factor, n)
-    return base_rate, decay_factor
diff --git a/vllm/v1/worker/gpu/states.py b/vllm/v1/worker/gpu/states.py
index 24d225886106..6268ea0ba673 100644
--- a/vllm/v1/worker/gpu/states.py
+++ b/vllm/v1/worker/gpu/states.py
@@ -57,6 +57,8 @@ def __init__(
         self.num_computed_tokens = StagedWriteTensor(
             self.max_num_reqs, dtype=torch.int32, device=device
         )
+        # Optimistic CPU mirror of num_computed_tokens (upper bound on GPU value).
+        self.num_computed_tokens_np = np.zeros(self.max_num_reqs, dtype=np.int32)
 
         # Last sampled tokens.
         self.last_sampled_tokens = torch.zeros(
@@ -100,7 +102,21 @@ def add_request(
         self.total_len.stage_write_elem(req_idx, prefill_len)
         self.all_token_ids.stage_write(req_idx, 0, all_token_ids)
         self.num_computed_prefill_tokens[req_idx] = num_computed_tokens
+        self.num_computed_tokens_np[req_idx] = num_computed_tokens
         self.num_computed_tokens.stage_write_elem(req_idx, num_computed_tokens)
+        self.num_computed_tokens_np[req_idx] = num_computed_tokens
+
+        if num_computed_tokens > 0 and num_computed_tokens <= prefill_len:
+            # For PD disagg or resumed requests: set last_sampled to the last
+            # computed token so the first decode step gets the right input_id.
+            # For fresh prefill requests (num_computed_tokens == 0) the tensor
+            # is not read by combine_sampled_and_draft_tokens so we skip the
+            # write. Use a slice assignment rather than scalar indexing so the
+            # write is dispatched through fill_ without a host/device sync.
+            self.last_sampled_tokens[req_idx : req_idx + 1] = all_token_ids[
+                num_computed_tokens - 1
+            ]
+        self.draft_tokens[req_idx].zero_()
 
     def apply_staged_writes(self) -> None:
         self.prompt_len.copy_to_uva()
diff --git a/vllm/v1/worker/gpu/warmup.py b/vllm/v1/worker/gpu/warmup.py
index 026b6a7d7eb9..83d87c74a4a0 100644
--- a/vllm/v1/worker/gpu/warmup.py
+++ b/vllm/v1/worker/gpu/warmup.py
@@ -29,13 +29,16 @@ def warmup_kernels(
     triton kernels. We must call the provided worker's execute_model for
     pipeline parallel coordination.
 
-    The first iteration simulates a prefill with requests of 2 prompt
-    tokens each. The second iteration simulates a decode step with all
-    requests generating 1 token each.
+    The first iteration simulates a prefill with requests of
+    2 + num_spec_steps prompt tokens each. The second iteration simulates
+    a decode step with all requests generating 1 + num_spec_steps tokens.
     """
-    prompt_token_ids = [0, 1]
-    prompt_len = len(prompt_token_ids)
     num_spec_steps = model_runner.num_speculative_steps
+    # Use 1 + num_spec_steps + 1 tokens so the prefill batch's per-request
+    # query length exceeds decode_query_len (= 1 + num_spec_steps), preventing
+    # it from being misclassified as a uniform decode batch.
+    prompt_len = 2 + num_spec_steps
+    prompt_token_ids = list(range(prompt_len))
     # After prefill, decode generates 1 verified + num_spec_steps draft tokens.
     decode_len = prompt_len + 1 + num_spec_steps
 
@@ -76,7 +79,7 @@ def _alloc_blocks(num_blocks: int) -> list[int]:
         nonlocal next_block_id
         return list(range(next_block_id, next_block_id := next_block_id + num_blocks))
 
-    # Step 1: Prefill all requests with 2 prompt tokens each.
+    # Step 1: Prefill all requests with 2 + num_spec_steps prompt tokens each.
     new_reqs = [
         NewRequestData.from_request(
             Request(req_ids[i], prompt_token_ids, sampling_params, pooling_params),
diff --git a/vllm/v1/worker/gpu_input_batch.py b/vllm/v1/worker/gpu_input_batch.py
index 89e63f3def7a..e9de08342b82 100644
--- a/vllm/v1/worker/gpu_input_batch.py
+++ b/vllm/v1/worker/gpu_input_batch.py
@@ -8,6 +8,7 @@
 import numpy as np
 import torch
 
+from vllm.config.reasoning import ReasoningConfig
 from vllm.lora.request import LoRARequest
 from vllm.multimodal.inputs import MultiModalFeatureSpec
 from vllm.pooling_params import PoolingParams
@@ -22,6 +23,9 @@
     MoveDirectionality,
 )
 from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.sample.thinking_budget_state import (
+    maybe_create_thinking_budget_state_holder,
+)
 from vllm.v1.utils import copy_slice
 from vllm.v1.worker.block_table import MultiGroupBlockTable
 
@@ -45,6 +49,12 @@ class CachedRequestState:
 
     lora_request: LoRARequest | None = None
     prompt_embeds: torch.Tensor | None = None
+    # To accumulate prompt logprobs tensor chunks across prefill steps.
+    in_progress_prompt_logprobs_cpu: LogprobsTensors | None = None
+
+    # Per-position mask for mixed-mode inputs (e.g chat completion with
+    # prompt_embeds content parts). See `Request.prompt_is_token_ids`.
+    prompt_is_token_ids: list[bool] | None = None
 
     # Used when both async_scheduling and spec_decode are enabled.
     prev_num_draft_len: int = 0
@@ -92,12 +102,20 @@ def __init__(
         max_num_blocks_per_req: list[int] | None = None,
         logitsprocs: LogitsProcessors | None = None,
         logitsprocs_need_output_token_ids: bool = False,
-        is_spec_decode: bool = False,
+        num_spec_tokens: int = 0,
         is_pooling_model: bool = False,
         cp_kv_cache_interleave_size: int = 1,
+        reasoning_config: ReasoningConfig | None = None,
     ):
+        self.thinking_budget_state_holder = maybe_create_thinking_budget_state_holder(
+            reasoning_config,
+            max_num_reqs,
+            num_spec_tokens,
+            device,
+            pin_memory,
+        )
+        self.thinking_token_budget_reqs: set[str] = set()
         self.is_pooling_model = is_pooling_model
-        self.is_spec_decode = is_spec_decode
         self.max_num_reqs = max_num_reqs
         self.max_model_len = max_model_len
         self.max_num_batched_tokens = max_num_batched_tokens
@@ -239,9 +257,6 @@ def __init__(
         # More efficient than num_logprobs=-1 when only a few tokens are needed
         self.logprob_token_ids: dict[str, list[int]] = {}
 
-        # To accumulate prompt logprobs tensor chunks across prefill steps.
-        self.in_progress_prompt_logprobs_cpu: dict[str, LogprobsTensors] = {}
-
         # Internal representation of per-step batch state changes, used for
         # reordering persistent batch and generating logitsprocs batch state
         # updates. Should reset each step.
@@ -344,7 +359,12 @@ def add_request(
         end_idx = start_idx + len(request.output_token_ids)
         if request.prompt_token_ids is not None:
             self.token_ids_cpu[req_index, :num_prompt_tokens] = request.prompt_token_ids
-            self.is_token_ids[req_index, :num_prompt_tokens] = True
+            if request.prompt_is_token_ids is not None:
+                self.is_token_ids[req_index, :num_prompt_tokens] = (
+                    request.prompt_is_token_ids
+                )
+            else:
+                self.is_token_ids[req_index, :num_prompt_tokens] = True
         else:
             self.is_token_ids[req_index, :num_prompt_tokens] = False
         if request.prompt_embeds is not None:
@@ -484,6 +504,7 @@ def update_req_spec_token_ids(
         start_index = self.num_tokens_no_spec[req_index]
         end_token_index = start_index + num_spec_tokens
         self.token_ids_cpu[req_index, start_index:end_token_index] = spec_token_ids
+        self.is_token_ids[req_index, start_index:end_token_index] = True
         cur_spec_token_ids.extend(spec_token_ids)
 
     def remove_request(self, req_id: str) -> int | None:
@@ -531,7 +552,6 @@ def remove_request(self, req_id: str) -> int | None:
         self.generators.pop(req_index, None)
         self.num_logprobs.pop(req_id, None)
         self.logprob_token_ids.pop(req_id, None)
-        self.in_progress_prompt_logprobs_cpu.pop(req_id, None)
         if self.prev_req_id_to_index is not None:
             self.prev_req_id_to_index.pop(req_id, None)
 
@@ -540,6 +560,7 @@ def remove_request(self, req_id: str) -> int | None:
             # False means we don't fill with -inf.
             self.allowed_token_ids_mask_cpu_tensor[req_index].fill_(False)
         self.bad_words_token_ids.pop(req_index, None)
+        self.thinking_token_budget_reqs.discard(req_id)
         return req_index
 
     def swap_states(self, i1: int, i2: int) -> None:
@@ -800,6 +821,8 @@ def refresh_metadata(self):
         # reset batch update tracking.
         # Update sampling metadata if batch state is changed.
         batch_update = self.batch_update_builder.get_and_reset(self.num_reqs)
+        if self.thinking_budget_state_holder is not None and batch_update:
+            self.thinking_budget_state_holder.sync_batch(batch_update)
         for logit_proc in self.logitsprocs.all:
             logit_proc.update_state(batch_update)
         if batch_update:
@@ -853,10 +876,15 @@ def _make_sampling_metadata(self) -> SamplingMetadata:
 
         # Only set output_token_ids if required by the current requests'
         # sampling parameters.
+        holder = self.thinking_budget_state_holder
+        thinking_budget_tracks_reqs = (
+            holder is not None and holder.has_tracked_requests()
+        )
         needs_output_token_ids = (
             not self.no_penalties
             or bool(self.bad_words_token_ids)
             or self.logitsprocs_need_output_token_ids
+            or not thinking_budget_tracks_reqs
         )
         output_token_ids = (
             cast(list[list[int]], self.req_output_token_ids)
@@ -902,6 +930,7 @@ def _make_sampling_metadata(self) -> SamplingMetadata:
             allowed_token_ids_mask=allowed_token_ids_mask,
             bad_words_token_ids=self.bad_words_token_ids,
             logitsprocs=self.logitsprocs,
+            thinking_budget_state_holder=self.thinking_budget_state_holder,
         )
 
     def get_pooling_params(self) -> list[PoolingParams]:
@@ -1076,6 +1105,13 @@ def no_penalties(self) -> bool:
             and len(self.repetition_penalties_reqs) == 0
         )
 
+    @property
+    def no_thinking_budget(self) -> bool:
+        return (
+            self.thinking_budget_state_holder is None
+            or len(self.thinking_token_budget_reqs) == 0
+        )
+
     @property
     def max_num_logprobs(self) -> int | None:
         return max(self.num_logprobs.values()) if self.num_logprobs else None
diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index 4c573f79e97a..98b2fbf120eb 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -54,7 +54,11 @@
 from vllm.model_executor.layers.attention import Attention, MLAAttention
 from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
 from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
-    RoutedExpertsCapturer,
+    extract_routed_experts_for_current_batch,
+    free_routing_buffers,
+    get_global_experts_capturer,
+    init_routed_experts_capturer_with_shared_cache,
+    issue_routing_d2h_copy,
 )
 from vllm.model_executor.layers.mamba.ops.ssu_dispatch import (
     initialize_mamba_ssu_backend,
@@ -169,6 +173,7 @@
 from vllm.v1.spec_decode.draft_model import DraftModelProposer
 from vllm.v1.spec_decode.eagle import EagleProposer
 from vllm.v1.spec_decode.extract_hidden_states import ExtractHiddenStatesProposer
+from vllm.v1.spec_decode.gemma4 import Gemma4Proposer
 from vllm.v1.spec_decode.medusa import MedusaProposer
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
 from vllm.v1.spec_decode.ngram_proposer_gpu import (
@@ -524,6 +529,7 @@ def __init__(
                 | DraftModelProposer
                 | MedusaProposer
                 | ExtractHiddenStatesProposer
+                | Gemma4Proposer
             )
             if self.speculative_config.method == "ngram":
                 from vllm.v1.spec_decode.ngram_proposer import NgramProposer
@@ -552,6 +558,8 @@ def __init__(
                 self._ngram_pinned_val_buf = torch.zeros(
                     self.max_num_reqs, dtype=torch.int32, pin_memory=True
                 )
+            elif self.speculative_config.use_gemma4_mtp():
+                self.drafter = Gemma4Proposer(self.vllm_config, self.device, self)
             elif self.speculative_config.use_dflash():
                 self.drafter = DFlashProposer(self.vllm_config, self.device, self)
                 self.use_aux_hidden_state_outputs = True
@@ -577,7 +585,9 @@ def __init__(
                     "Unknown speculative decoding method: "
                     f"{self.speculative_config.method}"
                 )
-            self.rejection_sampler = RejectionSampler(self.sampler)
+            self.rejection_sampler = RejectionSampler(
+                self.sampler, self.speculative_config, self.device
+            )
 
         self.num_spec_tokens = 0
         self.valid_sampled_token_count_gpu: torch.Tensor | None = None
@@ -627,7 +637,7 @@ def __init__(
             vocab_size=self.model_config.get_vocab_size(),
             block_sizes=[placeholder_block_size],
             kernel_block_sizes=[placeholder_block_size],
-            is_spec_decode=bool(self.vllm_config.speculative_config),
+            num_spec_tokens=self.num_spec_tokens,
             logitsprocs=build_logitsprocs(
                 self.vllm_config,
                 self.device,
@@ -643,6 +653,7 @@ def __init__(
             or self.vllm_config.reasoning_config is not None,
             is_pooling_model=self.is_pooling_model,
             cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
+            reasoning_config=self.vllm_config.reasoning_config,
         )
 
         # Separate cuda stream for overlapping transfer of sampled token ids from
@@ -881,6 +892,9 @@ def reset_encoder_cache(self) -> None:
         self.encoder_cache.clear()
         self.late_interaction_runner.clear()
 
+    def post_kv_cache_wake_up(self) -> None:
+        self.init_fp8_kv_scales()
+
     @torch.inference_mode()
     def init_fp8_kv_scales(self) -> None:
         """
@@ -1082,6 +1096,12 @@ def _update_states(self, scheduler_output: "SchedulerOutput") -> Callable | None
         for req_id in scheduler_output.finished_req_ids:
             self.input_batch.remove_request(req_id)
 
+        if self.routed_experts_initialized:
+            free_routing_buffers(
+                scheduler_output.finished_req_ids,
+                scheduler_output.preempted_req_ids,
+            )
+
         # Zero GPU memory for freshly allocated cache blocks to prevent
         # stale NaN/data from corrupting attention or SSM computation.
         if scheduler_output.new_block_ids_to_zero:
@@ -1157,6 +1177,7 @@ def _update_states(self, scheduler_output: "SchedulerOutput") -> Callable | None
                 req_id=req_id,
                 prompt_token_ids=new_req_data.prompt_token_ids,
                 prompt_embeds=new_req_data.prompt_embeds,
+                prompt_is_token_ids=new_req_data.prompt_is_token_ids,
                 mm_features=new_req_data.mm_features,
                 sampling_params=sampling_params,
                 pooling_params=pooling_params,
@@ -1336,13 +1357,27 @@ def _update_states(self, scheduler_output: "SchedulerOutput") -> Callable | None
             # For the last rank, we don't need to update the token_ids_cpu
             # because the sampled tokens are already cached.
             if not is_last_rank:
-                # Add new_token_ids to token_ids_cpu.
-                start_token_index = num_computed_tokens
-                end_token_index = num_computed_tokens + len(new_token_ids)
-                self.input_batch.token_ids_cpu[
-                    req_index, start_token_index:end_token_index
-                ] = new_token_ids
-                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
+                start_token_index = self.input_batch.num_tokens_no_spec[req_index]
+                # For chunked prefill, num_computed_tokens may less
+                # than num_tokens_no_spec.
+                # Async scheduled PP: no new_token_ids, advance num_tokens_no_spec
+                # according to num_computed_tokens.
+                end_token_index = max(
+                    start_token_index,
+                    num_computed_tokens + len(new_token_ids),
+                )
+                if end_token_index > start_token_index:
+                    if new_token_ids:
+                        # Add new_token_ids to token_ids_cpu.
+                        num_new_tokens = end_token_index - start_token_index
+                        tokens_to_append = new_token_ids[-num_new_tokens:]
+                        self.input_batch.token_ids_cpu[
+                            req_index, start_token_index:end_token_index
+                        ] = tokens_to_append
+                    self.input_batch.is_token_ids[
+                        req_index, start_token_index:end_token_index
+                    ] = True
+                    self.input_batch.num_tokens_no_spec[req_index] = end_token_index
 
             # Add spec_token_ids to token_ids_cpu.
             self.input_batch.update_req_spec_token_ids(req_state, scheduled_spec_tokens)
@@ -1499,10 +1534,16 @@ def _init_mrope_positions(self, req_state: CachedRequestState):
         )
         mrope_model = cast(SupportsMRoPE, model)
 
+        # `prompt_embeds` is a passthrough modality (no grid_thw), models'
+        # M-RoPE code assumes per-feature grid info, so filter it out. The
+        # prompt_embeds positions are treated as text positions for M-RoPE.
+        mrope_features = [
+            f for f in req_state.mm_features if f.modality != "prompt_embeds"
+        ]
         req_state.mrope_positions, req_state.mrope_position_delta = (
             mrope_model.get_mrope_input_positions(
                 req_state.prompt_token_ids,
-                req_state.mm_features,
+                mrope_features,
             )
         )
 
@@ -2144,10 +2185,6 @@ def _get_block_table(kv_cache_gid: int):
         block_table_gid_0 = _get_block_table(0)
         slot_mapping_gid_0 = slot_mappings[0]
 
-        if self.routed_experts_initialized:
-            attn_gid = self.routed_experts_attn_gid
-            slot_mapping_attn = slot_mappings[attn_gid]
-            self.slot_mapping = slot_mapping_attn[:num_tokens].cpu().numpy()
         num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
             :num_reqs_padded
         ]
@@ -2155,6 +2192,7 @@ def _get_block_table(kv_cache_gid: int):
             :num_reqs_padded
         ]
         seq_lens_cpu = self.optimistic_seq_lens_cpu[:num_reqs_padded]
+        seq_lens_cpu_upper_bound = seq_lens_cpu
 
         # is_prefilling: True if request is still in prefill phase.
         # Used by mamba backends to distinguish actual decodes from
@@ -2172,6 +2210,7 @@ def _get_block_table(kv_cache_gid: int):
             seq_lens=self.seq_lens[:num_reqs_padded],
             _seq_lens_cpu=seq_lens_cpu,
             _num_computed_tokens_cpu=num_computed_tokens_cpu,
+            seq_lens_cpu_upper_bound=seq_lens_cpu_upper_bound,
             num_reqs=num_reqs_padded,
             num_actual_tokens=num_tokens_padded,
             max_query_len=max_query_len,
@@ -2180,6 +2219,7 @@ def _get_block_table(kv_cache_gid: int):
             slot_mapping=slot_mapping_gid_0,
             causal=True,
             is_prefilling=is_prefilling,
+            positions=self.positions[:num_tokens_padded],
         )
 
         if self.dcp_world_size > 1:
@@ -2294,11 +2334,18 @@ def _build_attn_group_metadata(
                 cm.slot_mapping = slot_mappings[kv_cache_gid]
 
             if self.speculative_config and spec_decode_common_attn_metadata is None:
-                if isinstance(self.drafter, (EagleProposer, DFlashProposer)):
+                if isinstance(
+                    self.drafter, (EagleProposer, DFlashProposer, Gemma4Proposer)
+                ):
                     if self.drafter.kv_cache_gid == kv_cache_gid:
                         spec_decode_common_attn_metadata = cm
                 else:
                     spec_decode_common_attn_metadata = cm
+            # Capture per-group block tables for multi-group proposers.
+            if self.speculative_config and isinstance(self.drafter, Gemma4Proposer):
+                self.drafter.set_per_group_block_table(
+                    kv_cache_gid, cm.block_table_tensor
+                )
 
             for attn_gid in range(len(self.attn_groups[kv_cache_gid])):
                 if ubatch_slices is not None:
@@ -2310,13 +2357,26 @@ def _build_attn_group_metadata(
 
         if self.is_mm_prefix_lm:
             req_doc_ranges = {}
+
+            # Gemma4 bidi: skip ranges that exceed the sliding
+            # window. When image tokens > sliding_window, bidi causes
+            # early image tokens to attend to the entire image
+            # (e.g. 6 → 1092 targets), degrading spatial precision.
+            # Per-range filtering keeps bidi for small images/video
+            # frames while skipping oversized images.
+            hf_text_config = self.model_config.hf_text_config
+            _bidi_sw = getattr(hf_text_config, "sliding_window", None)
+
             for req_id in self.input_batch.req_ids:
                 image_doc_ranges = []
                 req_state = self.requests[req_id]
                 for mm_feature in req_state.mm_features:
                     pos_info = mm_feature.mm_position
                     img_doc_range = pos_info.extract_embeds_range()
-                    image_doc_ranges.extend(img_doc_range)
+                    for r in img_doc_range:
+                        if _bidi_sw is not None and (r[1] - r[0] + 1) > _bidi_sw:
+                            continue
+                        image_doc_ranges.append(r)
                 req_idx = self.input_batch.req_id_to_index[req_id]
                 req_doc_ranges[req_idx] = image_doc_ranges
 
@@ -2722,6 +2782,33 @@ def _execute_mm_encoder(
         if not mm_kwargs:
             return []
 
+        # `prompt_embeds` is a passthrough modality, the tensor is already in
+        # the model embedding space, so no encoder runs. Inject each
+        # `prompt_embeds` tensor directly into the encoder cache here so that
+        # `_gather_mm_embeddings` can splice it via the standard `is_mm_embed`
+        # path.
+        pe_indices = [
+            i
+            for i, (modality, _) in enumerate(mm_kwargs)
+            if modality == "prompt_embeds"
+        ]
+        if pe_indices:
+            for i in pe_indices:
+                pe_tensor = mm_kwargs[i][1]["embedding"].data
+                assert isinstance(pe_tensor, torch.Tensor)
+
+                self.encoder_cache[mm_hashes[i]] = pe_tensor.to(self.device)
+                self.maybe_save_ec_to_connector(self.encoder_cache, mm_hashes[i])
+            # Filter out `prompt_embeds` items from mm_kwargs/mm_hashes/mm_lora_refs
+            # since they don't require further encoder processing.
+            mm_hashes = [h for i, h in enumerate(mm_hashes) if i not in pe_indices]
+            mm_kwargs = [k for i, k in enumerate(mm_kwargs) if i not in pe_indices]
+            mm_lora_refs = [
+                r for i, r in enumerate(mm_lora_refs) if i not in pe_indices
+            ]
+            if not mm_kwargs:
+                return []  # nothing left to encode after filtering out `prompt_embeds`
+
         should_time = bool(
             self.observability_config
             and self.observability_config.enable_mm_processor_stats
@@ -3042,7 +3129,7 @@ def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
 
         return tuple(tasks)
 
-    def sync_and_slice_intermediate_tensors(
+    def sync_and_gather_intermediate_tensors(
         self,
         num_tokens: int,
         intermediate_tensors: IntermediateTensors | None,
@@ -3053,24 +3140,23 @@ def sync_and_slice_intermediate_tensors(
         tp = self.vllm_config.parallel_config.tensor_parallel_size
         is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
 
-        # When sequence parallelism is enabled, the "residual" tensor is sharded
-        # across tensor parallel ranks, so each rank only needs its own slice.
+        # When sequence parallelism is enabled, the "residual" tensor is
+        # sharded across TP ranks. All-gather it here because downstream
+        # QKV + Attention needs the full residual before the SP split point.
         if sync_self:
             assert intermediate_tensors is not None
             for k, v in intermediate_tensors.items():
                 is_scattered = k == "residual" and is_rs
-                copy_len = num_tokens // tp if is_scattered else num_tokens
-                self.intermediate_tensors[k][:copy_len].copy_(
-                    v[:copy_len], non_blocking=True
+                if is_scattered:
+                    local_len = num_tokens // tp
+                    v = get_tp_group().all_gather(v[:local_len], dim=0)
+
+                self.intermediate_tensors[k][:num_tokens].copy_(
+                    v[:num_tokens], non_blocking=True
                 )
 
         return IntermediateTensors(
-            {
-                k: v[: num_tokens // tp]
-                if k == "residual" and is_rs
-                else v[:num_tokens]
-                for k, v in self.intermediate_tensors.items()
-            }
+            {k: v[:num_tokens] for k, v in self.intermediate_tensors.items()}
         )
 
     def eplb_step(self, is_dummy: bool = False, is_profile: bool = False) -> None:
@@ -3286,7 +3372,7 @@ def _preprocess(
             intermediate_tensors = None
         else:
             assert intermediate_tensors is not None
-            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
+            intermediate_tensors = self.sync_and_gather_intermediate_tensors(
                 num_input_tokens, intermediate_tensors, True
             )
 
@@ -3345,7 +3431,6 @@ def _bookkeeping_sync(
         logits: torch.Tensor | None,
         hidden_states: torch.Tensor,
         num_scheduled_tokens: int,
-        spec_decode_metadata: SpecDecodeMetadata | None,
     ) -> tuple[
         dict[str, int],
         LogprobsLists | None,
@@ -3613,7 +3698,6 @@ def dispatch_cudagraph(num_tokens, disable_full=False, valid_modes=None):
                     allow_microbatching=allow_microbatching,
                     num_tokens_padded=num_tokens_padded,
                     uniform_decode=uniform_decode,
-                    num_scheduled_tokens_per_request=num_scheduled_tokens_np,
                     cudagraph_mode=cudagraph_mode.value,
                 )
             )
@@ -3758,6 +3842,15 @@ def _get_slot_mapping(kv_cache_gid: int):
 
         return slot_mappings_by_gid, slot_mappings_by_layer
 
+    def _is_all_reqs_chunked_prefill(self) -> bool:
+        """Check if all scheduled requests are marked to discard sampled tokens.
+
+        This is true when `discard_request_mask` is set for every scheduled
+        request (e.g., for chunked prefill requests that are not the last
+        prefill chunk)."""
+        num_reqs = self.input_batch.num_reqs
+        return bool(self.discard_request_mask.np[:num_reqs].all())
+
     @torch.inference_mode()
     def execute_model(
         self,
@@ -3771,11 +3864,9 @@ def execute_model(
             )
 
         if self.routed_experts_initialized:
-            capturer = RoutedExpertsCapturer.get_instance()
+            capturer = get_global_experts_capturer()
             if capturer is not None:
-                capturer.clear_buffer()  # noqa
-            else:
-                logger.error("RoutedExpertsCapturer not initialized.")
+                capturer.finalize_pending_copy()
 
         # If ngram_gpu is used, we need to copy the scheduler_output to avoid
         # the modification has influence on the scheduler_output in engine core process.
@@ -4119,7 +4210,7 @@ def sample_tokens(
             kv_connector_output = self.kv_connector_output
             self.kv_connector_output = None
             # receive sampled token ids from the last PP rank.
-            if self.use_async_scheduling and get_pp_group().world_size > 1:
+            if self.use_async_scheduling and not get_pp_group().is_last_rank:
                 self._pp_receive_prev_sampled_token_ids_to_input_batch()
             if not kv_connector_output:
                 return None  # type: ignore[return-value]
@@ -4213,7 +4304,8 @@ def propose_draft_token_ids(sampled_token_ids):
                     EagleProposer
                     | DFlashProposer
                     | DraftModelProposer
-                    | ExtractHiddenStatesProposer,
+                    | ExtractHiddenStatesProposer
+                    | Gemma4Proposer,
                 )
                 sampled_token_ids = sampler_output.sampled_token_ids
                 if input_fits_in_drafter:
@@ -4282,7 +4374,14 @@ def propose_draft_token_ids(sampled_token_ids):
                 logits,
                 hidden_states,
                 scheduler_output.total_num_scheduled_tokens,
-                spec_decode_metadata,
+            )
+
+        if self.routed_experts_initialized:
+            issue_routing_d2h_copy(
+                input_batch_req_ids=self.input_batch.req_ids,
+                num_scheduled_tokens=scheduler_output.num_scheduled_tokens,
+                positions=self.positions,
+                positions_cpu=self._positions_cpu,
             )
 
         if propose_drafts_after_bookkeeping:
@@ -4304,12 +4403,15 @@ def propose_draft_token_ids(sampled_token_ids):
         self.kv_connector_output = None
 
         with record_function_or_nullcontext("gpu_model_runner: ModelRunnerOutput"):
+            routed_experts_dict = None
             if self.routed_experts_initialized:
-                capturer = RoutedExpertsCapturer.get_instance()
-                if capturer is not None:
-                    capturer.save_captured_experts(indices=self.slot_mapping)  # noqa
-                else:
-                    logger.error("RoutedExpertsCapturer not initialized.")
+                routed_experts_dict = extract_routed_experts_for_current_batch(
+                    req_ids=req_ids_output_copy,
+                    requests=self.requests,
+                    req_id_to_index=self.input_batch.req_id_to_index,
+                    num_tokens_no_spec=self.input_batch.num_tokens_no_spec,
+                    max_model_len=self.max_model_len,
+                )
 
             output = ModelRunnerOutput(
                 req_ids=req_ids_output_copy,
@@ -4323,6 +4425,7 @@ def propose_draft_token_ids(sampled_token_ids):
                 else None,
                 num_nans_in_logits=num_nans_in_logits,
                 cudagraph_stats=cudagraph_stats,
+                routed_experts_dict=routed_experts_dict,
             )
 
         if not self.use_async_scheduling:
@@ -4361,9 +4464,12 @@ def _pp_broadcast_prev_sampled_token_ids(
         assert sampled_token_ids.dim() == 2 and sampled_token_ids.shape[-1] == 1, (
             "PP+async expects sampled_token_ids to have shape [num_reqs, 1]"
         )
-        torch.distributed.broadcast(
-            sampled_token_ids, src=pp.rank, group=pp.device_group
-        )
+        # Skip for chunked prefill: sampled tokens are dummy
+        # and will be discarded, no need to broadcast.
+        if not self._is_all_reqs_chunked_prefill():
+            torch.distributed.broadcast(
+                sampled_token_ids, src=pp.rank, group=pp.device_group
+            )
 
     def _pp_receive_prev_sampled_token_ids_to_input_batch(self) -> None:
         """Receive sampled token ids broadcast from last PP stage"""
@@ -4372,7 +4478,9 @@ def _pp_receive_prev_sampled_token_ids_to_input_batch(self) -> None:
         num_reqs = self.input_batch.num_reqs
         # `prev_sampled_token_ids` is expected to have shape [num_reqs, 1].
         recv = torch.empty((num_reqs, 1), dtype=torch.int32, device=self.device)
-        torch.distributed.broadcast(recv, src=pp.last_rank, group=pp.device_group)
+        # skip for chunked prefill.
+        if not self._is_all_reqs_chunked_prefill():
+            torch.distributed.broadcast(recv, src=pp.last_rank, group=pp.device_group)
         self.input_batch.prev_sampled_token_ids = recv
 
         # construct `prev_req_id_to_index` here so `_prepare_input_ids`
@@ -4605,7 +4713,8 @@ def propose_draft_token_ids(
             or spec_config.uses_draft_model()
         ):
             assert isinstance(
-                self.drafter, EagleProposer | DFlashProposer | DraftModelProposer
+                self.drafter,
+                EagleProposer | DFlashProposer | DraftModelProposer | Gemma4Proposer,
             )
 
             if spec_config.disable_padded_drafter_batch:
@@ -4643,6 +4752,16 @@ def propose_draft_token_ids(
                     next_token_ids, valid_sampled_tokens_count
                 )
 
+            # Let the target override the hidden state fed to the drafter
+            # (e.g. DeepSeek V4 MTP needs the pre-hc_head residual). Safe to
+            # rebind here: hidden_states was already consumed for sampling
+            # above and is not used again in this branch.
+            alt = getattr(
+                self.get_model(), "get_mtp_target_hidden_states", lambda: None
+            )()
+            if alt is not None:
+                hidden_states = alt
+
             num_rejected_tokens_gpu = None
             if spec_decode_metadata is None:
                 token_indices_to_sample = None
@@ -4807,6 +4926,23 @@ def load_model(self, load_dummy_weights: bool = False) -> None:
                         )
 
                     self.model.set_aux_hidden_state_layers(aux_layers)
+
+                if (
+                    is_mixture_of_experts(self.model)
+                    and self.parallel_config.enable_eplb
+                    and not load_dummy_weights
+                ):
+                    logger.info_once(
+                        "EPLB is enabled for model %s.",
+                        self.model_config.model,
+                    )
+                    assert self.eplb_state is not None
+                    self.eplb_state.add_model(
+                        self.model,
+                        self.model_config,
+                    )
+                    eplb_models += 1
+
                 time_after_load = time.perf_counter()
             self.model_memory_usage = m.consumed_memory
         except torch.cuda.OutOfMemoryError as e:
@@ -4824,7 +4960,6 @@ def load_model(self, load_dummy_weights: bool = False) -> None:
             "Model loading took %s GiB memory and %.6f seconds",
             format_gib(self.model_memory_usage),
             time_after_load - time_before_load,
-            scope="local",
         )
         if not load_dummy_weights:
             prepare_communication_buffer_for_model(self.model)
@@ -4846,15 +4981,10 @@ def load_model(self, load_dummy_weights: bool = False) -> None:
             is_mixture_of_experts(self.model)
             and self.parallel_config.enable_eplb
             and not load_dummy_weights
+            and self.eplb_state is not None
+            and self.eplb_state.is_async
         ):
-            logger.info_once("EPLB is enabled for model %s.", self.model_config.model)
-            assert self.eplb_state is not None
-            self.eplb_state.add_model(
-                self.model,
-                self.model_config,
-            )
-            if self.eplb_state.is_async:
-                self.eplb_state.start_async_loop()
+            self.eplb_state.start_async_loop()
 
         if (
             self.vllm_config.compilation_config.mode
@@ -4963,7 +5093,7 @@ def reload_weights(
             )
 
         # begin loading weights
-        logger.info_once("Reloading weights inplace...", scope="local")
+        logger.info_once("Reloading weights inplace...")
         if is_checkpoint_format:
             # load weights from checkpoint/ original model format
             initialize_layerwise_reload(model)
@@ -4975,7 +5105,6 @@ def reload_weights(
             logger.warning_once(
                 "Reloading with `is_checkpoint_format=True` requires that "
                 "weights be in kernel format and already sharded",
-                scope="local",
             )
             loaded_weights = set()
             for name, loaded_weight in weights_iterator:
@@ -4989,7 +5118,6 @@ def reload_weights(
         logger.info_once(
             "Reloading and processing weights took %.2f seconds",
             diff_seconds,
-            scope="local",
         )
         if self.model_config.quantization is None and loaded_weights is not None:
             weights_not_loaded = weights_to_load - loaded_weights
@@ -5008,7 +5136,6 @@ def _get_prompt_logprobs_dict(
         if not num_prompt_logprobs_dict:
             return {}
 
-        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
         prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
 
         # Since prompt logprobs are a rare feature, prioritize simple,
@@ -5032,14 +5159,14 @@ def _get_prompt_logprobs_dict(
             )
 
             # Set up target LogprobsTensors object.
-            logprobs_tensors = in_progress_dict.get(req_id)
-            if not logprobs_tensors:
+            logprobs_tensors = request.in_progress_prompt_logprobs_cpu
+            if logprobs_tensors is None:
                 # Create empty logprobs CPU tensors for the entire prompt.
                 # If chunked, we'll copy in slice by slice.
                 logprobs_tensors = LogprobsTensors.empty_cpu(
                     num_prompt_tokens - 1, num_prompt_logprobs + 1
                 )
-                in_progress_dict[req_id] = logprobs_tensors
+                request.in_progress_prompt_logprobs_cpu = logprobs_tensors
 
             # Determine number of logits to retrieve.
             start_idx = request.num_computed_tokens
@@ -5096,7 +5223,7 @@ def _get_prompt_logprobs_dict(
         # num_prompt_logprobs_dict.
         for req_id in completed_prefill_reqs:
             del num_prompt_logprobs_dict[req_id]
-            del in_progress_dict[req_id]
+            self.requests[req_id].in_progress_prompt_logprobs_cpu = None
 
         # Must synchronize the non-blocking GPU->CPU transfers.
         if prompt_logprobs_dict:
@@ -5461,7 +5588,7 @@ def _dummy_run(
                         )
                     )
 
-                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
+                intermediate_tensors = self.sync_and_gather_intermediate_tensors(
                     num_tokens_padded, None, False
                 )
 
@@ -5509,7 +5636,8 @@ def _dummy_run(
                     EagleProposer
                     | DFlashProposer
                     | DraftModelProposer
-                    | ExtractHiddenStatesProposer,
+                    | ExtractHiddenStatesProposer
+                    | Gemma4Proposer,
                 )
                 assert self.speculative_config is not None
                 # Eagle currently only supports PIECEWISE cudagraphs.
@@ -5615,6 +5743,26 @@ def _dummy_sampler_run(
             sampler_output = self.sampler(
                 logits=logits, sampling_metadata=dummy_metadata
             )
+            # Also warm forward_native (taken when generators dict is non-empty),
+            # but skip the extra call in 'processed_logits' / 'processed_logprobs'
+            # modes — there TopKTopPSampler binds forward = forward_native at
+            # init time, so the warmup call is redundant and only inflates peak
+            # memory during profile_run.
+            # No .clone() of logits: warmup output is discarded, so any in-place
+            # mutation by forward_native does not affect correctness.
+            if self.sampler.logprobs_mode not in (
+                "processed_logits",
+                "processed_logprobs",
+            ):
+                self.sampler(
+                    logits=logits,
+                    sampling_metadata=replace(
+                        dummy_metadata,
+                        generators={
+                            0: torch.Generator(device=self.device).manual_seed(0)
+                        },
+                    ),
+                )
         except RuntimeError as e:
             if "out of memory" in str(e):
                 raise RuntimeError(
@@ -5776,7 +5924,6 @@ def profile_run(self) -> None:
                             encoder_budget,
                             max_mm_items_per_batch,
                             dummy_modality,
-                            scope="local",
                         )
 
                         # Create dummy batch of multimodal inputs.
@@ -5850,6 +5997,20 @@ def _freeze_gc():
                 gc.unfreeze()
                 gc.collect()
 
+    def shutdown(self) -> None:
+        """Release GPU tensors (model weights, KV caches, workspace) so that
+        memory is reclaimable when running in the same process."""
+        from vllm.model_executor.layers.rotary_embedding import _ROPE_DICT
+        from vllm.v1.worker.workspace import reset_workspace_manager
+
+        # Calls torch.accelerator.synchronize()
+        self._cleanup_profiling_kv_cache()
+        self.compilation_config.static_forward_context.clear()
+        self.model = None  # type: ignore[assignment]
+        _ROPE_DICT.clear()
+
+        reset_workspace_manager()
+
     def _cleanup_profiling_kv_cache(self) -> None:
         torch.accelerator.synchronize()
         if hasattr(self, "kv_caches") and self.kv_caches:
@@ -5992,6 +6153,8 @@ def capture_model(self) -> int:
                 "Skipping CUDA graph capture. To turn on CUDA graph capture, "
                 "ensure `cudagraph_mode` was not manually set to `NONE`"
             )
+            if self.model_config.enable_return_routed_experts:
+                self.init_routed_experts_capturer()
             return 0
 
         # Initialize encoder CUDA graph manager if enabled.
@@ -6025,6 +6188,14 @@ def capture_model(self) -> int:
 
         start_time = time.perf_counter()
 
+        # Initialize the routed experts capturer once before any CUDA graph
+        # capture.  Must happen before graphs are captured so the buffer
+        # address is baked into the graph.  Do NOT call this inside
+        # _capture_cudagraphs() -- creating the capturer twice replaces
+        # the device buffer, causing graphs to write to a dead buffer.
+        if self.model_config.enable_return_routed_experts:
+            self.init_routed_experts_capturer()
+
         # Trigger CUDA graph capture for specific shapes.
         # Capture the large shapes first so that the smaller shapes
         # can reuse the memory pool allocated for the large shapes.
@@ -6073,7 +6244,6 @@ def capture_model(self) -> int:
             "Graph capturing finished in %.0f secs, took %.2f GiB",
             elapsed_time,
             cuda_graph_size / (1 << 30),
-            scope="local",
         )
         return cuda_graph_size
 
@@ -6278,7 +6448,8 @@ def initialize_metadata_builders(
             or self.speculative_config.uses_draft_model()
         ):
             assert isinstance(
-                self.drafter, EagleProposer | DFlashProposer | DraftModelProposer
+                self.drafter,
+                EagleProposer | DFlashProposer | DraftModelProposer | Gemma4Proposer,
             )
             self.drafter.initialize_attn_backend(kv_cache_config, kernel_block_sizes)
 
@@ -6331,7 +6502,10 @@ def _check_and_update_cudagraph_mode(
         ):
             assert isinstance(
                 self.drafter,
-                EagleProposer | DFlashProposer | ExtractHiddenStatesProposer,
+                EagleProposer
+                | DFlashProposer
+                | ExtractHiddenStatesProposer
+                | Gemma4Proposer,
             )
             self.drafter.initialize_cudagraph_keys(cudagraph_mode)
 
@@ -6444,10 +6618,11 @@ def may_reinitialize_input_batch(
                 block_sizes=block_sizes,
                 kernel_block_sizes=kernel_block_sizes,
                 max_num_blocks_per_req=max_num_blocks,
-                is_spec_decode=bool(self.vllm_config.speculative_config),
+                num_spec_tokens=self.num_spec_tokens,
                 logitsprocs=self.input_batch.logitsprocs,
                 logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
                 is_pooling_model=self.is_pooling_model,
+                reasoning_config=self.vllm_config.reasoning_config,
             )
 
         assert self._init_block_sizes == block_sizes, (
@@ -6502,7 +6677,6 @@ def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
 
     def _reshape_kv_cache_tensors(
         self,
-        kv_cache_config: KVCacheConfig,
         kv_cache_raw_tensors: dict[str, torch.Tensor],
         kernel_block_sizes: list[int],
     ) -> dict[str, torch.Tensor]:
@@ -6510,7 +6684,6 @@ def _reshape_kv_cache_tensors(
         Reshape the KV cache tensors to the desired shape and dtype.
 
         Args:
-            kv_cache_config: The KV cache config
             kv_cache_raw_tensors: The KV cache buffer of each layer, with
                 correct size but uninitialized shape.
             kernel_block_sizes: The kernel block sizes for each KV cache group.
@@ -6540,9 +6713,15 @@ def _reshape_kv_cache_tensors(
                     )
                     kernel_num_blocks = num_blocks * num_blocks_per_kv_block
 
+                    # For MLA with compression, storage_block_size != block_size
+                    if kv_cache_spec.storage_block_size != kv_cache_spec.block_size:
+                        shape_block_size = kv_cache_spec.storage_block_size
+                    else:
+                        shape_block_size = kernel_block_size
+
                     kv_cache_shape = attn_backend.get_kv_cache_shape(
                         kernel_num_blocks,
-                        kernel_block_size,
+                        shape_block_size,
                         kv_cache_spec.num_kv_heads,
                         kv_cache_spec.head_size,
                         cache_dtype_str=self.cache_config.cache_dtype,
@@ -6566,12 +6745,31 @@ def _reshape_kv_cache_tensors(
                         kv_cache_stride_order.index(i)
                         for i in range(len(kv_cache_stride_order))
                     ]
-                    kv_caches[layer_name] = (
-                        kv_cache_raw_tensors[layer_name]
-                        .view(dtype)
-                        .view(kv_cache_shape)
-                        .permute(*inv_order)
-                    )
+
+                    raw_tensor = kv_cache_raw_tensors[layer_name].view(dtype)
+                    if kv_cache_spec.page_size_padded is not None:
+                        # Use strided view to handle page_size_bytes that
+                        # include padding. This follows
+                        # the same pattern as MambaSpec handling below.
+                        # NOTE: This assumes kv_cache_shape[0] == num_blocks
+                        # (i.e. the first physical dimension is the block
+                        # index), which holds for MLA backends but NOT for
+                        # standard attention backends whose shape starts with
+                        # a K/V dimension of size 2.
+                        dtype_size = get_dtype_size(dtype)
+                        page_stride = kv_cache_spec.page_size_bytes // dtype_size
+                        strides = list(torch.empty(kv_cache_shape).stride())
+                        strides[inv_order[0]] = page_stride
+                        kv_cache = torch.as_strided(
+                            raw_tensor,
+                            size=kv_cache_shape,
+                            stride=tuple(strides),
+                        )
+                    else:
+                        # No padding — safe to use a contiguous view.
+                        kv_cache = raw_tensor.view(kv_cache_shape)
+                    kv_caches[layer_name] = kv_cache.permute(*inv_order)
+
                 elif isinstance(kv_cache_spec, MambaSpec):
                     has_mamba = True
                     raw_tensor = kv_cache_raw_tensors[layer_name]
@@ -6674,7 +6872,7 @@ def initialize_kv_cache_tensors(
 
             # Change the memory buffer to the desired shape
             kv_caches = self._reshape_kv_cache_tensors(
-                kv_cache_config, kv_cache_raw_tensors, kernel_block_sizes
+                kv_cache_raw_tensors, kernel_block_sizes
             )
 
         # Set up cross-layer KV cache sharing
@@ -6738,7 +6936,9 @@ def initialize_kv_cache(
         self.may_add_encoder_only_layers_to_kv_cache_config()
         self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
         self.initialize_attn_backend(kv_cache_config, is_profiling=is_profiling)
-        initialize_mamba_ssu_backend(self.vllm_config.mamba_config)
+        initialize_mamba_ssu_backend(
+            self.vllm_config.mamba_config, self.kv_cache_config
+        )
         # The kernel block size for all KV cache groups. For example, if
         # kv_cache_manager uses block_size 256 for a given group, but the attention
         # backends for that group only supports block_size 64, we will return
@@ -6790,45 +6990,44 @@ def init_routed_experts_capturer(self):
             "Initializing routed experts capturer, enable_return_routed_experts: %s",
             self.model_config.enable_return_routed_experts,
         )
-        routed_experts_capturer = RoutedExpertsCapturer.create()
-        self.routed_experts_attn_gid = self._get_attention_kv_cache_gid()
-        min_block_size = min(
-            [
-                group.kv_cache_spec.block_size
-                for group in self.kv_cache_config.kv_cache_groups
-            ]
-        )
-        num_groups = len(self.kv_cache_config.kv_cache_groups)
-        self.max_num_kv_tokens = (
-            self.kv_cache_config.num_blocks // num_groups
-        ) * min_block_size
-        dcp_size = self.vllm_config.parallel_config.decode_context_parallel_size
-        pcp_size = self.vllm_config.parallel_config.prefill_context_parallel_size
-        if pcp_size * dcp_size > 1:
-            self.max_num_kv_tokens *= pcp_size * dcp_size
+        if not self.model_config.enable_return_routed_experts:
+            self.routed_experts_initialized = False
+            return
+
+        from vllm.distributed import get_tp_group
+
+        if hasattr(self.model_config.hf_text_config, "n_shared_experts"):
+            num_fused_shared_experts = 1
+        else:
+            num_fused_shared_experts = 0
 
-        routed_experts_capturer.init_buffer(
+        tp_group = get_tp_group()
+        init_routed_experts_capturer_with_shared_cache(
+            enable=self.model_config.enable_return_routed_experts,
+            model_config=self.model_config,
+            num_fused_shared_experts=num_fused_shared_experts,
             max_num_batched_tokens=self.scheduler_config.max_num_batched_tokens,
-            max_num_kv_tokens=self.max_num_kv_tokens,
-            vllm_config=self.vllm_config,
+            max_model_len=self.max_model_len,
+            device=self.device,
+            rank=tp_group.rank_in_group,
+            world_size=tp_group.world_size,
         )
-        self._bind_routed_experts_capturer(routed_experts_capturer)
+        self._bind_routed_experts_capturer()
         self.routed_experts_initialized = True
 
-    def _bind_routed_experts_capturer(self, capturer: RoutedExpertsCapturer) -> None:
-        from vllm.model_executor.layers.fused_moe.layer import FusedMoE
-        from vllm.model_executor.layers.fused_moe.router.base_router import (
-            BaseRouter,
+        # Pinned CPU buffer for async positions D2H (avoids sync .cpu() call)
+        self._positions_cpu = torch.empty(
+            self.scheduler_config.max_num_batched_tokens,
+            dtype=torch.long,
+            pin_memory=True,
         )
 
-        for module in self.compilation_config.static_forward_context.values():
-            if isinstance(module, FusedMoE) and isinstance(module.router, BaseRouter):
-                layer_id = module.layer_id
-
-                def _capture_fn(topk_ids, _layer_id=layer_id, _capturer=capturer):
-                    _capturer.capture(_layer_id, topk_ids)
+    def _bind_routed_experts_capturer(self) -> None:
+        from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
+            bind_routing_capture_to_model,
+        )
 
-                module.router.set_capture_fn(_capture_fn)
+        bind_routing_capture_to_model(self.model)
 
     def may_add_encoder_only_layers_to_kv_cache_config(self) -> None:
         """
diff --git a/vllm/v1/worker/gpu_ubatch_wrapper.py b/vllm/v1/worker/gpu_ubatch_wrapper.py
index 01f18a11948d..a8c54c4f066e 100644
--- a/vllm/v1/worker/gpu_ubatch_wrapper.py
+++ b/vllm/v1/worker/gpu_ubatch_wrapper.py
@@ -71,8 +71,8 @@ def __init__(
                 A function that sets the number of SMs for computation.
         """
 
-        assert current_platform.is_cuda(), (
-            "SM control is currently only supported on CUDA"
+        assert current_platform.is_cuda() or current_platform.is_rocm(), (
+            "SM/CU control is supported on CUDA and ROCm platforms"
         )
         device = torch.accelerator.current_device_index()
         total_sms = num_compute_units(device)
diff --git a/vllm/v1/worker/gpu_worker.py b/vllm/v1/worker/gpu_worker.py
index 5381649f04dd..18c7941a0eed 100644
--- a/vllm/v1/worker/gpu_worker.py
+++ b/vllm/v1/worker/gpu_worker.py
@@ -5,12 +5,13 @@
 import gc
 import os
 from collections.abc import Callable
-from contextlib import AbstractContextManager, nullcontext
+from contextlib import AbstractContextManager, contextmanager, nullcontext
 from datetime import timedelta
 from types import NoneType
 from typing import TYPE_CHECKING, Any
 
 import numpy as np
+import regex as re
 import torch
 import torch.nn as nn
 
@@ -46,7 +47,7 @@
 from vllm.tracing import instrument
 from vllm.utils.mem_constants import GiB_bytes
 from vllm.utils.mem_utils import MemorySnapshot, format_gib, memory_profiling
-from vllm.utils.torch_utils import is_quantized_kv_cache, set_random_seed
+from vllm.utils.torch_utils import set_random_seed
 from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
 from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
 from vllm.v1.outputs import (
@@ -139,6 +140,8 @@ def __init__(
             if self.vllm_config.weight_transfer_config is not None
             else None
         )
+        self._weight_update_active = False
+        self._is_checkpoint_format = True
 
         # Torch/CUDA profiler. Enabled and configured through profiler_config.
         # Profiler wrapper is created lazily in profile() when start is called,
@@ -192,15 +195,8 @@ def wake_up(self, tags: list[str] | None = None) -> None:
                     buffer.data.copy_(self._sleep_saved_buffers[name].data)
             self._sleep_saved_buffers = {}
 
-        # If the KV cache has just been woken up,
-        # the internal state of cache_engine must be reset,
-        # especially the FP8 scaling factor.
-        if (
-            (tags is None or "kv_cache" in tags)
-            and is_quantized_kv_cache(self.cache_config.cache_dtype)
-            and hasattr(self.model_runner, "init_fp8_kv_scales")
-        ):
-            self.model_runner.init_fp8_kv_scales()
+        if tags is None or "kv_cache" in tags:
+            self.model_runner.post_kv_cache_wake_up()
 
     def _maybe_get_memory_pool_context(self, tag: str) -> AbstractContextManager:
         if not self.vllm_config.model_config.enable_sleep_mode:
@@ -215,6 +211,30 @@ def _maybe_get_memory_pool_context(self, tag: str) -> AbstractContextManager:
             )
         return allocator.use_memory_pool(tag=tag)
 
+    @contextmanager
+    def _scoped_allocator_max_split(self, max_split_size_mb: int):
+        """Temporarily set max_split_size_mb to reduce allocator fragmentation at the
+        cost of more cudaMalloc calls (negligible in practice). Restores the original
+        value on exit."""
+        if not current_platform.is_cuda():
+            yield
+            return
+
+        conf = os.environ.get("PYTORCH_CUDA_ALLOC_CONF", "")
+        match = re.search(r"max_split_size_mb:(\d+)", conf)
+        original_value = match.group(1) if match else None
+
+        torch._C._accelerator_setAllocatorSettings(
+            f"max_split_size_mb:{max_split_size_mb}"
+        )
+        try:
+            yield
+        finally:
+            # PyTorch defaults to SIZE_MAX (no limit).
+            _SIZE_MAX_MB = (2**64 - 1) // (1024 * 1024)
+            restore = original_value if original_value else str(_SIZE_MAX_MB)
+            torch._C._accelerator_setAllocatorSettings(f"max_split_size_mb:{restore}")
+
     @instrument(span_name="Init device")
     def init_device(self):
         if self.device_config.device_type == "cuda":
@@ -269,7 +289,7 @@ def init_device(self):
             )
 
             if self.use_v2_model_runner:
-                logger.info_once("Using V2 Model Runner", scope="local")
+                logger.info_once("Using V2 Model Runner")
 
             # Set random seed.
             set_random_seed(self.model_config.seed)
@@ -319,6 +339,8 @@ def load_model(self, *, load_dummy_weights: bool = False) -> None:
         with (
             self._maybe_get_memory_pool_context(tag="weights"),
             set_current_vllm_config(self.vllm_config),
+            # 20 MiB is the minimum PyTorch allows for max_split_size_mb.
+            self._scoped_allocator_max_split(max_split_size_mb=20),
         ):
             self.model_runner.load_model(load_dummy_weights=load_dummy_weights)
 
@@ -374,10 +396,14 @@ def determine_available_memory(self) -> int:
             )
 
             # Profile CUDA graph memory if graphs will be captured.
-            # Skip on ROCm/HIP as graph pool handles and mem_get_info behave
+            # Skip on ROCm/HIP/XPU as graph pool handles and mem_get_info behave
             # differently and can produce incorrect/negative estimates.
             cudagraph_memory_estimate = 0
-            if not self.model_config.enforce_eager and not current_platform.is_rocm():
+            if (
+                current_platform.is_cuda()
+                and self.vllm_config.compilation_config.cudagraph_mode
+                != CUDAGraphMode.NONE
+            ):
                 cudagraph_memory_estimate = self.model_runner.profile_cudagraph_memory()
 
         # Use the pre-cudagraph torch peak to avoid double-counting.
@@ -436,7 +462,6 @@ def determine_available_memory(self) -> int:
         logger.info_once(
             "Available KV cache memory: %s GiB",
             format_gib(self.available_kv_cache_memory_bytes),
-            scope="local",
         )
 
         if cudagraph_memory_estimate > 0:
@@ -450,14 +475,13 @@ def determine_available_memory(self) -> int:
                     1.0,
                 )
                 logger.info(
-                    "CUDA graph memory profiling is enabled "
-                    "(VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=1). "
-                    "This will become the default in v0.19. "
-                    "The current --gpu-memory-utilization=%.4f is equivalent "
-                    "to --gpu-memory-utilization=%.4f without CUDA graph "
-                    "memory profiling. To maintain the same effective KV "
-                    "cache size as before, increase "
-                    "--gpu-memory-utilization to %.4f.",
+                    "CUDA graph memory profiling is enabled (default since "
+                    "v0.21.0). The current --gpu-memory-utilization=%.4f is "
+                    "equivalent to --gpu-memory-utilization=%.4f without "
+                    "CUDA graph memory profiling. To maintain the same "
+                    "effective KV cache size as before, increase "
+                    "--gpu-memory-utilization to %.4f. To disable, set "
+                    "VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=0.",
                     current_util,
                     equiv_util,
                     suggested_util,
@@ -467,14 +491,14 @@ def determine_available_memory(self) -> int:
                     round(current_util + cg_util_delta, 4),
                     1.0,
                 )
-                logger.info(
-                    "In v0.19, CUDA graph memory profiling will be enabled "
-                    "by default (VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=1), "
-                    "which more accurately accounts for CUDA graph memory "
-                    "during KV cache allocation. To try it now, set "
-                    "VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=1 and increase "
-                    "--gpu-memory-utilization from %.4f to %.4f to maintain "
-                    "the same effective KV cache size.",
+                logger.warning(
+                    "CUDA graph memory profiling is disabled "
+                    "(VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=0). "
+                    "Without it, CUDA graph memory is not accounted for "
+                    "during KV cache allocation, which may require lowering "
+                    "--gpu-memory-utilization to avoid OOM. Consider "
+                    "re-enabling it (the default as of v0.21.0) and increasing "
+                    "--gpu-memory-utilization from %.4f to %.4f.",
                     current_util,
                     suggested_util,
                 )
@@ -689,6 +713,14 @@ def compile_or_warm_up_model(self) -> CompilationTimes:
         # the model initialization and profiling.
         set_random_seed(self.model_config.seed)
 
+        # All warmup is done — start monitoring for unexpected JIT
+        # compilations that would cause latency spikes during inference.
+        from vllm.triton_utils.jit_monitor import (
+            activate as activate_triton_jit_monitor,
+        )
+
+        activate_triton_jit_monitor()
+
         return CompilationTimes(
             language_model=self.compilation_config.compilation_time,
             encoder=self.compilation_config.encoder_compilation_time,
@@ -936,6 +968,13 @@ def save_tensorized_model(self, tensorizer_config: "TensorizerConfig") -> None:
             model_config=self.model_config,
         )
 
+    def _check_weight_transfer_engine(self) -> None:
+        if self.weight_transfer_engine is None:
+            raise RuntimeError(
+                "Weight transfer not configured. "
+                "Please set weight_transfer_config to enable weight transfer."
+            )
+
     def init_weight_transfer_engine(self, init_info: dict) -> None:
         """
         Initialize weight transfer mechanism.
@@ -944,26 +983,62 @@ def init_weight_transfer_engine(self, init_info: dict) -> None:
         Args:
             init_info: Dictionary containing backend-specific initialization info
         """
-        if self.weight_transfer_engine is None:
-            raise RuntimeError(
-                "Weight transfer not configured. "
-                "Please set weight_transfer_config to enable weight transfer."
-            )
+        self._check_weight_transfer_engine()
+        assert self.weight_transfer_engine is not None
         # Parse dict into backend-specific typed dataclass
         typed_init_info = self.weight_transfer_engine.parse_init_info(init_info)
         self.weight_transfer_engine.init_transfer_engine(typed_init_info)
 
+    def start_weight_update(self, is_checkpoint_format: bool = True) -> None:
+        """
+        Start a new weight update.
+
+        Prepares the model for receiving weights. For checkpoint format,
+        this initializes state for layerwise processing. For kernel format, this is
+        a no-op but must still be called for consistency.
+
+        Args:
+            is_checkpoint_format: Whether incoming weights are in checkpoint
+                format (need layerwise processing) or kernel format (direct
+                copy). Stored as state for finish_weight_update.
+        """
+        self._check_weight_transfer_engine()
+
+        if self._weight_update_active:
+            raise RuntimeError(
+                "start_weight_update called while a weight update is "
+                "already active. Call finish_weight_update first."
+            )
+
+        if is_checkpoint_format:
+            from vllm.model_executor.model_loader.reload import (
+                initialize_layerwise_reload,
+            )
+
+            model = self.model_runner.model
+            with torch.device(self.device):
+                initialize_layerwise_reload(model)
+
+        # Store state so update_weights/finish_weight_update can check
+        self._is_checkpoint_format = is_checkpoint_format
+        self._weight_update_active = True
+
     def update_weights(self, update_info: dict) -> None:
         """
-        Batched weight update from the trainer.
+        Receive weights from the trainer (one or more chunks).
+
+        start_weight_update must be called before update_weights and
+        finish_weight_update must be called after.
 
         Args:
             update_info: Dictionary containing backend-specific update info
         """
-        if self.weight_transfer_engine is None:
+        self._check_weight_transfer_engine()
+        assert self.weight_transfer_engine is not None
+
+        if not self._weight_update_active:
             raise RuntimeError(
-                "Weight transfer not configured. "
-                "Please set weight_transfer_config to enable weight transfer."
+                "start_weight_update must be called before update_weights."
             )
 
         # Parse dict into backend-specific typed dataclass
@@ -971,38 +1046,59 @@ def update_weights(self, update_info: dict) -> None:
 
         model = self.model_runner.model
 
-        if typed_update_info.is_checkpoint_format:
-            from vllm.model_executor.model_loader.reload import (
-                finalize_layerwise_reload,
-                initialize_layerwise_reload,
-            )
-
-            # Use layerwise reload pattern for checkpoint format weights
-            with torch.device(self.device):
-                initialize_layerwise_reload(model)
+        with torch.device(self.device):
+            if self._is_checkpoint_format:
                 self.weight_transfer_engine.receive_weights(
                     typed_update_info,
                     load_weights=model.load_weights,
                 )
-                finalize_layerwise_reload(model, self.model_config)
-        else:
-            # Weights are already in kernel format, copy directly
-            def load_weights_direct(
-                weights: list[tuple[str, torch.Tensor]],
-            ) -> None:
-                for name, weight in weights:
-                    param = model.get_parameter(name)
-                    param.copy_(weight)
-
-            self.weight_transfer_engine.receive_weights(
-                typed_update_info,
-                load_weights=load_weights_direct,
-            )
+            else:
+                # Weights are already in kernel format, copy directly
+                def load_weights_direct(
+                    weights: list[tuple[str, torch.Tensor]],
+                ) -> None:
+                    for name, weight in weights:
+                        param = model.get_parameter(name)
+                        param.copy_(weight)
+
+                self.weight_transfer_engine.receive_weights(
+                    typed_update_info,
+                    load_weights=load_weights_direct,
+                )
 
         # NCCL broadcast/packed path are asynchronous.
         # Sync here so the next step uses the new weights.
         torch.accelerator.synchronize()
 
+    def finish_weight_update(self) -> None:
+        """
+        Finish the current weight update.
+
+        For checkpoint format, this runs layerwise postprocessing.
+        Uses the is_checkpoint_format state stored by start_weight_update.
+        """
+        self._check_weight_transfer_engine()
+
+        if not self._weight_update_active:
+            raise RuntimeError(
+                "start_weight_update must be called before finish_weight_update."
+            )
+
+        is_checkpoint_format = self._is_checkpoint_format
+
+        if is_checkpoint_format:
+            from vllm.model_executor.model_loader.reload import (
+                finalize_layerwise_reload,
+            )
+
+            model = self.model_runner.model
+            with torch.device(self.device):
+                finalize_layerwise_reload(model, self.model_config)
+
+        # Reset state
+        self._weight_update_active = False
+        self._is_checkpoint_format = True
+
     def shutdown(self) -> None:
         # has_kv_transfer_group can be None during interpreter shutdown.
         if ensure_kv_transfer_shutdown is not None:
@@ -1013,6 +1109,11 @@ def shutdown(self) -> None:
         if weight_transfer_engine := getattr(self, "weight_transfer_engine", None):
             weight_transfer_engine.shutdown()
 
+        # Release GPU resources held by the model runner so that memory
+        # can be reclaimed when running in-process
+        if model_runner := getattr(self, "model_runner", None):
+            model_runner.shutdown()
+
     def elastic_ep_execute(self, execute_method: str, *args, **kwargs):
         return self.elastic_ep_executor.execute(execute_method, *args, **kwargs)
 
@@ -1025,11 +1126,10 @@ def init_worker_distributed_environment(
     backend: str = "nccl",
 ) -> None:
     """Initialize the distributed environment."""
-    attention_config = vllm_config.attention_config
     parallel_config = vllm_config.parallel_config
     from vllm.model_executor.layers.batch_invariant import init_batch_invariance
 
-    init_batch_invariance(attention_config.backend)
+    init_batch_invariance()
     override_envs_for_eplb(parallel_config)
     set_custom_all_reduce(not parallel_config.disable_custom_all_reduce)
 
diff --git a/vllm/v1/worker/lora_model_runner_mixin.py b/vllm/v1/worker/lora_model_runner_mixin.py
index 53873d156f88..3a14abfc3589 100644
--- a/vllm/v1/worker/lora_model_runner_mixin.py
+++ b/vllm/v1/worker/lora_model_runner_mixin.py
@@ -101,9 +101,12 @@ def maybe_setup_dummy_loras(
             assert self.lora_manager is not None, "LoRA is not enabled"
 
             num_loras = lora_config.max_loras
-            lora_warmup_rank = (
+            lora_warmup_rank: int = (
                 lora_config.max_lora_rank if lora_config.max_lora_rank < 8 else 8
             )
+            lora_warmup_rank = self.lora_manager.get_dummy_lora_warmup_rank(
+                lora_warmup_rank
+            )
             # Make dummy lora requests
             lora_requests: set[LoRARequest] = {
                 LoRARequest(
diff --git a/vllm/v1/worker/ubatch_utils.py b/vllm/v1/worker/ubatch_utils.py
index 7c41726472d5..f4a76529023c 100644
--- a/vllm/v1/worker/ubatch_utils.py
+++ b/vllm/v1/worker/ubatch_utils.py
@@ -177,7 +177,22 @@ def _make_metadata_with_slice(
         query_start_loc[1:] -= tokens_skipped
         query_start_loc_cpu[1:] -= tokens_skipped
     seq_lens = attn_metadata.seq_lens[request_slice]
-    seq_lens_cpu = attn_metadata.seq_lens_cpu[request_slice]
+    # Read raw fields to avoid triggering the deprecated D2H-syncing properties.
+    seq_lens_cpu = (
+        attn_metadata._seq_lens_cpu[request_slice]
+        if attn_metadata._seq_lens_cpu is not None
+        else None
+    )
+    seq_lens_cpu_upper_bound = (
+        attn_metadata.seq_lens_cpu_upper_bound[request_slice]
+        if attn_metadata.seq_lens_cpu_upper_bound is not None
+        else None
+    )
+    num_computed_tokens_cpu = (
+        attn_metadata._num_computed_tokens_cpu[request_slice]
+        if attn_metadata._num_computed_tokens_cpu is not None
+        else None
+    )
 
     if splits_last_request:
         # NOTE: We use start_locs (the original query_start_loc_cpu) to calculate
@@ -190,12 +205,18 @@ def _make_metadata_with_slice(
         # Make sure we don't modify the seq_lens tensors
         #  (not cudagraph compatible)
         seq_lens = seq_lens.clone()
-        seq_lens_cpu = seq_lens_cpu.clone()
         seq_lens[-1] -= tokens_skipped
-        seq_lens_cpu[-1] -= tokens_skipped
-
-    max_seq_len = int(seq_lens_cpu.max())
-    num_computed_tokens_cpu = attn_metadata.num_computed_tokens_cpu[request_slice]
+        if seq_lens_cpu is not None:
+            seq_lens_cpu = seq_lens_cpu.clone()
+            seq_lens_cpu[-1] -= tokens_skipped
+        if seq_lens_cpu_upper_bound is not None:
+            seq_lens_cpu_upper_bound = seq_lens_cpu_upper_bound.clone()
+            seq_lens_cpu_upper_bound[-1] -= tokens_skipped
+
+    assert seq_lens_cpu_upper_bound is not None
+    # Preserve the max_seq_len override set during CUDA-graph capture so
+    # the attention backend selects the correct kernel for SWA layers.
+    max_seq_len = max(int(seq_lens_cpu_upper_bound.max()), attn_metadata.max_seq_len)
 
     num_requests = request_slice.stop - request_slice.start
     num_actual_tokens = token_slice.stop - token_slice.start
@@ -221,6 +242,7 @@ def _make_metadata_with_slice(
         max_seq_len=max_seq_len,
         block_table_tensor=block_table_tensor,
         slot_mapping=slot_mapping,
+        seq_lens_cpu_upper_bound=seq_lens_cpu_upper_bound,
         _seq_lens_cpu=seq_lens_cpu,
         _num_computed_tokens_cpu=num_computed_tokens_cpu,
     )
diff --git a/vllm/v1/worker/utils.py b/vllm/v1/worker/utils.py
index 83fc12cb5c3b..b693adbf2771 100644
--- a/vllm/v1/worker/utils.py
+++ b/vllm/v1/worker/utils.py
@@ -120,7 +120,7 @@ def init_meta(
 
         for group in attn_groups_iter:
             spec = group.kv_cache_spec
-            if type(spec) is not FullAttentionSpec:
+            if not isinstance(spec, FullAttentionSpec):
                 continue
             if group.kv_cache_group_id >= len(kernel_block_sizes):
                 continue
@@ -519,12 +519,8 @@ def is_residual_scattered_for_sp(
     """Check if the residual tensor is scattered for sequence parallelism.
 
     The residual tensor is scattered across tensor parallel ranks when sequence
-    parallelism and tensor parallelism is enabled.
-
-    This follows the same logic as SequenceParallelismPass.is_applicable_for_range():
-    - In full-graph compilation mode (no splitting ops or using inductor graph
-      partition), SP is always applied
-    - Otherwise, SP is only applied for specific shapes in compile_sizes
+    parallelism and tensor parallelism is enabled. SP is only supported in
+    full-graph compilation mode.
     """
     if not vllm_config.compilation_config.pass_config.enable_sp:
         return False
@@ -534,16 +530,13 @@ def is_residual_scattered_for_sp(
     if tp == 1:
         return False
 
+    assert (
+        vllm_config.compilation_config.use_inductor_graph_partition
+        or not vllm_config.compilation_config.splitting_ops
+    ), "Sequence parallelism requires full-graph compilation"
+
     # When sequence parallelism is enabled, we always pad num_input_tokens
     # to be a multiple of tensor_parallel_size (tp) earlier.
     assert num_input_tokens % tp == 0
 
-    if (
-        not vllm_config.compilation_config.splitting_ops
-        or vllm_config.compilation_config.use_inductor_graph_partition
-    ):
-        return True
-    compile_sizes = vllm_config.compilation_config.compile_sizes
-    if compile_sizes is None:
-        return False
-    return num_input_tokens in compile_sizes
+    return True
diff --git a/vllm/v1/worker/workspace.py b/vllm/v1/worker/workspace.py
index 7e21d89f7036..1c502bfd8ff1 100644
--- a/vllm/v1/worker/workspace.py
+++ b/vllm/v1/worker/workspace.py
@@ -161,36 +161,33 @@ def get_caller_info() -> str:
                     "Workspace growth is not allowed after locking."
                 )
 
-            for ubatch_id in range(self._num_ubatches):
-                current_workspace = self._current_workspaces[ubatch_id]
-                if (
-                    current_workspace is None
-                    or self._workspace_size_bytes(current_workspace) < required_bytes
-                ):
-                    # Delete old tensor before allocating new one to avoid
-                    # memory spike from resize_(). resize_() allocates new
-                    # memory before freeing old, which can cause OOM.
-                    # Must clear the list reference first since local var
-                    # is just a copy of the reference.
-                    self._current_workspaces[ubatch_id] = None
-                    del current_workspace
-                    self._current_workspaces[ubatch_id] = torch.empty(
-                        (required_bytes,), dtype=torch.uint8, device=self._device
-                    )
+            # Only resize the requesting ubatch's workspace.  Other
+            # ubatches resize lazily on their next get_simultaneous call.
+            # Resizing all ubatches here would orphan the other ubatch's
+            # old tensor when it still holds views into it (DBO leak).
+            self._current_workspaces[ubatch_id] = None
+            del current_workspace
+            # Release the freed segment back to CUDA so the caching
+            # allocator can reuse the GPU memory for the larger
+            # allocation below. Without this, each resize may leave a
+            # dead segment in reserved memory which can cause higher peak
+            # memory usage.
+            torch.accelerator.empty_cache()
+            self._current_workspaces[ubatch_id] = torch.empty(
+                (required_bytes,), dtype=torch.uint8, device=self._device
+            )
+            current_workspace = self._current_workspaces[ubatch_id]
 
             if envs.VLLM_DEBUG_WORKSPACE:
                 logger.info(
                     "[WORKSPACE DEBUG] Resized workspace from '%s': %.2f MB -> "
-                    "%.2f MB (%d ubatches, total memory %.2f MB)",
+                    "%.2f MB (ubatch %d)",
                     get_caller_info(),
                     current_size / _MB,
                     required_bytes / _MB,
-                    self._num_ubatches,
-                    required_bytes * self._num_ubatches / _MB,
+                    ubatch_id,
                 )
 
-            current_workspace = self._current_workspaces[dbo_current_ubatch_id()]
-
         return current_workspace
 
 
diff --git a/vllm/v1/worker/xpu_worker.py b/vllm/v1/worker/xpu_worker.py
index 102a04f5d88a..0f01ee42a97b 100644
--- a/vllm/v1/worker/xpu_worker.py
+++ b/vllm/v1/worker/xpu_worker.py
@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import gc
 import os
-from typing import Any
 
 import torch
 
@@ -40,19 +39,35 @@ def __init__(
         assert device_config.device_type == "xpu"
         assert current_platform.is_xpu()
 
-        # Torch profiler. Enabled and configured through profiler_config.
-        self.profiler: Any | None = None
-        profiler_config = vllm_config.profiler_config
-        if profiler_config.profiler == "torch":
-            worker_name = f"{vllm_config.instance_id}-rank-{self.rank}"
-            self.profiler = TorchProfilerWrapper(
-                profiler_config,
-                worker_name=worker_name,
-                local_rank=self.local_rank,
-                activities=["CPU", "XPU"],
+    def init_device(self):
+        # In DP mode, XPU workers see all visible devices.
+        # Offset local_rank by the local DP shard.
+        parallel_config = self.parallel_config
+        if (
+            parallel_config.distributed_executor_backend
+            not in ("ray", "external_launcher")
+            and parallel_config.data_parallel_backend != "ray"
+            and parallel_config.nnodes_within_dp == 1
+        ):
+            dp_local_rank = parallel_config.data_parallel_rank_local
+            if dp_local_rank is None:
+                dp_local_rank = parallel_config.data_parallel_index
+            tp_pp_world_size = (
+                parallel_config.pipeline_parallel_size
+                * parallel_config.tensor_parallel_size
+            )
+            self.local_rank += dp_local_rank * tp_pp_world_size
+
+            visible_device_count = torch.accelerator.device_count()
+            assert self.local_rank < visible_device_count, (
+                f"DP adjusted local rank {self.local_rank} is out of bounds. "
+            )
+            assert parallel_config.local_world_size <= visible_device_count, (
+                f"local_world_size ({parallel_config.local_world_size}) must "
+                f"be less than or equal to the number of visible devices "
+                f"({visible_device_count})."
             )
 
-    def init_device(self):
         device = self.device_config.device
         if (
             isinstance(device, torch.device)
@@ -117,3 +132,30 @@ def init_device(self):
         if self.rank == 0:
             # If usage stat is enabled, collect relevant info.
             report_usage_stats(self.vllm_config)
+
+    def profile(self, is_start: bool = True, profile_prefix: str | None = None):
+        if self.profiler_config is None or self.profiler_config.profiler is None:
+            raise RuntimeError(
+                "Profiling is not enabled. Please set --profiler-config to enable "
+                "profiling. Example: "
+                "'--profiler-config.profiler=torch --profiler-config.torch_profiler_dir"
+                "=YOUR_DIR_PATH_TO_DUMP_TRACE'"
+            )
+
+        if is_start and self.profiler is None:
+            from vllm.distributed.utils import get_worker_rank_suffix
+
+            rank_suffix = get_worker_rank_suffix(global_rank=self.rank)
+            trace_name = (
+                f"{profile_prefix}_{rank_suffix}" if profile_prefix else rank_suffix
+            )
+
+            self.profiler = TorchProfilerWrapper(
+                self.profiler_config,
+                worker_name=trace_name,
+                local_rank=self.local_rank,
+                activities=["CPU", "XPU"],
+            )
+            logger.debug("Starting torch profiler with trace name: %s", trace_name)
+
+        super().profile(is_start=is_start, profile_prefix=profile_prefix)
diff --git a/vllm/vllm_flash_attn/flash_attn_interface.py b/vllm/vllm_flash_attn/flash_attn_interface.py
index eb0dbd42383f..33955bb239ef 100644
--- a/vllm/vllm_flash_attn/flash_attn_interface.py
+++ b/vllm/vllm_flash_attn/flash_attn_interface.py
@@ -387,6 +387,7 @@ def flash_attn_varlen_func(
             num_splits=num_splits,
             return_lse=return_softmax_lse,
             out=out,
+            learnable_sink=s_aux,
         )
     else:
         raise ValueError(f"Unsupported FA version: {fa_version}")